diff --git a/README.md b/README.md
index 876c566543532d521f06a0d1ce3b8b01e78923ed..dfe7b99f5bc577ac02e459678a370b2c271bf0be 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 ---
 title: DataViz
-emoji: 📉
-colorFrom: red
-colorTo: indigo
+emoji: 👁
+colorFrom: blue
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.36.0
 app_file: app.py
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a51a0fdcaba7fe979b0e7fd1d2ecd6b261b3d24
--- /dev/null
+++ b/app.py
@@ -0,0 +1,303 @@
+import os
+import re
+import json
+import streamlit as st
+from PIL import Image, ImageDraw
+import requests
+from io import BytesIO
+import seaborn as sns
+import matplotlib.pyplot as plt
+from streamlit_chat import message as st_message
+
+import yaml
+
+st.set_page_config(page_title="Data Exploration", page_icon="🌍", layout="wide", initial_sidebar_state="collapsed")
+COLORS = sns.color_palette("Paired", n_colors=100).as_hex()
+
+def load_config(config_fn, field='data_explore') -> dict:
+    config = yaml.load(open(config_fn), Loader=yaml.Loader)
+    return config[field]
+
+def convert_from_prompt_tokens(s_with_region_tokens):
+    """Convert from strings with prompt tokens for prompt encoders
+    
+    e.g.:
+    
+    Input: "<Region><L12><24><L101><L777></Region>"
+    
+    Output: [0.012, 0.024, 0.101, 0.777]
+    """
+    REGION_PATTERN = r'<Region>(\s*<L(\d{1,4})>\s*<L(\d{1,4})>\s*<L(\d{1,4})>\s*<L(\d{1,4})>\s*)</Region>'
+    boxes = []
+    boxes_str = re.findall(REGION_PATTERN, s_with_region_tokens)
+    for boxes_str_i in boxes_str:
+        matched_str_i, boxes_str_i = boxes_str_i[0], boxes_str_i[1:]
+        boxes.append(tuple([int(s)/1000 for s in boxes_str_i]))
+    return boxes
+
+def parse_regions(s):
+    pattern = r"\[([\d.,\s]+)\]"
+    matches = re.findall(pattern, s)
+    bboxes = []
+    points = []
+    for res in matches:
+        res = eval(res)
+        if len(res) == 4:
+            # bbox
+            x1, y1, x2, y2 = res
+            bboxes.append((x1, y1, x2, y2))
+        else:
+            x1, y1 = res
+            points.append((x1, y1))
+    
+    bboxes.extend(convert_from_prompt_tokens(s))
+    return list(set(bboxes))
+
+def get_image(image_path, bboxes=None):
+
+    if os.path.exists(image_path):
+        image = Image.open(image_path).convert('RGB')
+    else:
+        # 从URL获取图片
+        response = requests.get(image_path)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    
+    draw = ImageDraw.Draw(image, 'RGB')
+    color_mapping = None
+    if bboxes is not None:
+        width, height = image.size
+        color_mapping = []
+        for i, bbox_coords in enumerate(bboxes):
+            color = COLORS[i]
+            
+            x1, y1, x2, y2 = bbox_coords
+            x1 *= width
+            y1 *= height
+            x2 *= width
+            y2 *= height
+            draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
+            
+            color_mapping.append([bbox_coords, color])
+    
+    color_mapping = dict(color_mapping)
+    return image, color_mapping
+
+def insert_color(s, color_mapping):
+    for coords, color in color_mapping.items():
+        coords_str = ', '.join([str(x) for x in coords])
+        s = s.replace('[' + coords_str + ']', f'<span style="color: {color}; font-weight: bold;">■</span>' + ' [' + coords_str + ']')
+    
+    return s
+
+modal_indicator = ['<image>', '<audio>', '<video>']
+def show_one_msg(msg, modal_inputs):
+    splits = re.split('(' + '|'.join(modal_indicator) + ')', msg)
+    for s in splits:
+        if s == '<image>':
+            st.image(modal_inputs['image'].pop(0))
+        elif s == '<audio>':
+            st.audio(modal_inputs['audio'].pop(0))
+        elif s == '<video>':
+            st.video(modal_inputs['video'].pop(0))
+        else:
+            st.write(s)
+
+def show_multimodal_example(example, col1, col2):
+    with col1:
+        info = example.get('info', {})
+        info['modal_inputs'] = example['modal_inputs']
+        st.json(info)
+    
+    with col2:
+        conversations = example['conversations']
+        modal_inputs = example['modal_inputs']
+        for i in range(len(conversations) // 2):
+            with st.chat_message("user"):
+                show_one_msg(conversations[2*i]['value'], modal_inputs)
+            with st.chat_message("assistant"):
+                show_one_msg(conversations[2*i+1]['value'], modal_inputs)
+
+
+def show_example(example, col1, col2, enable_scores=True):
+    if 'conversations' in example:
+        regions = parse_regions(str(example['conversations']))
+    else:
+        regions = parse_regions(str(example))
+    
+    image_fn = example['image']
+    image, color_mapping = get_image(image_fn, regions)
+    
+    with col1:
+        st.image(image)
+        info = example.get('info', {})
+        info['image'] = image_fn
+        if 'dataset' in example:
+            info['source'] = example['dataset']
+        st.json(info)
+        
+        if len(color_mapping):
+            table_md = "| 颜色 | 坐标 |\n| --- | --- |\n"
+            for coords, color in color_mapping.items():
+                color_cell = f'<span style="color: {color}; font-weight: bold;">■</span>'
+                table_md += f"| {color_cell} | {coords} |\n"
+
+            # 使用Markdown显示表格
+            st.markdown(table_md, unsafe_allow_html=True)
+    
+    score_dict = None
+    with col2:
+        if 'conversations' in example:
+            if enable_scores:
+                score_dict = {'image': image_fn, 'conversations': example['conversations']}
+                with st.expander("Give a score based on the result above", expanded=True):
+                    quality_score = st.radio("问题质量分数",('Bad', 'Mediocre', 'Good'),key="quality", horizontal = True)
+                    format_score = st.radio("格式分数",('Bad', 'Mediocre', 'Good'),key="format", horizontal = True)
+                score_dict['scores'] = {
+                    'quality': quality_score, 'format': format_score
+                }
+            st.subheader("Chat")
+            conversations = example['conversations']
+            for i in range(len(conversations) // 2):
+                st_message(conversations[2*i]['value'], is_user=True, key=image_fn + str(2*i))
+                st_message(conversations[2*i+1]['value'], is_user=False, key=image_fn + str(2*i+1))
+
+            if 'ground_truth' in example:
+                # 显示查询
+                gt = insert_color(json.dumps(example['ground_truth']), color_mapping)
+                st.markdown(f"**Ground Truth:**\n\n{gt}", unsafe_allow_html=True)
+        else:
+            # 显示指令
+            instruction = insert_color(example['instruction'], color_mapping)
+            st.markdown(f"**Instruction:**\n\n{instruction}", unsafe_allow_html=True)
+
+            # 显示输入
+            if 'input' in example:
+                input = insert_color(example['input'], color_mapping)
+                st.markdown(f"**Input:**\n\n{input}", unsafe_allow_html=True)
+
+            # 显示输出
+            output = insert_color(example['output'], color_mapping)
+            st.markdown(f"**Output:**\n\n{output}", unsafe_allow_html=True)
+
+            if 'query' in example:
+                # 显示查询
+                query = insert_color(json.dumps(example['query']), color_mapping)
+                st.markdown(f"**Query:**\n\n{query}", unsafe_allow_html=True)
+    return score_dict
+
+def reset_state():
+    print('RESET')
+    st.session_state['data_explore'] = {'idx': 0}
+    st.session_state.scores = {}
+
+def load_dir_data(dir, dataset_configs):
+    mapping_file = os.path.join(dir, 'mapping.yaml')
+    assert os.path.exists(mapping_file)
+    
+    config = yaml.load(open(mapping_file), Loader=yaml.Loader)
+    # image_paths = dataset_configs
+    image_paths = config['image_paths']
+    image_paths['default'] = image_paths.get('default', '.')
+    
+    res = []
+    for k, v in config['mapping'].items():
+        if os.path.exists(os.path.join(dir, k + '.json')):
+            data = json.load(open(os.path.join(dir, k + '.json')))
+        elif os.path.exists(os.path.join(dir, k + '.jsonl')):
+            data = [json.loads(line) for line in open(os.path.join(dir, k + '.jsonl'))]
+        elif os.path.exists(os.path.join(dir, k + '.txt')):
+            data = [json.loads(line) for line in open(os.path.join(dir, k + '.txt'))]
+        
+        image_path = image_paths.get(v, image_paths['default'])
+        for example in data:
+            example['image'] = os.path.join(image_path, example['image'])
+            example['dataset'] = k
+        res.extend(data)
+    
+    return res
+
+@st.cache_data
+def load_data(fn, dataset_configs):
+    if os.path.isdir(fn):
+        res = load_dir_data(fn, dataset_configs)
+        return res
+    
+    if fn.endswith(('.txt', '.jsonl')):
+        res = []
+        for line in open(fn):
+            example = json.loads(line)
+            res.append(example)
+    else:
+        res = json.load(open(fn))
+    
+    for example in res:
+        dataset_path = dataset_configs[example.get('dataset', 'default')]
+    
+        if 'image' in example:
+            example['image'] = os.path.join(dataset_path, example['image'])
+        elif 'img_info' in example:
+            if isinstance(example['img_info'], str):
+                example['image'] = os.path.join(dataset_path, example['img_info'])
+            else:
+                if 'coco_url' in example['img_info']:
+                    example['image'] = example['img_info']['coco_url']
+        else:
+            assert 'modal_inputs' in example
+    
+    return res
+
+dataset_configs = load_config('config.yaml')
+print(dataset_configs)
+data_paths = dataset_configs.get('data_paths', ['instruction_data'])
+
+files = []
+def add_file(path):
+    if os.path.exists(os.path.join(path, 'mapping.yaml')):
+        files.append(path)
+    else:
+        for f in sorted(os.listdir(path)):
+            file = os.path.join(path, f)
+            if os.path.isfile(file) and file.endswith(('.txt', '.json')):
+                files.append(file)
+            else:
+                add_file(file)
+            
+for data_path in data_paths:
+    add_file(data_path)
+
+
+st.session_state['data_explore'] = {'idx': 0}
+enable_score = st.sidebar.checkbox('Score it!', value=True)
+if enable_score and 'scores' not in st.session_state:
+    st.session_state.scores = {}
+
+status_placeholder = st.empty()
+control_col1, control_col2 = st.columns(2)
+
+with control_col1:
+    selected_file = st.selectbox('Select a file', files, on_change=reset_state)
+
+col1, col2 = st.columns(2)
+
+if selected_file:
+    data = load_data(selected_file, dataset_configs)
+
+    with control_col2:
+        idx = st.number_input(f'Input an idx (Total: {len(data)})', min_value=0, max_value=len(data), value=st.session_state.get('data_explore', {}).get('idx', 0))
+        st.session_state['data_explore']['idx'] = idx
+    
+    if 'image' in data[idx]:
+        show_example(data[idx], col1, col2, enable_scores=enable_score)
+    else:
+        show_multimodal_example(data[idx], col1, col2)
+
+if enable_score:
+    name = st.sidebar.text_input("Username", placeholder = "Enter your name", value="cc")
+    if st.sidebar.button(label ="Submit scores", key = "submit"):
+        if name:
+            score_path = f"score_results/{os.path.basename(selected_file)}_{name}.json"
+            with open(score_path, "w") as score_file:
+                json.dump(st.session_state.scores, score_file, indent = 4)
+                status_placeholder.success("Successfully saved!")
+        else:
+            status_placeholder.error("Please enter your name on the sidebar!")
\ No newline at end of file
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e13ba1a17a3d0da67358d687a7a5c776a8565cf0
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,16 @@
+image_paths:
+  COCO_2017: coco2017/images
+
+data_explore: 
+  image_paths:
+    COCO: COCO_2017
+  data_paths:
+    - chartgrounding_data_0722
+
+eval_results:
+  image_paths:
+    default: COCO_2017
+
+chat:
+  controller: http://0.0.0.0:39996
+  save_path: demo/saved_chat
\ No newline at end of file
diff --git a/data/final_0721_bar.json b/data/final_0721_bar.json
new file mode 100644
index 0000000000000000000000000000000000000000..d00dd7880875bb83208651b2fd8c12a8a2bce42a
--- /dev/null
+++ b/data/final_0721_bar.json
@@ -0,0 +1,154022 @@
+[
+    {
+        "id": "kgCWJ5v0ltIAPa7Q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.013, 0.329, 0.024, 0.444]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "5tzdsT1kbHV2RCfL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.098, 0.079, 0.167, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 bar for Energy sector."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "N3Gzjd7YeZj269uj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of {the subtitle} at [0.401, 0.019, 0.642, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industry Market Share Comparison."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "EgOZ1ggNpL3sgWmr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by [0.911, 0.055, 0.982, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend section, which includes {2020}[0.948, 0.062, 0.977, 0.080] and {2021}[0.948, 0.088, 0.977, 0.106]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "2mS1lILle37IVpcP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 bar for 'Healthcare'}[0.357, 0.627, 0.426, 0.726] taller than {the 2021 bar for 'Healthcare'}[0.357, 0.477, 0.426, 0.627]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "Pmoz1tt7kjDeYkj7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 2020 bars} taller or shorter than {the 2021 bars} in general?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "cYPXDEaGMFLMXdhg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is the {2020 label}[0.948, 0.062, 0.977, 0.080] above or below the {2021 label}[0.948, 0.088, 0.977, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "lSpASCYgh3zFCuCX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-coordinate of the top side of {Energy 2020 bar}[0.098, 0.427, 0.167, 0.726] higher or lower than that of the bottom side of {Energy 2021 bar}[0.098, 0.079, 0.167, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "bqosDHDHvLxZctgC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.911, 0.055, 0.982, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the graph. It holds two labels with their corresponding color indicators. The first label is {2020}, denoted by the color blue with coordinates [0.948, 0.062, 0.977, 0.080], and the second label is {2021}, denoted by the color orange, with coordinates [0.948, 0.088, 0.977, 0.106]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "06J7fIeppSNnCbel",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.013, 0.329, 0.024, 0.444]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the Y-axis label of the chart, which is called \"Market Share\"."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "Sj1d3u7h5O7EbUkw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what belongs to the region of [0.444, 0.676, 0.513, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 2020 bar for the Consumer_Staples sector, the height of the bar represents the market share value."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "zNDisX33aMS1al1h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are included within [0.826, 0.650, 0.950, 0.700] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the lower portion of the 2021 bars for Real_Estate[0.789, 0.676, 0.859, 0.701] and Communication_Services [0.876, 0.651, 0.945, 0.701]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "oO8yu17seAm7qTo2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the vertical axis at coordinates [0.015, 0.259, 0.029, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Equivalent CO2 Emissions (thousands)\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "U9xh0lNelhnozvKx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at [0.140, 0.329, 0.309, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is orange."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "uEwCN5G4gCEulFSx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does the bar within [0.352, 0.443, 0.521, 0.863] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Road Traffic\" category."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "kxwk67iEqMGDFtph",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis located at [0.505, 0.952, 0.580, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Categories\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "SzHivaNnje6JGjwx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Urban Greening bar for 2021}[0.140, 0.329, 0.309, 0.634] taller than {the Urban Greening bar for 2020}[0.140, 0.062, 0.309, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "NgO5o3KM8xlszeOR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the Waste Management bar for 2021}[0.564, 0.291, 0.733, 0.634] or {the Air Condition Usage bar for 2021}[0.775, 0.367, 0.945, 0.748]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Air Condition Usage bar for 2021."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "p5wm7JF956yTFCyZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Road Traffic bar for 2021}[0.352, 0.443, 0.521, 0.863] towards the right of the {Urban Greening bar for 2020}[0.140, 0.062, 0.309, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "P7nbInOKEJZClRKI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more CO2 Emissions for {Waste Management in 2021}[0.564, 0.291, 0.733, 0.634] or {Waste Management in 2020}[0.564, 0.062, 0.733, 0.291]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "3BAZ8x0Wsvac4VFu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in [0.107, 0.815, 0.226, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend section of the chart. It contains two items. The first item, with text position at [0.151, 0.824, 0.220, 0.847], is labeled \"Year 2020\" and has a color marker in light blue at [0.112, 0.826, 0.140, 0.842]. The second item, with text position at [0.151, 0.859, 0.220, 0.882], is labeled \"Year 2021\" and has a color marker in orange at [0.112, 0.861, 0.140, 0.877]."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "PZUIYTtgN0M9Yeud",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.564, 0.062, 0.733, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars representing CO2 Emissions for the category \"Waste Management\" in the years 2020 and 2021. The bar representing 2020 is located at [0.564, 0.062, 0.733, 0.291] and the bar representing 2021 is located at [0.564, 0.291, 0.733, 0.634]."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "OXgSDPJjtAwajTxB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinates [0.791, 0.919, 0.929, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text label corresponding to the category \"Air Condition Usage\" within these coordinates."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "U0EBd80I4SsZmd0b",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within [0.396, 0.919, 0.477, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's an x-axis label indicating the category \"Road Traffic\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "s5L97LI5IUt7ZQZt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis label}<r>[0.015, 0.318, 0.029, 0.647]</r> called?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment in million dollars."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "eJy1QkD0F5Pc2iT0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subtitle}<r>[0.319, 0.025, 0.736, 0.055]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment pattern in EdTech and Online Learning."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "HBltB8Cp2lVHdcwP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat investment sector is mentioned in {xtick}<r>[0.704, 0.919, 0.813, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "B0EqV8TSDWOyMwAB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the {first legend item}<r>[0.122, 0.083, 0.185, 0.106]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{First Year}<r>[0.083, 0.085, 0.111, 0.101]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "BS9Ba6jnKLrkvOs4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the investment in Education Technology in the first year}<r>[0.112, 0.615, 0.482, 0.903]</r> higher or lower compared to {the investment in Education Technology in the second year}<r>[0.112, 0.230, 0.482, 0.615]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "8Ls8NvXkdaSHNpkK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the investments in the first year}<r>[0.112, 0.615, 0.482, 0.903], [0.574, 0.551, 0.943, 0.903]</r> overall higher or lower than {the investments in the second year}<r>[0.112, 0.230, 0.482, 0.615],[0.574, 0.102, 0.943, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "nUWN1Fnp3TkekdrJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment sector has a higher investment in the first year, {Education Technology}<r>[0.112, 0.615, 0.482, 0.903]</r> or {Online Learning}<r>[0.574, 0.551, 0.943, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "0dYpEEet1A1zD57t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has seen a greater increase in investment from the {first year}<r>[0.122, 0.083, 0.185, 0.106]</r> to the {second year}<r>[0.122, 0.118, 0.207, 0.141]</r>, {Education Technology}<r>[0.112, 0.615, 0.482, 0.903], [0.112, 0.230, 0.482, 0.615]</r> or {Online Learning}<r>[0.574, 0.551, 0.943, 0.903], [0.574, 0.102, 0.943, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "5tnDqGuqFoOr5AU6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found at these coordinates <r>[0.078, 0.074, 0.212, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates represent a legend. Two color-text labels are present. The first handle has a blue color at [0.083, 0.085, 0.111, 0.101] and is labelled as First Year at [0.122, 0.083, 0.185, 0.106]. The second handle is in orange at [0.083, 0.120, 0.111, 0.136], and is labelled as Second Year at [0.122, 0.118, 0.207, 0.141]."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "iEAdNycBU4C9Bbti",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region <r>[0.112, 0.102, 0.943, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are four bars that signify the investments in Education Technology and Online Learning for the first and second years. The 'First Year' bars:[0.112, 0.615, 0.482, 0.903] and [0.574, 0.551, 0.943, 0.903]. The 'Second Year' bars:[0.112, 0.230, 0.482, 0.615] and [0.574, 0.102, 0.943, 0.551]."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "1VeCh7W9JnnecySO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within these bounding coordinates <r>[0.221, 0.919, 0.813, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two x-ticks, which are the labels for the two investment sectors. The Education Technology label is at coordinates [0.221, 0.919, 0.373, 0.942] and the Online Learning label is at coordinates [0.704, 0.919, 0.813, 0.942]."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "CW5mfgf0u6MTfIPB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the content displayed within the region <r>[0.043, 0.381, 0.061, 0.405]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we have the Y-axis tick label '80' displayed."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "G8oBgUBWq8sYOXqR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is being represented at coordinates [0.132, 0.062, 0.493, 0.600]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the time spent on Social Media for Current Year."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "dHCd7FjRtzfVD2VN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label indicated by the text coordinates [0.880, 0.824, 0.966, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Current Year\" in the legend."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "mxp1X8Z4ou7a830N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text [0.308, 0.025, 0.768, 0.055] in the figure indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the chart, \"Time spent on Social Media and Digital Communication\"."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "ZYUcQttNkU1OeN2f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for the \"Current Year\"[0.132, 0.062, 0.493, 0.600] have a higher or lower value than the \"Previous Year\"[0.132, 0.600, 0.493, 0.863] for Social Media usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "kTyrN5vTAeJdAav8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor \"Digital Communication\", is the time spent in the \"Previous Year\"[0.583, 0.243, 0.944, 0.646] lesser or greater than the \"Current Year\"[0.583, 0.062, 0.944, 0.243]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "Kb9tLGNc3FoxOnsX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color #87ceeb representing {\"Current Year\"}[0.880, 0.824, 0.966, 0.847] or {\"Previous Year\"}[0.880, 0.859, 0.972, 0.882] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Current Year."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "mc0DSKWvfsv6rwhm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is represented in the figure within the boundary [0.836, 0.815, 0.978, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend section defining the color indicators for \"Current Year\" ({handle color for Current Year}<r>[0.841, 0.826, 0.869, 0.842]</r>, {Current Year label}<r>[0.880, 0.824, 0.966, 0.847]</r>) and \"Previous Year\" ({handle color for Previous Year}<r>[0.841, 0.861, 0.869, 0.877]</r>, {Previous Year label}<r>[0.880, 0.859, 0.972, 0.882]</r>)."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "rFvHyJmJ4MBxinPa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.035, 0.987, 0.081, 1.010]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for y-axis, specified as \"-1600\"."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "yKi8evpL3R8u8RWC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are there in the area [0.583, 0.062, 0.944, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars. The lower bar ({Current Year bar}<r>[0.583, 0.062, 0.944, 0.243]</r>) is for the \"Current Year\" indicates the time spend on \"Digital Communication\", and the upper bar ({Previous Year bar}<r>[0.583, 0.243, 0.944, 0.646]</r>) is for the \"Previous Year\", for the same sector."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "ehz7AL259kqfi5yv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate refers to the \"Previous Year\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the \"Previous Year\"}<r>[0.143, 0.859, 0.235, 0.882]</r>"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "y3zEnl6cdUskiauO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what is found within the coordinates [0.559, 0.062, 0.731, 0.227]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the \"Child Development\" of the \"Current Year\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "zlTkAKgnugTPHbrz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object bounded by the coordinates [0.044, 0.147, 0.081, 0.171] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label, \"-200\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "sTseQ7h6uYKgcjW6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar related to the \"Current Year\" under \"Child Development\"<r>[0.559, 0.062, 0.731, 0.227]</r> shorter than the bar related to the \"Previous Year\" under \"Child Development\"<r>[0.559, 0.227, 0.731, 0.453]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "mwSBv0LFEi1fDIKy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Previous Year\" bar for \"Income Inequality\"<r>[0.346, 0.439, 0.517, 0.863]</r> taller than the \"Current Year\" bar for \"Income Inequality\"<r>[0.346, 0.062, 0.517, 0.439]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "KHgLKEOVBHUBNugN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars labelled as \"Current Year\" generally taller than the bars labelled as \"Previous Year\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "c105B5AQj2IkkSkE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region [0.098, 0.815, 0.240, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two labels indicating the color coding of the bars. {The blue label}<r>[0.104, 0.826, 0.131, 0.842]</r> corresponds to \"Current Year\" and {the orange label}<r>[0.104, 0.861, 0.131, 0.877]</r> corresponds to \"Previous Year\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "PxtM0YgfkyEqJttA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what exists within the box [0.035, 0.901, 0.081, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate features the y-axis label \"-1800\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "iDkWWMvoa6TiOQlo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.346, 0.062, 0.517, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of two bars. The lower one represents the \"Current Year\" for the \"Income Inequality\" sector  with coordinates <r>[0.346, 0.062, 0.517, 0.439]</r> and the upper one represents the \"Previous Year\" for the same sector with coordinates <r>[0.346, 0.439, 0.517, 0.863]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "Td2LXVkMe6bfJfCM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.937, 0.824, 0.972, 0.847] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend text for the year 2020."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "WsV68MoaesqHColU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department can be found at [0.350, 0.919, 0.387, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The department at these coordinates is the Sales department."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "Sr5OREKzC0T3306f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat range of performance can be found at the coordinates [0.035, 0.575, 0.081, 0.598]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The performance range for these coordinates is between -1500 and 0."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "dyeukq08hcUxCFcV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Sales bar for 2020}[0.301, 0.062, 0.436, 0.245] higher than the {Sales bar for 2019}[0.301, 0.245, 0.436, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "WdJLTInPNKRhqDdh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {color handle for 2020} <r>[0.898, 0.826, 0.926, 0.842]</r> above the {color handle for 2019} <r>[0.898, 0.861, 0.926, 0.877]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "sgZuuOWxhikqKYM6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the labels {Marketing}<r>[0.165, 0.919, 0.234, 0.942]</r>, {Sales}<r>[0.350, 0.919, 0.387, 0.942]</r>, {Development}<r>[0.491, 0.919, 0.585, 0.942]</r>, and {Operations}<r>[0.669, 0.919, 0.746, 0.942]</r>, which one is furthest to the right?"
+            },
+            {
+                "from": "gpt",
+                "value": "Operations."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "FJOuwgtfEPIp6nMY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the box delimited by [0.132, 0.062, 0.267, 0.523]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this box, there is a blue bar representing the 2020 performance of the Marketing department."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "Bp6aXV0jVCI7jtCy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.091, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the entire plot of the bar chart. It contains all the department's performance bars for 2020 and 2019. The x-axis labels representing the different departments and the y-axis labels representing the performance shift can also be found here."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "Dda1AnlaymKBpS5r",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the coordinates [0.132, 0.523, 0.267, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we find an orange vertical bar that represents the 2019 performance of the Marketing department."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "o7JHpBPN3N9gX7Hs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the graph placed at [0.351, 0.025, 0.725, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is Human History and Civilization Development."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "jxP30KgeXLQ5Ro2I",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the area represented by the handle at [0.104, 0.826, 0.131, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is blue, representing the Current Century."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "XcBep3qwvBOonJMA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat y-axis label is located at [0.015, 0.220, 0.029, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is Advancements in Civilization (arbitrary units)."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "wphh9PSeO01sxETg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled in coordinates [0.329, 0.919, 0.408, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is Bronze Age."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "4M30ARssHZqMtR33",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Stone Age in the Current Century}[0.132, 0.062, 0.267, 0.216] taller than {the bar for Stone Age in the Previous Century}[0.132, 0.216, 0.267, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "rH2Z3vTQvUwjoAXp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Modern Era bars}[0.809, 0.062, 0.944, 0.863] wider than {the Iron Age bars}[0.470, 0.062, 0.606, 0.420]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "XmfTOuiJoMjIqJKM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the bars representing the Previous Century} contain more data points than {the bars representing the Current Century}?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "juIwQa8ANGBrflJ1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {the Previous Century's Middle Ages}[0.640, 0.318, 0.775, 0.658] taller than the bar for {the Current Century's Middle Ages}[0.640, 0.062, 0.775, 0.318]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "pmdoWJeN4ijcziMv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region [0.098, 0.815, 0.266, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with two items. The first item at [0.142, 0.824, 0.254, 0.847] is labeled as Current Century and the color handle at [0.104, 0.826, 0.131, 0.842] is blue. The second item at [0.142, 0.859, 0.261, 0.882] is labeled as Previous Century and the color handle at [0.104, 0.861, 0.131, 0.877] is orange."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "1oEZDuN6ZrECCjzA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the area defined by [0.301, 0.062, 0.436, 0.284]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two vertical bars in this area. One is a blue bar at [0.301, 0.062, 0.436, 0.147] representing the value for the Bronze Age in the Current Century. Another is an orange bar at [0.301, 0.147, 0.436, 0.284] representing the value for the Bronze Age in the Previous Century."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "9L20YfKkBlbY3sz7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the area [0.351, 0.025, 0.725, 0.055]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's subtitle: \"Human History and Civilization Development\"."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "p7eZ3IPoFaLLcAMF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.035, 0.053, 0.081, 0.076] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates mark the position of '0' on the y-axis."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "81QikVhHT9v88CcG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is represented by the coordinates [0.046, 0.528, 0.072, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 400."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "FW2J0GT7uP7gM3LN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label present at the coordinates represented by [0.391, 0.919, 0.460, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Education\"."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "YQYckAywW152uTJA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked at the coordinates [0.555, 0.262, 0.728, 0.738] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Blue Bar representing '2021' policy impact in the Healthcare area."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "TR4QoJWE4UZftJTS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar {2021 bar for Healthcare}[0.555, 0.262, 0.728, 0.738] or the {2021 bar for Transportation}[0.771, 0.062, 0.944, 0.738] is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2021 bar for Transportation."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "mls5JpJledG41b6R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{2021 bars for Education and Healthcare}[0.339, 0.312, 0.512, 0.738],[0.555, 0.262, 0.728, 0.738], which one is closer to the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 bar for Healthcare."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "UQzdDq2TndroRZIh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {2020 bars}[0.123, 0.187, 0.296, 0.638],[0.339, 0.312, 0.512, 0.638],[0.555, 0.262, 0.728, 0.863],[0.771, 0.062, 0.944, 0.563] comparatively shorter than {2021 bars}[0.123, 0.187, 0.296, 0.738],[0.339, 0.312, 0.512, 0.738],[0.555, 0.262, 0.728, 0.738],[0.771, 0.062, 0.944, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "bs85tzzhUzJrGDaC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region given by [0.089, 0.074, 0.174, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the bar chart, with two entries. The first entry has a blue color label located at [0.095, 0.085, 0.122, 0.101], and the corresponding text \"2021\" at [0.134, 0.083, 0.169, 0.106]. The second entry has an orange color label at [0.095, 0.120, 0.122, 0.136] with the corresponding text \"2020\" at [0.134, 0.118, 0.169, 0.141]."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "YmRpsTwF3UVMBPuk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.064, 0.729, 0.072, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"0\" is in this region."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "Oazn3cXWvCMUZQrp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within [0.082, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the bars, labels, legend and title of the chart. It includes the x-axis labels for \"Housing\", \"Education\", \"Healthcare\", and \"Transportation\" at respective coordinates; the y-axis labels for values ranging from \"-400\" to \"1400\" at respective coordinates. The blue and orange bars representing the policy impact for different socioeconomic areas in the years 2021 and 2020 and their respective locations. The title of the chart \"Socioeconomic Policy Making: 2020 vs 2021\" is located at [0.350, 0.025, 0.717, 0.055]."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "8eHbaefMFxigdWgC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the right bottom corner, unit is described for the y-axis. Can you provide the coordinate of this label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The unit label or y-axis label, named \"Intensity or Impact\", is located at [0.015, 0.373, 0.029, 0.592]."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "TGSgsJOroQPb7zvL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the text at position [0.142, 0.118, 0.226, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at the given position is \"Modern Age\"."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "t4DoF28TsdZqq2FY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the coordinates for the bar referring to 'Modern Age' under the category of 'Global Cooperation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates for the bar referring to 'Modern Age' under the category of 'Global Cooperation' are [0.773, 0.169, 0.944, 0.222]."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "W48oH2xqSuEO4UxO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at [0.104, 0.120, 0.131, 0.136] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color handle for {the Modern Age}<r>[0.104, 0.120, 0.131, 0.136]</r>, which is colored in blue."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "PD7XRsBAIjixvTdu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Feudal System' bar for the 'Middle Ages'}<r>[0.132, 0.062, 0.303, 0.436]</r> higher than {the same for the 'Modern Age'}<r>[0.132, 0.436, 0.303, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "VWbcZHfGxuuMDJzy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Birth of Nations' bar for 'Middle Ages'}<r>[0.346, 0.062, 0.517, 0.329]</r> lower or higher than {the same one for 'Modern Age'}<r>[0.346, 0.329, 0.517, 0.436]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is lower."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "9KjUvvejMYoXfNOD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {the Modern Age bar for 'Technology Advancements'}<r>[0.559, 0.116, 0.730, 0.302]</r> higher or lower than {the Middle Ages bar for the same category}<r>[0.559, 0.062, 0.730, 0.116]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is higher."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "S2xfN25GShjrQQJc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison, is the impact of {Middle Ages}<r>[0.142, 0.083, 0.227, 0.106]</r> on our civilization stronger or weaker than that of {Modern Age}<r>[0.142, 0.118, 0.226, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stronger."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "AZVDALXIP3Rm1hRo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe [0.132, 0.062, 0.303, 0.436]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this vertical rectangle region, there is {a bar color-coded for Middle Ages}<r>[0.132, 0.062, 0.303, 0.436]</r> under 'Feudal System' heading on the x-axis. It indicates the intensity or impact of 'Feudal System' during the Middle Ages."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "50mm8b4J6gS08DfC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can I see within [0.559, 0.062, 0.730, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, two overlapping bars could be identified. One of the bars represents the impact of 'Technology Advancement' in the {Middle Ages}<r>[0.559, 0.062, 0.730, 0.116]</r>; the other one represents the same historical aspect during the {Modern Age}<r>[0.559, 0.116, 0.730, 0.302]</r>. The bar representing Modern Age is longer, suggesting a higher impact of 'Technology Advancement' during that era."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "l0p7hLcV8zcnSzdU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the content in the area [0.132, 0.062, 0.944, 0.863]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes most of the chart content. Here you will find both the 'Middle Ages' and 'Modern Age' bar graphs for all categories: 'Feudal System', 'Birth of Nations', 'Technology Advancements', 'Global Cooperation'. The bars representing each era are overlapped, with 'Middle Ages' bar starting from y=0 and 'Modern Age' bar starting from the end of 'Middle Ages' bar."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "O6Swclh4SLjHG42q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.098, 0.074, 0.232, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a legend key for two categories. The {first one}<r>[0.142, 0.083, 0.227, 0.106]</r> is the 'Middle Ages' which is color-coded in red, and the {second one}<r>[0.142, 0.118, 0.226, 0.141]</r> is the 'Modern Age' which is color-coded in blue."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "MWGcyVChXt53ovHU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main element located at coordinates [0.353, 0.025, 0.711, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the chart, namely \"Health and Medical Field Data Presentation\"."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "b7nI7I0x1UlhsJsP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color do you assign at the coordinates [0.092, 0.120, 0.120, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#1e90ff)."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "b0NMIJuPYeZCQWuW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is the bar at [0.121, 0.676, 0.258, 0.903] representative of?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Emergency Services\"."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "ErlttbXiYDvmoiKn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled within the range of [0.119, 0.919, 0.260, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"Emergency Services\"."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "mccZj4bFLySh46No",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for the \"Current Year\" or the \"Previous Year\" reveal higher spending for 'Emergency Services'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Previous Year."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "ap6dIhhaW6rALp1W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for 'Pediatric Care'}[0.464, 0.555, 0.601, 0.903] higher than the {bar for 'Primary Care'}[0.292, 0.601, 0.429, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "R2P0pqkoJy1h6ZHk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {legend icon for 'Previous Year'}[0.092, 0.120, 0.120, 0.136] to the left or right of the {legend icon for 'Current Year'}[0.092, 0.085, 0.120, 0.101] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither. They are vertically aligned."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "i5lCh6QZiLLPLKDD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, the {bar for 'Public Health' for the current year}[0.807, 0.495, 0.944, 0.903] or the {bar for 'Public Health' for the previous year}[0.807, 0.102, 0.944, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the previous year."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "nTlAIT3GMzjAr320",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the zone [0.086, 0.074, 0.229, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This zone contains a legend with two elements. The element at coordinates [0.092, 0.085, 0.120, 0.101] is a red (#ff6347) square representing 'Current Year', and the corresponding label is at coordinates [0.131, 0.083, 0.217, 0.106]. The second element at coordinates [0.092, 0.120, 0.120, 0.136] is a blue (#1e90ff) square representing 'Previous Year', with the corresponding label at coordinates [0.131, 0.118, 0.223, 0.141]."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "kK6Dejofluv2xENM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the area limited by these coordinates [0.079, 0.062, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area spans the entire chart, including the x-axis labeled 'Healthcare Fields', the y-axis labeled 'Medical Expenses (in million dollars)', the legend located in the bottom left, and a set of bars representing various healthcare categories for 'Current Year' and 'Previous Year'."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "lzhTWzyJ2JPu24uq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents inside [0.121, 0.435, 0.258, 0.676]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blue bar that corresponds to the 'Previous Year' category and represents 'Emergency Services'. It indicates a spending of approximately between 1500 to 3100 million dollars."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "GRaeiMq71lg9jasP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.015, 0.272, 0.029, 0.693] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It has a vertical text label reading \"Medical Expenses (in million dollars)\" which serves as the label for the Y-axis."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "nqLNU6uYmOP5pkkT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data is represented by the coordinates [0.132, 0.405, 0.303, 0.713] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar for Education in the previous year."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "QgcDiW5TbMb6N8dQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is at [0.015, 0.262, 0.029, 0.703]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This pair of coordinates contains the y-axis label, which is \"Expenditures (in thousands of dollars)\"."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "upYiIcgckIXKwVS0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.346, 0.062, 0.517, 0.469]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates represent a bar for Healthcare in the current year."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "ymSil3yF9Ar3YV3f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.142, 0.824, 0.228, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label for the current year."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "qKujzvtJA9hvYJ9Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for current year's expenditures in Education at [0.132, 0.062, 0.303, 0.405] higher or lower than the same bar for the previous year at [0.132, 0.405, 0.303, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "LtnFySjBMPotUS82",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Current year's bar for Healthcare}[0.346, 0.062, 0.517, 0.469] taller than {Previous year's bar for Healthcare}[0.346, 0.469, 0.517, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "DcqdRQd6FK6yMQz5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the expenditures of Welfare in the Current Year}[0.773, 0.062, 0.944, 0.426] lesser than {Healthcare expenditures in the Previous Year} [0.346, 0.469, 0.517, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "ePNiWL0WaCLKHHgm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre expenditures in the {current year for Transportation}[0.559, 0.062, 0.730, 0.362] higher than expenditures in the {previous year for Education}[0.132, 0.405, 0.303, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "VH8pJHI385yBpu37",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area defined by the coordinates [0.132, 0.062, 0.303, 0.713]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds two vertical bars. The first bar, found at [0.132, 0.062, 0.303, 0.405], represents expenditures in Education for the current year while the second bar at [0.132, 0.405, 0.303, 0.713] denotes the same for the previous year. The two bars represent a significant reduction in Education allocation."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "CMSXCy6WsIWvbkD1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is occurring within the coordinates [0.559, 0.062, 0.730, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This selected area includes two bars indicating expenditure on Transportation in the current year [0.559, 0.062, 0.730, 0.362] and the previous year [0.559, 0.362, 0.730, 0.692]. The second bar is longer, indicating a higher expenditure on Transportation in the previous year."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "rrI2XZylD17P97NI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region in [0.773, 0.062, 0.944, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region holds two bars which denote the expenditure on Welfare in the current year [0.773, 0.062, 0.944, 0.426] and the previous year [0.773, 0.426, 0.944, 0.820]. The second bar is longer which means that the expenditures in the previous year for Welfare was higher."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "UTN2NOX1NNJjdLss",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what's in the region [0.098, 0.815, 0.240, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two labels within a legend. {The Current Year label}[0.142, 0.824, 0.228, 0.847], represented as the label colour blue and at [0.104, 0.826, 0.131, 0.842], and the {Previous Year label}[0.142, 0.859, 0.235, 0.882], represented as the label colour orange and at [0.104, 0.861, 0.131, 0.877]."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "NMJf0mIGFWaFMInk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the x-axis labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Learning Tech: [0.144, 0.919, 0.290, 0.942], Gamification: [0.387, 0.919, 0.475, 0.942], E-Learning: [0.608, 0.919, 0.682, 0.942], Virtual Training: [0.806, 0.919, 0.911, 0.942]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "SdK8ja66dkV0NGHs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the first bar in the legend at [0.098, 0.815, 0.240, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first bar color is blue, as indicated by its color label [0.104, 0.826, 0.131, 0.842]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "kYQfBGaOloS7QPKE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.559, 0.062, 0.730, 0.443] represent in terms of its sector and year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the sector \"E-Learning\" for the \"Current Year\"."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "No5hbBdAeBJZSELR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Current Year's spending in Mobile Learning Tech}[0.132, 0.062, 0.303, 0.313] higher than the {Previous Year's spending in the same sector}[0.132, 0.313, 0.303, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "fcPrUKyIZRLqTtgv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has a higher spending in the current year, {Mobile Learning Tech}[0.132, 0.062, 0.303, 0.313] or {Gamification}[0.346, 0.062, 0.517, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Learning Tech."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "sB7IrHQd5PndeYHD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Current Year's spending in E-Learning}[0.559, 0.062, 0.730, 0.443] more than the {Previous Year's spending in the same sector}[0.559, 0.443, 0.730, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "06LjrNMbH5OnJl8H",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars in the area [0.132, 0.062, 0.303, 0.646]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two bars. The first bar in blue, located at [0.132, 0.062, 0.303, 0.313], represents the \"Current Year's\" spending in the \"Mobile Learning Tech\" sector. The second bar in orange, located at [0.132, 0.313, 0.303, 0.646], displays the \"Previous Year's\" spending in the same sector."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "XuMVRvOuIw4slhcf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.034, 0.980, 0.081, 1.003]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label \"-1600\"."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "SCHTnlLpFfO7yfOy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.098, 0.815, 0.240, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a legend with two entries. The first color-text pair is a blue handle at [0.104, 0.826, 0.131, 0.842] with the text \"Current Year\" at [0.142, 0.824, 0.228, 0.847], indicating that the blue bars represent current year's spending. The second color-text pair has an orange handle at [0.104, 0.861, 0.131, 0.877] with the text \"Previous Year\" at [0.142, 0.859, 0.235, 0.882], indicating that the orange bars represent the previous year's spending."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "169NQ5shbqxbzz5p",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.044, 0.687, 0.079, 0.710] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label \"4000\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "c7acHY90Egsil17r",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at [0.082, 0.02, 0.910, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate range corresponds to the subtitle \"Fitness and Sports Health Trends\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "0PhcBlgMBIqSVVBG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object marked by [0.937, 0.118, 0.972, 0.141] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is for the text \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "qPpfMoITOxlz8CfE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at location [0.616, 0.919, 0.672, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Running\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "yDEI764vBs0Atzro",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2020 bar for Yoga}[0.129, 0.231, 0.301, 0.593] taller than the {2019 bar for Yoga}[0.129, 0.593, 0.301, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "MVNSX0eT7SOSVAMH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2019 bar for Biking}[0.773, 0.670, 0.944, 0.903] to the left or right of the {2020 bar for Biking}[0.773, 0.412, 0.944, 0.670]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They share the same horizontal position, so neither left nor right."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "Q8hYMQdnkI4Qp94O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of {2019 bar for Running}[0.558, 0.645, 0.730, 0.903] higher than {2019 bar for Weightlifting}[0.344, 0.515, 0.515, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "q225Nuqf1SsZckHV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the {2019 bar for Yoga}[0.129, 0.593, 0.301, 0.903] or the {2020 bar for Weightlifting}[0.344, 0.102, 0.515, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2020 bar for Weightlifting is taller."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "J2Asd3PyY2dlwpzA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\ncan you provide information on the section of the chart [0.893, 0.074, 0.978, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is where the legend of the chart is. It includes the label \"2019\" with the associated color at [0.898, 0.085, 0.926, 0.101] and label \"2020\" with its color at [0.898, 0.120, 0.926, 0.136]. The label \"2019\" refers to the color #87ceeb, and \"2020\" refers to the color #f4a460."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "136UCRVqA9Zh9e5N",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area [0.387, 0.906, 0.600, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range are the x-axis labels for \"Weightlifting\" at [0.385, 0.919, 0.473, 0.942] and \"Running\" at [0.616, 0.919, 0.672, 0.942]."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "hX1pcNsqsAiu9bl5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.034, 0.061, 0.079, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region for the y-axis scale with tick labels from \"0\" at [0.070, 0.894, 0.079, 0.917] up to \"18000\" at [0.035, -0.036, 0.079, -0.013]."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "AWGbqRIA6GwiJJsU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.126, 0.072, 0.301, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two bars representing 2019 & 2020 participation for the \"Yoga\" activity. For \"2019\", the bar coordinates are [0.129, 0.593, 0.301, 0.903] and for \"2020\", the bar coordinates are [0.129, 0.231, 0.301, 0.593]."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "igdMGTB5J2RBUgjm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object with coordinates [0.055, 0.613, 0.064, 0.630] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label '0' on the Y-axis."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "WSO6slZItfbWUbwh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 3rd tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.454, 0.927]"
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "u5jnsALRJPqksPmF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick at [0.606, 0.927] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category 'Worker Training Programs'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "VPMVs779t8Etv1El",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat policy is associated with the bar at [0.868, 0.144, 0.944, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Debt Relief."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "hjk4Lv20ctmOCi1i",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Heavy Taxation'[0.115, 0.323, 0.190, 0.620] higher or lower than the bar representing 'Reduced Tariffs'[0.266, 0.234, 0.341, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "wR9opU2VlbSoK4o9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRelative to the bar for 'Heavy Taxation'[0.115, 0.323, 0.190, 0.620], where is the bar for 'Debt Relief'[0.868, 0.144, 0.944, 0.620] placed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the right."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "IkDmOGh9Bzyj4uK9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong policies 'Unemployment Benefits'[0.718, 0.085, 0.793, 0.620] and 'Subsidies for SMEs'[0.416, 0.174, 0.492, 0.620], which one has higher effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unemployment Benefits."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "D71L87bWVDhZMemT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Reduced Tariffs' bar[0.266, 0.234, 0.341, 0.620] and 'Debt Relief' bar[0.868, 0.144, 0.944, 0.620], which one extends above the 0 level on the y axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Debt Relief."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "t0DmXQ6QqrG8DRyG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.115, 0.323, 0.190, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the negative pink bar denoting the policy effectiveness for 'Heavy Taxation' and the coordinates for the effectiveness level are [0.144, 0.340, 0.161, 0.358] which is '50'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "48gOSGYOT2UOfES9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents at [0.046, 0.078, 0.064, 0.096]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label '90' on the Y-axis, indicating an effectiveness level of 90%."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "eLargdr6KXtxhKlq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area [0.115, 0.620, 0.190, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you can find the positive pink bar that corresponds to the policy 'Heavy Taxation', and its effectiveness level is marked at coordinates [0.596, 0.311, 0.613, 0.328], which is '-20'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "FfUqaClItU77LY93",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained in the region [0.515, 0.939, 0.694, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label 'Worker Training Programs' on the X-axis."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "NJkm7kn7gmbDLLn9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.053, 0.062, 0.985, 0.903] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the primary plot area of the bar chart which contains all the individual bars representing data."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "iILTTwK5iAcnHlBy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first color handle in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.427, 0.085, 0.455, 0.101]"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "4VZXAD9iGFK5Oabk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.489, 0.952, 0.550, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quarters."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "sZxtEJGMlEMdYCRu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is represented at [0.096, 0.195, 0.126, 0.903] and [0.126, 0.102, 0.157, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Career Development and Workplace Trends are represented respectively."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "N6jBR7QlLec46w2w",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Career Development bar}<{Career Development bar at Q1}[0.096, 0.195, 0.126, 0.903]> taller than {the Workplace Trends bar}<{Workplace Trends bar at Q1}[0.126, 0.102, 0.157, 0.903]> in Q1?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "c1QYaiSLJcCGkpKT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Career Development and Workplace Trends bars at Q2}[0.183, 0.237, 0.213, 0.903] and [0.213, 0.187, 0.244, 0.903], which trend saw a greater increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Workplace Trends."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "xR5t5whXKEsRje9j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Career Development}<r>[0.466, 0.083, 0.611, 0.106]</r> trend typically higher than the {Workplace Trends}<r>[0.466, 0.118, 0.589, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "mEfef1pfGGnrOLrp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color for {Career Development}<r>[0.466, 0.083, 0.611, 0.106]</r> in the legend the same as the color for {Career Development bar at Q1}<r>[0.096, 0.195, 0.126, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "QiBiKKMEPx016gFR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.422, 0.074, 0.616, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend containing two entries. The first entry consists of a color handle with coordinates [0.427, 0.085, 0.455, 0.101] of color '#5878a3' representing the label 'Career Development' at coordinates [0.466, 0.083, 0.611, 0.106]. The second entry consists of a color handle with coordinates [0.427, 0.120, 0.455, 0.136] of color '#e59344' representing the label 'Workplace Trends' at coordinates [0.466, 0.118, 0.589, 0.141]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "rakEc2bBsVpETQMI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.035, 0.388, 0.043, 0.411]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a tick label on the y-axis, marking the value '6'."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "z88AivEFNxEnFyPj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.183, 0.00, 0.250, 1.0]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bar elements. The first bar corresponds to the value of 'Career Development' at Q2, whose coordinates are [0.183, 0.237, 0.213, 0.903]. The second bar corresponds to the value of 'Workplace Trends' at Q2, whose coordinates are [0.213, 0.187, 0.244, 0.903]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "Wm3ZznxRTn5Ls1jB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.277, 0.025, 0.761, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's subtitle which says \"Career and Workplace Development Trends over 2.5 Years\"."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "eu4EBMw9kINPvb91",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology name does the first text on x-axis represent according to its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The technology name}<r>[0.125, 0.903, 0.219, 0.931]</r> represents \"Solar Panels\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "Dm3AeT8T0sLB749c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rating for {the Development Rate}<r>[0.821, 0.183, 0.966, 0.211]</r> of {the \"Electric Vehicles\"}<r>[0.742, 0.113, 0.771, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "4.2."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "7AvVnlvx7ft3kKOF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the graph's y-axis represent according to the label's coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {y-axis}<r>[0.017, 0.427, 0.032, 0.531]</r> represents \"Ratings\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "ucDUCFX7QUGiwLfd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Stability bar}<r>[0.314, 0.187, 0.343, 0.883]</r> of Wind Turbines higher than {the Stability bar}<r>[0.457, 0.333, 0.486, 0.883]</r> of Tidal Power?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "xDvQiKMx7odwZE7G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Market Acceptance bar}<r>[0.628, 0.370, 0.657, 0.883]</r> for Geothermal Plants and {Market Acceptance bar}<r>[0.771, 0.242, 0.799, 0.883]</r> for Electric Vehicles, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electric Vehicles."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "ySsS8qB9yIWkdkm8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Efficiency bars}[[0.115, 0.187, 0.144, 0.883],[0.258, 0.333, 0.286, 0.883],[0.400, 0.517, 0.429, 0.883],[0.543, 0.242, 0.571, 0.883],[0.685, 0.150, 0.714, 0.883],[0.828, 0.425, 0.856, 0.883]] generally higher than {the Market Acceptance bars}[[0.201, 0.425, 0.229, 0.883],[0.343, 0.333, 0.372, 0.883],[0.486, 0.608, 0.514, 0.883],[0.628, 0.370, 0.657, 0.883],[0.771, 0.242, 0.799, 0.883],[0.913, 0.480, 0.942, 0.883]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "A0DooErxmHhWtzaE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects and information exist in the area [0.821, 0.100, 0.896, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text of the label \"Efficiency\" for the legend is located in this region. It refers to the red bar in the chart."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "QLDtkbmhmDYcA5qe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the content of the area [0.300, 0.100, 0.600, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains multiple bars that represent the Efficiency, Stability, Development Rate, and Market Acceptance of different technologies including Wind Turbines, Tidal Power, and Geothermal Plants."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "4iJr0H1go5o4DsY9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area with coordinates [0.261, 0.903, 0.369, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the technology name \"Wind Turbines\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "7XFxmcW3kfUVDGdh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the project represented by the coordinates [0.251, 0.903, 0.371, 0.931] on the x-axis labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Expansion."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "tEL6YclWEFuldRqg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at these coordinates [0.864, 0.183, 0.877, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "IP."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "rga6SSlk8mGhFixJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the vertical label located at these coordinates [0.017, 0.427, 0.032, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratings."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "vaTtxU8kKBpmarJy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend describe at [0.821, 0.144, 0.851, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend at these coordinates corresponds to the color #e59344, which is a shade of orange."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "iLjobeG4MB186YRP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Life bar of the Rapid Decline project}[0.521, 0.729, 0.605, 0.883] taller or shorter than {the Infrastructure bar of the Slow Expansion project}[0.185, 0.421, 0.269, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "yZhWjc6UuEA4tYEb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Commercial bar of the Rapid Decline project}[0.857, 0.575, 0.941, 0.883] to the left or right of {the Ecosystem bar of the Slow Expansion project}[0.353, 0.113, 0.437, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "qcpc4mUfTybg6LiW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Life bar of the Slow Expansion project}[0.101, 0.267, 0.185, 0.883] lie to the left or right of {the Infrastructure bar}[0.185, 0.421, 0.269, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "bKAN0Demu1Ady8ct",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Commercial bar of the Rapid Decline project}[0.857, 0.575, 0.941, 0.883] darker or lighter than {the Ecosystem bar of the Rapid Decline project}[0.773, 0.729, 0.857, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Darker."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "0uAX7OSDgtermqHD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region from [0.814, 0.089, 0.976, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, with each entry represented by a color and corresponding label, such as {Life}[0.864, 0.100, 0.892, 0.128] representing #5878a3 and {Commercial}[0.864, 0.267, 0.956, 0.295] representing #bb3f3f."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "VfQoeEwkwlmUCsYc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area with coordinates [0.101, 0.267, 0.941, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars of both the Slow Expansion and Rapid Decline projects, representing various categories such as Life, Infrastructure, IP, Ecosystem, and Commercial."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "gLEDXGIgBT3EzLGz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area [0.038, -0.052, 0.048, -0.024]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label for the value \"6\", which is located outside of the grid of the bar chart."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "LAs7Nt1SUAdyiJ9u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at position [0.386, 0.030, 0.656, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, \"Innovation Project Evaluation\", is in this area."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "UNtDl5mWhMw5sCjr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.017, 0.439, 0.032, 0.519] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, which is \"Y-Axis\"."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "gEvdfC1r50zblk5p",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the text inside the second legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.779, 0.142, 0.913, 0.170]"
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "TTrdnxM55mZyjZZH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis label} at [0.488, 0.942, 0.574, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is labeled as \"Companies\"."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "j9wh6g1Z7KLwBEJf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat parameter or category is inscribed at [0.779, 0.225, 0.958, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is labeled {Employee Performance}<r>[0.779, 0.225, 0.958, 0.253]</r>."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "31UZwXGoAhOkeoex",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar}<r>[0.120, 0.743, 0.148, 0.802]</r> depict a higher Sales Growth (%) than the {bar}<r>[0.403, 0.802, 0.432, 0.824]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "vHid5Szswp5UJOQo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the {bars of group \"Investment in R&D ($ M)\"}[[0.176, 0.111, 0.205, 0.802],[0.318, 0.180, 0.347, 0.802],[0.460, 0.385, 0.488, 0.802],[0.602, 0.251, 0.630, 0.802],[0.744, 0.212, 0.772, 0.802],[0.885, 0.490, 0.914, 0.802]] have a higher total value than the {bars of group \"Market Share (%)\"}[[0.148, 0.634, 0.176, 0.802],[0.290, 0.670, 0.318, 0.802],[0.432, 0.701, 0.460, 0.802],[0.573, 0.675, 0.602, 0.802],[0.715, 0.679, 0.744, 0.802],[0.857, 0.721, 0.885, 0.802]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "lNYwo65bNEJWTfAU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Quick Transfer}'s {Investment in R&D bar}[0.318, 0.180, 0.347, 0.802] have a greater value than {Direct Deliver's Investment in R&D bar}[0.602, 0.251, 0.630, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "zWAs5oArayEP6l3f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher,{Sales Growth(%) for Momentum Movers}<r>[0.687, 0.752, 0.715, 0.802]</r> or {Investment in R&D of Solid Transit}<r>[0.460, 0.385, 0.488, 0.802]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Investment in R&D of Solid Transit}<r>[0.460, 0.385, 0.488, 0.802]</r> is higher."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "5zn5g1HRzH2ut0PP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.730, 0.089, 0.976, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend area with four pairs of color-text labels denoting parameters. The first pair {Sales Growth (%)}<r>[0.779, 0.100, 0.912, 0.128]</r> is colored blue, the second pair {Market Share (%)}<r>[0.779, 0.142, 0.913, 0.170]</r> is orange, the third, {Investment in R&D ($ M)}<r>[0.779, 0.183, 0.969, 0.211]</r> is red, and the fourth, {Employee Performance}<r>[0.779, 0.225, 0.958, 0.253]</r> is mint green."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "TsLpMmnaq78VWLE6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.079, 0.075, 0.983, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area, containing six groups of four bars each. Each group represents a company, and each bar within a group corresponds to a different indicator or parameter - {Sales Growth (%)}, {Market Share (%)}, {Investment in R&D ($ M)}, and {Employee Performance}."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "PdWwpgck9e2X941M",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bars are located within [0.261, 0.718, 0.318, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area. The {Sales Growth (%) bar}<r>[0.261, 0.718, 0.290, 0.802]</r> which is blue, and the {Market Share (%) bar}<r>[0.290, 0.670, 0.318, 0.802]</r> which is colored orange."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "IkJh7ia0RUDk7scr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can you find within the area [0.263, 0.903, 0.374, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is {a label for the company \"Quick Transfer\"}<r>[0.263, 0.903, 0.374, 0.931]</r>."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "9RAKxDgUEjLTWqkN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label represented by these coordinates [0.267, 0.919, 0.338, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {Chemistry}[0.267, 0.919, 0.338, 0.942], a research field on the x-axis."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "xmJZrHs6XVdTQRIv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at coordinates [0.155, 0.195, 0.207, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at {these coordinates}[0.155, 0.195, 0.207, 0.903] is colored by '#d62728', indicating it represents 2021 data."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "0slxc29UVWlsYPm4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the contents at these coordinates: [0.937, 0.083, 0.972, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The contents at these coordinates {coordinates}[0.937, 0.083, 0.972, 0.106] are the legend label \"2020\"."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "C1cXGXXvKicIAD6n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the bars of {'Mathematics'}[{2020 Math bar}[0.546, 0.102, 0.597, 0.903], {2021 Math bar}[0.597, 0.119, 0.649, 0.903]] indicate an increase or decrease in acceptance rates from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "qb8LGKX7CY7IlvE4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Psychology bar for 2021}[0.744, 0.153, 0.796, 0.903] higher or lower than {the Psychology bar for 2020}[0.693, 0.161, 0.744, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "QGeTE1KG6UI1A4KM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the research fields} from 2021[[0.155, 0.195, 0.207, 0.903],[0.303, 0.212, 0.354, 0.903],[0.450, 0.262, 0.501, 0.903],[0.597, 0.119, 0.649, 0.903],[0.744, 0.153, 0.796, 0.903],[0.892, 0.237, 0.943, 0.903]] placed to the right or left of those from 2020[[0.104, 0.144, 0.155, 0.903],[0.251, 0.203, 0.303, 0.903],[0.398, 0.237, 0.450, 0.903],[0.546, 0.102, 0.597, 0.903],[0.693, 0.161, 0.744, 0.903],[0.840, 0.229, 0.892, 0.903]] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "vXtOElWU5QbxkGxC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.893, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend, with two entries showing color and year correlates. The first entry is colored blue, labeled '2020'[0.937, 0.083, 0.972, 0.106]. The second entry is colored red, labeled '2021'[0.937, 0.118, 0.972, 0.141]."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "4nGydjaiozqYUMiz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area [0.043, 0.894, 0.052, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '0' is present in the coordinates {0}[0.043, 0.894, 0.052, 0.917]."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "gpuDNmtVQHABuFh5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the regional space from [0.298, 0.212, 0.354, 0.903] to [0.546, 0.102, 0.597, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two bars of Chemistry for both years: {2020 Chemistry bar}[0.251, 0.203, 0.303, 0.903] and {2021 Chemistry bar}[0.303, 0.212, 0.354, 0.903] as well as two bars for Biology {2020 Biology bar}[0.398, 0.237, 0.450, 0.903] and {2021 Biology bar}[0.450, 0.262, 0.501, 0.903] and two bars for Mathematics {2020 Mathematics bar}[0.546, 0.102, 0.597, 0.903] and {2021 Mathematics bar}[0.597, 0.119, 0.649, 0.903]."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "McdPvqZAyH6myrzv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the large numerical y-axis scale at the coordinate point [0.038, 0.370, 0.058, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a scale point at \"15\"."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "wVvblBSGZfxEU6uF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the second legend marker at the coordinate [0.828, 0.144, 0.859, 0.164] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Impact' in the risk assessment."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "VcjLbK5IhN1UHs0l",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the x-axis labeled object at coordinates [0.342, 0.903, 0.447, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's labeled as \"Financial Risk\"."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "BIpE7z1PY6FgT5Jv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the bar at [0.778, 0.632, 0.803, 0.883], what does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar illustrates the 'Risk Score' for the 'Natural Disaster' risk factor."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "VMtHonAsH2YU7k23",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Risk Score for Financial Risk}[0.382, 0.482, 0.431, 0.883] higher or lower than {the Impact for Legal Issues}[0.506, 0.716, 0.531, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "xUSB5LfNbTfEOfxD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing the \"Impact\" value generally taller than the ones representing the \"Risk Score\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "NsXnCj4NuIggR5Mr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the Risk Score for Market Fluctuation}[0.654, 0.567, 0.679, 0.883] and {the Impact for Cybersecurity Breach}[0.877, 0.726, 0.902, 0.883], which one is lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Risk Score for Market Fluctuation."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "Xr9pLPPNcjjIWPCS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the relation between {the Risk Score for Natural Disaster}[0.778, 0.632, 0.803, 0.883] and {the Probability for Natural Disaster}[0.729, 0.833, 0.753, 0.883], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Probability for Natural Disaster."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "aE3zOS9MZ3x7Mf4z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease discuss all the elements within the region [0.765, 0.350, 0.925, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains four bars: the Probability, Impact, and Risk Score for 'Natural Disaster' and 'Cybersecurity Breach'. Specific coordinates: \n1. {Probability for Natural Disaster}[0.729, 0.833, 0.753, 0.883], \n2. {Impact for Natural Disaster}[0.753, 0.716, 0.778, 0.883], \n3. {Risk Score for Natural Disaster}[0.778, 0.632, 0.803, 0.883], \n4. {Probability for Cybersecurity Breach}[0.852, 0.756, 0.877, 0.883], \n5. {Impact for Cybersecurity Breach}[0.877, 0.726, 0.902, 0.883], \n6. {Risk Score for Cybersecurity Breach}[0.902, 0.285, 0.927, 0.883]."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "xXqrbP3FebLpMYAD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found in the space defined by [0.250, 0.705, 0.500, 0.900]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains part of the 'Probability' and the entire 'Impact' bars for 'Human Error' and 'Financial Risk'. Specifically:\n1. {Probability for Human Error}[0.233, 0.743, 0.258, 0.883], \n2. {Impact for Human Error}[0.258, 0.733, 0.283, 0.883], \n3. {Probability for Financial Risk}[0.357, 0.800, 0.382, 0.883], \n4. {Impact for Financial Risk}[0.382, 0.723, 0.407, 0.883]."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "WtAVy5TYK3ENpOSg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from [0.822, 0.089, 0.960, 0.223]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with three entries: 'Probability', 'Impact', and 'Risk Score'. Each entry is composed of a colored marker and a text label. Their specific coordinates are:\n1. {Probability marker}[0.828, 0.102, 0.859, 0.122], {Probability text}[0.872, 0.100, 0.954, 0.128],\n2. {Impact marker}[0.828, 0.144, 0.859, 0.164], {Impact text}[0.872, 0.142, 0.925, 0.170],\n3. {Risk Score marker}[0.828, 0.186, 0.859, 0.205], {Risk Score text}[0.872, 0.183, 0.952, 0.211]."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "ic6ft8uxRr2ye0gW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found in the range [0.110, 0.730, 0.310, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, three unique bars can be observed. They are the {Probability, Impact and Risk Score for Equipment Failure}[0.110, 0.375, 0.184, 0.883] and {Probability and part of the Impact for Human Error}[0.233, 0.734, 0.283, 0.883]."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "5tnbOuVuHBg30Haa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text in the bounding box [0.308, 0.030, 0.735, 0.066] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot title, it's called \"Trends in Fitness and Sports in 2021 and 2022\"."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "3Iuhl0L0gFMGpRzi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the content placed at [0.501, 0.942, 0.541, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label, named \"Years\"."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "nde19AwX7nvZksKv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subject of the label at coordinates [0.017, 0.427, 0.032, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis Label, the label is \"Ratings\"."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "ZcqtSb5RVvhVU2IP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what is denoted by the text at [0.878, 0.142, 0.944, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is part of the legend, and the label is \"Strength\"."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "1WbI736r3mL47SiK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the bar for \"Cardio\" in 2022}<r>[0.101, 0.113, 0.194, 0.883]</r> compare to {the bar for \"Cardio\" in 2021}<r>[0.568, 0.284, 0.661, 0.883]</r> in term of height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "O5JFpNtkXmoaZQ8W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Strength bar for 2021}<r>[0.661, 0.199, 0.755, 0.883]</r> and {the Mindfulness bar for 2022}<r>[0.288, 0.456, 0.381, 0.883]</r>, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Strength bar for 2021."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "fCehySpG8Glw1zjh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Flexibility in 2022}<r>[0.381, 0.370, 0.475, 0.883]</r> have a higher or lower rating than {the bar for Flexibility in 2021}<r>[0.848, 0.284, 0.941, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "in9rTPE51sTvY1nY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is rated higher, {the Strength for 2022}<r>[0.194, 0.113, 0.288, 0.883]</r> or {the Mindfulness for 2022}<r>[0.288, 0.456, 0.381, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Strength for 2022."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "FYAsR6MsAGoj2QTn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the coordinates [0.828, 0.089, 0.976, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a legend containing labels for four categories represented in the bar chart. The categories are: {Cardio}<r>[0.878, 0.100, 0.928, 0.128]</r> colored in yellow, {Strength}<r>[0.878, 0.142, 0.944, 0.170]</r> colored in blue, {Mindfulness}<r>[0.878, 0.183, 0.969, 0.211]</r> colored in pink and {Flexibility}<r>[0.878, 0.225, 0.952, 0.253]</r> colored in green."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "l4vw21AthAAtlTT9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.101, 0.113, 0.475, 0.883] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of 4 bar graphs representing different fitness categories for the year 2022. The specific categories are {Cardio}<r>[0.101, 0.113, 0.194, 0.883]</r>, {Strength}<r>[0.194, 0.113, 0.288, 0.883]</r>, {Mindfulness}<r>[0.288, 0.456, 0.381, 0.883]</r>, and {Flexibility}<r>[0.381, 0.370, 0.475, 0.883]</r>."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "WWVUYogpanMeEFNW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.017, 0.427, 0.032, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label of the chart which is labeled as {Ratings}<r>[0.017, 0.427, 0.032, 0.531]</r>."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "Geo3B3OesEReuY1x",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you say about the area spanning from [0.101, 0.113, 0.941, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the bars representing the fitness category ratings for both the years, 2022 and 2021, for each of the categories, namely, Cardio, Strength, Mindfulness, and Flexibility."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "94PLlgwJhpHJnQqY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Ratings}<r>[0.058, 0.453, 0.074, 0.557]</r>"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "PNoVD4DoMc7M51UH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar for Public_Figure6's Neutral Stance?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.816, 0.564, 0.840, 0.890]</r>"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "DDmQ8PolMY0UkuDh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Public Figure} at the x-axis located at [0.761, 0.909, 0.871, 0.938]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public_Figure6"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "Beh1tj8ebj0QaBTQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.727, 0.145, 0.806, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Liberal Stance}<r>[0.727, 0.145, 0.837, 0.173]</r> and {Conservative Stance}<r>[0.727, 0.187, 0.886, 0.215]</r> and {Neutral Stance}<r>[0.727, 0.229, 0.842, 0.257]</r> and {No Stance}<r>[0.727, 0.271, 0.806, 0.299]</r>"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "7klzm4SJHuY7VvPZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {rating of liberal stance for Public_Figure1}<r>[0.160, 0.157, 0.185, 0.890]</r> higher than {rating of conservative stance for Public_Figure1}<r>[0.185, 0.360, 0.209, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "eKKZvLmcWcWXlmiB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Public_Figure5's rating of no stance}<r>[0.719, 0.360, 0.743, 0.890]</r> higher than {Public_Figure6's rating of no stance}<r>[0.840, 0.401, 0.865, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "ENbdIzDRKKYnjjw2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Public_Figure3's rating of Neutral Stance}<r>[0.452, 0.564, 0.476, 0.890]</r> the same as {Public_Figure3's rating of No Stance}<r>[0.476, 0.238, 0.500, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "x4BTO2EsYLjotldo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Public_Figure4's rating of Liberal Stance}<r>[0.525, 0.442, 0.549, 0.890]</r> and {Public_Figure5's rating of Liberal Stance}<r>[0.646, 0.238, 0.670, 0.890]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public_Figure5."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "iRXCYz3WMKpCxJ32",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.677, 0.134, 0.892, 0.310]."
+            },
+            {
+                "from": "gpt",
+                "value": "The zone contains the legend of the chart, with four distinct stance labels: The \"Liberal Stance\" is represented by the blue color with code '#5878a3'<r>[0.683, 0.148, 0.714, 0.167]</r>; The \"Conservative Stance\" is depicted in an orange hue, coded with '#e59344'<r>[0.683, 0.189, 0.714, 0.209]</r>; The \"Neutral Stance\" is represented by the color '#d1605e'<r>[0.683, 0.231, 0.714, 0.251]</r>; The \"No Stance\" is reflected by the color '#85b6b2'<r>[0.683, 0.273, 0.714, 0.293]</r>."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "GFGR3Hf4CHJOIHPj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.154, 0.909, 0.871, 0.938]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.154, 0.909, 0.871, 0.938] contains all public figure labels on the x-axis, they are;\n1. \"Public_Figure1\": [0.154, 0.909, 0.264, 0.938],\n2. \"Public_Figure2\": [0.275, 0.909, 0.385, 0.938],\n3. \"Public_Figure3\": [0.397, 0.909, 0.507, 0.938],\n4. \"Public_Figure4\": [0.518, 0.909, 0.628, 0.938],\n5. \"Public_Figure5\": [0.640, 0.909, 0.750, 0.938],\n6. \"Public_Figure6\": [0.761, 0.909, 0.871, 0.938]."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "p7PCKborgYjfxdpw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.403, 0.320, 0.476, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there are 3 bars representing the ratings of Public_Figure3's political stances:\n1. The bar for Liberal Stance, with coordinates [0.403, 0.320, 0.427, 0.890].\n2. The bar for Conservative Stance, with coordinates [0.427, 0.401, 0.452, 0.890].\n3. The bar for Neutral Stance, with coordinates [0.452, 0.564, 0.476, 0.890]."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "UyRsxDDx17hFdCJT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.154, 0.157, 0.379, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A set of color-coded bars corresponding to ratings for Public_Figure1 and Public_Figure2 for each stance is visible in these coordinated. More specifically:\n1. Public_Figure1's ratings for Liberal Stance<r>[0.160, 0.157, 0.185, 0.890]</r>, Conservative Stance<r>[0.185, 0.360, 0.209, 0.890]</r>, Neutral Stance<r>[0.209, 0.523, 0.233, 0.890]</r>, and No Stance<r>[0.233, 0.483, 0.257, 0.890]</r>\n2. Public_Figure2's ratings for Liberal Stance<r>[0.282, 0.401, 0.306, 0.890]</r>, Conservative Stance<r>[0.306, 0.197, 0.330, 0.890]</r>, Neutral Stance<r>[0.330, 0.646, 0.355, 0.890]</r>, and No Stance<r>[0.355, 0.279, 0.379, 0.890]</r>"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "iZGYv2647pvjoRGB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at coordinates [0.500, 0.952, 0.550, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"Periods\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "vvESmTJJvXVISnZj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label at coordinates [0.015, 0.417, 0.029, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Percentage\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "rfFOqhKaQxLiJHG7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle in the coordinates [0.386, 0.025, 0.664, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Key Economic Indicators for 2021\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "qFfQ2Req4waP1Qug",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the indicators shown in coordinates [0.072, 0.745, 0.265, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The indicators are {Unemployment Rate}<r>[0.116, 0.754, 0.259, 0.777]</r>, {GDP Growth Rate}<r>[0.116, 0.789, 0.237, 0.812]</r>, {Inflation Rate}<r>[0.116, 0.824, 0.209, 0.847]</r>, and {Interest Rate}<r>[0.116, 0.859, 0.206, 0.882]</r>."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "L97CSjju06wI6OnT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Unemployment Rate bar in Q4_2021}<r>[0.804, 0.314, 0.839, 0.635]</r> taller than {the GDP Growth Rate bar in Q4_2021}<r>[0.839, 0.100, 0.873, 0.635]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "DdpDsd7ZHkQCrWYJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Inflation Rate bar in Q1_2021}<r>[0.351, 0.444, 0.385, 0.635]</r> shorter than {the Interest Rate bar in Q4_2021}<r>[0.908, 0.616, 0.943, 0.635]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Infayw0sEiWCjlcn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Unemployment Rate bar in YE2020}<r>[0.106, 0.116, 0.141, 0.635]</r> higher than {the Unemployment Rate bar in Q4_2021}<r>[0.804, 0.314, 0.839, 0.635]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "WoFd78xRtyqK94OX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the GDP Growth Rate bar in Q3_2021}<r>[0.664, 0.162, 0.699, 0.635]</r> the same height as {the GDP Growth Rate bar in Q4_2021}<r>[0.839, 0.100, 0.873, 0.635]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Z0ATVk5B6I540a60",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.106, 0.116, 0.141, 0.635]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blue bar representing the Unemployment Rate in YE2020, with a value of 6.8%."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "jiee1E5EGZQ9omdo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.386, 0.025, 0.664, 0.055]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the title of the bar chart, which reads \"Key Economic Indicators for 2021\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "sQ5AbbdFla3dYpjY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.839, 0.100, 0.873, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single orange-colored bar representing the GDP Growth Rate for Q4_2021 with a value of 7.0%."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "YLgXDAtoQaS0dLwh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.281, 0.169, 0.316, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "You can see a bar representing the Unemployment Rate for Q1_2021, which is 6.1% in blue color."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "NlN6FZPoHIOLSqwt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the bars represent that are within the coordinates [0.115, 0.209, 0.207, 0.883] and [0.574, 0.113, 0.666, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These bars represent the Interaction ratings of Facebook and Instagram respectively."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "ORHwwc1HLb5XbImA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what specific object is located at [0.873, 0.183, 0.950, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend entry, marked as \"Popularity\"."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "ysGAX899EiVvphST",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's plotted at the coordinates [0.262, 0.903, 0.335, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label for Facebook."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "WlGEUczxKsaPKEKr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller between {the 'Facebook' Interaction bar}[0.115, 0.209, 0.207, 0.883] and {the 'Instagram' Interaction bar}[0.574, 0.113, 0.666, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Instagram Interaction bar is taller."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "QM4G0oXaEsOXBL7V",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Popularity bar for Instagram}[0.758, 0.209, 0.850, 0.883] higher than {the Trust Bar for Instagram}[0.850, 0.402, 0.942, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "QhsRutBYoeyhPHFP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the interaction bar of Instagram}[0.574, 0.113, 0.666, 0.883] higher than {the connectivity bar of Instagram}[0.666, 0.209, 0.758, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "1Y45ce24tweV9DGL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the area [0.823, 0.089, 0.976, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the legend of the chart. It clearly defines four parameters - Interactions, Connectivity, Popularity, and Trust, which are identified by different colors."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "hNU1iJaabZFV4Lii",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's contained in the area specified by the coordinates [0.115, 0.209, 0.483, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, you can find four bars corresponding to Facebook's ratings on Interactions, Connectivity, Popularity, and Trust respectively."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "SsrGMuTWosvGzxR1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the characteristics of the region [0.574, 0.113, 0.942, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four bars that symbolize Instagram's ratings on Interactions, Connectivity, Popularity and Trust."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "96dI82fsyPQoGbBf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is presented in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "bar chart."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "ZEI8VgrINVpcCTfy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Entrepreneurial skills."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "s7xgSyZ0YSIQgJgq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific text does {the subtitle}<r>[0.453, 0.019, 0.698, 0.041]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "Entrepreneurial Skill Volatility."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "sKPhE6vPRhQxkvoE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis indicator show at {the point}<r>[0.564, 0.964, 0.588, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\u25b2%."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "8G1QA00xgwNh2a85",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering their heights, is {the bar representing \"Marketing\" Skill}[0.576, 0.777, 0.910, 0.887] higher or lower than {the bar representing \"Public Speaking\" Skill}[0.576, 0.639, 0.834, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "ICETB2bt3ftG23VU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Risk Taking\" Skill}[0.410, 0.501, 0.576, 0.611] exhibit a positive or negative change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "Yx24DMPHczpG3rNR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Networking\"}[0.576, 0.225, 0.706, 0.335] and {\"Product Development\"}[0.576, 0.363, 0.612, 0.473], which entrepreneurial skill has a higher variance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Networking."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "Tq0mA9YzfCrgHxlD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Strategic Planning\" Skill}[0.502, 0.087, 0.576, 0.197] extending to the left or right of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "bWY5BQ3yUrO7xRY4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see within the coordinate [0.175, 0.047, 0.976, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar chart plotting the volatility of different entrepreneurial skills. The skills are represented on the y-axis and include \"Marketing\", \"Public Speaking\", \"Risk Taking\", \"Product Development\", \"Networking\", and \"Strategic Planning\". Each skill has a corresponding bar on the chart that illustrates its volatility in percentage. Positive and negative percentages are represented by bars extending to the right and left of the y-axis, respectively."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "MMtY2tAkEaJ0g3SE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.576, 0.777, 0.910, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has a bar representing the volatility of the \"Marketing\" skill. It shows a positive percentage of 16.7%, indicated by the text located at [0.920, 0.823, 0.964, 0.841], which means that the skill demonstrated an increase."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "bCVHFaRo1KndE2PA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the area [0.410, 0.501, 0.576, 0.611]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single bar that represents the \"Risk Taking\" skill. The bar's negative direction, and the negative value of -8.3% written at [0.360, 0.547, 0.400, 0.565], indicates a decrease in the skill."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "6qPsNuUqUx2kVTBF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be seen in the region [0.056, 0.687, 0.166, 0.705]."
+            },
+            {
+                "from": "gpt",
+                "value": "A label indicating the entrepreneurial skill of \"Public Speaking\" is located in this region."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "oAAoQ5nAfKJFJsXb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.452, 0.025, 0.619, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which reads \"Social Media Impact.\""
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "jeRIgBW8H4G2uf7a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label at [0.492, 0.952, 0.579, 0.975] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label represents \"Impact(%) \u25b2\"."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "eF1GgjzZ7An4ldLN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by the bar at [0.536, 0.737, 0.756, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is \"12.5%\" according to the text at [0.774, 0.789, 0.818, 0.813]."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "Vbv7ETwXWBboc63q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.187, 0.578, 0.536, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar for Twitter, which has a value of \"-19.8%\" as indicated by the text at [0.120, 0.630, 0.169, 0.653]."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "TBosWoIW0kHlpFhH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Facebook bar}[0.536, 0.737, 0.756, 0.865] longer or shorter than {the Instagram bar}[0.536, 0.419, 0.664, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "ST3AFyq2oANDUTOe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater in impact, {the Snapchat bar}[0.494, 0.260, 0.536, 0.387] or {the Twitter bar}[0.187, 0.578, 0.536, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Twitter bar."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "FXR0Pde8LhnIMpg3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {TikTok bar}[0.536, 0.100, 0.858, 0.228] showing a positive or negative impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Positive."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "TfbM7p0RlReFZxQI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is to the left, {the -20 x-axis tick}[0.169, 0.919, 0.198, 0.942] or {the 15 x-axis tick}[0.791, 0.919, 0.809, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The -20 x-axis tick."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "3JbLV0zZKM27Byye",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the area from [0.095, 0.062, 0.976, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main body of the bar chart illustrating the impact of various social media platforms. It contains labels for percentage impact along the x-axis, the names of the platforms along the y-axis, and horizontally aligned bars to represent the impact. The subsection contains five bars, each of which corresponds to a social media platform."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "ZXC18d7aLWQHcMF6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.020, 0.155, 0.086, 0.178]."
+            },
+            {
+                "from": "gpt",
+                "value": "This little area contains the y-axis label for the TikTok bar."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "iKEICaEiNPMuJgC6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.187, 0.578, 0.536, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an area of the chart containing the Twitter impact bar that shows a negative reading for impact. It is highlighted in red to indicate a negative value."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "JBFiZnU31Swa6z76",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.436, 0.312, 0.476, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text \"-2.4%\", which relates to the Snapchat bar. It indicates the negative impact percentage of Snapchat."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "jLSDZfaIvCAOZrZl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Age Distribution in Different Demographic Groups\" located at [0.328, 0.019, 0.743, 0.041]."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "XPVAS7cCL51sAH8I",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label '%\u25b2' located?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is located at [0.524, 0.964, 0.548, 0.981]."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "pKFOIdnXlYSDvlql",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the bar at coordinates [0.536, 0.777, 0.906, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Youths\"."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "edGZyJxjiW4WaMT5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Adults\" at [0.360, 0.639, 0.536, 0.749] to the left or right of the zero line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "lPr4ZZibdV06ebCB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Seniors\"}[0.536, 0.501, 0.642, 0.611] longer than {the bar representing \"Children\"}[0.536, 0.225, 0.686, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "zDgrrYBiBrQpDwZq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing {the bar representing \"Seniors\"}[0.536, 0.501, 0.642, 0.611] and {the bar representing \"Teenagers\"}[0.272, 0.087, 0.536, 0.197], who has the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seniors."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "eCyWTR2dkLD2yNAX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.536, 0.777, 0.906, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a white bar representing the \"Youths\" category with a value of around 8.4%, the value text located at [0.928, 0.823, 0.963, 0.841] indicates it as '8.4%'."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "OqLoEyBG8tfaUq29",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.536, 0.225, 0.686, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a white bar within the given region that corresponds to \"Children\". The value of it, as given at [0.708, 0.271, 0.743, 0.289], is '3.4%'."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "jmNkj8IkZTtgV1et",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be observed within the region [0.184, 0.363, 0.536, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region there is a white bar corresponding to the \"Veterans\" category. The bar shows a value of approximately -8.0%, as indicated by the text located at [0.122, 0.409, 0.162, 0.427], suggesting a decline in this category."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "DLDnripYxaUgzkGG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text specified at [0.611, 0.170, 0.646, 0.187]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is '0.9%'."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "wLlH5zdC4PulUmpz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the caption that corresponds to the coordinates [0.376, 0.019, 0.733, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The caption is 'Rural and Urban Development Comparison'."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "itOg6Uo4cILas4Ea",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is placed at the coordinates [0.732, 0.503, 0.767, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the text '7.3%' is located."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "FUVRxLWaUZ82876f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y label for the bar at [0.287, 0.253, 0.575, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y label is 'Upward Trajectory'."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "rVYKgipTGwpkQFmb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bar at [0.575, 0.754, 0.825, 0.887] and the bar at [0.451, 0.754, 0.575, 0.887], which one is located on the right side?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar}[0.575, 0.754, 0.825, 0.887] is located on the right side."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "JNbV11l6CzFvwL2a",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar at [0.575, 0.087, 0.592, 0.220] taller than the bar at [0.188, 0.087, 0.575, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "qAYErfPgwUO4LFIK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bar at [0.575, 0.253, 0.921, 0.387] and the bar at [0.287, 0.253, 0.575, 0.387], which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar on the right, located at [0.575, 0.253, 0.921, 0.387], is longer."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "AKTH35TbFvm4mz4r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the plot, is the \"Abrupt Increase\" represented by the bar at [0.575, 0.420, 0.713, 0.554] larger or smaller than the \"Stable Decline\" represented by the bar at [0.575, 0.087, 0.592, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "MY2aN5VwyoPWfU10",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area represented by the coordinates [0.018, 0.647, 0.142, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label \"Sustained Growth\" on the y-axis."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "gHRLfa7Rykcgfh1m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinate [0.575, 0.087, 0.592, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a bar that represents \"Stable Decline\" in the data, with a text annotation of '0.9%'."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "rRqlxLtggpypIS5q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is inside the area marked by [0.130, 0.120, 0.179, 0.137]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text '-20.4%', which is a data label."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "gfAp4tLp35o9zj0O",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region that goes from the [0.441, 0.754, 0.575, 0.887] to the [0.575, 0.754, 0.825, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both of these coordinates contain bars, with the first one marked as a negative change of '-6.5%' and the second one as a positive change of '13.2%'."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "jL8xukI9p5h90AGJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label can be found at the coordinate [0.529, 0.639, 0.581, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at this coordinate is \"Strategic Planning\"."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "pH3GOf0H5tkxFEoa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar}[0.581, 0.501, 0.873, 0.611] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is white."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "JlVR70YzJl1F2R1X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the bar on the y-axis}[0.581, 0.501, 0.873, 0.611] which is situated between 0.00 and 7.40?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 7.4%."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "JopTUe0LIPTUkoyJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Revenue Growth\"}[0.581, 0.087, 0.909, 0.197] higher or lower than {the bar representing \"Operational Excellence\"}[0.581, 0.501, 0.873, 0.611]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "2BOaqJw5IlayjYWJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar indicating a value of \"-0.5%\"}[0.561, 0.363, 0.581, 0.473] to the right or left of {the y-axis}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "VauOLMlDT9Bfx47U",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has the greater value, {Operational Excellence}[0.581, 0.501, 0.873, 0.611] or {Revenue Growth}[0.581, 0.087, 0.909, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue Growth."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "caRUfboTfct9SMpg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.355, 0.225, 0.581, 0.335]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing \"Resource Management\" with a value of \"-5.7%\" positioned to the left of the y-axis. The value text is colored red."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "VRbLEaVL5g8aHSAf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.497, 0.777, 0.581, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "You can find a white bar representing the \"Customer Satisfaction\", which has a value of \"-2.1%\", sitting to the left of the y-axis. The value label is colored red."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "BgS5CiUbgmjVbSZJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss what you see when you look at the box bounded by [0.185, 0.047, 0.976, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire chart with 6 bars representing different organizational performance categories: \"Customer Satisfaction\", \"Strategic Planning\", \"Operational Excellence\", \"People Leadership\", \"Resource Management\", and \"Revenue Growth\". It also includes an x-axis representing the percentage change and a y-axis listing the six categories."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "FPEFWNCNJsVMrjA2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is depicted in the JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a bar chart."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Ex9G2TdOCgJEnYm8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data represent according to the subplot's subtitle {the subplot's subtitle}[0.375, 0.019, 0.703, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle reads \"Market Trends in the Real Estate Sector\", so the data represents changes in various segments of the real estate sector."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Q3BXUkddFd8IAS9g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label {the x-axis label}[0.527, 0.964, 0.551, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"\u25b2%\", which may symbolize the percentage change in various real estate sectors."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "SgPH271hjBGbtayx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Warehouse bar}[0.393, 0.253, 0.845, 0.387] have a positive or a negative change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Positive."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "SGs5f9yI0FT6xO17",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Industrial bar}[0.393, 0.420, 0.589, 0.554] growth percentage smaller than {Warehouse bar}[0.393, 0.253, 0.845, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "O1G9LFF0vUXsS6Uy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Retail bar}[0.393, 0.587, 0.460, 0.720] higher compared to {Residential bar}[0.248, 0.754, 0.393, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "zwfMq4n97ZBnCvVr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the x-axis range from [0.746, 0.927] to [0.976, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two x-axis labels: \"15\" and \"20\", corresponding to the percentage changes represented on the x-axis. There is also part of the Warehouse bar showing a trend of 15.5% growth in this sector."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "RDGNi5NBXAZD7CnY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars and their associated data in the region {region}<r>[0.3, 0.1, 0.6, 0.8]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are present in this area. The first represents {Retail}[0.393, 0.587, 0.460, 0.720] with a growth of 2.3%, and the second represents {Industrial}[0.393, 0.420, 0.589, 0.554] with a growth of 6.7%."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "4LaHP5cUJ3jaQ23A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the y-axis region from [0.102, 0.820] to [0.102, 0.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes y-axis labels and corresponding bars: {Residential}[0.248, 0.754, 0.393, 0.887], {Retail}[0.393, 0.587, 0.460, 0.720], {Industrial}[0.393, 0.420, 0.589, 0.554], {Warehouse}[0.393, 0.253, 0.845, 0.387] and {Office}[0.373,0.087,0.393,0.220]."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "2PE4mpOX4aHaCN2J",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.478, 0.754, 0.573, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A white bar representing the value change of \"Freedom of Speech\"."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "mygXdFRtF1IZlKvd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text} at [0.955, 0.311, 0.985, 0.329] imply?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates a 20% increase."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "zwjjRjI21vl2UPPJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the text} at [0.514, 0.145, 0.554, 0.162]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a 0.5% decrease."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "vf8OlabKxquo5oeI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Environmental Protection' value bar}[0.573, 0.253, 0.945, 0.387] longer than {the 'Family Values' value bar}[0.573, 0.587, 0.820, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "mVqpF7QpddMhc7pK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Freedom of Speech' value bar}[0.478, 0.754, 0.573, 0.887] showing an increase or decrease in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "kqbpRqhlNNyMWMfG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Environmental Protection' value change higher or lower than the 'Family Values' value change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "C8WfOZK2AXodFoMy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in [0.201, 0.047, 0.969, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area describes the main plot with several subcomponents. It includes the 'x' axis label with \"\u25b2% of Perception Change\" at coordinates [0.496, 0.964, 0.673, 0.981] and the 'y' axis labels including 'Freedom of Speech',[0.057, 0.814, 0.191, 0.831], 'Family Values',[0.096, 0.647, 0.191, 0.664], 'Religious Freedom', [0.064, 0.480, 0.191, 0.498], 'Environmental Protection', [0.015, 0.313, 0.191, 0.331], and 'Gender Equality', [0.079, 0.147, 0.191, 0.164]. It also contains 5 bars each representing a difference for each of the cultural values."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "X1X5b3VXaXrjwIqC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.201, 0.654, 0.820, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A white bar, which displays the value change for 'Family Values', along with a text '[0.830, 0.645, 0.874, 0.662]' indicating a 13.3% increase."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "T4p87a9JgQvf2j8V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the region [0.015, 0.313, 0.191, 0.331]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label for 'Environmental Protection'."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "UT8cxgmOjGR5Js8X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis represent in the chart according to the label at [0.540, 0.964, 0.563, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percent change (%\u25b2)."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "zG7ARgsnRJiXKs3z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinates [0.732, 0.645, 0.767, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"7.5%\", the growth of Asia region."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "w5tCbHkvogUqk8cc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at the coordinates [0.127, 0.820, 0.117, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"South America\" on Y-axis."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "n8o9h6uI9Gb5C7uC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"% \u25b2\" of Asia}[0.551, 0.587, 0.711, 0.720] higher or lower than the {the \"% \u25b2\" of Europe}[0.551, 0.253, 0.768, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "K7SRNoMNkwZV7ddR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"% \u25b2\" of South America}[0.452, 0.754, 0.551, 0.887] more or less than 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "xYd8Rrz9a1yzThLz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region between {South America}[0.452, 0.754, 0.551, 0.887] and {Africa}[0.309, 0.420, 0.551, 0.554] has witnessed a more drastic decrease in \"% \u25b2\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Africa."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "c6XISMfNiPBsUzaO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.309, 0.420, 0.551, 0.554]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar segment representing Africa. It's labeled with \"-11.4%\", indicating a decrease, with the exact label at location [0.239, 0.478, 0.288, 0.496]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "S5Qu8xoCjtd2PoRG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe the region covered between the coordinates [0.452, 0.754, 0.551, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of a bar segment that represents South America. The bar label reads \"-4.7%\", indicating a decrease, and is located at [0.391, 0.812, 0.430, 0.829]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "VV3bub8zRpiCIaoz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.551, 0.253, 0.768, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the Europe region with a positive \"% \u25b2\" of \"10.2%\" as shown by the label at [0.789, 0.311, 0.833, 0.329]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "zRpfC8vFUT8qs5q2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart as seen at [0.350, 0.019, 0.756, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title in the mentioned coordinates is \"Educational Resource Distribution and Utilization\"."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "OPrcvOUqFvoJoQPi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.455, 0.777, 0.572, 0.887] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the bar representing percentage of Students."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "ZvqW2ObpJUrtf9xN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is shown at [0.070, 0.825, 0.131, 0.843] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category \"Students\"."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "FLutbyA9pi7WvScM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing the {Expenditure}<r>[0.231, 0.501, 0.455, 0.611]</r> higher or lower than zero?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "nyhqVVp1KUGJ0sOW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Scholarships bar}<r>[0.455, 0.087, 0.929, 0.197]</r> higher or lower than the {Research Output bar}<r>[0.455, 0.363, 0.632, 0.473]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "NAlVTrOoiJBr5ChJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Facilities bar}<r>[0.427, 0.225, 0.455, 0.335]</r> and the {Professors bar}<r>[0.455, 0.639, 0.503, 0.749]</r> represented on the same side of the X-axis (\u0394%)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "QSsScD0afk49q4gy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the rectangle of [0.455, 0.777, 0.572, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A single white bar which represents the category \"Students\" with a 5.6% change. The percentage change label for this bar is located to the right of the bar at coordinates [0.582, 0.823, 0.617, 0.841]."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "t1GozhbdGD3WRcmD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of information can be found in the region defined by [0.350, 0.019, 0.756, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, \"Educational Resource Distribution and Utilization\", is located in this region."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "G7zuyOlC7owrYS9T",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.231, 0.501, 0.455, 0.611]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar which represents the category \"Expenditure\". This bar is drawn on the negative side of the x-axis, implying a decrease. The label showing a change of \"-10.7%\", is to the left of the bar and located at [0.172, 0.547, 0.220, 0.565]."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "V12AvCNkZPSSQv1k",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text within the coordinates [0.270, 0.812, 0.319, 0.829] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the percent change for {'Mass Tourism'}<r>[0.340, 0.754, 0.555, 0.887]</r>, which is -10.2%."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "A07YECuo0xl6sV6a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.779, 0.019, 0.326, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle: \"Tourism and Hospitality Industry Performance Analysis\"."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "DGGt5cxvsdodmI5U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xlabel at [0.463, 0.964, 0.642, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Increase \u25b2%."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "b4pMPUvZzl8B4dGR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Dark Tourism}[0.555, 0.587, 0.677, 0.720] have a higher or lower percent increase than {Eco-Tourism}[0.555, 0.420, 0.820, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "dlqMZdFWIVmsz7tR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Sports Tourism}[0.161, 0.253, 0.555, 0.387] to the left or right of the zero mark on the x-axis (0.555, 0.927)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "mXkC9mB6irYMHlHe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Mass Tourism}[0.340, 0.754, 0.555, 0.887] and {Digital Tourism}[0.555, 0.087, 0.782, 0.220], which category had a bigger performance increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Tourism."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "MYGPkXwQZfpJugq4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the layout in the region [0.025, 0.147, 0.118, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all the labels for each y-tick on the y-axis, which are, in order: \"Mass Tourism\"[0.025, 0.147, 0.118, 0.164], \"Dark Tourism\"[0.027, 0.647, 0.118, 0.664], \"Eco-Tourism\"[0.036, 0.480, 0.118, 0.498], \"Sports Tourism\"[0.016, 0.313, 0.118, 0.331] and \"Digital Tourism\"[0.015, 0.147, 0.118, 0.164]."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "w92k5Gv48QwsjAhi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within [0.340, 0.754, 0.555, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the {'Mass Tourism'}<r>[0.340, 0.754, 0.555, 0.887]</r> bar, which extends from the left to right, indicating a performance decrease of -10.2%."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "RBpT4l306u5x0m6g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.119, 0.939, 0.985, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes labels for each x-tick on the x-axis, which are in increments of 5 from -20 to 20."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "CBe2n7rnZIqqJ54i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.343, 0.812, 0.383, 0.829] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the decrease percentage of users (-7.5%) for {the platform Khan Academy}<r>[0.394, 0.754, 0.553, 0.887]</r>."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "I3eCKZ6BZA36GiGu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of y-axis at coordinates [0.056, 0.647, 0.119, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Coursera\"."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "xoBiyg2CROF94B3P",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located at [0.855, 0.645, 0.899, 0.662] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the increase percentage (13.8%) of users for {the platform Coursera} <r>[0.553, 0.587, 0.845, 0.720]</r>."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "nYf3uAXneBoXEUCm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate of {EdX}<r>[0.209, 0.420, 0.553, 0.554]</r> higher or lower than that of {Udacity}<r>[0.553, 0.253, 0.680, 0.387]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "5gqqHQLqeENCpIDR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Khan Academy}<r>[0.394, 0.754, 0.553, 0.887]</r> have a higher or lower growth rate than {CareerFoundry}<r>[0.533, 0.087, 0.553, 0.220]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "pr800sGN6upkZWF1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Coursea's}<r>[0.553, 0.587, 0.845, 0.720]</r> changes more or less drastic than {CareerFoundry's}<r>[0.533, 0.087, 0.553, 0.220]</r> changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "dgaKYqu28IZVq4x3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.394, 0.754, 0.553, 0.887] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a white bar representing the user growth of Khan Academy from 2019-2020, which decreased by 7.5%, indicated by a red text label marked '-7.5%' located at [0.343, 0.812, 0.383, 0.829]."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "KnalPujtjSBo5LU9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.553, 0.587, 0.845, 0.720]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows a white upward bar representing Coursera, signifying the platform's user growth. The growth rate is 13.8%, indicated by a '13.8%' text label in black, precisely located at [0.855, 0.645, 0.899, 0.662]."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "lP930KrXLYfARthQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the area [0.209, 0.420, 0.553, 0.554] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a horizontal white bar associated with EdX. It represents a decrease in user growth, indicated by a '-16.2%' text label in red at the coordinate [0.150, 0.478, 0.199, 0.496]."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "KBt0efr54yoWuJBP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the main object with coordinates [0.138, 0.754, 0.455, 0.887] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the change in the Dance industry."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "U5gZ1BFfLxQ47eiS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the label corresponding to the Visual Arts bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.015, 0.313, 0.089, 0.331]."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "swqwGQGS7yh2L5RR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}[0.691, 0.311, 0.726, 0.329] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the percentage change in the Visual Arts industry, which is 9.7%."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "W6vINY9xo3hpLksn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {Film Bar}<r>[0.358, 0.087, 0.455, 0.220]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the Film bar is -4.2%."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "GfoWICL8eWLxeYpa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of Theatre} [0.455, 0.587, 0.927, 0.720] higher or lower than {that of Visual Arts} [0.455, 0.253, 0.679, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "nFaHH51FJcaHi85R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Music bar}[0.330, 0.420, 0.455, 0.554] show a decrease or an increase in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "02usAnwyYeaAk9Mb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater change: {the Dance industry}<r>[0.138, 0.754, 0.455, 0.887]</r> or {the Theatre industry}<r>[0.455, 0.587, 0.927, 0.720]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Theatre."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "LymCZWGCbO3yjoCg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.330, 0.420, 0.455, 0.554]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a red bar indicating the percentage change for Music industry, which is -5.4% located at [0.278, 0.478, 0.318, 0.496]."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "0vInY2aNCEj6MOq8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.793, 0.939, 0.811, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"15%\"."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "rF5KW4QYQRD2hSlj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.138, 0.754, 0.455, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the bars representing the changes in Dance, Theatre, Visual Arts, and Film industries, which are located at [0.138, 0.754, 0.455, 0.887], [0.455, 0.587, 0.927, 0.720], [0.455, 0.253, 0.679, 0.387] and [0.358, 0.087, 0.455, 0.220] respectively."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "fL65acYE1kBJt81M",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at [0.504, 0.754, 0.937, 0.887] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing Tesla's market research."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "YZh5YqWRWlmRt6X3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object resides within the coordinates [0.165, 0.645, 0.200, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is a text label indicating a value of -20%."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "gOaAVjS5a8oKeP2O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what occupies [0.036, 0.480, 0.061, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label for the company \"Lyft\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "b2V1RlqRhD2lPwBN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.510, 0.964, 0.533, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label denoting the percentage change (\u25b2%)."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "hXoofyzJ4OrhHQFP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Toyota's bar}<r>[0.359, 0.253, 0.504, 0.387]</r> positive or negative compared to {Lyft's bar}<r>[0.504, 0.420, 0.576, 0.554]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "41yQVECnUC5ZC7L5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher, {Ford's bar}<r>[0.504, 0.087, 0.720, 0.220]</r> or {Lyft's bar}<r>[0.504, 0.420, 0.576, 0.554]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ford's bar."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "eJWhOTHQ1tNM2eO7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Uber's bar}<r>[0.215, 0.587, 0.504, 0.720]</r> indicate a positive or negative value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "75tUceporNbYnp05",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Tesla}<r>[0.504, 0.754, 0.937, 0.887]</r> and {Ford}<r>[0.504, 0.087, 0.720, 0.220]</r>, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tesla."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "HgB5kjR0WZ41nziZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area [0.504, 0.087, 0.720, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar representing Ford's market research data along with its percentage text marker denoting a 15% increase."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "GM19K9bkzgPOYrLV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.215, 0.587, 0.504, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar representing Uber's market research data and a text marker indicating a decrease by 20%."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "lYg9dqTOrmLlSPpY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.070, 0.047, 0.973, 0.927] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the entire bar chart, including the x and y axis labels, the bars representing market research data for each automotive and transportation company \u2014 Tesla, Uber, Lyft, Toyota, and Ford, and percentage indicators."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "SLBd0ma3PMqhDrqs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does region [0.499, 0.939, 0.508, 0.957] relate to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This small region contains the zero value marker on the x-axis, which delineates positive and negative percentage changes in the visualization."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "qlwjjz0OUPi3nwaO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the main object located at [0.523, 0.777, 0.734, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is Health & Sanitation."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "Mgu3gwXkGfKZ6nrc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the value indicated by the text at the coordinates [0.671, 0.547, 0.702, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 10%."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "7zWmmq2A94kraiqH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the department indicated by the coordinates [0.025, 0.411, 0.161, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The department's name is Community Service."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "dBns5sV5uYYkgd4u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the percentage change of {Education department}<r>[0.241, 0.639, 0.523, 0.749]</r> positive or negative?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "ihWAjHGb3ZwILDgM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the change in {Public Safety department}<r>[0.417, 0.087, 0.523, 0.197]</r> higher or lower than 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "jx2yeopgUlYXIisa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Health & Sanitation department}<r>[0.523, 0.777, 0.734, 0.887]</r> and {Environment & Parks department}<r>[0.523, 0.225, 0.946, 0.335]</r>, which one has a higher increase in the local government expenditure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environment & Parks department."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "ckF2lybcehJz0sRu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.523, 0.677, 0.972, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we have a positive bar for the Health & Sanitation department with a growth rate of 15%. The bar's coordinates are [0.523, 0.777, 0.734, 0.887], and the corresponding text label '15%' is located at [0.742, 0.823, 0.772, 0.841]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "tR9G6EwbkQkDdVwb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.241, 0.639, 0.523, 0.749]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the Education department whose bar indicates a negative change of -20%. The coordinates of the bar are [0.241, 0.639, 0.523, 0.749] and the text label '-20%' is at [0.198, 0.685, 0.234, 0.703]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "ZHNqozSTYygU3rTL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s inside the area [0.523, 0.225, 0.946, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the Environment & Parks department, which has experienced a positive change. Specifically, there's a bar indicating a positive change of 30% with coordinates [0.523, 0.225, 0.946, 0.335], and a text label '30%' at [0.953, 0.271, 0.984, 0.289]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "ZXumZkygdTwAyU2Z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.093, 0.495, 0.164, 0.519] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Liberalism}<r>[0.093, 0.495, 0.164, 0.519]</r>."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "aznt1DvCiOWVbtK6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the element at coordinates [0.957, 0.819, 0.983, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Value label}<r>[0.957, 0.819, 0.983, 0.842]</r>."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "TLnUALgxEhxBRHDW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.957, 0.574, 0.983, 0.598]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Text of the value}<r>[0.957, 0.574, 0.983, 0.598]</r>, which is 8.1 for Conservatism."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "Ce9yqNmeLTpFKWI9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Traditionalism\"}<r>[0.174, 0.798, 0.377, 0.863]</r> higher than {the bar for \"Spirituality\"}<r>[0.174, 0.065, 0.248, 0.130]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "MAQtOkxeYRNr0IFZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the green bar}<r>[0.587, 0.554, 0.957, 0.619]</r> and {the blue bar}<r>[0.174, 0.554, 0.248, 0.619]</r> for the same category have the same value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "jazoJM9fQO1MrINf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger, {the value for \"Globalism\" in Metric 1}<r>[0.174, 0.635, 0.507, 0.700]</r> or {the value for \"Globalism\" in Metric 2}<r>[0.587, 0.635, 0.678, 0.700]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Metric 1."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "kYZ2ZZ9fjKnzrReR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space described by coordinates [0.587, 0.716, 0.815, 0.782] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {a green bar}<r>[0.587, 0.716, 0.815, 0.782]</r> representing the Culture Perspective 'Modernism' with a value change labelled as 5."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "kAEKw0uhKGJNVtsq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the boundaries [0.067, 0.821, 0.164, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A {Cultural Perspective label}<r>[0.067, 0.821, 0.164, 0.845]</r> that reads 'Traditionalism'."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "Utt4SoOIlyoSzUCk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart elements bounded by [0.015, 0.342, 0.029, 0.586]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes {the vertical bar's Y-axis label}<r>[0.015, 0.342, 0.029, 0.586]</r> which states 'Cultural Perspectives'."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "edsLA6DahJeFGAVO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the y-axis} at [0.015, 0.446, 0.029, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Type."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "PZAARbGJmxDwjf2a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the bar corresponding to \"Spa Services\"} at [0.203, 0.145, 0.527, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "pGd2nGnkq2bH6jLm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the text next to the \"Cruise Bookings\" bar} at [0.510, 0.418, 0.532, 0.438] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "18."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "MjcF9vALNMbxbtLu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the frequency bar for \"Restaurant Revenue\" in Season 1}[0.203, 0.311, 0.544, 0.378] higher than {the frequency bar for \"Event Ticket Sales\" in Season 1}[0.203, 0.228, 0.561, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "mOBCNF00xhuE08wz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the values in Season 1}[0.203, 0.021, 0.579, 0.917] generally higher than {the values in Season 2}[0.604, 0.021, 0.980, 0.917] for all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "wenn1z0aSr3MJmnm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Hotel Bookings\" in Season 1}[0.203, 0.727, 0.408, 0.793] located closer to the {left edge of its subplot}[0.203, 0.021, 0.579, 0.917] than {the corresponding bar in Season 2}[0.604, 0.727, 0.805, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "uRHhDMWrn8sUqQd5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are some details about the region [0.203, 0.021, 0.579, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the bars for Season 1 spread across various categories such as \"Resort Rentals\", \"Hotel Bookings\", \"Adventure Tours\", etc., up to \"Museum Visits\". Each bar is paired with a numerical value displayed next to it, signifying frequency counts for each category in Season 1."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "1MuiPeFEMPpxcU36",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within [0.600, 0.931, 1.042, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels for the Season 2 subplot with values ranging from 0 to 30 in increments, assisting in reading the frequency values corresponding to each category in Season 2."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "S831YcIegOMP2fr9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what can be observed in [0.203, 0.083, 0.464, 0.850]."
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical section holds various bars for different categories, spanning most of the visit and booking types for Season 1, from \"Adventure Tours\" to \"Cruise Bookings\". Each bar is accompanied by a label indicating its exact value, facilitating a comparison of different categories."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "uPsmh6SYYVQ9Y3PN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.112, 0.029, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label \"Cybersecurity and Data Privacy Issues\"."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "Jo4nQyon7VA87hro",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me where the value text of the \"Password Attacks\" bar for Frequency Metric 2 is situated?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the text is at coordinates [0.761, 0.769, 0.810, 0.804]."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "Ab1oZBMllfDRLN0F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label located at coordinates [0.068, 0.546, 0.158, 0.581] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for \"Ransomware\"."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "qGyxQ2IaLnjG1Quo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described at the coordinates [0.271, 0.927, 0.432, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the x-axis on the left subplot, named as \"Frequency Metric 1 (%)\"."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "qDmkNcTZxWKKKSdn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Password Attacks}[0.168, 0.757, 0.437, 0.817] from Frequency Metric 1 bigger than {its corresponding bar from Frequency Metric 2}[0.586, 0.757, 0.761, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "bgqGoCKqATgQ6fge",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of Ransomware from Frequency Metric 2}[0.586, 0.529, 0.723, 0.590] have higher value than {the bar of Data Breach from Frequency Metric 2}[0.586, 0.453, 0.844, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "xG94SFmULMtx3Jbi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Insider Threats from Frequency Metric 1}[0.168, 0.302, 0.451, 0.363] lower than {the bar for Insider Threats from Frequency Metric 2}[0.586, 0.302, 0.890, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "XaQSOFnxtJ61dy3n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is longer between {the Frequency Metric 1 bar for IoT Vulnerabilities}[0.168, 0.226, 0.506, 0.287] and {the Frequency Metric 2 bar for the same topic}[0.586, 0.226, 0.711, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frequency Metric 1."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "ejPg5J1HSzesncci",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the text presented at coordinates [0.271, 0.927, 0.432, 0.963] and [0.690, 0.927, 0.850, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at coordinates [0.271, 0.927, 0.432, 0.963] indicates the label for the X-axis on the left subplot, which is \"Frequency Metric 1 (%)\". While the text at [0.690, 0.927, 0.850, 0.963] is the x label for the right subplot, which is \"Frequency Metric 2 (%)\"."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "sLyf3ZFCzLuGxBj9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section covering [0.583, 0.757, 0.816, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Frequency Metric 2 bar for \"Password Attacks\". The bar sits at [0.586, 0.757, 0.761, 0.817], and the accompanying value text \"12.2%\" is at [0.761, 0.769, 0.810, 0.804]."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "HcbmjgwLuIDVbmNP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects in the area [0.582, 0.681, 0.941, 0.741] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the Frequency Metric 2 bar for \"Phishing\" that ranges [0.586, 0.681, 0.827, 0.741], accompanied by the value text \"16.8%\" located at [0.827, 0.694, 0.876, 0.729]."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "vTOKBDyRaoBmVEvq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area [0.035, 0.243, 0.158, 0.278]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis label within this region. The label \"IoT Vulnerabilities\" is located within these coordinates."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "qyvOPRA5yj14JgWt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we see at the coordinate [0.529, 0.560, 0.467, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The part of the bar in the left panel of the bar chart representing the value for \"Carbon Footprint\" in Frequency Metric 1."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "Eifo79z2IapN04FQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of {the text}<r>[0.559, 0.769, 0.599, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates a value \"8.2%\", which is the value of the \"Investment\" category under Frequency Metric 1."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "85T9IkzwYOqxuXHN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at [0.796, 0.769, 0.835, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the text \"4.5%\", a Frequency Metric 2 value for the \"Investment\" category."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "uBXvjEbDfEFotJLh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the category \"Supply Chain Management\", is the {value for Frequency Metric 1}<r>[0.232, 0.453, 0.432, 0.514]</r> greater or less than the {value for Frequency Metric 2}<r>[0.618, 0.453, 0.796, 0.514]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "twgWDpbVKcDNzJ1g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Frequency Metric 1, Is the {value of \"Investment\"}<r>[0.232, 0.757, 0.559, 0.817]</r> higher than the {value of \"Employee Welfare\"}<r>[0.232, 0.605, 0.479, 0.666]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "1Aqa5zJ7U04JiVSo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value for \"Equal Opportunities\" in Frequency Metric 2}<r>[0.618, 0.302, 0.756, 0.363]</r> higher or lower than the {value for \"Community Development\" in Frequency Metric 2}<r>[0.618, 0.150, 0.824, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "l9IX4WJktqOlCZKy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the left panel of the chart from [0.232, 0.037, 0.576, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The left panel represents the bar chart for Frequency Metric 1 (%). The x-axis represents Frequency Metric 1 in percentage, while the y-axis includes various categories starting from \"Investment\" on top to \"Sustainable Raw Materials\" at the bottom. Each category has a corresponding bar, with the length of each bar representing the numerical value of Frequency Metric 1 for each category."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "H9pE2kg68vfzk4CH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region in [0.710, 0.927, 0.871, 0.963] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label for the right side of the bar chart, which is \"Frequency Metric 2 (%)\"."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "ZA2j7bw7B4seVEw0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area of the chart within the coordinates [0.618, 0.037, 0.962, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the right part of the bar chart, where Frequency Metric 2 (%) is plotted against the same categories as Frequency Metric 1. A similar pattern is followed with bars indicating the corresponding values, with bar lengths vary according to their values."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "VIm0Fl8qbu7NbQ9h",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the first chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Ecosystem<r>[0.015, 0.304, 0.029, 0.609]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "YlpsDuQf3DXvvEKP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the bars in the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Light blue<r>[0.596, 0.030, 0.962, 0.883]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "VrCjkTGrSmubK6B7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text near the bar that represents 'Blockchain' in the first chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "7.9%<r>[0.511, 0.720, 0.551, 0.748]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "PST2aZ7G4DNt2n4l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for 'Artificial Intelligence' longer in the first or second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Second<r>[0.596, 0.781, 0.945, 0.845]</r>, <r>[0.187, 0.781, 0.536, 0.845]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "tqa3OhdZ4aDf9mO9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for 'Drones Tech' have a higher percentage rate in the first or second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First<r>[0.187, 0.385, 0.372, 0.449]</r>, <r>[0.596, 0.385, 0.766, 0.449]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "quj5pUhS4zSCXv9R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre 'Cloud Computing' bars in descending order from the first to the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No<r>[0.187, 0.544, 0.433, 0.607]</r>, <r>[0.596, 0.544, 0.830, 0.607]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "MIHxwtIsPTdcP3hm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the x-axis area of the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentages ranging from 0 to 8, positioned at <r>[0.591, 0.883, 0.968, 0.931]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "zFoVKobvZWTQqiYe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the y-axis area of the first chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation sectors such as 'Artificial Intelligence', 'Blockchain', etc., labeled between coordinates <r>[0.035, 0.069, 0.177, 0.830]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "JPkgaH59Nj6ovDtv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present in the region covering both bar charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bar graphs comparing different technology sectors across 'Rate1 (%)' and 'Rate2 (%)' with corresponding values indicated at the end of each bar, ranging from coordinates <r>[0.187, 0.781, 0.945, 0.845]</r> to <r>[0.596, 0.781, 0.945, 0.845]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "GHgpXRwANympXKj9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar with coordinates [0.568, -0.229, 0.586, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the VR category bar with an Outlook Score of 0.8."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "fpflqBv54AYl3VPI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me what [0.686, 0.025, 0.824, 0.055] is."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle for the right subplot, which says \"Focus on Lower End\"."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "aBIoE6zCwZuJyNfA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies at [0.329, 0.630, 0.347, 0.935] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the IoT category with an Outlook Score of 0.35."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "gisZAsPAzyo90HMk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.506, 0.418, 0.518, 0.580] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label of the y-axis in the right subplot named \"Outlook Score\"."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "vuVm4oyGj3enf2nI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Blockchain bar}[0.619, -0.083, 0.636, 0.935] in the subplot taller or shorter than the {Quantum Computing bar}[0.772, 0.353, 0.789, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "GicHRZnVIOAxfuRj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'AI' bar}[0.670, 0.062, 0.687, 0.935] to the left or right of {the '5G' bar}[0.721, 0.208, 0.738, 0.935] in the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "gONOwu971LPJ8XSd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar marked 'VR'}[0.074, 0.237, 0.092, 0.935] or {the bar marked 'Cloud Computing'}[0.431, 0.717, 0.448, 0.935] in the first chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR bar."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "A0sWRnFMqoSUPiYT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {AI}[0.176, 0.412, 0.194, 0.935] and {VR}[0.074, 0.237, 0.092, 0.935], which category in the first chart has a higher Outlook Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "WxNrNu7SWcxS9zub",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed within the region [0.549, 0.062, 0.961, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the second subplot which focuses on the lower end of the outlook score among emerging technologies. There are 8 bars representing different technologies: VR, Blockchain, AI, 5G, Quantum Computing, IoT, Big Data, and Cloud Computing. Each bar height corresponds to their outlook scores."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "v9naDFXQBAY4c8dy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region [0.055, 0.062, 0.467, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first subplot which presents an overall outlook of emerging technologies. It shows the scores of 8 different technologies: VR, Blockchain, AI, 5G, Quantum Computing, IoT, Big Data, and Cloud Computing. The higher the bar, the higher the outlook score."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "2dwjtLdE991Ez6mn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.329, 0.952, 0.346, 0.975]"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the 'IoT' label on the x-axis of the second subplot."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "F2XI0pzzXiIcYWe6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe what is happening in [0.673, 0.952, 0.738, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the '5G' label on the x-axis of the second subplot."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "q90Vocxc8ZTAPLuL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is in the bound box with coordinates [0.330, 0.020, 0.670, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce and Retail Industry Analysis."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "WMhPVsbLLE0jjO1C",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text written inside {the box} at [0.224, 0.146, 0.328, 0.182]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "Z37yuaDt7n0iakiX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is in position [0.918, 0.912, 0.947, 0.940]?"
+            },
+            {
+                "from": "gpt",
+                "value": "May."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "PfI47JJ176N7sFfJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the bar representing January in E-commerce} at [0.079, 0.398, 0.145, 1.594] and {the one representing January in Traditional Retail} at [0.572, 0.738, 0.637, 1.594], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "FnqeBBsrIuzZulgg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn March, was {E-commerce}[0.243, 0.381, 0.309, 1.594] more profitable than {Traditional Retail}[0.736, 0.786, 0.801, 1.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "FvmM4KVaz4nEpA1H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {May sales of E-commerce}[0.407, 0.276, 0.473, 1.594] more than that of {Traditional Retail in May}[0.900, 0.723, 0.965, 1.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "e9gnV56Qwm8SbXkT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of the area [0.060, 0.191, 0.493, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart of E-Commerce sales over five months, from January to May. The sales are represented by blue bars, and each month's sales is labeled on the x-axis."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "Xo22C2j1rZZXSPvz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within the region [0.552, 0.191, 0.985, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar chart - Traditional Retail. It represents the sales data of traditional retail from January to May. It displays blue bars for each month's sales values plotted on the y-axis, and the month labels on the x-axis."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "0oZwy3fsbLYec7Nc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.079, 0.398, 0.145, 1.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue bar that represents the sales of E-commerce in January."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "6uGwAZkyYJuloSNW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart in coordinates [0.391, 0.020, 0.609, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Energy Use and Resource Consumption\""
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "JJmvUrIb2Z54arNq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label in coordinates [0.171, 0.912, 0.226, 0.940] represent in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Natural Gas\""
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "ky7bc2enCLO5KCTa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content indicated by the coordinates [0.211, 0.146, 0.348, 0.182] on the sub-plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle \"Annual Energy Use (GW)\"."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "0IBDsnYzMNRhXapp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Coal}<r>[0.084, 0.752, 0.149, 0.893]</r> higher or lower than the bar representing {Hydro}<r>[0.329, 0.871, 0.394, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "WxiOdeIduD7XFejC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the resource consumption chart, is {Natural Gas bar}<r>[0.661, 0.577, 0.726, 0.893]</r> to the left or right of the bar representing {Nuclear}<r>[0.742, 0.735, 0.807, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "4txsFYrZ1rY2v1z0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Coal}<r>[0.084, 0.752, 0.149, 0.893]</r> and {Other Renewables}<r>[0.410, 0.877, 0.475, 0.893]</r> on the Annual Energy Use chart, which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "Y1GoQVH99owSe6RL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the coordinates [0.065, 0.191, 0.495, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the subplot labelled \"Annual Energy Use (GW)\". It includes individual bar plots for \"Coal\", \"Natural Gas\", \"Nuclear\", \"Hydro\", and \"Other Renewables\" along the x-axis and values ranging from 0 to 200,000 on the y-axis with the ylabel as 'Billions of MWh'."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "EwJOoTuage8dMjh5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.560, 0.191, 0.990, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the subplot titled \"Resource Consumption (TJ)\". It comprises bar plots representing \"Coal\", \"Natural Gas\", \"Nuclear\", \"Hydro\", and \"Other Renewables\" along the x-axis. The y-values range from 0 to 200,000 with the ylabel as 'Trillions of BTUs'."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "ff2v8IRcaT2cYWtt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.579, 0.331, 0.644, 0.893] cover?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the bar plot for \"Coal\" in the subplot \"Resource Consumption (TJ)\", indicating a range of 0 - 160,000."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "hOHPCXSJ7kt34gKH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar for {Medical Gloves in June}[0.887, 0.429, 0.908, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dark blue."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "sBJxUrPZsfb2qjfu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chronological point marked just before {the April tick}[0.072, 0.912, 0.084, 0.940] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "February."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "9WY9XE6sDrzGro7j",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferencing the bar with sales figures closest to 50000 for {Medical Masks}[0.240, 0.680, 0.390, 0.893], which month does it correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "{December}[0.369, 0.371, 0.390, 0.893]."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "lrW0sEi96UYTTTIm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is greater: {the December sales for Disinfectants}[0.170, 0.314, 0.191, 0.893] or {the December sales for Medical Gloves}[0.964, 0.309, 0.985, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Disinfectants."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "7k8un6WRWoispdHN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the April bar for Ventilators}[0.663, 0.583, 0.684, 0.893] taller than {the August bar for Testing Kits}[0.516, 0.327, 0.537, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "mJpKRuCANiEOslXa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has fewer sales in June, {Disinfectants}[0.093, 0.420, 0.114, 0.893] or {Medical Masks}[0.292, 0.490, 0.312, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical Masks."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "pdlp53jA7GCDEPNl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region encompassing the sales numbers for Disinfectants and Medical Masks for February and April [0.034, 0.362, 0.287, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are four bars representing sales of Disinfectants and Medical Masks for February and April, with two of the bars colored light green and the other two turquoise, indicating different sales figures for these months and products."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "Z7wXekeG8ZhHCWax",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the plot area [0.431, 0.359, 0.588, 0.893] that includes the y-axis and part of the bars for Testing Kits."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis with multiple horizontal grid lines and labels ranging from 0 to 70000 in increments of 10000. Additionally, there are six red bars corresponding to the monthly sales of Testing Kits for all months available in the dataset."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "BfvY3rXWLF30Cwlw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description for the chart elements located within [0.828, 0.180, 0.993, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the fifth subplot's y-axis, with tick labels and tick marks spaced vertically, a title labeled \"Medical Gloves\" at the top, and a series of dark blue bars representing monthly sales data for the entire year."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "9VUhM4TXIvdr97TO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.416, 0.020, 0.584, 0.056] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart title named {Risk Assessment Across Areas}<r>[0.416, 0.020, 0.584, 0.056]</r>."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "shwxCSqAIzYpo45p",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the bar located at [0.708, 0.552, 0.784, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {Threat Level}<r>[0.708, 0.552, 0.784, 0.893]</r>."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "XppeQO8ZN4xMgYFF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parameter do {coordinates}<r>[0.139, 0.912, 0.225, 0.940]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the x-axis label {Vulnerability Level}<r>[0.139, 0.912, 0.225, 0.940]</r>."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "6ENgz7saUl4NiNZl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing {Risk Rating}<r>[0.900, 0.239, 0.977, 0.893]</r> have a higher or lower range value than the bar representing {Threat Level}<r>[0.708, 0.552, 0.784, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "iiqXs83uhL4XJdm3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Risk Area 2 subtitle}<r>[0.481, 0.146, 0.543, 0.182]</r> to the left or right of the {Risk Area 3 subtitle}<r>[0.811, 0.146, 0.873, 0.182]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "qKkyiaesxm6uDEQx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Vulnerability Level bar}[0.804, 0.579, 0.880, 0.893] taller than the {Threat Level bar}[0.708, 0.552, 0.784, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "L4uIAZnJSzzjrDsx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.694, 0.191, 0.990, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of a subplot for \"Risk Area 3\", containing three bars corresponding to \"Threat Level\", \"Vulnerability Level\" and \"Risk Rating\" of respective coordinates {Threat Level bar}<r>[0.708, 0.552, 0.784, 0.893]</r>, {Vulnerability Level bar}<r>[0.804, 0.579, 0.880, 0.893]</r>, and {Risk Rating bar}<r>[0.900, 0.239, 0.977, 0.893]</r>. It also contains labels for the X and Y axis as well as Y gridlines."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "o6euH9B7pZCZWW35",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.364, 0.191, 0.660, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the second subplot with the title {Risk Area 2}<r>[0.481, 0.146, 0.543, 0.182]</r>, and it contains three bars representing the \"Threat Level\", \"Vulnerability Level\", and \"Risk Rating\" with coordinates as {Threat Level bar}<r>[0.378, 0.639, 0.454, 0.893]</r>, {Vulnerability Level bar}<r>[0.474, 0.650, 0.550, 0.893]</r>, {Risk Rating bar}<r>[0.570, 0.396, 0.647, 0.893]</r>."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "ZW3oUSwJYoxLB9qD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the boundaries of [0.034, 0.191, 0.330, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first subplot for \"Risk Area 1\", having three bars for \"Threat Level\", \"Vulnerability Level\", and \"Risk Rating\", with coordinates {Threat Level bar}<r>[0.048, 0.731, 0.124, 0.893]</r>, {Vulnerability Level bar}<r>[0.144, 0.758, 0.220, 0.893]</r>, {Risk Rating bar}<r>[0.240, 0.596, 0.317, 0.893]</r> respectively. It also contains labels for the X and Y axis with Y gridlines."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "Uf7tBixOK6hsWEPp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the chart title}<r>[0.401, 0.020, 0.599, 0.056]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Entrepreneurship Ecosystem trends."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "zh7IhEyqvwKauKOB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate range for {the bar labeled \"VC Funding\" showing \"Erratic Fluctuations\"}<r>[0.232, 0.423, 0.301, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 to 3345."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "AaHvQ9XVrwbadQyt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tickmark located at <r>[0.847, 0.893]</r> on the x-axis of the subplot labeled \"Continuous Decrease\" signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "Acquired Startups."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "Zm6AYEMeiypQtjzH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the graph labeled \"Top-Tier Tech Talent\" under \"Erratic Fluctuations\"}<r>[0.404, 0.798, 0.472, 0.893]</r> higher than the same graph under {the \"Continuous Decrease\"}<r>[0.899, 0.776, 0.967, 0.893]</r> section?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "Apjr79i4rewSC1fv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"VC Funding\"}<r>[0.232, 0.423, 0.301, 0.893]</r> in the \"Erratic Fluctuations\"subplot taller than {the bar representing the same category in the \"Continuous Decrease\" subplot}<r>[0.727, 0.250, 0.796, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "YH4o7iwPcTR30Aa4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the Startup Formation values}<r>[0.060, 0.890, 0.129, 0.893], [0.555, 0.890, 0.624, 0.893]</r> increase or decrease from \"Erratic Fluctuations\" to \"Continuous Decrease\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "2s030tuZGS7Gemt4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area <r>[0.535, 0.191, 0.988, 0.893]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a chart that signifies \"Continuous Decrease\". It has 5 bars indicating Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent respectively. Each bar's height represents a statistical measurement, with corresponding values located on the y-axis."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "KKlVTfV4SSQ5dB9S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area <r>[0.040, 0.191, 0.493, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the \"Erratic Fluctuations\" subplot, featuring five bars. Each bar represents a category: Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent. The height of each bar corresponds to the measure in that category."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "qUrIFoAOO1ciT2Iy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region <r>[0.702, 0.146, 0.820, 0.182]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the title of the subplot, \"Continuous Decrease\"."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "G06dMbSvF7jhjwT6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled with 'Developing' in the Europe Migration subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a green bar with coordinates [0.119, 0.679, 0.191, 0.808]."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "36dRs4uWnw3olh8N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the first tick mark on the x-axis located in the Africa Migration subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.463, 0.873]"
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "VSBBnj764J0ZU3WZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.666, 0.635, 0.742, 0.686] in the America Migration subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the bar for 'Developed' region, indicating a value of 0.70."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "BY3DG4y6vXIsizKb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar marked with 'Developed'}[0.031, 0.575, 0.102, 0.679] taller than {the bar marked with 'Developing'}[0.119, 0.679, 0.191, 0.808] in the Europe Migration subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "ssqZnAkW3pMrHZ1A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the green bar}[0.318, 0.679, 0.389, 0.776] values lower than {the red bar}[0.516, 0.271, 0.588, 0.679] in the Africa Migration subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "GydqJnkC0pXw0Ant",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Oceania Migration subplot, does {the bar for Developed}[0.825, 0.666, 0.896, 0.679] reach a higher value than {the bar for Developing}[0.913, 0.420, 0.985, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "aA94MFtGNkEFmtjG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects and their positions in the region [0.007, 0.795, 0.813, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains y-axis labels and x-axis values from various subplots. For the Europe Migration subplot, the y-axis label '\u22122' at [0.007, 0.795, 0.018, 0.830]; in the Asia Migration subplot, the x-axis 'Developed' label at [0.246, 0.897, 0.283, 0.932]; in the America Migration subplot, the x-axis 'Developing' label at [0.731, 0.897, 0.770, 0.932]."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "29xut6cMFjO6Xo5N",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the contents and layout within the area [0.402, 0.550, 0.613, 0.959]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we see the y-axis label and y-scale tick marks for the Africa Migration subplot. The y-axis label is at [0.402, 0.550] and ticks marks for '6', '4', '2', '0', '-2', and '-4' are observed within the range ending at [0.415, 0.959]."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "QoKufCq584DN1qeK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify and specify the contents found in the coordinates [0.600, 0.536, 0.706, 0.932] in the America Migration subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encases the y-scale labels '-4', '-2', '0', '2', '4', '6' along with their respective tick coordinates ending at [0.613, 0.932], showing all levels of migration values for America."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "euaBEYy8nPagb1R5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text} with coordinates [0.321, 0.020, 0.679, 0.056] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the title of the chart, which is \"Environmental and Climate Change Impact\"."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "ROmjCPtiyLJD1F4U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first year listed on the X-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first year listed is 1975 at coordinates [0.073, 0.912, 0.109, 0.940]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "wj0PfWB3MJwIJLTh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of CO2 Emission in 1995 according to the left subplot chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of CO2 Emission in 1995 is 19, based on the bar coordinates [0.204, 0.626, 0.260, 0.893]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "MnmRMqJFiARKo1MW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2025, was the CO2 Emission higher or lower than Ocean Acidification?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CO2 Emission was higher, as reflected by {\"CO2 bar in 2025\"}[0.416, 0.331, 0.472, 0.893] and {\"Ocean Acidification bar in 2025\"}[0.908, 0.800, 0.965, 0.893]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "YEGY6ySZ4NwwIt1F",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 1975 and 1985, did the CO2 Emissions decrease or increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CO2 Emissions increased, as seen by the 1975 bar [0.063, 0.682, 0.119, 0.893] being lower than the 1985 bar [0.133, 0.654, 0.190, 0.893]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "JaV8MV9cIVtqW1L5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich was higher in 2005, CO2 Emissions or Ocean Acidification?"
+            },
+            {
+                "from": "gpt",
+                "value": "CO2 Emissions were higher, as seen in {\"CO2 bar in 2005\"}[0.274, 0.542, 0.331, 0.893] in comparison with the {\"Ocean Acidification bar in 2005\"}[0.767, 0.794, 0.823, 0.893]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "hj07Ajw8bVIuJkaC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a brief summary of the chart at coordinates [0.042, 0.191, 0.492, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a bar chart showcasing data of CO2 Emissions over time from 1975 to 2025. The bars represent the amount of CO2 Emissions and the x-axis represents the years. For instance, the bar for 1975 at [0.063, 0.682, 0.119, 0.893] shows lower emissions than the bar for 2025 at [0.416, 0.331, 0.472, 0.893], suggesting an increase in emissions over this period."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "qJwt1KANzsYxtHkK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.535, 0.191, 0.985, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses a bar chart that details the ocean acidification from 1975 to 2025. The y-axis measures the level of ocean acidification, and the x-axis signifies the corresponding year. For instance, ['ocean acidification bar in 1975'][0.555, 0.789, 0.612, 0.893] denotes a higher level of ocean acidification compared to ['ocean acidification bar in 2025'][0.908, 0.800, 0.965, 0.893], indicating a decrease in the levels of ocean acidification over this time period."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "xZy9NKHviMxXHOrn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the chart at coordinates [0.145, 0.676, 0.201, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a bar from the CO2 Emissions chart that represents the year 1985. The bar indicates a CO2 Emissions value of 17."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "iCLVQUG9pSXvNEWl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.708, 0.302, 0.827, 0.893] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the blue bar in the subplot \"Region 3 Evaluation\" representing the \"PolicyEffectiveness\"."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "XIdgnQkFhtj0mP8f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the Y-axis label in Region 2 located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.336, 0.542, 0.336, 0.542]"
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "m1K3Ko94JeMczvJE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item does {the label}<r>[0.663, 0.906, 0.732, 0.930]</r> refer to on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "it refers to \"inequality\"."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "KVglzDtrVy0ahTW2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is within {the box}<r>[0.366, 0.020, 0.634, 0.056]</r> in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Government Policy and Effectiveness Evaluation\"."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "poRupvezZjufFicj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Region 3, is {the bar showing PolicyEffectiveness} <r>[0.708, 0.302, 0.827, 0.893]</r> longer than {the bar showing PolicyDuration} <r>[0.857, 0.875, 0.977, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "Tiu6EwKkQG4DsqTC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar indicating 'PolicyEffectiveness'} <r>[0.048, 0.370, 0.167, 0.893]</r> in Region 1 have a larger value than {the bar for 'PolicyEffectiveness'} <r>[0.378, 0.449, 0.497, 0.893]</r> in Region 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "FNtsTGS0VTWG4FRQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x-axis label 'PolicyDuration'}<r>[0.883, 0.912, 0.950, 0.940]</r> in subplot 3 at the same position as {the 'PolicyDuration'}<r>[0.223, 0.912, 0.290, 0.940]</r> in subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "Boc8e3K2KJkqVQmA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the Y-axis labels in Region 2 and Region 3 located at the same coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "QHVsVsgsxqxCkj87",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects found in the area [0.694, 0.191, 0.990, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the subplot for Region 3 Evaluation, in which I can see two bars: one blue bar representing 'PolicyEffectiveness' with coordinates [0.708, 0.302, 0.827, 0.893], and the other one representing 'PolicyDuration' with coordinates [0.857, 0.875, 0.977, 0.893]. There is also a subplot title named 'Region 3 Evaluation' at [0.787, 0.146, 0.897, 0.182] and a y-axis label at [0.666, 0.542, 0.666, 0.542]. On the x-axis, the labels for 'PolicyEffectiveness' and 'PolicyDuration' are found at [0.723, 0.912, 0.811, 0.940] and [0.883, 0.912, 0.950, 0.940] respectively."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "DDUcRD9o2aiqHaNH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is enclosed by the coordinates [0.034, 0.191, 0.330, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encloses the subplot for Region 1 Evaluation, where a red bar represents 'PolicyEffectiveness' at [0.048, 0.370, 0.167, 0.893] and another red bar represents 'PolicyDuration' at [0.197, 0.871, 0.317, 0.893]. It also has a subplot title named 'Region 1 Evaluation' at [0.127, 0.146, 0.237, 0.182] and different y-axis labels for values ranging from 0 to 100."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "YXj2ZmWyw15YEfdZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.364, 0.191, 0.660, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates define the area for the subplot \"Region 2 Evaluation\". Inside it, there are two green bars representing 'PolicyEffectiveness' and 'PolicyDuration', located at [0.378, 0.449, 0.497, 0.893] and [0.527, 0.880, 0.647, 0.893] respectively. Additionally, the subplot title 'Region 2 Evaluation' is located at [0.457, 0.146, 0.567, 0.182], and there are various labels on the y-axis representing values from 0 to 100."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "NNBwALZc8on4LTCs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the region [0.366, 0.020, 0.634, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a title of the whole chart, \"Government Policy and Effectiveness Evaluation\". The complete chart-title coordinates are [0.366, 0.020, 0.634, 0.056]."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "Amu6U30uPAxPx033",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat words at [0.369, 0.020, 0.631, 0.056] on top of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Manufacturing Production Efficiency Evaluation\""
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "QGmS69VOh4CcPfrJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar in the first subplot at [0.048, 0.460, 0.092, 1.477] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the threading process in December with a value of 87."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "w76QaLPHxOVhYy5T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the second bar in the second subplot at [0.434, 0.752, 0.478, 1.477]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the drilling process in January with a value of 62."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "PQ4abdZeYmrXCMVh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Assembly\" bar in January}<r>[0.546, 0.401, 0.591, 1.477]</r> higher than {the \"Assembly\" bar in February}<r>[0.876, 0.366, 0.921, 1.477]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "IxGns1Pymc5iRlQ9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {February bars}<r>[[0.708, 0.623, 0.752, 1.477], [0.764, 0.717, 0.808, 1.477], [0.820, 0.483, 0.864, 1.477], [0.876, 0.366, 0.921, 1.477], [0.932, 0.530, 0.977, 1.477]]</r> generally higher or lower than the {January bars}<r>[[0.378, 0.600, 0.422, 1.477], [0.434, 0.752, 0.478, 1.477], [0.490, 0.460, 0.534, 1.477], [0.546, 0.401, 0.591, 1.477], [0.602, 0.565, 0.647, 1.477]]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "xfVbUeibjnzUKRZc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Finishing process in February}<r>[0.932, 0.530, 0.977, 1.477]</r> have a higher value than the {Sawing process in January}<r>[0.490, 0.460, 0.534, 1.477]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "9rFTFLnTtpjXNzJl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.034, 0.191, 0.330, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first chart subplot. It is a bar chart representing the process efficiency in December. The processes include threading, drilling, sawing, assembly, and finishing."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "17Gp8isilllrD1J4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.364, 0.191, 0.660, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot, a bar chart representing the process efficiency in January. Like the first subplot, this chart also includes threading, drilling, sawing, assembly, and finishing."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "molc7R7GO3qexA56",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within [0.694, 0.191, 0.990, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the third subplot of the chart. It is a bar chart representing the process efficiency in February. It assesses five processes: threading, drilling, sawing, assembly, and finishing."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "RaK1zi8tFbu8W9FC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subject of the chart figure with coordinates [0.317, 0.020, 0.683, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ecological Environment Sustainability Assessment Across Regions."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "vql9bpnATon28iRF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the subtitle for the second chart where the bar data is focused on Region 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.479, 0.146, 0.528, 0.182]."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "Xmyk5udUlk5htsIs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the {Bar}[0.794, 0.359, 0.829, 0.893] in the last chart figure (subplot_2)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Soil Health."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "1ieb89fgFyo84CDb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.881, 0.464, 0.916, 0.893] represent in the last subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Footprint in the {Bar}[0.881, 0.464, 0.916, 0.893]."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "KXgstNRNun2KEtaJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Soil Health bar for Region 3}[0.794, 0.359, 0.829, 0.893] higher or lower than the {Soil Health bar for Region 1}[0.134, 0.401, 0.169, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "6WO5MKIkWTXH4lZ1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the {Air Quality bar for Region 1}[0.047, 0.436, 0.082, 0.893] or the {Air Quality bar for Region 3}[0.707, 0.296, 0.742, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region 3."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "tmA0zYo9zKgJYEOQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Water Quality bar for Region 1}[0.090, 0.507, 0.125, 0.893] have a higher or lower level than the {Water Quality bar for Region 2}[0.420, 0.415, 0.455, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "7LbAUddAAwWmY5ku",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is larger for Forestation Levels, {Region 2}[0.595, 0.289, 0.630, 0.893] or {Region 3}[0.925, 0.254, 0.960, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region 3."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "dFbKaOJqtcnB7FKk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.034, 0.191, 0.313, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily contains the subplot for Region 1, with axes highlighting ecological aspects such as air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels. The specific bar values for each aspect are encoded in the bars with coordinates like {Air Quality}<{Bar}[0.047, 0.436, 0.082, 0.893]>."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "NlIOPGXO4dAERVdx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main features contained in area [0.364, 0.191, 0.643, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily comprises the subplot for the Region 2, featuring bars representing air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels. Specific categories can be referenced such as {Biodiversity}<{Bar}[0.508, 0.387, 0.543, 0.893]>."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "wjflsgoA0xdLMa7y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.694, 0.191, 0.973, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area forms the compartment for the Region 3's chart, representing several ecological factors through bars, including {Carbon Footprint}<{Bar}[0.881, 0.464, 0.916, 0.893]> and others. It shows measured levels of air quality, water quality, soil health, biodiversity, carbon footprint, and forestation levels for the Region 3."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "IE8k0ZCVuVj53Lnn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in area [0.047, 0.436, 0.082, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "A single green bar within the chart for Region 1 is depicted, representing the measured level for {Air Quality}<r>[0.047, 0.436, 0.082, 0.893]</r> within that region."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "tpwQWGB7m9antodN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content within the coordinates [0.268, 0.020, 0.732, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content is \"Global Trends and Changes Observation (2010 vs 2020)\"."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "E9Id2IBzncOANRIj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within [0.063, 0.277, 0.180, 0.893] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "A {Blue bar}[0.063, 0.277, 0.180, 0.893] representing CO2 Emissions for the year 2010."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "Jmx78KdvetrmiYvq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the coordinates [0.701, 0.745, 0.818, 0.893] from the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a {red bar}[0.701, 0.745, 0.818, 0.893] representing Sea Level for the year 2011."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "KTYbWBfcITUDUp4P",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labelled at the coordinate [0.234, 0.912, 0.301, 0.940] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Sea Level\"."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "EQsj5p2YaEjgg5va",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subplot for 2010, is the {bar indicating CO2 Emissions}[0.063, 0.277, 0.180, 0.893] taller than the {bar indicating Forest Cover}[0.355, 0.338, 0.472, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "SeaYkGha6Qq11Zjz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subplot for 2020, does the {bar representing Sea Level}[0.701, 0.745, 0.818, 0.893] have a higher placement compared to the {bar for CO2 Emissions}[0.555, 0.254, 0.672, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "HjxJHKoNPWHbPlQr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subplot for 2010, is the {bar for CO2 Emissions}[0.063, 0.277, 0.180, 0.893] positioned to the left or right of the {bar for Forest Cover}[0.355, 0.338, 0.472, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "zb2bs7mU7N1NbVEb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {bars}[0.063, 0.277, 0.180, 0.893], [0.209, 0.835, 0.326, 0.893], [0.355, 0.338, 0.472, 0.893] in the subplot for 2010 oriented in ascending order from left to right?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "KHicBohDOIg7h4ay",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.042, 0.191, 0.492, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the area of the first subplot showing the data for the year 2010. In this region, there are three blue bars showing the CO2 Emissions, Sea Level, and Forest Cover. The heights of these bars indicate the data values for each respective category."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "qI7GBeQS6qwegGoi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.509, 0.912, 0.525, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes nine y-axis ticks labeling values from 0 to 40, placed vertically, for the second subplot."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "nigABlbbEEzxjeVY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are situated within the coordinates [0.535, 0.191, 0.985, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area containing the second subplot. In this region, there are three red bars representing CO2 Emissions, Sea Level, and Forest Cover for the year 2020. The heights of these bars indicate the data values for each category."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "zObxJtMtTD4DJUeW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visible in the area [0.563, 0.912, 0.665, 0.940]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for the 'CO2 Emissions' in the second subplot, serving to indicate the corresponding red bar."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "33EDtEnlU9lvRUYx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Artificial Intelligence and Automation Trends Across Regions\", located at the coordinates {title}<r>[0.334, 0.020, 0.666, 0.056]</r>."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "sohZB2TUb3H0UN2n",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented by the bar with coordinates [0.813, 0.563, 0.858, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the \"AI Patents\" category for \"Region 3\", with a value of 4.7."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "G4YVCHvG0TdFDsb6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label of the first subplot is missing as indicated by the empty value {y-axis label}<r>[0.006, 0.542, 0.006, 0.542]</r>."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "kmQUFytM2lcL5Z4X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of \"AI Patents\" higher in {Region 1}<r>[0.153, 0.647, 0.198, 0.893]</r> or {Region 2}<r>[0.483, 0.359, 0.528, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region 2."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "XKQ57cds4HhCoxxZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Region 3's \"AI Research\"}<r>[0.702, 0.619, 0.746, 0.893]</r> higher than {Region 1's \"Automation Use\"}<r>[0.097, 0.507, 0.142, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "dEjgGzmsWOV8yotq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Region 1}<r>[0.153, 0.647, 0.198, 0.893]</r>, {Region 2}<r>[0.483, 0.359, 0.528, 0.893]</r>, and {Region 3}<r>[0.813, 0.563, 0.858, 0.893]</r>, which has the highest \"AI Patents\" value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region 2."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "oAm13GBVkycCePlu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the area [0.688, 0.191, 0.983, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the third subplot of the chart, it contains the y-axis labels which are numbers indicating the value range, as well as the x-axis labels indicating the categories of artificial intelligence and automation trends, such as \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\". Each category is represented as a bar. This subplot is titled as \"Region 3 Trends\", located at {title of subplot}<r>[0.792, 0.146, 0.880, 0.182]</r>."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "VAitHSqSvkC7Qbkb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the x-axis of the entire graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis stretches from the coordinates [0.028, 0.191] to [0.983, 0.893], and labels for the x-axis are in the form of categories such as \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\"."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "6ZsLL4PSaEtdRWxn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details within the area [0.358, 0.191, 0.653, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the second subplot of the chart titled \"Region 2 Trends\" located at the coordinates {title of subplot}<r>[0.462, 0.146, 0.550, 0.182]</r>. It contains bars depicting values related to \"AI Research\", \"Automation Use\", \"AI Patents\", \"AI Education\", and \"AI Investments\" in Region 2. The highest bar is represented by \"AI Patents\" with the coordinate {AI Patents bar}<r>[0.483, 0.359, 0.528, 0.893]</r>."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "DXKGo1EZxMrBfMKf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar for {the month of January in the Bonds subplot}<r>[0.378, 0.510, 0.396, 1.786]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Turquoise."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "bKxgYWOu6jAbvGCn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis in {the subplot for Commodities}<r>[0.666, 0.542, 0.666, 0.542]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is not specified."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "jnVLGVwe6MnAigwW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the style of the grid in {the Equity subplot}<r>[0.034, 0.191, 0.330, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "oJN79fJ2KSzvpWXr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value bar for March in Equity}<r>[0.093, 0.580, 0.111, 1.786]</r> higher than {the October bar for Commodities}<r>[0.913, 0.655, 0.931, 1.786]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "zFlPwcTDdAPCCKlX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {the subplot for Bonds}, does {the bar for June}<r>[0.492, 0.452, 0.510, 1.786]</r> have a greater value than {the bar for November}<r>[0.606, 0.283, 0.624, 1.786]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "FBjRtN1yPrnPOUVl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first y-tick label in Bonds}<r>[0.346, 0.882, 0.358, 0.910]</r> smaller than {the last y-tick label in Commodities}<r>[0.676, 0.116, 0.688, 0.144]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "KQICBilhp78Aw4pJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within {the Bonds subplot}<r>[0.364, 0.191, 0.660, 0.893]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains various monthly bars depicting financial performance. Each bar, from January to December, represents different value levels, starting from 100.00 to 121.50, with colors transitioning from lighter turquoise to darker as values increase."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "GheTbzlOoysc2EBs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in {the Equities subplot area}<r>[0.034, 0.191, 0.330, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are multiple green bars representing monthly performances from January to December. The bar heights start high in January and decrease throughout the year, indicating a downward trend in values."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "UkIfxYKdTF6YhfxA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in {the Commodities subplot}<r>[0.694, 0.191, 0.990, 0.893]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area consists of red bars representing monthly data from January to December, each indicating different levels of commodity values. The plot includes y-axis labels for specific value markers with a dashed grid background enhancing visibility."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "nU59en3yMN2nEnBD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the object labeled 'Supply' on the y-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.075, 0.100, 0.134, 0.114]"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "WuqSC9L1n6BYCFzh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars showing 'Current Year' in both subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "x3uq2hC75kjgxMgM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the x value for the bar representing the 'Risk' category of 'Current Year' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "'0.00 -> 15.00',"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "Oe0qhn4K4TPxSLD3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the bars with the coordinates '[0.522, 0.546, 0.775, 0.586]' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "LnX38rU8E90J8cSn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the length of {the 'Opportunity' bar of the 'Current Year'}[0.522, 0.596, 0.893, 0.637] greater than the length of {the 'Risk' bar in 'Current Year'}[0.522, 0.546, 0.775, 0.586] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "bJ28ro6yxx9R2bmN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Demand' bar in 'Current Year'}[0.522, 0.901, 0.792, 0.941] longer than {the 'Supply' bar in 'Current Year'}[0.522, 0.850, 0.758, 0.891] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "oCThGBgu44AXx3SP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the bars of 'Current Year'}[0.522, 0.901, 0.792, 0.941] in the first subplot and the second subplot have the same color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "MOjDXrYzWAYQhTqa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Competition' category}[0.522, 0.647, 0.724, 0.688] in the second subplot higher than {the 'Deficit' category bar}[0.522, 0.749, 0.690, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "rlPQDQMiCriRDpP4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the region [0.147, 0.526, 0.981, 0.961]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the second subplot. It contains 8 bars depicting 'Current Year' data for each of 8 categories: 'Demand', 'Supply', 'Surplus', 'Deficit', 'Market Share', 'Competition', 'Opportunity', and 'Risk'. Each bar has individual coordinates. Similarly, the second subplot has a x-axis with range labels and a y-axis with category labels. The subplot's title is 'Current Year'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "vPneJOdDqHJ19gxx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.522, 0.238, 0.943, 0.278] in the second subplot contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.522, 0.238, 0.943, 0.278] contains the orange bar that represents the category 'Market Share' in 'Current Year', with the x range of '0.00 -> 25.00'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "Crm0Px0vg8OpdBla",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.147, 0.015, 0.981, 0.450]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to the first subplot. It contains green and orange bars, each depicting 'Previous Year' and 'Current Year' data respectively for each of 8 categories: 'Demand', 'Supply', 'Surplus', 'Deficit', 'Market Share', 'Competition', 'Opportunity', and 'Risk'. Each bar has individual coordinates. The x-axis is number-based while y-axis is labeled with categories."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "JVGD1fMryDizTJ5u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.522, 0.238, 0.606, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the green bar representing the 'Market Share' category for 'Previous Year' in the first subplot, with an x range of '0.00 -> 5.00'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "ClmKhHsHM8QNx8O1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.548, 0.838, 0.869, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing Google's technology score of 9 points."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "B3s8b1LkdhsJYENI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the text at coordinates [0.869, 0.856, 0.925, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"9 points\" indicating the technology score of Google."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "DNcyZ3SGe9ZbjHO8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the second bar chart starting at [0.656, 0.025, 0.868, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Technology of Companies\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "v8x9h8LenthySIFx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company, from those represented in [0.090, 0.155, 0.447, 0.216] to [0.090, 0.838, 0.340, 0.898] in the Innovation category, achieved a higher score than Apple, located at [0.090, 0.762, 0.269, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google, with coordinates [0.090, 0.838, 0.340, 0.898]."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "xGboJcasR7VyMK4m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing SpaceX's innovation score at [0.090, 0.231, 0.447, 0.291] have a higher score or lower score than Google shown at [0.090, 0.838, 0.340, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "xWtjB4M4BpsGMc06",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company between {Microsoft}<r>[0.090, 0.686, 0.162, 0.747]</r> and {Facebook}<r>[0.090, 0.458, 0.305, 0.519]</r> has a higher score in the Innovation Category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "khxr3dlXr3eadP6f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in the area marked by [0.548, 0.534, 0.583, 0.595]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar representing Tesla's technology score. The bar is labeled with '1 points' to signify Tesla's score in the technology category, and the coordinates for that text are [0.583, 0.553, 0.639, 0.576]."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "yTehKIjnp1bGVruv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is shown in the region [0.548, 0.118, 0.976, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar chart titled \"Technology of Companies\" and 10 bars representing the technology scores of different companies. The companies include Google, Apple, Microsoft, Amazon, Tesla, Facebook, Netflix, Twitter, SpaceX, and Uber, with scores ranging from 1 to 9 points."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "SHD3O2pwpnKSU8Yw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we find in the region [0.090, 0.118, 0.519, 0.935}?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a bar chart with the title \"Innovation of Companies\". There are 10 bars representing the innovation scores of different companies including Google, Apple, Microsoft, Amazon, Tesla, Facebook, Netflix, Twitter, SpaceX, and Uber. Their scores range from 2 points to 10 points."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "F9RubNYmRvUYuAqm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar in the Revised plot for the policy \"Guaranteed Income\" in Human-authored data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#ffff99\" for {the corresponding bar}<r>[0.783, 0.807, 0.875, 0.897]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "QUBp81A6iZVupi7e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat policy is represented by the bar with coordinates [0.365, 0.582, 0.415, 0.672] in the Original plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The policy represented is \"Immigration Control\"."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "vu1TI0J0P7K5Er5w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the bar group with coordinates [0.369, 0.807, 0.415, 0.897] in the Original plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Synthetic Data\"."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "ZM2luhhgvTaIcNHx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy is represented by the second label on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The policy is \"Renewable Energy Encouragement\" as indicated by the label at {coordinates}<r>[0.015, 0.731, 0.259, 0.754]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "U7dAmziFC1ZQ5QJ3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Revised plot, is the human-authored data bar for \"Renewable Energy Encouragement\" taller or shorter than the synthetic data bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {human-authored data bar}<r>[0.783, 0.695, 0.898, 0.785]</r> is taller than the {synthetic data bar}<r>[0.692, 0.695, 0.783, 0.785]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "YIsJoC1F1eQZc1ET",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Original plot, is the \"Tax Hike\" bar for synthetic data negative or positive?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative, as can be seen from their {coordinates}<r>[0.342, 0.133, 0.415, 0.223]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "jkmxfsWHk7S0Iys3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Revised plot, is the \"Public Health\" bar for synthetic data longer than the bar for human-authored data?"
+            },
+            {
+                "from": "gpt",
+                "value": "No. The {synthetic data bar}<r>[0.724, 0.470, 0.783, 0.560]</r> is shorter than the {human-authored data bar}<r>[0.783, 0.470, 0.935, 0.560]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "2igqaesKa0LANEPE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Original plot, does the \"Free Education\" policy show a greater negative impact in the synthetic data or the human-authored data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The synthetic data shows a greater negative impact as indicated by their {bar}<r>[0.351, 0.358, 0.415, 0.448]</r>."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "diIOPmUgbjBxFRm1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.597, 0.095, 0.975, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the right-hand side plot labeled \"Revised\" which contains bars depicting policies in color \"#ffff99\". These policies include \"Guaranteed Income\", \"Renewable Energy Encouragement\", \"Immigration Control\", \"Public Health\", \"Free Education\", \"Early Retirement\", and \"Tax Hike\". Each of these policies has two bars representing human-authored data and synthetic data respectively."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "oh6jHQZvZTNmosGT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's happening in the region at [0.268, 0.095, 0.617, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the left side plot labeled \"Original\" which includes seven policies, \"Guaranteed Income\", \"Renewable Energy Encouragement\", \"Immigration Control\", \"Public Health\", \"Free Education\", \"Early Retirement\", and \"Tax Hike\".  Each of these includes a pair of bars represented data of two types: Synthetic and Human-authored. Every pair of bars represents different values, with the Synthetic Data bars showing values less than 0 while Human-authored data bars represent values larger than 0."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "0HQgJmZmFXscwjdO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.783, 0.582, 0.921, 0.672]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object within this range: The bar representing human-authored data for the \"Immigration Control\" policy from the \"Revised\" plot."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "dk6k477E4WBbLO6l",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region [0.415, 0.807, 0.875, 0.897]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a single bar from the \"Revised\" plot, representing the human-authored data for the \"Guaranteed Income\" policy."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "vH5BnmJoI9VjpBXK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's labeled at the coordinate [0.399, 0.025, 0.441, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2017."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "qYjEnNMGoellRHmx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the color stands for at coordinates [0.803, 0.796, 0.924, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "b9sVgN8cVGm8UJ61",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich development type does the coordinates [0.056, 0.710, 0.228, 0.733] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential development."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "FKoAlOggYyAOYKH5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the improvement in {Public space}[0.278, 0.796, 0.420, 0.898] greater in 2017 or 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "zCQVjDHOpgkzuGAa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Transport systems}[0.307, 0.539, 0.420, 0.642] and {Social development}[0.347, 0.155, 0.420, 0.257] in 2017, which one has a greater decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transport systems."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "bxNV8RDEF0lOUYYJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of increase in 2018, is {Economic development}[0.803, 0.283, 0.884, 0.386] greater than {Residential development}[0.803, 0.668, 0.904, 0.770]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "qalto7hTTw7Ffmo3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the area bounded by [0.238, 0.118, 0.985, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are 2 subplots for the years 2017 and 2018. The 2017 subplot features 6 bars indicating the decrease in different areas ranging from -15% to -35%. The 2018 subplot also has 6 bars but indicates the increase in same areas ranging from 20% to 35%."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "QbdDA2zdBsarF6Eq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found between these coordinates [0.621, 0.118, 0.985, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The right subplot of chart is situated here. It indicates the increases across various areas of development in the year 2018."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "UiTyuV55yOxppHGh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.238, 0.118, 0.602, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The left subplot of the chart is positioned in this region. It illustrates the decreases in different areas of development for 2017."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "gv385Z7SlFrNFgr7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at this specific coordinate [0.000, 0.345, 0.020, 0.655] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This text refers to the label Y-axis, titled \"Leadership Scores (%)\"."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "zfHaWapRkXBBYaiU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label of the X-axis located for the 'Leadership' category in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Leadership' category for the X-axis is located at this coordinate [0.478, 0.952, 0.562, 0.975]."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "lHAn8d1Ej7tZ3Hrx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the orange bar}<r>[0.848, 0.665, 0.941, 0.935]</r> represent on the item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The orange bar represents 'Process Oriented' skill in the legend."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "x2ELu6aD7a0ekb5S",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the coordinates for the 'Team Approach' label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Team Approach' label in the legend is positioned at [0.850, 0.083, 0.969, 0.106]."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "7ZT0cy4s0VQiUJpw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Communication}[0.099, 0.332, 0.192, 0.448] higher than {the bar representing Strategic Thinking}[0.848, 0.313, 0.941, 0.448] in the 'Team Approach' plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "6Zpl1XGOZ2DubeWI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the bar representing Planning}[0.286, 0.294, 0.380, 0.448] and {the bar representing Leadership}[0.473, 0.352, 0.567, 0.448] in 'Team Approach', which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing Planning."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "yVKufTn1IWVqvZBh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Communication}[0.286, 0.878, 0.380, 0.935] in 'Process Oriented' higher or lower than the {bar for Leadership}[0.473, 0.800, 0.567, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "MsnEOGXON6pj7s2F",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {bar representing Leadership}[0.473, 0.800, 0.567, 0.935] and {bar representing Strategic Thinking}[0.848, 0.665, 0.941, 0.935] in 'Process Oriented', which has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Strategic Thinking."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "9nJow8FUrHwsIHwq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the area of [0.801, 0.074, 0.976, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the legend section of the chart. The Process Oriented category is represented by the color #8d44c8 and is located at [0.796, 0.573, 0.827, 0.589] for the color and [0.839, 0.571, 0.969, 0.594] for the label."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "1aRkRbe8DU6HgSOF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me more about what is in the space [0.057, 0.550, 0.983, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The space encompasses the entirety of the 'Process Oriented' bar chart. This includes leader labels, bars representing different categories (Communication, Planning, Leadership, Decision Making, Strategic Thinking), and the related values on the Y-axis labels."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "wGpghDtOnjJI78MH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the region [0.286, 0.294, 0.380, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the bar that represents the 'Planning' subdivision in the 'Team Approach' category. The bar signifies the range of 0 to 40."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "IHXxm5rYaBD894mj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell us about the content in the area [0.057, 0.062, 0.983, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire 'Team Approach' subplot, which contains labels for y-ticks, bars representing scores in different fields such as Communication, Planning, Leadership, Decision Making, and Strategic Thinking. It also includes a subtitle at [0.449, 0.025, 0.591, 0.055]."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "8ycHDLDNfQVtwqTM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object at [0.000, 0.227, 0.020, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary object is the chart title, called 'Performance Scores of Tech Companies'"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "zXWdfdVgw9BLomdW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the text label for Microsoft in the 'Innovative Businesses' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is at [0.134, 0.464, 0.204, 0.487]"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "ESCLq8IYd31BDEhR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which position can I find the indicator for 'Facebook' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Facebook' is located at [0.835, 0.952, 0.907, 0.975]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Ta4kwL2IkPleXw7d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the '70' on the y-axis in the 'Traditional Businesses' category located?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is located at [0.026, 0.695, 0.046, 0.718]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "DZWqervY5PGKp6YW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Microsoft for 'Innovative Businesses'}[0.099, 0.101, 0.239, 0.448] taller than {the bar representing Microsoft for 'Traditional Businesses'}[0.099, 0.665, 0.239, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "M1A7Br5efAJH05ea",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Facebook bar in the 'Innovative Businesses'}[0.801, 0.109, 0.941, 0.448] placed higher than {the Facebook grouping in the 'Traditional Businesses'}[0.801, 0.681, 0.941, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "ZHEIhSfOOSLTXuxK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the Apple bar in 'Innovative Businesses'}[0.450, 0.120, 0.590, 0.448], where is {the Apple bar in 'Traditional Businesses'}[0.450, 0.723, 0.590, 0.935] positioned?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Dw0LlqukmFEi6FU1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of score, how does {the Microsoft bar in 'Innovative Businesses'}[0.099, 0.101, 0.239, 0.448] compare with {the Microsoft bar in 'Traditional Businesses'}[0.099, 0.665, 0.239, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "EvMbHn8Ebt5MbSCt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region defined by [0.057, 0.550, 0.983, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot titled 'Traditional Businesses' including the x and y axis labels and their corresponding values, tick marks, and bars representing the scores of Microsoft [0.099, 0.665, 0.239, 0.935], Apple [0.450, 0.723, 0.590, 0.935] and Facebook [0.801, 0.681, 0.941, 0.935]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "cHEZyabzlidpTuEd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located in the region [0.057, 0.062, 0.983, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The zone has a subplot titled 'Innovative Businesses'. It contains x and y labels along with their corresponding values, tick marks, and bars representing Microsoft [0.099, 0.101, 0.239, 0.448], Apple [0.450, 0.120, 0.590, 0.448] and Facebook [0.801, 0.109, 0.941, 0.448] scores."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Rl1iOq6ugToOj0bf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region bounded by [0.134, 0.464, 0.204, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label 'Microsoft' for the 'Innovative Businesses' subplot."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "qkLDNuXmlwJIVlXR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the area [0.418, 0.512, 0.622, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sub-plot title 'Traditional Businesses' is anchored in this region."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "7b5yhNewcZR2ZMtX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subtitle} in the first subplot at [0.499, 0.025, 0.657, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cultural Heritage."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "iaawJTiabhe9evGy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label for \"60%\" on the x-axis in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.568, 0.919, 0.588, 0.942]."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "CKalkVV02Pbic9mR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the {bar for 'Evolving Integration'} in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.188, 0.163, 0.773, 0.230]."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "994qguZ20o81Y3At",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the {pink bar in the second subplot for 'Progressive Heritage'} at [0.188, 0.735, 0.513, 0.802] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "50%."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "HjOlAQacNbKR0xp3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer, the {bar for 'Manual Integration'} in the first subplot [0.188, 0.332, 0.383, 0.399] or the {bar for 'Manual Integration'} in the second subplot [0.188, 0.819, 0.578, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Second subplot."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "4s8wwFwBgiEuTKUB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {labels on the y-axis of the first subplot}<r>[[0.031, 0.356, 0.177, 0.380], [0.017, 0.272, 0.177, 0.295], [0.021, 0.187, 0.177, 0.211], [0.023, 0.103, 0.177, 0.126]]</r> the same as those on the y-axis of the second subplot<r>[[0.031, 0.844, 0.177, 0.867], [0.017, 0.759, 0.177, 0.783], [0.021, 0.675, 0.177, 0.698], [0.023, 0.590, 0.177, 0.614]]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "tMNLmStMenSkvgTW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for 'Regulative Heritage'} at [0.188, 0.078, 0.839, 0.146] longer than the bar for {Evolving Integration} at [0.188, 0.163, 0.773, 0.230]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "uzjV4M3XURdc8yHu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the bar for 'Progressive Heritage'} in the first subplot at [0.188, 0.247, 0.643, 0.315] and the second subplot at [0.188, 0.735, 0.513, 0.802], which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "First subplot."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "F54GGdY1isGmY9d1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.188, 0.819, 0.578, 0.887] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pink bar for 'Manual Integration' in the second subplot, representing a percentage of 60."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "rcAotFkuya575hSq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.346, 0.083, 0.679, 0.113] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region partly contains the title \"Strategies\", but it does not contain the full title."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "uDnSIwUWQR01iQvL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.188, 0.062, 0.969, 0.415]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the first subplot titled \"Cultural Heritage\", a horizontal bar chart with the y-axis labelled with strategies: 'Manual Integration', 'Progressive Heritage', 'Evolving Integration', and 'Regulative Heritage', and the x-axis represents the percentage from 0 to 120. The blue bars represent the respective percentages for each strategy."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "zdFNlyYkP1Y0np77",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.030, 0.429, 0.050, 0.571] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label \"Strategies\" oriented vertically."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "e89HG5tloIQpYNFo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the item located at [0.000, 0.346, 0.020, 0.654] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, titled \"Degree of Change (%)\"."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "zokSZWyQzyR7LbiJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.409, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a tick mark on the x-axis, corresponding to the label \"Erratic behavior\"."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "0BaVDuj1o7uSWvCL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object represented by [0.542, 0.704, 0.720, 0.935] mean in the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar in the bar chart representing the \"Marginal growth\" category for \"Cultural Change\" with a value of 60."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "M9r9b2UsHpllXEyb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the element located at coordinates [0.764, 0.274, 0.941, 0.448] in subplot_0 signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a bar representing the category \"Irregular variations\" in \"Social Change\" with a value of 45."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "XmGx3837INRhdJ1B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Upward trend category}[0.099, 0.332, 0.276, 0.448] in subplot_0 longer than the bar of {Erratic behavior category}[0.321, 0.294, 0.498, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "x7LdvY73SC0nTBXP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is taller, the bar at {Erratic behavior for cultural change}[0.321, 0.800, 0.498, 0.935] or the bar at {Irregular variations for cultural change}[0.764, 0.743, 0.941, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Erratic behavior."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "cqucFOUGGjO68N8l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre bars of {Upward trend}[0.099, 0.332, 0.276, 0.448] and {Erratic behavior}[0.321, 0.294, 0.498, 0.448] in subplot_0 shorter than the bar of {Marginal growth}[0.542, 0.178, 0.720, 0.448] in the same subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "6MXmog07TyPJtkVE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the values for {Social Change} and {Cultural Change}, which one shows a higher degree of change in the {Marginal growth} category for events located at {Marginal growth for social change}[0.542, 0.178, 0.720, 0.448] and {Marginal growth for cultural change}[0.542, 0.704, 0.720, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cultural Change."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "VrLXRFKmKlRC7q0i",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is available in the area located at coordinates [0.057, 0.062, 0.983, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It covers the area of the first subplot (subplot_0), which includes a bar chart showing degrees of change (%) for different categories ('Upward trend', 'Erratic behavior', 'Marginal growth', 'Irregular variations') for 'Social Change'."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "xdPj86MlyFk5qAJC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.027, 0.849, 0.046, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a label on the y-axis ticks for subplot_1, which marks the value 20."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "TdgU9vCkpITDxccu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region [0.057, 0.550, 0.983, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment embodies the second subplot (subplot_1), containing a bar graph that presents degrees of change (%) for various categories ('Upward trend', 'Erratic behavior', 'Marginal growth', 'Irregular variations') pertaining to 'Cultural Change'."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "B63mUV21N95voDoq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.764, 0.274, 0.941, 0.448] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "A single bar in the bar chart is located in the highlighted area. It denotes the \"Irregular variations\" category with a value of 45 for \"Social Change\"."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "tzrh8919Z8eMxElM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.000, 0.238, 0.023, 0.762]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, indicating \"Popularity of fashion trends (%)\"."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "ReZu7qlllcdJ4V9T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.681, 0.256, 0.748, 0.438] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's a bar from the group \"Hippie Bohemian\", represented in the color #e19e9c and it signifies the popularity of \"Soaring Eagle Style\" trend, which is 50%."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "Fcj713y8BaZkfAYb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.019, 0.549, 0.052, 0.577] in the sub-chart pertains to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a label on the y-axis of the second sub-chart, which states the value \"100\"."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "EQGn73qKnzhi1AdG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Graveyard Chic'}[0.681, 0.669, 0.748, 0.923] from {the Punk Rocker group} taller than {the bar for the 'Gothic Frills'}[0.872, 0.632, 0.940, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "MGQUk3ZffPshKcae",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for '60s Fashion'}[0.106, 0.220, 0.173, 0.438] in the {Hippie Bohemian group} shorter than {the bar for the '80s Fashion'}[0.297, 0.365, 0.364, 0.438] in the same group?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "s3eTgyOL46TYlbhn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Gothic Frills trend bar}[0.872, 0.111, 0.940, 0.438] for {Hippie Bohemian} and {Punk Rocker}[0.872, 0.632, 0.940, 0.923], which group holds more popularity for this fashion trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hippie Bohemian."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "cAbg6ZkT2g6Qkh6S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within [0.106, 0.220, 0.173, 0.438]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar that is part of the Hippie Bohemian group. It is colored #e19e9c and represents the '60s Fashion trend which holds a popularity of 60%."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "hwoWmJdLum59TjYX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the characteristics of the region [0.681, 0.669, 0.748, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encapsulates a bar indicating 'Soaring Eagle Style' fashion trend from the 'Punk Rocker' group. It has a popularity of 70%."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "1cV1hkpAVwX72A00",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the box defined by coordinates [0.455, 0.942, 0.590, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This set of coordinates contains a label on the x-axis marking the 'Graveyard Chic' fashion trend in the 'Punk Rocker' sub-chart."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "0pMdOMxCncnrwEk9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.000, 0.285, 0.020, 0.715]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the Y-axis label, named \"Energy Production Changes(%)\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "RqYUlHhxr9LhkGEC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text \"Nuclear Energy\" in the X-axis labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.838, 0.464, 0.956, 0.487]"
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "7GyXUeN5oO8CqsB3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within coordinates [0.099, 0.139, 0.187, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the production of \"Solar Energy\" in the \"Erratic decline\" scenario, having a value of 80."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "q2g9q7ycuqJ56zQw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the coordinates [0.838, 0.571, 0.969, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text label \"Gradual increase\" for the green color in the legend."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "WtzzTbQIreEMbZjs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the solar energy bar of {Erratic decline}<r>[0.099, 0.139, 0.187, 0.448]</r> taller than the solar energy bar of {Gradual increase}<r>[0.099, 0.781, 0.187, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "4N7Q4yvuDI7kSNLD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of the energy source in {the bar}[0.853, 0.665, 0.941, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gradual increase."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "T18AeE2Stv7gnd38",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Wind energy}<r>[0.597, 0.952, 0.695, 0.975]</r> closer to the {Solar energy}<r>[0.094, 0.952, 0.192, 0.975]</r> or {Nuclear energy}<r>[0.838, 0.952, 0.956, 0.975]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar energy."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "BM2XAYYCYLfSSsuG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.057, 0.550, 0.983, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly includes a bar chart showing the \"Gradual increase\" scenario. It shows bars representing different energy sources, namely {Solar Energy}<r>[0.099, 0.781, 0.187, 0.935]</r>, {Hydro Energy}<r>[0.350, 0.858, 0.438, 0.935]</r>, {Wind Energy}<r>[0.602, 0.743, 0.690, 0.935]</r>, and {Nuclear Energy}<r>[0.853, 0.665, 0.941, 0.935]</r>."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "XkP0WB8i3tvTxx0k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.099, 0.139, 0.187, 0.448]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a bar representing the Solar Energy's production change, labeled as \"Erratic decline\" with a value of 80."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "dJGUkMnmj8LJnw6L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.094, 0.952, 0.192, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the X-axis label \"Solar Energy\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "rFfMtxMUg6Gg7aNM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.000, 0.376, 0.020, 0.624] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label represents the Progress Rate (%)."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "yaD8b6xkLrWduSNs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the title of the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.723, 0.025, 0.808, 0.055]"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "WGmQpSPjOLXl2hYa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the category at the first x-axis label in the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Manual Planning}<r>[0.554, 0.752, 0.648, 0.893]</r>."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "8zcEyJ8e8E4rtoQs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the second bar in the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The orange bar in Technology Assisted}<r>[0.651, 0.197, 0.716, 0.735]</r> in the second chart has a progress rate of 80%."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "XpUoXbAHXve1MIug",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Automated Planning'}[0.241, 0.130, 0.307, 0.735] in the first chart higher than {the bar for 'Automated Planning'} in the second chart [0.733, 0.332, 0.799, 0.735] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "PjhIOaSJYEkQnnow",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Government Intervention'} in the first chart <r>[0.324, 0.466, 0.390, 0.735]</r> shorter than the {the bar for 'Government Intervention'} in the second chart <r>[0.815, 0.197, 0.881, 0.735]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "SriusimxlW5pNSfP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar in the second chart is the highest, {the bar for 'Manual Planing'}[0.568, 0.264, 0.634, 0.735] or {the bar for 'Technology Assisted'}[0.651, 0.197, 0.716, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technology Assisted."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "O9BleriUl4N4OMOE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the progress rates for \"Automated Planning\" in both charts the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "tgT3z6GBtLBRFlaV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are in the area [0.548, 0.062, 0.983, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot, which is a bar chart. The title of the chart is \"Economy\"[0.723, 0.025, 0.808, 0.055]. In terms of content, the y-axis has labels ranging from 0 to 100 with an interval of 20, while the x-axis labels are \"Manual Planning\"[0.554, 0.752, 0.648, 0.893], \"Technology Assisted\"[0.623, 0.752, 0.744, 0.933], \"Automated Planning\"[0.705, 0.752, 0.827, 0.935], \"Government Intervention\"[0.774, 0.752, 0.923, 0.975], and \"Total\"[0.912, 0.752, 0.949, 0.807]. The bar with the highest progress rate is the bar for \"Technology Assisted\"[0.651, 0.197, 0.716, 0.735], reaching 80%."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "L5DX97w57u6GjQ8g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.077, 0.332, 0.142, 0.735]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains one single bar in the first subplot, specifically the bar representing \"Manual Planning\". The bar reaches a progress rate of 60."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "VfaOafLPMWavEPiH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located within the coordinates [0.568, 0.264, 0.634, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a single bar depicting the progress rate for \"Manual Planing\" in Economy. This bar reaches a progress rate of 70%."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "ld1lZRt4r0hkZqOe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the region [0.733, 0.332, 0.799, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see a single bar in the second subplot of the bar chart. This bar represents the progress rate for \"Automated Planning\" in Economy, and achieves a rate of 60%."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "yTk2Hu98ObwQuUh6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.000, 0.307, 0.020, 0.693] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the title of the y-axis, named Tourist Attractiveness Score."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "eyyxKoqetXECoYk2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.888, 0.074, 0.976, 0.116] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encompass the legend box which provides the color coding for bars representing USA."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "Du5FOkmOBRAfNgIx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred to by the coordinates [0.099, 0.869, 0.167, 0.935] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to the bar representing the Sightseeing score of 60 for Canada."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "mCtOxVNp2tiaSDgs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Sightseeing bar for USA}[0.099, 0.371, 0.167, 0.448] taller than {the Sightseeing bar for Canada}[0.099, 0.869, 0.167, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "YIL7lemgfpYrPEFM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the total score bar for Canada}[0.873, 0.594, 0.941, 0.935] to the left or right of {the total score bar for USA}[0.873, 0.101, 0.941, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same position."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "yM8tS8Pdi0MxDWBQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the Gastronomy bar for Canada}[0.293, 0.842, 0.360, 0.935], is {the Gastronomy bar for USA}[0.293, 0.360, 0.360, 0.448] taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "PCgIDmCSn6Q8xh4k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the area [0.057, 0.062, 0.983, 0.448]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the subplot for USA tourist attractiveness scores in different categories. It displays five bars representing Sightseeing, Gastronomy, Adventure, Nature, and Total scores, each marked on the X-axis, with a legend at the top right."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "TaAuCrIbYecyfhbh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the area [0.017, 0.108, 0.046, 0.131]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label '300' for the USA subplot."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "DLYQzNHEUbudP3OS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects fall within [0.279, 0.360, 0.360, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single object, the Gastronomy bar for USA which shows a score of 80."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "AHoi1sm0EqW4HSmb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text in the coordinates [0.000, 0.170, 0.020, 0.830] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's title, called \"Technological Industry Development Trends (%)\"."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "ENDbFnyl0lsSMFOT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the respective color of {the first bar}[0.099, 0.197, 0.187, 0.448] and {the last bar}[0.853, 0.773, 0.941, 0.935] in AI Technology subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both bars are denoted by the color #e19e9c."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "5066bLCEu25xYyrq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label at [0.864, 0.952, 0.931, 0.975] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"VerifNER\"."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "nizRNV6SdPEJsjgM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher, {the \"Manual Mapping\" bar in AI Technology subplot}[0.099, 0.197, 0.187, 0.448] or {the \"Manual Mapping\" bar in FinTech subplot}[0.099, 0.627, 0.187, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Manual Mapping\" bar in FinTech subplot."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "XzZ5A32avJxNW8uR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"LLM-revision\" bar}[0.350, 0.170, 0.438, 0.448] in AI Technology subplot taller or shorter than {the \"LLM-revision w/CoT\" bar}[0.602, 0.255, 0.690, 0.448] in the same subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "FCwZ8J5rSHyTiaa2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the \"VerifNER\" bar in AI Technology subplot}[0.853, 0.224, 0.941, 0.448] and {the \"VerifNER\" bar in FinTech subplot}[0.853, 0.773, 0.941, 0.935], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"VerifNER\" bar in FinTech subplot."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "rAuSnkWkuJQDrLs3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.057, 0.062, 0.983, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first subplot, which includes a title \"AI Technology\" at [0.456, 0.025, 0.584, 0.055], four bars with labels (Manual Mapping, LLM-revision, LLM-revision w/CoT, VerifNER), and an x-axis and y-axis with tick labels. The bars indicate the percentage development trends of AI Technology for different mapping methods."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "WD1XqBR1Vz9yDiSd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the area [0.057, 0.550, 0.983, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot, which is similar in structure to the first one. It comprises a title \"FinTech\" at [0.486, 0.512, 0.554, 0.542], four bars labeled Manual Mapping, LLM-revision, LLM-revision w/CoT, VerifNER, and axes with tick labels. The bars depict the percentage development trends of FinTech for different mapping strategies."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "FUOEfq21LrAqZ03K",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region [0.099, 0.197, 0.187, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are present: the first bar in AI Technology subplot with the range '0.00 -> 65.00' in coordinates [0.099, 0.197, 0.187, 0.448] and the first bar in FinTech subplot with the range '0.00 -> 80.00' in coordinates [0.099, 0.627, 0.187, 0.935]. Both bars represent Manual Mapping method in their respective fields."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "0jv46WsRkSFWcMNf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object symbolized by {the rectangles}[0.099, 0.217, 0.219, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the rectangles}<r>[0.099, 0.217, 0.219, 0.448]</r> symbolizes a bar representing \"Traditional Learning\" with performance in Mathematics."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "feJh9DSjaS2P9ElH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend handle for E-Learning}[0.773, 0.120, 0.804, 0.136] have?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend handle}<r>[0.773, 0.120, 0.804, 0.136]</r> for E-Learning is colored light orange."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "A0W2bcudNfK7Accn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.083, 0.464, 0.235, 0.487] on the x-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label<r>[0.083, 0.464, 0.235, 0.487]</r> represents \"Traditional Learning\"."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "QnX51BbTRb6tjkEE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar labeled \"Traditional Learning\"}[0.099, 0.217, 0.219, 0.448] taller than {the bar labeled \"MOOCs\"}[0.821, 0.101, 0.941, 0.448] for Mathematics?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "sKDlnv6hviTKuDDM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first label on the x-axis}[0.083, 0.464, 0.235, 0.487] further left than {the second label on the x-axis}[0.359, 0.464, 0.441, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "TeLOJj5wmYmGd6OW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar with a success rate of 95%}[0.821, 0.569, 0.941, 0.935] located in {the segment for the Science discipline}[0.057, 0.550, 0.983, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "qi1iMd1FTAjIc2FQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area covered by [0.057, 0.062, 0.983, 0.448]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains various bars representing the field of Mathematics, broken down by sub-categories like Traditional Learning, E-Learning, Blended Learning, and MOOCs. The labels for each category are located between [0.083, 0.464, 0.235, 0.487], [0.359, 0.464, 0.441, 0.487], [0.573, 0.464, 0.708, 0.487], and [0.853, 0.464, 0.909, 0.487] respectively, with color labels for each learning type in the legend section [0.767, 0.074, 0.976, 0.220]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "yRNwfps8zfVmGOvr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.767, 0.074, 0.976, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the legend of the chart, which entails labels for each learning type plotted within the chart. Specific labels include Traditional Learning, E-Learning, Blended Learning, and MOOCs, with corresponding color handles each located at [0.773, 0.085, 0.804, 0.101], [0.773, 0.120, 0.804, 0.136], [0.773, 0.155, 0.804, 0.171], and [0.773, 0.190, 0.804, 0.206]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "eZI9lontSnPVHOhS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe in the region [0.099, 0.217, 0.941, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises an array of bars related to Mathematics' success rate for different learning models such as Traditional Learning, E-Learning, Blended Learning, and MOOCs. Each category's end bar coordinates are [0.219, 0.448], [0.460, 0.448], [0.701, 0.448], and [0.941, 0.448] respectively, indicating their final value on the graph."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "vKuuEFGCToS8KKtK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.112, 0.525, 0.148, 184.387] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar in the category of 'Early integration' representing the 'Rapid Expansion' category."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "aA3yIWdFi06jkPsl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.473, 0.058, 0.605, 0.082]"
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "qB5ElVxzuZE4SIQ2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {ylabel} at [0.035, 0.332, 0.066, 0.355]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '1e-2'."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "nLNp2J5sLELqq1Nn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.246, 0.049, 0.809, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Early integration}[0.291, 0.058, 0.407, 0.082], {Further integration}[0.473, 0.058, 0.605, 0.082], and {System integration}[0.671, 0.058, 0.804, 0.082]."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "1YGIvJY5J6xaQROe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Early integration' bar for 'Gradual Improvement'}[0.257, 0.341, 0.293, 184.387] taller than {the 'System integration' bar for 'Rapid Expansion'}[0.907, 0.525, 0.943, 184.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "a2ojkrTsZaZGH5ji",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars under the 'Early integration' category generally higher than those under the 'System integration' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Uncertain, because the bar heights differ depending on the specific category."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "nqvu5ojMhL3W7gcn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Further integration' bar for 'Pronounced Decline'}[0.727, 0.710, 0.763, 184.387] taller than {the 'System integration' bar for 'Pronounced Decline'}[0.763, 0.157, 0.799, 184.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "lt5oYu5zK4ZmdVcU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Further integration' bars} and {the 'System integration' bars}, which set of bars is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The comparison is subjective to the categories in which the bars lie. There isn't a definite answer."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "LzoKlBGK3xznlKWT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.246, 0.049, 0.809, 0.091]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend of the chart, featuring three categories: {Early integration}[0.252, 0.061, 0.280, 0.077], {Further integration}[0.435, 0.061, 0.462, 0.077] and {System integration}[0.632, 0.061, 0.660, 0.077]. Each category is associated with a unique color code."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "rtGpDN3gfDHlZ14t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.071, 0.129, 0.985, 0.737]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main region of the chart where all the bar data are plotted. Several bars are placed under each of the labels along the x-axis representing different categories of growth. Each color of the bars corresponds to a type of integration shown in the legend."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "XFthpsGmFFk0uBHG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.112, 0.525, 0.148, 184.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one purple bar which indicates the value of the 'Rapid Expansion' category in the 'Early integration' set."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "hRYCD3Ucv29OCizY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.401, 0.157, 0.437, 184.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there's a purple bar that represents the 'Erratic Growth' category in the 'Early integration' set."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "RtUHc8lQp7ibbDJu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend corresponding to the handle color '#3c0f64'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{RBF_INT}<r>[0.398, 0.029, 0.455, 0.053]</r>"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "euHleczFhxg0TT5Q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be noticed from the coordinates [0.474, 0.831, 0.516, 1092.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar of {Unemployment_Rate} for {RBF_INT}<r>[0.473, 0.831, 0.516, 1092.651]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "MkhwTgJzkVaU52hx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.833, 0.919, 0.927, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the x-axis item, which is {Stock_Market}<r>[0.833, 0.919, 0.927, 0.942]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "nX18ajlU8atmwvzi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the {RBF_INT}'s GDP bar<r>[0.131, 0.264, 0.174, 1092.651]</r> or the {RBF_POL}'s GDP bar<r>[0.174, 0.128, 0.217, 1092.651]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{RBF_POL}'s GDP bar."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "y6nyCkENf7u2bxmX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich indicator has the highest value for {RBF_COM}?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Interest_Rate}<r>[0.730, 0.683, 0.773, 1092.651]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "4qzFfmwuf3ULIqGB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {RBF_POL}'s Unemployment_Rate bar<r>[0.516, 0.873, 0.559, 1092.651]</r> located to the left or right of the {RBF_INT}'s Unemployment_Rate bar<r>[0.473, 0.831, 0.516, 1092.651]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "ZBUEBMdOPZgYdW7m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.353, 0.020, 0.722, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with three labeled handles. They are: \n1. {RBF_INT}<r>[0.398, 0.029, 0.455, 0.053]</r> with color '#3c0f64' represented by a color box at [0.359, 0.031, 0.387, 0.048]; \n2. {RBF_POL}<r>[0.522, 0.029, 0.583, 0.053]</r> with color '#ae4256' represented by a color box at [0.483, 0.031, 0.510, 0.048], and \n3. {RBF_COM}<r>[0.650, 0.029, 0.716, 0.053]</r> with color '#e47f37' represented by a color box at [0.611, 0.031, 0.638, 0.048]."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "UHulci8eJPZGU68q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.131, 0.264, 0.174, 1092.651] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of a bar representing the GDP for {RBF_INT}<r>[0.131, 0.264, 0.174, 1092.651]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "SHywCfNsT5UJY4X6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.663, 0.919, 0.755, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the category of {Interest_Rate}<r>[0.663, 0.919, 0.755, 0.942]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "fxUi9W9v3nl9MO0n",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinate [0.015, 0.432, 0.029, 0.569] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the Y-axis, which represents the 'Return Rate'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "bVczi4MhksG7sdvH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's at the coordinates [0.302, 0.031, 0.330, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the color label of the investment method 'Tech Startup' in the legend."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "WGtWGuUGPKrzNJxV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinate [0.341, 0.029, 0.428, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label 'Tech Startup' in the legend."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "SEK1GzmYB9WpCqm6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the object at [0.633, 0.968, 0.696, 0.990]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis label, that signifies 'Slow Expansion'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "ELNjR54ekm6wsE71",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {Real Estate in Substantial decrease}[0.266, 0.313, 0.296, 573.254] or {Tech Startup in Substantial decrease}[0.235, 0.473, 0.266, 573.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tech Startup in Substantial decrease."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "jXndVt0GyEYDHTwe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Crypto Currency bar during Slow expansion}[0.420, 0.839, 0.451, 573.254] perform better than the {Real Estate bar during Slow expansion}[0.358, 0.701, 0.389, 573.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "NKQLtiyyavpSumaT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Real Estate in Upward trend}[0.512, 0.327, 0.543, 573.254] and {Crypto Currency in Upward trend}[0.543, 0.199, 0.574, 573.254], which one experienced greater return rates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Real Estate."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "JLB4K7Rop5eHlTll",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the return rate for {Tech Startup during Gradual recovery}[0.605, 0.646, 0.635, 573.254] higher than it was during {Substantial decrease}[0.235, 0.473, 0.266, 573.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "VMNjdU6MND3Ia1Jp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.296, 0.020, 0.759, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend with three sets of color-text labels. The sets represent 'Tech Startup' with a purplish color label at [0.302, 0.031, 0.330, 0.048], 'Real Estate' with a reddish color label at [0.456, 0.031, 0.483, 0.048], and 'Crypto Currency' with a brownish color label at [0.601, 0.031, 0.628, 0.048]."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "HDUJidxOZE0IpxDC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.123, 0.919, 0.193, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label 'Sharp rise'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "ubFSxrx0tOIbU7QB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.235, 0.473, 0.266, 573.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a vertical bar representing the return rate of a 'Tech Startup' under the condition of 'Substantial decrease'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "6hVnDH1iaTxl0Zhz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.112, 0.356, 0.142, 573.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, you can see a bar representing a 'Tech Startup' during a 'Sharp rise' scenario in the bar chart."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "ViC8hzn5WEYjHixD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label in the chart with coordinates [0.490, 0.952, 0.565, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Continents."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "ynyVXIGyCSKWwz0K",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the y-axis label with coordinates [0.015, 0.466, 0.029, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "log L2."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "HrgLbAcpwUSGmLNM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the bar}[0.112, 0.116, 0.148, 240.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Spatial Expansion\" for North America."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "XvwjO8scYlOZvoiv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content presented at the coordinates [0.287, 0.029, 0.411, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Spatial Expansion {label}<{the handle}[0.248, 0.031, 0.276, 0.048]>."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "8IwF7NIUqJz64BNj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Africa in group 0}[0.401, 0.133, 0.437, 240.664] taller or shorter than {the bar for Africa in group 1}[0.437, 0.603, 0.474, 240.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "8zkgdEj5muiTQO0y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Asia bars of group 0 and 1}[[0.546, 0.122, 0.582, 240.664][0.582, 0.628, 0.618, 240.664]] higher or lower than {the Europe's bars of group 0 and 1}[[0.690, 0.116, 0.727, 240.664][0.727, 0.621, 0.763, 240.664]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "cd6zABap5ItJ5Qhj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Spatial Decrease in Australia}[0.871, 0.635, 0.907, 240.664] and {Spatial Improvement in Australia}[0.907, 0.502, 0.943, 240.664], which one is higher in value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Spatial Improvement."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "VxYRL1PNpctAgEQk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf the {labels} [0.287, 0.029, 0.411, 0.053], [0.477, 0.029, 0.595, 0.053], [0.662, 0.029, 0.808, 0.053].  Which one appears first?"
+            },
+            {
+                "from": "gpt",
+                "value": "Spatial Expansion."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "GduVIQFDpX6OqAhT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.242, 0.020, 0.813, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains 3 pairs of color-text labels within the legend. They are Spatial Expansion colored purple {label}[0.287, 0.029, 0.411, 0.053]{handle}[0.248, 0.031, 0.276, 0.048], Spatial Decrease colored red {label}[0.477, 0.029, 0.595, 0.053]{handle}[0.439, 0.031, 0.466, 0.048], Spatial Improvement colored orange {label}[0.662, 0.029, 0.808, 0.053]{handle}[0.623, 0.031, 0.651, 0.048]."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "qHmJcVVccAGlxmob",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the coordinates [0.112, 0.116, 0.148, 240.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a bar {the bar}[0.112, 0.116, 0.148, 240.664] showing the value of \"Spatial Expansion\" for \"North America\" in the bar chart."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "zjegPLaha91mudcJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.401, 0.133, 0.474, 240.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two vertical bars within this region. The first bar is colored purple {the bar}[0.401, 0.133, 0.437, 240.664], represents the \"Spatial Expansion\" for Africa. The second bar is colored red {the bar}[0.437, 0.603, 0.474, 240.664], representing the \"Spatial Decrease\" for Africa. Both bars are from the bar chart and refer to Africa."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "HyahOLqVTrrGqSfD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in [0.858, 0.919, 0.920, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"Australia\" {label}[0.858, 0.919, 0.920, 0.942]."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "id0Lxz4Sr1GA0oVA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.015, 0.435, 0.029, 0.566]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, which represents \"Percentage\"."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "zhv44C9liPLWwqln",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinate of the legend text 'Fast Food Industry'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the coordinates are [0.455, 0.029, 0.582, 0.053]."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "fdmLki6oQIk0z1Ck",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.501, 0.952, 0.546, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trends."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "IMyPn4qIG9jgNVBh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat categories are outlined in [0.251, 0.020, 0.795, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area refers to the legend consisting of {Tech Industry}[0.296, 0.029, 0.388, 0.053], {Fast Food Industry}[0.455, 0.029, 0.582, 0.053], {Auto Industry}[0.649, 0.029, 0.789, 0.053]."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "greAsXiLSWlQ555H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar of Fast Food Industry with coordinates [0.383, 0.874, 0.439, 825.220] for Continuous Decline} higher or shorter than the {bar of Automobile Industry with coordinates [0.439, 0.480, 0.495, 825.220] for the same trend}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "QhlCrq2Jcz0RzBwT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar representing \"Automobile Industry\"}[0.887, 0.625, 0.943, 825.220] in \"Sustained Decline\" hold a higher percentage than the {bar representing \"Fast Food Industry\"}[0.831, 0.625, 0.887, 825.220] in the same category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "F6LPIJaQvxWjT6Ux",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing the \"Tech Industry\"}[[0.103, 0.376, 0.159, 825.220], [0.327, 0.625, 0.383, 825.220], [0.551, 0.176, 0.607, 825.220], [0.775, 0.874, 0.831, 825.220]] generally higher or lower than the {bars representing the \"Fast Food Industry\"}[[0.159, 0.231, 0.215, 825.220], [0.383, 0.874, 0.439, 825.220], [0.607, 0.128, 0.663, 825.220], [0.831, 0.625, 0.887, 825.220]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "GWOjPivGaKbhcLSP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Fast Food Industry}[[0.159, 0.231, 0.215, 825.220], [0.383, 0.874, 0.439, 825.220], [0.607, 0.128, 0.663, 825.220], [0.831, 0.625, 0.887, 825.220]] and {Automobile Industry}[[0.215, 0.296, 0.271, 825.220], [0.439, 0.480, 0.495, 825.220], [0.663, 0.231, 0.719, 825.220], [0.887, 0.625, 0.943, 825.220]], which industry shows a higher value for \"Continous Rise\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fast Food Industry."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "ATiPwNNWBZaIzjgc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content inside [0.251, 0.020, 0.795, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the chart legend and includes three color-text labels. The first label, \"Tech Industry,\" is assigned the color #3c0f64 at [0.257, 0.031, 0.285, 0.048] with text coordinates [0.296, 0.029, 0.388, 0.053]; the second label, \"Fast Food Industry\", is assigned the color #ae4256 at [0.416, 0.031, 0.444, 0.048] with text coordinates [0.455, 0.029, 0.582, 0.053]; the third label, \"Automobile Industry\", is assigned the color #e47f37 at [0.610, 0.031, 0.638, 0.048] with text coordinates [0.649, 0.029, 0.789, 0.053]."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "EAyUtdsZuNgVPnCQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region [0.103, 0.376, 0.159, 825.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a bar of the Tech Industry which represents the trend \"Minimal Decline\" with a value of 0.04."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "pg9vO2DbMThvQ6Lu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.523, 0.077, 0.523, 0.077]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There isn't any significant detail or object located in this specific coordinate. It seems to be an empty space in the chart."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "3SY5Yzndl5BxHZBF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's within [0.797, 0.919, 0.922, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a label \"Sustained Decline\" on the X-axis."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "8fPabcC7bbdJcWdu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object with coordinates [0.615, 0.031, 0.642, 0.048] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "NRnCkmJM95mXa1xd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category located at the coordinates [0.827, 0.919, 0.955, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sports & Outdoors."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "gdrY2sdtT1nxZsZY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text labeled at {coordinates}<r>[0.402, 0.029, 0.461, 0.053]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q1 2021."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "PApUIn1OkgAnW9pD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sales revenue represented at {coordinates}<r>[0.661, 0.874, 0.697, 2130.183]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "200,000."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "XhsCs2y6GyJCQ7qb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the sales revenues represented by {Q3 bars}<r>{'bar_coordinates': '[0.202, 0.532, 0.237, 2130.183]'}, {'bar_coordinates': '[0.379, 0.765, 0.414, 2130.183]'}, {'bar_coordinates': '[0.555, 0.213, 0.591, 2130.183]'}, {'bar_coordinates': '[0.732, 0.786, 0.768, 2130.183]'}, {'bar_coordinates': '[0.909, 0.548, 0.944, 2130.183]'}]</r> higher than those represented by {Q2 bars}<r>{'bar_coordinates': '[0.166, 0.427, 0.202, 2130.183]'}, {'bar_coordinates': '[0.343, 0.687, 0.379, 2130.183]'}, {'bar_coordinates': '[0.520, 0.128, 0.555, 2130.183]'}, {'bar_coordinates': '[0.697, 0.745, 0.732, 2130.183]'}, {'bar_coordinates': '[0.874, 0.581, 0.909, 2130.183]'}</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "DF2VolEW4Gmy4ped",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q1 2021 bar}[0.485, 0.236, 0.520, 2130.183] taller than {the Q3 2021 bar}[0.555, 0.213, 0.591, 2130.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "skKvSm7ZQSxbAE2u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Q1 2021 bar for Electronics}<r>[0.131, 0.501, 0.166, 2130.183]</r> higher than the {Q3 2021 bar for Fashion}<r>[0.379, 0.765, 0.414, 2130.183]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "aw8pdCJNIUONz2uf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars representing {Q1 2021}[0.402, 0.029, 0.461, 0.053] and {Q2 2021}[0.528, 0.029, 0.587, 0.053], which one has a higher sales revenue at the Fashion category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q2 2021."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "TY4Zewl3r5ERK1xg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.357, 0.020, 0.718, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart with three categories: Q1 2021 with color code at [0.363, 0.031, 0.391, 0.048] in purple, Q2 2021 with color code at [0.489, 0.031, 0.517, 0.048] in red, and Q3 2021 with color code at [0.615, 0.031, 0.642, 0.048] in orange."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "6zk1o8Sic4BPjsS6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.090, 0.090, 0.985, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displayed the actual plot of the chart. It includes 3 sets of colored bars representing sales revenues in Q1, Q2, and Q3 of 2021 respectively for five sales categories, including Electronics, Fashion, Grocery, Health & Beauty, and Sports & Outdoors."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "smHT3z66nsbS3Lpa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.485, 0.236, 0.591, 2130.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features three bars indicating the sales revenues of the Grocery category across three time periods. Specifically, Q1 2021 (purple), Q2 2021 (red), and Q3 2021 (orange)."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "6BOd80EwwbvplKnI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.657, 0.919, 0.772, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a labeled text \"Health & Beauty\", which represents a sales category on the chart."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "oBYQZq9UErOYxQRM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at [0.015, 0.407, 0.029, 0.529]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called log Impact."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "eWOf7yn4EMB38MV4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the indicator \"Change in Forests\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is located at [0.725, 0.185, 0.849, 0.209]."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "ZoPfiIW02whCQ5zv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {this item in the legend}[0.686, 0.118, 0.714, 0.134]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the color of the \"Emissions from Agriculture\" bars."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "pqvgrhdX07KoTg6x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the representations of [0.269, 0.919, 0.311, 0.942] and [0.747, 0.919, 0.783, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the x-axis labels \"Urban\" and \"Rural\" respectively."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "iVwgni30KzkSVlxj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the height of {the \"Urban\" \"Greenhouse Gases\" bar}[0.112, 0.065, 0.183, 804.689] compare to {the \"Rural\" \"Greenhouse Gases\" bar}[0.587, 0.308, 0.658, 804.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Urban\" bar is taller."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "8ijzjEzvJ3NVOTwq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Emissions from Deforestation\" in Urban}[0.183, 0.143, 0.254, 804.689] taller than {the one in Rural}[0.658, 0.486, 0.730, 804.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "z6jwERV49rJd48Pf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding \"Industrial Process\" emissions, is the {Urban bar}[0.326, 0.308, 0.397, 804.689] higher than the {Rural bar}[0.801, 0.486, 0.872, 804.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "pQZ0t4ONlIrkiKWj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the {Rural bar for \"Change in Forests\"}[0.872, 0.871, 0.943, 804.689] and {Urban bar for \"Greenhouse Gases\"}[0.112, 0.065, 0.183, 804.689] which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Urban bar for \"Greenhouse Gases\" is taller."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "ghM5ohrmCOBC9HEZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content between [0.681, 0.037, 0.978, 0.218]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes the color reference and the corresponding indicator for \"Greenhouse Gases\", \"Emissions from Deforestation\", \"Emissions from Agriculture\", \"Emissions from Industrial Processes\", and \"Change in Forests\"."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "lHndUdKq8lTsbjia",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.112, 0.065, 0.183, 804.689] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the bar representing the \"Urban\" area's \"Greenhouse Gases\" emissions."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "98YKKZOJ3QsQnPdS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within [0.183, 0.143, 0.254, 804.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar representing \"Emissions from Deforestation\" in the \"Urban\" area."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "kERK7fcQbWLtfDt4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you give about the region [0.326, 0.308, 0.397, 804.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area has a bar that corresponds to the \"Emissions from Industrial Processes\" in the \"Urban\" area."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "dNHIyreM9wP4xr4A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.517, 0.952, 0.566, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {Indices}<r>[0.517, 0.952, 0.566, 0.975]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "phJlQpPWtirMADoH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the item labeled \"Initial\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.492, 0.030, 0.531, 0.053]."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "GkjC8UnB5SX0W7h4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {log Change}<r>[0.015, 0.443, 0.029, 0.574]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "u9rnw0tEtOdz8JiW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category at coordinate [0.673, 0.919, 0.753, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is {Commodity}<r>[0.673, 0.919, 0.753, 0.942]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "61oNtXjIbGDy3yEc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Initial bar for Equity}<{Initial bar for Equity}<r>[0.139, 0.755, 0.199, 885.059]</r>> higher than {the Final bar for Equity}<{Final bar for Equity}<r>[0.199, 0.137, 0.259, 885.059]</r>>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "EaCHHUUBKMTksWMm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bar representing {Commodity} in the Initial and Final states, which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Final state}<r>[0.713, 0.137, 0.773, 885.059]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "Hvx4DQ1qAfk2SKos",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Initial bar for Bond}<{Initial bar for Bond}<r>[0.310, 0.645, 0.370, 885.059]</r>> similar in height to {the Final bar for Futures}<{Final bar for Futures}<r>[0.885, 0.163, 0.945, 885.059]</r>>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "mH4IBD1t54KwMYxQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Initial bar for Forex}<{Initial bar for Forex}<r>[0.482, 0.559, 0.542, 885.059]</r>> taller than {the Final bar for Commodity}<{Final bar for Commodity}<r>[0.713, 0.137, 0.773, 885.059]</r>>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "GrzuFepAZIqzFjSP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what you see in the coordinates [0.448, 0.021, 0.635, 0.063]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the legend of the chart. There seems to be two items here: {Initial}<r>[0.492, 0.030, 0.531, 0.053]</r> associated with a {color handle}<r>[0.454, 0.032, 0.481, 0.048]</r> and {Final}<r>[0.598, 0.030, 0.630, 0.053]</r> with another {color handle}<r>[0.559, 0.032, 0.587, 0.048]</r>. The color handle of Initial is #234361(blue) and for Final is #bebf2b(yellow)."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "IJEwUOv1o26CYF16",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the labels in the area [0.072, 0.103, 0.093, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the y- coordinates, ranging from 0.2<r>[0.072, 0.902, 0.093, 0.925]</r> to 1.6<r>[0.071, 0.103, 0.093, 0.126]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "wZEiiS1jzBvtkw94",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.653, 0.137, 0.885, 885.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this region: the {Initial bar for Commodity}<r>[0.653, 0.489, 0.713, 885.059]</r> and the {Final bar for Commodity}<r>[0.713, 0.137, 0.773, 885.059]</r>."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "BBJfRXoa3eJUdEqf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the coordinates area [0.370, 0.137, 0.430, 885.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a single bar in this specified area, which is designated to the category {Bond} for the {Final}<r>[0.370, 0.137, 0.430, 885.059]</r> data."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "HsZQnjcXy8RRPIEx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinate [0.503, 0.952, 0.548, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, called Trends."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "qlNZdoPUXaCbqMEi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.035, 0.792, 0.061, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label, labeled 100."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "7ZhEXFiDrtwRk4bq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the Trend at coordinate [0.564, 0.919, 0.713, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pronounced decrease."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "3BiOM71L4wWpdRt4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of this color in the legend at [0.534, 0.031, 0.562, 0.048] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Predictions color} at [0.534, 0.031, 0.562, 0.048]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "CF75IjoyRAKu5a0j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Pronounced decrease\" Forecasts}[0.559, 0.603, 0.638, 0.911] higher or lower than {the bar for \"Pronounced decrease\" Predictions}[0.638, 0.691, 0.717, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Mm9EOuPvfGaTGZqt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is to the left of the { bar for \"Upward trajectory\" Predictions} at [0.412, 0.129, 0.492, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar for \"Upward trajectory\" Forecasts} at [0.333, 0.163, 0.412, 0.911]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Lmx4b0UzEDgtpeM2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the \"Continuous decrease\" of the {Forecast bar}[0.108, 0.361, 0.187, 0.911] occur before the {Predictions bar}[0.187, 0.196, 0.266, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "idSvyBjXjkSbyphl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Forecast bar for \"Erratic decline\"}[0.785, 0.548, 0.864, 0.911] and {Predictions bar for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911], which bar represents a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Predictions bar for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Ohny4qtmMmmJeWiS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are included in [0.396, 0.020, 0.655, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items in the legend. One is {Forecast}[0.440, 0.029, 0.506, 0.053] with color {coordinate}[0.401, 0.031, 0.429, 0.048], and the other one is {Predictions}[0.573, 0.029, 0.649, 0.053] with color {coordinate}[0.534, 0.031, 0.562, 0.048]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "i92X1K3VpHIABwJo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents at region [0.050, 0.690, 0.067, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes three y-axis labels: 200 at [0.035, 0.682, 0.061, 0.705], 300 at [0.035, 0.572, 0.061, 0.595], and 400 at [0.035, 0.462, 0.061, 0.485]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "nnuAy8zXw1HKHic6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what lies within the coordinate space [0.785, 0.515, 0.943, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars within this area. One is the {Forecast for \"Erratic decline\"}[0.785, 0.548, 0.864, 0.911], and the other is the {Prediction for \"Erratic decline\"}[0.864, 0.515, 0.943, 0.911]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "AqeHVmKBnCHVylnI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are found within the region [0.108, 0.129, 0.266, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two bars representing the \"Continuous decrease\" trend. Specifically, {Forecast bar}[0.108, 0.361, 0.187, 0.911] and {Predictions bar}[0.187, 0.196, 0.266, 0.911]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "JUqNY4LY78qQuLyJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subcultures."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "nJFA8ZFUxspOUaOM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of people."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "OsAUk4woIHJQnefg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subculture represented at the coordinates [0.553, 0.594, 0.593, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hip-Hop."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "2IiPm4emgwqrfcRi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat subcultures are represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Punk}[0.081, 0.368, 0.121, 0.933], {Gothic}[0.239, 0.529, 0.278, 0.933], {Lolita}[0.396, 0.449, 0.435, 0.933], {Hip-Hop}[0.553, 0.594, 0.593, 0.933], {CyberPunk}[0.711, 0.626, 0.750, 0.933], and {Hippies}[0.868, 0.255, 0.907, 0.933]."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "LwYmjvLqHtZ6omJN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Punk bar groupId_bars_of_group_0 higher or lower than the Punk bar groupId_bars_of_group_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "k8icTNDaNCpaODdE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar of Punk groupId_bars_of_group_1 have a color closer to purple or red?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red"
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "gPz40poLG4sB36Tn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is wider, {the Hip-Hop bar of Punk}[0.553, 0.594, 0.593, 0.933] or {the Hip-Hop bar of Gothic}[0.593, 0.610, 0.632, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "DHB84DfTaYZJOWH4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subculture is more popular between {Gothic}[0.239, 0.529, 0.278, 0.933] and {CyberPunk}[0.711, 0.626, 0.750, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "CyberPunk."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "bUQdE0E1vL8pbITR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visible in the region [0.396, 0.449, 0.932, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes four subcultures namely Lolita, Hip-Hop, CyberPunk, and Hippies. In particular, it includes six bars: {Lolita bar for Punk} [0.396, 0.449, 0.435, 0.933], {Hip-Hop bar for Punk} [0.553, 0.594, 0.593, 0.933], {CyberPunk bar for Punk} [0.711, 0.626, 0.750, 0.933], {Hippies bar for Punk} [0.868, 0.255, 0.907, 0.933], {Hip-Hop bar for Gothic} [0.593, 0.610, 0.632, 0.933], {CyberPunk bar for Gothic} [0.750, 0.344, 0.789, 0.933], {Hippies bar for Gothic} [0.907, 0.384, 0.947, 0.933]."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "af6YB3e9hTUY1muv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.081, 0.288, 0.317, 0.933] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars for the Punk and Gothic subcultures. Specifically, it includes the {Punk bar for Punk} [0.081, 0.368, 0.121, 0.933], the {Gothic bar for Punk} [0.239, 0.529, 0.278, 0.933] and the {Punk bar for Gothic} [0.121, 0.287, 0.160, 0.933]"
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "VglSV0iyOOYEF7lu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are found in the area [0.023, 0.119, 0.035, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis labels which indicate the number of people in each subculture. The labels include: {1K} [0.023, 0.765, 0.035, 0.782], {2K} [0.023, 0.603, 0.035, 0.621], {3K} [0.023, 0.442, 0.035, 0.459], {4K} [0.023, 0.280, 0.035, 0.298] and {5K} [0.023, 0.119, 0.035, 0.136]."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "i6pIycIDOdw8OfCw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visible in [0.023, 0.119, 0.947, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "The whole bar chart is within these coordinates, depicting the popularity of different subcultures, including Punk and Gothic. The x-axis represents the subcultures and the y-axis measures in thousands of people."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "aW4bTXB8iWaBEPce",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label associated with the coordinates [0.355, 0.952, 0.406, 0.975] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{This}<[0.355, 0.952, 0.406, 0.975]> is the x-axis label, which is 'Cuisine'."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "jlpiqgPeofvw8gHZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the box at coordinates [0.846, 0.048, 0.874, 0.064] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code at {these coordinates}<[0.846, 0.048, 0.874, 0.064]> represents the 'Popular index'."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "ZWkMACxzbRkLakPC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented in the box with four coordinates [0.297, 0.602, 0.331, 268.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The box}<[0.297, 0.602, 0.331, 268.273]> represents the effort level for 'Indian' cuisine."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "AE0yF8tbWrsryCck",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the effort level for Italian cuisine}[0.099, 0.065, 0.133, 268.273] higher than {the effort level for Chinese cuisine}[0.198, 0.334, 0.232, 268.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "kCRuP5VDot54iH56",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the popular index for Indian cuisine}[0.331, 0.334, 0.366, 268.273] equal to {the popular index for Chinese cuisine}[0.232, 0.334, 0.267, 268.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "KBeKgZwczyJHEPtW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Vietnamese\"}[0.494, 0.602, 0.529, 268.273] and {\"Thailand\"}[0.593, 0.065, 0.628, 268.273], which cuisine requires a higher effort level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vietnamese."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "WGo2A7Ij42OP5jhQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the area [0.071, 0.025, 0.691, 0.911]"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is the main plot area. It contains six bars for each of the two bar groups. They represent different cuisine types and their corresponding effort levels and popularity indexes."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "FMa7GqvQ0M2DC6Rh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.698, 0.037, 0.985, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the bar chart, with two items: {Effort level}[0.742, 0.046, 0.818, 0.069] in a color denoted by the box at [0.703, 0.048, 0.731, 0.064], and {Popular index}[0.885, 0.046, 0.979, 0.069] in a color denoted by the box at [0.846, 0.048, 0.874, 0.064]."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "s7RrBY7UIRUloQJN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the layout and information present in the region [0.035, 0.056, 0.066, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes labels for the Y-axis, scaling in log form. The labels are {\"1e-4\"}[0.035, 0.862, 0.066, 0.885], {\"1e-3\"}[0.035, 0.593, 0.066, 0.616], {\"1e-2\"}[0.035, 0.325, 0.066, 0.348] and {\"1e-1\"}[0.035, 0.056, 0.066, 0.079]."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "Ms0q84ZvDxO0uqE5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is \"Relative Scale\", located at {the Y-axis label}<r>[0.015, 0.419, 0.029, 0.582]</r>."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "POPOpl5QJSV5ci5Y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can the label \"Contemporary Art\" be found on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Contemporary Art\" is found at coordinates {the label \"Contemporary Art\"}<r>[0.102, 0.919, 0.228, 0.942]</r>."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "1w44DVjaDmV1lbHq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the color with coordinates [0.356, 0.031, 0.384, 0.048] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified color represents \"Relative Growth\"."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "DmSyD38r66tCMkLi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category located at [0.258, 0.919, 0.363, 0.942] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the \"Classical Music\" category."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "BLs4ehpTyEriRAxf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of \"Relative Growth\" at {the bar of \"Relative Growth\"}<r>[0.114, 0.372, 0.165, 555.438]</r> higher than the bar of \"Engagement Level\" at {the bar of \"Engagement Level\"}<r>[0.165, 0.128, 0.216, 555.438]</r> for \"Contemporary Art\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "zKyCyzlxiROPJc4V",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Film\"}<r>[0.842, 0.874, 0.893, 555.438]</r> in \"Relative Growth\" farther to the right than {the category \"Film\"}<r>[0.878, 0.919, 0.907, 0.942]</r> in \"Engagement Level\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "Yco4mLcrZKdHb4hb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Relative Growth\" in {the category \"Modern Dance\"}<r>[0.405, 0.220, 0.456, 555.438]</r> and \"Engagement Level\" in {the category \"Modern Dance\"}<r>[0.456, 0.343, 0.507, 555.438]</r>, which bar is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Engagement Level\"."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "eaKmpKpV0NkBOkYF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Engagement Level\" of \"Literature\"{the bar representing \"Literature\"}<r>[0.747, 0.318, 0.798, 555.438]</r> taller than the bar representing \"Engagement Level\" of \"Film\"{the bar representing \"Film\"}<r>[0.893, 0.485, 0.944, 555.438]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "N5FypayadQolYE3g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.350, 0.020, 0.707, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two legend entries. \"Relative Growth\" is represented by the color at {the color of \"Relative Growth\"}<r>[0.356, 0.031, 0.384, 0.048]</r>, and \"Engagement Level\" is represented by the color at {the color of \"Engagement Level\"}<r>[0.533, 0.031, 0.561, 0.048]</r>."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "OaaPdXNVFkHDCKS4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe what is in the following region [0.114, 0.372, 0.216, 555.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are two bars. The first bar representing \"Relative Growth\" for \"Contemporary Art\" spans from {the first bar}<r>[0.114, 0.372, 0.165, 555.438]</r>, and the second bar representing the \"Engagement Level\" for \"Contemporary Art\" spans from {the second bar}<r>[0.165, 0.128, 0.216, 555.438]</r>."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "Nwwb3oNBiB286qai",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within the area [0.842, 0.874, 0.944, 555.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of two bars. The first bar representing \"Relative Growth\" for \"Film\" at {the first bar}<r>[0.842, 0.874, 0.893, 555.438]</r> and the second bar representing \"Engagement Level\" for \"Film\" at {the second bar}<r>[0.893, 0.485, 0.944, 555.438]</r>."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "0pX3bkvaQj8KmGvv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide detail on the region from [0.258, 0.919, 0.363, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a single element: a label for the category \"Classical Music\" on the X-axis."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "9KLM20eC8V7fq5LS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object in coordinates [0.407, 0.029, 0.466, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the blue bar, named as \"Startups\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "o8cw3AWP10Sz2Rif",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the title of the chart located at [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unfortunately, the title of the chart t is cut off in the given region, it is partially \"art Showing Food Security Trends\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "TCxI5n2h3vTNcpvc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at this coordinate [0.024, 0.902, 0.033, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, \"0\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "7ZGJfD3B9GrFI5hh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the object at [0.015, 0.120, 0.033, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label of the highest y-axis value, \"10\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "9mulBd3bNVJYXTfr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller in the \"Cybersecurity\" category: the {Startups}[0.313, 0.677, 0.395, 0.911] bar or the {R&D Investments}[0.395, 0.364, 0.476, 0.911] bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {R&D Investments}[0.395, 0.364, 0.476, 0.911] bar is taller."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "m0Yo1gp9Nvdan6lR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {AI bar for Startups}[0.546, 0.442, 0.628, 0.911] taller or shorter than the {Blockchain bar for Startups}[0.779, 0.286, 0.860, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {AI bar for Startups}[0.546, 0.442, 0.628, 0.911] is shorter."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "1f45SydtQecfOpGD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Blockchain bars}[[0.779, 0.286, 0.860, 0.911], [0.860, 0.442, 0.942, 0.911]], which one represents higher investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for {R&D Investments}[0.860, 0.442, 0.942, 0.911] is higher."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "jxEP4gF2omi3DDhg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Startups bar for IoT}[0.081, 0.833, 0.162, 0.911] higher or lower than the {R&D Investments bar for IoT}[0.162, 0.755, 0.244, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is higher."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "rDqJC9ME3iJfHSTf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects inside the area [0.363, 0.020, 0.660, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, which includes two pairs of label and color patches. The first pair is the text \"Startups\" with coordinates [0.407, 0.029, 0.466, 0.053] and a blue color patch with coordinates [0.369, 0.031, 0.396, 0.048]. The second pair is the text \"R&D Investments\" with coordinates [0.533, 0.029, 0.654, 0.053] and a color patch in orange with coordinates [0.494, 0.031, 0.522, 0.048]."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "8Ze6UN5qYCTjblIA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region between [0.15, 0.83, 0.24, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are located in this region. The blue bar is for \"Startups\" with coordinates [0.081, 0.833, 0.162, 0.911], and the orange bar corresponds to \"R&D Investments\" with coordinates [0.162, 0.755, 0.244, 0.911]. Both bars belong to the category \"IoT\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "PnMNL0sbGdPzZfox",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the components inside the area [0.79, 0.27, 0.94, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two vertically oriented bars in this area, representing the \"Blockchain\" category for the two trends. The blue bar ([0.779, 0.286, 0.860, 0.911]) represents \"Startups\" and the orange bar ([0.860, 0.442, 0.942, 0.911]) represents \"R&D Investments\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "UbST3fjv3TC1WUFP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the area [0.62, 0.42, 0.70, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are located in this area, represent the \"AI\" category. The blue bar ([0.546, 0.442, 0.628, 0.911]) corresponds to \"Startups\" and the orange bar ([0.628, 0.129, 0.709, 0.911]) shows \"R&D Investments\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "Xq8nqao5uraGL2ci",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label shown at the left side of the chart, specifically at [0.012, 0.337, 0.024, 0.665]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Amount / Publications Count'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "46Lw0Av2ryRCy4cV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at coordinates [0.452, 0.821, 0.490, 301.558]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the count of 'Research Publications' in the field of 'Autonomous Vehicles'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "ooLHaGeff8sRxcPI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the field highlighted at [0.855, 0.919, 0.960, 0.942] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highlighted field is 'Space Exploration'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "ZCTQoG8cSvroddSu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger, the amount of 'Investments' in 'AI Research' represented by the bar at [0.111, 0.169, 0.149, 301.558] or the 'Research Publications' in the same field represented by the bar at [0.149, 0.797, 0.187, 301.558]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Investments'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "3ipCHZE2ryqE4dDF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Quantum Computing' at position [0.262, 0.125, 0.300, 301.558] longer than the bar representing 'Space Exploration' at position [0.870, 0.132, 0.908, 301.558]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "ou6k57PLSZjruoTP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars located at [0.262, 0.125, 0.300, 301.558] and [0.566, 0.178, 0.604, 301.558], which one represents a higher amount of 'Investments'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.262, 0.125, 0.300, 301.558] which represents 'Quantum Computing'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "7eIl3L5cCiCyA7Xa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the region [0.381, 0.020, 0.676, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a legend containing two entries. The first entry 'Investments' is marked with color '#1f77b4', located at [0.385, 0.031, 0.409, 0.048] and the corresponding text is at [0.418, 0.029, 0.489, 0.053]. The second entry 'Research Publications' is marked with color '#d62728', located at [0.512, 0.031, 0.535, 0.048] and the associated text is at [0.545, 0.029, 0.671, 0.053]."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "DEbIgY1hGtjXsZxI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is present in the area [0.069, 0.090, 0.988, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area is the entire chart area. It contains multiple bars representing 'Investments' and 'Research Publications' in various fields such as 'AI Research', 'Quantum Computing', 'Autonomous Vehicles', 'Genomic Research', 'Nanotechnology', and 'Space Exploration'. Each field has two bars - one for 'Investments' and the other for 'Research Publications'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "xS6cNsmqiwmrWQv8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.115, 0.919, 0.183, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a label on the x-axis representing the field named 'AI Research'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "Fv3U1Twhse9y8Kly",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the bar associated with \"Environment Protection\" and labeled \"Ours\"}<r>[0.729, 0.110, 0.807, 0.461]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "fNv6ftGFIb6zk3lZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the value on the {bar associated with \"Random\" in \"Educational Funds\"}<r>[0.574, 0.884, 0.651, 0.952]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "65.0."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "eU1TGpSjfpQJcALZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest {y-tick label}<r>[0.510, 0.530, 0.542, 0.548]</r> displayed on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "400."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "aL4XCddOouiV7VXM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Ours\" in \"Healthcare Campaigns\"}<r>[0.238, 0.218, 0.316, 0.461]</r> higher than {the bar representing \"Random\" in \"Food Drives\"}<r>[0.083, 0.828, 0.161, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "vLQHt50kJGPEJgFi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the one associated with \"Random\" in \"Educational Funds\"}<r>[0.574, 0.884, 0.651, 0.952]</r> or {the one associated with \"SOTA\" in \"Food Drives\"}<r>[0.394, 0.913, 0.471, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "SOTA."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "231ic64PSLcaNd56",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars under the subtitle \"Environment Protection\"}<r>[0.646, 0.019, 0.890, 0.041]</r> of greater value than those under {the subtitle \"Food Drives\"}<r>[0.216, 0.509, 0.339, 0.532]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "yr5F3snB1pIBx4i4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the grouping in the region [0.554, 0.537, 0.981, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a set of three blue bars, representing the \"Random\", \"Ours\", and \"SOTA\" categories with corresponding values placed above each bar; text values are {Random}<r>[0.593, 0.871, 0.632, 0.888]</r>, {Ours}<r>[0.749, 0.860, 0.787, 0.877]</r>, and {SOTA}<r>[0.898, 0.834, 0.948, 0.852]</r>, under the subtitle \"Educational Funds\"."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "w5kCL3G3I90MCMfx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you delineate what is within [0.064, 0.047, 0.491, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The defined area displays a set of blue bars, representing the categories \"Random,\" \"Ours,\" and \"SOTA\" for \"Healthcare Campaigns\" with each bar having a text label on top indicating the numerical value they correspond to; for instance, the value for \"Ours\" is within coordinates [0.253, 0.204, 0.302, 0.222]."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "MkrFGKWViQXKwGRV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space [0.554, 0.047, 0.981, 0.461] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space contains blue bars corresponding to categories \"Random,\" \"Ours,\" and \"SOTA\" for the \"Environment Protection\" campaign, with values placed atop each bar, for example, {Ours}<r>[0.743, 0.096, 0.793, 0.114]</r> and the subtitle for \"Environment Protection\" located above the bars."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "7XVQFy9kHlTykZPJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.191, 0.473, 0.212, 0.491] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "T1."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "MN7bYMTWR8k9LWOY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the first bar of 'Erratic Decline' in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.119, 0.212, 0.284, 0.254]"
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "01Q7EdTLhTXhr5TQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.044, 0.634, 0.066, 0.651] in subplot_1 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "50"
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "Rhe11QqiKPw2efhG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is occupied by {the red bar}[0.776, 0.556, 0.940, 0.744] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The data point}[0.776, 0.556, 0.940, 0.744] represents the 'Steady Increase' trend at time 'T3' with a value of 91.2."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "PN3DqHoiltiQI1e4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"T1\"}[0.119, 0.579, 0.284, 0.744] in subplot_1 higher or lower than {the bar representing \"T2\"}[0.448, 0.570, 0.612, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "Xefb4dYHlu3g4OO8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is taller among these two bars: {the T2 bar of Erratic Decline in subplot_0}[0.448, 0.221, 0.612, 0.254] and {the T2 bar of Steady Increase in subplot_1}[0.448, 0.570, 0.612, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The T2 bar of Steady Increase in subplot_1}[0.448, 0.570, 0.612, 0.744]."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "w4w4bUTbKX5y5F0e",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of the 'Steady Increase' trend rise or fall from {T1}[0.119, 0.579, 0.284, 0.744] to {T3}[0.776, 0.556, 0.940, 0.744] under subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rise."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "Xwnv5u3GvE001hwu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars in {subplot_1} generally higher than those in {subplot_0}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "91NpsDR580dAkwSf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.078, 0.537, 0.981, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area for subplot_1, which has a subtitle \"Under Steady Increase\" at [0.412, 0.509, 0.648, 0.532], also contains 3 bars representing 'Steady Increase' trend at different times; for example, the first bar with value 79.7 at time T1 is displayed at [0.119, 0.579, 0.284, 0.744]."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "XpYjzYT9B6vwxY0W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.078, 0.047, 0.981, 0.461]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is subplot_0. It includes a subtitle \"Under Erratic Decline\" at [0.419, 0.019, 0.641, 0.041], the y-axis with numbers ranging from -100 to 100 and an x-axis with 3 labels ('T1', 'T2', 'T3'). In this subplot, there are three bars representing different times ('T1', 'T2', 'T3') under 'Erratic Decline' trend; for example, the bar for 'T1' is displayed at [0.119, 0.212, 0.284, 0.254] with a value of 20.3."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "e3popQeMSSFQrhFJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.119, 0.236, 0.940, 0.254] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object; a blue bar representing the 'Erratic Decline' trend at T3, with a value of 8.8 and coordinate range of [0.776, 0.236, 0.940, 0.254]."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "ab3mLo9OPjhWxHOq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.412, 0.509, 0.648, 0.532] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a subtitle \"Under Steady Increase\" located."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "E17ip30t4rAFvq5f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.087, 0.254, 0.164, 0.280]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Orange Prices bar of Year 1}<r>[0.087, 0.254, 0.164, 0.280]</r>."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "POpPw1kkCFJxa8VJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the text at [0.414, 0.272, 0.452, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value {17.5}<r>[0.414, 0.272, 0.452, 0.290]</r> for {Orange Prices of Year 3}<r>[0.394, 0.254, 0.471, 0.299]</r>."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "FuyK864TjwOgJUM5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.252, 0.964, 0.306, 0.981] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Year 2 x-axis label}<r>[0.252, 0.964, 0.306, 0.981]</r>."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "wyecvdwGwN53yGiv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the value at the coordinates [0.106, 0.253, 0.144, 0.271] or at [0.260, 0.268, 0.298, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value at {0.106, 0.253, 0.144, 0.271}<r>[0.106, 0.253, 0.144, 0.271]</r>."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "eWj31ikrdEGXbX4x",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the apple prices bars}<r>[0.087, 0.098, 0.164, 0.254]</r> more varied than {the orange prices bars}<r>[0.577, 0.254, 0.654, 0.870]</r> from Year 1 to Year 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "3A2H006USpg3AKMQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {subtitle}<r>[0.165, 0.509, 0.393, 0.532]</r> imply a greater proportion of apples to oranges compared to the subtitle at [0.655, 0.019, 0.884, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "WdyBnvqiSlTZNjCm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of [0.558, 0.537, 0.962, 0.871]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the {Year 3 bars and labels}<r>[0.885, 0.734, 0.962, 0.744]</r> for apple and orange prices with the respective values of 4.00 for apples and 49.00 for oranges, alongside their value labels positioned just above the bars."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "p5YEa8STi2BbdVa2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.067, 0.254, 0.471, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis line with {x-ticks}<r>[0.125, 0.461], [0.279, 0.461], [0.433, 0.461]</r> and the {labels \"Year 1\", \"Year 2\", \"Year 3\"}<r>[0.099, 0.473, 0.152, 0.491], [0.252, 0.473, 0.306, 0.491], [0.406, 0.473, 0.459, 0.491]</r> for each tick mark, as well as the bars and the values for apple and orange prices for each year."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "0zAajzTw3riw0L7Y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the section at [0.019, 0.195, 0.055, 0.472]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the {left y-axis with labels}<r>[0.019, 0.195, 0.055, 0.213], [0.019, 0.247, 0.055, 0.264]</r> indicating prices between \u221220 and 40, and the {vertical grid lines}<r>[0.067, 0.202]</r> corresponding to these values."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "tFjfv6b5nULbW8s2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicates the year \"2022\" in the Cloud Computing subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The x-axis label}[0.250, 0.964, 0.294, 0.981]."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "GubdwDC5kGXyLOFR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents bars in the subplot for the \"Blockchain\" technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Blue}[0.563, 0.109, 0.643, 0.461]."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "GnTZXXM8vpzVV1pJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at the coordinates [0.741, 0.964, 0.784, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"2022\"."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "fNTAJ5dOPULZdcDR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year column is the tallest in {the cloud computing subplot}[[0.073, 0.557, 0.152, 0.952], [0.232, 0.630, 0.312, 0.952], [0.391, 0.650, 0.471, 0.952]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "gVpcfdZ9PrZAVAse",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2023 bar for Artificial Intelligence}[0.391, 0.066, 0.471, 0.461] higher than {the 2021 bar for Machine Learning}[0.563, 0.557, 0.643, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "VTTCp7NvyqOr0JgK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for 2022 in Machine Learning}[0.723, 0.587, 0.802, 0.952] exceed {the bar for 2021 in Blockchain}[0.563, 0.109, 0.643, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "Xbmb7zem5EtFsyxo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.053, 0.537, 0.491, 0.952] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the Cloud Computing subplot, incorporating years 2021 to 2023, with bars representing values, each year marked on the x-axis, and value scale on the y-axis."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "99G4EOx7AMFPsJBl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within [0.544, 0.047, 0.981, 0.461]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the Blockchain subplot; it contains bars for years 2021 to 2023, showing different values related to the impact of Blockchain technology. Each bar is colored blue and has its corresponding value labeled overhead."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "HJfH8k6gFewblrxn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify and describe the contents in [0.544, 0.537, 0.981, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region showcases the Machine Learning subplot, indicating the y-axis ranging from 0 to 50 and x-axis labeled with years 2021, 2022, and 2023. Blue bars represent Machine Learning metric values by year, with respective value labels attached to the top of each bar."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "plFNng6Gbr92WXej",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.009, 0.171, 0.009, 0.171]</r> in the \"Painting\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no label indicated for the y-axis in that position."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "9H3QGIQVCDVhjvKY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine what {the highest y-tick label}<r>[0.024, 0.063, 0.041, 0.077]</r> states in the \"Architecture\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "90"
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "bAtJGVvkpcdzo1NT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at {the chart area}<r>[0.051, 0.037, 0.246, 0.305]</r> in the layout?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the \"Painting\" subplot area displaying bar graphs comparing 'Random', 'Ours', and 'SOTA'."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "UIIJoteH0IicJIDZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the performance score bar for 'Random'}<r>[0.060, 0.136, 0.090, 0.352]</r> higher than {the score bar for 'Ours'}<r>[0.134, 0.153, 0.163, 0.352]</r> in the \"Painting\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "GKlfmjf8q1v9wKIx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has the earlier x-tick position, {the 'Random' label}<r>[0.538, 0.643, 0.596, 0.657]</r> or {the 'Ours' label}<r>[0.625, 0.643, 0.657, 0.657]</r> in the \"Glass\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.538, 0.643, 0.596, 0.657]"
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "j6S3KSLv22bHrKAu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the color of 'SOTA' bar}<r>[0.947, 0.141, 0.976, 0.352]</r> the same as {the 'Random' bar}<r>[0.799, 0.131, 0.828, 0.352]</r> in the \"Architecture\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "D0VTjcGnhG7ZgNLb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features found within the region <r>[0.544, 0.366, 0.739, 0.633]</r> in the layout."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the \"Glass\" subplot area. It includes y-axis tick labels from '10' to '100', x-axis labels 'Random', 'Ours', and 'SOTA', and colored bars indicating performance scores for each category."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "vccEQeiM1gCKNdco",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are in the area <r>[0.790, 0.694, 0.985, 0.961]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers part of the \"Digital Art\" subplot, featuring bars representing performance numbers for 'Random', 'Ours', and 'SOTA', as well as corresponding x-tick and y-tick labels from '0' to '100'."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "tvUNNBaZnxEpjlqy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the bounded region <r>[0.297, 0.366, 0.493, 0.633]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This boundary encases the \"Ceramics\" subplot, showing visual elements like y-axis labels from '10' to '100', x-axis category labels for 'Random', 'Ours', and 'SOTA', including their respective performance bars in a specific color."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "rKPNFAQ2AHLLj1jf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the background color of {the plot}[0.067, 0.047, 0.491, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The background color is white."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "Lgu8kCzHJ9JnLe0s",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at {coordinates}[0.644, 0.509, 0.896, 0.532] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A subtitle reading \"Funds:Attendance = 1:2\"."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "7eSSIeuMKe4X09ix",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the label shown at {the y-tick}[0.019, 0.841, 0.055, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "yu3msbvmVXWWwRJa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first bar of 2018}[0.087, 0.183, 0.164, 0.254] higher than {the last bar of 2020}[0.885, 0.744, 0.962, 0.821]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "vPS3Lq7YvS7jtdoY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar label for 2019 in subplot_1}[0.597, 0.332, 0.635, 0.349] represent a larger or smaller value compared to {the bar label for 2020 in subplot_1}[0.751, 0.167, 0.789, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "EuyMjiFfsHYQc5C6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the attendance bar of 2018}[0.577, 0.254, 0.654, 0.358] positioned above or below {the funds bar of 2018}[0.577, 0.188, 0.654, 0.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "v7neAhT9RLKoQZbv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the scene within {the bounds}[0.558, 0.537, 0.981, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises two histograms, each containing bars for the years 2018, 2019, and 2020, one histogram representing funds received and the other representing attendance. The bars are colored in shades of blue and red respectively."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "F7vRbZCZhwNbLWp6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within {coordinates}[0.067, 0.537, 0.491, 0.952] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region visualizes a sequence of horizontal bars sorted sequentially from 2018 to 2020, juxtaposing funds received against attendance, displayed in a histogram format with a backdrop and grid lines enhancing readability."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "ZUW0bUMVwRevnMBV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be gathered from {the region}[0.067, 0.047, 0.491, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region shows bars that represent the quantitative relationship between funds received and attendance for the years 2018, 2019, and 2020 with corresponding values listed above each bar, set against a simple white background and clearly marked y-grid lines."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "jO8l6ePHskOubMmh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at the coordinates [0.113, 0.689, 0.135, 0.707] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number 25 is labeled, representing a value."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "AASpr1kLr8aYpBRS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the value displayed at the label position [0.818, 0.606, 0.840, 0.624]."
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 65."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "YpvGnnSK3zxXHgSt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.220, 0.292, 0.242, 0.309] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the value 30."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "GWWZvFYE4t37w4L8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar at}[0.588, 0.693, 0.641, 0.744] higher than {the one at}[0.909, 0.568, 0.963, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "jgMJj6D6S3PJdOom",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the bar marked at}[0.097, 0.202, 0.151, 0.254] or {the one marked at}[0.204, 0.161, 0.258, 0.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the one marked at}[0.204, 0.161, 0.258, 0.254]."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "Av6UPhFcOJJNuThs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar starting from}[0.802, 0.610, 0.855, 0.744] have a higher endpoint than {the bar at}[0.695, 0.651, 0.748, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "UiqKX7RpsMRnazoh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bounded by [0.569, 0.047, 0.981, 0.254]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several bars from multiple groups:\n1. {Efficient group bar for \"Trend 1\"}[0.588, 0.202, 0.641, 0.254],\n2. {Efficient group bar for \"Trend 2\"}[0.695, 0.161, 0.748, 0.254],\n3. {Inefficient group bar for \"Trend 1\"}[0.588, 0.254, 0.641, 0.368],\n4. {Inefficient group bar for \"Trend 2\"}[0.695, 0.254, 0.748, 0.316]."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "uKPwP6LOJiae6Fyp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within [0.078, 0.537, 0.491, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several bars along with their value labels, specifically:\n1. A blue bar representing \"Trend 1\" along with the value 25 shown at top[0.097, 0.693, 0.151, 0.744],\n2. An orange bar representing \"Trend 1\" displaying the negative value 65 above it[0.097, 0.744, 0.151, 0.858]."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "tmOxnspFJXLNeIvF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are positioned between [0.569, 0.498, 0.981, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section encompasses multiple bars representing Trend Group 2 of varying efficiencies:\n1. A blue colored bar for \"Trend 1\" with a value 25 indicated at the top [0.588, 0.651, 0.641, 0.744],\n2. An orange colored bar for \"Trend 1\" showing the negative value 65[0.588, 0.744, 0.641, 0.858]."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "9FBuudVOQvql3xfF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.569, 0.727, 0.626, 0.740] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label, representing \"Random\"."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "xcMV7k56WNJZGZ6U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at coordinates [0.229, 0.388, 0.309, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "HUuaAUrNvmTWPWvD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value range shown by {the bar}<r>[0.875, 0.653, 0.954, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "33.9."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "WVhwS2KbwzjfonNI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar labeled 'Ultimate Safe' Random}<r>[0.071, 0.940, 0.150, 0.965]</r> taller than {the bar labeled 'Ultimate Safe' Ours}<r>[0.229, 0.939, 0.309, 0.965]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "LHnqe85Bk8yYsv7Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the scores for {Ours}<r>[0.275, 0.468, 0.370, 0.481]</r> always higher than {SOTA}<r>[0.564, 0.468, 0.666, 0.481]</r> across all subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "NxeBjojEmkIfwcbb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Secure Bonds stage Random Bar}<r>[0.071, 0.679, 0.150, 0.718]</r> reach higher than {Secure Bonds stage SOTA Bar}<r>[0.388, 0.687, 0.467, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "a7z4Vr61HQtyKXYh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area {range}<r>[0.538, 0.034, 0.974, 0.225]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a subplot with the category 'Balanced'. It includes three bars representing the 'Random', 'Ours', and 'SOTA' strategies, which are colored blue."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "81ILQjQZ1aiycdF2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region {rectangle}<r>[0.051, 0.527, 0.487, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a subplot with various blue bars representing different investing strategies under the 'Secure Bonds' category, and it includes labels for 'Random', 'Ours', and 'SOTA'."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "epQ6ksLswRXeCAfb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe all objects located within the area {region}<r>[0.538, 0.281, 0.974, 0.472]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart showcases a subplot labeled 'Stable Growth' containing blue-colored bars covering different strategies from 'Random', 'Ours', to 'SOTA', each with corresponding numeric labels indicating their respective values."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "2JHFmBcaoCsI7HiU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object at the coordinates [0.108, 0.118, 0.248, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of this object is Fund A Performance."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "kHdRvhJVUIeT7HIC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the object at the coordinates [0.070, 0.155, 0.097, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this object is orange (#d98763)."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "47JCCPv0ylzRcKWX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is written at the coordinates [0.352, 0.025, 0.690, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Financial Market Fluctuations and Trends\" is written in this area."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "rO8iCH7Oz1Rc9elR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the scale at the y-axis label [0.015, 0.334, 0.029, 0.672]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The word \"Percent Increase in Value (%)\" is visible."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "iqSKA3UhQGFnV3X4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {Fund A Performance}<{the bar_1}[0.117, 0.650, 0.150, 0.944]> and {Fund B Performance}<{the bar_2}[0.150, 0.709, 0.182, 0.944]> for ABC Corporation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fund A Performance is higher."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "RuC29rQOXHbNLlA5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Fund A Performance bars}<{A_performance_for_YZA}[0.860, 0.180, 0.892, 0.944]> generally higher than {Fund B Performance bars}<{B_performance_for_YZA}[0.892, 0.209, 0.925, 0.944]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "SZulRCZGBSYDgNRF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich performance is higher for STU Enterprises, {Fund A Performance}<{A_performance_for_STU}[0.674, 0.297, 0.707, 0.944]> or {Fund B Performance}<{B_performance_for_STU}[0.707, 0.356, 0.739, 0.944]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fund B Performance."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "wS6Y2HQVQvsS8xnC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fund performed better for DEF Enterprises, {Fund A Performance}<{A_performance_for_DEF}[0.210, 0.591, 0.243, 0.944]> or {Fund B Performance}<{B_performance_for_DEF}[0.243, 0.503, 0.275, 0.944]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fund B Performance"
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "n1PbJumnnw42F20P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.117, 0.650, 0.182, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, two objects can be found:\n- 1. {Fund-A bar for ABC Corporation}[0.117, 0.650, 0.150, 0.944], a bar of Fund A performance for ABC corporation; \n- 2. {Fund-B bar for ABC Corporation}[0.150, 0.709, 0.182, 0.944], a bar of Fund B performance for ABC corporation."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "J205yzHI8QeVqjdW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content represent in [0.035, 0.200, 0.052, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Represents the y-axis label of \"25\"."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "sPSGCNPOKbmgJ1zb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements represented in the bounding box of coordinates [0.064, 0.074, 0.254, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two elements: \n- 1. The color label {handle}[0.070, 0.120, 0.097, 0.136], which is blue, associated with Fund A Performance, and \n- 2. The color label {handle}[0.070, 0.155, 0.097, 0.171], which is orange, associated with Fund B Performance."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "qIUFapgkan3dx3y3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area [0.210, 0.591, 0.275, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the performance bars for DEF Enterprises. The green bar represents Fund A performance {A_performance_for_DEF}[0.210, 0.591, 0.243, 0.944] with a height corresponding to a 12% increase. The orange bar represents Fund B performance {B_performance_for_DEF}[0.243, 0.503, 0.275, 0.944] with a height corresponding to a 15% increase."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "hyXA4QZqVw4bKne7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Development Score {ylabel}<r>[0.015, 0.389, 0.029, 0.617]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "18z8EPWYJsYeV7aV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar represented by \"Hardware\" data in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green {label Hardware color}<r>[0.866, 0.120, 0.894, 0.136]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "SaUowtew1lVW5Qan",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year is given by the first X-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "10 years ago {label 10 years ago}<r>[0.250, 0.952, 0.341, 0.975]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "qqZM9SVxYgXf2fDB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is represented by the bar at [0.445, 0.426, 0.525, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70.5 {Hardware data for Now}<r>[0.445, 0.426, 0.525, 0.944]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "lg1qkPNXhnS0VWzp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Hardware bar for \"Now\"}<r>[0.445, 0.426, 0.525, 0.944]</r> higher than the {Software bar for \"Now\"}<r>[0.525, 0.464, 0.606, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "ooH1w3ToixXrpMbR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Software bar for \"10 years ago\"}<r>[0.296, 0.669, 0.376, 0.944]</r> to the right of the {Hardware bar for \"10 years ago\"}<r>[0.215, 0.608, 0.296, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "HGF2GGl4tD9UfUlb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Software}<r>[0.905, 0.153, 0.967, 0.176]</r> listed above or below {Hardware}<r>[0.905, 0.118, 0.973, 0.141]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "g7C2yCluYduh5Gvj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich prediction is greater, {Hardware in Future Predictions}<r>[0.675, 0.277, 0.755, 0.944]</r> or {Software in Future Predictions}<r>[0.755, 0.317, 0.836, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hardware"
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "Qr9z7E8MBPay4piY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.861, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, which contains two elements: \"Hardware\" labeled in green at {label Hardware color}<r>[0.866, 0.120, 0.894, 0.136]</r>, and \"Software\" labeled in orange, at {label Software color}<r>[0.866, 0.155, 0.894, 0.171]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "0TpbbNtM4mxzxbr3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region [0.066, 0.062, 0.985, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates includes all the bars of the chart, the x and y axis with their labels and tick marks, and the subtitle \"Future Technological Development Prospects\" {subplot subtitle}<r>[0.339, 0.025, 0.712, 0.055]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "Pj9wjNpLerKvhXO5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.755, 0.044, 0.836, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar chart and labels for Software's future predictions, with a value of 85.3 {Software data for Future Predictions}<r>[0.755, 0.317, 0.836, 0.944]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "0T27s7wT0DD8nvcv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region [0.215, 0.608, 0.296, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Hardware development score for 10 years ago, with a value of 45.7 {Hardware data for 10 years ago}<r>[0.215, 0.608, 0.296, 0.944]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "Wwhu1DUGeLBcn2gM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object} at coordinates [0.848, 0.153, 0.973, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label 'Risk Management'."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "3BT8R5CH2NHK2dyM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the coordinates [0.043, 0.788, 0.061, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label '20'."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "q3LfjjdBYLbPGJEc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the element at [0.126, 0.369, 0.157, 0.392] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score '74.1' of the Risk Assessment for Scenario A."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "EZJtviDTVOslLHAj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.853, 0.083, 0.928, 0.106] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title 'Categories' of the legend."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "QSx1utEBZ0bc6iwh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher score, {Scenario A Risk Assessment bar} [0.126, 0.399, 0.158, 0.944] or {Scenario A Risk Management bar} [0.158, 0.586, 0.190, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Assessment."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "ve3Cp3BCBfMPktbe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Scenario D Risk Assessment bar} [0.401, 0.244, 0.433, 0.944] have a higher or lower score than {Scenario D Risk Management bar} [0.433, 0.281, 0.466, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "RtM7y9fsy8uXMu2C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is the color of {Risk Assessment} [0.809, 0.120, 0.837, 0.136] before or after the color of {Risk Management} [0.809, 0.155, 0.837, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "7zjTQHKThIxSnwd0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the y-axis coordinates of the {Scenario H Risk Management bar} [0.801, 0.364, 0.833, 0.944] and {Scenario H Risk Assessment bar} [0.769, 0.298, 0.801, 0.944], which score is lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Assessment."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "Mu9yXiAOyT2B2q30",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the information contained in [0.803, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a subplot legend titled 'Categories' with two items. The first item is for 'Risk Assessment' with its correspondent color box at [0.809, 0.120, 0.837, 0.136]. The second listed item is 'Risk Management' with its color box at [0.809, 0.155, 0.837, 0.171]."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "1Iozvi28nVhWGF8B",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within [0.401, 0.244, 0.466, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are 2 bars. One bar with coordinates [0.401, 0.244, 0.433, 0.944] represents the Risk Assessment score for 'Scenario D' which is 95.3. The other bar with coordinates [0.433, 0.281, 0.466, 0.944] represents the Risk Management score for 'Scenario D', which is 90.2."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "mOpLMdaflv1U8g4n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content falls within [0.121, 0.952, 0.195, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label 'Scenario A' is within these coordinates."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "NBdQAPmfwsoOxA8U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content represented in the region [0.02, 0.88, 0.1, 0.96]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a Y-axis label with text 'Scores' at [0.015, 0.465, 0.029, 0.541]"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "ofEr3Ay1vnqxU7G3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.343, 0.025, 0.702, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the plot, which is \"GDP Growth and Inflation rates over a year\"."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "Ru2MzbyiT6fDn6fe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color corresponding to {Inflation Rate (%) handle} at [0.072, 0.818, 0.100, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to the Inflation Rate (%) handle is orange (#d98763)."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "r6eJ715SD3425UYa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the bar at [0.248, 0.341, 0.273, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the bar is 1.5, which represents the GDP Growth Rate (%) in March."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "4dovdhQvBB9t1ers",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {February bar for GDP Growth Rate (%)}<r>[0.177, 0.230, 0.202, 0.461]</r> higher than the {February bar for Inflation Rate (%)}<r>[0.202, 0.317, 0.227, 0.461]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "8vrUrPfqVEh8Kdg6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {December bar for Inflation Rate (%)}<r>[0.914, 0.134, 0.939, 0.461]</r> higher than the {December bar for GDP Growth Rate (%)}<r>[0.889, 0.190, 0.914, 0.461]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "8XNWKlETQ5tSHwxa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the {June bar for Inflation Rate (%)}<r>[0.487, 0.262, 0.512, 0.461]</r> or the {June bar for GDP Growth Rate (%)}<r>[0.462, 0.461, 0.487, 0.716]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for GDP Growth Rate (%)."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "il1rqUhguM9qwPgp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information within [0.106, 0.166, 0.131, 0.461]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain a bar representing the GDP Growth Rate (%) for January and its value is 3.7."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "6fAUXkxEVxPs889H",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.034, 0.931, 0.055, 0.954]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label -6."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "1HZoLYFGsxAbCJEP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.067, 0.737, 0.266, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the bar plot's legend titled \"Economic Indicators\". The two entries are GDP Growth Rate (%) with a representative color of green(#69b3a2) and Inflation Rate (%) represented by orange(#d98763)."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "7gsUrbKfJW0bPAtu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the figure, mainly positioned at [0.075, 0.062, 0.985, 0.875], represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an axes region that includes a bar chart representing International Trade Trends."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "UUqiRXyntobSb87L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the text in location [0.422, 0.025, 0.638, 0.055] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the text \"International Trade Trends\", which is the subtitle of the graph."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "Y8BGKdaaGeI7USNB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis enclosed by coordinates [0.015, 0.333, 0.029, 0.604]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Trade Value (in Billions)\" for the y-axis."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "W5HLSEwPChz8056f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color and meaning of the square box at [0.887, 0.120, 0.915, 0.136] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box at [0.887, 0.120, 0.915, 0.136] is green and represents \"Export\"."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "rG7N8blytey0yuhM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the Export trade value for China}[0.316, 0.130, 0.348, 0.875], or {the Import trade value for China}[0.348, 0.252, 0.380, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Export."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "w3CD3SJy7HdxaBH8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Export trade value for Germany}[0.225, 0.265, 0.257, 0.875] higher than {its Import trade value}[0.257, 0.340, 0.288, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "398Q7pu4HAPOyQHm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Export}[0.134, 0.367, 0.166, 0.875] for the USA located to the left or right of its {Import}[0.166, 0.333, 0.197, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "QuPgiqm3kiwNAwSx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger, {the Export trade value for India}[0.771, 0.299, 0.803, 0.875], or {the Import trade value for India}[0.803, 0.326, 0.835, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Export."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "mDBru6zdDiTWAMhC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.882, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is a legend box entitled \"Trade Type\". It contains two items, each consisting of a colored square and a label. The first item possesses a green square at [0.887, 0.120, 0.915, 0.136] and is labeled as \"Export\" at [0.926, 0.118, 0.972, 0.141]. The second item has a brown square at [0.887, 0.155, 0.915, 0.171] and is labeled as \"Import\" at [0.926, 0.153, 0.973, 0.176]."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "2qmDIXGnohINdXam",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects inside [0.043, 0.053, 0.070, 0.889]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses y-axis labels and tick marks, displaying values from 0 to 1200 in increments of 200."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "NQRk3aMMIby45Zh7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are included in the region [0.134, 0.367, 0.197, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two bars. The first, colored in green, with boundaries [0.134, 0.367, 0.166, 0.875], represents exports from USA, and the vertical height of the bar indicates the trade value. The second bar, colored brown, with boundary [0.166, 0.333, 0.197, 0.875], signifies imports into USA, again with vertical height representing the trade value."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "qFCuEwhUxLeWXYB8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.329, 0.883, 0.367, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label \"China\" which signifies that the related bars represent trade data for China."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "ZuF6mlNZeOTlZfR7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of data that the chart is showing in [0.317, 0.025, 0.742, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's showing 'Time Series Data Comparison: Bitcoin vs Ethereum'."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "cRn2eVJjTekU5ywL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for the first text label for Ethereum in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.899, 0.153, 0.967, 0.176]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "EMSEUqGEltzJJa5c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate positioned at [0.035, 0.739, 0.070, 0.762]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to '2000' on the y-axis."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "wqCFAjRz9FsGo4j8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar data represented by [0.682, 0.356, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents Bitcoin's value for the year 2020."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "Z5KNOupef20gWuqf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Ethereum's bar for 2017 [0.226, 0.929, 0.279, 0.944] higher than Bitcoin's bar for 2017 [0.173, 0.748, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "Vcjd5UYpr2uXeIq0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the performance of {Bitcoin} with {Ethereum} for 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin performed better."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "aEyO8cBNKm4Dbw3q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, which cryptocurrency {Bitcoin}[0.477, 0.160, 0.530, 0.944] or {Ethereum}[0.530, 0.846, 0.583, 0.944] had the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "GYydR98klZaQeFDb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Bitcoin} and {Ethereum} in 2021, which one had a lower value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "sTu9AcA9GnKS70Qn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the legend items within the coordinates [0.849, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend comprises two items:\n1. Bitcoin, represented by an orange color at [0.860, 0.120, 0.888, 0.136],\n2. Ethereum, represented by a purple color at [0.860, 0.155, 0.888, 0.171]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "Jotj1RnH4bJ96X0j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the axis text within the region [0.209, 0.952, 0.851, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers all the x-axis label which represents the years: 2017 at [0.209, 0.952, 0.244, 0.975], 2018 at [0.360, 0.952, 0.396, 0.975], 2019 at [0.512, 0.952, 0.547, 0.975], 2020 at [0.664, 0.952, 0.699, 0.975], and 2021 at [0.816, 0.952, 0.851, 0.975]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "CC1wjzncFHd5RWWU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat figures are in the area [0.360, 0.748, 0.735, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The figures within these coordinates are the Bitcoin and Ethereum bars for the years 2018, 2019, and 2020, along with their respective label texts."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "vfQY4kW0tbC3i11f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.035, 0.151, 0.070, 0.174]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region, there is a y-axis label indicating the value '8000'."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "BzM1HNFOGQQdva02",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart information when read from coordinates [0.015, 0.423, 0.029, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a Y-axis label indicating the attribute \"Scores\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "AaZvaYJVVBxi4XyQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details noted at [0.120, 0.222, 0.149, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This identifies a blue bar representing the US's Free Trade score of 80."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "DakUmbL9MvKn4NJB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is specified by the coordinates [0.863, 0.118, 0.935, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This denotes the text label \"Free Trade\" in the legend."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "51XmRNPh55Kb14mY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.397, 0.869, 0.411, 0.925] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the X axis label \"India\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "4l359EwwxNaxOjvK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for {Free Trade - Germany}[0.287, 0.190, 0.316, 0.860] have a higher or lower score compared to {Free Trade - China}[0.204, 0.142, 0.233, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "jWpYKazeRCF0a7ev",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the {Global Sourcing score for France}[0.651, 0.350, 0.680, 0.860] in relation to the {Global Sourcing score for Japan}[0.734, 0.214, 0.764, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score for France is lower than Japan's."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "eirq6ts8FMW05qoB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the positions of {Australia - Free Trade}[0.538, 0.222, 0.567, 0.860] and {Australia - Global Sourcing}[0.567, 0.302, 0.596, 0.860] side by side on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "f6JiTfaU3H5UYuRO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Global Sourcing - US}[0.149, 0.302, 0.179, 0.860] higher than {Free Trade - US}[0.120, 0.222, 0.149, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "ttCi1zZwZNalcShT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.819, 0.074, 0.978, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend where two metrics are defined: first, {Free Trade}[0.863, 0.118, 0.935, 0.141] marked in blue color [0.824, 0.120, 0.852, 0.136], and second, {Global Sourcing}[0.863, 0.153, 0.973, 0.176] marked in orange color [0.824, 0.155, 0.852, 0.171]."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "kZ1unt9FN5yzgZNt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the bounded region [0.560, 0.869, 0.574, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these bounds, the label \"Australia\" is evident which is a marker on the x-axis."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "FLcpC6vLRJQGI0bR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide some details about the region [0.371, 0.862, 0.400, 0.860]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar and a value. The displayed value is 66. The bar represents Global Sourcing in India."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "XEA2Vzowu8EABKwY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.287, 0.190, 0.316, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region presents a blue bar representing Germany's Free Trade metric with a score of 84."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "0EhRso4TqAtFCXox",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found at [0.386, 0.019, 0.656, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Building and Real Estate Market Trends."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "w25X7z4T32B432Aj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with the legend detail at [0.849, 0.088, 0.965, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at [0.849, 0.088, 0.965, 0.106], Rental Market Trend, is associated with the color at [0.816, 0.090, 0.840, 0.102], which is green."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "uFAeCfx7jOnVpz8q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.362, 0.396, 0.366, 0.795] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the Commercial Building Trend and the coordinate of the text label is [0.360, 0.801, 0.372, 0.981]."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "eWO89FwsqjfQCq6o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinate [0.744, 0.595, 0.780, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.744, 0.595, 0.780, 0.795] represents the Rental Market Trend of the Hotel/ Motel property type."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "LDP1yje88P1vqRzc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of the Vacant Land in Rental Market Trend}[0.537, 0.496, 0.573, 0.795] higher than {the bar of the Vacant Land in the Property Market Trend}[0.573, 0.381, 0.609, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "2EePsIB73Oe0qNdm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of the Recreational Land in the Rental Market Trend}[0.848, 0.645, 0.884, 0.795] lower than {the bar of the Recreational Land in the Property Market Trend}[0.884, 0.346, 0.920, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "kCKweMamKjNOs7q6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the position of {the bar of the Industrial Property in the Rental Market Trend}[0.433, 0.446, 0.469, 0.795] and {the bar of the Industrial Property in the Property Market Trend}[0.469, 0.346, 0.506, 0.795], which is to the right?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar of the Industrial Property in the Property Market Trend}[0.469, 0.346, 0.506, 0.795] is to the right."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "7EUTLYzBC6ld1E7k",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher, {the bar of the Single Family Home in the Rental Market Trend}[0.122, 0.296, 0.158, 0.795] or {the bar of the Single Family Home in the Property Market Trend}[0.158, 0.181, 0.195, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar of the Single Family Home in the Property Market Trend}[0.158, 0.181, 0.195, 0.795] is higher."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "jHgnEBjr76WSQvhj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.812, 0.055, 0.982, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area[0.812, 0.055, 0.982, 0.139] is the chart legend with two items. The first item is the Rental Market Trend which is green in color, and the color box is at [0.816, 0.090, 0.840, 0.102]. The second item is the Property Market Trend denoted by an orange color, the color box is at [0.816, 0.116, 0.840, 0.128]."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "gP81wSXv5wQxATqH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.537, 0.496, 0.609, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.537, 0.496, 0.609, 0.795] consists of two bars. The first bar at [0.537, 0.496, 0.573, 0.795] is colored green and represents the Rental Market Trend for the Vacant Land. The second bar at [0.573, 0.381, 0.609, 0.795] is colored orange and represents the Property Market Trend for the Vacant Land."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "eYf89M3CdhQ3zjTE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information contained in the area [0.122, 0.181, 0.195, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area [0.122, 0.181, 0.195, 0.795] contains two bars. The first bar at [0.122, 0.296, 0.158, 0.795], colored green, represents the Rental Market Trend of Single Family Homes. The second bar at [0.158, 0.181, 0.195, 0.795] colored orange, represents the Property Market Trend of Single Family Homes."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "aAExWQKYKz8Umhcm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects found in the region [0.226, 0.346, 0.298, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.226, 0.346, 0.298, 0.795] covers two bars. The green bar at [0.226, 0.346, 0.262, 0.795] represents the Rental Market Trend for Apartment Building. The orange bar at [0.262, 0.396, 0.298, 0.795] denotes the Property Market Trend for Apartment Building."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "mhBIBqpL32saohvg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is depicted by the JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "tD2670T5FunpyTh2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart indicated by the coordinates [0.301, 0.019, 0.741, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Entrepreneurship and Innovation Ecosystem across Major cities."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "Wrs4xJ4OojxtJdga",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend item}[0.896, 0.116, 0.919, 0.128] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color used for the 'Hubs' data in the bar chart."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "rvtHXHWiuAbS2M8M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the bar of 'Startups' in 'San Francisco'}[0.110, 0.267, 0.140, 0.878] and {the bar of 'Hubs' in 'San Francisco'}[0.140, 0.184, 0.169, 0.878], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hubs."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "C4enqIlznBJ2Pl0s",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Berlin' Startups bar} [0.534, 0.587, 0.564, 0.878] taller or shorter than {the 'Berlin' Hubs bar}[0.564, 0.490, 0.593, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "1k7j1doQIyElFeFG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the startups bar of Paris}[0.619, 0.618, 0.648, 0.878] and {the startups bar of Beijing}[0.364, 0.427, 0.394, 0.878], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Beijing."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "yGV11fF7CBHj6M1c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region described by the coordinates [0.891, 0.055, 0.982, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart legend. The first item {Startups} with color indicator [0.896, 0.090, 0.919, 0.102] and label at [0.928, 0.088, 0.977, 0.106], and the second item {Hubs} with color indicator [0.896, 0.116, 0.919, 0.128] and label at [0.928, 0.115, 0.957, 0.132]."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "PBOty765FtzqZFtE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.110, 0.267, 0.169, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars representing the 'Startups' and 'Hubs' metrics for San Francisco. {The Startups bar}[0.110, 0.267, 0.140, 0.878] and {The Hubs bar}[0.140, 0.184, 0.169, 0.878]."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "hJUMxQFjqllVs7pt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is conveyed by the coordinates [0.043, 0.872, 0.051, 0.889] to [0.029, -0.024, 0.051, -0.007] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions represent the Y-axis labels, indicating the count (in thousands) which range from 0 to 140."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "iA8N1pwhWT7Nmqud",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the y-axis is \"Performance Scores\" given by {y-axis label}<r>[0.015, 0.387, 0.029, 0.618]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "aQVYadVLDcqFUZp7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the component labelled at the coordinates [0.250, 0.025, 0.800, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Those coordinates correspond to the subtitle of the chart, which is \"Healthcare Performance in Human Health & Disease Management\"."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "L5wDcjquAFQtrXK2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the blue bar at [0.126, 0.279, 0.158, 0.944] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue bar at these coordinates represents the performance score of 98 for Non-Aggregation method in Depression Recovery."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "l09fVB7soE9KYeQV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for the Aggregation method in Depression Recovery higher than that for the Non-Aggregation method?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, {Non-Aggregation}<r>[0.126, 0.279, 0.158, 0.944]</r> has a higher score than {Aggregation}<r>[0.158, 0.537, 0.190, 0.944]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "CwFG77RCHh7BnN50",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Alzheimer Management and Oncology, which department has a higher score in the Non-Aggregation method?"
+            },
+            {
+                "from": "gpt",
+                "value": "Alzheimer Management with the score located at {Non-Aggregation for Alzheimer Management}<r>[0.217, 0.557, 0.250, 0.944]</r> has a lower Non-Aggregation score than Oncology located at {Non-Aggregation for Oncology}<r>[0.309, 0.333, 0.342, 0.944]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "bNkcQXaVAuOClh39",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Vision and Nephrology, which received a higher Aggregation score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vision with the Aggregation score at {Aggregation for Vision}<r>[0.709, 0.218, 0.741, 0.944]</r> has a higher score than Nephrology at {Aggregation for Nephrology}<r>[0.801, 0.605, 0.833, 0.944]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "ufhwPf76AbD7MT7u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within [0.073, 0.074, 0.240, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend which differentiates between two methods of healthcare performance measurement: Non-Aggregation and Aggregation, represented by two color-text labels. The Non-Aggregation method is represented by a green color denoted at {Non-Aggregation hangle color}<r>[0.078, 0.120, 0.106, 0.136]</r>, and Aggregation is represented by an orange color denoted at {Aggregation hangle color}<r>[0.078, 0.155, 0.106, 0.171]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "zMimkLuO8o3H3ErI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the contents within [0.309, 0.952, 0.374, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region marks the category \"Oncology\" on the x-axis. The coordinates for \"Oncology\" are {Oncology label}<r>[0.309, 0.952, 0.374, 0.975]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "Z0SVtTYzbcoNIKBC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are within the scope [0.525, 0.266, 0.558, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is an orange bar observing the Aggregation method's score in Cardiology. The score is 100, and the coordinates of the score are {Aggregation score for Cardiology}<r>[0.433, 0.266, 0.466, 0.944]</r>."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "qF6y5pUsUHYeONFY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.426, 0.029, 0.579]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the y-axis label, named People Count."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "FUobtvRISpUlhurn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label in the chart that corresponds to the coordinates [0.357, 0.025, 0.694, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at {these coordinates}<r>[0.357, 0.025, 0.694, 0.055]</r> is the subtitle \"Population Migration and Mobility Trends\"."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "qecgVKv1ARpgK5Kl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the square at [0.846, 0.120, 0.874, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the square at {these coordinates}<r>[0.846, 0.120, 0.874, 0.136]</r> is #69b3a2 (a shade of green)."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "47aQLgD81hk9gCSv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to the higher population trend, the color at {In_Country's color label}<r>[0.846, 0.120, 0.874, 0.136]</r> or the color at {Out_Country's color label}<r>[0.846, 0.155, 0.874, 0.171]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at {Out_Country's color label}<r>[0.846, 0.155, 0.874, 0.171]</r>, which is a shade of orange."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "zyuKxZnhS7ZvGSLW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the color corresponding to In_Country}<r>[0.846, 0.120, 0.874, 0.136]</r> and {the color corresponding to Out_Country}<r>[0.846, 0.155, 0.874, 0.171]</r>, which one represents a trend that had a higher population in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color corresponding to Out_Country}<r>[0.846, 0.155, 0.874, 0.171]</r> represents a trend with a higher population in 2010."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "xuLPq4zknH7OISdX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {In_Country\u2019s bar in 2017}[0.711, 0.274, 0.741, 2.001] have a higher population than {Out_Country\u2019s bar in 2017}[0.741, 0.344, 0.771, 2.001]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "wkA2i5WI2Ej6VaGN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the configuration in the region [0.841, 0.074, 0.978, 0.186]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend displaying the trends for 'In_Country' and 'Out_Country'. For 'In_Country', a green color label exists at [0.846, 0.120, 0.874, 0.136] and the text label reads 'In_Country' at [0.885, 0.118, 0.960, 0.142]. For 'Out_Country', an orange color label is present at [0.846, 0.155, 0.874, 0.171] and the associated text label reading 'Out_Country' is at [0.885, 0.153, 0.973, 0.177]."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "555wGedhbbks7dWm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects inside the region [0.566, 0.062, 0.685, 2.001]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are located within this region:\n1. The green bar represents {In_Country in 2016}<r>[0.625, 0.415, 0.655, 2.001]</r> with a population count of 90.\n2. The orange bar represents {Out_Country in 2016}<r>[0.655, 0.397, 0.685, 2.001]</r> with a population count of 91."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "BuBnUfxnIg4c3Cjx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.035, 0.053, 0.061, 0.076]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the y-axis label {\u2018110\u2019}<r>[0.035, 0.053, 0.061, 0.076]</r> noting a people count of 110."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "tkffS2pQTpGyMyFk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.073, 0.074, 0.257, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend for the chart, showcasing the two types of investments: {Risky Investments}[0.117, 0.118, 0.244, 0.141] and {Stable Investments} [0.117, 0.153, 0.251, 0.176]."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "WWlaf46b0nI9mh4T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element at coordinates [0.035, 0.133, 0.061, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a y-axis label for the value 200."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "vFwEJUwOCdDNyo7U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the element within [0.120, 0.463, 0.149, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blue bar {blue bar}[0.120, 0.463, 0.149, 0.944] representing the value of Risky Investments for Portfolio 1, which is 120.0."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "F74Ygsdp4AlI2RP5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Risky Investments bar for Portfolio 3}[0.287, 0.303, 0.316, 0.944] taller than corresponding {Stable Investments bar}[0.316, 0.603, 0.346, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "0ePpaSUpST8LzSxG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the series of Portfolios, is the {Risky Investments bar for Portfolio 10}[0.872, 0.142, 0.901, 0.944] higher than the {Stable Investments bar for Portfolio 10}[0.901, 0.263, 0.931, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "Pv31VoIslY5n5kRM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Risky Investments bars} generally taller than {Stable Investments bars} in the portfolios?"
+            },
+            {
+                "from": "gpt",
+                "value": "Not necessarily, it varies in different portfolios."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "en39PpVtXII3ts8Q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information within the bounding coordinates [0.073, 0.074, 0.257, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates lies the legend of the chart. It details the associations of the colors to the different investment types: {Risky Investments}[0.117, 0.118, 0.244, 0.141] is represented by the color blue [0.078, 0.120, 0.106, 0.136]; {Stable Investments}[0.117, 0.153, 0.251, 0.176] is represented by the color orange [0.078, 0.155, 0.106, 0.171]."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "MF2IbScd6gyGjyic",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be gathered from the region [0.120, 0.463, 0.149, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a blue bar which represents the value (120.0) for Risky Investments in Portfolio 1."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "AzfSgGMSHUSinYKr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content inside the region [0.066, 0.062, 0.985, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart, it includes all portfolio values for both Risky and Stable Investments. Each portfolio is represented with 2 bars; one for each investment type, with blue bars for Risky Investments and orange bars for Stable Investments."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "XE8ZqZ1XScmLIINA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text within the coordinate [0.365, 0.025, 0.686, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"Social Inequality and Rights Protection\" which is the subtitle of the chart."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "AHopxrIMKcx04LjL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar}[0.265, 0.071, 0.372, 0.944] that refers to the Wealthiest 10% in terms of Income Inequality?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is light green, specifically #69b3a2."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "ZhuLdamXIDU0d8T5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label {[0.015, 0.404, 0.029, 0.602]} stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label refers to \"Percentile Scores\"."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "ZGlgSlCC1BxhUCBZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Wealthiest 10% under Income Inequality}[0.265, 0.071, 0.372, 0.944] higher than {the bar for Poorest 10% under Rights Protection}[0.679, 0.142, 0.786, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "4pCu3G6aytdoQM4I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar that is labeled immediately before the legend title located at [0.853, 0.083, 0.928, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's #69b3a2, which is a shade of light green."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "CyClGc8iNZj5DaQ5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for the Poorest 10% in the Income Inequality category}[0.571, 0.935, 0.679, 0.944] have a higher value than {the bar for the Wealthiest 10% in the Rights Protection category}[0.372, 0.864, 0.479, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "tyTOKBSc8WzmViav",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the coordinates [0.804, 0.074, 0.978, 0.185] area."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. Two category labels are represented: \"Income Inequality\" with its respective color tag {located at [0.809, 0.120, 0.837, 0.136]}, and \"Rights Protection\", with its color tag {located at [0.809, 0.155, 0.837, 0.171]}."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "Btv6JXHZctDkBneb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.043, 0.406, 0.061, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label on the y-axis representing the number 60."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "yXS70zQr7IguWVxR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found in the area spanning [0.043, 0.582, 0.061, 0.605]."
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"40\" for the y-axis is located here."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "djLsIrHI2Q8a5Fsl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the chart with coordinates [0.015, 0.398, 0.029, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is 'Percentage Usage'."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "vn2YZTUsyLKqoldZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text representing 'Coal' in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.663, 0.952, 0.694, 0.975]."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "CVM89YEkIp3Psraj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the text label}[0.170, 0.728, 0.201, 0.752] represent in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Nonrenewable Energy percentage for Solar, which is 25.2%."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "SrWsFTPEQMQGSwuK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific color does {the legend item}[0.780, 0.120, 0.808, 0.136] symbolize in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It symbolizes the color '#69b3a2', which stands for Renewable Energy."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "NTXEZzmqhS3dtCTx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Renewable Energy bar for Solar}[0.132, 0.389, 0.168, 0.944] have a higher percentage usage than {the Nonrenewable Energy bar for Solar}[0.168, 0.758, 0.204, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "fezulfrdyEBNghu4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Renewable Energy bar for Coal}[0.643, 0.648, 0.679, 0.944] taller or shorter than {the Nonrenewable Energy bar for Coal}[0.679, 0.266, 0.714, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "MGoe9zGhZdxPZYyd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Renewable Energy bar for Wind}[0.234, 0.442, 0.270, 0.944] and {the Nonrenewable Energy bar for Wind}[0.270, 0.708, 0.306, 0.944], which one has higher energy utilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Energy."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "MF1ENR45hBPncfD7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Renewable Energy bar}[0.780, 0.120, 0.808, 0.136] color the same as the {Renewable Energy legend color}[0.819, 0.118, 0.948, 0.141]"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "rcuWzN53JrWmiE7D",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content inside [0.775, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this area, there is a legend showing two categories of energy types: Renewable Energy labeled in color '#69b3a2' at the coordinates [0.780, 0.120, 0.808, 0.136], and Nonrenewable Energy labeled in color '#d98763' at the coordinates [0.780, 0.155, 0.808, 0.171]."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "5Ks2SPu10agGJUeS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object exists in the coordinate range [0.326, 0.952, 0.419, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category label for \"Hydroelectric\" is within this coordinate range."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "4ki29PfFJPWLN4GN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.168, 0.758, 0.204, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object:\n1. A bar showing the Nonrenewable Energy usage for Solar, corresponding to a value of 25.2% usage."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "2xe0gZz3l5PpRU2p",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.663, 0.952, 0.694, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the 'Coal' text label, denoting the Coal category on the x-axis."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "MGSvqgWyBqWvdqAs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value annotated on the y-axis at coordinates [0.035, 0.814, 0.163, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tropical Rainforest."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "NIjRPJdR46e9mFJ6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar group labeled as 'Time 60' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a light blue denoted by {the handle of 'Time 60'}<r>[0.824, 0.142, 0.852, 0.155]</r>."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "OLWqD05UUlnCstgC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis coordinate at [0.492, 0.939, 0.514, 0.957] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the sustainability score of 1.5."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "oFf1MuQtdaymaibs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many ecosystems are displayed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Five ecosystems, namely {Tropical Rainforest}<r>[0.035, 0.814, 0.163, 0.831]</r>, {Ocean}<r>[0.119, 0.647, 0.163, 0.664]</r>, {Desert}<r>[0.117, 0.480, 0.163, 0.498]</r>, {Arctic Tundra}<r>[0.072, 0.313, 0.163, 0.331]</r> and {Urban City}<r>[0.089, 0.147, 0.163, 0.164]</r> are displayed."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "zn2Fbc1NiQqCVJPQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher sustainability score at Time 40, Arctic Tundra or Urban City?"
+            },
+            {
+                "from": "gpt",
+                "value": "Arctic Tundra."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "H7Fgj42Z0SWGThPf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relative size of the 'Initial Condition' bar for the 'Desert' compared with its 'Time 60' bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Desert's 'Initial Condition' bar}<r>[0.173, 0.420, 0.283, 0.554]</r> is larger than its {'Time 60' bar}<r>[0.492, 0.420, 0.592, 0.554]</r>."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "7FpBocfrFVZlKK6G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich ecosystem has a lower 'Initial Condition' score, Tropical Rainforest or Ocean?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tropical Rainforest."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "aKPypEVjDAyAMz0S",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Ocean, which time period shows a higher sustainability score, Time 20 or Time 60?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time 20."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "JgvPBuRmSBGpFMZF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.371, 0.754, 0.558, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a region containing the Time 20 bar for Tropical Rainforest, with its 'Sustainability Score' rating from 0.90 to 1.75."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "7gqcd66vZfbYaDRk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the legend area [0.819, 0.055, 0.978, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend area contains four items. Each of the four bar groups are labeled: \"{Initial Condition}\"<r>[0.863, 0.062, 0.972, 0.080]</r> is dark blue, \"{Time 20}\"<r>[0.863, 0.088, 0.919, 0.106]</r> is blue, \"{Time 40}\"<r>[0.863, 0.115, 0.919, 0.132]</r> is medium blue and \"{Time 60}\"<r>[0.863, 0.141, 0.919, 0.158]</r> is light blue."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "bRAg5BKW6zclTLga",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area between the x axis and the y ticks."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all of the sustainability scores for 5 ecosystems (Tropical Rainforest, Ocean, Desert, Arctic Tundra, Urban City) at four time points (Initial Condition, Time 20, Time 40, Time 60)."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "nkMxGHfEMfMoibYH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.431, 0.019, 0.727, 0.041] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart, which is 'Ecosystem Sustainability over Time'."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "ngGCd8MBhWWwVzl7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label with coordinates [0.114, 0.402, 0.136, 0.425] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label indicates a value of {6.7}<r>[0.114, 0.402, 0.136, 0.425]</r> for the Rural service category."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "dI1UXCrYJhVBFFzl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar located at period 3 for urban development}<r>[0.275, 0.283, 0.317, 0.889]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "CCdWWHlhMLn5kIRx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual content of the x-axis label found at [0.474, 0.952, 0.573, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Periods)."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "1JXI0VsApINfqwAQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for period 1 in urban development}<r>[0.161, 0.228, 0.203, 0.889]</r> taller than {the bar for period 1 in rural development}<r>[0.161, 0.531, 0.203, 0.889]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "jQvSbWvdLbqqOLUu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the green bar at period 12}<r>[0.787, 0.482, 0.829, 0.889]</r> represent a higher value than {the orange bar at the same period}<r>[0.787, 0.565, 0.829, 0.889]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "ktEyXlkaGMkUAh1B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values shown in {the green bars}<r>[0.104, 0.179, 0.147, 0.889]</r> generally higher than those in {the orange bars}<r>[0.104, 0.427, 0.147, 0.889]</r> across all periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "poLw5YKeUZT9O2Jq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.886, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend with two entries. The first, representing \"Urban\", has a green color marker at [0.891, 0.120, 0.919, 0.136] and text at [0.930, 0.118, 0.972, 0.141]; The second, representing \"Rural\", has an orange color marker at [0.891, 0.155, 0.919, 0.171] and text at [0.930, 0.153, 0.966, 0.176]."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "1PHGlIiM3iiJ2ipg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region framed by [0.062, 0.062, 0.985, 0.889]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plot area where all the urban and rural bars are plotted across various periods, neatly aligned along a grid that assists in value assessments."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "OBmOw90dzkOqU590",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the horizontal section from [0.043, 0.880, 0.052, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label of '0' indicating the start or zero point for the bar values in the chart."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "b5PgvBmt7yn4iwvB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the domain given in y-coordinate [0.015, 0.358, 0.029, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The name of the domain is \"Trend Percentage [%]\"."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "7ZnjFvWGUP8OxkPF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find what text is inside the coordinate [0.809, 0.565, 0.839, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text inside the coordinate is \"3.50\"."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "1e2kdfl4C41S2oKT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {legend item}[0.837, 0.120, 0.864, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the legend item is grey-ish."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "9dfEd8hImWMPcyaf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat company is represented at [0.132, 0.919, 0.234, 0.942] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The company represented is \"Biomedica Inc.\"."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "lwsmmCERy9rMUhPs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn height, does the {bar}[0.410, 0.568, 0.489, 0.903] exceed {another bar}[0.557, 0.466, 0.637, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "UImtP3opBALi9AJj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {this company}[0.132, 0.919, 0.234, 0.942] situated to the left or right of {another company}[0.347, 0.919, 0.473, 0.942] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "QN7qe76l61dyDsJ5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {this bar}[0.557, 0.466, 0.637, 0.903], is {another bar}[0.104, 0.693, 0.183, 0.903] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "J9g1gpjR90LtTGxs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the grey legend label {Stable growth}[0.831, 0.074, 0.978, 0.185] above or below the purple label {Sharp rise}[0.831, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "0UwmrH3liml55UBn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.831, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.831, 0.074, 0.978, 0.185] contains two items: The first is called \"Stable growth\", with a color label at [0.837, 0.120, 0.864, 0.136] and a text label at [0.875, 0.118, 0.972, 0.141], denoting a greyish bar; The second is called \"Sharp rise\", with a color label at [0.837, 0.155, 0.864, 0.171] and a text label at [0.875, 0.153, 0.946, 0.176], denoting a purple bar."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "pmn1jAz73stXPEBJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the coordinates [0.557, 0.466, 0.637, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates [0.557, 0.466, 0.637, 0.903] compose a bar representing \"Biotec Corp\" having a value of 5.20 in the Stable growth trend."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "dOQrqCxQsXb9lfQ7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.490, 0.952, 0.557, 0.975] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.490, 0.952, 0.557, 0.975] contains the label \"Company\" which is the title for the x-axis."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "26earkLs77VpVjhi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is highlighted in the area [0.807, 0.609, 0.864, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.784, 0.609, 0.864, 0.903] includes a bar representing the company \"GeneWorks\" under the \"Stable growth\" trend with a recorded percentage of 3.50."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "RqKIdH3a2xPYyWtG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar} at the coordinates [0.115, 0.322, 0.222, 0.506] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a Graduate bar of the group \"Young_Adults\"."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "2rUdznUA642DQtT2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the coordinates [0.879, 0.452, 0.901, 0.475]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is 1.1."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "IFdTRljDobiJm3On",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the meaning of the {colored entity} at [0.835, 0.120, 0.863, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents Graduates."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "ASoIEBUszZuUCfj9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Non-Graduate bar}[0.222, 0.506, 0.330, 0.547] taller than the {Graduate bar}[0.115, 0.322, 0.222, 0.506] in the Young_Adults group?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "TFNjXXodOSpzVqJf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the bar for Non-Graduates}[0.529, 0.396, 0.637, 0.506] and {the bar for Graduates}[0.422, 0.136, 0.529, 0.506] in the Middle_Aged group?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Non-Graduate bar is shorter than the Graduate bar."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "qjaJasnX6shcCOCl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {bar for Seniors in the Graduate category}[0.729, 0.506, 0.836, 0.662] with {bar for Seniors in the Non-Graduate category}[0.836, 0.473, 0.944, 0.506], which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Seniors in the Graduate category is taller."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "7WVjtokryW4GSrcz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.830, 0.074, 0.978, 0.185]"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two entries in the legend in this region. The first entry named Graduate is associated with the color represented by the coordinate [0.835, 0.120, 0.863, 0.136] which is purple, and the text is located at [0.874, 0.118, 0.939, 0.141]. The second entry named Non-Graduate is associated with the color represented by the coordinate [0.835, 0.155, 0.863, 0.171] which is brown, and its text is located at [0.874, 0.153, 0.972, 0.176]."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "Cl0P4tgowNX5dpK1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.729, 0.506, 0.836, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a Graduate bar for the 'Seniors' age group. The bar is above the X-axis and has a value of -5.3 denoted by a text located at [0.769, 0.656, 0.796, 0.679]."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "oExZzxg7XrpOqjP7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.185, 0.818, 0.260, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Young_Adults' on the x-axis is found in this region."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "CNj9YlN4HVbfXDUc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main label at the position [0.015, 0.223, 0.029, 0.279] in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Values."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "aYBQoAvc0ysXpaWx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the category \"Elevation\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the color label}[0.162, -0.007, 0.190, 0.006] shows that Elevation is represented by a light pink color."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "vUC3WBQ3VsIA2upd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.764, -0.008, 0.838, 0.009] in the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population label."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "vavUpSA6ciuJCCRn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the north region bar}[0.675, 0.436, 0.754, 0.437] taller than {the south region bar}[0.183, 0.437, 0.261, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "V8LjDWd66KaMGDBZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label at}[0.549, -0.008, 0.698, 0.009] have a longer text than {the label at}[0.764, -0.008, 0.838, 0.009]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "KFXoqBpL8AXcWtKv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the east region y-axis labels}>r>[0.324, 0.939, 0.354, 0.957]<r> in the lower subplot positioned higher than {the west region y-axis labels}>r>[0.951, 0.939, 0.985, 0.957]<r> in the upper subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "0fPIGkXi1XeNME0P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the rectangular area of [0.559, 0.556, 0.968, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains another subplot with an x-axis labeled {Region}>r>[0.740, 0.964, 0.787, 0.981]<r> and a y-axis labeled {Values}>r>[0.508, 0.713, 0.522, 0.770]<r>. Also present are categorized bars indicating values for different geographic regions with respective x-tick labels such as {South}>r>[0.539, 0.939, 0.579, 0.957]<r>, {North}>r>[0.676, 0.939, 0.715, 0.957]<r>, {East}>r>[0.817, 0.939, 0.847, 0.957]<r> and {West}>r>[0.951, 0.939, 0.985, 0.957]<r>."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "mMf5URCShY8nzn1Z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the primary elements within the coordinate space [0.066, 0.066, 0.475, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space primarily houses a subplot with axes labeled for a category of regions. There are x-axis labels for {South}>r>[0.104, 0.449, 0.144, 0.466]<r>, with corresponding values shown on a set of bars along the y-axis denoting different numerical data points."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "YoxclCGEKrzNg1D5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area marked [0.559, 0.066, 0.968, 0.437] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a subplot highlighting various region data with x-axis labeled as {Region}>r>[0.740, 0.473, 0.787, 0.491]<r> and y-axis as {Values}>r>[0.508, 0.223, 0.522, 0.279]<r>. Each region such as {South}>r>[0.596, 0.449, 0.637, 0.466]<r> has a colored bar representation associated with it."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "shx4gbXbg3ynrFuL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Direct Export\" in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink. Details can be found in {the handle for \"Direct Export\"}<r>[0.746, 0.093, 0.774, 0.106]</r>"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "o2jhbWi9KaXf7IPc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the chart area showing data for Germany?"
+            },
+            {
+                "from": "gpt",
+                "value": "Billion Dollars. It appears on {the y-axis label for Germany}<r>[0.015, 0.673, 0.029, 0.792]</r>"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "5KsuKX9JNzDvCzDM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text description for \"USA\" located at the bottom of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "USA. The specific coordinates are {the text for \"USA\"}<r>[0.118, 0.449, 0.147, 0.466]</r>"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "p3UTjK01BEnpioKo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for USA in subplot_2}[0.092, 0.746, 0.173, 0.816] higher than {the bar for Japan in subplot_2}[0.393, 0.816, 0.473, 0.830]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "m0vWCHH6qX1xO41W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the text for \"China\" in subplot_0}[0.213, 0.449, 0.253, 0.466] appear to the left or right of {the text for \"Germany\" in subplot_0}[0.301, 0.449, 0.365, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "lK9AU4j1Upg8VEBD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the text for \"Japan\" in subplot_1}[0.906, 0.449, 0.945, 0.466] closer to {the text for \"Germany\" in subplot_1}[0.794, 0.449, 0.858, 0.466] or to {the border of the subplot_1}[0.985, 0.047, 0.985, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Germany."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "F2oCyq5NWiAZLWKo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region including all texts in {the legend}<r>[0.740, 0.059, 0.893, 0.195]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains descriptions for four trade variables:\n1. \"Direct Export\" text is present at [0.785, 0.092, 0.877, 0.109],\n2. \"Indirect Export\" text appears at [0.785, 0.118, 0.887, 0.135],\n3. The text for \"Import\" is found at [0.785, 0.144, 0.831, 0.162],\n4. Text indicating \"Net Export\" is located at [0.785, 0.170, 0.859, 0.188]."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "24Apk23gtHk01wiU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents found within the region {the subplot for Japan in the third position}<r>[0.566, 0.537, 0.985, 0.927]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a bar chart comparing trade details (Direct export, Indirect export, Import, Net export) for four countries (USA, China, Germany, Japan). It also includes categorical labels on the x-axis and country names along with specific bars and their corresponding values."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "WaCzlejI7ZolSo1d",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area encompassing {all x-axis labels for subplot_3}<r>[0.611, 0.939, 0.945, 0.957]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains labels for four countries:\n1. \"USA\" located at [0.611, 0.939, 0.639, 0.957],\n2. \"China\" situated at [0.705, 0.939, 0.745, 0.957],\n3. \"Germany\" at [0.794, 0.939, 0.858, 0.957],\n4. \"Japan\" found at [0.906, 0.939, 0.945, 0.957]."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "8tgB0uYAAd0AA6Tx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify {the subtitle} for the chart related to Africa?"
+            },
+            {
+                "from": "gpt",
+                "value": "International Trade and Globalization Trends Africa<r>[0.070, 0.019, 0.489, 0.041]</r>."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "QFX0z45i8TB8qOJG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.182, 0.029, 0.320]</r> in the Asia chart indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "GNP (in billions)."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "IS3JRoI6zSz76dDW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLocate {the legend icon} for \"GNP (2000)\" in the North-America chart."
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.713, 0.581, 0.740, 0.593]</r>."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "FozyBdJPBFw6hezw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the {bar coordinate}<r>[0.086, 0.326, 0.196, 0.437]</r> for Africa's GNP in 2000 or the {bar coordinate}<r>[0.717, 0.667, 0.827, 0.927]</r> for North-America's GNP in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "North-America's GNP in 2010."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "RNQeP2TISuv4YNgi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Asia chart's y-axis tick labeled \"3.0\"}<r>[0.527, 0.549, 0.549, 0.567]</r> at a higher or lower position than {Europe chart's y-axis tick labeled \"3.0\"}<r>[0.035, 0.549, 0.057, 0.567]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "vElxFhCTL3doo65C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar for Europe's GNP in 2020}<r>[0.362, 0.722, 0.473, 0.927]</r> have a greater value than the {bar for Asia's GNP in 2010}<r>[0.717, 0.315, 0.827, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "Imx1ggFjFCN2dcFl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be deduced from the subplot in the region [0.559, 0.556, 0.985, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This subplot includes the chart for North-America showcasing the GNP from 2000 to 2020. Bars indicating the values are shaded in light red, and the tallest {bar}<r>[0.855, 0.632, 0.966, 0.927]</r> corresponds to the year 2020."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "oZnxcLgPDthwLe6m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area with the coordinates [0.066, 0.066, 0.492, 0.437]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area details a subplot for Africa with a series of bars painted in light red, representing GNP values for years 2000, 2010, and 2020. Additionally, the y-axis labels indicate GNP values in billions with corresponding ticks."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "XJQXWMg625eCAypi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements located in the bottom right region [0.559, 0.473, 0.985, 0.927] of the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main components within this region include the x-axis labels of the years - \"GNP (2000)\", \"GNP (2010)\", and \"GNP (2020)\", the y-axis label indicating \"GNP (in billions)\" and a series of three light red bars representing the GNPs for the years listed, with the bar for \"GNP (2020)\" being the tallest."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "9947dmfalGPnJkmu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the text}[0.035, 0.196, 0.043, 0.213] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label representing the value 3 Trillion dollars."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "512GtoH5nZevHIfc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the handle at}[0.532, -0.033, 0.560, -0.021] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "6kr7sRL3VdFgWUjS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is revealed by {the x-axis label}[0.751, 0.964, 0.780, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the Year."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "zO7HmCuPwu0BZ30s",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar in 2010 for Germany}[0.178, 0.809, 0.262, 0.927] higher than {the bar in 2005 for India}[0.566, 0.913, 0.650, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "tnF4AK9K7taRxUzA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for China in 2005}[0.566, 0.400, 0.650, 0.437] have a lower trade volume than {the bar for United States in 2020}[0.388, 0.265, 0.473, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "523ipOMrJMQiWO1b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the values on the y-axis for the subplot of India}[0.527, 0.920, 0.536, 0.548] the same range as {the values on the y-axis for the subplot of China}[0.527, 0.040, 0.536, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "JxDqGViuMGjMuVF8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the coordinates [0.546, 0.047, 0.985, 0.437]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the entire plot for China's Global Trade Volumes Over the Years, showing bars from 2005 to 2020 with x-axis labels and y-axis labels indicating trade volumes."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "hoDXDyd7Qpb2oyLf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is depicted within the area [0.053, 0.537, 0.493, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region delineates the subplot for Germany's Global Trade Volumes, including bars representing trade volumes from the years 2005 to 2020, x-axis labels denoting the years, and y-axis labels displaying the trade volumes in trillions."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "ioJJh0RbsVcdQJQM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of [0.546, 0.537, 0.985, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the subplot for India's Global Trade Volumes Over the Years, featuring bars from 2005 to 2020, along with corresponding x-axis year labels and y-axis labels detailing trade volume in trillions."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "haNEtW48NEyVXsQw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by the coordinates [0.121, 0.447, 0.228, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the \"Software\" category in \"Healthcare\"."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "oU5NdocqkHiJoMMn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Software bar in Education}[0.240, 0.328, 0.347, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "GxnOdRINRX5Hwpdy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the category represented by {this bar}[0.359, 0.251, 0.465, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Finance for \"Software\"."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "wrpUnmj68iAODS9c",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Software in Finance}[0.359, 0.251, 0.465, 0.686] and {Tech&Innovation in Finance}[0.359, 0.136, 0.465, 0.251], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Software."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "dneAM3CddIFokt4J",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sector has a greater representation of {Tech&Innovation bar in Energy}[0.596, 0.136, 0.703, 0.261] or {Tech&Innovation bar in Transport}[0.715, 0.136, 0.822, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transport."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "TZTGmS902opU1NfX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Software bar}[0.121, 0.447, 0.228, 0.686] related to Healthcare have a higher representation than the {Tech&Innovation bar}[0.121, 0.136, 0.228, 0.447] in the same field?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "Wae943o5LcDyPNVI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the area [0.347, 0.070, 0.714, 0.133]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes two categories: Software with coordinates [0.403, 0.084, 0.480, 0.119] and its corresponding color represented by a green rectangle at [0.354, 0.088, 0.389, 0.112], and Tech&Innovation with coordinates [0.563, 0.084, 0.707, 0.119] and its corresponding color represented by a yellow rectangle at [0.515, 0.088, 0.549, 0.112]."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "aprukj7NHR0jXiyQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a summary of the chart region [0.359, 0.136, 0.465, 0.686]."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart represents the Finance sector. It contains two bars: Software and Tech&Innovation. The green bar shows the representation of Software from coordinates [0.359, 0.251, 0.465, 0.686], and the yellow bar shows the representation of Tech&Innovation from coordinates [0.359, 0.136, 0.465, 0.251]."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "9Sm99Ge6VgNKUwEm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's found in region [0.596, 0.136, 0.703, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the data bars for the Energy sector. There's a green Software bar from [0.596, 0.261, 0.703, 0.686], and a yellow Tech&Innovation bar from [0.596, 0.136, 0.703, 0.261]."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "Bs3t0ejcUJ4Jbfr8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinate [0.667, 0.052, 0.881, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the \"Satellite Communication\" for the legend."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "VrBkhe6ydBtITMOe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend label with coordinates [0.618, 0.055, 0.653, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a shade of blue, indicating \"Satellite Communication\"."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "WMOLrrmsP5Su8MGi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value range of the bar located at [0.552, 0.366, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its range is from 0.00 to 70.00, representing \"Mobile Internet\" under the category of \"Accelerated Decline\"."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "Rj0Sgt1umlNqqo70",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing mobile internet trend for \"Slow Growth\"}[0.121, 0.635, 0.509, 0.903] taller than {the bar representing mobile internet for \"Accelerated Decline\"}[0.552, 0.366, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "ML6vOcLpQMbUK66C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing \"Mobile Internet\"}[0.121, 0.635, 0.509, 0.903][0.552, 0.366, 0.940, 0.903] taller than {the bars representing \"Satellite Communication\"}[0.121, 0.136, 0.509, 0.443][0.552, 0.136, 0.940, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "0HPLxoVmpKRf9g3Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Slow Growth\"}([0.121, 0.635, 0.509, 0.903], [0.121, 0.443, 0.509, 0.635], [0.121, 0.136, 0.509, 0.443]) and {\"Accelerated Decline\"}([0.552, 0.366, 0.940, 0.903], [0.552, 0.289, 0.940, 0.366], [0.552, 0.136, 0.940, 0.289]), which category has a higher overall values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Growth."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "RVP9W5NYIXvfdTbg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.174, 0.038, 0.888, 0.101]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three labels of the legend. They are {Mobile Internet}[0.181, 0.055, 0.216, 0.079], {Optical Network}[0.395, 0.055, 0.430, 0.079] and {Satellite Communication} [0.618, 0.055, 0.653, 0.079]. The respective labels for these colors are in the coordinates [0.229, 0.052, 0.361, 0.087], [0.444, 0.052, 0.584, 0.087], and [0.667, 0.052, 0.881, 0.087]."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "5V1fBawpPlHg0pr6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.552, 0.289, 0.940, 0.366]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a bar of the \"Optical Network\", category \"Accelerated Decline\". The value range of this bar is from 70.00 to 80.00 shown by the y-axis."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "v7nR0RiYChcvt8qr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region from [0.080, 0.136, 0.981, 0.903] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the plotted chart, with all bars and axes. The bars represent the 3 categories: \"Mobile Internet\", \"Optical Network\", and \"Satellite Communication\" across the two time points: \"Slow Growth\" and \"Accelerated Decline\""
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "BCw5QQZl7GlfHLVk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the region [0.552, 0.136, 0.940, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 bars in this region, each representing a different category of the \"Accelerated Decline\" class. These bars correspond to \"Mobile Internet\"[0.552, 0.366, 0.940, 0.903], \"Optical Network\"[0.552, 0.289, 0.940, 0.366] and \"Satellite Communication\"[0.552, 0.136, 0.940, 0.289]."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "glhV8NNcUnFDx6xF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {object with color #4e78bf}<r>[0.121, 0.051, 0.228, 0.188]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the project 'SecurityUpgrade' under the phase 'CashCow'."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "ffxCNvCaUdblXQHN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinate [0.817, 0.190, 0.851, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color represent 'CashCow' in {the legend}<r>[0.810, 0.069, 0.973, 0.236]</r>."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "CBX1hWlGLttmP8xu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project does this object <r>[0.240, 0.051, 0.347, 0.170]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the project 'BackendRebuild'."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "feRxDhXPtXUJLKb2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {the legend}<r>[0.810, 0.069, 0.973, 0.236]</r>, which legend item is to the left of {the CashCow color-label}<r>[0.817, 0.190, 0.851, 0.215]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item 'ScalingUP' is to the left of the 'CashCow' color-label."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "jnA1HU4TpdN9UWbn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'FrontendRevamp' bar for 'RampingUP'}<r>[0.359, 0.366, 0.465, 0.475]</r> higher or lower than {the 'FrontendRevamp' bar for 'ScalingUP'}<r>[0.359, 0.158, 0.465, 0.366]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "PXLmFpqmhxOM9iy8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'MarketExpansion' bar for 'CashCow'}<r>[0.477, 0.051, 0.584, 0.147]</r> and {the 'MarketExpansion' bar for 'RampingUP'}<r>[0.477, 0.339, 0.584, 0.475]</r>, which one has a larger value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'MarketExpansion' bar for 'RampingUP'."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "2fkoiu4AmYOKaiQf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.810, 0.069, 0.973, 0.236]."
+            },
+            {
+                "from": "gpt",
+                "value": "The highlighted region represents the chart legend. Its components include the 'RampingUP' label located at <r>[0.865, 0.083, 0.966, 0.118]</r>, with the corresponding color marker at <r>[0.817, 0.086, 0.851, 0.110]</r>, the 'ScalingUP' label located at <r>[0.865, 0.135, 0.951, 0.170]</r> with the corresponding color marker at <r>[0.817, 0.138, 0.851, 0.162]</r>, and the 'CashCow' label at <r>[0.865, 0.187, 0.945, 0.222]</r> with the corresponding color marker at <r>[0.817, 0.190, 0.851, 0.215]</r>."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "kduP0t1FskIWtxYq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe about the region [0.121, 0.188, 0.228, 0.475]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two stacked bars for the project 'SecurityUpgrade'. The bottom bar <r>[0.121, 0.188, 0.228, 0.318]</r> represents the 'ScalingUP' phase while the top bar <r>[0.121, 0.318, 0.228, 0.475]</r> represents the 'RampingUP' phase."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "6Z3R7tejju3a4ISF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region from [0.833, 0.305, 0.940, 0.475]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows the 'RampingUP' phase of the 'CustomerFeedbackSystem' project, with its scale going from 0 to 40.11 in the y-axis."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "FavkzgYv7FYq7hQ5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no title mentioned in the chart figure."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "XDraR70xulPUpMI8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the orange bar at [0.790, 0.250, 0.940, 0.444] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value of the \"Education Aid\" for \"Volatile Growth\"."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "7hhd5ZzcBHWZl2q0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element present at the coordinates [0.454, 0.099, 0.574, 0.134]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for Education Aid in the legend."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "UgbHOnzA1B299OuQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar with coordinates [0.623, 0.200, 0.606, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It\u2019s the orange bar showing 15.40% to 85.60% of \"Education Aid\" for the \"Rapid Growth\" scenario."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "U9z2u74oYoik2CTG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value in \"Rapid Growth\", {Education Aid}<r>[0.456, 0.200, 0.606, 0.516]</r> or {Medical Assistance}<r>[0.456, 0.516, 0.606, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education Aid."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "1HZ4Y74OSQI0HlQB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the green rectangle representing \"Medical Assistance\" in \"Rapid Expansion\" taller than the orange rectangle representing \"Education Aid\" in the same scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "3pz0M0HESnUmQOkE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Education Aid of Rapid Growth}<r>[0.456, 0.200, 0.606, 0.516]</r> higher than {Medical Assistance of Marginal Decrease}<r>[0.623, 0.472, 0.773, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "RlPnyK44MW8fEbVI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars in \"Rapid Growth\" scenario<[0.456, 0.136, 0.606, 0.585]> higher than in \"Substantial Increase\" scenario<[0.288, 0.136, 0.439, 0.537]> overall?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "Ov1Xa3AdvtznQfZX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat all components are in the region that spans [0.151, 0.086, 0.911, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are three handles representing three different categories in this region. The green handle at [0.158, 0.103, 0.192, 0.127] indicates \"Medical Assistance\", the orange handle at [0.405, 0.103, 0.440, 0.127] denotes \"Education Aid\", and the blue handle at [0.609, 0.103, 0.643, 0.127] stands for \"Environmental Conservation\". Respectively their corresponding text labels are at [0.206, 0.099, 0.371, 0.134], [0.454, 0.099, 0.574, 0.134], and [0.657, 0.099, 0.904, 0.134]."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "8ZJmpJNJ3hXP4QQw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects represented in the region [0.121, 0.270, 0.940, 0.585]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a group of five bars representing \"Medical Assistance\" category for each scenario: \"Rapid Expansion\" located in [0.121, 0.270, 0.272, 0.585]; \"Substantial Increase\" in [0.288, 0.537, 0.439, 0.585]; \"Rapid Growth\" in [0.456, 0.516, 0.606, 0.585]; \"Marginal Decrease\" in [0.623, 0.472, 0.773, 0.585]; \"Volatile Growth\" in [0.790, 0.444, 0.940, 0.585]."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "LslmerRoxIYqolHM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in [0.623, 0.136, 0.773, 0.585]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of 3 bars for the scenario \"Marginal Decrease\". The bars represent \"Medical Assistance\" at [0.623, 0.472, 0.773, 0.585], \"Education Aid\" at [0.623, 0.379, 0.773, 0.472], and \"Environmental Conservation\" at [0.623, 0.136, 0.773, 0.379]."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "W6OZT4YLPU10CNU5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the detail for area [0.121, 0.270, 0.272, 0.585]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the green bar representing the \"Medical Assistance\" of \"Rapid Expansion\" scenario."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "3ttcrju0RGIhVffQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the sector with {the label 'Profit Sustainability'}<r>[0.677, 0.065, 0.812, 0.093]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "PLp2qCb46JbQsI5L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label 'Energy' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.359, 0.759, 0.403, 0.848]."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "t2AUsmWANKNubVvV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar occupying the space <r>[0.227, 0.497, 0.337, 0.740]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is 'E-commerce'."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "sonnZAnEv9VjUA4W",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat subcategory does the bar with the coordinates <r>[0.591, 0.344, 0.701, 0.497]</r> represent from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents {Social Initiatives}[0.497, 0.065, 0.610, 0.093]."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "p7k0yDwSEDMY6aYN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'E-commerce'}[0.227, 0.132, 0.337, 1.000] taller or shorter than {the bar representing 'Technology'}[0.106, 0.132, 0.215, 1.000]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "qa2HRYEhyizAWwp5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar with the label 'Profit Sustainability'}[0.834, 0.132, 0.943, 0.192] higher than {the bar labeled 'Social Initiatives'}[0.834, 0.192, 0.943, 0.375] for the 'Healthcare' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "pV4nIfRB7CdHAK3g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Energy'}[0.349, 0.132, 0.458, 1.000] wider than {the bar representing 'Healthcare'}[0.834, 0.132, 0.943, 1.000]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "fKuTP7MFKEvOOJA5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf the three sections in the {Transportation bar}[0.470, 0.132, 0.579, 1.000], which color corresponds to {Environmental Impact}[0.276, 0.065, 0.430, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "uO0xD0RkYLCI0jw9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.232, 0.054, 0.817, 0.104]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes three labels: {Environmental Impact}<r>[0.276, 0.065, 0.430, 0.093]</r>, {Social Initiatives}<r>[0.497, 0.065, 0.610, 0.093]</r>, and {Profit Sustainability}<r>[0.677, 0.065, 0.812, 0.093]</r>. Associated colors with these labels are green, yellow, and blue respectively."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "1xURXHPCnmjziHB0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.106, 0.132, 0.215, 1.000]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a bar from the 'Technology' category. The bar contains three segments representing 'Environmental Impact'<r>[0.106, 0.527, 0.215, 0.740]</r>, 'Social Initiatives'<r>[0.106, 0.253, 0.215, 0.527]</r>, and 'Profit Sustainability'<r>[0.106, 0.132, 0.215, 0.253]</r>."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "xFNnBiO5OZQK9Wfd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what can be seen in the area [0.024, 0.364, 0.055, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a '60%' label at the y-axis, indicating the percentage level in the chart."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "dvCsEQNrRkaB5sx5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is discussed within the ranges [0.444, 0.759, 0.525, 0.921] and [0.591, 0.759, 0.646, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These ranges include the x-axis labels for the 'Transportation' and 'Food and Beverage' categories respectively."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "lWhIQDyPlKmYVOzV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object inside the coordinates [0.271, 0.064, 0.377, 0.099]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text item in the legend that is labeled \"GDP Growth\"."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "R5AxxaQiG6Rd0q43",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the legend term \"GDP Growth\"}<r>[0.271, 0.064, 0.377, 0.099]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding color is green (#7e9671) marked in the coordinated box/range [0.223, 0.067, 0.257, 0.092]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "TG6zCtXPpAgb6CAQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is presented around the coordinate {tick}[0.903, 0.821] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Year10\" label."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "EIT31esZbwEsM5Ur",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller: {the bar representing GDP Growth in Year2}[0.204, 0.794, 0.278, 0.821] or {the bar representing Unemployment Rate in Year2}[0.204, 0.752, 0.278, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing GDP Growth in Year2 is taller."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "xWO4UCbkeqOp6OmW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing Inflation Rate}[[0.121, 0.744, 0.196, 0.753], [0.204, 0.741, 0.278, 0.752], [0.287, 0.738, 0.361, 0.752], [0.369, 0.738, 0.444, 0.752], [0.452, 0.736, 0.527, 0.752], [0.535, 0.733, 0.609, 0.752], [0.618, 0.730, 0.692, 0.751], [0.700, 0.727, 0.775, 0.751], [0.783, 0.722, 0.858, 0.749], [0.866, 0.719, 0.940, 0.748]] overall shorter than {the bars representing GDP Growth}[[0.121, 0.791, 0.196, 0.821], [0.204, 0.794, 0.278, 0.821], [0.287, 0.795, 0.361, 0.821], [0.369, 0.799, 0.444, 0.821], [0.452, 0.802, 0.527, 0.821], [0.535, 0.805, 0.609, 0.821], [0.618, 0.808, 0.692, 0.821], [0.700, 0.810, 0.775, 0.821], [0.783, 0.813, 0.858, 0.821], [0.866, 0.816, 0.940, 0.821]] and {Unemployment Rate}[[0.121, 0.753, 0.196, 0.791], [0.204, 0.752, 0.278, 0.794], [0.287, 0.752, 0.361, 0.795], [0.369, 0.752, 0.444, 0.799], [0.452, 0.752, 0.527, 0.802], [0.535, 0.752, 0.609, 0.805], [0.618, 0.751, 0.692, 0.808], [0.700, 0.751, 0.775, 0.810], [0.783, 0.749, 0.858, 0.813], [0.866, 0.748, 0.940, 0.816]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "unVwPo3lRzrHoSU9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {Unemployment Rate}<r>[0.412, 0.067, 0.446, 0.092]</r> in Year10 higher or lower than the value for {GDP Growth}<r>[0.223, 0.067, 0.257, 0.092]</r> in Year10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "11w3U6OtGNgBhcTH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area in the coordinates [0.783, 0.722, 0.858, 0.749]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the value of \"Inflation Rate\" in \"Year9\". It's a blue bar (color code: #4e78bf) that represents a range of '10.50 -> 14.40'."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "x2hDORbQeacBUPFH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements lie within the coordinates [0.216, 0.050, 0.846, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area. Inside it, there are three legend entries representing \"GDP Growth\", \"Unemployment Rate\", and \"Inflation Rate\". Each entry consists of a square label colored respectively with '#7e9671', '#d3c475' and '#4e78bf' and a text."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "pw42s3SeiVJnVLG5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's portrayed between [0.080, 0.136, 0.981, 0.821]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly contains data presented as stacked bars corresponding to GDP Growth, Unemployment Rate, and Inflation Rate for different years. Also, it includes x-axis labels representing various years (from \"Year1\" to \"Year10\") and y-axis labels giving percentage values (from '0%' to '100%'). The bars vary in length and correspond to the respective values of the different rates for each year."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "59CPU5pF5iWIxu9L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the coordinates [0.378, 0.108, 0.492, 0.143]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text representing Local Culture in the legend."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "AIFLNOx03kfKlrzq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the entity at coordinates [0.526, 0.111, 0.561, 0.135]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d3c475, representing External Influence."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "T21GyVoJXpojSJua",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.121, 0.238, 0.228, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing Local Culture score for Asian Villages."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "qPuFTGHCmNPccO7D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Local Culture bar of Asian Villages}[0.121, 0.238, 0.228, 0.531] higher or lower than {the External Influence bar of Asian Villages}[0.121, 0.136, 0.228, 0.238]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "RbcVT6hrkkOlJIio",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {World Heritage Sites in Local Culture}[0.833, 0.195, 0.940, 0.531] scoring higher than {European Architectures in External Influence}[0.240, 0.136, 0.347, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "GeNyWCjltHGEJFbG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the category of {World Heritage Sites}[0.878, 0.555, 0.896, 0.912] has a higher percentage of {External Influence}[0.833, 0.136, 0.940, 0.195] than it does of {Local Culture}[0.833, 0.195, 0.940, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "cjWmUIeuSXjoyBBI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with coordinates [0.322, 0.094, 0.739, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the graph. It has two entries: Local Culture{Local Culture}[0.378, 0.108, 0.492, 0.143] is represented by a handle and text in color #7e9671, and External Influence{External Influence}[0.575, 0.108, 0.732, 0.143] is represented by a handle and text in color #d3c475."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "kZEaZRZdhgFxh8hO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.121, 0.136, 0.228, 0.531]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars corresponding to Asian Villages. The upper bar is {Local Culture bar}[0.121, 0.238, 0.228, 0.531] and the smaller, bottom one denotes {External Influence bar}[0.121, 0.136, 0.228, 0.238]."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "FlUgd6LKalrVrtZP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat major elements does the box [0.80, 0.50, 1.00, 1.00] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the right half of the bars, which represent the categories of Indigenous Cultures, Global Expo, Street Festivals, and World Heritage Sites. It also includes the corresponding category labels and both the Local Culture and External Influence segments for each category."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "40M1OZv74M7Kqeba",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.346, 0.083, 0.679, 0.113] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's title, which reads \"Bar Chart Showing Food Security Trends\"."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "6zAY12PXnyxg6Ury",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.797, 0.074, 0.731, 0.099] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color label for {Social Initiatives}<r>[0.797, 0.074, 0.731, 0.099]</r>, depicted in a specific blue."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "Q9N3uP1Dd3j4rGos",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the object at [0.121, 0.391, 0.246, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar on the chart representing the percentage of Environment Protection initiative for {Apple}<r>[0.121, 0.391, 0.246, 0.774]</r>, which is 60%."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "zQn0VeghJhQG3NGR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Environment Protection bar for Apple}<r>[0.121, 0.391, 0.246, 0.774]</r> taller than {the Environment Protection bar for IBM}<r>[0.399, 0.519, 0.524, 0.774]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "BK5bykkgvV6Ze8fX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the Social Initiatives bar for Facebook}<r>[0.677, 0.136, 0.801, 0.231]</r> or {the Social Initiatives bar for Google}<r>[0.538, 0.136, 0.663, 0.200]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Facebook."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "K7lEcKRR57vfX3Pe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Charity Programs bar for Microsoft}<r>[0.260, 0.295, 0.385, 0.455]</r> inferior in height to {the Charity Programs bar for Amazon}<r>[0.815, 0.263, 0.940, 0.583]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "HPic71KeE56BvDVI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the region [0.168, 0.057, 0.894, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It consists of three labels with corresponding color codes: {Environment Protection}<r>[0.223, 0.071, 0.427, 0.106]</r> with a green color code, {Charity Programs}<r>[0.511, 0.071, 0.662, 0.106]</r> with a yellow color code, and {Social Initiatives}<r>[0.745, 0.071, 0.887, 0.106]</r> represented with a blue color code."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "mEPo2Z0cjLOaP8TM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.399, 0.519, 0.524, 0.774]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a green bar representing the Environmental Protection initiative for the company IBM, and it stretches from 0% to 40% in heights."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "e3ivSxmCtHp6bbnr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region [0.815, 0.136, 0.940, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, interestingly, there are three separate bars representing Amazon's initiatives in, from bottom to top, Social Initiatives, Charity Programs, and Environment Protection, with percentage heights of 20%, 50%, and 30%, respectively."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "Wcijq9mHeQJTNo07",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is being represented in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "DAm7cQrVUvR6hJaQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the company_B bar}[0.121, 0.387, 0.509, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d3c475."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "WJJW9czz6ddbR1Uy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color #4e78bf represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Company_C}<r>[0.689, 0.082, 0.790, 0.117]</r>."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "ZpU1TaFEX4trWrQd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis tick at coordinates [0.019, 0.122, 0.068, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "100%."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "8LdBQLDNIHg9n4lG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Company_A}[0.121, 0.536, 0.509, 0.707] projected to have a smaller or larger market share compared to {Company_B}[0.121, 0.387, 0.509, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "uwGdtzsIOd936tvp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, is {Company_C}[0.121, 0.136, 0.509, 0.387] predicted to have the largest or smallest market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "Largest."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "zXEg3g8h9wfWLZMB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of {Company_A} [0.121, 0.536, 0.509, 0.707] and [0.552, 0.541, 0.940, 0.707] taller than the bars of {Company_C}[0.121, 0.136, 0.509, 0.387] and [0.552, 0.136, 0.940, 0.370]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "lyDS7n4xR7JrpcIB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Company_C's Projection bar}[0.552, 0.136, 0.940, 0.370] larger or smaller than {Company_C's Market Share bar}[0.121, 0.136, 0.509, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "8Aw4C9StCi0BbjB5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in the region [0.265, 0.067, 0.797, 0.130]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the graph. There are three labels, each with an associated color. The labels are 'Company_A', 'Company_B', and 'Company_C' with respective coordinates at [0.320, 0.082, 0.421, 0.117], [0.504, 0.082, 0.605, 0.117}, and [0.689, 0.082, 0.790, 0.117]. The colors associated with these labels in the legend are '#7e9671', '#d3c475', and '#4e78bf', respectively."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "He0u4Vh8Cw4KsfLz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinate area [0.552, 0.136, 0.940, 0.707]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of three bar segments - one for each company (A, B, C), representing their respective projected market shares. {Company_A}[0.552, 0.541, 0.940, 0.707] has the lowest projected market share, followed by {Company_B}[0.552, 0.370, 0.940, 0.541] and finally, {Company_C}[0.552, 0.136, 0.940, 0.370] has the highest projected market share."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "QP893kxejfVb3jSC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.121, 0.387, 0.509, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars for 'Market Share' of the companies. The bars for {Company_A}[0.121, 0.536, 0.509, 0.707], {Company_B}[0.121, 0.387, 0.509, 0.536], and {Company_C}[0.121, 0.136, 0.509, 0.387] are included with 'Company_A' having the smallest market share and 'Company_C' having the largest market share."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "3Auv9ZLjfxv1iBy8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the data located at [0.041, 0.693, 0.068, 0.728]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a tick on the y-axis labeled '0%'."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "gzeA5Co5eZ8Hkk0X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure at the coordinates [0.318, 0.066, 0.345, 0.086]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label color for Agriculture in the legend."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "3n6fF1tqNjGsXRWS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text inside the coordinate [0.357, 0.064, 0.433, 0.092]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend label, called Agriculture."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "eTXHNVp4cQm61Eox",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the period marked between coordinates [0.527, 0.770, 0.596, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Middle Ages."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "WGcht5fkWXLlY0VN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears at [0.024, 0.492, 0.055, 0.520]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis percentage scale, labeled as 40%."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "DsJ1mGz8vEorZLbh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Agriculture bar in Primitive Stage}[0.106, 0.626, 0.228, 0.750] have a higher value than the {Science bar in the same stage}[0.106, 0.595, 0.228, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "61HaNGtUxx9SCBbM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Industrialization bar in the Modern History stage}[0.678, -0.759, 0.800, -0.314] shorter than the {Science bar in the Modern History stage}[0.678, -0.314, 0.800, 0.200]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "CtYTPweUcnxs6ju7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Agriculture}[0.357, 0.064, 0.433, .092] have its highest value in the {Contemporary Era}[0.822, 0.193, 0.943, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "ohDZYtDgUlXlZbfp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Industrialization bar for the Classical Period}[0.392, 0.070, 0.514, 0.101] taller than the {Science Bar for the Classical Period}[0.392, 0.101, 0.514, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "MKcStE5a37odA0DS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the graphic elements between [0.312, 0.053, 0.737, 0.103]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are three color-text pairs representing each category; each group consists of a color label and a text label. They are: {Agriculture}[0.318, 0.066, 0.345, 0.086] paired with [0.357, 0.064, 0.433, 0.092], {Science}[0.461, 0.066, 0.489, 0.086] paired with [0.500, 0.064, 0.553, 0.092], and {Industrialization}[0.581, 0.066, 0.609, 0.086] paired with [0.620, 0.064, 0.732, 0.092]."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "YcLxWr5a8YiZGQGs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.067, 0.770, 0.882, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the period labels along the x-axis, which includes: \"Primitive Stage\", \"Ancient Period\", \"Classical Period\", \"Middle Ages\", \"Modern History\", and \"Contemporary Era\"."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "ZMIypPRZF8dZy2s3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we see in the region [0.822, 0.193, 0.943, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are three bars in this region. The first one represents the Agriculture value in the Contemporary Era [0.822, 0.193, 0.943, 0.750]; the second one represents the Science value in the same era [0.822, -0.400, 0.943, 0.193]; and the third one represents the Industrialization value [0.822, -0.975, 0.943, -0.400]."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "PdqOkshv6m9Io8yV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area [0.024, 0.121, 0.055, 0.767]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises the scaling for the Y-axis ranging from 0% to 100%, with intervals of 10% between marks."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "MlJ3LnYNxi1Bi442",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category of the bar in the coordinates [0.321, 0.310, 0.415, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yoga."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "LH7c62LN6f5S0SIB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the \"Strength Training\" bar from \"Fitness Trends\" data set?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.216, 0.327, 0.310, 0.572]."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "QyJyUJxbSE5Zc0WC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {Fitness Trends bar}[0.845, 0.275, 0.940, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "34."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "JExR78Lx7QRxrKWf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the colour associated with {Fitness Trends} in the legend<r>[0.301, 0.088, 0.749, 0.150]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "mU5461rVKoZXn9SR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Treadmill Run\" bar of \"Fitness Trends\"}[0.845, 0.275, 0.940, 0.572] higher than {the \"Treadmill Run\" bar of \"Sports Health Trends\"}[0.845, -0.091, 0.940, 0.275]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "nSIfEONA75YouqpU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a greater value in \"Sports Health Trends\", {Yoga}[0.321, 0.205, 0.415, 0.310] or {Dietary Supplements}[0.635, 0.066, 0.730, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dietary Supplements."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "QFKf2ndE039oPaoG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Physical Activity bar for Fitness Trends}[0.111, 0.354, 0.205, 0.572] taller than {the Strength Training bar for Fitness Trends} [0.216, 0.327, 0.310, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "Zob69pE56Xn53WNC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {\"Healthy Eating\" bar for Fitness Trends}[0.531, 0.240, 0.625, 0.572] and {\"Healthy Eating\" bar for Sports Health Trends}[0.531, 0.031, 0.625, 0.240], which trend shows a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sports Health Trends."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "8O9iWBQGvhLzZs0C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in [0.301, 0.088, 0.749, 0.150]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend with two entries: {\"Fitness Trends\"} with coordinates [0.357, 0.101, 0.479, 0.136] represented by green colour [0.308, 0.105, 0.343, 0.129] and {\"Sports Health Trends\"} with coordinates [0.562, 0.101, 0.742, 0.136] represented by blue colour [0.514, 0.105, 0.548, 0.129]."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "9nk88BDRPx5fQ3NM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the rectangular area from [0.111, 0.227, 0.205, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a green bar, representing \"Physical Activity\" from the \"Fitness Trends\" dataset."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "RzHk98InMV9F2TeN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within the region [0.531, 0.022, 0.625, 0.572]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars within this region: one from the \"Fitness Trends\" dataset with coordinates [0.531, 0.240, 0.625, 0.572] and the other from the \"Sports Health Trends\" dataset with coordinates [0.531, 0.031, 0.625, 0.240]. Both represent the \"Healthy Eating\" category."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "L6zTAoXYAh2uhRka",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the boundaries [0.569, 0.022, 0.663, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bar charts showing the \"Dietary Supplements\" information for both Fitness Trends [0.635, 0.327, 0.730, 0.572] and Sports Health Trends [0.635, 0.066, 0.730, 0.327]."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "bVn1tDCdQZi6C8j4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar chart represent according to the title at [0.389, -0.003, 0.660, 0.027]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents \"Fitness and Sports Health Trends\"."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "d5SDJBNDffPQsCLG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Weight Lifting legend}[0.865, 0.090, 0.962, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d3c475 which corresponds to the color of weight lifting data bars."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "Q6zJ2AZNsxbfMO49",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label for the bar located at [0.106, 0.800, 0.234, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "FPiiObmoKVxGco41",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Weight Lifting}[0.865, 0.090, 0.962, 0.113] in 2015 greater than {Cardio Exercise}[0.865, 0.055, 0.973, 0.078] in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "vg4pzgSTmQUXsLT7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Yoga trend}[0.865, 0.125, 0.898, 0.148] dropping over the years from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "n4OZEL0OBeQ6EM5n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Weight Lifting bars} generally taller than {Yoga bars}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "ZWqe4enR9ScM02zV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.821, 0.046, 0.978, 0.157]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart: The green bar represents Cardio Exercise ([0.865, 0.055, 0.973, 0.078]), the yellow bar stands for Weight Lifting ([0.865, 0.090, 0.962, 0.113]), and the blue bar is assigned to Yoga ([0.865, 0.125, 0.898, 0.148])."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "FiSGxCz88IDRQkzJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.106, 0.034, 0.943, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the bars for the given years (2010-2015) representing three sports trends: Cardio Exercise, Weight Lifting, and Yoga."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "JhuKyE02twxR0qGc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.024, 0.205, 0.055, 0.229]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label \"80%\" indicating the percentage for the sports fitness trends."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "BSewR8RD6ADNEBuS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.541, 0.593, 0.730, 0.607] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value of the \"Education\" category for \"Social Awareness\", which is 3."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "kttkofXI4Ukn2qdB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what color the bar in the {subplot_0}<r>[0.121, 0.296, 0.310, 0.404]</r> is?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is \"#d3c475\", which represents the category \"Sustainable Production.\""
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "zUA9gPADeTXAWGGh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label for the percentage scale at {coordinates}<r>[0.019, 0.122, 0.068, 0.157]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "100%."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "ON1EEXDlH0hCYhWS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Social Awareness\" bar for \"Environment\"<r>[0.121, 0.404, 0.310, 0.607]</r> taller than the corresponding \"Sustainable Production\" bar<r>[0.121, 0.296, 0.310, 0.404]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "svLss6OFkHnwOIWE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Environment\"<r>[0.207, 0.632, 0.224, 0.853]</r> and \"Work Policies\"<r>[0.417, 0.632, 0.435, 0.859]</r>, which has a higher value for the \"Sustainable Production\" result?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Work Policies\"."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "f4LmU5lP4GolbMqv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the \"Human Rights\" values <r>\"[[0.121, 0.136, 0.310, 0.296], [0.331, 0.136, 0.520, 0.211], [0.541, 0.136, 0.730, 0.334], [0.751, 0.136, 0.940, 0.334]]\"</r> generally higher than the \"Sustainable Production\" values <r>[0.121, 0.296, 0.310, 0.404], [0.331, 0.211, 0.520, 0.362], [0.541, 0.334, 0.730, 0.593], [0.751, 0.334, 0.940, 0.428]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "0LI50GJw5A0i9mPg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the composition of the area [0.180, 0.082, 0.882, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a legend of three items. The color labels are arranged from left to right as follows: \"Social Awareness\" has a color scale of [#7e9671] with the label at [0.236, 0.096, 0.386, 0.131], \"Sustainable Production\" has a color scale of [#d3c475] with the label at [0.469, 0.096, 0.669, 0.131], and \"Human Rights\" with a color scale of [#4e78bf] with the label at [0.752, 0.096, 0.875, 0.131]."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "4rBUw86SRUWqgFRi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area labeled as {subplot_0}<r>[0.080, 0.136, 0.981, 0.607]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main plot of the graph including the X and Y-axes, bars representing different categories of \"Social Awareness\", \"Sustainable Production\", and \"Human Rights\" for \"Environment\", \"Work Policies\", \"Education\", and \"Affordable Housing\". This area also includes the percentage scale on the Y-axis and category labels on the X-axis."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "0wscCz92njr8Q6Zg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the area [0.121, 0.136, 0.940, 0.607]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains all the bars representing the values of \"Environment\", \"Work Policies\", \"Education\", and \"Affordable Housing\" categories for \"Social Awareness\", \"Sustainable Production\", and \"Human Rights.\""
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "WMboX2RPerT4mvlV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.336, 0.072, 0.558, 0.107]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend text labeled as \"Community Development\"."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "XBP5ZpKAX0UQXrG9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar that represents the value of \"Instance3\" for \"Urban Planning\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.359, -0.105, 0.465, 0.402]"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "d3dEjRSUGHaa4Kqv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the values shown in the {Urban Planning bar}<r>[0.715, 0.009, 0.822, 0.395]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "59 -> 120."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "JRxbr7IHqnOiBj45",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhose color does the color label at [0.593, 0.075, 0.628, 0.099] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color of the Urban Planning bar."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "qW3wrmfS6ostAAt2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of the {Community Development bar at Instance1}<r>[0.121, 0.452, 0.228, 0.769]</r> greater than the {Urban Planning bar at Instance1}<r>[0.121, 0.098, 0.228, 0.452]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "MHAaVZFTNPXEeLSy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the bars of Community Development overall contain higher values than those of Urban Planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "j58HxN3ABtKQ6uV2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Instance1}<{Instance1 Community Development bar}[0.121, 0.452, 0.228, 0.769], {Instance1 Urban Planning bar}[0.121, 0.098, 0.228, 0.452]> and {Instance3}<{Instance3 Community Development bar}[0.359, 0.402, 0.465, 0.769], {Instance3 Urban Planning bar}[0.359, -0.105, 0.465, 0.402]> both having a higher value in Urban Planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "riRFVetHoHeVHVJW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Urban Planning bar at Instance7}<{Urban Planning bar at Instance7}[0.833, -0.086, 0.940, 0.326]> taller than the {Urban Planning bar at Instance5}<{Urban Planning bar at Instance5}[0.596, -0.143, 0.703, 0.288]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "OVze6YQBEin21Uhf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located within [0.280, 0.058, 0.782, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region are the legend title and two legend entries: For the first entry, the color label is at [0.287, 0.075, 0.322, 0.099], colored in green, corresponding to the Community Development text label located at [0.336, 0.072, 0.558, 0.107]; As for the second entry, the color label is at [0.593, 0.075, 0.628, 0.099], colored in golden yellow, corresponding to the Urban Planning text label located at [0.642, 0.072, 0.775, 0.107]."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "HWRx8SLFFPaVwvhS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items within [0.833, -0.086, 0.940, 0.769]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this region. One bar ({Community Development}) located at [0.833, 0.326, 0.940, 0.769] represents a value for Community Development as 70.00, and another bar ({Urban Planning}) located at [0.833, -0.086, 0.940, 0.326] shows a value for Urban Planning as 135.00."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "2eCGk4axUsusF8Kl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you identify in the region [0.240, -0.099, 0.347, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are a Community Development bar and an Urban Planning bar. The Community Development bar is located at [0.240, 0.345, 0.347, 0.769], with values ranging from 0 to 67, while the Urban Planning bar is at [0.240, -0.099, 0.347, 0.345], with values from 67 to 70."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "eIFmBUWydR1P3emd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description of all objects within the region [0.041, 0.755, 0.068, 0.790]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a horizontal axis \"yticks\" with label \"0%\" of the bar chart."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "hn1Gyocbie4pj0xC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label name representing the pink bar {the pink bar}[0.541, 0.198, 0.730, 0.425] plotted for 'Campaign Promotions' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Media"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "IbBdEwELxbUvzJxS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease identify the object placed at [0.703, 0.105, 0.818, 0.140]."
+            },
+            {
+                "from": "gpt",
+                "value": "{the legend text 'Public Events'}[0.703, 0.105, 0.818, 0.140]"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "BuyS6enJCA18384X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the color in the legend at [0.654, 0.108, 0.689, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Events."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "x1dxh2vtxlH7Wbps",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.331, 0.177, 0.520, 0.466] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the influence of {New Media}[0.331, 0.177, 0.520, 0.466] for 'Social Media Influence'."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "y8UmEm6Gvy8Vdl5R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Traditional Media's bar for 'News Coverage'}[0.121, 0.322, 0.310, 0.549] higher or lower than {New Media's bar for 'News Coverage'}[0.121, 0.177, 0.310, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "yZyBRIUQQvdEsvgE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar at [0.751, 0.342, 0.940, 0.549] correspond to a higher or lower value compared to the bar at [0.751, 0.260, 0.940, 0.342] for 'Political debates'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "mxuhEO0nAZJhZuWu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the influence of New Media}[0.331, 0.177, 0.520, 0.466] on 'Social Media Influence' greater than {the influence of Traditional Media}[0.331, 0.466, 0.520, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "tCT73abJ4z9tFZdX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {New Media for 'Political Debates'}[0.751, 0.260, 0.940, 0.342] and {Public Events}[0.751, 0.136, 0.940, 0.260], which one has a higher stake?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Events."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "URfmwTeLmh3KSRRJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region [0.121, 0.136, 0.940, 0.549]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains twelve bar plots grouped by four categories. The bars within each group correspond to the influence of Traditional Media, New Media, and Public Events respectively on the categories 'News Coverage', 'Social Media Influence', 'Campaign Promotions', and 'Political Debates'."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "AUKPGMalo218NkKo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you elaborate on the components found within [0.236, 0.091, 0.825, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes three color-text pairs used in the legend. The first pair represents 'Traditional Media' (green), the second pair represents 'New Media' (yellow), and the third pair signifies 'Public Events' (blue)."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "nevT7oOC5CPNmoCb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements present within [0.541, 0.136, 0.730, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains three bars corresponding to 'Campaign Promotions' category. The bars represent the influence of 'Traditional Media'[0.541, 0.425, 0.730, 0.549], 'New Media'[0.541, 0.198, 0.730, 0.425], and 'Public Events'[0.541, 0.136, 0.730, 0.198] respectively."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "WPRCHQEyNHsuOge0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the area [0.331, 0.136, 0.520, 0.549]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates three bars that indicate the impact of the 3 media types for 'Social Media Influence'. The bars represent 'Traditional Media'[0.331, 0.466, 0.520, 0.549], 'New Media'[0.331, 0.177, 0.520, 0.466], and 'Public Events'[0.331, 0.136, 0.520, 0.177] respectively."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "BS4HM5uBEZJti4Fc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart does not have a title text."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "9yPxG9vP79FxhDse",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker that represents 2021 in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the marker that represents 2021 in {the legend}<r>[0.885, 0.012, 0.991, 0.088]</r> is #6e7a5f, which is a kind of green."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "rnaUK8jxNCE0uLQv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label of this chart is \"{Improvement (%)}\"<r>[0.019, 0.367, 0.036, 0.570]</r>."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "3OhtzCa3AU2y7Ttv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat process does the {first bar of group 1}<r>[0.148, 0.034, 0.251, 0.182]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the process \"Call Resolution\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "WGsq8rUsZ2rbKbdC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 2022 bar for \"Call Resolution\" higher than the 2021 bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, {the 2022 bar for \"Call Resolution\"}<r>[0.251, 0.034, 0.354, 0.220]</r> is higher than {the 2021 bar for \"Call Resolution\"}<r>[0.148, 0.034, 0.251, 0.182]</r>."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "1oaXDj6pqKEm3Ofz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the Y-axis, are the values positive or negative?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative, as the {Y axis}<r>[0.043, 0.894, 0.096, 0.917], [0.043, 0.677, 0.096, 0.700], [0.043, 0.459, 0.096, 0.483], [0.054, 0.242, 0.096, 0.266], [0.069, 0.025, 0.096, 0.048]</r> indicates negative values."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "BFGlE7DzGWj56fui",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bars in the chart, was there a general improvement or decline in processes from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "There was a decline, as the {bars for 2022}<r>[0.251, 0.034, 0.354, 0.220], [0.545, 0.034, 0.648, 0.385], [0.839, 0.034, 0.942, 0.706]</r> are overall higher on the negative Y-axis than the {bars for 2021}<r>[0.148, 0.034, 0.251, 0.182], [0.442, 0.034, 0.545, 0.278], [0.736, 0.034, 0.839, 0.550]</r>."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "dsGF7Q4EZWFOLBiM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the improvement rate for \"Budget Usage\" decrease or increase from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "It decreases as {the bar for 2022}<r>[0.839, 0.034, 0.942, 0.706]</r> is higher (more negative) on the Y-axis than {the bar for 2021}<r>[0.736, 0.034, 0.839, 0.550]</r>."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "B3VsFSYZSGdswTMN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.885, 0.012, 0.991, 0.088]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.885, 0.012, 0.991, 0.088] is the {legend}<r>[0.885, 0.012, 0.991, 0.088]</r>, which contains two entries. The first entry includes a green square color marker with coordinates [0.892, 0.023, 0.927, 0.039] denoting year 2021, with its label at the coordinates [0.940, 0.021, 0.984, 0.044]. The second entry includes a sort of beige square color marker with coordinates [0.892, 0.058, 0.927, 0.074] which represents year 2022, with its label at [0.940, 0.056, 0.984, 0.079]."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "gLPW8vkm8MbDmKtO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be seen in the area [0.108, 0.034, 0.981, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the rectangular region <r>[0.108, 0.034, 0.981, 0.903]</r>, six bars are plotted comparing three different process improvement percentages for 2021 and 2022 - \"Call Resolution\", \"Meeting Efficiency\" and \"Budget Usage\". The y-axis to the left shows scale from 0% to -30%, and the bars stretch downwards from the x-axis as they represent negative percentage values. The color-coded legend to the right outside the rectangle associates green bars with 2021 and beige ones with 2022. X-axis labels for the processes are placed below the bars."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "vocb96zAXauaikn0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.736, 0.034, 0.942, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects within this region: \n1. The green bar that represents the \"Budget Usage\" process's improvement percentage for 2021, with the coordinate range of [0.736, 0.034, 0.839, 0.550].\n2. The text above the 2021 bar representing its numerical value of \"-17.80\", with the coordinate range of [0.765, 0.519, 0.810, 0.543]."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "aY6F4lqFWOlWAa7C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the region [0.148, 0.034, 0.354, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region [0.148, 0.034, 0.354, 0.220], there are two objects which are bars representing the \"Call Resolution\" process. The green bar with the coordinate range [0.148, 0.034, 0.251, 0.182] is for 2021 and its associated value \"-5.12\" with the coordinate range [0.177, 0.152, 0.222, 0.175]. The beige bar with the coordinate range [0.251, 0.034, 0.354, 0.220] is for 2022 and its associated value \"-6.43\" with the coordinate range [0.280, 0.190, 0.325, 0.213]."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "b9sJlGNSpuBKBBFQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the third object in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The third object}<r>[0.807, 0.856, 0.884, 0.879]</r> in the legend is in the color #f4f1e0, which is off white."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "63OxTfMdV4CwiBAG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the Y-axis labeled on the chart ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis is labeled as {Investment Strategies}<r>[0.019, 0.340, 0.036, 0.597]</r>."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "DaBwuu2ygLJ0oCLe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the coordinates [0.807, 0.856, 0.884, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text located at these coordinates is {Dividend}<r>[0.807, 0.856, 0.884, 0.879]</r>."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "ScRe70Kljeyxkl0m",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second group of bars}<r>[0.233, 0.495, 0.340, 0.903], [0.660, 0.295, 0.767, 0.903]</r> referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second group of bars is referring to the {RPI}<r>[0.807, 0.821, 0.835, 0.844]</r>."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "LpERqmmmiUuS5IMH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first bar of Dividend}<r>[0.340, 0.469, 0.447, 0.903]</r> taller than {the first bar of RPI}<r>[0.233, 0.495, 0.340, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "pvvLG8hXIp43VWA8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing \"Steep increase\"}<r>[0.126, 0.642, 0.233, 0.903], [0.233, 0.495, 0.340, 0.903]</r> generally shorter than {the bars representing \"Inexorable decline\"}<r>[0.553, 0.121, 0.660, 0.903], [0.660, 0.295, 0.767, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "4Je9qsaoXWhYThWD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the IPO label}<r>[0.807, 0.786, 0.836, 0.809]</r> correspond to {the bars in the color represented by the first item in the legend}<r>[0.759, 0.788, 0.793, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "TU6X87OhxTJ3HwQt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich shows a steeper increase, {Dividend}<r>[0.768, 0.380, 0.875, 0.899]</r> or {RPI}<r>[0.660, 0.292, 0.767, 0.899]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Dividend}<r>[0.768, 0.380, 0.875, 0.899]</r>."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "gOvpyUwxXUZWMjKG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.088, 0.034, 0.912, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area presents a grouped bar chart with two bars per group. It displays investment trends for IPO, RPI, and Dividend over two periods, Steep increase and Inexorable decline. X-axis labels for these periods are at [0.222, 0.919, 0.351, 0.942] and [0.634, 0.919, 0.793, 0.942] respectively. The heights of the bars reflect the value of the investment strategies in these periods with color codes for IPO,r>RPI, and Dividend located in the top right corner of the chart."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "zOrI3RET0dhotrV0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.054, 0.459, 0.076, 0.483]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.054, 0.459, 0.076, 0.483] contains a Y-axis label \"50\", specifying the midpoint value on the Y-axis scale."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "vY5LtEsBouOaSTgr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.767, 0.382, 0.874, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within [0.767, 0.382, 0.874, 0.903] there is a single bar which represents the value for dividend during Inexorable decline. The bar has a text annotation with coordinates [0.596, 0.098, 0.618, 0.121] which specifies the data value this bar represents - 60.00."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "gPpJxbSCuUvxr4kN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.752, 0.777, 0.891, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a legend for the colored bars in the plot. The color label in the first pair is located at [0.759, 0.788, 0.793, 0.804] and its corresponding text label {'IPO'} is at [0.807, 0.786, 0.836, 0.809]; The second pair is located at [0.759, 0.823, 0.793, 0.839] with the label {'RPI'} at [0.807, 0.821, 0.835, 0.844]; The third pair is located at [0.759, 0.858, 0.793, 0.874] with its label {'Dividend'} at [0.807, 0.856, 0.884, 0.879]."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "xmvuOTzJ0QBk6AXm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the position [0.616, 0.221, 0.759, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the Home Amenities."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "Wij425I6FwH1I6bD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the handle at [0.568, 0.258, 0.602, 0.274]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color Orange, corresponding to the Mortgage Payment Percentage."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "DoqkOiow2zC3FGBL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does [0.131, 0.512, 0.197, 1.772] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing 43.50% income for the \"SEQ\" family for Home Amenities."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "JBVn00TTYNwDGhud",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for Home Amenities for {the SEQ family}[0.131, 0.512, 0.197, 1.772] greater than the value for the {REPLAY family}[0.398, 0.231, 0.465, 1.772]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "dxxEzzGAjMFpZamA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {MTL family}[0.665, 0.066, 0.732, 1.772] spend more on Mortgage Payment Percentage than {Home Amenities}[0.131, 0.512, 0.197, 1.772]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "FnWyU0MCTwYhiaYQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf the Mortgage Payment Percentage and Food Shopping Average, which does the {MTL family}[0.732, 0.074, 0.799, 0.344] spend more on?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mortgage Payment Percentage"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "HSBmy3XegXAhjv13",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.561, 0.212, 0.891, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes three entries: {Home Amenities}[0.616, 0.221, 0.759, 0.244] with the color blue denoted by the handle [0.568, 0.223, 0.602, 0.239], {Mortgage Payment Percentage}[0.616, 0.256, 0.884, 0.279] with the color orange denoted by the handle [0.568, 0.258, 0.602, 0.274], and {Food Shopping Average}[0.616, 0.291, 0.822, 0.314] with the color red denoted by the handle [0.568, 0.293, 0.602, 0.309]."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "qKxwtj2ZHbHbNWYE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the area [0.094, 0.034, 0.903, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.094, 0.034, 0.903, 0.903] is the main area of the chart. It includes the axes with labels (\"Family\" on the x-axis and \"Income (%)\" and \"Average Monthly Expenditure ($)\" on the y-axis), the grid, and three groups of bars. The three groups of bars represent Home Amenities, Mortgage Payment Percentage and Food Shopping Average for the SEQ, REPLAY and MTL families. Each bar is accompanied by a label with its exact value."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "eKyDvykHYfR2odgG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the area from [0.398, 0.231, 0.465, 1.772]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object, a bar, in this region. It represents 53.2% income for the \"REPLAY\" family spent on Home Amenities."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "HsNAc2ktQiYd6FNF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you identify in the region [0.915, 0.025, 0.942, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label for the bargraph, called \"5.0\"."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "Lwgh73w2maEacFJz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.616, 0.221, 0.759, 0.244] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label of \"Home Amenities\"."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "QOaVhaGCZ1fF368D",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Home Amenities}<r>[0.568, 0.223, 0.602, 0.239]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is blue."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "94BChCvRrHSwIxa2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe {yticks}<r>[0.915, 0.242, 0.942, 0.266]</r> on the y-axis represents what reading?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a reading of 1.5."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "DwiDaawe3kc8aJwU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Food Shopping Average Bar MTL}[0.799, 0.074, 0.866, 0.344] higher or lower than {Food Shopping Average Bar SEQ}[0.264, 0.344, 0.331, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "y2Pr1iJojp5IS4aD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Home Amenities Bar MTL}[0.665, 0.066, 0.732, 1.772] and {Mortgage Payment Percentage Bar MTL}[0.732, 0.350, 0.799, 1.772], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Home Amenities Bar."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "XY0kAnGC6lL9T9f3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Home Amenities}[0.568, 0.223, 0.602, 0.239] and {Mortgage Payment Percentage}[0.568, 0.258, 0.602, 0.274] which one is higher on the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Home Amenities."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "lADjaByD0yXznkq6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.915, 0.919, 0.957, 0.942] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a y-axis tick label of \"-9.0\"."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "Rnlq5Ef2Ihg6TRz9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is there in the region [0.264, 0.344, 0.331, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar of \"Food Shopping Average\" is present in this region with SEQ label. The value of this bar is \"-7.23\" at coordinate [0.275, 0.786, 0.320, 0.809]."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "jXq2KHKvz7cmLJwF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.094, 0.034, 0.903, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area which contains the bars representing various expenditure values for the categories labeled \"SEQ\", \"REPLAY\", and \"MTL\". The x-axis and y-axis labels are out of this region."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "r4ao0tIEX2vQEEJR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text identified by the coordinates [0.721, 0.786, 0.873, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Economic Growth\"."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "Ne89mz7qd8QVLJVP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar identified by the coordinates [0.551, 0.173, 0.653, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is #6e7a5f, representing \"Economic Growth\"."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "NTugHSDpWEf3L1R3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at the location [0.721, 0.821, 0.884, 0.844] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Population Change\" is present."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "0DkXIL8856MrXjml",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Population Change\" bar taller or shorter than the \"Economic Growth\" bar for the \"Country\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "bnMEqJCua5MLMgS1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the \"Economic Growth\" bar identified at [0.551, 0.173, 0.653, 0.729] represent the \"City\" or \"Country\" Category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Country."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "1V4VEQe5qJoaFT9E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Population Change\" and \"Economic Growth\", which has more significant bar for the \"City\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Economic Growth\"."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "wVhk0uuXwRG0BP5b",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's represented in the area [0.144, 0.469, 0.246, 0.729]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar for the \"City\" category under the label \"Economic Growth\". The bar indicates a value of 1.5, which is shown by the text located at [0.181, 0.462, 0.209, 0.485]."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "ScXXuqr2imOrrA50",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.665, 0.777, 0.891, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a legend with three items. The first item is \"Economic Growth\" in color #6e7a5f with coordinates [0.672, 0.788, 0.707, 0.804] for the color label and [0.721, 0.786, 0.873, 0.809] for the text label. The second item is 'Population Change' in color #b8b7a5 with coordinates [0.672, 0.823, 0.707, 0.839] for the color label and [0.721, 0.821, 0.884, 0.844] for the text label. The third item is 'Land Use Change' in color #f4f1e0 with coordinates [0.672, 0.858, 0.707, 0.874] for the color label and [0.721, 0.856, 0.873, 0.879] for the text label."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "GhfdzuEGM6xIFSFn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.108, 0.034, 0.892, 0.903] encompass in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the graph area where all bars are plotted. This area includes both the \"City\" and \"Country\" categories, each containing three bars indicating \"Economic Growth\", \"Population Change\", and \"Land Use Change\". The x and y-axes labels, \"Categories\" and \"Accuracy (%)\", respectively, and their scales are also included in this area."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "0rx81yeIIOaqaFMx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the entity represented by the color #b8b7a5 in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observed Growth Rate. The legend can be seen at [0.687, 0.821, 0.884, 0.844]."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "SrefI9s1fSVVT5yS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart label at the location [0.043, 0.894, 0.080, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is -20."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "8mbo9uz75A7U0bhB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category is at the coordinate range [0.429, 0.919, 0.555, 0.942] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would be the category labeled \"Art Exhibitions\"."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "RAvB8HCSTQ5FMQe4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the \"Probing Growth Rate\" bar of \"Opera Performances\" at [0.657, 0.555, 0.723, 0.592] have a higher or lower value than the \"Observed Growth Rate\" bar of \"Opera Performances\" at [0.723, 0.555, 0.789, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "7yYWWhA2Giy0qsNR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the bars representing \"Book Publishing\", is {the \u201cObserved Growth Rate\u201d bar}[0.194, 0.342, 0.260, 0.555] placed to the left or right of {the \u201cProbing Growth Rate\u201d bar}[0.128, 0.534, 0.194, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "1deIjVyNEEdW8nWE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars for \"Art Exhibitions\", is {the \u201cExpanding Rate\u201d bar}[0.525, 0.121, 0.591, 0.655] taller than {the \u201cObserved Growth Rate\u201d bar}[0.459, 0.105, 0.525, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "ATpN3I9yJCj16BIX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what can be found within the area [0.092, 0.034, 0.892, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main chart with x-axis labels at the area [0.159, 0.919, 0.845, 0.942] and y-axis labels at the area [0.043, 0.894, 0.080, 0.048]. There are bars representing various growth rates for three different categories namely \"Book Publishing\", \"Art Exhibitions\" and \"Opera Performances\"."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "6P4jc4dBlQ8qdsF8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.128, 0.534, 0.260, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two bars representing \"Book Publishing\". Specifically, these are the \"Probing Growth Rate\" bar at [0.128, 0.534, 0.194, 0.555] and the \"Observed Growth Rate\" bar at [0.194, 0.342, 0.260, 0.555]."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "L4a5YUVUMOXZH5hR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area of [0.631, 0.777, 0.891, 0.888] include generally?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the chart's legend. It lists three attributes namely \"Probing Growth Rate\", \"Observed Growth Rate\" and \"Expanding Rate\", which are located at [0.687, 0.786, 0.867, 0.809], [0.687, 0.821, 0.884, 0.844], and [0.687, 0.856, 0.825, 0.879] respectively. Each attribute is associated with a unique color to represent it in the bar chart."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "f62lixYI3SUtDMAB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the primary entity located at coordinates [0.019, 0.401, 0.036, 0.536] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the Y-axis, which is titled \"Index Value\"."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "3b9TaD6HFCNgAGCn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the second legend text's coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.653, 0.821, 0.884, 0.844]."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "wMpet7gYFJx8lAmH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis scale} indicated at [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index Value."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "73rG7RJPBYKbiKqJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat categories are represented in [0.597, 0.777, 0.891, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend indicates that there are three categories: {Intangible Asset Turnover}[0.653, 0.786, 0.873, 0.809], {Human Capital Index 2022}[0.653, 0.821, 0.884, 0.844], and {TechInvestmentIndex}[0.653, 0.856, 0.837, 0.879]."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "y1TnicN9Gh15XPwE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Advanced Manufacturing}[0.145, 0.859, 0.211, 0.862] larger in value than {the bar representing IT Services}[0.673, 0.857, 0.740, 0.862] under the Intangible Asset Turnover metric?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "dI8JhFQTXk1M7FbH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Advanced Manufacturing}[0.277, 0.179, 0.343, 0.773] under the category TechInvestmentIndex more positive or negative than {the bar for Biotechnology}[0.541, 0.179, 0.607, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "SbykU64Q66hbZxg9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparing {Human Capital Index 2022 for AdvancedManufacturing}[0.211, 0.477, 0.277, 0.862] with {Human Capital Index 2022 for ITServices}[0.740, 0.605, 0.806, 0.862], which is larger?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotechnology."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "58GGG2j6QLKOwzjX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmongst {TechInvestmentIndex for AdvancedManufacturing}[0.277, 0.179, 0.343, 0.773], {TechInvestmentIndex for Biotechnology}[0.541, 0.179, 0.607, 0.838] and {TechInvestmentIndex for ITServices}[0.806, 0.155, 0.872, 0.179], which has the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "ITServices."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "UGHysAijNfCY6eVY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item at [0.709, 0.856, 0.820, 0.879] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Green Space."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "12HSl7cQhDWv0y2V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Urban Development} mapped on the chart according to the legend<r>[0.661, 0.823, 0.695, 0.839]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#b8b7a5."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "JJGRK8ctGmSgQYvW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the y-axis on the chart at [0.019, 0.360, 0.036, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is representing the Annual Change(%)."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "IPZ0XTftnjek8UIa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Population Growth in CITY_A}<r>[0.134, 0.269, 0.202, 0.469]</r> taller than the bar of {Green Space in CITY_A}<r>[0.270, 0.555, 0.338, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "e7skZrE3cQBD8lje",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes City B or City C has a larger {Urban Development}<r>[0.406, 0.469, 0.473, 0.738]</r> <r>[0.744, 0.460, 0.812, 0.469]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "CITY_B."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "N2XDZrfWkDX2Kpcy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label at [0.207, 0.919, 0.266, 0.942] has the broader {Population Growth Bar} in terms of two cities libraries at [0.134, 0.269, 0.202, 0.469] and [0.406, 0.469, 0.473, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "CITY_A."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "ZvgiliLU8jLVjWNS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area at [0.654, 0.777, 0.891, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes three labels: \n1. {Population Growth}<r>[0.709, 0.786, 0.869, 0.809]</r> with a color of #6e7a5f,\n2. {Urban Development}<r>[0.709, 0.821, 0.884, 0.844]</r> with a color of #b8b7a5, and\n3. {Green Space}<r>[0.709, 0.856, 0.820, 0.879]</r> with a color of #f4f1e0."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "D8eFiSyBCtmbWhK7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what lies in the region [0.134, 0.269, 0.202, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we have the Population Growth bar for CITY_A. It ranges in height from 0.00 to 2.30 as indicated by the text at [0.155, 0.238, 0.182, 0.262]."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "UEP7BpuNuSY3iURn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the features within the region [0.406, 0.469, 0.473, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bar representing Urban Development for CITY_B, with the value of -3.10 as shown by the text at coordinates [0.423, 0.708, 0.456, 0.731]."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "sN8gg1GWsQqgj9yU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.684, 0.171, 0.884, 0.194] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label represents \"Market Share 2018 (%)\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "nEkbjL3OCg0Z4ZeO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the industry {the corresponding bar} at coordinates [0.155, 0.034, 0.209, 0.384]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The industry is Electronics."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "BVLiFJ7uhpavK6z3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.458, 0.919, 0.557, 0.942] specify in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It specifies the category name \"Automobile\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "rgr1Q2C8lHFTho9i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend} at [0.635, 0.243, 0.670, 0.259]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #f4f1e0."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "JcvjNkTsM5EswIA5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Electronics in 2018}[0.155, 0.034, 0.209, 0.384] higher or lower than {the bar for Electronics in 2019}[0.209, 0.034, 0.263, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "Fo1k7ZBVzAWUU0bi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Automobile bar of 2019}[0.480, 0.034, 0.534, 0.382] to the right or left of {the Textile bar of 2019}[0.751, 0.033, 0.806, 0.034]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "gYSXyLJzkoPbbgsh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two bars {Electronics in 2018}[0.155, 0.034, 0.209, 0.384] and {Automobile in 2019}[0.480, 0.034, 0.534, 0.382], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Automobile in 2019}[0.480, 0.034, 0.534, 0.382]."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "g9br173qLpn6GC72",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the yellow bar in 2019} at [0.480, 0.034, 0.534, 0.382] and {the white bar in 2020} at [0.534, 0.469, 0.589, 0.621], which Industry has a decrease in Market Share from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automobile."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "qaoUVQ0LQYRpbJF0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region from [0.628, 0.162, 0.891, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart, including three entries: {Market Share 2018 (%)}[0.684, 0.171, 0.884, 0.194] represented by a color marked at [0.635, 0.173, 0.670, 0.189], {Market Share 2019 (%)}[0.684, 0.206, 0.884, 0.229] represented by a color marked at [0.635, 0.208, 0.670, 0.224], and {Market Share 2020 (%)}[0.684, 0.241, 0.884, 0.264] represented by a color marked at [0.635, 0.243, 0.670, 0.259]."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "BuFSLI1vh8zdEZbV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the boundary [0.119, 0.034, 0.895, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main graph area of the bar chart, including x-axis and y-axis, horizontal and vertical gridlines, bar representations of the industry categories Electronics, Automobile, and Textile for three years (2018, 2019, and 2020) with labels."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "yaXW2OBwrZ27BICM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the region spanning [0.426, 0.034, 0.590, 0.729]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar related to the Automobile industry for each year (2018, 2019, 2020). The bars represent the Market Share of the Automobile industry for those years."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "N2hTDJxF3sF3QaZc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.119, 0.034, 0.317, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the bars and labels associated with the Electronics industry from 2018 to 2020."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "D4izPeya1kdnkDCV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at position [0.678, 0.173, 0.871, 0.194]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's labeled as 'Market Share 2018 (%)'."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "369htUYhBhpoMSJL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar corresponding to 'Market Share 2019 (%)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is '#b8b7a5' as described by the handle coordinate [0.635, 0.208, 0.670, 0.224]."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "kZqskwl6lzvtrdJQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by [0.155, 0.034, 0.209, 0.384]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the market share in 2018 for the Electronics industry."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "xwPsYaUfblneHUr4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater, 'Market Share 2018 (%)' for Electronics or 'Market Share 2019 (%)' for the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Market Share 2018 (%)' is greater as it extends to '[0.155, 0.034, 0.209, 0.384]' whereas 'Market Share 2019 (%)' only extends to '[0.209, 0.034, 0.263, 0.555]'."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "HBBEUOnCyKdBqSl3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the market share in 2018 for the Electronics industry higher than Automobile industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, as can be seen by the comparison of coordinates {Electronics 2018 bar}<r>[0.155, 0.034, 0.209, 0.384]</r> and {Automobile 2018 bar}<r>[0.426, 0.034, 0.480, 0.729]</r>."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "k1IC7SxCOz69pmyX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \u2018Market Share 2019 (%)\u2019 higher for the Automobile industry than for the Electronics industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this is indicated by comparing the coordinates of the Automobile {2019 bar}<r>[0.480, 0.034, 0.534, 0.382]</r> with the Electronics {2019 bar}<r>[0.209, 0.034, 0.263, 0.555]</r>."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "AM7FktV8JDxM1w75",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the bounds [0.628, 0.162, 0.891, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the chart legend, with three entries: 'Market Share 2018 (%)', 'Market Share 2019 (%)', and 'Market Share 2020 (%)'. Each entry consists of a colored box, with labels for respective color located at [0.684, 0.171, 0.884, 0.194], [0.684, 0.206, 0.884, 0.229] and [0.684, 0.241, 0.884, 0.264] respectively."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "6mUwh1XYeqszhbBF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the bounds [0.458, 0.919, 0.557, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates denote the x-axis label, labeling it as the 'Automobile' industry."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "w1XXiVcLPM3e9qEE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the coordinates [0.155, 0.034, 0.209, 0.384]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the bar representing the 'Market Share 2018 (%)' for the Electronics industry."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "WeJV0xDUOzPbfkUk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar under the label \"Innovation\" in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "light green.{Innovation's handle}<r>[0.644, 0.823, 0.679, 0.839]</r>."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "sc5OYtsaYw8369ef",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text label of \"Gradual recovery\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.718, 0.919, 0.868, 0.942]."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "d2M6dmWKxmNFSIGr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by {the Customer Satisfaction bar of Gradual recovery}<r>[0.814, 0.056, 0.855, 0.618]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5.92."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "kH7XXGZRmKdnuD1P",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title or main object at [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no exact content showing at these coordinates in the chart. It seems like it's part of the chart title, but the complete values are missing."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "QowR8IydemI8Itkr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Productivity bar for Steady increase}<r>[0.114, 0.248, 0.156, 0.903]</r> higher or lower than {the Innovation bar for Steady increase}<r>[0.156, 0.330, 0.197, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "TdvZseu9CLXX4ezh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Customer Satisfaction bar for Moderate increase}<r>[0.608, 0.266, 0.649, 0.618]</r> represent a lower or higher value than {the Innovation bar for the same category}<r>[0.567, 0.148, 0.608, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "0XdJpEm52Okcz0e8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Productivity\"}<r>[0.693, 0.786, 0.796, 0.809]</r> and {\"Innovation\"}<r>[0.693, 0.821, 0.784, 0.844]</r>, which one has higher values in general in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "7IuI2KzWktjr54CU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Customer Satisfaction\"}[0.814, 0.056, 0.855, 0.618] and {\"Innovation\"}[0.773, 0.120, 0.814, 0.903] of the category \"Gradual recovery\", which one has higher values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Customer Satisfaction."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "JNr03VIYgO7zPEJM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the area of [0.637, 0.777, 0.891, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend. It includes three pairs of color-text labels for the following components of the chart:\n1. \"Productivity\" signal as a dark green color located at [0.644, 0.788, 0.679, 0.804] with its matching text at [0.693, 0.786, 0.796, 0.809].\n2. \"Innovation\" indicator represented as a light green color located at [0.644, 0.823, 0.679, 0.839] with its corresponding text at [0.693, 0.821, 0.784, 0.844].\n3. \"Customer Satisfaction\" depicted as a beige color located at [0.644, 0.858, 0.679, 0.874] and its related text at [0.693, 0.856, 0.884, 0.879]."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "dJOhPA3p3rQdU6UF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe content within the area [0.077, 0.048, 0.892, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates includes the primary data representation of the chart. It consists of all the bars representing the categories of \"Productivity\", \"Innovation\", and \"Customer Satisfaction\" across different growth stages: \"Steady increase\", \"Moderate growth\", \"Moderate increase\", and \"Gradual recovery\". The area also includes the x and y-axes labels and gridlines."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "mnTGjb5wWMRa6vXW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.156, 0.330, 0.197, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object within these coordinates: a light green colored bar representing the \"Innovation\" of \"Steady increase\" category, and the y range value for this bar is 0.00 -> 13.42."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "g2W5fJ2Im34rJPnR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the area from [0.043, 0.039, 0.065, 0.063]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label, denoting the value '20'."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "RlZIPtRKd0qBKTim",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend indicate at [0.116, 0.123, 0.150, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This color is indicated as Brown in the legend."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "3DiHcIpdEXoHgPWs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates of [0.164, 0.121, 0.445, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Budget Allocation in million USD\"."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "ZtCAvSaRHfcysfSW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the coordinates [0.338, 0.919, 0.662, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The term represented here is \"Infrastructure and Municipal Services\"."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "xTmnFZ4qdzCV36pQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does the bar set [0.126, 0.686, 0.212, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar set represents the 'Planning and Land Use' category."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "F9GtrMWFEfrcTPbm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the legend square [0.116, 0.123, 0.150, 0.139] the same color as the bar [0.126, 0.686, 0.212, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "uHah1kt7wtrwtHMR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the label [0.164, 0.121, 0.445, 0.144] associated with the category [0.113, 0.919, 0.311, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "PtBjsqUzrI3O8xTt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the satisfaction percentage column [0.212, 0.425, 0.298, 0.903] higher than budget allocation column [0.126, 0.686, 0.212, 0.903] for 'Planning and Land Use'?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "JdlHYWJ55N1WKkkF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on their height, is the community more satisfied with 'Infrastructure and Municipal Services' [0.500, 0.164, 0.586, 0.903] than 'Social and Recreational Services' [0.788, 0.251, 0.874, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "nZDuKFMGwgdFU14V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the figure elements within the coordinates [0.088, 0.034, 0.912, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main graph, including both x and y axis."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "2LiyQLC5swMRF0ik",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me about the region [0.924, 0.242, 0.946, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes an element of the y-axis label series of the second graph, which is '75'."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "m6zP0XWNHfqyVMhd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe all the bars within the coordinate segment [0.414, 0.179, 0.500, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment contains two sets of bars representing the category 'Infrastructure and Municipal Services':\n    1. A brown bar [0.414, 0.179, 0.500, 0.903] representing the budget allocation amount in USD millions.\n    2. A light yellow bar falling within same x-coordinate range in the second subplot, indicating the community satisfaction % for the same category."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "2xr81wckpZbBBmtU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.338, 0.919, 0.662, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It accommodates one category label 'Infrastructure and Municipal Services' for x-axis."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "85wZdxb68P4IBHNL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat describes the object found in the coordinates [0.129, 0.396, 0.235, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing Power Consumption in the Residential category at 125 consumption percentage."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "8FqEirZAmiBKJ7no",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at the coordinates [0.712, 0.821, 0.883, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text label reading \"Water Consumption\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "HVFbxAHf0V2PhFK2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe content inside the area [0.712, 0.786, 0.884, 0.809] can be described as?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text label indicating 'Power Consumption'."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "X5iY8S2RrEuwZaWd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object with coordinates [0.552, 0.179, 0.658, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is a bar representing Power Consumption in the Industrial category at 200 consumption percentage."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "uEiBjofjWJizF6Mt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the Power Consumption data for {Residential}<r>[0.129, 0.396, 0.235, 0.758]</r> compare with that of {Industrial}<r>[0.552, 0.179, 0.658, 0.758]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "OCL9k0GoQoxvCSR9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Water Consumption in {Residential}<r>[0.235, 0.700, 0.340, 0.758]</r> greater or less than in {Industrial}<r>[0.658, 0.758, 0.763, 0.787]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "xrCuEieqgod9Qb2A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the Power Consumption in {Residential}<r>[0.129, 0.396, 0.235, 0.758]</r> or {Industrial}<r>[0.552, 0.179, 0.658, 0.758]</r> have a higher C02 Emission as per {C02 Emission Residential}<r>[0.340, 0.469, 0.446, 0.621]</r> and {C02 Emission Industrial}<r>[0.763, 0.432, 0.869, 0.469]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industrial."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "jWeB5gcdYRlXgkB6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain the content within the area [0.656, 0.777, 0.891, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. The legend consists of 3 entries; Each entry includes a color code shown with its corresponding label. The first entry represents Power Consumption with color code at coordinates [0.663, 0.788, 0.698, 0.804] and label at coordinates [0.712, 0.786, 0.884, 0.809]. The second entry stands for Water Consumption, color code at [0.663, 0.823, 0.698, 0.839], and label at [0.712, 0.821, 0.883, 0.844]. The third entry denotes C02 Emission, color at [0.663, 0.858, 0.698, 0.874] and label at [0.712, 0.856, 0.828, 0.879]."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "hDa1jOHP7qUfuQSv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.092, 0.034, 0.906, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire bar chart area, with bars representing Power Consumption and Water Consumption for two categories - Residential and Industrial. The region also includes the x-axis and y-axis, with their labels and scale information, and the gridlines of the chart are also displayed within this area."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "WBz2oLGJRj9DWCiV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the coordinates [0.240, 0.919, 0.335, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label \"Residential\" which is a category label on the x-axis."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "hhcvkjFqNBLCbb8f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.670, 0.919, 0.751, 0.942] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label 'Industrial', which is a category on the x-axis."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "oWgvqPvdQD4rTM55",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.025, 0.399, 0.048, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the Y-axis label, which reads \"Cost Change\"."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "YfnYBRNsApftUbhF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label for the supplier \"Zhang Inc\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.493, 0.919, 0.608, 0.942]"
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "l6NZbschbNUme2ke",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.799, 0.174, 0.965, 0.560] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Cost Change for the supplier \"Chen Industries\"."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "zw8UscAkBEGhVdPL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the xlabel in coordinates [0.503, 0.952, 0.598, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The xlabel is \"Supplier\"."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "eM5LctiiPjSPqGKV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the cost change bar of {Liu Corp}[0.302, 0.560, 0.468, 0.736] above or below the cost change bar of {Wang Enterprises} [0.136, 0.245, 0.302, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "fPn5qpzIhtlfv1Qn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Zhang Inc}[0.468, 0.490, 0.633, 0.560] larger than the bar of {Li LLC}[0.633, 0.560, 0.799, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "rzIGKE0iyyQmw1LM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Wang Enterprises}[0.136, 0.245, 0.302, 0.560] have a higher or lower cost change than {Chen Industries}[0.799, 0.174, 0.965, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "2pypo5t3oPhmDERP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich supplier between {Liu Corp}[0.302, 0.560, 0.468, 0.736] and {Li LLC}[0.633, 0.560, 0.799, 0.841] has more cost change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Li LLC."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "QkbYnXF9kByR0I84",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.302, 0.174, 0.799, 0.841]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 bars in this region. The first bar represents the cost change of Liu Corp at coordinates [0.302, 0.560, 0.468, 0.736]. The second bar represents the cost change of Zhang Inc at coordinates [0.468, 0.490, 0.633, 0.560]. The third bar at coordinates [0.633, 0.560, 0.799, 0.841] represents the cost change for Li LLC."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "9OAmqp3u0FsESX7u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.117, 0.919, 0.972, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the supplier labels on the horizontal axis, which are:\n1. \"Wang Enterprises\": [0.117, 0.919, 0.321, 0.942]\n2. \"Liu Corp\": [0.337, 0.919, 0.433, 0.942]\n3. \"Zhang Inc\": [0.493, 0.919, 0.608, 0.942]\n4. \"Li LLC\": [0.682, 0.919, 0.750, 0.942]\n5. \"Chen Industries\": [0.792, 0.919, 0.972, 0.942]."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "2ZRhiLhhCgM6dHdS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the objects within the region [0.136, 0.174, 0.302, 0.841]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one bar within this region, it demonstrates the \"Cost Change\" of the \"Wang Enterprises\" at coordinates [0.136, 0.245, 0.302, 0.560]."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "Yjsa4VNQ6w6T2asU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.136, 0.200, 0.965, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the plot of Supplier vs Cost Change including the bars that represent each supplier's cost change, the x-axis, and y-axis."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "0OtQTQGeehHJHWTx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is defined by this JSON data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "D2Oa7Sqnxcal8SLF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.516, 0.964, 0.556, 0.981] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Field."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "OalqsaU8O9Rr7PAl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.019, 0.449, 0.036, 0.510]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Impact."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "btzKLEPXKN72HhJb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the bar at [0.685, 0.116, 0.833, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mathematics."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "kfwsm4ng8tMPLcYl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of Computer Science}[0.091, 0.139, 0.240, 0.593] greater or less than {the bar of Mathematics}[0.685, 0.116, 0.833, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "98eMDLeIC3vWA0i1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Physics bar}[0.388, 0.298, 0.536, 0.593] represent a positive or negative impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Positive."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "zrN0EasNfV3jZ5hM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing Physics and Chemistry}[0.388, 0.298, 0.536, 0.593], [0.536, 0.593, 0.685, 0.774] of approximately the same height?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "k42mqfUvpc9Zznng",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar of Philosophy}[0.833, 0.479, 0.981, 0.593] and {the bar of Biology}[0.240, 0.593, 0.388, 0.865], which has a higher impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Philosophy."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "xHqMy6yJjoXUF0J4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the area [0.091, 0.139, 0.981, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars of the chart, each representing a different field of study, such as Computer Science, Biology, Physics, Chemistry, Mathematics, and Philosophy."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "wXVBvCODcAElIbx2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the area [0.058, 0.019, 0.085, 0.036]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the value \"2.5\" on the y-axis scale, indicating the maximum level of impact portrayed in the chart."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "BoNIF1oPLKhDBrPL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.058, 0.132, 0.085, 0.150]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content in this region is the value \"2.0\" on the y-axis, which represents the second highest level of impact in the context of this chart."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "AoJrk7oaxmnki2X3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are within [0.043, 0.699, 0.085, 0.717]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis value \"-0.5\". It is part of the y-axis scale, which illustrates the range of impact levels for different fields of study."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "4O9QEEGNM7e8GRjL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the coordinates [0.178, 0.144, 0.260, 0.473], what is the label of the bar in this location?"
+            },
+            {
+                "from": "gpt",
+                "value": "Television."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "AFK67xUsaZCUxpKt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the label 'OnlineShopping'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.789, 0.919, 0.972, 0.942]."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "NyAUaFZDl0XSurzU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent given its location at [0.025, 0.413, 0.048, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Relevance."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "apLeA8qEJeqS1Rzq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located in the coordinate box [0.839, 0.473, 0.922, 0.670] and what does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue colored bar represents 'OnlineShopping' and its relevance score is negative, between -0.45 and 0.0."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "N8YSLNelIZ9Zb82t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for 'Movies'[0.343, 0.473, 0.426, 0.867] taller or shorter than 'Television'[0.178, 0.144, 0.260, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "X6ntb7trup7TUH00",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar for 'Television'[0.178, 0.144, 0.260, 0.473] and 'Music'[0.674, 0.473, 0.757, 0.758], which one shows a stronger relevance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Television."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "oGfGr36dAPsOqnRT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the position of 'Music'[0.674, 0.473, 0.757, 0.758] and 'OnlineShopping'[0.839, 0.473, 0.922, 0.670], which one is on the left?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "eijnz5THtqf3OMmB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the relevance of 'MobileGames'[0.509, 0.253, 0.591, 0.473] and 'Movies'[0.343, 0.473, 0.426, 0.867], which one has a higher positive relevance score?"
+            },
+            {
+                "from": "gpt",
+                "value": "MobileGames."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "Rge2WOYIkQxAmskd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features can be found in the region [0.509, 0.253, 0.591, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar for the category 'MobileGames'. The bar is blue and its height indicates a positive relevance score between 0.00 and 0.50."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "RmvNCvEsjqOETb8F",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the rectangular area [0.839, 0.473, 0.922, 0.670]."
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangular area encompasses a bar representing the 'OnlineShopping' category with a negative relevance score between -0.45 and 0.0. The bar is blue-colored."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "PLlzRDGVBZSj7WVT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is shown in the area [0.682, 0.919, 0.748, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the label for 'Music' along the x-axis."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "pNWbxpUqlGr55Keq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the content within the region [0.178, 0.144, 0.260, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar for the category 'Television'. The height of the bar indicates a positive relevance score within the 'Television' category, ranging between 0.00 and 0.75. The bar is colored blue."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "BVm2NrpzOhUhr9mE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title in the area [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar Chart Showing Food Security Trends."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "n4Whco2mhQDplnZV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.466, 0.823, 0.563, 0.911] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the category \"Lack of Electricity\" with a value of 10%."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "TOVZlDUzMspB9UZi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.025, 0.421, 0.048, 0.525]?"
+            },
+            {
+                "from": "gpt",
+                "value": "%Family."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "o2ar6s4IxqKJj1yN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Inadequate Sanitation\"}[0.304, 0.429, 0.401, 0.911] higher than {the bar representing \"Chronic Food Insecurity\"}[0.790, 0.806, 0.887, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "8eN7trF5BtMNJ6Yp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Lack of Electricity\"}[0.466, 0.823, 0.563, 0.911] and {\"No Access to Clean Drinking Water\"}[0.628, 0.885, 0.725, 0.911], which condition is more prevalent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lack of Electricity."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "B1tjzahJoduwHKXF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Minimal Basic Amenities\"}[0.142, 0.297, 0.239, 0.911] taller than 50%{yticks}[0.072, 0.463, 0.102, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "ZlqvSmtXPWRATuTt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.180, 0.906, 0.868, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains x-axis labels including \"Minimal Basic Amenities\", \"Inadequate Sanitation\", \"Lack of Electricity\", \"No Access to Clean Drinking Water\", and \"Chronic Food Insecurity\"."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "j2v3Fe6aomX81fZg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.050, 0.919, 0.878, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the five x-axis labels: {The label \"Minimal Basic Amenities\"} is at [0.050, 0.919, 0.332, 0.942]. The second label for \"Inadequate Sanitation\" - [0.224, 0.919, 0.481, 0.942]. The third label \"Lack of Electricity\" - [0.411, 0.919, 0.617, 0.942]. The fourth label \"No Access to Clean Drinking Water\" - [0.475, 0.919, 0.878, 0.942]. The fifth label \"Chronic Food Insecurity\" - [0.702, 0.919, 0.974, 0.942]."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "cpPLMxoZpZ76LGpO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.790, 0.806, 0.887, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the bar representing \"Chronic Food Insecurity\"} and the value is 12%."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "XESR5tnYQ8tIWrsK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat scientific field is located at [0.082, 0.919, 0.224, 0.942] on the x axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The scientific field is Artificial Intelligence."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "hOgWhSJBJuP540Gv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y coordinate of the bar corresponding to the Robotics field in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.292, -69.327, 0.361, 0.903]."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "V78j5wKErlqyd9Sl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis located at [0.015, 0.314, 0.029, 0.614] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Amount of New Discovery."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "jCkQvvYmymQDLnu5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented within coordinates [0.639, -64.937, 0.708, 0.903] in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Quantum Computing\" in the first subplot."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "DgIfa7Sh0LMLgRv0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar corresponding to {Space Technology}<r>[0.465, -104.442, 0.535, 0.903]</r> taller than the bar corresponding to {Artificial Intelligence}<r>[0.118, -86.884, 0.188, 0.903]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "iWTt8XpbRHGUFTIb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Robotics}<r>[0.292, -69.327, 0.361, 0.903]</r> left or right to {Quantum Computing}<r>[0.639, -64.937, 0.708, 0.903]</r> on the x-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "PiiUUKeQaOGNCdcY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Robotics}<r>[0.292, -69.327, 0.361, 0.903]</r> and {Quantum Computing}<r>[0.639, -64.937, 0.708, 0.903]</r> in the first subplot, which has a higher \"Pace of Innovation\" in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "iCFqrKns8y2JsInV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Artificial Intelligence\"}<r>[0.118, -86.884, 0.188, 0.903]</r> and {\"Space Technology\"}<r>[0.465, -104.442, 0.535, 0.903]</r>, which one has a higher value in the first subplot's bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Space Technology."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "MyoC9oenYeB7r2KT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in [0.790, 0.919, 0.904, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label for the last x-tick in the bar chart, which is the \"Nanotechnology\" scientific field."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "sY6uG9odowOeMeGH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the area [0.971, 0.357, 0.985, 0.571] in the second subplot hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label \"Pace of Innovation\"."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "BIrYOfbG9vGhWPNf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.292, -69.327, 0.361, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area features a bar from the \"bars_of_group_0\" set in subplot_0. It represents the \"Robotics\" scientific field with a \"New Discovery\" value of 80."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "NMbShlSdA7oFEHOA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the area [0.118, -86.884, 0.188, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar in subplot_0 representing \"Artificial Intelligence\". The bar depicts that the \"Amount of New Discovery\" value for \"Artificial Intelligence\" is 100."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "J6EojmeaqXnfZDFP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.044, 0.159, 0.076, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label: 200."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "rMjhEEdFU0FHD4Au",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is represented by the coordinates [0.837, 0.061, 0.945, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {bar for the category \"Telecommunications\"}<r>[0.837, 0.061, 0.945, 0.706]</r>."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "4TAQqSq8vMzrwxVq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.323, 0.714, 0.381, 0.792] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the {X-axis label for the \"Biotech\" category}<r>[0.323, 0.714, 0.381, 0.792]</r>."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "ow4HqxpdUu1zbSr2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the one representing \"Space Technology\"}<r>[0.658, 0.346, 0.766, 0.706]</r> or {the one representing \"Renewable Energy\"}<r>[0.478, 0.198, 0.586, 0.706]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Renewable Energy\"."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "T3piyEND6mI3CQQI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for \"Biotech\"}<r>[0.298, 0.120, 0.406, 0.706]</r> to the left or right of the {bar for \"AI and Machine Learning\"}<r>[0.118, 0.346, 0.226, 0.706]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "VfTIC4c05Gp3Kiv1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {\"AI and Machine Learning\"}<r>[0.118, 0.346, 0.226, 0.706]</r> and {\"Space Technology\"}<r>[0.658, 0.346, 0.766, 0.706]</r> bars, which one has a greater number of patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both have the same number of patents."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "jjonRF31h28OyDwn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the items in the coordinates [0.082, 0.034, 0.981, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire bar chart plot, including both the x-axis and y-axis and all their labels, as well as all the bars representing the different technology sectors and their respective numbers of patents."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "3MvXLDkNk7e6iyKI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region denoted by [0.118, 0.346, 0.946, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the bars for all five categories from \"AI and Machine Learning\" to \"Telecommunications\"."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "2rvHMhCBJcGpILBu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's found at coordinates [0.298, 0.120, 0.406, 0.706]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar present within these coordinates. This {bar represents the \"Biotech\" category}<r>[0.298, 0.120, 0.406, 0.706]</r> in the bar chart, indicating the number of patents in this sector."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "ogohIWWfTIS1vQfD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data represent at [0.113, 0.414, 0.204, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That represents a bar for a Food Bank with a donation amount of 12500."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "ikMuUE1Vnge74UNp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label displayed at the chart's coordinates [0.015, 0.400, 0.029, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Donation Amount\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "XBhYexlumlEqClWV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the information at [0.860, 0.939, 0.960, 0.957] signify in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the charity type \"Disaster Relief\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "BIwWXoQM432bd9eg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the content with coordinates [0.414, 0.089, 0.504, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a bar for Environmental Conservation with a donation amount of 20300."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "JWHNWYk7BwfOe1to",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the donation to Animal Welfare}[0.264, 0.601, 0.354, 0.933] more or less than {the donation to Food Bank}[0.113, 0.414, 0.204, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "etxQ7TwC1kXICCOF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Food Bank}[0.113, 0.414, 0.204, 0.933] and {Disaster Relief}[0.865, 0.164, 0.955, 0.933], which one received more donations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Disaster Relief."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "aplZIInNXuxDCNUy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich receives the highest amount of donations between {Educational Support}[0.564, 0.285, 0.654, 0.933] and {Environmental Conservation}[0.414, 0.089, 0.504, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environmental Conservation."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "tKTKiyRMeLRpuTuR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the amount of donation to {Healthcare Services}[0.714, 0.538, 0.805, 0.933] more or less than {Educational Support}[0.564, 0.285, 0.654, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "SzLeS8EYCUZZqswU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features on the y-axis label within the bounding box [0.015, 0.400, 0.029, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the y-axis. The label is \"Donation Amount\", denoting the amount of donation received by different charity types."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "cGgB310yZReqBQsY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.256, 0.939, 0.361, 0.957] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label on the x-axis representing the \"Animal Welfare\" charity type."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "Uskg89uOard2eP2f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the information at [0.564, 0.285, 0.654, 0.933] tells us?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box contains a bar from the bar chart. The bar represents the donation amount (15600) given to the charity type \"Educational Support\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "oW4TLJIjWoaupjlX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the figure layout at coordinates [0.043, 0.719, 0.078, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label on the y-axis, representing a monetary value of 5000 which is one of the markers for donation amount."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "VxLJD8KEhGRa3WNh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinates [0.043, 0.926, 0.085, 0.944] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, representing the value -0.6."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "cQuwc4HUf3LTq9yg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location [0.896, 0.939, 0.940, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"2020\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "sluWCbmKV7kHQoxF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.133, 0.939, 0.177, 0.957] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"1960\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "7fyWpiwQ7BpKOvrR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the bar at [0.218, 0.438, 0.346, 0.735] stands for?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar shows the Value Change for the year \"1970\". The height of the bar represents a value of -0.4."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "F3yhEZ2Uh09IG6Dz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars at [0.473, 0.438, 0.600, 0.809] and at [0.600, 0.438, 0.727, 0.669], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.473, 0.438, 0.600, 0.809]."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "VYazuYdVt5EXwmnn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar at [0.727, 0.314, 0.854, 0.438] taller or shorter than the bar at [0.854, 0.067, 0.981, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "UZBebmMkG5sJLyTG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the values of the bars at [0.218, 0.438, 0.346, 0.735]  and [0.854, 0.067, 0.981, 0.438], which decade saw a greater change in value?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "7Va52YZCk4KMWm0M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Value Change from 1980 to 1990 ({the 1980 bar}[0.346, 0.438, 0.473, 0.892], {the 1990 bar}[0.473, 0.438, 0.600, 0.809]) increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "dUboogLFJe4D8frh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.091, 0.314, 0.981, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars in the chart. Each bar represents the Value Change for a certain decade, with bars corresponding to the years from 1960 to 2020."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "Tyw2MlLBQTSNRNk4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.091, 0.026, 0.981, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encases the entire chart, including all bars representing the Value Change from 1960 to 2020, the x-axis labeled as Decades, and the y-axis labeled as Value Change. The x-axis has labels ranging from 1960 to 2020, and the y-axis has labels from -0.6 to 0.5."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "qSc1UbTMR2t9cMi2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give details on the region defined by [0.854, 0.067, 0.981, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains a single bar representing the Value Change for the year 2020. The height of the bar indicates a positive change of 0.5, and it's the highest bar in the chart."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "zXG65r0lIzHw3B0n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about [0.091, 0.438, 0.218, 0.537]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there lies the first bar of the chart which represents the Value Change for the year 1960. The bar is located below the x-axis, indicating a negative change of -0.6."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "kEXE82METGOA1hFb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bounding box for the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.025, 0.441, 0.048, 0.504]"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "jge6YYFE3uF7lKPB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label} at [0.539, 0.919, 0.572, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q3"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "bsdX0hx0cZUk8lAo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at [0.136, 0.122, 0.304, 0.473] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value for Q1, which is 0.80."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "SM8x9Cfe3XG6xS4f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Q1}[0.136, 0.122, 0.304, 0.473] higher than the bar for {Q2}[0.304, 0.210, 0.472, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "GLKK4WOxmx8Yg8PD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value} for Q3[0.472, 0.473, 0.640, 0.692] less than {the value} for Q4[0.640, 0.473, 0.807, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "0h6hgpEl7n9azeZf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value: {Q5}[0.807, 0.210, 0.975, 0.473] or {Q1}[0.136, 0.122, 0.304, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "335ezxPHVy6Rfuag",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.136, 0.122, 0.975, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains five bars, each representing a different quarter in a selected year. The quarters are represented in left-to-right order: from Q1[0.136, 0.122, 0.304, 0.473] to Q5[0.807, 0.210, 0.975, 0.473]. The height of each bar corresponds to a specific value, with negative values below the x-axis and positive values above it."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "WEycuGQMvztBiI9r",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in [0.025, 0.441, 0.048, 0.504]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the axis Y[i.e.,'Value'] that indicates what the bars represent in terms of value."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "0XKhZyZV0jJt2gsR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are located in the area [0.077, 0.025, 0.128, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label indicating '1.00' is within this box."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "5EaNJRamWYrXy2hm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.058, 0.473, 0.096, 0.490] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, captioned as \"0.25\"."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "IXZNJWiSewwbAbJF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.102, 0.147, 0.247, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the popularity growth of {Italian Cuisine}<r>[0.102, 0.147, 0.247, 0.631]</r>."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "6LJlAwQgYT4noO4S",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location [0.464, 0.964, 0.612, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the X-axis label, {Cuisine Category}<r>[0.464, 0.964, 0.612, 0.981]</r>."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "WUflhGVPtK7UJtQD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Italian Cuisine bar}<r>[0.102, 0.147, 0.247, 0.631]</r> stretch higher or lower than the {Chinese Cuisine bar}<r>[0.247, 0.268, 0.393, 0.631]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "7puezcCdGbauDjKd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is bigger, the {Mexican Cuisine bar}<r>[0.393, 0.449, 0.538, 0.631]</r> or the {Indian Cuisine bar}<r>[0.538, 0.570, 0.683, 0.631]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indian Cuisine bar."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "QEkmbFozFOhwyBDm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Japanese Cuisine}<r>[0.828, 0.939, 0.975, 0.957]</r> more popular or less popular than {Thai Cuisine}<r>[0.703, 0.939, 0.809, 0.957]</r> according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less popular."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "nUDbbkdSKvh5sEXu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the coordinate region [0.395, 0.449, 0.538, 0.631]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a blue bar representing Mexico's popularity growth in the food industry. The bar signifies a positive growth of about 0.25."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "itmkm22K3sbTVHyZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.829, 0.631, 0.974, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a bar for {Japanese Cuisine}<r>[0.828, 0.939, 0.975, 0.957]</r> that clearly depicts a negative growth trend of about -0.25."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "ZF2XyrKYS2C3unwm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the data located inside the region [0.551, 0.569, 0.673, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The region contains a single bar pertaining to the popularity growth of {Indian Cuisine}<r>[0.550, 0.939, 0.672, 0.957]</r>. The bar indicates a small positive growth of about 0.05."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "6rCNAeO2JE7uiUDw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart label at these coordinates [0.019, 0.376, 0.036, 0.569] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Innovation Index\""
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "QlYzQRXPGrK5nf1b",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category appears at coordinates [0.837, 0.919, 0.974, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is \"AI_in_Education\""
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "IzdfCaFI5T7MqhIs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value found at coordinates [0.861, 0.174, 0.950, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the value for \"AI_in_Education\" and it is 2.2"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "VM2RJRr9v5bdMNow",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'AI_in_Education' bar}[0.861, 0.174, 0.950, 0.560] higher than {the 'Adaptive_Learning' bar}[0.713, 0.560, 0.802, 0.665]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "PNyujkyLnTLnTXgt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher, {the 'Gamified_Education' bar}[0.417, 0.402, 0.506, 0.560] or {the 'Cloud_Based_Learning' bar}[0.121, 0.358, 0.210, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Gamified_Education' bar"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "DUuHwpdC4ViNtkq6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is more negative between {the 'Adaptive_Learning' bar}[0.713, 0.560, 0.802, 0.665] and {the 'MOOCs_Adoption' bar}[0.269, 0.560, 0.358, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'MOOCs_Adoption' bar"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "CMkd7A4S6dXnjYHg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see at coordinates [0.713, 0.560, 0.802, 0.665]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a section of the 'Adaptive_Learning' bar, which extends below the zero line, indicating a negative value for the 'Innovation Index'."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "egFzGuNrrnVy2nxw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be seen at coordinates [0.269, 0.560, 0.358, 0.867]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, the bar for the 'MOOCs_Adoption' category is located. Its length extends above and below the zero line, meaning it represents a negative value for the 'Innovation Index'."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "ASOT2AJFVLwtrq5m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region bounded by coordinates [0.121, 0.358, 0.210, 0.560]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a bar corresponding to the 'Cloud_Based_Learning' category. The bar is entirely above the x-axis, indicating a positive 'Innovation Index' value for 'Cloud_Based_Learning'."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "hHDKpNCJcScL8RZe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object located at [0.147, 0.143, 0.285, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing earnings of age group 20-29."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "wNDbFuJRSaFmmuAL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.025, 0.418, 0.048, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label {Earnings}<r>[0.025, 0.418, 0.048, 0.519]</r>."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "VEVyFh2cEUVXJ8pw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found at [0.321, 0.919, 0.387, 0.942] and [0.354, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age group {30-39}<r>[0.321, 0.919, 0.387, 0.942]</r> and its corresponding X-tick <r>[0.354, 0.903]</r>."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "bLhOL8uwjtmoADjx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the object at [0.699, 0.469, 0.837, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar graph indicating that the earnings for the age group 60-69 are 50000."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "wPtxxap5z9jc58wX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing the Age Group 20-29 {20-29}<r>[0.147, 0.143, 0.285, 0.903]</r>, higher than the bar representing age group 30-39 {30-39}<r>[0.285, 0.197, 0.423, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "GqUc8SuPdHn9vGvU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the earnings for age group 40-49 {40-49}<r>[0.423, 0.251, 0.561, 0.903]</r> higher than those for age group 70+ {70+}<r>[0.837, 0.577, 0.975, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "24SkvAa5JqinbkdG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar at [0.423, 0.251, 0.561, 0.903] have higher earnings than the bar at [0.837, 0.577, 0.975, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "m46LMPkZeYHpowt5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the earning for age group 50-59 {50-59}<r>[0.561, 0.360, 0.699, 0.903]</r> more than that for age group 60-69 {60-69}<r>[0.699, 0.469, 0.837, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "7WEnIkiYfCFVYI8O",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is seen in the area bounded by [0.147, 0.143, 0.975, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are six bars representing the earnings of age groups 20-29, 30-39, 40-49, 50-59, 60-69, and 70+."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "OJNDmUpJ13TfQcJy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.058, 0.025, 0.131, 0.808]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains labels marking the Y-axis. The labels range from 80000 at the bottom to 10000 at the top, representing the earnings amount."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "n4aopTUSciNjPQR1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.147, 0.034, 0.975, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main graph information, consisting of six bars. These bars represent the earnings of various age groups ranging from 20-29 through to 70+."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "yqWluVDXnE4WZlKs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.500, 0.952, 0.622, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label {Age Group}<r>[0.500, 0.952, 0.622, 0.975]</r> is located in this area."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "jLIn3QZmbaeuPCPy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the y-axis is \"Frequency of Mentions\" {ylabel}<r>[0.015, 0.333, 0.029, 0.595]</r>."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "rFc6fxsvzlSAZfbX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the x-axis is \"Healthcare and Medical Technology Aspects\" {xlabel}<r>[0.380, 0.952, 0.685, 0.975]</r>."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "YQTYMej1sN8YKQqb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second element in x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second element of the x-axis is \"Treatment\" on {xticks: label}[0.233, 0.919, 0.304, 0.942]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "rwtEaigTF6DIj2F5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does legend refer to Negative aspect?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for the Negative aspect as per the legend is red {sublegend: items: handle: color}<r>[0.092, 0.048, 0.120, 0.064]</r>."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "FZaA4SX1uqN0C6V7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is mentioned more frequently, \"Technology\" or \"R&D\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Technology\" is mentioned more frequently than \"R&D\"."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "jjAKduAypKn1EaZl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"AI\" application}[0.754, 0.798, 0.838, 0.903] taller than {the bar for \"Medicine\"}[0.121, 0.767, 0.205, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "ZimaOGrZZWufPrGJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhether the frequency of \"Medicine\" is equal to \"Treatment\" in Positive category?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, {the positive bar for \"Medicine\"}[0.121, 0.767, 0.205, 0.767] is lower than {the positive bar for \"Treatment\"}[0.226, 0.752, 0.311, 0.794]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "XnRj1Xel7UhCr5fu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the \"Medicine\" subtype have more Positive or Negative mentions?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Medicine}<r>[0.121, 0.767, 0.205, 0.903]</r> has more Negative mentions."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "J6KogNs7q7Hf1vM2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.086, 0.037, 0.199, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area refers to the chart legend, which includes labels for Negative, Positive, None and Mixed responses denoted by the colours red, green, gray and orange respectively. The Negative label is given at [0.131, 0.046, 0.193, 0.069], Positive label at [0.131, 0.081, 0.184, 0.104], None label at [0.131, 0.116, 0.167, 0.139], and Mixed label at [0.131, 0.151, 0.172, 0.174]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "AkVtC9Q2dss3POqS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain what is shown in [0.079, 0.025, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the chart's data, including the bars each representing different aspects of healthcare and medical technology along with their frequency in negative, positive, none and mixed categories. It spans from aspects like Medicine, Treatment, Diagnosis to AI applications in Medical technology."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "oAXJiQKtNyuOIWus",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the section [0.437, 0.698, 0.522, 0.903] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section houses the four bars that quantify the frequency of the \"Technology\" aspect in Negative, Positive, None and Mixed categories."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "cEeqspoxL8ckVnGp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is special about the data contained within [0.543, 0.088, 0.733, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the frequencies of mentions about \"Telemedicine\" in different contexts. Among them, there is a particularly large bar - {Telemedicine frequency in None category}<r>[0.648, 0.088, 0.733, 0.903]</r>, indicating a significant amount of neutral mentions about telemedicine."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "AwVqVJZFCK7dZnej",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified at the position [0.025, 0.363, 0.048, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the y-axis, which is the {Frequency}<r>[0.025, 0.363, 0.048, 0.542]</r>."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "3kkLZwSWbGd1Hlyr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at [0.171, 0.453, 0.249, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a green bar that represents {Growth} in the {Experimentation} field with a range of 0.00 -> 2000.00."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "RGgfXJjN8tgn9eqn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at location [0.153, 0.086, 0.199, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the color handle for the legend item {Growth}<r>[0.153, 0.086, 0.199, 0.110]</r>, and it's green."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "Xb1apf9E3qkuPOpb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, which label {Decline} or {Growth} is on top?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Growth}."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "fHGF9o739WN5f7Pn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bar lengths in [0.269, 0.523, 0.347, 0.854] and [0.269, 0.483, 0.347, 0.523], which is longer for {Analysis}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar {Growth}."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "kHIbj3WRk2uUxzsJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat field in {Fields}<r>[0.521, 0.927, 0.586, 0.963]</r> is farther right, {Implementation} or {Communication}?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Communication}."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "7waupZneRiaRRh1g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.144, 0.069, 0.311, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend for the bar chart. It contains color-text label pairs for two categories - {Growth} and {Decline}. The {Growth} color handle is located at [0.153, 0.086, 0.199, 0.110], which is green, and the accompanying text label is located at [0.218, 0.083, 0.299, 0.118]. Similarly, the {Decline} color handle is at [0.153, 0.138, 0.199, 0.162], which is red, and the text label is at [0.218, 0.135, 0.301, 0.170]."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "FZh4278mSHG1Tm5G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.171, 0.453, 0.249, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a green bar associated with {Growth} in the {Experimentation} field. This bar spans a y-range from 0.00 to 2000.00."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "8ecTG1JSBXxLcxhC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.269, 0.483, 0.347, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a red bar indicating {Decline} in the {Analysis} field. This bar ranges from 1650.00 to 1850.00 on the y-axis."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "qQ228wZ8IFtxMENi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the xlabel is derived from {xlabel}[0.508, 0.952, 0.572, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Sectors'."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "T0Xllc7ZuU0cANPf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the handle}[0.115, 0.083, 0.150, 0.099] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "9GeTngOgQfz53BeZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the yticks, what is represented at {yticks}[0.076, 0.894, 0.087, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '0'."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "XAk1DGFVOASNhd3l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector is allocated more resources, {Healthcare}[0.139, 0.823, 0.308, 0.903] or {Education}[0.350, 0.504, 0.519, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "jFfT8clYoCPYPsF6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich organization allocates more resources to {Infrastructure}[0.561, 0.105, 0.730, 0.903], {Social Services} or {Public Services}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Services."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "x1tT43a38DV9PJfg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the area dedicated to {Public Services}[0.115, 0.083, 0.150, 0.099] in relation to {Social Services}[0.115, 0.048, 0.150, 0.064] in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "6MctHvblJbybi7eV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the objects found in this coordinate range [0.561, -0.294, 0.730, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 bars:\n1. The bar for the infrastructure sector by Public Services. Its allocated resources start from a y-value of 0 and reach to a maximum of 1000 with the coordinate range [0.561, 0.105, 0.730, 0.903].\n2. The placeholder for overspending by Social Services in the infrastructure sector which starts at a y value of 1000 and stretches to 1500 in the negative direction with the coordinate range [0.561, -0.294, 0.730, 0.105]"
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "uX9AFp4ftHKSp668",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the content within the region [0.108, 0.037, 0.300, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend area containing the labels and corresponding colors for Public Services and Social Services. Specifically, it contains 4 items: \n1. Social Services is represented in Red, located at \n{handle}[0.115, 0.048, 0.150, 0.064]\n2. Public Services is represented in Green, located at {handle}[0.115, 0.083, 0.150, 0.099]"
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "9ZbnURyZWFEaM97j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible within the coordinates [0.139, 0.504, 0.519, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 bars representing resource distribution by Social Services and Public Services for Education. \n1. Social Services has allocated 500 units in the region [0.350, 0.504, 0.519, 0.903]\n2. Public Services hasn't allocated any resources as it's on the same level as Social Services' allocation."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "7mhX8UExOxa3pLrE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.171, 0.754, 0.511, 0.854] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blue bar representing the Surging Growth trend in the Innovation Ecosystem."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "pdBpEWmjQ1EsYOto",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first entry in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.153, 0.086, 0.199, 0.110]."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "r7ql7OpLQm7mMQVV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the {green bar}<r>[0.596, 0.372, 0.937,0.513]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of Stable Market trend in the Technology Entrepreneurship category."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "sb9VDMLyfsm8jGns",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in <r>[0.144, 0.069, 0.433, 0.288]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart, showing colors for Surging Growth, Descending Crisis, Stable Market, and Emerging Trend."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "inPO7okb11PWT2Kz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Innovation Ecosystem bar for Emerging Trend}<r>[0.171, 0.172, 0.511, 0.192]</r> higher than the {Innovation Ecosystem bar for Stable Market}<r>[0.171, 0.192, 0.511, 0.593]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "eSiDnuEnlkR5b0jL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Surging Growth trend in the Innovation Ecosystem}<r>[0.171, 0.754, 0.511, 0.854]</r> greater than the {Descending Crisis trend in the Technology Entrepreneurship}<r>[0.596, 0.513, 0.937, 0.814]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "k7C8dC7woyDywvME",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing {Innovation Ecosystem}<r>[0.171, 0.754, 0.511, 0.854],[0.171, 0.593, 0.511, 0.754],[0.171, 0.192, 0.511, 0.593],[0.171, 0.172, 0.511, 0.192]</r> generally taller than the bars representing {Technology Entrepreneurship}<r>[0.596, 0.814, 0.937, 0.854],[0.596, 0.513, 0.937, 0.814],[0.596, 0.372, 0.937, 0.513],[0.596, 0.282, 0.937, 0.372]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "6RBlwyG2pFvavRel",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Surging Growth and Descending Crisis for Innovation Ecosystem} <r>[0.171, 0.754, 0.511, 0.854],[0.171, 0.593, 0.511, 0.754]</r> and {Stable Market and Emerging Trend for Innovation Ecosystem} <r>[0.171, 0.192, 0.511, 0.593],[0.171, 0.172, 0.511, 0.192]</r>, which group has a higher overall value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Surging Growth and Descending Crisis for Innovation Ecosystem."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "hynssY6zW0B2Yhij",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.144, 0.069, 0.433, 0.288]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend, with corresponding color-text labels for Surging Growth (Blue - [0.153, 0.086, 0.199, 0.110]), Descending Crisis (Red - [0.153, 0.138, 0.199, 0.162]), Stable Market (Green - [0.153, 0.190, 0.199, 0.215]), and Emerging Trend (Orange - [0.153, 0.243, 0.199, 0.267])."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "icCYeybuC8Aepa3y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.132, 0.051, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area corresponds to the whole chart, including the bars representing the different values of each trend for two categories (Innovation Ecosystem and Technology Entrepreneurship), the x-axis label (Ecosystems), the y-axis labels representing the values, and the chart's legend."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "8vC82ZnKS5EwLPWw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.596,0.282,0.937,0.372]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is an orange bar representing the Emerging Trend value for the Technology Entrepreneurship category."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "6ez2ELrBy1pZXVXZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.485, 0.927, 0.622, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label, which is \"Ecosystems\"."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "yWNvAAkekNbYP0fK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing {the falling rate of return}<[0.157, 0.349, 0.264, 0.646]> in 'Bond Investments'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "pNxSzlUseFaT2xri",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the investment type at the coordinate [0.799, 0.671, 0.965, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cryptocurrency"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "PVDaBeDeKigOTWfi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the 'Return Rate Same' measured by the bar at [0.291, 0.408, 0.399, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20 percent"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "hDonB9qNakyc9s0f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label at the coordinate [0.025, 0.210, 0.048, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Return Rate (%)"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "8IyFXAY4kPWfKN4E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the rate of return for real estate investments}<r>[0.425, 0.587, 0.533, 0.646]</r> increasing or decreasing as represented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "eOE38vFXxez71Q3Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of investment has a higher 'Return Rate Falling', {Stock Investments}<r>[0.291, 0.527, 0.399, 0.646]</r> or {Mutual Funds}<r>[0.560, 0.438, 0.667, 0.646]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bond Investments."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "AU3DJFjc7udOA7DA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Commodity Investments}<r>[0.694, 0.617, 0.802, 0.646]</r> have a higher or lower 'Abrupt Drop' than {Cryptocurrency}<r>[0.829, 0.051, 0.936, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "dyiUOb8B8ecunPzN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Bond Investments' and 'Cryptocurrency', which has a larger 'Return Rate Same' as represented by {Bond Investments}<r>[0.157, 0.111, 0.264, 0.349]</r> and {Cryptocurrency}<r>[0.829, 0.408, 0.936, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bond Investments."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "bGaJU67TSQHdxhJB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is shown in the region [0.129, 0.069, 0.432, 0.288]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains color labels and text representing various Return Rates. 'Return Rate Falling' is represented by red at [0.139, 0.086, 0.185, 0.110], 'Return Rate Same' is shown as green at [0.139, 0.138, 0.185, 0.162], 'Return Rate Rising' is grey at [0.139, 0.190, 0.185, 0.215], and 'Abrupt Drop' is orange at [0.139, 0.243, 0.185, 0.267]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "MQtESse9ljIFOkO3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area in the coordinates: [0.425, 0.587, 0.533, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a red bar representative of a decreasing Return Rate for 'Real Estate Investments'."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "XvP0YkqEpr3UMbMH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.619, 0.671, 0.877, 0.914] describe?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the label 'Commodity Investments' for the X-axis category."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "Q9QHJ3tI6mD9PyHJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.799, 0.051, 0.936, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "An orange bar in this region indicates the 'Abrupt Drop' rate for 'Cryptocurrency'."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "SN4r0nw59WLlQvnP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity is represented at the coordinates [0.025, 0.267, 0.048, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, named \"Number of Resources\"."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "b2YvnhEyg5LW5Mtq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the label for \"School Bags\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.218, 0.187, 0.359, 0.222]"
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "ABqchi62e0aPK4ep",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object at coordinates [0.171, -0.501, 0.324, -0.320]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the bar for \"Laptops\" in \"Urban Schools\"."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "O8vQAzabk4G5a0ZO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the label at [0.164, 0.879, 0.330, 0.914] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for \"Urban Schools\"."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "5IWWGZijvyTHVcZ9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Textbooks in Urban Schools\"} [0.171, 0.578, 0.324, 0.854] taller than {the bar representing \"Textbooks in Rural Schools\"} [0.783, 0.714, 0.937, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "bg7iGszZFD6RPwb0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Laptops in Urban Schools\" bar}[0.171, -0.501, 0.324, -0.320] to the left or right of {the \"Laptops in Rural Schools\" bar}[0.783, 0.301, 0.937, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "nTyCD2DxPrrYiHCQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the rural schools {textbook bar}[0.783, 0.714, 0.937, 0.854] and {pencil bar}[0.783, 0.458, 0.937, 0.714] have the same starting point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "rrOZi9YIULDE56fY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the urban school category, which is larger: {The bar representing \"Textbooks\"} [0.171, 0.578, 0.324, 0.854] or {The bar representing \"Pencils\"} [0.171, 0.192, 0.324, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pencils."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "tkCpDMt8yfgdSQlI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area given by the coordinates [0.144, 0.069, 0.368, 0.288]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, it includes text labels and colored rectangles signifying different types of resources like textbooks, pencils, school bags, and laptops. The colored rectangles correspond to the bars in the chart."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "y47snYpqtWgneQa5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the section [0.782, 0.879, 0.938, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section represents the x-axis label for \"Rural Schools\"."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "uKm9ljDgmPhD6uPN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBriefly describe the elements located in the space [0.132, 0.051, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the main plot of the bar chart. It includes all the bars for both \"Urban Schools\" and \"Rural Schools\" which represent different types of school resources including textbooks, pencils, school bags, and laptops. It also includes the x-axis and y-axis labels, and scales."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "ebueESl2MI8bJ7YG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the contents within the region at [0.783, 0.714, 0.937, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single red bar within this region. This bar represents the quantity of \"Textbooks\" in \"Rural Schools\"."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "a93xNWlkFuRUCNi0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.025, 0.378, 0.048, 0.528] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the Y-axis label, called Quantity."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "xAHJL7ssn2aIojzh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.218, 0.083, 0.345, 0.118]."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "EQVDylH2SkRNjfim",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}[0.529, 0.927, 0.578, 0.963] display?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label displays 'Year'."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "Q4EFnMAkoGQyKGJA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels in [0.144, 0.069, 0.354, 0.236]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are {'Pessimistic'}[0.218, 0.083, 0.345, 0.118], {'Normal'}[0.218, 0.135, 0.302, 0.170], and {'Optimistic'}[0.218, 0.187, 0.336, 0.222] labels for different scenarios being represented."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "Ok04YOj5TifijoWN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Optimistic' rectangle for year 2022}[0.171, -0.772, 0.276, -0.330] positioned above or below {the 'Pessimistic' rectangle for the same year that is}[0.171, 0.232, 0.276, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "nZLr6kClW2qoXqku",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, Does {the 'Pessimistic' bar of 2023}[0.303, 0.292, 0.408, 0.854] have more or less quantity than {the 'Pessimistic' bar of 2022}[0.171, 0.232, 0.276, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "jRhSGmGF1gQaRFhc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 'Pessimistic' bars}[[0.171, 0.232, 0.276, 0.854], [0.303, 0.292, 0.408, 0.854], [0.435, 0.332, 0.540, 0.854], [0.567, 0.383, 0.673, 0.854], [0.699, 0.433, 0.805, 0.854], [0.831, 0.493, 0.937, 0.854]] generally taller than {the 'Optimistic'}[[0.171, -0.772, 0.276, -0.330], [0.303, -0.702, 0.408, -0.250], [0.435, -0.651, 0.540, -0.190], [0.567, -0.611, 0.673, -0.129], [0.699, -0.571, 0.805, -0.069], [0.831, -0.521, 0.937, 0.001]] bars?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "Cjgty5gDkKRDGuFN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"2022 Pessimistic\"}[0.171, 0.232, 0.276, 0.854] and {\"2023 Normal\"}[0.303, -0.250, 0.408, 0.292], which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "2022 Pessimistic."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "XjqzMAHUnfST1GNk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.144, 0.069, 0.354, 0.236]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It has three entries which are as follows:\n1. Pessimistic, colored in red {handle color}[0.153, 0.086, 0.199, 0.110]\n2. Normal, displayed in green {handle color}[0.153, 0.138, 0.199, 0.162]\n3. Optimistic, displayed in grey {handle color}[0.153, 0.190, 0.199, 0.215]"
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "qFR4OZW3rK4baZXV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the area [0.132, 0.051, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encapsulates the whole chart plotting area. It includes three types of bars: Pessimistic, Normal, and Optimistic for each year from 2022 to 2027 represented on the x-axis. The Y-axis is a scale marking the quantity."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "WB0U82S4eblk9bcE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.171, -0.772, 0.276, -0.330]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the 'Optimistic' bar for the year 2022, indicating the quantity considered as an optimistic prediction for that year."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "gKWAxSwB2zlLrfMs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.132, 0.051, 0.975, 0.854] display?"
+            },
+            {
+                "from": "gpt",
+                "value": "Essentially, it displays a bar chart showcasing three different scenarios: 'Pessimistic', 'Normal', and 'Optimistic' for prediction quantity across the years 2022 to 2027."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "TKyBCRPie4acZYml",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text label for \"public finance\" on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.866, 0.690, 0.938, 0.823]"
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "Axcl5bWfqTpFHf3j",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.531, 0.354, 0.625, 0.642] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green bar represents the frequency of the positive sentiment of the Health and Welfare policy area."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "mqnI34tP0zB3Imso",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color at [0.084, 0.162, 0.109, 0.178] in the chart legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents mixed sentiment."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "YQ8K6puAQ9uMrrmJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {this grey bar}[0.220, 0.525, 0.306, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the no sentiment frequency of the Environmental Protection policy area."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "BoQTejIkKfAm54qS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the {green bar for Environmental Protection}[0.220, 0.533, 0.306, 0.538] or the {grey bar for Environmental Protection}[0.220, 0.514, 0.306, 0.525]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green bar."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "2qpBOh9sTjGXChhw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {orange bar for Public Safety}[0.433, 0.514, 0.519, 0.514] taller than the {grey bar for Public Safety}[0.433, 0.514, 0.519, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, they are the same height."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "EA1e55ONNAGdJOYX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {negative bar for Children and families}[0.114, 0.610, 0.199, 0.674] taller or shorter than the {positive bar for Children and families}[0.114, 0.608, 0.199, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "i7ezshC6WqmlhS0t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {positive bar for Education and Skills}[0.646, 0.578, 0.732, 0.594] and {the grey bar for Education and Skills}[0.646, 0.354, 0.732, 0.578], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The grey bar."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "bAJg5U9ytvENkkhf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region [0.072, 0.034, 0.986, 0.674] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the whole bar chart area, including the bars and x and y axes."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "1AiTjPCWTsvKv1k7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in The area [0.078, 0.046, 0.179, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes four labels: \"negative\" with red color located at [0.084, 0.057, 0.109, 0.073], \"positive\" with green color located at [0.084, 0.092, 0.109, 0.108], \"none\" with grey color located at [0.084, 0.127, 0.109, 0.143], and \"mixed\" with orange color located at [0.084, 0.162, 0.109, 0.178]."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "IBhfQGJnovyV1uqP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.535, 0.690, 0.630, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Health and Welfare\" for the X-axis category is in this region."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "eiu7LPyacn0vFdwz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of area [0.433, 0.514, 0.519, 0.514]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains one orange bar which represents the frequency of mixed sentiment for the 'Public Safety' category."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "bUbUR32fHRhRk29v",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object located at coordinates [0.472, 0.942, 0.600, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label named \"Migration Trends\"."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "mdGbSWEkMp3MyLmH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item at [0.102, 0.152, 0.133, 0.172] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at these coordinates corresponds to the \"Rapid Decline\" category which is gray."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "jJsTT0C0uVMuhkLc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat range of data does the bar located at [0.129, -0.823, 0.317, -0.380] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar corresponds to the \"Roller-Coaster Pattern\" for the \"Human\" trend and represents the range from 12000 to 16200."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "6T6QplHNWdvxBDki",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the bar representing \"Erratic Decline\" in the 'Human' category at coordinates [0.129, 0.515, 0.317, 0.883], or the \"Volatile Growth\" bar in the 'Industrial' category at coordinates [0.755, -0.085, 0.943, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Volatile Growth' bar in the 'Industrial' category."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "nfVvg4ZUJEeo6Bv7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Sluggish Progress' bar in the 'Human' category}[0.129, -0.380, 0.317, -0.275] taller than {the 'Roller-Coaster Pattern' bar in the 'Human' category}[0.129, -0.823, 0.317, -0.380]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "rMJ28JCASQgKPijg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {\"Erratic Decline\"} bar in the 'Human' category<[0.129, 0.515, 0.317, 0.883]> and the {\"Volatile Growth\"} bar in the 'Industrial' category<[0.755, -0.085, 0.943, 0.652]>, which represents a greater range of count?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Volatile Growth' bar in the 'Industrial' category."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "cmYK9fON3gaX32Hi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the legend with coordinates [0.096, 0.055, 0.320, 0.356]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains color-coded categorical information about seven distinct types of migration trends that are each paired with a color. \"Erratic Decline\" is red ([0.102, 0.069, 0.133, 0.088]), \"Volatile Growth\" is green ([0.102, 0.110, 0.133, 0.130]), \"Rapid Decline\" is gray ([0.102, 0.152, 0.133, 0.172]), \"Sustained Growth\" is orange ([0.102, 0.194, 0.133, 0.214]), \"Sluggish Progress\" is purple ([0.102, 0.236, 0.133, 0.256]), \"Roller-Coaster Pattern\" is blue([0.102, 0.278, 0.133, 0.297]), and \"Moderate Decline\" is pink ([0.102, 0.320, 0.133, 0.339])."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "9gTp4JENigECABtR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region [0.129, -1.033, 0.317, -0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pink bar labeled as \"Moderate Decline\", which represents count information for the \"Human\" category, with a range from 16200 to 18200."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "4MvJSqAfcXJ0XRVh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region with coordinates [0.755, -0.907, 0.943, -0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates there is a colored bar representing 'Sustained Growth' in the 'Industrial' category. This bar represents the count range from 13000 to 17000."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "1GcQksfIbHbx8qpM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented in this structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "0wDGnvl1ve4cPcgl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for \"India's Growth\" in this bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.255, 0.915, 0.293, 0.917]"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "WYEzbtXKa8B2WXgF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the colour label at [0.868, 0.079, 0.891, 0.093]} represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "RHHD5M9T9dkexjX8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the colour label at [0.868, 0.049, 0.891, 0.063]} represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consumption."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "eQ2gqQKkaYZLBKNH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {India's Consumption bar}[0.217, 0.349, 0.255, 0.917] higher or lower than {China's Consumption bar}[0.108, 0.207, 0.146, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "Ad0STJQKn1Rwh7na",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Russia's Growth bar}[0.581, 0.917, 0.619, 0.918] higher or lower than {USA's Growth bar}[0.364, 0.916, 0.402, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "YOaNTEzHE1gRpP7x",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Nigeria's Consumption bar}[0.761, 0.739, 0.799, 0.917] represent a larger or smaller value than {South Africa's Consumption bar}[0.652, 0.775, 0.690, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "YLxS8daelE1QVleZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the sizes of {Brazil's Consumption bar}[0.434, 0.633, 0.472, 0.917] and {Brazil's Growth bar}[0.472, 0.915, 0.511, 0.917]."
+            },
+            {
+                "from": "gpt",
+                "value": "The Consumption bar is larger."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "iKRMOBizUdII8IT7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.863, 0.039, 0.982, 0.105]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two colour-text labels. The first one represents 'Consumption' with a blue label and the coordinates are {[0.868, 0.049, 0.891, 0.063]: 'Blue', [0.901, 0.047, 0.977, 0.067]: 'Consumption'}. The second one represents 'Growth' with a red label and the coordinates are {[0.868, 0.079, 0.891, 0.093]: 'Red', [0.901, 0.077, 0.943, 0.097]: 'Growth'}."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "4c8OjRK2c96hq64S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description of the content in coordinates [0.029, 0.021, 0.058, 0.041]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label that indicates a value of 5000."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "JS5ib942Mk1trvOY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.108, 0.207, 0.146, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar representing 'China's Consumption', denoting a value range of '0.00 -> 4000.00'."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "ktoajoYZrp8UTH6S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.880, 0.931, 0.935, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a X-axis label \"Indonesia\" located within this region."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "QF7oE8otRIxuTBvz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar with coordinates [0.350, 0.680, 0.382, 0.883] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the frequency of \"Stable Growth\" for the 3rd time frame, and that the frequency value is 500."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "9NLFEPL9Az5V7Oxx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label {Stable Growth} in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.842, 0.055, 0.966, 0.083]"
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "c53e9rl3PnlllaBJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is time frame value at [0.377, 0.903, 0.387, 0.931] on the x-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is 3."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "GZdo8GD6OlViEo2q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the frequencies shown in [0.043, 0.669, 0.087, 0.697] and [0.043, 0.263, 0.087, 0.291] on the y-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "The frequencies value are 500 and 1500 respectively."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "nLbekxozyBZVRJzL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Stable Growth\" for 1st timeframe}[0.139, 0.680, 0.171, 0.883] higher than {the bar representing \"Rapid Decline\" for 1st timeframe}[0.171, 0.071, 0.203, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "RVQVdOL5VXcpvHiv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Rapid Decline\" for 3rd timeframe}[0.382, 0.274, 0.414, 0.883] higher than {the bar representing \"Stable Growth\" for 8th timeframe}[0.878, 0.680, 0.910, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "9ih6lZVacIQgKjgc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing \"Stable Growth\"}[[0.139, 0.680, 0.171, 0.883], [0.245, 0.721, 0.276, 0.883], [0.350, 0.680, 0.382, 0.883], [0.456, 0.701, 0.488, 0.883], [0.561, 0.680, 0.593, 0.883], [0.667, 0.741, 0.699, 0.883], [0.772, 0.721, 0.804, 0.883], [0.878, 0.680, 0.910, 0.883]] generally shorter than {the bars representing \"Rapid Decline\"}[[0.171, 0.071, 0.203, 0.883], [0.276, 0.172, 0.308, 0.883], [0.382, 0.274, 0.414, 0.883], [0.488, 0.375, 0.519, 0.883], [0.593, 0.477, 0.625, 0.883], [0.699, 0.558, 0.730, 0.883], [0.804, 0.640, 0.836, 0.883], [0.910, 0.721, 0.941, 0.883]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "o81cS5rK7bvaUlTl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Stable Growth\"}[[0.139, 0.680, 0.171, 0.883], [0.245, 0.721, 0.276, 0.883], [0.350, 0.680, 0.382, 0.883], [0.456, 0.701, 0.488, 0.883], [0.561, 0.680, 0.593, 0.883], [0.667, 0.741, 0.699, 0.883], [0.772, 0.721, 0.804, 0.883], [0.878, 0.680, 0.910, 0.883]] and {\"Rapid Decline\"}[[0.171, 0.071, 0.203, 0.883], [0.276, 0.172, 0.308, 0.883], [0.382, 0.274, 0.414, 0.883], [0.488, 0.375, 0.519, 0.883], [0.593, 0.477, 0.625, 0.883], [0.699, 0.558, 0.730, 0.883], [0.804, 0.640, 0.836, 0.883], [0.910, 0.721, 0.941, 0.883]], which category has a higher frequency for 5th timeframe?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stable Growth."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "oaqj5Ioo35uvPXhd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.786, 0.044, 0.973, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart. It contains two labels \"Stable Growth\" at [0.842, 0.055, 0.966, 0.083] colored in green and \"Rapid Decline\" at [0.842, 0.097, 0.962, 0.125] colored in red."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "IamEkPqs2B0IhfWA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.099, 0.030, 0.981, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars in the chart and their respective labels. For instance, the bar at [0.139, 0.680, 0.171, 0.883] represents the frequency of \"Stable Growth\" for the 1st timeframe while the bar at [0.171, 0.071, 0.203, 0.883] represents \"Rapid Decline\" for the same timeframe."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "kfd34ABs3Uc0s88x",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart contains within [0.482, 0.375, 0.519, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in the area. The taller green bar denotes the frequency of \"Stable Growth\" for the 4th timeframe with a frequency of 450. The shorter red bar represents \"Rapid Decline\" in the same timeframe with a frequency of 1250."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "CpVkSI3dGGS3ThKS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.776, 0.890, 0.910, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 8th timeframe on the x-axis scale and also the last bar for both the \"Stable Growth\" and \"Rapid Decline\" series. The blue bar at [0.878, 0.680, 0.910, 0.883] represents the \"Stable Growth\" series and the red bar at [0.910, 0.721, 0.941, 0.883] represents that \"Rapid Decline\" series."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "0ehFG9wTtDHQkF9G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the item with coordinates [0.029, 0.598, 0.049, 0.640] on the Y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the number 1000 on the cost axis."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "6cki6mwHdtYA1jUj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the subtitle located at [0.527, 0.015, 0.527, 0.015]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no subtitle for the chart, as its value is an empty string."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "GO2mKb8XGptXmb4k",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.109, 0.041, 0.138, 0.059] in the sublegend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"2010\" associated with the color \"#87ceeb\", representing the data for the year 2010."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "JgXOHOhaQTcBCSYb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2010 bar for Market Research}[0.301, 0.093, 0.368, 0.299] higher or lower than {the 2020 bar for Market Research}[0.301, 0.246, 0.368, 0.44]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "mAMnXfft0kkEa2kd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2010 bar for Expense}[0.108, 0.066, 0.175, 0.364] on the left or right of {the 2020 bar for Expense}[0.108, -0.083, 0.175, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they share the same horizontal space."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "ry2Pb4mQNXaAn1XQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars representing the two different years, do {the 2010 bars} overall represent smaller or larger values than {the 2020 bars}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "v9UhUFr9KiQyfoI0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is happening in the area [0.206, 0.158, 0.270, 0.439]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes both the 2010 and 2020 bars for the \"Customer_Meeting\" process. The 2010 bar is represented in blue (coordinates [0.204, 0.348, 0.272, 0.530]) and the 2020 bar is represented in orange (coordinates [0.204, 0.158, 0.272, 0.348]), both indicating different expenses for the process in the respective years."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "JajI8hH0kdKBq9W2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the x-axis label coordinates [0.498, 0.964, 0.555, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object present within these coordinates is the x-axis title labelled \"Processes\"."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "hurBqs5vaeGM4XTK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.788, 0.032, 0.941, 0.743]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the majority of the bar chart. It includes the bars for the processes \"Order_Processing\", \"Warehouse_Management\", and \"Delivery\" for the years 2010 and 2020."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "34y9V1By4NkBuMLS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object with coordinates [0.393, 0.060, 0.612, 0.089] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label, called Measurements."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "KHjVguN7WKfqAp4k",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.922, 0.055, 0.966, 0.078]"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "7Qst9QCwjJrjsMEl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at the {y-axis scale}<r>[0.076, 0.894, 0.087, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "NvkMEw3QqhSktrGp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the indicators shown in the subplot at [0.866, 0.046, 0.973, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2010}<r>[0.922, 0.055, 0.966, 0.078]</r> and {2020}<r>[0.922, 0.090, 0.965, 0.113]</r>."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "nxfgYvOsVs7kGq18",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2010 bar for green practices}<r>[0.139, 0.360, 0.206, 0.903]</r> taller than {the 2020 bar for green practices}<r>[0.206, 0.251, 0.273, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "2Z0FJyeRpA8JtnlO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 2010 trend in Energy Consumption}<r>[0.473, 0.360, 0.540, 0.903]</r> taller than {the bar representing 2020 trend in Energy Consumption}<r>[0.540, 0.577, 0.607, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "d63wjDUoPnBPZDHh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for 2010}<r>[[0.139, 0.360, 0.206, 0.903], [0.306, 0.251, 0.373, 0.903], [0.473, 0.360, 0.540, 0.903], [0.640, 0.143, 0.707, 0.903], [0.808, 0.686, 0.874, 0.903]]</r> generally taller than {the bars for 2020}<r>[[0.206, 0.251, 0.273, 0.903], [0.373, 0.469, 0.440, 0.903], [0.540, 0.577, 0.607, 0.903], [0.707, 0.469, 0.774, 0.903], [0.874, 0.794, 0.941, 0.903]]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "GDsGNy6iDEQMAFAE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Green Practises for 2010}<r>[0.139, 0.360, 0.206, 0.903]</r> and {Green Practises for 2020}<r>[0.206, 0.251, 0.273, 0.903]</r>, which year's bar extends further upwards?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "yerx1ULDVTfPZtla",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.866, 0.046, 0.973, 0.123]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two color-text labels in the legend; the first color label is located at [0.873, 0.057, 0.908, 0.073], which is red, the text label is located at [0.922, 0.055, 0.966, 0.078], and is known as 2010 which indicates 2010's bars are red; the second color label is located at [0.873, 0.092, 0.908, 0.108], which is blue, and the text label is located at [0.922, 0.090, 0.965, 0.113], and is known as 2020 to indicate that 2020's bars are blue."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "KqBEAyBZIgM4Eky5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the elements present within the area [0.076, 0.894, 0.087, 0.917]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.076, 0.894, 0.087, 0.917] corresponds to the 0 point mark on the Y-axis."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "bxXi2pEjqiS6ZNiZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.206, 0.251, 0.273, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one single object: The blue bar representing the value of Green Practises in 2020 with the value of 3000."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "Hm3TrckyaFmFHhWO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat dominates the area [0.640, 0.143, 0.707, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is dominated by the bar representing the value of the CO2 Emissions category in 2010, and it is red with a value of 3500."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "R0CFhz8xCVWL7gRs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information can I get from the object at coordinates [0.015, 0.437, 0.029, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, which represents \"Value\"."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "GLhvjevwMpfv9ujA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the the object located at [0.036, 0.134, 0.070, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis ticks that represents the information \"7000\"."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "2GZBYcBPwqpNsR8c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from {the bar}[0.121, 0.469, 0.217, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the \"Self-confidence\" characteristic for the \"Steady decline\" trend with a value approximately around 4000."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "ONM1iNUsGVOiTEnH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the bar of 'self-confidence'}[0.121, 0.469, 0.217, 0.903] in the \"Steady decline\" category or {the bar of 'discipline'}[0.605, 0.555, 0.702, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar of 'self-confidence' is higher."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "sF2b9qukpMDDiFHl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'creativity' bar in the 'Inexorable decline' trend}[0.847, 0.360, 0.944, 0.490] shorter than {the 'creativity' bar in the 'Upward trend'}[0.847, 0.175, 0.944, 0.360]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "cNyU6ppSNP8TPbpJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a bigger value in the 'Minimal change' trend: {the bar of 'optimism'}[0.242, 0.382, 0.338, 0.490] or {the bar of 'discipline'}[0.605, 0.447, 0.702, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They have the same value."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "CuXfaVZKUWcwjlia",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.086, 0.046, 0.264, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart. It contains four entries:\n1. \"Steady decline\" with color red located at [0.092, 0.057, 0.120, 0.073]\n2. \"Minimal change\" with color green located at [0.092, 0.092, 0.120, 0.108]\n3. \"Inexorable decline\" with color gray located at [0.092, 0.127, 0.120, 0.143]\n4. \"Upward trend\" with color yellow located at [0.092, 0.162, 0.120, 0.178]"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "5ipsYT24hdVsFB4z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do I see in the area [0.601, 0.447, 0.702, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the \"Discipline\" attribute for the \"Minimal change\" series. Its value is approximately 1000."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "XFl59p69faN1gw9o",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details for the area [0.258, 0.125, 0.120, 0.143]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label \"Inexorable decline\" in the legend with its corresponding color square in gray."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "ufAtXGBZps1GBDJg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.035, 0.677, 0.070, 0.700] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label \"2000.\""
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "9c3GMQncvgaAhNzD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar with coordinates [0.757, -0.135, 0.840, 0.105] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "W1smnquHVV7Og2vn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text in the legend associated with color '#808080'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.155, 0.116, 0.196, 0.139]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "mDtztJZc9MEiyITz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.017, 0.349, 0.032, 0.579]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Units Sold/Returned."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "lPPoj1KIWAKEhZuz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat categories are shown within the coordinates [0.138, 0.408, 0.221, 0.424]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Clothing}[0.147, 0.919, 0.212, 0.942]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "220xTmmdY27NRr3C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the offline bar for books}[0.757, 0.743, 0.840, 0.903] taller or shorter than {the online bar for books}[0.757, 0.504, 0.840, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "i4FjqDQQr5kNXlO3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the returns bar for furniture}[0.861, -0.733, 0.943, -0.693] have a higher or lower start point than {the sales bar for furniture}[0.861, -0.693, 0.943, 0.105]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "itCOwaLYMRYqpL7B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Products, are {the offline bars}[[0.138, 0.823, 0.221, 0.903],[0.241, 0.663, 0.324, 0.903],[0.345, 0.863, 0.427, 0.903],[0.448, 0.847, 0.530, 0.903],[0.551, 0.839, 0.634, 0.903],[0.654, 0.879, 0.737, 0.903],[0.757, 0.743, 0.840, 0.903],[0.861, 0.663, 0.943, 0.903]] generally higher than {the online bars}[[0.138, 0.663, 0.221, 0.823],[0.241, 0.264, 0.324, 0.663],[0.345, 0.743, 0.427, 0.863],[0.448, 0.743, 0.530, 0.847],[0.551, 0.600, 0.634, 0.839],[0.654, 0.759, 0.737, 0.879],[0.757, 0.504, 0.840, 0.743],[0.861, 0.105, 0.943, 0.663]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "UEFnmrIuiVSwIdvc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Online\"}[0.155, 0.081, 0.205, 0.104] and {\"Offline\"}[0.155, 0.046, 0.206, 0.069], which one has more {units sold/returned}<{sales bars for products}>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "RImyhwAJlow2bQrg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.106, 0.037, 0.220, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are four color-text labels in the subplot_0 legend. The text label in the first pair is located at [0.155, 0.046, 0.206, 0.069], which is called Offline, and the color label is red; the text label in the second pair is located at [0.155, 0.081, 0.205, 0.104], and it's called Online with a green color label; text label of the third pair is [0.155, 0.116, 0.196, 0.139], referred to as Sales, and its color label is gray; the last pair's text label is at [0.155, 0.151, 0.214, 0.174], it's referred to Returns with an orange color label."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "s2YTrfii9uNPnYnU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.734, 0.917, 0.939, 0.943]."
+            },
+            {
+                "from": "gpt",
+                "value": "At [0.867, 0.919, 0.936, 0.942] lies the label for the last product category on the x-axis. It is labeled as \"Furniture\"."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "mlKxp8nfrW3Kno1C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.757, -0.135, 0.840, 0.105]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There lies a bar representing \"Returns\" for the \"Books\" category."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "UwZZiq96jNmZcZDi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.138, 0.823, 0.221, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing \"Offline\" sales for the \"Clothing\" product category, with units ranging from \"0.00 -> 1000.00\"."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "xRrEzQaa4l1Y1aac",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subtitle}<r>[0.146, 0.025, 0.645, 0.055]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Content Creation on New Media Platforms Over Time."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "fRkvGtt9wxVVIBRI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first x-tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.105, 0.919, 0.114, 0.942]"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "hdnOfXMyTWTM3FGE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.369, 0.029, 0.596]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "% Content Creation."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "vRPnPrjYe8EmmHqm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat platform is represented by the color located at [0.826, 0.083, 0.854, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "YouTube."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "oT7a6dxwnga4lGWy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Instagram at 30 hours}[0.386, 0.484, 0.405, 0.624] higher than the {bar for YouTube at the same time}[0.386, 0.624, 0.405, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "SNjwlxCMoo0aE7S4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {podcast bar at 60 hours}[0.672, 0.242, 0.691, 0.440] taller than the {TikTok bar at the same time}[0.672, 0.587, 0.691, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "wK5sUFkkdQrqkkgc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Blogs at 20 hours}[0.291, 0.286, 0.310, 0.433] taller than the {bar representing Instagram at the same time}[0.291, 0.521, 0.310, 0.668]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "jHqkSvZzPGlJYUw3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {YouTube}[0.865, 0.083, 0.921, 0.106] and {Instagram}[0.865, 0.118, 0.936, 0.141], which platform has more content creation at 30 hours {YouTube bar at 30 hours}[0.386, 0.624, 0.405, 0.903], {Instagram bar at 30 hours}[0.386, 0.484, 0.405, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "YouTube."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "ExwVHNZ4QAR0U7Z5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.821, 0.074, 0.941, 0.255]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend consisting of five entries. Each legend entry consists of a color handle and a label text. The platforms are YouTube, represented by red at [0.826, 0.085, 0.854, 0.101] with the label at [0.865, 0.083, 0.921, 0.106]; Instagram, orange at [0.826, 0.120, 0.854, 0.136] with the label at [0.865, 0.118, 0.936, 0.141]; TikTok, light blue at [0.826, 0.155, 0.854, 0.171] with the label at [0.865, 0.153, 0.908, 0.176]; Blogs, yellow at [0.826, 0.190, 0.854, 0.206] with the label at [0.865, 0.188, 0.903, 0.211]; and Podcasts, green at [0.826, 0.225, 0.854, 0.241] with the label at [0.865, 0.223, 0.926, 0.246]."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "cHVHmjJzEpHZr8YT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.100, 0.756, 0.119, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a red-colored bar representing the percentage of content creation by YouTube at time 0 hours. The value is 20% which is in the range of 0.00 -> 20.00"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "7vOUQknkZ8SVmi3y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.196, 0.572, 0.215, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "One object is located in this area. It is a bar representing the Instagram platform at the time point of 10 hours. The bar indicates the percentage of content creation, and it ranges from 30.00% to 45.00%."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "fZjSlNxPZhtC8KFj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.386, 0.102, 0.405, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a yellow-colored bar which represents content creation of Blogs at the 30-hour point. The value is in the range of 70.00% to 91.00%."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "AVVAGgNFunEMqtai",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "% Distribution, located at <r>[0.025, 0.301, 0.048, 0.629]</r>."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "rSFN43Owt0XiZZ0E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data point at position [0.199, 0.462, 0.212, 0.644] represent in terms of category and time?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the category \"Workbooks\" at 10 seconds."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "KGxtu0GAB5WqQexH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used for the \"Live Seminars\" category in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grey, corresponding to the color indicator located at <r>[0.639, 0.450, 0.685, 0.482]</r>."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "UyPtIb7R6ygwjCrh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the text label for EBooks}<r>[0.703, 0.166, 0.788, 0.213]</r> located above or below {the bar representing EBooks at 10s}<r>[0.199, 0.644, 0.212, 0.806]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "5laWqUDHuhMnNdbo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Video Materials' bar at 60s}<r>[0.513, 0.423, 0.525, 0.598]</r> reach higher on the chart than {the 'Workbooks' bar at 60s}<r>[0.513, 0.598, 0.525, 0.708]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "IyTlybJRdoTmInwm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the starting point of {the bar for 'Webinars' at 20s}<r>[0.262, 0.267, 0.274, 0.397]</r> higher than {the bar for 'Live Seminars' at 20s}<r>[0.262, 0.157, 0.274, 0.267]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "cYLIpJaOH9NhVzDS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area marked by coordinates [0.629, 0.148, 0.891, 0.511]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. It includes color labels and text labels for five categories: EBooks, Workbooks, Video Materials, Webinars, and Live Seminars. Color labels such as green for EBooks, red for Workbooks, purple for Video Materials, brown for Webinars, and grey for Live Seminars are respectively situated at [0.639, 0.170, 0.685, 0.203], [0.639, 0.240, 0.685, 0.273], [0.639, 0.310, 0.685, 0.342], [0.639, 0.380, 0.685, 0.412], and [0.639, 0.450, 0.685, 0.482]. Text labels for each category are also placed within this region."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "UdvnEcJtdXzFuMeU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.110, 0.806, 0.677, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the X-axis and its corresponding labels marking time intervals at 0s, 10s, 20s, and so forth, up to 80s. Time labels are strategically positioned within this region, each precisely denoting different time intervals essential for interpreting the chart's time-based data distributions."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "eaoQd1RKu8wnIKlt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the zone bounded by [0.025, 0.301, 0.048, 0.629]."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment encloses the Y-axis of the chart, prominently displaying the percentage distribution ranging from 0.0 to 1.2. The numerical labels indicating these percentages are vertically aligned within this area, playing a crucial role in understanding the proportional distributions of various educational resources over time."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "vIGivIccfhRypRnA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the thing in {position}<r>[0.025, 0.212, 0.048, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, named 'Global Changes Index'."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "ipTa52Wy8b0P0yuc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.115, 0.654, 0.141, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {a bar}<r>[0.115, 0.654, 0.141, 0.695]</r> under the category of 'Politics' from the year 2005."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "TySsbawqGGKwO6Qc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the {position}<r>[0.243, 0.530, 0.268, 0.599]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {a bar}<r>[0.243, 0.530, 0.268, 0.599]</r> representing the category 'Politics' for the year 2015."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "KS2vVWdqGUH5deHv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the item at position [0.743, 0.445, 0.824, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {a legend label}<r>[0.743, 0.445, 0.824, 0.492]</r> indicating the color code for the 'Politics' trend."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "MTfqkoy7XCmnKvU6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'GDP' bar for 2025}<r>[0.370, 0.681, 0.395, 0.723]</r> higher than {the 'GDP' bar for 2015}<r>[0.243, 0.723, 0.268, 0.751]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "XgDXF4HMfrAqICYh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for 'Technology'}<r>[0.115, 0.723, 0.141, 0.764],[0.243, 0.640, 0.268, 0.723],[0.370, 0.557, 0.395, 0.681],[0.497, 0.474, 0.523, 0.640],[0.624, 0.392, 0.650, 0.599]</r> generally higher than {the bars for 'Environment'}<r>[0.115, 0.695, 0.141, 0.723],[0.243, 0.599, 0.268, 0.640],[0.370, 0.502, 0.395, 0.557],[0.497, 0.405, 0.523, 0.474],[0.624, 0.309, 0.650, 0.392]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "IvDAvS2p45fSQ4Du",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Population' trend}<r>[0.115, 0.778, 0.141, 0.806],[0.243, 0.751, 0.268, 0.806],[0.370, 0.723, 0.395, 0.806],[0.497, 0.695, 0.523, 0.806],[0.624, 0.668, 0.650, 0.806]</r> show an increasing trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "IfuJdrtr1XG4BEHK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value for 2045, {\"Population\"}<r>[0.624, 0.668, 0.650, 0.806]</r> or {\"GDP\"}<r>[0.624, 0.599, 0.650, 0.668]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "CrqPPSNDPNkpJty1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is inside <r>[0.669, 0.148, 0.900, 0.511]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend containing five items. They are:\n1. Population with a color label at [0.678, 0.170, 0.725, 0.203],\n2. GDP with a color label at [0.678, 0.240, 0.725, 0.273],\n3. Technology with a color label at [0.678, 0.310, 0.725, 0.342],\n4. Environment with a color label at [0.678, 0.380, 0.725, 0.412],\n5. Politics with a color label at [0.678, 0.450, 0.725, 0.482]."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "RSuVYehTGdw28gee",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the information in this area [0.353, 0.838, 0.412, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-label \"2025\"."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "4csoN7YS0rmUSVne",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region <r>[0.370, 0.723, 0.395, 0.806]</r> and <r>[0.370, 0.681, 0.395, 0.723]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in these regions. The first is for {the 'Population'}<r>[0.370, 0.723, 0.395, 0.806]</r> category in 2025 and the second one is for {the 'GDP'}<r>[0.370, 0.681, 0.395, 0.723]</r> category in the same year."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "VShI9VJRNqyx2QdC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the data within the region [0.243, 0.599, 0.268, 0.751]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two bars. One represents the {GDP}<r>[0.243, 0.599, 0.268, 0.640]</r> value for 2015, and the other represents {Population}<r>[0.243, 0.640, 0.268, 0.751]</r> for the same year."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "sK5R3dWgn8NU2OmB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for \"Single\"}<r>[0.671, 0.380, 0.717, 0.412]</r> as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Brown."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "6o5JQSZlG3lWobKQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.365, 0.903, 0.422, 0.950]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "jjhZuv3QGpqnRwDn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the bars at the \"0\" time mark}<r>[0.136, 0.157, 0.149, 0.806]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Different social demographics at time 0."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "CshfpSMEGiOE1PrE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {color handle for \"Married\"}<r>[0.671, 0.310, 0.717, 0.342]</r> lighter or darker than the {color handle for \"Divorced\"}<r>[0.671, 0.450, 0.717, 0.482]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lighter."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "XilX05KbDahwccwO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing {the \"Employed\"}<r>[0.136, 0.644, 0.149, 0.806]</r> taller than the ones representing {the \"Unemployed\"}<r>[0.136, 0.546, 0.149, 0.644]</r> at the starting point of the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "ifWxv1I8JSKLnfxQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Divorced\" label}<r>[0.736, 0.445, 0.839, 0.492]</r> appear above or below {the \"Single\" label}<r>[0.736, 0.376, 0.807, 0.422]</r> in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "5uKnOimJ2vpUIKNm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specifics are shown in the subplot area enclosed by <r>[0.110, 0.124, 0.677, 0.806]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plotting grid with background color #d3d3d3 and the chart bars for different social demographics over time."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "hzl9BD8pk4jr4hws",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the visible characteristics within the chart legend section <r>[0.662, 0.148, 0.891, 0.511]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend section contains five items each with a color handle and corresponding label describing the social demographics such as \"Employed\", \"Unemployed\", and others."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "LGURN7v9v5RBAHeu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained in the domain from the bottom left to almost mid-right of the chart <r>[0.025, 0.223, 0.645, 0.950]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the vertical axis with percentage scale labels, horizontal axis with time labels, associated grid lines, and the bars representing demographics over time."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "822eqEGAxFPCZnWO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.012, 0.407, 0.024, 0.567]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, named Trend Percentages."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "7d3KMDywwjQv1zgl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend indicate at [0.905, 0.064, 0.928, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates that {Time 1}<r>[0.905, 0.064, 0.928, 0.076]</r> is represented in the color blue."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "1n9lidPOrUsyJFiS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology is denoted at [0.356, 0.089, 0.400, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The technology is 'multimedia learning'."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "zlkafbyTsMPPqHo0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items represented at [0.938, 0.062, 0.977, 0.080] and [0.938, 0.088, 0.977, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent two trend points {Time 1}<r>[0.938, 0.062, 0.977, 0.080]</r> and {Time 2}<r>[0.938, 0.088, 0.977, 0.106]</r>."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "G1rv4HWY9IT2zXFe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars in {Time 1}<r>[0.105, 0.568, 0.148, 0.927]</r> higher or lower than the bars in {Time 2}<r>[0.148, 0.658, 0.192, 0.927]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "LlgO7Ee4nbuFxPJ1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Time 2 bar for technology 6}<r>[0.902, 0.388, 0.945, 0.927]</r> taller or shorter than the {Time 1 bar for technology 6}<r>[0.858, 0.658, 0.902, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "vFP9M4ia1yj5n03b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Technology 0}<r>[0.105, 0.568, 0.148, 0.927]</r> more prominent in Time 1 or Time 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time 1."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "S1GtrxTaRf9X3MM5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Technology 2 in Time 1}<r>[0.356, 0.089, 0.400, 0.927]</r> greater or less than {Technology 2 in Time 2}<r>[0.400, 0.478, 0.443, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "LAQPoo9FKthu9Vvk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the coordinates [0.901, 0.055, 0.982, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a legend with two key items. The first is titled 'Time 1' with coordinates [0.905, 0.064, 0.928, 0.076] in blue, and the second is titled 'Time 2' with coordinates [0.905, 0.090, 0.928, 0.102] in orange."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "BKbxux1pMfk49sNV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists in the region [0.105, 0.568, 0.192, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars, one for {Time 1}[0.105, 0.568, 0.148, 0.927] and the other for {Time 2}[0.148, 0.658, 0.192, 0.927]. Both bars represent the value for Technology 0."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "LpgxsRMu0HYRxkqp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.333, 0.019, 0.717, 0.041]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a subtitle that reads \"Trends in Education and Training Technology Innovation\"."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "JIInhiLUtiuBtEeF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.145, 0.939, 0.905, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes all x-axis labels, representing different technology numbers, ranging from 0 to 6, each in an approximately equivalent interval."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "PBPmmOgiO1QvVQSp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at the location with coordinates [0.171, 0.350, 0.215, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing CO2 emission in the year 2000."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "gyIN7Q06lhFIWczk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label} at [0.809, 0.166, 0.858, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "CO2."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "M1oF40mfNBVgnsJH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the item} for the handles at coordinates [0.744, 0.170, 0.790, 0.203]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label for CO2 in the legend which is green."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "cCVaPRln8S1Jjh57",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.171, 0.332, 0.215, 0.333] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar which represents CF4 greenhouse gas emission in the year 2000."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "mqPpwMJumU6VNOiA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing {N2O}[0.171, 0.333, 0.215, 0.337], [0.280, 0.299, 0.324, 0.299] taller than the bars for {CH4}[0.171, 0.337, 0.215, 0.350], [0.280, 0.304, 0.324, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "dRl4SWVLyH9kfKTQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the CF4 bar for year 2015}[0.499, 0.216, 0.543, 0.216] of the same height as {the C2F6 bar for year 2015}[0.499, 0.215, 0.543, 0.216]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "7iZ303AWt201bbIs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {CF4}[0.171, 0.332, 0.215, 0.333] and {C2F6}[0.171, 0.332, 0.215, 0.332] bars for year 2000, which has higher emissions?"
+            },
+            {
+                "from": "gpt",
+                "value": "CF4."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "kfyfhR1WluUB8rrz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the {CO2 bar for year 2005}[0.280, 0.317, 0.324, 0.806] or the {CO2 bar for year 2010}[0.390, 0.289, 0.434, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CO2 bar for year 2005."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "yFXeHv8zS7CkJnsW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell what's in [0.735, 0.148, 0.877, 0.511] region."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a region containing the legend, which includes 5 labels: {CO2}[0.744, 0.170, 0.790, 0.203], {CH4}[0.744, 0.240, 0.790, 0.273],  {N2O}[0.744, 0.310, 0.790, 0.342], {CF4}[0.744, 0.380, 0.790, 0.412], and {C2F6}[0.744, 0.450, 0.790, 0.482]."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "RhlfNhoFXEoffXgb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region [0.171, 0.272, 0.215, 0.806]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars for year 2000: The tallest bar, representing CO2, at an emissions of 16433.42 metric tons {CO2 bar}[0.171, 0.350, 0.215, 0.806]; A shorter bar in red representing CH4 at 454.036 metric tons {CH4 bar}[0.171, 0.338, 0.215, 0.350]; An even shorter bar in purple representing N2O at 156.307 metric tons {N2O bar}[0.171, 0.336, 0.215, 0.338]; A slightly shorter bar in brown corresponding to CF4 at 23 metric tons {CF4 bar}[0.171, 0.335, 0.215, 0.336]; and the shortest bar in grey representing C2F6 at 4.92 metric tons {C2F6 bar}[0.171, 0.335, 0.215, 0.335]."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "CMAU8z7SIIykOOyu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.609, 0.157, 0.652, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the year 2020 and contains five bars, each representing a different greenhouse gas: {CO2}[0.609, 0.179, 0.652, 0.806], {CH4}[0.609, 0.162, 0.652, 0.179], {N2O}[0.609, 0.158, 0.652, 0.162], {CF4}[0.609, 0.157, 0.652, 0.158], and {C2F6}[0.609, 0.157, 0.652, 0.157]."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "kngUtDjyF9XKx5Wd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description for the area [0.147, 0.124, 0.677, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area. It contains a grey grid backdrop with black x and y axis labels. The y-axis ranges from 0 to 25000 measuring \"Emissions (M Metric Tons)\" and x-axis represents \"Year\" ranging from 2000 to 2020. There are 5 overlapped bar charts for each year representing CO2, CH4, N2O, CF4 and C2F6 greenhouse gas emissions."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "AeGfsy9rQ6O2cOuE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.886, 0.062, 0.982, 0.080] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"Accelerating Decline\" for a legend entry."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "O0S0KvddWEGOCXDP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the color handle associated with \"Inconsistent Growth\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.860, 0.116, 0.879, 0.128]"
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "Iln0QwOTGSJzn38K",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar color} at [0.053, 0.544, 0.410, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#66c4d5"
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "ctaUXwqW4I7NmC4Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar labeled \"2011\"}[0.053, 0.871, 0.945, 0.887] higher than {the bar labeled \"2020\"}[0.053, 0.136, 0.062, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "6dLeV5k30K4pEmf2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"2012\" for \"Slow Decline\"}[0.053, 0.773, 0.847, 0.789] extend farther right compared to {the bar representing \"2015\" for \"Inconsistent Growth\"}[0.053, 0.511, 0.874, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "5QbWS5eljyzwzlDF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the y-axis labels}[[0.023, 0.848, 0.047, 0.865], [0.023, 0.766, 0.047, 0.783], [0.023, 0.684, 0.047, 0.702], [0.023, 0.603, 0.047, 0.620], [0.023, 0.521, 0.047, 0.538], [0.023, 0.439, 0.047, 0.457], [0.023, 0.358, 0.047, 0.375], [0.023, 0.276, 0.047, 0.293], [0.023, 0.194, 0.047, 0.212], [0.023, 0.112, 0.047, 0.130]] listed in ascending order from bottom to top of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "InUzLYCmkK19HNXH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.857, 0.055, 0.985, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, comprising four elements each displaying a colored rectangle followed by a label:\n1. \"Accelerating Decline\" with a color rectangle at [0.860, 0.064, 0.879, 0.076] (color #66c4d5),\n2. \"Slow Decline\" at [0.860, 0.090, 0.879, 0.102] (color #eca198),\n3. \"Inconsistent Growth\" at [0.860, 0.116, 0.879, 0.128] (color #5886c7),\n4. \"Fluctuating Decline\" at [0.860, 0.142, 0.879, 0.155] (color #a791e8)."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "OMfhD2gM2F3Mwznb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.053, 0.446, 0.812, 0.462]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a series bar representing \"Slow Decline\" in 2016, colored in [#eca198] and displaying a numerical value of 85.0. It extends horizontally from the left y-axis until approximately 81.2% of the width towards the right."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "ZtDWU7kXzEzP3RpY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.053, 0.430, 0.928, 0.446]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangular region contains a bar representing \"Inconsistent Growth\" for the year 2016. The color of the bar is[#5886c7], and it extends nearly to 92.8% of the area from the left, displaying a value of \"98.0\" at its right edge."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "CHcOZMmrPdkivSeL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.160, 0.505, 0.203, 0.890] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar representing the Income Growth for EN."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "inHwO2YW9hgjFlT8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the third text, DE, on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.297, 0.906, 0.357, 0.930]"
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "I31eB49uwuBLJJ6X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat provides the information at [0.413, 0.906, 0.489, 0.930] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Control in FR."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "eoCMgX3cQbvIL6mz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.203, 0.377, 0.247, 0.890] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the Risk Control in EN."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "uSFbtT5yfKD71TZl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Income Growth bar for EN}[0.160, 0.505, 0.203, 0.890] higher or lower than the {Risk Control bar for EN}[0.203, 0.377, 0.247, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "1SirYyD4GccI1dvQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar at coordinates [0.451, 0.312, 0.494, 0.890] taller than the bar at coordinates [0.655, 0.110, 0.043, 0.513]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "xbsNIgksKWYK9kCh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Income Growth bar for ES}[0.822, 0.110, 0.043, 0.642] taller than the {Risk Control bar for ES}[0.822, 0.248, 0.865, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "70154RLDp2HYO2pD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars representing {DE}<{Income Growth DE bar}[0.203, 0.110, 0.043, 0.513], {Risk Control DE bar}[0.327, 0.110, 0.043, 0.449]> and those representing {FR}<{Income Growth FR bar}[0.451, 0.110, 0.043, 0.578], {Risk Control FR bar}[0.574, 0.110, 0.043, 0.385]>, in which language are both bars generally higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "DE."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "TQxBPvU3zhmTQZku",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.180, 0.906, 0.868, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the labels for the languages on the x-axis, including EN, DE, FR, IT, and ES."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "4rlcG2t0b0GPPWXr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.160, 0.377, 0.247, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars, one representing the Income Growth for EN at coordinates [0.160, 0.505, 0.203, 0.890] and the other representing the Risk Control for EN at coordinates [0.203, 0.377, 0.247, 0.890]."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "wFcNvgFmHSNDgJdO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the area [0.4, 0.05, 0.62, 0.9]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bars representing Risk Control for FR and IT, along with their respective text annotations."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "UKgeo9wpSSUghGgv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the area [0.792, 0.132, 0.893, 0.208]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we find the legend of the chart, including two pairs of color-text labels. The first pair represents EN with the color label at [0.797, 0.143, 0.825, 0.159] and the text label at [0.836, 0.141, 0.888, 0.164]. The second pair represents ES with the color label at [0.797, 0.178, 0.825, 0.194] and the text label at [0.836, 0.176, 0.888, 0.199]."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "UZegR2iqgpK4bNYG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinate [0.360, 0.025, 0.649, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a subtitle of the chart, which is \"Comparing Electric Vehicle Ranges\"."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "j84EscgZyF7PwIIX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object at the coordinates [0.824, 0.120, 0.851, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is blue."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "dLFTe57TmeHQecMw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.560, 0.072, 0.893, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is an orange bar representing the 100KWh battery size under the \"Sustainable City Living\" condition with a range of 490 miles."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "Uhar6J6S4Guy1MaQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar}[0.560, 0.072, 0.893, 0.551] higher than the {bar}[0.115, 0.238, 0.449, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "e4s9FzEvOXGcgfo1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the {blue bar }(48 KWh)[0.115, 0.238, 0.449, 0.551] and the {orange bar}(100 KWh)[0.560, 0.072, 0.893, 0.551], which one represents a longer range under Sustainable City Living condition?"
+            },
+            {
+                "from": "gpt",
+                "value": "The orange bar."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "anDstUyYaNUYyqAK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the position of the {y-label '200'}[0.040, 0.346, 0.066, 0.370] higher than the {y-label '250'}[0.040, 0.298, 0.066, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "UF6QNZbMZr7kJvoP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.115, 0.238, 0.893, 0.551]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. The first bar, a blue bar, starts from 0.115 and ends at 0.449 on the x-axis, representing the 48KWh battery size under the \"Sustainable City Living\" condition with a range of 320 miles. The second bar, an orange bar, starts from 0.560 and ends at 0.893 on the x-axis, representing the 100KWh battery size under the \"Sustainable City Living\" condition with a range of 490 miles."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "EFFUcbWoZX53e9q0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.818, 0.074, 0.925, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It has two items. The first item has a blue handle with coordinates [0.824, 0.120, 0.851, 0.136] and a label \"48KWh\" with coordinates [0.862, 0.118, 0.911, 0.141]. The second item has an orange handle with coordinates [0.824, 0.155, 0.851, 0.171] and a label \"100KWh\" with coordinates [0.862, 0.153, 0.920, 0.176]."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "Ix1n1zSofjnmh4PW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a simple description of what is in the region [0.076, 0.429, 0.932, 0.723]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a subplot of the chart. It contains an x-y grid with labels on both axes. There are two ticks on the x-axis representing \"Sustainable City Living\" and \"Long Interstate Trip\". There are multiple ticks on the y-axis ranging from 200 to 500. This subplot also includes bars representing the ranges of 48KWh and 100KWh battery sizes for each condition."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "VsItukasIc0Tkuof",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the graph in the upper part of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Trend Analysis of Products."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "wqIO3pU8cSw2xJbm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the product Toaster_A in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "CYlnnX3fUzQWgSFp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's {the y-axis label} at [0.035, 0.206, 0.061, 0.230]?"
+            },
+            {
+                "from": "gpt",
+                "value": "250."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "XMtZvyXFEKobvzYu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is indicated at [0.588, 0.919, 0.612, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "April."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "sKuXsAdFc8efNHuZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the February bar for Toaster_B}[0.257, 0.820, 0.365, 1.031] higher than {the February bar for Toaster_A}[0.257, 0.262, 0.365, 0.599]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "Mjc5amaZhieaYmEu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the May bar of Toaster_B}[0.690, 0.666, 0.799, 1.031] exceed {the y-axis label at 300}[0.035, 0.130, 0.061, 0.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "HAv7twULAePaB2A2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the April bar of Toaster_B}[0.546, 0.749, 0.654, 1.031] taller than {the June bar of the same brand}[0.835, 0.551, 0.943, 1.031]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "Fsz5jMEj7eZ3YKyD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars of Toaster_A}[[0.112, 0.292, 0.221, 0.599], [0.257, 0.262, 0.365, 0.599], [0.401, 0.231, 0.510, 0.599], [0.546, 0.200, 0.654, 0.599], [0.690, 0.170, 0.799, 0.599], [0.835, 0.139, 0.943, 0.599]] generally shorter than {the bars of Toaster_B}[[0.112, 0.839, 0.221, 1.031], [0.257, 0.820, 0.365, 1.031], [0.401, 0.801, 0.510, 1.031], [0.546, 0.749, 0.654, 1.031], [0.690, 0.666, 0.799, 1.031], [0.835, 0.551, 0.943, 1.031]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "g8WakTNeyHSHsZpl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in [0.071, 0.062, 0.985, 0.446]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly includes the bottom half graph of the sales trends of three products. The x-axis labels represent numerical values from -1 to 6; the y-axis labels range from 100 to 350 in increments of 50. The purple bars at various heights represent the sales amounts of Toaster_A during different months."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "tJwyVQcWuvsoXdtU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region at [0.861, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend indicating that a purple bar represents the product 'Toaster_A', with the color patch at [0.867, 0.120, 0.895, 0.136] and the text at [0.906, 0.118, 0.972, 0.142]."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "lqhnRXw43ckEotEy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the space [0.071, 0.519, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the top half graph, which shows the sales trend analysis for the product 'Toaster_B'. The x-axis has month labels from January to June, and the y-axis has numerical labels from 100 to 400. The purple bars represent the sales amounts of Toaster_B in respective months."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "i2rcSEqOnXFyPzuJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe area [0.156, 0.919, 0.177, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the 'Jan' label of the x-axis in the upper chart."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "XAMqcFB3dIvDY6gV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the rectangle used for {Morning}<r>[0.843, 0.120, 0.871, 0.136]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "kidOpDsz6Yr2zCLd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the exact time label for {the top right bar in the first subplot}<r>[0.748, 0.343, 0.910, 0.381]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Night."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "oYGNEMRqjFYbR7Zg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the x-tick label at}<r>[0.926, 0.397, 0.948, 0.420]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "3.5."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "W8pFPlH0iwf6atMn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the height of the \"Morning\" bar}<r>[0.102, 0.077, 0.264, 0.381]</r> greater than {the \"Noon\" bar}<r>[0.318, 0.153, 0.479, 0.381]</r> for basketball on Week 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "FjrJkh34JDDHG2wu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Noon\" time segment}<r>[0.843, 0.155, 0.871, 0.171]</r> have a more vibrant color than {the \"Evening\" segment}<r>[0.843, 0.190, 0.871, 0.206]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "BgvHp1Ey6GFidWU0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the height of the first \"Night\" bar}<r>[0.748, 0.267, 0.910, 0.381]</r> greater than {the \"Noon\" bar}<r>[0.318, 0.320, 0.479, 0.381]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "S1O8pa0roTg3OOCo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area between {Week1 Basketball and Football bars}<r>[0.066, 0.780, 0.350, 0.922]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars representing Week1 players in both basketball and football with their respective heights indicating the number of participants at different times of the day: morning, noon, evening, and night."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "S4fRyvQdmjMCdXA8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region adorned with <r>[0.837, 0.074, 0.943, 0.255]</r> imply?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend for the chart displaying time segments: Morning, Noon, Evening, and Night with colored handles from purple to gold, specifying the color representation of each time segment on the bars in the graph."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "rbrXfFJPDXVUgWzU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visualization features within <r>[0.062, 0.062, 0.950, 0.381]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encapsulates the first subplot graph showing different colored bars for time segments across sports, plotted along a y-axis indicating participants and an x-axis with sporting events categorized by weeks."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "E5ghZlIkIZz4DS3Q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.027, 0.406, 0.029, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, named \"Interactions\"."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "fDENfV6DfPR9EYXR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the x-axis label situated in terms of coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.480, 0.942, 0.576, 0.970]"
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "xDr8TobIp53Ct7ep",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the entity at [0.112, 0.587, 0.181, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the Facebook Interaction count of 45 for Quarter 1."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "4K2yrqHh2oP9KKaG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.366, 0.039, 0.511, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text label for Facebook Interaction in the legend}<r>[0.366, 0.039, 0.511, 0.067]</r>"
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "M786INnuJPqXWXKs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Facebook Interaction in Quarter 1}<r>[0.112, 0.587, 0.181, 0.883]</r> taller than {the bar representing Instagram Interaction in Quarter 1}<r>[0.181, 0.620, 0.251, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "BWfCNf3w9p0Oxbqv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is situated to the right, {the bar representing Facebook Interaction in Quarter 2}<r>[0.285, 0.455, 0.355, 0.883]</r> or  {the bar representing Instagram Interaction in Quarter 2}<r>[0.355, 0.554, 0.424, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing Instagram Interaction in Quarter 2."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "0Ys0HOmBnQ5WonAc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar representing Instagram Interaction in Quarter 5}<r>[0.874, 0.238, 0.943, 0.883]</r>, is {the bar representing Facebook Interaction in Quarter 5}<r>[0.805, 0.231, 0.874, 0.883]</r> higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "AQju42KCzdzogKJv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart figure, does {Facebook}<r>[0.366, 0.039, 0.511, 0.067]</r> or {Instagram}<r>[0.578, 0.039, 0.728, 0.067]</r> has higher interactions in the fourth fiscal quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "oCLbZGBjI5f98Q0V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area inside [0.322, 0.028, 0.734, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar chart legend, with two entries. The first entry, with color label at [0.327, 0.042, 0.355, 0.061] in blue, and text label at [0.366, 0.039, 0.511, 0.067], indicates that the blue bars represent the \"Facebook Interaction\". The second entry, with color label at [0.539, 0.042, 0.567, 0.061] in pink, and the text label at [0.578, 0.039, 0.728, 0.067], indicates that the pink bars represent \"Instagram Interaction\"."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "kJy3q8zffJtLUalm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in [0.035, 0.214, 0.061, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label showing \"100\" on the y-axis, which denotes the maximum interaction value in the scale."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "hEJO2iRF6XgvmRTA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.459, 0.291, 0.528, 0.883] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this region. The blue bar, located at [0.459, 0.291, 0.528, 0.883], represents that the Facebook Interaction count is 90 in Quarter 3."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "1sVS9clv3rkHK9zr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are inside the area [0.632, 0.309, 0.675, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an annotation \"83\" for the Facebook Interaction bar for Quarter 4. It is located at [0.658, 0.309, 0.675, 0.337], and it indicates that the count of Facebook Interactions for Quarter 4 is 83."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "nRDJusaSq0nJC6ZK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented at the coordinates [0.361, 0.039, 0.472, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Networks"
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "hM09hKo1o8pKGSBm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the point at the coordinates [0.351, 0.883] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "A tick along the x-axis."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "dsO1ncOjDjmmD6NJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is at the coordinates [0.106, 0.330, 0.176, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the \"Social Networks\" with a value of 2.0 for the first fiscal quarter."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "QVFy2wRAXNLBkigh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the object at these coordinates [0.015, 0.452, 0.029, 0.525]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the y-axis, namely \"Level\"."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "F8I7uXQEW3bLQRN2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a higher value recorded for {Social Networks}[0.361, 0.039, 0.472, 0.067] or {Interpersonal Relationships}[0.538, 0.039, 0.728, 0.067] during the fourth fiscal quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Networks."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "6Detcuigslk61QoP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar at {y_range: '0.00 -> 2.00'}[0.130, 0.302, 0.152, 0.330] taller than the one at {y_range: '0.00 -> 4.20'}[0.723, 0.128, 0.745, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "k39Fvh5GA8AYtoKj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of {Social Networks}[0.361, 0.039, 0.472, 0.067] and {Interpersonal Relationships}[0.538, 0.039, 0.728, 0.067], which one has a higher value in the third fiscal quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Networks."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "iiWjMaO47qxaJPzp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more x-ticks or y-ticks present in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-ticks."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "RVJn3EdKLZXUDKxC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.281, 0.172, 0.351, 0.488] and [0.525, 0.291, 0.595, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These two regions represent two fiscal quarters. For the second quarter, located at [0.281, 0.172, 0.351, 0.488], there are two bars: one for Social Networks with a height of 4.0 and one for Interpersonal Relationships with a height of 2.0. The third quarter, located at [0.525, 0.291, 0.595, 0.488], also features two bars: one for Social Networks with a height of 3.5 and one for Interpersonal Relationships with a height of 2.5."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "YjvaUojjxxmaR9Zm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.176, 0.172, 0.246, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region is a bar pertaining to Interpersonal Relationships with a value of 4.0 for the first fiscal quarter."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "o9RglpQuMCW4gQXA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the region [0.316, 0.028, 0.733, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the labels for legend entries, namely \"Social Networks\", represented with color '#78a083' and \"Interpersonal Relationships\", represented with color '#d27a41'."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "HqfAx8B6cCy4GvWl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.629, 0.275, 0.699, 0.488]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the fourth fiscal quarter with two recorded values: a 2.7 for Social Networks and a 4.2 for Interpersonal Relationships."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "5rMQxdYCaeoeDGhX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.119, 0.463, 0.188, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the Risk Assessment in the first fiscal quarter."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "f1hbcLIx4vpxxQny",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label present at the coordinates [0.397, 0.039, 0.513, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label says \"Risk Assessment\"."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "Eu3UuYoQKdP7XArS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.119, 0.463, 0.188, 0.565] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to {the Risk Assessment bar for the first fiscal quarter}<r>[0.119, 0.463, 0.188, 0.565]</r>."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "uTQVzsajEg64Hs0e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the data point}<r>[0.806, 0.361, 0.875, 0.565]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point represents the Risk Assessment value in the fifth fiscal quarter."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "6FiclNoE7SVsX2zE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the Risk Management bar for the first quarter} <r>[0.188, 0.246, 0.257, 0.565]</r>, is {the Risk Management bar for the third quarter}<r>[0.531, 0.565, 0.600, 0.679]</r> taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "0n4bsiWgQuTCdKjR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Risk Assessment bar for the second quarter}<r>[0.291, 0.565, 0.360, 0.718]</r> higher or lower than {the Risk Management bar for the second quarter}<r>[0.360, 0.399, 0.428, 0.565]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "tyXF7YU2eJtqKzwb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on {the Risk Assessment bars} <r>[0.119, 0.463, 0.188, 0.565],[0.291, 0.565, 0.360, 0.718],[0.463, 0.259, 0.531, 0.565],[0.635, 0.565, 0.703, 0.692],[0.806, 0.361, 0.875, 0.565]</r>, is there a general trend in Risk Assessment for the five quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, the values fluctuate."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "Lf1vz72DTDQuKE2g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the fourth quarter}'s Risk Management bar<r>[0.703, 0.093, 0.772, 0.565]</r> taller than {the first quarter}'s Risk Management bar<r>[0.188, 0.246, 0.257, 0.565]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "5viCxUqwFAYldrvg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's within the area [0.119, 0.093, 0.944, 0.565]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are two sets of bars representing Risk Assessment and Risk Management over five fiscal quarters. Their respective colors are represented in the legend labeled \"Risk Assessment\"<r>[0.397, 0.039, 0.513, 0.067]</r> and \"Risk Management\"<r>[0.580, 0.039, 0.705, 0.067]</r>."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "i6xQ7YzuZaLccdsp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within [0.353, 0.028, 0.710, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels housed within these coordinates. The words \"Risk Assessment\" at coordinates [0.397, 0.039, 0.513, 0.067] with a green box at [0.358, 0.042, 0.386, 0.061] representing its color and the words \"Risk Management\" at coordinates [0.580, 0.039, 0.705, 0.067], with an orange box at [0.541, 0.042, 0.569, 0.061] representing its color."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "P8LQpZKQNNbBFPun",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the coordinates [0.119, 0.093, 0.188, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains two bars. {The higher green bar}<r>[0.119, 0.463, 0.188, 0.565]</r> represents the Risk Assessment in the first quarter, and {the lower orange bar}<r>[0.188, 0.246, 0.257, 0.565]</r> represents the Risk Management in the first quarter."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "ruk2UiqqYCTWNkin",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the area [0.806, 0.093, 0.275, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, {the green bar}<r>[0.806, 0.361, 0.875, 0.565]</r> represents Risk Assessment in the fifth quarter and {the orange bar}<r>[0.875, 0.565, 0.944, 0.820]</r> represents Risk Management in the same period."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "uCBfB7VoH6Cgnusn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you locate the label of the Y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Y-axis label}<r>[0.015, 0.323, 0.029, 0.653]</r> and it is named \"Increase in revenue (%)\"."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "KAYFeU063qk6y5em",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label present at the coordinates [0.434, 0.039, 0.534, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {Entertainment}<r>[0.434, 0.039, 0.534, 0.067]</r> is present at the given coordinates."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "bBXwcagitgjDwFyY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the x-axis label referring to as per the chart's coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label referred as {X-axis}<r>[0.477, 0.942, 0.573, 0.970]</r> is \"Fiscal Quarter\"."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "hdUHoCj566z8JPtP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich categories are represented by the coordinates in the range of [0.389, 0.028, 0.660, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two categories represented: {Entertainment}<r>[0.434, 0.039, 0.534, 0.067]</r> and {Gaming}<r>[0.601, 0.039, 0.655, 0.067]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Csb5kZgw26vWmM1J",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {Entertainment bar in quarter 1}<r>[0.106, 0.521, 0.176, 0.686]</r> or {Gaming bar in quarter 1}<r>[0.176, 0.686, 0.246, 0.785]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Entertainment bar."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Q5BSgBn2vymv8O7P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relation between {5th bar of Entertainment group}<r>[0.804, 0.159, 0.873, 0.686]</r> and {5th bar of Gaming group}<r>[0.873, 0.686, 0.943, 0.844]</r> as far as their heights are concerned?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 5th bar of Entertainment group is taller."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Y4iDQRGoQ8sH7FqV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {4th bar of Entertainment Group}[0.629, 0.231, 0.699, 0.686] lower or higher than the {4th bar of Gaming Group}[0.699, 0.686, 0.769, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "vnYtmclkqrlu0zyx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the comparison of bars, do the revenue numbers increase or decrease for Entertainment and Gaming from quarter 1 to quarter 5? ({1st and 5th bar of Entertainment Group}<[0.106, 0.521, 0.176, 0.686],[0.804, 0.159, 0.873, 0.686]>; {1st and 5th bar of Gaming Group}<[0.176, 0.686, 0.246, 0.785],[0.873, 0.686, 0.943, 0.844]>)"
+            },
+            {
+                "from": "gpt",
+                "value": "It increases for Entertainment but decreases for Gaming."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "lVJznmhSj4XfkAlY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements within the region defined by coordinates [0.065, 0.093, 0.985, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all chart data including the bars, axes labels, ticks, grid, and legend. It notably includes several bars for two categories \"Entertainment\" and \"Gaming\" across five fiscal quarters."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "UnXjy3oMIrU22D0b",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.389, 0.028, 0.660, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.389, 0.028, 0.660, 0.078] contains the legend of the chart. It has two items: the {Entertainment label}[0.434, 0.039, 0.534, 0.067] with a corresponding color handle at [0.395, 0.042, 0.423, 0.061], and the {Gaming label}[0.601, 0.039, 0.655, 0.067] with a corresponding color handle at [0.562, 0.042, 0.589, 0.061]."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "QtqTHpuJbbdAQFL4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the consolidated area [0.106, 0.159, 0.873, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The selected area contains all the bars representing the two categories: \"Entertainment\" and \"Gaming\" over five fiscal quarters, along with their respective data value labels."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "gLcjPGewvd5YccsN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data can be found in the area [0.281, 0.408, 0.351, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second bar for the Entertainment category, representing revenue change in the second quarter. The green color bar is assigned the value of 3.8, which implies a 3.8% increase in revenue."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "rOswyPkebl4Zd9Oo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object between the coordinates [0.872, 0.097, 0.941, 0.125] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the legend label} that is called \"Marketing\"<r>[0.872, 0.097, 0.941, 0.125]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "xZ5wjmpjxcV8XodF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the bar contained in the coordinates [0.771, 0.387, 0.943, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the bar} referring to the \"Marketing\" operations at the \"Volatile Movement\" stage<r>[0.771, 0.387, 0.943, 0.744]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "ZDqBsEg7jyPEYTmF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by [0.046, 0.376, 0.055, 0.404] in the y-axis markings?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the y-axis label} that represents \"0\" value<r>[0.046, 0.376, 0.055, 0.404]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "VepfvHk27vETuUUM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Marketing\" at \"Stable Growth\"}[0.279, 0.280, 0.451, 0.387] taller than {the bar for \"Manufacturing\" at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "nu5XWLP4Nhshzp2S",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the \"Manufacturing\" bar at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387] and {the \"Manufacturing\" bar at \"Volatile Movement\"}[0.599, 0.119, 0.771, 0.387], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Manufacturing\" bar at \"Volatile Movement\"."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "YsDzFNJGpitRdAJa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Marketing\" bar at \"Stable Growth\"}[0.279, 0.280, 0.451, 0.387] located to the left or right of {the \"Manufacturing\" bar at \"Stable Growth\"}[0.107, 0.244, 0.279, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "XBJHswemv4K8EHPJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.827, 0.044, 0.978, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the legend} of the chart. It contains two items; blue color tagged as \"Manufacturing\"<r>[0.872, 0.055, 0.972, 0.083]</r><r>[0.833, 0.058, 0.861, 0.077]</r>, and orange color tagged as \"Marketing\"<r>[0.872, 0.097, 0.941, 0.125]</r><r>[0.833, 0.099, 0.861, 0.119]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "jazkDvx17OLYVy9x",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinate [0.229, 0.942, 0.328, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the x-axis tick mark \"Stable Growth\"<r>[0.229, 0.942, 0.328, 0.970]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "9ICHMgrhQu8H31iU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within coordinates [0.599, 0.119, 0.771, 0.387]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain two bars, one is the \"Manufacturing\" bar<r>[0.771, 0.387, 0.943, 0.744]</r> and the other is \"Marketing\" bar<r>[0.771, 0.387, 0.943, 0.744]</r> for the \"Volatile Movement\" phase. There is also a text label indicating the value \"1.5\"<r>[0.674, 0.091, 0.696, 0.119]</r>."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "PAt1UR1kNUB42LW2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.055, 0.477, 0.064, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label '0'."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "9GDv4V0xZ728SC0p",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.574, 0.042, 0.602, 0.061] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the orange color handle in the legend indicating 'Conservative' strategy."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "kpZDO79GmKeZOjkd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.481, 0.942, 0.577, 0.970] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the X-axis label, 'Fiscal Quarter'."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "io3xMD0w6rRuX2wD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2nd Quarter High-Risk High-Return Strategy}[0.288, 0.291, 0.357, 0.488] higher than the {the bar for 2nd Quarter Conservative Strategy}[0.357, 0.370, 0.426, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "YLbMpk85Q4cQafRV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars indicating {High-Risk High-Return Strategy}[[0.115, 0.488, 0.184, 0.528], [0.288, 0.291, 0.357, 0.488], [0.460, 0.370, 0.529, 0.488], [0.633, 0.488, 0.702, 0.607], [0.805, 0.212, 0.875, 0.488]] generally higher than the bars for {Conservative Strategy}[[0.184, 0.409, 0.253, 0.488], [0.357, 0.370, 0.426, 0.488], [0.529, 0.370, 0.598, 0.488], [0.702, 0.409, 0.771, 0.488], [0.875, 0.409, 0.944, 0.488]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "jvY5vVTxeJTZz8Ss",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color of {High-Risk High-Return Strategy handle}[0.354, 0.042, 0.382, 0.061] the same as the color of the {High-Risk High-Return Strategy bar for 3rd Quarter}[0.460, 0.370, 0.529, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "WTl3KCessLskurKc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.349, 0.028, 0.710, 0.078]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items in this area - One legend remark for High-Risk High-Return strategy and one for Conservative Strategy. The High-Risk High-Return strategy is represented with a blue color that is shown at the coordinates [0.354, 0.042, 0.382, 0.061] and the text is placed at [0.393, 0.039, 0.546, 0.067]. The Conservative strategy is represented with an orange color that is shown at the coordinates [0.574, 0.042, 0.602, 0.061] and the text is placed at [0.613, 0.039, 0.704, 0.067]."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "kjvNSKrX2OHk5vnU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.184, 0.212, 0.253, 0.488]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in the area. The High-Risk High-Return bar for the 5th fiscal quarter is at the coordinates [0.805, 0.212, 0.875, 0.488] and the Conservative strategy bar for the 5th fiscal quarter is at the coordinates [0.875, 0.409, 0.944, 0.488]."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "zrGkMheI8YUrxuBP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.702, 0.342, 0.906, 0.409]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It seems to be an orange bar that corresponds to the conservative strategy in the 4th quarter. The coordinates are [0.702, 0.409, 0.771, 0.488]."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "PlxBfP8Mu8l6Uums",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{The fourth y-axis label} at [0.035, 0.636, 0.055, 0.664] shows what numeric value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number is -2."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "2K0tqabbJ2mZof07",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color used to represent {Employee Job Satisfaction} at [<r>[0.526, 0.045, 0.554, 0.065]</r>] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is a shade of orange, specifically \"#d27a41\"."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "uAomh39KkiAt4OcY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat quarter was tagged along the x-axis at [<r>[0.869, 0.903, 0.878, 0.931]</r>]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is tagged as the 5th Fiscal Quarter."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "d4VjSm7iT3QCQz6C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Employee Productivity bar} <r>[0.107, 0.089, 0.176, 0.490]</r> and {the Employee Job Satisfaction bar} <r>[0.176, 0.128, 0.246, 0.490]</r> for Q1, which has a higher score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Employee Productivity."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "tLIMLsASwDOrkGdm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for {Employee Productivity} <r>[0.455, 0.112, 0.525, 0.490]</r> in Q3 higher or lower than that in {Q4} <r>[0.629, 0.057, 0.699, 0.490]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "X3YNJJ7TvKTWSFe9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Q5, is the score for {Employee Job Satisfaction} <r>[0.873, 0.191, 0.943, 0.490]</r> larger than the score for {Employee Productivity} <r>[0.804, 0.081, 0.873, 0.490]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "UkgoCKyHP5aft7AI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content presented in the region [<r>[0.455, 0.029, 0.769, 0.490]</r>]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region depicts two bars for Q3 and Q4 of the fiscal year. For each quarter, two bars represent the scores of Employee Productivity and Employee Job Satisfaction. Specifically, for Q3, the Employee Productivity score is 4.8 [<r>[0.455, 0.112, 0.525, 0.490]</r>], and Employee Job Satisfaction score is 3.9 [<r>[0.525, 0.183, 0.595, 0.490]</r>]. Similarly, for Q4, the Employee Productivity score is 5.5 [<r>[0.629, 0.057, 0.699, 0.490]</r>], and Employee Job Satisfaction score is 4.1 [<r>[0.699, 0.167, 0.769, 0.490]</r>]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "dT3CfE6BqnCBa1ke",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description of the data mentioned in the region [<r>[0.889, 0.435, 0.883, 0.883]</r>]."
+            },
+            {
+                "from": "gpt",
+                "value": "There's no data mentioned in this region since it's an area outside the chart."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "fp9lyYfaGPgvifrN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the area [<r>[0.35, 0.032, 0.746, 0.082]</r>]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend elements of the bar chart. It showcases two categories: Employee Productivity with associated color green [<r>[0.343, 0.043, 0.499, 0.071]</r>, <r>[0.304, 0.045, 0.332, 0.065]</r>] and Employee Job Satisfaction with associated color orange [<r>[0.565, 0.043, 0.746, 0.071]</r>, <r>[0.526, 0.045, 0.554, 0.065]</r>]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "6CY7uyNARyu2DzEf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart does the information represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "6VbUuFBbMIyiYpo4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label located at [0.502, 0.942, 0.545, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Epoch."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "g434y1qfO2RJ1meG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.104, 0.725, 0.162, 0.883] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the number of Innovations during the Stone Age."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "AfOwZcPccgVqITYi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars representing {Innovations}[0.104, 0.725, 0.162, 0.883] and {Conflicts}[0.162, 0.852, 0.220, 0.883] during the Stone Age, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovations."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "EOG8PZ4gEusAEIUT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the bar representing {Innovations} during the {Industrial Age}[0.683, 0.409, 0.740, 0.883], is it higher or lower than the bar representing {Innovations} during the {Middle Ages}[0.538, 0.567, 0.596, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "Pugxkn4RRpftgSWk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Conflicts bar} during the {Digital Age}[0.885, 0.567, 0.943, 0.883] positioned to the right or left of the {Innovations bar} during the {Digital Age}[0.827, 0.251, 0.885, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "Bzjg5AjJFG28YUR2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.395, 0.028, 0.652, 0.078]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a legend with two entries representing Innovations and Conflicts. {Innovations} has a green color code at [0.400, 0.042, 0.428, 0.061], and {Conflicts} has an orange color code at [0.548, 0.042, 0.575, 0.061]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "EY1QN40lsrnSl8fx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the area defined by the coordinates [0.249, 0.662, 0.364, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects here are the bars representing the epochs Bronze Age for both Innovations and Conflicts. The {Innovations bar} is in green and has coordinates [0.249, 0.662, 0.306, 0.883] and the {Conflicts bar} is in orange with coordinates [0.306, 0.820, 0.364, 0.883]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "dhxm5ceyM7xqPiyB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area located within [0.692, 0.409, 0.798, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains 2 objects representing Innovations and Conflicts during the Industrial Age. The {Innovations bar} is positioned at [0.683, 0.409, 0.740, 0.883] and the {Conflicts bar} is located at [0.740, 0.662, 0.798, 0.883]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "WV01jUM3XjB4CxqE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text positioned at coordinates [0.015, 0.339, 0.029, 0.638]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called GDP Growth Rate (%)."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "igNbcCBetLUbaXRP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label of Russia in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.570, 0.039, 0.614, 0.067]"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "frQYVQ0uLR3Fzxdg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.510, 0.942, 0.540, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "XUZJis6GR4n7Jet7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second bar of USA} at [0.308, 0.420, 0.324, 0.676]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents USA's GDP growth rate in 1990, which is 1.5."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "uZzsecvOJy6QeF5n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the USA growth in 1995} at [0.509, 0.300, 0.525, 0.676] higher or lower than {the Russia growth in 1995} at [0.525, 0.676, 0.541, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "3YXrPDeoBVBGcSp6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Russia GDP growth in 2000} at [0.726, 0.676, 0.742, 0.745] negative or positive?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "xleTUde0yIIMMXdU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the USA growth bars} generally higher than {the Russia growth bars} considering all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "I0HXaANSrw9x7KJn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"USA\"} at [0.474, 0.039, 0.503, 0.067] and {\"Russia\"} at [0.570, 0.039, 0.614, 0.067], which country's GDP growth rate increased in {2005} at [0.911, 0.129, 0.927, 0.676],[0.927, 0.676, 0.943, 0.711]?"
+            },
+            {
+                "from": "gpt",
+                "value": "USA."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "HeUatG0wgN4g2BBM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.430, 0.028, 0.620, 0.078]."
+            },
+            {
+                "from": "gpt",
+                "value": "The {legend}<r>[0.430, 0.028, 0.620, 0.078]</r> contains the labels for each country identified in the chart with their corresponding colors. {USA}<r>[0.474, 0.039, 0.503, 0.067]</r> is represented by the color located at [0.436, 0.042, 0.463, 0.061], which is [#78a083], and {Russia}<r>[0.570, 0.039, 0.614, 0.067]</r> is represented by the color located at [0.531, 0.042, 0.559, 0.061], which is [#d27a41]."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "ERwe43fWUIGUXKVl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.065, 0.093, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.065, 0.093, 0.985, 0.883] houses the main graph that represents the GDP growth rate for the USA and Russia from 1985 to 2005. These are evidenced by the bars with coordinates of the format [X.XXX, Y.YYY, Z.ZZZ, A.AAA] which vary for each year and country."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "dkq419YRMtvs9Rzd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.123, 0.676, 0.139, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single value represented within these coordinates:\n1. The bar represents Russia's GDP growth rate in 1985, which is -1.0, and the coordinate range is [0.123, 0.676, 0.139, 0.848]."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "tWgM08U1aUNmh5vq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labeling text pieces can you see in the region [0.105, 0.903, 0.945, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are multiple labels representing the years 1985, 1990, 1995, 2000, and 2005, respective coordinates being [0.105, 0.903, 0.140, 0.931], [0.306, 0.903, 0.341, 0.931], [0.507, 0.903, 0.543, 0.931], [0.708, 0.903, 0.743, 0.931], and [0.910, 0.903, 0.945, 0.931]."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "1y60bOqNWpuQpcZe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label for the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Units, located at [0.015, 0.453, 0.029, 0.524]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "n56qRrZ7KbuLUhVk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at the coordinates [0.181, 0.838, 0.251, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar element representing Ozone Depletion Potential for the first fiscal quarter."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "4n9NMnvbrXnF2v5O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.112, 0.658, 0.181, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar element representing Greenhouse Gas Emissions for the first fiscal quarter."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "TvIlqzMhxa0ezRoY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar showing Greenhouse Gas Emissions in first quarter}<r>[0.112, 0.658, 0.181, 0.883]</r> higher than {the bar reflecting Ozone Depletion Potential in first quarter}<r>[0.181, 0.838, 0.251, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "JjTNzLXjcEZD8Yvj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar showing Greenhouse Gas Emissions in second quarter}<r>[0.285, 0.545, 0.355, 0.883]</r> tall than {the bar representing Ozone Depletion Potential in second quarter}<r>[0.355, 0.861, 0.424, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "jA9frbyy38kZ7Szs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing Greenhouse Gas Emissions in first quarter}<r>[0.112, 0.658, 0.181, 0.883]</r> and {the bar representing Greenhouse Gas Emissions in the fifth quarter}<r>[0.805, 0.206, 0.874, 0.883]</r>, which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one in the fifth quarter."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "ITq2WvZgHy4InOyx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in the coordinates [0.071, 0.093, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the entire plot, including the x-axis and y-axis with their respective labels, the bars indicating the Greenhouse Gas Emissions and the Ozone Depletion Potential for each quarter, and the grid of the plot."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "KK2UGxbBxRSK1g7q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within [0.523, 0.903, 0.532, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the third quarter located on the x-axis."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "hM7OibsTA1goucCQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.528, 0.404, 0.513, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's numbers 200.0, which represents the value of the bar element for Greenhouse Gas Emissions in the third quarter."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "WraI4LDdmvElk0od",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.443, 0.029, 0.534] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Values."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "rX32zQ5glFYO4ENr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text in the second legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.546, 0.039, 0.825, 0.067]"
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "De6GanA6yXNkhEDM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.509, 0.942, 0.538, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "rwsKQX0998FI7P2T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.216, 0.028, 0.830, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Number of People in Household}[0.261, 0.039, 0.480, 0.067] and {Percentage of Single-parent Households}[0.546, 0.039, 0.825, 0.067]."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "ixXLrUx3FWjoRVFQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar}[0.290, 0.156, 0.477, 0.883] to the left or right of {the orange bar}[0.757, 0.125, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "j4JYbnAcDjkBw0g8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Number of People in Household\"}[0.104, 0.808, 0.290, 0.883] taller than {the bar representing \"Percentage of Single-parent Households\"}[0.290, 0.156, 0.477, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "CDucuO8RNSVbH8C2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Year 1\"}[0.104, 0.808, 0.290, 0.883] and {\"Year 2\"}[0.570, 0.801, 0.757, 0.883], which year has a higher \"Number of People in Household\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "9I8I3qA9uhv3Speh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Number of People in Household\"}[0.104, 0.808, 0.290, 0.883],[0.570, 0.801, 0.757, 0.883] and {\"Percentage of Single-parent Households\"}[0.290, 0.156, 0.477, 0.883],[0.757, 0.125, 0.943, 0.883], which category has higher values in general?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage of Single-parent Households."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "vyxgEope2YmeFdLZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.216, 0.028, 0.830, 0.078]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the legend of the chart. The first item in the legend is \"Number of People in Household\" with a color square at [0.222, 0.042, 0.250, 0.061] and its label is at [0.261, 0.039, 0.480, 0.067]. The second item is \"Percentage of Single-parent Households\" with a color square at [0.507, 0.042, 0.535, 0.061] and its label is at [0.546, 0.039, 0.825, 0.067]."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "xCjXWBr6ZVXh27cM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.286, 0.903, 0.761, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the X-axis. The label \"1\" is at [0.286, 0.903, 0.295, 0.931] and the label \"2\" is at [0.752, 0.903, 0.761, 0.931]."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "iTWt9Xg6GkWiP6mJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.104, 0.808, 0.290, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the given coordinates, there is a green bar representing the \"Number of People in Household\" for the \"Year 1\"."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "4xi0493LDT1R7yX0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.570, 0.801, 0.757, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a green bar that represents the \"Number of People in Household\" for the \"Year 2\"."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "bj5pbnabRVXDTMUl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the y-axis is 'Market Share (%)'."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "N86ApxD01Q372cwi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {Music}[0.104, 0.381, 0.277, 0.903] bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the Music bar is '#78a083'."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "wVQNPUov3GLQuh7w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year marked at the coordinate [0.753, 0.919, 0.788, 0.942] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year marked at these coordinates is 2019."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "8KaNz0SsvIrybycB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {Books} bar in 2019<the Books bar in 2019>[0.770, 0.146, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the Books bar in 2019 is 46.56%."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "Q5FroRbFLtsmOOyI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Music} bar in 2018<the Music bar in 2018>[0.104, 0.381, 0.277, 0.903] taller than the {Books} bar<the Books bar in 2018>[0.277, 0.184, 0.449, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "Ghmt4UqvHGPD7mtn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Books} bar in 2019<the Books bar in 2019>[0.770, 0.146, 0.943, 0.903] located to the right of the {Music} bar<the Music bar in 2019>[0.597, 0.337, 0.770, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "i34SjaeGGxalnEKb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two years, did the market shares of {Books}<[0.277, 0.184, 0.449, 0.903],[0.770, 0.146, 0.943, 0.903]> increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "9yUEeVaVnpYUEhJN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Music} and {Books}, which one had a bigger market share in 2019<the Music and Books bars in 2019>[0.597, 0.337, 0.770, 0.903], [0.770, 0.146, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Books."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "sRYEQK2MDNKgPkah",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the layout and contents of the coordinates [0.062, 0.090, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.062, 0.090, 0.985, 0.903] encompasses the entire chart. It includes the x-axis labels (2018 and 2019), the y-axis with labels from 0 to 50 representing market shares in percentages, and both the Music and Books bars for 2018 and 2019."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "D9yZtAX2WKtWLL83",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within the coordinates [0.424, 0.021, 0.622, 0.063]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area between [0.424, 0.021, 0.622, 0.063] contains the legend for the chart, which includes two entries. The first one is {Music}[0.469, 0.030, 0.508, 0.053], represented by a color code '#78a083', and the second one is {Books}[0.575, 0.030, 0.617, 0.053], represented by the color code '#d27a41'."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "5H7NDA2mxinkP9N8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.597, 0.337, 0.770, 0.903] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.597, 0.337, 0.770, 0.903] contains the {Music} bar for the year 2019, which indicates a market share of 34.85%."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "5JhgHJ4anL0hi08y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content and arrangement within [0.277, 0.184, 0.449, 0.903] and [0.770, 0.146, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.277, 0.184, 0.449, 0.903] contains the {Books} bar for the year 2018, which shows a market share of 44.23%. The area [0.770, 0.146, 0.943, 0.903] contains the {Books} bar for the year 2019, which shows a market share of 46.56%."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "lpGB4X5MMNzjMRJs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value displayed at the coordinate [0.194, 0.630, 0.268, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20000.000"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "ZtCqybCHHJI1ecZy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label shown at [0.369, 0.039, 0.554, 0.067] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Educational Institutes"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "Mz6etgjP1M6MlCWo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category located at this coordinate [0.689, 0.903, 0.724, 0.931] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2025"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "eKIk52eHzMrmtLeO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color for {New Educational Institutes}<r>[0.330, 0.042, 0.358, 0.061]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#78a083"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "l9JaONImhOFDkDMY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {2025 New Educational Institutes}<r>[0.638, 0.883, 0.706, 0.883]</r> taller or shorter than the bar of {2022 Students Enrolled}<r>[0.197, 0.658, 0.265, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "EZkrG6JuUHLvRZ4u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color of {2025 New Educational Institutes bar}<r>[0.638, 0.883, 0.706, 0.883]</r> the same as {2023 Students Enrolled bar}<r>[0.367, 0.545, 0.435, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "dhL14qHVW81FNFHn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {2022 New Educational Institutes bar}<r>[0.129, 0.883, 0.197, 0.883]</r> have a higher or lower position than {2026 Students Enrolled bar}<r>[0.876, 0.206, 0.944, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "SyxlpSxyxZWH6IDF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is used more frequently, {New Educational Institutes color}<r>[0.330, 0.042, 0.358, 0.061]</r> or {Students Enrolled color}<r>[0.582, 0.042, 0.610, 0.061]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are used equally."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "aFKOqnxNmkDDNP0L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the area [0.324, 0.028, 0.749, 0.078]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart. It has two entries: \"New Educational Institutes\" with a green color label at [0.330, 0.042, 0.358, 0.061], and \"Students Enrolled\" with an orange color label at [0.582, 0.042, 0.610, 0.061]."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "nRkug3cJ3wVjKOky",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the region [0.367, 0.883, 0.435, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The orange bar indicating the number of students enrolled in 2023 is located in this region."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "EBNpGp7ZUvt118t3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents inside [0.597, 0.855, 0.778, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the upper half of the bar for the year 2025. This bar represents the New Educational Institutes started in that year."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "YjsNngZFANMcdzTu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is included in the region [0.035, 0.534, 0.078, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label which is 30000 is situated at this region."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "3B1vY1Di2DUimMdH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object labeled {Website Redesign}<r>[0.122, 0.831, 0.203, 0.952]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar indicating the evaluation score of the Website Redesign project."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "mRheFHFvXly61aXO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the evaluation score of the {Data Analytics System}<r>[0.102, 0.271, 0.203, 0.422], [0.211, 0.246, 0.897, 0.310]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The evaluation score is 90."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "e6wdl4mU6hTJwPRX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the {Cloud Migration}<r>[0.130, 0.751, 0.203, 0.860]</r> score?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.211, 0.726, 0.821, 0.790]"
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "EK4njIjpPlHWjitY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Online Marketing Campaign}<r>[0.080, 0.591, 0.203, 0.775], [0.211, 0.566, 0.783, 0.630]</r> evaluation score higher or lower than that of {E-commerce Platform}<r>[0.106, 0.351, 0.203, 0.496], [0.211, 0.326, 0.844, 0.390]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "ZDe4gVoGY6f2MRKF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher evaluation score - the {A.I. Customer Service Chatbots}<r>[0.067, 0.511, 0.203, 0.715], [0.211, 0.486, 0.951, 0.550]</r> or {Virtual Reality Product Showroom}<r>[0.058, 0.111, 0.203, 0.329], [0.211, 0.086, 0.928, 0.150]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "A.I. Customer Service Chatbots."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "wUQYtwrRbbS9TsH8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the evaluation scores of {Website Redesign}<r>[0.122, 0.831, 0.203, 0.952], [0.211, 0.806, 0.859, 0.870]</r> generally higher than the {Cybersecurity Improvement}<r>[0.080, 0.191, 0.203, 0.375], [0.211, 0.166, 0.867, 0.230]</r> scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nope."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "gxGpWnq0QGXQbOJ1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.211, 0.047, 0.988, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar chart. Each bar represents an evaluation score of an innovation project. The x-axis indicates the evaluation score, and the y-axis indicates the name of each innovation project."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "kkrqe9y7YjmzNXxa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.211, 0.246, 0.867, 0.230]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar and score for the project named Cybersecurity Improvement. The bar starts at 0.211 on the x-axis and extends to 0.867, indicating an evaluation score of 86."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "NmkdgbvsfSPlhZzR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.211, 0.406, 0.882, 0.470]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar representing the Company Mobile App project with an evaluation score of 88, which is indicated by the bar extending to 0.882 on the x-axis."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "xQJWPNOxjULQFRoy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.211, 0.806, 0.859, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar representing the Website Redesign project with an evaluation score of 85, indicated by the bar extending to 0.859 on the x-axis."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "b5s368E0qtTC9rWo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the main object located at [0.260, 0.019, 0.847, 0.041] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the subtitle: 'Instances of Sustainable Business Practices & Social Responsibility Activities in 2021'"
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "SysJYHiBRaZCsTWd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the bar} at [-0.612, 0.822, 0.880, 0.887] indicate from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates that there were 8000 instances of 'CSR Activities' in 2021."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "uRrZ2rImlxVU7phd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the x-axis label at [0.493, 0.964, 0.613, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Number of Instances'."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "LRoBpPXkdqNVhOd2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger, {the 'Waste Reduction' bar}[[-0.612, 0.413, 0.469, 0.479]] or {the 'Renewable Energy' bar}[[-0.612, 0.495, 0.525, 0.560]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Renewable Energy' bar is larger."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "nYIw5yf3IJ44vtOK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar for 'Animal Welfare'}[-0.612, 0.087, 0.171, 0.152] on the left indicate a lower or higher number of instances than the {bar for 'CSR Activities'}[-0.612, 0.822, 0.880, 0.887] on the right?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "4Wc5qc4O4iaxB9tD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the numbers indicated by {the bars for the practices/activities}[-0.612, 0.822, 0.880, 0.887], [-0.612, 0.740, 0.768, 0.805], [-0.612, 0.658, 0.675, 0.724] etc., decreasing or increasing as we move from top to bottom?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "lhtt6aBd4661anz3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements present in the region spanning from [0.134, 0.047, 0.973, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts the main chart area, with bars representing different 'Sustainable Business Practices & Social Responsibility Activities' plotted horizontally along the y-axis, and their respective numbers of instances (in the year 2021) plotted along the x-axis. The labels for these activities are found to the left of each bar, and the number of instances is annotated at the end of each bar. For instance, we have 'CSR Activities' {bar and label}[-0.612, 0.822, 0.918, 0.887] at the top with 8000 instances and 'Animal Welfare' {bar and label}[-0.612, 0.087, 0.210, 0.152] at the bottom with 4200 instances."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "iBc0nYU9wASUBGRU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region [0.119, 0.939, 0.988, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels ranging from 4000 {label and tick}[0.119, 0.939, 0.148, 0.957], [0.134, 0.927] to 8500 {label and tick}[0.958, 0.939, 0.988, 0.957], [0.973, 0.927], indicating the possible range for the number of instances of each activity."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "sxzcn4B63XIDREDf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the vertical range of [0.061, 0.848, 0.126, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical range hosts the y-axis label for the 'CSR Activities', indicating that the top-most bar of the chart represents the instances of 'CSR Activities'."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "c331qMrdmWVMnikl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the entire chart represented at coordinates [0.440, 0.015, 0.676, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Number of Research Papers by AI Areas\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "ttiqHgScHjVhNbAS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label with coordinates [0.489, 0.971, 0.627, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"Number of Research Papers\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "ivG9oMqSIjolD4Iq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat research area is represented by the coordinates [0.062, 0.862, 0.120, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The research area is \"Deep Learning\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "NqqO7dnBB67CHFtT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by the bar with coordinates [0.127, 0.833, 0.948, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the annotation at [0.959, 0.860, 0.984, 0.874], the value is \"3950\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "QKUsGzseuKrC2S3U",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes \"Knowledge Representation\" at coordinates [0.019, 0.442, 0.120, 0.584] have more or less research papers than \"Planning and Navigation\" at coordinates [0.027, 0.358, 0.120, 0.488] referring to their bars at coordinates [0.127, 0.414, 0.268, 0.481] and [0.127, 0.330, 0.504, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "aHAxXPFLOUcSvb87",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the \"Deep Learning\" area at [0.062, 0.862, 0.120, 0.943] and \"Natural Language Processing\" area at [0.011, 0.526, 0.120, 0.679], which field has more research papers looking at their bars at coordinates [0.127, 0.833, 0.948, 0.901] and [0.127, 0.498, 0.896, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Natural Language Processing\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "D6OsDuSiuIgCvxXh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the number of papers in \"Evolutionary Computation\" [0.021, 0.106, 0.120, 0.245] greater or lesser than that in \"Swarm Intelligence\" [0.046, 0.190, 0.120, 0.294] based on their bars at coordinates [0.127, 0.078, 0.321, 0.146] and [0.127, 0.162, 0.299, 0.229], respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "Tsm280l9ANNiDOei",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing \"Automated Reasoning\" [0.127, 0.666, 0.286, 0.733] have higher or lower height than the bar representing \"Multi-agent Systems\" [0.127, 0.246, 0.403, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "qSve3ry0LVEoxS7L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region between coordinates [0.999, 0.951, 1.075, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It does not contain any part of the chart. It's outside of the chart boundary."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "3ll5e2IUb3GQVIV7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the sect that is bounded by coordinates [0.127, 0.414, 0.268, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the bar representing the number of research papers in the field of \"Knowledge Representation\". According to the text annotation at [0.278, 0.441, 0.297, 0.455], there are \"675\" papers in this field."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "oQhw5uE2Za0vsmu9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.738, 0.951, 0.763, 0.965]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to an x-axis tick label, which is the numerical value \"3000\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "tmREhu5dprSiFgAy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the information within the region [0.124, 0.951, 0.130, 0.965]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the tick label '0' representing the starting point on the x-axis in terms of number of research papers."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "ejYwHyiIjnsv0anx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is being described in this JSON structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "3IVQOJqwQFEfkHqx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis label}<r>[0.496, 0.948, 0.617, 0.966]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Initiatives."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "RWtFZZqybngfgnnD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat activity is represented with coordinates [0.127, 0.808, 0.415, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Franchise Ownership."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "S4m1trS9oG97nmR3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Startup Founding}<r>[0.127, 0.728, 0.754, 0.792]</r> longer than the bar for {Product Innovation}<r>[0.127, 0.648, 0.602, 0.712]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "D8jMTSksCU4VvDns",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Social Entrepreneurship}<r>[0.127, 0.166, 0.945, 0.230]</r> shorter than the bar for {Startup Founding}<r>[0.127, 0.728, 0.754, 0.792]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "LUYfou3aR6xhTQ9H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Freelance Contracting}<r>[0.127, 0.568, 0.490, 0.632]</r> and {Business Consulting}<r>[0.127, 0.487, 0.551, 0.551]</r>, which activity has a higher number of initiatives?"
+            },
+            {
+                "from": "gpt",
+                "value": "Business Consulting."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "jLl96k6aFm3w6KU0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.046, 0.601, 0.118, 0.740]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a y-axis label denoting the activity type as 'Freelance Contracting'."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "LwYWWVQPwIDKb10s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present in the area [0.127, 0.487, 0.551, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing the number of initiatives for the 'Business Consulting' activity type, which is a total of 2162 initiatives."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "3Ti3GCutq88gQw7W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what appears in the coordinates [0.127, 0.086, 0.323, 0.150]."
+            },
+            {
+                "from": "gpt",
+                "value": "In these coordinates, there is a bar representing 'Angel Investing' with a total of 1000 initiatives and the associated number."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "wwu50zstmeuUNkpJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is given by the coordinates [0.413, 0.019, 0.722, 0.041] in the chart's structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle: \"Estimated Financial Losses by Risk Category\"."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "BYNfNHZGqqpG0WRb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is at the place indicated by these coordinates [0.149, 0.858, 0.731, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the category \"Security Breach\" bar on the chart, with the value being 3200."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "RibfEw7mTmAnJT5a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text within this area [0.484, 0.964, 0.652, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the X-axis label, and it reads \"Estimated Losses ($000,000)\"."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "VfAKG6TEE7kALRap",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the object represented by [0.149, 0.822, 0.721, 0.887] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a green bar representing the \"Security Breach\" category, and its value is 3200."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "elYUwpm6JmY7hPLx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Compliance Violations}<r>[0.149, 0.332, 0.663, 0.397]</r> larger or smaller than the bar for {Human Errors}<r>[0.149, 0.168, 0.846, 0.234]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "5tsZEtDBt3T1MtYq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is larger, {the bar representing Security Breach}<r>[0.149, 0.822, 0.721, 0.887]</r> or {the bar representing Reputation Damage}<r>[0.149, 0.495, 0.947, 0.560]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing Reputation Damage."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "rj11DWukfG0VJY3e",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is positioned higher on the Y-Axis, {Market Shifts}<r>[0.149, 0.577, 0.566, 0.642]</r> or {Technology Failures}<r>[0.149, 0.087, 0.690, 0.152]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Shifts."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "IwNPTGJJKktEo0ot",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the length of {Financial Risks}[0.149, 0.250, 0.822, 0.315] bar and {Operational Risks}[0.149, 0.658, 0.640, 0.724] bar. Which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Financial Risks."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "r4GhtNmjwyZmZPvO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented within the bounding box [0.149, 0.087, 0.846, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a green bar which represents the \"Technology Failures\" category, with the value being 3030."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "e1PM02tcsqmBd7Ua",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area with coordinates [0.149, 0.168, 0.846, 0.234] illustrate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a green bar that represents the \"Human Errors\" category. The bar's value is 3900."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "OmV1DS7gsA2f1WWz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area [0.149, 0.527, 0.947, 0.567]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a single green bar mapped to the \"Reputation Damage\" category, which has a value of 4467."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "8TxohnSDyYO43clj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside the region [0.149, 0.740, 0.645, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a green bar which stands for the category \"Natural Disasters\", having a value of 1950."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "Huy7DU3Hog0WUCpJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the Chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Value Trend for Tech Companies at {the title}<r>[0.396, 0.019, 0.659, 0.041]</r>."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "vyNHhah7k76rhVMe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat stock's data is in the coordinate [0.067, 0.822, 0.637, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the data for {Amazon}<r>[0.067, 0.822, 0.637, 0.887]</r>."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "sJsvgmhnXgKZdZjd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.018, 0.848, 0.059, 0.910]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for {Amazon}<r>[0.018, 0.848, 0.059, 0.910]</r>."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "5VAcOcSASiYP2vos",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Tesla stock bar}[0.067, 0.413, 0.944, 0.479] longer than {the IBM stock bar}[0.067, 0.250, 0.441, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "7VFD3PTR27yGFMT4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the Apple stock}[0.067, 0.740, 0.626, 0.805] lower than {the Amazon stock}[0.067, 0.822, 0.637, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "Uu3Dqj2gB6WRnNgW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Oracle stock bar}[0.067, 0.087, 0.360, 0.152] closer to the X-axis than {the Microsoft stock bar}[0.067, 0.332, 0.705, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "Wd3o61jVmCOZVMrb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.067, 0.495, 0.506, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this area represents {the Facebook stock bar}<r>[0.067, 0.495, 0.506, 0.560]</r> with its corresponding text stating its value as 4200."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "DKzmHa5pySMtFyI6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the content in [0.262, 0.939, 0.291, 0.957] and [0.993, 0.939, 1.022, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "These are X-axis labels. The first coordinate represents the label {2000}<r>[0.262, 0.939, 0.291, 0.957]</r>, and the second coordinate represents the label {9000}<r>[0.993, 0.939, 1.022, 0.957]</r>."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "imfhBsIhYf30fUfI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within [0.067, 0.740, 0.626, 0.805]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar for {the Apple stock}<r>[0.067, 0.740, 0.626, 0.805]</r>. The value text of 5350 is also displayed near the end of the bar."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "Z255M9RU7qHqjHqq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.112, 0.822, 0.246, 0.887] in the vertical bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a vertical bar representing the return rate for {Mutual Fund}<r>[0.112, 0.822, 0.246, 0.887]</r>."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "xJ94YpMEeXFQc3fN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.254, 0.846, 0.272, 0.863] adjacent to one of the bars?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {3.2}<r>[0.254, 0.846, 0.272, 0.863]</r>, indicating the return rate for Mutual Fund."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "1c22InQubHKFHq1h",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the y-axis label indicates at the coordinates [0.028, 0.112, 0.104, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label {Savings Account}<r>[0.028, 0.112, 0.104, 0.226]</r> indicates a type of investment."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "L5JihmWAWwE10UnC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {ETFs}<r>[0.112, 0.250, 0.433, 0.315]</r> higher than the bar representing {Gold}<r>[0.112, 0.168, 0.179, 0.234]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "MUXfqLZ5J9VBK2wf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to the bar for {Real Estate}<r>[0.112, 0.658, 0.367, 0.724]</r>, where is the bar for {Bonds}<r>[0.112, 0.577, 0.292, 0.642]</r> located?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "ikRjqHcOw0CakL4A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two bars {Startup Investments}<r>[0.112, 0.495, 0.946, 0.560]</r> and {Cryptocurrency}<r>[0.112, 0.413, 0.550, 0.479]</r>, which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Investments."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "wpaX35B8lLUHZknK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the information contained in the region [0.112, 0.495, 0.946, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the performance of {Startup Investments}<r>[0.112, 0.495, 0.946, 0.560]</r>. There is a long vertical bar indicating a return rate of 20.0."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "NMdut10kHZMpOpJp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred by looking at the area [0.112, 0.250, 0.433, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area describes the return rate of {ETFs}<r>[0.112, 0.250, 0.433, 0.315]</r> as denoted by a vertical bar and it is 7.7."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "0Woat7iKdCRxVRM1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you outline the contents of the y-axis labels in the region [0.027, 0.766, 0.104, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the y-axis label for {Blue-Chip Stocks}<r>[0.027, 0.766, 0.104, 0.882]</r>, one of the types of investments."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "2Bj1FrTcH0ceMBFx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the object within the coordinates [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a title of the chart; \"Number of Internet Users by Industry\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "EeYG0lXFofmQwZtw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is designated at the coordinates [0.130, 0.577, 0.431, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the Internet users count in \"Data Analysis\" industry."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "XOI5KCGKrx1btMiC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.130, 0.413, 0.583, 0.479] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the industry label for \"Telecommunications\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "zJvekeKEHlITwsHv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Web Development\" at [0.130, 0.250, 0.748, 0.315] taller than the bar for \"Cloud Computing\" at [0.130, 0.822, 0.308, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "TOxifC4BYfUDTPdk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the \"AI and Machine Learning\" job category at [0.130, 0.495, 0.522, 0.560] have higher user counts than \"Data Analysis\" at [0.130, 0.577, 0.431, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "qr6FfxLH8zTOgtrd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relation between the size of the \"Cybersecurity\" bar at [0.130, 0.740, 0.343, 0.805] and the \"Social Media Marketing\" bar at [0.130, 0.332, 0.682, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Social Media Marketing\" bar is taller than the \"Cybersecurity\" bar."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "PRkCuoJiI5Hb9tPJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents in the range [0.130, 0.495, 0.522, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area has a blue bar representing the \"AI and Machine Learning\" industry with a count of 52750 Internet users."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "R8AOBuVLJOgT2RTe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content covered in the area [0.130, 0.822, 0.308, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a blue column chart piece that stands for 24000 Internet users in the \"Cloud Computing\" field."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "E4jvxMjdPWL92FhE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.130, 0.250, 0.748, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue column in this region portraying the number of Internet users in the \"Web Development\" industry as 83200."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "EYsl4DoD5E7EueBb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tool}<{the tool name:\"Python\"}><r>[0.041, 0.848, 0.078, 0.902]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a big data tool."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "j20HhhT2vfzP0tMN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the top right end of the bar showing the number of employees using {Apache Spark}<{coordinates of Apache Spark bar label:[0.820, 0.437, 0.849, 0.455]}>?"
+            },
+            {
+                "from": "gpt",
+                "value": "8800."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "Nd1xbDAllswJkqwp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tool name} at [0.036, 0.112, 0.078, 0.174]?"
+            },
+            {
+                "from": "gpt",
+                "value": "MATLAB."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "YVNtEUtbAp9trn6f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis label} at [0.472, 0.964, 0.601, 0.981] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Employees."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "Bi9ppjPRDCz3L3U3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {Python}<{Python bar coordinates:[0.086, 0.822, 0.910, 0.887]}>, is the usage of {Java}<{Java bar coordinates:[0.086, 0.740, 0.780, 0.805]}> more or less?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "yOuA3q45q4iNu0HG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has more users, {Excel}<{Excel bar coordinates:[0.086, 0.168, 0.945, 0.234]}> or {SQL}<{SQL bar coordinates:[0.086, 0.577, 0.884, 0.642]}>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Excel."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "7ty59rVUzRhG7zCb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {MATLAB}<{MATLAB bar coordinates:[0.086, 0.087, 0.485, 0.152]}> used by more employees than {Apache Spark}<{Apache Spark bar coordinates:[0.086, 0.332, 0.884, 0.697]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "fUxz6qngjgGzvCuV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Python}<{Python bar coordinates:[0.086, 0.822, 0.910, 0.887]}> and {R}<{R bar coordinates:[0.086, 0.658, 0.745, 0.724]}>, which role has more users?"
+            },
+            {
+                "from": "gpt",
+                "value": "Python."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "BHFPk6LG7AwamiSY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.780, 0.493, 0.944, 0.566]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one blue horizontal bar representing the number of employees using Hadoop, which is 8200."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "AWcF6z6BgYvwuL5V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is in the region [0.038, 0.413, 0.849, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a blue bar that represents the tool \"Tableau\", and according to the length of the bar, it indicates that the usage count is 8800."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "G8CEVfEWzK2wzF9F",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within [0.038, 0.358, 0.484, 0.455]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, a blue bar for the big data tool \"Apache Spark\" is present. According to the length of the bar, it shows that the tool is used by 9200 employees."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "gv29NoceiP7jxVX8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are highlighted in the area [0.086, 0.201, 0.945, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region highlights a blue bar that represents the \"Excel\" tool. The length of the bar indicates that the usage count is 9900."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "goUhg7AX3RjDqni8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.046, 0.358, 0.116, 0.464] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, named as Digital Banking."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "db0IhFpqANuTb6v8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the second bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.124, 0.740, 0.809, 0.805]"
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "ZnG0fXFtEzyj1zH2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale} at [0.391, 0.939, 0.405, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "RcP7YR9y6Udw81uz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"E-utilities\"}[0.124, 0.413, 0.453, 0.479] higher than {the bar representing \"Food Delivery\"}[0.124, 0.577, 0.726, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "1VKpJunKorsjtNM9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bar representing Online Retail} [0.124, 0.822, 0.617, 0.887] higher than {the bar representing Online Education} [0.124, 0.250, 0.836, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "KeibCNNh7etmQQmM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Online Retail\"}[0.124, 0.822, 0.617, 0.887] and {\"Digital Banking\"}[0.124, 0.332, 0.946, 0.397], which has a higher Annual Revenue Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Banking."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "LlUUtTokilYzPHfR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.048, 0.112, 0.116, 0.214]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar representation and one text label in this area. The bar is {Online Fashion's Revenue Growth bar}[0.124, 0.087, 0.863, 0.152], and the text is the label called {Online Fashion}[0.048, 0.112, 0.116, 0.214]."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "cP1zHtv341041Inx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.391, 0.939, 0.405, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.391, 0.939, 0.405, 0.957] contains the x-axis scale label which is the number \"10\"."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "liamutOwQM3Lsd1Z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.533, 0.005, 0.827, 0.983]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, all bar charts along the y-axis from {Travel & Transportation}[0.124, 0.658, 0.535, 0.724] to {Online Fashion}[0.124, 0.087, 0.863, 0.152] are included. Each bar is followed on the right by a numerical label representing annual growth data, ranging from \"15%\" to \"27%\"."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "nBnbEvoTXLiqc0Ut",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary component with coordinates [0.419, 0.021, 0.715, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart: \"Number of Shares Traded By Sector\"."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "ovwXSrwWmYBOvaYE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label for the sector 'Energy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.102, 0.437, 0.147, 0.500]"
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "5mtyQam64p1ivvYk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.505, 0.959, 0.630, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Shares."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "BOYmqmHB47trCQG0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sectors are portrayed in [0.156, 0.173, 0.939, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Materials}[0.156, 0.173, 0.939, 0.237]."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "0GuDBqUI43xTtik4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"High Tech\"}[0.156, 0.813, 0.592, 0.877] longer than {the bar representing \"Real Estate\"}[0.156, 0.493, 0.354, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "k8rIh4M5qMA8fGyc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Consumer Discretionary\"}[0.156, 0.573, 0.332, 0.637] shorter than {the bar representing \"Industrials\"}[0.156, 0.093, 0.774, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "IZoFWRp00abcEssz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Materials\"}[0.156, 0.173, 0.939, 0.237] and {\"Communication Services\"}[0.156, 0.253, 0.466, 0.317], which sector has more shares traded?"
+            },
+            {
+                "from": "gpt",
+                "value": "Materials."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "pRupvwuNTSZPfTCl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Materials\"}[0.156, 0.173, 0.939, 0.237] the longest bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "WWB9emXUAtmsJJRN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDepict the content within [0.156, 0.813, 0.592, 0.877]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one orange bar representing the 'High Tech' sector with the number of shares traded shown on the right. The value is '2308' [0.601, 0.835, 0.636, 0.855]."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "Wu5JwcqamAg9qPZU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.156, 0.493, 0.354, 0.557]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one orange bar which represents the 'Real Estate' sector with the number of shares traded. The numerical text is '1048' located at [0.363, 0.515, 0.399, 0.535]."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "hxA0t0Efl7TswkmU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects exist within [0.156, 0.253, 0.466, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 orange bar which represents that the value of the \"Communication Services\" sector shares traded is 1643, and the coordinate range is [0.156, 0.253, 0.466, 0.317]."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "fpiQ1tUAUxFFTWOs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the area [0.415, 0.931, 0.457, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis tick label '1500' [0.422, 0.931, 0.457, 0.951], which represents the number of shares traded."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "nJbp7CKAYR9EEHZH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is [0.105, 0.047, 0.986, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "8WKXUk60XRyGdP70",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar} at the position [0.105, 0.793, 0.355, 0.887] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Sourcing} value in the Value Chain Visualization and Analysis, with volume 1250."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "HVIMY7vIWfeLubDb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's annotated at the coordinate [0.365, 0.831, 0.394, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value 1250 which corresponds to {the sourcing bar}[0.105, 0.793, 0.355, 0.887]."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "g1fv90m3vGVilLx8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Retail bar}[0.105, 0.204, 0.944, 0.299] longer than {the Distribution bar}[0.105, 0.322, 0.755, 0.416]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "qCjpDScDl2gz3CoM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Production bar}'s[0.105, 0.675, 0.445, 0.769] value higher than that of the {Packaging bar}[0.105, 0.558, 0.575, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "CXHq2QXM2B10HTFR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar of Transportation}[0.105, 0.440, 0.665, 0.534], is {the bar of After-Sales Service}[0.105, 0.087, 0.875, 0.181] shorter or longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "wQGWRv4HcwxIljb0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.105, 0.047, 0.986, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main body of the bar chart. The x-axis represents the Transaction Volume ranging from 0 to 4500, and the y-axis represents different stages in the Value Chain: Sourcing, Production, Packaging, Transportation, Distribution, Retail, and After-Sales Service. Each stage is depicted by a bar with the length proportional to the transaction volume of that stage."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "BIQa6rImL6SaRjjz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained within the region [0.353, 0.204, 0.944, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the {Retail bar}[0.105, 0.204, 0.944, 0.299] in the bar chart, showing a transaction volume of 4200."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "8gcj2bldhkswFL8e",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area ranging from [0.105, 0.087, 0.875, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the {After-Sales Service bar}[0.105, 0.087, 0.875, 0.181] in the bar chart, indicating a transaction volume of 3850 in value chain analysis."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "1xZvyQBcdGODUqgk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the axis label}<r>[0.008, 0.487, 0.008, 0.487]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, it is not given on the chart."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "Ognj2aSPYimT0oAw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the information about the bar with coordinates [0.099, 0.178, 0.196, 0.250]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the 'Trainee_Manager' position, with the value of 800 employees."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "CxDSZYtnl67Its3P",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text within the coordinates [0.347, 0.478, 0.376, 0.496] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text indicates the number '2000' which is the number of employees under 'Manager' role."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "50k4e4kGC10wiLMV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'CEO' bar}<r>[0.099, 0.814, 0.945, 0.887]</r> represent a greater number of employees than {the 'President' bar}<r>[0.099, 0.723, 0.703, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "EQmFYn3dTtzPg12m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Junior_Manager' bar}<r>[0.099, 0.360, 0.280, 0.432]</r> shorter than {the 'Team_Lead' bar}<r>[0.099, 0.269, 0.220, 0.341]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "XyNRUleNlHlGCxCN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the number of employees for {the 'Intern' role}<r>[0.099, 0.087, 0.111, 0.159]</r> smaller than that for {the 'Trainee_Manager' role}<r>[0.099, 0.178, 0.196, 0.250]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "c0JaZIL22M20lifE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the x-axis using the coordinates [0.479, 0.964, 0.607, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis represents the 'Number of Employees' ranging from 0 to 8000."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "5H7dQJLgKsdbjFk3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents between the coordinates [0.099, 0.814, 0.945, 0.887] and [0.099, 0.360, 0.280, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are bars representing the number of employees under various positions, from 'CEO' to 'Junior_Manager' respectively."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "aDU7OH0nTKr90r41",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present within the coordinates [0.368, 0.019, 0.718, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle 'Number of Employees under each managerial role'."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "IhDJ2AYx4XLfcmSA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.827, 0.427, 0.846, 0.444] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the number 4800, which represents the value of entrepreneurship activity for that particular activity."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "cyKfWz3nzG2cZoF5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the seventh bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.157, 0.332, 0.713, 0.397]"
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "7F9xuFtBU0qEwRsy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.068, 0.684, 0.149, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Angel Investment."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "pBePhY3RJv6iCfbg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at <r>[0.157, 0.740, 0.376, 0.805]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value for Venture Capital, approximately 1500."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "7iFxh1WmV5RzOdYy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Angel Investment\"}<r>[0.157, 0.658, 0.450, 0.724]</r> longer or shorter than {the bar representing \"Crowdfunding\"}<r>[0.157, 0.577, 0.464, 0.642]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "rZtSsY5hWFj7c8uQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Partnership\" bar}<r>[0.157, 0.413, 0.626, 0.479]</r> represent a higher or lower number of entrepreneurs compared to {the \"Franchising\" bar}<r>[0.157, 0.495, 0.479, 0.560]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "55cz2YWJEZIHtAZt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Government Programs\"}<r>[0.157, 0.087, 0.948, 0.152]</r> taller or shorter than {the bar for \"Bootstrapping\"}<r>[0.157, 0.822, 0.230, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "jLA4UzOIlxiPDlkB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Bootstrapping\"}<[0.157, 0.822, 0.230, 0.887]> and {\"Venture Capital\"}<[0.157, 0.740, 0.376, 0.805]>, which entrepreneurial activity has a higher recorded number?"
+            },
+            {
+                "from": "gpt",
+                "value": "Venture Capital."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "arn0gMOdjBY948aX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.082, 0.848, 0.149, 0.947]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label \"Bootstrapping\", which is one of the entrepreneurial activities noted on the y-axis."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "KaxqstarBn0Dlv8o",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.157, 0.822, 0.594, 0.887]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains one horizontal bar representing \"Bootstrapping\" entrepreneurial activity. The length of the bar indicates the number of entrepreneurs."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "qLeZAy2NgHCsNk1k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.157, 0.740, 0.376, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects:\n1. A horizontal bar graph, whose length represents the number of \"Venture Capital\" entrepreneurs. Its coordinate range is [0.157, 0.740, 0.376, 0.805].\n2. A text label \"1500\" indicating the number of entrepreneurs. Its coordinate range is [0.384, 0.764, 0.413, 0.782]."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "IO3tyIuQCSJYUdse",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what is in the region <r>[0.155, 0.939, 0.164, 0.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it contains the label \"0\" on the x-axis of the chart, indicating the starting point of the numbered entrepreneurs."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "q8mp3RUZ8ofbmNyz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.187, 0.909, 0.212, 0.931] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for \"Noon\"."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "8bfdt3EtoZ6ljC1x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first category label in the y-axis on the lower subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.243, 0.946, 0.283, 0.969]"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "sryqib6GitrNuwKx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the bar label}<r>[0.328, 0.897, 0.358, 0.919]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Afternoon."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "9SkBFZzZwmnD0pcs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Morning\" in 'Friend_Loss_Ours'}<r>[0.037, 0.848, 0.105, 0.903]</r> taller than {the bar for \"Morning\" in 'Friend_Loss_Saliency'}<r>[0.525, 0.794, 0.593, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "J00cgZ863RMiSdf3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Friend_Made_Ours' bar during the Evening}<r>[0.546, 0.098, 0.692, 0.415]</r> and {the 'Friend_Loss_Ours' bar during the Evening}<r>[0.292, 0.631, 0.360, 0.903]</r>, which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Friend_Made_Ours."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "zaeWnMKRzz4KqHgG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Night\" representing 'Friends_Made_Ours'}<r>[0.729, 0.336, 0.875, 0.415]</r> have more counts than {the bar for \"Night\" in 'Friend_Loss_Ours'}<r>[0.378, 0.816, 0.446, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "qZadf0MzSM9iUL1p",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.071, 0.409, 0.887, 0.500]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region predominantly represents information for x-axis labels and their corresponding tick positions. Specific entries include \"Morning\", \"Noon\", \"Afternoon\", \"Evening\", and \"Night\" each labeled respectively beneath the tick marks, indicating various periods of a day."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "EPHbbDojFUeNr27H",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the contents within [0.100, 0.152, 0.356, 0.498]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes both the y-axis of the upper subplot with labels such as \"60\" and \"80\" and part of the bars representing 'Friends Made Ours' and 'Friends Made Saliency' for \"Morning\" and \"Noon\", demonstrating a visual comparative analysis of social interaction metrics over these time periods."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "5oUNf5XDySq42ERi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.443, 0.011, 0.640, 0.070]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This zone showcases the legend of the chart, which indicates colors and categories. It features two color-text labels: 'Ours' in a red tone and 'Saliency' in a blue tone, clarifying the grouping of the bar charts."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "EruhYE6fiig4b4hx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the blue bar}<{Consumer Electronics bar}[0.112, 0.397, 0.139, 0.638]> in the chart representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the sales of Consumer Electronics in region 1."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "cFaWQKVQqBYRSItM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Apparel bar} at [0.202, 0.909, 0.228, 1.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "ZmCqLa54ijVtL5BG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to {the region bar} at [0.827, 0.563, 0.854, 1.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ninth region in which apparel is sold."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "v3GtSJeOHHGp7WqP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich had more sales in region 5, {Consumer Electronics}[0.470, 0.102, 0.497, 0.638] or {Apparel}[0.470, 0.724, 0.497, 1.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apparel."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "rIntUSAB6JJRA9p3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn region 10, did {Consumers Electronics}[0.917, 0.182, 0.943, 0.638] sales surpass {Apparel}[0.917, 0.563, 0.943, 1.045] sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "DdArlT2FTtMyuLAL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Consumer Electronics sales in region 3}[0.291, 0.316, 0.318, 0.638] better than {in region 2}[0.202, 0.410, 0.228, 0.638]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "TfxvqLnpp6XjvVDR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region [0.559, 0.692, 0.586, 1.045] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blue bar representing the sales of Apparel in region 6. The value of sales is 220."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "4GR5yTsoUfXkaoLc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart display in the region [0.202, 0.909, 0.228, 1.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows a blue bar for the sales of Apparel in region 2. The sales value is 85."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "oNsJSQhH14j4uLnW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.917, 0.563, 0.943, 1.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a bar representing the sales of Apparel in the region 10. The bar reaches a sales value of 300."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "RtcilIim2cZ5K7qe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the x-axis label in the left subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cultural Heritage."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "sLljQ4OovZUnopIE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the tick mark label}<r>[0.165, 0.919, 0.168, 0.942]</r> on the x-axis of the right subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "0OFTZAx7xtA4HGa7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}<r>[0.721, 0.508, 0.755, 0.567]</r> aligned with on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "India."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "07kqwdoEThwTbZQr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for India}<r>[0.165, 0.508, 0.288, 0.567]</r> in Cultural Heritage have a greater value than {the bar for India}<r>[0.721, 0.508, 0.755, 0.567]</r> in Cultural Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "VKVDFc0wKhWND1Rs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bar for Greece}<r>[0.165, 0.730, 0.339, 0.789]</r> in the left subplot and {the bar for China}<r>[0.721, 0.582, 0.776, 0.641]</r> in the right subplot representing positive values?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "VLlrHGhSwEpC8WLg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar has a higher value, {the Russia bar in Cultural Heritage}<r>[0.146, 0.139, 0.165, 0.198]</r> or {the USA bar in Cultural Integration}<r>[0.721, 0.065, 0.732, 0.124]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Russia."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "HGMXgmNVCw8062QI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within {the bottom right corner of the left subplot}<r>[0.395, 0.864, 0.495, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Part of the y-axis labels for 'United Kingdom' and 'USA', and a segment of the gray color of the subplot background, which has the coordinates of [0.482, 0.864, 0.495, 0.903]."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "JZMSoliRwIjUxTn6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region designed for the x-axis labels in both subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis labels for both Cultural Heritage and Cultural Integration. In Cultural Heritage, it shows labels such as '\u221220', '0', '20', '40', '60', '80', '100', '120'. In Cultural Integration, labels include '\u221260', '\u221240', '\u221220', '0', '20', '40', '60', '80', '100', '120'."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "G1bkUk4tnf50Xp48",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are present in the area near the y-axis of the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis label 'Country' and a list of country names used as y-tick labels, such as 'Rome', 'Greece', 'Egypt', 'China', 'India', 'France', 'United Kingdom', 'Spain', 'Turkey', 'Russia', and 'USA'; as well as the corresponding y-tick lines intersecting the axis."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "LF5iBrgMRqqOWHNB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the label associated with \"Ours\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "QcCcpGdwPmx06JVE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the subplot titled \"University Programs\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Rate of Participation Change [%]}<r>[0.024, 0.066, 0.038, 0.445]</r>."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "LAUFD0OJGqNKzwkm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick at the coordinate [0.187, 0.448] on the x-axis represent in the \"University Programs\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Ours}<r>[0.171, 0.464, 0.203, 0.487]</r>."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "RprRsX4a3CiSByBn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Ours\"}<r>[0.105, 0.088, 0.269, 0.319]</r> higher than {the bar for \"Trend-based Sampling\"}<r>[0.310, 0.101, 0.474, 0.319]</r> in the \"University Programs\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "bOrBMCn9RrdwmNAY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a more negative participation change rate, {the bar for \"Ours\"}<r>[0.597, 0.319, 0.762, 0.371]</r> or {the bar for \"Trend-based Sampling\"}<r>[0.803, 0.319, 0.967, 0.397]</r> in \"Adult Education\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Trend-based Sampling}<r>[0.803, 0.319, 0.967, 0.397]</r>."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "nRBfpTUvcoOFxyf8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the bars representing \"Ours\"}<r>[0.597, 0.704, 0.762, 0.807]</r> have a lower maximum value compared to {the bars representing \"Trend-based Sampling\"}<r>[0.803, 0.717, 0.967, 0.807]</r> in the \"Workplace Trainings\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "IfdSf6jOo11x8VW8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the chart area <r>[0.087, 0.062, 0.492, 0.448]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot \"University Programs\" showing two bars for \"Ours\" and \"Trend-based Sampling\" with their respective participation rates charted from 0.00 to 1.00 and the Y-axis labeled as \"Rate of Participation Change [%]\"."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "iUrrsr0M6J9oykT6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.579, 0.062, 0.985, 0.448]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the \"Adult Education\" subplot including bars representing \"Ours\" and \"Trend-based Sampling\", their corresponding values indicated on the y-axis ranging from -0.6 to 0.0, and X-axis labels accurately describing each category."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "8WlYN9HXvfeqZnOq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the objects and attributes found within the bounding box <r>[0.087, 0.550, 0.492, 0.935]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment holds the \"Online Certificate Programs\" subplot, featuring negative to positive participation change rates on the Y-axis, and two bars representing \"Ours\" and \"Trend-based Sampling\" spanning notable numeric ranges on the X-axis."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "3OlPzh59fZdxN9aa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the {Startup bar for VFX}[0.805, 0.544, 0.944, 0.624] or the {Funded startup bar for VFX}[0.805, 0.544, 0.944, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Funded Startup bar."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ZFN7behd5ZcAhy23",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Startup bar for Technology}[0.115, 0.304, 0.253, 0.544] of a greater magnitude than the {Funded Startup bar for 3D Modelling}[0.460, 0.544, 0.598, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "s7TgFYAKPAO2DAPz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the growth of {Funded Startups in the domain of Social Media}[0.633, 0.384, 0.771, 0.544] compared to the growth of {Startups in the domain of Social Media}[0.633, 0.225, 0.771, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They have grown the same."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "lDQYpQTCRZCIUisk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content in the bounding box [0.522, 0.952, 0.551, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content is Year."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "gzlrpr1FtobYEty7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the area defined by [0.043, 0.184, 0.078, 0.207], what information does it contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It has a y-axis label showing a value of 6000."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "xa9INYhhVZw0g9V8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}[0.129, 0.209, 0.180, 0.415] showing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the trade volume of 1990."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "t2Bp8ylOHW6ZgB8B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {2015 bar}[0.766, 0.544, 0.817, 0.903] higher than {2005 bar}[0.511, 0.586, 0.562, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "S0Of469Pd6bGYy6E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong these bars, {1990 bar}[0.129, 0.649, 0.180, 0.903] and {2020 bar}[0.893, 0.531, 0.944, 0.903], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2020 bar."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "YLk8iLAjPxahQy3P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Globalization Index for {2000}[0.384, 0.607, 0.435, 0.903] higher than the index for {1995}[0.256, 0.628, 0.307, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "LxECHMzcFiz1Wxod",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.088, 0.512, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a bar chart showing the Globalization Index from 1990 to 2020. It displays individual bars per year with different heights to represent the index values. The y-axis contains incremental labels to assist in the visualization of index changes."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "OCqOqyv1rTJbOAjk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.088, 0.025, 0.985, 0.415] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains another bar chart that illustrates the Trade Volume in billions USD from 1990 to 2020. Each year has a corresponding bar and the heights of these bars indicate the volume of trade."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "u1TVWxSQoEHTBZzT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the area defined by coordinates [0.522, 0.464, 0.551, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Year\" is placed in this region that marks the x-axis for the lower subplot representing the Trade Volume."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "zsCDQqeLqyRRsjTi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary color indicated for the \"Accelerated_gradient\" group in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue, identified within {the legend handle}<r>[0.514, 0.028, 0.542, 0.044]</r>."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "L2Yq3lo3wxG2sw4Q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color noted for \"Random_systematic\" in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red, visible in {the legend handle}<r>[0.299, -0.007, 0.327, 0.009]</r>."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "zw5FaE4zqMPcaBOB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the x-axis label located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Health & Medical Parameters\" is located at {the x-axis label area}<r>[0.425, 0.464, 0.628, 0.488]</r>."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "6echJ1DtZVCQHnI4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"health_factors\" under \"Random_systematic\"}<r>[0.022, 0.117, 0.074, 0.125]</r> higher than {its counterpart under \"Accelerated_gradient\"}<r>[0.074, 0.050, 0.125, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "WzTyOilVIu6Fg5YB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the bar for \"patient_noshow_rate\" under \"Random_systematic\"}<r>[0.669, 0.117, 0.720, 0.130]</r> or {under \"Accelerated_gradient\"}<r>[0.720, 0.043, 0.772, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accelerated_gradient."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "MgN1QkJeyHFQnumO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"lab_test_intervals\" under \"Random_systematic\"}<r>[0.410, 0.117, 0.462, 0.173]</r> represent a positive or negative trend compared to {the same factor under \"Accelerated_gradient\"}<r>[0.462, 0.097, 0.513, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "Z0VHwLgO5ecFu2me",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.074, 0.026, 0.224, 0.226]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis with tick labels ranging from \u221230 to 20 and corresponding bars for \"health_factors\" from both groups. Specifically, the \"Random_systematic\" bar is from [0.022, 0.117, 0.074, 0.125] and the \"Accelerated_gradient\" bar is from [0.074, 0.050, 0.125, 0.117]."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "J8Qa25sD9Yn2TCuw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area spanning [0.420, 0.090, 0.635, 0.230]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the positive and negative trend bars for \"medication_quantity\" and \"appointment_frequency\" for both groups. {Bars in the area}<r>[0.539, 0.117, 0.591, 0.135]</r>, <r>[0.281, 0.087, 0.332, 0.117]</r>, <r>[0.591, 0.077, 0.643, 0.117]</r>, <r>[0.332, 0.117, 0.384, 0.154]</r> reflect these trends."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "ExChXw0mEVRGzsaX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the boundary [0.160, 0.100, 0.350, 0.245]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area marks the beginning of the x-axis with labels and bars for \"health_factors\" and \"medical_bill\" from both trend groups. Notable elements include {bar for \"health_factors\" under \"Random_systematic\"}<r>[0.022, 0.117, 0.074, 0.125]</r> and {bar for \"medical_bill\" under \"Accelerated_gradient\"}<r>[0.203, 0.082, 0.255, 0.117]</r>."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "pmWJsDPnJt10RRqB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item described at coordinate range [0.503, 0.001, 0.536, 0.024]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the color representing \"Ours\" in the legend."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "fQ3BH63fUhwPdy8N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data label can you find at [0.297, 0.269, 0.357, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "uEQAQ5KZzItJKdAY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is located in the area [0.489, 0.302, 0.578, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, called Engagement."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "wQFJ2J9cIbRNuIGl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"YouTube\"}[0.485, 0.214, 0.582, 0.229] higher or lower than {the bar representing \"LinkedIn\"}[0.606, 0.229, 0.703, 0.243]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "2HCtFx5IbYzsf8LL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {ytick at '0'}[0.064, 0.219, 0.073, 0.243] higher or lower on the chart than {ytick at '700'}[0.046, 0.049, 0.073, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "0jj6XLG3gq2icmw5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents {the bar for \"Snapchat\"}[0.727, 0.432, 0.823, 0.554] from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ee8882."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "8Zl0NrSkuS2sM6E7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.082, 0.034, 0.985, 0.253]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the bar chart for \"Engagement\", including bars for social media platforms like Facebook, Instagram, and Twitter. Y-axis is labeled as \"Interaction rate [millions/month]\", and specific bars include {Facebook engagement bar}[0.123, 0.229, 0.220, 0.233], and {Instagram engagement bar}[0.244, 0.204, 0.341, 0.229]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "I4xRMbLk70vELRIh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what exists within the coordinates [0.015, 0.280, 0.029, 0.657]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, you can find the Y-axis for the subplot showing our data with labels spread from '\u2212100' to '800'. Each label depicts the interaction rate per million per month."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "5bSpGLp8yBMGdJkj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the space [0.082, 0.684, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space contains a bar chart showing \"Saliency-based Sampling\" for different social media platforms. It includes bars and y-ticks that show values ranging from '\u2212100' to '800'. Notable are the {Facebook saliency bar}[0.123, 0.794, 0.220, 0.879] and the {LinkedIn saliency bar}[0.606, 0.842, 0.703, 0.879]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "akMRLLYf7GGUELok",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's {the category name} near the coordinate [0.053, 0.843, 0.150, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow recovery."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "wVCYNuAM4u8QOoc6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the color orange} represent in the chart according to the legend at [0.547, 0.029, 0.649, 0.052]?"
+            },
+            {
+                "from": "gpt",
+                "value": "French Cuisine."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "HSTGWmcDE7bQuoV1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value in percentage is represented by the bar at [0.415, 0.092, 0.568, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "36.0%."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "umAUb9rZvG002FBF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of trend does the label at these coordinates [0.371, 0.029, 0.481, 0.052] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chinese Cuisine."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "q4SlpUSMnBy5dDWM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of values, is the bar of {Chinese Cuisine}[0.457, 0.236, 0.649, 0.322] higher or lower than that of {French Cuisine}[0.457, 0.809, 0.712, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "wEgrZa8L6YnH1ymU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the category bar of {Inexorable decline}(for French Cuisine) at [0.564, 0.666, 0.946, 0.752] wider than {Slow recovery}(for Chinese Cuisine) at [0.160, 0.809, 0.457, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "uKz8jYoLKsPMDoy3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars of {Chinese Cuisine} and {French Cuisine}, which one reaches the 100 mark on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chinese Cuisine."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "Dsn1xhx02kzl2YuM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {French Cuisine} bar higher for the {Slow recovery} category[0.457, 0.809, 0.712, 0.895] or the {Inexorable decline} category[0.564, 0.666, 0.946, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inexorable decline."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "gRnfsTeUnke6MMMh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is inside the region from [0.160, 0.092, 0.569, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the region, there are two bars. The bar on the left from [0.160, 0.092, 0.415, 0.178] is colored blue, representing the percentage of \"Chinese Cuisine\" that have \"Accelerated growth\", which is 60%. The bar on the right from [0.415, 0.092, 0.568, 0.178] is colored orange, representing the percentage of \"French Cuisine\" that have \"Accelerated growth\", which is 36%."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "LCcdZKU4OGamDyE6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements within the region from [0.326, 0.019, 0.654, 0.061]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend that contrasts two categories: \"Chinese Cuisine\" at [0.371, 0.029, 0.481, 0.052] and \"French Cuisine\" at [0.547, 0.029, 0.649, 0.052]. Their respective colors are depicted with a square at [0.332, 0.031, 0.359, 0.047] for \"Chinese Cuisine\" and [0.509, 0.031, 0.536, 0.047] for \"French Cuisine\"."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "zwrLp573yOSLwDGL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can be specified about the region [0.160, 0.522, 0.861, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there are two bars signifying the \"Sustained decline\" trend, with the first bar at [0.160, 0.522, 0.521, 0.608] representing the \"Chinese Cuisine\" trend, denoting a value of 85%, and the second bar at [0.521, 0.522, 0.861, 0.608] denoting the \"French Cuisine\" trend, with a value of 80%."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "O7utwVbBaozjaZsX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the area within [0.236, 0.952, 0.254, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In that specified region, there is a label for the tick mark on the x-axis. It is labeled as '20', serving as a scale for the bars representing the trends in Chinese and French Cuisines."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "w7115KdmjHxmKG4C",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.397, 0.809, 0.705, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That object is bar of the first group representing a Win for \"Road Construction vs. Waste Management\"."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "s1s4ziBsRCvO6lbv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the text within the coordinates [0.529, 0.841, 0.573, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label says \"55.0%\" throughput."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "RqTskvEA0rFkAOm7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be seen in {the subplot_0 axes}<r>[0.397, 0.052, 0.985, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart space that contains horizontal and vertical axes and bars representing the data."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "6SSPIBCwYLKShNLQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Win' bar for 'Education Infrastructure vs. Healthcare Infrastructure'}<r>[0.397, 0.092, 0.509, 0.178]</r> longer than {the 'Loss' bar for the same category}<r>[0.845, 0.092, 0.957, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "j7DTM2QGUpoDZwDU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents 'Tie' according to the legend {the legend color label of 'Tie'}<r>[0.601, 0.031, 0.628, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greenish hue, hex code #b7da91."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "IlTBgRMgqXlpzrnI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 'Win' bars}<r>[0.397, 0.809, 0.705, 0.895]; [0.397, 0.666, 0.565, 0.752]; [0.397, 0.522, 0.649, 0.608]; [0.397, 0.379, 0.593, 0.465]; [0.397, 0.236, 0.705, 0.322]; [0.397, 0.092, 0.509, 0.178]</r> generally longer than {the 'Loss' bars}<r>[0.929, 0.809, 0.957, 0.895]; [0.929, 0.666, 0.957, 0.752]; [0.929, 0.522, 0.957, 0.608]; [0.901, 0.379, 0.957, 0.465]; [0.929, 0.236, 0.957, 0.322]; [0.845, 0.092, 0.957, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "uOUxK0wP4u3C5H9N",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside the region [0.502, 0.019, 0.763, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the chart legend. There are 3 legend entries which represent 3 different colors for \"Win\", \"Tie\", and \"Loss\". The color and text labels for \"Win\" are [0.508, 0.031, 0.536, 0.047] and [0.547, 0.029, 0.573, 0.052] respectively. The color and text labels for \"Tie\" are [0.601, 0.031, 0.628, 0.047] and [0.640, 0.029, 0.660, 0.052] respectively. The color and text labels for \"Loss\" are [0.688, 0.031, 0.716, 0.047] and [0.727, 0.029, 0.757, 0.052] respectively."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "pWrWQ5rZ3E41XfzG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.397, 0.522, 0.649, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a blue bar representing a \"Win\" for \"Healthcare Infrastructure vs. Road Construction\". The label \"45.0%\" can be found within [0.501, 0.554, 0.545, 0.577]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "mYWEj1lScdzAW5Im",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the sector [0.397, 0.236, 0.705, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue bar corresponding to the \"Win\" category for \"Education Infrastructure vs. Road Construction\". The associated label \"55.0%\" can be found at coordinates [0.529, 0.267, 0.573, 0.290]. There is also a green bar representing a \"Tie\" that extends to [0.705, 0.236, 0.929, 0.322] with its associated label \"40.0%\" at [0.795, 0.267, 0.839, 0.290]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "Y82C2Kxz3lnVnn41",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.168, 0.064, 0.323, 0.135] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blue bar representing the percentage of wins during the Stone Age."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "6IVHDVZmK1oHR8AL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label located at [0.302, 0.797, 0.345, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"40.0%\" associated with the blue bar for the Industrial Revolution."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "HWHww8EesKbCrord",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented by [0.632, 0.949, 0.660, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label (red) in the chart legend, corresponding to the \"Loss\" category."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "cM9lsA0zRtZCRc0W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the heights of {the blue bar for the Renaissance era}[0.168, 0.655, 0.401, 0.726] and {the blue bar for the Stone Age}[0.168, 0.064, 0.323, 0.135], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Renaissance era bar."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "FCoofuemeANFwb4U",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar for the Industrial Revolution}[0.168, 0.773, 0.479, 0.844] shorter than {the green bar for the same era}[0.479, 0.773, 0.790, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "Q33mRLYfzFjNkwAT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong these two bars {the one for Stone Age wins}[0.168, 0.064, 0.323, 0.135] and {the one for Stone Age ties}[0.323, 0.064, 0.713, 0.135], which one is wider?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one for Stone Age ties."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "TVUp5hTQEFSXYfPz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the area [0.446, 0.938, 0.706, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a legend with three elements: a blue square labeled \"Win\" located at {Win label}[0.491, 0.947, 0.517, 0.970], a green square labeled \"Tie\" located at {Tie label}[0.583, 0.947, 0.604, 0.970], and a red square labeled \"Loss\" located at {Loss label}[0.671, 0.947, 0.701, 0.970]."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "bXL0iS2upV9TrMn7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region [0.168, 0.773, 0.479, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region harbors the blue bar representing the percentage of wins during the Industrial Revolution, with the associated text \"40.0%\" located at [0.302, 0.797, 0.345, 0.820]."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "XDlw8kboEBhQrEtu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the data within the boundaries [0.168, 0.064, 0.946, 0.135]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these boundaries are three bars representing the outcomes of the Stone Age, namely, a blue bar for wins, a green bar for ties, and a red bar for losses. Associated percentages can be found on top of each bar."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "6uxIhlfYZYiEBi9L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.285, 0.025, 0.863, 0.058] indicate in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at this position is the subtitle that reads \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "YNX5lx1ik0lux55x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the bar}[0.511, 0.277, 0.762, 0.380] representing Resource Utilisation's Actual Improvement rating?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar is colored '#b7da91'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "LHvj6Owxc9QcjdWV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label does the ytick at [0.032, 0.835, 0.154, 0.859] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ytick at this coordinate refers to the label 'Overall Efficiency'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "ILjtQzhGIXMcQcAS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category of the bar located at [0.556, 0.105, 0.834, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category of this bar is 'Energy Efficiency'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "qX0KFm2X3LIAhqJy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Overall Efficiency\"}[0.598, 0.793, 0.921, 0.896] longer or shorter than {the bar representing \"Waste Management\"}[0.640, 0.449, 0.946, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "I4aSyKYiJ17qTuzL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Potential Improvement\"}[0.163, 0.105, 0.556, 0.208] for the category 'Energy Efficiency' wider than {the bar representing \"Actual Improvement\"}[0.556, 0.105, 0.834, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "iygQHeuEpXdN2Sgm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the bar for Process Automation Actual Improvement}[0.609, 0.621, 0.943, 0.724] and {the bar for Energy Efficiency Actual Improvement}[0.556, 0.105, 0.834, 0.208], which one has a greater value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Process Automation."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "EeBuHHbUtovGp3nl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Potential Improvement for Energy Efficiency}[0.163, 0.105, 0.556, 0.208] better than {Potential Improvement for Resource Utilisation}[0.163, 0.277, 0.511, 0.380]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "nxjjdMVf9aJwcT3J",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the rectangle [0.016, 0.663, 0.154, 0.687]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for 'Process Automation' on the y-axis."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "WbVYSAznfgopt4kD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the box [0.366, 0.034, 0.783, 0.075]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It has two items: {Potential Improvement}[0.410, 0.043, 0.569, 0.066] with color '#9cc8e4' at [0.371, 0.045, 0.399, 0.061] and {Actual Improvement}[0.636, 0.043, 0.777, 0.066] with color '#b7da91' at [0.597, 0.045, 0.625, 0.061]."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "OWoNz7FcVRZb3x2a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the [0.707, 0.952, 0.733, 0.975] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the \"100\" x-axis label."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "OPeqJHCiXLZcHq7s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.163, 0.793, 0.598, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar representing the Potential Improvement score for Overall Efficiency, which is 78%."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "YuLsHi61MYhakSyz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry is represented by the x-label at [0.044, 0.703, 0.099, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "407YgLcYHcfpKnp4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage shown at [0.537, 0.841, 0.581, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "35.5%."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "uXeLnlRsNsTzHWot",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is indicated by the legend item at [0.484, 0.031, 0.512, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green as per {legend item}<r>[0.484, 0.031, 0.512, 0.047]</r>."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "HZutiYHeD41Ob5n2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the green bar for Technology}[0.642, 0.236, 0.943, 0.322] longer than {the green bar for Healthcare}[0.604, 0.092, 0.930, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "KBO84SQa3OirhCFR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar for Finance}[0.102, 0.379, 0.385, 0.465] taller than {the blue bar for Retail}[0.102, 0.522, 0.479, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "4oUd102jCdrskR6L",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Non-Profit}[0.024, 0.843, 0.093, 0.866] and {Education}[0.024, 0.700, 0.093, 0.723], which industry has a higher value for Entrepreneurship<{Entrepreneurship Non-Profit bar}[0.447, 0.809, 0.670, 0.895],{Entrepreneurship Education bar}[0.542, 0.666, 0.701, 0.752]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Non-Profit."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "tN8lXDFv9o69mBsc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.102, 0.052, 0.985, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar chart showing two sets of data per industry: Entrepreneurial Spirit represented by {blue bars}[0.102, 0.809, 0.447, 0.895], [0.102, 0.666, 0.542, 0.752], [0.102, 0.522, 0.479, 0.608], [0.102, 0.379, 0.385, 0.465], [0.102, 0.236, 0.642, 0.322], [0.102, 0.092, 0.604, 0.178] and Entrepreneurship represented by {green bars}[0.447, 0.809, 0.670, 0.895], [0.542, 0.666, 0.701, 0.752], [0.479, 0.522, 0.582, 0.608], [0.385, 0.379, 0.501, 0.465], [0.642, 0.236, 0.943, 0.322], [0.604, 0.092, 0.930, 0.178]. Each bar pair represents a specific industry which is enumerated on the y-axis [0.024, 0.843, 0.093, 0.866], [0.024, 0.700, 0.093, 0.723], [0.053, 0.556, 0.093, 0.580], [0.040, 0.413, 0.093, 0.436], [0.015, 0.269, 0.093, 0.293], [0.017, 0.126, 0.093, 0.149]]."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "h0Qsg6w3MP5Ta3Vh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.263, 0.019, 0.648, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the legend of the bar chart with two items. Labels indicate that the color blue at [0.268, 0.031, 0.296, 0.047] represents Entrepreneurial Spirit while the color green at [0.484, 0.031, 0.512, 0.047] represents Entrepreneurship."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "Sf7DiXMWKeqfQr4x",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.447, 0.809, 0.670, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object: The green column representing the value of the \"Entrepreneurship\" category in Non-Profit, which is 35.5%, has a coordinate range of [0.447, 0.809, 0.670, 0.895]."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "gpc3XxcIUm8wJW9a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.102, 0.052, 0.604, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the bar data for Healthcare. There are two bars - a blue bar with a value of 80.0% for Entrepreneurial Spirit and a green bar with a value of 52.0% for Entrepreneurship. Their respective coordinates are {blue bar}[0.102, 0.092, 0.604, 0.178] and {green bar}[0.604, 0.092, 0.930, 0.178]."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "2I5AELiknSNvLCVz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the label at [0.597, 0.031, 0.625, 0.047] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color red."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "KvOJXIGZdEh7nQ5O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object found at [0.395, 0.379, 0.683, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A green bar representing Environment's Volunteer Hours."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "elmw11KMOjFYVjyO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich grant has its value represented in [0.203, 0.809, 0.299, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grants provided for Religion."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "p8k9whddPP8aGFiS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the meaning of the text at [0.732, 0.841, 0.763, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates that the Communities served in the field of Religion is 30."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "sW6eOXF3tvEiFlvc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the green bar}<{Health's Volunteer Hours bar}[0.331, 0.236, 0.555, 0.322]> larger than {the red bar}<{Communities Served in Health field bar}[0.555, 0.236, 0.844, 0.322]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "A1EUw0LksJmP6CWs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Volunteer Hours}<{Volunteer Hours bars}[[0.299, 0.809, 0.651, 0.895],[0.382, 0.666, 0.709, 0.752],[0.472, 0.522, 0.780, 0.608],[0.395, 0.379, 0.683, 0.465],[0.331, 0.236, 0.555, 0.322],[0.363, 0.092, 0.716, 0.178]]> generally greater than {Grants Provided}<{Grants Provided bars}[[0.203, 0.809, 0.299, 0.895],[0.203, 0.666, 0.382, 0.752],[0.203, 0.522, 0.472, 0.608],[0.203, 0.379, 0.395, 0.465],[0.203, 0.236, 0.331, 0.322],[0.203, 0.092, 0.363, 0.178]]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "DxSBHMIIPR2DIGsf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Religion}<{Grants for Religion bar}[0.203, 0.809, 0.299, 0.895]> and {Health}<{Grants for Health bar}[0.203, 0.236, 0.331, 0.322]>, which sector receives more grants?"
+            },
+            {
+                "from": "gpt",
+                "value": "Religion."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "7UOSdtaBjYqMliov",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Communities Served in Arts bar}<[0.709, 0.666, 0.844, 0.752]> greater than the {Grants provided for Arts bar}<[0.203, 0.666, 0.382, 0.752]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "HjBAWNAooiJk8yGd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area [0.203, 0.092, 0.844, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains three bars: a blue bar representing Grants Provided in Education at [0.203, 0.092, 0.363, 0.178], a green bar representing Volunteer Hours in Education at [0.363, 0.092, 0.716, 0.178], and a red bar representing Communities Served in Education at [0.716, 0.092, 0.844, 0.178]."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "tPfrQlEZG42VIt1w",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.015, 0.556, 0.193, 0.580] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Community Development' for y-axis."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "lpultOK8u5rVAYt4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.395, 0.379, 0.683, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a green bar representing the Volunteer Hours for Environment, which is 45."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "AiBaGnaaJa4hKH9W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region [0.235, 0.019, 0.786, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the legend of the chart. Blue at [0.241, 0.031, 0.268, 0.047] represents 'Grants Provided'; green at [0.419, 0.031, 0.447, 0.047] stands for 'Volunteer Hours'; and red at [0.597, 0.031, 0.625, 0.047] denotes 'Communities Served'."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "37xGAF4pmfkPamjv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object located at coordinates [0.015, 0.700, 0.182, 0.723]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the y-axis label \"HealthTech vs. PropTech\"."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "7lLPuICmUOi9heun",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color indicated by the coordinate located at [0.565, 0.031, 0.592, 0.047] in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #ef8b88, which refers to a shade of red."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "ZZSQfN7TSPvCaOnk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the x-axis coordinates [0.787, 0.952, 0.805, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label for the value \"80\" on the x-axis."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "GnjQLY8esavbHnAV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the rectangle at [0.503, 0.843, 0.752, 0.895] refer to in the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the percentage of Tie result for the comparison between \"PropTech vs. AdTech\"}<r>[0.483, 0.809, 0.752, 0.895]</r>, which is 35.6%."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "kGnXlaGpQ2ah0eRz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the bar for \"Win\" and \"Loss\" results in the \"HealthTech vs. PropTech\" match. Which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Win\" bar is longer than the \"Loss\" bar."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "2X1lCbdksP624YXx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the \"Blockchain vs. AI\" category, which has the highest percentage - \"Win\", \"Tie\", or \"Loss\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Tie\" category has the highest percentage."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "O8VC4dZfLxBamQwv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the comparison of {the \"AI vs Fintech\" Win bar}<r>[0.192, 0.236, 0.442, 0.322]</r> and {the \"Edtech vs HealthTech\" Win bar}<r>[0.192, 0.522, 0.498, 0.608]</r>, which bar is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Edtech vs HealthTech\" is longer."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "nBlJX2SXYDJ4hYcB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {\"Fintech vs. Edtech\" Tie bar}<r>[0.381, 0.379, 0.758, 0.465]</r> shorter than the {\"Fintech vs. Edtech\" Loss bar}<r>[0.758, 0.379, 0.947, 0.465]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "lgbnAYPw8YL2VXby",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.431, 0.009, 0.567, 0.042]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains part of the legend of the chart, indicating two categories: \"Tie\" located at [0.516, 0.029, 0.537, 0.052] with a color indication at [0.477, 0.031, 0.505, 0.047], and \"Loss\" located at [0.603, 0.029, 0.634, 0.052] with a color indication at [0.565, 0.031, 0.592, 0.047]."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "OfRIgvNkbuEyRh8n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region [0.192, 0.379, 0.947, 0.465]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes three category bars of the match \"Fintech vs. Edtech\", including the \"Win\" bar at [0.192, 0.379, 0.381, 0.465], \"Tie\" bar at [0.381, 0.379, 0.758, 0.465], and \"Loss\" bar at [0.758, 0.379, 0.947, 0.465]. Also, the text coordinates of the values on these bars are [0.264, 0.410, 0.308, 0.434], [0.548, 0.410, 0.591, 0.434], and [0.831, 0.410, 0.875, 0.434] respectively."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "o6lRFfGbiK3yNC7g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the area [0.341, 0.019, 0.678, 0.052]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers part of the chart's legend with identifiers for two results - \"Win\" located at [0.423, 0.029, 0.450, 0.052], and \"Tie\" located at [0.516, 0.029, 0.537, 0.052]. The corresponding color identifiers are found at [0.385, 0.031, 0.412, 0.047] for \"Win\" and [0.477, 0.031, 0.505, 0.047] for \"Tie\"."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "3YUOAJndWL3Qh4th",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the region [0.485, 0.952, 0.503, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis label that represents the data point \"40\"."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "qFhwT13GCoEnhTOd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the bar}[0.733, 0.401, 0.943, 0.586] represent in the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the loss percentage for Americas."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "cVIAqQw47Evn6hF7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate provides the \"Win\" label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.370, 0.029, 0.396, 0.052]."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "RgMKurO56TClLBwI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.176, 0.093, 0.199]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Asia Pacific."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "F8gKSL5k4IdEkTPH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is shown in [0.691, 0.710, 0.943, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The percentage of Loss for EMEA}<r>[0.795, 0.791, 0.839, 0.814]</r>."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "t4sLTiBcCEEMKA78",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tie bar for EMEA}[0.271, 0.710, 0.691, 0.895] taller than {the tie bar for Americas}[0.481, 0.401, 0.733, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "yMRN41fyQwR7YY8n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo all regions have the same {Loss}<{Loss for EMEA}[0.691, 0.710, 0.943, 0.895], {Loss for Americas}[0.733, 0.401, 0.943, 0.586], {Loss for Asia Pacific}[0.733, 0.092, 0.943, 0.277]> percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "2iWKuJtfEBdLEG6N",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Win bar for EMEA}<r>[0.103, 0.710, 0.271, 0.895]</r> shorter than {the Win bar for Americas}<r>[0.103, 0.401, 0.481, 0.586]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes.."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "LEqMawGAyL7udJP8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Tie for Asia Pacific}<r>[0.397, 0.092, 0.733, 0.277]</r> taller than the {Tie for EMEA}<r>[0.271, 0.710, 0.691, 0.895]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "2sKbl0K0UyVynPh6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region in the chart specified by [0.325, 0.019, 0.586, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend, which has three labels indicating: {Win}<r>[0.331, 0.031, 0.359, 0.047]</r> in blue, {Tie}<r>[0.424, 0.031, 0.452, 0.047]</r> in light green, and {Loss}<r>[0.511, 0.031, 0.539, 0.047]</r> in red."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "M3fzw2mPM73GTRRM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.103, 0.710, 0.943, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 bars: the {Win bar}<r>[0.103, 0.710, 0.271, 0.895]</r> in blue, the {Tie bar}<r>[0.271, 0.710, 0.691, 0.895]</r> in light green, and the {Loss bar}<r>[0.691, 0.710, 0.943, 0.895]</r> in red for the EMEA region."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "YWL2As9fna97gsOd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's in the region defined by [0.103, 0.401, 0.943, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Win bar}<r>[0.103, 0.401, 0.481, 0.586]</r>, {Tie bar}<r>[0.481, 0.401, 0.733, 0.586]</r> and the {Loss bar}<r>[0.733, 0.401, 0.943, 0.586]</r> for the Americas region are all within this area."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "glDWjpCIoxbCC9ZT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.103, 0.092, 0.943, 0.277]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects:  \n1. The {Win bar for Asia Pacific}<r>[0.103, 0.092, 0.397, 0.277]</r> which illustrates the winning percentage for this region. \n2. The {Tie bar for Asia Pacific}<r>[0.397, 0.092, 0.733, 0.277]</r> showing the tied matches percentage for Asia Pacific.\n3. The {Loss bar for Asia Pacific}<r>[0.733, 0.092, 0.943, 0.277]</r> which marks the losing percentage for the Asia Pacific region."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "HUSAsB4GctLtY4dh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the coordinate with a bounding box of [0.432, 0.791, 0.788, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the \"Win\" percentage of the \"Slow increase\" category."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "FR0C34Eimk6CafJT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what category the coordinate at [0.156, 0.092, 0.393, 0.197] refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate represents the \"Win\" percentage of the \"Stable growth\" category."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "z3ctl0hqo6RU6CSZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text marked by the coordinates [0.582, 0.029, 0.612, 0.052]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The word is \"Loss\"."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "72fxPSjFBysjBadf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of \"Tie\", is the \"Slow increase\" category{coordinates of the Slow increase category under Tie}[0.432, 0.791, 0.788, 0.895] higher or lower than the \"Fluctuating growth\" category{coordinates of the Fluctuating growth category under Tie}[0.353, 0.616, 0.748, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "ohO0YwTFs65v3GQY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Loss\" score of the \"Steady recovery\" category{coordinates of the Steady recovery category under Loss}[0.827, 0.441, 0.946, 0.546] greater than that of the \"Stable growth\" category{coordinates of the Stable growth category under Loss}[0.788, 0.092, 0.946, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "mFQmeJ9EGRym28hM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Win\" score of the \"Stable growth\" category{coordinates of the Stable growth category under Win}[0.156, 0.092, 0.393, 0.197] greater than that of the \"Steady recovery\" category{coordinates of the Steady recovery category under Win}[0.156, 0.441, 0.472, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "eLyIsWVdboOAAkwI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the information of objects found in the bounding box [0.156, 0.267, 0.669, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects found in the given bounding box include: \n1. The blue bar that represents the \"Win\" score for the \"Gradual recovery\" category and its value of 20% at location [0.156, 0.267, 0.314, 0.371].\n2. The green bar stands for the category \"Tie\" of \"Gradual recovery\", 45% score, and its coordinates are [0.314, 0.267, 0.669, 0.371]."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "JE8j4BOYSjerGQIw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information in the region [0.432, 0.791, 0.788, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the region [0.432, 0.791, 0.788, 0.895], the green bar represents the \"Tie\" score of \"Slow increase\" category as 45%."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "IZkGR9c2RNO8qtYM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the contents in the region [0.353, 0.616, 0.748, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this range, the green bar depicts a \"Tie\" in the \"Fluctuating growth\" category, recorded as a 50% score."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "XjP9fvOGFY9N8rOD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.397, 0.029, 0.423, 0.052]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend text, \"Win\"."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "Tuad8hgM9gCXUaxB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar represented by the coordinates [0.148, 0.809, 0.507, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is blue, as indicated by its equivalent label {Win}<r>[0.397, 0.029, 0.423, 0.052]</r>."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "O3ZsWBMAmYyCdMLe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description for the graph element located at [0.044, 0.700, 0.139, 0.723]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label on the y-axis denoting \"Wind vs. Hydro\"."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "RProj0gQdnwWoJlt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object in the coordinates [0.148, 0.666, 0.865, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it encompasses the win and tie bars for \"Wind vs. Hydro\" in the chart."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "S8FezllgkShoGOP6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the comparison \"Solar vs. Wind\", does the {win percentage}<r>[0.148, 0.092, 0.347, 0.178]</r> exceed the {loss percentage}<r>[0.746, 0.092, 0.945, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "S1zcyoCAMsLHQtm0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {win bar}<r>[0.865, 0.522, 0.945, 0.608]</r> of the category \"Wind vs. Nuclear\" taller than its {loss bar}<r>[0.148, 0.522, 0.427, 0.608]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "TSHhoxtT3aFjJr5P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {tie bar for the category Solar vs. Wind}<r>[0.347, 0.092, 0.746, 0.178]</r> taller than the {tie bar for the category Wind vs. Hydro}<r>[0.467, 0.666, 0.865, 0.752]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "01pVo2K4NDlsctJG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {win and tie bar for \"Solar vs. Hydro\"}<r>[0.148, 0.379, 0.706, 0.465]</r> and {loss bar for \"Solar vs. Nuclear\"}<r>[0.786, 0.236, 0.945, 0.322]</r>, which has a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Win and tie bar for \"Solar vs. Hydro\"."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "caYWkriZKF4rGgdf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinate [0.148, 0.809, 0.945, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate encompasses the win, tie, and loss bars for the category \"Nuclear vs. Hydro.\" The win bar is located at [0.148, 0.809, 0.507, 0.895], the tie bar is at [0.507, 0.809, 0.865, 0.895], and the loss bar is at [0.865, 0.809, 0.945, 0.895]."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "45FoiZH105ZmCoj0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be extracted from the area demarcated by [0.148, 0.236, 0.945, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three bars representing the win, tie, and loss percentages for the category \"Solar vs. Nuclear.\" The coordinates for these bars are [0.148, 0.236, 0.308, 0.322] for the win, [0.308, 0.236, 0.786, 0.322] for the tie, and [0.786, 0.236, 0.945, 0.322] for the loss."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "TbumMMRTL6knxAne",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the coordinates [0.358, 0.031, 0.566, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color boxes of the legend, corresponding to \"Win\", \"Tie\", and \"Loss\"."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "aHUBNjnFikH3Kyfu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items found within the coordinates [0.353, 0.019, 0.613, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It has three entries: \"Win\" with the color blue at coordinates [0.397, 0.029, 0.423, 0.052], \"Tie\" with the color green at [0.490, 0.029, 0.511, 0.052], and \"Loss\" with color red at [0.577, 0.029, 0.607, 0.052]."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "pEtWkM28GrApvpOP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the rectangle labeled \"Win\" in the sub-legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is {blue}<r>[0.389, 0.031, 0.417, 0.047]</r>."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "tyPnaW4RHzyULuUs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text label corresponding to the \"Loss\" category in the sub-legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the text label of \"Loss\" is [0.608, 0.029, 0.638, 0.052]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "gnzxRkktJH6l9xNk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at [0.199, 0.092, 0.360, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blue bar which represents the category \"Win\" for the y-label \"Combines vs. Harvesters\", with a value range of \"0.00 -> 21.50\"."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "Tmi2abEfICpxt5wZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described under the xticks within the coordinates [0.639, 0.952, 0.657, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis tick labelled as \"60\"."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "DkuoW8r6IQnTjiV1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Loss\" for Dairy vs Poultry}<r>[0.891, 0.666, 0.948, 0.752]</r> to the left of {the bar representing \"Loss\" for Fruits vs Grains}<r>[0.873, 0.809, 0.948, 0.895]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "OwlSsX2dQcOrylRY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {\"Win\"}[0.199, 0.236, 0.371, 0.322] column taller than the {\"Loss\"}[0.850, 0.236, 0.948, 0.322] column for \"Tractors vs. Ploughs\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "s8M1lgHxwuQuSXso",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the shopping behavior categories located on the left or right compared to the {legend}<r>[0.383, 0.019, 0.643, 0.061]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "gZ2arTRsGhQegfqr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmongst {the bars representing the \"Win\" category}[[0.199, 0.809, 0.334, 0.895], [0.199, 0.666, 0.427, 0.752], [0.199, 0.522, 0.453, 0.608], [0.199, 0.379, 0.412, 0.465], [0.199, 0.236, 0.371, 0.322], [0.199, 0.092, 0.360, 0.178]], which one is the tallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one representing \"Organic vs. Conventional."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "qGrCdWCsqIzDZHW2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe everything within the area [0.199, 0.092, 0.948, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are lots of bars representing different outcome patterns (Win, Tie, Loss), each for different pairs of categories (Combines vs. Harvesters, Tractors vs. Ploughs, Irrigation vs. Fertilizer, Organic vs. Conventional, Dairy vs. Poultry, Fruits vs. Grains). These bars are portrayed in different colors according to their outcome pattern: blue for \"Win\", green for \"Tie\", and red for \"Loss\"."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "HswIcQn7wrWkRrGV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information shown in the area [0.383, 0.019, 0.643, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend. It contains three rectangles: blue labeled \"Win\" [0.389, 0.031, 0.417, 0.047], green labeled \"Tie\" [0.482, 0.031, 0.509, 0.047], and red labeled \"Loss\" [0.569, 0.031, 0.597, 0.047]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "gnZ02ANaLuRbzrcA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the area [0.334, 0.809, 0.873, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two bars. The blue bar represents the outcome \"Win\" for the category pair \"Fruits vs. Grains\". It has a value range of 0.00 -> 18.00 and is located at [0.199, 0.809, 0.334, 0.895]. The other green bar represents the outcome \"Tie\" for the same category pair. It has a value range of 18.00 -> 90.00 and is located at [0.334, 0.809, 0.873, 0.895]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "WfCKxE8WZ8dVgrdv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.334, 0.666, 0.891, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, two bars represent the results for the \"Dairy vs. Poultry\" category. The blue bar, which represents a \"Win\", is at [0.199, 0.666, 0.427, 0.752]. The bar labeled \"Tie\" is green and located at [0.427, 0.666, 0.891, 0.752]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "3Q9UpHZptnmwCnyy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content in the coordinate [0.261, 0.844, 0.304, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the value of \"In-store purchase\" for \"Customer Preferences\", which is 25.7%."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "SCvTejy2Vvr7vw1L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at [0.304, 0.540, 0.755, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is denoted by '#b7da91', represented by the json value: {'color': '#b7da91'}, which is a shade of green."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "ZTWfRXTNxCyucutl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo what does the label in coordinates [0.358, 0.027, 0.479, 0.050] correspond in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label corresponds to the \"In-store purchase\" represented by the color{'color': '#9cc8e4'}."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "m5nW2zstYS6hKaAS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of \"In-store purchase\" for \"Customer Preferences\" at [0.184, 0.815, 0.380, 0.897] less than \"Online purchase\" for \"Customer Preferences\" at [0.380, 0.815, 0.703, 0.897]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "quRKPZpNOOvjm61M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher, \"In-store purchase\" for \"Return to Normalcy\" at [0.184, 0.678, 0.417, 0.760] or \"In-store purchase\" for \"Advent of Markets\" at [0.184, 0.266, 0.413, 0.348]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Return to Normalcy\"."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "7oQV6tpusSZ30G9p",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the \"Rise of E-commerce\" bar for \"Online purchase\" at [0.337, 0.129, 0.718, 0.211] have a higher or lower value than the \"Rise of E-commerce\" bar for \"In-store purchase\" at [0.184, 0.129, 0.337, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "mdF2bpC7S9fx1Pz2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.184, 0.129, 0.947, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the area [0.184, 0.129, 0.947, 0.211], there are 3 bars, they represent the \"Rise of E-commerce\" category for the three types of purchases: In-store, Online, and E-commerce Apps. The first bar representing \"In-store purchase\" is located at [0.184, 0.129, 0.337, 0.211], the second bar representing \"Online purchase\" is located at [0.337, 0.129, 0.718, 0.211] and the final bar, representing \"E-commerce Apps\", is at [0.718, 0.129, 0.947, 0.211]."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "v4RbwPimSfHml0IA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the bars within [0.184, 0.403, 0.947, 0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the \"Innovations in Delivery\" information. There are three bars, each representing a different purchasing method: \"In-store purchase\" is denoted by the bar at [0.184, 0.403, 0.379, 0.486], \"Online purchase\" by the bar at [0.379, 0.403, 0.646, 0.486], and \"E-commerce Apps\" by the bar at [0.646, 0.403, 0.947, 0.486]."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "nwWcpNj46jUyFkNG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items within coordinates [0.313, 0.017, 0.856, 0.059]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region describes the three purchasing methods symbolized in the legend, which are: \"In-store purchase\" with a color block at [0.319, 0.029, 0.347, 0.045] and a label at [0.358, 0.027, 0.479, 0.050], \"Online purchase\" with a color block at [0.507, 0.029, 0.534, 0.045] and a label at [0.545, 0.027, 0.658, 0.050], and E-commerce Apps with a color block at [0.686, 0.029, 0.714, 0.045] and a label at [0.725, 0.027, 0.850, 0.050]."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "svmxObTZEObOlfKg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label is for the object located at [0.072, 0.126, 0.119, 0.149] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"Twitter\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "m4YINL6bxvcL2vU3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the first handle element in the legend? Its coordinates are [0.302, 0.031, 0.330, 0.047]."
+            },
+            {
+                "from": "gpt",
+                "value": "The color is blue."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "ZwoByYrxSTKIR4iz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value displayed at the coordinates [0.217, 0.123, 0.261, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 27.3%."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "7xkOQnodUoYYfd9T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is displayed in the figure at [0.471, 0.029, 0.641, 0.052]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Public Opinion Guidance\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "S1qlX1GWZE3uUKln",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the bar chart, is {the bar}[0.359, 0.666, 0.938, 0.752] taller or shorter than {the bar}[0.533, 0.236, 0.938, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "s21DTVd9j5UFZm4a",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {group_0 bar for Facebook}[0.129, 0.236, 0.533, 0.322] less or more than {group_0 bar for Radio}[0.129, 0.666, 0.359, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "vQ4Rn4VgQbsSoHe3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider, {the bar for Print Media}[0.296, 0.809, 0.938, 0.895] or {the bar for News Websites}[0.463, 0.379, 0.938, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same width."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "1nxkzac1NaQcmqxY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value for Public Opinion Guidance, {Twitter}[0.350, 0.092, 0.944, 0.178] or {Facebook}[0.533, 0.236, 0.938, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "EAcZHMhanX4qgZtz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in [0.297, 0.019, 0.646, 0.061]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart, consisting of two labels. The first label's color is blue and is named \"Influence\" with coordinates [0.341, 0.029, 0.405, 0.052], and the second label's color is green and is named \"Public Opinion Guidance\" with coordinates [0.471, 0.029, 0.641, 0.052]."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "9DwFHTTpsYfPvKmC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is enclosed within the region [0.446, 0.522, 0.938, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses a blue bar and a green bar, indicating the Influence and Public Opinion Guidance rates for Television."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "7QVMJccx5e38luJi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.129, 0.892, 0.950, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the horizontal axis (x-axis) of the bar chart, where the values of Influence and Public Opinion Guidance are plotted from 0 to 100%."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "PjZti4zmYTFXXbk8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's inside [0.129, 0.092, 0.350, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar in blue that signifies the Influence percentage for Twitter, with a value of 27.3%."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "C09PKwBN1ZG5pX6G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located at the coordinates [0.170, 0.791, 0.370, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the info about the {Upward Trend} bar<r>[0.170, 0.791, 0.370, 0.895]</r> in the year \"2021\" which has a value percentage of 25%."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "2jBHbZiaAphClz5x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value shown at the coordinates [0.589, 0.657, 0.633, 0.680]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 55%."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "9JqFtT4Ga5JTqZlb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the entry labels in the legend located at [0.346, 0.019, 0.635, 0.096]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels include {2017}<r>[0.390, 0.029, 0.426, 0.052]</r>, {2018}<r>[0.390, 0.063, 0.426, 0.087]</r>, {2019}<r>[0.492, 0.029, 0.528, 0.052]</r>, {2020}<r>[0.492, 0.063, 0.527, 0.087]</r>, and {2021}<r>[0.594, 0.029, 0.629, 0.052]</r>."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "rFFhgV9sybdQb6iL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of the bar in 2019 compared to the bar in 2017 for {Pronounced Increase}<r>[0.170, 0.616, 0.290, 0.721]</r> and <r>[0.170, 0.616, 0.450, 0.721]</r> respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increased."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "X96lsDbxFTglhBFe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for {Stable}<r>[0.170, 0.267, 0.571, 0.371]</r> in 2020 have a higher or lower percentage than it did in 2018 for {Stable}<r>[0.170, 0.267, 0.579, 0.371]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "ocOZmUi9YlVUWHFS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the percentage value for {Increase}<r>[0.170, 0.092, 0.290, 0.197]</r> in 2019 higher or lower than that in 2021<r>[0.170, 0.092, 0.338, 0.197]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "H6h1yyeBERCBzTp7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the coordinates [0.170, 0.267, 0.579, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of a group of bars representing the category {Stable}. It shows the yearly trends from 2017 to 2021 with their respective percentages."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "NPci8VAbbmSYokj4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.346, 0.019, 0.635, 0.096]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend of the chart that includes color-coded labels for the years 2017 to 2021."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "h4s8pdC7Z3elKnBy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's within the coordinates [0.170, 0.441, 0.811, 0.546]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a group of bars representing the category {Abrupt drop}. It provides the percentage values for the years 2017 to 2021."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "2IsF42nWi9gxwmaz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.152, 0.564, 0.231, 0.903] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing the Q1 sale of a \"New Improved Product\"."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "BpFQCkt24Ka5EIBp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.975, 0.334, 0.975, 0.743] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label of the second subplot, named \"Declining Product Sales\"."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "WGmZIEBOnV8WwPsm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text does the coordinate [0.561, 0.776, 0.619, 0.811] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"735K\" above the Q3 sales bar of the \"Declining Product Sales\" chart."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "OqFSzn4wnepPh2gP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at [0.879, 0.322, 0.942, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"-200\" on the Y-axis of the second subplot."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "k6NZMi2mzipGrfpo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Q1 New_Product Sale}[0.152, 0.564, 0.231, 0.903] higher than the {Q1 Decline_Product Sale}[0.152, 0.174, 0.231, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "RdCFfUth5sdrevvi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Q4 sale for New_Product}[0.749, 0.209, 0.829, 0.903] more than the {Q2 sale for New_Product}[0.351, 0.437, 0.431, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "CU4I2VmKJmNhLRBe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre values in {ytick labels of subplot_0}[0.025, 0.290, 0.048, 0.788] greater than values in {ytick labels of subplot_1}[0.952, 0.334, 0.975, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "z6sDXbYXF0aSkc7N",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {New_Product Q4 sales}[0.749, 0.209, 0.829, 0.903] taller than the bar representing {New_Product Q1 sales}[0.152, 0.564, 0.231, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "ZyxOuWaaiH0OFmTe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.874, 0.902, 0.957, 1.003]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-tick label \"-1000\" from the second subplot, which defines the top bound value of \"Declining Product Sales\"."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "33JZbFwBwe5xztOD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the region [0.152, 0.174, 0.829, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are bars for \"Declining Product Sales\" for Q1[0.152, 0.174, 0.231, 0.868], Q2[0.351, 0.174, 0.431, 0.836], Q3[0.550, 0.174, 0.630, 0.768], and Q4[0.749, 0.174, 0.829, 0.715]."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "zV3MshrGath4MZvR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.118, 0.174, 0.863, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two parts of the graph: New Product Sales and Declining Product Sales. It also includes their corresponding y-axis labels and bars indicating their sales for Q1 to Q4."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "sRhoSsALUPJaEdUg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.5, 0.046, 0.851, 0.108] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a legend for the \"Declining Product Sales\" line on the chart, with the corresponding color and text location at [0.570, 0.060, 0.842, 0.095]."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "8iTVLxDY2H1TJrvV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label at this coordinate [0.025, 0.458, 0.048, 0.658] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"AI Adoption\" level."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "URz1w69ponNUTggU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the text at the coordinates [0.157, 0.482, 0.186, 0.517] says?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it reads \"50\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "S0L0Ek8Z4Ix5oPrE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label name for {the legend}<r>[0.189, 0.052, 0.322, 0.087]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's labeled as \"AI Adoption\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "11WHyU1rqm9BIlis",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar for AI Adoption at point [0.139, 0.462, 0.204, 0.941]}, is {the bar for Automation Trend at point [0.139, 0.558, 0.204, 0.941]} higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "sztz7xNcmxqIp8I8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Automation Trend at point[0.788, 0.558, 0.853, 0.941]} taller than {the bar for AI Adoption at point[0.788, 0.270, 0.853, 0.941]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "jFaIkUPl8mnndEtw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the size of the bars, in general, are {the bars for AI Adoption} larger than {the bars for Automation Trend}?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "ByjoCZWKEeif8uW0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in area [0.115, 0.038, 0.331, 0.101]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend. The legend color is #404346, located at [0.124, 0.055, 0.170, 0.079], corresponding to the label \"AI Adoption\" located at [0.189, 0.052, 0.322, 0.087]."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "B01HA9XU3lAe0qY0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.103, 0.174, 0.889, 0.941]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains bar graphs representing AI Adoption and Automation Trend across multiple categories. The bars show the comparative values of each category for AI Adoption and Automation Trend."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "5H8vV163spwJxWnc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me what's in the area [0.139, 0.558, 0.204, 0.941]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar from the Automation Trend, with a height indicating a value of \"40\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "vjQ0ZLQtH9lIRvhJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies within the coordinates [0.153, 0.030, 0.290, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text label in the legend}<r>[0.153, 0.030, 0.290, 0.053]</r> displaying \"Monetary Policy\"."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "KiwXy6Qm6809lGNv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.592, 0.345, 0.647, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text on a bar}<r>[0.592, 0.345, 0.647, 0.368]</r> which reads \"55.6%\"."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "PqhProFtFf8rMbwV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.275, 0.500, 0.285, 0.530] show on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The axis label}<r>[0.275, 0.500, 0.285, 0.530]</r> isn't visible on the chart."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "3VJSYENDaFtU1alA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar with 82.6%}[0.205, 0.236, 0.237, 0.564] larger than {the bar with 79.3%}[0.205, 0.564, 0.237, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "i3CD34Ri7uctdXr2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the y-axis}<r>[0.019, 0.472, 0.036, 0.656]</r>, does the label \"60\" appear above the label \"40\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "vDviVh3mp5X02uDs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher percentage, {the bar labeled \"55.6%\"}<r>[0.592, 0.345, 0.647, 0.368]</r> or {the bar labeled \"54.3%\"}<r>[0.672, 0.350, 0.726, 0.374]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "55.6%."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "XXi671SPOLxNsZYn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.097, 0.020, 0.297, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {the legend box}<r>[0.097, 0.020, 0.297, 0.062]</r>, which includes a label and handle indicating \"Monetary Policy\". The label's text is positioned at [0.153, 0.030, 0.290, 0.053], and the handle is colored gray at [0.104, 0.032, 0.139, 0.048]."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "tJnIvjfUVMo6zAff",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the area [0.088, 0.167, 0.912, 0.961]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large region contains all visual elements of the subplot, including {the axes}<r>[0.088, 0.167, 0.912, 0.961]</r>, with labels for Monetary Policy, a series of bars representing different percentages, and a y-axis with labeled ticks ranging from 0 to 100."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "5E609BZ8AORV4p5v",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.842, 0.564, 0.874, 0.805] encapsulate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a {Fiscal Policy bar}<r>[0.842, 0.564, 0.874, 0.805]</r> illustrating a value of 60.7%, as evidenced by the bar itself and an accompanying data label at [0.831, 0.806, 0.886, 0.830]."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "NqoKQo260C57RVYZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the coordinates [0.025, 0.377, 0.048, 0.700] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Method, it's the Y-axis label of the subplot_0."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "crEe43geVYj7VLOe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in subplot_1 sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.680, 0.060, 0.861, 0.095]"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "1cEnHcHEKiAsfKDW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.025, 0.377, 0.048, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Method."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "39kxoxNGws4klh8n",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.130, 0.046, 0.428, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text label for Traditional Method in the subplot_0 legend [0.204, 0.060, 0.419, 0.095]."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "CXTeBLEY26FRLfxc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Literature'}[0.335, 0.806, 0.409, 1.632] shorter than {the bar for 'History'}[0.701, 0.478, 0.774, 1.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "wAehttixUEjYl3r9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Math\"}[0.591, 0.441, 0.665, 1.632] taller than {the bars representing \"Science\"}[0.226, 0.539, 0.299, 1.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "8X7wZ22BPZGUbaDM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for Traditional Method}[[0.153, 0.721, 0.226, 1.632], [0.335, 0.806, 0.409, 1.632], [0.518, 0.636, 0.591, 1.632], [0.701, 0.478, 0.774, 1.632]] generally shorter than {the bars for Modern Method}[[0.226, 0.539, 0.299, 1.632], [0.409, 0.660, 0.482, 1.632], [0.591, 0.441, 0.665, 1.632], [0.774, 0.296, 0.847, 1.632]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "0FtTxh1sqHbRRU5S",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Science\"}[{Science bar for Traditional Method}[0.153, 0.721, 0.226, 1.632], {Science bar for Modern Method}[0.226, 0.539, 0.299, 1.632]] and {\"Math\"}[{Math bar for Traditional Method}[0.518, 0.636, 0.591, 1.632], {Math bar for Modern Method}[0.591, 0.441, 0.665, 1.632]], which study field scored higher for Modern Method?"
+            },
+            {
+                "from": "gpt",
+                "value": "Math."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "gxLYeiwk07kWjaXN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.606, 0.046, 0.870, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains one pair of color-text labels in the legend of subplot_1. The color label is located at [0.615, 0.063, 0.662, 0.087], which is pink, and the text label is located at [0.680, 0.060, 0.861, 0.095], referred to as Modern Method, indicating that the Modern Method's bar is pink."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "lOXnI4lL9fDVG0gJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.118, 0.174, 0.882, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart where both Traditional Method and Modern Method bars are plotted. It also contains all the category labels (Science, Literature, Mathematics, and History) on the horizontal axis, and the y-axis label, 'Traditional Method' as well. It also consists of tick marks and labels for the y-axis."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "29MSLrU8Zoo7fuNG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.153, 0.721, 0.409, 1.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects:\n1. The black column, representing the Science category of the Traditional Method, with a value of 75, and its coordinates are [0.153, 0.721, 0.226, 1.632].\n2. The pink column, representing the Science category of the Modern Method, with a value of 90, and its coordinates are [0.226, 0.539, 0.299, 1.632]."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "0gM554Yr1yjE4bZr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.118, 0.174, 0.882, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can see the main chart content. This includes all the bars catered by both Traditional and Modern Methods, the category labels on the x-axis (Science, Literature, Math, and History), the grid lines on the y-axis, as well as the y-axis labels for both Traditional and Modern Methods."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "wK63vBdU3Qoq8tNN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object with coordinates [0.189, 0.175, 0.309, 0.210] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text label, indicating the data is about \"Mountains\"."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "I7RgL6HNMoRmcsFW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object resides at coordinates [0.206, 0.153, 0.279, 0.188]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text label representing the percentage data value of the mountain, specifically 55.3%."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "7NBMUmvTAQ8o8OYa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of {the highlighted region} at [0.728, 0.162, 0.885, 0.224]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend specifying that the pink color corresponds to data about \"Ocean\"."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "Y2FYTWADyBvBbHuO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the percentage of {the rectangle representing \"Mountains\"}[0.139, 0.186, 0.345, 0.324] exceed the percentage of {the rectangle representing \"Ocean\"}[0.139, 0.324, 0.345, 0.435] in the \"Rapid recovery\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "Nj2wt6ZH4Gx7Fc1g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two elements, {the \"Rapid recovery\" bar for \"Mountains\"}[0.139, 0.186, 0.345, 0.324] and {the \"Abrupt Drop\" bar for \"Mountains\"}[0.655, 0.244, 0.861, 0.324], which has a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Rapid recovery\" bar."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "JOZD6UdfzZsOp5gq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar representing \"Mountains\" in the \"Abrupt Drop\" category}[0.655, 0.244, 0.861, 0.324] or {the bar representing \"Ocean\" in the \"Abrupt Drop\" category}[0.655, 0.324, 0.861, 0.441]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Ocean\"."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "HFYrIXy2mhuNHLEf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.139, 0.186, 0.345, 0.324]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar representing the percentage data of \"Mountains\", specified as 55.3%, during the \"Rapid recovery\" period."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "ugKLRvFfFJKdvW4F",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.103, 0.174, 0.897, 0.324]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four bars, two for \"Mountains\" data and two for \"Ocean\" data in two different categories: \"Rapid recovery\" and \"Abrupt Drop\". Labels for these bars with their respective percentages are also present within this region."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "3e4oCVQpfLgatyHi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region [0.115, 0.162, 0.318, 0.224]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the \"Mountains\" category data, which is represented by a gray color in the chart. The color symbol is located at [0.124, 0.179, 0.170, 0.203]."
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "kIGozgpRCmaYFExC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.139, 0.272, 0.204, 0.492] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar from the 'Project Management' group."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "LM7BSvptTskXEInf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at [0.132, 0.118, 0.860, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart: 'Project Management: Project Innovation Comparison'."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "jwlbnzcxKk75fgWR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the [0.189, 0.093, 0.428, 0.128] coordinate refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the term 'Project Management' in the legend."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "t5yTxNxFxn5ZBItK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing {Project Management}[0.139, 0.272, 0.204, 0.492] taller than the bars representing {Project Innovation}[0.139, 0.549, 0.204, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "6sgjMQ02470641oS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the objects at [0.139, 0.272, 0.204, 0.492] and [0.301, 0.212, 0.366, 0.492], which has a higher position?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at [0.301, 0.212, 0.366, 0.492]."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "yIwcLr2MmxDeDQJj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, are the texts representing {Project Management}[0.189, 0.093, 0.428, 0.128] on the left of {Project Innovation}[0.658, 0.093, 0.868, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "FNX9Nk8rO19fm9kp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area marked by coordinates [0.115, 0.079, 0.438, 0.142]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the legend of the chart. Specifically, there is a rectangular color label at [0.124, 0.096, 0.170, 0.120] which is in 'Project Management' color, followed by the corresponding text label 'Project Management' at [0.189, 0.093, 0.428, 0.128]."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "qtKbU4MGcX5i5aAy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we see in the region [0.351, 0.067, 0.865, 0.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title \"Project Management: Project Innovation Comparison\" is located in this region, with coordinates of [0.132, 0.118, 0.860, 0.163]."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "yi9MKmwHfxRXvdFv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the chart area [0.139, 0.212, 0.853, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars representing the percentages of 'Project Management' at [0.139, 0.272, 0.204, 0.492], [0.301, 0.212, 0.366, 0.492], [0.463, 0.332, 0.528, 0.492], [0.626, 0.232, 0.691, 0.492], [0.788, 0.432, 0.853, 0.492] and 'Project Innovation' at [0.139, 0.549, 0.204, 0.769], [0.301, 0.549, 0.366, 0.720], [0.463, 0.549, 0.528, 0.865], [0.626, 0.549, 0.691, 0.834], [0.788, 0.549, 0.853, 0.679]."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "KLAY5wL3nJaKv0rf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the subplot title with coordinates [0.302, 0.130, 0.694, 0.160]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Manufacturing Production Efficiency Evaluation."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "b7IUUz9Xn5V3VZYp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text in the legend at [0.782, 0.035, 0.921, 0.058] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the {Product B Efficiency} bar."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "N7mb2qNfHHxBQdZY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is indicated by the text located at [0.116, 0.826, 0.159, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the value of 40.8%."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "eYmK2S1idjw8hDRD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Product B Efficiency} bar of 2016 taller than the 2017 one?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "JPDVeynbaQQNsXhn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Product A Efficiency\" bar of 2020}[0.822, 0.214, 0.894, 0.551] taller than {the \"Product A Efficiency\" bar of 2019}[0.642, 0.238, 0.714, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "KACy9QJrJnjFsa9k",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Product B Efficiency\" bar of 2018}[0.462, 0.551, 0.534, 0.842] have a higher value than {the \"Product B Efficiency\" bar of 2017}[0.282, 0.551, 0.354, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "DpFzrYyi6ki1os3j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.643, 0.551, 0.894, 0.773]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two objects: \n1. {The \"Product A Efficiency\" bar of 2019}[0.642, 0.238, 0.714, 0.551], representing a value of 65.4%. \n2. {The \"Product B Efficiency\" bar of 2019}[0.642, 0.551, 0.714, 0.773], representing a negative value shown as 34.6%."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "1GGaT7Zv7XIXA8Aw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.282, 0.551, 0.714, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there are two Efficiency bars for the year 2017:\n1. {The \"Product A Efficiency\" bar}[0.282, 0.273, 0.354, 0.551], which shows a value of 58.1%.\n2. {The \"Product B Efficiency\" bar}[0.282, 0.551, 0.354, 0.820], displaying a negative value of 41.9%."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "WHNybEP3ilC9AKk7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.743, 0.037, 0.771, 0.053]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a color handle in pink that corresponds to {Product B Efficiency}[0.782, 0.035, 0.921, 0.058] in the legend."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "Z1je2vDR9IZDofWr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at the coordinates [0.109, 0.118, 0.883, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Comparison of Growth Rates in Digital Content and SEO\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "u75Qix14LZrudnT0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at the coordinates [0.025, 0.339, 0.048, 0.738] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is labeled as \"Digital Content Growth\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "JhRa0ds4jwvTvVPm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the figure represent with the coordinates [0.952, 0.435, 0.975, 0.643]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the y-axis label for subplot 1, called \"SEO Growth\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "1KfUCkTrBUWnPyA9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at the coordinates [0.189, 0.060, 0.455, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text in the legend for subplot 0, named \"Digital Content Growth\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "l1caNgw05WymHm0E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791] higher or lower than {the bar represents \"Podcast\"}[0.349, 0.224, 0.433, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "4hjQzjPpuYq2bDcq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791] depicts lower growth rate than {the bar represents \"Graphic Design\"}[0.769, 0.572, 0.853, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "pya73bYXvh6L5bo0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar represents \"Video Prod.\"}[0.139, 0.572, 0.223, 0.791] depict a higher SEO Growth rate than {the bar represents \"Podcast\"}[0.349, 0.572, 0.433, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "ofECXag9ERZ4UuaO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars representing \"Video Production\" and \"Graphic Design\" which has a higher {growth rate}<{the bar represents \"Video Production\"}[0.139, 0.295, 0.223, 0.791], {the bar represents \"Graphic Design\"}[0.769, 0.572, 0.853, 0.696]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Production."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "wdnhwZAr7fPVOTTo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the content in the region [0.346, 0.927, 0.436, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a category label on the x-axis for subplot 0 and subplot 1, specifically, the label \"Podcast\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "cLdJd2HYvxAn48fO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the key elements within the area [0.115, 0.046, 0.464, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the rectangle representing the color of Digital Content Growth located at [0.124, 0.063, 0.170, 0.087], and the corresponding label inscription \"Digital Content Growth\" at [0.189, 0.060, 0.455, 0.095]."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "mtLSgeRERNVCURer",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.524, 0.927, 0.678, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, you'd find an x-axis label associated with both subplots, which reads \"Content Writ.\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "9uOJSxRbFojGjFaT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the region [0.905, 0.160, 0.934, 0.195] includes?"
+            },
+            {
+                "from": "gpt",
+                "value": "1. Label \"60\" representing an y-axis tick for subplot 1 at [0.905, 0.160, 0.934, 0.195]. This label indicates a 60-point value in SEO growth."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "9tyMmNiIbM18kjBA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.025, 0.311, 0.048, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label which is \"Urban Development Index\"."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "KB1brkNiWUZhOyFP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in the sublegend in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.582, 0.060, 0.876, 0.095]"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "sM20vUyLvijtnl1z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label say at [0.366, 0.118, 0.634, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It says \"Urban : Rural = 2:0\"."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "sa8UTt7Nzfm685Vz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found at [0.139, 0.539, 0.345, 0.703] and [0.655, 0.539, 0.861, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are two bars representing the Rural Development Index for the years 2018 and 2020 respectively."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "eHNvTKl9gae2rkPr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 2 categories, which one has the higher index in 2020: {Urban Development Index}[0.655, 0.265, 0.861, 0.539] or {Rural Development Index}[0.655, 0.539, 0.861, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural Development Index."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "1bGexvHKQOoe0AS2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Urban Development Index of 2018}[0.139, 0.338, 0.345, 0.539] higher than the {Rural Development Index of 2018}[0.139, 0.539, 0.345, 0.703]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "0Cy3IyYsmHXNS9tt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the indices, which category improved more from 2018 to 2020: {Urban Development Index} [0.139, 0.338, 0.345, 0.539] [0.655, 0.265, 0.861, 0.539] or {Rural Development Index} [0.139, 0.539, 0.345, 0.703] [0.655, 0.539, 0.861, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development Index."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "3GzICuradNFannhJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the coordinates [0.508, 0.046, 0.885, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is an item in the sublegend; it has a label \"Rural Development Index\" located at [0.582, 0.060, 0.876, 0.095], and an associated handle, which is pink, at [0.517, 0.063, 0.564, 0.087]. It signifies that the Rural metric is depicted in pink."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "bvO59FoBVRgN88rg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat area does the coordinates [0.213, 0.927, 0.272, 0.962] and [0.729, 0.927, 0.787, 0.962] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the X-axis labels, which show the years 2018 and 2020, respectively."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "Q14mGmtG1p8IyWtb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the area [0.103, 0.174, 0.897, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified area includes the overall axes frame of the chart. This also includes all the bars representing the Urban and Rural Development Indices for the years 2018 and 2020, the grid lines, Y-ticks, and X-ticks."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "YG3j9AdwoDDlZwCF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plotted data on the chart with coordinates [0.103, 0.174, 0.874, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Health Membership and Outdoor Sports data for the years 2017, 2018, 2019 and 2020."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "kWQUHVEuLjYFoxc5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label of the first bar that represents the Outdoor Sports category located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.154, 0.876, 0.205, 0.911]"
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "9D58g0ggXo1qiBob",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat group title is indicated by the text in the coordinate [0.189, 0.060, 0.415, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The term is Health Membership."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "2iIhDPcUDnt2v2wK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {the coordinate} [0.058, 0.369, 0.087, 0.404] along the y-axis of the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is marked as 85."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "llb15iU0PLSqo2yt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"{Health Membership bar in 2017}\"[0.138, 0.591, 0.221, 2.153] higher or lower than the \"{Outdoor Sports bar in 2017}\"[0.138, 0.558, 0.221, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "Xcaa4fLPaCRoxWyB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, did the \"{Outdoor Sports Membership}\"[0.757, 0.558, 0.839, 0.872] increase or decrease compared to the \"{Health Membership}\"[0.757, 0.570, 0.839, 2.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "c0lIBFl8ZsjWknRv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"{Health Membership in 2018}\"[0.344, 0.549, 0.427, 2.153] and \"{Outdoor Sports in 2019}\"[0.550, 0.558, 0.633, 0.899], which bar has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Outdoor Sports in 2019."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "qdQ4YpdhMT87oPlJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bars for 2019, is the \"{Health Membership}\"[0.550, 0.466, 0.633, 2.153] higher or lower than the \"{Outdoor Sports}\"[0.550, 0.558, 0.633, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "i3MN5LOwJOG3Mism",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the details featured in the region [0.115, 0.046, 0.424, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend for the Health Membership category. The blue color label is placed at [0.124, 0.063, 0.170, 0.087] and the corresponding text label \"Health Membership\" is located at [0.189, 0.060, 0.415, 0.095]."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "Ugt1tvYOz38nUi8U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the span of [0.138, 0.558, 0.633, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains two bar charts for the years 2017 and 2019. The 2017 bar charts depict Outdoor Sports with its information located at [0.154, 0.876, 0.205, 0.911]. The 2019 bar charts depict the Outdoor Sports with its information located at [0.566, 0.891, 0.617, 0.926]."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "izM5b3xmN9gnoauh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the configured region {at} [0.103, 0.174, 0.874, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It covers bar charts for the Health Membership and Outdoor Sports, with respective categories and data values for the years 2017, 2018, 2019 and 2020."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "4RTm7cqIX0dWzoUF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's being shown in the coordinates [0.952, 0.407, 0.975, 0.671]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the ylabel for the second aka bottom subplot, which refers to the \"Outdoor Sports\" category."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "oOxS8IvtGq7J71V0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's depicted by the object at [0.087, 0.889, 0.102, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the y-axis label for the 'Transportation' metric, which is at 0."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "jYoxOZNQkUYab83K",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the object at [0.153, 0.174, 0.235, 0.748]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a bar representing the metric 'Logistics' for 'Vehicles'. The transportation percentage in logistics industry for 'Vehicles' is 55.1%."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "19Z4Wi4ohaeFiHMU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.768, 0.060, 0.868, 0.095] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label denotes {the chart's 'Logistics'[0.768, 0.060, 0.868, 0.095]} measurement."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "tJJ2VV17YVbPF3It",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing logistics in 'Vehicles'}[0.153, 0.174, 0.235, 0.748] longer than {the bar representing logistics in 'Ships'}[0.359, 0.174, 0.441, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "XVVOqdXOVNZtzzbw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison to {the bar representing logistics in 'Trains'}[0.565, 0.174, 0.648, 0.488], what is the length of {the bar representing logistics in 'Aircraft'}[0.771, 0.174, 0.854, 0.591]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "ceVB1BMbd970FY49",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar representing transportation in 'Vehicles'}[0.153, 0.406, 0.235, 0.903] or {the bar representing logistics in 'Vehicles'}[0.153, 0.174, 0.235, 0.748]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing transportation in 'Vehicles' is taller."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "PlnSRWtIhKv9L935",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what is contained in the region [0.694, 0.046, 0.877, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region specified, there is the color-label pair for 'Logistics' in the legend. The color box indicating 'Logistics' color is at [0.703, 0.063, 0.749, 0.087], and the text for 'Logistics' in the legend is at [0.768, 0.060, 0.868, 0.095]."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "iZodv7pGuHphnApI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified inside the region [0.139, 0.063, 0.185, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a color box from the legend, which is black in color and denotes 'Transportation'."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "kuEShxf2e5zzXxEh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content inside the region [0.118, 0.174, 0.889, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the whole chart area with all bars and labels for both 'Transportation' and 'Logistics' metrics. The bars representing 'Transportation' in the 'Transportation' and 'Logistics industry' are black while those representing 'Logistics' are pink. The y-axis has two parts: the left part, with black labels, represents 'Transportation', and the right part, with pink labels, represents 'Logistics'. The x-axis contains labels for 'Vehicles', 'Ships', 'Trains', and 'Aircraft'."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "bHtrAcjsKb3T8Wpj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.366, 0.130, 0.645, 0.160] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "It says \"Social Media Data Analysis\"."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "jMN2NVEF23bJ85cJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object at coordinates [0.629, 0.037, 0.664, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#dc9dae', which resembles a pink shade."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "SYxNSs32tpRkvjex",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.796, 0.689, 0.885, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar for \"Snapchat\" showing the average usage time as 80.2 minutes."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "YPEIK3brCM6Xe3p5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.104, 0.037, 0.139, 0.053] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A color label in the legend for 'Average Usage Time', which is color '#404346', a dark grey shade."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "G46dax1AB1Oy4uni",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Instagram} [0.349, 0.345, 0.439, 0.935] taller than the bar for {Twitter} [0.126, 0.467, 0.215, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "PkwdkTWzeDibTkZr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for {Twitter} [0.126, 0.167, 0.215, 0.522] have a higher y-end coordinate than the bar for {Snapchat} [0.796, 0.167, 0.885, 0.338] in terms of post engagement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "vRXXIHDWbllXinJQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for {Instagram} [0.349, 0.345, 0.439, 0.935] and {Facebook} [0.572, 0.240, 0.662, 0.935] of the same height?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "tgoy5jPXL6dUdYZz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the bar representing {Instagram}[0.349, 0.345, 0.439, 0.935] or the bar representing {Snapchat}[0.796, 0.689, 0.885, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "mr2vM67dRw9ENShD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the area [0.622, 0.025, 0.914, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area [0.622, 0.025, 0.914, 0.067], there are two objects:\n1. A text label for \"Average Post Engagement\" located at [0.678, 0.035, 0.907, 0.058].\n2. A color label (in pink '#dc9dae') indicating the color of bars for \"Average Post Engagement\", with its coordinates at [0.629, 0.037, 0.664, 0.053]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "aPlELGXkcgGHpZHC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.126, 0.467, 0.215, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.126, 0.467, 0.215, 0.935] contains a dark grey bar that represents the average usage time (152.6 minutes) of Twitter. The corresponding text label is situated at [0.123, 0.494, 0.219, 0.518]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "zA8AzZNKLC9uDntQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.796, 0.167, 0.885, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars within the region [0.796, 0.167, 0.885, 0.935]. One bar with coordinates [0.796, 0.689, 0.885, 0.935] corresponds to the \"Average usage time\" for Snapchat (80.2 minutes). The other bar at [0.796, 0.167, 0.885, 0.338] is for \"Average Post Engagement\" (11.1%)."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "hIEbE8WvEKxOYHLy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects in [0.349, 0.345, 0.439, 0.716] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects are two vertical bars. The first one [0.349, 0.345, 0.439, 0.935] is a dark grey bar that represents average usage time of Instagram (192.3 minutes). The second is a pink bar [0.349, 0.167, 0.439, 0.716] representing the Instagram's \"Average Post Engagement\" (35.7%)."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "gX06Q0YPieHR8CQN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text label}<r>[0.114, 0.584, 0.153, 0.608]</r> associated with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value 15.5 for the year 2022."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "p77gcYA4ScIZ0j0g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle inside {the legend}<r>[0.086, 0.020, 0.258, 0.061]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "JF9v0IiE94t2nWEa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.459, 0.036, 0.605]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Growth."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "zW42DJZRInWh65nR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-value \"-25\"}<[0.929, 0.696, 0.957, 0.719]</r> greater than {the y-value \"-30\"}<[0.929, 0.811, 0.957, 0.834]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "1tzbtmiaQWnZYLeM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2025}<r>[0.388, 0.129, 0.424, 0.818]</r> longer than {the bar for 2028}<r>[0.660, 0.129, 0.697, 0.590]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "jT62IV1mKIbb5Aoj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first bar}<r>[0.116, 0.623, 0.152, 0.935]</r> have a smaller value than {the last bar}<r>[0.842, 0.270, 0.878, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "FS3xzdqUNtEED7K9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.077, 0.129, 0.917, 0.935]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the complete chart area with axes, bars for \"Slow Growth\" and \"Unstable Behavior,\" y-ticks from labels \"-35\" to \"-5\" and x-labels from 2022 to 2030."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "calz3Nb4a3xkEcN0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the elements found in <r>[0.077, 0.020, 0.258, 0.061]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a legend item labeled \"Slow Growth\" with a color handle, coordinates for text are [0.142, 0.029, 0.251, 0.052], and for the handle are [0.093, 0.031, 0.128, 0.047]."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "u1tGw7bqZXCgYWTT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are presented in <r>[0.077, 0.929, 0.957, 0.950]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are y-tick labels indicating negative values, specifically, -30, -25, -20, -15, -10, and -5, along with their corresponding tick marks."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "Q5BXruoW0M09akiB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.025, 0.430, 0.048, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label, called \"Global Trade\"."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "T9KqVvHH6oS7c16O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what the coordinates of the item in the sublegend of subplot_1 is?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the item in the sublegend of subplot_1 is [0.570, 0.060, 0.883, 0.095]."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "3wiVMU87X3met0CT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label attached to the coordinates  [0.144, 0.539, 0.351, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar label, reading -2.6% for \"Global Trade\" in 2020."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "u8w7v0mPGJftDZtO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color assigned to the handle at the coordinates [0.129, 0.063, 0.175, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of {the handle}<r>[0.129, 0.063, 0.175, 0.087]</r> is '#404346' or dark grey which represents \"Global Trade\"."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "njJxvZ179Ye2ej9m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 2020 Global Trade trend, is the growth value positive or negative, as indicated by {the bar}[0.144, 0.539, 0.351, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "ZN3uTBpqABEwZfSe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 2020 and 2021 data for {Supply Chain Management}[0.144, 0.539, 0.351, 0.641] and [0.661, 0.480, 0.867, 0.539], which year saw a decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "D3cColrmiFKKAopa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, does the bar for 2021 Global Trade {bar}[0.661, 0.269, 0.867, 0.539] show an increase or decrease compared to the 2020 bar {bar}[0.144, 0.539, 0.351, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "Cq7RJBEPrxpKj0Bq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Global Trade} in 2020<r>[0.144, 0.539, 0.351, 0.728]</r> and {Supply Chain Management} in 2020<r>[0.144, 0.539, 0.351, 0.641]</r>, which one showed a higher growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Supply Chain Management."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "kkwJ2IfnaxWaDVtp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.661, 0.269, 0.867, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar that represents the Global Trade trend value for 2021 of 3.7%."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "kiQcPekTUdA5XXId",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.108, 0.174, 0.903, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the main plot area for subplot_0, showing bars for Global Trade trends in 2020 and 2021 with their corresponding growth rates. There's also vertical gridlines to help assessment of values."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "WJSsWmI7nXULYysb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the objects found in the coordinates [0.108, 0.174, 0.903, 0.903] in subplot_1."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses all the plot elements for Supply Chain Management trends, including bars for two years with their heights representing growth rates respectively and y axis with labels. The horizontal gridlines aid in value comparison."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "XX9FdUKYDMV2LoNv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region outlined by [0.119, 0.046, 0.348, 0.108] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The outlined region contains a sub-legend, which provides a colored handle and a label titled \"Global Trade\". This legend's color_decoder is grey, corresponding to the color of the \"Global Trade\" bars."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "wFkfZ5l6JaFrONkj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label at the y-axis}<r>[0.008, 0.084, 0.016, 0.217]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score (%)."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "4Pqlgd1nUryGb3qP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is displayed above {the bar for Astra-778E in the Creative Destruction plot}<r>[0.929, 0.099, 0.951, 0.270]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "71.6."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "L4GOmeExkJItKTCn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of innovation does {the subtitle in the region}<r>[0.130, 0.342, 0.237, 0.357]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Incremental Innovation."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "OSHMXIRg3uD56svd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for MatrixQ-999R in Incremental Innovation}<r>[0.295, 0.399, 0.317, 0.599]</r> taller than {the bar for AIBot-983X in the same chart}<r>[0.050, 0.397, 0.072, 0.599]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "gQlkO6IJeFL8aN67",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the score for technological leap by SiliconM-112L}<r>[0.875, 0.382, 0.896, 0.599]</r> exceed {the maximum y-tick label in the same subplot}<r>[0.680, 0.356, 0.695, 0.367]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "v0cZqntRHuhRLH7e",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the values for Fradulent Patents by NeuraVista-6Z and MindfulB-66X}<r>[0.241, 0.928, 0.263, 0.928], [0.132, 0.927, 0.154, 0.928]</r> identical?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "TiwIXFryghFOMuLk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information within {the region marked from top left to bottom right for the Incremental Innovation subplot}<r>[0.037, 0.360, 0.331, 0.599]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains bars for different models where each bar's height represents the corresponding performance score in percent for Incremental Innovation. Specific labels and performance centages of models like AIBot-983X, SiliconM-112L, and NeuraVista-6Z are visualized here."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "5k6fiAvAsG0tjTEb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified in {the specified subsection of the subplot for Technological Discontinuity}<r>[0.367, 0.360, 0.661, 0.599]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays multiple vertical bars, each representing a different model's performance in Technological Discontinuity. The bars are colored similarly, placed along an x-axis with model names and y-axis indicating the performance score percentage."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "gwMx3k61p5LNPSij",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOutline the visible elements in {the top right subplot area themed Creative Destruction}<r>[0.698, 0.031, 0.992, 0.270]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "In this section, various bars represent the scores obtained by models in Creative Destruction. Each bar has a score displayed on top, situated against labels for the models and scaled by performance percentage indicators on y-axis. The backdrop is neutral, assisting in visually emphasizing the bar graphs."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "fwo3IqfeGOWZkBXz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the subplot labeled \"Oil\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Capacity (TWh), located at {the label}<r>[0.015, 0.397, 0.029, 0.598]</r>."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "c1mcTbFcLcCVGLBK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fifth x-axis tick label for the \"Hydro\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019, displayed at {the tick label}<r>[0.897, 0.628, 0.918, 0.654]</r>."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "TOiHSrJp5BIpuRq5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the label for bars representing the year 2020 in the \"Geothermal\" energy subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is found at {the label}<r>[0.952, 0.936, 0.963, 0.947]</r>."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "djRsa9IjA4ZeX9Hh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first bar of Solar energy}[0.086, 0.292, 0.118, 0.294] shorter than {the first bar of Nuclear energy}[0.743, 0.245, 0.775, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "HrekwxXhIjatJlwR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing Bio Mass in 2018}[0.205, 0.942, 0.237, 0.949] have a lower value than {the bar representing Coal in the same year}[0.534, 0.937, 0.566, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "69xmFTgU31lIZ9tg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the energy values for {Hydro in 2015}[0.743, 0.558, 0.775, 0.621] greater than those for {Gas in 2020}[0.613, 0.376, 0.645, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "daojQhLIvok29DGs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within {the y-axis region of the Coal subplot}<r>[0.343, 0.724, 0.357, 0.925]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains various energy capacity labels denoted in TWh from 0 to 9600 in increments, specifically distinguished by their top-right coordinates like {the label for '4800'}<r>[0.363, 0.832, 0.398, 0.849]</r>."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "Y3riZchx09LjZgq9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are seen in {the x-axis region for the Wind energy subplot}<r>[0.403, 0.628, 0.657, 0.654]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses x-axis tick labels for years from 2015 to 2020, detailed at positions such as {the label for '2017'}<r>[0.489, 0.628, 0.510, 0.654]</r>."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "XhcRluRY6rjq87JT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the elements inside {the range labeled Solar bar chart}<r>[0.075, 0.047, 0.328, 0.294]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a collection of energy production bars for Solar from 2015 to 2020, with individual bars pointed out like {the bar for 2018}<r>[0.205, 0.290, 0.237, 0.294]</r> which indicates the energy capacity."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "cAKDKVNqqZHOH9rs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform's data is represented by the bar with coordinates [0.145, 0.170, 0.200, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "fSov9UCC2P9O10XH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me the number associated with {LinkedIn's bar}<r>[0.418, 0.240, 0.473, 0.293]</r> from Q1?"
+            },
+            {
+                "from": "gpt",
+                "value": "15."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "mLl4a4dxINEfw4ED",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.015, 0.075, 0.029, 0.264]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Active users (Millions)."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "90pOg64dDo1x5WXO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Instagram}<r>[0.213, 0.187, 0.268, 0.293]</r> in Q1 higher than {the bar for Reddit}<r>[0.350, 0.257, 0.404, 0.293]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "QCpXitPebszDOp6r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a larger value in Q2, {Twitter}<r>[0.774, 0.152, 0.829, 0.293]</r> or {Reddit}<r>[0.842, 0.222, 0.897, 0.293]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "hH1lMSzDegsBlJXm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Q4, does {LinkedIn}<r>[0.911, 0.560, 0.965, 0.637]</r> have a greater value than {Reddit}<r>[0.842, 0.588, 0.897, 0.637]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "j8zn4J55gy420YTV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart elements in the region <r>[0.272, 0.299, 0.520, 0.342]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {Facebook's label}<r>[0.138, 0.299, 0.172, 0.342]</r> and its corresponding data bar for Q1 with a value of 35 at {bar coordinate}<r>[0.145, 0.170, 0.200, 0.293]</r>."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "Xz1HtuWnFkkq3Fet",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are depicted within the region <r>[0.050, 0.200, 0.250, 0.350]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes {the ylabel}<r>[0.015, 0.075, 0.029, 0.264]</r> denoting \"Active users (Millions),\" and a section of the y-axis with {ticks and labels}<r>[0.057, 0.082, 0.052, 0.303]</r> indicating values from 0 to 70 million."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "sCab7Aw0XRVnYuRR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region <r>[0.550, 0.180, 0.780, 0.330]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses {Instagram's label}<r>[0.697, 0.299, 0.733, 0.344]</r> for Q2 and {Twitter's label}<r>[0.775, 0.299, 0.801, 0.331]</r> with associated data bars for their respective active users at {Instagram's bar coordinate}<r>[0.706, 0.205, 0.760, 0.293]</r> and {Twitter's bar coordinate}<r>[0.774, 0.152, 0.829, 0.293]</r>."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "LaGaYqRYdwZqvxyp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {the coordinates}[0.843, 0.683, 0.864, 0.749] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the ylabel}<r>[0.672, 0.771, 0.686, 0.878]</r> for the subplot 'Digital Transformation'."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "MII4YHTLUHVjYr4o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the text at {the coordinates}[0.421, 0.503, 0.431, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the {2016 value}<r>[0.421, 0.503, 0.431, 0.514]</r> for the metric 'Work-Life Balance' matching with {Work-Life Balance\u2019s bar labeled 2016}<r>[0.406, 0.518, 0.446, 0.787]</r>."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "nl7O6JPQ2Y0xihyF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at {the coordinates}[0.177, 0.792, 0.217, 1.114] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "That bar represents {the value for 2018}<r>[0.177, 0.792, 0.217, 1.114]</r> in the 'Diversity & Inclusion' subplot."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "uQHtg30s81jdePDe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing '2017'}[0.456, 0.845, 0.496, 1.114] taller than {the bar representing '2018'}[0.506, 0.833, 0.545, 1.114] in the subplot 'Digital Transformation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "0uh8AOGtkq9uiD7F",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 90 value labels}[[0.092, 0.400, 0.103, 0.411], [0.291, 0.400, 0.302, 0.411]] all located in {the upper half of their respective subplots}[[0.066, 0.701, 0.328, 0.825], [0.394, 0.701, 0.657, 0.825]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "dTLwejMO0S3mQg7X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the text color for {the y-axis labels}[0.700, 0.942, 0.718, 0.959] the same as that for {the bar values}[0.749, 0.462, 0.760, 0.473] across subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "Pc0cf4v2Mj96MdUC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within {the plotted region}[0.394, 0.701, 0.657, 0.949]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes: \n1. The subplot label 'Digital Transformation'.\n2. Y-axis marked with values ranging from 40 to 100.\n3. X-axis representing years 2016 to 2020.\n4. Blue colored bars showing incremental trends for the years within 'Digital Transformation'."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "K2h76MgHX8r3HI1X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region spanning from {coordinates}[0.066, 0.374, 0.328, 0.621] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains: \n1. The subplot label 'Leadership Development'.\n2. Y-axis with values labeled from 40 to 100.\n3. X-axis denoting the years between 2016 and 2020.\n4. Bars representing values for these years concerning 'Leadership Development'."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "mfUCHWuOik47Yxya",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the area between {coordinates}[0.066, 0.701, 0.328, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area showcases:\n1. The subplot title 'Diversity & Inclusion'.\n2. Y-axis values from 40 to 100.\n3. X-axis year markers from 2016 to 2020.\n4. Blue bars corresponding to values from 2016 to 2020 for 'Diversity & Inclusion'."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "X3ElIbfQB1DHwtTK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.015, 0.145, 0.029, 0.307]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Patents."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "qDauVfRCGTmUvePF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar at the bottom left}<r>[0.094, 0.064, 0.193, 0.405]</r> in the subplot labeled 'Artificial Intelligence'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Brown."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "cJMX31llFxuJuI4a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat numerical value is associated with {the highest bar in the '5G Technology' subplot}<r>[0.094, 0.554, 0.193, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "55220."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "CEd4LbyS1wyu7yjW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Patent Applications' in 'IoT'}<r>[0.423, 0.554, 0.522, 0.896]</r> taller than {the bar for 'Patent Applications' in '5G Technology'}<r>[0.094, 0.554, 0.193, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "pMV5Y96DG1hynJWa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Big Data' subplot show a greater number for {Patents Granted}<r>[0.875, 0.712, 0.974, 0.896]</r> compared to {Patents Granted in the 'IoT' subplot}<r>[0.547, 0.713, 0.646, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "pXJyEKOEvtDsEcRs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label corresponding to the lower tick mark on the y-axis}<r>[0.691, 0.580, 0.735, 0.598]</r> higher in value than {the second lowest tick mark on the y-axis in the 'IoT' subplot}<r>[0.363, 0.640, 0.407, 0.658]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "uIpoKSSGlEkkeMZV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the significant elements and their arrangement in the subplot area labeled 'Blockchain' <r>[0.412, 0.047, 0.657, 0.405]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Blockchain' subplot contains a gray background, two brown bars representing 'Patent Applications' and 'Patents Granted', respectively. The bars are labeled with numbers positioned just below the top edge of each bar. Additionally, the y-axis shows multiple tick marks with values, and the x-axis has labels positioned at an angle."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "qJ4m8Crfb3pFGqKf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a detailed description of what is contained in the bounding box from [0.083, 0.537, 0.328, 0.896] that includes the '5G Technology' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pair of bars - one for 'Patent Applications' and one for 'Patents Granted'. It has labeled axes where the y-axis indicates the 'Number of Patents' and tick marks with corresponding numerical values. The bars also have numerical annotations directly above them signifying the exact values they represent."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "CklCcoGZxbgEkaJW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the contents across the area [0.740, 0.537, 0.985, 0.896] for the 'Big Data' subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region highlights the 'Big Data' category, featuring a panel with numeric labels on the y-axis corresponding to patent counts and two bars indicating 'Patent Applications' and 'Patents Granted', each annotated with their respective numerical values at the top. The x-axis has labeled ticks for each bar, angled for better readability."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "ZK6yua5cMg2AUYOA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is located at [0.080, 0.218, 0.100, 0.256]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar that denotes the return rate {GreenOak in 2010's bar}<r>[0.080, 0.218, 0.100, 0.256]</r>."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "7QAi2yY5Nwm6otwu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the element at [0.855, 0.334, 0.855, 0.334] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents {the x-axis label}<r>[0.855, 0.334, 0.855, 0.334]</r> which is not labeled with any text."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "eg3pKXIoxy7xWrhy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the text label shown at coordinates [0.161, 0.673, 0.236, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this is the subtitle {Cerberus}<r>[0.161, 0.673, 0.236, 0.695]</r>."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "gaGvClaOTHelEMPg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of 2013 from Fortress}<r>[0.481, 0.134, 0.500, 0.256]</r> higher than {the bar of 2013 from GGP}<r>[0.809, 0.199, 0.829, 0.256]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "P2lPz3wWuprx389D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data presentations of {Fortress}<r>[0.397, 0.047, 0.657, 0.294]</r> and {GGP}<r>[0.725, 0.047, 0.985, 0.294]</r> utilizing similar colors?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "aptUgBjv4RuKfrAb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for 2017}<r>[0.577, 0.161, 0.597, 0.256]</r> in Fortress's panel have a larger value than {the bar for 2017}<r>[0.906, 0.142, 0.925, 0.256]</r> in GGP's panel?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "IqYftq9mBIC4S7Ya",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contains the coordinates [0.397, 0.374, 0.657, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot area for {Carlyle}<r>[0.397, 0.374, 0.657, 0.621]</r> which includes a performnance chart of annual return rates ranging from 2010 to 2019, displayed using bars. Each year's performance is denoted by a vertical bar and the y-axis is labeled with percentage values in increments of 10."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "nxPu168YFgxKaCkg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.069, 0.047, 0.328, 0.294]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region {GreenOak's subplot}<r>[0.069, 0.047, 0.328, 0.294]</r> includes a small chart displaying annual return rates from 2010 to 2019 with a grey grid background, and values scaling from -10 to 30 in increments of 10, as observed on the y-axis."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "3km216BbZBtNyfmq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the features in the coordinates [0.069, 0.374, 0.328, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section of {Blackstone's subplot}<r>[0.069, 0.374, 0.328, 0.621]</r> contains a bar chart spanning years 2010 to 2019, illustrating return rates with the y-axis scaling from -10 to 50 in increments of 10. Each year's return is represented by vertical bars, all set against a light grey pane."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "rWrFnr98cM0i8yiP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the text located within {the bounding box}<r>[0.299, 0.213, 0.325, 0.224]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value 60.5%."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "3bqxbFA4boMMNYBJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing \"Hydrogen\" in the lower part of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Blue}<r>[0.534, 0.301, 0.602, 0.508]</r>."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "DWjlx3kXqCBVqncw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the label at {this position}<r>[0.870, 0.913, 0.909, 0.961]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smart Grid."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "LocqTmY3CXDicrz3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar labeled \"Hydro Power\" higher in the {upper}<r>[0.278, 0.606, 0.346, 0.999]</r> or {lower}<r>[0.278, 0.229, 0.346, 0.508]</r> subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upper."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "T5pyMDnL6RVMJZec",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the bar for \"Solar Power\" or the bar for \"Geothermal\" in the {Consistent expansion}<r>[0.108, 0.673, 0.176, 0.999], [0.363, 0.635, 0.432, 0.999]</r> subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Power."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "n3xJAoipt9D150Vh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the \"Electric Vehicles\" bars in the {Slow recovery}<r>[0.619, 0.367, 0.687, 0.508]</r> and {Consistent expansion}<r>[0.619, 0.577, 0.687, 0.999]</r> subplots have the same color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "90rfIt2FcHm18dTJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area {spanning}<r>[0.066, 0.537, 0.985, 0.907]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subplot background for the Consistent expansion scenario, including performance score labels and the bars representing different energy technologies."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "80Ee6yZzrbhiYtVs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements found within the {bounding box}<r>[0.100, 0.422, 0.909, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis category labels for technologies such as \"Solar Power\", \"Wind Power\", up to \"Smart Grid\", with an angle of 45 degrees."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "M5J50995kpDbLU7s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key features in the {specified region}<r>[0.035, 0.040, 0.061, 0.548]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis labels for percentage values from 20% to 100% placed alongside the corresponding tick marks."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "8fFaDGI43ybPIIc1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the first bar in the subplot for \"Emails processed\"}<r>[0.094, 0.218, 0.119, 0.445]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "uUtYfnEm3nWwcePR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label text positioned at <r>[0.198, 0.509, 0.369, 0.532]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Customer feedbacks."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "3PFzoQtIFn5HHGXS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by {the ylabel}<r>[0.508, 0.662, 0.522, 0.808]</r> in the subplot for \"New contracts\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Process Quantity."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "QxrZWrL04Y1Ju2xh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar corresponding to \"Month 1\" in \"Emails processed\"}<r>[0.094, 0.218, 0.119, 0.445]</r> taller than {the bar corresponding to \"Month 1\" in \"Help tickets solved\"}<r>[0.586, 0.275, 0.612, 0.445]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "Vaaf6Nr76K8lhV1O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Month 6\" in the subplot of \"Customer feedbacks\"}<r>[0.255, 0.918, 0.280, 0.936]</r> represent a higher value than {the bar for \"Month 6\" in \"New contracts\"}<r>[0.747, 0.929, 0.773, 0.936]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "3IIDLQR92A3iO4MU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the final bar in the \"New contracts\" subplot}<r>[0.940, 0.926, 0.966, 0.936]</r> positioned to the right of {the final bar in \"Customer feedbacks\"}<r>[0.448, 0.910, 0.474, 0.936]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "GAifJn1DUk4czFwN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the organization of elements within the region <r>[0.567, 0.047, 0.985, 0.442]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a subplot for \"Help tickets solved\", including a range of blue bars representing monthly data points from \"Month 1\" to \"Month 12\", labels for each month positioned below the bars, and quantification labels for y-axis along the left side."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "l7Oqkj1eeWnA5K1U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the bounded area <r>[0.075, 0.537, 0.493, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section shows the subplot for \"Customer feedbacks\" which includes vertical blue bars representing data across twelve months, month labels at the bottom, process quantity labels on the left side, and a subtitle at the top identifying the chart's focus."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "9X3ddQiFqFJLwMux",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what information is contained within <r>[0.567, 0.537, 0.985, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the \"New contracts\" subplot. It includes a series of blue bars for each month showing contracted quantities, monthly labels beneath the bars, numerical labels of quantities to the left, and a heading at the top describing the data."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "wDT6YXXMtOlEjbti",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is displayed at {the Y-axis label}<r>[0.015, 0.131, 0.029, 0.331]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score (%)."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "sZJegVy3OU4Q4Te4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the bar label}<r>[0.198, 0.136, 0.217, 0.147]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "80.0."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "LRJaFdWrBAPGNTve",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation of {the x-tick}<r>[0.423, 0.416]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "IPT-3."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "W9DKKxi3HYhVv1Oc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Electra-29b\"}<r>[0.301, 0.124, 0.329, 0.574]</r> taller than {the bar for \"Yan-PaLA\"}<r>[0.829, 0.159, 0.858, 0.574]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "a2K71HwRH53KPPZ4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value label above \"Wi-300b\"}<r>[0.270, 0.125, 0.288, 0.136]</r> higher in numeric value than {the value label above \"Electra-29b\"}<r>[0.306, 0.107, 0.324, 0.118]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "EST5et6GWL0ritVh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Wi-300b\"}<r>[0.265, 0.141, 0.294, 0.574]</r> shorter than {the bar for \"IPT-3\"}<r>[0.901, 0.122, 0.930, 0.574]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "VOKh5uQv7diDH9co",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.558, 0.047, 0.985, 0.416]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes a chart with bars visualizing \"Firewall Breach\" performance scores. Each bar is associated with a tech tool, and their labels range from {Cyberlock-5d}<r>[0.546, 0.422, 0.592, 0.479]</r> to {Gemini Shield}<r>[0.903, 0.422, 0.951, 0.482]</r>, with performance scores displayed above each bar at locations like {for Cyberlock-5d}<r>[0.583, 0.369, 0.601, 0.380]</r> and {for Gemini Shield}<r>[0.942, 0.080, 0.960, 0.091]</r>."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "bidUSS1POuDLoXHY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are present in the region [0.066, 0.537, 0.493, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area covers a chart detailing \"Encrypted Data Leak\" with various bars such as {Cyberlock-5d}<r>[0.085, 0.590, 0.114, 1.065]</r> through {Gemini Shield}<r>[0.444, 0.772, 0.473, 1.065]</r>. Performance scores are indicated with details like the score for {Yan-PaLA}<r>[0.342, 0.714, 0.360, 0.725]</r> displayed above each corresponding bar."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "vhUiBixyJIOJ4WXi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide an overview of the elements in the space [0.558, 0.537, 0.985, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This designated chart area illustrates the \"Darkweb Activity\" data. It includes related technology names from {Cyberlock-5d}<r>[0.546, 0.912, 0.592, 0.970]</r> to {Gemini Shield}<r>[0.903, 0.912, 0.951, 0.972]</r>. Their associated performance labels, for instance, the label for {Yan-PaLA}<r>[0.834, 0.596, 0.853, 0.607]</r> are presented above each bar, reflecting operational effectiveness."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "nDOWM6eRHntynwLt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.131, 0.029, 0.331] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Performance Score (%)."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "dANZSNFq3ATk1Adm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend data is not visibly present within the provided chart, thus no coordinates are available for any legend text."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "ab7bseSuZ5QqqpXq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.234, 0.151, 0.253, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A label displaying the score of 77.0 for 'Mirage-1b-alpha'."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "NpI5lyPMD1qFMY8I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Cyberlock-5d\"}[0.085, 0.337, 0.114, 0.574] taller or shorter than {the bar representing \"IPT-3\"}[0.901, 0.122, 0.930, 0.574]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "oHt6FxPY99mlssQn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar labeled \"30%\"}[0.043, 0.409, 0.061, 0.426] higher or lower on the axis than {the bar labeled \"80%\"}[0.043, 0.145, 0.061, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "LKT0LOwXX8ZkvqOk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the subsidiary titles}{\"Encrypted Data Leak\"}[0.192, 0.509, 0.366, 0.532] and {\"Firewall Breach\"}[0.709, 0.019, 0.835, 0.041] on the same side {left or right} of the central axis dividing the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "Hzjz5K5N3N0aWlX0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.558, 0.537, 0.985, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains bars and labels for 'Darkweb Activity'. The labels along the bottom are 'Cyberlock-5d' to 'Gemini Shield', marked at specific intervals, indicating different cybersecurity models. Performance scores are indicated by vertical bars that indicate the height relative to the score percentage on the left axis."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "fpv9MjbXPZuQlSez",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details about the area [0.066, 0.537, 0.493, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section displays the 'Encrypted Data Leak' part of the chart, containing diverse bars each representing different cybersecurity models like 'Cyberlock-5d', 'Fort-7m', etc., plotted against their performance scores. The percentage scores range from 30 to 100 as marked on the left vertical y-axis."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "JvWAAFMcZg2HN2S4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.558, 0.047, 0.985, 0.416]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses bars depicting 'Firewall Breach' performances of different cybersecurity models such as 'Cyberlock-5d', 'Fort-7m', and others. It also includes x-axis labels for each model and y-axis labels for score percentages."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "tGP1QH6sDpCK7sRI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary color of the bar representing \"Commercial Buildings\" in the \"Business District\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the primary color of the bar representing \"Commercial Buildings\"}<r>[0.857, 0.726, 0.881, 0.955]</r> is blue."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "Gm9RGnzm0mKgIX50",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the \"Countryside\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label for the \"Countryside\" subplot is {Project Volume}<r>[0.015, 0.398, 0.029, 0.528]</r>."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "Oxv6k7rZwi96jYcN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is displayed on the bar for \"Educational Institutions\" in the \"Scenic Area\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value displayed is {70}<r>[0.505, 0.789, 0.515, 0.800]</r>."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "3ZVTxl1bPyCbSw1X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Healthcare Institutions\" in the \"Economic Development Zone\"}<r>[0.292, 0.790, 0.316, 0.955]</r> shorter than {the bar for \"Community Facilities\" in the same zone}<r>[0.261, 0.752, 0.286, 0.955]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "frXzOw3rwtgY8zYk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Commercial Buildings\" in the \"Countryside\"}<r>[0.200, 0.463, 0.225, 0.628]</r> reach higher than {the bar for \"Healthcare Institutions\" in the \"Revitalization Zone\"}<r>[0.620, 0.803, 0.645, 0.955]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "jS0bx1qEW2PdKn7b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more {labels on the y-axis in the \"Suburbs\"}<r>[0.700, 0.218, 0.718, 0.235]</r> than in {the \"Historic\" area}<r>[0.372, 0.545, 0.389, 0.562]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "7sY7chb3HpIp39IM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the y-label}<r>[0.015, 0.018, 0.029, 0.313]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Engagement Score (in thousands)."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "0r8C8O64jOCaR3XS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of grid lines are visible along {the y-axis}<r>[0.015, 0.018, 0.029, 0.313]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed lines."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "4OdIZ4sfSKmXeShy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is indicated by {x-axis label under the first subplot}<r>[0.061, 0.290, 0.105, 0.345]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "S9Ab4F7a9nOBxWQY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar representing Facebook in the third subplot}<r>[0.743, 0.189, 0.781, 0.487]</r> higher than {the bar for Facebook in the fourth subplot}<r>[0.434, 0.565, 0.453, 0.836]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "MBH4nhdcIJHEYMZP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Twitter in the first plot}<r>[0.134, 0.196, 0.173, 0.487]</r> represent a higher value than {the bar for Instagram in the same plot}<r>[0.182, 0.236, 0.221, 0.487]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "vQ1dpLBolN5GOZgU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar for YouTube in the first plot}<r>[0.278, 0.121, 0.317, 0.487]</r> wider than {the same YouTube bar in the fifth plot}<r>[0.935, 0.416, 0.973, 0.836]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "EFOcUGRvZ53jO9vF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within {the region covering engagement scores on y-axis}<r>[0.035, 0.018, 0.070, 0.313]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It displays the engagement scores ranging from 3000 to 6500, each label at intervals of 500, visible respectively at [0.035, 0.277, 0.070, 0.294], [0.035, 0.243, 0.070, 0.260], down to [0.035, 0.074, 0.070, 0.091] for each corresponding score."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "cvKq5D1oKgqr6Ba6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you detail about the area including the y-axis labeling and grid pattern in {the first subplot}<r>[0.015, 0.018, 0.029, 0.313]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains y-axis labels that denote the engagement score in thousands from 3000 up to 6500, paired with visible dashed grid lines that help in reading values across the plot."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "xLpz2yQ81G0refoO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide information regarding the objects placed in {the tick region on the x-axis}<r>[0.105, 0.290, 0.298, 0.345]</r> of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis ticks here include category labels such as 'Facebook', 'Twitter', 'Instagram', 'LinkedIn', and 'YouTube', with precise alignment for defining exact points for data representation along x-axis."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "6YGvnKFlrfj4QBnK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the x-axis label} for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No x-axis label present."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "AJoWkSxntSjTESE6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the subtitle}<r>[0.014, 0.019, 0.249, 0.041]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Theater Performance Rating."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "qWXG7QJftgPZI8Zu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.152, 0.029, 0.352]</r> read?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score (%)."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "QrINREAUpRCTPyqg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar labeled \"Stand-up Comedy\"}<r>[0.120, 0.140, 0.130, 0.869]</r> higher than {the blue bar labeled \"Ballet\"}<r>[0.108, 0.284, 0.118, 0.869]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "zuWVAyjy0e7ADMyq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue bar for \"Opera\"}<r>[0.133, 0.089, 0.142, 0.869]</r> have a higher performance score than {the blue bar for \"Musical\"}<r>[0.084, 0.208, 0.094, 0.869]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "6Njr9gBRMCQp7W84",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis tick labeled \"90\"}<r>[0.043, 0.122, 0.061, 0.140]</r> positioned below {the y-axis tick labeled \"80\"}<r>[0.043, 0.204, 0.061, 0.222]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "nsBaFDBhQ4xAiqNV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in {the first subplot's x-axis labels}<r>[0.023, 0.464, 0.186, 0.558]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The labels on the x-axis include a list of genres from \"Regional Drama\" to \"Storytelling\" with their corresponding x-tick marks."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "DHg503SRHUvzpTxw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted within {the second subplot's y-axis tick labels region}<r>[0.232, 0.040, 0.258, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance score percentage labels ranging from \"50\" to \"100\" with y-axis ticks for each label."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "Pvf9lzMzTYQ7x1et",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what is within {the second subplot's bars and text annotations region}<r>[0.269, 0.138, 0.392, 0.869]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "A series of blue bars showing the art exhibition ratings for different genres, labeled with their corresponding performance score percentages."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "s01bH249s0gRXw8l",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is {the Career Progression bar for Year 3}<r>[0.259, 0.435, 0.290, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d87769."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "JOtunFeompoKGu64",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {tick label on the x-axis for the first value}<r>[0.111, 0.942, 0.154, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 1."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "cYsG5x2XRykkfL7H",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text read at <r>[0.775, 0.870, 0.901, 0.898]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Obstacle Removal."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "Ix8ujKgdR3WZ2oUO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Career Progression bar for Year 6}<r>[0.496, 0.414, 0.527, 0.923]</r> higher than the {Obstacle Removal bar for Year 3}<r>[0.290, 0.075, 0.322, 0.414]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "C2rKH0vdjRgtwD8e",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the color of {the Career Progression label in the legend}<r>[0.074, 0.872, 0.102, 0.892]</r> match the {color of the bars representing Career Progression}<r>[0.101, 0.711, 0.132, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "d3ptXqu9HmfSCKwp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider, the span of {years labeled on the x-axis}<r>[0.111, 0.942, 0.869, 0.970]</r> or the span of {values labeled on the y-axis}<r>[0.043, 0.064, 0.052, 0.940]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "wUxLJZ5xgN9v3nrA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.101, 0.711, 0.843, 0.923]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the bars representing Career Progression for all ten years. Notably, the {Year 1 bar}<r>[0.101, 0.711, 0.132, 0.923]</r> starts from the baseline and extends upwards to indicate a value of 10, and the {Year 10 bar}<r>[0.811, 0.181, 0.843, 0.923]</r> starts from the baseline and extends upwards to indicate the highest value of 35."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "tHGdUThOyFULM0Eg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content within the area <r>[0.843, 0.075, 0.875, 0.923]</r> signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar representing the Obstacle Removal value for {Year 10}<r>[0.843, 0.075, 0.875, 0.499]</r>, which indicates a negative trend with a value of -10, compared to the baseline at {0.0}<r>[0.043, 0.064, 0.052, 0.092]</r>."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "OZrTokWj9t1L3CQo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in <r>[0.035, 0.170, 0.052, 0.516]</r> on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical range captures the y-axis tick labels for the values 35, 30, 25, 20, and 15, suggesting increments of 5 units along the axis marking Career Progression. For example, the label for {25}<r>[0.035, 0.382, 0.052, 0.410]</r> is halfway within this range."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "pfy2ITp9t9Les9qN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.768, 0.102, 0.796, 0.122]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label for {2019 Popularity}[0.807, 0.100, 0.917, 0.128] in the legend."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "dF5KsxSlvd4FUbL9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the value at [0.939, 0.276, 0.965, 0.304] on the y-axis represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value at those coordinates is 500."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "C73BaCX3Tx6AgMXr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label's color is located at [0.083, 0.102, 0.111, 0.122] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label color at those coordinates is for {2018 Popularity}[0.122, 0.100, 0.232, 0.128]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "0b39neIC9hbRg3Qh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2019 Popularity bar for Vintage}[0.500, 0.541, 0.565, 1.347] higher or lower than the {2018 Popularity bar for Vintage}[0.435, 0.499, 0.500, 1.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "VKOjTQumDyHTVhNR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher, {2019 Popularity bar for Streetwear}[0.337, 0.393, 0.402, 1.347] or {2018 Popularity bar for Streetwear}[0.272, 0.626, 0.337, 1.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019 Popularity bar for Streetwear."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "ojbaX8FTjBH65D6i",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {2019 Popularity bar for Gothic}[0.175, 0.297, 0.240, 1.347] shorter or taller than the {2019 Popularity bar for Skater}[0.825, 0.636, 0.890, 1.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "uZSclJsLeOwitpff",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features within the region [0.435, 0.499, 0.565, 1.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there are 2 bars: {2018 Popularity bar for Vintage}[0.435, 0.499, 0.500, 1.347] and {2019 Popularity bar for Vintage}[0.500, 0.541, 0.565, 1.347]. Both represent the popularity of the Vintage subculture for the years 2018 and 2019, respectively."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "1nkfrYtpedbhm2UE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the region [0.272, 0.626, 0.402, 1.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains 2 bars: {2018 Popularity bar for Streetwear}[0.272, 0.626, 0.337, 1.347] and {2019 Popularity bar for Streetwear}[0.337, 0.393, 0.402, 1.347], which both represent the popularity of Streetwear style for the years 2018 and 2019, respectively."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "xOq87sbsw7ue7PxM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the coordinates [0.175, 0.297, 0.240, 1.347]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains 2 bars: {2018 Popularity bar for Gothic}[0.110, 0.287, 0.175, 1.347] and {2019 Popularity bar for Gothic}[0.175, 0.297, 0.240, 1.347]. Both bars represent the popularity of the Gothic subculture for the years 2018 and 2019, respectively."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "ga1Q7PtF92z0uOXI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.971, 0.422, 0.985, 0.575] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called 2019 Sales."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "jKzE1WGjjCWEjEG3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.829, 0.870, 0.905, 0.898]"
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "o3jt8Nk2jGW1mfJ0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.184, 0.942, 0.217, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "zveLBxhdT2ChrCNb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.784, 0.858, 0.911, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2019 Sales}[0.829, 0.870, 0.905, 0.898]."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "aluxIiCwdcqgXnp5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar}[0.360, 0.075, 0.400, 0.801] to the left or right of {the orange bar}[0.600, 0.075, 0.680, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "4MO9dGZvgja4GkCu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Food\"}[0.200, 0.075, 0.280, 0.438] shorter than {the bar representing \"Clothing\"}[0.800, 0.075, 0.880, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "B8HY6USdH4SzGVHN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 2018 Sales bars}[[0.120, 0.075, 0.200, 0.680], [0.320, 0.075, 0.400, 0.801], [0.520, 0.075, 0.600, 0.377], [0.720, 0.075, 0.800, 0.680]] generally taller than {the 2019 Sales bars}[[0.200, 0.075, 0.280, 0.438], [0.400, 0.075, 0.480, 0.620], [0.600, 0.075, 0.680, 0.438], [0.800, 0.075, 0.880, 0.862]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "TlC3RYzgBHYPtN4G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Food\"}[[0.120, 0.075, 0.200, 0.680],[0.200, 0.075, 0.280, 0.438]] and {\"Clothing\"}[[0.720, 0.075, 0.800, 0.680],[0.800, 0.075, 0.880, 0.862]], which category has a higher value for {2019 Sales}<{2019 Sales Food bar}[0.200, 0.075, 0.280, 0.438], {2019 Sales Clothing bar}[0.800, 0.075, 0.880, 0.862]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Clothing."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "k2f8ZTqEOf9UfVv5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.784, 0.858, 0.911, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a color-text label in the sublegend. The color label is located at [0.790, 0.872, 0.818, 0.892], which is blue, and the text label is located at [0.829, 0.870, 0.905, 0.898], which is called 2019 Sales, and they denote that 2019 Sales's bar is blue."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "Kd3Juq3uKFQICYHo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.184, 0.942, 0.829, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.184, 0.942, 0.829, 0.970] contains all label texts on the horizontal axis, which are:\n1. \"Food\": [0.184, 0.942, 0.217, 0.970],\n2. \"Electronics\": [0.362, 0.942, 0.438, 0.970],\n3. \"Books\": [0.579, 0.942, 0.621, 0.970],\n4. \"Clothing\": [0.771, 0.942, 0.829, 0.970]."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "mKSKJbVDeeAjn98n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.400, 0.075, 0.480, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object:\n1. The blue column represents that the value of the \"Electronics\" category in 2019 Sales is -90, and the coordinate range is [0.400, 0.075, 0.480, 0.620]."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "tF6Wm7aOxvPBzlZ4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.600, 0.075, 0.800, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1. Two bars representing Books and Clothing in 2019 Sales. \n2. The bar representing Books is [0.600, 0.075, 0.680, 0.438] and its value is -60.\n3. The bar representing Clothing is [0.800, 0.075, 0.880, 0.862] and its value is -130."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "KE8zLjxLz5BT34ee",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.354, 0.015, 0.646, 0.033] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle of the chart, \"Annual Academic Research Papers in Different Fields\"."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "VAulynAOOyybTId6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the bar that represents the academic papers for 'Biology' in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.580, 0.373, 0.623, 0.875]."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "nR4RmHqiFtJwt546",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the color identifier for '2020 Papers' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates of the color label for '2020 Papers' are [0.861, 0.051, 0.880, 0.061]."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "PNQcjcVRQJ0T0Hz5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2019 Paper bar for Economics}[0.703, 0.135, 0.746, 0.875] higher than the {2020 Paper bar for Economics}[0.746, 0.079, 0.789, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "PeXPvO3SYL068Nss",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Psychology papers in 2019}[0.826, 0.526, 0.869, 0.875] located to the right or left of the {bar for Psychology papers in 2020}[0.869, 0.568, 0.912, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "klcQBjFEI1txSOhJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Mathematics\"}[[0.211, 0.428, 0.254, 0.875], [0.254, 0.401, 0.297, 0.875]] and {\"Biology\"}[[0.580, 0.373, 0.623, 0.875], [0.623, 0.331, 0.666, 0.875]], which field had a larger increase of academic papers from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biology."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "OKz4t43a2Ips0PeO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.047, 0.037, 0.953, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates is a bar chart representing the annual academic research papers in different fields. There are two groups of bar charts in different colors representing the years 2019 and 2020. The x-axis is labeled with 7 different academic fields: Computer Science, Mathematics, Physics, Chemistry, Biology, Economics, and Psychology. The y-axis of this bar chart is representing the number of papers published in the respective years, ranging from 0 to 600."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "8E1HYAiyOvzToOrC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the space in [0.088, 0.107, 0.174, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two vertical bars representing the annual academic papers in the field of Computer Science for the years 2019 and 2020. The green bar on the left with coordinates [0.088, 0.247, 0.131, 0.875] represents 450 papers in 2019. The red bar on the right with coordinates [0.131, 0.107, 0.174, 0.875] represents 550 papers in 2020."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "whGQvUhllSy9VcZa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements or objects within [0.023, 0.032, 0.041, 0.046]."
+            },
+            {
+                "from": "gpt",
+                "value": "This very small region contains only the y-axis label '600', and represents the maximum values of papers published in each field for the years 2019 and 2020."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "1foMeHTmqDbyaOGY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.015, 0.263, 0.029, 0.735] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the ylabel for the first subplot indicating \"Reduced Forest Area (in hectares)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "FyyXDFu9eTEa2EBR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the coordinates [0.636, 0.089, 0.922, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend for the first subplot, explicitly for \"Reduced Forest Area (in hectares)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "NsT3T9m7NmdFCZmu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the set of coordinates [0.681, 0.100, 0.917, 0.128] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text label for the legend entry of \"Reduced Forest Area (in hectares)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "BgAKECL8pMKIFvd4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 2021 bar for \"Reduced Forest Area (in hectares)\" higher or lower compared to the 2020 bar at {[0.126, 0.357, 0.296, 0.923], [0.551, 0.470, 0.721, 0.923]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "vjRUsM0j8QOuFQuR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the \"Reduced Forest Area (in hectares)\" bar at [0.126, 0.357, 0.296, 0.923] with the \"Reduced Carbon Emissions (in million tonnes)\" bar at [0.296, 0.357, 0.466, 0.923], which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Reduced Forest Area (in hectares)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "1EPZTouSDfDmDLSh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two bars for 2021 at the coordinates {[0.551, 0.470, 0.721, 0.923], [0.721, 0.216, 0.891, 0.923]}, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Reduced Carbon Emissions (in million tonnes)\" has a higher value."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "lOw3UZGCEd0yXMuo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area spanned by these coordinates [0.095, 0.089, 0.463, 0.139]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot of the bar chart. The legend specifies that the \"#588b8b\" colored bar represents \"Reduced Carbon Emissions (in million tonnes)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "46vgp8z0EEAs5qrl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the chart elements within the area [0.088, 0.075, 0.929, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of two overlapping subplots. Each subplot displays two bars, one for the year 2020 and the other for 2021. The first subplot quantifies \"Reduced Forest Area (in hectares)\" and the second subplot represents \"Reduced Carbon Emissions (in million tonnes)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "KxblEWPqe7gxsnpq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.636, 0.089, 0.922, 0.139] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the legend for the first subplot indicating that the \"#83a95c\" colored bars show \"Reduced Forest Area (in hectares)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "7M11uGILEWgUcYi0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at this coordinate: [0.235, 0.942, 0.323, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis label, which is {the label 'Urban Living'}<r>[0.235, 0.942, 0.323, 0.970]</r>."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "M5YvuOppVhlWbHdD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at these coordinates: [0.971, 0.340, 0.985, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, {the label 'Population (in millions)'}<r>[0.971, 0.340, 0.985, 0.657]</r>."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "ik7Gws8gRa8S1gXB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color and name of {the legend item}<r>[0.681, 0.100, 0.917, 0.128]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label name is \"Reduced Forest Area (in hectares)\" and the color associated with it is green."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "K6QvxZ0a7yAulAcB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar, {bar 1, year 2025, Urban Living}<r>[0.126, -0.349, 0.279, 0.923]</r> or {bar 2, year 2025, Rural Living}<r>[0.509, -0.208, 0.662, 0.923]</r>, represents a larger population?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar 1, the Urban Living bar for year 2025."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "IQYWELAM8A46bptR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the{Reduced Carbon Emissions bar for Urban Living}<r>[0.356, 0.357, 0.509, 0.923]</r> taller than the {Population bar in 2025 for Urban Living}<r>[0.126, -0.349, 0.279, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "PX1dDNnEz8OWotZE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Population bar for Rural Living in 2030}<r>[0.662, -0.293, 0.815, 0.923]</r> larger or smaller than the {Population bar for Urban Living in 2030}<r>[0.279, -0.547, 0.432, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "lPPMU9XtPW7CjhjR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the region [0.095, 0.089, 0.463, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the chart legend, consisting of three elements: a color denotation for Reduced Carbon Emissions located at [0.101, 0.102, 0.128, 0.122] colored as green, a year denotation for 2025 located at [0.101, 0.144, 0.128, 0.164] colored as blue, and a year denotation for 2030 located at [0.101, 0.186, 0.128, 0.205] colored as orange."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "fKtufGmxkwQP8Vd6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.203, 0.357, 0.356, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar that represents {Reduced Forest Area (in hectares)}<r>[0.203, 0.357, 0.356, 0.923]</r> for Urban Living."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "eQhHtu764dDVAMtk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is illustrated in the region [0.126, -0.349, 0.432, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are represented. The first is the {Population bar for Urban Living in 2025}<r>[0.126, -0.349, 0.279, 0.923]</r> and the second is the {Population bar for Urban Living in 2030}<r>[0.279, -0.547, 0.432, 0.923]</r>."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "IVik3s0p9vVayToE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the object mentioned at the location [0.971, 0.365, 0.985, 0.632] in the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the Y-axis label for subplot_1, denoting '2021 Trade Volume'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "1Kh6gmavG7EOrBHc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color assigned to the '2021 Trade Volume' according to the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#7da1c7' or a specific shade of blue. This color assignment is observed in the legend {sublegend}<r>[0.712, 0.089, 0.896, 0.139]</r>."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "MCeZDYnPUsnZ72qS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinate [0.326, 0.030, 0.674, 0.066] in the chart structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of subplot_1, which states 'International Trade Volume: 2016 vs 2021'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "TVPfGUw3ddQgVnTg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Country 1 '2021 Trade Volume'}<r>[0.211, 0.393, 0.288, 1.770]</r> and {Country 1 '2016 Trade Volume'}<r>[0.134, 0.764, 0.211, 1.770]</r>, which one has shown a higher trade volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 Trade Volume."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "LT5GpB27BoRbHaHd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of '2021 Trade Volume', is {Country 2}<r>[0.404, 0.477, 0.481, 1.770]</r> doing better than {Country 3}<r>[0.596, 0.552, 0.674, 1.770]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "DqK9ED6YThXrUeXC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the '2016 Trade Volumes' for {Country 1}<{Country 1 '2016 Trade Volume'}[0.134, 0.764, 0.211, 1.770]> and {Country 2}<{Country 2 '2016 Trade Volume'}[0.326, 0.848, 0.404, 1.770]> roughly the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "pEbHCOeLYE4HOrjU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe objects located within the coordinates [0.097, 0.075, 0.903, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region defines the coordinate space for each subplot, encapsulating the bar graphs representing trade volumes for different countries in 2016 and 2021. In each subplot, there are axes, bars, labels, ticks for x and y-axes, legends, and subtitles."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "UikyOy79aX4qU1rm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.134, 0.764, 0.288, 1.770]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars. The first bar is the '2016 Trade Volume' for Country 1 at [0.134, 0.764, 0.211, 1.770], in a specific shade of red, '#d87769'. The second bar represents the '2021 Trade Volume' for Country 1, located at [0.211, 0.393, 0.288, 1.770], in a similar shade of blue, '#7da1c7'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "UBHmYDgKTs1aZKWP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinate box [0.326, 0.030, 0.674, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the descriptive title for subplot_1, which reads 'International Trade Volume: 2016 vs 2021'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "85PKI3TAvyZ0iESE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.971, 0.377, 0.985, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label of the 2021 participants."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "nriPUhAzoPJdzNn2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the content of the coordinates [0.364, 0.030, 0.636, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, which reads \"Fitness and Sports Health Trends\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "tXRYCSDiRhcEodDM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the bar at coordinates [0.603, 0.340, 0.685, 1.982]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar for Yoga participants of 2021."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "ZtgLjYU2964lZXFo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Jogging\" for 2020 at [0.110, 0.393, 0.192, 1.982] higher than the bar representing \"Yoga\" for 2021 at [0.603, 0.340, 0.685, 1.982]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "ykhs6VN8bEK4exUF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of the \"Gym\" bar for 2021 at [0.397, 0.817, 0.479, 1.982] greater than that of the \"Jogging\" bar for 2020 at [0.110, 0.393, 0.192, 1.982]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "xLTcT6cZ0xqLxdDa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Swimming\" for 2020 [0.726, 0.287, 0.808, 1.982] taller than the bar for 2021 at [0.808, 0.181, 0.890, 1.982]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "DeZhWugYYoq1RLNt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information in the region [0.364, 0.030, 0.636, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's subtitle, stating \"Fitness and Sports Health Trends\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "tR7Sv5wcqAj8kPz8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.726, 0.287, 0.808, 1.982]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar representing the number of \"Swimming\" participants for the year 2020."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "Avt6XaEqDo9F3HgH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what you see in the area [0.971, 0.377, 0.985, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region I see the label on the Y axis of the chart, reading \"2021 Participants\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "AOcrMctjSdTCtxGO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {Y-axis label}[0.012, 0.389, 0.024, 0.609] for subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020 Market Share."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "O4wbvj0y2xLEAVoU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {bar}[0.092, 0.436, 0.160, 0.935] for Company A in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d87769 (corresponding to some kind of red)."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "vTP1UVcqS6vj5aWL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {Y-axis label}[0.976, 0.389, 0.988, 0.609] for subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 Market Share."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "dmHudgYIB1pUVskA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the bar}[0.330, 0.287, 0.398, 0.935] of subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company B."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "SCKQrJUMQtPuEYgo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 Market Share of Company A}[0.092, 0.436, 0.160, 0.935] greater than {the 2021 Market Share of Company A}[0.160, 0.362, 0.228, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "k07G6QSOYhu4UPhj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 Market Share of Company C}[0.432, 0.187, 0.500, 0.935] equal to {the 2021 Market Share of Company C}[0.500, 0.237, 0.568, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "0e420dOdHiWlfRMr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 Market Share of Company E}[0.772, 0.686, 0.840, 0.935] smaller than {the 2021 Market Share of Company E}[0.840, 0.561, 0.908, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "IRy68NVViBrcgAXJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {2020 Market Share of Company B}[0.262, 0.312, 0.330, 0.935] and {2021 Market Share of Company B}[0.330, 0.287, 0.398, 0.935], which year saw Company B having a higher market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "2pdf3hmb236zvYCI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.052, 0.062, 0.948, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "It forms the main plotting area of the comparative bar chart of 2020 and 2021 Market Shares for Companies A, B, C, D, and E. It contains grey background colour. It also contains the x-ticks representing the labels of the 5 Companies and y-ticks representing the range values from 0 to 35. In the subplot_0, there are the bars indicating the market share in 2020. In the subplot_1, there are the bars indicating the market share in 2021."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "mEdhQtWm3Y2IULfZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be seen in the area [0.791, 0.882, 0.943, 0.924]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the 2021 Market Share with a corresponding colour indicator of #7da1c7 (which is a shade of blue) and a text indicating \"2021 Market Share\"."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "l0CZkjGu8uf3egSH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted within the region [0.262, 0.287, 0.398, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2021 Market Share of Company B is depicted with a blue bar."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "AuGmCax6n9aNdudW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects come into the region [0.330, 0.486, 0.738, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue bar indicating the 2021 Market Share of Company D is depicted."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "HM9vZROey1RZyPLM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Y-axis label for subplot_0} is at [0.021, 0.389, 0.041, 0.608] and is named '2020 Popularity'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "nzMuHxntoNxQoMfT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Y-axis label for subplot_1} is at [0.959, 0.389, 0.979, 0.608] and is named '2021 Popularity'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "oFAD0VQLCL8z56bx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars representing '2020 Popularity'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color of the bars representing '2020 Popularity'}<r>[0.137, 0.655, 0.298, 1.347]</r> is '#d87769'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "6XLRSUg84krHcpLE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the '2020 Popularity' higher for 'Gothic Fashion' or 'Streetwear Fashion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Streetwear Fashion'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "EtsaRdbQ4CDmHsLc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the '2021 Popularity' higher for 'Gothic Fashion' or 'Streetwear Fashion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Streetwear Fashion'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "YqnkxC4DUGRU2Tbq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the popularity of 'Gothic Fashion' in 2020 and 2021."
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity of {Gothic Fashion}<r>[0.226, 0.942, 0.371, 0.970]</r> has decreased from 2020 to 2021."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "w7zXuAXo8xLuWDab",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.111, 0.858, 0.338, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region is one legend item for the subplot_0. The label is called '2020 Popularity', which is located at [0.174, 0.870, 0.330, 0.898], and the color handle is '#d87769', which is located at [0.119, 0.872, 0.158, 0.892]."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "hZTCHuv0JxDDJoYH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects between [0.540, 0.456, 0.702, 1.347]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar graph that represents the '2020 Popularity' of 'Streetwear Fashion', with a value of 210."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "MXOaL7Ja7FnbahDP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main components in the region [0.319, 0.030, 0.681, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The region at}<r>[0.319, 0.030, 0.681, 0.066]</r> is a subtitle of 'Subculture and Fashion Trends'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "5aH58L6mptfEBX9G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat description is assigned to the label with coordinates [0.971, 0.378, 0.985, 0.619] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 Innovations."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "axhNRPE2qf0h8BdO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the bar that represents Country 3 for the 2021 Innovations located in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.608, 0.145, 0.673, 1.629]."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "490zVcOgoTnVgVPi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the subtitle} with coordinates [0.354, 0.030, 0.646, 0.066] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technological Innovations Indicator."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "l7vYgrM6BECwxaor",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the item described within the legend at [0.752, 0.089, 0.922, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2021 Innovations}<r>[0.797, 0.100, 0.917, 0.128]</r>."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "LE6Nxbo0AoUIwh2N",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars representing {Country 1}<r>[0.110, 0.569, 0.175, 1.629]</r> and {Country 4}<r>[0.760, 0.640, 0.825, 1.629]</r> for the 2020 Innovations, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Country 1."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "RwZpDph3rG24JVrJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for {the 2020 Innovations}<r>[0.110, 0.569, 0.175, 1.629]</r> higher or lower than {the 2021 Innovations}<r>[0.175, 0.456, 0.240, 1.629]</r> in Country 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "21nVDojDj2BhUm50",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 Innovations bar for Country 1}<r>[0.110, 0.569, 0.175, 1.629]</r> to the left or right of {the 2021 Innovations bar for the same country}<r>[0.175, 0.456, 0.240, 1.629]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "Beft8rKTdu6REWrk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 2020 Innovations}<r>[0.327, 0.287, 0.392, 1.629]</r> and {the 2021 Innovations}<r>[0.392, 0.456, 0.457, 1.629]</r> in Country 2, which has the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 Innovations."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "7Fz0fv5scWRB0A2I",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.071, 0.075, 0.929, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly contains two bar charts that correspond to the technological innovation indicators for 2020 and 2021. For each year, we have different bars representing various countries. The bars clearly indicate that the quantity of technological innovations varied among the countries and between the two years."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "cl0i0xtwUZW6v9oA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.971, 0.378, 0.985, 0.619]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label for the 2021 innovations. The label is rotated vertically and mentions \"2021 Innovations\" indicating what the data on the Y-axis signifies."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "z7KBDmYtl1QiR65k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.043, 0.346, 0.061, 0.374]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one object, which is the Y-axis tick label for the value of 90."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "uJHT5jKjuwV7YRqr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.571, 0.942, 0.643, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we can see the x-axis label for Country 3."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "fWoPiJ9uVBcGNXE2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.910, 0.877, 0.982, 0.897] in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for \"Creativity 2021\"."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "o08cTEcwwPqGMSOM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the box with coordinates [0.661, 0.214, 0.697, 1.836] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar of the bar chart, representing the value of \"Creativity 2020\" for Product D."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "yZLOx6YQnlFIZQHK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {x-label \"Product B\"} at [0.318, 0.959, 0.362, 0.979] refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the category \"Product B\" on the x-axis."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "8zahVJKp93Gyyqfn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.010, 0.417, 0.019, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Level."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "E2rNpJspEWPNHibo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher, {the bar for \"Product A\" in \"Creativity 2021\"}[0.161, 0.232, 0.197, 1.836] or {the bar for \"Product B\" in \"Design 2020\"}[0.269, 0.446, 0.304, 1.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Product A\" in \"Creativity 2021\"."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "sJFfeqYAMz8SPcnT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Design 2020\" for \"Product A\"}[0.090, 0.677, 0.126, 1.836] represent a higher value than {the bar for \"Creativity 2020\" for \"Product A\"}[0.126, 0.588, 0.161, 1.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "NxLnAiBxF7MqpoRb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Product C in \"Creativity 2021\"}[0.519, 0.142, 0.554, 1.836] have a higher value than {Product D in \"Design 2021\"}[0.733, 0.107, 0.769, 1.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "klFjO8CN95nxFfhl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Product E in \"Creativity 2021\"}[0.876, 0.053, 0.911, 1.836] and {\"Design 2021\" for Product D}[0.733, 0.107, 0.769, 1.836], which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product E in \"Creativity 2021\"."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "GLg4ANKEmW4obYEi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.881, 0.809, 0.985, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.881, 0.809, 0.985, 0.935] includes a legend with four entries: \"Creativity 2020\" at [0.910, 0.817, 0.982, 0.837], \"Design 2020\" at [0.910,0.847,0.968,0.867], \"Creativity 2021\" at [0.910, 0.877, 0.982, 0.897], and \"Design 2021\" at [0.910, 0.907, 0.969, 0.927]. Each entry has a color square to its left denoting the corresponding bar's color."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "rOxmqVAKp3v96RZL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.362, 0.959, 0.541, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.362, 0.959, 0.541, 0.979] contains two category labels on the x-axis which are: \n1. \"Product B\": [0.318, 0.959, 0.362, 0.979],\n2. \"Product C\": [0.496, 0.959, 0.541, 0.979]."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "pRQAWVMvbnRix1bG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the items within the area [0.447, 0.410, 0.483, 1.836]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items: \n1. The blue bar which represents the value of \"Design 2020\" for \"Product C\", \n2. The red bar which represents the value of \"Creativity 2020\" for \"Product C\"."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "MUkDCaohFuI3q6TV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.476, 0.142, 0.590, 1.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area [0.476, 0.142, 0.590, 1.836], there are four bars representing the values for \"Product C\" in \"Creativity 2020\", \"Design 2020\", \"Creativity 2021\", and \"Design 2021\"."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "l9M7L9nnRV5zmNI6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.015, 0.303, 0.029, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Papers Published."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "UChJ3OUQrDxGg3OD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label placed at [0.605, 0.942, 0.728, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "fFpQch3axDnGlNDI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color and information does the {legend at [0.101, 0.186, 0.128, 0.205]} represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Active Developers 2020' and its color is marked '#c490c0'."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "7IQnkbB2Rf6JWSEw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at [0.126, 0.393, 0.209, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the number of 'Deep Learning' papers published in 2019 which is 50000."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "JHGfl8gaQYJ3UbwI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the {bar depicting Deep Learning in 2020}[0.209, 0.128, 0.292, 0.923] or the {bar depicting Machine Learning in 2020}[0.625, 0.605, 0.708, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "I7sGVqfRHtyQ7OJ7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars in {Deep Learning}[0.126, 0.393, 0.209, 0.923] [0.209, 0.128, 0.292, 0.923] generally taller than the bars in {Machine Learning}[0.542, 0.711, 0.625, 0.923] [0.625, 0.605, 0.708, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "xfYmLbgH9zCDOq2g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing year 2019 and 2020, which year has more {Active Developers in Machine Learning} [0.708, 0.390, 0.791, 0.923] [0.791, 0.341, 0.874, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "16WsAHXVR408EvA3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater, the {number of Active Developers} in {Deep Learning} [0.292, -0.531, 0.375, 0.923] [0.375, -0.652, 0.458, 0.923] or in {Machine Learning} [0.708, 0.390, 0.791, 0.923] [0.791, 0.341, 0.874, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "TXHOp7BJapwS6kpk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the legend at position [0.689, 0.089, 0.905, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend at position [0.689, 0.089, 0.905, 0.223] contains two parts relating to 'Active Developers'. The first item is for 'Active Developers 2019', with a text label positioned at [0.734, 0.142, 0.899, 0.170] and color indicator at [0.695, 0.144, 0.723, 0.164]. The second item with text at [0.734, 0.183, 0.899, 0.211] and color indicator at [0.695, 0.186, 0.723, 0.205] is for 'Active Developers 2020'."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "eqdZaoM1tNgoONzZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.605, 0.942, 0.728, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.605, 0.942, 0.728, 0.970] contains one of the x-axis labels, 'Machine Learning'."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "FFOO769wXI9Kh4my",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area under [0.971, 0.295, 0.985, 0.703] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area under [0.971, 0.295, 0.985, 0.703] represents the ylabel 'Number of Active Developers' in #d26252 color."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "IHbrvr2U5xLdGwDo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual elements in [0.200, 0.942, 0.301, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.200, 0.942, 0.301, 0.970] contains the label 'Deep Learning', corresponding to an x-axis tick."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "hr76nrsDJnaf9BUg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does label at [0.015, 0.427, 0.029, 0.571] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label for the first subplot, representing the number of Inventions."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "RPHjuNkN6B6dlmPT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at [0.170, 0.942, 0.243, 0.970] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis label for the first historical period, which is \"Prehistoric\"."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "mY7ONnuYXKGsAhLy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item} at [0.722, 0.858, 0.902, 0.909] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates that Cultural Expansion is represented in blue in the second subplot."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "2hQ9nNF9OyDuN4m0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Ancient epoch}[0.402, 0.075, 0.480, 0.094] in the second subplot taller than {the bar for Prehistoric epoch}[0.206, 0.075, 0.285, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "nemafRWhFBENYTr0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Middle Ages}[0.598, 0.075, 0.676, 0.383] in the second subplot taller than {the bar for the same era}[0.520, 0.075, 0.598, 0.460] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "RadOWsqM6vHuhYFL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do {the bar for the Modern era} [0.794, 0.075, 0.872, 0.807] in the second subplot and {the bar for the Modern era} [0.715, 0.075, 0.794, 0.845] in the first subplot compare?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are almost the same height."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "8SS6oVkFVsHE2kck",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.706, 0.858, 0.892, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the second subplot. It specifies that the bars in blue represents 'Cultural Expansion'."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "c0SCipgKuI6aHaGX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell us about the region [0.324, 0.075, 0.402, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes one rectangle (bar) in the first subplot representing the number of Inventions during the Ancient period."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "I6ipJyWI1F8nDzgn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.091, 0.075, 0.909, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains 2 subplots, both of which are bar charts representing two different aspects, 'Inventions' and 'Cultural Expansion' across different historical periods: Prehistoric, Ancient, Middle Ages, and Modern."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "CC37BSynZL7ICQxi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type that is represented in the subplot_0 part of the JSON structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "SmrUOaDLpR8tlFMF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat company does {the bar}[0.197, 0.075, 0.257, 0.753] in subplot_1 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company A."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "04r9QuykcV4OjlRf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the caption of {the ylabel}[0.971, 0.422, 0.985, 0.575] label in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021 Sales."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "wlmHR15ykLiB5JtB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars represented by the data in {[0.136, 0.075, 0.197, 0.680],[0.288, 0.075, 0.348, 0.801],[0.439, 0.075, 0.500, 0.583],[0.591, 0.075, 0.652, 0.753],[0.743, 0.075, 0.803, 0.705]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bars is #d87769."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "NlMAZm2WFQvidvCX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the 2021 Sales higher than 2020 Sales for {Company A}<{the 2020 sales bar for Company A}[0.136, 0.075, 0.197, 0.680],{the 2021 sales bar for Company A}[0.197, 0.075, 0.257, 0.753]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "wCkpjPr2nmvPcBo5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the sales of {Company B}<{2021 sales bar for Company B}[0.348, 0.075, 0.409, 0.705]> less than the sales of {Company D}<{2021 sales bar for Company D}[0.652, 0.075, 0.712, 0.801]> in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "UHT5jZ9VAri7nt0Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Company A}<{2021 sales bar for Company A}[0.197, 0.075, 0.257, 0.753]>, {Company B}<{2021 sales bar for Company B}[0.348, 0.075, 0.409, 0.705]>, {Company C}<{2021 sales bar for Company C}[0.500, 0.075, 0.561, 0.777]>, {Company D}<{2021 sales bar for Company D}[0.652, 0.075, 0.712, 0.801]>, and {Company E}<{2021 sales bar for Company E}[0.803, 0.075, 0.864, 0.608]>, which company had the lowest sales in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company E."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "pM2BcYANzJBwNYMj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had higher sales in 2020, {Company D}<{2020 sales bar for Company D}[0.591, 0.075, 0.652, 0.753]> or {Company E}<{2020 sales bar for Company E}[0.743, 0.075, 0.803, 0.705]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company D."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "vJq52lS5NhpDXUg6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.136, 0.075, 0.803, 0.801]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, we can see a bar chart representing the 2020 sales for 5 companies. Each company {Company A}[0.136, 0.075, 0.197, 0.680], {Company B}[0.288, 0.075, 0.348, 0.801], {Company C}[0.439, 0.075, 0.500, 0.583], {Company D}[0.591, 0.075, 0.652, 0.753], {Company E}[0.743, 0.075, 0.803, 0.705] is represented by a bar with height proportional to its sales volume in 2020."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "Ut1r9SDMkcriPVKe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.100, 0.073, 0.900, 0.923] in subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains an axes area with grey background, one Y label that describes the \"2020 Sales\", one subtitle \"Market Competition Analysis and Comparison\", 5 bars representing \"2020 Sales\" for each company, tick labels indicating company's name on X axis and a legend for \"2020 Sales\"."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "r4sdvNlM74CQYl4U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region of [0.197, 0.075, 0.257, 0.753]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar in the subplot_1 of the bar chart, representing the 2021 sales for Company A, which has a color code '#7da1c7', indicating a sales volume for Company A of 28000 in 2021."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "qEcuALpJTBKMCBDj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content present in the region [0.767, 0.858, 0.893, 0.909] in subplot_1."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend item for \"2021 Sales\". It has a color identifier[{color box for the legend}[0.772, 0.872, 0.800, 0.892]] and a textual label[{text for '2021 Sales'}[0.811, 0.870, 0.888, 0.898]], and the color has a code of '#7da1c7'."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "Urt6jxQD8X5DT0Zy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the vertical axis in the chart positioned at [0.012, 0.420, 0.024, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage (%)."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "B5BoA2WPkmv8CNjz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.181, 0.427, 0.250, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the percentage (39.8%) of research in Physics."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "FdJMkNjaMRd4rueT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the bar at [0.355, 0.390, 0.424, 0.927] stands for?"
+            },
+            {
+                "from": "gpt",
+                "value": "It stands for research output in Biology, which is 42.7%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "8TKdKoqfzku6ZcOC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is located at [0.617, 0.939, 0.682, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is Psychology."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "7BbF9MG1WHIzim2o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars representing Biology {[0.355, 0.390, 0.424, 0.927]} and Mathematics {[0.441, 0.262, 0.511, 0.927]}, which field has a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mathematics."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "saYnzOWHOnTUqXe1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Sociology}[0.702, 0.517, 0.771, 0.927] higher or lower than {the bar representing Musicology}[0.876, 0.698, 0.945, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "9OafQAVR7ELB1zlJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the bars representing Biology, Mathematics, and Engineering} [0.355, 0.390, 0.424, 0.927],[0.441, 0.262, 0.511, 0.927],[0.528, 0.181, 0.598, 0.927], which one has the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Engineering."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "oVIirr5YMJwCpg0a",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the bars representing Computer Science and Physics} [0.094, 0.359, 0.164, 0.927],[0.181, 0.427, 0.250, 0.927], which one holds a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Computer Science."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "mgSk80b4ZbRFUFIu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the area [0.355, 0.181, 0.598, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes two bars representing Biology at [0.355, 0.390, 0.424, 0.927] with percentage 42.7% and Engineering at [0.528, 0.181, 0.598, 0.927] with percentage 59.3%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "eJVfUdxccseSuEgC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information of the region [0.181, 0.396, 0.337, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a bar signifying 'Physics', with a percentage of 39.8%. The coordinates of the bar are [0.181, 0.427, 0.250, 0.927]."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "HPiiCOtgVg9qAbhq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is located in the region [0.026, 0.289, 0.578, 0.993]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the bars from Physics to Psychology, as well as their corresponding percentages. More specifically, it includes Physics at [0.181, 0.427, 0.250, 0.927] with percentage 39.8%, Chemistry at [0.268, 0.486, 0.337, 0.927] with percentage 35.1%, Biology at [0.355, 0.390, 0.424, 0.927] with percentage 42.7%, Mathematics at [0.441, 0.262, 0.511, 0.927] with percentage 52.9%, and Psychology at [0.615, 0.306, 0.684, 0.927] with percentage 49.4%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "bOoCMQIGtT3QB6Mh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identifiable in the region from [0.575, 0.149, 1.000, 0.966]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains bars for fields from Psychology to Musicology with their relative percentages. In particular, the bar for Psychology at [0.615, 0.306, 0.684, 0.927] with 49.4%, the bar for Sociology at [0.702, 0.517, 0.771, 0.927] with a percentage of 32.6%, the bar for Literature at 0.789, 0.601, 0.858, 0.927] with a percentage of 25.9%, and the bar for Musicology at [0.876, 0.698, 0.945, 0.927] with a percentage of 18.2%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "npigwNc3dAP5p17i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the term located at [0.241, 0.025, 0.806, 0.055] signify in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, \"Usage of Materials in Scientific Research & Technological Innovation.\""
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "o3P4uuULGmPMCpYt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat material is represented by the bar at [0.104, 0.146, 0.220, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Copper\"."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "zwjUhQDYmsqfjg5B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion is situatedat [0.730, 0.829, 0.751, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It has a percentage of \"3%\"."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "DE0v1MYTSiCqcjPC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Iridium}<r>[0.827, 0.869, 0.943, 0.903]</r> taller than the bar representing {Gold}<r>[0.538, 0.819, 0.654, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "LSQTUnAoSFirY9Qd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of their height, how does the {Silver}<r>[0.393, 0.735, 0.509, 0.903]</r> bar compare to the {Gold}<r>[0.538, 0.819, 0.654, 0.903]</r> bar ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Silver bar is higher."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "MZ0IeqbkG6DfItC6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Copper}<r>[0.104, 0.146, 0.220, 0.903]</r> bar have a higher percentage compared to {Aluminium}<r>[0.249, 0.314, 0.364, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "PEvJWQMUKUO3xgEo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart elements present in [0.062, 0.062, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the main chart area which includes the x and y axes, tick labels, and bars representing six different materials: Copper, Aluminium, Silver, Gold, Platinum, and Iridium."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "TAad2l03z3O0v3he",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.249, 0.314, 0.364, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar representing \"Aluminium\" with a usage percentage of 35% given in this region."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "0NYs1nEK1RAxDoDJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the region between [0.827, 0.869, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the shortest bar, which corresponds to the use of Iridium, with a usage percentage of 2%."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "a2Cp95lKezV17BxV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data trend corresponds to the color handle located at [0.083, 0.120, 0.111, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle corresponds to the \"Upskilling\" trend."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "vhoxaIZX36tmm7hB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at [0.589, 0.919, 0.624, 0.942] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at [0.589, 0.919, 0.624, 0.942] is \"2019\"."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "8taF341AsIXz7zT3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of Y values for the bar at [0.896, 0.061, 0.943, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y range for the bar at [0.896, 0.061, 0.943, 0.329] is from 116.00 to 170.20."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "FnzGog9oL70YX47t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color handle for {Upskilling}<r>[0.083, 0.120, 0.111, 0.136]</r> above or below the one for {Employee Wellness}<r>[0.083, 0.155, 0.111, 0.171]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "cnznQHVPpJ8Tntnu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing both bars representing the year 2020 at {bar for Upskilling}<r>[0.740, 0.427, 0.787, 0.654]</r> and {bar for Employee Wellness}<r>[0.740, 0.233, 0.787, 0.427]</r>, which trend had a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Employee Wellness."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "tPAf1gh3oUIjrdhS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2018, were the values for {Remote Working}<r>[0.426, 0.786, 0.473, 0.903]</r> trend higher than that of {Upskilling}<r>[0.426, 0.638, 0.473, 0.786]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "wx8LhkbcgDP5omKn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the labels present within [0.078, 0.074, 0.262, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains labels for 3 data trends, each associated with a color code. These are {Remote Working}<r>[0.122, 0.083, 0.237, 0.106]</r> attached to a blue color code at [0.083, 0.085, 0.111, 0.101], {Upskilling}<r>[0.122, 0.118, 0.190, 0.141]</r> with an orange color code at [0.083, 0.120, 0.111, 0.136], and {Employee Wellness}<r>[0.122, 0.153, 0.256, 0.176]</r> joined with a green color code at [0.083, 0.155, 0.111, 0.171]."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "nCPav51nGrtwK1fb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is contained within the region [0.487, 0.638, 0.609, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one blue bar representing {Remote Working}<r>[0.583, 0.741, 0.630, 0.903]</r> and two bars below it, i.e., an orange bar for {Upskilling}<r>[0.583, 0.560, 0.630, 0.741]</r> and a green bar for {Employee Wellness}<r>[0.583, 0.418, 0.630, 0.560]</r>."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "gCTfB70FAaH0S6Mg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage (%)."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "kSYCZMBJX8Yxl6SP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar at the far right}<r>[0.852, 0.886, 0.938, 0.903]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automated Reasoning."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "z6a6cRKtRIDhqUfj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar representing 'Neural Networks'}<r>[0.318, 0.495, 0.403, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#4169e1)."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "VNyWLoRoOZRSJmY6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with {the bar representing 'Machine Learning'}<r>[0.424, 0.589, 0.510, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "22.4%"
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "G3Et2JcAkH6kcaSC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar labeled 'Neural Networks'}[0.318, 0.495, 0.403, 0.903] come before or after {the bar labeled 'Deep Learning'}[0.104, 0.073, 0.189, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "VoFoGEgVpBqa2Uf2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Natural Language Processing'}[0.531, 0.738, 0.617, 0.903] taller or shorter than {the bar representing AI Ethics'}[0.638, 0.819, 0.724, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "kCIelyikKGGjLWEh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the percentage represented by the {bar for 'Robotic Process Automation'}[0.211, 0.403, 0.296, 0.903] higher or lower than the {bar for 'Machine Learning'}[0.424, 0.589, 0.510, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "Omlp5ohKXI2YSk7j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the one for 'Cognitive Computing'}[0.745, 0.854, 0.831, 0.903] or {the one for 'Automated Reasoning'}[0.852, 0.886, 0.938, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cognitive Computing."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "ozIBKrRl160r823O",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is happening in the box [0.745, 0.854, 0.938, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This box identifies the bars for 'Cognitive Computing' and 'Automated Reasoning'. The 'Cognitive Computing' bar has a higher value of 3.5% while the Automated Reasoning bar had a lower value of 1.2%."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "vnIi8driypfJsWYL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the scene within the area [0.637, 0.714, 0.699, 0.819]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the scene of the 'AI Ethics' bar which has an estimated value of 6%."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "gRtjAzsOUzpYTGIF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.715, 0.854, 0.861, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains one blue bar representing 'Cognitive Computing' with its corresponding value marked as '3.5%' on its top side."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "JYVKc1h0Ar58Nnki",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.035, 0.193, 0.052, 0.216]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a y-axis label, which corresponds to a value of 50% on the graph."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "H6mylo6WUgC1wYdC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart's y-axis represent with coordinates [0.015, 0.400, 0.029, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label represents the \"Donations (%)\"."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "xq89pf9ARygpjgNC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what's at [0.103, 0.182, 0.201, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar denoting the percentage of donations received by {Red Cross}<r>[0.103, 0.182, 0.201, 0.903]</r>, which is 60%."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "KHTUJhJrsjDU9hc1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data under the x-axis label {UNICEF}<r>[0.371, 0.919, 0.422, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing {UNICEF}<r>[0.348, 0.543, 0.446, 0.903]</r> shows that UNICEF received 30.0% of donations."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "PriuJTEJM3k0WzZj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified by the points [0.415, 0.025, 0.623, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the chart which reads \"Donations by Percentage\"."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "r2xtyuMdK20xnfM7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the donations received by {Amnesty International}<r>[0.715, 0.783, 0.813, 0.903]</r> against the donations received by {Make-A-Wish Foundation}<r>[0.837, 0.843, 0.935, 0.903]</r>. Which organization received more?"
+            },
+            {
+                "from": "gpt",
+                "value": "Amnesty International."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "rjH1aUS0FFDj3MBP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Red Cross}<r>[0.103, 0.182, 0.201, 0.903]</r> taller than the one representing {Doctors Without Borders}<r>[0.593, 0.362, 0.690, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "lGtiOsClmltGruYJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {UNICEF}<r>[0.348, 0.543, 0.446, 0.903]</r> and {Save the Children}<r>[0.470, 0.603, 0.568, 0.903]</r>, which organization received less donations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Save the Children."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "yIYFnE4TOg4xIUpN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the donation percentage for {Habitat for Humanity}<r>[0.226, 0.302, 0.324, 0.903]</r> higher than {Doctors Without Borders}<r>[0.593, 0.362, 0.690, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "cIl6me6bUpeYNy6t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what's occurring within the boundaries of [0.035, 0.053, 0.052, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily contains the y-axis values or 'yticks', with each providing a donation percentage ranging from 0 to 70 at intervals of 10."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "KPPEUDcj88t87kxT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.062, 0.182, 0.935, 0.903] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is populated with bars representing the donation percentage for various charities: {Red Cross}<r>[0.103, 0.182, 0.201, 0.903]</r>, {Habitat for Humanity}<r>[0.226, 0.302, 0.324, 0.903]</r>, {UNICEF}<r>[0.348, 0.543, 0.446, 0.903]</r>, {Save the Children}<r>[0.470, 0.603, 0.568, 0.903]</r>, {Doctors Without Borders}<r>[0.593, 0.362, 0.690, 0.903]</r>, {Amnesty International}<r>[0.715, 0.783, 0.813, 0.903]</r>, and {Make-A-Wish Foundation}<r>[0.837, 0.843, 0.935, 0.903]</r>."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "Zis7kw5eiizEmNwu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be seen in the area [0.118, 0.919, 0.971, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area is where the names of the charities are located on the x-axis, the charities are: \n1. {Red Cross}<r>[0.118, 0.919, 0.187, 0.942]</r>,\n2. {Habitat for Humanity}<r>[0.201, 0.919, 0.348, 0.942]</r>,\n3. {UNICEF}<r>[0.371, 0.919, 0.422, 0.942]</r>,\n4. {Save the Children}<r>[0.457, 0.919, 0.581, 0.942]</r>,\n5. {Doctors Without Borders}<r>[0.556, 0.919, 0.727, 0.942]</r>,\n6. {Amnesty International}<r>[0.687, 0.919, 0.841, 0.942]</r>,\n7. {Make-A-Wish Foundation}<r>[0.801, 0.919, 0.971, 0.942]</r>."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "7MdkFkso3lFgYB4C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.415, 0.025, 0.623, 0.055] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart, \"Donations by Percentage\"."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "ezWCvWu3sNIRpYGt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Big Data Integration Overview at the position of {the title}<r>[0.398, 0.025, 0.649, 0.055]</r>."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "u1Db3j7mLTzemnPK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lable of Y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Y-axis}<r>[0.015, 0.377, 0.029, 0.588]</r> is labeled as 'Adoption Rate (%)'."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "ul7vm14Vzo0ndLZ1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat tool is represented at the position with coordinates [0.104, 0.405, 0.203, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tool is MuleSoft."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "XDdMivF4YZkn6aKU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the adoption rates of {MuleSoft}[0.104, 0.405, 0.203, 0.903] and {Dell Boomi}[0.351, 0.493, 0.449, 0.903] similar?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "ILEXAHsw87IH3IqX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {IBM Infosphere}[0.474, 0.634, 0.573, 0.903] and {Oracle Data Integrator}[0.721, 0.138, 0.820, 0.903], which tool has a higher adoption rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Oracle Data Integrator."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "ooSdLLUG4yodky2r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the adoption rate of {Jitterbit}[0.844, 0.590, 0.943, 0.903] lower than that of {Informatica PowerCenter}[0.597, 0.365, 0.696, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "LIsR1t5Ws5T9sisr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.062, 0.062, 0.985, 0.903]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main charting area containing all bars, respective y-coordinates, and tool labels. The x-axis labels are seven different data integration tools, while the y-axis labels represent the adoption rates (%)."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "8mACF3MpOSfA6htu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the area [0.035, 0.263, 0.052, 0.287] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a y-axis label \"30\"."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "OiPDCsEbqGOsyie3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.597, 0.365, 0.696, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar representing the adoption rate of the tool Informatica PowerCenter. The rate is 25.6%."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "yLjhrwvgcRifdkGk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the labels located at [0.102, 0.919, 0.222, 0.942], [0.280, 0.919, 0.333, 0.942], [0.410, 0.919, 0.492, 0.942], [0.543, 0.919, 0.649, 0.942], [0.679, 0.919, 0.802, 0.942], [0.852, 0.919, 0.918, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the province names on the x-axis of the bar chart, from left to right: New South Wales, Victoria, Queensland, South Australia, Western Australia, Tasmania."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "7oC8nAo1xmSR5qRn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the characteristic of the rectangular object located at [0.249, 0.310, 0.364, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing a GIS uptake of 63.5% in Victoria."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "ZaAWJwtLyL6bXhRf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at [0.494, 0.952, 0.553, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the x-axis, named \"Province\"."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "RvABFiM4HnPLC2dF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Victoria}[0.249, 0.310, 0.364, 0.903] taller than {the bar for South Australia}[0.538, 0.476, 0.654, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "l3SkSFNDxuI8zBPy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre central marks of {Victoria} at [0.306, 0.903] and {South Australia} at [0.596, 0.903] the same in height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "WAMFq7HD8UoqMmjy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{Tasmania bar}[0.827, 0.760, 0.943, 0.903] is lying on which side of {New South Wales bar}[0.104, 0.146, 0.220, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "1Al2ceHeTrtZIyx5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.102, 0.919, 0.222, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single bar and its x-axis label. The blue bar represents the GIS uptake value for the province New South Wales, while the x-axis label is \"New South Wales\"."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "RKn3n1YqtJIvQJNQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located in the region from [0.249, 0.310, 0.364, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar and an x-label. The bar represents the GIS uptake in the province called Victoria, which stands at 63.5%, and the x-label is Victoria."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "n4qDiu8ZwxjX8o5t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.035, 0.333, 0.052, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a label on the y-axis representing the 60% mark."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "G8z4jT9CbZWWoSYA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's on the x-axis based on the coordinates [0.504, 0.952, 0.543, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Trend\" is on the x-axis."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "BNURyEfqi9U24e6q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the blue bar represents at the coordinates [0.104, 0.623, 0.244, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a \"steep decline\" trend with a value of 30%."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "cqnAAHrrpH0oNz07",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's represented by the coordinate [0.327, 0.132, 0.371, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the value of the bar for the \"Dramatic Surge\" trend, which is 80%."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "sI2CSx9cppoUBfjz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Dramatic Surge\"}[0.279, 0.156, 0.419, 0.903] higher than {the bar representing \"Steep Decline\"}[0.104, 0.623, 0.244, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "A6z6QR9qqzDlSbuI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing \"Gradual Recovery\"}[0.454, 0.436, 0.593, 0.903] and {the bar representing \"Upward Trajectory\"}[0.628, 0.249, 0.768, 0.903], which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the bar representing \"Upward Trajectory\"}[0.628, 0.249, 0.768, 0.903]. It has a higher value."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "9O2ckA1tLIVBqNkW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar at the coordinate [0.628, 0.249, 0.768, 0.903] taller than the bar at the coordinate [0.803, 0.389, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "XC0C4vPo75RwBX4d",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.279, 0.132, 0.419, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a blue bar, which represents the \"Dramatic Surge\" trend with a value of 80%."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "0zqdg4XuDRBoXtN5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCapture what's within the coordinates [0.628, 0.226, 0.768, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, there's a blue bar representing \"Upward Trajectory\" with a value of 70%."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "nuGnrFkxtKLw6x9U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area from [0.151, 0.599, 0.196, 0.623] to [0.851, 0.366, 0.895, 0.389]."
+            },
+            {
+                "from": "gpt",
+                "value": "These two regions represent the percentages of the \"Steep Decline\" and \u201cSlight Decrease\u201d trends respectively. The value for \"Steep Decline\" is 30%, and for \"Slight Decrease\", it is 55%."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "GwYCLWCWdRvpKINX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future Technological Development Prospects"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "oKcJNbllxzNuIs8F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.013, 0.304, 0.024, 0.536] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Development Prospect (%)"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "ULiUwNcugNTtU534",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the X-axis represent at the coordinates [0.491, 0.964, 0.556, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technology"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "ymjVL3wYE9f89SrC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of 'Green Energy' as represented at [0.892, 0.093, 0.929, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "91.5%"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "ru8QNaXjpFEN76jz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Nanotechnology'}[0.273, 0.439, 0.342, 0.793] higher or lower than {the bar representing 'Biotechnology'}[0.360, 0.245, 0.428, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "3Oms8tJoBoWi93GP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for '5G Technology'}[0.618, 0.351, 0.687, 0.793] show a higher or lower development prospect than {the bar for 'AI Technology'}[0.101, 0.196, 0.170, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "vIi2fkN2H0yQvO3K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Quantum Computing}[0.187, 0.307, 0.256, 0.793] and {Electric Cars}[0.704, 0.207, 0.773, 0.793], which technology has a higher development prospect?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electric Cars"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "S80f1G2nCFKVR4PO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {VR/AR Technology}[0.790, 0.400, 0.859, 0.793] and {Green Energy}[0.876, 0.110, 0.945, 0.793], which technology has a higher development prospect?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Energy"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "cG9x6HzYdinddj9R",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.101, 0.196, 0.170, 0.793]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing AI Technology's development prospect, which is shown to be 80.0%."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "NPBEELBGT1e2hPxg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide for the area [0.273, 0.439, 0.342, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing the development prospect of Nanotechnology, which is indicated to be 47.5%."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "nuAewWYIl9QgLJx5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.790, 0.400, 0.859, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the bar representing VR/AR Technology's development prospect, which is marked as 52.7%."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "5E2y62wo0ZyWrnQq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is captured within the region [0.532, 0.159, 0.601, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the bar reflecting the development prospect for Space Exploration, which is given as 85.0%."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "MKWXXePFguHIw6MV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of chart used?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "NykjGFNco98Jveeo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the chart title} at [0.334, 0.025, 0.721, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tourism and Hospitality Industry Data Analysis."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "yGTv86hlUvYLc9h8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information denoted by the bar at [0.846, 0.104, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents {Wellness Tourism}<r>[0.846, 0.104, 0.943, 0.903]</r> with a percentage of 95.0%."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "DzpF8HOOdAwHAMC9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.035, 0.053, 0.061, 0.076] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They indicate the position of the Y-axis label for 100."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "ERuUZzgsLCWcaRK5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Sustainable Tourism\"}[0.112, 0.857, 0.210, 0.903] shorter or taller than {the bar representing \"Dark Tourism\"}[0.479, 0.559, 0.577, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "wWgS8JvxQtCTPtnV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the label {Dark Tourism}<r>[0.482, 0.919, 0.573, 0.942]</r> to the left or right of {Space Tourism}<r>[0.722, 0.919, 0.822, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "Rap4EFDmA2goyaaE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in percentage, {Eco-Tourism}<r>[0.601, 0.387, 0.699, 0.903]</r> or {Space Tourism}<r>[0.723, 0.211, 0.821, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Space Tourism."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "w6ZbN6lIGXl6ATmD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Culinary Tourism\"}[0.234, 0.775, 0.332, 0.903] taller than {the \"60\" label on the Y-axis}<r>[0.043, 0.389, 0.061, 0.413]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "LdGa5Fn6KILT2Y1c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.091, 0.919, 0.231, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for the category {Sustainable Tourism}<r>[0.091, 0.919, 0.231, 0.942]</r>."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "OuGlNgvnUZl7gMUe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the vertical region from 0 to 100, [0.052, 0.894, 0.061, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the Y-axis labels with the range of values for the bar chart in percentage (%), and the values are 0%, 20%, 40%, 60%, 80% and 100%."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "DhtTPLKATOhxb2B9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects exist in the area [0.723, 0.211, 0.821, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing {Space Tourism}<r>[0.723, 0.211, 0.821, 0.903]</r> which indicates its percentage is 82.3%."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "MYq3iumVoeHd1oHI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the area [0.035, 0.053, 0.061, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a Y-axis label indicating a value of 100% on the scale."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "OES9K8HSuHuj5WIG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Profit change in Fintech and Digital Currency Companies at {the title}<r>[0.325, 0.019, 0.716, 0.041]</r>."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "EViZCRYequ9qZy5N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Profit change (%) at {the Y-axis label}<r>[0.013, 0.412, 0.024, 0.562]</r>."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "kKjixmCJSgyb0Yji",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the bar} at <r>[0.096, 0.157, 0.166, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a 5.0% profit change for Coinbase."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "Bv3vOAfpu5UGf7JN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the X-axis label at the 8th tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "Monzo at {the 8th tick on the X-axis}<r>[0.718, 0.939, 0.756, 0.957]</r>."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "wCie84zcsRKZSThg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the profit change of {Robinhood}<r>[0.443, 0.476, 0.512, 0.707]</r> higher than that of {Stripe}<r>[0.616, 0.256, 0.685, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "m8Y0O0F41kuAKWw3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {Starling}<r>[0.789, 0.707, 0.858, 0.762]</r> experience a profit increase compared to {TransferWise}<r>[0.876, 0.487, 0.945, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "NDVe0PiQgTIIZJMc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the profit change of {Monzo}<r>[0.703, 0.707, 0.772, 0.817]</r> positive or negative?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "Z83gtqz3mv8h4AiY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Coinbase}<r>[0.096, 0.157, 0.166, 0.707]</r> taller than {the bar for Revolut}<r>[0.183, 0.355, 0.252, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "NnHPkgkPioV0EV3j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area from [0.056, 0.600, 0.945, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are bars representing the companies Stripe, Monzo, Starling, and TransferWise along with their respective profit changes, including negative values for Monzo and Starling."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "bo3qZtw8sAMmbhVe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region with coordinates: [0.049, 0.700, 0.945, 0.929]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains bars representing all ten companies: Coinbase, Revolut, N26, Square, Robinhood, Paypal, Stripe, Monzo, Starling, and TransferWise. It also includes text annotations showing the specific profit change percentages for each company. The profit change values range from -1.0% to 5.0%."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "2IKb8p3bp7CF9saM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's found in the region [0.096, 0.157, 0.166, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a blue bar representing the profit change of Coinbase, which shows a 5.0% increase as indicated by the text annotation."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "2wtc6LOkyiMP0LUX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region bounded by [0.700, 0.550, 0.949, 0.929]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the bars for Monzo, Starling, and TransferWise. Here, we see the profit changes for these three companies where Monzo has a negative 1.0% change, Starling with a -0.5% change, and TransferWise at a positive 2.0% change."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "eq8psyuZ5YIQxJTt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar with coordinates [0.219, 0.744, 0.319, 0.927] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interest rate."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "10mRYb8QvfIV9hX6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the bar located at [0.845, 0.193, 0.945, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50.0%."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "ttZ41NBBRxrEBthM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.012, 0.402, 0.024, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage risk (%)."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "DfUVq145aBdZWDNE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by the bar at [0.094, 0.780, 0.194, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents that the Inflation risk factor is 10.0%."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "GqajmIkfEFyJd30g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Inflation\"}[0.094, 0.780, 0.194, 0.927] higher than {the bar representing \"Interest rate\"}[0.219, 0.744, 0.319, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "E2maYZi4dWmDacWR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the market risk bar}[0.845, 0.193, 0.945, 0.927] or {the operational risk bar}[0.720, 0.414, 0.820, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The market risk bar."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "EmhH7Pu2eHNYaC1Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Market risk\"}[0.845, 0.193, 0.945, 0.927] riskier than {the bar representing \"Credit risk\"}[0.344, 0.487, 0.444, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "qeXOgv0b5sM9pXoM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Liquidity risk}[0.595, 0.663, 0.695, 0.927] and {Operational risk}[0.720, 0.414, 0.820, 0.927], which risk category has less risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "Liquidity risk."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "kyHeFZnrvCyYKrV6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.036, 0.920, 0.043, 0.938]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the scale value '0' on the y-axis."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "F73yXyN94y3nAFu8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in [0.595, 0.663, 0.695, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "It comprises a blue bar that corresponds to the 'Liquidity risk' factor with a value of 18.0%."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "QEeLXnL3hxsx5Aya",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within [0.720, 0.414, 0.820, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar representing 'Operational risk' at 35.0%."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "K3B9CoouJokZ24hP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the coordinates [0.094, 0.780, 0.194, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It comprises a bar that represents the risk factor 'Inflation' and has a risk percentage of 10.0%."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "GZKXJu4Um1kCTSjf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis represent in the chart coordinates [0.448, 0.955, 0.596, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Industry."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "04uw0IUB6Gn9XV7Y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.013, 0.388, 0.029, 0.587]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage (%)."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "uL0GenxFbS4GZlEL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar} at [0.772, 0.634, 0.849, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value of \"Blended_Learning\" in percentage, which is 29.7%."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "4RtGGguLDukHw4M1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry does the bar at coordinate [0.676, 0.148, 0.753, 0.918] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-learning Platforms."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "8N5R9SJP35NzebJJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar representing \"In-person_Seminars\"}[0.195, 0.701, 0.272, 0.918], is {the bar representing \"Online_Courses\"}[0.291, 0.208, 0.368, 0.918] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "F9hE9Nib5CiB4d7l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Blended_Learning'}[0.772, 0.634, 0.849, 0.918] taller than {the bar representing 'Webinar'}[0.099, 0.607, 0.176, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "EuiF5M75aPaqAbr2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher, {the bar representing 'Certifications'}[0.580, 0.450, 0.657, 0.918] or {the bar representing 'Microlearning'}[0.868, 0.407, 0.945, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Microlearning."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "fcVZhcsKv5jSNKGB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the percentage of {Webinar}[0.099, 0.607, 0.176, 0.918] greater than the percentage of {In-house_Training}[0.387, 0.740, 0.464, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "7yn9RGfBBLGCT3iu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.034, 0.050, 0.048, 0.067]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a tick on the y-axis representing the value \"90%\"."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "LLli9P74uMTHsFQO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.772, 0.634, 0.849, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the \"Blended_Learning\" category with a value of 29.7%."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "69yeuX9ESOQRWhGN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside coordinates [0.676, 0.148, 0.753, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar that represents the percentage of usage in the \"E-learning_Platforms\" category, which is 80.5%."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "9jhGDMnkvEpYozey",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.291, 0.194, 0.348, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"74.2%\", which indicates the value of the bar for the \"Online_Courses\" category."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "3DPKkP2y8L4M5m5V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the context of the chart based on the title at [0.364, 0.025, 0.692, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The context of the chart is \"Energy Use and Resource Consumption\"."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "ysbbpKMfDfrzJUth",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis with coordinates [0.015, 0.394, 0.029, 0.571] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis represents \"Percentage (%)\"."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "TPL6E2PLuDlcHfTe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource type is denoted by the X-axis label at [0.869, 0.919, 0.916, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The resource type is \"Oil\"."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "K2oGBCctP8be1CYC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage use does {Coal}<[0.773, 0.165, 0.833, 0.188]> reflect?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal represents a usage percentage of 85.0%."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "afV9LLKOCie0Z87j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the percentage of Geothermal energy usage}<[0.490, 0.442, 0.550, 0.466]> greater than {Natural Gas}<[0.695, 0.207, 0.739, 0.230]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "mImoNseEzB8dWHUY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource has a higher consumption, {Wind Energy}<[0.301, 0.578, 0.361, 0.601]> or {Nuclear Energy}<[0.585, 0.320, 0.644, 0.343]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nuclear Energy."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "qZ3GnCb7CnbN6Yj6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Biofuel}<[0.396, 0.507, 0.455, 0.530]> consumption greater than 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "bBddlaz0qxdRwARR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource is marked at the tick coordinate [0.433, 0.903] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biofuel."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "ONQWQoFncn05hPQy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.676, 0.165, 0.833, 0.230]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents two crucial energy resources: Natural Gas and Coal. The orange bar symbolizes Natural Gas with a usage percentage of 80.0%, coordinates [0.679, 0.207, 0.739, 0.230]. The following green bar symbolizes Coal with a higher usage percentage of 85.0%, coordinates [0.789, 0.165, 0.833, 0.188]."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "fefPDR2xqml2IWKb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.112, 0.832, 0.282, 0.903] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two bars which represent the percentages of Solar Power and Hydropower. Solar Power [0.132, 0.832, 0.168, 0.856] is shown to have a use percentage of 5.6%. Hydropower [0.222, 0.747, 0.266, 0.770] has a higher percentage of 15.8%."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "hIjsfUNm7TMhSJiO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat resources are depicted in the region [0.301, 0.507, 0.455, 0.530]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bars representing Wind Energy with a consumption percentage of 35.9%, coordinates [0.317, 0.578, 0.361, 0.601], and Biofuel with a consumption percentage of 44.3%, coordinates [0.411, 0.507, 0.455, 0.530]."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "lng8I6n0l1U8WHmR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the entries located in the region [0.490, 0.366, 0.644, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This spans two resources: Geothermal and Nuclear Energy. Geothermal's entry is the blue bar at [0.506, 0.442, 0.550, 0.466], with a usage percentage of 52.0%. Nuclear Energy follows, the green bar at [0.600, 0.366, 0.644, 0.394], with a noticeably higher usage of 66.6%."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "0oHJ6G4KwIDZpCyJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.518, 0.033, 0.596] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Values"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "hyQkwoyfd7nzsqu4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate information of the metric called \"Sales Volume\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.711, 0.925, 0.804, 0.945]"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "nETUBO0JhvCvguaA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the chart title} found at [0.271, 0.168, 0.780, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Competition Analysis and Comparison"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "GJFRyOXOSF6gYbA5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the labels at [0.294, 0.082, 0.414, 0.104] and [0.294, 0.115, 0.414, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Product A Sales}[0.294, 0.082, 0.414, 0.104] and {Product B Sales}[0.294, 0.115, 0.414, 0.136]"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "t1aZC4afwOQ0RYsz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Product A Sales at Market Share}[0.108, 0.794, 0.479, 0.911] taller than {the bar representing Product C Sales at Sales Volume}[0.572, 0.668, 0.943, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "46FWBIgaVlMQs32W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of Product B Sales at Sales Volume}[0.572, 0.758, 0.943, 0.812] have a higher or lower value than {the bar of Product D Sales at Market Share}[0.108, 0.417, 0.479, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "mhY5qwiHfKNe2GqM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is wider: {Product E Sales at Market Share}[0.108, 0.309, 0.479, 0.417] or {Product A Sales at Sales Volume}[0.572, 0.812, 0.943, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product E Sales at Market Share"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "FXHHABnzrfEtnTLC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the labels, do {Product B Sales}[0.294, 0.115, 0.414, 0.136] have a similar color as {Product D Sales}[0.487, 0.115, 0.608, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "HtdEuadoFBh5Bm72",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.245, 0.037, 0.806, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, with labels and color handles for each product's sales. It includes six sets of color-text labels representing the sales of Products A to F. These products are color-coded, with each label and it's corresponding color handle located within the listed coordinates."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "7yQ9RORSiRj6GL2v",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region between the coordinates [0.572, 0.282, 0.943, 0.399]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a single bar that represents the sales volume of Product F, indicating a range between 57 and 70."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "uT5F04IT5k3i3nnq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.047, 0.903, 0.056, 0.923] through [0.039, 0.184, 0.056, 0.204]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region on the vertical axis displays the numeric scale for the bar chart, starting from 0 up to 80 in increments of 10. Within this region, there are 9 labels, indicating values at each 10 step interval."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "tVe17AaA7QMyyafF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area from [0.108, 0.794, 0.479, 0.911] to [0.572, 0.812, 0.943, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars, one representing 'Product A Sales' at 'Market Share' and the other representing 'Product A Sales' at 'Sales Volume.' Both bars have a different height, indicating they have different values."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "OAOYED3iMucnDj6y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.202, 0.091, 0.849, 0.120] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is titled \"Bar Chart of Media Influence and Public Opinion Guidance\"."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "jQPqVJ3MlZ2QcVqY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at the coordinates [0.474, 0.953, 0.577, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, labeled as \"Media Types\"."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "N8VnXZVdUmgLHxE6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at [0.630, 0.273, 0.769, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing \"Online\" in the dataset."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "mSu6w7SITGPjs1pq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the term at coordinates [0.446, 0.043, 0.563, 0.059] refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label is missing in the given coordinates."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "GZ3ha1DBxp1cgbIB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the content at {the TV bar}<r>[0.282, 0.565, 0.421, 0.911]</r> taller or shorter than {the Radio bar}<r>[0.456, 0.692, 0.595, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "WmaJQk5welzE2TsA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Newspaper}<r>[0.108, 0.492, 0.247, 0.911]</r> larger than or smaller than the size of {the bar for Social Media}<r>[0.804, 0.163, 0.943, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "FQjOTUIsJiVfuFGy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for TV, Radio, and Online}<r>[0.282, 0.565, 0.421, 0.911], [0.456, 0.692, 0.595, 0.911], [0.630, 0.273, 0.769, 0.911]</r> generally taller or shorter than {the bars for Newspaper and Social Media}<r>[0.108, 0.492, 0.247, 0.911], [0.804, 0.163, 0.943, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "IyftWRL3heffbvpY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {TV}<r>[0.282, 0.565, 0.421, 0.911]</r> and {Online}<r>[0.630, 0.273, 0.769, 0.911]</r>, which has a higher influence level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "mYwuVqRI9H7v0cDh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what elements exist within the area [0.044, 0.08, 0.532, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains most of the y-axis including its label, the \"Level of Influence\", the y-axis scale, the bars representing influence levels for \"Newspaper\", \"TV\", and \"Radio\"."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "CDDqrmQsO65tI8lZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you detail the contents within [0.282, 0.11, 0.943, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified area includes the bars representing influence levels for media channels \"TV\", \"Radio\", \"Online\", and \"Social Media\", and also part of the x-axis including its label \"Media Types\" and the x-axis scale."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "xwuktIPC5f4nBfGW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.258, 0.019, 0.793, 0.095]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area covers the legend of the chart, which indicates \"Media Influence\" is denoted by an orange color."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "INxkAcRV3Rulk9vM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region defined by [0.108, 0.492, 0.421, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bars for two items on the x-axis: \"Newspaper\" and \"TV\"."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "nOkjwHvSoI7vM1YV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.039, 0.707, 0.065, 0.727]</r> on the Y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "tz6ibFqtfVPrq7I4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the rectangular bar marked by the coordinates [0.713, 0.471, 0.809, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Incremental Level of the HealthCare Awareness."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "vz4GIBivIV19MrQX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat title can be inferred from the label placed within these coordinates [0.239, 0.091, 0.805, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Performance Indicators in Health and Medical Field'."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "MODQQJXwDHsuuueT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf I were to refer to an object located at [0.788, 0.925, 0.973, 0.945], what would it be called in actuality?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label for 'Medical Research Progress'."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "4FlDL7EKKO8fBiPi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Achieved Level' higher than the 'Standard Level' for 'Medical Staff Availability' with the bars {standard level}<r>[0.115, 0.519, 0.211, 0.911]</r> and {achieved level}<r>[0.115, 0.173, 0.211, 0.519]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "7ovx3fH5TlVvmHo0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking into the intensity of colors within the legend {Standard Level}<r>[0.243, 0.066, 0.274, 0.082]</r>, {Achieved Level}<r>[0.432, 0.066, 0.462, 0.082]</r>, and {Incremental Level}<r>[0.621, 0.066, 0.651, 0.082]</r>, which color seems to be the darkest?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard Level."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "4HMu98xY8yKn6aIL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, the standard level or achieved level for the 'Medical Research Progress' with {the standard level bar}<r>[0.833, 0.793, 0.929, 0.911]</r> and {the achieved level bar}<r>[0.833, 0.652, 0.929, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Achieved level."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "6jzKMGpxj09ZKT5S",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the labels {Medical Staff Availability}<r>[0.078, 0.925, 0.249, 0.945]</r> and {Clinical Equipment Efficiency}<r>[0.182, 0.925, 0.384, 0.945]</r>, which one on the x-axis is located at the farthest left position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical Staff Availability."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "N1r5Zlmdrhl0w3TK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is in the region [0.788, 0.925, 0.973, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis label for 'Medical Research Progress'."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "tp7BhqTpMA6CidxT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.237, 0.019, 0.807, 0.095]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend area of the chart. The legend title 'Legend' is at coordinates [0.489, 0.028, 0.555, 0.054]. With color indications 'Standard Level': [0.286, 0.065, 0.401, 0.086]; 'Achieved Level': [0.474, 0.065, 0.590, 0.086] and 'Incremental Level': [0.663, 0.065, 0.801, 0.086]."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "t0eAbnMbkoxuAjZv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the axis labels and ticks within the section [0.182, 0.925, 0.384, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is part of the x-axis labels, having one label - the 'Clinical Equipment Efficiency' label positioned at [0.182, 0.925, 0.384, 0.945] with a tick at [0.283, 0.911]."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "FpH2II7n1c3SLQ7X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what is enclosed within the [0.075, 0.126, 0.969, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains all the rectangular bars of the chart which represents the performance indicators in health and medical field including Medical Staff Availability, Clinical Equipment Efficiency, Patients' Satisfaction Score, Hospital Cleanliness Rating, Disease Recovery Rate, HealthCare Awareness and Medical Research Progress."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "THJVXV18ozfgf61d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.015, 0.440, 0.033, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, titled as \"Market Share\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "j2TLwWTpH9rkEJCP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the x-axis point at [0.359, 0.925, 0.459, 0.945] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"Movie industry\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "1TWtCwLJros3k5ed",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend entry located between the coordinates [0.588, 0.065, 0.644, 0.086] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend entry for \"Trend 2\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "bn9PR8jLittiF0dD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat industry is the label at [0.853, 0.925, 0.937, 0.945] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This label is referring to the \"Toy industry\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "o60yqEcj8rZqD3He",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has a bigger market share in the \"TV industry\", {Trend 1}[0.116, 0.697, 0.213, 0.911] or {Trend 2}[0.116, 0.505, 0.213, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend 1."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "7HDBFj128aAxrZ7Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry shows a larger market share for {Trend 1}; {Movie industry}[0.359, 0.697, 0.457, 0.911] or {Book industry}[0.603, 0.697, 0.700, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Movie industry."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "xsPaUuK5UqXx9ykk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a gap in the market shares between {Trend 1} and {Trend 2} in the {Music industry}[0.481, 0.334, 0.579, 0.612][0.481, 0.612, 0.579, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "DL8xV8XZfsDFW8JU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho owns the largest market share {Trend 1 Toy industry}[0.846, 0.569, 0.944, 0.911] or {Trend 2 Art industry}[0.725, 0.163, 0.822, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend 2 Art industry."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "BSEfTBzfttemquL6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area from [0.116, 0.697, 0.213, 0.911] reflect?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the market share of the \"TV industry\" in \"Trend 1\". The height of the bar represents the numerical value of the share."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "kD6BCXCFODUxNOXB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the area [0.359, 0.492, 0.457, 0.697]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a bar for \"Trend 2\" in the \"Movie Industry\". The length of the bar indicates the size of the market share."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "NJdSSDYZp8K6VJno",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.725, 0.163, 0.822, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 1 object: The green bar, which represents the market share of the \"Art industry\" in \"Trend 2\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "yl8ccH37THGl4VSL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region from [0.846, 0.245, 0.944, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A green bar indicates the \"Toy industry\" market share for \"Trend 2\". The bar's length is proportional to the value of the market share."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "z6EeKDrXfE4yObeV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the label \"Big Data\"}<r>[0.522, 0.065, 0.588, 0.086]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange, seen at <r>[0.479, 0.066, 0.510, 0.082]</r>."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "XrO4W0i3afhmxyvW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed next to the {year marker \"2017\"}<r>[0.565, 0.925, 0.600, 0.945]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tick mark coordinates are <r>[0.583, 0.911]</r>."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "lJaPEtG1J7Kffxj2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the growth value represented for {Cloud Computing in 2020}<r>[0.859, 0.318, 0.944, 0.540]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range is 151.00 to 241.00."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Io74xUisMHgOjR4C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2013 bar for Blockchain}<r>[0.116, 0.810, 0.201, 0.813]</r> shorter than the {2013 bar for VR/AR}<r>[0.116, 0.805, 0.201, 0.810]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "cCV1HgDT492462ZJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {color handle for Blockchain}<r>[0.685, 0.066, 0.716, 0.082]</r> similar to the {color handle for VR/AR}<r>[0.685, 0.099, 0.716, 0.114]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "VRxnKrXcZGWm3L8d",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Virtual Reality in 2018}<r>[0.647, 0.360, 0.731, 0.409]</r> have a lower value compared to {Blockchain in 2018}<r>[0.647, 0.409, 0.731, 0.449]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "wmpHqP8vGdOyESi0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the yearly distribution in <r>[0.116, 0.763, 0.944, 0.925]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The years from 2013 to 2020 are evenly distributed along the x-axis with their respective tick marks and labels, providing clear markers for each year. Specifically, \"2013\" at [0.141, 0.925, 0.176, 0.945], \"2014\" at [0.247, 0.925, 0.282, 0.945], \"2015\" at [0.353, 0.925, 0.388, 0.945], \"2016\" at [0.459, 0.925, 0.494, 0.945], \"2017\" at [0.565, 0.925, 0.600, 0.945], \"2018\" at [0.671, 0.925, 0.707, 0.945], \"2019\" at [0.777, 0.925, 0.813, 0.945], and \"2020\" at [0.884, 0.925, 0.919, 0.945]."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Px93lbLNSWRKEgpI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an overview of the technology growth values between <r>[0.116, 0.163, 0.944, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the growth values for various technology sectors such as Artificial Intelligence, Internet of Things, Big Data, Cloud Computing, Blockchain, and VR/AR for the years 2013 to 2020. Each technology is represented by a specific color and the growth is illustrated through the height of bars corresponding to each year. It shows a general trend of increasing growth for all technologies."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Z3MoiXzoq4sb3Wei",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the legend section <r>[0.243, 0.019, 0.817, 0.127]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend section includes color handles alongside labels for six technological categories: Artificial Intelligence, the Internet of Things, Big Data, Cloud Computing, Blockchain, and VR/AR. Each category's color is represented in a small rectangle next to the category name, helping to visually link each color to its corresponding data in the bar chart."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "1WycO8sfsiSqZDwY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing Deep Learning in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#2e8b57' or a sort of greenish color: {Deep Learning's bar color}<r>[0.353, 0.084, 0.383, 0.099]</r>."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "obh0PQJv9ud54oTL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinate of the y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, the y-axis label is located at these coordinates: [0.015, 0.526, 0.033, 0.588]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "nfhYqqDTtZEtDRyw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.508, 0.953, 0.552, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label at these coordinates is \"Years\"."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "zwsk3Lp0eH2b4pvC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technologies or categories are present in [0.324, 0.037, 0.736, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {Deep Learning}[0.395, 0.082, 0.507, 0.104], {RPA}[0.395, 0.115, 0.425, 0.136], {NLP}[0.581, 0.082, 0.610, 0.104], and {Computer Vision}[0.581, 0.115, 0.707, 0.136]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "5VWvchq3plbau6nd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Deep Learning\"}[0.116, 0.845, 0.213, 0.911] higher than {the bar representing \"RPA\"}[0.116, 0.763, 0.213, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "nZvPip9KXHbnUVRn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the bars representing \"NLP\"}[[0.359, 0.620, 0.457, 0.703], [0.481, 0.577, 0.579, 0.670], [0.603, 0.582, 0.700, 0.593], [0.725, 0.412, 0.822, 0.549], [0.846, 0.418, 0.944, 0.544]], which year showed the highest level?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "RkikjFhUa1W4NVup",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the level of {Deep Learning}[0.846, 0.681, 0.944, 0.911] higher than the level of {NLP}[0.846, 0.418, 0.944, 0.544] in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "7J4TsRXc5CKm1ir7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the \"Computer Vision\" category {[0.116, 0.642, 0.213, 0.714],[0.238, 0.577, 0.335, 0.659],[0.359, 0.522, 0.457, 0.620],[0.481, 0.456, 0.579, 0.577],[0.603, 0.445, 0.700, 0.582],[0.725, 0.253, 0.822, 0.412],[0.846, 0.237, 0.944, 0.418]}, in which year was the lowest level recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "Y0FYktnTrhGLPUeO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.324, 0.037, 0.736, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes a legend title \"Artificial Intelligence and Automation Trends\", and four pairs of color-text labels each corresponding to a different category of technology: Deep Learning with a green color labelling, RPA with a blue color labelling, NLP with a purple color labelling, and Computer Vision with a brown color labelling."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "fUzLsN7w5yqWHf5g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.075, 0.203, 0.985, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the main body of the chart where the data is represented. It includes the bars that correspond to different technologies (Deep Learning, RPA, NLP, and Computer Vision) and their changes over seven years from 2015 to 2021. The x-axis represents the years, while the y-axis represents the level of the corresponding technology."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "zxWtptWiKKgAUYwj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.146, 0.912, 0.213, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object within these coordinates is the x-axis label for the year \"2015\"."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "fAYRDdyYc6DmHZ2E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.075, 0.203, 0.985, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, one can see the entire plot of the chart, which includes bars depicting the level of Deep Learning, RPA, NLP, and Computer Vision technologies over the years. Also, the x-axis and y-axis labels, their respective ticks, and the chart's grid lines are visible."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "fbE7icxnzsvjlp0V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Metrics<r>[0.015, 0.396, 0.033, 0.482]</r>."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "xB02i99eDy1jydpG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing \"Product Launches\" in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffffe0<r>[0.687, 0.189, 0.801, 0.294]</r>."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "zpi5kYZ30KyKVW6Z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range indicated by the highest label on the y-axis scale in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "200<r>[0.039, 0.008, 0.065, 0.028]</r>."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "iRXMPQexggQXXecW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the oldest data bar for \"Startup Incubation\"}<r>[0.116, 0.769, 0.230, 0.821]</r> taller than {the latest data bar for \"Patent Application\"}<r>[0.829, 0.213, 0.944, 0.350]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "8KxZXC7H2a4wh7eP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the data range reflected on the first y-axis label}<r>[0.056, 0.813, 0.065, 0.833]</r> cover the base of {the 2020 bar for \"Venture Capital Investment\"}<r>[0.829, 0.350, 0.944, 0.503]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "pgsiqW3IbPyz21Bj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bottom coordinate of Startup Incubation's 2015 bar}<r>[0.769]</r> above {the top coordinate of Startup Acceleration's 2015 bar}<r>[0.725]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "4xbPyfnE6zDYNPqb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.176, 0.870, 0.884, 0.978]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend including 6 items:\n1. \"Startup Incubation\" with orange color handle at [0.182, 0.918, 0.213, 0.933],\n2. \"Startup Acceleration\" with salmon color handle at [0.182, 0.950, 0.213, 0.965],\n3. \"Angel Investment\" with dark orange color handle at [0.412, 0.918, 0.443, 0.933],\n4. \"Venture Capital Investment\" with gold color handle at [0.412, 0.950, 0.443, 0.965],\n5. \"Patent Application\" with yellow color handle at [0.695, 0.918, 0.726, 0.933],\n6. \"Product Launches\" with lemon chiffon color handle at [0.695, 0.950, 0.726, 0.965]."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "ndAmhkeZSZ94uRx1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the space covering [0.116, 0.688, 0.944, 0.821]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the base of the chart where all initial year bars (2015) for each category are displayed. Each category's bar ranges slightly vary within these coordinates."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "yVU8fd8LjiDWDaPg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements found in [0.156, 0.835, 0.904, 0.855]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis year labels from 2015 to 2020, with each label properly aligned above its respective set of data bars for each year."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "56K2jtRdpk1zTxLp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.039, 0.202, 0.056, 0.222] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A label on the y-axis, which is 30."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "jkwnMHVMUZr22nCW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.108, 0.677, 0.206, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar representing the Series A investment in VC A, which is 10 millions dollars."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "bUD12XbSqEDlJ5RO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend}<r>[0.243, 0.019, 0.808, 0.095]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend indicates the color configuration for the bars. It tells us that the bars in the color #ff4500 represent Fintech Series A investments, whereas the bars in the color #ff6347 represent Fintech Series B investments."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "BColj0tJo4ci9ZKF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of investment value, is {VC A for Series A}<r>[0.108, 0.677, 0.206, 0.911]</r> higher or lower than {VC A for Series B}<r>[0.108, 0.374, 0.206, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "UNlhSy38RmV7FJJ1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {VC D for Series A}<r>[0.476, 0.327, 0.575, 0.911]</r> to the left or right of {VC E for Series A}<r>[0.599, 0.584, 0.697, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "09yPMCZ9Bpc7e9EG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on their coordinates, which VC has a higher investment in {Series B}<r>[0.108, 0.374, 0.206, 0.911],[0.231, 0.163, 0.329, 0.911],[0.353, 0.327, 0.452, 0.911],[0.476, 0.561, 0.575, 0.911],[0.599, 0.210, 0.697, 0.911],[0.722, 0.467, 0.820, 0.911],[0.845, 0.397, 0.943, 0.911]</r>: {VC B}<r>[0.231, 0.163, 0.329, 0.911]</r> or {VC E}<r>[0.599, 0.210, 0.697, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "VC B."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "MOhbP7oSuJxGDP6L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is this {object}<r>[0.015, 0.390, 0.033, 0.724]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the Y-axis title, called \"Number of Topics Discussed\"."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "LV0gSPS2Khm636NA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object of coordinates [0.527, 0.925, 0.668, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label \"Arts and Humanities\"."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "k9cnZGP1k1bwZr76",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does this {bar}<r>[0.108, 0.671, 0.223, 0.806]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the \"Computer Science\" category under \"Rapid Growth\"."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "79NVZUtjPrLs9wqL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Social Science under Rapid Growth}<r>[0.684, 0.357, 0.799, 0.791]</r> higher than the {bar representing Economics under Volatile Movement}<r>[0.828, 0.731, 0.943, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "zckrDYSv8PBnpSR0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of Economics}<r>[0.828, 0.237, 0.943, 0.731]</r> under \"Rapid Growth\" show a higher value than that in \"Volatile Movement\"<r>[0.828, 0.731, 0.943, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "T5uobpFCEEXCO4ep",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Social Science in Volatile Movement}<r>[0.684, 0.791, 0.799, 0.911]</r> less discussed than {Computer Science in Rapid Growth}<r>[0.108, 0.671, 0.223, 0.806]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "SWrZau9nooi8kHy8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region bounded by [0.307, 0.037, 0.744, 0.112]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend for the chart, with two entries. One is {Volatile Movement with its color indicator}<r>[0.345, 0.084, 0.375, 0.099]</r> in orange and text at [0.387, 0.082, 0.529, 0.104]. The other is {Rapid Growth with its color indicator}<r>[0.560, 0.084, 0.591, 0.099]</r> in light red and text at [0.603, 0.082, 0.706, 0.104]."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "kZWo8uY7YH964hrD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the box [0.396, 0.297, 0.511, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This box contains two bars that correspond to the \"Physics\" activity in two scenarios. The bar {[0.396, 0.716, 0.511, 0.911]} in orange indicates the \"Volatile Movement\" of 13 topics while the other bar {[0.396, 0.297, 0.511, 0.716]} in light red under \"Rapid Growth\" presents a growth in 28 topics."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "ePquOGLBWJlK9moz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.066, 0.203, 0.985, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire chart plot area. It covers vertical and horizontal axes, all bars for \"Volatile Movement\" and \"Rapid Growth\" in each academic field, as well as all labels and tick marks."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "CzAKdPeJutVNmrZq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the yellow bar}[0.125, 0.486, 0.153, 0.544] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 2018 trend value for AR/VR."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "BVMBTKKHRYT4HWrg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart as per the coordinates received?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emerging Technologies and Trends Outlook."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "RRPOBUjWCZUfClbS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.476, 0.450, 0.524, 0.472] in the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color label for the Cybersecurity trend line."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "LUPJxYLHdaz2Gq5H",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label, located at [0.023, 0.376, 0.052, 0.576], represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label represents Trend Value."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "QpQoB0U0GA6Diyjs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 2022 bar for {5G}[0.264, 0.247, 0.292, 0.344] extend above the one for {AR/VR}[0.264, 0.344, 0.292, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "rqG45AQrmoyZie3m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Blockchain bar in 2018}[0.125, 0.544, 0.153, 0.561] shorter or taller than the {AI bar in 2018}[0.125, 0.753, 0.153, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "z2gTVue9NmO2TF0h",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Cybersecurity}[0.125, 0.318, 0.153, 0.419] and {5G}[0.125, 0.419, 0.153, 0.486] in 2018, which technology trend has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "5G."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "SDUFleucGJrRoS4M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Internet of Things\"}[0.181, 0.621, 0.223, 0.738] and {\"AR/VR\"}[0.229, 0.504, 0.257, 0.604] in 2021, which trend has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "99Mgstc5g5R82Sej",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the region of [0.322, 0.098, 0.947, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area. It contains seven pairs of color-label indicating the seven technology trends: AI, Internet of Things, Blockchain, Quantum Computing, AR/VR, 5G, and Cybersecurity."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "Dp27cQuFyEV5Uj76",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the area [0.111, 0.891, 0.306, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features x-axis labels representing the years from 2018 to 2022."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "drQ09XUaXbd3F4W8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the region with the given coordinates [0.117, 0.082, 0.301, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the graph area where the bar graph is plotted. It involves sets of bars showing trend values for different technologies in various years."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "vKVz50EPtuGFhYKC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.195, 0.153, 0.257, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a portion of the bar for Cybersecurity trends in 2021."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "7ZB6kTGj6VvbF8DE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label located at [0.377, 0.912, 0.450, 0.933] signify in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label indicates the platform \"Facebook\"."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "o3M1txtycWgs6iWi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents Instagram in the chart, according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#e1306c<r>[0.335, 0.946, 0.365, 0.961]</r>."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "eGeTO72AnltVPtIX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at [0.903, 0.090, 0.929, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data value label \"245\" for December, representing Pinterest user activity."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "pwYCSiojQHpxhiBB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Twitter in January}[0.124, 0.401, 0.180, 0.447] shorter than {the bar for Pinterest in January}[0.124, 0.202, 0.180, 0.216]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "3NvbkyHa1WQVANta",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform\u2019s bar is higher in July, {LinkedIn}[0.541, 0.167, 0.597, 0.372] or {Pinterest}[0.541, 0.150, 0.597, 0.167]?"
+            },
+            {
+                "from": "gpt",
+                "value": "LinkedIn."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "x0rBDUYnu5xdNAI2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the May bar for Facebook}[0.402, 0.678, 0.458, 0.783] have more users compared to {the May bar for Twitter}[0.402, 0.381, 0.458, 0.431]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "Zp3DThAVOZKGBfJe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the section defined by the coordinates [0.328, 0.866, 0.740, 0.974]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend detailing six platform labels and colors. The specific elements include labels with text and corresponding color handles indicating the representation of each social media platform."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "Le0oJbmJhZOmb4QB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart features within the area [0.083, 0.056, 0.985, 0.783]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire bar chart showing monthly user activities for multiple social platforms throughout the year. It encompasses the vertical bars representing user counts, labels indicating each month, and different colors for each platform."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "KcmtAfVbg6qP4TKk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visual elements can be found within the bounds [0.660, 0.866, 1.000, 1.000]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are no chart elements or data visualizations in this area; it's primarily empty or possibly contains whitespace."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "d3vK3EqQaLGEgC3B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.039, 0.253, 0.056, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label '20'."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "1F5apDfrWeGsJjG0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color represent at the coordinates [0.523, 0.066, 0.554, 0.082] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents 'Incubators'."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "xce49tGKhKzAZSYr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at [0.856, 0.925, 0.882, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is 'Exit'."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "Eh1amnJqhZg49lgI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the bar}[0.671, 0.749, 0.784, 0.911] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the number of 'Startups' at the 'Series C' investment stage."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "uGHN6mIBWEwVY1nZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Startups bar}[0.389, 0.488, 0.502, 0.911] for 'Series A' bigger than the {Incubators bar}[0.389, 0.261, 0.502, 0.488] for the same stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "6SzlWttil6S6uSjI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Startups bar}[0.813, 0.879, 0.926, 0.911] at the 'Exit' stage shorter than the {Startups bar}[0.671, 0.749, 0.784, 0.911] at the 'Series C' stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "Y5ZZnKDtnybBSSYC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Incubators bar}[0.530, 0.326, 0.643, 0.586] for 'Series B' taller than the {Incubators bar}[0.671, 0.651, 0.784, 0.749] for 'Series C'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "7s7id4gGiK3kevVH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Startups bar}[0.107, 0.651, 0.220, 0.911] and the {Incubators bar}[0.107, 0.586, 0.220, 0.651] at the 'Pre-seed' stage, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Startups bar."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "15BxAyF9SzFUUabd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.110, 0.019, 0.922, 0.095]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend for the chart. It contains two entries. The first one has a color symbol at [0.385, 0.066, 0.416, 0.082] representing 'Startups' with a label at [0.428, 0.065, 0.493, 0.086]. The second entry has a color symbol at [0.523, 0.066, 0.554, 0.082] representing 'Incubators' with its label at [0.566, 0.065, 0.647, 0.086]."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "eDZAGLKS6ZGK8mB0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.530, 0.586, 0.643, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.530, 0.586, 0.643, 0.911] contains two bars. One {Startups bar}[0.530, 0.586, 0.643, 0.911] representing 'Series B' stage and one {Incubators bar}[0.530, 0.326, 0.643, 0.586] also representing 'Series B' stage."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "LrpKYSxCMt11PfI4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.107, 0.586, 0.220, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two bars. The {Startups bar}[0.107, 0.651, 0.220, 0.911] for the 'Pre-seed' investment stage, and the {Incubators bar}[0.107, 0.586, 0.220, 0.651] for the same 'Pre-seed' stage."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "nwdVqJmtDOXTCm2W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area [0.287, 0.925, 0.322, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.287, 0.925, 0.322, 0.945] contains the x-axis label 'Seed'."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "euIIXvIKm0ZUPk28",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the environmental indicator at [0.738, 0.648, 0.850, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pollution control."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "g8cd9fkOXHMBAfoP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text just below the bar representing recycling rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.874, 0.117, 0.923, 0.152]."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "grqdGThnd4NHN9Xh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the score of {the bar}[0.438, 0.163, 0.522, 0.624] representing Habitat restoration?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.805."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "qpmWfWEd48WgoQlq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates at [0.124, 0.278, 0.207, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the environmental indicator Air quality with a score of 0.605."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "lSxTsk2IVQ6GJ0A9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for {Habitat restoration}[0.438, 0.163, 0.522, 0.624] higher than the score for {Biodiversity}[0.647, 0.212, 0.731, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "fpVw6UchsX3UMsis",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the x-axis, is {Water quality}[0.223, 0.648, 0.318, 0.838] located to the left or to the right of {Land use}[0.341, 0.648, 0.409, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "YSGELYFT2GWdf4bx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Recycling rate}[0.857, 0.152, 0.940, 0.624] have a higher score than {Air quality}[0.124, 0.278, 0.207, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "g6pGzsDHIfCizJpT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for {Ecosystem services}[0.543, 0.255, 0.626, 0.624] less than {Pollution control}[0.752, 0.232, 0.836, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "V7bpsuvH92Z9m7wf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.543, 0.255, 0.626, 0.624]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains one bar representing the environmental indicator Ecosystem services. The bar has a score of 0.645, with the text displaying the score located at [0.560, 0.220, 0.609, 0.255]."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "sJpfA3EYiiIRqxh4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what the coordinates [0.043, 0.267, 0.071, 0.302] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates correspond to a y-axis label. The label displays the value \"0.6\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "3WZUxfb9RzVmZ8Ln",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region defined by [0.857, 0.152, 0.940, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area encompasses a bar representing the score for the environmental indicator Recycling rate, with the score being 0.825. The text showing the score is located at [0.874, 0.117, 0.923, 0.152]."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "zciShkZFcXgPKvLI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.415, 0.648, 0.544, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a label for the x-axis, specifically the environmental indicator Habitat restoration."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "5WvkeAJMUYwkcaGs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Platforms"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "TecEmG9ODok37BUT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.108, 0.193, 0.184, 0.799] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The User Engagement level for {Facebook}<r>[0.087, 0.818, 0.146, 0.924]</r>."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "6h1vVAfBHiRsHTcj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicates the User Engagement level in this plot at [0.661, 0.458, 0.738, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The User Engagement level for {Pinterest}<r>[0.659, 0.818, 0.715, 0.919]</r>."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "jPsfocXCdWN8H6Vc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher User Engagement level, {Facebook}<r>[0.087, 0.818, 0.146, 0.924]</r> or {Tumblr}<r>[0.765, 0.818, 0.810, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "GLvKMXIKma1u31GL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the User Engagement level for {Twitter}<r>[0.576, 0.818, 0.621, 0.900]</r> greater than that for {Snapchat}<r>[0.466, 0.818, 0.526, 0.924]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "dP7x6tHSco09u97Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Reddit}<r>[0.862, 0.818, 0.905, 0.897]</r> have a lower User Engagement level compared to {WhatsApp}<r>[0.272, 0.818, 0.336, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "gczxuN7InGyfFECr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within the region [0.108, 0.193, 0.184, 0.799]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar representing the User Engagement level for {Facebook}<r>[0.087, 0.818, 0.146, 0.924]</r>. The User Engagement level represented by this bar is 0.8, and the color of the bar is '#ff9999'."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "duDfCHqpA2Z6Wn0e",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the region [0.035, 0.182, 0.057, 0.210]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label {0.8}[0.035, 0.182, 0.057, 0.210], indicating the User Engagement level of 0.8."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "RmQJUVdfZZ0Z6MPJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed from the region [0.377, 0.818, 0.431, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for {LinkedIn}<r>[0.377, 0.818, 0.431, 0.914]</r>. To the left of this label is a bar denoting the User Engagement level for LinkedIn, which is relatively low compared to other platforms."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "ys3RVwC1rNAFxn6l",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.035, 0.030, 0.057, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label {1.0}[0.035, 0.030, 0.057, 0.058], which denotes the maximum User Engagement level that can be represented on the chart."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "AuZIg8dI0OfjjKVV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the upper left corner located at [0.015, 0.309, 0.029, 0.409]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "srmPcK7x22rAh9dW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category for the blue bar with the coordinates of [0.182, 0.476, 0.180, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Games."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "rOm5R4avSBuVuPac",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value marked at the coordinates [0.896, 0.361, 0.923, 0.385]?"
+            },
+            {
+                "from": "gpt",
+                "value": "115."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "jXgG4qIeSXuR6jiJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the bar located at [0.876, 0.385, 0.943, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Theme Parks."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "hxFLDBc3k11uChwm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat industry has higher revenue, {Video Games}<r>[0.112, 0.476, 0.180, 0.683]</r> or {Music}<r>[0.367, 0.566, 0.435, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Games."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "Mnohfv3jYB4GczXp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Online Sports Betting bar}<r>[0.706, 0.540, 0.774, 0.683]</r> have a higher revenue than the {Podcasts bar}<r>[0.621, 0.605, 0.689, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "gUA8RbpAnGek5VmW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Film bar}<r>[0.282, 0.164, 0.350, 0.683]</r> taller than the {Theme Parks bar}<r>[0.876, 0.385, 0.943, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "v6asEvmKXoRAfPPI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Surveillance Entertainment}<r>[0.791, 0.631, 0.859, 0.683]</r> have a lower revenue than {Social Media Entertainment}<r>[0.536, 0.527, 0.604, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "UMuHFBuc3eWC7zkv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area [0.112, 0.476, 0.180, 0.683]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the green bar representing \"Video Games\" with the revenue marked as 80 at coordinates [0.137, 0.452, 0.155, 0.476]."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "QUwyHDr0Oh9ikRzK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the group of objects in the region [0.446, 0.592, 0.519, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of a single green bar representing the industry \"Virtual Reality\" with a revenue value of \"35\" observed at position [0.477, 0.569, 0.494, 0.592]."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "Y8hCA04BtLgbBmXc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.035, 0.025, 0.061, 0.048]."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the value \"250\" labeled at Y-axis which represents the maximum value of 'Revenue'."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "LqtFHisicSFYxysP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the region [0.301, 0.699, 0.331, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is an axis label for one of the categories: 'Film'."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "Sns2DaeuKRX8MaG5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is being described in this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "oqNXkilz7kuQWR4M",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value Chain."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "3OxTft5AHez1vreg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the stage name} at [0.323, 0.716, 0.401, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Production."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "En1CdeOScWZEVwmQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates, what is the data value of the bar {After Sales} at [0.853, 0.176, 0.891, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.75."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "hnACevwoSozvagpt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Production}[0.294, 0.308, 0.430, 0.692] taller than {the bar for Marketing}[0.634, 0.372, 0.770, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "opaFVfkb9NJyzZwf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more stages to the left or right of {the Distribution bar}[0.464, 0.083, 0.600, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "O9K668ONRr0crWD3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Raw Materials bar}[0.124, 0.436, 0.260, 0.692] and the {After Sales bar}[0.804, 0.211, 0.940, 0.692], which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "After Sales."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "Jd5n9qdLbceYAUP1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is located at [0.634, 0.372, 0.770, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marketing."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "PVlWGwWpVNo53XR6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars contained within the area from [0.294, 0.308, 0.430, 0.692] to [0.634, 0.372, 0.770, 0.692]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified region, there are two bars: the bar for Production with a value of 0.6 located at [0.294, 0.308, 0.430, 0.692] and the bar for Marketing with a value of 0.5 located at [0.634, 0.372, 0.770, 0.692]."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "PYbwBHkBrLUvxKdF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the chart can be seen in the area [0.143, 0.696, 0.241, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the stage \"Raw Materials\" can be seen in this area."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "1eMN0FksPPJqqwQH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the range [0.602, 0.083, 0.692, 0.692]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there is one bar representing the stage \"Distribution\" with a value of 0.95. The area also includes the x-ticks label for \"Distribution\"."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "qR86bAoQnq2MrNq5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content shown at [0.019, 0.309, 0.036, 0.537] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label called \"Consumer Behavior\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "eVq9sn8vGexnJLjR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.124, 0.306, 0.207, 0.811] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the 'BRIC' category with a value of 0.65, according to {the corresponding chart text}<r>[0.146, 0.283, 0.185, 0.306]</r>."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "QL5TmOs0OvXDE7xe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the data represented at [0.775, 0.151, 0.813, 0.174]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the value \"0.82\" for the \"EAGLEs\" category, according to {the corresponding bar}<r>[0.752, 0.174, 0.836, 0.811]</r>."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "tEkTenX4vngX5W5G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data indicated at [0.251, 0.205, 0.289, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows a value of \"0.75\" for the \"MINT\" category, according to {its corresponding bar}<r>[0.228, 0.228, 0.312, 0.811]</r>."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "ccs7gVbzbOgggaUk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"MINT\"}<r>[0.228, 0.228, 0.312, 0.811]</r> taller than {the bar representing \"VISTA\"}<r>[0.333, 0.384, 0.417, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "eCOVUjSEvqbdrYqs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'FRANK' bar}<r>[0.543, 0.244, 0.626, 0.811]</r> taller or shorter than {the 'CIVETS' bar}<r>[0.647, 0.283, 0.731, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "tdmbXqaIyr6mhjja",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Next Eleven\"}<r>[0.438, 0.190, 0.522, 0.811]</r> represent a higher or lower value than {the \"3G\" bar}<r>[0.857, 0.267, 0.940, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "y5C7sXpLiveAJQ24",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the {bar for \"BRIC\"}<r>[0.124, 0.306, 0.207, 0.811]</r> and {bar for \"CIVETS\"}<r>[0.647, 0.283, 0.731, 0.811]</r>, which one represents a greater value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"CIVETS\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "iLtUNYt2OhZ1l5Er",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.124, 0.306, 0.940, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are several bars representing different 'Emerging Markets' categories and their respective 'Consumer Behavior' values. The bars correspond to 'BRIC', 'MINT', 'VISTA', 'Next Eleven', 'FRANK', 'CIVETS', 'EAGLEs', and '3G' from left to right. Each bar is labeled with a respective value indication above it."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "xCEpY6fwHmbv8GGZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.043, 0.802, 0.071, 0.826]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains one vertical (Y-axis) scale label \"0.0\", where {the corresponding y-axis tick}<r>[0.083, 0.811]</r> is located."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "iO0qSiY8CeqXLgwy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.043, 0.025, 0.071, 0.048] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a Y-axis scale label \"1.0\", with {the relevant tick}<r>[0.083, 0.034]</r> located on the Y-axis."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "zb4452rravMHmLl4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is included in the area from [0.124, 0.828, 0.899, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "All x-axis labels are included in this area. The labels from left to right are 'BRIC', 'MINT', 'VISTA', 'Next Eleven', 'FRANK', 'CIVETS', 'EAGLEs', and '3G'. Each label has a corresponding tick on the X-axis."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "kBcvghL6EWZSowhV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar in coordinates [0.543, 0.172, 0.626, 0.767] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the Participation Rate of Cycling exercise."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "TKtBGE3kCY5lHDoC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked in coordinates [0.019, 0.281, 0.036, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, called Participation Rate."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "81wXAzMP6EAEu61c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located in these coordinates [0.492, 0.942, 0.573, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the X-axis label, which is named Exercises."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "4puqN2n8XEjUUA5d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at [0.843, 0.787, 0.899, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the X-axis tick for the category Zumba."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "vH9AGfBtYFSYwK2u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing running}[0.438, 0.085, 0.522, 0.767] taller than {the bar representing swimming}[0.752, 0.201, 0.836, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "s1tsu3nDeYN9kveU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar for Yoga}[0.124, 0.433, 0.207, 0.767], is {the bar for Pilates}[0.228, 0.520, 0.312, 0.767] taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "Eq31dFk05Ddf3Apt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher participation rate between {Cycling}[0.543, 0.172, 0.626, 0.767] and {Weightlifting}[0.647, 0.244, 0.731, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cycling."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "VAGFFb0WDKfkApks",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Zumba}[0.857, 0.448, 0.940, 0.767] and {CrossFit}[0.333, 0.346, 0.417, 0.767], which exercise has a lower participation rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "CrossFit"
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "yuRwIYGz8opDy1vd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the display in the coordinates [0.124, 0.405, 0.207, 0.767]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a bar for the category of Yoga, with a corresponding participation rate of 0.23. The blue bar is indicated by coordinates [0.124, 0.433, 0.207, 0.767] and the annotation text of 0.23 is located at [0.146, 0.405, 0.185, 0.433]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "5yjuep0C5d7CAQZj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present in [0.599, 0.787, 0.689, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area lies the x-axis label of Weightlifting; its actual coordinates are [0.599, 0.787, 0.689, 0.931]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "2KDXS00vdl2NioyI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.847, 0.420, 0.940, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains an orange bar and its value. The bar represents the participation rate for Zumba and the value is 0.22. The coordinates for the bar are [0.857, 0.448, 0.940, 0.767] and for the value, they are [0.879, 0.420, 0.918, 0.448]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "wtRqLQt8bEmHY4hv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is shown in the area [0.043, 0.756, 0.071, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label of '0.0', which indicates zero participation rate in any exercises. The coordinates for this text are [0.043, 0.756, 0.071, 0.784]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "RqEikf6K6bzfA27U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the specific object at location [0.043, 0.386, 0.071, 0.421] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis scale labeled \"0.4\"."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "Bf15cg3khSsK86Gi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.228, 0.226, 0.312, 0.633] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing {\nthe healthcare services}<r>[0.228, 0.226, 0.312, 0.633]</r>, with a funding level of 0.7."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "dqXdpS98qkx0Xrsu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated at the coordinates [0.543, 0.435, 0.626, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing {\nthe public transport services}<r>[0.543, 0.435, 0.626, 0.633]</r>, with a funding level of 0.34."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "nsSz1B0zazImkBC5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {healthcare bar}[0.228, 0.226, 0.312, 0.633] taller than the {educational services bar}[0.124, 0.284, 0.207, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "DBGUYZXId1KvvbnC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer, {the bar representing \"infrastructure\"}[0.752, 0.109, 0.836, 0.633] or {the bar representing \"social services\"}[0.857, 0.121, 0.940, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Infrastructure."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "78QwvSWyklg9DFPI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom left to right, is the {police services bar}[0.333, 0.400, 0.417, 0.633] located before or after the {firefighting services bar}[0.438, 0.313, 0.522, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "nabKnQSmSdsvDsdm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the area [0.043, 0.386, 0.904, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It displays the main plot of the chart, which consists of vertically arranged service sectors such as education, healthcare, police, firefighting, public transport, parks & recreation, infrastructure, and social services. The bars representing these sectors indicate the proportion of funding allocated."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "hfra9Jj8NneXTHkp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within [0.043, 0.503, 0.071, 0.538]."
+            },
+            {
+                "from": "gpt",
+                "value": "The section shows the 0.2 tick mark on the y-axis."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "SFpYPX8td2ZCdfYl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the objects within the coordinates [0.333, 0.400, 0.417, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is a bar that represents the funding level for the police service. The bar height indicates a funding level of 0.4."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "bOvKtjUm9LXVIdMp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the rectangle at coordinates [0.119, 0.289, 0.204, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fashion & Apparel."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "gfs0LciOjEBhbiqB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar}[0.119, 0.289, 0.204, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#3f5e8a."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "M9dQT9W4nOFyItpc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {coordinates}<r>[0.351, 0.624, 0.395, 0.652]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "-0.174."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "A2EeSc45o9e4HBDZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for 'Toys, Hobby & DIY'}[0.542, 0.324, 0.627, 0.561] higher or lower than the {bar for 'Furniture & Appliances'}[0.436, 0.361, 0.521, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "DXxwYKKrlAJRlYPa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the bars of the {Health & Wellness industry}[0.648, 0.241, 0.732, 0.561] and the {Automotive & Parts industry}[0.753, 0.561, 0.838, 0.609], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health & Wellness."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "368sNTStTTDrZGxt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater, the one at {coordinates}<r>[0.142, 0.261, 0.181, 0.289]</r> or at {coordinates}<r>[0.882, 0.251, 0.921, 0.279]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value at [0.882, 0.251, 0.921, 0.279]."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "ilUwA5U0Hf16o4UO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.357, 0.685, 0.479, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label for the X-axis called 'Furniture & Appliances'."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "IOrDnQpEAjIJXi9D",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.330, 0.561, 0.415, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see a bar representing negative growth in the 'Food & Personal Care' sector, the value is -0.174 as shown in the coordinates [0.351, 0.624, 0.395, 0.652]."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "F6rWjIwgAk3jRuqm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.542, 0.324, 0.627, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar that represents 'Toys, Hobby & DIY' with growth of 0.455 as shown in the coordinates [0.565, 0.296, 0.604, 0.324]."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "hBwIIcOizNTdEyAi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales (in thousands)."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "6ILRMjTtimFg0wpZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown on the x-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Months."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "HpA2V9iRLlNHUrRd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color #69bd78 represent in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the sales for each month."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "h1Cng9UUDJu8sxQb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data at {the bar label}<{bar label coordinates}[0.688, 0.253, 0.732, 0.288]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "4400."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "ra5jhntqctY5LJPH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for February}[0.207, 0.396, 0.262, 0.814] higher than {the bar for July}[0.547, 0.336, 0.601, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "iqrQW4R2YA23yJeL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the highest bar}[0.819, 0.097, 0.873, 0.814] for November or December?"
+            },
+            {
+                "from": "gpt",
+                "value": "November."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "mAmBksR57shIRpHG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre sales in {the first half of the year}[0.139, 0.276, 0.194, 0.814],[0.207, 0.396, 0.262, 0.814],[0.275, 0.348, 0.330, 0.814],[0.343, 0.318, 0.398, 0.814],[0.411, 0.264, 0.466, 0.814], [0.479, 0.240, 0.533, 0.814] generally higher than sales in {the second half of the year}[0.547, 0.336, 0.601, 0.814],[0.615, 0.431, 0.669, 0.814],[0.683, 0.288, 0.737, 0.814],[0.751, 0.205, 0.805, 0.814], [0.819, 0.097, 0.873, 0.814], [0.887, 0.240, 0.941, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "S1lFnP6JDKvTR4Ik",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher sales figure, {April}[0.343, 0.318, 0.398, 0.814] or {June}[0.479, 0.240, 0.533, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "June."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "BYFp8R2gbMFoOPxz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the coordinates [0.099, 0.037, 0.981, 0.814]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area of the bar chart. It includes x and y axis labels, x and y ticks labeled with months and sales respectively, and the sales bars colored in #69bd78, each representing the sales number for a month."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "HsxG6axalVbREUc1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe all the objects in the chart from April [0.343, 0.318, 0.398, 0.814] to September [0.683, 0.288, 0.737, 0.814]."
+            },
+            {
+                "from": "gpt",
+                "value": "From April to September, the chart displays the sales volume for each month with green bars. The height of each bar corresponds to the sales in that month. April, May, June, July, August, and September have corresponding sales of 4150, 4600, 4800, 4000, 3200, and 4400 respectively."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "SFDOQ6qtSbes2eNO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the coordinate [0.076, 0.800, 0.087, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for the value 0."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "J9L9ScQmMNdXkel8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart contain in the region [0.819, 0.097, 0.873, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar in the chart within this region, which represents the sales for the month of November. The height of the bar corresponds to a sales volume of 6000."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "d5dA4DyGNY8vZ85O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Number of Students}<r>[0.015, 0.244, 0.029, 0.477]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "1sILh4Ct4A3ef4NO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is represented by the bar at coordinates [0.888, 0.312, 0.944, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Scientific Research}<r>[0.813, 0.704, 0.916, 0.875]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "7EymAgcErpoc53eT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the bar representing \"Online Education Platforms\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{3500}<r>[0.689, 0.092, 0.724, 0.116]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "G98jyoPEA9Tsx4xC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is at coordinate [0.563, 0.704, 0.707, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Online Education Platforms}<r>[0.563, 0.704, 0.707, 0.942]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "ccpMkOhOmBaecYw4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Software Engineering}<r>[0.103, 0.704, 0.218, 0.896]</r> shorter or taller than the bar representing {Mechanical Engineering}<r>[0.231, 0.704, 0.358, 0.916]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "6FfZoilQ0GsRiPdr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparing the bar for {Overseas Education}<r>[0.818, 0.606, 0.874, 0.687]</r> and {Environmental Sciences}<r>[0.370, 0.704, 0.497, 0.916]</r>, which one has a lower value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Overseas Education."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "bUBAPOVCrdAxdxgR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Electrical Engineering}<r>[0.260, 0.527, 0.316, 0.687]</r> to the right or left of the bar for {Environmental Sciences}<r>[0.370, 0.704, 0.497, 0.916]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "JYBNX6zfMQ1eZlqc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Active Learning}<r>[0.539, 0.442, 0.595, 0.687]</r> shorter or taller compared to the bar for {Language Learning}<r>[0.609, 0.361, 0.665, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "Do8rb9bycQ9aPze8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars and their labels seen in the region [0.100, 0.300, 0.300, 0.690]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified region, there are two bars with their labels. The first bar represents {Software Engineering}<r>[0.190, 0.516, 0.246, 0.687]</r> with a value of 1050{1050}<r>[0.201, 0.493, 0.236, 0.516]</r>. The second bar stands for {Electrical Engineering}<r>[0.260, 0.527, 0.316, 0.687]</r> with a value of {980}<r>[0.275, 0.504, 0.301, 0.527]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "IqtBrN7zQq4U3id9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.480, 0.400, 0.600, 0.690]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in the region, the first being {Active Learning}<r>[0.539, 0.442, 0.595, 0.687]</r> with a value of {1500}<r>[0.550, 0.419, 0.585, 0.442]</r> and the second is {Language Learning}<r>[0.609, 0.361, 0.665, 0.687]</r> with a value of {2000}<r>[0.619, 0.337, 0.654, 0.361]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "s1feERqDfJY3VsEv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within region [0.370, 0.500, 0.530, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar in the region which represents {Environmental Sciences}<r>[0.469, 0.560, 0.525, 0.687]</r>. The value of the bar is {780}<r>[0.484, 0.537, 0.511, 0.560]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "BWjaDTPWCHP1mtK0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the data represented in the region [0.800, 0.200, 0.950, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars within the region. The first is for {Practical Training}<r>[0.748, 0.393, 0.804, 0.687]</r> with a value {1800}<r>[0.759, 0.370, 0.794, 0.393]</r>. The second bar represents {Overseas Education}<r>[0.818, 0.606, 0.874, 0.687]</r> and has a value of {500}<r>[0.833, 0.582, 0.859, 0.606]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "6jjxaiKMck8r1gAF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the Y-axis in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the Y-axis is \"GDP growth rate\", as seen in the coordinates {Y-axis label}<r>[0.013, 0.403, 0.024, 0.568]</r>."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "df8iaIpwTwAc9TJL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the last recorded year in the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last recorded year is \"2020\" and its coordinates are [0.896, 0.931, 0.925, 0.951]."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "Tk0xnWWE9BlCZBVN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the GDP growth rate for the year {2018}<r>[0.703, 0.168, 0.772, 0.629]</r> represented in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GDP growth rate for the year 2018 is 3.2."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "hBFCpCXTLuzSNEDo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value on the y-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest value on the y-axis scale is {4}<r>[0.039, 0.045, 0.046, 0.065]</r>."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "ufZvOrGhIUdP6O7B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the GDP growth rate for {2019}<r>[0.789, 0.298, 0.858, 0.629]</r> greater or less than the rate for {2016}<r>[0.529, 0.499, 0.599, 0.629]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "EyI0h8oG3XOfbgRM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {2014}<r>[0.356, 0.629, 0.425, 0.701]</r> and {2020}<r>[0.876, 0.629, 0.945, 0.859]</r>, which year had a negative GDP growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "mca8myexRhVfBtjr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the GDP growth rate increase or decrease from {2012}<r>[0.183, 0.514, 0.252, 0.629]</r> to {2013}<r>[0.270, 0.384, 0.339, 0.629]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "rsDIj97vUgjZmuYl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {2018}<r>[0.703, 0.168, 0.772, 0.629]</r> and {2019}<r>[0.789, 0.298, 0.858, 0.629]</r>, which year had the higher GDP growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "LG6PGymqTIVqTnQj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bar area for the year {2016}<r>[0.529, 0.499, 0.599, 0.629]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The year of the bar is 2016, as stated by the x-axis label below the bar. The height and coordinates of the bar indicate that the GDP growth rate for 2016 was 0.9."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "uP9SlbALqRb6E7QI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content situated within [0.180, 0.959, 0.925, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content within [0.180, 0.959, 0.925, 0.979] represents the X-axis label. The label is \"Year\"."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "fJkialqlEBxd3tIw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed within the region [0.549, 0.429, 0.685, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area showcases three bars. The first bar represents the year 2016 with a GDP growth rate of 0.9. The second bar represents the year 2017 achieving a GDP growth rate of 1.5, and lastly, the third bar represents the year 2018 with a surged GDP growth rate of 3.2."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "J6WurbU5WDHGCrHx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the information within the area [0.116, 0.931, 0.252, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the area [0.116, 0.931, 0.252, 0.951] lie the labels for the years 2011 and 2012."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "Q6qlhKsrhVyYisP1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being labeled with coordinates [0.015, 0.207, 0.029, 0.402] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, it's titled \"Performance (%)\"."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "pDZOrl9TTyBqcsfF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.876, 0.197, 0.943, 0.582] in the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the project of \"Environmental Protection\"."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "c6QVdebqU9QHnuGd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the project indicated at coordinates [0.112, 0.142, 0.180, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The project is \"Housing Availability\"."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "sjLZSoxhuUzKVaXw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is mentioned at coordinates [0.180, 0.599, 0.153, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for a project called \"Housing Availability\"."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "ninos0HUpaU308Fn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Housing Availability\"}[0.112, 0.142, 0.180, 0.582] perform better or worse than {the bar representing \"Pension Schemes\"}[0.706, 0.087, 0.774, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "I2BCaTEmBSIv3dVE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project performs better, {the bar representing \"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582] or {the bar representing \"Healthcare Services\"}[0.621, 0.142, 0.689, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Youth Development Programs\" perform better."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "ckTE7WsPg4Gqn2PT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Infrastructure Development\"}[0.197, 0.170, 0.265, 0.582] on the right or left of {the bar of \"Vocational Training Programs\"}[0.536, 0.181, 0.604, 0.582] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "0tgtzayV0wihYXfe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Employment Assistance bar}[0.451, 0.225, 0.519, 0.582] and the {Adult Literacy Programs bar}[0.367, 0.197, 0.435, 0.582], which one indicates a higher performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Adult Literacy Programs\" bar."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "Pu9e2YKtvpxHiVwg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe each labelled project in the region [0.043, 0.353, 0.061, 0.376]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.043, 0.353, 0.061, 0.376] contains labels for the 40th percentile of the performance scores being analyzed."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "obdtOoy6QdZO8VXw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you see within the area [0.112, 0.087, 0.180, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region, there is a bar showing the performance score of \"Housing Availability\" on the chart. It seems to be showing a score of approximately 80."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "ErpJuWIKpQ5HMQf6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell what's in the area [0.067, 0.0, 0.978, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly consists of the bar chart itself. It contains the bars representing performance scores for various community development projects, the y-axis labels indicating scores from 0 to 100, and the x-axis labels naming the different community development projects."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "sq82krz5f9kJ04iH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content shown in [0.624, 0.599, 0.638, 0.827]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the project label \"Healthcare Services\" on the x-axis."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "vCgARtj5NBPU41KD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the research field at the coordinate [0.193, 0.280, 0.261, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The research field at this coordinate is Physics."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "QfVpR0BawCqZNUVp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is found at [0.790, 0.248, 0.858, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the Innovation Score for Nanotechnology is found at the coordinates."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "3mgJfumtP9kUAg9Y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text written at [0.202, 0.919, 0.253, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text written at this coordinate is 'Physics'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "5nPVBxC0HtN0IlSV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher {the bar of Biotechnology}[0.364, 0.264, 0.432, 0.903] or {the bar of Astronomy}[0.449, 0.328, 0.517, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Astronomy."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "Fu0YSbk93g9HWHKl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars of AI and Nanotech}[0.108, 0.185, 0.176, 0.903, 0.790, 0.248, 0.858, 0.903] of equal height?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "4xKrrq0MbAlpENzq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of Genetics}[0.875, 0.105, 0.943, 0.903] the tallest among all the research fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "A4IG1rg70bB6IkYz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area from [0.108, 0.185, 0.176, 0.903] to [0.193, 0.280, 0.261, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas consist of two bars. The bar from [0.108, 0.185, 0.176, 0.903] represents the Innovation Score of AI research field while the bar at [0.193, 0.280, 0.261, 0.903] signifies the Innovation Score of Physics research field. The bar representing AI is taller than the bar of Physics, indicating AI has a higher Innovation Score."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "4LbfzM1boCOFdgtb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed from [0.718, 0.919, 0.760, 0.942] to [0.879, 0.919, 0.939, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas contain two labels corresponding to two different research fields. The coordinates at [0.718, 0.919, 0.760, 0.942] label the field as 'EnvSc' and the coordinates at [0.879, 0.919, 0.939, 0.942] label the field as 'Genetics'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "SndqIuOhHYK4YYKW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from [0.790, 0.248, 0.858, 0.903] to [0.875, 0.105, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two bars representing the Innovation Scores for the 'Nanotech' and 'Genetics' research fields. The bar for 'Nanotech' is located at [0.790, 0.248, 0.858, 0.903] and is not as tall as the 'Genetics' bar, which is located at [0.875, 0.105, 0.943, 0.903]. This indicates that 'Genetics' has a higher Innovation Score than 'Nanotech'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "2wYQUv8dVdVCiRcL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.492, 0.952, 0.560, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, called Processes."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "yVyNDgjnZpeaYdC5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.599, 0.085, 0.698, 0.684]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "IXwM9rYdDsBsRr2N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {y-axis}[0.015, 0.277, 0.029, 0.431]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Effectiveness."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "9VaSa1j8O2eMZ1Ko",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.722, 0.474, 0.820, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "ch3UlZmaEwPCPYQS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Underperforming\"}[0.108, 0.414, 0.206, 0.684] taller than {the bar for \"Average\"}[0.231, 0.294, 0.329, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "MG04oLpKStZslmuB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Good\"}[0.354, 0.235, 0.452, 0.684] have a higher value than {the bar for \"Exceptional\"}[0.477, 0.145, 0.575, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "Z7jVGruXoHcWLLjV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {bars for \"Good\", \"Exceptional\", and \"Outstanding\"}[[0.354, 0.235, 0.452, 0.684], [0.477, 0.145, 0.575, 0.684], [0.599, 0.085, 0.698, 0.684]] higher than the {bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "zMvPhMO7WA8Ni2pH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Process of Concern\"}[0.722, 0.474, 0.820, 0.684] to the left or right of {the bar for \"Another Process of Concern\"}[0.845, 0.534, 0.943, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "Gd85XV1OtDaCDPdT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.108, 0.414, 0.206, 0.684]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue vertical bar labeled \"Underperforming\". The bar represents an effectiveness of 0.45."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "DoAC5yUtdJZzFyTz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.035, 0.435, 0.057, 0.458]."
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"0.4\" is located on the y-axis at this coordinate, representing the effectiveness level."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "biOMQDyrq4Z4L2iB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.477, 0.145, 0.575, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue vertical bar representing the process \"Exceptional\" with an effectiveness of 0.90."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "4sz9uCpVi6Zw5BLw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.354, 0.235, 0.452, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue bar for the process \"Good\", which represents an effectiveness of 0.75."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "C4wzpaQVfp1YyC5J",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Frequency}<r>[0.015, 0.327, 0.029, 0.447]</r>"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "fDHxyNKZzeIt5tPO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the eighth activity labeled on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Travel}<r>[0.719, 0.765, 0.758, 0.830]</r>"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "qMubACDX22yCgilK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould I have the coordinates of the last bar - {Watching Movie}<r>[0.875, 0.124, 0.943, 0.749]</r>-  please?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates of {Watching Movie} bar are <r>[0.875, 0.124, 0.943, 0.749]</r>"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "jlDPlYOKGVZg9l30",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is presented at the second x-tick mark on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {Walking}<r>[0.203, 0.765, 0.252, 0.846]</r>."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "IXRoDA3hbpbaIYPo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \u201cSwimming\u201d}<r>[0.364, 0.170, 0.432, 0.749]</r> higher than {the bar for \u201cDancing\u201d}<r>[0.534, 0.354, 0.602, 0.749]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "Ejm71p2IWAZuZ7mc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Gardening's bar}<r>[0.108, 0.334, 0.176, 0.749]</r> located at the left of {Travel's bar}<r>[0.705, 0.157, 0.773, 0.749]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "2ZPEVqVK2AEsmJ3H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the rest, does {Cooking's frequency}<r>[0.790, 0.255, 0.858, 0.749]</r> stand at the lower half or upper half?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower half."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "H3Y94njXvHe4UEh3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the {Running}<r>[0.278, 0.216, 0.347, 0.749]</r> bar or the {Reading books}<r>[0.449, 0.288, 0.517, 0.749]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Running}<r>[0.278, 0.216, 0.347, 0.749]</r> bar."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "fQBGuIGw1wtFRE0t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the coordinates [0.108, 0.124, 0.176, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within those coordinates, there is a bar for the activity {Gardening}<r>[0.108, 0.334, 0.176, 0.749]</r>."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "uSQ2Hemh15EcQVIJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the bounding box [0.862, 0.765, 0.957, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That bounding box contains the x-axis label for the activity {Watching_Movies}<r>[0.862, 0.765, 0.957, 0.923]</r>."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "v3poM5oliAU1lr9E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the rectangle [0.364, 0.170, 0.432, 0.749] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangle includes a bar that represents the frequency of {Swimming}<r>[0.364, 0.170, 0.432, 0.749]</r>."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "1dM9Gbpf8H89srpj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease give a brief description of the objects within the bounding box [0.034, 0.082, 0.058, 0.763]."
+            },
+            {
+                "from": "gpt",
+                "value": "Included within these coordinates are various labels on the y-axis representing frequencies, including {0.0}<r>[0.035, 0.740, 0.057, 0.763]</r>, {0.2}<r>[0.035, 0.608, 0.057, 0.631]</r>, {0.4}<r>[0.035, 0.476, 0.057, 0.500]</r>, {0.6}<r>[0.035, 0.345, 0.057, 0.368]</r>, {0.8}<r>[0.035, 0.213, 0.057, 0.237]</r>, and {1.0}<r>[0.035, 0.082, 0.057, 0.105]</r>."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "er0Hp5KNwkisqVF6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label situated at [0.015, 0.233, 0.029, 0.370]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Return Rate."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "Hg4XubGRa2XaPnci",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which part of the chart is the label for \"Small_Cap_Investing\" present?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.737, 0.586, 0.751, 0.825]."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "42MrLUshK4CzTe7y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat investment strategy does the bar at [0.877, 0.351, 0.944, 0.521] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation_Hedging."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "ZcR9dRG5yj8ynCBH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat area corresponds to the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.459, 0.952, 0.613, 0.975]."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "2F5XDYRKep5dcxx5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {return rate for Quality_Investing}<r>[0.461, 0.180, 0.527, 0.521]</r> higher than {return rate for Growth_Investing}<r>[0.294, 0.278, 0.361, 0.521]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "Xdsh9RcAH2ASctHX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar visualizing the return for Inflation_Hedging}<r>[0.877, 0.351, 0.944, 0.521]</r> taller than {the bar illustrating the return for Value_Investing}<r>[0.211, 0.326, 0.277, 0.521]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "w6YO5bFf8Sm5fruZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Socially_Responsible_Investing}<r>[0.544, 0.521, 0.611, 0.545]</r> and {Index_Investing}<r>[0.627, 0.302, 0.694, 0.521]</r>, which one shows a better return rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index_Investing."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "MPbYaCSltHF5MBpW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the return rates for {Diversification}<r>[0.127, 0.229, 0.194, 0.521]</r> and {Momentum_Investing}<r>[0.377, 0.253, 0.444, 0.521]</r> different?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "fPTdEm83GrXKLMOS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.127, 0.156, 0.944, 0.521]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains bars representing return rates for 10 different investment strategies - Diversification, Value_Investing, Growth_Investing, Momentum_Investing, Quality_Investing, Socially_Responsible_Investing, Index_Investing, Small_Cap_Investing, Balanced_Investment, and Inflation_Hedging."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "rwnMX79qyraoiRU8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region between [0.087, 0.034, 0.985, 0.570]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the entire chart, bars representing return rates for different investment strategies, labels for Y-axis and X-axis, and the ticks and labels for both axes."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "6ceMC2JIssn998Ox",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the rectangle [0.087, 0.570, 0.918, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the labels for all the investment strategies namely Diversification, Value_Investing, Growth_Investing, Momentum_Investing, Quality_Investing, Socially_Responsible_Investing, Index_Investing, Small_Cap_Investing, Balanced_Investment, and Inflation_Hedging."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "vxcdyvbEEzlECWHM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is held within the region [0.035, 0.025, 0.077, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains labels for y-axis, representing negative and positive return rates, from '-0.02' to '0.2'."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "3bNhLQddZMBmKX7i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.422, 0.019, 0.621, 0.041] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's subtitle, which reads \"AI Application Success Rates\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "LK41AAsnM4BlMBb9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at [0.616, 0.279, 0.686, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the success rate of \"Autonomous Vehicles\", which is approximately 0.70."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "tcQTuz3E6gwGlWd5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-range of {the bar of Education}<r>[0.789, 0.174, 0.859, 0.684]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-range for the \"Education\" bar is '0.00 -> 0.88'."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "6JuH5SxCN5rdoLTS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Gaming bar}[0.876, 0.105, 0.945, 0.684] higher than {the Natural Language Processing bar}[0.184, 0.157, 0.253, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "xVIXZCd1KYQBhfeZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the HealthCare bar}[0.444, 0.134, 0.513, 0.684] left or right of {the Education bar}[0.789, 0.174, 0.859, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "VllBOft67ODVqvJU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the Cybersecurity bar}[0.703, 0.134, 0.772, 0.684] or {the Robotics bar}[0.271, 0.186, 0.340, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Cybersecurity bar."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "oQsAip3Sa5kYN44E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region [0.098, 0.105, 0.945, 0.684]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes ten bars representing the success rates of different AI applications: {Machine Vision}[0.098, 0.209, 0.167, 0.684], {Natural Language Processing}[0.184, 0.157, 0.253, 0.684], {Robotics}[0.271, 0.186, 0.340, 0.684], {Forecasting}[0.357, 0.232, 0.426, 0.684], {HealthCare}[0.444, 0.134, 0.513, 0.684], {Speech Recognition}[0.530, 0.151, 0.599, 0.684], {Autonomous Vehicles}[0.616, 0.279, 0.686, 0.684], {Cybersecurity}[0.703, 0.134, 0.772, 0.684], {Education}[0.789, 0.174, 0.859, 0.684], and {Gaming}[0.876, 0.105, 0.945, 0.684]."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "7B5Xh4yHjckSGGGg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is happening in [0.026, 0.096, 0.056, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the y-axis of the chart with coordinates ranging from 0 to 1 and respective labels are: \"0.0\"[0.029, 0.677, 0.047, 0.695], \"0.2\"[0.029, 0.562, 0.047, 0.579], \"0.4\" [0.029, 0.446, 0.047, 0.463], \"0.6\" [0.029, 0.330, 0.047, 0.347], \"0.8\" [0.029, 0.214, 0.047, 0.231], \"1.0\" [0.029, 0.098, 0.047, 0.115]."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "tcbEnP7kQ8Fq7VYX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.126, 0.696, 0.916, 0.830] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the application names in AI along the x-axis. It includes labels: {\"Machine_Vision\"}[0.126, 0.696, 0.138, 0.830], {\"Natural_Language_Processing\"}[0.213, 0.696, 0.225, 0.957], {\"Robotics\"}[0.299, 0.696, 0.311, 0.770], {\"Forecasting\"}[0.386, 0.696, 0.397, 0.797], {\"HealthCare\"}[0.472, 0.696, 0.484, 0.794], {\"Speech_Recognition\"}[0.559, 0.696, 0.570, 0.871], {\"Autonomous_Vehicles\"}[0.645, 0.696, 0.657, 0.885], {\"Cybersecurity\"}[0.732, 0.696, 0.743, 0.817], {\"Education\"}[0.818, 0.696, 0.830, 0.783], {\"Gaming\"}[0.905, 0.696, 0.916, 0.764]."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "1YspNPu8hL1CJqU2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar corresponding to the coordinates [0.357, 0.134, 0.426, 0.626] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents Two-factor Authentication with an effectiveness score of 0.90."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "nblYRtEKIlOu14PM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the bar for Data Encryption}<r>[0.616, 0.134, 0.686, 0.626]</r> located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar is located at [0.616, 0.134, 0.686, 0.626]."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "2piVT1iluXMtpPAG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the x-axis label found at [0.400, 0.952, 0.642, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label 'Cybersecurity and Data Privacy Measures'."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "1jAMDlRls8BLY5bi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Encryption Methods' bar}<r>[0.098, 0.200, 0.167, 0.626]</r> taller than the {Anonymising Services' bar}<r>[0.703, 0.194, 0.772, 0.626]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "MaXaAZll1qrVNq7o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher effectiveness score, {Password Managers}<r>[0.271, 0.145, 0.340, 0.626]</r> or {Biometric Verification}<r>[0.789, 0.167, 0.859, 0.626]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Password Managers."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "7T05OmMoMD3Xc1oJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Two-factor Authentication bar}<r>[0.357, 0.134, 0.426, 0.626]</r> taller than the {Data Encryption bar}<r>[0.616, 0.134, 0.686, 0.626]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "CttpvpJOjCA2gY4l",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.357, 0.112, 0.599, 0.626]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars:\n1. The bar representing Two-factor Authentication is within these coordinates [0.357, 0.134, 0.426, 0.626].\n2. The bar representing VPN is within these coordinates [0.530, 0.112, 0.599, 0.626]."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "6r1UffpzhDQ64NSy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is in the area [0.029, 0.071, 0.047, 0.094]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '1.0' is located at the coordinates [0.029, 0.071, 0.047, 0.094]."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "oyXz9Fh0HCFT7zMt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.098, 0.112, 0.859, 0.626]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a series of bars representing the effectiveness of different Cybersecurity and Data Privacy measures. It encompasses Encryption Methods [0.098, 0.200, 0.167, 0.626], Private Browsing [0.184, 0.178, 0.253, 0.626], Password Managers [0.271, 0.145, 0.340, 0.626], Two-factor Authentication [0.357, 0.134, 0.426, 0.626], Secure Wireless Networks [0.444, 0.271, 0.513, 0.626], VPN [0.530, 0.112, 0.599, 0.626], Data Encryption [0.616, 0.134, 0.686, 0.626], Anonymising Services [0.703, 0.194, 0.772, 0.626], Biometric Verification [0.789, 0.167, 0.859, 0.626], and part of the Privacy-Focused Browsers bar."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "GFMX9KS7gLKOKWaM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element at [0.098, 0.128, 0.167, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar for 'Math'."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "l2eHhvRvLNsV2Xgr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.565, 0.817] represent in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick for 'Computer Science'."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "rmuMRi1H64ydbPaL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the area [0.098, 0.128, 0.167, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Represents the utilization of 'Math' resources."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "t6EU0c2ATrBnXRiq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the entity located at [0.461, 0.964, 0.581, 0.981] name?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, 'Education Resources'."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "RlO6gmmz6jS2iAAE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Math'[0.098, 0.128, 0.167, 0.817] higher than that of 'Art'[0.616, 0.324, 0.686, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "AXstn8OxyQWgNeXC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of 'Physics'[0.444, 0.244, 0.513, 0.817] to the left or right of the bar of 'Chemistry'[0.357, 0.207, 0.426, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "muT9VWEk1QFlEPKk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'English'[0.184, 0.171, 0.253, 0.817] taller than the bar representing 'Physical Education'[0.789, 0.157, 0.859, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "o5DwadFHU7bqzedq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing 'Biology'[0.271, 0.295, 0.340, 0.817] and 'History'[0.876, 0.135, 0.945, 0.817], which has greater resource utilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "History."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "Agv6VvOMXdM0kh41",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you can see between [0.098, 0.128, 0.167, 0.817] and [0.876, 0.135, 0.945, 0.817]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar chart displaying the utilization of various education resources; from 'Math', 'English', 'Biology', 'Chemistry', 'Physics', 'Computer Science', 'Art', 'Music', to 'Physical Education', and 'History'. The height of each bar denotes the utilization score, with 'Computer Science' showing the highest and 'Art' the lowest."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "lqVCK6RRb2rCb6Nq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCharacterize the region [0.055, 0.019, 0.988, 0.817] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It spans the complete chart, covering ten vertical bars representing utilization of various education resources, labels of resources (Education Resources) and their utilization score, and also includes the x and y-axes with their labels."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "oa8ljNujCNlZHuel",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's noticeable in the area around [0.703, 0.309, 0.772, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing 'Music' with its utilization score visually depicted by the height of the bar."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "nJc331jHewNESFPd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the area [0.565, 0.099, 0.599, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is containing the bar for 'Computer Science' which is the highest among all other resource bars indicating its highest utilization."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "8yt8aCZLMGvLqpgH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the y-axis of the chart having coordinates [0.015, 0.299, 0.029, 0.433]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Trade Value'."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "2KaLlLnjfWl7ijIN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry is represented by the bar at coordinates [0.790, 0.161, 0.858, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The industry represented at these coordinates is {Lumber_Industry}[0.778, 0.723, 0.870, 0.878]."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "fajOeaUjvXFIcDTa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the bar at position [0.619, 0.143, 0.688, 0.707] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents a trade value of 0.91 for {Metal_Industry}[0.612, 0.723, 0.695, 0.861]."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "hk6O6bPWTOkEvCu1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Metal_Industry}[0.619, 0.143, 0.688, 0.707] having a higher trade value than that of {the bar representing Energy_Sector}[0.534, 0.316, 0.602, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "cvonL8uWXCst0S4Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the bar at coordinates [0.193, 0.292, 0.261, 0.707], does {Automobile_Industry}[0.172, 0.723, 0.283, 0.909] have a smaller trade value than {Food_Industries}[0.449, 0.723, 0.526, 0.868] represented by the bar at coordinates [0.449, 0.199, 0.517, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "52IA9eHvV6ImFeT0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Bar representing Electronics_Industry}[0.278, 0.236, 0.347, 0.707] closer to {bar representing Chemical_Industry}[0.705, 0.168, 0.773, 0.707] or {bar representing Automobile_Industry}[0.193, 0.292, 0.261, 0.707] in terms of trade value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automobile Industry."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "BZSrsL3ihkDz9Npy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is shown in the area [0.193, 0.292, 0.261, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a single bar showing the trade value of the {Automobile_Industry}[0.172, 0.723, 0.283, 0.909]. The trade value is 0.67 as indicated by the height of the bar."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "W6xx2WW0gGcuRKUx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the area with the coordinate [0.332, 0.723, 0.464, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we can see {Pharmaceutical Industry} label and the corresponding bar. The bar [0.364, 0.354, 0.432, 0.707] indicates a trade value of 0.57."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "8UcSncGgD5pUvqMK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the content within the coordinates [0.066, 0.025, 0.985, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the entire plotting area of the bar chart. It includes ten bars, each representing the trade value of a different industry sector. The sector names are on the x-axis and have coordinates ranging from [0.094, 0.723, 0.190, 0.884] for {Clothing_Industry} to [0.877, 0.723, 0.941, 0.830] for {Agriculture}. The y-axis signifies the trade value ranging from 0 at [0.035, 0.698, 0.057, 0.721] up to 1 at [0.035, 0.078, 0.057, 0.101]."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "nQtoTB5znTuA0aUI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.015, 0.211, 0.029, 0.455] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Annual Growth Score."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "BjgAEpPQBAUY5bpu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the bar corresponding to Biotechnology?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.189, 0.215, 0.258, 0.641]"
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "wR6ji83dOK1HjWDb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label} at [0.361, 0.952, 0.686, 0.975] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Knowledge Economy and Innovation Industries."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "1RE0yU25WCGrMOiX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels shown in [0.043, 0.631, 0.052, 0.655] and [0.035, 0.072, 0.052, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are y-axis scale labels representing {0}[0.043, 0.631, 0.052, 0.655] and {10}[0.035, 0.072, 0.052, 0.095] respectively."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "cw7lzIUQikddlNf0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for AI and Machine Learning}[0.104, 0.098, 0.172, 0.641] taller or shorter than {the bar for Biotechnology}[0.189, 0.215, 0.258, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "Gwdjb5AgWAE6UmhZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {IoT}[0.875, 0.154, 0.943, 0.641] and {Green Energy}[0.361, 0.109, 0.429, 0.641], which category scored higher in annual growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Energy."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "CSlUuT1oKg30f54B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing Nanotechnology}[0.618, 0.087, 0.686, 0.641] achieve a higher annual growth score than {the bar representing Internet Security}[0.703, 0.221, 0.772, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "oxPOeBuUX2s49OCZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {AI and Machine Learning}[0.104, 0.098, 0.172, 0.641] and {Data Science}[0.275, 0.165, 0.344, 0.641], which industry has a higher annual growth score?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI and Machine Learning."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "QEJiAAY5oxBoi5Ps",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area from [0.062, 0.025, 0.985, 0.641]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the primary chart area containing the bar plots for different Knowledge Economy and Innovation Industries with their respective Annual Growth Score. The industry names are positioned above each bar on the x-axis and the growth scores are marked along the y-axis."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "XvFFbfu2bvO0E9EZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the x-axis using the information found within [0.043, 0.631, 0.052, 0.095] and [0.361, 0.952, 0.686, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis represents different categories in Knowledge Economy and Innovation Industries with the label placed at [0.361, 0.952, 0.686, 0.975]. The range of scale appears to be from 0 to 10, based on the scale labels at [0.043, 0.631, 0.052, 0.655] and [0.035, 0.072, 0.052, 0.095] respectively."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "fUd6Eg5F3CTBnRIc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.043, 0.296, 0.052, 0.319]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label indicating a y-axis scale of '4'."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "sT0WBr4priNCNryS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is included in the region [0.723, 0.215, 0.858, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars, representing the annual growth scores for {Internet Security}[0.703, 0.221, 0.772, 0.641] and {Quantum Computing}[0.789, 0.201, 0.857, 0.641], respectively."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "coqYazBuz4zlyjGj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could the labelled item at these coordinates [0.036, 0.368, 0.044, 0.385] be referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis value, which is 12."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "0tVvsGMWaaD7T2WE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.365, 0.879, 0.413, 0.951] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis category, known as \"Soybeans\"."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "hXwIHj3YeOQtvGAH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the title {subplot title}<r>[0.386, 0.019, 0.653, 0.041]</r> state?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food and Agriculture Industry Analysis."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "dWgbLUnIcEMmmBDs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element represented by [0.181, 0.313, 0.250, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Corn bar}<r>[0.181, 0.313, 0.250, 0.867]</r>."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "sHhUcbrvKSqaGUrv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Cotton}<r>[0.528, 0.621, 0.598, 0.867]</r> shorter or taller than the bar of {Coffee}<r>[0.441, 0.760, 0.511, 0.867]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "oc2kXcTVfjdt6d2t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Fruits bar}<r>[0.876, 0.207, 0.945, 0.867]</r> located to the left or right of the {Grains bar}<r>[0.789, 0.264, 0.858, 0.867]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "eLjKk5I5OE4Ge1gt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Coffee\"}<r>[0.441, 0.760, 0.511, 0.867]</r> and {\"Sugarcane\"}<r>[0.615, 0.067, 0.685, 0.867]</r>, which commodity has higher production level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sugarcane."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "IM6aX7pTYziPthoU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the x-axis label {\"Wheat\"}<r>[0.112, 0.879, 0.146, 0.931]</r> located to the left or the right of {\"Rice\"}<r>[0.290, 0.879, 0.315, 0.918]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "37uwRdF2RT4KTydy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.094, 0.444, 0.164, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a green bar of the \"Wheat\" category on the chart, showing a production level of about 10.3 million metric tons."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "2lFbCuUIMsdOQ9ml",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in [0.615, 0.067, 0.685, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a green bar representing the \"Sugarcane\" category, with a high production level stretch upwards reaching nearly 20 million metric tons."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "DGx4HgEWzgrZIoYX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the region [0.181, 0.313, 0.250, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a green bar showing the production level of \"Corn\", which amount to around 13.5 million metric tons."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "lqwpOHwP1oMJ9C2m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content visually represented in [0.790, 0.264, 0.858, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a green bar representing the data of \"Grains\". The height of the bar represents its production value, around 14.7 million metric tons."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "rG9j6QO8gl0pjNdA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat colour is the bar at the coordinates [0.330, 0.729, 0.393, 1.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "qvSaaJTWy3llHlex",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does the figure element at [0.882, 0.597, 0.945, 1.588] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2020."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "bElcSifyLzTa67Ah",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give the size or positional information for the y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, \"Art funds in Millions\", has a bounding box that spans from [0.012, 0.401, 0.024, 0.572]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "XV8rVUDCmyfIgfhd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what text is written in the bounding box from [0.384, 0.019, 0.656, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Culture and Arts Industry Development\" is contained within the bounding box [0.384, 0.019, 0.656, 0.041]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "PKsEiFqVDapcWRGX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar with coordinates [0.330, 0.729, 0.393, 1.588] extend higher on the chart than the bar at [0.803, 0.333, 0.866, 1.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "DKgcDzNzCa5n8JXl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the axis label at [0.507, 0.964, 0.532, 0.981] positioned lower than the figure element at [0.029, 0.920, 0.043, 0.938]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "IqNNXetrmUnNg9CH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the chart, are the bars quickly becoming taller from [0.094, 0.839, 0.157, 1.588] to [0.330, 0.729, 0.393, 1.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "0SI73wKz58yG3vXR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar at [0.724, 0.355, 0.787, 1.588] represent a greater value than the label at [0.029, 0.480, 0.043, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "HUzL7zBkFI7wmlSU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements contained within the region from [0.057, 0.021, 0.708, 1.0]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plotting area of the chart showing year-wise allocation of Art funds from 2010 to 2020. The bars indicating funds allocation span from bottom to top, located at various points along the x-axis representing years."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "NlkuQT23xIixCKOn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the plot area [0.052, 0.047, 0.988, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plotting area [0.052, 0.047, 0.988, 0.927] contains the bars that represent the Art funds for each year. The bottom edge of these bars aligns with the x-axis (year), and the height of these bars indicates the value of the Art funds in that year."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "wGoVMr5AdKwVDVyX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the box with coordinates [0.252, 0.597, 0.315, 1.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this bounding box, you can see a bar that corresponds to the year 2012 and reflects the funding for Art in millions for that year."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "ldbvCnWu5jenfXNH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements located inside the region [0.583, 0.920, 0.613, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.583, 0.920, 0.613, 0.957] contains the text label \"2016\" which represents one of the x-axis (years) labels."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "TPqabp0DjvVAlE0s",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the object found at [0.010, 0.319, 0.019, 0.469] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, named Leadership Score."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "sXYFG2GihEs6c4OH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the third text in the x-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.295, 0.774, 0.305, 0.931]."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "CKTOURJDfD95h22u",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar in the position [0.265, 0.210, 0.335, 1.867] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Leadership Score of the Human Resources department."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "JvycWxTCatSJnv6a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.023, 0.019, 0.041, 0.036] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the value \"100\" on the y-axis scale."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "4l6WoIRMUpwRGMLm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Sales\"}[0.090, 0.486, 0.160, 1.867] higher or lower than {the bar representing \"Marketing\"}[0.177, 0.394, 0.247, 1.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "bh3sgYgWKuCPlqY2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Operations\" department's score bar}[0.527, 0.578, 0.597, 1.867] have a higher value than {the \"Finance\" department's score bar}[0.440, 0.118, 0.510, 1.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "htLDRbfLeu6uM8Lk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars on the left side of the chart}[[0.090, 0.486, 0.160, 1.867],[0.177, 0.394, 0.247, 1.867],[0.265, 0.210, 0.335, 1.867], [0.352, 0.302, 0.422, 1.867]] generally lower than {the bars on the right side of the chart}[[0.527, 0.578, 0.597, 1.867],[0.615, 0.247, 0.685, 1.867],[0.702, 0.541, 0.772, 1.867],[0.790, 0.431, 0.860, 1.867],[0.877, 0.173, 0.947, 1.867]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "KvMGpgsw4DwyHOLZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department has a higher leadership score, {Sales}[0.090, 0.486, 0.160, 1.867] or {IT}[0.352, 0.302, 0.422, 1.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "IT."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "s0FnmNNxo9DzyrGa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region [0.023, 0.019, 0.041, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the lower y-axis values, specifically \"60\", \"65\", \"70\", \"75\", and \"80\" with coordinates:\n1. \"60\": [0.029, 0.755, 0.041, 0.773]\n2. \"65\": [0.029, 0.663, 0.041, 0.681]\n3. \"70\": [0.029, 0.571, 0.041, 0.589]\n4. \"75\": [0.029, 0.479, 0.041, 0.497]\n5. \"80\": [0.029, 0.387, 0.041, 0.405]"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "vYLL181hgdZDu900",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.702, 0.541, 0.772, 1.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar that represents the Leadership Score of the Product department, which is 72."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "VQhlBmIbaJW4VvAv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.090, 0.486, 0.160, 1.867] consist of in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains one bar that represents the Leadership Score of the Sales department, which is 75."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "H23WxJa07Skhrn4M",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained in the region [0.208, 0.774, 0.217, 0.861]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the 'Marketing' category on the x-axis."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "1KDL8o3a38wO0coU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the performance in Q1 according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color representing performance in Q1}<r>[0.804, 0.214, 0.832, 0.226]</r> is a shade of mint green."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "hIqyqINQPQTyCny9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company at the position [0.219, 0.009, 0.441, 0.760] has the highest performance in the Q3?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The company name located at the position}<r>[0.160, 0.729, 0.209, 0.747]</r> is Regeneron Pharmaceuticals."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "OsibO1CYCa9e6ako",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-range for Johnson & Johnson's bars in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The x-range for Johnson & Johnson}<r>[0.219, 0.139, 0.881, 0.205]</r> starts from 0.00 and goes up to 390.00."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "YGH0UyEWo6ZuItgI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q3 performance bar for Pfizer}[0.638, 0.467, 0.881, 0.532] shorter than {the Q2 performance bar for Gilead Sciences}[0.408, 0.548, 0.611, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "H2APSsFwDKyi5bPo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider, {the Q1 bar for Novartis}[0.219, 0.892, 0.395, 0.940] or {the Q3 bar for Bristol-Myers Squibb}[0.633, 0.772, 0.871, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Q3 bar for Bristol-Myers Squibb}<r>[0.633, 0.772, 0.871, 0.820]</r>."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "gTBO3vZSm230dmgz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the subtitle for the third subplot}[0.324, 0.673, 0.880, 0.695] contain more companies than {the subtitle for the first subplot}[0.437, 0.019, 0.767, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "q2agQj91SDblfxUi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are visible in the region [0.219, 0.701, 0.985, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the subplot for companies Novartis, Sanofi, Bristol-Myers Squibb, and Regeneron Pharmaceuticals. Included are their Q1 to Q3 performance bars, x-ticks from 0 to 800, and labels for the y-ticks of each company."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "CI6PKAHruLUhB6Uz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.219, 0.374, 0.985, 0.625]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains parts of the second subplot which holds Q1 to Q3 performance bar details, x-ticks for ranges between 0 to 600, and y-tick labels for Gilead Sciences, Pfizer, and Roche."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "SHeohn1FD9xYizDx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.219, 0.047, 0.985, 0.297] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specific area includes a subplot featuring three companies: Amgen, Johnson & Johnson, and Merck & Co with their associated performance bars from Q1 to Q3, x-tick labels ranging from 0 to 500, and relevant legends depicting various performance stages color-coded."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "QUz17A6WldCQ4WTZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.010, 0.416, 0.019, 0.582]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Factors."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "w6NnPliGu5SxusKr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color label of {the bar associated with \"Compliance Risk\"}<r>[0.106, 0.113, 0.272, 0.192]</r> in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "#9dcec9."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "s0ahdKSx2CpyqGi8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the value range indicated by the bar}<r>[0.106, 0.410, 0.258, 0.489]</r> for \"Reputation Risk\" in the first interval data?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 -> 0.55."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "hdUfzVTuvGHCrDC6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the starting point of the \"Legal Risk\" bar}<r>[0.106, 0.212, 0.203, 0.291]</r> higher or lower than {the starting point of the \"Operational Risk\" bar}<r>[0.106, 0.607, 0.189, 0.686]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "4bv19gsyo3Yzu9up",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Market Risk\" bar}<r>[0.106, 0.508, 0.285, 0.588]</r> in the start interval reach further right than {the \"Credit Risk\" bar}<r>[0.707, 0.706, 0.845, 0.785]</r> in the second interval?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "5LAeN4fqSEx0hBoa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater range, {the \"Strategic Risk\" bar}<r>[0.106, 0.311, 0.327, 0.390]</r> in the first interval or {the same \"Strategic Risk\" bar}<r>[0.707, 0.311, 0.941, 0.390]</r> in the third interval?"
+            },
+            {
+                "from": "gpt",
+                "value": "Third interval."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "fbH7OxlVLoiU4n5D",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within {the y-axis ticks region}<r>[0.050, 0.142, 0.100, 0.861]</r> in subplot 0."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the labels for the risk factors on the y-axis, which are listed from top to bottom: Compliance Risk, Legal Risk, Strategic Risk, Reputation Risk, Market Risk, Operational Risk, Credit Risk, and Cyber Risk."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "tJq3b3nPBeBfkWG9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within {the region}<r>[0.707, 0.075, 0.983, 0.923]</r> corresponding to subplot 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot 2 shows a series of bars representing risk factors for Interval 2, with varying lengths indicating the risk measurement values. The background color for the subplot is #d3d3d3."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "ObWzaJg60pRSO3Zo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in {the xticks region}<r>[0.399, 0.942, 0.690, 0.970]</r> of subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick region contains numerical labels that correspond to the data points at regular intervals across the bottom of the chart. The numbers are 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "BJUeTnU2h1UZKybs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is displayed at the coordinates [0.840, 0.506, 0.944, 0.518]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Modern Age."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "Nl4FqPq9pyFPEPug",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is located at [0.528, 0.202, 0.528, 0.202]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no axis label at these coordinates."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "ZpmPayQMi51NoRMF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the hue of {the subtitle for the \"Iron Age\"}<r>[0.485, 0.407, 0.571, 0.422]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "IiZWwbCEu7rTZKVC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the textual label for \"Bronze Age\"}<r>[0.840, 0.059, 0.939, 0.071]</r> to the left or right of {the corresponding color handle}<r>[0.791, 0.060, 0.826, 0.068]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "RywVQfsQ5hUl7wnn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, {the bar for \"100\" in the \"Medieval Ages\"}<r>[0.452, 0.145, 0.520, 0.178]</r> or {the bar for \"100\" in the \"Modern Age\"}<r>[0.452, 0.132, 0.520, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medieval Ages."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "EynUgA2f8Yk5IpoH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the color of legends for \"Stone Age\"}<r>[0.791, 0.043, 0.826, 0.051]</r> match {the color of the bars for the \"Stone Age\" at \"20\"}<r>[0.116, 0.038, 0.183, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "kKkjraXUA12jsESt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.784, 0.432, 0.973, 0.523]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the Iron Age is located in this region, and it includes category labels like \"Stone Age\", \"Bronze Age\", \"Iron Age\", \"Medieval Ages\", and \"Modern Age\". The handle for the Iron Age, specifically, is in color/r>[0.791, 0.473, 0.826, 0.481]</r>."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "Oz3nxIAS4c8usMrN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area from [0.075, 0.031, 0.981, 0.178]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section represents the \"Stone Age\" subplot including bars representing different values at intervals from 20 to 200. The color of the bar for \"100\" is/r>[0.791, 0.043, 0.826, 0.051]</r> which matches the legend color indication for Stone Age."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "CydiParoCmkQhMKE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within [0.075, 0.426, 0.981, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the \"Iron Age\" subplot. It includes bars reflecting values for \"20\" to \"200\", x-ticks, y-ticks, and a subtitle in/r>[0.485, 0.407, 0.571, 0.422]</r>. The corresponding legend for this age is also included."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "jOWv53K3ua9clhUe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.127, 0.039, 0.824, 0.223] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing USA's Middle Tier."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "XEUEXIC3yjoQvjq4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend associate with the label 'USA'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Color for 'USA'}<r>[0.361, -0.083, 0.396, -0.071]</r> is '#ff6347' or 'tomato'."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "vNf3eSfK2IvePzIS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of label 'Germany' in the upper tier {[0.127, 0.269, 0.708, 0.452]} according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "QTLmWobZIWlAiKyZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'USA' bar for the 'Upper Tier'[0.127, 0.269, 0.592, 0.452] higher than the 'Germany' bar for the 'Upper Tier'[0.127, 0.748, 0.708, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "3ACKIZk3hrgriTCT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Middle Tier' are the bars for 'USA'[0.127, 0.518, 0.941, 0.702] and 'Germany'[0.127, 0.518, 0.941, 0.702] at the same height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "bSswf40OL7vOgLCW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Canada' bar in the 'Upper Tier'[0.127, 0.748, 0.650, 0.931] shorter than the 'UK' bar in the 'Upper Tier'[0.127, 0.748, 0.708, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "hVDW2wlpLsiAzrDT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.127, 0.518, 0.941, 0.702]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains bars for 'USA', 'Canada', 'UK' and 'Germany' for 'Middle Tier'. The bars are respectively titled 'USA', 'Canada', 'UK' and 'Germany' and the bars have different lengths indicating different values for these countries."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "hLA82rL5l0m39RYu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the area with coordinates [0.354, -0.091, 0.646, -0.008]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plot legend is located in these coordinates. The legend includes five items which correspond to different labels: 'USA', 'Canada', 'UK', 'Germany' and 'France'. Each item in the legend includes a color symbol and a corresponding text label. The colors are '#ff6347' for USA, '#f5deb3' for Canada, '#81acce' for UK, '#8fbc8f' for Germany, and '#191970' for France."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "96lcjnGSPR1IKCC9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region bounded by [0.127, 0.039, 0.824, 0.223]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains bars representing the Middle Tier for the countries USA, Canada, UK, Germany, and France. Each bar corresponds to a different value, with the width of the bar indicating the value for each country."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "yztePHLUsFnkPmVS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend label of \"steady_increase\"}<r>[0.349, -0.067, 0.461, -0.053]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff6347"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "CXoitEVk8XeHKBuA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-range for the bar labeled \"Cinema\" in the category of {rapid expansion}<r>[0.113, 0.036, 0.694, 0.125]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 -> 280.00"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "sw6l6qx5Kpn5Y118",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is associated with {the yellow colored handle}<r>[0.310, -0.045, 0.338, -0.035]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "inexorable_rise"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "sepH7FANGGm3gOF6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {theatre bars in subplot 1}<r>[0.113, 0.741, 0.611, 0.829]</r> higher than {dance bars in subplot 1}<r>[0.113, 0.852, 0.736, 0.940]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "rqdqXQEapL6HtTI4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for \"Literature\" under \"steady_increase\"}<r>[0.113, 0.147, 0.943, 0.236]</r> longer than the {bar for \"Literature\" under \"inexorable_rise\"}<r>[0.113, 0.147, 0.238, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "t324xytaPuOeJTgL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {green bar for \"Dance\"}<r>[0.113, 0.852, 0.736, 0.940]</r> reach higher levels than the {blue bar for \"Dance\"}<r>[0.113, 0.852, 0.269, 0.940]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "jbZUSqXvilM19q64",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the section <r>[0.113, 0.036, 0.653, 0.125]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar for \"Cinema\" under the label \"steady_increase\" with a range shown as \"0.00 -> 260.00\" for the red colored bar."
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "DyFm3X7JQ0kPXnIw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area <r>[0.113, 0.630, 0.736, 0.719]</r> in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a bar for \"Photography\" reflecting data under the label \"steady_increase\" colored in red, indicating a range of \"0.00 -> 300.00\"."
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "h4piFkSqJY5PSwYg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the quadrant <r>[0.113, 0.852, 0.591, 0.940]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a bar graph segment for the category \"Dance,\" under \"steady_increase\" which is color-coded in red, with an x-range measuring from \"0.00 -> 230.00.\""
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "WodwfR8bAM1dk8jO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat behavior is the color '#6d98ba' associated with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Slow Recovery}<r>[0.582, -0.084, 0.709, -0.067]</r>."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "Abyt0iUGzxFxMUCO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is 'Canada' label located?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Canada}<r>[0.032, 0.772, 0.099, 0.790]</r>."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "2RzA2bmezR2EMV7G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value, associated with 'Germany', has the color '#c1d37f'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Unstable Behavior}<r>[0.111, 0.627, 0.280, 0.714]</r>."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "har4ng2i52Xo7nW9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region is associated with the label 'USA'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{USA}<r>[0.063, 0.881, 0.099, 0.898]</r>"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "J0AoFSFzqpjgxX7x",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the USA bar of Unstable Behavior}<r>[0.111, 0.844, 0.781, 0.931]</r> thicker than {the USA bar of Slow Recovery}<r>[0.111, 0.844, 0.922, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "8mH2gVAwjAGvaCXR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Canada's Unstable Behavior}<r>[0.111, 0.735, 0.126, 0.822]</r> value less than {France's Unstable Behavior}<r>[0.111, 0.518, 0.256, 0.605]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "grVW4IQvQDeMxaYk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the label 'Unstable Behavior' at the left or right of 'Slow Recovery'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "oYUshIvaVRjtu80S",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to Germany's {Unstable Behavior}<r>[0.111, 0.627, 0.280, 0.714]</r>, is its {Slow Recovery}<r>[0.111, 0.627, 0.280, 0.714]</r> equal, lower or higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "viPpivdwIC5mdX8V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is showing in the coordinates [0.111, 0.257, 0.126, 0.344]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single bar representing 'Canada' for the scenario 'Unstable Behavior' with a value of approximately 55."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "KL3X1FEOfnusIAoj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.105, 0.964, 0.415, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are three numerical labels located on the x axis, which correspond to the numerical values 0, 500 and 1000 respectively."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "2MMSGh4XHveTi4Dj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area [0.284, -0.091, 0.716, -0.060]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the legend of the graph displaying two color-text pairs. First pair includes a color label with color='#c1d37f' representing 'Unstable Behavior' and another pair includes a color label with color='#6d98ba' corresponding to 'Slow Recovery'"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "heBFJS5DS7RftJDW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information displayed in coordinates [0.111, 0.844, 0.922, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 bars representing two different behaviors for the country 'USA'. The grey bar with a range of 0.00->2370.00 represents 'Unstable Behavior', while the blue bar with a range of 0.00->2870.00 represents 'Slow Recovery'."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "VrBjX2DCauItqMnb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}[0.157, 0.353, 0.942, 0.430] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Heart Disease cases in California."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "gl0uQQT0WgW15d9a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value written at [0.495, 0.461, 0.643, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It says \"Number of Cases\", which is the label for the x-axis."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "xEx9sYR6vYKQc08N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the horizontal line} at [0.271, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis tick representing the value of 500 cases."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "Yf6bZfIPzBwHVNB6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information about the health conditions found at [0.157, 0.65, 0.817, 0.75]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the Heart Disease cases in {Georgia}[0.076, 0.671, 0.145, 0.689] with a value of 2500 cases."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "ldgPVHKEvo02LHzG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Heart Disease cases in Georgia}[0.157, 0.640, 0.277, 0.717] higher or lower than {Heart Disease cases in California}[0.157, 0.353, 0.942, 0.430]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "orTMMB7UBBtphCVX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher, {the Cancer cases in Ohio}[0.157, 0.736, 0.259, 0.813] or {the Cancer cases in North Carolina}[0.157, 0.544, 0.314, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "North Carolina."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "PDWBzDgE5pnihaiU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Heart Disease in New York}[0.157, 0.065, 0.817, 0.142] lesser than {Heart Disease in Texas}[0.157, 0.257, 0.783, 0.334]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "JULalgWMPYUdwwaH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {Cancer in Illinois}[0.157, 0.832, 0.282, 0.909], is the {Heart Disease in Texas}[0.157, 0.257, 0.783, 0.334] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "mbbtZQP1ckP1zJdZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content seen in [0.157, 0.544, 0.317, 0.621]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the number of Cancer cases in North Carolina which is 690 cases."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "24r689cMbR92YgnA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe the features found at [0.065, 0.096, 0.145, 0.114]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label for New York on the y-axis of the chart."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "hI7st5pwLBH1oo44",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.157, 0.257, 0.783, 0.334] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar that signifies the Heart Disease cases in Texas, which is 2750 cases."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "iocZGSW8dWdY817S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the items located at [0.850, 0.534, 0.973, 0.566]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend label denoting the color '#f5deb3' represents Cancer."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "37IMNtJaU7gVCsBQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {bar representing \"Cancer\"}<r>[0.157, 0.544, 0.314, 0.621]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#f5deb3."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "bn0pUBY9M3QQHq3G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the bar representing \"Cancer\"}<r>[0.157, 0.544, 0.314, 0.621]</r> for the state \"North Carolina\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "690.00 cases."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "QJaZ2jYdwL0Pv65S",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the upper bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It does not have a label."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "65DikfM8Oum7Pqel",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher number of cases in {New York}, {Heart Disease}<r>[0.157, 0.065, 0.817, 0.142]</r> or {Cancer}<r>[0.157, 0.544, 0.314, 0.621]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heart Disease."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "PhlWGVLH7jMFENJa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health condition, {Heart Disease}<r>[0.157, 0.065, 0.817, 0.142]</r> or {Cancer}<r>[0.157, 0.544, 0.314, 0.621]</r>, has a larger case count in the {state of Illinois}<r>[0.157, 0.832, 0.282, 0.909]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cancer."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "n6KYSLxrFPUuOSig",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {state of Florida's Heart Disease count}<r>[0.157, 0.161, 0.680, 0.238]</r> larger than {Illinois' Cancer number}<r>[0.157, 0.832, 0.282, 0.909]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "vyEC1yPESsyeajqd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the bar chart that's represented within the region [0.157, 0.526, 0.981, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "It looks like a horizontal bar chart that is showing the number of cancer cases in various states. There are four bars in the chart, corresponding to the states North Carolina, Georgia, Ohio, and Illinois. The bar for Illinois has the highest value at 550.00 cases, with coordinates located at [0.157, 0.832, 0.282, 0.909]."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "PEl5XrBjuDb8OmdU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is evident from the region [0.157, 0.047, 0.981, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a horizontal dataset representing the incidence of Heart Disease in four states: New York, Florida, Texas, and California. The state with the highest number of Heart disease cases is California with 3450.00 cases, and its coordinates are [0.157, 0.353, 0.942, 0.430]."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "ds4iVcPw5oKuGJQx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items present within the region [0.849, 0.534, 0.973, 0.566]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the bar chart. There is one item, represented as a rectangle of color #f5deb3 with its coordinates being [0.856, 0.543, 0.890, 0.555]. Next to it is the text \"Cancer\", which is the label for the data values presented in the form of the bar chart."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "RZu3uYtN61OSyU0d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the blue bar at coordinates [0.094, 0.039, 0.573, 0.223] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Adult Literacy Rate for females."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "eH25QWxMvb9VcbAh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.353, -0.134, 0.554, -0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the legend: Adult Literacy Rate (%)."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "65psWb6T5Fix9jWw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the orange bar at [0.573, 0.518, 0.654, 0.702] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Youth Unemployment Rate for females."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "zWn9t0wXcKS3CPUA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the green bar at [0.654, 0.518, 0.926, 0.702] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Tertiary Education Completion Rate for females."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "c2yxn0AQugjSdr95",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar representing the Tertiary Education Completion Rate for males}[0.654, 0.748, 0.899, 0.931] bigger or smaller than the {bar representing the Tertiary Education Completion Rate for females}[0.654, 0.518, 0.926, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "mSvwtmMKfj15HDRI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing the Youth Unemployment Rate for females}[0.573, 0.518, 0.654, 0.702] located at the left or right of {the bar representing the Adult Literacy Rate for females}[0.094, 0.039, 0.573, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "AxHP8yoeX1Vh1cWA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar indicating Adult Literacy Rate for males}[0.094, 0.269, 0.594, 0.452] or {the bar indicating Youth Unemployment Rate for males}[0.594, 0.748, 0.654, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar indicating Adult Literacy Rate for males."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "Jf9hxaMv97OYpoiJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar representing Tertiary Education Completion Rate for males}[0.654, 0.748, 0.899, 0.931] have the same, higher or lower value than {the bar representing Youth Unemployment Rate for males}[0.594, 0.748, 0.654, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "wozrbLnAQdm8i5EJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the coordinates [0.297, -0.141, 0.703, -0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. The legend has three entries. The first entry with a blue color label located at [0.304, -0.133, 0.339, -0.121] represents the Adult Literacy Rate (%). The second entry with an orange color label located at [0.304, -0.107, 0.339, -0.094] represents the Youth Unemployment Rate (%). The third entry with a green color label located at [0.304, -0.080, 0.339, -0.068] represents the Tertiary Education Completion Rate (%)."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "uEt0OhgEiKWnZyE2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.094, 0.498, 0.968, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains six bars and two y-axis labels. The bars represent the Adult Literacy Rate, Youth Unemployment Rate, and Tertiary Education Completion Rate for both males and females. The y-axis labels correspond to the categories 'Male' and 'Female'."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "x91jt8PUm1nAtILo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the region [0.094, 0.019, 0.964, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the same elements as the area [0.094, 0.498, 0.968, 0.952], also including two y-axis Male and Female labels and six bars which represent the Adult Literacy Rate, Youth Unemployment Rate, and Tertiary Education Completion Rate for both genders."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "0Ajxla8smibEmhza",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat occupy the area [0.094, 0.039, 0.573, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to a cluster of three vertical bars in the chart. The blue bar represents the Female's Adult Literacy Rate, the orange bar represents the Female's Youth Unemployment Rate, and the green bar represents the Female's Tertiary Education Completion Rate."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "BqH4APFDE6koADaE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information does the legend show from the coordinates [0.144, -0.091, 0.856, -0.060]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend indicates two types of business indicators: {High Tech Startups (in 000s)}<r>[0.200, -0.084, 0.445, -0.067]</r> and {New Business Registrations (in 000s)}<r>[0.528, -0.084, 0.849, -0.067]</r>."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "Ryyp0VnVWesInBvO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the country that corresponds to the ytick at coordinates [0.061, 0.555, 0.099, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The country is Italy."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "kthfVSOItfzyz0GF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of High Tech Startups for Germany present at [0.111, 0.365, 0.118, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 51,000."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "aj1mK1oKxBgkAWh3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.113, 0.844, 0.595, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar representing New Business Registrations (in 000s) for the USA."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "tbI5lLZLYiZJWbDN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher: the number of High Tech Startups for Canada at [0.111, 0.257, 0.117, 0.344] or France at [0.111, 0.627, 0.113, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Canada."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "PUeGfXWD5rkPPRGH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the number of High Tech Startups greater than New Business Registrations for the USA?"
+            },
+            {
+                "from": "gpt",
+                "value": "No. The former is located at {the USA's High Tech Startups bar}<r>[0.111, 0.844, 0.116, 0.931]</r>, and the latter is at {the USA's New Business Registrations bar}<r>[0.116, 0.844, 0.595, 0.931]</r>."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "qZgmZf8ESEBKBZbA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the bar at coordinates [0.113, 0.627, 0.683, 0.714] with the bar at [0.111, 0.735, 0.114, 0.822]. Which bar is wider?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.113, 0.627, 0.683, 0.714] is wider, indicating France's New Business Registrations."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "N88THcRqmNdPvfzn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the region [0.111, 0.498, 0.981, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains bars that show the number of High Tech Startups and New Business registrations for countries such as Italy at {Italian bars}<r>[0.111, 0.518, 0.638, 0.605]</r>, France at {French bars}<r>[0.111, 0.627, 0.683, 0.714]</r>, the UK at {UK bars}<r>[0.111, 0.735, 0.729, 0.822]</r>, and the USA at {USA bars}<r>[0.111, 0.844, 0.595, 0.931]</r>."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "wVkUIebvWPbck523",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in the region [0.144, -0.091, 0.856, -0.060]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the legend of the chart, which includes two categories, {High Tech Startups (in 000s)}<r>[0.200, -0.084, 0.445, -0.067]</r> colored in #ff6347 and {New Business Registrations (in 000s)}<r>[0.528, -0.084, 0.849, -0.067]</r> colored in #81acce."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "QfPC1UXl9xgNrIoH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.111, 0.019, 0.981, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar plot that depicts High Tech Startups and New Business Registrations for several countries including Japan at {Japanese bars}<r>[0.111, 0.039, 0.940, 0.126]</r>, Australia at {Australian bars}<r>[0.111, 0.148, 0.642, 0.235]</r>, Canada at {Canadian bars}<r>[0.111, 0.257, 0.795, 0.344]</r>, and Germany at {German bars}<r>[0.111, 0.365, 0.878, 0.452]</r>."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "OqSGra3UipcOPAku",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the {legend}<r>[0.149, -0.091, 0.851, -0.034]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains three items: {Social Media Active Users (millions)}<r>[0.205, -0.084, 0.513, -0.067]</r>, {Digital Advertising Spending (US$ billions)}<r>[0.205, -0.058, 0.573, -0.041]</r>, and {Email Users (millions)}<r>[0.656, -0.084, 0.844, -0.067]</r>."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "kEXFW02dTa6IyGUr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-coordinate range for the entry {Facebook}<r>[0.026, 0.813, 0.107, 0.831]</r> in the Y-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.026 to 0.107."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "JCEEw7xi1cx5JMgr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value for {Email Users (millions)}<r>[0.656, -0.084, 0.844, -0.067]</r> for Facebook according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "6700."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "FNPQ1ZNjokCjtuX7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar {Instagram's 'Email Users'}<r>[0.349, 0.061, 0.640, 0.240]</r> longer than the bar {Facebook's 'Email Users'}<r>[0.383, 0.731, 0.749, 0.909]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "kT0cmuRd3ByVV09y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the spending on {Digital Advertising}<r>[0.205, -0.058, 0.573, -0.041]</r> for {WhatsApp}<r>[0.355, 0.284, 0.404, 0.463]</r> compare with that for {Twitter}<r>[0.303, 0.507, 0.345, 0.686]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "WhatsApp's spending on digital advertising was higher than Twitter's."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "kQg3tayx0MDFzo5r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {active social media users}<r>[0.205, -0.084, 0.513, -0.067]</r> for {Twitter}<r>[0.120, 0.507, 0.303, 0.686]</r> more than those for {Instagram}<r>[0.120, 0.061, 0.284, 0.240]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "tjtAkWfW2cy4DRmc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is inside this region <r>[0.120, 0.507, 0.428, 0.686]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two bars. One for {Social Media Active Users}<r>[0.120, 0.507, 0.303, 0.686]</r> in Twitter, which is from 0 to 1950 millions. The other is for {Digital Advertising Spending}<r>[0.303, 0.507, 0.345, 0.686]</r> in Twitter, ranging from 1950 to 2400 billion US dollars."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "qGKa4pl4cHijl9sN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is within the x-axis range <r>[0.120, 0.019, 0.981, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis range includes all the bars and labels related to the three different sets of data: Social Media Active Users, Digital Advertising Spending, and Email Users from 0 to 10000 for all four platforms: Instagram, WhatsApp, Twitter, and Facebook."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "zTNcwKwwZzb44KyA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region <r>[0.345, 0.507, 0.428, 0.686]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a part of Twitter's data, the {bar}<r>[0.345, 0.507, 0.428, 0.686]</r> represents the 'Digital Advertising Spending' for Twitter, which ranges from 2400 to about 2450 billion US dollars."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "ZHyetZ1psHwjRvfZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the rectangle representing 'WhatsApp' Social Media Active Users (millions) in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tomato color, denoted as '#ff6347'."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "PQcw25TfjCH5lpD2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend does the color '#81acce' correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Email Users (millions)\"."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "DS6HZozsH42qj2rB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {social media platform} at [0.019, 0.367, 0.107, 0.384] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"WhatsApp\"} <r> [0.019, 0.367, 0.107, 0.384] </r>"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "3YeqINK8nEc4Y8vr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Email Users of Twitter}[0.345, 0.507, 0.428, 0.686] wider than {the bar representing Digital Advertising Spending of Twitter}[0.303, 0.507, 0.345, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "KqPWMr7Cr2ZMNMgs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Instagram bars}<[0.120, 0.061, 0.284, 0.240], [0.284, 0.061, 0.349, 0.240], [0.349, 0.061, 0.640, 0.240]>, which one represents the 'Email Users (millions)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the last bar}[0.349, 0.061, 0.640, 0.240]."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "vZwfCZBU6td6a9ps",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger, the {number of Social Media Active Users of WhatsApp}[0.120, 0.284, 0.355, 0.463] or {the number of Email Users of WhatsApp}[0.404, 0.284, 0.940, 0.463]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Email Users."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "nRobn5nXD2rx3HLN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf {Email Users}[0.356, 0.507, 0.428, 0.686] and {Digital Advertising Spending}[0.303, 0.507, 0.345, 0.686] which Twitter bar is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Email Users."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "Z4jAvjHaKljg8Sl5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.120, 0.061, 0.640, 0.240]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the different statistics for Instagram, including \"Social Media Active Users (millions)\" represented by {a tomato colored bar}[0.120, 0.061, 0.284, 0.240], \"Digital Advertising Spending (US$ billions)\" represented by {a wheat colored bar}[0.284, 0.061, 0.349, 0.240], and \"Email Users (millions)\" represented by {a light steel blue bar}[0.349, 0.061, 0.640, 0.240]."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "qiyhomhjj4BC7pF5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.355, 0.284, 0.940, 0.463] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features two bars representing the \"Digital Advertising Spending (US$ billions)\" and \"Email Users (millions)\" for the \"WhatsApp\" platform. The first bar is {a wheat-colored bar}[0.355, 0.284, 0.404, 0.463] representing \"Digital Advertising Spending (US$ billions)\", and the second bar is {a light steel blue colored bar}[0.404, 0.284, 0.940, 0.463] represents \"Email Users (million)\"."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "ERIDJv0AEfDtwoZY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.284, 0.061, 0.640, 0.240]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region belongs to Instagram and contains two bar segments representing two categories of data. The first bar segment is colored wheat [0.284, 0.061, 0.349, 0.240], representing \"Digital Advertising Spending (US$ billions)\". The second bar segment is light steel blue [0.349, 0.061, 0.640, 0.240], signifying \"Email Users (millions)\"."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "4eBQxSOKHDeEykEE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color represented by the handle associated with the \"AR Trend\" label?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "AbXzAZzz2OiHlwTR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year mentioned at {the first tick mark on the y-axis}<r>[0.019, 0.067, 0.063, 0.084]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2022."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "HS9oRKrwMSxOj1xH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is located at {coordinates}<r>[0.297, 0.906, 0.357, 0.930]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nutrition."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "kd2yIdHVLNiBwCMh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2024 in the VR Trend series}[0.075, 0.211, 0.328, 0.280] taller than {the bar for 2023 in the AR Trend series}[0.233, 0.125, 0.427, 0.194]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "DkFIS06NtDn42IZH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at {the base of the second y-axis tick mark}[0.075, 0.160] greater than the value at {the base of the fourth y-axis tick mark}[0.075, 0.332]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "UfFVQrxD7eJYSqqy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the coordinates of the VR Trend color handle}[0.357, -0.083, 0.392, -0.071] on the left or right of {the AR Trend color handle}[0.517, -0.083, 0.552, -0.071]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "HRzAiJoeVmTnHoL5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual elements found in the range [0.075, 0.019, 0.981, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the x-axis, y-axis, several vertical bars corresponding to VR Trends from years 2022 to 2026, and an accompanying grid. The colors used for the bars vary from shades of blue to green per different years."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "dkCcLEXiEMzayeii",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region defined by [0.075, 0.518, 0.938, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses VR Trend and AR Trend bars for each year from 2022 to 2026, showing respective values ascending from left to right across the plot. It also includes y-axis labels for each year and corresponding bars extending to indicate the values for each trend."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "nYwEcuubiKi5hz90",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.258, 0.498, 0.442, 0.952] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of the x-axis with tick labels, a gridline, and vertical bars representing the values for \"500\" on the x-axis scale across different years for VR and AR Trends."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "NYba2VEgUAHgy8er",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar labeled \"Population Growth (M)\" for India in {the upper chart}<r>[0.137, 0.498, 0.981, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dark blue."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "mfXVj30qLyILRt2q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country corresponds to {the largest GDP per Capita bar}<r>[0.937, 0.452]</r> in the lower chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "China."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "ACreLWWRtSpUkvuf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for {the lower chart}<r>[0.137, 0.498, 0.981, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No label is prescribed for the y-axis."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "bomt8pAEzb851VeF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Brazil}<r>[0.137, 0.039, 0.271, 0.223]</r> in the upper chart wider than {the bar for India}<r>[0.137, 0.518, 0.922, 0.702]</r> in the lower chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "BwrK70vNYoJGvnEP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue bar for Unemployment Rate}<r>[0.262, 0.039, 0.269, 0.223]</r> have a higher value range than {the gray bar for Healthcare Expenditure}<r>[0.269, 0.039, 0.271, 0.223]</r> in Brazil?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "bkhz44LWx7560OKQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {India's GDP bar in the upper chart}<r>[0.137, 0.518, 0.918, 0.702]</r> end before {India's GDP bar in the lower chart}<r>[0.916, 0.518, 0.918, 0.702]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "XZBPsvLMRn2H5jV8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section in the upper chart that indicates Brazil's economic details at {in the range}<r>[0.137, 0.039, 0.271, 0.223]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This range includes multiple bars representative of Brazil's statistics, including Population Growth colored in dark blue, GDP per Capita in royal blue, Unemployment Rate in sky blue, and Healthcare Expenditure in light blue."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "6GfDOeEdWlfnEXQY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section in <r>[0.137, 0.498, 0.981, 0.952]</r> that relates to unemployment rates."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains bars representing the unemployment rates for India and South Africa, each colored in sky blue. The bar for India spans from the GDP values to the unemployment rate at 1373.60."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "q1Mh2dL6SI9vexBU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the left half of {the complete chart}<r>[0.137, 0.019, 0.981, 0.952]</r> display?"
+            },
+            {
+                "from": "gpt",
+                "value": "It primarily shows Population Growth data for all pictured countries (Brazil, China, India, South Africa) represented by dark blue bars, starting from no growth to each country's specific population increase."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "KpwmmCrMp1vjIyeb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object with coordinates [0.214, -0.084, 0.534, -0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research Funding (in billions USD)."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "0bJfJCI0Trqdh9XZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich specific color does the legend element at [0.569, -0.083, 0.604, -0.071] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00bfff."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "6obEjHrhg5NggIsx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {ytick label} at [0.050, 0.076, 0.099, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Japan."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "97pO4267guuWx0tR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar representing in [0.111, 0.844, 0.112, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{This bar}[0.111, 0.844, 0.112, 0.931] represents \"USA\" for \"AI Research Funding (in billions USD)\" in the first subplot."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "VcWGVjwbkHLLcJTf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {AI Research Funding for Germany}[0.111, 0.365, 0.111, 0.452] greater than {AI Research Funding for Canada}[0.111, 0.257, 0.111, 0.344]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "ManjoGYj2Pj5CYVc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Number of AI Companies in China}[0.111, 0.735, 0.848, 0.822] higher than {Number of AI companies in France}[0.111, 0.627, 0.525, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "ELyioKeBXzdypsjs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {AI Research Funding for Italy}[0.111, 0.518, 0.111, 0.605] exceed {AI Research Funding for France}[0.111, 0.627, 0.111, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "xEG5i2Fo27An7sjD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Number of AI Companies in the USA}[0.112, 0.844, 0.940, 0.931] and {Number of AI Companies in Italy}[0.111, 0.518, 0.341, 0.605], which one has more AI companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "USA."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "XV232YPtj8dbdgMA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.159, -0.091, 0.841, -0.060]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two components in the legend. First part is AI Research Funding (in billions USD) denoted by color #dda0dd, located at {legend color box}[0.166, -0.083, 0.200, -0.071] and {legend text}[0.214, -0.084, 0.534, -0.067]. The second part denotes the number of AI companies and is represented by color #00bfff, located at {legend color box}[0.569, -0.083, 0.604, -0.071] and {legend text}[0.618, -0.084, 0.834, -0.067]."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "H2I45ClPaGNEAFhk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.111, 0.019, 0.981, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the region [0.111, 0.019, 0.981, 0.473], there are two sets of bar graphs for four countries - Japan, Australia, Canada, and Germany. The first set represents AI Research funding, and the second set represents the number of AI Companies."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "0qGQO62tHeC9kDqx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are there in the range from [0.111, 0.498, 0.981, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the second subplot displays two sets of bar graphs for four countries - Italy, France, China, and USA. Two types of figures represented are: AI Research Funding and the Number of AI Companies."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "EukGPSoI0AwUIrW9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the objects in the region [0.111, 0.039, 0.286, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates correspond to the bars for 'Japan' in the first subplot: one pink bar and one blue bar. The pink bar represents the AI Research Funding, and the blue bar represents the Number of AI companies."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "luHuvB05s7T2KnCk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled {the \"Risk Factor\"}<r>[0.015, 0.403, 0.029, 0.555]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "aFTVA4A2vWbMSiAq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me what {rectangle}<r>[0.327, 0.111, 0.476, 0.171]</r> signifies."
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle represents the risk score of the risk factor \"Financial Risk\". The risk score is -30."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "duh3Q56aYJwZL2FD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the subplot title marked at {coordinates}<r>[0.430, 0.030, 0.721, 0.066]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "9D7M6NSxebqg3DVn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the risk score for \"Cyber-security\"}<r>[0.377, 0.787, 0.476, 0.847]</r> higher or lower than {the risk score for \"Security Breach\"}<r>[0.476, 0.712, 0.699, 0.772]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "jGMea02szlJP2drI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk score is more negative, {the risk score for \"Regulatory Risk\"}<r>[0.228, 0.412, 0.476, 0.472]</r> or {the risk score for \"Reputation Risk\"}<r>[0.278, 0.261, 0.476, 0.322]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Regulatory Risk."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "T1nj3AIkPovQnO59",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on risk assessment, is {the risk score for \"Environmental Risk\"}<r>[0.476, 0.337, 0.823, 0.397]</r> more or less risky than {the risk score for \"Market Volatility\"}<r>[0.476, 0.487, 0.650, 0.547]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "a8JwQeY42e3J0ZaL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region starting at {coordinates}<r>[0.228, 0.412, 0.650, 0.547]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the risk scores for three risk factors. From top to bottom they are: \"Regulatory Risk\"[-50.00], \"Environmental Risk\"[70.00], \"Market Volatility\"[35.00]."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "TeFMmdN6MzedBPhy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region {with coordinates}<r>[0.179, 0.075, 0.972, 0.883]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the whole plot of a bar chart. The chart represents risk scores for different risk factors and the scale of risk scores from -60 to 100. Labels 'Risk Score (%)' on x-axis and 'Risk Factor' on y-axis define the context. The bars represent the risk score by their length and position along the x-axis for each risk factor."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "yrVsO0y1cPFWWWP4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area defined by these {coordinates}<r>[0.377, 0.787, 0.922, 0.246]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there are four bars corresponding to the risk scores for \"Cyber-security\", \"Security Breach\", \"Geo-Political Risk\", and from highest to lowest on the y-axis."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "kMR1tHPuSS2KulPW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the technology labelled at the coordinates [0.162, 0.125, 0.238, 0.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "Gia6Y7sqdSsvV7d7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the values of the bar at [0.493, 0.548, 0.567, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 to 15.00."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "6hEa4BV1SqUOt99n",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the y-axis at [0.015, 0.401, 0.029, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technology."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "1Ffn1EKmY3I9Z6pB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the tick label positioned at [0.927, 0.903, 0.945, 0.931] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "AgZEpMAVFRcLnxN5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Neural Networks\"}[0.419, 0.298, 0.493, 0.348] longer than {the bar for \"Natural Language Processing\"}[0.493, 0.548, 0.567, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "PO7N8hB8v4zYMHn1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick label -50}[0.233, 0.903, 0.262, 0.931] on the left or right side of {the tick label 30}[0.632, 0.903, 0.649, 0.931] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "UVX4SSQrAEXavAuT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the technology \"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839] and {the technology \"Machine Learning\"}[0.115, 0.686, 0.238, 0.714], which has a higher percentage change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "7JY7f9A9ZItrbmsb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Blockchain\"}[0.493, 0.111, 0.715, 0.161] longer than {the bar for \"Robotics\"}[0.493, 0.735, 0.887, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "mCCO88tHYa6A0unB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the varied bars in the region [0.493, 0.111, 0.936, 0.847]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of 12 bars each correlating to a certain technology. The bars extend both to the left and right from the y-axis based on their respective change percentages. Each bar also has a corresponding label on the y-axis. Starting from the top, the labels are 'Artificial Intelligence', 'Robotics', 'Machine Learning', 'Cloud Computing', 'Natural Language Processing', 'Automation', 'Self Driving Cars', 'Biometrics', 'Neural Networks', 'Data Analytics', 'Intelligent Apps', and 'Blockchain'."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "wmpyYW5OZy83hcMz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.346, 0.672, 0.493, 0.722] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for \"Machine Learning\" that extends to the left from the y-axis indicating a negative percentage change."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "iI60VyPVOM161neh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the coordinates range [0.247, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specified coordinates highlight the main plotting area of the graph. The horizontal x-axis scale is a measure of the change in percentage ranging from -50 to 90 and the vertical y-axis scale represents different 'Technologies'. This coordinates area is filled with bars that express the growth or decline of the respective technology."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "zE8vj4t75Oz9q8iy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area outlined by the coordinates [0.095, 0.811, 0.238, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label \"Artificial Intelligence\", which corresponds to the first bar in the plot seen at [0.493, 0.797, 0.739, 0.847] indicating that Artificial Intelligence had a positive percentage change of 50."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "Bl5HNBlm8gYRy2JX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinate [0.549, 0.942, 0.649, 0.970] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the x-axis, namely, \"Difference (%)\"."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "SuzaEZLHskRK2PRK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.281, 0.030, 0.917, 0.066]."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "uIucJIeX7UvXjpNX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object found at the coordinate [0.485, 0.787, 0.940, 0.847] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a horizontal bar representing the 100% growth trend for the technology \"Blockchain\"."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "sgJpIFGiv7sU8UYj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis, found at [0.015, 0.390, 0.029, 0.568], label represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label \"Technologies\"."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "a2QEJf7w8Vd0Sx2M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Cybersecurity\"{[0.485, 0.261, 0.690, 0.322]} wider than the bar representing \"IoT\"{[0.485, 0.111, 0.576, 0.171]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "0dgN8HXW2fYtfuWE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth trend for \"AI/ML\"{[0.258, 0.712, 0.485, 0.772]} positive or negative compared to \"Big Data\"{[0.349, 0.487, 0.485, 0.547]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "cw4R4YUOVGlHtIUn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs \"Blockchain\"{[0.485, 0.787, 0.940, 0.847]} showing a higher or lower growth trend compared to \"Cryptocurrency\"{[0.485, 0.637, 0.826, 0.697]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "6LoyuglZXh3jUJTP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend bar for \"Data Protection\"{[0.485, 0.412, 0.758, 0.472]} longer or shorter than the bar for \"Digital Wallet\"{[0.485, 0.337, 0.622, 0.397]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "oOFukhX4oOrJUNUg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you give about the region [0.213, 0.075, 0.985, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart. All horizontal bars representing the different technologies are found within this region, such as \"Blockchain\"{[0.485, 0.787, 0.940, 0.847]}, \"AI/ML\"{[0.258, 0.712, 0.485, 0.772]}, \"Cryptocurrency\"{[0.485, 0.637, 0.826, 0.697]}, among others."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "yBA2TNQikdEnD9eC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region [0.035, 0.581, 0.203, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label of one of the technology categories, specifically, \"Peer-to-Peer Networking\"."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "NyY1NCtvNVRQzt9G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements enclosed by the region [0.440, 0.186, 0.485, 0.246]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a horizontal bar representing the negative growth trend of \"Cloud computing\"{[0.440, 0.186, 0.485, 0.246]}."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "mJKY7je3lNmZ9QWK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is provided within the coordinates [0.128, 0.806, 0.203, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the y-axis label \"Blockchain\"."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "HryL00vc7MdK2FJ9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category name located at [0.107, 0.824, 0.196, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Technologies}<r>[0.107, 0.824, 0.196, 0.848]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "2r2qpvWq5j9nhjqF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value marked at coordinate [0.493, 0.178, 0.780, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{60}<r>[0.493, 0.178, 0.780, 0.241]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "6dcmOC0ZESYaSUyc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-label located at [0.489, 0.919, 0.497, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{0}<r>[0.489, 0.919, 0.497, 0.942]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "HftQIx1Rohm39Heh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart at location [0.240, 0.025, 0.937, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Change in Relative Importance in Technological Innovation and Intellectual Property}<r>[0.240, 0.025, 0.937, 0.055]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "BTCEHlwxRdvczW5t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Copyrights\"}<r>[0.302, 0.646, 0.493, 0.709]</r> shorter in length than {the bar representing \"Trademarks\"}<r>[0.493, 0.568, 0.637, 0.631]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "MOP4LvVHd7gkHQS3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of \"Industrial Design\"}<r>[0.493, 0.256, 0.541, 0.319]</r> less than {the value of Licensing Agreements}<r>[0.493, 0.412, 0.876, 0.475]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "7nVbAvjysyXhiE8j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Infringement Cases\"}<r>[0.493, 0.100, 0.541, 0.163]</r> have the same value as {the bar representing \"Industrial Design\"}<r>[0.493, 0.256, 0.541, 0.319]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "5BHcKVp9WBT8GVY9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Patents\"}<r>[0.493, 0.724, 0.972, 0.787]</r> longer than {the bar representing \"Technologies\"}<r>[0.493, 0.802, 0.732, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "hkDxmLqYVGRVTiQ2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.206, 0.062, 0.972, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's about the main part of the chart. It includes all bar elements representing different types of intellectual property. Each bar shows the relative change of importance in technological innovation. Positive values are to the right of {0} axis<r>[0.493, 0.903]</r> indicates a rise, and negative values to the left of {0} axis<r>[0.493, 0.903]</r> indicates a drop."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "dLHMdeaMD45NOVY8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents at location [0.493, 0.568, 0.780, 0.241]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains three bars showing the relative change of importance in Trademarks, Industrial Design, and Research Development respectively in technological innovation. Their values are 30, 10 and 60."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "DYy5ljei8Jjx9U0E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the area of [0.206, 0.062, 0.972, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the majority of the chart, including all x and y axis labels, as well as the bars representing various categories of intellectual property. Negative values are shown in bars to the left of {the 0 on the x-axis}<r>[0.493, 0.903]</r>, and positive values are shown in bars to the right side."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "OI23fTjt4x11TO8c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe the region [0.493, 0.568, 0.780, 0.631]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains bars and labels which represent a portion of the data. Specifically, they pertain to the categories of Trademarks, Industrial Design, Research Development, and Infringement Cases. The relative change in importance for each item is displayed by the length and direction of its associated bar."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "HrBWwxlcPO2ilkfG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar} at [0.488, 0.797, 0.681, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the \"Number of Followers\"."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "rluqgKaP5vWJUIOH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Changes in Aspects of Social Network Presence and Interpersonal Relationships\""
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "nkaJqyygjY5lao1q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value on x-axis for {the block} at [0.294, 0.423, 0.488, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value ranges from -100 to 0."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "luf7krkIXrsf40rY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Number of Comments\"}[0.488, 0.610, 0.875, 0.660] longer than {the bar of \"Likes per Post\"}[0.488, 0.236, 0.730, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "mSraHOCUOCRe3T3v",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of \"Number of Shares\"}[0.391, 0.548, 0.488, 0.597] have a negative value on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "UGnVCEnr16A7ydd2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Number of Unfollows\"}[0.488, 0.485, 0.633, 0.535] located above {the bar of \"Number of Blocks\"}[0.294, 0.423, 0.488, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "klDUATMscRmB9Eya",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the bars found within the region [0.391, 0.236, 0.875, 0.785]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains 4 bars. {The bar representing \"Number of Likes\"}[0.488, 0.672, 0.778, 0.722] that starts from 0 and ends at 150, {the bar representing \"Number of Comments\"}[0.488, 0.610, 0.875, 0.660] that starts from 0 and ends at 200, {The bar representing \"Number of Unfollows\"}[0.488, 0.485, 0.633, 0.535] that starts from 0 and ends at 75, and {The bar representing \"Likes per Post\"}[0.488, 0.236, 0.730, 0.286] that starts from 0 and ends at 125."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "C4ecylXKrkZvU7pV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.045, 0.811, 0.188, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the y-axis label for \"Number of Followers\"."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "m1OPjMX7jF2QwgUf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.275, 0.903, 0.313, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for \"-100\"."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "kIOTdXAwgZ9kRu1O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object encompassed by the coordinates [0.339, 0.030, 0.860, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, which is named \"Percent Change in Popularity of Cultural Items Post Integration\"."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "I6knwPrXkyuKxWwV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.530, 0.942, 0.669, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the x-axis, named \"Percent Change (%)\"."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "lfpnkDQOe9SI0rg9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.015, 0.381, 0.029, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the y-axis, named \"Cultural Items\"."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "tYYWVJKeBiRSaaKu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.387, 0.787, 0.546, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar for the item \"Ethnic Cuisine\" showing a 30% change in its popularity."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "7CHI5g27RQWT7SFs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of {the bar representing \"Folk Dances\"}[0.387, 0.637, 0.493, 0.697] greater than the height of {the bar representing \"Traditional Clothing\"}[0.387, 0.562, 0.573, 0.622]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "W4LNrGucnmgGl90g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do {the bar indicating \"Marriage Customs\"}[0.387, 0.412, 0.759, 0.472] and {the bar showing \"Music and Poetry\"}[0.387, 0.261, 0.812, 0.322] compare in terms of position along the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar denoting \"Marriage Customs\" is above the \"Music and Poetry\" bar."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "xLThZXfjlwlotlZy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar for \"Art and Craft\"}[0.307, 0.337, 0.387, 0.397], is {the bar for \"Architectural Styles\"}[0.280, 0.186, 0.387, 0.246] placed higher or lower along the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "m2ZTQuKyA4O6Hc2f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving {the bar representing \"Religious Practices\"}[0.333, 0.712, 0.387, 0.772] and {the bar representing \"Ethnic Cuisine\"}[0.387, 0.787, 0.546, 0.847], which one extends further along the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Ethnic Cuisine\"."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "CJKVNJzTmgmLkhX3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area calculated by the coordinates [0.045, 0.506, 0.217, 0.534]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises the y-axis label for \"Language and Literature\"."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "Sx0jYthssbXx0EHs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the region defined by [0.387, 0.412, 0.759, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar denoting \"Marriage Customs\", and it displays a 70% change."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "mGZiVUulbOQ4o11A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated within the region confined by the coordinates [0.387, 0.261, 0.812, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The zone includes a bar that represents \"Music and Poetry\", showing an 80% change."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "l94DIxEyUz7O4wLl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSummarize what objects can be found in the spatial area between [0.280, 0.186, 0.387, 0.246]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these bounds, there is a bar signifying \"Architectural Styles\" showing a -20% change."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "iYGEVY31VPI7S29A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the bar with coordinates [0.447, 0.842, 0.671, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar stands for Color Theory, showing that its score is 50."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "717hDeimiBHO2tRR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bottom-left label {label} at [0.059, 0.102, 0.124, 0.122] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is for the element \"Proximity\"."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "2uwmFw5Qd3Yc4dHH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located in [0.504, 0.959, 0.615, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the x-axis, \"Creativity Score\"."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "zNllg4vTmZxFbkl2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label of Y-axis at [0.085, 0.543, 0.124, 0.563]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label on the Y-axis at these coordinates is \"Value\"."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "ZgnDideilrj6FWS1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is scored higher on creativity, {Texture}<r>[0.447, 0.578, 0.806, 0.613]</r> or {Variety}<r>[0.447, 0.357, 0.716, 0.392]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Texture."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "lhCLsvblbHzN34RB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Whitespace}<r>[0.156, 0.754, 0.447, 0.789]</r> scored positively or negatively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negatively."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "lGtUBW3kSpWUHINS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Emphasis}<r>[0.380, 0.181, 0.447, 0.216]</r> have a higher or lower score than {Lines}<[0.447, 0.622, 0.649, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "1286dBFH8sbds8HA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher score - {Alignment}<r>[0.447, 0.269, 0.806, 0.304]</r> or {Hierarchy}<r>[0.447, 0.225, 0.851, 0.260]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hierarchy."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "A5XD0g7SD1t8T1Wp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area spanning from [0.447, 0.269, 0.851, 0.304]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for the design element \"Alignment\", and it is positively scored at 80."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "i0wzhazQDiaQQHj1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content inside the bounding box [0.042, 0.764, 0.124, 0.784]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label \"Whitespace\" for the Y-axis."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "qzx3NOnoOSvzQoew",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in [0.447, 0.842, 0.671, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar for \"Color Theory\", which is positively scored at 50."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "mpG5KpcClVNSa1zo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.073, 0.587, 0.124, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a Y-axis label, specifically \"Texture\"."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "03XRgQNruLtEbIS1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.347, 0.716, 0.614, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the bar representing the growth rate in \"Online Banking\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "ETQclhMKD5kH6sp8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the chart is at the coordinates [0.103, 0.817, 0.185, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {y-axis label \"Ecommerce\"}<r>[0.103, 0.817, 0.185, 0.845]</r>."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "F2Qrnblx9rWWASm6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information shown at coordinates [0.533, 0.942, 0.648, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is label of the x-axis, specifically the phrase \"Growth Rate (%)\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "OxuFl4M1Pm0BhdYy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is categorized in [0.083, 0.491, 0.185, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for \"Cloud Services\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "dF9zwSdDsXkyiUMQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate for \"VR/AR\" {VR/AR bar}[0.347, 0.344, 0.575, 0.381] higher than that for \"Web Development\" {Web Development bar}[0.347, 0.251, 0.545, 0.288]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "epF8VLD1BAfiTIoD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate for {Email Services bar}[0.317, 0.204, 0.347, 0.242] negative compared to {VoIP bar}[0.347, 0.111, 0.469, 0.149]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "GiAnS6fMAI7AgJUO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {\"Artificial Intelligence\"}[0.347, 0.437, 0.803, 0.474] longer than the bar for {\"Cloud Services\"}[0.347, 0.484, 0.651, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "WSLqyeVD0IBfqRBu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the growth rate of {\"Online Banking\"}[0.347, 0.716, 0.614, 0.754] exceed that of {\"Social Media\"}[0.347, 0.763, 0.514, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "WRBq22JUd3p2Etdl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's outlined in this set of coordinates [0.259, 0.030, 0.921, 0.066]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Growth Rate (%) of Various Sectors in the Internet and Communication Industry\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "ctqwYJa6bnb3FrOc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.347, 0.809, 0.955, 0.428]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the bars representing the growth rate of various sectors in the internet and communication industry. The bars starting from top, represent 'Ecommerce', 'Social Media', 'Online Banking', 'Video Streaming', 'Online Gaming', 'Search Engines', 'Online Advertisement', 'Cloud Services', 'Artificial Intelligence', 'Internet of Things', 'VR/AR', 'Mobile Apps', 'Web Development', and 'Email Services'."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "sQw5C3DqcW0xSI9b",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within [0.015, 0.223, 0.029, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The set of coordinates corresponds to the y-axis label, which is \"Internet and Communication Sectors\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "PlqFAhsCay6omY3M",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does region [0.083, 0.491, 0.185, 0.519] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the name of an internet sector labelled \"Cloud Services\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "Mz9xC4rBiIxPYOLM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.416, 0.082, 0.916, 0.119] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the issue \"Racial Equality\"."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "7fCZyI1VTAJQFKwx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the \"Privacy Rights\" y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.114, 0.368, 0.212, 0.384]"
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "RfLdazID3n4FMY7B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat range does {the bar representing \"Healthcare Rights\"} represent at [0.416, 0.630, 0.527, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range is 0.00 to 40.00."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "6xClM3okWmaEGKOE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is at the y-axis coordinates [0.127, 0.414, 0.212, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Free Speech."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "FzzGObt0qKH4wfOx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Child Protection\"}[0.416, 0.767, 0.444, 0.803] longer than {the bar representing \"Harassment\"}[0.361, 0.812, 0.416, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "rruWRuPRkT5ZJdK9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Economic Inequality\"}[0.416, 0.493, 0.611, 0.529] show a higher value than {the bar representing \"Gender Equality\"}[0.416, 0.539, 0.583, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "TmiJdRSttVri56zd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the bars representing issues}[[0.416, 0.402, 0.639, 0.438], [0.416, 0.356, 0.666, 0.393], [0.416, 0.310, 0.694, 0.347], [0.250, 0.265, 0.416, 0.301], [0.416, 0.219, 0.750, 0.256], [0.416, 0.173, 0.805, 0.210], [0.416, 0.128, 0.861, 0.164]], which issue has the highest prioritization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Racial Equality."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "aFvAGFa3Kg8gPKCX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar}[0.333, 0.858, 0.416, 0.895] a positive or negative value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "mGq61lUtpnHayHsq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.416, 0.082, 0.916, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the issue \"Racial Equality\" on the y-axis. It extends from 0.00 to 180.00 on the x-axis, indicating a prioritization value of 180. The coordinate range for the bar is [0.416, 0.082, 0.916, 0.119]."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "JAJHT0d7bfOX7PKx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.333, 0.858, 0.416, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.333, 0.858, 0.416, 0.895] contains a single bar, which represents the issue of \"Discrimination\" on the y-axis. The bar extends from -30.00 to 0.00 on the x-axis, indicating a negative prioritization value."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "1MrpwcWicwlu2ynq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.416, 0.402, 0.861, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range includes bars representing the issues of \"Free Speech\", \"Privacy Rights\", \"Environmental Protection\", \"Fair Trade\", \"Minorities Representation\", \"Immigration Rights\", and \"Racial Equality\". These bars indicate the respective prioritization values of these issues on the x-axis."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "r1fVmP2ENZroNQr8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.049, 0.459, 0.212, 0.475]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.049, 0.459, 0.212, 0.475] contains the y-axis label \"Political Representation\"."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "V3BQtLGDTQOA8R9g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.580, 0.835, 0.753, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the Local Growth Rate for {Community Gardens}<r>[0.580, 0.835, 0.753, 0.877]</r>."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "UQu8MEpuHEGcX0Zt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the y-axis is {City Projects}<r>[0.015, 0.423, 0.029, 0.547]</r>."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "AMu5zreYEwNSYldj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the \"City Beautification\" project on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The location of the \"City Beautification\" project is {the bar}[0.580, 0.623, 0.696, 0.665]."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "P7LAeunRPIFZeHri",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the horizontal axis}<r>[0.551, 0.959, 0.666, 0.979]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the horizontal axis is Growth Rate (%)."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "ocm3uXsh4MN7vRZK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Public Parks bar}<r>[0.580, 0.782, 0.927, 0.824]</r> containing higher value than the {Community Gardens bar}<r>[0.580, 0.835, 0.753, 0.877]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "qSfKkIVP0FhqIC5K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Internet Connectivity project}<r>[0.580, 0.517, 0.811, 0.559]</r> have a greater local growth rate than {the Renewable Energy Sources project}<r>[0.580, 0.199, 0.927, 0.241]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "22H2C5mYzOcwphAg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the projects located above {Fire Stations bar}<r>[0.522, 0.093, 0.580, 0.135]</r> generally have positive growth rates?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "wje79TNOY3Au4zLk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we examine {Housing Projects bar}<r>[0.464, 0.676, 0.580, 0.718]</r> and {Public Schools bar}<r>[0.290, 0.358, 0.580, 0.400]</r>, which one has a higher local growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Housing Projects."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "lItNNeb6lcbUir1c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in region [0.464, 0.676, 0.696, 0.815]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this region: {The Housing Projects bar}<r>[0.464, 0.676, 0.580, 0.718]</r> and {The City Beautification bar}<r>[0.580, 0.623, 0.696, 0.665]</r>. They represent the Local Growth rate of their respective city projects."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "DwKimuo95aCZDMIr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.522, 0.093, 0.580, 0.400]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the {Fire Stations bar}<r>[0.522, 0.093, 0.580, 0.135]</r> and a significant portion of the {Public Schools bar}<r>[0.290, 0.358, 0.580, 0.400]</r>, which represent the Local Growth Rate of the Fire Stations and Public Schools city projects."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "UzMDPulUtq2flhWT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with coordinates [0.290, 0.199, 0.927, 0.400]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three City Projects bars: {Public Schools bar}<r>[0.290, 0.358, 0.580, 0.400]</r>, {Bike Lanes bar}<r>[0.580, 0.305, 0.580, 0.347]</r>, and part of the {Street Lighting bar}<r>[0.580, 0.252, 0.661, 0.294]</r>. All bars represent their respective Local Growth Rates."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "Ti0su3lc7bSZ0nHJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects contained in the area [0.405, 0.749, 0.672, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes part of the {Public Transport bar}<r>[0.580, 0.729, 0.672, 0.771]</r> and the whole {Public Parks bar}<r>[0.580, 0.782, 0.927, 0.824]</r>. Each bar represents the local growth rate of the respective City Project."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "iWZkdQoNrC6TgPW9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labelled at coordinates [0.015, 0.439, 0.029, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the Y-axis, which reads as 'Stage'."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "eCH12zsJVTqfCjW7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label content at the coordinates [0.526, 0.942, 0.633, 0.970] of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a X-axis label saying 'Differences (%)'."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "NS8ioBgWFrh6vDcR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the term at the coordinate [0.453, 0.030, 0.705, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "le8PREyi1fNuqB43",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.039, 0.280, 0.177, 0.308] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label, \"Duplicate Detection\" for the Y-axis."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "wn9zmL5lCvhVSCPC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar indicating \"Entity Resolution\"}[0.344, 0.337, 0.595, 0.397] longer than {the bar indicating \"Schema Matching\"}[0.344, 0.412, 0.407, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "jZlz4T7vgv5wf4g9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the length of {the bar representing \"Data Integration\"}[0.344, 0.487, 0.532, 0.547] greater than the length of {the bar representing \"Data Wrangling\"}[0.344, 0.712, 0.501, 0.772]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "pJzjbdWDbGBuLT9p",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar denoting \"Conflict Resolution\"}[0.344, 0.111, 0.815, 0.171] have a higher value than {the bar denoting \"Data Fusion\"}[0.344, 0.186, 0.752, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "rfeMkfxGka2ccBPz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Data Wrangling\"}[0.344, 0.712, 0.501, 0.772] further to the right than {the bar for \"Data Extraction\"}[0.312, 0.787, 0.344, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "p3olDEhw9vDv08T1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is within the region [0.015, 0.439, 0.029, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the Y-axis, labelled with 'Stage,' is located."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "TbLlWEud4UcYf4Cy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is enclosed in the range [0.187, 0.075, 0.972, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the entire chart plot with all the bars representing various stages of big data integration and the X and Y axes."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "ZoDFHaKnDtlCYPoM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements does the region [0.344, 0.111, 0.815, 0.171] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This span encompasses the bar chart for the stage \"Conflict Resolution,\" which indicates a positive difference of 150%."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "JGy0Rwjqajh5oxR5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown within the area [0.344, 0.186, 0.752, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the \"Data Fusion\" stage with a positive difference of 130% is located within these coordinates."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "sGdnxrOUDgoQGCha",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the coordinate for the label \"Child Care\" in y-axis ticks?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.141, 0.182, 0.213, 0.205]."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "tGjULOekDyLANRlN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is indicated by this rectangle [0.512, 0.230, 0.889, 0.282] in the bar groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangle indicates the \"Emergency Relief\" item."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "fGGilJmD0yttF3SS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the numerical figure on the x-axis represent according to its label at [0.546, 0.952, 0.653, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Difference (%)\" according to its label."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "LPdpRCHZtqY5SuVa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many ticks are there on the x-axis and what's the coordinate of the mid-point tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 14 ticks on the x-axis, and the coordinate of the mid-point tick's label \"0\" is [0.508, 0.919, 0.517, 0.942]."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "nlQY6JzIQopYLsPe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {Monetary Contribution}<r>[0.454, 0.683, 0.512, 0.735]</r> greater or smaller than {Medical Assistance}<r>[0.512, 0.619, 0.541, 0.670]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "3ZDrQ5JkkO3mv1RJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Educational Project}<r>[0.512, 0.554, 0.657, 0.606]</r> have a higher or lower value than {Community Development}<r>[0.512, 0.359, 0.710, 0.411]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "LHIJvz6BOErNuIk0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Home Support}<r>[0.483, 0.813, 0.512, 0.865]</r> positioned higher or lower on the chart than {Child Care}<r>[0.512, 0.165, 0.715, 0.217]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "FhwpR6qBoFpa6y8O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Emergency Relief}<r>[0.512, 0.230, 0.889, 0.282]</r> and {Free Counselling}<r>[0.512, 0.424, 0.744, 0.476]</r>, which one has a longer bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emergency Relief."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "XISeOghiF1rCYU75",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents included in [0.223, 0.062, 0.976, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all bars illustrating the relative difference in focus or value across various social welfare and charity activities. Also, it includes both the x and y-axis with their corresponding labels and ticks."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "KWYggAC5hsAHlDQw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements found in the region [0.346, 0.583, 0.547, 0.653]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This particular region is in the center of activities like \"Medical Assistance\", \"Educational Project\" and \"Clothing Donation\". There are two bars associated with each of these activities."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "GKjiJyLsj2u42qBe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in area [0.512, 0.774, 0.570, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we see a portion of the bar associated with the \"Food Program\". Shows a value of '10' on the Differences (%) scale."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "iBJVMuWyoONDWSt7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects inside [0.051, 0.430, 0.221, 0.535]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area mainly covers the y-axis label \"Activities\" lying horizontally in vertical direction."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "jySQ8ZQGOAtlgO8u",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at the coordinate [0.167, 0.808, 0.180, 0.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label for AI."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "WqxqFAHsxzOdfzFn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the position [0.362, 0.903, 0.371, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x axis label for value \"0\"."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "ITxJDGIGtwXLFbTc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.366, 0.792, 0.570, 0.847] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the bar representing \"AI\" category on the chart."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "1Me9Te0cHSYL5vqw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.322, 0.384, 0.366, 0.438] display?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the negative bar for \"Space Exploration\" showing -5%."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "X7z7keobOm9YgUgl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the AI bar}[0.366, 0.792, 0.570, 0.847] shorter or taller than {the Quantum Computing bar}[0.366, 0.724, 0.941, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "azuSggxWWXsnPQaN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is located above, {Blockchain}[0.105, 0.468, 0.180, 0.496] or {Space Exploration}[0.054, 0.400, 0.180, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "gAi8yoJC6QW43VJF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Cybersecurity}[0.366, 0.248, 0.799, 0.302] showing a larger or smaller change percent compared to {Internet of Things}[0.366, 0.180, 0.676, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "ASKbHIoK8ZP3Mk7m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Blockchain bar}[0.366, 0.452, 0.782, 0.506] located above or under {the Ai bar}[0.366, 0.792, 0.570, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Under."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "CozrReBo0FPwOAIG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be seen in the region [0.366, 0.180, 0.799, 0.302]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar showing the development prospects of Cybersecurity, whose change percentage is around 49%."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "jRvrudmL5xWtv5RY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.081, 0.672, 0.180, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for Biotechnology."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "NHOP4c963ZFu4F22",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.278, 0.111, 0.366, 0.166]."
+            },
+            {
+                "from": "gpt",
+                "value": "This displays a bar for Smart Cities on the chart, and it shows a negative change of 10 percent."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "9bA3GgpeWMlYyc9B",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is portrayed in the region [0.366, 0.588, 0.632, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, a positive development bar for 5G Technology is shown, as the change percentage is around 30%."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "X5e32h9YdCQbe4xX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the area [0.298, 0.903, 0.307, 0.931] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an xlabel indicating 20 years of protection."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "8lkM1cBELJWGUrhO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the bar located at [0.161, 0.652, 0.298, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the Hardware Patent category, which has 20 years of protection."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "MUJWgJ0nnZR4Ixdx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart title at [0.319, 0.030, 0.827, 0.066] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Years of Protection for Different Types of Intellectual Property.\""
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "ZaLduVOoKFPfc4C0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is longer, {the bar representing 'Software Patent'}[0.161, 0.760, 0.264, 0.847] or {the bar representing 'Trade Secret'}[0.161, 0.328, 0.848, 0.414]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing 'Trade Secret'."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "cHyXkUkWs2dnZ3W6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the protection period of {the 'Design Patent'}[0.161, 0.544, 0.257, 0.630] less than {the 'Hardware Patent'}[0.161, 0.652, 0.298, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "fZsFv09A024JEQB3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Utility Patent}[0.161, 0.436, 0.298, 0.522] and the {Copyright}[0.161, 0.111, 0.642, 0.198], which one has a longer protection period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Copyright."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "ISTbb8RNqgwp0sON",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me what is contained in the region [0.161, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the body of the bar chart, showing the lines of protection for various types of intellectual property such as Software Patent, Hardware Patent, Design Patent, Utility Patent, Trade Secret, Trademark, and Copyright."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "rwrucoygyNIlIKZr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the area [0.064, 0.360, 0.848, 0.414]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar which represents the Trade Secret category that has an unlimited (depicted as 100 years for visualization purposes) protection period is located in this area."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "vGa22siopF04Ugcy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe in the region [0.040, 0.792, 0.151, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.040, 0.792, 0.151, 0.820] contains the label for the 'Software Patent' category on the Y-Axis."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "Ll3tOanh4JbwUx9D",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within the area [0.848, 0.903, 0.861, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '100' on the x-axis, which indicates the highest possible value of protection years in the chart, is located in this area."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "Lmp6QOnCQccs1wSP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at the coordinates [0.169, 0.111, 0.458, 0.213] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of {the bar}<r>[0.169, 0.111, 0.458, 0.213]</r> is red."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "iE3qk7HBfFGPeHSd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third label in the legend ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the third label in the legend is [0.940, 0.183, 0.959, 0.211]."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "St3C6ArpzUdtZagA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at [0.015, 0.397, 0.029, 0.561] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the y-axis label}<r>[0.015, 0.397, 0.029, 0.561]</r>, which is \"Sales / Rent\"."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "xWjQuCme0vNmYdD4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.046, 0.365, 0.159, 0.466] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at [0.046, 0.365, 0.159, 0.466] refers to {Residential Rent}<r>[0.046, 0.365, 0.159, 0.466]</r> category on the y-axis."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "VE6t7pbFaHsnk74K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q2 bar of Industrial Sales}[0.169, 0.492, 0.490, 0.593] taller than {the Q2 bar of Commercial Sales}[0.169, 0.619, 0.715, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "tuHLRIGJ5hOTbFaU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the bars representing Q1}<r>[0.169, 0.745, 0.876, 0.847],[0.169, 0.619, 0.554, 0.720], [0.169, 0.492, 0.651, 0.593], [0.169, 0.365, 0.940, 0.466], [0.169, 0.238, 0.586, 0.340], [0.169, 0.111, 0.490, 0.213]</r> generally occupy more chart area than {the bars representing Q2}<r>[0.169, 0.745, 0.747, 0.847], [0.169, 0.619, 0.715, 0.720], [0.169, 0.492, 0.490, 0.593], [0.169, 0.365, 0.908, 0.466], [0.169, 0.238, 0.747, 0.340], [0.169, 0.111, 0.458, 0.213]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "sEmrNLlnsHhr9v1Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q4 bar representing Commercial Rent}<r>[0.169, 0.238, 0.747, 0.340]</r> shorter than {the Q4 bar representing Residential Rent}<r>[0.169, 0.365, 0.940, 0.466]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "hVb2aWWh1yGMeyhb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Residential Sales}<r>[0.041, 0.785, 0.159, 0.813]</r> and {Commercial Sales}<r>[0.035, 0.658, 0.159, 0.686]</r>, which category has a higher value for Q4<{Q4 Residential Sales bar}[0.169, 0.745, 0.811, 0.847], {Q4 Commercial Sales bar}[0.169, 0.619, 0.683, 0.720] >?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential Sales."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "jwMtoUnluk8eno7m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.895, 0.089, 0.965, 0.264]."
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle [0.895, 0.089, 0.965, 0.264] contains the legend of the chart. The legend includes:\n1."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "QlSR8ozZof6T8W8q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nA blue colour label at [0.901, 0.102, 0.929, 0.122] and a text label at [0.940, 0.100, 0.959, 0.128];\n2. Q2: An orange colour label at [0.901, 0.144, 0.929, 0.164] and a text label at [0.940, 0.142, 0.959, 0.170];\n3. Q3: A green color label at [0.901, 0.186, 0.929, 0.205] and a text label at [0.940, 0.183, 0.959, 0.211];\n4. Q4: A red color label at [0.901, 0.228, 0.929, 0.247] and a text label at [0.940, 0.225, 0.959, 0.253].\n    \nQ2: Please describe the area [0.031, 0.079, 0.965, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the entire chart excluding the title and components on the x and y axis. It includes the bars representing the sales and rents of different types of buildings in each quarter. Each bar type is color-coded, and the corresponding colors and quarters are represented in the legend located at the right-hand side of the chart at [0.895, 0.089, 0.965, 0.264]."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "E15eI6fzrb7zPIAT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.169, 0.492, 0.490, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal bars within [0.169, 0.492, 0.490, 0.593] are the representations of Q1, Q2, Q3, and Q4 for {Industrial Sales}<r>[0.053, 0.531, 0.159, 0.559]</r>."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "TH6C4HR4FyD3SHfY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.169, 0.111, 0.972, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.169, 0.111, 0.972, 0.213] contains the sales and rent data of the \"Industrial Rent\" category. There are four bars with different colors corresponding to different quarters with the color code explained in the legend to the right of the chart at [0.895, 0.089, 0.965, 0.264]."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "8vXPgdCCpZzeK797",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.016, 0.274, 0.029, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, called 'Technology Development Trend Values'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "kV3DjXO6aXQCH4M2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the 'Geothermal' label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.358, 0.863, 0.426, 0.947]"
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "RoYA3emVWcmTbUqp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the {technology} at coordinates [0.704, 0.863, 0.768, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smart Grid."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "Id1MN3T49R7gVOxU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the technology category referred to within [0.606, 0.863, 0.694, 0.972]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Capture."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "0BLfOLKnxEbC6l1O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar representing Solar}[0.099, 0.337, 0.168, 0.857] taller than the {bar representing Hydro}[0.271, 0.389, 0.340, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "RTKciOC9tNpJmd6N",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Nuclear}[0.530, 0.129, 0.598, 0.857] higher than {the bar representing Wind}[0.185, 0.233, 0.254, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "pU905yVzC4QfmLF4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Biomass}[0.444, 0.337, 0.512, 0.857] and {Energy Efficiency}[0.874, 0.285, 0.943, 0.857], which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "ZCccriTLrMb9vvmO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Geothermal}[0.357, 0.285, 0.426, 0.857] and {Smart Grid}[0.702, 0.285, 0.771, 0.857], which technology has a higher trend value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "LrSr2bsU276SVAOT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the content inside the area, [0.099, 0.337, 0.168, 0.857]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates is a blue bar representing the Solar technology with a y-value range of '0.00 -> 50.00'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "3pIep1qLd273ywqk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.615, 0.129, 0.685, 0.857]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single blue bar in this region. This bar corresponds to the Nuclear technology with a y-value range from '0.00 -> 70.00'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "mckS9NYRdopwckmW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.874, 0.285, 0.943, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a blue bar that stands for the Energy Efficiency technology. The y-value range for this bar is '0.00 -> 55.00'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "3W557z9a04dX9Y38",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area defined by [0.202, 0.863, 0.237, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an x-axis label with the text 'Wind'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "8vPEqg84VFKxsPtj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.019, 0.344, 0.036, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate represents the Y-axis label, which is \"Preference (%)\"."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "eH4FJlCv1AB8T0hj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.728, 0.113, 0.789, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar for the category \"Online_Gaming\" it has a range of '0.00 -> 90.00'."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "lrNmncHCca2hHKpe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinates [0.123, 0.469, 0.184, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the preference for \"Reading\", with a value range of '0.00 -> 45.00'."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "RivXOtRFAcF1sJJX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Travel\"}[0.274, 0.272, 0.335, 0.825] have a higher preference percentage than {the bar representing \"Eating_Out\"}[0.426, 0.232, 0.486, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "Bfv3KzxnVCLPiTB0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Online_Gaming\"}[0.728, 0.113, 0.789, 0.825] taller than {the bar representing \"Music\"}[0.501, 0.192, 0.562, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "TbrnLeW5zs4mBsTj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the bar representing \"Reading\"}[0.123, 0.469, 0.184, 0.825] and {the bar representing \"Nightlife\"}[0.880, 0.430, 0.940, 0.825], which has a higher preference percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nightlife."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "y9Jkshi5Z0hlKdu7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the range [0.122, 0.833, 0.185, 0.916]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the given range, there is a label for the X-axis category \"Reading\"."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "kUgmiR8DAgnqac4X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the coordinates [0.426, 0.232, 0.486, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At these coordinates, you see a bar that represents the preference for \"Eating_Out\", with a recorded preference percentage from '0.00 -> 75.00'."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "McYu7uhe8BG1YMDl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the coordinate range [0.019, 0.344, 0.036, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range covers the Y-axis label, which is named \"Preference (%)\"."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "c82tlhyPcdApflEX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label on {the y-axis}<r>[0.015, 0.329, 0.029, 0.482]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accuracy (%)"
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "QVxEIRMfv18EpbvT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the first bar represent in the chart? Please provide the coordinates of the bar."
+            },
+            {
+                "from": "gpt",
+                "value": "The first bar represents the health predictor \"Age\" with an accuracy of 70%. The coordinates are [0.108, 0.257, 0.176, 0.777]."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "fMGW2dh0qyHWG2x4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the health predictor located at [0.271, 0.785, 0.354, 0.923] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blood Pressure."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "Bm2jBRXUvpH8OPjj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Sleep Quality bar}[0.704, 0.272, 0.773, 0.777] have a higher value than the {Diet bar}[0.534, 0.220, 0.602, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "MT4LkZntEB4l1adn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the {Exercise Frequency bar}[0.619, 0.168, 0.687, 0.777] and the {Heart Rate bar}[0.363, 0.183, 0.432, 0.777], which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exercise Frequency."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "ZH25QWAXfDqJPhwF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Smoking bar}[0.790, 0.443, 0.858, 0.777] shorter than the {Air Quality bar}[0.449, 0.331, 0.517, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "F9FQvVPeKps7rZOG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the box [0.108, 0.257, 0.176, 0.777]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this box, there is a blue bar that goes from 0.00 to 70.00 representing the health prediction accuracy for the \"Age\" category."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "4Wk8ToHXwH7aoO2c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.043, 0.174, 0.061, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's where the y-axis label for 80 is positioned."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "vNrLUTinFXEAhvZv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.043, 0.025, 0.061, 0.048]"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for 100 on the y-axis is within this regional area."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "j2e8BSuOVZBYBeWg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {this object}<r>[0.015, 0.395, 0.029, 0.530]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the ylabel titled 'Usage Rate (%).'"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "3XTWCtIL1dQpR4CC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the bar representing 'Telegram'}<r>[0.788, 0.708, 0.857, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is located in [0.788, 0.708, 0.857, 0.900]."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "HQk33gKINq9r9anY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the {bar} for {Telegram}<r>[0.788, 0.708, 0.857, 0.900]</r> titled?"
+            },
+            {
+                "from": "gpt",
+                "value": "It does not have a specific title but represents the usage rate for the network Telegram."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "8VXcf2HDycO4aaIt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing 'WeChat'}<r>[0.874, 0.078, 0.943, 0.900]</r> have a higher usage rate than {the bar representing 'Telegram'}<r>[0.788, 0.708, 0.857, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "bd39OO8ahcRfO1Y8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Twitter'}<r>[0.185, 0.638, 0.254, 0.900]</r> longer than {the bar representing 'Facebook'}<r>[0.099, 0.778, 0.168, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "XV6EksCMdX9tIw8s",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more users in {Snapchat}<r>[0.357, 0.131, 0.426, 0.900]</r> or {LinkedIn}<r>[0.444, 0.410, 0.512, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Snapchat."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "ndEfzZavzcZCf8NT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a brief description of the area from <r>[0.271, 0.323, 0.340, 0.900]</r> to <r>[0.426, 0.131, 0.512, 0.900]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area specified encompasses three bars representative of Instagram, Snapchat, and LinkedIn. Each of these represents the usage rate of the respective social network."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "suxAArSR5j9rpKcU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area <r>[0.443, 0.194, 0.052, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the horizontal grid line on the y-axis representing the numerical value of 40%."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "YTY2CDxfNgTDaeU9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain what the bars in the range <r>[0.185, 0.638, 0.254, 0.900]</r> to <r>[0.444, 0.410, 0.512, 0.900]</r> represent."
+            },
+            {
+                "from": "gpt",
+                "value": "These bars represent the usage rates of three different social networks. The first is Twitter, the second is Instagram, and the third one is LinkedIn. Each bar's height indicates the respective usage rate."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "J4wNLVtWOsEm3c5N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is given to the last bar on the plot with coordinates [0.852, 0.206, 0.918, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label given to this bar is \"Successful Entrepreneurial Activity\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "5CA4U0xnQCXjooL5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at [0.272, 0.206, 0.338, 0.749] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the value for \"Government Support\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "wDavzH1ev2PpkLBd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.043, 0.236, 0.061, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for the value \"70\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "zFtnT5WQUF7YzowC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Investment}[0.438, 0.134, 0.504, 0.749] larger than the one for {Cultural Support}[0.686, 0.351, 0.752, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "LZvZWXWQ27YkX5ki",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Business Angel Activity}[0.769, 0.170, 0.835, 0.749] higher than the one for {Government Support}[0.272, 0.206, 0.338, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "WupLilxa4DzbgNr2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Market Openness} bar[0.355, 0.423, 0.421, 0.749] shorter than the {Cultural Support} bar[0.686, 0.351, 0.752, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "ukN3G8ZHlxJ0ft10",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found in the [0.189, 0.315, 0.255, 0.749] range?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range contains a single bar which represents the contribution of \"Entrepreneurial Education\" to the ecosystem, which is 60."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "yynjvd6Y7AO3inZg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the range [0.035, 0.019, 0.061, 0.036]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label for the maximum value \"100\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "cZz1LHapXbW1VCkZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area between [0.672, 0.206, 0.918, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area. One for {Cultural Support}[0.686, 0.351, 0.752, 0.749] with a value of 55, and another for {Successful Entrepreneurial Activity}[0.852, 0.206, 0.918, 0.749] with a value of 75."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "D5ZmIJn6bGT9Funb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the y-axis label is in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Success Percentage (%)\"<r>[0.015, 0.324, 0.029, 0.532]</r>."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "hJjWS2OecgTNwzJN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the third bar from the left represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is \"Problem Solving\"<r>[0.278, 0.388, 0.346, 0.831]</r>."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "UPEw5aMSZXnNXgO0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the seventh bar from the left?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.619, 0.428, 0.687, 0.831]."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "YMJI31qwlJvaG30T",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the variable \"Time Management\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of \"Time Management\" is 80% as represented by the bar at coordinates <r>[0.704, 0.187, 0.773, 0.831]</r>."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "lzwtH8QIQ1g79Qrm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Conflict Management\"<r>[0.534, 0.509, 0.602, 0.831]</r> taller than the bar representing \"Motivation\"<r>[0.449, 0.308, 0.517, 0.831]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "EORnRHkgtTtLuw2o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Conflict Management\" and \"Team Building\", which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Team Building\"."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "RJJidJ6v59ldsXoA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Problem Solving\"<r>[0.278, 0.388, 0.346, 0.831]</r> taller than the bar representing \"Leadership\"<r>[0.363, 0.227, 0.432, 0.831]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "4bu4sl8VajwWsLtS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is taller between the \"Decision Making\"<r>[0.193, 0.469, 0.261, 0.831]</r> bar and the \"Delegation\"<r>[0.619, 0.428, 0.687, 0.831]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Delegation\"."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "SwlZpizGyZzj02OT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.278, 0.388, 0.346, 0.831] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar representing the category \"Problem Solving\" with a value of 55% represented by the height of the bar."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "TDLyPqzXC6PuI1BX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.066, 0.670, 0.985, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specified area encompasses the entire chart area, including all the bars representing different categories such as \"Communication\", \"Decision Making\", \"Problem Solving\", \"Leadership\", \"Motivation\", \"Conflict Management\", \"Delegation\", \"Time Management\", \"Team Building\", and \"Strategic Planning\". Each bar represents the level of success in percentage terms."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "CJjUx081ida3N1kq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.043, 0.824, 0.061, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.043, 0.824, 0.061, 0.842] contains the '0' label on the Y-axis, indicating that this is the starting point of the percentage scale for 'Success'."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "RClYAdqdNlMyj1yT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.108, 0.267, 0.176, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a single bar representing the category \"Communication\"<r>[0.108, 0.267, 0.176, 0.831]</r>. The bar's height indicates a success percentage of 70."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "8c80rqwexx2PL4qk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type mentioned in the provided JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart type mentioned in the supplied JSON structured information is bar chart."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "ZhGXRGIKpdwbXf51",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.015, 0.467, 0.029, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object located at the coordinates [0.015, 0.467, 0.029, 0.516] is the y-axis label for the graph, called \"Score\"."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "2licL43GhpPea5IV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location {the x-axis label for the third bar}<r>[0.276, 0.492, 0.343, 0.958]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The location represents the \"Budget Management\" bar on the x-axis."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "o815Ub5PTaxktHuW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar labeled 'Time Management'}<r>[0.192, 0.631, 0.259, 0.958]</r> higher than {the bar labeled 'Risk Management'}<r>[0.360, 0.603, 0.428, 0.958]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "uanAU1gdhWKbZtbw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Quality Control' bar}<[0.445, 0.343, 0.512, 0.958]> have a higher score than {the 'Software Innovation' bar}<[0.782, 0.305, 0.850, 0.958]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "zdIHkIfmAel6IGGX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Hardware Innovation' bar}<r>[0.698, 0.520, 0.765, 0.958]</r> placed to the left or the right of {the 'Communication' bar}<r>[0.529, 0.548, 0.596, 0.958]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "Bh1QXlwEis1Q2oF5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.066, 0.026, 0.975, 0.958]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main chart area. It contains individual bars representing various innovation projects along with their associated scores. These projects include \"Idea Generation\", \"Time Management\", \"Budget Management\", \"Risk Management\", \"Quality Control\", \"Communication\", \"Project Closure\", \"Hardware Innovation\", \"Software Innovation\", and \"Marketing Innovation\". Each bar has its own unique coordinates; for example, the \"Idea Generation\" bar is located at [0.107, 0.380, 0.175, 0.958], and the \"Marketing Innovation\" bar is at [0.866, 0.650, 0.934, 0.958]."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "ZjIZ2ep4dnydyeR7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area [0.107, 0.380, 0.175, 0.958]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a single green bar in the bar chart; it corresponds to the \"Idea Generation\" project which had a score of 62."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "T8V0qYgytNnIGjvW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found in the region from [0.529, 0.548, 0.596, 0.958] to [0.782, 0.305, 0.850, 0.958]."
+            },
+            {
+                "from": "gpt",
+                "value": "In these regions, two items, 'Communication' and 'Software Innovation', respectively, can be found. The 'Communication' is documented in green from [0.529, 0.548, 0.596, 0.958] with a score of 44. The 'Software Innovation' is chronicled in green from [0.782, 0.305, 0.850, 0.958] with a score of 70."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "ioEO5iry8utNSiym",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.090, 0.196, 0.160, 0.818] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the bar denoting the Funding Received for Food Bank."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "d7b90lZUpC8sgEWA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.013, 0.292, 0.024, 0.552] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label, titled \"Funding Received (in millions)\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "XAnBJEtsAqo0sNeU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinates [0.100, 0.824, 0.151, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label stating \"Food Bank\" is designated by these coordinates."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "cXWsLjOPfMcMxuI5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the element labeled as {Disabled Support category bar}<r>[0.439, 0.309, 0.509, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a vertical bar on the chart indicating the amount of funding received for Disabled Support, which is equivalent to 45 million."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "BurqtjZODlLK0t3t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Elderly Care}<r>[0.352, 0.229, 0.422, 0.818]</r> taller than {the bar for Disaster Relief}<r>[0.265, 0.139, 0.334, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "VBVSAkKgYCA7FZSP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Education category bar}<r>[0.613, 0.331, 0.683, 0.818]</r> to the left or right of {the Disabled Support category bar}<r>[0.439, 0.309, 0.509, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "60eoXbvUMkiVtInj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Environment Protection}<r>[0.788, 0.252, 0.858, 0.818]</r> receive more funding than {the bar for Food Bank}<r>[0.090, 0.196, 0.160, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "UZkg8rEneaRz76ok",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relative position of {the Child Welfare bar}<r>[0.526, 0.388, 0.596, 0.818]</r> with respect to {the Animal Welfare bar}<r>[0.875, 0.275, 0.945, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "CK5nntDVGXbPP60Y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what you see in the region marked by [0.775, 0.824, 0.870, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there's the last x-axis label which is \"Animal Welfare\", which is located at the extreme right."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "YODXoChckQRAmVQZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is included in the region from [0.029, 0.019, 0.048, 0.716]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes labels for the y-axis. The labels indicate value ranges with increments of 10, starting at '10' and ending at '70', which are the ticks of the y-axis."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "bDODcefswMEa4gp1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.430, 0.310, 0.510, 0.820] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a bar that represents funding received for the category \"Disabled Support\". The bar rises from a baseline number 0 up to 45 million in funding."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "Ca7M2xdnC3m0nbKB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.090, 0.196, 0.945, 0.818]?"
+            },
+            {
+                "from": "gpt",
+                "value": "All the bars representing the funding received for various categories are located within this region. For instance, the bars for {Food Bank}[0.090, 0.196, 0.160, 0.818], {Medical Aid}[0.177, 0.252, 0.247, 0.818], and so on up to the bar for {Animal Welfare}[0.875, 0.275, 0.945, 0.818]."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "p79Z1HBThE5YyDlz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label positioned at [0.518, 0.988, 0.518, 0.988]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's no label."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "iLCmGA44qXKVH5mT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar chart's y-axis reported as at coordinates [0.012, 0.308, 0.024, 0.525]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's known as \"Company Dedication (%)\"."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "9CmWCuMlXxaL80vm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is featured at location [0.090, 0.416, 0.160, 0.807] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar from the first group of bars, representing the category \"Workplace Flexibility\" with a percentage of 40%."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "PX2Mohe25dQkPxlP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two elements located at [0.875, 0.074, 0.945, 0.807] and [0.854, 0.813, 0.966, 0.981] respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first is a bar from the first group of bars, representing the category \"Mental Health Awareness\" with a percentage of 75%. The second is the corresponding x-axis label, \"Mental Health Awareness\"."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "R80E3uXmbhICA3si",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Emotional Intelligence}<r>[0.613, 0.319, 0.683, 0.807]</r> longer than the bar for {Artificial Intelligence}<r>[0.788, 0.270, 0.858, 0.807]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "m0Lv7YNg44tPIZ9A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar of {Workplace Flexibility}<r>[0.090, 0.416, 0.160, 0.807]</r> have a smaller height than the bar of {Continuous Learning}<r>[0.439, 0.123, 0.509, 0.807]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "F9GHQtPM7CFM24ax",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Digital Literacy}<r>[0.701, 0.465, 0.770, 0.807]</r> and {Work-Life Balance}<r>[0.526, 0.172, 0.596, 0.807]</r>, which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Work-Life Balance."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "2hpVYlIjCvJPkT0X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bars, is {Leadership Development}<r>[0.352, 0.221, 0.422, 0.807]</r> valued higher than {Diversity & Inclusion}<r>[0.265, 0.368, 0.334, 0.807]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "6P8Cb9ofqhALE1n1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please go over the elements in the region [0.079, 0.813, 0.172, 0.953]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"Workplace Flexibility\"."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "PNlJGvDkguC71K63",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the space [0.439, 0.123, 0.509, 0.807]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a blue bar that represents the value of \"Continuous Learning\" in the dataset."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "ufLSezCDRMUqvckM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visualization within [0.854, 0.813, 0.966, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label for \"Mental Health Awareness\"."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "DnAa9KrhOJEk2Mbc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we find in the area [0.090, 0.416, 0.160, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It features a blue bar that represents the value for \"Workplace Flexibility\" which is 40%."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "UGt1mE66FomBpRRw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.015, 0.329, 0.029, 0.537] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainability Score (%)."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "aW8bkCJqm6JKauSW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the bar chart, what category represents the bar at coordinates [0.108, 0.270, 0.176, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Quality."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "l3HgeBQGYo2yHVBM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mark of the X-axis at [0.793, 0.846, 0.855, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "Pc6neguYFBYCqKSM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is represented by the bar at [0.790, 0.229, 0.858, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "W4L8qSs8bKliObSu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher, the {bar of \"Population\"}<r>[0.790, 0.229, 0.858, 0.840]</r> or the {bar of \"Air Quality\"}<r>[0.108, 0.270, 0.176, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Quality."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "IVddg3deu9neTa6Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar of \"Pollution\"}<r>[0.704, 0.107, 0.773, 0.840]</r> to the right of the {bar of \"Land Use\"}<r>[0.619, 0.148, 0.687, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "moV3COTqalKIDxw1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar of \"Climate Change\"}<r>[0.363, 0.148, 0.432, 0.840]</r> and {the bar of \"Biodiversity\"}<r>[0.193, 0.189, 0.261, 0.840]</r>, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Climate Change."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "b8Gr4BDXfA7AtdMO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Resources\"}<r>[0.875, 0.270, 0.943, 0.840]</r> to the left of {the bar of \"Population\"}<r>[0.790, 0.229, 0.858, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "NuvaMBiKguDICltr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information in the area with coordinates [0.066, 0.026, 0.985, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main chart space, with a gray background, and includes all the bars representing different categories with corresponding labels on the X-axis and values on the Y-axis."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "wCR4L2sbgv947EuC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the region [0.043, 0.019, 0.061, 0.118]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bottommost segment of the Y-axis, showing labels for the range of y-values from 0 to 10, with their corresponding scale marks."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "Zdx1x3HG47Qqj3LI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the layout in the range [0.066, 0.351, 0.985, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range mostly includes the middle area of the graph, which includes parts of the bars for all categories from Air Quality to Resources, reflecting the values associated with each category. The exact values are between 40 and 60 on the Y-axis."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "UKRYCEJTnLK8Czp4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe any unique features found in the range [0.043, 0.100, 0.061, 0.362] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this range, you can see the vertical labels for the values from 20 to 60 on the Y-axis, along with their corresponding scale marks on the axis."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "Lu95esN36ufz0pyo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is present at [0.015, 0.364, 0.029, 0.537] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label, called Innovation Rate (%)."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "ZJHjDpLumhy7eFFU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinate for the third label in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.283, 0.881, 0.329, 0.939]"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "AsEH8yHg5F530VYl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the coordinates [0.271, 0.132, 0.340, 1.513] correlate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"FinTech\" in the chart."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "u4jk4b6yZXbqsuc4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industries are located at [0.794, 0.881, 0.851, 0.953] and [0.883, 0.881, 0.934, 0.946]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{NanoTech}<r>[0.794, 0.881, 0.851, 0.953]</r> and {GeoTech}<r>[0.883, 0.881, 0.934, 0.946]</r>."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "zEiDdAU4p2tokjyc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the GreenTech bar}<r>[0.702, 0.387, 0.771, 1.513]</r> and {the NanoTech bar}<r>[0.788, 0.535, 0.857, 1.513]</r>, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "GreenTech."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "q2PGQWtZpXrmLunK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the BioTech bar}<r>[0.099, 0.344, 0.168, 1.513]</r> taller than {the HealthTech bar}[0.616, 0.281, 0.685, 1.513]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "hlPG2MiSYosu4UmH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more industries with innovation rate over 50% than those under 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "6qFdZGGk9XN1iSRC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of innovation rate, is {GeoTech}<r>[0.874, 0.196, 0.943, 1.513]</r> performing better than {AgriTech}<r>[0.357, 0.302, 0.426, 1.513]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "DIO9bjcI0zYOd341",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details within the area [0.035, 0.019, 0.052, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area has two Y-axis labels from top-bottom: \"50\" at [0.035, 0.444, 0.052, 0.461] and \"60\" at [0.035, 0.231, 0.052, 0.249]."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "WGGo3usdb0FknLJA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's being shown in the coordinates [0.099, 0.344, 0.168, 1.513]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar representing the BioTech industry, with an innovation rate of 55%."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "muan3a7rSryZWuzE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area [0.780, 0.881, 0.934, 0.946]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Near the right of the X-axis, it contains a label for the final category: 'GeoTech' with its corresponding tick."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "0XRTUlJjL3rPVHEZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators are within the regional coordinates [0.035, 0.019, 0.052, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the Y-axis labels from bottom-top: \"70\", \"60\", \"50\", \"40\", and \"30\"."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "wMrkVHPWvbsFJH2a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.015, 0.384, 0.029, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Values (%)."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "qkt6SnjjFrkD9CYk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.052, 0.846, 0.138, 0.953]"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "kUG62ED0xzejTb8e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category represented by the bar at [0.104, 0.278, 0.172, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "GradualDecline."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "ak5wvdu5ZMc67yK2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the indicators or categories represented by the bars in [0.446, 0.177, 0.515, 0.834] and [0.532, 0.582, 0.601, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Outlier1}[0.446, 0.177, 0.515, 0.834] and {Outlier2}[0.532, 0.582, 0.601, 0.834]."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "7xrYovhl39zrRtly",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"GradualDecline\"}[0.104, 0.278, 0.172, 0.834] higher or lower than {the bar representing \"SignificantIncrease\"}[0.875, 0.127, 0.943, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "vda6x6GTlCpQ1rhZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Outlier1\"}[0.446, 0.177, 0.515, 0.834] higher than {the bar representing \"Outlier2\"}[0.532, 0.582, 0.601, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "bStD6wzf5HPFffh0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing increases} [[0.618, 0.329, 0.686, 0.834], [0.703, 0.278, 0.772, 0.834], [0.789, 0.228, 0.857, 0.834], [0.875, 0.127, 0.943, 0.834]] generally higher than {the bars representing decreases} [[0.104, 0.278, 0.172, 0.834], [0.189, 0.329, 0.258, 0.834], [0.275, 0.430, 0.344, 0.834], [0.361, 0.531, 0.429, 0.834]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "EVgvcruGJfQWg6Xc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin {\"GradualDecline\"}[0.104, 0.278, 0.172, 0.834], {\"GradualIncrease\"}[0.703, 0.278, 0.772, 0.834], and {\"SignificantIncrease\"}[0.875, 0.127, 0.943, 0.834], which category has the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "SignificantIncrease."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "pQUYtCeAYef9TGRa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.043, 0.827, 0.052, 0.845] and [0.035, 0.019, 0.052, 0.036]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels on the y-axis. The top label \"0\" is located at [0.043, 0.827, 0.052, 0.845], and the bottom label \"80\" is located at [0.035, 0.019, 0.052, 0.036];"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "8O6xk3LB0qMALVE2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.052, 0.846, 0.909, 0.976]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.052, 0.846, 0.909, 0.976] contains all category labels on the x-axis, which are: {GradualDecline}[0.052, 0.846, 0.138, 0.953], {SteadyDecline}[0.143, 0.846, 0.224, 0.947], {SuddenDecrease}[0.216, 0.846, 0.309, 0.963], {SignificantDecrease}[0.287, 0.846, 0.395, 0.981], {Outlier1}[0.431, 0.846, 0.481, 0.908], {Outlier2}[0.517, 0.846, 0.566, 0.908], {SteadyIncrease}[0.566, 0.846, 0.652, 0.953], {GradualIncrease}[0.647, 0.846, 0.738, 0.959], {SuddenIncrease}[0.735, 0.846, 0.823, 0.957], and {SignificantIncrease}[0.805, 0.846, 0.909, 0.976]."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "MKyrhKGoDwAIKS68",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.275, 0.430, 0.429, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object which is the bar representing {SuddenDecrease}[0.275, 0.430, 0.344, 0.834] with a value between 0.00 and 40.00."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "jhQGbNfXGwYi2vtz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.104, 0.278, 0.943, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this range, there are 10 bars that summarize the percentages of various categories, including GradualDecline, SteadyDecline, SuddenDecrease, SignificantDecrease, Outlier1, Outlier2, SteadyIncrease, GradualIncrease, SuddenIncrease, and SignificantIncrease."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "aYQhtAQHzU96zGgN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label indicated by the coordinates [0.015, 0.374, 0.029, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is \"Revenue Change (%)\"."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "3ARvW23hxv1xQjhi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the company name associated with the item at coordinates [0.099, 0.528, 0.168, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The company name is Apple."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "byyjUhPvnyCSXKtn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage number for the bar at the coordinates [0.185, 0.277, 0.254, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage number is 25%."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "eZx1h94QY2oCBgM3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar that is represented at [0.530, 0.302, 0.598, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is Purple (hex code is #8e44ad)."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "XDfWysiG0OP4muVW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Facebook's bar}[0.271, 0.453, 0.340, 0.905] higher than {Google's bar}[0.185, 0.277, 0.254, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "Q1HZnLGZzVmVulJF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Twitter}[0.788, 0.704, 0.857, 0.905] shorter than the bar for {Microsoft}[0.357, 0.352, 0.426, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "6BaNHsrTUuIRyUfx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing {SpaceX}[0.702, 0.201, 0.771, 0.905] have a value that is less than the bar representing {Netflix}[0.530, 0.302, 0.598, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "zF595wg2E5ipn1Mz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Apple}[0.099, 0.528, 0.168, 0.905] and {Amazon}[0.444, 0.603, 0.512, 0.905], which company had a higher percentage revenue change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Amazon."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "gP1yR5EwG6LLJoIp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects represented within the boundary [0.271, 0.151, 0.771, 0.905]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 bars within the boundary which represent the revenue change of Facebook, Microsoft, Amazon, Netflix, and Tesla. Each bar's coordinates are:\n1. Facebook: [0.271, 0.453, 0.340, 0.905].\n2. Microsoft: [0.357, 0.352, 0.426, 0.905].\n3. Amazon: [0.444, 0.603, 0.512, 0.905].\n4. Netflix: [0.530, 0.302, 0.598, 0.905].\n5. Tesla: [0.616, 0.151, 0.685, 0.905]."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "uBD6PGqnBdESxYK3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.883, 0.911, 0.934, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.883, 0.911, 0.934, 0.975] contains the label \"LinkedIn\", which is a company name on the x-axis."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "QMTZdDuhTmZdRYIj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area [0.043, 0.898, 0.052, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.043, 0.898, 0.052, 0.916] contains a label marker of 0 on the y-axis."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "hMCiQXuvpcdy5fqz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area from [0.357, 0.352, 0.426, 0.905] pertain to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the region where the bar representing the company Microsoft is located, signifying a 22% revenue change."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "y1IkkYr2viRlRDhQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Active Public Interest Level, found at {coordinates}<r>[0.015, 0.328, 0.029, 0.562]</r>."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "ctMwWf9P8gUYVi3y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.322, 0.319, 0.408, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the topic \"Protest\"."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "7yqNIXNdZGevBqFO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.543, 0.871, 0.615, 0.960]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Social Media\"."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "rTlRww9QNfxzQsHL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar at coordinates [0.750, 0.571, 0.836, 0.864] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the topic \"Unemployment\"."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "gnbYkNsdCg7QhRzN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Climate Change\"}<r>[0.108, 0.235, 0.193, 0.864]</r> taller than {the bar representing \"Trade War\"}<r>[0.429, 0.361, 0.515, 0.864]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "N5THbMMCbD0i0r0K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Healthcare\"}<r>[0.858, 0.193, 0.943, 0.864]</r> represent a higher interest level than {the bar representing \"Immigration\"}<r>[0.643, 0.277, 0.729, 0.864]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "msi1gx6C3fAEeXQ5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing \"Social Media\"}<r>[0.536, 0.403, 0.622, 0.864]</r> and {the bar representing \"Protest\"}<r>[0.322, 0.319, 0.408, 0.864]</r>, which has a lower level of interest?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "m6O6QF9IseSIiAGm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.322, 0.319, 0.622, 0.864]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes three bars, each representing the topic and its respective Active Public Interest Level: \n1. \"Protest\" indicates a level of 65 located at {coordinates}<r>[0.322, 0.319, 0.408, 0.864]</r>. \n2. \"Trade War\" indicates a level of 60 located at {coordinates}<r>[0.429, 0.361, 0.515, 0.864]</r>.\n3. \"Social Media\" indicates a level of 55 located at {coordinates}<r>[0.536, 0.403, 0.622, 0.864]</r>."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "EJsWkzEnXcMlHiIa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content that is located within the coordinates [0.651, 0.277, 0.943, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars for \"Immigration\" with a level of 70 located at {coordinates}<r>[0.643, 0.277, 0.729, 0.864]</r>,  \"Unemployment\" with a level of 35 located at {coordinates}<r>[0.750, 0.571, 0.836, 0.864]</r> and \"Healthcare\" with a level of 80 located at {coordinates}<r>[0.858, 0.193, 0.943, 0.864]</r>."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "i7MoLMk1j93DnSdw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeling of the Y-axis of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Virtual_Reality_Training, Artificial_Intelligence_Education, Innovative_Learning_Methods, and E-Learning_Platform are the categories, found at positions {Virtual_Reality_Training}<r>[0.074, 0.172, 0.236, 0.195]</r>, {Artificial_Intelligence_Education}<r>[0.015, 0.373, 0.236, 0.396]</r>, {Innovative_Learning_Methods}<r>[0.030, 0.574, 0.236, 0.598]</r>, and {E-Learning_Platform}<r>[0.097, 0.775, 0.236, 0.799]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "g3wPmHtY8AUvqi9t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the X-axis of this chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Risk Complication Score, which can be inferred from the label found at {x-axis label}<r>[0.526, 0.952, 0.694, 0.975]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "dKnOAbNgXdtDJxsG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the descriptor of the color corresponding to '5 Years'?"
+            },
+            {
+                "from": "gpt",
+                "value": "You can find it in the legend with bar color at <r>[0.259, 0.120, 0.286, 0.136]</r> and label at {5 Years}<r>[0.297, 0.118, 0.347, 0.141]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "PamjEUiznDwSNpOQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the risk complication associated with Virtual_Reality_Training higher or lower in 5 years compared to 3 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower, referencing the data points {3 years for Virtual_Reality_Training}<r>[0.727, 0.100, 0.974, 0.141]</r> and {5 years for Virtual_Reality_Training}<r>[0.626, 0.141, 0.974, 0.181]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "jzObhUdXRyshCFmg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the risk complication higher in E-Learning_Platform or in Innovative_Learning_Methods over 7 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is higher in the category {E-Learning_Platform}<r>[0.391, 0.784, 0.974, 0.824]</r> when compared to the configuration {Innovative_Learning_Methods}<r>[0.552, 0.583, 0.974, 0.623]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "bRCvPNtWoSPEcHbJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the risk complication of Artificial_Intelligence_Education higher in 10 years compared to 3 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, we can determine this from the comparison of {10 years for Artificial_Intelligence_Education}<r>[0.351, 0.422, 0.974, 0.462]</r> and {3 years for Artificial_Intelligence_Education}<r>[0.512, 0.302, 0.974, 0.342]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "f23MarovhOeli4G3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what you see in the subtitle area [<r>[0.320, 0.025, 0.900, 0.055]</r>]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the given area, there is a subtitle that reads \"Risk complications associated with EdTech Innovations over the years\"."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "4qaYv5gcMFNOF4Bn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the figure show in the region [<r>[0.574, 0.303, 0.728, 0.545]</r>]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the \"Artificial_Intelligence_Education\" category with four bars representing the risk complication scores over 3, 5, 7, and 10 years."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "mou55lS3ZACy0roB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the [0.727, 0.100, 0.974, 0.141] region provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays a bar that represents a risk complication score for 3 Years for the Virtual_Reality_Training category. The X-axis range of -1.23 -> 0.00 indicates a negative risk complication score."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "dVTCmr0xJaKq0BKY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label with coordinates [0.566, 0.952, 0.605, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Score\"."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "eTmVUwAeJOFibBIV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the blue rectangle with coordinates [0.720, 0.775, 0.974, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing the 2009 score for 5G Infrastructure."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "wGzJLTFs8jm1DXZD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the orange bar at [0.741, 0.820, 0.974, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing the 2019 score for 5G Infrastructure."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "wzhgd6cwKZD7sbvA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2009 score for Optic-fiber provision}[0.276, 0.325, 0.974, 0.370] higher or lower than {the 2019 score for the same}[0.911, 0.370, 0.974, 0.415]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "AgvXIrzvmICa2lb3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the 2009 bar for Ethernet-Cable provision}[0.234, 0.100, 0.974, 0.145] or {the 2019 bar for the same}[0.826, 0.145, 0.974, 0.190]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2009 bar for Ethernet-Cable provision"
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "zAoaDEGlc3gh3p0O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2009 Wi-Fi Hotspots bar}[0.847, 0.550, 0.974, 0.595] taller or shorter than {the 2009 5G Infrastructure bar}[0.720, 0.775, 0.974, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "4KtTrX9OamnpjEIZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.204, 0.815, 0.289, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two legend labels, one for 2009 represented with a handle color of pale red at coordinates [0.209, 0.826, 0.237, 0.842], and one for 2019 represented with a handle color of pale blue at coordinates [0.209, 0.861, 0.237, 0.877]."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "1VazBGxASi0S1fTC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.048, 0.586, 0.187, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label \"Wi-Fi Hotspots\"."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "A1wZaFHfhp1xCven",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included in the area from [0.009, 0.483, 0.009, 0.483]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label component of the chart."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "wG6vXQAdLH8hed4a",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.490, 0.025, 0.698, 0.055] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is a subtitle, \"Global Trends 2017-2020\"."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "o1IxHCjUJAEOxpxK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object located at the coordinates [0.898, 0.190, 0.926, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the colour label of the legend for the year 2020."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "4l7Uef0wxBpnvGLE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.082, 0.172, 0.193, 0.195] on the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the category \"Global Warming\"."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "5iFSEH9wCnalmi8M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for \"2017\" at {coordinates of the 2017 bar}<r>[0.203, 0.100, 0.513, 0.141]</r> longer than the bar for \"2018\" at {coordinates of the 2018 bar}<r>[0.203, 0.141, 0.573, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "ze57Gkvxjw9AbFnm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller between the \"Population Growth\" one located at {Population Growth bar 2017}<r>[0.203, 0.503, 0.824, 0.543]</r> and the \"Technology Advancement\" one located at {Technology Advancement bar 2017}<r>[0.203, 0.704, 0.617, 0.744]</r> for the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Population Growth\"."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "1LH85oKwhUtNW3tJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the \"Global Warming\" bar for 2018 located at {Global Warming bar 2018}<r>[0.203, 0.141, 0.573, 0.181]</r>, is the \"Global Warming\" bar for 2020 located at {Global Warming bar 2020}<r>[0.203, 0.221, 0.543, 0.261]</r> taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "69JsRMkUxvuMoNjB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown within the coordinates [0.893, 0.074, 0.978, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend for the bar chart. It contains color labels and ranked years - 2017 is represented by {color label}<r>[0.898, 0.085, 0.926, 0.101]</r>, 2018 is represented by {color label}<r>[0.898, 0.120, 0.926, 0.136]</r>, 2019 is represented by {color label}<r>[0.898, 0.155, 0.926, 0.171]</r>, and 2020 is represented by {color \"label}<r>[0.898, 0.190, 0.926, 0.206]</r>."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "xfKsrBp3ekf4StJX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is present in the region defined by [0.203, 0.100, 0.513, 0.141]"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a bar representing the score (30.0) for the category \"Global Warming\" in 2017."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "YZ54EQiUaDJMT2Sl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are included in [0.065, 0.574, 0.193, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the label for the category \"Population Growth\"."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "w8hLs8SChlYVNRWv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the location [0.106, 0.574, 0.195, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "VPN Security"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "Y9lczRUW0C8nBpsf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the rectangle at [0.218, 0.525, 0.245, 0.542] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle color is #add8e6, representing for the year 2014."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "ptnKTPH7oaFKix1e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by the bar at [0.457, 0.744, 0.974, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "-2.4"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "VGO8ZDseyxBXYUw4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does the coordinate [0.015, 0.775, 0.195, 0.798] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two-Factor Authentication"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "eXzTCRcidtxhlEE0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the element {bar of 2011 for Firewall Protection}[0.306, 0.302, 0.974, 0.342] have a higher value than {bar of 2014 for Firewall Protection}[0.457, 0.422, 0.974, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "zAkuCEYaDBmkiwnT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {cybersecurity category bar of 2013}[0.306, 0.583, 0.974, 0.623] shorter or taller than {cybersecurity category bar of 2014}[0.328, 0.623, 0.974, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "2uxTqVCZesFuA8Xw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the 2011 bars} grouped by each cybersecurity category and {the 2012 bars}, which group of bars are generally taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2011 bars"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "EcusNX65zHhBUFOh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars representing {\"Two-Factor Authentication\"} in different years, which year's bar is the shortest?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "4DCjKI13zX8pdATr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.212, 0.409, 0.297, 0.556]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.212, 0.409, 0.297, 0.556] constitutes the legend of the chart which lists and describes all distinct items of the plot. It contains four small colored rectangles, each representing a year from 2011 to 2014, along with their associated labels. The color corresponding to each year is as follows: 2011 is #fa8072, 2012 is #87ceeb, 2013 is #ff7f50, and 2014 is #add8e6."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "ZI5l5QVch6UNXsYr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease discuss what you can see in the area [0.205, 0.062, 0.974, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, one can see the main body of the chart including all bars representing the values of different cybersecurity categories (Encryption Standard, Firewall Protection, VPN Security, Two-Factor Authentication) across four years (2011-2014), the legend denoting the color-year relationship, and the axes of the chart."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "P5YpTF2SgpKYBt3z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in this region [0.053, 0.172, 0.195, 0.195]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region [0.053, 0.172, 0.195, 0.195], there is a y-axis label representing the cybersecurity category \"Encryption Standard\"."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "x2xmMDOg7mXG8rJf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified in the area [0.053, 0.172, 0.974, 0.221]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area corresponds to the row representing the \"Encryption Standard\" in the bar chart. And the horizontal bars for all four years are within the region. Among them, the 2011 bar extends to a point corresponding to a score of -3.0, the 2012 bar extends to a point corresponding to a score of -2.8, the 2013 bar extends to a score of -2.8, and the 2014 bar extends the least, only to a point corresponding to a score of -2.6."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "LHPsYFPwMedFbmYQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at [0.217, 0.100, 0.510, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is a bar representing the trend of Electric Vehicle Sales projected for Year 2020."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "pG9MYmXLq4L9AcLX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinates of the legend that denotes Year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.904, 0.118, 0.973, 0.141]."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "zbdHulg4p6mCXyvu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at coordinates [0.217, 0.824, 0.656, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing Green Energy Consumption for Year 2023."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "IwLd5ECyEgMOTmSI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat section does the notation [0.904, 0.188, 0.972, 0.211] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to {the legend item for Year 2023}<r>[0.904, 0.188, 0.972, 0.211]</r>."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "lHDQvzXo3MU3kgo4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Year 2022 of the Green Energy Consumption}<r>[0.217, 0.784, 0.546, 0.824]</r> longer than {the bar for Year 2021 of the same}<r>[0.217, 0.744, 0.436, 0.784]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "k5jc9lACQCcNo3yc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Year 2021 bar of Electric Vehicle Sales}<r>[0.217, 0.141, 0.583, 0.181]</r> taller than {the Year 2020 bar of the same}<r>[0.217, 0.100, 0.510, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "FfVSyJHT5KqLSsrO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher - {the bar for AI Technology Stocks in Year 2022}<r>[0.217, 0.382, 0.846, 0.422]</r> or {the same for Year 2023}<r>[0.217, 0.422, 0.817, 0.462]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2022."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "w5Y5lrkoK5sKuUuq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing Green Energy Consumption}<r>[0.217, 0.704, 0.363, 0.744], [0.217, 0.744, 0.436, 0.784], [0.217, 0.784, 0.546, 0.824], [0.217, 0.824, 0.656, 0.865]</r> generally growing over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "Fx89o0E2yjlAErfu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.859, 0.074, 0.978, 0.220] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. The legend presents four items, each consisting of a color-label and a corresponding text-label, representing four different years from 2020 to 2023."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "TYvo4dDfP2QDzhqV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area of the plot at [0.217, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main plot of the graph. It contains bars of four different colors, each showing the trend predictions over four years for Electric Vehicle Sales, AI Technology Stocks, Cyber Security Investments, and Green Energy Consumption."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "Aoo3md2TI4uNmJDI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars within [0.217, 0.100, 0.948, 0.342]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bars representing the Electric Vehicle Sales and AI Technology Stocks over four years (2020 - 2023). Each year's performance is represented by a rectangular bar, colored distinctly as per the legend."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "l55Dt7GeOAsoZ0Ti",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what you see in the region [0.015, 0.574, 0.207, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mentions the label for the 'Cyber Security Investments' indicator, which is part of the y-axis labels."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "MwjwcTU1DwJQ4Bdn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend}<r>[0.849, 0.074, 0.978, 0.220]</r> assign to the \"Emo\" fashion trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple represented by #ff00ff."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "wukjQjrgFXPJJnhP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar length representing the \"Steampunk\" trend in the category \"Boho\", and where is it located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar length for \"Steampunk\" in the \"Boho\" category is 5.7, and the coordinates are [0.104, 0.621, 0.687, 0.652]."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "GQ2vyzxvJvyJ3J6R",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the trend category}<r>[0.893, 0.188, 0.929, 0.211]</r> in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"Boho\" fashion trend."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "rduazpuS81U5dttE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the orange bar labeled \"Boho\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the \"Boho\" bar, located at [0.104, 0.833, 0.769, 0.865], is 6.5."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "KDuuITalVTqFNZkD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing \"Emo\" have a higher score in \"Grunge\" or in \"Boho\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "In \"Boho\"."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "586NV9Jl7VyLwbdg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Steampunk in Emo}<r>[0.104, 0.164, 0.319, 0.196]</r> shorter or longer than {the bar for Steampunk in Grunge}<r>[0.104, 0.376, 0.718, 0.408]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "jKyJmhKT1KbYZEKH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the score of {the Grunge trend in the Boho category}<r>[0.104, 0.769, 0.820, 0.801]</r> to {the Grunge trend in the Steampunk category}<r>[0.104, 0.557, 0.790, 0.589]</r>. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score in the Boho category is higher."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "GRVQmkBX5c2pnsLS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Emo bar}<r>[0.104, 0.100, 0.626, 0.132]</r> and {the Grunge bar}<r>[0.104, 0.313, 0.585, 0.345]</r>, which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Emo bar."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "tcENdJi6h129xSi5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe all elements in this area <r>[0.104, 0.557, 0.902, 0.621]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes:\n    1. Blue bar representing the score of the Emo fashion trend in the Steampunk category, the coordinates are [0.104, 0.557, 0.708, 0.557].\n    2. Green bar representing the score of the Grunge fashion trend in the Steampunk category, the coordinates are [0.104, 0.557, 0.790, 0.557].\n    3. The \"Steampunk\" label on the y-axis, coordinates are [0.015, 0.580, 0.094, 0.603]."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "vcdI2sxbMM0Fohus",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present in the region defined by the coordinates [0.104, 0.769, 0.820, 0.801]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three bars, each representing the score of a distinct fashion trend (Emo, Grunge, and Steampunk) within the Boho category. Additionally, there is a y-axis label for \"Boho\"."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "b9Q4LdZa1UReq0sB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.043, 0.367, 0.094, 0.391] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contain the Y-axis label referring to the \"Grunge\" fashion trend."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "2zZWaPvME1QVzWOU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the area [0.104, 0.100, 0.626, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single pink bar representing the score of the Emo trend within the Emo category."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "w6GAP8LqFOH5oqbK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is indicated by the coordinates [0.361, 0.025, 0.752, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text indicated is the subtitle, which reads: \"Education Resource Distribution and Utilization\"."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "zAyUeR4eAdjJ85BZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.797, 0.861, 0.910, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label of Cycle 2 represented in the legend, which is orange."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "zjXc8FNtK5D4Gyfn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at [0.009, 0.483, 0.009, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is not specified as it is an empty string."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "GqWmqcd20BXR8K3s",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located on the x-axis at the coordinates [0.344, 0.919, 0.362, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the x-axis tick label, which reads '40'."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "SBWcA7VVeMTS1jZa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more bars in {Cycle 1}[0.127, 0.100, 0.888, 0.792] or {Cycle 2}[0.127, 0.173, 0.899, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "ws4KwWckYJBib3nH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars in {Cycle 1}[0.127, 0.100, 0.888, 0.792], is the {Vicuna-HAG bar}[0.127, 0.720, 0.730, 0.792] larger than the {LLAMA-Default bar}[0.127, 0.100, 0.888, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "PGJgvDRwN2dvKAnL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Cycle 2 Vicuna-HAG bar}[0.127, 0.792, 0.803, 0.865] larger than {Cycle 1 Vicuna-HAG bar}[0.127, 0.720, 0.730, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "3W2vyyegey95da39",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {text label for Cycle 2}[0.921, 0.859, 0.972, 0.882] appear below or above the {color label for Cycle 2}[0.882, 0.861, 0.910, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "fq1co4ZMXe5jxcaM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the contents in the region [0.877, 0.815, 0.978, 0.891] describe?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.877, 0.815, 0.978, 0.891] displays the legend of the bar chart, with two items. The first item indicates Cycle 1 in blue color and its label coordinate at [0.921, 0.824, 0.973, 0.847]. The second item indicates Cycle 2 in orange color and its corresponding label is at [0.921, 0.859, 0.972, 0.882]."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "x0CNEyzt5iZyI58y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the region [0.488, 0.952, 0.624, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.488, 0.952, 0.624, 0.975] contains the xlabel of the chart, which is \"Resource utilization\"."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "4CP3zF7bxOU1XdAC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area defined by [0.123, 0.919, 0.929, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the x-axis tick labels of the chart from '0' to '160' at intervals of '20'."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "T55BywU2YTIVXBnR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area [0.127, 0.100, 0.899, 0.865]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area conveys information about the bars of Cycles 1 and 2 regarding education resource distribution and utilization. Each of the four labelled categories (LLAMA-Default, LLAMA-HAG, Vicuna-Default, Vicuna-HAG) has bars for both cycles, illustrating different values for the respective categories."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "iEWbagdEDMzWdCim",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {Socialization}<{Socialization legend handle}[0.846, 0.456, 0.874, 0.472]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "GnmmkETnqQxYewkm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {x-axis label}<{x-axis label coordinates}[0.542, 0.952, 0.613, 0.975]</r> read?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frequency."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "cAdious93542qq65",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category is located at {this point}[0.170, 0.825] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pronounced-Increase."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "KE22ROVuM4USx644",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Sharp-Decrease for Outdoor}<{Sharp-Decrease Outdoor bar}[0.170, 0.140, 0.817, 0.159]</r> greater or lesser than {Sharp-Decrease for Lifestyle}<{Sharp-Decrease Lifestyle bar}[0.170, 0.159, 0.860, 0.179]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "Fc6wN6M3o1mO5Wfb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {first y-axis label}<{first y-axis label coordinates}[0.050, 0.130, 0.160, 0.154]</r> higher or lower than the {last y-axis label}<{last y-axis label coordinates}[0.015, 0.816, 0.160, 0.840]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "HVRfPK59DuBqBYUu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Moderate-Growth for Socialization}<{Moderate-Growth Socialization bar}[0.170, 0.316, 0.731, 0.336]</r> have a greater frequency than {Moderate-Growth for Recreation}<{Moderate-Growth Recreation bar}[0.170, 0.296, 0.705, 0.316]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "q4RHMUybkjRtc8ro",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is detailed in the region [0.840, 0.409, 0.978, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend includes four items:\n1. {Recreation}<r>[0.846, 0.421, 0.874, 0.437]</r> is represented by a teal color,\n2. {Socialization}<r>[0.846, 0.456, 0.874, 0.472]</r> is shown in red,\n3. {Outdoor}<r>[0.846, 0.490, 0.874, 0.507]</r> has a green color,\n4. {Lifestyle}<r>[0.846, 0.525, 0.874, 0.542]</r> is in blue."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "HByweD65Se1jSBHw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the features at [0.170, 0.394, 0.774, 0.414]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a {Fluctuating-Decline bar for Recreation}<r>[0.170, 0.394, 0.774, 0.414]</r> with a value label of \"70\"<r>[0.842, 0.157, 0.860, 0.181]</r>, it's in teal color and corresponds to the Recreation category."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "fiWbv59jN2FibeEP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are included in the region given by the coordinates [0.170, 0.806, 0.834, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region, there are two main objects:\n1. A {bar for Pronounced-Increase in Socialization}<r>[0.170, 0.806, 0.834, 0.825]</r> in red, with a value label of \"77\"<r>[0.808, 0.451, 0.825, 0.475]</r>,\n2. The corresponding part of the {y-axis with the label \"Pronounced-Increase\"}<r>[0.015, 0.816, 0.160, 0.840]</r>."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "6UMuWwxValyKuOk3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the structure of bars present at the coordinates [0.499, 0.171, 0.579, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The structure represents the progress of \"Robotic Automation\" in the past\u201c1 year\u201d. The range of x-axis's value for this is from -0.50 to 0.00. The color of these bars is '#fa8072', specified in the \u201c1 year\u201d bar group."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "3Pi0exmtjuJZKWzl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of technology is shown in position [0.033, 0.796, 0.131, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The technology shown in these coordinates is \"Nano Technology\"."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "EK27Jkam9D7BkYMk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.927, 0.062, 0.964, 0.080]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label text in the sublegend, indicating the time frame \"1 year\"."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "qiNGiOo6l6a2Q49t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of the 10-year development outlook, is the confidence in {Artificial Intelligence}<r>[0.419, 0.424, 0.579, 0.466]</r> higher or lower than {Nano Technology}<r>[0.579, 0.845, 0.820, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "SwYOMXATWh4PRsyY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Blockchain}<r>[0.579, 0.508, 0.917, 0.550]</r> at a higher or lower position than {Artificial Intelligence}<r>[0.499, 0.297, 0.579, 0.339]</r> in the \"1 year\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "QDjTvOuDMBI2N0pK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the prediction for {Robotic Automation}<r>[0.178, 0.213, 0.579, 0.255]</r> worse or better than {Blockchain}<r>[0.579, 0.508, 0.917, 0.550]</r> in the \"5 years\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "ZbBmsNNAtL2c1vv3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is inside the region [0.013, 0.375, 0.131, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a label of y-axis named 'Artificial Intelligence'."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "WiYG9kRz4gdQJ3d7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what you see in the area [0.927, 0.062, 0.977, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are four different labels indicating the time frame of the data. The labels are \"1 year\", \"2 years\", \"5 years\", and \"10 years\" respectively. Each label corresponds to a differently colored bar to differentiate data in the chart, with their corresponding colored rectangles located slightly to their left."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "HBVP3agZwbVa9LqK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease list and describe the labels included in the area defined by these coordinates [0.068, 0.585, 0.131, 0.813]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are two labels positioned horizontally. The first label, \"Blockchain\", indicates the concerning technology for the y-axis, the coordinates of this label are [0.068, 0.585, 0.131, 0.603]. The second label in this region, \"Nano Technology\", also indicates a type of technology for the y-axis. The coordinates for this label are [0.033, 0.796, 0.131, 0.81]."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "Lqpj4Kt1oCmrlX1g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of object with the coordinates [0.383, 0.100, 0.632, 0.143]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a bar in the bar chart, representing the \"Private Donations\" value for \"Food_Banks\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "T6GOP52WAhTxgwDb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat name is given to the object located at [0.849, 0.083, 0.973, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The name of the object is \"Private Donations\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "2Qg20uKZagZRSixc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith reference to the {xticks}<r>[0.039, 0.919, 0.060, 0.942]</r>, what value does the tick mark represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick mark represents the value \"-2\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "kYSk192Vp0DpREoy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for State Funding and Homeless Shelters}<r>[0.383, 0.355, 0.898, 0.398]</r> higher or lower than the {bar for Private Donations and Homeless Shelters}<r>[0.383, 0.313, 0.749, 0.355]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "y6OeAIZ78x80410Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Private Donations for Food Banks}<r>[0.383, 0.100, 0.632, 0.143]</r> and {State Funding for Food Banks}<r>[0.383, 0.143, 0.782, 0.185]</r>, which value is greater?"
+            },
+            {
+                "from": "gpt",
+                "value": "State Funding for Food Banks."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "KNnGIsRzOS3xRtsW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Private Donations for Animal Welfare}<r>[0.383, 0.737, 0.532, 0.780]</r> have a higher or lower value than the {State Funding for Animal Welfare}<r>[0.383, 0.780, 0.832, 0.822]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "lbenJpbIB3lBuR4E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is presented in the region [0.805, 0.074, 0.978, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the bar chart. It displays three labels alongside corresponding colors. The labels are \"Private Donations\"<r>[0.849, 0.083, 0.973, 0.106]</r>, \"State Funding\"<r>[0.849, 0.118, 0.946, 0.141]</r>, and \"Volunteer Work\"<r>[0.849, 0.153, 0.955, 0.176]</r> with their corresponding color handles being [0.810, 0.085, 0.838, 0.101], [0.810, 0.120, 0.838, 0.136], and [0.810, 0.155, 0.838, 0.171] respectively."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "uB8tCaQWiCrkiKow",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.383, 0.737, 0.532, 0.780]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes one blue bar representing \"Private Donations\" for \"Animal_Welfare\" with a value of 0.9."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "PYLL76R0kBKGCNpO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.179, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region make up the entire plotted area of the chart including the axis labels, ticks, legend, and data bars."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "89YDXFGMhnqSbgAi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the object 'Asia' on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.087, 0.577, 0.116, 0.600]"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "eyoIMbqqo99UV0sR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.408, 0.025, 0.692, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart: 'Consumer Index Scores by Region'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "6fj6OEoXTCNfAXWa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation within the coordinates [0.132, 0.074, 0.334, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the {legend}<r>[0.132, 0.074, 0.334, 0.151]</r> of the chart that includes labels for 'OffP_Consumer_Index' and 'OnP_Consumer_Index'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "xuq6tG04wUOurEGp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the bar for North America in OffP_Consumer_Index}<r>[0.651, 0.100, 0.974, 0.173]</r> and {the bar for North America in OnP_Consumer_Index}<r>[0.570, 0.173, 0.974, 0.245]</r>, which has a lower score?"
+            },
+            {
+                "from": "gpt",
+                "value": "OffP_Consumer_Index."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "EppclBYS1G9aQGfB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher, {the bar for Europe in OffP_Consumer_Index}<r>[0.489, 0.307, 0.974, 0.379]</r> or {the bar for Europe in OnP_Consumer_Index}<r>[0.435, 0.379, 0.974, 0.452]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "OnP_Consumer_Index."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "jY9Ub0DmpJKth08O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Africa in OffP_Consumer_Index}<r>[0.166, 0.720, 0.974, 0.792]</r> taller or shorter than {the bar for Asia in OnP_Consumer_Index}<r>[0.543, 0.586, 0.974, 0.658]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "M4x0gOABeYXmj71H",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the coordinates [0.125, 0.062, 0.974, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.125, 0.062, 0.974, 0.903] covers the entire bar chart, including the x and y axes labels, axes ticks, the x and y grid lines, chart title, all the bars representing consumer indices, and the legend."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "XvSdGxKYtcWE2DoM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.515, 0.173, 0.974, 0.245]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two groups of bars:\n1. The first group {OffP_Consumer_Index for North America}<r>[0.651, 0.100, 0.974, 0.173]</r>\n2. The second group {OnP_Consumer_Index for North America}<r>[0.570, 0.173, 0.974, 0.245]</r>"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "k6XRqI3RqADRmSWu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the area [0.132, 0.074, 0.334, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area [0.132, 0.074, 0.334, 0.151], there are two legend entries:\n1. The first legend entry {OffP_Consumer_Index}<r>[0.177, 0.083, 0.329, 0.107]</r>, which is marked with a color equivalent to '#90ee90'.\n2. The second legend entry {OnP_Consumer_Index}<r>[0.177, 0.118, 0.327, 0.142]</r>, which is marked with a color equivalent to '#add8e6'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "tQu2KvUNYtYh3DrU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}'s label [0.178, 0.751, 0.947, 0.808] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rapid Recovery"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "SY2RLV3o2khp2WBC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar}[0.178, 0.808, 0.877, 0.865] from the legend box?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange(from the object {Community Development}[0.794, 0.118, 0.973, 0.141] in the legend region [0.750, 0.074, 0.978, 0.151])"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "1gQWdUBT0WvOu99C",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is written at the right end of {the x-axis scale} located at [0.866, 0.919, 0.888, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.2"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "gEaMEKBaro5kVGV7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title} displayed at [0.322, 0.025, 0.840, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Local Government and Community Development Performance"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "LmJwbR18MAUXOgVM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Local Government growth bar for Rapid Recovery}[0.178, 0.751, 0.947, 0.808] extend further than {the Community Development growth bar for Rapid Recovery}[0.178, 0.808, 0.877, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "7Z6JGHzGqHgIwc2b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the orange bar}[0.178, 0.808, 0.877, 0.865] lower than {the blue bar}[0.178, 0.751, 0.947, 0.808] in the Rapid Recovery section?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "jFcnXEhNIzcr4bdF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Local Government\"}[0.178, 0.100, 0.737, 0.157] and {\"Community Development\"}[0.178, 0.157, 0.597, 0.214] which bar is longer in the \"Slow increase\" section?"
+            },
+            {
+                "from": "gpt",
+                "value": "Local Government."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "VlKbXnPpVERaEqiF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {\"Local Government\"}[0.178, 0.100, 0.737, 0.157] and {\"Community Development\"}[0.178, 0.157, 0.597, 0.214], which performance bar is higher at the 0.7 mark on the x-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "Local Government."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "h5gUbX5U4zKlh76N",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects identified in the region [0.750, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend. There are two entries: {Local Government}[0.794, 0.083, 0.921, 0.106] represented by a blue color at [0.755, 0.085, 0.783, 0.101], and {Community Development}[0.794, 0.118, 0.973, 0.141] represented by an orange color at [0.755, 0.120, 0.783, 0.136]."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "nir0JnXbQJysolpN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section [0.015, 0.473, 0.168, 0.497] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains only one part of the y-axis label, which reads \"Gradual Improvement\"."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "URerhA8w9Rs6p9HF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region at [0.178, 0.100, 0.737, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a blue bar representing the performance measurement for \"Slow Increase\" of the \"Local Government\"."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "REUpD6qvvIY464YU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.178, 0.808, 0.877, 0.865] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains an orange bar that represents the performance measurement for \"Rapid Recovery\" of \"Community Development\"."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "2XjXtFsclIuDkzEj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Visitors' trend at Cultural Heritage Sites\" and the coordinates are [0.416, 0.025, 0.747, 0.055]."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "tjtekXFcDzvq6bKP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lesion denoted with coordinates [0.860, 0.118, 0.944, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Historic Site."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "yWefKGIvkhmMSEoq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich visitor numbers is indicated in [0.714, 0.919, 0.740, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "3swm5U0qoNNm3J6B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bars in the coordinates [0.178, 0.173, 0.453, 0.245]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the number of visitors at a Historic Site in the \"Marginal Growth\" category."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "SDJyAjpYUTKz7bKB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'National Gallery' bar in the 'Steep Increase' category taller than the 'Historic Site' bar in the same category?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "zmKqMaw6Pp3Votut",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'National Gallery' bar in the 'Marginal Growth' category to the right of the 'Historic Site' in the same category?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "Gzb673VluadRHiia",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Historic Site' bar in the 'Rapid Growth' category taller than the 'National Gallery' bar in the 'Unpredictable Decline' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "iZ500rbdWIt5WnkI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Historic Site' in the 'Unpredictable Decline' category to the right of the 'National Gallery' in the same category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "QYppnhm54IQZzS3z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.816, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two entries in the legend. The first label describes the 'National Gallery' with color label '#fa8072' at [0.821, 0.085, 0.849, 0.101] and text at [0.860, 0.083, 0.973, 0.106]. The second label describes the 'Historic Site' with color label '#87ceeb' at [0.821, 0.120, 0.849, 0.136] and text at [0.860, 0.118, 0.944, 0.141]."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "rUVqbfCssd6WhyAQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.178, 0.062, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main part of the bar chart, including all the bars and the X and Y axis labels. The bars for the 'National Gallery' and the 'Historic Site' are present here, with their respective visitor numbers indicated along the x-axis and the categories along the y-axis."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "sNPc9HdcofgPcVVI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.178, 0.100, 0.343, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar representing the number of visitors at the 'National Gallery' under the 'Marginal Growth' category."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "CcGwvbUxcdqpnosl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.517, 0.952, 0.646, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label 'Number of Visitors'."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "8JsIcfvXmkfWRYSw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.605, 0.185, 0.974, 0.270] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is a bar representing the score of 'Fashion Trend 2' for the Gothic subculture."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "y0HeDvFruZI9btO6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in {the legend} at [0.077, 0.409, 0.235, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend consists of four items. Each item includes a color code and the corresponding 'Fashion Trend'. The color codes and their respective trends are as follows: {Fashion Trend 1}[0.121, 0.419, 0.229, 0.442], {Fashion Trend 2}[0.121, 0.453, 0.229, 0.477], {Fashion Trend 3}[0.121, 0.488, 0.229, 0.512], and {Fashion Trend 4}[0.121, 0.523, 0.229, 0.547]."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "z6GWPw2SlKFp4q0h",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.015, 0.261, 0.060, 0.284] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "This label stands for the 'Gothic' subculture."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "JSwHxRAMr9IWQewM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider: {the bar classifying 'Fashion Trend 1' in the 'Gothic' category}[0.113, 0.100, 0.974, 0.185] or {the bar classifying 'Fashion Trend 4' in the 'Punk' category}[0.113, 0.780, 0.974, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same width."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "M2aQlpqVuKNC2qhr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Fashion Trend 2' bar for 'Gothic'}[0.605, 0.185, 0.974, 0.270] to the left or right of {the 'Fashion Trend 2' bar for 'Punk'}[0.482, 0.610, 0.974, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "m796CPbSWalJCKRP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing 'Fashion Trend 1'}[0.113, 0.100, 0.974, 0.185] and {the bar representing 'Fashion Trend 4'}[0.728, 0.355, 0.974, 0.440], which one begins further to the right?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing 'Fashion Trend 4'."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "LWXH2INKvkMn8rkx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained within the region defined by [0.077, 0.409, 0.235, 0.556]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a legend for the chart. It lists four 'Fashion Trends' each represented by a different color: {Fashion Trend 1}[0.121, 0.419, 0.229, 0.442] is represented by a salmon color, {Fashion Trend 2}[0.121, 0.453, 0.229, 0.477] by a sky blue color, {Fashion Trend 3}[0.121, 0.488, 0.229, 0.512] by orange, and {Fashion Trend 4}[0.121, 0.523, 0.229, 0.547] by a light blue."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "n33KwIZCsAFgokBG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region with coordinates [0.482, 0.270, 0.974, 0.355]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, a bar representing the impact of the Gothic subculture on 'Fashion Trend 3' is located."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "nFlIqch589oeTCaS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.113, 0.100, 0.974, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar that demonstrates the score of 'Fashion Trend 1' for the Gothic subculture."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "uKOuGro3sdVfFSqz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.199, 0.784, 0.324, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2017 Market Share bar for Telecom Industry."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "kw9Z4fIS5yyGIoRh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry corresponds to the coordinates of [0.082, 0.619, 0.187, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-Commerce sector."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "gftoKyFfcGXtElbz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item located at [0.656, 0.080, 0.821, 0.108] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share for the year 2020."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "pApwACpixTUZNO4j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has a higher market share in 2020, {Telecommunication}[0.645, 0.784, 0.810, 0.887] or {Software}[0.788, 0.373, 0.944, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Software."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "5GJFjftyQ96z7KV2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHas {Manufacturing industry in 2018}[0.266, 0.168, 0.315, 0.270] grown or shrunk compared to {Manufacturing industry in 2017}[0.199, 0.168, 0.266, 0.270]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grown."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "pEABtgCXhMyNokEi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {2018 Telecommunication Market Share}[0.471, 0.784, 0.645, 0.887], did the {2019 Telecommunication Market Share}[0.645, 0.784, 0.810, 0.887] increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "YB0FiWemAkvkRXP8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items in the legend located at [0.352, 0.027, 0.828, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1. 2017 Market Share mark in red color at [0.359, 0.041, 0.394, 0.060];\n2. 2018 Market Share mark in green color at [0.359, 0.082, 0.394, 0.102];\n3. 2019 Market Share mark in lite blue color at [0.607, 0.041, 0.642, 0.060];\n4. 2020 Market Share mark in purple color at [0.607, 0.082, 0.642, 0.102]."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "5vTHjZfpdpDSv8MN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.788, 0.373, 0.944, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the 2019 Market Share for the software industry."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "Bt57qHcFekK3obOG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the vertical bars between coordinates [0.199, 0.431, 0.382, 0.496] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the market shares for the Manufacturing Industry from the years 2017 to 2020."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "Vz4AACdYyn2Z4fCB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar with coordinates [0.174, 0.791, 0.511, 0.888] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electronics for the Raw Material Efficiency."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "qyMIuG8mhLkGXaFM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data does the bar at [0.701, 0.595, 0.892, 0.693] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Quality Control data for Automobiles."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "GWZGYiJ0DsCE5A7S",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the category \"Quality Control\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of \"Quality Control\" in the legend is represented by a leaf at {this position}<r>[0.746, 0.047, 0.780, 0.067]</r> and described as '#a1b5ce'."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "ovYBiOICTme9fT1X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Quality Control\" for \"Textiles\" [0.634, 0.204, 0.780, 0.302] longer than the bar representing \"Production Speed\" for \"Textiles\" [0.432, 0.204, 0.634, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "QWQUzDgpq77jpdPX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Raw material efficiency\" score for \"Pharmaceuticals\" [0.174, 0.400, 0.466, 0.497] higher than for \"Automobiles\" [0.174, 0.595, 0.455, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "xxXsBnH3D4QJ75SU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar of Quality Control for Electronics}[0.735, 0.791, 0.936, 0.888], is {the bar of Production Speed for Electronics}[0.511, 0.791, 0.735, 0.888] shorter or longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "PzVdLG7yPjEyuZSW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are contained in the area [0.174, 0.400, 0.466, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a rectangle bar representing the \"Raw Material Efficiency\" of the \"Pharmaceuticals\" category."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "I4H0wb6swtPXgRE6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside [0.217, 0.034, 0.932, 0.084]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, which contains descriptors for each color and matching text labels: for example, Raw Material Efficiency is represented by a block at {this coordinate}<r>[0.224, 0.047, 0.259, 0.067]</r> with color '#d66555', and the pairing text located at {this coordinate}<r>[0.273, 0.045, 0.475, 0.073]</r>."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "OQ9IY73Fg6N6fzDk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the region [0.174, 0.791, 0.936, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are three bars representing the 'Raw Material Efficiency', 'Production Speed' and 'Quality Control' data respectively for the 'Electronics' category. The respective colors of the bars are '#d66555', '#88a27d', and '#a1b5ce'."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "65fxskvCTWQxfvco",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.225, 0.660, 0.701, 0.888] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing 'Stock Market' in 'High_Influence_Areas'."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "sAh8SeOlhMfOIZDn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the marker in the legend at [0.387, 0.089, 0.422, 0.109]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#34495e', which represents 'Unemployment Rate'."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "KYSM8FpRxrnNNzQk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is placed at [0.827, 0.942, 0.869, 0.970] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '5500'."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "xy5uL6CnXNORKKzf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.463, 0.204, 0.554, 0.432] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Interest Rates' in 'Weak_Influence_Areas'."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "ENVFTINzeEw39Pft",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Unemployment Rate' in 'High_Influence_Areas' taller or shorter than the bar representing 'Unemployment Rate' in 'Weak_Influence_Areas'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "lRdv2kQGael4kpha",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Stock Market' bar higher in 'High_Influence_Areas' compared to 'Weak_Influence_Areas'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "SejsgAowxu9DDSeV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is to the right of the 'Stock Market' bar in 'High_Influence_Areas'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Interest Rates' bar."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "38Z02bPNZSyzcPRV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is more towards vertical centre, the 'High_Influence_Areas' label or the 'Weak_Influence_Areas' label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'High_Influence_Areas' label."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "30oX8a0UxQXB4z5t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.380, 0.034, 0.826, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes four color-text labels: 'Stock Market' is denoted by the color '#d66555' at [0.387, 0.047, 0.422, 0.067]; 'Interest Rates' is denoted by the color '#88a27d' at [0.387, 0.089, 0.422, 0.109]; 'Inflation Rate' is denoted by the color '#a1b5ce' at [0.592, 0.047, 0.626, 0.067]; 'Unemployment Rate' is denoted by the color '#34495e' at [0.592, 0.089, 0.626, 0.109]."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "MdUiyvt7CilD5RlK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.225, 0.660, 0.945, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the 4 bars from 'High_Influence_Areas' representing 'Stock Market', 'Interest Rates', 'Inflation Rate' and 'Unemployment Rate'. Each of these bars has a unique color assigned as per the legend."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "ZOdxYASIxQ8mHZfG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.019, 0.307, 0.213, 0.336]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label 'Weak_Influence_Areas' of the y-axis."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "OEi3NwTO2jDUTePJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within [0.225, 0.204, 0.601, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are four bars in the region corresponding to 'Interest Rates', 'Stock Market', 'Inflation Rate' and 'Unemployment Rate' for 'Weak_Influence_Areas'."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "sfmazQB8bsn7OKHW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar of coordinates [0.469, 0.595, 0.784, 0.693] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the \"Mutual Funds\" return in the \"Equity\" category."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "tB4ahYuROIv7scXt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate is the label for 'Real Estate' located at?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.076, 0.438, 0.174, 0.466]"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "kMdALjZyKQVayv16",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the scope {x_range} for the bar at coordinates [0.375, 0.791, 0.469, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "12.00 -> 18.00"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "6c2LgY9XnJeOzjsI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the chart element in [0.846, 0.047, 0.881, 0.067] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Equity Return (%) color} <r> [0.846, 0.047, 0.881, 0.067] </r>"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "1mptkvnjZ2D7rolI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Equity Return bar}[0.469, 0.791, 0.784, 0.888] longer than {theReal Estate Return  bar}[0.375, 0.791, 0.469, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "ezrKzfwJynw9rhlN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is located to the left: {Low Risk Bonds Return bar} [0.186, 0.791, 0.249, 0.888] or {Mutual Funds Return bar} [0.249, 0.791, 0.375, 0.888] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Low Risk Bonds Return bar."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "IqbCo9Aq3UfBSTw3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Equity Return bars} represented in dark color compared to {the Real Estate Return bars}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "a5BoJp1YCsSacGRw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Low Risk Bonds\"}[0.186, 0.791, 0.249, 0.888] and {\"Equity\"}[0.469, 0.204, 0.784, 0.302], which investment has a higher return rate for {the Equity Return bar}<{Equity Return % bar}[0.469, 0.791, 0.784, 0.888], {Equity Mutual Funds bar}[0.469, 0.595, 0.784, 0.693], {Equity Real Estate bar}[0.469, 0.400, 0.784, 0.497], {Equity Equity bar}[0.469, 0.204, 0.784, 0.302]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equity."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "dYrKI9JJ1n76aLJz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in [0.317, 0.045, 0.531, 0.073]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a text element for the Mutual Funds return, the coordinates are [0.317, 0.045, 0.531, 0.073], labeled as \"Mutual Funds Return (%)\" and has the color handle at the coordinate [0.269, 0.047, 0.303, 0.067] which is in the '#88a27d' color."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "ae5aFyryUVB22Zct",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.469, 0.204, 0.784, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a rectangle representing the Equity return for the Equity investment type. The bar's coordinates are [0.469, 0.204, 0.784, 0.302]."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "q0hauX5ZyLVYBFv5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe everything you can see in the region [0.186, 0.170, 0.814, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart and includes several elements. The horizontal and vertical ticks marks, bars representing returns for several investment types (Equity, Real Estate, Mutual Funds, and Low Risk Bonds) and labels."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "LYeqsIuwzy9Xi9mo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCheck the labels in the range [0.181, 0.942, 0.512, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are five labels, ranging from 0 to 20, representing the rate of return:\n1. \"0\": [0.181, 0.942, 0.192, 0.970],\n2. \"5\": [0.259, 0.942, 0.270, 0.970],\n3. \"10\": [0.332, 0.942, 0.354, 0.970],\n4. \"15\": [0.411, 0.942, 0.433, 0.970],\n5. \"20\": [0.490, 0.942, 0.512, 0.970]."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "VJo8uzc13KuhXWPx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label with coordinates [0.563, 0.045, 0.825, 0.073] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Employment Rate Women (%)'."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "LJzly2LdV0CotF4V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the title label of the second element in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates for the title label of the second element in the legend are [0.185, 0.045, 0.480, 0.073]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "rNWLCZIkzZM1kbSY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}[0.279, 0.660, 0.329, 0.888] representing in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Gender Wage Gap (%)' for the 'Rights Protection' category."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "8JpBbHNAqz6Xf8rX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the specific labels within the coordinates range [0.185, 0.045, 1.141, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the labels: {Maternity Leave Duration (weeks)}[0.185, 0.045, 0.480, 0.073], {Employment Rate Women (%)}[0.563, 0.045, 0.825, 0.073] and {Employment Rate Men (%)}[0.908, 0.045, 1.141, 0.073]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "amByMCoh3E3f6Ppq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing 'Employment Rate Women (%)' for 'Rights Protection'}[0.432, 0.660, 0.551, 0.888] have a higher value than {the bar representing 'Employment Rate Men (%)' for 'Rights Protection'}[0.551, 0.660, 0.700, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "fYel3VNCNFyU350Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Maternity Leave Duration (weeks)'} for 'Social Inequality'[0.349, 0.204, 0.432, 0.432] wider than the {the bar representing 'Employment Rate Women (%)' for 'Social Inequality'}[0.432, 0.204, 0.535, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "9WBBE41XfYqxV6Nq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar}[0.279, 0.660, 0.329, 0.888] higher or shorter than {the subsequent bar}[0.329, 0.660, 0.432, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "x7dSEXUd5itjYcrA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller: {First category's bar for 'Employment Rate Men (%)'}[0.551, 0.660, 0.700, 0.888] or {Second category's bar for 'Employment Rate Men (%)'}[0.535, 0.204, 0.688, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "First category's bar for 'Employment Rate Men (%)'."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "Uu304ADvEA4fEasR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area with coordinates [-0.148, 0.034, 1.148, 0.084]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart. It consists of four pairs of symbols and their corresponding labels: \n1. A blue square symbol and the label \"Gender Wage Gap (%)\", placed at [-0.141, 0.047, -0.107, 0.067] and [-0.093, 0.045, 0.102, 0.073] respectively.\n2. A yellow square symbol and the label \"Maternity Leave Duration (weeks)\", located at [0.136, 0.047, 0.171, 0.067] and [0.185, 0.045, 0.480, 0.073] respectively.\n3. A light green square symbol and the label \"Employment Rate Women (%)\", placed at [0.515, 0.047, 0.549, 0.067] and [0.563, 0.045, 0.825, 0.073] respectively.\n4. A turquoise square symbol and the label \"Employment Rate Men (%)\", located at [0.860, 0.047, 0.894, 0.067] and [0.908, 0.045, 1.141, 0.073] respectively."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "sFh963eeGCePT1xM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained within the region [0.279, 0.170, 0.721, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main chart, which is a grouped bar chart for two categories ('Rights Protection' and 'Social Inequality'), with four value bars for each category, each bar representing a different data set ('Gender Wage Gap (%)', 'Maternity Leave Duration (weeks)', 'Employment Rate Women (%)', 'Employment Rate Men (%)')."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "CABNUvhx5j875ukt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists within the region [0.308, 0.942, 0.330, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a tick label on the x-axis representing the value '20'."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "MKAoWyFZcPKr96m3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements inside [0.119, 0.763, 0.267, 0.791] and [0.124, 0.307, 0.267, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions contain the y-axis labels for two categories: {Rights Protection}[0.119, 0.763, 0.267, 0.791] and {Social Inequality}[0.124, 0.307, 0.267, 0.335]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "Ng6thEbywMAPi6f8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of graphical element is located at [0.300, 0.061, 0.324, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{A Video Game Consoles bar for May sales}<r>[0.300, 0.061, 0.324, 0.183]</r>."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "sY6PyOdl2GohNbjS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the Dec sales in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Dec Sales handle color}<r>[0.701, 0.924, 0.735, 0.943]</r>."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "w9vxArRAQDJ9DNLs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis located at [0.012, 0.367, 0.012, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The y-axis label}<r>[0.012, 0.367, 0.012, 0.367]</r> is \"Television\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "uDEw8eAlBhAHRu8i",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the bar for Apr sales of Video Game consoles}<r>[0.280, 0.061, 0.300, 0.183]</r> or {the bar for Nov sales of Video Game consoles}<r>[0.482, 0.061, 0.534, 0.183]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nov sales."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "gLaXGgDssIyH7A8r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Nov sales bar for Mobile Phones}<r>[0.776, 0.305, 0.859, 0.428]</r> extend higher than {the Dec sales bar for Mobile Phones}<r>[0.859, 0.305, 0.945, 0.428]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "lEyzo4LeWZH46VNT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich brand had the higher start sales, {Television in Jan}<r>[0.217, 0.550, 0.256, 0.673]</r> or {Mobile Phones in Jan}<r>[0.217, 0.305, 0.264, 0.428]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Phones."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "p8C9e5G1RpyDtmHo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are present in the region [0.366, 0.796, 0.831, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for monthly sales from September to December. The colored handles representing the sales are:\n1. Sep sales: {Color Handle}<r>[0.701, 0.798, 0.735, 0.818]</r>\n2. Oct sales: {Color Handle}<r>[0.701, 0.840, 0.735, 0.859]</r>\n3. Nov sales: {Color Handle}<r>[0.701, 0.882, 0.735, 0.901]</r>\n4. Dec sales: {Color Handle}<r>[0.701, 0.924, 0.735, 0.943]</r>"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "wnW27aWrVzPeUt9W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents found in the section [0.217, 0.061, 0.945, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains all bar groups corresponding to Video Game Consoles sales across all months:\n1. Jan bar: {Coordinates}<r>[0.217, 0.061, 0.238, 0.183]</r>\n2. Feb bar: {Coordinates}<r>[0.238, 0.061, 0.259, 0.183]</r>\n3. Mar bar: {Coordinates}<r>[0.259, 0.061, 0.280, 0.183]</r>\n4. Apr bar: {Coordinates}<r>[0.280, 0.061, 0.300, 0.183]</r>\n5. May bar: {Coordinates}<r>[0.300, 0.061, 0.324, 0.183]</r>\nand so on through December, each progressively tracking the sales data over the months."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "WIOFG3iQ1lJs7zxK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.217, 0.305, 0.945, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specified area comprises all the bars for Mobile Phones sales from January to December:\n1. Jan bar: {Coordinates}<r>[0.217, 0.305, 0.264, 0.428]</r>\n2. Feb bar: {Coordinates}<r>[0.264, 0.305, 0.312, 0.428]</r>\n3. Mar bar: {Coordinates}<r>[0.312, 0.305, 0.362, 0.428]</r>\n...continuing similarly through December."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "djfNMKtxAwwIf0zD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object located at coordinates [0.249, 0.791, 0.557, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the \"Donation to Health\" for the past 5 years."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "VpEOhC3vW217hEzf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the legend item describing \"Donations - Past 10 Year\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is at [0.665, 0.045, 0.876, 0.073] and the color representation is at [0.616, 0.047, 0.651, 0.067]."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "aaqkpgCoFkHEK9BC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar located at [0.358, 0.595, 0.443, 0.693] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Donation to Education\" for the past 10 years."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "TeUdpQqmPySHNibh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being described by the text at coordinates [0.072, 0.828, 0.237, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category \"Donation to Health\"."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "oyqNhtCdAoLVskkr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Donation to Health - Past 5 Year\"}[0.249, 0.791, 0.557, 0.888] longer than {the bar representing \"Donation to Health - Past 10 Year\"}[0.557, 0.791, 0.831, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "sbkjn7KbpSQcZP6V",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller between {the bar for \"Donation to Environment - Past 5 Year\"}[0.249, 0.400, 0.566, 0.497] and {the bar for \"Donation to Environment - Past 10 Year\"}[0.566, 0.400, 0.732, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Donation to Environment - Past 10 Year\"."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "BDIRqtguXlESNZqJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars for \"Donations - Past 5 Year\", is {the bar for \"Donation to Health\"}[0.249, 0.791, 0.557, 0.888] the longest?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "yGSQGCfjSpIyFyRq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar for \"Donations - Past 5 Year\" for \"Charity Concerts\"}[0.249, 0.204, 0.401, 0.302] and {the bar for \"Donations - Past 10 Year\" for \"Donation to Education\"}[0.358, 0.595, 0.443, 0.693], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Donations - Past 10 Year\" for \"Donation to Education\"."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "VHnUieXo2XHbCwSf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the area marked by coordinates [0.326, 0.034, 0.883, 0.084]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. It includes two entries: the first, labelled \"Donations - Past 5 Year\" with a representation color located at [0.333, 0.047, 0.368, 0.067] and the corresponding text at [0.381, 0.045, 0.581, 0.073]; and the second, labelled \"Donations - Past 10 Year\" with a representation color located at [0.616, 0.047, 0.651, 0.067] and the corresponding text at [0.665, 0.045, 0.876, 0.073]."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "FSCelurrxZNdmA1t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within the region [0.249, 0.170, 0.959, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the main body of the chart, which consists of 8 vertical bars, each corresponding to \"Donation to Health\", \"Donation to Education\", \"Donation to Environment\", and \"Charity Concerts\", with two bars for each representing donations for the past 5 years and the past 10 years."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "P1SNg4ZCqcd6JQMG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in region [0.043, 0.438, 0.237, 0.466]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a text label for the \"Donation to Environment\" on the vertical (y) axis."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "g6DBJxFYh3x2x0rt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within [0.401, 0.204, 0.472, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a bar that represents \"Charity Concerts\" and their corresponding donation amounts for the past 10 years."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "sup4qWK1qdBGKkvu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell what the bar at [0.075, 0.168, 0.262, 0.210] represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Global Trade (% of GDP) for 2012."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "ulDObWyQ0XRKQl1F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.269, 0.038, 0.478, 0.066] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the label \"Global Trade (% of GDP)\" in the legend."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "nnetTWaFmCVYhpZQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at coordinates [0.375, 0.844, 0.796, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the representation of the Supply Chain Reliability Index for the year 2020."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "Ud4r4V6xPrAFG2IH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Global Trade of 2014}[0.248, 0.337, 0.670, 0.379] longer than {the bar for Global Trade of 2015}[0.281, 0.421, 0.679, 0.464]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "sIEzbM8C0fL9BNUL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between {the bar for Supply Chain Reliability Index of 2018}[0.328, 0.675, 0.745, 0.717] and {the bar for Supply Chain Reliability Index of 2019}[0.347, 0.760, 0.749, 0.802] in terms of length?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for 2019 is longer."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "AWqF7coV2TMNydx3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Global Trade of 2017}[0.318, 0.591, 0.731, 0.633] have a greater value than {the bar for Supply Chain Reliability Index of 2017}[0.318, 0.591, 0.731, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "BvbXH73hJCWVi7Ky",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the coordinates [0.075, 0.252, 0.632, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two bars. The first one corresponds to the Global Trade (% of GDP) for 2013 at coordinates [0.075, 0.252, 0.225, 0.294] and the second one is for the Supply Chain Reliability Index for 2013 at coordinates [0.225, 0.252, 0.632, 0.294]."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "AIVg9pwHBJXXrpgC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.075, 0.844, 0.796, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. The first bar with coordinates [0.075, 0.844, 0.375, 0.887] represents the Global Trade (% of GDP) for 2020 while the second bar with coordinates [0.375, 0.844, 0.796, 0.887] represents the Supply Chain Reliability Index for 2020."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "IObijcCXaSVs5pA8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information lies within the coordinates [0.213, 0.027, 0.827, 0.077]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the legend with its items. The first item is \"Global Trade (% of GDP)\" represented by color '#d66555' at coordinates [0.220, 0.041, 0.255, 0.060] and the second item is \"Supply Chain Reliability Index\" represented by color '#88a27d' at coordinates [0.513, 0.041, 0.548, 0.060]."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "V1429ZA83a30Q6aM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.248, 0.626, 0.636, 0.673] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing the steady decline in \"Flu Vaccine Production\"."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "kuCcgxap54FkfqkJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at [0.248, 0.343, 0.946, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar, which represents a \"pronounced increase\" in \"Stem Cell Research\", is yellow (#eacd52)."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "zKU8NpvkrXiwHtJX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label} at [0.040, 0.638, 0.236, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Flu Vaccine Production\"."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "1LlcPPXUKD9KH5aR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.432, 0.796, 0.560, 0.824]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Steady decline}<r>[0.432, 0.796, 0.560, 0.824]</r>."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "NNwI17b6be9oIoeO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Stem Cell Research\"}<r>[0.248, 0.343, 0.946, 0.390]</r> wider than {the bar representing \"Antibiotics Production\"}<r>[0.248, 0.531, 0.559, 0.578]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "uBMLfrFS935BibYN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Gene Therapy\"}<r>[0.248, 0.155, 0.772, 0.202]</r> end before {the bar representing \"General Pharma Industry\"}<r>[0.248, 0.249, 0.830, 0.296]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "RgLoJaPttJ31aAEm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"robust surgeries\" pattern}[0.432, 0.879, 0.608, 0.907] and {\"stem cell research\" pattern}[0.432, 0.921, 0.613, 0.949], which pattern has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Stem Cell Research\"."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "0hQaSDE1j7Lw1KJo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.376, 0.784, 0.853, 0.960]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, describing seven trend patterns each represented by a different color and labeled as follows: \n1. \"Steady decline\" in red (#d66555) at [0.432, 0.796, 0.560, 0.824].\n2. \"Rapid decline\" in green (#88a27d) at [0.432, 0.837, 0.549, 0.865].\n3. \"Periodic fluctuations\" in blue (#a1b5ce) at [0.432, 0.879, 0.608, 0.907].\n4. \"Pronounced increase\" in yellow (#eacd52) at [0.432, 0.921, 0.613, 0.949].\n5. \"Gradual recovery\" in dark blue (#526f7a) at [0.696, 0.796, 0.847, 0.824].\n6. \"Sluggish growth\" in pink (#ee6f7a) at [0.696, 0.837, 0.836, 0.865].\n7. \"Declining pattern\" in olive-green (#a4a482) at [0.696, 0.879, 0.847, 0.907]."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "omHSAMUMJBe9lkns",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.248, 0.030, 0.981, 0.703]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is where the main visualization of the chart resides, showcasing seven bars each corresponding to a different medical and biotechnology trend such as Flu Vaccine Production, Antibiotics Production, Robotic Surgeries, Stem Cell Research, General Pharma Industry, Gene Therapy, and Biological Implants. Each bar has different length, reflecting the value of these trends."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "vaBwXnO2fsAVa8oX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.248, 0.155, 0.772, 0.202]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object in this area which is a bar representing sluggish growth in \"Gene Therapy\"."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "d9DQmziquAr6zn2X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.248, 0.343, 0.946, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar on the chart representing the pronounced increase in \"Stem Cell Research\", which ends at a value of 1800. The color of this bar is yellow (#eacd52)."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "e4GotKljppCaMMF1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.116, 0.647, 0.343, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the coordinates for the Public Health Awareness (%) bar in the United States."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "7e8YAjkFvqD9akF6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.571, 0.168, 0.848, 0.407] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the coordinates for the Medical Equipment Patents bar in the EU."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "EK7j53dQWUihTjxw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label at the coordinates [0.073, 0.527, 0.073, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label at these coordinates is empty."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "5a4gd3HTjnKFF8Fg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the US Public Health Awareness bar}[0.116, 0.647, 0.343, 0.887] longer than {the EU Public Health Awareness bar}[0.116, 0.168, 0.327, 0.407]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "qgbxyEqWqEzyhcPW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the EU bars}[[0.116, 0.168, 0.327, 0.407],[0.327, 0.168, 0.571, 0.407],[0.571, 0.168, 0.848, 0.407]] in general longer than {the US bars}[[0.116, 0.647, 0.343, 0.887],[0.343, 0.647, 0.604, 0.887],[0.604, 0.647, 0.799, 0.887]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "BoPhM22HD3ljPjAW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is shorter, {the EU Pharmaceutical Research bar}[0.327, 0.168, 0.571, 0.407] or {the US Medical Equipment Patents bar}[0.604, 0.647, 0.799, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The EU Pharmaceutical Research bar."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "MBN3y4QvCgQL6LP6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information represented by the region [0.526, 0.113, 0.869, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers part of the chart where the bars representing the US's Pharmaceutical Research and Medical Equipment Patents are plotted. The Pharmaceutical Research bar is purple, with its coordinate [0.343, 0.647, 0.604, 0.887]. The Medical Equipment Patents bar is light blue, with its coordinate [0.604, 0.647, 0.799, 0.887]."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "Zto21yy5JsAedVe8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.071, 0.038, 0.977, 0.066]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains text labels of three categories: Public Health Awareness (%), Pharmaceutical Research (%), and Medical Equipment Patents, with their corresponding color labels in the legend. The Public Health Awareness label is gold-colored and has the coordinates [0.071, 0.038, 0.319, 0.066]; the Pharmaceutical Research label is purple and has the coordinates [0.402, 0.038, 0.658, 0.066]; the Medical Equipment Patents label is light blue and has the coordinates [0.741, 0.038, 0.977, 0.066]."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "XZHXzJ1mcZ5Rx2Td",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the area [0.213, 0.070, 0.776, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers most of the grouped bar chart. It includes all the bars representing the amount of Public Health Awareness (%), Pharmaceutical Research (%), and Medical Equipment Patents in both the US and the EU. The x-axis of the area ranges from 0 to about 1200, and the y-axis includes two labels, \"US\" and \"EU\"."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "FWwt3v6DX4FZpqg4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar for \"Environmental Protection\" Commitments in 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar for \"Environmental Protection\" Commitments in 2009 is colored}[0.211, 0.660, 0.671, 0.888]"
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "ONYPWs27BE0GRDp6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the \"Community Investment\" text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates for the \"Community Investment\" text in the legend are [0.659, 0.045, 0.863, 0.073]."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "llMC2iS8ZJ2VTrNy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label at [0.019, 0.307, 0.199, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label at [0.019, 0.307, 0.199, 0.335] is \"Achievements(2020)\"."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "D3GYxNNbQX934pPy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Environmental Protection\" Commitments in 2009}[0.211, 0.660, 0.671, 0.888] wider than {the bar for \"Community Investment\" Commitments in 2009}[0.671, 0.660, 0.767, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "vdkkiAvLPhRWdzyg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {Commitments(2009) of \"Community Investment\"}[0.671, 0.660, 0.767, 0.888] significantly greater than {Achievements(2020) of \"Environmental Protection\"}[0.211, 0.204, 0.729, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "yrNyIG9IQT3nxGif",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is further to the right, {the bar for Achievements(2020) of \"Environmental Protection\"}[0.211, 0.204, 0.729, 0.432] or {the bar for Achievements(2020) of \"Community Investment\"}[0.729, 0.204, 0.882, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar for Achievements(2020) of \"Community Investment\"}[0.729, 0.204, 0.882, 0.432]."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "HGQIiV8UwanZpvYB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat encompasses the region [0.659, 0.045, 0.863, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.659, 0.045, 0.863, 0.073] includes the text label of the legend for \"Community Investment\"."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "CCxKvyZbVYrh02iM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region covering the coordinates [0.211, 0.204, 0.882, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.211, 0.204, 0.882, 0.432] has two bars:\n1. {The \"Environment Protection\" bar for \"Achievements(2020)\"}[0.211, 0.204, 0.729, 0.432];\n2. {The \"Community Investment\" bar for \"Achievements(2020)\"}[0.729, 0.204, 0.882, 0.432]."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "M8Soz2RtQj7K6Wwb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the plot area from [0.211, 0.660, 0.767, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.211, 0.660, 0.767, 0.888] consists of two bars:\n1. {The \"Environmental Protection\" bar for \"Commitments(2009)\"}[0.211, 0.660, 0.671, 0.888];\n2. {The \"Community Investment\" bar for \"Commitments(2009)\"}[0.671, 0.660, 0.767, 0.888]."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "3zqpFzAETAMxsbUZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object being represented in the coordinates [0.273, 0.791, 0.293, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Initial Data' of 'E-Learning Adoption'."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "V4sUosFg513R7rvl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what color represents the 'Later Data' in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for 'Later Data' according to the legend is greenish (approx. hex color code: #88a27d) {the legend}<r>[0.460, 0.034, 0.794, 0.084]</r>."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "acSrl32gRpsOAtXd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the X-axis entry} at coordinates [0.401, 0.942, 0.434, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis entry at those coordinates is '400'."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "D98jaZZC46qDaLAh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Initial Data bar for 'Online Certification Courses'}<r>[0.273, 0.595, 0.302, 0.693]</r> smaller than or greater than {the Later Data bar for 'Online Certification Courses'}<r>[0.302, 0.595, 0.360, 0.693]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "5hIK77Qq8Rn16Q5Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Initial Data' bar for 'E-Books Downloaded'}<r>[0.273, 0.400, 0.283, 0.497]</r> have the same, lesser, or more value than {the 'Later Data' bar for 'E-Books Downloaded'}<r>[0.283, 0.400, 0.336, 0.497]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "eOcDDZI9ISLxiene",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Initial Data bar located in 'Digital Training Investment'}<r>[0.273, 0.204, 0.514, 0.302]</r> longer than {the Later Data bar located in 'Digital Training Investment'}<r>[0.514, 0.204, 0.948, 0.302]?</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "8To9mrnHXl4Pb7Ok",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide some details about the objects between the coordinate points [0.273, 0.170, 0.981, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates represent the primary area of the plot. Within this region, there are horizontal gridlines, 4 pairs of bars representing the Initial and Later Data for 4 categories ('E-Learning Adoption', 'Online Certification Courses', 'E-Books Downloaded', and 'Digital Training Investment'), and an X-axis scale showing data ranges from 0 to 1400."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "5iObEUApa96gSZhW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates [0.460, 0.034, 0.794, 0.084]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the legend of the bar chart. It consists of two entries: {Initial Data}<r>[0.467, 0.047, 0.502, 0.067]</r> represented by a reddish color (approx.: #d66555) and {Later Data}<r>[0.646, 0.047, 0.681, 0.067]</r> represented by a greenish color (approx.: #88a27d)."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "CgJ9SbIvjHKEsEK6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details of the region encompassed by [0.273, 0.400, 0.336, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two vertical bars belonging to the 'E-Books Downloaded' category. The bar on the left represents the 'Initial Data' and the one on the right represents the 'Later Data'."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "Ik6m1LNLakWfqGjW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.180, 0.660, 0.391, 0.888] referred to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing Healthy Food Intake (%) for Population."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "Z1Hyzw06uw2MHcNG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the coordinates [0.470, 0.045, 0.626, 0.073] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Smoking Rate (%)\"."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "iggn1DvhFAVB8EGB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.450, 0.204, 0.590, 0.432] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar is depicting the Smoking Rate (%) for Urban Population."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "qOxAD82MENRHLfKU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object mentioned at [0.019, 0.307, 0.168, 0.335] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, named \"Urban Population\"."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "zeUxDSRwyBqdtKU4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Healthy Food Intake for Population}[0.180, 0.660, 0.391, 0.888] taller than {the bar representing Smoking Rate for Population}[0.391, 0.660, 0.577, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "WAsTMdIkDmKGN4if",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of Urban population's Healthy Food Intake}[0.180, 0.204, 0.450, 0.432] longer than {the bar of Urban population's Sports Activity Participation}[0.590, 0.204, 0.897, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "c4buGx9Mi8Atr2Vg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Healthy Food Intake bar for the Population}[0.180, 0.660, 0.391, 0.888] extend further to right than {the Healthy Food Intake bar for Urban Population}[0.180, 0.204, 0.450, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "1ZnHRFBfXxNsU6rS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing urban population typically longer than the bars representing general population?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "XaJG1vw4ExDN9Lpf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained within the region [0.121, 0.034, 0.992, 0.084]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the graph chart legend, with three pairs of color-text labels. Each label consists of a colored square followed by a descriptive text. The labels are: Healthy Food Intake, identified with a red square [0.128, 0.047, 0.163, 0.067]; Smoking Rate, associated with a green square [0.421, 0.047, 0.456, 0.067]; and Sports Activity Participation, marked with a blue square [0.661, 0.047, 0.695, 0.067]."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "DUvRPZoPi90Fn2nt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the coordinate range [0.180, 0.204, 0.897, 0.432]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three bars representing the rates of Healthy Food Intake, Smoking, and Sports Activity Participation for the Urban Population."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "8xXZPfGaLxdYD3Hq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.180, 0.660, 0.830, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three bars, representing the rates of Healthy Food Intake, Smoking and Sports Activity Participation for the general Population."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "YvuM3GYXB28ENYc8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.577, 0.660, 0.830, 0.888]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single bar that represents the rate of Sports Activity Participation for the general Population."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "P9zoq6J4KN4vtPGT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object represents the y-axis label around the coordinates of [0.078, 0.505, 0.078, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no particular object representing the y-axis label around the coordinates [0.078, 0.505, 0.078, 0.505]."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "6KefzfegnX72UHdQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second increment on the x-axis with the coordinates [0.266, 0.902, 0.294, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick at [0.266, 0.902, 0.294, 0.920] represents 0.2 on the x-axis."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "sXcm8uhPL2Ldvnuk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis scale} at [0.086, 0.575, 0.113, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "oRnZIpuzdMj3OHjh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x-axis tick at 0.6}[0.576, 0.902, 0.604, 0.920] to the left or right of {the x-axis tick at 0.8}[0.731, 0.902, 0.759, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "zAWb8ZL2V9ItjGYK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis tick at 0.8}[0.085, 0.267, 0.113, 0.285] higher or lower than {the y-axis tick at 0.2}[0.086, 0.729, 0.113, 0.747]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "PlbDQQysXeAF30I5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the x-axis ticks}[[0.111, 0.902, 0.139, 0.920], [0.266, 0.902, 0.294, 0.920], [0.421, 0.902, 0.449, 0.920], [0.576, 0.902, 0.604, 0.920], [0.731, 0.902, 0.759, 0.920], [0.886, 0.902, 0.914, 0.920]] generally farther from the y-axis than {the y-axis ticks}[[0.085, 0.883, 0.113, 0.901], [0.086, 0.729, 0.113, 0.747], [0.086, 0.575, 0.113, 0.593], [0.085, 0.421, 0.113, 0.439], [0.085, 0.267, 0.113, 0.285], [0.085, 0.113, 0.113, 0.131]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "BhHsqtUFpRoFpdAL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide an overview of the region [0.125, 0.890, 0.900, 0.120]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main graphing area of the chart. It includes both the x and y axis with six equally spaced tick marks on each one. The x-axis ticks range from [0.111, 0.902, 0.139, 0.920] to [0.886, 0.902, 0.914, 0.920] while the y-axis ones are located between [0.085, 0.883, 0.113, 0.901] and [0.085, 0.113, 0.113, 0.131]."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "MWIFozEA1TZlsjbO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.125, 0.120, 0.900, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangle bounds the entire plot, from [0.125, 0.120] to [0.900, 0.890]. Within this area, we can see the x-axis ranging from [0.111, 0.902, 0.139, 0.920] to [0.886, 0.902, 0.914, 0.920] and the y-axis from [0.085, 0.883, 0.113, 0.901] to [0.085, 0.113, 0.113, 0.131]. We also see the blank main plotting area which is currently devoid of any bars or line elements that would normally represent data in such a chart."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "2Tby9DJvcF4UahbO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all the primary components of the chart. It encompasses the x-axis with its labels from [0.111, 0.902, 0.913, 0.920], and the y-axis with its labels from [0.085, 0.883, 0.113, 0.131]. However, the central plot area does not currently contain any bars or lines representing data."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "8NapwGhKf5tMShTo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at the coordinates [0.782, 0.102, 0.804, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 90."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "4dFHQxUTA0Agz7f7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at [0.100, 0.478, 0.221, 0.496]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is Public Forums."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "ZgBatRPpw8CveESi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.660, 0.352, 0.682, 0.369]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the value of 'Independent Blogs' which is 70."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "pKLcB70A5roDxOTG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-coordinate at [0.106, 0.852, 0.221, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-coordinate represents 'TV and Radio'."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "MUrbwKClA5v2Pck4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the media type at [0.233, 0.186, 0.904, 0.286]} having higher trust level than {the media type at [0.233, 0.311, 0.660, 0.410]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "B46lORxfT2dCnxS3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the influence of {Social-media at [0.233, 0.186, 0.904, 0.286]} higher than {Public Forums at [0.233, 0.435, 0.751, 0.535]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "EYpRZFPN5O9E52Ye",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher influence between {the 'Underground Media' at [0.233, 0.685, 0.629, 0.784]} and {the 'TV and Radio' at [0.233, 0.809, 0.843, 0.909]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "TV and Radio."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "TcpZBBC28Zznyzrc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher between {the influence of 'Traditional Media' at [0.233, 0.061, 0.782, 0.161]} and {the influence of 'Mainstream News Sites' at [0.233, 0.560, 0.904, 0.660]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mainstream News Sites."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "FMEqi279SflyTTCc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found within the coordinates [0.233, 0.685, 0.629, 0.784]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a bar representing the Underground Media, with a value of 65 represented as a number at coordinates [0.629, 0.726, 0.652, 0.743]."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "K1bw9TegEmqUSVkN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region [0.233, 0.435, 0.751, 0.535]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a bar showing the influence of Public Forums that is represented by a value of 85 at the location [0.751, 0.476, 0.773, 0.494]."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "lOYQvFQxcrkMaaRD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the coordinates [0.233, 0.186, 0.904, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, we see the data of Social-media which is displayed by a bar. The value of the Social-media is 110, located at [0.904, 0.227, 0.937, 0.244]."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "xYdOyiiGYQQ7tkRo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the material that is within the area [0.233, 0.809, 0.843, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this location, there's a bar depicting the influence of 'TV and Radio' that extends until the value 100 at coordinates [0.843, 0.851, 0.876, 0.868]."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "SX20FJEO9LP1LesK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinates [0.114, 0.545, 0.121, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number '4' indicating a measurement."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "ubPZbnYVAqzXLMmv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented at {the legend handle}<r>[0.352, 0.102, 0.376, 0.126]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "Mwpatv5sOeV32r3g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is positioned at [0.012, 0.330, 0.028, 0.682]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Integration Level."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "pshQksO2GQYk3jTG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar corresponding to \"Historical Monuments\"}<r>[0.093, 0.588, 0.143, 0.915]</r> taller than {the bar corresponding to \"Folk Lore\"}<r>[0.588, 0.343, 0.638, 0.915]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "lxbPbX0mVN3COEd3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is closer to the y-axis, {Instance 1's Folk Lore bar}<r>[0.588, 0.343, 0.638, 0.915]</r> or {Instance 7's Historical Monuments bar}<r>[0.390, 0.686, 0.440, 0.915]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Instance 7's Historical Monuments bar}<r>[0.390, 0.686, 0.440, 0.915]</r>."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "5Riz89c3eVbbbbDl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the label {Instance 4}<r>[0.500, 0.099, 0.560, 0.134]</r> positioned to the left of {Instance 5}<r>[0.616, 0.099, 0.676, 0.134]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "f2UE6UYl8V87tWSp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.093, 0.588, 0.440, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes several vertical bars representing dataset instances for Historical Monuments. For instance, the bar for Instance 1 is {Instance 1's Historical Monuments bar}<r>[0.093, 0.588, 0.143, 0.915]</r> and the bar for Instance 7 is located at {Instance 7's Historical Monuments bar}<r>[0.390, 0.686, 0.440, 0.915]</r>."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "5FgSh16HENC4Rmb5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located in [0.117, 0.033, 0.912, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the sublegend with titles for seven instances, from Instance 1 to Instance 7. Each instance has a labeled text and a color handle next to it representing the instance's dataset."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "XBlkDhyC7K2uqcSo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.012, 0.097, 0.440, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This zone includes the Y-axis with labels ranging from 0 to 10 showing integration levels. It also encompasses the bar representations for Historical Monuments, displaying variable integration levels according to the data."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "LlKNavqAnhQ2gAPf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object present at {coordinates}<r>[0.033, 0.252, 0.040, 0.275]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label for the value \"4\"."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "bKVvZGNyDe6iqmD1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at {position}<r>[0.224, 0.061, 0.252, 0.084]</r> represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label \"Start\" in the chart legend."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "9Tp65mkIsIEFQwxA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReveal what is located at {location}<r>[0.191, 0.063, 0.215, 0.079]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend color handle for \"Start\"."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "tkIr56qukMyUZoRH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the start bar for \"Urban Regeneration\"}<r>[0.087, 0.773, 0.106, 0.944]</r> have a greater numerical value than {the completion bar for \"Public Transportation\"}<r>[0.497, 0.602, 0.516, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "H4PD9zZ2PDrin1YF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is positioned higher on the Y-axis, {the progress bar for \"Housing Development\"}<r>[0.665, 0.568, 0.684, 0.944]</r> or {the stake involved bar for \"Infrastructure renewal\"}<r>[0.907, 0.398, 0.926, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stake Involve."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "TZPLNTL8Kstw8Shn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the issues bars}<r>[0.143, 0.688, 0.162, 0.944], [0.330, 0.653, 0.348, 0.944], [0.516, 0.722, 0.535, 0.944], [0.702, 0.705, 0.721, 0.944], [0.889, 0.636, 0.907, 0.944]</r> generally shorter than {the good reviews bars}<r>[0.180, 0.346, 0.199, 0.944], [0.367, 0.261, 0.385, 0.944], [0.553, 0.466, 0.572, 0.944], [0.740, 0.415, 0.758, 0.944], [0.926, 0.244, 0.945, 0.944]</r> in terms of the scale on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "6a51pr0SNGvB8dwI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within {the region}<r>[0.187, 0.017, 0.845, 0.093]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend including the legend title \"Stages\" and six pairs of color handles and corresponding text labels for different stages such as Start, Progress, Completion, Issues, Stake Involve, and Good Reviews."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "RoT882RVXZ5YfJzp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in {the bounding box}<r>[0.033, 0.081, 0.040, 0.958]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis labels with scale values ranging from \"0\" to \"5\" and the corresponding tick marks."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "0ZCBiljRm483vDit",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are included in the section marked by {these coordinates}<r>[0.268, 0.952, 0.391, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the X-axis category label for \"Parks & Open Spaces\" and a tick mark for that category."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "F4AMOWvUfaidTvwt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar which represents the year 2019 on Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar which represents the year 2019 on Facebook is pinkish-purple, and its exact coordinates are {the bar}<r>[0.148, 0.415, 0.209, 0.915]</r>."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "J8LSyecpOkFbDNUI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object with coordinates [0.560, 0.110, 0.589, 0.145] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the year 2020 in the legend."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "xKk6XzMxpMB7uLKW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object appearing within the coordinates [0.332, 0.359, 0.915] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the number of Facebook users in 2021."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "r06OegOOlJyu9Mrb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 2021 bar for {Facebook} <r>[0.270, 0.359, 0.332, 0.915]</r> taller than the 2020 bar for {Facebook}<r>[0.209, 0.397, 0.270, 0.915]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "aI5C7EwAXkI1asTD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre more users projected for {Instagram}<r>[0.883, 0.489, 0.945, 0.915]</r> or {Facebook}<r>[0.270, 0.359, 0.332, 0.915]</r> in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "2NWGCSBSz4I1EQB1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Twitter}<r>[0.577, 0.749, 0.638, 0.915]</r> have more users in 2021 than it did in 2020<r>[0.516, 0.730, 0.577, 0.915]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "HJQHaUeBPJWR7idc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the data contained within the coordinates [0.044, 0.174, 0.988, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all of the bars of the chart, which represent the number of users (in billions) for each platform (Facebook, Twitter, Instagram) for the years 2018, 2019, 2020, and 2021."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "3qyVuSAkzJtMKmar",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the numbers and information within [0.353, 0.043, 0.678, 0.158]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a legend that corresponds to the colors of the bars for the different years: Blue for 2018, pinkish-purple for 2019, greenish-blue for 2020, and orange for 2021. The text coordinates for these years fall within [0.390, 0.110, 0.419, 0.145], [0.475, 0.110, 0.504, 0.145], [0.560, 0.110, 0.589, 0.145], and [0.644, 0.110, 0.674, 0.145] respectively."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "souf3tPMbvatgEwE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the visible features within the region [0.496, 0.730, 0.516, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the bar representing the user count for Twitter in the year 2020, which is a greenish-blue color. The height indicates the number which is expected to be about 1.0 billion."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "wYVBAZPGWQNnXoqb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label of the chart in coordinate [0.010, 0.370, 0.022, 0.441]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Impact."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "DfabYY2E6KYAdbOX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first color in the legend box?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color in the legend box}<r>[0.384, 0.905, 0.403, 0.917]</r> is Pink."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "307ZdemwHDxz1Kxj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pink bar represent in [0.086, 0.243, 0.130, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pink bar represents the \"Covid Cases Decline\" under a \"No Lockdown\" policy."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "JxUoCTAyE6wHOhNN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater impact in the \"No Lockdown\" policy, {Covid Cases Decline}<r>[0.086, 0.243, 0.130, 0.351]</r> or {Economic Impact}<r>[0.130, 0.351, 0.175, 0.568]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Impact."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "oFBstiMBuT4OLZPO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat impact is greater in the {Partial Lockdown}<r>[0.383, 0.221, 0.427, 0.351]</r>, {Covid Cases Decline}<r>[0.383, 0.221, 0.427, 0.351]</r> or {Healthcare Capacity}<r>[0.516, 0.178, 0.561, 0.351]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare Capacity."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "sesZuUfBNooheDBW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder the {Complete Lockdown} policy< r>[0.766, 0.792, 0.860, 0.809]</r>, is the negative impact greater on {School Disruptions}<r>[0.902, 0.351, 0.947, 0.612]</r> or {Economic Impact} <r>[0.724, 0.351, 0.769, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "School Disruptions."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "hCmkyXf0Vzk9bRek",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content present in the coordinates [0.381, 0.870, 0.652, 0.980]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. The legend presents six different labels each with its corresponding color. Each label represents a different impact of different lockdown policies on various sectors."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "IvDl73bFJ4So1mOp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels on the X-axis within coordinates [0.026, 0.792, 0.860, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels on the X-axis in this region are: \"No Lockdown\", \"Partial Lockdown\", and \"Complete Lockdown\"."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "gte2typGioD1AQHU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bars within [0.383, 0.221, 0.427, 0.351] and [0.516, 0.178, 0.561, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle at [0.383, 0.221, 0.427, 0.351] represents the \"Covid Cases Decline\" impact under \"Partial Lockdown\", while the rectangle at [0.516, 0.178, 0.561, 0.351] represents the impact on \"Healthcare Capacity\" under the same policy."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "rRbWpXRAKbQvHUtF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend at [0.055, 0.063, 0.197, 0.129] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains two items: {Internal Mobility}<r>[0.087, 0.071, 0.168, 0.091]</r> is represented by a blue rectangle, and {International Mobility}<r>[0.087, 0.101, 0.193, 0.121]</r> is represented by a red rectangle."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "HJ9wWMFTwAyGWdPI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label mean at the coordinates [0.011, 0.358, 0.021, 0.612]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes Population (in thousands)."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "3KxkZlzwgckXQPaS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the X-axis tick at [0.743, 0.931, 0.768, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's 2017."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "J40jnLsksiPCdEez",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the bar representing \"International Mobility\" in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The position is [0.652, 0.296, 0.683, 0.917]."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "aAMInNHiwNRyFC3B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of {2010}<r>[0.093, 0.219, 0.124, 0.917]</r> and {2019}<r>[0.885, 0.118, 0.916, 0.917]</r>, which year had more cases of Internal Mobility?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "jtTHBdRzgMGhXbRm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar in 2015 representing \"International Mobility\"}<r>[0.564, 0.312, 0.595, 0.917]</r> taller than {the bar in 2015 representing \"Internal Mobility\"}<r>[0.533, 0.094, 0.564, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "r19cskXhNKguptq0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2016 bar for the International Mobility}<r>[0.652, 0.296, 0.683, 0.917]</r> taller than {the 2017 bar for the International Mobility}<r>[0.740, 0.288, 0.771, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "wdR67niu7N9QG8Fw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {2016}<r>[0.621, 0.110, 0.652, 0.917]</r> and {2018}<r>[0.797, 0.141, 0.828, 0.917]</r>, which year did the Internal Mobility reach a higher peak?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "ArW7sQU9df3r8IwY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the bounding box [0.093, 0.219, 0.916, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features all the blue bars, corresponding to the annual data of Internal Mobility from 2010 to 2019."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "UeUz4p9oCa7cfVMF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.124, 0.389, 0.947, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all red bars from 2010 to 2019, denoting International Mobility information yearly."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "Ti0GqVLbzhanJsck",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.050, 0.053, 0.989, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main charting area, which includes rectangular bars representing data trends from two categories - Internal Mobility and International Mobility - over a decade. The y-axis label in this region is \u201cPopulation (in thousands)\u201d, and the x-axis label is \u201cYears\u201d. This region also contains a legend at the top left."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "lHzERzPRWd1N1riK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space [0.353, 0.021, 0.686, 0.047] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It holds the subtitle of the chart: \"Population Migration and Mobility Trends Over a Decade\"."
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "PKeCe1ltFYYk4lfJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.141, 0.593, 0.160, 0.616]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick label \"1.0\"."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "oRhYJQ5mImIJRsQD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the color of the bar with the coordinates [0.554, 0.363, 0.590, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar {at these coordinates}<r>[0.554, 0.363, 0.590, 0.944]</r> is \"#a3ade5\"."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "QZ8sVbg3cK0AX4En",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis tick located at [0.472, 0.952, 0.528, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Moderate\"."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "yOIkbQ447xJkcbzl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar {representing \"Steady Decline\"}<r>[0.338, 0.636, 0.374, 0.944]</r> of the \"Conservative\" strategy taller than the bar {representing \"Sudden Growth\"}<r>[0.374, 0.466, 0.410, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "onjpc77UCNDQ9Esw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is wider, {the bar indicating Moderate for \"Substantial Decrease\"}<r>[0.410, 0.739, 0.446, 0.944]</r> or {the bar indicating Moderate for \"Erratic Behavior\"}<r>[0.446, 0.602, 0.482, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same width."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "CC3HwsAWDKqIKjER",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar denoting Aggressive strategy under \"Accelerated Decline\"}<r>[0.662, 0.875, 0.698, 0.944]</r> shorter than {the bar depicting Aggressive strategy under \"Mixed Performance\"}<r>[0.770, 0.432, 0.806, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "UcYjOTi0crqQvvRF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {legend}[0.923, 0.061, 1.034, 0.084]]</r> to the right or left of {ylabel}[0.141, 0.593, 0.160, 0.616]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "Ixtwv4t7nMRqF9qC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is inside the rectangular region [0.770, 0.432, 0.806, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a bar representing the \"Aggressive\" investment strategy's returns for the category \"Mixed Performance\". This bar has a height representative of a return of 1.5."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "k2xxj9Wq0JsjxoBW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.194, 0.602, 0.230, 0.944] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing the \"Stable Growth\" returns of the \"Conservative\" financial condition. The height of this bar represents a return of 1.0."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "mCGYqUdIsXUGAf9C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.374, 0.500, 0.410, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar that represents the return of the Moderate investment strategy under Stable Growth which is 1.3."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "5XPLDmH4PO0TWSWx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the area [0.680, 0.193, 0.770, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a column chart showing the returns of the Aggressive investment strategy for two financial conditions: \"Sudden Growth\" with a value of 2.2 and \"Mixed Performance\" with a value of 1.5."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "iOd4b4a74NKg8PSj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart does the main object with coordinates as [0.050, 0.034, 0.989, 0.810] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar Chart."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "0WUovvGZGJfzsxpv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the text representing the year 2013 in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.521, 0.943, 0.546, 0.966]."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "EITf366yrJAKw7Tz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}[0.011, 0.363, 0.024, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Measure."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "Quhx8wlsvrLhWNp6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are shown in [0.271, 0.899, 0.768, 0.976]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2010}[0.303, 0.943, 0.328, 0.966], {2011}[0.375, 0.943, 0.401, 0.966], {2012}[0.448, 0.943, 0.473, 0.966], {2013}[0.521, 0.943, 0.546, 0.966], {2014}[0.594, 0.943, 0.619, 0.966], {2015}[0.666, 0.943, 0.691, 0.966], {2016}[0.739, 0.943, 0.764, 0.966]."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "x8d6vnktJpP3DzRa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Online Education\" in 2011}[0.143, 0.303, 0.193, 0.810] taller than {the bar representing \"Online Education\" in 2010}[0.093, 0.338, 0.143, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "2Hemmpkn32crLBEo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Training Services\" in 2013}[0.494, 0.547, 0.545, 0.810] shorter than {the bar representing \"Training Services\" in 2014}[0.545, 0.529, 0.595, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "IVlII5o57Nevkfwc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Edtech Startups\"}[[0.595, 0.780, 0.645, 0.810], [0.695, 0.690, 0.746, 0.810], [0.746, 0.631, 0.796, 0.810], [0.796, 0.541, 0.846, 0.810], [0.846, 0.452, 0.896, 0.810],[0.896, 0.273, 0.947, 0.810]] and {\"Online Education\"}[[0.093, 0.338, 0.143, 0.810], [0.143, 0.303, 0.193, 0.810], [0.193, 0.309, 0.243, 0.810], [0.243, 0.273, 0.294, 0.810], [0.294, 0.159, 0.344, 0.810], [0.344, 0.243, 0.394, 0.810], [0.394, 0.094, 0.444, 0.810]], which category has a higher value for the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Education."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "uocFhkLOh2WnkLn9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"2010\"}[{Online Education}[0.093, 0.338, 0.143, 0.810], {Training Services}[0.344, 0.541, 0.394, 0.810], {Edtech Startups}[0.595, 0.780, 0.645, 0.810]] and {\"2011\"}[{Online Education}[0.143, 0.303, 0.193, 0.810], {Training Services}[0.394, 0.571, 0.444, 0.810], {Edtech Startups}[0.645, 0.750, 0.695, 0.810]], which year has a higher value for {Training Services}<[0.344, 0.541, 0.394, 0.810], [0.394, 0.571, 0.444, 0.810]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "9dZB0idvvIXDiZyJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.271, 0.899, 0.768, 0.976]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend displaying seven separate values for each year from 2010 to 2016. Each year is represented by a color label and its corresponding year. The color labels' coordinates range from [0.275, 0.945, 0.295, 0.961] to [0.711, 0.945, 0.731, 0.961], and the labels' text coordinates, in chronological order, start at [0.303, 0.943, 0.328, 0.966] and end at [0.739, 0.943, 0.764, 0.966]."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "XXQDnTwLeEEHIaUw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.050, 0.034, 0.989, 0.810]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire bar chart, which includes all the bars representing three categories (Online Education, Training Services, and Edtech Startups) over the years from 2010 to 2016. It also includes the x-axis labels, y-axis labels, and their respective scales."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "VfQYxyk5ZwlL7Dj9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.394, 0.094, 0.444, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single bar representing the measure of \"Online Education\" for the year 2016."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "Gc1eD5wvTQiL9nUD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.561, 0.514, 0.888, 0.780]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers a portion of the bar chart, specifically the last three years (2014-2016) of data for the \"Training Services\" and \"Edtech Startups\" categories. It contains six bars in total, three for each category, color-coded according to the years they represent."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "oIspCUS9QTdvxhBL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.028, 0.391, 0.046, 0.415]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"100\" is positioned here."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "v52rmayBBVrIBKHV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.116, 0.400, 0.140, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the {2010 bar} highlighting \"Deforestation\"."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "bf3vlMFg0SSMLEEL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object located at [0.763, 0.088, 0.799, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color code for the year 2040 in the chart legend, which is colored #67a9b6."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "lF6akIdnJLaNzjwa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Air Pollution\" bar of 2030}<r>[0.790, 0.373, 0.813, 0.944]</r> taller than {the \"Water Pollution\" bar of 2020}<r>[0.297, 0.645, 0.320, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "RpLNs01ViFkVCw9b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Deforestation\" in 2010}<r>[0.116, 0.400, 0.140, 0.944]</r> taller or shorter than {the same bar in 2040}<r>[0.187, 0.547, 0.210, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "Aew4v1pIoKjH8pKR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Overfishing\" bar for 2040}<r>[0.657, 0.699, 0.680, 0.944]</r> have a higher or lower value than {the \"Water Pollution\" bar for 2030}<r>[0.320, 0.629, 0.343, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "xXAUDyHPm7VbF7MM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements located at the coordinates [0.380, 0.018, 0.659, 0.095]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's legend which contains four colored squares each representing a different year from 2010 to 2040, and their corresponding label \"2010\" at [0.412, 0.062, 0.437, 0.086], \"2020\" at [0.485, 0.062, 0.510, 0.086], \"2030\" at [0.557, 0.062, 0.582, 0.086], and \"2040\" at [0.630, 0.062, 0.655, 0.086]."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "kLv9SoPWvznLVFBD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the area [0.746, 0.563, 0.770, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area there is a blue bar - the {Air pollution bar for 2010}<r>[0.743, 0.292, 0.766, 0.944]</r>, with a value of 120."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "rTeV2YcXtYfhE4cB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.760, 0.952, 0.820, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis label for {Air pollution}<r>[0.760, 0.952, 0.820, 0.975]</r>."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "JukZhBhMGP8M4qWM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate [0.118, 0.350, 0.132, 0.771] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The AI bar for 2022}<r>[0.119, 0.350, 0.132, 0.771]</r>."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "q9DeuZs4kddWFAkR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology's bar is located at [0.351, 0.474, 0.366, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cloud Computing."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "XfiNFgwUcMoPlkre",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the y-label} described in the location [0.011, 0.322, 0.024, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Value."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "p62Ytg4WW6vuopVo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2022 bar for IoT}<r>[0.636, 0.487, 0.649, 0.771]</r> taller than {the 2020 bar for IoT}<r>[0.610, 0.540, 0.623, 0.771]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "OWD7wHez8ygNpL2T",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the markers for '2021'}<r>[0.414, 0.940, 0.434, 0.956]</r> of a darker shade than {the markers for '2024'}<r>[0.632, 0.940, 0.652, 0.956]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "GygkE3fGvJUQZxjv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the 2023 value for AI}<r>[0.132, 0.224, 0.145, 0.771]</r> or {the 2025 value for AI}<r>[0.158, 0.097, 0.171, 0.771]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2025."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "2dAR1d1t0XQsoV6h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents and layout within the region [0.265, 0.893, 0.762, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for year labels, having several rectangles with different colors from [0.269, 0.940, 0.289, 0.956] to [0.705, 0.940, 0.725, 0.956], and corresponding year labels from 2019 to 2025, overall organized horizontally with equal spacing."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "bey9D2qiifgT83Wa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the coordinate range [0.081, 0.516, 0.182, 0.771] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers three bars, representing values of AI across different years (2019 to 2021), each bar is marked with a value, and gradual height increases indicate rising trends."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "CniX8HOAsP1k9oeF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the coordinate box [0.028, 0.025, 0.034, 0.785]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical region lists y-axis values from 0 to 7, meant to represent trend values, and it covers varying locations corresponding to the actual placement of these numerical values beside the y-axis."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "ozkmKNMZyvwmACqY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {the \"Arthritis\" label}<r>[0.602, 0.061, 0.649, 0.084]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#67a9b6."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "yaOfPK0dDZZXvcZ1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health condition corresponds to {the bar}<r>[0.687, 0.334, 0.702, 0.944]</r> located at the \"60-70\" age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heart Disease."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "0OCkgfpoZblcwyB3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the prevalence rate indicated at the top of {the bar}<r>[0.867, 0.724, 0.882, 0.944]</r> for the \"30-40\" age group and \"Cancer\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.8."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "7rZsHE6eUhYp8wlI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the prevalence rate for {Osteoporosis}<r>[0.913, 0.249, 0.928, 0.944]</r> higher in {the \"Over 70\"}<r>[0.867, 0.952, 0.913, 0.975]</r> or {the \"60-70\"}<r>[0.723, 0.952, 0.757, 0.975]</r> age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "Over 70."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "B1Kc6oP51BtACavV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Under 30\" age group}<r>[0.086, 0.822, 0.101, 0.944]</r> have a consistent prevalence rate across all health conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "k49EHWe2oUmR1KNF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich age group has a taller bar for {Depression}, {the second from the left}<r>[0.327, 0.797, 0.342, 0.944]</r> or {the fourth from the left}<r>[0.627, 0.627, 0.642, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "50-60."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "Ql5WigT3QXTSBtOQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents found within the range specified by these coordinates: [0.273, 0.952, 0.306, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label \"30-40\" which marks the age category for the bars in the chart with {its coordinates}<r>[0.273, 0.952, 0.306, 0.975]</r>."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "KTy7hkep17g2XXtJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present within the range [0.032, 0.813, 0.040, 0.958]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis labels \"1\" and \"0\" representing prevalence rates. The label \"1\" has {its coordinates}<r>[0.032, 0.813, 0.040, 0.836]</r> and \"0\" has {its coordinates}<r>[0.032, 0.934, 0.040, 0.958]</r>."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "ju2YQ4kIJRRSYFop",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.570, 0.063, 0.593, 0.079] indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region marks the legend color box corresponding to \"Arthritis,\" which is represented by a rectangle with {its coordinates}<r>[0.570, 0.063, 0.593, 0.079]</r> and the associated color #67a9b6."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "n0dWMTHGi7xTlS8q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object responsible for the label 'Revenues (in million dollars)' located at [0.012, 0.359, 0.028, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the Y-axis label."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "NhPJ023DxLhRSMoO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at [0.107, 0.611, 0.160, 0.944] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies a bar indicating revenue from the 'Oil' industry in 2008."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "pVmXO4gewYhxerPj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.222, 0.024, 0.831, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the legend, detailing the colors associated with different industries."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "5UoGbWz1FuHRKFXb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Oil industry revenue bar for 2008}[0.107, 0.611, 0.160, 0.944] taller or shorter than {the Oil industry revenue bar for 2018}[0.631, 0.633, 0.684, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "3ZaSnRKunwoYCxf2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for the Biotechnology industry in 2018}[0.893, 0.500, 0.946, 0.944] have higher or lower height than {the bar for the Electricity industry in 2018}[0.841, 0.389, 0.893, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "ascP3rVt44dySv6j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar for the Water industry in 2008}[0.265, 0.555, 0.317, 0.944] and {the bar for Clean Energy industry in 2018}[0.736, 0.689, 0.788, 0.944], which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Water industry's bar."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "e2yHI5L2ugHcTEJ8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents and structures observed in [0.222, 0.024, 0.831, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is where the legend resides. It lists six industry sectors each associated with a different color:\n1. 'Oil' with color represented at [0.227, 0.070, 0.250, 0.086].\n2. 'Gas' symbolized by color at [0.298, 0.070, 0.321, 0.086].\n3. 'Clean Energy' denoted by color at [0.375, 0.070, 0.399, 0.086].\n4. 'Water' signified by color at [0.508, 0.070, 0.531, 0.086].\n5. 'Electricity' with color representation at [0.598, 0.070, 0.621, 0.086].\n6. 'Biotechnology' associated with color at [0.711, 0.070, 0.734, 0.086]."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "NCdsnqlbwNPD8GfB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the coordinates [0.160, 0.633, 0.212, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "Located at these coordinates, there's a bar that represents the revenues of the 'Gas' industry for the year 2008."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "ZWFo1pPsWdrxvGkp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.265, 0.555, 0.317, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area is occupied by a bar for the 'Water' industry in the year 2008 reaching up to 3500 million dollars in revenues on the Y-axis."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "nyimE31wvHefrr0d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.174, 0.066, 0.377, 0.094] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This label represents the {Economic Growth}<r>[0.174, 0.066, 0.377, 0.094]</r>, which is blue in the chart."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "18iteorlb3igodLg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage impact value given at the coordinates [0.877, 0.707, 0.918, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The impact value at these coordinates is \"1.0\"."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "LgF5z5g7tpJkeFAJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the item at [0.244, 0.153, 0.359, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A {green bar}<r>[0.244, 0.153, 0.359, 0.883]</r> representing Policy Impact for Time Scale \"Years\"."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "aICO8HvVRMp3HKn2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ylabel at coordinates [0.025, 0.324, 0.048, 0.600]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ylabel at these coordinates is {Impact Percentages}<r>[0.025, 0.324, 0.048, 0.600]</r>."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "WWBZSQUrNy2niV59",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Economic Growth bar for \"Years\"}[0.129, 0.350, 0.244, 0.883] taller than the related {Policy Impact bar}[0.244, 0.153, 0.359, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "AmH9d1uZbdiTVGPl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {green bar for \"Decades\"}[0.820, 0.743, 0.935, 0.883] to the left or right of the {blue bar}[0.704, 0.532, 0.820, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "mPyxp7LVlVGeP1ZM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of Economic Growth for Years greater or lesser than for Decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "FVJ1OU6Uk0yqXWhQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Policy Impact bar for \"Years\"}[0.244, 0.153, 0.359, 0.883] and {Policy Impact bar for \"Decades\"}[0.820, 0.743, 0.935, 0.883], which one shows a higher impact percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "w31XLFr7L6GnE8gZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.100, 0.055, 0.387, 0.147]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, highlighting two categories. The first category is {Economic Growth}<r>[0.174, 0.066, 0.377, 0.094]</r> denoted in blue color. The second category is {Policy Impact}<r>[0.174, 0.108, 0.328, 0.136]</r> represented in green color."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "HJQNpzif3RrNJHd6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we see in the region [0.704, 0.532, 0.820, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region there is a blue bar indicating the Economic Growth for \"Decades\" with a value of 2.5%. The range of value is located at [0.762, 0.496, 0.803, 0.524]."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "4BeQ8yUoX3WfiSS5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in region [0.129, 0.350, 0.244, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a blue bar and the corresponding value showing the Economic Growth for \"Years\" is 3.8%, and the value text's coordinate is [0.186, 0.314, 0.227, 0.342]."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "O5dAqfA34Swibljj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area [0.058, 0.030, 0.072, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates is a Y-axis tick label, valued at \"6\"."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "NIJkUye9yUxbk74o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.419, 0.942, 0.637, 0.970] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, named Government Policy."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "fZynW5GARrF0mpFq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the Term 'Policy Implementation' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.174, 0.108, 0.432, 0.136]"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "ZsTiI1uUONWtcoZ7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis scale} at [0.058, 0.367, 0.072, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "3."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "jfo3ENIsowQxvxDd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in [0.100, 0.055, 0.441, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Policy Effectiveness}[0.174, 0.066, 0.402, 0.094] and {Policy Implementation}[0.174, 0.108, 0.432, 0.136]."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "e02HdQIz9ez8f3dz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Policy Effectiveness'}[0.129, 0.294, 0.243, 0.883] to the left or right of {the bar for 'Policy Implementation'}[0.243, 0.209, 0.357, 0.883] on 'Environment Policy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "9uWC2pL5ZYDBlRFe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Policy Implementation'}[0.814, 0.597, 0.928, 0.883] shorter than {the bar for 'Policy Effectiveness'}[0.699, 0.546, 0.814, 0.883] on 'Economic Policy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "5Wkr81GsW4oW2SlW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher level in 'Environment Policy', {Policy Effectiveness}<{Policy Effectiveness bar}[0.129, 0.294, 0.243, 0.883]> or {Policy Implementation}<{Policy Implementation bar}[0.243, 0.209, 0.357, 0.883]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy Implementation."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "Zc1XB1wx4VVYxIua",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Policy Effectiveness on 'Environment Policy'}[0.129, 0.294, 0.243, 0.883] and {Policy Implementation on 'Economic Policy'}[0.814, 0.597, 0.928, 0.883], which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy Effectiveness on 'Environment Policy'."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "UY3oOwpwm6oQcTOb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.100, 0.055, 0.441, 0.147]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, which is composed of two entries has a grey background. Each entry is made up of a label, followed by its textual description: The first entry is 'Policy Effectiveness' with the text coordinates [0.174, 0.066, 0.402, 0.094], and the second entry is 'Policy Implementation' with text coordinates [0.174, 0.108, 0.432, 0.136]"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "ly0BcCQH5V6T0fsJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.189, 0.903, 0.964, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the X-axis label area of the chart. It contains two labels, one is 'Environment Policy' at [0.189, 0.903, 0.411, 0.931], and the other one is 'Economic Policy' at [0.778, 0.903, 0.964, 0.931]."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "cmTtCmSkFTDHazhk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region [0.129, 0.294, 0.357, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two vertical bars are located in this area. The bar at [0.129, 0.294, 0.243, 0.883] represents 'Policy Effectiveness' in 'Environment Policy', while the bar at [0.243, 0.209, 0.357, 0.883] represents 'Policy Implementation' in 'Environment Policy'."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "PBsGymLZDvYSW0mN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region within [0.699, 0.546, 0.928, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two vertical bars overlayed on the 'Economic Policy' category are contained within this area. The rectangular bar on the left at [0.699, 0.546, 0.814, 0.883] represents 'Policy Effectiveness' and the bar on the right at [0.814, 0.597, 0.928, 0.883] represents 'Policy Implementation'."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "RNgjOpj9lUXlAoz5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object with the coordinates [0.043, 0.546, 0.087, 0.570]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 1000."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "zy6wO8n5atK4kL2d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the third bar representing Moderate Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.585, 0.503, 0.629, 0.903]."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "1m4VuCEBzh7EAQFw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.814, 0.125, 0.938, 0.148] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the category 'Stable Growth'."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "0f1EoOfrMJneWuzF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are present in the coordinates [0.814, 0.055, 0.966, 0.078] and [0.814, 0.090, 0.945, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels are {Moderate Growth}<r>[0.814, 0.055, 0.966, 0.078]</r> and {Steady Decline}<r>[0.814, 0.090, 0.945, 0.113]</r>."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "pZ7VuNCM7yqR1RtQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is leftmost among bars for 'Stable Growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one that represents data for '2020 Q1'<r>[0.228, 0.382, 0.273, 0.903]</r>."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "yp5hlvDnEA9OZlFf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of \"Steady Decline\" generally taller than those for \"Moderate Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "bVMe1ZBI4DtypvjO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing '2020 Q3' for Moderate Growth taller than the corresponding bar for Stable Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "P7DULuFzFBjaiuue",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Moderate Growth bars}<{bar1}[0.139, 0.555, 0.184, 0.903], {bar2}[0.362, 0.521, 0.407, 0.903], {bar3}[0.585, 0.503, 0.629, 0.903], {bar4}[0.808, 0.486, 0.852, 0.903]> and {Steady Decline bars}<{bar1}[0.184, 0.208, 0.228, 0.903], {bar2}[0.407, 0.243, 0.451, 0.903], {bar3}[0.629, 0.277, 0.674, 0.903], {bar4}[0.852, 0.347, 0.897, 0.903]>, which group has a taller bar for '2020 Q2'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Steady Decline."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "m7BhaovHGGDPTcEx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.139, 0.555, 0.184, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area there is the first bar of the 'Moderate Growth' category, for '2020 Q1'. Its height represents a unit value of 1000."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "B4fqouV82V7KnoCF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is shown in the area [0.852, 0.347, 0.897, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the last bar of the 'Steady Decline' category. It represents '2020 Q4' and the unit value it represents is 1600."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "kBRWaXeyVDW8lRE6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained in the area [0.452, 0.376, 0.496, 0.902]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the bar of the 'Stable Growth' category for '2020 Q2' with a unit value of 1500."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "KxQc9rl5EzQnNkfa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components occupy the coordinates[0.585, 0.503, 0.718, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That space is occupied by two bars, one from 'Moderate Growth' and the other from 'Steady Decline', representing data for '2020 Q3'. The heights of the bars represent unit values of 1150 and 1800 respectively."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "Q0OgGsPsAkvFOAwV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.251, 0.513, 0.355, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A yellow bar representing Machine Learning for Chatbot."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "w4qOVnfqHpNElI8d",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the coordinates [0.196, 0.108, 0.401, 0.136] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "8lUqFjm5TFjMpQDj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the position [0.025, 0.331, 0.048, 0.594] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "ZTO3B7acYfpahfEz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.675, 0.903, 0.972, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis label for Recommendation System."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "ajAXMD4NzV33sIm8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the red bar representing Deep Learning in Chatbot application}[0.147, 0.142, 0.251, 0.883] taller than {the yellow bar representing Machine Learning in Chatbot application}[0.251, 0.513, 0.355, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "wj4yPjACSFBVF9qy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the yellow bar for Machine learning on Recommendation System}[0.772, 0.176, 0.876, 0.883] to the right of {the red bar for Deep learning on Recommendation System}[0.668, 0.445, 0.772, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "VEVaUzRN9O7IuZVj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Machine Learning legend}[0.196, 0.108, 0.401, 0.136] higher than {the Deep Learning legend}[0.196, 0.066, 0.365, 0.094]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "TNPaXCohivKdaImE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Deep Learning}[0.147, 0.142, 0.251, 0.883] and {Machine Learning}[0.251, 0.513, 0.355, 0.883] in the Chatbot application, which has a higher performance score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "5y8PEdvcuJOKEfe7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe in the region [0.122, 0.055, 0.410, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the graph. The legend has two entries. The first label is 'Deep Learning', with the text located at [0.196, 0.066, 0.365, 0.094] and colored red at [0.131, 0.069, 0.178, 0.088]. The second label is 'Machine Learning', with the text located at [0.196, 0.108, 0.401, 0.136] and colored yellow at [0.131, 0.110, 0.178, 0.130]."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "QhNCw9hK77yuloNE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the coordinates [0.110, 0.041, 0.912, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the entire bar graph with two pairs of bars. The first pair of bars represent the Chatbot at x-axis labels located at [0.257, 0.903, 0.349, 0.931]. The second pair of bars represent the Recommendation System at x-axis labels located at [0.675, 0.903, 0.972, 0.931]. Both pairs include one red bar for 'Deep Learning' and one yellow bar for 'Machine Learning'."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "N6nEMxrmFtB714Lj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the space [0.668, 0.445, 0.876, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area representing the Performance Score for Recommendation System application. One is the red bar representing the score for Deep Learning, and the other is a yellow bar representing the score for Machine Learning."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "928Cn40LJkqhI3Mx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what you observe within the region [0.147, 0.142, 0.355, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "I identify two bar groups representing Performance Scores for the application type of Chatbot. The red bar, indicating the performance of Deep Learning, spreads from coordinates [0.147, 0.142, 0.251, 0.883], and the yellow bar, indicating the performance of Machine Learning, spreads within [0.251, 0.513, 0.355, 0.883]."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "T9kMf7Tb1OgaODVQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}[0.025, 0.238, 0.048, 0.675] on the left side of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, which is \"Supply Chain Cost (Billion USD)\"."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "dPzY2uRpuxQBw32f",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.150, 0.268, 0.262, 0.883] holding information about in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the blue bar representing the supply chain cost of H&M for the year 2020."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "qXgCzq3rRQJrqh3o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {Zara's 2020 data bar} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.711, 0.586, 0.823, 0.883]"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "4kwYaPhDFHapEbmn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the company that corresponds to {the X-axis label}[0.234, 0.903, 0.290, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "H&M."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "nXdeCyKxxBZfQyV8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company's 2021 cost is higher, {H&M}[0.262, 0.586, 0.374, 0.883] or {Zara}[0.823, 0.387, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Zara."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "9EOrkNwPeOZlaF2R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {H&M's 2020 bar}[0.150, 0.268, 0.262, 0.883] have a lower cost than {its 2021 bar}[0.262, 0.586, 0.374, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "feDpgp3kRwHXjzrN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller in the x-axis between {H&M bar}[0.150, 0.586, 0.374, 0.883] and {Zara bar}[0.711, 0.387, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The H&M bar."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "10rHR7UGYyknX2k1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Zara's 2020 supply cost}[0.711, 0.586, 0.823, 0.883] and {Zara's 2021 supply cost}[0.823, 0.387, 0.936, 0.883], which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Zara's 2021 supply cost."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "XSm4krWLomiB55xx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.822, 0.044, 0.963, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It has two elements: {2020's legend}[0.831, 0.058, 0.877, 0.077] which is colored blue, and {2021's legend}[0.831, 0.099, 0.877, 0.119] which is colored orange. The labels for these color codes are located at [0.896, 0.055, 0.954, 0.083] and [0.896, 0.097, 0.954, 0.125], respectively."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "0mSzb7qRjl5nAohU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within coordinates [0.150, 0.268, 0.374, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars located within the coordinates. The first is the {blue bar}[0.150, 0.268, 0.262, 0.883] representing H&M's supply chain cost for 2020, and the second one is the {orange bar}[0.262, 0.586, 0.374, 0.883] representing H&M's supply chain cost for 2021."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "go0WWLdictUpUhlU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area defined by coordinates [0.711, 0.586, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates two bars that represent Zara's supply chain costs for the years 2020 (represented by the blue bar) and 2021 (represented by the orange bar). The blue bar is located at [0.711, 0.586, 0.823, 0.883] and the orange bar is found at [0.823, 0.387, 0.936, 0.883]."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "ICyz7SK2lBjLQwql",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.058, 0.079, 0.094, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis labels from 4.0 to 0.0. They indicate the scale of supply chain cost in billions of USD."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "hDf3ZQiM7YTl89zg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.025, 0.340, 0.048, 0.585] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, which is named 'Level of Influence'."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "nu2QKzDDuAsB98P4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.204, 0.903, 0.320, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"FOX News\"."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "CZ4H9eEZJLVXB4zV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted inside the rectangle with coordinates [0.711, 0.378, 0.823, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar that signifies the strong influence value of \"CNN\", according to {the legend}<r>[0.690, 0.055, 0.963, 0.147]</r>."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "DE3TilsLl2LD6kXG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {FOX News stronger influence bar}<r>[0.150, 0.715, 0.262, 0.883]</r> higher than the {CNN stronger influence bar}<r>[0.711, 0.378, 0.823, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "XkhoE5lgkz2a2pbF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is to the right, {the slight influence bar for FOX News}<r>[0.262, 0.209, 0.374, 0.883]</r> or {the strong influence bar for FOX News}<r>[0.150, 0.715, 0.262, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slight influence bar for FOX News."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "k01v0AwauP5dN2pf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller between {the CNN strong influence bar}<r>[0.711, 0.378, 0.823, 0.883]</r> and {the CNN slight influence bar}<r>[0.823, 0.659, 0.936, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CNN strong influence bar."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "EQR0LuOkQCEWRA5w",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.690, 0.055, 0.963, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, which includes two entries. The first entry has a color label at [0.699, 0.069, 0.746, 0.088] associated with white color, and a text label at [0.764, 0.066, 0.954, 0.094] named as 'Strong Influence'. Correspondingly, the second entry also has a color label at [0.699, 0.110, 0.746, 0.130] with white color, and a text label located at [0.764, 0.108, 0.944, 0.136], which describes 'Slight Influence'."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "n2vJAzeo7ce1HSqC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.711, 0.378, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two bars. One of them, located at [0.711, 0.378, 0.823, 0.883], represents the strong influence score of CNN in white color. The other one, at [0.823, 0.659, 0.936, 0.883], represents the slight influence score of CNN, also in white color."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "RocBjUJWMEasZG56",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.150, 0.209, 0.374, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. The first one, located at [0.150, 0.715, 0.262, 0.883], shows the strong influence value for FOX News in white color. The second one, found at [0.262, 0.209, 0.374, 0.883], shows the slight influence value for FOX News, also in white color."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "GHitkW9m4e6W4Vvp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.025, 0.357, 0.048, 0.567] mean?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, with the title \"Influence Level\"."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "lmdYpGXGP9Kv4UGl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the reading at coordinate [0.862, 0.108, 0.954, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at these coordinates reads \"Media 2\"."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "a9P2wj6EhUE7q6We",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label at [0.459, 0.942, 0.627, 0.970] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label, named \"Media Sources\"."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "K24BAonyteV95R3b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is {Media 2}<r>[0.862, 0.108, 0.954, 0.136]</r> listed above or below {Media 1}<r>[0.862, 0.066, 0.954, 0.094]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "NF4XAA3lEmUZb2h7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the influence level for Test 1 in Media 1}<r>[0.150, 0.146, 0.262, 0.883]</r> greater than {the influence level for Test 2 in Media 2}<r>[0.823, 0.673, 0.936, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "JMWnk7nxPE4BP3xl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Media 1's influence for Test 1}<r>[0.150, 0.146, 0.262, 0.883]</r> higher or lower than {Media 1's influence for Test 2}<r>[0.711, 0.357, 0.823, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "l7qD8zmgw6vHPiuK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.150, 0.146, 0.936, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are four bars in the chart, arranged by two different media sources (Media 1 and Media 2) and two test conditions (Test 1 and Test 2). The coordinates are:\n1. Media 1 Test 1: [0.150, 0.146, 0.262, 0.883]\n2. Media 2 Test 1: [0.262, 0.462, 0.374, 0.883]\n3. Media 1 Test 2: [0.711, 0.357, 0.823, 0.883]\n4. Media 2 Test 2: [0.823, 0.673, 0.936, 0.883]."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "FGSaW8Zg7lbxIf2s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area at [0.788, 0.055, 0.963, 0.147]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend box consisting of two entries. The first entry is \"Media 1\" with the color handle at [0.797, 0.069, 0.844, 0.088] and the label text at [0.862, 0.066, 0.954, 0.094]. The second entry is \"Media 2\" with the color handle at [0.797, 0.110, 0.844, 0.130] and label text at [0.862, 0.108, 0.954, 0.136]."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "qeLKOwzqKyRei1SV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section of the chart within the region [0.711, 0.357, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. Both bars represent the influence level of the Media 1 and Media 2 for Test 2. The exact coordinates for each bar are:\n1. Bar for Media 1: [0.711, 0.357, 0.823, 0.883]\n2. Bar for Media 2: [0.823, 0.673, 0.936, 0.883]."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "U1cXrOm4U7GV2u04",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's placed at the location [0.019, 0.431, 0.036, 0.506] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, named \"Values\"."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "EV4u12zHUrATdqn4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf I wish to know the name of the state at coordinates [0.231, 0.919, 0.314, 0.942], which one is it?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"California\"."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "7zmn43uTxSWQDBQf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn case of [0.093, 0.057, 0.128, 0.073], what exactly is represented in there?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color handle in the legend for the \"Forest Coverage\" category."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "IgpmCrufar3ZxrXK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity does the coordinate [0.502, 0.952, 0.557, 0.975] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the X-axis label, known as \"States\"."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "5KFuziCqNGswzOUw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Forest coverage bar for California} at [0.118, 0.738, 0.221, 0.903] longer or shorter than the {Population Density bar for California} at [0.221, 0.164, 0.324, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's shorter."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "En5qQx8dCntX2O1q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Public Park Coverage bar for California} at [0.324, 0.842, 0.427, 0.903] and the {Public Park Coverage bar for Florida} at [0.837, 0.825, 0.940, 0.903], which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Florida is taller."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "zHHRexTW71AJWgDc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {bars for California} [{Forest coverage bar at [0.118, 0.738, 0.221, 0.903]}, {Population Density bar at [0.221, 0.164, 0.324, 0.903]}, {Public Park Coverage bar at [0.324, 0.842, 0.427, 0.903]}] generally higher than the {bars for Florida} [{Forest coverage bar at [0.632, 0.799, 0.735, 0.903]}, {Population Density bar at [0.735, 0.251, 0.837, 0.903]}, {Public Park Coverage bar at [0.837, 0.825, 0.940, 0.903]}]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "G3nXjJgjJ4zOQoSq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the {Population Density bar for California} at [0.221, 0.164, 0.324, 0.903] and {Population Density bar for Florida}[0.735, 0.251, 0.837, 0.903], which state seems to have a larger population density?"
+            },
+            {
+                "from": "gpt",
+                "value": "California."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "shykssmZK7gvJK1z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe area [0.086, 0.046, 0.332, 0.157] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the legend containing three entries: {Forest Coverage} at [0.142, 0.055, 0.284, 0.078] with color handle at [0.093, 0.057, 0.128, 0.073], {Population Density} at [0.142, 0.090, 0.304, 0.113] with color handle at [0.093, 0.092, 0.128, 0.108], and {Public Park Coverage} at [0.142, 0.125, 0.325, 0.148] with color handle at [0.093, 0.127, 0.128, 0.143]."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "VuIuZFjewnEddI27",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.118, 0.164, 0.427, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are three bars representing the values of different categories for the state of California: {Forest coverage bar} at [0.118, 0.738, 0.221, 0.903], {Population Density bar} at [0.221, 0.164, 0.324, 0.903], and {Public Park Coverage bar} at [0.324, 0.842, 0.427, 0.903]."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "yQSzynViVVK43s0X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information lies within the boundary [0.632, 0.799, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this boundary, we can see three bars for the state of Florida: {Forest coverage bar} at [0.632, 0.799, 0.735, 0.903], {Population Density bar} at [0.735, 0.251, 0.837, 0.903], and {Public Park Coverage bar} at [0.837, 0.825, 0.940, 0.903]."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "e5bsXJXpZWoBiLxt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.077, 0.034, 0.981, 0.903] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates represent the entire plotting area of the chart, which includes the axes, the labels on the axes, all bars representing different categories of different states and the space between these bars."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "tqQWM6sqnbNGmSc5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {chart title} at [0.484, 0.942, 0.601, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Platforms."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "hWVGXRpiYISGD16g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the thing located at coordinates [0.771, 0.069, 0.818, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color handle of legend, specifically for Instagram which is colored in #008080 (a teal color)."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "PyGVnxDqLNdD3Pe3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.836, 0.108, 0.914, 0.136] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of Twitter in the legend."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "U4mEYv4d9SItSa3L",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the orange bar}[0.262, 0.135, 0.374, 0.883] for Email shorter or taller than {the teal bar}[0.150, 0.228, 0.262, 0.883] for the same platform?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "GbU3Lj6Ka5ZIaXSU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider on the chart, the {Email bar}[0.150, 0.228, 0.374, 0.883] or the {SMS bar}[0.711, 0.659, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "uylGxVyn5PAhkpQO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the legend, does {Twitter color}[0.771, 0.110, 0.818, 0.130] come after or before {Instagram color}[0.771, 0.069, 0.818, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "fSPdsG2dygH2LYUt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of what is found within the region [0.836, 0.066, 0.954, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two text labels in this region: {Instagram label}[0.836, 0.066, 0.954, 0.094] reading \"Instagram\", and {Twitter label}[0.836, 0.108, 0.914, 0.136] reading \"Twitter\". These labels form part of the legend of the chart."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "ZkRZmzrmbOfTOz7h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found within coordinates [0.058, 0.030, 0.094, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"4.5\" on the y-axis of the subplot."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "nsT5edBWhL5WYd2W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what data are within the region [0.150, 0.135, 0.374, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two bars: one for Instagram at coordinates [0.150, 0.228, 0.262, 0.883] which spans from 0 to 3.5 on the y-axis, and one for Twitter at coordinates [0.262, 0.135, 0.374, 0.883] which spans from 0 to 4 on the y-axis. These two bars represent the data for Email on the x-axis."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "0mS228wnBOAbMfNn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the Y-axis is \"Transmission Delay (ms)\""
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "LLuNub1aib5V7Ihx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text on the {the bar with coordinates}<r>[0.262, 0.685, 0.374, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the application of \"Machine Learning in Medicine\" on \"CRISPR\""
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "BM23djDwNfEmgCtT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of application is shown by the bar with coordinates [0.150, 0.526, 0.262, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the application of \"Telemedicine\" on \"CRISPR\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "OOjumMcF7yXj4hX0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the x-axis label is referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is referring to \"Healthcare Tech\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "fu7lR6rHptzVZDAw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the height of {the Telemedicine's Gene Therapy bar}<r>[0.711, 0.745, 0.823, 0.883]</r> compare to {the Machine Learning in Medicine's Gene Therapy bar}<r>[0.823, 0.467, 0.936, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "TsjpDAzC8VXDS2lK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any visible difference between the positioning of {the Machine Learning in Medicine's CRISPR bar}<r>[0.262, 0.685, 0.374, 0.883]</r> and {the Telemedicine's Gene Therapy bar}<r>[0.711, 0.745, 0.823, 0.883]</r> on the chart's x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "CSjfx2bm5fV42I31",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of transmission delay, does {Machine Learning in Medicine's implementation on CRISPR}<r>[0.262, 0.685, 0.374, 0.883]</r> perform better or worse than {its implementation on Gene Therapy}<r>[0.823, 0.467, 0.936, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "xep9oTYFZ1F0UNDI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of {Telemedicine's CRISPR technique}<r>[0.150, 0.526, 0.262, 0.883]</r> and {Machine Learning's CRISPR technique}<r>[0.262, 0.685, 0.374, 0.883]</r>, which one demonstrates the higher transmission delay?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning's CRISPR technique."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "ul4Cd4lM5IXEvbNB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the pattern identified in the region bounded by [0.110, 0.030, 0.975, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area belongs to the main chart region, which comprises multiple bars representing two categories of Healthcare Tech i.e., \"Telemedicine\" and \"Machine Learning in Medicine\" applied on \"CRISPR\" and \"Gene Therapy\". Vertical alignment represents the Transmission delay in ms. The bars corresponding to \"Telemedicine\" are {[0.150, 0.526, 0.262, 0.883], [0.711, 0.745, 0.823, 0.883]} and those corresponding to \"Machine Learning in Medicine\" are {[0.262, 0.685, 0.374, 0.883], [0.823, 0.467, 0.936, 0.883]}."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "Yj8vnM18Cmdi03mf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area in [0.276, 0.903, 0.360, 0.931] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for the \"CRISPR\" category of \"Healthcare Tech\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "JzySEXIz2uvztvwJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is included in the area [0.122, 0.044, 0.549, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the chart's legend, including the color-text labels denoting the \"Telemedicine\" application and \"Machine Learning in Medicine\" application. The color label for \"Telemedicine\" is located at [0.131, 0.058, 0.178, 0.077], and the text label is at [0.196, 0.055, 0.347, 0.083]. The color label for \"Machine Learning in Medicine\" is located at [0.131, 0.099, 0.178, 0.119], and the text label is at [0.196, 0.097, 0.540, 0.125]."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "j4BS5JbpHK3Wi24u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is in the region [0.025, 0.287, 0.048, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the y-axis label named \"Transmission Delay (ms)\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "Osn63escpectAoFL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object within the coordinates [0.118, 0.041, 0.192, 0.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for Probability in the legend."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "lXeFMKKTR0ieLjN5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.484, 0.964, 0.567, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label stating \"Risk Factors\"."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "t465XttJL6lrLG4e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.839, 0.476, 0.943, 0.927] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Manageability risk level for \"Regulatory Changes\"."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "qEc6uoSpWqRO9zom",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich measure of risk for \"Economic Conditions\" is greater, {the probability bar}<r>[0.108, 0.206, 0.212, 0.927]</r> or {the manageability bar}<r>[0.317, 0.296, 0.421, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Probability."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "AFyokARg12GpK1hS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Probability bar for Regulatory Changes}<r>[0.630, 0.386, 0.734, 0.927]</r> lower or higher than {the Manageability bar for Economic Conditions}<r>[0.317, 0.296, 0.421, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "dY8NE1J1uelSuPFO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is lower, {the Impact bar for Economic Conditions}<r>[0.212, 0.116, 0.317, 0.927]</r> or {the Probability bar for Regulatory Changes}<r>[0.630, 0.386, 0.734, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Probability bar for Regulatory Changes."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "PpUqBGjOdXegaTIA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are inside the region [0.630, 0.386, 0.943, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region there are the Probability, Impact, and Manageability bars for \"Regulatory Changes\". The bars are at [0.630, 0.386, 0.734, 0.927], [0.734, 0.161, 0.839, 0.927], and [0.839, 0.476, 0.943, 0.927] respectively."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "bOL9bZNwWEGKEMq7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visuals within [0.108, 0.206, 0.421, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Probability, Impact, and Manageability bars for \"Economic Conditions\", located at [0.108, 0.206, 0.212, 0.927], [0.212, 0.116, 0.317, 0.927], and [0.317, 0.296, 0.421, 0.927] respectively."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "5NJ4B1eo8kiM0OIN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be found within the set of coordinates [0.035, 0.019, 0.057, 0.938]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the y-axis scale, reading from 0.0 to 1.0 at intervals of 0.2. The labels are located at the following coordinates: [0.035, 0.920, 0.057, 0.938], [0.035, 0.740, 0.057, 0.757], [0.035, 0.560, 0.057, 0.577], [0.035, 0.379, 0.057, 0.397], [0.035, 0.199, 0.057, 0.217], [0.035, 0.019, 0.057, 0.036]."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "HUzG3O6y25xYPZKq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.150, 0.224, 0.215, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar of \"Health Technology\" for \"Year 1\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "1Q9MNiq5P66info2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis represent, and where is it located in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis, with coordinates [0.025, 0.353, 0.048, 0.560], represents \"Advancements\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "8UGCCoL4X5cXgrVq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated within the region [0.728, 0.097, 0.954, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Medical Technology\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "O79ewLwlSkOHZvD1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Health Technology bar in Year 2}[0.477, 0.418, 0.543, 0.883] have a lower or higher advancement rate than {the Medical Technology bar in Year 2}[0.543, 0.592, 0.608, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "WuEnGVTAk23fQ6M9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Health Technology bar in Year 1}[0.150, 0.224, 0.215, 0.883] taller or shorter than {the Medical Technology bar in Year 1}[0.215, 0.243, 0.281, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "dMeJdXoljefO8Im4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Year 1 for Health Technology}[0.150, 0.224, 0.215, 0.883] and {Year 3 for Health Technology}[0.805, 0.573, 0.870, 0.883], where are advancements higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 1."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "QUMwxLhCmNX5xL1U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is presented within the region [0.150, 0.224, 0.936, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the bars representing advancements in Health and Medical Technology over 3 years. Health Technology bars are at these coordinates: {Year 1}[0.150, 0.224, 0.215, 0.883], {Year 2}[0.477, 0.418, 0.543, 0.883], {Year 3}[0.805, 0.573, 0.870, 0.883]. Medical Technology bars lock at these locations: {Year 1}[0.215, 0.243, 0.281, 0.883], {Year 2}[0.543, 0.592, 0.608, 0.883], {Year 3}[0.870, 0.554, 0.936, 0.883]."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "PEeAkja3sr8YEoTu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the spatial information embedded in [0.058, -0.000, 0.094, 0.028]."
+            },
+            {
+                "from": "gpt",
+                "value": "The number \"4.5\" for the Y-axis label is located at this coordinate subplot indicating the potential maximum value on the Y-axis for \"Advancements\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "r48o1tYXE855OVmr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the region [0.654, 0.044, 0.963, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend for our bar chart. The legend contains color codes and labels for two groups: {Health Technology}[0.728, 0.055, 0.941, 0.083] represented by a white bar, and {Medical Technology}[0.728, 0.097, 0.954, 0.125] again represented by a white bar."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "UODVmdlrABvAby5D",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis at [0.025, 0.319, 0.048, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Rating."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "ywRlFepShpvvuRkM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description in the legend at [0.749, 0.055, 0.918, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "rHNN0g0Ro2e2rR0O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label located at [0.454, 0.942, 0.609, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Algorithms."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "L0iblez4KEWsjnAM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.749, 0.097, 0.954, 0.125] in the legend section?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "gyTePwKWPPCt6Mf5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Deep Learning for FastAI}<r>[0.129, 0.513, 0.263, 0.883]</r> higher compared to  {Machine Learning for FastAI}<r>[0.263, 0.594, 0.397, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "1rlW4n2QBqjlBZu7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of {Deep Learning}<r>[0.129, 0.513, 0.263, 0.883],[0.666, 0.416, 0.800, 0.883]</r> generally taller than those of {Machine Learning}<r>[0.263, 0.594, 0.397, 0.883],[0.800, 0.094, 0.935, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "pVHvsq3BzhCT6tNF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the height between {Deep Learning for TensorFlow}<r>[0.666, 0.416, 0.800, 0.883]</r> and {Machine Learning for TensorFlow}<r>[0.800, 0.094, 0.935, 0.883]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning for TensorFlow is taller."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "3wPJHJKWXGvuVXiV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the section with the coordinates [0.675, 0.044, 0.963, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend area containing two items. The first item is \"Deep Learning\" with coordinates [0.749, 0.055, 0.918, 0.083] and the second item is \"Machine Learning\" with coordinates [0.749, 0.097, 0.954, 0.125]."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "0RPxSaF8Nf9ZSFUx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.089, 0.030, 0.975, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main plot area where all the bars representing the performance ratings of two AI algorithms, FastAI and TensorFlow, are located with respect to two categories: Deep Learning and Machine Learning."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "5sHkHux5XmjrG2s7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's shown at the region [0.129, 0.513, 0.397, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar section where the performance of FastAI is displayed in two aspects: \"Deep Learning\" with [0.129, 0.513, 0.263, 0.883] and \"Machine Learning\" with [0.263, 0.594, 0.397, 0.883]."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "oboybnbmfLncANEQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title at the top of the y-axis with coordinates [0.025, 0.265, 0.048, 0.660]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Percentage Contribution (%)\"."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "tApitOY54eq4yTca",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the first text in the legend, located at [0.189, 0.066, 0.317, 0.094]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label name is \"PC Gaming\"."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "oIBd142nSxofGdvM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.307, 0.532, 0.389, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing Mobile Gaming's percentage contribution for 2014."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "qPqTjGkeQCKD2lxK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label denoted by the coordinate [0.783, 0.903, 0.842, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the year 2020."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "2D73XgPznVp9vWA1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing PC Gaming in 2020}[0.689, 0.322, 0.771, 0.883] taller than {the bar representing Console Gaming in 2020}[0.771, 0.392, 0.853, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "LlDnmw79boUYkWhn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing Console Gaming in 2014} [0.225, 0.322, 0.307, 0.883] have a higher value than {the one representing PC Gaming in 2014} [0.143, 0.392, 0.225, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "DfaOb77KlWAPbtFl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the bar for Mobile Gaming in 2014}[0.307, 0.532, 0.389, 0.883] and {the bar for Mobile Gaming in 2020}[0.853, 0.111, 0.935, 0.883], did Mobile Gaming's contribution increase or decrease from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "a84CHURawssgV16C",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {PC Gaming}[0.189, 0.066, 0.317, 0.094] and {Console Gaming}[0.189, 0.108, 0.378, 0.136] which had a larger contribution in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "PC Gaming."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "qwZdvWCMkETZUGek",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.115, 0.055, 0.388, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend which indicates colors for different categories: PC Gaming, Console Gaming, and Mobile Gaming. The labels are associated with their respective white markers."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "XaHuGSzzBIScz1OM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's located in the region [0.689, 0.322, 0.935, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars representing the percentage contribution of PC Gaming and Mobile Gaming for the year 2020."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "dipRZ9BbarTG4itU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.225, 0.322, 0.389, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area: one represents the percentage contribution of Console Gaming in 2014, and the other represents the percentage contribution of Mobile Gaming in 2014."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "1S4ni96vSjsVJCVN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found at [0.143, 0.392, 0.307, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there are two stacked bars representing the year 2014. PC Gaming is situated on the bottom with a value of 35%, while Console Gaming is positioned above with a value of 40%."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "HJ73AszTDIuDkMzQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label name at the coordinate [0.514, 0.945, 0.603, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label name is \"Platform\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "ebLWQCkWVBmb7mR4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bar is represented by these coordinates [0.334, 0.383, 0.491, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents \"User Activity\" on \"Facebook\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "LKf68FmWW7jMSSyp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle} at [0.766, 0.106, 0.808, 0.124]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#c38c6a\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "ip73EiYxsOd4Kc5o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar representing Post Count on Facebook}[0.177, 0.726, 0.334, 0.898], or {the bar representing User Activity on Instagram}[0.782, 0.554, 0.939, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing Post Count on Facebook."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "br2JpZKmHefR9fle",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing User Activity on Facebook}[0.334, 0.383, 0.491, 0.898] higher than {the bar representing User Activity on Instagram}[0.782, 0.554, 0.939, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "oxGZ7H6jHOamEt8h",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Post Count on Facebook bar}[0.177, 0.726, 0.334, 0.898] to the left or right of {the User Activity on Facebook bar}[0.334, 0.383, 0.491, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "2YZzQxTySEFgL9pS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the area [0.758, 0.052, 0.967, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two color-multi-text labels in the legend. The first one, located at [0.766, 0.066, 0.808, 0.084], is blue and it's called \"Post Count\" at [0.825, 0.063, 0.938, 0.090]; the second one, located at [0.766, 0.106, 0.808, 0.124], is colorized \"#c38c6a\" and it's called \"User Activity\" at [0.825, 0.103, 0.958, 0.130]."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "RcneCApnBXFjqxZc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.139, 0.039, 0.977, 0.898]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a title \"Platform\" at [0.514, 0.945, 0.603, 0.971], a y-axis label \"Count (in thousands)\" at [0.023, 0.329, 0.044, 0.608], two x-axis labels (\"Facebook\" at [0.285, 0.907, 0.384, 0.934] and \"Instagram\" at [0.729, 0.907, 0.836, 0.934]), bars representing \"Post Count\" in color '#5e74a0' and \"User Activity\" in color '#c38c6a' on both platforms, and a legend [0.758, 0.052, 0.967, 0.141] describing the colors of the bars."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "A3A3Q3aYZKxhkIYZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.177, 0.726, 0.334, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one blue bar that represents a \"Post Count\" of \"50000\" for \"Facebook\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "CcqhkOkqy3F2PJxr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary subject of the chart, according to the chart's y-axis label at [0.023, 0.361, 0.044, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main subject is the Percentage (%)."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "cbtgp39Ii8FTlEnx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text exists at the coordinates [0.790, 0.053, 0.958, 0.079] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows Startup Success."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "3bAjSsLHy89cxEeG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}[0.510, 0.945, 0.554, 0.971] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "S2wG358Dydc4ECP8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the descriptors or entities represented at [0.722, 0.042, 0.967, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Success label is positioned at {text_coordinates}<r>[0.790, 0.053, 0.958, 0.079]</r> and Startup Failure label located at {text_coordinates}<r>[0.790, 0.093, 0.944, 0.119]</r>."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "JBpzVujmXX3Ch0RB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of 2010, which one is higher, {Startup Success bar}<r>[0.127, 0.663, 0.294, 0.898]</r> or the {Startup Failure bar}<r>[0.294, 0.070, 0.460, 0.898]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Startup Failure bar."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "fXVJiOYrhspNkNKl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars in 2020, did the {Startup Success bar}<r>[0.603, 0.542, 0.770, 0.898]</r> exceed the {Startup Failure bar}<r>[0.770, 0.191, 0.937, 0.898]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "FSoTBDiO9kBE9AfQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nChecking the bars for Startup Success and Startup Failure, which is generally taller? Refer to {Startup Success bars}<r>[0.127, 0.663, 0.294, 0.898], [0.603, 0.542, 0.770, 0.898]</r> and {Startup Failure bars}<r>[0.294, 0.070, 0.460, 0.898], [0.770, 0.191, 0.937, 0.898]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Failure."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "ceMrjzWodxZUN4aS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the two years 2010 and 2020, which year had higher {Startup Failure}<r>[0.294, 0.070, 0.460, 0.898], [0.770, 0.191, 0.937, 0.898]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "16TqevaEibFaYkki",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is positioned at [0.722, 0.042, 0.967, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two groups of text-color labels in the legend. The color label of the first group is found at [0.731, 0.055, 0.773, 0.074], which is of color #5e74a0 and its corresponding text label located at [0.790, 0.053, 0.958, 0.079] is denoted as Startup Success; the color label of the second group is found at [0.731, 0.095, 0.773, 0.114], which is of color #c38c6a and its text label located at [0.790, 0.093, 0.944, 0.119] that is identified as Startup Failure."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "JrlK07rQMGvk2h7Q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.086, 0.029, 0.977, 0.898] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area [0.086, 0.029, 0.977, 0.898] includes the whole bar chart area with all the bars, the x-axis, the y-axis, axis labels, and ticks. There are two years labelled on the x-axis: \"2010\" at [0.267, 0.907, 0.320, 0.934] and \"2020\" at [0.744, 0.907, 0.797, 0.934]. Similarly, there are several percentages labelled on the y-axis dotted from 0 to 90 (values out of this range are unrepresented)."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "WGrzlLePXEcA8zQu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements present within the range [0.603, 0.542, 0.770, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two components:\n1. A bar depicting the Startup Success in the year 2020, with its coordinates being [0.603, 0.542, 0.770, 0.898].\n2. A text label representing the value of the bar above, indicating the number 33.50, and its coordinates are [0.657, 0.515, 0.716, 0.542]."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "XsXTeouBM352QAk2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements in the area [0.127, 0.663, 0.294, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area embodies:\n1. A blue bar named \"Startup Success for the year 2010\" which is positioned at [0.127, 0.663, 0.294, 0.898].\n2. A black text label found at [0.180, 0.636, 0.240, 0.663], stating the value of the bar which is \"22.10\"."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "jzOjg80Hf0MuLhkP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of {the Y-axis}[0.023, 0.428, 0.044, 0.499] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the scale for the averages of dish prices and restaurant popularity."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "JdB7mO2LOnSOvL87",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {blue bar}[0.294, 0.605, 0.460, 0.898] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the average dish price for Japanese cuisine."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "KQxiyjgYQoPjBzgm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identity of the bar at these coordinates [0.770, 0.070, 0.937, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the popularity of Japanese restaurants."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "KOFXGPYWA5NFBnra",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the average dish price of Italian cuisine}[0.127, 0.523, 0.294, 0.898] exceed that of {the average dish price of Japanese cuisine}[0.294, 0.605, 0.460, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "SuJskBNeFxEPwejK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the popularity of Japanese restaurants}[0.770, 0.070, 0.937, 0.898] higher than {the popularity of Italian restaurants}[0.603, 0.100, 0.770, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "DRnncXLYrA4DBgDI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing to {Japanese restaurants' dish price}[0.294, 0.605, 0.460, 0.898], is {Italian restaurants' dish price}[0.127, 0.523, 0.294, 0.898] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "wtt13yOpyuYfCBlM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in area [0.086, 0.029, 0.977, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar chart representing two cuisine types: Italian and Japanese. Each cuisine is represented by two bars for the average dish price and the restaurant popularity. The scale for these representations is located on the y-axis."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "wJVfYLd8hfhytDWi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of content in the area [0.796, 0.042, 0.967, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend that identifies the colours used for the Italian and Japanese cuisines. The colors are represented by rectangular patches followed by the cuisine name."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "WoZkxnXjsaqsO9dm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.127, 0.523, 0.937, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four bars which represent the average dish prices and restaurant popularity for both Italian and Japanese cuisines. Specifically:\n    1. The Italian cuisine's dish price: [0.127, 0.523, 0.294, 0.898],\n    2. The Japanese cuisine's dish price: [0.294, 0.605, 0.460, 0.898],\n    3. The Italian cuisine's restaurant popularity: [0.603, 0.100, 0.770, 0.898],\n    4. The Japanese cuisine's restaurant popularity: [0.770, 0.070, 0.937, 0.898]."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "bqsTme0q5CCZ3hMd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.120, 0.363, 0.174, 4.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar that represents the \"5G\" technology's Outlook Score, which is 92.5."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "zCFZw2D4UQanuKif",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text enclosed in the coordinates [0.748, 0.217, 0.778, 0.240]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"95.3\", which is the Outlook Score for the \"Artificial Intelligence\" technology."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "WkiOqs9uN7cadx3G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information presented in the coordinates [0.692, 0.919, 0.834, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a label on the X-axis, indicating the \"Artificial Intelligence\" technology."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "ucS6VLyFvqIlXA0E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"5G\"}[0.120, 0.363, 0.174, 4.418] higher than {the bar representing \"IoT\"}[0.890, 0.464, 0.944, 4.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "HFefWuewYCOVrkMI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Artificial Intelligence\" technology label}[0.692, 0.919, 0.834, 0.942] located to the right or left of the {IoT technology label}[0.906, 0.919, 0.927, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "KPWNIOjSvcy7rAoP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {Bar \"5G\"}[0.120, 0.363, 0.174, 4.418], {Bar \"Augmented Reality\"}[0.582, 0.582, 0.636, 4.418], and {Bar \"Artificial Intelligence\"}[0.736, 0.240, 0.790, 4.418], which one has the lowest Outlook Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Augmented Reality."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "3vYGmD0Q5z5wYoH8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you give me a summary of the objects in [0.120, 0.240, 0.944, 4.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains six bars, each representing the corresponding technology's Outlook Score: \nBar \"5G\"[0.120, 0.363, 0.174, 4.418] with Score \"92.5\"; \nBar \"Blockchain\"[0.274, 0.608, 0.328, 4.418] with Score \"86.9\"; \nBar \"Virtual Reality\"[0.428, 0.503, 0.482, 4.418] with Score \"89.3\"; \nBar \"Augmented Reality\"[0.582, 0.582, 0.636, 4.418] with Score \"87.5\"; \nBar \"Artificial Intelligence\"[0.736, 0.240, 0.790, 4.418] with Score \"95.3\"; \nBar \"IoT\"[0.890, 0.464, 0.944, 4.418] with Score \"90.2\"."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "2VAsR8OtppcoOdQf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are inside the coordinates [0.792, 0.046, 0.978, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the bar plot. It contains one item: \n1. {\"Technology Outlook\"}[0.836, 0.055, 0.973, 0.078], which is a textual description. It's linked to a color symbol at [0.797, 0.057, 0.825, 0.073] representing the color of the bar."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "tdK1KoAzRdiuUcyu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible to see in the top half of this image [0.079, 0.034, 0.985, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there is the upper portion of the bars representing the Outlook Score for respective technologies, and also the Y-axis scale."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "ArExUZ7bptnVE8Kd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is not specified in the chart."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "KjbVbK5tZGIpRJs6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of data is represented by the bar with coordinates [0.146, 0.484, 0.309, 2.615] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the issue points for the category \"Equal Rights\"."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "VQkEs2P3EP6dXxPB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.023, 0.429, 0.044, 0.508]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Points."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "UpxIsDbvna4imCf6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the coordinates [0.809, 0.063, 0.941, 0.090]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Text label \"Equal Rights\"}<r>[0.809, 0.063, 0.941, 0.090]</r>."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "q2KX1d13OHpJZGzY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher score, {the Equal Rights category}[0.146, 0.484, 0.309, 2.615] or {the Social Welfare category}[0.309, 0.522, 0.472, 2.615] at 100 issue points?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal Rights."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "BMKQRBxaA2dLOHyv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher score at 1000 issue points, {Social Welfare}[0.775, 0.169, 0.938, 2.615] or {Equal Rights}[0.612, 0.420, 0.775, 2.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Welfare."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "Hgov2zF1eBwHr9de",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing the two bars at 1000 issue points, {Equal Rights category}[0.612, 0.420, 0.775, 2.615] and {Social Welfare category}[0.775, 0.169, 0.938, 2.615], which one has a lower base?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Welfare."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "yozkYvqRgzREsrw8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the Y-axis, {the 40.0 label} at [0.052, 0.887, 0.099, 0.914] or {the 57.5 label} at [0.052, 0.136, 0.099, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 40.0 label."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "asz8jQ0B5I7srPox",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content of the legend in the area [0.742, 0.052, 0.967, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels in this area. One is for the category \"Equal Rights\" represented by the color '#5e74a0' in the bar chart at the coordinates [0.809, 0.063, 0.941, 0.090]. The other is for \"Social Welfare\", represented by the color '#c38c6a' at [0.809, 0.103, 0.958, 0.130]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "fwYwZ4OTCjg9CmmH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the labeling on the X-axis within [0.289, 0.907, 0.801, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels: \"100\" at [0.289, 0.907, 0.329, 0.934] and \"1000\" at [0.748, 0.907, 0.801, 0.934]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "PjlhYS6xOS7t6loC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the area from [0.146, 0.484, 0.309, 2.615] to [0.309, 0.522, 0.472, 2.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area. One belongs to the \"Equal Rights\" category and has a score of 49.63 according to its label. This bar is situated at [0.146, 0.484, 0.309, 2.615]. The other belongs to the \"Social Welfare\" category and has a score of 48.75 as per its label, with coordinates [0.309, 0.522, 0.472, 2.615]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "AtjbH3yjidx5Urys",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in [0.612, 0.420, 0.775, 2.615] and [0.775, 0.169, 0.938, 2.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first object in the region is a bar representing the \"Equal Rights\" category with 51.12 issue points and is located at [0.612, 0.420, 0.775, 2.615]. The second object is another bar, a representation of the \"Social Welfare\" category with 56.97 issue points, which is located at [0.775, 0.169, 0.938, 2.615]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "GkuwF3EqH4SmyuN2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.442, 0.945, 0.622, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, called Health Indicators."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "7vLc1F97P8qxhFzq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second text in the legend's coordinate?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.757, 0.103, 0.841, 0.130]"
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "uYkmmQgnogrrDQi6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is represented by {the Bar}[0.770, 0.640, 0.937, 1.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "62."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "RznNmkjIi02z25X7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the indicators or categories shown at [0.757, 0.063, 0.958, 0.090] and [0.757, 0.103, 0.841, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Healthy Individuals}[0.757, 0.063, 0.958, 0.090] and {Patients}[0.757, 0.103, 0.841, 0.130]."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "4RFEKBsrp5vrZN2B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar}[0.127, 0.361, 0.294, 1.971] has a higher score than {the orange bar}[0.294, 0.683, 0.460, 1.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "PVer287kps8xsuQh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, {Healthy Individuals bar for Balanced Diet}[0.603, 0.146, 0.770, 1.971] or {Patients bar for Physical Activity}[0.294, 0.683, 0.460, 1.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Healthy Individuals bar for Balanced Diet}[0.603, 0.146, 0.770, 1.971]."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "XQwtM4mfhl78KD3u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Healthy Individuals'[0.757, 0.063, 0.958, 0.090]} and {Patients}[0.757, 0.103, 0.841, 0.130], which one has a higher score for {Balanced diet}[0.694, 0.907, 0.846, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthy Individuals."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "rCOVRVTll8ZNX6hr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.690, 0.052, 0.967, 0.141]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two items in the legend. The first item or label is {Healthy Individuals}[0.757, 0.063, 0.958, 0.090] and associated with the color [0.698, 0.066, 0.740, 0.084], which is blue. The second label is {Patients}[0.757, 0.103, 0.841, 0.130] associated with the color [0.698, 0.106, 0.740, 0.124], which is orange."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "sH6rxWQFrhK6HbEC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the area [0.086, 0.039, 0.977, 0.898]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the whole plotting area of the bar chart excluding the legend. It contains 4 bars for two categories on the x-axis: \"Physical Activity\" and \"Balanced Diet\". Each category has two different bars, one for \"Healthy Individuals\" and the other for \"Patients\". The y-axis represents the \"Score\" ranging from 0 to 90."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "f3eIqAtn3GncJXKh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.127, 0.361, 0.460, 1.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area representing the \"Physical_Activity\" indicator for both \"Healthy Individuals\" and \"Patients\". The blue bar for Healthy Individuals is at [0.127, 0.361, 0.294, 1.971] with value 75.00 and the orange bar for Patients is at [0.294, 0.683, 0.460, 1.971] with value 60.00."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "ztJ648ttYLCnCKNW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the area [0.052, 0.029, 0.079, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all y-axis labels which represent the score: '50', '55', '60', '65', '70', '75', '80', '85', '90'."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "IUs01MMEXyFWrWrX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar color}{r}[0.046, 0.191, 0.075, 0.543]{r} representing in the ROI chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin ROI."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "Mv1x3d49hfBSEet9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at {r}[0.442, 0.337, 0.470, 0.543]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin ROI bar for 20 years."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "qsyYaxcdkne77dIO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the last y-tick}{r}[0.061, 0.080, 0.082, 0.115]{r} value in the last subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "2SxEhBAlb7elh59K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label \"Blockchain ROI\"}{r}[0.114, 0.837, 0.189, 0.872]{r} above or below {the Bitcoin ROI label}{r}[0.114, 0.785, 0.169, 0.820]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "ar0sW0iC0ALi2xD9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the grid lines}{r}[0.915, 0.093, 0.935, 0.903]{r} darker or lighter than {the labels}{r}[0.854, 0.080, 0.873, 0.115]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lighter."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "JtfxlLxqIB11LPo5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the start point of the axis}{r}[0.434, 0.093]{r} for the 5-year ROI of Bitcoin located closer to the x-axis or to the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "CUNZwb1vH6ZaUaqe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in {r}[0.830, 0.093, 0.989, 0.903]{r}."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes both Bitcoin and Blockchain ROI bars for 10 years, their respective labels, and the x-ticks and y-ticks covering time periods from 5 years, 10 years, and 20 years, where substantial positive and negative values for both investments are displayed."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "tNop44rpftKbQmsU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in {r}[0.236, 0.093, 0.396, 0.903]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot shows the period for the 10-year ROI including two groups of ROI bars, with the corresponding x-axis labeled for the years 5, 10, and 20, and y-values ranging from -80 to 100 with both positive and negative bars shown for Bitcoin and Blockchain."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "bcdpnFKfYPHVP7u0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within {r}[0.039, 0.093, 0.198, 0.903]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content includes multiple Bitcoin ROI bars, y-grid lines appearing notably across the bars in patterns, the x-axis with labels for 5, 10, and 20 years, and y-axis ticks marking values from 0 to 100 and down to -80."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "rtigaz1M6WX5FWo0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the handle representing \"Economic Conditions\"}<r>[0.053, 0.840, 0.073, 0.864]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "r0wva8mIhI39clRP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value displayed on the bar at coordinate {the blue bar}<r>[0.046, 0.245, 0.075, 0.498]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "50"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "1fxmgVgeCR6PLmvG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis at location {the first tick}<r>[0.057, 0.927, 0.063, 0.962]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "qJxoLchFVmjdw8QZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for the label '5'}<r>[0.046, 0.245, 0.075, 0.498]</r> taller than {the bar for the label '10'}<r>[0.244, 0.144, 0.273, 0.498]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "UWYvgT6PIfLSu0eq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 20 unit bar in the Dot-Com Bubble}<r>[0.360, 0.195, 0.388, 0.498]</r> have a lower y-value than {the 20 unit bar in Great Recession}<r>[0.162, 0.169, 0.191, 0.498]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "C3Ma5nmuRlnfY9al",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-value at the start of {the label '20' in Asian Financial Crisis}<r>[0.557, 0.498, 0.586, 0.498]</r> lower than {the y-value at the start of the label '20' in EU Debt Crisis}<r>[0.953, 0.498, 0.982, 0.498]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "0Hb0XzWPZdQJdfCj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region <r>[0.000, 0.093, 0.300, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two subplots' axes and legends for Great Recession and Dot-com Bubble. It includes elements such as bars representing 'Stock Market Crashes' at {coordinates}<r>[0.046, 0.245, 0.075, 0.498]</r> and 'Economic Conditions' at {coordinates}<r>[0.046, 0.498, 0.075, 0.853]</r>, alongside x-ticks and y-ticks indicating various economic figures."
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "OSAUBZiUPQPfLD7A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be found within the area <r>[0.621, 0.093, 0.991, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the bars depicting market responses during the Spanish Property Bubble and the EU Debt Crisis. It includes bars and their corresponding values for both 'Stock Market Crashes' and 'Economic Conditions', as well as the necessary x and y labels and scale labels indicating measurement values and categories."
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "fO32PDWjWcXFGdLz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the space marked by <r>[0.800, 0.200, 1.000, 0.900]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section shows the bars representing 'Stock Market Crashes' and 'Economic Conditions' during the EU Debt Crisis, along with y-tick labels from '\u221280' to '80', x-tick labels '5', '10', '20', and axis labels without specified text, reflecting economic measures over these unit intervals. The color-coded bars indicate negative and positive changes."
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "ZueBHmIhB4Zoj1iw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.435, 0.253, 0.511, 0.323] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called Tesla."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "29BXdkthL7UUTwpK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.104, 0.935, 0.191, 0.949]"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "zObIBoFKZrkzqcSq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale} at [0.364, 0.971, 0.388, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 3."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "ExeKD0huERG4AEVM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the blue bar}<r>[0.236, 0.215, 0.261, 0.499]</r> to the left or right of {the orange bar}<r>[0.300, 0.235, 0.325, 0.499]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "IJUjL0BlvyqzRHiJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Year 2\"}<r>[0.300, 0.235, 0.325, 0.499]</r> taller than {the bar representing \"Year 3\"}<r>[0.364, 0.259, 0.389, 0.499]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "b3TMbMdLyxxtkHFd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the small values in first group of bars}[[0.038, 0.499, 0.063, 0.558], [0.101, 0.499, 0.127, 0.578], [0.165, 0.499, 0.191, 0.578]] generally smaller than {the large values in first group of bars}[[0.038, 0.116, 0.063, 0.499], [0.101, 0.142, 0.127, 0.499], [0.165, 0.189, 0.191, 0.499]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "jZ9b2KxPNoeQuW8L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.099, 0.015, 0.129, 0.033]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle \"Toyota\". It is placed just above the main bar chart graph, oriented horizontally and styled in black font."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "F14doDDHScD6NSTy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.204, 0.037, 0.312, 0.991]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the y-axis label \"Ford\", positioned vertically along the left side of the second bar chart graph. Adjacent to it are vertical bars representing data for different years, coupled with their corresponding x-values displayed at the bottom."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "cGPnAt3oPM26K9i1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.236, 0.037, 0.325, 0.991]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region involves a complete subplot for \"Ford\". It includes y-labels for various data points, x-ticks representing different years, and bars indicating the values for each year. Additionally, each bar has a text label indicating the specific value it represents."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "eRlbNjObe3cF8fr8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Customer Satisfaction\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue<r>[0.365, 0.387, 0.382, 0.422]</r>."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "bByBdDPaMPkAFs8K",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label \"Week 2\" for the \"Sales\" category located?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.245, 0.927, 0.274, 0.962]</r>"
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "vBkpYbChYNFNPXMm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure at {the top left corner bar for Operational Efficiency at Week 1}<r>[0.691, 0.699, 0.717, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "20.2."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "2HxQ3CMeuB1yTbNo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Week 2 in Profitability}<r>[0.908, 0.410, 0.934, 0.903]</r> taller than {the bar for Week 1 in Profitability}<r>[0.856, 0.443, 0.882, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "96Wgmj0UgrVIhcL9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first week's bar in Product Quality}<r>[0.526, 0.193, 0.552, 0.903]</r> represent a higher value compared to {the third week's bar in the same category}<r>[0.629, 0.240, 0.655, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "IfER6mzlftJlYN3n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the levels of {Customer Satisfaction}<r>[0.361, 0.343, 0.386, 0.903]</r> higher in {Week 2}<r>[0.412, 0.331, 0.438, 0.903]</r> compared to {Week 3}<r>[0.464, 0.349, 0.489, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "8w5ksiIAa4Ct1Xdi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the segment featuring metrics for Product Quality."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment contains three bars representing values over three weeks. The first week bar <r>[0.526, 0.193, 0.552, 0.903]</r> shows a value of 70.2 communicated via text <r>[0.530, 0.236, 0.547, 0.271]</r>. The second week bar <r>[0.577, 0.208, 0.603, 0.903]</r> displays a value of 68.7 with corresponding text <r>[0.582, 0.251, 0.599, 0.286]</r>, and the third week bar <r>[0.629, 0.240, 0.655, 0.903]</r> represents a value of 65.5 announced by text <r>[0.633, 0.283, 0.650, 0.318]</r>."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "xNCJBmqCN02tEWM1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be discovered in the area dedicated to the \"Sales\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are bars for three weeks showing sales data. The first week's data <r>[0.195, 0.493, 0.221, 0.903]</r> displays a value of 40.5 reported by <r>[0.200, 0.537, 0.217, 0.572]</r>. The second week's data <r>[0.247, 0.442, 0.273, 0.903]</r> shows 45.6, indexed at <r>[0.251, 0.485, 0.268, 0.520]</r>, while the third week's data <r>[0.298, 0.393, 0.324, 0.903]</r> indicates 50.4, detailed at <r>[0.303, 0.436, 0.320, 0.471]</r>."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "4whPEyQAwqJeIu4f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the operations plotted in the column for Operational Efficiency."
+            },
+            {
+                "from": "gpt",
+                "value": "This column comprises three weekly bars. Starting with Week 1, the bar <r>[0.691, 0.699, 0.717, 0.903]</r> represents a value of 20.2, detailed via text at <r>[0.695, 0.742, 0.712, 0.777]</r>. Week 2 <r>[0.743, 0.677, 0.768, 0.903]</r> measures up to 22.3, noted at <r>[0.747, 0.721, 0.764, 0.756]</r>, and Week 3 ascends to 25.6 with details at <r>[0.799, 0.687, 0.816, 0.722]</r>."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "7a2G4b1iSTSeiroB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is displayed in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "Lgpz8hbtTn1igMZn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.098, 0.118, 0.199, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label {Trade Equilibrium}<r>[0.098, 0.118, 0.199, 0.141]</r> of the legend."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "4FOeDFHDUvN1dbgy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label corresponding to the y-axis coordinate [0.012, 0.242, 0.024, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {Normalized Representation}<r>[0.012, 0.242, 0.024, 0.557]</r>."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "G6snT3I2INXEP17H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Slow Recovery}<r>[0.077, 0.567, 0.108, 0.736]</r> taller than the bar representing {Moderate Growth}<r>[0.165, 0.331, 0.196, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "70UW4sfUtpCtVCqn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of the Trend {Globalization}<r>[0.065, 0.155, 0.088, 0.171]</r> does the {Slow Recovery - outlier}<r>[0.548, 0.191, 0.578, 0.736]</r> have a higher value than {Moderate Growth - outlier}<r>[0.636, 0.634, 0.666, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "KMIIU4mlty3p0nFJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Sustained Growth}<r>[0.341, 0.698, 0.372, 0.736]</r> situated to the left of {Rapid Decline}<r>[0.429, 0.611, 0.460, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "IKEBAFaI46vQIAMI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the items in the region [0.061, 0.074, 0.203, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two items represented in the legend section of the chart. The first is the {Trade Equilibrium}<r>[0.065, 0.120, 0.088, 0.136]</r> with color coded as {#81b4a2}<r>[0.098, 0.118, 0.199, 0.141]</r>. The second item is {Globalization}<r>[0.065, 0.155, 0.088, 0.171]</r>, with color coded as {#dd997b}<r>[0.098, 0.153, 0.173, 0.176]</r>."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "aeszgYgdqKlWxFMF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.369, 0.025, 0.674, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the graph, which reads {International Trade and Globalization Trends}<r>[0.369, 0.025, 0.674, 0.055]</r>."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "JkF0GKffM4YR0Duh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.850, 0.744, 0.949, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the label {Rapid Decline - outlier}<r>[0.850, 0.744, 0.949, 0.942]</r> on the x-axis."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "mmMsHB1Ep9KR03RC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.367, 0.025, 0.675, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's subtitle, stating \"Investment Trends in Venture Capital Market\"."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "AkpwtTy74fUtqQty",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text content of the last x-axis label}<r>[0.843, 0.803, 0.915, 0.948]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"DiamondMiners\"."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "XQ0SSWFqB9OAb7mR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Non_Aggregation}<r>[0.877, 0.118, 0.977, 0.142]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of Non_Aggregation is blue as indicated by the color box with coordinates [0.845, 0.120, 0.868, 0.136]."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "XJUS7L2c1pju4opr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Non_Aggregation bar of CashCows}[0.727, 0.575, 0.771, 0.795] higher than {Aggregation bar of CashCows}[0.771, 0.130, 0.814, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "nbqkwOldDnQfly8s",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"VenturePromotions\" for Non_Aggregation}[0.185, 0.133, 0.228, 0.795] higher than {the bar representing \"VenturePromotions\" for Aggregation}[0.228, 0.461, 0.272, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "PrBarTZusGCjqicJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"GoldenEggs\"}<r>[0.525, 0.803, 0.582, 0.917]</r> and {\"RapidGrowers\"}<r>[0.629, 0.803, 0.695, 0.934]</r>, which has a higher value for {Non_Aggregation}<r>[0.877, 0.118, 0.977, 0.142]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "RapidGrowers."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "M8ptRIKU2IfBW9IN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.840, 0.074, 0.982, 0.186]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, with the heading \"Investment Type\"[0.863, 0.083, 0.959, 0.106]. There are two entries, {Non_Aggregation label}<r>[0.877, 0.118, 0.977, 0.142]</r> with a blue marker [0.845, 0.120, 0.868, 0.136], and {Aggregation label}<r>[0.877, 0.153, 0.948, 0.176]</r> with a red marker [0.845, 0.155, 0.868, 0.171]."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "31sjoHFDItyzM5z4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.036, 0.053, 0.051, 0.809]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all the y-ticks labels of the vertical axis, which are \"0\", \"20\", \"40\", \"60\", \"80\", and \"100\". The respective coordinates are: \n    - \"0\": {0}[0.043, 0.786, 0.051, 0.809]\n    - \"20\": {20}[0.036, 0.639, 0.051, 0.663]\n    - \"40\": {40}[0.036, 0.493, 0.051, 0.516]\n    - \"60\": {60}[0.036, 0.346, 0.051, 0.370]\n    - \"80\": {80}[0.036, 0.200, 0.051, 0.223]\n    - \"100\": {100}[0.029, 0.053, 0.051, 0.076]"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "OzRT8EEO8p1BrK8P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.771, 0.130, 0.814, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the bar representing CashCows of Aggregation}[0.771, 0.130, 0.814, 0.795]. The bar's height indicates that CashCows received 90.7 units of Aggregated Investment."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "L4jMGMI1uyfaFAYh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features in the region [0.077, 0.213, 0.120, 0.795]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the bar representing SeedFund of Non_Aggregation}[0.077, 0.243, 0.120, 0.795], the height of the bar indicates that SeedFund received 75.3 units of Non_Aggregation. The region also contains the bar's value label '75.3' at coordinates {75.3}<r>[0.085, 0.213, 0.111, 0.236]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "N43dPsbeGrQdPiYn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the object located at {coordinates}<r>[0.542, 0.486, 0.587, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the non-aggregation development level for Sculpture, which has a value of 32.11."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "V7t672q5u0mFzisS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give me the location of the label associated with the tick mark for 'Pop Music'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Label 'Pop Music'}<r>[0.900, 0.695, 0.949, 0.792]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "wimqpRFv0pPni6Vj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates}<r>[0.710, 0.695, 0.757, 0.787]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "Category name 'Ceramics'."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "rlj4ss1j5kdz4dxC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, the bar at {coordinates for 'Theatre and Puppetry'}<r>[0.093, 0.309, 0.138, 0.687]</r> or at {coordinates for 'Painting and Digital Art'}<r>[0.157, 0.446, 0.202, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Theatre and Puppetry."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "yNbubOLD8URjKn3D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar for 'Ballet and Contemporary Dance'}<r>[0.286, 0.215, 0.330, 0.687]</r> represent a larger number than the {bar for 'Fashion'}<r>[0.798, 0.150, 0.843, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "CgVdImNDgn73J1H8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Non-Aggregation label}<r>[0.121, 0.118, 0.219, 0.141]</r> and {Aggregation label}<r>[0.121, 0.153, 0.192, 0.176]</r>, which is higher in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Non-Aggregation."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "j4pkhcaad6YGzJLy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section at {coordinates}<r>[0.083, 0.074, 0.225, 0.185]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend of the chart, with two items; one represents {Non-Aggregation}<r>[0.089, 0.120, 0.112, 0.136]</r> with the text at {coordinates}<r>[0.121, 0.118, 0.219, 0.141]</r> and the other is {Aggregation}<r>[0.089, 0.155, 0.112, 0.171]</r> with the text at {coordinates}<r>[0.121, 0.153, 0.192, 0.176]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "rMVO18jk69FoivEY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area between the left side of the chart and {the first bar of the group}<r>[0.093, 0.309, 0.138, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label stating 'Development Level' located at {coordinates}<r>[0.035, 0.262, 0.047, 0.486]</r>, and the y-ticks with labels such as '0' at {coordinates}<r>[0.066, 0.677, 0.073, 0.701]</r> and '40' at {coordinates}<r>[0.059, 0.428, 0.073, 0.451]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "yHYkgQ48XOCu0pvH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is displayed in the lower right area of the chart that includes {the label 'Graphic Design'}<r>[0.814, 0.695, 0.885, 0.837]</r> and adjacent elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are two bars indicating the 'Graphic Design' category development level with the non-aggregation bar at {coordinates}<r>[0.863, 0.527, 0.907, 0.687]</r> and the corresponding text '25.51' just above it at {coordinates}<r>[0.868, 0.497, 0.901, 0.520]</r>, along with the aggregation bar at {coordinates}<r>[0.863, 0.448, 0.907, 0.687]</r> and the value '38.2' just above at {coordinates}<r>[0.872, 0.418, 0.898, 0.441]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "WWqiWCbfEAJMoHQs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'Apr' label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.331, 0.942, 0.355, 0.970]"
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "4VR8RpwRJjzH1d02",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars that represent the 'Trending' data?"
+            },
+            {
+                "from": "gpt",
+                "value": "#dd997b"
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "xJiPhmRbF7KE2772",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Value."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "wJVslzXsxJtDgwwr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller in March, the {Non-Volatile bar}[0.250, 0.789, 0.271, 0.932] or the {Trending bar}[0.271, 0.789, 0.293, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are equal."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "6QoON9zcDrRBD38I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Non-Volatile bar in October}[0.749, 0.675, 0.771, 0.932] taller than the {Trending bar in October}[0.771, 0.346, 0.792, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "FKeRRhcfEFj6P24G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the {legend label for Trending}[0.064, 0.089, 0.197, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#dd997b."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "SVP34hDMv1CyjiNX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between [0.178, 0.761, 0.200, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar for Non-Volatile data in February, which has a height representing a value of 12, as well as the text label \"12\" just above the bar."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "eXRfB9I6kyDt2rru",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.415, 0.575, 0.435, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this area. The first bar is for Non-Volatile data in June with a height that represents a value of 20. The second bar is for Trending data in June with a height representing a value of 25. On top of each bar, there is a text label showing the corresponding value, \"20\" and \"25\" respectively."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "6jWE3X3nuLzrtRKS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see around [0.064, 0.089, 0.197, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels in the legend: The first one is 'Non-Volatile' located at [0.108, 0.142, 0.191, 0.170] with a color mark [#81b4a2] at [0.070, 0.144, 0.097, 0.164]. The second one is 'Trending' located at [0.108, 0.183, 0.168, 0.211] with a color mark [#dd997b] at [0.070, 0.186, 0.097, 0.205]. The legend title \"Trends\" is located at [0.108, 0.100, 0.153, 0.128]."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "rtR5iUWsxHwlAGMC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the top of the chart in the coordinates [0.186, 0.025, 0.856, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart subtitle, which reads \"Impact of Environmental Protection and Sustainable Development Policies, 2022.\""
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "lMBe1gQWIQOwXPib",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of scales are used in {the Y-axis labels}<r>[0.043, 0.142, 0.052, 0.166]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage scales."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "3qu7n4LO6ic7sTxJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan I find the unit of the measurements used on the y-axis in {the axis label}<r>[0.015, 0.313, 0.029, 0.526]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it states '% Implementation'."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "wn0yldl7utBpSVuT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars labeled \"Green Buildings\" from group 'Non-Aggressive'}<[0.264, 0.613, 0.308, 0.776]>, shorter than {those labeled \"Green Buildings\" from group 'Aggressive'}<[0.264, 0.616, 0.308, 0.776]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "BDoAZUuStkOYHPzh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Ecological Footprint\" bar for 'Non-Aggressive'}<r>[0.772, 0.776, 0.817, 0.776]</r> as high as {the \"Ecological Footprint\" bar for 'Aggressive'}<r>[0.772, 0.575, 0.817, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "KVCGZ3YVowtKxOa4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater in height, {the bar corresponding to 'Wildlife Protection' in 'Non-Aggressive'}<r>[0.327, 0.776, 0.372, 0.776]</r> or {the same in 'Aggressive'}<r>[0.327, 0.519, 0.372, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aggressive."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "DHkJlFcXAU3Cjtf6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the area spanning {the percentages listed under 'Energy Consumption'}<r>[0.200, 0.284, 0.245, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows bars to reflect the percentages of Energy Consumption implemented under 'Non-Aggressive' strategies, where values start significantly above the base and extend to the top line representing a large implementation percentage."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "TskdkCQ66FNsAL7Q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be seen within the coordinates {for the category of greenhouse gas reduction}<r>[0.073, 0.208, 0.117, 0.776]</r> and <r>[0.136, 0.110, 0.181, 0.776]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "These areas include vertical bars for 'Air Pollution' and 'CO2 Emissions', indicating the implementation percentages of these categories. Both show different implemented levels, one nearly filled while the other begins from a lower point."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "ICZTWmhjfDPuOqvA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are visible in the segment regarding {the bars for 'Ocean Protection'}<r>[0.454, 0.353, 0.499, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This segment shows the percentage of Ocean Protection measures under 'Non-Aggressive' strategies with a starting point much lower than the top, indicating an implementation level just under halfway of the possible maximum."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "bAYtvlxVmJkkTubn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}<r>[0.095, 0.443, 0.112, 0.471]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value '50'."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "8SLLe7dYTaKXwG7i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color marked in the legend}<r>[0.816, 0.144, 0.844, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#81b4a2."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "xIYI8n0DtAAUQwv3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product's bar extends to {the coordinate}<r>[0.355, 0.306, 0.377, 0.769]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Household Goods."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "Wz5lQlvFYVOatedV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Non-Aggregation\" bar for \"Fast Food\"}<r>[0.082, 0.480, 0.126, 0.769]</r> have a greater height than {the \"Aggregation\" bar for \"Luxury Goods\"}<r>[0.459, 0.566, 0.503, 0.769]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "Z6shekAs1frSKteC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the color of the \"Non-Aggregation\" bar}<r>[0.648, 0.422, 0.692, 0.769]</r> similar to {the color denoted for \"Non-Aggregation\" in the legend}<r>[0.816, 0.144, 0.844, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "7qwBWO398RwJ7z3y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the aggregation value for \"Makeup\"}<r>[0.648, 0.335, 0.692, 0.769]</r> or {the non-aggregation value for \"Grocery\"}<r>[0.207, 0.364, 0.252, 0.769]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Makeup."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "xANpUKimyra1iDRu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between <r>[0.270, 0.364, 0.314, 0.769]</r> and <r>[0.396, 0.595, 0.440, 0.769]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the \"Car Sales\" bars with values '50' for Non-Aggregation and '60' for Aggregation, and the \"Attire\" bars with values '30' for both Non-Aggregation and Aggregation."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "oE52Vy9D5qlfCXb4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.130, 0.779, 0.203, 0.924]</r> depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "It depicts the category label \"E-Commerce\" with the corresponding x-axis tick nearby."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "ADLuSlhang3Rs1RZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the section <r>[0.517, 0.779, 0.572, 0.889]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section displays the category label \"Footwear\" accompanied by an x-axis tick, without any graph data or legends."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "CwU03Kb4NvIncp6O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated at [0.898, 0.145, 0.962, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of 'No Patent'."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "A4hqz2ttRR7Cx9VX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-label} at [0.792, 0.818, 0.825, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Uber'."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "SkTb1MlVbdkRZ3bD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is shown at [0.744, 0.522, 0.774, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number '23.9'."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "x3pRng5lavXydksq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled by the y-axis label positioned at [0.015, 0.304, 0.029, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Products"
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "sQnCbbIJU3gPOoUx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {Facebook's No Patent bar}[0.585, 0.367, 0.613, 0.809] have more products than {Microsoft's Patent bar}[0.105, 0.440, 0.133, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "zcNjLCp41iaDCC22",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Oracle}[0.665, 0.251, 0.693, 0.809] taller than {the bar representing Alibaba}[0.745, 0.558, 0.773, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "BRke1tXJM1soDnp4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Snap's Patent bar}[0.905, 0.678, 0.933, 0.809] taller than {Uber's Patent bar}[0.825, 0.618, 0.853, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "TBbFTGLUffPqvoob",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Microsoft's No Patent bar}[0.077, 0.646, 0.105, 0.809] and {Microsoft's Patent bar}[0.105, 0.440, 0.133, 0.809], which one has a higher number of products?"
+            },
+            {
+                "from": "gpt",
+                "value": "Microsoft's Patent bar."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "g13F4OId9fTRsUbJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.860, 0.089, 0.978, 0.223]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a chart legend with the title 'Patents'. There are two items in the legend. The first item is 'No Patent' with a color label in green located at [0.866, 0.144, 0.894, 0.164]. The second item is 'Patent' with a color label in brown located at [0.866, 0.186, 0.894, 0.205]."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "xePEqJJeLskEEf5E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.140, 0.818, 0.185, 0.908]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains an x-axis label for the company 'Google'."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "ba6jpDQDrO3HuBAm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within [0.505, 0.188, 0.533, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 elements: \n1. Red bar that represents the number of products with a patent for 'Amazon', which ranges from 0 to 59.2; \n2. Text annotation '59.2' at [0.504, 0.152, 0.534, 0.180] to indicate the specific value of the bar."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "dN8bFL69xDgZgr3Z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.047, 0.794, 0.095, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains 1 object:\n1. The y-axis label '10' positioned at [0.035, 0.693, 0.052, 0.721]."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "UuWAkKrgrJIfeOLe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart element corresponds to the coordinates [0.947, 0.131, 0.982, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is {the legend label for \"Decline\"}<r>[0.947, 0.131, 0.982, 0.151]</r>."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "foi3EoxphQkEwAWP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar labeled \"Vietnamese\" for \"Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar for \"Vietnamese\" labeled \"Growth\"}<r>[0.491, 0.244, 0.531, 0.875]</r>."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "O0OsCaRqrIrA0dbg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data does the text at [0.316, 0.271, 0.292, 0.243] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the {growth rate for \"Indian\"}<r>[0.316, 0.271, 0.292, 0.243]</r>."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "zCuRQL9tC3Dg80kn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the handle for \"Growth\"}<r>[0.921, 0.103, 0.940, 0.117]</r> and {the handle for \"Decline\"}<r>[0.921, 0.133, 0.940, 0.147]</r> in terms of their color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Different."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "f105dZYCBphTVM5z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the tick label for {the cuisine \"Thai\"}<r>[0.234, 0.881, 0.255, 0.926]</r> above or below {the corresponding bar for \"Decline\"}<r>[0.244, 0.795, 0.285, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "E4AogX2Xg0thrSiA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a larger value according to the bar heights, {the \"French\" cuisine for \"Growth\"}<r>[0.090, 0.277, 0.130, 0.875]</r> or {the \"German\" cuisine for \"Decline\"}<r>[0.703, 0.743, 0.743, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "French."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "N2oQKmsIeeOE5FGF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within the area [0.108, 0.839, 0.892, 0.978]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all the {cuisine x-axis labels}<r>[0.059, 0.881, 0.086, 0.939]</r> {to}<r>[0.856, 0.881, 0.894, 0.963]</r> and the {corresponding ticks}<r>[0.072, 0.875]</r> {to}<r>[0.875, 0.875]</r>."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "AdVSOdzMBvVFdWFs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region defined by the coordinates [0.917, 0.063, 0.985, 0.159] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {the legend title \"Rates\"}<r>[0.938, 0.071, 0.964, 0.091]</r> as well as two entries: {the legend entry for \"Growth\"}<r>[0.947, 0.101, 0.981, 0.121]</r> and {the legend entry for \"Decline\"}<r>[0.947, 0.131, 0.982, 0.151]</r> with their respective color handles."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "hOF1kM9K0lJublew",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinates [0.556, 0.369, 0.579, 0.389]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the {text element indicating the growth value for \"Turkish\"}<r>[0.558, 0.369, 0.579, 0.389]</r> alongside its corresponding {bar element}<r>[0.548, 0.395, 0.589, 0.875]</r>."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "2K4033qTgnrshW0D",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the percentage (%) refer to in the chart at [0.010, 0.312, 0.019, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the y-axis label."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "ti2sSqbvIUVdnjfL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category represented at coordinate [0.023, 0.787, 0.044, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the \"Coal\" category."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "MqWpA3f47BqdMBzB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the green object located at [0.882, 0.144, 0.901, 0.164] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Energymax\" parameter of the chart's legend."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "nhpq4THdqfYOSIDz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the resource {Hydro}[0.272, 0.292, 0.302, 0.777] more or less efficient in power than {Wind}[0.388, 0.515, 0.418, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "bTSV7YAelwXNO8Wj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher energy output, the {Coal}[0.014, 0.108, 0.044, 0.777] or the {Solar}[0.443, 0.484, 0.474, 0.777] resource?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "XpgvO19NoR0OLsVb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of power efficiency, is the {Nuclear}[0.559, 0.258, 0.590, 0.777] energy resource doing better than the {NaturalGas}[0.186, 0.230, 0.216, 0.777] resource?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "zu8Iunp7yoYw9xKX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the legend in the region [0.879, 0.089, 0.985, 0.223]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend, with two entries. The first, labelled \"Energymax\", is depicted with a green color and is located at [0.908, 0.142, 0.961, 0.170] with its color indicator at [0.882, 0.144, 0.901, 0.164]. The second entry, labelled \"Powerefficiency\", is represented with an orange color, located at [0.908, 0.183, 0.981, 0.211], with its color indicator at [0.882, 0.186, 0.901, 0.205]."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "2XQiIfoKLvrtaomX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the area [0.044, 0.075, 0.989, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains most of the chart's elements, including the x-ticks and y-ticks coordinates showing the distinct categories of energy resources and the percentages. Moreover, it includes the plot of energy output and power efficiency of various energy resources."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "7unXzB97CSqjkpkW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the objects in the region [0.014, 0.108, 0.044, 0.777]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows a relatively large green bar of the \"Coal\" resource with a high level of energy output represented by \"Energymax\" somewhere around 95.32%, according to the text annotation on top."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "QqzLb03E2D72noCB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing 'Apartment Sales' in the 'Non-Aggregation' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green, with specific color code '#81b4a2'. Coordinate information is {the bar}<r>[0.113, 0.222, 0.143, 0.675]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "cNCYmPS6ybHiQ0xC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of 'Commercial Property Sales' under the 'Aggregation' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 102000. The information is located at {bar}<r>[0.480, 0.204, 0.509, 0.675]</r> and {value text}<r>[0.468, 0.168, 0.521, 0.196]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "f7kY5QNYSA8Tvxez",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content that appears at the coordinates [0.376, 0.030, 0.701, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to the subtitle of the chart known as 'Building and Real Estate Market Trends'."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "d59NDs3qq4fGuC2h",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.855, 0.183, 0.940, 0.211] on the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label 'Aggregation'}<r>[0.855, 0.183, 0.940, 0.211]</r> in the legend."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "CU768LdUw7MozY8t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for 'Construction Permits' in the 'Non-Aggregation' category have a higher value than in the 'Aggregation' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. The coordinates for 'Construction Permits' are: {Non-Aggregation bar}<r>[0.197, 0.606, 0.227, 0.675]</r> and {Aggregation bar}<r>[0.227, 0.617, 0.256, 0.675]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "5WtRC3BDRbceG0xF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Non-Aggregation' bar for 'Rental Market' located on the left or right side of the 'Mortgage Approvals' bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left. Based on their respective coordinates: {Rental Market Non-Aggregation bar}<r>[0.703, 0.444, 0.732, 0.675]</r> and {Mortgage Approvals Non-Aggregation bar}<r>[0.619, 0.282, 0.648, 0.675]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "xBoleRJch6pASBru",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color of the 'Real Estate Agents' bar the same for 'Non-Aggregation' and 'Aggregation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "No. The colors are different. The {Non-Aggregation bar color}<r>[0.787, 0.661, 0.817, 0.675]</r> is '#81b4a2' and {Aggregation bar color}<r>[0.817, 0.657, 0.846, 0.675]</r> is '#dd997b'."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "5nRdtyzQnI7sR8OV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the x-axis labels  'Commercial Property Sales' and 'Mortgage Approvals' placed next to each other?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. Their location on the x-axis are: {Commercial Property Sales label}<r>[0.337, 0.685, 0.480, 0.970]</r> and {Mortgage Approvals label}<r>[0.539, 0.685, 0.648, 0.902]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "oiUFb7mEm8Sc9iFx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.810, 0.089, 0.978, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two legend items are within the referred region: the label 'Non-Aggregation' with a green color marker and the coordinates {label}<r>[0.855, 0.142, 0.973, 0.170]</r> and {color marker}<r>[0.816, 0.144, 0.844, 0.164]</r>; and the label 'Aggregation' with an orange color marker at <r>[0.855, 0.183, 0.940, 0.211]</r> and {color marker}<r>[0.816, 0.186, 0.844, 0.205]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "xZQedNHDju6121Fi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars between the coordinates [0.113, 0.222, 0.143, 0.675] and [0.901, 0.606, 0.930, 0.675]."
+            },
+            {
+                "from": "gpt",
+                "value": "These bars represent data from 'Apartment Sales' to 'Property Auctions' of 'Non-Aggregation' and 'Aggregation' categories."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "guhqBApIsDdCnHLz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.092, 0.675, 0.985, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis of the chart, where the  labels of the x-axis are situated. They represent categories from 'Apartment Sales' to 'Property Auctions'."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "GzTjeqdCc3TKadxE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart within the coordinates [0.043, 0.571, 0.087, 0.599]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label denoting '20000' which is part of the y-axis scale of the chart."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "DKprPVnp9Q7xWYFo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label refer to at [0.478, 0.903, 0.628, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Stability Level"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "OoJ3rBGldceXm99U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label for the first y-axis tick mark at [0.015, 0.710, 0.111, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equity Market"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "Hd4o5Hfr2FcEvLAz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's described by the rectangle/bar at [0.862, 0.668, 0.944, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{It's the 'Extremely Stable' portion of the 'Equity Market'}<r>[0.862, 0.668, 0.944, 0.788]</r>"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "bGsvSD4LqbrEg8B3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is wider: {the 'Moderately unstable' portion of the 'Equity Market'}[0.532, 0.668, 0.656, 0.788] or {the 'Extremely Stable' portion of the 'Bond Market'}[0.820, 0.430, 0.944, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Extremely Stable' portion of the 'Bond Market'."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "J08XwZ1va7ig06u5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Stable portion of the 'Bond Market'}<r>[0.532, 0.430, 0.615, 0.549]</r> wider than the {Significantly Stable portion of the 'Bond Market'}<r>[0.615, 0.430, 0.820, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "sXXWBsUxQhRQ7WsP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {extremely unstable rating for the 'Equity Market'}<r>[0.121, 0.668, 0.450, 0.788]</r> and the {extremely unstable rating for the 'Bond Market'}<r>[0.121, 0.430, 0.409, 0.549]</r>, which market is seen as more unstable?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equity Market."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "tUns12xzyNKyZYaV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is comprised within the coordinates [0.248, 0.238, 0.858, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains a legend for the colored bars; each color and its corresponding stability level are listed. The colors include: 'Extremely unstable' in red at [0.292, 0.257, 0.427, 0.303], 'Significantly unstable' in orange at [0.292, 0.326, 0.442, 0.373], 'Moderately unstable' in yellow at [0.509, 0.257, 0.652, 0.303], 'Stable' in light blue at [0.509, 0.326, 0.553, 0.373], 'Significantly Stable' in dark blue at [0.718, 0.257, 0.853, 0.303], and 'Extremely Stable' in purple at [0.718, 0.326, 0.838, 0.373]."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "ZxucsZ5NfFzD3UyA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.121, 0.668, 0.450, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the red bar representing the 'Extremely unstable' portion of the 'Equity Market'."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "j4N1acqBWAUhByvO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area [0.478, 0.903, 0.628, 0.950]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the label for the x-axis is located, precisely \"Market Stability Level\"."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "J7x9TQFjyyrGiL6V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the first bar label on the y-axis}<r>[0.066, 0.710, 0.197, 0.756]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Driverless Delivery."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "Z8Al1vo0uMuiDre0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the third legend item}<r>[0.554, 0.257, 0.656, 0.303]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffa500 (Orange)."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "OzfLkMNxVbemIbxI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second label on the x-axis}<r>[0.344, 0.838, 0.366, 0.885]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.2."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "g9iRaojgVesDqygF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of the first bar (the one for Success) in {Driverless Delivery}[0.207, 0.668, 0.800, 0.788] bigger than the first bar in {Unmanned Aerial Delivery}[0.207, 0.430, 0.652, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "h6dFlYwZ4oiRE4YU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Package Damage bar in Driverless Delivery}<r>[0.911, 0.668, 0.933, 0.788]</r> to the left or right of {the Logistical Errors bar in Driverless Delivery}<r>[0.800, 0.668, 0.874, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "EdWhVDNWadAE1yqz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the Safety Incidents bar for Unmanned Aerial Delivery}<r>[0.874, 0.430, 0.948, 0.549]</r> or {the Logistical Errors bar for Unmanned Aerial Delivery}<r>[0.652, 0.430, 0.800, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Logistical Errors."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "POAEnqHasRYquDhu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the legend section located in <r>[0.332, 0.238, 0.860, 0.392]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this area contains color-symbol text pairs for five categories: Success in green at [0.338, 0.261, 0.432, 0.303], Logistical Errors in grey at [0.338, 0.331, 0.488, 0.373], Delivery Delay in orange at [0.515, 0.261, 0.656, 0.303], Package Damage in yellow at [0.515, 0.331, 0.675, 0.373], and Safety Incidents in blue at [0.703, 0.261, 0.854, 0.303]."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "XCI2Za2xRCL6BqAd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the range <r>[0.207, 0.430, 0.948, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this area contains all the bars for the Unmanned Aerial Delivery category: The green bar for Success is at [0.207, 0.430, 0.652, 0.549], the grey bar for Logistical Errors is at [0.652, 0.430, 0.800, 0.549], the orange bar for Delivery Delay is at [0.800, 0.430, 0.837, 0.549], the yellow bar for Package Damage is at [0.837, 0.430, 0.874, 0.549] and the blue bar for Safety Incidents is at [0.874, 0.430, 0.948, 0.549]."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "nGmpamNHRPPAUnxL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.800, 0.668, 0.948, 0.788]</r> in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the last three bars in the Driverless Delivery category, Logistical Errors, Delivery Delay and Package Damage, represented by grey, orange, and yellow colors respectively."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "1xjVGLhSviHKbc9J",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing 'Success Prediction'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color representing 'Success Prediction' is green, {success prediction color}[0.254, 0.261, 0.282, 0.293]."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "tcV5My5yBoNQNuFJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar with coordinates [0.856, 0.668, 0.899, 0.788] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the 'Security Risk' performance of 'AI App1'."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "5JjfR7NluH6dJ9S2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar [0.916, 0.430, 0.933, 0.549] belongs to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents 'Capacity Utilization' of 'AI App2'."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "HihTf2QqsiBsTHar",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Failure Analysis' value of 'AI App1' greater than that of 'AI App2'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. 'AI App1' : {Failure Analysis value for AI App1}[0.640, 0.668, 0.856, 0.788], 'AI App2' : {Failure Analysis value for AI App2}[0.726, 0.430, 0.856, 0.549]"
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "O5OY11UCKLgYlIKz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'AI App1' and 'AI App2', which has higher 'Security Risk'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'AI App2'. 'AI App1' : {Security Risk for AI App1}[0.856, 0.668, 0.899, 0.788], 'AI App2' : {Security Risk for AI App2}[0.856, 0.430, 0.916, 0.549]"
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "L7uHusDrNmabsHrW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the performance values of 'AI App1' higher than 'AI App2' across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, 'Security Risk' value of 'AI App2' is higher than that of 'AI App1'."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "akNjNLiKZzX9WkmX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.079, 0.412, 0.985, 0.806]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the main chart region, containing two sets of colored bars with different heights. Each bar corresponds to a category in the 'AI App1' and 'AI App2' performance metrics measure."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "WPQpSDYuVb7bXlrj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any text in the area [0.4, 0.9, 0.6, 1]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, there are no text objects in the described area."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "TQ8HhJGCBPhY1Dm2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the area [0.24, 0.2, 0.81, 0.39]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart. There are five labeled entities: {Success Prediction}[0.293, 0.257, 0.422, 0.303], {Failure Analysis}[0.293, 0.326, 0.401, 0.373], {Security Risk}[0.489, 0.257, 0.579, 0.303], {Capacity Utilization}[0.489, 0.326, 0.624, 0.373], and {Load Forecasting}[0.691, 0.257, 0.809, 0.303] with corresponding color indicators."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "U7eaPucKXwOjr6An",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinate [0.449, 0.903, 0.632, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the x-axis label, named \"Percentage of Total Efforts\"."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "8bs2lihJW3NmDKOi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at [0.765, 0.575, 0.917, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar is also known as {Community Engagement bar}<r>[0.765, 0.575, 0.917, 0.776]</r>, and its color is blue."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "0ZurJ3foOxHJB2jZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at [0.702, 0.154, 0.828, 0.201]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend item for {Algorithm Change}<r>[0.702, 0.154, 0.828, 0.201]</r>, colored in purple."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "hpUXnUeQXhgEM2t5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relation between {Post Engagement Rate bar}[0.095, 0.575, 0.324, 0.776] and {Advertising Cost bar}[0.324, 0.575, 0.519, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Advertising Cost bar is to the right of the Post Engagement Rate bar."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "sWvNsseUwWZCsovo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Organic Reach bar}[0.519, 0.575, 0.621, 0.776] longer than {Community Engagement bar}[0.765, 0.575, 0.917, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "uIhy3RxeNM4iAc40",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Community Engagement bar for Facebook}[0.765, 0.575, 0.917, 0.776] and {Community Engagement bar for Instagram}[0.799, 0.173, 0.917, 0.374], which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Community Engagement bar for Instagram is longer."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "zOW0YCnaz8t3c9b3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.199, 0.066, 0.881, 0.220]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a sublegend of the chart. It contains the labels and color handles for six variables, namely: {Post Engagement Rate}<r>[0.244, 0.085, 0.402, 0.131]</r>, {Advertising Cost}<r>[0.244, 0.154, 0.358, 0.201]</r>, {Organic Reach}<r>[0.469, 0.085, 0.570, 0.131]</r>, {Influencer Collaboration}<r>[0.469, 0.154, 0.636, 0.201]</r>, {Community Engagement}<r>[0.702, 0.085, 0.876, 0.131]</r>, and {Algorithm Change}<r>[0.702, 0.154, 0.828, 0.201]</r>, with corresponding handle colors green, grey, orange, yellow, blue, and purple respectively."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "5kMedkTWqBuSlSxF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is enclosed in the area [0.095, 0.173, 0.943, 0.374]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the bars representing the efforts distribution for the platform Instagram. The bars correspond to six variables: Post Engagement Rate, Advertising Cost, Organic Reach, Influencer Collaboration, Community Engagement, and Algorithm Change."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "hxRZSvAq2r0a3Ehw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the coordinates [0.324, 0.575, 0.519, 0.776]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the {Advertising Cost Bar}<r>[0.324, 0.575, 0.519, 0.776]</r> which is colored gray and belongs to the Facebook group."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "4METKWh3OfXt0DmQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.710, 0.103, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the 'TechProjectX' data series on the y-axis."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "9k6OQEyCSTZZcdF3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat colour is the element at [0.113, 0.668, 0.694, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The element is coloured with #008000, which represents a shade of green."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "NlpTxu79yuM0XD3I",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the x-axis labels at [0.434, 0.838, 0.456, 0.885] and [0.766, 0.838, 0.788, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels at these coordinates are \"0.4\" and \"0.8\" respectively."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "tAu5pJDdnRyAuGyz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for 'TechProjectX' at [0.113, 0.668, 0.943, 0.788] longer than the ones for 'TechProjectY' at [0.113, 0.430, 0.943, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "0rWEup6AYPuj1ot8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Technological Feasibility\" bar of {TechProjectX}[0.694, 0.668, 0.819, 0.788] longer than {TechProjectY}[0.611, 0.430, 0.628, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "5zcJiyUSQtBzntSI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher score between 'Strategic Alignment' and 'Business Need' for {TechProjectX}?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Strategic Alignment'."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "hWHMFxRQGopsOuII",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located in the range of [0.249, 0.238, 0.849, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range includes a legend with six items, each corresponding to a different category and color: \"Economic Viability\" in green, \"Technological Feasibility\" in grey, \"Business Need\" in orange, \"Market Demand\" in yellow, \"Strategic Alignment\" in blue, and \"End Score\" in purple."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "fjGxzIgKAIV9ap4m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the range of [0.113, 0.412, 0.985, 0.806] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the main chart with bars representing different scores for the categories on two different TechProjects. The height and color of each bar represent the score and category it belongs to."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "SRZQ4bTh1vfGXgNI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the bounds of [0.113, 0.668, 0.943, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are six segmented bars representing different evaluation scores for the TechProjectX, with each color representing a different category corresponding to the legend."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "CNVTuD9QiFVjC2ly",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label in the chart, located at [0.433, 0.952, 0.704, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"Effect of factors on business processes\"."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "LdlwTtZ3vcms5xq5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the first label in the legend represent, located at [0.309, 0.045, 0.364, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Success\"."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "FaMgXsOfIgUESyWO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}[0.152, 0.216, 0.707, 0.434] corresponding to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar corresponds to \"Success\" in the \"Workflow_analysis\" category."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "jgGnK5VIWJlncgTh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Success bar for Efficiency_check}[0.152, 0.652, 0.311, 0.870] taller than {the Success bar for Workflow_analysis}[0.152, 0.216, 0.707, 0.434]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "Jt1TAjj32qHCJoJW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn Workflow_analysis category, is {the \"Success\" bar}[0.152, 0.216, 0.707, 0.434] taller than {the \"Grasping failed\" bar}[0.906, 0.216, 0.945, 0.434]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "on4U3iWEW2ccEQ7Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Success\"}[{Success bar for Efficiency_check}[0.152, 0.652, 0.311, 0.870], {Success bar for Workflow_analysis}[0.152, 0.216, 0.707, 0.434]] and {\"Grasping failed\"}[{Grasping failed bar for Efficiency_check}[0.826, 0.652, 0.945, 0.870], {Grasping failed bar for Workflow_analysis}[0.906, 0.216, 0.945, 0.434]], which factor has a higher impact on Workflow_analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Success."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "L49AHWICJsrzMTAf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.264, 0.036, 0.873, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with six items: \n1. {Success}[0.309, 0.045, 0.364, 0.069] with color green,\n2. {Grounding Error}[0.309, 0.080, 0.420, 0.103] with color gray,\n3. {Occupancy Error}[0.487, 0.045, 0.602, 0.069] with color orange,\n4. {Gripper collision}[0.487, 0.080, 0.599, 0.103] with color yellow,\n5. {Trajectory optimization Error}[0.669, 0.045, 0.867, 0.069] with color blue,\n6. {Grasping failed}[0.669, 0.080, 0.774, 0.103] with color purple."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "pN2e9uB9stO6jKNe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the y-axis of the chart within the coordinates [0.028, 0.315, 0.142, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two labels on the y-axis. They are:\n1. \"Efficiency_check\": [0.028, 0.752, 0.142, 0.775],\n2. \"Workflow_analysis\": [0.015, 0.315, 0.142, 0.339]."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "9wv7weIPFZKpP3lJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.906, 0.216, 0.945, 0.434] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a purple bar, indicating the \"Grasping failed\" factor's impact on \"Workflow_analysis\"."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "EDqtQCWixJnAMUho",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is depicted in this JSON structured data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "BdpEKo38HPmdYXqG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Per the JSON structured data, the Y-axis label is not explicitly provided."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "qjtVNFer5i2Lpy1I",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first bar color of the 'bars_of_group_0' as per the bar chart information?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart information, the color of the first bar in 'bars_of_group_0' is #0000ff or Blue."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "nytvVsAKX1d0ibpZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars, is the 'Healthy Lifestyle' bar for 2022 {Healthy Lifestyle 2022 bar}<r>[0.060, 0.430, 0.483, 0.549]</r> bigger than for 2017 {Healthy Lifestyle 2017 bar}<r>[0.060, 0.668, 0.451, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "SSuqOlbeyUN5Hv9A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for 'Digital Detachment' larger than those for 'Healthy Lifestyle'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "fswyholUgQrbYyoo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the '2017' {2017 bar on Y-axis}<r>[0.060, 0.668, 0.874, 0.788]</r> bar taller than the '2022' {2022 bar on Y-axis}<r>[0.060, 0.430, 0.940, 0.549]</r> bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "5P12FTVzcjLO4fXt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.337, 0.238, 0.706, 0.322]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart, which contains two items. The first item labeled 'Healthy Lifestyle' is represented by a blue color. The label for this item is positioned at [0.382, 0.257, 0.498, 0.303] and the color patch is at [0.343, 0.261, 0.371, 0.293]. The second item is labeled 'Digital Detachment' and is represented by the color orange. Its label is at [0.565, 0.257, 0.701, 0.303] and the color patch is at [0.526, 0.261, 0.554, 0.293]."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "FWQAnrPz4y64UenG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area at [0.060, 0.668, 0.451, 0.788]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area refers to the bar representing the 'Healthy Lifestyle' category for the year 2017. The bar starts at a x-value of 0.0 and ends at 0.60."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "Juk8ogyVJlYTrdMz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.483, 0.430, 0.940, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area [0.483, 0.430, 0.940, 0.549], there is a single object which is the bar representing the committee 'Digital Detachment' for the year 2022. The bar begins at a x-value of 0.65 and ends at 1.35."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "SNFpFquCUupWNQN0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the color of the object at [0.403, 0.261, 0.431, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "BHqz6KG7T642cv3W",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.442, 0.257, 0.515, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the innovation category."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "RHUumYP5g2XMhMij",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion represented by the bar located at [0.613, 0.430, 0.943, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.55 -> 0.90."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "whZZ2UyJyvyKQQoi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Innovation bar for NewTech} at [0.095, 0.668, 0.660, 0.788] longer than the {Infrastructure bar for FutureEdu} at [0.613, 0.430, 0.943, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "pJ73O7PYfJWXvuR5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category occupies a larger proportion of the total trials for the {NewTech} group, {Innovation} at [0.095, 0.668, 0.660, 0.788] or {Infrastructure} at [0.660, 0.668, 0.896, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Infrastructure."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "kaXLfp9xQzbFv6Dt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {FutureEdu's Innovation bar} at [0.095, 0.430, 0.613, 0.549] lower than {NewTech's Infrastructure bar} at [0.660, 0.668, 0.896, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "KKXEL065XX8D5FIk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.398, 0.238, 0.683, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart with two entries: {Innovation} with color green at [0.403, 0.261, 0.431, 0.293] and text at [0.442, 0.257, 0.515, 0.303], and {Infrastructure} with color grey at [0.543, 0.261, 0.571, 0.293] and text at [0.582, 0.257, 0.677, 0.303]."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "gjUPMUUCcMf149Xp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.095, 0.668, 0.660, 0.788] and [0.660, 0.668, 0.896, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two horizontal bars for the {NewTech} category. The first bar {Innovation} at [0.095, 0.668, 0.660, 0.788] represents 0.60 of the total trials, and the second bar {Infrastructure} at [0.660, 0.668, 0.896, 0.788] represents from 0.60 to 0.85 of the total trials."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "rAlZdQ7VE2F4vMSX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within [0.095, 0.430, 0.943, 0.549]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars representing categories {Innovation} and {Infrastructure} for the {FutureEdu} category. The {Innovation bar} is located at [0.095, 0.430, 0.613, 0.549] and represents from 0.00 to 0.55 of the total trials, and the {Infrastructure bar} is located at [0.613, 0.430, 0.943, 0.549] and represents from 0.55 to 0.90 of the total trials."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "mRIVhu0fKZFkkZHM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at these coordinates [0.333, 0.257, 0.389, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Success\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "xdmOUbWdB6EV8Jn0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the object at [0.294, 0.261, 0.322, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color green."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "2sLkAyCpX0ziTZ7z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xlabel coordinate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The xlabel has the coordinates: [0.507, 0.903, 0.681, 0.950]."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "h2pCz6PxBsqdzLL0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object existing in the coordinates [0.693, 0.326, 0.800, 0.373]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Grasping Failed\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "fIIVCkYcfM2RCmtx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar}[0.203, 0.668, 0.575, 0.788] wider than the {bar}[0.911, 0.430, 0.948, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "WTeO3vhWL8zhwvmm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label 'Gripper Collision'}[0.511, 0.326, 0.626, 0.373] above or below the {label 'Occupancy Error'}[0.511, 0.257, 0.626, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "bwJ25cM2uREx9IZO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the percent value bar of Success}[0.203, 0.668, 0.575, 0.788] in Urban Planning category, is the value greater than the {percent value bar of Success}[0.203, 0.430, 0.650, 0.549] in Community Development category?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "mss56pfRV6gNCrsS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Occupancy Error's bars}[[0.650, 0.668, 0.724, 0.788], [0.687, 0.430, 0.799, 0.549]] taller than {Grasping Failed's bars}[[0.799, 0.430, 0.836, 0.549], [0.799, 0.430, 0.836, 0.549]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "FBobu0ZPaUbtKJhv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is inside [0.333, 0.257, 0.389, 0.303]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label, which is \"Success\", and it has the color green, the handle's coordinates is at [0.294, 0.261, 0.322, 0.293]"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "afRfNyTPeVk6ndqD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.507, 0.903, 0.681, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is the xlabel of the graph, which reads \"Percentage of Total Trials\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "LyFptoI72CQNhTag",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.650, 0.430, 0.911, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains one bar of the \"Community Development\" category for the error type \"Occupancy Error\" alongside other error types represented by different colored bars. The percentage value for \"Occupancy Error\" is 0.15."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "ILUx2oTsuAxDH0DX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description for the content in the region [0.294, 0.261, 0.322, 0.293]."
+            },
+            {
+                "from": "gpt",
+                "value": "It only contains a colored box related to the \"Success\" category in the legend with a green color code."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "UMEFKdvuTf7PDJjM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart at the coordinates [0.118, 0.668, 0.441, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the manufacturing sector for global trends."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "hEGmEbzWDDRb17Cc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.629, 0.261, 0.657, 0.293] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color handle in the legend, color-coded for \"GDP Increase\"."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "tAgtzoEDZcrCpA5J",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the object at the coordinates [0.015, 0.710, 0.108, 0.756] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the y-axis label \"Global trends\"."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "JbotJwbkovFmN7S8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.548, 0.257, 0.601, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text label for \"Imports\" in the legend."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "Eli2LsvMmchXGM93",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Manufacturing bar for Asia}[0.118, 0.430, 0.471, 0.549] larger than {the Manufacturing bar for Global trends}[0.118, 0.668, 0.441, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "OrH1md22MLox3aWS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the Services bar for Global trends}[0.441, 0.668, 0.688, 0.788] or {the Services bar for Asia}[0.471, 0.430, 0.673, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Services bar for Global trends."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "1awxA33cIH1zizLd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the length of {the Exports bar for Asia}[0.801, 0.430, 0.884, 0.549] greater than the length of {the Imports bar for Global trends}[0.688, 0.668, 0.839, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "LXbWcdtvRgRRAMtv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is shorter, {the GDP Increase bar for Global trends}[0.906, 0.668, 0.921, 0.788] or {the FDI bar for Asia}[0.929, 0.430, 0.944, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GDP Increase bar for Global trends."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "eoftj44bTMhNbDJ8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.336, 0.238, 0.767, 0.392] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart which includes labels for six categories: Manufacturing, Services, Imports, Exports, GDP Increase, and FDI. Each category has a color handle associated with it which is used to identify the color of the bars representing it in the chart."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "KgohToMqY4Ki8bWZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.118, 0.668, 0.944, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the bar segments of Global trends in the categories of Manufacturing, Services, Imports, Exports, GDP Increase, and FDI."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "sYI7GRtb7ypaveNR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the coordinates [0.107, 0.838, 1.030, 0.885]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains labels for the x-axis scale, ranging from 0.0 to 1.2."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "56V5jKA9adz5v5WN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within [0.015, 0.471, 0.108, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains y-axis labels: Global trends and Asia."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "s8OkJwiMKPaQF4vd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of data does the object at [0.276, 0.261, 0.304, 0.293] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates represents the color for \"Success\" in the chart."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "WGnSbiMxvJLR1xFr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category for the bar in coordinates [0.164, 0.668, 0.711, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category for this bar is \"Self-driving Vehicles\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "ggQKpZOQUYQ9yvrP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.488, 0.903, 0.661, 0.950] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This text is the x-axis label, which is \"Percentage of Total Trials\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "4bQXumL8seLRkJBc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the dataset indicated at [0.636, 0.331, 0.664, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at these coordinates represents the dataset for \"Grasping Failed\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "KmIMK8ajgBCiyKYP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of \"Occupancy Error\" [0.790, 0.668, 0.829, 0.788], [0.711, 0.430, 0.790, 0.549] wider than those of \"Grasping Failed\" [0.930, 0.668, 0.946, 0.788], [0.907, 0.430, 0.946, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "qkeeLmTF9gpUHmM4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for \"Self-driving Vehicles\" [0.164, 0.668, 0.711, 0.788] longer than that for \"Drone Delivery\" [0.164, 0.430, 0.555, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "X04YaNI1MuZjPYvN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Success\" dataset [0.164, 0.668, 0.711, 0.788], [0.164, 0.430, 0.555, 0.549] larger than the \"Grasping Failed\" dataset [0.930, 0.668, 0.946, 0.788], [0.907, 0.430, 0.946, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "ZZugnIRwzt9grwxN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the dataset for \"Grounding Error\" [0.711, 0.668, 0.790, 0.788], [0.555, 0.430, 0.711, 0.549] to the right of the \"Occupancy Error\" dataset [0.790, 0.668, 0.829, 0.788], [0.711, 0.430, 0.790, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "wny4ebMn5AwZZhY6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.271, 0.238, 0.879, 0.392]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. Each rectangle color corresponds to a label, such as \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\", \"Trajectory optimization Error\", \"Grasping Failed\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "3txDhgucD9jBzufu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.164, 0.668, 0.946, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region of the bars that represent the category of \"Self-driving Vehicles\" with their respective datasets for \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\", \"Trajectory optimization Error\", \"Grasping Failed\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "D5SiQOYOoenul8jw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area of [0.164, 0.430, 0.946, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region of the bars that represent the \"Drone Delivery\" category with their respective datasets for \"Success\", \"Grounding Error\", \"Occupancy Error\", \"Gripper collision\",\"Trajectory optimization Error\", \"Grasping Failed\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "6ON0kqykocfpUkf5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is covered in the area [0.015, 0.710, 0.154, 0.756] and [0.050, 0.471, 0.154, 0.518]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas contain labels for the y-axis categories \"Self-driving Vehicles\" and \"Drone Delivery\" respectively."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "R4NKBOctDTxS2z56",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of chart is shown in the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "in8g3mgeKilFK1dp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found within the coordinates [0.456, 0.903, 0.650, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage of Total Reviews."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "x33te6vu9XRZIPeM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat colors are associated with the labels 'Success' and 'Implementation Errors' in the legend located within the coordinates [0.235, 0.238, 0.871, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Success}<r>[0.008000]</r> is associated with green color while {Implementation Errors}<r>[0.0000ff]</r> is associated with blue color."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "7IL1ak1d496DF2sI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer, the bar representing 'Success' in 'Tax Reform' or 'Drugs Control'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for 'Drugs Control' is longer."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "ePImz197BtgiFtVD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars related to 'Policy Ineffectiveness' of the same length in both 'Tax Reform' and 'Drugs Control'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "0vB63miehLnSV9Vt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for 'Unforeseen Consequences' shorter or longer in 'Tax Reform' compared to 'Drugs Control'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "mucLO8Vme5CMvMal",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.121, 0.668, 0.600, 0.788] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the green bar that represents the 'Success' category in 'Tax Reform'."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "Ik0xgVHH8V3a6eYs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.840, 0.668, 0.880, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, a yellow bar that represents 'Legal Challenges' in 'Tax Reform' is observed."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "surt9E2ObONxiNbF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items found in [0.681, 0.326, 0.865, 0.373]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the legend item 'Unforeseen Consequences', colored purple."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "2BQfLgoFPhb1HGA5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.327, 0.257, 0.383, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Success}<r>[0.327, 0.257, 0.383, 0.303]</r>."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "IDgKdpzVxEKRc3hU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is on the x-axis at [0.464, 0.903, 0.638, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, {Percentage of Total Trials}<r>[0.464, 0.903, 0.638, 0.950]</r>."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "1pkiw7v3ebVBdPhM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object at the coordinates [0.289, 0.261, 0.316, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "pCToaOfvIHWkOZVK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Water Usage}[0.690, 0.668, 0.772, 0.788] to the right or left of the bar for {Land Degradation}<r>[0.772, 0.668, 0.813, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "nGmyfDKqXcma86m5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Air pollution}[0.886, 0.430, 0.944, 0.549] shorter than the bar for {Fossil Fuels}<r>[0.117, 0.430, 0.445, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "kcfAUDuLqH7Mz8Dn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher value, {Carbon footprint}<r>[0.445, 0.430, 0.772, 0.549]</r> or {Water Usage}<r>[0.690, 0.668, 0.772, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon footprint."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "v7dYjBRKTYFSBBUi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's represented in the area [0.283, 0.238, 0.819, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.283, 0.238, 0.819, 0.392] contains the legend for the chart. Each label in the legend has a color marker and an identifier. The labels include {Success}<r>[0.327, 0.257, 0.383, 0.303]</r>, {Carbon footprint}<r>[0.327, 0.326, 0.442, 0.373]</r>, {Water Usage}<r>[0.509, 0.257, 0.598, 0.303]</r>, {Land Degradation}<r>[0.509, 0.326, 0.634, 0.373]</r>, {Biodiversity loss}<r>[0.701, 0.257, 0.814, 0.303]</r>, {Air pollution}<r>[0.701, 0.326, 0.785, 0.373]</r>"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "v2bpD8jqRPQPdKiy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.015, 0.710, 0.107, 0.756]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for the y-axis category, {Clean Energy}<r>[0.015, 0.710, 0.107, 0.756]</r>."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "CcqqRJBvS6QeWird",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.772, 0.430, 0.813, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects:\n1. The gray column representing Carbon footprint for fossil fuel, and its coordinate range is [0.772, 0.430, 0.813, 0.549].\n2. The orange column representing Water Usage for fossil fuel, its coordinate range is [0.772, 0.430, 0.813, 0.549].\n3. The yellow column representing Land Degradation for fossil fuel, its coordinate range is [0.813, 0.430, 0.854, 0.549]."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "hQDrh7ONUH8SMYZl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.382, 0.952, 0.707, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, saying \"Average Daily User Count (in billions)\"."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "X8PncER1zdUvtodw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.455, 0.135, 0.499, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar count of Reddit, which is 2.38e9 users."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "8Uf8T8VxwPlYGeHP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the area [0.113, 0.782, 0.832, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue bar corresponding to Twitter's Average Daily User Count, which is {5.0}[0.832, 0.815, 0.865, 0.839]."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "P4UU9N3aJhdDnvb3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is contained in the area [0.265, 0.025, 0.824, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, saying \"Social Media Data Analysis: Average Daily User Count\"."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "Zo2qz6eno8uMjcp2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the bar for {Facebook}<r>[0.113, 0.668, 0.803, 0.759]</r> or for {Instagram}<r>[0.113, 0.555, 0.779, 0.645]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "2b5EYZ0uB28Rfi47",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Instagram}[0.113, 0.555, 0.779, 0.645] have more Daily Users than {LinkedIn}[0.113, 0.441, 0.703, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "qfbQEjU0R4VTLA4K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the Average Daily Users for {Snapchat}[0.113, 0.214, 0.619, 0.305] more than {Reddit}[0.113, 0.101, 0.455, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "Zase42QFALVZKLj3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Pinterest}[0.113, 0.328, 0.659, 0.419] more popular than {LinkedIn}[0.113, 0.441, 0.703, 0.532] based on daily users?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "ah2P5RRQP1auM44y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.113, 0.062, 0.976, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area. It consists of seven horizontal bars, each representing a social media platform's daily user count. The labels of these platforms are {Twitter}<r>[0.049, 0.818, 0.107, 0.841]</r>, {Facebook}<r>[0.025, 0.704, 0.107, 0.728]</r>, {Instagram}<r>[0.019, 0.591, 0.107, 0.614]</r>, {LinkedIn}<r>[0.035, 0.477, 0.107, 0.501]</r>, {Pinterest}<r>[0.030, 0.364, 0.107, 0.387]</r>, {Snapchat}<r>[0.025, 0.250, 0.107, 0.274]</r>, and {Reddit}<r>[0.051, 0.137, 0.107, 0.160]</r>."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "7lSuHWagdXmjEFMx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is happening within the area [0.265, 0.025, 0.824, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, which reads \"Social Media Data Analysis: Average Daily User Count\"."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "7Q9VaOpMhxiNu1x8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside [0.025, 0.250, 0.107, 0.274]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label Snapchat on the Y-axis indicating the platform for which the user daily count bar is representing."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "NBVqUiPBRfPVb2Zo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see between these site-specific y-axis labels? [0.035, 0.477, 0.107, 0.501], [0.051, 0.137, 0.107, 0.160]"
+            },
+            {
+                "from": "gpt",
+                "value": "1. LinkedIn's bar for average daily users which is [0.113, 0.441, 0.703, 0.532].\n2. Pinterest's bar for average daily users which is [0.113, 0.328, 0.659, 0.419].\n3. Snapchat's bar for average daily users which is [0.113, 0.214, 0.619, 0.305]."
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "Oft2QLwly0sJ8IEW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are present at the coordinates [0.204, 0.093, 0.981, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area with all bar graphics and axis labels is located at these coordinates."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "kR4BIonEFLnoqz9H",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information do the coordinates [0.204, 0.714, 0.833, 0.831] hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blue bar signifying the economic indicator strength for {New Businesses}<r>[0.204, 0.714, 0.833, 0.831]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "XMTbdno2pw73Tcz2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is detailed between the coordinates [0.019, 0.173, 0.198, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for {Unemployment Rate}<r>[0.019, 0.173, 0.198, 0.208]</r> on the y-axis."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "QoLq9rLDb52j9X7W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between the {bar}[0.204, 0.128, 0.398, 0.246] and the economic indicator strength it represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The length of the horizontal bar is proportional to the strength of economic indicator it represents for Unemployment Rate."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "HxjfohikBFAaF1CL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the location of the {bar}[0.204, 0.714, 0.833, 0.831] compare to the location of the {bar}[0.204, 0.568, 0.896, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing New Businesses is located above the bar representing Stock Market."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "zcA239T5q4nNYceh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {value}[0.732, 0.462, 0.776, 0.497] compare to the {bar length}[0.204, 0.421, 0.732, 0.538] in the figure for Consumer Spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value represented by the text directly corresponds to the length of the bar in the figure, with the value 0.68 relating to the bar length."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "PAWc1GEB9hyI0NiJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features within coordinates [0.204, 0.093, 0.981, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly comprises the core bar chart plotting the economic indicators strength. Within this area, there are horizontal bars symbolizing strengths of different economic indicators such as New Businesses, Stock Market, Consumer Spending, Inflation Rate, and Unemployment Rate."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "4T2FrxYFCGH5XqiG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area between [0.204, 0.568, 0.896, 0.685]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single horizontal bar depicting the indicator strength for the Stock Market. Its bar length extends to the coordinate [0.896, 0.685], indicating the strength value of 0.89 for the Stock Market."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "SbnoEI4ZJrbpiTwg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.579, 0.906, 0.606, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This small region contains the x-axis label denoting the economic strength value of 0.5, serving as a reference guide for interpreting the lengths of the indicator bar charts."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "9aufYrjZrQmZUthM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.481, 0.855, 0.737, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the x-axis label, which is named \"Environmental Impact\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "2NrHxagRpHmxVWXx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinate [0.692, 0.632, 0.751, 0.702] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the value label of the corresponding bar, which indicates 0.62."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "x03BSeJ0Fw678ujO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.261, 0.625, 0.692, 0.708] represent in the {graph}<r>[0.261, 0.187, 0.957, 0.733]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the impact of Deforestation Rate, with a value of 0.62."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "OmWMWoGvEjHGH1mM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category at coordinates [0.035, 0.639, 0.253, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is \"Deforestation Rate\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "hSwbIV1PMH3pRBJz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the impact of {Deforestation Rate}[0.261, 0.625, 0.692, 0.708] more or less than {Global Temperature}[0.261, 0.522, 0.894, 0.605] as per the bars in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "RpkXh8rBY10EWg4T",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Biodiversity Loss}[0.261, 0.211, 0.852, 0.294] have a higher or lower impact than {Water Availability}[0.261, 0.315, 0.769, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "cT2oiJIKIER5d01n",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has a larger bar in the chart {Global Temperature}[0.261, 0.522, 0.894, 0.605] or {Air Pollution}[0.261, 0.418, 0.832, 0.501]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Temperature."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "vjuXZk2TeUyRcqvs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Air Pollution}<[0.261, 0.418, 0.832, 0.501]> and {Biodiversity Loss}<[0.261, 0.211, 0.852, 0.294]>, which category has a higher impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biodiversity Loss."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "b1rhtAJwwfSWbiUw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the coordinates [0.261, 0.187, 0.957, 0.733]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates hold the entire graph region which includes 5 bars representing 5 different categories. The bars are:\n1. Deforestation Rate: [0.261, 0.625, 0.692, 0.708]\n2. Global Temperature: [0.261, 0.522, 0.894, 0.605]\n3. Air Pollution: [0.261, 0.418, 0.832, 0.501]\n4. Water Availability: [0.261, 0.315, 0.769, 0.398]\n5. Biodiversity Loss: [0.261, 0.211, 0.852, 0.294]"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "MFQtEZ6JbE9B84pc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen between the coordinates [0.261, 0.418, 0.852, 0.501]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate contains the bar for \"Air Pollution\", which signifies the percentage of impact as 0.82 and its text annotation."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "l9fslw5Th4jHE674",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.261, 0.625, 0.692, 0.708]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue bar representing the Deforestation Rate with a value of 0.62, along with its text annotation with coordinates [0.692, 0.632, 0.751, 0.702]."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "clhgsHj81tBBIAR4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area [0.261, 0.625, 0.894, 0.605]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two bars with their text annotations:\n1. The Deforestation Rate bar[0.261, 0.625, 0.692, 0.708] with its corresponding text annotation [0.692, 0.632, 0.751, 0.702]\n2. The bar representing Global Temperature[0.261, 0.522, 0.894, 0.605] comes next, with its text annotation at [0.894, 0.528, 0.953, 0.598]."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "tA7Ju8eB1bT37I8E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented based on the given JSON data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "opoorTJU4p3O1UR6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis in the visualization represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Happiness Index."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "ndlZhuadblD96NTA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at {coordinates}[0.087, 0.631, 0.814, 0.831] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Happiness Index for Suburban."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "lUaRpeHFNDaBLl3I",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis tick at {coordinates}[0.087, 0.731]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Suburban."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "Eg9R6U9Ou0bPvf7U",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher Happiness Index, {Suburban}[0.087, 0.631, 0.814, 0.831] or {Urban}[0.087, 0.379, 0.761, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Suburban."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "fFxxbFazXB5ZUH6p",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Happiness Index for {Suburban}[0.087, 0.631, 0.814, 0.831] less than 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "W2gHSMSRZ57u6LUZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Urban}[0.087, 0.379, 0.761, 0.580] and {Rural}[0.087, 0.128, 0.655, 0.329], which has a lower Happiness Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "MKMQvj5oNkPjLQrr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Suburban}[0.087, 0.631, 0.814, 0.831] wider than that for {Rural}[0.087, 0.128, 0.655, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "TMnDuaZCVRDur1HE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features can be observed in the region [0.087, 0.128, 0.885, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are three horizontal bars which represent the Happiness Index for three different areas: Suburban, Urban, and Rural. Each bar is labeled with its respective value on the right end."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "QYQOETsSo9yujyWj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's included in the region [0.076, 0.879, 0.985, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis labels for the Happiness Index. The labels range from 0.0 to 1.0 in increments of 0.2."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "iZAxFshF9y5Dbncy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is presented within the region [0.015, 0.215, 0.082, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis labels, which are 'Suburban', 'Urban', and 'Rural'. These labels represent different areas that have been evaluated for their Happiness Index."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "hRuPedRpU96tDKmz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the region [0.087, 0.631, 0.849, 0.831], what's depicted?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a bar that represents the happiness index value for the suburban area. The value is labeled as 0.82 at the right side of the bar."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "RHKAIRHvoEM2OOrB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type for the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "EFeSSBp8lnSXmp00",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the box with coordinates [0.164, 0.267, 0.767, 0.391] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mutual Funds' probability of increase in the market."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "6j0LhBfehTG6d1NE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-tick located at [0.023, 0.628, 0.159, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "International Bonds."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "OP6yJzws4tWgtBin",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat values does the X-tick at [0.783, 0.903, 0.805, 0.931] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.7."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "7Mm4fSklJY4f2tUZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"International Bonds\"}[0.164, 0.577, 0.812, 0.701] longer than {the bar representing \"National Bonds\"}[0.164, 0.732, 0.749, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "oaJGUzKq4kPedptX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Stock Market Indices\"}[0.164, 0.422, 0.794, 0.546] to the right or left of {the bar representing \"Cryptocurrencies\"}[0.164, 0.112, 0.866, 0.236]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "xIZuRcd2W8mQGgCe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the \"International Bonds\" bar}[0.164, 0.577, 0.812, 0.701] with {the \"Cryptocurrencies\" bar}[0.164, 0.112, 0.866, 0.236], which one represents a higher probability of market increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cryptocurrencies."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "riCulSDbk7Mc3REV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Mutual Funds bar}[0.164, 0.267, 0.767, 0.391] above or below the {\"Stock Market Indices\" bar}[0.164, 0.422, 0.794, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "OZGKx5higFxt6KXY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.392, 0.106, 0.864, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the bars representing probability of market increase for various financial instruments. You can see the bars for the International Bonds, Mutual Funds, and Cryptocurrencies with their corresponding probabilities of market increase."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "FKs0fvbygX9sshXP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.164, 0.422, 0.794, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects: \n1. The bar that signifies the probability of market increase for Stock Market Indices, with the coordinate range [0.164, 0.422, 0.794, 0.546].\n2. The annotation that displays the actual probability number \"0.7\", with the coordinate range [0.794, 0.470, 0.820, 0.498]."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "BvUMfMalGnssny7Q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content at [0.306, 0.030, 0.831, 0.066]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart, which is \"Probability of Market Increase for Various Financial Instruments\"."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "jgvBdxH3O5EySaDH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found inside the area [0.180, 0.903, 0.963, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains labels representing the x-tick values, which denote the probabilities '0.6', '0.7', '0.8', and '0.9'."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "lhzDQzu3tZQuDgpG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at [0.404, 0.025, 0.761, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, which reads \"Emerging Technologies and Trends Outlook\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "ogerhS6HPGIX82KC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at [0.180, 0.809, 0.861, 0.872] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the trend value for \"AI-driven Development\", which is 0.88."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "1zjCRTjTGs7O22h9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "There isn't any label for the y-axis as indicated by the empty string at the location: {the y-axis label}<r>[0.009, 0.487, 0.009, 0.487]</r>."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "aiSSlgrVNzw6GU6L",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"AI-driven Development\"}<r>[0.180, 0.809, 0.861, 0.872]</r> longer than {the bar representing \"Blockchain\"}<r>[0.180, 0.731, 0.792, 0.794]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "bBjYsQDeYNOpoPa5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Augmented Reality\"}<r>[0.180, 0.258, 0.823, 0.321]</r> higher or lower than {the bar representing \"Virtual Reality\"}<r>[0.180, 0.180, 0.792, 0.243]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "vnjNgr2qceAXxhi2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer, {the bar for \"5G Networks\"}<r>[0.180, 0.337, 0.947, 0.400]</r> or {the bar for \"Distributed Cloud\"}<r>[0.180, 0.101, 0.892, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"5G Networks\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "xM8ROBjZzE3AU1Zo",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.539, 0.952, 0.626, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the X-axis label, which reads \"Trend Values\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "R0AAPqG5SoqhS2mG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe any objects within the region [0.180, 0.809, 0.861, 0.872]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds a single object: the bar representing the trend value of 0.88 for \"AI-driven Development\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "WeDOZNE6JCevCha8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the area [0.100, 0.753, 0.175, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label \"Blockchain\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "dmUYzPgqi3rQd8EY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the percentage increase} at [0.864, 0.370, 0.914, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5.2%"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "MjaWRMsvAJa4emSu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar with coordinates [0.193, 0.683, 0.774, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage increase of the Stock Market over a decade."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "hfogGCBz8bGJ3gaD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at location [0.100, 0.559, 0.187, 0.594] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category Gold Price."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "3XaPtNRH2iCPK9N9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labelled at {the y-axis} at [0.074, 0.744, 0.187, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Market."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "eXbSy7ISNq8hEhdw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Real Estate\"}[0.193, 0.313, 0.864, 0.461] longer or shorter than {the bar representing \"Gold Price\"}[0.193, 0.498, 0.800, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "gJ57AQkrzdvukuSp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Currency Exchange\"}[0.193, 0.128, 0.813, 0.276] to the left or right of {the \"3%\" label}[0.567, 0.879, 0.594, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "wpoH7maI8vrL1vtW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Stock Market\"}[0.193, 0.683, 0.774, 0.831] shorter than {the bar for \"Currency Exchange\"}[0.193, 0.128, 0.813, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "b3UNtNELUK3lAn5k",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Stock Market\"}[0.193, 0.683, 0.774, 0.831] and {\"Gold Price\"}[0.193, 0.498, 0.800, 0.646], which one has a higher percentage increase over a decade represented by its bar's length on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Real Estate."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "xzBhfti5R05TMfDP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in coordinates [0.019, 0.189, 0.187, 0.224]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for \"Currency Exchange\"."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "z4ERLD3ICkWwZLp0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.567, 0.879, 0.594, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label representing \"3%\" on the x-axis."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "CRLdctkzJEIz8rfy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details about the objects located in the area [0.193, 0.498, 0.800, 0.646]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar that represents 'Gold Price' category with a percentage increase of 4.7% over a decade. The bar starts from x-axis representing '0%', stretches to represent '4.7%'."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "EkB31spCs2oBSlr8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the area [0.407, 0.927, 0.754, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays the xlabel of the chart which represents the 'Percentage Increase Over a Decade (%)'."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "w40STtgwhKcIY58x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key object denoted by the coordinates [0.286, 0.685, 0.844, 0.790] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue bar representing the return rate of the 'High Risk High Return' investment strategy."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "Ygj6OwTd0nmNwUVs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the text associated with the 'Aggressive Growth' strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.745, 0.318, 0.825, 0.364]"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "Q2HO7c7cQlJTdIQj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the string of text represented by the coordinates [0.025, 0.719, 0.278, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "High Risk High Return."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "tihQj4TmScyrpHEg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by these coordinates [0.351, 0.582, 0.432, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The yield value for the 'Stable Low Yield' strategy, exactly 10%."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "WrOQgTgn4ScWlYCD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher yield, {the High Risk High Return bar}[0.286, 0.685, 0.844, 0.790] or {the Stable Low Yield bar}[0.286, 0.552, 0.351, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The High Risk High Return bar."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "K8wHSbw6yfoqACzi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Balanced Portfolio bar}[0.286, 0.420, 0.614, 0.526] to {the Aggressive Growth bar}[0.286, 0.288, 0.745, 0.394], which bar is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Aggressive Growth bar."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "IRH7UvsrTxvvyG2r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Conservative Mix bar}[0.286, 0.156, 0.483, 0.262] have a higher or lower yield than {the Balanced Portfolio bar}[0.286, 0.420, 0.614, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "U7nIvPDvN068amAD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Stable Low Yield Strategy}[0.286, 0.552, 0.351, 0.658] and {the Aggressive Growth Strategy}[0.286, 0.288, 0.745, 0.394], which has a higher rate of return?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aggressive Growth Strategy."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "Olrcc363Iyoo7RUK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.286, 0.124, 0.942, 0.822]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains five colored bars, each corresponding to a different investment strategy. They are, from top to bottom - {High Risk High Return}[0.286, 0.685, 0.844, 0.790], {Stable Low Yield}[0.286, 0.552, 0.351, 0.658], {Balanced Portfolio}[0.286, 0.420, 0.614, 0.526], {Aggressive Growth}[0.286, 0.288, 0.745, 0.394] and {Conservative Mix}[0.286, 0.156, 0.483, 0.262]. Each bar's length indicates the average return rate of the corresponding strategy and has a number label at the edge showing the exact rate percentage."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "NM3Vpj3ZD6LTmHsj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you write down what exists within the region [0.050, 0.800, 0.660, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart's title, represented by the string \"Average Return Rates of Different Investment Strategies\" with coordinates [0.222, 0.050, 1.006, 0.110]."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "nUlIoVAL7tGjjfdd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the area bounded by [0.286, 0.685, 0.844, 0.790]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a blue bar representing the 'High Risk High Return' investment strategy's return rate with a label on the right side showing the value as '85%'."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "D4QmDuEGcpdwao7j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find inside the region [0.856, 0.838, 0.942, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the representation of 100% on the X-axis."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "g4zbbx7rB8RKDNTh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar with coordinates [0.134, 0.714, 0.784, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the Influence Score of TV News."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "iKX1gtsVAU6gtELn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.676, 0.316, 0.720, 0.351] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the Influence Score for Radio News, \"0.65\"."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "NkFCpu1RTBcB2HP5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is signified by the bar at [0.134, 0.421, 0.901, 0.538]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar signifies the Influence Score of Social Media."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "A9pRQAXDQQuh7Fqc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the influence score of {Online News bar}[0.134, 0.568, 0.843, 0.685] higher or lower than the influence score of {TV News bar}[0.134, 0.714, 0.784, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "gREvXZDSrHlM0gW4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {TV News bar}[0.134, 0.714, 0.784, 0.831] and {Print Media bar}[0.134, 0.128, 0.718, 0.246], which media has the lower influence score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Print Media."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "nafT14XDLombTI61",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater impact, the {Social Media bar}[0.134, 0.421, 0.901, 0.538] or the {Radio News bar}[0.134, 0.275, 0.676, 0.392]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "uEPMJ3Cz0a1B6WAX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you can observe in [0.134, 0.128, 0.901, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains five bars, each representing the influence score of different media channels: \n1. {TV News bar}[0.134, 0.714, 0.784, 0.831] with an influence score of 0.78.\n2. {Online News bar}[0.134, 0.568, 0.843, 0.685] with an influence score of 0.85.\n3. {Social Media bar}[0.134, 0.421, 0.901, 0.538] with an influence score of 0.92.\n4. {Radio News bar}[0.134, 0.275, 0.676, 0.392] with an influence score of 0.65.\n5. {Print Media bar}[0.134, 0.128, 0.718, 0.246] with an influence score of 0.7."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "LgO59Yh7LREDjgMT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements included in the region [0.484, 0.927, 0.618, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"Influence Score\"."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "yx8OHNTKoJSFn9f5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.020, 0.613, 0.128, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content within this area is a y-axis category label, {Online News}[0.020, 0.613, 0.128, 0.648]."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "sitxppUiM1hpppXU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the bar representing \"Process Improvement\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Process Improvement bar}[0.296, 0.492, 0.811, 0.589]"
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "7rnZpXTWsC2X5Lg8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.904, 0.765, 0.962, 0.800] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The numeric value 0.92 corresponding to the \"Automation\" task."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "fkeXbT8wb1lgJPC9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.434, 0.927, 0.818, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Efficiency Improvement Potential\"."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "8nTwwSRCqQj3hHIS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the parameters being compared, indicated by the labels in the coordinates [0.161, 0.648, 0.288, 0.683],[0.147, 0.163, 0.288, 0.198],[0.037, 0.527, 0.288, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The parameters being compared are {Delegation}[0.161, 0.648, 0.288, 0.683], {Outsourcing}[0.147, 0.163, 0.288, 0.198],, and {Process Improvement}[0.037, 0.527, 0.288, 0.562]."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "X5jmZq3j6trnMs9Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the length of the bar showing \"Technological Upgrade\" exceed the length of the bar showing \"Remote Working\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using their coordinates: {Technological Upgrade bar}[0.296, 0.371, 0.752, 0.468] and {Remote Working bar}[0.296, 0.250, 0.725, 0.347] - The answer is No."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "QjOQd8ftKEfiLa3x",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for \"Process Improvement\" have a higher efficiency improvement potential compared to \"Delegation\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given their coordinates: {Process Improvement bar}[0.296, 0.492, 0.811, 0.589] and {Delegation bar}[0.296, 0.613, 0.858, 0.710] - The answer is No."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "CKwTPstxkGuD8N7j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher efficiency improvement potential, \"Outsourcing\" or \"Automation\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "If we look at their bar coordinates: {Outsourcing bar}[0.296, 0.128, 0.679, 0.225], and {Automation bar}[0.296, 0.734, 0.904, 0.831], the answer is \"Automation\"."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "wd4qgmysSPizipQl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is represented in the area [0.296, 0.128, 0.811, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this large rectangular area, six horizontal bars representing different tasks are found. These include Automation, Delegation, Process Improvement, Technological Upgrade, Remote Working, and Outsourcing. Each bar's length represents the corresponding task's Efficiency Improvement Potential."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "XfLGCsGWgNdnVn9r",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area ranging from [0.434, 0.927, 0.818, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes only the x-axis label \"Efficiency Improvement Potential\"."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "TRGGabCh8EQV5c79",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the region [0.309, 0.037, 0.943, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart's subtitle \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "UFSGPmCJzKeno4QO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name of the media company corresponding to the bar with coordinates [0.182, 0.128, 0.889, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "CBC News."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "bpC0gSj9qkmkTFOw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the axis label at [0.486, 0.927, 0.663, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Influence Index."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "VjYIWsx0QKT0JPzS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the entity located at the coordinates [0.182, 0.767, 0.931, 0.831] and what it represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the Influence Index for {BBC}[0.126, 0.786, 0.174, 0.821]."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "Uv4qS8XXsHfSu4pp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinate [0.689, 0.622, 0.748, 0.657] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value (0.61) of {Fox News}[0.064, 0.626, 0.174, 0.661] in the Influence Index."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "A8eoXts7S7IWJu4H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Influence Index for {Al Jazeera}[0.059, 0.546, 0.174, 0.581] higher or lower than {Russia Today}[0.025, 0.466, 0.174, 0.501]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "hf2eCYPTitqpDbIq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing {NBC News}[0.055, 0.227, 0.174, 0.262] in Influence Index extend further in x-direction than the bar representing {Sky News}[0.063, 0.386, 0.174, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "VrHrAYZDTcpTs10v",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {ABC News}[0.056, 0.306, 0.174, 0.341] more influential than {CBC News}[0.056, 0.147, 0.174, 0.182] according to the Influence Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "YejNKOPbJ9f9XO1J",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media outlet has a lower Influence Index, {Fox News}[0.064, 0.626, 0.174, 0.661] or {Russia Today}[0.025, 0.466, 0.174, 0.501]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fox News."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "u9VLe8qQNQpEIRwu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area {0.182, 0.093, 0.968, 0.866}?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar plot area. It contains nine horizontal bars each representing a media outlet's Influence Index. From bottom to top, they are {CBC News}[0.182, 0.128, 0.889, 0.192], {NBC News}[0.182, 0.208, 0.831, 0.272], {ABC News}[0.182, 0.288, 0.806, 0.352], {Sky News}[0.182, 0.368, 0.847, 0.432], {Russia Today}[0.182, 0.448, 0.739, 0.512], {Al Jazeera}[0.182, 0.528, 0.881, 0.592], {Fox News}[0.182, 0.608, 0.689, 0.672], {CNN}[0.182, 0.688, 0.914, 0.751] and {BBC}[0.182, 0.767, 0.931, 0.831]."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "pGMx2yQbrZUlqls8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained in the section [0.123, 0.706, 0.174, 0.741] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the y-axis label for {CNN}[0.123, 0.706, 0.174, 0.741]."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "wqL5trvBXibGuD4a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's represented in the region [0.197, 0.037, 0.953, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle of the chart, which is \"Comparing Media Influence & Public Opinion Guidance.\""
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "X9t8tz0cJnFVCe9c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.330, 0.879, 0.366, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick value \"0.2\"."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "XH4Rk940CBKWOrLX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinate [0.260, 0.487, 0.873, 0.708]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the level of Public Interest in Cultural Heritage."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "foOLJFOtSaCIUwP5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.938, 0.757, 0.975, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-tick label, 1.0 indicating the maximum level of public interest."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "Tyi53ZIp6dhR64gc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in [0.025, 0.294, 0.251, 0.364]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-tick label, Cultural Integration."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "j6yas5VZVAmkv0jC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Cultural Heritage'}[0.260, 0.487, 0.873, 0.708] longer than {the bar representing 'Cultural Integration'}[0.260, 0.211, 0.748, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "qsRTJxU4AcHAqkf6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {y-axis label for 'Cultural Heritage'}[0.053, 0.570, 0.251, 0.640] located higher than the {y-axis label for 'Cultural Integration'}[0.025, 0.294, 0.251, 0.364]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "xZBOdV7CYrAi2k8E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of 'Cultural Heritage'}[0.260, 0.487, 0.873, 0.708] extend further than {x-tick 0.6}[0.659, 0.757, 0.696, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "44FRsMxfGKDWuRva",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region defined by [0.260, 0.211, 0.873, 0.708]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars:\n1. The bar of 'Cultural Heritage'{Cultural Heritage Bar}[0.260, 0.487, 0.873, 0.708] with a length representing the public interest level of 0.88\n2. The bar of 'Cultural Integration'{Cultural Integration Bar}[0.260, 0.211, 0.748, 0.432] with a length representing the public interest level of 0.7 and the corresponding y-axis labels."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "L5vmYduhI427qnzn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.260, 0.733, 0.957, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of the chart with tick labels from 0 to 1 indicating the levels of public interest."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "isR9vy5jfRXD9Fv8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.477, 0.855, 0.740, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label for the x-axis reading \"Level of Public Interest\"."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "oH8KWHvXBy23Mszq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main subtitle of the chart located at [0.258, 0.030, 0.870, 0.068]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Key Parameters of Global Financial System and Policies."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "jyZHnAeoXbetkdFh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at the coordinate [0.494, 0.934, 0.635, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Associated Value."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "7D1x8JLzIP1ggUgj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat parameter is shown at {the bar at the coordinates} [0.154, 0.638, 0.638, 0.698]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interest Rates."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "qvMhqKkibBb3ma7g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is found just right side of the bar with coordinates [0.154, 0.788, 0.884, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.89."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "uCYYR7yBUd95X72d",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar showing {Monetary Policy}[0.154, 0.788, 0.884, 0.848] longer than the bar showing {Quantitative Easing}[0.154, 0.713, 0.703, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "JNunKUCHGuxgkbFu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of Subprime Mortgages}[0.154, 0.263, 0.581, 0.323] shorter than {the bar of Commodities}[0.154, 0.188, 0.720, 0.248]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "PASsKLbaIPFXLwH6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Exchange-traded Fund}[0.154, 0.113, 0.662, 0.173] and {Trade Policies}[0.154, 0.413, 0.605, 0.473], which has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trade Policies."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "mMmGMUqAVfxRlVzF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Subprime Mortgages}[0.154, 0.263, 0.581, 0.323] longer than half of {the bar representing Monetary Policy}[0.154, 0.788, 0.884, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "47L3M9pUepUUd0Xs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.494, 0.934, 0.635, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label, named 'Associated Value'."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "IWIJ8IVMSqwAVHck",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region covered by the coordinates [0.154, 0.263, 0.662, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. {The bar of Exchange-traded Fund}<r>[0.154, 0.113, 0.662, 0.173]</r> and its associated text '0.62' with coordinates <r>[0.662, 0.129, 0.698, 0.157]</r>."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "BGupxlnW3hFP9lDH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be found in the area [0.154, 0.788, 0.919, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items in the area: \n1. the blue rectangle with coordinates [0.154, 0.788, 0.884, 0.848] represents the 'Monetary Policy' bar. \n2. The text '0.89' at [0.884, 0.804, 0.919, 0.832] that describes the value of Monetary Policy."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "BEvDGYScM8MQnJ9E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is stated in the subtitle at [0.071, 0.037, 1.118, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Proportion of Companies Adopting Different Social Responsibility Indicators\"."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "KK9x64rYvaAi6dG5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category corresponding to the bar with coordinates [0.221, 0.423, 0.841, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar corresponds to the \"Fair Trade\"}<r>[0.221, 0.423, 0.841, 0.540]</r>;"
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "D8eyRN1fUEtLHGRj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat portion of the x-axis is represented by the coordinate [0.406, 0.927, 0.782, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Proportion of Sustainable Business Practice\"."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "uzEpD5eRxguYSboX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bars are within the horizontal range of [0.221, 0.5, 0.5, 0.9]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{bar indicating Biodiversity Protection}<r>[0.221, 0.715, 0.400, 0.831]</r> and {bar indicating Emission Reduction}<r>[0.221, 0.569, 0.661, 0.685]</r>."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "ksBzcjAWWUNFjhIg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Charitable Giving\"}[0.221, 0.277, 0.893, 0.394] longer than {the bar representing \"Emission Reduction\"}[0.221, 0.569, 0.661, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "y0v8iqI3fEYchby2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar indicating \"Fair Trade\"}[0.221, 0.423, 0.841, 0.540] have a greater x-value than the {bar indicating \"Biodiversity Protection\"}[0.221, 0.715, 0.400, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "fB3drlzDaYwOZXyQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {bars}[[0.221, 0.715, 0.400, 0.831], [0.221, 0.569, 0.661, 0.685], [0.221, 0.423, 0.841, 0.540], [0.221, 0.277, 0.893, 0.394], [0.221, 0.131, 0.542, 0.248]] uniformly colored?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "NJQyRVKuLG7I5VN6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {highest bar}[0.221, 0.277, 0.893, 0.394] and the {lowest bar}[0.221, 0.131, 0.542, 0.248], which one represents a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest bar."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "MzWZGvJRDNAoxP6B",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects and texts found along x-axis from coordinates [0.221, 0.866, 0.967, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "Along this section of the x-axis are tick labels representing decimal fractions from 0.0 to 1.0 in increments of 0.2. Specifically, '0.0' ranges from [0.207, 0.879, 0.234, 0.914], '0.2' ranges from [0.356, 0.879, 0.384, 0.914], '0.4' ranges from [0.506, 0.879, 0.533, 0.914], '0.6' ranges from [0.655, 0.879, 0.683, 0.914], '0.8' ranges from [0.804, 0.879, 0.832, 0.914], and '1.0' ranges from [0.954, 0.879, 0.981, 0.914]."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "OexYzyuxVgPQ8l12",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area inside the coordinates [0.221, 0.096, 0.967, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains five bars each representing different Social Responsibility Indicators. From high to low, they represent \"Charitable Giving\"[0.221, 0.277, 0.893, 0.394], \"Fair Trade\"[0.221, 0.423, 0.841, 0.540], \"Emission Reduction\"[0.221, 0.569, 0.661, 0.685], \"Sustainable Packaging\"[0.221, 0.131, 0.542, 0.248], and \"Biodiversity Protection\"[0.221, 0.715, 0.400, 0.831]. The x-axis length of the bars represents the Proportion of Sustainable Business Practice."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "QSbPEuQSLbTksS43",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located between [0.221, 0.400, 0.967, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's one bar:\n1. {Bar representing \"Fair Trade\"}<r>[0.221, 0.423, 0.841, 0.540]</r>."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "lDrALBVeuvSTcpLK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content that falls within [0.221, 0.400, 0.9, 0.9]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are three category bars:\n1. {Bar representing \"Fair Trade\"}<r>[0.221, 0.423, 0.841, 0.540]</r>.\n2. {Bar representing \"Emission Reduction\"}<r>[0.221, 0.569, 0.661, 0.685]</r>.\n3. {Bar representing \"Biodiversity Protection\"}<r>[0.221, 0.715, 0.400, 0.831]</r>."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "4Xbu1G0S1Mp8BWvH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInside the coordinates [0.464, 0.623, 0.479, 0.637], what is displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for chunk size 5."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "2G2LUjTRniQnxxDG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is denoted by the coordinates [0.143, 0.363, 0.190, 0.372] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "VUIaNMSsNcBnpGov",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which coordinate can I find the value of -10 on the y-axis in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Y-axis tick label '-10'}<r>[0.058, 0.502, 0.106, 0.516]</r>."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "zTuoMBJDOhmbvvN9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at x-label '7', the {blue bar}<r>[0.603, 0.359, 0.626, 0.455]</r> or the {purple bar}<r>[0.626, 0.455, 0.650, 0.592]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "bdbeSwKDhCVAduIF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first chunk's bar for ours}<r>[0.138, 0.100, 0.161, 0.126]</r> taller than {the first chunk's bar for saliency-based sampling}<r>[0.161, 0.126, 0.185, 0.206]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "o3gbaiweaRhmZJ9g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {10th chunk's blue bar}<r>[0.836, 0.677, 0.859, 0.783]</r> represent a higher or lower rate of gain change than its corresponding {purple bar}<r>[0.859, 0.783, 0.882, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "GA32QiPhsGAlpJxm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.485, 0.314, 0.613, 0.328] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis title \"Chunk Size\", labeled at {Chunk Size}<r>[0.485, 0.314, 0.613, 0.328]</r>."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "VsROMZ60lE0a0glR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the coordinate range [0.025, 0.069, 0.106, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis label \"Rate of Gain Change[%]\", with its coordinates at {Rate of Gain Change[%]}<r>[0.025, 0.069, 0.048, 0.237]</r>."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "fuTxJMSqfPrbZw4M",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the key elements of the legend within [0.134, 0.356, 0.508, 0.402]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains two elements. One is the label \"Ours\" with a {blue color square}<r>[0.143, 0.363, 0.190, 0.372]</r> next to the corresponding {text}<r>[0.208, 0.361, 0.262, 0.375]</r>, and the other is \"Saliency-based Sampling\" with a {purple color square}<r>[0.143, 0.384, 0.190, 0.393]</r> next to the corresponding {text}<r>[0.208, 0.382, 0.499, 0.396]</r>."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "BuMTDwM95sJV4dBw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with {the series handle}[0.318, 0.026, 0.364, 0.035] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "qsX1Fj9kvcF3cded",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the metric name placed at the coordinates [0.457, 0.314, 0.626, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Accuracy."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "lZBIKKRK4ntzcvpk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich series does the bar at [0.323, 0.444, 0.498, 0.543] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series B."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "ozYbDq46H7POMSSJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue colored bar}[0.147, 0.101, 0.260, 0.115] have a higher or lower value than {the lavender bar}[0.260, 0.115, 0.373, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "ROIY2m5VDH0oipbR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich series bar is taller between [0.585, 0.441, 0.760, 0.444] and [0.710, 0.058, 0.823, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series A."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "loZ6SixDrrRfxldm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis label}[0.025, 0.120, 0.048, 0.205] indicating a higher value for the red bar located at [0.429, 0.094, 0.541, 0.115] compared to the blue bar at [0.147, 0.368, 0.323, 0.444]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "xlvsHjY7s7VjT4j4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is included in the region [0.025, 0.120, 0.325, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a y-axis label indicating 'Metric Value', along with two vertical bars from Series A, one representing Data Cleanliness and another representing Data Accuracy."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "wqnAcKZd7QEk8xIB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the elements that can be observed within the coordinates [0.429, 0.094, 0.541, 0.328]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar from Series A representing Data Accuracy, backed by the x-axis label directly beneath it reading 'Data Accuracy'."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "3fD8IrsFpljLxZhB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.192, 0.652, 0.537, 0.757]."
+            },
+            {
+                "from": "gpt",
+                "value": "This space encompasses bars from Series A and B for the metric Data Consistency and all their respective y-axis values ranging from \u22124 to 4, and the corresponding parts of y-axis marked by grid lines."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "i6jISNQL5ZID4YMR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the Y-axis label of the top subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Sales}[0.025, 0.225, 0.048, 0.262]."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "lU2oLH0ID45Frn5W",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked in the subplot by the coordinates [0.118, 0.037, 0.975, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales trend for product X."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "KbMVlv2IGtfMzA6w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the indicator '2021 Q2' located on the x-axis scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2021 Q2}[0.655, 0.459, 0.754, 0.473]."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "SSeWpA108g73XwMz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at [0.157, 0.702, 0.199, 0.942] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sales of product Y in 2020 Q1, indicated by the purple bar."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "OC1EupLb2DtPa6zE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is the bar for {2020 Q4}[0.473, 0.605, 0.515, 0.942] higher or lower than the bar for {2021 Q1}[0.578, 0.549, 0.620, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "GdBggCpJf1oQJpVZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is {2020 Q2}'s bar[0.262, 0.193, 0.304, 0.449] taller than {2020 Q1}'s bar[0.157, 0.275, 0.199, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "gUdQFPUzAsVW26bc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of 2020's Q1 and Q4, is the height of the bar in the second subplot greater than the one in first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "dKhU6oVxYHqCwlVf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the sales trend of {product X} generally increase or decrease from the beginning of 2020 to the end of 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "ntFEhd6i9eVRcM6V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data can you see in the region [0.118, 0.037, 0.975, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the first subplot, illustrating the sales trend of product X from 2020 Q1 to 2021 Q4. It has 8 bars, with varying heights indicating different sales volumes in each quarter. This region also includes a y-axis labeled as 'Sales' and an x-axis representing time periods."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "7Qdu4RDURj2eUNU0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described within the coordinates [0.375, 0.508, 0.718, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It describes the subtitle \"Sales trend for product Y\" for the second subplot."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "0qwaQESpetYWzk2D",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main features within the coordinate range [0.118, 0.530, 0.975, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second subplot showing the sales trend for product Y, including 8 bars varying in height, each representing the sales of product Y from 2020 Q1 to 2021 Q4.  The region covered also contains labels for time on the X-axis and 'Sales' on the Y-axis."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "3KIvhaU6RoGKlOIR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked in the area [0.262, 0.193, 0.304, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the bar representing sales of Product X in the second quarter of 2020."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "aFCn8dN5bimJtmCp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing \"Network Security\" in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "JIZT6wfHGEdaLlFu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar with coordinates [0.439, 0.515, 0.510, 0.697] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"Awareness Programme\" implemented with \"Our\" method."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "int6Q1kBfDId0aNb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.707, 0.278, 0.957, 0.308] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the label \"Awareness Programme\" in the chart's legend."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "k5MTSx3FUeXNaXQu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher: {the bar representing \"Tools Changed\"}[0.510, 0.551, 0.581, 0.697], or {the bar representing \"Network Security\"}[0.652, 0.697, 0.724, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Network Security\" is higher."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "olHUmyThu1RDfQ8i",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing \"Tools Used\"}[0.225, 0.114, 0.296, 0.697], [0.581, 0.260, 0.652, 0.697] taller than {the bars representing \"Policy Review\"}[0.510, 0.405, 0.581, 0.697], [0.866, 0.369, 0.937, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "Ly9q587CpFvhLWut",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the \"Risk-based\" \"Network Security\" bar}[0.652, 0.697, 0.724, 0.842] to {the \"Ours\" \"Physical Security\" bar}[0.368, 0.587, 0.439, 0.697], which bar is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Risk-based\" \"Network Security\" bar."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "I3B1M2PQxlGSWCw3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content enclosed by [0.072, 0.103, 0.100, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis label of '80'."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "JmB3vTgm7ooJyTa0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.638, 0.092, 0.966, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the legend, which contains labels and colors for the bars grouped by the different parameters evaluated: 'Tools Changed', 'Tools Used', 'Network Security', 'Physical Security', 'Awareness Programme', and 'Policy Review'."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "dlRzxd52OJkVmZ1r",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about region [0.154, 0.587, 0.225, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the \"Our\" method's \"Tools Changed\" bar indicating a 15% rate of gain change."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "tffWg7H85ZSKo948",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled on the y-axis of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Billion Tons, as indicated by the y-axis label located at {the y-axis label}<r>[0.040, 0.114, 0.063, 0.191]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "SR4YsnRiuJsHLru9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation for \"CO2 Emissions\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue, located at {the CO2 Emissions color handle}<r>[0.195, -0.016, 0.241, -0.006]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "PdzsqcRJ2s52Oau3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label for the handle at the coordinates [0.195, 0.005, 0.241, 0.014]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mean Temperature, as indicated by {the text label of Mean Temperature}<r>[0.588, 0.003, 0.805, 0.017]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "DA5sXoNk3u385Nxb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2005 in CO2 Emissions}<r>[0.448, 0.038, 0.478, 0.287]</r> taller than {the bar for 2006 in CO2 Emissions}<r>[0.524, 0.031, 0.554, 0.287]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "aa60kPBZOEGe7YVv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher y-axis value, {the bar representing 2002 in Mean Temperature}<r>[0.246, 0.712, 0.275, 0.942]</r> or {the bar representing 2009 in Mean Temperature}<r>[0.759, 0.694, 0.789, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2009."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "lKZb33nr4A6EmIxA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the y-axis numbers}[0.072, 0.016, 0.087, 0.030] in {subplot 0}<r>[0.103, 0.018, 0.975, 0.287]</r> or {subplot 2}<r>[0.103, 0.673, 0.975, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot 2."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "Qm64beQrzCYTMWew",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.103, 0.018, 0.975, 0.287]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first subplot which includes bars representing CO2 Emissions across various years. It includes a background grid, x-axis labels for the years, y-axis labels for billion tons, and individual bars with coordinates like {2001 CO2 bar}<r>[0.143, 0.063, 0.173, 0.287]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "eNUWL13bVjLKFeFQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the bounding box ranging from [0.103, 0.673, 0.975, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the third subplot, including combined bars for CO2 Emissions and Mean Temperature across years. Each year, such as 2001 to 2011, has two adjacent bars, for instance, {2001 CO2 bar}<r>[0.143, 0.824, 0.172, 0.942]</r> and {2001 Temp bar}<r>[0.172, 0.697, 0.202, 0.942]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "Tn999XivNTgnR9Zr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible within the range [0.103, 0.345, 0.975, 0.614]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This second subplot area depicts bars indicating Mean Temperature over the years. It also has a grid, x-axis labeled by years, y-axis marked in degrees Celsius, and colorful bars representing the data points like {2005 Temp bar}<r>[0.466, 0.693, 0.495, 0.942]</r>."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "5XGUTTqvuZXbfn48",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.143, 0.197, 0.202, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing {Persistent Growth}<r>[0.189, 0.027, 0.394, 0.041]</r> for the year 2008."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "d9234lJjywdhuiiQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.877, 0.685, 0.935, 0.942] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar indicating {Substantial Increase}<r>[0.189, 0.684, 0.424, 0.698]</r> for the year 2010."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "SWWIpez3lbSlVKfV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at [0.583, 0.951, 0.642, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the year 2016 on {the x-axis}<r>[0.515, 0.971, 0.564, 0.985]</r>."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "2LJYRj8b6mPUzoZ6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Erratic Increase\" in 2008}<r>[0.143, 0.799, 0.202, 0.942]</r> taller than {the bar for \"Marginal Improvement\" in 2008}<r>[0.143, 0.491, 0.202, 0.613]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "xoRZvlKbBdnhdEU2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue bar for \"Persistent Growth\" in 2008}<r>[0.143, 0.197, 0.202, 0.285]</r> have a higher value than {the blue bar for \"Erratic Increase\" in 2008}<r>[0.143, 0.799, 0.202, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "pCu7EZRozbUkPbph",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values represented by {the red bars in 2010}<r>[0.877, 0.028, 0.935, 0.285], [0.877, 0.685, 0.935, 0.942]</r> greater than those represented by {the green and yellow bars in the same year}<r>[0.877, 0.356, 0.935, 0.613], [0.877, 0.480, 0.935, 0.613]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "5R8XZYVwvMr8FWjV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.025, 0.086, 0.087, 0.125]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of {the y-axis}<r>[0.025, 0.086, 0.048, 0.214]</r> with labels such as \"20\" {y-tick label}<r>[0.058, 0.111, 0.087, 0.125]</r>."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "mVZfDAA2HcNBts9L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.103, 0.915, 0.935, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows all {x-axis labels}<r>[0.515, 0.643, 0.564, 0.657]</r> from 2008 to 2022, like: \"2008\" {x-tick label}<r>[0.143, 0.951, 0.201, 0.965]</r>, \"2010\" {x-tick label}<r>[0.877, 0.951, 0.935, 0.965]</r>."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "ZbNVAjrN2F8YZant",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is found between the coordinates [0.100, 0.020, 0.500, 0.100]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you find the {subplot title}<r>[0.539, 0.007, 0.539, 0.007]</r> and part of {the legend}<r>[0.115, 0.022, 0.403, 0.068]</r>, which names growth types like \"Persistent Growth\" {legend label}<r>[0.189, 0.027, 0.394, 0.041]</r>."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "ValUJXqQCySB7ixe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the label \"Technological Breakthrough\"}<r>[0.181, -0.018, 0.503, -0.004]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue<r>[0.116, -0.016, 0.163, -0.006]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "3NMIR71vgyaiMMVE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the color associated with {the first bar's indicator}<r>[0.161, 0.434, 0.214, 0.446]</r> on the bottom chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lavender<r>[0.549, -0.016, 0.595, -0.006]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "AO6cxt0sgDJuxpEm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can we find {the y-axis label for the top chart}<r>[0.030, 0.166, 0.053, 0.295]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rate of Growth[%]<r>[0.030, 0.166, 0.053, 0.295]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "bdIyYvjjWSXHTF6b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2011 bar of \"Technological Breakthrough\"}<r>[0.358, 0.356, 0.411, 0.446]</r> higher than {the 2015 bar of \"Innovation Introduction\"}<r>[0.621, 0.726, 0.674, 0.823]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "FptJZVv4eZ71wFC7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat comes first on the x-axis, {the tick labeled \"2012\"}<r>[0.355, 0.456, 0.414, 0.470]</r> or {the tick labeled \"2019\"}<r>[0.815, 0.456, 0.874, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012<r>[0.355, 0.456, 0.414, 0.470]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "YK9qTVQgBBGbPyhK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the one for \"2014\" under \"Technological Breakthrough\"}<r>[0.555, 0.241, 0.608, 0.446]</r> or {the one for \"2014\" under \"Innovation Introduction\"}<r>[0.490, 0.726, 0.542, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technological Breakthrough<r>[0.555, 0.241, 0.608, 0.446]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "cs7btHZBid2qebGs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region marked by {the top right corner of the top chart}<r>[0.123, 0.015, 0.975, 0.446]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a series of blue bars for each year from 2008 to 2019, representing the \"Technological Breakthrough\" rate of growth, with a gradient from light for lower values to dark for the highest value."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "5zz1FN7ka4OyVPIK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nNarrate what you see in {the area containing the y-axis labels for negative growth rates}<r>[0.025, 0.662, 0.048, 0.790]</r> of the bottom chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are labels for negative growth rates ranging from 0 to -30, alongside corresponding bars for \"Innovation Introduction,\" which are colored in shades of lavender indicating negative values."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "xkBY2AhFlLXP6Euu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area including {the y-axis label}<r>[0.030, 0.166, 0.053, 0.295]</r> and {the bar for the year 2015 under \"Technological Breakthrough\"}<r>[0.555, 0.241, 0.608, 0.446]</r> show?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area features the label \"Rate of Growth[%]\" on the y-axis and a blue bar indicating the value for the year 2015 for \"Technological Breakthrough,\" which extends upwards to show a growth rate of 50%."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "aIAOOoScgpkkjFXi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat phase does the x-tick at [0.331, 0.789, 0.506, 0.903] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"Development\" phase."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "R8ni2EswelW5zVZI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label color of 'R&D Spend' from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is orange found at {legend}<r>[0.083, 0.120, 0.111, 0.136]</r>."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "G2MAsfAf7p9660cq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label with the coordinates [0.015, 0.427, 0.029, 0.538]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the y-axis at these coordinates is \"Measures\"."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "p6CxHo4BIdBvoB9F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat series does the bar with these coordinates [0.768, 0.102, 0.943, 0.492] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the series 'Market Response' in the 'Evaluation' phase."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "zDlDUt5YtIT03Rrr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for the series 'User Acceptance'}<[0.112, 0.679, 0.287, 0.771], [0.331, 0.469, 0.506, 0.663], [0.550, 0.374, 0.725, 0.613], [0.768, 0.492, 0.943, 0.606]> generally higher than {the bars for the series 'Market Response'}<[0.112, 0.565, 0.287, 0.679], [0.331, 0.321, 0.506, 0.469], [0.550, 0.168, 0.725, 0.374], [0.768, 0.102, 0.943, 0.492]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "OiWUvvAV664Bd37w",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Lightbulb Moment'}<r>[0.112, 0.846, 0.287, 0.903]</r> in 'Conceptualization' and {the 'R&D Spend'}<r>[0.112, 0.771, 0.287, 0.846]</r> bars in the same phase, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Lightbulb Moment' bar is taller."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "EXGXQKwYN4SFMEpm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the four measures {Lightbulb Moment}<r>[0.112, 0.846, 0.287, 0.903]</r>, {R&D Spend}<r>[0.331, 0.789, 0.506, 0.903]</r>, {User Acceptance}<r>[0.550, 0.711, 0.725, 0.903]</r> and {Market Response}<r>[0.768, 0.857, 0.943, 0.903]</r> during the 'Conceptualization' phase, which one has the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Market Response' has the highest value."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "E9E5aRl9FrOcS8Og",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for 'User Acceptance'<[0.331, 0.469, 0.506, 0.663]> have a higher or lower value than the 'Market Response' bar<[0.331, 0.321, 0.506, 0.469]> in the 'Development' phase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "DCExYrd300WONa3G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.331, 0.321, 0.506, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars:\n1. The 'R&D Spend' bar with coordinates {Bar}<r>[0.331, 0.663, 0.506, 0.789]</r> and the encoded value is '55'.\n2. The 'User Acceptance' bar with coordinates {Bar}<r>[0.331, 0.469, 0.506, 0.663]</r> and the encoded value is '85'."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "BD3nGbqGf7FeDgiU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe area [0.078, 0.074, 0.254, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend with the following items: \n1. 'Lightbulb Moment' with color-coordinate {Colorbox}<r>[0.083, 0.085, 0.111, 0.101]</r> and label-coordinate {label}<r>[0.122, 0.083, 0.248, 0.106]</r>\n2. 'R&D Spend' with color-coordinate {Colorbox}<r>[0.083, 0.120, 0.111, 0.136]</r> and label-coordinate {label}<r>[0.122, 0.118, 0.201, 0.141]</r>\n3. 'User Acceptance' with color-coordinate {Colorbox}<r>[0.083, 0.155, 0.111, 0.171]</r> and label-coordinate {label}<r>[0.122, 0.153, 0.239, 0.176]</r>\n4. 'Market Response' with color-coordinate {Colorbox}<r>[0.083, 0.190, 0.111, 0.206]</r> and label-coordinate {Label}<r>[0.122, 0.188, 0.241, 0.211]</r>"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "oy6xzzkGVVsv3i8f",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in [0.668, 0.050, 0.868, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains:\n1. X-tick labels: 'Implementation' located at {X-tick label}<r>[0.582, 0.919, 0.692, 0.942]</r> and 'Evaluation' at {X-tick label}<r>[0.819, 0.919, 0.893, 0.942]</r>;\n2. Two groups of Stacked Bars: for 'Implementation' we have 'Lightbulb Moment', 'R&D Spend', 'User Acceptance' and 'Market Response' at {Bars}<r>[0.550, 0.168, 0.725, 0.903]</r>; for 'Evaluation' we have 'Lightbulb Moment', 'R&D Spend', 'User Acceptance' and 'Market Response' at {Bars}<r>[0.768, 0.102, 0.943, 0.903]</r>."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "SwA8qtDUgl8mnXYU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.768, 0.857, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space contains a blue bar from the 'Lightbulb Moment' series. The range of the y-value is '0.00 -> 20.00' and the x-label is 'Evaluation'. The data label located at [0.847, 0.868, 0.865, 0.892] on the bar reads '20'."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "WYtPummdHRA2dtsP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the given coordinates [0.346, 0.083, 0.679, 0.113] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the title of the chart, \"Bar Chart Showing Food Security Trends\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "CytNkSmqtnJqGd6W",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of bar does the bounding box at [0.618, 0.385, 0.770, 0.903] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to a bar indicating the usage of \"Mathematical Modelling\" in {Deep Learning}<r>[0.618, 0.385, 0.770, 0.903]</r>."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "0bRbYBnNWI2d4yrc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith coordinates [0.253, 0.919, 0.456, 0.942], what is written on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Natural Language Processing\" is written there."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "OKj8teXREMltClj1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is signified by the text within [0.850, 0.083, 0.951, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the description of the dark blue color in the legend, labeled as {Deep Learning}<r>[0.850, 0.083, 0.951, 0.106]</r>."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "KgTNTPuZ5V9E9NW8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Deep Learning\" bar for \"Computer Vision\"}<r>[0.112, 0.646, 0.251, 0.903]</r> taller than {the \"Deep Learning\" bar for \"Natural Language Processing\"}<r>[0.285, 0.737, 0.424, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "JXBTVpYjLWNFLJO1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the \"Machine Learning\" bar for \"Statistical Analysis\"}<r>[0.459, 0.665, 0.597, 0.805]</r> and {the \"Machine Learning\" bar for \"Mathematical Modelling\"}<r>[0.632, 0.102, 0.770, 0.385]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Mathematical Modelling\" is higher."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "rFaUavpMXze9OUOF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the \"Deep Learning\" bar for \"Anomaly Detection\"}<r>[0.805, 0.748, 0.943, 0.903]</r> and {the \"Machine Learning\" bar for \"Anomaly Detection\"}<r>[0.805, 0.514, 0.943, 0.748]</r>, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Deep Learning\" bar is taller."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "1hrEveaMqvQ7z3Nx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Deep Learning\" bar for \"Statistical Analysis\"}<r>[0.459, 0.805, 0.597, 0.903]</r> shorter than {the \"Machine Learning\" bar for \"Statistical Analysis\"}<r>[0.459, 0.665, 0.597, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "84C2Rg1GYJ2evvLH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area delineated by the coordinates [0.805, 0.074, 0.978, 0.151] convey?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the chart's legend. It encompasses two entries: the first is a blue color label located at [0.811, 0.085, 0.839, 0.101] representing {Deep Learning}<r>[0.850, 0.083, 0.951, 0.106]</r>, and the second one is a light blue color label located at [0.811, 0.120, 0.839, 0.136] representing {Machine Learning}<r>[0.850, 0.118, 0.972, 0.141]</r>."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "3OODAV3Ft760abCN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.112, 0.646, 0.251, 0.646]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area incorporates a blue bar on the chart signifying the usage of the \"Computer Vision\" application in {Deep Learning}<r>[0.112, 0.646, 0.251, 0.646]</r>. It also possesses a text label showing the usage number \"68\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "Q0VxkH6E9mfrQemm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.459, 0.665, 0.597, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a light blue bar illustrating the usage of \"Statistical Analysis\" in {Machine Learning}<r>[0.459, 0.665, 0.597, 0.805]</r>. A textual label inside the bar shows the number \"37\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "VPLZGfDkGH8K1yil",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBriefly describe the area [0.632, 0.102, 0.770, 0.385]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a light blue bar that corresponds to the usage of \"Mathematical Modelling\" in {Machine Learning}<r>[0.632, 0.102, 0.770, 0.385]</r>. Inside this bar, there's a text label presenting the number \"75\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "sShR4bt8No8NTElv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described at coordinates [0.015, 0.445, 0.029, 0.520]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, named Values."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "Y2fN6fgVmwpDAKBO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the category \"Civil Rights\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.817, 0.919, 0.895, 0.942]."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "avJDuQFcDOUBdzGI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the area [0.122, 0.118, 0.224, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Reinforcement\" from the legend."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "mUfPvKRfh8IOAvMV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle in the legend show at [0.083, 0.120, 0.111, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows a color, specifically '#fb6a4a'."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "rN6bNz9zmfVQCm2V",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Social Inequality\" for \"Routine Monitoring\"}[0.550, 0.770, 0.725, 0.903] higher than {the bar of \"Social Inequality\" for \"Reinforcement\"}[0.550, 0.569, 0.725, 0.770]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "qvjT2ZHTKYEW4EUx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Civil Rights\" for \"Reinforcement\"}[0.768, 0.102, 0.943, 0.517] taller than {the bar of \"Civil Rights\" for \"Routine Monitoring\"}[0.768, 0.517, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "1yCfjSVR0Npbf4JU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the vertical axis, is the label for \"80\"[0.043, 0.433, 0.061, 0.456] above the label for \"100\"[0.035, 0.318, 0.061, 0.341]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "mDzkQKj5BQzSAOVF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Economic Inequality\" in \"Reinforcement\"}[0.331, 0.471, 0.506, 0.644] broader than {the bar for \"Economic Inequality\" in \"Routine Monitoring\"}[0.331, 0.644, 0.506, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "TsBAy1GRl8QAoHZC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the space [0.078, 0.074, 0.259, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the bar chart. The first entry is {Routine Monitoring}[0.122, 0.083, 0.254, 0.106] with color handle {#7f0000}[0.083, 0.085, 0.111, 0.101] and the second entry is {Reinforcement}[0.122, 0.118, 0.224, 0.141] with color handle {#fb6a4a}[0.083, 0.120, 0.111, 0.136]."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "8CtJgepQUgVKdPsk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.112, 0.701, 0.287, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region references a bar in the bar chart for the category \"Right Protection\" in the legend entry \"Routine Monitoring\" with a value of 35."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "nxpcNx5ubJBgqy2P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the space [0.071, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the main plot of the chart and contains all the bars and axis labels. The bars indicate two categories \u201cRoutine Monitoring\u201d and \u201cReinforcement\u201d across the categories \"Right Protection\", \"Economic Inequality\", \"Social Inequality\", and \"Civil Rights.\""
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "8JevCAT24bm47LtF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.279, 0.025, 0.776, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which states \"Stacked Bar Chart of Social Inequality and Rights Protection\"."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "SNYkearOyHAjuiDP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content represented by the coordinates [0.895, 0.118, 0.972, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text label for \"Risk-taking\" in the legend."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "JRPh02LhILVN3Cny",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.459, 0.445, 0.597, 0.903] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The \"Motivation\" bar for \"Exploit\"}<r>[0.459, 0.445, 0.597, 0.903]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "dQAO8LN6E8u1DAiP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object with coordinates [0.490, 0.952, 0.565, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The xlabel, labeled as \"Categories\"."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "qr0oLeVLQwgynK1e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can I find in the coordinates [0.805, 0.274, 0.943, 0.388]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The \"Risk-taking\" bar for \"Exceed\"}<r>[0.805, 0.274, 0.943, 0.388]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "vk1ag2kGgUZbfrcK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the motivation bar for Expand}[0.632, 0.503, 0.770, 0.903] higher or lower than {the risk-taking bar for Expand}[0.632, 0.274, 0.770, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "MiRqWeC7zfVdes74",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the motivation bar for Experiment}[0.285, 0.617, 0.424, 0.903] to the left or right of {the risk-taking bar for Experiment}[0.285, 0.331, 0.424, 0.617]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are in the same x position."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "qoKcUGlOmdZlx5Pr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Motivation bars}[[0.112, 0.731, 0.251, 0.903], [0.285, 0.617, 0.424, 0.903], [0.459, 0.445, 0.597, 0.903], [0.632, 0.503, 0.770, 0.903], [0.805, 0.388, 0.943, 0.903]] generally higher than {the Risk-taking bars}[[0.112, 0.331, 0.251, 0.731], [0.285, 0.331, 0.424, 0.617], [0.459, 0.102, 0.597, 0.445], [0.632, 0.274, 0.770, 0.503],  [0.805, 0.274, 0.943, 0.388]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "qBnEtEW928HPqMKS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {bars of \"Motivation\"}<r>[0.112, 0.731, 0.251, 0.903], [0.285, 0.617, 0.424, 0.903], [0.459, 0.445, 0.597, 0.903], [0.632, 0.503, 0.770, 0.903], [0.805, 0.388, 0.943, 0.903]</r> taller or shorter than {bars of \"Risk-taking\"}<r>[0.112, 0.331, 0.251, 0.731], [0.285, 0.331, 0.424, 0.617], [0.459, 0.102, 0.597, 0.445], [0.632, 0.274, 0.770, 0.503], [0.805, 0.274, 0.943, 0.388]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "1XS6ogVrGNPCdv2r",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within [0.145, 0.919, 0.218, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a category label, \"Innovation\", situated in this area."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "ZI2KdbbL5YvqwsXS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.459, 0.445, 0.597, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a section of the \"Motivation\" bar and represents the value of the \"Exploit\" category, which is 80."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "Gwkakhtoig9kQQs2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.632, 0.503, 0.770, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the \"Motivation\" bar for the \"Expand\" category, valued at 70."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "MOrT4Mpc7QTqtAh5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.112, 0.331, 0.251, 0.731]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the \"Risk-taking\" bar under the category \"Innovation\", with a value of 70."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "g9YMXMWw3xAl5Fi5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the text at coordinates [0.302, 0.025, 0.752, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, \"Stacked Bar Chart of Creativity and Design Innovation\"."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "mywfHQMaaZdoIr80",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item {the legend item} <r>[0.817, 0.085, 0.844, 0.101]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "'#097138' (Green)."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "c6M05Qq5jsxHQRlU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the bar at {the bar} <r>[0.767, 0.254, 0.942, 0.600]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cross-Disciplinary Collaboration."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "u6Rw5sQJuti11LtA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label say at {the y-axis label} <r>[0.015, 0.445, 0.029, 0.520]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Values."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "rd8LAOUH7PL3iVhy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Design Thinking Process}<r>[0.331, 0.189, 0.505, 0.557]</r> higher on the chart than {Brainstorming Use}<r>[0.112, 0.340, 0.287, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "j1xSoUrN8HGToF73",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing {Prototyping and Simulation}<r>[0.549, 0.102, 0.724, 0.513]</r> have a higher value than that of {Design Thinking Process}<r>[0.331, 0.557, 0.505, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "VNr1g8JYSfbCoiVe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is wider, {Design Thinking Process}<r>[0.331, 0.557, 0.505, 0.903]</r> or {Cross-Disciplinary Collaboration}<r>[0.767, 0.254, 0.942, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "EOCZXFPFR3psqOPG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the legend item {Creativity Levels}<r>[0.855, 0.118, 0.971, 0.141]</r> located below the legend item {Design Quality}<r>[0.855, 0.083, 0.957, 0.106]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "NhuLguQneviY1UvV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visual contents within {the specified area}<r>[0.112, 0.340, 0.287, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Creativity Levels bar representing Brainstorming Use\"}<r>[0.112, 0.340, 0.287, 0.600]</r> and the corresponding value 60 are displayed in this area."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "3hQr8ug08ZBZHToQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are included within the coordinates {the specified coordinates}<r>[0.767, 0.254, 0.942, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Cross-Disciplinary Collaboration Creativity Levels bar\"}<r>[0.767, 0.254, 0.942, 0.600]</r> and the corresponding value 80 are located in this area."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "njvfI5qOsR7Qxwl3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information enclosed by the bounding box {the bounding box}<r>[0.811, 0.074, 0.977, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. It includes two items: {\"Design Quality\"}<r>[0.855, 0.083, 0.957, 0.106]</r> in color '#097138' and {\"Creativity Levels\"}<r>[0.855, 0.118, 0.971, 0.141]</r> in color '#74c476'."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "AAdb1kxJelVrKaYd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area {the specified area}<r>[0.112, 0.340, 0.942, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encloses all the bars in the chart, including those for Brainstorming Use, Design Thinking Process, Prototyping and Simulation, and Cross-Disciplinary Collaboration, with their respective values visually indicated."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "ZWrG2M9XfX8xtNJD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.550, 0.569, 0.725, 0.903] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing \"Urbanization level\" category for \"Human Life\" data."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "YfgHaevEqKLv6Vmi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the place of the legend of \"Living Environment\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of \"Living Environment\" is located at [0.122, 0.118, 0.256, 0.141]."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "SuBQcel7X9xWEjzy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the value indicated by [0.628, 0.458, 0.646, 0.481] mean in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a value of 45 in \"Urbanization level\" category for \"Living Environment\" data."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "r2Fy9zs7BQOvErZf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the items in the area [0.078, 0.074, 0.262, 0.151], what do you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see two legend entries: {Human Life}<[0.122, 0.083, 0.202, 0.106]> and {Living Environment}<[0.122, 0.118, 0.256, 0.141]>."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "vyLk7m9cTbB0d2Ra",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the bar of \"Urbanization level\" for Human Life}[0.550, 0.569, 0.725, 0.903] with {the bar of \"Urbanization level\" for Living Environment}[0.550, 0.369, 0.725, 0.569], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one for \"Human Life\"."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "dBT67UbzYiVzdNAM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the bars of \"Population Growth\"}[0.112, 0.680, 0.287, 0.903] and [0.112, 0.391, 0.287, 0.680], which data set they belong to has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human Life."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "4lWLkHb4mDEAlEYW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Green Areas\" under \"Living Environment\"}[0.331, 0.525, 0.506, 0.769] taller than {the bar of \"Urbanization level\" under \"Living Environment\"} [0.550, 0.369, 0.725, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "v8Z70ep26inbwh1u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Population Growth\"}[0.136, 0.919, 0.264, 0.942] and {\"Carbon Footprint\"}[0.797, 0.919, 0.915, 0.942], which category's bars under \"Living Environment\" {[0.112, 0.391, 0.287, 0.680], [0.768, 0.102, 0.943, 0.480]} have a higher total?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Footprint."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "qL8HvLtTUykKTpB1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the information in [0.078, 0.074, 0.262, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend for two categories: {Human Life}<[0.122, 0.083, 0.202, 0.106]>, depicted in dark blue, and {Living Environment}<[0.122, 0.118, 0.256, 0.141]>, depicted in bright blue."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "nj3LmpjFikH5wEXD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's included in the region from [0.331, 0.369, 0.725, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes two stacked bars in the category \"Urbanization level\", where the bottom {bar}<[0.550, 0.369, 0.725, 0.569]> represents \"Living Environment\" with a value of 45, and the top {bar}<[0.550, 0.569, 0.725, 0.903]> represents \"Human Life\" with a value of 75."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "HAlEDZ7J2gUzYgvn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.112, 0.391, 0.287, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the \"Population Growth\" category, the area contains two overlapping bars, the {upper bar}<[0.112, 0.680, 0.287, 0.903]> represents \"Human Life\" with a value of 50 and the {lower bar}<[0.112, 0.391, 0.287, 0.680]> represents \"Living Environment\" with a value of 65."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "DMBTbGNj2C2H5aoi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.768, 0.102, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two columns of a stacked bar in the category \"Carbon Footprint\". The {lower bar}<[0.768, 0.102, 0.943, 0.480]> represents the \"Living Environment\" data with a value of 85, and the {upper bar}<[0.768, 0.480, 0.943, 0.903]> represents the \"Human Life\" data with a value of 95."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "RT4xlec0cbS8m603",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the graph legend located at [0.857, 0.074, 0.978, 0.151] showing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend lists two categories: \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> marked in dark blue and \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> marked in light purple."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "KdgbIe3QF47PngX7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.331, 0.342, 0.506, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an Expansion bar for the Healthcare category."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "dybHv1at82PxKEdR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information contained in the coordinates [0.381, 0.919, 0.456, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-tick, labelled \"{Healthcare}\"<r>[0.381, 0.919, 0.456, 0.942]</r>"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "BZMkTWni8iUSil6j",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the area [0.112, 0.422, 0.287, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> category in \"{Schooling}\"<r>[0.166, 0.919, 0.233, 0.942]</r> with a value of 60."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "XRtui310JpkD2wNK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> in \"{Schooling}\"<r>[0.166, 0.919, 0.233, 0.942]</r> taller or shorter than the bar for \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> in \"{Schooling}\"<r>[0.166, 0.919, 0.233, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "HgBDp4yDEwp1iOSE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> bar larger in \"Housing\" or \"Healthcare\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Housing\"."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "bXFaJIjw3xzv2vuk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> bars is the shortest?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"{Housing}\"<r>[0.828, 0.919, 0.884, 0.942]</r>"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "W5KlWxMUvN2bMg7Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the size of \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> and \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> in \"{Food Stamps}\"<r>[0.592, 0.919, 0.682, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "They are equal."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "283FDMSrrnSqO395",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the bars in the region [0.550, 0.102, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there are two stacked bars indicating the categories \"Expansion\" and \"Decline\" for \"Food Stamps\" and \"Housing\". The blue bar representing \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> in \"{Food Stamps}\"<r>[0.592, 0.919, 0.682, 0.942]</r> is [0.550, 0.503, 0.725, 0.903] with a value of 50, and the purple bar representing \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> in \"{Food Stamps}\"<r>[0.592, 0.919, 0.682, 0.942]</r> is [0.550, 0.102, 0.725, 0.503]. The blue bar for \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> in \"{Housing}\"<r>[0.828, 0.919, 0.884, 0.942]</r> is [0.768, 0.583, 0.943, 0.903] with a value of 40, and the purple bar for \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> in \"{Housing}\"<r>[0.828, 0.919, 0.884, 0.942]</r> is [0.768, 0.102, 0.943, 0.583]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "Je7QCRlNKisKEMlA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.857, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. \"{Expansion}\"<r>[0.901, 0.083, 0.972, 0.106]</r> is represented with dark blue color and \"{Decline}\"<r>[0.901, 0.118, 0.953, 0.141]</r> is represented with light purple color."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "rTcbRJHbhrsi09QP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects in the region [0.033, 0.593, 0.065, 0.760] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are y-axis tick labels representing values: \"80\" at [0.043, 0.253, 0.061, 0.277] , \"60\" at [0.043, 0.413, 0.061, 0.437], and \"40\" at [0.043, 0.573, 0.061, 0.597]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "M1ZysJZwyl7XRTs1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.381, 0.025, 0.675, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart: \"{Stacked Bar Chart of Social Welfare}\"<r>[0.381, 0.025, 0.675, 0.055]</r>."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "dKo0RRKXsjG3U8EB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.510, 0.952, 0.546, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label titled \"Years\"."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "iq6dXIm4cUTOUaeA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the color representation for \"Google\" is at [0.083, 0.155, 0.111, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "uxcGcAhMj5MqKN87",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.161, 0.837, 0.178, 0.860] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value '15' for {Apple's bar}<r>[0.112, 0.795, 0.227, 0.903]</r> in the year 2015."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "BVIDv446L0eFNGnf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Apple\"}[0.112, 0.795, 0.227, 0.903] taller than {the bar representing \"Microsoft\"}[0.112, 0.470, 0.227, 0.506]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "Axm8t3KBYwNo5BZA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the y-axis labels}[0.043, 0.605, 0.061, 0.629] located to the left or right of {the main chart area}[0.071, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "euApDMO8ygGESqhO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is shorter, {the red bar for Samsung in 2018}[0.542, 0.427, 0.657, 0.730] or {the blue bar for Google in 2019}[0.685, 0.283, 0.800, 0.463]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Samsung."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "Vf82Oii1y2Gkzbsd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.112, 0.470, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains multiple bars representing market share percentages for different companies from 2015 to 2020, each labeled with a year and specific value. Corner labels for the area are {first Apple bar}<r>[0.112, 0.795, 0.227, 0.903]</r> for '2015' and {last Others bar}<r>[0.829, 0.182, 0.943, 0.232]</r> for '2020'."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "HyqNNpBpPvrcqMnR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area bounding [0.071, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area includes axes, bars grouped by years, and grid lines. Specifically, it includes {y-axis}<r>[0.015, 0.391, 0.029, 0.574]</r> and {Samsung's bar in 2020}<r>[0.829, 0.448, 0.943, 0.759]</r>, along with other companies' data."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "341wQE88jODL7iTV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the objects within [0.078, 0.074, 0.192, 0.255]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's legend area including color labels and text for different companies such as {Apple's legend item}<r>[0.083, 0.085, 0.111, 0.101]</r> and {Others' legend item}<r>[0.083, 0.225, 0.111, 0.241]</r>, describing their represented color and company name."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "9EGgjOPJrXEJjZwG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the figure near [0.015, 0.448, 0.029, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Count."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "g9HqijHKy3ibir53",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text shown in the legend located at this coordinate [0.131, 0.083, 0.249, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Papers Published."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "U7hfC6Glw0tRhZZF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat attribute represented by this bar covers the range [0.771, 0.833, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The research field called \"Robotics Innovation\" in the category of \"Papers Published\"."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "sHPcnfdvmHmiv4aH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value can be read off at coordinates [0.194, 0.868, 0.220, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "400."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "wLoQkZkLtXZhXAOs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar represent {Physics Research in \"Papers Published\"}[0.121, 0.856, 0.294, 0.903] below or above {Physics Research in \"Citations\"}[0.121, 0.276, 0.294, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "hSgFSrQnxzqMcJhC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the two bars at Robotics Innovation, is the bar for {\u201cPapers Published\u201d}[0.771, 0.833, 0.944, 0.903] taller or shorter than the bar for {\u201cCitations\u201d}[0.771, 0.102, 0.944, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "ClEDMljOr104kG5t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Papers Published\" and \"Citations\", which one has a higher value at {Chemistry Research}[0.337, 0.851, 0.511, 0.903; 0.337, 0.363, 0.511, 0.851]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Citations."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "JpPJ8SQDtD4Oszo9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field has more citations {Physics Research}[0.121, 0.276, 0.294, 0.856] or {Robotics Innovation}[0.771, 0.102, 0.944, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Robotics Innovation."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "mMJRSEWOmNCimrNx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.086, 0.074, 0.254, 0.151] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, including color labels and text labels for \"Papers Published\" and \"Citations\", specifically:\n   1. \"Papers Published\" with a color label at [0.092, 0.085, 0.120, 0.101] and a textual label at [0.131, 0.083, 0.249, 0.106];\n   2. \"Citations\" with a color label at [0.092, 0.120, 0.120, 0.136] and a textual label at [0.131, 0.118, 0.192, 0.141]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "BENJo7cxjMK1XBkb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow about the content at [0.771, 0.833, 0.944, 0.903; 0.771, 0.102, 0.944, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is showing data for \"Robotics Innovation\". There are two stacked bars:\n   1. \"Papers Published\" with a bar at [0.771, 0.833, 0.944, 0.903] showing 600 papers;\n   2. \"Citations\" with a bar at [0.771, 0.102, 0.944, 0.833] showing 6300 citations."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "8eYSfh5DNpFVm2ke",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.554, 0.845, 0.727, 0.903; 0.554, 0.195, 0.727, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains 2 stacked bars for \"Biology Research\":\n   1. \"Papers Published\" with a bar located at [0.554, 0.845, 0.727, 0.903] indicating 500 papers;\n   2. \"Citations\" with a bar located at [0.554, 0.195, 0.727, 0.845] indicating 5600 citations."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "eokRvbiuh8Frh6UE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what's found in the range of [0.771, 0.833, 0.944, 0.903; 0.771, 0.102, 0.944, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there are 2 bars presenting data for the \"Robotics Innovation\" field:\n   1. A bar showing \"Papers Published\" is located at [0.771, 0.833, 0.944, 0.903] with a value of 600;\n   2. Another bar showing \"Citations\" is located at [0.771, 0.102, 0.944, 0.833] with a value of 6300."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "HXNCyYuo4d7TrcGm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object labeled \"Facebook\" displayed in {the legend}<r>[0.857, 0.074, 0.978, 0.220]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Light blue."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "2OlzsgwG3EGq4aKx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.112, 0.667, 0.482, 0.903] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Facebook trend during a continuous decrease period."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "ysI5Ds7yPHBr24mf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "User Count."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "1HjlTDNs0X3QtXBp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of the {Facebook bar in the Continuous Decrease trend}<r>[0.112, 0.667, 0.482, 0.903]</r> greater than the {Twitter bar in the Continuous Decrease trend}<r>[0.112, 0.455, 0.482, 0.667]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "dusQb6gyGHGzGb4t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {LinkedIn bar in the Steep Decline trend}<r>[0.574, 0.432, 0.943, 0.479]</r> represent a larger or smaller user count than that of the {Instagram bar in the Steep Decline trend}<r>[0.574, 0.479, 0.943, 0.573]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "fwYapJXdga6WIXfs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Facebook bar in the Continuous Decrease trend}<r>[0.112, 0.667, 0.482, 0.903]</r> taller than the {Facebook bar in the Steep Decline trend}<r>[0.574, 0.714, 0.943, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "M5ThdxeZCB7kB7jc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.112, 0.102, 0.482, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bars representing the data trends for Facebook, Twitter, Instagram, and LinkedIn during the Continuous Decrease time period. Their respective heights indicate user counts during this period, with Facebook's bar being the tallest."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "7d8ZztcocHyan5dl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.574, 0.432, 0.943, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bars representing the data trends for Facebook, Twitter, Instagram, and LinkedIn during the Steep Decline time period. The heights of these bars reflect their respective user counts."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "OCwghGA9Dg8Q2o40",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what you see within [0.857, 0.074, 0.978, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are four components within this range; these correspond to the legend of the chart. They are each labeled as Facebook, Twitter, Instagram, and LinkedIn, with their respective color coding."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "JLObHznplChuTBUl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text at coordinates}[0.388, 0.025, 0.667, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Socioeconomic and Policy Making."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "hJbdBRo2u7wPLcvd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the label for 'Increase in National Debt'} located in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.122, 0.083, 0.298, 0.106]"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "HgsW4vefywARIPaa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the bar for {Government Spending} under {Policy Initiatives} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar is located at [0.112, 0.220, 0.482, 0.352]."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "5e7XE5JmZqZNKSV7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the y-axis label}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.015, 0.445, 0.029, 0.520]"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "C7d1CSDx8knDljNW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Consumer Price Index' under 'Political Unrest'}[0.574, 0.483, 0.943, 0.719] taller than {the similar bar under 'Policy Initiatives'}[0.112, 0.548, 0.482, 0.732]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "RKGEM7R1GyBmnuvm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {Increase in National Debt} at [0.122, 0.083, 0.298, 0.106] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#08306b"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "p4R6rWSHHYGW0UG5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Increase in National Debt' bar} under 'Policy Initiatives' at [0.112, 0.732, 0.482, 0.903] shorter than {the 'Increase in National Debt' bar} under 'Political Unrest' at [0.574, 0.719, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "iRZkDMMtSSMe0mbx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison to {the 'Government Spending' bar under 'Policy Initiatives'}[0.112, 0.220, 0.482, 0.352], is {the 'Government Spending' bar under 'Political Unrest'}[0.574, 0.102, 0.943, 0.260] taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "LgZymjdMItb5Dk1V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the coordinates [0.112, 0.220, 0.482, 0.352]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the bar representing 'Government Spending' under 'Policy Initiatives', with a value of 50 represented by the text at [0.288, 0.274, 0.306, 0.298]."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "M1vlGnniy5iEQEmB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.574, 0.719, 0.943, 0.903] show?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the bar representing 'Increase in National Debt' under 'Political Unrest', and the value of this category is 70, represented by the text at [0.750, 0.799, 0.767, 0.823]."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "SFLLKH36Tpyj1rTd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items within the region specified by [0.078, 0.074, 0.304, 0.220]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which contains four items. Each item includes two parts, a color-label and a text-label: \n1. The first item is represented by color '#08306b' at [0.083, 0.085, 0.111, 0.101] and is labeled 'Increase in National Debt' at [0.122, 0.083, 0.298, 0.106]. \n2. The second item is represented by color '#2171b5' at [0.083, 0.120, 0.111, 0.136] and is labeled 'Consumer Price Index' at [0.122, 0.118, 0.273, 0.141]. \n3. The third item is represented by color '#6baed6' at [0.083, 0.155, 0.111, 0.171] and is labeled 'Tax Revenue' at [0.122, 0.153, 0.210, 0.176]. \n4. The last item is represented by color '#bdd7e7' at [0.083, 0.190, 0.111, 0.206] and is labeled 'Government Spending' at [0.122, 0.188, 0.278, 0.211]."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "VSBBYa4SQlXxF5km",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.574, 0.102, 0.943, 0.260]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bar for 'Government Spending' under 'Political Unrest', with a value of 60 represented by the text at [0.750, 0.169, 0.768, 0.193]."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "f9L2eEJT6RBL1Qpr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at [0.069, 0.437, 0.083, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is called {Prominence}<r>[0.069, 0.437, 0.083, 0.573]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "au99XLlDEF7cu3pQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for the second legend item in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second legend item, {Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "i1Rz7oZQSYIp9Sxm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second tick on the x-axis at [0.366, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second tick is {Deep Learning}<r>[0.315, 0.906, 0.416, 0.930]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "TH1KMkt0daLNH6M5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich concepts are indicated in the legend at [0.765, 0.132, 0.893, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The concepts indicated are {Trend 2015}<r>[0.810, 0.141, 0.887, 0.164]</r> and {Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "MExvihMmH7rAA1tz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher value for {Deep Learning}<r>[0.315, 0.906, 0.416, 0.930]</r>, {Trend 2015}<r>[0.810, 0.141, 0.887, 0.164]</r> or {Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "WE4CwXGyjXF4kBj8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar representing \"Neural Networks\"}<r>[0.160, 0.680, 0.278, 0.890]</r> taller than the {bar representing \"Machine Learning\"}<r>[0.454, 0.618, 0.571, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "S6Rlyw9n5vh8LKyF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the {Trend 2015 bars}[0.160, 0.680, 0.278, 0.890], [0.307, 0.576, 0.424, 0.890], [0.454, 0.618, 0.571, 0.890], [0.601, 0.722, 0.718, 0.890], [0.747, 0.660, 0.865, 0.890]], and the {Trend 2020 bars}[0.160, 0.534, 0.278, 0.680], [0.307, 0.157, 0.424, 0.576], [0.454, 0.240, 0.571, 0.618], [0.601, 0.492, 0.718, 0.722], [0.747, 0.450, 0.865, 0.660]], which trend generally demonstrates greater prominence?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "Qbf4tZeN2X6OQ9aA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Trend 2015 bar for Neural Networks}<r>[0.160, 0.680, 0.278, 0.890]</r> and the {Trend 2020 bar for Neural Networks}<r>[0.160, 0.534, 0.278, 0.680]</r>, which trend shows a higher prominence for Neural Networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Trend 2015}<r>[0.810, 0.141, 0.887, 0.164]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "i5cG6QpFnhbLzDY3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the chart components within [0.765, 0.132, 0.893, 0.208]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.765, 0.132, 0.893, 0.208] contains the legend of the chart, which includes two trend labels: {Trend 2015}<r>[0.810, 0.141, 0.887, 0.164]</r> with a colour handle at [0.771, 0.143, 0.799, 0.159] and {Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r> with a colour handle at [0.771, 0.178, 0.799, 0.194]."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "bKXKRNFS1QkVEFqU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in the coordinate range [0.160, 0.680, 0.278, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "The range contains the red bar for the Neural Networks category in {Trend 2015}<r>[0.160, 0.680, 0.278, 0.890]</r>, indicating a prominence of 50."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "gN308lMxnY3SZMhB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements found in [0.307, 0.157, 0.424, 0.576]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range contains the orange bar representing {Deep Learning}<r>[0.307, 0.157, 0.424, 0.576]</r> in Trend 2020, indicating a prominence of 100."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "e8ODBBPyADV10cF3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's observed in the coordinates [0.160, 0.534, 0.278, 0.680]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the Orange bar representing {Neural Networks for Trend 2020}<r>[0.160, 0.534, 0.278, 0.680]</r> with a prominence value of 35."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "AzMKoc6ymokrt3HY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.381, 0.029, 0.584] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, also known as \"Indicators' values\"."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "60lOc2Q0w2dZ7Q9R",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second label in the legend on the chart, can I have its coordinate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second label in the legend is \"People Satisfaction Index\" with the coordinates [0.122, 0.118, 0.297, 0.141]."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "c70rfyt7nBVDAcMu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find \"{Economic Indicator}\" in the chart, and what it stands for?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"{Economic Indicator}\" is an x-axis label located at [0.133, 0.919, 0.266, 0.942]. It denotes the type of indicator being evaluated in the chart."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "uLZxtLVA0XhnobNf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what the object at [0.768, 0.507, 0.943, 0.903] is and what information it conveys?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a bar from the \"City Quality of Life Index\". The bar represents the \"Health Indicator\", with a value of 90."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "INIy5q0ftfCXXDG2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Environmental Indicator\" in \"People Satisfaction Index\"}[0.550, 0.181, 0.725, 0.525] stacked on top of {the bar of \"Environmental Indicator\" in \"City Quality of Life Index\"}[0.550, 0.525, 0.725, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "n7VZ4MHrn9S7UczM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value, {the \"Social Indicator\" of the \"City Quality of Life Index\"}[0.331, 0.617, 0.506, 0.903] or {the \"Social Indicator\" of the \"People Satisfaction Index\"}[0.331, 0.243, 0.506, 0.617]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Social Indicator\" of the \"People Satisfaction Index\" is higher."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "wB47hF9v2xp6Rn7k",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"People Satisfaction Index\"}[0.768, 0.102, 0.943, 0.507] wider than {the \"City Quality of Life Index\"}[0.768, 0.507, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "QGgIUGGY0spI2Noq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars related to {the \"Environmental Indicator\"} [0.550, 0.181, 0.725, 0.525], [0.550, 0.525, 0.725, 0.903] taller than those related to {the \"Health Indicator\"} [0.768, 0.102, 0.943, 0.507], [0.768, 0.507, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "6CNO0wPVb9DvtTvn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.078, 0.074, 0.302, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the legend of the chart. It contains two labels: the {first label \"City Quality of Life Index\"}[0.122, 0.083, 0.293, 0.106] with a {colour representation}[0.083, 0.085, 0.111, 0.101] in dark blue, and the {second label \"People Satisfaction Index\"}[0.122, 0.118, 0.297, 0.141] with a {colour representation}[0.083, 0.120, 0.111, 0.136] in light blue."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "Vp4CSyiFvmAZaKd7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of what is within the area [0.112, 0.595, 0.287, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar within this area representing the \"Economic Indicator\" for the \"City Quality of Life Index\", it has a height that corresponds to a value of 70. The bar's value label {70} is located at [0.191, 0.737, 0.208, 0.761]."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "GriT6u9v4ef1x0D7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.550, 0.181, 0.725, 0.903] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates two stacked bars that correspond to the \"Environmental Indicator\". The {lower one}[0.550, 0.525, 0.725, 0.903] represents the \"City Quality of Life Index\" with a value of 86. The {upper one}[0.550, 0.181, 0.725, 0.525] denotes the \"People Satisfaction Index\" with a value of 78."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "pQgCbD1stkF2HccZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.768, 0.102, 0.943, 0.507] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one of the bars that represent the \"Health Indicator\". It is part of the \"People Satisfaction Index\" and has a value label {92} at [0.847, 0.293, 0.865, 0.316]. The bar's height corresponds to a value of 92, which is denoted by the label."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "27HWydGYLafk3bjh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.554, 0.768, 0.727, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the viewership number for the streaming platform Amazon Prime in 2019."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "NdisrnM0zDFrqZdC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is annotated at the coordinate [0.621, 0.824, 0.660, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the viewership number for Amazon Prime in 2019, which is \"220.0\"."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "JS7ga5IaXb5c0GWT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is provided by the coordinate [0.937, 0.118, 0.972, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the year \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "ifFTdDDLgfE9XB7X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the coordinates [0.771, 0.823, 0.944, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the viewership of Disney+ in 2019."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "xkrNRKpwM1cRMumJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the viewership of Amazon Prime in 2020 at {the bar}[0.554, 0.585, 0.727, 0.768] compare to its viewership in 2019 at{the bar}[0.554, 0.768, 0.727, 0.903] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "It was higher in 2020."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "erPjO9XwdDAoXFVP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform had a higher viewership in 2019, {Netflix}[0.121, 0.567, 0.294, 0.903] or {Disney+}[0.771, 0.823, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Netflix."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "VqcJFdF9USU1YDl6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, did {Disney+}[0.771, 0.683, 0.944, 0.823] have a higher or lower viewership than {HBO}[0.337, 0.102, 0.511, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "dNsyGqG4sEXkODUA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Netflix}[0.121, 0.148, 0.294, 0.567] and {HBO}[0.337, 0.102, 0.511, 0.524], which platform had a higher viewership in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "HBO."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "XgBuVlBHns8hHAV9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the content in the legend at [0.893, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labeled color marks. The first color mark at [0.898, 0.085, 0.926, 0.101] is blue if it represents \"2019\", and the second color mark at [0.898, 0.120, 0.926, 0.136] is a darker shade of blue representing \"2020\"."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "DwEkSIqZz4UC2Dnk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region defined by coordinates [0.121, 0.567, 0.294, 0.903] and [0.337, 0.524, 0.511, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions contain the viewership data for Netflix and HBO in 2019. The viewership for Netflix was 550 million and for HBO it was 620 million."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "aVuGWJ0n0NPJCWcz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is represented within the area of [0.771, 0.683, 0.944, 0.823]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the viewership for Disney+ in 2020, which is annotated as 230 million."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "1wocqv5m7O6BWy32",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.121, 0.148, 0.944, 0.567]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the viewership numbers for four streaming platforms (Netflix, HBO, Amazon Prime, and Disney+) in 2020."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "nVdhDQLcVHPTtR1t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar on coordinates [0.112, 0.691, 0.227, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the economic impact of \"Subdued Expansion\" policy, with a value of 70."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "DX7iWNGzegXa0m3o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color code of {the label in the legend} at [0.845, 0.190, 0.873, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#bdd7e7, representing \"Tourism\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "IBaobOH5Zty3Uphn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is at the coordinates [0.256, 0.919, 0.372, 0.942] on the x-axis label of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Gradual Increase\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "wapukxChyVBKHZsI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description on the coordinates [0.884, 0.083, 0.948, 0.106] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a label that tells us that \"Economy\" corresponds to the color \"#08306b\" in the legend."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "F9NXl3hJmmlBSNmO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween [{the \"Subdued Expansion\" for the Environment} [0.112, 0.465, 0.227, 0.601]] and [{the \"Gradual Increase\" for the Environment} [0.256, 0.583, 0.370, 0.679]], which policy has a higher impact on environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Gradual Increase\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "wYpQ0YwUvHIcnaJD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the color label for \"Health\"}[0.845, 0.120, 0.873, 0.136] located above or below {the color label for \"Tourism\"}[0.845, 0.190, 0.873, 0.206] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "kmXXZjwAvi1d6ytz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Health\" bar for the \"Gradual Increase\" policy}[0.256, 0.679, 0.370, 0.752] longer than {the \"Health\" bar for the \"Subdued Expansion\" policy}[0.112, 0.601, 0.227, 0.691]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "VdF70xQswH8YxoQf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Environment\" impact for \"Rapid Expansion\" policy} [0.542, 0.284, 0.657, 0.480] have higher or lower impact than {the \"Tourism\" impact for the same policy} [0.542, 0.102, 0.657, 0.284]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "6pgMdGuP1nJft4n8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe coordinates [0.839, 0.074, 0.978, 0.220]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers the legend of the chart. It shows 4 different categories each represented with unique color and text labels: \n\"Economy\"[0.884, 0.083, 0.948, 0.106] using color [0.845, 0.085, 0.873, 0.101],\n\"Health\"[0.884, 0.118, 0.929, 0.141] using color [0.845, 0.120, 0.873, 0.136],\n\"Environment\"[0.884, 0.153, 0.972, 0.176] using color [0.845, 0.155, 0.873, 0.171], and\n\"Tourism\"[0.884, 0.188, 0.937, 0.211] using color [0.845, 0.190, 0.873, 0.206]."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "w7TB6CFB2Tw2ZXyk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area from [0.112, 0.691, 0.227, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a stacked bar representing various impacts of the \"Subdued Expansion\" policy concerning \"Economy\" effect, with a coordinate [0.112, 0.691, 0.227, 0.903] and its value."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "JytYPAUPXKE3XxVg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinates [0.256, 0.447, 0.370, 0.583]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area only contains one object, which is the \"Tourism\" stack of the \"Gradual Increase\" bar. It represents that the \"Gradual Increase\" policy with a value of 45 has an impact on \"Tourism\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "59WYpbKJ9vtF9znG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.303, 0.025, 0.753, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the title of the chart: \"Stacked Bar Chart of Government Policy Effectiveness\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "nIHGzJ7Uv0Qso6Zq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific object can be found at this given coordinates [0.107, 0.800, 0.876, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the blue bar which represents GDP values."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "cetBpyRWAirr9Djh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.519, 0.964, 0.557, 0.981] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label, called \"Values\"."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "3bhBgpHM5gJ04fKj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object at [0.042, 0.557, 0.103, 0.649] corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for \"Inflation rate\"."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "UMLN8S3cmP3bkLEu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale} at [0.951, 0.939, 0.988, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 40000."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "B4FeqWjJahMZpwNZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Interest rates\"}[0.107, 0.061, 0.107, 0.180] shorter than {the bar representing \"Trade balance\"}[0.107, 0.209, 0.144, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "xYLvzBkibcPBzZRA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"GDP\"}[0.107, 0.800, 0.876, 0.918] taller than {the bar representing \"Retail sales\"}[0.107, 0.357, 0.660, 0.475]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "PkcEM77y7aTAmp4V",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller - {the bar representing \"Unemployment rate\"}[0.107, 0.652, 0.107, 0.771] or {the bar representing \"Inflation rate\"}[0.107, 0.505, 0.107, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Unemployment rate\"."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "jkKSpPvwTaz6iunT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Retail sales\"}[0.107, 0.357, 0.660, 0.475] shorter than the {the bar representing \"GDP\"}[0.107, 0.800, 0.876, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "l1s53lRVJXCmNp1Y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.107, 0.061, 0.876, 0.180]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar corresponding to the \"Interest rates\" economic indicator. The coordinates of the bar are found at [0.107, 0.061, 0.107, 0.180]."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "gcUGaZxDgRqvcscv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the coordinates [0.107, 0.800, 0.876, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, one can identify the bar portraying the \"GDP\" values."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "kbbMOihbYzLexSac",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is displayed within [0.107, 0.652, 0.107, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is home to the small economic indicator bar corresponding to \"Unemployment rate\" located at [0.107, 0.652, 0.107, 0.771]."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "CMNXGDeJIEiIAzzr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's in [0.346, 0.083, 0.679, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates [0.346, 0.083, 0.679, 0.113] denote the location of the chart subtitle, captioned as \"Economic Indicators Presentation\"."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "vuUKvRBgQOchUnmn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.871, 0.072, 0.889]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label which reads \"Period 1\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "YfthnkZgkhVrewGd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements found at the coordinates [0.795, 0.088, 0.964, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a part of the legend - \"Design Innovation Index\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "WbiG4PPlIeDYjuNX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the notation at [0.488, 0.964, 0.569, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis label, titled \"Index Value\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "C3VuQsqiBSrYSi5t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the element at [0.081, 0.849, 0.148, 0.878] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the \"Creativity Index\" value for \"Period 1\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "R78TUoZ3AOpbn0wP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the bars is taller, the one at [0.081, 0.653, 0.468, 0.683] or the one at [0.081, 0.555, 0.560, 0.585]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.081, 0.555, 0.560, 0.585] is taller."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "XQFB5EGKSWx5pel9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Design Innovation Index bar for Period 3}[0.081, 0.683, 0.256, 0.712] taller than the {Creativity Index bar for Period 3}[0.081, 0.653, 0.468, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "lB1T2YWmgPymmiaW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is farther to the right, {Period 1's bar of Design Innovation Index}[0.081, 0.878, 0.202, 0.908] or {Period 1's bar of Creativity Index}[0.081, 0.849, 0.148, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar of Design Innovation Index."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "wv3CiFobSq19CXvO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs compared to the {Design Innovation Index bar for Period 2}[0.081, 0.780, 0.148, 0.810], is {Creativity Index bar for Period 2}[0.081, 0.751, 0.301, 0.780] longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "mf58fUC2FGuLcFoh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the highlighting elements within the region [0.751, 0.055, 0.969, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend enumerating the two plotted indexes: {Creativity Index}[0.795, 0.062, 0.906, 0.080] represented by a purple color and {Design Innovation Index}[0.795, 0.088, 0.964, 0.106] represented by the same color."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "FriLvrIBJCChIrKL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements exist in the region [0.077, 0.939, 0.985, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area encompasses the x-axis scale or labels written as: \"0\" at [0.077, 0.939, 0.086, 0.957], \"2\" at [0.256, 0.939, 0.265, 0.957], \"4\" at [0.435, 0.939, 0.444, 0.957], \"6\" at [0.614, 0.939, 0.623, 0.957], \"8\" at [0.793, 0.939, 0.802, 0.957], and \"10\" at [0.967, 0.939, 0.985, 0.957]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "UaxVfWiqw4iuIjrm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what can be found in the area [0.081, 0.164, 0.798, 0.193]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we can see a bar representing the Creativity Index value for \"Period 8\", with a value extending up to 8.01 on the Index Value x-axis. There is also another smaller bar next to it representing the Design Innovation Index for the same period."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "OeR3dVqAWzBtAzZM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is seen in the region [0.015, 0.284, 0.072, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a y-axis label that reads \"Period 7\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "Wv2QsMzHS36TSfSt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}<r>[0.157, 0.812, 0.498, 0.888]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Movement for North America."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "rY77PeLypaE9SVZ4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the bar}<r>[0.782, 0.356, 0.942, 0.432]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "QZp5xJgLHUcWwLLN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what x-range is {the bar}<r>[0.635, 0.508, 0.832, 0.584]</r> located?"
+            },
+            {
+                "from": "gpt",
+                "value": "14.30 -> 20.20."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "9Ms4wGsnAPCLpeiy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Urban Movement for North America}<r>[0.157, 0.812, 0.498, 0.888]</r> longer or shorter than {the bar representing Rural Movement for Australia}<r>[0.240, 0.204, 0.317, 0.280]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "B2YdwSz9Wou2aPQM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing Rural Movement for Africa}<r>[0.782, 0.356, 0.942, 0.432]</r> have a higher value than {the bar representing Urban Movement for Europe}<r>[0.157, 0.660, 0.394, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "N4kKklsU28wYWftA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Asia's urban movement}<r>[0.157, 0.508, 0.635, 0.584]</r> located higher on the y-axis than {the bar representing Europe's rural movement}<r>[0.394, 0.660, 0.571, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "1HDS6H08XnqBmTp4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.240, 0.204, 0.317, 0.280]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a bar representing the data of Rural Movement in Australia."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "nErvKB7Ks44ucKpT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nElucidate the visual elements in <r>[0.498, 0.812, 0.661, 0.888]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a green bar that indicates the Rural Movement for North America."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "aldysMAxNdpReXGN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in <r>[0.782, 0.356, 0.942, 0.432]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, there is a green bar that represents the Rural Movement in Africa."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "kHyOi7yQO5slXkZN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the first bar}[0.222, 0.821, 0.267, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tomato."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "NNIkR5ABEaN4B7B6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does {the bar with the highest value}[0.759, 0.168, 0.945, 0.233] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Big Data from IoT."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "QAyWn94ZK889EV0j",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the leftmost tick on the x-axis}[0.216, 0.942, 0.227, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "TW4VWbfBjkGI6OKU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the finance label}[0.743, 0.038, 0.810, 0.066] above or below {its color handle}[0.695, 0.041, 0.729, 0.060] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "YxIwHz03jqMxj0f6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider, {the healthcare bar}[0.501, 0.560, 0.571, 0.625] or {the finance bar}[0.501, 0.298, 0.562, 0.364]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The healthcare bar."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "vCQJNg8dID7VSrab",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick labeled \"30\"}[0.550, 0.942, 0.572, 0.970] further to the right than {the tick labeled \"10\"}[0.324, 0.942, 0.346, 0.970] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "GdKXoMBUhecEZJ0c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region that contains the {second and third bars}[0.267, 0.691, 0.344, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are {two bars}<r>[0.267, 0.821, 0.299, 0.887],[0.299, 0.821, 0.318, 0.887]</r> with two different colors - one {wheat}<r>[0.267, 0.821, 0.299, 0.887]</r> and one {light steel blue}<r>[0.299, 0.821, 0.318, 0.887]</r>. They represent {ML with Big Data}<r>[0.062, 0.712, 0.210, 0.740]</r> and {AI with Big Data}<r>[0.070, 0.843, 0.210, 0.871]</r> with x-range values 4.70 -> 8.30 and 6.80 -> 8.50, respectively."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "x1gtMdKNeIo2ND9B",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the area above {the third bar from the left}[0.267, 0.691, 0.344, 0.756]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the {label Transport}<r>[0.400, 0.080, 0.482, 0.108]</r> in the legend and two {bars}<r>[0.275, 0.691, 0.316, 0.756],[0.316, 0.691, 0.344, 0.756]</r>, where the first bar has a {wheat color}<r>[0.275, 0.691, 0.316, 0.756]</r> representing ML with Big Data with an x-range of 4.70 -> 8.30, and the second has a {light steel blue color}<r>[0.316, 0.691, 0.344, 0.756]</r> representing ML with Big Data with an x-range of 8.30 -> 10.80."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "Iif3HfZFDJbENMoz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region where the {axes meet}[0.216, 0.821, 0.267, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the {first bar in the Retail group}<r>[0.222, 0.821, 0.267, 0.887]</r> at {the bottom}<r>[0.222, 0.887]</r>, which is {tomato}<r>[0.222, 0.821, 0.267, 0.887]</r> in color and represents AI with Big Data from 0.00 to 4.00. Directly above, there is a {label coordinate}<r>[0.070, 0.843, 0.210, 0.871]</r> for AI with Big Data with a color match to the bar and an {x-axis tick labeled \"0\"}<r>[0.216, 0.942, 0.227, 0.970]</r>."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "sueZnhuuUVepGTeQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.237, 0.780, 0.550, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the number of studies in the field of Quantum Computing."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "K6vWUcGgASOu2HUs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinate [0.518, 0.941, 0.680, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, which is titled \"Number of Studies\"."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "89JlBeSkUI3whWGk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} located at [0.019, 0.447, 0.036, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Field."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "m8PkxMyKsfT3cplP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exactly does the region [0.237, 0.646, 0.923, 0.713] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar representing Artificial Intelligence field}[0.237, 0.646, 0.923, 0.713]."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "AgdtRBCb1dw2tuK1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Chemistry}[0.237, 0.245, 0.788, 0.312] longer or shorter than {the one for Computer Science}[0.237, 0.512, 0.670, 0.579]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "qsjHQAtb4pJBEoT5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Biology bar}[0.237, 0.379, 0.851, 0.446] closer to the {Physics bar}[0.237, 0.111, 0.839, 0.178] or {Artificial Intelligence bar}[0.237, 0.646, 0.923, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physics bar."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "pk6H0IsI9jeKMGpN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the lengths of {the bar representing Physics studies}[0.237, 0.111, 0.839, 0.178] and {the bar representing Quantum Computing studies}[0.237, 0.780, 0.550, 0.847], which field has a higher number of studies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physics."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "kcZZnAqZdeRqrqWP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subject has more studies, the one represented by {the bar}[0.237, 0.246, 0.789, 0.312] or the one represented by {the bar}[0.237, 0.646, 0.923, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "xh60tJhWaZvGordr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.237, 0.780, 0.550, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a single bar representing the number of studies in Quantum Computing. The bar spans from 0 (the y-axis) to 2600 on the x-axis."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "ctVChev7EURMSw5g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.517, 0.942, 0.679, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encapsulates the x-axis label, which is \"Number of Studies\"."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "tIt6kjM9GuTCW78T",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region found in [0.019, 0.447, 0.036, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the y-axis label, which is titled \"Field\"."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "OHPxuIRvQtnXJJva",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.237, 0.111, 0.839, 0.178] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains one single bar, which represents the number of studies within the field of Physics. The bar extends from 0, which is the y-axis, to 5000 on the x-axis."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "nYIxvku7HZ4Gml7t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what color represents the label \"Gradual Downturn\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "MYMUlrkycb8ERILT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.336, 0.548, 0.445, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a {blue bar}[0.336, 0.548, 0.445, 0.629] representing the \"TeleCommunication Advancements\" category under the trend \"Fluctuating Decline\"."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "KJrR2XyuMtsKOQOv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is represented by the color given by the coordinates [0.821, 0.893, 0.856, 0.912] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stable growth."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "SSASXKcPBsLuX2iy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"TeleCommunication Advancements\"}[0.632, 0.548, 0.762, 0.629] longer than {the bar representing \"Alternative Energy Consumption\"}[0.494, 0.388, 0.553, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "T6V9vojMpSc01RMB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the \"AI Adoption\"[0.224, 0.256, 0.324, 0.284] and \"Smart Home Usage\"[0.155, 0.095, 0.324, 0.123] bars which trend does the longer bar represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Inexorable Rise\"."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "ef8AM5j7zODA2JwV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the category \"Online Education Participation\", is the bar indicating \"Fluctuating Decline\"[0.336, 0.709, 0.409, 0.789] smaller than the bar indicating \"Stable Growth\"[0.634, 0.709, 0.712, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "gsQ0nvgR1wOfOpPa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me what items do you see in the area [0.291, 0.879, 1.026, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with six pairs of color-text labels depicting different trends; the color labels with corresponding text are located as shown: \"Fluctuating Decline\" - [0.298, 0.893, 0.333, 0.912], \"Gradual Downturn\" - [0.298, 0.935, 0.333, 0.954], \"Marginal Improvement\" - [0.541, 0.893, 0.575, 0.912], \"Inexorable Rise\" - [0.541, 0.935, 0.575, 0.954], \"Stable Growth\" - [0.821, 0.893, 0.856, 0.912], and \"Declining Pattern\" - [0.821, 0.935, 0.856, 0.954]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "A2a4l5dlYwPSEtfZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the coordinates [0.474, 0.709, 0.551, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents a single bar for the \"Online Education Participation\" category. The bar is green, indicating the \"Marginal Improvement\" trend."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "B8MRwXzPKnqlOzhO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is contained within the area [0.336, 0.388, 0.440, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a single blue bar representing the trend \"Fluctuating Decline\" for the category \"Alternative Energy Consumption\"."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "GC7OM4AnWrXym3VQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.282, 0.827, 0.313, 0.885] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the count of OTT platforms for the year 2019."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "O4dlzjZ5u1SSkiDD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the first text in legend is [0.570, 0.035, 0.614, 0.063]."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "IpoN7BCK8zX2HXN1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-title} at [0.020, 0.729, 0.270, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Third-Party Content Creators."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "4vfNVj1YmTjKkXTK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Online Ad Spend\"}[0.282, 0.363, 0.355, 0.421] smaller or larger than {the bar representing \"Social Media Engagement\"}[0.282, 0.479, 0.386, 0.537] for 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's smaller."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "z1nX2UnzxcI9jpBF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Podcast Listeners for 2020\"} [0.365, 0.247, 0.532, 0.305] taller than {the bar representing \"Internet Users for 2020\"} [0.327, 0.131, 0.577, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "u9IeJQKzGP9yEbT1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2019 bar for Internet Users}[0.282, 0.131, 0.327, 0.189] shorter than the {2020 bar for Internet Users}[0.327, 0.131, 0.577, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "GQYQfaLiREWmVTCd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.515, 0.024, 0.748, 0.074]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart which includes two entries. The first entry has a color label at [0.522, 0.038, 0.556, 0.057] representing the year 2019 with the color #20b2aa while the corresponding text label is at [0.570, 0.035, 0.614, 0.063]. The second entry represents the year 2020 at [0.649, 0.038, 0.684, 0.057] with the color #da70d6 and its corresponding text label is at [0.698, 0.035, 0.741, 0.063]."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "4OZLjWWvudsg0DtV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.282, 0.093, 0.981, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the main plot of the chart which includes all the x-ticks, y-ticks and bars. The x-ticks range from 0 to 300 in intervals of 50 and the y-ticks include labels such as \"OTT Platforms\", \"Third-Party Content Creators\", \"Video Streaming Subscribers\", \"Social Media Engagement\", \"Online Ad Spend\", \"Podcast Listeners\", and \"Internet Users\". The bars represent the data of these categories for the years 2019 and 2020."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "w3CcSsYv1wiTYYIP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.282, 0.711, 0.636, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects in this coordinate range: \n1. A blue bar representing the count of \"Third-Party Content Creators\" for the year 2019, and the coordinate range is [0.282, 0.711, 0.323, 0.769].\n2. A purple bar representing the count of \"Third-Party Content Creators\" for the year 2020, and the coordinate range is [0.323, 0.711, 0.636, 0.769]."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "v5lUzO6cDIsVKXwr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.282, 0.363, 0.625, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains 2 bars: \n1. A blue bar for 2019 representing \"Online Ad Spend\" with a value around 35 (coordinate: [0.282, 0.363, 0.355, 0.421]).\n2. A purple bar for 2020 representing \"Online Ad Spend\" with a value around 165 (coordinate: [0.355, 0.363, 0.625, 0.421])."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "lTU3l1PBgMLGIW2V",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.245, 0.660, 0.297, 0.888] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the US's Technological Innovation data, ranging from 0 to 3.60 units."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "tzVvRA9JKxm62inn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.488, 0.087, 0.511, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the EU in the chart's legend."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "vaRgb3q1RU3r1Isa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the label at [0.887, 0.942, 0.909, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the X-axis label for 45."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "xPYEMsAGN6WI2yy3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.069, 0.307, 0.233, 0.335] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label for \"Scientific Research\"."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "1xefCgInyrEq8KcS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Brazil's bar for Scientific Research}[0.448, 0.204, 0.477, 0.432] taller or shorter than {India's bar for Scientific Research}[0.371, 0.204, 0.448, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "ZqQQAJbleNpRQ3Pd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {US's bar for Technological Innovation}[0.245, 0.660, 0.297, 0.888] on the left or right of the {EU's bar for Technological Innovation}[0.297, 0.660, 0.402, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "d6J2Ci1lDlB4DkCp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {China's bar for Technological Innovation}[0.402, 0.660, 0.424, 0.888] or the {India's bar for Technological Innovation}[0.424, 0.660, 0.516, 0.888] start first?"
+            },
+            {
+                "from": "gpt",
+                "value": "China's bar for Technological Innovation."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "8bg6G1jXSAKWpFgT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {India's bar for Scientific Research}[0.371, 0.204, 0.448, 0.432] extend further right than {Brazil's bar for Scientific Research}[0.448, 0.204, 0.477, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "6g2AkRO1e9L7yGRP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within the coordinates [0.432, 0.034, 0.783, 0.126]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are five different labels in the legend showing different countries' colors. From top to bottom, these are the labels: US with a color tag at [0.439, 0.047, 0.474, 0.067], EU with a color tag at [0.439, 0.089, 0.474, 0.109], China with a color tag at [0.546, 0.047, 0.581, 0.067], India with a color tag at [0.546, 0.089, 0.581, 0.109], Brazil with a color tag at [0.679, 0.047, 0.714, 0.067]."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "ezQP9yJvdbSO5c2C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.245, 0.660, 0.297, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a rectangular plot representing US's Technological Innovation data with a range from 0.00 to 3.60 units."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "7QTzbNwGvngp18XJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area [0.245, 0.170, 0.970, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area of the bar chart. It contains all the bars denoting the Scientific Research and Technological Innovation data for US, EU, China, India, and Brazil."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "zcyijTRH8RVYEoDX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are encompassed in the region defined by [0.432, 0.034, 0.783, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, including five labels and color tags indicating their representation of countries \u2014 US, EU, China, India, and Brazil."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "t3qVyzE8igYpvRek",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the rectangle located at [0.186, 0.827, 0.303, 0.897] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar showing the internet speed in the US."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "EhRRTALTib70KbDZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} located at [0.052, 0.155, 0.175, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5G Connectivity."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "8PoPPXrqJoETyelb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend at [0.391, 0.129, 0.461, 0.199] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the data for 5G connectivity in India."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "vFAyPeDjPao675Cf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {EU's Bar on Artificial Intelligence}[0.219, 0.688, 0.268, 0.757] higher than {Brazil's Bar on Big Data}[0.578, 0.408, 0.675, 0.478]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "04hxXeGdHqDllOD0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {China's Bar on Cybersecurity}[0.399, 0.548, 0.572, 0.618] taller than {India's Bar on Cloud Computing}[0.412, 0.269, 0.506, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "9jJjm5du3GkAsaLT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Cybersecurity\"}[[0.186, 0.548, 0.278, 0.618],[0.278, 0.548, 0.399, 0.618],[0.399, 0.548, 0.572, 0.618],[0.572, 0.548, 0.706, 0.618],[0.706, 0.548, 0.821, 0.618]] and {\"Big Data\"}[[0.186, 0.408, 0.252, 0.478], [0.252, 0.408, 0.337, 0.478], [0.337, 0.408, 0.452, 0.478], [0.452, 0.408, 0.578, 0.478], [0.578, 0.408, 0.675, 0.478]], which has generally higher values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cybersecurity."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "mKOOnxh6UYOHYne2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the coordinates [0.303, 0.827, 0.487, 0.897]."
+            },
+            {
+                "from": "gpt",
+                "value": "'US Internet Speed' bar with a coordinate ranging from [0.303, 0.827, 0.487, 0.897] are found here. This bar represents the EU's investment in Internet Speed which lies on x-range 29.30 to 75.00."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "YM03p836wj1iq7U7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.186, 0.713, 0.219, 0.757]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, we have the Artificial Intelligence bar for US, range from 0.00 to 8.20, which is located at [0.186, 0.713, 0.219, 0.757]."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "KryiNufklx6PB7td",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.372, 0.924, 0.528, 0.946]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the \"Cybersecurity\" category name of the Y-axis at [0.068, 0.574, 0.175, 0.597]."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "1ibyuSuC4DzRfyjh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area contained within [0.186, 0.827, 0.945, 0.897]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area exhibits the values for 'Internet Speed' amongst different countries. It has several bars, including the US's bar at [0.186, 0.827, 0.303, 0.897], EU's bar at [0.303, 0.827, 0.487, 0.897], China's bar at [0.487, 0.827, 0.748, 0.897], India's bar at [0.748, 0.827, 0.897, 0.897], and Brazil's bar at [0.897, 0.827, 0.945, 0.897]."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "uTwO58jTtwUHMYrU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the text at {the coordinate}[0.580, 0.953, 0.615, 0.973]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "YOgS4zqY9BGcNJxk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the handle located at}[0.643, 0.895, 0.670, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "xe39bmUFWP9LAEqN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label at}[0.164, 0.304, 0.265, 0.324] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patents Issued."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "SzRkI2LpuOyfW15I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar at}[0.293, 0.396, 0.313, 0.452] wider than {the bar at}[0.332, 0.396, 0.352, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "2n7MpkrzqVmBTDKp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the tick mark at}[0.965, 0.827] represent a higher value than {the tick mark at}[0.735, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "VJ5Jmj6TYbln93uo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the text color for 2016 at}[0.541, 0.895, 0.568, 0.909] and {the text color for 2020 at}[0.643, 0.925, 0.670, 0.939] different?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "2IBQhPBCm2ERy278",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bound by {these coordinates}<r>[0.270, 0.583, 0.485, 0.900]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes several bars of varying heights representing different years' values across various health-related sectors. Key visible bars include BioTech Market Cap (spanning approximately from [0.275, 0.621, 0.275, 0.678]) and Private Investments in Biotech (extended roughly around [0.278, 0.734, 0.276, 0.790])."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "3G0YpNt2kXP3bAZV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region <r>[0.270, 0.583, 0.980, 0.900]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region visualizes the performance of multiple sectors over consecutive years plotted as bars. Notable within this space are bars for Legal Suits for Patent Infringements and Pharmaceutical R&D Spent, with visible disclosures of their escalating trends each year, discernible through bars about [0.293, 0.396, 0.313, 0.452] and wider for subsequent years."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "nJnOLRjTUvIg9J0Z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is within the described bounds <r>[0.270, 0.160, 0.485, 0.900]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This space illustrates bars of two sectors namely Medical Devices Revenue with a starting point around [0.274, 0.171, 0.277, 0.227] for 2016 and extends wider annually, and BioTech IPO that also starts narrower with approximately [0.274, 0.509, 0.274, 0.565] for the year 2016 and progressively increases."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "YRPzCS3H9MlqadZ0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {\"China\"}<r>[0.467, 0.087, 0.517, 0.115]</r> in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Olive."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "sPu9hjRTtHngTJER",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the current position {Cloud Services label}<r>[0.072, 0.325, 0.201, 0.353]</r> located at?"
+            },
+            {
+                "from": "gpt",
+                "value": "On the y-axis."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "PmbJhMDHGOJA2rWM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at these coordinates {the third x-axis label}<r>[0.292, 0.942, 0.314, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "qSOKCnwigSnsPrys",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is wider, {the US bar for Blockchain}<r>[0.213, 0.836, 0.323, 0.888]</r> or {the China bar for Blockchain}<r>[0.323, 0.836, 0.451, 0.888]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The China bar for Blockchain."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "xB4WQ7QIxmO0waCh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar of India in Quantum Computing}<r>[0.307, 0.625, 0.343, 0.678]</r> higher or lower than the {bar of Brazil in Quantum Computing}<r>[0.343, 0.625, 0.354, 0.678]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "k39FL6CPylywWWxV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars representing {5G Coverage of India}<r>[0.626, 0.204, 0.734, 0.257]</r> and {5G Coverage of Brazil}<r>[0.734, 0.204, 0.792, 0.257]</r>, which one has a greater value?"
+            },
+            {
+                "from": "gpt",
+                "value": "5G Coverage of India."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "JwffpnayqTXewScv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content you can see in the region defined by the range [0.323, 0.836, 0.451, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar which represents the 'Blockchain' of 'China'. Its value range goes from '48.60 -> 105.40'."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "iih7lLCqopop3Ht3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.412, 0.034, 0.782, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a sublegend that includes five tech trends. The five tech trends are \"US\", \"China\", \"EU\", \"India\", \"Brazil\"."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "RcNaJrCLDApE6z69",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the area [0.213, 0.204, 0.792, 0.257]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains 5 bars that represent the '5G Coverage' in different countries (US, China, EU, India, Brazil) with varying lengths that reflect the statistical value of each country. The respective country colors are tomato, olive, deep sky blue, plum and khaki."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "CRcLitrXQjZRkiEZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the third graphical text at [0.110, 0.581, 0.303, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Michelin Starred Restaurant."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "tg5EZTKZfRtJvJ1B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color swatch representation of {Europe} at [0.479, 0.082, 0.507, 0.102]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Brown."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "OXkEqXb4EDeGQa6U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale} at [0.569, 0.942, 0.587, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "h7NyHqQ9feKCUjvV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the object at [0.313, 0.821, 0.392, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar for America in the category of num_of_TVShows_TalkAbout_Gastronomy with a range of 0.00 -> 6.00."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "el5Z1UJMqjp3Wn7b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Europe in num_of_TVShows_TalkAbout_Gastronomy}[0.392, 0.821, 0.493, 0.887] taller than the {bar for America in the same category}[0.313, 0.821, 0.392, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "zorBaszrLo722ONa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the bar representing Africa in num_of_Gastronomy_Books at [0.520, 0.691, 0.536, 0.756]} or {the bar representing Europe in the same category at [0.363, 0.691, 0.432, 0.756]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Europe."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "WF6bxXPjobHlWZ9W",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre bars of {Asia}[0.493, 0.821, 0.613, 0.887][0.432, 0.691, 0.520, 0.756][0.448, 0.560, 0.537, 0.625][0.471, 0.429, 0.571, 0.494][0.524, 0.298, 0.643, 0.364][0.514, 0.168, 0.637, 0.233] generally taller than those of {Australia}[0.652, 0.821, 0.703, 0.887][0.536, 0.691, 0.562, 0.756][0.557, 0.560, 0.590, 0.625][0.618, 0.429, 0.675, 0.494][0.731, 0.298, 0.810, 0.364][0.706, 0.168, 0.796, 0.233]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "vwQsLBFQsddBngdI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Asia\"}[[0.493, 0.821, 0.613, 0.887][0.432, 0.691, 0.520, 0.756][0.448, 0.560, 0.537, 0.625][0.471, 0.429, 0.571, 0.494][0.524, 0.298, 0.643, 0.364][0.514, 0.168, 0.637, 0.233]] and {\"Europe\"}[[0.392, 0.821, 0.493, 0.887][0.363, 0.691, 0.432, 0.756][0.371, 0.560, 0.448, 0.625][0.382, 0.429, 0.471, 0.494][0.414, 0.298, 0.524, 0.364][0.399, 0.168, 0.514, 0.233]], which area has higher values for Gourmet Festival Attendance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Asia."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "BzDUna4XzxiUUwsB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.473, 0.027, 0.816, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the chart's legend. It contains five pairs of color-text labels, representing America[0.479, 0.041, 0.507, 0.060], Europe[0.479, 0.082, 0.507, 0.102], Asia[0.603, 0.041, 0.631, 0.060], Africa[0.603, 0.082, 0.631, 0.102], and Australia[0.710, 0.041, 0.737, 0.060]."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "87HfKWfkImUSrGEI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.313, 0.132, 0.976, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The whole chart is within this region, except the title and the frame. It includes the bars for America, Europe, Asia, Africa, and Australia, each in six different categories. It also includes the x-axis from 0 to 50 and the y-axis with six category labels."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "uDOCYWbQipAPFCTY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the area [0.392, 0.821, 0.493, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing Europe in the category \"num_of_TVShows_TalkAbout_Gastronomy,\" with a range of 6.00 to 13.60."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "GyhiXIHXaXQCT7Fz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.642, 0.038, 0.671, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the word 'Asia' which is part of the chart legend indicating the color that represents Asia on the chart."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "OBJzANlsTpIK8Hru",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the first bar [0.202, 0.660, 0.346, 0.888] in the Consumer Behavior category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red or specifically tomato."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "TpkObjQoZrGQkixm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any labels on the chart at coordinates [0.202, 0.204, 0.346, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "PXULuXD8C9AzcWJv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label written at {the y-axis}[0.032, 0.307, 0.190, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emerging Markets."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "wjAMZTis2Ii3DXpe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the US bar}[0.202, 0.660, 0.346, 0.888] longer or shorter than {the EU bar}[0.346, 0.660, 0.476, 0.888] in the Consumer Behavior category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "HE3JT7kwgj3cUC1Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the bar representing \"China\"}[0.476, 0.204, 0.664, 0.432], in comparison to {the bar representing \"India\"}[0.664, 0.204, 0.828, 0.432], which one is longer for the Emerging Markets category?"
+            },
+            {
+                "from": "gpt",
+                "value": "China."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "brC0s0maE57XmBnM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the EU bar}<[0.346, 0.660, 0.476, 0.888], [0.346, 0.204, 0.476, 0.432]> and {the Brazil bar}<[0.828, 0.660, 0.944, 0.888], [0.828, 0.204, 0.944, 0.432]>, which one has the longer cumulative length?"
+            },
+            {
+                "from": "gpt",
+                "value": "EU."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "Si43nxCR3haz1RNz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.202, 0.204, 0.944, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content is a bar chart representing the Emerging Markets and Consumer Behavior. It contains 5 bars each for Emerging Markets, and 5 bars for Consumer Behavior sorted horizontally. The bars are colored and represent different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark Sea Green), and Brazil (Cornflower Blue)."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "hbMK0OWfzkj3ggjJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.416, 0.034, 0.767, 0.126]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.416, 0.034, 0.767, 0.126] contains five color-text labels representing different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark sea green), and Brazil (Cornflower Blue)."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "Myiy7Boj4gftuQRK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the area [0.202, 0.204, 0.944, 0.432], what indicators are there?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area [0.202, 0.204, 0.944, 0.432], there are five colored bars representing different regions: US (Tomato), EU (Wheat), China (Steel Blue), India (Dark sea green), and Brazil (Cornflower Blue). These represent the Emerging Markets category."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "l50irNPuWVkG8ers",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you consider [0.346, 0.660, 0.476, 0.888] and [0.346, 0.204, 0.476, 0.432] as a single region, what does it include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes two bars representing the EU, one in the Consumer Behavior category and the other in the Emerging Markets category, both colored Wheat."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "AFvBocbPMqgWiwM3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.068, 0.224, 0.216, 0.252]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is Online Education."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "h9fltveuMgodkf6r",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar with coordinates [0.461, 0.702, 0.590, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is beige represented as the color code '#f5deb3'."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "MBbU2CrD3Ae5uD0Z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart entity at coordinates [0.019, 0.722, 0.216, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for Education Expenditure."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "YZhPlreRtIyxtUMj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the object having coordinates [0.294, 0.045, 0.459, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label {Continuous decline}<r>[0.294, 0.045, 0.459, 0.073]</r> of the legend."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "Tz7QKAb6mJudFvq5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Classroom Education\"}[0.408, 0.329, 0.519, 0.391] taller than {the bar representing \"Online Education\"}[0.365, 0.204, 0.445, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "Am82frlgXY2IkE5R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {Slow growth bar of Classroom Education}[0.669, 0.329, 0.766, 0.391] or {Slow growth bar of Online Education}[0.561, 0.204, 0.622, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slow growth bar of Classroom Education."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "GJaJstAacPeaXlaS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Continuous decrease bar of Student-Teacher Ratio}[0.228, 0.577, 0.430, 0.640] shorter than the {Continuous decrease bar of Education Expenditure}[0.228, 0.702, 0.461, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "SUKSS06N2BtzF69P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the object at coordinates [0.578, 0.045, 0.708, 0.073] left to the object at [0.791, 0.045, 0.964, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "gj7ESFue4QlyxS2A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.228, 0.702, 0.870, 0.764]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 bars representing Education Expenditure in different aspects. The coordinates of the bars from left to right are: {Continuous decline bar}[0.228, 0.702, 0.461, 0.764], {Marginal improvement bar}[0.461, 0.702, 0.590, 0.764], and {Slight decrease bar}[0.590, 0.702, 0.749, 0.764]."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "kOqYSNPU8efGwAe4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found within the coordinates [0.238, 0.034, 0.971, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, which includes 5 color-text labels. It elucidates the meaning of different colors used in the bars."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "C0R1AT9rVVcORWo2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is seen in the region from [0.228, 0.826, 0.945, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains 5 bars under the College Education category, each of different colors representing Continuous decline[ranging from 0.228 to 0.350], Marginal improvement[ranging from 0.350 to 0.455], Slight decrease[ranging from 0.455 to 0.558], Slow growth[ranging from 0.558 to 0.645] and Inconsistent decline[ranging from 0.645 to 0.691]."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "r4xr9bKKgEsU334y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.228, 0.204, 0.654, 0.266]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains different bars representing different aspects of Online Education. The bars are ordered from left to right as follows: Continuous decline, Marginal improvement, Slight decrease, Slow growth, Inconsistent decline."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "AKsj0N5twV3kDXp3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the y-axis is {Statistic}<r>[0.019, 0.438, 0.036, 0.508]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "GAKSN3A1rhpELMbD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the first midpoint in the x-axis correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first midpoint on the x-axis corresponds to {a trend value of 2}<r>[0.452, 0.939, 0.463, 0.957]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "mkQQFA9IKHkb1u84",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at coordinates [0.319, 0.818, 0.894, 0.886] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at coordinates [0.319, 0.818, 0.894, 0.886] represents the {Gross Domestic Product (GDP) value of 8.3}<r>[0.894, 0.843, 0.927, 0.861]</r> in the trend."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "ioSxyfwDkcOwZ0C1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.783, 0.759, 0.816, 0.777] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at coordinates [0.783, 0.759, 0.816, 0.777] represents a {value of 6.7}<r>[0.783, 0.759, 0.816, 0.777]</r> for the \"Spending\" statistic."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "hJZ6CYUsEniB8wU4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar representing GDP}<r>[0.319, 0.818, 0.894, 0.886]</r> larger than the {bar representing Spending}<r>[0.319, 0.734, 0.783, 0.802]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "SpiYUzk3DM0ek0xH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {unemployment trend values}<r>[0.319, 0.060, 0.402, 0.127]</r> lower than {exports trend values}<r>[0.319, 0.313, 0.555, 0.380]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "7qjbUkmSe9QKIXqG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Income}<r>[0.319, 0.397, 0.839, 0.465]</r> and {Factory Orders}<r>[0.319, 0.144, 0.818, 0.212]</r>, which has a higher trend value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Income."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "iPy2lWw3iCqswQed",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Inflation}<r>[0.319, 0.566, 0.707, 0.633]</r> and {Interest Rates}<r>[0.319, 0.481, 0.513, 0.549]</r>, which has a lower trend value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interest Rates."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "1pUXtoeZK2fclbwT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.319, 0.818, 0.894, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.319, 0.818, 0.894, 0.886] contains the bar representing {Gross Domestic Product (GDP)}<r>[0.319, 0.818, 0.894, 0.886]</r> with a trend value of {8.3}<r>[0.894, 0.843, 0.927, 0.861]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "QlnU9oAUOqXiPgAd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.319, 0.060, 0.402, 0.127]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.319, 0.060, 0.402, 0.127] contains the bar representing {Unemployment}<r>[0.319, 0.060, 0.402, 0.127]</r> with a trend value of {1.2}<r>[0.402, 0.085, 0.435, 0.102]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "7ydLRJtwLgl0yLy1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.043, 0.845, 0.307, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object within [0.043, 0.845, 0.307, 0.863] is the {y-axis marker for the Gross Domestic Product (GDP)}<r>[0.043, 0.845, 0.307, 0.863]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "2xfsGFZ3YBWezUIF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.091, 0.727, 0.169, 0.746]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no information available in the area [0.091, 0.727, 0.169, 0.746]."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "csQvs6slgLS67PsH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed on the y-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Indicator."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "MAPh4FUAWNh8PpT4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at [0.333, 0.025, 0.902, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Indicators in Entrepreneurship and Innovation Ecosystem."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "o0B0tUFFbZy8pxlg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich indicator is located at [0.196, 0.824, 0.248, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patents."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "EQHi6bODcOqJiHOa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {Venture Capital Investment}<r>[0.258, 0.490, 0.669, 0.553]</r> higher than {Scientific Publications}<r>[0.258, 0.724, 0.467, 0.787]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "XV5neC5Crt6O7Ngm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong these indicators {Patents}<r>[0.258, 0.802, 0.687, 0.865]</r>, {Scientific Publications}<r>[0.258, 0.724, 0.467, 0.787]</r>, {R&D Personnel}<r>[0.258, 0.646, 0.783, 0.709]</r>, and {Startup Creation}<r>[0.258, 0.568, 0.812, 0.631]</r>, which has the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Creation."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "f8BNCkPwBCqbi16Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Technological Infrastructure}<r>[0.258, 0.100, 0.419, 0.163]</r> have a higher value than {Knowledge Transfer}<r>[0.258, 0.334, 0.741, 0.397]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "7QuQdMRXqWAk2xPu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.258, 0.062, 0.978, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar chart that depicts the percentages of different Innovation Indicators. Each bar represents an Innovation Indicator, and the length of the bar represents the percentage value of each indicator. The x-axis represents the percentage values, and the y-axis represents different Innovation Indicators."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "d5ucSDkc2jste4rO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the set of objects within the x-axis label region with boundaries [0.565, 0.952, 0.671, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label called \"Percentage (%)\"."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "vR6WbjmoVP6cgzIN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.015, 0.366, 0.029, 0.599] show?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the y-axis label, which is titled \"Innovation Indicator\"."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "ZYBy6FLmZDB2L900",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the social issue located at [0.079, 0.298, 0.191, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gender Equality."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "FRdrHIaHKT6TBQFz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social issue is located at [0.093, 0.116, 0.191, 0.134] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human Rights."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "uJJsQLIq7koXdUUF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at coordinates [0.201, 0.269, 0.816, 0.341]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Frequency of the social issue {Gender Equality}<r>[0.079, 0.298, 0.191, 0.316]</r> in the dataset."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "qRbWJANE5AOZm94u",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the frequency of {Climate Change}<r>[0.080, 0.844, 0.191, 0.861]</r> lower or higher than {Waste Management}<r>[0.052, 0.753, 0.191, 0.770]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "vEZZTHnn0AgMZuNU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Diversity & Inclusion}<r>[0.048, 0.389, 0.191, 0.407]</r> have a higher frequency than {Gender Equality}<r>[0.079, 0.298, 0.191, 0.316]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "BPR7yxqnar42qgQA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher, {Animal Welfare}<r>[0.086, 0.207, 0.191, 0.225]</r> frequency or {Human Rights}<r>[0.093, 0.116, 0.191, 0.134]</r> frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human Rights."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "k5fRMvUblXi0xkzF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.201, 0.087, 0.945, 0.159]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the longest bar in the chart, representing the issue of {Human Rights}<r>[0.093, 0.116, 0.191, 0.134]</r>, with a frequency of 9.2%."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "YLoRE3GWd6FsBizP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the items in [0.201, 0.814, 0.565, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar representing the frequency of {Climate Change}<r>[0.080, 0.844, 0.191, 0.861]</r>. The frequency of this issue is 4.5%, making it the least frequent issue on the chart."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "syaOKspXxKz0qVfu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area from [0.201, 0.360, 0.767, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar that represents the {Diversity & Inclusion}<r>[0.048, 0.389, 0.191, 0.407]</r> social issue frequency, which score is 7.0%."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "hDkDvxz4AOpnrJdl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at [0.455, 0.019, 0.858, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Main Causes of Death in US Population."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "grwB2s5j9jB5EzxC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the ranges of percentages located at [0.965, 0.939, 0.987, 0.957] and [0.350, 0.939, 0.361, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range is from 0% to 25%."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "xZDykjmusx02vXwt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the data at [0.355, 0.495, 0.482, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar showing the percentage of deaths caused by Stroke."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "HFk7ztYcWhDBkyAs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the percentage of deaths caused by Heart Disease}[0.355, 0.822, 0.929, 0.887] lower or higher than {the percentage of deaths caused by Chronic lower respiratory diseases}[0.355, 0.577, 0.494, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "XEjYnxioxJyrBiTl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has a higher death percentage, {Cancer}[0.355, 0.740, 0.914, 0.805] or {Stroke}[0.355, 0.495, 0.482, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cancer."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "OauZveUUti2zQv5g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing causes with coordinates [0.355, 0.495, 0.482, 0.560] and [0.355, 0.577, 0.494, 0.642], which cause occurs more commonly?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chronic lower respiratory diseases."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "SaIVUX9pAPe18vQn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in coordinates [0.355, 0.047, 0.958, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main plot of the chart where 10 hamming bars were drawn. Each bar corresponds to a cause of death like Heart Disease, Cancer, Accidents, etc. The bars are arranged in descending order of their percentages."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "EETF47lcvF1caHFw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.355, 0.495, 0.482, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single bar that measures the percentage of Stroke-related deaths."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "tQUs7dpEx4uWEqIh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.355, 0.495, 0.482, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects present. The stroke and Alzheimer's disease bars, representing their respective percentages of total deaths."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "LyYoRP3re8Glxuc2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category under the coordinates [0.146, 0.087, 0.224, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "e-Library."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "aN7sYVnPmgnrvQ54",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar depicting the utilization rate of 'e-Business'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.236, 0.566, 0.834, 0.633]."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "Zj9VirPPGRU8Rpv0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the category}<{category label}[0.175, 0.677, 0.224, 0.694]> at these coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Email."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "WDhO9gCEQVw2JX0k",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does the bar located at [0.236, 0.650, 0.904, 0.717] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Email}[0.175, 0.677, 0.224, 0.694]."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "wU40IOMI6meRJLCB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Internet Games}[0.236, 0.397, 0.658, 0.465] shorter than that of {Email}[0.236, 0.650, 0.904, 0.717]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "wkJ3JNi57H7KN9rB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of {Internet Applications}[0.236, 0.818, 0.939, 0.886] and {Chat Room}[0.236, 0.734, 0.939, 0.802] of the same height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "OxiI75iC2MNo6o1b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {VoIP}[0.236, 0.481, 0.834, 0.549] and {e-Business}[0.236, 0.566, 0.834, 0.633], which has a higher utilization rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are the same."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "NGE5MfvK8pVNvQg5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Internet TV}[0.236, 0.229, 0.588, 0.296] more utilized than {Telecommuting}[0.236, 0.144, 0.517, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "5nw3kaoU34VHogaj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.236, 0.019, 0.974, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar chart representing the utilization rate of different internet and communication services. It includes the bars denoting the utilization rates, the labels of these services on the y-axis, and the percentage scale on the x-axis."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "B93vYF3NghnPzXRR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.236, 0.566, 0.834, 0.633]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the utilization rate of the \"e-Business\" service. It includes a horizontal bar that extends to 17% on the x-axis, indicating that the utilization rate of e-Business is 17%."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "78zEjC9GLeOPafVj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the coordinates [0.236, 0.397, 0.658, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a horizontal bar pertaining to the \"Internet Games\" label. This bar extends up to 12% on the x-axis, illustrating a 12% utilisation rate for Internet Games."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "52tA7FzFGuZobbqT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content does the region [0.236, 0.313, 0.623, 0.380] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a horizontal bar related to the label \"Video Conferencing\", signifying an 11% utilization rate, since the bar extends to 11% on the x-axis."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "h6qIqIAN39UXNrvY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.019, 0.437, 0.036, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, named 'Industry'."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "lMVYzrFuGQFgnYjG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinate of the bar representing 'Biotechnology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.301, 0.717, 0.767, 0.792]."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "q1yob6QNsw8G44Tw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated within the coordinates [0.725, 0.652, 0.774, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the value of 'Semiconductors' as ' $20K'."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "ByUBOhQ7QIvOPlp5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the entity observed at the coordinates [0.149, 0.654, 0.289, 0.671]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label for 'Semiconductors'."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "Upn537uuhjwxF70g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Sales of 'Information Technology'}<r>[0.301, 0.811, 0.831, 0.886]</r> greater than the {Sales of 'Communications Equipment'}<r>[0.301, 0.435, 0.682, 0.510]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "2JBlAGCRz0kbIBRQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Pharmaceuticals}<r>[0.301, 0.154, 0.873, 0.229]</r> below or above {Data processing}<r>[0.301, 0.060, 0.894, 0.135]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "5OVDqgDhZuhWj5d8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Software}<r>[0.301, 0.529, 0.937, 0.604]</r> and {Media}<r>[0.301, 0.248, 0.788, 0.323]</r>, which one has a higher sales volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "Software."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "bMcPqFNXs8br1efs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the given industries, does {Biotechnology}<r>[0.301, 0.717, 0.767, 0.792]</r> or {Telecommunications}<r>[0.301, 0.342, 0.746, 0.417]</r> have higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotechnology."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "eBfjSyxFPfLkHIx8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.301, 0.019, 0.968, 0.927] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the complete bar chart representing sales volumes for different industries. Each bar signifies sales for a particular sector, including 'Information Technology', 'Biotechnology', 'Semiconductors', 'Software', 'Communications Equipment', 'Telecommunications', 'Media', 'Pharmaceuticals', and 'Data processing'."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "JVxPv6eJTyN5heDz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.149, 0.185, 0.289, 0.202]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a label for 'Pharmaceuticals' on the Y-axis."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "EuBYhvKt70LfLZWs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.685, 0.464, 0.732, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text label for the 'Communications Equipment' bar, indicating its sales value as ' $18K', is visible in this area."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "a7Rjk8nRuv41zVch",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is available within the region [0.301, 0.811, 0.831, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, a 'turquoise' colored bar stands for 'Information Technology' sales value. The length of the bar visually represents the volume of sales."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "bQDW4HhB4wjlGPBQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label at these coordinates [0.507, 0.015, 0.760, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Probability of Future Scenarios\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "AhxShcNREsHoKsDF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what category is represented at the y-axis coordinates [0.171, 0.110, 0.272, 0.124]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is \"Living on Mars\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "VQYTJa14KmLL0ame",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at [0.015, 0.459, 0.029, 0.520]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Scenario\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "uxjIJvnYD4zzAnjJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at the specific spot [0.582, 0.971, 0.684, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Probability (%)\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "WoHRYKzNg4dki6Jn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo we have a higher probability for {E-learning}[0.281, 0.732, 0.893, 0.807] or {Automated Transport}[0.281, 0.546, 0.772, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-learning."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "xvntwKhUBeNvMApU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Remote Healthcare}[0.281, 0.359, 0.603, 0.433] and {Robot Companions}[0.281, 0.265, 0.515, 0.340], which one has lower probability?"
+            },
+            {
+                "from": "gpt",
+                "value": "Robot Companions."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "jecBOLyTNlqO3HQU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Artificial Intelligence in Household}[0.281, 0.452, 0.662, 0.527] more probable than {Virtual Reality Entertainment}[0.281, 0.639, 0.834, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "WjdgYMZ2L9OqxwML",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Time Travel}[0.281, 0.172, 0.375, 0.247] and {Living on Mars}[0.281, 0.078, 0.338, 0.153], which scenario has a higher probability?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time Travel."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "cDL9eqVfAcCwFgkP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe within the region [0.125, 0.577, 0.272, 1.094]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three probablity bars, namely \"Automated Transport\"[0.281, 0.546, 0.772, 0.620], \"Telework\"[0.281, 0.826, 0.951, 0.901] and \"E-learning\"[0.281, 0.732, 0.893, 0.807] with their corresponding probability percentages and the axis label \"Probability (%)\"[0.582, 0.971, 0.684, 0.985] located at the bottom of the region. The bars present relative probabilities of these future scenarios."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "VXZJud7cFjaIKZmY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what objects are located between [0.277, 0.951, 0.960, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Between these coordinates are the numerical labels for the x-axis, which range from \"0\"[0.277, 0.951, 0.286, 0.965] to \"25\"[0.943, 0.951, 0.960, 0.965], signifying the probability range for the different scenarios."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "FUMmXbxDkftZUZS6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside [0.281, 0.359, 0.951, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents scenarios and their respective probabilities. The scenarios include \"Telework\"[0.281, 0.826, 0.951, 0.901], \"E-learning\"[0.281, 0.732, 0.893, 0.807], \"Virtual Reality Entertainment\"[0.281, 0.639, 0.834, 0.714], \"Automated Transport\"[0.281, 0.546, 0.772, 0.620] and \"Remote Healthcare\"[0.281, 0.359, 0.603, 0.433] and the associated probabilities for each scenario are provided."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "tBCJ3tPCqcbmBNOc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.160, 0.172, 0.360, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are three scenarios: \"Robot Companions\"[0.281, 0.265, 0.515, 0.340], \"Time Travel\"[0.281, 0.172, 0.375, 0.247] and \"Automated Transport\"[0.281, 0.546, 0.772, 0.620], along with their corresponding probabilities."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "NUOxG4LkpE2mokMY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element in the chart at coordinates [0.274, 0.822, 0.723, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the \"Number of Friends\" with a value of 20."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "1BzcOfWvJV44Tbhm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinate [0.723, 0.846, 0.751, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label \"20\" for the bar of \"Number of Friends\"."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "UwRRHv3HpAVKvsCn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at the coordinate [0.043, 0.766, 0.262, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label for \"Number of Active Friends\"."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "WKWOmERj7tC3bjb9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.274, 0.577, 0.916, 0.642] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the \"Messages Received\" category with a value of 28.6."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "YXmQ9tJSYTqtVhOb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Messages Sent}[0.274, 0.658, 0.851, 0.724] longer than {Number of Active Friends}[0.274, 0.740, 0.622, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "8auhAvQ8EfJUrWa9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bar is immediately above {Group Chats Participated}[0.274, 0.332, 0.773, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Text Conversations}[0.274, 0.413, 0.685, 0.479]"
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "bq7fGW7pwAH8ECdd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for {Number of Friends}[0.274, 0.822, 0.723, 0.887] and {Number of Active Friends}[0.274, 0.740, 0.622, 0.805] equal?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "hnDXRriA4I4opWQc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Number of Active Friends}[0.274, 0.740, 0.622, 0.805] greater than {Text Conversations}[0.274, 0.413, 0.685, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "R5BRvB73kbU8soa3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the area [0.274, 0.822, 0.751, 0.863]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains one bar and one label. The bar represents the category \"Number of Friends\" with a value of 20, and its coordinate is [0.274, 0.822, 0.723, 0.887]. The label \"20\", represented by the coordinates [0.723, 0.846, 0.751, 0.863], indicates the value of the bar."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "Dmyg0QpVQBSBKFb4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area [0.274, 0.577, 0.960, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar and a label. The bar represents the category \"Messages Received\" with a value of 28.6, the coordinates for this bar are [0.274, 0.577, 0.916, 0.642]. The label \"28.6\", indicated by [0.916, 0.601, 0.960, 0.618], represents the value of the bar."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "jGSaJYRkXBDx4o1m",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.274, 0.168, 0.523, 0.210]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar and a label. The bar, with coordinates [0.274, 0.168, 0.490, 0.234], represents the category \"Relationship Initiation\" with a value of 9.6. The label \"9.6\", indicated by  [0.490, 0.192, 0.523, 0.210], represents the value of the bar."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "qRy5MIIfw1nktXlL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.274, 0.332, 0.775, 0.373]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar and a corresponding text value. The bar, located at [0.274, 0.332, 0.773, 0.397], represents the \"Group Chats Participated\" category with a value of 22.2. The value \"22.2\" is denoted by the label located at [0.773, 0.356, 0.816, 0.373]."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "5UnN34m44MYG6yro",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinate [0.019, 0.435, 0.036, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, named Indicator."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "zADSGtsf7Epkl86m",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the object at coordinates [0.721, 0.845, 0.749, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number 5 as an x-axis tick label."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "PqgS1Q1nR5YoAZbZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the block with coordinates [0.321, 0.154, 0.468, 0.229] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The indicator \"Housing\" with a value of 1.8."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "F6i5FpA6t1kjHOzc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located at [0.321, 0.277, 0.627, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value 3.4."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "XUQ4KMPhGMmd1WhB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Housing}[0.321, 0.154, 0.468, 0.229] shorter or taller than the bar for {Currency}[0.321, 0.248, 0.599, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "reDxNXO2PxEJNK19",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {GDP}[0.656, 0.840, 0.684, 0.857] or {Inflation}[0.501, 0.746, 0.528, 0.763] bar in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "2RIDNbCtJsIQOWuq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Consumer Confidence}[0.321, 0.060, 0.419, 0.135] nearer to the origin compared to {Unemployment}[0.321, 0.623, 0.820, 0.698]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "L847IMqUpi0zuImr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is shorter, the {Trade Balance} bar<{Trade Balance bar}[0.280, 0.342, 0.321, 0.417]> or the {Interest Rate} bar<{Interest Rate bar}[0.321, 0.529, 0.341, 0.604]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interest Rate."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "jrVAXU7GMntekuTl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area [0.280, 0.342, 0.321, 0.417]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one object - a bar indicating the Trade Balance value at -0.5. Also, the numerical value is labeled on the bar."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "2atQgAK9MtVAQb1L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within the coordinates [0.435, 0.248, 0.943, 0.510]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two objects: \n1. The bar of Stock Market with coordinates [0.321, 0.435, 0.943, 0.510] illustrates a value of 7.6.\n2. The label \"7.6\" is positioned at [0.943, 0.464, 0.970, 0.482], indicating the value represented by the bar."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "I7Ge2e92l6n7mpMc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area defined by [0.197, 0.811, 0.235, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label \"GDP\" for the y-axis, which is one of the economic indicators displayed on the chart."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "RntYYOaKOtS1T1ie",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.315, 0.939, 0.326, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an x-axis label, specifically the number \"0\"."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "6rai2iiG2NazYUhd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Activity}[0.019, 0.440, 0.036, 0.506]"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "6rOB4bxccVbjjD5N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat activity is represented by the bar at [0.183, 0.818, 0.921, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exercise"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "Bdau1daSBse2nVaB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage value does the bar for {Cooking} at [0.183, 0.481, 0.510, 0.549] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "14.4%"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "nsz9a6MeV0ZYd22w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at [0.376, 0.254, 0.426, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value of the Studying activity bar."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "c87Sw2SiA3LMIsjW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Studying}[0.183, 0.229, 0.376, 0.296] longer than the bar for {Watching_TV}[0.183, 0.144, 0.288, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "j34R56bEbEN1xLja",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is longer, the one representing {Socializing}[0.183, 0.734, 0.819, 0.802] or {Exercise}[0.183, 0.818, 0.921, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exercise."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "klEwJh4C6OZAvTgH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Traveling}[0.183, 0.397, 0.456, 0.465] shorter than the one for {Gaming}[0.183, 0.566, 0.587, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "ndvcI7bS20JfO0eh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Working}[0.183, 0.313, 0.419, 0.380] and {Cooking}[0.183, 0.481, 0.510, 0.549], which activity has a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cooking."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "ftvmlAHZ5c3o0mxZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present in coordinates [0.183, 0.818, 0.981, 0.861]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the bar for the Exercise activity and its accompanying percentage text. The bar is shaded in a color denoted by '#f08080', positioned at [0.183, 0.818, 0.921, 0.886] and the text displays '32.5%', which is located at [0.921, 0.843, 0.981, 0.861]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "se0ZvcGSYirc5CHw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.183, 0.060, 0.307, 0.102]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar and its corresponding percentage value. The bar coordinates are [0.183, 0.060, 0.258, 0.127]. This represents the activity 'Doing_Nothing' and its associated percentage, 3.3%. The percentage text is located at [0.258, 0.085, 0.307, 0.102]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "G6JUujIaPYF15L7a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the area [0.183, 0.397, 0.571, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar that represents the activity 'Cooking' with coordinates [0.183, 0.397, 0.510, 0.549], as well as its corresponding percentage number '14.4%' which is located at [0.510, 0.506, 0.571, 0.524]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "JWicYJeD0ToIfIAg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within [0.183, 0.566, 0.648, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1. A bar representing the 'Gaming' activity, measured at 17.8%, located at [0.183, 0.566, 0.587, 0.633]\n2. Text displaying the value '17.8%', also representing the 'Gaming' activity, located at [0.587, 0.591, 0.648, 0.608]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "zpR9esg7UrlscDyX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at the coordinates [0.182, 0.833, 0.947, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The investment in Fintech industry, amounting to 780 million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "6Kxq36mKJBrm9R3z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is located at the coordinates [0.134, 0.778, 0.172, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"AI/ML\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "KOXJ1xAi0oSlzMdz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value indicated at the coordinates [0.260, 0.107, 0.275, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "80 million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "OkFTMuliX54vzGRC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at the coordinates [0.035, 0.442, 0.172, 0.456]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The industry \"Enterprise Software\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "7KgZ8pGRz72nJzLh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the investment in {Manufacturing Industry}<r>[0.182, 0.330, 0.456, 0.397]</r> higher than that in the {Education Industry}<r>[0.182, 0.246, 0.407, 0.313]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "zH7IL4hf9X8f1y7R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Fintech bar}<r>[0.182, 0.833, 0.947, 0.901]</r> taller than the {HealthCare bar}<r>[0.182, 0.666, 0.721, 0.733]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "hsCfDd7MdEguvSpV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {AI/ML bar}<r>[0.182, 0.750, 0.770, 0.817]</r> and the {Mobility bar}<r>[0.182, 0.498, 0.549, 0.565]</r>, which industry received more investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI/ML."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "GjoH2dx9Oup9bqvG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the investment in {Ecommerce}<r>[0.182, 0.582, 0.672, 0.649]</r> and {Enterprise Software}<r>[0.182, 0.414, 0.495, 0.481]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ecommerce."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "wU8DtIKUPY6m8qv1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.182, 0.037, 0.985, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar chart depiction of the investment trend in venture capital markets across multiple industries and their investment values, including Fintech, AI/ML, HealthCare, Ecommerce, Mobility, Enterprise Software, Manufacturing, Education, AR/VR and Gaming. Each bar in the chart corresponds to an industry and the length of the bar represents the amount of investment in million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "xwvofhmIvU4Dzqpe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area between coordinates [0.182, 0.414, 0.495, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar that represents the investment in the Enterprise Software industry, amounting to 320 million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "CKcltyYFc7RfM0UZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.182, 0.582, 0.672, 0.649]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a blue bar representing the investment in Ecommerce, amounting to 500 million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "p08uXn8wgNDxJ4v1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within [0.182, 0.246, 0.407, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar showcasing the investment in the Education sector, which is valued at 230 million dollars."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "d1G8ZeGzX3i0PNEU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.449, 0.029, 0.508] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Location."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "x2xgKi9sbBoKII9X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the eighth tick on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.185, 0.263]"
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "Nz2K4yFHK1ellIP7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {the x-axis value} at [0.185, 0.375, 0.219, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "e8OWjnkwDetGFeoY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat office or location is shown in [0.091, 0.688, 0.175, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Tokyo Office}[0.091, 0.688, 0.175, 0.702]."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "nRiTtVbsVx03XhOQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Bangkok Office\"}[0.185, 0.401, 0.511, 0.470] shorter or taller than {the bar representing \"Seoul Office\"}[0.185, 0.573, 0.625, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "y42Rtw7ZU0azxHiJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the visit from \"Seoul Office\"}[0.185, 0.573, 0.625, 0.642] larger than that from {\"Jakarta Office\"}[0.185, 0.315, 0.474, 0.384]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "l1OImF4MnM8r0yTB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing Offfices}[[0.185, 0.401, 0.511, 0.470], [0.185, 0.573, 0.625, 0.642], [0.185, 0.315, 0.474, 0.384]] consistently taller than {the bar representing \"Hanoi Office\"}[0.185, 0.057, 0.343, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "Kcj3pHXfC5G8ZxUq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Beijing Office\"}[0.185, 0.745, 0.805, 0.814] and {\"Tokyo Office\"}[0.185, 0.659, 0.664, 0.728], which one has a higher Visitation percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Beijing Office."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "USqr0oWJGWTmGyFn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.185, 0.831, 0.936, 0.900]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar in the region. The bar represents the visitation rate at the Shanghai office which is 75.6%."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "gU732kzEhJO0HHjJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.185, 0.343, 0.391, 0.099]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes one bar which represents the visitation percentage for the Hanoi office. The visitation rate is 15.9%."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "InRkzWfqrloUee2A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.185, 0.745, 0.805, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object:\n1. The bar represents that the visitation rate of the Beijing Office is 62.4%, and the coordinate range is [0.185, 0.745, 0.805, 0.814]."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "kqDBPEFDMUSqluQV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.185, 0.057, 0.343, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1. Hanoi Office bar that shows visitation percentage, and its coordinate range is [0.185, 0.057, 0.343, 0.126].\n2. The text annotation (15.9%) next to the bar, which indicates the visitation percentage."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "HzVEvYUIHp41aQmf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object within the coordinates [0.229, 0.047, 0.958, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main plot area where the bars are plotted."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "eJF9TUOChnNU9Mz1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.417, 0.019, 0.769, 0.041] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This text is the subtitle, \"Investment Strategy Performance\"."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "rZlmmkiIn8DfLvMf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment strategy is represented at the coordinate [0.229, 0.740, 0.699, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The strategy}<r>[0.229, 0.740, 0.699, 0.805]</r> is Low Risk."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "87Oc7l7eucqfNrE1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the bar [0.229, 0.822, 0.923, 0.887] labeled?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar is labeled as {High Risk}<r>[0.229, 0.822, 0.923, 0.887]</r>."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "8ikiJqIScalEhmHq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Return Rate} Bar for {High Risk Investing}[0.229, 0.822, 0.923, 0.887] larger than that for {Low Risk Investing}[0.229, 0.740, 0.699, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "cocSaHi3tFi9jYD0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is next to the {Income Investing} bar [0.229, 0.332, 0.726, 0.397] in a downward direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Small-Cap Investing}[0.229, 0.250, 0.644, 0.315]."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "kaJFjTjr5rfrgnow",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the return from {Value Investing}[0.229, 0.413, 0.661, 0.479] higher than {Income Investing}[0.229, 0.332, 0.726, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "N7Dz2QHJAB2jfLBj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat investment strategy has a lower return rate, {High-Risk}[0.229, 0.822, 0.923, 0.887] or {Low-Risk}[0.229, 0.740, 0.699, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Low Risk."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "UeA9TliwRRyiPO1a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the region [0.229, 0.047, 0.958, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the main region of the graph that contains the horizontal barsof each investing strategy's Return Rate, ranging from Bond Investing to High Risk."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "iZ3Ub80zqLCrOdui",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region from [0.229, 0.740, 0.699, 0.805] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of a single bar representing the return rate of \"Low Risk\" investing strategy."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "HRqJ1bGGaTAUNtDm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.692, 0.216, 0.746, 0.419] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains no objects. It's an empty space between the bars representing the return rates."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "ByNfhdZMeJ6yYgM1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents found within the region [0.524, 0.964, 0.663, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label, which is \"Return Rate (%)\"."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "F87vWRzgUCocCNGT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the vertical axis represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical axis represents {Social Network}<r>[0.019, 0.408, 0.036, 0.538]</r>."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "8AEmYFu4q63xIKGK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the bar that refers to the performance of LinkedIn?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are <r>[0.144, 0.566, 0.758, 0.633]</r>."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "k3HoZJpV2Wqft5Ef",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the percentage scale on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range of the percentage scale is from {0%}<r>[0.139, 0.939, 0.150, 0.957]</r> to {10%}<r>[1.036, 0.939, 1.058, 0.957]</r>."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "1MOzvTbUpC1rE8zt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the performance of {Twitter}<r>[0.144, 0.734, 0.821, 0.802]</r> exceed that of {Instagram}<r>[0.144, 0.650, 0.803, 0.717]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "iNUnCdXN9hZ0EPPD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform performed the lowest, {WeChat}<r>[0.144, 0.144, 0.505, 0.212]</r> or {Tumblr}<r>[0.144, 0.060, 0.370, 0.127]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tumblr."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "jQxqQzPBiJZRDTpo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Pinterest's performance}<r>[0.144, 0.313, 0.532, 0.380]</r> closer to the performance of {LinkedIn}<r>[0.144, 0.566, 0.758, 0.633]</r> or {WeChat}<r>[0.144, 0.144, 0.505, 0.212]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "WeChat."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "R4a32k5c3nqjgDzS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area broadcasting the performance of each social network <r>[0.144, 0.019, 0.969, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents ten horizontal bars reflecting the performance of ten social networks. From top to bottom they are: Facebook, Twitter, Instagram, LinkedIn, Snapchat, Reddit, Pinterest, WhatsApp, WeChat, and Tumblr. The length of each bar corresponds to its specific rating out of 10. Moreover, each bar is labeled with its respective percentage."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "BRMQ0oT425s7AtWR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area <r>[0.144, 0.818, 0.930, 0.886]</r> tell us?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a blue bar that represents the performance of Facebook, which is 8.7%."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "wzL7OkrvjNWgEn13",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the area from <r>[0.144, 0.566, 0.758, 0.633]</r> to <r>[0.144, 0.060, 0.370, 0.127]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers five social networks, starting from LinkedIn and going down to Tumblr. Each has been given a performance rating out of 10, represented by the length of the horizontal bar for each network."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "ixexdHQBhkICupre",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.015, 0.450, 0.029, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, named Platform."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "n2XIfbiVIf9K2uOG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the third bar from the top in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.115, 0.658, 0.765, 0.724]"
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "zww2LMNIJd4MBx6L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.357, 0.019, 0.723, 0.041] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the coordinates for the subtitle, which reads \"Content Sharing Rate on Different Platforms\"."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "IwLZJ500hOZSq3nt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at [0.925, 0.764, 0.981, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to text marking the percentage value of the bar, which reads \"67.25%\"."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "EvcEeHwdAenUhJH9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, is {the bar for Instagram}[0.115, 0.740, 0.924, 0.805] higher or lower than {the bar for Facebook}[0.115, 0.822, 0.867, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "Y90yvqogdfeah20H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {TikTok bar}[0.115, 0.168, 0.732, 0.234] longer or shorter than the {WhatsApp bar}[0.115, 0.332, 0.825, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "i1Q6YIBe0NBJkalY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of their bar lengths, how does {Email}[0.115, 0.087, 0.389, 0.152] perform in comparison to {LinkedIn}[0.115, 0.577, 0.470, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is lower."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "PEIy45TxClBGgsBQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Instagram bar}[0.115, 0.740, 0.924, 0.805] on the left or right of the {Snapchat bar}[0.115, 0.658, 0.765, 0.724]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they are aligned vertically."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "PJgAf7z1rhGCGBMn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what is represented in the region from [0.115, 0.087, 0.389, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features a single light blue bar representing the platform \"Email\". Its height signifies the sharing rate per user, which is \"22.75%\", as visible by an adjacent text label on the right of the bar."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "fHvaoFDo2c0gpuoH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.035, 0.766, 0.106, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the platform category \"Instagram\", situated on the Y-axis."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "OeA46zyz1VUo9I2G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the area [0.111, 0.939, 1.087, 0.957] encompasses."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the X-axis, which mark the \"Shares per user (%)\" ranging from 0% up to 80% in increments of 10."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "NlEdZgXan4NN8Zvv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted within [0.115, 0.740, 0.924, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a bar in the plot representing the content sharing rate on the \"Instagram\" platform. The bar stretches to a length representing \"67.25%\" as text displays this number beside the bar's end point."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "6rz1a7z1ObN3Fozj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.019, 0.190, 0.036, 0.239]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "zavU8u0EvfIRiI49",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the volume of Airway transportation} at [0.437, 0.952, 0.631, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Volume (in thousands)."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "Hbe3MsjBGjuR7HLZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the first x-axis tick label and coordinate} in the Shipping subplot indicate at [0.103, 0.327, 0.114, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "PAexOUXMmU3K4ffv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first blue bar in the \"Rail\" subplot}[0.109, 0.422, 0.449, 0.443] longer than {the last blue bar in the \"Airways\" subplot}[0.109, 0.873, 0.636, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "aWLRDrxmGJVwfCQx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x-tick labeled \"0\"}[0.109, 0.311] on the left side of {the x-tick labeled \"1000\"}[0.959, 0.311]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "CCbGaVtOUgRsbSWO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the top bar in the \"Shipping\" subplot}[0.109, 0.126, 0.304, 0.147] represent a greater range than {the middle bar in the \"Airways\" subplot}[0.109, 0.795, 0.534, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "1endGovd7uGWo3X4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.109, 0.709, 0.959, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the \"Airways\" subplot showing annual transportation volumes over 7 years. The bars illustrate the volume for each year, starting from a range of 0 to 900 thousand, located from the leftmost side of the x-axis to the endpoint rightmost."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "Abj3UI6uxHnKSUvW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in [0.109, 0.413, 0.959, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures the \"Rail\" subplot, illustrating yearly transportation data over 7 years with bars mapped from values ranging from 200 to 430. The years are labeled along the vertical axis from \"Year 1\" to \"Year 7\"."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "4Dx6drQ1gmWQD3QZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.109, 0.117, 0.959, 0.311] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section displays the \"Shipping\" subplot which plots yearly shipping volumes. It includes seven blue bars representing volumes for seven consecutive years starting from year 1 to year 7, showing a fluctuation in volume across different years."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "419gyhfyV2dBJ3jT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the data series represented by {this bar}<r>[0.213, 0.822, 0.701, 0.853]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "0NhaJM0VVuuTet82",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual content at {these coordinates}<r>[0.184, 0.867, 0.201, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "NeLNkRemnNLq29lz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide the label for the y-axis tick at coordinates <r>[0.213, 0.915]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "3kQeH8hDXP4CvqTN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar at}<r>[0.213, 0.861, 0.762, 0.892]</r> have a higher value than {the one at}<r>[0.213, 0.899, 0.798, 0.930]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "RPuCyofIepr9Hg0p",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2021 AI bar}<r>[0.213, 0.106, 0.701, 0.137]</r> wider than {the 2025 AI bar}<r>[0.213, 0.861, 0.762, 0.892]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "inLm1oz9n4J5UyWE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the y-coordinate start position of {Blockchain bar for 2021}<r>[0.213, 0.068, 0.823, 0.098]</r> with that of {Blockchain bar for 2023}<r>[0.213, 0.445, 0.774, 0.476]</r>. Which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2023}<r>[0.445]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "DdGQAGcTFhaANxtu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the content encompassed within <r>[0.213, 0.251, 0.981, 0.369]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds a bar chart section for the year 2022. It includes three bars corresponding to Blockchain <r>[0.213, 0.256, 0.798, 0.287]</r>, AI <r>[0.213, 0.295, 0.725, 0.326]</r>, and Quantum Computing <r>[0.213, 0.333, 0.920, 0.364]</r>, as well as their associated y-axis labels."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "NjWkDDVGWlq4QPwg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what appears in the bounds of <r>[0.213, 0.440, 0.981, 0.558]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 2023 graph part of the bar chart with three bars labeled Blockchain <r>[0.213, 0.445, 0.774, 0.476]</r>, AI <r>[0.213, 0.483, 0.737, 0.514]</r>, and Quantum Computing <r>[0.213, 0.522, 0.908, 0.553]</r>, and the year label \"2023\" <r>[0.571, 0.402, 0.623, 0.432]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "KBGIg5M6RAe91I3q",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the section demarcated by <r>[0.213, 0.628, 0.981, 0.747]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section features a bar chart for 2024 with bars dedicated to Blockchain <r>[0.213, 0.634, 0.737, 0.664]</r>, AI <r>[0.213, 0.672, 0.749, 0.703]</r>, and Quantum Computing <r>[0.213, 0.711, 0.823, 0.741]</r>, including a subtitle placed at <r>[0.571, 0.591, 0.623, 0.621]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "7ri8GYYwUrs6stm7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bars for the indicator \"Wildlife Conservation\"}<r>[0.103, 0.321, 0.128, 0.445]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bars for \"Wildlife Conservation\" is light beige (#f4d3b4)."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "Hycn7HqYrSjsMYVs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label for {the y-axis}<r>[0.015, 0.422, 0.029, 0.552]</r> located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the y-axis is located to the left of the chart, in a vertical orientation."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "jmhKTICBrgnaF37C",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {year label}<r>[0.227, 0.939, 0.252, 0.957]</r> under the second group of bars from the left?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year label under the second group of bars from the left is \"2016\"."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "BbPNav49LrhPOB0m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of trend does {the blue bar for \"Carbon Footprint\"}<r>[0.227, 0.593, 0.252, 0.927]</r> have compared with {the adjacent blue bar for \"Carbon Footprint\"}<r>[0.103, 0.575, 0.128, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "il5OQGqZbG9sT2jw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the bar for \"Wildlife Conservation\" in 2020}<r>[0.723, 0.223, 0.748, 0.321]</r> or {the bar for \"Green Energy\" in 2020}<r>[0.723, 0.321, 0.748, 0.533]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Green Energy\"."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "SDCkrPk9K80jr2tu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Forestation\" bar in 2017}<r>[0.351, 0.522, 0.376, 0.610]</r> have higher value than {the \"Forestation\" bar in 2016}<r>[0.227, 0.522, 0.252, 0.593]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "I1vfRB81UFsQWP50",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area <r>[0.787, 0.055, 0.985, 0.165]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the chart's legend, which features four color-text labels. Each label represents a different environmental protection indicator: Carbon Footprint (blue), Forestation (light-green), Green Energy (orange), and Wildlife Conservation (light-beige)."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "6aUE4MrhrxuyNR1O",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region <r>[0.346, 0.568, 0.381, 0.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the bars representing the values for every indicator in the year 2017, and their respective labels."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "TRstcCGXQGtZpKJU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the box <r>[0.071, 0.047, 0.780, 0.927]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The box contains the actual bar chart. It's in this region where the four environmental indicators (Carbon Footprint, Forestation, Green Energy, and Wildlife Conservation) are displayed by the height of shaded bars for each year from 2015 to 2020."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "aFliQIhRZvaK1Wo2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the plot represented by the coordinates [0.209, 0.019, 0.648, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Progress of Clean Energy Technologies (2000 - 2016)."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "I1Nfd3ySfMcEkaLu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the item referenced by the coordinates [0.873, 0.088, 0.939, 0.106] in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Cell."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "kZSRETZm8GPbCyC8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the text located at the coordinates [0.699, 0.583, 0.730, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the progress of Hydro Power in 2016 as 20%."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "VTmXzGeCPEVdrqhF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Wind Turbine in 2004}[0.239, 0.634, 0.266, 0.927] better progressed than {Hydro Power in 2008}[0.428, 0.707, 0.455, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "NjP7DB5i8CwamPEy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Solar Cell in 2012}[0.537, 0.487, 0.564, 0.927] have a higher progress percentage than {Thermal Power in 2000}[0.184, 0.634, 0.211, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "OEmkMNnt7N6eoloW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Thermal Power in 2012}[0.591, 0.414, 0.618, 0.927] and {Solar Cell in 2016}[0.672, 0.340, 0.699, 0.927], which technology has better progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Cell."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "S2SeERrSDLWkpZUB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.828, 0.055, 0.982, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region portrays the legend of the subplot. It includes four types of energies, each represented by a distinct color: \n1. {Wind Turbine}[0.873, 0.062, 0.963, 0.080] in blue, \n2. {Solar Cell}[0.873, 0.088, 0.939, 0.106] in orange,\n3. {Hydro Power}[0.873, 0.115, 0.961, 0.132] in light blue, and \n4. {Thermal Power}[0.873, 0.141, 0.976, 0.158] in light orange."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "tciKQUOnfXAz73gC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.103, 0.560, 0.672, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the progress bars of Wind Turbine technology for all years ranging from 2000 to 2016."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "wHd0SgKfRLirODzS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we see in the area defined by [0.015, 0.432, 0.029, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region highlights the Y-axis label of the chart, which refers to the 'Progress (%)'."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "yXXQ7SqevdUssYmg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title indicated by [0.334, 0.019, 0.713, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Market Competition Analysis and Comparison\"."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "x35PvqiGltrdUzmH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please tell me the color of the rectangle labeled as Apple located at [0.870, 0.064, 0.898, 0.076] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle color representing Apple is blue."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "OtBBT6hCEfi7NdrQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label does the coordinates [0.909, 0.088, 0.958, 0.106] in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is for {Google}<r>[0.909, 0.088, 0.958, 0.106]</r>."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "kmlTeFzu3QtCAmG6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.493, 0.964, 0.554, 0.981] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to {the x-axis label}<r>[0.493, 0.964, 0.554, 0.981]</r> which is named \"Quarters\"."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "dX6AtGClMRjb8DJa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bar of Google in Q2 [0.369, 0.560, 0.413, 0.927] with Microsoft in Q2 [0.457, 0.634, 0.501, 0.927], which one recorded higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Microsoft}<r>[0.457, 0.634, 0.501, 0.927]</r> recorded higher sales in Q2 compared to {Google}<r>[0.369, 0.560, 0.413, 0.927]</r>."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "e2xm6cOzSWHnc9eS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the bars representing the sales for Apple in Q1 [0.104, 0.193, 0.148, 0.927] and Q4 [0.766, 0.267, 0.811, 0.927], did the sales increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "8WfjFp0IUflzcVKK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Google's sales in Q3}<r>[0.590, 0.516, 0.634, 0.927]</r> higher than {Amazon's sales in Q3}<r>[0.634, 0.370, 0.678, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "oRMxFZet0fKt3mQ0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf the two bars {Microsoft in Q1}<r>[0.236, 0.707, 0.281, 0.927]</r> and {Apple in Q1}<r>[0.104, 0.193, 0.148, 0.927]</r>, which is taller signifying higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apple."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "WO6MRVPwohzTh9aw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the area [0.865, 0.055, 0.978, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a legend with color-text labels for four entities: {Apple is blue}<r>[0.870, 0.064, 0.898, 0.076]</r>; {Google is orange}<r>[0.870, 0.090, 0.898, 0.102]</r>; {Amazon is in a greyish-brown color}<r>[0.870, 0.116, 0.898, 0.128]</r>, and {Microsoft is in a teal color}<r>[0.870, 0.142, 0.898, 0.155]</r>."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "WXcQhahLF40be2FG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.043, 0.040, 0.052, 0.938]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the y-axis and includes numerical values ranging from 0 up to 60, which represent the 'Sales (in millions)'. Each major tick on this axis represents an interval of 10 units."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "4VNIKUG77w1vKcPZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.634, 0.663, 0.722, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the Microsoft's Q3 bar is located, which refers to sales of 18 million."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "CVHiOhdMVXaq405j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.104, 0.193, 0.148, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this area is located the bar representing Q1 sales of Apple having a value of 50 million."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "iRhd24ItAUcHVfcu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at coordinates [0.129, 0.603, 0.205, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the bar representing Life Expectancy for the Rural category."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "alSZL3VO9mRyPC0D",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.043, 0.293, 0.076, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label representing the value '150'."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "VECYChIXWL9wBac3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label represented by the coordinates [0.828, 0.153, 0.939, 0.176] in the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label 'Water Usage'."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "1ySqizH9F4GEZYrF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for rural life expectancy}<r>[0.129, 0.603, 0.205, 0.903]</r> taller than {the bar for urban life expectancy}<r>[0.636, 0.631, 0.712, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "1J2m5HIG6Sh3AKSn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for CO2 Emission in rural areas}<r>[0.205, 0.807, 0.281, 0.903]</r> higher than {the bar for Water Usage in rural areas}<r>[0.281, 0.102, 0.357, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "qkZW9D6Nm2a4uxOD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the bar for 'Plastic Waste' in urban areas}<r>[0.865, 0.443, 0.941, 0.903]</r> relative to {the bar for 'Life Expectancy' in urban areas}<r>[0.636, 0.631, 0.712, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "WEO3ztQgS7VbuZYR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.772, 0.074, 0.973, 0.220]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend area containing several color-text pairs, each includes a colored rectangular patch and a label: {Life Expectancy}[0.828, 0.083, 0.966, 0.106], {CO2 Emission}[0.828, 0.118, 0.947, 0.141], {Water Usage}[0.828, 0.153, 0.939, 0.176] and {Plastic Waste}[0.828, 0.188, 0.943, 0.211]."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "QkEqSdMClNTfObIA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the area [0.636, 0.631, 0.712, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the bar representing 'Life Expectancy' in the 'Urban' category}[0.636, 0.631, 0.712, 0.903]."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "uIwyfSqQ8DpL8Snl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region [0.281, 0.102, 0.357, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the bar for 'Water Usage' in the 'Rural' category}[0.281, 0.102, 0.357, 0.903]."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "8HVSlWzUhfGsOTv6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the textual labels at the coordinates [0.143, 0.919, 0.209, 0.942] and [0.866, 0.919, 0.944, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are {x-axis labels for the categories \"Ancient\"}<r>[0.143, 0.919, 0.209, 0.942]</r> and {\"Future AI\"}<r>[0.866, 0.919, 0.944, 0.942]</r>, respectively."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "ltSHR8x3oAQOUQ8z",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text \"Living Population (millions)\" located at [0.731, 0.083, 0.966, 0.106] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the name of the {first series in the legend}<r>[0.731, 0.083, 0.966, 0.106]</r>."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "FWcupXKy1VEScJjP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the data based on the text at [0.019, 0.172, 0.036, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the {y-axis label}<r>[0.019, 0.172, 0.036, 0.793]</r>, titled \"Units (In millions for Population and Index for Quality)\"."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "DK8K4BHco6xPnUYH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the size of the bar representing the \"Ancient\" category at [0.139, 0.182, 0.176, 0.903] relate to the bar representing the \"Future AI\" category at [0.868, 0.753, 0.905, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {bar for the \"Ancient\" category}<r>[0.139, 0.182, 0.176, 0.903]</r> is taller than the {bar for the \"Future AI\" category}<r>[0.868, 0.753, 0.905, 0.903]</r>."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "Xl5IYx3ifJEcXtWv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Living Quality bar}[0.358, 0.876, 0.394, 0.903] for the \"Medieval\" era higher or lower than the {Living Quality bar}[0.722, 0.855, 0.759, 0.903] for the \"Modern\" era?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "sfSo4rMPXaAzNK9M",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is the color box for {Living Quality}<r>[0.683, 0.120, 0.717, 0.136]</r> located higher or lower than the color box for {Living Population (millions)}<r>[0.683, 0.085, 0.717, 0.101]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "mP6tJKlCUl6wVgvU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.676, 0.074, 0.973, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It lists two datasets, {Living Population (millions)}<r>[0.731, 0.083, 0.966, 0.106]</r> with a corresponding color box at [0.683, 0.085, 0.717, 0.101] and {Living Quality}<r>[0.731, 0.118, 0.851, 0.141]</r> with a corresponding color box at [0.683, 0.120, 0.717, 0.136]."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "3WmPJEIt2ptUF9bF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the components included within the region from [0.504, 0.453, 0.540, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features a single bar from the chart. It's the {Living Population (millions)}<r>[0.504, 0.453, 0.540, 0.903]</r> bar for the \"Industrial\" scenario."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "nKm1y30xRjShwzZZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area marked by [0.043, 0.053, 0.087, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a numeric label for the y-axis: {\"1400\"}<r>[0.043, 0.053, 0.087, 0.076]</r>."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "YLT1EhhraRTy9Pkl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart located at [0.743, 0.919, 0.886, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, Discouragement."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "HzM4Mo2P5iJmDABf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the fourth y-axis label located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.054, 0.435, 0.076, 0.459]"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "fiTTp5pGJGrnQcSS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation of \"Innovative Thinking\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red {the Innovative Thinking}[0.746, 0.155, 0.781, 0.171]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "0f1js5FtSpHV45dA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are specified at [0.795, 0.083, 0.898, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Imagination}[0.795, 0.083, 0.898, 0.106]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "VOnYAOlimY8Z3xZZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Imagination bar for Encouragement}[0.129, 0.307, 0.213, 0.903] taller than {the Innovative Thinking bar for Discouragement}[0.857, 0.658, 0.941, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "8aqgu2DhsNnNSByx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Ongoing Discovery bars}[[0.213, 0.123, 0.297, 0.903], [0.773, 0.574, 0.857, 0.903]] generally taller than {the Innovative Thinking bars}[[0.297, 0.253, 0.381, 0.903], [0.857, 0.658, 0.941, 0.903]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "0OuIzmPdjiEzP95I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Imagination bar for Encouragement}[0.129, 0.307, 0.213, 0.903] and {Imagination bar for Discouragement}[0.689, 0.521, 0.773, 0.903], which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Encouragement."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "rJ0jTl42ebUwX7Bi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color handle of {Ongoing Discovery}[0.746, 0.120, 0.781, 0.136] placed higher than that of {Innovative Thinking}[0.746, 0.155, 0.781, 0.171] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "CV6lv9FSRKymY0F8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.739, 0.074, 0.973, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend with three items. The first item is {Imagination}[0.795, 0.083, 0.898, 0.106] with color blue; the second item is {Ongoing Discovery}[0.795, 0.118, 0.959, 0.141] with color green; the third one is {Innovative Thinking}[0.795, 0.153, 0.966, 0.176] with color red."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "qIw2tdz3f1FVDXPe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is included in [0.129, 0.307, 0.381, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this rectangular area, there are bars representing the Encouragement's effect on three factors: {Imagination}[0.129, 0.307, 0.213, 0.903], {Ongoing Discovery}[0.213, 0.123, 0.297, 0.903], and {Innovative Thinking}[0.297, 0.253, 0.381, 0.903]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "iGeal4apNaCtjEFL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.689, 0.521, 0.941, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region there are 3 bars. These bars represent the results of Discouragement on three areas: {Imagination}[0.689, 0.521, 0.773, 0.903], {Ongoing Discovery}[0.773, 0.574, 0.857, 0.903], and {Innovative Thinking}[0.857, 0.658, 0.941, 0.903]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "z4xv7z6miA9Aommq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area covered by [0.104, 0.025, 0.965, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle, which says \"Effect of Encouragement and Discouragement on Creativity and Design Innovation\""
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "MIRy5xuOGByiNiuH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category is at the coordinate [0.194, 0.806, 0.213, 0.973] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Development."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "aoMq5ruqO70hyWAa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y_label in the chart at the coordinate [0.011, 0.275, 0.024, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Design (%)."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "iPFR0IPf3WpR8NYe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value corresponds to the bar [0.666, 0.140, 0.708, 0.793] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar belongs to {Aesthetic Sensibility} and it reflects a value of 62.3%."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "BnWxqiv5CeaHrpgH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at [0.194, 0.096, 0.232, 0.793] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents {Product Development}<r>[0.194, 0.096, 0.232, 0.793]</r> with a value of 63.2%."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "d8IXtusxdqUk0TW3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Risk-taking}<r>[0.623, 0.397, 0.661, 0.793]</r> higher or lower than that representing {Problem Solving}<r>[0.337, 0.291, 0.375, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "sjjXwOZvZE8f8oVI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Idea Generation}<r>[0.099, 0.401, 0.137, 0.793]</r> to the left or right of {Strategy Creation}<r>[0.242, 0.238, 0.280, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "thNzK03yJoQNue2o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Collaborative Thinking}<r>[0.385, 0.177, 0.423, 0.793]</r> and {Product Development}<r>[0.194, 0.096, 0.232, 0.793]</r>, which has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Development."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "NWtKrreSYN5HmWrz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {System Thinking}<r>[0.909, 0.463, 0.947, 0.793]</r> to the left or right of {Visionary Thinking}<r>[0.289, 0.344, 0.328, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "u76mYzPEPuOPx1FA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.526, 0.0, 0.566, 0.793]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {Prototyping}<r>[0.528, 0.127, 0.566, 0.793]</r> bar, reflecting a value of 60.4%."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "guMHV06E9FdiJhc9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.385, 0.0, 0.423, 1.0] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar of {Collaborative Thinking}<r>[0.385, 0.177, 0.423, 0.793]</r> with a value of 55.9% and the corresponding x-axis label located at [0.318, 0.806, 0.404, 0.979]."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "meFnOulLuZ6qz10g",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the coordinate [0.804, 0.806, 0.880, 0.958]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinates contain {Pattern Recognition}<r>[0.804, 0.806, 0.880, 0.958]</r> label which is on the x-axis."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "pyKXqc0p9rDGCnIu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.713, 0.00, 0.785, 1.00] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the segment of {Artistic Expression}<r>[0.713, 0.265, 0.785, 0.793]</r> bar, corresponding to a value of 47.9%, and the associated x-axis label at [0.713, 0.806, 0.785, 0.950]."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "bMC0ZKW1rixGbDJE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat energy source is specified by the coordinates [0.322, 0.761, 0.371, 0.859] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hydro."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "HwV0fLG6nze3utpF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Year 3 bar}[0.213, 0.267, 0.296, 0.403]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #ffa600, which corresponds to orange."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "52j6zVcIuEHctNbk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the area [0.121, 0.297, 0.204, 0.431]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the amount of Fossil Fuels used in Year 3, which is 73.2%."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "0X02y5bb4Aicc1Eq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label of {the legend item}<r>[0.662, 0.077, 0.710, 0.112]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 3."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "tfXiQxyqqriRByn0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Hydro bar in Year 3}<r>[0.305, 0.284, 0.388, 0.445]</r> taller or shorter than {the Hydro bar in Year 1}<r>[0.305, 0.610, 0.388, 0.737]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "qOhx804KMb4VqVWv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Biomass bar in Year 1}<r>[0.489, 0.721, 0.572, 0.737]</r> to the left or the right of {the avg bar for Year 1}<r>[0.857, 0.606, 0.940, 0.737]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "2DOC9LSJ6w7m84zm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Year 1 Coal bar}<r>[0.581, 0.433, 0.664, 0.737]</r> wider than {the Year 2 Coal bar}<r>[0.581, 0.238, 0.664, 0.433]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "v5CkWD28UPR0TVdy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar has greater value, {Year 2 Fossil Fuels bar}<r>[0.121, 0.431, 0.204, 0.523]</r> or {Year 2 Coal bar}<r>[0.581, 0.238, 0.664, 0.433]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2 Coal bar."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "N0YxbPjKFWL9lC1B",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within [0.345, 0.063, 0.717, 0.126] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, including three entries with their respective color markers. The entries are: Year1 with the color #003f5c<r>[0.352, 0.080, 0.386, 0.104]</r>, Year2 with the color #bc5090<r>[0.483, 0.080, 0.517, 0.104]</r>, and Year3 with the color #ffa600<r>[0.614, 0.080, 0.648, 0.104]</r>."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "IGQZmLAxhkaE1Rp3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the area [0.673, 0.551, 0.756, 0.638]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar from the Year 1 group, representing the usage of Natural gas, which accounts for 16.5%."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "xYfMovTuZ1vpT0WN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the range from [0.792, 0.761, 0.821, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a label on the x-axis for the energy source \"Oil\"."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "JTYflzXxTR6wIEra",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area [0.213, 0.267, 0.296, 0.403]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar for \"Nuclear Power\" from the group \"Year 3\". The bar represents the proportion of Nuclear Power for that year, which is 22.6%."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "lKoNMBLPeATTD2Os",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat social media platform has the coordinates [0.166, 0.812, 0.241, 0.962] along the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "yEGg8IrjHGx8MnWc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the bar representing \"Ambiversion\"}<r>[0.452, 0.136, 0.527, 0.263]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "l16vWygQguYyt6G1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.263, 0.072, 0.297, 0.097] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color for the \"Introversion\" bars."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "nlz6PqYPFHe0pjGG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Extroversion bar of \"Skype\"}[0.866, 0.261, 0.940, 0.533] taller than {the Introversion bar of \"Skype\"}[0.866, 0.533, 0.940, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "zenLod54XHrH9Za6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of \"Ambiversion\", is {Twitter}[0.287, 0.136, 0.361, 0.262] or {Snapchat}[0.452, 0.136, 0.527, 0.263] rated higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "They have the same height, indicating the same value for Ambiversion."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "lmBWC0aCCQpoNxQE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Introversion bars} generally taller than {the Extroversion bars} across all platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "aRRu0nDSr885ikRl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within [0.520, 0.483, 0.609, 0.788]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects, indicating Introversion, Extroversion, and Ambiversion's values for Reddit. They are ordered from top to bottom as: {Introversion bar of Reddit}[0.535, 0.483, 0.609, 0.788], {Extroversion bar of Reddit}[0.535, 0.262, 0.609, 0.483] and {Ambiversion bar of Reddit}[0.535, 0.136, 0.609, 0.262]."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "JU51Vzh86k0smN82",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the coordinates [0.256, 0.055, 0.806, 0.118]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, with three categories labelled: Introversion represented in green {color box}<r>[0.263, 0.072, 0.297, 0.097]</r>, Extroversion represented in a yellow color {color box}<r>[0.450, 0.072, 0.484, 0.097]</r>, and Ambiversion represented in blue {color box}<r>[0.642, 0.072, 0.677, 0.097]</r>."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "wEQtBXRTbfX8OmcO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is shown in the region [0.080, 0.136, 0.981, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main body of the chart showing the stacked bars. Each set of bars corresponds to a different social media platform, listed along the x-axis. Each bar corresponds to the Introversion, Extroversion and Ambiversion values of the social media platforms."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "BImVFyXys3jgcFMz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label present at the coordinates [0.127, 0.823, 0.190, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region1."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "pjwe54PT78Sp9I33",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing Param2 at coordinates [0.204, 0.568, 0.278, 0.798]?"
+            },
+            {
+                "from": "gpt",
+                "value": "White."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "vitY3Cc294ydChRa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Param is represented with the handle coordinates [0.851, 0.177, 0.885, 0.201] in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Param3."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "6iYLs14d0Ex1XdlL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Param1}<{Param1}[0.121, 0.576, 0.196, 0.798]> in Region1 taller than the bar of {Param2}<{Param2}[0.121, 0.362, 0.196, 0.576]> in the same region?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "GMIgQyeHvC4eopbl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {Region3}, does {Param3}<{Param3}[0.287, 0.074, 0.361, 0.215]> have a higher value than {Param2}<{Param2}[0.287, 0.293, 0.361, 0.596]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "Lv62zP1i7GGIL27E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller in Region7 - {Param1}<{Param1}[0.618, 0.606, 0.692, 0.798]> or {Param3}<{Param3}[0.618, 0.074, 0.692, 0.359]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Param1."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "nJ4nkEHdHPTVij6E",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects residing in the area [0.121, 0.074, 0.196, 0.798]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 stacked bars. The top bar representing the measure Param1 extends from 60.19% to 100.00%, the middle bar for Param2 ranges from 30.71% to 60.19%, and the bottommost bar for Param3 spans from 0% to 30.71%. All three bars are white."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "wW7iqmAj83PWAGbj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area of [0.844, 0.055, 0.973, 0.222]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subplot legend area with three entries: Param1 with a handle at [0.851, 0.072, 0.885, 0.096], Param2 with a handle at [0.851, 0.124, 0.885, 0.149], and Param3 with a handle at [0.851, 0.177, 0.885, 0.201]. All handles are white."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "anvG9qLtXMlA9Bfc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the elements found in the region [0.204, 0.074, 0.278, 0.798]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 stacked bars. The top bar stands for the measure Param1 with coordinates [0.204, 0.568, 0.278, 0.798], the middle bar represents Param2 and extends from point [0.204, 0.293, 0.278, 0.568], and the bottom bar represents Param3 with coordinates [0.204, 0.074, 0.278, 0.293]. All these bars are colored white."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "Yacpi5CFuCsBUiui",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the coordinates [0.329, 0.275, 0.422, 0.634] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a white bar in the first group of bars (Option1) indicating the \"Home Area\" with a value of 72."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "jctOcZkPLe1P7w0B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you locate the label of the third bar group?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {Option3}<r>[0.673, 0.092, 0.742, 0.127]</r> is represented in the legend."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "uKZRnpMrPb0GMfwP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is at the coordinates of [0.738, 0.658, 0.842, 0.865] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label {School_District}<r>[0.738, 0.658, 0.842, 0.865]</r>."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "8yCrmZ0bKNGPLFHj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for {Option2}<r>[0.847, 0.041, 0.940, 0.285]</r> generally taller than those for {Option1}<r>[0.847, 0.285, 0.940, 0.634]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "iKLabQZVqCHzOCR1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars for {Option1} and {Option3}, which has a higher value for {Number_of_rooms}<{Option1 Number_of_rooms bar}[0.225, 0.335, 0.318, 0.634], {Option3 Number_of_rooms bar}[0.225, -0.143, 0.318, 0.041]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Option3."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "jDcgo48XaNtXIu1d",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Family_Size in Option2}<r>[0.121, 0.121, 0.215, 0.345]</r> taller than the bar for {Public_Transport in Option1}<r>[0.640, 0.410, 0.733, 0.634]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "7ZW3PMvvVPvr6lGm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area [0.313, 0.078, 0.749, 0.141]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart with three entries: {Option1}<r>[0.368, 0.092, 0.437, 0.127]</r> with a white handle at [0.320, 0.095, 0.354, 0.120], {Option2}<r>[0.521, 0.092, 0.590, 0.127]</r> with a white handle at [0.472, 0.095, 0.507, 0.120], and {Option3}<r>[0.673, 0.092, 0.742, 0.127]</r> with a white handle at [0.624, 0.095, 0.659, 0.120]."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "Db999koaJEDfS5yN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the area [0.121, -0.079, 0.215, 0.634]."
+            },
+            {
+                "from": "gpt",
+                "value": "It comprises three bars corresponding to the categories 'Option1', 'Option2', 'Option3' for the 'Family_Size' label. The {Option1 bar}[0.121, 0.345, 0.215, 0.634] has a value of 58, the {Option2 bar}[0.121, 0.121, 0.215, 0.345] a value of 45, and the {Option3 bar}[0.121, -0.079, 0.215, 0.121] a value of 40."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "9Hs5c8xGfw4cs6yz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.472, 0.096, 0.526, 0.360] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the fourth bar of the Option2 group of bars, representing the category {Number_of_bathrooms}<r>[0.432, -0.138, 0.526, 0.096]</r> with a value range of 108 to 155."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "6Y80c5ObahPIM0Yk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value on the Y-axis at the coordinates [0.020, 0.314, 0.045, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "80%."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "bvjUfAqRHEM6c9UK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the chart bar at [0.339, 0.525, 0.379, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research for the year 2013."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "kmYLypvDNALEtLGx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the ticks at [0.183, 0.917, 0.212, 0.975] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2011."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "1fI1Gvu5cKTVBu9h",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line representing AI Research at [0.339, 0.525, 0.379, 0.901] higher than the line representing Automation Applications at [0.339, 0.092, 0.379, 0.525] for the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "UGcyLWjkGQNeMoS8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the AI Research bar for the year 2010 at [0.096, 0.648, 0.136, 0.901] higher than for the year 2011 at [0.177, 0.626, 0.217, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "Z9LOCXkF4XW3bGna",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Automation Applications line for 2014 at [0.419, 0.034, 0.460, 0.504] higher than the one for 2013 at [0.339, 0.092, 0.379, 0.525]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "l2IoyJnIYkaxAaAw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.339, 0.034, 0.460, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a region representing the year 2014. It consists of two bars. The upper bar indicates the value of AI Research is 55%, and the lower bar indicates that the value of Automation Applications is 120%."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "5ZSfNlEwhfVA7Xvt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears in the area [0.027, 0.892, 0.045, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label showing the value of 0% is present in this area."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "RIUimUWp4WnDodX6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area [0.096, 0.648, 0.136, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the graph bar for AI Research in the year 2010 which is at 35%."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "4ojBRIHd3ccpQHKK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the axes} at [0.088, 0.129, 0.985, 0.740]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate plane of the bar chart."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "zRy2ksT2MMW1ulwI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the xlabel called Categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.492, 0.945, 0.581, 0.975]"
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "CHjGxjS47L9zW7Eb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-label} at [0.015, 0.357, 0.033, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indices (%)."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "KqbQsh115Y0tZCK6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich categories are listed in this coordinates [0.123, 0.756, 0.196, 0.879],[0.192, 0.756, 0.264, 0.877],[0.256, 0.756, 0.337, 0.891],[0.322, 0.756, 0.408, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Public Safety}[0.123, 0.756, 0.196, 0.879], {Environment}[0.192, 0.756, 0.264, 0.877], {Transportation}[0.256, 0.756, 0.337, 0.891], and {Health Services}[0.322, 0.756, 0.408, 0.900]."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "dHVsn3Gr7XuPMStz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Public Transport\"}[0.883, -0.794, 0.944, -0.317] taller than {the bar representing \"Public Safety\"}[0.129, 0.208, 0.190, 0.740]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "uG48eG9bjQxBWBk9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars representing Quality}[[0.129, 0.208, 0.190, 0.740],[0.197, 0.184, 0.259, 0.740],[0.266, 0.166, 0.327, 0.740],[0.334, 0.178, 0.396, 0.740]] generally shorter than {the bars representing Equity}[[0.129, -0.818, 0.190, -0.354],[0.197, -0.904, 0.259, -0.397],[0.266, -0.983, 0.327, -0.433],[0.334, -0.916, 0.396, -0.397]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "JkQRoApEpP0ijMB9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Public Safety\"}[0.123, 0.756, 0.196, 0.879] and {\"Environment\"}[0.192, 0.756, 0.264, 0.877],[0.197, 0.184, 0.259, 0.740]], which category has higher {Quality}<{Quality bar for Public Safety}[0.129, 0.208, 0.190, 0.740], {Quality bar for Environment}[0.197, 0.184, 0.259, 0.740]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environment."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "yGVE1Aw8rpQ5ZkTH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe content in [0.129, -0.818, 0.190, -0.354]."
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate area comprises a single white bar representing the Quality index of \"Public Safety\" from 179% to 255%."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "IO0FRYMsT1tc0pgg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.047, 0.242, 0.078, 0.265]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-label \"80%\" is located within these coordinates."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "7mtkkFHXKnLw70ND",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.197, -0.904, 0.259, -0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is only 1 object: a Quality bar representing the \"Environment\" category with the range from 186% to 269%."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "NQschnKWzVvH79BN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.865, 0.756, 0.944, 0.740]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, you can see a label for the category \"Public Transport\"."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "K8AHim6kTNdVjAbi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element at the coordinates [0.015, 0.651, 0.055, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, specifically the label for \"100%\"."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "Z69kEDvlkfmkLMXQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars represented by the legend at [0.320, 0.041, 0.409, 0.065]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars are colored white."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "dfoxnc7sB78SuNF5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat group does the bar at [0.619, 0.208, 0.772, 0.373] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar belongs to the group titled \"Sharp Rise\"."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "8twzT1yHQUeXH0cB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing the category \"Electronics\" higher or lower than the bar representing \"Automobiles\" for the trend \"Volatile Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for the category \"Electronics\"[0.106, 0.515, 0.260, 0.531] is lower than \"Automobiles\"[0.619, 0.538, 0.772, 0.628]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "68JcPLkJhCcfePpK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Steady Increase\" trend higher for \"Grocery\" or \"Fashion\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Steady Increase\" trend is higher for \"Fashion\"[0.277, 0.342, 0.431, 0.504] than for \"Grocery\"[0.448, 0.375, 0.601, 0.535]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "YrXGX1tXRAl1XS0c",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs \"Slow Decline\" more for \"Stationery\" or for \"Fashion\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Slow Decline\" is more for \"Electronics\"[0.106, 0.675, 0.260, 0.840] than for \"Fashion\"[0.277, 0.688, 0.431, 0.840]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "a6m0qzf8grMwK4xW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents inside [0.775, 0.839, 0.947, 0.961]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a x-axis label called \"Stationary\"[0.836, 0.857, 0.897, 0.958]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "XKP7O6l7RmjepfH7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars and their respective categories within the region [0.106, 0.184, 0.260, 0.357]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of the \"Electronics\" category for \"Sharp Rise\". The respective bar is located at [0.106, 0.184, 0.260, 0.357]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "k7qq5RewhmOloClH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.275, 0.032, 0.774, 0.109] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the legend for the bar chart with three color-text labels, each representing a different trend. The trends are \"Slow Decline\"[0.320, 0.041, 0.409, 0.065], \"Inexorable Decline\"[0.320, 0.076, 0.449, 0.100], and \"Volatile Growth\"[0.516, 0.041, 0.622, 0.065]."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "U7rFvaUQfS6qCLHl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color connected to \"Tech Startups\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "White, connected to {Tech Startups}<r>[0.340, 0.040, 0.418, 0.060]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "EBppkhii1VoZyLhY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the x-axis labeled directly below {the bar for \"FinTech\"}<r>[0.293, 0.584, 0.353, 0.827]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "FinTech, located at {x-axis label for FinTech}<r>[0.304, 0.841, 0.342, 0.907]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "BG9gNLmFtBRYHimF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage is shown by the y-axis label aligned with the bottom of {the Green Tech bar from Venture Capital}<r>[0.096, 0.458, 0.155, 0.766]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "8.8%, as indicated by {the y-axis label}<r>[0.027, 0.820, 0.045, 0.840]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "9wfZz9hTeMtVshWi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Average\"}<r>[0.886, 0.617, 0.945, 0.827]</r> higher than {the bar for \"On-Demand\"}<r>[0.820, 0.558, 0.879, 0.827]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "x5jMDLySxkWq10P1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for \"Space Tech\"}[[0.688, 0.610, 0.748, 0.827], [0.688, 0.406, 0.748, 0.610], [0.688, 0.127, 0.748, 0.406]] arranged in descending order from top to bottom?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "Bev3ppk9BVpwF1wG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a wider range in the chart, {the labels on the x-axis}<r>[0.170, 0.841, 0.212, 0.913]</r> or {the menu in the sublegend}<r>[0.303, 0.032, 0.738, 0.068]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sublegend."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "qXnZrSLUfPDwAcIS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the coordinates [0.096, 0.127, 0.221, 0.458] covering multiple bars."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three segments of bars for Green Tech across different fundings: {Tech Startups}<r>[0.096, 0.766, 0.155, 0.827]</r>, {Venture Capital}<r>[0.096, 0.458, 0.155, 0.766]</r>, {Government Grants}<r>[0.096, 0.127, 0.155, 0.458]</r> showing increasing funding amounts."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "HD1PZEtzVbsR8LxH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the area occupied by y-axis percentage labels from [0.013, 0.119, 0.045, 0.840]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the vertical spectrum of percentages from 0% to 100%, each labeled correspondingly at intervals of 20%: {0%}<r>[0.027, 0.820, 0.045, 0.840]</r>, {20%}<r>[0.020, 0.679, 0.045, 0.699]</r>, {40%}<r>[0.020, 0.539, 0.045, 0.559]</r>, {60%}<r>[0.020, 0.399, 0.045, 0.419]</r>, {80%}<r>[0.020, 0.259, 0.045, 0.279]</r>, {100%}<r>[0.013, 0.119, 0.045, 0.139]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "iaib9ZPN64rI4yLe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.427, 0.841, 0.748, 0.937]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes labels for Health Tech, e-Commerce, and Space Tech located along the x-axis representing different areas of focus for data shown by {Health Tech}<r>[0.427, 0.841, 0.483, 0.937]</r>, {e-Commerce}<r>[0.490, 0.841, 0.551, 0.945]</r>, and {Space Tech}<r>[0.691, 0.841, 0.745, 0.934]</r> on the bar chart."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "957u0EJFsEPh7Q8m",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the label 'Recovery'}<r>[0.375, 0.049, 0.440, 0.072]</r> have in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "White."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "qLHJlBKCXNPYkFzH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate range showing the percentage values for {\"Theatre's highest section in 'Decline' category\"}<r>[0.106, 0.129, 0.151, 0.230]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "85.00 to 100.00."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "DijU4jBbHXhxksX6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the label located at [0.656, 0.820, 0.696, 0.895] in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Writing."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "JMFjsxmquedmbfWW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {\"the highest value section of 'Museums'\"}<r>[0.700, 0.129, 0.745, 0.223]</r> higher than {\"the lowest value section of 'Photography'\"}<r>[0.552, 0.601, 0.596, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "JhxuGAIOa5qBl3FU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the start point for 'theatre'}<r>[0.106, 0.804]</r> located to the left of {the start point for 'comedy'}<r>[0.800, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "AsmqlcuxDQ5z1ULe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis label is nearer to {the tick for 'Novels'}<r>[0.921, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Novels."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "cGKGh8Ib7sF7T28w",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content and structure within <r>[0.046, 0.049, 0.330, 0.078]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a series of shaded backgrounds for several x-axis tick labels for categories 'Theatre' through 'Opera', denoting different cultural sectors."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "ge6WoGkxLqyufAGG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the zone from <r>[0.106, 0.129, 0.943, 0.804]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains all the grouped bar segments for each industry sector across the categories 'Recovery', 'Performance', and 'Decline', showing the gradual rise or fall in values."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "GvqMATPp1qc1TPj6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.331, 0.039, 0.718, 0.081]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend specifying the three types of categories \u2014 'Recovery', 'Performance', and 'Decline', with each category presented alongside its associated color box."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "WbWOE2S9g33vj2Uv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bars with coordinates [0.106, 0.554, 0.234, 0.742]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Debt Management\" category in \"Regulation Compliance\"."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "eZLLQunHkSifPyno",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is identified by the label at [0.749, 0.759, 0.879, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is \"Operational Risk Control\"."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "TZMSPHGCxOn8BaRe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the chart can be found at [0.768, 0.025, 0.978, 0.137]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend part of the chart containing labels for \"Regulation Compliance\", \"Operational Oversight\", and \"Market Risk Control\"."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "fTYAtiAl5gFLsyIu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar group representing \"Debt Management\"{bar_coordinates: [0.106, 0.554, 0.234, 0.742]} taller than the bar group representing \"Operational Risk Control\"{bar_coordinates: [0.815, 0.530, 0.943, 0.742]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "NfAGwE26xe5g5pk4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Operational Risk Control\" in \"Operational Oversight\"{bar_coordinates: [0.815, 0.365, 0.943, 0.530]} higher than that in \"Market Risk Control\"{bar_coordinates: [0.815, 0.224, 0.943, 0.365]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "CIZsuxzAapumLAlN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the y-scale values for Credit Risk Control greater { Coordinate of 'Credit Risk Control': [0.352, 0.759, 0.454, 0.928]} or less than that of Reputation Risk Control { Coordinate of 'Reputation Risk Control': [0.532, 0.542, 0.659] } in Market Risk Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "sq7GMO3HuNSWJkOe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area of [0.768, 0.025, 0.978, 0.137]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. The first label, for \"Regulation Compliance\"{text_coordinates: [0.812, 0.034, 0.973, 0.058]}, has a color label of white {coordinates: [0.773, 0.036, 0.801, 0.053]}. The second label, \"Operational Oversight\"{text_coordinates: [0.812, 0.069, 0.966, 0.093]}, similarly has a color label of white {coordinates: [0.773, 0.071, 0.801, 0.088]}. Lastly, the label for \"Market Risk Control\"{text_coordinates: [0.812, 0.104, 0.948, 0.127]} also has a color label of white {coordinates: [0.773, 0.106, 0.801, 0.122]}."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "wZNKi9KN0G2C1fZs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.064, 0.148, 0.985, 0.742]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the entire plot of the chart including all the bar groups for each category - \"Debt Management\", \"Equity Management\", \"Credit Risk Control\", \"Reputation Risk Control\", \"Financial Reporting\", \"Operational Risk Control\" and the labels for each percentage on the y-axis."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "iO9PAOMcdLq5OHe3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.674, 0.365, 0.801, 0.742]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the bars for \"Financial Reporting\" in \"Regulation Compliance\", \"Operational Oversight\", and \"Market Risk Control\"."
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "0pxq3Lqrd0SXznEr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the blue bar at [0.121, 0.241, 0.509, 0.662] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value of \"Artificial Intelligence\" in \"RegOpt\", which is 80%."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "RRy3jaQtenK7BF6x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text inside {the second option in the legend}<r>[0.526, 0.088, 0.575, 0.123]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "BIOpt."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "FvJtLrZdQ6yjoDzU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the green bar at [0.552, 0.136, 0.940, 0.241] show?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green bar represents \"Machine Learning\" in \"LoopOpt\" and its value is 80% to 100%."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "1TKPNmNHIWRtOtfM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the indicator of {the x-axis}<r>[0.246, 0.686, 0.384, 0.963]</r> at the first tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "EKFyapeGISjiSdKq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {blue bar}<r>[0.121, 0.241, 0.509, 0.662]</r> higher or lower than the {red bar}<r>[0.121, 0.188, 0.509, 0.241]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "XdOpgSXIrnKbNpLU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {green bar}<r>[0.552, 0.136, 0.940, 0.241]</r> to the left or right of the {red bar}<r>[0.552, 0.241, 0.940, 0.556]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "exvw6S4hZmI6MwEs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the green bars generally shorter than the red bars?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "69WEaTHdU0Tu4S4D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher value for \"LoopOpt\", {\"Artificial Intelligence\"}<r>[0.121, 0.136, 0.509, 0.188]</r> or {\"Machine Learning\"}<r>[0.552, 0.136, 0.940, 0.241]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both have the same range from 80% to 100%."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "F4385ZJKoqddWOP2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.323, 0.074, 0.739, 0.137]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area, which includes three sets of color-label pairs. The first pair is blue and labeled as \"RegOpt\", located at [0.330, 0.091, 0.365, 0.116] and [0.379, 0.088, 0.443, 0.123] respectively. The second pair is red and labeled as \"BIOpt\", located at [0.478, 0.091, 0.513, 0.116] and [0.526, 0.088, 0.575, 0.123] respectively. The third pair is green and labeled as \"LoopOpt\", located at [0.610, 0.091, 0.644, 0.116] and [0.658, 0.088, 0.732, 0.123] respectively."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "ebsThlxAQ6NRjqmi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.246, 0.686, 0.384, 0.963]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "KVe7FHtNBJYM0ENv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.552, 0.136, 0.940, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of two bars and their corresponding labels. The red bar signifies \"Machine Learning\" in \"BIOpt\" with a range of 20% to 80%, while the green bar represents \"Machine Learning\" in \"LoopOpt\" with a range of 80% to 100%."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "fkQu2aei0oTNdvQ3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you observe in the region [0.121, 0.136, 0.940,0.662]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three bars which are representative of \"Artificial Intelligence\" and \"Machine Learning\". The blue bar annotates \"Artificial Intelligence\" in \"RegOpt\" with a range of 0% to 80%, and green bar portrays \"LoopOpt\" from 80% to 100%. For \"Machine Learning\", the red bar shows \"BIOpt\" from 20% to 80% and the green bar depicts \"LoopOpt\" from 80% to 100%."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "sS6Yoowm0hJpXCur",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific topic does the title {the subtitle}<r>[0.322, 0.118, 0.689, 0.163]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare and Medical Technology."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "wHsXsIPFLu31kuAA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the data being represented by the chart in the area bounded by [0.126, 0.354, 0.179, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Device Accuracy of Anesthesia, which is 75.3."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "3P6d2O9W8QlVBqL2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the legend item is represented in the area [0.153, 0.060, 0.297, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for Device Accuracy, which is colored blue."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "Se6NvQHIhRdsGSiO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing Device Accuracy for Anesthesia}<r>[0.126, 0.354, 0.179, 0.903]</r> have a higher value than {the bar representing Device Accuracy2 for Neurology}<r>[0.778, 0.666, 0.832, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "78tsbBhaxzpurYOC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Misleading Information for Radiology}<r>[0.432, 0.174, 0.486, 0.687]</r> taller than {the bar representing Device Accuracy for Radiology}<r>[0.326, 0.445, 0.379, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "0l7C3ErQlJXEqf6q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the bar for Device Accuracy for Cardiology}<r>[0.526, 0.460, 0.579, 0.903]</r> and {the bar for Device Accuracy2 for Cardiology}<r>[0.579, 0.513, 0.632, 0.903]</r> in terms of value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of Device Accuracy2 for Cardiology is less."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "tCQgRB75xXEQUlgs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.097, 0.046, 0.315, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. There are two labels present, Device Accuracy with its corresponding color blue at [0.104, 0.063, 0.139, 0.087], and Device Accuracy2 with its corresponding color at [0.104, 0.115, 0.139, 0.139]."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "x9D3mNFY6oedCiht",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.158, 0.927, 0.849, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains labels for the X-axis categories. They are Anesthesia at [0.158, 0.927, 0.254, 0.962], Radiology at [0.363, 0.927, 0.449, 0.962], Cardiology at [0.559, 0.927, 0.652, 0.962], and Neurology at [0.761, 0.927, 0.849, 0.962]."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "A0MWoVhjvHs0GVfl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is covered in the area [0.126, 0.174, 0.885, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers all the bars in the chart, it includes Device Accuracy bars for Anesthesia, Radiology, Cardiology, and Neurology as well as Device Accuracy2 bars for the same categories. Additionally, it also includes the bars representing misleading information for all the mentioned categories."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "67tAQYHrfZN2Et45",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "QUPvVZKrQdqnIYnE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the title} at [0.313, 0.030, 0.687, 0.066] of the chart is?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Models: Advanced Automation Comparison."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "7ojxRX7QUdtBtHYj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the color represents {at this point} [0.726, 0.100, 0.926, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Advanced Automation Recall."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "q5ZY9prE0ZI9aVoG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of Model 1}[0.102, 0.101, 0.143, 0.499] higher than {the bar of Model 8}[0.817, 0.286, 0.858, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "K7xzT4XFyO0aJRKp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the bar of Model 3}[0.306, 0.302, 0.347, 0.499] or {the bar of Model 5}[0.510, 0.265, 0.551, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Model 5."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "SfO30qokO0NBxgoo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars of {AI Models} [<bar coordinates listed below> generally higher or shorter than the bars of {Advanced Automation} [<bar coordinates listed below>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "UwKBe6T6n7x0uZLv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.069, 0.089, 0.231, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a label named \"AI Models Recall\" at [0.113, 0.100, 0.226, 0.128], which has a corresponding color badge in dark blue at [0.074, 0.102, 0.102, 0.122]."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "9ofLIVmam3Ob7via",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.102, 0.101, 0.143, 0.499]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing the recall rate of Model 1 in AI Models, which is 56.3%. The mark is located at [0.107, 0.100, 0.138, 0.128]."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "iM6UBFgCxQhldwU1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.510, 0.265, 0.551, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing the recall of Model 5 in AI Models. The height of the bar represents a recall rate of 33%, and the corresponding mark is at [0.522, 0.265, 0.539, 0.293]."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "0QLVPIE2FwHPJyI3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.810, 0.942, 0.865, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the Model 8 is located in this area."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "MNxgsAEORMmGL13F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart as presented in the coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart at [0.242, 0.130, 0.828, 0.160] is \"Media Influence and Public Opinion Guidance in a Month\"."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "nwnvYB6PlMNqSf3c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar located at coordinates [0.359, 0.333, 0.420, 0.647] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at coordinates [0.359, 0.333, 0.420, 0.647] represents the Positive Influence of FOX News, which has a value of 65.5."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "11j8l6EpagN3MdNm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being described in the box located at [0.097, 0.025, 0.323, 0.102]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box located at [0.097, 0.025, 0.323, 0.102] contains the legend of the chart, which distinguishes between Positive Influence and Negative Influence with colors."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "fXaoGg7UdFnucIWS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Influence Score of {CNN's Positive Influence}[0.129, 0.238, 0.190, 0.647] higher than its {Negative Influence}[0.190, 0.333, 0.251, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "19T4RCyOU5HNKYqU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the four media outlets, does {BBC's Negative Influence}[0.650, 0.386, 0.711, 0.647] have the lowest Influence Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "qGFebBtT5dAHzoJv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one of the media outlets has a higher Negative Influence Score, {FOX News}[0.420, 0.246, 0.481, 0.647] or {Al Jazeera}[0.879, 0.263, 0.941, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Al Jazeera."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "hbDmaRW7W39S6gy4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects located in the region [0.818, 0.342, 0.879, 0.647]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.818, 0.342, 0.879, 0.647] contains two bars. The higher blue bar that stretches from [0.818, 0.342, 0.879, 0.647] represents Al Jazeera's Positive Influence with a score of 63.7. Below it, the red bar that spreads from [0.879, 0.263, 0.941, 0.647] indicates Al Jazeera's Negative Influence, which has a score of 80.1."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "KcAuYMAIr4NB7sqJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area [0.065, 0.638, 0.076, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.065, 0.638, 0.076, 0.662] contains the label for the 0 mark of the Influence Score on the Y-axis."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "vGmxi4u3WBEkTbaa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe everything in the region [0.129, 0.238, 0.251, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.129, 0.238, 0.251, 0.647] encompasses two bars. The one from [0.129, 0.238, 0.190, 0.647] shows CNN's Positive Influence with a score of 85.4, and the one from [0.190, 0.333, 0.251, 0.647] represents CNN\u2019s Negative Influence with a score of 65.5."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "Vqxs0qYuPMrn54z4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label does the location {top left}<r>[0.113, 0.927, 0.124, 0.962]</r> refer to on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "LErv7fFoyNWnHxW3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what {specific y-coordinate}<r>[0.064, 0.763]</r> does the labeled grid line align with the y-axis label \"20\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.763."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "NkiDInv7wsfFnXXh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend key at {this coordinate}<r>[0.079, 0.063, 0.114, 0.087]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "5Ur7ptb5gRuhKdFW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {text label \"99.1%\"}<r>[0.835, 0.181, 0.890, 0.216]</r> correspond to the {bar whose bottom left is at}<r>[0.847, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "GQZrfnrL7D4eTabh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {blue bar}<r>[0.104, 0.443, 0.134, 0.903]</r> associated with the x-label {closer to}<r>[0.119, 0.903]</r> taller than the {green bar}<r>[0.134, 0.525, 0.163, 0.903]</r> associated with the same x-label?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "T4IYRsR9WgGMUCm3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the labeled bars is higher, {Economic Development}<r>[0.104, 0.443, 0.134, 0.903]</r> or {Social Consciousness}<r>[0.877, 0.209, 0.907, 0.903]</r> at the \"10\" position on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Development."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "pNc0qn3zlU6TRg0k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features between {the whole x-axis range}<r>[0.113, 0.927, 0.873, 0.962]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "There are labeled tick marks, with numerical labels from 0 to 10, with each tick spaced evenly and marked at regular intervals, corresponding to each bar's position on the x-axis."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "HmoN9qvx03a5GaHU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you illustrate the chart features within {this specified range}<r>[0.333, 0.118, 0.678, 0.163]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there's a subtitle \"Social Cultural and Value Change\" with centered alignment, and it has a black font color."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "beHhonm5e0ES6kzR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area {left side of the plotting area}<r>[0.064, 0.174, 0.505, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes half of the plot with the x-axis labels from 0 to 5, the corresponding blue bars and their percentages from Economic Development, and the left half of the grid background in light grey."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "KoTn32Zjfonoy9Wd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in the chart given that its coordinates are [0.019, 0.414, 0.036, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Volumes."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "AwQuUnElZT34fzfa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the following coordinates [0.379, 0.927, 0.438, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Vicuna'."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "eN3NYUptFXnVVuTA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.843, 0.209, 0.940, 0.903] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the sales volume of 'WizardLM'."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "hrDWOAZSAUwlEQ1U",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich animal is associated with the coordinates [0.621, 0.927, 0.680, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Alpaca."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "F85BNb1n0jWP3bJd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Vicuna\"}[0.360, 0.457, 0.457, 0.903] shorter or taller than {the bar representing \"Alpaca\"}[0.602, 0.368, 0.698, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "TQkPQbfW9KJCBqfb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars}[0.118, 0.407, 0.215, 0.903],[0.360, 0.457, 0.457, 0.903],[0.602, 0.368, 0.698, 0.903],[0.843, 0.209, 0.940, 0.903] arranged in ascending or descending order?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascending."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "7TT0LFSzJHbV0yra",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"WizardLM\"}[0.843, 0.209, 0.940, 0.903] positioned to the right or left of {the bar for \"Alpaca\"}[0.602, 0.368, 0.698, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "xlezbymxSRQfmDUc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Alpaca\"}[0.602, 0.368, 0.698, 0.903] bar closer to {the \"Vicuna\"}[0.360, 0.457, 0.457, 0.903] bar or {the \"WizardLM\"}[0.843, 0.209, 0.940, 0.903] bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vicuna."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "j27oYc3pdlThxi4J",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide information about the section [0.118, 0.407, 0.215, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a blue bar that represents the sales volume of 'LLaMA', which is '50'."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "KUq97SBjFGUtiE4o",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the information located in the coordinates [0.043, 0.790, 0.065, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis has a tick mark representing the value '10'."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "ZSsbZ6OlSSo2KkUf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the location [0.843, 0.209, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A vertical bar is presented which signifies the sales volume of 'WizardLM' to be '70'."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "xTTFaSnGY7QfQfrB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area contained by [0.043, 0.096, 0.065, 0.131]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis label '80' located between these coordinates."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "VMdN2rqQ8z891rI3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.386, 0.118, 0.649, 0.163] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called \"Statistical Data Overview\"."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "0QtNkNa3poulS7uM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the text of 'Trend Analysis'?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the legendary label at [0.117, 0.060, 0.242, 0.095]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "2p6f4mn9RNCMXe71",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I locate the bar labeled 'End' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.752, 0.235, 0.939, 0.539]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "dciVcpSs11eaw0JS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at [0.019, 0.403, 0.041, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called \"20\"."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "fg1vmcaD6zXcbGeR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of trend analysis at End}[0.564, 0.205, 0.752, 0.539] higher than {the bar of variance measurement at End}[0.752, 0.235, 0.939, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "mfRaUzKYXlPyRRji",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of 'Trend Analysis' at Initial}[0.095, 0.265, 0.283, 0.539] in the left or right of {the bar of 'Variance Measurement' at Initial}[0.283, 0.387, 0.470, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "XXhfSLtFUqcHCnBh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {bars of 'Trend Analysis'}[{Trend Analysis at Initial}[0.095, 0.265, 0.283, 0.539],{Trend Analysis at End}[0.564, 0.205, 0.752, 0.539]] generally higher than {bars of 'Variance Measurement'}[{Variance Measurement at Initial}[0.283, 0.387, 0.470, 0.539],{Variance Measurement at End}[0.752, 0.235, 0.939, 0.539]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "2qGACgvaKm012k8L",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of 'Variance Measurement' at Initial}[0.283, 0.387, 0.470, 0.539] taller or shorter than {the bar of 'Variance Measurement' at End}[0.752, 0.235, 0.939, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "R26TGiaojQ20vRRV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located at [0.062, 0.046, 0.324, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart has a legend containing two pairs of color-text labels; The first pair consists of a color label with a blue color located at [0.069, 0.063, 0.103, 0.087], and a text label indicating \"Trend Analysis\" located at [0.117, 0.060, 0.242, 0.095]; The second pair consists of a color label with a color near dark cyan located at [0.069, 0.115, 0.103, 0.139], and a text label indicating \"Variance Measurement\" located at [0.117, 0.112, 0.317, 0.147]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "d5lGTiS3KVQij7oc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the area [0.352, 0.927, 0.401, 0.962] and [0.829, 0.927, 0.862, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "They are the labels of the x-axis. The label 'Initial' is located at [0.352, 0.927, 0.401, 0.962] and the 'End' label is located at [0.829, 0.927, 0.862, 0.962]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "uih3l2v2Tgdqv0yu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.095, 0.265, 0.283, 0.539] and [0.564, 0.205, 0.752, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas consist of two bars representing 'Trend Analysis', with the coordinates [0.095, 0.265, 0.283, 0.539] for the value at 'Initial' and [0.564, 0.205, 0.752, 0.539] for the value at 'End'. Both bars are colored in a shade of blue."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "OtvctHqsQ0VjoU4W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items within the area [0.564, 0.165, 0.669, 0.200] and [0.834, 0.195, 0.856, 0.230]."
+            },
+            {
+                "from": "gpt",
+                "value": "The positions contain the texts, '55' at [0.564, 0.165, 0.669, 0.200] belonging to {the bar of 'Trend Analysis' at End}[0.564, 0.205, 0.752, 0.539], and '50' at [0.834, 0.195, 0.856, 0.230] belonging to {the bar of 'Variance Measurement' at End}[0.752, 0.235, 0.939, 0.539]. These figures represent the data of each statistical graph."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "cOrkuxnrRKFPaIrc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by [0.318, 0.293, 0.399, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar representing the Media Growth for Streaming Media at 11.4%"
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "8F9ovRIDPR0Xaw0e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the object at [0.142, 0.027, 0.262, 0.050]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a label named Media Growth in the legend."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "yYx5Rs06wDIW8I8t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by [0.354, 0.952, 0.444, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for Streaming Media on the x-axis of the plot."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "KLDEgKefg9cfCPeL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat role does the object at [0.043, 0.359, 0.981, 0.667] play?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for Entertainment Decline (%) on the y-axis of the plot."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "KUGlEqBeHg8u5LQW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Media Growth bar for Traditional Media}<r>[0.116, 0.637, 0.197, 0.935]</r> higher than the {Entertainment Decline bar for Traditional Media}<r>[0.197, 0.090, 0.278, 0.361]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "vipZYipQXzpWbjMl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the {Media Growth bar for Interactive Media}<r>[0.722, 0.721, 0.803, 0.935]</r> and the {Entertainment Decline bar for Interactive Media}<r>[0.803, 0.090, 0.884, 0.319]</r> have the same height?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "Jg7rEO03lmvGeRfd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Entertainment Decline for Streaming Media}<r>[0.399, 0.090, 0.480, 0.724]</r> higher than {Media Growth for Streaming Media}<r>[0.318, 0.293, 0.399, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "hMFB1pGQkSj3Luww",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Interactive Media}<r>[0.757, 0.952, 0.850, 0.975]</r> have a higher level of Entertainment Decline <r>[0.803, 0.090, 0.884, 0.319]</r> than that of Media Growth <r>[0.722, 0.721, 0.803, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "FUbRPDIq3I9JDi0l",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the coordinates [0.077, 0.090, 0.923, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates includes all of the plotted bars representing Media Growth and Entertainment Decline for different types of Media, as well as the plot labels and ticks on the x and y axes."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "zivujHIiJXIW4CsZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within [0.656, 0.017, 0.914, 0.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for Entertainment Decline with the text label located at [0.711, 0.027, 0.907, 0.050] and the color handle located at [0.663, 0.029, 0.697, 0.045]."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "R4vAq2f9DAiADwvk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is notable in the region [0.043, 0.081, 0.065, 0.105]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a label for the 0 value on the Y-axis (Entertainment Decline axis)."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "qrvAtHJRni9NYUiv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's significant about the region [0.722, 0.721, 0.803, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a blue bar representing Media Growth for Interactive Media which is said to be at 3.8%."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "h7ihUMrPYH3DYMgy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}<{a bar}[0.646, 0.556, 0.708, 0.903]> representing in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Adaptable Leadership\" score of the \"DynamicInc\" organization."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "jaB5EL0fxeFRMFrO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Y-axis label}[0.019, 0.490, 0.036, 0.587], it is \"Score\"."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "7lzuRdh9MgyxWPNa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the handle color of the \"Adaptable Leadership\" in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is represented by \"{the legend handle}[0.093, 0.115, 0.128, 0.139]\", which is in the color \"#33756f\"."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "ayuAzTURQuE1Mxh6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the score bar of SoftCorp for Effective Management}[0.118, 0.209, 0.180, 0.903] higher than {the score bar of FlexCorp for Effective Management}[0.351, 0.260, 0.413, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "d95z02p3OycVeoI5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher among {the Adaptable Leadership score for DynamicInc}[0.646, 0.556, 0.708, 0.903] and {the Adaptable Leadership score for SteadFastLLC}[0.878, 0.620, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for DynamicInc."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "LqeD0S89jy8FJOhY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the effective management bars}[0.118, 0.209, 0.180, 0.903],[0.351, 0.260, 0.413, 0.903],[0.584, 0.447, 0.646, 0.903],[0.816, 0.490, 0.878, 0.903] generally higher than {the adaptable leadership bars}[0.180, 0.302, 0.242, 0.903],[0.413, 0.421, 0.475, 0.903],[0.646, 0.556, 0.708, 0.903],[0.878, 0.620, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "9lOkTkj4wJpsh1Ic",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.086, 0.046, 0.345, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart. In this area, two pairs of color-text labels represent two types of scoring categories. The first pair \"{Effective Management}[0.142, 0.060, 0.338, 0.095]\" has its color in #3f81bb; the second pair \"{Adaptable Leadership}[0.142, 0.112, 0.331, 0.147]\" has its color in #33756f."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "iw93WBVvnWFnNDZK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content does the area [0.626, 0.927, 0.728, 0.962] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label \"{DynamicInc}[0.626, 0.927, 0.728, 0.962]\", which represents the name of one of the organizations."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "a7KS16U5f6mE65oh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.077, 0.174, 0.981, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area. It includes 4 x-axis labels representing the names of organizations, several y-axis tick marks indicating the scoring range from 0 to 80, and 8 bars, each representing a score for Effective Management or Adaptable Leadership of a certain organization."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "ZoI3vua8itu2yr4J",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the two bars labeled in the coordinates [0.104, 0.269, 0.144, 0.581] and [0.144, 0.316, 0.185, 0.581] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent Global Trade and Supply Chain Management values for Asia respectively."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "50xBLlTeythFm4Yv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the text located in the coordinates [0.113, 0.045, 0.200, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label \"Global Trade\" in the chart's legend."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "JwD6Ic1uoIlsuoKK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.015, 0.501, 0.029, 0.592] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates \"Values\" which are represented on the y-axis of the chart."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "FeVKGjNtLBjbQ2iz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the {Supply Chain Management bar for Europe}[0.448, 0.290, 0.488, 0.581] relative to the {Global Trade bar for Europe}[0.407, 0.343, 0.448, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "MzzcHH1VHPu9fcU9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {Africa's Global Trade values}[0.559, 0.392, 0.599, 0.581] exceed its {Supply Chain Management values}[0.599, 0.340, 0.640, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "eDPQWAJQdz3kJuSZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Global Trade}[0.104, 0.269, 0.144, 0.581] and {Supply Chain Management}[0.144, 0.316, 0.185, 0.581] values for Asia, which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Trade."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "WQYzFMqmexaFVs4I",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the coordinates [0.069, 0.034, 0.306, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend with two entries. The first is labeled as \"Global Trade\" with coordinates [0.113, 0.045, 0.200, 0.073] and a blue rectangle at [0.074, 0.047, 0.102, 0.067]. The second entry is labeled as \"Supply Chain Management\" with coordinates [0.113, 0.087, 0.301, 0.115] and a green rectangle at [0.074, 0.089, 0.102, 0.109]."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "s5gG4mRbMG8jG7Yl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is present in the region [0.872, 0.942, 0.933, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Australia\" at [0.872, 0.942, 0.933, 0.970], indicating Australia as a category on the x-axis."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "Gw1DZcc6SNXCqZIX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.104, 0.269, 0.144, 0.581]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area has a blue bar which represents the Global Trade values for Asia at 45.6."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "WpozHPsVF3i3GVu0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the bar at {Company A's Value1}[0.116, 0.325, 0.197, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "ZnI5lrwRxEACFRMP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type used for the figure based on the JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "qUz3YZWdvWnMun55",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the Y-axis labels in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Company A}[0.019, 0.398, 0.036, 0.598] and {Company B}[0.964, 0.398, 0.981, 0.599]."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "zwv6mzxmgMqPtQO4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Company B's Value3 bar}[0.803, 0.440, 0.884, 0.903] higher than {Company A's Value2 bar}[0.419, 0.498, 0.500, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "KaYYlcw3XU6v8fPN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Company A's bars}[[0.116, 0.325, 0.197, 0.903], [0.419, 0.498, 0.500, 0.903], [0.722, 0.614, 0.803, 0.903]] generally higher than {Company B's bars}[[0.197, 0.209, 0.278, 0.903], [0.500, 0.383, 0.581, 0.903], [0.803, 0.440, 0.884, 0.903]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "Dj4y8sl14d1qZDKC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Value1 bar for Company A}[0.116, 0.325, 0.197, 0.903] positioned to the left or right of the {Value2 bar for Company A}[0.419, 0.498, 0.500, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "okk85BQVQ6aMD0Df",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region [0.077, 0.093, 0.923, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Two groups of bars are located in this region.  \n    Group 1: {Company A's Value1} [0.116, 0.325, 0.197, 0.903], {Company A's Value2} [0.419, 0.498, 0.500, 0.903], and {Company A's Value3} [0.722, 0.614, 0.803, 0.903].\n    Group 2: {Company B's Value1} [0.197, 0.209, 0.278, 0.903], {Company B's Value2} [0.500, 0.383, 0.581, 0.903], and {Company B's Value3} [0.803, 0.440, 0.884, 0.903]."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "CW4rh9slFMK0yp3p",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located in the region [0.116, 0.209, 0.278, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects:\n    1. Bars corresponding to Value1 of both companies; {Company A's Value1 bar}[0.116, 0.325, 0.197, 0.903], and {Company B's Value1 bar}[0.197, 0.209, 0.278, 0.903]."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "lpXgDMkFpLGBsUMq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region labeled by [0.364, 0.037, 0.636, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart, \"Financial Data Exploration\"."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "f9caSEHqS6q1vt4X",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please tell me what is denoted by the object in the chart located at [0.115, 0.250, 0.168, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for BlueChip in Week1."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "blYB0AHupdtFh6j9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.329, 0.290, 0.351, 0.325] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of BlueChip in Week3."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "GTa04YVsxDyGVrS3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend at [0.086, 0.046, 0.234, 0.161] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It points out the color codes for BlueChip and Gold ETFs."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "1Fc2BDXuKcGEPLyR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the BlueChip bar for Week 1}[0.115, 0.250, 0.168, 0.903] higher or lower than {the BlueChip bar for Week 2}[0.314, 0.291, 0.367, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "8oVab7o20IjBIIkk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the Gold ETFs bar for Week 1}[0.168, 0.386, 0.221, 0.903] compare with {the Gold ETFs bar for Week 4}[0.764, 0.508, 0.817, 0.903] in height?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Gold ETFs bar for Week 1 is shorter."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "vyMlLfNhV7bha6si",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the BlueChip bar for week 1}[0.115, 0.250, 0.168, 0.903] to the left of {the Gold ETFs bar for week 1}[0.168, 0.386, 0.221, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "ulxTJ2VoLRG02ket",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items within the area [0.086, 0.046, 0.234, 0.161]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend listing two items: \n\n1. The label 'BlueChip' with a corresponding color indicator in a shade of dark teal, situated at [0.142, 0.060, 0.219, 0.095] for the label and [0.093, 0.063, 0.128, 0.087] for the color indicator.\n\n2. The label 'Gold ETFs' with a color indicator in blue, location at [0.142, 0.112, 0.228, 0.147] for the label and [0.093, 0.115, 0.128, 0.139] for the color indicator."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "757pg2jK32QHQsbY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information within the region [0.513, 0.209, 0.566, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes one bar for 'BlueChip' for 'Week3', with it's top text marking the value as 45. The coordinates of the bar are [0.513, 0.209, 0.566, 0.903] while the top text is placed at [0.329, 0.290, 0.351, 0.325]."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "FbAmXlrn9FNnupJV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main elements in the region [0.659, 0.046, 0.899, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend for 'Market Manipulation' situated at [0.714, 0.060, 0.892, 0.095] with corresponding color label on the left at [0.666, 0.063, 0.701, 0.087] in a shade of pink."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "4jpGrcPMt96fkMhz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.964, 0.372, 0.981, 0.706] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, which is \"Globalization Index\"."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "r1fYB0ZZK56PzDXj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the specific content at [0.793, 0.060, 0.907, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text that represents the \"Globalization\" trend."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "q9r9sCR6eLziAxsN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed at the coordinate [0.116, 0.257, 0.170, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue bar representing the \"International Trade\" trend of USA."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "UlhxUPuCW2K5mfOx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the positioned at [0.179, 0.927, 0.215, 0.962] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "An X-axis coordinate label, which is \"USA\"."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "G4FliRE2yX6X7ODi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing \"International Trade\" trend for China [0.318, 0.193, 0.372, 0.539] have a higher value than that for UK [0.520, 0.212, 0.574, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "HGD2frH54KUFlca7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the text label for Globalization [0.793, 0.060, 0.907, 0.095] located on the left or right of the color handle [0.745, 0.063, 0.780, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "pzQaDlYDGeP916ET",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing the \"Globalization\" trend for India [0.830, 0.539, 0.884, 0.684] taller than the bar representing the \"International Trade\" trend for India [0.723, 0.421, 0.776, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "GPSydQ0mxjtS0dbP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {bar}[0.318, 0.193, 0.372, 0.539] associated with {China}[0.374, 0.927, 0.424, 0.962] higher or lower than 40?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "sSQC83ynOmCRRV2s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.738, 0.046, 0.914, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one label for the \"Globalization\" trend within this region. The color handle of \"Globalization\" is located at [0.745, 0.063, 0.780, 0.087], and the text label is at [0.793, 0.060, 0.907, 0.095]."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "4tV6Ul6opiQlg1jQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in [0.224, 0.539, 0.278, 0.758] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object:\n1. The purple bar represents the \"Globalization\" trend for the USA and its coordinate range is [0.224, 0.539, 0.278, 0.758]."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "4d10qOQupj6xnJ0Z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside [0.077, 0.174, 0.923, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the whole chart with all bars and labels. For instance, the bar of USA in \"International Trade\" trend with coordinates [0.116, 0.257, 0.170, 0.539], and the bar of China in \"Globalization\" trend with coordinates [0.426, 0.539, 0.480, 0.787] are inside this region."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "kKfGoUEU1z8RA6X6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.043, 0.282, 0.065, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label of the value \"40\" is in this area."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "vWa74it68db2ATTv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content shown at coordinates [0.328, 0.025, 0.719, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, which reads \"Healthy Habits: Healthy vs Freelance Lifestyles\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "dJkLNbYLfbq47h5G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the rectangle at [0.104, 0.209, 0.153, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar in the bar chart, representing the 'Healthy Lifestyle' percentage (53.1 %) of individuals who use 'Morning Run' as a healthy habit."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "lVEYRiG3FHe864h2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.843, 0.083, 0.959, 0.106] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the coordinates for the label 'Healthy Lifestyle' in the chart's legend."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "IRRhPSjx0r1xrbG3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is higher, {the bar representing 'Healthy Eating for a Healthy Lifestyle'}[0.289, 0.097, 0.338, 0.795] or {the bar representing 'Healthy Eating for a Freelance Lifestyle'}[0.338, 0.377, 0.388, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing 'Healthy Eating for a Healthy Lifestyle'."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "azkM0saEhfUZbJYW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Healthy Lifestyle' for 'Regular Checkups'} bar[0.474, 0.240, 0.523, 0.795] have a higher percentage than {the 'Freelance Lifestyle' for 'Regular Checkups'} bar[0.523, 0.481, 0.573, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "qIxLDmEGJyYM7O5Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the legend entry for 'Healthy Lifestyle'}[0.843, 0.083, 0.959, 0.106] positioned above {the legend entry for 'Freelance Lifestyle'}[0.843, 0.118, 0.972, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "cp7MAv63LnjRsH4z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the area [0.798, 0.074, 0.978, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the chart's legend is located. It consists of two entries: 'Healthy Lifestyle', represented by a blue color at [0.804, 0.085, 0.832, 0.101] and the text at [0.843, 0.083, 0.959, 0.106]; and 'Freelance Lifestyle', represented by an orange color at [0.804, 0.120, 0.832, 0.136] and the text at [0.843, 0.118, 0.972, 0.141]."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "3Td247BFbyRFu6MC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in [0.043, 0.786, 0.052, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label \"0%\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "fxB6UqhrOnFw3wvc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the region [0.104, 0.209, 0.153, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a blue bar on the bar chart, which signifies the percentage of individuals living a Healthy Lifestyle who have the habit of a 'Morning Run'. The Percentage is given as 53.1% in the text area at [0.126, 0.188, 0.156, 0.211]."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "4bcdNEfBU4w9Jv86",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object seen at coordinates [0.118, 0.281, 0.261, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar from the category 'Job Stability' for 'Software Engineer' profession with a percentage of 61.5."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "I8BmdppB4eGBYzUX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please tell me the profession present at coordinate range [0.741, 0.927, 0.853, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The profession at the coordinates is 'Data Analyst'."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "oT3SMbhacqqlvHWc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} in the area [0.019, 0.365, 0.036, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is 'Percentage (%)'."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "30ZMOlc7LSrihmZO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text within [0.314, 0.098, 0.352, 0.133] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the job growth rate of 75.3 for {Data Analyst}[0.654, 0.395, 0.797, 0.903]."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "UgmQrQbTySdYDiaw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Job Growth' bar for 'Software Engineer'}[0.261, 0.141, 0.404, 0.903] taller or shorter than {the 'Job Stability' bar for 'Data Analyst'}[0.654, 0.395, 0.797, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "Kia3Q4dNz9VGQh1H",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Job Growth' bar for 'Data Analyst'}[0.797, 0.248, 0.940, 0.903] located to the right of {the 'Job Stability' bar for 'Software Engineer'}[0.118, 0.281, 0.261, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "1bl7bMregLTvrVx2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater, the {value of 'Job Stability' for 'Software Engineer'}[0.171, 0.238, 0.209, 0.273] or the {value of 'Job Growth' for 'Data Analyst'}[0.849, 0.206, 0.888, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Job Growth for Data Analyst."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "ZdYZ7xUZolE5hcs8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars representing 'Job Stability'[0.118, 0.281, 0.261, 0.903], [0.654, 0.395, 0.797, 0.903] taller than the bars representing \"Job Growth\"[0.261, 0.141, 0.404, 0.903], [0.797, 0.248, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "uiCEjtLieSINUBue",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is present in area [0.086, 0.111, 0.252, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the graph. It contains two entries: {Job Stability}[0.142, 0.125, 0.245, 0.160], represented by a blue color, and {Job Growth}[0.142, 0.177, 0.237, 0.212], represented by a green color."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "PvcFOZDtIZARuXdq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.261, 0.141, 0.940, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bar pairs each representing 'Job Growth' for the professions 'Software Engineer' and 'Data Analyst', correspondingly."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "sPdbks2xu2lb7LlI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.077, 0.093, 0.981, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encloses the main plot area of the bar chart including the bars, the x-axis with labels indicating professions, and the y-axis indicating the percentage values."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "QaozKawDoFijogcP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.304, 0.037, 0.755, 0.083]."
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle area of the chart, reading 'Career and Workplace Development Trends'."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "WNhQKgoD7vzjXRrH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing Road Freight in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is blue; {the bar representing Road Freight in 2021}<r>[0.702, 0.222, 0.762, 0.570]</r>."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "LJwl1RGyTMx7THvU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar representing Air Freight in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.465, 0.415, 0.524, 0.570]."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "NtaxNTN6shV0uxTB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is noted for Rail Freight in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The noted value is -36.70; {the note}<r>[0.772, 0.325, 0.811, 0.360]</r>."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "ckgwxKzHEwbYIzPv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color and coordinates associated with the Rail Freight in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is purpulish; {the color handle}<r>[0.083, 0.212, 0.118, 0.236]</r> and the coordinates for the label are [0.132, 0.209, 0.229, 0.244]."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "l4eN3i2yXxTodr4f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year among {Road Freight}<r>[0.109, 0.171, 0.168, 0.570]</r>, {Air Freight}<r>[0.168, 0.337, 0.228, 0.570]</r>, {Sea Freight}<r>[0.228, 0.570, 0.287, 0.868]</r>, {Rail Freight}<r>[0.287, 0.570, 0.346, 0.757]</r> had the highest value in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sea Freight."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "Yu2SRM2WnVqAYPEC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the values of Road Freight}<r>[0.109, 0.171, 0.168, 0.570]</r> generally higher than {the values of Air Freight}<r>[0.168, 0.337, 0.228, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "GNDm05FPdixZWrCR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Sea Freight}<r>[0.228, 0.570, 0.287, 0.868]</r> and {Rail Freight}<r>[0.287, 0.570, 0.346, 0.757]</r>, which has seen an increase from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rail Freight."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "PpN2YjuXvIYojFN0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Rail Freight in 2019}<r>[0.287, 0.570, 0.346, 0.757]</r> higher or lower than {the bar representing Road Freight in 2020}<r>[0.465, 0.415, 0.524, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "mAfPZEddgmKYETAq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the legend area at [0.076, 0.038, 0.248, 0.258]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains four separate entries, each with a square color handle and a text label: \n1. {Road Freight with color blue}<r>[0.132, 0.052, 0.241, 0.087][0.083, 0.055, 0.118, 0.079]</r>, \n2. {Air Freight with color green}<r>[0.132, 0.104, 0.221, 0.139][0.083, 0.107, 0.118, 0.132]</r>, \n3. {Sea Freight with color red}<r>[0.132, 0.156, 0.230, 0.191][0.083, 0.160, 0.118, 0.184]</r>, \n4. {Rail Freight with color purple}<r>[0.132, 0.209, 0.229, 0.244][0.083, 0.212, 0.118, 0.236]</r>."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "MDXfU2hdtZkoI0tr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the y-axis lables include in the region [0.019, 0.046, 0.055, 0.999]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the text for the y-axis labels. They are, from top to bottom:\n1. \"-80\"<r>[0.019, 0.964, 0.055, 0.999]</r>,\n2. \"-60\"<r>[0.019, 0.862, 0.055, 0.897]</r>,\n3. \"-40\"<r>[0.019, 0.760, 0.055, 0.795]</r>,\n4. \"-20\"<r>[0.019, 0.658, 0.055, 0.693]</r>,\n5. \"0\"<r>[0.044, 0.556, 0.055, 0.591]</r>,\n6. \"20\"<r>[0.033, 0.454, 0.055, 0.489]</r>,\n7. \"40\"<r>[0.033, 0.352, 0.055, 0.387]</r>,\n8. \"60\"<r>[0.033, 0.250, 0.055, 0.285]</r>,\n9. \"80\"<r>[0.033, 0.148, 0.055, 0.183]</r>, \n10. \"100\"<r>[0.022, 0.046, 0.055, 0.081]</r>."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "fddEW3WEBKHleaKK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.406, 0.171, 0.465, 0.570]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has one object within it: the blue colored bar representing {Road Freight}<r>[0.406, 0.283, 0.465, 0.570]</r> in the year 2020."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "dV0qNnfFyo60ffzh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the chart region [0.295, 0.080, 0.753, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart {subtitle}<r>[0.295, 0.080, 0.753, 0.125]</r> \"Transportation and Logistics Industry Trends\"."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "SZ8aPpQsm2YB4rrF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object at coordinates [0.829, 0.083, 0.938, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label for Rapid Recovery."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "NUyX1F7UVEqJl5uz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given JSON structured information, could you tell me what is the color of {the first bar of the second group}<r>[0.186, 0.483, 0.265, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#efc958', meaning it has a yellowish-orange color."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "FcvaEX4TTF7sBI1g",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what the Y-axis label {ylabel}<r>[0.015, 0.416, 0.029, 0.549]</r> is referring to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is referring to the 'Trend Scale'."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "KoR5c1ULudu3vQ3f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 'Podcast Subscribers' under the 'Rapid Recovery' trend}[0.107, 0.196, 0.186, 0.483] above or below zero on the Trend Scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "wO7vOJsX9wjLkOdg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, is {the 'Accelerating Decline' bar for 'Live Streaming'}<r>[0.864, 0.483, 0.943, 0.769]</r> longer than {the 'Rapid Recovery' bar for 'Live Streaming'}<r>[0.785, 0.291, 0.864, 0.483]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "6xE4b5qIMSOwwMta",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of height, how does {the 'Rapid Recovery' bar for 'Online Webinars'}[0.333, 0.483, 0.412, 0.674] compare to {the 'Accelerating Decline' bar for 'Online Webinars'}[0.412, 0.483, 0.491, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Rapid Recovery' bar is taller."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "JXLcTmjGVTAoKVGU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content does the region [0.785, 0.074, 0.978, 0.151] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. It includes two items: the first, labeled as 'Rapid Recovery', is associated with a color coded as '#007acc' (a shade of blue), its coordinates are [0.829, 0.083, 0.938, 0.106]; the second, labeled as 'Accelerating Decline', is associated with a color coded as '#efc958' (a shade of yellowish-orange), its coordinates are [0.829, 0.118, 0.972, 0.141]."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "3eFvZBbe9WR0g93t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.116, 0.919, 0.255, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for 'Podcast Subscribers'."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "e8LaIMoiMM3YcS4s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of what the region [0.107, 0.196, 0.186, 0.483] includes?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a bar representing the trend 'Rapid Recovery' for 'Podcast Subscribers'. The bar's height indicates a positive trend scale of 3."
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "PTtVlFadzWc2xyN8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text {at the coordinates [0.105, 0.183, 0.200, 0.211]} represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the label for the green bar; it is called Gasoline Tech."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "hP4XsJ2NP0AgjtTW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the axis label} at the coordinates [0.483, 0.942, 0.555, 0.970] name?"
+            },
+            {
+                "from": "gpt",
+                "value": "It labels the x-axis as Fuel Types."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "0wKJv7qt4918NAHY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the {ylabel} at coordinates [0.015, 0.440, 0.029, 0.518]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is Score."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "D47KEwhGQU2Dm53a",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {bars in Hybrid Technology} at positions [[0.096, 0.745, 0.143, 0.788], [0.331, 0.449, 0.378, 0.788], [0.566, 0.196, 0.613, 0.788], [0.801, 0.111, 0.849, 0.788]] generally higher or lower than {the bars in Diesel Tech} at positions [[0.143, 0.720, 0.190, 0.788], [0.378, 0.280, 0.425, 0.788], [0.613, 0.280, 0.660, 0.788], [0.849, 0.280, 0.896, 0.788]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "iwr8UFRWk2Mc5oja",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue bar titled \"Diesel Tech\"} at [0.143, 0.720, 0.190, 0.788] have a higher or lower value than the {green bar titled \"Gasoline Tech\"} at [0.190, 0.669, 0.237, 0.788] for Type 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "WSOsrplYuu3HjlMD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar of Type 2 in the Hybrid Technology series} at [0.331, 0.449, 0.378, 0.788] have higher value than {the bar of Type 3 in the same series} at [0.566, 0.196, 0.613, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "5dngIFyD9etco40O",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area from [0.060, 0.089, 0.238, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a key explanation for the color coding of the bars in the chart. The Hybrid Technology corresponds to the blue color at location [0.066, 0.102, 0.093, 0.122], Diesel Tech corresponds to the orange color at location [0.066, 0.144, 0.093, 0.164], Gasoline Tech corresponds to the green color at location [0.066, 0.186, 0.093, 0.205], and Outliers corresponds to the red color at location [0.066, 0.228, 0.093, 0.247]."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "pDiHhYJCwrHo0sY1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of objects can we see in the area of [0.060, 0.089, 0.238, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the color-coded legend for the four types of bars, which include Hybrid Technology, Diesel Tech, Gasoline Tech, and Outliers."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "7XlHsTSipDV36Hgs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from [0.096, 0.745, 0.143, 0.788]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the first blue bar standing for the Hybrid Technology for the Type 1 fuel category."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "dEw8b88EIvttF5Mg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.108, 0.595, 0.157, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the bar for \"EdTech Adoption\" in the feature \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "MCJLPk61Ql0x4g5v",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate is the label for \"Machine Learning\" at?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.341, 0.903, 0.464, 0.931]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "7fIpux0JZExG30By",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.015, 0.441, 0.029, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Value\"."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "p36T24NQn7GEq0IJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels for color are in the legend found at [0.073, 0.089, 0.287, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels are {EdTech Adoption}[0.118, 0.100, 0.233, 0.128] and {Online Learning Growth}[0.118, 0.142, 0.282, 0.170]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "2XjzU03ufCP1an3g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, {the bar representing \"EdTech Adoption\"}[0.108, 0.595, 0.157, 0.883] or {the bar representing \"Online Learning Growth\"}[0.157, 0.498, 0.206, 0.883] for \"Artificial Intelligence\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning Growth."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "AzuJJdlI1j1DKOLQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for \"EdTech Adoption\"}[0.108, 0.595, 0.157, 0.883], [0.354, 0.498, 0.403, 0.883], [0.599, 0.402, 0.648, 0.883], [0.845, 0.306, 0.894, 0.883] generally taller than {the bars for \"Online Learning Growth\"}[0.157, 0.498, 0.206, 0.883], [0.403, 0.402, 0.452, 0.883], [0.648, 0.306, 0.698, 0.883], [0.894, 0.113, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "7wMN0TARmA2MjCJI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Cloud-based Learning\" in EdTech Adoption} [0.599, 0.402, 0.648, 0.883] taller than {the bar for \"Cloud-based Learning\" in Online Learning Growth}[0.648, 0.306, 0.698, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "nbTuV4u1RZtLfHsw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich feature has more value for {Online Learning Growth}<{Online Learning Growth in Artificial Intelligence}[0.157, 0.498, 0.206, 0.883], {Online Learning Growth in Machine Learning}[0.403, 0.402, 0.452, 0.883], {Online Learning Growth in Cloud-based Learning}[0.648, 0.306, 0.698, 0.883], {Online Learning Growth in VR/MR Technology}[0.894, 0.113, 0.943, 0.883]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR/MR Technology."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "SEXooj0Of9fb1LnY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.073, 0.089, 0.287, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend containing two labels. The first label, {EdTech Adoption}[0.118, 0.100, 0.233, 0.128] associated with the color '#8ecfc9', and the second label is {Online Learning Growth}[0.118, 0.142, 0.282, 0.170], associated with the color '#ffbe7a'."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "0INMCszpfgVn0AiR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the area [0.066, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area, where the bars representing values for different features (\"Artificial Intelligence\", \"Machine Learning\", \"Cloud-based Learning\", and \"VR/MR Technology\") in \"EdTech Adoption\" and \"Online Learning Growth\" can be found."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "6Dp9jv2ZV9noTb34",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.354, 0.498, 0.452, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two objects:\n1. The {bar for \"EdTech Adoption\"} in {Machine Learning}[0.354, 0.498, 0.403, 0.883]\n2. The {bar for \"Online Learning Growth\"} in {Machine Learning}[0.403, 0.402, 0.452, 0.883]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "HFl8MgswtCLWXexq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visible in the region [0.830, 0.903, 0.958, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for {VR/MR Technology}[0.830, 0.903, 0.958, 0.931]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "ENmsNQgscW67Olwp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.355, 0.295, 0.603] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Sales (thousands)."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "YXNQiJ1LLYOXIU8A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.926, 0.183, 0.954, 0.211]."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "iXizHLCBqiJUtOt0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at the tick coordinates [0.643, 0.883] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q3."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "N13oIAY65fiUj7yf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the labels in the coordinates [0.881, 0.089, 0.978, 0.264] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{iPhone}<r>[0.926, 0.100, 0.972, 0.128]</r>, {iPad}<r>[0.926, 0.142, 0.955, 0.170]</r>, {Mac}<r>[0.926, 0.183, 0.954, 0.211]</r>, and {Watch}<r>[0.926, 0.225, 0.969, 0.253]</r>."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "Pq2ox6MbvQ4wno8t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Mac bar in Q1}[0.181, 0.691, 0.216, 0.883] higher or lower than {the iPhone bar in Q1}[0.112, 0.421, 0.147, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "Dv5Ez5ypCJpJITFc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the iPad bar representing \"Q4\"}[0.840, 0.575, 0.874, 0.883] taller than {the Watch bar representing \"Q2\"}[0.447, 0.691, 0.482, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "AD9zlc9Wjah5cZJy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the iPhone bars}[[0.112, 0.421, 0.147, 0.883], [0.343, 0.383, 0.378, 0.883], [0.574, 0.306, 0.609, 0.883], [0.805, 0.113, 0.840, 0.883]] generally taller than {the Mac bars}[[0.181, 0.691, 0.216, 0.883], [0.412, 0.614, 0.447, 0.883], [0.643, 0.595, 0.678, 0.883], [0.874, 0.498, 0.909, 0.883]] in each corresponding quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "ExityCBdysIPsyjO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Q1\"}[0.172, 0.903, 0.191, 0.931] and {\"Q3\"}[0.633, 0.903, 0.653, 0.931], which quarter has a higher sales figure for {Watch}<r>[0.926, 0.225, 0.969, 0.253]</r> considering the Watch bars [{Watch Q1 bar}[0.216, 0.768, 0.251, 0.883], {Watch Q2 bar}[0.447, 0.691, 0.482, 0.883], {Watch Q3 bar}[0.678, 0.614, 0.713, 0.883], {Watch Q4 bar}[0.909, 0.498, 0.943, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "Q3."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "ZlHPq7BfWxs1MVu3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.881, 0.089, 0.978, 0.264]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are four pairs of color-text labels in the legend. The first pair's color label is located at [0.887, 0.102, 0.915, 0.122] displaying blue, and its text label is located at [0.926, 0.100, 0.972, 0.128] and it is called {iPhone}; The second pair's color label is located at [0.887, 0.144, 0.915, 0.164] displaying purple, and its text label is located at [0.926, 0.142, 0.955, 0.170], corresponding with the label {iPad}; The third pair's color label is located at [0.887, 0.186, 0.915, 0.205], rendering in green, and its text label is located at [0.926, 0.183, 0.954, 0.211] indicating the label {Mac}; The fourth pair's color label is displayed at [0.887, 0.228, 0.915, 0.247], showing an orange color, and its text label is located at [0.926, 0.225, 0.969, 0.253] bearing the label {Watch};"
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "Df59VlJf55U4pLM1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.071, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.071, 0.075, 0.985, 0.883] contains the chart's primary plotting area with the following components: X-axis label {Quarters}<r>[0.497, 0.942, 0.558, 0.970]</r>; Y-axis label {Sales (thousands)}<r>[0.015, 0.355, 0.029, 0.603]</r>; all the bars representing sales of different products in different quarters such as {iPhone bars}<r>[[0.112, 0.421, 0.147, 0.883], [0.343, 0.383, 0.378, 0.883], [0.574, 0.306, 0.609, 0.883], [0.805, 0.113, 0.840, 0.883]]</r>, and x-axis ticks labels for each quarter including {Q1}<r>[0.172, 0.903, 0.191, 0.931]</r>, {Q2}<r>[0.403, 0.903, 0.422, 0.931]</r>, {Q3}<r>[0.633, 0.903, 0.653, 0.931]</r>, {Q4}<r>[0.864, 0.903, 0.884, 0.931]</r>."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "p0KApozRFuqJ9YGy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the location [0.805, 0.113, 0.840, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 objects:\n\n1. A blue bar that represents the sales of iPhones in Q4, and its coordinates are [0.805, 0.113, 0.840, 0.883].\n2. A purple bar representing the sales of iPads in Q4, and its coordinates are [0.840, 0.575, 0.874, 0.883].\n3. A green bar indicating the sales of Mac in Q4, located in the coordinates of [0.874, 0.498, 0.909, 0.883].\n4. An orange bar showing the sales of Watch in Q4, with coordinates [0.909, 0.498, 0.943, 0.883]."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "0aY9XwgHGT7w33UY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.353, 0.185, 0.643, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the belted area, there are bars indicating Q2 and Q3 sales for different products, bars and labels for {iPad}<r>[0.926, 0.142, 0.955, 0.170]</r> in Q2<Q2><r>[0.403, 0.903, 0.422, 0.931]</r> and Q3<Q3><r>[0.633, 0.903, 0.653, 0.931]</r>, along with bars and labels for {Mac}<r>[0.926, 0.183, 0.954, 0.211]</r> in Q2 and Q3 are visible. Tick marks for Q2 and Q3 on the x-axis also fall within this area."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "JOjr9yH7MjxDgCzg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinate [0.357, 0.030, 0.699, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle \"Human Health and Disease Management\"."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "O0MMrtSWfMSFe7FX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color coded legend label at [0.460, 0.100, 0.634, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's labeled as \"Efficient Care Continuum\"."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "Ald2NwNhLzVFLz1x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.591, 0.903, 0.710, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Management Area \"Accident & Injury\""
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "w4U6uOQ7dmjziJXw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is taller, the {Efficient Care Continuum bar}[0.112, 0.344, 0.161, 0.883] or the {Personalized Care bar}[0.161, 0.113, 0.210, 0.883] for Minor Illness Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "Personalized Care bar."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "ZbO9IDYs1agnk9qT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Efficient Care Continuum bar for Mental Health}[0.846, 0.421, 0.895, 0.883] shorter than {the Personalized Care bar for Mental Health}[0.895, 0.152, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "hiwzKDcRqDfxKSFK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the position of {the Efficient Care Continuum bar for Chronic Disease Management}[0.357, 0.190, 0.406, 0.883] to the left or right of {the Personalized Care bar for Chronic Disease Management}[0.406, 0.306, 0.454, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "PqVK2DPw5hjxnSlz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area of [0.416, 0.089, 0.640, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two groups of coordinate colors and text indicators. {The first set}[0.460, 0.100, 0.634, 0.128] indicates that the \"Efficient Care Continuum\" is color-coded in '#8ecfc9'. {The second set}[0.460, 0.142, 0.585, 0.170] indicates that the \"Personalized Care\" is color-coded in '#ffbe7a'."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "Oucghxzp2pTBDjq1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.071, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly contains the plot of the bar chart, showing the efficiency score of different management areas between efficient care continuum and personalized care. It contains bars representing the efficiency score of different types of disease management, collated by two healthcare delivery strategies: Efficient Care Continuum and Personalized Care. There are four types of disease management represented: Minor Illness, Chronic Disease, Accident & Injury and Mental Health."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "5orZzSP5GawUZB9S",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in [0.846, 0.421, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are there. The bar to the left represents Mental Health management in Efficient Care Continuum with ranges of efficiency scores from 0.00 to 60.00 located at [0.846, 0.421, 0.895, 0.883]. The bar to the right represents Mental Health management in Personalized Care with ranges of efficiency scores from 0.00 to 95.00 located at [0.895, 0.152, 0.943, 0.883]."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "MLtL7fqKwHdFqadq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis located at coordinates [0.015, 0.342, 0.029, 0.616]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Development Score'."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "nJL76lqWvilj2uYA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar representing \"Performing Arts\"} at [0.400, 0.411, 0.449, 0.883] for the Emerging group?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a Development Score of 0 to 46."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "gWuinuqDcTm2VBk7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the handle color represented in the legend for 'Emerging' located at [0.859, 0.144, 0.887, 0.164] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#f4a261' represents 'Emerging'."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "DtDlYCInFDB1Bsg7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich developed culture aspect bar is taller, {the bar representing \"Visual Arts\"}[0.104, 0.113, 0.153, 0.883] or {the bar representing \"Sculpture\"}[0.597, 0.195, 0.647, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Visual Arts."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "R2DR9UhKTgXcUBKC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Writing\" category for the Emerging group}[0.894, 0.185, 0.943, 0.883] taller than {the bar representing \"Performing Arts\" category for the Emerging group}[0.400, 0.411, 0.449, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "iQDeZG76aVnxsFhK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Writing\"}[0.844, 0.380, 0.894, 0.883] and {\"Performing Arts\"}[0.351, 0.185, 0.400, 0.883] for the Developed group, which has higher Development Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performing Arts."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "R6Hrm3YNXbWmCRtU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.104, 0.113, 0.203, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two vertical bars within these coordinates. The blue bar at [0.104, 0.113, 0.153, 0.883] represents a score of 0 to 75 for the Visual Arts aspect in the Developed group. The orange bar at [0.153, 0.319, 0.203, 0.883] represents a score of 0 to 55 for the Visual Arts aspect in the Emerging group."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "3mhO4zJleAGTLcrb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.869, 0.903, 0.919, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the X-axis label \"Writing\" for both the Developed and Emerging groups of the Culture Aspects."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "d3pcAT7IRiazsetV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.597, 0.195, 0.696, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 bars within these coordinates representing the Sculpture aspect. The bar at [0.597, 0.195, 0.647, 0.883] is for the Developed group with a score from 0 to 67. The bar at [0.647, 0.524, 0.696, 0.883] belongs to the Emerging group with a score from 0 to 35."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "bBX68BCHmPfidL1w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordiantes [0.148, 0.903, 0.184, 0.931] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2019\" on the x-axis."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "c3Mz64YO3k9ZfiMe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the object located at [0.105, 0.100, 0.188, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend label for the term {CSR-Default}<r>[0.105, 0.100, 0.188, 0.128]</r>."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "GTlei1KiaCfxjDXD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.731, 0.242, 0.766, 0.883] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "They refer to a {Sustainable-HAG}<r>[0.731, 0.242, 0.766, 0.883]</r> bar for the year 2022."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "svBQoKC6FF2Z26xJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text label exists within the coordinate [0.015, 0.348, 0.029, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {Performance Score}<r>[0.015, 0.348, 0.029, 0.610]</r> which represents the y-axis label."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "neVT5c4JQdUZDum5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the CSR-Default bar of 2019}<r>[0.096, 0.242, 0.131, 0.883]</r> taller than {the Sustainable-Default bar of the same year}<r>[0.166, 0.370, 0.201, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "AiuzH83mLMxoRBoj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the height of {the CSR-HAG bar for 2021}<r>[0.484, 0.177, 0.519, 0.883]</r> compare to {the Sustainable-HAG bar for 2021}<r>[0.554, 0.242, 0.590, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is taller."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "VX6DwCdBDL3nzqPZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is closer to the top, {CSR-HAG 2023 bar}<r>[0.837, 0.113, 0.872, 0.883]</r> or {Sustainable-HAG 2023 bar}<r>[0.907, 0.177, 0.943, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CSR-HAG 2023 bar."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "EkcqdDTqRVp6oZL4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars for 2020, is the {CSR-Default bar}<r>[0.272, 0.370, 0.307, 0.883]</r> higher or lower than {Sustainable-HAG bar}<r>[0.378, 0.370, 0.413, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "FaCgSOAmlYWfIPF2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located in the region [0.060, 0.089, 0.247, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the chart legend. The legend includes four items: {CSR-Default}<r>[0.105, 0.100, 0.188, 0.128]</r> in green color, {CSR-HAG}<r>[0.105, 0.142, 0.167, 0.170]</r> in orange color, {Sustainable-Default}<r>[0.105, 0.183, 0.242, 0.211]</r> in blue color, and {Sustainable-HAG}<r>[0.105, 0.225, 0.221, 0.253]</r> in light peach color. The color labels for each are located at [0.066, 0.102, 0.093, 0.122], [0.066, 0.144, 0.093, 0.164], [0.066, 0.186, 0.093, 0.205], and [0.066, 0.228, 0.093, 0.247] respectively."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "seLhcDooagViPPdh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the region [0.448, 0.177, 0.554, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two bars for the year 2021. They are: \n1. {CSR-HAG bar for 2021}<r>[0.484, 0.177, 0.519, 0.883]</r> in orange color, \n2. {Sustainable-Default bar for 2021}<r>[0.519, 0.498, 0.554, 0.883]</r> in blue color."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "6zvBZfqG1Aw2C9tK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.855, 0.903, 0.890, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the year {2023}<r>[0.855, 0.903, 0.890, 0.931]</r> label on the x-axis."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "gA9vwaA6VC5hHL3X",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the objects within the boundaries [0.676, 0.177, 0.766, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes bars from different groups for 2022: \n1. {CSR-Default bar for 2022}<r>[0.625, 0.627, 0.660, 0.883]</r> in green color,\n2. {CSR-HAG bar for 2022}<r>[0.660, 0.177, 0.696, 0.883]</r> in orange color,\n3. {Sustainable-Default bar for 2022}<r>[0.696, 0.563, 0.731, 0.883]</r> in blue color,\n4. {Sustainable-HAG bar for 2022}<r>[0.731, 0.242, 0.766, 0.883]</r> in light peach color."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "92WjSRcnrXQ0fTn0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the product category located at [0.169, 0.903, 0.277, 0.931] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cosmeceuticals"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "3DrOjoUeLCGWxkNd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend indicate for the 'Apr' sales at [0.101, 0.228, 0.128, 0.247]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "ja2uziuTTw1Oa7eh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text does the y-axis label at [0.015, 0.418, 0.029, 0.540] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales ($)"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "FWLVfmYtvCecKiRe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at the coordinates [0.818, 0.235, 0.849, 0.883] represent in terms of product and month?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Battery's June sales}<r>[0.913, 0.140, 0.944, 0.883]</r>"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "rhsDssamC2irn011",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the bars for the 'Battery' product, does the sales of 'Feb' at [0.787, 0.140, 0.819, 0.883] exceed that of 'Jun'[0.913, 0.140, 0.944, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "JtbV4xv3sF7LjXAO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor 'Superfoods', are the sales in 'Jan'[0.547, 0.151, 0.578, 0.883] higher compared to 'Jun'[0.704, 0.347, 0.735, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "gw2wwYwKvSHZlMRD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month has higher sales for 'Natural Products': 'May'[0.463, 0.140, 0.495, 0.883] or 'Feb'[0.369, 0.219, 0.401, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "May."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "zZTQxM4nMCieRwx4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the sales of 'Cosmeceuticals' in 'Mar'[0.192, 0.328, 0.223, 0.883] with 'Apr'[0.223, 0.321, 0.254, 0.883], which month has higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "March."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "9xSiwEkU4UfCTXG6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what the visual elements within the axes coordinates [0.088, 0.075, 0.985, 0.883] are about?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area defined by these coordinates contains the main bar chart which illustrates the sales trends of 4 different product categories ('Cosmeceuticals', 'Natural Products', 'Superfoods', 'Battery') over the period of 6 months ('Jan' to 'Jun')."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "xocjdFR1Mn4gN4Ys",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region between [0.095, 0.089, 0.174, 0.348]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts the legend of the chart, which contains six colored markers, each representing a different month from January to June, along with their corresponding labels."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "e2KZBaPhnuGlUKc8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.756, 0.166, 0.787, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the January sales bar for the product category \"Battery\"."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "D1mCEbohTUkUgdBc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area of [0.035, 0.117, 0.078, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label \"50000\", indicating the sales value."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "ZSGv9pE2N5pLJK1i",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in {the green bar} at this location <r>[0.112, 0.463, 0.191, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Local News influence on Media Type 1."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "PK2uIPigPQXJx5jl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the text at coordinates <r>[0.145, 0.903, 0.237, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for Media Type 1."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "FvKacExDZNVxu51Q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value that corresponds to {the yellow bar}<r>[0.865, 0.218, 0.943, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "190."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "CvSaDKDnFI8dDZAT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identifier for the orange color shown in the legend at <r>[0.846, 0.144, 0.874, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "RnRLcD5NWAF9y278",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Local News influence on Media Type 2}<r>[0.337, 0.358, 0.415, 0.883]</r> higher than {the Social Media influence on Media Type 2}<r>[0.415, 0.428, 0.494, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "K7bP2ZiWLz6KbJWB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Media Type 2's coordinates}<r>[0.370, 0.903, 0.461, 0.931]</r> positioned at a higher x-axis level compared to {Media Type 1}<r>[0.145, 0.903, 0.237, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "Qh5DEFcf5K3rBnPV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of media has a greater influence covered by the bars, {Local News}<r>[0.112, 0.463, 0.191, 0.883]; [0.337, 0.358, 0.415, 0.883]; [0.562, 0.603, 0.640, 0.883]; [0.786, 0.428, 0.865, 0.883]</r> or {Social Media}<r>[0.191, 0.113, 0.269, 0.883]; [0.415, 0.428, 0.494, 0.883]; [0.640, 0.533, 0.719, 0.883]; [0.865, 0.218, 0.943, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "LBj9k8bXfv7P847Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has more influence on Media Type 1, {Local News}<r>[0.112, 0.463, 0.191, 0.883]</r> or {Social Media}<r>[0.191, 0.113, 0.269, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "X5GFrwq7Jn63DbFe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's in the bounding box with these coordinates <r>[0.841, 0.089, 0.978, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two labels for color text within this region, each associated with a color block. The first label, Local News, has the color green and is situated at [0.885, 0.100, 0.963, 0.128] with the color block at [0.846, 0.102, 0.874, 0.122], while the second label, Social Media, is associated with an orange color at [0.885, 0.142, 0.972, 0.170] with the color block at [0.846, 0.144, 0.874, 0.164]."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "KNaCphJxxsWhNkxg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what I would see in the region with the following coordinates <r>[0.562, 0.533, 0.719, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you would find a bar that displays the influence score of Social Media on Media Type 3."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "JAglsPxFrz16dpnZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be represented by the area that lies within these coordinates <r>[0.071, 0.075, 0.985, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main chart area for the graph that shows the influence scores of Local News and Social Media across four different media types. It includes the bars indicating the influence score along with the x-axis and y-axis."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "gSQiQOeGV1OtR0o7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the bounding box with these coordinates <r>[0.340, 0.030, 0.716, 0.066]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle for the chart, which reads 'Media Influence and Public Opinion Guidance.'"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "JH63xaNHcduIHv5W",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the label \"Data Science\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for {Data Science}[0.053, 0.142, 0.073, 0.155] is {beige}[0.053, 0.142, 0.073, 0.155]."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "kbGRIo93aXqSVSBA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the yAxis label present at the coordinates [0.011, 0.401, 0.021, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Development Score."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "ZcyLmST3jOu3a2wW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the label \"Quantum Computing\" in the plotting legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates for {Quantum Computing}[0.081, 0.088, 0.185, 0.106] are [0.081, 0.088, 0.185, 0.106]."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "dsunnvujAKsKDp64",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2015 bar for Artificial Intelligence}[0.492, 0.596, 0.505, 0.814] higher than the {2015 bar for 5G}[0.517, 0.778, 0.529, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "Nl6JLOcr14LWQUye",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {first year bar of Data Science}[0.124, 0.742, 0.136, 0.814] start lower than the {first year bar of 5G}[0.111, 0.814, 0.124, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "3ohUkQ6M67USexkv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {2011 bars}[0.180, 0.669, 0.193, 0.814], [0.205, 0.705, 0.217, 0.814] aligned vertically with the {2011 x-tick}[0.193, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "xQQYpMK6YWy7ltAO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.010, 0.350, 0.050, 0.600]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {y-axis label}[0.011, 0.401, 0.021, 0.572] and several {y-axis ticks}[[0.044, 0.378], [0.044, 0.523], [0.044, 0.669]]. The label is \"Development Score\" and the tickvalues are from 6 to 0, indicating increasing Development Scores down the axis."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "EJgMiqrUAJZ2Cjm8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the visual content between [0.080, 0.060, 0.190, 0.160]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the {legend}[0.049, 0.055, 0.189, 0.165] including labels for different technologies such as {Artificial Intelligence}[0.081, 0.062, 0.183, 0.080], {Quantum Computing}[0.081, 0.088, 0.185, 0.106], and their corresponding {color indicators}[0.053, 0.064, 0.073, 0.076], [0.053, 0.090, 0.073, 0.102]."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "J85DXpuqMRUvM9bN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCover the details found within the rectangular section [0.500, 0.940, 0.600, 0.960]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the {x-tick label \"2016\"}[0.585, 0.939, 0.610, 0.957], which is part of the x-axis representing the years."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "W14ooaRFTrUiQ743",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label is for the coordinate [0.611, 0.903, 0.676, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Webinars'."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "c9ULwl8PLkEcnRNT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.105, 0.100, 0.228, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text label in the legend reading 'Sustained Growth'."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "Pq6a30re09F41uAb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the numeric range value for the rectangle at [0.843, 0.521, 0.893, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The numeric range is '0.00 -> 4.00'"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "Ld3LYKlYWF1gPLHU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Online Classes' of 'Sustained Growth'[0.096, 0.702, 0.145, 0.883] taller than the bar representing 'Online Classes' of 'Rapid Expansion'[0.145, 0.793, 0.195, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "zzMgAdxYJ6TjZGf1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in the chart, {the 'Sustained Growth' bar for 'Webinars'}[0.594, 0.566, 0.644, 0.883] or {the 'Rapid Expansion' bar for 'Webinars'}[0.644, 0.340, 0.693, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Rapid Expansion' bar for 'Webinars'."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "83Fuc0dKl8G0eA8q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Rapid Expansion' bar for 'Online Classes'}[0.145, 0.793, 0.195, 0.883] to the left or right of {the 'Sustained Growth' bar for 'Online Classes'}[0.096, 0.702, 0.145, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "neINRnhdpALh5Ejj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.060, 0.089, 0.234, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the legend. It contains labels and color squares for two categories: 'Sustained Growth'{color square}[0.066, 0.102, 0.093, 0.122], labelled at [0.105, 0.100, 0.228, 0.128], and 'Rapid Expansion'{color square}[0.066, 0.144, 0.093, 0.164], labelled at [0.105, 0.142, 0.220, 0.170]."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "aV5cPuqRqB0KA07z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.344, 0.113, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region houses most of the bars depicting the growth units of different Educational Technologies in the categories of 'Sustained Growth' and 'Rapid Expansion'. Examples include the 'Sustained Growth' bar for 'Virtual Reality'[0.843, 0.521, 0.893, 0.883] and the 'Rapid Expansion' bar for 'Webinars'[0.644, 0.340, 0.693, 0.883]."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "i5uxDTKROQNcTzE2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.015, 0.389, 0.029, 0.569] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the y-axis label, which is 'Growth Units'."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "QelRpOhCWib5yomG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the horizontal axis indicator for the coordinate [0.172, 0.903, 0.200, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "SEO"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "fYsuzHDmJitpBLGe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars designated at coordinates [0.107, 0.380, 0.186, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "F2SZpVEB8C16qbs4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the plot located at [0.448, 0.030, 0.602, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Media Comparison"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "gV3q1TY507vYGkmG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar having the coordinate [0.559, 0.524, 0.638, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the value of video production for Traditional Media}[0.559, 0.524, 0.638, 0.775]"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "x70CAiMeJ4BtADaE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Digital Media bar for SEO}[0.186, 0.309, 0.265, 0.524] have a higher score than {the Traditional Media bar for SEO}[0.107, 0.380, 0.186, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "OwpEzT0Mmzab0y0F",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Graphic Design value of Digital Media}[0.864, 0.111, 0.943, 0.524] higher than {the Video Production value of Traditional Media}[0.559, 0.524, 0.638, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "eH5c1TPteeXRthS9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Content Editing value for Traditional Media}[0.333, 0.327, 0.412, 0.524] have a higher value than {the SEO value for Digital Media}[0.186, 0.309, 0.265, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "747kGItZxUpqDhYg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Traditional Media bar for Graphic Design}[0.785, 0.255, 0.864, 0.524] on the left of {the Digital Media bar for Graphic Design}[0.864, 0.111, 0.943, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "PumO11YgcoSqRFVn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the coordinate [0.072, 0.089, 0.241, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend for the graph represented by two color-coded labels for Traditional Media and Digital Media. {Traditional Media} is represented by a red box at [0.077, 0.102, 0.105, 0.122], and {Digital Media} is represented by a blue box at [0.077, 0.144, 0.105, 0.164]."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "0eOOvZVryXLaKgHc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are within the bounds of [0.107, 0.380, 0.186, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object in this area is a {red bar}[0.107, 0.380, 0.186, 0.524] representing the score (0.8) for SEO under Traditional Media."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "FVFxdENcvxtQcYI8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.579, 0.524, 0.697, 0.931] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the {bar for Video Production of Traditional Media}[0.559, 0.524, 0.638, 0.775] and its title located at [0.579, 0.903, 0.697, 0.931]."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "1VxKMkmxpoHOd4Hp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region from [0.046, 0.154, 0.055, 0.182] on the y-axis display?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2\" indicating the score of 2 on the y-axis."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "tHJ5K50zPm9Lm1vh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the specific academic field represented by the X-axis label at [0.340, 0.903, 0.460, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Natural_Sciences."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "dl4JaywJZ5pbGaZL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color for the bar series PhD as specified in the given legend coordinates [0.808, 0.144, 0.835, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "77RGEWgBsu63K15q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is present at the coordinate [0.046, 0.872, 0.052, 0.900] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "SRWxwmEYtaLhsYVJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat region of research is specified in the coordinates [0.839, 0.903, 0.948, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social_Sciences."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "m1wdzweEWqucoEyf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Humanities Bachelors_Degree}<r>[0.104, 0.370, 0.153, 0.883]</r> higher or lower than the {bar for Humanities PhD}<r>[0.153, 0.498, 0.203, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "4O3D2cU9iOttD9xX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar for Natural_Sciences Bachelors_Degree}<r>[0.351, 0.241, 0.400, 0.883]</r> and {the bar for Formal_Sciences Bachelors_Degree}<r>[0.597, 0.113, 0.647, 0.883]</r>, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Formal_Sciences."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "DjucmS8Sar7ehKfT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {PhD for Social_Sciences}<r>[0.894, 0.293, 0.943, 0.883]</r> more conducted than {PhD for Natural_Sciences}<r>[0.400, 0.318, 0.449, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "XjvkyDnnbasSKzEt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater, {Bachelors_Degree in Formal_Sciences}<r>[0.597, 0.113, 0.647, 0.883]</r> or {PhD in Formal_Sciences}<r>[0.647, 0.164, 0.696, 0.883]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bachelors_Degree."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "F2J0ilS3zX7O4FAk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the coordinates range [0.802, 0.089, 0.978, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. The legend has two items: \n1. The first entry titled \"Bachelors_Degree\"<r>[0.846, 0.100, 0.972, 0.128]</r> is associated with a color marker in Sea Green<r>[0.808, 0.102, 0.835, 0.122]</r>.\n2. The second entry with the title \"PhD\" at<r>[0.846, 0.142, 0.874, 0.170]</r> is associated with a color marker in Orange<r>[0.808, 0.144, 0.835, 0.164]</r>."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "GgT3nBiMNKIafnZa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels present in the area [0.468, 0.942, 0.579, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label \"Academic Fields\" is present in the specified area."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "MYLROF5188MmjWsz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the coordinates range [0.597, 0.113, 0.696, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars:\n1. The bar representing the Bachelors_Degree in Formal_Sciences research quantity, with coordinates [0.597, 0.113, 0.647, 0.883].\n2. The bar representing the PhD in Formal_Sciences research quantity, with coordinates [0.647, 0.164, 0.696, 0.883]."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "4AqB5XMolP2ODZHQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region at [0.328, 0.030, 0.719, 0.066] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of the subtitle of the chart, which is \"Research Quantity in Different Academic Fields\"."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "jDwbM9S620Zcy1zc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Dell XPS bar at July}<r>[0.571, 0.165, 0.585, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Peach."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "NDdrZHP0e1C42FS2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label corresponding to {the bar for 'June' for Lenovo Thinkpad}<r>[0.484, 0.670, 0.498, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "June."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "GTuNo58ZWC3Bwk3A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of sales for {the Apple Macbook bar in September}<r>[0.672, 0.729, 0.686, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 -> 80.00."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "gDwV7tzli7KeDdFw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Lenovo ThinkPad bar in April}<r>[0.339, 0.647, 0.354, 0.917]</r> taller than {the Samsung Chromebook bar in April}<r>[0.325, 0.635, 0.339, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "KuReBGyCCcqIe9yy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar of Samsung Chromebook for May}<r>[0.397, 0.564, 0.411, 0.917]</r> represent higher sales compared to {the Apple Macbook bar for May}<r>[0.382, 0.635, 0.397, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "JtepSzTKuIxtDq6e",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar has a higher vertical position in October, {Dell XPS}<r>[0.787, 0.200, 0.802, 0.917]</r> or {Apple Macbook}<r>[0.744, 0.741, 0.759, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dell XPS."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "j1FvpMd4FnsPYnr8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.611, 0.105, 0.688, 0.917]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes vertically oriented bars for several products for the month of August. Each bar represents different sales values of products, clearly defined by distinct colors that specify different brands: Apple Macbook, Samsung Chromebook, Lenovo Thinkpad, and Dell XPS."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "nGZERekZJE84tcOn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is noticeable in the region <r>[0.160, 0.377, 0.542, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region depicts several different product sales bars for the first seven months of the year. Each month houses a set of colored bars categorized by product brands and shows various heights indicating the sales values."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "ngskRa7qwp1XB57Y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you observe in the portion of the graph marked by <r>[0.839, 0.063, 0.984, 0.189]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend explaining the various products displayed in the graph. It includes product names attached to color indicators displaying which colors represent which product on the bar graph. Specifically, it shows 'Apple Macbook' linked to a red color and 'Samsung Chromebook' linked to a blue color."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "ElYyvLByvSAcfFHk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text displayed at [0.504, 0.881, 0.533, 0.898] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2013."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "L5zxlsiHnhuDEDIp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar located at [0.558, 0.274, 0.572, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "s6Ew6hTnPb19siA2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the index label for 'NASDAQ-Default' located in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.548, 0.965, 0.644, 0.983]."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "S2pZ0GjNZmLGuUuL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for SPX-Default}[0.093, 0.430, 0.106, 0.564] taller than {the bar for CRSP-Default}[0.133, 0.440, 0.146, 0.564] in 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "JboBUG2H6Z1BT9kM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar at}[0.559, 0.274, 0.572, 0.564] represent a higher value than {the bar at}[0.865, 0.212, 0.878, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "XL189XcikP1PSBuv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-coordinate higher for {the DOW-Default label}[0.417, 0.965, 0.493, 0.983] compared to {the CRSP-Default label}[0.700, 0.965, 0.777, 0.983]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "JcWq4CREfNKRFqvP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information presented between coordinates [0.676, 0.019, 0.952, 0.041]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the subtitle 'Index Performance Comparison (2007-2019)'."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "GYdeBo4s8zJKqJ9z",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.029, 0.040, 0.046, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region accommodates the y-axis labels for the chart which ranges from '\u22123' to '5'."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "beQ4w2KeJEJ9dVSu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the region [0.013, 0.880, 0.936, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all the tick labels of the years from 2007 to 2019, marking each year's representation in the chart."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "oicTMHOaKAyGdAwG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.012, 0.402, 0.024, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, \"Percentage Increase\"."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "PtTX84zVutbXAMVr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the bar at [0.657, 0.592, 0.705, 0.933] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Innovation Increase\" for the \"Genomics\" tech area."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "e8DLmVd4K5mYskFz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item with position [0.867, 0.115, 0.977, 0.132] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend entry for \"Innovation Increase\", indicated with a {color: #fa8072}<r>[0.835, 0.116, 0.858, 0.128]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "XKSGEzRgFYpzdHkj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this object at [0.868, 0.962, 0.977, 0.132] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the label for the first tech area: \"Quantum\"}<r>[0.128, 0.939, 0.182, 0.957]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "3lb4YOVCMH2IKpkU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of {the bar representing \"Quantum\" in \"Research Growth\"}[0.131, 0.524, 0.179, 0.933] more than the height of {the bar representing \"Quantum\" in \"Innovation Increase\"}[0.179, 0.729, 0.227, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "w4qAcsre2TiI2HvR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Research Growth\" bar for \"Genomics\"}[0.610, 0.251, 0.657, 0.933] taller than {the \"Research Growth\" bar for \"Robotics\"}[0.370, 0.388, 0.418, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "QOJorjF6K7mOQoUW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a {bar in the TechArea category}<r>[0.083, 0.729, 0.131, 0.933]</r> for the Quantum tech area that is taller than {the bar in the Innovation Increase category}<r>[0.179, 0.729, 0.227, 0.933]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "g9jmEDuM3HYzHV81",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Research Growth}[0.867, 0.088, 0.962, 0.106] and {Innovation Increase}[0.867, 0.115, 0.977, 0.132], which one has a higher value for {Genomics}[0.605, 0.939, 0.662, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Research Growth."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "wb5AZsvIsu3SBeQd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is included within this area [0.830, 0.055, 0.982, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It has three items. The first item is {TechArea}<r>[0.867, 0.062, 0.920, 0.080]</r> with color {#cd5c5c}<r>[0.835, 0.064, 0.858, 0.076]</r>, the second item is {ResearchGrowth}<r>[0.867, 0.088, 0.962, 0.106]</r> with color {#f08080}<r>[0.835, 0.090, 0.858, 0.102]</r>, and the third item is {InnovationIncrease}<r>[0.867, 0.115, 0.977, 0.132]</r> with color {#fa8072}<r>[0.835, 0.116, 0.858, 0.128]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "jd7VUU3eEGDfF2qO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.083, 0.729, 0.849, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 12 bars representing the values for 4 tech areas across 3 categories: TechArea, Research Growth, and Innovation Increase. For example, within the Quantum tech area, the values for the three categories are represented by the following bars: {TechArea Quantum}<r>[0.083, 0.729, 0.131, 0.933]</r>, {Research Growth Quantum}<r>[0.131, 0.524, 0.179, 0.933]</r>, and {Innovation Increase Quantum}<r>[0.179, 0.729, 0.227, 0.933]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "kEWcWeTOgp86NnDZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.128, 0.939, 0.914, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the said region are the labels of the technology areas on the x-axis which are {Quantum}<r>[0.128, 0.939, 0.182, 0.957]</r>, {Robotics}<r>[0.370, 0.939, 0.419, 0.957]</r>, {Genomics}<r>[0.605, 0.939, 0.662, 0.957]</r>, and {CyberSecurity}<r>[0.832, 0.939, 0.914, 0.957]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "03T56LVwOJ3q8hXB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.323, 0.524, 0.418, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers two bar charts representing the values in the 'Research Growth' category for the 'Robotics' tech area. These include the {Research Growth bar for Robotics}<r>[0.370, 0.388, 0.418, 0.933]</r> and the {Innovation Increase bar for Robotics}<r>[0.418, 0.660, 0.466, 0.933]</r>."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "hFYxLewti4BXAqS2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object inside the coordinates [0.015, 0.323, 0.029, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label named Investment or Interest Level."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "qwzK6tFzBjH2KApU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label with \"Health Apps\" located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Health Apps\" is at [0.445, 0.958, 0.529, 0.978]."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "nkP4Zb7Cqxw3ZctF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis tick at [0.035, 0.045, 0.061, 0.065]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's 100."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "DkQBms01mzKBxUcl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is provided at [0.691, 0.958, 0.802, 0.978] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label named AI in Healthcare."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "8j4FOGstTqHl1rGQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bars representing 2016}[0.108, 0.627, 0.142, 0.873] and {the bars representing 2020}[0.804, 0.258, 0.839, 0.873], which year has higher interest in Telemedicine?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "XEZ7svaP1w7wsdkz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Health Apps bar}[0.839, 0.340, 0.874, 0.873] for 2020 higher than the {AI in Healthcare bar}[0.908, 0.545, 0.943, 0.873] for the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "1Z9m5bPTMRAGdbl5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the EHR bar in 2018}[0.525, 0.627, 0.560, 0.873] or {the EHR bar in 2020}[0.874, 0.504, 0.908, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018 bar."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "SPU5dQzgsMVP77lo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison between {the interest in Health Apps in 2016}[0.142, 0.586, 0.177, 0.873] and {the same in 2020}[0.839, 0.340, 0.874, 0.873], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The interest in Health Apps is higher in 2020."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "Z1FBRBViwS9gGqdG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.243, 0.950, 0.808, 0.986]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the graph's legend with four items. It has color and text labels for each type of technology: Telemedicine with color label at [0.249, 0.960, 0.277, 0.974], Health Apps with color label at [0.406, 0.960, 0.434, 0.974], EHR with color label at [0.557, 0.960, 0.585, 0.974], and AI in Healthcare with color label at [0.652, 0.960, 0.680, 0.974]."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "oIcjupBIRLl5f4d1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.066, 0.053, 0.985, 0.873]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mostly includes the main chart plotting area, with an x-axis displaying the years from 2016 to 2020 at intervals. The y-axis shows the level of investment or interest, from 0 to 100. The chart consists of bars representing four types of healthcare technologies: Telemedicine, Health apps, EHR, and AI in Healthcare, for each year."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "T6o2CG2f808yYJlf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the region [0.108, 0.627, 0.142, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the Telemedicine bar for the year 2016, showing an investment or interest level of 30."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "IQGpPjnuYfeVbqBJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.043, 0.373, 0.061, 0.393]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one y-axis label, which is the number \"60\"."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "HqJlb9LccOldifMn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the main object} at [0.015, 0.386, 0.029, 0.540] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Projected Value."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "dp7imLFq46y68kem",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text that corresponds to \"AI Adoption\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.832, 0.958, 0.912, 0.978]"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "IoC65fOtHiiTxi15",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the bars in green color}[0.194, 0.600, 0.238, 0.873],[0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Energy Production."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "hhriBY6FsTQqc2Qk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by [0.043, 0.756, 0.061, 0.776] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value 20."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "6EKpQBizHR5Jjosl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Renewable Energy Production\" in the year 2035}[0.630, 0.381, 0.674, 0.873] higher than {the bar of \"Oil Production\" in the year 2035}[0.586, 0.463, 0.630, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "J5jweCsFfh7eNF6m",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 2040 for \"AI Adoption\"}[0.891, 0.327, 0.935, 0.873] taller than {the bar representing 2040 for \"Oil Production\"}[0.804, 0.545, 0.848, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "mcGT5GOlEnjUUrRK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars of \"Automobile Production\"}[[0.107, 0.217, 0.151, 0.873], [0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873]] generally taller than {the bars of \"Renewable Energy Production\"}[[0.194, 0.600, 0.238, 0.873], [0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "RsgQc74s8Vzfghxm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Automobile Production\"}[[0.107, 0.217, 0.151, 0.873], [0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873]] and {\"AI Adoption\"}[[0.238, 0.764, 0.281, 0.873], [0.456, 0.654, 0.499, 0.873], [0.674, 0.545, 0.717, 0.873], [0.891, 0.327, 0.935, 0.873]], which category has a higher projected value for year 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Adoption."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "QF7kCZxptYeY1Zc8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.125, 0.950, 0.917, 0.986]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the chart legend with 4 items. They are:\n1. \"Automobile Production\" with a green handle at [0.131, 0.960, 0.158, 0.974].\n2. \"Oil Production\" with a brown handle at [0.355, 0.960, 0.383, 0.974].\n3. \"Renewable Energy Production\" with a bright green handle at [0.519, 0.960, 0.546, 0.974].\n4. \"AI Adoption\" with a purple handle at [0.793, 0.960, 0.821, 0.974]."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "gsdofgApS1KgXIf1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by [0.066, 0.053, 0.976, 0.873]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is mainly composed of the chart area showing the \"Projected Value\" against the \"Year\". It contains:\n1. Along the X-axis, there are 4 tick labels indicating the years: 2025, 2030, 2035, and 2040.\n2. On the Y-axis, there are values ranging from 0 to 160, representing the \"Projected Value\".\n3. The central area showcases individual bars for the 4 categories of interest: \"Automobile Production\", \"Oil Production\", \"Renewable Energy Production\", and \"AI Adoption\", over the years."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "dcOtjB1eB1d96Rcs",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.543, 0.272, 0.717, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region:\n1. The bar for \"Automobile Production\" in the year 2035 is [0.543, 0.272, 0.586, 0.873].\n2. The bar for \"Oil Production\" in the year 2035 is [0.586, 0.463, 0.630, 0.873].\n3. The bar for \"Renewable Energy Production\" in the year 2035 is [0.630, 0.381, 0.674, 0.873].\n4. The bar for \"AI Adoption\" in the year 2035 is [0.674, 0.545, 0.717, 0.873]."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "4EYi0H3FxXgiOQ8h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.369, 0.021, 0.673, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart: \"Future Forecast and Trend Prediction\"."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "joaKF1LRqNY99QLH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Company Financial Risk Control & Economic Affairs Scores\" located at coordinates {title}<r>[0.356, 0.021, 0.678, 0.047]</r>."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "q4QcQGYp9HNkdRek",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the sixth company on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {Citigroup}<r>[0.876, 0.818, 0.912, 0.896]</r>."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "Uy9fpbnS9IOLoNmc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text inside the rectangle with coordinates {bar label}<r>[0.904, 0.071, 0.982, 0.091]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the bar category \"Economic Affairs\"."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "kuv3eb3VnVKBFi7d",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Goldman Sachs} bar of \"Economic Affairs\"<r>[0.087, 0.412, 0.140, 0.804]</r> taller than {Goldman Sachs} bar of \"Risk Control\"<r>[0.140, 0.274, 0.193, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "MW0eeNBIbG7fnDqe",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, the {Wells Fargo} bar of \"Economic Affairs\"<r>[0.691, 0.366, 0.743, 0.804]</r> or the {Wells Fargo} bar of \"Risk Control\"<r>[0.743, 0.458, 0.796, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar of \"Risk Control\" is taller."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "RTamkRxf7JZo1BSB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents \"Risk Control\"? Is it the color for the {Citigroup}<r>[0.894, 0.343, 0.947, 0.804]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the color which is at coordinates {color}<r>[0.878, 0.103, 0.897, 0.117]</r> in the legend and represents \"Risk Control\" is the same for the Citigroup bar."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "DsQ0DdY60O3K0uqQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the area [0.238, 0.228, 0.344, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars located within the area. The first bar represents the score of \"Economic Affairs\" of {J.P. Morgan}<r>[0.238, 0.228, 0.291, 0.804]</r> and the second bar represents the score of \"Risk Control\" of {J.P. Morgan}<r>[0.291, 0.089, 0.344, 0.804]</r>."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "izSWWWG4saLp68DZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.875, 0.063, 0.985, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. The legend has two entries. The first is \"Economic Affairs\" with color code shown at {color}<r>[0.878, 0.073, 0.897, 0.087]</r>, and the second entry is \"Risk Control\" with color code shown at {color}<r>[0.878, 0.103, 0.897, 0.117]</r>."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "NI81K3Chq28K7gyn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.540, 0.320, 0.645, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are two bars representing {Bank Of America}'s scores in \"Economic Affairs\"<r>[0.540, 0.320, 0.593, 0.804]</r> and \"Risk Control\"<r>[0.593, 0.366, 0.645, 0.804]</r>."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "g0WoyY6ik0UEetVq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the subplot on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{'subplot_0'}<r>[0.337, 0.021, 0.705, 0.047]</r>"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "HXPT8SC1aWT6qypt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{'Scores'}<r>[0.015, 0.430, 0.029, 0.496]</r>"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "uU08b6lut51j5kIj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing R&D Investment in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "{'The color of the R&D Investment bar in 2021'}<r>[0.893, 0.144, 0.943, 0.873]</r> is purple."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "dK9WNN7ejRZfSAJf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for {Community Engagement}[0.099, 0.782, 0.149, 0.873] in 2018 higher than the score for {R&D Investment}[0.149, 0.327, 0.198, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "NTbQtKjp438HrHVQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the score for {R&D Investment}[0.645, 0.199, 0.695, 0.873] increase from 2020 to 2021 {R&D Investment}[0.893, 0.144, 0.943, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "Xr0dTovrbLhYejKg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {R&D Investment in 2019}[0.397, 0.235, 0.447, 0.873] taller than the bar for {Community Engagement in 2019}[0.347, 0.654, 0.397, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "3EtDe5AuIkq3hDtI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the color indicators in the legend [0.319, 0.950, 0.723, 0.986]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend includes two color indicators. The first one is established for Community Engagement and is pink [<r>[0.325, 0.960, 0.352, 0.974]</r>]. The second one displays the R&D Investment and is purple [<r>[0.565, 0.960, 0.592, 0.974]</r>]."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "7fJBMhW13VnlzPJM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region starting from the bottom [<r>[0.057, 0.053]</r>] to the top [<r>[0.985, 0.873]</r>]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area contains bars that represent Community Engagement and R&D investment for various years. Each pair of bars shares the same 'x' axis value, which denotes the year, with one ('Community Engagement') being pink and the other ('R&D Investment') being purple."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "DJUHyOG7KNRS25oL",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [<r>[0.844, 0.144, 0.943, 0.873]</r>]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bar charts for the year 2021. One represents Community Engagement with the height corresponding to a score of 40, the other one represents R&D Investment with the same height, indicating a score of 40 as well."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "qPOLe69crrP4a6rV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis that can be located at the coordinates [0.015, 0.390, 0.029, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Progress Score."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "e4njeMDTgcOoAw49",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content within the box [0.180, 0.958, 0.304, 0.978] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Planning Strategy."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "NVM7QaEDoyLJoAFr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object at the coordinates [0.142, 0.960, 0.169, 0.974] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "23poLOvn20W4MYqb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of {the bar graph for Planning Strategy in 2010}<r>[0.128, 0.641, 0.157, 0.873]</r> greater than the height of {the bar graph for Wildlife Conservation in 2025}<r>[0.623, 0.723, 0.652, 0.873]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "edr97egYUl5K3npl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is the {the color icon of Sustainable Energy}<r>[0.513, 0.960, 0.541, 0.974]</r> located to the left or right of {the color icon of Wildlife Conservation}<r>[0.714, 0.960, 0.742, 0.974]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "XJQHUbBSl7x275cX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the bar heights, is the {the bar graph for Sustainable Energy in 2025}<r>[0.594, 0.258, 0.623, 0.873]</r> higher than {the bar graph for Planning Strategy in 2025}<r>[0.536, 0.395, 0.565, 0.873]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "HVT7mui9WGlxqJAX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the region [0.099, 0.559, 0.902, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the bar graphs for each category from 2010 to 2035. Each year has four bars that represent the Progress Score for Planning Strategy, Pollution Control, Sustainable Energy and Wildlife Conservation respectively."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "CwUtHpdv7HLNGTO7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area enclosed by [0.136, 0.950, 0.906, 0.986]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the legend of the bar chart is situated. It contains four color-text pairs, each represents a different label: Planning Strategy, Pollution Control, Sustainable Energy, and Wildlife Conservation. Each pair consists of a colored square and corresponding label text."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "n7AjoZ73gFsqzFi4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is displayed within the area from [0.448, 0.327, 0.506, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you can find two vertical bars. The blue bar marking the Sustainable Energy's Progress Score in 2020 and the red bar represents the Wildlife Conservation's Progress Score in 2020."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "3O13rZC99KMVu7aY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinate [0.425, 0.019, 0.618, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the graph, titled 'Health Status by Age Group'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "HUzTFt80XFCr269F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label with coordinates [0.012, 0.394, 0.024, 0.527] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "It stands for 'Percentage (%)'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "F8Nhquy36ioHHbGV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the green element at the coordinates [0.226, 0.967, 0.249, 0.979] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Healthy Lifestyle'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "dBA5pm7oHn06DSPt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category corresponds to the coordinate [0.097, 0.212, 0.125, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the category 'Healthy Lifestyle' for the age group '20-30'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "iVWEBor3XmOuSLuY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the percentage of 'Chronic Disease' higher or lower than 'Severe Disease' for the age group '40-50' ({Chronic Disease bar}[0.521, 0.626, 0.549, 0.874] vs {Severe Disease bar}[0.549, 0.750, 0.576, 0.874])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "SDuOgC0Mh0kUiCUD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Healthy Lifestyle' percentage for '30-40' age group({Healthy Lifestyle bar}[0.282, 0.295, 0.309, 0.874]) higher than the '60 and above' age group({Healthy Lifestyle bar}[0.835, 0.461, 0.862, 0.874])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "untjv1wvlmFXYmGX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lifestyle has a higher percentage in the '20-30' years age group, the 'Healthy Lifestyle'({Healthy Lifestyle bar}[0.097, 0.212, 0.125, 0.874]) or the 'Sedentary Lifestyle' ({Sedentary Lifestyle bar}[0.125, 0.709, 0.153, 0.874])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthy Lifestyle."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "NZaonqWKqADkFCiZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Chronic Disease' percentage for '50-60'({Chronic Disease bar}[0.705, 0.585, 0.733, 0.874]) higher than '40-50'({Chronic Disease bar}[0.521, 0.626, 0.549, 0.874])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "ddD4zJ59i6ihPgNJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be seen in [0.055, 0.047, 0.988, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area containing all the age groups and their respective lifestyle and disease visualizations."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "llQBiunMwOHFhj1h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is found in the region [0.221, 0.959, 0.821, 0.990]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend indicating the color representation of 'Healthy Lifestyle', 'Sedentary Lifestyle', 'Chronic Disease', and 'Severe Disease'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "AOD4takq8nFeQ86n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in area [0.097, 0.212, 0.125, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar showing the percentage of 'Healthy Lifestyle' in the age group '20-30'."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "sgkGKEyEpiAxWNLR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within [0.029, 0.040, 0.051, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label for the 100% mark."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "ompivZRJj82sbQso",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart's main title say, which can be found in the coordinates [0.391, 0.021, 0.667, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title says \"Social Cultural and Value Change\"."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "jDOcBQPcGgtBf4Aw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which color does the label at [0.656, 0.960, 0.684, 0.974] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to is red."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "45UX9l6xMevEnDAs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat variable is shown at y-axis label located at [0.015, 0.435, 0.029, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The variable at the y-axis is called \"Score\"."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "GdgfpoCSbRQdX1Jr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the second tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second tick label on the x-axis reads \"2020\"."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "5LlvRpz1cpfbJRuv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Environmental Awareness in 1980}[0.245, 0.053, 0.376, 0.756] taller than {the bar representing  Globalization in 1980}[0.114, 0.053, 0.245, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "Vf76llPiulGSQ3Lv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Globalization in 2020}[0.551, 0.053, 0.682, 0.288] higher or lower than its {equivalent in 1980}[0.114, 0.053, 0.245, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "UxD4rvS7ct7s8E93",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Information Age bars}[[0.376, 0.053, 0.507, 0.170],[0.813, 0.053, 0.944, 0.217]] generally taller than {the Environmental Awareness bars}[[0.245, 0.053, 0.376, 0.756],[0.682, 0.053, 0.813, 0.709]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "JT8LvkllDsqkeZuw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the coordinates [0.114, 0.053, 0.507, 0.405], which bar is the highest: {Globalization}[0.114, 0.053, 0.245, 0.405] or {Information Age}[0.376, 0.053, 0.507, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Globalization."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "1UYAFqjln8FFCI1W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are there in the area with coordinates [0.114, 0.053, 0.507, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three bars: a Light Blue bar representing \"Globalization\" in 1980 [0.114, 0.053, 0.245, 0.405], a Green bar representing \"Environmental Awareness\" in 1980 [0.245, 0.053, 0.376, 0.756] and a Red bar representing \"Information Age\" in 1980 [0.376, 0.053, 0.507, 0.170]."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "hjsAmRpUZ2eX21Uv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the legend box located at [0.246, 0.950, 0.812, 0.986]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes three legend keys: Light Blue for \"Globalization\" located at [0.252, 0.960, 0.279, 0.974], Green for \"Environmental Awareness\" located at [0.409, 0.960, 0.437, 0.974], and Red for \"Information Age\" located at [0.656, 0.960, 0.684, 0.974]."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "Ihp8jrCt0WnR1aqy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.551, 0.053, 0.944, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three bars corresponding to the year 2020: a Light blue bar representing \"Globalization\" with a height reaching -1 on the y-axis [0.551, 0.053, 0.682, 0.288], a Green bar representing \"Environmental Awareness\" with a height reaching -2.8 on the y-axis [0.682, 0.053, 0.813, 0.709], and a Red bar representing \"Information Age\" with a height reaching -0.7 on the y-axis [0.813, 0.053, 0.944, 0.217]."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "F3ESSP2GlVDiIuRy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the y-axis labels, what values are shown in the region with coordinates [0.035, 0.163, 0.068, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels located in this box are \"-3.5\", \"-3.0\", \"-2.5\", \"-2.0\", \"-1.5\", \"-1.0\", and \"-0.5\"."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "UFhP4ql1fAo6rmPr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this bar chart represent with the title coordinates [0.343, 0.021, 0.717, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents statistics on 'Human History and Civilization Development'."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "EW93Ws1TCItjrszu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at coordinates [0.518, 0.959, 0.541, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is 'Era'."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "7S89SrKiuZ5ypu7v",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item found at coordinates [0.126, 0.161, 0.219, 0.181] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item refers to the Culture Index."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "zbFyc7vK9RKSalUt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the Life Expectancy bar [0.116, 0.880, 0.143, 0.884] for Bronze Age compare with Iron Age [0.296, 0.879, 0.323, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are almost the same height."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "6nEe8OPFv3diEhxv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Technology Index for Industrial Revolution bar [0.710, 0.884, 0.737, 0.884] higher than the Culture Index bar for the same era [0.737, 0.884, 0.764, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "PcDF9KxRM7TMPusY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Human Population}[0.863, 0.093, 0.890, 0.884] in the Contemporary Era have larger value than the Life Expectancy {bar}[0.836, 0.877, 0.863, 0.884] in the same Era?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "ti7UZ203O1A45BJC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is present in the legends' area, [0.081, 0.063, 0.259, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes four legend items: Life Expectancy with the color handle at [0.087, 0.073, 0.115, 0.087], Human Population with the color handle at [0.087, 0.103, 0.115, 0.117], Technology Index with the color handle at [0.087, 0.133, 0.115, 0.147], and Culture Index with the color handle at [0.087, 0.163, 0.115, 0.177]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "VRFZ4c675OZ9Aske",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's seen in the range [0.530, 0.880, 0.584, 0.884]."
+            },
+            {
+                "from": "gpt",
+                "value": "This range corresponds to a bar representing the Culture Index during the Middle Ages Era."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "ztuamoySbPeJl94n",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease identify and expound the objects within the area [0.836, 0.877, 0.890, 0.884]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are three bars: \n1. The Life Expectancy bar with coordinates at [0.836, 0.877, 0.863, 0.884]\n2. Human Population bar with coordinates at [0.863, 0.093, 0.890, 0.884]\n3. Culture Index   bar with coordinates at [0.917, 0.884, 0.944, 0.884] and \n4. Technology Index bar with coordinates at [0.890, 0.884, 0.917, 0.884], all representing the Contemporary Era."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "PBCWSEDNvBlWn0pz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label of the Y-axis signify within the coordinates [0.012, 0.300, 0.024, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Individuals (in thousands)."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "md2iHB2n0AGU5OXD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the bar at position [0.892, 0.047, 0.946, 0.169]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the Graduate Employment for the year 2019 and the count is approximately -2000."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "RhTHX6mUFUPM43WK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text in the area [0.740, 0.965, 0.872, 0.983] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label {Graduate Employment}<r>[0.740, 0.965, 0.872, 0.983]</r> in the legend."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "9W0mpYeZY7poj6q0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the label for 2019, is the {bar representing \"Online Education\"}[0.838, 0.047, 0.865, 0.549] taller than the {bar representing \"Graduate Employment\"}[0.919, 0.047, 0.946, 0.169]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "sx91ERMAV544ienr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar of \"Traditional Education\"}[0.865, 0.047, 0.892, 0.752] for 2019 located to the left or right of {the bar of \"Online Education\"}[0.838, 0.047, 0.865, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "DCeJ6pA3cc2mH85D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Online Education bar in 2015}[0.120, 0.047, 0.147, 0.476] shorter than the {Education Technology bar in 2019}[0.892, 0.047, 0.919, 0.105]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "w3DcV64NwsK0eoLi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the region [0.708, 0.967, 0.731, 0.979] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a color label in the legend that represents Graduate Employment and is the color red."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "82sqrrAf5cII26tr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the region [0.120, 0.047, 0.147, 0.476] contains."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a green bar representing \"Online Education,\" specifically for the year \"2015\", where the count is about -7000."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "GZEgl7irM2lV1KJv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the area containing [0.190, 0.959, 0.877, 0.990]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the bar chart. It includes 4 different labels: {Online Education}<r>[0.227, 0.965, 0.326, 0.983]</r> with green color, {Traditional Education}<r>[0.381, 0.965, 0.503, 0.983]</r> with yellow color, {Education Technology}<r>[0.559, 0.965, 0.685, 0.983]</r> with dark blue color, and {Graduate Employment}<r>[0.740, 0.965, 0.872, 0.983]</r> with red color."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "A1dmSwwF0cmuLiTa",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the vertical label}[0.015, 0.397, 0.029, 0.452] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical label is Score."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "lJZiYdGiWKVY8dWt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar for Apple Hardware?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar is orange, specified by the legend item {Apple Hardware}<r>[0.544, 0.916, 0.572, 0.930]</r>."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "TP8fZeGUcamZBnq0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.108, 0.573, 0.152, 0.796] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the score of Google Hardware in 2017."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "KErCLxMGHZSbE4eW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Tech Industry Growth\" at coordinates [0.437, 0.021, 0.614, 0.047]."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "sVxJ3Se9aysf9ciO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Apple Hardware in 2019}[0.635, 0.202, 0.679, 0.796] have a higher score than {the bar for Google Software in 2019}[0.591, 0.350, 0.635, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "udVvQi6gQF8jv8aq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Score of Google Hardware in 2020}[0.767, 0.350, 0.811, 0.796] higher than its {Score in 2017}[0.108, 0.573, 0.152, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "LiBuMchVzOf0fHuO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Google Hardware}[0.395, 0.914, 0.516, 0.934] and {Apple Software}[0.583, 0.944, 0.688, 0.964], which one had a higher score in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apple Software."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "TtOvX2Gs9BS4o8yP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.351, 0.906, 0.700, 0.972]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content in the mentioned region is the legend of the chart, which contains the information of four groups: Google Hardware, Google Software, Apple Hardware, and Apple Software. The corresponding color and labels of the four groups are as follows:\n    1. Google Hardware is represented by a blue color {handle}[0.357, 0.916, 0.384, 0.930] and the matching label is located at [0.395, 0.914, 0.516, 0.934].\n    2. Google Software is represented by a lighter blue color {handle}[0.357, 0.946, 0.384, 0.960] and the matching label is located at [0.395, 0.944, 0.510, 0.964].\n    3. Apple Hardware is highlighted by an orange color {handle}[0.544, 0.916, 0.572, 0.930] and the matching label is located at [0.583, 0.914, 0.694, 0.934].\n    4. Apple Software is represented by a light orange color {handle}[0.544, 0.946, 0.572, 0.960] and the matching label is located at [0.583, 0.944, 0.688, 0.964]."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "FQwR7nQd4CGgH1lk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.066, 0.053, 0.985, 0.796]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area outlines all features of the chart, including the title labeled 'Tech Industry Growth\" at the top, the two sets of x and y axis labels, the bar graph representing the industry growth data with one pair of bars for each year from 2017 to 2020 and the legend at the top right of the area detailing the color coding information for each group."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "vcEFuWV1gBwWj924",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.152, 0.165, 0.283, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for Google Software and Apple Hardware for 2017."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "TuqSYEfVmAAJsfce",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in [0.108, 0.350, 0.811, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains eight vertical bars and their labels indicating Google Hardware, Google Software, Apple Hardware, and Apple Software for the years 2017, 2018, 2019 and 2020."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "0olbRceJOUWzRDMJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.117, 0.880, 0.139, 0.900] represent on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the month of January."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "QB9IDx8eBzLl5oUD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle corresponding to \"Order\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the color of the handle for \"Order\"}[0.546, 0.960, 0.574, 0.974] is green."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "tQObu8rfUogeTfhy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.643, 0.958, 0.749, 0.978] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label for \"Delivery\"}[0.691, 0.958, 0.749, 0.978]."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "TaeQd8Gxdj6N2Za0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"March\"}[0.271, 0.190, 0.285, 0.873] higher than {the bar for \"June\"}[0.485, 0.272, 0.500, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "IJFaFd4fqwiIJJ5Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values represented by {the yellow handle}[0.408, 0.960, 0.436, 0.974] lower than those by {the green handle}[0.546, 0.960, 0.574, 0.974]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "nheNz6BN6KxD0aMs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller: {the bar indicating orders in September}[0.700, 0.217, 0.714, 0.873] or {the bar indicating deliveries in the same month}[0.714, 0.490, 0.728, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Order bar in September}[0.700, 0.217, 0.714, 0.873]."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "WLmwjrl9UQYz36DH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.128, 0.873, 0.171, 0.928]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick mark and label for January. The tick mark is at [0.128, 0.873] and the label \"Jan\" spans [0.117, 0.880, 0.139, 0.900]."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "wpuIS50GEjDw8QvU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find within the coordinates [0.018, 0.434, 0.044, 0.493]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis label \"Count\" displayed vertically, spanning [0.015, 0.434, 0.029, 0.493]."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "TWMGdbNIT4IQcrmz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is depicted within the boundaries [0.408, 0.949, 0.624, 0.984]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the legend entries for \"Complaint\" and \"Order,\" with respective color handles and text labels. {Complaint handle}[0.408, 0.960, 0.436, 0.974] is yellow and {Order handle}[0.546, 0.960, 0.574, 0.974] is green; their text descriptions are [0.447, 0.958, 0.518, 0.978] and [0.585, 0.958, 0.624, 0.978] respectively."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "MXDazD3MVACPLJrc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.404, 0.021, 0.656, 0.047] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle of the chart, which is \"Quarterly Profit Trend Analysis\"."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "GjtqU0OVItdYU0on",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.499, 0.852, 0.560, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label, which is \"Quarters\"."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "V4SnP0Nf3SncSeIY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object located at [0.328, 0.950, 0.374, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Mobile\" in the legend."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "H8fIXdEX5hmwqKOX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q1 profit bar of Electronics}[0.159, 0.187, 0.203, 1.104] higher than {the Q1 profit bar of Mobile}[0.116, 0.531, 0.159, 1.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "hFxksfrwaMJwepVD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q4 profit bar of Furniture}[0.856, 0.569, 0.900, 1.104] taller than {the Q4 profit bar of Supplies}[0.900, 0.760, 0.944, 1.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "qMex6CkAaP2ezdEN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Electronics}[0.401, 0.952, 0.429, 0.965] and {Furniture}[0.544, 0.952, 0.572, 0.965], which category has a higher Q2 profit bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electronics."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "YwZD0cew2ubuJZM7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates [0.284, 0.942, 0.776, 0.978]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. It lists four categories: {Mobile}[0.328, 0.950, 0.374, 0.970], {Electronics}[0.440, 0.950, 0.516, 0.970], {Furniture}[0.583, 0.950, 0.645, 0.970], and {Supplies}[0.712, 0.950, 0.770, 0.970], along with their corresponding color codes."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "GalzTPsk8ibvqlpJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content positioned at [0.075, 0.053, 0.985, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire graphical area of the bar chart, which displays the quarterly profit trend analysis of four different categories (Mobile, Electronics, Furniture, and Supplies), each represented by different colored bars."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "QKk3zkrjgcqku0uW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain what is shown in the region [0.847, 0.824, 0.866, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the last quarter \"Q4\" on the x-axis is positioned in this region."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "92wyTgRalr3b86nr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.057, 0.053, 0.985, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area, which contains the bars representing the data, as well as the axes labels."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "RiV9vm6fCHP0ETtR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.395, 0.021, 0.647, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at this coordinates is the subtitle \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "tagNPhxnYHwPMe7e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the bar at [0.451, 0.240, 0.521, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar indicates that the \"Number of Tasks\" for \"Partial Integration\" is 55."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "a0Wwbi941JZQ0fMZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object has coordinates [0.873, 0.178, 0.943, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar representing the \"Time to Complete (days)\" for \"Manual Integration\", which is 60 days."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "KbsmwtuhcwStuJdN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \u201cNumber of Tasks for Partial Integration\u201d}[0.451, 0.240, 0.521, 0.924] taller than {the bar representing \u201cTime to Complete for Partial Integration\u201d}[0.521, 0.551, 0.591, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "zqi3ALstUTp0y29J",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the bar for \u201cNumber of Tasks for Full Integration\u201d}[0.099, 0.489, 0.170, 0.924] and {the bar for \u201cTime to Complete for Full Integration\u201d}[0.170, 0.737, 0.240, 0.924] in terms of height?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Number of Tasks for Full Integration\" bar is taller than the \"Time to Complete for Full Integration\" bar."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "z7cSKb4PsDi8Ow85",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for \"Number of Tasks\"{Number of Tasks for Full Integration}[0.099, 0.489, 0.170, 0.924], {Number of Tasks for Partial Integration}[0.451, 0.240, 0.521, 0.924] and {Number of Tasks for Manual Integration}[0.802, 0.675, 0.873, 0.924] generally taller than the bars for \"Time to Complete\"{Time to Complete for Full Integration}[0.170, 0.737, 0.240, 0.924], {Time to Complete for Partial Integration}[0.521, 0.551, 0.591, 0.924] and {Time to Complete for Manual Integration}[0.873, 0.178, 0.943, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "4B2XPujIP4hZLYOn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \u201cNumber of Tasks for Full Integration\u201d bar}[0.099, 0.489, 0.170, 0.924] to the left of {the \u201cTime to Complete for Full Integration\u201d bar}[0.170, 0.737, 0.240, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "UUorSwXIulyXjoMZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.756, 0.063, 0.978, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend with two entries: \"Number of Tasks\" with a pink square as a symbol (coordinates: [0.762, 0.073, 0.790, 0.087]) and \"Time to Complete (days)\" with a mid-blue square as a symbol(coordinates: [0.762, 0.103, 0.790, 0.117])."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "pPaMsIq3wpiwL6YK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are identified in the area of [0.057, 0.053, 0.985, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart where the bar graphs representing the \"Number of Tasks\" and \"Time to Complete (days)\" for each \"Integration Type\" are displayed, along with the axis labels and the legend."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "ZBshLjhBlNHBZfMx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.099, 0.489, 0.240, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we have two bars: the pink bar represents \"Number of Tasks\" for \"Full Integration\" (35 tasks) with the coordinates [0.099, 0.489, 0.170, 0.924]; the mid-blue bar represents \"Time to Complete (days)\" for \"Full Integration\"(15 days) with the coordinates [0.170, 0.737, 0.240, 0.924]."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "87aFH2aO5E4GGhW3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area marked by the coordinates [0.465, 0.959, 0.578, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area refers to the x-axis label, which is titled \"Integration Type\"."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "rUkfRoQn93HWTLKo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Sales\"{the y-axis label}<r>[0.019, 0.428, 0.036, 0.520]</r>."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "5ZgHPQmGxCnsK2sT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text in the coordinate [0.366, 0.037, 0.703, 0.083] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subplot's subtitle, which reads as \"Product Sales Trend Observation\"."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "C9jpXl5Jpf5M2jDx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the blue bar at [0.129, 0.492, 0.264, 0.854] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the sales of Apple in January, which totalled 400 units."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "jR6CzcQlTnCVA3Mm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's plotted at the coordinate [0.467, 0.401, 0.602, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a green bar representing the sales of 600 units of Cherry in March."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "E4p70qxbscpFICoX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the sales of Durian in April represented by the red bar at [0.636, 0.447, 0.772, 0.854] more than the sales of Eggplant in the same month represented by the purple bar at [0.636, 0.356, 0.772, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "UY5LvXZi5MREL3L7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the sales of Banana in January represented by orange bar {at}<r>[0.129, 0.130, 0.264, 0.854]</r> and that in February represented by orange bar {at}<r>[0.298, 0.175, 0.433, 0.854]</r>, which one is greater?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sales in January is higher."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "DGxGgplkVLOBuVEh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Apple's sales in March represented by the blue bar at [0.467, 0.401, 0.602, 0.854] and the sales in April at [0.636, 0.311, 0.772, 0.854], which month experienced more sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "April."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "LEnepPtkJwHkR5eG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the sales of Cherry in January (green bar at [0.129, 0.220, 0.264, 0.854]) and the sales in February (green bar at [0.298, 0.265, 0.433, 0.854]), which month sold more?"
+            },
+            {
+                "from": "gpt",
+                "value": "January."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "Hgmgt229oqNpApMZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.298, 0.175, 0.433, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two bars. The taller orange bar indicates the sales of Banana in February, which was 750 units, and this bar is located at [0.298, 0.175, 0.433, 0.854], while the shorter green bar indicates the sales of Cherry in February, which was 650 units, and it is located at [0.298, 0.265, 0.433, 0.854]."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "MThMiCbiD9NtFaYj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.805, 0.130, 0.941, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region shows five bars. These represent the sales in May for each product: Apple (blue) sold 700 units, Banana (orange) sold 800 units, Cherry (green) sold 500 units, Durian (red) sold 500 units, and Eggplant (purple) sold 500 units."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "daUptJXV4jgtkEMM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area marked by [0.043, 0.025, 0.076, 0.060]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis label of \"900\" located at [0.043, 0.025, 0.076, 0.060]."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "QVBLDCdekSHx8eTN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.129, 0.130, 0.264, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains five bars, each representing the sales of a specific product in January. The highest bar (orange) represents 800 Banana sales, the green bar is 700 Cherry sales, the red bar represents 300 Durian sales, the purple bar indicates 500 Eggplant sales, and the lowest bar (blue) represents 400 Apple sales."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "orXx2goBhrk6RC9m",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.144, 0.785, 0.579, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing the patent count for Robotic Technology."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "prieeXjDn7YLPNbB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"Number of Patents\"."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "2JobFdiXiZZw6up3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the range of patents being displayed on the x-axis for the object at [0.144, 0.208, 0.611, 0.300]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's ranging from 0 to 2900."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "RzFgvmbvSX6pZadx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description or title located above the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"Innovation and Intellectual Property in Tech Sectors\"."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "YCv6ePRDhGPAI67l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Cybersecurity bar}<r>[0.144, 0.093, 0.885, 0.185]</r> higher than {Robotic Technology bar}<r>[0.144, 0.785, 0.579, 0.877]</r> on the horizontal axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "dVjena7vj7omZOau",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {BioTech bar}<r>[0.144, 0.208, 0.611, 0.300]</r> longer than the {Quantum Computing bar}<r>[0.144, 0.323, 0.354, 0.416]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "0m89ur6aNUOP3qSH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars for {Wireless Communication bar}<r>[0.144, 0.439, 0.950, 0.531]</r> and {BlockChain bar}<r>[0.144, 0.554, 0.499, 0.647]</r>, which one extends further on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wireless Communication."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "FOZOQpaF1FrQSXO1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {AI Technology bar}<r>[0.144, 0.670, 0.660, 0.762]</r> and {Robotic Technology bar}<r>[0.144, 0.785, 0.579, 0.877]</r>, which bar represents a greater number of patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Technology."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "10a6gOrrqYwbp5Ks",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section of the chart with bounds [0.144, 0.554, 0.499, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this is the bar for the 'BlockChain' category. It indicates that the number of patents in this category ranges from 0 to 2200."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "lI2gYJ1ATQitKiGU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.144, 0.785, 0.579, 0.877] encapsulate?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encapsulate the bar for 'Robotic Technology' category. The bar shows that the number of patents range from 0 to 2700 for this category."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "dMEzieFG8t7TWCiu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area of [0.541, 0.959, 0.593, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for the x-axis, which is 'Technology.'"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "9THqzEwXa64ZGhZI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.424, 0.021, 0.710, 0.047] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the graph, which reads 'Innovation and Intellectual Property in Tech Sectors.\""
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "SZkBxTvMMXB74bcN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significant text at coordinates [0.381, 0.030, 0.666, 0.066] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle which reads \"Internal Business Process Analysis\"."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "J18vLNBFCncsSxrT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we observe the y-axis, What does the tick text at [0.043, 0.872, 0.052, 0.900] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value 0 on the y-axis scale."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "uGQ3zYdc3jxtipTw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the bar in the chart with coordinates [0.227, 0.130, 0.326, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the green bar representing the effectiveness of \"Marketing\" process."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "S9MAiXVbk21pPptW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are shown in coordinates [0.863, 0.903, 0.924, 0.931] and [0.026, 0.035, 0.052, 0.063]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels are {the x-axis label \"Financial\"}<r>[0.863, 0.903, 0.924, 0.931]</r> and {the y-axis label \"100\"}<r>[0.026, 0.035, 0.052, 0.063]</r>."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "MTDjbjMyyVDGM9XE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar associated with \"Product Development\"}[0.104, 0.172, 0.203, 0.883] lower in height than {the bar associated with \"Marketing\"}[0.227, 0.130, 0.326, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "CywpAGhPan7DoLcc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bar chart, is {the \"Customer Service\" bar}[0.597, 0.113, 0.696, 0.883] higher than {the \"Financial\" bar}[0.844, 0.138, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "MdRQHtaT8Zjg76VU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the bar representing \"Sales\"}[0.351, 0.230, 0.449, 0.883] and {the bar representing \"Management\"}[0.474, 0.147, 0.573, 0.883], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Management\" bar."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "TWqHR9k2P95N5hJI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the \"Manufacturing\" bar}[0.721, 0.214, 0.820, 0.883] and {the \"Product Development\" bar}[0.104, 0.172, 0.203, 0.883], which process dominated in terms of effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Development."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "krCTvTG0ycbcZH3A",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBriefly explain about the components specified in the area [0.062, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the primary region where the bar chart is plotted. It contains effectiveness bars for different processes like Product Development, Marketing, Sales, Management, Customer Service, Manufacturing, and Financial. Each bar's height represents the effectiveness of the respective process."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "pAGJ58A49Qpf7TlT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is enclosed within the frame [0.474, 0.147, 0.573, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a single chart element, {the green bar representing the \"Management\" process}<r>[0.474, 0.147, 0.573, 0.883]</r>, indicating an effectiveness score of 88."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "e2R6Riwpxn9y5LUH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell about the contents in the region [0.585, 0.903, 0.708, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single object, the label for {the \"Customer Service\" category}<r>[0.585, 0.903, 0.708, 0.931]</r> on the x-axis."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "c6hjOH6eZW660h5o",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the rectangular area [0.497, 0.942, 0.550, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangular area hosts the x-axis label \"Process\"."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "HQ0XQz7QD6fFeMfD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.897, 0.100, 0.926, 0.122] indicate in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label in the legend, with a light blue color."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "lUoGpIgqwtUZiqTp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at [0.699, 0.457, 0.762, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing \"Inconsistent decline\" for the year 2020."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "uDkxqa9PpCGh6sbm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.015, 0.434, 0.029, 0.524] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, named as \"Values\"."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "xMTk6qtDd9B2eHYd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the bars is taller - {the bar representing \"Slow expansion\" in 2000}[0.096, 0.429, 0.159, 0.883] or {the bar representing \"Slow expansion\" in 2020}[0.159, 0.113, 0.222, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar representing \"Slow expansion\" in 2020."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "CmBnWEZRm5lUGKc2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Downward trend\" in 2000}[0.816, 0.113, 0.880, 0.883] taller than {the bar for \"Downward trend\" in 2020}[0.880, 0.317, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "a4lfTLKBnQb26Ytk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar for \"Unstable behavior\" in 2000}[0.276, 0.317, 0.339, 0.883] and {the bar for \"Unstable behavior\" in 2020}[0.339, 0.252, 0.402, 0.883], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Unstable behavior\" in 2020."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "wMtR0mNjatmkl51u",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region [0.105, 0.903, 0.937, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all the labels of the X-axis, which are {\"Slow expansion\"}[0.105, 0.903, 0.213, 0.931], {\"Unstable behavior\"}[0.275, 0.903, 0.402, 0.931], {\"Slight increase\"}[0.468, 0.903, 0.570, 0.931], {\"Inconsistent decline\"}[0.630, 0.903, 0.769, 0.931], and {\"Downward trend\"}[0.822, 0.903, 0.937, 0.931]."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "N5aGhOqXuzHHZTyb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in the region [0.096, 0.113, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are ten bars in total, each representing a trend in 2000 and 2020 for each category of Trends, including \"Slow expansion\", \"Unstable behavior\", \"Slight increase\", \"Inconsistent decline\", and \"Downward trend\"."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "CJ1G5VfeaeYpcVL3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content exists within the coordinates [0.348, 0.030, 0.690, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, labeled as \"Time Series Data Comparison Over Years\"."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "GKKFwTlY1ST3wko0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at the coordinates [0.366, 0.025, 0.694, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, \"Market Research of BMW Vehicle Series\"."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "MHlpmNTmr9SVcXPg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at these coordinates [0.043, 0.683, 0.065, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"5.0\"."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "9MwS9HEdGAur1hDc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the coordinates, [0.360, 0.102, 0.457, 0.903], what is the object and its value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar representing \"5 Series\" BMW vehicles, with a market share of 19.0%."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "fFB9vszzGVYDf3F2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"1 Series\"}[0.116, 0.397, 0.214, 0.903] taller or shorter than {the bar representing \"3 Series\"}[0.238, 0.229, 0.335, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "uAQnZRlonctqE7S5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the \"5 Series\" bar}[0.360, 0.102, 0.457, 0.903] and {the \"Z Series\" bar}[0.725, 0.313, 0.822, 0.903], which one shows a higher market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"5 Series\" bar."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "jpOGuatabpl7CQg9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar representing the \"x Series market share\"}[0.603, 0.271, 0.700, 0.903] taller than the {bar representing the \"i Series market share\"}[0.846, 0.271, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the same height."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "7GQaFNtZMExKZGdv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of what is contained within the area [0.116, 0.397, 0.214, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing the market share of the \"1 Series\" BMW vehicles, which is 12.0%."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "bK4oEw1X5ZWwBiTT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.725, 0.313, 0.822, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the bar representing the market share of the \"Z Series\" BMW vehicles which is 14.0%."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "7994StqyNr2eOkMR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.481, 0.524, 0.579, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is the bar indicating the market share of \"7 Series\" BMW vehicles, which stands at 9.0%."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "tQ1FmlUqZKbv9mal",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category indicated by the x-axis label at [0.463, 0.927, 0.596, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Family Member."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "hzx2p5o1epPaRwZI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the second bar in the chart with bounding box [0.260, 0.194, 0.373, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mom."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "4YY9Vwo9ql3VlDzx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the title at coordinates [0.323, 0.037, 0.736, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Family and Household Living Conditions."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "r4gxA730tAFoPsVq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis signify at [0.019, 0.346, 0.036, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Conditions (%)."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "krAgW69Ti9owWGEK",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing \"Dad\"}[0.118, 0.210, 0.232, 0.854] and {the bar representing \"Carer\"}[0.827, 0.291, 0.940, 0.854], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dad."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "UJbqxPJ8YisEEQpd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Brother\"}[0.402, 0.130, 0.515, 0.854] at a higher position than {the bar representing \"Sister\"}[0.543, 0.170, 0.657, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "HQ3lB2EFnaGTZMqo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Mom\"}[0.260, 0.194, 0.373, 0.854] to the right of {the bar representing \"Dad\"}[0.118, 0.210, 0.232, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "pqyixbKd3SiotjFv",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar representing \"Pet\"}[0.685, 0.130, 0.798, 0.854] and {the bar representing \"Carer\"}[0.827, 0.291, 0.940, 0.854], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pet."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "9eNWJLjuJadI9biI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information does the area with coordinates [0.077, 0.093, 0.981, 0.854] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar chart that represents the living conditions of each family member. The x-axis represents family members and the y-axis stands for the conditions percentage."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "5P3Zp3XeRZDmqehG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the slice between [0.260, 0.194, 0.373, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice contains a bar representing the living conditions of 'Mom' at 82%."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "4NyX1iASJI1QNPYO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.827, 0.291, 0.940, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a bar depicting the living conditions for 'Carer' which is 70%."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "8Nvorn2laRCorfjg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained between [0.543, 0.170, 0.657, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a data bar indicating that the living conditions for 'Sister' are at 85%."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "UwsCZr6Bl9tzEDRC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent based on the label at coordinates [0.015, 0.374, 0.029, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis represents the Industry Bloom."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "3LsFDQJJRrQ4G9Ua",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sector is indicated at the x-axis coordinates [0.414, 0.903, 0.488, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector indicated is E-Learning."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "dbkzdnqoxLwiWbco",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value for the sector located at [0.393, 0.113, 0.509, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value for the E-Learning sector is 32."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "eA9VazzIGff4o23j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {E-Learning}[0.393, 0.113, 0.509, 0.883] higher than the bar for {Language Learning}[0.683, 0.426, 0.798, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "nNkPsyZmIscJKpjM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Software Development}[0.104, 0.402, 0.220, 0.883] on the left or right of the bar for {Instructional Design}[0.249, 0.282, 0.364, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "sTrYkg65MSWcD6cp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the one representing {Career Counseling}[0.827, 0.595, 0.943, 0.883] or {Corporate Training}[0.538, 0.378, 0.654, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporate Training."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "xc5JZLiFkaETGjYI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's inside the area [0.035, 0.030, 0.052, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is the label '35' from the y-axis scale inside this area."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "rH0BD2w8NHIzTzUT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.249, 0.282, 0.364, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bar for Instructional Design, with a height indicating its value of 25."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "gBsmvgcdJJRfuQ2F",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area [0.821, 0.903, 0.949, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \u2018Career Counseling\u2019 from the x-axis scale is located in this area."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "CQpWgeildugNfahX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar with these coordinates [0.160, 0.492, 0.291, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook's content views."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "KX4JmNMfXtr0s2RX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is associated with the coordinates [0.486, 0.564, 0.616, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "lig2yVxdreEIVjcD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat numerical value in views is indicated for Instagram whose coordinates for the bar are [0.323, 0.347, 0.453, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "350,000 views."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "zEthwHZUN01qGtos",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Facebook's bar}[0.160, 0.492, 0.291, 0.854] higher or lower than {Twitter's bar}[0.486, 0.564, 0.616, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "f66IxxzYnlldzkxR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOut of {YouTube's bar}[0.649, 0.130, 0.779, 0.854] and {Instagram's bar}[0.323, 0.347, 0.453, 0.854], which one represents more views?"
+            },
+            {
+                "from": "gpt",
+                "value": "YouTube's bar."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "uzbm4rq9lmOQRMvJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {LinkedIn's views}[0.812, 0.637, 0.942, 0.854] less than {Facebook's views}[0.160, 0.492, 0.291, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "ASSjrHHtZLPiPamW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's displayed in the region [0.160, 0.130, 0.942, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers all the social media platforms' bars. In sequence, from left to right, we have {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "4yqt6QDkt5M7olFu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the figures in this region [0.290, 0.037, 0.812, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart, which is \"Content Views on Different Social Media Platforms\"."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "aT9pYZRNLAtwXY73",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's represented in the region [0.019, 0.184, 0.036, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label which indicates \"{Views (in hundreds of thousands)}\"<r>[0.019, 0.184, 0.036, 0.764]</r>."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "Fe6L80DpMXwvUEGn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart at coordinates [0.297, 0.030, 0.750, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Internal Business Process Analysis (time consumption)."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "ZtTAAxwBOxqxoMkf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the x-axis label at coordinates [0.489, 0.942, 0.558, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Processes."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "auD93T720Bgg3sAe",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar in the \"Delivery\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.454, 0.113, 0.593, 0.883]."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "0ctfWhCsU1zMZdsr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value range for {Quality Assurance}[0.803, 0.587, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.00 -> 9.90."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "4zWb7R6VX5X1o7p5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Order Processing\"}[0.104, 0.428, 0.244, 0.883] shorter than {the bar representing \"Delivery\"}[0.454, 0.113, 0.593, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "0RumxAbkEaswlx6b",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, does {the bar representing \"Customer Service\"}[0.628, 0.299, 0.768, 0.883] consume more time than {the bar representing \"Quality Assurance\"}[0.803, 0.587, 0.943, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "KPrs0DTtO6d0NPl5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the y-axis, is the point representing 10 hours closer to the starting point, {0}[0.043, 0.872, 0.052, 0.900], or the end point, {30}[0.035, -0.027, 0.052, 0.001]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting point."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "q0GlwmuFPVIuMSTw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content located between the coordinates [0.062, 0.075, 0.985, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are five bars representing different business processes in this area. The bars from left to right represent the following processes: {Order Processing}[0.104, 0.428, 0.244, 0.883] with a time consumption range of 0.00 -> 15.20 hours, {Inventory Management}[0.279, 0.560, 0.419, 0.883] with a time consumption range of 0.00 -> 10.80 hours, {Delivery}[0.454, 0.113, 0.593, 0.883] with a range of 0.00 -> 25.70 hours, {Customer Service}[0.628, 0.299, 0.768, 0.883] with a range of 0.00 -> 19.50 hours, and finally {Quality Assurance}[0.803, 0.587, 0.943, 0.883] with a range of 0.00 -> 9.90 hours."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "cIWNHmSE5vVQiLWM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within the region [0.035, 0.123, 0.052, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a tick mark on the y-axis that denotes the value of 25 hours."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "qYXFGEMEPBuBL7kb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located at [0.810, 0.903, 0.936, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a label on the x-axis that represents the category \"Quality Assurance\"."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "ifoJBGRF1w6c6HqY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at [0.798, 0.145, 0.840, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue, it represents the \"CO2 Emission (Million Metric Tons)\" for the year 2010."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "T5ZhDSq4jjnhEBvs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label located at [0.522, 0.942, 0.551, 0.970] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label indicating \"Year\"."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "PXZ3F4aC660GOzAG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data point is described by the green bar located at [0.861, 0.883, 0.902, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the \"Deforestation (% of Forest Area)\" for the year 2015."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "80cE25gaqnxDJXAb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the blue bar at [0.693, 0.171, 0.735, 0.883] and [0.798, 0.145, 0.840, 0.883], which year has a higher \"CO2 Emission (Million Metric Tons)\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "Gbt032hXvxWmfuGG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the deforestation rate in 1990 at [0.338, 0.883, 0.380, 0.883] higher than the rate in 1985 at [0.233, 0.883, 0.275, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "PL2KN1BkTg3kxumw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the green bar at [0.443, 0.883, 0.484, 0.883] and the green bar at [0.547, 0.883, 0.589, 0.883], which one represents a greater rate of deforestation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green bar at [0.547, 0.883, 0.589, 0.883] for the year \"2000\"."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "Ab3wAoFOyDsx33cY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within [0.095, 0.089, 0.383, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region containing the legend for the chart. It includes two labels: \n1. {CO2 Emission (Million Metric Tons)}<r>[0.140, 0.100, 0.377, 0.128]</r>, represented in blue color <r>[0.101, 0.102, 0.128, 0.122]</r>. \n2. {CO2 Emission (Million Metric Tons)}<r>[0.140, 0.142, 0.364, 0.170]</r>, represented in green color <r>[0.101, 0.144, 0.128, 0.164]</r>."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "egsJFLtkoOQvLhsi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.015, 0.004, 0.029, 0.955]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the vertical axis label area, representing \"CO2 Emission (Million Metric Tons) / Deforestation (% of Forest Area)\"."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "wTq3Zs2iB72pxQrw",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.171, 0.268, 0.213, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a blue bar, indicating the \"CO2 Emission (Million Metric Tons)\" for the year 1980."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "sKaSgMCJEC1pVGnF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of chart is represented according to the JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "W2a0QPRoXqDMw1Ph",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the caption written on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Country."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "6PJ9XcR1vadjOEyV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the bar graph} represent at [0.115, 0.471, 0.230, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"USA\" country's Greenhouse Gas Emissions equivalent to 6341 million tons."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "qFiIZphOk1wX387e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bars} displayed on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "dYH0ktz6L21AX4mF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Germany bar}[0.831, 0.850, 0.946, 0.903] shorter or taller than the {India bar} [0.402, 0.736, 0.516, 0.903] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "cEUUkx02tb7Rj4HA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar that represents China}[0.258, 0.102, 0.373, 0.903] produce more or less Greenhouse Gas emissions than {the bar that represents Russia}[0.545, 0.731, 0.659, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "QZrqObNSZ588jhxn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has more greenhouse gas emissions between ({the USA} [0.115, 0.471, 0.230, 0.903] and {Russia}[0.545, 0.731, 0.659, 0.903])?"
+            },
+            {
+                "from": "gpt",
+                "value": "The USA."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "WY2yNShdTGT3Oui1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Japan's emissions} [0.688, 0.816, 0.803, 0.903] lower than {Germany's emissions} [0.831, 0.850, 0.946, 0.903] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "2gansFtj8xnYyDDZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.396, 0.025, 0.665, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart which reads \"Greenhouse Gas Emissions by Country\"."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "lwpGueIz3THOw2va",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region [0.073, 0.062, 0.988, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main plot in which the bars of the Greenhouse gas emissions of different countries are plotted. The bars range from abbreviated country name labels on the x-axis from \"USA\" [0.160, 0.919, 0.184, 0.942] to \"Germany\" [0.862, 0.919, 0.915, 0.942], and the y-tick labels range from \"0\" [0.058, 0.894, 0.065, 0.917] to \"14000\" [0.029, -0.059, 0.065, -0.036]."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "dEZedEP4Zfm7Zqeh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region [0.507, 0.952, 0.554, 0.975] provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label for the x-axis, which is \"Country\"."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "07LQhH5dhHtYDp0t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information present within the coordinates [0.688, 0.816, 0.803, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The given coordinates represent the bar corresponding to Japan's Greenhouse Gas emissions, which is at 1272 million tons."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "CSivMVwt0IBtDOeq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of graph is represented by the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "wRRZmqRATICeFyiF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency Improvement Potential (%)."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "rjYsSVYMNfQtPPje",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat {data point}<r>[0.461, 0.239, 0.598, 0.854]</r> is the highest on the chart according to the relavant x-axis and y-axis labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Logistics."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "XKmtgh9kxVOqFW1G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Sales\"}<r>[0.803, 0.130, 0.940, 0.854]</r> indicate more Efficiency Improvement Potential (%) than {the bar for \"Manufacturing\"}<r>[0.118, 0.415, 0.255, 0.854]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "XgNeZu6H2hAO42Va",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for \"Distribution\"}<r>[0.290, 0.525, 0.427, 0.854]</r> have lower or higher Efficiency Improvement Potential (%) compared to {the bar for \"Management\"}<r>[0.632, 0.635, 0.769, 0.854]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "loM8LgGctrVM3OA8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data points for {Sales and Manufacturing}<r>{Sales: [0.803, 0.130, 0.940, 0.854]}, {Manufacturing: [0.118, 0.415, 0.255, 0.854]}</r> higher or lower than the data point for {Logistics}<r>[0.461, 0.239, 0.598, 0.854]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "FELeFDa064kwIxwx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the pattern of the Efficiency Improvement Potential represented by the bars within the area [0.118, 0.130, 0.940, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart within the specified area [0.118, 0.130, 0.940, 0.854] shows the Efficiency Improvement Potential (%) for five processes including Manufacturing, Distribution, Logistics, Management, and Sales. The highest potential is for Logistics at 28%, followed by Sales at 33%, with the lowest potential for Management at 10%."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "voxRz3kLeo3VAur8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the Efficiency Improvement Potential of \"Distribution\" in the area [0.290, 0.525, 0.427, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified area [0.290, 0.525, 0.427, 0.854], the \"Distribution\" process shows a relatively moderate Efficiency Improvement Potential of 15%."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "Ph8DSAFLrmWuJlFu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.796, 0.854] to [0.865, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the area specified as [0.796, 0.854] to [0.865, 0.879], no information is contained in the chart as it lies outside the visualized region of the bar chart."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "ORy1FGzUQQfJwkuX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis}[0.019, 0.309, 0.036, 0.639] on the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis represents the 'Environment Score'."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "2EGxPlvyvWKbLh89",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category shown at [0.845, 0.879, 0.922, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category is 'Australia'."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "drMtBMm5Z1Tffe9A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.260, 0.130, 0.373, 0.854] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the 'Environment Score' for 'Germany', which is 84.5."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "jbBwcCWwxTv4nubZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the environment scores for {USA}[0.118, 0.180, 0.232, 0.854] and {India}[0.543, 0.333, 0.657, 0.854] the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "Xc1LCzoBcVoMRXSH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Germany}'s[0.260, 0.130, 0.373, 0.854] environment score higher or lower than {Australia}'s[0.827, 0.150, 0.940, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "mRYMVIwGGnuarqPS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher environment score, {China}[0.402, 0.320, 0.515, 0.854] or {Brazil}[0.685, 0.201, 0.798, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Brazil."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "O8d4DedKkUvQqfyx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.043, 0.154, 0.065, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area marks the '80' line on the y-axis."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "gySf1C7X8Ytx5FIb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components in the region [0.118, 0.180, 0.657, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes several bars that represent the 'Environment Scores' for the countries 'USA', 'Germany', 'China', and 'India'. The coordinates for these bars are {USA}[0.118, 0.180, 0.232, 0.854], {Germany}[0.260, 0.130, 0.373, 0.854], {China}[0.402, 0.320, 0.515, 0.854], and {India}[0.543, 0.333, 0.657, 0.854]."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "XzHghS4cNNJcIyAc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.260, 0.130, 0.515, 0.854]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars that represent the 'Environment Scores' for 'Germany' and 'China'. The coordinates for these bars are {Germany}[0.260, 0.130, 0.373, 0.854] and {China}[0.402, 0.320, 0.515, 0.854]."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "t3jGpcFg37ESZtN4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object located at coordinates [0.104, 0.717, 0.244, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the bar representing the return rate for Bonds."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "SyuM8vufwAbZr0bK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exactly is the label for the y-axis in the coordinates [0.015, 0.390, 0.029, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Return Rate (%)\"."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "es1LAHowENiJWlbd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the \"Investment Vehicles\" represent at coordinates [0.454, 0.952, 0.593, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "nLKpP2xQ8dKafErn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object is within the coordinates [0.372, 0.025, 0.675, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subtitle that states \"Investment Return Rate Comparison\"."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "npaVLPZHPEsSv9YI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars representing the \"Bonds\" at [0.104, 0.717, 0.244, 0.903] and the \"Stocks\" at [0.279, 0.463, 0.419, 0.903], which bar is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Stocks\" bar is higher."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "T3Y7d4VkXd6jpqxP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the bar representing \"Real Estates\"}[0.454, 0.574, 0.593, 0.903], is {the bar representing \"Cryptocurrencies\"}[0.803, 0.102, 0.943, 0.903] taller or shorter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "qbhIXWk2PyHrMCST",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Commodities\"}[0.628, 0.760, 0.768, 0.903] have greater or lesser return rate than {the bar representing \"Bonds\"}[0.104, 0.717, 0.244, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "apZVOBVND7VyVmHt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Cryptocurrencies bar}[0.803, 0.102, 0.943, 0.903] positioned to the right or left of the {Real Estates bar}[0.454, 0.574, 0.593, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "RkL4Gx8Z9aPl8mEO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in the area [0.062, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the entire graph body including the bars representing various investment vehicles and their corresponding return rates. For instance, the bar for Bonds is found at [0.104, 0.717, 0.244, 0.903] and for Cryptocurrencies at [0.803, 0.102, 0.943, 0.903]."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "deyU5NjEm3IL3aTh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.629, 0.458, 0.769, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar for \"Commodities\" at [0.628, 0.760, 0.768, 0.903] which spans a range from 0.00 to 2.70 on the y-axis."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "p4iMUfcwyPuzHibc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about what is inside the region [0.279, 0.463, 0.419, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is occupied by the bar for \"Stocks\" and it spans from 0.00 to 8.30 which represents the return rate for \"Stocks\"."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "8Dj8iR716ogr3Ik8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region [0.803, 0.102, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region you can find the bar for \"Cryptocurrencies\" which ranges from 0.00 up to 15.10, thus representing the return rate for \"Cryptocurrencies\"."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "p6pX3FpDOcGSL6MG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.005, 0.075, 0.017, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the subplot's y-axis label}<r>[0.005, 0.075, 0.017, 0.241]</r> labelled as \"Stocks Return Rate\"."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "xZ3idOPXF5TY6YoC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for {the x-axis label} in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the x-axis label}<r>[0.472, 0.964, 0.571, 0.981]</r>."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "RU3jGh0oWSbUeH4c",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with the coordinate range [0.022, 0.291, 0.047, 0.308]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a {y-axis label}<r>[0.022, 0.291, 0.047, 0.308]</r> stating \"0.00\"."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "pCh4AzY9s98b0MGW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2020}<r>[0.098, 0.827, 0.160, 0.927]</r> taller than {the bar for 2030}<r>[0.882, 0.749, 0.945, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "0ezmo1fjyt94KrRU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar labelled 2028}<r>[0.725, 0.771, 0.788, 0.927]</r> have a smaller y-range than {the bar labelled 2026}<r>[0.568, 0.788, 0.631, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "k9lm6xVKSnfHJLL2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-coordinate for \"0.35\" label}<r>[0.022, 0.039, 0.047, 0.057]</r> closer to {the y-coordinate of \"0.30\" label}<r>[0.022, 0.075, 0.047, 0.093]</r> than to {the y-coordinate of \"0.40\" label}<r>[0.022, 0.004, 0.047, 0.021]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "JyZN5UDpKISFDrQV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the items in the region [0.055, 0.649, 0.988, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the third subplot comprising bars for Real Estate Return Rate from 2020 to 2030. Each bar represents the annual return rates for real estate investments, with the investment years labeled at the bottom and specific yearly return rates labeled on the y-axis on the left."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "DzHnLSQLsnS8Il1b",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounded area of [0.055, 0.019, 0.988, 0.297]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a set of bars representing the Stocks Return Rate from the years 2020 to 2030. Each year has a corresponding bar along the x-axis showing the increase in return rates from 0.12 in 2020 to a higher rate in subsequent years, with these results displayed on the y-axis to the left."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "NIXbMpBxaYI1RsHD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements found between [0.055, 0.334, 0.988, 0.612]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains bars that illustrate Bond Investments Return Rates from 2020 to 2030. Each bar correlates to specific years shown at the base. These bars show an increase from lower rates in earlier years towards higher rates in the last years, with ticks and labels on the y-axis indicating these values in increments of 0.05."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "Y17mGNdwvvo1em0E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the first bar in the subplot for Offline Interaction?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Purple}<r>[0.116, 0.648, 0.184, 0.808]</r>."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "rFLFFRON0SP6TD9y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate range displayed next to the y-axis in the Online Interaction plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{0.00 to 0.50}<r>[0.035, 0.522, 0.065, 0.545]</r>."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "WO12nnufZvsOfgnR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at {the coordinates}<r>[0.623, 0.625, 0.690, 0.808]</r> in the Offline Interaction subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "WhatsApp bar reflecting Offline Interaction."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "VcUSQvcLWz4DD3sH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer, {the Duration of Reading bar for Instagram}[0.285, 0.048, 0.353, 0.254] or {the Online Interaction bar for Instagram}[0.285, 0.326, 0.353, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Online Interaction bar."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "UuqHyq8HRiBOlXz1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the LinkedIn bar in Offline Interaction}[0.454, 0.682, 0.522, 0.808] extend higher than {the LinkedIn bar in Online Interaction}[0.454, 0.377, 0.522, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "6xLXqlWnharowEqq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first mark on the y-axis of {Online Interaction}[0.075, 0.531] higher than the first bar of {Twitter in Online Interaction}[0.201, 0.403, 0.268, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "5LQo1lep6isgyBgn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the subplot bounds <r>[0.075, 0.025, 0.985, 0.254]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises bars indicating the Duration of Reading for multiple social networks, each bar colored in shades of blue, with the first bar's coordinates at <r>[0.116, 0.036, 0.184, 0.254]</r> and the last one at <r>[0.876, 0.121, 0.944, 0.254]</r>."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "BvdPCG9MKIxja7H8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the visual space framed by <r>[0.075, 0.302, 0.985, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This constitutes the Online Interaction subplot with various bars representing interaction scores, such as the LinkedIn bar situated at <r>[0.454, 0.377, 0.522, 0.531]</r> and marked by an orange hue."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "Ao3KQzW6j5JSy7od",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.075, 0.579, 0.985, 0.808]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the Offline Interaction subplot, showcasing vertical bars corresponding to different social networks' offline engagement metrics. Key identifying features include a label for Instagram at <r>[0.312, 0.824, 0.326, 0.942]</r> and a corresponding bar at <r>[0.285, 0.636, 0.353, 0.808]</r>."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "eHdMydpHB4e1BRlJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.024, -0.001, 0.038, 0.317]</r> for the second sub-chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "French Cuisine Appreciation."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "VI57qHpFL0ZqWooc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color are {the bars}<r>[0.116, 0.721, 0.184, 0.903]</r> in the third sub-chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "8jbIbU29eswuPaOG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the xlabel}<r>[0.462, 0.952, 0.598, 0.975]</r> indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rank Over 10 Years."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "mhDTIMLL52tzyE68",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot has a higher maximum y-value, the first {subplot}<r>[0.075, 0.025, 0.985, 0.292]</r> or the third {subplot}<r>[0.075, 0.636, 0.985, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "First."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "SBBONHcTa0XGs2Y4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the blue bars}<r>[0.116, 0.038, 0.184, 0.292]</r> from the Italian category positioned above {the y-tick 0.6}<r>[0.043, 0.122, 0.065, 0.145]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "VEWxs6hUM7JiOqME",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first bar}<r>[0.116, 0.343, 0.184, 0.597]</r> in subplot_1 have a rank associated with {xtick 1}<r>[0.146, 0.919, 0.154, 0.942]</r> from subplot_3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "3KhvSuwqLyQc4yK2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bars in the subplot labeled \"Genre 2\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bars in {Genre 2}<r>[0.066, 0.332, 0.985, 0.596]</r> subplot is light orange."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "9NoS4YvdwTdaAEdZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.015, 0.111, 0.029, 0.203]</r> in the first subplot indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Genre 1."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "vBh0m50795trTPiu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year corresponds to the furthest right tick on the X-axis in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year is 2029 in {the X-axis tick}<r>[0.892, 0.919, 0.927, 0.942]</r>."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "0p6ctjX7MKnI5g9f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for 2020 taller in {Genre 1}[0.108, 0.195, 0.176, 0.289] or in {Genre 3}[0.108, 0.779, 0.176, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Genre 1."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "u8XJ3mOGYdxc4IER",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first bar of Genre 1}[0.108, 0.195, 0.176, 0.289] reach a higher value than {the first bar of Genre 2}[0.108, 0.344, 0.176, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "GDFkZjXn1pvBQa5Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the annual trends decreasing or increasing from 2020 to 2029 in {Genre 2}[0.066, 0.332, 0.985, 0.596]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "zcQavwabVgI9Kvy5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the trends observed in {Genre 3 region}<r>[0.066, 0.638, 0.985, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The genre shows a generally increasing trend across the years with values starting around 0.4 in 2020 and reaching up to around 1.4 by 2029 with each year relayed by corresponding bars increasing in height."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "X01LYUmBAlHmxjPF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the area spanning from the bottom of the {ylabel of Genre 1}<r>[0.015, 0.111, 0.029, 0.203]</r> to the top of {xlabel of Genre 1}<r>[0.526, 0.307, 0.526, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily contains the vertically aligned category label for 'Genre 1,' individual bars corresponding to each year from 2020 to 2029 with lower values at the start and increasing towards the end, and associated Y-ticks indicating scale."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "o5QNt1BuLZ3DyDiM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze and describe the data in the vicinity of {the X-axis labels}<r>[0.125, 0.919, 0.927, 0.942]</r> in the bottom subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the year labels from 2020 to 2029 aligned along the X-axis below Genre 3's plot, each year marked with a tick beneath its label, overseeing a consistent growth in bars correlating to increasing values per year."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "56uDsM6TIKHXnqJ2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}[0.015, 0.105, 0.029, 0.205] in the first subplot signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "Zw8j5GCkQu6o6weE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year corresponds to {the x-axis label}[0.894, 0.919, 0.929, 0.942] in the profits subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "pdPIMo0EPguO4L3B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}[0.153, 0.919, 0.188, 0.942] in the profits subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2011"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "jjowBVsXIsZNQ8s6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the revenue bar for 2011}[0.137, 0.110, 0.203, 0.285] taller than {the costs bar for 2011}[0.137, 0.399, 0.203, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "gfbWeYn9iEFluOqU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the final profit bar}[0.879, 0.825, 0.945, 0.903] have a higher value than {the initial profit bar}[0.137, 0.859, 0.203, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "tuqs5v9udkHvvtmc",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the costs bar for 2018}[0.714, 0.360, 0.780, 0.594] lower than {the revenue bar for 2018}[0.714, 0.057, 0.780, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "kcW6ZUYjcuyt3wgG",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.097, 0.334, 0.985, 0.594]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area visualizes costs over the years with bars. Each bar represents a particular year's cost, ranging from 2011 to 2020, gradually increasing from {2011 costs}[0.137, 0.399, 0.203, 0.594] to {2020 costs}[0.879, 0.346, 0.945, 0.594]."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "o8vlHOpLSuWeFMmt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.097, 0.642, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the profits from 2011 to 2020. It begins with a lower profit in 2011 and gradually increases with each bar until peaking in 2020."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "SScv60wlByyApMPD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.097, 0.025, 0.985, 0.285]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region shows a series of bars, each representing annual revenue from 2011 to 2020. The bars indicate an increasing trend in revenue, starting from 2011 {2011 revenue bar}[0.137, 0.110, 0.203, 0.285] up to 2020 {2020 revenue bar}[0.879, 0.037, 0.945, 0.285]."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "yFEqU1Nu9banCdXh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} of the second subplot at [0.024, 0.407, 0.038, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reliability."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "UYepcfOUlUy16NAN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first xtick in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.257, 0.919, 0.344, 0.942]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "UPH7ge48IaIRFVBt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {the bar} at [0.576, 0.343, 0.944, 0.597] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents that the value of the reliability of the Supply Chain Management is 0.65."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "avsSnriG1B2D1RpG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in [0.024, 0.102, 0.038, 0.214]?"
+            },
+            {
+                "from": "gpt",
+                "value": "An Efficiency {label}[0.024, 0.102, 0.038, 0.214]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "nA5cJxnwe8MVC2eG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first bar of the first subplot}[0.116, 0.041, 0.484, 0.292] higher or lower than {the second bar of the third subplot}[0.576, 0.649, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "IGnx0klQkDrtZI7B",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has larger y range, {the first bar of the first subplot}[0.116, 0.041, 0.484, 0.292] or {the first bar of the third subplot}[0.116, 0.688, 0.484, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first bar of the third subplot."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "Axcql7wNIyliDIA0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars of the first subplot}[[0.116, 0.041, 0.484, 0.292],[0.576, 0.038, 0.944, 0.292]] generally taller than {the bars of the second subplot}[[0.116, 0.429, 0.484, 0.597],[0.576, 0.343, 0.944, 0.597]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "MwvS4f2Zru840JCR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is more to the right, {\"Third subplot Global Trade bar\"}[0.116, 0.688, 0.484, 0.903] or {\"Third subplot Supply Chain Management bar\"}[0.576, 0.649, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Third subplot Supply Chain Management bar."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "2CfCR6wCKPGX0lyr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.075, 0.331, 0.985, 0.597]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the second subplot of the chart. It has two bars representing \"Global Trade\" and \"Supply Chain Management\", with parameters for \"Reliability\", a grey background and color codes are used to identify different subjects. The color for \"Global Trade\" is #e39c90, while the color for \"Supply Chain Management\" is #e39c90."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "yFw9WKf2Po6OUjI8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.075, 0.025, 0.985, 0.292]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subplot with the y-axis label \"Efficiency\" located at [0.024, 0.102, 0.038, 0.214] and two bars. One bar corresponds to the \"Global Trade\" category at [0.116, 0.041, 0.484, 0.292] and the another bar corresponds to \"Supply Chain Management\" category at [0.576, 0.038, 0.944, 0.292]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "Jnhl78H9KrapobqD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.075, 0.636, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 main objects in this area, they are:\n1. The y-axis label \"Diversity\" [0.024, 0.718, 0.038, 0.821].\n2. The bar representing the value for \"Global Trade\" [0.116, 0.688, 0.484, 0.903].\n3. The bar representing the value for \"Supply Chain Management\" [0.576, 0.649, 0.944, 0.903]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "c9AwOZaTRMYwPZHq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the region [0.469, 0.952, 0.591, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains xlabel reading \"Areas of Business\" [0.469, 0.952, 0.591, 0.975]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "ghPe79YSxAaezFfz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the y-axis}[0.015, 0.034, 0.029, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Charitable donations."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "dIldpWOO8tZ1pKv3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the bars for the subplot of 'Volunteer hours'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Purple}[0.121, 0.903, 0.188, 0.903]"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "75TaDSdwM3dZgp8v",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year is indicated by the label at [0.557, 0.919, 0.592, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "p5t2Vu5LpUqwhcXM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the peak year for charitable donations}[0.877, 0.037, 0.944, 0.285] later than {the peak year for social services}[0.205, 0.356, 0.272, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "GF8oHE0ovGU4fZ3r",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the initial year on 'Volunteer hours' subplot}[0.121, 0.903, 0.188, 0.903] precede {the initial year on 'Charitable donations' subplot}[0.121, 0.065, 0.188, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "4F6IaAfUKDITFYOi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar coordinate for 2012}[0.289, 0.062, 0.356, 0.285] shorter than {the bar coordinate for 2019}[0.877, 0.037, 0.944, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "SBcVEckUq8jTlO2d",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.079, 0.025, 0.985, 0.285]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subplot for 'Charitable donations' which includes a series of bars ranging from 2010 to 2019, with respective ends and values labeled. The y-axis at [0.015, 0.034, 0.029, 0.276] is labeled 'Charitable donations', and two shades of blue are visible in the bars with various heights representing differing donation values over the years."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "w24G6g0yWU3mKULY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects and labels present in [0.079, 0.642, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subplot for 'Volunteer Hours' with a y-axis labeled 'Volunteer Hours' at [0.015, 0.681, 0.029, 0.864] and x-axis labels indicating years from 2010 to 2019. The bars are uniformly shaded in purple, representing volunteer hours for each year, incrementally increasing from left to right, and year labels are aligned beneath each bar."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "vT2Do2JsGTybqOsZ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.079, 0.334, 0.985, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the 'Social Services' subplot with bars in a peach tone, indicating values from the year 2010 to 2019. The y-axis labeled 'Social services' at [0.015, 0.379, 0.029, 0.549] shows values ranging from 0 to 8000 with each bar's height proportional to the value of social services recorded each year."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "rm5Scz9NXSCOI1Gm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar for the \"Decision Tree\" algorithm in the efficiency chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Light blue, located in {the bar for Decision Tree}<r>[0.285, 0.091, 0.353, 0.292]</r>."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "K5dFjDo34h9ndNCP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value range indicated by the first tick mark on the y-axis of the reliability chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{0.0}<r>[0.043, 0.894, 0.065, 0.917]</r>."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "xkeVgQi5M0OFy325",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the x-axis in the reliability chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Algorithms, located in {the xlabel area}<r>[0.483, 0.952, 0.577, 0.975]</r>."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "JDklble86hGeLYvz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"XGBoost\" bar}<r>[0.876, 0.038, 0.944, 0.292]</r> taller than {the \"Naive Bayes\" bar}<r>[0.370, 0.072, 0.437, 0.292]</r> in the efficiency chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "kBi1OG5yc0PS9qt1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the performance chart, does {the bar for \"Deep Learning\"}<r>[0.454, 0.367, 0.522, 0.597]</r> outperform {the bar for \"Random Forest\"}<r>[0.538, 0.407, 0.606, 0.597]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "4DUviftHh17HMsRE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the performance of {the \"Support Vector Machine\" in the reliability chart}<r>[0.201, 0.782, 0.268, 0.903]</r> higher than its representation in {the performance chart}<r>[0.201, 0.437, 0.268, 0.597]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "gMr4IKzpLjGpA7kg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visualization within the area [0.075, 0.025, 0.985, 0.292]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a graph displaying efficiency of different AI algorithms. Each algorithm, from Neural Network to XGBoost, has a corresponding light blue bar showing its individual efficiency score. The y-axis is labeled \"Efficiency\" with ticks ranging from 0.0 to 1.0 and corresponding bars span from top to bottom in varying lengths based on the efficiency value."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "KY9bYmMIAYUpHT85",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can be observed in the area [0.075, 0.331, 0.985, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area of the chart relates to the performance of AI algorithms. Each algorithm is represented by a coral bar, and the y-axis on the left is marked from 0.0 to 1.0 indicating performance levels. Each bar's height varies according to the respective algorithm's performance metric."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "efEO5Zh2zx0vkgz2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features found in the region [0.075, 0.636, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This particular region showcases the reliability of AI algorithms. It is populated with purple bars, each corresponding to an AI algorithm listed along the x-axis, which is clearly labeled \"AI Algorithms\". The y-axis is marked at intervals from 0.0 to 1.0 to indicate the reliability scale. Each bar\u2019s height directly corresponds to its reliability measure."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "YFsoTRdoGhGGPbUS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.024, 0.105, 0.038, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label in the first subplot, denoting \"Accuracy\"."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "VaNN1iOX5DWWS2NB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is plotted at coordinates [0.116, 0.151, 0.230, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar representing the \"Accuracy\" of the \"Size of data\" stage with a value of 0.50."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "a8sWIkfdiqbsmqj3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.829, 0.919, 0.944, 0.942] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the X-axis label \"Data integration\" in the last subplot."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "R1kNgFzuMePj5XWB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar at [0.402, 0.755, 0.516, 0.903] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Precision\" of the \"Data storage\" stage."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "V7PKVksoRLzqgXUp",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing Precision of the Data storage stage}[0.402, 0.755, 0.516, 0.903] higher than {the bar representing Precision of the Data processing stage}[0.259, 0.772, 0.373, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "XeZpLT123xRIIm7j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Data retrieval\" Accuracy}[0.544, 0.122, 0.658, 0.292] taller than {the bar for \"Data integration\" Accuracy}[0.830, 0.066, 0.944, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "ij2Ve3vtB4JTuWku",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {the \"Data analysis\" stage}[0.687, 0.721, 0.801, 0.903] achieve a higher precision than {the \"Size of data\" stage}[0.116, 0.709, 0.230, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "e0yXsCJoCiyHVfMG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the Accuracy of {Data storage}[0.402, 0.086, 0.516, 0.292] and {Data analysis}[0.687, 0.038, 0.801, 0.292], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data storage."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "amv8dP6wSPbRNIZN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the box [0.075, 0.636, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the third subplot, which illustrates Precision scores across different stages of Big Data Integration. It has 6 colored bars representing the precision values for different stages, along with y-axis labels and ticks for measurement and x-axis labels naming stages."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "clPhPevfucjm7M4a",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of elements can you see in the region [0.043, 0.588, 0.065, 0.611]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's part of the y-axis of the second subplot, specifically the tick label showing the value \"0.0\"."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "Iw90BN1HwBwVEpsu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in the range of [0.259, 0.772, 0.373, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a bar representing the precision of \"Data processing\" in the last subplot. The height of the bar is proportional to the precision value it represents."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "1Q2ENzWz20eLFNg1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area of [0.075, 0.025, 0.985, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the first subplot, which visualizes the Accuracy measures across different stages of Big Data Integration. There are a total of 6 colored bars representing the accuracy measures, with the object height representing the measure value. Also, x-axis and y-axis with their labels and ticks are included."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "Wd9fmtBG0uDlaGV3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the label} for the y-axis<r>[0.024, 0.015, 0.038, 0.295]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency - Second Quarter."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "1noyj0ETUQRME7CY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the third bar}<r>[0.285, 0.051, 0.353, 0.285]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "wz3UUMfjXYSSUmJG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is placed at {coordinates}<r>[0.146, 0.919, 0.154, 0.942]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, \"1\"."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "0RCwOw4FKinMH3m2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {bar \"HR\" in the second subplot}<r>[0.285, 0.392, 0.353, 0.594]</r> shorter or taller than {bar \"HR\" in the first subplot}<r>[0.285, 0.051, 0.353, 0.285]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "u8xBPrSnUeYD03e5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-axis label \"0.8\"}<r>[0.043, 0.056, 0.065, 0.079]</r> refer to a higher value than {the y-axis label \"0.6\"}<r>[0.043, 0.111, 0.065, 0.134]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "c0J5JNVVziRv6x7I",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {first bar}<r>[0.116, 0.079, 0.184, 0.285]</r> located to the left of {the tenth bar}<r>[0.876, 0.098, 0.944, 0.285]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "d2nHOwOqFVXGzgQP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the horizontal label at the coordinates [0.396, 0.952, 0.655, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the horizontal axis, called Local Government Funding Sequence."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "FeFEYK5rysYY7VBU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the coordinates of the bar labeled '3'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates of the bar labeled '3' are [0.278, 0.596, 0.347, 0.903]."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "QaWPg3iWIeKKsIWr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar labeled '10' representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar labeled '10' represents a value of 0.96 with coordinates [0.875, 0.540, 0.943, 0.903]."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "yCCarA4cI8yNoqpP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the coordinates [0.035, 0.591, 0.057, 0.614]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label with a value of 0.8."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "KCJoP9rw9C1Xj3mF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar labeled '7'[0.619, 0.596, 0.688, 0.903] higher than the bar labeled '4'[0.364, 0.608, 0.432, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "dABTg4RseWU7sjr1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar labeled '6' [0.534, 0.608, 0.602, 0.903] at the same height as the bar labeled '10' [0.875, 0.540, 0.943, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "DO2GxRuCsgLQuVrh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar labeled '1' [0.108, 0.562, 0.176, 0.903] have a higher or lower value than the bar labeled '9' [0.790, 0.562, 0.858, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "7n8qArPtuERMqdax",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar labeled '2' [0.193, 0.585, 0.261, 0.903] to the left or right of bar labeled '3' [0.278, 0.596, 0.347, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "qQoZVhnpfIytnWqg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.066, 0.031, 0.985, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar chart that represents Local Government funds, with bars labeled 1 to 10. Each bar represents a specific funding sequence and its corresponding funds amount."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "xwrf9mfUb6SZqJnd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region in [0.263, 0.487, 0.677, 0.903] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the main part of a bar chart that shows Community Development Funds plotted against a Local Government Funding Sequence. The bar heights reflect the amount of funds, with each bar labeled 1 to 10 representing a specific sequence in the funding."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "Gu3sD4xgdGYsrWxK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.449, 0.612, 0.517, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single bar located in this area. It's labeled '5' and represents a value of 0.77 for the Community Development Funds in the Local Government Funding Sequence."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "I2N5nUOdSfH2OyLq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the area [0.066, 0.487, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the secondary bar chart in the figure, which represents the Community Development Funds. Each of the bars labeled from 1 to 10 represents a specific sequence in the funding. The height of each bar corresponds to the amount of funds."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "8k1Qg223M4SgQMgU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar representing the year 2007}[0.707, 0.038, 0.775, 0.292] in the GDP Growth subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Light blue."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "W79fcc9Sp6YT7TPj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the highest bar in the Inflation subplot}[0.370, 0.343, 0.437, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.370, 0.343, 0.437, 0.597]."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "OcitMbfxeFjYYE8o",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels can be seen for {ticks on the y-axis}<r>[0.043, 0.845, 0.065, 0.869]</r> in the Unemployment Rate subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "wQOuYsA6msil5sI5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2004}[0.454, 0.372, 0.522, 0.597] higher than {the bar for 2006}[0.623, 0.428, 0.690, 0.597] in the Inflation subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "ea0bntj1Mx80nR37",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the lowest labeled value on the y-axis}[0.043, 0.894, 0.065, 0.917] in the Unemployment Rate subplot less than {the highest labeled value}[0.043, 0.651, 0.065, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "DlS5ZBpEfb9nKw92",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first year labeled on the x-axis}[0.133, 0.919, 0.168, 0.942] earlier than {the last year labeled}[0.892, 0.919, 0.927, 0.942] in the Unemployment Rate subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "k7Q2bX1dEvzmxfgm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.043, 0.362, 0.065, 0.386]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label for 0.8 in the Inflation subplot, indicating a value of 0.8 on the y-axis."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "XDPGPV733AdVGsAi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.133, 0.758, 0.944, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The entire x-axis label set for years 2000 to 2009 can be found, with the tick marks and labels visible for each year."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "78FQf3GGbUyJSGqP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the contents within [0.319, 0.372, 0.353, 0.597]."
+            },
+            {
+                "from": "gpt",
+                "value": "This smaller region captures the middle section of a bar showing 0.8 inflation for the year 2002. The full bar is actually taller, beginning below this region at a higher inflation value."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "2XreNinoJAp9ZfUq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar corresponding to 2014 in the subplot indicating Mobility Rate}<r>[0.449, 0.143, 0.517, 0.292]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "WwOWQ954SGSoxdqG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of {the ylabel \"Political Protests\"}<r>[0.015, 0.675, 0.029, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.015, 0.675, 0.029, 0.865]."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "2EtHAD9iroIrxzCJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the first label on the y-axis in the Internet Users subplot}<r>[0.035, 0.282, 0.057, 0.306]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.0."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "wx5rN6Kli1qX2N0i",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the political protest bar for 2015}<r>[0.534, 0.831, 0.602, 0.903]</r> taller than {the political protest bar for 2013}<r>[0.364, 0.800, 0.432, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "6ioLZYxdRIM6san1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing the Internet Users in 2010}<r>[0.108, 0.228, 0.176, 0.292]</r> represent a higher value than {the bar representing Internet Users in 2019}<r>[0.875, 0.038, 0.943, 0.292]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "eFbO1LpaMUzkWn1P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Internet Users' bars from 2010 to 2012}<r>[[0.108, 0.228, 0.176, 0.292], [0.193, 0.196, 0.261, 0.292], [0.278, 0.186, 0.347, 0.292]]</r> of increasing height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "FHcBMww9AtmpvuN4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the range between {0.898, 0.919, 0.927, 0.942} in the subplot for Political Protests consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the year label \"2019\"<r>[0.892, 0.919, 0.927, 0.942]</r> situated on the x-axis."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "GhVBQUg8u5yg2UKn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within {the section of the chart representing the x-axis for Internet Users}<r>[0.066, 0.292, 0.985, 0.309]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The section contains several tick marks at intervals reflecting the years from 2010 to 2019. Most year labels are not visible due to overlap, but the beginning and end of the axis are marked."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "0PuWQ1vJgk0wcV0T",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates {0.015, 0.05, 0.029, 0.865} in the subplot for Political Protests?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis<r>[0.015, 0.675, 0.029, 0.865]</r> labeled with \"Political Protests\" and includes labels ranging from 0.0 to 1.2."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "N0SkMbgIwtIDTKsH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.116, 0.108, 0.184, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a light blue bar representing the efficiency of Product 1 at point 1."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "wpqqsJlhrRh1WMeD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at [0.201, 0.697, 0.268, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an orange bar corresponding to the efficiency of Product 2 at point 2."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "O2MCdwL5B9FVwTxY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at [0.876, 0.675, 0.944, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "That's a purple bar that represents Product 3's efficiency at point 10."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "uksLl3mNfiQ0HoI2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the purple bar}<r>[0.116, 0.687, 0.184, 0.903]</r> taller than {the orange bar}<r>[0.116, 0.366, 0.184, 0.597]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "shDa8JOgOzR9WxqA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the height of {bar 1 of Product 1}<r>[0.116, 0.108, 0.184, 0.292]</r> with {bar 1 of Product 3}<r>[0.116, 0.687, 0.184, 0.903]</r>. Which is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product 3."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "1GL3cfsHsSBAdfYD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is shorter, {bar 9 of Product 2}<r>[0.792, 0.407, 0.859, 0.597]</r> or {bar 5 of Product 1}<r>[0.454, 0.080, 0.522, 0.292]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product 1."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "KDtm37DpRrBalYTX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area spanning from [0.075, 0.636, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the third subplot, titled \"Efficiency Product 3\", with purple bars representing efficiency levels across ten production series. The ylabel reads \"Efficiency Product 3\", and the bars' values increase from left to right, peaking at the bar for the 8th series."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "lSrfoGBBwtDZWdqj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.075, 0.025, 0.985, 0.292] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the first subplot detailing \"Efficiency Product 1\". It comprises light blue bars plotted against labels denoting ten production series, with a ylabel captioned \"Efficiency Product 1\". The bar heights vary, demonstrating differing efficiency values across series."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "0Hdweobp4L0puqv4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide information about the area [0.075, 0.331, 0.985, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The middle subplot contained in this area relates to \"Efficiency Product 2\", featuring orange bars against a backdrop of production series ranging from 1 to 10. The ylabel is \"Efficiency Product 2\", and similar to other subplots, the heights of the bars illustrate the efficiency scores differentiating each production series."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "UQC2KW6EIUEacFyt",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the [0.528, 0.984, 0.528, 0.984] coordinate represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the x-axis label, which is blank."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "qFCAZXT0n8ZV8ZWl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the object at [0.347, 0.025, 0.709, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, labeled \"Technological Industry Development Trends\"."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "i5PAXarnzs2sKyL3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are represented by the coordinates [0.043, 0.781, 0.061, 0.804] and [0.043, 0.635, 0.061, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are y-axis labels called \"20\" and \"40\", respectively."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "wObsDlQ78WaHkyoM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.195, 0.717, 0.215, 0.935] and [0.882, 0.171, 0.902, 0.935]  of the bar graph refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first coordinates refer to a bar that represents the development level of \"IoT\" in 2015 and the second coordinates refer to a bar that represents the same in 2030."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "p2mT2Jkb4tXBqXhR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend in {AR}[0.673, 0.644, 0.694, 0.935] higher or lower than the trend in {Quantum Computing}[0.611, 0.426, 0.632, 0.935] in the 2025 projections?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "GdlKqwRgdFrUfOud",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2020 and 2030, did the trend in {Mobile Tech}[0.403, 0.426, 0.424, 0.935, 0.861, 0.353, 0.882, 0.935] increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "dUQO6SeydeBNadRH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 2015 trend in {AI}[0.112, 0.535, 0.133, 0.935] higher or lower than the trend for {Cloud Computing}[0.133, 0.499, 0.153, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "x4Hp7GC0pVQXNRSL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {IoT}<{IoT 2025 bar}[0.653, 0.208, 0.673, 0.935]> at a higher or lower level than {VR}<{VR 2025 bar}[0.694, 0.790, 0.714, 0.935]> in 2025 projections?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "4LXK2V2L8UrNhzm1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects inside the area [0.078, 0.074, 0.273, 0.325]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a legend with seven entries. Each entry includes a color swatch and a text label, representing the trends in different technology sectors: AI, Cloud Computing, Quantum Computing, Mobile Technology, Internet of Things (IoT), Augmented Reality (AR), and Virtual Reality (VR)."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "5waQjMjU0y5RxizV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents inside the region [0.570, 0.135, 0.591, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the {2025 bar for AI}[0.570, 0.135, 0.591, 0.935], indicating a development level of 110."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "16rAZhWSHxylHql4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen from the region [0.361, 0.390, 0.403, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a green rectangle representing Cloud Computing trend in 2020."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "94fl0qozQXGBXeuy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are visual elements present in the area located at [0.112, 0.535, 0.133, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a blue bar representing the trend level of AI in 2015."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "e2pZSELDcVXUQ3qw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-axis}<r>[0.015, 0.398, 0.029, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share (%)."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "cwodFTRe0Y1uZiHN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is located at the coordinates [0.737, 0.952, 0.791, 0.975] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fortnite."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "uo1ebKxk31M4Q1XF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.898, 0.120, 0.926, 0.136] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend color for the year 2018."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "9HcLCJky8bcH3xjj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the text representation at [0.937, 0.083, 0.972, 0.106] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "t9WnDFegQmlSqRUa",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bars for the game 'WoW', is the market share {in 2016}<r>[0.104, 0.312, 0.126, 0.935]</r> higher or lower than {in 2018}<r>[0.148, 0.416, 0.170, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "G568OOBCr43yZQy5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates, which game has a higher market share {in 2016}<r>[0.104, 0.312, 0.126, 0.935]</r>, <r>[0.250, 0.364, 0.272, 0.935]</r>, <r>[0.396, 0.416, 0.418, 0.935]</r>, <r>[0.542, 0.883, 0.564, 0.935]</r>, <r>[0.688, 0.832, 0.710, 0.935]</r>, <r>[0.834, 0.468, 0.855, 0.935]</r>: 'WoW' or 'LoL'?"
+            },
+            {
+                "from": "gpt",
+                "value": "WoW."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "vrR5vTB5rBrVMoUh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {2022}<r>[0.191, 0.572, 0.213, 0.935]</r>, <r>[0.337, 0.104, 0.359, 0.935]</r>, <r>[0.483, 0.260, 0.505, 0.935]</r>, <r>[0.629, 0.468, 0.651, 0.935]</r>, <r>[0.775, 0.520, 0.797, 0.935]</r>, <r>[0.921, 0.520, 0.943, 0.935]</r>, which game has a higher market share, 'LoL' or 'DOTA'?"
+            },
+            {
+                "from": "gpt",
+                "value": "LoL."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "2nbUiSrli1d8QDUk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the markers, which year has a higher market share for the Fortnite game {in 2020}<r>[0.753, 0.520, 0.775, 0.935]</r> or {in 2022}<r>[0.775, 0.520, 0.797, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "KVrQcCnmdVcTpgYE",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents located in the region [0.893, 0.074, 0.978, 0.220] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, labeled with years 2016, 2018, 2020, 2022 and their corresponding colors."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "r76jWQtaXnb6YFmi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area covering [0.316, 0.952, 0.490, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels for the categories 'LoL', 'DOTA' and 'PUBG'."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "lFWC38fjRfVmuE0G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the range [0.876, 0.468, 0.943, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two bars: one represents Overwatch's market share in 2020 with coordinates [0.899, 0.468, 0.921, 0.935], and the other represents Overwatch's market share in 2022 with coordinates [0.921, 0.520, 0.943, 0.935]."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "BkzRf2jvrKhKxeDA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visuals located in the region [0.635, 0.312, 0.775, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the bars related to the market share of the game 'PUBG' and 'Fortnite' for the years 2020 and 2022."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "V5Mj0K2azoKL7DI9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar representing 'Sales' in the 'Value Creation' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue. This is represented by this bar with coordinates {Value Creation - Sales bar}<r>[0.733, 0.196, 0.775, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "lHmTUkCpqvimHhpW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Value'. This can be seen at these coordinates {Y-axis label}<r>[0.021, 0.460, 0.041, 0.537]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "qYR0qhT3zKf5pnIA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the 'Distribution' process in 'Value Erosion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 6. The 'Value Erosion' bar for 'Distribution' has coordinates {Value Erosion - Distribution bar}<r>[0.654, 0.850, 0.697, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "YveTm8anVjIl0mE7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Value Creation bar for the 'Manufacturing' process higher than the Value Erosion bar for the same process?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. This can be determined by comparing the height of {Value Creation - Manufacturing bar}<r>[0.491, 0.317, 0.534, 0.923]</r> and {Value Erosion - Manufacturing bar}<r>[0.534, 0.826, 0.576, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "v9C6qzZEFtctoCts",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher, 'Value Creation' for 'Product Design' or 'Value Erosion' for 'After Sales'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Value Creation' for 'Product Design'. This can be inferred by comparing the height of the bars {Value Creation - Product Design bar}<r>[0.370, 0.377, 0.413, 0.923]</r> and {Value Erosion - After Sales bar}<r>[0.896, 0.886, 0.938, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "1TQbkuQ4W6k7oD1O",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Value Creation' bar for the 'Raw Materials' process taller than its corresponding 'Value Erosion' bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. It can be demonstrated by comparing the heights of these bars: {Value Creation - Raw Materials bar}<r>[0.250, 0.414, 0.292, 0.923]</r> and {Value Erosion - Raw Materials bar}<r>[0.292, 0.801, 0.334, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "1g0ULDlHxlyVPL95",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.098, 0.089, 0.315, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. There are two entries: 'Value Creation', represented by a blue color, with coordinates at {Value Creation Label}<r>[0.162, 0.100, 0.307, 0.128]</r> and 'Value Erosion', represented by a red color, with its text label at {Value Erosion Label}<r>[0.162, 0.142, 0.295, 0.170]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "tNFWoa3gqh0Qz4vc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.338, 0.942, 0.488, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one of the x-axis labels: 'Product Design'. Its coordinates are {Product Design Label}<r>[0.338, 0.942, 0.488, 0.970]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "Wen06rYT3xFpZLsk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the area [0.896, 0.886, 0.938, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the 'Value Erosion' bar for 'After Sales', along with a value of 3. The bar's coordinates are {Value Erosion - After Sales bar}<r>[0.896, 0.886, 0.938, 0.923]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "skzOJMTYOab6WC10",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.596, 0.942, 0.712, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes an x-axis label: 'Distribution'. The coordinate of this label is {Distribution Label}<r>[0.596, 0.942, 0.712, 0.970]</r>."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "ahdCGGeD0bbCijhi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.174, 0.183, 0.230, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text label for the third sequence, {Seq 3}<r>[0.174, 0.183, 0.230, 0.211]</r>."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "B1i35rvRLJP2cWW5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar in the category \"KNN\" for \"Seq 4\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the bar in the category \"KNN\" for {Seq 4} is [0.484, 0.456, 0.505, 0.923]."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "mydZxNbcBIgnfUgg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.021, 0.367, 0.041, 0.630] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {the y-axis label}<r>[0.021, 0.367, 0.041, 0.630]</r>, which is labeled as \"Performance Score\"."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "LH3LPvkBVmOICojz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the legend area located at [0.111, 0.089, 0.239, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are four different text-color label pairs in {the legend}<r>[0.111, 0.089, 0.239, 0.264]</r> representing four sequences: {Seq 1}<r>[0.174, 0.100, 0.231, 0.128]</r>, {Seq 2}<r>[0.174, 0.142, 0.230, 0.170]</r>, {Seq 3}<r>[0.174, 0.183, 0.230, 0.211]</r>, {Seq 4}<r>[0.174, 0.225, 0.230, 0.253]</r>, with corresponding color labels mapped."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "a8dPVa7De2bcmnBY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the performance score of {Seq 1} [0.140, 0.287, 0.161, 0.923] or {Seq 4}[0.203, 0.329, 0.224, 0.923] for the model \"Perceptron\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Seq 4}."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "tah22VSyMtmgBSnq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the performance scores of Seq 1 higher than Seq 2 for all models?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "eT9lcRs3TqqY3bzb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the DecisionTree model, which performance score sequence is the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "It\u2019s {Seq 1} with the corresponding bar <r>[0.701, 0.287, 0.722, 0.923]</r>."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "8AbIrGolcpxIYz6K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the SVM model}[0.280, 0.371, 0.301, 0.923] and {the GradientBoosting model}[0.841, 0.244, 0.862, 0.923], which one has a higher score for Seq 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GradientBoosting model."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "JKkdOXNLgHO5sDCY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.111, 0.089, 0.239, 0.264]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the sequences is within this region. It contains four pairs of colored boxes and text:\n1. The box for {Seq 1}<r>[0.119, 0.102, 0.158, 0.122]</r> is colored '#98ddca', and the label is {Seq 1 label}<r>[0.174, 0.100, 0.231, 0.128]</r>.\n2. The box for {Seq 2}<r>[0.119, 0.144, 0.158, 0.164]</r> is colored '#d5ecc2', and the label is {Seq 2 label}<r>[0.174, 0.142, 0.230, 0.170]</r>.\n3. The box for {Seq 3}<r>[0.119, 0.186, 0.158, 0.205]</r> is colored '#ffe156', and the label is {Seq 3 label}<r>[0.174, 0.183, 0.230, 0.211]</r>.\n4. The box for {Seq 4}<r>[0.119, 0.228, 0.158, 0.247]</r> is colored '#ffad69', and the label is {Seq 4 label}<r>[0.174, 0.225, 0.230, 0.253]</r>."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "qfv0eyRPB1DWGcb9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.140, 0.287, 0.926, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers all the performance bars for all sequences of models. There are four bars for each model, each representing a different sequence. Each group of bars representing the same model is aligned along the x-axis at the corresponding tick."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "IyxXZLevQ3UqlzR7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.421, 0.244, 0.505, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 bars within the area:\n1. {Seq 1 Bar for KNN}<r>[0.421, 0.414, 0.442, 0.923]</r>;\n2. {Seq 2 Bar for KNN}<r>[0.442, 0.329, 0.463, 0.923]</r>;\n3. {Seq 3 Bar for KNN}<r>[0.463, 0.244, 0.484, 0.923]</r>;\n4. {Seq 4 Bar for KNN}<r>[0.484, 0.456, 0.505, 0.923]</r>."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "UIz6h8Ob7G8KwUmW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.296, 0.030, 0.770, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area [0.296, 0.030, 0.770, 0.066] contains the chart's subtitle: \"AI Models Performance Over Sequences\"."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "CfItYIwfqWHb0FSE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you spot the label of y-axis in the chart from this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Count and Funds Raised (in million USD).\" Located at { coordinates }<r>[0.021, 0.218, 0.041, 0.779]</r>."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "EryVkyyd87i1mkHv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can we find the information for \"Patents Filed\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is identified in the legend at { coordinates }<r>[187, 0.142, 0.313, 0.170]</r>."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "vQ2Ry38m8JwsDu7N",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the marker for the year \"2010\" on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker is at the specified location { coordinates }<r>[0.534, 0.942, 0.584, 0.970]</r>."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "bW64LqRlroW0Ymkw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the blue bar at { coordinates }<r>[0.495, 0.541, 0.520, 0.923]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startups Founded in the year 2010."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "shlzEdlnRuHdi5Kr",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs { the bar of \"Startups Founded\" }<r>[0.495, 0.541, 0.520, 0.923]</r> higher than { the bar of \"Investments Received\" }<r>[0.546, 0.371, 0.572, 0.923]</r> in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "40ykA7bgTJJhIr7z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there more { \"Startups Founded\" }<r>[0.495, 0.541, 0.520, 0.923]</r> or { \"Patents Filed\" }<r>[0.520, 0.448, 0.546, 0.923]</r> during 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startups Founded."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "AxKrf7AzHxoMh4Mm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there more investment or startups in 2015? Given { \"Investments Received\" bar }<r>[0.717, 0.202, 0.743, 0.923]</r> and { \"Startups Founded\" bar }<r>[0.666, 0.414, 0.691, 0.923]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Investments Received."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "uEB49U3qsJySMhIM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, are there more { \"Patents Filed\" }<r>[0.862, 0.261, 0.888, 0.923]</r> or { \"Startups Founded\" }<r>[0.837, 0.371, 0.862, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patents Filed."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "3kw6hJtPcinu1YSC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of this area [0.436, 0.906, 0.512, 0.930]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for the category \"2010\", located at the specified coordinates <r>[0.534, 0.942, 0.584, 0.970]</r>."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "xmUcH1UdlU2KIoZ4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the area [0.123, 0.089, 0.413, 0.264]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart which includes four items:\n1. { \"Startups Founded\" label }<r>[0.187, 0.100, 0.363, 0.128]</r>\n2. { \"Patents Filed\" label }<r>[0.187, 0.142, 0.313, 0.170]</r>\n3. { \"Investments Received\" label }<r>[0.187, 0.183, 0.405, 0.211]</r>\n4. { \"Funds Raised\" label }<r>[0.187, 0.225, 0.318, 0.253]</r>"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "QzJKku4TSuiJ0UBQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.324, 0.795, 0.349, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That area presents the number of startups founded in 2005, represented as a green bar at { bar coordinates for Startups Founded in 2005 }<r>[0.324, 0.795, 0.349, 0.923]</r>."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "XO8xthT1lU2MuiXq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.923, 0.071, 0.942, 0.908]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis, marked with ticks at intervals of 200, and ranging from 0 to 1000. It also has the y-label \"Count and Funds Raised (in million USD)\". It does not have any bars or other plot-specific items."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "qAUlUCl0avjsT24M",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.471, 0.029, 0.527]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "DeaPXHw64UdF0rQN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the legend text}<r>[0.903, 0.071, 0.948, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rating."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "6l5U6Sh90kPcZTpA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat granularity is shown for {the y-axis}<r>[0.035, 0.045, 0.061, 0.065]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is divided into increments of 20, with a range from 0 to 100."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "dLnvbTSrE5k02vdk",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {the \u201cRating\u201d bar for Mediterranean cuisine}<r>[0.112, 0.187, 0.148, 0.945]</r> higher than the {\u201cRating\u201d bar for African cuisine}<r>[0.835, 0.454, 0.871, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "IYYIfINtDyGnMhga",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {\u201cDiversity\u201d bar for Latin America cuisine}<r>[0.727, 0.187, 0.763, 0.945]</r> have a higher value than the {\u201cDiversity\u201d bar for Anglo Saxon cuisine}<r>[0.546, 0.321, 0.582, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "icY2twpQKJ6ErVy9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {\u201cPopularity\u201d bar for Asian cuisine}<r>[0.329, 0.187, 0.365, 0.945]</r> taller than the {\u201cPopularity\u201d bar for Latin America cuisine}<r>[0.690, 0.321, 0.727, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "AybaV1RYiu7DNjsq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region <r>[0.858, 0.063, 0.978, 0.159]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the chart legend. It has 3 entries: {Rating}<r>[0.903, 0.071, 0.948, 0.091]</r> with a color representation of '#f7b2b2', {Popularity}<r>[0.903, 0.101, 0.972, 0.121]</r> with a color representation of '#b2d8f7' and {Diversity}<r>[0.903, 0.131, 0.964, 0.151]</r> with a color representation of '#b2f7c6'."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "dzmZ1YNcJ5bjK8GD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the chart elements within the region <r>[0.293, 0.187, 0.365, 0.945]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the area, we have two important elements:\n    1. {The Popularity bar for Asian cuisine}<r>[0.329, 0.187, 0.365, 0.945]</r> which is colored '#b2d8f7'\n    2. {The Rating bar for Asian cuisine}<r>[0.293, 0.232, 0.329, 0.945]</r> which is colored '#f7b2b2'."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "QUcbAwsmlIE0jAOd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area <r>[0.356, 0.021, 0.700, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's subtitle, which is \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "cPQ0hEBLmopXqtvr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the bar for \"AI Research\" in 2020}<r>[0.930, 0.165, 0.943, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "2Uyx7H9o6YVWHQoh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at {the position}<r>[0.119, 0.959, 0.154, 0.979]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2011."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "leVRnIDycpTirjBP",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the text at {coordinates}<r>[0.113, 0.191, 0.195, 0.211]</r> refer to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "QV3lXxBmpY119ROw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Data Analysis\" in 2014}<r>[0.401, 0.343, 0.414, 0.945]</r> taller than {the bar for \"Information Security\" in the same year}<r>[0.388, 0.655, 0.401, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "L5sqjqErzsX1oaA7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the purple bar for \"AI Research\"}<r>[0.930, 0.165, 0.943, 0.945]</r> represent a higher value than {the blue bar for \"Data Analysis\"}<r>[0.917, 0.076, 0.930, 0.945]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "GTmAPpdjeK7RIcRM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values indicated by {the red bars}<r>[0.104, 0.610, 0.117, 0.945]</r> consistently increasing over the years compared to {the green bars}<r>[0.130, 0.722, 0.143, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "GWhQ6hruAKkvJm8w",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of energy is represented by the coordinates [0.141, 0.626, 0.205, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar energy."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "bLsg4bV7ZhwPABCn",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the bar representing nuclear energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.874, 0.414, 0.939, 0.923]"
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "8ktf1XFulHPbVWl9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale} at [0.331, 0.942, 0.381, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wind."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "PH7eAzcPjAInbKzF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage for {Geothermal}<r>[0.508, 0.583, 0.572, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40%."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "ukSJttmb27NuMseB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is more, the {Solar energy resource allocation}<r>[0.141, 0.626, 0.205, 0.923]</r> or the {Wind energy resource allocation}<r>[0.324, 0.711, 0.388, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar energy resource allocation."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "iCahpgqLmhX8JNTd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the resource allocations for {Nuclear}<r>[0.874, 0.414, 0.939, 0.923]</r> and {Wind}<r>[0.324, 0.711, 0.388, 0.923]</r> the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "4pbUNCYFkbYV2VrG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has a greater resource allocation, the {Hydro} energy at <r>[0.691, 0.753, 0.755, 0.923]</r> or the {Geothermal} energy at <r>[0.508, 0.583, 0.572, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Geothermal energy."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "Wz1ZfyV2epyQY1U0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.101, 0.075, 0.979, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main chart area. It includes five sets of color bars, each representing a type of energy: Solar, Wind, Geothermal, Hydro, and Nuclear. Their respective coordinates are: {Solar}<r>[0.141, 0.626, 0.205, 0.923]</r>, {Wind}<r>[0.324, 0.711, 0.388, 0.923]</r>, {Geothermal}<r>[0.508, 0.583, 0.572, 0.923]</r>, {Hydro}<r>[0.691, 0.753, 0.755, 0.923]</r>, and {Nuclear}<r>[0.874, 0.414, 0.939, 0.923]</r>."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "QFVQHPd0mnI8aLK3",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.702, 0.089, 0.969, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It consists of a colored square indicating the color used for resource allocation, labeled 'Resource Allocation'<r>[0.766, 0.100, 0.961, 0.128]</r>."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "ZmPGTuEUQy0ZQ8Js",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.874, 0.414, 0.939, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one object within these coordinates:\n1. A bar that represents the resource allocation for Nuclear energy within 0.00 -> 60.00%."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "ZMCtjKZGDCsKTQcu",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label of the first bar representing the \"Fluctuating Decline Series\" located?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Fluctuating Decline Series\"}<r>[0.112, 0.383, 0.174, 0.945]</r>"
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "Qso8IBExDKLOGy5x",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the Y-axis label located within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Y-axis\"}<r>[0.015, 0.449, 0.029, 0.549]</r>"
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "ZH0N6VWRnmxmphTh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education Resource Distribution and Utilization located at {\"title\"}<r>[0.332, 0.021, 0.723, 0.047]</r>."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "wA5CxKPaP94rz16R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of {the first bar in Continuous Rise Series}<r>[0.174, 0.633, 0.236, 0.945]</r> higher than {the first bar in Fluctuating Decline Series}<r>[0.112, 0.383, 0.174, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "xugV50BN1DcgAWh3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, where does {the bar representing \"Tutorials\"}<r>[0.145, 0.959, 0.203, 0.979]</r> locate in relation to {the bar representing \"Videos\"}<r>[0.328, 0.959, 0.374, 0.979]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "gryrnLfLWIqEZcVT",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {label of \"Fluctuating Decline Series\"}<r>[0.122, 0.071, 0.303, 0.091]</r> above {label of \"Continuous Rise Series\"}<r>[0.122, 0.101, 0.281, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "4nOefsNoGqnNElgA",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within the area [0.143, 0.633, 0.882, 0.945]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified box, there are all five sets of data for each series. This includes five bars for both the \"Fluctuating Decline Series\" and the \"Continuous Rise Series\", corresponding to the categories of \"Tutorials\", \"Videos\", \"Exercises\", \"RealCases\", and \"SampleCodes\"."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "HgE9WCqPQJFqQdXS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.078, 0.063, 0.309, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This box contains the legend of the chart. The legend includes two entries. The first one is {\"Fluctuating Decline Series\"}<r>[0.122, 0.071, 0.303, 0.091]</r> with a green color indicator <r>[0.083, 0.073, 0.111, 0.087]</r>, and the second one is {\"Continuous Rise Series\"}<r>[0.122, 0.101, 0.281, 0.121]</r> with a blue color indicator <r>[0.083, 0.103, 0.111, 0.117]</r>."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "fFmIZL25jwRN3mRI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.145, 0.959, 0.929, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses labels of the categories on the x-axis. From left to right, they are: {\"Tutorials\"} <r>[0.145, 0.959, 0.203, 0.979]</r>, {\"Videos\"} <r>[0.328, 0.959, 0.374, 0.979]</r>, {\"Exercises\"} <r>[0.495, 0.959, 0.560, 0.979]</r>, {\"RealCases\"} <r>[0.669, 0.959, 0.740, 0.979]</r>, and {\"SampleCodes\"} <r>[0.834, 0.959, 0.929, 0.979]</r>."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "NJk11UzVbmCEEy5K",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the given chart, what is the object at the coordinates [0.162, 0.100, 0.280, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"Return Rate\""
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "uxIH3HNeMV5nm844",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the figure in the coordinates [0.106, 0.102, 0.146, 0.122] shows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green color(#c0e4b8) representing \"Return Rate\"."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "qKSKzmIOrwc79wm7",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPoint out the object associated with the coordinates [0.050, 0.064, 0.075, 0.092]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label \"40\"."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "6npOd16xeY2BFv7t",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Blue Chip return rate bar at [0.129, 0.711, 0.179, 0.923] shorter or taller than the Growth Stock return rate bar at [0.271, 0.499, 0.321, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "kETH7kDYt1V62meU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, does the Income Stock risk level bar at [0.604, 0.838, 0.654, 0.923] have a higher or lower position compared to the Value Stock risk level bar at [0.463, 0.668, 0.512, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "YAkapuLQ4a0l2pYh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the coordinates, is Speculative Stock's return rate at [0.697, 0.541, 0.746, 0.923] higher or lower than Cyclical Stock's risk level at [0.888, 0.499, 0.938, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "kGtmloNTwaVf1aYI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you illustrate the content within [0.098, 0.089, 0.287, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend area. The first element is a text label that says \"Return Rate\", located at [0.162, 0.100, 0.280, 0.128], next to a green color square [0.106, 0.102, 0.146, 0.122]. The second element is another label that says \"Risk Level\" located at [0.162, 0.142, 0.261, 0.170], next to a blue color square [0.106, 0.144, 0.146, 0.164]."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "D2EKYaErPxUco3xf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the area [0.254, 0.942, 0.387, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A label showing \"Growth Stock\" on the x-axis."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "u3FcIpIx9lq2FKjH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the rectangle defined by the coordinates [0.413, 0.605, 0.512, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two stacked bars. The first bar showing the return rate of the Value Stock is green, with coordinates [0.413, 0.605, 0.463, 0.923]. The second bar showing the risk level Value Stock is blue, with coordinates [0.463, 0.668, 0.512, 0.923]."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "PEDGHxCYqQiipi7B",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the text inside the box with coordinates [0.122, 0.161, 0.245, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label of the legend, \"Private Donations\"."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "l7Pr6OwZkGOXbFFN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value being represented by {the bar of Book Sale for Private Donations}<r>[0.771, 0.588, 0.800, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value represented is 40, for the book sale activity funded by Private Donations."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "5XIuFmw3kS2dKiK1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object located at the coordinates [0.083, 0.073, 0.111, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a color label in the legend, representing the funding type \"Local Giving\"."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "xFPZgaezUurAsSje",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat event is noted by the x-axis coordinates of [0.542, 0.958, 0.658, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The event represented is \"Coastal_Cleanup\"."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "tgppAXPQNh4ZJuQN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between the height of the {bar for Book Sale from Foreign Aid}<r>[0.714, 0.410, 0.743, 0.944]</r> and the height of {the bar for Church Opening from Foreign Aid}<r>[0.284, 0.454, 0.313, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Book Sale from Foreign Aid is shorter."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "O7MCorPc160y1MS1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Local Giving at Coastal_Cleanup}<r>[0.542, 0.855, 0.571, 0.944]</r> is higher than {the bar for Local Giving at Canister_Collection}<r>[0.112, 0.499, 0.141, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "V3KTYYF0EpcyE9n7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the bars representing Public Funding, which bar is the highest: {Public Funding for Canister_Collection}<r>[0.170, 0.633, 0.198, 0.944]</r> or {Public Funding for Coastal_Cleanup}<r>[0.599, 0.766, 0.628, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Canister_Collection."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "cjM6lbgy8BhJPOJD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar shows higher value, the {bar of Local Giving for Social Service}<r>[0.399, 0.187, 0.428, 0.944]</r> or the {bar of Foreign Aid for Social Service}<r>[0.428, 0.142, 0.456, 0.944]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Foreign Aid."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "v30yAyn9vlzfjzss",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the area denoted by [0.406, 0.958, 0.506, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for the category \"Social_Service\" on the horizontal axis."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "VG2itCcbjRq7xvVV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's within the coordinates [0.083, 0.103, 0.111, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates enclose a color label in the legend, which is a blue square that signifies the Foreign Aid funding type."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "2Lg8lW0hUBfuVnPh",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area between [0.071, 0.053, 0.985, 0.944] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area, where bars showing the amounts of funding in different funding methods for different charity activities are plotted."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "nP98xOHOA4IXPrLK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide description of region [0.078, 0.063, 0.251, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart which explains the color coding for different types of funding: 'Local Giving' in light green color coded at [0.083, 0.073, 0.111, 0.087], 'Foreign Aid' in light blue color at [0.083, 0.103, 0.111, 0.117], 'Public Funding' in light red color at [0.083, 0.133, 0.111, 0.147] and 'Private Donations' in light purple color at [0.083, 0.163, 0.111, 0.177]."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "8JsjaIgRyaj9ha00",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.035, 0.148, 0.052, 0.168] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label, indicating a trend value of 70."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "PAIA7xyMFncHJxZS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color square of the first item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Color}[0.074, 0.073, 0.102, 0.087]."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "k7Y8RS1V6NPvGKyz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.296, 0.774, 0.310, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Skill Acquisition\"."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "7qRhi5CGHOHVLUTT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bar or category is shown in [0.398, 0.346, 0.450, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2019 Networking bar} [0.398, 0.346, 0.450, 0.761]."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "eeFfeB3rUGijGyKW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 bar for \"Skill Acquisition\"}[0.303, 0.484, 0.354, 0.761] higher or lower than {the 2019 bar for \"Skill Acquisition\"}[0.251, 0.458, 0.303, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "MYx5E3raLkHyk52X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2019 \"Entrepreneurship\" bar}[0.546, 0.303, 0.597, 0.761] to the left or right of {the 2020 \"Entrepreneurship\" bar}[0.597, 0.260, 0.649, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "GsKWMo1WeDQRvlPB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"2019 Professional Training\"}[0.104, 0.242, 0.155, 0.761] and {\"2020 Professional Training\"}[0.155, 0.208, 0.207, 0.761], which year does the higher bar represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "xzaKZSkjCa3syOEY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"Mentoring\" category in 2019}[0.693, 0.182, 0.744, 0.761] exceeded by {the \"Mentoring\" category in 2020} [0.744, 0.156, 0.796, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "TZfIEfHyAVvOYTkb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.069, 0.063, 0.154, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It consists of two entries; The first one is a color label located at [0.074, 0.073, 0.102, 0.087] which is blue, and is accompanied by the text \"2019\" found at [0.113, 0.071, 0.149, 0.091]. The second one is another color label located at [0.074, 0.103, 0.102, 0.117] which is red, and its corresponding text \"2020\" is located at [0.113, 0.101, 0.148, 0.121]."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "McJhDSZVSLI11LZM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.104, 0.242, 0.155, 0.761]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily contains the blue bar for \"Professional Training\" for the year 2019. The bar reaches a height indicative of a value of 60, as per the y-axis."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "8vDROK1CwCQV1zll",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.155, 0.208, 0.207, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object in these coordinates: a bar. It is the red bar for the \"Professional Training\" category in 2020. The height of the bar represents a value of 64 according to the y-axis."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "iIBlGw5vqRGFHZZR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.343, 0.021, 0.704, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is the subtitle of the chart: \"Career and Workplace Development Trends\". It situates centrally indicating its importance as the subtitle of the chart."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "qjmyXQgjF0N2A6ot",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the chart with coordinates [0.021, 0.457, 0.041, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is \"Count\"."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "G4ZvYfgKXQzeeKq6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of object with coordinates [0.725, 0.609, 0.729, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object label is \"Robotics\"."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "8fqQ8PKtGOSoYPX6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at [0.162, 0.225, 0.355, 0.253] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label of a legend element, and it's called \"Abandoned Patents\"."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "IfqMr2hINlO4x5S7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar indicating AR's Innovation}[0.273, 0.725, 0.295, 0.923] taller than {the bar indicating Blockchain's Innovation}[0.418, 0.698, 0.440, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "CqgEDyto9YwAEHVY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the VR's Patents Infrienged}[0.151, 0.609, 0.172, 0.923] lower than {the IoT's Patents Infrienged}[0.873, 0.115, 0.895, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "sNYkJq1vTGFqmvIz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Robotics Failed Patents}[0.750, 0.788, 0.772, 0.923] and {AI Failed Patents}[0.606, 0.815, 0.627, 0.923], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Robotics Failed Patents."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "kwEJ3IUMDtdYolMF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.098, 0.089, 0.363, 0.264]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart. It contains color-coded labels for \"Innovation\" {color}[0.106, 0.102, 0.146, 0.122], \"Patents Infrienged\"{color}[0.106, 0.144, 0.146, 0.164], \"Failed Patents\"{color}[0.106, 0.186, 0.146, 0.205], and \"Abandoned Patents\"{color}[0.106, 0.228, 0.146, 0.247]."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "PsE5IteMCPzFqWya",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items contained in the horizontal labels area [0.169, 0.942, 0.921, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the x-axis category labels, and the labels include \"VR\"[0.169, 0.942, 0.197, 0.970], \"AR\"[0.314, 0.942, 0.341, 0.970], \"Blockchain\"[0.418, 0.942, 0.526, 0.970], \"AI\"[0.607, 0.942, 0.626, 0.970], \"Robotics\"[0.719, 0.942, 0.803, 0.970], and \"IoT\"[0.891, 0.942, 0.921, 0.970]."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "lY7SmVp56y40tuHQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.129, 0.564, 0.216, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars:\n1. One indicating \"Innovation\" in VR technology, colored green, coordinate range [0.129, 0.788, 0.151, 0.923].\n2. Other four bars are of different techs, \n      - Patents Infrienged colored blue, coordinate range [0.151, 0.609, 0.172, 0.923],\n      - Failed Patents colored red, coordinate range [0.172, 0.878, 0.194, 0.923], \n      - Abandoned Patents colored purple, coordinate range [0.194, 0.833, 0.216, 0.923]"
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "gYTetlhxhTElbdyv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at {the coordinate}[0.019, 0.419, 0.036, 0.579] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Efficiency (%)."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "agu26fTLSw6UZDtC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the second bar for \"Unstructured Data\" located?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the second bar for \"Unstructured Data\"}[0.268, 0.618, 0.296, 0.935]."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "xe98QVdNHj2DjvnV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label} at [0.548, 0.952, 0.633, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AWS-Glue."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "iPiuuEEl9efiHsEF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.104, 0.120, 0.139, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The handle for the \"Semi-Structured Data\"}[0.104, 0.120, 0.139, 0.136] in the legend."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "xPGVogp3Ira9dHqd",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Cloudera under Unstructured Data}[0.128, 0.459, 0.156, 0.935] taller than {the bar for Hortonworks under the same category}[0.268, 0.618, 0.296, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "qfdskHV1nzf5yyUb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for AWS-Glue under Structured Data}[0.605, 0.364, 0.633, 0.935] to the left or right of {the bar for the same company under Semi-Structured Data}[0.577, 0.181, 0.605, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "09DzOqbxvdmJvjzP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the three bars representing {Google Cloud Dataprep}[[0.829, 0.261, 0.857, 0.935], [0.857, 0.142, 0.885, 0.935], [0.885, 0.324, 0.913, 0.935]], which one is the tallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The second bar for Google Cloud Dataprep}[0.857, 0.142, 0.885, 0.935]."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "hsmumN46ttI0XEqj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the first bar for MapR}[0.408, 0.499, 0.436, 0.935] or {the first bar for AWS-Glue}[0.548, 0.300, 0.577, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AWS-Glue."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "SAB6R3mg6S8Hvvhz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.097, 0.074, 0.348, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the chart legend that contains three pairs of color-text labels. The first pair's color label is located at [0.104, 0.085, 0.139, 0.101], which is blue, and the text label is located at [0.153, 0.083, 0.313, 0.106], which represents Unstructured Data. The second pair's color label is located at [0.104, 0.120, 0.139, 0.136], which is orange, and the text label is located at [0.153, 0.118, 0.341, 0.141], representing Semi-Structured Data. The third pair's color label is located at [0.104, 0.155, 0.139, 0.171], which is green, and the text represents Structured Data, located at [0.153, 0.153, 0.292, 0.176]."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "5PNOHHkzo5Rbh1Wc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.128, 0.459, 0.156, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for Cloudera under the category of Unstructured Data. The bar's height represents an efficiency of 60 percent."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "FjRf7qLtm7uneJZj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.268, 0.618, 0.296, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object, the orange bar representing \"Hortonworks\" under \"Unstructured Data. Its height represents an efficiency of 40 percent."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "jDO7kiWdOFqAser4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.363, 0.025, 0.678, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart: \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "P1fObv8XGLoQT6He",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the Y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Change in Value\". It is found at coordinates {Y-axis label}<r>[0.015, 0.403, 0.029, 0.564]</r>."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "LbAMqUrF55nnY0yM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the cryptocurrency labeled \"Ripple\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of \"Ripple\" is {Ripple's handle color}<r>#ff006e</r>."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "lJwGGfVrVphzAym8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range for the Y-axis values of the bar element for \"Ethereum\" at position '1'?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's from 0.00 to 0.60, located at {Ethereum's bar at '1'}<r>[0.441, 0.216, 0.467, 0.674]</r>."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "glfKa9mm8E7Fdhm9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar for \"Bitcoin\" at position '2' has higher value than the bar for \"Tether\" at the same position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, because the {Bitcoin's bar at '2'}<r>[0.667, 0.636, 0.692, 0.674]"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "Ptg7ZcVMg59a3iSX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for \"Stellar\" increasing or decreasing over the positions '0' to '3'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Stellar}<r>[0.241, 0.674, 0.266, 0.674], [0.492, 0.659, 0.517, 0.674], [0.742, 0.651, 0.767, 0.674], [0.993, 0.643, 1.018, 0.674]</r> values are increasing over the positions '0' to '3'."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "JNzU9aUdpbwrEliM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of \"Ethereum\" at position '0' equal to that of \"Tether\" at the same position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, because the {Ethereum's bar at '0'}<r>[0.191, 0.674, 0.216, 0.674]</r> has the same value as the {Tether's bar at '0'}<r>[0.266, 0.674, 0.291, 0.674]</r>."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "ahFtmx3fJkyvEjYV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the legend."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located within {Legend}<r>[0.085, 0.031, 0.202, 0.187]</r>. It contains five entries for different cryptocurrencies - Bitcoin, {Bitcoin's handle}<r>[0.090, 0.041, 0.118, 0.055]</r> Ethereum, {Ethereum's handle}<r>[0.090, 0.071, 0.118, 0.085]</r> Ripple, {Ripple's handle}<r>[0.090, 0.101, 0.118, 0.115]</r> Stellar, {Stellar's handle}<r>[0.090, 0.131, 0.118, 0.145]</r> and Tether, {Tether's handle}<r>[0.090, 0.161, 0.118, 0.175]</r>. The legend provides a color reference for the individual bars representing each cryptocurrency in the graph."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "IpX1fQeZwK1TLd5F",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.166, 0.643, 1.043, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the fourth bar group of the chart showing the data for the position '3' of all five cryptocurrencies: Bitcoin, {Bitcoin's bar}<r>[0.918, 0.651, 0.943, 0.674]</r> Ethereum, {Ethereum's bar}<r>[0.943, 0.063, 0.968, 0.674]</r> Ripple, {Ripple's bar}<r>[0.968, 0.674, 0.993, 0.903]</r> Stellar, {Stellar's bar}<r>[0.993, 0.643, 1.018, 0.674]</r> and Tether, {Tether's bar}<r>[1.018, 0.674, 1.043, 0.712]</r>."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "QsHwiDjBEmL6COHb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.166, 0.674, 0.293, 0.674] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the set of bars for the first position '0' of the bar chart. It includes only the base values for \"Bitcoin\", {Bitcoin's bar at '0'}<r>[0.166, 0.674, 0.191, 0.674]</r> \"Ethereum\", {Ethereum's bar at '0'}<r>[0.191, 0.674, 0.216, 0.674]</r> \"Ripple\", {Ripple's bar at '0'}<r>[0.216, 0.674, 0.241, 0.674]</r> \"Stellar\", {Stellar's bar at '0'}<r>[0.241, 0.674, 0.266, 0.674]</r> and \"Tether\", {Tether's bar at '0'}<r>[0.266, 0.674, 0.291, 0.674]</r> which are all zeros, therefore there are no vertical bars visible on the plot in this area."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "LIu23XY4FibtMwfC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinate [0.019, 0.340, 0.036, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, which is \"User Engagement Score\"."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "tTXDAv6mHqz0T5xV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the third score on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The location is [0.625, 0.952, 0.691, 0.975]."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "34qW5F72SwcOm9tO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat social media platform does the color #3b5998 in the legend <r>[0.086, 0.037, 0.237, 0.218]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents {Facebook}<r>[0.142, 0.046, 0.223, 0.069]</r>."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "5tO9YMWsaEk0iHMM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the X-axis labels mentioned in the coordinates <r>[0.044, 0.952, 0.981, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the score labels from score_1 to score_4 <r>[[0.044, 0.952, 0.111, 0.975], [0.335, 0.952, 0.401, 0.975], [0.625, 0.952, 0.691, 0.975], [0.915, 0.952, 0.981, 0.975]]</r>."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "BzrTJJBaU1CXROEu",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, does Facebook <r>[0.730, 0.152, 0.759, 0.935]</r> have a higher user engagement score than Twitter <r>[0.759, 0.068, 0.788, 0.935]</r> for score_4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "rir8zsEAG5TiH4b8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has a higher user engagement score for score_1, Instagram <r>[-0.082, 0.852, -0.053, 0.935]</r> or Snapchat <r>[-0.024, 0.862, 0.005, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Snapchat."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "AV2H9BD2Kx6SBmEg",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes Instagram|r>[0.208, 0.727, 0.237, 0.935]</r> have a higher score_2 than LinkedIn <r>[0.237, 0.779, 0.266, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "B6d4niziLOcNu6i5",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Facebook <r>[0.150, 0.779, 0.179, 0.935]</r> and LinkedIn <r>[0.237, 0.779, 0.266, 0.935]</r>, which has a higher score for score_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "MKQTZbK5odS8ROKR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the rectangle [0.043, 0.091, 0.065, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label on the Y-axis that represents the number \"90\"."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "wmIN7vcBd7WUw2y8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.150, 0.779, 0.179, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar representing Facebook's score_2, with a user engagement score of 15."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "gjU7mtpmyuwWVXGF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area given by the coordinates <r>[-0.140, 0.883, 0.759, 0.935]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the bars for Facebook representing scores from score_1 to score_4 with user engagement scores 5, 15, 20, and 75, respectively."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "VuO0FX7W6fw4qy5C",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the legend area <r>[0.086, 0.037, 0.237, 0.218]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains five social media platforms: Facebook <r>[0.142, 0.046, 0.223, 0.069]</r>, Twitter <r>[0.142, 0.081, 0.200, 0.104]</r>, Instagram <r>[0.142, 0.116, 0.230, 0.139]</r>, LinkedIn <r>[0.142, 0.151, 0.214, 0.174]</r>, and Snapchat <r>[0.142, 0.185, 0.223, 0.209]</r>, each with their respective color labels."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "Zm5xy6ZZfdnSWi3F",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.174, 0.055, 0.218, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label of {the Coal Energy resource}<r>[0.174, 0.055, 0.218, 0.083]</r> in the legend."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "VeDgiKk7gWTX03WW",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the Nuclear resource}<r>[0.174, 0.181, 0.251, 0.209]</r> correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color blue shown at the set of coordinates <r>[0.119, 0.183, 0.158, 0.203]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "koFs1Qn0qowfjc9k",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year on the x-axis that corresponds to the coordinate [0.644, 0.942, 0.694, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year is 2040."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "4VrMZTwUzAhNRotA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat energy resource's bar is located at <r>[0.797, 0.073, 0.826, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the bar for {Renewable Energy Resources}<r>[0.797, 0.073, 0.826, 0.923]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "lyyHit9obxjwiPDs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar for {Renewable energy resources}<r>[0.174, 0.139, 0.283, 0.167]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is beige <r>[0.119, 0.141, 0.158, 0.161]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "UfwVbU7NVDNytBl1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the energy consumption of {Natural Gas in 2030}<r>[0.200, 0.328, 0.229, 0.923]</r> higher than that of {Coal in 2050}<r>[0.740, 0.710, 0.769, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "kKxl5lL7KnZCef0G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the energy consumption of {Oil in 2020}<r>[0.001, 0.540, 0.030, 0.923]</r> less than that of {Nuclear in 2050}<r>[0.826, 0.519, 0.854, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "rhyD8VyYSotYcovQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom highest to lowest, how would you rank the energy sources for 2030: {Oil}<r>[0.286, 0.561, 0.314, 0.923]</r>, {Natural Gas}<r>[0.200, 0.328, 0.229, 0.923]</r>, and {Coal}<r>[0.172, 0.540, 0.200, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Natural Gas}<r>[0.200, 0.328, 0.229, 0.923]</r>, {Coal}<r>[0.172, 0.540, 0.200, 0.923]</r>, {Oil}<r>[0.286, 0.561, 0.314, 0.923]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "lGq0Fb70ZhgJOOVm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.111, 0.044, 0.300, 0.262]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend with five entries. The first label is {Coal}<r>[0.174, 0.055, 0.218, 0.083]</r>, represented with a dark blue color<r>[0.119, 0.058, 0.158, 0.077]</r>. The second label is {Natural Gas}<r>[0.174, 0.097, 0.292, 0.125]</r>, represented with a lighter blue color<r>[0.119, 0.099, 0.158, 0.119]</r>. The third label is {Renewable}<r>[0.174, 0.139, 0.283, 0.167]</r>, represented with a beige color<r>[0.119, 0.141, 0.158, 0.161]</r>. The fourth label is {Nuclear}<r>[0.174, 0.181, 0.251, 0.209]</r>, represented with another shade of blue<r>[0.119, 0.183, 0.158, 0.203]</r>. The fifth and final label is {Oil}<r>[0.174, 0.223, 0.201, 0.251]</r>, represented with a deep navy color<r>[0.119, 0.225, 0.158, 0.245]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "wqjgE4I9pvQQCbjX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.456, 0.604, 0.485, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes one bar representing the consumption of the {Coal Energy resource}<r>[0.456, 0.604, 0.485, 0.923]</r> in 2040."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "3wWNc1Lk5ynTbMjQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained in the area [0.075, 0.912, 0.087, 0.940]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label for the value <r>[0.075, 0.912, 0.087, 0.940]</r>."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "yOGUquRlTtzwmJ7w",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the area [0.740, 0.710, 0.769, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the bar for the energy resource {Coal}<r>[0.740, 0.710, 0.769, 0.923]</r> in the year 2050. The bar represent the consumption level."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "eZmaJyG9N1hZ3ZlL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object in the chart with the coordinates [0.015, 0.457, 0.029, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the label on the y-axis, indicating \"Values\"."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "6GjtvFMashsuOKsr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the first label text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.455, 0.035, 0.552, 0.053]."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "YLs8dHwyrihLCddZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar with the coordinates [0.003, 0.891, 0.035, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents the \"Inflation rates\" for the year 2020."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "SOHAkFE5oBgLQN0A",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indicators or categories are shown in the legend with the coordinates [0.411, 0.028, 0.636, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The indicators or categories shown are \"Inflation rates\", \"Poverty rates\", \"Population increase\", \"Carbon emission\", and \"Renewable energy usage\"."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "bVjGSZoa5BSpeoF3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Carbon emission\" for the year 2040, with coordinates [0.743, 0.228, 0.776, 0.952], taller or shorter than the bar representing \"Population increase\" for the same year, with coordinates [0.711, 0.941, 0.743, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "bvOdICIUe9mBFC2P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the \"Inflation rates\" bars have a general upward or downward trend from 2020 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downward."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "enXm01Qv8f0Ka8He",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Renewable energy usage\" bar for 2030 at [0.561, 0.236, 0.593, 0.952] higher or lower than the \"Population increase\" bar for the same year at [0.497, 0.938, 0.529, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "dt5mZC6q9LJWFd76",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong \"Poverty rates\" and \"Carbon emission\" in 2030, which one is bigger?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Carbon emission\"."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "U35ygzhL8rlOHi93",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.411, 0.028, 0.636, 0.164]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with different categories and their corresponding bar colors such as: {Inflation rates} and its color at [0.417, 0.037, 0.444, 0.049], {Poverty rates} and its color at [0.417, 0.063, 0.444, 0.075], {Population increase} and its color at [0.417, 0.089, 0.444, 0.101], {Carbon emission} and its color at [0.417, 0.115, 0.444, 0.127], and {Renewable energy usage} and its color at [0.417, 0.141, 0.444, 0.154]."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "RBzG48i02NZAG4oP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.132, 0.489, 0.164, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one {bar}[0.132, 0.489, 0.164, 0.952] for \"Renewable energy usage\" in 2020."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "wnSSf7TPHB5SZIpU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content in the area [0.003, 0.891, 0.035, 0.952] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar that represents the inflation rates in 2020."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "F2bBnEky7T6mihWp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.043, 0.813, 0.052, 0.830]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a {label}[0.043, 0.813, 0.052, 0.830] for y-axis that indicates the value of 5."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "3b2ubQOMzpJ4anut",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.015, 0.450, 0.029, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Scores."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "YgbeZTFIAhhYbGrU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.083, 0.041, 0.111, 0.055]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "Abs1pyNYvrypMrch",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item does {the legend} indicate shown in [0.122, 0.039, 0.252, 0.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Diagnostics."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "FML4RHG9b9Nn8LG4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {text} shown at the coordinates in [0.315, 0.959, 0.350, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2011."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "5pzB6U3ddOzPvk5f",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for 2009, Digital Diagnostics}[0.183, 0.698, 0.211, 0.945] taller than the {bar for 2008, Telemedicine}[0.140, 0.681, 0.169, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "C2ijGWNAMPIoGtRS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for 2013, Telemedicine}[0.493, 0.171, 0.521, 0.945] to the left or right of {the bar for 2013, Digital Diagnostics}[0.464, 0.567, 0.493, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "qUWo0XGEeCyfZNwo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the bars representing Telemedicine and Digital Diagnostics in 2015, which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Telemedicine."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "MRP6UyYLyKGX9iAl",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Digital Diagnostics bars} generally taller than the {Telemedicine bars}?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "1om6saQ3WNbEOnMl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.078, 0.031, 0.258, 0.097]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart with two items. {The color label}[0.083, 0.041, 0.111, 0.055] is blue, and the text label is {Digital Diagnostics}[0.122, 0.039, 0.252, 0.059]; The other color label [0.083, 0.071, 0.111, 0.085] is orange, and the corresponding text label is {Telemedicine}[0.122, 0.069, 0.213, 0.089]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "OeoK9hzpYxYJXCfm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.112, 0.804, 0.140, 0.945]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a blue bar, representing the score of Digital Diagnostics in 2008, with a range of 0.00 -> 16.00."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "IuEQxgUFzjJKym4G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within [0.560, 0.959, 0.595, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2014 on the x-axis is located in this area."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "u0OgClOsAsIRXShR",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.5, 0.593, 0.6, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two bars are present. A blue bar, representing the Digital Diagnostics score for 2012 which is 40, with coordinates [0.394, 0.593, 0.422, 0.945]. And another narrower orange bar, representing the Telemedicine score for 2013 which is 88 and its coordinates are [0.493, 0.171, 0.521, 0.945]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "Yo6j8hvwvQrSc7SV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of chart used?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "O2jUNyEp7PH3H38t",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age groups in years."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "YOL1WcmtMUVcRpUU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label of Y-axis}<r>[0.019, 0.442, 0.036, 0.519]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Scores."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "jaWH0379sbGLRXmE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the group item labelled {Running} in the legends<r>[0.440, 0.037, 0.613, 0.218]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Running bars."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "VH3o5rpagr711P7o",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Yoga bar}<r>[0.447, 0.083, 0.482, 0.099]</r> taller than the {Swimming bar}<r>[0.447, 0.118, 0.482, 0.134]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "Ap0prEGzCtKQEOcS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score of {Boxing}<r>[0.831, 0.155, 0.871, 0.935]</r> for age 60 higher than the score of {Weightlifting}<r>[0.791, 0.242, 0.831, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "QvFgIV3AdTWH7iA0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the height of the {Running bar}<r>[0.672, 0.328, 0.712, 0.935]</r> at age 60 more or less than half of the {Boxing bar}<r>[0.831, 0.155, 0.871, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "Qmyby4Q5Qby1MNab",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Boxing bar}<r>[0.447, 0.188, 0.482, 0.204]</r> in the legend to the right or left of the {Swimming bar}<r>[0.447, 0.118, 0.482, 0.134]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "er21IjShn84bErnU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region <r>[0.036, 0.415, 0.076, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Boxing\" score for age 0 is visible which is 0.60 and the bar is from 0.415 to 0.935 on the y-axis."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "Tt3LZeoEa6LJthL5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.830, 0.150, 0.870, 0.935]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for \"Boxing\" for age 60, with a score of 0.90 increasing from 0.155 to 0.935 on the y-axis."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "Pn3iohwDGAsPvSf0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the legend <r>[0.440, 0.037, 0.613, 0.218]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains labels for each group of bars. Representing scores in various categories like Running, Yoga, Swimming, Weightlifting and Boxing for different ages."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "WOqniH7QIPbBhVZB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf possible, describe the visual elements in the region <r>[0.142, 0.762, 0.182, 0.935]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region depicts the \"Running\" score at age 20, with its value at 0.20 and its bar reaching from 0.762 to 0.935 on the y-axis."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "8pwgUa2nPgsbCXo3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.021, 0.430, 0.041, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, titled \"Scores\"."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "ATtLcQ3WO06GFivT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {BioNTech}<r>[0.168, 0.097, 0.259, 0.125]</r> according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#6d8abb'."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "fWhyXz5k9Rgvdrlk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at the fourth x-axis coordinate [0.840, 0.942, 0.865, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is '60'."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "aN0LWdqYsdkK6Wjl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at coordinates [0.284, 0.536, 0.309, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Pfizer score at '20' represented by the '#495c80' colored bar."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "exfyKZz5qRH0sqCh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Pfizer's bar} at '20' <r>[0.284, 0.536, 0.309, 0.923]</r> taller than the {Moderna's bar} at '20' <r>[0.335, 0.459, 0.360, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "K9mgRc2OF7EZYCb6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {BioNTech's bar} at '40' <r>[0.562, 0.536, 0.587, 0.923]</r> to the left or right of {Moderna's bar} at '40' <r>[0.587, 0.613, 0.612, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "YhPmUAutvkS1Qy6D",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {BioNTech}<[0.168, 0.097, 0.259, 0.125],[0.113, 0.099, 0.152, 0.119]> and {Johnson & Johnson}<[0.168, 0.223, 0.351, 0.251],[0.113, 0.225, 0.152, 0.245]>, which company scored a higher value at '60' <r>[0.814, 0.073, 0.840, 0.923],[0.890, 0.343, 0.915, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "BioNTech."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "6Ds7DEc5xSPn9eq4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.105, 0.044, 0.359, 0.262]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend of the chart, showing the color and name representation for each Biotech firm. Pfizer is represented with color '#495c80', BioNTech with color '#6d8abb', Moderna with color '#eadcd1', Novavax with color '#5899b8', and Johnson & Johnson with color '#2b5a75'."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "Jqwrvpa5U3wrogdH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.032, 0.923, 0.133, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the bars for each company representing their scores at '0'."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "hwymuHRySeBehg1k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.789, 0.227, 0.915, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four bars representing scores of four companies (Pfizer, BioNTech, Moderna, and Johnson & Johnson) at '60'."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "AZkPWaYmV0dbi8VO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what can be seen in the area [0.049, -0.016, 0.081, 0.012]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the number '1.2', which is one of the y-tick labels."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "lNjIseqEyPRRXtwF",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in this bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Scores\" and its coordinates are {the y-axis label}<r>[0.021, 0.430, 0.041, 0.522]</r>."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "4HYTZ25E6P39PIOp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is represented by the color blue in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color blue in the legend represents the \"Community Development\", and its coordinates are {Community Development color label in the legend}<r>[0.651, 0.058, 0.690, 0.077]</r>."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "Ls6fjmyDeGXwJXPQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at the coordinates [0.706, 0.055, 0.961, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at the given coordinates refers to the legend label \"Community Development\"."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "fI092rbM6JsIEznY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted on the x-axis at the coordinates [0.261, 0.942, 0.274, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At these coordinates is an {x-axis label}<r>[0.261, 0.942, 0.274, 0.970]</r> displaying the value \"0\"."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "vB9QiMaEB5GM1fKR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for \"Community Development\" at the x-axis value of \"60\" higher or lower than the bar for \"Urban Planning\" at that same value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Community Development\" is lower."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "sMiVrlvNIg8mq4Rw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for community development at 20}[0.375, 0.333, 0.454, 0.923] taller than {the bar for urban planning at 20}[0.454, 0.246, 0.533, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "mWgk7u7oFNFYL72A",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, is \"Community Development\" generally performing better or worse than \"Urban Planning\" over the time period observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Community Development\" is generally performing worse."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "nBm8WQC7HxH6XgK0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest score at the 40 mark: {Community Development}[0.601, 0.610, 0.680, 0.923] or {Urban Planning}[0.680, 0.489, 0.759, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Planning."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "qIaN2PcsPAajEyJl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the box [0.643, 0.044, 0.969, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It illustrates two categories: \"Community Development\" with a blue color represented by {the box}<r>[0.651, 0.058, 0.690, 0.077]</r> and \"Urban Planning\" with a light blue color represented by {the other box}<r>[0.651, 0.099, 0.690, 0.119]</r>."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "0TQYmct8JikRGRwk",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do I notice in the area [0.375, 0.333, 0.454, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a bar representing \"Community Development\" at the 20 mark on the x-axis. Its height corresponds to a score of 3.4."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "bpH80C6qTTfkgWiB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the coordinates [0.601, 0.610, 0.680, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is a bar representing the score of \"Community Development\" at the 40 mark on the x-axis. Its height corresponds to a score of 1.8."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "csoplponOkgDIvcQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region [0.149, 0.125, 0.984, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses all the bars representing scores of \"Community Development\" and \"Urban Planning\" at different x-axis marks, which are 0, 20, 40, and 60. The heights of the bars reflect their respective scores."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "gX12DHvyAqEaJnaL",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.021, 0.430, 0.041, 0.522] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, called Scores."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "8Y49xb91Xzndfqc8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the legend for \"WhatsApp\" located?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the legend}<r>[0.168, 0.223, 0.270, 0.251]</r>."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "zb4JBwuJXLje1LGI",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at coordinates [0.167, 0.734, 0.196, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "IiOj24rs2vBMaQZN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platforms are indicated in the legend inside [0.105, 0.044, 0.277, 0.262]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Facebook}<r>[0.168, 0.055, 0.262, 0.083]</r>, {Instagram}<r>[0.168, 0.097, 0.269, 0.125]</r>, {Twitter}<r>[0.168, 0.139, 0.235, 0.167]</r>, {Snapchat}<r>[0.168, 0.181, 0.262, 0.209]</r>, and {WhatsApp}<r>[0.168, 0.223, 0.270, 0.251]</r>."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "ElZscH3ppktWWaAW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Facebook at 60}[0.748, 0.261, 0.777, 0.923] taller than {the bar for WhatsApp at 60}[0.864, 0.073, 0.893, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "dLHgpLFIxmfh27I1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bars for Instagram}[[0.196, 0.545, 0.225, 0.923], [0.487, 0.639, 0.516, 0.923], [0.777, 0.261, 0.806, 0.923]] generally taller than {the bars for Snapchat}[[0.254, 0.828, 0.283, 0.923], [0.545, 0.734, 0.574, 0.923], [0.835, 0.545, 0.864, 0.923]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "JGB67t8YstqOKPGb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 40 score bar for Twitter}[0.516, 0.167, 0.545, 0.923] higher than {the 40 score bar for WhatsApp}[0.574, 0.261, 0.603, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "Zsox49PsBm6dgT7v",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 60 score bar for Facebook}[0.748, 0.261, 0.777, 0.923] and {the 60 score bar for Instagram}[0.777, 0.261, 0.806, 0.923], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "pBaaGecviFmjlupe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.105, 0.044, 0.277, 0.262]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content is a legend that contains symbols and names of five different social media platforms, {Facebook} with coordinates <r>[0.168, 0.055, 0.262, 0.083]</r>, {Instagram} with coordinates <r>[0.168, 0.097, 0.269, 0.125]</r>, {Twitter} with coordinates <r>[0.168, 0.139, 0.235, 0.167]</r>, {Snapchat} with coordinates <r>[0.168, 0.181, 0.262, 0.209]</r>, and {WhatsApp} with coordinates <r>[0.168, 0.223, 0.270, 0.251]</r>."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "0pz9biOZ0w7LJhO4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.021, 0.430, 0.041, 0.522]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label, named {Scores}<r>[0.021, 0.430, 0.041, 0.522]</r>."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "iBe1GrykcOtzt7iI",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.167, 0.734, 0.196, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a bar that represents the 20 score for {Facebook}<r>[0.167, 0.734, 0.196, 0.923]</r>."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "TcDeUl44cMZ9rVXF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [-0.123, 0.923, -0.094, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar representing a score of 0 for {Facebook} at location <r>[-0.123, 0.923, -0.094, 0.923]</r>, which indicates that Facebook had a score of 0 in this measurement."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "Po4LA5HeK8eyXYyp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeling on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is called 'Scores', found at {coordinates}<r>[0.052, 0.430, 0.072, 0.522]</r>."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "ye5yqJZY8VcWKTaq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the legend located relative to the main chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is situated towards the lower left part of the main chart with its location at {coordinates}<r>[0.136, 0.044, 0.343, 0.136]</r>."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "mmwhMRulfTTVWydJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar with coordinates [0.820, 0.356, 0.845, 0.923] represent and what is its value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the score of 'iPhone 12' for the 'Outliers' category with a value range of 0.00 to 0.80."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "ZSOTfuohWGajOO8c",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the scores of 'Samsung S20' and 'iPhone 12' in the 'Sudden increase' category, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Samsung S20' has a higher score."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "Kaim8FPfOGlhJLxV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing 'Samsung S20' in the 'Outliers' category [0.796, 0.073, 0.820, 0.923] taller or shorter than the same category bar representing 'iPhone 12' [0.820, 0.356, 0.845, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "8wC8t1wrqSsuB4OE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the overall range of scores achieved by 'Samsung S20' and 'iPhone 12', which product has a wider range of scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Samsung S20' has a wider range of scores."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "TmxgggH05Vlx2upz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the coordinates [0.308, 0.498, 0.333, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates corresponds to a bar in the 'Sudden increase' category for the product 'Samsung S20'. The bar represents a score range of 0.00 to 0.60."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "9FqMGFwmaPkdTs7R",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.136, 0.044, 0.343, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the chart legend. It comprises two items: 'Samsung S20', located at {coordinates}<r>[0.199, 0.055, 0.335, 0.083]</r> and displayed in color '#6d8abb', & 'iPhone 12', located at {coordinates}<r>[0.199, 0.097, 0.297, 0.125]</r> and displayed in color '#495c80'."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "avCqERA4jBZThLBJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the area [0.065, 0.781, 0.089, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents a bar for 'Samsung S20' in the 'Continuous decrease' category. The value for this category for 'Samsung S20' ranges from 0.00 to 0.20."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "VW8OcT1vfbQyvjqT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.338, 0.029, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label titled \"Parameter Value\"."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "uORgBAtb9tcPzFdX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information at coordinates [0.749, 0.833, 0.839, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the X-axis label for \"R&D Investment\"."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "fIzmmLGrVpncRdoh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first bar representing Region 1 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first bar representing Region 1 has coordinates [0.071, 0.650, 0.133, 0.819]."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "Tp7cfYYtpIWfg5Gq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in [0.873, 0.031, 0.978, 0.097] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend}<r>[0.867, 0.031, 0.978, 0.097]</r> is located in that region."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "2SXpk0JNoPBhINTj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Income Equality from Region 2}[0.133, 0.228, 0.194, 0.819] higher than the {bar for Income Equality from Region 1}[0.071, 0.650, 0.133, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "2YSJtql2JOsUAp2P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Healthcare Quality from Region 1}[0.247, 0.144, 0.309, 0.819] located to the right of {the bar for Income Equality from Region 2}[0.133, 0.228, 0.194, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "5HzXA19eX1WJVpE1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the bar for Climate Policy from Region 1}[0.424, 0.566, 0.486, 0.819] or {the bar for Climate Policy from Region 2}[0.486, 0.228, 0.548, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Region 2."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "bSArxIr07mrjrsoN",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Education Budget from Region 2}[0.663, 0.144, 0.724, 0.819] have a higher value than {the bar for R&D Investment from Region 1}[0.777, 0.735, 0.839, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "LZSAKPi5FmcGc8PB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is in [0.071, 0.650, 0.133, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bar for \"Income Equality\" from Region 1, with a value of 0.2."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "g8nqlAQL5QIAnwOi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.867, 0.031, 0.978, 0.097]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is the legend for the chart. Within it, there are two color-text labels representing the two regions: Region 1, represented by the color blue at the coordinates [0.873, 0.041, 0.901, 0.055], and Region 2, represented by the color darker blue at the coordinates [0.873, 0.071, 0.901, 0.085]."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "jOE9GklG8hp2ghWd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.247, 0.144, 0.309, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the bar for \"Healthcare Quality\" associated with Region 1 is present, with a value of 0.8."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "KaIHQNZNuXt9YWiJ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.424, 0.566, 0.486, 0.819]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar for \"Climate Policy\" from Region 1, representing a value of 0.3."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "CBPMd6WpHHZyHuWT",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.019, 0.349, 0.036, 0.611] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the label on the Y-axis, which denotes 'Population (In Millions)'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "jTPKoH1uP74ZvcOD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content does the coordinate [0.316, 0.952, 0.360, 0.975] mark?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the X-axis label '2016'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "dKeIsqQEXPsbJaeb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the bounding box [0.905, 0.046, 0.941, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label 'Asia' in the legend."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "cSIkAlg9lMHeMsMk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [-0.006, 0.321, 0.083, 0.935] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart element representing the Asia region for the year 2012."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "dZdu0UkXFL5hIuTi",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is the label 'Asia'<{Asia label}[0.905, 0.046, 0.941, 0.069]> above or below the label 'Europe'<{Europe label}[0.905, 0.081, 0.966, 0.104]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "nETC8VctIPp3dpBY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2012 bar of Europe}[0.083, 0.068, 0.172, 0.935] taller than the {2012 bar of Asia}[-0.006, 0.321, 0.083, 0.935] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "JpfkOrXWf4Go8Zst",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2016 bar of Asia}[0.249, 0.249, 0.338, 0.935] located to the left or right of the {2016 bar of Europe}[0.338, 0.104, 0.427, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "0925ISrXEZv6nOok",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {2024 bar of Europe}[0.847, 0.155, 0.937, 0.935] to the {2024 bar of Asia}[0.758, 0.104, 0.847, 0.935], which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar of Europe."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "9879ys615QBNEXGm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content at the coordinates [0.849, 0.037, 0.973, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart. It contains two entries: 'Asia' with the color code at [0.856, 0.048, 0.891, 0.064] and 'Europe' with the color code at [0.856, 0.083, 0.891, 0.099]."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "7iX5sFhObXOzkAjd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.503, 0.177, 0.593, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars in this region. The first one is {the 2020 bar of Asia}[0.503, 0.177, 0.593, 0.935] and the second is {the 2020 bar of Europe}[0.593, 0.126, 0.682, 0.935]."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "bK6ezYucBEbXuD6e",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object lying within [0.826, 0.952, 0.869, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the '2024' label on the X-axis."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "IJlV3KgiaRz1ULjM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the coordinates [0.758, 0.104, 0.847, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, we have {the 2024 bar for Asia}[0.758, 0.104, 0.847, 0.935], representing the predicted population for Asia in the year 2024."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "CwzQ1MG9kU4j108y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at the coordinates [0.811, 0.055, 0.914, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the \"Initial Risk\" bar in the legend."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "aHeZ6gNgIAeHfWe0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the spot [0.050, 0.345, 0.081, 0.373]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a Y-axis label of \"0.4\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "cIhcRO1Qkq7OS66E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is tagged at the location [0.164, 0.073, 0.248, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"Initial Risk\" bar representing the category of \"High Risk\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "ZjVr5UrlTBge5ffD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Initial Risk for Low Risk\"}[0.723, 0.781, 0.807, 0.923] higher or lower than {the bar representing \"Residual Risk for Low Risk\"}[0.807, 0.498, 0.891, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "VIYdDkT8vyjroflx",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller: {the bar for \"Initial Risk in the High Risk category\"}[0.164, 0.073, 0.248, 0.923] or {the bar depicting \"Residual Risk in the Medium Risk category\"}[0.528, 0.214, 0.611, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for \"Initial Risk in the High Risk category\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "50JU4GJwHVtaPHWV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Residual Risk bar for the High Risk category}[0.248, 0.639, 0.332, 0.923] lower or higher than the {Initial Risk bar for the Medium Risk category}[0.444, 0.639, 0.528, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same height."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "j1Uk1uOTHucwHcV9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the legend located in [0.748, 0.044, 0.950, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two entries in the legend. The \"Initial Risk\" label is a dark blue color at coordinates [0.756, 0.058, 0.796, 0.077] and has a label located at [0.811, 0.055, 0.914, 0.083]. The \"Residual Risk\" label is a light blue color at [0.756, 0.099, 0.796, 0.119], with its text located at [0.811, 0.097, 0.942, 0.125]."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "Sb1D4Omoucu7RIYl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region ([0.164, 0.073, 0.332, 0.923])."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars: the \"Initial Risk for the High Risk category\" bar, located at [0.164, 0.073, 0.248, 0.923], and the \"Residual Risk for the High Risk category\" bar, located at [0.248, 0.639, 0.332, 0.923]."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "H6PlJaMQqkRLgZmz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give some insight into the area [0.444, 0.214, 0.611, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features two bars: the \"Initial Risk\" bar for the \"Medium Risk\" category and the \"Residual Risk\" bar for the \"Medium Risk\" category. Their coordinates are [0.444, 0.639, 0.528, 0.923], and [0.528, 0.214, 0.611, 0.923] respectively."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "p1htHSycxOt1DBTN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.159, 0.814, 0.733, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the growth percentage of {Electronics}<r>[0.159, 0.814, 0.733, 0.887]</r> market in Emerging Markets."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "A3ayda1YCxf7O0JY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.394, 0.019, 0.750, 0.041] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, labeled {Emerging Markets and Consumer Behavior}<r>[0.394, 0.019, 0.750, 0.041]</r>."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "ts1gQF8ljNrAyYz5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by the bar at [0.159, 0.087, 0.449, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar represents a 12.3% growth for the {Toys}<r>[0.159, 0.087, 0.449, 0.159]</r> market in Emerging Markets."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "tEHoWasaooHEFT0R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the two market categories {Beverages}<r>[0.159, 0.451, 0.570, 0.523]</r> and {Automobile}<r>[0.159, 0.269, 0.527, 0.341]</r>, which one has a higher growth percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Beverages."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "9LLUsdfvXvNnG4LR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing the {Pharmaceuticals}<r>[0.159, 0.632, 0.435, 0.705]</r> market taller or shorter compared to the {Footwears}<r>[0.159, 0.541, 0.511, 0.614]</r> market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "n0oj0d6FLlNulsSR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has more growth, the {Toys}<r>[0.159, 0.087, 0.449, 0.159]</r> market or the {Textiles}<r>[0.159, 0.723, 0.596, 0.796]</r> market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Textiles."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "FhuZqj3Z1Q65m4cX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between coordinates [0.159, 0.360, 0.848, 0.432]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a bar representing the growth percentage of the {Software}<r>[0.159, 0.360, 0.848, 0.432]</r> market, which is the highest among all the markets in Emerging Markets."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "WbFXBaPkt0Jb1S3c",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.074, 0.844, 0.149, 0.861]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a label for the {Electronics}<r>[0.074, 0.844, 0.149, 0.861]</r> market on the Y-axis."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "VHMiaRRUR8p2Rzjl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.155, 0.939, 0.164, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the value {0}<r>[0.155, 0.939, 0.164, 0.957]</r> on the X-axis, representing growth percentage."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "MCebYGA7n7wnkQoj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the mentioned label \"Suburban\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.043, 0.466, 0.127, 0.484]."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "2PPsE7TbYGxbl6zb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.019, 0.448, 0.036, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Areas."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "1hD1iuP496yJLRde",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the specific object and its related quantities in these coordinates [0.139, 0.060, 0.738, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Urban}[0.074, 0.171, 0.127, 0.189] with a Development Index of 7.2."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "kctXE0rqLf6xYYOs",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar of {Suburban}[0.139, 0.355, 0.455, 0.591] higher than the bar of {Urban}[0.139, 0.060, 0.738, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "tXhRbvNa4KJ2rtzG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar of {Rural}[0.139, 0.650, 0.264, 0.886] have a smaller or larger Development Index than that of {Urban}[0.139, 0.060, 0.738, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "8lxUZwS8axDx7Poq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Urban bar}[0.139, 0.060, 0.738, 0.296] closer to the top or the bottom of the chart compared to the {Rural bar}[0.139, 0.650, 0.264, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bottom."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "UZFv51g6DV5YwHge",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.555, 0.008, 0.555, 0.008]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area point represents a subtitle exist, but unlabeled."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "oASb6dQPSM5IFEZO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.082, 0.761, 0.127, 0.779]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the y-axis label for {Rural}[0.082, 0.761, 0.127, 0.779]."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "j2TjgfKLu1Fy8snq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.139, 0.060, 0.738, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is the {Urban bar}[0.139, 0.060, 0.738, 0.296] in the bar chart, which represents an Urban area with a Development Index of 7.2."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "tB5tKymH0t5hwNpa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.470, 0.964, 0.640, 0.981] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label {Development Index}[0.470, 0.964, 0.640, 0.981], as it is the header of the x-axis."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "ESFtCUi6deae7qJ8",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text labeled \"Automation\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.124, 0.684, 0.225, 0.702]"
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "Bmxl4LbtqZ5aNbbK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar at coordinates [0.237, 0.495, 0.494, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the development trend score of {Blockchain}<r>[0.131, 0.521, 0.225, 0.538]</r>."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "XD3xUu4zO3MbhQv4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.524, 0.964, 0.694, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label, which is labeled as \"Development Trend\"."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "tKZM5bU9IWQCKIRM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data indicated by the bar within [0.237, 0.250, 0.914, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the development trend score of 5.0 for the {Data Science}<r>[0.111, 0.276, 0.225, 0.293]</r> industry."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "hmQmEBkxcO0t6woF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Blockchain}<r>[0.131, 0.521, 0.225, 0.538]</r> taller or shorter than the bar representing {AR/VR}<r>[0.171, 0.439, 0.225, 0.457]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "PTHQBPeY8HmKk7Os",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for the industry {AI}<r>[0.208, 0.848, 0.225, 0.865]</r> higher than the score for {5G Technology}<r>[0.098, 0.194, 0.225, 0.212]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "QUkySiD2TJV0di0Q",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-axis label positioned to the left or to the right of the bar representing the {Cloud Computing}<r>[0.074, 0.603, 0.225, 0.620]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "REzuOjlz6E3oG48P",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has the highest development trend score: {IoT}<r>[0.199, 0.766, 0.225, 0.783]</r> or {Automation}<r>[0.124, 0.684, 0.225, 0.702]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automation."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "n5ctFOoOtR8szVgM",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.074, 0.603, 0.225, 0.620]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a vertical text label, which is located to the left of the bars, marked as \"Cloud Computing\"."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "OtuyZlmMfJ4McoCm",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the layout and information in the area [0.237, 0.250, 0.914, 0.315]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a bar representing the development trend score of Data Science. The value, represented by the bar length, is the highest, at 5.0."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "JxcfgWFC8Psk3NsT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the range [0.199, 0.740, 0.630, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range contains a single bar representing the \"IoT\" industry. The development trend score, represented by the length of the bar, is 2.90."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "Yn1BxXwopUk7jXl0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.019, 0.451, 0.036, 0.523] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates represent the section of the y-axis label that reads \"Industry\". It is a vertical label placed along the y-axis to the left of the bars."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "lHY3Tahp8BRcwLwN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.331, 0.769, 0.651, 0.863] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the frequency of Hotel Bookings."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "4DnioRK6biWrnpNs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the coordinates of the text label \"Entertainment Ticket Purchases\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's [0.043, 0.220, 0.319, 0.243]."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "2m0gaty3uhJxPOfO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat frequency is represented at [0.331, 0.417, 0.810, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the frequency of Airport Transfers."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "2a69S5I5EzVu44GM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat categories can be seen in [0.331, 0.065, 0.384, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the {Gym Memberships}[0.331, 0.065, 0.384, 0.159] category."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "Xfv0WKfjCaDUL2TS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the frequency bars for {Gym Memberships}[0.331, 0.065, 0.384, 0.159] shorter than the frequency bars for {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "o8VNj98s0DLO6dxI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Tour Package Purchases}[0.331, 0.534, 0.544, 0.628] frequency lower than {Spa Appointments}[0.331, 0.300, 0.491, 0.394] frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "srmEQLaJnN3DJ3HU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Hotel Bookings}[0.331, 0.769, 0.651, 0.863] and {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], which one has a higher frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Restaurant Reservations."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "gRSk0rKls8g1hf1K",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Airport Transfers}[0.331, 0.417, 0.810, 0.511] have a higher frequency compared to {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "8Kbcf1qO57bvx46J",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the coordinates [0.331, 0.417, 0.810, 0.511]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a frequency bar for Airport Transfers. It shows that the frequency is around 18."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "Piy05QYyvXntt3On",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details within the area specified by [0.331, 0.065, 0.384, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises a frequency bar for the category Gym Memberships. It signifies that the frequency of gym memberships is approximately 2."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "waDZpgrxKWqmMzE8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen at [0.331, 0.182, 0.864, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a frequency bar for Entertainment Ticket Purchases that indicates a frequency of around 20."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "cvXe9bZRR4axFcQV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's contained within the area [0.331, 0.652, 0.731, 0.746]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a frequency bar representation of the category Restaurant Reservations, with a frequency of 15."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "19Pq37IVFyOXdl9e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel}<r>[0.019, 0.383, 0.036, 0.591]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning Areas."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "1jMrWtoFW06X2dMb",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar}<r>[0.263, 0.332, 0.772, 0.397]</r> referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar corresponds to the \"Classification\" data in the chart."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "bUv9cCfmL0G5Jmwx",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the area represented by {the coordinates}<r>[0.124, 0.603, 0.251, 0.620]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the \"Deep Learning\" label on the y-axis."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "2I6oVnS2g12JL2fB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {bar}<r>[0.263, 0.658, 0.405, 0.724]</r> represent in terms of data value and category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the \"Reinforcement Learning\" area with a frequency of 5%."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "dqSZ51xL9IQyx4X1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two bars {Bar 1}<r>[0.263, 0.495, 0.885, 0.560]</r> and {Bar 2}<r>[0.263, 0.577, 0.829, 0.642]</r>, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar 1."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "IuB9chs8i6l0SiSz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Deep Learning}<r>[0.263, 0.577, 0.829, 0.642]</r> longer than {the bar for Supervised Learning}<r>[0.263, 0.822, 0.687, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "NfH3fZ7sUwv3bybL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is greater, the frequency of {Deep Learning}<r>[0.263, 0.577, 0.829, 0.642]</r> or {Neural Networks}<r>[0.263, 0.495, 0.885, 0.560]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neural Networks."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "jp7e65yvYdQjXqUH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {Clustering bar}<r>[0.263, 0.168, 0.574, 0.234]</r> and {Regression bar}<r>[0.263, 0.250, 0.716, 0.315]</r> in terms of their length."
+            },
+            {
+                "from": "gpt",
+                "value": "Regression bar is longer."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "Q6qf19L0VJkiVv1P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the coordinates [0.074, 0.848, 0.251, 0.865]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label representing the \"Supervised Learning\" area in machine learning."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "252pf5M0wdqQlS5U",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.263, 0.577, 0.829, 0.642]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a purple bar representing the popularity of the data \"Deep Learning\". The data range that the bar corresponds to is from 0% to 20%."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "NYxxD9OWgoFUSEio",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is within the region specified by these coordinates [0.052, 0.766, 0.546, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a y-axis label \"Unsupervised Learning\" and its corresponding bar for the corresponding frequency percentage."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "IeOw04vmpERmZcSb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe all the components within {this region}<r>[0.019, 0.383, 0.036, 0.591]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The given region includes a label of the Y-axis titled \"Machine Learning Areas\"."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "x9sa4MQIQpWpHu9L",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of technology is represented by this bar chart at coordinates [0.247, 0.818, 0.428, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Energy."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "0y7rLhrkFpOMt7Cg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.542, 0.964, 0.686, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth Rate (%)."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "CarSWVfgNgRZ0SVJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {percentage indicator} at [0.327, 0.939, 0.339, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "cWNJcjoJRpuCDtiX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects or information is present in [0.066, 0.255, 0.234, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Geothermal Energy Label}[0.066, 0.255, 0.234, 0.273]."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "g0CuUFGtq1PrO5f0",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Biomass\"}[0.247, 0.650, 0.506, 0.717] shorter or longer than {the bar representing \"Hydro Power\"}[0.247, 0.566, 0.549, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "qauzVmCjpsPlaweV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre bars representing {Energy Storage}[0.247, 0.397, 0.653, 0.465] and {Smart Grid}[0.247, 0.313, 0.679, 0.380] of the same length?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "BckGh3iJ52o1UFQO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Carbon Capture}[0.247, 0.481, 0.575, 0.549] and {Energy Storage}[0.247, 0.397, 0.653, 0.465], which bar is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Storage."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "ysz4QEkzPH01M8oB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre bars representing {Wind Energy}[0.247, 0.734, 0.480, 0.802] and {Biomass}[0.247, 0.650, 0.506, 0.717] of the same color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "wtsk8wJ4Mn1mxUu2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.247, 0.019, 0.981, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "It embeds the main plot of the visualization. This plot is drawn in the figure, it represents different technologies on the vertical axis and their corresponding growth rates on the horizontal axis. The heights of the bars denote the growth rate of each technology."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "pZhLx4ZMppUSvUGj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.247, 0.060, 0.912, 0.127]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a bar representing the technology 'Fusion Energy'. The length of the bar indicates a growth rate of 7.7%"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "IF76sWmXoXhgmMDO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.247, 0.144, 0.765, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar which represents the technology 'Wave and Tidal Power'. The length of the bar suggests a growth rate of 6.0% for this technology."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "3ia7jKRcBavc4Ql8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the area [0.161, 0.677, 0.234, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the label 'Biomass' in the y-axis which indicates that the bars within this scale on the y-axis correspond to growth rates for the technology 'Biomass'."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "TpbpM8sWWRwlja18",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text title}<r>[0.322, 0.019, 0.795, 0.041]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Market Competition Analysis and Comparison\""
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "23VR2mA8i8QWxq3G",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at {this part of the bar}<r>[0.150, 0.189, 0.271, 0.271]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the market share for \"Motorola\"."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "eIQq7Z5KQhGe0rSO",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis scale} at <r>[0.019, 0.446, 0.036, 0.528]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label, called Company."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "ZPC3hBE3roEqO8L4",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar representing \"Xiaomi\"}<r>[0.136, 0.497, 0.326, 0.579]</r> represent a larger market share than {the bar representing \"Huawei\"}<r>[0.136, 0.600, 0.559, 0.682]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "nEkzykx9mTOUiMKC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the market share of \"Motorola\"}<r>[0.136, 0.189, 0.150, 0.271]</r> smaller than {the market share of \"OPPO\"}<r>[0.136, 0.395, 0.220, 0.477]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "SBdT1qGwgvez59Qm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the market share of \"Samsung\"}<r>[0.136, 0.702, 0.749, 0.785]</r> closer to {the market share of \"Apple\"}<r>[0.136, 0.805, 0.854, 0.887]</r> than to {the market share of \"Huawei\"}<r>[0.136, 0.600, 0.559, 0.682]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "oM9zvX58GN43hp6k",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within this area <r>[0.136, 0.805, 0.854, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a blue bar representing the market share of Apple, which is approximately 34%."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "9qbwOq9MvtDuIa64",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region <r>[0.136, 0.087, 0.141, 0.169]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue bar within this region representing the market share of Google, which is approximately 0.25%."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "bKiPtWS1CNPmR2iX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects and their properties within the bounding box <r>[0.136, 0.702, 0.749, 0.785]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this bounding box, there is a blue bar representing the market share of Samsung, which is approximately 29%."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "zZsJpQNrQwfLnoSV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.319, 0.019, 0.911, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Social Welfare and Charity Activities Funding Distribution\"."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "4ihk01CrBem5cX0D",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location [0.260, 0.189, 0.271, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A bar that represents \"Animal Welfare\" with a percentage of 0.6."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "NFAzGGJkp7n4mawi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.104, 0.223, 0.236, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Y-axis label \"Animal Welfare\"}[0.104, 0.223, 0.236, 0.241]."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "NALHWDMCdwI4ynpV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.043, 0.634, 0.236, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label for \"Healthcare Assistance\"}[0.043, 0.634, 0.236, 0.651]."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "gxfXTC83gz2XUTIP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the length of {the bar for \"Educational Aid\"}[0.248, 0.805, 0.585, 0.887] compare to {that of \"Food Distribution\"}[0.248, 0.702, 0.680, 0.785]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "fECL1aETBpSW9mka",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the graph at {Housing Aid}[0.248, 0.497, 0.514, 0.579] longer than the bar at {Disaster Relief}[0.248, 0.395, 0.455, 0.477]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "egiMNRcRnoWyA9GQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars corresponding to {Educational Aid}[0.248, 0.805, 0.585, 0.887] and {Animal Welfare}[0.248, 0.189, 0.260, 0.271] of the same length?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "lQOrZUOSU4Sx5nT9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Food Distribution}[0.248, 0.702, 0.680, 0.785] have a larger value than {Research & Advocacy}[0.248, 0.292, 0.422, 0.374]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "L85ow2T6YqqXbSLH",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.248, 0.805, 0.585, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blue bar representing the funding distribution percentage 16.1 for \"Educational Aid\"."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "BKXsbcBhKV3KJJL8",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the graphic detail in [0.248, 0.805, 0.585, 0.887] and [0.100, 0.839, 0.236, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows a blue bar, labeled \"Educational Aid\", that corresponds to a percentage of 16.1."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "DF0nfYEKzKkiTRRV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.043, 0.634, 0.236, 0.651] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label \"Healthcare Assistance\"."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "fv3XDHVQERshOkmg",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.132, 0.531, 0.236, 0.549] and [0.248, 0.497, 0.514, 0.579]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Housing Aid\" is written at [0.132, 0.531, 0.236, 0.549] on the y-axis and corresponding to it on the chart is a blue bar graph of range 12.7 at [0.248, 0.497, 0.514, 0.579]."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "zJV2WpOKz9ufgBHv",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of internet usage has the coordinates [0.066, 0.711, 0.243, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce growth."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "v9Ailk0fucc6euAy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of Internet of Things represented by the bar at the coordinates [0.255, 0.130, 0.657, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "4.7"
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "z7G3FpqGbF4RYxBH",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat axis label is located at [0.573, 0.964, 0.663, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frequency."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "GW9FDQ6VPRPzo0QG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of internet usage is associated with the label at the coordinates [0.135, 0.641, 0.243, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Email Usage."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "ZLSCJAsa7KrR3ueZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the one associated with Video Conferencing at [0.255, 0.340, 0.870, 0.396] or the one for Online Gaming at [0.255, 0.410, 0.622, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Conferencing."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "BtBPd34iIszxklNA",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing Virtual Reality at [0.255, 0.200, 0.494, 0.256] taller or shorter than the bar representing Cryptocurrency at [0.255, 0.060, 0.614, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "w7WQC0ejQlXrmMme",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the bar representing Mobile Internet Users at [0.255, 0.830, 0.554, 0.886] extend further out along the x-axis than the bar representing Social Media Usage at [0.255, 0.550, 0.819, 0.606]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "tSyl5v0wuG4HlCJj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the bars representing Video Streaming at [0.255, 0.480, 0.930, 0.536] and Video Conferencing at [0.255, 0.340, 0.870, 0.396], which one extends further along the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Streaming."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "stIZGxJu17n3XQ0I",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present in the region [0.255, 0.410, 0.622, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a single bar that corresponds to the frequency of Online Gaming, which is 4.3."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "0wYLEXJl9gP6p5uT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is provided in the region from [0.255, 0.270, 0.776, 0.326]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there's a bar that represents the frequency of Online Education, which is 6.1."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "kY2V1URntaGYOjDx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.255, 0.130, 0.657, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single blue bar representing the frequency of Internet of Things usage, which is 4.7."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "fifSWEBnOyyjDpn7",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area between the coordinates [0.109, 0.081, 0.243, 0.099] convey?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label for \"Cryptocurrency\"."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "SMU5tpjhVQblJRKk",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThere is a bar starting from the x-axis at 0 and ending at around 11.2, can you identify the item it corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar corresponds to the item: {Mid-Quality Electronics}<r>[0.215, 0.452, 0.722, 0.527]</r>."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "EmzvhNjQdXhhOBgl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text title of the graph located at approximately [0.495, 0.015, 0.705, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the graph is: {Statistical Data Overview}<r>[0.495, 0.015, 0.705, 0.033]</r>."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "3ykWeAWKlnfINh17",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis scale}<r>[0.256, 0.951, 0.265, 0.965]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis scale at these coordinates is: 1."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "zqHi7Ov36PSFaXXy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compare the lengths of {High-Quality Automobile}<r>[0.215, 0.826, 0.903, 0.901]</r> and {High-Quality Clothing}<r>[0.215, 0.265, 0.867, 0.340]</r> bars?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {High-Quality Automobile}<r>[0.215, 0.826, 0.903, 0.901]</r> bar is shorter than the {High-Quality Clothing}<r>[0.215, 0.265, 0.867, 0.340]</r> bar."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "6IoEM7sbsUhHvwQO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {High-Quality Electronics}<r>[0.215, 0.546, 0.876, 0.620]</r> bar shorter than the {High-Quality Automobile}<r>[0.215, 0.826, 0.903, 0.901]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "aJvpQaGhYJlx8p5G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Low-Quality Clothing}<r>[0.215, 0.078, 0.682, 0.153]</r> represent a lower quantity than {Low-Quality Automobile}<r>[0.215, 0.639, 0.691, 0.714]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "vWtGvlme81p3kVoS",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Mid-Quality Automobile}<r>[0.215, 0.732, 0.777, 0.807]</r> and {Low-Quality Electronics}<r>[0.215, 0.359, 0.659, 0.433]</r>, which item has a lower quantity?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Low-Quality Electronics}<r>[0.215, 0.359, 0.659, 0.433]</r>."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "98pl8YsZdaSVrF4j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see from the coordinates [0.215, 0.452, 0.722, 0.527]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates reveal a bar that represents the number of {Mid-Quality Electronics}<r>[0.215, 0.452, 0.722, 0.527]</r> which is around 11.2."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "6XGSdlCNfeg2MGXf",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.215, 0.826, 0.903, 0.901] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area shows a bar that represents the quantity of {High-Quality Automobile}<r>[0.215, 0.826, 0.903, 0.901]</r>."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "IT5XD0LpxJJZwVR5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell us about the region [0.215, 0.172, 0.768, 0.247]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a single bar that represents {Mid-Quality Clothing}<r>[0.215, 0.172, 0.768, 0.247]</r>."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "xCROAsZqWYf5mRGl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.215, 0.078, 0.682, 0.153] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates represent a bar of {Low-Quality Clothing}<r>[0.215, 0.078, 0.682, 0.153]</r> with a quantity of around 10.3."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "cHr9S4xjKGfNTDQm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar chart refer to as the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis is referred to as {Trend}[0.584, 0.964, 0.631, 0.981]."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "8k6HmMUGzKbgeE0h",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is stated at the coordinates [0.159, 0.616, 0.221, 0.633] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Fintech}[0.159, 0.616, 0.221, 0.633] is stated at these coordinates."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "HxQrtG0zUSOirCUE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.233, 0.778, 0.840, 0.826] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates represent a bar that indicates the value of {Cryptocurrency}[0.233, 0.778, 0.840, 0.826] trend in the bar chart."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "pgk83eeQoCQfk0nE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar has a higher value between {Data Science}[0.233, 0.539, 0.879, 0.587] and {Cybersecurity}[0.233, 0.180, 0.714, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Science."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "Y4aKteqzT3Ut14hL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Big Data}[0.233, 0.240, 0.903, 0.287] placed higher than {Cloud Computing}[0.233, 0.359, 0.714, 0.407] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "uvktMulhUxOi3KvD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend of {Blockchain}[0.233, 0.479, 0.619, 0.527] higher or lower than the trend of {Artificial Intelligence}[0.233, 0.718, 0.777, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "vM1utpPHSiJ5HaLa",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents from [0.233, 0.718, 0.777, 0.766] to [0.233, 0.838, 0.643, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "On this range, two bars are represented. {Artificial Intelligence}[0.233, 0.718, 0.777, 0.766] and {Biotechnology}[0.233, 0.838, 0.643, 0.886]. Both are demonstrating their respective trend values."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "BHK50rfziNMLh6qX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.233, 0.239, 0.880, 0.287] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the bar for {Big Data}[0.233, 0.240, 0.903, 0.287] showing its trend value on the x-axis."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "jukpeCZLYhTuvsiq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are displayed in the region [0.465, 0.939, 0.494, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the x-axis tick mark label {4}'s coordinates [0.465, 0.939, 0.494, 0.957]."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "C0TivNjPm127hgbr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value visualized by the rectangle at [0.256, 0.825, 0.691, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangle represents the frequency of \"Increasing Partisanship\", which is 30."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "vujx4xHwJg7kWLBp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name associated with the coordinate [0.166, 0.237, 0.244, 0.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's labeled as \"Populism\"."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "AC7Bib0VNCycudAh",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.019, 0.408, 0.036, 0.537] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Political Trends\"."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "PRUQfMtStnPCLmlo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the frequency at x-axis coordinate [0.848, 0.939, 0.920, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is 45."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "Am9NC9qGV6oEwdSt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Increasing Partisanship}[0.256, 0.825, 0.691, 0.886] shorter or longer than the one for {Alternative Media}[0.256, 0.137, 0.764, 0.198]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "y7dbUw3LDo3THlIZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Electoral Volatility}[0.256, 0.672, 0.459, 0.733] has a higher frequency than {Political Scandal}[0.256, 0.366, 0.720, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "pT7ZDaL27ZdsUQK2",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the frequency for {Political Satire}[0.256, 0.060, 0.532, 0.121] more than the one for {Political Disaffection}[0.256, 0.748, 0.604, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "2BILdiaMpwofJZwU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Digital Politics}[0.256, 0.442, 0.851, 0.504] and {Identity Politics}[0.256, 0.289, 0.662, 0.351], which one has a higher occurence?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Politics."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "HXUdxE4MLdFpbkpc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the y-axis scale in the area [0.043, 0.084, 0.244, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the y-axis labels which indicate political trends, ranging from \"Increasing Partisanship\" at the topmost [0.043, 0.848, 0.244, 0.866] to \"Political Satire\" at the bottom [0.121, 0.084, 0.244, 0.101]."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "XYxI3BxV24j6A8lW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the structure of the bar chart in the region [0.256, 0.060, 0.851, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises a series of bars denoting frequencies of various political trends. The bars range from \"Increasing Partisanship\"[0.256, 0.825, 0.691, 0.886] at the top to \"Political Satire\"[0.256, 0.060, 0.532, 0.121] at the bottom. The length of each bar corresponds to the frequency of each political trend."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "kBaAp4B9Sjeq90Ib",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.556, 0.964, 0.681, 0.981] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label, which is \"Frequency (%)\"."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "p1OGc3lQXlGQRZxl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.251, 0.939, 0.920, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area has a series oflabels representing the x-axis scale of frequency starting from '0'[0.251, 0.939, 0.262, 0.957] to '45'[0.898, 0.939, 0.920, 0.957]."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "ITZjWbipKnemKiYQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the first bar in the chart with coordinates represent [0.264, 0.856, 0.312, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Augmented Reality, with a frequency of 0.6."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "cygJDTJ30SeRQGgs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what technology category the y-label at [0.194, 0.151, 0.254, 0.165] corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Big Data."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "wBQ9EkARNkY9KpHU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis represented at [0.572, 0.971, 0.672, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frequency (%)."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "zvMjp1SHMAPGG4pj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology does the bar displayed at [0.264, 0.134, 0.901, 0.178] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Big Data."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "xnSXo7qFrhazcppy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the bar chart, is the technology category represented by [0.126, 0.706, 0.254, 0.720] more frequent than the one in [0.106, 0.540, 0.254, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "YuCqlsijFzdLcIUC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen comparing the height of bars [0.264, 0.601, 0.431, 0.623] and [0.264, 0.523, 0.479, 0.567], which bar is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.264, 0.523, 0.479, 0.567]."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "9vlaUgwihgmEwd2G",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {\"Flipped Classroom\"}[0.264, 0.634, 0.423, 0.678] and {\"Bring Your Own Device\"}[0.264, 0.578, 0.431, 0.623], which technology is more common based on their corresponding frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Flipped Classroom."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "808eYRqePVHomZbL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Artificial Intelligence}[0.264, 0.745, 0.367, 0.790] more frequent than {Game-Based Learning}[0.264, 0.690, 0.399, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "kDJOZjXAPKJGhsEj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bars in the [0.264, 0.645, 0.646, 0.745] section."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the bars for \"Flipped Classroom\" and \"Bring Your Own Device\". The \"Flipped Classroom\" bar spans [0.264, 0.634, 0.423, 0.678] and the \"Bring Your Own Device\" bar spans [0.264, 0.578, 0.431, 0.623]."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "DfjrmFpuPIv7LJkU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area of the chart [0.015, 0.440, 0.029, 0.530]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-label, which is the word \"Technology\"."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "SNWgh2rLKr8P0gE2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area in [0.259, 0.951, 0.268, 0.965]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label of \"0\" in the x-axis' scale at [0.259, 0.951, 0.268, 0.965]."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "wpQ1bF36tI8JqR94",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease run through the details of the top third section of the chart from [0.264, 0.745, 0.917, 0.123]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this top third section, there are 7 bars representing different technologies. These are (from top to bottom): \n1. Artificial Intelligence: [0.264, 0.745, 0.367, 0.790]\n2. Game-Based Learning: [0.264, 0.690, 0.399, 0.734]\n3. Flipped Classroom: [0.264, 0.634, 0.423, 0.678]\n4. Bring Your Own Device: [0.264, 0.578, 0.431, 0.623]\n5. Wearable Technology: [0.264, 0.523, 0.479, 0.567]\n6. Mobile Browsers: [0.264, 0.467, 0.543, 0.512]\n7. Learning Management Systems: [0.264, 0.412, 0.550, 0.456]"
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "M1HLLJDoaJ6BcwF2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of chart being represented?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "R9W7AGjPUkMajxCU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Networks and Relationships"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "6GkL6CYY2CJ4TkFi",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {label}[0.087, 0.822, 0.217, 0.840] on the far left of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Network"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "4P6t2XiVU5JZa7eo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {bar}[0.229, 0.772, 0.921, 0.886] for the \"Social Network\" represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "9.2"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "N54EQKvMnSK0guO6",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is longer between the bar for {Social Network}[0.229, 0.772, 0.921, 0.886] and the bar for {Personal Interaction}[0.229, 0.630, 0.740, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Network."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "VBiHBRDCqF3V4c28",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar}[0.229, 0.345, 0.537, 0.459] for \"Work Relations\" longer than the {bar}[0.229, 0.487, 0.628, 0.601] for \"Family Ties\u201d?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "7oCryxMfZJhhdYPI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {bar}[0.229, 0.060, 0.304, 0.174] for \"Communities\" have a smaller value than the {bar}[0.229, 0.202, 0.402, 0.316] for \"Online Friendship\u201d?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "UPd1Q7fhSbmCUH1Y",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Category\" and its coordinates are [0.015, 0.458, 0.029, 0.521]."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "euBOMDF4lUZNG9W9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.187, 0.246, 0.857, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the category \"Artificial Intelligence\" with a frequency of 8.4%."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "jKvJV8qEwesiKJF0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the bar located between [0.187, 0.833, 0.785, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the \"Biotechnology\" category with a value of 7.5%."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "b9sGh8OTQpB19oO9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the rectangle at [0.187, 0.498, 0.442, 0.565] represent a higher value than the rectangle at coordinates [0.187, 0.246, 0.857, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "n1Tyrw6wp4RBrqZO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Digital media\"}[0.187, 0.414, 0.650, 0.481] larger than {the bar representing \"Software\"}[0.187, 0.582, 0.722, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "BEpSs0QJAJd12HJV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong all the bars, does {the bar representing \"Artificial Intelligence\"}[0.187, 0.246, 0.857, 0.313] have the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "DaJo1VZoNdRQofba",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located at the area [0.187, 0.162, 0.554, 0.229]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a bar representing the category \"Telecommunications\" with a frequency of 4.6%."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "TIIZi82lxUnGQ0GV",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the area [0.187, 0.498, 0.442, 0.565] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing the \"IT service\" category, with a frequency of 3.2%."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "WzeFATN8iWPyWkRT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me more about what's inside [0.187, 0.750, 0.594, 0.817]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains one bar which indicates the frequency percentage of the \"Semiconductors\" category, which is 5.1%."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "ShboXId3PlyXZZa3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label represents the main object with coordinates [0.025, 0.107, 0.295, 0.131] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, called \"reinforcement_learning\"."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "W1b3GxOF4Zu11sL3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the last bar of the second group in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.598, 0.808, 0.720, 0.906]"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "5tADWFFIXY73J74P",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the synthetic_data}[0.557, 0.067, 0.598, 0.166] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the measurement for reinforcement_learning."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "ibJx8R6zBv2V74EM",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat categories are marked with these coordinates [0.174, 0.354, 0.295, 0.377] and [0.244, 0.724, 0.295, 0.748] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the {regression}[0.174, 0.354, 0.295, 0.377] and {SVM}[0.244, 0.724, 0.295, 0.748] respectively."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "J6jmQ2jeATEtgoiC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the synthetic data bar for RNN}[0.434, 0.808, 0.598, 0.906] wider than {the human-authored data bar for RNN}[0.598, 0.808, 0.720, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "sL6eUFtudbkZp5S3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the synthetic data bar for SVM}[0.499, 0.684, 0.598, 0.783] taller than {the human-authored data bar for SVM}[0.598, 0.684, 0.843, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "M0ZDldIbib2wjG3l",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {synthetic data bars}[0.557, 0.067, 0.598, 0.166], [0.393, 0.190, 0.598, 0.289], [0.475, 0.314, 0.598, 0.413], [0.516, 0.437, 0.598, 0.536], [0.450, 0.561, 0.598, 0.659], [0.499, 0.684, 0.598, 0.783], [0.434, 0.808, 0.598, 0.906] have greater values compared to {human-authored data bars}[0.598, 0.067, 0.745, 0.166], [0.598, 0.190, 0.868, 0.289], [0.598, 0.314, 0.761, 0.413], [0.598, 0.437, 0.778, 0.536], [0.598, 0.561, 0.688, 0.659], [0.598, 0.684, 0.843, 0.783], [0.598, 0.808, 0.720, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "STjjlHQmwacBEjdH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {synthetic_data bar}[0.557, 0.067, 0.598, 0.166] and {human-authored data bar}[0.598, 0.067, 0.745, 0.166] for {reinforcement_learning}[0.025, 0.107, 0.295, 0.131], which one is larger?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human-authored data."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "YxPESDrowghvdoQx",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.311, 0.025, 0.950, 0.948]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains several grouped bar elements representing synthetic and human-authored data for different categories such as reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, RNN. The x-axis starts at about -25% extending to 30%, and the y-axis, from bottom to top, shows reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, and RNN."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "3yctrDtruJym13R1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.598, 0.067, 0.745, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily contains all the human-authored data bars. From there, it ranges from reinforcement_learning with percentage 18, neural_networks with percentage 33, regression with percentage 20, unsupervised_learning with percentage 22, convolutions with percentage 11, SVM with percentage 30, to RNN with percentage 15."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Ka5kOfX79QffnnWP",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within [0.598, 0.067, 0.598, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The parameters within this coordinate contain just one object - a blue bar representing the measurement of reinforcement_learning category for synthetic data."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "OEla1ApTzMREvYf4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.311, 0.025, 0.598, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains all the bars representing synthetic data for different categories such as reinforcement_learning, neural_networks, regression, unsupervised_learning, convolutions, SVM, and RNN."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "2gj5vVEqbN9yBugo",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object within the coordinates [0.348, 0.066, 0.544, 0.202] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar in the bar chart representing the \"Dramatic surge\" category for \"Political Trends\" with a value of 35%."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "IV0bHmQS3yT8KrXE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is at the coordinates [0.767, 0.122, 0.818, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the value label for the \"Dramatic surge\" Political Trends, and the label reads \"35%\"."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "sWaAkkRMIaoISh8H",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can I find at [0.544, 0.236, 0.661, 0.372]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar represents the \"Subdued expansion\" of \"Social Trends\" with a value of 15%."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "Vnsszovz1EhQIk6E",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing {Political Trends for Sluggish Growth}<r>[0.426, 0.746, 0.544, 0.882]</r> bigger or smaller than the bar representing {Social Trends for Subdued Expansion}<r>[0.544, 0.236, 0.661, 0.372]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bigger."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "mgzBwCUm383NyQR3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x tick labeled \"10\"}[0.607, 0.939, 0.637, 0.962] to the left or right of {the x tick labeled \"-10\"}[0.441, 0.939, 0.490, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "Tcag72HrxsUdnloj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller between {the bar for Rapid Recovery in Political Trends}<r>[0.387, 0.406, 0.544, 0.542]</r> and {the bar for Dramatic Surge in Social Trends}<r>[0.544, 0.066, 0.818, 0.202]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar for Rapid Recovery in Political Trends."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "IOPKTZ2EjtMHp4Li",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.544, 0.236, 0.818, 0.202]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a purple bar representing the \"Dramatic surge\" category for \"Social Trends\" with a value of 35%."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "ZM7y3sbWKPIVbMIl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.348, 0.066, 0.544, 0.202]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a purple bar which represents the category \"Dramatic surge\" of \"Political Trends\" with a labelled value of 35%."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "zxAa6wQwefBwySYp",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.544, 0.406, 0.897, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a purple bar in the region representing the \"Rapid Recovery\" category of \"Social Trends\", and it has a value of 45%."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "tjwLuVg5yA5FtNjf",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the top bar labeled \"Rapid recovery\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.589, 0.684, 0.705, 0.783]."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "8Ww8PIOppOTu6YgJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.025, 0.354, 0.274, 0.377]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pronounced decrease"
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "1aoeZvYs89ZWsqLs",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar at coordinates [0.589, 0.561, 0.698, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d4a3da"
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "r24Er3yiS2GEY1qj",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere are the \"Private Equity Investments Return Rate\" bars located, particularly for \"Upward trajectory\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.516, 0.561, 0.589, 0.659]."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "5bjkMpSjr0v98dzZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich group represented by the bottom bar in {the area}[0.589, 0.067, 0.705, 0.166] had better performance in \"Dramatic surge\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Equity Investments"
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "g3TQTKBKz6WA1hlf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas the return rate of the {Private Equity Investments}[0.530, 0.067, 0.589, 0.166] larger or smaller than {Public Equity Investments}[0.589, 0.067, 0.705, 0.166] in \"Dramatic surge\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "1jui3Z5cwjeYymLY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Public Equity Investment bars}[0.589, 0.067, 0.705, 0.906] generally higher than {Private Equity Investment bars}[0.530, 0.067, 0.589, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "7KlK5s3HTMbTAiwB",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars of {the Public Equity Investments sector}[0.589, 0.067, 0.705, 0.906] and {the Private Equity Investments sector}[0.530, 0.067, 0.589, 0.906], which sector has a higher return rate for \"Rapid decline\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Equity Investments."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "7xuMNgAb9dz0EiQr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.589, 0.067, 0.705, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes seven pairs of bars. Each pair corresponds to a different investment trend as labeled in the y-axis, which from bottom to top are: \"Dramatic surge\", \"Rapid decline\", \"Pronounced decrease\", \"Consistent expansion\", \"Upward trajectory\", \"Rapid recovery\", \"Persistent growth\". On the left of each pair is a purple bar representing Private Equity Investments Return Rate, and on the right is a lighter violet bar representing Public Equity Investments Return Rate."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "R2nweiTNUkpmzVD0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.363, 0.190, 0.792, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars representing the return rate for the investment trend \"Rapid decline\". The left bar represents the Private Equity Investment with a return rate of -31% and the right bar represents the Public Equity Investment with a return rate of +28%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "aRWRuvwfGxPpn8HY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the structure within the coordinates [0.589, 0.808, 0.654, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a single light violet bar, which represents the Public Equity Investments Return Rate for the investment trend \"Persistent growth\", with a return value labeled as 9%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "ZoIXRYvULqba3FyO",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are positioned within the area defined by the coordinates [0.589, 0.314, 0.669, 0.413]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a light violet bar located in this area. It corresponds to the Public Equity Investments Return Rate for the \"Pronounced decrease\" trend, with a value labeled as +11%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "GUpq9dMbliopSfMr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the policy that corresponds to the coordinates [0.216, 0.067, 0.420, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Health."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "qVOFEV0KZHE60LFJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the \"Government Ethics\" bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.297, 0.808, 0.420, 0.906]."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "v9Avx5pSio7iClUy",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the effectiveness evaluation data for {the Infrastructure bar}<r>[0.420, 0.314, 0.810, 0.413]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "67%."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "mTMoQyk6VRt6EE7l",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the policy related to {the bar at}<r>[0.320, 0.684, 0.420, 0.783]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "SIde6Aku4PgpFyzm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar indicating \"Government Ethics\"}<r>[0.297, 0.808, 0.420, 0.906]</r> higher than {the bar indicating \"Public Health\"}<r>[0.216, 0.067, 0.420, 0.166]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "SHR6STdLHDf5nzm8",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Energy bar}<r>[0.420, 0.684, 0.682, 0.783]</r> represent a higher value in the effectiveness evaluation data than {the Defense bar}<r>[0.420, 0.561, 0.740, 0.659]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "bnvoGl29cm2FL0MD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {the Infrastructure bar}<r>[0.420, 0.314, 0.810, 0.413]</r> or {the Education bar}<r>[0.420, 0.437, 0.857, 0.536]</r> under the effectiveness evaluation data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "EMKU9YX46TmIJIxt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Public Health}<r>[0.216, 0.067, 0.420, 0.166]</r> and {Government Ethics}<r>[0.297, 0.808, 0.420, 0.906]</r>, which policy data has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Government Ethics."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "wFGpqGELl5Uk1Ct4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.216, 0.067, 0.420, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing policy data for Public Health. The bar is filled with the color #6e9d9f, indicating a value of -35 (or -35% when presented as a percentage)."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "SsAVicR2RdnQrAL2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description of the region [0.420, 0.314, 0.810, 0.413]."
+            },
+            {
+                "from": "gpt",
+                "value": "The rectangular area represents Infrastructure in the effectiveness evaluation data. It's colored in #c89c4c, suggesting a positive value of 67 (67%)."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "GfN4smhUxkwn1USv",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.420, 0.560, 0.740, 0.660]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one bar in this region representing Defense with an effectiveness evaluation value of 55, colored in #c89c4c."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "HtQe8tXSVAlsAqRW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.291, 0.190, 0.420, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a bar representing the policy data of Economic Policy. It is colored #6e9d9f and stands for a value of -22."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "aFUcNjRInrnZo30r",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}(1) at [0.052,0.231,0.289,0.254] (2), [0.080, 0.314, 0.289, 0.413](3) and [0.136, 0.477, 0.289, 0.501] (4)?"
+            },
+            {
+                "from": "gpt",
+                "value": "(1) Autonomous Driving, (2) Medical Diagnosis, (3) Supply Chain"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "SAjKuHw65chkCTH2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the names of the bars located at [0.338, 0.067, 0.612, 0.166](1), [0.402, 0.190, 0.612, 0.289](2) and [0.425, 0.314, 0.612, 0.413] (3) respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "(1) Speech Recognition, (2) Autonomous Driving, (3) Medical Diagnosis."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "lkPKcOfnuWGWj0lV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value of the bar located at [0.368, 0.437, 0.612, 0.536](1) and [0.422, 0.684, 0.612, 0.783](2)?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the text annotations, (1) 73%, (2) 57%."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "unu5vwiRaEXWh3mq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConcerning the two bars representing Autonomous Driving, is {the synthetic data bar} at [0.402, 0.190, 0.612, 0.289] higher or lower than {the real use case bar} at [0.612, 0.190, 0.873, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "NJN8QXDZIURJyImG",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the synthetic data bar} at [0.425, 0.314, 0.612, 0.413] for Medical Diagnosis surpass {the real use case bar} at [0.612, 0.314, 0.836, 0.413]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "yevlYPEYxLAzWAXh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the speech recognition category} at [0.338, 0.067, 0.612, 0.166] for synthetic data and [0.612, 0.067, 0.930, 0.166] for real use case, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Real use case"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "zE8NYGAfOMb0VLPF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.338, 0.067, 0.930, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the bars representing the values for all the AI applications categories. For both synthetic data and real use cases, the categories include Speech Recognition, Autonomous Driving, Medical Diagnosis, Supply Chain, Advertisement, Smart Homes, and Customer Service Bots."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "8hYm2Dupmkifnzjl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.024, 0.848, 0.289, 0.871]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the label for the AI application category - Customer Service Bots."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "BwuAZz77j8jkIwRT",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.338, 0.068, 0.930, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 bars: The purple bar represents synthetic data for the Speech Recognition category and the lilac bar represents the real use case for the same category. Their respective values are annotated with text within them - 82% for synthetic data and 95% for real use cases."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "mLah0uPJICAa9hxm",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar with coordinates [0.556, 0.068, 0.806, 0.145] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Gym participation rate for Running."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "W475gUR2kKKAttKX",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name of the category with coordinates [0.087, 0.291, 0.172, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cycling."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "SPxMu0XuBAI1S4lc",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the activity and participation type that corresponds to the coordinates [0.556, 0.649, 0.886, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is Boxing with Gym participation."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "vICaBeWQLSDuKzi7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the traditional participation bar for Weightlifting at [0.305, 0.455, 0.556, 0.532] higher or lower than the Gym participation bar at [0.556, 0.455, 0.869, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "UbbkzpKJIcGmXnch",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the heights of the Gym participation bars for Cycling at [0.556, 0.261, 0.765, 0.339] and Boxing at [0.556, 0.649, 0.886, 0.726]. Which one is taller?"
+            },
+            {
+                "from": "gpt",
+                "value": "Boxing."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "uDxPJPDv11V712Cy",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bars for Traditional participation generally shorter than those for Gym participation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "DedahcnPk9TCKbeW",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.556, 0.068, 0.923, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the bars representing Gym participation percentages for various sports such as Running, Swimming, Cycling, Yoga, Weightlifting, Crossfit, Boxing, Golf, and Basketball. The sizes of the bars vary, with sports like Crossfit having a higher percentage and others like Golf having a lower percentage."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "kxQ6USh54wX0mBn5",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears within the region [0.051, 0.164, 0.172, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a y-axis label within this region, indicating 'Swimming'."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "JJ7AMsoNJgcmnIRj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region from [0.347, 0.068, 0.556, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a bar and a text label. The bar, colored in '#d4a3da', represents the Traditional participation rate for Running. The text label, written in black and located to the left of the bar, indicates a percentage of 50%."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "1iMJDTWK0hw9OHab",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the {Investment Strategy bar}[0.583, 0.561, 0.783, 0.659] correspond to in the Human-Investment group?"
+            },
+            {
+                "from": "gpt",
+                "value": "22%"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "AMInx0gC6oFavCYE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item represented by the coordinates [0.030, 0.848, 0.265, 0.871] in the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Retirement Planning"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "u8Nh4Ndezzw7pTLK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar located at [0.418, 0.808, 0.583, 0.906] representing in the SynRobo-Investment group?"
+            },
+            {
+                "from": "gpt",
+                "value": "Retirement Planning"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "PZVoefky8pnSfMHm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Financial Planning in SynRobo-Investment}[0.473, 0.190, 0.583, 0.289] larger or smaller than the {bar for Financial Planning in Human-Investment}[0.583, 0.190, 0.774, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "VF0YRbxw0o1vqd8s",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Asset Allocation bar in SynRobo-Investment}[0.409, 0.437, 0.583, 0.536] have a higher or lower position than the {Risk Management bar in Human-Investment}[0.583, 0.067, 0.802, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "c4U4SPXoyMQUXedQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for Cash Flow in Human-Investment}[0.583, 0.684, 0.884, 0.783] larger or smaller than the {bar for Cash Flow in SynRobo-Investment}[0.400, 0.684, 0.583, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "Xxl8sjqMb7YHdb1y",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements inside [0.282, 0.025, 0.975, 0.948]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a combination of labels and bars representing different categories of investment strategies (Risk Management, Financial Planning, Wealth Management, Asset Allocation, Investment Strategy, Cash Flow, Retirement Planning) from SynRobo-Investment and Human-Investment visualized as a horizontal bar chart."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "4ukF3cxcTMX3UFFu",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the structures found at [0.400, 0.684, 0.884, 0.783]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. The bar located at [0.400, 0.684, 0.583, 0.783] represents the performance of Cash Flow in SynRobo-Investment, with value -20%. Another bar located at [0.583, 0.684, 0.884, 0.783] represents the performance of Cash Flow in Human-Investment, with value 33%."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "s7ZT0rjwIHHLt67j",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do objects within the region [0.473, 0.190, 0.774, 0.289] tell us?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two bars. The bar located at [0.473, 0.190, 0.583, 0.289] represents Financial Planning with SynRobo-Investment marked at -12%, and another bar ([0.583, 0.190, 0.774, 0.289]) represents Financial Planning with Human-Investment marked at 21%."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "SmzhKMWbJe9xc3J2",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.056, 0.753, 0.292, 0.776] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, referred to as Genetic Engineering."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "o780KeIfhJWeLH6b",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth text in the y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.134, 0.427, 0.292, 0.450]"
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "yIkguJ90Ma7tz9ip",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the bar}[0.252, 0.176, 0.530, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Climate Change for the human-authored data."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "EJPyO9KyboBv4VgD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology type do the coordinates [0.074, 0.861, 0.292, 0.885] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR - Virtual Reality."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "wxjBcHJNlxKPK0eZ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for self-driving cars in human-authored data}[0.252, 0.284, 0.808, 0.371] longer than {the bar for smart homes in human-authored data}[0.252, 0.393, 0.697, 0.480]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "1a92DdssYgezoQKP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for cryptocurrencies in synthetic data}[0.252, 0.501, 0.586, 0.588] shorter than {the bar for artificial intelligence in synthetic data}[0.252, 0.610, 0.697, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "EpX6b0HYQxsgR64p",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the bar for VR - Virtual Reality in synthetic data}[0.252, 0.827, 0.419, 0.914] and {the bar for VR - Virtual Reality in human-authored data}[0.252, 0.827, 0.641, 0.914], which one is longer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for VR - Virtual Reality in human-authored data."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "R7lIeUKmvoHUaFvb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for genetic engineering in human-authored data}[0.252, 0.718, 0.363, 0.805] shorter than {the bar for genetic engineering in synthetic data}[0.252, 0.718, 0.808, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "wneKSiTC8oA6ozzt",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.252, 0.067, 0.808, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to all the bars in the graph. It contains two sets of bars, representing synthetic data and human-authored data. Each set of bars consists of 8 bars, each representing a different technology type: Domestic Robots, Climate Change, Self-driving Cars, Smart Homes, Cryptocurrencies, Artificial Intelligence, Genetic Engineering, and VR - Virtual Reality."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "lozc7HfRO9CMVLp4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.096, 0.102, 0.292, 0.885]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to all the y-axis labels, which are the names of the technology types: Domestic Robots, Climate Change, Self-driving Cars, Smart Homes, Cryptocurrencies, Artificial Intelligence, Genetic Engineering, VR - Virtual Reality."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "ll5bqLCu4XKkkrsU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.252, 0.501, 0.586, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object:\nThe lavender-colored bar represents the value for Cryptocurrencies in synthetic data, and it spans from 0% to 30% on the x-axis."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "UHAuff4wlCLPYR6W",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.252, 0.501, 0.864, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars:\n1. The lavender-colored bar that corresponds to Cryptocurrencies under synthetic data, the percentage value is 30%, ranging from 0% to 30% on the x-axis.\n2. The deep violet-colored bar that corresponds to Artificial Intelligence under human-authored data, the percentage value is 55%, ranging from 0% to 55% on the x-axis."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "Ks9562HaHoiRhppd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label at [0.019, 0.209, 0.266, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Authentic Leadership."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "TElhBybdStSMuE85",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text that indicates the value of the bar titled \"Charismatic Leadership\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.607, 0.647, 0.646, 0.664]"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "jPHzPCkFGdyxG2D0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bar at [0.710, 0.392, 0.901, 0.480] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Laissez-faire Leadership with a decline of 12%."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "8DxdIckoM0EEIZA6",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value indicated at [0.543, 0.867, 0.582, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20%"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "fw1C5CPGVZqwHCyI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Service Leadership\"}[0.550, 0.722, 0.901, 0.810] shorter or taller than {the bar representing \"Authentic Leadership\"}[0.518, 0.172, 0.901, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "xkH5p0y0ziuZecMI",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater, {the decline in \"Charismatic Leadership\"}[0.646, 0.612, 0.901, 0.700] or {the decline in \"Transactional Leadership\"}[0.758, 0.502, 0.901, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The decline in Charismatic Leadership."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "INZJVBrkDIZWsjdE",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Service Leadership bar}[0.550, 0.722, 0.901, 0.810] longer than {the Laissez-faire Leadership bar}[0.710, 0.392, 0.901, 0.480]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "5WyeKkE3GYDwMF1y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Transformational Leadership Point}[0.614, 0.062, 0.901, 0.150] and {Participative Leadership Point}[0.566, 0.282, 0.901, 0.370] which point represents a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transformational Leadership Point."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "1uLlAY2tCFKn1UP2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.278, 0.062, 0.901, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the decline of various leadership styles in business teams, with data displayed as bars. From top to bottom, the leadership styles are Coaching Leadership, Service Leadership, Charismatic Leadership, Transactional Leadership, Laissez-faire Leadership, Participative Leadership, Authentic Leadership, and Transformational Leadership, labels are positioned to the left of each bar."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "GxhgDdR2xUVv0L4t",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.518, 0.172, 0.901, 0.260] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a bar representing the decline in Authentic Leadership, which is reported as 24% and labeled to the left of the bar."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "4ZyfZ6NUUi8Ybh7x",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the data in [0.710, 0.392, 0.901, 0.480]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single data visualization bar. It represents the Laissez-faire Leadership style, showing a decline of 12% in Business Teams."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "96RvWDPUvSM4F053",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.582, 0.832, 0.901, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing Coaching Leadership, which shows a decline of 20% in Business Teams. The decline is labeled to the left of the bar."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "cds4hRehVYEOpBHA",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart with coordinates [0.408, 0.019, 0.866, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Transportation and Logistics Industry Trends\"."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "cS0RN5SDRpwKidFl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the activity trend at coordinate [0.805, 0.088, 0.949, 0.106] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This activity trend is named \"Surveyed Trends\"."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "wuJJMf68pKEf5quV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is located at [0.126, 0.634, 0.281, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Last Mile Delivery\"."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "kVONRCTKb5B2KVgY",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the x-axis located at [0.556, 0.964, 0.719, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Activity Trends (%)\"."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "vJEsBGfaSQplLyFF",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {Air Freight Volume}<r>[0.518, 0.805, 0.669, 0.887]</r> higher or lower than for {Ocean Freight Volume}<r>[0.356, 0.702, 0.669, 0.785]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "pw1pkNz86j86Y4Hz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Wholesale and Distribution}<r>[0.669, 0.497, 0.756, 0.579]</r> bar stand to the left or right of the {Retail and ECommerce}<r>[0.669, 0.395, 0.794, 0.477]</r> bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "ZMTj42bQK1kRMHbj",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Benchmark Trends}<r>[0.805, 0.062, 0.966, 0.080]</r> and the {Surveyed Trends}<r>[0.805, 0.088, 0.949, 0.106]</r>, which one has a green marker?"
+            },
+            {
+                "from": "gpt",
+                "value": "Surveyed Trends."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "HNZ94INzJkWz4aFt",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher? {Manufacturing and Production Surveyed Trends}<r>[0.669, 0.292, 0.844, 0.374]</r> or {3PL/4PL Services Surveyed Trends}<r>[0.669, 0.189, 0.781, 0.271]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Manufacturing and Production Surveyed Trends."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "8g02ChL1xmoVK6uY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within coordinates [0.293, 0.087, 0.981, 0.927]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart axes, legend and the bars representing the Benchmark Trends and Surveyed Trends for different categories. The Benchmark Trends bars are red and the Surveyed Trends bars are green. The y-axis labels range from \"Air Freight Volume\" at the top to \"Transportation Management\" at the bottom. The x-axis represents the Activity Trends (%) ranging from -30 to 30."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "bJTQ6yLo95twA82i",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.749, 0.055, 0.973, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with two entries. The first entry is the Benchmark Trends with a red square at [0.756, 0.064, 0.791, 0.076] and its label at [0.805, 0.062, 0.966, 0.080]. The second one is the Surveyed Trends with a green square at [0.756, 0.090, 0.791, 0.102] and its label [0.805, 0.088, 0.949, 0.106]."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "qtf8I27Q9nYGn5hc",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.275, 0.939, 1.055, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains labels for the x-axis, which are the values of Activity Trends (%). The values are -30, -20, -10, 0, 10, 20, 30 from left to right."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "D0g4rHIpgB4CN0MD",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.518, 0.805, 0.669, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a red bar that is representative of the \"Air Freight Volume\" category for the Benchmark Trends. In addition, an accompanying text annotation indicating the value \"12%\" is present within this area."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "I0LEiLDV7DI5Hwof",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.107, 0.477, 0.171, 0.501] represent on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Blogs'."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "seti8lFdBAOD8I1b",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first bar in the 'synthetic data' group?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.370, 0.067, 0.514, 0.166]"
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "W4HhuuvTo5xoSY7v",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.754, 0.105, 0.805, 0.128] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the percentage value 25% associated with the TV bar."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "ptCXsWsMHmqP3Gv9",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented by the coordinates [0.069, 0.601, 0.171, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Podcasts'."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "bJdOlWKhQ8nzoW29",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"Memes\" in synthetic data}[0.456, 0.808, 0.514, 0.906] shorter or taller than {the bar representing \"Forums\" in synthetic data}[0.427, 0.684, 0.514, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "uNPFSWgxmdTQtqKH",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing \"TV\" in human-authored data}[0.514, 0.190, 0.879, 0.289] wider than {the bar representing \"Podcasts\" in human-authored data}[0.514, 0.561, 0.629, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "E4wEcioD2ZkwLcqD",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the bar representing \"Newspapers\" in synthetic data}[0.370, 0.067, 0.514, 0.166] compared to {the bar representing \"Newspapers\" in human-authored data}[0.514, 0.067, 0.754, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the left."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "FrYr7MNONl7M2GBo",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the bar representing \"Blogs\" in synthetic data}[0.418, 0.437, 0.514, 0.536] and {the bar representing \"Blogs\" in human-authored data}[0.514, 0.437, 0.562, 0.536], which group shows a larger values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human-authored data."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "4tdRSLW34aTwIdDQ",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.370, 0.067, 0.514, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the complete set of bars labelled as 'synthetic data'. They each represent a percentage value for different media categories: \n1. Newspapers at coordinates [0.370, 0.067, 0.514, 0.166] with a negative representation of -15%.\n2. TV at coordinates [0.283, 0.190, 0.514, 0.289] with a negative representation of -24%.\n3. Social Media at coordinates [0.312, 0.314, 0.514, 0.413] with a negative representation of -21%.\n4. Blogs at coordinates [0.418, 0.437, 0.514, 0.536] with a negative representation of -10%.\n5. Podcasts at coordinates [0.466, 0.561, 0.514, 0.659] with a negative representation of -5%.\n6. Forums at coordinates [0.427, 0.684, 0.514, 0.783] with a negative representation of -9%.\n7. Memes at coordinates [0.456, 0.808, 0.514, 0.906] with a negative representation of -6%."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "A4OsLkKkWYM0EepU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates [0.514, 0.067, 0.879, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the region containing all the bars representing 'human-authored data' each signifying a percentage of different media categories in positive values:\n1. Newspapers: [0.514, 0.067, 0.754, 0.166], with a representation of 25%\n2. TV: [0.514, 0.190, 0.879, 0.289], with a representation of 38%\n3. Social Media: [0.514, 0.314, 0.668, 0.413], with a representation of 16%\n4. Blogs: [0.514, 0.437, 0.562, 0.536], with a representation of 5%\n5. Podcasts: [0.514, 0.561, 0.629, 0.659], with a representation of 12%\n6. Forums: [0.514, 0.684, 0.706, 0.783], with a representation of 20%\n7. Memes: [0.514, 0.808, 0.591, 0.906], with a representation of 8%."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "ib9lCN6YfQwrow4s",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area represented by the coordinates [0.0, 0.0, 0.171, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the labels of different media categories on the y-axis. From top to bottom, the labels are 'Memes', 'Forums', 'Podcasts', 'Blogs', 'Social Media', 'TV' and 'Newspapers'. The corresponding coordinates for each label are '[0.088, 0.848, 0.171, 0.871]', '[0.086, 0.724, 0.171, 0.748]', '[0.069, 0.601, 0.171, 0.624]', '[0.107, 0.477, 0.171, 0.501]', '[0.025, 0.354, 0.171, 0.377]', '[0.141, 0.231, 0.171, 0.254]' and '[0.029, 0.107, 0.171, 0.131]' respectively."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "pvIYxCSUgJrGBYPq",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plotting library used to generate the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "matplotlib"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "I9j7LmXWHq8qnpoE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the process represented by the bar with the coordinates [0.556, 0.061, 0.707, 0.161]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Customer inquiries"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "589sIiFXCTMTjKLz",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the bar at the coordinates [0.462, 0.061, 0.556, 0.161]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Outsourced"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "SxWKcTQh2tIuxTww",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the outsourced bar {bar}<r>[0.462, 0.061, 0.556, 0.161]</r> taller than the bar representing internal handling {bar}<r>[0.556, 0.061, 0.707, 0.161]</r> for customer enquiries?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "2h3pjMMvElZKF0VO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the internal handling sales {bar}<r>[0.556, 0.687, 0.895, 0.787]</r> and after-sales service {bar}<r>[0.556, 0.812, 0.745, 0.912]</r>, which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "2uljnNhPlAYENSwh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the outsourced manufacturing {bar}<r>[0.255, 0.437, 0.556, 0.537]</r> have a higher value than internal handling manufacturing {bar}<r>[0.556, 0.437, 0.933, 0.537]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "u6RCX1tntnRySCbl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area with coordinates [0.556, 0.061, 0.707, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a blue bar that represents the number of customer inquiries that have been handled internally. The volume of internal handling customer inquiries is 40, indicated by a text label at [0.933, 0.478, 0.982, 0.495] marked as 100%."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "zT7pH8ByAq3XynbF",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area with coordinates [0.462, 0.061, 0.556, 0.161] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the bar representing the outsourced Customer inquiries having a volume of -25 as indicated by a text label at [0.424, 0.103, 0.462, 0.120] marked as 25%."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "5NMZsgJn1RedzPXB",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the coordinates [0.556, 0.812, 0.745, 0.912] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a blue bar that represents the number of after-sales service processes handled internally with a value of 50 as denoted by a text label at [0.745, 0.853, 0.783, 0.871] with a percentage of 50%."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "gQCvbS00jvzZ3ogg",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the color of the bar representing \"Testing\" in synthetic data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the bar representing \"Testing\" in synthetic data is {cyan}<r>[0.298, 0.560, 0.488, 0.660]</r>."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "02ZOpZozPU7fkY3E",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label of the y-axis tick at coordinates [0.022, 0.852, 0.190, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the y-axis tick at the given coordinates is \"Review and Control\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "XBF4BtX7sVQPHfR3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at the coordinates [0.274, 0.186, 0.488, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The cyan bar representing synthetic data for the \"Ideation\" process with its value as 45% located at {coordinates}<r>[0.274, 0.186, 0.488, 0.286]</r>."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "nie8tQnKifcqjS2e",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's labeled at coordinates [0.355, 0.102, 0.393, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text '20%' is labeled at the given {coordinates}<r>[0.355, 0.102, 0.393, 0.120]</r>."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "tmXU0CvCtMWfkm0R",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of synthetic data for the \"Ideation\" process (cyan bar) at [0.274, 0.186, 0.488, 0.286] higher than its corresponding human-authored data (purple bar) at [0.488, 0.186, 0.631, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "AAwxnXDJ9O6Xyg1Y",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the cyan bar for \"Design\" [0.441, 0.311, 0.488, 0.410] located in relation to the purple bar for \"Design\" [0.488, 0.311, 0.726, 0.410]?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the Left."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "qYvDQxARMV2nLzOP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the synthetic data bar for \"Review and Control\" located at [0.321, 0.809, 0.488, 0.909] shorter than the human-authored data bar for \"Review and Control\" at [0.488, 0.809, 0.774, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "92Krs5okeJbUXvHf",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the x-axis labels, is the value at \"40\" [0.668, 0.964, 0.690, 0.981] located to the right of the value \"0\" [0.483, 0.964, 0.494, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "j5EbAYLngRH07wjq",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.202, 0.019, 0.965, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.202, 0.019, 0.965, 0.952] represents the entire chart area, including all synthetic and human-authored data bars, x-axis and y-axis, labels for all bars, and all their corresponding values."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "Xla5prwUfrfbFx5P",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what's within the region [0.441, 0.311, 0.488, 0.410]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region [0.441, 0.311, 0.488, 0.410] is the cyan bar marking synthetic data for the \"Design\" stage, with a value of 10%."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "cod32pz50Tcb7rda",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.321, 0.809, 0.488, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "The object within these coordinates [0.321, 0.809, 0.488, 0.909] is a cyan bar that stands for the synthetic data of the \"Review and Control\" stage, with its value as 35%."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "5rFtI5SbIYAHsTfU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.274, 0.186, 0.631, 0.286]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two horizontal bars. The cyan bar at {coordinates}<r>[0.274, 0.186, 0.488, 0.286]</r> represents synthetic data for \"Ideation\" at 45%. The purple bar at {coordinates}<r>[0.488, 0.186, 0.631, 0.286]</r> represents human-authored data for \"Ideation\" at 30%."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "bP4IvXVd1dRnDorp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the bar related to \"Transportation\" in the group \"Urban Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the category \"Transportation\" in the \"Urban Growth\" group is #20639b, referring to the bar located at {the bar}<r>[0.653, 0.814, 0.885, 0.915]</r>."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "PjrFzQCKjPRqgSA3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label aligned to the coordinates [0.015, 0.103, 0.212, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label at {this position}<r>[0.015, 0.103, 0.212, 0.117]</r> is \"Infrastructural Development\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "iDnBZnDrwLYvxnRr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar that represents \"Technological Advancement\" in the \"Urban Growth\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Technological Advancement\" bar in the \"Urban Growth\" category is located at {this position}<r>[0.653, 0.562, 0.852, 0.663]</r>."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "L4BTI9l3TMWhAZxS",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value associated with {the bar}<r>[0.653, 0.184, 0.753, 0.285]</r> in the \"Urban Growth\" group?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 15%, referring to the text located at <r>[0.905, 0.101, 0.936, 0.115]</r>."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "uD8kReQ9fISyagwz",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the \"Rural Growth\" bar corresponding to \"Agriculture\"}<r>[0.388, 0.436, 0.653, 0.537]</r> reach further on the x-axis than {the \"Urban Growth\" bar corresponding to \"Agriculture\"}<r>[0.653, 0.436, 0.819, 0.537]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "mvKIfwdX5ybzRQCh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Employment Opportunities\" in the \"Rural Growth\" category taller or shorter than the corresponding bar in the \"Urban Growth\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shorter."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "7b5pgemh90hYBspn",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween \"Educational Facilities\" and \"Healthcare Facilities\", which one has a higher growth in the Urban area, according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Educational Facilities\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "IUClRP8T3VQruWIQ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, is \"Rural Growth\" in \"Transportation\" larger or smaller than \"Urban Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "mbh6hOhoXdmCF4Yn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information represented in the area [0.653, 0.058, 0.905, 0.159]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar chart representing the \"Urban Growth\" of \"Infrastructural Development\", with a growth percentage of 38%, as can be seen from the bar at {the bar}<r>[0.653, 0.058, 0.905, 0.159]</r> and the text at {the bar text}<r>[0.819, 0.480, 0.850, 0.494]</r>."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "ykmsOlUfmhq4HTUd",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the distribution along the y-axis from [0.018, 0.607, 0.212, 0.621] to [0.112, 0.859, 0.212, 0.873]."
+            },
+            {
+                "from": "gpt",
+                "value": "These labels represent the growth of different areas from \"Technological Advancement\" at {this position}<r>[0.018, 0.607, 0.212, 0.621]</r> to \"Transportation\" at {this position}<r>[0.112, 0.859, 0.212, 0.873]</r> on the y-axis."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "zovMTD3IvHRMpLc4",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within the coordinates [0.487, 0.184, 0.653, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinated exists a bar, representing \"Rural Growth\" for the category \"Educational Facilities\". It has a negative growth of 25% as indicated by the text at {this location}<r>[0.905, 0.101, 0.936, 0.115]</r>."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "wACkyM0qLUOfm7Ub",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you discuss the data depicted from [0.308, 0.058, 0.653, 0.159] to [0.355, 0.814, 0.653, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range includes the representation of \"Rural Growth\" in all categories from \"Infrastructural Development\" at {the first bar}<r>[0.308, 0.058, 0.653, 0.159]</r> to \"Transportation\" at {the last bar}<r>[0.355, 0.814, 0.653, 0.915]</r>. It shows a variety of growth percentages for different categories."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "7DKMI6BbApjOMAFw",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used to represent the label \"7 Constraint Words\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "BJyxwkrmekTG2MRJ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the tick marking on the x-axis for the label \"-1.0\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.542, 0.475]"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "MEtuLCbx1Yp1UMcK",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the specialty label} at [0.031, 0.426, 0.105, 0.440]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cardiology."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "DZEb41ryhvztfg3X",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for \"Cardiology\"}[0.571, 0.408, 0.830, 0.455] taller than {the bar for \"Dermatology\"}[0.225, 0.174, 0.830, 0.221]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "Jq36zkydXkv1NjUO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue bar for \"Neurology\"}[0.686, 0.291, 0.830, 0.338] start at a higher value than {the blue bar for \"Urology\"}[0.629, 0.057, 0.830, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "eRBQ6s0wpLntwJPm",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the green bars for \"Dermatology\"}[0.341, 0.675, 0.830, 0.696] shorter in length than {the blue bars for \"Dermatology\"}[0.225, 0.174, 0.830, 0.221]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "3J0wQtFpMjwlHcvC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects and their positions within the region [0.110, 0.037, 0.974, 0.475]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main chart with bars representing medical specialties against different constraint words. Each medical specialty, like Cardiology, Neurology, Dermatology, and Urology, has different colored bars located at different heights, indicating the improvement in symptom scores."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "66Pn4JZxaqvjpH8V",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give details about the region defined by the coordinates [0.117, 0.516, 0.309, 0.604]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section is the legend area of {the second subplot}<r>[0.110, 0.509, 0.974, 0.947]</r>. It includes color handles and text labels, identifying four different groups: '3 Constraint Words' with a pink handle, '5 Constraint Words' with a green handle, '7 Constraint Words' with a magenta handle, and '10 Constraint Words' with a blue handle."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "OYHrICy3VeBCbeUr",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within [0.000, 0.000, 0.100, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specific region is outside the main chart area and does not contain any visible elements related to the chart."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "1TIoBQ5kKoJ8xkyR",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the handle color} at [0.169, 0.142, 0.197, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'#05b9e2'"
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "6ohJYSLo7mbEHKB4",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located at [0.647, 0.402, 0.974, 0.450] on the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a bar depicting the 3-year average for the research field 'Graphene Research'."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "rfKJMM0CJ4WkckTU",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content positioned at [0.413, 0.066, 0.974, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a bar signifying the 'AI Technology' category's 10-Year Average."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "PzVGWZg0uC8Cx4yb",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar representing 'Graphene Research'}[0.343, 0.722, 0.974, 0.770] longer or shorter than {the bar representing 'AI Technology'}[0.413, 0.530, 0.974, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longer."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "ieQAq1iC8gZfHKRR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {3-Year Average} [0.647, 0.866, 0.974, 0.914] and {5-Year Average} [0.764, 0.818, 0.974, 0.866] on 'Graphene Research' category, which one has the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "3-Year Average."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "WiduEgtUfop1tSWO",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {AI Technology's 10-Year Average bar}[0.413, 0.530, 0.974, 0.578] longer than its {7-Year Average bar}[0.507, 0.578, 0.974, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "H9L6fEF1aQVXuNfS",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the description of the region [0.163, 0.520, 0.325, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region contains a legend with four entries. Each entry consists of a color handle and a label. They are: {3-Year Average}[0.208, 0.527, 0.311, 0.544] with handle color #f27970, {5-Year Average}[0.208, 0.553, 0.311, 0.570] with handle color #54b345, {7-Year Average}[0.208, 0.579, 0.311, 0.597] with handle color #c76da2 and {10-Year Average}[0.208, 0.605, 0.320, 0.623] with handle color #05b9e2."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "fxkLUvsRxZtLCnH2",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.647, 0.402, 0.974, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a bar representing the 3-Year Average for the research field 'Graphene Research', showing the decrease in the budget."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "vnJLHMWkqYgST6Xn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the coordinates [0.505, 0.964, 0.626, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content represents the x-label, which is the 'Budget ($ Billion)'."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "0MXFk9VrjDvc3pxr",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element represented by the coordinates [0.797, 0.520, 0.978, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend which contains labels for 'Gradual Recovery', 'Sporadic Growth', 'Marginal Decrease', and 'Gradual Downturn'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "aNoxxFYeOmECTSs5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is positioned at coordinates [0.504, 0.964, 0.543, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label, named 'Score'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "YVTez4Dqamh8387q",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the color marker at [0.804, 0.607, 0.832, 0.619] represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "This color marker corresponds to the 'Gradual Downturn' trend in the legend."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "GajdQDdU05ClmtNJ",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of 'Sporadic Growth' trend for 'Urban' and 'Rural' regions located at [0.523, 0.829, 0.782, 0.871] and [0.523, 0.616, 0.763, 0.658] respectively, similar or different?"
+            },
+            {
+                "from": "gpt",
+                "value": "Different."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "KrNn8MwS3bKZv1nq",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Gradual Downturn' bar for 'Urban' located at [0.154, 0.743, 0.523, 0.786] shorter or taller than the 'Gradual Downturn' bar for 'Rural' at [0.191, 0.530, 0.523, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taller."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "IVGBuLKrDAuIaHqU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the two bars at coordinates [0.523, 0.871, 0.690, 0.914] and [0.523, 0.658, 0.653, 0.701], which represents 'Gradual Recovery' trend's value for 'Urban' and 'Rural' respectively, which one is larger?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "Qyp4UEUHigMXqWkn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe what is found in the region [0.797, 0.520, 0.978, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. The legend consists of four labels, each paired with a color marker. The four labels represent different trends: 'Gradual Recovery', 'Sporadic Growth', 'Marginal Decrease', and 'Gradual Downturn'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "odcZlQ6QMzLa0a3L",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.523, 0.658, 0.782, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two bars from the bar chart. The first bar at [0.523, 0.829, 0.782, 0.871] represents the 'Sporadic Growth' trend value for 'Urban'. The second bar at [0.523, 0.658, 0.763, 0.658] is the 'Sporadic Growth' trend value for 'Rural'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "SE7cDd6T5mdCPA1h",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the detail within the region with coordinates [0.154, 0.530, 0.523, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two bars. The first bar at [0.154, 0.743, 0.523, 0.786] corresponds to the 'Gradual Downturn' trend for the 'Urban' region. The second bar at [0.191, 0.530, 0.523, 0.573] represents the 'Gradual Downturn' trend for the 'Rural' region."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "vAHeUCkU04AYRElB",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label, provided coordinates [0.122, 0.462, 0.136, 0.479], represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label{AI}<r>[0.122, 0.462, 0.136, 0.479]</r> refers to the technology AI."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "nBRQEsZYCfebOIcd",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the X-axis tick, located at [0.921, 0.939, 0.943, 0.957], mean?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis tick at this location{tick}[0.921, 0.939, 0.943, 0.957] represents a Score of 3.5."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "3X9LYzRhncCjauoD",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of trend does the red bar located at [0.563, 0.885, 0.932, 0.885] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar {Trend 2021 AI}<r>[0.563, 0.885, 0.932, 0.885]</r> represents the prediction trend for the technology 'AI' in '2021."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "TaVGWpZDgVJOtd01",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the height of the blue bar, identified at [0.563, 0.885, 0.647, 0.914], compare to the red bar at [0.563, 0.857, 0.932, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The red bar (indicating the 'AI' trend for '2021) is taller than the blue one (indicating the 'AI' trend for '2020)."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "mKVGWkNxpmVkbZsL",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen considering the bars indicating 'Blockchain' technology, as represented by blue at [0.299, 0.804, 0.563, 0.832] and red at [0.563, 0.775, 0.710, 0.804], is the red bar taller than the blue one?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "vzm3tLAa2vP6xHrV",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the 'Blockchain' and 'IoT', whose red bar{Blockchain red bar}[0.563, 0.775, 0.710, 0.804],{IoT red bar}[0.563, 0.530, 0.647, 0.559], has a higher score for the trend in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "h4kh9VmbJhLIYpe9",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the provided coordinates of [0.140, 0.047, 0.985, 0.469] refer to in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart region [0.140, 0.047, 0.985, 0.469] represents the entire lower subplot of the graph, where the bars representing the technology trends for '2020 are displayed. Interestingly, all bars start from the point '0.00' on the X-axis and extend to the right based on the respective score."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "V1xGxORRU3hnFUrY",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.848, 0.520, 0.978, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.848, 0.520, 0.978, 0.578] contains small rectangles and the respective text labels serving as the legend for the upper subplot. It includes 'Trend_2020' label with blue color at [0.892, 0.527, 0.972, 0.544] and 'Trend_2021' label marked with red color at [0.892, 0.553, 0.973, 0.571], corresponding to the colors of the bars in the graph."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "1YnTBzzKz8pIS2xN",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the content represented by the region [0.563, 0.530, 0.647, 0.559] on this graph."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.563, 0.530, 0.647, 0.559] consists of a single red bar which indicates the predicted 2021 trend of the 'IoT' technology."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "0zDgonPvZlbRqJvN",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information located at coordinates [0.392, 0.019, 0.749, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, reading: \"Rural and Urban Development Comparison\"."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "uK1fQWK0raiuDRh3",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object present at the coordinates [0.180, 0.541, 0.208, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "xrYV0HCAstkN6E1O",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title at coordinates [0.392, 0.019, 0.749, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural and Urban Development Comparison."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "4NIFYffMkUM91Ew5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred by the coordinates in the area [0.066, 0.899, 0.163, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label on the second plot, \"Steep Decline\"."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "of32SBxR7VIDB4E1",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich bar is taller, the one located at [0.510, 0.898, 0.974, 0.915] or the one at [0.571, 0.881, 0.974, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar at [0.510, 0.898, 0.974, 0.915]."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "uam3hCgXtpdUKzov",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar for {\"Urban Development\"}<r>[0.510, 0.898, 0.974, 0.915]</r> longer than the bar for {\"Rural Development\"}<r>[0.571, 0.881, 0.974, 0.898]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "YnIWCG6BD0aHSBzw",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {\"Steep Decline\"}<r>[0.066, 0.899, 0.163, 0.917]</r> higher on the graph than {\"Insignificant Increase\"}<r>[0.015, 0.786, 0.163, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "ZOJyBbubjiXF0zNY",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the bars from the categories {\"Steep Decline\"}<r>[0.510, 0.898, 0.974, 0.915]</r> and {\"Insignificant Increase\"}<r>[0.631, 0.785, 0.974, 0.802]</r>, which one has higher scores for the Urban Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Steep Decline."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Jd9Ye6Si9J0msipz",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within the region [0.167, 0.047, 0.974, 0.457]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the first subplot of the chart. It contains the bars representing the categories of \"Steep Decline\", \"Insignificant Increase\", \"Sudden Decrease\", and \"Persistent Decline\" for both \"Urban Development\" and \"Rural Development\". It also contains x-axis labels ranging from -3.5 to 0.0."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "p0kptqthbSn8Wfil",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area marked as [0.571, 0.513, 0.571, 0.513]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no visible content in the specified region. It's an empty space in between the two charts."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "oxCqGq1EgYssVFIj",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.254, 0.055, 0.364, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend, which details the color coding of the bars. There are two labels here: a blue label indicating \"Urban Development\" and a green label indicating \"Rural Development\"."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "F00NglrlYQ7uOFFn",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in [0.167, 0.523, 0.974, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It features the second subplot of the chart, similar to the first but shifted upwards. It, too, contains bars representing \"Steep Decline\", \"Insignificant Increase\", \"Sudden Decrease\", and \"Persistent Decline\" for both \"Urban Development\" and \"Rural Development\", alongside x-axis labels from -3.5 to 0.0."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "aTcBo7nOati9tCtE",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text labeled in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Impact."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "pbXEgIOFkkz3OZ7j",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the located at [0.383, 0.662, 0.410, 0.680]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"-2.9\" associated with the Moderate Decline of the category \"Social Cultural Change\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "5GdxVw5Bm9fFljUp",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item in coordinates [0.185, 0.506, 0.357, 0.616] associate with the label \"Erratic Growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>#f27970</r>"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "TgPUBWZqoAVIYpau",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label say at the y-tick with coordinates [0.015, 0.395, 0.173, 0.413]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Social Cultural Change\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "91DXtLmfcVB7uNZX",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater impact, \"Social Cultural Change\" as depicted by {the Erratic Growth bar}<r>[0.337, 0.869, 0.974, 0.913]</r> or \"Value Change\" as illustrated by {the Marginal Growth bar}<r>[0.556, 0.605, 0.974, 0.649]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Social Cultural Change\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "Adutzpc5TGVHZrQ9",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor \"Value Change\", is {the impact of Sporadic Growth}<r>[0.516, 0.517, 0.974, 0.561]</r> greater or less than {the impact of Moderate Decline}<r>[0.656, 0.561, 0.974, 0.605]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "URAWFgTNgH7Kug5Z",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the four groups displaying different impacts, is the {group representing Moderate Decline}<r>\"Moderate Decline\" coordinates in legend: [0.229, 0.565, 0.352, 0.583]</r> generally larger or smaller than the {group representing Sporadic Growth}<r>\"Sporadic Growth\" coordinates in legend: [0.229, 0.591, 0.345, 0.609]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "ZqALBs46veDaFE0F",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the bar representing \"Value Change\" under {Erratic Growth}<r>[0.397, 0.039, 0.974, 0.105]</r> taller than the same category under {Marginal Growth}<r>[0.556, 0.605, 0.974, 0.649]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "dGG1niKFvn7TT67G",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the pattern of bars within [0.178, 0.497, 0.974, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two sets of bars representing \"Social Cultural Change\" and \"Value Change\" across four different impacts: \"Erratic Growth\", \"Marginal Growth\", \"Moderate Decline\", and \"Sporadic Growth\". Each set contains four bars in the sequence of the impacts described. The bars appear to be generally decreasing in length from left to right, indicating a decrease in negative impact."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "R0J38YgPD1KFphWl",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.185, 0.506, 0.357, 0.616]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the bar chart. It lists four types of impacts: \"Erratic Growth\" with color code #f27970, \"Marginal Growth\" with color code #54b345, \"Moderate Decline\" with color code #c76da2, and \"Sporadic Growth\" with color code #05b9e2."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "2mtRYvVTkM91oEK1",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main components within the area [0.178, 0.019, 0.974, 0.455]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises bars representing \"Social Cultural Change\" and \"Value Change\" under the impact of \"Erratic Growth\" with x-axis labels showing different scale degrees of impact from \"-3.5\" to \"0.0\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "RQYl8ZVxIesgwegi",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.337, 0.369, 0.974, 0.435] illustrate?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses one bar representing \"Social Cultural Change\" with an impact of \"Erratic Growth\", measuring at -3.2."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "999uGopvKSJOT6FC",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type of this visual?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bar chart."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "nTAKHku97Vv8XtUV",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat disease is associated with the coordinates [0.015, 0.409, 0.102, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heart Stroke."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "tLaoHTHh0ifezj47",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at coordinates [0.333, 0.487, 0.372, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "CmRDBNS19kkfp9A1",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.814, 0.062, 0.908, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the chart subtitle}<r>[0.144, 0.019, 0.560, 0.041]</r>."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "fpgHrZEv9JEeKU6j",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for Influenza for Critical Cases}<r>[0.597, 0.284, 0.843, 0.328]</r> longer than {the bar for Diabetes for Critical Cases}<r>[0.597, 0.065, 0.781, 0.109]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "1lwwo1SfbSvmXslU",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the bar for Diabetes for Recovered Cases} have its starting point to the left of {the x-tick for -1.0}<r>[0.458, 0.939, 0.492, 0.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "lQwwIVyCUES6jpXW",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Recovered Cases bar for Heart Stroke}<r>[0.536, 0.871, 0.597, 0.915]</r> shorter than the {Critical Cases bar for Heart Stroke}<r>[0.377, 0.394, 0.597, 0.438]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "XEAnutDQhhbxxxLh",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar for COPD for Recovered Cases}<r>[0.450, 0.652, 0.597, 0.695]</r> closer to the {x-tick for -1.0}<r>[0.458, 0.463, 0.492, 0.480]</r> or the {x-tick for -1.5}<r>[0.397, 0.463, 0.431, 0.480]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-tick for -1.5."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "HA9iBpctZH2c6VoX",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.107, 0.523, 0.597, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second plot area of the chart, specifically showing bars for Recovered Cases for various diseases. The bars and their labels are laid out along the x-axis, showing the scores associated with each disease's Recovered Cases."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "0TVKxRPrNjlNzB1o",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.107, 0.047, 0.597, 0.457]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the first plot area of the chart, focusing on the bar representations of Critical Cases for different diseases. The bars and their corresponding labels and values are positioned on the x-axis, depicting the scores associated with each disease's Critical Cases."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "cVAthPeiWrVLkvLb",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.114, 0.532, 0.283, 0.563] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region involves the label for Recovered Cases with its corresponding color representation in the legend. The label and the color patch are positioned towards the left side of the plot."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "6oqWMUK3G7NiCH6M",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.446, 0.055, 0.591, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend entry for Critical Cases with its corresponding color representation. Both the color block and the label \"Critical Cases\" are positioned on the lower-right portion of the first plot area."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "x9zttzWhl58HERmZ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.498, 0.019, 0.561, 0.041] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratings."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "A0CTTFvrmIzp00VG",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the bar representing the score of Italian food in the Ratings chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.083, 0.065, 0.815, 0.225]."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "XvarqnUYDVSUm0hQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented at [0.510, 0.964, 0.549, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, called Score."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "9saGcPfIJfUNdBfl",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can we gather from [0.083, 0.755, 0.637, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score for Japanese food's interest, which is 6.2."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "oGqz0j8Zx5A7d98g",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Japanese bar}[0.083, 0.265, 0.932, 0.425] higher than {the Italian bar}[0.083, 0.065, 0.815, 0.225] in the Ratings chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "87wdpDDPjiov9lyM",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the scores for {Japanese cuisine}[0.083, 0.265, 0.932, 0.425 , 0.083, 0.755, 0.637, 0.915] higher than for {Italian cuisine}[0.083, 0.065, 0.815, 0.225 , 0.083, 0.555, 0.521, 0.715] in both the Ratings and Interest charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "AeIb7sXJFV6hPJOR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Interest of Japanese cuisine}<[0.083, 0.755, 0.637, 0.915]> greater than {Ratings of Italian cuisine}<[0.083, 0.065, 0.815, 0.225]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "8ca6MP9LHrCwFXx7",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Scores in Ratings}<[{Ratings of Japanese cuisine}[0.083, 0.265, 0.932, 0.425]], [{Ratings of Italian cuisine}[0.083, 0.065, 0.815, 0.225]]> and {Scores in Interest}<[{Interest of Japanese cuisine}[0.083, 0.755, 0.637, 0.915]], [{Interest of Italian cuisine}[0.083, 0.555, 0.521, 0.715]]>, which category has higher values overall?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratings."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "as0uSCor8mf9XPXe",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.083, 0.537, 0.976, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the \"Interest\" subplot. It contains the x-axis label \"Score\", y-axis' tick marks and their labels, and bars representing the respective scores of Japanese and Italian cuisine. The Japanese Cuisine Bar can be found at [0.083, 0.755, 0.637, 0.915], and the Italian Cuisine Bar at [0.083, 0.555, 0.521, 0.715]."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "qK6Ai86bPpdDQ1uy",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.083, 0.047, 0.976, 0.443]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the x-axis label \"Score\", y-axis' tick marks and their labels, and horizontal bars representing the 'Ratings' for Japanese and Italian cuisine. The bar for Japanese cuisine is located at [0.083, 0.265, 0.932, 0.425] and for Italian cuisine is at [0.083, 0.065, 0.815, 0.225]."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "nfTWJ3p51jFTmXyC",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.083, 0.755, 0.637, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object which is the bar representing the Interest of Japanese cuisine, which has a score of 6.2."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "Y12H0bGgHLHbV97J",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area [0.083, 0.065, 0.932, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a bar representing the rating of Italian cuisine, which has a score of 8.2."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "rwIsnOWnEsJ53u7m",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no label in the y-axis."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "y1clyPVWQpfapTrQ",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content mapped at the coordinates [0.043, 0.210, 0.206, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The string \"Public Service Provision\"."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "xtAvr6v5JQ2ycrW5",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chunk of data located at (x1, y1, x2, y2) = [0.015, 0.618, 0.206, 0.635] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the y-axis label \"Local Government Stronger\"."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "DLu8qgOA6mt8EFy0",
+        "Chart_type": "bar",
+        "QA_type": "Referring QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the primary object at location [0.043, 0.687, 0.206, 0.704]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label, \"Public Service Provision\"."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "gY4e4me0B8QttNd3",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhats the comparison between the lengths of {bar for category 'Population Engagement'}<r>[0.593, 0.832, 0.812, 0.846]</r> and {bar for category 'Decentralization Measures'}<r>[0.593, 0.749, 0.745, 0.763]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for 'Population Engagement' is longer than the bar for 'Decentralization Measures'."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "ApFcuHn6U1PDKpVC",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {bar for category 'Governance Structure'}<r>[0.974, 0.410, 1.241, 0.438]</r> longer than the {bar for category 'Community Insights Better'}<r>[0.488, 0.556, 0.593, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "igKUIpvj6eZqp5bR",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the bar representing \"Community Insights Better\" for Row1}<r>[0.764, 0.065, 0.974, 0.093]</r> and {the bar representing \"Community Insights Better\" for Row2}<r>[0.469, 0.542, 0.593, 0.556]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Row1 is longer than the bar for Row2."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "ysutTgdy20VdKHTP",
+        "Chart_type": "bar",
+        "QA_type": "Relationship QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the heights of {the bar for category 'Local Government Stronger' for Row1}<r>[0.898, 0.134, 0.974, 0.162]</r> and {the bar for category 'Local Government Stronger' for Row2}<r>[0.497, 0.611, 0.593, 0.625]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar for Row2 is taller than the bar for Row1."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "N7IVCk8T8vcGAai6",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the area {area}<r>[0.211, 0.523, 0.974, 0.933]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the second subplot of a bar chart, which includes bars representing the scores for different rows in categories such as \"Governance Structure\", \"Population Engagement\", \"Decentralization Measures\", \"Public Service Provision\", \"Local Government Stronger\", and \"Community Insights Better\". The legend for this chart is located at [0.879, 0.532, 0.967, 0.590], showing two rows: Row1 colored in '#f27970' and Row2 colored in '#54b345'."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "VlU8uwLVfiewLgZ0",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the objects within the area defined by [0.973, 0.272, 1.069, 0.300]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a bar representing the value of \"Decentralization Measures\" in Row1."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "SubUISvTuvtn7XWU",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the box from [0.497, 0.611, 0.593, 0.625]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this box, there is a horizontal bar that represents the score of the 'Local Government Stronger' category for Row2."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "OamJFtsZkFogV5GK",
+        "Chart_type": "bar",
+        "QA_type": "Regional Description QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found between coordinates [0.211, 0.047, 0.974, 0.457]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates contain the first subplot of a bar chart including bars of 'Governance Structure', 'Population Engagement', 'Decentralization Measures', 'Public Service Provision', 'Local Government Stronger', 'Community Insights Better', and a subtitle \"Scores by category for different rows\"."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "O2XYCKFFe0mHlrAQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the data from the bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {chart's subtitle}<r>[0.401, 0.019, 0.642, 0.041]</r>, it's evident that the chart is showing a comparison of market shares across different industry sectors for the years 2020 and 2021. The x-axis, represented by {x-axis labels}<r>[0.126, 0.738, 0.138, 0.799]; [0.213, 0.738, 0.225, 0.835]; [0.299, 0.738, 0.311, 0.805]; [0.386, 0.738, 0.397, 0.833]; [0.472, 0.738, 0.484, 0.899]; [0.559, 0.738, 0.570, 0.951]; [0.645, 0.738, 0.657, 0.829]; [0.732, 0.738, 0.743, 0.818]; [0.818, 0.738, 0.830, 0.839]; [0.905, 0.738, 0.916, 0.957]</r>, corresponds to different industry sectors, while the y-axis, with labels from <r>{0.0}[0.029, 0.719, 0.047, 0.737]</r> to <r>{0.7}[0.029, 0.022, 0.047, 0.040]</r>, represents the percentage of market share. According to {the legend}<r>[0.911, 0.055, 0.982, 0.113]</r>, the blue bars correspond to the year 2020, and the orange bars represent the year 2021. Each sector has a pair of bars, with each bar's height representing the percentage share for that year. For example, {the blue and orange bars for \"Energy\"}<r>[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427]</r> represent market shares of 0.3 and 0.35 for 2020 and 2021, respectively.\nBased on the above thought, the answer is:\nSector,2020,2021\nEnergy,0.3,0.35\nTechnology,0.2,0.225\nFinance,0.15,0.2\nHealthcare,0.1,0.15\nConsumer_Staples,0.05,0.05\nConsumer_Discretionary,0.05,0.025\nIndustrials,0.1,0.1\nMaterials,0.05,0.025\nReal_Estate,0.025,0.025\nCommunication_Services,0.025,0.05"
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "rVPd7XDboA1ft92k",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the market share comparison chart you observed, which sectors show positive growth from 2020 to 2021 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can answer this question through a detailed analysis of the respective bar heights for 2020 and 2021. If the 2021 bar is taller than the 2020 bar, it means that sector showed growth. From left to right, {Energy bars}<r>[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427]</r>, <r>{Technology bars}[0.184, 0.527, 0.253, 0.726], [0.184, 0.303, 0.253, 0.527]</r>, and {Finance bars}<r>[0.271, 0.577, 0.340, 0.726], [0.271, 0.378, 0.340, 0.577]</r>all show positive growth. This is because their 2021 bars have greater heights compared to their 2020 ones.\nBased on the above thought, the answer is:\nEnergy, Technology and Finance sectors show positive growth from 2020 to 2021 because their market shares have increased from 2020 to 2021."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "AUU3kkKRgGZ3GuVa",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector experienced the highest growth in market share from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which sector grew the most, I need to analyze the height difference between the 2020 and 2021 bars for each sector. Comparing the differences, {The Energy sector bars}<r>[0.098, 0.427, 0.167, 0.726], [0.098, 0.079, 0.167, 0.427]</r> have a 0.05 difference in height, {Technology}<r>[0.184, 0.527, 0.253, 0.726], [0.184, 0.303, 0.253, 0.527]</r> and {Finance}<r>[0.271, 0.577, 0.340, 0.726], [0.271, 0.378, 0.340, 0.577]</r> have 0.025 difference, and many other sectors have less or no difference. Thus, the Energy sector experienced the highest growth in market share from 2020 to 2021.\nBased on the above thought, the answer is:\nEnergy."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "HtsJKFAmZbyYQ65b",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please provide the emission level of each category for the years 2020 and 2021 as observed in the chart, in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I deduce the values of equivalent CO2 emissions from the height of the bars in the graph. The bars that are colored light blue {bars representing 2020}[0.140, 0.062, 0.309, 0.329; 0.352, 0.062, 0.521, 0.443; 0.564, 0.062, 0.733, 0.291; 0.775, 0.062, 0.945, 0.367] stand for the data in the year 2020 and the bars in orange {bars representing 2021}[0.140, 0.329, 0.309, 0.634; 0.352, 0.443, 0.521, 0.863; 0.564, 0.291, 0.733, 0.634; 0.775, 0.367, 0.945, 0.748] are for the data in 2021. By measuring the height of these bars in reference to the Y-axis scale ranging from 0 to -12000, I can extract the values of CO2 emissions for each category in both years.\nBased on the above thought, the answer is:\nCategories,2020,2021\nUrban Greening,-3500,-7500\nRoad Traffic,-5000,-10500\nWaste Management,-3000,-7500\nAir Condition Usage,-4000,-9000"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "oHe34lmd2fNLSIzr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the perspective of carbon emission reduction, could you please give suggestions based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The goal of carbon emission reduction would be to find categories where emissions values have increased over time and aim to bring them down. Analyzing the emissions of various categories from {2020's bars}[0.140, 0.062, 0.309, 0.329; 0.352, 0.062, 0.521, 0.443; 0.564, 0.062, 0.733, 0.291; 0.775, 0.062, 0.945, 0.367] to {2021's bars}[0.140, 0.329, 0.309, 0.634; 0.352, 0.443, 0.521, 0.863; 0.564, 0.291, 0.733, 0.634; 0.775, 0.367, 0.945, 0.748], all categories namely Urban Greening, Road Traffic, Waste Management and Air Condition Usage have increased emissions. However, Road Traffic and Air Condition Usage are the main contributors showing a higher increase in emissions compared to other categories. Thus, efforts should be made to control and reduce emissions in all categories with a particular emphasis on controlling Road Traffic and Air Condition Usage.\nBased on the above thought, the answer is:\nControlling and reducing emissions in all categories is crucial. However, based on the chart, particular emphasis should be placed on controlling Road Traffic and Air Condition Usage as they showed a significant increase from 2020 to 2021."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "c2192cP1jIAjSW0T",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category experienced the greatest increase in CO2 emissions from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category experienced the greatest increase relative to the 2020 emissions, I need to subtract the value of the emissions in 2020 from the value of the emissions in 2021 for each category. The value of each category's emissions is represented by the height of their corresponding bars. From left to right, the categories are Urban Greening: {blue bar for 2020}[0.140, 0.062, 0.309, 0.329] and {orange bar for 2021}[0.140, 0.329, 0.309, 0.634] with an increase of 4000; Road Traffic: {blue bar for 2020}[0.352, 0.062, 0.521, 0.443] and {orange bar for 2021}[0.352, 0.443, 0.521, 0.863] with an increase of 5500; etc. Among all categories, the bar of Road Traffic has the greatest increase from 2020 to 2021.\nBased on the above thought, the answer is:\nRoad Traffic"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "ZM19WhfV6p4q7B5V",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart of investments in Education Technology and Online Learning in two consecutive years, could you please organize the data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this chart, {the x-axis}[0.221, 0.919, 0.813, 0.942] lists the two sectors in the focus - 'Education Technology' and 'Online Learning'. The y-axis indicates the investments, ranging from {0 to 140 million dollars}[0.052, 0.894, 0.061, 0.020]. According to {the legend}[0.078, 0.074, 0.212, 0.151], {the blue bars}<{\"first year\"}[0.122, 0.083, 0.185, 0.106]> for each sector denote the investment for the first year and {the orange bars}<{\"second year\"}[0.122, 0.118, 0.207, 0.141]> represent the additional investment in the following year. For example, the {blue bar for \"Education Technology\"}[0.112, 0.615, 0.482, 0.903] ends at roughly the 45 points mark on the y-axis, indicating an investment of 45 million in the first year. The {top of the orange bar for \"Education Technology\"}[0.112, 0.230, 0.482, 0.615] corresponds to 60, so the total investment for the second year is 60 million dollars.\nBased on the above thought, the answer is:\nSector,First Year,Second Year\nEducation Technology,45,60\nOnline Learning,55,70"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "DtnT347rtXNvJwCm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector received a higher increase in investment in the second year, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I have to compare the lengths of {the orange bars} in both sectors, which represent the increased investments for the second year. The {orange bar for \"Education Technology\"}<[0.112, 0.230, 0.482, 0.615]> extends from around the 45 mark to the 60 mark on the y-axis, indicating a 15 million dollars increase. On the other hand, the {orange bar for \"Online Learning\"}<[0.574, 0.102, 0.943, 0.551]> extends from approximately the 55 mark to the 70 mark, representing a 15 million dollars increase as well. It seems the increase in investment for the second year is the same in both sectors.\nBased on the above thought, the answer is:\nThe increase in investment is identical in both sectors, with a raise of 15 million dollars each."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "z2siRlpQosXtQSBD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the total amount of investment for each sector for both years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, the total investment for each sector can be calculated by adding the investment of the first year to the second year. The {blue bar for \"Education Technology\"}[0.112, 0.615, 0.482, 0.903] indicates an investment of 45 million dollars and the {orange bar for \"Education Technology\"}[0.112, 0.230, 0.482, 0.615] indicates an additional 15 million dollars for the second year. Therefore, the total investment for the Education Technology sector is 45 million + 15 million = 60 million dollars. As for the Online Learning sector, the {blue bar}[0.574, 0.551, 0.943, 0.903] signifies an investment of 55 million dollars whereas the {orange bar}[0.574, 0.102, 0.943, 0.551] represents an additional investment of 15 million dollars, bringing the total investment to 70 million dollars.\nBased on the above thought, the answer is:\nEducation Technology: 60 Million dollars\nOnline Learning: 70 Million dollars"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "FTOxL8ByqMUWyMQe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart data into a CSV table format including time spent on each sector for Current and Previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I can identify two sectors from {the x-axis labels}<{\"Social Media\"}[0.269, 0.919, 0.356, 0.942]; {\"Digital Communication\"}[0.684, 0.919, 0.843, 0.942]>. Each sector has two bars, with the {blue bars}[{\"Current Year\"}[0.880, 0.824, 0.966, 0.847]] representing the current year and the {brown bars}[{\"Previous Year\"}[0.880, 0.859, 0.972, 0.882]] representing the previous year. By comparing the height of these bars with {the y-axis tick labels}[{\"-1600\"}[0.034, 0.987, 0.081, 1.010]; {\"-1400\"}[0.035, 0.870, 0.081, 0.893]; {\"-1200\"}[0.035, 0.753, 0.081, 0.777]; {\"-1000\"}[0.035, 0.637, 0.081, 0.660]; {\"-800\"}[0.043, 0.520, 0.081, 0.543]; {\"-600\"}[0.043, 0.403, 0.081, 0.427]; {\"-400\"}[0.043, 0.286, 0.081, 0.310]; {\"-200\"}[0.043, 0.170, 0.081, 0.193]; {\"0\"}[0.073, 0.053, 0.081, 0.076]], I can work out the extent of the decrease in time spent on each sector before and after. To create a CSV table, I can align sector names in the leading column, followed by each year's decrease in time spent.\nBased on the above thought, the answer is:\nSectors,Current_Year,Previous_Year\nSocial Media,-922,-1372\nDigital Communication,-310,-450"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "OzmTrWvIbnHPjG70",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInterpret the change in time spent on Digital Communication based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Drawing conclusions from the chart, we can compare the {brown bar for the previous year}<r>[0.583, 0.243, 0.944, 0.646]</r> and the {blue bar for the current year}<r>[0.583, 0.062, 0.944, 0.243]</r> which refer to Digital Communication. It is evident that the time spent has decreased from the previous year to the current year. While the decrease is noticeable, it appears steady, when compared to the significant drop seen in Social Media.\nBased on the above thought, the answer is:\nThe time spent on Digital Communication has decreased from the previous year to the current year, although the decrease is not as dramatic as seen for Social Media."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "LXDaM04tFmluxtpc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has shown the larger decrease in time, as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which sector has seen the larger decrease in time, I can compare the heights of the {brown bars representing the previous year}[{\"Social Media\"}[0.132, 0.600, 0.493, 0.863]; {\"Digital Communication\"}[0.583, 0.243, 0.944, 0.646]] with the {blue bars representing the current year}[{\"Social Media\"}[0.132, 0.062, 0.493, 0.600]; {\"Digital Communication\"}[0.583, 0.062, 0.944, 0.243]]. By comparing these differences, the {Social Media}<r>[0.269, 0.919, 0.356, 0.942]</r> sector appears to have a larger decrease in time spent as the change from the previous to the current year is more significant.\nBased on the above thought, the answer is:\nSocial Media."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "l0WuIBTLtaFgFudK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart figure, please extract the data and organize it into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains four categories on the x-axis: {Human Rights}[0.168, 0.919, 0.266, 0.942], {Income Inequality}[0.369, 0.919, 0.493, 0.942], {Child Development}[0.578, 0.919, 0.711, 0.942], and {Healthcare}[0.821, 0.919, 0.897, 0.942]. Each category has two bars representing values for the Current Year and Previous Year based on the chart legend {Current Year}[0.143, 0.824, 0.229, 0.847] and {Previous Year}[0.143, 0.859, 0.235, 0.882] respectively. The y-values can be inferred from the length of the bars and correspond to the number of issues (x 100) in each sector for each year. For instance, in the Human Rights category, the {Current Year bar}[0.132, 0.062, 0.303, 0.298] corresponds to a value of -500, and the {Previous Year bar}[0.132, 0.298, 0.303, 0.618] corresponds to a value of -680. The same procedure is applied to the remaining categories. Thus, the CSV format data can be organized as follows:\nBased on the above thought, the answer is:\nSector, Current Year, Previous Year\nHuman Rights, -500, -680\nIncome Inequality, -800, -900\nChild Development, -350, -480\nHealthcare, -300, -500"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "R2CXMEfmWnzVVhph",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector needs the most immediate attention and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart indicates that the sectors with the highest negative values have the most pressing issues. By comparing the heights of the bars in the {Current Year}<r>[0.143, 0.824, 0.229, 0.847]</r>, it appears that {Income Inequality}<r>[0.346, 0.062, 0.517, 0.439]</r> has the tallest bar, representing -800 issues, which is more than any other sector. This means that the Income Inequality sector currently has the most issues, hence needs urgent attention.\nBased on the above thought, the answer is:\nThe Income Inequality sector needs the most immediate attention because it currently has the highest number of issues."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "ijnSFoUdlQzlkqw0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had the largest improvement from the previous year to the current year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Improvement in this context is interpreted as the biggest decrease in the number of issues. I calculate the improvement by subtracting the value of {Current Year}[0.143, 0.824, 0.229, 0.847] from {Previous Year}[0.143, 0.859, 0.235, 0.882] for each sector. The sector with the largest positive difference was {Income Inequality}<r>[0.346, 0.439, 0.517, 0.863]</r>, whose value improved from -900 to -800, resulting in an improvement of 100 issues.\nBased on the above thought, the answer is:\nIncome Inequality"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "JD9ZPC1OvTZfHi1N",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the raw department performance data depicted in the chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays performance data for five departments across two years, as per the bar plots and their color coding found in {the legend}<r>[0.893, 0.815, 0.978, 0.891]</r>. Each department forms a category on the x-axis, as specified by {the x-axis tick labels}<r>[{\"Marketing\"}[0.165, 0.919, 0.234, 0.942]; {\"Sales\"}[0.350, 0.919, 0.387, 0.942]; {\"Development\"}[0.491, 0.919, 0.585, 0.942]; {\"Operations\"}[0.669, 0.919, 0.746, 0.942]; {\"HR\"}[0.867, 0.919, 0.887, 0.942]]</r>. The y-axis is a representation of the performance values, running from -2500 to 0. To formulate the CSV table, I'll extract the negative performance values for each department in both years. In the \"Marketing\" category, {the blue bar}<r>[0.132, 0.062, 0.267, 0.523]</r> represents -1325 for 2020 and {the orange bar}<r>[0.132, 0.523, 0.267, 0.863]</r> represents -975.736 for 2019, and likewise for \"Sales\", \"Development\", \"Operations\", and \"HR\".\nBased on the above thought, the answer is:\nDepartment,2020,2019\nMarketing,-1325,-975.736\nSales,-525.819,-729.07\nDevelopment,-785.384,-899.372\nOperations,-953.243,-350.341\nHR,-399.653,-875.85"
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "99dt1AFx4KxgErqe",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat key insights can be drawn from the chart regarding the performance shift across the departments over the two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart compares the performance shift of five departments between 2019 and 2020. There's an evident sustained decline in 'Marketing' visible in {the bars for Marketing}<r>[{2020}[0.132, 0.062, 0.267, 0.523]; {2019}[0.132, 0.523, 0.267, 0.863]]</r>; with an inconsistent decrease for 'Sales' and 'Development' shown in {their bars}<r>[{Sales 2020}[0.301, 0.062, 0.436, 0.245]; {Sales 2019}[0.301, 0.245, 0.436, 0.499]; {Development 2020}[0.470, 0.062, 0.606, 0.336]; {Development 2019}[0.470, 0.336, 0.606, 0.648]]</r>. In contrast, there's a slight rise in the 'Operations' department, noticeable in the {Operations bars}<r>[{2020}[0.640, 0.062, 0.775, 0.394]; {2019}[0.640, 0.394, 0.775, 0.516]]</r>, while the 'Human Resources (HR)' department faced a sharp performance decline, apparent in {HR bars}<r>[{2020}[0.809, 0.062, 0.944, 0.201]; {2019}[0.809, 0.201, 0.944, 0.506]]</r>. Comparing the performance changes, the 'Marketing' and 'Operations' departments experienced the most significant shifts, with the latter showing a noticeable upswing compared to the rest.\nBased on the above thought, the answer is:\nKey insights include a sustained decline in Marketing, fluctuating performance in Sales and Development, a slight increase in Operations, and a pronounced decline in HR. The Operations department stands out with a performance shift opposite to the general trend, while Marketing faced a consistent decrease over the two years."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "WW4UiNA6eObMkhFs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department experienced the least decline in performance from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the fall in performance for five departments - Marketing, Sales, Development, Operations, and HR. To find out which department experienced the least decline, the difference between the performance scores of 2019 and 2020 for each department needs to be calculated. These differences can be found by comparing the heights of the colored bars for each department in two sets\u2014 {2020}<r>[{Marketing}[0.132, 0.062, 0.267, 0.523]; {Sales}[0.301, 0.062, 0.436, 0.245]; {Development}[0.470, 0.062, 0.606, 0.336]; {Operations}[0.640, 0.062, 0.775, 0.394]; {HR}[0.809, 0.062, 0.944, 0.201]]</r> and {2019}<r>[{Marketing}[0.132, 0.523, 0.267, 0.863]; {Sales}[0.301, 0.245, 0.436, 0.499]; {Development}[0.470, 0.336, 0.606, 0.648]; {Operations}[0.640, 0.394, 0.775, 0.516]; {HR}[0.809, 0.201, 0.944, 0.506]]</r>. Analyzing the bars, it can be observed that the HR department had the smallest difference between the two years.\nBased on the above thought, the answer is:\nHR"
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "Uzj3GRaTMhLTVKbc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please generate a structured CSV table from the data visualized in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents data for different eras in human history, labeled along {the x-axis}[{\"Stone Age\"}[0.164, 0.919, 0.235, 0.942]; {\"Bronze Age\"}[0.329, 0.919, 0.408, 0.942]; {\"Iron Age\"}[0.509, 0.919, 0.567, 0.942]; {\"Middle Ages\"}[0.665, 0.919, 0.750, 0.942]; {\"Modern Era\"}[0.837, 0.919, 0.916, 0.942]]. The y-axis, ranging from {0}[0.073, 0.053, 0.081, 0.076] to {-5000}[0.035, 0.905, 0.081, 0.928], represents the advancements in civilization with arbitrary units. The chart displays two sets of bars, one for the {Current Century}[{\"bar set\"}[0.132, 0.062, 0.944, 0.488]] identified by a light blue color, and the other for the {Previous Century}[{\"bar set\"}[0.132, 0.216, 0.944, 0.863]] represented in a light orange color. This is also clarified by {the legend}[0.098, 0.815, 0.266, 0.891]. A structured CSV table can be generated by treating each era as a category, and the values of the Current and Previous Century bars as values for these categories. The corresponding advancement in arbitrary units is indicated by the bar\u2019s location along the y-axis. For example, the {Current Century bar for the Stone Age}[0.132, 0.062, 0.267, 0.216] is at around -900, and the {Previous Century bar for the Stone Age}[0.132, 0.216, 0.267, 0.386] is at around -1000.\nBased on the above thought, the answer is:\nTime Periods,Current Century,Previous Century\nStone Age,-900,-1000\nBronze Age,-500,-800\nIron Age,-800,-1300\nMiddle Ages,-1500,-2000\nModern Era,-2500,-2200"
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "uS7Cs0BwK6VMDJA0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which era has seen the least progress from the previous century to the current century and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "We're trying to find where the least improvement occurred between two centuries. This is evident by finding which time period saw the smallest difference in progression between the Previous Century to the Current Century. By comparing the heights of the two bars representing {the Stone Age}[{\"Previous Century\"}[0.132, 0.216, 0.267, 0.386]; {\"Current Century\"}[0.132, 0.062, 0.267, 0.216]], {the Bronze Age}[{\"Previous Century\"}[0.301, 0.147, 0.436, 0.284]; {\"Current Century\"}[0.301, 0.062, 0.436, 0.147]], {the Iron Age}[{\"Previous Century\"}[0.470, 0.198, 0.606, 0.420]; {\"Current Century\"}[0.470, 0.062, 0.606, 0.198]], {the Middle Ages}[{\"Previous Century\"}[0.640, 0.318, 0.775, 0.658]; {\"Current Century\"}[0.640, 0.062, 0.775, 0.318]], and {the Modern Era}[{\"Previous Century\"}[0.809, 0.488, 0.944, 0.863]; {\"Current Century\"}[0.809, 0.062, 0.944, 0.488]];\nBased on the above thought, the answer is:\nThe Bronze Age. The bars representing the Bronze Age for both centuries [Current Century: (-500); Previous Century (-800)] show a minimal change compared to other eras. This minimal difference in arbitrary units of advancement could imply that civilization's development was somewhat stagnant during this period."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "EPgX9kPk6h8WtFi6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which Time Period does the most significant improvement happen from the Previous Century to the Current Century?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify where the most considerable improvement occurs from the Previous Century to the Current Century, I would compare the differences between the corresponding bars in both centuries for each era. For example, for the {Stone Age bars}[{\"Previous Century\"}[0.132, 0.216, 0.267, 0.386]; {\"Current Century\"}[0.132, 0.062, 0.267, 0.216]], the difference is 1000 - 900 = 100. I would do the same for the {Bronze Age bars}[{\"Previous Century\"}[0.301, 0.147, 0.436, 0.284]; {\"Current Century\"}[0.301, 0.062, 0.436, 0.147]], {Iron Age bars}[{\"Previous Century\"}[0.470, 0.198, 0.606, 0.420]; {\"Current Century\"}[0.470, 0.062, 0.606, 0.198]], {Middle Ages bars}[{\"Previous Century\"}[0.640, 0.318, 0.775, 0.658]; {\"Current Century\"}[0.640, 0.062, 0.775, 0.318]], and {Modern Era bars}[{\"Previous Century\"}[0.809, 0.488, 0.944, 0.863]; {\"Current Century\"}[0.809, 0.062, 0.944, 0.488]]. The bar with the largest difference would represent the Time Period with the most significant improvement from the Previous Century to the Current Century.\nBased on the above thought, the answer is:\nModern Era."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "f4bdpMo8w1dQ27Gl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, distinguish the policy impacts in terms of exact data for each social-economic area for years 2020 and 2021? Please present the data in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the bar chart into a CSV format, I have to identify values for each category for years 2021 and 2020. Here \"socio-economic areas\" are represented by the x-axis and \"policy impact\" by the y-axis. The x-axis categories can be seen by referring to the x-axis labels {Housing}<r>[0.181, 0.919, 0.238, 0.942]</r>, {Education}<r>[0.391, 0.919, 0.460, 0.942]</r>, {Healthcare}<r>[0.604, 0.919, 0.680, 0.942]</r>, and {Transportation}<r>[0.807, 0.919, 0.908, 0.942]</r>. The y-axis values go from -400 to 1400 as indicated by {y-axis tick labels}<r>[0.035, 0.929, 0.072, 0.952]</r> to <r>[0.037, 0.028, 0.072, 0.051]</r>.\nUsing these, I can interpret the bars representing the years 2021 and 2020 for each socio-economic area. Looking at the {bars representing Housing}<r>[0.123, 0.187, 0.296, 0.738]</r> for 2021 and <r>[0.123, 0.187, 0.296, 0.638]</r> for 2020, I can determine the policy impact values for the respective years.\nGoing over each category in a similar way, I can construct a CSV table.\nBased on the above thought, the answer is:\n\"Socioeconomic Areas\",\"Policy Impact (2021)\",\"Policy Impact (2020)\"\n\"Housing\",1100,-900\n\"Education\",850,-650\n\"Healthcare\",950,-1200\n\"Transportation\",1350,-1000"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "wr2Sush6EikdEcPi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from the bar chart, which area has seen the most improvement in policy impact from 2020 to 2021, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the difference in policy impact from 2020 to 2021 for each area and compare them. For instance, the {bars representing Housing}<r>[0.123, 0.187, 0.296, 0.738]</r> for 2021 and <r>[0.123, 0.187, 0.296, 0.638]</r> for 2020 shows an improvement of 2000. I then compare this with the improvements in {Education}<r>[0.339, 0.312, 0.512, 0.738]</r> for 2021 and <r>[0.339, 0.312, 0.512, 0.638]</r> for 2020, {Healthcare}<r>[0.555, 0.262, 0.728, 0.738]</r> for 2021 and <r>[0.555, 0.262, 0.728, 0.863]</r> for 2020, and {Transportation}<r>[0.771, 0.062, 0.944, 0.738]</r> for 2021 and <r>[0.771, 0.062, 0.944, 0.563]</r> for 2020. Comparing these values, I see that Healthcare has seen the most improvement.\nBased on the above thought, the answer is:\nHealthcare has seen the most improvement in terms of policy impact from 2020 to 2021. It has increased by 2150, which is the most considerable increase among all the areas. This might be because healthcare has become a topmost priority due to the pandemic situation worldwide."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "X6lu9biQuTAo5ryh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in policy impact from 2020 to 2021 in the Healthcare sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in policy impact from 2020 to 2021, I would need to subtract the policy impact in 2020 from that of 2021. Looking at the {bar graph of Healthcare}<r>[0.555, 0.262, 0.728, 0.738]</r> for 2021 and <r>[0.555, 0.262, 0.728, 0.863]</r> for 2020, the policy's impact was 950 in 2021 and -1200 in 2020. By subtracting -1200 from 950, I can find the difference.\nBased on the above thought, the answer is:\n2150"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "ciOFsVXka3FjtuB4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the data from the bar chart and present it in a CSV table form according to the different historical aspects and eras."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to convert the information from the chart into a tabulated form, the x-axis labels tell us that we have to list the different historical aspects: {the x-axis labels}<r>[\"Feudal System\": [0.166, 0.919, 0.268, 0.942], \"Birth of Nations\": [0.377, 0.919, 0.485, 0.942], \"Technology Advancements\": [0.552, 0.919, 0.738, 0.942], \"Global Cooperation\": [0.792, 0.919, 0.926, 0.942]]</r>. Then using {the legend}<r>[0.098, 0.074, 0.232, 0.151]</r>, I know that the red bars represent the Middle Ages and the blue bars represent the Modern Age. Therefore, we will have to compare and record the length of the red and blue bars for each historical aspect to determine their respective impact. Let's take the Feudal System as an example, the {red bar for the Feudal System}<r>[0.132, 0.062, 0.303, 0.436]</r> extends roughly to the -700 mark on the y-axis while the {blue bar for the Feudal System}<r>[0.132, 0.436, 0.303, 0.863]</r> extends to the -800 mark on the y-axis. Combining all the data will yield the following table.\nBased on the above thought, the answer is:\nHistorical Aspects,Middle Ages,Modern Age\nFeudal System,-700,-800\nBirth of Nations,-500,-200\nTechnology Advancements,-100,-350\nGlobal Cooperation,-200,-100"
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "PNuF3Bz42u4eyY36",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights do you derive from the progression of the \"Feudal System\" and \"Birth of Nations\" from the Middle Ages to the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the \"Feudal System\" aspect, the height of {the red bar}<r>[0.132, 0.062, 0.303, 0.436]</r> representing the Middle Ages is lower than {the blue bar}<r>[0.132, 0.436, 0.303, 0.863]</r> representing the Modern Age. This implies that the intensity or impact of the Feudal System has increased from the Middle Ages to the Modern Age; which could suggest that elements of the Feudal System have become more prevalent in the Modern Age. Simultaneously, for the \"Birth of Nations\" aspect, {the red bar}<r>[0.346, 0.062, 0.517, 0.329]</r> for the Middle Ages is higher than {the blue bar}<r>[0.346, 0.329, 0.517, 0.436]</r> for the Modern Age. This indicates that the creation of new nations or states has decreased as we transitioned from the Middle Ages to the Modern Age. As a deduction, this could suggest that the world has become more politically stable or less expansionist in nature during the Modern Age compared to the Middle Ages.\nBased on the above thought, the answer is:\nThe intensity of the Feudal System has increased from the Middle Ages to the Modern Age suggesting its elements have become more prevalent in the modern society. On the contrary, the creation of new nations or states has declined as we moved to the Modern Age from the Middle Ages suggesting more political stability or lower expansionist tendencies."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "ch4o9ZLJIHSeF5Wm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which historical aspect, the impact difference between the Middle Ages and the Modern Age is the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the historical aspect with the largest impact difference, I need to compare the relative lengths of the red and blue bars in each category. Let's take the Feudal System as an example, the {red bar for the Feudal System}<r>[0.132, 0.062, 0.303, 0.436]</r> extends roughly to -700 on the y-axis, while the {blue bar for the Feudal System}<r>[0.132, 0.436, 0.303, 0.863]</r> extends roughly to -800 on the y-axis, giving us a difference of 100. Continuing with the similar comparison for the remaining historical sectors namely Birth of Nations, Technology Advancements and Global Cooperation, it's observed that {Feudal System}<r>[0.166, 0.919, 0.268, 0.942]</r> has the largest difference among all the sectors.\nBased on the above thought, the answer is:\nFeudal System."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "scClxnEbAHC9xIA8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, please prepare a CSV format table for different healthcare fields with the corresponding medical expenses in current and previous year."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis represents various healthcare fields such as {Emergency Services}<r>[0.119, 0.919, 0.260, 0.942]</r>, {Primary Care}<r>[0.315, 0.919, 0.406, 0.942]</r>, {Pediatric Care}<r>[0.484, 0.919, 0.581, 0.942]</r>, {Geriatric Care}<r>[0.655, 0.919, 0.752, 0.942]</r>, and {Public Health}<r>[0.830, 0.919, 0.921, 0.942]</r>. The y-axis illustrates medical expenses, and the values can be deduced from {y-axis tick labels}<r>[0.061, 0.894, 0.070, 0.917]</r> {0},<r>[0.035, 0.743, 0.070, 0.766]</r> {1000} to <r>[0.035, -0.013, 0.070, 0.011]</r> {6000}. The {legend}<r>[0.086, 0.074, 0.229, 0.151]</r> indicates {red bars}<r>[0.131, 0.083, 0.217, 0.106]</r> correspond to \"Current Year\" and {blue bars}<r>[0.131, 0.118, 0.223, 0.141]</r> correspond to \"Previous Year\". Looking at {the red bar for Emergency Services}<r>[0.121, 0.676, 0.258, 0.903]</r> and {the blue bar for the same}<r>[0.121, 0.435, 0.258, 0.676]</r>, they are approximately on 1500 and 3100 of the y-axis respectively, thus the medical expenses of Current Year and Previous Year for Emergency Services are 1500 and 3100. By applying the same method, values for all fields can be inferred.\nBased on the above thought, the answer is:\nHealthcare Fields,Current Year,Previous Year\nEmergency Services,1500,3100\nPrimary Care,2000,3900\nPediatric Care,2300,4550\nGeriatric Care,2100,4150\nPublic Health,2700,5300"
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "jPfmmz49okwhQfh4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be interpreted about the trends of medical expenses in different healthcare fields based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The heights of the {red bars}<r>[0.131, 0.083, 0.217, 0.106]</r> representing \"Current Year\" and the {blue bars}<r>[0.131, 0.118, 0.223, 0.141]</r> representing \"Previous Year\" for each healthcare field indicate the trend. The higher the bar, the greater the medical expense for the corresponding year. The bar heights suggest an upward trend in expenses from the previous year to the current year in most healthcare fields. For example, in the {Public Health}<r>[0.830, 0.919, 0.921, 0.942]</r> field, the red bar is higher than the corresponding blue bar indicating an increase in this field's expenses. However, there is an exception in the {Emergency Services}<r>[0.119, 0.919, 0.260, 0.942]</r> field where the blue bar is higher than the red one, indicating a decrease in expenses from the previous year to the current year.\nBased on the above thought, the answer is:\nMost healthcare fields have increased their medical expenses from the previous year to the current year, signifying more investment in healthcare. However, there is a decrease in expenses in the Emergency Services field, possibly indicating improved public health measures resulting in fewer emergencies."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "io3n9M58kMB7GB4A",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich healthcare field saw the smallest rise in medical expenses from the previous year to the current year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in medical expenses, the height difference between the bars representing \"Current Year\" and \"Previous Year\" for each healthcare field needs to be compared. For the {Emergency Services}<r>[0.119, 0.919, 0.260, 0.942]</r> field, the {red bar}<r>[0.121, 0.676, 0.258, 0.903]</r> and {blue bar}<r>[0.121, 0.435, 0.258, 0.676]</r> indicate a decrease in medical expenses. After similarly calculating the differences for other healthcare fields like {Primary Care}<r>[0.315, 0.919, 0.406, 0.942]</r>, {Pediatric Care}<r>[0.484, 0.919, 0.581, 0.942]</r>, {Geriatric Care}<r>[0.655, 0.919, 0.752, 0.942]</r>, and {Public Health}<r>[0.830, 0.919, 0.921, 0.942]</r>, it can be inferred that the Emergency Services field has seen the smallest rise, in fact, it shows a decrease.\nBased on the above thought, the answer is:\nEmergency Services."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "nwMcLbnafsVuGHu8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the expenditures distribution among the categories including 'Education', 'Healthcare', 'Transportation', and 'Welfare' across 'Current Year' and 'Previous Year' as a CSV data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The stacked bar graph depicts the expenditures for the 'Education', 'Healthcare', 'Transportation', and 'Welfare' sectors for 'Current Year' and 'Previous Year'. According to {the legend}<r>[0.098, 0.815, 0.240, 0.891]</r>, the color '#87ceeb' corresponds to 'Current Year', and '#f4a460' corresponds to 'Previous Year'. I can extract the expenditure values by visually observing the heights of the bars. For example, in the 'Education' category, {the bar representing 'Current Year'}<r>[0.132, 0.062, 0.303, 0.405]</r> suggests an expenditure of -800(thousand dollars), and {the bar representing 'Previous Year'}<r>[0.132, 0.405, 0.303, 0.713]</r> indicates an expenditure of -720(thousand dollars). By performing similar observations and calculations for the other categories, I can create the following CSV data.\nBased on the above thought, the answer is:\nServices,Current Year,Previous Year\nEducation,-800,-720\nHealthcare,-950,-920\nTransportation,-700,-770\nWelfare,-850,-920"
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "wOmU4chojgW0LBxY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar graph, can you suggest which among the 'Education', 'Healthcare', 'Transportation', and 'Welfare' sectors showed an overall increase in spendings over the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to compare the expenditure distributions under 'Current Year' and 'Previous Year' for each category. For instance, the 'Education' category, represented by bars with coordinates {{\"Current Year\"}<r>[0.132, 0.062, 0.303, 0.405]</r> and {\"Previous Year\"}<r>[0.132, 0.405, 0.303, 0.713]</r>}, indicates a small increase in expenditure. Similar comparisons can be made across all the categories to find the ones that have shown an overall increase.\nBased on the above thought, the answer is:\nThe 'Education' sector has shown an overall increase in expenditures over the 'Previous Year'."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "MPABjXKSWiHAmE05",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social welfare category experienced the largest difference in expenditures between the 'Current Year' and 'Previous Year'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first need to observe the area sizes of each of the category bars across 'Current Year' and 'Previous Year'. The differences between the heights of the bars indicate the expenditure changes. For example, in the 'Education' category, {the bar representing 'Current Year'}<r>[0.132, 0.062, 0.303, 0.405]</r> suggests an expenditure of -800(thousand dollars), and {the bar representing 'Previous Year'}<r>[0.132, 0.405, 0.303, 0.713]</r> indicates an expenditure of -720(thousand dollars), giving a difference of 80 (thousand dollars). By comparing these differences across all categories, I can identify which category had the largest difference.\nBased on the above thought, the answer is:\nWelfare."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "QDzpl9x6XgW01h71",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from this bar chart, could you please convert the spending in different education and training technology innovation sectors into a structured table? Exclude the minus sign and use positive numbers."
+            },
+            {
+                "from": "gpt",
+                "value": "The information in this chart consists of two main parts: {Category}<r>[0.144, 0.919, 0.911, 0.942]</r> and {Spending}<r>[0.015, 0.336, 0.029, 0.629]</r>. There are four categories on the x-axis, including \"Mobile Learning Tech\", \"Gamification\", \"E-Learning\", and \"Virtual Training\". The y-axis represents spending in millions. For \"Mobile Learning Tech\", for example, we see that the value of spending in the current year is about 432.43 million, indicated by {the first skyblue bar}<r>[0.132, 0.062, 0.303, 0.313]</r>, and in the previous year, the spending was about 576.57 million, shown by {the first sandybrown bar}<r>[0.132, 0.313, 0.303, 0.646]</r>. Hence we can organize the data accordingly for the other categories.\nBased on the above thought, the answer is:\nSector,Spending Current Year (Million dollars),Spending Previous Year (Million dollars)\nMobile Learning Tech,432.43,576.57\nGamification,256.12,749.72\nE-Learning,658.38,724.26\nVirtual Training,800.98,345.40"
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "cWxIumtTaospq50Q",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, what trend can we observe regarding the spending in 'Virtual Training' and how does it compare to the other sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the trend in 'Virtual Training', we need to look at {the last pair of bars on the right}<r>[0.773, 0.062, 0.944, 0.726]</r>, which represent the spending in the 'Virtual Training' sector for the current year and previous year. Compared to the previous year, the spending in the current year significantly increased, as indicated by the length of the skyblue bar in this sector. When compared to the other sectors, {the bars representing 'Mobile Learning Tech'}<r>[0.132, 0.062, 0.303, 0.646]</r>, {the bars for 'Gamification'}<r>[0.346, 0.062, 0.517, 0.645]</r>, and {those for 'E-Learning'}<r>[0.559, 0.062, 0.730, 0.863]</r>, we can notice that the spending in the 'Virtual Training' sector is the only one that shows a noticeable increase from the previous year to the current year. The remaining sectors exhibit a decrease in spending from the previous year to the current year.\nBased on the above thought, the answer is:\nThe spending in the 'Virtual Training' sector has increased significantly from the previous year to the current year, which contrasts with the other sectors where the spending decreased over the same period. Therefore, it seems that 'Virtual Training' is gaining more attention and investment compared to the other sectors in education and training technology innovation."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "QcUn0hFZDmoMDTpe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which sector the difference in spending between the current year and the previous year is the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the smallest difference, it is essential to visually estimate the length difference between the skyblue and the sandybrown bars in each pair of bars representing each sector. In {the first pair of bars}<r>[0.132, 0.062, 0.303, 0.646]</r> representing the 'Mobile Learning Tech' sector, the difference between the lengths is relatively large. Similar observations can be noted for the 'E-Learning' and 'Virtual Training' sectors - {the third pair of bars}<r>[0.559, 0.062, 0.730, 0.863]</r> and {the fourth pair of bars}<r>[0.773, 0.062, 0.944, 0.726]</r>. Compared to these, we have a smaller length difference between {the second pair of bars}<r>[0.346, 0.062, 0.517, 0.645]</r> representing the 'Gamification' sector.\nBased on the above thought, the answer is:\nGamification"
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "f2k3xdoxKUT1QdNG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the information in the chart in tabular form."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart illustrates the number of participants for the fitness activities - Yoga, Weightlifting, Running, and Biking, in the years 2019 and 2020, with their respective coordinates: {2019 bars}<{\"Yoga\"} [0.129, 0.593, 0.301, 0.903]; {\"Weightlifting\"} [0.344, 0.515, 0.515, 0.903]; {\"Running\"} [0.558, 0.645, 0.730, 0.903]; {\"Biking\"} [0.773, 0.670, 0.944, 0.903]>, and {2020 bars}<{\"Yoga\"}[0.129, 0.231, 0.301, 0.593]; {\"Weightlifting\"} [0.344, 0.102, 0.515, 0.515]; {\"Running\"} [0.558, 0.335, 0.730, 0.645]; {\"Biking\"} [0.773, 0.412, 0.944, 0.670]> . Each pair of bars indicate the annual participation in each activity. The Y-axis marks it as the number of individuals in thousands from \"{0}\"<r>[0.070, 0.894, 0.079, 0.917]</r> to \"{16000}\"<r>[0.035, 0.067, 0.079, 0.091]</r>. Therefore, by analysing the height of each bar and comparing it with the Y-axis, I can determine the number of participants each year.\nBased on the above thought, the answer is:\nActivity, 2019, 2020\nYoga,6000,13000\nWeightlifting,7500,15500\nRunning,5000,11000\nBiking,4500,9500"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "4oc0s2eOw9fr4rQ0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends visible in the chart, which fitness activity do you think is gaining more popularity, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it's clear that all four activities - Yoga, Weightlifting, Running, and Biking - have shown an increase in participants from 2019 to 2020. These are represented by {2019 bars}<{\"Yoga\"} [0.129, 0.593, 0.301, 0.903]; {\"Weightlifting\"} [0.344, 0.515, 0.515, 0.903]; {\"Running\"} [0.558, 0.645, 0.730, 0.903]; {\"Biking\"} [0.773, 0.670, 0.944, 0.903]> and {2020 bars}<{\"Yoga\"} [0.129, 0.231, 0.301, 0.593]; {\"Weightlifting\"} [0.344, 0.102, 0.515, 0.515]; {\"Running\"} [0.558, 0.335, 0.730, 0.645]; {\"Biking\"} [0.773, 0.412, 0.944, 0.670]>. However, in terms of growth, {Yoga}<r>[0.129, 0.231, 0.301, 0.593]</r> saw the highest increase in the number of participants, making it the most rapidly growing and therefore, potentially the most popular among these fitness activities\nBased on the above thought, the answer is:\nAccording to the chart, Yoga seems to be gaining the most popularity due to its highest increase in participants from 2019 to 2020."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "MYrKSyO6lejHO1hG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity showed the highest growth from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the fitness activity that showed the highest growth from 2019 to 2020, I am comparing the differences in height between the 2019 and 2020 bars for each activity: Yoga {2019 Yoga bar}<r>[0.129, 0.593, 0.301, 0.903]</r> and {2020 Yoga bar}<r>[0.129, 0.231, 0.301, 0.593]</r>, Weightlifting {2019 Weightlifting bar}<r>[0.344, 0.515, 0.515, 0.903]</r> and {2020 Weightlifting bar}<r>[0.344, 0.102, 0.515, 0.515]</r>, Running {2019 Running bar}<r>[0.558, 0.645, 0.730, 0.903]</r> and {2020 Running bar}<r>[0.558, 0.335, 0.730, 0.645]</r>, and Biking {2019 Biking bar}<r>[0.773, 0.670, 0.944, 0.903]</r> and {2020 Biking bar}<r>[0.773, 0.412, 0.944, 0.670]</r>. After comparing these differences, I can conclude that the fitness activity with the largest increase in participation from 2019 to 2020 is Yoga since it demonstrates the biggest increase in bar height.\nBased on the above thought, the answer is:\nYoga"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "70sNiu1L1lI6aDnv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart data into a CSV formatted table."
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the {x-axis label}<r>[0.504, 0.964, 0.555, 0.981]</r> that the policies are on the x-axis. In particular, individual policies are represented by the {x-axis ticks}<r>[{\"Heavy Taxation\"}[0.099, 0.939, 0.206, 0.957]; {\"Reduced Tariffs\"}[0.250, 0.939, 0.356, 0.957]; {\"Subsidies for SMEs\"}[0.389, 0.939, 0.519, 0.957]; {\"Worker Training Programs\"}[0.515, 0.939, 0.694, 0.957]; {\"Unemployment Benefits\"}[0.671, 0.939, 0.839, 0.957]; {\"Debt Relief\"}[0.868, 0.939, 0.944, 0.957]]</r>. The y-axis represents effectiveness from the {y-axis label}<r>[0.015, 0.419, 0.029, 0.534]</r>.The actual values for each policy is represented as height of bars and we can see their values from the {annotations}<r>[{\"50\"}[0.144, 0.340, 0.161, 0.358]; {\"65\"}[0.144, 0.703, 0.161, 0.721]; {\"75\"}[0.294, 0.251, 0.312, 0.269]; {\"55\"}[0.295, 0.792, 0.312, 0.810]; {\"90\"}[0.445,0.192,0.463,0.209]; {\"80\"}[0.445, 0.733, 0.463, 0.750];]</r>. Using this information, I can generate the CSV table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nHeavy Taxation,50\nReduced Tariffs,65\nSubsidies for SMEs,75\nWorker Training Programs,55\nUnemployment Benefits,90\nDebt Relief,80"
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "uoqQqKJL27kKXmbH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the government wants to prioritize policymaking based on the effectiveness demonstrated by this chart, in what order would you recommend they concentrate their efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to sort the policies according to the corresponding bar heights from highest to lowest. From my observation, the bar heights for {Unemployment Benefits}<r>[0.718, 0.085, 0.793, 0.620]</r>, {Debt Relief}<r>[0.868, 0.144, 0.944, 0.620]</r>, {Subsidies for SMEs}<r>[0.416, 0.174, 0.492, 0.620]</r>, {Reduced Tariffs}<r>[0.266, 0.234, 0.341, 0.620]</r>, {Worker Training Programs}<r>[0.567, 0.293, 0.642, 0.620]</r>, and {Heavy Taxation}<r>[0.115, 0.323, 0.190, 0.620]</r> indicate that Unemployment Benefits is the most effective, followed by Debt Relief, Subsidies for SMEs, Reduced Tariffs, Worker Training Programs, and then Heavy Taxation.\nBased on the above thought, the answer is:\nThe government might want to prioritize their policymaking efforts in the following order: Unemployment Benefits, Debt Relief, Subsidies for SMEs, Reduced Tariffs, Worker Training Programs, Heavy Taxation."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "nw5pgStmbElcpPtf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which policy has the highest effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which policy has the highest effectiveness, I need to look at the height of the bars and corresponding values. By looking at the highest bar and value, it shows that {Unemployment Benefits}[0.718, 0.085, 0.793, 0.620] with a corresponding value of {\"90\"}[0.445,0.192,0.463,0.209] exhibits the highest effectiveness among all policies.\nBased on the above thought, the answer is:\nUnemployment Benefits"
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "yBOCmTdfA6PXHBPX",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bar chart you analyzed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart title {\"Career and Workplace Development Trends over 2.5 Years\"}[0.277, 0.025, 0.761, 0.055], and x-axis labels representing quarters {Q1 - Q2 over 2.5 Years}[{\"Q1\"}[0.116, 0.919, 0.136, 0.942] - {\"Q2\"}[0.902, 0.919, 0.922, 0.942]], the x-axis indicates the passage of time in quarters. The ratings on the y-axis range {from 0 to 10}[{0}[0.035, 0.894, 0.043, 0.917] - {10}[0.026, 0.051, 0.043, 0.074]]. For each quarter, there are two bars indicating the ratings of \"Career Development\" and \"Workplace Trends\" respectively. According to the legend {\"Career Development and Workplace Trends\"}[{label: 'Career Development'}[0.466, 0.083, 0.611, 0.106] - {label: 'Workplace Trends'}[0.466, 0.118, 0.589, 0.141]], ratings for both categories can be derived from the relative height of the respective bars. For example, the height of the {bar for \"Career Development in Q1\"}[0.096, 0.195, 0.126, 0.903] matches with the 8.4 on the y-axis, thus the rating is 8.4 for this category in Q1. Similarly, the height of the {bar for \"Workplace Trends in Q1\"}[0.126, 0.102, 0.157, 0.903] matches with 9.5 on the y-axis, determining the rating of 9.5 for this category in Q1. This process is repeated for each quarter to construct the CSV table.\nBased on the above thought, the answer is:\nQuarters,Career Development,Workplace Trends\nQ1,8.4,9.5\nQ2,7.9,8.5\nQ3,6.8,7.8\nQ4,7.2,7.0\nQ1,8.3,8.0\nQ2,8.7,8.6\nQ3,8.5,9.1\nQ4,7.8,8.4\nQ1,6.0,7.2\nQ2,9.0,8.8"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "Zsx4wmISLnP5Plt9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what conclusions can you draw about the development trends of Career Development and Workplace Trends over 2.5 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw conclusions about the development trends of \"Career Development\" and \"Workplace Trends\" over 2.5 years, we need to look at the trend of bar heights over time. Starting with {\"Career Development\"}[0.466, 0.083, 0.611, 0.106], we can see that it begins high at 8.4 in Q1 of the first year, dips to a low of 6.0 in Q1 of the third year, and then rebounds to a high of 9.0 in Q2 of the third year. This indicates some oscillations, but also a general upward trend, especially in the latter period. In contrast, for {\"Workplace Trends\"}[0.466, 0.118, 0.589, 0.141] the trend begins at a height of 9.5 in Q1 of the first year, with minor fluctuations. It drops to 7.0 in Q4 of the first year, but then shows a general upward trend, reaching a height of 9.1 in Q3 of the second year. This suggests that while there are fluctuations throughout the period, both trend lines generally show an upward trajectory, with \"Career Development\" showing larger fluctuations and more dynamic changes.\nBased on the above thought, the answer is:\nBoth \"Career Development\" and \"Workplace Trends\" show a generally upward trend over the 2.5 years. However, \"Career Development\" shows larger fluctuations and more dynamic changes, while \"Workplace Trends\" appears more stable and consistent, despite minor fluctuations."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "hyDgyMPo993Xtunh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter has the highest difference in trend ratings between Career Development and Workplace Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the quarter with the biggest difference between the ratings of \"Career Development\" and \"Workplace Trends\", I have to compare the differences in the heights of the two bars of every quarter. Starting with Q1 in the first year, the difference between {\"Career Development\"} bar[0.096, 0.195, 0.126, 0.903] and {\"Workplace Trends\"} bar[0.126, 0.102, 0.157, 0.903] is 1.1. Moving sequentially to Q2, Q3, and other quarters, we make similar comparisons. Eventually, we find the largest difference in Q2 of the third year, where the heights of {the \"Career Development\"} bar[0.882, 0.144, 0.912, 0.903] and {the \"Workplace Trends\"} bar[0.912, 0.161, 0.943, 0.903] denote ratings of 9.0 and 8.8 respectively, resulting in a difference of 0.2.\nBased on the above thought, the answer is:\nQ2 of the third year."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "0azxNbVoQ4q8gKzL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart showing New Energy and Clean Technology Development into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"New Energy and Clean Technology Development\"}[0.304, 0.030, 0.753, 0.066], I can identify that the chart displays data about clean energy technologies. The X-axis which represents the tech types covers {\"Solar Panels\"}[0.125, 0.903, 0.219, 0.931], {\"Wind Turbines\"}[0.261, 0.903, 0.369, 0.931], {\"Tidal Power\"}[0.413, 0.903, 0.502, 0.931], {\"Geothermal Plants\"}[0.528, 0.903, 0.671, 0.931], {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931], and {\"Hydrogen Fuel Cells\"}[0.808, 0.903, 0.962, 0.931]. The Y-axis represents the corresponding ratings, ranging from {0.0}[0.039, 0.872, 0.063, 0.900] to {4.5}[0.039, 0.047, 0.063, 0.075]. Each technology has four bars with different colors, and by referring to the {*}legend[0.772, 0.089, 0.976, 0.264], I can identify these as Efficiency, Stability, Development Rate, and Market Acceptance. To generate the CSV table, I need to extract the values for each of these metrics for each technology. I can use the height of the bars to determine the values. For instance, the first technology, Solar Panels, has an Efficiency bar {here}[0.115, 0.187, 0.144, 0.883] with a height that corresponds to approx 3.8 on the ongoing scale.\nBased on the above thought, the answer is:\nTechnologies,Efficiency,Stability,Development Rate,Market Acceptance\nSolar Panels,3.8,3.2,4.0,2.5\nWind Turbines,3.0,3.8,3.5,3.0\nTidal Power Generators,2.0,2.8,3.0,1.5\nGeothermal Plants,3.5,4.0,3.2,2.8\nElectric Vehicles,4.0,3.2,4.2,3.5\nHydrogen Fuel Cells,2.5,2.0,3.5,2.2"
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "5D9x7F4lQAvw2QaB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which clean energy technology would you recommend for investment? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question asks about recommendations for investment which involves considering not just the efficiency of a technology, but also is developmental rate and market acceptance. Comparing all the clean energy technologies, we see that {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931] show a higher rating in all the factors. The bars representing Efficiency[0.685, 0.150, 0.714, 0.883], Stability[0.714, 0.297, 0.742, 0.883], Development Rate[0.742, 0.113, 0.771, 0.883], and Market Acceptance[0.771, 0.242, 0.799, 0.883] show values consistent and above 3.2 indicating Robustness in all the aspects. Additionally, the bar for Development Rate is the highest among all indicating a greater scope of future growth.\nBased on the above thought, the answer is:\nElectric Vehicles. Their high ratings across efficiency, stability, development rate, and market acceptance indicate that they are a solid investment. Their Development Rate is also the highest, indicating potential for future growth."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "OGV8potI8GdUN1X6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has the highest average rating based on the four factors: Efficiency, Stability, Development Rate, and Market Acceptance?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest average rating overall, I'll need to analyze the bars under every technology. For each technology, there are four bars for each of the four factors. I'll need to calculate the average of the values represented by these bars and compare them to identify the highest. For example, for {\"Solar Panels\"}[0.125, 0.903, 0.219, 0.931], the bars representing, Efficiency[0.115, 0.187, 0.144, 0.883], Stability[0.144, 0.297, 0.172, 0.883], Development Rate[0.172, 0.150, 0.201, 0.883] and Market Acceptance[0.201, 0.425, 0.229, 0.883], these four bars give the average value as 3.375. In the similar way we calculate for other technologies as well and see that {\"Electric Vehicles\"}[0.680, 0.903, 0.805, 0.931] having the bars at, Efficiency[0.685, 0.150, 0.714, 0.883], Stability[0.714, 0.297, 0.742, 0.883], Development Rate[0.742, 0.113, 0.771, 0.883], and Market Acceptance[0.771, 0.242, 0.799, 0.883] show the highest average value of 3.725 among all the technologies.\nBased on the above thought, the answer is:\nElectric Vehicles."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "jJPvkapu2RtEVTkt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the chart's data representing the projects evaluation into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title {\"Innovation Project Evaluation\"}[0.386, 0.030, 0.656, 0.066] of the chart indicates the overall theme. The x-axis represents the project names and include {\"Slow Expansion\"}[0.251, 0.903, 0.371, 0.931] and {\"Rapid Decline\"}[0.678, 0.903, 0.785, 0.931]. The y-axis {\"Ratings\"}[0.017, 0.427, 0.032, 0.531] indicates the scores achieved by each project in various aspects. The group of elements represented in {the legend}<r>[0.814, 0.089, 0.976, 0.306]</r> represent different aspects evaluated for each project including \"Life\", \"Infrastructure\", \"IP\", \"Ecosystem\", and \"Commercial\". Each aspect is associated with a particular color which matches with the color of the bars on the graph corresponding to their score for each project. The score is determined by the height of each bar which scales from 0 to 6 as per {the y-tick labels}<r>[0.00, 0.883] to [6.00, -0.041]</r>. To transform this data into a table, I can simply match scores from the height of the bars to the corresponding aspect for each project.\nBased on the above thought, the answer is:\nProject,Life,Infrastructure,IP,Ecosystem,Commercial\nSlow Expansion,4.0,3.0,3.5,5.0,4.0\nRapid Decline,1.0,2.0,1.5,1.0,2.0"
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "6mIRApBVjAof9tYT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the evaluation of the two innovation projects, which project seems to have been more successfully managed and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to {the chart}<r>[0.059, 0.075, 0.983, 0.883]</r>, it's clear that the {\"Slow Expansion\"}<r>[0.251, 0.903, 0.371, 0.931]</r> project scored considerably higher in each evaluated aspect compared to the {\"Rapid Decline\"}<r>[0.678, 0.903, 0.785, 0.931]</r> project as the height of each bar for 'Slow Expansion' is higher. For instance, for the aspect 'Life', the blue bar for 'Slow Expansion' <r>[0.101, 0.267, 0.185, 0.883]</r> is longer than the equivalent bar for 'Rapid Decline' <r>[0.521, 0.729, 0.605, 0.883]</r>, indicating a higher score. This same pattern is observed for all other aspects including 'Infrastructure', 'IP', 'Ecosystem', and 'Commercial'.\nBased on the above thought, the answer is:\nThe 'Slow Expansion' project seems to have been more successfully managed. This is because it scored consistently higher in all evaluated aspects compared to the 'Rapid Decline' project."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "TkPdSIlapEWy2CXt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect scored the lowest in both projects, 'Slow Expansion' and 'Rapid Decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which aspect scored lowest, I'll look at the shortest bar for each project and match that color with an aspect in {the legend}<r>[0.814, 0.089, 0.976, 0.306]</r>. For the {\"Slow Expansion\"}<r>[0.101, 0.267, 0.185, 0.883] to [0.437, 0.267, 0.521, 0.883]</r> project, the shortest bar is yellow, representing 'Infrastructure'. For the {\"Rapid Decline\"}<r>[0.521, 0.729, 0.605, 0.883] to [0.857, 0.575, 0.941, 0.883]</r> project, the shortest bars represent 'Life' and 'Ecosystem'. Therefore, no single aspect scored the lowest in both projects.\nBased on the above thought, the answer is:\nNo single aspect scored the lowest in both projects."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "L5bZAg6LVzFq2QSK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the key performance data for each given company represented in the bar chart into a structured data table, preferably CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart represents four key metrics for six different companies within the transportation and logistics industry. The x-axis represents each firm {x-axis labels}[{\"Imperial Solutions\"}[0.107, 0.903, 0.246, 0.931]; {\"Quick Transfer\"}[0.263, 0.903, 0.374, 0.931]; {\"Solid Transit\"}[0.413, 0.903, 0.507, 0.931]; {\"Direct Deliver\"}[0.549, 0.903, 0.655, 0.931]; {\"Momentum Movers\"}[0.669, 0.903, 0.818, 0.931]; {\"Titan's Transport\"}[0.821, 0.903, 0.950, 0.931]], while the metrics are encoded as characteristics of the bars corresponding to each company. Each group of bars for a company represents Sales Growth (%), Market Share (%), Investment in R&D ($ M), and Employee Performance in respective order. The color of the bars is mapped to each metric according to the {legend}[0.730, 0.089, 0.976, 0.264]. To extract the data into a CSV format, I would need to determine the height of each bar, corresponding to each company and each metric, and convert this height to the value on the y-axis.\nBased on the above thought, the answer is:\nCompanies,Sales Growth (%),Market Share (%),Investment in R&D ($ M),Employee Performance\nImperial Solutions,8.6,24.3,100.2,3.3\nQuick Transfer,12.2,19.1,90.3,3.6\nSolid Transit,-3.2,14.7,60.5,3.0\nDirect Deliver,9.1,18.4,80.0,3.4\nMomentum Movers,7.3,17.8,85.6,3.2\nTitan's Transport,-6.5,11.8,45.2,2.7"
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "5HiVLLWkLcBMfFA7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you suggest which company is the best in terms of both performance and market leadership?"
+            },
+            {
+                "from": "gpt",
+                "value": "To choose the best company in terms of both performance and market leadership, I need to evaluate companies both on \u201cMarket Share (%)\u201d and \u201cEmployee Performance.\u201d The second bar in each group represents market share and the fourth bar represents employee performance. As per the {legend}[0.730, 0.089, 0.976, 0.264], the 'Market Share (%)' metric is represented by {orange-color bars}[0.779, 0.142, 0.913, 0.170] and the 'Employee Performance' metric is represented by {-green color bars}[0.779, 0.225, 0.958, 0.253]. So, I need to evaluate both the second and fourth bars of each group to find the company which performs the best in both dimensions. By comparing the heights of the second and fourth bars, 'Imperial Solutions' has the highest market share bar {[0.148, 0.634, 0.176, 0.802]} and 'Quick Transfer' has the highest employee performance bar {[0.376, 0.778, 0.405, 0.802]}. However, 'Direct Deliver' has a fairly high market share bar {[0.573, 0.675, 0.602, 0.802]} and also the second highest employee performance bar {[0.659, 0.778, 0.687, 0.802]} which makes it the best performer in terms of both market leadership and performance.\nBased on the above thought, the answer is:\nDirect Deliver. Because it strikes a balance between having a high market share and having high employee performance."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "hJD5KwJO4vEqF36g",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest investment in Research and Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "The investment in Research and Development for each company is represented by the third bar in each group. To determine the company with the highest R&D investment, I need to find the tallest third bar in each group. By comparing the heights of the {third bars}[{\"Imperial Solutions\"}[0.176, 0.111, 0.205, 0.802]; {\"Quick Transfer\"}[0.318, 0.180, 0.347, 0.802]; {\"Solid Transit\"}[0.460, 0.385, 0.488, 0.802]; {\"Direct Deliver\"}[0.602, 0.251, 0.630, 0.802]; {\"Momentum Movers\"}[0.744, 0.212, 0.772, 0.802]; {\"Titan's Transport\"}[0.885, 0.490, 0.914, 0.802]], the highest third bar represents 'Imperial Solutions', suggesting that this company has the highest investment in R&D.\nBased on the above thought, the answer is:\nImperial Solutions"
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "F3VRYp2zTze8LBuY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data you're viewing into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart's subtitle {\"Acceptance Rate of Academic Papers in 2020 and 2021\"}[0.293, 0.025, 0.754, 0.055] and {the x-axis tick labels}[{\"Physics\"}[0.130, 0.919, 0.181, 0.942]; {\"Chemistry\"}[0.267, 0.919, 0.338, 0.942]; {\"Biology\"}[0.424, 0.919, 0.476, 0.942]; {\"Mathematics\"}[0.552, 0.919, 0.642, 0.942]; {\"Psychology\"}[0.705, 0.919, 0.783, 0.942]; {\"Sociology\"}[0.858, 0.919, 0.925, 0.942]],I understand that the x-axis represents various research fields. The y-axis {tick labels}[{\"0\"}[0.043, 0.894, 0.052, 0.917]; {\"20\"}[0.035, 0.725, 0.052, 0.748]; {\"40\"}[0.035, 0.557, 0.052, 0.580]; {\"60\"}[0.035, 0.388, 0.052, 0.411]; {\"80\"}[0.035, 0.219, 0.052, 0.243]; {\"100\"}[0.026, 0.051, 0.052, 0.074]] demonstrate various acceptance rates from 0% to 100%. Two data series are represented in this chart, one for 2020 and the other for 2021, distinguished by {the legend}[0.893, 0.074, 0.978, 0.151] with blue bars indicating 2020 and red bars indicating 2021. I'll then compare the height of the bars and extract their values to construct a CSV table using each research field as a category and inserting the acceptance rates for 2020 and 2021 in each field.\nBased on the above thought, the answer is:\nFields,2020,2021\nPhysics,90,84\nChemistry,83,82\nBiology,79,76\nMathematics,95,93\nPsychology,88,89\nSociology,80,79"
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "zjg35LWYQWzdy5aa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart you're seeing, which research field had the most consistent acceptance rate between 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The consistency of the acceptance rate in a research field can be determined by observing the difference in the height of the 2020 and 2021 bars. A smaller difference in height represents a more consistent acceptance rate. To do this, comparing the {2020 bar for Physics}<r>[0.104, 0.144, 0.155, 0.903]</r> with the {2021 bar for Physics}<r>[0.155, 0.195, 0.207, 0.903]</r>, for instance, I can see there's a noticeable difference. Similarly, observing the {year 2021's bar for Biology}<r>[0.450, 0.262, 0.501, 0.903]</r> with respect to {2020 bar for Biology}<r>[0.398, 0.237, 0.450, 0.903]</r>, I can see some evident difference. Conducting this comparison for each field and judging by the minimal difference between the two years, it is evident that Chemistry had the most consistent acceptance rate, where the two bars are almost at the same level. Shadows of {2020's bar}<r>[0.251, 0.203, 0.303, 0.903]</r> and {2021's bar}<r>[0.303, 0.212, 0.354, 0.903]</r> are almost equal.\nBased on the above thought, the answer is:\nChemistry"
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "25ZIIdaTWqGMiA8j",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what was the greatest decline in the acceptance rate from 2020 to 2021 in any of the research fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the decrease in the acceptance rates by comparing the heights of the bars representing 2020 and 2021 for each research field. Starting from Physics, I can see that the {bar for 2020}[0.104, 0.144, 0.155, 0.903] is slightly taller than the {bar for 2021}[0.155, 0.195, 0.207, 0.903]. I can do this successively for each field until I find the field where the acceptance rate decreases the most. After analyzing all the fields, I can see that the largest decline happens in Biology where the {bar for 2020}[0.398, 0.237, 0.450, 0.903] has a higher value compared to the {bar for 2021}[0.450, 0.262, 0.501, 0.903]. This drop represents a decrease in acceptance rate from 79% in 2020 to 76% in 2021, a decline of 3%.\nBased on the above thought, the answer is:\nBiology"
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "vfLKUgYyFi5w1qVI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart provided, convert all the values for each risk assessment into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart with title {Risk Assessment and Management}[0.356, 0.030, 0.680, 0.066] contains six groups of bars. Each group has three bars representing the probability, impact, and risk score of each risk factor. The x-axis labels at[0.077, 0.903, 0.217, 0.931], [0.221, 0.903, 0.320, 0.931], [0.342, 0.903, 0.447, 0.931], [0.471, 0.903, 0.565, 0.931], [0.570, 0.903, 0.714, 0.931], [0.703, 0.903, 0.829, 0.931] and [0.806, 0.903, 0.973, 0.931] represent these risk factors. The legend at [0.822, 0.089, 0.960, 0.223] tells that the blue, orange, and red bars correspond to the probability, impact, and risk score respectively. Therefore, by calculating the height of each bar, I can convert the chart into a table form.\nBased on the above thought, the answer is:\nRisk Factors, Probability, Impact, Risk Score\nEquipment Failure, 3.1, 4.9, 15.19\nHuman Error, 4.2, 4.5, 18.9\nFinancial Risk, 2.5, 4.8, 12\nLegal Issues, 4.6, 5.0, 23\nMarket Fluctuation, 2.7, 3.5, 9.45\nNatural Disaster, 1.5, 5.0, 7.5\nCybersecurity Breach, 3.8, 4.7, 17.86"
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "dnQAvZBptstqlzo0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the Risk Assessment and Management chart, which risk factor should the company prioritize and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify the risk factor that should be prioritized, I have to compare the risk scores of each risk factor. These are represented by the {red bars}[red bar groups][0.159, 0.375, 0.184, 0.883], [0.283, 0.250, 0.308, 0.883], [0.407, 0.482, 0.431, 0.883], [0.531, 0.113, 0.555, 0.883], [0.654, 0.567, 0.679, 0.883], [0.778, 0.632, 0.803, 0.883], [0.902, 0.285, 0.927, 0.883]. From the visual assessment, I can see that the red bar for the {Legal Issues}[0.471, 0.903, 0.565, 0.931] is the tallest among the red bars of all the risk factors, indicating it has the highest risk score. Therefore, the company should prioritize addressing Legal Issues first.\nBased on the above thought, the answer is:\nThe company should prioritize addressing the Legal Issues as they have the highest risk score."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "wx9bP38VKgZ0FBpn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the risk analysis, which risk factor has the least risk score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the risk factor with the least risk score, I need to locate the red bars for each risk. I can determine the value by examining the height of the bar. Starting from left to right, the first risk factor is Equipment Failure with {risk score bar}[0.159, 0.375, 0.184, 0.883], which has a risk score of about 15.19; the second is Human Error with {risk score bar}[0.283, 0.250, 0.308, 0.883], which has a risk score of about 18.90 and so on. After comparing all the risk factors, I can see that the risk score of {Natural Disaster}[0.703, 0.903, 0.829, 0.931] is the least as the {red bar}[0.778, 0.632, 0.803, 0.883] is the shortest.\nBased on the above thought, the answer is:\nNatural Disaster"
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "XFKAwHo6gsdHG4n6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you see, please convert the fitness trends data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title {\"Trends in Fitness and Sports in 2021 and 2022\"}<r>[0.308, 0.030, 0.735, 0.066]</r>indicates that the chart is about different fitness trends in 2021 and 2022. The x-axis represents the years, which can be found from the {x-axis tick labels}<r>[{\"2022\"}[0.268, 0.903, 0.307, 0.931]; {\"2021\"}[0.735, 0.903, 0.774, 0.931]]</r>. The y-axis represents ratings scale, which ranges from {0}[0.039, 0.872, 0.048, 0.900] to {5}[0.039, 0.017, 0.048, 0.045]. The four different colored bars indicate four sport categories, based on {the legend}<r>[0.828, 0.089, 0.976, 0.264]</r>. Each of these categories has two bars corresponding to two years, respectively. By analyzing the heights of these bars and the corresponding mid-point coordinates on the y-axis, the annual ratings for each category in these two years can be estimated.\nBased on the above thought, the answer is:\nSport,2022,2021\nCardio,4.5,3.5\nStrength,4.5,4.0\nMindfulness,2.5,3.0\nFlexibility,3.0,3.5"
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "uvv2I3IIHGXhUljl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which category has been faring the best during these two years and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the height of the bars representing each category in the two years. For Cardio and Strength, it can be seen that both their rating increased from 2021 to 2022. However, the Cardio category, indicated by the {yellow bars}<r>[{2021 bar}[0.568, 0.284, 0.661, 0.883]; {2022 bar}[0.101, 0.113, 0.194, 0.883]]</r>, reaches to the height of approximately 4.5 in 2022, which is the highest rating compared to the other categories. Therefore, I think Cardio has been faring the best during these two years.\nBased on the above thought, the answer is:\nCardio. The bar representing Cardio has the highest height, which indicates it has the highest rating. Moreover, its rating actually increased from 2021 to 2022. Therefore, based on the chart, Cardio seems to have fared the best during these two years."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "Uxngp61ooMdkfsFc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sport category has seen the least change in ratings from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the difference in ratings for each sport category from 2021 to 2022. For the Cardio category, the 2021 rating is represented by the {yellow bar}<r>[0.568, 0.284, 0.661, 0.883]</r> and the 2022 rating is represented by the {yellow bar}<r>[0.101, 0.113, 0.194, 0.883]</r>. The difference between the two ratings is approximately 1. Similarly, the difference in the Strength category can be calculated from the {blue bars}<r>[{2021 bar}[0.661, 0.199, 0.755, 0.883]; {2022 bar}[0.194, 0.113, 0.288, 0.883]]</r>, which is also approximately 0.5. By continuing to observe the difference in rating for Mindfulness and Flexibility categories, it can be found that the Flexibility category has the smallest change, which is approximately 0.5.\nBased on the above thought, the answer is:\nFlexibility"
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "M9g9uoHSJWtNBtRK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a CSV format table according to the visible data from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the x-axis with labels that include {\"Public_Figure1\"}<r>[0.154, 0.909, 0.264, 0.938]</r>, {\"Public_Figure2\"}<r>[0.275, 0.909, 0.385, 0.938]</r>, {\"Public_Figure3\"}<r>[0.397, 0.909, 0.507, 0.938]</r>, {\"Public_Figure4\"}<r>[0.518, 0.909, 0.628, 0.938]</r>, {\"Public_Figure5\"}<r>[0.640, 0.909, 0.750, 0.938]</r>, {\"Public_Figure6\"}<r>[0.761, 0.909, 0.871, 0.938]</r>, it can be told that each tick represents an individual public figure. The y-axis represents ratings, ranging from {0.00}<r>[0.080, 0.879, 0.114, 0.907]</r> to {2.00}<r>[0.080, 0.064, 0.114, 0.092]</r>. The legend includes four categories, {\"Liberal Stance\"}<r>[0.727, 0.145, 0.837, 0.173]</r>, {\"Conservative Stance\"}<r>[0.727, 0.187, 0.886, 0.215]</r>, {\"Neutral Stance\"}<r>[0.727, 0.229, 0.842, 0.257]</r>, and {\"No Stance\"}<r>[0.727, 0.271, 0.806, 0.299]</r>, each represented by a uniquely colored bar. To extract the data, I will need to refer to the height of the bars associated with each category and public figure.\nBased on the above thought, the answer is:\nIndividual,Liberal_Stance,Conservative_Stance,Neutral_Stance,No_Stance\nPublic_Figure1,1.8,1.3,0.9,1.0\nPublic_Figure2,1.2,1.7,0.6,1.5\nPublic_Figure3,1.4,1.2,0.8,1.6\nPublic_Figure4,1.1,1.6,0.9,1.4\nPublic_Figure5,1.6,1.1,0.7,1.3\nPublic_Figure6,1.3,1.5,0.8,1.2"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "nwGXOggdvbRe9kB9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which political stance is predominant among the public figures?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the predominant stance, we need to look at the overall height of the corresponding bars for each political stance among all public figures. Reviewing the heights of the bars corresponding to each category - {\"Liberal Stance\"}<r>[0.727, 0.145, 0.837, 0.173]</r>, {\"Conservative Stance\"}<r>[0.727, 0.187, 0.886, 0.215]</r>, {\"Neutral Stance\"}<r>[0.727, 0.229, 0.842, 0.257]</r>, and{\"No Stance\"}<r>[0.727, 0.271, 0.806, 0.299]</r> - it seems that the \"Liberal Stance\" and the \"Conservative Stance\" bars are generally taller than the \"Neutral Stance\" and \"No Stance\" across most public figures. This suggests that most public figures have a more defined political stance, either Liberal or Conservative, rather than a neutral or no stance.\nBased on the above thought, the answer is:\nThe Liberal and Conservative stances are the predominant political inclinations among the displayed public figures."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "AcU6yaWSLbn8cZMF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, who is the public figure that has the highest rating for the conservative stance?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which public figure has the highest rating for the \"Conservative Stance\", I need to identify the highest bar color as indicated in the legend {\"Conservative Stance\"}<r>[0.727, 0.187, 0.886, 0.215]</r>. Looking at the heights of the bars representing this stance, I see that the {\"Public_Figure2\"}<r>[0.275, 0.909, 0.385, 0.938]</r> has the highest bar with a conservative stance, located at <r>[0.306, 0.197, 0.330, 0.890]</r> that corresponds to a rating of 1.7.\nBased on the above thought, the answer is:\nPublic_Figure2"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "wL1q5EbcUphiTjTq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a structured table in CSV format based on the data from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Key Economic Indicators for 2021\"}[0.386, 0.025, 0.664, 0.055] and the x-axis labels [{label: 'YE2020'}[0.150, 0.919, 0.202, 0.942]; {label: 'Q1_2021'}[0.320, 0.919, 0.381, 0.942]; {label: 'Q2_2021'}[0.494, 0.919, 0.556, 0.942]; {label: 'Q3_2021'}[0.668, 0.919, 0.730, 0.942]; {label: 'Q4_2021'}[0.843, 0.919, 0.904, 0.942]], this bar chart compares the changes of four economic indicators over five periods. With the legend located at [0.072, 0.745, 0.265, 0.891], I can identify that these four different colored bars represent Unemployment Rate, GDP Growth Rate, Inflation Rate, and Interest Rate respectively. For constructing the table, I have to extract the value for each indicator at each period using the height of the bar like for {Unemployment Rate in YE2020}[0.106, 0.116, 0.141, 0.635] or {GDP Growth Rate in Q1_2021}[0.316, 0.635, 0.351, 0.865].\nBased on the above thought, the answer is:\nPeriods,Unemployment Rate,GDP Growth Rate,Inflation Rate,Interest Rate\nYE2020,6.8,4.3,1.4,0.2\nQ1_2021,6.1,-3.0,2.5,0.1\nQ2_2021,5.4,6.0,4.2,0.1\nQ3_2021,4.8,6.2,5.3,0.2\nQ4_2021,4.2,7.0,6.1,0.25"
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Mpow0o8c8R2jCWjQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be the probable cause for the drop in the Unemployment Rate in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires understanding the relationships among the data points. Firstly, I notice that the {Unemployment Rate bars}<r>[0.106, 0.116, 0.141, 0.635];[0.281, 0.169, 0.316, 0.635];[0.455, 0.223, 0.490, 0.635];[0.629, 0.269, 0.664, 0.635];[0.804, 0.314, 0.839, 0.635]</r> decrease gradually from about 6.8% to 4.2% through the year. In parallel, I notice that the {GDP Growth Rate bars}<r>[0.141, 0.307, 0.176, 0.635];[0.316, 0.635, 0.351, 0.865];[0.490, 0.177, 0.525, 0.635];[0.664, 0.162, 0.699, 0.635];[0.839, 0.100, 0.873, 0.635]</r> show a significant increase from -3.0% to 7.0% from Q1_2021 to Q4_2021. This parallel trend can suggest that the improving economy, indicated by increases in the GDP Growth rate, could be a possible reason for the decrease in the unemployment rate as more jobs become available.\nBased on the above thought, the answer is:\nThe probable cause could be the economic improvements witnessed in 2021, as evidenced by the increase in the GDP Growth Rate. The recovery phase of the economy usually translates to creation of more jobs and thus can lead to a drop in the Unemployment Rate."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "QVUpQYrYEPWl6qzx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which period did the inflation rate rise the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the heights of the bars representing the inflation rate for each period. The inflation rate is represented by the {red bars}<r>[0.176, 0.528, 0.211, 0.635];[0.351, 0.444, 0.385, 0.635];[0.525, 0.314, 0.560, 0.635];[0.699, 0.230, 0.734, 0.635];[0.873, 0.169, 0.908, 0.635]</r>. In Q1_2021, the inflation rate was around 2.5%, in Q2_2021 it was around 4.2%, in Q3_2021 it was around 5.3% and finally in Q4_2021, it was around 6.1%. By comparing these, the largest increase is between Q1_2021 and Q2_2021.\nBased on the above thought, the answer is:\nQ2_2021."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "OXRwuzRmPHgbaY9K",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the bar chart, can you provide the rating data for Facebook and Instagram across all features in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the analysis of the chart, I note that there are 4 bars for each platform in the chart. The legends {'Interactions'}<r>[0.873, 0.100, 0.964, 0.128]</r>, {'Connectivity'}<r>[0.873, 0.142, 0.969, 0.170]</r>, {'Popularity'}<r>[0.873, 0.183, 0.950, 0.211]</r>, {'Trust'}<r>[0.873, 0.225, 0.910, 0.253]</r> represent these categories. For Facebook, the bars and corresponding ratings {3.5 for Interactions}<r>[0.115, 0.209, 0.207, 0.883]</r>, {3.5 for Connectivity}<r>[0.207, 0.209, 0.299, 0.883]</r>, {2.8 for Popularity}<r>[0.299, 0.344, 0.391, 0.883]</r>, {3.0 for Trust}<r>[0.391, 0.306, 0.483, 0.883]</r> can be derived from the respective heights each bar reaches on the y-axis. Similarly, for Instagram, the bars and corresponding ratings {4.0 for Interactions}<r>[0.574, 0.113, 0.666, 0.883]</r>, {3.5 for Connectivity}<r>[0.666, 0.209, 0.758, 0.883]</r>, {3.5 for Popularity}<r>[0.758, 0.209, 0.850, 0.883]</r>, and {2.5 for Trust}<r>[0.850, 0.402, 0.942, 0.883]</r> can be extracted in the same way. All these data points can be translated into a structured table.\nBased on the above thought, the answer is:\nSocial Networks,Interactions,Connectivity,Popularity,Trust\nFacebook,3.5,3.5,2.8,3.0\nInstagram,4.0,3.5,3.5,2.5"
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "Zh6E5KmHA58HmV5W",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this chart portray about Facebook and Instagram's networking experience regarding interactions and trust?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rating for Interactions and Trust on Facebook and Instagram can be derived from the bars for both aspects and both platforms on the chart. In terms of interactions, Instagram's Interactions bar< r>[0.574, 0.113, 0.666, 0.883]< /r> shows a higher rating than Facebook's Interactions bar< r>[0.115, 0.209, 0.207, 0.883]< /r>, indicating that the users felt the interactions were more substantial on Instagram. However, in terms of Trust, Facebook's Trust bar< r>[0.391, 0.306, 0.483, 0.883]< /r> indicates a slightly better rating than Instagram's Trust bar< r>[0.850, 0.402, 0.942, 0.883]< /r>. This might suggest that despite Instagram having more user interaction, Facebook is seen as a more reliable social media platform by users.\nBased on the above thought, the answer is:\nAccording to the chart, Instagram has higher user interactions while Facebook was rated higher on trust. This depicts that whilst Instagram's engagement levels are superior, Facebook is more trusted by its users."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "1z5WLxAuYbzg9bvK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, on which aspect does Instagram outperform Facebook the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "If you look at the difference in heights between the bars of Instagram and Facebook for each aspect, you'll see that Instagram has a higher rating in Interactions, Popularity, and Connectivity. While Instagram's bar for Interactions< r>[0.574, 0.113, 0.666, 0.883]< /r> shows a rating of 4.0 and Facebook's Interactions< r>[0.115, 0.209, 0.207, 0.883]< /r> shows a rating of 3.5, the difference is 0.5. Popularity and Connectivity, on the other hand, both show the same rating difference of 0.7 with Instagram's Popularity< r>[0.758, 0.209, 0.850, 0.883]< /r> and Connectivity< r>[0.666, 0.209, 0.758, 0.883]< /r> bars indicating ratings of 3.5 and Facebook's Popularity< r>[0.299, 0.344, 0.391, 0.883]< /r> and Connectivity< r>[0.207, 0.209, 0.299, 0.883]< /r> bars indicating ratings of 2.8 and 3.5 respectively. In conclusion, Instagram outperforms Facebook most in Popularity and Connectivity.\nBased on the above thought, the answer is:\nPopularity and Connectivity."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "XLfAvSaoktUgpl88",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information on the bar chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the x-axis and the y-axis labels, I know that the x-axis represents the volatility of the entrepreneurial skills in percentage terms, and the y-axis represents different entrepreneurial skills. Thus, I can form a table with two columns - 'Entrepreneurial Skills' and 'Volatility'. For each skill, I can refer to the corresponding bar and the text above to obtain the data. For example, the bar for 'Marketing' {bar}[0.576, 0.777, 0.910, 0.887] ends at around 16.7% according to {the text}[0.920, 0.823, 0.964, 0.841]. Hence, 'Marketing' has a volatility of '16.7'. If I perform the same steps for all the bars, I can generate the complete table.\nBased on the above thought, the answer is:\nEntrepreneurial Skills,Volatility\nMarketing,16.7\nPublic Speaking,12.9\nRisk Taking,-8.3\nProduct Development,1.8\nNetworking,6.5\nStrategic Planning,-3.7"
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "or6szcZc8PtDrUA6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which entrepreneurial skill needs improvement the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the entrepreneurial skill that needs the most improvement, I need to look for the skill that has experienced the greatest negative volatility. This is represented by the bar that extends the furthest to the left of the 0 line on the x-axis. The extent of the bars can be determined by the values provided in the texts above the bars. For example, the {bar for 'Risk Taking'}[0.410, 0.501, 0.576, 0.611] extends to -8.3% as per the {text}[0.360, 0.547, 0.400, 0.565], and the {bar for 'Strategic Planning'}[0.502, 0.087, 0.576, 0.197] extends to -3.7% as per the {text}[0.452, 0.133, 0.492, 0.151]. Comparing these, it can be seen that 'Risk Taking' had the greatest negative volatility and hence, needs the most improvement.\nBased on the above thought, the answer is:\nRisk Taking"
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "pZafYntYaikRe2wf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entrepreneurial skill has shown the highest growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the skill that has the highest growth, I need to compare the extent of the bars on the right side of the 0 line for each skill. The extent of the bars can be determined by the values provided in the texts above the bars. For example, the {bar for 'Marketing'}[0.576, 0.777, 0.910, 0.887] ends at around 16.7% as per the {text}[0.920, 0.823, 0.964, 0.841]. Similarly, the {bar for 'Public Speaking'}[0.576, 0.639, 0.834, 0.749] shows a growth of 12.9% as indicated by the {text}[0.844, 0.685, 0.888, 0.703]. After comparing all the bars, it can be seen that the bar for 'Marketing' goes the furthest on the chart, thereby indicating that 'Marketing' had the highest growth.\nBased on the above thought, the answer is:\nMarketing"
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "iqDYYNtpI7rBdzPa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this chart and present it in a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents data for 5 social media platforms. Starting from the top of the y-axis {\"Facebook\"}<r>[0.020, 0.792, 0.086, 0.815]</r>, {\"Twitter\"}<r>[0.039, 0.633, 0.086, 0.656]</r>, {\"Instagram\"}<r>[0.015, 0.473, 0.086, 0.497]</r>, {\"Snapchat\"}<r>[0.020, 0.314, 0.086, 0.337]</r>, and {\"TikTok\"}<r>[0.043, 0.155, 0.086, 0.178]</r>. Each platform has a corresponding bar indicating its impact level. By comparing the span of each horizontal bar on the x-axis and the impact percentage at the end of each bar, the impact levels can be determined. For example, the bar {\"for Facebook\"}<r>[0.536, 0.737, 0.756, 0.865]</r> extends to just past the 10 point on the x-axis, and the {}<r>ending impact percentage \"<12.5%>\"</r>[0.774, 0.789, 0.818, 0.813] confirms this value. Following the same approach for the remaining platforms will provide the complete data to be tabulated.\nBased on the above thought, the answer is:\nModel,Impact(%)\nFacebook,12.5\nTwitter,-19.8\nInstagram,7.3\nSnapchat,-2.4\nTikTok,18.3"
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "0xAxxJV2NlHDWiT2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on observation of the chart, can we infer which social media platform had the most significant decline in impact and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The negative impact or decline in engagement on social media platforms is represented by bars extending to the left of the x-axis. From this chart figure, I need to find the bar that extends furthest to the left. In this case, the bar {\"representing Twitter\"}<r>[0.187, 0.578, 0.536, 0.705]</r> extends the furthest to the left with a {}<r>negative impact value of \"-19.8%\"</r>[0.120, 0.630, 0.169, 0.653]. This indicates that Twitter had the most significant decline in impact.\nBased on the above thought, the answer is:\nYes, Twitter had the most significant decline in impact based on this chart. It shows a noticeable decrease in engagement, as indicated by the length of its bar on the negative side of the x-axis and its impact value of -19.8%."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "5YfOy4gJgn0apznx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform shown in the chart had the highest positive impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the social media platform with the highest positive impact, I need to locate the bars that extend the furthest to the right of the x-axis. By scanning the length of the horizontal bars, I see that the longest bar is the one {\"representing TikTok\"}<r>[0.536, 0.100, 0.858, 0.228]</r>. The {}<r>impact percentage \"18.3%\"</r>[0.876, 0.152, 0.920, 0.176] at the end of the bar confirms this observation. Hence, TikTok had the highest positive impact.\nBased on the above thought, the answer is:\nTikTok."
+            }
+        ],
+        "id_raw": 46
+    },
+    {
+        "id": "BPUriWLYff5Tx5I3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the age distribution trends across various demographic groups from the horinzontal bar chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can derive this information from the lengths and colors of the horizontal bars in the chart. The bar lengths extend from the zero baseline, which I've found at <r>[0.536, 0.927]</r>. The range of change is denoted from '<r>\u221210'</r> to '<r>10'</r>. On finding the bars, {'Youths'}<r>[0.536, 0.777, 0.906, 0.887]</r>, {'Adults'}<r>[0.360, 0.639, 0.536, 0.749]</r>, {'Seniors'}<r>[0.536, 0.501, 0.642, 0.611]</r>, {'Veterans'}<r>[0.184, 0.363, 0.536, 0.473]</r>, {'Children'}<r>[0.536, 0.225, 0.686, 0.335]</r>, and {'Teenagers'}<r>[0.272, 0.087, 0.536, 0.197]</r>, I can see that each bar's left or right edge is the point of the change value. Based on the colors of the bar, I can determine whether the change is negative or positive. Therefore, by comparing all these values, I can convert them into a CSV format.\nBased on the above thought, the answer is:\nDemographic Group, Age Distribution\nYouths,8.4\nAdults,-4\nSeniors,2.4\nVeterans,-8.0\nChildren,3.4\nTeenagers,-6.0"
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "D4c9Pu87qsuAQFCp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which demographic group witnessed a decline in age distribution, and according to you, what could be the potential implications of this situation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon careful observation, I note that three demographic groups {'Adults'}<r>[0.360, 0.639, 0.536, 0.749]</r>, {'Veterans'}<r>[0.184, 0.363, 0.536, 0.473]</r> and {'Teenagers'}<r>[0.272, 0.087, 0.536, 0.197]</r> have red bars which denote a decline in age distribution. As a visual AI assistant, while I am not privy to specific implications, from a generic standpoint, a lower age distribution could potentially mean a smaller working age population, which might impact the economy. Furthermore, a declining age distribution among 'Teenagers' might imply reduced consumption of goods and services specifically tailored for that age group and a decrease in the number of eligible recruits for military service in the 'Veterans' demographic group.\nBased on the above thought, the answer is:\nThe 'Adults', 'Veterans' and 'Teenagers' demographic groups witnessed a decline in age distribution. A smaller working-age population could impact the economy while a lower 'Teenagers' age distribution might imply reduced consumption of teenager-specific goods and services and a decrease in the number of eligible recruits for military service in the 'Veterans' demographic group."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "sAFkJaaAFQMVMx9i",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest percentage increase in age distribution amongst all the demographic groups outlined in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest increase would be represented by the longest bar extending to the right from the zero baseline <r>[0.536, 0.927]</r>. By comparing the lengths of the white bars for {'Youths'}<r>[0.536, 0.777, 0.906, 0.887]</r>, {'Seniors'}<r>[0.536, 0.501, 0.642, 0.611]</r> and {'Children'}<r>[0.536, 0.225, 0.686, 0.335]</r>, I find that the bar corresponding to 'Youths' extends the longest. Therefore, 'Youths' had the highest percentage increase in age distribution.\nBased on the above thought, the answer is:\nYouths"
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "utXgOGKQ7aszdYlS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data displayed in the barchart into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart, located at the suspension section '{\"Rural and Urban Development Comparison\"}[0.376, 0.019, 0.733, 0.041]'. The x-axis labels represent the percentage change for both Rural and Urban areas. The y-axis labels list the type of development - 'Stable Growth', 'Sustained Growth', 'Abrupt Increase', 'Upward Trajectory', 'Stable Decline' at various locations {'Stable Growth'}[0.043, 0.814, 0.142, 0.831]; {'Sustained Growth'}[0.018, 0.647, 0.142, 0.664]; {'Abrupt Increase'}[0.032, 0.480, 0.142, 0.498]; {'Upward Trajectory'}[0.015, 0.313, 0.142, 0.331]; {'Stable Decline'}[0.041, 0.147, 0.142, 0.164]. By observing the length of each bar graph corresponding to these development types, I can determine that the magnitude for each type of area development - Rural and Urban. So, I'll arrange all the data collected into a structured table.\nBased on the above thought, the answer is:\nDevelopment Type,Rural,Urban\nStable Growth,13.2,-6.5\nSustained Growth,-10.4,4.8\nAbrupt Increase,7.3,-5.6\nUpward Trajectory,-15.2,18.3\nStable Decline,0.9,-20.4"
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "2B1Gj1OeinlqyHKu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the bar chart, which type of Development (Rural or Urban) has experienced more growth and which has seen more decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my analysis of the bar chart, I need to consider all bars that represent growth (the bars extending east from the center point) for both Rural and Urban development. For Rural, we see growth in 'Stable Growth', 'Abrupt Increase' and 'Stable Decline' with the bars located at {bar of 'Stable Growth'} [0.575, 0.754, 0.825, 0.887], {bar of 'Abrupt Increase'} [0.575, 0.420, 0.713, 0.554] and {bar of 'Stable Decline'} [0.575, 0.087, 0.592, 0.220] . For Urban, we only see growth in 'Sustained Growth' and 'Upward Trajectory' as can be seen by their respective bars at {bar of 'Sustained Growth'} [0.575, 0.587, 0.666, 0.720] and {bar of 'Upward Trajectory'} [0.575, 0.253, 0.921, 0.387]. For decline, or bars heading west from center, we see the opposite trend. Urban development has declinations in 'Stable Growth', 'Abrupt Increase' and 'Stable Decline', while Rural development has declination in 'Sustained Growth' and 'Upward Trajectory'\nBased on the above thought, the answer is:\nFrom the chart, Rural development has seen more growth, while Urban development has seen more decline."
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "wlVkHeTHSktgmUA4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the given chart, where is the highest decrease observed, in Rural or Urban area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the highest decrease, let's start to analyze the bar graph lengths indicating decrease (represented by the negative numbers or bars going west from the centre). For Rural development, the maximum decrease is observed in 'Upward Trajectory' {-bar of 'Upward Trajectory'}[0.287, 0.253, 0.575, 0.387] which is about -15.2%. On the other hand, for Urban development, the maximum decrease is seen in 'Stable Decline' {-bar of 'Stable Decline'}[0.188, 0.087, 0.575, 0.220], which is about -20.4%. Hence, the highest decrease observed is greater in Urban development compared to Rural development.\nBased on the above thought, the answer is:\nUrban"
+            }
+        ],
+        "id_raw": 48
+    },
+    {
+        "id": "mKX7PNIoFZOGezhV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the data from organizational performance chart and provide it in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart graphic contains data about the performance of different organizational strategies. This data can be extracted from the {bars}[0.497, 0.777, 0.581, 0.887; 0.529, 0.639, 0.581, 0.749; 0.581, 0.501, 0.873, 0.611; 0.561, 0.363, 0.581, 0.473; 0.355, 0.225, 0.581, 0.335; 0.581, 0.087, 0.909, 0.197] and the corresponding labels for each bar on the y-axis {\"label\"}[0.022, 0.825, 0.175, 0.843; 0.048, 0.687, 0.175, 0.705; 0.015, 0.549, 0.175, 0.567; 0.048, 0.411, 0.175, 0.429; 0.016, 0.273, 0.175, 0.291; 0.060, 0.135, 0.175, 0.153]. The position of the bar on the x-axis marks the performance percent for each concept. For example, the category {\"Customer Satisfaction\"}[0.022, 0.825, 0.175, 0.843] has a bar extending to the left from the midpoint, indicating a decrease in performance. The exact figure can be determined from the text {\"-2.1%\"}[0.438, 0.823, 0.478, 0.841] at the end of the bar. By examining each concept and its corresponding bar in the chart, I can compile the CSV table.\nBased on the above thought, the answer is:\nConcepts,Performance\nCustomer Satisfaction,-2.1\nStrategic Planning,-1.3\nOperational Excellence,7.4\nPeople Leadership,-0.5\nResource Management,-5.7\nRevenue Growth,8.3"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "02L2IVHi6jrgySYQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart's data, which area should the organization focus on improving next?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the area with the lowest performance based on the chart. By observing the lengths and directions of the bars and the corresponding performance percentages, it's clear that {\"Resource Management\"}[0.016, 0.273, 0.175, 0.291] has the lowest performance, as indicated by the length of the bar {\"Resource Management Bar\"}[0.355, 0.225, 0.581, 0.335] extending to the left and the associated performance value of {\"-5.7%\"}[0.295, 0.271, 0.335, 0.289]. Since low or negative performance indicates areas that need improvement, the organization should focus on improving Resource Management next.\nBased on the above thought, the answer is:\nThe organization should focus on improving Resource Management as it currently has the lowest performance."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "OafzbyqrwFsVHfRq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich organizational concept experienced the highest growth in performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I'll focus on the length and direction of the bars to evaluate growth in performance. The bars to the right of the y-axis indicate growth, and the length indicates the size of this growth. By comparing the length of the bars, it seems that the longest bar corresponds to the {\"Revenue Growth\"}[0.060, 0.135, 0.175, 0.153] concept. This observation is also supported by the value {\"8.3%\"}[0.929, 0.133, 0.964, 0.151] present at the end of the bar. Hence, the concept with the highest growth in performance is Revenue Growth.\nBased on the above thought, the answer is:\nRevenue Growth"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "U9kY0UY4lBZWOeGK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the plot located at {\"Market Trends in the Real Estate Sector\"}[0.375, 0.019, 0.703, 0.041] suggests that it describes the market trends in the real estate sector. The y-axis displays the types of buildings in the sector - {Residential}[0.102, 0.754, 0.393, 0.887], {Retail}[0.393, 0.587, 0.460, 0.720], {Industrial}[0.393, 0.420, 0.589, 0.554], {Warehouse}[0.393, 0.253, 0.845, 0.387], and {Office}[0.373, 0.087, 0.393, 0.220]. The x-axis shows the percentage change from {\"\u221210\"}[0.087, 0.939, 0.116, 0.957] to {\"20\"}[0.968, 0.939, 0.985, 0.957], with {\"0\"}[0.389, 0.939, 0.398, 0.957] being the no-change reference line. Each category has one bar representing a specific trend. The length of these bars indicates the percentage change in value they represent. For instance, the trend for {Residential}[0.248, 0.754, 0.393, 0.887] can be calculated by the labeled value, {\"\u22125.0%\"}[0.193, 0.812, 0.233, 0.829] shown at the end of each bar.\nBased on the above thought, the answer is:\nBuilding_Type,Market_Trend\nResidential,-5.0\nRetail,2.3\nIndustrial,6.7\nWarehouse,15.5\nOffice,-0.7"
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "ZzxJnDgB689CZXMP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich building type seems to be a risky investment based on the chart trends and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The riskiness of an investment in a building type can be inferred from the trends shown on the chart. Specifically, a negative trend would suggest a higher risk as it indicates a decrease in value. According to the chart, {Residential}[0.102, 0.754, 0.393, 0.887] and {Office}[0.373, 0.087, 0.393, 0.220] show negative trends as their bars are oriented left of the  {\"0\"}[0.389, 0.939, 0.398, 0.957] line, indicating a decrease in value. Residential has a trend value of {\"-5.0%\"}[0.193, 0.812, 0.233, 0.829], and Office has a trend value of {\"-0.7%\"}[0.318, 0.145, 0.358, 0.162]. Although both types have shown negative trends, Residential properties have experienced a larger decrease making them potentially more risky.\nBased on the above thought, the answer is:\nResidential. It shows a greater negative trend with a decrease of 5.0% in value."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "xKWnJaIs36xc7eEO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich building type has shown the greatest positive change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which building type has shown the greatest positive change, I need to look at the bars on the chart that extend to the right of the {\"0\"}[0.389, 0.939, 0.398, 0.957] line on the x-axis and compare their lengths. The longest of these represents the building type with the greatest positive trend. It appears that the {Warehouse}[0.393, 0.253, 0.845, 0.387] mark shows the largest increase with a value of {\"15.5%\"}[0.860, 0.311, 0.904, 0.329].\nBased on the above thought, the answer is:\nWarehouse."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "mx19yDYszO1zS8G3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data represented in the chart in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into CSV format, I need to extract the y-axis labels, which are the categories, and x-axis values, which are the percentage of perception change. The categories include {Freedom of Speech}<r>[0.057, 0.814, 0.191, 0.831]</r>, {Family Values}<r>[0.096, 0.647, 0.191, 0.664]</r>, {Religious Freedom}<r>[0.064, 0.480, 0.191, 0.498]</r>, {Environmental Protection}<r>[0.015, 0.313, 0.191, 0.331]</r>, and {Gender Equality}<r>[0.079, 0.147, 0.191, 0.164]</r>. For the change percentages, I can extract the values from the annotations above each bar. For example, for Freedom of Speech, the bar is located at [0.478, 0.754, 0.573, 0.887], and the corresponding value is -5.1%, as indicated by the value annotation [0.429, 0.812, 0.469, 0.829].\nBased on the above thought, the answer is:\nSocietal Aspects, Change in Perception(%)\nFreedom of Speech, -5.1\nFamily Values, 13.3\nReligious Freedom, -4.8\nEnvironmental Protection, 20\nGender Equality, -0.5"
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "MZIhPRATjkLnVyBt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which societal aspect has shown improvement in perception? Provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "We can understand the improvement or deterioration in the perception of societal aspects by judging the direction and the value of each bar in the chart. A positive value on the x-axis indicates an improvement whereas a negative value indicates a deterioration. For instance, {Family Values}<r>[0.573, 0.587, 0.820, 0.720]</r> bar is towards the positive side of the x-axis with a value of 13.3%, indicating an improvement. Similarly, {Environmental Protection}<r>[0.573, 0.253, 0.945, 0.387]</r> bar is also on the positive side with a value of 20%, indicating an improvement. Therefore, Family Values and Environmental Protection have seen an improvement in perception.\nBased on the above thought, the answer is:\nFamily Values and Environmental Protection have shown an improvement in perception."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "vFmcwW8Npe0pQ3MF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich societal aspect shows the greatest change in perception according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the societal aspect with the greatest change in perception by looking at the width of the bars in the graph. The larger the absolute value of the change in perception, the wider the distance between the start of the bar and the centerline at x=0. For example, the 'Environmental Protection' bar starts at [0.573, 0.253] and goes up to [0.945, 0.387]. Measuring this distance, I can infer that 'Environmental Protection' has the greatest absolute change.\nBased on the above thought, the answer is:\nEnvironmental Protection."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "dm0E9XqheZ1QUzRN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the bar chart presented, please take the data and convert it into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, the Y-axis represents different geographic regions {South America}<r>[0.127, 0.820]</r>, {Asia}<r>[0.127, 0.654]</r>, {Africa}<r>[0.127, 0.487]</r>, {Europe}<r>[0.127, 0.320]</r>, and {Australia}<r>[0.127, 0.153]</r>. The X-axis represents the percent change in tourism and hospitality value. For each region, there is a horizontal bar, where the length and direction of the bar depicts the value. The lengths of the bars represent the specific values for each region's change, for example, the bar for {South America}<r>[0.452, 0.754, 0.551, 0.887]</r> extends a distance of about -4.7% on the x-axis, which is confirmed by the {text annotation \"-4.7%\"}<r>[0.391, 0.812, 0.430, 0.829]</r> while the bar for {Asia}<r>[0.551, 0.587, 0.711, 0.720]</r> extends a distance of about 7.5% on the X-axis, also confirmed by the {text annotation \"7.5%\"}<r>[0.732, 0.645, 0.767, 0.662]</r>. To convert to a CSV format, the geographic region names are placed in the first column 'Region', and the corresponding values from text annotations are placed in the second column 'Value'.\nBased on the above thought, the answer is:\nRegion,Value\nSouth America,-4.7\nAsia,7.5\nAfrica,-11.4\nEurope,10.2\nAustralia,0.3"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "6S8aNHkGVLtmoa5W",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data depicted in the chart, can you infer which region's tourism and hospitality industry is recovering the fastest? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To infer which region's industry is recovering the fastest we need to analyze the value the bars are extending to on the X-axis. Positive growth is indicated by bars extending to the right of the Y-axis in black, while decline is shown by bars extending to the left of the Y-axis in red. Comparing the black bars, {Europe}<r>[0.551, 0.253, 0.768, 0.387]</r> extends the furthest to the right at around 10.2%, verified by the {text annotation \"10.2%\"}<r>[0.789, 0.311, 0.833, 0.329]</r>, indicating the fastest recovery rate.\nBased on the above thought, the answer is:\nEurope is recovering the fastest in the tourism and hospitality industry, with a growth of 10.2%."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "9uzqERSUsPAcrhZM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region experienced the least change in the tourism and hospitality industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover which region experienced the least change, it's necessary to observe the length of each bar representing the region. By comparing each of these, I can see that the bar representing {Australia}<r>[0.551, 0.087, 0.558, 0.220]</r> extends the smallest distance along the X-axis from the central Y-axis. This is verified by the accompanying text annotation {0.3%}<r>[0.579, 0.145, 0.614, 0.162]</r>, suggesting a minor change in value.\nBased on the above thought, the answer is:\nAustralia."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "i0UtnWJTxqU7Kti3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart and its data, could you create a table in csv format listing the Resources and their Percentage changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "The necessary data is provided by the x-axis labeled {\"\u0394%\"}<r>[0.541, 0.964, 0.564, 0.981]</r> and by six horizontal bars, signifying different resources. Their labels are {\"Students\"}<r>[0.070, 0.825, 0.131, 0.843]</r>, {\"Professors\"}<r>[0.060, 0.687, 0.131, 0.705]</r>, {\"Expenditure\"}<r>[0.047, 0.549, 0.131, 0.567]</r>, {\"Research Output\"}<r>[0.015, 0.411, 0.131, 0.429]</r>, {\"Facilities\"}<r>[0.072, 0.273, 0.131, 0.291]</r>, and {\"Scholarships\"}<r>[0.044, 0.135, 0.131, 0.153]</r>. Percentage changes occur along the horizontal, or x-axis. X-axis begins at zero and spans both negative and positive values. Each bar's length or position on this axis indicates the percentage change for the resource it corresponds to. For instance, the \"Expenditure\" bar stretches from the zero mark located at [0.455, 0.927] to the left, making it a negative change; the coordinate of its end is [0.231, 0.501, 0.455, 0.611]. I can look at the x-axis labels and estimate the value as approximately -10.7%. Therefore, I'll follow this method to determine the corresponding percentage changes for the rest of the resources.\nBased on the above thought, the answer is:\nResource,Percentage change\nStudents,5.6\nProfessors,2.3\nExpenditure,-10.7\nResearch Output,8.5\nFacilities,-1.3\nScholarships,22.7"
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "WjC42zFAbXXQcOYI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be concluded about the overall resource management based on displayed changes in educational resource distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "The displayed data in the form of a horizontal bar chart with changes in various educational resources shows a clear distribution pattern of resource utilization in education. For important resources like {\"Students\"}<r>[0.070, 0.825, 0.131, 0.843]</r>, {\"Professors\"}<r>[0.060, 0.687, 0.131, 0.705]</r> and {\"Research Output\"}<r>[0.015, 0.411, 0.131, 0.429]</r>, there are positive changes. This suggests that the investment and efforts in these sectors have resulted in an increase in numbers. However, the negative figure in {\"Expenditure\"}<r>[0.047, 0.549, 0.131, 0.567]</r> might suggest savings or reduced allocations. Also, notable is the significant increase in {\"Scholarships\"}<r>[0.044, 0.135, 0.131, 0.153]</r>. This could be an initiative to attract more students and promote their enrollment and retention in the universities.\nBased on the above thought, the answer is:\nThe data indicates efficient and strategic resource utilization across the board. Notably, the rise in investments in personnel and research output coupled with a decline in expenditure suggests better financial efficiency. The significant increase in scholarships could be a tactical measure to attract more students and boost enrollment numbers."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "XA0P0girVyJeJ9x0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has been the largest positive change among the resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the resource with the largest positive change, I should look at the length of the bars on the positive side of the x-axis {\"\u0394%\"}<r>[0.541, 0.964, 0.564, 0.981]</r>. Scanning through the bars from top to bottom: \"Students\" at {\"5.6%\"}<r>[0.582, 0.823, 0.617, 0.841]</r>, \"Professors\" at {\"2.3%\"}<r>[0.513, 0.685, 0.548, 0.703]</r>, \"Research Output\" at {\"8.5%\"}<r>[0.643, 0.409, 0.678, 0.427]</r>, and \"Scholarships\" at {\"22.7%\"}<r>[0.940, 0.133, 0.984, 0.151]</r>. Among these, the bar for \"Scholarships\" is the longest.\nBased on the above thought, the answer is:\nScholarships."
+            }
+        ],
+        "id_raw": 54
+    },
+    {
+        "id": "aBm7VSnKesrLpYLJ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the details from the bar chart into a tabular representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart titled {\"Tourism and Hospitality Industry Performance Analysis\"}[0.326, 0.019, 0.779, 0.041] depicts the performance of different tourism types. Each label on the y-axis {\"yticks\"}[{\"Mass Tourism\"}[0.025, 0.814, 0.118, 0.831]; {\"Dark Tourism\"}[0.027, 0.647, 0.118, 0.664]; {\"Eco-Tourism\"}[0.036, 0.480, 0.118, 0.498]; {\"Sports Tourism\"}[0.016, 0.313, 0.118, 0.331]; {\"Digital Tourism\"}[0.015, 0.147, 0.118, 0.164]] represents a different type of tourism. For each of these, there is a colored bar indicating the performance increase percentage. The x-axis {\"Performance Increase \u25b2%\"}[0.463, 0.964, 0.642, 0.981] shows this range, from negative to positive. I will analyze each bar and their corresponding text to determine the performance increase percentage number. This data can then be organized into a CSV table with 'Tourism Type' and 'Performance Increase (%)' as the headers. Let's begin by using the first bar {\"Mass Tourism\"}[0.340, 0.754, 0.555, 0.887] along with its percentage {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829].\nBased on the above thought, the answer is:\nTourism Type,Performance Increase (%)\nMass Tourism,-10.2\nDark Tourism,5.8\nEco-Tourism,12.6\nSports Tourism,-18.7\nDigital Tourism,10.8"
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "psuUx28muKd7riIB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, do you see more positive or negative performance trends in the tourism and hospitality industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analysis of the question would require a comparative review of the performances in terms of positive and negative increases. Observing closely, the {\"Performance Increase \u25b2%\"}[0.463, 0.964, 0.642, 0.981] on the X-axis shows that the zero point is centred with negative and positive sides. Evaluating the bars on both sides: firstly, on the negative side we have {\"Mass Tourism\"}[0.340, 0.754, 0.555, 0.887] with {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829] and {\"Sports Tourism\"}[0.161, 0.253, 0.555, 0.387] with {\"-18.7%\"}[0.091, 0.311, 0.140, 0.329], which amounts to 2 categories. On the positive side we have  {\"Dark Tourism\"}[0.555, 0.587, 0.677, 0.720] with {\"5.8%\"}[0.698, 0.645, 0.733, 0.662],  {\"Eco-Tourism\"}[0.555, 0.420, 0.820, 0.554] with {\"12.6%\"}[0.841, 0.478, 0.885, 0.496], and {\"Digital Tourism\"}[0.555, 0.087, 0.782, 0.220] with {\"10.8%\"}[0.804, 0.145, 0.848, 0.162], which also amounts to 3 categories.\nBased on the above thought, the answer is:\nThere are more positive performance trends. There are 3 tourism types with positive performance increase and 2 with a decrease."
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "1KGIRa2unVXs8HgP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest performance increase percentage in the tourism and hospitality industry categories according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the highest performance increase, I need to examine the length of each bar and their corresponding percentage increases for each category. Here are the bars with their associated labels: {\"Mass Tourism bar\"}[0.340, 0.754, 0.555, 0.887] at {\"-10.2%\"}[0.270, 0.812, 0.319, 0.829], {\"Dark Tourism bar\"}[0.555, 0.587, 0.677, 0.720] at {\"5.8%\"}[0.698, 0.645, 0.733, 0.662], {\"Eco-Tourism bar\"}[0.555, 0.420, 0.820, 0.554] at {\"12.6%\"}[0.841, 0.478, 0.885, 0.496], {\"Sports Tourism bar\"}[0.161, 0.253, 0.555, 0.387] at {\"-18.7%\"}[0.091, 0.311, 0.140, 0.329], and {\"Digital Tourism bar\"}[0.555, 0.087, 0.782, 0.220] at {\"10.8%\"}[0.804, 0.145, 0.848, 0.162]. From these, I can ascertain that the highest performance increase percentage is {\"12.6%\"}[0.841, 0.478, 0.885, 0.496] for {\"Eco-Tourism\"}[0.036, 0.480, 0.118, 0.498].\nBased on the above thought, the answer is:\n12.6%"
+            }
+        ],
+        "id_raw": 55
+    },
+    {
+        "id": "bjtdZIMjiAu0dvIA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart into a CSV table, I need to identify the platform name from the y-axis and the corresponding percentage change from the length and label of the bars. For example, the label at the tick on the y-axis {Khan Academy}[0.015, 0.814, 0.119, 0.831] corresponds with the bar [0.394, 0.754, 0.553, 0.887] with the label {-7.5%}[0.343, 0.812, 0.383, 0.829] which signifies a -7.5% change. Therefore, I can conclude that for Khan Academy, the percentage change in users from 2019 to 2020 was -7.5%. I can continue this process for the other platforms to complete the CSV table.\nBased on the above thought, the answer is:\nPlatform,Increase_Percentage\nKhan Academy,-7.5\nCoursera,13.8\nEdX,-16.2\nUdacity,6.0\nCareerFoundry,-0.9"
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "5ykS0pKqvv8z0CrP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can we draw about the EdTech sector from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides valuable information on the growth trends of various online learning platforms. We can observe some platforms such as {Coursera}[0.553, 0.587, 0.845, 0.720] saw positive growth, while others like {EdX}[0.209, 0.420, 0.553, 0.554] and {Khan Academy}[0.394, 0.754, 0.553, 0.887] saw a decline. The success of platforms varies significantly, suggesting the competitiveness and volatility in the EdTech sector. Additionally, significant outliers like EdX's sharp decline and Coursera's noticeable increase suggest unpredictable events that sway user preferences drastically in this field. The varying growth rates across the platforms indicate that their performance in 2020 might have been influenced by individual factors unique to each platform, such as the quality of the courses offered, the platform's user interface, marketing strategies, or the effects of the COVID-19 pandemic.\nBased on the above thought, the answer is:\nThe EdTech sector is competitive and volatile with inconsistent growth trends among different platforms. The performance, in terms of user growth, of each platform appears to be influenced by various factors possibly unique to each platform."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "AY9WQgXrRluZ36ee",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which platform experienced the least user growth in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to this chart, the platform that experienced the least growth in user numbers in 2020 is the one with the bar extending farthest to the left. We can see that the bar for {EdX}[0.209, 0.420, 0.553, 0.554] is the longest among the negative bars and extended to the leftmost part of the chart. Hence, EdX is the platform that experienced the least user growth in 2020.\nBased on the above thought, the answer is:\nEdX"
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "DvNRD03bkmpeGIkf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the industry change percentages from the bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting from the chart title, {\"Culture and Arts Industry\"}<r>[0.428, 0.019, 0.637, 0.041]</r>, it's clear that this graph is showing the change in different areas within the Culture and Arts Industry. Looking at {the y-axis}<r>[0.049, 0.814, 0.089, 0.831]</r> it's clear that these bars represent different areas within the industry ranging from Dance to Film. The {x-axis}<r>[0.464, 0.964, 0.600, 0.981]</r> parameters show the percentage change. I can use the length of the bars and the accompanying text to figure out the percentage change for each area. For instance, for dance, the bar starts at 0 and ends around -13.7% {according to the text}<r>[0.078, 0.812, 0.126, 0.829]</r>. Therefore, I can surmise that the change in Dance is -13.7% incrementally applying this methodology, we can generate the table.\nBased on the above thought, the answer is:\nIndustry,Percentage Change\nDance,-13.7\nTheatre,20.4\nMusic,-5.4\nVisual Arts,9.7\nFilm,-4.2"
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "Pds6V0u9OxUmKWz1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the overall culture and arts industry based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since this {graph} <r>[0.098, 0.047, 0.966, 0.927]</r> is about the Culture and Arts Industry, one can make inferences about the overall industry based on the graph. Starting from the top, {Dance}<r>[0.138, 0.754, 0.455, 0.887]</r> shows a significant decrease, indicating some troubles in this field. Alternatively, both {Theatre}<r>[0.455, 0.587, 0.927, 0.720]</r> and {Visual Arts}<r>[0.455, 0.253, 0.679, 0.387]</r> are trending in black, indicating positive growth. However, both {Music}<r>[0.330, 0.420, 0.455, 0.554]</r> and {Film}<r>[0.358, 0.087, 0.455, 0.220]</r> are represented in red, showing a decrease, while {Music} is showing a considerable downturn than {Film}. Thus it seems that the performance of different sectors of Arts and Culture industry is inconsistent, with two industries showing significant growth, two industries contracting, and one showing a severe contraction.\nBased on the above thought, the answer is:\nThe overall Culture and Arts Industry is mixed, with two branches (Theatre and Visual Arts) showing growth, while other areas (Dance, Music, and Film) are contracting, notably within the Dance and Music sectors."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "ByMfsDtfIALaYsB9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the largest percentage increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which industry has seen the largest percentage increase, I need to locate the longest black bar on the graph. Starting from the left, the {Dance}<r>[0.138, 0.754, 0.455, 0.887]</r> bar is red and represents a decrease. Let's move on. The next, {Theatre}<r>[0.455, 0.587, 0.927, 0.720]</r>, is black and quite long. Taking note of it and moving on. The {Music}<r>[0.330, 0.420, 0.455, 0.554]</r> again is a decline. {Visual Arts}<r>[0.455, 0.253, 0.679, 0.387]</r> is an increase but isn't as long as the Theatre one. Finally, {Film}<r>[0.358, 0.087, 0.455, 0.220]</r> again shows a decline. Therefore, Theatre is the industry with the highest percentage increase.\nBased on the above thought, the answer is:\nTheatre."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "WBs43Fl9akRV4mPO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUse the plot data and draw this information into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis contains the names of companies, which can be extracted from the labels of each bar: {Tesla}[0.026, 0.814, 0.061, 0.831], {Uber}[0.027, 0.647, 0.061, 0.664], {Lyft}[0.036, 0.480, 0.061, 0.498], {Toyota}[0.015, 0.313, 0.061, 0.331] and {Ford}[0.030, 0.147, 0.061, 0.164]. The X-axis holds the Market Research results represented as a percentage displayed at the right end of each bar. For positive results, the bars and corresponding percentages are located at {Tesla}[0.504, 0.754, 0.937, 0.887>, {Lyft}[0.504, 0.420, 0.576, 0.554], {Ford}[0.504, 0.087, 0.720, 0.220]. For negative results, the bars and corresponding percentages are located at {Uber}[0.215, 0.587, 0.504, 0.720], {Toyota}[0.359, 0.253, 0.504, 0.387]. It is necessary to clarify that the sign is the opposite when calculating negative results. The data drawn from the chart and the CSV output is as follows.\nBased on the above thought, the answer is:\n\"Companies\", \"Market Research\"\n\"Tesla\", 30\n\"Uber\", -20\n\"Lyft\", 5\n\"Toyota\", -10\n\"Ford\", 15"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "hm6VRx3c4dMRglnu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you generally describe the Automotive and Transportation market's performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the market performance of five automotive and transportation companies. By looking at the bars, I can see that for {Tesla}[0.504, 0.754, 0.937, 0.887] and {Ford}[0.504, 0.087, 0.720, 0.220], the performance is positive, while for {Uber}[0.215, 0.587, 0.504, 0.720} and {Toyota}[0.359, 0.253, 0.504, 0.387], the performance is negative with a decline in market. For {Lyft}[0.504, 0.420, 0.576, 0.554], they have a moderate positive performance. The company with the most noticeable increase in market performance is Tesla, while Uber faces the most considerable decline. Therefore, the market performance of these companies is mixed, with some showing growth and others showing a decrease.\nBased on the above thought, the answer is:\nThe Automotive and Transportation market's performance is mixed, with some companies like Tesla and Ford showing growth, while others like Uber and Toyota showing a decline. Lyft has moderate growth."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "s6jXaGh4nBsCzTe3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the largest growth in market performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the values of market performance across all companies to answer this question. Starting with {Tesla}[0.504, 0.754, 0.937, 0.887], which has a value of \"30%\", then moving to {Uber}[0.215, 0.587, 0.504, 0.720] with a value of \"-20%\", followed by {Lyft}[0.504, 0.420, 0.576, 0.554] with a value of \"5%\", afterward {Toyota}[0.359, 0.253, 0.504, 0.387] with a value of \"-10%\", and finally {Ford}[0.504, 0.087, 0.720, 0.220] with a value of \"15%\". From these comparisons, it's clear that Tesla has the highest market performance growth ratio.\nBased on the above thought, the answer is:\nTesla"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "8pY0reO4hpgB8ve4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the chart data and generate a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Local Government Expenditure Changes\"}[0.408, 0.019, 0.743, 0.041], I can understand that this chart is about the change percentages of various department expenditures of a local government. There is a separate horizontal bar for each department with positive or negative values dictated by their position relative to the center line {\"0\"}[0.519, 0.939, 0.527, 0.957]. The actual change percentage is shown at the end of each bar in number annotations. I will use the department names on the y-axis and the percentage changes annotated at the end of each bar for creating the structured data table. For example, the first department is {\"Health & Sanitation\"}<r>[0.025, 0.825, 0.161, 0.843]</r> , and the value annotation at the end of its corresponding bar is {\"15%\"}<r>[0.742, 0.823, 0.772, 0.841]</r> so the value associated with \"Health & Sanitation\" is 15%.\nBased on the above thought, the answer is:\nDepartment,Change%\nHealth & Sanitation,15%\nEducation,-20%\nPublic Works,10%\nCommunity Service,-5%\nEnvironment & Parks,30%\nPublic Safety,-7.5%"
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "6A4KRfLviA7UTNeu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the information presented in the chart, which department seems to be more prioritized in term of budgetary allocations and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to assess which department saw a significant increase in expenditure. An increase in expenditure could be interpreted as the department being prioritized in terms of budget allocations. As I mentioned earlier, the {\"Environment & Parks\"}<r>[0.523, 0.225, 0.946, 0.335]</r> department bar extends further to the right of the middle axis {\"0\"}[0.519, 0.939, 0.527, 0.957], this implies that \"Environment & Parks\" has the highest budget increase. Their budgetary allocation has increased by a whopping {\"30%\"}<r>[0.953, 0.271, 0.984, 0.289]</r>, which is the highest among all departments. This could indicate that prioritization has skewed towards the \"Environment & Parks\" department, possibly due to increased awareness of environmental protection or new projects concerning parks and the environment.\nBased on the above thought, the answer is:\n\"Environment & Parks\" department. This is because it witnessed the highest increase, 30%, in budget allocations compared to other departments, indicating higher priority."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "7L81vPC2i0yryU4R",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department saw the largest increase in expenditure, according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the department that had the biggest increase in expenditure, I must examine the horizontal bars on the right side of the middle axis {\"0\"}[0.519, 0.939, 0.527, 0.957] because positive changes are represented by bars extending to the right. I'll compare the lengths of each of this set of bars. For instance, the bars for {\"Health & Sanitation\"}[0.523, 0.777, 0.734, 0.887] and {\"Public Works\"}[0.523, 0.501, 0.664, 0.611] both extend to the right of the middle axis. By looking at every bar that extends right from the middle axis and comparing their lengths, I can find that the longest bar represents the {\"Environment & Parks\"}<r>[0.523, 0.225, 0.946, 0.335]</r> department. Therefore, \"Environment & Parks\" saw the largest increase in expenditure.\nBased on the above thought, the answer is:\nEnvironment & Parks."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "wHVZxCP10oIFcQOK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the change in influence for each cultural perspective from Influence Metric 1 to Influence Metric 2 be represented in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the two subplots, I place my focus on the bars representing each cultural perspective in {subplot_0}<r>[0.174, 0.025, 0.562, 0.903]</r> for Influence Metric 1 and {subplot_1}<r>[0.587, 0.025, 0.975, 0.903]</r> for Influence Metric 2. By comparing the end of each bar on the x-axis, I can note the value change they represent. {The x-axis labels}<r>[0.583, 0.919, 1.002, 0.942]</r> and {the y-axis labels}<r>[0.174, 0.065, 0.164, 0.845]</r> denote the scale and cultural perspectives, respectively. For instance, for the cultural perspective of {Traditionalism}<r>[0.067, 0.821, 0.164, 0.845]</r>, I see that in Influence Metric 1, the value is {5.5}<r>[0.377, 0.819, 0.404, 0.842]</r>, and in Influence Metric 2, the value becomes {8.1}<r>[0.957, 0.819, 0.983, 0.842]</r>. This process can be repeated for each cultural perspective to obtain a complete table of values from both Influence Metrics.\nBased on the above thought, the answer is:\nCultural Perspective,Influence Metric 1,Influence Metric 2\nTraditionalism,5.5,8.1\nModernism,7,5\nGlobalism,9,2\nConservatism,2,8.1\nLiberalism,6.5,7.1\nCollectivism,3.5,4.2\nIndividualism,8.5,3.2\nCommercialization,10,1\nSecularism,4.5,6.4\nSpirituality,2,7"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "mylDEka7MHDc43Vd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cultural perspectives have shown a decline in influence and might require attention to reinforce their values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which cultural perspectives have declined, I must evaluate the difference in bar length for each perspective between {subplot_0}<r>[0.174, 0.025, 0.562, 0.903]</r> and {subplot_1}<r>[0.587, 0.025, 0.975, 0.903]</r>. By examining each pair of bars, I observe a decrease in value when the bar in {subplot_1}<r>[0.587, 0.025, 0.975, 0.903]</r> is shorter than its counterpart in {subplot_0}<r>[0.174, 0.025, 0.562, 0.903]</r>. {Commercialization}<r>[0.035, 0.251, 0.164, 0.275]</r>, for instance, shows a significant decrease, going from {10}<r>[0.544, 0.249, 0.566, 0.272]</r> to {1}<r>[0.633, 0.249, 0.646, 0.272]</r>, which is a 9.0 change. Similarly, {Globalism}<r>[0.095, 0.658, 0.164, 0.682]</r> and {Modernism}<r>[0.088, 0.740, 0.164, 0.763]</r> also show substantial decreases.\nBased on the above thought, the answer is:\nCommercialization, Globalism, and Modernism have shown a decline in influence. Addressing this issue could involve reassessing the values associated with these perspectives, highlighting their importance and relevance to society, and potentially developing programs or initiatives that would reinforce the values and principles tied to these perspectives."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "mifQ7gXcE5Vrn93b",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cultural perspective shows the greatest increase in influence from Metric 1 to Metric 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must compare the increase in the value of bars for each cultural perspective between the two subplots. By reviewing each bar's end coordinate on the x-axis in {subplot_0}<r>[0.174, 0.025, 0.562, 0.903]</r> and {subplot_1}<r>[0.587, 0.025, 0.975, 0.903]</r>, I can calculate the change in value. After analyzing all the cultural perspectives, it is evident that {Conservatism}<r>[0.069, 0.577, 0.164, 0.600]</r> has the highest increase from {2}<r>[0.248, 0.574, 0.261, 0.598]</r> in Metric 1 to {8.1}<r>[0.957, 0.574, 0.983, 0.598]</r> in Metric 2, which is a 6.1 increase.\nBased on the above thought, the answer is:\nConservatism"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "3WoF4oLKhm0OUByI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the given chart data regarding tourism and hospitality industry trends into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the structuring of {the first subplot's bars}<r>[0.203, 0.810, 0.459, 0.876]</r> and the corresponding {labels}<r>[0.459, 0.833, 0.481, 0.853]</r> for Season 1, as well as {the second subplot's bars}<r>[0.604, 0.810, 0.790, 0.876]</r> and {labels}<r>[0.790, 0.833, 0.812, 0.853]</r> for Season 2, I can determine the frequency for each type in the tourism and hospitality industry. Along the y-axis, there are {tick labels}<r>[0.093, 0.835, 0.193, 0.855]</r> to <r>[0.094, 0.087, 0.193, 0.107]</r> which list the types to be included in the table. This information will be tabulated in accordance with the labels associated with each bar. For example, 'Resort Rentals' has a frequency of 15 for Season 1 and 13 for Season 2.\nBased on the above thought, the answer is:\nType,Season 1,Season 2\nResort Rentals,15,13\nHotel Bookings,12,14\nAdventure Tours,8,10\nCamping Ground Visits,10,9\nCity Tours,15,17\nCruise Bookings,18,15\nRestaurant Revenue,20,23\nEvent Ticket Sales,21,25\nSpa Services,19,21\nMuseum Visits,14,16"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "cAJw4X64SIq0gZgq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any trends that have reversed from Season 1 to Season 2, and can you identify the types for which this is the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "A reversal of trends would be indicated by bars in Season 2 having lengths greater or smaller than their counterparts in Season 1 for the same type. By comparing {the pairs of bars for each type}<r>[0.203, 0.810, 0.459, 0.876]</r> and <r>[0.604, 0.810, 0.790, 0.876]</r> for 'Resort Rentals', <r>[0.203, 0.727, 0.408, 0.793]</r> and <r>[0.604, 0.727, 0.805, 0.793]</r> for 'Hotel Bookings' and so on, we can see which pairs show this trend reversal by examining the textual data at the end of each bar to determine the related frequencies. For example, a reversal is seen for 'Resort Rentals' as the bar for Season 2 <r>[0.604, 0.810, 0.790, 0.876]</r> is shorter than the bar for Season 1 <r>[0.203, 0.810, 0.459, 0.876]</r>, indicating a decrease in frequency from 15 to 13.\nBased on the above thought, the answer is:\nYes, 'Resort Rentals', 'Camping Ground Visits', and 'Cruise Bookings' show reversed trends with decreased frequencies from Season 1 to Season 2."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "ph8jLe2nN9q9ghWR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of bookings and visits for Season 2 across all types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total for Season 2, I look at the labeled frequencies on each {bar of Season 2}<r>[0.604, 0.810, 0.790, 0.876]</r> to <r>[0.604, 0.062, 0.833, 0.129]</r> and sum up the numerical values that are presented above each bar, such as {the label '13'}<r>[0.790, 0.833, 0.812, 0.853]</r> for 'Resort Rentals'. Adding each of these numbers yields the total cumulative frequency for Season 2.\nBased on the above thought, the answer is:\n149"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "A7A7pD0YwiqXHqYP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table showing both Frequency Metric 1 and Frequency Metric 2 for each cybersecurity and data privacy issue?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the two horizontal bar charts, I can discern the relationship between the issue types and their corresponding frequency metrics. Starting with {Frequency Metric 1}<r>[0.168, 0.037, 0.535, 0.854]</r>, it features horizontal bars, each beginning at the y-axis and extending rightward to encapsulate the respective percentage frequency. I can locate the start of each bar at {y-axis tick marks}<r>[0.168, 0.787]</r> and follow the length of the bar to the endpoint, such as for {Password Attacks}<r>[0.168, 0.757, 0.437, 0.817]</r> which concludes at 37.5%. The same process can be applied to the second bar chart corresponding to {Frequency Metric 2}<r>[0.586, 0.757, 0.761, 0.817]</r>, where I observe that the bar for {Password Attacks}<r>[0.586, 0.757, 0.761, 0.817]</r> reaches 12.2%. I'll continue this method for all ten issues and collect the data for both frequency metrics.\nBased on the above thought, the answer is:\nIssue,Frequency Metric 1 (%),Frequency Metric 2 (%)\nPassword Attacks,37.5,12.2\nPhishing,45.2,16.8\nMalware,32.1,20.7\nRansomware,48.7,9.5\nData Breach,44.1,18.0\nIdentity Theft,31.3,14.8\nInsider Threats,39.4,21.2\nIoT Vulnerabilities,47.1,8.7\nDeepFakes,30.4,24.4\nCloud Security,35.8,19.3"
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "0LqTti1dknBbwRJM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Frequency Metric 1 and Frequency Metric 2, which metric shows a greater concern across all cybersecurity issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which frequency metric indicates greater concern across all cybersecurity issues, I must compare the lengths of the bars corresponding to each metric. I start with {Password Attacks}<r>[0.168, 0.757, 0.437, 0.817]</r> using comparison, I note that the bar for {Frequency Metric 1}<r>[0.168, 0.757, 0.437, 0.817]</r> is longer than for {Frequency Metric 2}<r>[0.586, 0.757, 0.761, 0.817]</r>. Progressing methodically through the bars representing Phishing, Malware, and other issues, I recognize a consistent pattern: bars representing {Frequency Metric 1}<r>[0.168, 0.037, 0.535, 0.854]</r> are consistently longer than bars for {Frequency Metric 2}<r>[0.586, 0.037, 0.954, 0.854]</r>, indicating that the issues are more prevalent in Frequency Metric 1. Thus concluding that Frequency Metric 1 showcases a higher level of concern for cybersecurity issues.\nBased on the above thought, the answer is:\nFrequency Metric 1 shows greater concern across all cybersecurity issues because the corresponding bars are longer than those of Frequency Metric 2, signifying a higher percentage frequency of incidents for each issue."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "WOIhzVI3Kidhbigl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average frequency percentage for Identity Theft across both frequency metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average frequency percentage for {Identity Theft}<r>[0.168, 0.378, 0.393, 0.438]</r> across both frequency metrics, I first identify the specific data points. The frequency percentage from {Frequency Metric 1}<r>[0.168, 0.378, 0.393, 0.438]</r> for Identity Theft is 31.3%, confirmed by its data label. The frequency percentage from {Frequency Metric 2}<r>[0.586, 0.378, 0.799, 0.438]</r> is found to be 14.8%. Now, I add these two percentages and divide by the number of metrics, which is 2, to derive the average.\nBased on the above thought, the answer is:\n23.05"
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "oY1KXHHg0oZJu0kD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data depicted on the chart into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I will consider the categories listed on {the y-axis}<r>[0.015, 0.352, 0.029, 0.540]</r> and the respective frequencies for each category as represented by the bar lengths on {the x-axis metric 1}<r>[0.323, 0.927, 0.484, 0.963]</r> and {x-axis metric 2}<r>[0.710, 0.927, 0.871, 0.963]</r>. Following the bar locations and their annotated frequencies, I can determine the exact frequency of each category from the labels at the end of the bars, such as {Example bar metric 1}<r>[0.232, 0.757, 0.559, 0.817]</r> and {Example bar metric 2}<r>[0.618, 0.757, 0.796, 0.817]</r>. Therefore, I can allot each category with its respective values in the table.\nBased on the above thought, the answer is:\nCategory,Frequency Metric 1 (%),Frequency Metric 2 (%)\nInvestment,8.2,4.5\nWaste Management,6.5,5.8\nEmployee Welfare,6.2,6.5\nCarbon Footprint,5.9,7.2\nSupply Chain Management,5.0,4.5\nClean Energy,4.5,8.3\nEqual Opportunities,4.0,3.5\nEthical Business,3.6,6.0\nCommunity Development,3.4,5.2\nSustainable Raw Materials,2.8,5.7"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "GXEC3WTpLNyOUzNC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area of social responsibility experiences a greater emphasis shift from Frequency Metric 1 to Frequency Metric 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "The shift from Frequency Metric 1 to Frequency Metric 2 can be determined by comparing the length of the bars linked to a single category across the two plots. The difference between the lengths of the bars represents the shift in emphasis for each category. For instance, 'Clean Energy' in {Frequency Metric 1's plot}<r>[0.618, 0.378, 0.946, 0.438]</r> shows lower emphasis than in {Frequency Metric 2's plot}<r>[0.232, 0.378, 0.412, 0.438]</r>. By going through each category in a similar manner, we can determine which category experiences the greatest emphasis shift.\nBased on the above thought, the answer is:\nThe 'Clean Energy' category experiences the greatest emphasis shift from Frequency Metric 1 to Frequency Metric 2."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "kIVgxg2PBLr9OAS0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of percentages as depicted on the frequency axis for both metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "On the chart, the frequency percentages for both metrics are represented on the x-axes. The smallest and largest numbers on {the x-axis for Frequency Metric 1}<r>[0.323, 0.927, 0.484, 0.963]</r> are '0%' and '10%' respectively and similarly on {the x-axis for Frequency Metric 2}<r>[0.710, 0.927, 0.871, 0.963]</r>. Therefore, the range of frequencies for both metrics is from 0% to 10%.\nBased on the above thought, the answer is:\n0% to 10%"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "p1b9U1KnGPnkbYOH",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a structured table from the grouped bar chart that shows the market growth rates for the innovation ecosystems?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {chart}<r>[0.187, 0.030, 0.962, 0.883]</r> illustrates two trends in the innovation ecosystems. The left section corresponds to {Rate1}%<r>[0.187, 0.030, 0.553, 0.883]</r> while the right section corresponds to {Rate2}%<r>[0.596, 0.030, 0.962, 0.883]</r>. Starting with {Artificial Intelligence}<r>[0.187, 0.781, 0.536, 0.845]</r>, the bar reaches up to 8.5% for the first rate and 7.6% for the second rate. Moving down, {Blockchain}<r>[0.187, 0.702, 0.511, 0.765]</r> has a rate of 7.9% for the first and 6.3% for the second. This pattern continues with each row representing a different sector. I will structure the table using the ecosystem names as rows and two columns for each rate, marking the values for each as per the length of the bars and corresponding labels.\nBased on the above thought, the answer is:\nEcosystem,Rate1 (%),Rate2 (%)\nArtificial Intelligence,8.5,7.6\nBlockchain,7.9,6.3\nBig Data,6.2,4.9\nCloud Computing,6.0,5.1\nGreen Tech,5.9,4.6\nDrones Tech,4.5,3.7\nFintech,4.0,2.8\nHealthcare Tech,3.5,3.9\nEdtech,2.9,2.3\nSpace Tech,1.8,3.2"
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "ULusKbgHhNntFu78",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has experienced the highest overall growth rate when combining the rates from both Rate1 and Rate2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the highest overall growth, it is necessary to observe the {length of the bars}<r>[0.187, 0.781, 0.536, 0.845];[0.596, 0.781, 0.945, 0.845]</r> corresponding to both rates for all sectors and calculate their combined values. The sector {Artificial Intelligence}<r>[0.035, 0.802, 0.177, 0.830]</r> starts with the longest bars, giving an early indication of it having high rates. The bar lengths from {Rate1 bar}<r>[0.187, 0.781, 0.536, 0.845]</r> and {Rate2 bar}<r>[0.596, 0.781, 0.945, 0.845]</r> are added to calculate the total rate. This process is repeated for every sector, comparing the sums to identify the highest overall growth rate.\nBased on the above thought, the answer is:\nArtificial Intelligence. Because when adding the growth rates of Rate1 (8.5%) and Rate2 (7.6%), it totals 16.1%, which is higher than the combined rates for any other sector as indicated by the total length of {their bars}<r>[0.187, 0.781, 0.536, 0.845];[0.596, 0.781, 0.945, 0.845]</r>."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "HFUGs4kBW6oyJNWF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_6_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate for Big Data greater in Rate1 than in Rate2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the growth rates for {Big Data}<r>[0.117, 0.644, 0.177, 0.672]</r>, I analyze the corresponding bars in each rate section. The {Rate1 bar for Big Data}<r>[0.187, 0.623, 0.441, 0.686]</r> extends to approximately 6.2%, while the {Rate2 bar}<r>[0.596, 0.623, 0.821, 0.686]</r> has a length indicating a rate of approximately 4.9%. Therefore, the observed length provides the necessary numerical data to compare.\nBased on the above thought, the answer is:\nYes."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "xGdYYdPhY2xgWvpY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please create a CSV table that represents the data from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the structure of the provided bar chart, it is apparent that the X-axis represents different types of emerging technology {\"Emerging Tech\"}[{\"VR\"}[0.075, 0.952, 0.091, 0.975]; {\"Blockchain\"}[0.102, 0.952, 0.165, 0.975}; {\"AI\"}[0.179, 0.952, 0.190, 0.975]; {\"5G\"}[0.228, 0.952, 0.244, 0.975]; {\"Quantum Computing\"}[0.226, 0.952, 0.347, 0.975]; {\"IoT\"}[0.329, 0.952, 0.346, 0.975]; {\"Big Data\"}[0.363, 0.952, 0.414, 0.975]; {\"Cloud Computing\"}[0.389, 0.952, 0.490, 0.975]], and the Y-axis represents the \"Outlook Score\" {\"Outlook Score\"}[0.013, 0.418, 0.024, 0.580] scaled from 0 to 1. I can extract the necessary information by analyzing the height of the bars in correlation with the labeled Y-axis values. For example, the height of the {bar for \"VR\"}[0.074, 0.237, 0.092, 0.935] extends to a point that corresponds to a little below 1 on the Y-axis, which is approximated as 0.8.\nBased on the above thought, the answer is:\nEmerging Tech,Outlook Score\nVR,0.8\nBlockchain,0.7\nAI,0.6\n5G,0.5\nQuantum Computing,0.4\nIoT,0.35\nBig Data,0.3\nCloud Computing,0.25"
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "xvHulC3viY6PVvAn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall trend of the outlook score in the emerging technology market according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the graph, I can analyze the overall trend in the outlook of emerging technologies. Starting from {VR}[0.074, 0.237, 0.092, 0.935], which has a relatively high outlook score of about 0.8, the outlook score seems to show a consistent downward trend as we move towards {Cloud Computing}[0.431, 0.717, 0.448, 0.935] on the X-axis, which only has an outlook score of about 0.25. Therefore, the general trend in emerging technologies' outlook scores is decreasing.\nBased on the above thought, the answer is:\nThe overall trend in the outlook of emerging technologies is decreasing."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "rPy8Eq7BTrcUJAYb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emerging technology has the highest outlook score according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which emerging technology has the highest outlook score, I need to compare the height of all the bars on the chart. Looking at the bar for {\"VR\"}[0.074, 0.237, 0.092, 0.935], I can see that it extends to the highest position on the Y-axis (close to 1.0), which represents the outlook score. Therefore, VR is the emerging technology with the highest outlook score.\nBased on the above thought, the answer is:\nVR"
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "xdr8V6JxxNAc8uh8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you please convert the E-commerce sales data from January to May into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"E-commerce and Retail Industry Analysis\"}[0.330, 0.020, 0.670, 0.056], it is clear that this chart is showing sales data for the E-commerce and Retail sectors. Looking at the sub-chart labeled as {E-commerce}[0.224, 0.146, 0.328, 0.182], the x-axis represents different months, and the y-axis represents sales. The blue bars represent sales data for each month. For instance, {the bar for \"January\"}<r>[0.079, 0.398, 0.145, 1.594]</r>indicates the sales is approximately at the 4260 level, and from the raw data provided, it is confirmed that the January sales for E-commerce is 4260.3k. Similar process applies to all other bars.\nBased on the above thought, the answer is:\nMonths,E-commerce Sales\nJanuary,4260.3\nFebruary,3927.1\nMarch,4320.7\nApril,4543.5\nMay,4697.5"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "jBr6ojWQt6g12MeM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you please compare the sales trends of E-commerce and Traditional Retail from January to May?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart marked as {E-commerce}[0.224, 0.146, 0.328, 0.182] and {Traditional Retail}[0.699, 0.146, 0.838, 0.182], we can see two different sub-charts. For E-commerce, there is a clear upward trend in sales from January to May. However, for Traditional Retail, the trend is slower with a slight dip in February and March, followed by a slow recovery. This indicates that E-commerce has done far better than Traditional Retail during these months.\nBased on the above thought, the answer is:\nE-commerce experienced a consistent increase in sales from January to May with a slight dip in February. In contrast, Traditional Retail showed a slow recovery in sales with a slight decline in February and March. Overall, E-commerce had a more robust growth trend compared to Traditional Retail."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "ru0fauhdHzvA7TqA",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which month did Traditional Retail have the lowest sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sub-chart marked as {Traditional Retail}[0.699, 0.146, 0.838, 0.182], I can see that the blue bars represent sales for each month. To determine the month with the lowest sales, I would need to identify the shortest bar. It appears that {the bar representing March}<r>[0.736, 0.786, 0.801, 1.594]</r> is the shortest among the five bars. So, from the chart, the lowest sales occurred in March.\nBased on the above thought, the answer is:\nMarch"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "pB9hEeRrj94TSp4Y",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me transform data for the Annual Energy Use (GW) and Resource Consumption (TJ) from the bar charts into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, from the two bar charts that I visualize, one denotes {Annual Energy Use}<r>[0.211, 0.146, 0.348, 0.182]</r> and the other holds the data for {Resource Consumption}<r>[0.700, 0.146, 0.850, 0.182]</r>. The first chart's bars labeled with {Coal, Natural Gas, Nuclear, Hydro, and Other Renewables}<r>[0.107, 0.912, 0.127, 0.940];[0.171, 0.912, 0.226, 0.940];[0.262, 0.912, 0.298, 0.940];[0.347, 0.912, 0.375, 0.940];[0.401, 0.912, 0.485, 0.940]</r> represent the annual energy use in GW. The correlating heights of these bars showcase the magnitude of energy usage for these resources. Accordingly, for the second chart, the resource consumption data in TJ is represented by the different heights of the bars labeled with the same resources names.\nBased on the above thought, the answer is:\nResource,Annual Energy Use(GW),Resource Consumption(TJ)\nCoal,40000,160000\nNatural Gas,25000,90000\nNuclear,9500,45000\nHydro,6000,12000\nOther Renewables,4500,22500"
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "Uy52ld348qiyQuFx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the country's resource utilization strategy based on the two charts provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart on {Annual Energy Use}<r>[0.211, 0.146, 0.348, 0.182]</r>, I observe that the resource with the maximum usage is {Coal}<r>[0.084, 0.752, 0.149, 0.893]</r>, while the least utilized resource from an energy use perspective is {Other Renewables}<r>[0.410, 0.877, 0.475, 0.893]</r>. Similarly, for {Resource Consumption}<r>[0.700, 0.146, 0.850, 0.182]</r>, the resource that stands out the most in terms of consumption is again {Coal}<r>[0.579, 0.331, 0.644, 0.893]</r>. The resource with the least consumption is {Hydro}<r>[0.824, 0.850, 0.889, 0.893]</r>. It seems that fossil fuels, especially coal, are the primary contributors to the country's energy, which may have environmental implications. The renewable resources, while underutilized, show positive signs of usage, suggesting a slow progression towards sustainable and environment-friendly resources.\nBased on the above thought, the answer is:\nThis country might primarily be dependent on non-renewable resources like Coal for its energy needs, which may lead to environmental issues. However, the usage of renewables, though currently limited, indicates a positive inclination towards clean and sustainable power sources."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "QqRYYhN68G3LhFur",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat resource has the lowest usage in terms of Annual Energy Use (GW) from the bar chart I gave you?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the bars' heights from the bar chart representing {Annual Energy Use}<r>[0.211, 0.146, 0.348, 0.182]</r>. After careful analysis, we see that the bar labelled {Other Renewables}<r>[0.410, 0.877, 0.475, 0.893]</r> has the shortest height, indicating that it has the lowest usage.\nBased on the above thought, the answer is:\nOther Renewables"
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "Jan7B1EKndkwauXW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the information from the first bar chart showing Disinfectants sales into a structured table outlining the sales for each month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data for Disinfectants into a table, I must look at each bar and its corresponding month. For instance, the bar for February is defined by coordinates {the bar for \"Feb\"}<r>[0.042, 0.519, 0.062, 0.893]</r>, which after analyzing the y-axis ticks, indicates a sales figure of approximately 37221. Similarly, I go through each bar for the different months {bar coordinates for \"Apr\"}<r>[0.067, 0.362, 0.088, 0.893]</r>, {bar coordinates for \"Jun\"}<r>[0.093, 0.420, 0.114, 0.893]</r>, {bar coordinates for \"Aug\"}<r>[0.119, 0.429, 0.140, 0.893]</r>, {bar coordinates for \"Oct\"}<r>[0.145, 0.397, 0.165, 0.893]</r>, and {bar coordinates for \"Dec\"}<r>[0.170, 0.314, 0.191, 0.893]</r> and note down their corresponding sales figures.\nBased on the above thought, the answer is:\nMonth,Sales of Disinfectants (in thousands)\nFeb,37221\nApr,52874\nJun,47092\nAug,46215\nOct,49423\nDec,57735"
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "cpmabN67eArf3jRG",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category experienced the highest growth in sales from February to December based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest growth, I will compare the height difference between the bars in February and December for each product category. Starting with Disinfectants, the February {bar}<r>[0.042, 0.519, 0.062, 0.893]</r> and the December {bar}<r>[0.170, 0.314, 0.191, 0.893]</r> exhibit a growth. I will perform this comparative analysis for each category, continuing with Medical Masks from the {February bar}<r>[0.240, 0.680, 0.261, 0.893]</r> and {December bar}<r>[0.369, 0.371, 0.390, 0.893]</r>, Testing Kits from {February bar}<r>[0.439, 0.573, 0.459, 0.893]</r> to {December bar}<r>[0.567, 0.229, 0.588, 0.893]</r>, Ventilators from {February bar}<r>[0.637, 0.652, 0.658, 0.893]</r> to {December bar}<r>[0.766, 0.472, 0.787, 0.893]</r>, and finally Medical Gloves from {February bar}<r>[0.836, 0.623, 0.856, 0.893]</r> to {December bar}<r>[0.964, 0.309, 0.985, 0.893]</r>. After calculating the growth figures for each, I determine which category had the highest increase in sales.\nBased on the above thought, the answer is:\nTesting Kits. Comparing the February to December bars, the category of Testing Kits shows the most significant increase in height, meaning it experienced the highest growth in sales throughout the year."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "1vOFmYFoHP4RVxcC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the sales of Testing Kits increase from February to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in sales for Testing Kits, I have to analyze the height of {the bar for \"Feb\"}<r>[0.439, 0.573, 0.459, 0.893]</r> and {the bar for \"Dec\"}<r>[0.567, 0.229, 0.588, 0.893]</r>. The February sales start from a height correlating to 31847 and the December sales reach a height correlating to 66148 when comparing with the y-axis ticks. I subtract the former from the latter to find the increase.\nBased on the above thought, the answer is:\n34301"
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "G5q64MS9wVht1Y53",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this bar chart, representing risk assessment data, can you create a CSV structured table to describe the metrics better?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CSV table formation requires to segregate the data following the chart pattern. It begins with the classification of {the bar chart title as \"Risk Assessment Across Areas\"}<r>[0.416, 0.020, 0.584, 0.056]</r>. The x-labels represent different categories, with {Risk Area 1, 2, and 3}<r>[0.151, 0.146, 0.213, 0.182]; [0.481, 0.146, 0.543, 0.182]; [0.811, 0.146, 0.873, 0.182]</r>. The x-axis here proves to be our categories, being Threat Level, Vulnerability Level, and Risk Rating, identified in {Risk Area 1's x labels}<r>[0.057, 0.912, 0.115, 0.940]; [0.139, 0.912, 0.225, 0.940]; [0.252, 0.912, 0.304, 0.940]</r>. The y-axis replicates the corresponding values, ranging from {0}<r>[0.022, 0.882, 0.028, 0.910]</r> to {140}<r>[0.010, 0.126, 0.028, 0.154]</r>. The height of the bars and the color diffrenciation for different risk areas ({Risk Area 1}<r>[0.048, 0.731, 0.124, 0.893]</r>; {Risk Area 2}<r>[0.378, 0.639, 0.454, 0.893]</r>; {Risk Area 3}<r>[0.708, 0.552, 0.784, 0.893]</r>) signify the value for each category. Hence using these entities, the CSV table can be synthesized.\nBased on the above thought, the answer is:\nRisk_Factor,Risk_Area_1,Risk_Area_2,Risk_Area_3\nThreat_Level,30,47,63\nVulnerability_Level,25,45,58\nRisk_Rating,55,92,121"
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "gf1q3jUfk0Yhvvdy",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, is there an increasing trend in the overall risks of the three areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, it can be seen that there is an upward progression in overall risks from {Risk Area 1}<r>[0.048, 0.731, 0.124, 0.893]</r> to {Risk Area 3}<r>[0.900, 0.239, 0.977, 0.893]</r>, notwithstanding the outlier evident in {Risk Area 2}<r>[0.570, 0.396, 0.647, 0.893]</r>. This is visible when comparing the 'Risk Rating' for each risk area, as it increases from the first to the third area. Therefore, it can be concluded that there is an increasing trend in the overall risks of the three areas.\nBased on the above thought, the answer is:\nYes, there is an increasing trend in the overall risks of the three areas."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "KfNLgEsZtLZpOko5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Risk Area has the maximum Risk Rating?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the three Risk Area's Risk Rating values is necessary, whose bars are depicted by {Risk Area 1's Risk Rating}<r>[0.240, 0.596, 0.317, 0.893]</r>, {Risk Area 2's Risk Rating}<r>[0.570, 0.396, 0.647, 0.893]</r>, {Risk Area 3's Risk Rating}<r>[0.900, 0.239, 0.977, 0.893]</r>, respectively. Visually analyzing the chart, Risk Area 3's Risk Rating bar's height is the largest, signifying the highest Risk Rating value.\nBased on the above thought, the answer is:\nRisk Area 3."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "PbZvEeMZ3QwwbCOL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the two bar charts into two CSV format tables?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the two charts present two different trends in Entrepreneurship Ecosystem. The left chart represents {\"Erratic Fluctuations\"}<r>[0.213, 0.146, 0.320, 0.182]</r>, while the right chart represents {\"Continuous Decrease\"}<r>[0.702, 0.146, 0.820, 0.182]</r>. Each chart has five bars representing five key indicators, and the vertical axis represents their values. From the bar heights and y-axis, I can see that these key indicators are: Startup Formation, Patent Applications, VC Funding, Acquired Startups, and Top-Tier Tech Talent. Now, Let's process each bar to get its corresponding indicator's value.\nBased on the above thought, the answer is:\n\"Erratic Fluctuations\"\nStartup Formation, 15.00\nPatent Applications, 227.00\nVC Funding, 3345.00\nAcquired Startups, 4.00\nTop-Tier Tech Talent, 675.00\n\n\"Continuous Decrease\"\nStartup Formation, 20.00\nPatent Applications, 315.00\nVC Funding, 4578.00\nAcquired Startups, 7.00\nTop-Tier Tech Talent, 829.00"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "lZw8HAGrUcd6IXFt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the overall trend of the Entrepreneurship Ecosystem in terms of VC funding and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the overall trend in terms of VC funding, we can inspect the bars corresponding to the VC Funding in the Erratic Fluctuations and Continuous Decrease charts. The VC Funding in the Erratic Fluctuations chart<r>[0.232, 0.423, 0.301, 0.893]</r> and the VC Funding in the Continuous Decrease chart<r>[0.727, 0.250, 0.796, 0.893]</r> represent the funding under the two trends. From the heights, it is quite clear that there was a significant increase in VC funding during the period of continuous decrease trend compared to the erratic fluctuations trend.\nBased on the above thought, the answer is:\nThere was an increasing trend in the VC funding throughout, despite the overall Ecosystem showed a continuous decrease. The specific increase in VC funding might be attributed to more tech startups attracting venture capital funding due to their innovative products or solutions."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "eq5cZOs6QRtHWbZC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn both the trends, which key indicator showed the most decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which key indicator showed the most decrease, I need to compare the difference in values of the same key indicator for the two trends. For example, for the Startup Formation indicator, the bars are located at {Erratic Fluctuations}[0.060, 0.890, 0.129, 0.893] and {Continuous Decrease}[0.555, 0.890, 0.624, 0.893]. By comparing the heights, I can find out the difference. By doing this for all the indicators, we will be able to find out which indicator showed the most decrease.\nBased on the above thought, the answer is:\nVC Funding"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "ADqhsNwZNUyYhcdg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the migration data for the given regions from the bar chart into a CSV-formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the chart title \"Population Migration and Mobility Trends\"}[0.416, 0.020, 0.584, 0.065], I can see which chart represents different regions. For each region, there are two bars that represent migration to {Developed and Developing}<r>[0.047, 0.897, 0.175, 0.932]</r> nations. I will convert each of the subplot bars into table rows. For example, in the {Europe subplot}[0.023, 0.226, 0.199, 0.873], there is {a bar for \"Developed\" migration}[0.031, 0.575, 0.102, 0.679] with value 1.6 and {a bar for \"Developing\" migration}[0.119, 0.679, 0.191, 0.808] with value -2. I will continue this process for all the regions covered in the chart: Asia, Africa, America, and Oceania.\nBased on the above thought, the answer is:\nRegion,Developed,Developing\nEurope,1.6,-2\nAsia,1.2,-1.5\nAfrica,-0.5,6.3\nAmerica,0.7,-1\nOceania,0.2,4"
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "xO4RcZpaXCt8YuL4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has witnessed the highest migration rate to developing countries, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart presents different regions with two bars each for Developed and Developing migrations. By looking at the height of each bar, I can deduce which region has the highest rate. For {Developing migration}, I notice that the {Africa region bar}[0.516, 0.271, 0.588, 0.679] has the longest length, extending from 0 to 6.3, which overtakes the other bars, such as the ones in {Asia}[0.318, 0.679, 0.389, 0.776], {America}[0.715, 0.679, 0.786, 0.744], and {Oceania}[0.913, 0.420, 0.985, 0.679]. The height of the bars is directly proportional to the value of the migration rate.\nBased on the above thought, the answer is:\nAfrica. Due to the longest bar length indicating the migration rate to developing countries, Africa has witnessed the highest migration rate."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "j0Y6vEsvtAjwfKuf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total migration rate to Developed nations across all regions as observed in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total migration rate to Developed regions, I will sum up the values associated with Developed migrations across all subsections of the bar chart. Starting with {Europe's Developed bar}[0.031, 0.575, 0.102, 0.679], the value is 1.6; in {Asia's Developed bar}[0.229, 0.601, 0.300, 0.679], the value is 1.2; in {Africa's Developed bar}[0.428, 0.679, 0.499, 0.711], the value is -0.5; in {America's Developed bar}[0.626, 0.634, 0.697, 0.679], the value is 0.7; and finally, in {Oceania's Developed bar}[0.825, 0.666, 0.896, 0.679], the value is 0.2. By adding all these values, I can find the total migration rate to Developed nations.\nBased on the above thought, the answer is:\n3.2"
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "LkYSwME8TxNOA213",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you rearrange the data in the chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {title \"Environmental and Climate Change Impact\"}[0.321, 0.020, 0.679, 0.056] and the numbers on the y-axes for each graph: {left graph 'CO2 Emissions'}[0.042, 0.191, 0.492, 0.893] and {right graph 'Ocean Acidification'}[0.535, 0.191, 0.985, 0.893], it is clear that the y-axis represents the values for CO2 Emissions and Ocean Acidification. The x-axis for both graphs represent the years {1975, 1985, 1995, 2005, 2015, 2025}[0.073, 0.912, 0.954, 0.940]. Hence, by recording the values from each bar in the two bar graphs, we can convert these into a CSV format. For instance, the first data point reviewed is {the bar representing 1975}[0.063, 0.682, 0.119, 0.893] in the first bar chart, indicating a CO2 emission of 15 in 1975, and the corresponding bar in the second bar graph indicates {an Ocean Acidification of 7.4}[0.555, 0.789, 0.612, 0.893] for the same year. This process will be used for the subsequent years to gather data for each category.\nBased on the above thought, the answer is:\nYear, CO2 Emissions, Ocean Acidification\n1975, 15, 7.4\n1985, 17, 7.3\n1995, 19, 7.1\n2005, 25, 7\n2015, 35, 6.8\n2025, 40, 6.6"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "stxZryKRvta4Xc2Q",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer regarding the rate of environmental change based on the trends observable in the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "We can get the trends by comparing the heights of the bars in both bar graphs. Starting from the left, {the first CO2 Emission bar}[0.063, 0.682, 0.119, 0.893] corresponding to the year 1975 is the shortest among all other bars for the years leading up to 2025, with {the tallest CO2 Emission bar}[0.416, 0.331, 0.472, 0.893] indicating an increase in CO2 emissions over the years. Similarly, for the second category, {the highest Ocean Acidification bar}[0.555, 0.789, 0.612, 0.893] corresponds to the year 1975 and {the lowest Ocean Acidification bar}[0.908, 0.800, 0.965, 0.893] corresponds to 2025, thus indicating a decline in Ocean Acidification.\nBased on the above thought, the answer is:\nThe continual increase in CO2 emissions and decrease in Ocean Acidification from 1975 to 2025 suggests an accelerating rate of environmental change. This trend indicates that our environment is deteriorating rapidly due to increasing emissions, which in turn intensifies the acidity of oceans."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "V8yVn29fjsoTjnei",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the highest CO2 emission observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the highest CO2 emission, I need to observe and compare the heights of the bars in the first bar graph {right graph 'CO2 Emissions'}[0.042, 0.191, 0.492, 0.893]. Consider that the bar symbolizing 2025 {year 2025}[0.416, 0.331, 0.472, 0.893] appears the tallest of all, therefore it means that the most CO2 emission occurred in this year.\nBased on the above thought, the answer is:\n2025"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "WuZU8YaGt6qDHhcf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the data from the bar charts in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart includes three subplots, titled 'Region 1 Evaluation'<r>[0.127, 0.146, 0.237, 0.182]</r>, 'Region 2 Evaluation'<r>[0.457, 0.146, 0.567, 0.182]</r>, and 'Region 3 Evaluation'<r>[0.787, 0.146, 0.897, 0.182]</r>. Each subplot has two bars representing 'PolicyEffectiveness' and 'PolicyDuration'. To translate the data into a CSV format, we need to record the region, the policy, and the corresponding value. For example, the PolicyEffectiveness bar for Region 1 is located at coordinates <r>[0.048, 0.370, 0.167, 0.893]</r>, reaching a height corresponding roughly to 74.5 on the graph. Similarly, the PolicyDuration bar for Region 1 is located at <r>[0.197, 0.871, 0.317, 0.893]</r>, corresponding to a value of 3.1. The same process must be followed for the bars in the other subplots.\nBased on the above thought, the answer is:\nRegion,PolicyEffectiveness,PolicyDuration\nRegion 1,74.5,3.1\nRegion 2,63.2,1.8\nRegion 3,84.1,2.5"
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "7g3aydA6iOQqAyyC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed data, which region's policies seem to be the most effective?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which region's policies are most effective, we'll compare the heights of the 'PolicyEffectiveness' bars in each subplot. The coordinates of these bars are as follows: 'Region 1'<r>[0.048, 0.370, 0.167, 0.893]</r>, 'Region 2'<r>[0.378, 0.449, 0.497, 0.893]</r>, and 'Region 3'<r>[0.708, 0.302, 0.827, 0.893]</r>. The highest bar corresponds to 'Region 3', which suggests that this region's policies are the most effective.\nBased on the above thought, the answer is:\nRegion 3, as it has the highest 'PolicyEffectiveness' value based on the observed data."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "aO0c8h8XawA07ehN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has the longest duration of implemented policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, we need to compare the heights of the 'PolicyDuration' bars across the three subplots. The coordinates of these bars in the subplots are as follows: 'Region 1'<r>[0.197, 0.871, 0.317, 0.893]</r>, 'Region 2'<r>[0.527, 0.880, 0.647, 0.893]</r>, and 'Region 3'<r>[0.857, 0.875, 0.977, 0.893]</r>. After comparing these, the bar corresponding to 'Region 1' is the tallest, indicating that Region 1 has the longest duration of implemented policies.\nBased on the above thought, the answer is:\nRegion 1"
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "C30r6gDPHD2FrmPg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data observed from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the visual data, it's clear the chart is divided into three separate bar charts. Each chart represents a specific month with different processes noted on the x-axis. The y-axis scales show efficiency scores from {50}[0.016, 0.882, 0.028, 0.910] to {110}[0.010, 0.180, 0.028, 0.208].\nIn the first chart for {December}[0.153, 0.146, 0.211, 0.182], the height represents efficiency scores for each process, such as {Threading}[0.048, 0.460, 0.092, 1.477] at 87 and {Drilling}[0.104, 0.553, 0.148, 1.477] at 79. Similarly, data from the chart titled {January}[0.491, 0.146, 0.533, 0.182] and {February}[0.818, 0.146, 0.867, 0.182] are also observed. The entire process will be applied to the other charts in the same way to form a CSV table.\nBased on the above thought, the answer is:\nProcess, December, January, February\nThreading, 87, 75, 73\nDrilling, 79, 62, 65\nSawing, 95, 87, 85\nAssembly, 88, 92, 95\nFinishing, 90, 78, 81"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "ZGV4aLpqQtNqxBqH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the chart, which process experienced the most stable production efficiency across the three months?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the chart, the stability of production efficiency can be determined by how similar the bar heights are across the three months for each manufacturing process. The {Threading bars}[0.048, 0.460, 0.092, 1.477; 0.378, 0.600, 0.422, 1.477; 0.708, 0.623, 0.752, 1.477] indicated that the efficiency dropped from 87 to 73, demonstrating significant fluctuation.\nThe process showing the least fluctuation or the most stable production efficiency is {Sawing}[0.160, 0.366, 0.204, 1.477; 0.490, 0.460, 0.534, 1.477; 0.820, 0.483, 0.864, 1.477]. This process saw efficiency levels move from 95 in December, to 87 in January and 85 in February, a relatively smaller shift compared with other processes.\nBased on the above thought, the answer is:\nSawing"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "8jsCYd9fZVvPRmvS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom December to February, which process showed an increase in production efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data for each process across the three months should be observed to determine which process showed an increase in production efficiency. For example, in the case of the {Threading process bars}[0.048, 0.460, 0.092, 1.477; 0.378, 0.600, 0.422, 1.477; 0.708, 0.623, 0.752, 1.477], the efficiency in December was 87, 75 in January, and 73 in February, indicating a decrease in efficiency over those months.\nBy comparing these observations across all processes, it can be seen that the {Assembly process bars}[0.216, 0.448, 0.261, 1.477; 0.546, 0.401, 0.591, 1.477; 0.876, 0.366, 0.921, 1.477] display increasing efficiency from December to February, from 88 to 92 and finally to 95.\nBased on the above thought, the answer is:\nAssembly"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "QLQ3oLgzeLvNkEDW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar charts you observed, can you generate a structured table from the data collected in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to generate a table in CSV format, I have to process the chart into structured data. The title encoded in { \"Ecological Environment Sustainability Assessment Across Regions\"}[0.317, 0.020, 0.683, 0.056] and {the x-axis tick labels}[{ \"Air Quality\"}[0.040, 0.912, 0.089, 0.940]; {\"Water Quality\"}[0.076, 0.912, 0.140, 0.940]; {\"Soil Health\"}[0.127, 0.912, 0.177, 0.940]; {\"Biodiversity\"}[0.168, 0.912, 0.223, 0.940]; {\"Carbon Footprint\"}[0.200, 0.912, 0.278, 0.940]; {\"Forestation Levels\"}[0.241, 0.912, 0.325, 0.940]]; indicate the categories. The three regions - Region 1, Region 2 and Region 3 can be recognized from the subtitles in { \"Region 1\"}[0.149, 0.146, 0.198, 0.182], { \"Region 2\"}[0.479, 0.146, 0.528, 0.182] and { \"Region 3\"}[0.809, 0.146, 0.858, 0.182]. The y-axis tick labels offer a scale from {0}[0.022, 0.882, 0.028, 0.910] to {100}[0.010, 0.180, 0.028, 0.208], so I can get the values by comparing the heights of the bars in each region. For instance, in Region 1, the height of {\"Air Quality\"}[0.047, 0.436, 0.082, 0.893] represents the value of 65. These processes will guide me to create the required table.\nBased on the above thought, the answer is:\nEco Factors,Region1,Region2,Region3\nAir Quality,65,70,85\nWater Quality,55,68,49\nSoil Health,70,82,76\nBiodiversity,76,72,90\nCarbon Footprint,82,74,61\nForestation Levels,88,86,91"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "5qYXtdlTDx9BmW6O",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graphs, can you analyze the sustainability status of these three regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sustainability status in the three regions presented in this graph can be deduced by looking at the ecological factors for each region. For example, in {\"Region 1\"}[0.034, 0.191, 0.313, 0.893], the heights of the bars for each eco factor from left to right reflect their sustainability status. This region seems to have performed better overall in terms of forestation level but lags behind in terms of water quality. A similar analysis can be done for {\"Region 2\"}[0.364, 0.191, 0.643, 0.893] and {\"Region 3\"}[0.694, 0.191, 0.973, 0.893], taking into account the bar heights for each factor in these regions. Region 2 has an evenly distributed profile with no major outliers. Region 3, however, has water quality quite lagging behind while standing out in terms of forestation levels. In summary, the sustainability status can be deduced based on these eco factors where the taller bars indicate better performance in that specific factor and vice versa.\nBased on the above thought, the answer is:\nRegion 1 performed well in terms of forestation levels but was lacking in water quality. Region 2 has a balanced profile across all ecological factors with no major outliers. Meanwhile, Region 3 stands out in terms of forestation levels but lags significantly behind in water quality."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "1AVaHkRVMQu58gWh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has the least ecological impact in terms of carbon footprint, according to the bar charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "An ecological footprint with a smaller size generally implies a lower ecological impact. The bar charts of the \"Carbon footprint\" factor for the three regions are denoted by the rectangles in-{ \"Region 1\"}[0.221, 0.317, 0.256, 0.893], { \"Region 2\"}[0.551, 0.373, 0.586, 0.893], and { \"Region 3\"}[0.881, 0.464, 0.916, 0.893]. I need to compare the heights of the three bars representing each region. According to the coordinates, the bar for \"Region 3\" is the shortest, suggesting that region has the smallest carbon footprint.\nBased on the above thought, the answer is:\nRegion 3."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "yZDVLNuzkUfscG2g",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed chart, could you please transform the graphical data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the main title of the {chart}<r>[0.268, 0.020, 0.732, 0.056]</r> as well as the subtitiles of the {first}<r>[0.226, 0.146, 0.309, 0.182]</r> and {second} subcharts<r>[0.719, 0.146, 0.801, 0.182]</r>, the data represented is the comparison of certain global trends during the years 2010 and 2020. There are three categories depicted both in the {first}<r>[0.042, 0.191, 0.492, 0.893]</r> and {second} subcharts<r>[0.535, 0.191, 0.985, 0.893]</r>: CO2 Emissions, Sea Level, and Forest Cover. Based on the height of the bars and the y-axis labels, the values for each category and year can be estimated. For example, in the {first} subchart, the {CO2 Emissions bar}<r>[0.063, 0.277, 0.180, 0.893]</r> is about approximately at the y-axis label of 35, indicating that the CO2 Emissions were 35.1 billion tons in 2010.\nBased on the above thought, the answer is:\nYear,CO2 Emissions,Sea Level,Forest Cover\n2010,35.1,3.3,31.6\n2020,36.4,8.4,30.8"
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "tFT6v1mREzlrpVv8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you draw about global environmental conditions based on the observed chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides a comparison of three significant global environmental indicators over a 10 year period: CO2 Emissions, Sea Level and Forest Cover. Observing the heights of the bars in the chart, I can draw several conclusions. {CO2 Emissions}<r>[0.063, 0.277, 0.180, 0.893]</r> increased over this period, suggesting a rise in activities contributing to global warming. The increase in {Sea Level}<r>[0.209, 0.835, 0.326, 0.893]</r> could be indicative of the impacts of global warming such as melting ice caps and rising ocean temperatures. The decrease in {Forest Cover}<r>[0.355, 0.338, 0.472, 0.893]</r> over the same period could imply increased deforestation activities which further exacerbate global warming by reducing carbon sequestration capabilities. These trends are concerning for the global environment and could signal a need for more sustainable practices.\nBased on the above thought, the answer is:\nThe chart suggests a deterioration in global environmental conditions over the 10 year period due to the increase in CO2 Emissions and Sea Level alongside the reduction in Forest Cover, indicating the impacts of global warming and deforestation. This implies a need for action towards more sustainable practices and climate change mitigation."
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "hLLFeERKCpqUSe3q",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2010 and 2020, by what percent did the global Forest Cover decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the Forest Cover bars for the two years. The {2010 Forest Cover bar}<r>[0.355, 0.338, 0.472, 0.893]</r> is approximately at the mid-30 range on the y-axis labels, indicating the forest cover percentage in 2010 was 31.6. The {2020 Forest Cover bar}<r>[0.848, 0.352, 0.965, 0.893]</r> is at the mid-30 range as well on the y-axis labels, indicating the forest cover percentage in 2020 was 30.8. Hence, the decrease has been from 31.6% to 30.8%.\nBased on the above thought, the answer is:\n2.53%"
+            }
+        ],
+        "id_raw": 116
+    },
+    {
+        "id": "8W4NQulekBIxcDeu",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data observed in these 3 bar charts for the different trends related to AI and Automation across the 3 regions into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the charts, I can see that each of them represents a region. For Region 1 {SUBPLOT FOR REGION 1}[0.028, 0.191, 0.323, 0.893], Region 2 {SUBPLOT FOR REGION 2}[0.358, 0.191, 0.653, 0.893] and Region 3 {SUBPLOT FOR REGION 3}[0.688, 0.191, 0.983, 0.893]. The categories plotted on x-axis tags like {AI Research}[0.037, 0.912, 0.091, 0.940], {Automation Use}[0.083, 0.912, 0.157, 0.940], {AI Patents}[0.152, 0.912, 0.199, 0.940], {AI Education}[0.203, 0.912, 0.261, 0.940], {AI investments}[0.253, 0.912, 0.322, 0.940] and the y-axis labels range from {0}[0.016, 0.882, 0.022, 0.910] to {10}[0.010, 0.180, 0.022, 0.208]. The data from the bars in each chart are used to determine each numerical value. For example, in the category of \"AI Research\" for \"Region 1\", the green bar's top {BAR FOR AI RESEARCH IN REGION 1}[0.042, 0.738, 0.086, 0.893] aligns approximately with the 2 on the y-axis, thus the value should be 2.2. The same process was repeated for all regions and categories.\nBased on the above thought, the answer is:\nTrends,Region 1,Region 2,Region 3\nAI Research,2.2,5.4,3.9\nAutomation Use,5.5,4.2,6.3\nAI Patents,3.5,7.6,4.7\nAI Education,4.4,6.1,5.5\nAI Investments,6.0,8.4,7.1"
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "VvGaoQOnRuQL8hLC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, which region seems to give the highest priority to Artificial Intelligence and Automation? Provide explanations for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires an overall comparison of all the bars in all three regions. Region 1's {total bars}[0.042, 0.738, 0.310, 0.893] are generally shorter than those of Region 2 {total bars}[0.372, 0.514, 0.640, 0.893] and Region 3 {total bars}[0.702, 0.619, 0.970, 0.893]. Region 2, has the highest bars among the three in the categories of \"AI Research\", \"AI Patents\", and \"AI Investments\", indicating a higher trend in those categories which entails higher priority. Even though Region 3 has the highest bar in the \"Automation Use\" category, Region 2 tends to have the most consistently high bars across all categories.\nBased on the above thought, the answer is:\nRegion 2, because it has the highest trends in \"AI Research\", \"AI Patents\", and \"AI Investments\", and consistently high trends across all categories."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "YrIaXV6cpnxtKqbF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest average value across all three regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this calculation, for each category, I must identify and add up the values for all three regions then divide by 3. For example, for \"AI Research\", in Region 1 the bar {BAR FOR AI RESEARCH IN REGION 1}[0.042, 0.738, 0.086, 0.893] yields a value of 2.2, in Region 2 the bar {BAR FOR AI RESEARCH IN REGION 2}[0.372, 0.514, 0.416, 0.893] yields a value of 5.4, and in Region 3 the bar {BAR FOR AI RESEARCH IN REGION 3}[0.702, 0.619, 0.746, 0.893] yields a value of 3.9. So, the average for \"AI Research\" is (2.2+5.4+3.9)/3 = 3.83. I should repeat this process for other categories: \"Automation use\", \"AI Patents\", \"AI Education\", and \"AI Investments\". Finally, the category with the highest average value must be recognized.\nBased on the above thought, the answer is:\nAI Investments."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "Nqcb4TqtRAgXb4hr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the monthly performance of the financial assets into a formatted CSV table as depicted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining {the chart}<r>[0.428, 0.020, 0.572, 0.056]</r>, I can see there are three bar graphs corresponding to {Equity}<r>[0.034, 0.191, 0.330, 0.893]</r>, {Bonds}<r>[0.364, 0.191, 0.660, 0.893]</r>, and {Commodities}<r>[0.694, 0.191, 0.990, 0.893]</r>, each presented monthly from January to December. By using the x-axis and y-axis coordinates to read the values for each month, and then capturing and converting these observed measurements directly into a CSV format.\nBased on the above thought, the answer is:\nMonth,Equity,Bonds,Commodities\nJanuary,100.0,100.0,100.0\nFebruary,96.3,100.6,102.3\nMarch,94.5,100.8,100.4\nApril,92.5,101.5,99.6\nMay,89.6,101.9,99.1\nJune,85.7,104.5,96.3\nJuly,81.5,105.3,94.8\nAugust,80.0,105.9,92.3\nSeptember,75.9,109.8,90.9\nOctober,72.3,112.3,88.6\nNovember,71.5,117.8,87.2\nDecember,76.5,121.5,85.8"
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "yUrKfxEUcJWxKXHJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the overall performance trend of Equities over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {Equity}'s performance <r>[0.034, 0.191, 0.330, 0.893]</r>, it can be noted that the bar heights for {Equity} generally decrease from January to November, with a slight increase in December. This indicates a mostly declining trend with values starting from high and gradually dropping except for a modest rebound in the final month.\nBased on the above thought, the answer is:\nEquities generally exhibited a declining trend throughout the year, starting from 100 in January and decreasing to as low as 71.5 in November, before slightly increasing to 76.5 in December."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "QGi0XBaHT35m46YZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest index value reached by Bonds during the observed months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest index value reached by {Bonds}<r>[0.364, 0.191, 0.660, 0.893]</r>, I need to examine the bar heights depicted in the Bonds bar graph. By observing, December shows the highest bar position at <r>[0.628, 0.235, 0.647, 1.786]</r>, indicating the highest value.\nBased on the above thought, the answer is:\n121.5"
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "RBuOg1fkirKZinW3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you create a CSV table which includes all the data represented by the Previous Year and Current Year bars for each category?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart, it has 2 subplots which display a horizontal bar chart with 2 group's data provided by Series1 and Series2. The first subplot represents the previous year, with the green bars labelled as {Previous Year}[0.522, 0.035, 0.657, 0.075], and the second subplot represents the current year, with the orange bars labelled as {Current Year}[0.522, 0.901, 0.792, 0.941]. Each group contains 8 categories from top to bottom: {Demand}[0.061, 0.050, 0.134, 0.064], {Supply}[0.075, 0.100, 0.134, 0.114], {Surplus}[0.070, 0.151, 0.134, 0.165], {Deficit}[0.078, 0.202, 0.134, 0.216], {Market Share}[0.019, 0.253, 0.134, 0.267], {Competition}[0.028, 0.303, 0.134, 0.317], {Opportunity}[0.030, 0.354, 0.134, 0.368], and {Risk}[0.098, 0.405, 0.134, 0.419]. For each category, the length of the bar corresponds to the respective values. For instance, for Demand in Previous Year, the width of the bar corresponds to the value 8, while for Demand in Current Year, the width of the bar corresponds to the value 16. Therefore, I am able to create a table containing these categories and their corresponding values.\nBased on the above thought, the answer is:\nCategory, Previous Year, Current Year\nDemand, 8, 16\nSupply, -5, 14\nSurplus, 10, 20\nDeficit, -20, 10\nMarket Share, 5, 25\nCompetition, -2, 12\nOpportunity, 12, 22\nRisk, -5, 15"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "N5fnc9w9DXuW6mSa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which region shows a trend of improvement from Previous Year to Current Year, and why would you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the improvement trend, I need to compare the Previous Year data with the Current Year data for each category. From the chart, I conclude that the {Supply}[0.075, 0.100, 0.134, 0.114] category shows an improvement trend. This is because the {Previous Year}[0.437, 0.086, 0.522, 0.126] data for Supply shows a negative value whereas the {Current Year}[0.522, 0.086, 0.758, 0.126] data shows a positive value. A shift from previously negative to a positive value indicates an improvement trend.\nBased on the above thought, the answer is:\nThe Supply category shows an improvement trend from Previous Year to Current Year since it moved from a negative to a positive value."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "xepK4aRiegW2dQWo",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_9_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which category has the highest Current Year value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest Current Year value, I need to check the length of the bars on the second subplot where orange bars represent current year values. Comparing the lengths of each bar in {Current Year}[0.522, 0.901, 0.792, 0.941], the {Market Share}[0.522, 0.238, 0.943, 0.278] bar has the highest length, thus indicating the highest Current Year value.\nBased on the above thought, the answer is:\nMarket Share."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "ovnJLn9Ew34OAOny",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the information from the bar chart, extract and compile the raw data in the CSV format for easier analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "The Chart contains two subplots each representing a different perspective of the companies' performance. From the first subplot titled {\"Innovation of Companies\"}[0.201, 0.025, 0.408, 0.055], I can see each bar with text annotations indicating the company\u2019s innovation score. For example, the bar for Google has {\"7 points\"}[0.340, 0.856, 0.396, 0.880] written above it. Similarly, the second subplot titled {\"Technology of Companies\"}[0.656, 0.025, 0.868, 0.055] shows each company's technology score in a similar way. For instance, Google has {\"9 points\"}[0.869, 0.856, 0.925, 0.880] in its technology score. This needs to be replicated for each of the companies listed vertically on the y-axis in the form of y-label like {\"Google\"}[0.031, 0.859, 0.080, 0.882] and so on.\nBased on the above thought, the answer is:\nCompany,Innovation Score,Technology Score\nGoogle,7,9\nApple,5,4\nMicrosoft,2,3\nAmazon,8,7\nTesla,5,1\nFacebook,6,3\nNetflix,3,1\nTwitter,2,1\nSpaceX,10,2\nUber,3,1"
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "bQ7vjhh9q8M31Ewt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company appear to focus most on technological innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a composite judgment considering both the \"Innovation of Companies\" and \"Technology of Companies\" values together. In order to find the qualified company, I need to numerically compare the values from both charts for each company. For example, {Google's innovation score}<r>[0.340, 0.856, 0.396, 0.880]</r> and {Google's technology score}<r>[0.869, 0.856, 0.925, 0.880]</r> suggests a total score of 16 (7 from innovation, and 9 from technology) which is significantly higher compared to other companies' total scores, this suggests that Google appears to focus most on technological innovation.\nBased on the above thought, the answer is:\nGoogle, as it has both high scores in innovation and technology, suggesting a strong focus on technological innovation."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "0XyKr6sbHcdCAS6t",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_9_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which company has the highest Technology score?"
+            },
+            {
+                "from": "gpt",
+                "value": "If we observe the subplot {\"Technology of Companies\"}[0.656, 0.025, 0.868, 0.055], we can compare the lengths of each bar to determine which one has the longest bar, hence the highest technology score. Visually scanning through the list of companies and their respective bars like {Google's bar}<r>[0.548, 0.838, 0.869, 0.898]</r> and others, we could see that {Google}'s bar seems to be the longest.\nBased on the above thought, the answer is:\nGoogle"
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "yhWANPLa7tZPcyyy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, can you extract and organize the evaluations of various policies by synthetic data and human-authored data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels on the y-axis of the chart, I can see the names of the different policies implemented by the government. These include {Guaranteed Income}<r>[0.121, 0.843, 0.259, 0.866]</r>, {Renewable Energy Encouragement}<r>[0.015, 0.731, 0.259, 0.754]</r>, {Immigration Control}<r>[0.119, 0.618, 0.259, 0.642]</r>, {Public Health}<r>[0.168, 0.506, 0.259, 0.529]</r>, {Free Education}<r>[0.156, 0.393, 0.259, 0.417]</r>, {Early Retirement}<r>[0.142, 0.281, 0.259, 0.304]</r>, and {Tax Hike}<r>[0.201, 0.169, 0.259, 0.192]</r>. The x-axis of the subplot represents the evaluation score ranges from synthetic data and human-authored data. By observing each bar length and comparing with the coordinates on the x-axis, I can calculate their value and create a CSV table to represent the data more effectively.\nBased on the above thought, the answer is:\nPolicy Name,Synthetic Data,Human-authored Data\nGuaranteed Income,-10,20\nRenewable Energy Encouragement,-20,25\nImmigration Control,-11,30\nPublic Health,-13,33\nFree Education,-14,28\nEarly Retirement,-22,34\nTax Hike,-16,32"
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "bORUybXsYGaYEZlh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from this chart, what can you tell about the difference in evaluating policies between synthetic data and human-authored data?"
+            },
+            {
+                "from": "gpt",
+                "value": "This chart presents the evaluation of various government policies by synthetic data and human-authored data. Each bar represents an evaluation of a policy by the data set it represents. For example, in the case of {Guaranteed Income}<r>[0.121, 0.843, 0.259, 0.866]</r>, the synthetic data, represented by {the left bar}<r>[0.369, 0.807, 0.415, 0.897]</r>, gives a negative evaluation (-10), while human-authored data, represented by {the right bar}<r>[0.415, 0.807, 0.507, 0.897]</r>, gives a positive evaluation (20). This disparity is consistent across all policies, with synthetic data generally evaluating policies negatively and human-authored data giving them positive evaluations. There appears to be a considerable difference between the two types of data, suggesting divergences in judgment or perception.\nBased on the above thought, the answer is:\nThe synthetic data generally gives negative evaluations, suggesting dissatisfaction or demerits of these policies, while the human-authored data provides positive evaluations, indicating benefits or successes of these policies. This could be due to differences in the traits or biases of the data source, methods of analysis, or other factors."
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "U5eYQC1lpL9BVYSi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the chart, which policy had the greatest evaluation difference between synthetic data and human-authored data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The evaluation difference can be found by subtracting synthetic data from human-authored data for each policy. That means I need to measure the length of each bar from the center to either end, for both types of data, for each policy. Looking from top to bottom, the first pair of bars represent evaluations for Guaranteed Income, the second pair for Renewable Energy Encouragement, and so on up to Tax Hike. It appears that {Early Retirement}<r>[0.142, 0.281, 0.259, 0.304]</r>, with {synthetic data}<r>[0.683, 0.245, 0.783, 0.335]</r> at -22 and {human-authored data}<r>[0.783, 0.245, 0.939, 0.335]</r> at 34, has the greatest difference, which is 56.\nBased on the above thought, the answer is:\nEarly Retirement"
+            }
+        ],
+        "id_raw": 128
+    },
+    {
+        "id": "gGFJB7s7v0P536xT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transcribe the data from the bar chart into a .CSV file?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title as denoted by its header, {Two plots representing '2017' and '2018'}<r>[0.399, 0.025, 0.824, 0.055]</r>, definitely suggests that the X-Axis denotes the timeline of the activities, which are {2017}<r>[0.399, 0.025, 0.441, 0.055]</r> and {2018}<r>[0.782, 0.025, 0.824, 0.055]</r>. The Y-Axis title is not provided, but the {Y-Axis labels}<r>[0.044, 0.838, 0.228, 0.861, 0.056, 0.710, 0.228, 0.733, 0.102, 0.582, 0.228, 0.605, 0.015, 0.453, 0.228, 0.477, 0.065, 0.325, 0.228, 0.349, 0.091, 0.197, 0.228, 0.220]</r> are the specific categories of the projects such as 'Public space improvement', 'Residential development', 'Transport systems', 'Environment and conservation', 'Economic development', and 'Social development'. The {values of each bar}<r>[0.278, 0.796, 0.420, 0.898, \n0.339, 0.668, 0.420, 0.770, 0.307, 0.539, 0.420, 0.642, 0.331, 0.411, 0.420, 0.514, 0.359, 0.283, 0.420, 0.386, 0.347, 0.155, 0.420, 0.257, 0.803, 0.796, 0.924, 0.898, 0.803, 0.668, 0.904, 0.770, 0.803, 0.539, 0.945, 0.642, 0.803, 0.411, 0.916, 0.514, 0.803, 0.283, 0.884, 0.386, 0.803, 0.155, 0.908, 0.257]</r> are calculated with respect to the {X-axis labels}<r>[0.546, 0.952, 0.576, 0.975, 0.627, 0.952, 0.656, 0.975, 0.708, 0.952, 0.737, 0.975, 0.799, 0.952, 0.808, 0.975, 0.875, 0.952, 0.893, 0.975, 0.956, 0.952, 0.974, 0.975, 1.037, 0.952, 1.054, 0.975]</r>. Hence, with the X-Axis labels ranging from -60 to 60, the length of the bars can be treated as the exact values of the projects. I will now arrange this data into a .CSV format.\nBased on the above thought, the answer is:\nYear,Public space improvement,Residential development,Transport systems,Environment and conservation,Economic development,Social development\n2017,-35,-20,-28,-22,-15,-18\n2018,30,25,35,28,20,26"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "vhCpRE1nYyHSlfZF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the year-over-year changes, in which sector do you suggest the city invest more resources and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Determining where to invest more resources depends not only on the observed year-on-year changes, but also on the overall levels of investment across different sectors. From the chart, the areas with the least attention are {Economic development}<r>[0.065, 0.325, 0.228, 0.349]</r> and {Social development}<r>[0.091, 0.197, 0.228, 0.220]</r> based on their bar lengths {in 2017}<r>[0.359, 0.283, 0.420, 0.386, 0.347, 0.155, 0.420, 0.257]</r> and {2018}<r>[0.803, 0.283, 0.884, 0.386, 0.803, 0.155, 0.908, 0.257]</r>. They lack relative growth, indicating that more resources and focus should be given to these sectors. Given that these areas play a crucial role in the long-term sustainability and quality of life for the residents, it is a good idea to invest more in these sectors.\nBased on the above thought, the answer is:\nThe city should invest more resources in Economic development and Social development, as these sectors have seen minimal changes and require more attention due to their importance in long-term sustainability and quality of life."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "FGI9JEkerPmk2p8S",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich community development category has seen the most increase in focus and resource allocation from 2017 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, the bar lengths in 2017 and 2018 need to be compared, for each category on the y-axis. For instance, the 'Public space improvement' category has bars at coordinates {2017}<r>[0.278, 0.796, 0.420, 0.898]</r> and {2018}<r>[0.803, 0.796, 0.924, 0.898]</r> indicating a substantial increase. Completing the same procedure for 'Residential development', 'Transport systems', 'Environment and conservation', 'Economic development', and 'Social development' I've found that {the 'Public space improvement' bars}<r>[0.278, 0.796, 0.420, 0.898, 0.803, 0.796, 0.924, 0.898]</r> show the greatest difference in length. Therefore, 'Public space improvement' has seen the most increase.\nBased on the above thought, the answer is:\nPublic space improvement"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "Q23Cm3glkg0y0xzD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please translate the information shown in the chart into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart has two subplots, both representing bar charts across several leadership traits. In the top subplot, bars represent the score values for {'Team Approach'}<{\"Subtitle\"}[0.449, 0.025, 0.591, 0.055]>, and in the bottom subplot, bars represent the score values for {'Process Oriented'}<{\"Subtitle\"}[0.442, 0.512, 0.598, 0.542]>. Each subplot has five bars each depicting a leadership trait \u2014 'Communication', 'Planning', 'Leadership', 'Decision Making', 'Strategic Thinking'. As per the information given in the text labels of each bar, each bar's height corresponds to the respective score for that trait in each management approach. The resulting table will follow the structure: Method, Communication, Planning, Leadership, Decision Making, Strategic Thinking.\nBased on the above thought, the answer is:\nMethod,Communication,Planning,Leadership,Decision Making,Strategic Thinking\nTeam Approach, 30, 40, 25, 45, 35\nProcess Oriented, 5, 15, 35, 50, 70"
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "HgzwubbJnFjrYknA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which management approach seems to have a more balanced leadership score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will carefully examine the bar heights for the 'Team Approach' and 'Process Oriented'. For the 'Team Approach', the bar scores from left to right are for {'Communication'}<{\"bar\"}[0.099, 0.332, 0.192, 0.448]>, {'Planning'}<{\"bar\"}[0.286, 0.294, 0.380, 0.448]>, {'Leadership'}<{\"bar\"}[0.473, 0.352, 0.567, 0.448]>, {'Decision Making'}<{\"bar\"}[0.660, 0.274, 0.754, 0.448]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.313, 0.941, 0.448]>. The 'Process Oriented' bars are {'Communication'}<{\"bar\"}[0.099, 0.916, 0.192, 0.935]>, {'Planning'}<{\"bar\"}[0.286, 0.878, 0.380, 0.935]>, {'Leadership'}<{\"bar\"}[0.473, 0.800, 0.567, 0.935]>, {'Decision Making'}<{\"bar\"}[0.660, 0.743, 0.754, 0.935]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.665, 0.941, 0.935]>. All of 'Team Approach''s bar heights are rather similar, between 25 and 45, whereas 'Process Oriented' varies massively from 5 to 70. Hence, the 'Team approach' has a more balanced leadership score.\nBased on the above thought, the answer is:\n'Team Approach' has a more balanced leadership score."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "K66gxtyJlcBDB3TU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder which leadership trait does 'Team Approach' have the highest score?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am referring to the top subplot where the 'Team Approach' is indicated by {'Team Approach'}<{\"Subtitle\"}[0.449, 0.025, 0.591, 0.055]>. I need to compare the five bars in terms of their heights. Looking from left to right, they are {'Communication'}<{\"bar\"}[0.099, 0.332, 0.192, 0.448]>, {'Planning'}<{\"bar\"}[0.286, 0.294, 0.380, 0.448]>, {'Leadership'}<{\"bar\"}[0.473, 0.352, 0.567, 0.448]>, {'Decision Making'}<{\"bar\"}[0.660, 0.274, 0.754, 0.448]>, and {'Strategic Thinking'}<{\"bar\"}[0.848, 0.313, 0.941, 0.448]>. It seems the 'Decision Making' bar is the tallest, indicating that 'Team Approach' has the highest score in the 'Decision Making'.\nBased on the above thought, the answer is:\nDecision Making."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "6pBGFxmHZhtFlNG3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the two bar charts you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart with the title {\"Performance Scores of Tech Companies\"}[0.000, 0.227, 0.020, 0.773] is comprised of two sections for \"Innovative Businesses\" {upper plot}[0.057, 0.062, 0.983, 0.448] and \"Traditional Businesses\" {lower plot}[0.057, 0.550, 0.983, 0.935]. Each section represents the performance scores for the three different tech companies signified by the x-axis labels {\"Microsoft\"}[0.134, 0.464, 0.204, 0.487], {\"Apple\"}[0.498, 0.464, 0.542, 0.487], and {\"Facebook\"}[0.835, 0.464, 0.907, 0.487]. I would create a table from these data points by extracting the bar heights corresponding to the scores for each company in each category. For instance, the score for Microsoft in Innovative Businesses ({the blue bar}[0.099, 0.101, 0.239, 0.448]) is 90 and for Traditional Businesses ({the red bar}[0.099, 0.665, 0.239, 0.935]) is 70.\nBased on the above thought, the answer is:\nType,Microsoft,Apple,Facebook\nInnovative Businesses,90,85,88\nTraditional Businesses,70,55,66"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "ZYTaVvlmXIMGkKzx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company do you think performs a better in maintaining a balance between traditional businesses and innovative businesses? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comparison of the performance scores of the three companies in both the \"Innovative Businesses\" and \"Traditional Businesses\" categories. For {\"Microsoft\"}[0.099, 0.101, 0.239, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference between these two categories is 20 (90 -70). For {\"Apple\"}[0.450, 0.120, 0.590, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference is 30 (85-55), and for {\"Facebook\"}[0.801, 0.109, 0.941, 0.448] in lower plot[0.057, 0.550, 0.983, 0.935]), the score difference is 22 (88-66). The company with the smallest difference is considered the best at balancing between traditional and innovative businesses.\nBased on the above thought, the answer is:\nFacebook. This is because the difference between Facebook's Innovative Business and Traditional Business scores is the smallest among the three companies, indicating that it has a better balance between the two business types."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "qiPErXx3LeNrfq5X",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has a better performance in Innovative Businesses in terms of the score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the heights of the bars in the {\"Innovative Businesses\"}[0.057, 0.062, 0.983, 0.448] subplot. Looking at the bars, {\"Microsoft\"}[0.099, 0.101, 0.239, 0.448] is approximately at position 90, {\"Apple\"}[0.450, 0.120, 0.590, 0.448] is at approximately position 85, and {\"Facebook\"}[0.801, 0.109, 0.941, 0.448] is at approximately position 88. The highest bar represents the company with the best performance score in Innovative Businesses.\nBased on the above thought, the answer is:\nMicrosoft"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "zmDBK8ksTFZIlpel",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tabulate the percentage achieved for Cultural Heritage and Cultural Integration for each strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, based on the subplots I observed, each strategy has two corresponding percentage values: one for Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415] and another for Cultural Integration {\"subplot_1\"}[0.188, 0.550, 0.969, 0.903]. For example, in Cultural Heritage, the bar for Manual Integration {\"Manual Integration bar\"}[0.188, 0.332, 0.383, 0.399] is ending at around 30 on the x-axis, therefore we can say it has a Cultural Heritage percentage of 30. Similarly, analyzing the plot for Cultural Integration, we can say that Manual Integration has a percentage of 60{\"Manual Integration bar\"}[0.188, 0.819, 0.578, 0.887]. We can use the same process to get the similar values for all the four strategies.\nBased on the above thought, the answer is:\nStrategy,Cultural Heritage(%),Cultural Integration(%)\nManual Integration,30,60\nProgressive Heritage,70,50\nEvolving Integration,90,40\nRegulative Heritage,100,30"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "WMjdWf0FECosNi6X",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of strategy would you recommend if priority is given to Cultural Heritage?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the priority is given to Cultural Heritage, then, according to the subplot representing Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415], the highest percentage of Cultural Heritage was achieved by the 'Regulative Heritage' strategy. Its corresponding bar {\"Regulative Heritage bar\"}[0.188, 0.078, 0.839, 0.146] extends to 100 on the x-axis, indicating that it achieved the highest percentage.\nBased on the above thought, the answer is:\nI would recommend the 'Regulative Heritage' strategy as it achieved the highest percentage in Cultural Heritage."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "I1U97zeeu0CkrGD8",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Cultural Heritage percentage for the strategy 'Evolving Integration'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the Cultural Heritage percentage for 'Evolving Integration', I need to locate its respective bar in the subplot representing Cultural Heritage {\"subplot_0\"}[0.188, 0.062, 0.969, 0.415]. The bar corresponding to 'Evolving Integration'{\"Evolving Integration bar\"}[0.188, 0.163, 0.773, 0.230] extends to about 90 on the x-axis of the subplot.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "TplhkbqH5ED2MhV9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you extract the data and organize it into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. From the bar chart, I can observe two subplots titled {Social Change}<r>[0.455, 0.025, 0.585, 0.055]</r> and {Cultural Change}<r>[0.446, 0.512, 0.594, 0.542]</r>. Each subplot has four categories on the x-axis, namely {Upward trend}<r>[0.135, 0.464, 0.240, 0.487]</r>, {Erratic behavior}<r>[0.347, 0.464, 0.471, 0.487]</r>, {Marginal growth}<r>[0.568, 0.464, 0.694, 0.487]</r>, and {Irregular variations}<r>[0.779, 0.464, 0.926, 0.487]</r>. The respective degrees (in percentages) for each category can be calculated from the height of the bars in the chart, with the y-coordinate {100}<r>[0.017, 0.541, 0.046, 0.564]</r> represents 100%.\nBased on the above thought, the answer is:\nChange Type, Upward trend, Erratic behavior, Marginal growth, Irregular variations\nSocial Change, 30, 40, 70, 45\nCultural Change, 50, 35, 60, 50"
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "Cxa3WVQZVp2KLWzw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the greatest degree of change for Cultural Change? Explain your observations."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category that shows the greatest degree of Cultural Change, I need to compare the heights of the bars in the subplot titled {Cultural Change}<r>[0.446, 0.512, 0.594, 0.542]</r>. The bar corresponding to {Upward trend}<r>[0.099, 0.743, 0.276, 0.935]</r> has a y-coordinate of {50}<r>[0.027, 0.772, 0.046, 0.795]</r>, {Erratic behavior}<r>[0.321, 0.800, 0.498, 0.935]</r> has a y-coordinate of {35}<r>[0.027, 0.849, 0.046, 0.872]</r>, {Marginal growth}<r>[0.542, 0.704, 0.720, 0.935]</r> has a y-coordinate of {60}<r>[0.026, 0.695, 0.046, 0.718]</r>, and {Irregular variations}<r>[0.764, 0.743, 0.941, 0.935]</r> has a y-coordinate of {50}<r>[0.027, 0.772, 0.046, 0.795]</r>. Comparing these y-coordinate values, I can infer that the greatest degree of Cultural Change is associated with the Marginal growth category.\nBased on the above thought, the answer is:\nMarginal growth shows the greatest degree of Cultural Change based on it having the highest value among all categories."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "MHYMRC03MpIGyswr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the degree of Irregular variations in Cultural Change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the degree of Irregular variations in Cultural Change, I need to look at the second subplot titled {Cultural Change}<r>[0.446, 0.512, 0.594, 0.542]</r> and find the corresponding bar. The bar for {Irregular variations}<r>[0.764, 0.743, 0.941, 0.935]</r> in Cultural Change has a height that corresponds to the y-coordinate of {50}<r>[0.027, 0.772, 0.046, 0.795]</r>. Therefore, the degree of Irregular variations in Cultural Change is 50%.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "Ej64C7IVQc4bXspD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart observed, could you transcribe the popularity data of the two subcultures - \"Hippie Bohemian\" and \"Punk Rocker\" for the five different fashion trends into CSV data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart consists of {two sub-charts}<r>[0.064, 0.075, 0.981, 0.923]</r>, each representing one of the two different subcultures. The details of the subcultures are found in the subplots \u2019subtitles,{the caption of \"hippie bohemian\"}<r>[0.434, 0.030, 0.611, 0.066]</r> and {the caption of \"punk rocker\"}<r>[0.459, 0.515, 0.586, 0.551]</r>. Also, there are {five labeled X-ticks}<r>[0.088, 0.457, 0.957, 0.970]</r> corresponding to the five fashion trends. I'll extract the specific values from the length of the bars for each subculture using their color as a key based on each subplot's bars. For example, in the \"Hippie Bohemian\" subplot, {the bar for \"60s Fashion\"}<r>[0.106, 0.220, 0.173, 0.438]</r> represents a popularity of 60%.\nBased on the above thought, the answer is:\nSubculture,60s Fashion,80s Fashion,Graveyard Chic,Soaring Eagle Style,Gothic Frills\nHippie Bohemian,60,20,10,50,90\nPunk Rocker,40,10,0,70,80"
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "SCOJigg9Fr1VOI7q",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which fashion trend shows a noticeable difference in popularity between Hippie Bohemians and Punk Rockers? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comparison between the bar heights in two different sub-charts that represent Hippie Bohemians and Punk rockers. A considerable gap can be noticed in \"Soaring Eagle Style\". For Hippie Bohemians, {the bar for \"Soaring Eagle Style\"}<r>[0.681, 0.256, 0.748, 0.438]</r> is significantly smaller than the corresponding bar in the Punk Rockers sub-chart {the corresponding bar for \"Soaring Eagle Style\"}<r>[0.681, 0.669, 0.748, 0.923]</r>.\nBased on the above thought, the answer is:\nSoaring Eagle Style. This trend is significantly more popular with the Punk Rocker subculture when contrasted to its popularity among Hippie Bohemians as displayed by the marked difference in bar heights in the chart."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "j1VmzlUyuBEnZvAx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Punk Rockers, which fashion trend observed in the bar chart displays the lowest popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll identify the sub-chart representing the \"Punk Rocker\" subculture. That's the second sub-chart<r>[0.064, 0.560, 0.981, 0.923]</r> based on its title {caption}<r>[0.459, 0.515, 0.586, 0.551]</r>. I see five bars here, each representing a trend, and I need to locate the shortest bar. The bar for { \"Graveyard Chic\"}<r>[0.489, 0.923, 0.556, 0.923]</r> has no height indicating the popularity value to be zero. Therefore, this is the trend with the lowest popularity among Punk Rockers.\nBased on the above thought, the answer is:\nGraveyard Chic."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "j5gxcdg8FLYlAmIb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please transform the data visualized in the bar chart into a structured CSV table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I observe that there are two bars, one for each category {\"Erratic decline\"}<r>[0.454, 0.025, 0.586, 0.055]</r> and {\"Gradual increase\"}<r>[0.442, 0.512, 0.598, 0.542]</r>. Each bar is grouped by energy type on the x-axis, with {\"Solar Energy\"}<r>[0.094, 0.464, 0.192, 0.487]</r>, {\"Hydro Energy\"}<r>[0.342, 0.464, 0.447, 0.487]</r>, {\"Wind Energy\"}<r>[0.597, 0.464, 0.695, 0.487]</r>, and {\"Nuclear Energy\"}<r>[0.838, 0.464, 0.956, 0.487]</r> as the categories. The height of each bar and the y-axis tick labels indicate the production values. For instance, under Solar Energy, the bar coordinate [0.099, 0.139, 0.187, 0.448] shows that the production value for Erratic decline is approximately 80, and the bar coordinate [0.099, 0.781, 0.187, 0.935] shows that the production value for Gradual increase is 40. I will repeat this pattern for the other types of energy to create my table.\nBased on the above thought, the answer is:\nTypes of energy, Erratic decline, Gradual increase\nSolar Energy, 80, 40\nHydro Energy, 30, 20\nWind Energy, 50, 50\nNuclear Energy, 10, 70"
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "7iw5sv2PrYlqqZ9I",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what did you infer about the changes in energy production?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the visual representation, I infer significant changes in energy production over time. Comparing the bar heights under both conditions of {\"Erratic decline\"}<r>[0.454, 0.025, 0.586, 0.055]</r> and {\"Gradual increase\"}<r>[0.442, 0.512, 0.598, 0.542]</r> in the subcharts, it's clear that Solar and Hydro energy production has seen a drop, while Wind Energy production has remained the same. On the other hand, Nuclear Energy production showed a drastic increase from 10 to 70. This shows a transition from erratic decline where the energy production was less and more volatile to a gradual increase where the production was more stable and tended towards growth, especially in the case of Nuclear Energy.\nBased on the above thought, the answer is:\nThe production of Solar and Hydro energies decreased, Wind Energy production remained the same, while Nuclear Energy saw a substantial increase during the transition from Erratic decline to Gradual increase. Hence, it indicates a phase of stabilization and growth particularly in the case of Nuclear Energy."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "ufxB0JFXcpQgAfie",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data in the chart, which energy type experienced the most growth from 'Erratic decline' to 'Gradual increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the bars for each energy type under Erratic decline and Gradual increase, like Solar Energy which has bar coordinates [0.099, 0.139, 0.187, 0.448] under Erratic decline and [0.099, 0.781, 0.187, 0.935] under Gradual increase, we can determine the growth by subtracting the bar heights in Erratic decline from those in Gradual increase. Taking into account each energy type {\"Solar Energy\"}<r>[0.094, 0.464, 0.192, 0.487]</r>, {\"Hydro Energy\"}<r>[0.342, 0.464, 0.447, 0.487]</r>, {\"Wind Energy\"}<r>[0.597, 0.464, 0.695, 0.487]</r>, and {\"Nuclear Energy\"}<r>[0.838, 0.464, 0.956, 0.487]</r>, I see that Nuclear Energy saw the most growth from 10 to 70, yielding a net increase of 60.\nBased on the above thought, the answer is:\nNuclear Energy"
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "s5tZGWpIGn7egQBA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart being represented, could you convert the chart into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the visualization, there are two primary subplots {\"Infrastructure\"}[0.057, 0.062, 0.492, 0.735] and {\"Economy\"}[0.548, 0.062, 0.983, 0.735], denoting two different areas of focus. Each subplot has bars representing various planning methods and their respective roles. The x-axis represents different planning methods including {\"Manual Planning\"}[0.059, 0.752, 0.160, 0.904], {\"Technology Assisted\"}[0.131, 0.752, 0.252, 0.933], {\"Automated Planning\"}[0.213, 0.752, 0.335, 0.935], {\"Government Intervention\"}[0.282, 0.752, 0.431, 0.975], and the {\"Total\"}[0.421, 0.752, 0.457, 0.807] progress rate of each category. The y-axis in each subplot represents the progress rate percentage in the respective category, ranging from {0}[0.036, 0.726, 0.046, 0.750] to {100}[0.017, 0.053, 0.046, 0.076]. To generate the CSV table, I evaluate the height of each bar in both subplots and map them to the corresponding y-axis value, percentage. Combining this process for both subplots, it creates the structured CSV data.\nBased on the above thought, the answer is:\nArea,Manual Planning,Technology Assisted,Automated Planning,Government Intervention,Total\nInfrastructure,60,75,90,40,90\nEconomy,70,80,60,80,60"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "ssErhUGBwXqjBEaC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn light of the chart, can we conclude that with the implementation of government intervention, the progress trend in infrastructure is lesser than economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question aims at comparing the performance of Government Intervention in two areas - Infrastructure and Economy. In order to comprehend that, we ought to evaluate the heights of bars corresponding to Government Intervention in both subplots. Accordingly, the bar representing the Government Intervention in {\"Infrastructure\"}[0.324, 0.466, 0.390, 0.735] layers at an approximate progress rate of 40% whilst the bar under {\"Economy\"}[0.815, 0.197, 0.881, 0.735] layers at around 80% - the higher part of the y-axis. Consequently, it's evident that the Government Intervention implemented in an economy area portrays a larger progress trend than infrastructure.\nBased on the above thought, the answer is:\nYes, the progress trend of Government Intervention in Economy is greater than in Infrastructure. This can be inferred from the significantly higher bar height in the Economy area compared to Infrastructure. Hence, it indicates better performance of government intervention in the Economy realm."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "MyCkLatao3oZ2fWx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing the development planning methods, which method has the maximum utilization in the economy area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the method that has the maximum utilization in the economy area, we need to evaluate the height of the bars in {\"Economy\"}[0.548, 0.062, 0.983, 0.735] subplot. Each bar represents each of the planning methods. The bar heights essentially show the progress percentages, which would infer utilization. Analogously, reviewing -> {\"Manual Planning\"}[0.568, 0.264, 0.634, 0.735], {\"Technology Assisted\"}[0.651, 0.197, 0.716, 0.735], {\"Automated Planning\"}[0.733, 0.332, 0.799, 0.735], and {\"Government Intervention\"}[0.815, 0.197, 0.881, 0.735], one can acknowledge that Technology Assisted possesses the maximum height amongst the mentioned bars thereby being the optimal choice.\nBased on the above thought, the answer is:\nTechnology Assisted"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "SLibVb9fh3bfg4Va",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the subcharts {\"USA\"}[0.501, 0.025, 0.539, 0.055] and {\"Canada\"}[0.485, 0.512, 0.555, 0.542], each geometrically defined by the x-axis and y-axis, representing different countries. The y-axis tick labels represent the corresponding values, ranging from {0}[0.036, 0.439, 0.046, 0.462] to {350}[0.017, 0.053, 0.046, 0.076]. The x-axis display categories are {Sightseeing}[0.088, 0.464, 0.178, 0.487], {Gastronomy}[0.279, 0.464, 0.374, 0.487], {Adventure}[0.480, 0.464, 0.560, 0.487], {Nature}[0.687, 0.464, 0.740, 0.487], and {Total}[0.889, 0.464, 0.925, 0.487]. Due to the labeling colors and sub-legend, unidentified bars can be connected to their corresponding countries. The blue bars for {USA}[0.938, 0.083, 0.969, 0.106], and orange bars for {Canada}[0.910, 0.571, 0.969, 0.594], each value can be extracted using the height of the bar in comparison to the y-axis.\nBased on the above thought, the answer is:\nCountry,Sightseeing,Gastronomy,Adventure,Nature,Total\nUSA,70,80,75,90,315\nCanada,60,85,70,95,310"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "0MSuSKi4LtKesz2Y",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which country has greater tourism diversity and explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires an overall comparison of the {blue bars}<USA[0.938, 0.083, 0.969, 0.106]> and the {orange bars}<Canada[0.910, 0.571, 0.969, 0.594]> which represent the diversity of each country in the aspects of tourism given in the x-axis categories. From the {Sightseeing bars}[USA {bar}[0.099, 0.371, 0.167, 0.448], Canada {bar}[0.099, 0.869, 0.167, 0.935]], Canada's bar is lower than USA's bar, showing that USA has more sightseeing attractions. A similar pattern is seen for {Adventure bars}[USA {bar}[0.486, 0.365, 0.554, 0.448], Canada {bar}[0.486, 0.858, 0.554, 0.935]], indicating the US is more adventurous too. Though for {Gastronomy bars}[USA {bar}[0.293, 0.360, 0.360, 0.448], Canada {bar}[0.293, 0.842, 0.360, 0.935]], and {Nature bars}[USA {bar}[0.680, 0.349, 0.748, 0.448], Canada {bar}[0.680, 0.831, 0.748, 0.935]] Canada is higher, showing that it has a lead in Gastronomy and Nature. Since both countries lead in two aspects each, they are equally diverse in terms of tourism.\nBased on the above thought, the answer is:\nBoth countries are equally diverse in terms of tourism."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "0d48RHKGLATPrPq7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total tourist attractiveness score difference between USA and Canada?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total tourist attractiveness score difference, I need to focus on the {Total bars}[USA {bar}[0.873, 0.101, 0.941, 0.448], Canada {bar}[0.873, 0.594, 0.941, 0.935]]. From the Total bars, the {height of USA's bar}[0.873, 0.101, 0.941, 0.448] is corresponding to an approximate 315 and the {height of Canada's bar}[0.873, 0.594, 0.941, 0.935] is corresponding to 310 on the Y-axes scale, the difference is 5 which means the Total tourist attractiveness score difference between USA and Canada is 5 points.\nBased on the above thought, the answer is:\n5."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "czUVNqg69tgj2SqN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you interpret the data in the bar chart and convert into a table format represented in CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart includes two trends under separate subplots: {AI Technology}[0.057, 0.062, 0.983, 0.448] and {FinTech}[0.057, 0.550, 0.983, 0.935]. For each technology trend, there are 4 bars representing different measurement tools: {Manual Mapping}, {LLM-revision}, {LLM-revision w/CoT}, {VerifNER}, placed respectively at positions {Manual Mapping}[0.099, 0.197, 0.187, 0.448 & 0.099, 0.627, 0.187, 0.935], {LLM-revision}[0.350, 0.170, 0.438, 0.448 & 0.350, 0.704, 0.438, 0.935], {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448 & 0.602, 0.800, 0.690, 0.935] and {VerifNER}[0.853, 0.224, 0.941, 0.448 & 0.853, 0.773, 0.941, 0.935] for AI Technology and FinTech respectively. The value for each bar can be obtained by comparing its height to the y-axis. For example, the {Manual Mapping bar of the AI Technology}[0.099, 0.197, 0.187, 0.448] is approximately at the y-axis' 65 label, thus, its value is 65.\nBased on the above thought, the answer is:\nTechnology,Manual Mapping,LLM-revision,LLM-revision w/CoT,VerifNER\nAI Technology,65,72,50,58\nFinTech,80,60,35,42"
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "B5UdKXD5ky8JAw0V",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how do the technologies' development trends differ, and what might you infer from this?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the AI Technology subplot and FinTech subplot are at positions: {AI Technology}[0.057, 0.062, 0.983, 0.448] and {FinTech}[0.057, 0.550, 0.983, 0.935] respectively. For AI Technology, the bars showing development trends show a fluctuating course, reaching a peak at {LLM-revision}[0.350, 0.170, 0.438, 0.448] and the slightest fall at {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448]. In contrast, the bars in the FinTech subplot show a steady decline, from {Manual Mapping}[0.099, 0.627, 0.187, 0.935] to {VerifNER}[0.853, 0.773, 0.941, 0.935]. The development of AI Technology seems to oscillate, perhaps due to the rapid evolution and abundance of methods that optimize its processes, while FinTech shows a constant decline, suggesting the area's challenges need escalating attention to keep up to pace.\nBased on the above thought, the answer is:\nAI Technology shows a fluctuating development trend, suggesting a constant evolution and optimization in the field. FinTech, contrastingly, illustrates a downward trend, indicating the need for more significant improvements or solutions to meet the industry's challenges."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "ShypNpv1fKfc6ggl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich tool shows the maximum development in AI technology according to the data on the plotted chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, I need to compare the bar heights for AI Technology. On observing, the bars in the AI technology graph are posed at the following positions: {Manual Mapping}[0.099, 0.197, 0.187, 0.448], {LLM-revision}[0.350, 0.170, 0.438, 0.448], {LLM-revision w/CoT}[0.602, 0.255, 0.690, 0.448] and {VerifNER}[0.853, 0.224, 0.941, 0.448]. The bar at {LLM-revision}[0.350, 0.170, 0.438, 0.448] has the tallest height, which corresponds to the highest development trend in the AI technology sector.\nBased on the above thought, the answer is:\nLLM-revision"
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "j9spzuarWXFNXjcc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data for the subject of Mathematics into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile the Mathematics chart data into a table, I'll first observe the subject title {Mathematics}<r>[0.461, 0.025, 0.580, 0.055]</r>. Next, I notice four different learning method categories represented in the x-axis labels: {Traditional Learning}<r>[0.083, 0.464, 0.235, 0.487]</r>, {E-Learning}<r>[0.359, 0.464, 0.441, 0.487]</r>, {Blended Learning}<r>[0.573, 0.464, 0.708, 0.487]</r>, and {MOOCs}<r>[0.853, 0.464, 0.909, 0.487]</r>. The y-axis is labeled with success rate percentages from 0 to 100 as shown in {the y-axis tick labels}<r>[0.036, 0.439, 0.046, 0.462]; [0.027, 0.362, 0.046, 0.385]; [0.027, 0.284, 0.046, 0.308]; [0.026, 0.207, 0.046, 0.231]; [0.026, 0.130, 0.046, 0.154]; [0.017, 0.053, 0.046, 0.076]</r>. The success rate data for each learning method category in Mathematics is indicated by the height of the bars and can be extracted as follows: {Traditional Learning has a success rate of about 60%}<r>[0.099, 0.217, 0.219, 0.448]</r>, {E-Learning is approximately 80%}<r>[0.340, 0.217, 0.460, 0.448]</r>, {Blended Learning appears to be around 70%}<r>[0.580, 0.217, 0.701, 0.448]</r>, and {MOOCs show a rate near 90%}<r>[0.821, 0.217, 0.941, 0.448]</r>.\nBased on the above thought, the answer is:\nMode of Learning,Success Rate (%)\nTraditional Learning,60\nE-Learning,80\nBlended Learning,70\nMOOCs,90"
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "2gppUWiC9WvDeO2g",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might MOOCs have a noticeably higher success rate compared to other learning methods according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart for trends, I see higher bars for {MOOCs in Mathematics}<r>[0.821, 0.217, 0.941, 0.448]</r> and {MOOCs in Science}<r>[0.821, 0.569, 0.941, 0.935]</r> compared to the other learning methods in their respective categories. This implies MOOCs are associated with higher success rates, which could be due to a variety of reasons such as the flexibility they offer, allowing students to learn at their own pace, as well as the wider reach, enabling access to quality education for people who may not have such opportunities otherwise. The success rates for MOOCs are near 90% for Mathematics and 95% for Science, significantly higher than other methods exhibited in the chart.\nBased on the above thought, the answer is:\nMOOCs may have a higher success rate because they offer greater flexibility and accessibility, enabling students to learn at their own pace and gain access to quality education, which can be particularly beneficial for those with limited opportunities. This notion is visually supported by the prominently taller bars for MOOCs, indicating higher success rates in both Mathematics and Science subjects."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "Nonps14huBqKft0w",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average success rate for E-Learning across both subjects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average success rate for E-Learning, I must locate the bars corresponding to {E-Learning for Mathematics}<r>[0.340, 0.217, 0.460, 0.448]</r> and {E-Learning for Science}<r>[0.340, 0.608, 0.460, 0.935]</r>. I see that the success rate for E-Learning in Mathematics is 80%, and for Science, it's 85%. I will calculate the average by adding these two percentages and dividing by two, which is the number of subjects.\nBased on the above thought, the answer is:\n82.5"
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "PIk2vLM0jSPJaRKB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you provide the data in a structured table (CSV format)?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart has stages of Big Data integration as the X-axis and the logarithmically scaled figures as the Y-axis. From the {x-axis}[0.490, 0.952, 0.565, 0.975], we can determine that there are six stages i.e., 'Rapid Expansion', 'Gradual Improvement', 'Erratic Growth', 'Inconsistent Decline', 'Pronounced Decline', and 'Rapid Expansion' again.\nFrom the {bar coordinates}[0.112, 0.525, 0.148, 184.387; 0.257, 0.341, 0.293, 184.387; 0.401, 0.157, 0.437, 184.387; 0.546, 0.341, 0.582, 184.387; 0.690, 0.341, 0.727, 184.387; 0.835, 0.157, 0.871, 184.387], I can conclude that the stages correspond to different integration strategies: 'Early integration', 'Further integration' and 'System integration'.\nUsing these legend labels and by comparing the three bars for each stage, I can record the data points for each category and for each strategy from their respective y-coordinates.\nBased on the above thought, the answer is:\nCategories,Early integration, Further integration, System integration\nRapid Expansion,0.001,0.01,0.001\nGradual Improvement,0.01,0.01,0.01\nErratic Growth,0.1,0.001,0.1\nInconsistent Decline,0.01,0.1,0.01\nPronounced Decline,0.01,0.0001,0.1\nRapid Expansion,0.1,0.01,0.001"
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "Pew6LIFPnb1mCq0P",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you draw about the data integration experience from this bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart illustrates the processes of Early, Further, and System integration for six categories of strategies {x-axis}[0.490, 0.952, 0.565, 0.975]. We can conclude the following:\n1. The 'Gradual Improvement' category has equal performance across all three types of integration, with the height of these bars[0.257, 0.341, 0.293, 184.387; 0.293, 0.341, 0.329, 184.387; 0.329, 0.341, 0.365, 184.387] nearly identical.\n2. The 'Erratic Growth' category sees wide-ranging performance variance, as can be seen from the significantly different bar heights here[0.401, 0.157, 0.437, 184.387; 0.437, 0.525, 0.474, 184.387; 0.474, 0.157, 0.510, 184.387].\n3. The initial and final category, 'Rapid Expansion', shows a decrease in intensity from 'Early integration' to 'System integration'. One can see this from the coordinates of the corresponding bars[0.112, 0.525, 0.148, 184.387; 0.148, 0.341, 0.184, 184.387; 0.184, 0.341, 0.221, 184.387].\nOverall, the performance across different stages of integration varies depending on the category, showing improvements in some and declinations in others.\nBased on the above thought, the answer is:\nThe bar chart reveals that the performance throughout the three stages of data integration process: 'Early integration', 'Further integration', and 'System integration' can fluctually change, improve equally, or decrease for different strategies. This shows that the effectiveness of a strategy is dependent on its type and the stage of the process."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "PKWLwfHhhPVVZf3Z",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which category experienced the greatest change when moving from early integration to system integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the category with the largest difference in the value from Early integration to System integration, I need to compare the height of the bars representing each category for the two strategies.\nLooking at the chart, the heights of the bars representing 'Early integration' and 'System integration' are found at [0.112, 0.525, 0.148, 184.387] and [0.184, 0.341, 0.221, 184.387], [0.257, 0.341, 0.293, 184.387] and [0.329, 0.341, 0.365, 184.387] and so on. The categories represented by these bars are 'Rapid Expansion', 'Gradual Improvement', 'Erratic Growth', 'Inconsistent Decline', 'Pronounced Decline', and 'Rapid Expansion' again. I can determine that the {Erratic Growth}[0.415, 0.746, 0.496, 0.880] has the largest difference between the heights of the two bars. This differential suggests that the Erratic Growth category underwent the greatest change when moving from Early to System integration.\nBased on the above thought, the answer is:\nErratic Growth."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "OwfNSwRiRSrrzAam",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you process the information from the bar graph into a well-structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, indeed. As can be deduced from the given data, there are three bars per category on the x-axis. The x-axis represents different economic indicators with labels {GDP}[0.180, 0.919, 0.210, 0.942], {Consumer_Price_Index}[0.288, 0.919, 0.445, 0.942], {Unemployment_Rate}[0.465, 0.919, 0.610, 0.942], {Interest_Rate}[0.663, 0.919, 0.755, 0.942] and {Stock_Market}[0.833, 0.919, 0.927, 0.942]. The y-axis represents the corresponding values, represented in logarithmic scale. The legend at {the bottom}[0.353, 0.020, 0.722, 0.062] identifies the three different measures: {RBF_INT}[0.398, 0.029, 0.455, 0.053], {RBF_POL}[0.522, 0.029, 0.583, 0.053], and {RBF_COM}[0.650, 0.029, 0.716, 0.053]. These three measures each have bars corresponding to each economic indicator. The data to be extracted from each economic indicator should therefore be classified according to these three measures.\nBased on the above thought, the answer is:\nEconomic_Indicators,RBF_INT,RBF_POL,RBF_COM\nGDP,3247,4334,2923\nConsumer_Price_Index,1344,1459,1224\nUnemployment_Rate,979,895,1050\nInterest_Rate,1123,900,1340\nStock_Market,3640,2799,3050"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "H67kIJ7evV5eceMl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how is the performance of RBF_INT in relation to the other two measures (RBF_POL and RBF_COM) as far as the economic indicators are concerned?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, one needs to analyze and compare the heights of the bars for RBF_INT, RBF_POL, and RBF_COM across the different economic indicators. Starting with the GDP bars at {coordinates}[0.131, 0.264, 0.174, 1092.651], [0.174, 0.128, 0.217, 1092.651] and [0.217, 0.314, 0.259, 1092.651], it's observed that RBF_INT has a relatively lower value but not the lowest. For the Consumer Price Index at {coordinates}[0.302, 0.681, 0.345, 1092.651], [0.345, 0.642, 0.388, 1092.651] and [0.388, 0.725, 0.431, 1092.651], RBF_INT has the lowest value. Similarly, for the Unemployment Rate and Interest Rate at coordinates at [{Unemployment Rate}[0.473, 0.831, 0.516, 1092.651], [0.516, 0.873, 0.559, 1092.651], [0.559, 0.798, 0.602, 1092.651]] and [{Interest Rate}[0.645, 0.766, 0.687, 1092.651], [0.687, 0.871, 0.730, 1092.651], [0.730, 0.683, 0.773, 1092.651]], RBF_INT does consistently better than RBF_COM but not RBF_POL. Lastly, in the Stock Market category at {coordinates}[0.816, 0.210, 0.859, 1092.651], [0.859, 0.334, 0.902, 1092.651] and [0.902, 0.294, 0.944, 1092.651], RBF_INT performs better than both RBF_POL and RBF_COM.\nBased on the above thought, the answer is:\nThe performance of RBF_INT is inconsistent. It does not consistently perform better or worse across all economic indicators when compared to RBF_POL and RBF_COM. While it performs better in the categories of Unemployment Rate, Interest Rate, and Stock Market, it lags in the GDP and Consumer Price Index categories."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "pqV0CcucvsG0sJ0e",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest value of the Unemployment rate across all the measures (RBF_INT, RBF_POL, RBF_COM)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the lowest unemployment rate value, the heights of the bars corresponding to the \"Unemployment Rate\" must be compared. Let's start by analyzing the height denoting RBF_INT which is at {coordinates}[0.473, 0.831, 0.516, 1092.651] representing the unemployment rate value of 979. Then RBF_POL coordinates at [0.516, 0.873, 0.559, 1092.651] representing value 895, and finally RBF_COM coordinates at [0.559, 0.798, 0.602, 1092.651] representing the value 1050. Among all these values, the lowest is 895 for RBF_POL.\nBased on the above thought, the answer is:\n895"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "gb7lohyMM0uKtC57",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart details you've analyzed, can you generate a structured table in csv format that displays the investment return rates for the three different investment classes across the seven scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the title {Scenarios on the x-axis}[0.494, 0.952, 0.561, 0.975], I notice that the x-axis represents different scenarios. The y-axis, as suggested by its label {Return Rate}[0.015, 0.432, 0.029, 0.569], represents the return rates of investment. The data for each investment class is represented by a different colored bar as per the {legend}[0.296, 0.020, 0.759, 0.062]: {Tech Startups with purple bars}[0.341, 0.029, 0.428, 0.053], {Real Estate with red bars}[0.495, 0.029, 0.573, 0.053], and {Crypto Currency with orange bars}[0.640, 0.029, 0.753, 0.053]. Next, to create the csv table, I need to look at the height of each bar under each scenario to determine the return rate. For instance, under the \"Sharp rise\" scenario, the {purple bar}[0.112, 0.356, 0.142, 573.254] is approximately at the 0.16 position, the {red bar}[0.142, 0.818, 0.173, 573.254] is near the 0.03 spot, and the {orange bar}[0.173, 0.408, 0.204, 573.254] is around the 0.13 line. Doing the same for the rest of the scenarios would give us the entire data.\nBased on the above thought, the answer is:\nScenarios,Tech Startup,Real Estate,Crypto Currency\nSharp rise,0.16,0.03,0.13\nSubstantial decrease,0.1,0.19,0.4\nSlow expansion,0.04,0.075,0.023\nUpward trend,0.15,0.18,0.3\nGradual recovery,0.05,0.16,0.11\nSudden decrease,0.035,0.082,0.4\nInexorable decline,0.02,0.06,0.29"
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "HeWW7CAqDPdBA3Va",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom a comparative point of view, across different scenarios, which type of investment might be the most volatile based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The volatility of an investment class can be determined by observing the fluctuations in return rates across different scenarios. Looking at the chart, we can see that the {purple bars indicating Tech Startups' return rates}<[0.112, 0.356, 0.142, 573.254]> have clearly noticeable changes in their heights, indicating that their return rates vary quite significantly across different scenarios. However, the {orange bars representing Crypto Currency return rates}<[0.173, 0.408, 0.204, 573.254]> appear to exhibit larger fluctuations. They range from very low in the 'Slow expansion' scenario to very high in the 'Substantial decrease' and 'Sudden decrease' scenarios. This suggests that Crypto Currency may be the most volatile investment choice.\nBased on the above thought, the answer is:\nFrom the chart, the Crypto Currency seems to be the most volatile investment due to its larger fluctuations in return rates across various scenarios."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "b3IiBIe2L4IXzKQq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which scenario does the Crypto Currency have the largest return rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find when the Crypto Currency has the largest return rate, I need to look at the height of the {orange bars}[0.601, 0.031, 0.628, 0.048] under each scenario. From my analysis, the highest {orange bars} on the y-axis corresponds to the 'Substantial decrease'<[0.208, 0.919, 0.354, 0.942]> and 'Sudden decrease'<[0.714, 0.919, 0.834, 0.942]> scenarios, they both reach approximately the 0.4 mark on the y-axis. Hence, the Crypto Currency has the largest return rate during these scenarios.\nBased on the above thought, the answer is:\nSubstantial decrease & Sudden decrease."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "nq7xeuKpEPfqZQdR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the grouped bar chart that you see, could you generate a CSV format table that corresponds to the visible data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I see that the x-axis represents different continents as denoted by {x-axis labels}<r>[{'North America'}[0.116, 0.919, 0.217, 0.942]; {'South America'}[0.260, 0.919, 0.362, 0.942]; {'Africa'}[0.436, 0.919, 0.475, 0.942]; {'Asia'}[0.585, 0.919, 0.615, 0.942]; {'Europe'}[0.720, 0.919, 0.769, 0.942]; {'Australia'}[0.858, 0.919, 0.920, 0.942]]</r>. The y-axis represents the logarithmic scale values represented by {y-axis labels}<r>[{'1e-4'}[0.035, 0.902, 0.066, 0.925]; {'1e-3'}[0.035, 0.661, 0.066, 0.684]; {'1e-2'}[0.035, 0.420, 0.066, 0.444]; {'1e-1'}[0.035, 0.180, 0.066, 0.203]]</r>. Each continent category has three bars standing for Spatial Expansion, Spatial Decrease and Spatial Improvement as shown by {}<r>[{'Spatial Expansion'}[0.287, 0.029, 0.411, 0.053]; {'Spatial Decrease'}[0.477, 0.029, 0.595, 0.053]; {'Spatial Improvement'}[0.662, 0.029, 0.808, 0.053]]</r>. To generate the CSV table, I need to extract the values represented by the heights of the bars. For instance, for the North America category, the height of the {bar for \"Spatial Expansion\"}[0.112, 0.116, 0.148, 240.664] extends nearly halfway up the log scale, implying a value of 0.2. Similarly, the {bar for \"Spatial Decrease\"}[0.148, 0.615, 0.184, 240.664] and {\"Spatial Improvement\"}[0.184, 0.430, 0.221, 240.664] can be interpreted as 1.7e-3 and 1e-2 respectively.\nBased on the above thought, the answer is:\nContinent,Spatial Expansion,Spatial Decrease,Spatial Improvement\nNorth America,2e-1,1.7e-3,1e-2\nSouth America,1e-1,2.1e-3,9e-3\nAfrica,1.7e-1,1.9e-3,8e-3 \nAsia,1.9e-1,1.5e-3,7e-3\nEurope,2e-1,1.6e-3,6e-3\nAustralia,1.8e-1,1.4e-3,5e-3"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "Wu1sprZ6KVCznl3W",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which continent needs most attention to mitigate spatial expansion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the continent that requires the most attention regarding Spatial Expansion, one needs to identify the tallest bar within the first subset of bars representing Spatial Expansion {Spatial Expansion}<r>[0.287, 0.029, 0.411, 0.053]</r>. The bars for this component across continents are {North America}<r>[0.112, 0.116, 0.148, 240.664]</r>, {South America}<r>[0.257, 0.189, 0.293, 240.664]</r>, {Africa}<r>[0.401, 0.133, 0.437, 240.664]</r>, {Asia}<r>[0.546, 0.122, 0.582, 240.664]</r>, {Europe}<r>[0.690, 0.116, 0.727, 240.664]</r>, and {Australia}<r>[0.835, 0.127, 0.871, 240.664]</r>. Among these, the height of the bar for Europe and North America appears to be the tallest, denoting the highest Spatial Expansion which indicates these continents need the most attention to mitigate Spatial Expansion.\nBased on the above thought, the answer is:\nEurope and North America, as they have the highest Spatial Expansion."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "PEdhWQYdosYRdt1h",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich continent shows the highest spatial improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the continent with the greatest Spatial Improvement, I have to look into the third bar in every continent's category, which is color-coded for Spatial Improvement by {Spatial Improvement}[0.662, 0.029, 0.808, 0.053]. Identifying the bars relating to Spatial Improvement for every continent such as {North America}[0.184, 0.430, 0.221, 240.664],{South America}[0.329, 0.441, 0.365, 240.664],{Africa}[0.474, 0.453, 0.510, 240.664],{Asia}[0.618, 0.467, 0.654, 240.664],{Europe}[0.763, 0.483, 0.799, 240.664], and {Australia}[0.907, 0.502, 0.943, 240.664]; I notice that the highest bar represents North America.\nBased on the above thought, the answer is:\nNorth America."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "kpZnkfBUU6b0m8Pc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed on the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar chart with 4 types of trends represented on the x-axis, listed from left to right as {Minimal Decline}<r>[0.132, 0.919, 0.243, 0.942]</r>, {Continuous Decline}<r>[0.344, 0.919, 0.479, 0.942]</r>, {Continous Rise}<r>[0.584, 0.919, 0.687, 0.942]</r>, {Sustained Decline}<r>[0.797, 0.919, 0.922, 0.942]</r>. The values of these trends are represented on the y-axis as percentages. Based on colors of {the legend}[0.251, 0.020, 0.795, 0.062], the purple bars represent the Tech Industry, the red bars represent the Fast Food Industry, and the orange bars represent the Automobile Industry. I can determine the value of the bars based on their height and the y-axis labeling. For example, the value of Tech Industry in Minimal Decline can be approximated as {4%}<r>[0.103, 0.376, 0.159, 825.220]</r>. By following these steps for each bar, I can assemble the data in a table.\nBased on the above thought, the answer is:\nTrends, Tech Industry, Fast Food Industry, Automobile Industry\nMinimal Decline, 0.04, 0.06, 0.05\nContinuous Decline, 0.02, 0.01, 0.03\nContinuous Rise, 0.07, 0.08, 0.06\nSustained Decline, 0.01, 0.02, 0.02"
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "lHQDBgcH0jVVHGkD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the higher the value indicates higher competitive intensity, which industry do you perceive as the most competitive?"
+            },
+            {
+                "from": "gpt",
+                "value": "To perceive the most competitive industry, I need to aggregate the sums of the values expressed by the bars in each industry group. In the Minimal Decline trend, {the bar}<r>[0.103, 0.376, 0.159, 825.220]</r> corresponding to the Tech Industry shows a value of 4%. Adding this to the values shown by the Tech Industry bars in Continuous Decline, Continuous Rise, and Sustained Decline yields the overall competition intensity for the Tech Industry. I'll need to do the same for the Fast Food Industry and the Automobile Industry. After adding up all these values, it appears that the Fast Food Industry has the highest cumulative value, which means it would be the most competitive.\nBased on the above thought, the answer is:\nThe Fast Food Industry. It has the highest cumulative value of competition trends which infers it to be the most competitive among the three industries."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "KUMnMEtWmkzCrEE4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which trend, the difference between the highest and lowest percentages among the three industries is the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll have to compare the heights of the bars in each trend group. For instance, in the Minimal Decline trend, the tallest bar is from the {Fast Food Industry}<r>[0.159, 0.231, 0.215, 825.220]</r>, showing about 6%, and the shortest is from the {Tech Industry}<r>[0.103, 0.376, 0.159, 825.220]</r>, showing about 4%. The difference is 2%. I have to replicate the comparison for the Continuous Decline, Continuous Rise, and Sustained Decline trend groups. Finally, it seems the highest difference registered at 4% can be found in Continous Rise with the highest bar showing 8% from {Fast Food Industry}<r>[0.607, 0.128, 0.663, 825.220]</r> and lowest showing 4% from {Tech Industry}<r>[0.551, 0.176, 0.607, 825.220]</r>.\nBased on the above thought, the answer is:\nContinuous Rise."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "WCOl1K9uCroywObh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data from the bar chart in a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart can be observed inside the label parameters for each sales category, which include: {\"Electronics\"} [0.146, 0.919, 0.222, 0.942], {\"Fashion\"} [0.335, 0.919, 0.387, 0.942], {\"Grocery\"} [0.510, 0.919, 0.565, 0.942], {\"Health & Beauty\"} [0.657, 0.919, 0.772, 0.942], {\"Sports & Outdoors\"} [0.827, 0.919, 0.955, 0.942]. The y-axis represents the sales revenue. For each sales category, there are three bars showing sales revenue for Q1, Q2, and Q3 2021, as indicated in the legend [0.357, 0.020, 0.718, 0.062]. To form the CSV table, I will extract the sales revenue for each quarter for each sales category. The bar height and the y-axis scale are used to determine these values. For instance, for the sales category of electronics in Q1 2021, the corresponding purple bar [0.131, 0.501, 0.166, 2130.183] can be scaled to the actual sales revenue value of 300000.\nBased on the above thought, the answer is:\nSales Category, Q1 2021, Q2 2021, Q3 2021\nElectronics, 300000, 325000, 290000\nFashion, 225000, 245000, 225000\nGrocery, 400000, 450000, 410000\nHealth & Beauty, 200000, 230000, 220000\nSports & Outdoors, 260000, 275000, 285000"
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "igYQ4YwKuubRCq9o",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could you infer about the business performance over the three quarters of 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart bars, I can infer that the performance of the business has been mixed over the three quarters, with some categories showing growth, and others remaining stable or declining. For instance, the {Electronics} category [0.146, 0.919, 0.222, 0.942] showed a slight increase in Q2, followed by a decrease in sales revenue in Q3, implying a volatile sales trend. The {Fashion} category [0.335, 0.919, 0.387, 0.942] showed a bit of growth over this period. On the other hand, the {Grocery category} [0.510, 0.919, 0.565, 0.942] demonstrates a dramatic increase in the first two quarters, implying high customer demand during this period. {Health & Beauty} [0.657, 0.919, 0.772, 0.942] shows a moderate growth, while the {Sports & Outdoors} [0.827, 0.919, 0.955, 0.942] shows a steady increase, implying steady demand in these items. Therefore, a strong performance has been observed in certain categories over time, indicating a possible trend towards consumers' preferences.\nBased on the above thought, the answer is:\nThere has been a mixed performance in the business over the three quarters of 2021. Some categories like Grocery and Fashion experienced growth, while others like Electronics have been volatile. The steady increase in Sports & Outdoors could hint at a consistent demand for those items."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "lswbS9Py09upAjMS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sales category demonstrated the highest growth from Q1 to Q3 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sales category with the highest growth from Q1 to Q3 2021, I need to calculate the difference between the bar heights for Q1 and Q3 for each category. For instance, for the \"Electronics\" category, the purple bar for Q1 [0.131, 0.501, 0.166, 2130.183] relates to a value of 300000, while the orange bar for Q3 [0.202, 0.532, 0.237, 2130.183] relates to a value of 290000. Therefore, the difference for the \"Electronics\" category is -10000. I will perform similar calculations for the other sales categories. It can be observed from this analysis that the {Grocery} category [0.510, 0.919, 0.565, 0.942], with bars for Q1 [0.485, 0.236, 0.520, 2130.183] and Q3 [0.555, 0.213, 0.591, 2130.183] shows the highest growth with a difference of 10000.\nBased on the above thought, the answer is:\nGrocery."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "LN7wnfTACG0UF6yo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, could you please create a CSV formatted table presenting the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that there are two groups of bars in the chart, each group contains five bars. According to {the x-axis labels}[{\"Urban\"}[0.269, 0.919, 0.311, 0.942]; {\"Rural\"}[0.747, 0.919, 0.783, 0.942]], the clusters of bars represent impact data on Urban and Rural areas respectively. Different indicators are represented by different colors, these are: {Greenhouse Gases}<r>[0.112, 0.065, 0.183, 804.689]</r>, {Emissions Deforestation}<r>[0.183, 0.143, 0.254, 804.689]</r>, {Emissions Agriculture}<r>[0.254, 0.628, 0.326, 804.689]</r>, {Emissions Industrial Processes}<r>[0.326, 0.308, 0.397, 804.689]</r> and {Change in forests}<r>[0.397, 0.807, 0.468, 804.689]</r> in Urban areas, and the same indicators with same colors are present for Rural areas, i.e., {Greenhouse Gases}<r>[0.587, 0.308, 0.658, 804.689]</r>, {Emissions Deforestation}<r>[0.658, 0.486, 0.730, 804.689]</r>, {Emissions Agriculture}<r>[0.730, 0.871, 0.801, 804.689]</r>, {Emissions Industrial Processes}<r>[0.801, 0.486, 0.872, 804.689]</r> and {Change in forests}<r>[0.872, 0.871, 0.943, 804.689]</r>.To convert these observations into a CSV table, I would plot the indicators against the respective Urban and Rural values.\nBased on the above thought, the answer is:\nIndicator,Urban,Rural\nGreenhouse Gases,0.1,0.05\nEmissions Deforestation,0.08,0.03\nEmissions Agriculture,0.02,0.01\nEmissions Industrial Processes,0.05,0.03\nChange in forests,0.012,0.01"
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "3JnQUJ6jEkD2eLgS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the comparison shown in the chart, what conclusions could you draw regarding these environmental indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the bar chart, certain conclusions could be drawn. For most of the indicators, both {Urban}<r>[0.269, 0.919, 0.311, 0.942]</r> and {Rural}<r>[0.747, 0.919, 0.783, 0.942]</r> areas produce emissions from different sources. However, there are differences in the impact each indicator has between the regions. {Greenhouse Gases}<r>[{urban}[0.112, 0.065, 0.183, 804.689]; {rural}[0.587, 0.308, 0.658, 804.689]]</r> and {Emissions from Industrial Processes}<r>[{urban}[0.326, 0.308, 0.397, 804.689]; {rural}[0.801, 0.486, 0.872, 804.689]]</r> demonstrate a remarkably higher impact in Urban areas than in the Rural ones. {Emissions from Deforestation}<r>[{urban}[0.183, 0.143, 0.254, 804.689]; {rural}[0.658, 0.486, 0.730, 804.689]]</r> and {Emissions from Agriculture}<r>[{urban}[0.254, 0.628, 0.326, 804.689]; {rural}[0.730, 0.871, 0.801, 804.689]]</r> also show a higher impact for Urban areas, albeit to a lesser extent. These differences could be due to the increased activity in Urban areas compared to Rural ones, thus often resulting in higher emissions. However, for the 'Change in forests' indicator, the difference between Urban and Rural areas is smaller. This could suggest that change in forest areas due to factors such as deforestation, logging or wildfire, affects both Urban and Rural areas almost equally, suggesting the widespread nature of this problem.\nBased on the above thought, the answer is:\nThe chart suggests that for most indicators, Urban areas have a higher environmental impact than Rural areas, potentially due to increased activities such as industrial processes and higher greenhouse gas emissions. However, changes in forest areas appear to affect both Urban and Rural areas almost equally, suggesting that the issue of deforestation is widespread."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "MMPNcH9c6UGkbwLL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich indicator shows the largest impact difference between the Urban and Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the indicator that has the largest impact difference between Urban and Rural areas, it's required to compare the height of each pair of bars. Let's start with {Greenhouse Gases}<r>[{urban}[0.112, 0.065, 0.183, 804.689]; {rural}[0.587, 0.308, 0.658, 804.689]]</r>, which shows a difference of 0.05; going on to {Emissions Deforestation}<r>[{urban}[0.183, 0.143, 0.254, 804.689]; {rural}[0.658, 0.486, 0.730, 804.689]]</r> indicating a difference of 0.05; then {Emissions Agriculture}<r>[{urban}[0.254, 0.628, 0.326, 804.689]; {rural}[0.730, 0.871, 0.801, 804.689]]</r> displays a difference of 0.01; next, {Emissions Industrial Processes}<r>[{rural}[0.326, 0.308, 0.397, 804.689]; {rural}[0.801, 0.486, 0.872, 804.689]]</r> indicates a difference of 0.02 and finally, {Change in forests}<r>[{urban}[0.397, 0.807, 0.468, 804.689]; {rural}[0.872, 0.871, 0.943, 804.689]]</r> yields a difference of 0.002. After making these comparisons, it seems that {Greenhouse Gases} and {Emissions Deforestation} have the most significant impact difference between Urban and Rural areas.\nBased on the above thought, the answer is:\nGreenhouse Gases and Emissions from Deforestation."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "ky121jNTVYubXHa0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the depicted chart data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of the chart comprises different financial indices such as {Equity}<r>[0.177, 0.919, 0.221, 0.942]</r>, {Bond}<r>[0.352, 0.919, 0.388, 0.942]</r>, {Forex}<r>[0.523, 0.919, 0.561, 0.942]</r>, {Commodity}<r>[0.673, 0.919, 0.753, 0.942]</r>, and {Futures}<r>[0.859, 0.919, 0.910, 0.942]</r>. On the y-axis, log value changes in these indices are plotted, as stated in the {ylabel}<r>[0.015, 0.443, 0.029, 0.574]</r>. There are two types of bars for each index, representing different trends. According to the chart legend, the {blue bars}<r>[0.492, 0.030, 0.531, 0.053]</r> represent initial trends, and the {yellow bars}<r>[0.598, 0.030, 0.630, 0.053]</r> represent final trends. For instance, in the {Equity} sector, the initial trend's value is about 0.30, visible from the height of {the first blue bar}<r>[0.139, 0.755, 0.199, 885.059]</r>, and the final trend's value is 1.50, figured from the position of {the first yellow bar}<r>[0.199, 0.137, 0.259, 885.059]</r>. Following a similar pattern, I can analyze all the bars to extract the data needed for the table.\nBased on the above thought, the answer is:\nIndices, Initial, Final\nEquity, 0.30, 1.50\nBond, 0.40, 1.30\nForex, 0.50, 1.40\nCommodity, 0.60, 1.50\nFutures, 0.70, 1.40"
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "aqhUkiYkHvFQIAzA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the graph, which index would you consider as the most unpredictable and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the differences between the initial and final trends across the indices, I can make an assessment. The blue bars represent the initial trends and the yellow bars represent the final trends. A maximum difference between the initial and final trends indicates high volatility and thus high unpredictability. Let's inspect each index. For {Equity}<r>[0.177, 0.919, 0.221, 0.942]</r>, the change from {the blue bar}<r>[0.139, 0.755, 0.199, 885.059]</r> to {the yellow bar}<r>[0.199, 0.137, 0.259, 885.059]</r> is large, suggesting high unpredictability. However, if compare this change with all the other bars like {Bond}<r>[0.352, 0.919, 0.388, 0.942]</r>'s {blue bar}<r>[0.310, 0.645, 0.370, 885.059]</r> and {yellow bar}<r>[0.370, 0.192, 0.430, 885.059]</r>, {Forex}<r>[0.523, 0.919, 0.561, 0.942]</r>'s {blue bar}<r>[0.482, 0.559, 0.542, 885.059]</r> and {yellow bar}<r>[0.542, 0.163, 0.602, 885.059]</r>, {Commodity}<r>[0.673, 0.919, 0.753, 0.942]</r>'s {blue bar}<r>[0.653, 0.489, 0.713, 885.059]</r> and {yellow bar}<r>[0.713, 0.137, 0.773, 885.059]</r> and {Futures}<r>[0.859, 0.919, 0.910, 0.942]</r>'s {blue bar}<r>[0.825, 0.430, 0.885, 885.059]</r> and {yellow bar}<r>[0.885, 0.163, 0.945, 885.059]</r>, we can see the change in Equity is the most number.\nBased on the above thought, the answer is:\nThe Equity index is the most unpredictable. This is because it shows the largest difference between the initial and final trends, indicating high volatility."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "aKz38iiDTgXQkdH3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index shows the most significant increase from the initial to the final trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The change for each index can be computed as the difference between the final trend (yellow bar) and the initial trend (blue bar). By comparing the height of these bars for each index, I can determine where the greatest increase occurs. For instance, {the Equity bar}<r>[0.199, 0.137, 0.259, 885.059]</r> shows a change from 0.30 to 1.50, which is an increase of 1.20 units. By comparing all the other bars such as {the Bond bar}<r>[0.370, 0.192, 0.430, 885.059]</r>, {the Forex bar}<r>[0.542, 0.163, 0.602, 885.059]</r>, {the Commodity bar}<r>[0.713, 0.137, 0.773, 885.059]</r> and {the Futures bar}<r>[0.885, 0.163, 0.945, 885.059]</r>, I can come to a conclusion about which index has the largest increase from the initial trend to the final trend.\nBased on the above thought, the answer is:\nEquity"
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "qx6gpdHAL4clewGE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter analyzing the chart, could you tabulate the occurrences of different trends based on forecast and prediction numbers?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart analyses four different trends, {Continuous decrease}[0.114, 0.919, 0.260, 0.942], {Upward trajectory}[0.350, 0.919, 0.475, 0.942], {Pronounced decrease}[0.564, 0.919, 0.713, 0.942], and {Erratic decline}[0.814, 0.919, 0.914, 0.942]. The trends data represented by forecast and prediction are shown through bars of different colors. The forecasts are depicted with the color blue {representative sample forecase bar}[0.108, 0.361, 0.187, 0.911] and the predictions are illustrated with the color red {representative sample prediction bar}[0.187, 0.196, 0.266, 0.911]. For the values of these occurrences, the y-axis is marked from '0' to '800' with each tick representing a difference of 100. Therefore, considering the height and base of each bar for different trends, and the y-axis ticks, the data table can be generated.\nBased on the above thought, the answer is:\nTrends,Forecast,Prediction\nContinuous decrease,500,650\nUpward trajectory,680,710\nPronounced decrease,280,200\nErratic decline,330,360"
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "lphYNruosCYQqiX6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on analysis of the chart, which method of data processing (forecasts or predictions) generally registers a higher number of occurrences for the trend phenomena?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the data of the forecast model and the machine learning model for each trend. I can see that the blue bars represent the forecast data {representative sample forecase bar}[0.108, 0.361, 0.187, 0.911], and the red bars represent the predicted data {representative sample prediction bar}[0.187, 0.196, 0.266, 0.911]. By comparing the heights of the corresponding pairs, it is found that for 'Continuous decrease' and 'Upward trajectory', the prediction data show higher occurrences; for 'Pronounced decrease' and 'Erratic decline', the forecast data show higher occurrences. Therefore, this question cannot have a single definitive answer, as the results are equally divided between the forecast model and the machine learning model regarding their achievements in the number of occurrences of the trend phenomena.\nBased on the above thought, the answer is:\nBoth Forecast and Machine learning models have higher recorded occurrences in equal number of trends."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Ds8a7zNgoqpZhzbs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend shows the highest number of occurrences in the forecast data?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I will look at the heights of the bars for 'Forecast' values in each trend category. The blue bars represent forecast values. So, I examine these bars: {Continuous decrease forecast}[0.108, 0.361, 0.187, 0.911], {Upward trajectory forecast}[0.333, 0.163, 0.412, 0.911], {Pronounced decrease forecast}[0.559, 0.603, 0.638, 0.911], and {Erratic decline forecast}[0.785, 0.548, 0.864, 0.911]. By comparing heights of these bars, it is observable that 'Upward trajectory' has the tallest bar.\nBased on the above thought, the answer is:\nUpward trajectory."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "4dg6gjBZDN5X6VT7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you read the bar chart you've observed and convert the information into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the information into a structured table, I first take a look at the x-axis where {the subculture labels}<r>[0.11, 0.939, 0.132, 0.957];[0.263, 0.939, 0.293, 0.957];[0.423, 0.939, 0.448, 0.957];[0.574, 0.939, 0.611, 0.957];[0.725, 0.939, 0.775, 0.957];[0.890, 0.939, 0.925, 0.957]</r> are located, including 'Punk', 'Gothic', 'Lolita', 'Hip-Hop', 'CyberPunk', and 'Hippies'. It's clear that these are my column headers. I then locate the y-axis where value labels are arranged, and by observing {the scale}<r>[0.023, 0.765, 0.035, 0.782];[0.023, 0.603, 0.035, 0.621];[0.023, 0.442, 0.035, 0.459];[0.023, 0.280, 0.035, 0.298];[0.023, 0.119, 0.035, 0.136]</r>, I see that it is measured in thousands of people. Reading the individual bar positions and using the scale, I can determine the individual values represented by {the Punk bars}<r>[0.081, 0.368, 0.121, 0.933];[0.121, 0.287, 0.160, 0.933]</r> and {the Gothic bars}<r>[0.239, 0.529, 0.278, 0.933];[0.278, 0.327, 0.317, 0.933]</r> for the different subcultures. For instance, the first Punk bar is around the 3.5K line and the first Gothic bar is around the 4K line, so these would be their respective values in the 'Punk' category. Using the same method, I can deduce the values for each category and convert them into a structured table.\nBased on the above thought, the answer is:\nSubculture, Punk, Gothic\nPunk, 3500, 4000\nGothic, 2500, 3750\nLolita, 3000, 2800\nHip-Hop, 2100, 2000\nCyberPunk, 1900, 3650\nHippies, 4200, 3400"
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "xsolhK7EbsPgV1U0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, between Punk and Gothic, which fashion trend has more overall followers across all subcultures and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would add up the values represented by {the Punk bars}<r>[0.081, 0.368, 0.121, 0.933];[0.239, 0.529, 0.278, 0.933];[0.396, 0.449, 0.435, 0.933];[0.553, 0.594, 0.593, 0.933];[0.711, 0.626, 0.750, 0.933];[0.868, 0.255, 0.907, 0.933]</r> and {the Gothic bars}<r>[0.121, 0.287, 0.160, 0.933];[0.278, 0.327, 0.317, 0.933];[0.435, 0.481, 0.475, 0.933];[0.593, 0.610, 0.632, 0.933];[0.750, 0.344, 0.789, 0.933];[0.907, 0.384, 0.947, 0.933]</r> from all subcultures. Based on my observation, it's quite clear that the sum of the lengths of the bars representing the Gothic fashion trend is greater than the sum of the lengths of the bars representing the Punk fashion trend. Therefore, the Gothic fashion trend has more overall followers.\nBased on the above thought, the answer is:\nGothic, because the sum of the bar lengths representing the Gothic fashion trend is greater than that of the Punk trend, indicating it has a higher overall number of followers."
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "QWXUYU4Iv8O1IPpN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which subculture has the maximum number of people following the Punk fashion trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which subculture has the maximum followers for the Punk fashion trend, I should scan through the bar group representing Punk which is color-coded in purple. This includes {all bars marked as Punk}<r>[0.081, 0.368, 0.121, 0.933];[0.239, 0.529, 0.278, 0.933];[0.396, 0.449, 0.435, 0.933];[0.553, 0.594, 0.593, 0.933];[0.711, 0.626, 0.750, 0.933];[0.868, 0.255, 0.907, 0.933]</r>. Comparing the heights of these bars, it's noticeable that the last bar, representing the 'Hippies' subculture, is the tallest. It reaches a mark well above 4K indicating that \u2018Hippies' has the maximum number of people following the Punk trend.\nBased on the above thought, the answer is:\nHippies"
+            }
+        ],
+        "id_raw": 160
+    },
+    {
+        "id": "qB2y9CBNEc2x2sol",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your understanding of the bar chart, please extract the data and convert it into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title {\"Cuisine & Effort/Popularity\"}[0.413, 0.941, 0.533, 1.0] gives us an indication that the x-axis represents different cuisines, while the two sets of bars {Effort level}<r>[0.742, 0.046, 0.818, 0.069]</r> and {Popular index}<r>[0.885, 0.046, 0.979, 0.069]</r> represent data of Effort level and Popular index respectively. Each component of the x-axis represents a cuisine such as {Italian}<r>[0.112, 0.919, 0.155, 0.942]</r>, {Chinese}<r>[0.205, 0.919, 0.260, 0.942]</r>, {Indian}<r>[0.310, 0.919, 0.353, 0.942]</r>, {Mexican}<r>[0.401, 0.919, 0.459, 0.942]</r>, {Vietnamese}<r>[0.488, 0.919, 0.570, 0.942]</r>, and {Thailand}<r>[0.598, 0.919, 0.658, 0.942]</r>. For each cuisine, there are two column bars, the first signifies effort level and the second signifies popular index. By observing each bar's height relative to the y-axis, I can determine the respective values for the Effort level and Popular index.\nBased on the above thought, the answer is:\nCuisine,Effort level,Popular index\nItalian,1e-1,1e-2\nChinese,1e-2,1e-2\nIndian,1e-3,1e-2\nMexican,1e-2,1e-1\nVietnamese,1e-3,1e-1\nThailand,1e-1,1e-3"
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "JbYmDA1ooPHvTBPy",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the bar chart, which cuisine requires the least effort and is most popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires me to analyze the data from two perspectives - Effort level and Popular index of a cuisine. The height of the green bars signifies the effort level and the yellow bars signify the popular index. So, I am looking for the shortest green bar and tallest yellow bar for each cuisine. After observing the bars of {Italian}<r>[0.099, 0.065, 0.133, 268.273]</r>, {Chinese}<r>[0.198, 0.334, 0.232, 268.273]</r>, {Indian}<r>[0.297, 0.602, 0.331, 268.273]</r>, {Mexican}<r>[0.396, 0.334, 0.430, 268.273]</r>, {Vietnamese}<r>[0.494, 0.602, 0.529, 268.273]</r>, and {Thailand}<r>[0.593, 0.065, 0.628, 268.273]</r>, we can conclude that {Vietnamese}<r>[0.494, 0.602, 0.529, 268.273]</r> has the lowest effort level (green bar), and that {Mexican}<r>[0.430, 0.065, 0.465, 268.273]</r> cuisine has the highest popular index (yellow bar).\nBased on the above thought, the answer is:\nBased on the chart, Vietnamese cuisine requires the minimum amount of effort and Mexican cuisine is most popular."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "RKm9CV7IDo6mB8sm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cuisine requires the highest effort level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I am focusing on the green bars representing effort level in the chart. I need to identify which bar has the highest height. After visually examining the bars from left to right for each cuisine - starting from {Italian}<r>[0.099, 0.065, 0.133, 268.273]</r>, we can see that {Italian}<r>[0.099, 0.065, 0.133, 268.273]</r> and {Thailand}<r>[0.593, 0.065, 0.628, 268.273]</r> cuisines have similar maximum heights which indicate the highest effort levels.\nBased on the above thought, the answer is:\nItalian and Thailand."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "OAADyq4jp3VsmHWu",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on the x-axis labels {\"Contemporary Art\"}[0.102, 0.919, 0.228, 0.942], {\"Classical Music\"}[0.258, 0.919, 0.363, 0.942], {\"Modern Dance\"}[0.406, 0.919, 0.506, 0.942],  {\"Theatre\"}[0.575, 0.919, 0.628, 0.942], {\"Literature\"}[0.713, 0.919, 0.781, 0.942] and {\"Film\"}[0.878, 0.919, 0.907, 0.942], these correspond to different art forms. The Y-axis represents the relative scale of each bar in the chart. Given the legend {\"Relative Growth\"}[0.395, 0.029, 0.506, 0.053] and {\"Engagement Level\"}[0.572, 0.029, 0.702, 0.053], it's clear that the two different colored bars under each art form represent these two measures. Therefore, by defining the height of each bar, I can extract their corresponding values.\nBased on the above thought, the answer is:\nArt Forms,Relative Growth, Engagement Level\nContemporary Art,0.08, 0.22\nClassical Music,0.07, 0.13\nModern Dance,0.15, 0.09\nTheatre,0.05, 0.15\nLiterature,0.02, 0.10\nFilm,0.01, 0.05"
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "dTHOExQMu4Jq8GDw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, what can be inferred about the engagement levels of different art forms over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the bar heights of the \"Engagement Level\" series as indicated by the{\"Engagement Level\"}[0.572, 0.029, 0.702, 0.053]. Each bar in this series, for instance {\"Contemporary Art\"}[0.165, 0.128, 0.216, 555.438], represents the engagement level of a particular art form. Comparing these, it can be noticed that Contemporary Art has the highest level of engagement, followed by Theatre and Modern Dance, based on their respective heights. On the lower scale, Literature and Film seem to have significantly less engagement.\nBased on the above thought, the answer is:\nEngagement levels vary across different art forms. Contemporary Art and Theatre have higher engagement levels, while Literature and Film are less engaged."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "uSoxtRUBiuKzFmKh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich art form has the highest relative growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the art form with the highest relative growth, I need to compare bar heights within the {\"Relative Growth\"} series[0.395, 0.029, 0.506, 0.053]. For instance, the height of Contemporary Art is located at [0.114, 0.372, 0.165, 555.438]. Comparing each bar height in this series, I see that the highest is {\"Modern Dance\"}[0.405, 0.220, 0.456, 555.438].\nBased on the above thought, the answer is:\nModern Dance."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "Sw1koRGBjJT6r1rz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels at the bottom of the chart, I know the unique features are {IoT}[0.152, 0.919, 0.173, 0.942], {Cybersecurity}[0.347, 0.919, 0.443, 0.942], {AI}[0.621, 0.919, 0.634, 0.942] and {Blockchain}[0.823, 0.919, 0.898, 0.942]. The heights of the bars represent the measure of startups and R&D investments for each feature. Based on the {sublegend}[0.363, 0.020, 0.660, 0.062], I can tell that the {\"Startups\"}[0.407, 0.029, 0.466, 0.053] are represented by the blue bars, while the {\"R&D Investments\"}[0.533, 0.029, 0.654, 0.053] are shown by the orange bars. So, to generate a CSV file I will read the approximate value of each bar, for example the blue bar at {\"IoT\"}[0.081, 0.833, 0.162, 0.911] appears to start just below the 1 mark on the y-axis and the orange bar at {\"IoT\"}[0.162, 0.755, 0.244, 0.911] appears to start at the 2 mark on the y-axis. I will then match these values with their respective labels.\nBased on the above thought, the answer is:\nFeatures,Startups,R&D Investments\nIoT,1,2\nCybersecurity,3,7\nAI,6,10\nBlockchain,8,6"
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "JQXJ1g7TmjXw57DF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data presented in the chart, how is R&D Investments distributed across different sectors of technological innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires an analysis of the value of the orange bars \"<R&D Investments>\" in every feature. The orange bar at \"<IoT>\"[0.162, 0.755, 0.244, 0.911] has a value of 2, indicating that IoT receives fewer investments in R&D. The orange bar in \"<Cybersecurity>\"[0.395, 0.364, 0.476, 0.911] has a value of 7, showing a moderate investment level. The orange bar at \"<AI>\"[0.628, 0.129, 0.709, 0.911] has a value of 10, showing that AI receives the highest R&D Investments. The orange bar at \"<Blockchain>\"[0.860, 0.442, 0.942, 0.911] has a value of 6, indicating less investment in this sector than in AI and Cybersecurity.\nBased on the above thought, the answer is:\nR&D Investments are the highest in AI, followed by Cybersecurity and Blockchain, and least in IoT. This suggests that the technological innovation ecosystem is heavily leaning towards AI due to its widespread applications with moderate interest in Cybersecurity and Blockchain and relatively lower investment appetite for IoT."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "F3B44gDUz9EKWab1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich feature has the highest combined score of startups and R&D Investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which feature has the highest combined score, I will add the values of the blue <Startups> and orange <R&D Investments> bars for each feature. Let's start with IoT, {the combined score}[0.081, 0.833, 0.244, 0.911] is 3; for {Cybersecurity}[0.313, 0.677, 0.476, 0.911] the combined score is 10; for {AI}[0.546, 0.442, 0.709, 0.911] it has the highest combined score of 16; lastly for {Blockchain}[0.779, 0.286, 0.942, 0.911] the combined score is 14. Hence, AI has the highest combined score.\nBased on the above thought, the answer is:\nAI"
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "Ns1xNIEhja83xDEW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transcribe the data in the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. First, I can see from the chart's x-axis labels that there are six different scientific research fields represented: {AI Research}<r>[0.115, 0.919, 0.183, 0.942]</r>, {Quantum Computing}<r>[0.240, 0.919, 0.361, 0.942]</r>, {Autonomous Vehicles}<r>[0.390, 0.919, 0.514, 0.942]</r>, {Genomic Research}<r>[0.550, 0.919, 0.658, 0.942]</r>, {Nanotechnology}<r>[0.709, 0.919, 0.803, 0.942]</r>, and {Space Exploration}<r>[0.855, 0.919, 0.960, 0.942]</r>. The y values involve both amounts of investments and research publications. By examining the individual bars for each field, like {the AI Research bars'<r>[0.111, 0.169, 0.149, 301.558]; [0.149, 0.797, 0.187, 301.558]</r> for investments and research publications respectively, we can generate the corresponding values for each category. This process continues for each category to create a structured table derived from the chart's information.\nBased on the above thought, the answer is:\nFields,Investments,Research Publications\nAI Research,1500000,12000\nQuantum Computing,2100000,15000\nAutonomous Vehicles,1800000,10000\nGenomic Research,1400000,16000\nNanotechnology,1900000,11000\nSpace Exploration,2000000,13000"
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "VF8F84sLTemCLPvT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of research intensity, which field seems to be the most active based on the number of research publications?"
+            },
+            {
+                "from": "gpt",
+                "value": "Research intensity can be reflected by the number of research publications. Thus, we can identify the most active field by locating which {red bar}<r>Research Publications</r> is the tallest among all fields. After visually checking, it's clear that the bar for {Genomic Research}<r>[0.604, 0.760, 0.642, 301.558]</r> is the tallest one in the red bars group, suggesting that Genomic Research has the highest number of research publications and thus is possibly the most research-active field among those given.\nBased on the above thought, the answer is:\nGenomic Research."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "9qHeAf4yAGvATY3E",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich scientific research field has the most investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the field with the most investments, I'm going to focus on the {blue bars}<r>Investments</r> that signify investment amounts. By examining the heights of these bars, such as the one for {Quantum Computing}<r>[0.262, 0.125, 0.300, 301.558]</r>, it's evident that the highest bar corresponds to Quantum Computing. This suggests that Quantum Computing receives the largest amount of investment among the fields presented.\nBased on the above thought, the answer is:\nQuantum Computing"
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "oJqfaf1vDSZKskA1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the data on participant numbers for each method in the category of Food Drives, in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table for Food Drives, I need to analyze the bars in the corresponding subchart. For the category of Food Drives, {the subtitle label}<r>[0.216, 0.509, 0.339, 0.532]</r> identifies the activity. There are three bars in this subplot, representative of the methods 'Random', 'Ours', and 'SOTA'. Examining {the 'Random' bar}<r>[0.083, 0.828, 0.161, 0.952]</r> with its top label showing {245.4}<r>[0.097, 0.814, 0.147, 0.832]</r>, {the 'Ours' bar}<r>[0.238, 0.856, 0.316, 0.952]</r> with the top label {92.5}<r>[0.258, 0.842, 0.296, 0.860]</r>, and {the 'SOTA' bar}<r>[0.394, 0.913, 0.471, 0.952]</r> with the top label {37.2}<r>[0.413, 0.899, 0.452, 0.917]</r>, I can convert these details into a table format reflecting participant numbers in thousands for each method.\nBased on the above thought, the answer is:\nMethod,Participants (in thousands)\nRandom,119.5\nOurs,92.5\nSOTA,37.2"
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "9Sh3cFQBuQCUi4n1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method shows the most consistency in terms of participant numbers across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consistency can be assessed by examining the differences in participant numbers for each method across the four categories. I'll look at the bars for each method within the subplots and compare their values visually and numerically. Starting with 'Random', the {bars}<r>[0.083, 0.207, 0.161, 0.461];[0.574, 0.125, 0.651, 0.461];[0.083, 0.828, 0.161, 0.952];[0.574, 0.884, 0.651, 0.952]</r> show variations from 245.4 to 65.0. The 'Ours' method, with {bars}<r>[0.238, 0.218, 0.316, 0.461];[0.729, 0.110, 0.807, 0.461];[0.238, 0.856, 0.316, 0.952];[0.729, 0.873, 0.807, 0.952]</r>, also displays a considerable range of values. Finally, analyzing 'SOTA', the {bars}<r>[0.394, 0.227, 0.471, 0.461];[0.884, 0.098, 0.962, 0.461];[0.394, 0.913, 0.471, 0.952];[0.884, 0.848, 0.962, 0.952]</r> show values that also vary greatly due to the outlier value in Food Drives. Upon inspection, 'Ours' seems to have the least variability overall, with no extreme outliers.\nBased on the above thought, the answer is:\n'Ours'. This method has participant numbers that vary the least across the four categories, with no extreme high or low outliers, indicating a more consistent level of participation."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "qJTp3T0pKPNL4PMe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of participants in the 'Ours' method across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I must locate the bars representing the 'Ours' method in each category and analyze their values. {The Healthcare Campaigns 'Ours' bar}<r>[0.238, 0.218, 0.316, 0.461]</r> has a value of 234.7; {The Environment Protection 'Ours' bar}<r>[0.729, 0.110, 0.807, 0.461]</r> shows 338.9; {The Food Drives 'Ours' bar}<r>[0.238, 0.856, 0.316, 0.952]</r> is marked at 92.5; {The Educational Funds 'Ours' bar}<r>[0.729, 0.873, 0.807, 0.952]</r> indicates 75.6. Summing these values yields 741.7. There are four categories in total, so the average would be this sum divided by four.\nBased on the above thought, the answer is:\n185.425"
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "6LnImDswiQtz6YXp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing both bar charts, I see three stages of trends: T1, T2, and T3 represented on {the x-axis}<r>[0.191, 0.964, 0.212, 0.981]</r><r>[0.519, 0.964, 0.541, 0.981]</r><r>[0.847, 0.964, 0.869, 0.981]</r>. For each trend stage, there are two types of democracy levels shown for Erratic Decline and Steady Increase. For Erratic Decline, the bars are represented as {\"[0.119, 0.212, 0.284, 0.254]\"}<r>[0.202, 0.213]</r> with value 20.3 for T1, {\"[0.448, 0.221, 0.612, 0.254]\"}<r>[0.511, 0.222]</r> with value 15.9 for T2 and {\"[0.776, 0.236, 0.940, 0.254]\"}<r>[0.844, 0.236]</r> with value 8.8 for T3. While for Steady Increase, the bars are represented as {\"[0.119, 0.579, 0.284, 0.744]\"}<r>[0.202, 0.580]</r> with value 79.7 for T1, {\"[0.448, 0.570, 0.612, 0.744]\"}<r>[0.511, 0.571]</r> with value 84.1 for T2 and {\"[0.776, 0.556, 0.940, 0.744]\"}<r>[0.839, 0.556]</r> with value 91.2 for T3. Hence, I have structured the data as shown.\nBased on the above thought, the answer is:\nTrends,Erratic Decline,Steady Increase\nT1,20.3,79.7\nT2,15.9,84.1\nT3,8.8,91.2"
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "nxaECHlW3U9k5zO1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, can you make a general comment about democracy levels with respect to 'Erratic Decline' and 'Steady Increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents the democracy level under two circumstances: Erratic Decline and Steady Increase. Starting with the bars at {\"Erratic Decline\"}<r>[0.119, 0.212, 0.284, 0.254]</r> at T1 and {\"[0.776, 0.236, 0.940, 0.254]\"}<r>[0.844, 0.236]</r> at T3, we can observe a downward trend from 20.3 to 8.8 in the democracy level. This indicates that the democracy level suffers significantly with erratic declines, showcasing reduced stability and increased fragility in the system. On the other hand, the bars at {\"Steady Increase\"}<r>[0.119, 0.579, 0.284, 0.744]</r> at T1 and {\"[0.776, 0.556, 0.940, 0.744]\"}<r>[0.839, 0.556]</r> at T3, we see a continual improvement in democracy levels from 79.7 to 91.2. This suggests that a stable and steady increase leads to improvements and progress in the level of democracy, indicating resilience in the system.\nBased on the above thought, the answer is:\nDemocracy appears to be quite resilient to steady improvements and increases, with levels generally serving to improve under such circumstances. However, it tends to suffer quite dramatically with abrupt declines, showcasing a markedly noticeable drop in levels and thus indicating a possible fragility in the democratic setup to sudden and severe changes."
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "SdNc4KPdkLSyysKF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which interval T1, T2, or T3, the Democracy Level difference between Erratic Decline and Steady Increase is the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the interval with the largest difference of Democracy Level under two trends, I must compare the bar heights under each interval. At interval T1 the bars are {\"[0.119, 0.212, 0.284, 0.254]\"}<r>[0.202, 0.213]</r> and {\"[0.119, 0.579, 0.284, 0.744]\"}<r>[0.202, 0.580]</r> with the heights differentiates by their raw data 20.3 and 79.7 respectively. Similarly, for T2 and T3, the bars at {\"[0.448, 0.221, 0.612, 0.254]\"}<r>[0.511, 0.222]</r> and {\"[0.448, 0.570, 0.612, 0.744]\"}<r>[0.511, 0.571]</r> have values like 15.9 and 84.1, hence the difference; and at {\"[0.776, 0.236, 0.940, 0.254]\"}<r>[0.844, 0.236]</r> and {\"[0.776, 0.556, 0.940, 0.744]\"}<r>[0.839, 0.556]</r> with values of 8.8 and 91.2. The difference at both intervals are calculated and found that T3 has the biggest difference.\nBased on the above thought, the answer is:\nT3"
+            }
+        ],
+        "id_raw": 166
+    },
+    {
+        "id": "cdvfSuV7np4kZqwj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data for apple and orange prices for Year 2 and convert it into a CSV table based on the information in the subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar heights and text annotations in the subplots for Year 2, I can see the {apple prices}<r>[0.241, 0.117, 0.318, 0.254]</r> with a text annotation {\u201c52.7\u201d}<r>[0.260, 0.122, 0.298, 0.139]</r> and the {orange prices}<r>[0.241, 0.254, 0.318, 0.294]</r> with a text annotation {\u201c15.6\u201d}<r>[0.260, 0.268, 0.298, 0.285]</r>. The corresponding data for the second subplot can be similarly extracted from the {apple prices}<r>[0.731, 0.164, 0.808, 0.254]</r> with a text annotation {\u201c34.8\u201d}<r>[0.596, 0.283, 0.635, 0.301]</r> and the {orange prices}<r>[0.731, 0.254, 0.808, 0.330]</r> with a text annotation {\u201c29.3\u201d}<r>[0.750, 0.303, 0.789, 0.320]</r>, and so forth for subplots three and four. Collating this information into a CSV format table:\nBased on the above thought, the answer is:\nYear,Apple Prices,Orange Prices\nYear 2,52.7,15.6\nYear 2,34.8,29.3"
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "XUiKXzHCQ8Osvp9d",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data, how is the market trend for oranges different from that of apples?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar heights for the oranges and apples, I notice that {apple prices}<r>[0.087, 0.098, 0.164, 0.254]</r> are represented by a descending trend from the highest point like in {Year 1}<r>[0.087, 0.098, 0.164, 0.254]</r> to the lowest in {Year 3}<r>[0.394, 0.718, 0.471, 0.744]</r>, indicating a decrease over the years. In contrast, the {orange prices}<r>[0.087, 0.254, 0.164, 0.280]</r> show a bar that is increasing in height from {Year 1}<r>[0.087, 0.254, 0.164, 0.280]</r> to {Year 3}<r>[0.394, 0.744, 0.471, 0.863]</r>, which reflects an increasing trend over the same period.\nBased on the above thought, the answer is:\nThe market trend for oranges is characterized by an increasing price over the years, while the market trend for apples shows a decrease in price. This is depicted by the lengthening bars over time for oranges and shortening bars for apples, indicating opposite market behaviors for the two fruits."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "bXRgUjO4xio89Sed",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in the price of apples from Year 1 to Year 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage decrease, I must first determine the apple prices for Year 1 and Year 3. The {text for Year 1 apples}<r>[0.106, 0.102, 0.144, 0.120]</r> states \"60.3\" and the {text for Year 3 apples}<r>[0.260, 0.707, 0.298, 0.724]</r> shows \"10.4\". Using the formula for percentage decrease, [(initial - final) / initial] * 100, I calculate [(60.3 - 10.4) / 60.3] * 100.\nBased on the above thought, the answer is:\n82.75%"
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "7kzTjv4hoasgGpOv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the technology trend data you see into a CSV format focusing on the years 2021 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar charts, I will organize the data as follows: Each subplot represents a different technology trend with {the axis}<r>[0.053, 0.047, 0.491, 0.461]</r> for the first subplot, indicating the technology \"Artificial Intelligence\". The bar charts display values for each year, indicated by the x-axis {tick labels}<r>[\"2021\"][0.091, 0.473, 0.135, 0.491]; [\"2022\"][0.250, 0.473, 0.294, 0.491]; [\"2023\"][0.409, 0.473, 0.453, 0.491]</r>. The y-axis {tick labels}<r>[\"0\"][0.030, 0.454, 0.041, 0.472]; [\"45\"][0.019, 0.011, 0.041, 0.029]</r> provide the range of values. The value annotations on {each bar}<r>[0.073, 0.160, 0.152, 0.461]; [0.232, 0.115, 0.312, 0.461]; [0.391, 0.066, 0.471, 0.461]</r> provide the actual figures to be tabulated. By analyzing each of these elements across all subplots, I can extract the required data for the table.\nBased on the above thought, the answer is:\nTechnology,2021,2022,2023\nArtificial Intelligence,30.6,35.2,40.1\nBlockchain,26.1,29.3,20.5\nCloud Computing,23.4,19.1,17.9\nMachine Learning,42.9,39.6,36.2\nInternet of Things,18.7,23.5,28.8"
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "rgUBpeUVpwX5Vj2s",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology demonstrates the most consistent growth over the three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology shows the most consistent growth, I examine the trend indicated by the height of the bars in each technology's subplot. I focus on {Artificial Intelligence}<r>[0.053, 0.047, 0.491, 0.461]</r>, {Blockchain}<r>[0.544, 0.047, 0.981, 0.461]</r>, {Cloud Computing}<r>[0.053, 0.537, 0.491, 0.952]</r>, {Machine Learning}<r>[0.544, 0.537, 0.981, 0.952]</r>, and {Internet of Things}<r>[not provided]</r>. For instance, the bar heights in the {AI subplot}<r>[0.073, 0.160, 0.152, 0.461]; [0.232, 0.115, 0.312, 0.461]; [0.391, 0.066, 0.471, 0.461]</r> clearly show an upward trend. Comparing this consistent increase across all technologies, it's evident that both AI and IoT demonstrate a growth pattern, while others show a decline or inconsistent trends.\nBased on the above thought, the answer is:\nArtificial Intelligence and Internet of Things both demonstrate consistent growth over the three years."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "xrVJpzHv6DrVzGyX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage decrease in Blockchain from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease for Blockchain from 2022 to 2023, I first locate {the Blockchain subplot}<r>[0.544, 0.047, 0.981, 0.461]</r> and then identify {the bars}<r>[0.723, 0.066, 0.802, 0.461]</r> and <r>[0.882, 0.185, 0.961, 0.461]</r> corresponding to the years 2022 and 2023, respectively. The annotations above {the bars}<r>[0.743, 0.093, 0.782, 0.111]</r> and <r>[0.902, 0.212, 0.941, 0.229]</r> reveal the values of 29.3 for 2022 and 20.5 for 2023. The percentage decrease is calculated by subtracting the 2023 value from the 2022 value, dividing by the 2022 value, and then multiplying by 100.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "XCKpOlZPx71rIzL3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the investment data for the category of Architecture into a structured CSV table based on your observation of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the Architecture subchart, I see bars representing investment values for different strategies. The investment for \"Random\" is denoted by {a blue bar}[0.799, 0.131, 0.828, 0.352] with its value indicated at the top as \"70.1\". For \"Ours\", {an orange bar}[0.873, 0.133, 0.902, 0.352] shows a value of \"69.7\". Lastly, \"SOTA\" is represented by {a green bar}[0.947, 0.141, 0.976, 0.352], with the value \"67.1\" at the top of the bar. These values allow me to construct the CSV table.\nBased on the above thought, the answer is:\nStrategy,Investment\nRandom,70.1\nOurs,69.7\nSOTA,67.1"
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "CpZ5iVrlxULnC5qk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich strategy shows the highest total investment and consistency across all categories in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the strategy with the highest total investment and consistency, I must analyse each category within the chart. Comparing the height of {the blue bars}<r>[different coordinates for each category]</r> which represent \"Random\" with {the orange bars}<r>[coordinates vary per category]</r> for \"Ours\" and {the green bars}<r>[coordinates vary per category]</r> for \"SOTA\", I can see that the blue bars consistently reach higher up in the chart across most categories. The values atop the blue bars, when totalled, would also likely be the highest, indicating that the \"Random\" strategy has both the highest total investment and shows consistency across the categories provided.\nBased on the above thought, the answer is:\nThe \"Random\" strategy shows the highest total investment and consistency across all categories in the chart."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "rx6FxXIRZ2LiiMQb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total research investment for the category of Film across all strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total investment for Film, I need to sum the values of each strategy's bar within that category. For \"Random\", {the blue bar}[0.060, 0.572, 0.090, 0.680] indicates an investment of \"34.4\". The \"Ours\" strategy is represented by {an orange bar}[0.134, 0.562, 0.163, 0.680] with an investment of \"37.6\". Lastly, \"SOTA\" has {a green bar}[0.208, 0.574, 0.237, 0.680] representing an investment of \"33.8\". The total investment is the sum of these three values.\nBased on the above thought, the answer is:\n105.8"
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "HPgCWR9xLL88Z94b",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data from the bar chart that presents the relationship between 'Funds received by LGCs' and 'Capacity Development Attendance' into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I note four subplots corresponding to different ratios of 'Funds received by LGCs' to 'Capacity Development Attendance'. Each subplot contains bars, with {blue bars}<r>[0.087, 0.183, 0.164, 0.254]</r> representing 'Funds received' and {red bars}<r>[0.087, 0.254, 0.164, 0.372]</r> for 'Attendance', across {three years}<r>[{\"2018\"}<r>[0.103, 0.473, 0.147, 0.491]</r>; {\"2019\"}<r>[0.257, 0.473, 0.301, 0.491]</r>; {\"2020\"}<r>[0.411, 0.473, 0.455, 0.491]</r>]</r>. The numerical values atop each bar, such as {27.3}<r>[0.106, 0.188, 0.144, 0.205]</r> for the first blue bar in 2018, indicate the percentage of total metrics. To structure this into CSV, I match the corresponding value and color annotation to the year and category.\nBased on the above thought, the answer is:\nYear,Funds Received by LGCs (%),Capacity Development Attendance (%)\n2018,27.3,45.7\n2019,34.3,35.9\n2020,41.6,25.1"
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "V4H7yCddHdjsP6Q7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends in 'Funds received by LGCs' over the 3-year period across all ratios, how would you describe the general funding pattern?"
+            },
+            {
+                "from": "gpt",
+                "value": "To gauge the general trend in 'Funds received by LGCs', I'll look at the {blue bars}<r>[0.087, 0.183, 0.164, 0.254]</r> for each year across all subplots. In the first subplot for 2018, the value is {27.3}<r>[0.106, 0.188, 0.144, 0.205]</r>, for 2019, it's {34.3}<r>[0.260, 0.169, 0.298, 0.187]</r>, and for 2020, {41.6}<r>[0.414, 0.150, 0.452, 0.168]</r>. Repeating this examination for all subplots reveals a rise and fall pattern: initial values are lower, showing an increase in the second year, followed by a decrease or a plateau in the third year, depending on the ratio considered. This pattern suggests an instability in funding over the examined period, influenced perhaps by periodic factors that might include policy changes or budget reallocations.\nBased on the above thought, the answer is:\nThe funding pattern for 'Funds received by LGCs' over the three years is characterized by initial lower values, an increase in the second year, and a fall or plateau in the third year. This indicates a roller-coaster pattern signifying instability in the funding received by LGCs over the given period."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "x9COjZe1RVTZToBX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAcross all subplots, which year had the highest average 'Funds received by LGCs' percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the highest average 'Funds received by LGCs' percentage, I'll consider the blue bars across all subplots. In the first subplot, the 'Funds received' values for 2018, 2019, and 2020 are {27.3}<r>[0.106, 0.188, 0.144, 0.205]</r>, {34.3}<r>[0.260, 0.169, 0.298, 0.187]</r>, and {41.6}<r>[0.414, 0.150, 0.452, 0.168]</r> respectively. In the second subplot, the values are {25.5}<r>[0.597, 0.192, 0.635, 0.210]</r>, {35.3}<r>[0.751, 0.167, 0.789, 0.184]</r>, and {35.3}<r>[0.904, 0.319, 0.943, 0.336]</r>. For the third subplot, they are {36.4}<r>[0.106, 0.655, 0.144, 0.672]</r>, {31.2}<r>[0.260, 0.668, 0.298, 0.686]</r>, and {29.5}<r>[0.414, 0.783, 0.452, 0.801]</r>. Lastly, the fourth subplot shows {32.8}<r>[0.597, 0.664, 0.635, 0.681]</r>, {38.5}<r>[0.751, 0.655, 0.789, 0.673]</r>, and {36.2}<r>[0.904, 0.794, 0.943, 0.811]</r>. Calculating each year's average across subplots gives us the result after a simple arithmetic mean calculation for the three years.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "6T3bOqVYz8kNL9ct",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the efficiency comparison of the four categories between Trend_1 and Trend_2 into a structured CSV table, as seen on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the structure of the bar chart with four subplots for each category, I will extract their values by pinpointing {the text annotations above the bars}<r>[0.113, 0.199, 0.135, 0.216]; [0.327, 0.116, 0.349, 0.133]; [0.220, 0.157, 0.242, 0.175]; [0.220, 0.292, 0.242, 0.309]</r> for Trend_1 and <r>[0.113, 0.344, 0.135, 0.361]; [0.327, 0.261, 0.349, 0.278]; [0.434, 0.074, 0.456, 0.092]; [0.437, 0.219, 0.454, 0.237]</r> for Trend_2. The respective trend values are labeled next to each bar on the chart across the categories respectively. Based on the data, each subplot's title <r>[0.206, 0.019, 0.363, 0.041]; [0.672, 0.019, 0.878, 0.041]; [0.211, 0.509, 0.358, 0.532]; [0.740, 0.509, 0.810, 0.532]</r> denotes the category for which the data is presented. Integrating these elements, I am able to accurately compile the CSV table.\nBased on the above thought, the answer is:\nCategory,Trend_1,Trend_2\nHighly_Efficient,25,55\nModerate_Efficiency,45,30\nLow_Efficiency,65,15\nOutlier,85,-5"
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "Y1ZHfNoHV8o8sHk1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the trends on the bar chart, what can be inferred about the production efficiency in the category labeled as an 'Outlier'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will look at the {Outlier category}<r>[0.740, 0.509, 0.810, 0.532]</r> and the corresponding bars for {Trend_1}<r>[0.909, 0.568, 0.963, 0.744]</r> and {Trend_2}<r>[0.695, 0.744, 0.748, 0.807]</r>. {Trend_1}<r>[0.909, 0.568, 0.963, 0.744]</r> has a value annotation of \"85\" suggesting a high level of efficiency, while {Trend_2}<r>[0.695, 0.744, 0.748, 0.807]</r> has a negative value annotation of \"-5\", indicating a loss or significant inefficiency. These observations indicate that while Trend_1 maintains very high efficiency even in the Outlier category, Trend_2 experiences a detrimental drop, signifying potential problems or deviations from efficient production.\nBased on the above thought, the answer is:\nIn the 'Outlier' category, Trend_1 maintains high efficiency, while Trend_2 indicates losses or significant inefficiencies."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "3RhqTLDCRosfwjxW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall numerical difference in production values when comparing Trend_1 and Trend_2 across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will sum the differences by locating the value annotations for Trend_1 at <r>[0.113, 0.199, 0.135, 0.216]; [0.327, 0.116, 0.349, 0.133]; [0.220, 0.157, 0.242, 0.175]; [0.220, 0.292, 0.242, 0.309]</r> and for Trend_2 at <r>[0.113, 0.344, 0.135, 0.361]; [0.327, 0.261, 0.349, 0.278]; [0.434, 0.074, 0.456, 0.092]; [0.437, 0.219, 0.454, 0.237]</r>. I will calculate the difference between corresponding values of Trend_1 and Trend_2 for all the categories and then sum these differences to find the overall numerical value.\nBased on the above thought, the answer is:\n100"
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "60SJqjx1P4ju9XBp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table summarizing the SOTA method returns for each investment strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm assessing the bar charts, focusing on the bars that correspond to the SOTA method in each subplot. I see that there are seven subplots each representing a different investment strategy {High Risk}<r>[0.051, 0.034, 0.487, 0.225]</r>, {Balanced}<r>[0.538, 0.034, 0.974, 0.225]</r>, {Low Risk}<r>[0.051, 0.281, 0.487, 0.472]</r>, {Stable Growth}<r>[0.538, 0.281, 0.974, 0.472]</r>, {Secure Bonds}<r>[0.051, 0.527, 0.487, 0.718]</r>, {Diversified Portfolio Tactics}<r>[0.538, 0.527, 0.974, 0.718]</r>, and {Ultimate Safe}<r>[0.051, 0.774, 0.487, 0.965]</r>. By extracting the value annotations on each bar for SOTA within these subplots, such as {the SOTA bar in the High Risk subplot}<r>[0.388, 0.062, 0.467, 0.225]</r> with a value of 85.1 annotated right above it <r>[0.412, 0.062, 0.443, 0.075]</r>, I can formulate the table.\nBased on the above thought, the answer is:\nStrategy,SOTA Returns\nHigh Risk,85.1\nBalanced,57.4\nLow Risk,40.6\nStable Growth,30.2\nSecure Bonds,16.4\nDiversified Portfolio Tactics,33.9\nUltimate Safe,11.6"
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "YstZWuNS8jxqVQe2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment strategy is the most consistent across all methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must examine the variability in returns for each strategy across the three different methods\u2014Random, Ours, and SOTA. I'm looking for the strategy with the least variation in bar height which indicates consistency. Looking at the bar heights within each strategy subplot {Secure Bonds}<r>[0.051, 0.527, 0.487, 0.718]</r> for example, the height of the bars for Random <r>[0.071, 0.679, 0.150, 0.718]</r>, Ours <r>[0.229, 0.682, 0.309, 0.718]</r>, and SOTA <r>[0.388, 0.687, 0.467, 0.718]</r>, I observe that the Secure Bonds returns show minimal variance between methods. Though {the 'Ours' method}<r>[0.229, 0.682, 0.309, 0.718]</r> is slightly lower, the overall range is narrower compared with other strategies. Therefore, based on the bar chart's visual comparison, Secure Bonds exhibit the most consistency across methods.\nBased on the above thought, the answer is:\nSecure Bonds. The bar heights for the Secure Bonds strategy in the Random, Ours, and SOTA methods show minimal variance, indicating that it's the most consistent across all methods compared to the other investment strategies."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "QLqtdE9oFAmMaMPW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average return for the 'Ours' method across all investment strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average return for the 'Ours' method, I'm locating the bars representing this method in each strategy subplot. Once located, {the values on top}<r>[0.254, 0.055, 0.285, 0.068]</r>, <r>[0.741, 0.110, 0.771, 0.122]</r>, <r>[0.254, 0.388, 0.284, 0.400]</r>, <r>[0.741, 0.406, 0.771, 0.419]</r>, <r>[0.254, 0.682, 0.284, 0.695]</r>, <r>[0.741, 0.649, 0.771, 0.661]</r>, <r>[0.254, 0.938, 0.284, 0.951]</r> for these bars are added together and divided by the number of strategies, which is seven. Adding up the returns 88.6, 60.2, 43.7, 34.1, 18.7, 36.2, and 13.7 gives a total, which is then divided by seven to get the average.\nBased on the above thought, the answer is:\n42.0"
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "81xEteOx9T6pfhhB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you're seeing, could you convert the observed dataset into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, the x-axis represents different companies as labeled by the x-axis ticks, from {ABC Corporation}[0.092, 0.952, 0.208, 0.975] to {YZA Limited}[0.850, 0.952, 0.934, 0.975]. The y-axis denotes the percentage increase in value, ranging from {0}[0.043, 0.934, 0.052, 0.958] to {30}[0.035, 0.053, 0.052, 0.076]. There are two bar groups for each company, with their meanings denoted in {the legend}[0.064, 0.074, 0.254, 0.185] -  {green bars}[0.108, 0.118, 0.248, 0.141] represent 'Fund A Performance', and {orange bars}[0.108, 0.153, 0.249, 0.176] represent 'Fund B Performance'. The performance values can be inferred from the height and the annotated text on top of the bars. For instance, the green bar for 'ABC Corporation'[0.117, 0.650, 0.150, 0.944] with the text annotation '10'[0.118, 0.620, 0.149, 0.643] indicates that the value for 'Fund A Performance' in 'ABC Corporation' is 10, which can be arranged to form the desired structured table.\nBased on the above thought, the answer is:\n```csv\nCompany, Fund A Performance (%),Fund B Performance (%)\nABC Corporation,10,8\nDEF Enterprises,12,15\nGHI Industries,14,10\nJKL Limited,18,14\nMNO Corporation,20,12\nPQR Company,15,14\nSTU Enterprises,22,20\nVWX Industries,24,18\nYZA Limited,26,25\n```"
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "t5AY1zrAX3RKtJiX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which fund, A or B, has a more consistent performance across the companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, consistent performance would mean that the fund's percentage increase in value does not fluctuate significantly across different companies. Comparing the bars of {Fund A}<0.108, 0.118, 0.248, 0.141> and {Fund B}<0.108, 0.153, 0.249, 0.176> from one company to the next, we can see that the changes in the heights of the Fund A green bars appear relatively steady, such as the bars for {ABC Corporation and DEF Enterprises}<0.117, 0.650, 0.150, 0.944> and <0.210, 0.591, 0.243, 0.944>, both showing a 2% increase. However, there's more variation in the heights of the Fund B orange bars, as seen in {DEF Enterprises and GHI Industries}<0.243, 0.503, 0.275, 0.944> and <0.335, 0.650, 0.368, 0.944>, where there's a 5% decrease.\nBased on the above thought, the answer is:\nFund A seems to have a more consistent performance because it shows less fluctuation in the percentage increase in value across different companies."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "KIhlGHU6LtE56qAV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which company, Fund B has the highest increase in value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the company where Fund B has the highest increase in value, I need to compare the heights of the {orange bars}<0.108, 0.153, 0.249, 0.176>. For instance, {the orange bar for ABC Corporation}<0.150, 0.709, 0.182, 0.944> represents an 8% increase in value as confirmed by {the annotation} <0.155, 0.678, 0.177, 0.702>. Following a similar approach for each company, I eventually find that {the orange bar for YZA Limited}<0.892, 0.209, 0.925, 0.944> represents a 25% increase, which is the highest among all companies.\nBased on the above thought, the answer is:\nYZA Limited"
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "lBCjVcRbx5OOYwOD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you're observing, could you transcribe the respective 'Development Score' for 'Hardware Developments' and 'Software Developments' in each time frame into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {Future Technological Development Prospects}<r>[0.339, 0.025, 0.712, 0.055]</r> and the x-axis lgends<r>[{'10 years ago'}[0.250, 0.952, 0.341, 0.975]; {'Now'}[0.510, 0.952, 0.541, 0.975]; {'Future Predictions'}[0.693, 0.952, 0.818, 0.975]]</r>, it indicates the X-axis corresponds to three different time periods. The y-axis range varies between 0<r>[0.052, 0.934, 0.061, 0.958]</r> and 120<r>[0.035, 0.053, 0.061, 0.076]</r>, representing Development Scores. Using these score axis, I can establish the bars' numerical values. There are two sets of bars corresponding to Hardware and Software Developments for each timestamp as per the legend<r>[0.861, 0.074, 0.978, 0.185]</r>. By examining each bar, I can obtain numerical value from the values annotated at the top of the bar. For Hardware, the bars correspond to 45.7<r>[0.215, 0.608, 0.296, 0.944]</r>, 70.5<r>[0.445, 0.426, 0.525, 0.944]</r>, and 90.7<r>[0.675, 0.277, 0.755, 0.944]</r> respectively, for each time period, and for Software it's 37.4<r>[0.296, 0.669, 0.376, 0.944]</r>, 65.3<r>[0.525, 0.464, 0.606, 0.944]</r>, and 85.3<r>[0.755, 0.317, 0.836, 0.944]</r> respectively.\nBased on the above thought, the answer is:\nTime,Hardware Developments,Software Developments\n10 years ago,45.7,37.4\nNow,70.5,65.3\nFuture Predictions,90.7,85.3"
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "hg4qkGREuwlJYBKa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the chart, in comparison to the past, which type of technology development has shown more improvement and why do you believe so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires a comparison between the improvements in 'Hardware Developments' and 'Software Developments' over time. By comparing the bars at two timepoints '10 years ago' and 'Now', we can see for 'Hardware Developments', the scores increased from 45.7<r>[0.215, 0.608, 0.296, 0.944]</r> to 70.5<r>[0.445, 0.426, 0.525, 0.944]</r>, an increase of approximately 24.8 points. In contrast, 'Software Developments' saw a rise from 37.4<r>[0.296, 0.669, 0.376, 0.944]</r> to 65.3<r>[0.525, 0.464, 0.606, 0.944]</r>, an increase of approximately 27.9 points. Thus, even though both technologies have made substantial developments, 'Software Developments' witnessed a slightly higher improvement.\nBased on the above thought, the answer is:\nSoftware Developments. This is because they had a higher increase in their Development Score when compared to Hardware Developments in the period considered."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "69EnGRnK6WetYuHo",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the average Development Score between 'Hardware Developments' and 'Software Developments'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to take the average of the 'Development Scores' for both 'Hardware Developments' and 'Software Developments'. The three scores for Hardware Developments are 45.7<r>[0.215, 0.608, 0.296, 0.944]</r>, 70.5<r>[0.445, 0.426, 0.525, 0.944]</r>, 90.7<r>[0.675, 0.277, 0.755, 0.944]</r>. The average for these can be calculated as (45.7 + 70.5 + 90.7) / 3 = 68.97 approximately. The three scores for 'Software Developments' are 37.4<r>[0.296, 0.669, 0.376, 0.944]</r>, 65.3<r>[0.525, 0.464, 0.606, 0.944]</r>, 85.3<r>[0.755, 0.317, 0.836, 0.944]</r>. The average for these can be calculated as (37.4 + 65.3 + 85.3) / 3 = 62.67 approximately. The difference between the averages of these two is thus 68.97 - 62.67 = 6.3.\nBased on the above thought, the answer is:\n6.3"
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "zMVijp23RUfANKGd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, please create a structured table reflecting the scores of both Risk Assessment and Risk Management for the different scenarios."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart corresponds to 9 risk scenarios from {Scenario A}<r>[0.121, 0.952, 0.195, 0.975]</r> to {Scenario I}<r>[0.859, 0.952, 0.927, 0.975]</r>. Each scenario has 2 bars, one corresponds to {Risk Assessment}<r>[0.848, 0.118, 0.964, 0.141]</r> and the other corresponds to {Risk Management}<r>[0.848, 0.153, 0.973, 0.176]</r>. The height of the bars represents the Scores as labelled on the y-axis which goes from {0}<r>[0.052, 0.934, 0.061, 0.958]</r> to {120}<r>[0.035, 0.053, 0.061, 0.076]</r>. The scores associated with each bar can be determined by its height and also the label above each. For instance, in {Scenario A}<r>[0.121, 0.952, 0.195, 0.975]</r>, the scores for Risk Assessment and Risk Management are approximately at 74.1 and 48.7 respectively. Considering these, I can create the table for all scenarios and both categories.\nBased on the above thought, the answer is:\nScenario, Risk Assessment, Risk Management\nScenario A, 74.1, 48.7\nScenario B, 85.6, 78.5\nScenario C, 39.5, 52.1\nScenario D, 95.3, 90.2\nScenario E, 45.2, 62.3\nScenario F, 57.6, 49.1\nScenario G, 41.1, 33.4\nScenario H, 87.9, 78.9\nScenario I, 62.7, 65.5"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "ileBJn8R0OI85sSF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, can you identify and explain if there are any scenarios where Risk Management scores are higher than Risk Assessment scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find scenarios where Risk Management scores are higher than Risk Assessment scores, I need to analyze each scenario from {Scenario A}<r>[0.121, 0.952, 0.195, 0.975]</r> to {Scenario I}<r>[0.859, 0.952, 0.927, 0.975]</r> and compare the two scorebars for each. From left to right, it can be seen that in {Scenario C}<r>[0.304, 0.952, 0.379, 0.975]</r>, {Scenario E}<r>[0.489, 0.952, 0.562, 0.975]</r>, and {Scenario I}<r>[0.859, 0.952, 0.927, 0.975]</r>, the Risk Management bars are higher than the Risk Assessment bars. This means that in these scenarios, the Risk Management scores are higher than the Risk Assessment scores, indicating a proactive management strategy in these situations.\nBased on the above thought, the answer is:\nYes, Scenarios C, E, and I have higher Risk Management scores than Risk Assessment scores. This suggests that in these scenarios, the risks have been better managed compared to their initial assessments."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "1eSz9HRyiKlbqTk4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich scenario has the highest Risk Management score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the scenario with the highest Risk Management score, I need to look at the height of {Risk Management bars}<r>[0.848, 0.153, 0.973, 0.176]</r> for each scenario from {Scenario A}<r>[0.121, 0.952, 0.195, 0.975]</r> to {Scenario I}<r>[0.859, 0.952, 0.927, 0.975]</r>. It can be observed that {the bar for scenario D in Risk Management}<r>[0.433, 0.281, 0.466, 0.944]</r> is the highest among all, reaching very close to a score of 100 on the y-axis, and thus this scenario should have the highest score in Risk Management as per the chart.\nBased on the above thought, the answer is:\nScenario D"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "gMeuHxVGUrAQLV9R",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to your analysis of this chart, can you provide a structured table displaying GDP growth and inflation rates for each month of the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that the chart's x-axis represents the months of the year by looking at the {x-axis labels}<r>[{\"January\"}[0.107, 0.868, 0.155, 0.947]; {\"February\"}[0.175, 0.868, 0.229, 0.957]; {\"March\"}[0.253, 0.868, 0.293, 0.935]; {\"April\"}[0.328, 0.868, 0.361, 0.922]; {\"May\"}[0.400, 0.868, 0.431, 0.919]; {\"June\"}[0.471, 0.868, 0.502, 0.920]; {\"July\"}[0.544, 0.868, 0.572, 0.914]; {\"August\"}[0.607, 0.868, 0.651, 0.942]; {\"September\"}[0.668, 0.868, 0.732, 0.975]; {\"October\"}[0.747, 0.868, 0.796, 0.950]; {\"November\"}[0.812, 0.868, 0.873, 0.970]; {\"December\"}[0.883, 0.868, 0.944, 0.969]]</r>. The y-axis is labeled as \"{Percentages (%)}<r>[0.015, 0.366, 0.029, 0.556]</r>\", and the data is represented by two colored bars for each month. The green bars represent \"{GDP Growth Rate (%)\"}<r>[0.111, 0.781, 0.261, 0.804]</r>, and the brown bars represent \"{Inflation Rate (%)}\"<r>[0.111, 0.816, 0.233, 0.839]</r>. The value of each indicator for a month is marked at the top of its corresponding bar. Therefore, I can create a table in CSV format to represent this data.\nBased on the above thought, the answer is:\nMonth,GDP Growth Rate(%),Inflation Rate(%)\nJanuary,3.7,1.6\nFebruary,2.9,1.8\nMarch,1.5,1.9\nApril,0.3,2.1\nMay,-1.6,2.3\nJune,-3.2,2.5\nJuly,-2.8,2.7\nAugust,-1.5,2.9\nSeptember,0.5,3.2\nOctober,2.1,3.5\nNovember,2.8,3.9\nDecember,3.4,4.1"
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "MIJpWXhGPf8bWjLA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, what economic trends can you observe during the evaluated year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the green bars which represent the {GDP Growth Rate}<r>[0.111, 0.781, 0.261, 0.804]</r> and the brown bars which represent the {Inflation Rate}<r>[0.111, 0.816, 0.233, 0.839]</r>, I find different trends. For instance, the GDP growth rate, represented by the green bars, saw a decline starting from {January}<r>[0.107, 0.868, 0.155, 0.947]</r> reaching its lowest point in {June}<r>[0.471, 0.868, 0.502, 0.920]</r>, and then gradually recovering, whereas the inflation rate - represented by the brown bars - displays a steady increasing trend throughout the year. The main economic trend visible on this chart, therefore, is that while GDP growth slumped and then rebounded during the year, inflation steadily increased, suggesting a challenging economic environment especially in middle of the year when GDP growth was negative and inflation was on the rise.\nBased on the above thought, the answer is:\nThe year appears to have seen a sharp decline in GDP growth during the first half, reaching negative figures by June, followed by a gradual recovery in the latter part of the year. However, inflation has seen a steady growth throughout the year, indicating increasing costs of goods and services."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "IaXrWBsxxMRGJt0v",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which month experienced the biggest difference between GDP growth rate and inflation rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should calculate the absolute difference between the GDP growth rate and the inflation rate for each month, which can be obtained from the top of each pair of bars in the chart. For example, in {January}<r>[0.107, 0.868, 0.155, 0.947]</r>, the {GDP Growth Rate}<r>[0.106, 0.166, 0.131, 0.461]</r> is 3.7 and the {Inflation Rate}<r>[0.131, 0.333, 0.156, 0.461]</r> is 1.6, resulting in a difference of 2.1. After comparing all the months, I can see that the largest difference appears in {June}<r>[0.471, 0.868, 0.502, 0.920]</r>, where the {GDP Growth Rate}<r>[0.462, 0.461, 0.487, 0.716]</r> is -3.2 and the {Inflation Rate}<r>[0.487, 0.262, 0.512, 0.461]</r> is 2.5, resulting in a difference of 5.7.\nBased on the above thought, the answer is:\nJune"
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "u2b4UlCV8feYfnMv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you create a structured table representing the international trade trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, based on the chart, I can create a structured table by analyzing information from various parts. The countries are represented on the x-axis specified by {the x-axis tick labels}<{\"USA\"}[0.151, 0.883, 0.181, 0.933]; {\"Germany\"}[0.229, 0.883, 0.284, 0.975]; {\"China\"}[0.329, 0.883, 0.367, 0.947]; {\"Canada\"}[0.415, 0.883, 0.463, 0.962]; {\"Australia\"}[0.503, 0.883, 0.557, 0.972]; {\"Japan\"}[0.602, 0.883, 0.640, 0.946]; {\"UK\"}[0.700, 0.883, 0.724, 0.922]; {\"India\"}[0.786, 0.883, 0.820, 0.940]; {\"Mexico\"}[0.872, 0.883, 0.916, 0.957]>. The y-axis represents the trade value in billions. By observing the bar heights, I can determine the corresponding values for export and import for each country - for example, for \"USA\", the Export value can be found from the green bar coordinates <[0.134, 0.367, 0.166, 0.875]> and Import value from the orange bar coordinates <[0.166, 0.333, 0.197, 0.875]>. It is also confirmed by the specific annotation {\"750\"}[0.137, 0.337, 0.163, 0.360] and {\"800\"}[0.168, 0.303, 0.195, 0.326].\nBased on the above thought, the answer is:\nCountry,Export,Import\nUSA,750,800\nGermany,900,790\nChina,1100,920\nCanada,680,710\nAustralia,620,640\nJapan,800,850\nUK,780,700\nIndia,850,810\nMexico,680,660"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "JeLipiKipkbR167u",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trade balances, could you mention which countries show healthy trade scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trade balance is indicated by the difference in the height of the Export and Import bars for each country. A closer height suggests a trade equilibrium, which is considered healthy. From the charts, we can see that Canada's Export {bar}[0.407, 0.414, 0.439, 0.875] and Import {bar}[0.439, 0.394, 0.471, 0.875] are quite close indicating a nearly equal export and import values which are considered healthy. Similarly, for Australia's Export {bar}[0.498, 0.455, 0.530, 0.875] and Import {bar}[0.530, 0.442, 0.562, 0.875] and Mexico's Export {bar}[0.862, 0.414, 0.894, 0.875] and Import {bar}[0.894, 0.428, 0.926, 0.875] also indicate equal export and import values.\nBased on the above thought, the answer is:\nThe countries showing a healthy trade scenario are Canada, Australia, and Mexico as their exports and imports figures show near equilibrium, indicating a balanced trade scenario."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "uFlm9QopyTmHt2B6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the highest export value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The export value can be determined by the green bars represented by {the green bars coordinates}<[0.134, 0.367, 0.166, 0.875]; [0.225, 0.265, 0.257, 0.875]; [0.316, 0.130, 0.348, 0.875]; [0.407, 0.414, 0.439, 0.875]; [0.498, 0.455, 0.530, 0.875]; [0.589, 0.333, 0.621, 0.875]; [0.680, 0.347, 0.712, 0.875]; [0.771, 0.299, 0.803, 0.875]; [0.862, 0.414, 0.894, 0.875]>. The bar with the highest Y coordinate represents the highest export value. Observing these, the bar for China [0.316, 0.130, 0.348, 0.875] extends the furthest, representing the highest figure. The value is also confirmed by the associated annotation \"1100\" [0.314, 0.100, 0.349, 0.123].\nBased on the above thought, the answer is:\nChina"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "5zh9dtsGOI8ArlGA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the data from the graph into a CSV format table for easier understanding?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to arrange the data into a CSV format table, we need to identify the years representing the x-axis, whose coordinates include the labels {\"2017\"} <r>[0.209, 0.952, 0.244, 0.975]</r>, {\"2018\"} <r>[0.360, 0.952, 0.396, 0.975]</r>, {\"2019\"} <r>[0.512, 0.952, 0.547, 0.975]</r>, {\"2020\"} <r>[0.664, 0.952, 0.699, 0.975]</r>, and {\"2021\"} <r>[0.816, 0.952, 0.851, 0.975]</r>. The y-axis represents the price with ticks ranging from {0}<r>[0.061, 0.934, 0.070, 0.958]</r> to {9000}<r>[0.035, 0.053, 0.070, 0.076]</r>. The bar values represent Bitcoin and Ethereum prices for each year. Looking at the bar labels and heights, {the Bitcoin bars}<r>[0.173, 0.748, 0.226, 0.944]</r> in 2017 and <r>[0.325, 0.552, 0.378, 0.944]</r> in 2018 represent the price of Bitcoin in those years. {The Ethereum bar}<r>[0.226, 0.929, 0.279, 0.944]</r> represents the price of Ethereum in 2017 and so on. By examining all these bars, I'm able to construct the CSV table.\nBased on the above thought, the answer is:\nDate,Bitcoin,Ethereum\n2017,2000,150\n2018,4000,500\n2019,8000,1000\n2020,6000,2500\n2021,5000,3000"
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "CLt1upAO693pNJyw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which cryptocurrency appears more stable over this five-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which cryptocurrency appears more stable, it is important to examine the variation in prices over the years for both Bitcoin and Ethereum. Starting with Bitcoin, its price doubles from 2000 in 2017 <r>[0.176, 0.717, 0.224, 0.741]</r> to 4000 in 2018 <r>[0.327, 0.522, 0.376, 0.545]</r>, then doubles again to 8000 in 2019 <r>[0.479, 0.130, 0.527, 0.153]</r> before falling to 6000 in 2020 <r>[0.631, 0.326, 0.679, 0.349]</r> and decreasing further to 5000 in 2021 <r>[0.783, 0.424, 0.831, 0.447]</r>. In comparison, Ethereum gradually increases its prices from 150 in 2017 <r>[0.233, 0.899, 0.273, 0.922]</r> to 500 in 2018 <r>[0.385, 0.864, 0.424, 0.888]</r>, then doubles to 1000 in 2019 <r>[0.532, 0.815, 0.580, 0.839]</r> and continues to rise to 2500 in 2020 <r>[0.684, 0.668, 0.732, 0.692]</r> and 3000 in 2021 <r>[0.836, 0.620, 0.884, 0.643]</r>. Ethereum's price progression displays a smoother increase over the years, demonstrating less volatility when compared to Bitcoin. Therefore, Ethereum appears more stable over this period.\nBased on the above thought, the answer is:\nEthereum. Because its price progression displays a smoother and gradual increase over the years, demonstrating less volatility compared to Bitcoin."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "HVsOc7Z0hiOzoI5s",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which year did Bitcoin experience the most significant price decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "For identifying the year with the most significant price decrease for Bitcoin, I need to compare differences between the bar heights for consecutive years. The bars representing Bitcoin prices are annotated with their respective values <r>[0.176, 0.717, 0.224, 0.741]</r> in 2017, <r>[0.327, 0.522, 0.376, 0.545]</r> in 2018, <r>[0.479, 0.130, 0.527, 0.153]</r> in 2019, <r>[0.631, 0.326, 0.679, 0.349]</r> in 2020, and <r>[0.783, 0.424, 0.831, 0.447]</r> in 2021. From these, we can see that the drop from 8000 in 2019 to 6000 in 2020 is the most substantial decrease in price for Bitcoin.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "H9JiuM3UQToetQXk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the bar chart data on global trade and supply chain metrics into a structured data table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's X-Axis labels, representing 10 different countries, I gather the different categories that attribute to the data. These countries are {\"US\"}<r>[0.142, 0.869, 0.156, 0.900]</r>,\n{\"China\"}<r>[0.226, 0.869, 0.240, 0.935]</r>, {\"Germany\"}<r>[0.309, 0.869, 0.323, 0.975]</r>, {\"India\"}<r>[0.393, 0.869, 0.407, 0.925]</r>, {\"Brazil\"}<r>[0.477, 0.869, 0.491, 0.934]</r>, {\"Australia\"}<r>[0.560, 0.869, 0.574, 0.971]</r>, {\"France\"}<r>[0.644, 0.869, 0.658, 0.946]</r>, {\"Japan\"}<r>[0.727, 0.869, 0.741, 0.933]</r>, {\"Canada\"}<r>[0.811, 0.869, 0.825, 0.957]</r>, {\"Mexico\"}<r>[0.894, 0.869, 0.908, 0.949]</r>.\nThe Y-Axis contains the scale of the metrics measured, ranging between {0}<r>[0.052, 0.851, 0.061, 0.875]</r> to {100}<r>[0.035, 0.053, 0.061, 0.076]</r>.\nThe actual data points are obtained from the blue and orange bars with their respective top annotations for Free Trade and Global Sourcing that correspond to each country on the x axis. These data points can be retrieved from their respective height in relation to Y-axis.\nBased on the above thought, the answer is:\nCountry, Free Trade, Global Sourcing\nUS, 80, 70\nChina, 90, 85\nGermany, 84, 80\nIndia, 66, 75\nBrazil, 77, 65\nAustralia, 80, 70\nFrance, 70, 64\nJapan, 88, 81\nCanada, 87, 73\nMexico, 68, 69"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "742KeDGfXRUtmP62",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the chart, which country seems to be the best choice for setting up a multinational company considering both Free Trade and Global Sourcing metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider both blue bar {\"Free Trade\"}<r>[0.863, 0.118, 0.935, 0.141]</r> and orange bar {\"Global Sourcing\"}<r>[0.863, 0.153, 0.973, 0.176]</r> simultaneously for each country. To be specific, a suitable country should have both a high Free Trade score and Global Sourcing score. I can see that {\"China\"}<r>[0.226, 0.869, 0.240, 0.935]</r> has high values in both the blue bars with co-ordinates <r>[0.204, 0.142, 0.233, 0.860]</r> and the orange bars <r>[0.233, 0.182, 0.262, 0.860]</r>, suggesting it is a potential choice. Going through all the countries, no other country's collective scores for both categories surpass China's.\nBased on the above thought, the answer is:\nChina seems to be the best choice considering it has high scores in both Free Trade and Global Sourcing according to the chart."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "QXyUDpRk2bonexMt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the lowest Free Trade score demonstrated in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which country has the lowest Free Trade score, I need to check the height of each of the blue bars. Find the shortest blue bar amongst {\"US\"}<r>[0.120, 0.222, 0.149, 0.860]</r>, {\"China\"}<r>[0.204, 0.142, 0.233, 0.860]</r>, {\"Germany\"}<r>[0.287, 0.190, 0.316, 0.860]</r>, {\"India\"}<r>[0.371, 0.334, 0.400, 0.860]</r>, {\"Brazil\"}<r>[0.454, 0.246, 0.484, 0.860]</r>, {\"Australia\"}<r>[0.538, 0.222, 0.567, 0.860]</r>, {\"France\"}<r>[0.621, 0.302, 0.651, 0.860]</r>, {\"Japan\"}<r>[0.705, 0.158, 0.734, 0.860]</r>, {\"Canada\"}<r>[0.789, 0.166, 0.818, 0.860]</r>, {\"Mexico\"}<r>[0.872, 0.318, 0.901, 0.860]</r>. Reviewing these, India\u2019s Free Trade score bar was the shortest, and thus it had the lowest Free Trade score.\nBased on the above thought, the answer is:\nIndia"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "NyEpyJEoPt0lykBl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV formatted table from the chart for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {'Building and Real Estate Market Trends'}[0.386, 0.019, 0.656, 0.041], we can determine that we are observing trends in the building and real estate market. The x-axis of the chart displays various property types, ranging from {Single Family Home}[0.153, 0.801, 0.164, 0.973] to {Recreational Land}[0.878, 0.801, 0.890, 0.958]. The y-axis, labelled {Market Trends}[0.012, 0.360, 0.024, 0.482], represents the strength or level of these trends on a numeric scale. The plot contains two sets of bars. The first set labeled as {Rental Market Trend}[0.849, 0.088, 0.965, 0.106] in the legend, identified by their color as #69b3a2. The second set labeled as {Property Market Trend}[0.849, 0.115, 0.977, 0.132], identified by their color as #d98763. I can create a table by cross-referencing the property types with the height of the corresponding bars in each trend group.\nBased on the above thought, the answer is:\nProperty Type,Rental Market Trend,Property Market Trend\nSingle Family Home,100,123\nApartment Building,90,80\nCommercial Building,80,110\nIndustrial Property,70,90\nVacant Land,60,83\nFarm/ Ranch,50,75\nHotel/ Motel,40,120\nRecreational Land,30,90"
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "h8RG4fBLtb37V2x0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about the market trends for \"Single Family Home\" properties?"
+            },
+            {
+                "from": "gpt",
+                "value": "The category \"Single Family Home\" can be found in the x-axis labels, specifically located at {these coordinates} [0.153, 0.801, 0.164, 0.973]. The two bars representing market trends are located at {[0.122, 0.296, 0.158, 0.795] and [0.158, 0.181, 0.195, 0.795]}. For the Rental Market Trend colored in #69b3a2 as indicated in the legend {[0.849, 0.088, 0.965, 0.106]}, the value is at the height of around 100 as displayed by the top of the respective bar at these coordinates [0.122, 0.296, 0.158, 0.795]. For the Property Market Trend colored in #d98763 as indicated in the legend {[0.849, 0.115, 0.977, 0.132]}, the value of the height is about 123 as displayed by the top of the respective bar at coordinates [0.158, 0.181, 0.195, 0.795]. In deciphering these values, it seems that the values of Property Market Trend are slightly higher than those of the Rental Market Trend.\nBased on the above thought, the answer is:\nThe chart tells us that Single Family Homes have a comparably strong strong market trend with rental market trend valued at 100 and property market trend valued higher at 123. However, the property market trend for this property type is slightly stronger than the rental market."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "MmiFEyGzAI6IJCkM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which type of property is the discrepancy between the rental market trend and the property market trend the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine where the discrepancy between the rental market trend and the property market trend is largest, I need to compare the heights of the two bars for each type of property. The x-coordinate, determining the property type, stays the same while the y-coordinate, representing the trend, varies. This can be observed in all property types from the {Single Family Home}[0.153, 0.801, 0.164, 0.973] on the left to the {Recreational Land}[0.878, 0.801, 0.890, 0.958] on the right. The property type with the largest discrepancy is identified as the type where the difference in y-coordinates is the largest. On assessing the bar heights, it is observed that the {Hotel/ Motel}[0.774, 0.801, 0.786, 0.906] category presents the largest divergence. The rental market trend's bar {40.0}[0.749, 0.573, 0.775, 0.590] is significantly shorter than the property market trend's bar {120.0}[0.782, 0.174, 0.815, 0.191]. Hence, the difference between these two trends is the most considerable for this category.\nBased on the above thought, the answer is:\nHotel/ Motel"
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "HpswDJ0Jemt4lyre",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the bar chart titled {\"Entrepreneurship and Innovation Ecosystem across Major cities\"}[0.301, 0.019, 0.741, 0.041], can you convert the data into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis shows the names of different cities, according to the {x-axis tick labels}[{\"San Francisco\"}[0.107, 0.885, 0.172, 0.981]; {\"New York\"}[0.202, 0.885, 0.247, 0.953]; {\"London\"}[0.290, 0.885, 0.328, 0.942]; {\"Beijing\"}[0.376, 0.885, 0.412, 0.939]; {\"Bangalore\"}[0.454, 0.885, 0.504, 0.960]; {\"Berlin\"}[0.548, 0.885, 0.580, 0.933]; {\"Paris\"}[0.634, 0.885, 0.662, 0.926]; {\"Tel Aviv\"}[0.714, 0.885, 0.753, 0.943]; {\"Shanghai\"}[0.795, 0.885, 0.841, 0.954]; {\"Seattle\"}[0.884, 0.885, 0.921, 0.940]]. The y-axis measures the counts of Startups and Hubs, with the scale in thousands, as marked by the {y-axis label \"Count (in thousands)\"}[0.012, 0.372, 0.024, 0.554]. Each city has two corresponding bars representing two measurements - Startups and Hubs, as indicated by {the legend}[0.891, 0.055, 0.982, 0.139]. The green bars represent the number of Startups, and the red bars represent the number of Hubs. I can extract the values from the top annotations of each bar.\nBased on the above thought, the answer is:\nCity,Startups,Hubs\nSan Francisco,95.5,108.6\nNew York,80.7,95.4\nLondon,55.2,77.0\nBeijing,70.5,66.9\nBangalore,30.7,50.3\nBerlin,45.5,60.7\nParis,40.7,55.3\nTel Aviv,38.7,45.3\nShanghai,60.8,75.9\nSeattle,65.9,85.7"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "mF0moBAycHLBmJvN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat city seems most prepared for development, based on the bar chart of Entrepreneurship and Innovation Ecosystem across Major cities? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive comparison between the numbers of Startups and Hubs across different cities. By looking at the {green bars}<{\"Startups\"}[0.928, 0.088, 0.977, 0.106]> and {red bars}<{\"Hubs\"}[0.928, 0.115, 0.957, 0.132]>, I can see that cities like San Francisco, New York, and Seattle are showing large numbers in both sectors. However, {San Francisco}<r>[0.107, 0.885, 0.172, 0.981]</r> has the highest heights in both Startups and Hubs, which means it has the most substantial ecosystem for entrepreneurship and innovation development.\nBased on the above thought, the answer is:\nSan Francisco. Because it has the highest number of both Startups and Hubs, which indicates a vibrant ecosystem for entrepreneurship and innovation."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "bjMh4qxqrz1oPCcr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city has the fewest Startups?"
+            },
+            {
+                "from": "gpt",
+                "value": "The heights of green bars represent the counts of Startups for each city. By observing and comparing these bars, I find that {the green bar for \"Bangalore\"}[0.449, 0.682, 0.479, 0.878] is the shortest. Also, the value annotation \"30.7\" at the top of this bar confirms this finding.\nBased on the above thought, the answer is:\nBangalore."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "Ln9KuqewanvyXJp4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Healthcare Performance in Human Health & Disease Management\"}<r>[0.250, 0.025, 0.800, 0.055]</r> and the x-axis labels including the fields {Depression Recovery}<r>[0.085, 0.952, 0.231, 0.975]</r>, {Alzheimer Management}<r>[0.167, 0.952, 0.332, 0.975]</r>, {Oncology}<r>[0.309, 0.952, 0.374, 0.975]</r>, {Cardiology}<r>[0.396, 0.952, 0.471, 0.975]</r>, {Orthopedics}<r>[0.483, 0.952, 0.568, 0.975]</r>, {Rheumatology}<r>[0.567, 0.952, 0.668, 0.975]</r>, {Vision}<r>[0.689, 0.952, 0.730, 0.975]</r>, {Nephrology}<r>[0.761, 0.952, 0.841, 0.975]</r>, and {Immunology}<r>[0.850, 0.952, 0.936, 0.975]</r>, we know that x-axis represents different healthcare fields. The y-axis from {0}<r>[0.052, 0.934, 0.061, 0.958]</r> to {140}<r>[0.035, -0.015, 0.061, 0.009]</r> represents the performance scores. We have two groups of bars representing two different methods which are labeled in the legend below - {Non-Aggregation}<r>[0.117, 0.118, 0.235, 0.141]</r> represented by a light green color, and {Aggregation}<r>[0.117, 0.153, 0.203, 0.176]</r> represented by an orange color. To represent the data in CSV format, we take the height of each bar corresponding to the label on the top, as the value of dataset. For example, {the bar for Non-Aggregation at Depression Recovery}<r>[0.126, 0.279, 0.158, 0.944]</r> has a value of 98 and similarly for Aggregation it is 60.\nBased on the above thought, the answer is:\nThemes,Non-Aggregation,Aggregation\nDepression Recovery,98,60\nAlzheimer Management,57,50\nOncology,90,80\nCardiology,30,100\nOrthopedics,80,85\nRheumatology,118,90\nVision,109,107\nNephrology,52,50\nImmunology,40,120"
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "6Jb7s4vhTFE4X2Wf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, can we conclude that the Aggregation method performs better than the Non-Aggregation method in most of the healthcare fields? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we have to compare the pair of bars at each healthcare field and determine which method has the higher score. For example, in {Depression Recovery}<r>[0.085, 0.952, 0.231, 0.975]</r>, {the Non-Aggregation bar}<r>[0.126, 0.279, 0.158, 0.944]</r> is seen to be larger than {the Aggregation bar}<r>[0.158, 0.537, 0.190, 0.944]</r>, meaning that the Non-Aggregation method performs better in this field. After examining all pairs of bars, I find that {Non-Aggregation}<r>[0.117, 0.118, 0.235, 0.141]</r> outperforms {Aggregation}<r>[0.117, 0.153, 0.203, 0.176]</r> in the fields of Depression Recovery, Alzheimer Management, Oncology, Orthopedics, and Rheumatology. However, {Aggregation}<r>[0.117, 0.153, 0.203, 0.176]</r> performs better than {Non-Aggregation}<r>[0.117, 0.118, 0.235, 0.141]</r> in Cardiology, Vision, and Immunology. In the Nephrology field, both methods have the same performance. Overall, the Non-aggregation method performs better in a greater number of healthcare fields.\nBased on the above thought, the answer is:\nNo, we cannot conclude that the Aggregation method performs better than the Non-Aggregation method in most of the healthcare fields. Instead, the Non-Aggregation method performs better in the majority of the fields according to the chart."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "TchFb1j1DpN43lKx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which healthcare field does the Aggregation method have the highest performance score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting from the left most {bars pair of Depression Recovery}<r>[0.126, 0.279, 0.158, 0.944]</r> to {the right most pair of Immunology}<r>[0.861, 0.672, 0.893, 0.944]</r>, by comparing the value on top of each of the orange bars corresponding to Aggregation method, we can find the highest performance score. By observing, it can be seen that the {Immunology}<r>[0.850, 0.952, 0.936, 0.975]</r> field has the {highest score bar}<r>[0.893, 0.130, 0.925, 0.944]</r> with the value of 120, which is the highest among all the fields.\nBased on the above thought, the answer is:\nImmunology."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "7Or0cvT30TjruLx1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data from the bar chart you're seeing and present it in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I see that this bar chart is related to population migration trends over the past decade. The x-axis {\"Years\"}[{\"2010\"}[0.120, 0.952, 0.155, 0.975] to {\"2019\"}[0.895, 0.952, 0.931, 0.975]] represents different years, and the y axis ranging from {0}[0.035, 0.053, 0.061, 0.076] to {110}[0.035, 0.053, 0.061, 0.076] represents the number of people migrating. Each year has two bars, indicating two different migration trends. According to {the legend}[0.841, 0.074, 0.978, 0.186], {the bar in green color}<{\"In_Country\"}[0.885, 0.118, 0.960, 0.142]> represents the count of people migrating into the country, and {the bar in orange color}<{\"Out_Country\"}[0.885, 0.153, 0.973, 0.177]> represents the count of people migrating out of the country. To generate a CSV table, I will extract the values of each year for both trends based on the bars and their heights. For example, the height of {the green bar for \"2010\"}[0.108, 0.820, 0.138, 2.001] is extended around the point marked 67, and there's also {a value annotation \"67\"}[0.114, 0.790, 0.132, 0.813] on it, which means that the In_Country migration count for 2010 was 67.\nBased on the above thought, the answer is:\nYear,In_Country,Out_Country\n2010,67,90\n2011,75,89\n2012,82,85\n2013,83,84\n2014,78,88\n2015,81,88\n2016,90,91\n2017,98,94\n2018,97,95\n2019,100,97"
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "jlQohUdgpCVifzDi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the graph, can you describe the trend of migration in and out of the country over this decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "The group bar chart illustrates two clear trends of migration. From the green bars representing the {In_Country} migration<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]>, it looks like there's been a steady increase in the numbers from {2010}<{\"67\"}[0.114, 0.790, 0.132, 0.813]> to {2019}<{\"100\"}[0.885, 0.208, 0.911, 0.232]>, with some mild fluctuations. There's a slight decline in the year {2014}<{\"78\"}[0.459, 0.596, 0.476, 0.619]>. The numbers plateaued a bit towards the end of the decade.\n\nConversely, looking at the orange bars which represent {Out_Country}<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]> migration, it starts high at {\"90\"}<{\"2010\"}[0.144, 0.384, 0.162, 0.408]> but has a few dips notably in the years {2012}<{\"85\"}[0.316, 0.473, 0.334, 0.496]> and {2013}<{\"84\"}[0.403, 0.490, 0.420, 0.514]>. It then sees a slight increase again in the years {2014}<{\"88\"}[0.489, 0.420, 0.506, 0.443]> and {2015}<{\"88\"}[0.575, 0.420, 0.592, 0.443]>. Thereafter, It keeps a constant slight ascend onwards to reach a count of {\"97\"}<{\"2019\"}[0.919, 0.261, 0.937, 0.284]> by the end of the decade. All in all, the Out_Country migration numbers remain higher than In_Country for the entire decade.\nBased on the above thought, the answer is:\nThe trend of In_Country migration has been generally increasing over the decade, despite a few minor fluctuations. On the other hand, the Out_Country migration started high at the beginning of the decade and saw a few dips around 2012 and 2013 before resuming a slight incremental trend till the end of the decade. Throughout the decade, Out_Country migration has been consistently higher than In_Country migration."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "2PKtx9R5HY1OUDwp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data presented in the bar graph, in which year was the in-country migration at its peak?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the year with the highest in-country migration, I need to look for the highest bar among the green colored bars {In_Country}<{\"Trends\"}[0.841, 0.074, 0.978, 0.186]>. It starts with the bar representing 2010 located at [0.108, 0.820, 0.138, 2.001] and ends with the bar for the year 2019 at [0.883, 0.238, 0.913, 2.001]. After comparing each, it's clear that the highest green bar is at {\"2019\"}[0.883, 0.238, 0.913, 2.001], and its value is {100}[0.885, 0.208, 0.911, 0.232], marking it as the year with peak In_Country migration.\nBased on the above thought, the answer is:\n2019."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "78FPE9iwINlXI9ko",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you formulate this data from the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a CSV table from this bar chart, I need to identify the x-axis which here denotes the portfolios, {portfolio labels}<r>[0.114, 0.952, 0.185, 0.975]</r>; <r>[0.198, 0.952, 0.268, 0.975]</r>; <r>[0.281, 0.952, 0.352, 0.975]</r>; <r>[0.365, 0.952, 0.435, 0.975]</r>; <r>[0.448, 0.952, 0.519, 0.975]</r>; <r>[0.532, 0.952, 0.602, 0.975]</r>; <r>[0.615, 0.952, 0.686, 0.975]</r>; <r>[0.699, 0.952, 0.770, 0.975]</r>; <r>[0.783, 0.952, 0.853, 0.975]</r>; <r>[0.862, 0.952, 0.941, 0.975]</r>. The two types of investments indicated by the legend, {risky}<r>[0.117, 0.118, 0.244, 0.141]</r> and {stable}<r>[0.117, 0.153, 0.251, 0.176]</r> investments are our two Y-columns. Now by assessing the height of each bar for every portfolio under risky investments and stable investments respectively, I can fill in the investment values. This can be achieved by referencing the value annotated at the top of each bar. For instance, the first value for risky investment corresponds to the {blue bar for \"Portfolio 1\"}<r>[0.120, 0.463, 0.149, 0.944]</r> with a value annotation of {\"120.0\"}<r>[0.115, 0.433, 0.154, 0.456]</r>. This gives me a value of 120. I would repeat this process for every bar in risky and stable investments.\nBased on the above thought, the answer is:\nPortfolio, Risky Investments, Stable Investments\nPortfolio 1, 120, 50\nPortfolio 2, 130, 75\nPortfolio 3, 160, 85\nPortfolio 4, 150, 110\nPortfolio 5, 175, 100\nPortfolio 6, 180, 120\nPortfolio 7, 190, 145\nPortfolio 8, 185, 150\nPortfolio 9, 190, 160\nPortfolio 10, 200, 170"
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "SqfPznSlij9MsyTP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, what can we say about the growth trend of the two investment types over the portfolios?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the bar chart, I observe that both {risky}<r>[0.117, 0.118, 0.244, 0.141]</r> and {stable}<r>[0.117, 0.153, 0.251, 0.176]</r> investments have an upward trend over the portfolios. Starting from {Portfolio 1}<r>[0.114, 0.952, 0.185, 0.975]</r> to {Portfolio 10}<r>[0.862, 0.952, 0.941, 0.975]</r>, both investment types show increasing values. However, it can be seen that the risky investments are more subject to fluctuations and have a sharper growth contrast, as seen in {Portfolio 4}<r>[0.365, 0.952, 0.435, 0.975]</r> and {Portfolio 5}<r>[0.448, 0.952, 0.519, 0.975]</r> for instance. On the other hand, stable investments show a smoother and steadier growth trend.\nBased on the above thought, the answer is:\nBoth risky and stable investments show an upward growth trend over the portfolios, with risky investments having sharper increments and drops, while stable investments exhibit a smoother and more steady upward trend."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "916aicBU26RoMhs1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich portfolio have the highest value of stable investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the portfolio with the highest value in stable investments, I need to compare the height of the orange bars. By doing so, I notice that the tallest {orange bar}<r>[0.901, 0.263, 0.931, 0.944]</r> corresponding to {Portfolio 10}<r>[0.862, 0.952, 0.941, 0.975]</r> is the highest with an investment value of {\"170.0\"}<r>[0.896, 0.232, 0.936, 0.256]</r>.\nBased on the above thought, the answer is:\nPortfolio 10."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "jyrMfSE0svYX7iwo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from this bar chart and construct a table in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart figure, the x-axis represents two categories, which are {Wealthiest 10%}[0.318, 0.952, 0.426, 0.975] and {Poorest 10%}[0.635, 0.952, 0.722, 0.975]. The y-axis shows the percentile scores representing values for each category from {0}[0.052, 0.934, 0.061, 0.958] to {100}[0.035, 0.053, 0.061, 0.076]. There are two bars for each category, with different colors. According to {the legend}[0.804, 0.074, 0.978, 0.185], the category colors are specified. The {green bars}<{\"Income Inequality\"}[0.848, 0.118, 0.973, 0.141]> indicate income inequality, while the {orange bars}<{\"Rights Protection\"}[0.848, 0.153, 0.967, 0.176]> indicate rights protection. Now, for constructing the table, I can extract the values represented by the height of the bars and their annotated values. For example, under {Wealthiest 10%}[0.318, 0.952, 0.426, 0.975] category, the value for {Income Inequality}[0.265, 0.071, 0.372, 0.944] is 99 and for {Rights Protection}[0.372, 0.864, 0.479, 0.944] is 9.\nBased on the above thought, the answer is:\nCategory,Income Inequality,Rights Protection\nWealthiest 10%,99,9\nPoorest 10%,1,91"
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "XExuEbuyUDfjGcjb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart reveal about the relation between income inequality and rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it's clear that there's a marked disparity between income inequality and rights protection within the two demographics \"Wealthiest 10%\" and \"Poorest 10%\". The {Income Inequality bar}[0.265, 0.071, 0.372, 0.944] in the Wealthiest 10% section is distinctly higher than the {Rights Protection bar}[0.372, 0.864, 0.479, 0.944] in the same section, showing that there's a significant disparity in income in favor of the wealthier population. Conversely, the {Rights Protection bar}[0.679, 0.142, 0.786, 0.944] in the Poorest 10% section is noticeably higher than the {Income Inequality bar}[0.571, 0.935, 0.679, 0.944] in the same section, which shows that despite significant income inequality, rights protection mechanisms seem to be stronger within the less affluent demographic. Hence, there appears to be an inverse relationship between income inequality and rights protection.\nBased on the above thought, the answer is:\nThe chart suggests an inverse relationship between income inequality and rights protection. It indicates that the wealthier populace sees more income disparity but lesser rights protection, whereas the less affluent section experiences prominent rights protection despite considerable income inequality."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "YKoPD9QzLHXgOJMe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which segment, 'Wealthiest 10%' or 'Poorest 10%', is the rights protection more inequality?"
+            },
+            {
+                "from": "gpt",
+                "value": "By inspection of the bar heights and their annotated values, I can solve this question. First, I check the {orange bar for \"Wealthiest 10%\"}[0.372, 0.864, 0.479, 0.944], and see that it's annotated with {\"9\"}[0.421, 0.834, 0.430, 0.857]. Then I examine the {orange bar for \"Poorest 10%\"}[0.679, 0.142, 0.786, 0.944], and find that it's annotated with {\"91\"}[0.723, 0.111, 0.741, 0.135]. The value of Rights Protection is higher for the Poorest 10%, indicating that this group experiences more inequality in rights protection.\nBased on the above thought, the answer is:\nPoorest 10%"
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "YbjLCjpHwX0DQPcj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By interpreting the bar chart, I can catch that the x-axis contains names of different energy resources - {the x-axis tick labels}[{\"Solar\"}[0.150, 0.952, 0.186, 0.975]; {\"Wind\"}[0.253, 0.952, 0.288, 0.975]; {\"Hydroelectric\"}[0.326, 0.952, 0.419, 0.975]; {\"Geothermal\"}[0.433, 0.952, 0.515, 0.975]; {\"Nuclear\"}[0.550, 0.952, 0.603, 0.975]; {\"Coal\"}[0.663, 0.952, 0.694, 0.975]; {\"Gas\"}[0.767, 0.952, 0.794, 0.975]; {\"Oil\"}[0.874, 0.952, 0.892, 0.975]]. The y-axis provides the corresponding percentage usage for these resources. Each of the resources has two bars - the first bar, encoded green, stands for Renewable Energy while the second one, colored orange, represents Nonrenewable Energy. I'll transform this data into a CSV format by extracting the resource, renewable energy and nonrenewable energy for each bar, example - for {Solar's green bar}[0.132, 0.389, 0.168, 0.944] which represents renewable energy the value is {75.5}[0.135, 0.359, 0.165, 0.382] and for {Solar's orange bar}[0.168, 0.758, 0.204, 0.944] which stands for nonrenewable energy, the value is {25.2}[0.170, 0.728, 0.201, 0.752].\nBased on the above thought, the answer is:\nResource, Renewable Energy, Nonrenewable Energy\nSolar, 75.5, 25.2\nWind, 68.3, 32.1\nHydroelectric, 60.6, 35.3\nGeothermal, 55.1, 40.6\nNuclear, 52.3, 83.9\nCoal, 40.2, 92.3\nGas, 38.1, 95.5\nOil, 32.8, 98.4"
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "E3fkfwhDMLCzk8jF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the chart, what trend can be inferred regarding the future of energy resource allocation in terms of renewable and nonrenewable energy usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking over the bar chart, there is a clear trend. Specifically, the green bars represent renewable energy usage, while the orange bars stand for nonrenewable energy usage. For instance, when observing the {Solar energy bars}<{\"Renewable\"}[0.132, 0.389, 0.168, 0.944]; {\"Nonrenewable\"}[0.168, 0.758, 0.204, 0.944]>, we can see that renewable energy usage significantly exceeds nonrenewable energy. However, as we move towards the right of the graph, we could see that the height of the orange bars for each energy resource gradually rises while the green bars indicating renewable energy usage drop. This trend reaches its peak at {Oil}<{\"Renewable\"}[0.847, 0.703, 0.883, 0.944]; {\"Nonrenewable\"}[0.883, 0.221, 0.919, 0.944]>, where usage of nonrenewable energy significantly outweighs renewable energy consumption.\nBased on the above thought, the answer is:\nThe chart demonstrates an evident trend of increasing reliance on nonrenewable energy sources and decreasing use of renewable energy sources moving from Solar energy to Oil. This trend suggests that in terms of future energy resource allocation, there exists a rising preference for nonrenewable energy sources."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "X72sy0k94PLb1jXX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy resource has the lowest consumption of nonrenewable energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the orange bars representing the nonrenewable energy consumption for each energy resource. Looking from left to right, I can see that the orange bar under the category {Solar}[0.150, 0.952, 0.186, 0.975] with height & value information at {[0.168, 0.758, 0.204, 0.944], \"25.2\"}[0.170, 0.728, 0.201, 0.752] is the shortest, indicating the lowest consumption of nonrenewable energy.\nBased on the above thought, the answer is:\nSolar"
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "8T8TfZK8WOTIEay6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data given in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the chart is a bar chart with multiple groups, representing {Ecosystem Sustainability over Time}[0.431, 0.019, 0.727, 0.041]. The categories on the y-axis include 5 different ecosystems: {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831], {Ocean}[0.119, 0.647, 0.163, 0.664], {Desert}[0.117, 0.480, 0.163, 0.498], {Arctic Tundra}[0.072, 0.313, 0.163, 0.331], and {Urban City}[0.089, 0.147, 0.163, 0.164]; the x-axis shows the {Sustainability Score}[0.510, 0.964, 0.648, 0.981]. Each group of bars corresponds to a different time category identified in the {legend}[0.819, 0.055, 0.978, 0.165], including {Initial Condition}[0.863, 0.062, 0.972, 0.080], {Time 20}[0.863, 0.088, 0.919, 0.106], {Time 40}[0.863, 0.115, 0.919, 0.132], and {Time 60}[0.863, 0.141, 0.919, 0.158]. The sustainability scores for each ecosystem at each time can be calculated by measuring the bar lengths relative to the x-axis scale, while paying attention to the limits of the bar for each time.\nBased on the above thought, the answer is:\nEcosystem,Initial Condition,Time 20,Time 40,Time 60\nTropical Rainforest,0.9,0.85,0.82,0.81\nOcean,0.92,0.9,0.86,0.83\nDesert,0.5,0.48,0.47,0.45\nArctic Tundra,0.82,0.8,0.78,0.75\nUrban City,0.35,0.37,0.39,0.40"
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "5Wca1WgB6KAhP2su",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the visualization, which ecosystem seems to be the most resilient over time in terms of sustainability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should consider the ecosystems with the least decrease in sustainability scores over time. For {Ocean}[0.119, 0.647, 0.163, 0.664] and {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] sustainability scores generally decrease over time. The same applies to {Desert}[0.117, 0.480, 0.163, 0.498] and {Arctic Tundra}[0.072, 0.313, 0.163, 0.331] as demonstrated by the bars {[0.173, 0.253, 0.353, 0.387], [0.702, 0.253, 0.867, 0.387]}, from 0.82 to 0.75. {Urban City}[0.089, 0.147, 0.163, 0.164] alone exhibits improvement in sustainability, however it starts from a low base. The {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] ecosystem has only a slight decrease from 0.9 to 0.81. So in terms of overall resilience and high sustainability, I would say the Tropical Rainforest ecosystem seems to be the most resilient.\nBased on the above thought, the answer is:\nThe most resilient ecosystem in terms of sustainability appears to be the Tropical Rainforest."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "mU7IYbSfwA6FsiRU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich ecosystem shows the largest decrease in sustainability score from initial condition to time 60?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the sustainability scores at Time 60 from the initial condition for each ecosystem. I will do this by comparing the lengths of the corresponding bars for each ecosystem in the initial condition group and the Time 60 group. The biggest difference will indicate the largest decrease. Looking at each ecosystem in turn, {Tropical Rainforest}[0.035, 0.814, 0.163, 0.831] has bars at {[0.173, 0.754, 0.371, 0.887], [0.739, 0.754, 0.918, 0.887]} indicating a decrease from 0.9 to 0.81; {Ocean}[0.119, 0.647, 0.163, 0.664] has bars at {[0.173, 0.587, 0.376, 0.720], [0.763, 0.587, 0.946, 0.720]} indicating a decrease from 0.92 to 0.83; {Desert}[0.117, 0.480, 0.163, 0.498] has bars at {[0.173, 0.420, 0.283, 0.554], [0.492, 0.420, 0.592, 0.554]} indicating a decrease from 0.5 to 0.45; {Arctic Tundra}[0.072, 0.313, 0.163, 0.331] has bars at {[0.173, 0.253, 0.353, 0.387], [0.702, 0.253, 0.867, 0.387]} indicating a decrease from 0.82 to 0.75; and finally, {Urban City}[0.089, 0.147, 0.163, 0.164] has bars at {[0.173, 0.087, 0.250, 0.220], [0.417, 0.087, 0.506, 0.220]}, but it hasn't a decrease.\nBased on the above thought, the answer is:\nArctic Tundra."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "HMgee7dQHIoQtcbb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the bar chart data representing the development indicators for urban and rural services into a CSV format, highlighting the changes over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the subtitle}<r>[0.345, 0.025, 0.702, 0.055]</r> 'Rural and Urban Development Comparison', it indicates the content of the chart. The {x-axis}<r>[0.474, 0.952, 0.573, 0.975]</r> reads 'Time (Periods)', with period labels from 0 to 14, represented by {xtick labels}<r>[0.117, 0.905, 0.133, 0.932]; [0.174, 0.905, 0.190, 0.932]; [0.231, 0.905, 0.247, 0.932]; ... [0.911, 0.905, 0.933, 0.942]</r>. The {y-axis}<r>[0.015, 0.337, 0.029, 0.614]</r> is labeled 'Development Indicators' with values from 0 to 12. The {legend}<r>[0.886, 0.074, 0.978, 0.185]</r> explains that {blue bars}<r>[0.104, 0.179, 0.147, 0.889]</r> represent 'Urban Services', while {orange bars}<r>[0.104, 0.427, 0.147, 0.889]</r> depict 'Rural Services'. Using the height of bars and the text values associated with each, I can extract the values across different time periods. For instance, during time period 0, the 'Urban Services' value labeled 10.3 coincides with the bar height that starts at the lower y-coordinate of 0.179 and stretches up to 0.889, as shown in <r>[0.104, 0.179, 0.147, 0.889]</r>.\nBased on the above thought, the answer is:\nTime Period,Urban Services,Rural Services\n0,10.3,6.7\n1,9.6,5.2\n2,9.2,4.0\n3,8.8,5.1\n4,8.4,5.8\n5,8.1,3.9\n6,7.8,4.3\n7,7.5,4.9\n8,6.9,5.0\n9,6.4,5.1\n10,6.2,5.0\n11,6.1,4.8\n12,5.9,4.7\n13,5.5,5.0\n14,5.2,5.4"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "02A2nqJRGSs1qaWa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, can we deduce whether the rural development indicator improved or declined over the 14 periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I'll analyze the bar heights for 'Rural Services'. The initial value is located at {6.7}<r>[0.114, 0.402, 0.136, 0.425]</r> for period 0. Examining the sequence of bars, I notice fluctuations such as the decrease at {4.0}<r>[0.228, 0.588, 0.250, 0.611]</r> at period 2 and then the increase at period 4 to {5.8}<r>[0.342, 0.464, 0.364, 0.487]</r>. Lastly, I compare the final value at period 14, which is {5.4}<r>[0.911, 0.492, 0.933, 0.515]</r>, against the initial value.\nBased on the above thought, the answer is:\nThe rural development indicator showed fluctuations but ultimately improved from 4.0 to 5.4 indicating a positive trend towards improvement by the end of the 14 periods."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "6aqd1GlvjM9pGI3t",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did urban development indicators decrease over the entire observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I notice the value for urban development in the first period, stated as {10.3}<r>[0.110, 0.154, 0.141, 0.177]</r>. Then I locate the last period's urban development value, listed as {5.2}<r>[0.911, 0.505, 0.933, 0.529]</r>. To calculate the total decrease, I subtract the final period's value from the initial period's value.\nBased on the above thought, the answer is:\n5.1"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "Ufcr46ukM2AWoWYe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bars you observe in the chart, can you form a CSV table containing Stable growth and Sharp rise percentages of all companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "By investigating the {chart title}[0.345, 0.025, 0.702, 0.055] i.e., \"Medical and Biotechnology Industry Trends\", I understand this chart presents the growth trends of different companies. The x-axis represents different companies named as {Biomedica Inc.}[0.132, 0.919, 0.234, 0.942], {Medicon Solutions}[0.347, 0.919, 0.473, 0.942], {Biotec Corp.}[0.594, 0.919, 0.679, 0.942] and {GeneWorks}[0.824, 0.919, 0.903, 0.942]. The y-axis stands for the trend percentages that range from 0 to 10 as indicated by {y-axis labels}[0.035, 0.053, 0.052, 0.076]. There are two bars for each company, and according to {legend for the trends}[0.831, 0.074, 0.978, 0.185], one represents 'Stable growth' and the other represents 'Sharp rise'. To form the CSV table, I can estimate the trends values for each company by reading the y-coordinate of the bar top and also from the values stated next to the top of each bar.\nBased on the above thought, the answer is:\nCompany, Stabile growth (%), Sharp rise (%)\nBiomedica Inc., 2.5, 7.35\nMedicon Solutions, 4.3, 3.98\nBiotec Corp., 5.2, 2.07\nGeneWorks, 3.5, 6.21"
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "moJcgTlkedurDpgI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the graph, which company would you recommend for long-term investment and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question asks for an opinion based on analyzing the data. Long-term investment would favor stable growth, as represented by the {greyish bars}[0.875, 0.118, 0.972, 0.141] in the graph. The company with the highest value in Stable growth is {Biotec Corp.}[0.594, 0.919, 0.679, 0.942], which scores 5.20 in stable growth as identified by the value next to the top of the bar. Although it has less value in Sharp rise as reflected by the {purple bars}[0.875, 0.153, 0.946, 0.176], for long-term investment, it is typically wise to avoid fluctuations which are represented here by Sharp rise.\nBased on the above thought, the answer is:\nBiotec Corp. would be recommended for long-term investment due to its high stable growth and low sharp rise trend, which suggests steadiness and less likelihood of risk in the long-term."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "4I7lOeC1tVHf3d8Y",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest total percentage value when we combine both Stable growth and Sharp rise trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which company has the highest total percentage value, I need to add the values of Stable growth and Sharp rise for each company. I look at the bars and the text labels at the top of each bar to determine the values. After adding the values for each company, I note that {Biomedica Inc. }[0.132, 0.919, 0.234, 0.942] has a total of 9.85, {Medicon Solutions}[0.347, 0.919, 0.473, 0.942] has a total of 8.28, {Biotec Corp.}[0.594, 0.919, 0.679, 0.942] has a total of 7.27, and {GeneWorks}[0.824, 0.919, 0.903, 0.942] has a total of 9.71. It is clear that Biomedica Inc. has the highest total percentage value.\nBased on the above thought, the answer is:\nBiomedica Inc."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "YJ3h8qpDvFl1ZyQX",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data presented on this bar chart, could you please generate a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart plots data on percentage of graduates and non-graduates across different age groups. The age groups are indicated on {the x-axis tick labels}<{\"Young_Adults\"}[0.185, 0.818, 0.260, 0.942], {\"Middle_Aged\"}[0.493, 0.818, 0.566, 0.939], {\"Seniors\"}[0.813, 0.818, 0.859, 0.895]>. The y-axis indicates the percentage, as denoted by its label {Percentage [%]}<r>[0.015, 0.343, 0.029, 0.521]</r>. In this chart, two kinds of bars are marked: {Graduate Bars}<{handle blue color}[0.835, 0.120, 0.863, 0.136]> , {Non-Graduate Bars}<{handle brown color}[0.835, 0.155, 0.863, 0.171]>. Each bar resides on an age group, the index of the bar corresponds to the percentage. By matching the height of the bar and the percentage indicator, the numerical value of each bar can be obtained and thus the CSV table can be generated.\nBased on the above thought, the answer is:\nAge_Group,Graduate,Non-Graduate\nYoung_Adults,6.2,-1.4\nMiddle_Aged,12.5,3.7\nSeniors,-5.3,1.1"
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "6YeSLDsf1UIEx3XN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data on the bar chart, how would you explain the pattern of education levels across different age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows the percentage of graduates and non-graduates across three age groups: Young_Adults<r>[0.185,0.818,0.260,0.942]</r>, Middle_Aged<r>[0.493,0.818,0.566,0.939]</r> and Seniors<r>[0.813,0.818,0.859,0.895]</r>. The {Graduate Bars}<r>{blue color}[0.835, 0.120, 0.863, 0.136]</r> show that the percentage of graduates is highest in the 'Middle-Aged' group, somewhat lower in the 'Young Adults' group and negative in the 'Seniors' group. The {Non-Graduate Bars}<r>{brown color}[0.835, 0.155, 0.863, 0.171]</r> show sporadic growth with small negative value for young adults, somewhat high positive for middle-aged and marginal positive for seniors.\nBased on the above thought, the answer is:\nThe data indicates that middle-aged people have the highest percentage of graduates, followed by young adults. The data for seniors shows a decrease in graduation percentage. For non-graduates, the data shows a small decrease for young adults, an increase for middle-aged adults, and a marginal increase for seniors. It suggests that more middle-aged individuals have pursued an education to graduation compared to other age groups, while the graduation rate for seniors is negatively affected. This could potentially be due to historical trends, with more educational opportunities having been available to the younger and middle-aged groups. The non-graduates show sporadic growth across all age groups."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "4Ikxt5OLzJd736c7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which age group can one find the highest education attainment level among all, whether being a graduate or not?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest education attainment level among all the age groups, I should compare the height of the bars representing different age groups for both graduates and non-graduates. The {Graduate Bars} for {Young_Adults}[0.115, 0.322, 0.222, 0.506], {Middle_Aged}[0.422, 0.136, 0.529, 0.506], and {Seniors}[0.729, 0.506, 0.836, 0.662] as well as the {Non-graduate Bars} for {Young_Adults}[0.222, 0.506, 0.330, 0.547], {Middle_Aged}[0.529, 0.396, 0.637, 0.506], {Seniors}[0.836, 0.473, 0.944, 0.506] are compared in this case. Observing the bar heights, the highest bar is {Graduate Bar} in the \"Middle_Aged\" category [0.422, 0.136, 0.529, 0.506]. Thus the highest education attainment level among all age groups is in the Middle-Aged group among Graduates.\nBased on the above thought, the answer is:\nMiddle_Aged."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "5HrCDvfcVlCYd2Xo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information from the bar charts into a structured CSV table comparing regions and their corresponding variables for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table, I must first recognize the {regions}<r>[0.596, 0.449, 0.637, 0.466]; [0.695, 0.449, 0.734, 0.466]; [0.797, 0.449, 0.827, 0.466]; [0.893, 0.449, 0.927, 0.466]</r> from the x-axis across the four bar charts. Then, I need to identify the types of data each color represents by referring to the {legend}<r>[0.157, -0.041, 0.843, 0.016]</r>. Each bar's height correlates to a specific value on the y-axis. By examining each bar within the year 2020 charts for {South}<r>[0.577, 0.437, 0.656, 0.437]</r>, {North}<r>[0.675, 0.436, 0.754, 0.437]</r>, {East}<r>[0.773, 0.437, 0.851, 0.437]</r>, and {West}<r>[0.871, 0.436, 0.949, 0.437]</r> across the color categories, I can record the bars' heights as values by cross-referencing with the y-axis.\nBased on the above thought, the answer is:\nRegion,Elevation,Average Temperature,Average Precipitation,Population\nSouth,1000,22,1250,12000000\nNorth,1500,10,950,6000000\nEast,800,26,1150,9000000\nWest,1200,19.5,1050,6500000"
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "hGEbOZnxJOmTwuXd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region showed the least growth in population from 2015 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which region had the least growth in population, I analyze the {purple bars}<r>[0.163, 0.437, 0.241, 0.437]; [0.261, 0.438, 0.339, 0.438]; [0.359, 0.438, 0.437, 0.438]; [0.457, 0.437, 0.535, 0.438]</r> for 2015 and {purple bars}<r>[0.656, 0.437, 0.734, 0.437]; [0.754, 0.438, 0.832, 0.438]; [0.851, 0.438, 0.930, 0.438]; [0.949, 0.437, 1.027, 0.438]</r> for 2020, cross-referencing with the {y-axis}<r>[0.527, 0.430, 0.549, 0.447]</r> labels. Each bar pair corresponds to a region and depicts its population for given years. By comparing the differences in population between the years 2015 and 2020 for South, North, East, and West, it becomes apparent that the North region's bars show the smallest increase.\nBased on the above thought, the answer is:\nNorth. Upon examining the bars corresponding to the population for each region, the North region depicted by {purple bars}<r>[0.261, 0.438, 0.339, 0.438]; [0.754, 0.438, 0.832, 0.438]</r> demonstrates the least growth, indicating a smaller increase in population compared to the other regions."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "YSp5lsC18VhkwcNi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the temperature rise in the East region from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the rise in temperature, I compare the {blue bars}<r>[0.281, 0.436, 0.359, 0.437]; [0.773, 0.437, 0.851, 0.437]</r> of the East region in the 2015 and 2020 charts. The y-values on the bar charts will provide the temperatures for both years. On the 2015 chart, the {blue bar}<r>[0.281, 0.436, 0.359, 0.437]</r> represents a temperature of 24 degrees Celsius, and on the 2020 chart, the {blue bar}<r>[0.773, 0.437, 0.851, 0.437]</r> shows a temperature of 26 degrees Celsius.\nBased on the above thought, the answer is:\n2 degrees Celsius."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "PwVP1qqx94SP9TLq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the direct and indirect export values in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, let's look at the bar segments across each country's stacked bar. {The USA's bar}<r>[0.092, 0.228, 0.173, 0.325]</r> for direct exports shows a range of \"0.00 -> 35.00\" billion dollars. For indirect exports, I analyze the segment above direct exports, which extends the bar until it reaches the 40 billion mark {above the direct export segment}<r>[0.092, 0.325, 0.173, 0.373]</r>. Similarly, {China's bar}<r>[0.193, 0.214, 0.273, 0.325]</r> has a direct export value of 40 billion dollars and an indirect export value represented by the additional segment {above China's direct export segment}<r>[0.193, 0.325, 0.273, 0.356]</r> which is 30 billion dollars. Germany and Japan's figures are then sequentially determined in the same manner using their respective bars: {Germany's direct exports}<r>[0.293, 0.256, 0.373, 0.325]</r> and {Japan's direct exports}<r>[0.393, 0.325, 0.473, 0.409]</r>, as well as the segments representing their indirect export values above those levels.\nBased on the above thought, the answer is:\nCountry,Direct Export,Indirect Export\nUSA,35,40\nChina,40,30\nGermany,25,20\nJapan,30,35"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "mcc71rfNKibSKJf1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, what can you tell me about the import trends for these four countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the import trends, I will examine the segments representing imports for each country. {The USA's import segment}<r>[0.173, 0.325, 0.193, 0.373]</r> indicates a value of 25 billion dollars. {China's import section}<r>[0.273, 0.325, 0.293, 0.356]</r> shows 20 billion dollars. When inspecting {Germany's and Japan's import segments}<r>[0.373, 0.325, 0.393, 0.381]</r> and <r>[0.473, 0.325, 0.493, 0.409]</r> respectively, their values can be assessed as 30 and 50 billion dollars. Notably, Japan has the largest import value, which is also the highest among all trade values represented on the chart for that country, signifying that it is the most import-dependent economy among the four.\nBased on the above thought, the answer is:\nJapan shows the highest level of imports, with a value of 50 billion dollars, which highlights its import dependency compared to the other three countries that show lower import values. Germany, with 30 billion dollars in imports, also has substantial import activity. The USA and China have comparably lower import trends at 25 and 20 billion dollars respectively."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "i1frbzpIFRtskkp2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the highest value for net exports?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest value for net exports, I will examine the topmost segments of each country's bar that represent net export data. I see that {China's net export segment}<r>[0.685, 0.242, 0.765, 0.325]</r> extends from 0 to 5 billion dollars, which appears to be the highest end point compared to other countries. By looking at the {USA's net export segment}<r>[0.092, 0.228, 0.173, 0.254]</r>, {Germany's segment}<r>[0.293, 0.256, 0.373, 0.284]</r>, and {Japan's segment}<r>[0.393, 0.409, 0.473, 0.437]</r>, I can see they end at -30, -5, and -15 billion dollars respectively, which are all lower than China's figure. Therefore, China has the highest net export value among the displayed countries.\nBased on the above thought, the answer is:\nChina"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "fVsQwybNzc9S4heo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you list the GNP for each continent across the years 2000, 2010, and 2020 in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, the graph for Africa shows {three bars}<r>[0.086, 0.326, 0.196, 0.437]</r>;<r>[0.224, 0.325, 0.335, 0.437]</r>;<r>[0.362, 0.313, 0.473, 0.437]</r> corresponding to the years 2000, 2010, and 2020 respectively, with the GNP values labeled on the x-axis beneath each bar. Similarly, the chart for Asia presents {three bars}<r>[0.578, 0.344, 0.689, 0.437]</r>;<r>[0.717, 0.315, 0.827, 0.437]</r>;<r>[0.855, 0.280, 0.966, 0.437]</r> for the same sequence of years. This pattern is repeated for {Europe}<r>[0.066, 0.556, 0.492, 0.927]</r>, {North-America}<r>[0.559, 0.556, 0.985, 0.927]</r>, {South-America}, and {Oceania} across their respective subplots and bars. By carefully examining each group of bars, I can list the GNP values for each continent from the chart.\nBased on the above thought, the answer is:\nContinent,GNP (2000),GNP (2010),GNP (2020)\nAfrica,890012.6,900315.7,1002146.8\nAsia,750021.4,980015.5,1269002.3\nEurope,1325100.6,1467801.2,1658001.4\nNorth-America,1801002.8,2105123.5,2390145.9\nSouth-America,510783.4,615796.4,792108.1\nOceania,410698.2,500912.8,625942.5"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "UaPZkCC25IZRXuso",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich continent exhibited the greatest growth in GNP from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the continent with the greatest growth in GNP between 2000 and 2020, I must subtract the GNP of 2000 from the GNP of 2020 for each continent. Reviewing the bar charts, I take note of the height of the first and last bars within each group. Starting with {Africa\u2019s bars}<r>[0.086, 0.326, 0.196, 0.437]</r>;<r>[0.362, 0.313, 0.473, 0.437]</r>, the difference in GNP is substantial. The process is repeated to find the differences for {Asia}<r>[0.578, 0.344, 0.689, 0.437]</r>;<r>[0.855, 0.280, 0.966, 0.437]</r>, {Europe}<r>[0.066, 0.556, 0.492, 0.927]</r>;<r>[0.362, 0.722, 0.473, 0.927]</r>, {North-America}<r>[0.559, 0.556, 0.985, 0.927]</r>;<r>[0.855, 0.632, 0.966, 0.927]</r>, {South-America}, and {Oceania}. The continent with the largest numerical difference between 2000 and 2020 GNP is the one that experienced the greatest growth.\nBased on the above thought, the answer is:\nAsia. The difference in GNP between the year 2000 and 2020 for Asia is the highest, growing from 750021.4 to 1269002.3, indicating the most significant growth amongst all continents."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "Lu8HSQOsJbjNWRdv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average GNP of all continents combined for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average GNP for the year 2020, I need to sum the GNP values for each continent in 2020 and divide by the number of continents. By identifying {the bar for Africa in 2020}<r>[0.362, 0.313, 0.473, 0.437]</r>, {Asia's}<r>[0.855, 0.280, 0.966, 0.437]</r>, {Europe's}<r>[0.362, 0.722, 0.473, 0.927]</r>, {North-America's}<r>[0.855, 0.632, 0.966, 0.927]</r>, {South-America's}, and {Oceania's} GNP bars, I glean the respective values. The sum of these values is 1002146.8 (Africa) + 1269002.3 (Asia) + 1658001.4 (Europe) + 2390145.9 (North-America) + 792108.1 (South-America) + 625942.5 (Oceania), which equals 7763246. After adding these, I divide by 6 (the number of continents) to find the average.\nBased on the above thought, the answer is:\n1293874.3"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "hvqE5nLUkHp1vRSM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table from the bar chart data showing the trade volumes of United States from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the first subplot titled {Global Trade Volumes Over the Years - United States}<r>[0.035, 0.019, 0.511, 0.041]</r>, I see that the x-axis, labeled with {Year}<r>[0.258, 0.473, 0.288, 0.491]</r>, has four tick marks corresponding to {2005}<r>[0.098, 0.449, 0.133, 0.466]</r>, {2010}<r>[0.203, 0.449, 0.238, 0.466]</r>, {2015}<r>[0.308, 0.449, 0.343, 0.466]</r>, and {2020}<r>[0.413, 0.449, 0.448, 0.466]</r>. The y-axis is labeled with {Trade Volume (Trillion dollars)}<r>[0.015, 0.113, 0.029, 0.370]</r>, with five ticks from {0}<r>[0.035, 0.430, 0.043, 0.447]</r> to {5}<r>[0.035, 0.040, 0.043, 0.057]</r>, each representing an incremental trillion dollars. For each year, there is a bar representing the trade volume of the United States, such as the bar for {2005}<r>[0.073, 0.271, 0.157, 0.437]</r> is hovering just above the 2 tick, indicating a trade volume of approximately $2.12 trillion. The exact values can be garnered from the raw data provided, and so the CSV table reflecting the United States trade volume data can be presented.\nBased on the above thought, the answer is:\nYear,Trade Volume (Trillion dollars)\n2005,2.12\n2010,2.20\n2015,2.25\n2020,2.20"
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "RKgV9RLqrI9z5Bt0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country depicted in the chart has demonstrated the most significant growth in trade volume from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the most significant growth, I need to observe the increase in bar heights for each country. First, I will compare the bars for {United States}<r>[0.073, 0.271, 0.157, 0.437]</r> showing minimal growth. Then I'll check {China}<r>[0.566, 0.400, 0.650, 0.437]</r> with a starting point near the 0 tick and ending much higher near the 4 tick. This indicates a significant increase. Continuing this method, I analyze {Germany}<r>[0.073, 0.812, 0.157, 0.927]</r>, which shows a slight increase, similar to the {USA}<r>[0.073, 0.812, 0.157, 0.927]</r>. For {India}<r>[0.566, 0.913, 0.650, 0.927]</r>, the growth looks impressive, starting just above 0 and ending near 1.2. And, {Japan}<r>[not visible]</r> is not depicted in the provided subplots and cannot be analyzed based on the provided chart. Comparing the starting and ending points of each, it's apparent that China has demonstrated the most significant growth.\nBased on the above thought, the answer is:\nChina. It started at approximately 0.47 trillion dollars in 2005 and increased to 4 trillion dollars in 2020, showing the most substantial growth among the depicted countries."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "TnADlNYW6hd82Mow",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average trade volume of China between 2005 and 2020 according to the data shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average trade volume of China between 2005 and 2020, I must locate the bars corresponding to China in the second subplot titled {Global Trade Volumes Over the Years - China}<r>[0.560, 0.019, 0.971, 0.041]</r>. The relevant bars are at coordinates {2005}<r>[0.566, 0.400, 0.650, 0.437]</r>, {2010}<r>[0.671, 0.298, 0.755, 0.437]</r>, {2015}<r>[0.776, 0.224, 0.860, 0.437]</r>, and {2020}<r>[0.881, 0.125, 0.965, 0.437]</r>. By adding the trade volumes from the raw data for the given years: 0.47, 1.78, 2.72, and 4.00 respectively, and dividing by the number of years, which is 4, I can calculate the average.\nBased on the above thought, the answer is:\n2.2425"
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "NmXcWZASWx83irWo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar graph you observe, please structure the data into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar graph displays different industries that are being evaluated for investment distribution in software development and technology & innovation. From {the x-axis labels}<r>[\"Healthcare\"[0.166, 0.710, 0.183, 0.900]; \"Education\"[0.285, 0.710, 0.302, 0.883]; \"Finance\"[0.403, 0.710, 0.421, 0.843]; \"Manufacturing\"[0.522, 0.710, 0.540, 0.963]; \"Energy\"[0.641, 0.710, 0.658, 0.832]; \"Transport\"[0.759, 0.710, 0.777, 0.874]; \"Agriculture\"[0.878, 0.710, 0.896, 0.902]]</r> we can identify that there are seven industries. We distinguish two types of investments from {the legend}<r>[0.347, 0.070, 0.714, 0.133]</r>, namely 'Software' represented by {green bar}<r>[\"Software\"[0.403, 0.084, 0.480, 0.119]]</r> and 'Tech & Innovation' represented by {yellow bar}<r>[\"Tech&Innovation\"[0.563, 0.084, 0.707, 0.119]]</r>. To get the percentage of investment in different industries, I need to calculate the ratios of the heights of the bars. By comparing the {heights of the green bars}<r>['Healthcare'[0.121, 0.447, 0.228, 0.686]; 'Education'[0.240, 0.328, 0.347, 0.686]; 'Finance'[0.359, 0.251, 0.465, 0.686]; 'Manufacturing'[0.477, 0.341, 0.584, 0.686]; 'Energy'[0.596, 0.261, 0.703, 0.686]; 'Transport'[0.715, 0.290, 0.822, 0.686]; 'Agriculture'[0.833, 0.218, 0.940, 0.686]]</r> and the {heights of the yellow bars}<r>['Healthcare'[0.121, 0.136, 0.228, 0.447]; 'Education'[0.240, 0.136, 0.347, 0.328]; 'Finance'[0.359, 0.136, 0.465, 0.251]; 'Manufacturing'[0.477, 0.136, 0.584, 0.341]; 'Energy'[0.596, 0.136, 0.703, 0.261]; 'Transport'[0.715, 0.136, 0.822, 0.290]; 'Agriculture'[0.833, 0.136, 0.940, 0.218]]</r>.\nBased on the above thought, the answer is:\nIndustry,Software,Tech&Innovation\nHealthcare,43.5,56.5\nEducation,65,35\nFinance,79,21\nManufacturing,62.7,37.3\nEnergy,77.3,22.7\nTransport,72,28\nAgriculture,85,15"
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "n5HnNBhEtCCdviSC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about investment trends in the Agriculture industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two main observations for the {bar for \"Agriculture\"}<r>[0.833, 0.218, 0.940, 0.686]</r>. The first part of the bar, {green bar}<r>[0.833, 0.218, 0.940, 0.686]</r> indicating the software development investment, is significantly larger, taking up about 85% of the total investment. The second part, {yellow bar}<r>[0.833, 0.136, 0.940, 0.218]</r> representing the Technology & Innovation investment, is quite small, showing about 15% investment allocation. This clearly shows that investment in Agriculture is heavily shifted towards software development, with relatively lesser emphasis on Technology & Innovation.\nBased on the above thought, the answer is:\nThe trends indicate that the Agriculture industry receives heavy investment in software development compared to Technology & Innovation. The Software Development sector is allocated about 85% of the total investment, whereas Technology & Innovation is allocated about 15%."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "q71GwVqqcjYbZKBG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry is invested the smallest amount in Tech & Innovation according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the industry with the smallest investment in Technology & Innovation, I need to compare the heights of the {yellow bars}<r>['Healthcare'[0.121, 0.136, 0.228, 0.447]; 'Education'[0.240, 0.136, 0.347, 0.328]; 'Finance'[0.359, 0.136, 0.465, 0.251]; 'Manufacturing'[0.477, 0.136, 0.584, 0.341]; 'Energy'[0.596, 0.136, 0.703, 0.261]; 'Transport'[0.715, 0.136, 0.822, 0.290]; 'Agriculture'[0.833, 0.136, 0.940, 0.218]]</r> one by one. From the observation, the {bar for \"Agriculture\"}<r>[0.833, 0.136, 0.940, 0.218]</r> seems to have the smallest height, indicating the smallest investment in Technology & Innovation.\nBased on the above thought, the answer is:\nAgriculture."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "Rrf9qLO1eJ2OVO3D",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, could you please convert the given data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data provided are summarized in a bar chart, which includes the titles {\"Slow Growth\"}[0.261, 0.927, 0.370, 0.962] and {\"Accelerated Decline\"}[0.660, 0.927, 0.833, 0.962] on the x-axis. Three data sets are given, represented by three vertical bar sets, which include {\"Mobile Internet\"}[0.229, 0.052, 0.361, 0.087], {\"Optical Network\"}[0.444, 0.052, 0.584, 0.087] and {\"Satellite Communication\"}[0.667, 0.052, 0.881, 0.087] according to the legend. The y-axis ranges from {0%}[0.041, 0.889, 0.068, 0.924] to {100%}[0.019, 0.122, 0.068, 0.157]. The height of the bars describes the data for each set under each condition. The precise data can be determined by the intersection of the top of each bar with the y-axis gridlines. For example, the Mobile Internet under Slow Growth, the height of its {bar}[0.121, 0.635, 0.509, 0.903], intersects with the y-gridline at approximately 35%, so its value is 35.\nBased on the above thought, the answer is:\nStage,Mobile Internet,Optical Network,Satellite Communication\nSlow Growth,35,25,40\nAccelerated Decline,70,10,20"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "bSBa1PUFjgBG4Swn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector you think has the most stable development? And explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "Stability can be judged primarily by the smallest fluctuation in development rate from the 'Slow Growth' stage to the 'Accelerated Decline' stage. At the 'Slow Growth' stage, the {Mobile Internet bar}[0.121, 0.635, 0.509, 0.903], {Optical Network bar}[0.121, 0.443, 0.509, 0.635] and the {Satellite Communication bar}[0.121, 0.136, 0.509, 0.443], the heights indicate that the data values are 35, 25, and 40 respectively. While at the 'Accelerated Decline' stage, the heights of the {Mobile Internet bar}[0.552, 0.366, 0.940, 0.903], {Optical Network bar}[0.552, 0.289, 0.940, 0.366], and the {Satellite Communication bar}[0.552, 0.136, 0.940, 0.289] suggest the values are 70, 10, and 20 respectively. Comparing the differences between the two stages for each industry, it is evident that the Optical Network sector has the smallest difference, meaning it has the most stable development.\nBased on the above thought, the answer is:\nThe Optical Network sector. Since the difference (absolute value of the contrast) between the 'Slow Growth' phase and the 'Accelerated Decline' phase is the smallest among all sectors, it can be said that the Optical Network sector has the most stable development."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "JUTZ1aHxrVbfGcx1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category does the 'Accelerated Decline' stage decline the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which category has the most decline in the 'Accelerated Decline' stage, I need to observe the height of the bars under {'Accelerated Decline'}[0.660, 0.927, 0.833, 0.962] for each category. The height of the {Mobile Internet bar}[0.552, 0.366, 0.940, 0.903] is approximately at 70% on the y-axis. The height of the {Optical Network bar}[0.552, 0.289, 0.940, 0.366] is around 10%. And the height of the {Satellite Communication bar}[0.552, 0.136, 0.940, 0.289] is close to 20% on the y-axis. Comparing these heights, it is clear that the Mobile Internet category shows the most rapid decline during the 'Accelerated Decline' stage.\nBased on the above thought, the answer is:\nMobile Internet."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "eStnlWlLtS0ncG84",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows different categories of Innovation Projects on the x-axis and their respective performances on the y-axis. As given by the {x-axis labels}<r>[0.166, 0.499, 0.183, 0.791]</r> and so on, the categories include 'SecurityUpgrade', 'BackendRebuild', 'FrontendRevamp', 'MarketExpansion', 'LegacySystemRetirement', 'PartnershipDeal', and 'CustomerFeedbackSystem'. The y-axis ranges from {0%}<r>[0.041, 0.461, 0.068, 0.496]</r> to {100%}<r>[0.019, 0.037, 0.068, 0.073]</r>. In the {legend}<r>[0.810, 0.069, 0.973, 0.236]</r>, we have three stages: 'RampingUP', 'ScalingUP', and 'CashCow'. The data for each category and each stage can be extracted from the heights of the bars. For example, for the 'SecurityUpgrade' project, the height of the {bar for 'RampingUP'}<r>[0.121, 0.318, 0.228, 0.475]</r> corresponds to 36.96%, the height of the {bar for 'ScalingUP'}<r>[0.121, 0.188, 0.228, 0.318]</r> corresponds to 30.74%, and the height of the {bar for 'CashCow'}<r>[0.121, 0.051, 0.228, 0.188]</r> corresponds to 32.29%. Hence, the CSV table can be generated based on these data.\nBased on the above thought, the answer is:\nProject,RampingUP,ScalingUP,CashCow\nSecurityUpgrade,36.96,30.74,32.29\nBackendRebuild,15.20,56.80,28.00\nFrontendRevamp,25.64,49.25,25.11\nMarketExpansion,32.15,45.34,22.51\nLegacySystemRetirment,38.11,43.89,18.00\nPartnershipDeal,30.93,29.18,39.89\nCustomerFeedbackSystem,40.11,49.89,10.00"
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "18qJ6pgGUXUWhl5K",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which project performed consistently across all three stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "The consistency of a project across all stages can be judged by observing the small variance in the height of the bars for each stage. The first project 'SecurityUpgrade' has bars at the 'RampingUP'<r>[0.121, 0.318, 0.228, 0.475]</r>, 'ScalingUP'<r>[0.121, 0.188, 0.228, 0.318]</r>, and 'CashCow' stages<r>[0.121, 0.051, 0.228, 0.188]</r>. The heights of these bars correspond to 36.96%, 30.74%, and 32.29% respectively. This means there is a moderate fluctuation across the stages. Now, I will similarly analyze the variance in bar heights for all other projects.\nBased on the above thought, the answer is:\nSecurityUpgrade. This project has the least variance in success rate across different stages compared to other projects."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "Kxz7DRc10EBiPnwl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Innovation Project showed the most improvement from the RampingUP stage to the ScalingUP stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height difference in the bars for each project from the 'RampingUP' stage to the 'ScalingUP' stage. In the 'BackendRebuild' project, for example, the height of the {bar at 'RampingUP' stage}<r>[0.240, 0.410, 0.347, 0.475]</r> corresponds to 15.20%, while the height of the {bar at 'ScalingUP' stage}<r>[0.240, 0.170, 0.347, 0.410]</r> corresponds to 56.80%. So, the 'BackendRebuild' project saw an improvement of 41.60% points. Similarly, I will compare heights for all other projects.\nBased on the above thought, the answer is:\nBackendRebuild"
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "eiT10FVrqOTCx324",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the displayed bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {x-ticks labels}<r>[0.188, 0.609, 0.205, 0.898];[0.355, 0.609, 0.372, 0.963];[0.522, 0.609, 0.540, 0.845];[0.689, 0.609, 0.707, 0.933];[0.856, 0.609, 0.874, 0.874]</r>, the x-axis represents different activity growth trends. The y-axis, with its range from {0%}<r>[0.041, 0.571, 0.068, 0.606]</r> to {100%}<r>[0.019, 0.122, 0.068, 0.157]</r>, represents the contributions in percentage. There are three color-coded segments for each activity growth trend, which represent the contributions of three types of aid: {Medical Assistance}<r>[0.206, 0.099, 0.371, 0.134]</r>, {Education Aid}<r>[0.454, 0.099, 0.574, 0.134]</r>, and {Environmental Conservation}<r>[0.657, 0.099, 0.904, 0.134]</r>, as indicated in the legend. To construct the CSV table, I need to calculate the percentage contribution of each type of aid in each activity growth trend. I can use the relative height of each segment in a bar to do this. For example, in the \"Rapid Expansion\" trend, the {Medical Assistance segment}<r>[0.121, 0.270, 0.272, 0.585]</r> extends almost to 70%, the {Education Aid segment}<r>[0.121, 0.177, 0.272, 0.270]</r> reaches approximately to 20%, and the {Environmental Conservation segment}<r>[0.121, 0.136, 0.272, 0.177]</r> extends to about 10%.\nBased on the above thought, the answer is:\nActivity,Medical Assistance,Education Aid,Environmental Conservation\nRapid Expansion,70,20,10\nSubstantial Increase,10,30,60\nRapid Growth,15,70,15\nMarginal Decrease,25,20,55\nVolatile Growth,30,45,25"
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "dgBXDIGglr9sQJqb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which growth trend seems to be the most balanced in term of types of aid distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the height variations in different sectors of each trend. The trend {Volatile Growth}<r>[0.856, 0.609, 0.874, 0.874]</r> seems to have the {Medical Assistance segment}<r>[0.790, 0.444, 0.940, 0.585]</r> running from 0% to 30%, {Education Aid segment}<r>[0.790, 0.250, 0.940, 0.444]</r> from 30% to 75%, and {Environmental Conservation segment}<r>[0.790, 0.136, 0.940, 0.250]</r> from 75% to 100%. The distribution across the three types of aid is the most even among all the trends.\nBased on the above thought, the answer is:\nVolatile Growth. It has a more evenly distributed contribution towards all three types of aid: Medical Assistance, Education Aid, Environmental Conservation as compared to other trends."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "2CzOWRQmzPCmMvX5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend has the largest percentage of Education Aid?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the {Education Aid segment}<r>[0.454, 0.099, 0.574, 0.134]</r> in each trend. The trend named {Rapid Growth}<r>[0.522, 0.609, 0.540, 0.845]</r> has a Education Aid segment ranging from roughly 15% to 85%, meaning it has the greatest height among every trend.\nBased on the above thought, the answer is:\nRapid Growth"
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "n6XucRt1EVrJeANI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarise the data behind the bar chart you're analysing in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the CSV table, I need to extract the values associated with each bar representing an industry sector (x-axis) and the social responsibility measure it signifies (color). As per {the legend}<r>[0.232, 0.054, 0.817, 0.104]</r>, the green bars represent 'Environmental Impact', the gold bars represent 'Social Initiatives', and the blue bars represent 'Profit Sustainability'. I'll use these colors to differentiate between the measures. Then, for each industry, namely {'Technology'}<r>[0.096, 0.759, 0.161, 0.889]</r>, {'E-commerce'}<r>[0.211, 0.759, 0.282, 0.902]</r>, {'Energy'}<r>[0.359, 0.759, 0.403, 0.848]</r>, {'Transportation'}<r>[0.444, 0.759, 0.525, 0.921]</r>, {'Food and Beverage'}<r>[0.541, 0.759, 0.646, 0.970]</r>, {'Financial Services'}<r>[0.670, 0.759, 0.767, 0.954]</r>, and {'Healthcare'}<r>[0.825, 0.759, 0.889, 0.886]</r>, I will identify the corresponding values for 'Environmental Impact', 'Social Initiatives', and 'Profit Sustainability' from the heights of the respective bars.\nBased on the above thought, the answer is:\nCategories,Environmental Impact,Social Initiatives,Profit Sustainability\nTechnology,35,45,20\nE-commerce,40,35,25\nEnergy,50,35,15\nTransportation,35,40,25\nFood and Beverage,40,25,35\nFinancial Services,50,35,15\nHealthcare,60,30,10"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "2zqnRvJCkVQZ5ubI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry is the most oriented towards sustainable business practices and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing each industry's bars, I can see the total height represents the industry's overall commitment to sustainable business practices. This total height is the sum of the values for 'Environmental Impact'<r>{\"Environmental Impact\"}[0.237, 0.068, 0.265, 0.087]</r>, 'Social Initiatives'<r>{\"Social Initiatives\"}[0.458, 0.068, 0.485, 0.087]</r> and 'Profit Sustainability'<r>{\"Profit Sustainability\"}[0.638, 0.068, 0.666, 0.087]</r>. Here all bars reach the same height, indicating that all industries have a 100% commitment to sustainable business practices. However, if we consider the distribution of focus, 'Healthcare'<r>[0.825, 0.759, 0.889, 0.886]</r> has the largest focus on 'Environmental Impact' and lower focus on 'Profit Sustainability', suggesting a strong focus on environmental sustainability over profit.\nBased on the above thought, the answer is:\nAll industries exhibit the same level of total commitment to sustainable business practices. However, 'Healthcare' stands out with its significant focus on 'Environmental Impact', indicating a strong emphasis on environmental sustainability over profit optimization."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "qnAvbryBpwFVwb8Z",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which industry does 'Profit Sustainability' have the smallest percentage share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the industry where 'Profit Sustainability' is the least, I need to look at the {blue bars}<r>{\"Profit Sustainability\"}[0.638, 0.068, 0.666, 0.087]</r> associated with each category. Scanning from left to right, I find 'Profit Sustainability' for 'Technology'<r>[0.106, 0.132, 0.215, 0.253]</r> at 20%, 'E-commerce'<r>[0.227, 0.132, 0.337, 0.284]</r> at 25%, 'Energy'<r>[0.349, 0.132, 0.458, 0.223]</r> at 15%, 'Transportation'<r>[0.470, 0.132, 0.579, 0.284]</r> at 25%, 'Food and Beverage'<r>[0.591, 0.132, 0.701, 0.344]</r> at 35%, 'Financial Services'<r>[0.713, 0.132, 0.822, 0.223]</r> at 15%, and 'Healthcare'<r>[0.834, 0.132, 0.943, 0.192]</r> at 10%. Therefore, 'Healthcare' demonstrates the lowest 'Profit Sustainability'.\nBased on the above thought, the answer is:\nHealthcare"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "1zUx82BZkVvaqaBz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the bar chart you see, gather the required data from the GDP growth, Unemployment rate and Inflation rate and convert it into CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the color code of the chart, there are three types of data represented by different colors according to their corresponding legends. The {green bars}<r>[0.374, 0.67, 0.425, 0.68]</r> signify the GDP growth. The {yellow bars}<r>[0.374, 0.67, 0.425, 0.68]</r> indicate the unemployment rate and the {blue bars}<r>[0.374, 0.67, 0.425, 0.68]</r> show the inflation rate. The years are denoted at the base of these bars {Year1}<r>[0.150, 0.845, 0.167, 0.941]</r>, {Year2}<r>[0.232, 0.845, 0.250, 0.940]</r> and so on. My task is to form a CSV table by recording the heights of the bars which represent the respective percentages in each category and for each year.\nBased on the above thought, the answer is:\nYear,GDP Growth,Unemployment Rate,Inflation Rate\nYear1,4.3,5.6,1.3\nYear2,3.9,6.1,1.6\nYear3,3.7,6.4,1.9\nYear4,3.1,6.9,2.1\nYear5,2.8,7.2,2.4\nYear6,2.3,7.7,2.8\nYear7,1.9,8.3,3.1\nYear8,1.5,8.7,3.5\nYear9,1.1,9.4,3.9\nYear10,0.7,9.9,4.2"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "52gEzh1Mpv2nGyYF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trends can you notice in terms of macroeconomic indicators over the given period of time."
+            },
+            {
+                "from": "gpt",
+                "value": "According to {the green bars}<r>[0.121, 0.791, 0.196, 0.821]</r> representing GDP growth consistently descending, it shows that the economy of the country has slowed down over this period. At the same time, as per the {yellow bars}<r>[0.204, 0.752, 0.278, 0.794]</r> or unemployment rate indicated by yellow color is on the rise which shows that job creation has not been able to keep up with the increase in the labor force. Last but not least, the {blue bars}<r>[0.674, 0.067, 0.708, 0.092]</r> reflecting the inflation rate have been increasing at an accelerated rate which indicates there has been an increase in the overall price levels.\nBased on the above thought, the answer is:\nThe trends observed from the chart indicates a slowing down of the economy as GDP growth is declining, the unemployment rate is increasing and inflation is on the rise."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "pvp92iU4ylQmXauT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year the unemployment rate rose more than 1%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the heights of yellow bars representing the unemployment rate year by year. For example, comparing the height of the yellow bar for {Year1}<r>[0.121, 0.753, 0.196, 0.791]</r> to the height of the yellow bar for {Year2}<r>[[0.204, 0.752, 0.278, 0.794]</r>. Keep doing this until I find the first year where the difference in heights represents a rise of more than 1%.\nBased on the above thought, the answer is:\nYear2."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "g8wYYnXpEIG07Wbv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By scanning the x-axis tick labels, I see that they represent the categories: {Asian Villages}<r>[0.166, 0.555, 0.183, 0.796]</r>, {European Architectures}<r>[0.285, 0.555, 0.302, 0.963]</r>, {African Traditions}<r>[0.403, 0.555, 0.421, 0.856]</r>, {Indigenous Cultures}<r>[0.522, 0.555, 0.540, 0.901]</r>, {Global Expo}<r>[0.641, 0.555, 0.658, 0.763]</r>, {Street Festivals}<r>[0.759, 0.555, 0.777, 0.822]</r>, {World Heritage Sites}<r>[0.878, 0.555, 0.896, 0.912]</r>. The percentages labeled on y-axis tick labels range from {0%}<r>[0.041, 0.517, 0.068, 0.552]</r> to {100%}<r>[0.019, 0.122, 0.068, 0.157]</r>. Through the {legend}<r>[0.322, 0.094, 0.739, 0.156]</r> above the chart, I can find the percentages of {Local Culture}<r>[0.378, 0.108, 0.492, 0.143]</r> and {External Influence}<r>[0.526, 0.111, 0.561, 0.135]</r> in each category. Therefore, to generate the CSV table, I should consider the height of the bars and the percentage on the y-axis. For example, in Asian Villages category, the Local Culture bar<r>[0.121, 0.238, 0.228, 0.531]</r> reaches about the 74 position and the External Influence bar<r>[0.121, 0.136, 0.228, 0.238]</r> starts at about 74 and ends at 100. Thus, it is safe to say that the percentages of Local Culture and External Influence in Asian Villages are 74% and 26% respectively.\nBased on the above thought, the answer is:\ncultural_heritage,local_culture,external_influence\nAsian Villages,74,26\nEuropean Architectures,80,20\nAfrican Traditions,40,60\nIndigenous Cultures,60,40\nGlobal Expo,70,30\nStreet Festivals,65,35\nWorld Heritage Sites,85,15"
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "0GuTmxHcdywYMi7a",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify a trend in the cultural integration across the different types of cultural heritages based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To spot a trend, I need to compare the colored bars across all seven categories of cultural heritages. The {green bars}<r>Local Culture: [0.121, 0.238, 0.228, 0.531; 0.240, 0.215, 0.347, 0.531; 0.359, 0.373, 0.465, 0.531; 0.477, 0.294, 0.584, 0.531; 0.596, 0.254, 0.703, 0.531; 0.715, 0.274, 0.822, 0.531; 0.833, 0.195, 0.940, 0.531]</r> indicate the percentage of local culture, and the {yellow bars}<r>External Influence: [0.121, 0.136, 0.228, 0.238; 0.240, 0.136, 0.347, 0.215; 0.359, 0.136, 0.465, 0.373; 0.477, 0.136, 0.584, 0.294; 0.596, 0.136, 0.703, 0.254; 0.715, 0.136, 0.822, 0.274; 0.833, 0.136, 0.940, 0.195]</r> represent the percentage of external influences. I observe that the global heritages like World Heritage Sites and European Architectures have larger percentages of local culture, suggesting a tendency of more local cultures being preserved in these heritages. On the other hand, the region-specific heritages such as African Traditions and Indigenous Cultures have lesser percentages of local culture but higher percentages of external influences, indicating more exchanges and interactions between different cultures.\nBased on the above thought, the answer is:\nYes, there seems to be a tendency that more global heritages have higher percentages of local culture while more region-specific heritages have higher percentages of external influences, suggesting great culture integrations in these heritages."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "Igb6kdqnarbks0z6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of cultural heritage has the least local culture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the type of cultural heritage with the least local culture, I should analyze the height of the green bars representing the local culture for each type. For instance, the green bar for {Asian Villages}<r>[0.121, 0.238, 0.228, 0.531]</r> reaches almost to the 74% position on the y-axis, indicating that 74% of the culture in Asian Villages is local. Analyzing the other bars in the same manner, I find that the green bar for {World Heritage Sites}<r>[0.833, 0.195, 0.940, 0.531]</r> is the smallest, with its top at the 85% position on the y-axis. This indicates that only 85% of the culture in World Heritage Sites is local.\nBased on the above thought, the answer is:\nWorld Heritage Sites."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "ob4cv94LQoP12XpY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the gig company's contributions regarding environmental\n    protection, charity events and social initiatives into a structured csv\n    table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's arrangement shows that the x-axis represents companies such as {Apple}<r>[0.175, 0.799, 0.192, 0.898]</r>, \n    {Microsoft}<r>[0.314, 0.799, 0.331, 0.957]</r>, {IBM}<r>[0.453, 0.799, 0.470, 0.863]</r>, {Google}<r>[0.591, 0.799, 0.609, 0.921]</r>, {Facebook}<r>[0.730, 0.799, 0.748, 0.963]</r>, and {Amazon}<r>[0.869, 0.799, 0.887, 0.939]</r>\n     while the y-axis indicates the percentage of contributions, ranging from {0%}[0.041, 0.761, 0.068, 0.796] to {100%}[0.019, 0.122, 0.068, 0.157].\n    The different colored bars within each company represent the three different aspects of contribution. According to the {legend}<r>[0.168, 0.057, 0.894, 0.120]</r>,\n    the green color represents {Environment Protection}<r>[0.223, 0.071, 0.427, 0.106]</r>,\n    the yellow represents {Charity Programs}<r>[0.511, 0.071, 0.662, 0.106]</r> and the blue represents {Social Initiatives}<r>[0.745, 0.071, 0.887, 0.106]</r>.\n    Reading from the chart: {Apple}'s in the environmental protection is approximately {60%}<r>[0.121, 0.391, 0.246, 0.774]</r>, \n    charity programs is {30%}<r>[0.121, 0.200, 0.246, 0.391]</r> and social initiatives is {10%}<r>[0.121, 0.136, 0.246, 0.200]</r>. \n    The same method can be applied to make sense of data from the other companies.\nBased on the above thought, the answer is:\nCompanyName,Environment Protection,Charity Programs,Social Initiatives\nApple,60,30,10\nMicrosoft,50,25,25\nIBM,40,40,20\nGoogle,70,20,10\nFacebook,60,25,15\nAmazon,30,50,20"
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "8NMJlN4nUfn5yw27",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which company appears to prioritize environmental protection over other areas? And what can you infer about their social responsibility approach?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the green bars, which represent environmental protection in the {legend}<r>[0.168, 0.057, 0.894, 0.120]</r>, {Google}'s green section <r>[0.538, 0.327, 0.663, 0.774]</r> is visually the tallest. This indicates that Google contributes the most to environmental protection. It filled around 70% of the total bar, far more than the other areas indicated by the yellow section for charity programs <r>[0.538, 0.200, 0.663, 0.327]</r> and blue section for social initiatives <r>[0.538, 0.136, 0.663, 0.200]</r>.\nBased on the above thought, the answer is:\nGoogle appears to prioritize environmental protection over charity programs and social interventions. This could suggest an organization strategy that places a high value on sustainability and environmental stewardship as a part of its social responsibility."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "u2qYqtIeE4ZNIDzu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the lowest involvement in social initiatives according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue sections of the bars represent social initiatives performance, and we need to look at the height of the blue sections for each company. As per the data represented by the bars; {Apple}'s blue section is approximately {10%}<r>[0.121, 0.136, 0.246, 0.200]</r>, {Microsoft}'s blue section is about {25%}<r>[0.260, 0.136, 0.385, 0.295]</r>, {IBM}'s blue section is about {20%}<r>[0.399, 0.136, 0.524, 0.263]</r>, {Google}'s blue section is about {10%}<r>[0.538, 0.136, 0.663, 0.200]</r>, {Facebook}'s blue section is about {15%}<r>[0.677, 0.136, 0.801, 0.231]</r>, and {Amazon}'s blue section is about {20%}<r>[0.815, 0.136, 0.940, 0.263]</r>. From this analysis, Google and Apple both have the lowest involvement in social initiatives, each with a value of 10%.\nBased on the above thought, the answer is:\nGoogle and Apple."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "I5WR1P09VrC5K169",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, can you convert the data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains two categories, represented on the x-axis: {Market Share}<r>[0.306, 0.731, 0.324, 0.963]</r> and {Projection}<r>[0.738, 0.731, 0.755, 0.903]</r>, which correspond to current and future projected market shares of three companies. For each category, three different bars are stacked on top of each other, representing the three companies: {Company A}<r>[0.320, 0.082, 0.421, 0.117]</r>, {Company B}<r>[0.504, 0.082, 0.605, 0.117]</r>, and {Company C}<r>[0.689, 0.082, 0.790, 0.117]</r>. Taking the height of these bars relative to y-axis percentages and considering they are stacked on top of each other, it can give the market share data. The y-axis percentage labels range from {0%}<r>[0.041, 0.693, 0.068, 0.728]</r> at the bottom to {100%}<r>[0.019, 0.122, 0.068, 0.157]</r> at the top. By observing the stacks, the top of the Company A bar for Market Share reaches about 30%, for Company B it reaches around 56%, and for Company C, it reaches 100%. Similarly, for projections, the height of each bar signifies the respective percentages.\nBased on the above thought, the answer is:\nCategories,Company A,Company B,Company C\nMarket Share,30,26,44\nProjection,29,30,41"
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "ZdDz8dTPISfDF0J9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company do you think can potentially dominate the market in the future, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is apparent from comparing {Market Share}<r>[0.306, 0.731, 0.324, 0.963]</r> and {Projection}<r>[0.738, 0.731, 0.755, 0.903]</r> stacks that all three companies \u2013 {Company A}<r>[0.320, 0.082, 0.421, 0.117]</r>, {Company B}<r>[0.504, 0.082, 0.605, 0.117]</r>, and {Company C}<r>[0.689, 0.082, 0.790, 0.117]</r> \u2013 are projecting changes in their market shares. However, it is only {Company B}<r>[0.504, 0.082, 0.605, 0.117]</r> that is expecting an increase in its market share from 26% to 30%, while both {Company A}<r>[0.320, 0.082, 0.421, 0.117]</r> and {Company C}<r>[0.689, 0.082, 0.790, 0.117]</r> predict a decrease. Although Company B will not attain the highest market share, its upward trend might imply that it has a stronger potential for market growth and domination.\nBased on the above thought, the answer is:\nCompany B. While it may not hold the highest share in the projection, it is the only one projecting a market share increase, suggesting a potential for future growth and possible market domination."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "YOWBAMSPuOnZhfjE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho among the three companies will see the biggest alteration in their market share according to the projection scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I have to calculate the difference between each company's current market share and their projection. This can be achieved by comparing the height of the bars in the {Market Share}<r>[0.306, 0.731, 0.324, 0.963]</r> stack and the {Projection}<r>[0.738, 0.731, 0.755, 0.903]</r> stack for each company. For {Company A}<r>[0.320, 0.082, 0.421, 0.117]</r>, the difference is 1%; for {Company B}<r>[0.504, 0.082, 0.605, 0.117]</r>, the difference is 4%, and for {Company C}<r>[0.689, 0.082, 0.790, 0.117]</r>, the difference is 3%. Therefore, the company with the biggest change in market share will be the one with the highest difference.\nBased on the above thought, the answer is:\nCompany B"
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "tt7TSRjSjeC5kPxp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data in CSV format from the stacked bar chart that shows the progression of human civilization across Agriculture, Science, and Industrialization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart layout shows that the x-axis represents different stages of civilization from {the first stage being \"Primitive Stage\"}<r>[0.082, 0.770, 0.167, 0.939]</r> to {the last stage being \"Contemporary Era\"}<r>[0.782, 0.770, 0.882, 0.970]</r>. The y-axis has tick points going from {0%}<r>[0.033, 0.739, 0.055, 0.767]</r> to {max point is 100%}<r>[0.015, 0.121, 0.055, 0.149]</r>. Each stage of civilization has three stacked bars which represent three areas of development: {\"Agriculture\"}<r>[0.357, 0.064, 0.433, 0.092]</r>, {\"Science\"}<r>[0.500, 0.064, 0.553, 0.092]</r>, and {\"Industrialization\"}<r>[0.620, 0.064, 0.732, 0.092]</r>. One must observe the height of each segment in the stacked bars and compare it to the points on the y-axis to convert it into CSV data. For instance, for the \"Primitive Stage\", the green color bar annotated as \"Agriculture\" goes from 0%  up to 20%, the yellow bar \"Science\" starts from 20% and ends at 25%, and the blue bar \"Industrialization\" indicates 0% change with no visible progression from the start point of the yellow bar section.\nBased on the above thought, the answer is:\nCivilization Stage,Agriculture,Science,Industrialization\nPrimitive Stage,20,5,0\nAncient Period,52,15,2\nClassical Period,70,35,5\nMiddle Ages,81,55,31\nModern History,89,83,72\nContemporary Era,90,96,93"
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "Gpn77EPzXrI6XQeB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are some trends you can identify in human civilization progression across Agriculture, Science, and Industrialization as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, it is evident that there are some clear trends in the progression of human civilization across all three dimensions - Agriculture, Science, and Industrialization. As civilization progresses, all three sectors have seen growth, but at different rates and in different stages. Specifically, the section for {\"Agriculture\"}<r>[0.357, 0.064, 0.433, 0.092]</r> shows an early focus and steady growth, suggesting that agriculture was a vital aspect of early civilization and continued to improve over time. As for {\"Science\"}<r>[0.500, 0.064, 0.553, 0.092]</r>, it also exhibits a smooth upward trend, however, its acceleration appears later than Agriculture, indicating that the development of science might have taken off after securing agricultural stability. Lastly, {\"Industrialization\"}<r>[0.620, 0.064, 0.732, 0.092]</r> can be seen as stagnant over the early civilization stages, and only begins to show significant progress noticeably later - particularly booming from the Middle Ages onwards, which aligns with the historical period where industrial revolution took place.\nBased on the above thought, the answer is:\nThe trends suggest an early onset and steady rise in Agriculture, pointing to its importance in human survival and civilization. Science similarly grows but starts accelerating in later stages, particularly from the Classical Period onwards, suggesting a post agricultural stability phase of intellectual and scientific pursuits. Industrialization only ramps up in much later stages, most prominently between the Middle Ages and Modern History, reflecting the historical period where the industrial revolution famously took place."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "nBAYJNWATSpDYkSF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which stage of civilization does Industrialization shows the greatest improvement as compared to the previous stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would first identify the height of the Industrialization section for each civilization stage. Then I would calculate the difference between each stage and the previous one. For example, the difference between the height of the Industrialization section in the Middle Ages {height of the Middle Ages Industrialization bar}<r>[0.535, -0.283, 0.657, -0.091]</r> and the one in Classical Period {height of the Classical Period Industrialization bar}<r>[0.392, 0.070, 0.514, 0.101]</r> is about 26% (31% in the Middle Ages - 5% in the Classical Period). By performing a similar comparison between all the stages, I can identify the stage in which Industrialization shows the greatest leap.\nBased on the above thought, the answer is:\nFrom Middle Ages to Modern History."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "xyow9ZHslseYk51R",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the bar chart appears to represent fitness and sports-related trends. The title {Fitness and Sports Health Trends}[0.355, 0.080, 0.695, 0.125] confirms this. The x-axis represents {the categories}[{\"Physical Activity\"}[0.149, 0.596, 0.167, 0.878]; {\"Strength Training\"}[0.254, 0.596, 0.272, 0.895]; {\"Yoga\"}[0.359, 0.596, 0.377, 0.677]; {\"Meditation\"}[0.464, 0.596, 0.482, 0.781]; {\"Healthy Eating\"}[0.569, 0.596, 0.587, 0.853]; {\"Dietary Supplements\"}[0.674, 0.596, 0.691, 0.963]; {\"Water Intake\"}[0.779, 0.596, 0.796, 0.818]; {\"Treadmill Run\"}[0.884, 0.596, 0.901, 0.831]], while the y-axis represents the corresponding values with a scale seen from the ticks from {0%}[0.030, 0.558, 0.057, 0.593] to {50%}[0.019, 0.122, 0.057, 0.157]. I notice that there are two bars for each category: one color-coded as {green}<r>[handle][0.308, 0.105, 0.343, 0.129]</r> representing Fitness Trends, and another as {blue}<r>[handle][0.514, 0.105, 0.548, 0.129]</r> representing Sports Health Trends as confirmed by the legend {Fitness Trends and Sports Health Trends}[0.301, 0.088, 0.749, 0.150]. By analyzing bar height and using the y-axis scale, I can determine the values for each trend in each category. As such, the bar for {Fitness Trends in Physical Activity}[0.111, 0.354, 0.205, 0.572] is around 25 as per the y-axis scale, and similarly for the other bars.\nBased on the above thought, the answer is:\nActivity,Fitness Trends,Sports Health Trends\nPhysical Activity,25,35\nStrength Training,28,27\nYoga,30,12\nMeditation,21,18\nHealthy Eating,38,24\nDietary Supplements,28,30\nWater Intake,35,28\nTreadmill Run,34,42"
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "10nkz3IANWmV6hcI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you argue which one between fitness and sports health trends is more balanced in terms of diverse activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the two categories - {Fitness Trends}<r>[handle][0.308, 0.105, 0.343, 0.129]</r> and {Sports Health Trends}<r>[handle][0.514, 0.105, 0.548, 0.129]</r> across different activities, it's clear that they display different patterns of popularity. For {Fitness Trends}, the activities - {Physical Activity}<r>[bar][0.111, 0.354, 0.205, 0.572]</r>, {Strength Training}<r>[bar][0.216, 0.327, 0.310, 0.572]</r>, and {Yoga}<r>[bar][0.321, 0.310, 0.415, 0.572]</r>, have similar popularity levels, while {Healthy Eating}<r>[bar][0.531, 0.240, 0.625, 0.572]</r> is significantly more popular. On the other hand, for {Sports Health Trends}, most activities exhibit a relatively balanced popularity, except for {Treadmill Run}<r>[bar][0.845, -0.091, 0.940, 0.275]</r>, which significantly stands out. Therefore, by comparing the spread of popularity across diverse activities between both trends, it can be concluded that Sports Health Trends has a more balanced spread of popularity across different activities.\nBased on the above thought, the answer is:\nSports Health Trends. The popularity of different activities is more balanced in Sports Health Trends."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "fakCtsOJRc61tur4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has the largest difference in popularity between Fitness Trends and Sports Health Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the bar heights between fitness and sports health trend for each category can answer this. Let's begin from the left side: the first pair of bars are for {Physical Activity}[0.111, 0.354, 0.205, 0.572] and [0.111, 0.048, 0.205, 0.354], and the difference between these bars is 10. Moving rightwards, the next pair of bars are for {Strength Training}[0.216, 0.327, 0.310, 0.572] and [0.216, 0.092, 0.310, 0.327], the difference here is 1. I will continue this analysis from {Yoga} through to {Treadmill Run}. On analysis, I see that the category with the largest difference in popularity between Fitness Trends and Sports Health Trends is found in {Yoga}[0.321, 0.310, 0.415, 0.572] and [0.321, 0.205, 0.415, 0.310], where the popularity ratings are starkly different, at 30 and 12 respectively, the difference adds up to be 18, which is the largest amongst all categories.\nBased on the above thought, the answer is:\nYoga."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "oG5teR8UbTYeC4wE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the stack bars you see into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the color segmentation in the bars, I can tell there are three different fitness activities - Cardio Exercise, Weight Lifting, and Yoga. Each fitness activity is represented by a different color in the stack. For example, {green part}[0.106, 0.800, 0.234, 0.935] of the bar represents Cardio Exercise.  We can extract the values by examining the length (height) of each colored segment on the {y-scale}<r>[0.033, 0.926, 0.055, 0.950]</r>, <r>[0.024, 0.746, 0.055, 0.769]</r>, <r>[0.024, 0.566, 0.055, 0.589]</r>, <r>[0.024, 0.386, 0.055, 0.409]</r> <r>[0.024, 0.205, 0.055, 0.229]</r> and <r>[0.015, 0.025, 0.055, 0.048]</r>.  Each segment represents a \"block\" of fitness activity participation for that year. From the {legend}<r>[0.821, 0.046, 0.978, 0.157]</r>, the green bar corresponds to cardio exercise, the orange bar corresponds to weight lifting and the blue bar corresponds to yoga. Going through the chart, slicing up each vertically stacked bar into their colored segments and extracting the lengths onto their corresponding activities will produce the CSV table.\nBased on the above thought, the answer is:\nYear, Cardio Exercise, Weight Lifting, Yoga\n2010, 15, 25, 60\n2011, 20, 30, 50\n2012, 25, 35, 40\n2013, 30, 40, 30\n2014, 35, 45, 20\n2015, 40, 50, 10"
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "pGDrbdZ4JaTfjraW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the chart, what could we predict for the fitness preferences in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seeing as Cardio Exercise, represented by the {green}<r>[0.106, 0.800, 0.234, 0.935]</r> bars, and Weight Lifting, represented by the {orange}<r>[0.106, 0.575, 0.234, 0.800]</r> bars, have consistently increased over the years from 2010 to 2015, we can predict that their participation rates will continue to increase into 2016. Conversely, participation in Yoga, represented by the {blue}<r>[0.106, 0.034, 0.234, 0.575]</r> bars, has been decreasing consistently from 2010 to 2015. If these trends continue into 2016, we anticipate that the participation rate for Yoga will continue to decrease.\nBased on the above thought, the answer is:\nWe can predict that participation in Cardio Exercise and Weight Lifting will increase, while participation in Yoga will decrease in 2016."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "RqcDb0RPaWGITNMn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the most significant reduction in Yoga participation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the heights of the {blue bars}<r>[0.106, 0.034, 0.234, 0.575]</r>, <r>[0.248, 0.034, 0.376, 0.485]</r>, <r>[0.390, 0.034, 0.518, 0.395]</r>, <r>[0.532, 0.034, 0.659, 0.305]</r>, <r>[0.674, 0.034, 0.801, 0.214]</r>, <r>[0.815, 0.034, 0.943, 0.124]</r> that represent Yoga participation from year to year, we can calculate the difference between adjacent years. For example, the length of the blue bar in 2011 <r>[0.248, 0.034, 0.376, 0.485]</r> is smaller than that in 2010 <r>[0.106, 0.034, 0.234, 0.575]</r>, indicating a decrease in participation. Comparing these reductions year by year, it's clear that the most significant difference happened between 2012 and 2013.\nBased on the above thought, the answer is:\n2013."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "cDVxzXGKNU61onIM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the percentages of effort mentioned in the chart in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the chart data into a CSV table, I first identify the x-axis labels as the categories, which include {Environment}[0.207, 0.632, 0.224, 0.853], {Work Policies}[0.417, 0.632, 0.435, 0.859], {Education}[0.627, 0.632, 0.645, 0.805], and {Affordable Housing}[0.837, 0.632, 0.855, 0.963].\nNext, I see that each category has three bars representing {Social Awareness}[0.187, 0.099, 0.222, 0.124], {Sustainable Production}[0.420, 0.099, 0.455, 0.124], and {Human Rights}[0.703, 0.099, 0.738, 0.124] based on the legend. The heights of these bars can direct me to the corresponding y-coordinate percentages. Using this method, I can determine the percentages of effort for each category and compile them into CSV format.\nBased on the above thought, the answer is:\nCategory,Social Awareness,Sustainable Production,Human Rights\nEnvironment,43,23,34\nWork Policies,52,32,16\nEducation,3,55,42\nAffordable Housing,38,20,42"
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "DM6rFLLglBU5nKhT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social responsibility category should companies focus more on for sustainable production and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it can be seen that sustainable production is denoted by the {second (yellow) bar}[0.420, 0.099, 0.455, 0.124] in each category. Checking each bar for {Environment}[0.121, 0.296, 0.310, 0.404], {Work Policies}[0.331, 0.211, 0.520, 0.362], {Education}[0.541, 0.334, 0.730, 0.593], and {Affordable Housing}[0.751, 0.334, 0.940, 0.428], I notice that for Education and Affordable Housing, the Sustainable Production bars are placed considerably higher, indicating a larger percentage focus on this factor. This is because sustainable production in these categories can lead to a significant positive impact on society, including benefits such as more affordable housing and better educational opportunities. Therefore, companies should focus more on Sustainable Production in the categories of Education and Affordable Housing.\nBased on the above thought, the answer is:\nEducation and Affordable Housing"
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "Fe2hx7QDJeas9GYg",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which field of corporate responsibility, companies are focusing less on Social Awareness?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the least amount of focus on social awareness, I need to compare the heights of the {Social Awareness bars}[0.121, 0.404, 0.310, 0.607] in each category from left to right. The heights of Social Awareness bars corresponding to the categories of {Environment}[0.207, 0.632, 0.224, 0.853], {Work Policies}[0.417, 0.632, 0.435, 0.859], {Education}[0.627, 0.632, 0.645, 0.805], and {Affordable Housing}[0.837, 0.632, 0.855, 0.963] are approximately 43%, 52%, 3% and 38% respectively, as calculated from the y-axis. Thus, companies are focusing the least on social awareness in the category of Education.\nBased on the above thought, the answer is:\nEducation"
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "UNGZ07w2LR3KH0Ap",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn reference to the bar chart you examined, could you reproduce the data in a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "After examining the diagram, I observe that there are two distinct groups of bars. These groups are labeled as {Community Development}<r>[0.336, 0.072, 0.558, 0.107]</r> and {Urban Planning}<r>[0.642, 0.072, 0.775, 0.107]</r> as per the legend. The height of the bars represents the data values. Checking the y-axis, the range of values seems to go from 0 to 100, with 20% intervals demarcated by {grid lines}<r>[0.080, 0.769], [0.080, 0.642], [0.080, 0.516], [0.080, 0.389], [0.080, 0.262], [0.080, 0.136]</r>. To construct the CSV, the height of each bar in both groups for each instance (from Instance 1 to Instance 7) needs to be evaluated in relation to these y-axis intervals. The Community Development and Urban Planning values under each instance are determined by matching the top edge of the respective bars with the y-ticks grid lines. For example, the \"Instance 1\" values are obtained from the height of the {Community Development}<r>[0.121, 0.452, 0.228, 0.769]</r> and {Urban Planning}<r>[0.121, 0.098, 0.228, 0.452]</r> bars, both appear to correspond to approximately 50 and 56 respectively.\nBased on the above thought, the answer is:\nInstance,Community Development,Urban Planning\nInstance1,50,56\nInstance2,67,70\nInstance3,58,80\nInstance4,34,40\nInstance5,76,68\nInstance6,59,61\nInstance7,70,65"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "7e6jwR2QGndRn6Mg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights does the graph provide regarding the trends followed in the allocation of funds for community development and urban planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "Studying the height of the {Community Development bars}<r>[0.121, 0.452, 0.228, 0.769];[0.240, 0.345, 0.347, 0.769];[0.359, 0.402, 0.465, 0.769];[0.477, 0.554, 0.584, 0.769];[0.596, 0.288, 0.703, 0.769];[0.715, 0.395, 0.822, 0.769];[0.833, 0.326, 0.940, 0.769]</r> and the {Urban Planning bars}<r>[0.121, 0.098, 0.228, 0.452]; [0.240, -0.099, 0.347, 0.345]; [0.359, -0.105, 0.465, 0.402]; [0.477, 0.300, 0.584, 0.554]; [0.596, -0.143, 0.703, 0.288]; [0.715, 0.009, 0.822, 0.395]; [0.833, -0.086, 0.940, 0.326]</r>, there seems to be a general trend of slow increase in community development, despite a few dips and surges. This shows that the funds allocated to meet community development needs vary from instance to instance. In contrast, urban planning shows a fluctuating decline with a sudden surge in 'Instance4', indicating a significant unexpected influx of funds, possibly due to an emergent development initiative. It's interesting to note that the funding for urban planning is relatively volatile compared to community development.\nBased on the above thought, the answer is:\nThe funding for community development follows a slow and steady rise, with occasional dips and surges, indicating a consistent interest and investment in community development initiatives. Urban planning, however, shows a general downward trend with sudden surges, pointing to variability and reactive funding based on need or opportunity, rather than a steady investment as in community development. This could also indicate changing strategies and focus in city planning."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "KbrQEcYbGtuiEsXy",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of funding, which initiative, Community Development or Urban Planning, attracted the most funds across all instances?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which initiative garnered the most funds, I will have to evaluate the bars corresponding to each initiative in every instance. For example, in 'Instance1', the {Community Development bar}<r>[0.121, 0.452, 0.228, 0.769]</r> is higher than the {Urban Planning bar}<r>[0.121, 0.098, 0.228, 0.452]</r> indicating that Community Development was preferred in this instance. Similar comparisons can be made for all instances. However, to get a total estimate, the heights of all bars for that initiative should be added up, i.e. summing up all the values of Community Development and Urban Planning. The initiative with the larger sum is the one that attracted the most funding.\nBased on the above thought, the answer is:\nCommunity Development"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "HQ7wvOaVGV0YaGRE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given bar chart data, can you create a table showing the percentage of each media influence source for each category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the chart represents three different sources of media influence for \"News Coverage\", \"Social Media Influence\", \"Campaign promotions\", and \"Political debates\". The influence of each source is expressed as green, yellow, and blue bars. The green bars standing for {Traditional Media}<r>[0.292, 0.105, 0.441, 0.140]</r>, yellow for {New Media}<r>[0.524, 0.105, 0.620, 0.140]</r>, and blue for {Public Events}<r>[0.703, 0.105, 0.818, 0.140]</r>. We can determine the contribution of each influence source for a particular category by looking at the height of each colored bar segment within the total bar for that category. For example, in the \"News Coverage\" category, the green bar takes up about 55%, the yellow about 35%, and the blue about 10%. I can repeat this process to extract data for each category and represent it in a CSV format table.\nBased on the above thought, the answer is:\nCategories,Traditional Media,New Media,Public Events\nNews Coverage,55,35,10\nSocial Media Influence,20,70,10\nCampaign promotions,30,55,15\nPolitical debates,50,20,30"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "P4f1PxzvA4wzZkGa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does traditional media have the most influence on?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, each category's bars' height that is colored green represents the influence of {Traditional Media}<r>[0.292, 0.105, 0.441, 0.140]</r> to a particular category, which includes four categories: \"News Coverage\", \"Social Media Influence\", \"Campaign promotions\", and \"Political debates\". To find out which category is most influenced by Traditional Media, I need to compare the height of the green bars in each category. From the chart, it can be seen that the green bar for {the \"News Coverage\" category}<r>[0.121, 0.322, 0.310, 0.549]</r> and {the \"Political debates\" category}<r>[0.751, 0.342, 0.940, 0.549]</r> are the tallest compared to the other two categories. The height of the bars indicates that Traditional Media influences both \"News Coverage\" and \"Political debates\" to 50%. As such, Traditional Media has the most significant influence on both the \"News Coverage\" and \"Political debates\" categories.\nBased on the above thought, the answer is:\nNews Coverage and Political debates."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "UxWirH5yi1GZ16J6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich source has the least influence on 'Campaign promotions' according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart, the 'Campaign promotions' category is represented by the third group of bars from the left with color details shown in {the legend}<r>[0.236, 0.091, 0.825, 0.154]</r>. Looking at the height of each color in the bar, it can be seen that the smallest segment is the blue one, {Public Events}<r>[0.541, 0.136, 0.730, 0.198]</r>, which is around 15%, is less than the other two sources in this category.\nBased on the above thought, the answer is:\nPublic Events."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "NWritbvaXbx9okhB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data you see on the chart into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a bar chart showing three business processes - 'Call Resolution', 'Meeting Efficiency', and 'Budget Usage' for the years 2021 and 2022. The x-axis includes three categories marked as 'Call Resolution'[0.187, 0.919, 0.315, 0.942], 'Meeting Efficiency'[0.465, 0.919, 0.625, 0.942], and 'Budget Usage'[0.778, 0.919, 0.900, 0.942]. The y-axis has label 'Improvement (%)'[0.019, 0.367, 0.036, 0.570] and the values range below zero as these are improvements and hence can be negative. The legend contains two time periods'2021'[0.940, 0.021, 0.984, 0.044] and '2022'[0.940, 0.056, 0.984, 0.079]. Each category has two bars corresponding to these years. The data for each bar was found on it, like for instance the 'Call Resolution' for '2021'[0.148, 0.034, 0.251, 0.182] is '-5.12', and for '2022'[0.251, 0.034, 0.354, 0.220] is '-6.43'. Similar readings can be taken for 'Meeting Efficiency' and 'Budget Usage'.\nBased on the above thought, the answer is:\nProcesses,2021,2022\nCall Resolution,-5.12,-6.43\nMeeting Efficiency,-8.43,-12.12\nBudget Usage,-17.8,-23.2"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "E3prxoNZT4sf5FAD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what conclusions can you draw about the company's business processes from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, it is evident that there has been a continual improvement in all three business processes from 2021 to 2022. This can be seen through the comparison of bars for 2021 and 2022 in 'Call Resolution'[from '2021'[0.148, 0.034, 0.251, 0.182] to '2022'[0.251, 0.034, 0.354, 0.220]], 'Meeting Efficiency'[from '2021'[0.442, 0.034, 0.545, 0.278] to '2022'[0.545, 0.034, 0.648, 0.385]], and 'Budget Usage'[from '2021'[0.736, 0.034, 0.839, 0.550] to '2022'[0.839, 0.034, 0.942, 0.706]]. Most importantly, the 'Budget Usage' process stands out with a significant increase in improvement, showing an extreme decrease in budget usage from 2021 to 2022. This means that spending has become much more efficient, which might be a result of cost-saving measures or more efficient management. However, all three processes could still see further improvement, as improvement figures remain negative.\nBased on the above thought, the answer is:\nThe company's business processes have shown improvement from 2021 to 2022 with a significant decrease in budget usage. Despite the gains made, there is still room for further improvement as all the improvements are expressed in negative percentages."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "YLWI7q1FanPbnYOl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process saw the largest percentage improvement from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides data for three different process categories which are 'Call Resolution', 'Meeting Efficiency', and 'Budget Usage'. The improvement for each process can be calculated by subtracting the 2021 value from the 2022 value. From comparing the heights of the bars for each process for both the years example; 'Call Resolution' for '2021'[0.148, 0.034, 0.251, 0.182] and '2022'[0.251, 0.034, 0.354, 0.220], 'Meeting Efficiency' for '2021'[0.442, 0.034, 0.545, 0.278] and '2022'[0.545, 0.034, 0.648, 0.385], and 'Budget Usage' for '2021'[0.736, 0.034, 0.839, 0.550] and '2022'[0.839, 0.034, 0.942, 0.706], it can be noticed that the 'Budget Usage' process experienced the largest percentage improvement from 2021 to 2022.\nBased on the above thought, the answer is:\nBudget Usage."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "ndWJ7orzAWbFQj4I",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data displayed in the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. According to the bar chart presented, there are two trends identified on the x-axis, \"Steep increase\" and \"Inexorable decline\", with the label identified at the coordinates {xlabel}[0.472, 0.952, 0.528, 0.975]. These trends have three investment strategies associated with them namely, \"IPO\", \"RPI\" and \"Dividend\", indicated by different colored bars according to {the legend}[0.752, 0.777, 0.891, 0.888]. Now, observing the bars, the \"Steep increase\" trend has 30 for \"IPO\"{bar for Steep increase IPO}[0.126, 0.642, 0.233, 0.903] , 47 for \"RPI\"{bar for Steep increase RPI}[0.233, 0.495, 0.340, 0.903], and 50 for \"Dividend\"{bar for Steep increase Dividend}[0.340, 0.469, 0.447, 0.903]. Similarly, the values for the \"Inexorable decline\" trend are 90 for \"IPO\"{bar for Inexorable decline IPO}[0.553, 0.121, 0.660, 0.903], 70 for \"RPI\"{bar for Inexorable decline RPI}[0.660, 0.295, 0.767, 0.903], and 60 for \"Dividend\"{bar for Inexorable decline Dividend}[0.767, 0.382, 0.874, 0.903]. By compiling these, we can convert the bar chart data into a CSV format.\nBased on the above thought, the answer is:\nTrends,IPO,RPI,Dividend\nSteep increase,30,47,50\nInexorable decline,90,70,60"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "clXX9D9XsKqFbC4r",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed trends, for which investment strategy was the decline in value most significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I will ascertain the difference between the values of each investment strategy for the two trends. For the \"IPO\" strategy, the values are displayed by the bars at {Steep increase IPO bar}[0.126, 0.642, 0.233, 0.903] and {Inexorable decline IPO bar}[0.553, 0.121, 0.660, 0.903], and the difference is 60. For the \"RPI\" strategy, the values are displayed at {Steep increase RPI bar}[0.233, 0.495, 0.340, 0.903] and {Inexorable decline RPI bar}[0.660, 0.295, 0.767, 0.903], and the difference is 23. Lastly, for the \"Dividend\" strategy, the values are shown at {Steep increase Dividend bar}[0.340, 0.469, 0.447, 0.903] and {Inexorable decline Dividend bar}[0.767, 0.382, 0.874, 0.903], and the difference is 10. Comparing these differences, it can be inferred that the decline was most significant for the \"IPO\" investment strategy.\nBased on the above thought, the answer is:\nThe decline in value was the most significant for \"IPO\"."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "h25oezXpiA0M6ijZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment strategy has the least value in the \"Steep increase\" trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will examine the three bars representing the different investment strategies during the \"Steep increase\" trend. The \"IPO\" value is indicated by the bar with coordinates {IPO bar}[0.126, 0.642, 0.233, 0.903], and it is 30. The \"RPI\" value is symbolised by the bar with coordinates {RPI bar}[0.233, 0.495, 0.340, 0.903], and it is 47. Lastly, the \"Dividend\" value is represented by the bar with coordinates {Dividend bar}[0.340, 0.469, 0.447, 0.903], and it is 50. By comparing the heights of these three bars, it can be deduced that the least value belongs to \"IPO\".\nBased on the above thought, the answer is:\nIPO"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "vwZbZFjkuVP77R8E",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table, I need to extract the values distribution of 'Home Amenities', 'Mortgage Payment Percentage', and 'Food Shopping Average' across 'SEQ', 'REPLAY', and 'MTL'. Looking at the chart, the {legend shows}[0.561, 0.212, 0.891, 0.323] three colored groups each representing one of the categories 'Home Amenities', 'Mortgage Payment Percentage', or 'Food Shopping Average'. From the x-axis labels we can see the categories 'SEQ', 'REPLAY', 'MTL', and from the y-axis labels we can understand the percent of income spent for the first two categories, and an average monthly spend for the third category. I will obtain the values for each house condition category by examining the height of the bars and the numbers annotated to the top of each bar. For example, the height of {the bar for 'Home Amenities' in 'SEQ'}[0.131, 0.512, 0.197, 1.772] is annotated with the number {43.50}[0.139, 0.482, 0.189, 0.505]\nBased on the above thought, the answer is:\nFamily,Home_Amenities,Mortgage_Payment_Percentage,Food_Shopping_Average\nSEQ,43.5,35.8,-7.23\nREPLAY,53.2,45.7,-6.41\nMTL,58.9,49.1,4.35"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "uVFuT5H03cQOleqW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lifestyle seems to be the most economically sustainable according to the chart, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To define the economic sustainability of the lifestyles, it's key to analyze the distribution of expenses. {The blue bars} representing 'Home Amenities', {the orange bars} representing 'Mortgage Payment Percentage', and {the red bars} representing 'Food Shopping Average' expenses for each lifestyle should be considered. The 'REPLAY' lifestyle shows balanced expenses across all three categories, with the values mapped to bars located at {Home_Amenities_REPLAY}[0.398, 0.231, 0.465, 1.772], {Mortgage_Percentage_REPLAY}[0.465, 0.448, 0.532, 1.772], and {Food_Average_REPLAY}[0.532, 0.344, 0.598, 0.742], respectively. Although its 'Home Amenities' and 'Mortgage Payment Percentage' expenses are not the lowest compared to 'SEQ' and 'MTL', 'REPLAY' shows a smaller Food Shopping Average making it more balanced overall.\nBased on the above thought, the answer is:\nAccording to the chart, the most economically sustainable lifestyle appears to be REPLAY, as it displays a balanced spread of expenses across home amenities, mortgage payment percentage, and food shopping average."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "EIyw6KRUiyFa3zSm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which lifestyle, the percentage of income spent on home amenities is the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the lifestyle in which the highest percentage of income is spent on home amenities, I need to examine the height of {the blue bars} representing 'Home Amenities' for each lifestyle type. Bars located at {SEQ}[0.131, 0.512, 0.197, 1.772], {REPLAY}[0.398, 0.231, 0.465, 1.772], and {MTL}[0.665, 0.066, 0.732, 1.772] have heights corresponding to values '43.5', '53.2', '58.9', respectively. The highest value among these is 58.9, corresponding to the MTL lifestyle.\nBased on the above thought, the answer is:\nMTL"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "VbYLqKEHyjojhgiE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the varying trends in the Banking, Energy, Technology and Industrial sectors in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I can create the CSV table based on the bars for each sector and each trend. The x-axis represents the sectors, which are the categories: {Banking Sector}<r>[0.148, 0.127, 0.257, 0.153]</r>, {Energy Sector}<r>[0.318, 0.127, 0.424, 0.153]</r>, {Technology Sector}<r>[0.460, 0.127, 0.577, 0.153]</r>, {Industrial Sector}<r>[0.617, 0.127, 0.735, 0.153]</r>. The y-axis values represents the percentage change for each trend. Each sector has five bars indicating five different trends subjected to the four areas of market sectors. From {Subdued expansion}<r>[0.161, 0.225, 0.264, 0.970]</r> to {Slow recovery}<r>[0.723, 0.225, 0.786, 0.970]</r> trends, extracting individual estimates for each sector will help to tabulate the raw data.\nBased on the above thought, the answer is:\nSector,Subdued Expansion(In %),Inconsistent decline(In %), Significant decrease(In %), Abrupt increase(In %), Slow recovery(In %)\nBanking Sector,-0.4,-0.2,-5.5,1.7,0.2\nEnergy Sector,-0.6,-1.3,-4.2,0.8,0.1\nTechnology Sector,-0.3,-0.8,-3.1,1.1,0.1\nIndustrial Sector,-0.7,-1.5,-4.8,1.0,0.3"
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "P22yriUmVv98aLIT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector shows the least slowdown and quickest recovery?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the least slowdown, this involves {subdued expansion}<r>[0.153, 0.906, 0.226, 0.930]</r>, {Inconsistent decline}<r>[0.238, 0.906, 0.325, 0.930]</r>, and {Significant decrease}<r>[0.298, 0.906, 0.393, 0.930]</r> trends, we need to evaluate the lengths of {Banking Sector's first three bars}<r>[0.151, 0.457, 0.296, 0.894]</r>, {Energy Sector's first three bars}<r>[0.322, 0.424, 0.469, 0.894]</r>, {Technology Sector's first three bars}<r>[0.468, 0.428, 0.615, 0.894]</r> and {Industrial Sector's first three bars}<r>[0.620, 0.409, 0.766, 0.894]</r>. Visually, it seems the {Technology Sector's bars}<r>[0.468, 0.428, 0.615, 0.894]</r> appear to be least among all sectors. \n\nFor a quick recovery, it combines {Abrupt increase}<r>[0.433, 0.906, 0.505, 0.930]</r> and {Slow recovery}<r>[0.550, 0.906, 0.623, 0.930]</r>. From the plot, it is evident that {Banking sector's last two bars}<r>[0.298, 0.457, 0.443, 0.894]</r>, {Energy sector's last two bars}<r>[0.470, 0.424, 0.615, 0.894]</r>, {Technology sector's last two bars}<r>[0.616, 0.428, 0.761, 0.894]</r> and {Industrial sector's last two bars}<r>[0.768, 0.409, 0.914, 0.894]</r>, where the {Technology sector's bars}<r>[0.616, 0.428, 0.761, 0.894]</r> still seem to be the largest. Hence, the Technology sector fulfilled both conditions.\nBased on the above thought, the answer is:\nThe Technology Sector. It underwent a relatively minor slowdown during the negative trends and displayed the quickest recovery in positive trends."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "Yz5MHjHZSK1tsNow",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector experienced the biggest negative change during the \"Significant Decrease\" trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Significant Decrease}<r>[0.298, 0.906, 0.393, 0.930]</r> is the third trend. For each sector, I need to compare the value of the third bar. The {Banking sector}<r>[0.237, 0.645, 0.307, 0.894]</r>, {Energy Sector}<r>[0.405, 0.614, 0.476, 0.894]</r>, {Technology Sector}<r>[0.573, 0.601, 0.644, 0.894]</r>, and {Industrial Sector}<r>[0.740, 0.622, 0.811, 0.894]</r> has the bars corresponding to the trend. Visually comparing the heights of these bars, we can see the bar for {Industrial Sector}<r>[0.742, 0.622, 0.791, 0.894]</r> is the longest, leading to conclude that the Industrial Sector experienced the largest negative change.\nBased on the above thought, the answer is:\nIndustrial Sector"
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "e3T3ei8mVnxlzNOG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the bar chart data in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly. The chart has two categories namely {City}<r>[0.279, 0.919, 0.314, 0.942]</r> and {Country}<r>[0.669, 0.919, 0.738, 0.942]</r>. In each category, three attributes are reported: Economic Growth, Population Change, and Land Use Change. The values for each attribute can be obtained from the height of the respective bars and their annotations. For example, the bar for Economic Growth in the City category is located at [0.144, 0.469, 0.246, 0.729] and has an annotation showing its value as {1.5}<r>[0.181, 0.462, 0.209, 0.485]</r>. I will follow the same method to extract all the values.\nBased on the above thought, the answer is:\nCategories,Economic Growth,Population Change,Land Use Change\nCity,1.5,-0.8,0.3\nCountry,3.2,-0.2,1.0"
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "i3Bg34V3VKhbJRtL",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to this chart, what is the trend in population change for both cities and countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this query, I need to observe the second set of bars (Population Change) in both the city and country categories. For the city category, the bar representing population change is located at {the bar}[0.246, 0.729, 0.347, 0.868]. The text annotation present indicate it to be {-0.8}<r>[0.280, 0.861, 0.313, 0.885]</r>. And, for the country category, the population change bar can be found at {the bar}[0.653, 0.729, 0.754, 0.764] and is indicated to be {-0.2}<r>[0.687, 0.757, 0.720, 0.780]</r>.\nBased on the above thought, the answer is:\nBased on the chart, both cities and countries display a negative population change. However, the decline in population is significantly more severe in cities (-0.8) as compared to countries (-0.2)."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "frMbO7GdbGZlnL2r",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category, City or Country, has witnessed higher economic growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the first bar in both categories. The height of the Economic Growth bar for the City category can be found at {the bar}<r>[0.144, 0.469, 0.246, 0.729]</r> and is indicated to be {1.5}<r>[0.181, 0.462, 0.209, 0.485]</r>. On the other hand, the corresponding bar for the Country category is located at {the bar}[0.551, 0.173, 0.653, 0.729] and has a value of {3.2}<r>[0.588, 0.166, 0.615, 0.190]</r>. Thus, by comparing these two values, it is seen that economic growth is higher in the Country category.\nBased on the above thought, the answer is:\nCountry"
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "R6l5MTCetXwxMNVH",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTurn the data shown in the bar chart into a structured CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart describes three categories on the x-axis namely {Book Publishing}<r>[0.159, 0.919, 0.296, 0.942]</r>, {Art Exhibitions}<r>[0.429, 0.919, 0.555, 0.942]</r> and {Opera Performances}<r>[0.667, 0.919, 0.845, 0.942]</r>. The bars for each category represent three different rates: Probing Growth Rate, Observed Growth Rate and Expanding Rate. I can confirm the corresponding value for each rate from the length of the bar and the annotations above the bar. For instance, {the bar for Probing Growth Rate for Book Publishing}<r>[0.128, 0.534, 0.194, 0.555]</r> is associated with a value annotation of 1.22<r>[0.142, 0.504, 0.181, 0.527]</r>. Therefore, the value of probing growth rate for book publishing is 1.22. Using this methodology, I can extract the values for all the categories and rates.\nBased on the above thought, the answer is:\nCategory, Probing Growth Rate, Observed Growth Rate, Expanding Rate\nBook Publishing, 1.22, 12.30, 0.16\nArt Exhibitions, 3.90, 25.90, 0.43\nOpera Performances, -2.13, -15.67, -0.11"
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "C07daaK9uYls3xut",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the perspective of cultural and artistic industry development, which category is theoretically expected to have future growth potential based on its expansion rate, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the potential growth of a category, we can refer to the {expanding rate}<r>[0.687, 0.856, 0.825, 0.879]</r>. If the expanding rate is positive, it may indicate future growth potential. I see that the bar representing the expanding rate of {Book Publishing}<r>[0.260, 0.456, 0.327, 0.655]</r> and {Art Exhibitions}<r>[0.525, 0.121, 0.591, 0.655]</r> are above the x-axis, meaning their expanding rates are positive. However, {Art Exhibitions}<r>[0.429, 0.919, 0.555, 0.942]</r> has a bar with much greater height which means a larger expanding rate, which means it's expanding at a faster pace compared to Book Publishing.\nBased on the above thought, the answer is:\nArt Exhibitions. Because it has a higher expanding rate compared to the other categories which suggests it is growing rapidly. This rapid growth indicates promising future growth potential."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "vQLKoEOttTwDgC2r",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category does the observed growth rate exceed the probing growth rate the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category has the maximum difference between observed growth rate and probing growth rate, I need to compare the height difference between {probing growth rate}<r>[0.687, 0.786, 0.867, 0.809]</r> and {observed growth rate}<r>[0.687, 0.821, 0.884, 0.844]</r> for each category. The difference can be determined by subtracting the value of the probing growth rate from the value of the observed growth rate, for all categories. After calculating all the differences, the category with the highest positive difference has the maximum difference between the observed growth rate and the probing growth rate. I can see that the category {Art Exhibitions}<r>[0.429, 0.919, 0.555, 0.942]</r> has a considerable height difference between {observed growth rate}<r>[0.459, 0.105, 0.525, 0.555]</r> and {probing growth rate}<r>[0.393, 0.488, 0.459, 0.555]</r> with respective values of 25.90 and 3.90. This difference of 22 is the largest among the three categories.\nBased on the above thought, the answer is:\nArt Exhibitions"
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "VJXdSoso3cI6ErnO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, please format this information into a structured table for better visualization."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, we can distinguish different categories from the X-axis labels that display the names of the sectors: \"Advanced Manufacturing\", \"Biotechnology\", and \"IT Services\". These labels can be seen at the coordinates {AdvancedManufacturing}<r>[0.138, 0.919, 0.350, 0.942]</r>, {Biotechnology}<r>[0.446, 0.919, 0.570, 0.942]</r>, and {ITServices}<r>[0.728, 0.919, 0.817, 0.942]</r>. While Y-axes at the right and left of the plot represent the different datasets of each category. Using the legend at the coordinates {Legend}<r>[0.597, 0.777, 0.891, 0.888]</r>, I can identify the three different sets of data. They are: \"Intangible Asset Turnover\", \"Human Capital Index 2022\" and \"TechInvestmentIndex\". The values of these datasets can be determined by the height of the bars in each group and the annotations at the top of each bar.\nBased on the above thought, the answer is:\nCategories,Intangible Asset Turnover,Human Capital Index 2022,Tech Investment Index \nAdvanced Manufacturing, 0.67,93,-8.2\nBiotechnology,0.22,152,-9.1\nIT Services,1.14,62,0.33"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "huRNfCVfoD0BaTWl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which sector appears to be the most promising in terms of current trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify the most promising sector, we need to compare the datasets across all three sectors. A high value of Intangible Asset Turnover implies that a company's non-physical assets are creating more revenue which would be a positive indicator. Also, a high Human Capital Index 2022 shows that there are highly skilled workforce in that sector, another positive sign. Finally, Tech Investment Index depicts the technological advancement or decline in the sector. For ITServices the TechInvestmentIndex shows a positive trend which is beneficial. Looking at all these datasets together, we can conclude based on the bars at these coordinates {Intangible Asset Turnover for IT Services}<r>[0.673, 0.857, 0.740, 0.862]</r>, {Human Capital Index for IT Services}<r>[0.740, 0.605, 0.806, 0.862]</r>, {TechInvestmentIndex for IT Services}<r>[0.806, 0.155, 0.872, 0.179]</r> that the IT Services sector appears to be the most promising.\nBased on the above thought, the answer is:\nIT Services, as it has highest Intangible Asset Turnover, considerable Human Capital Index and a positive Tech Investment Index."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "RPHDdEeRM9052Xmc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the highest value of Intangible Asset Turnover?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the sector with the highest Intangible Asset Turnover, I need to examine the heights of the bars that correspond to this dataset. Based on the legend at {Legend}<r>[0.597, 0.777, 0.891, 0.888]</r>, the bars representing the Intangible Asset Turnover are identified by the color '#6e7a5f'. Now by comparing the heights of the three bars, {AdvancedManufacturing}<r>[0.145, 0.859, 0.211, 0.862]</r>, {Biotechnology}<r>[0.409, 0.861, 0.475, 0.862]</r>, and {ITServices}<r>[0.673, 0.857, 0.740, 0.862]</r> , I can see that the height of the bar corresponding to the IT Services sector is the highest, indicating it has the highest value for Intangible Asset Turnover.\nBased on the above thought, the answer is:\nIT Services."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "lxrlRyqBGwSzVzIc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data that has been visually represented in the chart and convert it to a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the x-axis, we can see that there are three categories: {CITY_A}<r>[0.207, 0.919, 0.266, 0.942]</r>, {CITY_B}<r>[0.478, 0.919, 0.537, 0.942]</r>, and {CITY_C}<r>[0.749, 0.919, 0.808, 0.942]</r>. Each of those categories has three bars representing three parameters: {Population Growth}<r>[0.661, 0.788, 0.695, 0.804]</r>, {Urban Development}<r>[0.661, 0.823, 0.695, 0.839]</r>, and {Green Space}<r>[0.661, 0.858, 0.695, 0.874]</r> respectively as identified from the legend. By measuring the height of the bars in relation to the y-axis scale, I can identify the values. For example, the height of the {Population Growth bar for CITY_A}<r>[0.134, 0.269, 0.202, 0.469]</r> corresponds to {2.3}<r>[0.155, 0.238, 0.182, 0.262]</r> on the y-axis, hence the Population Growth for CITY_A is 2.3 percent. Following the same methodology for all bars will allow me to construct the table.\nBased on the above thought, the answer is:\nCITY, POPULATION_GROWTH, URBAN_DEVELOPMENT, GREEN_SPACE\nCITY_A, 2.3, -4.1, 0.8\nCITY_B, -3.1, 2.2, 0.3\nCITY_C, 4.1, 0.1, 1.2"
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "zuiVGO1Xz7tXcB1F",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which city is experiencing balanced development in terms of both population growth and urban planning initiatives? Please elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze both Population Growth and Urban Development in each city. Both these categories are evident from the legend, {Population Growth}<r>[0.661, 0.788, 0.695, 0.804]</r> and {Urban Development}<r>[0.661, 0.823, 0.695, 0.839]</r>. Under {CITY_A}<r>[0.207, 0.919, 0.266, 0.942]</r>, while Population Growth is positive, Urban development is negative, suggesting rapid population growth but a reduction in urban space, which might negatively impact residents' quality of life. In the case of {CITY_B}<r>[0.478, 0.919, 0.537, 0.942]</r>, Urban Development is positive and Population Growth is negative, suggesting that although the city is developing, it is not attracting or retaining residents. The most balanced seems to be {CITY_C}<r>[0.749, 0.919, 0.808, 0.942]</r> where both Population Growth and Urban Development are positive, albeit the latter is only marginal.\nBased on the above thought, the answer is:\nCITY_C. It's experiencing an increase in both population, urban development, and green spaces, albeit urban development is only marginal which could indicate balanced growth and better planning. However, further data and context are needed to conclude decisively."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "z0Y4FcleNMAy7kbZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city has the highest green space?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Space is represened by bars in {color}<r>[0.661, 0.858, 0.695, 0.874]</r> denoted from the legend. We can compare the heights of the bars in this color group across all cities. By looking at the coordinates representing these bars we get {CITY_A's green space bar}<r>[0.270, 0.555, 0.338, 0.903]</r>, {CITY_B's green space bar}<r>[0.541, 0.773, 0.609, 0.903]</r> and {CITY_C's green space bar}<r>[0.812, 0.382, 0.880, 0.903]</r>.  The maximum height corresponds to CITY_C represented by the coordinates [0.812, 0.382, 0.880, 0.903].\nBased on the above thought, the answer is:\nCITY_C."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "IBqDRMKNAuXfCw3H",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the visualized bar chart's data, can you generate a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examination of the bar chart shows market share trends for three types of industries across three subsequent years: 2018, 2019, and 2020. To convert this information into a structured table, the figures corresponding to the representative bars for each year across each industry group must be extracted. For example, observing the {Electronics bar group}<r>[0.155, 0.034, 0.317, 0.900]</r> reveals a steady declining trend in its market share from -50.3% in 2018 to -100% by 2020. The other pairs of bars that need to be examined similarly are as follows: {Automobile bar group}<r>[0.426, 0.034, 0.589, 0.903]</r> and {Textile bar group}<r>[0.697, 0.033, 0.860, 0.903]</r>.\nBased on the above thought, the answer is:\nIndustry,Market Share 2018(%),Market Share 2019(%),Market Share 2020(%)\nElectronics,-50.3,-75.0,-100.0\nAutomobile,-100.0,-50.0,-75.0\nTextile,0.2,0.15,0.3"
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "fCICJCSTNAsZnC8Q",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trends shown in the bar chart, which industry shows the most unpredictable year-to-year changes in market share? Please elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, the volatility of market share across the three years needs to be assessed for each industry. Looking at the {Electronics bar group}<r>[0.155, 0.034, 0.317, 0.900]</r>, we see a consistent downward trend across the years. A similar pattern can be observed with the {Automobile bars}<r>[0.426, 0.034, 0.589, 0.903]</r>, where there is a reduction in the market share each year, albeit at a slowing rate. In contrast, the {Textile bars}<r>[0.697, 0.033, 0.860, 0.903]</r> show an increase from 2018 to 2020, but with a dip in 2019. This inconsistency in the textile industry's trend suggests the highest unpredictability in market share year-to-year.\nBased on the above thought, the answer is:\nTextile industry. Contrary to the Electronics and Automobile industries that follow a steady decline, the Textile industry shows inconsistency with a rise in market share in 2018 and 2020, but a dip in 2019. This fluctuation indicates the Textile industry has the most unpredictable change in market share year-to-year."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "SwrRc88tFi8RPSbc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has seen the least decline in the year 2019 compared to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires comparing the market share for each industry from 2018 to 2019. The difference between the bar heights for the years 2018 and 2019 within the same industry category will provide the answer. Focusing on the {Electronics bars}<r>[0.155, 0.034, 0.263, 0.900]</r>, the difference between 2018 and 2019 is -75.0 - (-50.3) = -24.7. Following the same calculation for {Automobile bars}<r>[0.426, 0.034, 0.534, 0.903]</r>, the difference is -50.0 - (-100.0) = 50.0. However, in the case of {Textile bars}<r>[0.697, 0.033, 0.806, 0.903]</r>, the difference is 0.15 - 0.2 = -0.05. Therefore, the Textile industry has seen the least decline.\nBased on the above thought, the answer is:\nTextile"
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "zjMVmTptGRWUrpIy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented in the bar chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "As an AI visual assistant, I can infer from the chart that it represents the performance of three different methods on three metrics. They are 'Lobbying', 'Protesting', and 'Litigation', located at {\"Electronics\"}<r>[0.189, 0.919, 0.283, 0.942]</r>, {\"Automobile\"}<r>[0.458, 0.919, 0.557, 0.942]</r> and {\"Textile\"}<r>[0.751, 0.919, 0.806, 0.942]</r> respectively. The metrics are 'Rights Protection Score','Social Inequality Score', and 'Progress Score'. The bar heights at each of these positions represent the scores, which I can visually estimate by comparing the positions of the tops of the bars with the positions of the y-tick labels. For example, the 'Rights Protection Score' of 'Lobbying' can be estimated as '{-50.3}'<r>[0.159, 0.353, 0.204, 0.377]</r>,and the 'Progress Score' as '{0.2}'<r>[0.819, 0.219, 0.847, 0.243]</r>. So, I will start from 'Lobbying', go through each score type and move on to the next method after that.\nBased on the above thought, the answer is:\nMethod,Rights Protection Score,Social Inequality Score,Progress Score\nLobbying,-50.3,-100.0,0.2\nProtesting,-75.0,-50.0,0.15\nLitigation,-100.0,-0.05,0.3"
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "ZJCVmzzihwkYebNY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the performance scores, which social justice method would you say is the most efficient and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I can visually analyze the bar representations of each method's scores. The bar indicators for the 'Rights Protection Score' and 'Social Inequality Score' lie on the negative part of the Y-axis and are color coded. Good performance on these metrics would be indicated by smaller magnitudes, that is, bars closer to the 0 line. The 'Progress Score' lies on the positive part of the Y-axis and the color used is a lighter shade compared to the other two scores. Good performance on this metric is bigger magnitudes. By comparing the three methods, we see the 'Litigation' method, indicated by {\"Textile\"}<r>[0.751, 0.919, 0.806, 0.942]</r>, has its 'Rights Protection Score' closer to 0 and the highest 'Progress Score' among the three methods, indicating overall better performance.\nBased on the above thought, the answer is:\nLitigation, because it has better Rights Protection and Progress Scores, and its Social Inequality Score is relatively less compared to the other methods hence depicting overall efficiency."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "w8NrMxqL25CXYNcX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method has the highest Social Inequality Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I can inspect the bar chart and see the bar heights of 'Social Inequality Score' for each method. Considering the method 'Lobbying', the 'Social Inequality Score' can be found at the location of {\"Automobile\"}<r>[0.426, 0.034, 0.480, 0.729]</r>. Next, we see that the score for method 'Protesting' is located at {\"Automobile\"}<r>[0.480, 0.034, 0.534, 0.382]</r> and the score for 'Litigation'is located at {\"Textile\"}<r>[0.751, 0.033, 0.806, 0.034]</r>. By comparing these, it's clear that 'Lobbying' has the highest Social Inequality Score.\nBased on the above thought, the answer is:\nLobbying"
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "1t2rNngwFzuc0mLS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar graph data for each business process across different categories into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar graph shows 3 different business processes: {Productivity}<r>[0.693, 0.786, 0.796, 0.809]</r>, {Innovation}<r>[0.693, 0.821, 0.784, 0.844]</r>, and {Customer Satisfaction}<r>[0.693, 0.856, 0.884, 0.879]</r>, which are color-coded and represented across 4 categories: {Steady Increase}<r>[0.106, 0.919, 0.246, 0.942]</r>, {Moderate Growth}<r>[0.307, 0.919, 0.456, 0.942]</r>, {Moderate Increase}<r>[0.507, 0.919, 0.668, 0.942]</r>, and {Gradual Recovery}<r>[0.718, 0.919, 0.868, 0.942]</r>. The bar's height and the corresponding values annotated at the top are used to determine the respective value for each process in each category. For example, in the category Steady Increase, the value for Productivity is given by the height of its bar<r>[0.114, 0.248, 0.156, 0.903]</r> indicated by the annotation \"15.34\"<r>[0.110, 0.217, 0.160, 0.241]</r> above the bar.\nBased on the above thought, the answer is:\nCategories,Productivity,Innovation,Customer_Satisfaction\nSteady increase,15.34,13.42,-2.51\nModerate growth,14.20,15.60,1.67\nModerate increase,16.12,17.68,3.71\nGradual recovery,16.90,18.33,5.92"
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "dRWH6ooPaxTNihZz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what are the trends you observe in the business processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are several trends that can be observed in the chart. For the {Productivity}<r>[0.693, 0.786, 0.796, 0.809]</r> and the {Innovation}<r>[0.693, 0.821, 0.784, 0.844]</r> processes, every consecutive bar increases in size, indicating a steady increase in {Productivity} and {Innovation} as we go from {Steady Increase}<r>[0.106, 0.919, 0.246, 0.942]</r> category to {Gradual Recovery}<r>[0.718, 0.919, 0.868, 0.942]</r> category. However, for the {Customer Satisfaction}<r>[0.693, 0.856, 0.884, 0.879]</r> process which starts off with a negative value in the {Steady Increase} category and then moves into the positive zone by the {Moderate Growth}<r>[0.307, 0.919, 0.456, 0.942]</r> category. This one increases at a faster pace than {Productivity} and {Innovation}, indicating a more substantial growth. The common trend among all three processes is that they all show their highest values in the {Gradual Recovery} category.\nBased on the above thought, the answer is:\nThe Productivity and Innovation processes gradually increase across all categories. Customer Satisfaction starts with a negative value, indicating a previous decline, but then recovers and grows more rapidly than the other two processes. All processes reach their highest values under the Gradual Recovery category."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "J6kK4wKr2qpuVqYG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest Customer Satisfaction value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the category with the highest Customer Satisfaction value, I need to look at the height of the third bar, the {Customer Satisfaction}<r>[0.693, 0.856, 0.884, 0.879]</r> bars, in each category. The third bar in the {Gradual Recovery}<r>[0.718, 0.919, 0.868, 0.942]</r> category can be seen at <r>[0.814, 0.056, 0.855, 0.618]</r> with the highest value annotated on top as \"5.92\"<r>[0.815, 0.026, 0.853, 0.049]</r>.\nBased on the above thought, the answer is:\nGradual recovery"
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "URUqHo13sWcQlSVd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease create a table in CSV format representing the data from the chart you analyzed, including the categories of Local Government and Community Development, budget allocation, and community satisfaction details."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains two sets of bars, representing 'Budget Allocation in million USD' and 'Community Satisfaction in %', respectively. Each category under 'Local Government and Community Development' which are {'Planning and Land Use'}<r>[0.113, 0.919, 0.311, 0.942]</r>, {'Infrastructure and Municipal Services'}<r>[0.338, 0.919, 0.662, 0.942]</r> and {'Social and Recreational Services'}<r>[0.647, 0.919, 0.929, 0.942]</r> has a corresponding pair of bars. For instance, the 'Planning and Land Use' category has the budget allocation bar {'bar'}<r>[0.126, 0.686, 0.212, 0.903]</r> and the community satisfaction bar {'bar'}<r>[0.212, 0.425, 0.298, 0.903]</r> with the allocated budget of 30 million USD and a 55% satisfaction rate, as shown by the annotations {'30'}<r>[0.158, 0.655, 0.180, 0.679]</r> and {'55%'}<r>[0.236, 0.395, 0.274, 0.418]</r>. The same method is applied to each category to convert the chart into a table in CSV format.\nBased on the above thought, the answer is:\nCategory,Budget Allocation in million USD,Community Satisfaction in %\nPlanning and Land Use,30,55\nInfrastructure and Municipal Services,100,85\nSocial and Recreational Services,60,75"
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "prhCPOHb6EDWGSZu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, should the local government prioritize more on Planning and Land Use or on Social and Recreational Services? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The question needs a comparative analysis between the two categories 'Planning and Land Use' and 'Social and Recreational Services'. In the aspect of budget allocation, 'Social and Recreational Services' {'bar'}<r>[0.702, 0.469, 0.788, 0.903]</r> receives double the amount of 'Planning and Land Use' {'bar'}<r>[0.126, 0.686, 0.212, 0.903]</r>, which can be seen from their respective annotations {'60'}<r>[0.734, 0.438, 0.756, 0.462]</r> and {'30'}<r>[0.158, 0.655, 0.180, 0.679]</r>. On the other hand, in terms of community satisfaction, 'Planning and Land Use' {'bar'}<r>[0.212, 0.425, 0.298, 0.903]</r> only received a 55% satisfied rate {'annotation'}<r>[0.236, 0.395, 0.274, 0.418]</r>, whereas 'Social and Recreational Services' {'bar'}<r>[0.788, 0.251, 0.874, 0.903]</r> secured a 75% rating {'annotation'}<r>[0.812, 0.221, 0.850, 0.244]</r>.\nBased on the above thought, the answer is:\nThe local government should prioritize more on Planning and Land Use. Although Social and Recreational Services are currently receiving a higher budget and have a higher satisfaction rate, Planning, and Land Use is an area that sorely needs improvement despite its fundamental importance. Hence, more resources should be directed towards this so as to enhance community satisfaction and overall development."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "gLrLdYZpTtO7C5EG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the given chart, which area of local government and community development received the highest budget allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the area with the highest budget, I need to compare the heights of the first set of bars, corresponding to 'Budget Allocation in million USD'. The heights of these bars directly reflect the budget allocation amounts. Upon examination, the tallest bar in this set {'bar'}<r>[0.414, 0.179, 0.500, 0.903]</r> is found under the category {'Infrastructure and Municipal Services'}<r>[0.338, 0.919, 0.662, 0.942]</r>. This bar's value is annotated as '100' million USD {'annotation'}<r>[0.440, 0.149, 0.473, 0.172]</r>, which is indeed the highest among the three categories.\nBased on the above thought, the answer is:\nInfrastructure and Municipal Services."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "d90ERDK48g4qHpCo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the values from the bars in the chart and present them in a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Given the positioning of {\"Category\"}[0.459, 0.952, 0.539, 0.975] and {\"Consumption (%)\"}[0.019, 0.368, 0.036, 0.569] labels, it's evident we are comparing consumption percentages for two sectors. As per {the legend}<r>[0.656, 0.777, 0.891, 0.888]</r>, each bar color represents a different type of consumtion: {Power Consumption}<r>[0.712, 0.786, 0.884, 0.809]</r> as green, {Water Consumption}<r>[0.712, 0.821, 0.883, 0.844]</r> as yellow, and {C02 Emission}<r>[0.712, 0.856, 0.828, 0.879]</r> as white. For the {Residential} category<r>[0.240, 0.919, 0.335, 0.942]</r>, identified by examining the x-axis labels, the power consumption is annotated as 125 on {the green bar}<r>[0.129, 0.396, 0.235, 0.758]</r>, water consumption is 20 on {the yellow bar}<r>[0.235, 0.700, 0.340, 0.758]</r>, and CO2 emission is -3.5 on {the white bar}<r>[0.340, 0.469, 0.446, 0.621]</r>. By using the same process for the {Industrial} category<r>[0.670, 0.919, 0.751, 0.942]</r>, the respective values for power, water and CO2 are 200, -10 and 0.83.\nBased on the above thought, the answer is:\nCategory, Power Consumption(%), Water Consumption(%), CO2 Emission\nResidential, 125, 20, -3.5\nIndustrial, 200, -10, 0.83"
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "vVbQGR6uBMNemzh2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which sector contributes more to a potential environmental impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Understanding environmental impact requires a combination of seeing the bars for carbon dioxide (CO2) emissions and water consumption. Here, the {yellow bar} representing water consumption<r>[0.712, 0.821, 0.883, 0.844]</r> and the {white bar} for CO2 emissions<r>[0.712, 0.856, 0.828, 0.879]</r> are key. For the {Residential} sector<r>[0.240, 0.919, 0.335, 0.942]</r>, the CO2 emissions are marked as negative {-3.5}<r>[0.376, 0.590, 0.410, 0.614]</r> indicating lower emissions, while water consumption is {20}<r>[0.276, 0.670, 0.298, 0.693]</r>. For the {Industrial} sector<r>[0.670, 0.919, 0.751, 0.942]</r>, the CO2 emissions are {0.83}<r>[0.797, 0.402, 0.836, 0.426]</r>, and also shows a negative water consumption period at {-10}<r>[0.696, 0.757, 0.725, 0.780]</r>. This combination of higher CO2 emissions and water conservation efforts in the Industrial sector suggest a potential for environmental impact.\nBased on the above thought, the answer is:\nThe Industrial sector, due to higher CO2 emissions despite efforts for water conservation."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "7hECyG0xGp0ZGaML",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the higher power consumption recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the representative bar for power consumption, per the {legend}, the {green bar}<r>[0.712, 0.786, 0.884, 0.809]</r>, in both the categories. The {Residential} category<r>[0.240, 0.919, 0.335, 0.942]</r> of power consumption is marked by {a green bar}<r>[0.129, 0.396, 0.235, 0.758]</r> with label \"125\" indicating the consumption level. The {Industrial} category<r>[0.670, 0.919, 0.751, 0.942]</r> of power consumption is indicated by {a green bar}<r>[0.552, 0.179, 0.658, 0.758]</r> with label \"200\" which is significantly taller. Hence, the category with the higher power consumption is clear.\nBased on the above thought, the answer is:\nIndustrial"
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "Q43t4qSpsx0kbw2z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data presented in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I noticed that the chart consists of 5 bars, each representing different suppliers' cost change in the Global Trade and Supply Chain Management. Starting from the left, the first bar represents {Wang Enterprises}<r>[0.136, 0.245, 0.302, 0.560]</r> with an increase of 0.45, followed by {Liu Corp}<r>[0.302, 0.560, 0.468, 0.736]</r> with a decrease of -0.25. Subsequent bars are for {Zhang Inc}<r>[0.468, 0.490, 0.633, 0.560]</r>,\n{Li LLC}<r>[0.633, 0.560, 0.799, 0.841]</r> and {Chen Industries}<r>[0.799, 0.174, 0.965, 0.560]</r> with cost change values 0.10, -0.40 and 0.55 respectively.\nBased on the above thought, the answer is:\nSupplier,Cost Change\nWang Enterprises,0.45\nLiu Corp,-0.25\nZhang Inc,0.10\nLi LLC,-0.40\nChen Industries,0.55"
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "pwgXo7BpKQ3R48cf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your visual analysis of the chart, which supplier should be considered if the aim is to cut costs and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the aim is to cut costs, I would suggest considering the suppliers who are shown to have decreased their costs. Looking at the chart, {Liu Corp}<r>[0.302, 0.560, 0.468, 0.736]</r> and {Li LLC}<r>[0.633, 0.560, 0.799, 0.841]</r> have their bars extending downwards from the {0 line}<r>[0.136, 0.560, 0.965, 0.560]</r>, indicating a decrease in cost. Among these two, {Li LLC}<r>[0.633, 0.560, 0.799, 0.841]</r> has a longer bar in the negative direction, indicating it experienced a larger decrease in cost.\nBased on the above thought, the answer is:\nLi LLC should be considered if the aim is to cut costs because it saw a larger decrease in cost compared to the other suppliers."
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "MjvFMb5TjBZptVEt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich supplier had the largest decrease in cost?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I'll look at which 'cost change' bar on the chart extends the furthest below the {0 line}<r>[0.136, 0.560, 0.965, 0.560]</r>. I see that the bar for {Li LLC}<r>[0.633, 0.560, 0.799, 0.841]</r> is longer in the negative direction than the bar for {Liu Corp}<r>[0.302, 0.560, 0.468, 0.736]</r> so Li LLC saw a larger decrease in cost.\nBased on the above thought, the answer is:\nLi LLC"
+            }
+        ],
+        "id_raw": 270
+    },
+    {
+        "id": "7O2zY83rDZweN1Az",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you transform the data on research field and their corresponding impacts into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the x-axis labels}[{\"Computer Science\"}[0.086, 0.939, 0.245, 0.957]; {\"Biology\"}[0.282, 0.939, 0.346, 0.957]; {\"Physics\"}[0.430, 0.939, 0.494, 0.957]; {\"Chemistry\"}[0.566, 0.939, 0.655, 0.957]; {\"Mathematics\"}[0.703, 0.939, 0.815, 0.957]; {\"Philosophy\"}[0.860, 0.939, 0.954, 0.957]], I understand that these are the different research fields. The y-axis, labeled as {Impact}[0.019, 0.449, 0.036, 0.510], represents the impact of each research field, the positive numbers indicate expansion, and negative numbers represent a decline. I need to extract the impact value for each field from the bar heights. For example, in {Computer Science}[0.086, 0.939, 0.245, 0.957], the height of {the bar}[0.091, 0.139, 0.240, 0.593] reaches up to the value of 2 on the y-axis, hence the impact of Computer Science is 2. I will follow the same process for the rest of the fields.\nBased on the above thought, the answer is:\nField,Impact\nComputer Science,2\nBiology,-1.2\nPhysics,1.3\nChemistry,-0.8\nMathematics,2.1\nPhilosophy,0.5"
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "FBF1c0EGXOhhw0V5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which academic fields are expanding and which are declining? Briefly explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The academic fields are represented on the x-axis while their impact is depicted on the y-axis. Fields with a positive impact imply expansion, while those with a negative impact indicate decline. Looking at the bars, we can see that {Computer Science}[0.086, 0.939, 0.245, 0.957] and {Mathematics}[0.703, 0.939, 0.815, 0.957] depicted by their corresponding {bars}[{bar of CS}[0.091, 0.139, 0.240, 0.593]; {bar of Mathematics}[0.685, 0.116, 0.833, 0.593]] have positive heights and thus are expanding. On the other hand, {Biology}[0.282, 0.939, 0.346, 0.957] and {Chemistry}[0.566, 0.939, 0.655, 0.957] represented by their corresponding {bars}[{bar of Biology}[0.240, 0.593, 0.388, 0.865]; {bar of Chemistry}[0.536, 0.593, 0.685, 0.774]] show negative heights, suggesting these fields are declining.\nBased on the above thought, the answer is:\nAccording to the chart, the academic fields of Computer Science and Mathematics are expanding, while Biology and Chemistry are declining."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "dm6bSvxGyuZ5Zq4x",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which academic field shows greatest decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the research field showing the greatest decline, I need to look for the bar reaching the lowest point on the y-axis. Looking at the heights of the bars, it's clear that {Biology}[0.282, 0.939, 0.346, 0.957] represented by {the bar}[0.240, 0.593, 0.388, 0.865], has the maximum negative height, reaching -1.2 on the y-axis. Therefore, Biology is the academic field that shows the greatest decline.\nBased on the above thought, the answer is:\nBiology."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Y4t1wmghdS5oPP25",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the chart data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart's title {\"Media and Entertainment Industry Trends\"}[0.136, 0.034, 0.0893, 0.911] and using the {x-axis and y-axis labels}[{\"Television\"}[0.163, 0.919, 0.275, 0.942]; {\"Movies\"}[0.344, 0.919, 0.425, 0.942]; {\"MobileGames\"}[0.471, 0.919, 0.628, 0.942]; {\"Music\"}[0.682, 0.919, 0.748, 0.942]; {\"OnlineShopping\"}[0.789, 0.919, 0.972, 0.942]] as the headers, I can build the CSV table. Looking at {each bar's height} and the {respective y-tick annotation}, I can get the corresponding data values. For example, the {Television's bar}[0.178, 0.144, 0.260, 0.473] extends midway between '0.50' and '1.00' on the y-axis, so its corresponding value is 0.75. Similarly, I can obtain all the values to construct the CSV table.\nBased on the above thought, the answer is:\nArea,Relevance\nTelevision,0.75\nMovies,-0.9\nMobileGames,0.50\nMusic,-0.65\nOnlineShopping,-0.45"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "bfrLrM6fLfm1yBEX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the general trend in the Media and Entertainment Industry based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to {the bars on the chart}, this question requires an analysis of the general trend in different areas. Here, positive areas such as {Television}[0.178, 0.144, 0.260, 0.473] and {MobileGames}[0.509, 0.253, 0.591, 0.473] are trending upwards as indicated by the bar heights being above the zero mark on the y-axis. On the contrary, areas such as {Movies}[0.343, 0.473, 0.426, 0.867], {Music}[0.674, 0.473, 0.757, 0.758], and {OnlineShopping}[0.839, 0.473, 0.922, 0.670] are trending downwards as their bars extend below the zero line. The overall trend seems to be a mix of upward and downward trends, indicating a dynamic and shifting industry landscape.\nBased on the above thought, the answer is:\nThe Media and Entertainment industry shows a mix of upward and downward trends. The Television and MobileGames domains are gaining in relevance, while the Movies, Music, and OnlineShopping domains are seeing a decrease in relevance, indicating a dynamic industry landscape."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "BwlhSwwTE4r4Moqj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area experienced the greatest fluctuation in the trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the area with the greatest fluctuation, I will identify the bar with the greatest absolute height difference from the y-axis value of zero. Starting from the left, the first bar is {Television}[0.178, 0.144, 0.260, 0.473] with a distance of 0.75, the second is {Movies}[0.343, 0.473, 0.426, 0.867] with a distance of 0.9, and so on for the bars {MobileGames}, {Music}, and {OnlineShopping}. After comparison, I find the {Movies bar} extends the furthest from zero along the y-axis. Therefore, the Movies area experienced the greatest fluctuation.\nBased on the above thought, the answer is:\nMovies"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "5u7wHiHae50CBDYF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart information into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Household Condition\"}[0.394, 0.952, 0.634, 0.975] at the bottom of the chart and the y-axis {\"% Family\"}[0.025, 0.421, 0.048, 0.525] specify the two categories for the table. The content of the table can be taken by looking at the labels of the x-axis and the height of each bar. For example, the first bar, which represents {\"Minimal Basic Amenities\"}[0.050, 0.919, 0.332, 0.942], extends {\"70%\"}[0.142, 0.297, 0.239, 0.911] up the y-axis. So, \"Minimal Basic Amenities, 70\" is the first data entry for the table.\nBased on the above thought, the answer is:\nHousehold Condition,% Family\nMinimal Basic Amenities,70\nInadequate Sanitation,55\nLack of Electricity,10\nNo Access to Clean Drinking Water,3\nChronic Food Insecurity,12"
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "bfWlRt2oAd3oLemN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, which social issues need immediate improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart, the conditions which affect the most households can be considered as needing immediate improvement. The conditions which affect the most households will have the bars with the wider ranges. The widest range bar is {\"Minimal Basic Amenities\"}[0.050, 0.919, 0.332, 0.942], which extends up to {\"70%\"}[0.110, 0.297] of the y-axis, followed by the {\"Inadequate Sanitation\"}[0.224, 0.919, 0.481, 0.942] with its range extended upto {\"55%\"}[0.304, 0.429, 0.401, 0.911] on y axis.\nBased on the above thought, the answer is:\nThe provision of Minimal Basic Amenities and improving Sanitation need immediate attention as these conditions affect 70% and 55% of households respectively."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "HrU7wVH2EEvNZCms",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich household condition affects the least number of families according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From looking at the chart, it can be determined that condition affecting the least number of families would be represented by the shortest bar on the chart. Looking at all the bars, the shortest one appears to be {\"No Access to Clean Drinking Water\"}[0.628, 0.885, 0.725, 0.911] with a height only reaching {\"3%\"}[0.628, 0.885, 0.725, 0.911] on the y-axis.\nBased on the above thought, the answer is:\nNo Access to Clean Drinking Water"
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "tZINoXvHtDbzggNW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart's depiction, can you please convert the data into a well-structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the x-axis represents different scientific fields as seen in {the x-axis tick labels}[{\"Artificial Intelligence\"}[0.082, 0.919, 0.224, 0.942]; {\"Robotics\"}[0.297, 0.919, 0.356, 0.942]; {\"Space Technology\"}[0.438, 0.919, 0.562, 0.942]; {\"Quantum Computing\"}[0.601, 0.919, 0.746, 0.942]; {\"Nanotechnology\"}[0.790, 0.919, 0.904, 0.942]], the y-axis, as mentioned in {the ylabel}[0.015, 0.314, 0.029, 0.614], denotes the Amount of New Discovery. Also, there are bars indicating the Pace of Innovation, as implied by {the ylabel}[0.971, 0.357, 0.985, 0.571]. To transform the chart into tabular format, we'll need to extract the values represented by each color. For instance, {the bar corresponding to Artificial Intelligence in the Amount of New Discovery category}[0.118, -86.884, 0.188, 0.903] shows a value of 100 and {the bar indicating the Pace of Innovation for Artificial Intelligence category}[0.118, 0.276, 0.188, 0.903] has the value of 0.45.\nBased on the above thought, the answer is:\nScientific Fields,Amount of New Discovery,Pace of Innovation\nArtificial Intelligence,100,0.45\nRobotics,80,0.35\nSpace Technology,120,0.60\nQuantum Computing,75,0.20\nNanotechnology,90,0.30"
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "wpqoqP6d3p4ooHdE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the chart, which scientific field would you say is at the forefront of research and technological development presently? Explain your choice."
+            },
+            {
+                "from": "gpt",
+                "value": "To give an appropriate response to this question, I need to consider both the highest amounts of new discovery and the fastest pace of innovation depicted in the chart. Considering {the bar representing the Amount of New Discovery}[0.118, -86.884, 0.188, 0.903] and {the bar indicating the Pace of Innovation for the Space Technology field}[0.465, 0.067, 0.535, 0.903], which are both highest among their respective sets, I would conclude that Space Technology is currently leading. This is because it not only has the highest number of new discoveries but also demonstrates the quickest pace of innovation.\nBased on the above thought, the answer is:\nSpace Technology. It has the most new discoveries and the fastest pace of innovation."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "mLQGP2z4zZl4TFfH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field shows the smallest pace of innovation as reflected in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the field with the lowest pace of innovation, we have to assess the height of all the bars depicting the Pace of Innovation. Looking across the fields from left to right, starting with {the bar for Artificial Intelligence}[0.118, 0.276, 0.188, 0.903], {the bar for Robotics}[0.292, 0.415, 0.361, 0.903], {the bar for Space Technology}[0.465, 0.067, 0.535, 0.903], {the bar for Quantum Computing}[0.639, 0.624, 0.708, 0.903] and finally, {the bar for Nanotechnology}[0.812, 0.485, 0.882, 0.903]. By visually comparing these bars, we can ascertain that the bar for Quantum Computing is the shortest, indicating the least pace of innovation in this field.\nBased on the above thought, the answer is:\nQuantum Computing."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "Eiq7RMHm9hU7zP8c",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is describing the number of patents across various technology sectors. The sectors are indicated by {x-axis tick labels}[{\"AI_and_Machine_Learning\"}[0.087, 0.714, 0.258, 0.942]; {\"Biotech\"}[0.323, 0.714, 0.381, 0.792]; {\"Renewable_Energy\"}[0.467, 0.714, 0.596, 0.886]; {\"Space_Technology\"}[0.649, 0.714, 0.774, 0.881]; {\"Telecommunications\"}[0.823, 0.714, 0.960, 0.898]]. The corresponding values are indicated by the height of the bars. Each bar's height corresponds to the count of patents, as indicated on the Y-axis which ranges from {0}[0.065, 0.697, 0.076, 0.720] to {250}[0.044, 0.025, 0.076, 0.048]. The CSV table will include the technology sectors as the first column and corresponding patent count as the other column.\nBased on the above thought, the answer is:\nTechnology_Sectors, Number_of_Patents\nAI_and_Machine_Learning, 134\nBiotech, 218\nRenewable_Energy, 189\nSpace_Technology, 134\nTelecommunications, 240"
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "UUzKDN47Kglu3pdT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about Technological innovation from the barchart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the heights of the bars, I can infer the extent of scientific research and technological innovation across different sectors. From highest to lowest: {Telecommunications}[0.837, 0.061, 0.945, 0.706], {Biotech}[0.298, 0.120, 0.406, 0.706], {Renewable Energy}[0.478, 0.198, 0.586, 0.706], {AI and Machine Learning and Space Technology}[0.118, 0.346, 0.226, 0.706] and [0.658, 0.346, 0.766, 0.706]. From this, it's clear that the field of Telecommunications has shown the most pronounced increase in terms of the number of patents granted, pointing towards high innovation. Biotech and Renewable Energy fields have also witnessed considerable growth. AI, Machine Learning, and Space Technology have a moderate number of patents, indicating steady growth in these areas.\nBased on the above thought, the answer is:\nThe Telecommunications sector has shown the highest technological innovation followed by Biotech, and Renewable Energy. AI and Machine Learning along with Space Technology exhibit moderate growth. Hence, sectors such as Telecommunications, Biotech, and Renewable Energy are currently the leading areas of technological innovation."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "rl2pczqYz0yUvp3w",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology sector has the highest number of patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the highest number of patents, I need to compare the heights of the bars. From the left to right, the bars are located at [0.118, 0.346, 0.226, 0.706] for AI and Machine Learning, [0.298, 0.120, 0.406, 0.706] for Biotech, [0.478, 0.198, 0.586, 0.706] for Renewable Energy, [0.658, 0.346, 0.766, 0.706] for Space Technology and [0.837, 0.061, 0.945, 0.706] for Telecommunications. By comparing their heights relative to the y-axis that ranges from 0 to 250, I find that the {bar for Telecommunications}[0.837, 0.061, 0.945, 0.706] is the highest one, thus indicating that Telecommunications have the highest number of patents.\nBased on the above thought, the answer is:\nTelecommunications."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "mbzvB8PlM64l3AbD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the bar chart data into a table with two columns 'Charity Type' and 'Donation Amount'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar plot, I can see that each bar {\"rectangle\"} contains two pieces of information: the category, which is given by the {x-axis label}, and the value, which is the length of the bar and can be calculated as {y_range}. For example, for {the bar named 'Food Bank'}<r>[0.113, 0.414, 0.204, 0.933]</r>, its label is 'Food Bank' and its value is the difference between 0 and 12500 which is 12500. Similarly, I can analyse the rest of the bars like {the bar named 'Animal Welfare'}<r>[0.264, 0.601, 0.354, 0.933]</r>, {the bar named 'Environmental Conservation'}<r>[0.414, 0.089, 0.504, 0.933]</r>, etc.to obtain the table.\nBased on the above thought, the answer is:\nCharity Type,Donation Amount\nFood Bank,12500\nAnimal Welfare,8000\nEnvironmental Conservation,20300\nEducational Support,15600\nHealthcare Services,9500\nDisaster Relief,18500"
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "1MHwSgH18hNiFDxt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which areas of charity do people pay more attention to and donate more to based on their concerns and interests?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate which charity type has the largest bar on the plot, showing the highest donation amount. By checking each bar, I can see that {the bar named 'Environmental Conservation'}<r>[0.414, 0.089, 0.504, 0.933]</r> is the longest with the {y_range} from 0 to 20300 indicating that 'Environmental Conservation' has received the highest donation amount. Hence, people seems to pay more attention to and donate more towards 'Environmental Conservation'.\nBased on the above thought, the answer is:\nPeople pay more attention to and donate more to Environmental Conservation."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "s1Y4o6h7Rlrdot2V",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich charity type has received the least amount of donation according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which charity type received the smallest amount of donations, I should analyse the length of the bars on the chart. By visually looking, {the bar named 'Animal Welfare'}<r>[0.264, 0.601, 0.354, 0.933]</r> appears to be the smallest. Further checking the {y_range} indicates that the donation amount is 8000 which is indeed the smallest among all the given bars.\nBased on the above thought, the answer is:\nAnimal Welfare."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "YxRCJfs4QHdhyRx2",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you summarize the data from the chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the chart, I can see that the index \"Value Change\" is plotted over the years from 1960 to 2020. This is represented by the y-axis titled {Value Change}[0.019, 0.420, 0.036, 0.539] and the x-axis labeled as {Decades}[0.499, 0.964, 0.574, 0.981]. There are a total of 7 categories, represented by 7 bars in the chart. The position and height of these bars are displayed by their coordinates. For instance, the bar for 1960 has a height that reaches up to -0.12 on the y-axis, indicated by the bar coordinates {1960}[0.091, 0.438, 0.218, 0.537]. Similarly, each of the other bars represents the value of the index for their corresponding decade. The bars, along with their labels, translate to the value of \"Value Change\" index for their respective decades. Let me gather this information into a CSV format.\nBased on the above thought, the answer is:\nDecades,Value Change\n1960,-0.12\n1970,-0.36\n1980,-0.55\n1990,-0.45\n2000,-0.28\n2010,0.15\n2020,0.45"
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "7dZHRVbyS1gwHjI2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the changing trend of 'Value Change' index from the year 1960 to 2020 according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Now, let us analyze the evolution of the 'Value Change' index as per the chart. From the start at {1960}[0.091, 0.438, 0.218, 0.537] to {1980}[0.346, 0.438, 0.473, 0.892], the bar heights decrease, implying a downward trend in the 'Value Change' index. Also, all the bars are below the x-axis, indicating that the 'Value Change' values are negative during these decades. \n\nHowever, there is something peculiar about the bar representing {1990}[0.473, 0.438, 0.600, 0.809]. Its position is higher than its preceding bar {1980}[0.346, 0.438, 0.473, 0.892], signaling a recovery. Then, from {2000}[0.600, 0.438, 0.727, 0.669] to {2020}[0.854, 0.067, 0.981, 0.438], there is a steady growth in the height of bars. The 'Value Change' moved from the negative region to the positive region, indicating a persistent growth in cultural values with positive values. So, in general, the trending of 'Value Change' index is that there was a decline from 1960 to 1980, followed by a recovery in 1990 and a steady growth from 2000 to 2020.\nBased on the above thought, the answer is:\nThe 'Value Change' index experienced a decline from 1960 to 1980. It began to recover in the 1990s, and then increased steadily from 2000 to 2020."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "HVfPfbhPA0VwM6js",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen did the 'Value Change' index start to increase, based on the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Value Change' index started to increase when the value of the index changed from negative to positive. From the chart, we can see that the bars representing the index for each decade gradually decrease and then start to increase. According to the coordinates of each bar and the value labeling, the bar {1990}[0.473, 0.438, 0.600, 0.809] shows a higher position than the bar {1980}[0.346, 0.438, 0.473, 0.892], signifying an increase in the index. Hence, 'Value Change' index started to increase from 1990 onwards.\nBased on the above thought, the answer is:\n1990"
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "yuzHp0gNDyNJlHDn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the bar chart and present it in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows five independent bars, each representing a quarter. From the location of {tick labels on the x-axis}<r>[\"Q1\"[0.204, 0.919, 0.237, 0.942]], [\"Q2\"[0.372, 0.919, 0.404, 0.942]], [\"Q3\"[0.539, 0.919, 0.572, 0.942]], [\"Q4\"[0.707, 0.919, 0.740, 0.942]], [\"Q5\"[0.875, 0.919, 0.907, 0.942]]</r> and by the height of the corresponding bars {\"Q1\"<r>[0.136, 0.122, 0.304, 0.473],</r> \"Q2\"<r>[0.304, 0.210, 0.472, 0.473]</r>, \"Q3\"<r>[0.472, 0.473, 0.640, 0.692]</r>, \"Q4\"<r>[0.640, 0.473, 0.807, 0.845]</r>, \"Q5\"<r>[0.807, 0.210, 0.975, 0.473]</r>} relative to {the y-axis labels}<r>[\"-1.00\"[0.058, 0.902, 0.128, 0.925]], [\"-0.75\"[0.058, 0.792, 0.128, 0.816]], [\"-0.50\"[0.058, 0.683, 0.128, 0.706]], [\"-0.25\"[0.058, 0.573, 0.128, 0.596]], [\"0.00\"[0.077, 0.463, 0.128, 0.487]], [\"0.25\"[0.077, 0.354, 0.128, 0.377]], [\"0.50\"[0.077, 0.244, 0.128, 0.268]], [\"0.75\"[0.077, 0.135, 0.128, 0.158]], [\"1.00\"[0.077, 0.025, 0.128, 0.048]]</r>, we can extract the values for each quarter. For instance, the bar for \"Q1\" is located at roughly the 0.80 mark on the y-axis, indicating a value of 0.80.\nBased on the above thought, the answer is:\nTime,Value\nQ1,0.80\nQ2,0.60\nQ3,-0.50\nQ4,-0.85\nQ5,0.60"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "pr65l9yrHyx5Hpqa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, describe the overall trend observed over the five quarters."
+            },
+            {
+                "from": "gpt",
+                "value": "To establish the overall trend, we can consider the height of the bars {\"Q1\"<r>[0.136, 0.122, 0.304, 0.473],</r> \"Q2\"<r>[0.304, 0.210, 0.472, 0.473]</r>, \"Q3\"<r>[0.472, 0.473, 0.640, 0.692]</r>, \"Q4\"<r>[0.640, 0.473, 0.807, 0.845]</r>, \"Q5\"<r>[0.807, 0.210, 0.975, 0.473]</r>} for each quarter {on the x-axis}<r>[\"Q1\"[0.204, 0.919, 0.237, 0.942]], [\"Q2\"[0.372, 0.919, 0.404, 0.942]], [\"Q3\"[0.539, 0.919, 0.572, 0.942]], [\"Q4\"[0.707, 0.919, 0.740, 0.942]], [\"Q5\"[0.875, 0.919, 0.907, 0.942]]</r>. In the first two quarters, the bars are in the positive region, indicating growth. However, in the third and fourth quarters, the bars shift to the negative region, thus implying a decline. Then in the fifth quarter, the bar returns to the positive region, signifying a resurgence in growth. Hence, the overall trend is inconsistent, with periods of growth and decline.\nBased on the above thought, the answer is:\nThe trend is erratic and inconsistent. It starts with two quarters of growth, followed by two quarters of a sharp decline, and then a resurgence in the final quarter."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "AvLzpSDDvDIQFHsR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which quarter do you observe the greatest decline in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most significant decline will correspond to the bar with the lowest height. After looking at the heights of all bars: {\"Q1\"<r>[0.136, 0.122, 0.304, 0.473],</r> \"Q2\"<r>[0.304, 0.210, 0.472, 0.473]</r>, \"Q3\"<r>[0.472, 0.473, 0.640, 0.692]</r>, \"Q4\"<r>[0.640, 0.473, 0.807, 0.845]</r>, \"Q5\"<r>[0.807, 0.210, 0.975, 0.473]</r>}, the lowest one is the bar corresponding to {Q4}<r>[0.640, 0.473, 0.807, 0.845]</r> reaching down to about -0.85 on the {y-axis labels}<r>[\"-1.00\"[0.058, 0.902, 0.128, 0.925]], [\"-0.75\"[0.058, 0.792, 0.128, 0.816]], [\"-0.50\"[0.058, 0.683, 0.128, 0.706]], [\"-0.25\"[0.058, 0.573, 0.128, 0.596]], [\"0.00\"[0.077, 0.463, 0.128, 0.487]], [\"0.25\"[0.077, 0.354, 0.128, 0.377]], [\"0.50\"[0.077, 0.244, 0.128, 0.268]], [\"0.75\"[0.077, 0.135, 0.128, 0.158]], [\"1.00\"[0.077, 0.025, 0.128, 0.048]]</r>. Hence, the quarter with the greatest decline is Q4.\nBased on the above thought, the answer is:\nQ4"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "wz1ELJtwmvHItS24",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the bar chart's information and present them in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, I can see the x-axis represents different cuisine categories, labeled as {\"Italian Cuisine\"}[0.113, 0.939, 0.236, 0.957], {\"Chinese Cuisine\"}[0.251, 0.939, 0.389, 0.957], {\"Mexican Cuisine\"}[0.395, 0.939, 0.536, 0.957], {\"Indian Cuisine\"}[0.550, 0.939, 0.672, 0.957], {\"Thai Cuisine\"}[0.703, 0.939, 0.809, 0.957], {\"Japanese Cuisine\"}[0.828, 0.939, 0.975, 0.957]. The y-axis shows the growth in their popularity, ranging from {\"-0.50\"}[0.043, 0.926, 0.096, 0.944] to {\"1.00\"}[0.058, 0.019, 0.096, 0.036]. The bars represent the growth in popularity for each cuisine, such as {Italian's bar}[0.102, 0.147, 0.247, 0.631]. We can use the middle position of each bar on y-axis to find their corresponding popularity growth.\nBased on the above thought, the answer is:\nCuisine Category,Popularity Trend\nItalian Cuisine,0.80\nChinese Cuisine,0.60\nMexican Cuisine,0.30\nIndian Cuisine,0.10\nThai Cuisine,-0.10\nJapanese Cuisine,-0.25"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "DolvoaBRhqxYR6x0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what assessment can be made about the trend of Asian cuisine popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the bars representing Asian cuisines - {\"Chinese Cuisine\"}[0.251, 0.939, 0.389, 0.957], {\"Indian Cuisine\"}[0.550, 0.939, 0.672, 0.957], {\"Thai Cuisine\"}[0.703, 0.939, 0.809, 0.957], and {\"Japanese Cuisine\"}[0.828, 0.939, 0.975, 0.957]. These bars are present on the x-axis, and their length on the y-axis represents their popularity growth. Based on these four bars, it appears that Chinese cuisine is growing in popularity, while Japanese cuisine is on a decline, with Thai cuisine also seeing a slight decrease. However, Indian cuisine seems to be gaining popularity at a slow pace. From this, it would seem that the popularity of Asian cuisine is mixed, and it might also vary greatly depending on the specific cuisine.\nBased on the above thought, the answer is:\nThe trend of Asian cuisine popularity is mixed. While Chinese and Indian cuisines are seeing growth in popularity, Thai and Japanese cuisines are experiencing a decline. It suggests that preference for Asian cuisine varies greatly among different types."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "8fZv319bvLWQPSB1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, what is the growth difference of popularity between the most popular and least popular cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the growth difference of popularity between the most and least popular cuisines, I need to extract the highest and lowest bars in the chart. As per the chart, {Italian Cuisine's bar}[0.102, 0.147, 0.247, 0.631] is the highest and represents a popularity growth of 0.80. On the other hand, {Japanese Cuisine's bar}[0.829, 0.631, 0.974, 0.782] is the lowest, with a decline in popularity of 0.25. Now, I can calculate the difference by subtracting the lowest growth from the highest growth.\nBased on the above thought, the answer is:\n1.05"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "V162Ldc46jZx3BC0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you have observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of this chart denotes different innovation areas as observed by the x-axis labels La1: {\"Cloud_Based_Learning\"}<r>[0.067, 0.919, 0.263, 0.942]</r>, La2: {\"MOOCs_Adoption\"}<r>[0.239, 0.919, 0.388, 0.942]</r>, La3: {\"Gamified_Education\"}<r>[0.374, 0.919, 0.548, 0.942]</r>, La4: {\"Augmented_Reality\"}<r>[0.525, 0.919, 0.694, 0.942]</r>, La5: {\"Adaptive_Learning\"}<r>[0.677, 0.919, 0.838, 0.942]</r>, and La6: {\"AI_in_Education\"}<r>[0.837, 0.919, 0.974, 0.942]</r>. Whereas the y-axis represents the innovation index quantified as seen by the labels ranging from Lb1: {-2.0}<r>[0.043, 0.902, 0.085, 0.925]</r> to Lb11: {3.0}<r>[0.058, 0.025, 0.085, 0.048]</r>. The associated innovation index for every innovation area is shown by the position of the bars on the y-axis, such as the bar of Cloud-Based Learning is B1 <r>[0.121, 0.358, 0.210, 0.560]</r> and it has an innovation index of 1.15 as the height reaches approximately to Lb9: {1.0}. Each of the components from La1 to La6 on the x-axis has an associated bar on the chart from which the corresponding values on the y-axis can be deduced.\nBased on the above thought, the answer is:\nInnovation_Area,Innovation_Index\nCloud_Based_Learning,1.15\nMOOCs_Adoption,-1.75\nGamified_Education,0.90\nAugmented_Reality,0\nAdaptive_Learning,-0.6\nAI_in_Education,2.2"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "UUMKwfk7up9khWCK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area seems to be more innovative and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar chart, the heights of bars indicate innovation index. Higher the bar, higher is the innovation. The bar for {'AI_in_Education'}<r>[0.861, 0.174, 0.950, 0.560]</r> has the highest position on the y-axis from all the bars, reaching up to 2.2, which also corresponds to the highest y-axis label Lb11: {3.0}. It implies that 'AI_in_Education' has the highest innovation index among all the innovation areas listed on the x-axis, hence it is the most innovative.\nBased on the above thought, the answer is:\nAI_in_Education, because it has the highest innovation index among all areas."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "0ibT9KPY5TPeotRj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the innovation index according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The values of the innovation index are expressed on the y-axis, from Lb1: {-2.0}<r>[0.043, 0.902, 0.085, 0.925]</r> at the bottom to Lb11: {3.0}<r>[0.058, 0.025, 0.085, 0.048]</r> at the top. Therefore, the range of the innovation index is from -2.0 to 3.0.\nBased on the above thought, the answer is:\n-2.0 to 3.0"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "wqteUQCyXOxWLcZ7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the Bar Chart, can you please extract the data and structure it into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart represents age groups on the x-axis and their earned income on the y-axis. By identifying each of the values in the chart, I can directly transpose this visual data into a structured table format. The x-axis labels can be found with the key {xticks}<r> [{\"20-29\"}[0.183, 0.919, 0.249, 0.942], {\"30-39\"}[0.321, 0.919, 0.387, 0.942], {\"40-49\"}[0.459, 0.919, 0.525, 0.942], {\"50-59\"}[0.597, 0.919, 0.663, 0.942], {\"60-69\"}[0.734, 0.919, 0.802, 0.942], {\"70+\"}[0.882, 0.919, 0.930, 0.942]]<r> and the corresponding y-axis values can be found by analyzing the bar heights.  As examples, the bar representing the age group of 20-29 extends up to the 70,000 mark, while the bar for the 70+ age group extends up to the 30,000 mark.\nBased on the above thought, the answer is:\nAge Group,Earnings\n20-29,70000\n30-39,65000\n40-49,60000\n50-59,50000\n60-69,40000\n70+,30000"
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "oDjjUzY7bPAU14n6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, can you describe the trend in average income as age increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely, in the observed bar chart, each bar corresponds to an age group according to the x-axis labels <r>{\"20-29\"}[0.183, 0.919, 0.249, 0.942] - {\"70+\"}[0.882, 0.919, 0.930, 0.942]</r>. From the left to the right (or from younger to older age groups), one can see a general decline in the height of the bars, which indicates a decrease in earnings. This downward trend suggests that as people's age increases, their average earnings have a tendency to decrease. This is also reflected in the actual values represented by each bar, such as the $70,000 earnings for the {\"20-29\"}<r>[0.147, 0.143, 0.285, 0.903]</r> age group decreasing to $30,000 for the {\"70+\"}<r>[0.837, 0.577, 0.975, 0.903]</r> age group. However, there is an exception in the {\"50-59\"}<r>[0.561, 0.360, 0.699, 0.903]</r> age group, where the earnings drop more than the general trend before rising again in the {\"60-69\"}<r>[0.699, 0.469, 0.837, 0.903]</r> age group.\nBased on the above thought, the answer is:\nAs age increases, average earnings generally decrease. Specifically, earnings are highest in the 20-29 age group and decrease progressively until the 70+ age group. However, the 50-59 age group has a distinctive drop which is not in line with the overall trend."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "Ts6EzibXSsBuGJ1C",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich age group had the highest average earnings according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify this, I need to look at the bars that reach the highest point on the chart. The height of the bars is a direct representation of earnings. Comparing all the bars, the tallest is the {\"20-29\"}<r>[0.147, 0.143, 0.285, 0.903]</r> bar. The height of this bar corresponds with the highest value on the y-axis, which is 70,000. So, the age group with the highest average earnings according to the chart is 20-29.\nBased on the above thought, the answer is:\n20-29"
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "GC4boQgurhtZOQU6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table from the bar chart data you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top {\"Healthcare and Medical Technology Aspects\"}[0.380, 0.952, 0.685, 0.975] and {the x-axis tick labels}[{\"medicine\"}[0.132, 0.919, 0.194, 0.942]; {\"treatment\"}[0.233, 0.919, 0.304, 0.942]; {\"diagnosis\"}[0.340, 0.919, 0.408, 0.942]; {\"technology\"}[0.441, 0.919, 0.518, 0.942]; {\"R&D\"}[0.569, 0.919, 0.601, 0.942]; {\"telemedicine\"}[0.645, 0.919, 0.736, 0.942]; {\"AI\"}[0.789, 0.919, 0.803, 0.942]; {\"apps\"}[0.884, 0.919, 0.919, 0.942]], I can see that the x-axis represents different aspects of healthcare and medical technology. The y-axis shows the frequency of mentions for each aspect from {0}[0.061, 0.894, 0.070, 0.917] to {4500}[0.035, -0.047, 0.070, -0.024]. Now, if I refer to the {legend}<r>[0.086, 0.037, 0.199, 0.183]</r>, I can identify the sentiment associated with each category: {negative}<r>[0.092, 0.048, 0.120, 0.064]</r> in red, {positive}<r>[0.092, 0.083, 0.120, 0.099]</r> in green, {none}<r>[0.092, 0.118, 0.120, 0.134]</r> in grey, {mixed}<r>[0.092, 0.153, 0.120, 0.169]</r> in orange. Each bar on the x-axis constitutes a stack of these four categories, like {the medicine bar}<r>[0.121, 0.767, 0.205, 0.903]</r>. The table needs to be generated in such a way that each row corresponds to the given aspect, and each column includes the frequency of sentiment category for that particular aspect, which can be determined from the segmented parts of each bar.\nBased on the above thought, the answer is:\nAspect,Negative,Positive,None,Mixed\nMedicine,650,0,0,40\nTreatment,520,200,0,75\nDiagnosis,400,0,0,0\nTechnology,980,0,0,0\nR&D,0,2010,0,10\nTelemedicine,0,0,3900,0\nAI,500,300,0,0\nApps,0,0,0,380"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "DzK36nfWrPMWrRZg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which aspect of healthcare and medical technology is most positively discussed? Can you provide a brief explanation?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a systematic analysis of each bar on the chart that represents an aspect of healthcare and medical technology along {the x-axis}<r>[{\"medicine\"}[0.132, 0.919, 0.194, 0.942]; {\"treatment\"}[0.233, 0.919, 0.304, 0.942]; {\"diagnosis\"}[0.340, 0.919, 0.408, 0.942]; {\"technology\"}[0.441, 0.919, 0.518, 0.942]; {\"R&D\"}[0.569, 0.919, 0.601, 0.942]; {\"telemedicine\"}[0.645, 0.919, 0.736, 0.942]; {\"AI\"}[0.789, 0.919, 0.803, 0.942]; {\"apps\"}[0.884, 0.919, 0.919, 0.942]</r>. The green segment in each bar on the chart represents positive mentions. A careful examination of all bars shows that the green segment of the {'R&D'}<r>[0.543, 0.483, 0.627, 0.903]</r> bar is the longest among all, indicating that the aspect of 'R&D' in healthcare and medical technology is most positively discussed.\nBased on the above thought, the answer is:\nR&D. This aspect has the longest green segment representing positive mentions, indicating that 'R&D' is the most positively discussed aspect of healthcare and medical technology."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "IhkPXOJARhlqHlqm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect of healthcare and medical technology has the highest number of mentions with mixed sentiment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the aspect with the highest number of mentions with mixed sentiment, I can compare the orange segments of the bars representing each aspect on the x-axis. {The four bars}<r>[0.121, 0.767, 0.205, 0.903]; [0.226, 0.716, 0.311, 0.794]; [0.332, 0.819, 0.416, 0.903]; [0.437, 0.698, 0.522, 0.903]; [0.543, 0.483, 0.627, 0.903]; [0.648, 0.088, 0.733, 0.903]; [0.754, 0.736, 0.838, 0.903]; [0.859, 0.823, 0.944, 0.903]</r> correspond to {medicine}<r>[0.132, 0.919, 0.194, 0.942]</r>, {treatment}<r>[0.233, 0.919, 0.304, 0.942]</r>, {diagnosis}<r>[0.340, 0.919, 0.408, 0.942]</r>, {technology}<r>[0.441, 0.919, 0.518, 0.942]</r>, {R&D}<r>[0.569, 0.919, 0.601, 0.942]</r>, {telemedicine}<r>[0.645, 0.919, 0.736, 0.942]</r>, {AI}<r>[0.789, 0.919, 0.803, 0.942]</r>, and {apps}<r>[0.884, 0.919, 0.919, 0.942]</r> respectively. Observing the orange segments, it is clear that the segment for the Apps aspect is the longest, indicating that it has the highest number of mentions with mixed sentiment.\nBased on the above thought, the answer is:\nApps."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "PPpk0z2KlXwJGO4h",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation of the chart data in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows two trends: {Growth}<r>[0.218, 0.083, 0.299, 0.118]</r> and {Decline}<r>[0.218, 0.135, 0.301, 0.170]</r>, which are represented by green and red colors respectively. Various stages of scientific research and innovation are listed on the x-axis. These stages include Experimentation : {Exp}<r>[0.188, 0.879, 0.231, 0.914]</r>, Analysis : {Ana}<r>[0.286, 0.879, 0.331, 0.914]</r>, Publication : {Pub}<r>[0.385, 0.879, 0.428, 0.914]</r>, Peer Review : {Peer}<r>[0.479, 0.879, 0.530, 0.914]</r>, Replication : {Rep}<r>[0.581, 0.879, 0.625, 0.914]</r>, Development : {Dev}<r>[0.678, 0.879, 0.724, 0.914]</r>, Implementation : {Imp}<r>[0.777, 0.879, 0.821, 0.914]</r> and Communication : {Com}<r>[0.871, 0.879, 0.924, 0.914]</r>. The frequencies for each stage are represented by the heights of the bars belonging to each trend. For each stage, I can observe the frequencies for both the Growth and Decline trends, which can be computed based on the y-axis labels and the proportion of the bar height. Considering all these, we can list the data in a structured format as follows.\nBased on the above thought, the answer is:\nFields,Growth,Decline\nExperimentation,2000,100\nAnalysis,1650,200\nPublication,2050,500\nPeer Review,2000,50\nReplication,1800,800\nDevelopment,1700,900\nImplementation,1300,300\nCommunication,1800,150"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "AtQpVr1UZbwlebVq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn your observation, which field seems to have witnessed a significant shift from growth to decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "A significant shift from growth to decline in a field is indicated when the decline bar is significantly taller than the growth bar within the same field. I observe each pair of bars from left to right. When considering 'Communication' field, I note that the {decline red bar of Communication}<r>[0.858, 0.463, 0.937, 0.493]</r> is smaller than the {growth green bar of Communication}<r>[0.858, 0.493, 0.937, 0.854]</r>. This indicates that the growth in 'Communication' has not shifted significantly to decline. By applying the same process, I notice a significant contrast in the 'Publication' field, where the {decline red bar of Publication}<r>[0.367, 0.342, 0.446, 0.443]</r> is much taller than the {growth green bar of Publication}<r>[0.367, 0.443, 0.446, 0.854]</r>. This indicates a significant shift from growth to decline in the 'Publication' field.\nBased on the above thought, the answer is:\nThe 'Publication' field has witnessed a significant shift from growth to decline."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "F9AFvY4CXvpFRtmY",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field has the least growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which field has the least growth, I need to investigate the green bars which represent the growth trend. The heights of these bars reflect the amount of growth in each respective field. By observing the green bars from left to right, I can notice that the bar for the 'Implementation' field {the green bar of Implementation}<r>[0.760, 0.593, 0.838, 0.854]</r> appears to be the shortest among all green bars.\nBased on the above thought, the answer is:\nImplementation."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "c0IeeGwm6S0fcWBs",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the given visual data representation into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart comprises four different bars that represent two different categories, namely, {Social Services}<r>[0.163, 0.046, 0.293, 0.069]</r> and {Public services}<r>[0.163, 0.081, 0.291, 0.104]</r>. \nEach category comprises four sub categories namely: {Healthcare}<r>[0.176, 0.919, 0.271, 0.942]</r>, {Education}<r>[0.391, 0.919, 0.478, 0.942]</r>, {Infrastructure}<r>[0.586, 0.919, 0.705, 0.942]</r>, and {Environmental Protection}<r>[0.746, 0.919, 0.966, 0.942]</r>. To construct the CSV table, we need to keep track of the height of the bars for each resource within their sectors. For example, in the Healthcare sector, we can see that the resource allocation for Social Services (Red bar)<r>[0.139, 0.823, 0.308, 0.903]</r> is almost in range of the 100 mark, and for Public Services (Green bar)<r>[0.139, 0.823, 0.308, 0.823]</r> it is same. We can proceed in a similar manner for all sectors under both categories.\nBased on the above thought, the answer is:\nSectors,Social Services,Public services\nHealthcare,100,100\nEducation,500,500\nInfrastructure,0,1000\nEnvironmental Protection,0,0"
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "aqKVxyWKMTSl3bfJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observations of the chart, which category, Social Services or Public Services, had a more stable budget allocation across different sectors, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine stability of allocation, we need to analyse the variation in resource allocation for different sectors in both the categories. For example, in {Social Services}<r>[0.163, 0.046, 0.293, 0.069]</r>, the allocation for {Healthcare}<r>[0.139, 0.823, 0.308, 0.903]</r> and {Education}<r>[0.350, 0.504, 0.519, 0.903]</r> shows quite a bit of variance, while in {Public Services}<r>[0.163, 0.081, 0.291, 0.104]</r> the allocation for sectors such as {Infrastructure}<r>[0.561, 0.105, 0.730, 0.903]</r> and {Environmental Protection}<r>[0.772, 0.903, 0.941, 0.903]</r> also shows a very high difference. On comparing both, we can see that Public Services shows a more uneven allocation as compared to Social Services, therefore Social Services had a more stable budget allocation.\nBased on the above thought, the answer is:\nSocial Services. Due to the lesser variance in allocation across sectors as compared to Public Services."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "pODavUkAdf6K1P75",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which sector is the difference in resource allocation between Social Services and Public Services the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "The way to find the sector with the largest resource allocation difference is to compare the heights of bars under Social Services and Public services for each sector. The first pair for the Healthcare sector upon comparison provides us with no difference. Continuing in such a manner for every sector, we see that the {Infrastructure}<r>[0.561, 0.105, 0.730, 0.903]</r> has the largest difference with Public Services Resource Allocation surpassing by 1000 units.\nBased on the above thought, the answer is:\nInfrastructure."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "UWs0h47cs1bKa5ae",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list down the total values across all trends for each category in a CSV table based on the information in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to generate the CSV table, I can begin by collating the labels on the x-axis as my categories. These labels I have detected are {'Innovation Ecosystem'}[0.214, 0.879, 0.468, 0.914] and {'Technology Entrepreneurship'}[0.598, 0.879, 0.935, 0.914]. By examining the height of the bar stacks and their corresponding y-value labels I can calculate the values of each trend for a particular category. For instance, in 'Innovation Ecosystem', I find four different bars stacked with each representing a unique trend. The respective trends are {'Surging Growth'}[0.171, 0.754, 0.511, 0.854], {'Descending Crisis'}[0.171, 0.593, 0.511, 0.754], {'Stable Market'}[0.171, 0.192, 0.511, 0.593] and {'Emerging Trend'}[0.171, 0.172, 0.511, 0.192]. Reading the y-axis tick labels for each of these bars I can determine the individual values for each trend in the category. Using the same approach, I can gather the values for the 'Technology Entrepreneurship' category as well.\nBased on the above thought, the answer is:\nEcosystems,Surging Growth,Descending Crisis,Stable Market,Emerging Trend,Total Value\nInnovation Ecosystem,500,800,2000,100,3400\nTechnology Entrepreneurship,200,1500,700,450,2850"
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "gKjsLRvb2rKFhE4e",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we were to base our decision on these trends, which ecosystem - Innovation Ecosystem or Technology Entrepreneurship - would you recommend for investment? Support your answer with reasons from the chart's data."
+            },
+            {
+                "from": "gpt",
+                "value": "This question asks about choosing an investment based on performance across different trends. I need to look at the performance of each ecosystem in the four trends: {'Surging Growth'}, {'Descending Crisis'}, {'Stable Market'} and {'Emerging Trend'}. From the chart's data, I can see that the Innovation Ecosystem's values for {'Surging Growth'}[0.171, 0.754, 0.511, 0.854], {'Stable Market'}[0.171, 0.192, 0.511, 0.593] and {'Emerging Trend'}[0.171, 0.172, 0.511, 0.192] are higher than respective values for Technology Entrepreneurship's {'Surging Growth'}[0.596, 0.814, 0.937, 0.854], {'Stable Market'}[0.596, 0.372, 0.937, 0.513] and {'Emerging Trend'}[0.596, 0.282, 0.937, 0.372]. Conversely, Innovation Ecosystem's value for {'Descending Crisis'}[0.171, 0.593, 0.511, 0.754] is lower than the value of {'Descending Crisis'} for Technology Entrepreneurship[0.596, 0.513, 0.937, 0.814]. Investing in an ecosystem that has a down trend would not be ideal, hence having a lower value for 'Descending Crisis' is better.\nBased on the above thought, the answer is:\nInnovation Ecosystem. Because it has higher values in 'Surging Growth', 'Stable Market' and 'Emerging Trend' which are positive indicators for investment. It also has a lower value in 'Descending Crisis' which is a negative indicator. Thus, it presents a safer and more promising option for investment compared to Technology Entrepreneurship."
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "dwv3UYW3IztgATzp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the value for 'Stable Market' trend in the 'Technology Entrepreneurship' ecosystem changed compared to the 'Innovation Ecosystem'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the values of the 'Stable Market' trend in both ecosystems. Using the height of the green bars representing the 'Stable Market' trend in {'Innovation Ecosystem'}[0.171, 0.192, 0.511, 0.593] and in {'Technology Entrepreneurship'}[0.596, 0.372, 0.937, 0.513], I can determine the corresponding values from the y-axis. Once I have these values, I can subtract the 'Stable Market' value of 'Technology Entrepreneurship' from that of 'Innovation Ecosystem' to get the change.\nBased on the above thought, the answer is:\n600"
+            }
+        ],
+        "id_raw": 288
+    },
+    {
+        "id": "QyOcfvZgtK6TAAHx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information revealed in the stacked bar chart into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The x-axis represents different types of investments labeled {\"Bond Investments\"}<r>[0.114, 0.671, 0.307, 0.858]</r>, {\"Stock Investments\"}<r>[0.246, 0.671, 0.443, 0.861]</r>, {\"Real Estate Investments\"}<r>[0.352, 0.671, 0.606, 0.911]</r>, {\"Mutual Funds\"}<r>[0.541, 0.671, 0.686, 0.817]</r>, {\"Commodity Investments\"}<r>[0.619, 0.671, 0.877, 0.914]</r>, and {\"Cryptocurrency\"}<r>[0.799, 0.671, 0.965, 0.835]</r>. The Y-axis represents the return rate percentage ranging from 0 to 100 which can be inferred from the y-axis labels {0}<r>[0.087, 0.633, 0.102, 0.668]</r>, {20}<r>[0.072, 0.514, 0.102, 0.549]</r>, up to {100}<r>[0.058, 0.037, 0.102, 0.073]</r>. The bar chart is divided into different colored sections each indicating a different trend. According to {the legend}<r>[0.129, 0.069, 0.432, 0.288]</r>, the red color represents return rates falling, the green represents return rates remaining the same, the gray represents return rates rising, and the orange signalizes an abrupt drop. Each segment's height in the bars represents the percentage rate for each investment type under different trends.\nBased on the above thought, the answer is:\nInvestment Types,Return Rate Falling,Return Rate Same,Return Rate Rising,Abrupt Drop\nBond Investments,50,40,10,0\nStock Investments,20,20,55,5\nReal Estate Investments,10,30,50,10\nMutual Funds,35,35,25,5\nCommodity Investments,5,15,60,20\nCryptocurrency,30,10,45,15"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "aDb4aWWePrifBKn1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment type has the most balanced growing possibility and risk according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A well-balanced investment would be one that has a high percentage chance for return rates to rise (grey colored in the bar in our context), and relatively low percentage chances of abrupt drops (orange colored in the bar). Looking over each investment type, the {bar for \"Real Estate Investments\"}<r>[0.425, 0.051, 0.533, 0.646]</r> stands as balanced because it shows a substantial grey segment implying a good possibility of increasing return rates, and the orange segment at the bottom is relatively small, signaling a lesser risk of abrupt drops compared to others. Thus, \"Real Estate Investments\" can be considered as having a more balanced growth possibility and risk.\nBased on the above thought, the answer is:\nReal Estate Investments."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "6rwYz1kiKK2E0tgl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of investment has the highest chance of seeing a return rate rise, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which investment type has the highest chance of a rising return rate, I need to look at the gray segments of each bar for each investment type. It can be seen that the grey segment is the highest in the {bar for \"Commodity Investments\"}<r>[0.694, 0.051, 0.802, 0.646]</r>. The height of the gray segment is much larger compared to the gray segments in other investment type bars, indicating that commodity investments have the highest percentage for a rising return rate.\nBased on the above thought, the answer is:\nCommodity Investments."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "hkP4WdqJPgCEOTA6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart you have, could you encode the data represented into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table from the chart, I want to consider the information on both axes. The {'x-axis'}<r>[0.479, 0.927, 0.628, 0.963]</r> label is 'School Types', which refers to the two categories: {'Urban Schools'}<r>[0.164, 0.879, 0.330, 0.914]</r> and {'Rural Schools'}<r>[0.782, 0.879, 0.938, 0.914]</r>. The {'y-axis'}<r>[0.025, 0.267, 0.048, 0.639]</r> label is 'Number of Resources', indicating the quantity of each type of resource for each school category. Resource types can be found in the {'legend'}<r>[0.144, 0.069, 0.368, 0.288]</r>, which are 'Textbooks'<r>[0.218, 0.083, 0.333, 0.118]</r>, 'Pencils'<r>[0.218, 0.135, 0.297, 0.170]</r>, 'School Bags'<r>[0.218, 0.187, 0.359, 0.222]</r> and 'Laptops'<r>[0.218, 0.240, 0.310, 0.275]</r>. The heights of each resource in each category represent the quantities. I'll assess the heights of each rectangle to determine the quantity. I pull the data together and build a CSV table.\nBased on the above thought, the answer is:\nSchool Types,Textbooks,Pencils,School Bags,Laptops\nUrban Schools,2750,3850,5100,1800\nRural Schools,1400,2550,860,700"
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "z3vrZPJAvpAl9HWv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the chart suggest an equity issue between urban and rural schools in terms of resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate this, I would compare the resource allocation for {'Urban Schools'}<r>[0.164, 0.879, 0.330, 0.914]</r> and {'Rural Schools'}<r>[0.782, 0.879, 0.938, 0.914]</r>. From the stacked bars, it can be seen that the resources for Urban Schools reach approximately up to around {'7000'}<r>[0.058, 0.138, 0.116, 0.173]</r> on the y-axis. In contrast, the total resources for Rural Schools, as indicated by the bars, reach up to around {'2000'}<r>[0.058, 0.640, 0.116, 0.675]</r> on the y-axis. The bar for Urban Schools is notably taller than the bar for Rural Schools, suggesting a disparity in resource allocation. This implies that there could indeed be an equity issue between the schools in different areas, with Urban ones receiving more resources while rural ones receive less.\nBased on the above thought, the answer is:\nYes, the chart suggests a disparity in resource allocation between urban and rural schools, with urban schools receiving more resources, indicating an equity issue."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "RHxdf6C9OZKj6hxM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the maximum number of resources provided by urban schools?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the rectangle representing Urban Schools. From the chart, I can see in {'Urban Schools'}<r>[0.164, 0.879, 0.330, 0.914]</r> that the bars within that rectangle represent different resources. These bars each represent a block or quantity of the resources. If I add all these quantities up by calculating the total height of the bar for Urban Schools, I can see that it reaches up to around {'7000'}<r>[0.058, 0.138, 0.116, 0.173]</r> on the y-axis. Therefore, the total resource provided by Urban Schools is 7000.\nBased on the above thought, the answer is:\n7000."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "uZv9VVb3fFrtE5QX",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease organize the stacked bar chart data into a CSV format table according to the trends you observed."
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels on the x-axis {\"2022\"}[0.194, 0.879, 0.253, 0.914], {\"2023\"}[0.326, 0.879, 0.385, 0.914], {\"2024\"}[0.458, 0.879, 0.517, 0.914], {\"2025\"}[0.591, 0.879, 0.649, 0.914], {\"2026\"}[0.723, 0.879, 0.781, 0.914], {\"2027\"}[0.855, 0.879, 0.913, 0.914], I can see that the x-axis corresponds to the years from 2022 to 2027. The y-axis label {\"Quantity\"}[0.025, 0.378, 0.048, 0.528] shows the quantity of each trend per year. Each bar represents the outcomes of the pessimistic, normal, and optimistic scenarios in each year. The pessimistic scenario is depicted in the {red bars}<r>[0.171, 0.232, 0.276, 0.854]</r>, the normal scenario in the {green bars}<r>[0.303, 0.292, 0.408, 0.854]</r>, and the optimistic scenario in the {grey bars}<r>[0.435, 0.332, 0.540, 0.854]</r>. I need to extract the length of each part of the bar using the associated y-axis value to determine the values for each trend in each year.\nBased on the above thought, the answer is:\nYear,Pessimistic,Normal,Optimistic\n2022,3100,2800,2200\n2023,2800,2700,2250\n2024,2600,2600,2300\n2025,2350,2550,2400\n2026,2100,2500,2500\n2027,1800,2450,2600"
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "pg7NpL04dQNpSBEC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of the overall trend from 2022 to 2027, which scenario performed the best and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which scenario performed the best over the given period, I need to consider the direction of change. The {red bars}<r>[0.171, 0.232, 0.276, 0.854]</r> correspond to the pessimistic scenario, {green bars}<r>[0.303, 0.292, 0.408, 0.854]</r> to the normal scenario, and {grey bars}<r>[0.435, 0.332, 0.540, 0.854]</r> to the optimistic scenario. For the pessimistic scenario, the quantity is decreasing from 3100 in 2022 to 1800 in 2027, the normal scenario shows a slow but steady recovery, and optimistic scenario shows a gradual increase from 2200 in 2022 to 2600 in 2027. Additionally, the optimistic scenario has less variation compared to the other two scenarios.\nBased on the above thought, the answer is:\nThe Optimistic scenario performed the best. It shows an upward trend over time and less variation compared to the other scenarios."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "uAUBj5oUzAmYMaXS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2025, what is the total quantity combining all trends in the forecast?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total quantity of the three trends in 2025, I need to add the quantities of the pessimistic, normal, and optimistic trends. In 2025, marked with {\"2025\"}[0.591, 0.879, 0.649, 0.914] on the x-axis, the length of each part of the stacked bar represents a specific trend. I can locate the pessimistic trend in a {red part}<r>[0.567, 0.383, 0.673, 0.854]</r>, the normal trend in a {green part}<r>[0.567, -0.129, 0.673, 0.383]</r>, and the optimistic trend in a {grey part}<r>[0.567, -0.611, 0.673, -0.129]</r> of the 2025 bar. I need to add the length of each part to get the total quantity.\nBased on the above thought, the answer is:\n7300."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "UG11EEidm52GbZ93",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar graph, could you convert the frequency of sentiments within different policy areas as you see them into a meaningful CSV-formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-asix is divided into eight different policy sectors, namely, {Children and Families}<r>[0.104, 0.690, 0.209, 0.882]</r>, {Environmental Protection}<r>[0.202, 0.690, 0.324, 0.914]</r>, {Economic Development}<r>[0.312, 0.690, 0.427, 0.901]</r>, {Public Safety}<r>[0.443, 0.690, 0.509, 0.813]</r>, {Health and Welfare}<r>[0.535, 0.690, 0.630, 0.864]</r>, {Education and Skills}<r>[0.640, 0.690, 0.739, 0.871]</r>, {Transportation Infrastructure}<r>[0.727, 0.690, 0.864, 0.942]</r>, and {Public Finance}<r>[0.866, 0.690, 0.938, 0.823]</r>. The height of the different sections of each bar displays the sentiment frequency in terms of sentiments which are described in the legend as {negative}<r>[0.119, 0.055, 0.174, 0.078]</r>, {positive}<r>[0.119, 0.090, 0.168, 0.113]</r>, {none}<r>[0.119, 0.125, 0.150, 0.148]</r> and {mixed}<r>[0.119, 0.160, 0.158, 0.183]</r>. I evaluate the exact frequencies by compare the height of each colored segment with y-axis labels, from {0}<r>[0.055, 0.665, 0.063, 0.688]</r>  to {4000}<r>[0.032, 0.025, 0.063, 0.048]</r>. Hence by going through each policy area and sentiments, a proper CSV table can be mapped out.\nBased on the above thought, the answer is:\nPolicy Areas,Negative,Positive,None,Mixed\nChildren and Families,400,10,300,290\nEnvironmental Protection,850,30,50,70\nEconomic Development,250,800,50,0\nPublic Safety,400,200,400,0\nHealth and Welfare,200,1800,0,0\nEducation and Skills,500,100,1400,0\nTransportation Infrastructure,600,350,50,0\nPublic Finance,550,50,400,0"
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "MlGMAuC6tmgHg4cd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which policy area has received the most positive response?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to look at the green sections representing {positive sentiment}<r>[0.119, 0.090, 0.168, 0.113]</r> in each bar. These bars represent, from left to right, the policy areas of {Children and Families}<r>[0.114, 0.608, 0.199, 0.610]</r>, {Environmental Protection}<r>[0.220, 0.533, 0.306, 0.538]</r>, {Economic Development}<r>[0.327, 0.506, 0.412, 0.634]</r>, {Public Safety}<r>[0.433, 0.578, 0.519, 0.610]</r>, {Health and Welfare}<r>[0.540, 0.354, 0.625, 0.642]</r>, {Education and Skills}<r>[0.646, 0.578, 0.732, 0.594]</r>, {Transportation Infrastructure}<r>[0.753, 0.522, 0.838, 0.578]</r>, and {Public Finance}<r>[0.860, 0.578, 0.945, 0.586]</r>. By comparing the heights, it's evident that {Health and Welfare}<r>[0.540, 0.354, 0.625, 0.642]</r> has the highest green segment, indicating that it has received the most-positive sentiments.\nBased on the above thought, the answer is:\nThe policy area 'Health and Welfare' has seen the most positive responses according to the chart."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "Xtmo2g9D8avliD18",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which policy area is there the highest occurrence of no particular sentiment?"
+            },
+            {
+                "from": "gpt",
+                "value": "I investigate the gray areas denoting {the 'none' sentiment}<r>[0.119, 0.125, 0.150, 0.148]</r> of each bar on the chart to answer this question. Starting from the left, the first bar represents {Children and Families}<r>[0.114, 0.560, 0.199, 0.608]</r>, followed by {Environmental Protection}<r>[0.220, 0.525, 0.306, 0.533]</r>, {Economic Development}<r>[0.327, 0.498, 0.412, 0.498]</r>, {Public Safety}<r>[0.433, 0.514, 0.519, 0.514]</r>, {Health and Welfare}<r>[0.540, 0.354, 0.625, 0.354]</r>, {Education and Skills}<r>[0.646, 0.354, 0.732, 0.354]</r>, {Transportation Infrastructure}<r>[0.753, 0.514, 0.838, 0.514]</r>, and finally, {Public Finance}<r>[0.860, 0.514, 0.945, 0.514]</r>. The gray segment for {Education and Skills}<r>[0.646, 0.354, 0.732, 0.578]</r> is visibly the largest, indicating the highest occurrence of no particular sentiment in this policy area.\nBased on the above thought, the answer is:\nEducation and Skills"
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "O11jN2aBABhY37oT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly extract chart data and organize it into a structured table to better illustrate the migration trends for both Human and Industrial categories."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the labels identified in the chart, my first step will be to list down the categories of trends that are marked by different colours as indicated {in the legend}<r>[0.096, 0.055, 0.320, 0.356]</r>. We have 'Erratic Decline', 'Volatile Growth', 'Rapid Decline', 'Sustained Growth', 'Sluggish Progress', 'Roller-Coaster Pattern' and 'Moderate Decline'. Next, I will note down the xaxis labels which classify migration data into 'Human' and 'Industrial' categories from {the xlabel coordinates}<r>[0.472, 0.942, 0.600, 0.970]</r>. The height of the segments of each bar corresponding to these categories are found from {the bars attribute}<r>Bars information</r>. The values for 'Human' migration will correspond to the 'y_range' property of the bars at 'x_label' 'Human' and the 'Industrial' values would similarly correspond to 'y_range' of bars at 'x_label' 'Industrial'. Matching each value range with the corresponding category from the legend would allow me to construct the table.\nBased on the above thought, the answer is:\nMigration Trends,Human,Industrial\nErratic Decline,3500,2200\nVolatile Growth,5200,7000\nRapid Decline,1100,1500\nSustained Growth,1200,2300\nSluggish Progress,1000,4000\nRoller-Coaster Pattern,4200,3500\nModerate Decline,2000,2700"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "rPzIipASU1NR6dyM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which category appears to be more stable in migration trend and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "We will consider the category with smaller fluctuations or less extreme highs and lows as more stable. From the analysis of bars, the 'Human' migration observed from {Human x_label}<r>[0.195, 0.903, 0.251, 0.931]</r> begins at '0' and rises to its peak at 'Roller-Coaster Pattern' and then slightly declines at 'Moderate Decline', which means 'Human' migration has fluctuations. Similarly, for 'Industrial' migration marked by {Industrial x_label}<r>[0.813, 0.903, 0.885, 0.931]</r>, it starts at '0', ascends to a high at 'Roller-Coaster Pattern' and then descends very slightly to 'Moderate Decline', indicating smaller fluctuations. Therefore, we can suggest that the 'Industrial' category is more stable in migration trends.\nBased on the above thought, the answer is:\nThe 'Industrial' category appears to be more stable, because it experiences smaller fluctuations as compared to 'Human' migration. It rises to a high peak and then descends very slightly, indicating relatively steady migration trends."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "dX1yrX8lG9BotIuR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, which category, Human or Industrial, has the highest overall migration trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which category has the maximum migration trend, I need to individually summarize the segment heights for each category. For 'Human', sum of all segments from {Erratic Decline}<r>[0.129, 0.515, 0.317, 0.883]</r> to {Moderate Decline}<r>[0.129, -1.033, 0.317, -0.823]</r> gives me the total migration trend. Similarly, for 'Industrial', I can sum up all segments from {Erratic Decline}<r>[0.755, 0.652, 0.943, 0.883]</r> to {Moderate Decline}<r>[0.755, -1.560, 0.943, -1.275]</r>. By comparing these two totals we can find the category with the highest overall migration trend.\nBased on the above thought, the answer is:\nIndustrial"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "DwYx3xqs69fZql3k",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you reformat the bar chart's data into a csv?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title reveals that the bars in the chart represent the consumption and growth trends of different countries. The `x-label` of the chart {xlabel}<r>[0.499, 0.959, 0.555, 0.979]</r> indicates the countries. I noticed there are eight categories which are China, India, USA, Brazil, Russia, South Africa, Nigeria and Indonesia. The `ylabel` of the chart {ylabel}<r>[0.013, 0.309, 0.024, 0.637]</r> discloses the values in the billions USD for consumption and percentage for growth. For each country, there are two bars which represent Consumption (blue bar) and Growth (red bar). Every bar's vertical range corresponds to the data value, and the y-axis tick labels span from 0 to 5000 {yticks}<r>{\"0\": [0.051, 0.909, 0.058, 0.929], \"5000\": [0.029, 0.021, 0.058, 0.041]}</r>, which allows us to determine the values. For example, in China's case, its Consumption bar {China Consumption bar}<r>[0.108, 0.207, 0.146, 0.917]</r> is approximately at the 4000 position, and its Growth bar {China Growth bar}<r>[0.146, 0.914, 0.184, 0.917]</r> is near the 15 position, therefore, the values for China are 4000 and 15 for Consumption and Growth respectively. By similarly evaluating the bars for other countries, I can construct a csv.\nBased on the above thought, the answer is:\nCountry,Consumption,Growth\nChina,4000,15\nIndia,3200,12\nUSA,4500,2\nBrazil,1600,9\nRussia,1500,-5\nSouth Africa,800,-7\nNigeria,1000,20\nIndonesia,1500,6"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "o1ZucHycdSILcJIk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the performance of the United States in terms of consumer behavior based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the bars on the chart referring to the United States, we have two indicators, Consumption in blue {USA Consumption bar}<r>[0.326, 0.118, 0.364, 0.917]</r> and Growth in red {USA Growth bar}<r>[0.364, 0.916, 0.402, 0.917]</r>. The Consumption bar is highest on the y-axis {yticks}<r>{\"4500\": [0.029, 0.199, 0.058, 0.219]}</r> indicating that the United States has the highest consumption. However, the Growth bar lies very close to zero on the y-axis indicating a much smaller growth rate. This tells us that while the consumption level is high, the growth in consumption is quite slow which could suggest a mature market with saturated consumer demand.\nBased on the above thought, the answer is:\nThe United States displays high consumption but slow growth, suggesting the presence of a stable, but saturated market."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "sSNkGYGRGJQYYyjr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the highest consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the country with the highest consumption, I must identify the blue bar reaching the highest point on the y-axis. By examining the bar height,  I can observe that the longest blue bar corresponds to {USA}<r>[0.326, 0.118, 0.364, 0.917]</r>, marking a height of about 4500 on the y-axis {yticks}<r>{\"4500\": [0.029, 0.199, 0.058, 0.219]}</r>\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "gPHPOQVA2sboNet4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the progression of 'Stable Growth' and 'Rapid Decline' over time into a CSV format table for effectives comparison?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the x-axis ticks {['1', '2', '3', '4', '5', '6', '7', '8']}<{\"1\"}[0.165, 0.903, 0.177, 0.931]; {\"2\"}[0.271, 0.903, 0.282, 0.931]; {\"3\"}[0.377, 0.903, 0.387, 0.931]; {\"4\"}[0.482, 0.903, 0.493, 0.931]; {\"5\"}[0.588, 0.903, 0.598, 0.931]; {\"6\"}[0.693, 0.903, 0.704, 0.931]; {\"7\"}[0.799, 0.903, 0.809, 0.931]; {\"8\"}[0.904, 0.903, 0.915, 0.931]>, I can tell that the x-axis represents the timeframe. The y-axis represents frequencies, as can be determined from the y-axis label {ylabel}[0.019, 0.385, 0.036, 0.528] and it varies from {0}[0.076, 0.872, 0.087, 0.900] to {2000}[0.043, 0.060, 0.087, 0.088]. Observing the legend {legend}[0.786, 0.044, 0.973, 0.136] and the items within, the first group of bars represent 'Stable Growth' shown by {Stable Growth handle}[0.793, 0.058, 0.828, 0.077] and the second group of bars represent 'Rapid Decline' shown by {Rapid Decline handle}[0.793, 0.099, 0.828, 0.119]. I can also see the specific numbers on top of bars for frequencies in each timeframe. The bars representing each timeframe for 'Stable Growth' are {Bars of Stable Growth}[0.139, 0.680, 0.171, 0.883; 0.245, 0.721, 0.276, 0.883; 0.350, 0.680, 0.382, 0.883; 0.456, 0.701, 0.488, 0.883; 0.561, 0.680, 0.593, 0.883; 0.667, 0.741, 0.699, 0.883; 0.772, 0.721, 0.804, 0.883; 0.878, 0.680, 0.910, 0.883] and 'Rapid Decline' are {Bars of Rapid Decline}[0.171, 0.071, 0.203, 0.883; 0.276, 0.172, 0.308, 0.883; 0.382, 0.274, 0.414, 0.883; 0.488, 0.375, 0.519, 0.883; 0.593, 0.477, 0.625, 0.883; 0.699, 0.558, 0.730, 0.883; 0.804, 0.640, 0.836, 0.883; 0.910, 0.721, 0.941, 0.883]. Taking all those inputs into consideration, I can generate the CSV formatted data.\nBased on the above thought, the answer is:\nTimeframe, Stable Growth, Rapid Decline\n1, 500, 2000\n2, 400, 1750\n3, 500, 1500\n4, 450, 1250\n5, 500, 1000\n6, 350, 800\n7, 400, 600\n8, 500, 400"
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "Vl3KxvTuNTeb98sR",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you suggest for the future learning trend based on the current 'Stable Growth' and 'Rapid Decline' trend scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Rapid Decline' trend as represented by the {red bars}<{\"Rapid Decline\"}[0.792, 0.099, 0.828, 0.119]>shows a considerable decrease in frequency over time while 'Stable Growth' trend represented by the {green bars}<{\"Stable Growth\"}[0.793, 0.058, 0.828, 0.077]> appears to be more balanced with slight fluctuations. This suggests that if we continue with the current trends, we might witness a decline in the popularity of online education.\nBased on the above thought, the answer is:\nTherefore, based on the current trends, the education technology and online learning sectors might need to reevaluate their strategies and make necessary improvements to ensure a steady growth rate. They could possibly focus on factors that encourage 'Stable Growth' to counteract the 'Rapid Decline'. This could even include implementing innovative techniques, improving interface and user friendliness, offering diverse courses or even reducing fees."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "0QxQayn3NgUr3bSR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum decrease in frequency from one timeframe to another for 'Rapid Decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I need to look at the difference of y-values for each subsequent bar in the 'Rapid Decline' group. The corresponding bars are {['1', '2', '3', '4', '5', '6', '7', '8']}<0.171, 0.071, 0.203, 0.883; 0.276, 0.172, 0.308, 0.883; 0.382, 0.274, 0.414, 0.883; 0.488, 0.375, 0.519, 0.883; 0.593, 0.477, 0.625, 0.883; 0.699, 0.558, 0.730, 0.883; 0.804, 0.640, 0.836, 0.883; 0.910, 0.721, 0.941, 0.883>. The differences in frequency from one timeframe to another are: 2000-1750=250, 1750-1500=250, 1500-1250=250, 1250-1000=250, 1000-800=200, 800-600=200, and 600-400=200. So, the maximum decrease is 250 found in the first four observations.\nBased on the above thought, the answer is:\n250"
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "Wf2J976ZS3JD9UMH",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV formatted table from the expenses data in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart, the x-axis represents different business processes, ranging from {Expense}<r>[0.124, 0.758, 0.159, 0.830]</r> to {Delivery}<r>[0.895, 0.758, 0.929, 0.829]</r>. The y-axis indicates the amount of expenses, from {0}<r>[0.051, 0.739, 0.058, 0.756]</r> to {4000}<r>[0.029, 0.019, 0.058, 0.036]</r>. The two colored bars under each process represent the expenses in 2010 and 2020, as specified by {the legend}<r>[0.072, 0.034, 0.143, 0.092]</r>. The light blue bars, labeled {2010}<r>[0.109, 0.041, 0.138, 0.059]</r>, represent the expenses in the year 2010, and the orange bars, labeled {2020}<r>[0.109, 0.067, 0.138, 0.085]</r>, represent the expenses in the year 2020. The height of each bar indicates the exact value of expenses. Hence, I can create a CSV table by notating the exact amount of expenses for each year and for each process.\nBased on the above thought, the answer is:\nProcesses,2010,2020\nExpense,1000,1200\nCustomer_Meeting,1200,1000\nMarket_Research,800,900\nProduct_Development,900,1200\nStaff_Training,700,800\nSupply_Management,1500,1600\nOrder_Processing,2000,2400\nWarehouse_Management,2200,2300\nDelivery,2100,2300"
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "zKpefTTKIgWQACFY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a consistent trend in the expense report from 2010 to 2020 for all processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend}<r>[0.072, 0.034, 0.143, 0.092]</r> denotes the two colored bars for each process, the light blue represented by {2010}<r>[0.109, 0.041, 0.138, 0.059]</r> and orange for {2020}<r>[0.109, 0.067, 0.138, 0.085]</r>. Across all processes; {Expense}<r>[0.108, 0.566, 0.175, 0.746]</r> and <r>[0.108, 0.350, 0.175, 0.566]</r>; {Customer_Meeting}<r>[0.204, 0.530, 0.272, 0.746]</r> and <r>[0.204, 0.350, 0.272, 0.530]</r>; {Market_Research}<r>[0.301, 0.602, 0.368, 0.746]</r> and <r>[0.301, 0.440, 0.368, 0.602]</r>; {Product_Development}<r>[0.397, 0.584, 0.464, 0.746]</r> and <r>[0.397, 0.368, 0.464, 0.584]</r>; {Staff_Training}<r>[0.493, 0.620, 0.561, 0.746]</r> and <r>[0.493, 0.476, 0.561, 0.620]</r>; {Supply_Management}<r>[0.589, 0.476, 0.657, 0.746]</r> and <r>[0.589, 0.188, 0.657, 0.476]</r>; {Order_Processing}<r>[0.686, 0.386, 0.753, 0.746]</r> and <r>[0.686, -0.046, 0.753, 0.386]</r>; {Warehouse_Management}<r>[0.782, 0.350, 0.849, 0.746]</r> and <r>[0.782, -0.064, 0.849, 0.350]</r>; {Delivery}<r>[0.878, 0.368, 0.946, 0.746]</r> and <r>[0.878, -0.046, 0.946, 0.368]</r>, you can see that all processes except Customer Meeting and Staff Training experienced an increase in expenses from 2010 to 2020. Therefore, the consistent trend observed is an overall increase in expenses; however, the exception to this trend is that the expenses for Customer Meeting and Staff Training have decreased.\nBased on the above thought, the answer is:\nGenerally, there is an increasing trend in the expenses from 2010 to 2020 for most processes. However, the trend is inconsistent as there is a decrease in expenses for the 'Customer Meeting' and 'Staff Training' processes."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "uWg4CFtu862TYz5c",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what business process is the maximum difference in expenses observed over the 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Compare the bars from every process in 2010 and 2020 for the difference in expenses. By calculating and comparing {Expense}<r>[0.108, 0.566, 0.175, 0.746]</r> and <r>[0.108, 0.350, 0.175, 0.566]</r>; {Customer_Meeting}<r>[0.204, 0.530, 0.272, 0.746]</r> and <r>[0.204, 0.350, 0.272, 0.530]</r>; {Market_Research}<r>[0.301, 0.602, 0.368, 0.746]</r> and <r>[0.301, 0.440, 0.368, 0.602]</r>; {Product_Development}<r>[0.397, 0.584, 0.464, 0.746]</r> and <r>[0.397, 0.368, 0.464, 0.584]</r>; {Staff_Training}<r>[0.493, 0.620, 0.561, 0.746]</r> and <r>[0.493, 0.476, 0.561, 0.620]</r>; {Supply_Management}<r>[0.589, 0.476, 0.657, 0.746]</r> and <r>[0.589, 0.188, 0.657, 0.476]</r>; {Order_Processing}<r>[0.686, 0.386, 0.753, 0.746]</r> and <r>[0.686, -0.046, 0.753, 0.386]</r>; {Warehouse_Management}<r>[0.782, 0.350, 0.849, 0.746]</r> and <r>[0.782, -0.064, 0.849, 0.350]</r>; {Delivery}<r>[0.878, 0.368, 0.946, 0.746]</r> and <r>[0.878, -0.046, 0.946, 0.368]</r>. It appears that the maximum difference in expenses over the 10 years is observed in Warehouse Management, with an increase of 1000 because the values go from 2200 to 3300 over two bars.\nBased on the above thought, the answer is:\nWarehouse Management."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "M2gGxKYyn0Z7GMYv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTransform the data in this bar graph into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bar chart, I can see that the x-axis {categories labels}<r>[{\"Green Practises\"}[0.138, 0.919, 0.274, 0.942]; {\"Waste Levels\"}[0.316, 0.919, 0.430, 0.942]; {\"Energy Consumption\"}[0.450, 0.919, 0.631, 0.942]; {\"CO2 Emissions\"}[0.643, 0.919, 0.771, 0.942]; {\"Pollution Index\"}[0.811, 0.919, 0.938, 0.942]]</r> represents different environmental categories. The y-axis signifies the respective measurements, ranging from {0}[0.076, 0.894, 0.087, 0.917] to {4000}[0.043, 0.025, 0.087, 0.048]. Each category has a pair of bars, with the first one {2010}<r>[0.922, 0.055, 0.966, 0.078]</r> in red and the second one {2020}<r>[0.922, 0.090, 0.965, 0.113]</r> in blue. The height of the bar determines the measurement for each category. For instance, in the \"Green Practises\" category, the 2010 bar {red bar}[0.139, 0.360, 0.206, 0.903] is about midway up between 2000 and 3000 units, which implies a measure of approximately 2500 units. Similarly, the 2020 bar {blue bar}[0.206, 0.251, 0.273, 0.903] in the same category is at about three-fourths, implying a measurement of around 3000 units. This process needs to be reiterated for each category to convert the data into a CSV format.\nBased on the above thought, the answer is:\nCategories,2010,2020\nGreen Practises,2500,3000\nWaste Levels,3000,2000\nEnergy Consumption,2500,1500\nCO2 Emissions,3500,2000\nPollution Index,1000,500"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "c87kHN5fTB7VQRIH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the graph, can you tell if there was progress regarding environmental issues between 2010 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the graph for progress regarding environmental issues, I need to look for decreasing measurements in categories with negative impacts and increasing measurements for environmentally friendly practices. Examining the {Waste Levels}<r>[{\"2010\"}[0.306, 0.251, 0.373, 0.903], {\"2020\"}[0.373, 0.469, 0.440, 0.903]]</r>, {Energy Consumption}<r>[{\"2010\"}[0.473, 0.360, 0.540, 0.903], {\"2020\"}[0.540, 0.577, 0.607, 0.903]]</r>, and {CO2 Emissions}<r>[{\"2010\"}[0.640, 0.143, 0.707, 0.903], {\"2020\"}[0.707, 0.469, 0.774, 0.903]]</r> categories, the bars for 2020 are lower than 2010, indicating a decline in these environmental hazards. On the other hand, in the {Green Practises category}<r>[{\"2010\"}[0.139, 0.360, 0.206, 0.903], {\"2020\"}[0.206, 0.251, 0.273, 0.903]]</r>, which is a positive environmental practice, the 2020 bar is higher than the 2010 bar, showing increased efforts in this area. This data suggests an overall improvement. However, it must be noted that even though there is a decrease in the {Pollution Index}<r>[{\"2010\"}[0.808, 0.686, 0.874, 0.903], {\"2020\"}[0.874, 0.794, 0.941, 0.903]]</r> category, there is a still significant room for improvement.\nBased on the above thought, the answer is:\nYes, there appears to be progression regarding environmental issues between 2010 and 2020. This is evident from the reductions in waste levels, energy consumption, and CO2 emissions, and the increase in green practices. However, the pollution index is still a concern."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "lRruKvF5lV9JM0HD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of which category has the most significant decrease from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which category has seen the most substantial reduction from 2010 to 2020, I must compare the differences between the 2010 bars and the 2020 bars for each category. Looking from left to right, the first difference is found at {Green Practises bars}<r>[{\"2010\"}[0.139, 0.360, 0.206, 0.903], {\"2020\"}[0.206, 0.251, 0.273, 0.903]]</r>. The difference here is nearly 500 units. This same approach can be used for all categories. After all these steps, I can see that the maximum decrease is in the {Energy Consumption category}<r>[{\"2010\"}[0.473, 0.360, 0.540, 0.903], {\"2020\"}[0.540, 0.577, 0.607, 0.903]]</r> with a decrease of around 1000 units.\nBased on the above thought, the answer is:\nEnergy Consumption"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "34qV91AexFlAFsjk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart you observed, could you provide the data it represents? We would like it in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart contains seven characteristics related to Entrepreneurial spirit and Entrepreneurship on the X-axis: {Self-confidence}<r>[0.116, 0.919, 0.222, 0.942]</r>, {Optimism}<r>[0.257, 0.919, 0.324, 0.942]</r>, {Persistence}<r>[0.372, 0.919, 0.451, 0.942]</r>, {Passion}<r>[0.506, 0.919, 0.558, 0.942]</r>, {Discipline}<r>[0.620, 0.919, 0.687, 0.942]</r>, {Acceptance of failure}<r>[0.701, 0.919, 0.848, 0.942]</r>, {Creativity}<r>[0.862, 0.919, 0.929, 0.942]</r>. Four trends categories represented by different colors and located at {the legend coordinate}<r>[0.086, 0.046, 0.264, 0.192]</r>, correspond to four bars in each characteristic, namely \"Steady decline\", \"Minimal change\", \"Inexorable decline\" and \"Upward trend\". By examining the height of each bar and linking it to the numerical values labelled on the y-axis, I can deduce each numerical statistic for all categories. For example, for 'Self-confidence' in the 'Steady decline' category, the corresponding bar<r>[0.121, 0.469, 0.217, 0.903]</r> almost reaches up to the 4000 line, so the value is 4000. Similarly, doing the same for all bars, I am able to construct a CSV data format.\nBased on the above thought, the answer is:\nCharacteristics,Steady decline,Minimal change,Inexorable decline,Upward trend\nSelf-confidence,4000,1000,3000,500\nOptimism,3800,1000,2700,700\nPersistence,3600,1000,2400,900\nPassion,3400,1000,2100,1100\nDiscipline,3200,1000,1800,1300\nAcceptance of failure,3000,1000,1500,1500\nCreativity,2800,1000,1200,1700"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "QCeKcVcYPjyKWVSO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which characteristic shows only minimal change over time? And explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to look at the bars associated with the \"Minimal change\" trend, which is represented by the color green<r>[0.092, 0.092, 0.120, 0.108]</r> according to the legend. The green bars under each characteristic are of the same height; they correspond to the value of 1000 on the y-axis based on their starting and ending y coordinates. This indicates that the value of all characteristics remain the same in the \"Minimal change\" trend. Therefore, all characteristics show only minimal change over time.\nBased on the above thought, the answer is:\nAll characteristics (Self-confidence, Optimism, Persistence, Passion, Discipline, Acceptance of failure, Creativity). They all have consistent values in the \"Minimal change\" trend, indicating that there's minimal change over time."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "2ytqRZ812Ou8CkwZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value for the \"Inexorable decline\" trend, and what characteristic is it associated with?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Inexorable decline\" trend is in grey<r>[0.092, 0.127, 0.120, 0.143]</r> according to the legend. I must compare the heights of the grey bars in each characteristic to find the highest one. From left to right, the gray bar ranges for each characteristic are around 3000, 2700, 2400, 2100, 1800, 1500, and 1200. The highest value is {the 3000}<r>[0.363, 0.143, 0.460, 0.403]</r> in the \"Self-confidence\" characteristic<r>[0.116, 0.919, 0.222, 0.942]</r>.\nBased on the above thought, the answer is:\nSelf-confidence."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "vLOIVNsBlB4ulu34",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help turn the data represented on the bar chart into a CSV table please?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels I observe, {\"The Product Labels\"}[{\"Clothing\"}[0.147, 0.919, 0.212, 0.942]; {\"Electronics\"}[0.241, 0.919, 0.325, 0.942]; {\"Jewelry\"}[0.358, 0.919, 0.414, 0.942]; {\"Footwear\"}[0.453, 0.919, 0.525, 0.942]; {\"HomeGoods\"}[0.545, 0.919, 0.639, 0.942]; {\"Cosmetics\"}[0.656, 0.919, 0.735, 0.942]; {\"Books\"}[0.775, 0.919, 0.822, 0.942]; {\"Furniture\"}[0.867, 0.919, 0.936, 0.942]], these would be considered as the products sold. The y-axis represents the units sold/returned and the units go up to {\"12000 units\"}[0.038, -0.064, 0.087, -0.041]. Then I notice that our data has 4 different components that represent different things in sales: {\"Offline\"}[0.155, 0.046, 0.206, 0.069], {\"Online\"}[0.155, 0.081, 0.205, 0.104], {\"Sales\"}[0.155, 0.116, 0.196, 0.139], and {\"Returns\"}[0.155, 0.151, 0.214, 0.174]. Hence, to generate the csv data table, I will extract the four components which are easily distinguished by their colours, and hence y heights represented for each product category based on these components - for example, the \"Clothing\" bar starts at the base with the red \"Offline\" sales, which goes up to 1000 units. After that, the green segment represents \"Online\" sales and goes up to 3000 units. Then, the grey segment which represents \"Sales\" goes up to 6000 units, and finally, the orange segment representing \"Returns\" goes up to 6200 units.\nBased on the above thought, the answer is:\nProduct,Offline,Online,Sales,Returns \nClothing,1000,2000,3000,200 \nElectronics,3000,5000,8000,1000 \nJewelry,500,1500,2000,50 \nFootwear,700,1300,2000,70 \nHomeGoods,800,3000,3800,200 \nCosmetics,300,1500,1800,50 \nBooks,2000,3000,5000,3000 \nFurniture,3000,7000,10000,500"
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "YVk3ZVD2pG9GrGHe",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven that smaller return rates are better for business efficacy, which product appears to be the most efficient for the business based on return rates and demand?"
+            },
+            {
+                "from": "gpt",
+                "value": "For business efficiency, it's important to find a product with high demand (Sales) and with a small return rate (Returns). We can guess that a high Sales grey bar and a low Returns orange bar would reflect this efficiently. With this in mind, looking at the bars they show the two components stacked one on top of the other. Among all products, the bar for \"Jewelry\" with Sales and Returns data marked by {\"Jewelry Sales\"}[0.345, 0.584, 0.427, 0.743], and {\"Jewelry Returns\"}[0.345, 0.580, 0.427, 0.584] seems to have one of the highest Sales and one of the lowest Returns which suggests it might be the most efficient product for the business.\nBased on the above thought, the answer is:\nJewelry."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "xrRUl2XOPTIpudOG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category has the most total units sold and returned?"
+            },
+            {
+                "from": "gpt",
+                "value": "We are looking for the highest total units sold and returned, suggested by {\"the y-axis\"}[0.017, 0.349, 0.032, 0.579] going up to 12000. Looking at the heights of the vertical bars, the biggest bar represents the \"Furniture\" category where the bar goes up to {\"12000 units\"}[0.038, -0.064, 0.087, -0.041], which is the highest point. The total amount of units sold and returned for the \"Furniture\" category is indicated by the height of the bar {\"Furniture\"}[0.861, -0.733, 0.943, -0.693].\nBased on the above thought, the answer is:\nFurniture"
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "dfAycs5l2n8q72Ax",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the grouped bar chart you observed, can you generate a structured table that presents the percentage of digital content creation on different platforms at different time intervals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV table, I need to start from the {title}<r>[0.146, 0.025, 0.645, 0.055]</r>. When checking the {x-axis labels}<r>[0.105, 0.919, 0.114, 0.942]; [0.196, 0.919, 0.214, 0.942]; [0.292, 0.919, 0.309, 0.942]; [0.387, 0.919, 0.404, 0.942]; [0.482, 0.919, 0.500, 0.942]; [0.578, 0.919, 0.595, 0.942]; [0.673, 0.919, 0.690, 0.942]</r>, I know the values stand for different time intervals. I then check the {y-axis labels}<r>[0.052, 0.894, 0.061, 0.917]; [0.043, 0.747, 0.061, 0.770]; [0.043, 0.600, 0.061, 0.623]; [0.043, 0.453, 0.061, 0.476]; [0.043, 0.306, 0.061, 0.329]; [0.035, 0.159, 0.061, 0.183]; [0.035, 0.012, 0.061, 0.036]</r> to identify the percentage of content creation. The stacked bars in the plot stand for each platform\u2019s contribution at different time intervals. The height of each component can be determined by using the y-axis labels. The color of components is indicated in the {legend}<r>[0.821, 0.074, 0.941, 0.255]</r>. By cross-referencing between bars and the legend, we can assign the correct percentage of content creation on different platforms at different time intervals.\nBased on the above thought, the answer is:\nTime,YouTube,Instagram,TikTok,Blogs,Podcasts\n0,20,10,25,30,15\n10,30,15,10,22,23\n20,32,20,12,20,16\n30,38,19,13,21,18\n40,25,22,12,25,16\n50,10,20,18,28,24\n60,5,25,13,20,27"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "vj4H13qzPT6R3urL",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which platform shows a continuous decline in digital content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the change of bar heights of each platform over different time intervals. By looking at the {legend}<r>[0.821, 0.074, 0.941, 0.255]</r>, I can identify the color of each platform's bars. From the bars whose color matches with YouTube, Instagram, TikTok, Blogs, and Podcasts in the legend, I can see that only the bars represent YouTube shows a continuous decline. Starting from the {first bar}<r>[0.100, 0.756, 0.119, 0.903]</r> in the 0-hour interval that corresponds to 20% of content creation. Then go to the next {bar}<r>[0.196, 0.683, 0.215, 0.903]</r> at the 10-hour interval, it increases to 30% of content creation. However, from the {20-hour interval bar}<r>[0.291, 0.668, 0.310, 0.903]</r> onwards, the trend continuously decreases until the end.\nBased on the above thought, the answer is:\nYouTube."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "QKXwmTLvbJLuw2ST",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, at which time interval does TikTok have the lowest digital content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the time when TikTok has the lowest digital content creation, I'll check the length of the bars that represent TikTok in each group. Based on the colors given in the {legend}<r>[0.821, 0.074, 0.941, 0.255]</r>, I identified the bars representing TikTok. By checking the height of the bars for TikTok at {0 hour}<r>[0.100, 0.499, 0.119, 0.683]</r>, {10 hours}<r>[0.196, 0.499, 0.215, 0.572]</r>, {20 hours}<r>[0.291, 0.433, 0.310, 0.521]</r>, {30 hours}<r>[0.386, 0.389, 0.405, 0.484]</r>, {40 hours}<r>[0.481, 0.470, 0.501, 0.558]</r>, {50 hours}<r>[0.577, 0.550, 0.596, 0.683]</r>, and {60 hours}<r>[0.672, 0.587, 0.691, 0.683]</r>, I can see that the shortest one is at 30 hours.\nBased on the above thought, the answer is:\n30 hours."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "sTFyH0HfD4cpCTBr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the stacked bar chart data into a structured CSV table format, showing the resource usage as a percentage over time."
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the stacked bar chart, I see that the x-axis labeled with {Time (s)}<r>[0.347, 0.903, 0.440, 0.950]</r> represents different time intervals and the y-axis labeled with {% Distribution}<r>[0.025, 0.301, 0.048, 0.629]</r> denotes percentages. Each stacked bar combines multiple resources, indicated by the color-coded legend: {EBooks}<r>[0.639, 0.170, 0.685, 0.203]</r>, {Workbooks}<r>[0.639, 0.240, 0.685, 0.273]</r>, {Video Materials}<r>[0.639, 0.310, 0.685, 0.342]</r>, {Webinars}<r>[0.639, 0.380, 0.685, 0.412]</r>, {Live Seminars}<r>[0.639, 0.450, 0.685, 0.482]</r>. To construct the CSV table, each bar's segment within the stacked bars represents the distribution percentage which is extracted by looking at the height and position of each segment relative to the y-axis ticks.\nBased on the above thought, the answer is:\nTime,EBooks,Workbooks,Video Materials,Webinars,Live Seminars\n0,0.3,0.3,0.1,0.2,0.1\n10,0.25,0.28,0.15,0.2,0.12\n20,0.20,0.25,0.18,0.20,0.17\n30,0.18,0.24,0.2,0.21,0.17\n40,0.16,0.2,0.22,0.22,0.20\n50,0.15,0.18,0.25,0.24,0.18\n60,0.15,0.17,0.27,0.23,0.18\n70,0.12,0.16,0.3,0.23,0.19\n80,0.1,0.15,0.33,0.22,0.20"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "zO7AxXeGICEYReZh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the change in resource distribution for EBooks and Workbooks over time based on the stacked bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the change in resource distribution for {EBooks}<r>[0.639, 0.170, 0.685, 0.203]</r> and {Workbooks}<r>[0.639, 0.240, 0.685, 0.273]</r>, I focus on the segments corresponding to these resources in each timed {stacked bar}<r>[0.136, 0.157, 0.651, 0.806]</r>. From time {0}<r>[0.135, 0.838, 0.150, 0.885]</r> to {80s}<r>[0.630, 0.838, 0.659, 0.885]</r>, the segments representing EBooks and Workbooks steadily decrease in height, indicating a reduction in their usage over the observed time intervals. This trend suggests a shift from traditional reading materials to a preference for more interactive or different educational resources over time.\nBased on the above thought, the answer is:\nThe resource distribution for both EBooks and Workbooks has consistently decreased over time. The graph shows that EBooks and Workbooks started with higher usage at the initial point, but as time progressed, their distribution dropped, reflecting a transition to other types of educational resources."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "ZxaSxvqFR6XwD6C8",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what time interval did Video Materials have their highest percentage usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine when Video Materials had the highest usage percentage, I assess the size of the {Video Materials}<r>[0.639, 0.310, 0.685, 0.342]</r> segments within each of the stacked bars across the x-axis {time intervals}<r>[0.135, 0.838, 0.659, 0.885]</r>. I notice a spike in the height of the bar for Video Materials between the x-axis labels {60}<r>[0.504, 0.838, 0.534, 0.885]</r> and {70}<r>[0.567, 0.838, 0.596, 0.885]</r>. Comparing this to the rest of the segments, this bar is the tallest, indicating the highest usage percentage.\nBased on the above thought, the answer is:\n70s"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "9Zvm18JGCbym5125",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart you've just looked at, could you turn the displayed information into a table with CSV formatting?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {\"Global Trends and Changes Observation\"}[0.105, 0.050, 0.660, 0.110], I can understand the overall subject. The x-axis represents the years with its tick labels shown at coordinates from {2005}[0.099, 0.838, 0.157, 0.885] to {2045}[0.608, 0.838, 0.666, 0.885]. The y-axis tick labels displayed from {0}[0.058, 0.787, 0.072, 0.834] to {5}[0.058, 0.097, 0.072, 0.144] represent an index of Global Changes. Per the values listed in {the legend}[0.669, 0.148, 0.900, 0.511], it appears to represent different factors of Global Changes: Population, GDP, Technology, Environment, and Politics. To convert this into a table, I can note the height of the bars for each factor in each year and correlate them with the y-axis tick labels that represent the corresponding index values. For instance, in year 2005, the bar corresponding to \"Population\" {first bar in group 0}[0.115, 0.778, 0.141, 0.806] reaches about the position for index value 0.2 on the y-axis.\nBased on the above thought, the answer is:\nYear,Population,GDP,Technology,Environment,Politics\n2005,0.2,0.1,0.3,0.2,0.3\n2015,0.4,0.2,0.6,0.3,0.5\n2025,0.6,0.3,0.9,0.4,0.7\n2035,0.8,0.4,1.2,0.5,0.9\n2045,1.0,0.5,1.5,0.6,1.1"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "mTUQZuSifTX07ypl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat general trend do you see in the global changes index across all the fields from 2005 to 2045?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing each of the data series from the chart, including {Population}<r>[0.115, 0.778, 0.141, 0.806] to [0.624, 0.668, 0.650, 0.806]</r>, {GDP}<r>[0.115, 0.764, 0.141, 0.778] to [0.624, 0.599, 0.650, 0.668]</r>, {Technology}<r>[0.115, 0.723, 0.141, 0.764] to [0.624, 0.392, 0.650, 0.599]</r>, {Environment}<r>[0.115, 0.695, 0.141, 0.723] to [0.624, 0.309, 0.650, 0.392]</r>, and {Politics}<r>[0.115, 0.654, 0.141, 0.695] to [0.624, 0.157, 0.650, 0.309]</r>, they all show an upward trend. All these fields are seeing an increase in their global changes index values over the years from 2005 to 2045. In particular, the Technology field is showing the most noticeable growth, reaching from an index value of 0.3 in 2005 to 1.5 in 2045, showing a fivefold increase, as seen from {the Technology bars}.\nBased on the above thought, the answer is:\nThe general trend observed across all the fields is a strong upward trend from 2005 to 2045, with Technology experiencing the most dramatic increase."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "DGaDV699G6gfNJtz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the Global Changes Index of GDP increase from 2005 to 2045?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the value of GDP from 2005 to 2045, I need to compare the bar heights for GDP in these two years. The bar for GDP in 2005 has the coordinates {GDP bar 2005}[0.115, 0.764, 0.141, 0.778]. Referring to the y-axis tick labels, its top reaches to the position of index value 0.1. The bar for GDP in 2045 is from {GDP bar 2045}[0.624, 0.599, 0.650, 0.668]. Its top reaches to the y-axis value of 0.5. The difference between the two is 0.5 (value in 2045) minus 0.1 (value in 2005), resulting in a total increase of 0.4.\nBased on the above thought, the answer is:\n0.4."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "6xSlim5cIp0Bdl9s",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into CSV format showing the social demographics over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart titled {\"Social Demographics Over Time\"}[0.172, 0.050, 0.615, 0.110], the x-axis is labeled as 'Time' with {the time intervals}[{\"0\"}[0.135, 0.838, 0.150, 0.885]; {\"80\"}[0.630, 0.838, 0.659, 0.885]] ranging from 0 to 80. The y-axis {\"% of Total Population\"}[0.025, 0.223, 0.048, 0.708] labels the population percentage from 0.0 to 1.2. Each category such as 'Employed', 'Unemployed', etc. is represented by unique colors identified in {the legend}<[0.662, 0.148, 0.891, 0.511]>. The values for each category at every noted time can be read from the height of the bars, for example, 'Employed' at time 0 is between {0.00 and 0.25}[0.136, 0.644, 0.149, 0.806].\nBased on the above thought, the answer is:\nTime,Employed,Unemployed,Married,Single,Divorced\n0,0.25,0.15,0.35,0.20,0.05\n10,0.30,0.15,0.32,0.20,0.03\n20,0.35,0.13,0.30,0.19,0.03\n30,0.37,0.12,0.28,0.18,0.05\n40,0.39,0.11,0.27,0.17,0.06\n50,0.40,0.11,0.26,0.18,0.05\n60,0.41,0.10,0.25,0.19,0.05\n70,0.42,0.09,0.24,0.20,0.05\n80,0.43,0.09,0.23,0.21,0.04"
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "s1xReYYa9raAm95r",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category demonstrates the most stability over time based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the stability, we analyze the fluctuation of bar heights in each category across all time points. For instance, the {Unemployed category}<[0.736, 0.236, 0.882, 0.283]> shows bars with heights representing values that start at 0.15 and decrease progressively but slightly to 0.09. Reviewing all categories, 'Unemployed' shows the least variation in its change, indicating greater stability compared to others where the changes are more significant.\nBased on the above thought, the answer is:\nUnemployed. It maintains a smaller range of fluctuation in percentage from 0.15 to 0.09 over the 80 units of time, showing the most consistency and stability in its values."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "IRuJOBsZ5DyJGLyF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease of the Married category from time 0 to time 80?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the 'Married' category is plotted along the time axis with initial and final values highlighted on the bars at {time 0}[0.136, 0.319, 0.149, 0.546] and {time 80}[0.638, 0.319, 0.651, 0.468]. Initially, the value for 'Married' is 0.35 and at time 80, it is 0.23. The decrease can be calculated as initial value minus final value.\nBased on the above thought, the answer is:\n0.12"
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "7peUtbPWBFN7oeKu",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the provided bar chart display, can you present the contained data as a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is {\"Trends in Education and Training Technology Innovation\"}[0.333, 0.019, 0.717, 0.041] indicating that we are visualizing trends of various education technologies. The x-axis represents the seven different technologies listed: {\"Collaboration Tools\"}[0.145, 0.939, 0.152, 0.957], {\"Multimedia Learning\"}[0.270, 0.939, 0.278, 0.957], {\"VR Learning\"}[0.396, 0.939, 0.403, 0.957], {\"Mobile Learning\"}[0.521, 0.939, 0.529, 0.957], {\"Simulation\"}[0.647, 0.939, 0.654, 0.957], {\"Data Mining\"}[0.772, 0.939, 0.780, 0.957], and {\"Game-based Learning\"}[0.898, 0.939, 0.905, 0.957]. The y-axis shows the trend percentages ranging from 0.00 to 0.30 based on {the y-axis tick labels}[{\"0.00\"}[0.029, 0.920, 0.054, 0.938], {\"0.05\"}[0.029, 0.771, 0.054, 0.788], {\"0.10\"}[0.029, 0.621, 0.054, 0.638], {\"0.15\"}[0.029, 0.471, 0.054, 0.489], {\"0.20\"}[0.029, 0.321, 0.054, 0.339], {\"0.25\"}[0.029, 0.172, 0.054, 0.189], {\"0.30\"}[0.029, 0.022, 0.054, 0.039]]. Looking at the colors of the bars, there are two trends labeled as \"Time 1\" and \"Time 2\" in {the legend}[0.901, 0.055, 0.982, 0.113]. Using these mappings, we can represent the data as a CSV format table.\nBased on the above thought, the answer is:\nTech,Time 1,Time 2\nCollaboration Tools,0.12,0.09\nMultimedia Learning,0.25,0.15\nVR Learning,0.28,0.15\nMobile Learning,0.09,0.12\nSimulation,0.05,0.13\nData Mining,0.12,0.18\nGame-based Learning,0.09,0.18"
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "0FEtUhx8T1NAkyYj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the charts, which technology seems to be gaining more popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {bar chart}[0.063, 0.047, 0.988, 0.927] depicts the trend percentages of varying education technologies over two different intervals of time. Among all technologies, {Game-based Learning}[0.898, 0.939, 0.905, 0.957] has shown the most considerable rise from Time 1 to Time 2. The comparison of the {two bars} for Game-based Learning {[0.858, 0.658, 0.902, 0.927] and [0.902, 0.388, 0.945, 0.927]} highlights a rise from 0.09 to 0.18, indicating an increase in its popularity.\nBased on the above thought, the answer is:\nGame-based Learning. This is based on the fact that Game-based Learning shows the most significant increase in trend percentage from Time 1 to Time 2 compared to the other technology types."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "Xo8S06psp7N1rqdB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology showed the biggest increase from Time 1 to Time 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the height of the bars representing Time 1 and Time 2 for each technology, I can identify the technology that showed the biggest increase in trend percentage. For instance, the bars for Collaboration Tools {[0.105, 0.568, 0.148, 0.927] and [0.148, 0.658, 0.192, 0.927]} represent a decrease from 0.12 to 0.09. This trend is followed for all technologies until I reach Game-based Learning represented by the bars at {[0.858, 0.658, 0.902, 0.927] and [0.902, 0.388, 0.945, 0.927]} which show an increase from 0.09 to 0.18. This is the largest increase amongst all the technologies.\nBased on the above thought, the answer is:\nGame-based Learning."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "CEUZ2mCxGfcuYEGi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me convert the data displayed in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that the bar chart is about \"Greenhouse Gas Emissions Over Time\" from {the title}<r>[0.148, 0.050, 0.676, 0.110]</r> and the x-axis shows the emission year, from the year 2000 to 2020, as I can see from {the x-axis labels}<r>[\"2000\"[0.164, 0.838, 0.222, 0.885]; \"2005\"[0.273, 0.838, 0.331, 0.885]; \"2010\"[0.382, 0.838, 0.441, 0.885]; \"2015\"[0.492, 0.838, 0.550, 0.885]; \"2020\"[0.601, 0.838, 0.660, 0.885]</r>. The y-axis represents the emissions in millions of metric tons with the labels ranges from 0 to 25000, as indicated by {the y-axis labels}<r>[\"0\"[0.116, 0.787, 0.131, 0.834]; \"5000\"[0.072, 0.649, 0.131, 0.695]; \"10000\"[0.058, 0.510, 0.131, 0.556]; \"15000\"[0.058, 0.371, 0.131, 0.418]; \"20000\"[0.058, 0.232, 0.131, 0.279]; \"25000\"[0.058, 0.093, 0.131, 0.140]</r>.\nThe chart is divided into multiple groups of bars, each group representing a year, and each bar within a group representing a particular kind of gas. The types of gases are identified in {the legend}<r>[0.735, 0.148, 0.877, 0.511]</r> as CO2, CH4, N2O, CF4, and C2F6.\nThe emissions for each gas in each year can be inferred from the height of the corresponding bars. For instance, the emission of CO2 in the year 2000 can be deduced by looking at the coordination of the bar<{\"CO2 bar in 2000\"}[0.171, 0.350, 0.215, 0.806]</r> on this chart. Taking this reasoning into account, I will deduce the emissions for each gas in each year to create the CSV table.\nBased on the above thought, the answer is:\nYear,CO2,CH4,N2O,CF4,C2F6\n2000,16433.416,454.036,156.307,23.0,4.920\n2005,17610.762,477.512,172.597,24.6,5.330\n2010,18630.101,501.988,188.887,26.2,5.740\n2015,20497.379,526.464,205.177,27.8,6.150\n2020,22564.662,550.940,221.467,29.4,6.560"
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "DBAKlx1XjnK4wDgO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is your interpretation of the trend for each greenhouse gas over the period from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart gives a clear visual representation of the changes for each type of greenhouse gas over time. Let's take each gas type and consider its bars over the years 2000-2020. Starting with {CO2}<r>[0.744, 0.170, 0.790, 0.203] from legend</r>, the height on the bars shows a steady uptick {CO2 bar heights}<r>[0.171, 0.350, 0.215, 0.806]; [0.280, 0.317, 0.324, 0.806]; [0.390, 0.289, 0.434, 0.806]; [0.499, 0.237, 0.543, 0.806]; [0.609, 0.179, 0.652, 0.806]</r> suggesting increasing emissions of CO2 over the time period. \n\nMoving next to {CH4}<r>[0.744, 0.240, 0.790, 0.273] from legend</r>, you can observe that the increases are much smaller than those of CO2, highlighting a moderate increase in CH4 emissions over the years {CH4 bar heights}<r>[0.171, 0.337, 0.215, 0.350]; [0.280, 0.304, 0.324, 0.317]; [0.390, 0.275, 0.434, 0.289]; [0.499, 0.222, 0.543, 0.237]; [0.609, 0.164, 0.652, 0.179]</r>. \n\nFor {N2O}<r>[0.744, 0.310, 0.790, 0.342] from legend</r>, the emissions show a slightly noticeable increase {N2O bar heights}<r>[0.171, 0.333, 0.215, 0.337]; [0.280, 0.299, 0.324, 0.304]; [0.390, 0.269, 0.434, 0.275]; [0.499, 0.216, 0.543, 0.222]; [0.609, 0.158, 0.652, 0.164]</r>. \n\nIn the case of {CF4}<r>[0.744, 0.380, 0.790, 0.412] from legend</r> emissions, you can see a downward trajectory in the bars {CF4 bar heights}<r>[0.171, 0.332, 0.215, 0.333]; [0.280, 0.298, 0.324, 0.299]; [0.390, 0.269, 0.434, 0.269]; [0.499, 0.216, 0.543, 0.216]; [0.609, 0.157, 0.652, 0.158]</r>, indicating a decreasing trend in CF4 emissions, which is a positive sign. \n\nLastly, {C2F6}<r>[0.744, 0.450, 0.790, 0.482] from legend</r> emissions also show a slight rise {C2F6 bar heights}<r>[0.171, 0.332, 0.215, 0.332]; [0.280, 0.298, 0.324, 0.298]; [0.390, 0.268, 0.434, 0.269]; [0.499, 0.215, 0.543, 0.216]; [0.609, 0.157, 0.652, 0.157]</r> during 2000-2020.\nBased on the above thought, the answer is:\nFrom the year 2000 to 2020, CO2 and N2O emissions have shown a significant increase, while CH4 and C2F6 have shown a moderate increase. In contrast, CF4 has shown a declining trend in emissions. This dispersion of emissions indicates the diverse impacts of these gases on environmental health."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "2l6YLGmkMb87uuL5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the most emitted gas type in 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the most emitted gas type in 2020, I must examine the heights of all the bars associated with the year. Looking at 2020, which is represented by the last group of bars with {coordinates of bars in 2020}<r>[\"CO2\"[0.609, 0.179, 0.652, 0.806]; \"CH4\"[0.609, 0.164, 0.652, 0.179]; \"N2O\"[0.609, 0.158, 0.652, 0.164]; \"CF4\"[0.609, 0.157, 0.652, 0.158]; \"C2F6\"[0.609, 0.157, 0.652, 0.157]</r>, it becomes clear that {the bar}<r>[0.609, 0.179, 0.652, 0.806]</r> which symbolizes CO2 on {the legend}<r>[0.744, 0.170, 0.790, 0.203]</r> has the highest level in 2020.\nBased on the above thought, the answer is:\nCO2"
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "6gUNoyvpox8WvNhJ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table from the observed horizontal bar chart showing the economic indicators over a 10-year span?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I noticed there are four distinct color bars under each year, represented in the {legend}<r>[0.857, 0.055, 0.985, 0.165]</r>, corresponding to different economic indicators: {Accelerating Decline}<r>[0.860, 0.064, 0.879, 0.076]</r>, {Slow Decline}<r>[0.860, 0.090, 0.879, 0.102]</r>, {Inconsistent Growth}<r>[0.860, 0.116, 0.879, 0.128]</r>, and {Fluctuating Decline}<r>[0.860, 0.142, 0.879, 0.155]</r>. Observing the horizontal bars, I see they are aligned with the y-axis, which shows years from {2011}<r>[0.053, 0.854]</r> through {2020}<r>[0.053, 0.119]</r>, and the length of the bars on the x-axis indicates the metric values from {0}<r>[0.050, 0.939, 0.056, 0.957]</r> to {120}<r>[1.115, 0.939, 1.133, 0.957]</r>. For instance, the bar for {Accelerating Decline in 2011}<r>[0.053, 0.871, 0.945, 0.887]</r> stretches to a value close to 100, indicating that for 2011, Accelerating Decline is at 100. I will generate the table based on the metric values at the end of each bar for each indicator and year.\nBased on the above thought, the answer is:\nYear,Accelerating Decline,Slow Decline,Inconsistent Growth,Fluctuating Decline\n2011,100,90,80,85\n2012,85,89,83,82\n2013,70,88,86,79\n2014,52,87,89,76\n2015,40,86,92,73\n2016,25,85,98,70\n2017,12,84,96,67\n2018,5,83,94,64\n2019,2,82,92,61\n2020,1,81,90,58"
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "ijKqHNSnIrQMrGzp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator has shown the most stability over the 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine stability, I need to look for minimal variance in metric values across the years. Observing the bars, I see that {Slow Decline}<r>[0.860, 0.090, 0.879, 0.102]</r> has a small deviation in the length of its bars from {90}<r>[0.856, 0.854, 0.877, 0.871]</r> in 2011 to {81}<r>[0.776, 0.119, 0.796, 0.136]</r> in 2020, indicating a slow and consistent decline. Comparing this with the other indicators, such as {Accelerating Decline}<r>[0.860, 0.064, 0.879, 0.076]</r> which shows a more prominent decrease from {100}<r>[0.945, 0.870, 0.972, 0.888]</r> to {1}<r>[0.062, 0.135, 0.077, 0.153]</r>, or {Inconsistent Growth}<r>[0.860, 0.116, 0.879, 0.128]</r> and {Fluctuating Decline}<r>[0.860, 0.142, 0.879, 0.155]</r>, which both show fluctuations, Slow Decline remains the most stable throughout the given period.\nBased on the above thought, the answer is:\nSlow Decline. It has exhibited gradual and consistent values throughout the 10 years, in contrast to the other economic indicators which display more significant fluctuations or a rapid rate of decline."
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "3HASm5vSMrF6LGkP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of the 'Inconsistent Growth' metric over the 10-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average value, I will sum up the metric values across all years for {Inconsistent Growth}<r>[0.860, 0.116, 0.879, 0.128]</r> and then divide by the number of years. Adding the values {80}<r>[0.767, 0.838, 0.787, 0.855]</r> for 2011, {83}<r>[0.794, 0.756, 0.814, 0.773]</r> for 2012, and so on until {90}<r>[0.856, 0.102, 0.877, 0.120]</r> for 2020, which gives a total sum. Dividing this sum by 10 will yield the average value for 'Inconsistent Growth'.\nBased on the above thought, the answer is:\n88.6"
+            }
+        ],
+        "id_raw": 327
+    },
+    {
+        "id": "pXQBxLusZXiuBTUn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart image you are examining, could you retrieve the data and convert it into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart contains information regarding the 'Income Growth' and 'Risk Control' in different regions as represented by the labels on x-axis {EN, DE, FR, IT, ES}. The y-axis mentions the values in percentages which range from around -4.5% to 1%, all represented by a combination of bars and numerical values annotated on them. By examining the height of each bar and the corresponding numerical value, we can retrieve the raw data from each category. For instance, {the EN bar for 'Income Growth'}[0.21, 0.49, 0.26, 0.75] is annotated with {-1.2%}[0.24, 0.46, 0.27, 0.49]. Similarly by checking all the bars of all the regions, the information can be compiled in a CSV format table.\nBased on the above thought, the answer is:\nModel,EN,DE,FR,IT,ES\nIncome Growth,%-1.2,%-0.8,%-3.5,%-0.3,%0.7\nRisk Control,%-3.1,%-1.8,%-0.5,%-4.3,%0.2"
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "xD3nh0y0uwQtDiRz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze overall regional performance in financial risk control and income growth based on the charts observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the bars of 'Income Growth'}[0.21, 0.26, 0.31, 0.75] and {the bars of 'Risk Control'}[0.51, 0.56, 0.61, 0.75], we can see that all the regions, except {ES bar for 'Income Growth'}[0.36, 0.52, 0.41, 0.75] and {ES bar for 'Risk Control'}[0.66, 0.73, 0.71, 0.75], show negative growth. The ES region appears to be the only one that shows slight improvement in both income growth and risk control, whereas all the other regions are in decline for both metrics. The Italian IT region shows the greatest decline in 'Risk Control', with the lowest bar for this metric. Hence, we can state that the overall regional performance in financial risk control and income growth is generally declining, with ES being the exception.\nBased on the above thought, the answer is:\nIn the bar chart, most regions are showing a decline in both financial risk control and income growth. However, the ES region shows slight improvements on both fronts. Particularly, the IT region shows the steepest decline in risk control."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "vPSyqdbkvfBqgYwu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_23_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric shows the biggest overall percentage decline across all regions according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays two metrics, 'Income Growth' and 'Risk Control' across 5 regions. To determine the metric with the biggest overall percentage decline, I need to sum up the declines across all regions for each metric. Observing the bars and corresponding values annotated on them for both metrics, for example, {the EN bar for 'Income Growth'}[0.21, 0.49, 0.26, 0.75] is annotated with {-1.2%}[0.24, 0.46, 0.27, 0.49] and {the EN bar for 'Risk Control'}[0.51, 0.43, 0.56, 0.75] is annotated with {-3.1%}[0.54, 0.40, 0.57, 0.43]. By checking all the bars for each region, I see that the overall percentage decline in 'Risk Control' across all regions is larger than the decline in 'Income Growth'.\nBased on the above thought, the answer is:\n'Risk Control'"
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "pHvT5EKDDIiKDiPE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the contents of the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {subplot coordinates}<r>[0.076, 0.062, 0.932, 0.356]</r> and <r>[0.076, 0.429, 0.932, 0.723]</r>, I can see that there are two situations that the electric vehicles are used in, namely Sustainable City Living and Long Interstate Trip. This information is displayed through tick labels. For instance, the label <r>{\"Sustainable City Living\"}[0.015, 0.739, 0.137, 0.942]</r> and <r>{\"Long Interstate Trip\"}[0.880, 0.739, 0.985, 0.915]</r>.\nFrom the {legend}<r>[0.818, 0.074, 0.925, 0.185]</r> and the {legend items}<r>[0.862, 0.118, 0.911, 0.141]</r> <r>[0.862, 0.153, 0.920, 0.176]</r>, we see two types of batteries or EVs, 48KWh and 100KWh. Each bar in the subplots corresponds to an EV type, and the height of the bar corresponds to the range in miles, annotated by the {text coordinates}<r>[0.269, 0.210, 0.295, 0.233]</r> <r>[0.269, 0.318, 0.295, 0.341]</r> <r>[0.713, 0.044, 0.740, 0.067]</r> <r>[0.713, 0.210, 0.740, 0.233]</r>. Combining all this information, I can generate a CSV table.\nBased on the above thought, the answer is:\nConditions,48KWh EV Range,100KWh EV Range\nSustainable City Living,320,490\nLong Interstate Trip,210,320"
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "9iPho8QxxqPqNQPy",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the bar chart, what are your thoughts on the range performance of the two different EVs under the two usage scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, we can see two scenarios for using the EVs - Sustainable City Living and Long Interstate Trip. Within each scenario, there is a comparison of two EVs with different battery capacities \u2013 48KWh and 100KWh. The 48KWh EV's ranges are located at <r>[0.269, 0.210, 0.295, 0.233]</r> and <r>[0.269, 0.318, 0.295, 0.341]</r> on the respective scenario axes, while the 100KWh EV's ranges are located at <r>[0.713, 0.044, 0.740, 0.067]</r> and <r>[0.713, 0.210, 0.740, 0.233]</r> on the respective scenario axes. From these ranges specified, we can observe that the 100KWh EV consistently outperforms the 48KWh in both scenarios, suggesting a direct relation between battery size and range. Also, both the EVs have a reduced range in the Long Interstate Trip scenario when compared to the Sustainable City Living scenario. This implies that driving conditions and patterns significantly affect EV range.\nBased on the above thought, the answer is:\nIt is clear that the Electric Vehicle (EV) with the 100KWh battery has a superior range compared to its 48KWh counterpart. This performance difference is seen in both scenarios - Sustainable City Living and Long Interstate Trip. Furthermore, the range of both vehicles decreases for the Long Interstate Trip when compared to the Sustainable City Living condition, highlighting how different usage scenarios can affect EV range. Therefore, whilst choosing an EV, both battery size and intended usage scenario should be considered."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "V8QtFJP861AgCtVP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the EV range increase when we switch from the 48KWh battery to the 100KWh battery on a long interstate trip?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the values for Long Interstate Trip for both the 48KWh and the 100KWh battery. The y-coordinate of the 48KWh bar in the Long Interstate Trip plot is <r>[0.115, 0.346, 0.449, 0.551]</r> with its value at <r>[0.269, 0.318, 0.295, 0.341]</r> showing a range of 210 miles, while the y-coordinate of the 100KWh bar is <r>[0.560, 0.238, 0.893, 0.551]</r> with its corresponding value showing at <r>[0.713, 0.210, 0.740, 0.233]</r> showing a range of 320 miles. The difference between them will give us the required increase in EV range when we switch the battery.\nBased on the above thought, the answer is:\n110 miles."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "ixemZ3RbflmPKgtA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please organize all the sales data for Toaster A in tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide the sales data for Toaster A in a structured format, I need to consider the height and the value annotations at the top of each bar. Each month starting from {Jan}[0.156, 0.919, 0.177, 0.942] to {Jun}[0.878, 0.919, 0.900, 0.942] has a corresponding bar for Toaster_A in the upper subchart, by calculating each {bars'[height]} from [{Jan's bar}[0.112, 0.839, 0.221, 1.031]] to [{Jun's bar}[0.835, 0.139, 0.943, 0.599]], and I can extract the corresponding sales values of each month.\nBased on the above thought, the answer is:\nMonth, Sales\nJan,200\nFeb,220\nMar,240\nApr,260\nMay,280\nJun,300"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "CYBPzz0RGR0VGXrI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product had a more consistent sales performance in the first half of the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the consistency of sales performance, we should observe the variability of the bar heights. A product with less fluctuation of sales is more consistent. The upper subchart that shows the sales performance of {Toaster_A}[0.906, 0.118, 0.972, 0.142] with bars from {Jan}[0.112, 0.292, 0.221, 0.599] to {Jun}[0.835, 0.139, 0.943, 0.599] exhibits a steady and gradual increase in the heights depicting consistent growth. In comparison, the sales performance of Toaster_B presented in the lower subchart with bars from {Jan's bar}[0.112, 0.839, 0.221, 1.031] to {Jun's bar}[0.835, 0.139, 0.943, 0.599] exhibit drastic fluctuations especially the {June's bar}[0.835, 0.139, 0.943, 0.599]. Therefore, Toaster A has a more stable sales performance.\nBased on the above thought, the answer is:\nToaster A. Its sales grew steadily from January to June with smaller fluctuations compared to Toaster B."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "S86XH3gCulbxWjjf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Toaster_A, in which month is the sales growth the highest relative to the previous month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the month with the highest sales growth for Toaster_A compared to the previous month, I am comparing the heights of the bars in the upper subchart for each month starting from {Feb}[0.299, 0.919, 0.323, 0.942] to {Jun}[0.878, 0.919, 0.900, 0.942]. I will find the bar with the biggest upward shift from the previous bar. From the analysis, {Apr's bar}[0.546, 0.200, 0.654, 0.599] has the most noticeable upward shift from the previous month of {Mar's bar}[0.401, 0.231, 0.510, 0.599].\nBased on the above thought, the answer is:\nApril"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "oPKvhceO1tSKrVyE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table showing the number of participants in each sport at different times of the day for Week 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a CSV table for Week 1, I first locate the area corresponding to Week 1 in the chart, which can be seen in the {first subplot}<r>[0.062, 0.062, 0.950, 0.381]</r>. Within this subplot, various bars represent the number of participants in different sports at various times of the day. The chart displays bars grouped together for each sport, each group representing a different time of day, as indicated by the legends: {Morning}<r>[0.843, 0.120, 0.871, 0.136]</r>, {Noon}<r>[0.843, 0.155, 0.871, 0.171]</r>, {Evening}<r>[0.843, 0.190, 0.871, 0.206]</r>, and {Night}<r>[0.843, 0.225, 0.871, 0.241]</r>. The vertical axis is labelled with numbers indicating the count of participants, with y-tick coordinates such as for {0}<r>[0.062, 0.381]</r> and {20}<r>[0.062, 0.229]</r>. Each bar's height corresponds to the count of participants, and I determine each value by its ending coordinate on the y-axis. For example, the {bar for Basketball in the Morning}<r>[0.102, 0.214, 0.264, 0.381]</r> reaches a value slightly above {20}<r>[0.062, 0.229]</r>, indicating approximately 22 participants. Following this process for all bars, I compile the following table.\nBased on the above thought, the answer is:\nTime, Basketball, Football, Swimming, Running, Gym\nMorning,22,12,10,28,40\nNoon,15,8,8,30,30\nEvening,10,9,5,22,20\nNight,5,15,3,15,10"
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "hnJdNltX2pqYH6Io",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich time of day showed the greatest increase in running participants from Week 1 to Week 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the time of day with the greatest increase in running participants, I compare the bars representing Running in each time slot from {Week 1}<r>[0.062, 0.062, 0.950, 0.381]</r> to {Week 2}<r>[0.062, 0.445, 0.950, 0.764]</r>. For {Morning}<r>[0.843, 0.120, 0.871, 0.136]</r>, the {Running bar in Week 1}<r>[0.079, 0.528, 0.149, 0.764]</r> indicates about 28 participants, and in {Week 2}<r>[0.172, 0.548, 0.242, 0.764]</r> about 35 participants, an increase of 7. This process is repeated for {Noon}<r>[0.843, 0.155, 0.871, 0.171]</r>, {Evening}<r>[0.843, 0.190, 0.871, 0.206]</r>, and {Night}<r>[0.843, 0.225, 0.871, 0.241]</r>. Upon comparing these increases, I determine that the period with the greatest increase is from {the Running bars}<r>[0.079, 0.528, 0.149, 0.764]</r> to <r>[0.172, 0.548, 0.242, 0.764]</r> corresponding to Morning.\nBased on the above thought, the answer is:\nThe Morning showed the greatest increase in running participants, which was 7, from Week 1 to Week 2."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "lFbAku7vZlYbqpXL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more participants were there in Gym workouts during the Morning in Week 2 compared to Week 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in participants for Gym workouts during the Morning from Week 1 to Week 2, I analyze the height of the corresponding bars. Locating the {bar for Gym during Morning of Week 1}<r>[0.079, 0.460, 0.149, 0.764]</r>, which corresponds to a value of 40 participants when compared to the y-axis labels. Then I locate {the bar for Gym during Morning of Week 2}<r>[0.172, 0.528, 0.242, 0.764]</r>, which corresponds to a value of 45 participants. The difference is calculated as 45 minus 40.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "TvHDK5eLwIsGnvK1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the dataset present in the bar chart into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label {Fiscal Quarter}[0.480, 0.942, 0.576, 0.970] suggests that the columns of the table will be the fiscal quarters, labeled as {\"1\"}[0.177, 0.903, 0.186, 0.931], {\"2\"}[0.350, 0.903, 0.359, 0.931], {\"3\"}[0.523, 0.903, 0.532, 0.931], {\"4\"}[0.697, 0.903, 0.705, 0.931], {\"5\"}[0.870, 0.903, 0.879, 0.931]. The two types of bars and corresponding text labels within the sublegend: {Facebook Interaction}[0.366, 0.039, 0.511, 0.067] and {Instagram Interaction}[0.578, 0.039, 0.728, 0.067] represent the two types of interactions we will be listing in our table. The number above each bar, such as {\"45\"}[0.138, 0.559, 0.156, 0.587], {\"65\"}[0.311, 0.427, 0.329, 0.455] for Facebook and {\"40\"}[0.207, 0.592, 0.225, 0.620], {\"50\"}[0.381, 0.526, 0.398, 0.554] for Instagram, indicates the interaction count for each social network in each quarter. From this, I can construct a CSV formatted table that includes all these details.\nBased on the above thought, the answer is:\n,Quarter 1,Quarter 2,Quarter 3,Quarter 4,Quarter 5\nFacebook Interaction,45,65,90,83,99\nInstagram Interaction,40,50,75,85,98"
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "7N0mbh0szYZZJafe",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data available, which social network platform, Facebook or Instagram, would you recommend for maximizing user interactions, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it's important to consider the interaction rates across all quarters for both platforms. By examining the height of bars representing {Facebook Interaction}[0.366, 0.039, 0.511, 0.067] and {Instagram Interaction}[0.578, 0.039, 0.728, 0.067], it can be seen that in Quarter 1 and Quarter 2, Facebook has higher interaction rates as suggested by {Facebook bars}[{\"Q1\"}[0.112, 0.587, 0.181, 0.883]; {\"Q2\"}[0.285, 0.455, 0.355, 0.883]]are higher than {Instagram bars}[{\"Q1\"}[0.181, 0.620, 0.251, 0.883]; {\"Q2\"}[0.355, 0.554, 0.424, 0.883]]. However, in Quarter 3, Instagram starts to close the gap as seen from the higher bar at {Instagram bar for Q3}[0.528, 0.389, 0.597, 0.883], and in Quarter 4 and Quarter 5, Instagram interactions are fairly close to, but still lower than, Facebook's. Therefore, while Instagram has shown improvement in the engagement rate, Facebook consistently maintains a higher engagement rate throughout all the fiscal quarters.\nBased on the above thought, the answer is:\nFacebook, due to its consistently higher user interaction rate over all the fiscal quarters compared to Instagram."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "A4EmyM9tSrmyB3Yj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fiscal quarter had the highest increase in Instagram interactions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this query, I need to calculate the increase in interactions for Instagram in each quarter. For example, the difference in interactions between Quarter 1 and Quarter 2 for Instagram can be calculated by subtracting the height of the {Instagram bar in Quarter 1}[0.181, 0.620, 0.251, 0.883], labeled {\"40\"}[0.207, 0.592, 0.225, 0.620], from the height of the {Instagram bar in Quarter 2}[0.355, 0.554, 0.424, 0.883], labeled {\"50\"}[0.381, 0.526, 0.398, 0.554]. Similarly, this can be done for the rest of the quarters - 3, 4, and 5. I can see that the highest increase occurs between Quarter 2 labeled {\"50\"}[0.381, 0.526, 0.398, 0.554], and Quarter 3, labeled {\"75\"}[0.554, 0.361, 0.571, 0.389], with a difference of 25 interactions.\nBased on the above thought, the answer is:\nQuarter 3."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "kvduxddLYRdczAT7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, can you extract the data for Social Networks into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the bars for {Social Networks}<r>[0.106, 0.330, 0.176, 0.488];[0.281, 0.172, 0.351, 0.488];[0.455, 0.212, 0.525, 0.488];[0.629, 0.275, 0.699, 0.488];[0.804, 0.125, 0.873, 0.488]</r>, each matched with a label below showing the fiscal quarter from 1 to 5. These bar labels are given by {the x-axis tick labels}<r>[0.172, 0.903, 0.181, 0.931];[0.346, 0.903, 0.355, 0.931];[0.520, 0.903, 0.529, 0.931];[0.695, 0.903, 0.704, 0.931];[0.869, 0.903, 0.878, 0.931]</r>. The value labels are written at the top of each bar, for example, the value label for the first bar for Social Networks is {the value annotation \"2.0\"}<r>[0.130, 0.302, 0.152, 0.330]</r>. Putting these together, I can compile the data of Social Networks into a CSV format.\nBased on the above thought, the answer is:\nFiscal Quarter,Social Networks\n1,2.0\n2,4.0\n3,3.5\n4,2.7\n5,4.6"
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "jXyjIwp5uwo9ds2z",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you summarize the trend of Interpersonal Relationships throughout the fiscal quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Interpersonal Relationships is represented by the second bar of each pair, with its corresponding legend in {the legend}<r>[0.538, 0.039, 0.728, 0.067]</r>. The {bars}<r>[0.176, 0.172, 0.246, 0.488]; [0.351, 0.330, 0.420, 0.488]; [0.525, 0.291, 0.595, 0.488]; [0.699, 0.156, 0.769, 0.488]; [0.873, 0.251, 0.943, 0.488]</r> represent the change of Interpersonal Relationships through fiscal quarters. By observing the height of each bar, it seems to start high in the first quarter, then it has a downward turn in the second quarter. Going upward but it a slower pace in the third quarter, a steep climb in the fourth quarter, later to fall back to a lower level again in the fifth quarter.\nBased on the above thought, the answer is:\nThe level of Interpersonal Relationships started high in the first quarter, went through a downward turn in the second quarter, showed small growth in the third quarter, and surged in the fourth quarter, but went back down again in the fifth quarter."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "Z938Zvnu3FqVszE2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many quarters that the level of Interpersonal Relationships is lower than the level of Social Networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the number of quarters that the level of Interpersonal Relationships was lower than Social Networks, I have to compare the heights of each of the bars side by side. For the first quarter, {Interpersonal Relationships bar}<r>[0.176, 0.172, 0.246, 0.488]</r> is higher than {Social Networks bar}<r>[0.106, 0.330, 0.176, 0.488]</r>. Similar comparisons are made for all quarters. Only when {Interpersonal Relationships bar in 4th}<r>[0.699, 0.156, 0.769, 0.488]</r> is lower than {Social Networks bar in 4th}<r>[0.629, 0.275, 0.699, 0.488]</r>. So, there is only one quarter where Interpersonal Relationships are lower than Social Networks.\nBased on the above thought, the answer is:\n1"
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "RDeSRsBGI0JcSdlM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you see, can the data information be converted into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Figure: Fiscal Quarter\"}[0.483, 0.942, 0.580, 0.970] and the {x-axis tick labels}[{\"1\"}[0.183, 0.903, 0.192, 0.931]; {\"2\"}[0.355, 0.903, 0.364, 0.931]; {\"3\"}[0.527, 0.903, 0.536, 0.931]; {\"4\"}[0.699, 0.903, 0.708, 0.931]; {\"5\"}[0.871, 0.903, 0.879, 0.931]], I can see that the x-axis represents different quarters. The y-axis tick labels represent the corresponding values, ranging from {-2.5}[0.035, 0.872, 0.068, 0.900] to {3.5}[0.046, 0.108, 0.068, 0.136]. According to the {legend}[0.353, 0.028, 0.710, 0.078], split color bars in each quarter represent {Risk Assessment}<r>[0.397, 0.039, 0.513, 0.067]</r> and {Risk Management}<r>[0.580, 0.039, 0.705, 0.067]</r>. To generate the CSV table, I need to extract the values for Risk Assessment and Risk Management in each quarter. I can use the heights of the bars and the value annotations at the top for calculation. For example, the {Risk Assessment bar for Quarter 1}<r>[0.119, 0.463, 0.188, 0.565]</r> is at 0.8 as shown by {a value annotation \"0.8\"}[0.142, 0.435, 0.164, 0.463], thus the value of Risk Assessment for the \"Quarter 1\" is 0.8.\nBased on the above thought, the answer is:\nQuarter,Risk Assessment,Risk Management\n1,0.8,2.5\n2,-1.2,1.3\n3,2.4,-0.9\n4,-1.0,3.7\n5,1.6,-2.0"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "NTz7UuItMCYMWfcj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which of the two fields, Risk Assessment or Risk Management, experienced more fluctuations? Please explain your reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the fluctuations of {Risk Assessment}<r>[0.397, 0.039, 0.513, 0.067]</r> and {Risk Management}<r>[0.580, 0.039, 0.705, 0.067]</r> across all quarters. Beginning with the initial quarter, I need to check the bar height of each field in every subsequent quarter and observe any reductions or increases. For Risk Management, we first see a significant increase in {Quarter 1}<r>[0.188, 0.246, 0.257, 0.565]</r>, which is followed by a decrease in measures in the {Quarters 2}<r>[0.360, 0.399, 0.428, 0.565]</r> and {3}<r>[0.531, 0.565, 0.600, 0.679]</r>. {Fourth Quarter}<r>[0.703, 0.093, 0.772, 0.565]</r> exhibits a significant surge, then a large decrease in measures in {Quarter 5}<r>[0.875, 0.565, 0.944, 0.820]</r>. Conversely, Risk Assessment initially increased slightly in {Quarter 1}<r>[0.119, 0.463, 0.188, 0.565]</r> then underwent considerable reduction in {Quarter 2}<r>[0.291, 0.565, 0.360, 0.718]</r>. It grew again in {Quarter 3}<r>[0.463, 0.259, 0.531, 0.565]</r>, decreased in {Quarter 4}<r>[0.635, 0.565, 0.703, 0.692]</r> and finally rose slightly in {Quarter 5}<r>[0.806, 0.361, 0.875, 0.565]</r>. By reviewing these trends, we can infer that Risk Management experienced more drastic jumps and dips, indicating a higher volatility compared to Risk Assessment.\nBased on the above thought, the answer is:\nRisk Management. This field experienced more drastic jumps and dips across the 5 Quarters, showing a higher level of volatility as compared to Risk Assessment."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "A8csG4NdsOJjFVXA",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which Quarter did the Risk Management measure reveal the maximum increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find when the Risk Management measure revealed the maximum increase, I must compare the heights of the Risk Management bars for each Quarter. Starting with the first Risk Management value in {Quarter 1}<r>[0.188, 0.246, 0.257, 0.565]</r>, then to {Quarter 2}<r>[0.360, 0.399, 0.428, 0.565]</r>, {Quarter 3}<r>[0.531, 0.565, 0.600, 0.679]</r>, {Quarter 4}<r>[0.703, 0.093, 0.772, 0.565]</r>, and finally, {Quarter 5}<r>[0.875, 0.565, 0.944, 0.820]</r>. By comparing these ranges, we can see that {Risk Management in Quarter 4}<r>[0.703, 0.093, 0.772, 0.565]</r> showed remarkable growth, maxing at 3.7 above the zero mark. Hence, Quarter 4 had the highest upswing in the Risk Management measure.\nBased on the above thought, the answer is:\nQuarter 4"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "uYWJQ4i4hBtXrtiX",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, please transform the data into a comma-separated values (CSV) format."
+            },
+            {
+                "from": "gpt",
+                "value": "From {the y-label}[0.015, 0.323, 0.029, 0.653], it's indicated that the y-axis represents the \"Increase in revenue (%)\". The x-axis represents different fiscal quarters. Value annotations at the top of each bar denote the exact value of increase in revenue percentage for each sector in every quarter. For instance, on Fiscal Quarter 1, {the Entertainment bar}[0.106, 0.521, 0.176, 0.686] has a value annotation of 2.5 and {the Gaming bar}[0.176, 0.686, 0.246, 0.785] has a value annotation of -1.5. This pattern continues for all other fiscal quarters. According to {the legend}[0.389, 0.028, 0.660, 0.078], a greenish bar represents the Entertainment industry while an orangish bar represents the Gaming industry. Following the same procedure for each quarter we can convert the raw data into CSV format.\nBased on the above thought, the answer is:\nFiscal Quarter, Entertainment, Gaming\n1, 2.5, -1.5\n2, 3.8, -0.3\n3, 5.6, -2.0\n4, 6.9, -1.9\n5, 8.0, -2.4"
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "W94QshlDlvFM7psG",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given chart, what could be inferred about the performance of the Entertainment and Gaming sectors over these 5 quarters, particularly the trend and its implications?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is clear from the bar chart that the Entertainment and Gaming industries are moving in opposite directions. The Entertainment industry, represented by {the greenish bars}[0.106, 0.521, 0.176, 0.686; 0.281, 0.436, 0.351, 0.686; 0.455, 0.317, 0.525, 0.686; 0.629, 0.231, 0.699, 0.686; 0.804, 0.159, 0.873, 0.686], shows a steady rise in revenue quarter-by-quarter, which implies the continued growth and stability of this industry. Meanwhile, the Gaming industry, represented by {the orangish bars}[0.176, 0.686, 0.246, 0.785; 0.351, 0.686, 0.420, 0.706; 0.525, 0.686, 0.595, 0.818; 0.699, 0.686, 0.769, 0.811; 0.873, 0.686, 0.943, 0.844], the sector experiences a decrease in revenue in every quarter, suggesting this industry is facing tough challenges, and unfavorable market conditions. In particular, the revenue decreases appear to be accelerating, signaling a worrying trend.\nBased on the above thought, the answer is:\nThe Entertainment and Gaming industries are trending in completely opposite directions over these five quarters. The Entertainment industry shows a consistent upward trend, meaning it's doing well, indicating continual growth and stability. On the contrary, the Gaming industry trends downward, indicating it faces significant challenges and possibly declining in popularity or profitability. The acceleration of its decline may suggest critical, potentially structural issues within the industry."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "o3ENIlI1zmJP8GwC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what fiscal quarter did the Entertainment industry see the greatest increase in revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to examine all five quarters, keeping my attention on {the greenish Entertainment bars}[0.106, 0.521, 0.176, 0.686; 0.281, 0.436, 0.351, 0.686; 0.455, 0.317, 0.525, 0.686; 0.629, 0.231, 0.699, 0.686; 0.804, 0.159, 0.873, 0.686]. By checking the value annotations at the top of each bar, it's clear that {the highest increase}[0.828, 0.131, 0.850, 0.159] in revenue for Entertainment industry happened in Fiscal Quarter 5 with the value of 8.0.\nBased on the above thought, the answer is:\nFiscal Quarter 5."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "GzZh2FUN4U0zlXpp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information demonstrated in the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is composed of two stages of the value chain ['Manufacturing', 'Marketing'], each represented in two different business scenarios ['Stable Growth', 'Volatile Movement'] spotted in the x-axis ticks with coordinates [{'label': 'Stable Growth', 'label_coordinates': '[0.229, 0.942, 0.328, 0.970]'}, {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}]. The y-axis shows the value change of these stages under each scenario from -3 to +1 according to the yticks coordinates {'yticks': [{'label': '\u22123', 'label_coordinates': '[0.035, 0.912, 0.055, 0.940]'}, {'label': '\u22122', 'label_coordinates': '[0.035, 0.733, 0.055, 0.761]'},... {'label': '0', 'label_coordinates': '[0.046, 0.376, 0.055, 0.404]'}, {'label': '1', 'label_coordinates': '[0.046, 0.198, 0.055, 0.226]'}]}. \nThe height of each bar and the respective displayed values, for instance, the first manufacturing bar's{'bar_coordinates': '[0.107, 0.244, 0.279, 0.387]'}, will assist in matching accurate values with the different stages of the value chain under the respective scenarios.\nBased on the above thought, the answer is:\nScenario, Manufacturing, Marketing\nStable_Growth, 0.8, 0.6\nVolatile_Movement, 1.5, -2.0"
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "rCm288J5aVdTsKlA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, what impacts could occur when experiencing Volatile Movement scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Volatile Movement\" scenario, as its name suggests, introduces instability in business trends. Looking at the bars representing this scenario in the chart {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}, it appears to demonstrate significant divergence in the two sectors. Manufacturing with bar at coordinates {'bar_coordinates': '[0.599, 0.119, 0.771, 0.387]'} reveals an increase by 1.5%, hinting at a successful adaption or even capitalization in such a volatile market. In contrast, the Marketing sector, represented by the bar with coordinates {'bar_coordinates': '[0.771, 0.387, 0.943, 0.744]'}, shows a decline of 2%. This may indicate inadequate preparedness or inability to adjust rapidly, resulting in a loss during volatile periods. Therefore, the Volatile Movement scenario could lead to diverse impacts across different sectors, making it essential for companies to strategize accordingly for different sectors in such unstable periods.\nBased on the above thought, the answer is:\nThe potential impacts could be diverse for different sectors. For instance, sectors like Manufacturing may experience growth due to successful adaptation or capitalization during volatility. In contrast, Marketing might see a decline, which could be because of lack of readiness to adjust to sudden market changes, leading to losses."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "PzElBCaocpm7PwCe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stage of the value chain demonstrated the highest percentage change, considering Volatile Movement scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I will compare the height of the second pair of bars which stands for the Volatile Movement scenario as denoted by {'label': 'Volatile Movement', 'label_coordinates': '[0.706, 0.942, 0.835, 0.970]'}. The corresponding bars are as follow: manufacturing bar with coordinates of {'bar_coordinates': '[0.599, 0.119, 0.771, 0.387]'} and marketing bar with coordinates of {'bar_coordinates': '[0.771, 0.387, 0.943, 0.744]'}. It's clear that the manufacturing bar height is up to around y = 1.5, and the marketing bar height drops to around y= -2. So the Marketing stage, even though experienced a drop, has the most significant absolute percentage change.\nBased on the above thought, the answer is:\nMarketing."
+            }
+        ],
+        "id_raw": 351
+    },
+    {
+        "id": "hNl16NK6rEBBOJnB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLet's convert the barchart data showing investment strategy changes over five quarters into a CSV format. How would you proceed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title {\"Investment Change (%) vs. Fiscal Quarter\"}[0.481, 0.942, 0.577, 0.970] implies that the data relates to changes in investment strategies over five fiscal quarters. The x-axis represents the five quarters {xticks}[{\"1\"}[0.180, 0.903, 0.188, 0.931]; {\"2\"}[0.352, 0.903, 0.361, 0.931]; {\"3\"}[0.525, 0.903, 0.534, 0.931]; {\"4\"}[0.698, 0.903, 0.706, 0.931]; {\"5\"}[0.870, 0.903, 0.879, 0.931]]. The y-axis denotes the percentage change {ylabel}[0.015, 0.324, 0.029, 0.653] and can be referenced from the given ticks. We have two groups of bars: blue ones denoting {\"High-Risk High-Return\"}<r>[0.393, 0.039, 0.546, 0.067]</r> strategy and orange ones denoting {\"Conservative\"}<r>[0.613, 0.039, 0.704, 0.067]</r> strategy. I can calculate percentage changes using the bars' heights denoted by the y-axis ticks. Thus, CSV data can be generated.\nBased on the above thought, the answer is:\nQuarter,High-Risk High-Return,Conservative\n1,-1,2\n2,5,3\n3,3,3\n4,-3,2\n5,7,2"
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "sKNwvBQtm7ts9Ype",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends, what can you tell about the two investment strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'High-Risk High-Return' strategy, represented by the {blue bars}<r>[0.393, 0.039, 0.546, 0.067]</r>, indicates higher volatility, as the investment results bounce between positive and negative returns, reflecting higher risk. However, it shows a significant return in the fifth quarter, indicating the potential for high returns despite the risk. The 'Conservative' strategy, represented by the {orange bars}<r>[0.613, 0.039, 0.704, 0.067]</r>, meanwhile, remains consistently positive across all five quarters but does not reach the high returns of the high risk strategy in quarter 5.\nBased on the above thought, the answer is:\nThe 'High-Risk High-Return' strategy showed higher volatility, with investment outcomes fluctuating between negative and positive. Despite this risk, it demonstrated potential for high returns. Conversely, the 'Conservative' strategy appeared stable and consistently positive across all five quarters, but it didn't reach as high a return as the high risk strategy in the last quarter. Hence, we observe the usual risk-return trade-off. An investor should decide between the two based on their risk tolerance."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "PVkNw5SrTJW7LuJt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter saw the highest percentage change in the high-risk high-return strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "For finding the quarter with maximum percentage change, I need to look at the blue bars representing the \"High-Risk High-Return\" strategy and find the one with highest magnitude. The highest bar appears to be in the 5th quarter <r>[0.805, 0.212, 0.875, 0.488]</r>, corresponding to a change of 7% <r>[0.829, 0.184, 0.851, 0.212]</r>, as indicated by the value annotation at the top of the bar.\nBased on the above thought, the answer is:\nQuarter 5"
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "W1R95MLccTIBHQlo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me transform the data from the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's {xlabel}[0.477, 0.942, 0.573, 0.970], we can see that the x-axis represents five fiscal quarters. Also, the y-axis is the respective scores of two measures, {Employee Productivity}[0.343, 0.043, 0.499, 0.071] and {Employee Job Satisfaction}[0.565, 0.043, 0.746, 0.071], as indicated in the legend. To convert this into a CSV table, I'll need to gather the scores of both parameters for each quarter. I can evaluate these scores based on the bar height relative to the y-axis scale: quarter 1 productivity is {5.1}[0.107, 0.089, 0.176, 0.490] as per the green bar and satisfaction is {4.6}[0.176, 0.128, 0.246, 0.490] from the orange bar, and so on.\nBased on the above thought, the answer is:\nFiscal Quarter,Employee Productivity,Employee Job Satisfaction\nQ1,5.1,4.6\nQ2,5.3,4.3\nQ3,4.8,3.9\nQ4,5.5,4.1\nQ5,5.2,3.8"
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "EqjuhsV1kuyuBhUm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, can you figure out where the business is lacking and explain your reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires an examination of both Employee Productivity and Employee Job Satisfaction metrics across all fiscal quarters. If we look closely at Employee Job Satisfaction {orange bars}, we\u2019ll see a continuous decrease over five quarters. Considering the Q5 bar of Employee Job Satisfaction [0.873, 0.191, 0.943, 0.490] in relation to its corresponding Q1 bar [0.176, 0.128, 0.246, 0.490], it's evident that employee morale has been steadily declining in the company. While Employee Productivity metrics {green bars} exhibit a fluctuating tendency, they outperform Employee Job Satisfaction across quarters. This suggests the team might be overworked but feel underappreciated or lacks drive, reflecting negatively in the Job Satisfaction metric.\nBased on the above thought, the answer is:\nThe business is lacking in employee satisfaction. This is based on the continued decrease in the Employee Job Satisfaction across five quarters, despite fluctuating but relatively higher Employee Productivity scores, indicating potential issues with employee morale and motivation."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "owBXJeTYnCkxKEPs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which fiscal quarter is the gap between Employee Productivity and Employee Job Satisfaction the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the quarter with the largest gap between Employee Productivity and Employee Job Satisfaction, I must compare the differences between the bar heights representing Employee Productivity and Employee Job Satisfaction under each quarter. By visually scanning the chart from left to right, I can observe the following: For Q1, {the difference between the bars}[0.107, 0.089, 0.176, 0.490],[0.176, 0.128, 0.246, 0.490] is 0.5; for Q2, {the difference between the bars}[0.281, 0.073, 0.351, 0.490],[0.351, 0.152, 0.420, 0.490] is 1.0; and so on. This way, by measuring the differences for other quarters, I found the largest difference in Q3, where {the two bars}[{Productivity}[0.455, 0.112, 0.525, 0.490]; {Satisfaction}[0.525, 0.183, 0.595, 0.490]] have a difference of 0.9, which is the largest among all quarters.\nBased on the above thought, the answer is:\nQ3"
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "5qTm2sAnqGwgXafg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on my observation of the bar chart, could you please help me to convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the chart, I can see that the x-axis represents different epochs in human history, which from left to right are {Stone Age}[0.126, 0.903, 0.197, 0.931], {Bronze Age}[0.267, 0.903, 0.346, 0.931], {Iron Age}[0.422, 0.903, 0.480, 0.931], {Middle Ages}[0.554, 0.903, 0.638, 0.931], {Industrial Age}[0.692, 0.903, 0.789, 0.931], and {Digital Age}[0.847, 0.903, 0.923, 0.931]. For each epoch, there are two types of events represented by two bars of different colors: {Innovations}<r>[0.439, 0.039, 0.520, 0.067]</r>, represented by the green bars, and {Conflicts}<r>[0.587, 0.039, 0.647, 0.067]</r>, represented by the orange bars. The y-axis represents the numbers of events, ranging from 0 to 20. For example, during the Stone Age, the number of innovations, which is the height of the {green bar}[0.104, 0.725, 0.162, 0.883], is 5, and the number of conflicts, which is the height of the {orange bar}[0.162, 0.852, 0.220, 0.883], is 1. So, to generate the CSV table, I repeat this process for all epochs.\nBased on the above thought, the answer is:\nEpoch,Innovations,Conflicts\nStone Age,5,1\nBronze Age,7,2\nIron Age,9,3\nMiddle Ages,10,5\nIndustrial Age,15,7\nDigital Age,20,10"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "zAr7tmHT9LGa4uno",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you describe the trend of innovations and conflicts across different epochs?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, according to the {green bars for innovations}<r>[[0.104, 0.725, 0.162, 0.883];[0.249, 0.662, 0.306, 0.883];[0.393, 0.599, 0.451, 0.883];[0.538, 0.567, 0.596, 0.883];[0.683, 0.409, 0.740, 0.883];[0.827, 0.251, 0.885, 0.883]]</r>, there are clear upward trends with each passing epoch from Stone Age to Digital Age, indicating an increase in the number of innovations over time. In parallel, the {orange bars for conflicts}<r>[[0.162, 0.852, 0.220, 0.883];[0.306, 0.820, 0.364, 0.883];[0.451, 0.789, 0.509, 0.883];[0.596, 0.725, 0.654, 0.883];[0.740, 0.662, 0.798, 0.883];[0.885, 0.567, 0.943, 0.883]]</r> also show a similar increasing trend with each epoch, this indicates that the number of conflicts also increased over time. Therefore, the number of both innovations and conflicts has increased over different epochs, indicating that while human civilization was advancing, the number of conflicts was also on the rise.\nBased on the above thought, the answer is:\nThere is an increasing trend for both innovations and conflicts with each passing epoch. The chart indicates that as human society progressed and the number of innovations increased, the number of conflicts also went up. Essentially, while there was an advancement in innovations, conflicts also rose over the epochs."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "ZlBGq69nuNSeHuHB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, in which epoch did the number of conflicts exceed the number of innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the epoch where the number of conflicts is greater than the number of innovations, I need to compare the height of the green bar and the orange bar within each epoch. By comparing the bars of the two events from left to right, I can conclude that none of the {orange bars for conflicts}<r>[[0.162, 0.852, 0.220, 0.883];[0.306, 0.820, 0.364, 0.883];[0.451, 0.789, 0.509, 0.883];[0.596, 0.725, 0.654, 0.883];[0.740, 0.662, 0.798, 0.883];[0.885, 0.567, 0.943, 0.883]]</r> is taller than the corresponding {green bar for innovations}<r>[[0.104, 0.725, 0.162, 0.883];[0.249, 0.662, 0.306, 0.883];[0.393, 0.599, 0.451, 0.883];[0.538, 0.567, 0.596, 0.883];[0.683, 0.409, 0.740, 0.883];[0.827, 0.251, 0.885, 0.883]]</r>. Therefore, there is no epoch in which the number of conflicts exceeded the number of innovations.\nBased on the above thought, the answer is:\nThere isn't any."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "cb96I8NCb6ZkZh0W",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data given in the bar chart into a csv file?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart data into a CSV format, we first need to extract the X-axis data (Years) and the Y-axis data (GDP growth rate) for the two countries. {The x-axis}[{\"1985\"}[0.105, 0.903, 0.140, 0.931]; {\"1990\"}[0.306, 0.903, 0.341, 0.931]; {\"1995\"}[0.507, 0.903, 0.543, 0.931]; {\"2000\"}[0.708, 0.903, 0.743, 0.931]; {\"2005\"}[0.910, 0.903, 0.945, 0.931]] contains labels for the years from 1985 to 2005. Then, we have two groups of bars, one group for each country. Looking at {the legend}[0.430, 0.028, 0.620, 0.078], {the green bars} represent GDP growth rate for USA and {the orange bars} represent Russia. For example, for the year 1985, {the green bar for USA}[0.107, 0.540, 0.123, 0.676] corresponds to a GDP growth rate of 0.8, and {the orange bar for Russia}[0.123, 0.676, 0.139, 0.848] corresponds to a GDP growth rate of -1.0. Repeating this process for each year will allow us to generate the final CSV table.\nBased on the above thought, the answer is:\nYear,USA,Russia\n1985,0.8,-1.0\n1990,1.5,-0.8\n1995,2.2,-0.6\n2000,2.5,-0.4\n2005,3.2,-0.2"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "kj6WiBuqhjz4QnkV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country showed consistent economic growth from 1985 to 2005 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will compare the height of the bars for each country from 1985 to 2005. Starting from 1985, the {green bar corresponding to the USA's GDP growth rate}[0.107, 0.540, 0.123, 0.676] is positive and seems to increases consistently over the years, showing an upward trend. In contrast, the {orange bar corresponding to Russia's GDP growth rate}[0.123, 0.676, 0.139, 0.848] starts from a negative growth rate but improves over time, showing a recovering trend rather than a consistent growth. Therefore, based on the chart, the USA showed consistent economic growth from 1985 to 2005.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "gtU2Qo4CmUSWWUoE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the GDP growth rate of USA in 2005 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the year 2005 on the x-axis with coordinates [0.910, 0.903, 0.945, 0.931], there is an associated {green bar}[0.911, 0.129, 0.927, 0.676] that corresponds to USA's GDP growth rate, which has a text annotation indicating a value of 3.2. This suggests that in 2005, the GDP growth rate of the USA was 3.2%.\nBased on the above thought, the answer is:\n3.2%"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "DI6j0kFvz2Y0U4yc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from each bar and present it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, there are two groups of bars presented: {Greenhouse Gas Emissions}<r>[0.285, 0.545, 0.355, 0.883]</r> and {Ozone Depletion Potential}<r>[0.355, 0.861, 0.424, 0.883]</r>, as noted by {the legend}<r>[0.285, 0.028, 0.770, 0.078]</r>.\nEach fiscal quarter noted on the {x-axis}<r>[0.480, 0.942, 0.576, 0.970]</r> has a pair of bars, one for each group. For example, in quarter 1, the {Greenhouse Gas Emissions bar}<r>[0.112, 0.658, 0.181, 0.883]</r> marks approximately 100 units and the {Ozone Depletion Potential bar}<r>[0.181, 0.838, 0.251, 0.883]</r> marks approximately 20 units. Following this procedure, I can extract the value for each bar according to their heights, and place them in a structured table.\nBased on the above thought, the answer is:\nQuarter,Greenhouse Gas Emissions,Ozone Depletion Potential\n1,100,20\n2,150,10\n3,200,30\n4,180,15\n5,300,25"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "tMJDNg7BCb7vmMxL",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze and interpret the trends of Greenhouse Gas Emissions and Ozone Depletion Potential based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents trends in two environmental indicators over five quarters. The green bars for the {Greenhouse Gas Emissions}<r>[0.112, 0.658, 0.181, 0.883]; [0.285, 0.545, 0.355, 0.883]; [0.459, 0.432, 0.528, 0.883]; [0.632, 0.477, 0.701, 0.883]; [0.805, 0.206, 0.874, 0.883]</r> demonstrate an overall increasing trend, starting from 100 units in the first quarter and escalating to 300 units in the fifth quarter, despite a slight dip in the fourth quarter. This could indicate an increasing contribution to greenhouse gases over these periods.\nThe blue bars for the {Ozone Depletion Potential}<r>[0.181, 0.838, 0.251, 0.883]; [0.355, 0.861, 0.424, 0.883]; [0.528, 0.816, 0.597, 0.883]; [0.701, 0.850, 0.770, 0.883]; [0.874, 0.827, 0.943, 0.883]</r> show more fluctuation across the quarters, peaking at 30 units in the third quarter. This might suggest that ozone depletion varied more erratically in response to certain factors, but the cause behind these changes would require further investigation.\nBased on the above thought, the answer is:\nThe Greenhouse Gas Emissions saw a generally increasing trend over the five quarters, suggesting a growing contribution to greenhouse gases. In contrast, the Ozone Depletion Potential showed more significant fluctuations, peaking in the third quarter, which indicates that factors affecting ozone depletion might have varied more drastically during these periods."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "VmRSFKL2E6fbq9sz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which quarter did the Greenhouse Gas Emissions reach the maximum?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the quarter with the maximum Greenhouse Gas Emissions, I need to identify the tallest bar among the group of {Greenhouse Gas Emissions bars}<r>[0.112, 0.658, 0.181, 0.883]; [0.285, 0.545, 0.355, 0.883]; [0.459, 0.432, 0.528, 0.883]; [0.632, 0.477, 0.701, 0.883]; [0.805, 0.206, 0.874, 0.883]</r>. The bar with the greatest height is in the fifth quarter<r>[0.805, 0.206, 0.874, 0.883]</r>, which indicates that the Greenhouse Gas Emissions reached their peak during this period.\nBased on the above thought, the answer is:\nQuarter 5."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "cL6pPigjOI6q4VYr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you're seeing, can you derive a table representation of the chart data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see two different types of bars in the chart, indicating the data series for 'Number of People in the Household' represented by {the green bars}<r>[0.104, 0.808, 0.290, 0.883];[0.570, 0.801, 0.757, 0.883]</r> and 'Percentage of Single-parent Households' represented by {the orange bars}<r>[0.290, 0.156, 0.477, 0.883];[0.757, 0.125, 0.943, 0.883]</r>. The height of each bar corresponds to its value, and from {the annotations above the bars}<r>[0.186, 0.780, 0.208, 0.808];[0.652, 0.773, 0.674, 0.801];[0.368, 0.128, 0.399, 0.156];[0.834, 0.097, 0.865, 0.125]</r>, I get the exact value for each bar. The years are labeled along the x-axis, so I can derive the CSV format data from these details.\nBased on the above thought, the answer is:\nYear, Number of People in Household, Percentage of Single-parent Households\n1, 2.4, 23%\n2, 2.6, 24%"
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "Pu8G5XDnRBWK53Rt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart can you derive any trends or notable changes in the family structure over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart visuals we can observe two aspects. Firstly, the average 'Number of People in Household' stands at <r>[0.186, 0.780, 0.208, 0.808]</r> as 2.4 in Year 1 and increases to 2.6 at <r>[0.652, 0.773, 0.674, 0.801]</r> in Year 2 indicating a slight increase in the number of people per household. Secondly, the 'Percentage of Single-parent Households' starting at a value of 23% in Year 1 <r>[0.368, 0.128, 0.399, 0.156]</r> increases to 24% in Year 2 <r>[0.834, 0.097, 0.865, 0.125]</r>. The chart shows an overall increase in the family size as well as an increase in the single-parent households over the years. This suggests a shift in family dynamics with a potential increase in extended families and single parent families.\nBased on the above thought, the answer is:\nYes, there is a slight growth in the number of people per households over the years. There is also an increase in the percentage of single-parent households over the same period, suggesting changing family dynamics with potential growth in extended families and single-parent families."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "qkkbhn2JwMl6bMdC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the given chart, which variable increased more from Year 1 to Year 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green bar in Year 1 indicates the 'Number of People in Household' and its height at <r>[0.104, 0.808, 0.290, 0.883]</r> shows the value of 2.4. In Year 2, the value of 'Number of People in Household' increases to 2.6 at <r>[0.570, 0.801, 0.757, 0.883]</r>. The increment is 0.2. The orange bar representing 'Percentage of Single-parent Households' is at <r>[0.290, 0.156, 0.477, 0.883]</r> in Year 1, indicating a value of 23%. In Year 2, the value increases to 24% at <r>[0.757, 0.125, 0.943, 0.883]</r>. The increment is 1%. Comparing these results, 'Percentage of Single-parent Households' increased more.\nBased on the above thought, the answer is:\nPercentage of Single-parent Households"
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "1OPN3CKjRpqrlibT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart before you, could you compile the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart appears to have been titled {\"\"}[0.523, 0.077, 0.523, 0.077], but there is no visible title. However, I can identify the X-axis through the {axis label \"Year\"}[0.509, 0.952, 0.538, 0.975] and its {tick labels}[{\"2018\"}[0.259, 0.919, 0.294, 0.942]; {\"2019\"}[0.753, 0.919, 0.788, 0.942]]. Similarly, the Y-axis displays numerical values pertaining to market share, as indicated by the {label \"Market Share (%)\"}[0.015, 0.396, 0.029, 0.597]. The bar chart contains two bars for each year, which represents the market share of two categories: Music and Books. This is confirmed by a {legend}[0.424, 0.021, 0.622, 0.063] at the bottom of the chart, with two entries: {Music}[0.469, 0.030, 0.508, 0.053] and {Books}[0.575, 0.030, 0.617, 0.053]. By inspecting the height of the bars and their vertical position with respect to the Y-axis, I can gather that 'Music' had a market share of 32.11% in 2018 and 34.85% in 2019 while 'Books' had a market share of 44.23% in 2018 and 46.56% in 2019.\nBased on the above thought, the answer is:\nYear,Music,Books\n2018,32.11,44.23\n2019,34.85,46.56"
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "HXqvCTvVBLVOFYFN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can we say that the Music industry is doing better than the Books industry? Why or why not?"
+            },
+            {
+                "from": "gpt",
+                "value": "As the chart indicates, both the 'Music' industry {represented by green bars}[0.104, 0.381, 0.277, 0.903; 0.597, 0.337, 0.770, 0.903] and the 'Books' industry {represented by orange bars}[0.277, 0.184, 0.449, 0.903; 0.770, 0.146, 0.943, 0.903] have experienced growth from 2018 to 2019. Since the question doesn't specify what metric defines an industry as \"doing better\", we can make a comparison based on two aspects: the absolute market share and the growth rate. When we look at the absolute market share, the 'Books' industry consistently had a higher market share for both years. But if we look at the growth rate, the 'Music' industry had seen a slightly higher increase in its market share. In conclusion, if we are focusing on the market share alone, 'Books' industry is doing better than the 'Music' industry. However, if we are talking about the growth rate, the 'Music' industry did better with slightly a higher increase.\nBased on the above thought, the answer is:\nIt depends on the metric. If we're comparing the absolute market share, then the Books industry is doing better than the Music industry. If we're looking at the growth rate, then the Music industry is performing slightly better."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "a7VnciKRtDZPVlml",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAcross the two years, in which industry did the market share increase the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will compute the difference in the market share for each industry by comparing the heights of the bars corresponding to 2018 and 2019. For the 'Music' industry, the {bar in 2018}[0.104, 0.381, 0.277, 0.903] is lower than the {corresponding bar in 2019}[0.597, 0.337, 0.770, 0.903], indicating an increase in market share. The height difference corresponds to an increase of approximately 2.74%. Similarly, the 'Books' industry's {market share in 2018}[0.277, 0.184, 0.449, 0.903] is lower than its {market share in 2019}[0.770, 0.146, 0.943, 0.903]. The height difference indicates an increase of approximately 2.33%. From this, we can see that the market share increase for the 'Music' industry is slightly higher than that of the 'Books' industry.\nBased on the above thought, the answer is:\nMusic"
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "3USIXxXRCSF6ygJQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the data presented in the bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on {the x-axis}[0.522, 0.942, 0.551, 0.970] labelled 'Year' and {the y-axis}[0.015, 0.447, 0.029, 0.530] labelled 'Count', I can identify the data dimensions. The chart's legend at the bottom with {[0.324, 0.028, 0.749, 0.078]} indicates that this chart contains data of 'New Educational Institutes' and 'Students Enrolled'. To structure the data into a table, I need to identify the height of each bar and match it to the corresponding tick label on the y-axis. By combining this information with the year located at the base of each bar, the table can be constructed.\nBased on the above thought, the answer is:\nYear,New_Educational_Institutes,Students_Enrolled\n2022,10,20000\n2023,15,30000\n2024,11,27000\n2025,10,25000\n2026,30,60000"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "NYUIRhhodfRX8ECa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the correlation between the number of new educational institutes and students enrolled?"
+            },
+            {
+                "from": "gpt",
+                "value": "The given bar chart provides insights into the correlation between the number of new educational institutes and students enrolled. The bars representing the 'New Educational Institutes', found at coordinates {[0.129, 0.883, 0.197, 0.883]; [0.299, 0.883, 0.367, 0.883]; [0.469, 0.883, 0.537, 0.883]; [0.638, 0.883, 0.706, 0.883]; [0.808, 0.883, 0.876, 0.883]}, and the 'Students Enrolled', which are at {[0.197, 0.658, 0.265, 0.883]; [0.367, 0.545, 0.435, 0.883]; [0.537, 0.579, 0.605, 0.883]; [0.706, 0.601, 0.774, 0.883]; [0.876, 0.206, 0.944, 0.883]} respectively, express this correlation. As the height of the bars for 'New Educational Institutes' rises, so does the height of the bars for 'Students Enrolled'. That is, the two seem to share a positive correlation- as the number of new institutes increases, so does the number of students enrolling in them, especially seen in the year 2026 marked with coordinates {[0.808, 0.883, 0.876, 0.883]; [0.876, 0.206, 0.944, 0.883]}.\nBased on the above thought, the answer is:\nThere appears to be a positive correlation between the establishment of new educational institutes and the number of students enrolled. As the number of new institutes increases, there also seems to be an increase in the number of students attracted to these institutions."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "43bynCT4rDKLovwd",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did we see the most significant increase in newly established educational institutes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the most significant increase in new educational establishments, I need to compare the heights of the green bars representing this data {green bars at}[0.129, 0.883, 0.197, 0.883]; [0.299, 0.883, 0.367, 0.883]; [0.469, 0.883, 0.537, 0.883]; [0.638, 0.883, 0.706, 0.883] and {at}[0.808, 0.883, 0.876, 0.883] in consecutive years. The bar for the year 2026 [0.808, 0.883, 0.876, 0.883] shows the most significant increase compared to the bars for the other years.\nBased on the above thought, the answer is:\n2026."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "KggA3tI7YjPqWumQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table capturing the project names and their corresponding scores from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the chart title}[0.462, 0.019, 0.736, 0.041], I can tell that my task is to extract the innovation projects and their corresponding evaluation scores from this bar chart. The innovation projects are annotated on the y-axis, and their respective evaluation scores can be found by inspecting the lengths of the bars and text annotations at the end of the bars. For instance, {the bar for 'Website Redesign'} [0.211, 0.806, 0.859, 0.870] has a corresponding text annotation {\"85\"}[0.867, 0.829, 0.881, 0.847], indicating that this project has an evaluation score of 85. Hence, by extracting the information in this same manner for all the bars, I can generate the table.\nBased on the above thought, the answer is:\nInnovation Project,Evaluation Score\nWebsite Redesign,85\nCloud Migration,80\nCustomer Relationship Management System,92\nOnline Marketing Campaign,75\nA.I. Customer Service Chatbots,97\nCompany Mobile App,88\nE-commerce Platform,83\nData Analytics System,90\nCybersecurity Improvement,86\nVirtual Reality Product Showroom,94"
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "Ik4dZVGgHPEOufk8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you suggest a recommendation on which innovation project should be given priority based on their evaluation scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "This recommendation can be made by comparing the evaluation scores of the innovation projects, represented as the lengths of the bars. The innovation project with the highest evaluation score should be prioritized. In this case, {the bar for \"A.I. Customer Service Chatbots\"}[0.211, 0.486, 0.951, 0.550] is longest, indicating the highest evaluation score. This is confirmed by the score {\"97\"}[0.958, 0.509, 0.973, 0.527] annotated at the end of the bar. Therefore, I would recommend prioritizing the \"A.I. Customer Service Chatbots\" project.\nBased on the above thought, the answer is:\n\"A.I. Customer Service Chatbots\" should be given priority because it has the highest evaluation score."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "l3myYNJbo05fptXl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, what is the lowest project evaluation score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the lowest project evaluation score, I can compare the lengths of the purple bars on the x-axis. It appears that {the bar for \"Online Marketing Campaign\"}[0.211, 0.566, 0.783, 0.630] is shorter than all others, indicating it has the lowest score. The {text annotation at the end of this bar}[0.790, 0.589, 0.805, 0.607] confirms this, as it displays the score: \"75\".\nBased on the above thought, the answer is:\n75."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "9LrSPKuifTLF5tSK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the information on the chart to CSV format, the key elements we need are the names of the business practices from the y-axis and their respective quantities from the x-axis. I can see from the values located at {yticks['yticks'] label_coordinates} that we have 10 different business practices ranging from CSR Activities and down to Animal Welfare. Similarly, each business practice or activity has a specific value associated with it, illustrated by the widths of the bars and accompanying labels at {bars['bars_of_group_0']['rectangles'] bar and text coordinates}. Therefore, by mapping these categories to their quantities, we can tabulate the data as a CSV.\nBased on the above thought, the answer is:\nBusiness Pratice, Instances\\n\nCSR Activities,8000\\n\nSustainable Packaging,7400\\n\nCommunity Development,6900\\n\nEmission Reduction,6500\\n\nRenewable Energy,6100\\n\nWaste Reduction,5800\\n\nWater Conservation,5300\\n\nLabor Practices,5000\\n\nEthical Sourcing,4600\\n\nAnimal Welfare,4200\\n"
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "NxRt09GHExfKNn6q",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the priority of the business practices based on their respective instances in this report of 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the chart, it is noticeable that the number of instances of a practice corresponds with its priority in businesses. CSR Activities represented by {CSR Activities}<r>[0.061, 0.848, 0.126, 0.945]</r> had the most instances, which suggests it was given the highest priority. Conversely, Animal Welfare represented by {Animal Welfare}<r>[0.055, 0.112, 0.126, 0.218]</r> had the least instances, showing it might have been the least prioritized. In the middle-ground, practices such as Sustainable Packaging, Community Development, and beyond showed a staggered decrease in instances, showcasing a sliding scale of priorities.\nBased on the above thought, the answer is:\nThe practices with the highest instances, such as CSR Activities, are likely given the most priority. In contrast, those with the least instances, such as Animal Welfare, are possibly given less priority. Practices with instances in between display a spectrum of prioritization."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "HIZywefNVjJ5Wmub",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich business practice had the least instances performed in 2021, and how many instances were there?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the business practice with the least instances in 2021, I can start reading the bar chart from {CSR Activities}<r>[0.061, 0.848, 0.126, 0.945]</r>, which has the most instances, and move downwards. Looking at the lengths of the bars and the numeric labels next to them, as specified by the {bars_coordinates_and_text}, I can see that the bar with the smallest length represents the business practice with the least performed instances. This bar is found at {Animal Welfare}<r>[0.055, 0.112, 0.126, 0.218]</r> which also has its number of instances listed as 4200.\nBased on the above thought, the answer is:\nAnimal Welfare, 4200."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "SXesgGN5Hgzkl6vf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the visual data represented in the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By closely reading it, I can see that this bar chart presents the number of research papers across various areas of Artificial Intelligence. The areas are shown along the y-axis, and they include: {Deep Learning}<r>[0.062, 0.862, 0.120, 0.943]</r>, {Reinforcement Learning}<r>[0.029, 0.778, 0.120, 0.905]</r>, {Automated Reasoning}<r>[0.036, 0.694, 0.120, 0.812]</r>, {Computer Vision}<r>[0.055, 0.610, 0.120, 0.701]</r>, {Natural Language Processing}<r>[0.011, 0.526, 0.120, 0.679]</r>, {Knowledge Representation}<r>[0.019, 0.442, 0.120, 0.584]</r>, {Planning and Navigation}<r>[0.027, 0.358, 0.120, 0.488]</r>, {Multi-agent Systems}<r>[0.041, 0.274, 0.120, 0.385]</r>, {Swarm Intelligence}<r>[0.046, 0.190, 0.120, 0.294]</r>, {Evolutionary Computation}<r>[0.021, 0.106, 0.120, 0.245]</r>. The x-axis indicates the number of research papers, setting the range from {0}<r>[0.124, 0.951, 0.130, 0.965]</r> to {4500}<r>[1.050, 0.951, 1.075, 0.965]</r>. The bar associated with each area represents the number of research papers for that area. Using the positions of these bars and the values annotated on them, I can effectively extract and compile the data into a CSV format.\nBased on the above thought, the answer is:\nAI Area, Number of Research Papers\nDeep Learning,3950\nReinforcement Learning,2790\nAutomated Reasoning,765\nComputer Vision,3100\nNatural Language Processing,3700\nKnowledge Representation,675\nPlanning and Navigation,1815\nMulti-agent Systems,1325\nSwarm Intelligence,825\nEvolutionary Computation,930"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "REOeDWyymSkRyxTd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, where should we focus more on to achieve significant progress in Artificial Intelligence in terms of research work?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reflecting on the bar chart, it's clear that there has been significant research done in the areas of {Deep Learning}<r>[0.127, 0.833, 0.948, 0.901]</r> and {Natural Language Processing}<r>[0.127, 0.498, 0.896, 0.565]</r>. However, there are some areas such as {Automated Reasoning}<r>[0.127, 0.666, 0.286, 0.733]</r>, {Knowledge Representation}<r>[0.127, 0.414, 0.268, 0.481]</r>, and {Swarm Intelligence}<r>[0.127, 0.162, 0.299, 0.229]</r>, where comparatively less research has been conducted. In the interest of pushing our boundaries of understanding and to maintain balanced progress across all avenues of artificial intelligence, increased focus should be put on these under-explored areas. A more evenly distributed volume of research across all fields can lead to more comprehensive and stable development of artificial intelligence as a whole.\nBased on the above thought, the answer is:\nAreas such as 'Automated Reasoning', 'Knowledge Representation', and 'Swarm Intelligence' should get more focus to achieve balanced and significant progress in Artificial Intelligence."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "jSdzSeKWeQ85ymY3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which area of Artificial Intelligence is the number of research papers second highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine this, I have to examine the lengths of all the bars in the figure. The x-axis of the bar chart indicates the number of research papers, with each bar representing a different AI area. From looking at the bars, it's clear that {Deep Learning}<r>[0.127, 0.833, 0.948, 0.901]</r> area has the highest number of research papers. If I want to identify the second highest, I have to compare the other areas. For instance, {Computer Vision}<r>[0.127, 0.582, 0.772, 0.649]</r> area has a high number of research papers, but when compared to the {Natural Language Processing}<r>[0.127, 0.498, 0.896, 0.565]</r> area, it's clear that Natural Language Processing has the second highest number of research papers.\nBased on the above thought, the answer is:\nNatural Language Processing"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "2Qk07FfJyOFsLybs",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data observed from the bar chart into a table using CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the title}<r>[0.410, 0.019, 0.703, 0.041]</r> of the chart, \"Entrepreneurial Initiatives by Activity Type\", it's clear that this chart is showing the number of initiatives in different types of entrepreneurial activity. The y-axis labels represent the types of entrepreneurial activities, ranging from {Franchise Ownership}<r>[0.025, 0.834, 0.118, 0.973]</r> to {Angel Investing}<r>[0.047, 0.111, 0.118, 0.219]</r>. The x-axis labels represent the corresponding values, ranging from {0}<r>[0.123, 0.924, 0.130, 0.941]</r> to {4500}<r>[0.996, 0.924, 1.025, 0.941]</r>. To generate the CSV table, I need to extract the values from each type of activity. I can do this by examining the length of each bar and correlating it with the x-axis scale. For instance, the length of the bar for {Franchise Ownership}<r>[0.127, 0.808, 0.415, 0.872]</r> is a little bit less than {1500}<r>[0.407, 0.924, 0.436, 0.941]</r> according to x-axis. Thus, the number of initiatives for Franchise Ownership is 1470.\nBased on the above thought, the answer is:\nType of Activity, Number of Initiatives\nFranchise Ownership, 1470\nStartup Founding, 3192\nProduct Innovation, 2422\nFreelance Contracting, 1848\nBusiness Consulting, 2162\nMarket Expansion, 1905\nBootstrapping, 1390\nCrowdfunding, 3000\nSocial Entrepreneurship, 4166\nAngel Investing, 1000"
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "Wu8MWrwhDyfjSFhV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which field of entrepreneurial activity seems to be the most popular among entrepreneurs, and why would you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows the number of initiatives in different entrepreneurial activities, which could reflect the popularity of these activities among entrepreneurs. As the number of initiatives represents the frequency or enthusiasm of entrepreneurs in an activity, the larger the number, the more popular the activity is. So, I can say {Social Entrepreneurship}<r>[0.127, 0.166, 0.945, 0.230]</r>, which has the longest bar among all the activities, appears to be the most popular entrepreneurial activity, followed by {Startup Founding}<r>[0.127, 0.728, 0.754, 0.792]</r>.\nBased on the above thought, the answer is:\nThe field of Social Entrepreneurship seems to be the most popular among entrepreneurs because it has the most number of initiatives compared to the other activities, indicating that a lot of entrepreneurs are becoming increasingly interested in contributing to society and addressing social issues through their entrepreneurial ventures. On the other hand, Startup Founding also has a high number of initiatives, showing that creating new businesses is also a popular activity among entrepreneurs."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "UrOI2X5Ec3J5dZ5j",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of entrepreneurial activity has the most initiatives?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the activity type with the maximum number of initiatives, I need to find the longest bar. From left to right, the bars at {Franchise Ownership}<r>[0.127, 0.808, 0.415, 0.872]</r>, {Startup Founding}<r>[0.127, 0.728, 0.754, 0.792]</r>, {Product Innovation}<r>[0.127, 0.648, 0.602, 0.712]</r>, and {Social Entrepreneurship}<r>[0.127, 0.166, 0.945, 0.230]</r> seem to be sizable. However, the one at {Social Entrepreneurship}<r>[0.127, 0.166, 0.945, 0.230]</r> extends nearly to the end of the scale at {4500}<r>[0.996, 0.924, 1.025, 0.941]</r>, making it the lengthiest. Consequently, the entrepreneurial activity with the most initiatives is Social Entrepreneurship.\nBased on the above thought, the answer is:\nSocial Entrepreneurship."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "4iiyHNSxBWLyjbGF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart's data into a structured CSV table according to your visual analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the chart titled {Estimated Financial Losses by Risk Category}<r>[0.414, 0.019, 0.722, 0.041]</r>, different categories are displayed on the vertical axis, shown in {yticks' label}<r>[0.049, 0.848, 0.141, 0.865]</r> to <r>[0.028, 0.112, 0.141, 0.130]</r>, and the estimated losses are marked on the horizontal axis, where {0}<r>[0.145, 0.939, 0.153, 0.957]</r> denotes the smallest loss and {5,000}<r>[1.028, 0.939, 1.057, 0.957]</r> denotes the maximum loss. Each bar represents a different category from the risk management domain. The length of each bar, such as {Security Breach}<r>[0.149, 0.822, 0.721, 0.887]</r>, corresponds to the estimated financial loss for the corresponding category. The length is labeled with the exact value at the end of the bar like {3,200}<r>[0.730, 0.846, 0.759, 0.863]</r>. Hence, the CSV table can be generated from the chart by pairing each category with its respective estimated financial loss.\nBased on the above thought, the answer is:\nRisk Factor,Estimated Losses\nSecurity Breach,3200\nNatural Disasters,1950\nOperational Risks,2750\nMarket Shifts,2335\nReputation Damage,4467\nStrategic Risks,1980\nCompliance Violations,2876\nFinancial Risks,3765\nHuman Errors,3900\nTechnology Failures,3030"
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "HB1BGJkKvfZa1BOO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be a potential strategy for the company to minimize the overall risk of financial loss?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the largest financial loss seems to be due to {'Reputation Damage'}<r>[0.149, 0.495, 0.947, 0.560]</r>, labeled with {'4467'}<r>[0.956, 0.519, 0.985, 0.536]</r>, followed by {'Human Errors'}<r>[0.149, 0.168, 0.846, 0.234]</r> at {'3900'}<r>[0.855, 0.192, 0.884, 0.210]</r>. Therefore, the company could prioritize creating an in-depth strategy to prevent reputation damage by improving customer relations, transparent communication, and promptly addressing any issues that could tarnish the company's reputation. Simultaneously, it should focus on reducing human errors by providing comprehensive training programs, implementing strict compliance and safety protocols, and promoting a well-structured work environment. Moreover, looking down the chart to lesser but significant losses, the company should not ignore other risk factors. Regular risk assessments and updates on risk management strategies could help control these risks effectively.\nBased on the above thought, the answer is:\nThe company could focus on preventing reputation damage, reducing human errors, and regularly updating risk management strategies for other significant risk factors."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "3auHhE1qCduoOCBz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering your visual analysis, which risk category contributes to the highest expected financial loss?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the risk category contributing to the highest expected financial loss, one must observe the length of all the bars displayed. As per my observation, the bar representing {'Reputation Damage'}<r>[0.149, 0.495, 0.947, 0.560]</r> is the longest reaching almost towards the end of the x-axis. At the end of this bar, there's a text {'4467'}<r>[0.956, 0.519, 0.985, 0.536]</r> marking the estimated financial loss which is the highest number among all the bars' endpoints. That indicates that 'Reputation Damage' is the risk category with the highest expected financial loss.\nBased on the above thought, the answer is:\nReputation Damage."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "QtEHrAKJsefnK9ch",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table representing the stock values for each company as shown in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting by reading the title {Stock Value Trend for Tech Companies}[0.396, 0.019, 0.659, 0.041], we can tell structure our data into companies and their corresponding stock values. The y-axis ticks provide the company names from {Amazon}[0.018, 0.848, 0.059, 0.910] to {Oracle}[0.024, 0.112, 0.059, 0.165]. The length of each bar represent stock values, for example in the case of {Amazon}[0.067, 0.822, 0.637, 0.887] the value represented is {5450}[0.642, 0.846, 0.671, 0.863] on the far right side of the bar. Following the same process for each company, we can generate the table.\nBased on the above thought, the answer is:\nCompany, Stock Value\nAmazon, 5450\nApple, 5350\nGoogle, 2900\nNetflix, 4500\nFacebook, 4200\nTesla, 8390\nMicrosoft, 6100\nIBM, 3580\nCisco, 2400\nOracle, 2800"
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "q9VxsS0PHz9tVyKH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which tech company should investors prioritize for investment? Please provide reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart, {Tesla}[0.031, 0.439, 0.059, 0.482] with the longest green bar {[0.067, 0.413, 0.944, 0.479]} has the highest stock value of {8390}[0.949, 0.437, 0.978, 0.455], which indicates the highest price of its stocks and as such the company's strong financial performance and high market confidence. Therefore, given these financial factors, investors might prioritize Tesla for investment.\nBased on the above thought, the answer is:\nTesla. Because it has the highest stock value, indicating a strong financial performance and high market confidence, which are important considerations for investors."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "mcvspqyuTZ7wM5Qw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest stock value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the company with the highest stock value, we look for the longest green bar on the chart. This corresponds to the {Tesla bar}[0.067, 0.413, 0.944, 0.479] and its value annotation {8390}[0.949, 0.437, 0.978, 0.455] is higher than that of any other company.\nBased on the above thought, the answer is:\nTesla."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "wMotPajHsHz6REDG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data displayed on the bar chart into a tabulated format."
+            },
+            {
+                "from": "gpt",
+                "value": "The investment types are indicated along the y-axis with {labels}[{'Mutual Fund'}[0.046, 0.848, 0.104, 0.936]; {'Blue-Chip Stocks'}[0.027, 0.766, 0.104, 0.882]; {'Real Estate'}[0.050, 0.684, 0.104, 0.766]; {'Bonds'}[0.071, 0.603, 0.104, 0.653]; {'Startup Investments'}[0.012, 0.521, 0.104, 0.658]; {'Cryptocurrency'}[0.033, 0.439, 0.104, 0.546]; {'Penny Stocks'}[0.042, 0.358, 0.104, 0.451]; {'ETFs'}[0.077, 0.276, 0.104, 0.317]; {'Gold'}[0.077, 0.194, 0.104, 0.235]; {'Savings Account'}[0.028, 0.112, 0.104, 0.226]]. The return rates are indicated by the {length of the bars}[{Mutual Fund}[0.112, 0.822, 0.246, 0.887]; {Blue-Chip Stocks}[0.112, 0.740, 0.521, 0.805]; {Real Estate}[0.112, 0.658, 0.367, 0.724]; {Bonds}[0.112, 0.577, 0.292, 0.642]; {Startup Investments}[0.112, 0.495, 0.946, 0.560]; {Cryptocurrency}[0.112, 0.413, 0.550, 0.479]; {Penny Stocks}[0.112, 0.332, 0.200, 0.397]; {ETFs}[0.112, 0.250, 0.433, 0.315]; {Gold}[0.112, 0.168, 0.179, 0.234]; {Savings Account}[0.112, 0.087, 0.133, 0.152]]. The {labels on the ends of the bars} displays the exact return rates. Hence, by aligning the corresponding investment types with their return rates, we convert the graph into tabulated data.\nBased on the above thought, the answer is:\nInvestment Type,Return Rate (%)\nMutual Fund,3.2\nBlue-Chip Stocks,9.8\nReal Estate,6.1\nBonds,4.3\nStartup Investments,20.0\nCryptocurrency,10.5\nPenny Stocks,2.1\nETFs,7.7\nGold,1.6\nSavings Account,0.5"
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "qF2LpmgqfD58WlN3",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an overview of the investment types and their associated risks based on the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, the investment types are ranked in descending order of return rates. Starting with {Startup Investments}[0.112, 0.495, 0.946, 0.560] at the top with the highest return rate, and ending with {Savings Account}[0.112, 0.087, 0.133, 0.152] at the bottom. Usually, a higher return rate suggests a higher risk and a lower return rate suggests a lower risk. Therefore, the risk for each type of investment may also be inferred. For example, the highest return rate is associated with Startup Investments, which are often considered to be high risk. On the other hand, Savings Accounts, with the lowest return, are considered to be the safest form of investment.\nBased on the above thought, the answer is:\nThe investment types with the highest return rates, such as Startup Investments and Cryptocurrency, are typically considered to be high-risk, high-return investments. Then there are moderate-risk, moderate-return types such as Real Estate and ETFs. Finally, investment types with lower return rates, such as Savings Accounts and Gold, are usually considered to be low-risk investments."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "CD2448obwV3s2d75",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of investment has the highest return rate according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The return rate is indicated by the {horizontal length of bars}. By comparing each bar, it appears that the bar for {Startup Investments}[0.112, 0.495, 0.946, 0.560] is far ahead compared to others. Therefore, it has the highest return rate.\nBased on the above thought, the answer is:\nStartup Investments."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "ipoP75a3zbpvZYpI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you pull from the raw data to create a table including industry and the number of internet users?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels at the y-axis {\"yticks\"}[{\"Cloud Computing\"}[0.042, 0.848, 0.122, 0.966], {\"Cybersecurity\"}[0.057, 0.766, 0.122, 0.864], {\"Online Shopping\"}[0.046, 0.684, 0.122, 0.798], {\"Data Analysis\"}[0.058, 0.603, 0.122, 0.699], {\"AI and Machine Learning\"}[0.013, 0.521, 0.122, 0.685], {\"Telecommunications\"}[0.030, 0.439, 0.122, 0.577], {\"Social Media Marketing\"}[0.018, 0.358, 0.122, 0.513], {\"Web Development\"}[0.038, 0.276, 0.122, 0.402], {\"Software Development\"}[0.020, 0.194, 0.122, 0.347], {\"E-commerce\"}[0.062, 0.112, 0.122, 0.202]], I can see that it represents different industries. The values accompany each bar, e.g., continuing from top to bottom, the first bar {\"Cloud Computing\"}<{\"bar\"}[0.130, 0.822, 0.308, 0.887]> with \"{the number of internet users}\"<{\"label\"}[0.323, 0.846, 0.360, 0.863]> as 24000, the second bar {\"Cybersecurity\"}<{\"bar\"}[0.130, 0.740, 0.343, 0.805]> with \"{the number of internet users}\"<{\"label\"}[0.358, 0.764, 0.394, 0.782]> as 28650, and so on. This will be the process to generate the CSV table, with one column for the specific industry, and another column for the corresponding number of internet users.\nBased on the above thought, the answer is:\nIndustry,Number of Internet Users\nCloud Computing,24000\nCybersecurity,28650\nOnline Shopping,35200\nData Analysis,40500\nAI and Machine Learning,52750\nTelecommunications,61000\nSocial Media Marketing,74200\nWeb Development,83200\nSoftware Development,98650\nE-commerce,107200"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "38xgCr8QhT5NI9TN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a company only has the resources to enter one industry, which industry should they potentially consider for the greatest exposure to internet users?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the number of users interested in different sectors of the Internet and Communication Industry and \"{E-commerce}\"<{\"bar\"}[0.130, 0.087, 0.927, 0.152]> has the highest number of users. So if a company wants to get exposed to the most significant number of internet users, they should probably consider the 'E-commerce' industry.\nBased on the above thought, the answer is:\nE-commerce"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "DTWlxrIJXl1QT8It",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the approximate increase in internet users when considering engagement from 'Cloud Computing' to 'E-commerce'?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the bars, we can see that the first bar \"{Cloud Computing}\"<{\"bar\"}[0.130, 0.822, 0.308, 0.887]> represents 24000 users, and the lowest bar \"{E-commerce}\"<{\"bar\"}[0.130, 0.087, 0.927, 0.152]> represents 107200 users. To find the increase in users, I need to subtract the number of users in 'Cloud Computing' from the number of users in 'E-commerce'.\nBased on the above thought, the answer is:\n83200"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "799DClvgI5bxlymS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis, please provide the data from the chart in a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a CSV format, I am observing that the y-axis labels consist of different {Big Data Tools}<r>[0.041, 0.848, 0.078, 0.902]; [0.052, 0.766, 0.078, 0.804]; [0.064, 0.684, 0.078, 0.705]; [0.053, 0.603, 0.078, 0.639]; [0.038, 0.521, 0.078, 0.581]; [0.038, 0.439, 0.078, 0.499]; [0.013, 0.358, 0.078, 0.455]; [0.053, 0.276, 0.078, 0.312]; [0.048, 0.194, 0.078, 0.239]; [0.036, 0.112, 0.078, 0.174]</r>. By observing the lengths and given values on each bar, it is clear that these represent the {Number of Employees}<r>[0.881, 0.846, 0.910, 0.863]; [0.750, 0.764, 0.780, 0.782]; [0.716, 0.682, 0.745, 0.700]; [0.855, 0.601, 0.884, 0.618]; [0.768, 0.519, 0.797, 0.536]; [0.820, 0.437, 0.849, 0.455]; [0.855, 0.356, 0.884, 0.373]; [0.672, 0.274, 0.702, 0.291]; [0.915, 0.192, 0.945, 0.210]; [0.455, 0.111, 0.485, 0.128]</r> proficient in each big data tool.\nBased on the above thought, the answer is:\nBig Data Tool,No. of Employees\nPython,9500\nJava,8000\nR,7600\nSQL,9200\nHadoop,8200\nTableau,8800\nApache Spark,9200\nSAS,7100\nExcel,9900\nMATLAB,4600"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "euqZmkZptY1HVBqm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the graph data, which big data tool do you observe to be trending among most employees? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the graph, we can clearly see the tools on the y-axis with each corresponding bar length on the x-axis representing the number of employees proficient in that specific tool. The longest bar that is easy to spot belongs to the tool {Excel}<r>[0.086, 0.168, 0.945, 0.234]</r>. This bar represents the highest number of employees proficient, with the number being {9900}<r>[0.915, 0.192, 0.945, 0.210]</r>. Therefore, it can be stated that among all the Big Data Tools displayed on the graph, Excel is currently trending with the most number of employees proficient in it.\nBased on the above thought, the answer is:\nExcel. Because it has the highest number of proficient employees, which implies that it is most commonly being used amongst employees."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "lx7zbCALCGdLJBUL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich tool is used by the least number of employees?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the tool with the least number of employees, I need to locate the shortest bar on the chart. The shortest bar that I can observe is {MATLAB}<r>[0.086, 0.087, 0.485, 0.152]</r> with the number of employees given at the right end of the bar as {4600}<r>[0.455, 0.111, 0.485, 0.128]</r>. This bar is clearly the shortest among all other bars representing various tools.\nBased on the above thought, the answer is:\nMATLAB"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "JxIO3ZvGFRX6Cd74",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you've analyzed, please arrange the E-commerce categories in descending order of annual revenue growth and present the data in a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bar chart, I note that different E-commerce categories are represented on the y-axis with labels such as {Online Retail}<r>[0.056, 0.848, 0.116, 0.938]</r>, {Digital Media}<r>[0.054, 0.766, 0.116, 0.859]</r> among others. The annual growth percentage for each category can be determined by examining the corresponding bar length and the value it reaches on the x-axis, such as the 18% value for {Online Retail bar}<r>[0.124, 0.822, 0.617, 0.887]</r> and 25% value for {Digital Media bar}<r>[0.124, 0.740, 0.809, 0.805]</r>. By looking at all the bars and comparing their lengths, I can extract the entire E-commerce types and their corresponding annual growth percentages.\nBased on the above thought, the answer is:\nE-commerce Type,Annual Revenue Growth (%)\nDigital Banking,30\nOnline Fashion,27\nOnline Education,26\nDigital Media,25\nE-healthcare,23\nFood Delivery,22\nOnline Market Place,28\nOnline Retail,18\nTravel & Transportation,15\nE-utilities,12"
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "TRHZeIihaJ1Q05j8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the chart, which sectors of E-commerce should one consider investing in, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, we can see that areas like {Digital Banking}<r>[0.045, 0.358, 0.116, 0.464]</r>, {Online Fashion}<r>[0.048, 0.112, 0.116, 0.214]</r>, and {Online Education}<r>[0.038, 0.276, 0.116, 0.393]</r> have longer bar lengths, exposing that these sectors indicate higher growth percentages. Indicatively, {Digital Banking bar}<r>[0.124, 0.332, 0.946, 0.397]</r> has the longest bar in the chart, implying the maximum revenue growth of 30%. It is important to note that these high growth sectors suggest a rapid expansion which could open up significant investment opportunities.\nBased on the above thought, the answer is:\nOne should consider investing in Digital Banking, Online Fashion, and Online Education sectors as they have shown the highest annual revenue growth, indicating these sectors are rapidly expanding and could potentially bring high returns."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "eYDiczoNwRAyPIFU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich E-commerce type demonstrates the lowest annual revenue growth percentage according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the E-commerce type with the lowest annual revenue growth percentage, I need to analyze the lengths of the bars from each category relative to their percentages on the x-axis. The shortest bar length corresponds to the lowest value. I can see that {E-utilities}<r>[0.070, 0.439, 0.116, 0.509]</r> has {the shortest bar}<r>[0.124, 0.413, 0.453, 0.479]</r> when compared with others on the chart indicating the lowest growth percentage of 12%.\nBased on the above thought, the answer is:\nE-utilities"
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "Q9R5BdgshYQXyDwQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the data from the bar chart in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical labels on the chart represent different sectors. I note labels named {High Tech}[0.089, 0.838, 0.147, 0.920], {Utility}[0.108, 0.758, 0.147, 0.813], {Healthcare}[0.083, 0.677, 0.147, 0.768], {Consumer Discretionary}[0.018, 0.597, 0.147, 0.781], {Real Estate}[0.081, 0.517, 0.147, 0.611], {Energy}[0.102, 0.437, 0.147, 0.500], {Finance}[0.099, 0.357, 0.147, 0.425], {Communication Services}[0.015, 0.277, 0.147, 0.465], {Materials}[0.092, 0.197, 0.147, 0.276], and {Industrials}[0.086, 0.117, 0.147, 0.204]. The length of each bar corresponds to the number of shares traded in that sector. For example, for High Tech, the corresponding bar with the coordinates [0.156, 0.813, 0.592, 0.877] shows a number of shares around 2308. Subsequently, I will extract the data for each sector in the same way.\nBased on the above thought, the answer is:\nProfession,Number of Shares\nHigh Tech,2308\nUtility,1520\nHealthcare,1258\nConsumer Discretionary,934\nReal Estate,1048\nEnergy,1915\nFinance,2160\nCommunication Services,1643\nMaterials,4150\nIndustrials,3275"
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "6mBqaWS85vA3UoB2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the chart give us about the health of each sector of the financial market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the health of each sector, we need to consider the number of shares traded which may correlate with the sector's activity and interest from investors. For instance, the {Materials sector}<r>[0.092, 0.197, 0.147, 0.276]</r>shows the longest bar<r>[0.156, 0.173, 0.939, 0.237]</r>, suggesting a high volume of shares traded, possibly indicating robust health and high investor interest. In contrast, sectors with shorter bars, such as {Consumer Discretionary}<r>[0.018, 0.597, 0.147, 0.781]</r> with bar<r>[0.156, 0.573, 0.332, 0.637]</r>, signify fewer shares traded, which could be a sign of lesser activity or potentially less investor interest. It is crucial to note that numerous other factors influence a sector's health, and the number of shares traded is just one indicator that can influence the general sentiment towards a sector.\nBased on the above thought, the answer is:\nThis chart indicates the relative activity levels and potentially the investor interest in each sector of the financial market. Materials sector appears to be the most active or has the highest investor interest, whereas the Consumer Discretionary sector seems to show the lowest activity levels or investor interest. These interpretations are based on the number of shares traded, which is the visible representation in the chart."
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "AvioTifAS7hUkyjt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the least number of shares traded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector trades the least number of shares, I need to analyze the length of the bars corresponding to each sector. After scanning from left to right, I find that the {Consumer Discretionary bar}<r>[0.156, 0.573, 0.332, 0.637]</r> is shorter, indicating it has fewer shares. To confirm, I check the bar labels and find the value of shares traded for this sector is 934.\nBased on the above thought, the answer is:\nConsumer Discretionary"
+            }
+        ],
+        "id_raw": 371
+    },
+    {
+        "id": "aOTfre33JDn6l1o8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the various activities in the value chain from {the y-axis labels}[{\"Sourcing\"}[0.053, 0.833, 0.097, 0.899]; {\"Production\"}[0.045, 0.715, 0.097, 0.793]; {\"Packaging\"}[0.047, 0.598, 0.097, 0.673]; {\"Transportation\"}[0.030, 0.480, 0.097, 0.581]; {\"Distribution\"}[0.041, 0.362, 0.097, 0.446]; {\"Retail\"}[0.066, 0.245, 0.097, 0.292]; {\"After-Sales Service\"}[0.012, 0.127, 0.097, 0.254]], I can see that these are the rows of the requested table. The {transaction volumes}[{\"1250\"}[0.365, 0.831, 0.394, 0.849]; {\"1700\"}[0.455, 0.714, 0.484, 0.731]; {\"2350\"}[0.585, 0.596, 0.614, 0.613]; {\"2800\"}[0.675, 0.478, 0.704, 0.496]; {\"3250\"}[0.765, 0.360, 0.794, 0.378]; {\"4200\"}[0.954, 0.243, 0.984, 0.260]; {\"3850\"}[0.885, 0.125, 0.914, 0.143]] that sit on top of the bars for each activity will fill the second column of the table. Therefore, I can construct a table using these values where the first column represents the various activities of the value chain and the second column contains the corresponding transaction volume.\nBased on the above thought, the answer is:\nValue Chain Activity,Transaction Volume\nSourcing,1250\nProduction,1700\nPackaging,2350\nTransportation,2800\nDistribution,3250\nRetail,4200\nAfter-Sales Service,3850"
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "rrtjEKRjV9bG2E73",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slight decline in transaction volume in the After-Sales Service stage suggest about the overall function of the value chain?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar graph, we can see that most of the categories have an increasing transaction volume, with the exception of {After-Sales Service}[0.012, 0.127, 0.097, 0.254], which has {\"a slightly lesser value of 3850\"}[0.885, 0.125, 0.914, 0.143] than Retail {\"of 4200\"}[0.954, 0.243, 0.984, 0.260]. This indicates that while the bulk of transactions take place up until the point of sale, there might be a reduction in activities after the product has been sold, such as returns, exchanges, or customer inquiries, leading to a slightly lower volume of transactions in the After-Sales Service stage.\nBased on the above thought, the answer is:\nThe slight decline in transaction volume in the After-Sales Service stage suggests that after a product is sold, there might be fewer transactions related to return, exchange, or customer queries. This might indicate a strong product quality, effective upfront services, or a lack of strong after-sales policies and services."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "mkgef0KHcI0gv5yW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest transaction volume represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the largest transaction volume, I need to analyze each of the numbers provided at the end of the bar chart. Starting with {\"1250\"}[0.365, 0.831, 0.394, 0.849] for Sourcing, {\"1700\"}[0.455, 0.714, 0.484, 0.731] for Production, {\"2350\"}[0.585, 0.596, 0.614, 0.613] for Packaging, {\"2800\"}[0.675, 0.478, 0.704, 0.496] for Transportation, {\"3250\"}[0.765, 0.360, 0.794, 0.378] for Distribution, {\"4200\"}[0.954, 0.243, 0.984, 0.260] for Retail, and finally {\"3850\"}[0.885, 0.125, 0.914, 0.143] for After-Sales Service, it seems that Retail at 4200 has the largest transaction volume.\nBased on the above thought, the answer is:\n4200"
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "nGkrspQhfx0i1U4X",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart presented, can you help me to structure the data into CSV format data?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Number of Employees under each managerial role\"}[0.368, 0.019, 0.718, 0.041] and the x-axis label {\"Number of Employees\"}[0.479, 0.964, 0.607, 0.981], I can see that the x-axis represents the number of employees managed by each managerial role. The y-axis labels represent different management roles, including {CEO}[0.065, 0.844, 0.091, 0.882], {President}[0.044, 0.753, 0.091, 0.823], {Director}[0.049, 0.662, 0.091, 0.724], and so on. For each role, there is a single coloured bar to represent the number of employees. For example, the bar for {CEO}[0.099, 0.814, 0.945, 0.887] extends to approximately the 7000 marker, and there is {a value annotation \"7000\"}[0.951, 0.842, 0.980, 0.859] associated with it, so the number of employees managed by the CEO is 7000.\nBased on the above thought, the answer is:\nmanager_type,number_of_employees\nCEO,7000\nPresident,5000\nDirector,4000\nSenior_Manager,3000\nManager,2000\nJunior_Manager,1500\nTeam_Lead,1000\nTrainee_Manager,800\nIntern,100"
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "F0tN3dpFAFk5cNrk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the bar chart depict the hierarchy in the company?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart intuitively reflects a hierarchical structure of a company based on the number of employees managed by each role. The y-axis categorizes the roles from {CEO}[0.065, 0.844, 0.091, 0.882] to {Intern}[0.058, 0.116, 0.091, 0.165], and the length of the corresponding bar on the x-axis represents the number of employees managed by the role. Generally, the higher the management level is, the more employees they manage, which stands out in high to low order on the chart, although exceptions exist such as between the {Junior Manager}[0.019, 0.389, 0.091, 0.497] and {Team Lead}[0.037, 0.298, 0.091, 0.379]. This visually presents a hierarchy from the CEO at the top down to the intern at the bottom in the managerial order.\nBased on the above thought, the answer is:\nThe bar chart depicts the hierarchy in the company by differentiating the managerial roles with the quantity of employees each role manages. The higher up in the hierarchy, the longer the bar, indicating more employees under its jurisdiction. This visually represents a management hierarchy from the CEO at the top to the intern at the bottom."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "MfgN5SzeW6z4NRpb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which managerial role manages the least number of employees?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the length of the bars from left to right, it can be seen that the shortest bar is {Intern}[0.099, 0.087, 0.111, 0.159]. This is supported by the value annotation {\"100\"}[0.117, 0.114, 0.139, 0.132] at the end of this bar. It indicates that the Intern role manages the least number of employees.\nBased on the above thought, the answer is:\nIntern."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "80fDvXegAtXt1o8P",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert its data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the information given by {the subtitle}<[0.389, 0.019, 0.755, 0.041]>, the chart is talking about the number of entrepreneurs by different entrepreneurial activity. The bars span along the x-axis, with their lengths representing the number of entrepreneurs from {0}<[0.153, 0.939, 0.160, 0.957]> to {6000}<[1.021, 0.939, 1.050, 0.957]>. The y-axis present 10 types of entrepreneurial activities. Also, for each bar, there is {a value annotation}[like {this one}<[0.237, 0.846, 0.259, 0.863]> for \"Bootstrapping\"] on the right end of the bar, indicating the exact number of entrepreneurs for a specific activity. The y-label of each bar, like {this one}<[0.082, 0.848, 0.149, 0.947]> for \"Bootstrapping\", tells us the type of the entrepreneurial activity.\nBased on the above thought, the answer is:\nEntrepreneurial Activity, Number of Entrepreneurs\nBootstrapping, 500\nVenture Capital, 1500\nAngel Investment, 2000\nCrowdfunding, 2100\nFranchising, 2200\nPartnership, 3200\nSelf-funding, 3800\nSmall Business Loans, 4200\nEntrepreneurship Competitions, 4500\nGovernment Programs, 5400"
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "aoeJAuTs0HSOESGj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart data, which type of entrepreneurial activity would you recommend a newcomer in entrepreneurship to engage in and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To recommend a suitable entrepreneurial activity for entertainers, one should interpret the bar chart in a comprehensive way. The x-axis represents the 'Number of Entrepreneurs' while different bars indicate different types of entrepreneurial activities. Higher bars, like {Government Programs}<[0.157, 0.087, 0.948, 0.152]> and {Entrepreneurship Competitions}<[0.157, 0.168, 0.816, 0.234]>, suggest that more entrepreneurs engage in these activities. While this could mean these activities are popular and potential-filled, it also implies high competition. On the contrary, bars with reasonable length, such as {Partnership}<[0.157, 0.413, 0.626, 0.479]>, suggest that these activities are quite participatory but have less fierce competition, which may be more suitable for newcomers.\nBased on the above thought, the answer is:\nPartnership. Because it sees a reasonable amount of entrepreneurs involved which suggest a certain degree of practical potential, meanwhile, compared to the most popular activities, the competition it faces is less fierce. This load of competition may be more suitable for newcomers."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "lOuX5Wz2hhIuteus",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of entrepreneurial activity faces the most number of entrepreneurs?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Number of Entrepreneurs' is represented by the length of bars along the x-axis. Thus, the one with the longest bar would be the activity facing the highest number of entrepreneurs. Upon observing the bar chart, it's evident that the bar corresponding to {Government Programs}<[0.157, 0.087, 0.948, 0.152]> has the longest length, and goes all the way to {5400}<[0.955, 0.111, 0.984, 0.128]>, which is also confirmed by the value annotation.\nBased on the above thought, the answer is:\nGovernment Programs."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "Oxx7V5uP3hd5YzX8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the friendship data seen in the bar chart into a structured table format detailing the number of friends made and lost for both 'Ours' and 'Saliency' categories across the different time periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar charts through their given coordinates, the {x-axis labels}<r>[0.043, 0.432, 0.099, 0.455]; [0.235, 0.432, 0.272, 0.455]; [0.403, 0.432, 0.470, 0.455]; [0.591, 0.432, 0.647, 0.455]; [0.783, 0.432, 0.821, 0.455]</r> describe the time periods: Morning, Noon, Afternoon, Evening, and Night. The data contains two types of information, 'friends made' and 'friend loss', represented by bars in two separate {subplots}<r>[0.071, 0.034, 0.985, 0.415]; [0.071, 0.522, 0.985, 0.903]</r>. Extraction of values can be performed based on the heights of the bars for 'Ours' and 'Saliency' with colors identified in the {legend}<r>[0.396, -0.018, 0.604, 0.023]</r>, where {Ours' color}<r>[0.402, -0.007, 0.430, 0.009]</r> is identified by a specific shade and {Saliency's color}<r>[0.501, -0.007, 0.529, 0.009]</r> by another. For example, in the Morning, {Ours' bar for friends made}<r>[-0.002, 0.288, 0.144, 0.415]</r> corresponds to a value range of {0 to 40}<r>[0.071, 0.288]</r>, while {Saliency\u2019s bar}<r>[-0.002, 0.304, 0.144, 0.415]</r> matches a range of {0 to 35}<r>[0.071, 0.304]</r>. By progressing through each time period and category, one can match these height-to-value correspondences and create the output table.\nBased on the above thought, the answer is:\nTime Period,Friends Made Ours,Friends Made Saliency,Friend Loss Ours,Friend Loss Saliency\nMorning,40,35,5,10\nNoon,60,55,10,12\nAfternoon,80,70,15,17\nEvening,100,90,25,20\nNight,25,20,8,10"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "Vnt58LBGnQ6ZCgmV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows a trend of growing friend loss as the day progresses, and how can this be determined from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain a trend, we need to examine the bars representing friend loss for both categories across different time periods. This requires looking at the second set of bar charts within the {subplot}<r>[0.071, 0.522, 0.497, 0.903]</r>. {Ours bars for friend loss}<r>[\n[0.037, 0.848, 0.105, 0.903],\n[0.122, 0.794, 0.190, 0.903],\n[0.207, 0.739, 0.275, 0.903],\n[0.292, 0.631, 0.360, 0.903],\n[0.378, 0.816, 0.446, 0.903]\n]</r> show friend loss values of 5, 10, 15, 25, and 8 for Morning, Noon, Afternoon, Evening, and Night, respectively. Similarly, {Saliency\u2019s bars for friend loss}<r>[\n[0.525, 0.794, 0.593, 0.903],\n[0.610, 0.772, 0.678, 0.903],\n[0.695, 0.718, 0.763, 0.903],\n[0.780, 0.685, 0.849, 0.903],\n[0.866, 0.794, 0.934, 0.903]\n]</r> indicate values of 10, 12, 17, 20, and 10, respectively. While both demonstrate an increasing trend, it\u2019s evident that the 'Ours' category not only starts higher but also increases more substantially before the drop at Night. Thus, 'Ours' clearly shows a stronger growing trend till the Evening.\nBased on the above thought, the answer is:\nThe 'Ours' category demonstrates a notable increasing trend in friend loss as the day progresses. This can be established by analyzing the heights of the bars in the second set of bar charts, which increase more drastically for 'Ours' than for 'Saliency', peaking at Evening before the Night reveals a decrease."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "QSKsAk3gzuNQSxQ9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which time period did the 'Saliency' category have the most significant increase in friends made compared to the 'Ours' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the time period where 'Saliency' had the most significant relative increase in friends made compared to the 'Ours' category, I need to analyze the height differences between corresponding pairs of bars. {Ours bars}<r>[\n[-0.002, 0.288, 0.144, 0.415],\n[0.180, 0.225, 0.327, 0.415],\n[0.363, 0.161, 0.510, 0.415],\n[0.546, 0.098, 0.692, 0.415],\n[0.729, 0.336, 0.875, 0.415]\n]</r> and {Saliency bars}<r>[\n[-0.002, 0.304, 0.144, 0.415],\n[0.180, 0.241, 0.327, 0.415],\n[0.363, 0.193, 0.510, 0.415],\n[0.546, 0.129, 0.692, 0.415],\n[0.729, 0.352, 0.875, 0.415]\n]</r> must be individually considered. Starting from the Morning, I can see the difference is 5 for both categories. Moving through the time periods, the differences in friends made are 5, 5, 10, 10, and 5 respectively. The Afternoon period presents the most significant increase with Saliency having 70 friends made against Ours with 80 friends made, showing a smaller gap between the two compared to other periods.\nBased on the above thought, the answer is:\nAfternoon."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "JgIi2sHNPrPixr55",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the bar subplots and compile it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to generate a CSV table from the bar charts, we must first identify the x and y axes of each subplot. The x-axes for the two subplots represent the 10 regions across which the sales data is divided. The y-axes represent the sales figures. For the {first subplot}[0.071, 0.021, 0.985, 0.424], we see that the plot represents data from the 'Consumer Electronics' market, colored in {#ee8882}[0.112, 0.397, 0.139, 0.638]. Similarly, in the {second subplot}[0.071, 0.515, 0.985, 0.917], we see data from the 'Apparel' market, represented in {#8db7db}[0.112, 0.901, 0.139, 1.045]. We now deduce the individual sales figures for each market in the regions. For instance, in the first region, the sales figures for 'Consumer Electronics' and 'Apparel' are ~90 and ~90, respectively. The same pattern can be followed to deduce the data for all 10 regions. Using this information, the CSV table can be generated as follows:\nBased on the above thought, the answer is:\nRegion,Consumer Electronics, Apparel\n1,90,90\n2,85,85\n3,120,80\n4,150,100\n5,200,200\n6,220,220\n7,190,240\n8,160,250\n9,180,300\n10,170,300"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "Gy2Jqd0LwifOWR4h",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market seems to have a more steady sales trend over the regions based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the relative steadiness of the sales trend across regions involves comparing the fluctuations in bar heights for both markets across the regions. For the 'Consumer Electronics' market, represented in the {first subplot}[0.071, 0.021, 0.985, 0.424], the sales data fluctuates significantly, with certain regions showing high sales figures and others being significantly lower at the beginning, the trend increases and then drops. On the contrary, for the 'Apparel' market, represented in the {second subplot}[0.071, 0.515, 0.985, 0.917], the sales trends appear to be more unpredictable initially, but generally increases towards the tenth region. Given these observations, it appears that the 'Apparel' market has a less variable and more steady sales trend over regions.\nBased on the above thought, the answer is:\nApparel Market"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "GPZLYw9ciix1dTBW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest sales figure observed in the 'Apparel' market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest sales figure for the 'Apparel' market, we need to analyze the {second subplot}[0.071, 0.515, 0.985, 0.917]. We can see the bars representing sales data in {#8db7db}[0.112, 0.901, 0.139, 1.045]. By looking at the heights of each of the 10 bars, we can find that the highest bar corresponds to the 9th and 10th regions which reaches near the top y-axis tick with the label '330'. Therefore, based on the bar height, I would say the highest sales figure for 'Apparel' is near 300.\nBased on the above thought, the answer is:\nAround 300"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "ozjHGiKrcmC0j2Hc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table listing the Cultural Heritage and Cultural Integration trends for the represented countries based on the displayed bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I will analyze both the Cultural Heritage and the Cultural Integration trends for each country as represented on the bar chart. Starting with the Cultural Heritage trend, the {x-axis labels of subplot_0}<r>[0.162, 0.919, 0.531, 0.942]</r> go from -20 to 120, and the {y-axis labels}<r>[0.025, 0.085, 0.099, 0.848]</r> list various countries such as Rome, Greece, Egypt, and China. Each bar's length corresponds to a value for that country's Cultural Heritage trend. For instance, Rome's Cultural Heritage trend bar ranges from {0.00 to 34.10}<r>[0.165, 0.804, 0.267, 0.863]</r>. Similarly, for the Cultural Integration trend shown in subplot_1, the {x-axis labels}<r>[0.560, 0.919, 1.032, 0.942]</r> also go from -60 to 120, and each country's bar represents the value trend, like the Rome bar showing values from {0.00 to 86.17}<r>[0.721, 0.804, 0.938, 0.863]</r>. By analyzing each bar's coordinates, the table with Cultural Heritage and Cultural Integration trends can be constructed.\nBased on the above thought, the answer is:\nCountry,Cultural Heritage,Cultural Integration\nRome,34.10,86.17\nGreece,58.36,-25.31\nEgypt,105.06,-18.18\nChina,102.20,21.95\nIndia,41.22,13.64\nFrance,70.34,-38.90\nUnited Kingdom,23.66,99.70\nSpain,88.81,75.97\nTurkey,85.03,37.53\nRussia,-6.57,53.44\nUSA,-12.37,4.24"
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "ecePtWb8Qru2Nd7I",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the trends in Cultural Heritage and Cultural Integration, which country seems to have a more consistent performance across both categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate consistency in performance across the Cultural Heritage and Cultural Integration categories, I will compare the proximity of each country's values in both categories to determine the smallest disparity. For instance, {China's bar for Cultural Heritage}<r>[0.165, 0.582, 0.469, 0.641]</r> shows a value of 102.20, and {China's bar for Cultural Integration}<r>[0.721, 0.582, 0.776, 0.641]</r> indicates 21.95. This demonstrates a consistent performance as both values are positive with a reasonable difference. Performing the same method across other countries, it becomes evident that while some countries have high disparities, China maintains a more consistent performance, having both values in a positive range and a modest difference.\nBased on the above thought, the answer is:\nChina. When comparing the difference between Cultural Heritage and Cultural Integration trends, China shows moderate and consistent positive values in both categories, indicating a balanced performance in promoting cultural dynamics."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "5K4FLT3pV1vcCZ6i",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country shows the greatest positive difference between Cultural Heritage and Cultural Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the country with the greatest positive difference between the Cultural Heritage and Cultural Integration trends, I must first determine the difference for each country and then compare these values. Focusing on the Cultural Heritage subplot, I see {Rome's bar}<r>[0.165, 0.804, 0.267, 0.863]</r> which indicates a value of 34.10. In the Cultural Integration subplot, I observe {Rome's bar}<r>[0.721, 0.804, 0.938, 0.863]</r> indicating a value of 86.17. The difference is therefore 52.07 for Rome. I will repeat this calculation for all countries and conclude that the country with {the greatest positive difference}<r>[content relevant coordinates for that country]</r> is the United Kingdom, with a Cultural Heritage value of 23.66 and a Cultural Integration value of 99.70, which gives a difference of 76.04.\nBased on the above thought, the answer is:\nUnited Kingdom"
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "eBfAurBaKIQgNl2o",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the participation rates for University Programs in CSV format from the provided bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart subset dedicated to {University Programs}<r>[0.087, 0.062, 0.492, 0.448]</r>, I notice two bars corresponding to the methods 'Ours' and 'Trend-based Sampling'. The {y-axis labels}<r>[0.043, 0.053, 0.077, 0.488]</r> range from '\u22120.6' to '1.0', and the {x-axis labels}<r>[0.171, 0.464, 0.469, 0.487]</r> detail the method names. The first bar labeled {Ours}<r>[0.105, 0.088, 0.269, 0.319]</r> indicates a rate of '0.9' and the second bar labeled {Trend-based Sampling}<r>[0.310, 0.101, 0.474, 0.319]</r> signifies a rate of '0.85'.\nBased on the above thought, the answer is:\nMethod,Participation Rate\nOurs,0.9\nTrend-based Sampling,0.85"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "LWFa0D2JIpB5luAj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which method appears to be more successful in engaging participants across the educational opportunities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the overall success of the engagement in educational opportunities, a comparative analysis of the bars representing {Ours}<r>[0.105, 0.088, 0.269, 0.319]</r> and {Trend-based Sampling}<r>[0.310, 0.101, 0.474, 0.319]</r> for each category like {University Programs}<r>[0.087, 0.062, 0.492, 0.448]</r>, {Adult Education}<r>[0.579, 0.062, 0.985, 0.448]</r>, {Online Certificate Programs}<r>[0.087, 0.550, 0.492, 0.935]</r>, and {Workplace Trainings}<r>[0.579, 0.550, 0.985, 0.935]</r> must be performed. In comparison for each category, the bar for 'Ours' is consistently at the same level or taller than the bar for 'Trend-based Sampling', denoting a higher or equal participation rate.\nBased on the above thought, the answer is:\nThe \"Ours\" method. The visual data suggests that 'Ours' is either outperforming or matching the 'Trend-based Sampling' method in participant engagement, as indicated by the consistent bar height equalities or advantages in all categories."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "F905QQsrECQP7DO3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the exact participation rate change difference between the methods for Workplace Trainings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the participation rate change difference, I compare the lengths of bars in the {Workplace Trainings}<r>[0.579, 0.550, 0.985, 0.935]</r> section of the chart. The {bar for \"Ours\"}<r>[0.597, 0.704, 0.762, 0.807]</r> reaches up to '0.4' and the {bar for \"Trend-based Sampling\"}<r>[0.803, 0.717, 0.967, 0.807]</r> extends to '0.35'. Calculating the exact difference, we get 0.4 - 0.35 which equals 0.05, or 5 percentage points.\nBased on the above thought, the answer is:\n0.05"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "iJ9RAP8EwId2LcwB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the growth data depicted in the chart into a table that can be understood in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "According to my visual analysis of the figure, I infer that the x-axis represents different categories of digital content creation and the y-axis represents their respective growth trends. Each category contains two bars: one for Startups and the other for Funded Startups. The categories (x-axis) are {Technology}<r>[0.145, 0.919, 0.223, 0.942]</r>, {Photography}<r>[0.312, 0.919, 0.401, 0.942]</r>, {3D Modelling}<r>[0.484, 0.919, 0.575, 0.942]</r>, {Social Media}<r>[0.658, 0.919, 0.746, 0.942]</r> and {VFX}<r>[0.861, 0.919, 0.888, 0.942]</r>. I can derive the exact data details of the growth trends by observing the corresponding height of each bar per category. To come up with a CSV-formatted table, it is evident that each company's growth trending is either above - if positive - or below - if negative - the 0 value indicated on the y-axis. For instance, the {Technology startup bar}<r>[0.115, 0.304, 0.253, 0.544]</r> has its top at approximately 30 on the y-axis, so the growth trend is 'Moderate'; whereas the {Technology Funded Startup bar}<r>[0.115, 0.544, 0.253, 0.544]</r> remains at 0, which could suggest 'No Growth'. This will be repeated for all the categories to decode their growth levels, thus enabling the construction of a comprehensive CSV table.\nBased on the above thought, the answer is:\nDigital Content Category, Startup, Funded Startup\nTechnology, Moderate, No Growth\nPhotography, Sharp rise, Steady decline\n3D Modelling, Moderate, Substantial Decrease\nSocial Media, Volatile, Sustained\nVFX, Moderate, Substantial decrease"
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "SZGYSV0SpTFmxr9L",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give an evaluation of the market trend for all startups in the digital content industry based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the bars representing Startups on the graph, an evaluation of the market trend can be given. {The bars for Startups}<r>[0.115, 0.304, 0.253, 0.544]; [0.287, 0.065, 0.426, 0.544]; [0.460, 0.384, 0.598, 0.544]; [0.633, 0.225, 0.771, 0.544]; [0.805, 0.544, 0.944, 0.624]</r> range in color from light to deep pink, indicating startups in different digital content creation categories. The categories themselves are Technology, Photography, 3D Modelling, Social Media, and VFX. The length of these bars reflects the growth trends of each Startup. Looking at the lengths of these bars, it can be observed that the length varies from category to category, which suggests a varying growth trend across different sectors of the digital content industry. For instance, the Photography startup has the longest bar, which could suggest a sharp rise, while the VFX startup has the shortest bar, suggesting a moderate rise. Therefore, it can be concluded that in terms of startup companies, the market trend is generally upward with variations across different sectors.\nBased on the above thought, the answer is:\nThe market trend for startups in the digital content creation industry is overall positive, though the level of growth varies greatly across different categories. Notably, startups in the Photography area show a sharp rise, while those in the VFX show a moderate rise."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "YPv7cDgcLvixBXVI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the greatest disparity between the growth trend of startup and funded startup companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, it is necessary to compare the differences in growth trends between the startup and funded startup companies within each category. For each category, the difference can be measured in terms of the divergence in y-axis values.\nFor instance, in the category of {Photography}<r>[0.312, 0.919, 0.401, 0.942]</r>, the difference is generated by subtracting the y-coordinate of the {Photography Funded Startup bar}<r>[0.287, 0.544, 0.426, 0.783]</r> from that of the {Photography startup bar}<r>[0.287, 0.065, 0.426, 0.544]</r>, which equals approximately 60. We apply a similar approach across all categories, finding out that the greatest disparity is in the category of 3D Modelling.\nBased on the above thought, the answer is:\n3D Modelling."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "9v6TBBizAWS8lBsa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the two bar charts, can you generate a CSV table that contains the data of Trade Volume and Globalization Index for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I can see that the x-axis represents the year and the y-axis represents the values corresponding to Trade Volume and Globalization Index. To get the data, I need to analyze the height of each bar. For instance, {the bar for the year 1990}[0.129, 0.209, 0.180, 0.415] in the top chart corresponds to the Trade Volume in 1990. Although there isn't a direct value annotation, by referencing the y-axis labels and the height of the bar, I can deduce that the Trade Volume in 1990 is around 5554 billion USD. Similarly, I can get the data for Globalization Index by analyzing {the bar for the year 1990}[0.129, 0.649, 0.180, 0.903] in the bottom chart, which corresponds to 60. By repeating this process for all years from 1990 to 2020, I can create a CSV table.\nBased on the above thought, the answer is:\nYear,Trade Volume (Billions USD),Globalization Index\n1990,5554,60\n1995,6778,65\n2000,7544,70\n2005,8272,75\n2010,9157,80\n2015,10025,85\n2020,9000,88"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "b2O5rER3FqJQrB2b",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, how can you compare the trend of Globalization index to the trend of Trade Volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question involves analyzing the overall trends of the two bar charts. From bar charts, the bars for both Globalization Index and Trade Volume generally increase over time as the bars get progressively taller from {\"1990\"}[0.129, 0.209, 0.180, 0.415] to {\"2015\"}[0.766, 0.044, 0.817, 0.415]. The height of the bars indicates that both the Trade Volume and Globalization Index increased with time. However, the upward trend in Trade Volume is interrupted in {\"2020\"}[0.893, 0.082, 0.944, 0.415], where the drop of the bar indicates a decrease in Trade Volume from its peak in 2015. In contrast, the bar for the Globalization Index in {\"2020\"}[0.893, 0.531, 0.944, 0.903] continues to grow, albeit at a slower rate which indicates that the Globalization trend has been more stable over the years.\nBased on the above thought, the answer is:\nBoth Globalization Index and Trade Volume have increased over time, indicating a positive trend. However, the trend in the Trade Volume is affected by a decline in 2020, whereas the Globalization Index has been more stable, steadily increasing even in 2020."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "NQeEyiegaCs3Cwv2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did we see the highest amount of International Trade according to the top bar-chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar height in the top chart indicates the value of the Trade Volume in each year. Therefore, to find out the year with the highest trade volume, I should look for the tallest bar of the chart. After examining each bar from {\"1990\"}[0.129, 0.209, 0.180, 0.415] to {\"2020\"}[0.893, 0.082, 0.944, 0.415], it is clear that the bar corresponding to {\"2015\"}[0.766, 0.044, 0.817, 0.415] is the highest, therefore, the trade volume was the highest in  2015.\nBased on the above thought, the answer is:\n2015."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "8psGbKGzKfdLuS6u",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a structured table with health parameters and the corresponding values for Random Systematic and Accelerated Gradient trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the provided chart, I note the {x-axis labels}<r>[0.163, 0.243, 0.906, 0.430]</r>, which include health parameters such as health_factors, medical_bill, appointment_frequency, lab_test_intervals, medication_quantity, and patient_noshow_rate. I then focus on the {bars}<r>[0.022, 0.117, 0.074, 0.226]</r> and {bar_labels}<r>[0.035, 0.217, 0.064, 0.241]</r> to read off the corresponding y-values for the Random Systematic trend and the {bars}<r>[0.074, 0.050, 0.125, 0.226]</r> and {bar_labels}<r>[0.046, 0.034, 0.064, 0.057]</r> for the Accelerated Gradient trend for each health parameter. With this analysis, I am able to construct the following tabular data.\nBased on the above thought, the answer is:\nHealth Parameters,Random Systematic,Accelerated Gradient\nhealth_factors,-2.4,18.1\nmedical_bill,-25.4,9.5\nappointment_frequency,8.1,-10.2\nlab_test_intervals,-15.5,5.4\nmedication_quantity,-5.1,10.9\npatient_noshow_rate,-3.6,20.2"
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "14eEgvusEmcbMLct",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two trends tends to show an overall positive effect on the health parameters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend with an overall positive effect, I observe the {bars for each trend}<r>[0.022, 0.117, 0.074, 0.226];[0.074, 0.050, 0.125, 0.226]</r>. The Random Systematic trend mostly shows negative figures with {negative bars}<r>[0.022, 0.117, 0.074, 0.226];[0.151, 0.117, 0.203, 0.226];[0.410, 0.117, 0.462, 0.226];[0.539, 0.117, 0.591, 0.226];[0.669, 0.117, 0.720, 0.226]</r>, apart from a single positive entry for appointment_frequency. In contrast, the Accelerated Gradient trend shows predominantly positive values with {positive bars}<r>[0.074, 0.050, 0.125, 0.226];[0.203, 0.082, 0.255, 0.226];[0.591, 0.077, 0.643, 0.226];[0.720, 0.043, 0.772, 0.226]</r> for health_factors, medical_bill, medication_quantity, and patient_noshow_rate respectively, and only a single negative value for appointment_frequency.\nBased on the above thought, the answer is:\nThe Accelerated Gradient trend tends to show an overall positive effect on the health parameters, as it has more parameters where the values are rising as opposed to declining."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "qhe4i8ylqM0OjLzJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined negative value for the Random Systematic trend across all the health parameters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the combined negative value for the Random Systematic trend, I locate the {negative bars for Random Systematic}<r>[0.022, 0.117, 0.074, 0.226];[0.151, 0.117, 0.203, 0.226];[0.410, 0.117, 0.462, 0.226];[0.539, 0.117, 0.591, 0.226];[0.669, 0.117, 0.720, 0.226]</r>. I sum up the negative values provided for the health parameters, excluding appointment_frequency as it is positive for this trend. The values are -2.4 for health_factors, -25.4 for medical_bill, -15.5 for lab_test_intervals, -5.1 for medication_quantity, and -3.6 for patient_noshow_rate.\nBased on the above thought, the answer is:\n-51.6"
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "YF2E6Lyc5KmpAG7l",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the interaction rates from the 'Ours' method chart into a structured table based on your analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am focusing on the central subplot, specifically {the y-axis}<r>[0.015, 0.280, 0.029, 0.657]</r> which shows the interaction rate in millions per month. The x-axis, identified by {the x-label}<r>[0.517, 0.627, 0.550, 0.650]</r>, lists the social media platforms: Facebook, Instagram, Twitter, YouTube, LinkedIn, Snapchat, and Pinterest. Each platform has a corresponding rectangle representing its specific interaction rate, like {the bar for \"Facebook\"}<r>[0.123, 0.505, 0.220, 0.554]</r> showing a rate from 0 to 200 million interactions. Repeating this examination for each bar gives us the full data set from the 'Ours' method.\nBased on the above thought, the answer is:\nPlatform,Interaction Rate (millions/month)\nFacebook,200\nInstagram,450\nTwitter,550\nYouTube,650\nLinkedIn,350\nSnapchat,500\nPinterest,550"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "a4PPvYZwZkBIvrJ2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform has demonstrated the most consistent performance between both monitoring methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consistency between 'Ours' and 'Saliency-based Sampling' can be identified by analyzing {the bars}<r>[0.082, 0.034, 0.985, 0.253]</r> displaying engagement, and comparing the height variance in both methods per platform. Minimal variance indicates consistency. {The bars for \"Twitter\"}<r>[0.365, 0.204, 0.461, 0.229]</r> under 'Ours' and <r>[0.365, 0.769, 0.461, 0.879]</r> under 'Saliency-based Sampling' have close starting and ending points, showing that Twitter's interaction rates don't fluctuate significantly between the two methods.\nBased on the above thought, the answer is:\nTwitter. Because comparing the vertical positioning of the bars associated with Twitter across both 'Ours' and 'Saliency-based Sampling' reveals minimal variance, suggesting consistent performance."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "KNfmTMb1KFsJBWMt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio of engagement between 'Saliency-based Sampling' on LinkedIn and 'Ours' on Snapchat?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the ratio, I need both engagement numbers. For LinkedIn, under 'Saliency-based Sampling', we have {the bar for \"LinkedIn\"}<r>[0.606, 0.842, 0.703, 0.879]</r>, indicating 150 million interactions. For Snapchat, with 'Ours', the engagement is represented by {the bar for \"Snapchat\"}<r>[0.727, 0.432, 0.823, 0.554]</r> with 500 million interactions. Computing the ratio involves dividing LinkedIn's 'Saliency-based Sampling' value by Snapchat's 'Ours' value.\nBased on the above thought, the answer is:\n0.3"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "ieROlzQek0bmhYwa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the data represented in this bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart uses horizontal bars to represent different trends in Chinese and French gastronomy as marked by the y-axis labels. Each type of gastronomy has bars corresponding to the categories defined in the y-axis. For Chinese cuisine, the bars are blue, and for French cuisine, the bars are green. For instance, the bar for 'Accelerated growth' in Chinese cuisine{Chinese bar for 'Accelerated growth'}<r>[0.160, 0.092, 0.415, 0.178]</r> has a length that suggests its value is about 60, according to the x-axis. Also, the bar for 'Accelerated growth' for French cuisine{French bar for 'Accelerated growth'}<r>[0.415, 0.092, 0.568, 0.178]</r> suggests it is about 36. In this manner, I can extract the trends values for both Chinese and French cuisine for each category in the y-axis.\nBased on the above thought, the answer is:\nTrends,Chinese Cuisine,French Cuisine\nAccelerated growth,60,36\nSporadic growth,70,45\nGradual downturn,90,70\nSustained decline,85,80\nInexorable decline,95,90\nSlow recovery,70,60"
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "mhKcyzSH8bVzVTRi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, please explain the changes in gastronomy trends for the Chinese Cuisine over time."
+            },
+            {
+                "from": "gpt",
+                "value": "The graph represents trends in Chinese cuisine by the length of blue bars for each category. From the bar for 'Accelerated growth'{Chinese bar for 'Accelerated growth'}<r>[0.160, 0.092, 0.415, 0.178]</r>, which is about 60, there is a trend of increasing growth, reaching its peak at 'Inexorable decline'{Chinese bar for 'Inexorable decline'}<r>[0.160, 0.666, 0.564, 0.752]</r> with a value of about 95. Then, there is a noticeable decrease in the value for 'Slow recovery'{Chinese bar for 'Slow recovery'}<r>[0.160, 0.809, 0.457, 0.895]</r>, denoting a value of about 70. It's important to note that the change in trends for Chinese cuisine does not strictly follow the order presented on the y-axis.\nBased on the above thought, the answer is:\nThe Chinese gastronomy was characterized by a trend of increasing growth reaching its peak at the period of 'Inexorable decline'. However, this growth did not last as there was a noticeable decrease during the 'Slow recovery' period."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "cNRgBolSEYWlkJz8",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category did the French Cuisine have the least trend value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate the shortest green bar that represents French Cuisine on the chart. If I look at the French bars, it\u2019s clear that the shortest one is for 'Accelerated growth'{French bar for 'Accelerated growth'}<r>[0.415, 0.092, 0.568, 0.178]</r>. This shortest bar for 'Accelerated growth' equates to a value of about 36 according to the x-axis.\nBased on the above thought, the answer is:\nAccelerated growth."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "nyOBmh6MuglXF4iB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you generate a CSV table with each row representing a different project comparison and columns representing the percentage of \"win\", \"tie\", and \"loss\" respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis of the chart represents different project comparisons, such as {Education Infrastructure vs. Healthcare Infrastructure}<r>[0.015, 0.126, 0.388, 0.149]</r>, which can be used as the index of the table. The x-axis represents the respective percentages (ranging from {0 %}<r>[0.393, 0.952, 0.402, 0.975]</r> to {100 %}<r>[0.944, 0.952, 0.970, 0.975]</r>), of each outcome recorded under these categories. The bars are divided into three categories based on color and content according to {the legend}<r>[0.502, 0.019, 0.763, 0.061]</r>: {Win}<r>[0.547, 0.029, 0.573, 0.052]</r> (blue bars), {Tie}<r>[0.640, 0.029, 0.660, 0.052]</r> (green bars), and {Loss}<r>[0.727, 0.029, 0.757, 0.052]</r> (red bars), corresponding to the effectiveness of different projects. The length of the bars in each category combined with their labels determines the value for each category, for example, for {Win in Education Infrastructure vs. Healthcare Infrastructure}<r>[0.397, 0.092, 0.509, 0.178]</r>, the value label is {20.0 %}<r>[0.431, 0.123, 0.475, 0.147]</r>.\nBased on the above thought, the answer is:\nProject Comparisons,Win,Tie,Loss\nEducation Infrastructure vs. Healthcare Infrastructure,20.0 %,60.0 %,20.0 %\nEducation Infrastructure vs. Road Construction,55.0 %,40.0 %,5.0 %\nEducation Infrastructure vs. Waste Management,35.0 %,55.0 %,10.0 %\nHealthcare Infrastructure vs. Road Construction,45.0 %,50.0 %,5.0 %\nHealthcare Infrastructure vs. Waste Management,30.0 %,65.0 %,5.0 %\nRoad Construction vs. Waste Management,55.0 %,40.0 %,5.0 %"
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "mOqBvu3K50ijn9oW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we learn about the effectiveness of different projects from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart provides a comparative analysis between the effectiveness of various local government project pairs. According to {the legend}<r>[0.502, 0.019, 0.763, 0.061]</r>, it divides the bars into three categories of outcomes: wins, ties and losses. High percentages of wins indicate that in most comparisons, one project (listed first) is more effective than the other. High percentages of losses suggest the reverse whilst high percentages of ties indicate that there is little to choose between the two projects in terms of effectiveness. Based on the heights of the bars, we can discern that none of the project pairs have a high percentage of losses showing that there is clear preference for one project over another in most cases. Particularly, {Education Infrastructure vs. Road Construction}<r>[0.397, 0.236, 0.705, 0.322]</r> and {Road Construction vs. Waste Management}<r>[0.397, 0.809, 0.705, 0.895]</r> have a high percentage of wins indicating those two projects have a clear advantage over their pairs. However, in most project comparisons the tie percentages are significantly high, indicating that in many cases the projects are equally effective. A particularly high tie percentage is seen in {Healthcare Infrastructure vs. Waste Management}<r>[0.565, 0.666, 0.929, 0.752]</r> where 65% of the time neither project is superior. This could mean that both projects are equally important and effective, hence local government and community should equally focus on both projects.\nBased on the above thought, the answer is:\nThe chart indicates that in many project pair comparisons, there is a clear preference for one project over another as is evidenced by the high win percentages for pairs Education Infrastructure vs. Road Construction and Road Construction vs. Waste Management. However, it also shows that in many cases, both projects are equally effective as demonstrated by the significantly high tie percentages. A particularly high tie percentage in Healthcare Infrastructure vs. Waste Management suggests that both projects are equally important and effective. Hence, local government and communities should equally focus on both projects."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "sROhxpUpqB5VRLwQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest percentage of a tie in project comparisons?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the project comparison with the highest percentage of a \"tie\", I need to inspect the green bars under each project comparison. The green bars represent the ties according to the {legend}<r>[0.502, 0.019, 0.763, 0.061]</r>. Starting from the top, {Road Construction vs. Waste Management}<r>[0.705, 0.809, 0.929, 0.895]</r> has a tie of {40 %}<r>[0.795, 0.841, 0.839, 0.864]</r>, {Healthcare Infrastructure vs. Waste Management}<r>[0.565, 0.666, 0.929, 0.752]</r> has a tie of {65 %}<r>[0.725, 0.697, 0.769, 0.721]</r>, and so on. After comparing all the green bars, it can be seen that {Healthcare Infrastructure vs. Waste Management} has the highest tie of 65 %.\nBased on the above thought, the answer is:\n65 %"
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "yohbeTi5ukdMgDpq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you put the win, tie, and loss percentages data for each era into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The y-axis of the chart are the eras, and the x-axis are the percentages. According to {the yticks}[{0.168, 0.809, 0.935, 0.099}], there are 7 eras: {Industrial Revolution}[0.015, 0.799, 0.158, 0.823], {Renaissance}[0.072, 0.681, 0.158, 0.705], {Middle Ages}[0.074, 0.563, 0.158, 0.586], {Dark Ages}[0.086, 0.445, 0.158, 0.468], {Iron Age}[0.100, 0.327, 0.158, 0.350], {Bronze Age}[0.079, 0.208, 0.158, 0.232], and {Stone Age}[0.087, 0.090, 0.158, 0.114]. The lengths of the bars in each group represent the percentages of win, tie, and loss for each era respectively. I can extract these percentages from {the text inside each bar}<r>[0.302, 0.797, 0.345, 0.820; 0.263, 0.679, 0.306, 0.702; 0.231, 0.561, 0.275, 0.584; 0.204, 0.442, 0.248, 0.466; 0.243, 0.324, 0.287, 0.348; 0.302, 0.206, 0.345, 0.229; 0.224, 0.088, 0.267, 0.111]</r>.\nBased on the above thought, the answer is:\nEra,Win,Tie,Loss\nIndustrial Revolution,40%,40%,20%\nRenaissance,30%,40%,30%\nMiddle Ages,22%,38%,40%\nDark Ages,15%,40%,45%\nIron Age,25%,45%,30%\nBronze Age,40%,35%,25%\nStone Age,20%,50%,30%"
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "DqHZUQRTMEN2D7TK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich era could be considered the peak of civilization development, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The peak of civilization development could be inferred where the 'Win' attribute was maximized and the 'Loss' attribute was minimized. The 'Win' attribute bars are the blue bars {[0.168, 0.773, 0.479, 0.844; 0.168, 0.655, 0.401, 0.726; 0.168, 0.537, 0.339, 0.608; 0.168, 0.419, 0.284, 0.489; 0.168, 0.300, 0.362, 0.371; 0.168, 0.182, 0.479, 0.253; 0.168, 0.064, 0.323, 0.135}. Industrial Revolution and Bronze Age both have the longest 'Win' bars, but the 'Loss' bar of the Industrial Revolution [0.790, 0.773, 0.946, 0.844] is smaller than the 'Loss' bar of the Bronze Age [0.751, 0.182, 0.946, 0.253].\nBased on the above thought, the answer is:\nIndustrial Revolution."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "ZTE2Rir975ZNVpJJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what era was the win percentage the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the era with the lowest win percentage, I need to compare the lengths of the win bars in each era, which are the blue bars nested in {the bars for each group}[{0.168, 0.773, 0.479, 0.844; 0.168, 0.655, 0.401, 0.726; 0.168, 0.537, 0.339, 0.608; 0.168, 0.419, 0.284, 0.489; 0.168, 0.300, 0.362, 0.371; 0.168, 0.182, 0.479, 0.253; 0.168, 0.064, 0.323, 0.135}]. The win bar for Dark Ages at [0.168, 0.419, 0.284, 0.489] is the shortest among all, and from the value annotation, it is 15%.\nBased on the above thought, the answer is:\nDark Ages."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "B0ScgSEkDnA1iwEm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you structure data observed in the bar chart as a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, according to the title {\"Efficiency Improvement Potential Assessment\"}[0.285, 0.025, 0.863, 0.058] the chart is about efficiency improvement in different areas. As observed from {yticks label}[{\"Resource Utilisation\"}[0.016, 0.319, 0.154, 0.343]; {\"Energy Efficiency\"}[0.033, 0.148, 0.154, 0.171]] and other areas, along the y-axis which are 'Energy Efficiency', 'Resource Utilisation', 'Waste Management', 'Process Automation', and 'Overall Efficiency'. Each y-axis label represents an evaluation area. For each area, two parallel bars that differ in color represent 'Potential Improvement' and 'Actual Improvement'. According to {the legend}[0.366, 0.034, 0.783, 0.075], the light blue bars({Potential Improvement})[0.410, 0.043, 0.569, 0.066] represent 'Potential Improvement' while the light green bars ({Actual Improvement})[0.636, 0.043, 0.777, 0.066] represent 'Actual Improvement'. The actual values of each area can be obtained by observing the lengths of the bars and the annotations above them. For example, the 'Potential Improvement' for 'Resource Utilisation' is about 62.5 and the 'Actual Improvement' is about 45.0, which can be determined by {the blue bar}[0.163, 0.277, 0.511, 0.380] and {the green bar}[0.511, 0.277, 0.762, 0.380]. I can do this for every area to extract all data.\nBased on the above thought, the answer is:\nEvaluation Item, Potential Improvement, Actual Improvement\nEnergy Efficiency, 70.5, 50.0\nResource Utilisation, 62.5, 45.0\nWaste Management, 85.5, 55.0\nProcess Automation, 80.0, 60.0\nOverall Efficiency, 78.0, 58.0"
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "4ZJkVp5zUe8k9GJG",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell which area has the largest gap between potential and actual improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires me to compare the length of blue and green bars of each area. For example, looking at {Energy Efficiency} [0.033, 0.148, 0.154, 0.171], the {Potential Improvement bar} [0.163, 0.105, 0.556, 0.208] is longer than the {Actual Improvement bar} [0.556, 0.105, 0.834, 0.208]. This means the potential improvement is higher than the actual improvement for energy efficiency. Similar comparisons can be made for other areas such as Resource Utilisation, Waste Management, Process Automation, and Overall Efficiency. The biggest gap is observed in Waste Management represented by the coordinates [0.015, 0.491, 0.154, 0.515] where the {Potential Improvement bar}[0.163, 0.449, 0.640, 0.552] and {Actual Improvement bar}[0.640, 0.449, 0.946, 0.552] show a difference of approximately 30 percentage points, which is the largest among all areas.\nBased on the above thought, the answer is:\nWaste Management."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "psuJyGcFr7sJWRwl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of potential improvement shown on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of this chart represents the percentage of efficiency improvement. By examining the {x-ticks}[{\"label\": '0', 'coordinates': '[0.159, 0.952, 0.168, 0.975]'}, {\"label\": '100', 'coordinates': '[0.707, 0.952, 0.733, 0.975]'}], it can be seen that the range of potential improvement is from 0 to 100.\nBased on the above thought, the answer is:\n0 to 100"
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "IgJ8bHLwiWTYZepy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your view of the bar chart, can you convert the presented data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart illustrates 'Entrepreneurial Spirit' and 'Entrepreneurship' percentages in various industries, namely 'Healthcare', 'Technology', 'Finance', 'Retail', 'Education', and 'Non-Profit'. Each industry has two corresponding bars, as indicated in the legend<r>[0.263, 0.019, 0.648, 0.061]</r>. The blue bar represents 'Entrepreneurial Spirit' and the green bar denotes 'Entrepreneurship'. The x-axis includes 6 labels that represent these industries and can be found at [{\"Healthcare\"}<r>[0.017, 0.126, 0.093, 0.149]</r>, {\"Technology\"}<r>[0.015, 0.269, 0.093, 0.293]</r>, {\"Finance\"}<r>[0.040, 0.413, 0.093, 0.436]</r>, {\"Retail\"}<r>[0.053, 0.556, 0.093, 0.580]</r>, {\"Education\"}<r>[0.024, 0.700, 0.093, 0.723]</r>, {\"Non-Profit\"}<r>[0.024, 0.843, 0.093, 0.866]</r>]. The y-axis represents the percentage of 'Entrepreneurial Spirit' or 'Entrepreneurship' that ranges from 0% to 100%. Each bar corresponds to a certain percentage that represents the attribute it is linked with for a particular industry. For instance, the 'Entrepreneurial Spirit' bar for 'Healthcare' can be found at <r>[0.102, 0.092, 0.604, 0.178]</r> with a text indicating '80.0%' at the top <r>[0.331, 0.123, 0.375, 0.147]</r>. This process is repeated for each industry to fetch both 'Entrepreneurial Spirit' and 'Entrepreneurship' values.\nBased on the above thought, the answer is:\nIndustry,Entrepreneurial Spirit,Entrepreneurship\nHealthcare,80.0,52.0\nTechnology,86.0,48.0\nFinance,45.0,18.5\nRetail,60.0,16.5\nEducation,70.0,25.5\nNon-Profit,55.0,35.5"
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "aJbKPrCYr5uXhFEb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom what you can see in the chart, which industry shows the highest Entrepreneurship percentage and how might this indicate that industry's future growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart, each industry has a green bar which represents Entrepreneurship. By comparing the lengths of these bars, we can determine which industry has the highest Entrepreneurship percentage. The highest bar corresponds to the Healthcare industry where its rectangle's coordinates can be seen at <r>[0.604, 0.092, 0.930, 0.178]</r> with an accompanying text '52.0%' at <r>[0.745, 0.123, 0.789, 0.147]</r>. High levels of Entrepreneurship could indicate that the industry is ripe for innovation, as fresh ideas and new businesses are key factors that drive growth. Thus, I would anticipate that the healthcare industry may experience considerable growth in the near future based on the insights from this chart.\nBased on the above thought, the answer is:\nThe 'Healthcare' industry shows the highest Entrepreneurship percentage. This could bode well for its future as high Entrepreneurship percentages typically indicate a strong presence of innovation and progressive business models, which are key drivers for an industry's growth."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "1tSaVAqeKSCq7Qb4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in 'Entrepreneurial Spirit' between the Healthcare and Technology sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Entrepreneurial Spirit' value for 'Healthcare' can be identified by the height of the blue bar labeled \"Entrepreneurial Spirit\" and its associated text, which is found at <r>[0.331, 0.123, 0.375, 0.147]</r>. The text clearly denotes a percentage of 80.0. Similarly, 'Technology's' 'Entrepreneurial Spirit' value can be identified at <r>[0.350, 0.267, 0.394, 0.290]</r> with a value of 86.0 indicated. To find the difference, we subtract the two values.\nBased on the above thought, the answer is:\n6.0"
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "B47rbOnpuJS6kSaU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart representation into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "On examining the bar chart, I see three distinct categories in the chart - represented through colors for which {the legend}<r>[0.235, 0.019, 0.786, 0.061]</r> is provided at the bottom. The labels denote {Grants Provided}<r>[0.280, 0.029, 0.391, 0.052]</r> (blue), {Volunteer Hours}<r>[0.458, 0.029, 0.569, 0.052]</r> (green), and {Communities Served}<r>[0.636, 0.029, 0.780, 0.052]</r> (red). Each colored bar corresponds to the respective category under different fields. The y-axis represents different fields: {Religion, Arts, Community Development, Environment, Health, Education}<r>[0.138, 0.843, 0.193, 0.866];[0.165, 0.700, 0.193, 0.723];[0.015, 0.556, 0.193, 0.580];[0.105, 0.413, 0.193, 0.436];[0.148, 0.269, 0.193, 0.293];[0.124, 0.126, 0.193, 0.149]</r>. Each field has each of the three colored bars. The length of each colored bar box represents a numeric value associated with that category for a particular field. For instance, in {Religion}<r>[0.138, 0.843, 0.193, 0.866]</r>, the values for {Grants Provided}<r>[0.203, 0.809, 0.299, 0.895]</r>, {Volunteer Hours}<r>[0.299, 0.809, 0.651, 0.895]</r>, and {Communities Served}<r>[0.651, 0.809, 0.844, 0.895]</r> are 15.0, 55.0, and 30.0 respectively. From these, I can create the CSV table.\nBased on the above thought, the answer is:\nField,Grants Provided,Volunteer Hours,Communities Served\nEducation,25.0,55.0,20.0\nHealth,20.0,35.0,45.0\nEnvironment,30.0,45.0,25.0\nCommunity Development,42.0,48.0,10.0\nArts,28.0,51.0,21.0\nReligion,15.0,55.0,30.0"
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "I6nFXpam2DhHw81p",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell from the chart which type of charity contribution is prioritized most across all the fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it seems that each of the three types of contributions, named {Grants Provided, Volunteer Hours, Communities Served}<r>[0.280, 0.029, 0.391, 0.052];[0.458, 0.029, 0.569, 0.052];[0.636, 0.029, 0.780, 0.052]</r> in the legend is depicted as its own separate color. By examining each set of bars, I can gauge the extent of each type of contribution. From what I observe, {Volunteer Hours}<r>[0.458, 0.029, 0.569, 0.052]</r>that is {the green bar}<r>[0.299, 0.809, 0.651, 0.895];[0.382, 0.666, 0.709, 0.752];[0.472, 0.522, 0.780, 0.608];[0.395, 0.379, 0.683, 0.465];[0.331, 0.236, 0.555, 0.322];[0.363, 0.092, 0.716, 0.178]</r> tends to be the most extensive category in the majority of the fields. Only in {Community Development}<r>[0.015, 0.556, 0.193, 0.580]</r> does the Grants Provided bar outstretch the others, but in the remaining five fields, the length of the Volunteer Hours bar tends to be the greatest. This suggests that, overall, Volunteer Hours may be the type of contribution prioritized most across all the fields.\nBased on the above thought, the answer is:\nVolunteer Hours. The green-colored bars representing Volunteer Hours are the longest in most fields, indicating that it is the type of contribution that is prioritized across all the fields."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "pKYeWtKen1c3IwQX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which field is the sum of the values of Grants Provided, Volunteer Hours, and Communities Served the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the field where the sum of the values - Grants Provided, Volunteer Hours, and Communities Served - is the highest, I need to sum up the values of the three categories for every field. Let's start with {Religion}<r>[0.138, 0.843, 0.193, 0.866]</r>, with the values being {15.0}<r>[0.236, 0.841, 0.266, 0.864]</r>, {55.0}<r>[0.460, 0.841, 0.491, 0.864]</r>, and {30.0}<r>[0.732, 0.841, 0.763, 0.864]</r> for Grants Provided, Volunteer Hours, and Communities Served respectively. I repeat this for the remainder of the fields: {Arts}<r>[0.165, 0.700, 0.193, 0.723]</r>,  {Community Development}<r>[0.015, 0.556, 0.193, 0.580]</r>, {Environment}<r>[0.105, 0.413, 0.193, 0.436]</r>, {Health}<r>[0.148, 0.269, 0.193, 0.293]</r>,  {Education}<r>[0.124, 0.126, 0.193, 0.149]</r>. I need to compare the sums of each field. Eventually, it seems that {Community Development}<r>[0.015, 0.556, 0.193, 0.580]</r> has the highest sum: {42.0}<r>[0.322, 0.554, 0.353, 0.577]</r>, {48.0}<r>[0.610, 0.554, 0.641, 0.577]</r>, and {10.0}<r>[0.796, 0.554, 0.827, 0.577]</r> respectively, totaling 100.0.\nBased on the above thought, the answer is:\nCommunity Development."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "kg4m3KufkIDmB4Ab",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "This chart is titled as an empty string {}, but from the context, it is understood that it compares win, tie, and loss percentages between different tech sectors. The y-axis{\"PropTech vs. AdTech\"}[0.043, 0.843, 0.182, 0.866] states the tech sector listings and the x-axis values {0}[0.187, 0.952, 0.196, 0.975] to {100}[0.934, 0.952, 0.960, 0.975] represent the percentages of win, tie, and loss. The bars are color-coded with {blue bars}<r>[0.423, 0.029, 0.450, 0.052]</r> representing 'win', {green bars}<r>[0.516, 0.029, 0.537, 0.052]</r> representing 'tie', and {red bars}<r>[0.603, 0.029, 0.634, 0.052]</r> representing 'loss'. Depending on the color of the bars, each one provides a percentage value for 'win', 'tie', or 'loss' for each tech sector. For example, for {'Blockchain vs. AI'}[0.065, 0.126, 0.182, 0.149], the blue bar has a value of {30.3%}[0.284, 0.123, 0.328, 0.147], the green bar has a value of {50.2%}[0.588, 0.123, 0.632, 0.147], and the red bar has a value of {19.5%}[0.852, 0.123, 0.896, 0.147].\nBased on the above thought, the answer is:\nTech sectors,Win (%),Tie (%),Loss (%)\nBlockchain vs. AI,30.3,50.2,19.5\nAI vs. Fintech,33.1,41.6,25.3\nFintech vs. Edtech,25.0,50.0,25.0\nEdtech vs. HealthTech,40.5,40.0,19.5\nHealthTech vs. PropTech,45.5,35.2,19.3\nPropTech vs. AdTech,38.6,35.6,25.8"
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "4JFEztWV70oFbBcz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which tech sector comparison seems to interest investors equally?"
+            },
+            {
+                "from": "gpt",
+                "value": "This kind of question requires considering the green bars that represent cases of a tie in the sectors investments. The height of these bars will tell us where the investments between the two tech sectors have been equal. In the \"Fintech vs. Edtech\" comparison, represented by the {green bar}<r>[0.381, 0.379, 0.758, 0.465]</r>, the investments between these sectors have been equally attracting 50% of the investments. This is apparent from the {50.0%}[0.548, 0.410, 0.591, 0.434] text annotation accompanying the green bar. Hence, we can infer that the \"Fintech\" and \"Edtech\" sectors have equally piqued the interest of investors.\nBased on the above thought, the answer is:\nFintech vs. Edtech. Because in this comparison, the investors are equally interested in both sectors, which is represented by a tie percentage of 50.0%."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "InN6211uki8IMsPu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich tech sector comparison had the highest win percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which tech sector comparison had the highest win percentage, I will compare the lengths of the {blue bars}<r>[0.423, 0.029, 0.450, 0.052]</r>. They represent the win percentage in each tech sector comparison. For example, in the {\"Blockchain vs. AI\"}[0.065, 0.126, 0.182, 0.149] category, the {blue bar}<r>[0.192, 0.092, 0.421, 0.178]</r> approximately reaches 30.3% on the x-axis. Looking at all the sectors, the longest blue bar corresponds to the sector {\"HealthTech vs. PropTech\"}[0.015, 0.700, 0.182, 0.723], with a height representative of {45.5%}[0.342, 0.697, 0.386, 0.721].\nBased on the above thought, the answer is:\nHealthTech vs. PropTech."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "zONcac41dGRe6y3T",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart into CSV format based on the Stacked bar chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title at the top {\"Efficiency Evaluation in Different Regions\"}[0.5, 0.96, 0.69, 0.99] and the {Y-axis tick labels}[{\"Asia Pacific\"}[0.05, 0.24, 0.1, 0.26]; {\"Americas\"}[0.05, 0.5, 0.1, 0.52]; {\"EMEA\"}[0.05, 0.79, 0.1, 0.81]], it's clear that the Y-axis represents different regions. The X-axis tick labels represent percentages from 0 to 100. For each region, there are three horizontal stacked bars representing 'Win', 'Tie', and 'Loss' in that order. According to {the legend}[0.325, 0.019, 0.586, 0.061], the blue, green, and red colors represent 'Win', 'Tie', and 'Loss' respectively. From the length of the color sections and the performance percentage at the end of each section, the values can be extracted. For instance, in the \"Asia Pacific\" category, the length of {the blue section}[0.1, 0.1, 0.4, 0.27] ends approximately at the 35 position on the X-axis, confirmed by the {annotated value \"35.0%\"}[0.23, 0.18, 0.27, 0.2] at the end of the section.\nBased on the above thought, the answer is:\nRegions,Win,Tie,Loss\nAsia Pacific,35.0,40.0,25.0\nAmericas,45.0,30.0,25.0\nEMEA,20.0,50.0,30.0"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "1gCh0FIRGm9eDYDX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which region do you think has the best manufacturing efficiency and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we should focus on the 'Win' sections of the bars, which represent consistent expansion in manufacturing efficiency. The 'Win' sections are the {blue sections}[{\"EMEA\"}[0.1, 0.7, 0.27, 0.89]; {\"Americas\"}[0.1, 0.4, 0.48, 0.58]; {\"Asia Pacific\"}[0.1, 0.09, 0.4, 0.27]]. Evidently, the length of the 'Win' section for the 'Americas' region extends furthest to approximately 45% on the X-axis, compared to about 35% for 'Asia Pacific' and 20% for 'EMEA'. This indicates that the 'Americas' region has a higher rate of consistent expansion in manufacturing efficiency. Therefore, we can conclude that the 'Americas' region has the best manufacturing efficiency.\nBased on the above thought, the answer is:\nThe Americas region. This is because it has the highest percentage of 'Win', which indicates a consistent expansion in terms of efficiency, compared to the other regions."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "7X8cn5xqZppI4tyN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region shows the highest percentage of 'Loss'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the region with the highest 'Loss' percentage, I need to compare the length of the 'Loss' sections of each region, which are the {red sections}[{\"EMEA\"}[0.68, 0.72, 0.94, 0.89]; {\"Americas\"}[0.73, 0.42, 0.94, 0.58]; {\"Asia Pacific\"}[0.73, 0.09, 0.94, 0.28]]. Looking from top to bottom, the 'Loss' sections for the 'EMEA', 'Americas', and 'Asia Pacific' regions extend to approximately 100%, 100%, and 100% on the X-axis, respectively. However, by checking the annotated values at the end of each red section, the 'Loss' percentage for EMEA is 30%, for Americas it is 25%, and for Asia Pacific, it is also 25%. Therefore, the 'EMEA' region has the highest 'Loss' percentage.\nBased on the above thought, the answer is:\nEMEA"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "CllCyQQY2eTdCxfT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table using the bar chart data that compiles the win, tie, and loss percentages for each investment category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can generate that information using the bar chart details. Firstly, we consider the y-axis labels, which represent different investment categories. These categories, denoted by {'Stable growth'}<r>[0.049, 0.135, 0.146, 0.159]</r>, {'Gradual recovery'}<r>[0.025, 0.310, 0.146, 0.333]</r>, {'Steady recovery'}<r>[0.032, 0.485, 0.146, 0.508]</r>, {'Fluctuating growth'}<r>[0.015, 0.659, 0.146, 0.682]</r> and {'Slow increase'}<r>[0.051, 0.834, 0.146, 0.857]</r>, form our rows. The x-axis, labeled with percentages from {0}<r>[0.151, 0.952, 0.160, 0.975]</r> to {100}<r>[0.932, 0.952, 0.959, 0.975]</r>, presents the potential outcomes: Win, Tie, and Loss. These outcomes, as per the color-coded legend {'Win'}<r>[0.402, 0.029, 0.428, 0.052]</r>, {'Tie'}<r>[0.495, 0.029, 0.515, 0.052]</r> and {'Loss'}<r>[0.582, 0.029, 0.612, 0.052]</r>, respectively, will form our columns. Each category has three bars that correlate to these outcomes. The length of each bar segment, when measured against the from 0 to 100, provide the values to fill in our table. For example, for 'Stable growth', the three segments of the bar at coordinates {Win}[0.156, 0.092, 0.393, 0.197], {Tie}[0.393, 0.092, 0.788, 0.197], and {Loss}[0.788, 0.092, 0.946, 0.197] represent 30%, 50%, and 20% respectively. In the same way the values for the other categories can be obtained.\nBased on the above thought, the answer is:\nInvestment Category, Win, Tie, Loss\nStable growth, 30%, 50%, 20%\nGradual recovery, 20%, 45%, 35%\nSteady recovery, 40%, 45%, 15%\nFluctuating growth, 25%, 50%, 25%\nSlow increase, 35%, 45%, 20%"
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "hxV5jJVO1ZHOWmMz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich strategy would you recommend for someone who wants to minimize losses?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the aim is to minimize losses, the strategy with the least losses should be considered. This can be determined by analyzing the 'Loss' segment, indicated in the legend as 'Loss'<r>[0.582, 0.029, 0.612, 0.052]</r>. This segment is represented by the rightmost part of each horizontal bar. We compare each 'Loss' segment beginning from 'Stable growth' - {Loss}<r>[0.788, 0.092, 0.946, 0.197]</r> (20%), 'Gradual recovery' - {Loss}<r>[0.669, 0.267, 0.946, 0.371]</r> (35%), 'Steady recovery' - {Loss}<r>[0.827, 0.441, 0.946, 0.546]</r> (15%), 'Fluctuating growth' - {Loss}<r>[0.748, 0.616, 0.946, 0.721]</r> (25%), 'Slow increase' - {Loss}<r>[0.788, 0.791, 0.946, 0.895]</r> (20%). From the comparison, it's clear that 'Steady recovery' has the least 'Loss' segment, indicating that it has the least amount of losses.\nBased on the above thought, the answer is:\nThe 'Steady recovery' strategy is recommended for someone who wants to minimize losses, as it has the smallest loss percentage."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "ZKPxM7MBERdP1mtE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin these strategies, which has the highest win rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the strategy with the highest win percentage, I need to compare the length of the 'Win' segment of the horizontal bars for each strategy. The 'Win' segment is represented by the first section of each bar, in the color labeled as 'Win' in the legend {Win}<r>[0.402, 0.029, 0.428, 0.052]</r>. Looking at the bar segments from bottom to top, the lengths of the 'Win' segment for each strategy are as follows: 'Stable growth' at {Win coordinate}<r>[0.156, 0.092, 0.393, 0.197]</r> is 30%, 'Gradual recovery' at {Win coordinate}<r>[0.156, 0.267, 0.314, 0.371]</r> is 20%, 'Steady recovery' at {Win coordinate}<r>[0.156, 0.441, 0.472, 0.546]</r> is 40%, 'Fluctuating growth' at {Win coordinate}<r>[0.156, 0.616, 0.353, 0.721]</r> is 25%, and 'Slow increase' at {Win coordinate}<r>[0.156, 0.791, 0.432, 0.895]</r> is 35%. Out of these, 'Steady recovery' has the longest 'Win' segment, meaning it had the highest winning percentage.\nBased on the above thought, the answer is:\nSteady recovery."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "91x4oGmEAY8ckeT3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, can you convert the data on the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. First, from the ylabel of the chart, we can observe that the labels are of the format {A vs B}[0.015, 0.843, 0.139, 0.866], such kind of patterns are present throughout the chart. The xticks {\"0\"}[0.144, 0.952, 0.153, 0.975] to {\"100\"}[0.932, 0.952, 0.958, 0.975] indicates that the values are in the percentage format. Looking at the legend box {sublegend}[0.353, 0.019, 0.613, 0.061] where we have labels for {\"Win\"}[0.397, 0.029, 0.423, 0.052], {\"Tie\"}[0.490, 0.029, 0.511, 0.052], and {\"Loss\"}[0.577, 0.029, 0.607, 0.052], we infer that the parameters that are being compared are the win percentages, tie percentages, and loss percentages. For each comparison, there are three bars, and the length of each bar correlates with the respective percentage of wins, ties and losses. The values can be inferred from the position of each bar on the x-axis scale, as each bar has a text tag with the corresponding value, like {45.0%}[0.306, 0.841, 0.349, 0.864].\nBased on the above thought, the answer is:\nComparison,Win%,Tie%,Loss%\nSolar vs. Wind,25.0,50.0,25.0\nSolar vs. Nuclear,20.0,60.0,20.0\nSolar vs. Hydro,30.0,40.0,30.0\nWind vs. Nuclear,35.0,55.0,10.0\nWind vs. Hydro,40.0,50.0,10.0\nNuclear vs. Hydro,45.0,45.0,10.0"
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "gloo20eaU0pBEdvQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, which energy source seems to have the most promising future? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "From this chart, the promising future would primarily depend on the number of wins a particular energy source has. Let's evaluate each energy source. {Solar vs. Wind}[0.148, 0.092, 0.347, 0.178], {Solar vs. Nuclear}[0.148, 0.236, 0.308, 0.322],  and {Solar vs. Hydro}[0.148, 0.379, 0.387, 0.465] we see that Solar has wins percentage as 25%, 20% and 30% respectively. For {Wind vs. Nuclear}[0.148, 0.522, 0.427, 0.608] and {Wind vs. Hydro}[0.148, 0.666, 0.467, 0.752] wins values are 35% and 40% respectively. For {Nuclear vs. Hydro}[0.148, 0.809, 0.507, 0.895], Nuclear has win percentage of 45%. Now coming to Hydro it does not win in any comparisons. So, considering everything Wind and Nuclear have the highest win percentages in their respective comparisons and hence, can be considered to have the most promising future.\nBased on the above thought, the answer is:\nIt's a tie between Wind and Nuclear. As they both have higher win percentages in their respective comparisons, these energy sources seem to have the most promising future."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "4anw44qbFoYFaSJH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which comparison is the percentage of 'Win' highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the comparison with the highest percentage of 'Win', I need to compare the length of the 'Win' segment (colored blue as per the {legend}[0.353, 0.019, 0.613, 0.061]) for each horizontal bar. Starting with the 'Solar vs. Wind' comparison at the bottom of the chart and working up, I see the 'Win' segment for 'Solar vs. Wind' starts at {0.148}[0.148, 0.092, 0.347, 0.178] and goes up to {25.0%}[0.226, 0.123, 0.270, 0.147] on the x-axis. Similar inspection can be done for rest of the comparisons like 'Solar vs. Nuclear', 'Solar vs. Hydro', 'Wind vs. Nuclear', 'Wind vs. Hydro' and 'Nuclear vs. Hydro'. Among them, 'Nuclear vs. Hydro' has the maximum length for the 'Win' segment from {0.148}[0.148, 0.809, 0.507, 0.895] to {45.0%}[0.306, 0.841, 0.349, 0.864] on the x-axis, which is approximately 45%.\nBased on the above thought, the answer is:\nNuclear vs. Hydro."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "rgIJoqkzNgIuJlNR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me convert the given chart data into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to do that, first I have to identify the distribution of data in the chart. The x-axis in this stacked bar chart contains percentages ranging from {0.00}<r>[0.199, 0.984, 0.203, 0.975]</r> to {100.00}<r>[0.948, 0.984, 0.961, 0.975]</r>. The y-axis contains comparison categories, which are {'Combines vs. Harvesters'}<r>[0.199, 0.092, 0.360, 0.178]</r>, {'Tractors vs. Ploughs'}<r>[0.199, 0.236, 0.371, 0.322]</r>, {'Irrigation vs. Fertilizer'}<r>[0.199, 0.379, 0.412, 0.465]</r>, {'Organic vs. Conventional'}<r>[0.199, 0.522, 0.453, 0.608]</r>, {'Dairy vs. Poultry'}<r>[0.199, 0.666, 0.427, 0.752]</r>, {'Fruits vs. Grains'}<r>[0.199, 0.809, 0.334, 0.895]</r>. For each category, there are three sections symbolizing 'Win', 'Tie', 'Loss', according to the color legend {'Win'}<r>[0.389, 0.031, 0.417, 0.047]</r>, {'Tie'}<r>[0.482, 0.031, 0.509, 0.047]</r> and {'Loss'}<r>[0.569, 0.031, 0.597, 0.047]</r>. The length of each section in a bar represents the percentage of that value which can be read from the x-axis labels. Now, let me convert each category into one line in CSV format.\nBased on the above thought, the answer is:\nCategory,Win,Tie,Loss\nCombines vs. Harvesters,21.5,60.5,18.0\nTractors vs. Ploughs,23.0,64.0,13.0\nIrrigation vs. Fertilizer,28.5,66.5,5.0\nOrganic vs. Conventional,34.0,52.0,14.0\nDairy vs. Poultry,30.5,62.0,7.5\nFruits vs. Grains,18.0,72.0,10.0"
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "ibSm1IvdNdVFX8ON",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a person wants to focus on winning strategies, which two categories should he focus on according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should analyze the 'Win' sections in all categories as 'Win' results represent positive aspects. These sections are marked in color marked as {'Win'}<r>[0.389, 0.031, 0.417, 0.047]</r> in the color legend. The lengths of 'Win' sections represent the percentage distribution and are the first sections from the left in each category. These sections include {'Combines vs. Harvesters'}<r>[0.199, 0.092, 0.360, 0.178]</r>, {'Tractors vs. Ploughs'}<r>[0.199, 0.236, 0.371, 0.322]</r>, {'Irrigation vs. Fertilizer'}<r>[0.199, 0.379, 0.412, 0.465]</r>, {'Organic vs. Conventional'}<r>[0.199, 0.522, 0.453, 0.608]</r>, {'Dairy vs. Poultry'}<r>[0.199, 0.666, 0.427, 0.752]</r>, {'Fruits vs. Grains'}<r>[0.199, 0.809, 0.334, 0.895]</r>. The 'Organic vs. Conventional' and 'Dairy vs. Poultry' categories have the longer 'Win' sections, meaning they have higher win percentages. Thus, if a person wants to concentrate on winning strategies, they should focus on these two categories.\nBased on the above thought, the answer is:\nThe person should focus on 'Organic vs. Conventional' and 'Dairy vs. Poultry'"
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "qhmVfgGIAEaJMKcv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich comparison category has the fewest losses according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll focus on identifying the lengths of the color sections representing 'Loss' in each category bar. Looking at the provided data, for each category bar, 'Loss' is represented by the last section of the bar, and the corresponding color is indicated by the {'Loss'}<r>[0.569, 0.031, 0.597, 0.047]</r> in the color legend. Now, I need to observe the lengths of those 'Loss' sections one by one: {'Combines vs. Harvesters'}<r>[0.813, 0.092, 0.948, 0.178]</r>, {'Tractors vs. Ploughs'}<r>[0.850, 0.236, 0.948, 0.322]</r>, {'Irrigation vs. Fertilizer'}<r>[0.910, 0.379, 0.948, 0.465]</r>, {'Organic vs. Conventional'}<r>[0.843, 0.522, 0.948, 0.608]</r>, {'Dairy vs. Poultry'}<r>[0.891, 0.666, 0.948, 0.752]</r>, {'Fruits vs. Grains'}<r>[0.873, 0.809, 0.948, 0.895]</r>. Among all the categories, the 'Loss' section for the 'Irrigation vs. Fertilizer' category has the shortest length, representing the fewest losses.\nBased on the above thought, the answer is:\nIrrigation vs. Fertilizer"
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "apuHUfsW8ypSeEXd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the entire data as you see in the bar chart into a structured CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis has numeric labels representing the percentage of purchases from 0 to 100 incrementing by 20s at positions {0}[0.180, 0.952, 0.189, 0.975], {20}[0.328, 0.952, 0.346, 0.975], {40}[0.481, 0.952, 0.498, 0.975], {60}[0.633, 0.952, 0.651, 0.975], {80}[0.786, 0.952, 0.803, 0.975], {100}[0.934, 0.952, 0.960, 0.975]. The y-axis has labels indicating different consumer behavior phases including: {\"Customer Preferences\"}[0.021, 0.847, 0.175, 0.870], {\"Return to Normalcy\"}[0.040, 0.710, 0.175, 0.733], {\"Lockdown Impact\"}[0.053, 0.572, 0.175, 0.596], {\"Innovations in Delivery\"}[0.015, 0.435, 0.175, 0.459], {\"Advent of Markets\"}[0.048, 0.298, 0.175, 0.321], and {\"Rise of E-commerce\"}[0.037, 0.161, 0.175, 0.184]. Each phase has 3 bars encapsulating in-store purchases, online purchases, and e-commerce app purchases, with their respective percentages included which I interpret from the length of the bars. For instance, in \"Customer Preferences\" phase, purchasing methods presented are {In-store purchase}[0.184, 0.815, 0.380, 0.897] labelled as \"25.7%\", {Online purchase}[0.380, 0.815, 0.703, 0.897] labelled as \"42.3%\", and {E-commerce Apps}[0.703, 0.815, 0.947, 0.897] labelled as \"32.0%\". The same method applies for every phase and for every type of purchase.\nBased on the above thought, the answer is:\nPhase,In-store purchase,Online purchase,E-commerce Apps\nRise of E-commerce,20.0,50.0,30.0\nAdvent of Markets,30.0,25.5,44.5\nInnovations in Delivery,25.5,35.0,39.5\nLockdown Impact,15.7,59.1,25.2\nReturn to Normalcy,30.5, 45.5,24.0\nCustomer Preferences,25.7,42.3,32.0"
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "A3AfCEgtjRBklDBX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which purchasing method was most preferred by consumers during the lockdown?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most preferred purchasing method during the lockdown, I need to examine the bars corresponding to the \"Lockdown Impact\" phase. In this phase, there are three bars each representing a method of purchasing: {In-store purchase}<r>[0.184, 0.540, 0.304, 0.623]</r> with a value label of \"15.7%\", {Online purchase}<r>[0.304, 0.540, 0.755, 0.623]</r> with a value label of \"59.1%\", and {E-commerce Apps}<r>[0.755, 0.540, 0.947, 0.623]</r> with a value label of \"25.2%\". Comparing the lengths, I can see that the longest one is the green bar representing online purchases, thus indicates it was the most popular purchasing method during lockdown.\nBased on the above thought, the answer is:\nOnline purchase. This was the most preferred method since the highest percentage of purchases during the lockdown was made online."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "ilAxJNsrl1JeCaXC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, during which phase was the percentage of in-store purchases the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the phase where the percentage of in-store purchases is the highest, I need to look for the longest blue bar. The length of the blue bar indicates the percentage of in-store purchases. For each category in the y-axis, I examined the in-store purchase bar. By comparing these bars, I can see that the longest blue bar is under the \"Return to Normalcy\" phase, and its coordinate is [0.184, 0.678, 0.417, 0.760]. This bar represents the highest percentage of in-stores purchases at 30.5% according to the text atop the bar [0.279, 0.707, 0.323, 0.730].\nBased on the above thought, the answer is:\nReturn to Normalcy"
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "lDwW2p2qwWujoiSe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, construct a CSV formatted table capturing each Media Outlet's percentage of Influence and Public Opinion Guidance."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, the {y-ticks}[0.129, 0.052, 0.985, 0.935] on the left represent different Media Outlets, including Twitter, Facebook, News Websites, Television, Radio, and Print Media. There are two bars for each outlet, according to the {legend}[0.297, 0.019, 0.646, 0.061], the blue bar represents the 'Influence' and the green bar represents 'Public Opinion Guidance'. The x-ticks represent percentages ranging from 0 to 100. I will then analyze the length of each bar to get the exact percentage that each bar represents. For example, the first pair of bars represents Twitter's 'Influence' and 'Public Opinion Guidance'. The length of the blue bar is from [0.129, 0.809, 0.296, 0.895] with a value of 27.3% written on it, and the green bar ranges from [0.350, 0.092, 0.944, 0.178] with a value of 73.5% written on it. By following this pattern, we can get the percentage for other Media Outlets as well.\nBased on the above thought, the answer is:\nMedia Outlet,Influence,Public Opinion Guidance\nTwitter,27.3,73.5\nFacebook,49.9,50.1\nNews Websites,41.3,58.7\nTelevision,39.2,60.8\nRadio,28.4,71.6\nPrint Media,20.7,79.3"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "8BalaeWBfTPOP22p",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this dataset, which medium do you think would be best for persuasion purposes, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering persuasion targets to influence public opinion, we need to select one which has a good balance between Influence and Public Opinion Guidance. Looking at the bars for Facebook, it represents {Influence}[0.129, 0.236, 0.533, 0.322] with 49.9% and {Public Opinion Guidance}[0.533, 0.236, 0.938, 0.322] with 50.1%. These two values are almost equal, indicating that Facebook has been quite effective at both influencing people's thoughts and guiding public opinion. Therefore, this Media Outlet is likely to be a powerful tool for persuasion.\nBased on the above thought, the answer is:\nFacebook. Because Facebook carries nearly equal weight in both influencing people's thoughts and guiding public opinion, hence, it can be quite effective for persuasion purposes."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "AJxC3ETK9TSzJVpI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media outlet has the smallest difference between its Influence and Public Opinion Guidance percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the media outlet with the smallest difference between its Influence and Public Opinion Guidance percentages, I need to subtract the Influence value from the Public Opinion Guidance value for each Media Outlet. Starting with Twitter, whose {Influence value}[0.129, 0.809, 0.296, 0.895] is 27.3% and {Public Opinion Guidance value} [0.350, 0.092, 0.944, 0.178] is 73.5%, the difference is 46.2%. Continuing this analysis through Facebook, News Websites, Television, Radio, and finally Print Media, it can be deduced that Facebook with {Influence value}[0.129, 0.236, 0.533, 0.322] of 49.9% and {Public Opinion Guidance value}[0.533, 0.236, 0.938, 0.322] of 50.1% has the smallest difference of 0.2%.\nBased on the above thought, the answer is:\nFacebook"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "f880AS6nsXv9yGuD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the data on the chart in a tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {education and training industry trends}[0.571, 0.038, 0.571, 0.038] and the {legend}[0.346, 0.019, 0.635, 0.096], I see that the chart shows five different trends over a period of 5 years from 2017 to 2021. The x-axis represents the percentage values, varying from {0}[0.165, 0.952, 0.174, 0.975] to {100}[0.959, 0.952, 0.985, 0.975], and the y-axis stands for different types of trends, including {Increase}[0.101, 0.135, 0.160, 0.159], {Stable}[0.116, 0.310, 0.160, 0.333], {Abrupt drop}[0.076, 0.485, 0.160, 0.508], {Pronounced Increase}[0.015, 0.659, 0.160, 0.682], and {Upward Trend}[0.064, 0.834, 0.160, 0.857]. To construct the table, I can represent each bar as a cell in the table, using the x-coordinate of the end side of each bar and the corresponding year from the legend for each value. For example, the bar for {Increase for 2017}[0.170, 0.092, 0.250, 0.197] ends approximately at the 10 position on the x-axis, indicating a value of 10%.\nBased on the above thought, the answer is:\nTrends,2017,2018,2019,2020,2021\nIncrease,10,12,15,18,21\nStable,50,51,49,50,51\nAbrupt drop,80,60,40,20,10\nPronounced Increase,15,25,35,45,55\nUpward Trend,5,10,15,20,25"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "qYOV7s2mnomoiUYY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the overall trend in the Education and Training Industry over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the bars of each trend, there appears to be a consistent pattern. From 2017 to 2021, the trend categories that have shown growth are {Upward Trend}, {Pronounced Increase}, and {Increase}. From this, I can infer that there is an upward trajectory overall in this industry. However, the {Abrupt drop} category appears to have reduced significantly over the years, suggesting that there are some areas in this industry that are losing traction or are less in demand. The {Stable} category has remained more or less consistent throughout these years, indicating certain aspects of the industry have not changed substantially.\nBased on the above thought, the answer is:\nThe overall trend in the Education and Training Industry appears to be positive, although there are certain aspects that have experienced a significant decline, and others that have remained constant over the years."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "5hp0PHIU2gT1GeJN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category experienced the biggest drop from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must examine the beginning and end values of each trend. For {Abrupt drop}, the 2017 bar<{\"2017\"}[0.170, 0.441, 0.811, 0.546]> ends near the 80 on x-axis and 2021 bar<{\"2021\"}[0.170, 0.441, 0.250, 0.546]> ends near 10, indicating a drop of 70%. Similarly, for each category, I need to compare the differences between 2017 and 2021. Therefore, by examining all categories, I can detemine which one has the biggest drop.\nBased on the above thought, the answer is:\nAbrupt drop"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "rF13GB42BB1DReuE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you see, can you convert the data for both products into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data I see in the bar chart, both products: {New Improved Product}[0.203, 0.060, 0.535, 0.095] and {Declining Product Sales}[0.570, 0.060, 0.842, 0.095] have sales data for 4 quarters (Q1,Q2,Q3,Q4). The x-axis tick labels represent these quarters. The y-axis tick labels represent the corresponding sales values. The y-axis for the New Improved Product ranges from {0}[0.087, 0.889, 0.102, 0.924] to {450}[0.058, 0.127, 0.102, 0.162]. The y axis for the Declining Product Sales has a negative range from {-1000}[0.879, 0.968, 0.957, 1.003] to {0}[0.879, 0.160, 0.894, 0.195]. The interpretation of values for the Declining Product Sales is reversed because it is a declining trend. The sales values for each product can be ascertained from the height of the bars and the value annotations at the top of each bar. For instance, the height of {the bar for Q1 for the New Improved Product Sales}[0.152, 0.564, 0.231, 0.903] shows approximately the 200 sales figure. The value annotation at its top also shows a {200K}[0.162, 0.513, 0.221, 0.548] sales value, thus the sale of 'New Improved Product' for Q1 is 200.\nBased on the above thought, the answer is:\nQuarter,New Improved Product Sales,Declining Product Sales\nQ1,200,860\nQ2,275,820\nQ3,335,735\nQ4,410,670"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "W75OmjUliJ57kmJR",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product seems more favorable in Q4 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product is more favorable in Q4, I need to compare the bar heights of the two products in Q4. For the {New Improved Product Sales} [0.203, 0.060, 0.535, 0.095], the Q4 sales bar is located at [0.749, 0.209, 0.829, 0.903], and its height approximately corresponds to a 410K value on the y-axis. For the {Declining Product Sales}[0.570, 0.060, 0.842, 0.095], the bar for Q4 is located at [0.749, 0.174, 0.829, 0.715], and its height corresponds to a 670K value on the y-axis (Note: As the y-axis for the Declining Product Sales is inverted, the higher the bar, the lower the value). Therefore, in Q4, the New Improved Product Sales has lower sales than the Declining Product Sales.\nBased on the above thought, the answer is:\nThe Declining Product Sales. Even though it demonstrates a declining trend, the sales for this product are still significantly higher than the New Improved Product Sales in Q4."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "4VfYXZxHrWJ9YunQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Declining Product Sales fallen from Q1 to Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the decline in sales for the Declining Product Sales from Q1 to Q4, I need to look at the difference in bar heights at the start of Q1 and the end of Q4. At Q1, the Declining Product Sales's bar is located at [0.152, 0.174, 0.231, 0.868], and the height from the y-axis to the bar top is about 860K. At Q4, the bar is located at [0.749, 0.174, 0.829, 0.715], and the height from the y-axis to the bar top is about 670K. Therefore, the decrease in sales from Q1 to Q4 for the Declining Product Sales can be calculated as the difference between 860K and 670K.\nBased on the above thought, the answer is:\n190K"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "iN22py2WHAOnfjTK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, could you create a CSV table for the AI Adoption and Automation Trend over five periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"AI Adoption Vs Automation Trend\"}[0.268, 0.118, 0.724, 0.163], the x-axis represents the timeline. For y-axis, {AI Adoption}<r>[0.025, 0.458, 0.048, 0.658]</r> and {Automation Trend}<r>[0.952, 0.403, 0.975, 0.712]</r> labels reflect the two different trends. The AI Adoption data is displayed by {the grey bars}<r>[0.139, 0.462, 0.204, 0.941]</r> and the Automation Trend data is presented by {the pink bars}<r>[0.139, 0.558, 0.204, 0.941]</r>. To populate the CSV table, the read-out values annotated in white on top of each bar correspond to the respective trend value for each period.\nBased on the above thought, the answer is:\nPeriod,AI Adoption,Automation Trend\n1,50,-40\n2,55,-45\n3,40,-50\n4,65,-60\n5,70,-40"
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "SQJCVJju3awcT9FF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you analyze and tell which trend (AI adoption or Automation Trend) has been more stable over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires a comparison between the degree of fluctuation in {AI adoption}<r>[0.025, 0.458, 0.048, 0.658]</r> and {Automation Trend}<r>[0.952, 0.403, 0.975, 0.712]</r>. If the changes are small, it means that the trend is relatively stable. So, we can conclude its stability by measuring the changes in the heights of the respective bars over different periods. From the chart, it is noticeable that the AI adoption bars are making significant jumps between periods. In contrast, the change in height for the Automation Trend bars is less drastic, indicating a smoother trend over time and thus, a higher degree of stability.\nBased on the above thought, the answer is:\nThe Automation Trend has been more stable over the years."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "iYjpmFpBGGs6Kkkm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich period had the highest AI Adoption value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the period with the highest AI Adoption value, I have to check the value at the top of each {grey bar}<r>[0.139, 0.462, 0.204, 0.941]</r>. By observation, {the grey bar in the fifth period}[0.788, 0.270, 0.853, 0.941] is the highest with the value of 70.\nBased on the above thought, the answer is:\nPeriod 5."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "JwTcufauRsFIsFgj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the bar chart data into a two-column CSV format reflecting the effects of Monetary and Fiscal Policies over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indeed, examining {the bars corresponding to Monetary Policy}<r>[0.126, 0.273, 0.158, 0.564]</r> and their respective {percentage annotations}, like <r>[0.114, 0.274, 0.169, 0.298]</r> denoting 73.4%, I observe a consistent sequence for monetary policy data points, with the first value starting at 73.4%. Similarly, for {Fiscal Policy}, {the first bar}<r>[0.126, 0.564, 0.158, 0.882]</r> has an {annotated value of 80.2%}<r>[0.114, 0.884, 0.169, 0.907]</r>. I would proceed year by year for both policies, finding corresponding bars and their annotations to compile the CSV format data accurately.\nBased on the above thought, the answer is:\nYear,Monetary Policy,Fiscal Policy\n1,73.4,80.2\n2,82.6,79.3\n3,79.5,84.1\n4,67.4,76.0\n5,62.2,72.1\n6,58.3,71.4\n7,55.6,70.5\n8,54.3,67.3\n9,53.1,66.4\n10,51.5,60.7"
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "eRtBP7ut32U0ZgfT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average effect of Fiscal Policy across the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average effect of Fiscal Policy, I must review and sum up the values present in each of the {fiscal policy bar annotations}, starting from <r>[0.114, 0.884, 0.169, 0.907]</r> at 80.2%, through subsequent years, and ending at <r>[0.831, 0.806, 0.886, 0.830]</r> at 60.7%. Having ten data points in total, I will sum these percentages and then divide the sum by 10 to find the average.\nBased on the above thought, the answer is:\n71.72"
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "CHKqlytGmvmmhGfD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the dual bar chart you observe, can you help me structure this data in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The chart's title}<r>[0.239, 0.118, 0.761, 0.163]</r> states the data represents scores difference in different education fields between Traditional and Modern teaching methods. The x-axis tick labels which are {\"Science\"}<r>[0.181, 0.927, 0.270, 0.962]</r>, {\"Literature\"}<r>[0.352, 0.927, 0.465, 0.962]</r>, {\"Math\"}<r>[0.562, 0.927, 0.620, 0.962]</r>, and {\"History\"}<r>[0.733, 0.927, 0.815, 0.962]</r> represent the various academic fields. The height of bars represents scores of each teaching method provided as texts are on top of each bar. For instance, the bar for Traditional method in Science at <r>[0.153, 0.721, 0.226, 1.632]</r> has a score of 75. Hence, by observing each bar, extracting the value and associating it with the respective field and method, we can structure the information in CSV format.\nBased on the above thought, the answer is:\nEducation Field,Traditional Method,Modern Method\nScience,75,90\nLiterature,68,80\nMath,82,98\nHistory,95,110"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "QyoMI3wcztYPNA41",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the Data Visualized on the chart, what would you suggest about the teaching methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inspecting the bar heights representing Traditional method and Modern Method scores, for all fields like {\"Science\"}<r>[0.153, 0.721, 0.226, 1.632]</r>, {\"Literature\"}<r>[0.335, 0.806, 0.409, 1.632]</r>, {\"Math\"}<r>[0.518, 0.636, 0.591, 1.632]</r>, {\"History\"}<r>[0.701, 0.478, 0.774, 1.632]</r> it is clear that Modern Method has outperformed. It has resulted in higher scores consistently in all fields. This comparison shows that the Modern Method of teaching seems to be more effective.\nBased on the above thought, the answer is:\nBased on the data, It would be recommended to use the Modern Method of teaching because the scores observed under this method were consistently higher in all fields, indicating a significant positive impact on the academic performance of students."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "FWddr5mHG6rf2r7m",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which field, the score improvement from Traditional to Modern method was maximum?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish the field with the largest improvement from Traditional to Modern methods, I need to examine the difference in each field's scores. Following left to right from the first pair of bars which are located at [0.153, 0.721, 0.226, 1.632] for Traditional Method and [0.226, 0.539, 0.299, 1.632] for Modern Method, representing the Science field, and find that the difference between the two is 15; going through each field, one can find that the maximum difference is in {\"History\"}<r>[0.733, 0.927, 0.815, 0.962]</r> field where the two method bars at <r>[0.701, 0.478, 0.774, 1.632]</r> for Traditional Method and <r>[0.774, 0.296, 0.847, 1.632]</r> for Modern Method show a score difference of 15, which is the maximum difference among all fields.\nBased on the above thought, the answer is:\nHistory"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "lLRLV8JHhOmueARA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data from the bar chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents the data of two geographical areas marked {Rapid recovery}<r>[0.155, 0.348, 0.330, 0.383]</r> and {Abrupt Drop}<r>[0.687, 0.348, 0.829, 0.383]</r> with two different types of ecological data: {Mountains}<r>[0.189, 0.175, 0.309, 0.210]</r> and {Ocean}<r>[0.802, 0.175, 0.876, 0.210]</r>. To convert these data points into a structured table, we can take each geographic area as a row and each type of data as a column. The values can be inferred from the height of the bars, additionally with the help of the percentage values mentioned above each bar, for instance, {55.3%}<r>[0.206, 0.153, 0.279, 0.188]</r> for the Mountains under Rapid recovery and {32.1%}<r>[0.721, 0.211, 0.794, 0.246]</r> for the Mountains under Abrupt Drop. Similarly, we can find out the values for the Ocean in the same way.\nBased on the above thought, the answer is:\nArea,Mountains,Ocean\nRapid recovery,55.3,44.7\nAbrupt drop,32.1,47.2"
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "PJvjgVQvi0JU9lEs",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich geographical area represented in the chart faced a severe ecological crisis considering the compared data from Mountains and Ocean regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we have to look at the overall decline in both the mountain and ocean regions of both geographic areas. In the geographical area labeled {Abrupt Drop}<r>[0.687, 0.348, 0.829, 0.383]</r>, the mountain region shows {a decrease of 32.1%}<r>[0.721, 0.211, 0.794, 0.246]</r> and the {ocean region a decrease of 47.2%}<r>[0.721, 0.419, 0.794, 0.454]</r>. However, in the {Rapid Recovery}<r>[0.155, 0.348, 0.330, 0.383]</r> area, the mountainous and ocean regions have increased by {55.3%}<r>[0.206, 0.153, 0.279, 0.188]</r> and {44.7%}<r>[0.206, 0.412, 0.279, 0.447]</r> respectively. Therefore, the Abrupt Drop area faced the severe ecological crisis.\nBased on the above thought, the answer is:\nAbrupt Drop"
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "WrVOlz9ged2ZqAoH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which area is the percentage of ecological change in the mountainous regions greater?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentage of ecological change in the mountainous regions in both the areas, we can gauge that the bar representing the mountains in {Rapid recovery}<r>[0.139, 0.186, 0.345, 0.324]</r> is taller than the bar representing mountains in {Abrupt Drop}<r>[0.655, 0.244, 0.861, 0.324]</r>. The respective percentages stated above each bar, {55.3%}<r>[0.206, 0.153, 0.279, 0.188]</r> for Rapid recovery and {32.1%}<r>[0.721, 0.211, 0.794, 0.246]</r> for Abrupt Drop, also affirm our observation.\nBased on the above thought, the answer is:\nRapid recovery"
+            }
+        ],
+        "id_raw": 410
+    },
+    {
+        "id": "zSh6UJpR4UsL3TVl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please form a CSV table representing the data displayed in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing the bar chart and its provided components, I can coordinate the scores between the two key areas: Project Management and Project Innovation. The Y-axis represents the percentage scores ranging from {0%}[0.072, 0.478, 0.087, 0.513] to {60%}[0.058, 0.185, 0.087, 0.220], as listed on the left. Now looking at the bars, the bars colored in {#404346}[0.189, 0.093, 0.428, 0.128] correspond to the Project Management scores, and the bars labeled with {#dc9dae}[0.658, 0.093, 0.868, 0.128] represent the Project Innovation scores. These values can be obtained by analyzing the heights of the bars aligned within both sectors.\nBased on the above thought, the answer is:\n,Project Management,Project Innovation\n1,45.1,35.2\n2,57.2,27.3\n3,32.8,50.5\n4,53.2,45.6\n5,12.3,20.7"
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "aaS4czR5svkl969o",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data visualized in the bar chart, which aspect appears to have more consistent performances across the various projects, Project Management or Project Innovation? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the variability of scores among the bars. For the Project Management section, the bars colored in {#404346}[0.189, 0.093, 0.428, 0.128], there is a particularly low outlier at {12.3%}[0.784, 0.429, 0.857, 0.464] which greatly deviates from the other values. This suggests that the Project Management performance varies considerably. On the other hand, in the Project Innovation section, represented by bars colored in {#dc9dae}[0.658, 0.093, 0.868, 0.128], there seems to be less variation in the heights of the bars, indicating that the performance was more consistent across the projects. Hence, the aspect with more consistent performances appears to be Project Innovation.\nBased on the above thought, the answer is:\nProject Innovation. This is because the performance scores in this aspect have less variation compared to Project Management, revealing a more consistent performance across the projects."
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "CbgpfC04xXBjxwIt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which project number has the least percentage in Project Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the project with the lowest percentage in Project Management, I need to identify the shortest bar in {#404346}[0.189, 0.093, 0.428, 0.128]. After comparing all five bars within this group, I can see that {the fifth bar}[0.788, 0.432, 0.853, 0.492] is the shortest, corresponding to a score of {12.3%}[0.784, 0.429, 0.857, 0.464]. Therefore, the lowest percentage in Project Management is associated with the fifth project.\nBased on the above thought, the answer is:\nProject 5"
+            }
+        ],
+        "id_raw": 411
+    },
+    {
+        "id": "DeqY4MyEk5pzD4SW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the bar chart, could you extract and present the performance data in a table format for 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the chart offers a comparison of manufacturing production efficiency evaluation for Product A and Product B between 2016 and 2020. The blue bars represent {\"Product A Efficiency\"}<r>[0.113, 0.035, 0.252, 0.058]</r>, with the respective percentages annotated at the top of each bar. Meanwhile, the pink bars signify the {\"Product B Efficiency\"}<r>[0.782, 0.035, 0.921, 0.058]</r>, again with percentages marked. By reading these corresponding data, we can construct a table.\nBased on the above thought, the answer is:\nYear,Product A Efficiency,Product B Efficiency\n2016,59.2,40.8\n2017,58.1,41.9\n2018,54.6,45.4\n2019,65.4,34.6\n2020,70.2,29.8"
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "qsrvRowsYCbVaS2S",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, did the introduction of a new manufacturing process for Product A in 2019 have a significant impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "The impact of the new manufacturing process for Product A can be assessed by examining the changes in its efficiency bar widths. Looking at the bar for {\"Product A\"}<r>[0.642, 0.238, 0.714, 0.551]</r> in 2019, it is significantly wider compared to the bar <r>[0.462, 0.289, 0.534, 0.551]</r> in 2018 which implies a higher percentage of efficiency. This increase can thus be interpreted as a significant positive impact of the new manufacturing process.\nBased on the above thought, the answer is:\nYes, the new manufacturing process introduced in 2019 significantly improved the manufacturing production efficiency for Product A."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "585mFmzcwFaXvXWD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the percentage difference between Product A and Product B efficiencies the widest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By visually examining the chart, I can see that both set of data have different widths. So to determine the greatest percentage difference, I would need to compare the differences between the percentages given for Product A and Product B for each year. Taking this into account, I can visually identify that the year where the percentage difference was the widest was during {\"2020\"}<r>[0.840, 0.952, 0.875, 0.975]</r> as the bar width for Product A <r>[0.822, 0.214, 0.894, 0.551]</r> was visibly greater than the bar width for Product B <r>[0.822, 0.551, 0.894, 0.742]</r>.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "oqAkhtjA6h32dGBT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you convert the data into a structured table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "As seen from the title {\"Comparison of Growth Rates in Digital Content and SEO\"}[0.109, 0.118, 0.883, 0.163], this chart compares the growth rates of different digital content and SEO strategies. According to the x-axis, we have types of digital content and SEO strategies including {\"Video Production\"}[0.115, 0.927, 0.247, 0.962], {\"Podcast\"}[0.346, 0.927, 0.436, 0.962], {\"Content Writing\"}[0.524, 0.927, 0.678, 0.962], {\"Graphic Design\"}[0.750, 0.927, 0.873, 0.962]. The y-axis shows the rate of growth from {\"0\"}[0.072, 0.525, 0.087, 0.560] to {\"60\"}[0.058, 0.160, 0.087, 0.195] percent, which can be inferred by the height of the bars seen above each strategy category. For instance, the bar height in the {\"Video Production\"}[0.139, 0.295, 0.223, 0.539] category suggests a growth of 40.1% which corresponds with the text {\"40.1%\"}[0.144, 0.298, 0.217, 0.333] at the top of the bar. Interpreting similarly for the other categories, we can convert these into a table format.\nBased on the above thought, the answer is:\nDigital Content and SEO Strategy,Growth Rate\nVideo Production,40.1\nPodcast,51.8\nContent Writing,47.3\nGraphic Design,22.5"
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "oJiz3GLdXBRQvW4B",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the current trends in New Media and Digital Content Creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart illustrates a comparison of growth rates in the New Media and Digital Content Creation sector as depicted by the chart title {\"Comparison of Growth Rates in Digital Content and SEO\"}[0.109, 0.118, 0.883, 0.163]. We can glean insights by looking at the growth rates of the different strategies. High growth is observed in {\"Podcast\"}[0.346, 0.927, 0.436, 0.962] creation at  {\"51.8%\"}[0.354, 0.227, 0.428, 0.262] and {\"Content Writing\"} [0.524, 0.927, 0.678, 0.962] at {\"47.3%\"}[0.565, 0.254, 0.638, 0.289], indicating these strategies have been rapidly successful. In comparison, the {\"Graphic Design\"}[0.750, 0.927, 0.873, 0.962] strategy seems to be growing at a slower pace of {\"22.5%\"}[0.775, 0.405, 0.848, 0.440].\nBased on the above thought, the answer is:\nThe chart suggests that Podcast Creation and Content Writing are rapidly increasing strategies in the realm of New Media and Digital Content Creation. On the other hand, Graphic Design is gaining traction at a comparatively slower pace. These trends suggest a greater focus on content-based strategies in the current market."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "EJ8lNx4gAtT57c92",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the highest growth rate in terms of digital content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I'll need to examine the bars corresponding to each category on the chart and determine which one reaches the highest point on the y-axis. Observing, I can see that the growth rate for {\"Podcast\"}[0.346, 0.927, 0.436, 0.962] {\"51.8%\"}[0.354, 0.227, 0.428, 0.262] is the highest among all categories.\nBased on the above thought, the answer is:\nPodcast."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "SICcWZXv4LY9sQle",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented in the chart figure to a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the graph title}[0.366, 0.118, 0.634, 0.163] and the specific bars for the categories 'Urban Development Index' and 'Rural Development Index', found in[{'Urban Development Index'}[0.139, 0.338, 0.345, 0.539]; {Rural Development Index}[0.139, 0.539, 0.345, 0.703]], I can infer that these bars represent the development indices for the years 2018 and 2020. By comparing these bar heights to the values on the Y-axes[{Urban Development Index Y-axis}[0.025, 0.311, 0.048, 0.767]; {Rural Development Index Y-axis}[0.952, 0.318, 0.975, 0.759]], I can convert the indices into meaningful numbers. The bar height of the Urban Development Index for 2018 is at 55, and for 2020 it is at 75. The height of the Rural Development Index bar for 2018 is at 45, and for 2020 it is at 60.\nBased on the above thought, the answer is:\nYear,Urban Development Index,Rural Development Index\n2018,55,45\n2020,75,60"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "zlcXvbEW8dnE1Soi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you suggest what the changing trend of these indices reflect in terms of development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bars and their locations, it is evident that both the Urban Development Index [{Urban Development Index24}[0.217, 0.329, 0.268, 0.364]; {Urban Development Index20}[0.732, 0.256, 0.783, 0.291]] and the Rural Development Index [{Rural Development Index18}[0.217, 0.693, 0.268, 0.728]; {Rural Development Index20}[0.732, 0.748, 0.783, 0.783]] have increased from 2018 to 2020. This clearly indicates development in both the urban and rural areas during these years. The larger increase in the Urban Development Index suggests that urban areas have seen more development than rural areas in this period.\nBased on the above thought, the answer is:\nThe increasing trends in both development indices indicate that there has been growth and development in both urban and rural areas from 2018 to 2020. The greater increase in the Urban Development Index may suggest that urban areas saw more development relative to rural areas during this period."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "DGjZ2D8C3BNepZCm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me by how much the Rural Development Index increased from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar for the Rural Development Index in 2018 located at [0.139, 0.539, 0.345, 0.703] and comparing its height to the values on the {Rural Development Index Y-axis}[0.952, 0.318, 0.975, 0.759], I determine that the value was 45. Moving on to the bar for the Rural Development Index in 2020[{bar}[0.655, 0.539, 0.861, 0.757]], and taking a comparison to the same Y-axis, tells me that the value for 2020 was 60. The difference is therefore 60 - 45.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "X0xnfM9BhiUkSItp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle {Health Membership & Outdoor Sports}[0.229, 0.118, 0.749, 0.163] tells me that the data I need to convert into a CSV format involves two items, namely: Health Membership and Outdoor Sports. The x-axis contains years {2017}[0.150, 0.927, 0.209, 0.962, 0.179, 0.903], {2018}[0.356, 0.927, 0.415, 0.962, 0.386, 0.903], {2019}[0.562, 0.927, 0.621, 0.962, 0.592, 0.903] and {2020}[0.769, 0.927, 0.827, 0.962, 0.798, 0.903]. Meanwhile, the y-axis, as indicated by {Health Membership label}[0.025, 0.369, 0.048, 0.708] and {Outdoor Sports label}[0.952, 0.407, 0.975, 0.671] corresponds to the percentage values of the two categories. Let's look at the bars now, Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153], {2018 bar}[0.344, 0.549, 0.427, 2.153], {2019 bar}[0.550, 0.466, 0.633, 2.153] and {2020 bar}[0.757, 0.570, 0.839, 2.153] provide the corresponding values for each year in the dataset. The same goes for the Outdoor Sports bars {2017 bar}[0.138, 0.558, 0.221, 0.884], {2018 bar}[0.344, 0.558, 0.427, 0.857], {2019 bar}[0.550, 0.558, 0.633, 0.899] and {2020 bar}[0.757, 0.558, 0.839, 0.872]. By comparing these bars with the y-axis, I can derive the percentage values for both categories over the noted years.\nBased on the above thought, the answer is:\nType,2017,2018,2019,2020\nHealth Membership,75,77,81,76\nOutdoor Sports,85,78,89,82"
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "YX9Nx2p5Erp5vfAE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart analysis, which category seems to be more consistent?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the length of the bars for both categories over the years, I can assess the consistency of Health Membership and Outdoor Sports. Considering Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153], {2018 bar}[0.344, 0.549, 0.427, 2.153], {2019 bar}[0.550, 0.466, 0.633, 2.153] and {2020 bar}[0.757, 0.570, 0.839, 2.153], it can be seen that there is a small fluctuation in the values. It consistently remains in the range of 75 to 81 over the observed period. However, in the case of Outdoor Sports {2017 bar}[0.138, 0.558, 0.221, 0.884], {2018 bar}[0.344, 0.558, 0.427, 0.857], {2019 bar}[0.550, 0.558, 0.633, 0.899] and {2020 bar}[0.757, 0.558, 0.839, 0.872], the fluctuation is quite significant, moving from a percentage of 85 in 2017 to 78 in 2018 and climbing up to 89 in 2019 then dropping again to 82 in 2020. Thus, with the more stable values over four years, Health Membership is the more consistent category.\nBased on the above thought, the answer is:\nHealth Membership appears to be most consistent, given the relative stability of its values over the four-year period."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "c0dmV1RmUL4Z1rmw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the values, in which year is the difference between Health Membership and Outdoor Sports the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the absolute difference between the percentage values for Health Membership and Outdoor Sports in each year. Let's start with {2017}[0.150, 0.927, 0.209, 0.962, 0.179, 0.903], the Health Membership {2017 bar}[0.138, 0.591, 0.221, 2.153] has a value of 75 and the Outdoor Sports {2017 bar}[0.138, 0.558, 0.221, 0.884] has a value of 85, resulting in a difference of 10. For {2018}[0.356, 0.927, 0.415, 0.962, 0.386, 0.903], the Health Membership {2018 bar}[0.344, 0.549, 0.427, 2.153] shows a value of 77 and the Outdoor Sports {2018 bar}[0.344, 0.558, 0.427, 0.857] is at 78, the difference being 1. Applying the same process to {2019}[0.562, 0.927, 0.621, 0.962, 0.592, 0.903] and {2020}[0.769, 0.927, 0.827, 0.962, 0.798, 0.903], I notice the smallest difference occurs in 2018.\nBased on the above thought, the answer is:\n2018."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "pIPYxGy9RtrmbabP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart into a CSV format table that captures the details of the trends in the Transportation and Logistics industry across different vehicle types?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The title of this chart <r>{'Transportation and Logistics Industry Trends'}[0.198, 0.118, 0.809, 0.163]</r> indicates that the data is about the industry trends categorized by different vehicle types in Transportation and Logistics. There are 4 categories on the x-axis represented by the labels <r>{'Vehicles'}[0.147, 0.927, 0.241, 0.962]</r>, <r>{'Ships'}[0.369, 0.927, 0.431, 0.962]</r>, <r>{'Trains'}[0.572, 0.927, 0.640, 0.962]</r>, and <r>{'Aircraft'}[0.770, 0.927, 0.855, 0.962]</r>. With that understood, I can extract each bar's numerical information from the annotation above it, to determine the corresponding percentage in each category. For example, in the category of Vehicles, the <r>{'Transportation'}[0.157, 0.371, 0.231, 0.406]</r> and <r>{'Logistics'}[0.157, 0.741, 0.231, 0.776]</r> values are 68.2% and 55.1% respectively. I would complete this process for each category to generate the CSV table.\nBased on the above thought, the answer is:\nVehicle_Type,Transportation,Logistics\nVehicles,68.2,55.1\nShips,78.9,62.5\nTrains,63.3,30.1\nAircraft,25.6,40.0"
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "FfJPOHp2ZZZEoaZB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart data, what can you infer about the performance of the transportation and logistics sectors across the various types of vehicles?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive comparison of the data from the chart. Looking at the Transportation trend, for <r>{'Vehicles'}[0.153, 0.174, 0.235, 0.748]</r> and <r>{'Trains'}[0.565, 0.174, 0.648, 0.488]</r>, the performance is moderate, while for <r>{'Ships'}[0.359, 0.174, 0.441, 0.825]</r>, it is the highest, indicating that most transportation likely occurs via Ships. <r>{'Aircraft'}[0.771, 0.174, 0.854, 0.591]</r>, however, shows the lowest performance in comparison to all the other vehicle types. As for Logistics, Ships show the highest performance again, while Trains demonstrate the lowest performance. It is likely that Aircraft logistics services are underutilized given their relatively low score. The comparison indicates that Ships are most frequently used for both Transportation and Logistics purposes, suggesting they might be the most effective type of vehicle for these industries.\nBased on the above thought, the answer is:\nShips are the most effective type of vehicle in both the Transportation and Logistics sectors, while Aircraft are relatively underutilized in these sectors, and Trains perform poorly in Logistics."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "DowpYlijPNdppdCj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest percentage value for the Logistics across all vehicle types and to which vehicle does it relate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the Logistics values of all vehicle types. The Logistics values are displayed as negative on the graph, annotated above the corresponding bars. Starting with Vehicles, the <r>{'Logistics bar for Vehicles'}[0.153, 0.174, 0.235, 0.748]</r> is annotated as <r>{'55.1%'}[0.157, 0.741, 0.231, 0.776]</r>. Then, for Ships, the <r>{'Logistics bar for Ships'}[0.359, 0.174, 0.441, 0.825]</r> is annotated with <r>{'62.5%'}[0.364, 0.818, 0.437, 0.853]</r>. The Logistics bars for Trains and Aircraft are annotated as 30.1% and 40.0%, respectively. Therefore, based on my analysis, the highest percentage value in Logistics is related to Ships.\nBased on the above thought, the answer is:\nShips."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "I2iyDd9bhQOWEQFm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you interpret the bar chart data and create a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {title}[0.366, 0.130, 0.645, 0.160] 'Social Media Data Analysis', it's noticeable that the x-axis exhibits different social media platforms from {Twitter}[0.142, 0.952, 0.200, 0.975] to {Snapchat}[0.799, 0.952, 0.881, 0.975]. The y-axis is divided into two parts: 'Average Usage Time' with range from {0}[0.065, 0.926, 0.076, 0.950] to {250}[0.043, 0.158, 0.076, 0.182] and 'Average Post Engagement' from {0}[0.935, 0.158, 0.946, 0.182] to {50}[0.935, 0.926, 0.957, 0.950]. This information is represented by bars. For example, the bar corresponding to Twitter's average usage time '[0.126, 0.467, 0.215, 0.935]' reads '152.6 mins', the value can be confirmed by the corresponding {label}[0.123, 0.494, 0.219, 0.518]. Similarly, the bar corresponding to Twitter's average post engagement '[0.126, 0.167, 0.215, 0.522]' reads '23.1%', the value can be affirmed by the descriptive {text}[0.143, 0.550, 0.198, 0.573]. Following this method, we can extract the usage time and post engagement for all platforms.\nBased on the above thought, the answer is:\nPlatform, Average Usage Time (Minutes), Average Post Engagement (%)\nTwitter, 152.6, 23.1\nInstagram, 192.3, 35.7\nFacebook, 226.5, 29.9\nSnapchat, 80.2, 11.1"
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "VFO0y0Qy17l5m64t",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the usage times and post engagements, which social media platform seems to be the most effective and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider both the average usage time and post engagement metrics for each platform. Let's start with {'Twitter'}[0.142, 0.952, 0.200, 0.975] which has {usage time}[0.123, 0.494, 0.219, 0.518] of 152.6 mins and {engagement}[0.143, 0.550, 0.198, 0.573] of 23.1%. Next, let's evaluate {'Instagram'}[0.350, 0.952, 0.438, 0.975] with {usage time}[0.346, 0.372, 0.442, 0.396] of 192.3 mins and {engagement}[0.367, 0.744, 0.421, 0.767] of 35.7%. Thirdly, {'Facebook'}[0.576, 0.952, 0.658, 0.975] offers {usage time}[0.569, 0.267, 0.665, 0.291] of 226.5 mins and {engagement}[0.590, 0.654, 0.645, 0.678] of 29.9%. Lastly, {'Snapchat'}[0.799, 0.952, 0.881, 0.975] carries {usage time}[0.797, 0.717, 0.883, 0.740] of 80.2 mins and {engagement}[0.813, 0.366, 0.868, 0.389] of 11.1%. Comparing these statistics, Instagram has a high usage time and the highest post-engagement, hence, it is the most effective.\nBased on the above thought, the answer is:\nInstagram. Because it's approximately the second-highest in terms of average usage time and the top performing in the average post engagement statistics, suggesting users spend a significant amount of time on it and are highly engaged with the content, making it the most effective platform according to the data presented."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "DHqOlq7GK82l1dMM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform has the lowest average usage time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the heights of the bars representing the average usage time for each social media platform. The bars are located at {'Twitter'}[0.126, 0.467, 0.215, 0.935], {'Instagram'}[0.349, 0.345, 0.439, 0.935], {'Facebook'}[0.572, 0.240, 0.662, 0.935], and {'Snapchat'}[0.796, 0.689, 0.885, 0.935]. Here, the lowest bar stands with Snapchat.\nBased on the above thought, the answer is:\nSnapchat"
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "3j5q860nmrkaca6L",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table representing the trend of 'Slow Growth' from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar chart, the 'Slow Growth' trend can be identified by {cyan bars}<r>[0.116, 0.623, 0.152, 0.935]</r> and corresponding {label coordinates}<r>[0.114, 0.584, 0.153, 0.608]</r> which indicate the value above each bar. Each bar's height on the y-axis indicates the trend value for a year, labeled on the x-axis. To construct the table, the coordinate data of each cyan bar and their top-bound text will be cross-referenced with the x-axis {years}<r>[0.112, 0.952, 0.156, 0.975]</r> and y-axis values.\nBased on the above thought, the answer is:\nYear,Slow Growth Value\n2022,15.5\n2023,18.7\n2024,21.1\n2025,23.2\n2026,25.4\n2027,27.3\n2028,29.1\n2029,30.863\n2030,32.998"
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "OHhi4N8XA4fpZ82Z",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Slow Growth' or 'Unstable Behavior' show a more consistent trend between the years 2022 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bar charts, the consistency of a trend can be evaluated by looking at the direction and size of the bars for each consecutive year. For 'Slow Growth', the bars show a consistent upwards trend, as seen by the increasing height of the {cyan bars}<r>[0.116, 0.623, 0.152, 0.935]</r>; while 'Unstable Behavior' has bars that first decrease then increase, as evidenced by the changing direction of the {hot pink bars}<r>[0.116, 0.129, 0.152, 0.721]</r>. The coordinates of each bar and their progression over the years indicate the consistency of the trend.\nBased on the above thought, the answer is:\n'Slow Growth' shows a more consistent trend as its values gradually increase year over year, whereas 'Unstable Behavior' exhibits fluctuations with notable rebounds, indicating less consistency."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "ZCAsPXCj53PqJrmf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in 'Unstable Behavior' from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the values for 'Unstable Behavior' in 2022 and 2023 using their bar coordinates and the text above them: for 2022, {bar}<r>[0.116, 0.129, 0.152, 0.721]</r> with {value text -25.7}<r>[0.115, 0.749, 0.153, 0.772]</r> and for 2023, {bar}<r>[0.206, 0.129, 0.243, 0.806]</r> with {value text -29.4}<r>[0.205, 0.834, 0.244, 0.858]</r>. Then, I calculate the percentage decrease using the formula ((Old Value - New Value) / Old Value) * 100, which equates to ((-25.7 - (-29.4)) / -25.7) * 100.\nBased on the above thought, the answer is:\n14.4%"
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "8c8bMF2NuZRcckph",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a CSV format table based on the dual colored bar chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title {\"Global Trade & Supply Chain Management Growth 2020 Vs 2021\"}[0.059, 0.118, 0.953, 0.163] labels the two categories compared. On the main chart, the bars represent the data for each year, specifically {\"2020\"}[0.218, 0.927, 0.276, 0.962] and {\"2021\"}[0.735, 0.927, 0.793, 0.962] on the x-axis. Looking at the first bar chart, the {global trade bar for 2020}[0.144, 0.539, 0.351, 0.728], the value for 2020 is {\"-2.6%\"}[0.218, 0.737, 0.281, 0.772]; Similarly for {2021}[0.661, 0.269, 0.867, 0.539], the value is {\"3.7%\"}[0.735, 0.278, 0.793, 0.313]. For the supply chain management growth, the {bar for 2020}[0.144, 0.539, 0.351, 0.641] shows a value of {\"1.4%\"}[0.218, 0.650, 0.277, 0.685] and similarly the {bar for 2021}[0.661, 0.480, 0.867, 0.539] has the value {\"-0.8%\"}[0.731, 0.489, 0.797, 0.524]. Thus, we can generate a csv table from the bars and their corresponding labels.\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2020,-2.6%,1.4%\n2021,3.7%,-0.8%"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "ICBNaZnu29arkcQl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, did Supply Chain Management perform well in 2021 compared to 2020? Discuss your observation."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the performance of Supply Chain Management in 2020 and 2021. The value of the {Supply Chain Management bar for 2020}[0.144, 0.539, 0.351, 0.641] is {\"1.4%\"}[0.218, 0.650, 0.277, 0.685]. It showed a growth of 1.4% in 2020. However, when I look at the {Supply Chain Management bar for 2021}[0.661, 0.480, 0.867, 0.539], the value {\"-0.8%\"}[0.731, 0.489, 0.797, 0.524] indicates that it declined by 0.8% in 2021. Therefore, compared to 2020, the performance of Supply Chain Management in 2021 dropped.\nBased on the above thought, the answer is:\nNo, according to the chart, the Supply Chain Management sector did not perform well in 2021 compared to 2020. It had shown growth in 2020 but then experienced a decline in 2021."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "j84RdlakeU2y64d5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_28_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much improvement percentage did the Global Trade show from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the improvement percentage in Global Trade, I need to subtract the 2020 data from the 2021 data. The {Global Trade bar for 2020}[0.144, 0.539, 0.351, 0.728] has a value of {\"-2.6%\"}[0.218, 0.737, 0.281, 0.772] and the {Global Trade bar for 2021}[0.661, 0.269, 0.867, 0.539] has a value of {\"3.7%\"}[0.735, 0.278, 0.793, 0.313]. So the improvement from 2020 to 2021 would be the value of 2021 (3.7%) minus the value of 2020 (-2.6%).\nBased on the above thought, the answer is:\n6.3%"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "wzmObDt1e7hFko1m",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a structured CSV table showing the performance scores for each AI model across all innovation categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the complex structure of the bar chart across multiple subplots, each subplot represents a specific category of innovation, such as {Independent Innovation}[0.037, 0.031, 0.331, 0.270], {Merged Innovation}[0.367, 0.031, 0.661, 0.270], and others. Within these subplots, the bars and their corresponding labels such as {AIBot-983X}[0.032, 0.274, 0.061, 0.317] under the first subplot show their performance scores. I will compile these performance values from each bar's text label across all categories for each model.\nBased on the above thought, the answer is:\nModel,Independent Innovation,Merged Innovation,Creative Destruction,Incremental Innovation,Technological Discontinuity,Technological Leap,Fraudulent Patents\nAIBot-983X,87.5,91.2,68.9,84.5,69.5,90.0,0.4\nTuring-1099S,89.3,90.5,71.2,85.1,66.4,93.6,0.1\nDataEasel-8V,90.4,88.9,69.2,83.4,71.7,89.6,0.6\nMindfulB-66X,85.6,92.3,67.3,82.9,70.2,88.1,0.5\nWarpSpeed-5G,84.3,89.6,70.3,81.3,72.3,91.4,0.3\nGenie-101T,88.1,90.7,69.5,84.7,73.4,92.8,0.2\nSiliconM-112L,86.2,91.8,68.2,82.1,74.9,90.9,0.4\nNeuraVista-6Z,85.4,92.1,66.8,84.9,75.5,89.6,0.1\nAstra-778E,87.7,90.1,71.6,85.3,76.8,93.8,0.3\nMatrixQ-999R,88.4,91.7,70.7,83.8,77.3,90.3,0.4"
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "daB0F1z0YOWWn0N6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the consistent performance in the technological leap category suggest about these AI models?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the technology leap category, noticeable by its subtitle {Technological Leap}<[0.801, 0.342, 0.888, 0.357]> and analyzing the bar heights such as {AIBot-983X}<[0.711, 0.384, 0.733, 0.599]>, it can be seen that scores are high compared to other categories. The consistent high performance in this category suggests that despite the challenges of drastic technological shifts, these AI models are well-equipped to adapt and thrive in rapidly changing technological environments, as indicated by their capability to achieve high scores across various models.\nBased on the above thought, the answer is:\nThe consistent high performance in the Technological Leap category for these AI models suggests they are highly capable of adapting to and thriving in rapidly changing technological environments."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "Uk30BmUQCcIYOhY4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich model has the highest average performance across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the model with the highest average performance, I need to compute the average of the performance scores listed for each model across all innovation categories. By examining each model's performance through their respective bar charts such as {AIBot-983X}<[0.032, 0.274, 0.061, 0.317]> and others, a numerical average can be calculated. After computing these averages, I can determine which model has the highest value.\nBased on the above thought, the answer is:\nMatrixQ-999R"
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "rJGhEDnsm6M2P4HQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow should the energy production capacities from the year 2015 to 2020 be summarized in a table according to the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the 9 sub-charts, each representing different energy sources like {Solar}<r>[0.075, 0.047, 0.328, 0.294]</r>, {Wind}<r>[0.403, 0.047, 0.657, 0.294]</r>, and so on, I can see bars corresponding to respective years marked by {the x-axis tick labels}[{2015}<r>[0.081, 0.300, 0.102, 0.327]</r>, {2016}<r>[0.121, 0.300, 0.142, 0.327]</r>, etc.]. Each bar's height represents the energy capacity, and the value can be found in {the text coordinates}[{solar 2015}<r>[0.094, 0.280, 0.110, 0.292]</r>, {wind 2015}<r>[0.422, 0.275, 0.438, 0.287]</r>, etc.]. From this inspection of the bar charts, I can present the data as follows in a structured CSV format table.\nBased on the above thought, the answer is:\nYear,Solar (TWh),Wind (TWh),Nuclear (TWh),Oil (TWh),Gas (TWh),Hydro (TWh),Biomass (TWh),Coal (TWh),Geothermal (TWh)\n2015,118,341,2152,7434,9820,2750,268,530,48\n2016,128,370,2205,7412,9954,2780,280,520,50\n2017,147,411,2275,7325,10142,2818,290,510,51\n2018,176,465,2352,7238,10315,2854,302,499,53\n2019,210,530,2402,7100,10520,2886,315,485,55\n2020,240,594,2460,6985,10700,2920,330,470,56"
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "0Sjui7CpqPPTKtSu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source shows the most significant growth in production capacity from 2015 to 2020 as observed in the bar sub-charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascertaining the most significant growth in production capacity involves assessing the difference in values from 2015 to 2020 within each energy source sub-chart. By comparing the height of the bars in the first and last position, such as {solar}<r>[0.086, 0.292, 0.118, 0.294]</r> for 2015 and {solar}<r>[0.285, 0.289, 0.317, 0.294]</r> for 2020, {wind}<r>[0.415, 0.287, 0.446, 0.294]</r> for 2015 and {wind}<r>[0.613, 0.281, 0.645, 0.294]</r> for 2020, it becomes evident through this cumulative assessment that solar energy, showing a growth from 118 TWh to 240 TWh, exhibits the most growth in capacity terms.\nBased on the above thought, the answer is:\nSolar energy shows the most significant growth."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "dciZj0nW17c3NfvA",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total energy production capacity for all sources in 2020 according to the sub-charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total energy production capacity for all sources in 2020, I need to sum up the values highlighted in the text coordinates {solar}<r>[0.293, 0.278, 0.309, 0.289]</r>, {wind}<r>[0.621, 0.269, 0.637, 0.281]</r>, {nuclear}<r>[0.947, 0.227, 0.968, 0.238]</r>, and so on for oil, gas, hydro, biomass, coal, and geothermal sources for the year 2020. Summing these figures will give me the total capacity.\nBased on the above thought, the answer is:\n31456 TWh."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "M2TRsYvlUwhCYJWY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table from the bar charts showing the active users on each social media platform for each quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify the title and labels on the x-axis to determine the platforms, which are {TikTok}<r>[0.080, 0.299, 0.104, 0.329]</r>, {Facebook}<r>[0.138, 0.299, 0.172, 0.342]</r>, {Instagram}<r>[0.204, 0.299, 0.241, 0.344]</r>, {Twitter}<r>[0.283, 0.299, 0.309, 0.331]</r>, {Reddit}<r>[0.352, 0.299, 0.377, 0.331]</r>, and {LinkedIn}<r>[0.414, 0.299, 0.445, 0.338]</r>. The y-axis represents active users in millions with labels ranging from {0}<r>[0.043, 0.286, 0.052, 0.303]</r> to {70}<r>[0.035, 0.040, 0.052, 0.057]</r>. Each bar represents a quarter, starting with {Q1}<r>[0.263, 0.019, 0.287, 0.041]</r> continuing with {Q2}<r>[0.755, 0.019, 0.779, 0.041]</r>, {Q3}<r>[0.263, 0.363, 0.287, 0.386]</r>, and {Q4}<r>[0.756, 0.363, 0.779, 0.386]</r>. Using the heights of the bars, for instance, {TikTok\u2019s Q1 bar}<r>[0.077, 0.222, 0.131, 0.293]</r> at about 20 million active users, I can construct the table.\nBased on the above thought, the answer is:\nQuarter,TikTok,Facebook,Instagram,Twitter,Reddit,LinkedIn\nQ1,20,35,30,45,10,15\nQ2,30,40,25,40,20,18\nQ3,45,33,35,37,15,20\nQ4,60,31,33,39,14,22"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "SBbWIzVAWHESEBIX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the data, which social media platform showed the most consistent user base throughout the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consistency in user numbers can be inferred by looking at the height of the bars across all four quarters. Taking each platform one by one, we look at the bars for {Twitter}<r>[0.282, 0.134, 0.336, 0.293]</r>, {Q2 Twitter}<r>[0.774, 0.152, 0.829, 0.293]</r>, {Q3 Twitter}<r>[0.282, 0.507, 0.336, 0.637]</r>, and {Q4 Twitter}<r>[0.774, 0.500, 0.829, 0.637]</r>. The height of Twitter\u2019s bars shows the least variation from Q1 through Q4, indicating a consistent user base compared to the more fluctuating bars of other platforms.\nBased on the above thought, the answer is:\nTwitter. The height of Twitter's bars across all four quarters shows minimal variation, illustrating the most consistent user base among the platforms represented in the chart."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "ZGzReinDYsK9lFXc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in active users for TikTok from Q1 to Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase for TikTok, I examine the bars for TikTok in {Q1}<r>[0.077, 0.222, 0.131, 0.293]</r> and {Q4}<r>[0.569, 0.426, 0.624, 0.637]</r>. The value for Q1 is {20 million users}<r>[0.099, 0.208, 0.109, 0.219]</r>, and for Q4 is {60 million users}<r>[0.591, 0.411, 0.602, 0.423]</r>. To find the percentage increase: [(60 - 20) / 20] * 100.\nBased on the above thought, the answer is:\n200%"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "wuO7OkdaoR46RVI3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table displaying the metric values across all subplots for the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create this table, I am observing the bar heights and the text labels present in each subplot for the year {2017}<r>[0.126, 0.628, 0.147, 0.654]</r>. Starting with 'Career Development', the text label {\u201c85\u201d}<r>[0.142, 0.093, 0.153, 0.104]</r> suggests the score for this metric. Proceeding with 'Workplace Environment', the corresponding text label {\u201c75\u201d}<r>[0.470, 0.135, 0.481, 0.146]</r> indicates the score. I repeat this process for each subplot, including 'Upskilling', 'Leadership Development', 'Work-Life Balance', 'Employee Engagement', 'Diversity & Inclusion', 'Digital Transformation', and 'Company Culture' to compile the required values for the year 2017.\nBased on the above thought, the answer is:\nMetric,2017\nCareer Development,85\nWorkplace Environment,75\nUpskilling,52\nLeadership Development,88\nWork-Life Balance,67\nEmployee Engagement,77\nDiversity & Inclusion,74\nDigital Transformation,65\nCompany Culture,82"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "gLQ2bqKpzF6Y3M30",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric shows the most significant improvement from the year 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the metric with the most significant improvement, I will compare the increase in values from 2016 to 2020 for each metric. I begin with 'Career Development' which increased from {\u201c80\u201d}<r>[0.092, 0.114, 0.103, 0.125]</r> to {\u201c88\u201d}<r>[0.291, 0.081, 0.302, 0.092]</r>. I perform similar comparisons for each subsequent subplot by examining the text labels for 'Workplace Environment', 'Upskilling', 'Leadership Development', 'Work-Life Balance', 'Employee Engagement', 'Diversity & Inclusion', 'Digital Transformation', and 'Company Culture'. The metric with the largest difference in values between {2016}<r>[0.076, 0.628, 0.098, 0.654]</r> and {2020}<r>[0.275, 0.628, 0.297, 0.654]</r> will be considered to have the most significant improvement.\nBased on the above thought, the answer is:\nWork-Life Balance. The rise from \u201c65\u201d in {2016}<r>[0.421, 0.503, 0.431, 0.514]</r> to \u201c78\u201d in {2020}<r>[0.619, 0.449, 0.630, 0.460]</r> represents the largest increase among all the metrics from the provided data."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "MFcx7pex9rUh4Ru9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of metric values across all categories for the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total sum for the year 2018, I will add up the values indicated by the text labels on top of each bar corresponding to {2018}<r>[0.176, 0.628, 0.197, 0.654]</r>. I locate each label, starting with 'Career Development' {\u201c87\u201d}<r>[0.192, 0.085, 0.202, 0.096]</r>, 'Workplace Environment' {\u201c68\u201d}<r>[0.520, 0.163, 0.531, 0.175]</r>, 'Upskilling' {\u201c57\u201d}<r>[0.848, 0.209, 0.859, 0.220]</r>, and so on for other categories. Each value is added to form the cumulative sum.\nBased on the above thought, the answer is:\n626"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "MGpMxYL0Kfx36lCU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a CSV table format that includes technology types and their patent applications and grants?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar charts, I can identify six different technology types as subtitled in each subplot, with respective bar details capturing data on patent applications and patents granted. Observing closely, each bar has a label just beneath it naming the category and text above each bar indicating the numerical value. For instance, for Artificial Intelligence {the bars}[{\"Patent Applications\"}[0.094, 0.064, 0.193, 0.405]], which represents patent applications, has a number {28325}<r>[0.131, 0.052, 0.157, 0.064]</r> at the top, while {the bars}[{\"Patents Granted\"}[0.218, 0.223, 0.317, 0.405]], representing patents granted, has a corresponding number {15090}<r>[0.255, 0.212, 0.281, 0.223]</r>. I will systematically extract similar data for each technology from their respective subplots, and convert this information into a structured CSV format.\nBased on the above thought, the answer is:\nTechnology,Patent Applications,Patents Granted\nArtificial Intelligence,28325,15090\nBlockchain,13323,5602\nQuantum Computing,8743,4127\n5G Technology,55220,29127\nIoT,41205,22030\nBig Data,33184,17847"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "cXibKC8uCSocv35s",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain why artificial intelligence and 5G technology have higher numbers of patent applications and grants compared to other technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the charts, it\u2019s evident that both {Artificial Intelligence}{r>[0.083, 0.047, 0.328, 0.405]}</r> and {5G Technology}{r>[0.083, 0.537, 0.328, 0.896]}</r> have significantly higher bars corresponding to both patent applications and patents granted. The height of these bars, compared to those of other technologies like {Blockchain}<r>[0.412, 0.047, 0.657, 0.405]</r> or {Quantum Computing}<r>[0.740, 0.047, 0.985, 0.405]</r>, is quite substantial. The greater height represents a larger volume of patents, indicating a higher rate of innovation and industry investment in these fields. Artificial Intelligence and 5G are leading-edge technologies with broad applications in various sectors, driving the need for extensive patenting to protect novel inventions and competitive advantages.\nBased on the above thought, the answer is:\nArtificial intelligence and 5G technology show higher patent numbers due to their groundbreaking potential and wide applications across different industries. Increased investments and rapid innovations in these areas necessitate robust patenting to safeguard intellectual property, setting them apart from more nascent fields like Blockchain and Quantum Computing."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "AKfkAAqkUpuRoMBk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of patent applications for all technologies combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "For calculating the total number of patent applications, I will sum up the numbers from each technology's patent application bar. Observing {the number above patent applications}[{\"Artificial Intelligence\"}[0.131, 0.052, 0.157, 0.064]; {\"Blockchain\"}[0.459, 0.052, 0.486, 0.064]; {\"Quantum Computing\"}[0.790, 0.052, 0.811, 0.064]; {\"5G Technology\"}[0.131, 0.543, 0.157, 0.554]; {\"IoT\"}[0.459, 0.543, 0.485, 0.554]; {\"Big Data\"}[0.787, 0.543, 0.814, 0.554]], where each has the value for patent applications listed. Adding these values gives a combined total for the observed technologies.\nBased on the above thought, the answer is:\n168800"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "xm0L6lzsM90EuOET",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the return rate data for the investment company \"Clearbell\" from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the area designated for \"Clearbell\", {the subplot}<r>[0.397, 0.701, 0.657, 0.949]</r>, which includes {the bars}<r>[0.409, 0.910, 0.428, 0.910]</r> representing the year 2010 through {the bars}<r>[0.954, 0.701, 0.973, 0.910]</r> for the year 2019. Using the y-axis {tick labels}<r>[0.363, 0.942, 0.392, 0.959]</r> to <r>[0.375, 0.694, 0.392, 0.711]</r>, the data points can be extracted. I will list out the year along with the corresponding return rate, as visible from the chart, for each bar.\nBased on the above thought, the answer is:\nYear,Return Rate (%)\n2010,0\n2011,-5\n2012,-10\n2013,-10\n2014,-5\n2015,0\n2016,5\n2017,10\n2018,15\n2019,20"
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "JjgmG9C7irIlFONl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company experienced the most dramatic change in their return rate between 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the most dramatic change requires comparing the initial and final return rates for each company. Scanning through the subplots, starting with {GreenOak}<r>[0.069, 0.047, 0.328, 0.294]</r>, {Fortress}<r>[0.397, 0.047, 0.657, 0.294]</r>, {GGP}<r>[0.725, 0.047, 0.985, 0.294]</r>, and continuing with the others, I extract the rates for the first and last bars of each, such as {for GreenOak}<r>[0.080, 0.218, 0.100, 0.256]</r> and <r>[0.297, 0.161, 0.317, 0.256]</r>. The most significant change is noted by comparing these values for all companies and recognizing the largest difference.\nBased on the above thought, the answer is:\nNREP. Starting with {1% in 2010}<r>[0.737, 0.580, 0.756, 0.583]</r> and ending with {0% in 2019}<r>[0.954, 0.583, 0.973, 0.583]</r>, the difference is a complete decline of 1% over the decade, which is a considerable change rate-wise, given the initial low base rate."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "tMTjcS9pZc2M211T",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average return rate for Blackstone over the entire period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average return rate for \"Blackstone\", I need to sum up the individual yearly return rates and divide by the number of years. Examining {the subplot for Blackstone}<r>[0.069, 0.374, 0.328, 0.621]</r> including {bars}<r>[0.080, 0.583, 0.100, 0.621]</r> through <r>[0.297, 0.469, 0.317, 0.583]</r>, I obtain the following return rates: -10, -8, -5, 0, 5, 10, 15, 20, 27, and 30. Summing these values and dividing by 10 provides the average.\nBased on the above thought, the answer is:\n8.4"
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "2FN4ACuyI2fRbaU0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the percentage performance score for each energy technology in 'Slow recovery' trend as a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the lower half of the bar chart where 'Slow recovery' is indicated as the subtitle at {the bottom chart subtitle}<r>[0.467, 0.019, 0.584, 0.041]</r>, I see various bars corresponding to different energy technologies as labeled on the x-axis at {tick labels}<r>[0.100, 0.422, 0.909, 0.470]</r>. The values at the top of each bar represent the performance score percentages for each technology. For instance, the bar for 'Solar Power' has coordinates at {bar for Solar Power}<r>[0.108, 0.277, 0.176, 0.508]</r> with a height indicating a performance score that can be deciphered from the value labelled '50.2%' at {text above Solar Power bar}<r>[0.128, 0.261, 0.155, 0.272]</r>. By analyzing the bars' heights and their top values for each technology, I can construct a CSV structured table.\nBased on the above thought, the answer is:\nTechnology, Slow Recovery (%)\nSolar Power, 50.2\nWind Power, 35.2\nHydro Power, 60.5\nGeothermal, 27.0\nBioenergy, 50.9\nHydrogen, 45.0\nElectric Vehicles, 30.7\nEnergy Storage, 56.0\nCarbon Capture, 46.8\nSmart Grid, 41.9"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "U5kqwX6BJjQD2ohQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy technologies have a lower performance score in 'Consistent expansion' compared to 'Slow recovery'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the performance scores between the trends, I must look at the bars corresponding to each energy technology under both trends and note which ones have a lower height in 'Consistent expansion' compared to 'Slow recovery'. Starting with 'Solar Power', I can see that its bar in 'Slow recovery' at {bar for Solar Power in Slow recovery}<r>[0.108, 0.277, 0.176, 0.508]</r> is shorter than its counterpart in 'Consistent expansion' at {bar for Solar Power in Consistent expansion}<r>[0.108, 0.673, 0.176, 0.999]</r>. Investigating the bars of all other technologies using their coordinates, I will determine if any have lower values in the 'Consistent expansion' trend compared to 'Slow recovery'.\nBased on the above thought, the answer is:\nNone of the technologies have a lower performance score in 'Consistent expansion' compared to 'Slow recovery'. Each technology's performance score is higher or equal in the 'Consistent expansion' trend, which is apparent from the height of the bars being taller or at least equal in the upper chart compared to the lower chart for each respective technology."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "j8Iw8pMSCZihrbgs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for 'Hydro Power' combining both 'Slow recovery' and 'Consistent expansion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the bars corresponding to 'Hydro Power' for both trends to sum their performance scores. The bar for the 'Slow recovery' trend is found at {bar for Hydro Power in Slow recovery}<r>[0.278, 0.229, 0.346, 0.508]</r>, with its value label '60.5%' located at {text above Hydro Power bar in Slow recovery}<r>[0.299, 0.213, 0.325, 0.224]</r>. Similarly, the bar for the 'Consistent expansion' trend is found at {bar for Hydro Power in Consistent expansion}<r>[0.278, 0.606, 0.346, 0.999]</r>, with its corresponding value label '85.1%' at {text above Hydro Power bar in Consistent expansion}<r>[0.299, 0.590, 0.325, 0.601]</r>. Adding both values: 60.5 + 85.1 gives the total percentage.\nBased on the above thought, the answer is:\n145.6"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "Ext9uzrwNwTKO9DU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that shows the progression of 'Customer feedbacks' over the 12 months as displayed in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the specific subplot dedicated to 'Customer feedbacks' with its axes at {the axes}<r>[0.075, 0.537, 0.493, 0.932]</r>, I observe each of the 12 bars that correspond to a month of the year, starting from {Month 1 bar}<r>[0.094, 0.927, 0.119, 0.936]</r> to {Month 12 bar}<r>[0.448, 0.910, 0.474, 0.936]</r>. The height of each bar indicates the quantity of feedbacks received with the y-axis tick labels providing a scale from {0}<r>[0.075, 0.928, 0.097, 0.936]</r> to values over {50}<r>[0.427, 0.898, 0.449, 0.909]</r>. I can develop a CSV table structure reflecting each bar's label and height, which corresponds to the number of customer feedbacks in each month.\nBased on the above thought, the answer is:\nMonth,Customer Feedbacks\nMonth 1,50\nMonth 2,60\nMonth 3,75\nMonth 4,80\nMonth 5,90\nMonth 6,95\nMonth 7,100\nMonth 8,105\nMonth 9,110\nMonth 10,120\nMonth 11,130\nMonth 12,140"
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "gNlsemi7RSWKG3UU",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter shows the greatest increase in 'Emails processed' and by how much did it increase over the previous quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess quarterly increases, I will examine the 'Emails processed' subplot, specifically looking at month-wise bars and compare the last month of each quarter to the last month of the previous quarter. For example, comparing {Month 3}<r>[0.158, 0.208, 0.184, 0.445]</r> with {Month 6}<r>[0.255, 0.132, 0.280, 0.445]</r>, {Month 9}<r>[0.351, 0.104, 0.377, 0.445]</r>, and {Month 12}<r>[0.448, 0.066, 0.474, 0.445]</r>, I'll use the values annotated above the bars like for {Month 3 '1250'}<r>[0.160, 0.197, 0.181, 0.208]</r> and {Month 6 '1650'}<r>[0.257, 0.121, 0.278, 0.132]</r>. The greatest increase will be the quarter where the difference between these end-month values is the highest.\nBased on the above thought, the answer is:\nQ2 to Q1. The increase was by 400, from 1250 in {Month 3}<r>[0.158, 0.208, 0.184, 0.445]</r> to 1650 in {Month 6}<r>[0.255, 0.132, 0.280, 0.445]</r>."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "kVMrlP5lwl9MpXGM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total number of 'New contracts' signed across all 12 months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total number of 'New contracts' over the year, I shall sum up the values presented above each bar in the respective subplot, starting from {Month 1}<r>[0.586, 0.931, 0.612, 0.936]</r> through {Month 12}<r>[0.940, 0.926, 0.966, 0.936]</r>. The annotations, such as {Month 1 '25'}<r>[0.594, 0.920, 0.604, 0.931]</r> and {Month 12 '55'}<r>[0.948, 0.914, 0.958, 0.925]</r>, indicate the respective number of contracts. By summing all the annotated values, I'd get the total number of new contracts for the year.\nBased on the above thought, the answer is:\n444"
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "hyVOEbWoAeWdrKLm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a CSV-formatted table representing the performance of various softwares in the Encrypted Data Leak category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot for Encrypted Data Leak {\"Encrypted Data Leak\"}[0.192, 0.509, 0.366, 0.532] shows eleven bars representing different software performances. Each bar has a text label above it indicating the performance score. Based on the position of the bars on the y-axis, which has tick labels from {30}[0.043, 0.899, 0.061, 0.917] to {100}[0.035, 0.530, 0.061, 0.548], I will determine the scores for each software. For example, {the bar for \"Cyberlock-5d\"}[0.085, 0.590, 0.114, 1.065] has a text label above {\"90.0\"}[0.090, 0.573, 0.109, 0.585], indicating a performance score of 90.0. I'll repeat this process for all softwares listed along the x-axis, including their {labels}[0.054, 0.912, 0.459, 0.972].\nBased on the above thought, the answer is:\nSoftware,Encrypted Data Leak Performance Score (%)\nCyberlock-5d,90.0\nFort-7m,85.8\nAven-110z-1.3,82.8\nYz-54z,77.4\nMirage-1b-alpha,74.5\nWi-300b,70.1\nElectra-29b,68.2\nYan-PaLA,63.4\nYan-PaLA 2,63.0\nIPT-3,58.0\nGemini Shield,55.5"
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "agZCfjZToJfMGjQr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich software shows a decline in performance for both Encrypted Data Leak and Darkweb Activity categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the software that shows decline in both Encrypted Data Leak and Darkweb Activity, I need to analyze the bar charts for each category. In the {Encrypted Data Leak}<r>[0.066, 0.537, 0.493, 0.906]</r> category, I observe the height of the bars and the associated text labels placed above the bars to gauge the performance scores, such as the 74.5 score for {Mirage-1b-alpha}<r>[0.229, 0.672, 0.258, 1.065]</r> with its label {\"74.5\"}<r>[0.234, 0.655, 0.252, 0.666]</r>. Next, I move to the {Darkweb Activity}<r>[0.558, 0.537, 0.985, 0.906]</r> chart and do the same, where the performance score for {Mirage-1b-alpha} is {\"76.7\"}<r>[0.726, 0.644, 0.745, 0.655]</r>. By analyzing the scores for the same software across both categories, I can spot which ones show a decrease in scores. This process is repeated for all software listed on the x-axis.\nBased on the above thought, the answer is:\nMirage-1b-alpha. Its performance score decreased from 77.0 in Incognito Surveilance to 74.5 in Encrypted Data Leak and further to 76.7 in Darkweb Activity."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "pSPx9aAYZbdaTH7m",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average performance score for Darkweb Activity across all softwares?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average performance score for Darkweb Activity, I need to sum all individual software scores and divide by the total number of software. The scores are displayed as text labels above each bar in the Darkweb Activity subplot {\"Darkweb Activity\"}[0.701, 0.509, 0.842, 0.532]. The text labels range from {\"60.6\"}[0.583, 0.728, 0.601, 0.740] for {Cyberlock-5d} to {\"93.5\"}[0.942, 0.555, 0.960, 0.566] for {Gemini Shield}. I will add each of these values obtained from the text labels above the bars {[0.578, 0.745, 0.966, 1.065]}, and then calculate the average.\nBased on the above thought, the answer is:\n77.1"
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "4KcYsV9nWPuvvRIn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data into a table depicting the performance scores of each biotechnology model for Q1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, focusing on the first subplot titled {Incognito Surveilance}<r>[0.189, 0.019, 0.369, 0.041]</r> which signifies Q1 data, I scan the x-axis and locate the biotech models at the following coordinates: BioMed-X<r>[0.054, 0.422, 0.100, 0.479]</r>, GenoTech<r>[0.106, 0.422, 0.135, 0.459]</r>, MicroCure<r>[0.122, 0.422, 0.171, 0.483]</r>, PatholPro<r>[0.182, 0.422, 0.207, 0.454]</r>, HealthShape<r>[0.188, 0.422, 0.243, 0.491]</r>, VitaVascular<r>[0.248, 0.422, 0.279, 0.460]</r>, CelluFit<r>[0.275, 0.422, 0.315, 0.472]</r>, NeuroNet<r>[0.318, 0.422, 0.351, 0.463]</r>, ImmunInsight<r>[0.348, 0.422, 0.387, 0.470]</r>. I then cross-reference these labels with corresponding bar coordinates to extract performance values: BioMed-X<r>[0.085, 0.337, 0.114, 0.574]</r>, GenoTech<r>[0.121, 0.258, 0.150, 0.574]</r>, MicroCure<r>[0.157, 0.205, 0.186, 0.574]</r>, PatholPro<r>[0.193, 0.152, 0.222, 0.574]</r>, HealthShape<r>[0.229, 0.168, 0.258, 0.574]</r>, VitaVascular<r>[0.265, 0.141, 0.294, 0.574]</r>, CelluFit<r>[0.301, 0.124, 0.329, 0.574]</r>, NeuroNet<r>[0.337, 0.099, 0.365, 0.574]</r>, ImmunInsight<r>[0.373, 0.084, 0.401, 0.574]</r>.\nBased on the above thought, the answer is:\nModel,Q1 Score\nBioMed-X,87.6\nGenoTech,83.2\nMicroCure,67.9\nPatholPro,72.1\nHealthShape,76.5\nVitaVascular,91.5\nCelluFit,84.3\nNeuroNet,75.4\nImmunInsight,88.7"
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "ubOkRjpCKF9DdqSg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich biotech model demonstrated a consistent improvement over the four quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must analyze the trend of each biotech model's performance scores across the subplots representing each quarter. I'll observe the bar heights for each model in all four subplots \u2013 {Incognito Surveilance}<r>[0.189, 0.019, 0.369, 0.041]</r>, {Firewall Breach}<r>[0.709, 0.019, 0.835, 0.041]</r>, {Encrypted Data Leak}<r>[0.192, 0.509, 0.366, 0.532]</r>, and {Darkweb Activity}<r>[0.701, 0.509, 0.842, 0.532]</r>. Observing the bar coordinates: BioMed-X<r>[0.578, 0.745, 0.606, 1.065]</r>, GenoTech<r>[0.614, 0.732, 0.642, 1.065]</r>, MicroCure<r>[0.650, 0.704, 0.678, 1.065]</r>, PatholPro<r>[0.685, 0.682, 0.714, 1.065]</r>, HealthShape<r>[0.721, 0.660, 0.750, 1.065]</r>, VitaVascular<r>[0.757, 0.640, 0.786, 1.065]</r>, CelluFit<r>[0.793, 0.625, 0.822, 1.065]</r>, NeuroNet<r>[0.829, 0.613, 0.858, 1.065]</r>, ImmunInsight<r>[0.865, 0.601, 0.894, 1.065]</r>. The consistent improvement can be identified by noting the sequential increase in bar height across all quarters. According to the bar coordinates, MicroCure reflects a progressive rise in the bar heights representing an increase in performance scores from Q1 to Q4.\nBased on the above thought, the answer is:\nMicroCure. This model shows a consistent rise in its performance scores as visualized across all subplots, indicating an improvement in each successive quarter."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "gE4gOqRUyJGSC9Jp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest performance score observed in Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will review the second subplot representing Q2 data titled {Firewall Breach}<r>[0.709, 0.019, 0.835, 0.041]</r>. I will scan the y-axis for the highest numerical value by examining the bars. I locate the performance score of each biotech model by identifying the top edge of their respective bars: Cyberlock-5d<r>[0.578, 0.385, 0.606, 0.574]</r>, Fort-7m<r>[0.614, 0.280, 0.642, 0.574]</r>, Aven-110z-1.3<r>[0.650, 0.227, 0.678, 0.574]</r>, Yz-54z<r>[0.685, 0.192, 0.714, 0.574]</r>, Mirage-1b-alpha<r>[0.721, 0.221, 0.750, 0.574]</r>, Wi-300b<r>[0.757, 0.205, 0.786, 0.574]</r>, Electra-29b<r>[0.793, 0.178, 0.822, 0.574]</r>, Yan-PaLA<r>[0.829, 0.159, 0.858, 0.574]</r>, Yan-PaLA 2<r>[0.865, 0.149, 0.894, 0.574]</r>, IPT-3<r>[0.901, 0.122, 0.930, 0.574]</r>, Gemini Shield<r>[0.937, 0.097, 0.966, 0.574]</r>. According to text coordinates, the highest performance score is associated with IPT-3 with a value found at <r>[0.906, 0.106, 0.925, 0.117]</r>.\nBased on the above thought, the answer is:\n85.7"
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "1hGa0ftRaar5UWQ3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you organize the project volume data from the bar charts into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining the bar chart, I am analyzing each sub-chart for different community types, starting with {\"Uptown\"}[0.165, 0.019, 0.229, 0.041], and moving right along to {\"Business District\"}[0.784, 0.673, 0.923, 0.695]. I notice the x-axis lists the types of community projects: {Residential Buildings}[0.021, 0.231, 0.090, 0.317], {Recreational Facilities}[0.049, 0.231, 0.121, 0.320], and so forth. The y-axis, located at {ylabel coordinates}[0.015, 0.071, 0.029, 0.200], scales from 0 to 100, representing the project volumes. For each type of project within the \"Uptown\" community, for instance, {bar chart rectangles}[0.078, 0.148, 0.102, 0.301] and {value labels}[0.085, 0.135, 0.095, 0.146], I extract their respective heights and note down the numerical values to transform this visual information into a CSV table.\nBased on the above thought, the answer is:\nCommunity,Residential Buildings,Recreational Facilities,Transport Infrastructure,Educational Institutions,Commercial Buildings,Gardens & Parks,Community Facilities,Healthcare Institutions\nUptown,60,50,65,70,80,45,55,75\nDowntown,65,70,55,70,60,65,70,85\nSuburbs,70,75,55,65,70,70,80,65\nCountryside,45,50,60,60,65,75,80,70\nHistoric,50,55,55,70,85,70,65,75\nRevitalization Zone,45,50,70,75,80,50,60,70\nEconomic Development Zone,70,75,55,80,85,70,80,65\nScenic Area,40,80,60,60,70,80,85,60\nBusiness District,80,65,70,70,90,45,40,70"
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "Uq9pgEeGVU9A3Nfd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify which type of community has the most diversified investment in projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which type of community has the most diversified investment in projects, I analyze the spread of project volumes across all categories for each community. I observe {the bars}<r>[0.078, 0.148, 0.102, 0.301]</r> in each sub-chart, representing project volumes, and check for the community that has the least variation in bar heights, indicating that the investment is spread out more evenly across different types of projects. After comparing all the communities, I notice that the {Economic Development Zone}<r>[0.075, 0.673, 0.320, 0.695]</r> demonstrates a relatively even distribution of project investments across most categories as indicated by the consistent heights of its bars in comparison to other communities.\nBased on the above thought, the answer is:\nThe Economic Development Zone has the most diversified investment, as the project volumes across different categories are almost evenly distributed, which indicates a balanced spread of investment across various types of projects."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "O6AfZCDlfJlvHPhG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich community has the highest number of projects for Recreational Facilities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the community with the highest number of projects for Recreational Facilities, I need to compare the height of the bars corresponding to Recreational Facilities across all the sub-charts. I start with the {\"Uptown\"}[0.165, 0.019, 0.229, 0.041] sub-chart and go through each, looking specifically at the height of the bars related to Recreational Facilities, such as {Uptown's Recreational Facilities bar}[0.108, 0.174, 0.133, 0.301] and so on, until I find the tallest bar which indicates the highest project volume. Comparing the values as listed in the chart, {Scenic Area's Recreational Facilities bar}[0.437, 0.752, 0.461, 0.955] stands out as the tallest.\nBased on the above thought, the answer is:\nScenic Area."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "pVRJETx8fwuzljrc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table for the 'Subdued Expansion' trend with the engagement scores for each social media platform?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the first subplot corresponding to the 'Subdued Expansion' trend, I analyze the data for each platform. I find {the bars}<r>[0.086, 0.148, 0.125, 0.487]</r> each with a text label above that gives the engagement score for each platform\u2014{Facebook}<r>[0.086, 0.148, 0.125, 0.487]</r> has a score of 5000, {Twitter}<r>[0.134, 0.196, 0.173, 0.487]</r> has a score of 4300, {Instagram}<r>[0.182, 0.236, 0.221, 0.487]</r> at 3700, {LinkedIn}<r>[0.230, 0.175, 0.269, 0.487]</r> at 4600, and {YouTube}<r>[0.278, 0.121, 0.317, 0.487]</r> scoring 5400.\nBased on the above thought, the answer is:\nSocialMedia,SubduedExpansion\nFacebook,5000\nTwitter,4300\nInstagram,3700\nLinkedIn,4600\nYouTube,5400"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "dIfDmC3XBsb7A5h7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which trend did LinkedIn show the most significant increase in engagement compared to its previous period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine LinkedIn's most significant increase, I assess each period where LinkedIn's engagement score rises from the previous period's score. I begin with the 'Subdued Expansion' subplot and note {LinkedIn's score}<r>[0.230, 0.175, 0.269, 0.487]</r>. Moving to the next subplot labeled 'Stable Growth', LinkedIn's score does not show an increase. Progressing through the subsequent trends, I notice in the 'Rapid Recovery' subplot, LinkedIn shows a significant score increase from its 'Volatile Decline' score, changing from 4200 to 4800, shown by {LinkedIn's bars}<r>[0.559, 0.551, 0.597, 0.836]</r> and <r>[0.887, 0.511, 0.925, 0.836]</r> respectively. Comparing this with other increases, I conclude which trend exhibited the greatest increase for LinkedIn.\nBased on the above thought, the answer is:\nRapid Recovery. The transition from the 'Volatile Decline' trend to the 'Rapid Recovery' trend shows the most substantial increase in engagement for LinkedIn, shifting from an engagement score of 4200 to 4800."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "LPUxEjuyStZfnrP5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the engagement on Twitter reduced from the 'Stable Growth' trend to the 'Rapid Decline' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare Twitter's engagement between the two trends, I first locate Twitter's bars. In the 'Stable Growth' subplot, {Twitter's bar}<r>[0.462, 0.202, 0.501, 0.487]</r> indicates an engagement score of 4200. Next, in the 'Rapid Decline' subplot, I analyze {Twitter's bar}<r>[0.791, 0.223, 0.829, 0.487]</r> to find an engagement score of 3900. Now, by calculating the difference between 4200 and 3900, I can find the reduction in engagement.\nBased on the above thought, the answer is:\n300"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "Xdk0mblbl6cs826w",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table showing the genres and their corresponding theater performance and art exhibition ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the given data for different genres within the chart {subplot_0}<r>[0.066, 0.047, 0.197, 0.458]</r> and {subplot_1}<r>[0.263, 0.047, 0.394, 0.458]</r>, I observe that the genres are listed along the x-axis, with their corresponding ratings depicted as bars. The bars have text annotations indicating the exact scores. By extracting each genre's theater and art exhibition ratings from the top of the respective bars such as {Regional Drama}<r>[0.072, 0.252, 0.081, 0.869]</r>, I can compile the CSV table.\nBased on the above thought, the answer is:\nGenre,Theater Performance Rating,Art Exhibition Rating\nRegional Drama,75.0,69.0\nMusical,80.4,82.3\nContemporary Dance,81.8,82.0\nBallet,71.1,70.4\nStand-up Comedy,88.7,87.5\nOpera,94.8,88.9\nPantomime,78.5,68.6\nMimes,77.1,79.8\nImprovisational,92.0,76.9\nStorytelling,83.6,78.1"
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "KCowvA9JnlBSHNBl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich genre shows a noteworthy disparity between Theater Performance and Art Exhibition Ratings, and can you explain why it may be significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will locate the bars within {subplot_0}<r>[0.066, 0.047, 0.197, 0.458]</r> for Theater Performance Rating and within {subplot_1}<r>[0.263, 0.047, 0.394, 0.458]</r> for Art Exhibition Rating to observe marked differences. For instance, I identify that the {Opera genre}<r>[0.133, 0.089, 0.142, 0.869]; [0.330, 0.138, 0.339, 0.869]</r> has a significant lead in both categories with an especially high Theater Performance Rating. This exceptional performance in both categories could reflect a higher audience appreciation for the Opera genre, possibly due to its unique offering of both visual arts and live performance, thus resonating more strongly with the audience compared to other genres.\nBased on the above thought, the answer is:\nThe Opera genre. It indicates a strong audience preference for Opera that is consistent across both performance venues, setting it apart as a particularly popular art form potentially due to its grandeur and rich artistic experience."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "YZ4IUTcP5UJ1bfOQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Theater Performance Rating for all the genres represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate the average Theater Performance Rating, I need to sum the ratings from each genre noted in the bars within {subplot_0}<r>[0.066, 0.047, 0.197, 0.458]</r> and divide by the number of genres present. The performance ratings for genres {Regional Drama}<r>[0.072, 0.252, 0.081, 0.869]</r>, {Musical}<r>[0.084, 0.208, 0.094, 0.869]</r>, and others bring us to a total sum. There are 10 genres in total. After doing the math, I arrive at the average rating.\nBased on the above thought, the answer is:\n81.99"
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "odPBaAJ11C1MPqwg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the provided chart data into structured tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "The dual bar chart presents two different datasets over a 10-year period. {The left y-axis}<r>[0.015, 0.367, 0.029, 0.630]</r> of the chart shows the 'Career Progression' with increasing orange bars and the {right y-axis}<r>[0.971, 0.373, 0.985, 0.624]</r> shows the 'Obstacle Removal' with decreasing blue bars. There are ten sets of bars corresponding to each year labeled on the {x-axis}<r>[0.111, 0.942, 0.869, 0.970]</r>. For career progression, the bars start at the baseline <r>[0.062, 0.923]</r> and extend upwards, whereas for obstacle removal, the bars also start at the same baseline but extend downwards. By examining the height of each bar from the baseline to its top, I would tabulate the given values for all ten years.\nBased on the above thought, the answer is:\nYear,Career Progression,Obstacle Removal\nYear 1,10,-5\nYear 2,20,-7\nYear 3,23,-8\nYear 4,15,-6\nYear 5,28,-7\nYear 6,24,-9\nYear 7,29,-10\nYear 8,28,-12\nYear 9,30,-9\nYear 10,35,-10"
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "g3WlLaYTo9wXVAug",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a visible trend in the obstacle removal over the years, and if so, what does it indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the trend in obstacle removal, I will analyze the height and direction of the {blue bars}<r>[0.132, 0.075, 0.875, 0.499]</r> across all x-axis labels from {Year 1} to {Year 10}<r>[0.111, 0.942, 0.869, 0.970]</r>, in relation to the negative scale on the {right y-axis}<r>[0.971, 0.373, 0.985, 0.624]</r>. A downward trend in the height of the blue bars would signify an increase in the number of obstacles being removed over time.\nBased on the above thought, the answer is:\nYes, there is a visible trend of increasing obstacle removal over the years. The ascending height of the blue bars, especially noticeable in the 8th and 9th years, suggests that the individual is becoming progressively more effective at overcoming career obstacles, indicating a positive development in problem-solving and possibly a phase of accelerated career growth."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "MzxGSgpbm5XuvTXw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of obstacle removal over the 10-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total sum of the obstacle removal values, I will add up the negative values represented by each of the {blue bars}<r>[0.132, 0.075, 0.875, 0.499]</r> along the x-axis, which corresponds to the years from 'Year 1' to 'Year 10'. Each bar's value corresponds to the label on the {right y-axis}<r>[0.971, 0.373, 0.985, 0.624]</r>, allowing me to accurately sum the obstacle removal values for each year.\nBased on the above thought, the answer is:\n-84"
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "Taijq1Sy3A3J20Sa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you put the data from the bar chart representing the popularity of various fashion subcultures in 2018 and 2019 into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, we can start by dividing the chart by fashion subculture. Each subculture {Gothic}[0.152, 0.942, 0.197, 0.970], {Streetwear}[0.299, 0.942, 0.376, 0.970], {Vintage}[0.473, 0.942, 0.527, 0.970], {Grudge}[0.637, 0.942, 0.688, 0.970], and {Skater}[0.803, 0.942, 0.848, 0.970], has two bars that reflect popularity for the years 2018 and 2019. These are distinguished by different color bars according to {the legend}[0.763, 0.089, 0.922, 0.139]. The values can be estimated from the y-axis. For instance, for Gothic, {[2018 Popularity Bar][0.110, 0.287, 0.175, 1.347]} corresponds to approximately 500 and {[2019 Popularity Bar][0.175, 0.297, 0.240, 1.347]} corresponds to around 495. We can repeat this step for each subculture to determine the associated values.\nBased on the above thought, the answer is:\nSubculture,2018 Popularity,2019 Popularity\nGothic,500,495\nStreetwear,340,450\nVintage,400,380\nGrudge,360,385\nSkater,320,335"
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "aeK8R4lE3trUM4Qv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the popularity trend for the Streetwear category based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Streetwear category has two bars corresponding to 2018 and 2019. The {Streetwear bar for 2018}[0.272, 0.626, 0.337, 1.347] has a value around 340 and the {Streetwear bar for 2019}[0.337, 0.393, 0.402, 1.347] is around 450. This indicates an increase in popularity for this subculture from 2018 to 2019. Thus, the trend for the Streetwear subculture seems to be on the rise.\nBased on the above thought, the answer is:\nThe Streetwear subculture showed a rising trend in popularity from the year 2018 to 2019."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "LRYBa61JCuS5IjVw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fashion subculture has seen the most significant increase in popularity from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the subculture with the most significant increase, I need to look at the difference in height between the 2018 and 2019 bars for each subculture. Starting with {Gothic}'s bars<[2018 Popularity Bar][0.110, 0.287, 0.175, 1.347]; [2019 Popularity Bar][0.175, 0.297, 0.240, 1.347]>, it's a decreasing trend; Moving on to {Streetwear}'s bars<[2018 Popularity Bar][0.272, 0.626, 0.337, 1.347]; [2019 Popularity Bar][0.337, 0.393, 0.402, 1.347]>, it saw an increase of about 110 in popularity; and so on for each subculture. Comparing the differences of each subculture, I can see that {Streetwear} saw the most significant increase.\nBased on the above thought, the answer is:\nStreetwear"
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "07RoACsRV5KBU7pU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into CSV formatted rows and columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe that there are two sets of bars {2018 Sales}<r>[0.120, 0.075, 0.200, 0.680]</r>, {2019 Sales}<r>[0.200, 0.075, 0.280, 0.438]</r>. These bars represent the Sales in 2018 and 2019, respectively. {The x-axis tick labels}<r>{\"Food\"}[0.184, 0.942, 0.217, 0.970]; {\"Electronics\"}[0.362, 0.942, 0.438, 0.970]; {\"Books\"}[0.579, 0.942, 0.621, 0.970]; {\"Clothing\"}[0.771, 0.942, 0.829, 0.970]</r> suggest these are four different categories. The bars' heights assist in determining the corresponding values and extracting this data into a CSV format, where the first column is the category, the second column represents the 2018 sales, and the third column represents the 2019 sales.\nBased on the above thought, the answer is:\nCategory,2018 Sales,2019 Sales\nFood,-100,-60\nElectronics,-120,-90\nBooks,-50,-60\nClothing,-100,-130"
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "9czRpTsquF94MqvJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product category appeared to struggle the most over the given years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the chart, it's clear that all product categories recorded a decrease in sales. However, {Clothing}<r>[0.720, 0.075, 0.800, 0.680; 0.800, 0.075, 0.880, 0.862]</r> showed a significant decline as the bars carved a steeper graph compared to other categories. The sales went from -100 in 2018 to -130 in 2019, which indicates a larger decrease than the other categories. Furthermore, the 2019 sales bar for clothing is also the longest among all the categories suggesting that not only did it have the highest drop from the previous year but it was also the lowest performing category in 2019.\nBased on the above thought, the answer is:\nThe Clothing category struggled the most based on the data from the chart. The significant decline in sales along with the lowest performance in sales in 2019 highlights this struggle."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "KAtK3wtXgcDxkGBk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category shows the largest difference in sales from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the height difference between the two bars in each sales category. Starting with {Food category}<r>[0.120, 0.075, 0.200, 0.680]</r> and [0.200, 0.075, 0.280, 0.438], the difference is approximately 40; repeating this process for each of the remaining product categories. From these observations, it's visible that {Clothing}<r>[0.720, 0.075, 0.800, 0.680; 0.800, 0.075, 0.880, 0.862]</r> category shows the maximum change in sales from 2018 to 2019.\nBased on the above thought, the answer is:\nClothing"
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "3JCgnPwJouT64MB8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLet's derive a table with a comparison of the number of papers in each academic field for the years 2019 and 2020 as portrayed in the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title, {Annual Academic Research Papers in Different Fields}<r>[0.354, 0.015, 0.646, 0.033]</r>, I can see that the data pertains to annual academic research papers in different fields for the years 2019 and 2020. The x-axis labels represent the fields {Computer Science}<r>[0.098, 0.885, 0.165, 0.985]</r>, {Mathematics}<r>[0.230, 0.885, 0.279, 0.958]</r>, {Physics}<r>[0.362, 0.885, 0.392, 0.931]</r>, {Chemistry}<r>[0.480, 0.885, 0.520, 0.945]</r>, {Biology}<r>[0.607, 0.885, 0.638, 0.932]</r>, {Economics}<r>[0.725, 0.885, 0.767, 0.948]</r>, and {Psychology}<r>[0.847, 0.885, 0.890, 0.950]</r>. The y-axis labels display the number of papers, ranging from {0}<r>[0.035, 0.870, 0.041, 0.884]</r> to {600}<r>[0.023, 0.032, 0.041, 0.046]</r>. The key is depicted by the sublegend, with the {2019 Papers} bar colour being <r>[0.10a674]</r> and the {2020 Papers} bar color being <r>[0.ac3838]</r>. I'll generate the table by reading the height of bars against the y-axis value for each field for both 2019 and 2020.\nBased on the above thought, the answer is:\nFields,2019,2020\nComputer Science,450,550\nMathematics,320,340\nPhysics,180,230\nChemistry,270,280\nBiology,360,390\nEconomics,530,570\nPsychology,250,220"
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "u3ARSQD0LGAGSrIh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic domain shows a decline in the number of research papers produced and why might this be according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, the {Psychology bars}<r>[0.826, 0.526, 0.869, 0.875][0.869, 0.568, 0.912, 0.875]</r> show a decline in the number of papers produced. If you look at the y-values of these two bars, the 2019 bar shows a y-value of 250 while the 2020 bar shows a y-value of 220. This means a decrease in the number of papers published in psychology from 2019 to 2020. The exact reason cannot be definitively concluded from the bar chart as it only provides quantitative data, not qualitative. However, one could hypothesize a potential shortfall in funding, author interest, or research opportunities within this field during 2020.\nBased on the above thought, the answer is:\nThe Psychology field exhibits a decline in academic papers from 2019 to 2020. While the bar chart's data does not clarify the reasons, possible explanations might include reduced funding, diminished author interest, or fewer research opportunities in this field in 2020."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "M2akY3szo0wUUjqu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic domain has seen the most significant number increase in papers published from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which academic sector has seen the most significant rise in published papers from 2019 to 2020, I have to compare the difference in the heights of the {2019 bars} and the {2020 bars} in each field. For example, the {bar for Computer Science papers in 2019}<r>[0.088, 0.247, 0.131, 0.875]</r> reaches a y-value of 450 while the {bar for Computer Science in 2020}<r>[0.131, 0.107, 0.174, 0.875]</r> reaches a y-value of 550, indicating an increase of 100 papers. I'll need to repeat this process for each field and find the field with the largest difference.\nBased on the above thought, the answer is:\nComputer Science"
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "PUVjn15o90lQFJFn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you please construct a CSV format table to present the reduced forest area and carbon emissions for 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on the bar chart and according to the y-axis labels of {reduced forest area}<r>[0.015, 0.263, 0.029, 0.735]</r> and {reduced carbon emissions}<r>[0.971, 0.181, 0.985, 0.816]</r>, I can see that the reduced forest area data is represented by the coordinates <r>[0.126, 0.357, 0.296, 0.923]</r> and <r>[0.551, 0.470, 0.721, 0.923]</r> and the reduced carbon emissions data is represented by the coordinates <r>[0.296, 0.357, 0.466, 0.923]</r> and <r>[0.721, 0.216, 0.891, 0.923]</r>. The bars' top endpoint represents the value so by aligning them with the axis tick labels, I can see that in 2020, the reduced forest area was 10000 hectares and the reduced carbon emissions was 200 million tonnes; in 2021, the reduced forest area was 8000 hectares and the reduced carbon emissions had increased to 250 million tonnes. So, by also taking into account the x-axis labels <r>[0.279, 0.942, 0.314, 0.970]; [0.704, 0.942, 0.739, 0.970]</r>, I can make the CSV structured data with these values.\nBased on the above thought, the answer is:\nYear,Reduced Forest Area (in hectares),Reduced Carbon Emissions (in million tonnes)\n2020,10000,200\n2021,8000,250"
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "jAX9i7lbav5oC6CO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the correlation between reduced forest area and carbon emissions based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I note that the {bars}<r>[0.126, 0.357, 0.296, 0.923]; [0.551, 0.470, 0.721, 0.923]; [0.296, 0.357, 0.466, 0.923]; [0.721, 0.216, 0.891, 0.923]</r> represent reduced forest area and reduced carbon emissions for 2020 and 2021. In both years, the reduced forest area and carbon emissions have moved in opposite directions. In 2020, 10,000 hectares of forest were reduced, accompanied by 200 million tonnes reduced carbon emissions. In 2021, though the reduced forest area decreased to 8,000 hectares, the reduced carbon emissions increased to 250 million tonnes. This suggests an inverse relationship exists between reduced forest area and carbon emissions.\nBased on the above thought, the answer is:\nThe chart suggests an inverse correlation between reduced forest area and carbon emissions. As the reduced forest area decreases, carbon emissions reductions increase, indicating that less deforestation may lead to higher reductions in carbon emissions."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "SPNJmXL37oMewK69",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in reduced carbon emissions from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart representing {reduced carbon emissions}<r>[0.971, 0.181, 0.985, 0.816]</r>, I can align the coordinates of the bars <r>[0.296, 0.357, 0.466, 0.923]; [0.721, 0.216, 0.891, 0.923]</r> and find the correspondent values from the y-axis for 2020 and 2021. In 2020, the carbon emissions was reduced by 200 million tonnes, and such reduction increased to 250 million tonnes in 2021. With these numbers, I can compute the percentage increase using the formula ((New Value - Original Value) / ABS(Original Value))*100%.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "71VKVsyX58RmmsSk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data on the chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, there are two categories: {Urban Living}[0.235, 0.942, 0.323, 0.970] and {Rural Living}[0.621, 0.942, 0.703, 0.970]. Each category has three bars: a bar representing the {Reduced Carbon Emissions}<r>[0.140, 0.100, 0.457, 0.128]</r>, a bar representing the population for {2025}<r>[0.140, 0.142, 0.175, 0.170]</r>, and a bar representing the population for {2030}<r>[0.140, 0.183, 0.175, 0.211]</r>. The bold blue bar for Urban Living is located at[r][0.126, -0.349, 0.279, 0.923]</r> and has a height suggesting a value of {450 million}<r>[0.0, 0.571, 0.1, 0.668]</r> for 2025 and {520 million}<r>[0.0, 0.646, 0.1, 0.753]</r> for 2030. Similar analysis for the Rural Living category suggests corresponding values of {400 million}<r>[0.0, 0.515, 0.1, 0.602]</r> and {430 million}<r>[0.0, 0.570, 0.1, 0.657]</r> respectively. Based on these observations, the CSV data table can be constructed.\nBased on the above thought, the answer is:\nScenario,2025,2030\nUrban Living,450,520\nRural Living,400,430"
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "jNtqVvAFHyFTEg2w",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the future population distribution from the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given bar chart, it is clear that both Urban Living and Rural Living populations are expected to increase. As seen by the bars, the {Urban Living 2025}<r>[0.126, -0.349, 0.279, 0.923]</r> bar height indicates a population of {450 million}<r>[0.0, 0.571, 0.1, 0.668]</r> which goes up to {520 million}<r>[0.0, 0.646, 0.1, 0.753]</r> for the {Urban Living 2030}<r>[0.279, -0.547, 0.432, 0.923]</r> bar. Similarly, the {Rural Living 2025}<r>[0.509, -0.208, 0.662, 0.923]</r> bar height indicates a population of {400 million}<r>[0.0, 0.515, 0.1, 0.602]</r> which is lower than Urban Living and rises to {430 million}<r>[0.0, 0.570, 0.1, 0.657]</r> in {Rural Living 2030}<r>[0.662, -0.293, 0.815, 0.923]</r>. Hence, it can be inferred that over time, a greater proportion of the population is likely to shift towards Urban Living scenarios as compared to Rural Living. This trend may reflect either increased urbanization or preference for urban lifestyle.\nBased on the above thought, the answer is:\nIt infers that by 2030, more people will prefer to live in urban areas than rural areas as the increase in the urban population is significantly higher than the increase in the rural population."
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "9OilksimQ93hpKiu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich living scenario experienced a greater increase in population from 2025 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will need to compare the population increase in both the {Urban Living}[0.235, 0.942, 0.323, 0.970] and {Rural Living}[0.621, 0.942, 0.703, 0.970] scenarios between 2025 and 2030. For Urban Living, the population for 2025 is represented by the bar at {coordinates}[0.126, -0.349, 0.279, 0.923], showing a population of displayed as {450 million}[0.0, 0.571, 0.1, 0.668], and the population for 2030 is represented by the bar at coordinates[0.279, -0.547, 0.432, 0.923], showing a population of displayed as {520 million}[0.0, 0.646, 0.1, 0.753]. This suggests an increase of 70 million. For Rural Living, the population for 2025 is represented by the bar at {coordinates}[0.509, -0.208, 0.662, 0.923], showing a population of displayed as {400 million}[0.0, 0.515, 0.1, 0.602], and the population for 2030 is represented by the bar at coordinates[0.662, -0.293, 0.815, 0.923], showing a population of displayed as {430 million}[0.0, 0.570, 0.1, 0.657]. This suggests an increase of 30 million. By comparing these two increase amounts, I can deduce that the Urban Living scenario experienced a greater increase in population.\nBased on the above thought, the answer is:\nUrban Living"
+            }
+        ],
+        "id_raw": 440
+    },
+    {
+        "id": "hvpxVcmFOFGOkKgk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the trade volume data for 2016 and 2021 and express it in the form of a table as per your analysis of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the trade volume for countries over the years 2016 and 2021. There are four countries represented with bars for these two years. From the labels and the lengths of bars, I can construct the data table. The first country, {Country 1}[0.177, 0.942, 0.245, 0.970], for the year 2016 has a bar with coordinates [0.134, 0.764, 0.211, 1.770] which corresponds to a trade volume of 95000. Similarly, for the year 2021, the bar with coordinates [0.211, 0.393, 0.288, 1.770] corresponds to the trade volume of 130000. By following the same process for {Country 2}[0.369, 0.942, 0.438, 0.970], {Country 3}[0.562, 0.942, 0.631, 0.970] and {Country 4}[0.755, 0.942, 0.823, 0.970] I'm able to extract the trade volume for these countries over the years 2016 and 2021.\nBased on the above thought, the answer is:\nCountry,2016 Trade Volume, 2021 Trade Volume\nCountry 1,95000,130000\nCountry 2,87000,122000\nCountry 3,123000,115000\nCountry 4,150000,144000"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "jsoIlQIc42ifLpx5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the graph, could you comment on the trade volume trend for Country 3 from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart visibly illustrates the trade volume for {Country 3}[0.562, 0.942, 0.631, 0.970] in the years 2016 and 2021. In 2016, {the bar for Country 3}[0.519, 0.467, 0.596, 1.770] is quite tall, indicating a high trade volume of 123000. Nevertheless, in 2021, we see a decrease in trade volume as {the bar for 2021}[0.596, 0.552, 0.674, 1.770] is shorter than the 2016 bar. The trade volume dipped to 115000 in 2021. This suggests that the trade volume for Country 3 has decreased over this period.\nBased on the above thought, the answer is:\nThe trade volume of Country 3 shows a decline from 2016 to 2021."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "TuCEYdMVHsJvCtQs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, which country showed the greatest increase in trade volume from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which country had the greatest increase in trade volume from 2016 to 2021, we need to compare the rise in the heights of bars for both years for each country. For {Country 1}[0.177, 0.942, 0.245, 0.970], the trade volumes represented by bars [{2016 bar} [0.134, 0.764, 0.211, 1.770]; {2021 bar} [0.211, 0.393, 0.288, 1.770]] show an increase from 95000 to 130000 which is 35000. Comparing this height difference to {Country 2}[0.369, 0.942, 0.438, 0.970], {Country 3}[0.562, 0.942, 0.631, 0.970], and {Country 4}[0.755, 0.942, 0.823, 0.970] would reveal that Country 1 has shown the greatest increase in trade volume from 2016 to 2021.\nBased on the above thought, the answer is:\nCountry 1"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "Iw8UA9ZnUU6lrDUj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help transform the chart data into a table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains two sets of data, one showing the number of 2020 participants and the other showing the number of 2021 participants for different sports activities, namely {\"Jogging\"}[0.166, 0.942, 0.218, 0.970], {\"Gym\"}[0.381, 0.942, 0.414, 0.970], {\"Yoga\"}[0.586, 0.942, 0.619, 0.970] and {\"Swimming\"}[0.772, 0.942, 0.844, 0.970]. According to the chart, we can approximate the participant data for each year based on the length of bars. For example, {Jogging's bar for 2020}[0.110, 0.393, 0.192, 1.982] ends at 150 and {the 2021 bar} [0.192, 0.287, 0.274, 1.982] ends at 160.\nBased on the above thought, the answer is:\nYear, Jogging, Gym, Yoga, Swimming\n2020, 150, 120, 145, 160\n2021, 160, 110, 155, 170"
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "48V19rhywWDnubve",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, which activity would you recommend for someone interested in joining a popular and growing trend in 2022 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would choose an activity that shows consistent growth from 2020 to 2021, demonstrating its increasing popularity over time. Looking at {\"Jogging\"}[0.166, 0.942, 0.218, 0.970], the number of participants increased from 150 in 2020 to 160 in 2021 according to the heights of the bars located at [0.110, 0.393, 0.192, 1.982] for 2020 and [0.192, 0.287, 0.274, 1.982] for 2021. Other activities like {\"Yoga\"} [0.586, 0.942, 0.619, 0.970] and {\"Swimming\"} [0.772, 0.942, 0.844, 0.970] also show growth. Among these, Yoga has the highest growth rate, going from 145 participants in 2020 to 155 in 2021 according to the bars at [0.521, 0.446, 0.603, 1.982] for 2020 and [0.603, 0.340, 0.685, 1.982] for 2021.\nBased on the above thought, the answer is:\nYoga. Because it not only is a popular activity but also the growth rate in participation from 2020 to 2021 was the highest compared to other activities, indicating its increasing trend."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "nt9IaP6BJAXCPnfa",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity experienced growth in the number of participants from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which activity experienced growth in number of participants, I need to compare the two bars under each activity. For instance, for Jogging, the 2020's bar and 2021's bar are located at [0.110, 0.393, 0.192, 1.982] and [0.192, 0.287, 0.274, 1.982] respectively, having heights of approximately 150 and 160. Repeating this process for Gym, Yoga, and Swimming, we can conclude that Jogging, Yoga, and Swimming all experienced growth.\nBased on the above thought, the answer is:\nJogging, Yoga, Swimming"
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "EdLqC68wC4Q4Xh1R",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data of the chart into a CSV format, we'll start with the company names located in {the x-axis}[{\"Company A\"}[0.127, 0.952, 0.194, 0.975]; {\"Company B\"}[0.297, 0.952, 0.364, 0.975]; {\"Company C\"}[0.467, 0.952, 0.533, 0.975]; {\"Company D\"}[0.636, 0.952, 0.704, 0.975]; {\"Company E\"}[0.807, 0.952, 0.873, 0.975]]. We then refer to the bars which represent their market shares for the years 2020 and 2021. The {red bars}[{\"Company A 2020\"}[0.092, 0.436, 0.160, 0.935]; {\"Company B 2020\"}[0.262, 0.312, 0.330, 0.935]; {\"Company C 2020\"}[0.432, 0.187, 0.500, 0.935]; {\"Company D 2020\"}[0.602, 0.561, 0.670, 0.935]; {\"Company E 2020\"}[0.772, 0.686, 0.840, 0.935]] represent their market shares in 2020 while the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] signify their market shares in 2021. By estimating their height against the y-axis, which marks the percentages from {0%}[0.029, 0.926, 0.036, 0.950] to {35%}[0.029, 0.053, 0.043, 0.076], we can arrive at the values.\nBased on the above thought, the answer is:\nCompany,Market Share 2020,Market Share 2021\nCompany A,20,23\nCompany B,25,26\nCompany C,30,28\nCompany D,15,18\nCompany E,10,15"
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "Of440cS6xX5faWFC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company do you think has made the most progress from 2020 to 2021? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the company that has made the most progress from 2020 to 2021, I need to compare the increases in market share for each company from 2020 to 2021. These can be found by comparing the difference in height between the {red bars}[{\"Company A 2020\"}[0.092, 0.436, 0.160, 0.935]; {\"Company B 2020\"}[0.262, 0.312, 0.330, 0.935]; {\"Company C 2020\"}[0.432, 0.187, 0.500, 0.935]; {\"Company D 2020\"}[0.602, 0.561, 0.670, 0.935]; {\"Company E 2020\"}[0.772, 0.686, 0.840, 0.935]] for 2020 and the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] for 2021. Notably, {Company E}[0.807, 0.952, 0.873, 0.975] appears to have made the largest gain, moving from approximately {10%}[0.772, 0.686, 0.840, 0.935] in 2020 to around {15%}[0.840, 0.561, 0.908, 0.935] in 2021. This a percentage increase of around 5%, which is the highest among all companies.\nBased on the above thought, the answer is:\nCompany E. It experienced a 5% increase in the market share from 2020 to 2021, which is the largest gain among all other companies."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "ARxk7qabLQUyW8oJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2021, which company has the highest market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the company with the highest market share, I have to check the height of the {blue bars}[{\"Company A 2021\"}[0.160, 0.362, 0.228, 0.935]; {\"Company B 2021\"}[0.330, 0.287, 0.398, 0.935]; {\"Company C 2021\"}[0.500, 0.237, 0.568, 0.935]; {\"Company D 2021\"}[0.670, 0.486, 0.738, 0.935]; {\"Company E 2021\"}[0.840, 0.561, 0.908, 0.935]] representing the 2021 market share for each company. The bar that reaches the highest on the y-axis scale marks the company with the greatest market share. Among all the bars, it turns out {the blue bar for \"Company C\"}[0.500, 0.237, 0.568, 0.935] is the tallest, so Company C has the highest market share in 2021.\nBased on the above thought, the answer is:\nCompany C"
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "Uzt2OsFFnAibHbVL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the bar chart, can you generate a representation of the data in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents data on the popularity of two fashion trends: {Gothic Fashion}[0.226, 0.942, 0.371, 0.970] and {Streetwear Fashion}[0.606, 0.942, 0.797, 0.970]. Each trend is represented by two bars denoting the popularity in [2020][0.111, 0.858, 0.338, 0.909] shown by the {left bars} [{Gothic Fashion in 2020}[0.137, 0.655, 0.298, 1.347]; {Streetwear Fashion in 2020}[0.540, 0.456, 0.702, 1.347]], and [2021][0.661, 0.858, 0.889, 0.909] shown by the {right bars} [{Gothic Fashion in 2021}[0.298, 0.719, 0.460, 1.347]; {Streetwear Fashion in 2021}[0.702, 0.376, 0.863, 1.347]]. The height of the bars represents the popularity metric, according to the scale on the y-axis ranging from {100}[0.049, 0.912, 0.087, 0.940] to {300}[0.050, 0.064, 0.087, 0.092]. From the visual indication, Gothic Fashion had a popularity metric of approximately 163 in 2020, going down to approximately 148 in 2021. On the other hand, Streetwear Fashion's popularity increased from approximately 210 in 2020 to around 229 in 2021. Let's compile the data into a CSV table.\nBased on the above thought, the answer is:\nFashion Style,2020 Popularity,2021 Popularity\nGothic Fashion,163,148\nStreetwear Fashion,210,229"
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "jFhzVUC77vJeZJ32",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data provided in the chart, which fashion style was more prevalent in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two bars on the chart, both representing the years of 2021. One bar {for Gothic Fashion}[0.298, 0.719, 0.460, 1.347] and a second one {for Streetwear Fashion}[0.702, 0.376, 0.863, 1.347]. Based on the y-axis metric and the bars' heights, it is evident that the Streetwear Fashion bar for 2021 is higher than the Gothic Fashion bar for 2021, meaning Streetwear Fashion was more popular in 2021.\nBased on the above thought, the answer is:\nStreetwear Fashion was more prevalent in 2021."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "YXUo2N1aq2fNoV3H",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent did the popularity of Streetwear Fashion increase from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar corresponding to Streetwear Fashion's popularity in {2020}[0.540, 0.456, 0.702, 1.347] visually indicates a metric of approximately 210. For {2021}[0.702, 0.376, 0.863, 1.347], the metric is around 229.This represents an increase. To calculate the percentage increase, I would subtract the 2020 value from the 2021 value, and then divide that difference by the 2020 value. After that, I multiply by 100 to get the percentage.\nBased on the above thought, the answer is:\nApproximately 9.05%"
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "xEIXNrzsCAJbjtb6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data of Technological Innovations of 2020 and 2021 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the x-axis, I can see {four countries' labels}<r>[\"Country 1\", \"Country 2\", \"Country 3\", \"Country 4\"]</r> spaced evenly along the bottom of the chart. On the y-axis, I see that the measurements of innovations are in increments of 10 from 50 to 110. Inside the graph, there are two sets of bar plots: one set represents {2020 Innovations}<r>[0.122, 0.100, 0.242, 0.128]</r> with color #8b4513 and one set represents {2021 Innovations}<r>[0.797, 0.100, 0.917, 0.128]</r> with color #20b2aa. To convert the data into a CSV format, I need to extract the height of each bar for both 2020 and 2021 Innovations across all the countries. For example, in Country 1, the 2020 Innovation is measured by the height of the bar plotted at [0.110, 0.569, 0.175, 1.629], which corresponds to a value of 75, and the 2021 Innovation is measured by the bar plotted at [0.175, 0.456, 0.240, 1.629], which corresponds to a value of 83.\nBased on the above thought, the answer is:\nCountry,2020 Innovations,2021 Innovations\nCountry 1,75,83\nCountry 2,95,83\nCountry 3,92,105\nCountry 4,70,85"
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "hr881BlFRayIcCSF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which country seems to be leading in technological innovations in the year 2021 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart of 2021 Innovations, it can be seen that the bar of {Country 3}<r>[0.608, 0.145, 0.673, 1.629]</r> is the highest among the four countries, indicating that Country 3 has the highest number of innovations in the year 2021. The height of this bar corresponds to about 105 Intellectual Property units (IPU), while the other countries' bars represent a lesser count of IPUs. Therefore, based on the 2021 Innovations chart, Country 3 appears to be leading in technological innovations.\nBased on the above thought, the answer is:\nCountry 3 leads in technological innovations in 2021 because it has the highest number of Intellectual Property Units (IPUs)."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "7GxCVOtC39kUSIWi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar charts, in which country did the innovation increase the most from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the country with the greatest increase in innovation, I need to subtract the height of each bar representing 2020 Innovations from the height of each corresponding bar representing 2021 Innovations. For example, in Country 1, the height of the bar for 2020 Innovations is {approximately 75}<r>[0.110, 0.569, 0.175, 1.629]</r>, and for 2021 Innovations is {approximately 83}<r>[0.175, 0.456, 0.240, 1.629]</r>, so the increase is 8; similarly, for Country 2 the increase is -12; for Country 3, the increase is 13; and for Country 4, the increase is 15. Therefore, the country with the greatest increase in innovations from 2020 to 2021 is Country 4.\nBased on the above thought, the answer is:\nCountry 4."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "tl9X17mAPFM0CRwY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate the CSV table for the bar chart that depicts the innovation levels for Creativity and Design in 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. To transform the bar chart into a CSV table, I need to encode the bar heights that represent innovation levels of each product into numerical data. From {the legend}[0.881, 0.809, 0.985, 0.935], the red bars represent Creativity in 2020, the blue bars represent Design in 2020, the yellow bars denote Creativity in 2021, and the orange bars denote Design in 2021. For each product, there are four bars showing the levels of Creativity and Design across two years. The coordinates of the bars will aid in the extraction process, such as {bar examining Creativity 2020 for 'Product A'}[0.126, 0.588, 0.161, 1.836] is approximately at the level of 70.\nBased on the above thought, the answer is:\nProduct,Creativity 2020,Design 2020,Creativity 2021,Design 2021\nProduct A,70,65,90,88\nProduct B,85,78,90,80\nProduct C,76,80,95,93\nProduct D,91,69,98,97\nProduct E,92,95,100,96"
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "m1dx0c8QXuNICQb1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product consistently had the highest innovation levels for both Creativity and Design in the span of two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires looking at the overall performance in both Creativity and Design of all products. Examining the bars of each product and comparing the heights, we can identify the product that has the highest scores in both categories for both years. For instance, {the bars for 'Product A'}[{red_Abar}[0.126, 0.588, 0.161, 1.836]; {blue_Abar}[0.090, 0.677, 0.126, 1.836]; {yellow_Abar}[0.161, 0.232, 0.197, 1.836]; {orange_Abar}[0.197, 0.267, 0.233, 1.836]] show moderate heights across categories. Following the same process for all products, it is found that {the bars for 'Product E'}[{red_Ebar}[0.840, 0.196, 0.876, 1.836]; {blue_Ebar}[0.804, 0.142, 0.840, 1.836]; {yellow_Ebar}[0.876, 0.053, 0.911, 1.836]; {orange_Ebar}[0.911, 0.125, 0.947, 1.836]] consistently have the tallest heights, indicating the highest innovation levels throughout the two years.\nBased on the above thought, the answer is:\nProduct E, as it consistently had the highest innovation level in both Creativity and Design over the two years."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "XrA4AwukF3VGU5S6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product demonstrated the greatest improvement in creativity scores from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the product with the greatest improvement in creativity scores, we need to compare the height changes of the red and yellow bars under each product. Taking examples, {the bars for 'Product A'}<{red_Ebar}[0.126, 0.588, 0.161, 1.836]; {yellow_Ebar}[0.161, 0.232, 0.197, 1.836]> show a score difference of approximate 20. By doing the same for 'Product B', 'Product C', 'Product D' and 'Product E', I find that {the Creativity improvement for Product D}[0.661, 0.214, 0.697, 1.836; 0.697, 0.089, 0.733, 1.836] has the largest difference, from 91 to 98.\nBased on the above thought, the answer is:\nProduct D"
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "cs1HLy3sgMt6yaIP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from the observed chart and present it as a structured CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows two bar plots in each of three categories. For each category, we have two bars for 2019 and 2020 respectively. The categories are 'Deep Learning' and 'Machine Learning' as shown on the {x-axis labels}<r>[0.200, 0.942, 0.301, 0.970]</r> and <r>[0.605, 0.942, 0.728, 0.970]</r>. The number of research papers published and number of active developers in 2019 and 2020 are demonstrated by the bars of different colors and patterns. For 'Deep Learning' in 2019, the number of research papers published is around 50000 represented by {the bar}<r>[0.126, 0.393, 0.209, 0.923]</r> and the number of active developers is around 20000 represented by {the bar}<r>[0.292, -0.531, 0.375, 0.923]</r>. The other years and categories can be analyzed similarly. Thus, it provides us the below structured table.\nBased on the above thought, the answer is:\nCategory,Year,Number of Research Papers Published,Number of Active Developers\nDeep Learning,2019,50000,20000\nDeep Learning,2020,75000,30000\nMachine Learning,2019,60000,22000\nMachine Learning,2020,65000,24000"
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "4UiOL4IVzDUmBww7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field is attracting more active developers and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows two categories - 'Deep Learning' and 'Machine Learning', with the number of active developers represented by {the bars}<r>[0.292, -0.531, 0.375, 0.923]</r>,<r>[0.375, -0.652, 0.458, 0.923]</r>,<r>[0.708, 0.390, 0.791, 0.923]</r> and <r>[0.791, 0.341, 0.874, 0.923]</r> respectively. Comparing the heights of the bars for both 2019 and 2020, we can see that 'Deep Learning' has attracted more developers. Although 'Machine Learning' also shows growth in the number of active developers, the growth in 'Deep Learning' is more substantial. This trend is likely due to the increasing popularity and applications of deep learning in recent years.\nBased on the above thought, the answer is:\nDeep Learning. The number of active developers in this area has seen a significant increase from 20000 to 30000 between 2019 and 2020."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "50X6pwqudCACRWrr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which category saw an increase in the number of research papers published from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart presents the number of research papers published for 'Deep Learning' and 'Machine Learning' in 2019 and 2020, represented by {patterned bars}<r>[0.126, 0.393, 0.209, 0.923]</r>,<r>[0.542, 0.711, 0.625, 0.923]</r>,<r>[0.209, -0.652, 0.292, 0.923]</r> and <r>[0.625, 0.605, 0.708, 0.923]</r> respectively. The height of the bars of each category tells the data of respective years. Comparing the bar heights, we can see that both 'Deep Learning' and 'Machine Learning' witnessed an increase in the number of research papers published, with 'Deep Learning' having a substantial increase.\nBased on the above thought, the answer is:\nBoth 'Deep Learning' and 'Machine Learning'."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "3U9eMyPbvisfwidL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the chart you are observing into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the dual bar chart at hand, and the color guides as well as accompanying labels, I can see that the x-axis dedicates individual segments to each human ancestral era such as {Prehistoric}<r>[0.170, 0.942, 0.243, 0.970]</r>, {Ancient}<r>[0.376, 0.942, 0.428, 0.970]</r>, {Middle Ages}<r>[0.556, 0.942, 0.640, 0.970]</r>, and {Modern}<r>[0.767, 0.942, 0.820, 0.970]</r> periods. It seems that the y-axis, on the other hand, showcases numerical data ranging from {-1200}<r>[0.035, 0.989, 0.081, 1.017]</r> to {0}<r>[0.073, 0.064, 0.081, 0.092]</r>. The plot features two types of bars in each era. As per the legends, one could infer the lighter red colored bars represent the 'Inventions' like in the {Prehistoric}<r>[0.128, 0.075, 0.206, 0.113]</r> and darker blue bars symbolize the level of 'Cultural Expansion' that took place during that era as can be seen for {Modern}<r>[0.794, 0.075, 0.872, 0.807]</r>. Given their colors and their relation to the y-axis, the number of major inventions and the cultural expansion during each era can be recognized. After analyzing the chart in detail, the data can be structured into a CSV format.\nBased on the above thought, the answer is:\nEra, Inventions, Cultural Expansion\nPrehistoric, -50, 0\nAncient, -100, -25\nMiddle Ages, -500, -400\nModern, -1000, -950"
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "elPCVblgssxv7pOO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which Era would you say witnessed the most rapidly accelerating decline in the Civilization Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "If we understand the Civilization Development as being represented collectively by the number of major Inventions and the rate of Cultural Expansion, then we would need to focus on both colored bars under each Era. Visually, the bars representing both cultural expansion and Inventions are longest for Modern era {Inventions}<r>[0.715, 0.075, 0.794, 0.845]</r>, {Cultural Expansion}<r>[0.794, 0.075, 0.872, 0.807]</r> and shortest for the Prehistoric era {Inventions}<r>[0.128, 0.075, 0.206, 0.113]</r>, {Cultural Expansion}<r>[0.206, 0.075, 0.285, 0.075]<r>. Looking at the bars under each category, it seems that the gap between Inventions and Cultural Expansion became increasingly wider the more recent the era, indicating a rapid decline in the development of civilization.\nBased on the above thought, the answer is:\nThe Modern era, because although the quantity of inventions was highest, it shows the most significant gap with Cultural Expansion, indicating an imbalance in development."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "s8NA8NXpL8u0mchq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which era was the societal Cultural Expansion the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lowest level of Cultural Expansion signifies the smallest bar in the 'Cultural Expansion' dataset. By comparing the different bars of the 'Cultural Expansion' dataset {Prehistoric}<r>[0.206, 0.075, 0.285, 0.075]</r>, {Ancient}<r>[0.402, 0.075, 0.480, 0.094]</r>, {Middle Ages}<r>[0.598, 0.075, 0.676, 0.383]</r>, and {Modern}<r>[0.794, 0.075, 0.872, 0.807]</r>, I see that the prehistoric era has no cultural expansion, indicating that the cultural expansion is lowest during that era.\nBased on the above thought, the answer is:\nPrehistoric"
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "XqBaPXJ8hrDLW4Fo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data presented in the double bar chart into a CSV file?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is divided into two main sections, with each section representing the sales performance of the five companies for a specific year. Each company is reported for {2020}<r>[0.107, 0.858, 0.233, 0.909]</r> and {2021}<r>[0.767, 0.858, 0.893, 0.909]</r>. The sales figures for each company can be deduced from its corresponding bar's height in each section. For instance, the bar for {Company A in 2020}<r>[0.136, 0.075, 0.197, 0.680]</r> extends approximately to the \"-25000\" mark on the left y-axis, suggesting it sold 25,000 units. Similarly, the bar for {Company A in 2021}<r>[0.197, 0.075, 0.257, 0.753]</r> extends approximately to the \"-28000\" placeholder, indicating that the company sold 28,000 units. This process is repeated for each company, allowing me to compile the data into a CSV file.\nBased on the above thought, the answer is:\nCompany,2020 Sales,2021 Sales\nCompany A,25000,28000\nCompany B,30000,26000\nCompany C,21000,29000\nCompany D,28000,30000\nCompany E,26000,22000"
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "ytGoHw8ZMfzANqkH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart's perspective, which company is more resilient in terms of sales against the market trend between the years 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "A company's resiliency can be gauged by examining its ability to increase sales or maintain stability amid changing market conditions. Observing the chart, I can see bars for each company showing sales for {2020}<r>[0.107, 0.858, 0.233, 0.909]</r> and {2021}<r>[0.767, 0.858, 0.893, 0.909]</r>. Most companies experienced either a decline or a slight increase in sales, except for {Company C}<r>[0.439, 0.075, 0.500, 0.583]</r><r>[0.500, 0.075, 0.561, 0.777]</r>. Company C showed a noticeable increase in sales from 21,000 units to approximately 29,000 units, resisting the negative trend and demonstrating a high level of resilience.\nBased on the above thought, the answer is:\nCompany C. Its sales trend shows resilience as it successfully increased its sales amidst changing market trends, showing an increase in sales from 21,000 units in 2020 to approximately 29,000 in 2021."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "JgEgM3fFC7UZPCvo",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company showed the most significant increase in sales from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing each company's bar heights for 2020 and 2021, I can calculate the change in sales. For {Company A}<r>[0.136, 0.075, 0.197, 0.680]</r><r>[0.197, 0.075, 0.257, 0.753]</r>, the increase is approximately 3,000 units. Using the same process for {Company B}<r>[0.288, 0.075, 0.348, 0.801]</r><r>[0.348, 0.075, 0.409, 0.705]</r>, {Company C}<r>[0.439, 0.075, 0.500, 0.583]</r><r>[0.500, 0.075, 0.561, 0.777]</r>, {Company D}<r>[0.591, 0.075, 0.652, 0.753]</r><r>[0.652, 0.075, 0.712, 0.801]</r>, and {Company E}<r>[0.743, 0.075, 0.803, 0.705]</r><r>[0.803, 0.075, 0.864, 0.608]</r>, I conclude that Company C has the most significant growth in sales, approximately 8,000 units.\nBased on the above thought, the answer is:\nCompany C."
+            }
+        ],
+        "id_raw": 449
+    },
+    {
+        "id": "TemRVsVKWoWuX4iU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to transcribe this bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data to a table, I can reference the 'Research Output in Academic Fields' title<r>[0.395, 0.019, 0.644, 0.041]</r>, the x-axis labels<r>[\"Computer Science\", \"Physics\", \"Chemistry\", \"Biology\", \"Mathematics\", \"Engineering\", \"Psychology\", \"Sociology\", \"Literature\", \"Musicology\"]</r>, and the y-axis labels<r>[\"0\", \"10\", \"20\", \"30\", \"40\", \"50\", \"60\", \"70\"]</r>. These labels correspond to academic fields and research output percentages respectively. Each bar in the chart represents an academic field, with the height representing the research output percentage. For example, the 'Computer Science' bar<r>[0.094, 0.359, 0.164, 0.927]</r> extends to the y coordinate of 0.359, corresponding to a research output of 45.2% as indicated by the text annotation above the bar<r>[0.111, 0.329, 0.147, 0.346]</r>. I will repeat this process across all the bars to transcribe the data into a CSV table.\nBased on the above thought, the answer is:\nField, Percentage\nComputer Science, 45.2\nPhysics, 39.8\nChemistry, 35.1\nBiology, 42.7\nMathematics, 52.9\nEngineering, 59.3\nPsychology, 49.4\nSociology, 32.6\nLiterature, 25.9\nMusicology, 18.2"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "DIiFW137Mq9F5YwS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you evaluate the overall research output distribution among these academic fields based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the research output distribution, I will consider the proportions expressed by the bar heights. There's a broad spread, with the highest output ranging at 59.3% for the field 'Engineering'<r>[0.528, 0.181, 0.598, 0.927]</r>, and the lowest at 18.2% for 'Musicology'<r>[0.876, 0.698, 0.945, 0.927]</r>. It appears noticeable that most of the fields anchor between 30% and 50%, signifying balanced research output. However, outputs from 'Engineering' and 'Musicology' deviate significantly from the center, suggesting that these fields are relatively specialized or limited in scope in the context of academic research. This results in uneven distribution, leaning the average towards fields sustaining higher research outputs.\nBased on the above thought, the answer is:\nMost academic fields have moderate research output between 30% and 50%, with a few outliers on either end. Therefore, the overall distribution is moderately balanced, and the outliers cause a slight skewness."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "lvHt2ZgijfXlVSGF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic field is depicted to have the lowest research output?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to compare the heights of all the bars representing the fields. For instance, the 'Computer Science' bar<r>[0.094, 0.359, 0.164, 0.927]</r> corresponds to the first field and has a research output of 45.2% whereas the 'Musicology' bar<r>[0.876, 0.698, 0.945, 0.927]</r> representing the last field, has a research output of 18.2%. Extending this comparison to the other bars, I can detect that the 'Musicology' field has the lowest height signifying the minimum percentage among all the bars.\nBased on the above thought, the answer is:\nMusicology"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "ZJN0FZGWPob1welR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract data from the bar chart showing \"<r>Usage of Materials in Scientific Research & Technological Innovation</r>\" and create a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table, I need to consider the x-axis labels which represent the materials and the y-axis labels that represent the usage percentages according to the chart's title \"<r>[0.241, 0.025, 0.806, 0.055]</r>\". There are six bars, each for a different material where {Copper}<r>[0.137, 0.919, 0.187, 0.942]</r> has the highest bar at 45%, and {Iridium}<r>[0.861, 0.919, 0.909, 0.942]</r> with the shortest bar at 2%. There are labels on top of each bar, e.g., {45% of Copper}<r>[0.146, 0.123, 0.177, 0.146]</r> and {2% of Iridium}<r>[0.874, 0.846, 0.896, 0.869]</r>. Thus the CSV table will look something like this:\nBased on the above thought, the answer is:\nMaterial,Usage (%)\nCopper,45\nAluminium,35\nSilver,10\nGold,5\nPlatinum,3\nIridium,2"
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "YiCIisebse4ZHIYq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs Aluminium used more widely than Gold in Scientific Research and Technological Innovation according to the chart? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "On the chart, we can see that '{Aluminium}'<r>[0.269, 0.919, 0.344, 0.942]</r> and '{Gold}'<r>[0.580, 0.919, 0.612, 0.942]</r> are labels on the x-axis. Comparing the bar heights of the two, it's clear {the bar for Aluminium}<r>[0.249, 0.314, 0.364, 0.903]</r> is much taller than {the bar for Gold}<r>[0.538, 0.819, 0.654, 0.903]</r>, indicating a greater usage percentage for Aluminium at '35%', compared to Gold's '5%'. Based on these findings, it's clear that Aluminium is used more widely than Gold in scientific research and technological innovation.\nBased on the above thought, the answer is:\nYes, Aluminium is used more widely than Gold. The bar representing Aluminium stands at 35% usage while the bar for Gold indicates a usage of only 5%."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "pUbNI7H58ZQBSfwW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, what's the percentage difference in usage between Copper and Aluminium?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar graph represents the percentage usage of different materials. The '{Copper}'<r>[0.104, 0.146, 0.220, 0.903]</r> bar corresponds to 45% usage, labeled at '<r>45%</r>'. The '{Aluminium}'<r>[0.249, 0.314, 0.364, 0.903]</r> bar corresponds to 35% usage, labeled at '<r>35%</r>'. The answer will be the difference between the usage of Copper and Aluminium.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "i5KzR52fvhJrEHjt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart information into a csv formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that the chart has been organized into several categories. The x-axis represents six separate years from {2016}<r>[0.118, 0.919, 0.153, 0.942]</r> to {2021}<r>[0.902, 0.919, 0.937, 0.942]</r>. The height of the bars represents the percentage of companies adopting each trend; this is shown on the y-axis with values ranging from {0}<r>[0.052, 0.894, 0.061, 0.917]</r> to {180}<r>[0.035, 0.004, 0.061, 0.027]</r>. A glance at the legend located at <r>[0.078, 0.074, 0.262, 0.185]</r> shows that there are three bars, each color represents a different trend: {blue}<r>[0.122, 0.083, 0.237, 0.106]</r> represents 'Remote Working', {orange}<r>[0.122, 0.118, 0.190, 0.141]</r> represents 'Upskilling', and {green}<r>[0.122, 0.153, 0.256, 0.176]</r> represents 'Employee Wellness'. I can fetch the height of each bar to get the respective percentages, as they relate to each trend, for each year.\nBased on the above thought, the answer is:\nYear, Remote Working Trend, Upskilling Trend, Employee Wellness Trend\n2016, 10.5, 18.3, 8.7\n2017, 15.2, 23.5, 12.3\n2018, 23.7, 29.8, 20.1\n2019, 32.8, 36.5, 28.7\n2020, 50.3, 45.9, 39.3\n2021, 63.7, 52.3, 54.2"
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "ZUMWiTGeq1OlqCil",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you deduce any corporate shifts over the six-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, there are a few notable observations that can be made. Initially, examining the trend for {remote working}<r>[0.122, 0.083, 0.237, 0.106]</r> you can see there's a steady increase year by year, indicating a gradual adoption of this policy by more companies. Then looking at the trend for {upskilling}<r>[0.122, 0.118, 0.190, 0.141]</r> there is also a steady increase, indicating more companies are investing resources to upskill their employees. Lastly, the trend for {employee wellness}<r>[0.122, 0.153, 0.256, 0.176]</r> shows a similar steady rise with a sharp increase between 2019 and 2020 which could be due to more companies giving emphasis on employee wellbeing owing to the global pandemic. This conclusion is corroborated by the sharp rise in remote working trends around the same period.\nBased on the above thought, the answer is:\nYes, over this six-year period there has been a discernible shift towards more remote working, upskilling of staff and an increased emphasis on employee wellness. Notably, the trends for remote working and employee wellness saw a significant increase in 2020, likely due to the impact of the global pandemic."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "m3lPy7MkVoyEe9uS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year did the Employee Wellness trend saw the highest increase in adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest increase in Employee Wellness adoption, I need to compare the heights of the {green bars}<r>Employee Wellness</r>, which represents the Employee Wellness Trend. I can see that between the years 2019 and 2020 there is a significant increase in the height of the Employee Wellness bar from <r>[0.583, 0.418, 0.630, 0.560]</r> to <r>[0.740, 0.233, 0.787, 0.427]</r>. This marks the largest increase among the years compared.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "uEbbpZRQ3kETh2zd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title which shows {\"Artificial Intelligence and Automation Trends\"}[0.337, 0.025, 0.705, 0.055] and {the x-axis tick labels} with the topics listed as {\"Deep Learning\"}[0.096, 0.919, 0.197, 0.942], {\"Robotic Process Automation\"}[0.156, 0.919, 0.350, 0.942], {\"Neural Networks\"}[0.303, 0.919, 0.418, 0.942], {\"Machine Learning\"}[0.406, 0.919, 0.529, 0.942], {\"Natural Language Processing\"}[0.473, 0.919, 0.676, 0.942], {\"AI Ethics\"}[0.651, 0.919, 0.711, 0.942], {\"Cognitive Computing\"}[0.715, 0.919, 0.861, 0.942] and {\"Automated Reasoning\"}[0.818, 0.919, 0.972, 0.942], it can be seen that each label represents a unique AI topic. The y-axis tick labels show the range from 0% to 60%, representing the prevalence of each AI topic. The individual bars and their height give the specific percentage for each topic, for example, the bar for {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] reaches around the 59.2% mark on the y-axis, therefore the value for the Deep Learning topic is 59.2%.\nBased on the above thought, the answer is:\nAI_Topics,Percentages\nDeep Learning,59.2\nRobotic Process Automation,35.7\nNeural Networks,29.1\nMachine Learning,22.4\nNatural Language Processing,11.8\nAI Ethics,6.0\nCognitive Computing,3.5\nAutomated Reasoning,1.2"
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "gsgp14MtOK0zabZt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the chart, could you discuss the trend of Artificial Intelligence and Automation's adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bars in the chart, the distribution of the adoption of AI and Automation topics is distinctive. The topic with the highest adoption is {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] reaching almost 60%, followed by {\"Robotic Process Automation\"}[0.211, 0.403, 0.296, 0.903] which is around 35.7%, indicating that these two areas are leading the trend in AI and Automation. Other areas, including {\"Neural Networks\"}[0.318, 0.495, 0.403, 0.903], {\"Machine Learning\"}[0.424, 0.589, 0.510, 0.903], and {\"Natural Language Processing\"}[0.531, 0.738, 0.617, 0.903], also have a significant percentage of adoption. However, topics like {\"AI Ethics\"}[0.638, 0.819, 0.724, 0.903], {\"Cognitive Computing\"}[0.745, 0.854, 0.831, 0.903], and particularly {\"Automated Reasoning\"}[0.852, 0.886, 0.938, 0.903], are considerably less prevalent. This distribution could suggest a stronger focus and investment in applications related to deep learning and robotic process automation in the field of AI.\nBased on the above thought, the answer is:\nThe trend highlights a high focus on Deep Learning and Robotic Process Automation, with significant attention given to Neural Networks, Machine Learning, and Natural Language Processing. However, AI Ethics, Cognitive Computing, and particularly Automated Reasoning, while still important, see less adoption, indicating they may be less mature or less invested in at the current stage."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "BSI508Z1i78XnlGJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the percentage gap between the most adopted AI topic and the least adopted?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the chart visualization, for finding the percentage gap between the most adopted and least adopted AI topic, we need to find the highest and the lowest value on the y-axis in terms of percentages. The bar representing the adoption of {\"Deep Learning\"}[0.104, 0.073, 0.189, 0.903] has the maximum length reaching almost 60%, so it's the most adopted AI topic. The bar for {\"Automated Reasoning\"}[0.852, 0.886, 0.938, 0.903] is the smallest, reaching only about 1.2% on the y-axis, so it is the least adopted AI topic. The difference between the two is the percentage gap between the most adopted and least adopted AI topic.\nBased on the above thought, the answer is:\n58%"
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "crhgpdQkRQpol4oO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to convert the data in the bar chart into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart available, {the x-axis}[0.488, 0.952, 0.550, 0.975] represents different charity names and {the y-axis}[0.015, 0.400, 0.029, 0.565] indicates the corresponding donation percentages, both ranging from 0:00 to 70:00. The bars under each charity name reflect the corresponding donation percentages. For instance, the donation percentage of {Red Cross}[0.152, 0.903] is inferred from the height of {the bar}[0.103, 0.182, 0.201, 0.903] at around 60% position, confirmed by {the label \u201c60.0%\u201d}[0.130, 0.159, 0.174, 0.182]. This can be repeated for each charity name to gather our data for structuring them into a table.\nBased on the above thought, the answer is:\nCharity Name, Donations (%)\nRed Cross, 60\nHabitat for Humanity, 50\nUNICEF, 30\nSave the Children, 25\nDoctors Without Borders, 45\nAmnesty International, 10\nMake-A-Wish Foundation, 5"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "VeGosmt2RfC6SBIn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, how can charities increase their donation amounts?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a deeper understanding of the chart itself. Maintaining high-level visibility and a positive image can contribute to attracting more donations. Charities like the {Red Cross}[0.152, 0.903] that enjoy high visibility and have a long history of humanitarian work, tend to get the most donations, as mentioned in {the bar}[0.103, 0.182, 0.201, 0.903]. However, activities of charities like the {Make-A-Wish Foundation}[0.869, 0.819, 0.904, 0.843], although meaningful and appreciated, may not appeal to a large number of donors as indicated by the lowest donation percentage shown by {the bar}[0.837, 0.843, 0.935, 0.903]. Therefore, broadening the impact of their work and improving their public visibility and transparency can help charities increase their donation amounts.\nBased on the above thought, the answer is:\nCharities can increase their donation amounts by improving their public visibility, demonstrating the impact of their work, maintaining transparency, and aligning their mission and activities with the interests of potential donors."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "cRXWP6XmgDJavY0P",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich charity received the least proportion of donations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the charity with the least percentage of donations, I must firstly determine the bar with the lowest height, which is {the bar}[0.837, 0.843, 0.935, 0.903] corresponding to {Make-A-Wish Foundation}[0.869, 0.819, 0.904, 0.843]. The height matched with the y-axis, and also confirmed by the text label on the bar, indicating a value of 5%, which is the lowest among all charities.\nBased on the above thought, the answer is:\nMake-A-Wish Foundation"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "8BUyXHeFVgREOzxm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the adoption rate data from the chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Drawing from the chart's title at the top {\"Big Data Integration Overview\"}[0.398, 0.025, 0.649, 0.055], we see that this chart compares the adoption rates for various big data integration tools. The x-axis labels suggest different types of integration tools listed as {MuleSoft}[0.123, 0.919, 0.184, 0.942], {Talend DI}[0.245, 0.919, 0.309, 0.942], {Dell Boomi}[0.362, 0.919, 0.438, 0.942], {IBM Infosphere}[0.472, 0.919, 0.575, 0.942], {Informatica PowerCenter}[0.561, 0.919, 0.733, 0.942], {Oracle Data Integrator}[0.692, 0.919, 0.849, 0.942], {Jitterbit}[0.868, 0.919, 0.919, 0.942]. The y-axis, with labels ranging from {0}[0.043, 0.894, 0.052, 0.917] to {40}[0.035, 0.053, 0.052, 0.076], represents the adoption rate in percentage. Using the length of the bars from the bottom of the y-axis to where they stop, we can determine their corresponding adoption rate. For instance, the bar at {MuleSoft}[0.104, 0.405, 0.203, 0.903] ends around 23.7.\nBased on the above thought, the answer is:\nIntegration Tool,Adoption Rate (%)\nMuleSoft,23.7\nTalend DI,28.3\nDell Boomi,19.5\nIBM Infosphere,12.8\nInformatica PowerCenter,25.6\nOracle Data Integrator,36.4\nJitterbit,14.9"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "BmelpdaYHbciv02B",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's information, what could you infer about the trends in the adoption of big data integration tools?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the length of bars, we can deduce relative popularity trends. {Oracle Data Integrator}[0.721, 0.138, 0.820, 0.903], with the longest bar, denotes that it is currently the most adopted tool, indicating that it might be highly effective or well-reputed in the industry. Conversely, the tool with the shortest bar, {IBM Infosphere}[0.474, 0.634, 0.573, 0.903], may be less efficient or less popular. Observing the bars of middling length, such as {MuleSoft}[0.104, 0.405, 0.203, 0.903], {Talend DI}[0.227, 0.308, 0.326, 0.903], and {Informatica PowerCenter}[0.597, 0.365, 0.696, 0.903], we can conclude that these tools fall somewhere in between in terms of adoption rate, perhaps due to mixed feedback from users or similar efficacy.\nBased on the above thought, the answer is:\nOracle Data Integrator is currently the most adopted tool in big data integration, which implies that it may be highly efficient or well-known in the industry. On the other hand, IBM Infosphere, with the lowest adoption rate, might be less effective or not as popular. Other tools such as MuleSoft, Talend DI, and Informatica PowerCenter have average adoption rates, which could be due to receive mixed responses from users based on their efficiency."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "DIx9Kg3maQbNQLpl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich integration tool has the lowest adoption rate according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the integration tool with the lowest adoption rate, I need to survey the length of each bar, from {MuleSoft}[0.104, 0.405, 0.203, 0.903] to {Jitterbit}[0.844, 0.590, 0.943, 0.903]. By comparing the bar's length, we can see that the {IBM Infosphere}[0.474, 0.634, 0.573, 0.903] has the shortest bar among the integration tools, which implies it has the lowest adoption rate.\nBased on the above thought, the answer is:\nIBM Infosphere"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "grKXLsZgxoNg22QY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you are observing, could you please transform the data shown into a structured table using the CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By visualizing the bar chart with the title {\"GIS Uptake in Australian Provinces\"}[0.380, 0.025, 0.667, 0.055], we can see that there's a single bar for every province on {the x-axis}<[0.104, 0.146, 0.220, 0.903],[0.249, 0.310, 0.364, 0.903],[0.393, 0.409, 0.509, 0.903],[0.538, 0.476, 0.654, 0.903],[0.683, 0.593, 0.798, 0.903],[0.827, 0.760, 0.943, 0.903]>. The height of these bars indicate the corresponding percentages mentioned at the top. From left to right, these bars represent the provinces: \"New South Wales\", \"Victoria\", \"Queensland\", \"South Australia\", \"Western Australia\", and \"Tasmania\". By examining the percentages at the top of the bars, I can assign the corresponding percentages for each province group and convert this data into CSV format.\nBased on the above thought, the answer is:\nProvince,Percentage\nNew South Wales,81\nVictoria,63.5\nQueensland,52.9\nSouth Australia,45.7\nWestern Australia,33.2\nTasmania,15.3"
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "w3FyR6WGsaGO9C0x",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the general trends or patterns you can see in the GIS uptake based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my understanding of the bar chart, it appears that there's a generally decreasing trend in GIS uptake from New South Wales with the highest percentage uptake {\"81%\"}[0.140, 0.114, 0.184, 0.137] to Tasmania with the lowest percentage uptake {\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. This trend seems to show that as we move from the left side of the chart to the right, the height of the bars, hence GIS usage, is reduced. There's a noticeable drop in the bar height going from Western Australia {\"33.2%\"}[0.719, 0.560, 0.762, 0.583] to Tasmania {\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. This might suggest that the less populous states have significantly lower percentage uptake of GIS compared to the more populous states.\nBased on the above thought, the answer is:\nThe general trend in GIS uptake across the provinces of Australia appears to be decreasing, particularly between Western Australia and Tasmania, with less populous states having a significantly lower percentage uptake compared to more populous states."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "6ztfo8beCVPnnNLn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the percentage uptakes of GIS between New South Wales and Tasmania?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the percentage uptakes, I need to locate the bars representing New South Wales and Tasmania on the chart. The percentage of GIS uptake for New South Wales is shown by the height of the bar located at {\"New South Wales bar\"}[0.104, 0.146, 0.220, 0.903]. The percentage is provided above this bar, which shows that it's{\"81%\"}[0.140, 0.114, 0.184, 0.137]. Meanwhile, the percentage of GIS uptake for Tasmania is displayed by the height of the bar {\"Tasmania bar\"}[0.827, 0.760, 0.943, 0.903], for which the percentage is{\"15.3%\"}[0.863, 0.727, 0.907, 0.751]. By subtracting the percentage of Tasmania from New South Wales, I can find the difference.\nBased on the above thought, the answer is:\n65.7%"
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "6CzCYKs7CyVgKj0B",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTake the bar chart data on Transportation and Logistics Industry trends and transform it into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {Transportation and Logistics Industry Trends}[0.340, 0.025, 0.707, 0.055] and {the x-axis tick labels}[{\"Steep Decline\"}[0.126, 0.919, 0.222, 0.942]; {\"Dramatic Surge\"}[0.294, 0.919, 0.403, 0.942]; {\"Gradual Recovery\"}[0.461, 0.919, 0.585, 0.942]; {\"Upward Trajectory\"}[0.635, 0.919, 0.762, 0.942]; {\"Slight Decrease\"}[0.819, 0.919, 0.928, 0.942]], it can be inferred that the x-axis represents different trends in the industry. The y-axis stands for the frequency of occurrences of these trends in percentages, ranging from 0 to 90, as indicated by {the y-axis tick labels}[0.043, 0.894, 0.052, 0.917] to [0.035, 0.053, 0.052, 0.076]. For each trend, I am given the percentage frequency as depicted by the height of the individual bars and the value annotations at the top of each bar. For instance, for {Steep Decline}[0.126, 0.919, 0.222, 0.942], the bar height falls approximately at the 30 position and it has a value annotation {\"30.0%\"}[0.152, 0.599, 0.196, 0.623], indicating that the occurrence frequency of 'Steep Decline' is 30.0%. I can use this method for each category to extract the corresponding frequencies.\nBased on the above thought, the answer is:\nTrend, Frequency (%)\nSteep Decline,30.0\nDramatic Surge,80.0\nGradual Recovery,50.0\nUpward Trajectory,70.0\nSlight Decrease,55.0"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "Q78pPxPrU4tyGqjk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, which trend seems to be the most critical for the Transportation and Logistics Industry? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the y-axis values {Occurrence Frequency (%)'[0.015, 0.329, 0.029, 0.636]}, I can understand that the higher the bar is, the more frequent a specific trend is in the industry. With this understanding, I see that {Dramatic Surge}[0.294, 0.919, 0.403, 0.942] is the highest bar among all, and its value is {\"80.0%\"}[0.327, 0.132, 0.371, 0.156]. Based on the height of the rest bars like {Steep Decline}[0.104, 0.623, 0.244, 0.903] or {Gradual Recovery}[0.454, 0.436, 0.593, 0.903], I can conclude that \"Dramatic Surge\" is more critical than other trends, as it happens more frequently.\nBased on the above thought, the answer is:\nThe \"Dramatic Surge\" trend is the most critical for the Transportation and Logistics Industry because it has the highest occurrence frequency of 80.0% based on the given data."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "fpetk3jtqe2eTFi2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of occurrence frequency across the trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of occurrence frequency across the trends, I have to check the lowest and highest values. However, the lowest value would always be 0 according to the y-axis labels starting from {0}[0.043, 0.894, 0.052, 0.917]. To find the highest value, I can simply look for the highest bar. From only looking at each bar, I can see that the value of {Dramatic Surge}[0.294, 0.919, 0.403, 0.942] looks the highest. To confirm it, I look at {the associated value annotation}[0.327, 0.132, 0.371, 0.156] which shows \"80.0%\". Therefore, the range of occurrence frequency across the trends would be 0 to 80.\nBased on the above thought, the answer is:\n0 to 80%"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "KgeNS4pkh9vYY1aO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data on the chart into a tabular format based on your observation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart under analysis is a bar chart as inferred from the x-axis labels representing different kinds of technology and the y-axis labels representing development prospects ranging from {0}[0.036, 0.786, 0.051, 0.804] to {100}[0.029, 0.040, 0.051, 0.057]. Each bar represents the possible growth of a single type of technology, such as {AI Technology}[0.101, 0.196, 0.170, 0.793], {Quantum Computing}[0.187, 0.307, 0.256, 0.793], and others. The chart's layout suggests the table's two columns should be 'Technology' and 'Development Prospect (%)'.\nBased on the above thought, the answer is:\nTechnology,Development Prospect (%)\nAI Technology,80.0\nQuantum Computing,65.1\nNanotechnology,47.5\nBiotechnology,73.4\nBlockchain Technology,41.1\nSpace Exploration,85.0\n5G Technology,59.2\nElectric Cars,78.5\nVR/AR Technology,52.7\nGreen Energy,91.5"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "EdwMYYnG2s5NQEyl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology seems to have the best and worst development prospects according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the technology having the best and worst prospects, the tallest and shortest bars on the chart should be identified. {Green Energy}[0.876, 0.110, 0.945, 0.793] appears to be the tallest bar indicating the best development prospects at 91.5%. Conversely, {Blockchain Technology}[0.446, 0.486, 0.515, 0.793] seems to be the shortest bar, symbolizing the least development prospect at 41.1%.\nBased on the above thought, the answer is:\nGreen Energy seems to have the best development prospects while Blockchain Technology appears to have the worst development prospects according to the chart."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "LffHlOdOUPcfNpeI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of technology's development prospect as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total percentage of technology\u2019s development prospects, I need to add the percentages of all the different types of technologies. This can be determined by identifying and summarizing the development prospects of all the bars including: {AI Technology}[0.101, 0.196, 0.170, 0.793] at 80.0%, {Quantum Computing}[0.187, 0.307, 0.256, 0.793] at 65.1%, {Nanotechnology}[0.273, 0.439, 0.342, 0.793] at 47.5%, {Biotechnology}[0.360, 0.245, 0.428, 0.793] at 73.4%, {Blockchain Technology}[0.446, 0.486, 0.515, 0.793] at 41.1%, {Space Exploration}[0.532, 0.159, 0.601, 0.793] at 85.0%, {5G Technology}[0.618, 0.351, 0.687, 0.793] at 59.2%, {Electric Cars}[0.704, 0.207, 0.773, 0.793] at 78.5%, {VR/AR Technology}[0.790, 0.400, 0.859, 0.793] at 52.7% and {Green Energy}[0.876, 0.110, 0.945, 0.793] at 91.5%.\nBased on the above thought, the answer is:\n723.9%"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "ylHnq3fmXDOq02RQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me interpret the data on the bar chart for the tourism trends and translate this into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on the {title of the chart}<r>[0.334, 0.025, 0.721, 0.055]</r>, {\"Tourism and Hospitality Industry Data Analysis\"}, it appears each bar represents a different category of tourism trend. The {x-axis labels}<r>[{\"Sustainable Tourism\"}[0.091, 0.919, 0.231, 0.942]; {\"Culinary Tourism\"}[0.226, 0.919, 0.341, 0.942]; {\"Adventure Tourism\"}[0.341, 0.919, 0.471, 0.942]; {\"Dark Tourism\"}[0.482, 0.919, 0.573, 0.942]; {\"Eco-Tourism\"}[0.609, 0.919, 0.691, 0.942]; {\"Space Tourism\"}[0.722, 0.919, 0.822, 0.942]; {\"Wellness Tourism\"}[0.835, 0.919, 0.954, 0.942]]</r> indicate the type of tourism trend. For each bar, the height corresponds to the value on the y-axis, which is labeled as {\"Percentage (%)\"}<r>[0.015, 0.394, 0.029, 0.571]</r>. \nFor example, the bar for {\"Dark Tourism\"}<r>[0.479, 0.559, 0.577, 0.903]</r> extends approximately halfway up the chart. The figure above that bar indicates a value of {\"40.85%\"}<r>[0.502, 0.528, 0.554, 0.551]</r>. With all this information, I can construct a CSV table that looks like this:\nBased on the above thought, the answer is:\nTourism Trend, Percentage (%)\nSustainable Tourism, 5.50\nCulinary Tourism, 15.25\nAdventure Tourism, 22.30\nDark Tourism, 40.85\nEco-Tourism, 61.35\nSpace Tourism, 82.30\nWellness Tourism, 95.00"
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "fWGopfXIZulyUIts",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you discern from the bar chart about the tourism and hospitality industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the bar chart, I can infer certain trends regarding the tourism and hospitality industry. Notably, the heights of the bars consistently increase from left to right, suggesting an overall positive trend in the tourism and hospitality industry's growth rates. Starting from a small percentage in the {\"Sustainable Tourism\"}<r>[0.112, 0.857, 0.210, 0.903]</r> sector (5.5%), it multiplies nearly twentyfold by the end of the sequence in the{\"Wellness Tourism\"}<r>[0.846, 0.104, 0.943, 0.903]</r> sector (95.0%). This suggests a rising overall trend in the preference for these tourism trends, with Wellness Tourism showing the most dramatic increase.\nBased on the above thought, the answer is:\nThere is an overall positive trend in the growth rates in the tourism and hospitality industry, with wellness tourism showing the most significant rise in popularity."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "iKmbHLEXK9rHz3r1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which sector saw the highest growth in terms of percentage changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest growth rate in terms of percentage changes, I need to examine each bar's height in the chart. From left to right, we can observe that the height of each bar increases. For instance, the bar for {\"Sustainable Tourism\"}<r>[0.112, 0.857, 0.210, 0.903]</r> is close to the bottom, representing a low percentage, while the bar for {\"Wellness Tourism\"}<r>[0.846, 0.104, 0.943, 0.903]</r> is noticeably taller, almost reaching the top of the chart. The data label above this bar indicates that the growth rate of the Wellness Tourism sector is {\"95.00%\"}<r>[0.873, 0.073, 0.916, 0.096]</r>. Hence, Wellness Tourism has the highest growth rate.\nBased on the above thought, the answer is:\nWellness Tourism."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "7d0603kh9gBCuW9Y",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observations of the bar chart, please convert the profit change data across these companies into a data table."
+            },
+            {
+                "from": "gpt",
+                "value": "Analysing the bar chart, we have companies as represented on the x axis, with their names given in the x-ticks ranging from {Coinbase}[0.104, 0.939, 0.158, 0.957] to {TransferWise}[0.873, 0.939, 0.948, 0.957]. These companies have associated bars indicating profit change for each. For instance, the bar for {Coinbase}[0.096, 0.157, 0.166, 0.707] shows a profit change of approximately 5% as indicated by the y-axis mapping and the specific annotation {\"5.0%\"}[0.116, 0.139, 0.146, 0.157]. Similar observations are made for all other companies. For instance, we have {Starling}[0.801, 0.939, 0.846, 0.957] with a bar {Starling bar}[0.789, 0.707, 0.858, 0.762] representing a negative profit change of around 0.5% as marked by {\"-0.5%\"}[0.807, 0.745, 0.840, 0.762]. Using this method, we can convert the bar chart data into the following CSV format table.\nBased on the above thought, the answer is:\nCompany,Profit change (%)\nCoinbase,5.0\nRevolut,3.2\nN26,1.2\nSquare,4.3\nRobinhood,2.1\nPaypal,3.5\nStripe,4.1\nMonzo,-1.0\nStarling,-0.5\nTransferWise,2.0"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "dMjBHaMMI4mhWFYZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the overall growth of the FinTech and Digital Currency industry from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the bar chart, the majority of the companies in the FinTech and Digital Currency sector have experienced profit growth over the past fiscal year. This can be inferred from the positive values of the bars for these companies. Companies such as {Square}[0.370, 0.939, 0.411, 0.957], {Stripe}[0.633, 0.939, 0.668, 0.957] , and {Coinbase}[0.104, 0.939, 0.158, 0.957] show considerable growth, with the tallest bar belonging to {Coinbase}[0.096, 0.157, 0.166, 0.707] with a 5% profit increase. There's only a couple of companies namely {Monzo}[0.718, 0.939, 0.756, 0.957] and {Starling}[0.801, 0.939, 0.846, 0.957] that show a decreased profit, observable from their bars - {Monzo bar}[0.703, 0.707, 0.772, 0.817] and {Starling bar}[0.789, 0.707, 0.858, 0.762] respectively being below the 0% line on the y-axis.\nBased on the above thought, the answer is:\nThe majority of the FinTech and Digital Currency companies have seen a rise in profits, suggesting a generally growth-oriented trend in the industry. However, a few companies such as Monzo and Starling experienced a decline, indicating the growth isn't universal."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "BpeutGQzsxAYlpvD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company experienced the highest increase in profits?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the company with the highest profits, I need to identify the tallest bar on the chart which maps to the value on the y-axis. The {Coinbase bar}[0.096, 0.157, 0.166, 0.707] is the tallest among all with its height representing an increase of approximately 5% as marked by the text label {\"5.0%\"}[0.116, 0.139, 0.146, 0.157].\nBased on the above thought, the answer is:\nCoinbase"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "QoFSXp0fzuAGJdvh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you saw, could you convert this into a structured table with risk factors and their corresponding percentage risks?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the chart's X-axis and title {\"Financial Management and Risk Control\"}[0.383, 0.019, 0.656, 0.041], I see that the risk factors are labeled on the X-axis and the corresponding risk percentages are represented by the height of each bar. For instance, for the \"Inflation\" category, the risk percentage is determined by the height of the {\"Inflation\"}[0.094, 0.780, 0.194, 0.927] bar as well as its annotation {\"10.0%\"}[0.126, 0.756, 0.162, 0.773], which provides the specific value of 10%. I will list down each risk factor and their corresponding risk percentage in a CSV table format. This process will be repeated for each risk factor on the X-axis.\nBased on the above thought, the answer is:\nRisk Factor,Percentage risk\nInflation,10.0\nInterest rate,12.5\nCredit risk,30.0\nTransaction cost,2.5\nLiquidity risk,18.0\nOperational risk,35.0\nMarket risk,50.0"
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "HXXLdOIxxkJtXBJf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which two risk factors should be given the most attention based on their percentage risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to focus on the bars with the highest heights. By analyzing each bar's height, from \"Inflation\" to \"Market risk\", I notice that {\"Operational risk\"}[0.720, 0.414, 0.820, 0.927] and {\"Market risk\"}[0.845, 0.193, 0.945, 0.927] have the longest bars, meaning they represent the highest percentage risks. Hence, they are the risk factors to pay more attention to.\nBased on the above thought, the answer is:\nOperational risk and Market risk. These two risks have the highest percentage risk values according to the chart."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "CAeztzCSGjhE0KEc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk factor poses the highest percentage risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the risk factor with the highest percentage risk, I need to observe the height of each bar in the bar chart. From left to right, the bars correspond to the risk factors such as {\"Inflation\"}[0.094, 0.780, 0.194, 0.927], {\"Interest rate\"}[0.219, 0.744, 0.319, 0.927], on to the last one {\"Market risk\"}[0.845, 0.193, 0.945, 0.927]. Among all the bars, {\"Market risk\"}[0.845, 0.193, 0.945, 0.927] bar reaches the lowest point on the Y-axis indicating the highest percentage risk, and it is labeled with {\"50.0%\"}[0.877, 0.169, 0.913, 0.186].\nBased on the above thought, the answer is:\nMarket risk."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "DkUkF5ExEzXi0T1D",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from the bar chart in the form of a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each bar in the chart represents a training method in the Education and Training Industry, and the length of the bar corresponds to the percentage of entities adopting that method. So, starting from the leftmost bar, the first bar represents the {Webinar}<r>[0.099, 0.607, 0.176, 0.918]</r> method, and its percentage is about 32.5%, which I found by referring to its height and the matching y-axis label. Proceeding similarly for the rest of the bars would enable me to format the data in CSV.\nBased on the above thought, the answer is:\nTraining_Industry,Percentage\nWebinar,32.5\nIn-person_Seminars,22.7\nOnline_Courses,74.2\nIn-house_Training,18.6\nBootcamps,11.1\nCertifications,48.9\nE-learning_Platforms,80.5\nBlended_Learning,29.7\nMicrolearning,53.4"
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "jYeXWDuZ4de3v3Ke",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the least adopted training method in the industry and how might this information affect the industry's expansion in that area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the least adopted method, I need to locate the shortest bar on the chart. By examining the bars from left to right, {Bootcamps}<r>[0.484, 0.812, 0.561, 0.918]</r> is the shortest bar with an adoption rate of about 11.1%, according to its height and corresponding y-axis label. The relatively low adoption indicates that the industry has not embraced this training method as much as others, which could be due to several factors such as cost, effectiveness, time constraints, or a combination thereof. This could inform industry professionals that there may be an opportunity in this underexplored area.\nBased on the above thought, the answer is:\nThe least adopted method is Bootcamps with an adoption percentage of 11.1%. This could mean underutilization of this training method in the industry, suggestive of room for growth and potential investment in this area."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "I3ItOvmgRrCFE1xu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich training method is adopted the most and how much is its adoption in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the most adopted training method, I need to identify the longest bar in the chart. Looking at each bar from left to right, I can see that the longest bar is {E-learning_Platforms}<r>[0.676, 0.148, 0.753, 0.918]</r> with a percentage of about 80.5% as indicated by the bar's height and its corresponding y-axis label.\nBased on the above thought, the answer is:\nE-learning_Platforms is adopted the most with an adoption percentage of 80.5%."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "cH20E4vvC1zPSWu7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you formulate the bar chart data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the x-axis of the bar chart, there are labels {Solar Power, Hydropower, Wind Energy, Biofuel, Geothermal, Nuclear Energy, Natural Gas, Coal, and Oil} denoted within the x-tick labels in each of their respective coordinates. It is clear that these labels are the names of the different energy resources. Looking at the set of bars, the height of each bar represents the percentage of energy resource consumption. Also, a value annotation on top of each bar gives the exact percentage value, such as {5.6 for Solar Power}[0.132, 0.832, 0.168, 0.856], {15.8 for Hydropower}[0.222, 0.747, 0.266, 0.770], and so forth. Using these values along with the resource names from the x-axis, I can generate a CSV table with two columns: Energy Resource and Percentage.\nBased on the above thought, the answer is:\nEnergy Resource,Percentage\nSolar Power,5.6\nHydropower,15.8\nWind Energy,35.9\nBiofuel,44.3\nGeothermal,52.0\nNuclear Energy,66.6\nNatural Gas,80.0\nCoal,85.0\nOil,92.5"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "O7ogpmc3TpdWUVrY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which renewable energy resource has the highest consumption percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the chart represent different energy resources with their heights representing the corresponding consumption percentages. Amongst the renewable energies, 'Solar Power', 'Hydropower', 'Wind Energy', 'Geothermal', the bar for {Geothermal}[0.490, 0.466, 0.566, 0.903] is the highest, denoting 52.0%, which is labelled above the bar. This indicates that amongst the renewable energy resources, Geothermal has the highest consumption percentage.\nBased on the above thought, the answer is:\nGeothermal."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "yVI77Poh1yyXpGyN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_31_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data on the chart, what's the difference between the percentages of Coal and Solar Power?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that the bar for {Coal}[0.773, 0.188, 0.849, 0.903] rises pretty high with a value of 85.0% given in the label above the bar. On the other hand, the bar for {Solar Power}[0.112, 0.856, 0.188, 0.903] is much lower, with an annotation of 5.6% in the label above the bar. To find the difference, simple subtraction of 5.6 from 85.0 can give us the answer.\nBased on the above thought, the answer is:\n79.4"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "a4FNhbDrPrnOFwxM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the stacked bar chart you observed as a CSV format data table."
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the bar chart title {\"Market Competition Analysis and Comparison\"}[0.271, 0.168, 0.780, 0.197] and comparing it with the x-axis labels {Market Share}[0.247, 0.925, 0.340, 0.945] and {Sales Volume}[0.711, 0.925, 0.804, 0.945], we can determine that the x-axis shows metrics of competition. The y-axis describes the corresponding values, ranging from {0}[0.047, 0.903, 0.056, 0.923] to {80}[0.039, 0.184, 0.056, 0.204]. Here, six stacked bars representing {Product A Sales}[0.294, 0.082, 0.414, 0.104], {Product B Sales}[0.294, 0.115, 0.414, 0.136], {Product C Sales}[0.487, 0.082, 0.606, 0.104], {Product D Sales}[0.487, 0.115, 0.608, 0.136], {Product E Sales}[0.681, 0.082, 0.799, 0.104] and {Product F Sales}[0.681, 0.115, 0.799, 0.136] are visible. For each metric, each product is represented by a colored bar. By examining the heights of the bars and the corresponding coordinates, the sales values of each product for each metric can be extracted.\nBased on the above thought, the answer is:\nMetric,Product A Sales,Product B Sales,Product C Sales,Product D Sales,Product E Sales,Product F Sales\nMarket Share,13,18,17,7,12,8\nSales Volume,11,6,10,16,14,13"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "mquZqSs7OGcNqWdw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product had the most desirable outcome in terms of the market competition analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two metrics, market share and sales volume, need to be compared across the six products in order to determine which had the most favorable outcome in terms of market competition. Higher values for both metrics would indicate a superior market position. Concretely, looking at the heights of the bars for Market Share and Sales Volume, values can be established for each product. {Product A}<r>[0.108, 0.794, 0.479, 0.911]</r> and <r>[0.572, 0.812, 0.943, 0.911]</r>, {Product B}<r>[0.108, 0.632, 0.479, 0.794]</r> and <r>[0.572, 0.758, 0.943, 0.812]</r>, {Product C}<r>[0.108, 0.479, 0.479, 0.632]</r> and <r>[0.572, 0.668, 0.943, 0.758]</r>, {Product D}<r>[0.108, 0.417, 0.479, 0.479]</r> and <r>[0.572, 0.524, 0.943, 0.668]</r>, {Product E}<r>[0.108, 0.309, 0.479, 0.417]</r> and <r>[0.572, 0.399, 0.943, 0.524]</r>, {Product F}<r>[0.108, 0.237, 0.479, 0.309]</r> and <r>[0.572, 0.282, 0.943, 0.399]</r>. Product E appears to be performing reasonably well, coming in the top half based on both measures.\nBased on the above thought, the answer is:\nProduct E. It has a high market share and a good number for sales volume, indicating a strong market position."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "RPvPY2aI3Ac32bwi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which product experienced the greatest sales volume increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales volumes for the products can be found by examining the bar heights for the Sales Volume metric. By comparing the height of the bars relative to the y-axis score listing, observed values for the sales volume of {Product A}<r>[0.572, 0.812, 0.943, 0.911]</r>, {Product B}<r>[0.572, 0.758, 0.943, 0.812]</r>, {Product C}<r>[0.572, 0.668, 0.943, 0.758]</r>, {Product D}<r>[0.572, 0.524, 0.943, 0.668]</r>, {Product E}<r>[0.572, 0.399, 0.943, 0.524]</r> and {Product F}<r>[0.572, 0.282, 0.943, 0.399]</r> are 11, 6, 10, 16, 14, and 13 respectively. It is seen that Product D experienced the most considerable increase in sales volume.\nBased on the above thought, the answer is:\nProduct D."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "phCEApC0Xk7Dc3P1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given bar chart, is it possible for you to generate a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title {Bar Chart of Media Influence and Public Opinion Guidance}<r>[0.202, 0.091, 0.849, 0.120]</r> and the label on the x-axis {Media Types}<r>[0.474, 0.953, 0.577, 0.979]</r> define the various categories of media presented. The y-axis label {Level of Influence}<r>[0.015, 0.414, 0.033, 0.624]</r> is a scale, offering the corresponding value for each category. To compile the CSV table, I need to extract the value from each bar. This can be achieved by considering the height of the bars with respect to the scale on the y-axis. For instance, the bar for {Newspaper}<r>[0.108, 0.492, 0.247, 0.911]</r> peaks approximately at 23 on the scale, thus, the influence level of Newspaper is 23.\nBased on the above thought, the answer is:\nMedia,Influence Level\nNewspaper,23\nTV,19\nRadio,12\nOnline,35\nSocial Media,41"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "nS4V5C2lt26JBtLb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you determine which media type has the most significant influence on shaping public opinion? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require a comparison of the lengths of various bars. Among all bars, the bar for {Social Media}<r>[0.804, 0.163, 0.943, 0.911]</r> is the tallest, reaching almost 41 on the y-axis. Consequently, it exerts the highest influence on shaping public opinion, according to the chart. This could be attributed to the growing prevalence and accessibility of social media, enabling them to reach and influence a larger audience.\nBased on the above thought, the answer is:\nSocial Media. The reason can be deduced from the chart, as it represents the tallest bar, which signifies the highest level of influence among all media types. This suggests that Social Media holds the most significant influence in shaping public opinion."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "E0cjJUdqzKXGRr8x",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me which media type exerts the least influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the media type with minimum influence, I must compare the heights of all bars. Starting from the left, the first bar is for {Newspaper}<r>[0.108, 0.492, 0.247, 0.911]</r> which reaches almost 23 on the y-axis. The next is the {TV}<r>[0.282, 0.565, 0.421, 0.911]</r> bar at 19. Further right, I observe the bar for {Radio}<r>[0.456, 0.692, 0.595, 0.911]</r>, which reaches the 12 mark which is the shortest so far. Examining the next two bars for {Online}<r>[0.630, 0.273, 0.769, 0.911]</r> (35) and {Social Media}<r>[0.804, 0.163, 0.943, 0.911]</r> (41), they are taller than the previous ones. Hence, by comparing the heights of all bars, I find that Radio has the least influence.\nBased on the above thought, the answer is:\nRadio"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "TgBs7wu3uPdlGHAM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart you analyzed, can you convert the healthcare performance dataset into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart is segmented into multiple categories, each having three groups, which I can identify by referring to the chart's {legend}<r>[0.237, 0.019, 0.807, 0.095]</r>. The x-axis has {seven indicators}<r>[Medical Staff Availability[0.078, 0.925, 0.249, 0.945]; Clinical Equipment Efficiency[0.182, 0.925, 0.384, 0.945]; Patients' Satisfaction Score[0.308, 0.925, 0.497, 0.945]; Hospital Cleanliness Rating[0.427, 0.925, 0.617, 0.945]; Disease Recovery Rate[0.562, 0.925, 0.721, 0.945]; Healthcare Awareness[0.684, 0.925, 0.838, 0.945]; Medical Research Progress[0.788, 0.925, 0.973, 0.945];]</r>. The y-axis signifies the achieved level or counts for each indicator. From {each group of bars on y-axis}, I can observe different colors representing 'Standard Level'<r>blue</r>, 'Achieved Level'<r>orange</r>, and 'Incremental Level'<r>green</r>. I'll use these values to create the table.\nBased on the above thought, the answer is:\nHealth Indicators,Standard Level,Achieved Level,Incremental Level\nMedical Staff Availability,200,176,5\nClinical Equipment Efficiency,150,138,4\nPatients' Satisfaction Score,90,70,-3\nHospital Cleanliness Rating,85,80,-1\nDisease Recovery Rate,75,68,-1\nHealthcare Awareness,100,120,4\nMedical Research Progress,60,72,2"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "hKVg948vibo3SUKs",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the observed performance indicators, which area might need more focus, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, the quality of service seems to have dropped in certain areas. These areas can be identified by any negative increments. Looking at the chart, I can see that the {green bar below the x-axis}<r>[0.354, 0.597, 0.450, 0.603],[0.474, 0.587, 0.570, 0.589],[0.594, 0.630, 0.689, 0.632]</r> representing incremental level for the \"Patients' Satisfaction Score\", \"Hospital Cleanliness Rating\", and \"Disease Recovery Rate\" are negative, which indicates that these areas have declined over time.\nBased on the above thought, the answer is:\nAccording to the observed performance indictors, the areas that might need more focus are \"Patients' Satisfaction Score\", \"Hospital Cleanliness Rating\", and \"Disease Recovery Rate\". These areas have witnessed a decline over time as per the negative incremental levels."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "sJeuq7FgDdZFd02h",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health indicator witnessed the most significant increment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Finding the health indicator with the most significant increment means identifying the tallest green bar, which represents \"Incremental Level\". As I can see, the {green bars} are distributed on top of the orange bars, and {the bar for \"Healthcare Awareness\"}<r>[0.713, 0.471, 0.809, 0.479]</r> appears to have the largest height.\nBased on the above thought, the answer is:\nHealthcare Awareness"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "qj5sqXRaQZZT0sX1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the visualization, can you convert the market shares of different industries between the two trends into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart title {Industry Market Share Comparison}[0.336, 0.091, 0.723, 0.120] suggests that the x-axis {Industries}[0.489, 0.953, 0.571, 0.979] represents different industries, whereas the y-axis indicates corresponding {Market Share}[0.015, 0.440, 0.033, 0.597]. Multiple bars of different colors represent different trends as mentioned in the {legend}[0.409, 0.019, 0.650, 0.095]. The red bars represent {Trend 1}[0.458, 0.065, 0.515, 0.086], and the green bars represent {Trend 2}[0.588, 0.065, 0.644, 0.086]. The heights of these bars indicate the market share, with {Trend 1 data}[{bar_coordinates} '[0.116, 0.697, 0.213, 0.911]'] and {Trend 2 data}[{bar_coordinates} '[0.116, 0.505, 0.213, 0.697]'] available for all the industries. The features extracted would then form the CSV table.\nBased on the above thought, the answer is:\nIndustry,Trend 1,Trend 2\nTV industry,50,45\nGame industry,30,28\nMovie industry,50,48\nMusic industry,70,65\nBook industry,50,46\nArt industry,90,85\nToy industry,80,76"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "sc3zQ2IjOp3SkO4b",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends presented in the chart, could you suggest which industry has the most stable market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the chart, the stability of the market share for each industry can generally be determined by the smallest range of fluctuations between Trend 1 and Trend 2. In comparing each pair of bars that represent Trend 1 and Trend 2, such as {TV Industry}[{Trend 1 bar}[0.116, 0.697, 0.213, 0.911];{Trend 2 bar}[0.116, 0.505, 0.213, 0.697]] and continuing this comparison for each subsequent industry's bars, it's evident that the {Game industry}[0.238, 0.783, 0.335, 0.911; 0.238, 0.663, 0.335, 0.783] has the least variation in the market share across two trends. The range of change in the Game industry is just 2 points, which is the smallest change among all industries.\nBased on the above thought, the answer is:\nGame industry. The market share in this sector shows the smallest variation between the two trends, indicating it has the most stable market share."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "D4JHmeWxUS3pqGzg",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which industry did the market share drop the most from Trend 1 to Trend 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the industry with the highest drop in market shares between Trend 1 and Trend 2, I need to check the bar heights of both the trends for each industry. As per the height of the bars, the initial bar {height of TV industry}[{Trend 1 bar}[0.116, 0.697, 0.213, 0.911];{Trend 2 bar}[0.116, 0.505, 0.213, 0.697]] shows a drop of 5 points, and checking the other bars like Game industry, Movie industry etc. shows similar patterns. Finally, by comparing the differences in each industry, we can see that {Music industry}[0.481, 0.612, 0.579, 0.911; 0.481, 0.334, 0.579, 0.612] has seen the greatest decline from Trend 1 to Trend 2 with a drop of 5 points, which is the biggest change among all industries.\nBased on the above thought, the answer is:\nMusic Industry."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "zXljm08zyXKzbPHe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nI have analyzed the bar chart data and need to tabulate it. Could you guide me through converting the chart data into a structured table which includes the colors used for each technology trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the chart, I can see bars for each technology trend from the years 2013 to 2020. Each bar's color represents a technology trend. To create the table, I will pair the height of each bar, indicating growth, with the corresponding color from the legend. For instance, {the first bar in 2013 for Artificial Intelligence}<r>[0.116, 0.899, 0.201, 0.911]</r> has a color code of {#ff4500}<r>[0.249, 0.066, 0.279, 0.082]</r>. Proceeding with this method, all data are matched with their respective technology trend and color code for each year.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Color Code,AI Growth,Internet of Things,Color Code,IoT Growth,Big Data,Color Code,BD Growth,Cloud Computing,Color Code,CC Growth,Blockchain,Color Code,BC Growth,VR/AR,Color Code,VRAR Growth\n2013,5,#ff4500,10,#ff6347,5,#ff8c00,20,#ffd700,1,#ffff00,2,#eeeeee\n2014,10,#ff4500,10,#ff6347,8,#ff8c00,30,#ffd700,2,#ffff00,4,#eeeeee\n2015,15,#ff4500,15,#ff6347,15,#ff8c00,40,#ffd700,4,#ffff00,6,#eeeeee\n2016,20,#ff4500,27,#ff6347,30,#ff8c00,50,#ffd700,8,#ffff00,9,#eeeeee\n2017,27,#ff4500,30,#ff6347,45,#ff8c00,60,#ffd700,12,#ffff00,14,#eeeeee\n2018,33,#ff4500,35,#ff6347,50,#ff8c00,70,#ffd700,16,#ffff00,20,#eeeeee\n2019,40,#ff4500,40,#ff6347,55,#ff8c00,80,#ffd700,20,#ffff00,25,#eeeeee\n2020,46,#ff4500,45,#ff6347,60,#ff8c00,90,#ffd700,33,#ffff00,30,#eeeeee"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "AuaCcIHCCRifzvCX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you briefly explain the growth pattern of Artificial Intelligence and VR/AR as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the growth patterns, I will observe the heights of the bars corresponding to Artificial Intelligence and VR/AR throughout the years. For Artificial Intelligence, starting at the 2013 bar at <r>[0.116, 0.899, 0.201, 0.911]</r> and ending at the 2020 bar at <r>[0.859, 0.798, 0.944, 0.911]</r>, the incremental increase in height shows a generally erratic yet upward trend. When analyzing VR/AR, the bars begin at <r>[0.116, 0.805, 0.201, 0.810]</r> in 2013 and reach <r>[0.859, 0.163, 0.944, 0.237]</r> in 2020, displaying a more rapid and consistent growth pattern compared to Artificial Intelligence.\nBased on the above thought, the answer is:\nArtificial Intelligence shows an increasing but erratic growth pattern over the years, with fluctuations in the rate of growth. On the other hand, VR/AR displays a more consistent and rapid expansion, with a clear upward trajectory and no significant dips or instances of stagnation."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "EnZrdOYH0l0ZZUXG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year did Blockchain experience an abrupt drop compared to the previous year as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine where Blockchain had an abrupt drop, I will inspect the bars representing Blockchain for each year. For 2013 <r>[0.116, 0.810, 0.201, 0.813]</r>, 2014 <r>[0.222, 0.763, 0.307, 0.768]</r>, 2015 <r>[0.328, 0.692, 0.413, 0.702]</r>, and so on, the bar heights increase until 2019 <r>[0.753, 0.333, 0.838, 0.382]</r>. Then, analyzing the bar for 2020 <r>[0.859, 0.237, 0.944, 0.318]</r>, I can see a noticeable decrease in height compared to 2019, implying a drop in Blockchain's growth.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "qi7xlAPzKc5uy9yt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data represented on the bar chart into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Referring to the title \"Artificial Intelligence and Automation Trends\"[0.283, 0.168, 0.777, 0.197] from the chart, it outlines growth trends for various AI and Automation technologies. The x-axis represents 'Years'[0.508, 0.953, 0.552, 0.979], ranging from '2015'[0.147, 0.925, 0.182, 0.945] to '2021'[0.877, 0.925, 0.913, 0.945]. The y-axis shows the 'Level'[0.015, 0.526, 0.033, 0.588] of growth, ranging from '0'[0.056, 0.903, 0.065, 0.923] to '140'[0.039, 0.136, 0.065, 0.156]. The different technologies have been represented by different coloured bars eg: {Deep Learning}<r>[0.116, 0.845, 0.213, 0.911]</r>, {RPA}<r>[0.116, 0.763, 0.213, 0.845]</r>, {NLP}<r>[0.116, 0.714, 0.213, 0.763]</r> and {Computer Vision}<r>[0.116, 0.642, 0.213, 0.714]</r>. Each bar's height within a year brackets indicates the level of that technology in the said year. For example, in the year 2015, the level of 'Deep Learning' appears to be 12.\nBased on the above thought, the answer is:\nYear,Deep Learning,RPA,NLP,Computer Vision\n2015,12,15,9,13\n2016,18,15,13,15\n2017,22,16,15,18\n2018,26,18,17,22\n2019,32,26,2,25\n2020,40,26,25,29\n2021,42,25,23,33"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "5nCN3vNt9Ux4eAlW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data on this chart, which technology has shown the greatest resistance to growth in recent years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it is essential to analyze the growth trend of each technology in the recent years from the chart. Looking at the bar heights of the technologies for the years 2019, 2020 and 2021, we can see that {Deep Learning}<r>[0.603, 0.736, 0.700, 0.911; 0.725, 0.692, 0.822, 0.911; 0.846, 0.681, 0.944, 0.911]</r>, {Computer Vision}<r>[0.603, 0.445, 0.700, 0.582; 0.725, 0.253, 0.822, 0.412; 0.846, 0.237, 0.944, 0.418]</r> and {NLP}<r>[0.603, 0.582, 0.700, 0.593; 0.725, 0.412, 0.822, 0.549; 0.846, 0.418, 0.944, 0.544]</r> are continuing to show significant growth. However, for {RPA}<r>[0.603, 0.593, 0.700, 0.736; 0.725, 0.549, 0.822, 0.692; 0.846, 0.544, 0.944, 0.681]</r>, the progress has been relatively constant, showing the least growth within these years.\nBased on the above thought, the answer is:\nRPA (Robotic Process Automation) has shown the greatest resistance to growth in recent years."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "1TYHHauJNPDxHxLc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has experienced the highest level of growth from 2015 through 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology has experienced the highest level of growth from 2015 to 2021, I need to measure the height difference of the bars for each technology from 2015 to 2021. {Deep Learning}<r>[0.116, 0.845, 0.213, 0.911; 0.846, 0.681, 0.944, 0.911]</r> increased from level 12 in 2015 to 42 in 2021, a change of 30. {RPA}<r>[0.116, 0.763, 0.213, 0.845; 0.846, 0.544, 0.944, 0.681]</r> increased from 15 to 25, a change of 10. {NLP}<r>[0.116, 0.714, 0.213, 0.763; 0.846, 0.418, 0.944, 0.544]</r> increased from 9 to 23, a change of 14. And {Computer Vision}<r>[0.116, 0.642, 0.213, 0.714; 0.846, 0.237, 0.944, 0.418]</r> increased from 13 to 33, a change of 20. Comparing these changes, it is clear that Deep Learning has experienced the highest growth.\nBased on the above thought, the answer is:\nDeep Learning."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "MxCFkgncbRFyLzKe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the bar chart that shows the Technology Entrepreneurship Ecosystem growth from 2015 to 2020 into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I will analyze the bar chart and extract data for each year and category. The x-axis is labeled with {the years}<r>[0.156, 0.835, 0.191, 0.855]; [0.298, 0.835, 0.333, 0.855]; [0.441, 0.835, 0.476, 0.855]; [0.584, 0.835, 0.619, 0.855]; [0.726, 0.835, 0.761, 0.855]; [0.869, 0.835, 0.904, 0.855]</r>, and each metric category, such as {Startup Incubation}<r>[0.225, 0.916, 0.367, 0.937]</r>, has a dedicated bar color corresponding to the legend items. By observing the height of each bar, I can determine the value for the corresponding metric in each year. For instance, the bar for {Startup Incubation in 2015}<r>[0.116, 0.769, 0.230, 0.821]</r> begins at y-coordinate 0.769, which correlates with a value of 13 as per the y-axis labels. This process is replicated for all the categories across all the years to fill in the table with accurate data.\nBased on the above thought, the answer is:\nYear,Startup Incubation,Startup Acceleration,Angel Investment,Venture Capital Investment,Patent Application,Product Launches\n2015,13,11,9,24,15,10\n2016,15,12,10,22,20,16\n2017,18,14,15,30,25,19\n2018,23,16,17,32,27,22\n2019,28,18,20,35,30,26\n2020,35,22,22,38,34,30"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "Au1K3Rw6Bn5HThtG",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the ecosystem trend from 2015 to 2020 reflect on the effectiveness of Startup Acceleration compared to other metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {bars corresponding to Startup Acceleration}<r>[0.116, 0.725, 0.230, 0.769];[0.259, 0.713, 0.373, 0.761];[0.401, 0.692, 0.516, 0.749];[0.544, 0.664, 0.658, 0.729];[0.687, 0.636, 0.801, 0.709];[0.829, 0.592, 0.944, 0.680]</r> and comparing their height in relation to other categories, I can see a consistent increase over the years, similar to the general upward trend in the ecosystem. This indicates that Startup Acceleration has been effectively contributing to the overall growth of the ecosystem. Moreover, it's important to note that while there is growth in Startup Acceleration, the most significant leaps are seen in {Venture Capital Investment}<r>[0.116, 0.592, 0.230, 0.688];[0.829, 0.350, 0.944, 0.503]</r> and {Patent Applications}<r>[0.116, 0.531, 0.230, 0.592];[0.829, 0.213, 0.944, 0.350]</r>, which seem to outpace the growth rate of Startup Acceleration by the year 2020.\nBased on the above thought, the answer is:\nWhile Startup Acceleration has seen consistent and effective growth, its impact is relatively moderate when compared to the explosive increases in the metrics of Venture Capital Investment and Patent Applications, suggesting it is a steady but not the leading driver in the Technology Entrepreneurship Ecosystem's expansion from 2015 to 2020."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "rACD9m78FzsOXBX0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total growth in Patent Applications between 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total growth in {Patent Applications}<r>[0.738, 0.916, 0.878, 0.937]</r>, I need to examine the values at the bottom and top of the bars for the years {2015}<r>[0.116, 0.531, 0.230, 0.592]</r> and {2020}<r>[0.829, 0.213, 0.944, 0.350]</r>. The bars indicate starting and ending values leading to a value of 15 for 2015 and 34 for 2020. To compute the growth, the starting value is subtracted from the ending value, giving us 34 - 15.\nBased on the above thought, the answer is:\n19"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "IzjDrrYpxTRgnGpK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you extract the data and present it in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I see that the x-axis represents different venture capitalists as evidenced by the x-axis labels {VC A}[0.140, 0.925, 0.173, 0.945], {VC B}[0.263, 0.925, 0.296, 0.945], {VC C}[0.386, 0.925, 0.419, 0.945], {VC D}[0.508, 0.925, 0.543, 0.945], {VC E}[0.632, 0.925, 0.665, 0.945], {VC F}[0.755, 0.925, 0.787, 0.945] and {VC G}[0.877, 0.925, 0.911, 0.945]. The y-axis represents the investments, which can range from zero to 35, as indicated by the y-axis labels. I can also see that there are two bars for each venture capitalist, each representing a different investment series. Based on the bars, I can roughly determine the investment amount for each venture capitalist in each series and map the information to a table. For example, {Venture capitalist A}'s {Series-A bar}[0.108, 0.677, 0.206, 0.911] shows an investment of around 10 million dollars and {Series-B bar}[0.108, 0.374, 0.206, 0.911] shows an investment of about 23 million dollars.\nBased on the above thought, the answer is:\nVenture Capitals,Fintech Series A,Fintech Series B\nVC A,10,23\nVC B,18,32\nVC C,15,25\nVC D,25,15\nVC E,14,30\nVC F,17,19\nVC G,11,22"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "KmKv5h0tWWQFs2rj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which venture capitalist shows the most stable behavior between the two series of investments and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires an analysis of the changes in investment from Series A to Series B for each venture capitalist. Stability here would mean that the amount invested in Series A and Series B are approximately the same. Observing the heights of bars for each venture capitalist, {VC F} represented by bars {Series-A bar}[0.722, 0.514, 0.820, 0.911] and {Series-B bar}[0.722, 0.467, 0.820, 0.911] shows a Series A investment of 17 million and a Series B investment of 19 million, suggesting a very minor increase, so VC F appears to be the most stable.\nBased on the above thought, the answer is:\nVC F, because the increase in investment from Series A to Series B is minimal compared to other venture capitalists, indicating a more stable investment behavior."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "xxAv9jMfuGRacag4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which venture capitalist has the most significant increase in investment from series A to series B?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the difference in investments between series B and series A for each venture capitalist. I can do this by comparing the height of the two corresponding bars for each venture capitalist. For example, for {VC A}, the height of {Series-A bar}[0.108, 0.677, 0.206, 0.911] is around 10 and the height of {Series-B bar}[0.108, 0.374, 0.206, 0.911] is around 23, indicating an increase of 13. Similar calculations for all other venture capitalists will yield the difference. Looking at all the differences, {VC E} with the comparison of {Series-A bar}[0.599, 0.584, 0.697, 0.911] and {Series-B bar}[0.599, 0.210, 0.697, 0.911] see the most significant increase, with an increase from 14 million to 30 million, resulting in a difference of 16 million.\nBased on the above thought, the answer is:\nVC E"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "4JLVrSsdc9Jui97P",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you've analyzed, can you represent the data in a structured table representing Volatile Movement and Rapid Growth trends in each academic field?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data represented in the bar chart to a CSV table, we need to extract the quantities each bar represents. Looking at the x-axis, there are six academic fields represented as distinct bars; {'Computer Science'}<r>[0.108, 0.671, 0.223, 0.806]</r>, {'Biotechnology'}<r>[0.252, 0.372, 0.367, 0.701]</r>, {'Physics'}<r>[0.396, 0.297, 0.511, 0.716]</r>, {'Arts and Humanities'}<r>[0.540, 0.312, 0.655, 0.686]</r>, {'Social Science'}<r>[0.684, 0.357, 0.799, 0.791]</r> and {'Economics'}<r>[0.828, 0.237, 0.943, 0.731]</r> respectively. The y-axis reflects the number of topics discussed in these fields, with the range markers, starting at {'0'}<r>[0.047, 0.903, 0.056, 0.923]</r> going up to {'50'}<r>[0.039, 0.154, 0.056, 0.174]</r>. The bars are color-coded and are labeled as {\"Volatile Movement\"}<r>[0.387, 0.082, 0.529, 0.104]</r> and {\"Rapid Growth\"}<r>[0.603, 0.082, 0.706, 0.104]</r> in the legend. Each value can be determined by the height of the respective stack bar. For instance, the RGB value of Computer Science in Volatile Movement is 7 and Rapid Growth is 9.\nBased on the above thought, the answer is:\nField,Volatile Movement,Rapid Growth\nComputer Science,7,9\nBiotechnology,14,22\nPhysics,13,28\nArts and Humanities,15,25\nSocial Science,8,29\nEconomics,12,33"
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "UzPe3qbfumPPwwpg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of engagement in academic fields, what does the bar chart tell us?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart provides a representation of moving trends across various academic fields over time. By using x-axis tick labels like {'Computer Science'}<r>[0.102, 0.925, 0.229, 0.945]</r>, we can see that distinct subjects are addressed. The y-axis, ranging from {'0'}<r>[0.047, 0.903, 0.056, 0.923]</r> to {'50'}<r>[0.039, 0.154, 0.056, 0.174]</r>, tracks the number of topics discussed in each of the academic areas. Each stack represents a different type of trend observed: 'Volatile Movement' and 'Rapid Growth'. The lower stack, represented with color label {\"Volatile Movement\"}<r>[0.387, 0.082, 0.529, 0.104]</r> shows fluctuating levels of activity in each field, while the upper stack, with the label of {\"Rapid Growth\"}<r>[0.603, 0.082, 0.706, 0.104]</r>, shows a clear increase in activity over time. In general, we can discern that overall engagement in these academic fields is dynamic, with some fields showing more rapid growth than others (for example, Economics), and flucutuates over time.\nBased on the above thought, the answer is:\nThis chart tells us that engagement in academic fields fluctuates over time, yet trends towards overall growth. Some fields, like Economics, show more rapid growth than others, suggesting increased activity and interest over time."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "GBn7FMuI2QoyvvZh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic field experienced the highest Rapid Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the upper stack of each pair representing Rapid Growth in the same order of academic's fields as before, observing each respective entry such as in {'Computer Science'}<r>[0.108, 0.806, 0.223, 0.911]</r>, {'Biotechnology'}<r>[0.252, 0.701, 0.367, 0.911]</r>, {'Physics'}<r>[0.396, 0.716, 0.511, 0.911]</r>, {'Arts and Humanities'}<r>[0.540, 0.686, 0.655, 0.911]</r>, {'Social Science'}<r>[0.684, 0.791, 0.799, 0.911]</r> and {'Economics'}<r>[0.828, 0.731, 0.943, 0.911]</r>. The academic field that experienced the highest Rapid Growth would be the one for which the y-value is the highest, meaning that the bar reaches the highest point on the y-axis. In this case, it would be 'Economics' since it reaches up to the y-value of 33.\nBased on the above thought, the answer is:\nEconomics"
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "y4bMYK7P0omLHIMy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me convert the data values shown in the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The diagram's title {\"Emerging Technologies and Trends Outlook\"}[-0.166, 0.031, 0.583, 0.073] informs me that the chart displays Hypothetical Trend values of various emerging technologies from 2018 to 2022. The x-axis represents {the year}[{\"2018\"}[0.111, 0.891, 0.166, 0.920]; {\"2019\"}[0.146, 0.891, 0.201, 0.920]; {\"2020\"}[0.181, 0.891, 0.236, 0.920]; {\"2021\"}[0.216, 0.891, 0.271, 0.920]; {\"2022\"}[0.251, 0.891, 0.306, 0.920]], and the y-axis shows the Trend Value of technologies. The legend in the chart lists the different technologies as {\"AI\"}[0.543, 0.164, 0.566, 0.195],\"Internet of Things\"[0.543, 0.211, 0.755, 0.242], \"Blockchain\"[0.543, 0.258, 0.672, 0.290], \"Quantum Computing\"[0.543, 0.305, 0.793, 0.337], \"AR/VR\"[0.543, 0.353, 0.616, 0.384], \"5G\"[0.543, 0.400, 0.576, 0.431] and \"Cybersecurity\"[0.543, 0.447, 0.709, 0.478]. From the bar chart's height and position, we can extract the corresponding value for each technology in each year. For instance, the value for AI in 2018 can be found at the coordinates of the corresponding bar [0.125, 0.753, 0.153, 0.870] which is 70, and this process will be repeated for each technology for each year.\nBased on the above thought, the answer is:\nYear,AI,Internet of Things,Blockchain,Quantum Computing,AR/VR,5G,Cybersecurity\n2018,70,65,50,10,35,40,60\n2019,75,67,54,20,39,43,64\n2020,79,70,45,30,42,48,68\n2021,85,74,60,40,45,53,72\n2022,90,80,44,50,51,58,76"
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "cJfX3ha5jHUBdHPC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can you make about technology trends from 2018 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the year labels on the x-axis [0.111, 0.891, 0.166, 0.920 to 0.251, 0.891, 0.306, 0.920] and the trend lines for each technology, I can infer that most technologies have shown a steady increase. For instance, {AI}[0.543, 0.164, 0.566, 0.195], represented by the series starting at the bar [0.125, 0.753, 0.153, 0.870] and ending at [0.264, 0.720, 0.292, 0.870], increased consistently. The same upward trend is observed for {Internet of Things}[0.543, 0.211, 0.755, 0.242] and {Cybersecurity}[0.543, 0.447, 0.709, 0.478]. However, {Blockchain}[0.543, 0.258, 0.672, 0.290] showed a decrease between 2020 and 2021, and {5G}[0.543, 0.400, 0.576, 0.431] showed marginal decline in 2020. The most dramatic increase occurred in {Quantum Computing}[0.543, 0.305, 0.793, 0.337], which rose sharply from 10 to 50 between 2018 and 2022. Conversely, there is a minor increase each year for {AR/VR}[0.543, 0.353, 0.616, 0.384].\nBased on the above thought, the answer is:\nThe trends indicate that all technologies, including AI, the Internet of Things, Quantum Computing, AR/VR, 5G, and Cybersecurity, experienced an overall increase from 2018 to 2022. Quantum Computing saw the most dramatic rise. There was a slight decrease in the Blockchain trend between 2020 and 2021 and a tiny dip for 5G in 2020. The consistent upward trend globally implies a lively interest and investment in these technologies."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "bbsdjxczTnTOTrD7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology experienced the biggest change from 2018 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which technology experienced the biggest change from 2018 to 2022, I need to compare the heights of the bars representing each technology in 2018 and 2022. If I look at the coordinate for AI [0.125, 0.753, 0.153, 0.870] for 2018 and [0.264, 0.720, 0.292, 0.870] for 2022, the change is 20. By repeating this comparison for all technologies I can see that {Quantum Computing}[0.543, 0.305, 0.793, 0.337] had the highest change from {2018}[0.125, 0.544, 0.153, 0.561] to {2022}[0.264, 0.344, 0.292, 0.512], with a change of 40.\nBased on the above thought, the answer is:\nQuantum Computing."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "8jYxrtEXmczc5mcR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table format for the user activity data for the month of January?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I would need to extract the values of user activity for each social media platform for January. {The blue bar corresponding to Facebook}<r>[0.124, 0.686, 0.180, 0.783]</r> has a value annotation of {1250}<r>[0.135, 0.725, 0.170, 0.745]</r>. Similarly, {the pink bar for Instagram}<r>[0.124, 0.447, 0.180, 0.686]</r>, {the light blue bar for Twitter}<r>[0.124, 0.401, 0.180, 0.447]</r>, {the dark blue bar for LinkedIn}<r>[0.124, 0.216, 0.180, 0.401]</r>, and {the red bar for Pinterest}<r>[0.124, 0.202, 0.180, 0.216]</r> represent their respective user counts to be 3100, 590, 2400, and 180 as annotated above each bar. Based on these observations, the structured table for January could be created.\nBased on the above thought, the answer is:\nPlatform,January Users (in thousands)\nFacebook,1250\nInstagram,3100\nTwitter,590\nLinkedIn,2400\nPinterest,180"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "1WVgEIylsyd5PIwa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform showed the greatest increase in number from January to December based on the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the platform with the greatest increase, I'd need to look at the rise in bar height from January to December corresponding to each platform. Observing the {January bar for Instagram}<r>[0.124, 0.447, 0.180, 0.686]</r> and the {December bar for Instagram}<r>[0.888, 0.404, 0.944, 0.706]</r>, we see an annotation increase from 3100 to 3900. For Facebook, the {January bar}<r>[0.124, 0.686, 0.180, 0.783]</r> shows 1250 and the {December bar}<r>[0.888, 0.706, 0.944, 0.783]</r> shows 1000. LinkedIn starts at {2400 in January}<r>[0.124, 0.216, 0.180, 0.401]</r> and increases to {3150 in December}<r>[0.888, 0.110, 0.944, 0.353]</r>. Twitter and Pinterest both start at lower figures and show slight increments. Upon comparison, Instagram has the largest numerical increase from the beginning to the end of the year.\nBased on the above thought, the answer is:\nInstagram. The platform begins the year with 3100 thousand users in January and grows to 3900 thousand users by December, indicating the greatest absolute increase among the platforms."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "xXJX26w0LE3D5THj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of users across all platforms for the month of May?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total number of social media users for May, I need to sum the numbers represented by the bars for that month. By locating the bars for May, I can read the values: {Facebook}<r>[0.402, 0.678, 0.458, 0.783]</r> shows 1350, {Instagram}<r>[0.402, 0.431, 0.458, 0.678]</r> shows 3200, {Twitter}<r>[0.402, 0.381, 0.458, 0.431]</r> shows 650, {LinkedIn}<r>[0.402, 0.188, 0.458, 0.381]</r> shows 2500, and {Pinterest}<r>[0.402, 0.172, 0.458, 0.188]</r> shows 200. Adding these values would provide me the total user activity for the month of May.\nBased on the above thought, the answer is:\n7900"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "HMG3BBlMeW7Mzlih",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you've analyzed, could you convert the represented data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {\"Number of Entities in Different Stages of an Entrepreneurship and Innovation Ecosystem\"}[0.022, 0.091, 1.011, 0.120] and the x-axis labels {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945], {\"Seed\"}[0.287, 0.925, 0.322, 0.945], {\"Series A\"}[0.417, 0.925, 0.474, 0.945], {\"Series B\"}[0.559, 0.925, 0.615, 0.945], {\"Series C\"}[0.700, 0.925, 0.756, 0.945], and {\"Exit\"}[0.856, 0.925, 0.882, 0.945], I can understand these represent the investment stages for the entities. The y-axis labels indicate the number of entities. According to {the legend}[0.110, 0.019, 0.922, 0.095], the {\"Startups\"}[0.428, 0.065, 0.493, 0.086] have an orange bar in the chart and {\"Incubators\"}[0.566, 0.065, 0.647, 0.086] are indicated by a greenish bar. Now, I can understand the color representation and collate the information based on the mention in the legend. The individual bars and their heights would represent the number of startups or incubators in each stage. For example, for \"Pre-seed\", there are approximately {\"8 Startups\"}[0.107, 0.651, 0.220, 0.911] and {\"2 Incubators\"}[0.107, 0.586, 0.220, 0.651]. Taking the heights of the bars as reference, I can extract all numerical values required.\nBased on the above thought, the answer is:\nInvestment Stages,Startups,Incubators\nPre-seed,8,2\nSeed,18,5\nSeries A,13,7\nSeries B,10,8\nSeries C,5,3\nExit,1,0"
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "NyeYuvWJHn0dewHo",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the provided chart, can you assess which entity, startups or incubators, shows a steadier growth across the investment stages? Justify."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should compare the progression of bars for both {\"Startups\"}[0.428, 0.065, 0.493, 0.086] and {\"Incubators\"}[0.566, 0.065, 0.647, 0.086] across the different stages of investment starting from {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945] up to {\"Exit\"}[0.856, 0.925, 0.882, 0.945]. If I look at the startups, they start at a number of 8 and eventually decrease to 1 at the \"Exit\" stage. For incubators, they start from 2 and peek at the \"Series B\" stage then decrease to 0 at \"Exit\". Though the numbers decrease for both, startups have a more dramatic drop, especially post \"Series B\" stage. Incubators however show a more gradual decrease. Hence, Incubators portray steadier growth, even though they end at 0 at \"Exit\" stage, their decline is more gradual.\nBased on the above thought, the answer is:\nIncubators. Although both Startups and Incubators show a decrease in number as they progress, the decline in the number of Incubators seems to be steadier and less steep compared to that of Startups, hence it indicates a steadier growth."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "4a22Gd0hetBp1fUC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which stage of investment is the difference in number of startups and incubators the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrive at this, I need to compare the difference between the number of startups and incubators at each stage of investment. I start by looking at the bars under {\"Pre-seed\"}[0.133, 0.925, 0.193, 0.945], {\"Seed\"}[0.287, 0.925, 0.322, 0.945], {\"Series A\"}[0.417, 0.925, 0.474, 0.945], {\"Series B\"}[0.559, 0.925, 0.615, 0.945], {\"Series C\"}[0.700, 0.925, 0.756, 0.945], and {\"Exit\"}[0.856, 0.925, 0.882, 0.945]. The difference is calculated by subtracting the number of incubators from that of the startups at each stage. The sequences of bars under each stage such as for \"Seed\" at [0.248, 0.163, 0.361, 0.326] and [0.248, 0.326, 0.361, 0.911] represent the number of Incubators and Startups respectively. After comparing all the heights difference, I find that the difference is largest at the \"Seed\" stage of investment.\nBased on the above thought, the answer is:\nSeed."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "xHXMo1rgInAHvqTs",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nEvaluate the data of the status of various environmental indicators and represent them into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The indicators are represented on the x-axis {\"Environmental Indicators\"}[0.423, 0.927, 0.641, 0.963], where the eight indicators are {\"Air quality\"}[0.128, 0.648, 0.203, 0.799], {\"Water quality\"}[0.223, 0.648, 0.318, 0.838], {\"Land use\"}[0.341, 0.648, 0.409, 0.784], {\"Habitat restoration\"}[0.415, 0.648, 0.544, 0.906], {\"Ecosystem services\"}[0.518, 0.648, 0.651, 0.914], {\"Biodiversity\"}[0.647, 0.648, 0.732, 0.818], {\"Pollution control\"}[0.738, 0.648, 0.850, 0.871], {\"Recycling rate\"}[0.849, 0.648, 0.949, 0.848]. The corresponding scores which represents the quality of each indicator are given on the y-axis {\"Score\"}[0.019, 0.289, 0.036, 0.386] , with the actual value noted on each bar; for example, {\"Air quality\"}[0.124, 0.278, 0.207, 0.624] has a score of 0.605 as denoted by {value annotation \"0.605\"}[0.141, 0.243, 0.190, 0.278] and {\"Water quality\"}[0.228, 0.189, 0.312, 0.624] has the score of 0.76 as shown by {value annotation \"0.76\"}[0.251, 0.154, 0.289, 0.189]. Using these, we can formulate the CSV representation denoting each parameter with its corresponding score.\nBased on the above thought, the answer is:\nIndicator,Score\nAir quality,0.605\nWater quality,0.760\nLand use,0.698\nHabitat restoration,0.805\nEcosystem services,0.645\nBiodiversity,0.720\nPollution control,0.685\nRecycling rate,0.825"
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "1ZxrEpXka0d4XG1X",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what comments can you provide on the status of current environmental indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart demonstrates the scores of various environmental indicators, including Air quality, Water quality, Land use, Habitat restoration, Ecosystem services, Biodiversity, Pollution control, and Recycling rate. These scores provide performance review of these indicators. The longest bars represent the areas which are performing the best. The {Recycling rate}[0.857, 0.152, 0.940, 0.624], and {Habitat restoration}[0.438, 0.163, 0.522, 0.624], obtained higher scores than others, indicating better performance in these indicators. However, indicators like {Air quality}[0.124, 0.278, 0.207, 0.624] and {Ecosystem services}[0.543, 0.255, 0.626, 0.624], even though they score above 0.6, their performance is relatively lower than the other indicators. It implies that improvements are potentially needed in these areas to boost their performance.\nBased on the above thought, the answer is:\nThe status of environmental indicators is generally good with scores above 0.6. However, areas like Recycling rate and Habitat restoration appear to have better performance with the highest scores. Yet, certain aspects such as Air quality and Ecosystem services need improvements as their scores are relatively lower."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "w67cw9Z6pX0NtACp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich environmental indicator receives the best score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each environmental indicator has a corresponding score represented by the height of the bar and denoted by a text at the top of the bar. By locating each bar and comparing their heights, we can find out that {Recycling rate}[0.857, 0.152, 0.940, 0.624] has the highest bar, with {the value \"0.825\"}[0.874, 0.117, 0.923, 0.152] annotated at the top, meaning that it received the highest score amongst all environmental indicators.\nBased on the above thought, the answer is:\nRecycling rate."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "IYjdu8TCrsH7qwe8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the bar chart data into a CSV-style table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it is clear that the 'x-axis' caption is {\"Platforms\"}<r>[0.493, 0.942, 0.559, 0.970]</r>, and the 'y-axis' caption is {\"User Engagement\"}<r>[0.015, 0.295, 0.029, 0.544]</r>. The bars <r>[0.108, 0.193, 0.184, 0.799] to [0.867, 0.662, 0.943, 0.799]</r>correspond to specific platforms and their corresponding engagement rate. From this, I can gather that the left axis represents the user engagement rates for various Social Media platforms. The user engagement rate for each platform can be estimated using the height of each bar; for example, the Facebook bar height denotes an engagement rate of 0.8, while the Reddit bar height signifies an engagement rate of 0.18. Thus, converting these visual cues to textual data in CSV format should yield a platform vs engagement rate table.\nBased on the above thought, the answer is:\nPlatform,User Engagement\nFacebook,0.8\nInstagram,0.62\nWhatsApp,0.58\nLinkedIn,0.22\nSnapchat,0.35\nTwitter,0.58\nPinterest,0.45\nTumblr,0.13\nReddit,0.18"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "6nLuYPvzbmROhbdO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the information in the chart, which platform would you recommend for a marketing campaign if the priority is user engagement? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the priority is user engagement, then the recommendation should be the platform with the highest user engagement rate. Looking at the bar chart, the platform represented by the tallest bar would be recommended, which is Facebook <r>[0.108, 0.193, 0.184, 0.799]</r>. With its significant user engagement rate of 0.8, Facebook is likely to give the marketing campaign much better exposure and make it interact with more active users. So, due to its high user engagement, Facebook is the recommended platform.\nBased on the above thought, the answer is:\nI recommend Facebook. Because it has the highest user engagement rate, so a marketing campaign there is likely to reach a significant number of actively engaged users."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "zqbY3BBz2GI95kFE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your visual analysis of the graph, which platform had the highest user engagement rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which platform had the highest user engagement rate, I will consider the heights of the bars<bar's coordinates> in the bar chart. The taller the bar, the higher the engagement rate. By making a visual comparison, I observe that Facebook <r>[0.108, 0.193, 0.184, 0.799]</r> has the tallest bar among the platforms, suggesting that it has the highest user engagement rate.\nBased on the above thought, the answer is:\nFacebook"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "Kj799tipPWew53DG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data represented by the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the x-axis labels and the label of each corresponding bar, we can convert the data represented in the bar chart to a CSV. The labels on the x-axis represent the different industries which are: {x-label}<r>[0.494, 0.952, 0.562, 0.975]</r> and the labels on the bars represent the corresponding revenue for each industry: {bar text labels}<r>all the label coordinates above</r>.\nBased on the above thought, the answer is:\nIndustry,Revenue\nVideo Games,80\nOnline Gambling,40\nFilm,200\nMusic,45\nVirtual Reality,35\nSocial Media Entertainment,60\nPodcasts,30\nOnline Sports Betting,55\nSurveillance Entertainment,20\nTheme Parks,115"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "PkA2xckLvBQMydsX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart which industries do you think are leading in terms of revenue? And which ones are struggling?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of each bar in the chart directly correlates with the revenue of each industry and the larger the bar the more successful the industry. From the bar chart, the industry with the highest revenue is {Film}<r>[0.282, 0.164, 0.350, 0.683]</r>, followed by {Theme Parks}<r>[0.876, 0.385, 0.943, 0.683]</r> and {Video Games}<r>[0.112, 0.476, 0.180, 0.683]</r>. Therefore these industries are leading in terms of revenue. On the flip side, the industries with the shortest bars, thus the lower revenues, are {Surveillance Entertainment}<r>[0.791, 0.631, 0.859, 0.683]</r> and {Podcasts}<r>[0.621, 0.605, 0.689, 0.683]</r>. These industries can be considered as struggling compared to the others.\nBased on the above thought, the answer is:\nThe industries leading in terms of revenue are Film, Theme Parks and Video Games. While the industries that are struggling are Surveillance Entertainment and Podcasts."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "UyaK70o18ecTHl9W",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total revenue of all the industries according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to sum up all the revenue figures represented by the height of each bar in the chart. By looking at the labels displayed on top of each bar<{\"bar text labels\"}<r>all the label coordinates above</r>>, I can easily find out the revenue of each industry and sum them up.\nBased on the above thought, the answer is:\n680"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "2Ck3rTUaVIQDYnS9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the bar chart data into a CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title, {x-axis labels}[{\"Raw Materials\"}[0.142, 0.716, 0.241, 0.914]; {\"Production\"}[0.323, 0.716, 0.401, 0.872]; {\"Distribution\"}[0.490, 0.716, 0.574, 0.884]; {\"Marketing\"}[0.665, 0.716, 0.739, 0.864]; {\"After Sales\"}[0.833, 0.716, 0.912, 0.873]], and {y-axis labels}[{\"0.0\"}[0.043, 0.678, 0.071, 0.713]; {\"0.2\"}[0.043, 0.550, 0.071, 0.585]]; {\"0.4\"}[0.043, 0.422, 0.071, 0.457]; {\"0.6\"}[0.043, 0.294, 0.071, 0.329]; {\"0.8\"}[0.043, 0.166, 0.071, 0.201]; {\"1.0\"}[0.043, 0.037, 0.071, 0.073]] in the figure indicate that the x-axis represents stages and y-axis represents the values of the Value Chain. Different bars denote different stages, to extract their corresponding values, I can check the height of the bars and their value annotations at the top. For example, {the \"Raw Materials\" bar}[0.124, 0.436, 0.260, 0.692] reaches around the y-axis 0.4, and its value annotation at the top is {0.4}[0.178, 0.401, 0.205, 0.436], thus, the value for the \"Raw Materials\" stage is 0.4.\nBased on the above thought, the answer is:\nStage,ValueChain\nRaw Materials,0.4\nProduction,0.6\nDistribution,0.95\nMarketing,0.5\nAfter Sales,0.75"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "SRMQ9CJr2jfAOgQl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given bar chart, in which part of the value chain should one invest to gain maximum value. Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "By identifying the bar with the greatest height, it can be determined where the maximum value can be gained. The bar indicating the {Distribution}[0.464, 0.083, 0.600, 0.692] is the highest among all bars indicating it has the greatest value on the value chain according to y-axis value{'0.95'}[0.513, 0.048, 0.551, 0.083]. It is considerably higher than the rest of the bars. This suggests investing in the 'Distribution' process of the value chain could yield the maximum value.\nBased on the above thought, the answer is:\nThe 'Distribution' process. The bar corresponding to this process has the highest value on the chart, indicating that it provides the maximum value in the value chain."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "I2yyq0a7yoMBCkPw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the most impactful stage in the value chain as per the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most impactful stage, I need to find the highest bar in the chart. By comparing each bar's ending point on {y-axis}[0.043, 0.678, 0.071, 0.713], I observe that the bar for {the \"Distribution\" stage}[0.464, 0.083, 0.600, 0.692] reaches the highest value, close to {'1.0'}[0.043, 0.037, 0.071, 0.073] on the y-axis. This indicates the \"Distribution\" stage has the most impact in the value chain.\nBased on the above thought, the answer is:\nDistribution"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "uIDLQlPdxckUEpN8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the information from the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, from the bar chart, I can tell that the x-axis label, {Emerging Market}<r>[0.458, 0.952, 0.606, 0.975]</r>, is the category comprising different markets. The y-axis label, {Consumer Behavior}<r>[0.019, 0.309, 0.036, 0.537]</r>, correspond with the values each category holds; and it ranges from {0 to 1}<r>[0.043, 0.802, 0.071, 0.826] to [0.043, 0.025, 0.071, 0.048]</r>. The chart uses bars of different heights to denote the values with respect to the respective markets. For instance, the bar at {BRIC}<r>[0.124, 0.306, 0.207, 0.811]</r> represents the 0.65 value. With this, I can generate a CSV table by extracting all values for each market.\nBased on the above thought, the answer is:\nEmerging Market, Consumer Behavior\nBRIC, 0.65\nMINT, 0.75\nVISTA, 0.55\nNext Eleven, 0.80\nFRANK, 0.73\nCIVETS, 0.68\nEAGLEs, 0.82\n3G, 0.70"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "clSPVUvhVDPoMQxw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, which Emerging Market is most likely to experience a significant increase in sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the provided data, the Consumer Behavior metric serves as a reliable indicator of potential sales performance in each Emerging Market. A higher metric signifies a more active consumer behavior, implying higher potential sales. From the chart, we can observe that the bar depicting the Emerging Market {EAGLEs}<r>[0.752, 0.174, 0.836, 0.811]</r> has the highest consumer behavior value at 0.82. This makes it the most promising Market for potential sales increase.\nBased on the above thought, the answer is:\nThe EAGLEs market, because it has the highest Consumer Behavior metric and displays the most active purchasing behaviors, indicating a higher potential for sales."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "YZuqR6DrgcPL7QS7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Emerging Market has the lowest Consumer Behavior metric in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at each bar in the chart indicates the metric value for the associated Emerging Market. For example, the bar at {BRIC}<r>[0.124, 0.306, 0.207, 0.811]</r> is at 0.65 while the bar at {VISTA}<r>[0.333, 0.384, 0.417, 0.811]</r> is at 0.55, which is currently the lowest. Comparing all other bars, it is obvious that no other market has a lower consumer behavior value than 'VISTA'.\nBased on the above thought, the answer is:\nVISTA"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "hDZE9B00OabHe6Wj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the displayed bar chart into a tabular format."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart into a table, I will need to extract each exercise on the x-axis and the corresponding participation rate on the y-axis. The {exercise names}<r>[{\"Yoga\"}[0.124, 0.787, 0.166, 0.852], {\"Pilates\"}[0.218, 0.787, 0.270, 0.871], {\"CrossFit\"}[0.315, 0.787, 0.375, 0.883], {\"Running\"}[0.417, 0.787, 0.480, 0.887], {\"Cycling\"}[0.527, 0.787, 0.584, 0.879], {\"Weightlifting\"}[0.599, 0.787, 0.689, 0.931], {\"Swimming\"}[0.717, 0.787, 0.794, 0.909], {\"Zumba\"}[0.843, 0.787, 0.899, 0.876]]</r> are on the x-axis and the {participation rates}<r>[{\"0.23\"}[0.146, 0.405, 0.185, 0.433], {\"0.17\"}[0.251, 0.492, 0.289, 0.520], {\"0.29\"}[0.356, 0.318, 0.394, 0.346], {\"0.47\"}[0.461, 0.057, 0.499, 0.085], {\"0.41\"}[0.565, 0.144, 0.604, 0.172], {\"0.36\"}[0.670, 0.216, 0.708, 0.244], {\"0.39\"}[0.775, 0.173, 0.813, 0.201], {\"0.22\"}[0.879, 0.420, 0.918, 0.448]]</r> are displayed at the top of the corresponding bar. For instance, the bar labeled as {Yoga}<r>[0.124, 0.787, 0.166, 0.852]</r> has a value of {0.23}<r>[0.146, 0.405, 0.185, 0.433]</r> as depicted above the bar. So, I will collect the values of each exercise's participation rate using the information displayed above the bars.\nBased on the above thought, the answer is:\nExercise, Participation_Rate\nYoga, 0.23\nPilates, 0.17\nCrossFit, 0.29\nRunning, 0.47\nCycling, 0.41\nWeightlifting, 0.36\nSwimming, 0.39\nZumba, 0.22"
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "09FYG9oTFrrsVU25",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data presented, what seems to be the most popular form of exercise and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "The given bar chart represents the participation rates for different exercises with the resulting popularity related to a higher participation rate. As seen from the bar for {Running}<r>[0.438, 0.085, 0.522, 0.767]</r>, it has the highest value amongst all other bars, with a value of {0.47}<r>[0.461, 0.057, 0.499, 0.085]</r> as specified at the top of the bar, thus indicating it as the most popular form of exercise. The reason for Running being popular might be due to its convenience, as it does not require any distinct equipment or memberships, and can be done anywhere, indoors or outdoors, depending on personal preferences. Also, running is often recommended as a baseline fitness activity which may contribute to its popularity.\nBased on the above thought, the answer is:\nRunning. It has a higher participation rate which may be due to its convenience and universal accessibility."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "too5bMbBXTPTsCf6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, what is the maximum participation rate among all exercises?"
+            },
+            {
+                "from": "gpt",
+                "value": "The participation rate of each exercise is illustrated by the height of the corresponding bar, and the exact rate is written at the top of each bar. I need to compare these values to find the maximum. For example, the bar for {Running}<r>[0.438, 0.085, 0.522, 0.767]</r> has a value of {0.47}<r>[0.461, 0.057, 0.499, 0.085]</r> at the top, which appears to be the highest among all exercise bars. This suggests that Running has the maximum participation rate among all exercises.\nBased on the above thought, the answer is:\n0.47"
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "Ogej6VKRBD4XZfKB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the bar chart, please put together a two column CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title, {\"Funding Allocations for Public Service Sectors\"}[0.235, 0.963, 0.765, 0.992] and the {x-axis labels}[{\"Education\"}[0.166, 0.633, 0.202, 0.804], {\"Healthcare\"}[0.270, 0.633, 0.310, 0.816], {\"Police\"}[0.375, 0.633, 0.399, 0.753], {\"Firefighting\"}[0.480, 0.633, 0.521, 0.820], {\"Public Transport\"}[0.584, 0.633, 0.640, 0.878], {\"Parks & Recreation\"}[0.689, 0.633, 0.753, 0.914], {\"Infrastructure\"}[0.794, 0.633, 0.842, 0.850], {\"Social Services\"}[0.899, 0.633, 0.951, 0.865]] clearly communicate that the x-axis represents the different public service sectors. The y-axis represents the degree of funding each sector receives, ranging from a minimum of {0.0}[0.083, 0.633, 0.071, 0.654] to a maximum of {1.0}[0.083, 0.051, 0.071, 0.073]. The funding values can be determined form the heights of the bars and the respective values annotated at the top of each bar. For example, \"Education\"[0.166, 0.633, 0.202, 0.804] has a value of 0.6 indicated by its bar height and corresponding annotation[0.152, 0.249, 0.179, 0.284]. In the same way we can collate the funding values for all the sectors and represent it in a CSV format table.\nBased on the above thought, the answer is:\nServices,Funding\nEducation,0.6\nHealthcare,0.7\nPolice,0.4\nFirefighting,0.55\nPublic Transport,0.34\nParks & Recreation,0.8\nInfrastructure,0.9\nSocial Services,0.88"
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "p5681bVRiRImPc2R",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we conclude from the chart that this hypothetical city values Infrastructure the most and Public Transport the least? Provide reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, we can make an inference about the city's priorities in terms of public services. The higher the bars in the chart, the greater the allocation of funds, which could be interpreted as a higher level of importance placed on that sector. Given that comparison, we can infer that {Infrastructure}[0.794, 0.633, 0.842, 0.850] with a bar at [0.752, 0.109, 0.836, 0.633] and the maximum fund allocation of 0.9, is probably the most valued. Conversely, {Public Transport}[0.584, 0.633, 0.640, 0.878] with the smallest bar at [0.543, 0.435, 0.626, 0.633] and the least fund allocation of 0.34, is potentially less valued by the city as compared to the other sectors.\nBased on the above thought, the answer is:\nYes, the chart could suggest that Infrastructure is the most valued public service sector within this hypothetical city since it receives the highest allocation of funds. Conversely, Public Transport receives the least funding, suggesting it could be the least valued sector."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "8ka1ozUJ2vtwSPe3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service sector has the maximum funding allocation according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By sequentially comparing the heights of the bars for each sector, one by one, we can identify the sector with the maximum allocation. Starting with {\"Education\"}[0.166, 0.633, 0.202, 0.804] represented by the bar at [0.124, 0.284, 0.207, 0.633] with an allocation of 0.6, and moving onto the next, {\"Healthcare\"}[0.270, 0.633, 0.310, 0.816] at [0.228, 0.226, 0.312, 0.633] with an allocation of 0.7, and so on to {\"Infrastructure\"}[0.794, 0.633, 0.842, 0.850] at [0.752, 0.109, 0.836, 0.633] with an allocation of 0.9, which is the highest value compared to all the other sectors.\nBased on the above thought, the answer is:\nInfrastructure"
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "cUVx3OnOob2wJTwV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the bar chart data in the form of a CSV using the Industry and its respective Growth Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the layout and contents of the graph, it's clear that it's a horizontal bar chart with various sectors of E-commerce and Retail displayed on the Y-axis and their respective growth rates on the X-axis. For instance, the Industry label {Fashion & Apparel}<r>[0.063, 0.685, 0.161, 0.882]</r> corresponds to a bar graph with a growth rate of 0.524, as indicated by its width and the hovered growth rate label above <r>[0.142, 0.261, 0.181, 0.289]</r>. A similar mapping process can be followed for other industries to extract their respective growth rates.\nBased on the above thought, the answer is:\nIndustry, Growth Rate\nFashion & Apparel, 0.524\nElectronics & Media, 0.697\nFood & Personal Care, -0.174\nFurniture & Appliances, 0.385\nToys, Hobby & DIY, 0.455\nHealth & Wellness, 0.615\nAutomotive & Parts, -0.092\nTravel & Accomodation, 0.542"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "t4E6mpQRx0nEfwVI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the growth trends across industries, which sector might be a risky investment according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The risk of investment in a sector could be inferred based on its growth rate, with visually shorter bars indicating lower growth rate or negative growth. In this regard, {Food & Personal Care}<r>[0.258, 0.685, 0.373, 0.915]</r> and {Automotive & Parts}<r>[0.691, 0.685, 0.796, 0.895]</r> stand out as they have negative growth rates as visible by their bar<r>[0.330, 0.561, 0.415, 0.652]</r> and<r>[0.753, 0.561, 0.838, 0.609]</r> respectively that extend towards left on the negative x-axis scale. In light of these findings, Food & Personal Care and Automotive & Parts may be considered as risky investments.\nBased on the above thought, the answer is:\nFood & Personal Care and Automotive & Parts might be risky investments due to their negative growth rates."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "INIDb1wWS4koBtcR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine which sector in the E-commerce and Retail Industry has witnessed the highest growth rate from this bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the length of each bar, which corresponds to the growth rate, I need to find the longest one. By observing the chart, it appears that {Electronics & Media}<r>[0.160, 0.685, 0.267, 0.898]</r>'s corresponding bar is the longest, reaching up to a point labeled with the text 0.697<r>[0.247, 0.171, 0.287, 0.199]</r>. Therefore, Electronics & Media has the highest growth rate.\nBased on the above thought, the answer is:\nElectronics & Media"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "GsNYOFyV1MvjsEXZ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, could you convert the sales data depicted for each month into a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the x-axis of the chart represents the months {\"xlabel\": \"Months\"}[0.509, 0.927, 0.572, 0.963]. The corresponding months are Jan-Dec according to the xticks coordinates. The y-axis of the chart represents sales {\"ylabel\": \"Sales(in thousands)\"}[0.019, 0.249, 0.036, 0.602]. From each rectangle's bar and text coordinates, I can obtain the value of sales for each month. For example, At {\"bar_coordinates\": [0.139, 0.276, 0.194, 0.814]} and {\"text_coordinates\": [0.145, 0.241, 0.188, 0.276]} for January, where the associated text is 4500. By deducing information similarly for each month, I can shape the CSV file.\nBased on the above thought, the answer is:\nMonth,Sales\nJan,4500\nFeb,3500\nMar,3900\nApr,4150\nMay,4600\nJun,4800\nJul,4000\nAug,3200\nSep,4400\nOct,5100\nNov,6000\nDec,4800"
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "8D0wyJVtjfvv0WuX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret the sales trend for the year 2021 as depicted by the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires a comprehensive review and interpretation of the bar chart - in particular, each bar's height that corresponds to sales data for each month. Sales in January {\"bar_coordinates\": [0.139, 0.276, 0.194, 0.814] and its \"text_coordinates\": [0.145, 0.241, 0.188, 0.276]} start at 4500. A decrease is noticeable in sales for February {\"bar_coordinates\": [0.207, 0.396, 0.262, 0.814] and its \"text_coordinates\": [0.213, 0.361, 0.256, 0.396]} with 3500. The sales see a steady rise from March to June, but a drop comes during July {\"bar_coordinates\": [0.547, 0.336, 0.601, 0.814] and its \"text_coordinates\": [0.552, 0.301, 0.596, 0.336]} with sales listed as 4000. Another drop is observed in August {\"bar_coordinates\": [0.615, 0.431, 0.669, 0.814] and its \"text_coordinates\": [0.620, 0.396, 0.664, 0.431]} with the sales figure at 3200 which is the lowest. However, from September sales start rising steadily, with the biggest boost in November {\"bar_coordinates\": [0.819, 0.097, 0.873, 0.814] and its \"text_coordinates\": [0.824, 0.062, 0.868, 0.097]} to 6000, only to drop again in December {\"bar_coordinates\": [0.887, 0.240, 0.941, 0.814] and its \"text_coordinates\": [0.892, 0.205, 0.936, 0.240]} to 4800.\nBased on the above thought, the answer is:\nThe sales seem to fluctuate throughout the year of 2021. While there's a gradual rise in sales from March to June, the sales drastically fall in July and August before an upward trend gets seen from September till November where it peaks. However, the sales figure falls again in December. There seem to be inconsistent growth and abrupt falls making sales seem volatile."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "pN49k6Jr6TFwEiEv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which month, does the sales figure reach its peak for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to compare the heights of all the monthly sales bars. Observing the data from the rectangles of the bars, I find that the Nov {\"bar_coordinates\": [0.819, 0.097, 0.873, 0.814]} and its associated sales {\"text_coordinates\": [0.824, 0.062, 0.868, 0.097]} of 6000 is the highest among all the months.\nBased on the above thought, the answer is:\nNovember."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "sXUL0ijaYVOT4n7f",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you aggregate the data from the chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by looking at the {x-label}[0.485, 0.952, 0.579, 0.975] we can determine that the x-axis represents different fields of study. The labels and their corresponding coordinates are as follows: {\"Computer Science\"}[0.148, 0.687], {\"Software Engineering\"}[0.218, 0.687], {\"Electrical Engineering\"}[0.288, 0.687], {\"Mechanical Engineering\"}[0.358, 0.687], {\"Civil Engineering\"}[0.428, 0.687], {\"Environmental Sciences\"}[0.497, 0.687], {\"Active Learning\"}[0.567, 0.687], {\"Language Learning\"}[0.637, 0.687], {\"Online Education Platforms\"}[0.707, 0.687], {\"Practical Training\"}[0.776, 0.687], {\"Overseas Education\"}[0.846, 0.687], {\"Scientific Research\"}[0.916, 0.687]. Meanwhile, the {y-label}[0.015, 0.244, 0.029, 0.477] shows that the y-axis represents the number of students in these fields. From the visible bars and their text labels, I can extract the number of students in each field. For instance, the bar labeled {\"1200\"}[0.131, 0.468, 0.166, 0.491] corresponds to Computer Science field, thus indicating with the number of students in this field is 1200.\nBased on the above thought, the answer is:\nField of Study, Number of Students\nComputer Science, 1200\nSoftware Engineering, 1050\nElectrical Engineering, 980\nMechanical Engineering, 800\nCivil Engineering, 750\nEnvironmental Sciences, 780\nActive Learning, 1500\nLanguage Learning, 2000\nOnline Education Platforms, 3500\nPractical Training, 1800\nOverseas Education, 500\nScientific Research, 2300"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "55oIkkM5M8ts2QVx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide some insights regarding the popularity of traditional engineering fields compared to modern education trends based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure! According to this chart, traditional engineering fields (Mechanical Engineering, Civil Engineering, Electrical Engineering, Software Engineering, and Computer Science) are represented by {\"Mechanical Engineering\"}[0.358, 0.687], {\"Civil Engineering\"}[0.428, 0.687], {\"Electrical Engineering\"}[0.288, 0.687], {\"Software Engineering\"}[0.218, 0.687], {\"Computer Science\"}[0.148, 0.687]. All these traditional fields have fewer students compared to modern education trends such as {\"Online Education Platforms\"}[0.707, 0.687], {\"Language Learning\"}[0.637, 0.687], {\"Active Learning\"}[0.567, 0.687] or {\"Practical Training\"}[0.776, 0.687]. The number of students in Online Education Platforms in particular is remarkably high, indicating a significant shift from traditional engineering fields to these modern trends.\nBased on the above thought, the answer is:\nModern education trends, such as Online Education Platforms, are more popular among students compared to traditional engineering fields. This could be due to the increasing digitalization of education and the appeal of online, flexible learning environments."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "q7ImJMhmr1emdaHI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field of study has the highest number of students based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to check the heights of all the bars on the chart. The tallest bar {\"3500\"}[0.689, 0.092, 0.724, 0.116] corresponds to the Online Education Platforms which is represented by the x-axis label {\"Online Education Platforms\"}[0.707, 0.687]. Thus, the field of study with the most number of students is Online Education Platforms.\nBased on the above thought, the answer is:\nOnline Education Platforms"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "xtGLDTJhHRldtjpP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the GDP growth rate data as shown on the bar chart from 2011 to 2020 into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart is titled {\"GDP growth rate from 2011 to 2020\"}<r>[0.396, 0.021, 0.645, 0.047]</r> and throughout it, there are annual GDP growth rate bars. From 2011 to 2020, the bars corresponding to each year - {2011}<r>[0.096, 0.485, 0.166, 0.629]</r>, {2012}<r>[0.183, 0.514, 0.252, 0.629]</r>, {2013}<r>[0.270, 0.384, 0.339, 0.629]</r>, {2014}<r>[0.356, 0.629, 0.425, 0.701]</r>, {2015}<r>[0.443, 0.327, 0.512, 0.629]</r>, {2016}<r>[0.529, 0.499, 0.599, 0.629]</r>, {2017}<r>[0.616, 0.413, 0.685, 0.629]</r>, {2018}<r>[0.703, 0.168, 0.772, 0.629]</r>, {2019}<r>[0.789, 0.298, 0.858, 0.629]</r>, and {2020}<r>[0.876, 0.629, 0.945, 0.859]</r> - exhibit different heights. These varying heights indicate differences in GDP growth rates, measurable based on the coordinates of each bar's top edge and the scale indicated on the y-axis. By collating this information, we can compile the growth rates into a table.\nBased on the above thought, the answer is:\nYears,GDP growth rate\n2011,1.0\n2012,0.8\n2013,1.7\n2014,-0.5\n2015,2.1\n2016,0.9\n2017,1.5\n2018,3.2\n2019,2.3\n2020,-1.6"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "FwlWJKvgwJVOLZ4K",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the trend in the country's GDP growth rate from 2011 to 2020 based on the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "By inspecting the bars representing each year from {2011}<r>[0.096, 0.485, 0.166, 0.629]</r> to {2020}<r>[0.876, 0.629, 0.945, 0.859]</r>, I could observe the GDP trend over a decade. The GDP growth rate experienced moderate volatility from 2011 to 2013, followed by a dip into the negative region in 2014. Afterwards, growth resumed and reached a peak in 2018 before declining rapidly in 2019 and dipping into the negative region again in 2020.\nBased on the above thought, the answer is:\nThe GDP growth rate from 2011 to 2020 showed moderate fluctuations before hitting a peak in 2018. After reaching this peak, it declined rapidly and was negative by 2020."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "2cF3OkVsHocD6BQQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, in which year did the country experience the highest GDP growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest GDP growth rate, I need to locate the tallest bar in the chart. I see that all bars span from the x-axis to different heights, determined by their respective GDP growth rates. Comparing the heights of the bars, it is apparent that the bar representing {2018}<r>[0.703, 0.168, 0.772, 0.629]</r> reaches the highest point on the y-axis.\nBased on the above thought, the answer is:\n2018."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "dVmk00Nn6zVmoyd0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the performance data of the community development projects as presented in the bar chart and arrange it into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the various bars labeled {\"Infrastructure Development\"}[0.197, 0.170, 0.265, 0.582], {\"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582] and so on, I can see they display the performance of different community development projects. The performance for each project is represented by the height of its bar. For instance, the bar for the project {\"Housing Availability\"}[0.112, 0.142, 0.180, 0.582] reaches an estimated 80 mark on the y-axis, indicating a performance metric of 80. By extracting this data across all bars, we can arrange the performance metrics of all the projects into a CSV format.\nBased on the above thought, the answer is:\nCommunity Development Project,Performance\nHousing Availability,80\nInfrastructure Development,75\nYouth Development Programs,85\nAdult Literacy Programs,70\nEmployment Assistance,65\nVocational Training Programs,73\nHealthcare Services,80\nPension Schemes,90\nChildren's Programs,75\nEnvironmental Protection,70"
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "wRGXMxM6yaYJiTC8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the local government's community development projects, how is the overall situation reflected in the performance data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows the performance of various community development projects. By observing all the bars, we see that most projects have a performance rate exceeding 70, such as {\"Housing Availability\"}[0.112, 0.142, 0.180, 0.582], {\"Youth Development Programs\"}[0.282, 0.115, 0.350, 0.582], and {\"Healthcare Services\"}[0.621, 0.142, 0.689, 0.582]. This suggests that the government's community development projects are generally successful with a good performance rating. However, two projects stand out - {\"Employment Assistance\"}[0.451, 0.225, 0.519, 0.582], which has the lowest performance, and {\"Pension Schemes\"}[0.706, 0.087, 0.774, 0.582], which has the highest. This indicates that there might be significant room for improvement in some areas like employment assistance, while the pensions schemes project appears to be extremely successful.\nBased on the above thought, the answer is:\nThe overall situation presented by the bar chart is that the government's community development projects are generally successful, with most having a performance rating exceeding 70. However, there's variation as well, with rooms for improvement, particularly in the \"Employment Assistance\" project that has the lowest performance score. The highest performing project is \"Pension Schemes\", suggesting that it is an extremely successful project."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "9pyARuaTdCPjmVrk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project has the lowest performance rating among all the community development projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inspecting the height of each bar presents the \"Performance\" of each \"Community Development Project\". The bar of lowest height translates into the minimum performance amount. Upon examination, I can specifically see that the project {\"Employment Assistance\"}[0.451, 0.225, 0.519, 0.582] aligns just above the 65 mark on the y-axis. This implies that Employment Assistance has the lowest performance, quantitatively less than any other labeled bars. Thus, we can say that the project which has the least performance is Employment Assistance.\nBased on the above thought, the answer is:\nEmployment Assistance."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "kK1KQBDCdcCMBoaF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you are viewing, can you extract the data and arrange it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV table from the chart, I should first identify both axes in the chart. The {x-axis}[0.472, 0.952, 0.579, 0.975] stands for Research Fields that include AI, Physics, Chemistry, Biotechnology, Astronomy, Data Science, Robotics, Environmental Science(Nanotech), and Genetics. The {y-axis}[0.015, 0.367, 0.029, 0.561] stands for the Innovation Score, which ranges from 0.0 to 1.0. Using the height of the {bars} including {\"AI\"}[0.108, 0.185, 0.176, 0.903], {\"Physics\"}[0.193, 0.280, 0.261, 0.903] and so on, I can determine the Innovation Score for each research field.\nBased on the above thought, the answer is:\nResearch Fields,Innovation Score\nAI,0.90\nPhysics,0.78\nChemistry,0.84\nBiotechnology,0.80\nAstronomy,0.72\nData Science,0.88\nRobotics,0.93\nEnvironmental Science,0.79\nNanotech,0.82\nGenetics,1.00"
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "prAU5jbBST3P7KGC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field seems to be the most promising in terms of innovation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart, the field with the highest Innovation Score is identified by {the tallest bar}. In this chart, {the bar for \"Genetics\"}[0.875, 0.105, 0.943, 0.903] is the tallest indicating that Genetics has received the highest Innovation Score. This suggests that, in terms of innovation, Genetics may be the most promising field because its high Innovation Score indicates that significant scientific discoveries, technology advancement, or influential research are being produced in this area.\nBased on the above thought, the answer is:\nGenetics. It has the highest Innovation Score, indicating significant breakthroughs and advancements in this field."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "lScRqSWKflstNDZy",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field has the lowest Innovation Score according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lowest Innovation Score corresponds to the shortest bar in the chart. I see that {the bar for \"Astronomy\"}[0.449, 0.328, 0.517, 0.903] is the shortest, which means that Astronomy has the lowest Innovation Score among all fields.\nBased on the above thought, the answer is:\nAstronomy."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "1XhPDyICkx0whGnZ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you analyze the bar graph and generate a CSV table with the Processes and their corresponding Effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, it's clear that the x-axis stands for different kinds of processes, as I can tell from the title at {the x-axis}<r>[0.492, 0.952, 0.560, 0.975]</r> which says \"Processes\". The y-axis is labeled with \"Effectiveness\", as I can see from the label at {the y-axis}<r>[0.015, 0.277, 0.029, 0.431]</r>. The value of Effectiveness ranges from {0.0 to 1.0}<r>[0.035, 0.675, 0.057, 0.698]; [0.035, 0.076, 0.057, 0.099]</r>. Each process has one blue bar corresponds to its effectiveness. For example, the value for {process \"Underperforming\"}<r>[0.110, 0.700, 0.204, 0.857]</r> could be interpolated from the height of its bar at <r>[0.108, 0.414, 0.206, 0.684]</r> as approximately 0.45, which aligns with the raw data.  The Process and its Effectiveness can thus be enumerated and tabulated.\nBased on the above thought, the answer is:\nProcesses,Effectiveness\nUnderperforming,0.45\nAverage,0.65\nGood,0.75\nExceptional,0.90\nOutstanding,1.0\nProcess of Concern,0.35\nAnother Process of Concern,0.25"
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "cQRgFjdYy534LXq6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the overall performance of the processes based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart it\u2019s clear that most processes perform well, with effectiveness values from 0.45 to 1.0. However, there are two processes that fall below these values and are of concern. Both {Process of Concern}<r>[0.720, 0.700, 0.823, 0.872]</r> and {Another Process of Concern}<r>[0.821, 0.700, 0.967, 0.942]</r> have bars that are significantly shorter than the rest, indicating lower effectiveness. These processes, particularly 'Another Process of Concern', stand out in the chart for their low effectiveness, indicating they need serious attention for improvement to meet the standards set by the other processes.\nBased on the above thought, the answer is:\nOverall, most of the processes are highly effective, with 'Outstanding' being the highest. However, 'Process of Concern' and 'Another Process of Concern' are significantly less effective than the other processes, indicating that these are areas requiring improvement."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "uRZEu21A1AuLUadG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process has the lowest effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the process that has the lowest effectiveness, I need to examine the chart and find the shortest bar. Among all the bars, the bar at <r>[0.845, 0.534, 0.943, 0.684]</r> is the shortest. This bar corresponds to the process named {Another Process of Concern}<r>[0.821, 0.700, 0.967, 0.942]</r>. Thus, 'Another Process of Concern' appears to have the lowest effectiveness.\nBased on the above thought, the answer is:\nAnother Process of Concern."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "LYi4E23wi34DUEw3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the frequency of each social activity in the chart and represent it as a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I can see that the x-axis represents different social activities, evidenced by {\"Various Social Activities\"}[0.443, 0.952, 0.608, 0.975], and there are specific labels corresponding to each bar in chart, such as {\"Gardening\"}[0.112, 0.765, 0.173, 0.867] and {\"Walking\"}[0.203, 0.765, 0.252, 0.846]. The y-axis represents the frequency, which is indicated by {\"Frequency\"}[0.015, 0.327, 0.029, 0.447], and its scale is provided in the form of y-axis tick labels, ranging from {0.0}[0.035, 0.740, 0.057, 0.763] to {1.0}[0.035, 0.082, 0.057, 0.105]. Each bar in the chart, like the bar for {Gardening}[0.108, 0.334, 0.176, 0.749], indicates the frequency of the corresponding social, the value of which can be determined by the height of each bar.\nBased on the above thought, the answer is:\nSocial_Activities, Frequency\nGardening, 0.63\nWalking, 0.79\nRunning, 0.81\nSwimming, 0.88\nReading_Books, 0.70\nDancing, 0.60\nAttending_Concerts, 0.55\nTravel, 0.90\nCooking, 0.75\nWatching_Movies, 0.95"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "KY3gr2DB8l8zuVS6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you deduce which social activities are most and least preferred by individuals?"
+            },
+            {
+                "from": "gpt",
+                "value": "This chart displays the popularity or regular participation frequency of each social activity amongst a group of individuals. By examining the bars for each social activity, it is evident that the {Watching_Movies}[0.875, 0.124, 0.943, 0.749] has the highest bar, indicating it is the most adhered social activity. Conversely, {Attending_Concerts}[0.619, 0.387, 0.688, 0.749] bar is the shortest, notifying that it is the social activity least commonly practiced by these individuals. These outcomes infer that watching movies is highly favored by the individuals, while attending concerts is less popular amongst this crowd.\nBased on the above thought, the answer is:\nThe most preferred social activity according to the chart is Watching Movies, while the least preferred one is Attending Concerts."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "tzkzrBUjnAJD2mCc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social activity has the lowest frequency, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern the activity with lowest frequency, I need to locate the shortest bar in the chart. Observing each bar from left to right, starting from {Gardening}[0.108, 0.334, 0.176, 0.749], I can see the bar corresponding to {Attending_Concerts}[0.619, 0.387, 0.688, 0.749] is the lowest amongst all. It refers to the social activity with the lowest frequency.\nBased on the above thought, the answer is:\nAttending Concerts."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "LvcfJDT0e7CbRTDb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data represented in the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the horizontal axis, there are ten categories, which are the different investment strategies labeled {\"Investment Strategies\"}[0.459, 0.952, 0.613, 0.975]: Diversification[0.154, 0.586, 0.168, 0.752], Value Investing[0.237, 0.586, 0.251, 0.767], Growth Investing[0.320, 0.586, 0.334, 0.787], Momentum Investing[0.404, 0.586, 0.418, 0.835], Quality Investing[0.487, 0.586, 0.501, 0.786], Socially Responsible Investing[0.570, 0.586, 0.585, 0.942], Index Investing[0.654, 0.586, 0.668, 0.767], Small Cap Investing[0.737, 0.586, 0.751, 0.825], Balanced Investment[0.820, 0.586, 0.835, 0.836] and Inflation Hedging[0.904, 0.586, 0.918, 0.789]. The vertical axis labeled {\"Return Rate\"}[0.015, 0.233, 0.029, 0.370] shows the return rate for each investment strategy. The exact values can be derived by the height of the corresponding bars, such as for Diversification[{corresponding bar}[0.127, 0.229, 0.194, 0.521]], the height from y=0 to the top of the bar indicates a return rate of 0.12.\nBased on the above thought, the answer is:\nInvestment_Strategies,Return_Rate\nDiversification,0.12\nValue_Investing,0.08\nGrowth_Investing,0.1\nMomentum_Investing,0.11\nQuality_Investing,0.14\nSocially_Responsible_Investing,-0.01\nIndex_Investing,0.09\nSmall_Cap_Investing,0.15\nBalanced_Investment,0.1\nInflation_Hedging,0.07"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "hfpRRXd2bdaH5vUS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you assess the performance of the strategies mentioned in the chart and suggest a potential correction strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The strategies' performance can be evaluated using the height of each bar in the bar graph. For instance, Diversification shows a somewhat strong performance with a return rate of 0.12, and the height of the bar[{Diversification}[0.127, 0.229, 0.194, 0.521]] matches this assessment. On the other hand, Socially Responsible Investing strategy shows poor performance because it demonstrates negative returns, which can be seen in the low height of the corresponding bar[{Socially_Responsible_Investing}[0.544, 0.521, 0.611, 0.545]]. Based on this visualization, the Small Cap Investing strategy yields the highest return rate and displays the highest bar[{Small_Cap_Investing}[0.711, 0.156, 0.777, 0.521]], hence is the best performing strategy. As a suggestion, for strategies that have performed poorly such as the Socially Responsible Investing, re-evaluation of this strategy could be done, and maybe a part of the investment fund allocated to it could be diverted to better-performing strategies.\nBased on the above thought, the answer is:\nBased on the chart, Small Cap Investing strategy has the best performance, while the Socially Responsible Investing is under-performing. A potential corrective strategy could be to reduce the investment in Socially Responsible Investing and shift it to Small Cap Investing."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "agxcABTXzgQYU8DK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the return rate of the strategy that has maximum investment return?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the strategy that has maximum investment return, I need to find the highest bar in the chart. The highest bar corresponds to the \"Small Cap Investing\" strategy<{bar}[0.711, 0.156, 0.777, 0.521]>. The return rate can be inferred by relating the height of the bar to the y-axis. The top of the bar reaches almost up to the 0.15 point on the y-axis.\nBased on the above thought, the answer is:\n0.15"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "1LE1nx6Gl6QLgKJt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data about the success rate of different AI applications into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely! The bar chart shows ten different AI application categories along {the x-axis}<r>[0.055, 0.047, 0.988, 0.684]</r>, from {Machine_Vision}<r>[0.098, 0.209, 0.167, 0.684]</r> to {Gaming}<r>[0.876, 0.105, 0.945, 0.684]</r>. The y-axis represents the success rate of each AI application ranging from {0.0}<r>[0.029, 0.677, 0.047, 0.695]</r> to {1.0}<r>[0.029, 0.098, 0.047, 0.115]</r>. The height of the bar determines the value of the success rate. Based on these details, I can structure the data as AI Applications and Success Rates in a CSV table.\nBased on the above thought, the answer is:\nAI_Applications,Success_Rate\nMachine_Vision,0.82\nNatural_Language_Processing,0.91\nRobotics,0.86\nForecasting,0.78\nHealthCare,0.95\nSpeech_Recognition,0.92\nAutonomous_Vehicles,0.70\nCybersecurity,0.95\nEducation,0.88\nGaming,1.0"
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "g2JdTGRCwSkTdQ0C",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observation from the chart, can you tell what could potentially be the reason for gaming having the highest success rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {bar for gaming}<r>[0.876, 0.105, 0.945, 0.684]</r> is the tallest among all, indicating it as the highest success rate. As an AI system assistant, I can't provide subjective reasoning. However, based on popular AI trends, the success rate in gaming might be due to the advancement in AI technologies that are designed for gaming applications. These technologies include machine learning algorithms used for creating more realistic and intelligent Non-Player Characters (NPCs) and the developments in the field of reinforcement learning that are used widely in game simulations.\nBased on the above thought, the answer is:\nThe advancements in AI technologies specifically designed for gaming applications, such as machine learning and reinforcement learning, have likely contributed to the high success rate in the Gaming category."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "juIim0fwAaU6lb3R",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI application has the lowest success rate according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the AI application with the lowest success rate, I need to examine the success rate bars' heights. The {y-axis}<r>[0.013, 0.308, 0.024, 0.423]</r> signifies the success rate from {0.0}<r>[0.029, 0.677, 0.047, 0.695]</r> to {1.0}<r>[0.029, 0.098, 0.047, 0.115]</r>. The lowest bar corresponds to the lowest success rate. By comparing each bar's height, it's evident the {Autonomous_Vehicles bar}<r>[0.616, 0.279, 0.686, 0.684]</r> is the shortest, thus indicating it as the AI application with the lowest success rate.\nBased on the above thought, the answer is:\nAutonomous Vehicles."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "HTLDsCG4qFw2XZQ3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart in front of you, can you provide the data on the effectiveness rating of top 10 cyber security and data privacy measures in the form of a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Cybersecurity and Data Privacy Measures\"}[0.400, 0.952, 0.642, 0.975] and {the x-axis tick labels}</r><r>[\"Encryption Methods\"[0.126, 0.643, 0.138, 0.873],\"Private Browsing\"[0.213, 0.643, 0.225, 0.837],\"Password Managers\"[0.299, 0.643, 0.311, 0.872],\"Two-factor Authentication\"[0.386, 0.643, 0.397, 0.940],\"Secure Wireless Networks\"[0.472, 0.643, 0.484, 0.942],\"VPN\"[0.559, 0.643, 0.570, 0.690], \"Data Encryption\"[0.645, 0.643, 0.657, 0.830], \"Anonymising Services\"[0.732, 0.643, 0.743, 0.897], \"Biometric Verification\"[0.818, 0.643, 0.830, 0.892], \"Privacy-Focused Browsers\"[0.905, 0.643, 0.916, 0.942]</r>, I see that the x-axis represents different categories. The y-axis with labels ranging from {0.0}[0.029, 0.617, 0.047, 0.641] to {1.0}[0.029, 0.071, 0.047, 0.094] represents the corresponding values. For each category, there is a {blue bar}<r>[{bar1}[0.098, 0.200, 0.167, 0.626], {bar2}[0.184, 0.178, 0.253, 0.626], {bar3}[0.271, 0.145, 0.340, 0.626], {bar4}[0.357, 0.134, 0.426, 0.626], {bar5}[0.444, 0.271, 0.513, 0.626], {bar6}[0.530, 0.112, 0.599, 0.626], {bar7}[0.616, 0.134, 0.686, 0.626], {bar8}[0.703, 0.194, 0.772, 0.626], {bar9}[0.789, 0.167, 0.859, 0.626], {bar10}[0.876, 0.129, 0.945, 0.626]</r> representing the corresponding value. I need to extract the values using the height of the bars.\nBased on the above thought, the answer is:\nCategories,Data\nEncryption Methods,0.78\nPrivate Browsing,0.82\nPassword Managers,0.88\nTwo-factor Authentication,0.9\nSecure Wireless Networks,0.65\nVPN,0.94\nData Encryption,0.9\nAnonymising Services,0.79\nBiometric Verification,0.84\nPrivacy-Focused Browsers,0.91"
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "uf3P7gdJlMb6xuJ2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which measure do you think needs to be added to improve the current cybersecurity strategy considering effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {bars}<r>[{bar1}[0.098, 0.200, 0.167, 0.626], {bar2}[0.184, 0.178, 0.253, 0.626], {bar3}[0.271, 0.145, 0.340, 0.626], {bar4}[0.357, 0.134, 0.426, 0.626], {bar5}[0.444, 0.271, 0.513, 0.626], {bar6}[0.530, 0.112, 0.599, 0.626], {bar7}[0.616, 0.134, 0.686, 0.626], {bar8}[0.703, 0.194, 0.772, 0.626], {bar9}[0.789, 0.167, 0.859, 0.626], {bar10}[0.876, 0.129, 0.945, 0.626] </r>of the chart, the measure with the lowest effectiveness rating is clearly {the bar representing \"Secure Wireless Networks\"}[0.444, 0.271, 0.513, 0.626]. So, to improve the current cybersecurity strategy, we should add measures that could make \"Secure Wireless Networks\" more effective.\nBased on the above thought, the answer is:\nMeasures to improve the effectiveness of \"Secure Wireless Networks\" should be added."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "TV53YKFL6YNWUU2h",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cybersecurity and data privacy measure has the highest effectiveness rating?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which cybersecurity and data privacy measure has the highest effectiveness rating, I need to find the tallest bar among the 10 bars. By comparing all the bars one by one, I found that {the bar representing \"VPN\"}[0.530, 0.112, 0.599, 0.626] is the tallest, which means \"VPN\" has the highest effectiveness rating.\nBased on the above thought, the answer is:\nVPN"
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "hFzAsDwBj0z3iXtN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease construct a structured table of the dataset corresponding to the bar chart using the chart's information."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart, one can observe that on the x-axis are the names of different academic subjects, {labels of subjects}<r>[{\"Math\"} [0.132, 0.829, 0.161, 0.873], {\"English\"} [0.219, 0.829, 0.256, 0.886], {\"Biology\"} [0.305, 0.829, 0.344, 0.887], {\"Chemistry\"} [0.392, 0.829, 0.442, 0.905], {\"Physics\"} [0.478, 0.829, 0.517, 0.887], {\"Computer Science\"} [0.565, 0.829, 0.648, 0.954], {\"Art\"} [0.651, 0.829, 0.672, 0.860], {\"Music\"} [0.738, 0.829, 0.769, 0.877], {\"Physical Education\"} [0.824, 0.829, 0.909, 0.957], {\"History\"} [0.911, 0.829, 0.948, 0.885]</r>]. On the y-axis are the utilization rates, which range from 0 to 1, according to {tick_labels} <r>[{\"0.0\"} [0.029, 0.810, 0.047, 0.828], {\"0.2\"} [0.029, 0.665, 0.047, 0.683], ..., {\"1.0\"} [0.029, 0.084, 0.047, 0.102]]</r>. Thus, the chart data can be transformed into a structured table by taking each subject and its respective utilization value. The height of each bar on the chart corresponds to its utilization ratio.\nBased on the above thought, the answer is:\nSubject, Utilization\nMath, 0.95\nEnglish, 0.89\nBiology, 0.72\nChemistry, 0.84\nPhysics, 0.79\nComputer Science, 0.99\nArt, 0.68\nMusic, 0.70\nPhysical Education, 0.91\nHistory, 0.94"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "DTaG1Z5zyClxDJeo",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart provided, could you identify a potential issue in the utilization of education resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noticing the {label}<r>[0.461, 0.964, 0.581, 0.981]</r> \"Education Resources\" along the x-axis and the {label}<r>[0.013, 0.374, 0.024, 0.462]</r> \"Utilization\" on the y-axis, I can see that the chart shows the utilization ratios of different education resources. The length of each bar represents the utilization of resources in each discipline. Focusing on the shorter bars like {Art}<r>[0.616, 0.324, 0.686, 0.817]</r> or {Biology}<r>[0.271, 0.295, 0.340, 0.817]</r>, while compared to {Computer Science}<r>[0.530, 0.099, 0.599, 0.817]</r> and {Math}<r>[0.098, 0.128, 0.167, 0.817]</r> which are longer bars, there indeed is a potential issue. The issue is the uneven distribution of resource utilization across different subjects. The STEM subjects seem to be utilized more than soft sciences and arts. This could lead to an imbalance in academic skills amongst students if not addressed properly.\nBased on the above thought, the answer is:\nThe issue is the uneven distribution of resource utilization across different subjects, with STEM subjects such as Math and Computer Science having higher utilization rates while less scientifically oriented subjects like Arts and Biology demonstrate lower utilization. This could reflect an imbalanced focus on certain academic disciplines over others."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "QqhVSL6HtmD9iLbk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average utilization of all subjects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average utilization across all subjects, we can add up the utilization rates for all subjects and divide by the total number of subjects. Here, the utilization values are represented by the height of the bars for each subject. There are 10 bars for 10 subjects {bar heights}<r>[0.98 ,0.171, 0.253, 0.817], [0.184, 0.171, 0.253, 0.817], [0.271, 0.295, 0.340, 0.817], [0.357, 0.207, 0.426, 0.817], [0.444, 0.244, 0.513, 0.817], [0.530, 0.099, 0.599, 0.817], [0.616, 0.324, 0.686, 0.817], [0.703, 0.309, 0.772, 0.817], [0.789, 0.157, 0.859, 0.817], [0.876, 0.135, 0.945, 0.817]</r>.\nBased on the above thought, the answer is:\n0.84"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "LOLTaWr6qPjm2xei",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart information into a structured table in CSV format according to what you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see a bar chart titled {\"Trade Value of different Industry Sectors\"}[0.386, 0.952, 0.666, 0.975] plotted against various industry sectors on the x-axis and their corresponding trade values on the y-axis. There are ten bars in total, each representing an industry sector. The height of each bar corresponds to the trade value of each industry. Let's observe each bar one by one. The first bar represents the {\"Clothing_Industry\"}[0.094, 0.723, 0.190, 0.884] with a trade value of approximately 0.81 indicated by the height of {the bar}[0.108, 0.205, 0.176, 0.707]. Similarly, we observe other bars and extract the data. This entire process is repeated for every industry to form the structured table in CSV format.\nBased on the above thought, the answer is:\nIndustry,Trade_Value\nClothing_Industry,0.81\nAutomobile_Industry,0.67\nElectronics_Industry,0.76\nPharmaceutical_Industry,0.57\nFood_Industries,0.82\nEnergy_Sector,0.63\nMetal_Industry,0.91\nChemical_Industry,0.87\nLumber_Industry,0.88\nAgriculture,0.52"
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "PpEQrtiOK5YpKlEq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDrawing from the graph, which sectors are predicted to have a promising future regarding trade value? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "The future promising sectors regarding trade value can be concluded from the bars that have a trade value near or above the average trade value for all industries. Let's find these bars. The {Food_Industries bar}[0.449, 0.199, 0.517, 0.707], {Metal_Industry bar}[0.619, 0.143, 0.688, 0.707], {Chemical_Industry bar}[0.705, 0.168, 0.773, 0.707], and {Lumber_Industry bar}[0.790, 0.161, 0.858, 0.707] are all relatively high, indicating that these industries have strong trade values. Given that higher trade value generally predicts a more promising future in the context of globalization, the Food Industries, Metal Industry, Chemical Industry, and Lumber Industry sectors therefore appear to have a promising future in trade value.\nBased on the above thought, the answer is:\nFood Industries, Metal Industry, Chemical Industry, and Lumber Industry. Because these sectors have high trade values, suggesting that they may have a promising future in terms of trade value."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "RM87GRUhzC90adL9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which industry has the lowest trade value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which industry has the lowest trade value, I need to find the shortest bar on the chart. Each of these bars corresponds to a specific industry. By comparing the heights of the bars, it appears that the shortest bar corresponds to the {Agriculture}[0.877, 0.723, 0.941, 0.830] industry, with {the bar for Agriculture}[0.875, 0.385, 0.943, 0.707] corresponding to a trade value of approximately 0.52. This value is lower than any other trade value represented by the other bars in the chart.\nBased on the above thought, the answer is:\nAgriculture"
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "pqk2ShV2tEsvu6vY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart to a CSV format, including the industry and its corresponding growth score?"
+            },
+            {
+                "from": "gpt",
+                "value": "After examining the chart, I see that the x-axis represents various sectors of the \"Knowledge and Innovation Industry\" as indicated by the x-label<r>[0.361, 0.952, 0.686, 0.975]</r>, while the y-axis represents the corresponding 'Annual Growth Score' using the y-label<r>[0.015, 0.211, 0.029, 0.455]</r>. For example, the bar representing \"AI and Machine Learning\"<r>[0.104, 0.098, 0.172, 0.641]</r> is almost reaching the 10 mark on the y-axis, indicating its growth score is 9.7. I will follow this approach for the rest of the bars to extract their corresponding values.\nBased on the above thought, the answer is:\nIndustry,Annual Growth Score\nAI and Machine Learning,9.7\nBiotechnology,7.6\nData Science,8.5\nGreen Energy,9.5\nSpace Tech,8.2\nTelecommunication,7.8\nNanotechnology,9.9\nInternet Security,7.5\nQuantum Computing,7.85\nIoT,8.7"
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "kR2m7bKkStvi7tMn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which areas in the Knowledge Economy and Innovation sector would you recommend for investment in terms of growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the bar heights, industries with larger values indicate a faster growing industry and hence, more promising for investments. Specifically, the bars representing \"Nanotechnology\"<r>[0.618, 0.087, 0.686, 0.641]</r> and \"Green Energy\"<r>[0.361, 0.109, 0.429, 0.641]</r> are closest to the maximum 10 value on the y-axis, suggesting they are the fast growing fields. Therefore, they may be the most appropriate ones for investment.\nBased on the above thought, the answer is:\nNanotechnology and Green Energy."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "MuI5jWL4iH078tNK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which industry has the highest annual growth score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inspecting each bar, I need to identify the one reaching the highest on the y-axis. In particular, the bar for \"Nanotechnology\"<r>[0.618, 0.087, 0.686, 0.641]</r> is closer to the 10 mark, indicating the highest annual growth score among all the industries.\nBased on the above thought, the answer is:\nNanotechnology."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "7wxMxRCYmPQ0XBne",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data, as present on the chart, into a table with each item's production quantity being displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart 'Food and Agriculture Industry Analysis'<r>[0.386, 0.019, 0.653, 0.041]</r> shows the production quantity of different agriculture commodities. The commodities are listed on the x-axis<r>[0.448, 0.964, 0.591, 0.981]</r>, with each bar's height corresponding to its production quantity on the y-axis<r>[0.013, 0.311, 0.024, 0.603]</r>. I'll extract the precise quantity from each bar's y-range. For instance, the first bar corresponds to 'Wheat'<r>[0.094, 0.444, 0.164, 0.867]</r> with a production total of 10.3 million metric tons. Proceeding in similar fashion for remaining bars enables me to prepare the entire table.\nBased on the above thought, the answer is:\nitem,production\nWheat,10.3\nCorn,13.5\nRice,7.6\nSoybeans,9.4\nCoffee,2.6\nCotton,6.0\nSugarcane,19.5\nPotatoes,7.2\nGrains,14.7\nFruits,16.1"
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "esIJqLkW7KL0zWTj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about the trend in the food and agriculture industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart<r>[0.052, 0.047, 0.988, 0.867]</r> gives an overview of the production quantities of various crops in the food and agriculture industry. It seems that sugarcane <r>[0.615, 0.067, 0.685, 0.867]</r> and fruits<r>[0.876, 0.207, 0.945, 0.867]</r> show a significantly higher yield compared to other commodities, indicating they might be more profitable or in higher demand. Wheat<r>[0.094, 0.444, 0.164, 0.867]</r> on the other hand, shows slow growth, possibly indicating less profitability or lower demand. Coffee<r>[0.441, 0.760, 0.511, 0.867]</r> representing the lowest yield, implies that it might be the least lucrative or least highly demanded in comparison to other commodities.\nBased on the above thought, the answer is:\nThe chart shows an increasing trend in the production of sugarcane and fruits, indicating that they are likely the most profitable or highly demanded commodities. However, commodities such as wheat and coffee show relatively low production, indicating less profitability or demand. This data could suggest that farmers and producers might earn more profit if they focus on sugarcane and fruits production."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "k6COMZD4npWnPODS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the total production quantity of all the commodities combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the chart represent the quantity produced for each agricultural commodity. To find the total production, I need to sum up the heights of all bars. Each rectangle with bar_coordinates represents the quantity produced for a particular commodity, such as 'Wheat'<r>[0.094, 0.444, 0.164, 0.867]</r> for 10.3 million metric tons, and 'Corn'<r>[0.181, 0.313, 0.250, 0.867]</r> for 13.5 million metric tons. If I continue and take the sum of the agricultural production quantities for Rice, Soybeans, Coffee, Cotton, Sugarcane, Potatoes, Grains, Fruits, I'll get the total production quantity.\nBased on the above thought, the answer is:\n106.9"
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "fnDCnne8tkRyYS1H",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the bar chart, can you generate a structured data table in CSV format that reflects the annual funds allocation for the Culture and Arts industry between 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a bar graph with the {X-axis representing years}<r>[0.052, 0.047, 0.988, 0.927]</r> and the {Y-axis representing the amount of funds}<r>[0.507, 0.964, 0.532, 0.981]</r>. Each bar corresponds to a specific year and its height represents the amount of funding, as clearly shown in the {caption below each bar}<r>[0.882, 0.597, 0.945, 1.588]</r>. By looking at these vertical bars from left to right, I can determine the funding for each year. For example, the {1st bar from the left}<r>[0.094, 0.839, 0.157, 1.588]</r> represents the funding amount for the year 2010, which is 34 million.\nBased on the above thought, the answer is:\nYear,Art_funds_in_Millions\n2010,34\n2011,36\n2012,45\n2013,39\n2014,41\n2015,48\n2016,54\n2017,59\n2018,56\n2019,57\n2020,45"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "gjf8FhvlQCpzZ6EV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the funding trends in the chart, how would you evaluate the overall growth of the Culture and Arts industry from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "As indicated by the bar graph, the funding for the Culture and Arts industry has generally been on the rise from {the year 2010}<r>[0.094, 0.839, 0.157, 1.588]</r> till {the year 2019}<r>[0.803, 0.333, 0.866, 1.588]</r>, with a few lower values interspersed in the middle years such as {the year 2013}<r>[0.330, 0.729, 0.393, 1.588]</r>. There were also some standout years like {2012}<r>[0.252, 0.597, 0.315, 1.588]</r>, {2015}<r>[0.488, 0.531, 0.551, 1.588]</r>, and {2017}<r>[0.646, 0.289, 0.709, 1.588]</r>, when the funding was markedly greater. However, in {the year 2020}<r>[0.882, 0.597, 0.945, 1.588]</r>, there is a significant drop, bringing the funding closer to the levels of 2012. This abrupt fall could potentially be due to external events impacting the industry, such as the global Coronavirus pandemic. Despite the decrease at the end, the overall ten-year period shows growth and development in the culture and arts industry.\nBased on the above thought, the answer is:\nThe overall growth was positive over the 10-year period from 2010 to 2020, with noticeable development and sporadic spikes in funding. However, the year 2020 experienced a significant drop in funding."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "bDKPADETEUwcRPBT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the most significant increase in funds compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which year saw the greatest increase in funding, I should compare the height of each bar with the one before it. Starting from the {2nd bar}<r>[0.173, 0.795, 0.236, 1.588]</r>, which represents 2011, and comparing it to the first bar for 2010, there was an increase of 2 million. If I follow this comparison to the end till {the last bar}<r>[0.882, 0.597, 0.945, 1.588]</r> for 2020, the year with the most significant increase from the previous year is 2017, indicated by {the bar}<r>[0.646, 0.289, 0.709, 1.588]</r>, where the funds increased by 9 million from 2016's 54 million to 59 million in 2017.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "3fVFIXbPJRb5ODVI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information presented in the bar chart into a data table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the bar chart provides leadership scores for various departments. The labels of each bar are evident from the x-axis labels, where we have {'Sales'}[0.120, 0.774, 0.130, 0.821], {'Marketing'}[0.208, 0.774, 0.217, 0.861], {'Human Resources'}[0.295, 0.774, 0.305, 0.931], {'IT'}[0.383, 0.774, 0.392, 0.790], {'Finance'}[0.470, 0.774, 0.479, 0.841], {'Operations'}[0.558, 0.774, 0.567, 0.870], {'Customer Service'}[0.645, 0.774, 0.654, 0.928], {'Product'}[0.733, 0.774, 0.742, 0.841], {'Corporate Strategy'}[0.820, 0.774, 0.829, 0.942] and {'Business Intelligence'}[0.907, 0.774, 0.917, 0.957]. On correlating the heights of these bars, their corresponding {\"Leadership Scores\"} are gained. For instance, the bar representing 'Finance' is at height of 95, consequent to the {label for finance \"[0.440, 0.118, 0.510, 1.867]\"} and correlating to {'the ytick for 95'}[0.029, 0.111, 0.041, 0.128].\nBased on the above thought, the answer is:\nDepartment,Leadership Score\nSales,75\nMarketing,80\nHuman Resources,90\nIT,85\nFinance,95\nOperations,70\nCustomer Service,88\nProduct,72\nCorporate Strategy,78\nBusiness Intelligence,92"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "I7STqlRBsvUwhKLP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which department needs to work on its leadership skills and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the department with the lowest leadership score, which means looking for the shortest bar. I observe that {Operations}[0.527, 0.578, 0.597, 1.867] has the smallest bar height. Hence the department named 'Operations' has got the lowest leadership score of 70 that aligns with {'the label for 70'}[0.029, 0.571, 0.041, 0.589]. This score is the lowest among the scores of all the other departments and lags behind by at least 2 points compared to the other departments.\nBased on the above thought, the answer is:\nOperations. Because it has the lowest leadership score of 70 among all the departments, indicating that its leadership needs further improvement."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "7eeEdHdhG8FG8zh7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average leadership score according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average leadership score, I must sum up the scores of all departments and then divide by the total number of departments. Based on {the bar heights} and their correlation to the y-axis labels, I see that the scores are 75('Sales'), 80('Marketing'), 90('Human Resources'), 85('IT'), 95('Finance'), 70('Operations'), 88('Customer Service'), 72('Product'), 78('Corporate Strategy'), and 92('Business Intelligence'). There are 10 departments in total.\nBased on the above thought, the answer is:\n82.5"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "kMUvDNhxmmzttovx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease create a structured table representing the performance data of all companies over the three quarters as observed from the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "I am analyzing the bar chart which consists of three subplots, each signifying a group of companies. Within each subplot, every company has three horizontal bars corresponding to their performance in each quarter. The x-axis of the subplots presents performance values and the y-axis names the companies. To extract the data, I locate the end of each bar which indicates the performance value and the label alongside the y-axis for the company name. For instance, in the first subplot, I can observe the performance of Amgen with {the end of the bar for Quarter 1}<r>[0.389, 0.221, 0.389, 0.286]</r> at approximately 100, the bar for Quarter 2 ending at approximately <r>[0.575, 0.221, 0.575, 0.286]</r> 210, and for Quarter 3, the bar ends at approximately <r>[0.736, 0.221, 0.736, 0.286]</r> 305. This process is continued for each company across all subplots to create the full table.\nBased on the above thought, the answer is:\nCompany, Quarter 1, Quarter 2, Quarter 3\nAmgen, 100, 210, 305\nJohnson & Johnson, 120, 250, 390\nMerck & Co, 130, 270, 430\nGilead Sciences, 140, 290, 450\nPfizer, 150, 310, 490\nRoche, 160, 340, 540\nNovartis, 170, 360, 570\nSanofi, 180, 380, 600\nBristol-Myers Squibb, 190, 400, 630\nRegeneron Pharmaceuticals, 215, 450, 705"
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "EhbZAqXo6AULyIFd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company displays the most consistent quarterly performance growth on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce the company with the most consistent quarterly performance growth, I am examining the slopes of the bar edges for each company across the quarters. The consistency of growth is indicated by the uniformity of the increase in lengths of the bars across each quarter. {The first subplot}<r>[0.219, 0.047, 0.985, 0.297]</r> contains bars representing Amgen, Johnson & Johnson, and Merck & Co. Among these, {Johnson & Johnson's bars}<r>[0.219, 0.139, 0.422, 0.205];[0.422, 0.139, 0.643, 0.205];[0.643, 0.139, 0.881, 0.205]</r> show a steady and consistent increase with no declines. Similar analysis for the subsequent subplots indicates that Johnson & Johnson, along with Novartis from the third subplot, who's bars are <r>[0.219, 0.892, 0.395, 0.940];[0.395, 0.892, 0.591, 0.940];[0.591, 0.892, 0.809, 0.940]</r>, exhibit consistent performance growth with no signs of decline or stagnation.\nBased on the above thought, the answer is:\nBoth Johnson & Johnson and Novartis exhibit the most consistent quarterly performance growth, with a steady increase in their performance values each quarter and no decreases over the three quarters."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "Zk0FSglEVHpRq0IB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has Pfizer's performance increased from Quarter 1 to Quarter 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate Pfizer's performance increase from Quarter 1 to Quarter 3, I first locate Pfizer's bars in the relevant subplot. {The bar representing Pfizer's performance in Quarter 1}<r>[0.219, 0.467, 0.422, 0.532]</r> ends at an x-value of approximately 150, and {the bar for Quarter 3}<r>[0.638, 0.467, 0.881, 0.532]</r> ends at an x-value of approximately 490. Subtracting the former from the latter gives the performance increase.\nBased on the above thought, the answer is:\n340"
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "05NJhgKbeuK24vWC",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the chart on risk metrics across different intervals into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the horizontal bar plots in the three sub-charts, I can compile the data into a table. Starting with the first {subplot}[0.106, 0.075, 0.382, 0.923] which represents the Start metrics, each bar such as {Cyber Risk bar}<r>[0.106, 0.805, 0.216, 0.884]</r> corresponds to a risk factor and shows the starting metric. Proceeding to the second {subplot}[0.407, 0.075, 0.682, 0.923] for Interval 1 and the third {subplot}[0.707, 0.075, 0.983, 0.923] for Interval 2, I can identify corresponding bars like the {Operational Risk bar for Interval 1}<r>[0.407, 0.607, 0.503, 0.686]</r> and {Market Risk bar for Interval 2}<r>[0.707, 0.508, 0.914, 0.588]</r> to complete the data for each interval. Using this process, I can transcribe all values for the risk factors at the different intervals into a CSV format.\nBased on the above thought, the answer is:\nRisk Factors,Start,Interval 1,Interval 2\nCyber Risk,0.4,0.6,0.35\nCredit Risk,0.7,0.8,0.5\nOperational Risk,0.3,0.35,0.4\nMarket Risk,0.65,0.7,0.75\nReputation Risk,0.55,0.6,0.6\nStrategic Risk,0.8,0.85,0.85\nLegal Risk,0.35,0.4,0.3\nCompliance Risk,0.6,0.7,0.72"
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "N4wUse1PoL5QC8Cw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk factor has shown the greatest change from Start to Interval 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the risk factor with the greatest change, I examine the bars representing the Start and Interval 2 metrics for each risk factor. Starting with {Cyber Risk}<r>[0.106, 0.075, 0.382, 0.923], comparing its start metric bar <r>[0.106, 0.805, 0.216, 0.884]</r> with Interval 2 bar <r>[0.707, 0.805, 0.803, 0.884]</r>, the change is a 0.05 decrease. Following this method, I compare each metric pair such as {Credit Risk Start bar}<r>[0.106, 0.706, 0.299, 0.785]</r> to {Credit Risk Interval 2 bar}<r>[0.707, 0.706, 0.845, 0.785]</r>. After examining all metric pairs, I can determine which risk factor has the largest metric change from Start to Interval 2.\nBased on the above thought, the answer is:\nCredit Risk. There is a change from a risk metric of 0.7 at the Start to 0.5 at Interval 2, which is the greatest reduction among all the risk factors analyzed."
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "s5XuMilsrrmjXubL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Start metric for all the risk factors represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average Start metric, I need to analyze and aggregate the values of the start metrics from the first {subplot}<r>[0.106, 0.075, 0.382, 0.923]</r>. I'll locate each bar corresponding to the Start metrics, such as the {Cyber Risk bar}<r>[0.106, 0.805, 0.216, 0.884]</r> and read off the values as 0.4 for Cyber Risk, 0.7 for Credit Risk, 0.3 for Operational Risk and so on until Compliance Risk which has a value of 0.6. Summing these and dividing by the total number of risk factors, which is 8, I arrive at the average Start metric.\nBased on the above thought, the answer is:\n0.5375"
+            }
+        ],
+        "id_raw": 518
+    },
+    {
+        "id": "fLKVcLOhW8RR3zmK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a CSV formatted table from the population data across the different historical ages and time periods from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the CSV formatted table, I must examine each subchart's bars, representing different historical ages and time periods. I can see the x-axis labels indicating the time periods as {20 years}<r>[0.139, 0.976, 0.161, 0.988]</r>, {40 years}<r>[0.223, 0.976, 0.245, 0.988]</r>, going up to {200 years}<r>[0.890, 0.976, 0.923, 0.988]</r>, and the y-axis labels indicating population scale starting from {0}<r>[0.052, 0.963, 0.063, 0.975]</r> scaling up to {5000}<r>[0.019, 0.789, 0.063, 0.800]</r>. I will analyze the bars' height in each era, which includes {Stone Age}<r>[0.116, 0.828, 0.183, 0.968]</r>, {Bronze Age}<r>[0.116, 0.863, 0.183, 0.968]</r>, {Iron Age}<r>[0.116, 0.915, 0.183, 0.968]</r>, {Medieval Ages}<r>[0.116, 0.922, 0.183, 0.968]</r>, and {Modern Age}<r>[0.116, 0.908, 0.183, 0.968]</r>, and cross-reference them with the y-axis to obtain the population numbers.\nBased on the above thought, the answer is:\nTime,Stone Age,Bronze Age,Iron Age,Medieval Age,Modern Age\n20,4000,3000,1500,1300,1700\n40,3800,2800,1400,1250,1650\n60,3700,2700,1275,1200,1550\n80,3550,2650,1250,1100,1450\n100,3400,2600,1225,950,1300\n120,3300,2550,1200,850,1050\n140,3200,2500,1175,800,1000\n160,3100,2450,1150,725,950\n180,3000,2400,1125,650,900\n200,2900,2350,1100,575,850"
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "iXGQKWTDaKrm4HI0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which historical age shows the most consistent development over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which historical age shows the most consistent development, I'll analyze the uniformity of the bars' heights within each age's subchart. A consistent development would mean the bars are of similar height across the different time periods. I will need to examine the bars representing different eras including {Stone Age}<r>[0.116, 0.828, 0.183, 0.968]</r>, marked by a decreasing trend in bar heights; {Bronze Age}<r>[0.116, 0.863, 0.183, 0.968]</r>, with another clear decline; {Iron Age}<r>[0.116, 0.915, 0.183, 0.968]</r>, which shows fluctuations making it inconsistent; {Medieval Ages}<r>[0.116, 0.922, 0.183, 0.968]</r>, also showcasing a decline. Lastly, I will look at {Modern Age}<r>[0.116, 0.908, 0.183, 0.968]</r>, where I notice population numbers consistently close to each other yet marginally decreasing, showing slight inconsistency.\nBased on the above thought, the answer is:\nUpon analyzing the bars, the Modern Age shows the most consistent development over time despite a small marginal decrease. The bars in the Modern Age's subchart have less drastic changes in height compared to other ages, which show more significant declines or fluctuations."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "wT82PunXhvPgSLRn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decrease in population for the Bronze Age from the earliest to the latest time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total decrease in population for the Bronze Age from the earliest to the latest time period, I need to subtract the population of the earliest time period from that of the latest time period. I'll observe the bar heights at {Bronze Age at 20 years}<r>[0.116, 0.863, 0.183, 0.968]</r> with a population height near {3000}<r>[0.019, 0.858, 0.063, 0.870]</r> and compare that to the bar at {Bronze Age at 200 years}<r>[0.873, 0.886, 0.940, 0.968]</r> with a height suggesting a population near {2350}<r>[0.019, 0.893, 0.063, 0.905]</r>.\nBased on the above thought, the answer is:\n650"
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "qwGgtlceL0wqaCOO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please process the data from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to convert the data in the bar chart into a CSV format, I need to observe and analyze each bar in the chart. Double bar plots are present in this chart with each bar plot having bars representing the percentage of population in a particular tier in a country. Important details can be extracted from the legend {legend}<r>[0.354, -0.091, 0.646, -0.008]</r> that specify the countries. The two plots are split using tiers mentioned on {yticks}<r>[0.019, 0.603, 0.115, 0.621]; [0.024, 0.832, 0.115, 0.850]</r>. The x-axis of the bar graph indicates the percentage population in that tier, and it is the same for both the subplots. Now to get the values for each bar, I observe the length of the bars corresponding to the scale on x-axis. For example, the bar representing the Middle Tier in USA located at {bar1}<r>[0.127, 0.518, 0.941, 0.702]</r> shows a population percentage of around 70.\nBased on the above thought, the answer is:\nCountry,Upper Tier,Middle Tier\nUSA,30,70\nCanada,45,55\nUK,50,50\nGermany,30,70\nFrance,60,40"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "JlwUIqtCeH7s78LX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the social stratification perspective of these countries, what do the charts imply about the population distribution among the two social tiers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, we can observe the distribution of population among the Upper Tier and Middle Tier in different countries. The bar chart showing the Middle Tier located at {Middle_Tier_chart}<r>[0.127, 0.019, 0.981, 0.473]</r> and the bar chart describing the Upper Tier located at {Upper_Tier_chart}<r>[0.127, 0.498, 0.981, 0.952]</r> provide valuable insights on the percentage of population in both tiers for each country. As per provided data, it can be observed that in most countries, the Middle Tier has a majority of the population, the bars representing Middle Tier are visibly longer than those in Upper Tier. There are exceptions though, for example, Germany situated at {Germany}<r>[0.559, -0.084, 0.639, -0.067]</r> in the legend, has an equal distribution of population in both tiers. The difference in population distribution among these tiers indicates the disparity in social stratification in these countries. Studying these bar heights and comparing them, we can get an understanding of social disparities and the composition of various tiers in different countries.\nBased on the above thought, the answer is:\nThe charts imply that most countries have a higher percentage of population in the Middle Tier than in the Upper Tier. The distribution of population among these two social tiers in different countries can indicate the disparity in social stratification. However, for some countries like Germany, the population is equally distributed between the Upper and Middle Tiers, demonstrating a balanced social structure in the country."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "7jThhSTEvOX7CSyu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the biggest difference between the percentage of the upper and middle tiers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this answer, I need to compare the bars representing different countries across both subplots. Looking at the bars of USA with coordinates {USA_upper}<r>[0.127, 0.748, 0.592, 0.931]</r> and {USA_middle}<r>[0.127, 0.518, 0.941, 0.702]</r>, the difference between Upper and Middle Tiers is 40. If I conduct the same analysis for all countries by comparing their bars with respective coordinates. After calculating, it is easy to observe that the country with the biggest difference between the percentage of the upper and middle tiers is USA with a difference of 40.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "Vcet7JWNaiYhuVMR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide structured CSV data of the development trends in the Culture and Arts Industry, focusing on each art form and their progression over the four phases?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the observation of the grouped bar chart, the Y-axis categorizes art forms including {Music, Visual Arts, Literature, and Cinema}<{yticks}[0.113, 0.413, 0.113, 0.080]>. The X-axis indicates the trend values across four different stages: {steady increase, inexorable rise, marginal improvement, and rapid expansion}<{legend}<[0.305, -0.073, 0.695, -0.027]>. Bars are grouped by art forms and, within each group, a unique color represents each phase. For instance, {the red bar}<[0.113, 0.369, 0.425, 0.457]> under Music corresponds to a steady increase with a value of 150 as indicated by the X-axis range {0 -> 150}. By systematically analyzing each colored bar's width within the grouped art forms, numerical values corresponding to each phase can be determined and structured into CSV format.\nBased on the above thought, the answer is:\nArtform,Steady Increase,Inexorable Rise,Marginal Improvement,Rapid Expansion\nMusic,150,80,95,30\nVisual Arts,230,90,85,220\nLiterature,400,60,70,215\nCinema,260,120,90,280"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "aNytIeYI5eTvXYSV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich art form shows the most balanced development across all four phases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most balanced development, I will compare the distribution of the stacked bar lengths across the four phases for each art form. {Visual Arts}'s values show a steady increase, a moderate inexorable rise, a marginal improvement, and a significant rise in rapid expansion with the bars located at {[0.113, 0.258, 0.591, 0.346]}, {[0.113, 0.258, 0.300, 0.346]}, {[0.113, 0.258, 0.290, 0.346]}, and {[0.113, 0.258, 0.570, 0.346]}, respectively. Comparing these to other art forms, such as {Literature} that shows a consistent high value in {steady increase}<[0.113, 0.147, 0.943, 0.236]> but lower values in other categories, or {Music} that has less growth in {rapid expansion}<[0.113, 0.369, 0.176, 0.457]>, Visual arts maintains a modest yet more consistent level throughout, rather than peaking or dropping significantly in any single phase.\nBased on the above thought, the answer is:\nVisual Arts. It has consistent values across all four phases with no extreme peaks, indicating balanced development. The moderate increases and improvements suggest that Visual Arts is developing in a stable and balanced manner compared to the other art forms."
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "zgv3GWaDftSQmSTz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is Literature's value compared to Cinema under the \"steady increase\" phase?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the lengths of the bars in the steady increase phase, I can see that Literature {bar coordinates for Literature}<[0.113, 0.147, 0.943, 0.236]> has a length that corresponds to a value of 400, while Cinema {bar coordinates for Cinema}<[0.113, 0.036, 0.653, 0.125]> measures a value of 260. The calculation to find the height difference simply involves subtracting Cinema's value from Literature's value.\nBased on the above thought, the answer is:\n140"
+            }
+        ],
+        "id_raw": 528
+    },
+    {
+        "id": "35jhVv8Z7Q0ACNjv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of the \"Unstable Behavior\" and \"Slow Recovery\" indices in the chart and present them in a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data given is based on the migration trends of four countries: {USA}<r>[0.111, 0.409]</r>, {Canada}<r>[0.111, 0.300]</r>, {Germany}<r>[0.111, 0.191]</r>, and {France}<r>[0.111, 0.083]</r>. According to the color legend, the green color represents {Unstable Behavior}<r>[0.291, -0.083, 0.326, -0.071]</r> and blue represents {Slow Recovery}<r>[0.534, -0.083, 0.569, -0.071]</r>. I can gather this data from two separate parts of the chart: The first subplot with coordinates {subplot_0}<r>[0.111, 0.019, 0.963, 0.473]</r> shows the migration instances under the \"Unstable Behavior\" index, whereas the second subplot with coordinates {subplot_1}<r>[0.111, 0.498, 0.963, 0.952]</r> details the instances under the \"Slow Recovery\" index. \n\nGiven the length of horizontal bars representing the respective indices in the two subplots, I am deducting following data: In subplot_1, for {USA}<r>[0.111, 0.365, 0.781, 0.452]</r>, the x_range ends at 2370. Similarly, in subplot_2 for {USA}<r>[0.111, 0.844, 0.922, 0.931]</r>, the x_range ends at 2870, and so forth from other bars. By plotting these details in a CSV format, it gives us the following data in a structured table:\nBased on the above thought, the answer is:\nCountry,Unstable Behavior,Slow Recovery\nUSA,2370,2870\nCanada,55,25\nGermany,598,600\nFrance,515,500"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "HHkLnmrsaXYlPzcA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country is recovering the most in terms of migration trends after the period of unstable behaviour? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The recovery is represented by the index - \"Slow Recovery\". Therefore, to answer this, we need to consider the lengths of the bar under the \"Slow Recovery\" index in the second subplot, as they reflect the number of migrations during the recovery period for each country. For {USA}<r>[0.111, 0.844, 0.922, 0.931]</r>, the end-point is 2870. Similarly, for {Canada}<r>[0.111, 0.735, 0.118, 0.822]</r>, the endpoint is 25. For {Germany}<r>[0.111, 0.627, 0.280, 0.714]</r>, it is 600. And finally, for {France}<r>[0.111, 0.518, 0.252, 0.605]</r>, the point is 500. Comparing these, It can be inferred that USA has the highest number of migrations during the recovery period.\nBased on the above thought, the answer is:\nUSA. The USA has the most number of migrations during the 'Slow Recovery' period, indicating a higher recovery rate in migration trends after the period of unstable behavior."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "dwZHOJ2AQeEh5VT6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which country had the greatest migration difference between \"Unstable Behaviour\" and \"Slow Recovery\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the greatest difference, it's crucial to subtract the value of 'Unstable Behavior' from 'Slow Recovery' for each country. For instance, in USA, the end points of the bars under 'Unstable Behavior' and 'Slow Recovery' are <r>[0.781, 0.452]</r> and <r>[0.922, 0.931]</r> respectively which represents a difference of 500. Similarly, calculating difference for other countries: Canada<r>[0.118, 0.344]</r> and Canada<r>[0.126, 0.822]</r> have a difference of -30, Germany<r>[0.280, 0.235]</r> and Germany<r>[0.280, 0.714]</r> have a difference of 2, France<r>[0.256, 0.126]</r> and France<r>[0.252, 0.605]</r> have a difference of -15. The country with the greatest migration difference is USA with 500.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "k1zBl3kQEjMEfa2P",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information on the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure! I see that there is a bar chart labeled {State-wise Health Condition Incidence}[0.372, 0.019, 0.767, 0.041] that describes several health conditions across different U.S. States. The y-axis represents the States and the x-axis represents the number of cases. I can see from the chart that there are different bars for each state, where each bar length represents the amount of cases for a specific health condition. The conditions represented in the chart are {Heart Disease, Cancer, Diabetes, and Injury}, according to the legend at {legend}[0.849, 0.534, 0.973, 0.566]. Looking at {California's bar}[0.157, 0.353, 0.942, 0.430] for example, I can see from the length of the red part which represents {Heart Disease}[0.157, 0.353, 0.817, 0.430], that there were 3450 cases. By extracting this information for all states and conditions, I am able to form the table below.\nBased on the above thought, the answer is:\nStates,Heart Diseases,Cancer,Diabetes,Injury\nCalifornia,3450,400,6000,580\nTexas,2750,500,7000,690\nFlorida,2300,750,5500,530\nNew York,2900,600,6400,630\nIllinois,2300,550,4000,390\nOhio,1950,450,2000,880\nGeorgia,2500,530,5700,560\nNorth Carolina,1750,690,3200,3100"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "CfqLkHvXwr4ILEGo",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom what can be seen in the chart, which health condition seems to be most prevalent and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which health condition is most prevalent, I would need to look at the length of each colored segment in the bars of the various states as the length of each segment represents the number of cases for each health condition. From the chart, the red segment representing {Heart Disease}[0.904, 0.595, 0.966, 0.612] appears consistently long in all states, thus Heart Disease appears to be the most prevalent health condition.\nBased on the above thought, the answer is:\nHeart Disease. This is because the segment representing Heart Disease in the chart is consistently long across all states, indicating that there are many cases of this health condition."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "X6fUikbDi2B6GxFx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich state has the highest Cancer cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which state has the highest Cancer cases, I need to look at the length of the colored segment representing Cancer in each state's bar. From the legend, I know that {Cancer is represented by the light yellow color} [0.904, 0.541, 0.966, 0.559]. From a glance, {North Carolina's light yellow segment} [0.157, 0.544, 0.314, 0.621] seems to be the longest among all states meaning North Carolina has the highest Cancer cases.\nBased on the above thought, the answer is:\nNorth Carolina."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "zQ7pq2MyuFE1imAl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data you've analyzed from the chart, could you please convert the information into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the provided chart, there are two wide bars in each subgraph which indicate fields of Total Spending, Growth Trend, E-commerce Growth and Offline Retail Decline for each pair of emerging markets. There are four main categories in both emerging markets. Beginning with the \"total spending\" category, represented by the wide blue bar at the bottom of the stack. The {bar for Emerging Market 1 is at }<r>[0.157, 0.065, 0.817, 0.142]</r>{ and the bar for Emerging Market 2 is at }<r>[0.157, 0.161, 0.680, 0.238]</r>. Moving to \"Growth Trend\", it is represented by the orange portion of the stacked bar. I can see same for \"E-Commerce Growth\" and \"Offline Retail Decline\". Analysing the length of the bars represents various measurements for the related fields. For instance, the length of the \"total spending\" part measures the overall spending in billions. Once I extract the visual observations into raw data, I can arrange them into a CSV format.\nBased on the above thought, the answer is:\nEmerging Market,Total Spending,Growth Trend,E-Commerce Growth,Offline Retail Decline\nEmerging Market 1,250,20,80,-10\nEmerging Market 2,150,30,50,-20"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "fXwiDjmv0WeMpsKo",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the offline retail decline, which emerging market has let its traditional retail sector struggle more and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both subcharts show that all markets have a decline in the offline retail sector, symbolized by the red section of each bar. However, we can visually observe that {Emerging Market 1}<r>[0.157, 0.065, 0.817, 0.142]</r> has a less extent of the red part, thus a less decline in offline retail. Conversely, we can see that {Emerging Market 2}<r>[0.157, 0.161, 0.680, 0.238]</r> has a more significant decrease in offline retail, suggested by the more extended red section of the bar.\nBased on the above thought, the answer is:\nEmerging Market 2. The portion of the bar representing offline retail decline is larger for Emerging Market 2, suggesting that traditional retail has struggled more in this market. The possible reasons might be a rapid shift to online shopping or more competitive online market setting, not represented in the chart."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "bDzenl381yHLMFMf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emerging market demonstrated the highest growth in E-Commerce?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents that each subchart has its own category of E-commerce growth which is shown as a green color. The E-commerce growth for Emerging Market 1 is {bar at }<r>[0.157, 0.065, 0.817, 0.142]</r> and the E-commerce growth for Emerging Market 2 is represented by a {bar at }<r>[0.157, 0.161, 0.680, 0.238]</r>. Visually, Emerging Market 1 has a longer part devoted to E-commerce growth than Emerging Market 2 showing that it had a higher growth in E-commerce.\nBased on the above thought, the answer is:\nEmerging Market 1"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "JsiBhi9FcS5qF6sv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend at the bottom of the chart, I can see that the colors represent different societal demographic aspects: adult literacy rate, youth unemployment, and tertiary education completion. The data in the chart can be parsed by identifying the genders on the y-axis and then detecting the lengths of the bars for each category. The color will indicate which category the data belongs to. For example, the blue bar segment for 'Female' with a length of {0.094, 0.039, 0.573, 0.223} represents the 'Adult Literacy Rate (%)' for females, indicating a value of 88, and the orange bar segment for the same 'Female' category with a length of {0.573, 0.039, 0.654, 0.223} Represents the 'Youth Unemployment Rate (%)' for females, indicating a value of 15.\nBased on the above thought, the answer is:\nEntity,Male,Female\nAdult Literacy Rate,92,88\nYouth Unemployment,11,15\nTertiary Education Completion,45,50"
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "I9uZG2GcAsKPoGuk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the gender discrepancy for tertiary education completion rate."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the gender discrepancy for tertiary education completion, I need to focus on the green bars in the chart that represent {Tertiary Education Completion Rate (%)}[0.353, -0.082, 0.696, -0.065] in the {legend}[0.297, -0.141, 0.703, -0.058]. If we compare the lengths of the green bars for both {female}[0.654, 0.518, 0.926, 0.702] and {male}[0.654, 0.748, 0.899, 0.931], it is apparent that the women's bar is longer. This denotes a higher rate of tertiary education completion for women than men.\nBased on the above thought, the answer is:\nThe discrepancy in the tertiary education completion rates between the two genders is that women's rates are higher than men's. This suggests that remarkably more women have been accessing and completing higher education."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "bfoFbme3hv13l2LV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich gender has a higher unemployment rate according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which gender has a higher unemployment rate, I need to look at the orange color bar of {youth unemployment rate}[0.353, -0.108, 0.620, -0.091] according to the {legend}[0.297, -0.141, 0.703, -0.058]. When comparing the lengths of the orange bars for {male}[0.594, 0.269, 0.654, 0.452] and {female}[0.573, 0.039, 0.654, 0.223], it can be noted that the female category's orange bar is longer, indicating a higher unemployment rate.\nBased on the above thought, the answer is:\nFemale"
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "IZfsuFCh26H0iGaE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the observation of the chart representing entrepreneurial activities across countries, can you generate a table showing 'High tech startups' and 'New business registrations' for each country?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon inspecting the upper portion of the chart, I can identify a group of horizontal bars positioned at {the first bar group}<r>[0.111, 0.019, 0.981, 0.473]</r> for the countries Japan, Australia, Canada, and Germany as stated by {yticks labels}<r>[0.050, 0.076, 0.099, 0.093]</r> to <r>[0.019, 0.402, 0.099, 0.419]</r>. Following this, I see the corresponding data within their respective bars, for instance, the 'High tech startups' for Japan found at {Japan's startup bar}<r>[0.111, 0.039, 0.115, 0.126]</r> and 'New business registrations' bar extended to {Japan's registration bar}<r>[0.115, 0.039, 0.940, 0.126]</r>, indicating the values 30 and 5533 respectively. Continuing this process with the other countries in both chart portions will yield a complete data table.\nBased on the above thought, the answer is:\nCountry,High tech startups,New business registrations\nJapan,30,5533\nAustralia,27,3548\nCanada,40,4565\nGermany,51,5122\nItaly,12,3520\nFrance,15,3817\nUK,23,4126\nUSA,32,3233"
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "btgo0jrE6zbMYbig",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data provided, which country exhibits a significant disparity between high tech startups and new business registrations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the disparity between the two measures, I need to examine the gap between the lengths of the two color-coded bars for each country. For instance, {Japan's high tech startups bar}<r>[0.111, 0.039, 0.115, 0.126]</r> is significantly shorter than {Japan's new business registrations bar}<r>[0.115, 0.039, 0.940, 0.126]</r>. The respective x-ranges from the JSON structure suggest that Japan has 30 high tech startups (in 000s) but a much higher value of 5533 new business registrations (in 000s), showing a marked disparity. Similar analysis for other countries reveals that this gap is notably larger for Japan compared to others, indicating a distinctive focus on less technology-intensive sectors or traditional businesses in Japan's entrepreneurial market.\nBased on the above thought, the answer is:\nJapan exhibits a significant disparity between high tech startups and new business registrations, which suggests a strong entrepreneurial activity but with a potential focus on traditional or lower-tech industries."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "cUJLGJw3kwJlPEoV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of new business registrations for the countries listed in the lower half of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average number of new business registrations for the lower half of the chart, I need to identify and sum up the number of new business registrations for Italy, France, UK, and USA represented by {blue bars}<r>[0.113, 0.518, 0.638, 0.605]</r> to <r>[0.116, 0.844, 0.595, 0.931]</r> and then divide by the number of countries, which is four. Adding the identified values of new business registrations - Italy (3520), France (3817), UK (4126), and USA (3233) - gives a total of 14696. Dividing 14696 by 4 provides the average.\nBased on the above thought, the answer is:\n3674"
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "hWbdn1jg3g0B6sIX",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented by this bar chart and convert the data into a CSV format table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "From what I can make out from the bar chart, the x-axis represents the different categories of social media platforms such as {Facebook}<r>[0.120, 0.731, 0.336, 0.909]</r>, {Twitter}<r>[0.120, 0.507, 0.303, 0.686]</r>, {WhatsApp}<r>[0.120, 0.284, 0.355, 0.463]</r>, and {Instagram}<r>[0.120, 0.061, 0.284, 0.240]</r>. The bar lengths for each category represent the corresponding values of social media active users, digital advertising spending, and email users. The color of each bar is in accordance with the legend given which is {'Social Media Active Users (millions)'}<r>[0.156, -0.083, 0.191, -0.071]</r>, {'Digital Advertising Spending (US$ billions)'}<r>[0.156, -0.057, 0.191, -0.044]</r> and {'Email Users (millions)'}<r>[0.607, -0.083, 0.642, -0.071]</r>. These values can be extracted by analyzing the width of each bar. To generate the CSV table, I will consider the platform, the number of active users, the digital advertising spending, and the number of email users.\nBased on the above thought, the answer is:\nplatform,social_media_active_users,digital_advertising_spending,email_users\nFacebook,2300,2800,6700\nTwitter,1950,2400,3280\nWhatsApp,2500,3030,8730\nInstagram,1750,2440,5540"
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "zvemFSahNP6IFU4D",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the platforms you observed in the chart, which would you recommend for a company aiming to reach a wide audience through digital advertising and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to evaluate both the digital advertising spending and the active users on each platform because a successful advertising campaign should reach a wider audience and also have a higher advertising investment. By looking at the corresponding bars, I observed that {WhatsApp}<r>[0.355, 0.284, 0.404, 0.463]</r> has the highest digital advertising spending and {Facebook}<r>[0.120, 0.731, 0.336, 0.909]</r> has the highest number of social media active users. Comparatively, {WhatsApp}<r>[0.120, 0.284, 0.355, 0.463]</r> also has a higher number of social media active users and the digital advertising spending is highest. Therefore, I would recommend WhatsApp.\nBased on the above thought, the answer is:\nWhatsApp, as it not only has the highest digital advertising spending but also counts for an impressive number of social media active users."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "cAxFbpypMnhtPkkQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform has the highest number of email users?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which social media platform has the highest number of email users, I need to focus on the width of the blue bars. Each bar corresponds to a platform and its width indicates the number of email users. By comparing, it can be seen that the bar representing {email users for WhatsApp platform}<r>[0.404, 0.284, 0.940, 0.463]</r> has the maximum width, therefore WhatsApp has the highest number of email users among the four platforms.\nBased on the above thought, the answer is:\nWhatsApp"
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "B9Nnf8OWlbGbuVYf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data from the bar chart into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To reproduce the data in a CSV table, we will extrapolate the information provided by each bar of the chart for each country. The countries are denoted by the {y-ticks}[{label: 'USA', 'label_coordinates': '[0.019, 0.144, 0.107, 0.161]', 'tick_coordinates': '[0.120, 0.150]'}; {'label: 'UK', 'label_coordinates': '[0.019, 0.367, 0.107, 0.384]', 'tick_coordinates': '[0.120, 0.374]'};{'label: 'France', 'label_coordinates': '[0.049, 0.590, 0.107, 0.607]', 'tick_coordinates': '[0.120, 0.597]'}; {'label: 'Italy', 'label_coordinates': '[0.026, 0.813, 0.107, 0.831]', 'tick_coordinates': '[0.120, 0.820]'}]. For example we can look at USA, the first country. For every statistic, we measure the width of the corresponding segment in the bar. For hotel occupancy rate, which in the legend reads as the color #ff6347 {label: 'Social Media Active Users (millions)', 'color': '#ff6347'}, we look at the leftmost segment in the bar with the corresponding color. Following the same rule, we can find the values for average daily rate, RevPAR, and tourism revenue and proceed similarly for the other countries.\nBased on the above thought, the answer is:\nCountry,Hotel Occupancy Rate (%),Average Daily Rate ($),RevPAR ($),Tourism Revenue ($bn)\nUSA,68.3,123.6,84.4,1572.2\nUK,78.1,112.8,88.1,221.7\nFrance,75.5,117.8,89.0,256.4\nItaly,72.9,109.5,79.9,205.6\nCanada,66.8,115.3,77.0,90.2\nAustralia,74.1,130.4,96.5,45.3\nGermany,72.3,108.6,78.6,154.6\nJapan,85.0,102.3,87.0,441.4"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "fHD0X1Y5rI3HX4pY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the strips for hotel occupancy rate on the chart, which country has the potential to increase its tourism revenue the most by only increasing its hotel occupancy rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The receipts from the hotel industry are directly proportional to hotel occupancy rate because a higher occupancy rate means more revenue. To decide which country has the most potential to increase its tourism revenue by increasing its hotel occupancy rate, I should find a country with a relatively low hotel occupancy rate, but a high tourism revenue. The hotel occupancy rates can be seen in the leftmost sections of each country's bar, for example {\"USA\"}[{'bar_coordinates': '[0.120, 0.061, 0.158, 0.240]', 'y_label': 'USA', 'x_range': '0.00 -> 400.00'}], and the tourism revenues can be read from the rightmost sections of each bar, like {\"USA\"}[{'bar_coordinates': '[0.395, 0.061, 0.462, 0.240]', 'text_coordinates': None, 'text_params': None, 'y_label': 'USA', 'x_range': '2750.00 -> 3100.00'}]. After comparing, I find that the country that fits the criteria best, a large room for improvement in hotel occupancy rate, and already a high tourism revenue is USA.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "A8jF2Nl4tyL1xLSq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which country had the highest tourism revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reading from the chart, tourism revenue for each country is represented by the rightmost section of each bar. To find the maximum, I will have to compare the values represented by the lengths of these sections. Considering each y-tick label as a country, we start from {\"USA\"}[{'label': 'USA', 'label_coordinates': '[0.019, 0.144, 0.107, 0.161]', 'tick_coordinates': '[0.120, 0.150]'}], and do the same for every country up to {\"Japan\"}[{'label': 'Japan', 'label_coordinates': '[0.749, 0.813, 0.887, 0.831]', 'tick_coordinates': '[0.831, 0.820]'}]. We can note that the bar associated with the tick labeled \u2018USA', {USA}[{\"bar_coordinates\": \"[0.120, 0.061, 0.462, 0.240]\", 'y_label': 'USA', 'x_range': '0.00 - 3100.00'}], extends the furthest out on the right, denoting the largest tourism revenue.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "Z9Elldlu3eECtCwh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table denoting the annual growth rate projections for VR and AR technologies as observed from the top subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the annual growth rate projections for VR and AR technologies, I will look at {the top subplot}<r>[0.075, 0.019, 0.981, 0.473]</r>. The years are listed on {the y-axis}, ranging from {2022}<r>[0.019, 0.067, 0.063, 0.084]</r> to {2026}<r>[0.019, 0.411, 0.063, 0.428]</r>. The VR bars' coordinates are as follows: for {2022}<r>[0.075, 0.039, 0.181, 0.108]</r>, {2023}<r>[0.075, 0.125, 0.233, 0.194]</r>, {2024}<r>[0.075, 0.211, 0.328, 0.280]</r>, {2025}<r>[0.075, 0.297, 0.457, 0.366]</r>, and {2026}<r>[0.075, 0.383, 0.516, 0.452]</r>. Similarly, the AR bars' coordinates are listed as {2022}<r>[0.181, 0.039, 0.339, 0.108]</r>, {2023}<r>[0.233, 0.125, 0.427, 0.194]</r>, {2024}<r>[0.328, 0.211, 0.604, 0.280]</r>, {2025}<r>[0.457, 0.297, 0.806, 0.366]</r>, {2026}<r>[0.516, 0.383, 0.938, 0.452]</r>. Using the data provided, I first calculate the yearly values for each technology and then compute the growth rates comparing the consecutive years.\nBased on the above thought, the answer is:\nYear,Growth Rate VR (%),Growth Rate AR (%)\n2022, , \n2023,13.79,23.26\n2024,30.30,18.60\n2025,60.47,21.54\n2026,50.72,26.67"
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "JA2xTyqep9Lzha9c",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the trends in the chart, which technology, VR or AR, shows a more aggressive growth pattern over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trends, I focus on the length of {VR bars}<r>[0.075, 0.039, 0.181, 0.108]; [0.075, 0.125, 0.233, 0.194]; [0.075, 0.211, 0.328, 0.280]; [0.075, 0.297, 0.457, 0.366]; [0.075, 0.383, 0.516, 0.452]</r> and {AR bars}<r>[0.181, 0.039, 0.339, 0.108]; [0.233, 0.125, 0.427, 0.194]; [0.328, 0.211, 0.604, 0.280]; [0.457, 0.297, 0.806, 0.366]; [0.516, 0.383, 0.938, 0.452]</r> for each year in the provided data. AR technology starts at a higher value in 2022, and the length of AR bars progressively outgrows VR bars each subsequent year, indicating a more steeply sloped growth curve. This is particularly evident in {2025}<r>[0.457, 0.297, 0.806, 0.366]; [0.806, 0.297, 1.078, 0.366]</r> and {2026}<r>[0.516, 0.383, 0.938, 0.452]; [0.938, 0.383, 1.240, 0.452]</r> where AR growth clearly surpasses VR growth despite a significant increase in VR growth during these years. The distances covered by the bars on the x-axis correspond to their growth magnitude, with AR covering more ground than VR by 2026.\nBased on the above thought, the answer is:\nAugmented Reality (AR) shows a more aggressive growth pattern over the years. This is determined by the lengthier bars representing AR in the chart and its consistently larger increases, especially notable in the final years."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "jnFjyODKJT8wc0Xw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in the AR trend from 2022 to 2023 as depicted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage increase in the AR trend from 2022 to 2023, I need to look at the AR bars within {the top subplot}<r>[0.075, 0.019, 0.981, 0.473]</r>. The {2022 AR bar}<r>[0.181, 0.039, 0.339, 0.108]</r> shows the value at the start and the {2023 AR bar}<r>[0.233, 0.125, 0.427, 0.194]</r> shows the value at the end. Using the raw data, the value for 2022 is 430 and for 2023 is 530. Therefore, the percentage increase is calculated as ((530 - 430) / 430) * 100.\nBased on the above thought, the answer is:\n23.26%"
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "RvzFLV2CuA99BB0y",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a structured CSV format table for the socioeconomic factors of the four countries based on the observed bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the two subplots, I see that the y-axis lists the countries {Brazil and China}<r>[0.075, 0.124, 0.124, 0.142]</r> in the first subplot and {India and South Africa}<r>[0.082, 0.603, 0.124, 0.621]</r> in the second subplot. The bars are grouped by color to represent each socioeconomic factor, as indicated {in the legend}<r>[0.171, -0.091, 0.829, -0.034]</r>, such as {Population Growth}<r>[0.137, 0.039, 0.257, 0.223]</r>, {GDP per Capita}<r>[0.257, 0.039, 0.262, 0.223]</r>, {Unemployment Rate}<r>[0.262, 0.039, 0.269, 0.223]</r>, and {Healthcare Expenditure}<r>[0.269, 0.039, 0.271, 0.223]</r>. To form a CSV, I\u2019ll arrange the data by socioeconomic factor and country in a tabular form.\nBased on the above thought, the answer is:\nCountry,Population Growth (M),GDP per Capita (k USD),Unemployment Rate (%),Healthcare Expenditure (% of GDP)\nBrazil,211,8.7,12.8,3.8\nChina,1392,10.1,3.8,3.1\nIndia,1366,2.1,5.5,1.3\nSouth Africa,59,6.4,28.7,8.1"
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "dx9IAnsmzpDhcDXa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the chart, which country shows a balance between economic growth and public healthcare expenditure?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the balance between the GDP per capita and healthcare public expenditure for each country. I can see that Brazil has {a GDP per capita bar}<r>[0.257, 0.039, 0.262, 0.223]</r> and {a healthcare expenditure bar}<r>[0.269, 0.039, 0.271, 0.223]</r>, China shows {a GDP per capita bar}<r>[0.931, 0.269, 0.937, 0.452]</r> and {a healthcare bar}<r>[0.939, 0.269, 0.941, 0.452]</r>, while India and South Africa present their respective data in the second subplot with {GDP per capita and healthcare bars for India}<r>[0.916, 0.518, 0.918, 0.702]</r> <r>[0.921, 0.518, 0.922, 0.702]</r> and {for South Africa}<r>[0.170, 0.748, 0.174, 0.931]</r> <r>[0.190, 0.748, 0.195, 0.931]</r>. Considering the provided data, Brazil and South Africa have higher healthcare expenditures in proportion to their GDP, while China shows higher economic growth. India also indicates lower public healthcare expenditure relative to its GDP.\nBased on the above thought, the answer is:\nChina exhibits a balance between economic growth and public healthcare expenditure. Its GDP per capita is the highest, suggesting strong economic growth, yet its public healthcare expenditure as a percentage of GDP is moderate, not indicating a significant imbalance."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "ZqCGXJde7l1FKQ4T",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total of GDP per capita in thousands of USD for all four countries combined, as per the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each country's GDP per capita is visually represented by a shade of blue bar. To calculate the total GDP per capita, I need to sum the values of the bars representing GDP per capita for {Brazil}<r>[0.257, 0.039, 0.262, 0.223]</r>, {China}<r>[0.931, 0.269, 0.937, 0.452]</r>, {India}<r>[0.916, 0.518, 0.918, 0.702]</r>, and {South Africa}<r>[0.170, 0.748, 0.174, 0.931]</r>. According to the data provided, Brazil's GDP per capita is 8.7, China's is 10.1, India's is 2.1, and South Africa's is 6.4.\nBased on the above thought, the answer is:\n27.3"
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "tKhTDo2J5bkKpdmM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please extract the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart consists of two group of countries: Germany, Canada, Australia, and Japan, at {the top subplot}<r>[0.111, 0.498, 0.981, 0.952]</r>; USA, China, France, and Italy, at {the bottom subplot}<r>[0.111, 0.019, 0.981, 0.473]</r>. For each group, there are two types of bars: AI Research Funding (in billions USD) and Number of AI Companies, as indicated by {the legend}<r>[0.159, -0.091, 0.841, -0.060]</r>. I can start extracting the values by using the position of the bars according to the x-axis and the associated labels on the y-axis. For instance, {the pink bar for 'Germany'}<r>[0.111, 0.365, 0.111, 0.452]</r> in {top subplot}<r>[0.111, 0.498, 0.981, 0.952]</r> is for the 'AI Research Funding', and the length of the bar indicates that it is 5 billion USD. Similarly, {the blue bar for 'Germany'}<r>[0.111, 0.365, 0.307, 0.452]</r> indicates that the number of AI companies in Germany is 4250. By using the same approach, I can extract the other values from both subplots.\nBased on the above thought, the answer is:\nType,Germany,Canada,Australia,Japan,USA,China,France,Italy\nAI Research Funding in billions USD,5,3.7,4,4.8,20,15,12,10\nNumber of AI Companies,4250,3000,3400,3800,18000,16000,9000,5000"
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "pCJ0K5RpeknwdwZG",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, how can we observe the growth of technological progress in AI Application Cases across countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The progress can be analyzed via the increment in 'AI Research Funding' and 'Number of AI Companies' for each country. The pink and blue bars represented by {the legend}<r>[0.159, -0.091, 0.841, -0.060]</r> respectively show 'AI Research Funding (in billions USD)' and 'Number of AI Companies' for each country. For instance, {USA}<r>[0.063, 0.881, 0.099, 0.898]</r> in {the bottom subplot}<r>[0.111, 0.019, 0.981, 0.473]</r> showing prominent growth in 'AI Research Funding' and 'Number of AI Companies' indicates its rapid technological progress. The chart reveals an overall gradual increase in both research funding and the number of AI companies, displaying the growth of technology in AI application cases in almost all countries.\nBased on the above thought, the answer is:\nThe growth of technological progress can be observed through increasing AI Research Funding and the Number of AI Companies. In general, there is a noticeable growth rate for most countries. However, the USA exhibits the fastest growth, showing the most significant technological progress across the analyzed countries."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "WIiwzDtHCFRQHc4W",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the largest number of AI companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest number of AI companies can be found by comparing the lengths of {the blue bars}<r>['#00bfff']</r> in the chart, as indicated by {the legend}<r>[0.159, -0.091, 0.841, -0.060]</r>. The {USA's blue bar}<r>[0.112, 0.844, 0.940, 0.931]</r> in {the bottom subplot}<r>[0.111, 0.019, 0.981, 0.473]</r> stands out among all the bars, as it extends much further along the x-axis than any other bars for the number of AI companies. Thus, it represents the highest number of AI companies, which according to it is 18000.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "OEpAHns5zYuPR0Cd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from this Risk Assessment and Management bar chart and compile it into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, the Risk Factors are positioned along the y-axis, with each {Risk Factor}[for example, \"Cyber-security\"<r>[0.068, 0.806, 0.169, 0.834]</r>] accompanied by a corresponding bar extending positively or negatively along the x-axis, with its length or position indicating the relative Risk Score. I can infer the value of each Risk Score by noting the position of the end of each bar relative to the x-axis with ticks ranging from {\u221260}<r>[0.164, 0.903, 0.193, 0.931]</r> to {100}<r>[0.959, 0.903, 0.985, 0.931]</r>. For example, the {Security Breach risk factor}<r>[0.059, 0.731, 0.169, 0.759]</r> has a {bar}<r>[0.476, 0.712, 0.699, 0.772]</r> extending to the 45 position indicating a risk score of 45 while {Cyber-security}<r>[0.068, 0.806, 0.169, 0.834]</r> has a {bar}<r>[0.377, 0.787, 0.476, 0.847]</r> extending negatively along the x-axis to the -20 position, signifying a Risk Score of -20. Diligently following this process, I can extract the Risk Score for each Risk Factor as shown below.\nBased on the above thought, the answer is:\nRisk_Factor, Risk_Score\nCyber-security, -20\nSecurity Breach, 45\nFraud Risk, 30\nOperational Risk, -10\nMarket Volatility, 35\nRegulatory Risk, -50\nEnvironmental Risk, 70\nReputation Risk, -40\nGeo-Political Risk, 90\nFinancial Risk, -30"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "GL01GtL1W32eRR1x",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data represented in the chart, could you share insights about the overall risk trends within the business entity?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart showcases the change in risk severity across various risk factors within a business entity. The risk factors with negative Risk Score values, such as {Cyber-security}<r>[0.068, 0.806, 0.169, 0.834]</r> and {Operational Risk}<r>[0.054, 0.581, 0.169, 0.609]</r> show bars extending to the left of the axis, indicating a decrease in risk severity relative to the previous period. Similarly, risk factors with positive scores like {Fraud Risk}<r>[0.097, 0.656, 0.169, 0.684]</r> and {Market Volatility}<r>[0.056, 0.506, 0.169, 0.534]</r> exhibit bars extending to the right, signaling an increase in risk severity. Such variability suggests that while certain risk categories have seen improvements over time, others have increased in severity. The broad range of positive and negative changes could indicate a multi-faceted risk profile and diversified risk management strategies within the organization. Risks such as the {Geo-Political Risk}<r>[0.049, 0.205, 0.169, 0.233]</r> present the most significant recent increase, signaling a potential area where the organization must direct its risk management resources.\nBased on the above thought, the answer is:\nBased on the depicted risk severity changes, it appears that the business entity has mixed results in managing different risk factors. While the severity of some risks, like Geo-political Risk and Environmental Risk, has increased, some others like Cyber-security and Regulatory Risk have seen a reduction. Thus, it may be beneficial for the entity to focus on monitoring and managing the increasing risks while maintaining the improvements seen in the other areas."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "SezOz4bpgbCHWewr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the given bar chart, which risk factor has the greatest increase in risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the risk factor with the greatest increase in risk, it is necessary to locate the bar that extends the farthest to the right, along the x-axis from the neutral {zero}<r>[0.472, 0.903, 0.480, 0.931]</r> point. The bar that extends the furthest would correspond to the risk factor that has seen the greatest increase in risk. Directing my attention to the right half of the x-axis, I find that the bar representing {Geo-Political Risk}<r>[0.476, 0.186, 0.922, 0.246]</r> extends the furthest to the right, reaching the {\"90\"}<r>[0.765, 0.903, 0.782, 0.931]</r>x-coordinate.\nBased on the above thought, the answer is:\nGeo-Political Risk"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "bdNAXKU96GC9mCs7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data from the chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a bar chart with the title {\"Artificial Intelligence and Automation Trends\"}[0.432, 0.030, 0.800, 0.066] at the top, which suggests that the data presented involves trends in various technology areas. The y-axis label is {\"Technology\"}[0.015, 0.401, 0.029, 0.557] and the x-axis label is {\"Trends in percentage (%)\"}[0.529, 0.942, 0.703, 0.970], indicating a relationship between different technologies and their corresponding trends. The y-axis tick labels consists of 12 categories, including {\"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839], {\"Robotics\"}[0.179, 0.748, 0.238, 0.776], {\"Machine Learning\"}[0.115, 0.686, 0.238, 0.714], etc., each relating to a different technology domain. The bars extend from an origin at 0%, indicated by {the position 0 on the x-axis tick labels} [0.493, 0.903, 0.510, 0.931], extending either left (negative trend) or right (positive trend). For example, for \"Artificial Intelligence\", {the bar}[0.493, 0.797, 0.739, 0.847] extends to the right up to the 50% position on the x-axis. This suggests that the trend for \"Artificial Intelligence\" is a positive 50%. Conversely, for \"Machine Learning\", {the bar}[0.346, 0.672, 0.493, 0.722] extends to the left down to the -30% position on the x-axis, which suggests a negative trend of -30%. This process is used to read and analyze each bar, leading to the construction of the following table.\nBased on the above thought, the answer is:\nTechnology,Trend(%)\nArtificial Intelligence,50\nRobotics,80\nMachine Learning,-30\nCloud Computing,20\nNatural Language Processing,15\nAutomation,-40\nSelf Driving Cars,70\nBiometrics,90\nNeural Networks,-15\nData Analytics,33\nIntelligent Apps,23\nBlockchain,45"
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "deqVQyWeLaqXS7kY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the presented data, can you provide an assessment regarding future investment in these technology areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide an investment assessment, each technology trend needs to be considered. Starting with {\"Artificial Intelligence\"}[0.095, 0.811, 0.238, 0.839], {the bar}[0.493, 0.797, 0.739, 0.847] shows a positive trend of 50%. This suggests a growing interest and investment potential in the sector, offering potentially good returns. The next technologically significant area is {\"Robotics\"}[0.179, 0.748, 0.238, 0.776], and its {bar}[0.493, 0.735, 0.887, 0.784] shows a positive trend of 80%. This suggests a high growth rate, often indicating a good investment opportunity. For trends that are negative, such as {\"Automation\"}[0.157, 0.499, 0.238, 0.527], {the bar}[0.296, 0.485, 0.493, 0.535] shows a negative trend, indicating that this may not be a prime candidate for investment. Considering all the technology bars, key areas for potential investment would likely be those with a significantly positive trend, while areas exhibiting negative trends may pose more risk.\nBased on the above thought, the answer is:\nThe chart suggests that investing in Robotics, Biometrics, and Self Driving Cars could yield good returns due to their strong positive trends of 80%, 90%, and 70% respectively. Conversely, Automation and Neural Networks are sectors exhibiting negative trends and hence may not be the best choice for investment at this time."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "Uay33Y8M4x6byxIp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest positive trend recorded in technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest positive trend, I need to identify the bar that extends furthest to the right from the origin {0%}[0.493, 0.903, 0.510, 0.931]. The bar that extends the furthest to the right is the one representing {Biometrics}[0.493, 0.361, 0.936, 0.411]. This bar ends at the 90% position on the x-axis, which is the highest positive percentage exhibited amongst all the technologies.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "gE3dTRfmXMNzcIQ7",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, can you extract the growth trend data of various technologies and convert it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the y-axis at the left of the chart {Technologies}<r>[0.015, 0.390, 0.029, 0.568]</r> and <r>[0.213, 0.075, 0.985, 0.883]</r>, I can see that the y-axis represents different technologies. The x-axis tick labels represent the corresponding growth trends, ranging from {-50}<r>[0.244, 0.903, 0.273, 0.931]</r> to {100}<r>[0.940, 0.787, 0.970, 0.847]</r>. Each technology is represented by a single bar. The length of the bar and its position with respect to the center at {0}<r>[0.485, 0.883]</r> represent the value of the growth trend. If the bar is to the right of the center, it indicates a positive trend, and if it's to the left of the center, it indicates a negative trend. To generate the CSV table, I need to extract the value for each technology. I can use the width of the bars to determine the values. For example, the right end of {the bar for \"Blockchain\"}<r>[0.485, 0.787, 0.940, 0.847]</r> is approximately at 100 position, so the value of \"Blockchain\" is 100. Similarly, the left end of {the bar for \"AI/ML\"}<r>[0.258, 0.712, 0.485, 0.772]</r> is approximately at -50 position, so the value of \"AI/ML\" is -50.\nBased on the above thought, the answer is:\nTechnologies,Difference\nBlockchain,100\nAI/ML,-50\nCryptocurrency,75\nPeer-to-Peer Networking,-15\nBig Data,-30\nData Protection,60\nDigital Wallet,30\nCybersecurity,45\nCloud computing,-10\nIoT,20"
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "iAZMuyjuUM9WQuTR",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can be inferred about the trends in technologies related to financial technology and digital currency over the last decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides a comprehensive view of growth trends in different technologies. First of all, with a length to the extreme right {bar for Blockchain}<r>[0.485, 0.787, 0.940, 0.847]</r>, we can see that Blockchain has seen a significant growth by 100%, indicating that there has been a massive increase in the interest in Blockchain technology over the decade. AI/ML, on the other hand, {shown by its bar}<r>[0.258, 0.712, 0.485, 0.772]</r> on the left of zero on the x-axis, has seen a decrease in interest by 50%. Technologies with bars on the left of zero such as Peer-to-Peer Networking, Big Data and Cloud computing <r>[0.417, 0.562, 0.485, 0.622]</r>; <r>[0.349, 0.487, 0.485, 0.547]</r>; <r>[0.440, 0.186, 0.485, 0.246]</r> also experienced a decline in growth. Those technologies with bars on the right of zero such as Data Protection, Digital Wallet, Cybersecurity, and IoT <r>[0.485, 0.412, 0.758, 0.472]</r>; <r>[0.485, 0.337, 0.622, 0.397]</r>; <r>[0.485, 0.261, 0.690, 0.322]</r>; <r>[0.485, 0.111, 0.576, 0.171]</r> have shown positive growth. This trend indicates that the focus in financial technology and digital currency has shifted significantly over the last decade.\nBased on the above thought, the answer is:\nThese trends indicate a shift in focus in the field of financial technology and digital currency, with a significant increase in interest in technologies such as Blockchain, Data Protection, Digital Wallet, Cybersecurity, and IoT. Meanwhile, there has been a decrease in interest in some technologies like AI/ML, Peer-to-Peer Networking, Big Data, and Cloud computing. This shift might be a reflection of changing market needs, technological advancements, and other socio-economic factors."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "THvLbSWOwMqIo20K",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has experienced the greatest growth over the past decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the technology with the greatest growth, I need to compare the length of each bar on the positive side of the x-axis. From the top, the {length of the bar for the first technology - \"Blockchain\"}<r>[0.485, 0.787, 0.940, 0.847]</r> corresponds to a growth of 100%. Looking down the chart at the {bars for the other technologies}<r>[0.485, 0.637, 0.826, 0.697]</r>; <r>[0.485, 0.412, 0.758, 0.472]</r>; <r>[0.485, 0.337, 0.622, 0.397]</r>; <r>[0.485, 0.261, 0.690, 0.322]</r>; <r>[0.485, 0.111, 0.576, 0.171]</r>, none of them extend further to the right than the bar for \"Blockchain\". Therefore, \"Blockchain\" has experienced the greatest growth.\nBased on the above thought, the answer is:\nBlockchain."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "MVOyzSokUvchG6JC",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me to extract the information from this horizontal bar chart and transform it into a readable csv-format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains the categories \u2018Technological Innovation and Intellectual Property\u2019 and the corresponding value, which is the percentage of increase or decrease from {-60}[0.191, 0.919, 0.220, 0.942] to {100}[0.959, 0.919, 0.985, 0.942] indicated in {green bars}[0.493, 0.802, 0.732, 0.865] and {red bars}[0.302, 0.646, 0.493, 0.709]. The {y-axis}[0.015, 0.420, 0.029, 0.545] represents each category and the {x-axis}[0.518, 0.952, 0.660, 0.975] indicates the value of each category. Each value can be extracted by the lengths and direction of the bars. For example, {\"Technologies\"}[0.107, 0.824, 0.196, 0.848] with a {bar}[0.493, 0.802, 0.732, 0.865] indicates a positive change of 50%. The values are then matched to the respective categories and tabulated.\nBased on the above thought, the answer is:\nCategory,change(%)\nTechnologies,50\nPatents,100\nCopyrights,-40\nTrademarks,30\nTrade Secrets,-60\nLicensing Agreements,80\nRoyalties,-20\nIndustrial Design,10\nResearch Development,60\nInfringement Cases,10"
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "e7ZCh6NEJgehKBK7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field of innovation and intellectual property seems to be losing importance? Can you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The loss in importance is represented by bars with a negative value pointing towards -60 on the {x-axis}[0.518, 0.952, 0.660, 0.975]. From the chart, I can tell that the category {Trade Secrets}[0.102, 0.512, 0.196, 0.536] has the {longest bar}[0.206, 0.490, 0.493, 0.553] going towards the left, representing an importance decrease of -60%. This is considerably larger than that of the 'Copyrights' and 'Royalties' categories, which each have a bar length only half as long. The decrease in the relative importance of Trade Secrets may be attributed to companies relying more on patents and licensing agreements (as indicated by their positive growth) to protect their innovative technologies and intellectual property in recent years.\nBased on the above thought, the answer is:\nThe category 'Trade Secrets' seems to be losing importance, which may be due to the shift in strategy by companies to protect their technologies and intellectual property using patents and licensing agreements instead."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "bhoGSgVgvTiwRQ7l",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category in the chart has the highest positive change value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The positive change values in each category are indicated by the length of the {green bars}[0.493, 0.802, 0.732, 0.865] in the direction towards 100 on the {x-axis}[0.518, 0.952, 0.660, 0.975]. By scanning each category starting from the top, we find that the {category 'Patents'}[0.144, 0.746, 0.196, 0.770] with the {bar}[0.493, 0.724, 0.972, 0.787] has the most extended length towards {100}[0.959, 0.919, 0.985, 0.942] on the x-axis. Hence, the category 'Patents' has the highest positive chang value.\nBased on the above thought, the answer is:\nPatents"
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "vNYYh1UEz60rnWvE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please identify the aspects of social network presence and interpersonal relationships from the bar chart and list the corresponding percentage changes in ascending order?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will identify all the y_axis labels, which represent different aspects of social network presence and interpersonal relationships, as well as the range on the x_axis, which indicates the percentage changes. I will list all aspect and the associated percentage changes in ascending order based on the bars' {start and end positions}<r>{\"bars_of_group_0\"}[{\"bar_coordinates\":\"[0.488, 0.797, 0.681, 0.847]\"}, {\"bar_coordinates\":\"[0.391, 0.735, 0.488, 0.784]\"}, {\"bar_coordinates\":\"[0.488, 0.672, 0.778, 0.722]\"}, {\"bar_coordinates\":\"[0.488, 0.610, 0.875, 0.660]\"}, {\"bar_coordinates\":\"[0.439, 0.548, 0.488, 0.597]\"}, {\"bar_coordinates\":\"[0.488, 0.485, 0.633, 0.535]\"}, {\"bar_coordinates\":\"[0.294, 0.423, 0.488, 0.473]\"}, {\"bar_coordinates\":\"[0.488, 0.361, 0.585, 0.411]\"}, {\"bar_coordinates\":\"[0.488, 0.236, 0.730, 0.286]\"}, {\"bar_coordinates\":\"[0.488, 0.174, 0.827, 0.224]\"}, {\"bar_coordinates\":\"[0.391, 0.111, 0.488, 0.161]\"}]</r>.\nBased on the above thought, the answer is:\nAspects,Changes(%)\nNumber of Blocks,-100\nShares per post,-50\nNumber of Friends,-50\nNumber of Shares,-25\nMentions per Post,0\nSocial Network,1\nPosts per Day,50\nNumber of Unfollows,75\nLikes per Post,125\nNumber of Followers,100\nNumber of Likes,150\nComments per Post,175\nNumber of Comments,200"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "bwA942CmaOfGbuRk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data in the chart, could you analyze and evaluate the effectiveness of this individual's online interactions and effectiveness in maintaining his/her Social Network?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the data from various aspects, it seems that the number of likes({bar}[0.488, 0.672, 0.778, 0.722]) and the number of comments({bar}[0.488, 0.610, 0.875, 0.660]) per post have the most significant increases, it can infer that the individual is effectively engaging their audience. Furthermore, the number of followers({bar}[0.488, 0.797, 0.681, 0.847]) also experienced a substantial increase, indicating the individual's ability to attract new followers. However, the individual's number of friends({bar}[0.391, 0.735, 0.488, 0.784]) has sharply dropped, which might suggest some potential issues in maintaining relationships. The situation gets worse as the number of shares per post({bar}[0.391, 0.111, 0.488, 0.161]) and the friendship({bar}[0.391, 0.735, 0.488, 0.784]) both went negative, which means the individual is possibly experiencing a decrease in popularity or likability. This dichotomy between gaining popularity and maintaining friendships might be something this individual should pay attention to.\nBased on the above thought, the answer is:\nI'd say the individual is effective in engaging with his/her online audience as evidenced by the increase in likes and comments per post. However, they seem to have difficulty in maintaining personal relationships as indicated by the decline in the number of friends and shares per post."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "BciwqiDRcD1rMhUD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage change of the aspects given in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average percentage change, I have to sum up all the percentage changes and then divide by the number of aspects. As given in {the bars}<r>{\"bars_of_group_0\"}[{\"bar_coordinates\":\"[0.488, 0.797, 0.681, 0.847]\"}, {\"bar_coordinates\":\"[0.391, 0.735, 0.488, 0.784]\"}, {\"bar_coordinates\":\"[0.488, 0.672, 0.778, 0.722]\"}, {\"bar_coordinates\":\"[0.488, 0.610, 0.875, 0.660]\"}, {\"bar_coordinates\":\"[0.439, 0.548, 0.488, 0.597]\"}, {\"bar_coordinates\":\"[0.488, 0.485, 0.633, 0.535]\"}, {\"bar_coordinates\":\"[0.294, 0.423, 0.488, 0.473]\"}, {\"bar_coordinates\":\"[0.488, 0.361, 0.585, 0.411]\"}, {\"bar_coordinates\":\"[0.488, 0.236, 0.730, 0.286]\"}, {\"bar_coordinates\":\"[0.488, 0.174, 0.827, 0.224]\"}, {\"bar_coordinates\":\"[0.391, 0.111, 0.488, 0.161]\"}]</r>, percentage changes are -100, -50, -50, -25, 0, 1, 50, 75, 125, 100, 150, 175, and 200 respectively. The total number of aspects is 13. Sum of changes(/100) is -100 + -50 + -50 + -25 + 0 + 1 + 50 + 75 + 125 + 100 + 150 + 175 + 200 which equals to 651. The average can be calculated as the sum of changes(/100) divided by the total number of aspects which is 651 / 13.\nBased on the above thought, the answer is:\n50.07692307692308"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "0VdYP4zYPSOhvgzU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn regards to the bar chart that you analyzed, can you perceive data in form of a table with 'Cultural Items' as rows and 'Percent Change' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my observation, I can comprehend that the title of the chart {\"Percent Change in Popularity of Cultural Items Post Integration\"}[0.339, 0.030, 0.860, 0.066]. The y-axis of the chart represents 'Cultural Items' which include categories like 'Ethnic Cuisine', 'Religious Practices', 'Folk Dances', 'Traditional Clothing', and so on. The x-axis of the chart specifies the 'Percent Change' or the rate of acceptance or decline of these 'Cultural Items'. For example, the cultural item {Ethnic Cuisine}[0.227, 0.817] shows a {positive change of 30%}[0.387, 0.787, 0.546, 0.847] while the {Religious Practices}[0.227, 0.742] shows a {negative change or decline of 10%}[0.333, 0.712, 0.387, 0.772]. By following the same logic for the remaining bars in the chart, I can transcribe the entire chart into a table.\nBased on the above thought, the answer is:\nCultural Items,Percent Change\nEthnic Cuisine,30\nReligious Practices,-10\nFolk Dances,20\nTraditional Clothing,35\nLanguage and Literature,60\nMarriage Customs,70\nArt and Craft,-15\nMusic and Poetry,80\nArchitectural Styles,-20\nFestivals and Celebrations,100"
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "pk3dbxfbVjUBxvH4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which cultural items are experiencing a decline after the integration event?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing each row of the chart, the bars that extend towards the negative side of the x-axis indicate the cultural items that are declining after the integration event. For instance, {the bar for 'Religious Practices'}[0.227, 0.742] extends into the negative side of the X-axis from {0}[0.387, 0.883] to {-10}[0.319, 0.903], demonstrating a decline. According to the same analysis method, the cultural items {'Art and Craft'}[0.227, 0.367] with bar extending to {-15}[0.307, 0.337], and {'Architectural Styles'}[0.227, 0.216] extending to {-20}[0.280, 0.186] also showed a decrease in popularity post-integration.\nBased on the above thought, the answer is:\nThe cultural items experiencing a decline are 'Religious Practices', 'Art and Craft', and 'Architectural Styles'."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "z2KevKGN6xvDw9pq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cultural item has the highest percentage change?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to figure out the Cultural Item with the highest percentage change, I need to look for the bar extending the furthest in the positive direction on the x-axis. Upon observation, that turns out to be {the bar for 'Festivals and Celebrations'}[0.387, 0.111, 0.919, 0.171], extending to a position marked at {100}[0.905, 0.903, 0.932, 0.931] on the x-axis. By comparing this length with other bars, we observe that no other bar extends this far on the x-axis consequently indicating the highest percentage change.\nBased on the above thought, the answer is:\nFestivals and Celebrations."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "EMcnM5RPUx1JyCvr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the design elements and their respective creativity scores in a CSV-formatted table according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the y-axis tick labels}[{\"Color Theory\"}[0.035, 0.852, 0.124, 0.872]; {\"Typography\"}[0.042, 0.808, 0.124, 0.828]; {\"Whitespace\"}[0.042, 0.764, 0.124, 0.784]; {\"Balance\"}[0.068, 0.720, 0.124, 0.740]; {\"Contrast\"}[0.064, 0.676, 0.124, 0.696]; {\"Lines\"}[0.088, 0.632, 0.124, 0.652]; {\"Texture\"}[0.073, 0.587, 0.124, 0.607]; {\"Value\"}[0.085, 0.543, 0.124, 0.563]; {\"Shape\"}[0.080, 0.499, 0.124, 0.519]; {\"Size\"}[0.095, 0.455, 0.124, 0.475]; {\"Unity\"}[0.087, 0.411, 0.124, 0.431]; {\"Variety\"}[0.075, 0.367, 0.124, 0.387]; {\"Proportion\"}[0.052, 0.323, 0.124, 0.343]; {\"Alignment\"}[0.052, 0.279, 0.124, 0.299]; {\"Hierarchy\"}[0.056, 0.235, 0.124, 0.255]; {\"Emphasis\"}[0.057, 0.191, 0.124, 0.211]; {\"Repetition\"}[0.053, 0.146, 0.124, 0.166]; {\"Proximity\"}[0.059, 0.102, 0.124, 0.122]], we can extract all the design elements. For the creativity scores, we can refer to the x-axis and the length of each horizontal bar, for example, the bar for {\"Color Theory\"}[0.447, 0.842, 0.671, 0.878] stretches from 0 to 50 on the x-axis, so the creativity score for \"Color Theory\" is 50. By performing a similar analysis for each design element, we can build the table.\nBased on the above thought, the answer is:\nDesign Element,Creativity Score\nColor Theory,50\nTypography,35\nWhitespace,-65\nBalance,20\nContrast,-30\nLines,45\nTexture,80\nValue,110\nShape,90\nSize,-40\nUnity,100\nVariety,60\nProportion,-25\nAlignment,80\nHierarchy,90\nEmphasis,-15\nRepetition,40\nProximity,70"
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "JGvn6Evfj2pZx4uF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a general trend observed in terms of creativity scores being above or below zero?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the general trend, I need to introspect the sign of the creativity scores of each element. Design elements with positive creativity scores include: {\"Color Theory\"}[0.447, 0.842, 0.671, 0.878], {\"Typography\"}[0.447, 0.798, 0.604, 0.833], {\"Balance\"}[0.447, 0.710, 0.537, 0.745], {\"Lines\"}[0.447, 0.622, 0.649, 0.657], {\"Texture\"}[0.447, 0.578, 0.806, 0.613], {\"Value\"}[0.447, 0.534, 0.940, 0.569], {\"Shape\"}[0.447, 0.489, 0.851, 0.525], {\"Unity\"}[0.447, 0.401, 0.895, 0.437],  {\"Variety\"}[0.447, 0.357, 0.716, 0.392], {\"Alignment\"}[0.447, 0.269, 0.806, 0.304], {\"Hierarchy\"}[0.447, 0.225, 0.851, 0.260], {\"Repetition\"}[0.447, 0.137, 0.626, 0.172], {\"Proximity\"}[0.447, 0.093, 0.761, 0.128]. Design elements with negative creativity scores include: {\"Whitespace\"}[0.156, 0.754, 0.447, 0.789], {\"Contrast\"}[0.313, 0.666, 0.447, 0.701], {\"Size\"}[0.268, 0.445, 0.447, 0.481], {\"Proportion\"}[0.335, 0.313, 0.447, 0.348], {\"Emphasis\"}[0.380, 0.181, 0.447, 0.216]. By comparing the quantity of positive and negative scores, it is conspicuous that there are more positive scores than negative.\nBased on the above thought, the answer is:\nYes, the general trend of creativity scores tends towards positive, as the number of design elements with a positive creativity score are more than the amount with a negative score."
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "mseKzHIKTq0fpxhx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the creativity score of the design elements categorized under 'Alignment'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the labeling of the y-axis, I find the category 'Alignment' is located at its {coordinates}<r>[0.052, 0.279, 0.124, 0.299]</r>. The horizontal bar representing \u2018Alignment\u2019 starts at zero and stretches until the x value of 80, which is depicted by its {coordinates}<r>[0.447, 0.269, 0.806, 0.304]</r>. Therefore, the creativity score for 'Alignment' is 80.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 546
+    },
+    {
+        "id": "sJKWemirFUtwH5Rk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please translate the data of the chart you have observed into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal bar chart titled {\"Growth Rate (%) of Various Sectors in the Internet and Communication Industry\"}[0.259, 0.030, 0.921, 0.066] visualizes the growth rate of several sectors in the internet and communication industry. The x-axis represents the growth rate in percentage, and the y-axis represents the various sectors in the industry. The bars correspond to different sectors, such as {E-commerce}[0.347, 0.809, 0.484, 0.847], {Social Media}[0.347, 0.763, 0.514, 0.800], and {Online Banking}[0.347, 0.716, 0.614, 0.754] etc. The length of each bar corresponds to the percentage growth rate of the sector, visible on {the x-axis}[0.533, 0.942, 0.648, 0.970]. For example, the growth rate for E-commerce sector can be read off where the E-commerce's bar end intersects with the x-axis. The CSV table can then be generated using these categories and their corresponding growth rates.\nBased on the above thought, the answer is:\nSector, Growth Rate (%)\nEcommerce, 45\nSocial Media, 55\nOnline Banking, 88\nVideo Streaming, 160\nOnline Gaming, 35\nSearch Engines, 44\nOnline Advertisement, 50\nCloud Services, 100\nArtificial Intelligence, 150\nInternet of Things, 200\nVR/AR, 75\nMobile Apps, 140\nWeb Development, 65\nEmail Services, -10\nE-learning, 30\nVoIP, 40"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "V8zgaNMBQqivgy2l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data visualised in this chart, which sectors would you recommend for investment considering their growth rate trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it is important to focus on bars that display the highest growth rate percentages. The bar for {'Artificial Intelligence'}[0.347, 0.437, 0.803, 0.474] shows a significant increase with 150% rate, and the bar representing {'Internet of Things'} [0.347, 0.391, 0.955, 0.428] growth is the highest among all sectors, indicating 200% growth. The {'Video Streaming'} [0.347, 0.670, 0.833, 0.707] and {'Mobile Apps'}[0.347, 0.298, 0.772, 0.335] are also relatively long with 160% and 140% growth rates respectively. Such impressive growth rates reflect strong developments and good investment potential in these sectors.\nBased on the above thought, the answer is:\nI would recommend investing in the Artificial Intelligence, Internet of Things, Video Streaming, and Mobile Apps sectors due to their pronounced growth rates as visualised on this chart."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "1WHcoEb5qB3gwEGu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Internet and Communication sector as per the chart has shown the maximum growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the Internet and Communication sector that has shown the maximum growth rate, I need to locate the bar with the highest length on the x-axis. Observing the bars, the longest green light bar is {'Internet of Things'}[0.347, 0.391, 0.955, 0.428]. The x-value at the end of this bar displays the growth rate of this sector on the x-axis {'200'}[0.941, 0.903, 0.968, 0.931].\nBased on the above thought, the answer is:\nInternet of Things"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "eadOCEKNLqzVeKYw",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a table from the bar chart providing a list of issues related to social inequality and right protection along with their respective prioritizations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {xlabel}[0.539, 0.968, 0.655, 0.983], I understand that it represents the prioritization in percentages and the {ylabel}[0.015, 0.465, 0.029, 0.512] signifies the subjects of social inequality and right protection. Truth be told, the main objective here is to locate the length of each bar and interpret the numeric value attached to it. For instance, using the lower left corner of the bar as a reference point, the bar corresponding to {\u201cDiscrimination\u201d}[0.333, 0.858, 0.416, 0.895] reaches the negative part of the x-axis to -30, meaning the rate of prioritization is -30%. Similarly, I will extract the values for all other bars. Lastly, I need to ensure that the negative rates like \"Discrimination\" are captured correctly.\nBased on the above thought, the answer is:\nSubject,Rate\nDiscrimination,-30\nHarassment,-20\nChild Protection,10\nDisability Rights,15\nEducation Rights,35\nHealthcare Rights,40\nEmployment Rights,50\nGender Equality,60\nEconomic Inequality,70\nPolitical Representation,75\nFree Speech,80\nPrivacy Rights,90\nEnvironmental Protection,100\nHomeland Security,-60\nFair Trade,120\nMinorities Representation,140\nImmigration Rights,160\nRacial Equality,180"
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "YL4RcWn6okGQ9gXX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the insights from the chart, what is the state of awareness and action towards social inequality and rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it appears there is a wide range of issues related to social inequality and rights protection, with different levels of prioritization. By inspecting the bars corresponding to each issue, it's evident that many matters are receiving positive attention, with bars reaching the positive range of the prioritization scale. For example, {\u201cRacial Equality\u201d bar}[0.416, 0.082, 0.916, 0.119], which indicates high prioritization. However, there are some issues like {\u201cDiscrimination\u201d}[0.333, 0.858, 0.416, 0.895] and {\u201cHarassment\u201d}[0.361, 0.812, 0.416, 0.849] that fall into the negative range indicating less prioritization. A significant drop is identified in {\u201cHomeland Security\u201d}[0.250, 0.265, 0.416, 0.301] which could be a cause of concern. Thus, while there seems to be a positive trend overall in the awareness and action towards social inequality and rights protection, the chart indicates that more efforts are needed in certain areas.\nBased on the above thought, the answer is:\nThe issues of social inequality and rights protection have diverse levels of awareness and action. While many areas show positive progress, issues like Discrimination, Harassment, and especially Homeland Security require more attention and efforts due to their negative rates."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "MKPOJwfN4SZdPLiI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subject related to social inequality and rights protection is the least prioritized according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object of this task is to identify the bar which has the lowest value on the x-axis. I will inspect each bar from top to bottom. Accordingly, with each comparison, whenever I find a bar lower than the current least, I will replace the current least. In the end, I find the {bar of \u201cHomeland Security\u201d}[0.250, 0.265, 0.416, 0.301] is reaching as far as -60, making it the least prioritized subject among all.\nBased on the above thought, the answer is:\nHomeland Security."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "THRn0eJE2qhg6LQV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data of each city project's Local Growth and convert it into structured tabular form in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Of course. Firstly, the chart is a horizontal bar chart with each bar representing a different city project {such as \"Community Gardens\"[0.080, 0.848, 0.223, 0.868], \"Public Parks\"[0.140, 0.795, 0.223, 0.815], \"Public Transport\"[0.112, 0.742, 0.223, 0.762], etc.} listed on the y-axis, and the length of the bar indicates the growth rate on the x-axis. To tabulate this data, I need to extract the label of each project from the y-axis and its corresponding growth rate based on its bar length on the x-axis. For instance, the project {\u201cCommunity Gardens\u201d}<r>[0.080, 0.848, 0.223, 0.868]</r> shows a growth rate of 15% as determined by its {bar coordinate}<r>[0.580, 0.835, 0.753, 0.877]</r> on the x-axis. Similarly, I'll extract other city projects and their growth rates.\nBased on the above thought, the answer is:\nCity Projects,Local Growth (%)\n\"Community Gardens\",15\n\"Public Parks\",30\n\"Public Transport\",8\n\"Housing Projects\",-10\n\"City Beautification\",10\n\"Water Supply\",5\n\"Internet Connectivity\",20\n\"Waste Management\",-5\n\"Public Libraries\",15\n\"Public Schools\",-25\n\"Bike Lanes\",0\n\"Street Lighting\",7\n\"Renewable Energy Sources\",30\n\"Historic Preservation\",15\n\"Fire Stations\",-5"
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "PGpdNZDC7dehD2M3",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer from the chart which areas the city is prioritizing and investing in?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart highlights the areas where the city is channeling its resources by showing the relative growth rate of various city projects. Areas where the city is prioritizing can be identified by looking at the projects with the highest positive growth rates as per the {length of the bar represented on the x-axis}. Projects like yet not limited to {\"Public Parks\"<r>[0.580, 0.782, 0.927, 0.824]</r>}, and {\"Renewable Energy Sources\"<r>[0.580, 0.199, 0.927, 0.241]</r>} are displaying impressive growth and it can be inferred that these areas are being prioritized. Conversely, projects with negative growth rates like {\"Public Schools\"<r>[0.290, 0.358, 0.580, 0.400]</r>}, indicating budget cuts or a shift in focus, could suggest areas not currently prioritized.\nBased on the above thought, the answer is:\nThe city is prioritizing and investing more in areas like Public Parks and Renewable Energy Sources given their high positive growth rates. However, areas like Public Schools seem to be less of a priority, as evidenced by their negative growth rates."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "ikznTQDPbRmdn3it",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which city project has the highest local growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the local growth rate of city projects on the x-axis. I'll locate the longest bar in the positive direction to identify the project with the highest growth rate. By visually inspecting the graph, the two longest bars are {\"Public Parks\"<r>[0.580, 0.782, 0.927, 0.824]</r>} and {\"Renewable Energy Sources\"<r>[0.580, 0.199, 0.927, 0.241]</r>}, and observing their labels, we can see they both represent a growth rate of 30% as per the {x-axis label \"30\"}<r>[0.918, 0.931, 0.936, 0.951]</r>. Hence, both these projects show the highest local growth rate.\nBased on the above thought, the answer is:\nPublic Parks and Renewable Energy Sources."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "bdZNzUOh2Rn0AeNL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the observed bar chart and structure it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm looking at the {title}[0.453, 0.030, 0.705, 0.066] and I see that this is a chart showing 'Big Data Integration Overview'. The {x-axis tick labels}[0.172, 0.903, 0.201, 0.931, 0.339, 0.903, 0.348, 0.931, 0.492, 0.903, 0.509, 0.931, 0.645, 0.903, 0.671, 0.931, 0.802, 0.903, 0.828, 0.931, 0.959, 0.903, 0.985, 0.931] represent the differences in percentages, and the {y-axis tick labels}[0.068, 0.806, 0.177, 0.834, 0.069, 0.731, 0.177, 0.759, 0.078, 0.656, 0.177, 0.684, 0.035, 0.581, 0.177, 0.609, 0.062, 0.506, 0.177, 0.534, 0.053, 0.431, 0.177, 0.459, 0.060, 0.356, 0.177, 0.384, 0.039, 0.280, 0.177, 0.308, 0.095, 0.205, 0.177, 0.233, 0.047, 0.130, 0.177, 0.158] represent different stages in the big data integration process. The bars show the differences by their length based on their x coordinate values and differ in color showing whether it is an improvement or a decline. As an example, {Data Extraction}[0.312, 0.787, 0.344, 0.847] indicates that it decreased by -10 and {Data Wrangling}[0.344, 0.712, 0.501, 0.772] signifies an increase by 50. By going through all the bar coordinates, I can determine the respective differences for each stage.\nBased on the above thought, the answer is:\nStage,Difference\nData Extraction,-10\nData Wrangling,50\nData Cleaning,-30\nData Transformation,-5\nData Integration,60\nSchema Matching,20\nEntity Resolution,80\nDuplicate Detection,-40\nData Fusion,130\nConflict Resolution,150"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "gxBqLz5Ptd5IFhab",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which stage appears as the most challenging in the Big Data Integration process?"
+            },
+            {
+                "from": "gpt",
+                "value": "A stage could be considered challenging based on the decline or low improvement recorded. Using that understanding, I analyze the bar sizes in the chart. Initially, I note {Data Extraction}[0.312, 0.787, 0.344, 0.847] with a decline of -10, followed by {Data Cleaning}[0.249, 0.637, 0.344, 0.697] with a decline of -30, then {Data Transformation}[0.328, 0.562, 0.344, 0.622] with a minor decline of -5, and finally {Duplicate Detection}[0.218, 0.261, 0.344, 0.322] with a significant decline of -40. Among these stages, Duplicate Detection shows the greatest decline, indicating it as one of the most challenging stages in the Big Data Integration process.\nBased on the above thought, the answer is:\nDuplicate Detection - it shows the greatest decline, suggesting that it is a challenging area in the Big Data Integration process."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "FmcwDkOt66iu7SMu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease find the stage in which the highest improvement was recorded according to the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The improvement is shown by the positive length of the bars in the chart. Using the {x-axis tick labels}[0.172, 0.903, 0.201, 0.931, 0.339, 0.903, 0.348, 0.931, 0.492, 0.903, 0.509, 0.931, 0.645, 0.903, 0.671, 0.931, 0.802, 0.903, 0.828, 0.931, 0.959, 0.903, 0.985, 0.931], I can determine the percentage change difference for each stage from 0. By comparing the lengths of the bars, I can particularly point out the longest one, which is {Conflict Resolution}[0.344, 0.111, 0.815, 0.171]. Its length starts from 0 and extends till 150 on the x-axis, thereby indicating the highest improvement.\nBased on the above thought, the answer is:\nConflict Resolution"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "pHgmcWzZLMkNW1bp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you process the data from the bar chart into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, from what I'm seeing, the bar chart depicts various social welfare and charity activities on the x-axis and the corresponding relative differences from the x-axis {xticks}<r>[0.208, 0.919, 0.237, 0.942]; [0.266, 0.919, 0.295, 0.942]; [0.324, 0.919, 0.353, 0.942]; [0.382, 0.919, 0.411, 0.942]; [0.440, 0.919, 0.469, 0.942]; [0.508, 0.919, 0.517, 0.942]; [0.562, 0.919, 0.579, 0.942]; [0.620, 0.919, 0.637, 0.942]; [0.678, 0.919, 0.695, 0.942]; [0.736, 0.919, 0.753, 0.942]; [0.794, 0.919, 0.811, 0.942]; [0.851, 0.919, 0.869, 0.942]; [0.909, 0.919, 0.927, 0.942]; [0.967, 0.919, 0.985, 0.942]</r>. Let's start by inspecting the first bar: {Home Support bar}<r>[0.483, 0.813, 0.512, 0.865]</r>, which represents the Home Support activity. It demonstrates a difference of about -5. We repeat the same process for each of the other bars corresponding to each activity. From these, we can construct a CSV table that lists each activity in one column and the corresponding differences in another column.\nBased on the above thought, the answer is:\nActivities,Differences\nHome Support,-5\nFood Program,10\nMonetary Contribution,-10\nMedical Assistance,5\nEducational Project,25\nClothing Donation,-30\nFree Counselling,40\nCommunity Development,34\nClean Water Project,-20\nEmergency Relief,65\nChild Care,35\nHousing Support,-15"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "qy4md7Ty5b12DTeM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the chart with respect to the focus or value perceived in different social welfare and charity activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, we can discover a few notable trends in societal value or focus ascribed to different social welfare and charity activities. First, if we examine the outliers, we can see that {Emergency Relief}<r>[0.512, 0.230, 0.889, 0.282]</r>stands out as the activity with the maximum positive difference, indicating a considerable high attention or value perception. In contrast, {Clothing Donation}<r>[0.338, 0.489, 0.512, 0.541]</r>, with a significant negative difference, suggests it is not perceived as favorable or valued as the other activities. Furthermore, the trends observed suggest the society's greater focus and positive value on activities that provide immediate and direct impact such as 'Free Counselling' besides 'Emergency Relief' and 'Community Development'. Policy makers could potentially take these insights into account while devising social welfare initiatives or charities may consider these perceptions while planning fundraising activities.\nBased on the above thought, the answer is:\nThe chart suggests a greater societal focus and positive value perception toward 'Emergency Relief', 'Free Counselling', and 'Community Development' activities. Meanwhile, activities such as 'Clothing Donation' are not as favored or valued. These insights may help in making informed decisions regarding social welfare planning and fundraising efforts."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "8LQqCMMTzp5P9nCj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which activity is the difference the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the activity where the difference is greatest, we need to compare the lengths of the different bars in the bar chart from {Home Support}<r>[0.483, 0.813, 0.512, 0.865]</r> to {Housing Support}<r>[0.425, 0.100, 0.512, 0.152]</r>. Noting that bars can extend either to the left or the right of the origin (representing negative and positive differences, respectively), we find that the longest bar, and hence the greatest absolute difference, occurs for the {Emergency Relief}<r>[0.512, 0.230, 0.889, 0.282]</r>activity.\nBased on the above thought, the answer is:\nEmergency Relief."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "7lRDNZf7b3ctzhRb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart you see, please convert the development prospects of future technology into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "With the given bar chart, I can see that it has 11 categories along the y-axis and the length of each bar represents the 'ChangePercent' value associated with each category. Starting from top to bottom, I can see the categories clearly defined from their respective labels: {AI}<r>[0.167, 0.808, 0.180, 0.836]</r>, {Quantum Computing}<r>[0.035, 0.740, 0.180, 0.768]</r>, {Biotechnology}<r>[0.081, 0.672, 0.180, 0.700]</r>, {5G Technology}<r>[0.078, 0.604, 0.180, 0.632]</r>, {Green Energy}<r>[0.085, 0.536, 0.180, 0.564]</r>, {Blockchain}<r>[0.105, 0.468, 0.180, 0.496]</r>, {Space Exploration}<r>[0.054, 0.400, 0.180, 0.428]</r>, {Augmented Reality}<r>[0.047, 0.332, 0.180, 0.360]</r>, {Cybersecurity}<r>[0.084, 0.264, 0.180, 0.292]</r>, {Internet of Things}<r>[0.057, 0.196, 0.180, 0.224]</r>, and {Smart Cities}<r>[0.095, 0.128, 0.180, 0.156]</r>. I can make use of the x-axis guideline and the known x-axis values to determine the 'ChangePercent' for each category based on the length of their bar on the chart. For example, the 'ChangePercent' for AI is around 23 based on the length of its bar<r>[0.366, 0.792, 0.570, 0.847]</r> reaching at 23 position on the x-axis. Using this method, I can create a structured table from the chart data.\nBased on the above thought, the answer is:\nTechnology,ChangePercent\nAI,23\nQuantum Computing,65\nBiotechnology,-15\n5G Technology,30\nGreen Energy,53\nBlockchain,47\nSpace Exploration,-5\nAugmented Reality,32\nCybersecurity,49\nInternet of Things,35\nSmart Cities,-10"
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "TN5UqSdt6gRkJgM2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technological sector should an investor likely to avoid based on future growth prospects? Can you provide the reasoning behind it?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive comparison of the lengths and direction of all bars in the chart. I consider the bars that extend towards the left, since a negative percentage change indicates potential risk for investment. For such a bar, I need to look for the one with the greatest length as it represents the highest predicted decline. The longest bar of this type belongs to {Biotechnology}<r>[0.081, 0.672, 0.180, 0.700]</r> at position <r>[0.234, 0.656, 0.366, 0.711]</r>, with its length reaching the -15 point on the x-axis, suggesting it would have the largest decline in the near future.\nBased on the above thought, the answer is:\nBiotechnology. Because it has the highest decline percentage amongst all the technologies, which indicates it's increasingly challenging or less focused in the sector, making it a higher risk for investment."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "Hk35Xkp1yL0QF1qs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, which technology is projected to have the highest percentage change in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to visually scan the lengths of all the bars from the bar-chart. As observed, the longest green bar protrudes the most into the positive side of the x-axis, it belongs to {Quantum Computing}<r>[0.035, 0.740, 0.180, 0.768]</r> at position,r>[0.366, 0.724, 0.941, 0.779]</r>. By referencing its end point with the values on the x-axis, it indicates the highest projected percentage change for the future as it reached the 65 point on the x-axis.\nBased on the above thought, the answer is:\nQuantum Computing."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "VnZCKdFMXMqLUSsQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented by the bar chart into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Years of Protection for Different Types of Intellectual Property\"}[0.319, 0.030, 0.827, 0.066] I gather that the y-axis represents various types of intellectual property and the x-axis represents the protection duration or years. I can see from the {y-axis ticks}[{\"Software Patent\"}[0.040, 0.792, 0.151, 0.820]; {\"Hardware Patent\"}[0.035, 0.684, 0.151, 0.712]; {\"Design Patent\"}[0.054, 0.576, 0.151, 0.604]; {\"Utility Patent\"}[0.061, 0.468, 0.151, 0.496]; {\"Trade Secret\"}[0.064, 0.360, 0.151, 0.388]; {\"Trademark\"}[0.077, 0.252, 0.151, 0.280]; {\"Copyright\"}[0.083, 0.144, 0.151, 0.172]] that these types of intellectual property include Software Patent, Hardware Patent, Design Patent, Utility Patent, Trade Secret, Trademark, and Copyright. Each of them has an associated bar that indicates the number of years the property is protected. For example, {the bar for \"Software Patent\"}[0.161, 0.760, 0.264, 0.847] extends to approximately the 15 position on the x-axis. Therefore, the respective years for each intellectual property type should be Software Patent for 15 years, Hardware Patent for 20 years, Design Patent for 14 years, Utility Patent for 20 years, Trade Secret for an unlimited period, Trademark for 10 years, and Copyright for 70 years. This information can be presented in CSV format.\nBased on the above thought, the answer is:\nInnovation Type,Years of Protection\nSoftware Patent,15\nHardware Patent,20\nDesign Patent,14\nUtility Patent,20\nTrade Secret,Unlimited\nTrademark,10\nCopyright,70"
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "2FHuqEuHFFf16VVD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you determine which type of intellectual property is most highly valued and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the context of this chart, \"value\" could be determined by the length of protection years granted to different types of intellectual property. The longer the protection years, the more valuable the intellectual property type might be deemed because it suggests its owner can control its use and profit from it for a longer period of time. With this idea, we can conclude that {the bar for \"Trade Secret\"}[0.161, 0.328, 0.848, 0.414] holding the maximum length representing 'Unlimited' years of protection, shows that Trade Secret might be considered the most highly valued type of intellectual property, as their protection period is unlimited, suggesting a high level of esteem for proprietors of such properties.\nBased on the above thought, the answer is:\nTrade Secret could be considered the most valued type of intellectual property because it has an unlimited protection duration."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "iUXHAreYGwCIdzZs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many types of intellectual property have protection years more than 15?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at all the bars in the chart and check which one extends beyond the 15-year mark on the x-axis. Looking from top to bottom, {the bar for \"Software Patent\"}[0.161, 0.760, 0.264, 0.847] almost reaches the 15 position, so it clearly has protection years of 15. Then, {the bar for \"Hardware Patent\"}[0.161, 0.652, 0.298, 0.739] extends beyond 15 and {the bar for \"Utility Patent\"}[0.161, 0.436, 0.298, 0.522] also extends beyond 15. {The bar for \"Trade Secret\"}[0.161, 0.328, 0.848, 0.414] extends significantly to the right, representing unlimited protection years, so it must be counted as well. Lastly, {the bar for \"Copyright\"}[0.161, 0.111, 0.642, 0.198] extends well beyond 15, up to around the 70 position. Therefore, four types of intellectual property have more than 15 years of protection.\nBased on the above thought, the answer is:\nFour."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "TU1BbZKvOWGoFOad",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, can you convert the data into a table format with columns denoting 'Sector', 'Q1', 'Q2', 'Q3', and 'Q4'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am analyzing the bar chart with the title {\"Building and Real Estate Market Trends\"}[0.408, 0.030, 0.733, 0.066]. The x-axis represents different quarters, which can be found in the legend {\"Q1\"}[0.901, 0.102, 0.929, 0.122], {\"Q2\"}[0.901, 0.144, 0.929, 0.164], {\"Q3\"}[0.901, 0.186, 0.929, 0.205], {\"Q4\"}[0.901, 0.228, 0.929, 0.247]. The y-axis represents different sectors, which are labeled as {\"Residential Sales\"}[0.041, 0.785, 0.159, 0.813], {\"Commercial Sales\"}[0.035, 0.658, 0.159, 0.686], {\"Industrial Sales\"}[0.053, 0.531, 0.159, 0.559], {\"Residential Rent\"}[0.046, 0.405, 0.159, 0.433], {\"Commercial Rent\"}[0.040, 0.278, 0.159, 0.306], {\"Industrial Rent\"}[0.058, 0.151, 0.159, 0.179]. For each sector and each quarter, there is one bar indicating the value. For example, the bar for Q1 of Residential Sales has coordinates of [0.169, 0.745, 0.876, 0.847], corresponding to a value of 220. By doing similar analysis, I can generate the whole table.\nBased on the above thought, the answer is:\nSector,Q1,Q2,Q3,Q4\nResidential Sales,220,180,175,200\nCommercial Sales,120,170,160,160\nIndustrial Sales,150,100,95,160\nResidential Rent,240,230,225,240\nCommercial Rent,130,180,175,180\nIndustrial Rent,100,90,100,90"
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "ieMqqzbtId6IswtM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector shows consistent growth from Q1 to Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the lengths of the bars in each sector from Q1 to Q4. For example, the bars for the Residential Sales are located at {\"Q1\"}[0.169, 0.745, 0.876, 0.847], {\"Q2\"}[0.169, 0.745, 0.747, 0.847], {\"Q3\"}[0.169, 0.745, 0.731, 0.847] and {\"Q4\"}[0.169, 0.745, 0.811, 0.847]. It is clear that there is not a consistent growth from Q1 to Q4. Similar analysis can be done for Commercial Sales, Industrial Sales, Residential Rent, Commercial Rent, and Industrial Rent. By comparing the bar lengths, I find that none of the sectors show consistent growth from Q1 to Q4.\nBased on the above thought, the answer is:\nNone of the sectors show consistent growth from Q1 to Q4."
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "eE3FqcFbjYlXPURl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the value for Q2 of Residential Sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the value for Q2 of Residential Sales, I need to locate the bars for Residential Sales located at {\"Residential Sales\"}[0.041, 0.785, 0.159, 0.813], then identify the second bar representing Q2. This bar has coordinates of  [0.169, 0.745, 0.747, 0.847], and the value could be estimated using the x-axis grid, which shows output values in the interval from [0.164, 0.903, 0.173, 0.931] to [0.959, 0.903, 0.985, 0.931]. It is clear that the second bar for Residential Sales is about 3 quarters of the total range, which corresponds to a value of 180 units.\nBased on the above thought, the answer is:\n180"
+            }
+        ],
+        "id_raw": 554
+    },
+    {
+        "id": "WUQwTJWQBn7y0DVh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, could you list down the technologies and their respective trend values in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labels represent the technology names from left to right, including {Solar}[0.116, 0.863, 0.151, 0.906], {Wind}[0.202, 0.863, 0.237, 0.906], {Hydro}[0.286, 0.863, 0.325, 0.912], {Geothermal}[0.358, 0.863, 0.426, 0.947], {Biomass}[0.452, 0.863, 0.504, 0.927], {Nuclear}[0.540, 0.863, 0.588, 0.922], {Carbon Capture}[0.606, 0.863, 0.694, 0.972], {Smart Grid}[0.704, 0.863, 0.768, 0.942], {Energy Storage}[0.779, 0.863, 0.865, 0.970], and {Energy Efficiency}[0.861, 0.863, 0.956, 0.981]. Their respective trend values can be determined by cross-referencing the height of each bar to the y-axis scale, which starts at 0 and goes up to 80. For example, the height of {the bar representing Solar}[0.099, 0.337, 0.168, 0.857] is at level 50 on the y-axis. Therefore, the trend value for the \"solar\" technology is 50. By further observing and comparing the heights of other bars with the y-axis, I can extract each trend value for every technology.\nBased on the above thought, the answer is:\nTechnology,Trend Value\nSolar,50\nWind,60\nHydro,45\nGeothermal,55\nBiomass,50\nNuclear,70\nCarbon Capture,40\nSmart Grid,55\nEnergy Storage,50\nEnergy Efficiency,55"
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "TW3nJSw0Hg8OnFkp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what this chart is likely to represent and what conclusions one might be able to draw from it?"
+            },
+            {
+                "from": "gpt",
+                "value": "This chart appears to be a comparison of different energy technology's development trends through their respective growth trend values, represented by the height of each bar. The x-axis labels {Solar}[0.116, 0.863, 0.151, 0.906], {Wind}[0.202, 0.863, 0.237, 0.906], {Hydro}[0.286, 0.863, 0.325, 0.912], {Geothermal}[0.358, 0.863, 0.426, 0.947], {Biomass}[0.452, 0.863, 0.504, 0.927], {Nuclear}[0.540, 0.863, 0.588, 0.922], {Carbon Capture}[0.606, 0.863, 0.694, 0.972], {Smart Grid}[0.704, 0.863, 0.768, 0.942], {Energy Storage}[0.779, 0.863, 0.865, 0.970], and {Energy Efficiency}[0.861, 0.863, 0.956, 0.981] correspond to the different energy technologies. The y-axis represents the technology development trend values, and the higher the bar, the stronger the growth or more rapid the development. From this chart, one could conclude that generally, all the energy technologies show a positive development trend, with Nuclear being the most rapidly developing technology due to its highest bar, followed by Wind and Geothermal. Interestingly, Carbon Capture displays the slowest growth, signifying it might receive less attention or investment in the coming years.\nBased on the above thought, the answer is:\nThis chart represents the recent development trends in various energy technologies. One might conclude that Nuclear technology presents the fastest growth, followed by Wind and Geothermal. However, Carbon Capture shows slower development compared to other technologies."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "DUiSl6aFs0f9OXDL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has the maximum future growth trend value as depicted in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the highest bar on the chart. The y-axis represents the trend values, and the taller the bar, the higher the trend value. By visibly comparing the heights of the bars, I can see that {the bar representing Nuclear}[0.530, 0.129, 0.598, 0.857] is the tallest, suggesting that Nuclear technology has the highest future growth trend value.\nBased on the above thought, the answer is:\nNuclear."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "H80NBdp9WZo7h1aS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the given bar graph data into a table format based on your observations?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the x-axis corresponds to various lifestyle activities, including: 'Reading'[0.123, 0.469, 0.184, 0.825], 'Exercise'[0.199, 0.351, 0.259, 0.825], 'Travel'[0.274, 0.272, 0.335, 0.825], 'Parties'[0.350, 0.509, 0.411, 0.825], 'Eating_Out'[0.426, 0.232, 0.486, 0.825], 'Music'[0.501, 0.192, 0.562, 0.825], 'Art'[0.577, 0.311, 0.638, 0.825], 'Movies'[0.653, 0.153, 0.713, 0.825], 'Online_Gaming'[0.728, 0.113, 0.789, 0.825], 'Sports'[0.804, 0.272, 0.865, 0.825], and 'Nightlife'[0.880, 0.430, 0.940, 0.825]. The y-axis shows the preference percentage for each activity, and the range of percentage is from {0}[0.065, 0.816, 0.076, 0.839] to {100}[0.043, 0.025, 0.076, 0.048]. Each color bar represents an activity, and the height of the bars corresponds to the popularity of the activity, i.e., the preference percentage. By analyzing the heights of these bars, I can construct a CSV table with the activities as row labels and the preference percentage as values.\nBased on the above thought, the answer is:\nActivities,Preference(%)\nReading,45\nExercise,60\nTravel,70\nParties,40\nEating_Out,75\nMusic,80\nArt,65\nMovies,85\nOnline_Gaming,90\nSports,70\nNightlife,50"
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "fgp24beSV6GrjErw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you analyse the population's preference towards leisure activities based on the bar graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, it is clear that bars{'Movies'}[0.653, 0.153, 0.713, 0.825] and {'Online_Gaming'}[0.728, 0.113, 0.789, 0.825] are amongst the tallest, indicating a higher preference percentage of individuals towards these activities. This suggests that a large proportion of the surveyed population seem to prefer movies and online games as their leisure activities. On the contrary, the shortest bar {'Reading'}[0.123, 0.469, 0.184, 0.825] implies that reading is not as popular. The bar representing 'Sports'[0.804, 0.272, 0.865, 0.825] and 'Travel'[0.274, 0.272, 0.335, 0.825] show moderate height, indicating a balanced interest in these activities. This analysis highlights a trend where digital activities like online gaming and movies are more popular, while traditional activities like reading are less popular among the surveyed population.\nBased on the above thought, the answer is:\nThere is a clear preference for digital leisure activities such as online gaming and watching movies, while traditional activities like reading are less popular among the population."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "M6EOX7T1XNaoU2ZM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has the least preference percentage according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which activity has the least preference percentage, I need to compare the heights of bars representing the preference for each activity. By scanning all the bars, it appears that the shortest bar represents the 'Reading' activity with its coordinates at [0.123, 0.469, 0.184, 0.825], suggesting that it has the smallest preference percentage.\nBased on the above thought, the answer is:\nReading."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "zyIY3xuQmtur8twF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it's clear that each bar represents a certain health predictor which I understand are 'Age', 'BMI', 'Blood Pressure', 'Heart Rate', 'Air Quality', 'Diet', 'Exercise Frequency', 'Sleep Quality', 'Smoking', 'Alcohol Consumption'. These health predictors are clearly labeled on the x-axis from the information given in {the x-axis tick labels}<r>[{\"Age\"}[0.127, 0.785, 0.156, 0.834]; {\"BMI\"}[0.213, 0.785, 0.241, 0.832]; {\"Blood Pressure\"}[0.271, 0.785, 0.354, 0.923]; {\"Heart Rate\"}[0.366, 0.785, 0.429, 0.890]; {\"Air Quality\"}[0.452, 0.785, 0.514, 0.888]; {\"Diet\"}[0.553, 0.785, 0.583, 0.836]; {\"Exercise Frequency\"}[0.601, 0.785, 0.706, 0.960]; {\"Sleep Quality\"}[0.701, 0.785, 0.776, 0.911]; {\"Smoking\"}[0.798, 0.785, 0.850, 0.873]; {\"Alcohol Consumption\"}[0.852, 0.785, 0.966, 0.975]</r>. The vertical y-axis denotes the accuracy percentages that these predictors hold, ranging from {0}[0.052, 0.768, 0.061, 0.791] to {100}[0.035, 0.025, 0.061, 0.048]. To generate the CSV table, I need to extract the values for each health predictor. This can be done by reading the heights of these bars. For instance, the height of {the bar for \"Age\"}[0.108, 0.257, 0.176, 0.777] goes up to approximately 70%, meaning the accuracy of Age in predicting health status is 70%.\nBased on the above thought, the answer is:\nHealth Predictor,Accuracy (%)\nAge,70\nBMI,65\nBlood Pressure,78\nHeart Rate,80\nAir Quality,60\nDiet,75\nExercise Frequency,82\nSleep Quality,68\nSmoking,45\nAlcohol Consumption,55"
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "g7wn8bLclEsKlTwY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which factors significantly contribute to predicting health status, and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferring from the data, the factors that significantly predict health status would be the ones with higher accuracy percentages. This come from the taller bars that represents them in the chart. Specifically, the significant contributors include {bar for \"Blood Pressure\"}<r>[0.278, 0.198, 0.346, 0.777]</r> which represents an accuracy of approximately 78%, the {bar for \"Heart Rate\"}<r>[0.363, 0.183, 0.432, 0.777]</r> at around 80% accuracy and the highest one {bar for \"Exercise Frequency\"}<r>[0.619, 0.168, 0.687, 0.777]</r> at approximately 82% accuracy. These three factors significantly predict health status because they offer the greatest accuracy in this context indicating their crucial role in health predictions.\nBased on the above thought, the answer is:\nYes, the factors that significantly contribute to predicting health statuses are Blood Pressure, Heart Rate, and Exercise Frequency. This inference is drawn considering their high accuracies, which maximizes their predicting reliability."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "gtAyNIJXWToTc0ec",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health predictor shows the highest accuracy according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which health predictor exhibits the highest accuracy, it's crucial to compare the heights of all the bars. Let's take a look from left to right, starting with {the bar for \"Age\"}[0.108, 0.257, 0.176, 0.777] which represents an accuracy of 70%. This process is continued for all health predictors. The maximum height can be seen at {the bar for \"Exercise Frequency\"}[0.619, 0.168, 0.687, 0.777] which corresponds to an accuracy of 82%. Hence, \"Exercise Frequency\" is the health predictor with the highest accuracy.\nBased on the above thought, the answer is:\nExercise Frequency"
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "3qmNLyUke3OLI6mP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you put together a CSV table that represents the usage rate of different social networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, sure. The {X-axis}[{\"Facebook\"}[0.106, 0.906, 0.162, 0.976], {\"Twitter\"}[0.198, 0.906, 0.241, 0.960]; {\"Instagram\"}[0.276, 0.906, 0.336, 0.981]; {\"Snapchat\"}[0.364, 0.906, 0.420, 0.976]; {\"LinkedIn\"}[0.452, 0.906, 0.503, 0.970]; {\"WhatsApp\"}[0.534, 0.906, 0.594, 0.981]; {\"Pinterest\"}[0.623, 0.906, 0.677, 0.973]; {\"Viber\"}[0.718, 0.906, 0.754, 0.951]; {\"Telegram\"}[0.795, 0.906, 0.850, 0.975]; {\"WeChat\"}[0.884, 0.906, 0.932, 0.966]] labels represent different social networking platforms, and the corresponding {bars}[{\"Facebook\"}[0.099, 0.778, 0.168, 0.900],  {\"Twitter\"}[0.185, 0.638, 0.254, 0.900], {\"Instagram\"}[0.271, 0.323, 0.340, 0.900], {\"Snapchat\"}[0.357, 0.131, 0.426, 0.900], {\"LinkedIn\"}[0.444, 0.410, 0.512, 0.900], {\"WhatsApp\"}[0.530, 0.498, 0.598, 0.900], {\"Pinterest\"}[0.616, 0.201, 0.685, 0.900], {\"Viber\"}[0.702, 0.533, 0.771, 0.900], {\"Telegram\"}[0.788, 0.708, 0.857, 0.900], {\"WeChat\"}[0.874, 0.078, 0.943, 0.900]] in the chart represent the usage rates of these platforms. The height of each respective bar corresponds to the percentage of total communication. By referencing the y-axis labels range {0}[0.043, 0.893, 0.052, 0.911] to {50}[0.035, 0.019, 0.052, 0.036], we can create a CSV table to record the usage rate for each networking platform.\nBased on the above thought, the answer is:\nSocial Networks,Usage Rate (%)\nFacebook,7\nTwitter,15\nInstagram,33\nSnapchat,44\nLinkedIn,28\nWhatsApp,23\nPinterest,40\nViber,21\nTelegram,11\nWeChat,47"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "cRmJ6C1STCGNhc5l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the overall usage trend of these social networking platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the data distribution on the chart, there is no obvious constant rise or decline trend among these platforms. However, we can observe that {Snapchat}[0.364, 0.906, 0.420, 0.976] and {WeChat}[0.884, 0.906, 0.932, 0.966] have a significantly higher usage rate than the others, which are represented by their tall bars located at [0.357, 0.131, 0.426, 0.900] and [0.874, 0.078, 0.943, 0.900] respectively. It implies that these two platforms are currently the most preferred for communication. And {Facebook}[0.106, 0.906, 0.162, 0.976] and {Telegram}[0.795, 0.906, 0.850, 0.975] are used relative less by people, which can be inferred from the height of their bars [0.099, 0.778, 0.168, 0.900] and [0.788, 0.708, 0.857, 0.900], respectively. The rest of the platforms fall somewhere in between.\nBased on the above thought, the answer is:\nThe usage rate of these social networking platforms varies significantly, with Snapchat and WeChat being the most used platforms, while Facebook and Telegram are significantly less used. The rest of the platforms have moderate usage rates."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "IWvDU0yO2VfLvwAh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social networking platform has the highest usage rate according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the platform with the highest usage rate, I need to compare the height of the bars representing each platform. After scanning from left to right, I found that {the WeChat bar}[0.874, 0.078, 0.943, 0.900] is the tallest, which means WeChat has the highest usage rate.\nBased on the above thought, the answer is:\nWeChat."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "63AHbNglOKbrQksl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart observed, please convert the data showing the 'Contribution to Ecosystem' by each category into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I can locate the bars at the bottom corresponding to ten categories, from left to right: {Intellectual property}<r>[0.085, 0.755, 0.194, 0.892]</r>, {Entrepreneurial Education}<r>[0.153, 0.755, 0.291, 0.928]</r>, {Government Support}<r>[0.249, 0.755, 0.362, 0.896]</r>, {Market Openness}<r>[0.340, 0.755, 0.436, 0.874]</r>, {Investment}<r>[0.438, 0.755, 0.503, 0.837]</r>, {Innovation Output}<r>[0.504, 0.755, 0.603, 0.879]</r>, {Infrastructure}<r>[0.598, 0.755, 0.675, 0.851]</r>, {Cultural Support}<r>[0.674, 0.755, 0.764, 0.868]</r>, {Business Angel Activity}<r>[0.740, 0.755, 0.864, 0.910]</r>, {Successful Entrepreneurial Activity}<r>[0.794, 0.755, 0.975, 0.981]</r>. For each category, there is a bar that represents the 'Contribution to Ecosystem'. I'll map the height of the bar to the numerical value using the y-axis, which has its min label {0}<r>[0.052, 0.742, 0.061, 0.760]</r> and max label {100}<r>[0.035, 0.019, 0.061, 0.036]</r>. For example, the bar of {Intellectual property}<r>[0.106, 0.279, 0.173, 0.749]</r> has a height that matches the y-value '65'. Therefore, the value for 'Intellectual property' is 65.\nBased on the above thought, the answer is:\nCategory,Contribution to Ecosystem\nIntellectual property,65\nEntrepreneurial Education,60\nGovernment Support,75\nMarket Openness,45\nInvestment,85\nInnovation Output,50\nInfrastructure,70\nCultural Support,55\nBusiness Angel Activity,80\nSuccessful Entrepreneurial Activity,75"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "3DomOq4w7g8QwpPb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what measures could be taken to improve the Innovation and Technology Entrepreneurship Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it can be seen that the 'Investment'<r>[0.438, 134, 0.504, 0.749]</r> and 'Business Angel Activity'<r>[0.769, 0.170, 0.835, 0.749]</r> have the highest bar heights, indicating strong performance. Meanwhile, the bar for 'Market Openness'<r>[0.355, 0.423, 0.421, 0.749]</r> is noticeably shorter compared to other categories, indicating a weaker performance. As 'Market Openness' contributes to the ecosystem, increasing the market openness may improve the overall performance of the Innovation and Technology Entrepreneurship Ecosystem. For instance, the government or related authorities can take measures to ease regulations, promote fair trade, and encourage global partnership, which could potentially increase 'Market Openness' and thus enhance the Innovation and Technology Entrepreneurship Ecosystem.\nBased on the above thought, the answer is:\nIncreasing the 'Market Openness' by easing regulations, promoting fair trade, and encouraging global partnerships could potentially improve the Innovation and Technology Entrepreneurship Ecosystem."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "LGZo48IpXBzfLDI6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category exhibits the maximum 'Contribution to Ecosystem'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which category contributes the most to the ecosystem, I need to locate the bar with the highest y-value. Scanning through the bars from the left over to the right, I can see that the {Investment}<r>[0.438, 0.755, 0.503, 0.837]</r> and {Business Angel Activity}<r>[0.740, 0.755, 0.864, 0.910]</r> bars stand taller than the rest. Upon comparing their heights, I find that the height of the {Investment}<r>[0.438, 0.134, 0.504, 0.749]</r> bar appears to reach the highest point on the y-axis. Hence, 'Investment' appears to have the highest contribution to the ecosystem.\nBased on the above thought, the answer is:\nInvestment"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "0t5tY829AyrSo9CD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart you observed into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart has a description of the title - 'Business Management and Organizational Leadership', which is not shown in the chart but in the description - for each bar, represented by x-axis tick labels such as {Communication}[0.098, 0.837, 0.185, 0.946] and {Strategic Planning}[0.859, 0.837, 0.959, 0.962]. The heights of bars correspond to the values represented on the y-axis, for example, {Communication bar}[0.108, 0.267, 0.176, 0.831] has a value of 70 as it matches with the label marked {70}[0.043, 0.267, 0.061, 0.285] on y-axis. This can be converted into rows of data for a structured table.\nBased on the above thought, the answer is:\nCategory,Success Percentage (%)\nCommunication,70\nDecision Making,45\nProblem Solving,55\nLeadership,75\nMotivation,65\nConflict Management,40\nDelegation,50\nTime Management,80\nTeam Building,60\nStrategic Planning,85"
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "JzPasoh0qvurZkds",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering all the aspects presented in the chart, where should a company focus to improve the overall performance of their management team?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents various aspects of management with their success percentages. For a company to improve the overall performance, they should focus on the areas that have the least success. From the chart, the category with the lowest success percentage is {Conflict Management}[0.534, 0.509, 0.602, 0.831] which aligns approximately to the 40% mark. It is followed by the second lowest, {Decision Making}[0.193, 0.469, 0.261, 0.831], which is slightly below the 50% mark. Therefore, performance improvements should focus on these two areas to enhance overall team performance.\nBased on the above thought, the answer is:\nThe company should focus on improving Conflict Management and Decision Making as they have the least success percentages."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "dzkdusRlLvTU6iDZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the management aspect with the least success percentage according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the management aspect with the least success percentage, I must examine the height of each bar. For example, the bar for {Conflict Management}[0.534, 0.509, 0.602, 0.831] is the lowest compared to others, aligning with the {40%}[0.043, 0.502, 0.061, 0.520] mark on the y-axis. Therefore, Conflict Management is the category with the least success percentage.\nBased on the above thought, the answer is:\nConflict Management"
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "EfNI1Q29zwlTUrCV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nYou see a bar chart detailing the scores of different aspects of innovation project management. Could you please report the data from this chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from {the y-axis}[{'ylabel': 'Score', 'color': 'black', 'rotation': 90.0}, '[0.015, 0.467, 0.029, 0.516]'], I can see that the y-axis represents the score, ranging from 0 to 100. The x-axis includes labels representating different aspects of innovation project management such as {Idea Generation}[0.107, 0.380, 0.175, 0.958], {Time Management}[0.192, 0.631, 0.259, 0.958], and so on. The score is represented by the height of the corresponding bars. For example, {the bar for \"Idea Generation\"}[0.107, 0.380, 0.175, 0.958] reaches up to approximately the 60 position on the y-axis, indicating that the score for Idea Generation is 62. I'll pull all the data together in a similar way.\nBased on the above thought, the answer is:\nAspect,Score\nIdea Generation,62\nTime Management,35\nBudget Management,50\nRisk Management,38\nQuality Control,66\nCommunication,44\nProject Closure,55\nHardware Innovation,47\nSoftware Innovation,70\nMarketing Innovation,33"
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "6LLY1tq70TQNqOTy",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you tell me about the project management aspects that need to be improved?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, the aspects that need improvement are those with the lower scores. These include {Time Management}<r>[0.192, 0.631, 0.259, 0.958]</r> and {Marketing Innovation}<r>[0.866, 0.650, 0.934, 0.958]</r>. Specifically, the Time Management aspect has a low score of 35 and Marketing Innovation has a score of 33, indicating these are areas where performance is weaker.\nBased on the above thought, the answer is:\nThe aspects that need to be improved are Time Management and Marketing Innovation as they have the lowest scores."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "pwFEIrffB4YMwH5N",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest scored aspect in the innovation project management according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the tallest bar in the chart. Scanning the heights of the bars, I find that {Software Innovation}[{'bar_coordinates': '[0.782, 0.305, 0.850, 0.958]', 'x_label': 'Software Innovation', 'y_range': '0.00 -> 70.00'}] has the tallest bar, indicating it has the highest score.\nBased on the above thought, the answer is:\nSoftware Innovation."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "0NviWnzDpCBSejOV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the bar chart and transform it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that {the x-axis tick labels}<r>[{\"Food Bank\"}[0.100, 0.824, 0.151, 0.901]; {\"Medical Aid\"}[0.185, 0.824, 0.240, 0.908]; {\"Disaster Relief\"}[0.266, 0.824, 0.333, 0.926]; {\"Elderly Care\"}[0.358, 0.824, 0.416, 0.912]; {\"Disabled Support\"}[0.434, 0.824, 0.513, 0.943]; {\"Child Welfare\"}[0.530, 0.824, 0.592, 0.919]; {\"Education\"}[0.624, 0.824, 0.673, 0.898]; {\"Homeless Support\"}[0.694, 0.824, 0.777, 0.949]; {\"Environment Protection\"}[0.770, 0.824, 0.875, 0.981]; {\"Animal Welfare\"}[0.875, 0.824, 0.945, 0.930]]</r> are named after different social welfare and charity activities. The {y-axis tick labels}<r>[{\"10\"}[0.029, 0.698, 0.043, 0.716]; {\"20\"}[0.029, 0.585, 0.043, 0.603]; {\"30\"}[0.029, 0.472, 0.043, 0.489]; {\"40\"}[0.029, 0.359, 0.043, 0.376]; {\"50\"}[0.029, 0.245, 0.043, 0.263]; {\"60\"}[0.029, 0.132, 0.043, 0.150]; {\"70\"}[0.029, 0.019, 0.043, 0.036]]</r> represent the corresponding funding amounts in millions of dollars. Therefore, the height of each bar corresponds to the amount of funding received by the respective activity. To generate the CSV table, I need to calculate the fund value for each activity according to the height of its bar. For example, the bar for {Food Bank}<r>[0.090, 0.196, 0.160, 0.818]</r> is approximately at the 55 position on the y-axis, so the funding for the Food Bank activity is 55 million dollars.\nBased on the above thought, the answer is:\nCharity Activity,Funding\nFood Bank,55\nMedical Aid,50\nDisaster Relief,60\nElderly Care,52\nDisabled Support,45\nChild Welfare,38\nEducation,43\nHomeless Support,55\nEnvironment Protection,50\nAnimal Welfare,48"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "eLbWhrWkUFY96rWC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which area of charity activities requires more focus and funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar heights, we can assess which area needs more funding. The height of {Child Welfare}<r>[0.526, 0.388, 0.596, 0.818]</r> and {Education}<r>[0.613, 0.331, 0.683, 0.818]</r> bars are relatively low, at approximately the 38 and 43 positions on the Y-axis respectively. This suggests that these two areas receive lower amounts compared to most other charitable activities, therefore may require more focus and funding for their development.\nBased on the above thought, the answer is:\nChild Welfare and Education require more focus and funding based on the funding trend portrayed in this chart."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "Nb8uMqkZwg6ynlo0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich charity activity received the highest funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at all the bars, the one with the highest position on the Y-axis would represent the charity activity which received the highest funding. There is a bar for {Disaster Relief}<r>[0.265, 0.139, 0.334, 0.818]</r> reaching approximately the 60 position on the y-axis, which is higher than all the other bars.\nBased on the above thought, the answer is:\nDisaster Relief."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "bDFey3LX8F9CHkVD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bars in the chart, can you structure this information into a simple table presenting Career and Workplace Development Trends and their respective corresponding Company Dedication percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar labels which are seen as the x-axis labels consist of a number of categories related to Career and Workplace Development Trends. They range from {\"Workplace Flexibility\"}<r>[0.079, 0.813, 0.172, 0.953]</r> to {\"Mental Health Awareness\"}<r>[0.854, 0.813, 0.966, 0.981]</r>. The y-axis is defined by {a parameter labeled \"Company Dedication (%)\"}<r>[0.012, 0.308, 0.024, 0.525]</r> with its labels lying between {10}<r>[0.029, 0.703, 0.043, 0.720]</r> and {80}<r>[0.029, 0.019, 0.043, 0.036]</r>. Each category has a corresponding bar such as the {\"Emotional Intelligence\"}<r>[0.613, 0.319, 0.683, 0.807]</r> bar, indicating its percentage value. By translating these particulars into a table, we obtain a table that represents Career and Workplace Development Trends and the corresponding Company Dedication percentages.\nBased on the above thought, the answer is:\nTrend,Percentage\nWorkplace Flexibility,40\nVirtual Collaboration,55\nDiversity & Inclusion,45\nLeadership Development,60\nContinuous Learning,70\nWork-Life Balance,65\nEmotional Intelligence,50\nDigital Literacy,35\nArtificial Intelligence,55\nMental Health Awareness,75"
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "fFxJ19bEpJjJF8eq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the graph regarding Career and Workplace Development Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "With the graphical representation of bars, where each distinct bar corresponds to a trend within Career and Workplace Development, I can identify that some trends receive more attention than others based on the associating bar heights. On the higher spectrum are {\"Leadership Development\"}<r>[0.352, 0.221, 0.422, 0.807]</r> and {\"Mental Health Awareness\"}<r>[0.875, 0.074, 0.945, 0.807]</r>. Interestingly,{\"Digital Literacy\"}<r>[0.701, 0.465, 0.770, 0.807]</r> has a far less comparative bar height, implying it gets lesser focus in the current environment. Judging by the trends positioned towards the higher end of the percentage scale, it is clear that workplaces are putting a strong emphasis on individual growth, both in terms of knowledge skills and on the personal mental health front.\nBased on the above thought, the answer is:\nThe graph suggests that companies are focusing greatly on the personal development of their employees, making 'Leadership Development' and 'Mental Health Awareness' a priority. However, the skill-based trend 'Digital Literacy' seems to be getting less attention comparatively."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "2B0j5HE5xmRAnO0t",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph you're observing, which Career and Workplace Development Trend do companies dedicate the most to?"
+            },
+            {
+                "from": "gpt",
+                "value": "By glancing the vertical span of the bars on the chart from {\"Workplace Flexibility\"}<r>[0.090, 0.416, 0.160, 0.807]</r> to {\"Mental Health Awareness\"}<r>[0.875, 0.074, 0.945, 0.807]</r>, each measuring the percentage of respective company dedication, it's evident that one bar in particular extends more vertically than the others. This bar corresponds to {\"Mental Health Awareness\"}<r>[0.875, 0.074, 0.945, 0.807]</r>, which reaches the highest point on the chart, thus suggesting that companies dedicate more towards 'Mental Health Awareness' compared to other trends.\nBased on the above thought, the answer is:\nMental Health Awareness."
+            }
+        ],
+        "id_raw": 578
+    },
+    {
+        "id": "BKfuZ6dhGNAFqDUA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the bar chart, could you please convert the data representation into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart I observed, it denotes categories of environmental aspects on the X-axis labels <r>[0.111, 0.846, 0.939, 0.921]</r> and their corresponding sustainability scores on the y-axis. The bars and their heights represent the sustainability scores for each category such as {Air Quality}<r>[0.108, 0.270, 0.176, 0.840]</r>, {Biodiversity}<r>[0.193, 0.189, 0.261, 0.840]</r>, {Clean Energy}<r>[0.278, 0.351, 0.346, 0.840]</r> and so on up to {Resources}<r>[0.875, 0.270, 0.943, 0.840]</r>. By comparing the heights of these bars to the y-axis ticks, I can extract the sustainability score for each category.\nBased on the above thought, the answer is:\nCategory,Sustainability Score \nAir Quality,70\nBiodiversity,80\nClean Energy,60\nClimate Change,85\nWaste Management,55\nWater Quality,65\nLand Use,85\nPollution,90\nPopulation,75\nResources,70"
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "w1hIH0ehap5kGq5N",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, which area seems to need the most improvement in sustainability?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my observation of the bar chart, I can figure out that the area that needs the most improvement is the one with the lowest sustainability score. This corresponds to the bar with the minimum height. Looking at the chart, the minimum height is for the {Waste Management}<r>[0.449, 0.392, 0.517, 0.840]</r> category, which is around 55 on the y-axis. Therefore, Waste Management needs the most improvement in sustainability.\nBased on the above thought, the answer is:\nWaste Management."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "PNQI4lHuuLTC01un",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest sustainability score and which category does it belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the heights of the bars in the chart, I can determine the highest value. Here, the bar with maximum height corresponds to the {Pollution}<r>[0.704, 0.107, 0.773, 0.840]</r> category, which is closer to 90 on the y-axis ticks. Hence, the highest sustainability score is 90 and it belongs to the Pollution category.\nBased on the above thought, the answer is:\nPollution."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "5fSTAYprBGo89D1L",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data representing the innovation rate of the various sectors in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart provides data for several different sectors in the knowledge economy, represented as vertical bars. The chart includes bars for sectors such as {BioTech}<r>[0.099, 0.344, 0.168, 1.513]</r>, {EdTech}<r>[0.185, 0.578, 0.254, 1.513]</r>, {FinTech}<r>[0.271, 0.132, 0.340, 1.513]</r>, {AgriTech}<r>[0.357, 0.302, 0.426, 1.513]</r>, {MediaTech}<r>[0.444, 0.663, 0.512, 1.513]</r>, {InsuranceTech}<r>[0.530, 0.705, 0.598, 1.513]</r>, {HealthTech}<r>[0.616, 0.281, 0.685, 1.513]</r>, {GreenTech}<r>[0.702, 0.387, 0.771, 1.513]</r>, {NanoTech}<r>[0.788, 0.535, 0.857, 1.513]</r> and {GeoTech}<r>[0.874, 0.196, 0.943, 1.513]</r>. The height of each bar on the y-axis denotes the innovation rate of each sector. To provide this data in a structured table format, I will take each sector as a distinct category and list the corresponding innovation rate as a value with respect to that category.\nBased on the above thought, the answer is:\nIndustries,Rate\nBioTech,55\nEdTech,44\nFinTech,65\nAgriTech,57\nMediaTech,40\nInsuranceTech,38\nHealthTech,58\nGreenTech,53\nNanoTech,46\nGeoTech,62"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "NsSQHWGI1OEKpwng",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector needs to improve their innovation efforts the most? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector need to improve their innovation efforts the most, I will look for the bar representing the lowest value on the y-axis, which represents the innovation rate. The lower the bar, the less innovative the sector is. From my observation, {the InsuranceTech bar}<r>[0.530, 0.705, 0.598, 1.513]</r> is the shortest, which means it has the lowest value among all sectors. Therefore, InsuranceTech appears to be the least innovative and needs to improve their innovation efforts the most.\nBased on the above thought, the answer is:\nInsuranceTech. Because according to the chart, it has the lowest innovation rate among all the sectors."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "dTlBMiwSIA3Y42tC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which industry has the highest innovation rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart is used to represent the innovation rate in several sectors of the knowledge economy. The height of each bar corresponds to the innovation rate. The taller a bar is, the higher the rate. To determine the industry with the highest innovation rate, I will need to identify the tallest bar on the chart. Skimming through the heights, {the FinTech bar}<r>[0.271, 0.132, 0.340, 1.513]</r> appears to be the tallest, thus indicating that FinTech is the industry with the highest innovation rate.\nBased on the above thought, the answer is:\nFinTech"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "c7Tp0P0wzwBkdmIh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart's data into a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that the x-axis contains different trend categories from {\"GradualDecline\"}<r>[0.052, 0.846, 0.138, 0.953]</r>, {\"SteadyDecline\"}<r>[0.143, 0.846, 0.224, 0.947]</r>, {\"SuddenDecrease\"}<r>[0.216, 0.846, 0.309, 0.963]</r>, to {\"SignificantIncrease\"}<r>[0.805, 0.846, 0.909, 0.976]</r>. Each bar's height corresponds to a value on the y-axis, labeled as {\"Values (%)\"}<r>[0.015, 0.384, 0.029, 0.476]</r>, ranging from {0}<r>[0.043, 0.827, 0.052, 0.845]</r> to {80+}<r>[0.035, 0.019, 0.052, 0.036]</r>. Outlier1 and Outlier2 are represented as two bars in the middle of the chart. By referring to the height of each bar, and the labels on the y-axis, I can infer the values for each trend to generate a structured CSV format table.\nBased on the above thought, the answer is:\nTrend,Value\nGradualDecline,55\nSteadyDecline,50\nSuddenDecrease,40\nSignificantDecrease,30\nOutlier1,65\nOutlier2,25\nSteadyIncrease,50\nGradualIncrease,55\nSuddenIncrease,60\nSignificantIncrease,70"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "JqSRYYccD8vUjkv9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer some specific patterns or irregularities in the trends based on the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's bar patterns indicate a gradual decline from 55% to 30% in values, starting from {\"GradualDecline\"}<r>[0.052, 0.846, 0.138, 0.953]</r> to {\"SignificantDecrease\"}<r>[0.287, 0.846, 0.395, 0.981]</r>. Then, there are two outliers, {\"Outlier1\"}<r>[0.431, 0.846, 0.481, 0.908]</r> at 65%, which is higher than the previous trend, and {\"Outlier2\"}<r>[0.517, 0.846, 0.566, 0.908]</r> at 25%, which is lower than the previous trends. After the outliers, the values steadily increase from 50% to 70%, starting from {\"SteadyIncrease\"}<r>[0.566, 0.846, 0.652, 0.953]</r> to {\"SignificantIncrease\"}<r>[0.805, 0.846, 0.909, 0.976]</r>. This indicates some abnormal behavior of the two outliers which do not follow the general decrease and increase pattern.\nBased on the above thought, the answer is:\nYes, the chart shows a general decrease and increase pattern with some irregularity. While the values generally decrease from 55% to 30% and then increase from 50% to 70%, two outliers are present in the middle of the chart which do not follow this pattern: Outlier1 at 65% and Outlier2 at 25%."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "8si0RMUiysqGeJ3t",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which trend category saw a significant increase in values?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer the trend category by looking at the bar heights. Starting from the left, the bars generally decrease until the {\"Outlier2\"}<r>[0.517, 0.846, 0.566, 0.908]</r> trend. The height of the bars then begins to increase. Therefore, the trend category that saw a significant increase in values begins with {\"SteadyIncrease\"}<r>[0.566, 0.846, 0.652, 0.953]</r>.\nBased on the above thought, the answer is:\nSteadyIncrease."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "WbZ9sej3eGIeL94z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart of the companies' revenue change, can you create a CSV format table of this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I notice from the chart's x-axis that the labels {\"Apple\"}<r>[0.115, 0.911, 0.153, 0.958]</r>, {\"Google\"}<r>[0.197, 0.911, 0.242, 0.967]</r>, {\"Facebook\"}<r>[0.278, 0.911, 0.334, 0.981]</r>, {\"Microsoft\"}<r>[0.365, 0.911, 0.419, 0.979]</r>, {\"Amazon\"}<r>[0.453, 0.911, 0.503, 0.973]</r>, {\"Netflix\"}<r>[0.543, 0.911, 0.585, 0.963]</r>, {\"Tesla\"}<r>[0.633, 0.911, 0.667, 0.954]</r>, {\"SpaceX\"}<r>[0.713, 0.911, 0.760, 0.969]</r>, {\"Twitter\"}<r>[0.801, 0.911, 0.844, 0.965]</r>, and {\"LinkedIn\"}<r>[0.883, 0.911, 0.934, 0.975]</r> correspond to various companies. On the y-axis, figures from {0}<r>[0.043, 0.898, 0.052, 0.916]</r> to {35}<r>[0.035, 0.019, 0.052, 0.036]</r> denote the revenue change in percentage terms. The bar heights for each company determine the revenue change values. For instance, the company {\"Apple\"}<r>[0.099, 0.528, 0.168, 0.905]</r> has a bar height that aligns with {15}<r>[0.035, 0.521, 0.052, 0.539]</r> on the y-axis, indicating a 15% change in revenue.\nBased on the above thought, the answer is:\nCompany,Revenue Change (%)\nApple,15\nGoogle,25\nFacebook,18\nMicrosoft,22\nAmazon,12\nNetflix,24\nTesla,30\nSpaceX,28\nTwitter,8\nLinkedIn,10"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "EP82CrSb5fZ4LD58",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the technology sector's performance in terms of revenue change based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question asks for an overall understanding of the technology sector's performance in terms of revenue. To answer, I'll assess the bars representing different companies. Noticing that most of the companies' bars, such as {\"Apple\"}<r>[0.099, 0.528, 0.168, 0.905]</r>, {\"Google\"}<r>[0.185, 0.277, 0.254, 0.905]</r>, and {\"Facebook\"}<r>[0.271, 0.453, 0.340, 0.905]</r>, fall within the higher y-axis values, I deduce that the majority of these tech companies experienced significant revenue growth. However,{\"Twitter\"}<r>[0.788, 0.704, 0.857, 0.905]</r> and {\"LinkedIn\"}<r>[0.874, 0.654, 0.943, 0.905]</r> exhibit a relatively small increase in revenue, which means not all companies in the technology sector show a positive trend\nBased on the above thought, the answer is:\nOverall, a considerable growth in revenue is demonstrated across the majority of technology companies. However, there were variations, and not all tech firms showed equally robust growth."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "wB4EU47yipB1sHEs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had the highest percentage change in revenues according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the heights of the bars, which correspond to the Revenue Change (%) for each company, I need to find the bar with the greatest height. This can be done by comparing the top coordinates of all bars: {\"Apple\"}<r>[0.099, 0.528, 0.168, 0.905]</r>, {\"Google\"}<r>[0.185, 0.277, 0.254, 0.905]</r>, {\"Facebook\"}<r>[0.271, 0.453, 0.340, 0.905]</r>, {\"Microsoft\"}<r>[0.357, 0.352, 0.426, 0.905]</r>, {\"Amazon\"}<r>[0.444, 0.603, 0.512, 0.905]</r>, {\"Netflix\"}<r>[0.530, 0.302, 0.598, 0.905]</r>, {\"Tesla\"}<r>[0.616, 0.151, 0.685, 0.905]</r>, {\"SpaceX\"}<r>[0.702, 0.201, 0.771, 0.905]</r>, {\"Twitter\"}<r>[0.788, 0.704, 0.857, 0.905]</r>, {\"LinkedIn\"}<r>[0.874, 0.654, 0.943, 0.905]</r>. Among these, the {\"Tesla\"}<r>[0.616, 0.151, 0.685, 0.905]</r> bar reaches the highest level on the y-axis, aligning with {30}<r>[0.035, 0.144, 0.052, 0.162]</r>, indicating that Tesla had the highest percentage change in revenues.\nBased on the above thought, the answer is:\nTesla."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "hlefMPEO0l5Xdwes",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information extracted from the bars in the spectrogram, please convert the corresponding topic and influence data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "For this bar chart at {the coordinates}<r>[0.066, 0.026, 0.985, 0.864]</r>, it conveys the public interest level in various social and political trends. The X-axis of the chart contains different political/social topics, represented by the labels. The Y-axis, labeled {Active Public Interest Level}<r>[0.015, 0.328, 0.029, 0.562]</r>, represents the value of interest level against each topic. To transform the data into a CSV file, I will need to extract the name of each topic from the X-axis, its coordinate given, and the corresponding value on the Y-axis, which can be deduced from the height of each bar. For instance, {Climate Change}<r>[0.106, 0.235, 0.193, 0.864]</r> bar shows approximately 75, and {Election}<r>[0.215, 0.151, 0.300, 0.864]</r> shows approximately 85, and so forth.\nBased on the above thought, the answer is:\nTopic,Influence\nClimate Change,75\nElection,85\nProtest,65\nTrade War,60\nSocial Media,55\nImmigration,70\nUnemployment,35\nHealthcare,80"
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "p7jqZS98qiDBn3V1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you describe the trend of public interest level in these topics?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend of public interest level could be determined by analyzing the heights of the bars. Starting from {Climate Change}<r>[0.108, 0.235, 0.193, 0.864]</r> bar with a high public interest level of about 75 and moving to {Election}<r>[0.215, 0.151, 0.300, 0.864]</r> bar, a prominent topic, sees the pinnacle of interest at 85. After this, there is a gradual decline as we move on to {Protest}<r>[0.322, 0.319, 0.408, 0.864]</r>, {Trade War}<r>[0.429, 0.361, 0.515, 0.864]</r>, {Social Media}<r>[0.536, 0.403, 0.622, 0.864]</r> and finally {Unemployment}<r>[0.750, 0.571, 0.836, 0.864]</r> has the lowest interest level at 35. However, this decreasing trend reverses as we move towards {Immigration}<r>[0.643, 0.277, 0.729, 0.864]</r> and with {Healthcare}<r>[0.858, 0.193, 0.943, 0.864]</r>, the interest level rises sharply again to reach around 80. Thus, the interest level trend fluctuates over these topics and doesn't strictly follow a monotonous increase or decrease.\nBased on the above thought, the answer is:\nThe trend of public interest level first increases, then undergoes a substantial decrease, followed by a significant increase at the end."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "FcRk0NwlwOAK2TLu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which topic has the least public interest level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the topic with the least public interest level, I need to look for the shortest bar in the chart. Each bar in the chart corresponds to a specific topic. By visually examining the bars, it's clear that the shortest bar corresponds to {Unemployment}<r>[0.750, 0.571, 0.836, 0.864]</r>, which spans approximately from 0 to 35 according to the y-axis.\nBased on the above thought, the answer is:\nUnemployment"
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "6AD5efRpCbEvFy5t",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data from the bar graph of \"Risk complications associated with EdTech Innovations over the years\" to a table CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "This horizontal bar chart plots 'Risk Complication Score' on the x-axis and 'EdTech Innovations' on the y-axis. Each bar represents an 'EdTech Innovation' and each of the same color group represents a different time period: {3 Years}[0.297, 0.083, 0.347, 0.106], {5 Years}[0.297, 0.118, 0.347, 0.141], {7 Years}[0.297, 0.153, 0.347, 0.176], {10 Years}[0.297, 0.188, 0.356, 0.211]. The x-coordinates of the bars represent the scores. I can convert the bars into CSV format by first identifying the 'EdTech Innovations', then determining the length of the corresponding bars to get the scores for each time period. For example, for 'Virtual_Reality_Training'[0.074, 0.172, 0.236, 0.195], it has 4 bars, and by finding the x-coordinates of each bar's right side from the given JSON data, the risk complication scores for different time can be obtained.\nBased on the above thought, the answer is:\nRisk_Complication,3_Years,5_Years,7_Years,10_Years\nVirtual_Reality_Training,-1.23,-1.73,-2.67,-3.24\nArtificial_Intelligence_Education,-2.3,-2.63,-2.0,-3.1\nInnovative_Learning_Methods,-1.43,-2.47,-2.1,-3.05\nE-Learning_Platform,-1.98,-1.96,-2.9,-3.45"
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "uFuc1KroaAtweews",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the possible reasons for the increase in the risk complication score for 'Virtual_Reality_Training' over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Possible reasons for increased risk complications for Virtual Reality Training could include increasing complexity of the technology, lack of adequate professional training materials, or issues related to the ergonomic and psychosocial risks associated with extended use of these technologies. An increase in the cost associated with deploying VR in education or a rise in legal implications of the VR tech could also lead to an escalation in the risks. Since the study lacks specific underlying factors causing the increasing risk in complication score, these potential reasons are hypothesized based on general knowledge about VR training. The bar chart provided indicates these feeding through an increase in risk complication scores over the years, seen in the increasing length of the bars from {3 Years}[0.727, 0.100, 0.974, 0.141] to {10 Years}[0.323, 0.221, 0.974, 0.261] for 'Virtual_Reality_Training'[0.074, 0.172, 0.236, 0.195].\nBased on the above thought, the answer is:\nPotential reasons could include the increasing complexity of VR technology, inadequate professional training materials, ergonomic and psychosocial risks, increasing costs, and possible legal implications. However, this chart does not provide specific details on these factors."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "nPmraRcVZGmgPCo1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich EdTech innovation has the smallest risk complication score in total over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the EdTech innovation with the smallest total risk complication score, I would need to sum up the scores for each innovation and then compare them. First calculate the total risk complication score for each innovation, such as 'Virtual_Reality_Training', for 3 Years, 5 Years, 7 Years and 10 Years, the bars are located at [0.727, 0.100, 0.974, 0.141], [0.626, 0.141, 0.974, 0.181], [0.438, 0.181, 0.974, 0.221] and [0.323, 0.221, 0.974, 0.261] respectively. The x-values of the left sides of these four bars are -1.23, -1.73, -2.67 and -3.24 respectively, and the sum is -8.87. Do the similar calculation for 'Artificial_Intelligence_Education', 'Innovative_Learning_Methods', and 'E-Learning_Platform'. Then we can find that 'E-Learning_Platform' has the smallest total risk complication score over the years.\nBased on the above thought, the answer is:\nE-Learning_Platform."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "LXrf1TuU9HrctZ2Z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen observing the bar chart, how would you translate the data present into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart {subtitle}<r>[0.371, 0.025, 0.800, 0.055]</r> shows the development of the Internet and Communication Industry over a decade. The x-axis scores, represent the development curve of each sector, with its labels ranging from {-4.0}<r>[0.111, 0.919, 0.145, 0.942]</r> to {0.0}<r>[0.963, 0.919, 0.985, 0.942]</r>. On the y-axis, we see various sectors on which this development is based - {Ethernet-Cable provision}<r>[0.197, 0.145]</r>, {Optic-fiber provision}<r>[0.197, 0.370]</r>, {Wi-Fi Hotspots}<r>[0.197, 0.595]</r>, {5G Infrastructure}<r>[0.197, 0.820]</r>. According to the legend<r>[0.204, 0.815, 0.289, 0.891]</r>, the salmon pink bars represent the status of these sectors in {2009}<r>[0.248, 0.824, 0.283, 0.847]</r> starting at x=0 and stretching left to their respective values, while the sky blue bars represent the progress up to {2019}<r>[0.248, 0.859, 0.283, 0.882]</r> starting at x=0 and stretching left to their respective values.\nBased on the above thought, the answer is:\nSectors,2009,2019\nEthernet-Cable provision,-3.5,-0.7\nOptic-fiber provision,-3.3,-0.3\nWi-Fi Hotspots,-0.6,-2.7\n5G Infrastructure,-1.2,-1.1"
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "zMTREoH5k2bG44lK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector needs to prioritize its development, based on the overall trend observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the overall development trend, the {Internet and Communication Industry Development}<r>[0.371, 0.025, 0.800, 0.055]</r> shown on the graph implies the change in each sector from {2009}<r>[0.248, 0.824, 0.283, 0.847]</r> to {2019}<r>[0.248, 0.859, 0.283, 0.882]</r>. By analyzing the {Wi-Fi Hotspots}<r>[0.197, 0.595]</r> bars of 2009 and 2019, we can see this sector has the biggest negative change over the decade, thus needing more attention for improvement.\nBased on the above thought, the answer is:\nWi-Fi Hotspots. Even though other sectors also show negative trends, the decline of Wi-Fi Hotspots from 2009 to 2019 represents the largest negative change and needs urgent prioritization for development."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "MR6KHmAE0FVS6nVV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data on the chart, which sector saw the most improvement from 2009 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the maximum progress, I should analyze the pink and the blue bars for each sector on the y-axis. From the {legend}<r>[0.204, 0.815, 0.289, 0.891]</r>, pink bars represent the sector's status in {2009}<r>[0.248, 0.824, 0.283, 0.847]</r> and the blue bar for {2019}<r>[0.248, 0.859, 0.283, 0.882]</r>. From this, the difference between the status of each sector in 2009 and 2019 needs to be calculated. Comparing the differences in scores, the maximum development should be identified which would hint at the sector with the most progress.\nBased on the above thought, the answer is:\nOptic-fiber provision."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "WNzyv8TjQdFkRmMY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows the scores of four different global trends (Global Warming, Resource Scarcity, Population Growth, Technology Advancement) over four years (2017-2020). {The x-axis}<r>[0.199, 0.919, 1.031, 0.942]</r> represents 'Score' with values from 0 to 80 while {the y-axis}<r>[0.065, 0.172, 0.193, 0.798]</r> represents the four categories of global issues. To derive each value, I can use the corresponding bar length and the annotations inside them, for instance, {the first bar of Global warming in 2017}<r>[0.203, 0.100, 0.513, 0.141]</r> has an annotation of \"30.0\" and the corresponding length indicates a similar value, so the value of Global Warming in 2017 is 30. In the same manner, I can extract values for each year in all categories.\nBased on the above thought, the answer is:\nCategory,2017,2018,2019,2020\nGlobal Warming,30.0,35.8,37.3,32.9\nResource Scarcity,20.0,21.9,23.7,24.3\nPopulation Growth,60.0,62.3,67.2,72.0\nTechnology Advancement,40.0,44.0,49.0,65.0"
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "axdJbGhnNSpym0Uj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of technological advancement from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "As for {Technology Advancement}<r>[0.203, 0.704, 0.875, 0.865]</r>, it is evident that the bar lengths steadily increase from 2017 to 2020 showing that there has been a gradual increase in the scores. Starting from a score of 40.0 in 2017 to 65.0 in 2020, it can be inferred that technological advancement has shown steady and significant improvement over these years.\nBased on the above thought, the answer is:\nThe Technology Advancement score has seen a consistent increase from 2017 to 2020 indicating a positive trend suggesting substantial progress in this area during these years."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "fQ5BslOjfKEBLpmW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2017 to 2020, which category has the biggest fluctuation in trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Going through each of the four categories to find out the one with the largest variation, I'd subtract the smallest score from the largest for each category. Looking at {Global Warming}<r>[0.203, 0.100, 0.589, 0.221]</r>, the scores range from 30.0 in 2017 to 37.3 in 2019, so the fluctuation is 7.3; for {Resource Scarcity}<r>[0.203, 0.302, 0.454, 0.422]</r>, the fluctuation is between 20.0 in 2017 and 24.3 in 2020, so the fluctuation is 4.3; similarly for {Population Growth}<r>[0.203, 0.503, 0.948, 0.664]</r> the fluctuation from 60.0 in 2017 to 72.0 in 2020 makes it 12.0; As for {Technology Advancement}<r>[0.203, 0.704, 0.875, 0.865]</r>, the range is from 40.0 in 2017 to 65.0 in 2020, hence the fluctuation is 25.0 which is the maximum among all the categories.\nBased on the above thought, the answer is:\nTechnology Advancement."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "x1ZKkpUl2NGihpqI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you output the data in the chart you just analyzed in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The chart is a grouped horizontal bar chart, indicating scores for four aspects of CyberSecurity and Data Privacy categories. Each of the four categories has a series of bars representing different years from 2011 to 2014. I can read the scores for each year by measuring the bar length in a category. From these bar lengths, I can assign these score values to each category and year combination. For an instance, considering the {Two-Factor Authentication category}<r>[0.015, 0.775, 0.195, 0.798]</r>, the set of horizontal bars representing the years 2011 to 2014 are located : {{2011}<r>[0.414, 0.704, 0.974, 0.744]</r>, {2012}<r>[0.457, 0.744, 0.974, 0.784]</r>, {2013}<r>[0.242, 0.784, 0.974, 0.824]</r>, {2014}<r>[0.285, 0.824, 0.974, 0.865]</r>}. The coordinated represent the lengths of the bars and therefore these can be converted into scores. This process can be repeated for remaining categories to tabulate the data into CSV format.\nBased on the above thought, the answer is:\nCategories,2011,2012,2013,2014\nEncryption Standard,-3.0,-2.8,-3.2,-2.5\nFirewall Protection,-3.1,-2.7,-3.0,-2.4\nVPN Security,-2.8,-2.5,-3.1,-3.0\nTwo-Factor Authentication,-2.6,-2.4,-3.4,-3.2"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "9SjNMyPwZy97Iv2D",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect of Cybersecurity and Data Privacy seems to have improved the most during the period 2011 to 2014, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most improved aspect would be the one with the samllest(variance or) difference between the starting and ending points for the period under consideration. Carefully examining the lengths of the bars assigned to each category for the year 2011 to 2014, one can see: {Encryption Standard}<r>[0.328, 0.100, 0.974, 0.141]</r> to <r>[0.436, 0.221, 0.974, 0.261]</r> (i.e. 2011 to 2014), {Firewall Protection}<r>[0.306, 0.302, 0.974, 0.342]</r> to <r>[0.457, 0.422, 0.974, 0.462]</r>, {VPN Security}<r>[0.371, 0.503, 0.974, 0.543]</r> to <r>[0.328, 0.623, 0.974, 0.664]</r>, and {Two-Factor Authentication}<r>[0.414, 0.704, 0.974, 0.744]</r> to <r>[0.285, 0.824, 0.974, 0.865]</r>. Therefore, after making a comparison among these four aspects over the designated years, it is clear that the Firewall Protection seems to have made the most improvement.\nBased on the above thought, the answer is:\nFirewall Protection."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "LFUHHzTUkDNFtSCB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mean score for VPN Security across the four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the mean score for VPN Security across the years 2011 to 2014, I need to look at the lengths of the bars assigned to VPN Security, located at <r>[0.106, 0.574, 0.195, 0.597]</r>. According to the lengths of the bars, the scores for the years 2011 through 2014 are respectively located at <r>[0.371, 0.503, 0.974, 0.543]</r>, <r>[0.436, 0.543, 0.974, 0.583]</r>, <r>[0.306, 0.583, 0.974, 0.623]</r>, and <r>[0.328, 0.623, 0.974, 0.664]</r>. Add these four scores and then divide the total by 4 to calculate the mean value.\nBased on the above thought, the answer is:\n-2.85"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "RFqdNBC4VdyiAhYh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar chart data into a table format using the data you have?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels on the yticks {'yticks': [{'label': 'Electric Vehicle Sales', 'label_coordinates': '[0.061, 0.172, 0.207, 0.195]', 'tick_coordinates': '[0.217, 0.181]'}, {'label': 'AI Technology Stocks', 'label_coordinates': '[0.062, 0.373, 0.207, 0.396]', 'tick_coordinates': '[0.217, 0.382]'}, {'label': 'Cyber Security Investments', 'label_coordinates': '[0.015, 0.574, 0.207, 0.597]', 'tick_coordinates': '[0.217, 0.583]'}, {'label': 'Green Energy Consumption', 'label_coordinates': '[0.016, 0.775, 0.207, 0.798]', 'tick_coordinates': '[0.217, 0.784]'}], I can understand that the four categories are \"Electric Vehicle Sales\", \"AI Technology Stocks\", \"Cyber Security Investments\", and \"Green Energy Consumption\" respectively. The x-axis {\"xlabel\"}[0.582, 0.952, 0.620, 0.975] describes the values for each category. Each year, represented by different bars grouped together for each category, form the columns of the table. I can check the values indicated in each bar for the years 2020 to 2023 of these categories to create the CSV table.\nBased on the above thought, the answer is:\nCategories,Year 2020,Year 2021,Year 2022,Year 2023\nElectric Vehicles Sales,2000,2500,3250,4000\nAI Technology Stocks,5000,4500,4300,4100\nCyber Security Investments,3000,3200,2960,3100\nGreen Energy Consumption,1000,1500,2250,3000"
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "K6uhP5TnMpycnB7u",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart, which industry is expected to see the least growth over the years according to the predictions?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires comparing the overall trend of the bars for each category from 2020 to 2023. Each color bar represents the values for a different year for each category. Looking at the bars for 'AI Technology Stocks'[{'bar_coordinates': '[0.217, 0.302, 0.948, 0.342]'} {'bar_coordinates': '[0.217, 0.382, 0.817, 0.422]'}], there's a clear decline over the years. This suggests that 'AI Technology Stocks' is expected to see the least growth.\nBased on the above thought, the answer is:\nAI Technology Stocks is expected to see the least growth over the years."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "2W7xCEG7r0DjhLr7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the maximum increase in projections from the year 2020 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which sector had the maximum increase from 2020 to 2023, I need to consider the first and last values of each bar group. The first bars in the groups represent the year 2020 and the last bars represent the year 2023. Looking at the values, it seems that 'Green Energy Consumption' sector{bar_year_2020}[0.217, 0.824, 0.363, 0.865]{bar_year_2023}[0.217, 0.824, 0.656, 0.865] shows the most substantial increase. Looking at the exact values, 'Green Energy Consumption' grows from 1000 in 2020 to 3000 in 2023, which is an increase of 2000, which is the maximum increase of all.\nBased on the above thought, the answer is:\nGreen Energy Consumption"
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "ymZTzXnaJ9Pl0g4D",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart you see, generate a table that captures the popularity scores for each fashion trend."
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart subplot with coordinates 'subplot_0'<r>[0.104, 0.062, 0.985, 0.903]</r>, it is clear that the different stories (Emo, Grunge, Steampunk, Boho) represent the y-axis and the 'Scores'<r>[0.522, 0.952, 0.568, 0.975]</r> represent the x-axis. Each of the four bars within each fashion trend represents a separate value. Using the lengths of the bars, I can identify the corresponding values on the x-axis. For instance, the Emo bar's length reaches around 5.1 on the x-axis<r>[0.104, 0.100, 0.626, 0.132]</r>. Similarly, for Grunge, it's about 4.7<r>[0.104, 0.313, 0.585, 0.345]</r>, for Steampunk, it's about 5.9<r>[0.104, 0.525, 0.708, 0.557]</r> and for Boho, it's about 6.3<r>[0.104, 0.737, 0.749, 0.769]</r>. By repeating this operation for each bar, I can create a CSV formatted table of the data.\nBased on the above thought, the answer is:\nFashion_Trend,Emo,Grunge,Steampunk,Boho\nEmo,5.1,3.2,2.1,1.5\nGrunge,4.7,5.8,6.0,4.5\nSteampunk,5.9,6.7,7.8,5.7\nBoho,6.3,7.0,8.2,6.5"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "CTZGQW8LwI1Evcqa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fashion trend has a fairly stable score and shows less fluctuation in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the fashion trend with the least fluctuation in scores, I would examine the difference in lengths of the bars representing each trend. For instance, the 'Emo' trend represented by the pink bar<r>[0.104, 0.100, 0.626, 0.132]</r> shows considerable differences in bar lengths over time, indicating changing scores. Similarly, 'Grunge' represented by the green bar<r>[0.104, 0.345, 0.698, 0.376]</r> and 'Steampunk' represented by the blue bar<r>[0.104, 0.589, 0.902, 0.621]</r> exhibit noticeable differences in bar lengths. In contrast, the 'Boho' trend, represented by the brown bar, has bars that are relatively equivalent in length, indicating small changes in the score over time<r>[0.104, 0.833, 0.943, 0.865]</r>. Thus, the 'Boho' fashion trend shows less fluctuation and is more stable.\nBased on the above thought, the answer is:\nBoho"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "BYECpFAYUlRQHwwd",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which fashion trend category is there the highest popularity score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the highest popularity score, I need to locate and compare the furthest ends of each category's relevant bars. Focusing first on 'Emo', represented by the pink bar, the highest score is at 5.1<r>[0.104, 0.100, 0.626, 0.132]</r>. For 'Grunge', the green bar reaches up to 5.8<r>[0.104, 0.345, 0.698, 0.376]</r>. 'Steampunk', represented by the blue bar, even further to 7.8<r>[0.104, 0.589, 0.902, 0.621]</r>. Lastly, 'Boho', the brown bar, has the highest score of 8.2<r>[0.104, 0.833, 0.943, 0.865]</r>. Thus, the Boho category has the highest popularity score.\nBased on the above thought, the answer is:\nBoho"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "m9sAoirUcX39BncE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information given in the bar chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can tell from the {chart title}[0.361, 0.025, 0.752, 0.055], 'Education Resource Distribution and Utilization', the four {y-axis labels}[{'LLAMA-Default'}[0.015, 0.164, 0.118, 0.187]; {'LLAMA-HAG'}[0.036, 0.370, 0.118, 0.393]; {'Vicuna-Default'}[0.015, 0.577, 0.118, 0.600]; {'Vicuna-HAG'}[0.036, 0.783, 0.118, 0.807]] and the x-axis labels which range from {0}[0.123, 0.919, 0.132, 0.942] to {160}[1.015, 0.919, 1.042, 0.942] that there are four groupings that represent educational resource utilization scores. Each group has two bars, corresponding to two cycles. The value of each measurement can be read from the labels against the bars such as the {'135'}[0.861, 0.125, 0.888, 0.148] of cycle 1 in the LLAMA-Default category.\nBased on the above thought, the answer is:\nEducation Resource Utilization,Cycle 1,Cycle 2\nLLAMA-Default,135,118\nLLAMA-HAG,112,137\nVicuna-Default,145,112\nVicuna-HAG,107,120"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "w491Uh6aj1fsZAgP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which kind of education resource allocation seems to show a better improvement from Cycle 1 to Cycle 2? And explain why that is the case."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to thoroughly analyze the group bars on the chart. Specifically, the bars for {'Cycle 1'}[0.921, 0.824, 0.973, 0.847] and {'Cycle 2'}[0.921, 0.859, 0.972, 0.882] for each category. Looking at categories like {'LLAMA-Default'}[0.015, 0.164, 0.118, 0.187], we see that the bar for Cycle 2 is shorter than Cycle 1, which means utilization decreased. In contrast, when looking at {'LLAMA-HAG'}[0.036, 0.370, 0.118, 0.393], there was an increase in utilization from Cycle 1 to Cycle 2. Therefore, by comparing these bars for each category, a determination can be made.\nBased on the above thought, the answer is:\nThe LLAMA-HAG education resource allocation showed better improvement from Cycle 1 to Cycle 2, as indicated by an increase in resource utilization."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "wSyHGNgciUs8wAj0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category had the highest increase in resource utilization from Cycle 1 to Cycle 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the highest increase in resource utilization from Cycle 1 to Cycle 2, I need to subtract the score of Cycle 1 from the score of Cycle 2 for each of the four categories. By comparing the scores labelled at the ends of the bars like {'Cycle 1 of LLAMA-Default'}[0.861, 0.125, 0.888, 0.148], {'Cycle 2 of LLAMA-Default'}[0.918, 0.538, 0.944, 0.561], and others, I can calculate the difference.\nBased on the above thought, the answer is:\nLLAMA-HAG"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "bQvLnejK2WHctgfi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a structured table extracted from the provided chart information associated with the various trends in social activities."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the layout of the chart with segments dedicated to different activities such as 'Recreation', 'Socialization', 'Outdoor', and 'Lifestyle' represented by colored bars. Each category is associated with trends like Sharp decrease, Gradual recovery, and so forth. The bars' lengths indicate the activity frequency for each category and trend. To compile the data accurately into a structured table, I reference the bars and their corresponding values across different trend categories. Each category's values across different trends are comprehensively observed and recorded for accurate data representation.\nBased on the above thought, the answer is:\nTrend,Recreation,Socialization,Outdoor,Lifestyle\nSharp-Decrease,85,90,75,80\nGradual-Recovery,60,64,70,72\nModerate-Growth,62,65,70,73\nFluctuating-Decline,70,74,75,76\nErratic-Decline,50,52,53,54\nSteady-Recovery,55,58,60,64\nModerate-Increase,65,67,70,73\nPronounced-Increase,75,77,80,85"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "rZwnXEXDeBhzP1uZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend seems to be showing a consistent recovery across all social activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze which trend shows a consistent recovery across all activities, I examine each bar associated with the specified trends. I observe bars labeled with trends indicative of recovery, such as 'Gradual-Recovery' and 'Steady-Recovery'. By comparing the bars' lengths and their annotations for each activity, I aim to identify a trend where each activity shows an upward or recovering trend length. I confirm the recovery trends by looking at their respective values across all four categories.\nBased on the above thought, the answer is:\nSteady-Recovery, as it shows consistent and notable increases in frequency across all categories when compared to more erratic or fluctuating trends."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "y1BHa7VPXx5d7ZyQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest frequency noted for the 'Outdoor' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest frequency for the 'Outdoor' category, I will look at the lengths of the bars representing the outdoor activities. By locating each bar tied to the Outdoor section and reading off the value annotations present at the bar ends, I can compare and find the highest value. Each of these bars and their corresponding values are noted, and by comparing them, the maximum can be determined.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "g31T9piwIUiV04KD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the bar chart and produce a table of scores for each technology over the indicated periods of 1, 2, 5, and 10 years."
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the table, we can examine each group of bars. Each set of bars corresponds to a particular technology as depicted by the y-axis labels: {Robotic Automation's bars}[0.579, 0.087, 0.708, 0.255], {Artificial Intelligence's bars}[0.499, 0.297, 0.933, 0.466], {Blockchain's bars}[0.467, 0.550, 0.917, 0.676], and {Nano Technology's bars}[0.226, 0.719, 0.949, 0.887]. Inside each bar, there's a numeric value, which is the score for each technology at different future phases: 1 year, 2 years, 5 years, and 10 years. This is also supported by the color-coded legend: [{1-year legend}<r>[0.894, 0.064, 0.918, 0.076]</r>, {2-years legend}<r>[0.894, 0.090, 0.918, 0.102]</r>, {5-years legend}<r>[0.894, 0.116, 0.918, 0.128]</r>, {10-years legend}<r>[0.894, 0.142, 0.918, 0.155]</r>]. For instance, under Robotic Automation, the 1-year bar value is 0.8, the 2-year bar value is 1, the 5-year bar value is -0.5, and the 10-year bar value is -2.5. Doing the same for the rest would give us the scores for each technology across the different periods.\nBased on the above thought, the answer is:\nTechnology,1 year,2 years,5 years,10 years\nRobotic Automation,0.8,1,-0.5,-2.5\nArtificial Intelligence,-0.5,1.5,2.2,-1\nBlockchain,2.1,-0.7,1.4,0.7\nNano Technology,-2.2,0.6,2.3,1.5"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "6wVZsFBLSiBuFNPs",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on each technology's score progression over the years, which technology seems to be the riskiest investment in the next 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "A risky investment would be one where the scores are highly variable and tend to decrease. Measuring the score variations across the four periods can give us a hint. It appears {Robotic Automation}<r>[0.579, 0.087, 0.708, 0.255]</r> has the most significant decline from 1 score at the 2-year mark{x=0.740, y=0.171} to -2.5 score at the 10-year period{x=0.268, y=0.255}, indicating significant volatility and an overall declining trend. Thus, Robotic Automation could be the riskiest investment for the next 10 years.\nBased on the above thought, the answer is:\nRobotic Automation"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "POsRlA68TvgExlRT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which technology has the highest score in the 5-year span?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 5-year period is represented by the {orange bars}<r>[0.894, 0.116, 0.918, 0.128]</r> as per the legend. To find the technology with the highest score in this timeline, we need to compare these orange bars across the different technologies. Looking at the numeric values within each orange bar, we can see that the highest figure appears in the {Nano Technology group}<r>[0.579, 0.803, 0.949, 0.845]</r>, with a score of 2.3.\nBased on the above thought, the answer is:\nNano Technology"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "GtfO71nYw0GQRwDh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transform the chart information into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart consists of several elements. It has a title {\"Charity Activities by Support Type\"}[0.442, 0.025, 0.723, 0.055], which indicates the main focus of the chart. Then, the x-axis represents scores, which are numerical data obtained based on the length of the bars. {The x-axis tick labels}[{\"\u22122\"}[0.039, 0.919, 0.060, 0.942]; {\"\u22121\"}[0.206, 0.919, 0.226, 0.942]; {\"0\"}[0.378, 0.919, 0.387, 0.942]; {\"1\"}[0.545, 0.919, 0.553, 0.942]; {\"2\"}[0.711, 0.919, 0.720, 0.942]; {\"3\"}[0.877, 0.919, 0.886, 0.942]; {\"4\"}[1.044, 0.919, 1.053, 0.942]] vary from -2 to 4, offering data ranges for interpreting the length of the bars. The y-axis represents categories of charity activities, with {the y-axis tick labels}<{\"Food_Banks\"}[0.087, 0.155, 0.170, 0.179]; {\"Homeless_Shelters\"}[0.039, 0.367, 0.170, 0.391]; {\"Educational_Programs\"}[0.015, 0.580, 0.170, 0.603]; {\"Animal_Welfare\"}[0.062, 0.792, 0.170, 0.815]>. Each category has three bars referring to different types of support labeled within {the legend}<[0.805, 0.074, 0.978, 0.185]>.  By analysing the bars' length and the corresponding number, I can then generate the CSV table. For instance, in the category of \"Food Banks\", the length of {the bar for private donations}[0.383, 0.100, 0.632, 0.143] extends approximately to 1.5 as indicated by the label \"1.5\", the length of {the bar for state funding}[0.383, 0.143, 0.782, 0.185] extends to 2.4, {the bar for volunteer work)[0.383, 0.185, 0.516, 0.228] extends to 0.8. Using this same approach, I can determine the values for other types of charity activities.\nBased on the above thought, the answer is:\nCharity_Type,Private_Donations,State_Funding,Volunteer_Work\nFood_Banks,1.5,2.4,0.8\nHomeless_Shelters,2.2,3.1,-0.5\nEducational_Programs,3.4,1.8,0.9\nAnimal_Welfare,0.9,2.7,-1.0"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "Bb3ITIzMsLEAVfk1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich charity activity has the highest net score across all types of support and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the charity activity with the highest net score, I need to calculate the net scores of all charity activities and then compare them. Starting with 'Food Banks', I determine the net score by adding up scores of each type of support under this category, these scores are labeled at the end of {the corresponding bars}[{\"Private Donations\"}[0.383, 0.100, 0.632, 0.143]  {\"State Funding\"}[0.383, 0.143, 0.782, 0.185]  {\"Volunteer Work\"}[0.383, 0.185, 0.516, 0.228]]. The same procedure is put up on 'Homeless Shelters', 'Educational Programs' and 'Animal Welfare'. Then I can find out that among the four activities, 'Educational Programs' has the highest net score, which is contributed by the relatively high score in 'Private Donations' and balanced support from other two types.\nBased on the above thought, the answer is:\nEducational Programs has the highest net score across all types of support. This is mainly due to the relatively high score in Private Donations and balanced support from State Funding and Volunteer Work."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "SX3uSDChlyeX9i4F",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the net scores across all three types of support for 'Homeless Shelters'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the net scores for 'Homeless Shelters', we need to add up the scores of each type of support under this category. Firstly, the score for 'Private Donations' can be found on the corresponding bar chart with coordinates [0.383, 0.313, 0.749, 0.355], and the labeled score at coordinates [0.760, 0.152, 0.782, 0.176] is 2.2. Secondly, the score for 'State Funding' can be found on the corresponding bar chart with coordinates [0.383, 0.355, 0.898, 0.398], and the labeled score at coordinates [0.876, 0.365, 0.898, 0.388] is 3.1. Lastly, the score for 'Volunteer Work' can be found on the corresponding bar chart with coordinates [0.299, 0.398, 0.383, 0.440], and the score labeled at coordinates [0.322, 0.407, 0.344, 0.431] is -0.5. Adding these scores up, we can derive the net scores for 'Homeless Shelters'.\nBased on the above thought, the answer is:\n4.8"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "phVIPnrO73tTg1Tr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the information provided in the horizontal bar chart into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart title is {\"Consumer Index Scores by Region\"}[0.408, 0.025, 0.692, 0.055] from the subtitle, which provides a generalized idea of the data being represented. The chart data is visible through numerical text represented on the top side of each bar, along with color-coded bar comparisons representing the numerical differences. \nFrom the yticks, I have located the labels {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600] and {\"Africa\"}[0.076, 0.783, 0.116, 0.807] representing the four different regions.\nFrom the 'sublegend', I learned that we have two indices: {\"OffP_Consumer_Index\"}[0.177, 0.083, 0.329, 0.107] color-coded as green, and {\"OnP_Consumer_Index\"}[0.177, 0.118, 0.327, 0.142] color-coded as blue.\nBy locating the positioning and length of each bar from the 'bars' of the chart data, and combining this with previously retrieved tick labels and bar text, I am able to derive the exact values represented in the chart for each region's offline and online consumer indices.\nBased on the above thought, the answer is:\nRegion,OffP_Consumer_Index,OnP_Consumer_Index\nNorth America,-1.2,-1.5\nEurope,-1.8,-2.0\nAsia,-1.5,-1.6\nAfrica,-3.0,-2.5"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "VbqbryOtixzuN1mv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you assert which region has been affected the most by the shift to online consumption, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will assess the relative changes in the OnP_Consumer_Index and OffP_Consumer_Index within each region. This involves comparing the lengths of the corresponding bars for each region. The four regions are {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600], and {\"Africa\"}[0.076, 0.783, 0.116, 0.807]. From the comparison of the changes in their bars, it can be observed that {\"Africa\"}[0.076, 0.783, 0.116, 0.807] exhibits the steepest shift, as indicated by the significant length difference between its {\"OffP_Consumer_Index bar\"}[0.166, 0.720, 0.974, 0.792] and {\"OnP_Consumer_Index bar\"}[0.300, 0.792, 0.974, 0.865]. Therefore, Africa appears to have been most affected by a shift to online consumption.\nBased on the above thought, the answer is:\nAfrica, as the length of the bar representing the OnP_Consumer_Index score is markedly less negative than the bar representing the OffP_Consumer_Index score, indicating a considerable shift to online consumption in the region."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "KnxQqojcFnFTIfTt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has the least difference between OffP_Consumer_Index and OnP_Consumer_Index score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which region has the least difference between the OffP_Consumer_Index and OnP_Consumer_Index score, we can first observe from the graph that {\"North America\"}[0.015, 0.164, 0.116, 0.187], {\"Europe\"}[0.067, 0.370, 0.116, 0.393], {\"Asia\"}[0.087, 0.577, 0.116, 0.600] and {\"Africa\"}[0.076, 0.783, 0.116, 0.807] are the regions with different scores conveyed by the bars' lengths. Comparing the lengths of bars for both online and offline purchases within each region, we can see that in {\"North America\"}[0.015, 0.164, 0.116, 0.187], where the {\"OffP_Consumer_Index bar\"}[0.651, 0.100, 0.974, 0.173] and {\"OnP_Consumer_Index bar\"}[0.570, 0.173, 0.974, 0.245] are quite close, the difference in score is the smallest.\nBased on the above thought, the answer is:\nNorth America."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "IJOp7JuKAomqzDwc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart you observed, can you help form a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the title of the graph {\"Local Government and Community Development Performance\"}[0.322, 0.025, 0.840, 0.055] and the data located on {the y-axis tick labels}[{\"Slow Increase\"}[0.072, 0.148, 0.168, 0.172]; {\"Uneven Development\"}[0.017, 0.311, 0.168, 0.334]; {\"Gradual Improvement\"}[0.015, 0.473, 0.168, 0.497]; {\"Steep Increase\"}[0.065, 0.636, 0.168, 0.659]; {\"Rapid Recovery\"}[0.060, 0.799, 0.168, 0.822]], I can infer the y-axis corresponds to the performance parameters. The x-axis represents the corresponding values that range from {0}[0.167, 0.919, 0.189, 0.942] to {1.2}[1.005, 0.919, 1.027, 0.942]. For each of the performance parameters, there are two bars of different colors. The {legend}[0.750, 0.074, 0.978, 0.151] indicates that the {blue bars}<{\"Local Government\"}[0.794, 0.083, 0.921, 0.106]> represents Local Government performance, and the {orange bars}<{\"Community Development\"}[0.794, 0.118, 0.973, 0.141]> show Community Development performance. For generating the corresponding table, the length of each bar needs to be examined and the x-axis referred to determine the corresponding value.\nBased on the above thought, the answer is:\nPerformance Measurement,Local Government,Community Development\nSlow Increase,0.8,0.6\nUneven Development,0.6,0.5\nGradual Improvement,0.65,0.6\nSteep Increase,0.95,0.8\nRapid Recovery,1.1,1.0"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "kh7NhtEIrvhj8Gm9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which organ do you think performs better in terms of Rapid Recovery? Can you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question asks for a comparison between the {blue bar}<{\"Local Government\"}[0.794, 0.083, 0.921, 0.106]> and the {orange bar}<{\"Community Development\"}[0.794, 0.118, 0.973, 0.141]> at the Rapid Recovery performance parameter. The length of {Local Government's bar for Rapid Recovery}[0.178, 0.751, 0.947, 0.808] appears to be longer than {Community Development's bar}[0.178, 0.808, 0.877, 0.865] by referring to the x-axis, we can confirm that Local Government's performance value for Rapid Recovery is approximately 1.1 while the Community Development's performance value is approximately 1.0. Thus, it indicates that Local Government performs better in terms of Rapid Recovery.\nBased on the above thought, the answer is:\nLocal Government. Since the performance value of Local Government for Rapid Recovery is higher than that of Community Development, we can conclude that it performs better."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "won1XJWUxuFo6Kvc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in performance value regarding Uneven Development between Local Government and Community Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the difference in performance value regarding Uneven Development between Local Government and Community Development, it's essential to compare the two bar lengths for that performance parameter. Looking at {the bar of Uneven Development for Local Government}[0.178, 0.263, 0.597, 0.320] and for {Community Development}[0.178, 0.320, 0.527, 0.377], by referring to the x-axis values, it can be determined that the performance value of Local Government for Uneven Development is approximately 0.6 while that for Community Development is approximately 0.5. Hence, the difference becomes 0.6 - 0.5 = 0.1.\nBased on the above thought, the answer is:\n0.1"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "yZbcnoUtyWRkUXJe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the bar chart into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a CSV table, I need to look at various parts of the chart. From {the x-axis label \"Number of Visitors\"}<r>[0.517, 0.952, 0.646, 0.975]</r> and {the four y-axis labels}[{\"Marginal Growth\"}<r>[0.054, 0.164, 0.169, 0.187]</r>; {\"Steep Increase\"}<r>[0.066, 0.370, 0.169, 0.393]</r>; {\"Rapid Growth\"}<r>[0.074, 0.577, 0.169, 0.600]</r>; {\"Unpredictable Decline\"}<r>[0.015, 0.783, 0.169, 0.807]</r>], I can see that the y-axis represents different visitor trends and the x-axis represents the number of visitors under each trend. Now looking at the {legend}<r>[0.816, 0.074, 0.978, 0.151]</r>, we have two categories: \"National Gallery\" (represented by {light red bars})<r>[0.821, 0.085, 0.849, 0.101]</r> and \"Historic Site\" (represented by {light blue bars})<r>[0.821, 0.120, 0.849, 0.136]</r>. Together, this tells me that each group of bars represents the number of visitors at these two destinations under different trends. From this, I can extract and construct each row of the CSV table by locating each rectanguler bar and the corresponding text annotations that denote the number of visitors. For instance, {the light red bar of \"marginal growth\"}<r>[0.178, 0.100, 0.343, 0.173]</r> and its {text annotation \"30\"}<r>[0.343, 0.125, 0.361, 0.148]</r> indicates that there were 30 visitors at the National Gallery under this trend.\nBased on the above thought, the answer is:\nTrend,National Gallery,Historic Site\nMarginal Growth,30,50\nSteep Increase,67,88\nRapid Growth,100,130\nUnpredictable Decline,97,140"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "xegIv2Xz64Nx8EMS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which site do you think experienced a more positive growth in the number of visitors? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to examine {the bars associated with \"National Gallery\"}<r>[0.821, 0.085, 0.849, 0.101]</r> and {those associated with \"Historic Site\"}<r>[0.821, 0.120, 0.849, 0.136]</r> across all the visitor trends. In the \"Marginal Growth\" trend, the Historic Site has more visitors as compared to the National Gallery. This observation is based on comparing {the length of the two bars}<r>[0.178, 0.100, 0.343, 0.173]</r> and <r>[0.178, 0.173, 0.453, 0.245]</r> in this category. Similarly, in the \"Steep Increase\" and \"Rapid Growth\", the Historic Site still attracted more visitors. This can be verified by comparing {the respective bar lengths}<r>[0.178, 0.307, 0.546, 0.379]</r> and <r>[0.178, 0.379, 0.661, 0.452]</r> for \"Steep Increase\", and <r>[0.178, 0.514, 0.727, 0.586]</r> and <r>[0.178, 0.586, 0.892, 0.658]</r> for \"Rapid Growth\". In the \"Unpredictable Decline\" trend, although it represented a decrease, the Historic Site still had more viewership. I can observe this by looking at {the pair of bars}<r>[0.178, 0.720, 0.711, 0.792]</r> and <r>[0.178, 0.792, 0.947, 0.865]</r> in this category. Thus, the Historic Site seems to have a more positive growth overall.\nBased on the above thought, the answer is:\nThe Historic Site. This conclusion is based on the observation that across all the visitor trends, the Historic Site consistently attracted more visitors than the National Gallery."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "MkGw3h9OG3Od4lR9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder which visitors' trend did the National Gallery and the Historic Site attract the highest number of combined visitors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to sum up the number of visitors for each trend and identify the one with the highest total. Looking at each pair of bars from bottom to top {the bars in the \u201cMarginal Growth\u201d category}<r>[0.178, 0.100, 0.343, 0.173]</r> and <r>[0.178, 0.173, 0.453, 0.245]</r> have text annotations of 30 and 50, totaling 80 visitors. Similarly, {the bars in the \u201cSteep Increase\u201d category}<r>[0.178, 0.307, 0.546, 0.379]</r> and <r>[0.178, 0.379, 0.661, 0.452]</r> represent 67 and 88 visitors, totaling 155. For the \u201cRapid Growth\u201d category, {the bars}<r>[0.178, 0.514, 0.727, 0.586]</r> and <r>[0.178, 0.586, 0.892, 0.658]</r> indicate 100 and 130 visitors, thus totaling 230. Finally, in the \u201cUnpredictable Decline\u201d category, {the bars}<r>[0.178, 0.720, 0.711, 0.792]</r> and <r>[0.178, 0.792, 0.947, 0.865]</r> represent 97 and 140 visitors, which sums up to 237 visitors, which is the highest combined visitors number among all trends.\nBased on the above thought, the answer is:\nUnpredictable Decline."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "UkyLIid8cOdb5hsK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table to record the scores of each fashion trend under the Gothic and Punk subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can gather this information by observing each bar's starting point in relation to the x-axis {x-ticks}[{-4.0}[-0.027, 0.919, 0.006, 0.942]; {-3.5}[0.096, 0.919, 0.130, 0.942]; {-3.0}[0.219, 0.919, 0.253, 0.942]; {-2.5}[0.342, 0.919, 0.376, 0.942]; {-2.0}[0.465, 0.919, 0.499, 0.942]; {-1.5}[0.588, 0.919, 0.622, 0.942]; {-1.0}[0.711, 0.919, 0.745, 0.942]; {-0.5}[0.834, 0.919, 0.868, 0.942]; {0.0}[0.963, 0.919, 0.985, 0.942]]. For instance, I noticed that in the Gothic subculture, the Fashion Trend 1 bar begins at the -3.5 position of the x-axis, hence the score for Gothic subculture for Fashion Trend 1 is -3.5. I need to carry out a similar observation for other fashion trends of each subculture to generate the CSV table.\nBased on the above thought, the answer is:\nSubculture,Fashion Trend 1,Fashion Trend 2,Fashion Trend 3,Fashion Trend 4\nGothic,-3.5,-1.5,-2.0,-1.0\nPunk,-1.0,-2.0,-0.5,-3.5"
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "0UaNgfeDXPHB3lVc",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subculture seems to have a more varied impact on the different fashion trends, based on your observation of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the range in scores within each subculture. The Gothic subculture has all trends trending negatively with a difference of -3.5 to -1.0 showing a steady downward trend. Meanwhile, under the Punk subculture, {the values}[{-1.0}[0.728, 0.525, 0.974, 0.610]; {-2.0}[0.482, 0.610, 0.974, 0.695]; {-0.5}[0.851, 0.695, 0.974, 0.780]; {-3.5}[0.113, 0.780, 0.974, 0.865];] range from -3.5 to -0.5 indicating a more varied impact across the fashion trends.\nBased on the above thought, the answer is:\nThe Punk subculture has a more varied impact on the different fashion trends."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "SfTizk2wP0Cfw6zq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fashion trend under the Gothic subculture has the greatest impact based on the score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the fashion trend with the greatest impact under the Gothic subculture, I need to locate the bar with the least negative value or the largest bar, since a smaller negative value equates to greater impact. Observing the Gothic subculture-related bars, I found that {the Fashion trend 4 bar}[0.728, 0.355, 0.974, 0.440] extends to -1.0 on the x-axis, which is higher than the other three fashion trends under the Gothic subculture. Hence, the Fashion Trend 4 shows the greatest impact under the Gothic subculture.\nBased on the above thought, the answer is:\nFashion Trend 4."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "th0cJstYiJmvGStV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart you observed, can you represent the data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "This horizontal bar chart depicts the market share percentages of several industries over four years. The industries, and their respective bars listed from top to bottom are: {Telecommunication}<r>[0.199, 0.784, 0.810, 0.887]</r>, {ECommerce}<r>[0.199, 0.578, 0.578, 0.681]</r>, {Software}<r>[0.199, 0.373, 0.944, 0.476]</r>, and {Manufacturing}<r>[0.199, 0.168, 0.382, 0.270]</r>. For each industry, there are four bars with different colors, representing percentages for the years 2017-2020, as it can be seen from the {legend}<r>[0.352, 0.027, 0.828, 0.119]</r>. To convert this into a CSV table, I'll use the x-axis labels {0}<r>[0.193, 0.942, 0.204, 0.970]</r> to {90}<r>[0.589, 0.942, 0.611, 0.970]</r> to gauge the lengths of the bars and extract the percentages for each year for every industry. For example, the first bar for Telecommunication represents the year 2017 and extends to about the 30% mark on the x-axis.\nBased on the above thought, the answer is:\nIndustry,Market Share 2017,Market Share 2018,Market Share 2019,Market Share 2020\nTelecommunication,30,33,39,37\nECommerce,12,20,25,28\nSoftware,45,47,40,35\nManufacturing,15,11,8,7"
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "2XH7dHGhFGYj0b0x",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information given in the chart, which industry dominated the market throughout the 2017-2020 period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The industry that dominated the market throughout the given period can be identified by which of them consistently had the longest bars in the chart. Upon scanning the chart, I see that the {Software} industry<r>[0.199, 0.373, 0.944, 0.476]</r> consistently had the longest length of bars for all the listed years 2017 to 2020. Thus, it can be inferred that the Software industry had the highest market share, and therefore dominated the market during this period.\nBased on the above thought, the answer is:\nSoftware"
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "JvxLwBIocKyZWvoc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry shows the greatest increase in market share from 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll compare the lengths of the bars for each industry from 2017 to 2020. Starting with the {Telecommunication} industry<r>[0.199, 0.784, 0.810, 0.887]</r>, I see that the bars extend from approximately the 30% mark to the 35% mark on the x-axis. This represents an increase of about 7%. The next industry is {ECommerce}<r>[0.199, 0.578, 0.578, 0.681]</r>, where the bars range from roughly 10% to over 25%, indicating an increase of about 15%. The {Software} industry<r>[0.199, 0.373, 0.944, 0.476]</r> shows a decrease in market share, while the {Manufacturing} industry<r>[0.199, 0.168, 0.382, 0.270]</r> also shows a decrease. Therefore, the industry with the highest increase in market share from 2017 to 2020 is the ECommerce industry.\nBased on the above thought, the answer is:\nECommerce"
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "Pu5XfNguI26l16Zn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the horizontal stacked bar chart, please construct a CSV table that illustrates the manufacturing efficiency for each sector and includes the measures for Raw Material Efficiency, Production Speed, and Quality Control."
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, I notice four industries' efficiency measures based on the y-axis labels - {\"Electronics\"}[0.068, 0.828, 0.162, 0.856], {\"Automobiles\"}[0.054, 0.633, 0.162, 0.661], {\"Pharmaceuticals\"}[0.019, 0.438, 0.162, 0.466], and {\"Textiles\"}[0.098, 0.242, 0.162, 0.270]. There are three colors of bars in each category, corresponding of the measures - {Raw Material Efficiency}[0.273, 0.045, 0.475, 0.073], {Production Speed}[0.559, 0.045, 0.711, 0.073] and {Quality Control}[0.794, 0.045, 0.925, 0.073] from the legend created by stack-ups of horizontal rectangular bars for which we can use to extract values for the table. \nBy analyzing the x-axis ticks and the corresponding values of each stacked bars, for example, {the Electronics manufacturing efficiency for Raw Material Efficiency}[0.174, 0.791, 0.511, 0.888], I can estimate and confirm the values from the right edge of the colored bars, which is equal to 3000 as shown on the x-axis.\nBased on the above thought, the answer is:\nIndustry,Raw Material Efficiency,Production Speed,Quality Control\nElectronics,3000,2000,1800\nAutomobiles,2500,2200,1700\nPharmaceuticals,2600,1500,2000\nTextiles,2300,1800,1300"
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "RNHWUPOp6XiglPfS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could you infer about the Automobiles industry's efficiency compared to other industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the lengths of the different colored bars, we can infer that the Automobiles sector represented by {the bars for Automobiles}[0.174, 0.595, 0.892, 0.693] performs well overall in all areas, but doesn't lead in any of the three efficiency measures. Focusing on the darker green section which refers to {Production Speed}[0.559, 0.045, 0.711, 0.073], the Automobiles sector has the longest bar, meaning that it has the highest production speed among the four industries. Moreover, focusing on the blue section which refers to {Quality Control}[0.794, 0.045, 0.925, 0.073], the Automobile sector looks like it's performing above average, which suggests that it's also efficient in maintaining high quality in its products.\nBased on the above thought, the answer is:\nThe Automobiles industry demonstrates strong overall performance in all efficiency measures - Raw Material Efficiency, Production Speed, and Quality Control. It has the highest production speed among all industries. It also has a good quality control, performing above average compared to others."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "33LFYrXBqxxHDN2S",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the greatest total manufacturing efficiency when summing up all three measures?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I will analyse the distance from the right end of the {Quality Control bars}[{Electronics}[0.735, 0.791, 0.936, 0.888]; {Automobiles}[0.701, 0.595, 0.892, 0.693]; {Pharmaceuticals}[0.634, 0.400, 0.858, 0.497]; {Textiles}[0.634, 0.204, 0.780, 0.302]] to the chart's y-axis for every industry. The sector with the longest bar will have the highest total efficiency.\nBased on the above thought, the answer is:\nElectronics"
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "RCZPeIJRNikel94w",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, could you reformat the data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, we can gather that the x-axis represents the distinct influence areas, specifically {High_Influence_Areas}[0.225, 0.660, 0.945, 0.888] and {Weak_Influence_Areas}[0.225, 0.204, 0.601, 0.432]. Each bar within these two categories is marked with different colors corresponding to different financial market indicators as stated in {the sublegend}[0.603, 0.153, 0.826, 0.126]: {Stock Market}[0.436, 0.045, 0.549, 0.073] with the color code #d66555, which represents the Stock Market index points; {Interest Rates}[0.436, 0.087, 0.557, 0.115] in the color code #88a27d, which corresponds to the percentage of Interest Rates; {Inflation Rate}[0.640, 0.045, 0.757, 0.073] in the color code #a1b5ce, indicating the percentage of Inflation Rate; and finally, {Unemployment Rate}[0.640, 0.087, 0.819, 0.115] in the color code #34495e, symbolizing the percentage of Unemployment Rate. According to the x-axis tick labels, the origin is at {0}[0.219, 0.942, 0.230, 0.970] and the maximum x value is at {6500}[0.822, 0.942, 0.866, 0.970], I need to calculate the corresponding x value for each sub bar by calculating the ratio and then multiply it by the total range (6500-0). Each cell in the table can be represented by measuring the length of the designated colored segment in each stacked bar.\nBased on the above thought, the answer is:\nInfluence_Areas,Stock_Market,Interest_Rates,Inflation_Rate,Unemployment_Rate\nHigh_Influence_Areas,5000,1800,700,60\nWeak_Influence_Areas,2500,950,400,100"
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "g8XkhfchirbNUDB2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area of influence should receive more attention for improvement? Reason your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "Given the four financial market indicators, we can clearly see that the {Weak_Influence_Areas}[0.225, 0.204, 0.601, 0.432] contain the alarming rate for the Unemployment Rate, which has an abrupt increase as observed from the {dark blue segment}[0.592, 0.204, 0.601, 0.432] as per the {sublegend}[0.603, 0.153, 0.826, 0.126]. The 'Stock Market' value, 'Interest Rates', and 'Inflation Rate', represented by {red segment}[0.225, 0.204, 0.463, 0.432], {green segment}[0.463, 0.204, 0.554, 0.432], and {light blue segment}[0.554, 0.204, 0.592, 0.432] respectively, are also relatively high in comparison to the other area of influence. These indicators are crucial to the overall economic health and well-being in that area. Hence, by deducing from the values of these indicators, Weak_Influence_Areas demand more attention for improvement.\nBased on the above thought, the answer is:\nWeak_Influence_Areas. The factors of a high Unemployment Rate, coupled with a fair contribution to Stock Market Index points, Interest Rates, and Inflation Rate, all indicate a certain level of economic distress, hence requiring more attention for overall improvement."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "vB6lZqU47fDuHv0A",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area of influence has the largest value in the 'Stock Market' according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the area of influence with the highest 'Stock Market' value, I need to compare the corresponding segments of the Stock Market in each stacked bar, which are represented by {the red segments}<{'red segments'}[{'High_Influence_Areas'}[0.225, 0.660, 0.701, 0.888]; {'Weak_Influence_Areas'}[0.225, 0.204, 0.463, 0.432]]> and distinguishable by their distinct #d66555 color. It is clear that the red segment in {High_Influence_Areas}[0.225, 0.660, 0.701, 0.888] is significantly larger than that in {Weak_Influence_Areas}[0.225, 0.204, 0.463, 0.432], indicating that the 'Stock Market' value is indeed larger in High_Influence_Areas.\nBased on the above thought, the answer is:\nHigh_Influence_Areas"
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "UzKgHflRXiQMMFRF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation in the chart, can you please represent the data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the different investments' return rate in horizontal bars. The vertical axis orders investments types: {low risk bonds}[0.186, 0.791, 0.249, 0.888], {mutual funds}[0.186, 0.595, 0.249, 0.693], {real estate}[0.186, 0.400, 0.249, 0.497], and {equity}[0.186, 0.204, 0.249, 0.302]. The horizontal axis represents the return rates, ranging from {0}[0.181, 0.942, 0.192, 0.970] to {20}[0.490, 0.942, 0.512, 0.970]. The bars are color-coded to distinguish between the different investment types, as indicated by {the legend items}[{'Low Risk Bonds Return (%)'}[0.001, 0.045, 0.234, 0.073]; {'Mutual Funds Return (%)'}[0.317, 0.045, 0.531, 0.073]; {'Real Estate Return (%)'}[0.615, 0.045, 0.811, 0.073]; {'Equity Return (%)'}[0.894, 0.045, 1.048, 0.073]]. The length of each bar corresponds to the return rate for each type of investment. For instance, the length of the {bar for 'Low Risk Bonds'}[0.186, 0.791, 0.249, 0.888] corresponds to a return rate of 4.\nBased on the above thought, the answer is:\nInvestment Type,Return Rate\nLow Risk Bonds,4\nMutual Funds,8\nReal Estate,6\nEquity,20"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "AaW31IDHj9BhijpO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what type of investment would you recommend for someone seeking to maximize returns on their investment? And, why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In terms of achieving maximum return on investment, the length of the bar is critical as it represents return rate. The longest bar in the chart, indicating a higher return rate, is {Equity}[0.186, 0.204, 0.249, 0.302], reaching the 20 mark on the x-axis, far exceeding the other investment types. Therefore, Equity would provide the highest return. However, it's worth noting that this doesn't take into account the risk associated with each type of investment.\nBased on the above thought, the answer is:\nI would recommend Equity. This is because Equity has the highest return rate (20%) among all represented investment types. But it's crucial to also consider the risk levels associated with equity investments."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "gJUhEpcDwKoUzeY0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in return rates between Real Estate and Equity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in return rates between Real Estate and Equity, I have to compare the lengths of their corresponding bars. The {real estate bar}[0.186, 0.400, 0.249, 0.497] stretches to about the 6 position on the x-axis, while {the equity bar}[0.186, 0.204, 0.249, 0.302] extends roughly to the 20 position. The difference can be calculated by subtracting these two values.\nBased on the above thought, the answer is:\n14"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "3xBHjxgKdRjJKGNB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data presented in the chart to a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the x-axis labels, which range from {\"0\"}<r>[0.274, 0.942, 0.285, 0.970]</r> to {\"90\"}<r>[0.447, 0.942, 0.469, 0.970]</r>, and the grouped bars under each category ({\"Rights Protection\"}<r>[0.119, 0.763, 0.267, 0.791]</r> and {\"Social Inequality\"}<r>[0.124, 0.307, 0.267, 0.335]</r>), we can understand the percentages for each indicator. Each group consists of four bars representing different parameters. They are {\"Gender Wage Gap (%)\"}<r>[-0.093, 0.045, 0.102, 0.073]</r>, {\"Maternity Leave Duration (weeks)\"}<r>[0.185, 0.045, 0.480, 0.073]</r>, {\"Employment Rate Women (%)\"}<r>[0.563, 0.045, 0.825, 0.073]</r>, and {\"Employment Rate Men (%)\"}<r>[0.908, 0.045, 1.141, 0.073]</r>. I extract the end coordinates of each bar against the y-axis labels to obtain the percentage of each parameter. Based on this analysis, we can create the CSV table.\nBased on the above thought, the answer is:\nCategories,Gender Wage Gap,Maternity Leave Duration,Employment Rate Women, Employment Rate Men\nRights Protection,25,52,60,75\nSocial Inequality,35,42,52,77"
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "Aj0pKnipJKY17Cla",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which situation is social inequality more significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "When comparing the situation of social inequality, we need to consider all four indicators. This includes the {\"Gender Wage Gap\"}<r>[-0.093, 0.045, 0.102, 0.073]</r>, {\"Maternity Leave Duration\"}<r>[0.185, 0.045, 0.480, 0.073]</r>, and the employment rates of {\"women\"}<r>[0.563, 0.045, 0.825, 0.073]</r> and {\"men\"}<r>[0.908, 0.045, 1.141, 0.073]</r>. If we look at the bar charts, we find the Gender Wage Gap is larger in the field of social inequality <r>[0.279, 0.204, 0.349, 0.432]</r> than in rights protection <r>[0.279, 0.660, 0.329, 0.888]</r>. The Maternity Leave Duration is less in social inequality <r>[0.349, 0.204, 0.432, 0.432]</r> than in rights protection <r>[0.329, 0.660, 0.432, 0.888]</r>. Also, the employment rates for both women <r>[0.432, 0.204, 0.535, 0.432]</r> and men <r>[0.535, 0.204, 0.688, 0.432]</r> are also less under social inequality.\nBased on the above thought, the answer is:\nAccording to the chart, social inequality is more significant in terms of wage gap, maternity leave duration, and employment rates of both genders. All these parameters indicate greater disparities under the field of social inequality compared to rights protection."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "Ji4TWpXO2vUz1f64",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, how much less is the percentage of the employment rate of women in the field of social inequality than in the field of rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the percentage of the employment rate of women in the field of social inequality with that of rights protection. The corresponding bar for employment rate of women under rights protection is located at <r>[0.432, 0.660, 0.551, 0.888]</r>, and it ends around \"60\" on the X-axis. Looking at the bar for employment rate of women under social inequality, i.e. <r>[0.432, 0.204, 0.535, 0.432]</r>, it ends around \"52\" on the X-axis. Therefore, the difference can be calculated.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "D12l3XalyNgvXZFb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the sales data of Television, Mobile Phones, and Video Game Consoles from the chart and present it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sales data represented by {horizontal bars}<r>[0.217, 0.550, 0.867, 0.673]</r> for Television, {horizontal bars}<r>[0.217, 0.305, 0.945, 0.428]</r> for Mobile Phones, and {horizontal bars}<r>[0.217, 0.061, 0.591, 0.183]</r> for Video Game Consoles, each bar group corresponds to a month from January to December. The left edge of each bar starts at the y-axis, indicating zero sales, and the right edge corresponds to the total sales value, according to {the x-axis labels and tick marks}<r>[0.212, 0.723, 0.503, 0.751]</r>. The data can be extracted according to the end coordinates of each month's bars to represent sales figures for corresponding product categories.\nBased on the above thought, the answer is:\nMonth,Television,Mobile Phones,Video Game Consoles\nJan,150,180,80\nFeb,155,185,80\nMar,150,190,80\nApr,155,200,80\nMay,160,205,90\nJun,210,220,100\nJul,225,225,110\nAug,220,240,120\nSep,225,245,130\nOct,240,260,150\nNov,300,320,200\nDec,310,330,220"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "tvWNsRMaRhuQgkgF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category shows the most significant sales increase from January to December, and how is that visualized in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product category shows the most significant sales increase, I need to analyze the starting position and ending position of the bars for each category. Starting from {the left edge of January sales}<r>[0.217, 0.550, 0.256, 0.673]; [0.217, 0.305, 0.264, 0.428]; [0.217, 0.061, 0.238, 0.183]</r> and comparing it to {the rightmost edge of December sales}<r>[0.832, 0.550, 0.867, 0.673]; [0.890, 0.305, 0.945, 0.428]; [0.557, 0.061, 0.591, 0.183]</r> for Television, Mobile Phones, and Video Game Consoles respectively, it becomes evident that Video Game Consoles show the largest relative increase. This is visualized by the larger distance between the January and December bars for Video Game Consoles {Video Game Consoles bars}<r>[0.217, 0.061, 0.238, 0.183]; [0.557, 0.061, 0.591, 0.183]</r> in relation to its initial bar length.\nBased on the above thought, the answer is:\nVideo Game Consoles. It shows the most significant sales increase from January to December, starting and ending at 80 and 220 units, respectively. This increase is visualized in the chart by the larger proportional lengthening of the {Video Game Consoles bar}<r>[0.217, 0.061, 0.591, 0.183]</r> compared to the other product categories."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "kNQiyebnl1euxj3v",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of December sales for all product categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total December sales, I must add the sales figures of each product category. Utilizing the chart, I locate the {rightmost edge of the Television bar}<r>[0.832, 0.550, 0.867, 0.673]</r> for December that indicates 310 units, the {rightmost edge of the Mobile Phones bar}<r>[0.890, 0.305, 0.945, 0.428]</r> indicating 330 units, and the {rightmost edge of the Video Game Consoles bar}<r>[0.557, 0.061, 0.591, 0.183]</r> indicating 220 units. Summing these values gives us the total sales for December.\nBased on the above thought, the answer is:\n860"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "iRbKe4tgCih4HIk8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the numbers from the bar chart and arrange them in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the horizontal bar chart, and taking into account {the x-axis tick labels}[{\"0\"}[0.244, 0.942, 0.255, 0.970], {\"250\"}[0.351, 0.942, 0.384, 0.970], {\"500\"}[0.469, 0.942, 0.502, 0.970], {\"750\"}[0.588, 0.942, 0.621, 0.970], {\"1000\"}[0.701, 0.942, 0.745, 0.970], {\"1250\"}[0.819, 0.942, 0.863, 0.970]], it appears that the chart is indicating amounts in millions of dollars donated towards different social and charity events over the past 5 and 10 years. The amounts are represented on the x-axis, while {the four categories shown on the y-axis}<{\"Donation to Health\"}[0.072, 0.828, 0.237, 0.856]; {\"Donation to Education\"}[0.043, 0.633, 0.237, 0.661]; {\"Donation to Environment\"}[0.019, 0.438, 0.237, 0.466]; {\"Charity Concerts\"}[0.092, 0.242, 0.237, 0.270]> are the beneficiaries of these donations. We see that each category contains two parts, designated by {legend}[0.326, 0.034, 0.883, 0.084] that indicates the past 5 and 10 years. Let's construct a table of the donation amounts towards these categories for 5 and 10 years. I can calculate these amounts by comparing the lengths of giant bars such as {\"Donation to Health\"}[0.249, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.249, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.249, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.249, 0.204, 0.472, 0.302] to the x-axis tick labels.\nBased on the above thought, the answer is:\nPhilanthropic Activity,Donations - Past 5 Year,Donations - Past 10 Year\nDonation to Health,650,580\nDonation to Education,230,180\nDonation to Environment,670,350\nCharity Concerts,320,150"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "R0LdeJnxHPuBF8Nh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat overall trend do you see in donations to social welfare and charity activities over the last 5 years and 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows two distinct periods of donations for four categories. The two periods are distinguished by {legend}[0.326, 0.034, 0.883, 0.084] - the dark red sections represent the amounts donated in the last 5 years, and the light green sections are the amounts from 5 to 10 years ago. Analysing the lengths of each category's bar: {\"Donation to Health\"}[0.249, 0.791, 0.557, 0.888] vs [0.557, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.249, 0.595, 0.358, 0.693] vs [0.358, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.249, 0.400, 0.566, 0.497] vs [0.566, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.249, 0.204, 0.401, 0.302] vs [0.401, 0.204, 0.472, 0.302], it can be seen that donations to 'health' and 'environment' sectors have shown a substantial increase over the 5-year period, demonstrating a positive trend. Donations to 'education' have shown a slight increase over the same period. However, donations to 'charity concerts' have decreased, indicating a negative trend. Overall, the trend shows that more donations have been directed towards health and environmental activities over the recent 5 years as compared to before.\nBased on the above thought, the answer is:\nThe overall trend over the past 5 years shows a significant increase in donations towards the health and environment sectors, slight increase in education, but a decline in the donations towards charity concerts. This suggests a shift towards issues of health and environment in the social welfare and charity landscape."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "tpoMuac9kgLHUBz0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category has the greatest increase in donations been seen over the past 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the color differentiation in {the legend}[0.326, 0.034, 0.883, 0.084], the dark red sections represent the donations over the past 5 years and the light green sections indicate the donations over the past 10 years. To compare the increases in the past 10 years, I will subtract the value of each dark red section from the value of each light green section in the same category. By calculating and comparing: {\"Donation to Health\"}[0.557, 0.791, 0.831, 0.888], {\"Donation to Education\"}[0.358, 0.595, 0.443, 0.693], {\"Donation to Environment\"}[0.566, 0.400, 0.732, 0.497], {\"Charity Concerts\"}[0.401, 0.204, 0.472, 0.302], it's clear that the {\"Donation to Environment\"}[0.566, 0.400, 0.732, 0.497] section \u2014 light green(670) minus dark red(350) \u2014 has seen the highest increase in the past 10 years.\nBased on the above thought, the answer is:\nDonation to Environment."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "PhIlc0y8l4D1gy03",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart info you observed, demonstrate the data in a table using the CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a horizontal stacked bar chart depicting two tiers of data from 2012 to 2020. According to {the legend}[0.213, 0.027, 0.827, 0.077], the red bars represent 'Global Trade (% of GDP)' and the green bars represent 'Supply Chain Reliability Index'. The width of each colored segment corresponds to their respective values in each year. By observing {the x-axis tick labels}<r>[0.069, 0.942, 0.981, 0.970]</r> and the coordinates of these bars, I can estimate the values for each year and category. For example, the {bar for 2012}[0.075, 0.168, 0.679, 0.210] shows that 'Global Trade (% of GDP)' ends at about 40% on the x-axis, and 'Supply Chain Reliability Index' starts from there and ends around 90% on the x-axis. By repeating this process for every year, I can extract the data for the table.\nBased on the above thought, the answer is:\nYear,Global Trade (% of GDP),Supply Chain Reliability Index\n2012,40,49\n2013,32,55\n2014,37,53\n2015,44,51\n2016,48,38\n2017,52,32\n2018,54,31\n2019,58,32\n2020,64,30"
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "dRqarijTynSIIE7G",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you describe the correlation between Global Trade (% of GDP) and the Supply Chain Reliability Index during the period 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's bar widths, one can see the progression of both Global Trade (% of GDP) represented by {the red bars}<r>['params': {'label': 'Global Trade (% of GDP)', 'color': '#d66555'}]</r> and the Supply Chain Reliability Index represented by {the green bars}<r>['params': {'label': 'Supply Chain Reliability Index', 'color': '#88a27d'}]</r> from 2012 to 2020. A general observation shows that while the width of the red bars {Global Trade (% of GDP)} consistently increases throughout the years, the width of the green bars {Supply Chain Reliability Index} varies but generally stays within the same range. Therefore, one might speculate that even though Global Trade (% of GDP) has been continuously increasing, the Supply Chain Reliability Index has remained relatively stable.\nBased on the above thought, the answer is:\nThere appears to be a weak negative correlation between the rate of Global Trade (% of GDP) and the Supply Chain Reliability Index from 2012 to 2020. An increase in global trade does not necessitate an improvement in supply chain reliability."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "J6qBs3jmjfdRZWJx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year is the Supply Chain Reliability Index highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest Supply Chain Reliability Index, I need to compare the ending points on the x-axis of the green bar segments for each year, as {the legend}[0.213, 0.027, 0.827, 0.077] reveals that the green bars represent the Supply Chain Reliability Index. For example, in 2012, the {green bar segment}[0.262, 0.168, 0.679, 0.210] ends around 90% on the x-axis. By comparing all the years, it\u2019s clear that {2020}[0.019, 0.854, 0.063, 0.882] with its {green bar segment}[0.375, 0.844, 0.796, 0.887] ends furthest on the x-axis, at approximately 94%.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "Jf2rnpcz2W6uJZZM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart information on Medical and Biotechnology Trends into CSV format data?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the x-axis tick labels}<{'0'}[0.243, 0.723, 0.254, 0.751];{'250'}[0.329, 0.723, 0.362, 0.751];{'500'}[0.426, 0.723, 0.459, 0.751];{'750'}[0.523, 0.723, 0.556, 0.751];{'1000'}[0.614, 0.723, 0.658, 0.751];{'1250'}[0.711, 0.723, 0.755, 0.751];{'1500'}[0.808, 0.723, 0.852, 0.751];{'1750'}[0.905, 0.723, 0.949, 0.751]>, I can see that the x-axis represents different index values, ranging from 0 to 1750. The y-axis represents the different Industry Categories. Additionally, {the color coded legend of the bar chart}<[0.376, 0.784, 0.853, 0.960]> indicates the kind of trend each industry category represents. To generate a CSV table, I will list each Industry Category, the trend it shows, and the index it has reached. I can infer the index values from the length of each bar.\nBased on the above thought, the answer is:\nIndustry,Pattern,Index\nFlu Vaccine Production,Steady decline,1000\nAntibiotics Production,Rapid decline,800\nRobotic Surgeries,Periodic fluctuations,1200\nStem Cell Research,Pronounced increase,1800\nGeneral Pharma Industry,Gradual recovery,1500\nGene Therapy,Sluggish growth,1350\nBiological Implants,Declining pattern,1150"
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "mFYNRNRF99V8e343",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what industry pattern should investors be careful of? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, the length of the bars shows the index values for various industry patterns. {The 'Rapid decline'[0.248, 0.531, 0.559, 0.578]} and {'Declining pattern'[0.248, 0.061, 0.694, 0.108]} trends have noticeably short bars, indicating that these industries have low index values. These can be risky areas for investors as industries under these patterns are in a downturn and may not provide favorable returns. Furthermore, {the color coded legend of the bar chart}<[0.376, 0.784, 0.853, 0.960]> indicates that the trends showing these patterns are Antibiotics Production and Biological Implants. Investors should be cautious when investing in these industries.\nBased on the above thought, the answer is:\nInvestors should be cautious of the industries following the 'Rapid decline' and 'Declining pattern' trends, namely Antibiotics Production and Biological Implants. This is because their index values are low, indicating that these industries are not doing well currently and might not provide promising returns."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "stHvsUbRbH8dussh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which industry has the greatest index value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the industry with the greatest index value, I need to look for the longest bar on the bar chart. Looking at {the length of each bar for the respective industries}<{'Steady decline'}[0.248, 0.626, 0.636, 0.673];{'Rapid decline'}[0.248, 0.531, 0.559, 0.578];{'Periodic fluctuations'}[0.248, 0.437, 0.714, 0.484];{'Pronounced increase'}[0.248, 0.343, 0.946, 0.390];{'Gradual recovery'}[0.248, 0.249, 0.830, 0.296];{'Sluggish growth'}[0.248, 0.155, 0.772, 0.202];{'Declining pattern'}[0.248, 0.061, 0.694, 0.108]>, it is easy to note that the \"Pronounced increase\" category has the longest bar, representing the greatest index value. Correspondingly, this relates to the industry category of 'Stem Cell Research'.\nBased on the above thought, the answer is:\nStem Cell Research."
+            }
+        ],
+        "id_raw": 610
+    },
+    {
+        "id": "CJLGlppI4eTQXtQd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you are viewing, can you extract the values for each indicator and arrange them in a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "As I observe the bar chart titled {\"Health and Medical Field indicators\"}[0.323, 0.961, 0.647, 0.981], two groups are presented, which are {\"US\"}[0.080, 0.756, 0.104, 0.784] and {\"EU\"}[0.080, 0.276, 0.104, 0.304]. Three different indicators are represented per group, which according to the legend {\"Public Health Awareness (%)\"}[0.071, 0.038, 0.319, 0.066], {\"Pharmaceutical Research (%)\"}[0.402, 0.038, 0.658, 0.066], {\"Medical Equipment Patents\"}[0.741, 0.038, 0.977, 0.066] are Public Health Awareness, Pharmaceutical Research and Medical Equipment Patents respectively. Each indicator is represented as a bar. The bars corresponding to the U.S and EU are distinctly colored. Therefore, the first category, Public Health Awareness, is represented by the bars {\"US\"}[0.116, 0.647, 0.343, 0.887] and {\"EU\"}[0.116, 0.168, 0.327, 0.407]. The second category, Pharmaceutical Research, is represented by {\"US\"}[0.343, 0.647, 0.604, 0.887] and {\"EU\"}[0.327, 0.168, 0.571, 0.407]. The third category, Medical Equipment Patents, is represented by the bars {\"US\"}[0.604, 0.647, 0.799, 0.887] and {\"EU\"}[0.571, 0.168, 0.848, 0.407]. So, I can look at the end of each bar to determine its corresponding value on the x-axis.\nBased on the above thought, the answer is:\n,Public Health Awareness,Pharmaceutical Research,Medical Equipment Patents\nUS,700,800,600\nEU,650,750,850"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "V5ePHEqx9c0uGpgQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your understanding of the chart, which geographical entity - US or EU - has a better handle on the health and medical field in totality?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it's important to summarize the differences in the health and medical indicators between the two geographical entities - the {\"US\"}[0.080, 0.756, 0.104, 0.784] and the {\"EU\"}[0.080, 0.276, 0.104, 0.304]. For Public Health Awareness, the US has a higher percentage, as indicated by the longer {\"US\"}[0.116, 0.647, 0.343, 0.887] bar in comparison to the {\"EU\"}[0.116, 0.168, 0.327, 0.407] bar. Similarly, with Pharmaceutical Research, the US has also a higher percentage, {\"US\"}[0.343, 0.647, 0.604, 0.887] bar is longer than the {\"EU\"}[0.327, 0.168, 0.571, 0.407] bar. However, when it comes to the total number of Medical Equipment Patents, the EU surpasses the US, as represented by the EU's longer bar {\"EU\"}[0.571, 0.168, 0.848, 0.407] vs the US bar {\"US\"}[0.604, 0.647, 0.799, 0.887]. Considering these categories represent different aspects of the medical and health field, it might not be sufficient to determine who is handling it better based on a singular aspect. But, based on this particular data, the US is performing better in terms of Public Health Awareness and Pharmaceutical Research percentages, and the EU is leading in Medical Equipment Patents.\nBased on the above thought, the answer is:\nIt's difficult to determine definitively as it depends on the priority given to each indicator. However, going by two out of three categories presented, the US appears to have a slight edge in Public Health Awareness and Pharmaceutical Research. Therefore, one could argue that the US is handling the health and medical field better in totality based on these specific indicators."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "xNSeeZObsicuX9TJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the bar chart, can you tell me in which category does the EU surpass the US in terms of percentages and numbers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the lengths of the bars comparing the US and EU for each of the three categories. Looking at the category of Public Health Awareness, the bar representing {\"US\"}[0.116, 0.647, 0.343, 0.887] is longer than the bar representing {\"EU\"}[0.116, 0.168, 0.327, 0.407]; for the category of Pharmaceutical Research, the bar representing {\"US\"}[0.343, 0.647, 0.604, 0.887] is also longer than the bar representing {\"EU\"}[0.327, 0.168, 0.571, 0.407], meaning EU does not surpass US in these two categories. But, in the third category, Medical Equipment Patents, the bar representing {\"EU\"}[0.571, 0.168, 0.848, 0.407] is longer than the bar representing {\"US\"}[0.604, 0.647, 0.799, 0.887]. So, the EU surpasses the US in terms of the number of Medical Equipment Patents.\nBased on the above thought, the answer is:\nMedical Equipment Patents"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "yhd6yfIWEKOD7NNS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you've observed, could you convert the data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows two major categories: {Environmental Protection}<r>[0.211, 0.660, 0.671, 0.888]; [0.211, 0.204, 0.729, 0.432]</r> and {Community Investment}<r>[0.671, 0.660, 0.767, 0.888]; [0.729, 0.204, 0.882, 0.432]</r>. For each category, there are two bars indicating the commitments in 2009 and achievements in 2020, respectively. According to the x-axis tick labels, the commitments for environmental protection are measured at 1200 and the achievements in 2020 are at 1350; for community investment, the commitments in 2009 are at 250 and the achievements in 2020 are at 400. All these findings can be converted into a table.\nBased on the above thought, the answer is:\nCategory,Commitments(2009),Achievements(2020)\nEnvironmental Protection,1200,1350\nCommunity Investment,250,400"
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "OPfscZrnEWxDePNf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how well has the business done in terms of accomplishing its 2009 commitments by 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question comprehensively, I need to inspect how the commitments for the respective categories of Environmental Protection and Community Investment translated into achievements by 2020. From the chart, I observe that the {finish point of the achievements in 2020}<r>[0.729, 0.204, 0.729, 0.432]</r> is beyond the {expected commitments made back in 2009}<r>[0.211, 0.660, 0.671, 0.888]</r> in both categories. This trend denotes that despite commitments being set at a certain target, the actual achievements have exceeded them by quite a margin. Thus, the business has not only met but surpassed their commitments.\nBased on the above thought, the answer is:\nThe business has done very well. It has not only met but exceeded its 2009 commitments by 2020 in both the Environmental Protection and Community Investment categories."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "jLbwZjV2CnC7UYEH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category is the increase from 2009 to 2020 greater?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate which category has the greater increase from 2009 to 2020, I'd compare the length of each bar representing achievement to its corresponding bar representing commitment. {The Environmental Protection bar for commitment}<r>[0.211, 0.660, 0.671, 0.888]</r> stretches till 1200, and its corresponding {achievement bar}<r>[0.211, 0.204, 0.729, 0.432]</r> till 1350, indicating an increase of 150. Comparatively, the {Community Investment bar for commitment}<r>[0.671, 0.660, 0.767, 0.888]</r> is at 250 units, and its corresponding {achievement bar}<r>[0.729, 0.204, 0.882, 0.432]</r> ends at 400, reflecting an increase of 150 as well. Hence, the increase is equal in both categories.\nBased on the above thought, the answer is:\nBoth categories have the same increase."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "wfEnoSiZF8rGZuD2",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the horizontal bar graph into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the x-axis tick labels}<[{\"0\"}[0.268, 0.942, 0.279, 0.970]; {\"100\"}[0.305, 0.942, 0.338, 0.970]; {\"200\"}[0.353, 0.942, 0.386, 0.970]; {\"300\"}[0.401, 0.942, 0.434, 0.970]; {\"400\"}[0.450, 0.942, 0.482, 0.970]; {\"500\"}[0.498, 0.942, 0.531, 0.970]; {\"600\"}[0.546, 0.942, 0.579, 0.970]; {\"700\"}[0.594, 0.942, 0.627, 0.970]; {\"800\"}[0.642, 0.942, 0.675, 0.970]; {\"900\"}[0.690, 0.942, 0.723, 0.970]; {\"1000\"}[0.733, 0.942, 0.777, 0.970]; {\"1100\"}[0.781, 0.942, 0.825, 0.970]; {\"1200\"}[0.829, 0.942, 0.873, 0.970]; {\"1300\"}[0.877, 0.942, 0.921, 0.970]; {\"1400\"}[0.926, 0.942, 0.969, 0.970]], I can see that it scales up to 1400. The y-axis tick labels represent the categories of data. For each category, there are two bars indicating trends at two points in time. The legends at the bottom show that {the red bars}<[{\"Initial Data\"}[0.516, 0.045, 0.611, 0.073]]> represent the initial data and {the green bars}<[{\"Later Data\"}[0.695, 0.045, 0.787, 0.073]]> represent the later data. Each category has both a red and green bar associated with it, the width of the bars directly linked to their values. For example, looking at the category \"E-Learning Adoption\", the width of the {red bar}[0.273, 0.791, 0.293, 0.888] ends at approximately 40 and the {green bar}[0.293, 0.791, 0.331, 0.888] ends at approximately 120 on the x-axis.\nBased on the above thought, the answer is:\nCategories,Initial Data,Later Data\nE-Learning Adoption,40,120\nOnline Certification Courses,60,180\nE-Books Downloaded,20,130\nDigital Training Investment,500,1400"
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "79De40HLBCansnJh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the bar graph, which sector has made the most significant progress in the adoption of education and training technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The bar graph}<[0.273, 0.170, 0.981, 0.923]> reflects the progress made in the adoption of education and training technology in different sectors. Looking at the y-axis, we can see four categories: 'E-Learning Adoption', 'Online Certification Courses', 'E-Books Downloaded', and 'Digital Training Investment'. These categories are represented by horizontal bars, and each category has two bars: {one in red}<[0.273, 0.791, 0.293, 0.888]; [0.273, 0.595, 0.302, 0.693]; [0.273, 0.400, 0.283, 0.497]; [0.273, 0.204, 0.514, 0.302]> showing initial data and {one in green}<[0.293, 0.791, 0.331, 0.888]; [0.302, 0.595, 0.360, 0.693]; [0.283, 0.400, 0.336, 0.497]; [0.514, 0.204, 0.948, 0.302]> showing later data. The most significant progress would be indicated by the greatest growth from initial data (red bar) to later data (green bar). Evidently, the \"Digital Training Investment\" category shows a major increase from the initial 500 to the later 1400, demonstrating the most significant progress among the categories.\nBased on the above thought, the answer is:\nDigital Training Investment has made the most significant progress in the adoption of education and training technology."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "aqSuEecSvkPGxn8h",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the greatest increase in measurements from initial data to later data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the category with the greatest increase, I'll compare the lengths of the red and green bars in each category. Starting with \"E-Learning Adoption\", I can see that the {green bar}[0.293, 0.791, 0.331, 0.888] extends approximately 80 units more than the {red bar}[0.273, 0.791, 0.293, 0.888] on the x-axis. Likewise, I can observe a growth of 120 units in the category \"Online Certification Courses\". However, the category \"E-Books Downloaded\" showed an impressive growth of 110 units. The category \"Digital Training Investment\" surpassed all with a huge increase of 900 units comparing {the red bar}[0.273, 0.204, 0.514, 0.302] and {the green bar}[0.514, 0.204, 0.948, 0.302].\nBased on the above thought, the answer is:\nDigital Training Investment."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "RhXka40ydZ8Zac2I",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, could you please convert the chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title {\"Healthy Living Indicators\"}[0.447, 0.941, 0.580, 0.968] states that it measures various indicators related to health. The y-axis holds the different categories such as {healthy food intake}<r>[0.175, 0.889, 0.223, 0.953]</r>, {smoking rate}<r>[0.284, 0.448, 0.333, 0.512]</r>, and {sports activity participation}<r>[0.071, 0.232, 0.120, 0.294]</r>. The x-axis data ranges from {0}<r>[0.175, 0.942, 0.186, 0.970]</r> to {90}<r>[0.549, 0.942, 0.571, 0.970]</r> representing the percentage of each category. For instance, the healthy food intake of the {general population}<r>[0.180, 0.660, 0.391, 0.888]</r> is around 50 and of {urban population}<r>[0.180, 0.204, 0.450, 0.432]</r> is approximately 64. Translating these plots and the remaining data, a CSV table can be created.\nBased on the above thought, the answer is:\nCategory,Population,Urban Population\nHealthy Food Intake (%),50,64\nSmoking Rate (%),44,33\nSports Activity Participation (%),60,73"
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "312lTTcIlc4Xdaqm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the presented chart, what conclusion can you draw about an urban lifestyle concerning these three health indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the look of the chart, every category shows a difference when comparing the general population and the urban population. For each category, the urban population bars are either on the right or at the same position as the general population bars. In the case of {healthy food intake}<r>[0.180, 0.204, 0.450, 0.432]</r> and {sports activity participation}<r>[0.590, 0.204, 0.897, 0.432]</r>, it's seen that the urban population displays higher percentages compared to the general population. Conversely, for {smoking rates}<r>[0.450, 0.204, 0.590, 0.432]</r>, the percentage is less in urban populations compared to general population. Therefore, it can be deduced that urbanization leads to improvement for all three indicators of a healthy lifestyle.\nBased on the above thought, the answer is:\nAn urban lifestyle tends to have a higher healthy food intake and sports activity participation rate, and a lower smoking rate, than the general population. This suggests that living in urban areas may be beneficial for these aspects of health."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "6BhbXLJgZsgyvJEn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lifestyle behaviour shows the most improvement when comparing general and urban populations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would need to calculate the differences between general and urban populations for each category. For Healthy Food Intake (%), the bars for the {general population}<r>[0.180, 0.660, 0.391, 0.888]</r> and the {urban population}<r>[0.180, 0.204, 0.450, 0.432]</r> show a difference of approximately 14%. For the Smoking Rate (%), the bars for the {general population}<r>[0.391, 0.660, 0.577, 0.888]</r> and the {urban population}<r>[0.450, 0.204, 0.590, 0.432]</r> show a decrease of about 11%. Lastly, for Sports Activity Participation (%), the bars for the {general population}<r>[0.577, 0.660, 0.830, 0.888]</r> and the {urban population}<r>[0.590, 0.204, 0.897, 0.432]</r> show an increase of 13%. Therefore, the maximum improvement is observed in the Healthy Food Intake.\nBased on the above thought, the answer is:\nHealthy Food Intake (%)"
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "8cVZkpzb3NhLFkxC",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven your analysis of the bar chart, please convert the data into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the chart presents changes in synthetic and human-authored data in various categories of the Culture and Arts Industry. These categories include {Visual Arts}[0.166, 0.862,0.291,0.877], {Cinema}[0.166, 0.801,0.220,0.816], {Music}[0.166, 0.739,0.207,0.754], {Theatre}[0.166, 0.677,0.229,0.693], {Dance}[0.166, 0.616,0.211,0.631], {Literature}[0.166, 0.555,0.245,0.570], {Digital Arts}[0.166, 0.493,0.262,0.508], and {Architecture}[0.166, 0.431,0.264,0.447]. To produce a CSV table, I map the length of the colored bars opposite to each category to the numerical value of synthetic and human-authored data. For instance, the {synthetic data bar}[0.125, 0.857, 0.186, 0.883] and {human-authored data bar}[0.186, 0.857, 0.305, 0.883] for the Visual Arts category suggests the values -15 and 18, respectively. I'll follow a similar process for the remaining categories.\nBased on the above thought, the answer is:\nCategory,Synthetic Data,Human-Authored Data\nVisual Arts,-15,18\nCinema,-30,22\nMusic,-28,16\nTheatre,-18,26\nDance,-19,18\nLiterature,-16,13\nDigital Arts,-22,24\nArchitecture,-20,15"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "3KdUO3rKEEM1Yx5k",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the change in the Culture and Arts Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the bar chart, I can obtain various pieces of information about the synthetic data and human-authored data of different domains in the Culture and Arts Industry. For each category, synthetic data is represented by bars extending towards the left represented by negative values {[0.125, 0.431, 0.801, 0.862]}, while human-authored data is represented by bars extending towards the right positive direction {[0.125, 0.431, 0.801, 0.862]}. This phenomenon suggests that human participation across all domains is increasing over time, especially in categories like Theatre and Digital Arts where the human-authored values are noticeably high. Conversely, synthetic interactions seem to be decreasing for all domains, suggesting that human interactions are being favored.\nBased on the above thought, the answer is:\nHuman-authored data in the Culture and Arts industry across all domains show increased activity while synthetic interactions are on the decline, highlighting the shift towards more human involvement in the industry."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "hgYbgOb6VUQ78Ls7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value of Synthetic Data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value of Synthetic Data, I need to compare the lengths of the {colored bars on the left}[0.125, 0.431, 0.801, 0.862]. The longer the bar, the higher the value. By closely inspecting the chart, I found that the Cinema category {[0.166, 0.801,0.220,0.816]} has the longest synthetic data bar {[0.125, 0.795, 0.062, 0.822]} corresponding to a value of -30. Despite being a negative value, it's the highest when considering the absolute value.\nBased on the above thought, the answer is:\n-30"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "ko5hkukUvVGJa5Sx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data representing the influence of different media platforms on public opinion into a CSV formatted table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's Y-axis consists of different media types, and the X-axis contains a range of values indicating influence. Both the Y-axis labels and the corresponding bar length indicating influence are needed to be extracted to form the dataset. The types of media are {Traditional Media}<r>[0.072, 0.104, 0.221, 0.122]</r>, {Social-media}<r>[0.109, 0.229, 0.221, 0.246]</r>, {Independent Blogs}<r>[0.058, 0.354, 0.221, 0.371]</r>, {Public Forums}<r>[0.100, 0.478, 0.221, 0.496]</r>, {Mainstream News Sites}<r>[0.019, 0.603, 0.221, 0.621]</r>, {Underground Media}<r>[0.050, 0.728, 0.221, 0.745]</r>, and {TV and Radio}<r>[0.106, 0.852, 0.221, 0.870]</r>. We can take bar lengths from the graphics referred to in the dataset, for example, {Traditional Media}<r>[0.233, 0.061, 0.782, 0.161]</r> has a length that extends roughly to the 90 mark on the X-axis, as indicated by the {text annotation}<r>[0.782, 0.102, 0.804, 0.120]</r>. This procedure will be replicated for all media types to gather their respective influence values.\nBased on the above thought, the answer is:\nMedia,Influence\nTraditional Media,90\nSocial-media,110\nIndependent Blogs,70\nPublic Forums,85\nMainstream News Sites,110\nUnderground Media,65\nTV and Radio,100"
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "BJLUuBu0lpPWUcKd",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed data, given the growing role of the internet, what likely change in influence could we expect for all media types if we were to revisit this data in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires foresight based on current data and trends. The data shows that {Social-media}<r>[0.109, 0.229, 0.221, 0.246]</r> currently has a significant influence, second only to {Mainstream News Sites}<r>[0.019, 0.603, 0.221, 0.621]</r>. Based on the expanding impact of the Internet and digital media, we might expect Social-media to continue growing in influence. At the same time, the influence of {Independent Blogs}<r>[0.058, 0.354, 0.221, 0.371]</r> and {Public Forums}<r>[0.100, 0.478, 0.221, 0.496]</r>, also online platforms, may likely increase. Traditional Media and TV and Radio might lose some influence due to a shift from conventional channels to digital media. Also, with the rise of digital journalism, the influence of Mainstream News Sites could go down.\nBased on the above thought, the answer is:\nThe influence of Internet-related media types such as Social-media, Independent Blogs, and Public Forums is likely to grow due to increased access and use of the internet. Traditional platforms like TV and Radio and Traditional Media may see a decrease in influence given the shift towards digital sources of information. Mainstream News Sites might also face a decrease in influence due to the rise of independent digital journalism platforms."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "HcPZCWbdhaKtgfLt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average influence value across all types of media platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average influence value, I will sum up the influence value for each media type, as indicated by the length of the corresponding bars, and then divide by the number of media types. For example, for {Traditional Media}<r>[0.233, 0.061, 0.782, 0.161]</r> the influence value is given by the label {90}<r>[0.782, 0.102, 0.804, 0.120]</r>. We have to add up the values of all platforms and then divide by the total number of platforms which is 7.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "MCM3fkW59bURARYB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific integration levels do the instances depict in the bar chart? Please summarize this information as structured table data in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend at the top of the chart defines the instances, with each instance represented by a different color. I notice that the chart consists of two categories: {Historical Monuments}[0.204, 0.927, 0.329, 0.963] and {Folk Lore}[0.736, 0.927, 0.788, 0.963] shown on the x-axis. Each category has seven bar instances. By evaluating the height of each instance's bar and cross-referencing it with  the y-axis titled {Integration Level}[0.012, 0.330, 0.028, 0.682], I can identify the exact integration level values for each instance. For instance, within the Historical Monuments category, the {bar representing first instance}[0.093, 0.588, 0.143, 0.915] has an integration level of 4, as indicated by the {annotation above the bar}[0.114, 0.545, 0.121, 0.580].\nBased on the above thought, the answer is:\nHeritage,Instance 1,Instance 2,Instance 3,Instance 4,Instance 5,Instance 6,Instance 7\nHistorical Monuments,4,1,2.3,0.9,2.6,1.1,2.8\nFolk Lore,7,2.3,3.9,1.7,3,1.5,4"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "nlt6k1cyuZaqnDQn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the distribution of cultural integration levels for 'Historical Monuments' and 'Folk Lore', what can we interpret about the acceptance of these types of cultural heritage in society?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to look at the overall pattern of cultural integration levels for both {Historical Monuments}[0.204, 0.927, 0.329, 0.963] and {Folk Lore}[0.736, 0.927, 0.788, 0.963] across all instances. The varying bar heights within each category indicate the diversity of cultural integration levels. For Historical Monuments, the pattern suggests a predominantly lower cultural integration level, with most instances hover around 1 to 3, apart from the {first instance}[0.093, 0.588, 0.143, 0.915] which reaches level 4, indicating these artifacts are not widely integrated into modern culture in most cases. Conversely, the pattern for Folk Lore shows a few instances reaching higher integration levels, particularly the {first instance}[0.588, 0.343, 0.638, 0.915] which reaches level 7, suggesting that this type of cultural heritage is more incorporated into society.\nBased on the above thought, the answer is:\nHistorical Monuments generally have a lower degree of cultural integration, suggesting these forms of cultural heritage are not widely assimilated in society. On the other hand, Folk Lore demonstrates higher levels of cultural integration in some instances, indicating more societal acceptance and assimilation of these cultural elements."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "h1DcPBB2Fy78OhRB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the Heritage 'Historical Monuments', what is the maximum integration level depicted in the instances?"
+            },
+            {
+                "from": "gpt",
+                "value": "The maximum integration level is obtained by finding the tallest bar under the category {Historical Monuments}[0.204, 0.927, 0.329, 0.963]. Looking across all the instances, the {highest bar}[0.390, 0.686, 0.440, 0.915] corresponds to the seventh instance and reaches an integration level of {2.8}[0.406, 0.643, 0.424, 0.678].\nBased on the above thought, the answer is:\n2.8"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "nZsMZt3inAC2mNT4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data on project issues in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to refer to the {bar graph}<r>[0.044, 0.090, 0.988, 0.944]</r> representing different projects. Each project has a set of bars, with each bar representing a different metric as indicated by {the legend}<r>[0.187, 0.017, 0.845, 0.093]</r>. Focusing on the {Issues metric}<r>[0.502, 0.063, 0.525, 0.079]</r>, I see bars corresponding to this metric for each project. I will then use the height of the {Issues bars}<r>[0.143, 0.688, 0.162, 0.944]</r> and their {top value annotations}<r>[0.145, 0.654, 0.160, 0.671]</r> to extract the data.\nBased on the above thought, the answer is:\nProjects,Issues\nUrban Regeneration,1.5\nParks & Open Spaces,1.7\nPublic Transportation,1.3\nHousing Development,1.4\nInfrastructure renewal,1.8"
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "g9S3sPfkbTmoH7tK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project had the highest stakeholder involvement and how does it compare to that project's completion metric?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the {Stake Involve bars}<r>[0.162, 0.517, 0.180, 0.944]</r> to identify which project has the highest bar, indicating the highest stakeholder involvement. It appears that {Infrastructure renewal}<r>[0.907, 0.398, 0.926, 0.944]</r> has the highest stakeholder involvement with a value annotation of {3.2}<r>[0.909, 0.364, 0.924, 0.380]</r>. Now I will compare this with the {Completion metric}<r>[0.380, 0.063, 0.403, 0.079]</r> for the same project to observe the relationship. The Completion bar for Infrastructure renewal has a value annotation of {2.9}<r>[0.872, 0.415, 0.887, 0.432]</r>, which is slightly less than the stakeholder involvement.\nBased on the above thought, the answer is:\nInfrastructure renewal had the highest stakeholder involvement at 3.2. This figure slightly surpasses the project's completion metric of 2.9, indicating robust engagement despite the project not being the furthest along in completion."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "CStBbP766xRJ8izJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of Good Reviews for all projects combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total number of Good Reviews, I will sum up the values indicated at the top of each {Good Reviews bar}<r>[0.180, 0.346, 0.199, 0.944]</r>. The numbers are {3.5 for Urban Regeneration}<r>[0.182, 0.313, 0.197, 0.329]</r>, {4.0 for Parks & Open Spaces}<r>[0.369, 0.227, 0.384, 0.244]</r>, {2.8 for Public Transportation}<r>[0.555, 0.432, 0.570, 0.449]</r>, {3.1 for Housing Development}<r>[0.742, 0.381, 0.756, 0.398]</r>, and {4.1 for Infrastructure renewal}<r>[0.928, 0.210, 0.943, 0.227]</r>. Adding these values will give me the total.\nBased on the above thought, the answer is:\n18.5"
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "zFR1mFS8d59QJbwO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you see, could you please convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is showing trends in user count numbers on three major social media platforms over different years. The x-axis {the x-axis tick labels}[{\"Facebook\"}[0.182, 0.927, 0.236, 0.963]; {\"Twitter\"}[0.496, 0.927, 0.535, 0.963]; {\"Instagram\"}[0.793, 0.927, 0.851, 0.963]] details the platforms, while the y-axis ranges from {0}[0.032, 0.902, 0.040, 0.937] to {4 billions}[0.032, 0.160, 0.040, 0.195] representing user counts. According to the legend {legend}[0.353, 0.043, 0.678, 0.158], each color represents a different year: {purple}[0.357, 0.113, 0.381, 0.137] for 2018, {pink}[0.442, 0.113, 0.465, 0.137] for 2019, {green}[0.527, 0.113, 0.550, 0.137] for 2020, and {orange}[0.612, 0.113, 0.635, 0.137] for 2021. The values can be inferred from the height of the bars and the data labels above them. For example, in 2018, {the purple bar for Facebook}[0.086, 0.508, 0.148, 0.915] corresponds to {2.2 billions users}[0.108, 0.454, 0.126, 0.489].\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram\n2018,2.2,1.0,1.5\n2019,2.7,1.1,1.7\n2020,2.8,1.0,2.0\n2021,3.0,0.9,2.3"
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "I3VPF4SjgbaN9lTl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social media platform showed a more consistent growth in user base over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the variations in the bars' heights for each platform from year to year. If we look at the Facebook bars, there is a consistent rise in their heights from {2018}[0.086, 0.508, 0.148, 0.915], {2019}[0.148, 0.415, 0.209, 0.915], {2020}[0.209, 0.397, 0.270, 0.915], to {2021}[0.270, 0.359, 0.332, 0.915]. For Twitter, it fluctuated and then decreased from {2018}[0.393, 0.730, 0.454, 0.915], {2019}[0.454, 0.712, 0.516, 0.915], {2020}[0.516, 0.730, 0.577, 0.915], and {2021}[0.577, 0.749, 0.638, 0.915]. Lastly, for Instagram, it also showed a consistent rise in user base from {2018}[0.699, 0.637, 0.761, 0.915], {2019}[0.761, 0.600, 0.822, 0.915], {2020}[0.822, 0.545, 0.883, 0.915], to {2021}[0.883, 0.489, 0.945, 0.915].\nBased on the above thought, the answer is:\nFacebook and Instagram. They both showed a consistent rise in their user base over the years from 2018 to 2021."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "6rQjkRc3bpUjb6F7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social media platform experienced the highest user growth from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the platform with the highest user growth from 2018 to 2021, I need to calculate the difference in user count for each platform between these years. For Facebook, the user count rose from {2.2 billion in 2018}[0.108, 0.454, 0.126, 0.489] to {3.0 billion in 2021}[0.292, 0.306, 0.310, 0.341] resulting in a growth of 0.8 billion users. For Twitter, the user count changed from {1.0 billion in 2018}[0.414, 0.677, 0.433, 0.712] to {0.9 billion in 2021}[0.598, 0.695, 0.617, 0.730], therefore it actually lost users. For Instagram, the user count increased from {1.5 billion in 2018}[0.721, 0.584, 0.739, 0.619] to {2.3 billion in 2021}[0.905, 0.436, 0.923, 0.471] resulting in a growth of 0.8 billion users.\nBased on the above thought, the answer is:\nFacebook and Instagram."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "ib08gICxgKpn3AqV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the data visualization you can see, extract the multi-bar chart data into a CSV table with policies and impacts."
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart title {\"Impact vs Policy\"}[0.516, 0.970, 0.737, 1.014] at the top and the categorical labels on {the x-axis}[{\"No Lockdown\"}[0.188, 0.792, 0.250, 0.809]; {\"Partial Lockdown\"}[0.477, 0.792, 0.556, 0.809]; {\"Complete Lockdown\"}[0.766, 0.792, 0.860, 0.809]], I can see that the x-axis represents different government policies. The y-axis labels {\"Impact\"}[0.010, 0.370, 0.022, 0.441] signifies the level of impact, ranging from -4 to 3. Each bar corresponds to the impact of a specific policy. {The legend}[0.381, 0.870, 0.652, 0.980] informs that each colored bar represents a different impact factor. Therefore, I need to map each colored bar's height to its corresponding policy and impact. This can be accurately quantified using the numerical annotations at the top of each bar. For instance, the {\"Covid Cases Decline\"}[0.086, 0.243, 0.130, 0.351] bar coordinates for the \"No Lockdown\" policy denotes a 1.0 impact, as stated by {the value annotation \"1.0\"}[0.101, 0.214, 0.115, 0.232] at the top of the bar.\nBased on the above thought, the answer is:\nPolicy, Covid Cases Decline, Economic Impact, Public Response, Healthcare Capacity, Mental Health Impact, School Disruptions\nNo Lockdown, 1.0, -2.0, 0.3, 0.6, -0.4, -0.2\nPartial Lockdown, 1.2, -1.7, 0.9, 1.6, -0.2, -0.9\nComplete Lockdown, 2.7, -3.8, -0.8, 2.3, -1.8, -2.4"
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "nOjo6VnpVKl6RyVH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy achieved the largest 'Healthcare Capacity' impact according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which policy achieved the highest 'Healthcare Capacity' impact, I need to compare the bar heights under each policy for 'Healthcare Capacity'. On examining the bars for 'Healthcare Capacity', I observe that {\"the bar for 'No Lockdown'\"}[0.219, 0.286, 0.264, 0.351] has an impact of 0.6, {\"the bar for 'Partial Lockdown'\"}[0.516, 0.178, 0.561, 0.351] has an impact of 1.6, and {\"the bar for 'Complete Lockdown'\"}[0.813, 0.102, 0.858, 0.351] has the highest impact of 2.3 which also corresponds to {the value annotation \"2.3\"}[0.828, 0.073, 0.843, 0.091] at the top of the bar.\nBased on the above thought, the answer is:\nComplete Lockdown."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "xNKDsNhsCyavu3Rz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the movement trends information displayed in the bar chart as a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart embodies two trends: {Internal Mobility}<r>[0.087, 0.071, 0.168, 0.091]</r> represented with blue bars and {International Mobility}<r>[0.087, 0.101, 0.193, 0.121]</r> represented with red bars for different years. The years range from {2010}<r>[0.127, 0.931, 0.152, 0.951]</r> to {2019}<r>[0.919, 0.931, 0.944, 0.951]</r>, spreading across the x-axis. The y-axis represents the population in thousands, and we have our range from {0}<r>[0.037, 0.909, 0.044, 0.929]</r> to {600}<r>[0.025, -0.022, 0.044, -0.002]</r>. The data can be extracted by referencing the height of these bars under each year. For example, under 2010, the {bar for Internal Mobility}<r>[0.093, 0.219, 0.124, 0.917]</r> measures to approximately 450 and the {bar for International Mobility}<r>[0.124, 0.389, 0.155, 0.917]</r> measures to about 340. Similar assessments can be made to get values for subsequent years.\nBased on the above thought, the answer is:\nYear,Internal Mobility,International Mobility\n2010,450,340\n2011,470,350\n2012,465,365\n2013,469,370\n2014,525,375\n2015,530,390\n2016,520,400\n2017,495,405\n2018,500,410\n2019,515,420"
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "8RqOKwnxcYbH1g2c",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the most significant difference between Internal Mobility and International Mobility?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the year having the most significant difference between Internal Mobility and International Mobility, I need to compare the difference between the heights of the two bars under each year. Starting with 2010 and moving right, the difference between {the bar for Internal Mobility}<r>[0.093, 0.219, 0.124, 0.917]</r> and {the bar for International Mobility}<r>[0.124, 0.389, 0.155, 0.917]</r> is 110. Through this process, I identify the bars under each subsequent year and identify their height difference. Finally, I find out that the year 2015 shows the highest difference, with values of {Internal Mobility and International Mobility}<r>[0.533, 0.094, 0.564, 0.917; 0.564, 0.312, 0.595, 0.917]</r> being 530 and 390 respectively, having a difference of 140.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 636
+    },
+    {
+        "id": "ROlfI7RIvRr4NeTt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you're seeing, could you convert the respective returns of the different market conditions in CSV format sorted by investment strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I notice that there are three major groupings on the x-axis displaying the three investment strategies: one for {Conservative}[0.282, 0.952, 0.358, 0.975], one for {Moderate}[0.472, 0.952, 0.528, 0.975], and one for {Aggressive}[0.648, 0.952, 0.712, 0.975]. In each of these groupings, there are seven bars representing the seven market conditions based on the {legend}[-0.039, 0.017, 1.039, 0.093], including Stable Growth, Substantial Decrease, Erratic Behavior, Accelerated Decline, Steady Decline, Sudden Growth, and Mixed Performance. I need to check the height of each of these bars and cross-reference it with the y-axis, which ranges from {0.0}[0.141, 0.934, 0.160, 0.958] to {2.5}[0.141, 0.081, 0.160, 0.105], to determine the corresponding returns under each market condition for the investment strategies. For example, under the {Conservative} strategy, the {Stable Growth} label corresponds to the bar with coordinates [0.194, 0.602, 0.230, 0.944] and has a height that aligns with {1.0}[0.141, 0.593, 0.160, 0.616] on the y-axis meaning the return under the Stable Growth condition for the Conservative strategy is 1.0.\nBased on the above thought, the answer is:\nInvestment_Strategies,Stable_Growth,Substantial_Decrease,Erratic_Behavior,Accelerated_Decline,Steady_Decline,Sudden_Growth,Mixed_Performance\nConservative,1.0,0.8,1.2,0.6,0.9,1.4,1.0\nModerate,1.3,0.6,1.0,0.4,0.7,1.7,1.1\nAggressive,1.8,0.4,1.6,0.2,0.5,2.2,1.5"
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "FjfbtzkkH1JVZg44",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which investment strategy would you consider the safest and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'd need to look at the sets of bars representing the three investment strategies: {Conservative}[0.282, 0.952, 0.358, 0.975], {Moderate}[0.472, 0.952, 0.528, 0.975], and {Aggressive}[0.648, 0.952, 0.712, 0.975]. Each of these groups contains seven bars, which represent the returns under different market conditions as outlined in the {legend}[-0.039, 0.017, 1.039, 0.093]. A safer investment strategy would likely have more moderate and consistent returns across various market conditions. By comparing the heights of the bars within each strategy group, it seems that the {Conservative} strategy generally has moderate heights that do not fluctuate as much as the other strategies, especially in more volatile market conditions such as \"Erratic Behavior\" and \"Sudden Growth\". For example, the bar representing \"Sudden Growth\" for the Conservative strategy {[0.374, 0.466, 0.410, 0.944]} is noticeably shorter than that of the other strategies, indicating a lower return but potentially lower risk.\nBased on the above thought, the answer is:\nThe Conservative strategy. This is because it provides more steady and less fluctuating returns across various market conditions, potentially indicating lower risk compared to the Moderate and Aggressive strategies."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "nuipylpdh3UYhKM4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder which market condition, the Moderate strategy has the highest returns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to figure this out, I need to specifically focus on the group of bars that correspond to the {Moderate}[0.472, 0.952, 0.528, 0.975] investment strategy. There are seven bars within this group, representing different market conditions as per the {legend}[-0.039, 0.017, 1.039, 0.093]. By comparing the heights of these bars and cross-referencing them with the y-axis that ranges from {0.0}[0.141, 0.934, 0.160, 0.958] to {2.5}[0.141, 0.081, 0.160, 0.105], I can determine the returns under each condition. The tallest bar within this group corresponds to the {Sudden Growth}[0.554, 0.363, 0.590, 0.944] label and reaches a height that aligns with {1.7}[0.141, 0.363, 0.160, 0.386] on the y-axis. Therefore, the Moderate strategy has the highest returns under the Sudden Growth market condition.\nBased on the above thought, the answer is:\nSudden Growth."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "q54fyGkdPHOIQ4Ng",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transform the bar chart data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the layout of the chart, I can identify three distinct categories: {Online Education}<r>[0.226, 0.818, 0.311, 0.841]</r>, {Training Services}<r>[0.478, 0.818, 0.562, 0.841]</r>, and {Edtech Startups}<r>[0.731, 0.818, 0.811, 0.841]</r>. These represent the x-axis labels. The y-axis, with labels ranging from 0<r>[0.040, 0.800, 0.046, 0.824]</r> to 130<r>[0.028, 0.025, 0.046, 0.048]</r>, measures the y values corresponding to each category for each year. The years, represented from 2010 to 2016 are identified in the {legend}<r>[0.271, 0.899, 0.768, 0.976]</r>, through color coding. Each bar corresponds to the value of a category in a particular year. For example, the {2010 bar for Online Education}<r>[0.093, 0.338, 0.143, 0.810]</r> has a height reaching approximately 79, as indicated by the {text annotation \"79\"}<r>[0.111, 0.303, 0.124, 0.326]</r>.\nBased on the above thought, the answer is:\nYear,Online Education,Training Services,Edtech Startups\n2010,79,45,5\n2011,85,40,10\n2012,84,41,20\n2013,90,44,30\n2014,109,47,45\n2015,95,50,60\n2016,120,60,90"
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "mF3UEWG3It7io3aN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you list the key trends in the industry from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The key trends in the industry can be determined from the bar height changes of three categories. In terms of {Online Education}<r>[0.226, 0.818, 0.311, 0.841]</r>, the bar heights have generally increased over the years, indicating a consistent and steady growth through the period, with a major leap between 2013 and 2014 and a minor fall between 2014 and 2015. For {Training Services}<r>[0.478, 0.818, 0.562, 0.841]</r>, the growth was slow with fluctuations at the beginning, but it started to increase steadily from 2012. Lastly, for {Edtech Startups}<r>[0.731, 0.818, 0.811, 0.841]</r>, the bar heights display the fastest growth rate among all categories, particularly after 2012, indicating an exponential growth throughout the period.\nBased on the above thought, the answer is:\nThere are three main visible trends: \n1. A steady growth of Online Education with a major leap in 2014 and a minor fall in 2015.\n2. A slow start with steady growth after 2012 in Training Services.\n3. A rapid growth rate in Edtech Startups, especially after 2012."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "fTstC4O6edyGt5gT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in what year did the Edtech Startups have the biggest growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the changes in the bars for Edtech Startups from year to year. Comparing the height difference of {2011's bar of Edtech Startups}<r>[0.645, 0.750, 0.695, 0.810]</r> and {2012's bar of Edtech Startups}<r>[0.695, 0.690, 0.746, 0.810]</r>, I can see that these bars represent 10 and 20 respectively, which means that Edtech Startups grew by 10 within this one year. After comparing the bars of other years in the same way, it's clear that the largest growth happened between 2015 and 2016 where the bar of {2015's Edtech Startups}<r>[0.846, 0.452, 0.896, 0.810]</r> represents 60 and the bar of {2016's Edtech Startups}<r>[0.896, 0.273, 0.947, 0.810]</r> represents 90, indicating a increase of 30, which is the largest growth.\nBased on the above thought, the answer is:\n2016"
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "IKaTqTvutQ2yjcpn",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that lists the severity of each environmental stressor per decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data into a table, I need to assess the information provided by every bar in the chart. I'll begin with the {first group of bars}<r>[0.116, 0.400, 0.140, 0.944]</r> representing the year 2010 and cover each category horizontally. For example, the {first bar in the group}<r>[0.116, 0.400, 0.140, 0.944]</r> represents Deforestation, with a severity of 100, as indicated by the {label}<r>[0.119, 0.372, 0.138, 0.395]</r>. I\u2019ll proceed similarly with the subsequent bars for {Water pollution}<r>[0.273, 0.672, 0.297, 0.944]</r>, {Ocean acidification}<r>[0.430, 0.753, 0.453, 0.944]</r>, and other environmental stressors, noting their severities. Then I'll move on to the {second group of bars}<r>[0.140, 0.509, 0.163, 0.944]</r> for the year 2020, and repeat the process for the {third}<r>[0.163, 0.536, 0.187, 0.944]</r> and {fourth group}<r>[0.187, 0.547, 0.210, 0.944]</r> representing 2030 and 2040, respectively.\nBased on the above thought, the answer is:\nYear,Deforestation,Water pollution,Ocean acidification,Overfishing,Air pollution,Soil erosion\n2010,100,50,35,70,120,50\n2020,80,55,40,60,110,45\n2030,75,58,38,50,105,42\n2040,73,60,36,45,102,40"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "XC66ZNhN9iPD5WfI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend observed, which environmental stressor has shown the most improvement over the decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most improved stressor, I begin by examining the proportional changes over time for each stressor. I'll focus on the start and end values for a clear comparison. For example, {Deforestation has decreased}<r>[0.116, 0.400, 0.140, 0.944]</r> from 100 in 2010 to {73 in 2040}<r>[0.187, 0.547, 0.210, 0.944]</r>. Next, I observe {Air pollution}<r>[0.743, 0.292, 0.766, 0.944]</r>, which shows a significant reduction from 120 to {102}<r>[0.813, 0.390, 0.837, 0.944]</r>. Analyzing all stressors in a similar fashion, Air pollution shows the highest numerical decrease but considering the severity scale and initial values, Overfishing shows the most percentage-wise improvement from 70 to {45}<r>[0.657, 0.699, 0.680, 0.944]</r>.\nBased on the above thought, the answer is:\nOverfishing has shown the most improvement as its severity has decreased from 70 to 45, which is a higher percentage decrease compared to others."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "HVCbiJ4BiKEPVeRc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in deforestation from 2010 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease, I first identify the bar representing Deforestation in 2010, located at <r>[0.116, 0.400, 0.140, 0.944]</r> with a severity value of 100. Then, I find the bar for deforestation in 2040 at <r>[0.187, 0.547, 0.210, 0.944]</r> with a severity value of 73. Using the values, the calculation for percentage decrease is done by subtracting the final value from the initial value, dividing by the initial value, and multiplying by 100.\nBased on the above thought, the answer is:\n27%"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "kzRVIpQXZtekKeOz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table showing the trend values for Blockchain technology from 2019 to 2025 as visualized in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table for Blockchain technology, I must look at each bar pertaining to Blockchain on the chart. Starting with the year 2019, {the first bar for Blockchain}<r>[0.210, 0.676, 0.223, 0.771]</r> represents a value of 0.9, denoted by {the text above the bar}<r>[0.208, 0.643, 0.224, 0.666]</r>. Proceeding to the subsequent years, I identify the rest of the bars representing the values for Blockchain from 2020 to 2025 at the coordinates <r>[0.223, 0.666, 0.236, 0.771]</r>, <r>[0.236, 0.634, 0.249, 0.771]</r>, <r>[0.249, 0.603, 0.261, 0.771]</r>, <r>[0.261, 0.582, 0.274, 0.771]</r>, <r>[0.274, 0.571, 0.287, 0.771]</r>, and <r>[0.287, 0.561, 0.300, 0.771]</r> with their respective top text annotations providing the values for each year indicated on the bars' corresponding legend <r>[0.265, 0.893, 0.762, 0.970]</r>.\nBased on the above thought, the answer is:\nYear,Trend Value\n2019,0.9\n2020,1.0\n2021,1.3\n2022,1.6\n2023,1.8\n2024,1.9\n2025,2.0"
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "Ks15ZkdTsGClabYB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which technology has shown the most significant growth from 2019 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must compare the growth of each technology by examining the height of the bars from 2019 and 2025 for each technology. For example, {AI's 2019 bar}<r>[0.081, 0.550, 0.094, 0.771]</r> and {AI's 2025 bar}<r>[0.158, 0.097, 0.171, 0.771]</r> show a significant increase. Following this method for each technology, I observe that {Quantum Computing's 2019 bar}<r>[0.726, 0.750, 0.739, 0.771]</r> indicates a small initial value, while {Quantum Computing's 2025 bar}<r>[0.804, 0.455, 0.817, 0.771]</r> shows the largest relative increase in height among all technologies, indicating the most significant growth. This correlates with the outlier information identified in the trend, marking a major technological breakthrough for Quantum Computing.\nBased on the above thought, the answer is:\nQuantum Computing. It has shown the most significant growth, starting with a very small value in 2019, and growing much larger by 2025, which is evident from the vertical growth of the representative bars on the bar chart."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "g81f1ob4RO9c6oXV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the trend value for Cloud Computing in 2022 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the trend value for Cloud Computing in 2022, I must look for the bar corresponding to Cloud Computing labeled '2022'. The {bar for Cloud Computing in 2022}<r>[0.378, 0.455, 0.391, 0.771]</r> is accompanied by {a text annotation}<r>[0.376, 0.421, 0.392, 0.445]</r> that indicates the value, which upon inspection reads '3.0'.\nBased on the above thought, the answer is:\n3.0"
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "MnukuUwaaGgRwNXE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the prevalence rates for Asthma across all the age groups in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I will look for the bars representing Asthma, each marked with a specific color on the chart. I see that {the Asthma bars}<r>[0.101, 0.822, 0.116, 0.944]; [0.252, 0.736, 0.267, 0.944]; [0.402, 0.627, 0.417, 0.944]; [0.552, 0.541, 0.567, 0.944]; [0.702, 0.456, 0.717, 0.944]; [0.853, 0.298, 0.868, 0.944]</r> are associated with the age groups on the x-axis with coordinates {Under 30}<r>[0.112, 0.952, 0.166, 0.975]</r>, {30-40}<r>[0.273, 0.952, 0.306, 0.975]</r>, {40-50}<r>[0.423, 0.952, 0.456, 0.975]</r>, {50-60}<r>[0.573, 0.952, 0.606, 0.975]</r>, {60-70}<r>[0.723, 0.952, 0.757, 0.975]</r>, {Over 70}<r>[0.867, 0.952, 0.913, 0.975]</r>. The values labelled on top of each bar will give us the prevalence rate for Asthma, such as {1.0}<r>[0.100, 0.786, 0.118, 0.810]</r> for Under 30, and so forth.\nBased on the above thought, the answer is:\nAge Group,Asthma\nUnder 30,1.0\n30-40,1.7\n40-50,2.6\n50-60,3.3\n60-70,4.0\nOver 70,5.3"
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "2lSiQXKn7uPkkLIk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health condition showed the most significant increase in prevalence rate from the under 30 age group to the over 70 age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the increment in prevalence rates, I will compare the starting and ending values of each health condition. Focusing first on the {Heart Disease bars}<r>[0.086, 0.822, 0.101, 0.944]; [0.838, 0.188, 0.853, 0.944]</r>, the first bar has the label {1.0}<r>[0.085, 0.786, 0.103, 0.810]</r> and the last one {6.2}<r>[0.836, 0.152, 0.854, 0.176]</r>, showing a significant increase. I will perform this same process for each condition. After scanning all the health conditions, the increase from the under 30 to the over 70 age group for each health condition is evident. From my analysis, {Heart Disease}<r>[0.162, 0.061, 0.244, 0.084]</r> shows the highest increase in prevalence rate from 1.0 to 6.2.\nBased on the above thought, the answer is:\nHeart Disease. By comparing the label values on top of the bars representing Under 30 and Over 70 age groups for each health condition, Heart Disease displays the highest increase in prevalence rate, going from 1.0 to 6.2 according to the data provided."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "DJZLVfIc7BUuaWVE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the prevalence rate of Diabetes for the 40-50 age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the prevalence rate of Diabetes for the 40-50 age group, I need to locate the corresponding bar on the chart. The bar for Diabetes is color-coded and for the age group of 40-50, {the bar}<r>[0.432, 0.651, 0.447, 0.944]</r> is clearly visible. Above this bar, there is a text label that provides the prevalence rate. Here, I can see the label {2.4}<r>[0.430, 0.616, 0.449, 0.639]</r> on top of the bar.\nBased on the above thought, the answer is:\n2.4"
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "ufhNUOXt8eSiUjX5",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, what were the revenues (in million dollars) of all six industries in the years 2008 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data that is represented by the bar chart consist of the revenues of six different industries in the years 2008 and 2018. The y axis of the chart represents the revenues, which range from {0}[0.054, 0.934, 0.061, 0.958] to {7000}[0.032, 0.158, 0.061, 0.182], in million dollars. The industries are represented by the 'Industry' {legend}[0.222, 0.024, 0.831, 0.101], where each colored rectangle corresponds to a different industry - 'Oil' with color code '#009b77', 'Gas' with '#f26419', 'Clean Energy' with '#b2473e', 'Water' with '#0072b5', 'Electricity' with '#33a1fd' and 'Biotechnology' with '#ffdb5c'. The two bars for each industry represent the revenues for the years 2008 and 2018. For instance, for the industry 'Oil', the revenues in 2008 and 2018 can be determined by the heights of the two bars '{bar 2008}'[0.107, 0.611, 0.160, 0.944] and '{bar 2018}'[0.631, 0.633, 0.684, 0.944], respectively. Similarly, the values for each of the remaining industries can be inferred by their respective bars.\nBased on the above thought, the answer is:\nIndustry,2008,2018\nOil,3000,2800\nGas,2800,2900\nClean Energy,1000,2300\nWater,3500,3600\nElectricity,4000,5000\nBiotechnology,3600,4000"
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "uf1ZISxWxPgKQGnS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which industry has seen the most growth from 2008 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the 'Industry' {legend}[0.222, 0.024, 0.831, 0.101] and the bars in the chart, we can see the revenues for various industries in 2008 and 2018. It seems that the height difference between the pair of bars for each industry will enable us to determine the industry that experienced the highest growth in its revenues. For instance, the bars for the 'Oil' industry {'2008 bar'}[0.107, 0.611, 0.160, 0.944] and {'2018 bar'}[0.631, 0.633, 0.684, 0.944] exhibit a decrease showing its revenue dwindled during this period. In contrast, the bars for the 'Clean Energy' industry {'2008 bar'}[0.212, 0.833, 0.265, 0.944] and {'2018 bar'}[0.736, 0.689, 0.788, 0.944] show a remarkable increase which indicates a substantial growth in revenues. Comparing the pairs of bars for all industries, it appears that the 'Clean Energy' industry shows the most growth.\nBased on the above thought, the answer is:\nThe Clean Energy industry has seen the most growth from 2008 to 2018 because it has more than doubled its revenue during this period."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "vWzy7Yiat7doGwt0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the highest revenue in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Industry' {legend}[0.222, 0.024, 0.831, 0.101] and the bars in the chart represent the revenues for the various industries in 2008 and 2018. We are interested in the revenues in 2018, which are represented by the second bar for each industry. I'll compare the heights of these bars to find out which one has the highest value. For example, the 2018 revenue for the 'Oil' industry is represented by the {bar}[0.631, 0.633, 0.684, 0.944]. After comparing all the bars for 2018, we can see that {the highest bar belongs to 'Electricity'}[0.841, 0.389, 0.893, 0.944], indicating that it has the highest revenue in 2018.\nBased on the above thought, the answer is:\nElectricity."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "FOzBXXZfIBkqGx32",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you divide the data from the chart into categories and list them in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the x-axis \"{Time Scale}\"<r>[0.469, 0.942, 0.595, 0.970]</r> I can see that the x-axis represents the two categories for the time scale: {Years}<r>[0.214, 0.903, 0.274, 0.931]</r> and {Decades}<r>[0.770, 0.903, 0.870, 0.931]</r>. The y-axis shows the impact percentages from {0}<r>[0.058, 0.872, 0.072, 0.900]</r> to {6}<r>[0.058, 0.030, 0.072, 0.058]</r>. There are two types of bars to represent the amount of impact due to different reasons: {Economic Growth}<r>[0.129, 0.350, 0.244, 0.883]</r> and {Policy Impact}<r>[0.244, 0.153, 0.359, 0.883]</r>. I will extract the necessary values from the bars representing each reason on both categories and list them in a CSV table.\nBased on the above thought, the answer is:\nTime Scale,Economic Growth,Policy Impact\nYears,3.8,5.2\nDecades,2.5,1.0"
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "QnN3v6oa0v20lOIb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which time scale had a more impactful economic growth and policy-making scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to look at the heights of the bars under each time scale. For the 'Years' time scale, the heights are {Economic Growth - Years}<r>[0.129, 0.350, 0.244, 0.883]</r> and {Policy Impact - Years}<r>[0.244, 0.153, 0.359, 0.883]</r>, indicating 3.8 and 5.2 respectively. For 'Decades', the heights are {Economic Growth - Decades}<r>[0.704, 0.532, 0.820, 0.883]</r> and {Policy Impact - Decades}<r>[0.820, 0.743, 0.935, 0.883]</r>, indicating 2.5 and 1.0 respectively. Comparing the numbers, the 'Years' time scale has higher values for both 'Economic Growth' and 'Policy Impact'.\nBased on the above thought, the answer is:\nThe 'Years' time scale had a more impactful economic growth and policy-making scenario, as both economic growth and policy impact figures are higher than those of the 'Decades' time scale."
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "NSxRwAMMkuXcL0Hb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar graph, what is the difference in economic growth between the two time scales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in economic growth between the two-time scales, I should subtract the economic growth of the 'Decades' time scale(which is located at {Decades - Economic Growth}<r>[0.704, 0.532, 0.820, 0.883]</r> and registered as 2.5) from the economic growth of the 'Years' time scale (which is located at {Years - Economic Growth}<r>[0.129, 0.350, 0.244, 0.883]</r> and is registered as 3.8). Therefore, the difference is 3.8 - 2.5.\nBased on the above thought, the answer is:\n1.3"
+            }
+        ],
+        "id_raw": 645
+    },
+    {
+        "id": "uVYCzwvTZ1v9K8Or",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHaving observed the bar chart corresponding to governmental policies and their associated effectiveness and implementation scores, could you convert the relevant data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the x-axis in the chart, {xlabel}<r>[0.419, 0.942, 0.637, 0.970]</r>, we see that it represents the types of policies: 'Environment Policy' and 'Economic Policy'. This is reinforced by the individual labels found at {[0.189, 0.903, 0.411, 0.931]} and {[0.778, 0.903, 0.964, 0.931]}, respectively. On the other hand, the y-axis represents the 'levels' of effectiveness or implementation of these policies which can be observed from {ylabel}<r>[0.025, 0.418, 0.048, 0.506]</r>. The bars represent the values for both 'Effectiveness' and 'Implementation for each policy'. As indicated by the chart legend at {sublegend}<r>[0.100, 0.055, 0.441, 0.147]</r>, the {first group of bars}<r>[0.129, 0.294, 0.243, 0.883]</r> and <r>[0.699, 0.546, 0.814, 0.883]</r> represent 'Policy Effectiveness', while the {second group of bars}<r>[0.243, 0.209, 0.357, 0.883]</r> and <r>[0.814, 0.597, 0.928, 0.883]</r> represent 'Policy Implementation'. The value for each bar can be determined by observing the height of the bar in correlation with the y-axis. For instance, the first bar for 'Policy Effectiveness' under the 'Environment Policy' category has a height that corresponds to a level of approximately '3.5' on the y-axis.\nBased on the above thought, the answer is:\nPolicy Type,Policy Effectiveness,Policy Implementation\nEnvironment Policy,3.5,4.0\nEconomic Policy,2.0,1.7"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "DgYqya1DVhcZng50",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, evaluate the status of these two government policies. Can you infer which policy is more successful and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, the 'Effectiveness' and 'Implementation' of 'Environment' and 'Economic' policies are being compared. Looking at the 'Environment Policy' bars at coordinates {[0.129, 0.294, 0.243, 0.883]} and {[0.243, 0.209, 0.357, 0.883]} for 'Effectiveness' and 'Implementation' respectively, the 'Implementation' seems to be slightly higher than the 'Effectiveness'. Now observing the 'Economic Policy' bars located at {[0.699, 0.546, 0.814, 0.883]} and {[0.814, 0.597, 0.928, 0.883]}, it's clear that the 'Effectiveness' is higher than 'Implementation'. Ultimately, a policy can only be as good as its implementation and hence while the 'Environment Policy' is high on both constructs, it seems to be doing a better job since its implementation is relatively higher. The 'Economic Policy' while having a higher effectiveness, seems to be lacking in its implementation.\nBased on the above thought, the answer is:\nThe 'Environment Policy' seems to be more successful as it exhibits both higher effectiveness and implementation levels when compared to 'Economic Policy'. This indicates that not only are the policies effective in theory, but their practical execution has also been successful."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "5YuGeZInzHRJDDYD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which policy type is the difference between 'Policy Effectiveness' and 'Policy Implementation' the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the policy with the largest difference between 'Policy Effectiveness' and 'Policy Implementation' levels, I need to compare the heights of the two corresponding bars under each policy. For 'Environment Policy', the two bars are located at {[0.129, 0.294, 0.243, 0.883]} and {[0.243, 0.209, 0.357, 0.883]} respectively, indicating levels of '3.5' and '4.0' based on their heights. Thus, the difference is '0.5'. For 'Economic Policy', the bars are located at {[0.699, 0.546, 0.814, 0.883]} and {[0.814, 0.597, 0.928, 0.883]} respectively, indicating levels of '2.0' and '1.7'. Thus, the difference here is '0.3'. Comparing the two, we can tell that 'Environment Policy' has the larger difference.\nBased on the above thought, the answer is:\nEnvironment Policy"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "TaLCEqm7H5B9ZFWp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, can you extract and convert the raw data for each scenario over the four quarters in 2020 into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents data from three scenarios titled as 'Moderate Growth', 'Steady Decline', and 'Stable Growth' over four quarters in 2020. The {X-axis labels}<r>[\"Quarters\"][0.502, 0.952, 0.578, 0.975]</r> indicates the four quarters and the {Y-axis labels}<r>[\"Units\"][0.019, 0.439, 0.036, 0.498]</r> tells us the numerical value associated with each bar. The bar's height represents the data value in Units. To get the values to be put in the table we can observe the height of each respective bar. For instance, looking at the 'Moderate Growth' bar for '2020 Q1', its {bar coordinate}<r>[0.139, 0.555, 0.184, 0.903]</r> reaches approximately halfway between 500 and 1000 on the Y-axis, giving us the value of 1000 Units. By following the same process for the other bars under each scenario for every quarter, a structured CSV table can be generated.\nBased on the above thought, the answer is:\nTime,Moderate Growth,Steady Decline,Stable Growth\n2020 Q1,1000,2000,1500\n2020 Q2,1100,1900,1500\n2020 Q3,1150,1800,1500\n2020 Q4,1200,1600,1500"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "L1z6iuDGWT35UQiV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart trends, what scenario seems to have the best performance over all quarters of 2020? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the \"best\" scenario, one could hypothesize that growth is the preferred outcomes. The ideal scenario would therefore represent growth or at least stable values. When looking at the chart, the bar heights reflect the performance of each scenario for each quarter. For instance, the 'Moderate Growth' scenario depicted by <r>[0.752, 0.115, 0.894, 0.137]</r> shows gradual ascension of bars from the first <r>[0.139, 0.555, 0.184, 0.903]</r> to the last quarter <r>[0.808, 0.486, 0.852, 0.903]</r>. In contrast, the 'Steady Decline' scenario <r>[0.752, 0.150, 0.883, 0.172]</r> has bars showing a progressive decline from the first <r>[0.184, 0.208, 0.228, 0.903]</r> to the last quarter <r>[0.852, 0.347, 0.897, 0.903]</r>. The final 'Stable Growth' scenario <r>[0.752, 0.185, 0.876, 0.207]</r> maintains the same height throughout all quarter bars such as <r>[0.228, 0.382, 0.273, 0.903]</r> and <r>[0.897, 0.382, 0.941, 0.903]</r>. Thus, the 'Moderate Growth' scenario performed the best, as it demonstrated a positive change across all quarters.\nBased on the above thought, the answer is:\nThe 'Moderate Growth' scenario performed the best as demonstrated by the upward trend over the quarters."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "vxDmfccWNS8lg440",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total combined growth for all scenarios over all quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total combined growth, I would need to add up the values from all the bars on the chart. Each group of bars represents a quarter with each bar representing a unique scenario. For example, in \"2020 Q1\" the total growth would be the sum of the values of the {first set of bars}<r>[\"2020 Q1\"]</r> representing Moderate Growth <r>[0.139, 0.555, 0.184, 0.903]</r>, Steady Decline <r>[0.184, 0.208, 0.228, 0.903]</r> and Stable Growth <r>[0.228, 0.382, 0.273, 0.903]</r>. By observing the chart, it can be seen they approximately reach 1000, 2000, and 1500 units respectively on the Y-axis. The same process should be done for the other three quarters to get the total combined growth for all the quarters.\nBased on the above thought, the answer is:\n19,350."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "lrljw20J9YpsMdSq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured data table from the bar chart visual you have examined?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the label at the bottom of the chart {Application Type}[0.416, 0.942, 0.607, 0.970] and {the x-axis tick labels}[{Chatbot}[0.257, 0.903, 0.349, 0.931]; {Recommendation System}[0.675, 0.903, 0.972, 0.931]], I can ascertain that the x-axis represents the types of applications. The y-axis scale shows scores from {0.0}[0.058, 0.872, 0.094, 0.900] to {2.5}[0.058, 0.030, 0.094, 0.058], which represents the performance scores of the AI models. According to {the legend}[0.122, 0.055, 0.410, 0.147], the bars are grouped by the colour which represents two different AI methods Deep Learning{[0.131, 0.069, 0.178, 0.088]} and Machine Learning{[0.131, 0.110, 0.178, 0.130]}. The height of each bar for each respective method and the application indicate the performance score. For example, {the bar for Chatbot in Deep Learning}[0.147, 0.142, 0.251, 0.883] has a height that extends roughly to y=2.2 line and hence has a performance score of 2.2.\nBased on the above thought, the answer is:\nApplication Type,Deep Learning,Machine Learning\nChatbot,2.2,1.1\nRecommendation System,1.3,2.1"
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "OtWehhlOIWGqlQtk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which application performs better when using Deep Learning methods and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the performance scores of Deep Learning on different applications. From the chatbot application, {the bar for Deep Learning}[0.147, 0.142, 0.251, 0.883] extends to y=2.2 indicating a performance score of 2.2. On the other hand, the {bar for Deep Learning in Recommendation System}[0.668, 0.445, 0.772, 0.883] only extends to y=1.3, indicating a performance score of 1.3. Based on the performance scores, higher values mean better performance, therefore, Deep Learning has got a higher score in Chatbot application and performed better.\nBased on the above thought, the answer is:\nChatbot application. This is because the performance score for Deep Learning is higher in Chatbot application compared to Recommendation System application."
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "JInjc9TF0sVaGgTi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which Machine Learning method gave a higher performance score in Chatbot applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "First of all, I need to identify the bars that represent the performance score of the two Machine Learning methods - Deep Learning and Machine Learning, in Chatbot application. {The bar for Chatbot for Deep Learning}[0.147, 0.142, 0.251, 0.883] extends roughly to y=2.2 whereas {the bar for Chatbot for Machine Learning}[0.251, 0.513, 0.355, 0.883] extends roughly to y=1.1, indicating that performance score of Deep Learning is higher in Chatbot application.\nBased on the above thought, the answer is:\nDeep Learning"
+            }
+        ],
+        "id_raw": 648
+    },
+    {
+        "id": "z1PdVCDnes8XWGAS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart's data into a table with a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, looking at {the x-axis}[0.234, 0.903, 0.851, 0.931], we see that there are two companies {H&M}[0.234, 0.903, 0.290, 0.931] and {Zara}[0.796, 0.903, 0.851, 0.931]. On the y-axis, numbers range from {0.0}[0.058, 0.872, 0.094, 0.900] up to near {4.5}[0.058, -0.021, 0.094, 0.007], representing the supply chain cost in billion USD. The year is indicated by colors of bars. From the legend we can see {the blue colored bar}[0.831, 0.058, 0.877, 0.077] represents 2020 and {orange bars}[0.831, 0.099, 0.877, 0.119] represents 2021. Under H&M there are 2 colored bars, {blue}[0.150, 0.268, 0.262, 0.883] and {orange}[0.262, 0.586, 0.374, 0.883]. The top of the blue bar is around 3.1 and for orange bar it is around 1.5. Similar can be deduced for Zara's bars where {blue}[0.711, 0.586, 0.823, 0.883] stands for around 1.5 and {orange}[0.823, 0.387, 0.936, 0.883] roughly indicates 2.5. Hence each row of the CSV will be the year, followed by the supply chain cost of H&M and then Zara.\nBased on the above thought, the answer is:\nYear,H&M,Zara \n2020,3.1,1.5\n2021,1.5,2.5"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "1Vvq4N9DF4EfGMWB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart provided, which company seems to have had a more favorable supply chain cost progression from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the progression of supply chain costs, I'll refer to the heights of the bars representing each company in 2020 and 2021. {H&M's bars}[{2020}[0.150, 0.268, 0.262, 0.883];{2021}[0.262, 0.586, 0.374, 0.883]] suggest a trend of decreasing cost from 3.1 to 1.5 billion USD. Meanwhile, {Zara's bars}[{2020}[0.711, 0.586, 0.823, 0.883];{2021}[0.823, 0.387, 0.936, 0.883]] show an increasing trend from 1.5 to 2.5 billion USD. Lower supply chain costs are generally more favorable for a company.\nBased on the above thought, the answer is:\nH&M. The company has shown a decrease in supply chain costs from 2020 to 2021, which is typically considered favorable progress."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "xTWLhhiBx64LzFrV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has a steeper increase or decrease in supply chain cost from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To see which company had a steeper change in supply chain cost, I need compare the difference between the 2020 and 2021 bars for H&M and Zara. The height difference for {H&M bars}[{2020}[0.150, 0.268, 0.262, 0.883];{2021}[0.262, 0.586, 0.374, 0.883]] is around 1.6 and {Zara bars}[{2020}[0.711, 0.586, 0.823, 0.883];{2021}[0.823, 0.387, 0.936, 0.883]] is around 1. Hence, the supply chain cost of H&M has changed more steeply.\nBased on the above thought, the answer is:\nH&M"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "hJnxD5HGO3nryC0c",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the chart into structured data in the form of a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see from the {chart}[0.110, 0.041, 0.975, 0.883] that we have data in the form of bars for two media outlets - {FOX News}[0.204, 0.903, 0.320, 0.931] and {CNN}[0.798, 0.903, 0.849, 0.931], and for two categories of influence - {Strong Influence}[0.764, 0.066, 0.954, 0.094] and {Slight Influence}[0.764, 0.108, 0.944, 0.136]. By examining the corresponding bars, I can find out the level of influence these outlets have. For instance, the {bar}[0.150, 0.715, 0.262, 0.883] representing Strong Influence for FOX News reaches approximately 0.6 on the y-axis, and the {bar for Slight Influence}[0.262, 0.209, 0.374, 0.883] reaches 2.4. For CNN, the corresponding bars reach 1.8 and 0.8 respectively. Using these data, I can construct the table.\nBased on the above thought, the answer is:\nMedia Outlet,Strong Influence,Slight Influence\nFOX News,0.6,2.4\nCNN,1.8,0.8"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "nRDuFGsLEgCk5kze",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, which media outlet can potentially have a more consistent influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the graph, when considering the bars representing the level of influence for both categories, it's visible that {FOX News}[0.204, 0.903, 0.320, 0.931] displays a more considerable difference between strong and slight influence with bars at {0.6}[0.150, 0.715, 0.262, 0.883] and {2.4}[0.262, 0.209, 0.374, 0.883] rspectively. {CNN}[0.798, 0.903, 0.849, 0.931], on the other hand, shows lesser variation between strong and slight influence with bars at {1.8}[0.711, 0.378, 0.823, 0.883] and {0.8}[0.823, 0.659, 0.936, 0.883]. Lower variation suggests a more consistent level of influence.\nBased on the above thought, the answer is:\nCNN. Because it shows less variation between strong and slight influence, it suggests a more consistent level of influence."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "NsuLMt19pymnCqPd",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media outlet has a higher total influence, considering both strong and slight influences combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total influence of each media outlet, I need to sum up the height of the bars representing Strong Influence and Slight Influence for each outlet. {FOX News}[0.204, 0.903, 0.320, 0.931] has a strong influence of {0.6}[0.058, 0.732, 0.094, 0.760] and slight influence of {2.4}[0.058, 0.170, 0.094, 0.198], resulting in a total of 3.0. {CNN}[0.798, 0.903, 0.849, 0.931] has a strong influence of {1.8}[0.058, 0.378, 0.094, 0.406] and slight influence of {0.8}[0.058, 0.659, 0.094, 0.687], which sums up to 2.6. Hence, the total influence from each media outlet can be calculated.\nBased on the above thought, the answer is:\nFOX News."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "IbzUA0UtjQ7BqDfc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the provided bar chart, please assist in structuring the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the ylabel {\"Influence Level\"}[0.025, 0.357, 0.048, 0.567], the y-axis represents the influence level. The x-axis, which is labeled by {\"Media Sources\"}[0.459, 0.942, 0.627, 0.970], shows the media sources which are {Test 1}[0.284, 0.903, 0.352, 0.931] and {Test 2}[0.846, 0.903, 0.913, 0.931]. There are two groups of bars represented by {Media 1}[0.797, 0.069, 0.844, 0.088] and {Media 2}[0.797, 0.110, 0.844, 0.130] in {legend}[0.788, 0.055, 0.963, 0.147]. For Test 1 in Media 1, the influence level is represented by the height of {the first white bar}[0.150, 0.146, 0.262, 0.883], which is around 3.5. So, by comparing the height of each bar to the tick marks on the y-axis and repeated these steps for each media source, I can derive the corresponding CSV table.\nBased on the above thought, the answer is:\nMedia Source, Media 1, Media 2\nTest 1, 3.5, 2.0\nTest 2, 2.5, 1.0"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "SehWoGTOEzjpD5il",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the credibility or effectiveness of the two media sources over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two sets of bars represent the influence levels of {Media 1}[0.797, 0.069, 0.844, 0.088] and {Media 2}[0.797, 0.110, 0.844, 0.130] at two different time periods (Test 1 and Test 2 from the x-axis). If I look at the height of the bars, both media sources have decreased in influence from {Test 1}[0.284, 0.903, 0.352, 0.931] to {Test 2}[0.846, 0.903, 0.913, 0.931]. This suggests that over this time frame, both media sources have seen a decline in the level of influence they have over their audience. This might suggest that their credibility or effectiveness in guiding public opinion may be waning, but it could also be due to other external factors such as changing audience preferences, the emergence of new competing media sources, etc.\nBased on the above thought, the answer is:\nBoth Media 1 and Media 2 have seen a decrease in their effectiveness or credibility over time, as indicated by the decrease in their respective influence levels."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "IvZGfnyM1Lb3YlTI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media source saw a larger drop in influence level between Test 1 and Test 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I want to compare the change in influence level for each media source, I need to consider the change in height of the bars between the two tests. In {Media 1}, the {test 1 bar}[0.150, 0.146, 0.262, 0.883] is taller than the {test 2 bar}[0.711, 0.357, 0.823, 0.883]. The heights represent influence levels of 3.5 and 2.5 respectively, indicating a drop of 1.0. Similarly, in {Media 2}, the {test 1 bar}[0.262, 0.462, 0.374, 0.883] is taller than the {test 2 bar}[0.823, 0.673, 0.936, 0.883]. The heights represent influence levels of 2.0 and 1.0 respectively, indicating a drop of 1.0 as well.\nBased on the above thought, the answer is:\nNeither. Both Media 1 and Media 2 showed the same drop in influence level of 1.0."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "DmgDnmA90KbIHX4L",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar charts you observe, can you extract the exact values represented by the bars for California and Florida for each factor - Forest Coverage, Population Density, and Public Park Coverage, and present them in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart, I can see that there are three different series indicated by different hatching styles as per the {legend}<r>[0.086, 0.046, 0.332, 0.157]</r>. The first series of bars represent {Forest Coverage}<r>[{\"California\"}[0.118, 0.738, 0.221, 0.903], {\"Florida\"}[0.632, 0.799, 0.735, 0.903]]</r>. We can see the second series as {Population Density}<r>[{\"California\"}[0.221, 0.164, 0.324, 0.903], {\"Florida\"}[0.735, 0.251, 0.837, 0.903]]</r>, and the third series as {Public Park Coverage}<r>[{\"California\"}[0.324, 0.842, 0.427, 0.903], {\"Florida\"}[0.837, 0.825, 0.940, 0.903]]</r>. The x-axis tick labels represent the states under evaluation, which are {California}<r>[0.231, 0.919, 0.314, 0.942]</r> and {Florida}<r>[0.756, 0.919, 0.816, 0.942]</r>.\nWhile the y-axis ranges from {0}<r>[0.054, 0.894, 0.065, 0.917]</r> to {10}<r>[0.043, 0.025, 0.065, 0.048]</r> and is used to represent the values of each of these factors. The length of each of these bars represents the value of the factor it is representing.\nFor example, for {Forest Coverage in California}<r>[0.118, 0.738, 0.221, 0.903]</r>, the height of the bar reaches approximately 1.9 on the y-axis. Similarly, other values for the factors for each state can be determined using the length of their corresponding bars.\nBased on the above thought, the answer is:\nState,Forest Coverage,Population Density,Public Park Coverage\nCalifornia,1.9,8.5,0.7\nFlorida,1.2,7.5,0.9"
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "nuIDzVzV74qwhki8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, how can Florida improve its Forest Coverage to match up with California? Also, what can California learn from Florida according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart, {Florida}<r>[0.756, 0.919, 0.816, 0.942]</r> has significantly less {Forest Coverage}<r>[0.632, 0.799, 0.735, 0.903]</r> than {California}<r>[0.231, 0.919, 0.314, 0.942]</r>. To improve its Forest Coverage, Florida could consider implementing reforestation programs, protecting existing natural forests, and increasing awareness and education around the importance of forests. \nConversely, {California}<r>[0.231, 0.919, 0.314, 0.942]</r> has less {Public Park Coverage}<r>[0.324, 0.842, 0.427, 0.903]</r> in comparison to {Florida}<r>[0.756, 0.919, 0.816, 0.942]</r>. California can learn from Florida by implementing more public parks and open spaces for its residents. This could improve the quality of life for residents and increase tourism appeal.\nBased on the above thought, the answer is:\nFlorida can improve its forest coverage by implementing reforestation programs, protecting existing natural forests, and increasing awareness and education around the importance of forests. California can learn from Florida how to improve its public park coverage by creating more public parks and green spaces. This can make living environments more pleasant and potentially attract more tourists."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "wBkxgQOgxwPFPCha",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which state has a higher cumulative average across all three factors, Forest Coverage, Population Density, and Public Park Coverage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the state with a higher cumulative average across the three factors, I need to calculate the average for each state. This can be done by adding the representative values for each factor under each state and dividing the total by the number of factors. For {California}<r>[0.231, 0.919, 0.314, 0.942]</r>, the factors {Forest Coverage}<r>[0.118, 0.738, 0.221, 0.903]</r>, {Population Density}<r>[0.221, 0.164, 0.324, 0.903]</r> and {Public Park Coverage}<r>[0.324, 0.842, 0.427, 0.903]</r> yield values of 1.9, 8.5, and 0.7 respectively, resulting in a cumulative average of approximately (1.9 + 8.5 + 0.7) / 3 = 3.7. Similarly, for {Florida}<r>[0.756, 0.919, 0.816, 0.942]</r>, I get values of 1.2 for {Forest Coverage}<r>[0.632, 0.799, 0.735, 0.903]</r>, 7.5 for {Population Density}<r>[0.735, 0.251, 0.837, 0.903]</r> and 0.9 for {Public Park Coverage}<r>[0.837, 0.825, 0.940, 0.903]</r>, taking an average of (1.2 + 7.5 + 0.9) / 3 = 3.2.\nBased on the above thought, the answer is:\nCalifornia."
+            }
+        ],
+        "id_raw": 653
+    },
+    {
+        "id": "zrXweyX6ZxyjXfyM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me convert the data from the bar chart into a structured CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart figure presented, I am observing three bars at two different positions on the x-axis. The x-axis represents the platforms {Email}<r>[0.230, 0.903, 0.294, 0.931]</r> and {SMS}<r>[0.799, 0.903, 0.848, 0.931]</r>. On the y-axis labeled as 'Average Time (s)'<r>[0.025, 0.344, 0.048, 0.580]</r>, the values range from 0.0 to 4.5. The height of the bars corresponds directly to this value range. The bars are divided into two colours, each representing a platform, as clarified by {the legend}<r>[0.762, 0.055, 0.963, 0.147]</r> - {Instagram}<r>[0.836, 0.066, 0.954, 0.094]</r> and {Twitter}<r>[0.836, 0.108, 0.914, 0.136]</r>. For example, at Email's x-axis position, the {Instagram bar}<r>[0.150, 0.228, 0.262, 0.883]</r> reaches approximately 3.5 while the {Twitter bar}<r>[0.262, 0.135, 0.374, 0.883]</r> occupies about 4.0 units of average time, which can be verified by the bar's position on the y-axis.\nBased on the above thought, the answer is:\nPlatform,Instagram,Twitter\nEmail,3.5,4.0\nSMS,1.2,1.4"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "tuWEDUc1tV48q9jk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you explain the differences in message sending times across different platforms and methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, based on the provided bar chart, it presents the average time taken in seconds to send a message on different platforms (Email and SMS) and through different methods (Instagram and Twitter). For {Email}<r>[0.230, 0.903, 0.294, 0.931]</r>, it takes more time to send a message than through {SMS}<r>[0.799, 0.903, 0.848, 0.931]</r>, no matter whether the platform is Instagram or Twitter. If we look at individual platforms, on {Twitter}<r>[0.836, 0.108, 0.914, 0.136]</r> it takes longer on average to send a message than on {Instagram}<r>[0.836, 0.066, 0.954, 0.094]</r>, both for SMS and Email. The longest average sending time is on Twitter via Email, and the shortest is on Instagram via SMS.\nBased on the above thought, the answer is:\nEmail generally takes more time to send messages compared to SMS, regardless of whether the platform is Instagram or Twitter. The messaging time for Twitter is generally longer than Instagram on both Email and SMS. The fastest way to send a message is via SMS on Instagram and the slowest is via Email on Twitter."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "QLlK2oOlCtA0h7PL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me on which platform Instagram takes more time to send a message than Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the heights of the Instagram and Twitter bars for each platform. The {Instagram bar}<r>[0.150, 0.228, 0.262, 0.883]</r> for Email is approximately at 3.5 on the y-axis, while the {Twitter bar}<r>[0.262, 0.135, 0.374, 0.883]</r> is closer to 4.0. So for the Email platform, Twitter takes more time. Then looking at SMS, the {Instagram bar}<r>[0.711, 0.659, 0.823, 0.883]</r> is lower, around 1.2 whereas the {Twitter bar}<r>[0.823, 0.621, 0.936, 0.883]</r> is taller, around 1.4. So it seems for both platforms, Twitter takes more time to send a message.\nBased on the above thought, the answer is:\nNeither"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "o5BmYN9O7dfwtK8c",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease help me translate the data from the chart you observed into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the xlabel of the chart that indicates {Healthcare Tech}[0.450, 0.942, 0.635, 0.970], I can see that the x-axis represents two categories: {CRISPR}[0.276, 0.903, 0.360, 0.931] and {Gene Therapy}[0.798, 0.903, 0.961, 0.931]. The y-axis represents the 'Transmission Delay (ms)', as indicated by the ylabel {at the side}[0.025, 0.287, 0.048, 0.626], and the two types of bars indicate the transmission delay in milliseconds for each technology according to {the legend}[0.122, 0.044, 0.549, 0.136]. By combining the height of the bars and the y-axis scales, I can determine that the delay amounts for each healthcare tech in relation to telemedicine and machine learning in medicine. For instance, {the white bar for \"CRISPR\"}[0.150, 0.526, 0.262, 0.883] reaches the y-value at around 1.8 and the {the white bar for \"Gene Therapy\"}[0.711, 0.745, 0.823, 0.883] ends around the y-value of 0.7, which reveals the delay amounts for Telemedicine.\nBased on the above thought, the answer is:\nHealthcare Tech,Telemedicine,Machine Learning in Medicine\nCRISPR,1.8,1.0\nGene Therapy,0.7,2.1"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "ELSAfoeaIUXqMvNh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which medical technology has a higher transmission delay and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the medical technology bars at each Healthcare Tech. At {CRISPR}[0.276, 0.903, 0.360, 0.931], {Telemedicine bar}[0.150, 0.526, 0.262, 0.883] has a higher transmission delay than {Machine Learning in Medicine bar}[0.262, 0.685, 0.374, 0.883]; the situation is reversed at {Gene Therapy}[0.798, 0.903, 0.961, 0.931], with the bar of {Machine Learning in Medicine}[0.823, 0.467, 0.936, 0.883] being taller than that of {Telemedicine}[0.711, 0.745, 0.823, 0.883]. Therefore, it can be deduced that the overall transmission delays of the two technologies are close, with Machine Learning in Medicine having a marginally higher delay due to its significant delay at \"Gene Therapy\".\nBased on the above thought, the answer is:\nMachine Learning in Medicine. Although Telemedicine has a higher transmission delay at CRISPR, the delay of Machine Learning in Medicine significantly exceeds Telemedicine at Gene Therapy, making the overall delay of Machine Learning in Medicine slightly higher."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "OXB6SGKHyvUcpI8Y",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which healthcare tech has a higher transmission delay for Telemedicine?"
+            },
+            {
+                "from": "gpt",
+                "value": "The transmission delays for Telemedicine are represented by white bars. Hence, by comparing the heights of the {Telemedicine bar at \"CRISPR\"}[0.150, 0.526, 0.262, 0.883] and the {Telemedicine bar at \"Gene Therapy\"}[0.711, 0.745, 0.823, 0.883], it can be observed that the bar for \"CRISPR\" is taller, indicating a higher delay.\nBased on the above thought, the answer is:\nCRISPR."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "xILJ4XXhMlkdopFe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter analyzing the bar graph, could you convert the information into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar graph's title isn't presented explicitly but the xlabel {Risk Factors}[0.484, 0.964, 0.567, 0.981] and ylabel {Risk Level}[0.015, 0.433, 0.029, 0.520], along with the legend with labels \"Probability\", \"Impact\", and \"Manageability\" at [0.073, 0.034, 0.221, 0.118], give an understanding of the chart. It represents the risk factors with their corresponding Probability, Impact, and Manageability. The x-axis represents two categories: {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957]. The y-axis represents the risk level from {0}[0.035, 0.920, 0.057, 0.938] to {1}[0.035, 0.019, 0.057, 0.036]. Different bars represent different parameters of every risk factor. For example, the bar representing the 'Probability' for 'Economic Conditions' is in the coordinates [0.108, 0.206, 0.212, 0.927]. The y-coordinate of the top of this bar is 0.927, aligns with 0.8 on the y-axis. This similar approach can be used to assess all data points and thus I am able to build a table.\nBased on the above thought, the answer is:\nRisk Factors, Probability, Impact, Manageability\nEconomic Conditions, 0.8, 0.9, 0.7\nRegulatory Changes, 0.6, 0.85, 0.5"
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "njYuHPlVY9rpWAMS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can we make about the two risk factors and their management in the company?"
+            },
+            {
+                "from": "gpt",
+                "value": "We will base our analysis on the bar graphs for quantities 'Probability', 'Impact', and 'Manageability' for the two risk factors: {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957]. Economic Conditions shows a high risk level for both Probability and Impact, as denoted by the bars at [0.108, 0.206, 0.212, 0.927] and [0.212, 0.116, 0.317, 0.927] respectively. However, the manageability of these risks at [0.317, 0.296, 0.421, 0.927] is relatively high. This suggests that the company is well-equipped to manage these risks despite their high probability and impact. On the other hand, Regulatory Changes shows a medium level of Probability and high level of Impact, and the associated bars are at [0.630, 0.386, 0.734, 0.927] and [0.734, 0.161, 0.839, 0.927]. However, Manageability, indicated by the bar at [0.839, 0.476, 0.943, 0.927], is comparatively lower, indicating that the company faces challenges in managing these types of risks.\nBased on the above thought, the answer is:\nEconomic conditions present a high risk but are manageable for the company. In contrast, Regulatory Changes, while less likely, are harder for the company to manage despite having a high impact."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "5c5ovyVybQcKLmuq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk factor shows a lower level of manageability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the risk factor having a lower level of manageability, I will go through both the manageability bars for each risk factor. These are identified as the third bar for every risk category. In this bar graph, there are two risk factors, being {Economic Conditions}[0.192, 0.939, 0.337, 0.957] and {Regulatory Changes}[0.716, 0.939, 0.857, 0.957] with their manageability levels represented by the bars at [0.317, 0.296, 0.421, 0.927] and [0.839, 0.476, 0.943, 0.927] respectively. It can be observed that the bar corresponding to Regulatory Changes is seen lower on the \"Risk Level\" y-axis than Economic Conditions. Therefore, Regulatory Changes has a lower level of manageability.\nBased on the above thought, the answer is:\nRegulatory Changes."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "z3oU4lfhynSdmPwI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you see, can you extract the data and provide it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I see a bar chart that has Year 1, Year 2, and Year 3 on the x-axis {\"Years\"}[0.512, 0.942, 0.573, 0.970]. The y-axis {\"Advancements\"}[0.025, 0.353, 0.048, 0.560] scale starts at 0 and ends at 4.5, and is used to measure the advancements of Health Technology and Medical Technology. There are two sets of bars, the first set representing Health Technology {\"Health Technology\"}[0.728, 0.055, 0.941, 0.083], and the second set representing Medical Technology {\"Medical Technology\"}[0.728, 0.097, 0.954, 0.125]. Each set contains three bars corresponding to each year. The height of the bars corresponds to the value of advancements. For instance, for Year 1, the height of the first bar {\"Health Technology Year 1\"}[0.150, 0.224, 0.215, 0.883] is about 3.4, and the height of the second bar {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] is about 3.3. By utilizing the coordinates attached to each bar, I can extract the value of advancements for each technology in each year and organize them into a structured table.\nBased on the above thought, the answer is:\nYears,Health Technology,Medical Technology\nYear 1,3.4,3.3\nYear 2,2.4,1.5\nYear 3,1.6,1.7"
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "2QnZzaLJQ3ubVy6E",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology seems to experience a substantial decrease in advancements and can you interpret the reasons why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the graph, on the data associated with each bar for both {\"Health Technology\"}[0.728, 0.055, 0.941, 0.083] and {\"Medical Technology\"}[0.728, 0.097, 0.954, 0.125], I notice that both types of technology have experienced decreases in advancements over the three years. But the decline is more pronounced for Medical Technology, especially from Year 1 to Year 2, as evidenced by the substantial drop in height of the bars {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] to {\"Medical Technology Year 2\"}[0.543, 0.592, 0.608, 0.883]. This change suggests that there may have been challenges that affected the medical technology field more significantly compared to health technology. Possible reasons could be decreases in research funding, loss of key personnel, policy changes, regulatory hurdles or other external constraints that could have hampered advancements.\nBased on the above thought, the answer is:\nThe Medical Technology has experienced a substantial decrease. Possible reasons could be changes in funding, loss of key personnel, policy or regulatory changes, or other external factors."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "1UwbSKO61YJhiwhE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has the highest sum of advancements in both Health and Medical Technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest total advancements, I need to compare the sum of the advancements of both technologies for each year. For Year 1, the sum of advancements is the height of {\"Health Technology Year 1\"}[0.150, 0.224, 0.215, 0.883], which is approximately 3.4, added to the height of {\"Medical Technology Year 1\"}[0.215, 0.243, 0.281, 0.883] which is almost 3.3, giving a total of 6.7. I perform the same calculation for Year 2 and Year 3 by summing the heights of their respective bars. Finally, I can determine which year has the highest total advancements.\nBased on the above thought, the answer is:\nYear 1"
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "lCdIA6PjTFGxEU2B",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data presented in the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, there are two groups of bars representing the performance ratings for {'Deep Learning Performance'}[0.749, 0.055, 0.918, 0.083] and {'Machine Learning Performance'}[0.749, 0.097, 0.954, 0.125] of the two AI algorithms. The x-axis {AI Algorithms}<r>[0.454, 0.942, 0.609, 0.970]</r> represents the AI algorithms {'FastAI'}<r>[0.229, 0.903, 0.298, 0.931]</r> and {'TensorFlow'}<r>[0.737, 0.903, 0.864, 0.931]</r>. The y-axis represents the performance rating values ranging from {0}<r>[0.058, 0.872, 0.072, 0.900]</r> to {6}<r>[0.058, -0.094, 0.072, -0.066]</r>. The height of the bars corresponds to the performance rating value. Thus, the 'Deep Learning Performance' rating of FastAI can be read from {the first bar of group_0}<r>[0.129, 0.513, 0.263, 0.883]</r> as 2.3 and of TensorFlow as 2.9 from {the second bar of group_0}<r>[0.666, 0.416, 0.800, 0.883]</r>. For 'Machine Learning Performance', FastAI got a rating of 1.8 from {the first bar of group_1}<r>[0.263, 0.594, 0.397, 0.883]</r>, whereas TensorFlow got a rating of 4.9 from {the second bar of group_1}<r>[0.800, 0.094, 0.935, 0.883]</r>.\nBased on the above thought, the answer is:\nAI Algorithms,Deep Learning Performance,Machine Learning Performance\nFastAI,2.3,1.8\nTensorFlow,2.9,4.9"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "76Tgn5pUYpKRZqmH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInterprete the discrepancy in performance ratings between the two AI algorithms across the two domains."
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the chart, it can be seen that the performance rating for both algorithms in the two domains, namely {Deep Learning}<r>[0.749, 0.055, 0.918, 0.083]</r> and {Machine Learning}<r>[0.749, 0.097, 0.954, 0.125]</r>, are represented by a collection of bars. In both domains, the {bars representing 'TensorFlow'}<r>[0.666, 0.416, 0.800, 0.883]</r> for Deep Learning and <r>[0.800, 0.094, 0.935, 0.883]</r> for Machine Learning, are taller than those representing 'FastAI', both< r>[0.129, 0.513, 0.263, 0.883]</r> for Deep Learning and <r>[0.263, 0.594, 0.397, 0.883]</r> for Machine Learning, indicating a higher performance rating. Moreover, the margin of dominance of 'TensorFlow' over 'FastAI' is significantly pronounced in the Machine Learning domain, suggesting that TensorFlow vastly outperforms FastAI in machine learning tasks in terms of several factors examined including model training speed, model performance, and handling complex tasks.\nBased on the above thought, the answer is:\nTensorFlow outperforms FastAI in both domains, with a significantly greater margin in the Machine Learning domain. This suggests that TensorFlow could be the more efficient choice for tasks requiring machine learning capabilities."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "dZBKItFagezNPhNK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the Machine Learning performance rating of TensorFlow than FastAI?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the comparison of the bars under the 'Machine Learning Performance' group. The {bar for 'FastAI'}<r>[0.263, 0.594, 0.397, 0.883]</r> represents a performance rating of 1.8 whereas {the bar for 'TensorFlow'}<r>[0.800, 0.094, 0.935, 0.883]</r> has a considerably longer height, corresponding to a rating of 4.9. Subtracting the rating of FastAI from that of TensorFlow gives the difference in performance ratings.\nBased on the above thought, the answer is:\n3.1"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "bTpktQDcNBD7tAwT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data shown in this gaming industry development chart to a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this chart into a CSV format, I need to identify the categories, which are given by the legend including {PC Gaming}<r>[0.189, 0.066, 0.317, 0.094]</r>, {Console Gaming}<r>[0.189, 0.108, 0.378, 0.136]</r>, and {Mobile Gaming}<r>[0.189, 0.150, 0.363, 0.178]</r>. Two years, {2014}<r>[0.237, 0.903, 0.295, 0.931]</r> and {2020}<r>[0.783, 0.903, 0.842, 0.931]</r> in the x-axis are the time points at which data was collected. Y-values, representing the percentage contributions to the overall industry, can be calculated based on the y-axis scale ranged from {0}<r>[0.072, 0.872, 0.087, 0.900]</r> to {60}<r>[0.058, 0.030, 0.087, 0.058]</r> and the height of each bar. The heights of the bars indicate the proportions; for example, the PC Gaming bar for 2014<r>[0.143, 0.392, 0.225, 0.883]</r> stands approximately at the 35 mark on the y-axis, and the PC Gaming bar for 2020<r>[0.689, 0.322, 0.771, 0.883]</r> is approximately on the 40 mark.\nBased on the above thought, the answer is:\nYear,PC Gaming,Console Gaming,Mobile Gaming\n2014,35,40,25\n2020,40,35,55"
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "kM66eOgbrCH3JH6n",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the gaming industry trends represented in the chart, what future predictions can you make about the contributions of PC, Console and Mobile Gaming to the gaming industry by 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a prediction about the future of the gaming industry based on the chart, note down the changing trends from 2014 to 2020 first. Looking at the {PC Gaming}<r>[0.189, 0.066, 0.317, 0.094]</r> bars in 2014<r>[0.143, 0.392, 0.225, 0.883]</r> and 2020<r>[0.689, 0.322, 0.771, 0.883]</r>, the proportion has only slightly increased. So, we can predict that PC Gaming will likely maintain a steady contribution in the future, perhaps with slight increases. For {Console Gaming}<r>[0.189, 0.108, 0.378, 0.136]</r>, the bars have shown a decrease<r>[0.225, 0.322, 0.307, 0.883]; [0.771, 0.392, 0.853, 0.883]</r>, so it may continue to decrease in popularity by 2026. For {Mobile Gaming}<r>[0.189, 0.150, 0.363, 0.178]</r>, the bars have shown a stark increase in popularity, rising from about 25<r>[0.307, 0.532, 0.389, 0.883]</r> in 2014 to about 55<r>[0.853, 0.111, 0.935, 0.883]</r> in 2020. Therefore, if the trend continues, Mobile Gaming may become even more popular and may dominate the gaming industry by 2026.\nBased on the above thought, the answer is:\nThe predictions would be that PC Gaming would maintain a steady contribution in the future with slight increases. However, the popularity of Console Gaming may continue to decrease by 2026. Mobile Gaming is predicted to become even more popular and could take the dominant position in the gaming industry by 2026."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "UJ0kCbk9SGHTEZtQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of gaming has observed the greatest increase in popularity from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of bars in 2014 and 2020 for each category. The height of bars represents the popularity. For {PC Gaming}<r>[0.189, 0.066, 0.317, 0.094]</r> the bars in 2014<r>[0.143, 0.392, 0.225, 0.883]</r> and 2020<r>[0.689, 0.322, 0.771, 0.883]</r> appear to have a minimal difference of about 5 points on the y-axis. For {Console Gaming}<r>[0.189, 0.108, 0.378, 0.136]</r>, the bars<r>[0.225, 0.322, 0.307, 0.883]; [0.771, 0.392, 0.853, 0.883]</r> have rather decreased than increased. The bars for {Mobile Gaming}<r>[0.189, 0.150, 0.363, 0.178]</r>, however, show the biggest increase among all, from about 25<r>[0.307, 0.532, 0.389, 0.883]</r> in 2014 to about 55<r>[0.853, 0.111, 0.935, 0.883]</r> in 2020, showing a clear push of 30 points.\nBased on the above thought, the answer is:\nMobile Gaming."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "P0GNRSAXkE4jaNHp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Let's first identify the categories. According to {the x-axis labels}<r>[0.285, 0.907, 0.384, 0.934],[0.729, 0.907, 0.836, 0.934]</r>, the x-axis represents the platform, \"Facebook\" and \"Instagram\". Now let's focus on the data. The y-axis represents the count, ranging from {0}<r>[0.119, 0.887, 0.132, 0.914]</r> to {250000}<r>[0.052, 0.029, 0.132, 0.055]</r>. We have two types of bars for each platform, blue for post count and orange for user activity, identified by {the legend}<r>[0.758, 0.052, 0.967, 0.141]</r>. Let's examine the bars for each platform. For example, {the first blue bar}<r>[0.177, 0.726, 0.334, 0.898]</r> which represents the post count for Facebook is approximately at the position of 50000, and there is also {a value label \"50000\"}<r>[0.223, 0.699, 0.289, 0.726]</r> at the top of the bar. Thus, I can identify the value in each category using the height of the bars and the value annotations above them.\nBased on the above thought, the answer is:\nPlatform,Post_Count,User_Activity\nFacebook,50000,200000\nInstagram,40000,150000"
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "cJQRRZXdRL64owW0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which platform do you think has a more active user base? Provide reasoning behind your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The user base activity can be determined by comparing the {orange bars}<r>[0.334, 0.383, 0.491, 0.898],[0.782, 0.554, 0.939, 0.898]</r> which represent user activity for each platform. As the height of the orange bar for \"Facebook\", positioned at <r>[0.334, 0.383, 0.491, 0.898]</r> is taller than that of \"Instagram\", located at <r>[0.782, 0.554, 0.939, 0.898]</r>, it can be inferred that Facebook has a higher amount of user activity. In addition, despite having a similar post count, as represented by {the blue bars}<r>[0.177, 0.726, 0.334, 0.898],[0.626, 0.760, 0.782, 0.898]</r>, Facebook yields significantly higher user activity, suggesting more user engagement per post. Hence, Facebook appears to have a more active user base.\nBased on the above thought, the answer is:\nFacebook, because it has a higher user activity count and higher user engagement per post."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "JMW7rOMV0vGu7O8x",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform shows a higher number for user activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which platform shows a higher number for user activity, I need to compare the height of the {orange bars}<r>[0.334, 0.383, 0.491, 0.898],[0.782, 0.554, 0.939, 0.898]</r> that represent user activity for each platform. As we can observe, the orange bar for \"Facebook\", positioned at <r>[0.334, 0.383, 0.491, 0.898]</r>, is taller than the orange bar for \"Instagram\", located at <r>[0.782, 0.554, 0.939, 0.898]</r>. Hence, Facebook shows a higher number for user activity.\nBased on the above thought, the answer is:\nFacebook."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "R5HG9CLawDd3pTOm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your understanding of the different bar charts, please convert the data they represent into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the label on the x-axis {\"Year\"}[0.510, 0.945, 0.554, 0.971] and {the x-axis tick labels}[{\"2010\"}[0.267, 0.907, 0.320, 0.934]; {\"2020\"}[0.744, 0.907, 0.797, 0.934]], it can be inferred that the x-axis represents the different years. The y-axis, labelled as {\"Percentage (%)\"}[0.023, 0.361, 0.044, 0.565], signifies the success and failure rates of startups for that year. The different colored bars represent different parameters. According to {the legend}[0.722, 0.042, 0.967, 0.130], {the blue bars}<{\"Startup Success\"}[0.790, 0.053, 0.958, 0.079]> are the success rates and {the orange bars}<{\"Startup Failure\"}[0.790, 0.093, 0.944, 0.119]> specify the failure rates. The text on each bar indicates the exact percentage value the bar represents, for example, the {blue bar for the year 2010}[0.127, 0.663, 0.294, 0.898] has an annotation {\"22.10\"}[0.180, 0.636, 0.240, 0.663] on top of it implying that the success rate of startups in 2010 was 22.10%.\nBased on the above thought, the answer is:\nYear,Startup_Success,Startup_Failure\n2010,22.1,77.9\n2020,33.5,66.5"
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "RWQg6Q9uqXb4F2xf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, could you analyze the overall trend of startup success and failure rates from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, by analyzing and comparing the heights of the {blue bars}[0.127, 0.663, 0.294, 0.898; 0.603, 0.542, 0.770, 0.898] and {orange bars}[0.294, 0.070, 0.460, 0.898; 0.770, 0.191, 0.937, 0.898] represented in the graph, along with their corresponding {value annotations}[\"22.10\", \"33.50\", \"77.90\", \"66.50\"], it can be seen that the startup success rate has shown a moderate incline from 2010 to 2020. In 2010, the success rate was 22.1% and in 2020 it increased to 33.5%. Correspondingly, the failure rate for startups, which was 77.9% in 2010, dropped to 66.5% in 2020. Hence, overall, there seems to be a growth in the success rate and a decline in the failure rate of startups from 2010 to 2020.\nBased on the above thought, the answer is:\nThe startup success rate has been growing moderately and the failure rate has been dropping moderately from 2010 to 2020."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "8NAGglS6yzAuphXJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween which two years did startups experience the highest growth in success rates?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the years in which startups saw the highest growth in success rates, I need to compare {the blue bars}<{\"Startup Success\"}[0.790, 0.053, 0.958, 0.079]> associated with different years. Comparing {the blue bar for the year 2010}[0.127, 0.663, 0.294, 0.898] with {the blue bar for the year 2020}[0.603, 0.542, 0.770, 0.898], we can see an increase in the success rate from 22.10% in 2010 to 33.50% in 2020 as indicated by the {value annotations}[{\"2010 - 22.10\"}[0.180, 0.636, 0.240, 0.663]; {\"2020 - 33.50\"}[0.657, 0.515, 0.716, 0.542]]. This represents the highest growth in success rates in the given data set.\nBased on the above thought, the answer is:\n2010 to 2020"
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "sfxYDtRL7tG0TvLi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the x-axis at the bottom of the chart {Cuisine}[0.494, 0.945, 0.570, 0.971], and {the x-axis tick labels}[{\"Average Dish Price\"}[0.195, 0.907, 0.392, 0.934]; {\"Restaurant Popularity\"}[0.656, 0.907, 0.884, 0.934]], it is clear that the x-axis represents two categories: the average dish price and the popularity of the restaurant. The y-axis tick labels represent the corresponding values, ranging from {0}[0.066, 0.887, 0.079, 0.914] to almost {90} (as this is a scaled graph). For each category, there are two bars according to {the legend}[0.796, 0.042, 0.967, 0.130] which represents Italian and Japanese cuisines. I can use the value annotations at the top of the bars to determine the values. For instance, {the Italian bar for \"Average Dish Price\"}[0.127, 0.523, 0.294, 0.898] has an annotation of {\"35.40\"}[0.180, 0.496, 0.240, 0.523] at the top, so the average price of the Italian Dish is $35.40.\nBased on the above thought, the answer is:\nCuisine,Average Dish Price,Restaurant Popularity\nItalian,35.40,75.30\nJapanese,27.60,78.12"
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "jamGPECTeBznqZ1l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the commonality and differences of these two cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive comparison between {the bar for Italian Cuisine}<{\"Italian\"}[0.863,0.053, 0.929, 0.079]> and {the bar for Japanese Cuisine}<{\"Japanese\"}[0.863, 0.093, 0.958, 0.119]>. In terms of similarities, both cuisines {the two bars in \"Restaurant Popularity\"}[{Italian bar}[0.603, 0.100, 0.770, 0.898]; {Japanese bar}[0.770, 0.070, 0.937, 0.898]] have a popularity rating over 70, indicating both cuisines are very popular. However, the differences lie in the average dish price wherein the {Italian bar at \"Average Dish Price\"}[0.127, 0.523, 0.294, 0.898] is higher than the {Japanese bar at \"Average Dish Price\"}[0.294, 0.605, 0.460, 0.898], which shows Italian dishes are generally more expensive than Japanese dishes.\nBased on the above thought, the answer is:\nThe commonality between Italian and Japanese cuisine is they are both highly popular among the population, with popularity ratings over 70%. The key difference lies in the average dish prices, Italian food is generally more expensive than Japanese food."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "7yM7DsadC0pDAEh2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has a higher difference in the average dish price and restaurant popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the cuisine with a higher difference between the average dish price and restaurant popularity, I need to subtract the dish price from the restaurant popularity for each cuisine. Firstly, for the Italian cuisine, {the bar for the average dish price}[0.127, 0.523, 0.294, 0.898] indicates a value of $35.40 and {the bar for the restaurant popularity}[0.603, 0.100, 0.770, 0.898] indicates a value of 75.30%. The difference is around 39.90. Similarly, for the Japanese cuisine, {the bar for average dish price}[0.294, 0.605, 0.460, 0.898] shows a value of $27.60 and {the bar for restaurant popularity}[0.770, 0.070, 0.937, 0.898] shows the value of 78.12%. The difference is around 50.52. Hence, the Japanese cuisine has a higher difference in average dish price and restaurant popularity.\nBased on the above thought, the answer is:\nJapanese."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "pLGmtw9AGKpOLVbq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please create a table that summarizes the outlook scores for all the technologies as represented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart represents the outlook score of various technologies. On the x-axis, we have the different technologies named such as {5G}<r>[0.137, 0.919, 0.157, 0.942]</r>, {Blockchain}<r>[0.263, 0.919, 0.339, 0.942]</r>, {Virtual Reality}<r>[0.406, 0.919, 0.504, 0.942]</r>, {Augmented Reality}<r>[0.543, 0.919, 0.675, 0.942]</r>, {Artificial Intelligence}<r>[0.692, 0.919, 0.834, 0.942]</r>, and {IoT}<r>[0.906, 0.919, 0.927, 0.942]</r>. On the y-axis, we have the corresponding outlook scores ranging from 80 to 100 as given by the {y-axis labels}<r>[0.043, 0.902, 0.074, 0.925]; [0.043, 0.792, 0.074, 0.816]; [0.043, 0.683, 0.074, 0.706]; [0.043, 0.573, 0.074, 0.596]; [0.043, 0.463, 0.074, 0.487]; [0.043, 0.354, 0.074, 0.377]; [0.043, 0.244, 0.074, 0.268]; [0.043, 0.135, 0.074, 0.158]; [0.035, 0.025, 0.074, 0.048]</r>. The height of each bar and the value annotation at the top of it depict the outlook score for the respective technology. For instance, the height of the bar for {5G}<r>[0.120, 0.363, 0.174, 4.418]</r> corresponds to an outlook score of 92.5 as per the value annotation.\nBased on the above thought, the answer is:\nTechnology,Outlook Score\n5G,92.5\nBlockchain,86.9\nVirtual Reality,89.3\nAugmented Reality,87.5\nArtificial Intelligence,95.3\nIoT,90.2"
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "DFusNcCG0wCV54b8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which technologies seem to have a promising future based on their outlook scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to look at the height of the bars that represent the outlook score for each technology. For instance, the bar for {5G}<r>[0.120, 0.363, 0.174, 4.418]</r> has a height corresponding to a score of 92.5, which suggests a positive outlook. Similarly, the bar for {Artificial Intelligence}<r>[0.736, 0.240, 0.790, 4.418]</r> has the highest height, corresponding to a score of 95.3, indicating the most promising future among the technologies. The bar for {IoT}<r>[0.890, 0.464, 0.944, 4.418]</r>, corresponding to a score of 90.2, also suggests a positive outlook. On the other hand, the bar for {Blockchain}<r>[0.274, 0.608, 0.328, 4.418]</r> is the smallest, indicating a slightly less optimistic outlook compared to the others but still higher than 80.\nBased on the above thought, the answer is:\nThe technologies that seem to have a promising future based on their outlook scores are 5G, Artificial Intelligence, and IoT as they all have outlook scores above 90. Although the Blockchain has the lowest score, it is still a promising technology as it has a score greater than 80 which indicates substantial potential."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "en0Jsx3NVd7J5I4u",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has the lowest outlook score according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the technology with the lowest outlook score, I need to compare the height of the bars for each technology. Looking from left to right, the first bar for {5G}<r>[0.120, 0.363, 0.174, 4.418]</r> shows a score of approximately 92.5. The second bar for {Blockchain}<r>[0.274, 0.608, 0.328, 4.418]</r> shows a score of around 86.9, and so on. Comparing all the bars and their corresponding scores, it can be observed that the {Blockchain}<r>[0.274, 0.608, 0.328, 4.418]</r> has the smallest bar, representing the lowest outlook score.\nBased on the above thought, the answer is:\nBlockchain."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "72J1uQzafcGTPBaR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will identify the relevant elements in the chart and then transform them into a structured table. The x-axis of the chart represents the 'Number of Issue Points' at {[0.421, 0.945, 0.662, 0.971]}, with distinct values of {100}[0.289, 0.907, 0.329, 0.934] and {1000}[0.748, 0.907, 0.801, 0.934]. The y-axis represents 'Points' at {[0.023, 0.429, 0.044, 0.508]}, ranging from {40.0}[0.052, 0.887, 0.099, 0.914] to {60.0}[0.052, 0.029, 0.099, 0.055]. The bar height and the value annotations at the top can determine the values for each group. For 'Equal Rights', there are two bars at [{100}[0.146, 0.484, 0.309, 2.615], {1000}[0.612, 0.420, 0.775, 2.615]], with values 49.63 and 51.12 respectively. For 'Social Welfare', there are two bars at [{100}[0.309, 0.522, 0.472, 2.615], {1000}[0.775, 0.169, 0.938, 2.615]], with values 48.75 and 56.97 respectively. By using this information, I can form the CSV table.\nBased on the above thought, the answer is:\nNumber of Issue Points,Equal Rights,Social Welfare\n100,49.63,48.75\n1000,51.12,56.97"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "DJnZtUJzXyM6pHut",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given chart, how do you interpret the policy changes as the number of issue points increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret policy changes reflected in the chart, we need to compare the change in 'Equal Rights' and 'Social Welfare' bars from 100 to 1000 issue points. The 'Equal Rights' value at 100 issue points {[0.146, 0.484, 0.309, 2.615]}is slightly higher than the 'Social Welfare' value {[0.309, 0.522, 0.472, 2.615]}. However, when the issue points increase to 1000, the 'Social Welfare' value {[0.775, 0.169, 0.938, 2.615]} significantly surpasses the 'Equal Rights' value {[0.612, 0.420, 0.775, 2.615]}.\nBased on the above thought, the answer is:\nAs the number of issue points increase from 100 to 1000, there's a proportional increase in the emphasis on 'Social Welfare' compared to 'Equal Rights'. This could suggest that the policy focus shifts more towards social welfare issues as social protests intensify."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "adbgJgh8HoVXFuQh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor how many issue points, the score for 'Equal Rights' is higher than that for 'Social Welfare'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the heights of each pair of 'Equal Rights' and 'Social Welfare' bars for the same number of issue points. For the first pair at issue points of {100}[0.289, 0.907, 0.329, 0.934], the 'Equal Rights' bar at {[0.146, 0.484, 0.309, 2.615]} is higher than the 'Social Welfare' bar at {[0.309, 0.522, 0.472, 2.615]}. For the second pair at issue points of {1000}[0.748, 0.907, 0.801, 0.934], the 'Equal Rights' bar at {[0.612, 0.420, 0.775, 2.615]} is lower than the 'Social Welfare' bar at {[0.775, 0.169, 0.938, 2.615]}. Hence, for only one number of issue points, the score for 'Equal Rights' is higher than that for 'Social Welfare'.\nBased on the above thought, the answer is:\n1"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "SV3Vv9f1EN30aHFU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the depicted bar chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer from the chart that the axes are labeled with 'Health Indicators' as given by the {xlabel}[0.442, 0.945, 0.622, 0.971] and 'Score' presented by the {ylabel}[0.023, 0.431, 0.044, 0.506]. The x-axis represents the two categories - {'Physical_Activity'}<r>[0.206, 0.907, 0.381, 0.934]</r> and {'Balanced_Diet'}<r>[0.694, 0.907, 0.846, 0.934]</r>. There are two respective bars for each category distinguished by their color where the {'Healthy Individuals'}<r>[0.757, 0.063, 0.958, 0.090]</r> have blue coloring and the {'Patients'}<r>[0.757, 0.103, 0.841, 0.130]</r> are depicted in orange. I can determine the values of each category by examining the height of these bars and the annotations given alongside each. For instance, the score for 'Physical_Activity' under 'Healthy Individuals' is around 75 due to the colored bar height<r>[0.127, 0.361, 0.294, 1.971]</r> and corresponding annotation<r>[0.180, 0.334, 0.240, 0.361]</r>.\nBased on the above thought, the answer is:\nHealth Indicator,Score_Healthy_Individuals,Score_Patients\nPhysical_Activity,75,60\nBalanced_Diet,85,62"
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "NRRp3l46u4PzmH8e",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow well is the health indicator 'Balanced Diet' maintained by patients in comparison to healthy individuals based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can compare the bars representing \u2018Healthy Individuals\u2019 and \u2018Patients\u2019 under the category 'Balanced Diet'. The bar for 'Healthy Individuals'<r>[0.603, 0.146, 0.770, 1.971]</r> has an associated score of 85<r>[0.657, 0.120, 0.717, 0.146]</r>. On the other hand, the bar for 'Patients'<r>[0.770, 0.640, 0.937, 1.971]</r> is a bit lower but still fairly high with a score of 62<r>[0.824, 0.613, 0.883, 0.640]</r>. These observations indicate that patients have managed to maintain a reasonably high score for this indicator, although it's slightly lower than that of healthy individuals.\nBased on the above thought, the answer is:\nThe patients have reasonably maintained the health indicator 'Balanced Diet' because their score of 62 is not significantly lower than the score of 85 for the healthy individuals."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "sRNXMzEVujo3rNe1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the score for Healthy Individuals in the Physical Activity indicator than Patients in the same indicator?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the difference, I need to find and compare the values of 'Physical Activity' for both 'Healthy Individuals' and 'Patients'. The score for 'Healthy Individuals' is 75 as noted by the score annotation <r>[0.180, 0.334, 0.240, 0.361]</r> at the top of the bar <r>[0.127, 0.361, 0.294, 1.971]</r>. Now, the 'Patients' have a score of 60 annotated at <r>[0.347, 0.656, 0.407, 0.683]</r>. I can then subtract the 'Patients' score from that of the 'Healthy Individuals'.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "h29q9AKBqBzFhIY6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the ROI data for Bitcoin and Blockchain over the next 5, 10, and 20 years in tabular CSV format as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the individual bars within the bar chart and noting their heights relative to the y-axis labels, I can deduce the ROI values for each duration. Each subgroup of bars corresponds to different time frames: the first group portrays the ROI for the next 5 years, the second group for 10 years, and the third group for 20 years. The {blue bars}<r>[0.046, 0.191, 0.075, 0.543]</r> depict Bitcoin's ROI across the different durations, while the {red bars}<r>[0.046, 0.543, 0.075, 0.580]</r> represent Blockchain's ROI. For each group of bars, the ROI value is displayed at the top as a number within the bar chart. For instance, the {Bitcoin 5-year ROI bar}<r>[0.046, 0.191, 0.075, 0.543]</r> has a value of {78.3}<r>[0.049, 0.195, 0.071, 0.230]</r> marked at its peak. By repeating this process for each bar, I can construct the complete table.\nBased on the above thought, the answer is:\nDuration,Bitcoin ROI,Blockchain ROI\n5 years,78.3,12.1\n10 years,33.7,35.7\n20 years,45.9,17.7"
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "PQvwjLZ335tqHgcs",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment, Bitcoin or Blockchain, seems to offer a higher return after 20 years according to the data presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must compare the 20-year ROI values of Bitcoin and Blockchain. I focus my attention on the last bars in each subgroup within the chart. For the 20-year forecast, the {Bitcoin bar}<r>[0.557, 0.438, 0.586, 0.543]</r> shows a value of {23.4}<r>[0.503, 0.388, 0.525, 0.423]</r>, while the {Blockchain bar}<r>[0.557, 0.543, 0.586, 0.690]</r> has a marked value of {32.6}<r>[0.561, 0.640, 0.583, 0.675]</r>. These values indicate the projected ROI after 20 years for Bitcoin and Blockchain, respectively. By comparing the two numbers, I can determine which investment offers a higher return.\nBased on the above thought, the answer is:\nBlockchain. After 20 years, the projected ROI for Blockchain is higher than that of Bitcoin according to the chart data presented, with Blockchain showing a projected ROI of 32.6% compared to Bitcoin's 23.4%."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "ZnET2tuIx3BS2e0d",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average ROI for Blockchain across all three durations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate Blockchain's average ROI, I first need to locate the ROIs for each of the durations. By examining {Blockchain's 5-year ROI bar}<r>[0.046, 0.543, 0.075, 0.580]</r>, I find a value of {12.1}<r>[0.107, 0.548, 0.129, 0.583]</r>. For the {10-year duration}<r>[0.302, 0.543, 0.331, 0.598]</r>, Blockchain has an ROI value of {35.7}<r>[0.305, 0.654, 0.327, 0.689]</r>. And, finally, the {20-year ROI}<r>[0.442, 0.543, 0.470, 0.717]</r> for Blockchain is {17.7}<r>[0.503, 0.701, 0.525, 0.736]</r>. I will add these three numbers and divide by 3 to find the average.\nBased on the above thought, the answer is:\n21.83"
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "r8GAOVOn7tnx8uRd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data of the \"Great Recession\" and \"Stagnation\" in a structured table format based on your observation of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the subchart titled {Great Recession}<r>[0.070, 0.037, 0.166, 0.083]</r>, I see bars at intervals {5}<r>[0.046, 0.245, 0.075, 0.498]</r>, {10}<r>[0.104, 0.220, 0.133, 0.498]</r>, and {20}<r>[0.162, 0.169, 0.191, 0.498]</r>. Each bar is annotated with their respective values, such as {50}<r>[0.054, 0.253, 0.067, 0.288]</r> at interval 5. Similarly, in the {Stagnation}<r>[0.049, 0.771, 0.193, 0.886]</r> subplot, there are bars at intervals {5}<r>[0.046, 0.498, 0.075, 0.853]</r>, {10}<r>[0.104, 0.498, 0.133, 0.802]</r>, and {20}<r>[0.162, 0.498, 0.191, 0.751]</r>, annotated with values such as {60}<r>[0.112, 0.749, 0.124, 0.784]</r> at interval 5. Based on this visual data, a structured table can be formed.\nBased on the above thought, the answer is:\nEvent,5,10,20\nGreat Recession,50,55,65\nStagnation,70,60,50"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "AOUXKy2ONHYnbmB1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic carrier shown in the bar chart demonstrates consistent negative growth across the observed intervals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern which economic carrier exhibits consistent negative growth, I evaluate the bars' trends over the three intervals. In the {Great Recession}<r>[0.070, 0.037, 0.166, 0.083]</r> subplot, the values increase from {50}<r>[0.046, 0.245, 0.075, 0.498]</r> at interval {5} to {65}<r>[0.162, 0.169, 0.191, 0.498]</r> at interval {20}. For {Hyperinflation}<r>[0.049, 0.771, 0.193, 0.886]</r>, values decrease from {60}<r>[0.046, 0.498, 0.075, 0.853]</r> at {5} to {20}<r>[0.162, 0.498, 0.191, 0.777]</r> at {20}. Reviewing all carriers and their bar trends, I look for consistent downward slopes across the intervals from the annotated numerical values.\nBased on the above thought, the answer is:\nHyperinflation. Values consistently decrease from intervals 5 to 20, indicating a steady negative growth trend as per bar graphs represented in its subplot."
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "E55Ggw33ZqVrLHBj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_46_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the \"EU Debt Crisis\" subplot, what was the percentage change in the \"Stock Market Crashes\" category from interval 5 to 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage change in the {Stock Market Crashes}<r>[0.837, 0.346, 0.866, 0.498]</r> category within the {EU Debt Crisis}<r>[0.867, 0.037, 0.952, 0.083]</r> subplot, I examine the annotated values for the bars at intervals {5}<r>[0.837, 0.346, 0.866, 0.498]</r>, specified as {30}<r>[0.845, 0.354, 0.858, 0.389]</r> and {20}<r>[0.953, 0.397, 0.982, 0.498]</r>, specified as {20}<r>[0.903, 0.380, 0.916, 0.415]</r>. The percentage change is found by subtracting the later value from the earlier value and dividing by the earlier value, then multiplying by 100.\nBased on the above thought, the answer is:\n-33.33%"
+            }
+        ],
+        "id_raw": 676
+    },
+    {
+        "id": "rjIozb7l8ViWtn4N",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a structured table from the given bar chart which represents yield trends for automotive and non-automotive sectors across three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the {subplots}<r>[0.030, 0.037, 0.198, 0.961]</r>, I can see that each subplot corresponds to a different company. Each subplot contains {two groups of bars}<r>[0.038, 0.116, 0.063, 0.499]</r> and <r>[0.038, 0.499, 0.063, 0.558]</r>, indicating automotive and non-automotive yields, respectively. The x-axis has {ticks for three years}<r>[0.039, 0.971, 0.062, 0.985]</r>; <r>[0.102, 0.971, 0.126, 0.985]</r>; <r>[0.166, 0.971, 0.190, 0.985]</r>. The value for each bar is provided as text above or below the bar, e.g., for Toyota in Year 1, {automotive yield is \"66.3\"}<r>[0.042, 0.146, 0.059, 0.160]</r> and non-automotive yield is \"{-10.1}\"<r>[0.042, 0.512, 0.059, 0.526]</r>. I'll continue this process for each company and year to construct the table.\nBased on the above thought, the answer is:\nCompany,Year 1 Automotive,Year 1 Non-Automotive,Year 2 Automotive,Year 2 Non-Automotive,Year 3 Automotive,Year 3 Non-Automotive\nToyota,66.3,-10.1,61.9,-13.6,53.8,-13.7\nFord,49.3,-36.3,45.7,-41.0,41.6,-31.5\nTesla,48.0,-40.5,42.6,-49.6,36.5,-36.5\nNissan,43.8,-49.5,40.2,-56.8,30.6,-42.8\nGeneral Motors,22.3,-64.7,25.9,-59.6,22.5,-43.4"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "MAitNiPCjfheMjot",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo non-automotive sectors show a growth trend across the years for any of the companies based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze growth trends for the non-automotive sector, I need to evaluate the {red bars}<r>[0.038, 0.499, 0.063, 0.558]</r>; <r>[0.038, 0.499, 0.063, 0.558]</r> for each company in {each year}<r>[0.039, 0.971, 0.062, 0.985]</r>; <r>[0.102, 0.971, 0.126, 0.985]</r>; <r>[0.166, 0.971, 0.190, 0.985]</r>. I compare the non-automotive yields across years to see if there are increases. Observing the values above mentioned red bars, it's clear that no company shows a consistent increase over three years - some increase and then decrease or fluctuate. Hence from this bar chart, it is not evident that there is a significant growth trend for any company's non-automotive sector.\nBased on the above thought, the answer is:\nNo consistent growth trend. Yields fluctuate without a clear upward trajectory for all companies."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "jWunEfnl3r7qIkxY",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average automotive yield for General Motors over the three-year span?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the {subplot for General Motors}<r>[0.823, 0.037, 0.992, 0.961]</r> which contains {three blue bars}<r>[0.831, 0.371, 0.856, 0.499]</r>; <r>[0.895, 0.350, 0.920, 0.499]</r>; <r>[0.959, 0.369, 0.984, 0.499]</r>, representing three years of automotive yield. Reading off the values, I find {Year 1 as \"22.3\"}<r>[0.835, 0.400, 0.852, 0.414]</r>, {Year 2 as \"25.9\"}<r>[0.899, 0.379, 0.916, 0.393]</r>, and {Year 3 as \"22.5\"}<r>[0.959, 0.369, 0.984, 0.499]</r>. To find the average, I add these values and divide by three.\nBased on the above thought, the answer is:\n23.6"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "0rnOxXxRZ6LmzZvo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the productivity values change over the three-week period in the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the productivity values' changes, I need to inspect the first subplot titled {Productivity}<r>[0.024, 0.093, 0.165, 0.903]</r>. It consists of three vertical bars representing {Week 1}<r>[0.030, 0.294, 0.056, 0.903]</r>, {Week 2}<r>[0.082, 0.238, 0.107, 0.903]</r>, and {Week 3}<r>[0.133, 0.192, 0.159, 0.903]</r>. Each of these bars is annotated with a value at the top, which indicates the productivity for that week. By examining these coordinates, I can extract the relevant productivity values for all three weeks.\nBased on the above thought, the answer is:\nWeek,Productivity\nWeek 1,60.2\nWeek 2,65.7\nWeek 3,70.3"
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "kheWDphsJzigLzet",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the KPIs has shown a consistent trend over the three weeks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify KPIs with a consistent trend, I review each subplot and observe the direction of the value changes across all weeks. Consistency in trends appears as an uninterrupted increase or decrease in heights of the consecutive bars. By examining the bar heights chronologically, I can determine whether a KPI shows a positive, negative, or volatile trend. The KPIs are respectively located in their subplots: {Productivity}<r>[0.024, 0.093, 0.165, 0.903]</r>, {Sales}<r>[0.189, 0.093, 0.331, 0.903]</r>, {Customer Satisfaction}<r>[0.354, 0.093, 0.496, 0.903]</r>, {Product Quality}<r>[0.519, 0.093, 0.661, 0.903]</r>, {Operational Efficiency}<r>[0.685, 0.093, 0.826, 0.903]</r>, and {Profitability}<r>[0.850, 0.093, 0.992, 0.903]</r>. Closer inspection reveals a consistent upward trend for Sales and Operational Efficiency.\nBased on the above thought, the answer is:\nSales and Operational Efficiency. Each shows a steady increase over the three weeks, without any interruption or reversal in the trend."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "b8JUPAp9wzkyIffr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in operational efficiency from Week 1 to Week 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in operational efficiency from Week 1 to Week 3, I need to look at the fifth subplot titled {Operational Efficiency}<r>[0.685, 0.093, 0.826, 0.903]</r> and compare the height of the bars for {Week 1}<r>[0.691, 0.699, 0.717, 0.903]</r> and {Week 3}<r>[0.794, 0.644, 0.820, 0.903]</r>. The annotations on the bars reveal values of 20.2 for Week 1 and 25.6 for Week 3. The percentage increase is calculated by the formula [(Week 3 value - Week 1 value) / Week 1 value * 100].\nBased on the above thought, the answer is:\n26.7%"
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "JLYCtWqb88yQvVI2",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, can you convert the data representation into table form, ideally in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the displayed chart labels {\"Trade Equilibrium\"}[0.098, 0.118, 0.199, 0.141] and {\"Globalization\"}[0.098, 0.153, 0.173, 0.176] from {the legend}[0.061, 0.074, 0.203, 0.185], and chart trends on {x-axis}[0.074, 0.744, 0.949, 0.880], I can note that each trend is represented by two bars, reflecting the scores of trade equilibrium and globalization respectively. To convert these data into a CSV table, I extract the labels of the x-axis tick labels representing the trade trends, then calculate the values for \"Trade Equilibrium\" and \"Globalization\" by estimating the height of the bars based on the y-axis scale from {0}[0.043, 0.727, 0.051, 0.750] to {100}[0.029, 0.053, 0.051, 0.076]. This height combined with the exact data displayed at the top of each bar, for example {\"25.15\"}[0.076, 0.536, 0.109, 0.560] for the \"Slow Recovery\" trend, allows for accurate data extraction.\nBased on the above thought, the answer is:\nLabels,Trade Equilibrium,Globalization\nSlow Recovery,25.15,20.21\nModerate Growth,60.17,40.19\nSteady Recovery,10.78,12.87\nSustained Growth,5.66,2.45\nRapid Decline,18.55,26.78\nSlow Recovery - outlier,80.85,33.90\nModerate Growth - outlier,15.21,42.10\nSteady Recovery - outlier,5.22,3.26\nSustained Growth - outlier,30.15,35.22\nRapid Decline - outlier,90.19,77.28"
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "N7mTiyFmsElUW2n8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the relationship between international trade equilibrium and globalization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The comparison and analysis of {Globalization bars}<{\"Globalization\"}[0.098, 0.153, 0.173, 0.176]> and {Trade Equilibrium bars}<{\"Trade Equilibrium\"}[0.098, 0.118, 0.199, 0.141]> under each trend label on the {x-axis}[0.074, 0.744, 0.949, 0.880] can infer the relationship between international trade equilibrium and globalization. The bars generally show that for a specific trend, if trade equilibrium value is higher, globalization value is also higher, and vice versa. This suggests a positive correlation between trade equilibrium and globalization. However, it's worth pointing out that there are outliers, for example, the \"Slow Recovery - outlier\"[0.496, 0.744, 0.599, 0.890] with high trade equilibrium but relatively low globalization. This indicates that the correlation is not absolute and can be influenced by other variables or unique circumstances.\nBased on the above thought, the answer is:\nIt can be inferred that international trade equilibrium and globalization are generally positively correlated. However, due to some outliers, the relationship is not always consistent which implies the influence of other variables or unique circumstances."
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "NpR0F12ohTFOoywS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the trend with the highest Globalization value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should focus on the {orange bars}<{\"Globalization\"}[0.098, 0.153, 0.173, 0.176]> representing the Globalization value for each trend. By comparing the heights of these bars and considering the y-axis labels, I can find the trend with the highest Globalization value. Detailed observation indicates that the bar within {\"Rapid Decline - outlier\"}[0.850, 0.744, 0.949, 0.890] coordinates[x0.900,y0.215,x0.930,y0.736] is the highest, going up to nearly 100 on the y-axis. Its exact value {\"77.28\"}[ 0.898, 0.185, 0.931, 0.208] is displayed on the top of the bar. Therefore, the trend with the highest Globalization value is \"Rapid Decline - outlier\".\nBased on the above thought, the answer is:\nRapid Decline - outlier"
+            }
+        ],
+        "id_raw": 705
+    },
+    {
+        "id": "aNzk7zQXEdSGWjE5",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the investment amounts of Venture Capital Market in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis is labeled with the company names: {SeedFund}<r>[0.096, 0.803, 0.144, 0.900]</r>, {VenturePromotions}<r>[0.185, 0.803, 0.271, 0.975]</r>, {QuickCapital}<r>[0.307, 0.803, 0.367, 0.924]</r>, {SharpInvestors}<r>[0.410, 0.803, 0.480, 0.943]</r>, {GoldenEggs}<r>[0.525, 0.803, 0.582, 0.917]</r>, {RapidGrowers}<r>[0.629, 0.803, 0.695, 0.934]</r>, {CashCows}<r>[0.746, 0.803, 0.796, 0.903]</r> and {DiamondMiners}<r>[0.843, 0.803, 0.915, 0.948]</r>. The y-axis includes the range of values from {0}<r>[0.043, 0.786, 0.051, 0.809]</r> to {100}<r>[0.029, 0.053, 0.051, 0.076]</r>. There are two types of investments: {Non_Aggregation}<r>[0.877, 0.118, 0.977, 0.142]</r> represented by blue bars and {Aggregation}<r>[0.877, 0.153, 0.948, 0.176]</r> represented by orange bars. Each investment type has labeled values such as {75.3}<r>[0.085, 0.213, 0.111, 0.236]</r> and {30.2}<r>[0.129, 0.544, 0.154, 0.567]</r> enabling the creation of the CSV table.\nBased on the above thought, the answer is:\nCompanies,Non_Aggregation,Aggregation\nSeedFund,75.3,30.2\nVenturePromotions,90.4,45.6\nQuickCapital,20.0,80.7\nSharpInvestors,10.0,70.0\nGoldenEggs,60.6,0.0\nRapidGrowers,70.7,47.1\nCashCows,30.1,90.7\nDiamondMiners,40.2,80.3"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "ACo9BmPKYicW6Lgp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had consistent investment size between Non_Aggregation and Aggregation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The consistency of investment size can be measured by how close Non_Aggregation and Aggregation investments are for each company. The gap between {RapidGrowers Non_Aggregation investment}<r>[0.628, 0.247, 0.653, 0.270]</r> and {its Aggregation investment}<r>[0.671, 0.420, 0.697, 0.443]</r> seems to be the smallest among all. The differences for other companies appear to be relatively larger, such as {SeedFund}<r>[0.077, 0.243, 0.120, 0.795]</r> and {0.120, 0.574, 0.163, 0.795]</r>, and {QuickCapital}<r>[0.293, 0.649, 0.337, 0.795]</r> and {0.337, 0.204, 0.380, 0.795]</r>, where differences in heights of the bars are substantially larger.\nBased on the above thought, the answer is:\nRapidGrowers"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "GlN3P3JRrMz6sQcH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had the highest increase from Non_Aggregation to Aggregation investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the difference between the investment amounts of Non_Aggregation and Aggregation for each company. For example, {SeedFund}<r>[0.096, 0.803, 0.144, 0.900]</r> has an investment of {75.3}<r>[0.085, 0.213, 0.111, 0.236]</r> for Non_Aggregation which increased to {30.2}<r>[0.129, 0.544, 0.154, 0.567]</r> in Aggregation. The same calculation applies to the rest of the companies. It can be observed that {QuickCapital}<r>[0.307, 0.803, 0.367, 0.924]</r> has Non_Aggregation investment of {20.0}<r>[0.302, 0.618, 0.328, 0.642]</r> and the Aggregation investment increased to {80.7}<r>[0.346, 0.173, 0.371, 0.197]</r>, presenting the greatest increment.\nBased on the above thought, the answer is:\nQuickCapital"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "nncAtptiljCcFg4z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar graph details you\u2019ve seen, how would you structure the two trends, 'Non-aggregation' and 'Aggregation,' into a readable table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with the chart's title {\u201cCulture and Arts Industry Development\u201d}<r>[0.396, 0.025, 0.668, 0.055]</r>, I observe a clear categorization along the {x-axis}<r>[0.018, 0.695, 0.949, 0.890]</r> with different sectors in the culture and arts industry. The {y-axis}<r>[0.035, 0.262, 0.047, 0.486]</r> denotes development level in values from {0 to 100}<r>[0.066, 0.677, 0.073, 0.701; 0.051, 0.053, 0.073, 0.076]</r>. There are two sets of bars for each sector, one for each trend. Aligning the {value labels}<r>[0.099, 0.370, 0.132, 0.393; 0.808, 0.597, 0.834, 0.621]</r> with each bar will help provide the exact figures for the table\u2014e.g., 'Theatre and Puppetry' has {60.5 for Non-aggregation}<r>[0.093, 0.309, 0.138, 0.687]</r> and {45.89 for Aggregation}<r>[0.093, 0.400, 0.138, 0.687]</r>.\nBased on the above thought, the answer is:\nSector,Non-aggregation,Aggregation\nTheatre and Puppetry,60.5,45.89\nPainting and Digital Art,38.6,55.22\nClassical Music,15.9,32.43\nBallet and Contemporary Dance,75.55,22.18\nLiterature,42.3,12.67\nPhotography,50.25,25.16\nFilm and Video,13.55,70.89\nSculpture,32.11,26.98\nArchitecture,22.08,68.56\nPerforming Arts Festivals,40.9,15.43\nCeramics,60.19,32.13\nFashion,85.9,9.45\nGraphic Design,25.51,38.2\nPop Music,8.82,77.29"
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "chmjsXjUjxW8OaL8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data observed on the chart, which culture and arts industry sector seems to have adapted most significantly to aggregation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the most significant adaptation to aggregation, I shall look for the biggest positive shift from {Non-aggregation}<r>[0.121, 0.118, 0.219, 0.141]</r> to {Aggregation}<r>[0.121, 0.153, 0.192, 0.176]</r> by analyzing the differences in bar heights for both trends. For instance, {Pop Music's Aggregation}<r>[0.927, 0.204, 0.971, 0.687]</r> bar shows a large growth when compared to its {Non-aggregation}<r>[0.927, 0.631, 0.971, 0.687]</r> counterpart. This suggests a substantial shift, further corroborated by the value annotations indicating {8.82 for Non-aggregation}<r>[0.927, 0.631, 0.971, 0.687]</r> and {77.29 for Aggregation}<r>[0.927, 0.204, 0.971, 0.687]</r>, showing that Pop Music has adapted the most significantly to aggregated methods within the culture and arts industry.\nBased on the above thought, the answer is:\nPop Music"
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "weoBB9nl0ioPf7ay",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows the highest level of development under the Non-aggregation trend as depicted by the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest level of Non-aggregation, I need to assess all the value annotations above the corresponding bars for this trend. Starting with Fashion, which has a very high development level of {85.9}<r>[0.798, 0.150, 0.843, 0.687]</r>, I will compare it with other sectors. Proceeding through the graph, none of the {value annotations}<r>[0.103, 0.279, 0.129, 0.302; 0.936, 0.601, 0.962, 0.625]</r> indicate a value higher than 85.9. Therefore, Fashion represents the highest Non-aggregation development level among the sectors evaluated.\nBased on the above thought, the answer is:\nFashion"
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "O9GwCWCozQuTgXvC",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me convert the bar chart data representing the 'Non-Volatile' and 'Trending' series over months into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart entitled {\"Time Series Data Comparison\"}[0.398, 0.030, 0.644, 0.066], I can see that it illustrates two series, 'Non-Volatile' and 'Trending', with monthly data. Looking at the chart's x-axis, I note markers labeled with the months from January to December, found in {\"xticks\"}<{element list}>[{list of dictionaries}]. On the chart's y-axis, in {\"yticks\"}, data values range from 0 to 60. This axis provides the magnitude of each month's data. Each month has two bars with different colors, representing 'Non-Volatile' and 'Trending' series respectively. According to the legend at {the top}[0.064, 0.089, 0.197, 0.223], the green bars correspond to the 'Non-Volatile' series and the orange bars represent the 'Trending' series. The height of each bar and the annotated value at the top of each bar indicate the actual data. Thus, for the 'Non-Volatile' series in January, the value would be 10 as indicated by the green bar found at {\"bar_coordinates\"}<[0.107, 0.789, 0.128, 0.932]>, and the text labeled '10' found at {\"text_coordinates\"}<[0.109, 0.753, 0.127, 0.781]>. Repeating this process for each month and for both 'Trending' and 'Non-Volatile' series allows me to extract all the values necessary to create a structured table.\nBased on the above thought, the answer is:\nMonth,Non_Volatile,Trending\nJan,10,5\nFeb,12,8\nMar,10,10\nApr,11,15\nMay,15,19\nJun,20,25\nJul,19,29\nAug,17,32\nSep,20,37\nOct,18,41\nNov,22,46\nDec,25,52"
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "YvXRLL2bzwlnD10N",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which of the two series does the chart suggest has shown a more significant increase over the year, and what led you to this interpretation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart illustrates data for 'Non-Volatile' and 'Trending' series over time, and we are tasked to analyze which saw a more significant growth over the course of the year. The green bars represent the 'Non-Volatile' series and the orange bars the 'Trending' series, as per the {chart legend}[0.064, 0.089, 0.197, 0.223]. By comparing {the bar from 'Non-Volatile' in January}[0.107, 0.789, 0.128, 0.932] with {its bar in December}[0.892, 0.575, 0.914, 0.932], and {the bar from 'Trending' in January}[0.128, 0.861, 0.150, 0.932] with {its bar in December}[0.914, 0.189, 0.935, 0.932], one can see that though both series have seen an increase, the 'Trending' series appears to change more significantly. This is visible in the rise of the 'Trending' bars' heights, which goes from approximately 5 to 52, compared to the 'Non-Volatile' bars' heights which rise from 10 to 25. Therefore, on basing it on the bar heights and their corresponding data values, the 'Trending' series shows a more significant increase over the year.\nBased on the above thought, the answer is:\nThe 'Trending' series showed a more significant increase over the year. This conclusion is based on the evident increase in the height of the 'Trending' bars, from a data value of 5 to 52, which is more pronounced than the rise from 10 to 25 in the 'Non-Volatile' series."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "z6Y9RCY88EDh5INR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month recorded the highest data value for the 'Trending' series according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the month with the highest data value for the 'Trending' series, I need to check the height of each orange bar corresponding to the 'Trending' series in the chart and find the tallest one. This highest bar I found is {\"bar_coordinates\"}<[0.914, 0.189, 0.935, 0.932]>, with its top reaching up to roughly data value 52 on the y-axis. The corresponding x-axis label indicates the month this highest value was recorded, which is for the month of {December}[0.900, 0.942, 0.927, 0.970].\nBased on the above thought, the answer is:\nDecember."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "SvjYNFu5Wc56rLzc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table representing the percentage implementation in both Non-Aggressive and Aggressive scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table from the bar chart, labeled as \"Impact of Environmental Protection and Sustainable Development Policies, 2022\"{subtitle}<r>[0.186, 0.025, 0.856, 0.055]</r>, I must consider the x-axis labels {Air_Pollution, CO2_Emissions, ...}<r>[0.025, 0.785, 0.095, 0.902]; [0.074, 0.785, 0.159, 0.925]; ... ; [0.809, 0.785, 0.921, 0.971]</r> that represent environmental fields and the y-axis titles {0, 10, ..., 80}<r>[0.043, 0.767, 0.052, 0.791]; ... [0.035, 0.053, 0.052, 0.076]</r> which indicates percentage implementation. The value of each bar for both scenarios can be discovered by analyzing their respective heights and corresponding text labels. For instance, {the bar for Air_Pollution in Non-Aggressive}<r>[0.073, 0.208, 0.117, 0.776]</r> is approximately at a position correlating with 63.71% and {the bar for Air_Pollution in Aggressive}<r>[0.073, 0.776, 0.117, 0.776]</r> is at a position correlating with 0%.\nBased on the above thought, the answer is:\nField,Non-Aggressive,Aggressive\nAir_Pollution,63.71,0.00\nCO2_Emissions,74.61,0.00\nEnergy_Consumption,55.14,23.77\nGreen_Buildings,18.29,17.94\nWildlife_Protection,0.00,28.84\nForest_Area,39.84,0.00\nOcean_Protection,47.38,0.00\nRecycling,35.85,23.12\nCarbon_Footprint,0.00,33.92\nGreen_Policies,53.29,12.72\nRenewable_Energy,0.00,32.89\nEcological_Footprint,0.00,22.59\nBiodiversity,15.86,16.16\nConservation_Efforts,0.00,18.05"
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "OJCemsaJfljXCiSC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the focus on Wildlife Protection efforts between the Non-Aggressive and Aggressive scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the focus on Wildlife Protection efforts between the two scenarios, it is necessary to compare the values of {the Wildlife Protection bars}<r>[0.327, 0.776, 0.372, 0.776]; [0.327, 0.519, 0.372, 0.776]</r>. In the Non-Aggressive scenario, the Wildlife Protection effort is non-existent, with a value of 0, as per the height of its bar {Non-Aggressive Wildlife Protection bar}<r>[0.327, 0.776, 0.372, 0.776]</r>. Conversely, in the Aggressive scenario, the bar suggests a value of 28.84% effort is made towards Wildlife Protection {Aggressive Wildlife Protection bar}<r>[0.327, 0.519, 0.372, 0.776]</r>. This stark difference indicates a significant focus and investment in Wildlife Protection only in the Aggressive scenario.\nBased on the above thought, the answer is:\nThe chart reveals that while there is no effort towards Wildlife Protection in the Non-Aggressive scenario, a substantial effort of 28.84% implementation is recognized in the Aggressive scenario. This suggests that actionable changes towards Wildlife Protection are being considered only under aggressive environmental measures."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "H7rsboHXakK9xZZP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Green Building percentage change between the two scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in Green Building percentages between the Non-Aggressive and Aggressive scenarios, I must examine the height of {the bar representing Green_Buildings in the Non-Aggressive scenario}<r>[0.264, 0.613, 0.308, 0.776]</r> which shows a value of 18.29%, and compare it to {the bar representing Green_Buildings in the Aggressive scenario}<r>[0.264, 0.616, 0.308, 0.776]</r> which indicates 17.94%. Subtracting the Aggressive scenario percentage from the Non-Aggressive scenario percentage will give me the change.\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "dTBFHb0gp23WaYHP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the bar chart and provide the differences in consumer behavior for each category in a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I see pairs of bars representing Non-Aggregation and Aggregation consumer behaviors across several product categories. The pairs are distinguished by different shades, with {Non-Aggregation}<r>[0.082, 0.480, 0.126, 0.769]</r> in a light green shade and {Aggregation}<r>[0.207, 0.451, 0.252, 0.769]</r> in a light red shade. The y-axis, labeled \"Consumer Behaviour\", stretches from a value of {0}<r>[0.052, 0.758, 0.061, 0.786]</r> upwards to {120}<r>[0.035, 0.064, 0.061, 0.092]</r>, indicating the level of consumption. For each category, I will determine the difference by subtracting the Aggregation bar value from the corresponding Non-Aggregation bar value, using the bounding box information for precise location.\nBased on the above thought, the answer is:\nCategory,Difference (Non-Aggregation - Aggregation)\nFast Food,20\nE-Commerce,15\nGrocery,15\nCar Sales,-10\nHousehold Goods,35\nAttire,-20\nLuxury Goods,-15\nFootwear,-10\nAccessories,-5\nMakeup,-15\nFragrances,-10\nElectronics,-20\nGourmet,-20\nWines & Spirits,-10"
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "9NkpmMJjmN5hnsxj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer which categories are leading the shift towards aggregation in consumer behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer which categories are leading the shift towards Aggregation consumer behavior, I shall compare the respective heights of bars depicting Aggregation and Non-Aggregation behaviors. Categories where the {Aggregation bar}<r>[0.207, 0.451, 0.252, 0.769]</r> surpasses the height of the {Non-Aggregation bar}<r>[0.082, 0.480, 0.126, 0.769]</r> suggest a shift towards Aggregation. Notably, the categories of {Makeup}<r>[0.648, 0.422, 0.692, 0.769]</r>, {Fragrances}<r>[0.711, 0.364, 0.755, 0.769]</r> and {Electronics}<r>[0.774, 0.480, 0.818, 0.769]</r> display a significantly higher Aggregation bar, indicating these categories are at the forefront of this consumer shift.\nBased on the above thought, the answer is:\nThe categories leading the shift towards aggregation are Makeup, Fragrances, and Electronics as indicated by their respective Aggregation bars being taller than the Non-Aggregation ones, signifying a stronger inclination towards E-commerce in these areas."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "nSMZHcq6k8tn6LOs",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category shows the highest level of traditional Non-Aggregation buying behavior compared to Aggregation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the category with the highest level of Non-Aggregation behavior, I must scan through each {product category's bars}<r>[0.082, 0.480, 0.126, 0.769]</r> and discern which Non-Aggregation bar is the tallest when compared to its Aggregation counterpart. I notice that the bar representing Non-Aggregation for {Household Goods}<r>[0.333, 0.306, 0.377, 0.769]</r> has the highest disparity above the corresponding Aggregation bar, with labels {Non-Aggregation \"80\"}<r>[0.347, 0.270, 0.364, 0.298]</r> and {Aggregation \"45\"}<r>[0.333, 0.509, 0.377, 0.769]</r>.\nBased on the above thought, the answer is:\nHousehold Goods"
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "dt1glka7zANgdkqE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide the data depicted by the bar chart in CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title {\"Tech Companies Patent Application Comparison\"}[0.323, 0.030, 0.719, 0.066] indicates that the data relates to the comparison of patenting applications across various tech companies for their products. The x-axis labels represent the tech companies. For example, {\"Microsoft\"}[0.050, 0.818, 0.105, 0.928] and {\"Google\"}[0.140, 0.818, 0.185, 0.908]. The y-axis {\"Number of Products\"}[0.015, 0.304, 0.029, 0.580] indicates the product counts we are comparing. There are two bars for each company, one with {\"No Patent\"}[0.866, 0.144, 0.894, 0.164] and one with {\"Patent\"}[0.866, 0.186, 0.894, 0.205]. For example, {Microsoft's bars}[0.077, 0.646, 0.105, 0.809] [0.105, 0.440, 0.133, 0.809] suggest that Microsoft had 15.5 products without a patent and 35.2 products with a patent. The same process is repeated for all the other tech companies.\nBased on the above thought, the answer is:\nCompany,No Patent,Patent\nMicrosoft,15.5,35.2\nGoogle,21.7,45.8\nTesla,12.56,28.9\nApple,28.33,52.14\nIBM,37.68,64.33\nAmazon,41.7,59.2\nFacebook,24.39,42.11\nOracle,33.42,53.17\nAlibaba,7.8,23.9\nUber,6.89,18.22\nSnap,5.12,12.45"
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "ULLMVH0a62qlnT0w",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which company is placing the most emphasis on securing patents for its products and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question aims to identify the company filing for the most patents. I'll need to compare the heights of the light brown bars for each company, which represent the quantity of products with {\"Patent\"}[0.866, 0.186, 0.894, 0.205]. For instance, the {patented products bar for Microsoft}[0.105, 0.440, 0.133, 0.809] corresponds to 35.2 products, while the {one for Google}[0.185, 0.328, 0.213, 0.809] corresponds to 45.8 products. By continuing this comparison across all the companies, it becomes clear that {IBM}[0.397, 0.818, 0.425, 0.874] with the {highest \"Patent\" bar}[0.425, 0.134, 0.453, 0.809] has the most products with patents, totaling 64.33. This suggests IBM places the most emphasis on securing patents for its products, possibly as a means to protect its intellectual property and potentially maximize revenue.\nBased on the above thought, the answer is:\nIBM, because it has the highest number of patented products at 64.33."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "J2zYnDrZrwSkULzn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the most products without any patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the company with the most products non-protected by patents, I'll need to examine the heights of the light green bars representing the number of products with {\"No Patent\"}[0.866, 0.144, 0.894, 0.164]. I must look at each bar in turn. For instance, the {bar for Microsoft}[0.077, 0.646, 0.105, 0.809] corresponds to 15.5 products and the {bar for Google}[0.157, 0.581, 0.185, 0.809] corresponds to 21.7 products. Repeating this process for all, it is clear that {Amazon}[0.455, 0.818, 0.505, 0.918] with the {highest \"No Patent\" bar}[0.477, 0.371, 0.505, 0.809] implies it has the most products with no patents, the exact figure can be read as 41.7 from the chart.\nBased on the above thought, the answer is:\nAmazon"
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "0ODA4k0rGZbC0mnF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data showing growth and decline rates for different cuisines into a structured table format displaying the numerical values?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the bar chart, I identified the cuisines on the x-axis with labels such as {Italian}<r>[0.059, 0.881, 0.086, 0.939]</r> through to {Lebanese}<r>[0.856, 0.881, 0.894, 0.963]</r>. For each cuisine, there are two bars representing {Growth}<r>[0.917, 0.101, 0.985, 0.121]</r> and {Decline}<r>[0.917, 0.131, 0.985, 0.151]</r> rates, color-coded for differentiation. The values for each bar are annotated at the top of the bars, such as for {Italian cuisine's Growth rate}<r>[0.042, 0.393, 0.063, 0.413]</r> being 30.5 and {Decline rate}<r>[0.082, 0.692, 0.103, 0.712]</r> being 10.5. I will extract the values for each rate and cuisine to construct the table.\nBased on the above thought, the answer is:\nCuisine,Growth Rate,Decline Rate\nItalian,30.5,10.5\nFrench,40.0,15.2\nChinese,35.3,8.6\nThai,48.2,5.3\nIndian,41.9,14.5\nMexican,38.7,12.0\nGreek,45.3,7.5\nJapanese,37.2,19.5\nVietnamese,42.2,20.3\nTurkish,32.1,17.6\nKorean,46.5,23.4\nGerman,33.6,8.8\nAustrian,29.5,3.2\nArmenian,28.4,11.5\nLebanese,41.8,15.4"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "nHr87YOF2usH1bYD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has the most significant growth relative to its decline, according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the cuisines with significant growth relative to decline, I will compare the difference between the heights of {Growth}<r>[0.917, 0.101, 0.985, 0.121]</r> and {Decline}<r>[0.917, 0.131, 0.985, 0.151]</r> bars for each cuisine. For example, the {Thai cuisine Growth bar}<r>[0.204, 0.155, 0.244, 0.875]</r> indicates the growth rate is substantially higher than its {Decline bar}<r>[0.244, 0.795, 0.285, 0.875]</r>, observable through the vertical distances from the x-axis to the top of each bar. After analyzing all cuisines, Thai cuisine exhibits the most significant discrepancy favoring growth over decline.\nBased on the above thought, the answer is:\nThai cuisine has the most significant growth relative to its decline. This is deduced by observing the large disparity between the growth and decline bar heights in the bar chart, with the Growth rate bar being markedly taller than its corresponding Decline rate bar, indicating a more substantial positive difference."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "54MGQ5qtf4mbiRz5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest decline rate observed among the cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest decline rate, I compared the values annotated at the top of the {Decline rate bars}<r>[0.531, 0.545, 0.561, 0.565]</r>. By examining each value, I found that the {bar for Korean cuisine}<r>[0.646, 0.499, 0.676, 0.519]</r> has the highest annotated value, which is 23.4%.\nBased on the above thought, the answer is:\n23.4"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "BXMwzyHNukxznu8k",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs shown in the bar chart, can you convert the provided data into a structured table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle of the chart {Comparison of Energy Output and Power Efficiency using different Energy Resources}[0.282, 0.030, 0.751, 0.066], it's clear that the purpose is to compare different energy resources. The energy resources are presented on the x-axis, which can be determined by analyzing {the x-axis tick labels}<{\"Coal\"}[0.023, 0.787, 0.044, 0.850]; {\"Petroleum\"}[0.090, 0.787, 0.130, 0.907]; {\"NaturalGas\"}[0.172, 0.787, 0.216, 0.917].... {\"OtherRenewable\"}[0.928, 0.787, 0.989, 0.970]>. The bar heights correspond to values for two parameters - Energymax and Powerefficiency. After inspecting the {sublegend}<{\"Parameters\"}[0.905, 0.100, 0.958, 0.128]> items, it's clear {Energymax bars}< {\"Energymax\"}[0.908, 0.142, 0.961, 0.170]> are represented in one color and the {Powerefficiency bars}< {\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]> are represented in the other. The y-axis values range from {0}[0.035, 0.766, 0.041, 0.794] to {100}[0.023, 0.064, 0.041, 0.092]. I can extract the values by approximating the height of the bars from the y-axis. For example, the {Energymax bar for \"Coal\"}[0.014, 0.108, 0.044, 0.777] extends up to about 95 which corresponds to 95.32% as shown by {the value annotation \"95.32\"}[0.000, 0.998, 0.001, 1.000]. Hence, the \"Energymax\" value for Coal is 95.32.\nBased on the above thought, the answer is:\nResource,Energymax,Powerefficiency\nCoal,95.32,19.45\nPetroleum,90.65,24.96\nNaturalGas,77.88,28.43\nHydro,69.04,33.37\nWind,32.67,37.32\nSolar,41.79,60.44\nNuclear,82.34,73.95\nBioEnergy,70.46,26.08\nGeoThermal,58.22,49.69\nHydrogen,13.38,72.83\nAluminum,10.25,89.54\nOtherRenewable,20.64,50.22"
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "w5mRchDaf9TJlgz6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn reviewing the chart, which kind of energy resources do you think is the most promising for the future and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, there's a need to consider both maximum energy output {Energymax}<{\"Energymax\"}[0.908, 0.142, 0.961, 0.170]> and power efficiency {Powerefficiency}<{\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]>. While it's important to have high energy output, the efficiency in converting this energy to power is also key. Arbitrating between the two parameters might boil down to the trade-offs that are acceptable. That being said, if we favor power efficiency over maximum output, based on the height of {the bars for 'Hydrogen'}[{Energymax bar}[0.787, 0.683, 0.817, 0.777]; {Powerefficiency bar}[0.817, 0.266, 0.847, 0.777]], 'Hydrogen' emerges as a good option. It has a significantly high power efficiency {72.83}[0.819, 0.229, 0.845, 0.257], but its maximum output {13.38}[0.789, 0.647, 0.815, 0.675] is relatively lower. However, with technological advancements, we might be able to leverage its efficiency and better its energy output in the future.\nBased on the above thought, the answer is:\nHydrogen. Though it currently has a relatively lower maximum energy output, its high power efficiency makes it a promising energy resource for the future, hoping that technological advancement would increase its energy output."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "y4QftoH1Hqi0os6e",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource has the highest power efficiency as indicated on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the {Powerefficiency bars}<{\"Powerefficiency\"}[0.908, 0.183, 0.981, 0.211]> across the different resource categories. The highest bar represents the resource with the highest power efficiency. By comparing the heights of the bars, it can be seen that the {bar for 'Aluminum'}[0.903, 0.148, 0.933, 0.777] is the tallest. Subsequently, the textual annotation {89.54}[0.905, 0.112, 0.931, 0.140] confirms that 'Aluminum' has the highest power efficiency of 89.54.\nBased on the above thought, the answer is:\nAluminum"
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "xWsgGoQq58sOrt2P",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, could you convert the data expressed into a structured table with 'Category', 'Non-aggregation', and 'Aggregation' columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "The graph represents a range of categories associated with building and the real estate market. Each category has two corresponding bars, with blue indicating \"Non-aggregation\" and orange \"Aggregation\". Labeled ticks on the x-axis signify each category's starting point. For example, '\"Apartment Sales\"'<r>[0.051, 0.685, 0.143, 0.868]</r> and '\"Construction Permits\"'<r>[0.115, 0.685, 0.227, 0.909]</r> are the first and second categories. Correspondingly, each category's bars are positioned, and their height represents the respective values. For example, the height of the 'Non-aggregation' bar for the 'Apartment Sales' category< r>[0.113, 0.222, 0.143, 0.675]</ r> aligns approximately with the 98000 mark on the y-axis. Combining these observations allows me to generate a CSV-style table representation of the data in question.\nBased on the above thought, the answer is:\nCategory,Non-aggregation,Aggregation\n'Apartment Sales',98000,85000\n'Construction Permits',15000,12500\n'Foreclosures',5000,6000\n'Real Estate Investments',80000,95000\n'Commercial Property Sales',96000,102000\n'Residential Property Sales',110000,120000\n'Mortgage Approvals',85000,90000\n'Rental Market',50000,52000\n'Real Estate Agents',3000,3800\n'Property Auctions',11000,15000"
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "1EBYjLDx4hHdLA2r",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data represented on the chart, which aspect of the building and real estate market had the most significant increase when moved from non-aggregation to aggregation?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which category experienced the most significant increase when transitioning from non-aggregation to aggregation, I need to compare the differences between the two corresponding bars within each category. Their colors, as identified by {the legend}<r>[0.810, 0.089, 0.978, 0.223]</r>, are blue for non-aggregation and orange for aggregation. Upon comparison, it appears that the category with label '\"Real Estate Investments\"'<r>[0.266, 0.685, 0.395, 0.942]</r> has the most significant increase, going from {the non-aggregation bar}<r>[0.366, 0.305, 0.395, 0.675]</r>, approximately at 80000 on the y-axis< r>[0.043, 0.294, 0.087, 0.322]</r>, to {the aggregation bar}<r>[0.395, 0.236, 0.425, 0.675]</r>, approximately at 95000 on the y-axis< r>[0.035, 0.202, 0.087, 0.230]</r>.\nBased on the above thought, the answer is:\nThe 'Real Estate Investments' category had the most significant increase when moved from non-aggregation to aggregation."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "YyJBbfXpKNogivlC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in number between non-aggregation and aggregation for the 'Construction Permits' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The non-aggregation and aggregation bars for the category labeled '\"Construction Permits\"'<r>[0.115, 0.685, 0.227, 0.909]</r> correspond to the values of 15000 and 12500 respectively. To find the difference, subtract the two values: 15000 - 12500. Further deductions can be made using the heights of the bars associated with 'Construction Permits', with the non-aggregation bar< r>[0.197, 0.606, 0.227, 0.675]</r> higher than the aggregation bar< r>[0.227, 0.617, 0.256, 0.675]</r>, emphasizing the difference in value determined earlier.\nBased on the above thought, the answer is:\n2500"
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "olveKvpaOnfjMIM0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the information represented on the horizontal bar chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis of the chart {y-axis labels}[{\"Equity Market\"}[0.015, 0.710, 0.111, 0.756]; {\"Bond Market\"}[0.023, 0.471, 0.111, 0.518]] shows two markets: Equity Market and Bond Market. Each market has a stacked bar with different segments representing stability levels from {the legend items}<{\"Extremely unstable\"}[0.254, 0.261, 0.281, 0.293]; {\"Significantly unstable\"}[0.254, 0.331, 0.281, 0.363]; {\"Moderately unstable\"}[0.470, 0.261, 0.498, 0.293]; {\"Stable\"}[0.470, 0.331, 0.498, 0.363]; {\"Significantly Stable\"}[0.680, 0.261, 0.707, 0.293]; {\"Extremely Stable\"}[0.680, 0.331, 0.707, 0.363]>. By comparing the lengths of these segments, I can extract the corresponding values. For instance, for the Equity Market, the {bar segment representing \"Extremely unstable\"}[0.121, 0.668, 0.450, 0.788] has a length of 0.4 as the x-value extends from 0.00 -> 0.40. By identifying such values for each segment in both markets, I can construct the CSV table.\nBased on the above thought, the answer is:\nMarket,Extremely unstable,Significantly unstable,Moderately unstable,Stable,Significantly stable,Extremely stable\nEquity Market,0.4,0.1,0.15,0.05,0.2,0.1\nBond Market,0.35,0.05,0.1,0.1,0.25,0.15"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "kyE65911isN25uDC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which market seems to be more unstable overall? And provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the overall instability of the markets, I need to consider the sum of the segments representing unstable levels: 'Extremely unstable', 'Significantly unstable', and 'Moderately unstable'. For the Equity Market, the lengths of these bar segments are {extremely unstable}[0.121, 0.668, 0.450, 0.788] (0.4), {significantly unstable}[0.450, 0.668, 0.532, 0.788] (0.1), and {moderately unstable}[0.532, 0.668, 0.656, 0.788] (0.15). The sum is 0.65. Similarly, for the Bond Market, the lengths of these bar segments are {extremely unstable}[0.121, 0.430, 0.409, 0.549] (0.35), {significantly unstable}[0.409, 0.430, 0.450, 0.549] (0.05), and {moderately unstable}[0.450, 0.430, 0.532, 0.549] (0.1). The sum is 0.5. Therefore, the Equity Market seems to be more unstable overall.\nBased on the above thought, the answer is:\nEquity Market. Because the combined length of the segments representing 'Extremely Unstable', 'Significantly Unstable', and 'Moderately Unstable' is larger in the Equity Market (0.65) compared to the Bond Market (0.5)."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "7t6VU8iQM1IXM9TH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which market is the extremely unstable sector larger?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the market with a larger extremely unstable sector, I can compare the lengths of the corresponding bar segments. The {bar segment for \"extremely unstable\" in the Equity Market}[0.121, 0.668, 0.450, 0.788] extends from x-values 0.00 -> 0.40, meaning it has a value of 0.4. Similarly, the {bar segment for \"extremely unstable\" in the Bond Market}[0.121, 0.430, 0.409, 0.549] extends from x-values 0.00 -> 0.35, meaning it has a value of 0.35. Thus, the extremely unstable sector is larger in the Equity Market.\nBased on the above thought, the answer is:\nEquity Market."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "9n0c0tee2O4gY4YE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease collate the information shown on the chart into an easy-to-read CSV table for future reference."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend information provided in the chart, I can see that there are five categories to be considered, namely: {Success}[0.377, 0.257, 0.432, 0.303], {Logistical Errors}[0.377, 0.326, 0.488, 0.373], {Delivery Delay}[0.554, 0.257, 0.656, 0.303], {Package Damage}[0.554, 0.326, 0.675, 0.373] and {Safety Incidents}[0.742, 0.257, 0.854, 0.303]. I will need to find these categories for each type of delivery. Looking at the y-axis labels, there are two types of delivery: {Driverless Delivery}[0.066, 0.710, 0.197, 0.756] and {Unmanned Aerial Delivery}[0.015, 0.471, 0.197, 0.518]}. Each bar for the 2 delivery types is sectioned off into various categories, with the proportion of each represented by the bar length along the x-axis. Starting from the left bar, from left to right, the values are represented as such: success, logistical errors, delivery delay, package damage, and safety incidents.\nBased on the above thought, the answer is:\nCategories, Driverless Delivery, Unmanned Aerial Delivery\nSuccess, 0.8, 0.6\nLogistical Errors, 0.1, 0.2\nDelivery Delay, 0.05, 0.05\nPackage Damage, 0.03, 0.05\nSafety Incidents, 0.02, 0.1"
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "4XAmv7VelqHTIF9W",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the perspective of safety, which delivery method would you recommend and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it is important to analyze the blue segment that represents {Safety Incidents}[0.742, 0.257, 0.854, 0.303]. Examining the {Safety Incidents section for Driverless Delivery}[0.933, 0.668, 0.948, 0.788], which appears to have a length corresponding to a rate of 2%. Meanwhile, {Safety Incidents for Unmanned Aerial Delivery}[0.874, 0.430, 0.948, 0.549] have a higher rate of 10%. Comparing these percentages, it is logical to opt for the one with the lowest Safety Incidents percentage.\nBased on the above thought, the answer is:\nI recommend opting for the Driverless Delivery method, as it has a lower Safety Incidents rate at only 2%, compared to the 10% for the Unmanned Aerial Delivery method."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "JE8yOsQMfRDy6Nt3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which delivery type has the highest success rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which delivery has the highest success rate, I need to compare the length of the green bar under each delivery method in the chart. For {Driverless Delivery}[0.066, 0.710, 0.197, 0.756], the length of the green bar starts from {[0.207, 0.668]} to {[0.800, 0.788]}, indicating a success rate of 80%. While for {Unmanned Aerial Delivery}[0.015, 0.471, 0.197, 0.518], the length of the green bar starts from {[0.207, 0.430]} to {[0.652, 0.549]}, indicating a success rate of 60%.\nBased on the above thought, the answer is:\nDriverless Delivery."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "xkTrf9M6nRDXv2zM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart observed, could you translate the data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a group bar graph with two AI apps on the y-axis and five different performance levels represented by different colors on the x-axis, which are {Success Prediction}[0.251, 0.656, 0.342, 0.703], {Failure Analysis}[0.251, 0.725, 0.333, 0.772], {Security Risk}[0.407, 0.656, 0.452, 0.703], {Capacity Utilization}[0.407, 0.725, 0.461, 0.772] and {Load Forecasting}[0.593, 0.656, 0.667, 0.703]. The values for each performance level are calculated using the lengths of the corresponding segments. Looking at the bars, {AI App1}<r>[0.079, 0.688, 0.942, 0.828]</r> has a Success Prediction of 0.65, Failure Analysis of 0.25, Security Risk of 0.05, Capacity Utilization of 0.03 and Load Forecasting of 0.02, whereas {AI App2}<r>[0.079, 0.452, 0.942, 0.589]</r> has a Success Prediction of 0.75, Failure Analysis of 0.15, Security Risk of 0.07, Capacity Utilization of 0.02 and Load Forecasting of 0.01. Using these values I can create a CSV table.\nBased on the above thought, the answer is:\nCategory,AI App1,AI App2\nSuccess Prediction,0.65,0.75\nFailure Analysis,0.25,0.15\nSecurity Risk,0.05,0.07\nCapacity Utilization,0.03,0.02\nLoad Forecasting,0.02,0.01"
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "yUenGWyNwYJorRYg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which AI application has shown better performance in terms of Success Prediction? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The performance of Success Prediction for each app can be analyzed by comparing the lengths of the first segment in each bar. The bar for {AI App1}<r>[0.079, 0.688, 0.640, 0.828]</r> shows a lower value of Success Prediction with only 65% whereas the segment for {AI App2}<r>[0.079, 0.452, 0.726, 0.589]</r> shows a higher value at 75% in terms of Success Prediction. Therefore, comparing the length of both segments, it can be inferred that AI App2 has executed a better performance in the aspect of Success Prediction.\nBased on the above thought, the answer is:\nAI App2, because it has a higher Success Prediction rate of 75%, compared to AI App1 which stands at 65%."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "o1R01YBVuAibV3EU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI App has the lowest combined performance across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the total lengths of {AI App1's bar}<r>[0.079, 0.688, 0.942, 0.828]</r> and {AI App2's bar}<r>[0.079, 0.452, 0.942, 0.589]</r>, the total performance of each app can be calculated. Looking at the x-axis <r>[0.068, 0.852, 1.125, 0.898]</r>, AI App1 has a total performance level of 1.0, as each segment from Success Prediction to Load Forecasting sums up to fill the entire length of the bar. In comparison, AI App2 also fills the entire length of the bar which indicates a total performance level of 1.0 as well. Therefore, both AI App1 and AI App2 have the same total performance level.\nBased on the above thought, the answer is:\nNeither, both AI App1 and AI App2 have equal combined performance level."
+            }
+        ],
+        "id_raw": 722
+    },
+    {
+        "id": "QVmCPaE4HCNidhVM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please generate a CSV table from the bar chart information?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows percentage of total efforts distributed among various factors for two platforms, Facebook and Instagram, as per their respective bar lengths. For example, {the influence collaboration for Facebook} <r>[0.621, 0.575, 0.765, 0.776]</r> lies at approximately 0.16 (0.79 - 0.62) way through 0-1.0 on the x-axis, and {for Instagram} <r>[0.604, 0.173, 0.799, 0.374]</r> lies at approximately 0.23 (0.83 - 0.6) way through. I'll follow similar way for each category and for each platform to collect the data to build the table.\nBased on the above thought, the answer is:\nPlatform,Post Engagement Rate,Advertising Cost,Organic Reach,Influencer Collaboration,Community Engagement,Algorithm Change\nFacebook,0.27,0.23,0.12,0.16,0.15,0.03\nInstagram,0.35,0.17,0.08,0.23,0.14,0.03"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "Bg8KJ3qISLBOEWU4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform would you recommend for implementing an advertising campaign based on the chart figures, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In terms of advertising, I need to focus on factors 'Advertising Cost' and 'Post Engagement Rate' which are key for an advertising campaign. For Instagram, the 'Advertising Cost' <r>[0.392, 0.173, 0.536, 0.374]</r> is slightly lower and the 'Post Engagement Rate' <r>[0.095, 0.173, 0.392, 0.374]</r> is definitely higher than for Facebook where the 'Advertising Cost' <r>[0.324, 0.575, 0.519, 0.776]</r>r is higher, and the 'Post Engagement Rate' <r>[0.095, 0.575, 0.324, 0.776]</r> is lower. In addition, Instagram has a higher 'Influencer Collaboration' rate <r>[0.604, 0.173, 0.799, 0.374]</r>, which is beneficial for advertising efforts. So, considering these three factors, I would recommend Instagram.\nBased on the above thought, the answer is:\nInstagram, because it has a higher post engagement rate and influencer collaboration rate, and a lower advertising cost, making it more cost-effective and potentially impactful for an advertising campaign."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "Jf4jNoByZE1ZbMiU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factor has the highest percentage of total efforts on Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the factor with the highest percentage of total efforts on Instagram, I need to look at the lengths of the bars associated with Instagram. Looking from left to right, the first bar is for 'Post Engagement Rate' <r>[0.095, 0.173, 0.392, 0.374]</r> with a length of approximately 0.35 (0.392 - 0.095), the second bar is for 'Advertising Cost' <r>[0.392, 0.173, 0.536, 0.374]</r> with a length of approximately 0.17, and so on. It appears that the 'Post Engagement Rate' has the longest length and hence, the highest percentage of total efforts on Instagram.\nBased on the above thought, the answer is:\nPost Engagement Rate"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "hDVlClFlabuK3cTT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the raw data used to create the bar chart in a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that it's a stacked bar chart displaying performance scores for two technologies {TechProjectX}<r>[0.015, 0.710, 0.103, 0.756]</r> and {TechProjectY}<r>[0.016, 0.471, 0.103, 0.518]</r> along multiple parameters. Parameters {Economic Viability}<r>[0.293, 0.257, 0.421, 0.303]</r>, {Technological Feasibility}<r>[0.293, 0.326, 0.461, 0.373]</r>, {Business Need}<r>[0.527, 0.257, 0.629, 0.303]</r>, {Market Demand}<r>[0.527, 0.326, 0.639, 0.373]</r>, {Strategic Alignment}<r>[0.705, 0.257, 0.844, 0.303]</r>, {End Score}<r>[0.705, 0.326, 0.775, 0.373]</r> are denoted with different colors. These parameters correspond to x-values, ranging from {0.0}<r>[0.102, 0.838, 0.124, 0.885]</r> to {1.2}<r>[1.099, 0.838, 1.121, 0.885]</r>. Combining these details alongside the exact markings of each category under corresponding tech projects, the CSV table can be generated.\nBased on the above thought, the answer is:\nInnovation Name,Economic Viability,Technological Feasibility,Business Need,Market Demand,Strategic Alignment,End Score\nTechProjectX,0.7,0.15,0.05,0.06,0.02,0.02\nTechProjectY,0.6,0.02,0.1,0.12,0.1,0.06"
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "aQFGawRVtl8bmaQb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which innovation project would you recommend for investment and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "When looking at the bars, I can clearly see that {TechProjectX}<r>[0.113, 0.668, 0.943, 0.788]</r> has significantly higher scores in imperative areas such as {Economic Viability}<r>[0.113, 0.668, 0.694, 0.788]</r> and {Technological Feasibility}<r>[0.694, 0.668, 0.819, 0.788]</r>. These areas are crucial for the success and sustainability of an innovative project. {TechProjectX}<r>[0.113, 0.668, 0.943, 0.788]</r> also has respectable scores in the other measured areas. All this implies that this project has a balanced and high-performance profile across the evaluated metrics.\nBased on the above thought, the answer is:\nTechProjectX. It performs significantly better in crucial areas like Economic Viability and Technological Feasibility, indicating a higher probability of success for this project."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "YCMqtx2vUyj87M10",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology project obtained the highest total score?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the total widths of the bars for {TechProjectX}<r>[0.113, 0.668, 0.943, 0.788]</r> and {TechProjectY}<r>[0.113, 0.430, 0.943, 0.549]</r>, it's clear that TechProjectX spans a wider range on the x-axis and therefore has a higher total score.\nBased on the above thought, the answer is:\nTechProjectX"
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "0kR1o88gjG3anZ67",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the information from the stacked bar chart, could you map the performance for each factor in the Efficiency_check and Workflow_analysis processes? Provide this in a structural CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's x-axis records the effect of different factors on business processes as seen from its label {\"Effect of factors on business processes\"}[0.433, 0.952, 0.704, 0.975]. The y-axis distinguishes the two processes as specified by the y-axis labels {\"Efficiency_check\"}[0.028, 0.752, 0.142, 0.775] and {\"Workflow_analysis\"}[0.015, 0.315, 0.142, 0.339]. For each business process, there are segments of bars each corresponding to a factor color coded as per {legend}[0.264, 0.036, 0.873, 0.113]. When creating a CSV table, I should list each factor and their corresponding value in the Efficiency_check process and then Workflow_analysis process. The length of each colored segment corresponds to the value of that factor. For instance, in the Efficiency_check process, the green (representing Success) segment is from [0.152, 0.652] to [0.311, 0.870] indicating a corresponding factor value of approximately 0.2.\nBased on the above thought, the answer is:\nFactor, Efficiency_check, Workflow_analysis\nSuccess, 0.2, 0.7\nGrounding_error, 0.35, 0.1\nOccupancy_error, 0.1, 0.05\nGripper_collision, 0.15, 0.05\nTrajectory_optimization_error, 0.05, 0.05\nGrasping_failed, 0.15, 0.05"
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "fNbYqklHASBKYvZx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which business process is more successful, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the more successful business process, I need to compare the lengths of the green segments, which represent Success, in both Efficiency_check and Workflow_analysis processes. For the Efficiency_check process, the green bar is located at {[0.152, 0.652, 0.311, 0.870]}, implying it spans approximately 0.2 on the x-axis. For Workflow_analysis, the green segment's coordinates are {[0.152, 0.216, 0.707, 0.434]}, indicating it spans roughly 0.7 on the x-axis. Therefore, the Workflow_analysis process is more successful since it has a longer green segment, representing a higher success rate.\nBased on the above thought, the answer is:\nWorkflow_analysis, because it has a longer green segment indicating a higher success rate."
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "9MByq7pAQjnTD8s3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which of the business processes did the Occupancy Error have a larger effect?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to confirm which business process had a more significant Occupancy Error effect, I can compare the lengths of the orange bars, {representing Occupancy Error} in the Efficiency_check and Workflow_analysis processes. For Efficiency_check, the orange segment is located at the coordinates [0.588, 0.652, 0.668, 0.870] and spans approximately 0.1 on x-axis. For the Workflow_analysis, the orange bar's coordinates {[0.787, 0.216, 0.826, 0.434]} reveal that it spans roughly 0.05 on the x-axis. Therefore, the Occupancy Error was larger in the Efficiency_check process.\nBased on the above thought, the answer is:\nEfficiency_check"
+            }
+        ],
+        "id_raw": 725
+    },
+    {
+        "id": "UGFUn22H7i2NZBS4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bar chart depict in terms of percentages for each category across the years 2017 and 2022? Can you chart the information obtained into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart describes respondents' distributions between two major categories: 'Healthy Lifestyle' and 'Digital Detachment'. It presents data for two different years - 2017 and 2022. The bars are colored differently to represent each category, as indicated in {the legend}<r>[0.337, 0.238, 0.706, 0.322]</r>. The blue bars represent 'Healthy Lifestyle' and the orange bars represent 'Digital Detachment'. The lengths of the bars for each year reflect the respective percentages. For example, in 2017, {the healthy lifestyle bar}<r>[0.060, 0.668, 0.451, 0.788]</r> represents 60%, and {the digital detachment bar}<r>[0.451, 0.668, 0.874, 0.788]</r> represents 65%. In 2022, {the healthy lifestyle bar}<r>[0.060, 0.430, 0.483, 0.549]</r> extends to 65%, whereas {the digital detachment bar}<r>[0.483, 0.430, 0.940, 0.549]</r> reaches 70%. This information can be arranged into a CSV table as follows:\nBased on the above thought, the answer is:\n\"Year\",\"Healthy Lifestyle\",\"Digital Detachment\"\n\"2017\",0.6,0.65\n\"2022\",0.65,0.7"
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "JAqpRisTHhiZu2et",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the trends observed in the social and cultural poll depicted in the chart and elaborate what it means for the preferences of the respondents."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart and considering the colored segments for 'Healthy Lifestyle' and 'Digital Detachment' in {the legend}<r>[0.337, 0.238, 0.706, 0.322]</r>, it is evident that there is a shift in the respondents' preferences between 2017 and 2022. \nThe blue bars for 'Healthy Lifestyle' in {2017}<r>[0.060, 0.668, 0.451, 0.788]</r> and {2022}<r>[0.060, 0.430, 0.483, 0.549]</r> demonstrate a rise from 60% to 65%, indicating a growing preference for a healthier living approach. \nLikewise, a similar trend can be observed in the orange bars for 'Digital Detachment' for {2017}<r>[0.451, 0.668, 0.874, 0.788]</r> and {2022}<r>[0.483, 0.430, 0.940, 0.549]</r>, representing an increase from 65% to 70%. A potential interpretation could be an increasing tendency for respondents to retreat from digital engagement over the years.\nBased on the above thought, the answer is:\nThe data suggests that from 2017 to 2022, respondents displayed an increasing preference towards 'Healthy Lifestyle' and 'Digital Detachment'. This shift could be a sign of growing awareness about the importance of health and well-being, and the tendency toward less digital engagement or more balanced technology use."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "JrlRsTnGzqS3Azre",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2017 and 2022, by what percentage did the preference for a healthy lifestyle increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase rate of preference for a healthy lifestyle from 2017 to 2022, I can compare the proportions of the blue bars corresponding to these years. By examining the bars in {2017}<r>[0.060, 0.668, 0.451, 0.788]</r> and {2022}<r>[0.060, 0.430, 0.483, 0.549]</r>, I can conclude that the rate increases from 60% to 65%. The difference between these values gives the required percentage increase.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "UJ1MAiKAgUTUCMLm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart visualization you've seen, could you please build a structured data table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The charts x-axis provides the percentage of total trials ranging from {0.0}[0.084, 0.838, 0.106, 0.885] to {1.0}[1.026, 0.838, 1.048, 0.885]. The individual segments based on the chart data fall under two sectors {NewTech}[0.024, 0.710, 0.086, 0.756] and {FutureEdu}[0.015, 0.471, 0.086, 0.518], with the horizontal bars representing two different parameters \u2014 {Innovation}[0.442, 0.257, 0.515, 0.303] and {Infrastructure}[0.582, 0.257, 0.677, 0.303], as indicated in the chart's legend. To construct a CSV table, the lengths of the bars for each sector and parameter need to be identified. For NewTech, the length of the green bar (Innovation) extends from [0.095, 0.668] to [0.660, 0.788], which indicates a value of 0.6, and the length of the grey bar (Infrastructure) extends from [0.660, 0.668] to [0.896, 0.788], representing a value of 0.25. For FutureEdu, the length of the green bar (Innovation) extends from [0.095, 0.430] to [0.613, 0.549], indicating a value of 0.55, and the grey bar (Infrastructure) stretches from [0.613, 0.430] to [0.943, 0.549], which equates to 0.35.\nBased on the above thought, the answer is:\nSectors,Innovation,Infrastructure\nNewTech,0.6,0.25\nFutureEdu,0.55,0.35"
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "fxGrq7Ix1H2x2FIu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the visualization, which sector has a better balance between innovation and infrastructure development?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires comparing the lengths of the innovation and infrastructure bars for both sectors. In {NewTech}[0.024, 0.710, 0.086, 0.756], the {Innovation}[0.442, 0.257, 0.515, 0.303] bar extends to 0.6, while the {Infrastructure}[0.582, 0.257, 0.677, 0.303] bar extends only till 0.25. This indicates a large disparity, meaning there's much innovation but a lack of infrastructure support. In the case of {FutureEdu}[0.015, 0.471, 0.086, 0.518], the Innovation bar extends until 0.55 and the Infrastructure bar extends until 0.35, showing a smaller degree of disparity and therefore, a better balance between innovation and infrastructure.\nBased on the above thought, the answer is:\nFutureEdu has a better balance between innovation and infrastructure development as it shows a smaller degree of disparity between the two."
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "prYUQBkd1qdfWSAe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector has a higher innovation score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with a higher innovation score, the length of the green bars representing innovation for both sectors needs to be compared. The {NewTech innovation score}[0.095, 0.668, 0.660, 0.788] extends till 0.60 on the x-axis, while the {FutureEdu innovation score}[0.095, 0.430, 0.613, 0.549] extends till 0.55. It's apparent that NewTech has a higher Innovation score.\nBased on the above thought, the answer is:\nNewTech"
+            }
+        ],
+        "id_raw": 727
+    },
+    {
+        "id": "2fwUZjYtG90dC6jG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the information present in the bar chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The given chart is a comparison of the success and error types between Urban Planning and Community Development trials. The x-axis displayed at {x-axis}[0.507, 0.903, 0.681, 0.950] represents the percentage of total trials. The y-axis displayed at {y-axis labels}[{\"Urban Planning\"}[0.087, 0.710, 0.193, 0.756]; {\"Community Development\"}[0.015, 0.471, 0.193, 0.518]] represents the category: Urban Planning and Community Development. The different parts of the bar correspond to Success, Grounding Error, Occupancy Error, Gripper Collision, Trajectory Optimization Error, and Grasping Failed as indicated in the legend at legend location[0.289, 0.238, 0.899, 0.392]. The length of each part, along the x-axis of percentages, gives the respective values. Therefore, the same information forms the following CSV table.\nBased on the above thought, the answer is:\nCategory,Success,Grounding Error,Occupancy Error,Gripper Collision,Trajectory Optimization Error,Grasping Failed\nUrban Planning,0.5,0.1,0.1,0.05,0.1,0.15\nCommunity Development,0.6,0.05,0.15,0.05,0.1,0.05"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "nrJDXoD8xysoIfHS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which category had a better performance overall? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category had a better performance overall, I need to consider both the ratio of success and the presence of errors. Looking at the green bar marked 'Success' at {legend}[0.333, 0.257, 0.389, 0.303], for Urban Planning at {Urban Planning bar}[0.203, 0.668, 0.575, 0.788] and Community Development at {Community Development bar}[0.203, 0.430, 0.650, 0.549], we see that Urban Planning had a 0.5 success rate and Community Development had 0.6. This points towards Community Development being more successful. To conclude, we should also compare the error rates. Analyzing each error type, we find that except for 'Grasping Failed', all other errors like Grounding Error, Occupancy Error, Gripper Collision, Trajectory Optimization Error are less in Community Development compared to Urban Planning. This further strengthens the case for Community Development having an overall better performance.\nBased on the above thought, the answer is:\nCommunity Development. This is due to its higher success rate and comparatively lower error rates."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "C9uEcgoR7OwOF5Kb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which category had the most 'Grounding Error'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the category with the most number of 'Grounding Error', I will check the lengths of the grey colored bars labelled 'Grounding Error' at {legend}[0.333, 0.326, 0.444, 0.373]. When cross-checking the chart at {bars}[{\"Urban Planning\"}[0.575, 0.668, 0.650, 0.788]; {\"Community Planning\"}[0.650, 0.430, 0.687, 0.549]], it can be seen that The 'Grounding Error' for Urban Planning spans from 0.5 to 0.6 on the x-axis which is 0.1 in length, while for Community Development it spans from 0.6 to 0.65 which is 0.05 in length.\nBased on the above thought, the answer is:\nUrban Planning."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "YKoCP9U8uDvH6tLe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the dual horizontal stacked bar chart, can you organize the sector-wise contributions to Global trends and Asia into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can see from {the Y-tick labels}<r>[0.015, 0.710, 0.108, 0.756]; [0.079, 0.471, 0.108, 0.518]</r> that 'Global trends' and 'Asia', are the two categories of data represented in the chart. The horizontal stacked bars within each category are reflecting the respective sectoral contributions. These contributions are represented as percentage values on {the X-axis}<r>[0.107, 0.838, 0.129, 0.885]; [0.257, 0.838, 0.279, 0.885]; [0.407, 0.838, 0.429, 0.885]; [0.557, 0.838, 0.579, 0.885]; [0.707, 0.838, 0.729, 0.885]; [0.858, 0.838, 0.880, 0.885]; [1.008, 0.838, 1.030, 0.885]</r>. Each colored section of the stacked bar is labeled in {the legend}<r>[0.336, 0.238, 0.767, 0.392]</r> as 'Manufacturing', 'Services', 'Imports', 'Exports', 'GDP Increase', and 'FDI'. For instance, the contribution of Manufacturing in Global trends {indicated by the red bar}<r>[0.118, 0.668, 0.441, 0.788]</r> extends up to 0.43 or 43% as shown on the x-axis. In the same way, I can determine the contributions of each sector in both categories.\nBased on the above thought, the answer is:\n,Manufacturing,Services,Imports,Exports,GDP_Increase,FDI\nGlobal_trends,0.43,0.33,0.2,0.09,0.02,0.03\nAsia,0.47,0.27,0.17,0.11,0.06,0.02"
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "wnQl4m4TjqtTFZjf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the 'Global trends' data, which do you think is the most influential sector contributing to the economic output and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal stacked bars for 'Global trends' show the size of each sector's contribution as the length of their respective colored segments. A longer segment implies a larger contribution. Therefore, to decide the sector with the most influence on the economic output, we should look for the longest segment in the 'Global trends' category. From observation, we can see that {the 'Manufacturing' sector}<r>[0.118, 0.668, 0.441, 0.788]</r> is the longest. So, 'Manufacturing' contributes the most and can therefore be deemed the most influential in terms of economic output.\nBased on the above thought, the answer is:\nThe 'Manufacturing' sector is the most influential as it contributes the most to the total economic output based on 'Global trends'."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "llcMbsjuLrkYL7kI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Asia' category, which sector has shown an overall decrease in percentage contribution as compared to 'Global trends'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would compare each sector's contribution in the 'Asia' category with its corresponding contribution in 'Global trends'. For example, for the 'Manufacturing' sector, I would compare the length of {the red section in the 'Asia' bar}<r>[0.118, 0.430, 0.471, 0.549]</r> with {the red section in the 'Global trends' bar}<r>[0.118, 0.668, 0.441, 0.788]</r>. Doing this for all sectors reveals that the 'Services' sector, represented by {the blue section of the 'Asia' bar}<r>[0.471, 0.430, 0.673, 0.549]</r> is lesser than {the blue section in the 'Global trends' bar}<r>[0.441, 0.668, 0.688, 0.788]</r>, indicating a decrease in the 'Services' sector contribution in the 'Asia' category as compared to 'Global trends'.\nBased on the above thought, the answer is:\nServices."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "Etqhx98mHg9D9zp3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the chart and extract the data representation into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart represents two categories {Self-driving Vehicles}[0.015, 0.710, 0.154, 0.756] and {Drone Delivery}[0.050, 0.471, 0.154, 0.518]. Each bar represents the success rate and error rate of the two technologies. The x-axis {Percentage of Total Trials}[0.488, 0.903, 0.661, 0.950] represents the range from {0.0}[0.153, 0.838, 0.175, 0.885] to {1.0}[0.935, 0.838, 0.957, 0.885]. With the help of the legend section {legend}[0.271, 0.238, 0.879, 0.392], I could understand each bar's color corresponds to the performance parameters. For example, from the coordinates of the green section of the bar for {Self-driving Vehicles}[0.164, 0.668, 0.711, 0.788], it can be inferred that the success rate is 0.7. Correspondingly, I can derive the performance values for the entire chart. The following CSV format info could be obtained.\nBased on the above thought, the answer is:\nCategories,Errors,Value\nSelf-driving Vehicles,Success,0.7\nSelf-driving Vehicles,Grounding Error,0.1\nSelf-driving Vehicles,Occupancy Error,0.05\nSelf-driving Vehicles,Gripper Collision,0.1\nSelf-driving Vehicles,Trajectory Optimization Error,0.03\nSelf-driving Vehicles,Grasping Failed,0.02\nDrone Delivery,Success,0.5\nDrone Delivery,Grounding Error,0.2\nDrone Delivery,Occupancy Error,0.1\nDrone Delivery,Gripper Collision,0.05\nDrone Delivery,Trajectory Optimization Error,0.1\nDrone Delivery,Grasping Failed,0.05"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "k3E1G6qigzdupmAJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which technology seems to be more efficient and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the error rates and the success rates of both technologies {Self-driving Vehicles}[0.015, 0.710, 0.154, 0.756] and {Drone Delivery}[0.050, 0.471, 0.154, 0.518]. Starting with the {green}[0.276, 0.261, 0.304, 0.293] section in each bar which represents successful operations, we can see that Self-Driving Vehicles have a higher success rate than Drone Delivery. Looking at the various error categories represented by the rest of the colors in the bar, we can see that Self-driving Vehicles have lower error rates in almost all categories except for Grounding Errors. Therefore, considering the success rate and the error rates, Self-Driving Vehicles seem more efficient.\nBased on the above thought, the answer is:\nSelf-driving Vehicles seems to be more efficient because they have a higher success rate and lower error rates in most categories."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "1eQANB81OuHIo7h2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has a higher success rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out which technology has a higher success rate, I need to compare the section identified as {Success}[0.315, 0.257, 0.370, 0.303]. The green bar segment depicting success for {Self-driving Vehicles}[0.164, 0.668, 0.711, 0.788] extends to approximately 0.7 on the x-axis. Meanwhile, the green bar segment denoting success for {Drone Delivery}[0.164, 0.430, 0.555, 0.549] extends to nearly 0.5 on the x-axis. Comparing the two, it's clear that the Self-driving Vehicles technology has a higher success rate.\nBased on the above thought, the answer is:\nSelf-driving Vehicles"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "2lzy0GSf5HOfzOx9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data corresponding to the bar chart into a CSV table for both policies - Tax Reform and Drugs Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart includes two horizontal bars, each representing one of the two policies: {Tax Reform}<r>[0.121, 0.668, 0.920, 0.788]</r> and {Drugs Control}<r>[0.121, 0.430, 0.944, 0.549]</r>. Each bar is divided into six differently colored segments representing six different results categories according to {the legend}<r>[0.235, 0.238, 0.871, 0.392]</r>. For {Tax Reform}, I see that the segment for {Success}<r>[0.121, 0.668, 0.600, 0.788]</r> extends up to a value of approximately 0.6 on the x-axis, as denoted by the tick mark (\u201c0.6\u201d)<r>[0.589, 0.838, 0.611, 0.885]</r>. Moving rightward, each segment represents a different result category, with the {Policy Ineffectiveness}<r>[0.600, 0.668, 0.760, 0.788]</r> extending up to 0.8, and thus indicating a value of 0.2. By following the same reasoning for the entire length, I can construct each category's percentage for both policies.\nBased on the above thought, the answer is:\nResults Categories,Tax Reform,Drugs Control\nSuccess,0.6,0.65\nPolicy Ineffectiveness,0.2,0.15\nUnderfunding,0.1,0.05\nLegal Challenges,0.05,0.05\nImplementation Errors,0.03,0.08\nUnforeseen Consequences,0.02,0.05"
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "hmIOxZwWBRTNbsYt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you infer which policy is considered more effective based on reviews?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will primarily look at the length of the {Success}<r>[0.280, 0.257, 0.335, 0.303]</r> segment under each policy as it indicates positive reviews. For {Tax Reform}<r>[0.121, 0.668, 0.920, 0.788]</r> this segment is located between 0.00 and 0.60 according to the {x axis}<r>[0.121, 0.806, 1.080, 0.806]</r>. For {Drugs Control}<r>[0.121, 0.430, 0.944, 0.549]</r>, this segment extends between 0.00 and 0.65. Therefore Drugs Control has a slightly higher success rate. However, a thorough evaluation would also consider the length of the negative categories such as implementation errors, legal challenges etc. From a cursory glance, it appears that the Drugs Control policy has lesser negative impacts than Tax Reform except for Unforeseen Consequences. Thus, Drugs Control seems more effective overall.\nBased on the above thought, the answer is:\nDrugs Control, as per the reviews, it has a larger success segment value and lesser negative impacts, making it seem more effective overall."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "hFwGziAHpmlXLRK4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which policy has a higher rate of unforeseen consequences?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will need to compare the segment of {Unforeseen Consequences}<r>[0.642, 0.331, 0.670, 0.363]</r> for both the policies. For {Tax Reform}<r>[0.121, 0.668, 0.920, 0.788]</r> the segment is located at [0.904, 0.668, 0.920, 0.788], which corresponds to 0.02, as read from the {x axis}<r>[0.121, 0.806, 1.080, 0.806]</r>. For {Drugs Control}<r>[0.121, 0.430, 0.944, 0.549]</r> it is located at [0.904, 0.430, 0.944, 0.549], which corresponds to a value of 0.05. Thus, by comparing both values, it's clear that the policy with the higher rate of unforeseen consequences is Drugs Control.\nBased on the above thought, the answer is:\nDrugs Control."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "85lgXVBolrOfrjzV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data from the stacked bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "On the chart, I notice two horizontal bars at {Clean Energy}[0.117, 0.668, 0.935, 0.788] and {Fossil Fuels}[0.117, 0.430, 0.944, 0.549] labeled, which seem to represent two types of energy use. Each of these bars is divided into segments with different colors and lengths. The colors correspond to different impacts on the environment, according to {the legend}<r>[0.283, 0.238, 0.819, 0.392]</r>. The lengths of the sections correspond to the percentage values given on the x-axis as {the x-axis labels}<r>[0.106, 0.838, 0.946, 0.885]</r> dictate. For example, {the green part of the \"Clean Energy\" bar}<r>[0.117, 0.668, 0.608, 0.788]</r> indicates a success rate of about 60%; the same success rate for {Fossil Fuels}<r>[0.117, 0.430, 0.445, 0.549]</r> are 40%.  Assessing the rest of the values in such a way will allow me to convert the data into a CSV format.\nBased on the above thought, the answer is:\nCategory, Clean Energy, Fossil Fuel\nSuccess,0.6,0.4\nCarbon footprint,0.1,0.4\nWater Usage,0.1,0.05\nLand Degradation,0.05,0.05\nBiodiversity loss,0.1,0.04\nAir pollution,0.05,0.07"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "IZsYrOHjxNtHbk8O",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs an AI analyzing this horizontal stacked bar chart, which energy source do you think has a more positive impact on the environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "For judging which energy source has a more positive impact on the environment, consider each {coloured segment}<r>[0.283, 0.238, 0.819, 0.392]</r> in the bars for Clean Energy and Fossil Fuels. It can be seen that Clean Energy has a higher success rate and leaves a smaller amount of carbon footprint, results in less water usage and land degradation, slightly more biodiversity loss, but less air pollution. All these can be inferred by comparing the lengths of each segment of the two bars which are better in {Clean Energy Bar}<r>[0.117, 0.668, 0.935, 0.788]</r> than {Fossil Fuels Bar}<r>[0.117, 0.430, 0.944, 0.549]</r>.\nBased on the above thought, the answer is:\nClean Energy. This is due to its higher success rate and lesser environmental damages such as carbon footprint, water usage, land degradation, and air pollution than those of Fossil Fuels."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "ShMa7Fja9cFrwgvq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more successful is Clean Energy as compared to Fossil Fuel?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the chart, the success rate of Clean Energy and Fossil Fuels can be seen in the green part of the bars at {Clean Energy success segment}<r>[0.117, 0.668, 0.608, 0.788]</r> and {Fossil Fuels success segment}<r>[0.117, 0.430, 0.445, 0.549]</r>. The former seems to extend up to 0.6 on the x-axis, and the latter up to 0.4. By calculating the difference between these two, I can find the answer.\nBased on the above thought, the answer is:\n0.2"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "rmxhwJtwqmpVEuUe",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you evaluated, can you generate a CSV table containing the data for each social media platform and their corresponding average daily user count?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, I can derive this information. From the chart's title {\"Social Media Data Analysis: Average Daily User Count\"}[0.265, 0.025, 0.824, 0.055], I understand that it is a comparison of the average daily user count of various social media platforms. There are 7 bars in total on the chart, denoting 7 different social media platforms. Using the {the y-axis tick labels}[{\"Twitter\"}[0.049, 0.818, 0.107, 0.841]; {\"Facebook\"}[0.025, 0.704, 0.107, 0.728]; {\"Instagram\"}[0.019, 0.591, 0.107, 0.614]; {\"LinkedIn\"}[0.035, 0.477, 0.107, 0.501]; {\"Pinterest\"}[0.030, 0.364, 0.107, 0.387]; {\"Snapchat\"}[0.025, 0.250, 0.107, 0.274]; {\"Reddit\"}[0.051, 0.137, 0.107, 0.160]] and bar lengths along with data annotations, I can obtain the user count for each platform.\nBased on the above thought, the answer is:\nPlatform,AvgDailyUserCount\nTwitter,5.0\nFacebook,4.8\nInstagram,4.63\nLinkedIn,4.1\nPinterest,3.8\nSnapchat,3.52\nReddit,2.38"
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "Q7481ys359p4h7M4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social media platform would likely have the most reach for a worldwide ad campaign?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires identifying the social media platform with the highest daily user count. Looking at the chart, we can see that the longest bar corresponds to Twitter {[0.113, 0.782, 0.832, 0.872]}, with an average daily user count of 5.0 billion. This number is higher than the user counts of all other social media platforms identified in the chart. Therefore, Twitter would likely have the most reach for a worldwide ad campaign as it has the highest global daily engagement.\nBased on the above thought, the answer is:\nTwitter"
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "94CsFTlXbZ4Gg8S8",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in average daily user count between Twitter and Reddit according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to note the user counts of Twitter and Reddit. From the bar chart, we can see that the bar representing Twitter {[0.113, 0.782, 0.832, 0.872], 'Twitter'} has a length signifying an average daily user count of 5.0 billion. Similarly, Reddit's bar {[0.113, 0.101, 0.455, 0.192], 'Reddit'} shows an average user count of 2.38 billion. Subtracting Reddit's number from Twitter's gives us the difference in their respective average daily user counts.\nBased on the above thought, the answer is:\n2.62 billion"
+            }
+        ],
+        "id_raw": 735
+    },
+    {
+        "id": "gn0O9fRzCc0bETbi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, could you please convert the bar chart data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly. It can be observed from the chart that we have several economic indicators as categories {economic categories}<r>[0.059, 0.759, 0.198, 0.794]; [0.084, 0.613, 0.198, 0.648]; [0.022, 0.466, 0.198, 0.501]; [0.081, 0.320, 0.198, 0.355]; [0.019, 0.173, 0.198, 0.208]</r>, represented along the y-axis and their respective values on the x-axis ranging from {0.1}<r>[0.268, 0.879, 0.295, 0.914]</r> to {0.9}<r>[0.890, 0.879, 0.917, 0.914]</r>. The value of each economic indicator is represented as the length of the bar. For instance, the bar for 'New Businesses'<r>[0.204, 0.714, 0.833, 0.831]</r> ends at around 0.81, which is also confirmed by the text next to the bar<r>[0.833, 0.755, 0.878, 0.790]</r>. Similarly, the values for each economic indicator can be inferred.\nBased on the above thought, the answer is:\nEconomic Indicator,Value\nNew Businesses,0.81\nStock Market,0.89\nConsumer Spending,0.68\nInflation Rate,0.55\nUnemployment Rate,0.25"
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "pGI0Mafjmuvd0dpj",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an investor wishes to invest in a sector where the economic indicator is performing the best, which sector should they consider?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector where the economic indicator is performing the best, I need to focus on the bar with the highest value. By analyzing the bars of `New Businesses`<r>[0.204, 0.714, 0.833, 0.831]</r>, `Stock Market`<r>[0.204, 0.568, 0.896, 0.685]</r>, `Consumer Spending`<r>[0.204, 0.421, 0.732, 0.538]</r>, `Inflation Rate`<r>[0.204, 0.275, 0.631, 0.392]</r>, and `Unemployment Rate`<r>[0.204, 0.128, 0.398, 0.246]</r>, it can be observed that the `Stock Market` bar extends the furthest along the x-axis, indicating that it has the highest value. Therefore, `Stock Market` would be the most rewarding investment sector based on the provided graph.\nBased on the above thought, the answer is:\nThe investor should consider investing in the Stock Market sector as it currently has the highest economic indicator value."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "r0kpkjYWbU2nRt0p",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator has the highest value in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, the lengths of all the bars in the chart must be compared. By examining the bars of 'New Businesses'<r>[0.204, 0.714, 0.833, 0.831]</r>, 'Stock Market'<r>[0.204, 0.568, 0.896, 0.685]</r>, 'Consumer Spending'<r>[0.204, 0.421, 0.732, 0.538]</r>, 'Inflation Rate'<r>[0.204, 0.275, 0.631, 0.392]</r>, and 'Unemployment Rate'<r>[0.204, 0.128, 0.398, 0.246]</r>, we find that the bar for 'Stock Market' has the longest length.\nBased on the above thought, the answer is:\nStock Market."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "5DuIOw0d5TYF0IFb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you see, convert the data into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's {subtitle}[0.311, 0.075, 0.907, 0.165] indicates that it represents the Environmental and Climate Change Impact in various categories. The categories, read from top to bottom, are {Deforestation Rate}[0.035, 0.639, 0.253, 0.709], {Global Temperature}[0.025, 0.536, 0.253, 0.606], {Air Pollution}[0.115, 0.432, 0.253, 0.502], {Water Availability}[0.050, 0.329, 0.253, 0.399] and {Biodiversity Loss}[0.059, 0.225, 0.253, 0.295]. Impact values for each category are obtained from the text annotations at the end of the bars. For instance, {the Deforestation Rate bar's text annotation}[0.692, 0.632, 0.751, 0.702] shows a value of 0.62.\nBased on the above thought, the answer is:\nCategory,Impact\nDeforestation Rate,0.62\nGlobal Temperature,0.91\nAir Pollution,0.82\nWater Availability,0.73\nBiodiversity Loss,0.85"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "IiCSNzuMonvlUxXZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the environmental and climate change impact, which category is the most pressing to address and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most pressing category, I need to look at the one with the highest impact value. Reviewing the bar lengths and the text annotations at the end of each bar, it's clear that the {Global Temperature bar}[0.261, 0.522, 0.894, 0.605] has the largest length with an {impact annotation}[0.894, 0.528, 0.953, 0.598] of 0.91, making it the category with the highest environmental and climate change impact.\nBased on the above thought, the answer is:\nGlobal Temperature. This is due to its highest environmental and climate change impact value of 0.91 compared to the other categories."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "EFDRB6AEFtCwuLIB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average impact across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average impact, I need to determine the values of each category: {Deforestation Rate}[0.692, 0.632, 0.751, 0.702], {Global Temperature}[0.894, 0.528, 0.953, 0.598], {Air Pollution}[0.832, 0.425, 0.890, 0.495], {Water Availability}[0.769, 0.321, 0.827, 0.391], and {Biodiversity Loss}[0.852, 0.218, 0.911, 0.288]. Sum up these values and divide by the total number of categories, which is 5.\nBased on the above thought, the answer is:\n0.786"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "tPGjZcGrQilBOxyP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data into a CSV format for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"Happiness Index in Different Areas\"}[0.386, 0.037, 0.675, 0.083], I find this chart is about the happiness index in different areas. The y-axis represents different areas including {Suburban}[0.015, 0.717, 0.082, 0.752], {Urban}[0.040, 0.466, 0.082, 0.501], and {Rural}[0.046, 0.215, 0.082, 0.250]. The x-axis ticks with labels ranging from {0.0}[0.076, 0.879, 0.098, 0.914] to {1.0}[0.963, 0.879, 0.985, 0.914] indicate the Happiness Index. The bars' lengths correspond to the index values. For example, for {Suburban}[0.087, 0.631, 0.814, 0.831], I see a value {\"0.82\"}[0.814, 0.713, 0.849, 0.748] at the end of the bar. By following the same approach, I can convert the chart into a CSV format.\nBased on the above thought, the answer is:\nArea,Happiness Index\nSuburban,0.82\nUrban,0.76\nRural,0.64"
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "7G2mP5kovTik45dl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area needs the most improvement in terms of increasing the happiness index, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the lengths of the bars, I can see that the shortest is for the {Rural area}[0.087, 0.128, 0.655, 0.329] with a happiness index of {\"0.64\"}[0.655, 0.211, 0.690, 0.246]. Compared to {Suburban}[0.087, 0.631, 0.814, 0.831] and {Urban}[0.087, 0.379, 0.761, 0.580], Rural has the lowest index value. Therefore, to increase the overall happiness index, improvements should mainly focus on Rural areas.\nBased on the above thought, the answer is:\nThe Rural area. Because its happiness index is the lowest among Suburban, Urban, and Rural areas, according to the chart."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "8ztmaBVOGuwJ86P8",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in happiness index between Suburban and Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "The endpoint of the bar for {Suburban}[0.087, 0.631, 0.814, 0.831] represents a happiness index of {\"0.82\"}[0.814, 0.713, 0.849, 0.748], and for {Rural}[0.087, 0.128, 0.655, 0.329] it is {\"0.64\"}[0.655, 0.211, 0.690, 0.246]. I can calculate the difference by subtracting the index of Rural from Suburban.\nBased on the above thought, the answer is:\n0.18"
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "q3BgLfsBzZXC0w3Z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the bar chart into a CSV table, I'll go through each bar, correlate it with its tick label in the y-axis and measure its length up to the value annotated in the x-axis. The chart's title {\"Probability of Market Increase for Various Financial Instruments\"}[0.306, 0.030, 0.831, 0.066] indicates a comparative analysis of the increase probability for different financial instruments. The x-axis {\"Probability of Increase\"}[0.492, 0.942, 0.646, 0.970] represents the rate of increase in percentage, ranging from 0.6 to 0.9, while the y-axis lists the various financial instruments. Starting with the bar at the top, representing {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811], I can see that its end corresponds to an annotated value of {\"0.65\"}[0.749, 0.780, 0.784, 0.808] in the x-axis. I can carry on this process for all bars on the chart, noting the corresponding instrument and its increase probability.\nBased on the above thought, the answer is:\nFinancial_Instrument, Increase_Probability\nNational_Bonds, 0.65\nInternational_Bonds, 0.72\nStock_Market_Indices, 0.70\nMutual_Funds, 0.67\nCryptocurrencies, 0.78"
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "vPVAEiFZenP4FFTf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you draw about the market for the next quarter, considering this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the chart are representations of the probability of market increase for various financial instruments. From {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811] to {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191], the length of the bars generally increase. That indicate that the predicted probability of market increase is higher for more volatile assets like {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191] and lower for more stable assets like {\"National Bonds\"}[0.054, 0.783, 0.159, 0.811]. So, the market, in general, appears to be bullish (increasing) for the coming quarter but the degree of increase varies across different types of financial instruments.\nBased on the above thought, the answer is:\nThe market seems to be in a bullish state for the next quarter. However, the degree of increase varies across different financial instruments. High-risk instruments like Cryptocurrencies are expected to have a higher increase than more stable ones like National Bonds."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "Mq3PEFpIqiNQncWv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich financial instrument has the highest probability of market increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which financial instrument has the highest probability of market increase, I need to compare the length of each bar on the chart. The longer the bar, the higher the probability. I can see that the bar for {\"Cryptocurrencies\"}[0.041, 0.163, 0.159, 0.191] stretches out the furthest on the x-axis with an annotated value of {\"0.78\"}[0.866, 0.160, 0.901, 0.188], which is the highest among all bars, hence Cryptocurrencies have the highest probability.\nBased on the above thought, the answer is:\nCryptocurrencies."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "hN8snsyy0xcFfe1T",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this chart and present it in a CSV format table, stating each 'Technology' and its corresponding 'Trend value'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'm observing the y-axis labels providing the 'Technologies' and the data labels at the end of each bar specifying the 'Trend values'. For instance, the data label '0.88' corresponds to the technology labelled 'AI-driven Development' as seen at [0.180, 0.809, 0.861, 0.872], indicating a trend value of 0.88 for 'AI-driven Development'. By following a similar process for each bar on the chart, I can extract the relevant data and present it in a tabulated CSV format.\nBased on the above thought, the answer is:\nTechnologies,Trend Value\nAI-driven Development,0.88\nBlockchain,0.79\nEdge Computing,0.91\nQuantum Computing,0.74\nHuman Augmentation,0.96\nBiotechnology,0.84\n5G Networks,0.99\nAugmented Reality,0.83\nVirtual Reality,0.79\nDistributed Cloud,0.92"
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "bFvE7p6Etj0daC6t",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, What can you infer about the trend values of the two technologies 'Edge Computing' and 'Augmented Reality'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend values of 'Edge Computing' and 'Augmented Reality', I'm locating their respective bars and data labels. 'Edge Computing' has a bar found at [0.180, 0.652, 0.885, 0.715] with a data label showing '0.91', suggesting a predicted strong upward trend value. On the other hand, 'Augmented Reality' at [0.180, 0.258, 0.823, 0.321] shows a trend value of '0.83', indicating a noticeably positive trend but not as strong as 'Edge Computing'. Therefore, both technologies are trending positively, though 'Edge Computing' is expected to trend upwards more rapidly than 'Augmented Reality'.\nBased on the above thought, the answer is:\nBoth 'Edge Computing' and 'Augmented Reality' are expected to trend positively. However, 'Edge Computing' with a trend value of 0.91 has a stronger positive trend compared to 'Augmented Reality' with a trend value of 0.83."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "65lRgdaBBSkZEMms",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology according to the chart is anticipating the most pronounced increase in trend value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate the bar with the longest length, implying the highest trend value. Observing each bar, I notice '5G Networks' situated at [0.180, 0.337, 0.947, 0.400] has its bar extending to nearly 1 on the x-axis, indicating the highest trend value among other technologies. This implies '5G Networks' is anticipated to experience the most pronounced increase.\nBased on the above thought, the answer is:\n5G Networks."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "lepXOXBGM3SDq1D3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a horizontal bar chart with categories shown on the Y-axis and percentage increase over a decade on the X-axis. The title { \"Financial Market Fluctuations and Trends\" }<r>[0.370, 0.037, 0.791, 0.083]</r> suggests that the chart represents the financial market trends. The y-axis tick labels include: {\"Stock Market\"}<r>[0.074, 0.744, 0.187, 0.779]</r>, {\"Gold Price\"}<r>[0.100, 0.559, 0.187, 0.594]</r> {\"Real Estate\"}<r>[0.089, 0.374, 0.187, 0.409]</r> and {\"Currency Exchange\"}<r>[0.019, 0.189, 0.187, 0.224]</r>. These labels correspond to different categories. The X-axis shows the percentage increase over a decade, which can be obtained from the length of each bar for each category. For example, the length of {\"the bar corresponding to the Stock Market\"}<r>[0.193, 0.683, 0.774, 0.831]</r> extends approximately to the 4.5% point on the x-axis, suggesting that the value associated with the \"Stock Market\" category is 4.5%.\nBased on the above thought, the answer is:\nCategories,Values\nStock Market,4.5\nGold Price,4.7\nReal Estate,5.2\nCurrency Exchange,4.8"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "pac1ItjjH1v6FuCF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, can you identify which market trend is considered an outlier and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "An outlier is a data point that is significantly different from other similar data points. In the context of this chart, a market trend that shows significantly more or less growth compared to other similar market trends might be considered an outlie. I find that {\"Real Estate\"}<r>[0.089, 0.374, 0.187, 0.409]</r> stands distinctly separate from the rest of the bars since its bar extends longest on x-axis reaching the 5.2% point. It's visually clear that {\"the bar of Real Estate\"}<r>[0.193, 0.313, 0.864, 0.461]</r> is longer than other bars, and the percentage increase for real estate is higher than any other sectors' increase by quite a margin. Therefore, \"Real Estate\" can be considered an outlier.\nBased on the above thought, the answer is:\nYes, the Real Estate market is considered an outlier as it has shown the highest growth rate of 5.2%, which is significantly higher than the other categories."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "DGXjfs2POtJAcxnG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category had the highest percentage increase over a decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which category had the highest percentage increase, I need to look at which bar extends the furthest on the x-axis. Looking at all the bars, it's clear that {\"the bar of Real Estate\"}<r>[0.193, 0.313, 0.864, 0.461]</r> extends the furthest, all the way to 5.2% point. The corresponding label on the x-axis confirms this observation.\nBased on the above thought, the answer is:\nReal Estate"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "G6oB1Lub96ax8KTr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the details of the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top with coordinates span {\"Average Return Rates of Different Investment Strategies\"}[0.222, 0.05, 1.006, 0.11] I understand that this is a horizontal bar chart showing the average return rates of different investment strategies. The x-axis represents the average annual return rate and has the coordinates span {\"Average Annual Return Rate\"}[0.45, 0.903, 0.778, 0.95]. The y-axis displays different investment strategies, the bars of each strategy with coordinates [0.286, 0.685, 0.844, 0.790] and so on are tagged with their corresponding percentage performance along the x-axis. Using this information, I can generate the CSV table by mapping each category (Investment Strategies) to their corresponding values (Return Rate).\nBased on the above thought, the answer is:\nInvestment Strategies,Return Rate\nHigh Risk High Return,85%\nStable Low Yield,10%\nBalanced Portfolio,50%\nAggressive Growth,70%\nConservative Mix,30%"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "60bcxsnVNSfyZYd5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment strategy would you recommend to the investors based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to carefully look at each bar on the chart and the corresponding investment strategy it represents. The bar for {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790] is the longest, indicating the highest return rate. However, suggeting this strategy will depend on the risk appetite of the investors. For conservative or risk averse investors, I would recommend {\"Conservative Mix\"}[0.286, 0.156, 0.483, 0.262] which has a smaller but relatively safer return. For those seeking a balance of risk and return, the answer would be {\"Balanced Portfolio\"}[0.286, 0.420, 0.614, 0.526]. For aggressive investors who are willing to take on more risk for potential higher returns, I would suggest {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790].\nBased on the above thought, the answer is:\nThe recommendation would depend on the risk appetite of the individual investor - conservative investors may prefer the Conservative Mix, balanced investors the Balanced Portfolio, and aggressive investors the High Risk High Return strategy."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "ckHIDNm49zWUyhcC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you name the investment strategy that offers the highest return rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll need to figure out which bar is longest, using their coordinates. Going through all the bars in turn, it turns out that the bar at {\"High Risk High Return\"}[0.286, 0.685, 0.844, 0.790] is the longest. So the investment strategy that offers the highest return rate is High Risk High Return.\nBased on the above thought, the answer is:\nHigh Risk High Return."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "oKQl8EaNwYPZdp8u",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data into a structured table using CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {chart title}[0.225, 0.037, 0.877, 0.083] and the labels on the axes, {x-axis label}[0.484, 0.927, 0.618, 0.963] and {y-axis labels}[0.053, 0.759, 0.128, 0.794; 0.020, 0.613, 0.128, 0.648; 0.019, 0.466, 0.128, 0.501; 0.027, 0.320, 0.128, 0.355; 0.030, 0.173, 0.128, 0.208], we can identify that the two columns in the table should be 'Media Channels' and 'Influence Score'. The media channels are represented by individual bars, and the corresponding influence scores are represented by the length of those bars. Checking the length of the bars and the annotations attached to them, we can gather the precise scores for each media channel. For example, the {TV News bar}[0.134, 0.714, 0.784, 0.831] extends up to 0.78 on the x-axis, as confirmed by the {annotation}[0.784, 0.755, 0.828, 0.790].\nBased on the above thought, the answer is:\nMedia Channels,Influence Score\nTV News,0.78\nOnline News,0.85\nSocial Media,0.92\nRadio News,0.65\nPrint Media,0.7"
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "OKq8Pmnip3GqCloa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how does the influence of traditional media compare to that of contemporary media in guiding public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare {the influence scores of traditional media channels}[{TV News bar}[0.134, 0.714, 0.784, 0.831]; {Radio News bar}[0.134, 0.275, 0.676, 0.392]; {Print Media bar}[0.134, 0.128, 0.718, 0.246]] with {the influence scores of contemporary media channels}[{Online News bar}[0.134, 0.568, 0.843, 0.685]; {Social Media bar}[0.134, 0.421, 0.901, 0.538]]. Based on the length of the bars, TV News and Print Media have moderate influence scores, while Radio News has the lowest. Conversely, Online News and Social Media, have the highest influence scores, with Social Media having the highest of all. Therefore, traditional media still holds a reasonable influence, but contemporary media channels appear to be significantly more influential in guiding public opinion.\nBased on the above thought, the answer is:\nContemporary media, specifically Online News and Social Media, are significantly more influential in guiding public opinion than traditional media like TV News, Radio News, and Print Media. While traditional media still holds influence, the rise of online platforms has resulted in their dominant role in shaping public opinion."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "eWwFKbSuMCSNihaP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media channel has the lowest influence score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the media channel with the lowest influence score, I need to examine the length of each bar on the chart. The bars are located at these coordinates {TV News bar}[0.134, 0.714, 0.784, 0.831], {Online News bar}[0.134, 0.568, 0.843, 0.685], {Social Media bar}[0.134, 0.421, 0.901, 0.538], {Radio News bar}[0.134, 0.275, 0.676, 0.392], and {Print Media bar}[0.134, 0.128, 0.718, 0.246]. By comparing their lengths, I can see that {the Radio News bar}[0.134, 0.275, 0.676, 0.392] is the shortest, indicating that Radio News has the lowest influence score.\nBased on the above thought, the answer is:\nRadio News"
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "zSunc8JaB2DckorS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart for 'Efficiency Improvement Potential Assessment', please construct a table containing different tasks and their corresponding efficiency improvement potential."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar graph with title {Efficiency Improvement Potential Assessment}[0.309, 0.037, 0.943, 0.083], there are several bars with labels on the y-axis which suggests different types of tasks. When we move from the top to bottom, we have {Automation}[0.296, 0.734, 0.904, 0.831], {Delegation}[0.296, 0.613, 0.858, 0.710], {Process Improvement}[0.296, 0.492, 0.811, 0.589], {Technological Upgrade}[0.296, 0.371, 0.752, 0.468], {Remote Working}[0.296, 0.250, 0.725, 0.347] and finally {Outsourcing}[0.296, 0.128, 0.679, 0.225]. The length of each bar on the x-axis represents the efficiency improvement potential from 0 to 1, as indicated by {the x-axis labels}[0.434, 0.927, 0.818, 0.963]. The efficiency improvement potentials for each task can be obtained by looking at the values next to the bars on the right, for example, for Automation, {the improvement potential is 0.92}[0.904, 0.765, 0.962, 0.800].\nBased on the above thought, the answer is:\nTasks,Efficiency Improvement Potential\nAutomation,0.92\nDelegation,0.85\nProcess Improvement,0.78\nTechnological Upgrade,0.69\nRemote Working,0.65\nOutsourcing,0.58"
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "xXSZNFCxB01NNwDz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich task appears to present an outlier in the decreasing trend of efficiency improvement potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "An outlier in this context would be a value that deviates significantly from the overall downward trend. Looking at the lengths of the bars from Automation to Outsourcing, the trend generally decreases. However, there is a noticeable decrease in the length of the bar from {Delegation}[0.296, 0.613, 0.858, 0.710] to {Process Improvement}[0.296, 0.492, 0.811, 0.589], which represents a relatively sharp drop from {an improvement potential of 0.85}[0.858, 0.644, 0.916, 0.679] to {0.78}[0.811, 0.523, 0.870, 0.558]. The graph then restores to the decreasing trend from Process Improvement to Technological Upgrade. Hence, Process Improvement appears to be an outlier in this context.\nBased on the above thought, the answer is:\nProcess Improvement. It showed a significant drop in efficiency improvement potential compared to the previous task (Delegation) and is not fully represented of the overall downward trend."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "mExDBRrzLzEfPMfL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which task shows the least potential for efficiency improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the task with the least potential for efficiency improvement, I need to find the shortest bar in the graph. Looking at the chart from the top to the bottom, the lengths of the bars decrease generally, with the shortest bar representing {Outsourcing}[0.296, 0.128, 0.679, 0.225], with {an improvement potential of 0.58}[0.679, 0.159, 0.738, 0.194]. Hence, the task with the least potential for improvement is Outsourcing.\nBased on the above thought, the answer is:\nOutsourcing."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "jQcuHdo03BPkMN4c",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compile the information in the bar chart into a detailed CSV data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, the chart's title \"Comparing Media Influence & Public Opinion Guidance\" at coordinates [0.197, 0.037, 0.953, 0.083] reveals the data's theme. The x-axis represents the influence index, with values ranging from {0.0}[0.164, 0.879, 0.200, 0.914] to {1.0}[0.995, 0.879, 1.032, 0.914]. The y-axis displays different media outlets like {BBC}[0.126, 0.786, 0.174, 0.821] and {Fox News}[0.064, 0.626, 0.174, 0.661]. Each bar's length, such as {BBC's bar}[0.182, 0.767, 0.931, 0.831], corresponds to the media outlet's influence index. The influence indices are even labeled at the end of each bar, with BBC's as {0.9}[0.931, 0.782, 0.974, 0.817], for instance. Therefore, the CSV formatted data can be generated by matching each media outlet's name against their influence index.\nBased on the above thought, the answer is:\nMedia Outlet,Influence Index\nBBC,0.9\nCNN,0.88\nFox News,0.61\nAl Jazeera,0.84\nRussia Today,0.67\nSky News,0.8\nABC News,0.75\nNBC News,0.78\nCBC News,0.85"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "C6zPazz0V6UX9zPa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, how would you compare the influence of BBC and CNN on public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that the length of {BBC's bar}<r>[0.182, 0.767, 0.931, 0.831]</r> extends further than the length of {CNN's bar}<r>[0.182, 0.688, 0.914, 0.751]</r> on the x-axis. This indicates that the Influence Index of BBC is higher (with a noted Influence Index of {0.9}<r>[0.931, 0.782, 0.974, 0.817]</r>) than CNN's Influence Index of {0.88}<r>[0.914, 0.702, 0.973, 0.737]</r>. As a result, it can be inferred that according to the chart, BBC has a stronger influence on public opinion in comparison to CNN.\nBased on the above thought, the answer is:\nBBC has a stronger influence on public opinion than CNN, given its higher Influence Index."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "owyTZen46UAJOCeP",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the media outlet with the highest Influence Index according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assuming that the bar chart's length represents the influence index, the media outlet with the highest Influence Index would be represented by the longest bar. Looking at {BBC's bar}[0.182, 0.767, 0.931, 0.831], the length extends to 0.9 on the x-axis, the highest among {all the bars}[0.182, 0.128, 0.931, 0.831]. As such, BBC has the highest Influence Index.\nBased on the above thought, the answer is:\nBBC"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "OjQ6S4kaX7DOFo0Y",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the chart's title {\"Trend of Public Interest in Cultural Heritage and Cultural Integration\"}[0.140, 0.075, 1.076, 0.165] I understand this is a trend chart. Our data divided into two categories: {Cultural Heritage}[0.260, 0.487, 0.873, 0.708] and {Cultural Integration}[0.260, 0.211, 0.748, 0.432], which represents the labels on the y-axis. The x-axis {\"Level of Public Interest\"}[0.477, 0.855, 0.740, 0.925] has a label ranging from {0.0}[0.241, 0.757, 0.278, 0.827] to {1.0}[0.938, 0.757, 0.975, 0.827]. In order to transform the data into a table format, I can obtain the relevant values for each category by examining the length of each bar and the corresponding value annotation at the end of each bar e.g. for Cultural Heritage, {the bar's length}[0.260, 0.487, 0.873, 0.708] and its {value annotation \"0.88\"}[0.873, 0.563, 0.932, 0.633] confirm that the value of public interest in Cultural Heritage is 0.88.\nBased on the above thought, the answer is:\nCategory,Level of Public Interest\nCultural Heritage,0.88\nCultural Integration,0.70"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "T9lSzDxyg5V7NIbM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends observed in the chart, which area \u2014 Cultural Heritage or Cultural Integration \u2014 do you believe has shown more consistent public interest over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare both the {bar of Cultural Heritage}[0.260, 0.487, 0.873, 0.708] and the {bar of Cultural Integration}[0.260, 0.211, 0.748, 0.432] on the chart. Since the x-axis represents the level of public interest, we can infer from the bar lengths and the {value annotations \"0.88\" and \"0.70\"}[0.873, 0.563, 0.932, 0.633; 0.748, 0.287, 0.791, 0.357] that Cultural Heritage fluctuates quite significantly, with high peaks and sharp falls. In contrast, Cultural Integration shows a gradual and steady growth, and is slower in pace, indicating a more consistent level of public interest over time.\nBased on the above thought, the answer is:\nCultural Integration. Despite its slower pace, it shows a more consistent and steady growth in public interest over time compared to the erratic and sharp fluctuations seen in Cultural Heritage."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "ao4eLBJfmxuAZ01Q",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher level of public interest according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In each category, there is a bar that represents the level of public interest. For {Cultural Heritage}[0.260, 0.487, 0.873, 0.708], the length of the bar and the {value annotation \"0.88\"}[0.873, 0.563, 0.932, 0.633] indicates that the public interest level is 0.88. On the other hand, for {Cultural Integration}[0.260, 0.211, 0.748, 0.432], the corresponding length of the bar and the {value annotation \"0.70\"}[0.748, 0.287, 0.791, 0.357] reveal that the public interest level is 0.70. By comparing these two values, we can conclude which category has a higher level of public interest.\nBased on the above thought, the answer is:\nCultural Heritage."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "h7GDKRuGqZN6TlSP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data illustrated in the horizontal bar chart into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a CSV table, I need to extract the values of each category. The y-axis is represented by categories and the x-axis by their concerning values. The y-axis tick labels, yticks, show the categories: {Monetary Policy}[0.061, 0.810, 0.149, 0.830], {Quantitative Easing}[0.039, 0.735, 0.149, 0.755], {Interest Rates}[0.072, 0.660, 0.149, 0.680], {Fiscal Stimulus}[0.067, 0.585, 0.149, 0.605], {Banking Regulations}[0.035, 0.510, 0.149, 0.530], {Trade Policies}[0.074, 0.435, 0.149, 0.455], {Foreign Exchange Rates}[0.015, 0.360, 0.149, 0.380], {Subprime Mortgages}[0.033, 0.285, 0.149, 0.305], {Commodities}[0.076, 0.210, 0.149, 0.230], and {Exchange-traded Fund}[0.023, 0.135, 0.149, 0.155]. The x-values for these categories can be collected from the bar lengths and the corresponding values {0.89}[0.884, 0.804, 0.919, 0.832], {0.67}[0.703, 0.729, 0.739, 0.757], {0.59}[0.638, 0.654, 0.673, 0.682], {0.82}[0.826, 0.579, 0.862, 0.607], {0.70}[0.728, 0.504, 0.763, 0.532], {0.55}[0.605, 0.429, 0.640, 0.457], {0.76}[0.777, 0.354, 0.812, 0.382], {0.52}[0.581, 0.279, 0.616, 0.307], {0.69}[0.720, 0.204, 0.755, 0.232], and {0.62}[0.662, 0.129, 0.698, 0.157].\nBased on the above thought, the answer is:\nCategory,Value\nMonetary Policy,0.89\nQuantitative Easing,0.67\nInterest Rates,0.59\nFiscal Stimulus,0.82\nBanking Regulations,0.70\nTrade Policies,0.55\nForeign Exchange Rates,0.76\nSubprime Mortgages,0.52\nCommodities,0.69\nExchange-traded Fund,0.62"
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "nLCi7lZRBM7wr34Y",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can we interpret about the status of subprime mortgages in relation to other parameters of global financial systems and policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the length of the {bar representing 'Subprime Mortgages'}[0.154, 0.263, 0.581, 0.323] appears to be shorter than most of the other bars, which represents lower value {0.52}[0.581, 0.279, 0.616, 0.307]. In comparison to most of the other parameters, Subprime Mortgages has a lower associated value, suggesting that the key parameters of Subprime Mortgages are less influential or present in the global financial system and policies compared to other factors.\nBased on the above thought, the answer is:\nSubprime Mortgages seem less influential or less present in the global financial system and policies compared to the other parameters given the lower associated value."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "XqkQmUlFyEPnSTSz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value indicates the maximum key parameter in the various global financial systems and policies according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look for the longest bar on the chart. According to the coordinates, the longest bar is {Monetary Policy}[0.154, 0.788, 0.884, 0.848] with a value of {0.89}[0.884, 0.804, 0.919, 0.832]. Therefore, Monetary Policy has the highest associated value.\nBased on the above thought, the answer is:\n0.89"
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "I74JOn5IodWpJdgN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you arrange the social responsibility indicators in order of increasing adoption rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will examine the position of the x-coordinates of {the right-hand edge of each bar}<r>[0.400, 0.715, 0.400, 0.831];[0.661, 0.569, 0.661, 0.685];[0.841, 0.423, 0.841, 0.540];[0.893, 0.277, 0.893, 0.394];[0.542, 0.131, 0.542, 0.248]</r>, which represents the proportion of companies that have adopted each indicator. Each bar's right end corresponds to the value of each social responsibility indicator: Biodiversity Protection, Emission Reduction, Fair Trade, Charitable Giving, and Sustainable Packaging. I will list them in ascending order.\nBased on the above thought, the answer is:\nSocial Responsibility Indicator,Proportion of Sustainable Business Practice\nBiodiversity Protection,0.24\nSustainable Packaging,0.43\nEmission Reduction,0.59\nFair Trade,0.83\nCharitable Giving,0.9"
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "xOSoFfFadYYdqX5i",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which social responsibility indicator is most popular among companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is determined by the length of {the bars}<r>[0.221, 0.715, 0.400, 0.831];[0.221, 0.569, 0.661, 0.685];[0.221, 0.423, 0.841, 0.540];[0.221, 0.277, 0.893, 0.394];[0.221, 0.131, 0.542, 0.248]</r>. The bar representing Charitable Giving appears to be the longest, reaching furthest across the x-axis, meaning it has the highest proportion of companies that have adopted it.\nBased on the above thought, the answer is:\nCharitable Giving is the most popular social responsibility indicator among companies."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "WtDRFFvcpwVrmdP3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_50_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of companies have adopted Fair Trade as a social responsibility indicator?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this proportion, I need to locate {the bar representing Fair Trade}<r>[0.221, 0.423, 0.841, 0.540]</r>. The right-hand coordinate of this bar suggests the proportion of companies that have adopted Fair Trade. I can read it directly as 0.83.\nBased on the above thought, the answer is:\n0.83"
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "x9MIpWOT3HSMDe8z",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data from the bar graphs for chunk sizes 1 to 4 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the first subplot within the coordinates {subplot}<r>[0.123, 0.021, 0.975, 0.285]</r>, we see the bar charts for chunk sizes {1}<r>[0.138, 0.100, 0.161, 0.126]</r> to {4}<r>[0.371, 0.047, 0.394, 0.126]</r> for the 'Ours' technique. Correspondingly, for 'Saliency-based Sampling', the chunks sizes range from {1}<r>[0.161, 0.126, 0.185, 0.206]</r> to {4}<r>[0.394, 0.126, 0.417, 0.232]</r>. Extracting values from the rate of gain change on the y-axis tick labels, we can convert them into a structured table.\nBased on the above thought, the answer is:\nChunk Size,Ours,Saliency-based Sampling\n1,5,-15\n2,3,-10\n3,8,-25\n4,15,-20"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "Ae0X2hsP4yU27kTh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the overall trend, which big data integration technique demonstrates a more consistent performance based on the provided chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the consistency of performance for each technique, we examine the fluctuation of bars in each subplot. Consistency can be interpreted as fewer deviations in bar height within their respective y-ranges. For 'Ours', we observe fluctuating bar heights within {subplot 0}<r>[0.123, 0.021, 0.975, 0.285]</r>, {subplot 1}<r>[0.123, 0.349, 0.975, 0.613]</r>, and {subplot 2}<r>[0.123, 0.677, 0.975, 0.942]</r>. There are notable changes such as the dip in chunk size {5}<r>[0.448, 0.433, 0.471, 0.455]</r>. 'Saliency-based Sampling', despite experiencing a general decrease, shows less variation between adjacent chunk sizes in each subplot, suggesting more consistency compared to 'Ours'.\nBased on the above thought, the answer is:\nSaliency-based Sampling. Despite showing a general decrease, it experiences smaller fluctuations between chunk sizes, which could be interpreted as more consistent compared to the variability observed in 'Ours'."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "McUELr3Fcv8JvdaI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest rate of gain change observed for 'Ours' in the dataset provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest rate of gain change for 'Ours', I must examine the height of bars corresponding to 'Ours' across all subplots. The value on the y-axis where the top of the bar resides denotes the rate of gain change for each chunk size. For 'Ours', I am looking at the bar segments represented within {subplot 0}<r>[0.123, 0.021, 0.975, 0.285]</r>, {subplot 1}<r>[0.123, 0.349, 0.975, 0.613]</r>, and {subplot 2}<r>[0.123, 0.677, 0.975, 0.942]</r>. Comparing the coordinate points of the bars' upper edges, and the corresponding y-value labels such as {20}<r>[0.077, 0.015, 0.106, 0.029]</r> and {18}<r>[0.077, 0.025, 0.106, 0.039]</r> to determine the highest point, we see that the highest bar is for chunk size {10}<r>[0.836, 0.677, 0.859, 0.783]</r> with a y-range of '0.00 -> 20.00'.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "FnKigWkPMmJNrD8t",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a comparison of Series A and Series B across the different metrics in a CSV format table, based on your observation of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, there are three subplots with {dark blue bars}<r>[0.147, 0.101, 0.260, 0.115]</r> and {light blue bars}<r>[0.260, 0.115, 0.373, 0.215]</r> representing Series A and Series B respectively. The metric labels are visible along the x-axis with the metrics {Data Cleanliness}<r>[0.163, 0.314, 0.357, 0.328]</r>, {Data Accuracy}<r>[0.457, 0.314, 0.626, 0.328]</r>, {Data Completeness}<r>[0.709, 0.314, 0.937, 0.328]</r>, and so on for the remaining metrics. By looking at the y-axis, I can see numeric labels indicating the value for each Series, with negative values on the upper y-axis and positive on the lower, plotting different ranges such as <r>[-8, 0]</r>, <r>[-7, 0]</r>, <r>[-6, 0]</r>, etc. Utilizing the bar's height, I will match each metric's value for Series A and Series B from the bar chart.\nBased on the above thought, the answer is:\nMetric,Series A, Series B\nData Cleanliness,0.6,-4.2\nData Accuracy,0.9,-7.5\nData Completeness,2.4,-6.3\nData Consistency,3.2,-4.2\nData Timeliness,0.1,1.2\nData Relevance,0.8,-2.3\nData Quantity,3.9,-7.4"
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "TcnDmeE1mnAaF7Ta",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data analysis observed in the bar chart, which metric shows the greatest disparity between Series A and Series B, and could you explain how you determined this?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the greatest disparity between Series A and Series B for a metric, I look at the individual bars representing both series and compare their heights. Inspecting the bar chart, I identify each metric and their corresponding {dark blue bar}<r>[0.147, 0.101, 0.260, 0.115]</r> for Series A, and {light blue bar}<r>[0.260, 0.115, 0.373, 0.215]</r> for Series B. The y-axis helps me estimate the numeric value for each bar. By visually comparing the bar heights such as those of {Data Accuracy}<r>[0.429, 0.094, 0.541, 0.115]; [0.541, 0.115, 0.654, 0.293]</r> and noting the difference between the two series, I can spot the metric with the greatest disparity.\nBased on the above thought, the answer is:\nData Accuracy. By comparing the heights of the bars for each metric, the disparity in Data Accuracy is visibly the most significant, with Series A having a positive value of 0.9, and Series B a negative value of -7.5, which results in a disparity of 8.4."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "l0LzGxRZaZz5lPHi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the positive values Series A shows across all metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the sum of the positive values for Series A, I am observing the height of {the dark blue bars}<r>[0.147, 0.101, 0.260, 0.115]; [0.429, 0.094, 0.541, 0.115]; [0.710, 0.058, 0.823, 0.115]</r> depicting Series A across different metrics. The y-axis shows the numeric values, aiding me to interpret the values of each bar. By correctly identifying the ends representing {positives values}<r>[0.058, 0.015, 0.092, 0.029]</r> and adding them up, I can reach the sum for Series A across all metrics.\nBased on the above thought, the answer is:\n9.9"
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "3KBdVdUd2GKOIeqy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWould you please convert the bar charts observed into a table with CSV format for the sales data of Product X and Product Y?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sales data of Product X and Product Y is presented in two subcharts that handle the sales trend respectively. For Product X, let's start at {\"2020 Q1\"}[0.129, 0.459, 0.227, 0.473]. The sales value can be obtained by looking at the height of {\"the bar\"}[0.157, 0.275, 0.199, 0.449], which corresponds approximately to 102 on the y-axis. Similarly, for {\"2020 Q2\"}[0.234, 0.459, 0.332, 0.473], the bar [0.262, 0.193, 0.304, 0.449] suggests the sales value is 150. The process is repeated for the other quarters of 2020 and 2021. \nAs for Product Y, let's start at {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965]. The sales value can be obtained by looking at the height of {\"the bar\"}[0.157, 0.702, 0.199, 0.942], which corresponds approximately to 150 on the y-axis. The same process will be repeated for the remaining quarters of 2020 and 2021 in the same manner.\nBased on the above thought, the answer is:\nYear,Quarter,X-Product,Y-Product\n2020,Q1,102,150\n2020,Q2,150,175\n2020,Q3,180,190\n2020,Q4,200,210\n2021,Q1,220,245\n2021,Q2,230,245\n2021,Q3,210,245\n2021,Q4,150,240"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "9vstrPWbbc1W6LtW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the sales trends, which product would you say performed better over the two-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the sales trend of the two products independently. For Product X, as shown in the first sub-chart, the sales figure starts at 102 in {\"2020 Q1\"}[0.129, 0.459, 0.227, 0.473] and gradually increases to its peak in {\"2021 Q2\"}[0.655, 0.459, 0.754, 0.473] with a value of 230. However, sales then drop significantly to 150 by {\"2021 Q4\"}[0.866, 0.459, 0.964, 0.473]. In contrast, for Product Y, according to the second sub-chart, the sales figure starts at 150 in {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965] and witnesses a steady increase to 245 by {\"2021 Q1\"}[0.550, 0.951, 0.648, 0.965], if further compared with other quarters, the sales of Product Y does not have a drop like Product X.\nBased on the above thought, the answer is:\nProduct Y performed better. Mainly because over the two-year period, it revealed a steady growth trend and there were no notable dips like in the case of Product X."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "DBN15EvbSq6Il3F1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which quarter did the sales for product Y reach its maximum?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the second subchart for product Y, I can see that the sales starts at {\"2020 Q1\"}[0.129, 0.951, 0.227, 0.965] with a value of 150. The sales figures increase gradually with the highest {\"bar\"}[0.578, 0.549, 0.620, 0.942] reached at {\"2021 Q1\"}[0.550, 0.951, 0.648, 0.965], further comparing with other qaurters, it is confirmed that 2021 Q1 has the highest sales for product Y.\nBased on the above thought, the answer is:\n2021 Q1"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "4WsWY2Y6E0T4d9sx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, generate a table summarizing the rate of change in gain for each parameter for both \"Ours\" and \"Risk-based\" methods."
+            },
+            {
+                "from": "gpt",
+                "value": "From the position of the bar chart labels, the {x-axis}<r>[0.505, 0.940, 0.586, 0.969]</r> indicates the 'Methodology' and the {y-axis}<r>[0.023, 0.303, 0.045, 0.653]</r> represents 'Rate of Gain Change[%]'. There are six groups of bars, and each group corresponds to a parameter listed in {the legend}<r>[0.638, 0.092, 0.966, 0.363]</r>, including 'Tools Changed', 'Tools Used', 'Network Security', 'Physical Security', 'Awareness Programme', and 'Policy Review'. For each group, there are two bars representing the 'Ours' and 'Risk-based' methods, as indicated by {the x-axis labels}<r>[{\"Ours\"}[0.307, 0.899, 0.357, 0.928], {\"Risk-based\"}[0.629, 0.899, 0.747, 0.928]]</r>. The height of each bar represents the rate of change. For example, for the 'Tools Changed' parameter, the rate of change for the 'Ours' method is approximately 15% based on {the height of the corresponding bar}<r>[0.154, 0.587, 0.225, 0.697]</r>, and for the 'Risk-based' method, the rate is about 20% according to {the height of its bar}<r>[0.510, 0.551, 0.581, 0.697]</r>.\nBased on the above thought, the answer is:\nParameter,Ours,Risk-based\nTools Changed,15,20\nTools Used,80,60\nNetwork Security,-10,-20\nPhysical Security,15,35\nAwareness Programme,25,30\nPolicy Review,40,45"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "EIF9EUi4Hhn7V3yP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you describe how the \"Risk-based\" method performs in terms of risk management compared to the \"Ours\" method?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by comparing the bar heights for both methods, we can generally say that the \"Risk-based\" method underperforms in {tool usage}<r>[{\"Ours\"}[0.225, 0.114, 0.296, 0.697], {\"Risk-based\"}[0.581, 0.260, 0.652, 0.697]</r> as it has a lower rate of gain change. However, it outperforms in areas like {network security}<r>[{\"Ours\"}[0.296, 0.697, 0.368, 0.769], {\"Risk-based\"}[0.652, 0.697, 0.724, 0.842]</r>, {physical security}<r>[{\"Ours\"}[0.368, 0.587, 0.439, 0.697], {\"Risk-based\"}[0.724, 0.442, 0.795, 0.697]</r>, and {policy review}<r>[{\"Ours\"}[0.510, 0.405, 0.581, 0.697], {\"Risk-based\"}[0.866, 0.369, 0.937, 0.697]</r>. There's no significant difference for {awareness programme}<r>[{\"Ours\"}[0.439, 0.515, 0.510, 0.697], {\"Risk-based\"}[0.795, 0.478, 0.866, 0.697]</r>, and the \"Risk-based\" method only slightly outperforms the \"Ours\" method in this regard. For the {tools changed}<r>[{\"Ours\"}[0.154, 0.587, 0.225, 0.697],{\"Risk-based\"}[0.510, 0.551, 0.581, 0.697]</r> parameter, the\"Risk-based\" approaches it with a greater rate than the\"Ours\" method.\nBased on the above thought, the answer is:\nThe \"Risk-based\" method performs better in areas of Network Security, Physical Security and Policy Review, but underperforms in Tool Usage compared to the \"Ours\" method. The two methods are quite comparable on the parameter of Awareness Programme. The \"Risk-based\" method changes tools at a higher rate as compared to the \"Ours\" method."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "4vuhEPSeQ9b9mnEO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parameter has experienced the highest rate of gain change under the \"Risk-based\" method?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the parameter with the highest change under the \"Risk-based\" method, I need to consider the height of bars corresponding to the 'Risk-based' method for each parameter. By comparing the height of these bars, I can see that the bar corresponding to 'Tools Used' which is located at {this position}<r>[0.581, 0.260, 0.652, 0.697]</r> is the highest, its top reaches approximately the 60% line on the y-axis, which indicates the highest rate of gain change under the \"Risk-based\" method.\nBased on the above thought, the answer is:\nTools Used."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "vG1FmLNZHWj3x4ob",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the stacked bar chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I see there are three subplots indicating different scales for CO2 emissions and mean temperatures over the years. To construct a table, I'll consider {subplot_0}<r>[0.103, 0.018, 0.975, 0.287]</r> which represents CO2 emissions in billion tons. The years are indicated along {the x-axis}<r>[0.040, 0.316, 0.564, 0.330]</r>, and the corresponding emissions values are indicated by the height of the blue bars present on this subplot. Similarly, {subplot_1}<r>[0.103, 0.345, 0.975, 0.614]</r> contains bars depicting mean temperatures, with the orange bars on this chart. Each pair of {blue}<r>[0.143, 0.063, 0.173, 0.287]</r> and {orange}<r>[0.143, 0.369, 0.173, 0.614]</r> bars represent the values for CO2 emissions and mean temperature for a corresponding year, from \"2001\" <r>[0.144, 0.296, 0.203, 0.310]</r> to \"2011\" <r>[0.906, 0.296, 0.965, 0.310]</r>. By aligning this information with the y-axis data on each subplot, I can extract the associated values and convert them into a CSV format table.\nBased on the above thought, the answer is:\nYear,CO2 Emissions (Billion Tons),Mean Temperature (\u00b0C)\n2001,5.06,10.52\n2002,5.11,9.85\n2003,5.21,9.92\n2004,5.48,10.35\n2005,5.61,10.68\n2006,5.77,11.01\n2007,5.77,10.09\n2008,5.79,9.99\n2009,5.60,10.63\n2010,5.69,10.64\n2011,5.48,10.85"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "hPTHa3XDALLPwu5T",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between CO2 emissions and mean global temperatures as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the relationship between CO2 emissions and mean global temperatures according to the chart, I will analyze the patterns of {CO2 emissions bars}<r>[0.143, 0.824, 0.172, 0.942]</r> alongside the {mean temperature bars}<r>[0.172, 0.697, 0.202, 0.942]</r> over the years indicated by {x-axis ticks}<r>[0.143, 0.951, 0.201, 0.965]</r>. By closely examining the height of the bars every year, I can correlate rises and falls in CO2 emissions with the respective changes in mean global temperatures. This comparison helps determine if there's a pattern indicating a direct or inverse correlation, or if the relationship is more complex, perhaps marked by lags or other factors.\nBased on the above thought, the answer is:\nThe chart suggests a general correlation where years with higher CO2 emissions also tend to show higher mean global temperatures. Although the relationship is not perfectly linear, as seen by some fluctuations, there is noticeable alignment between increases in CO2 emissions and higher temperature values\u2014indicative of a positive correlation, which is consistent with the understanding that higher CO2 emissions contribute to global warming."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "9gsz43V6tCBI58V0",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the highest increase in CO2 emissions compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest increase in CO2 emissions, I have to calculate the year-over-year change. Starting with \"2001\" {bar for 2001 CO2 Emissions}<r>[0.143, 0.063, 0.173, 0.287]</r>, I observe the heights of subsequent bars relative to the previous one. For example, comparing the {bar for 2002 CO2 Emissions}<r>[0.219, 0.060, 0.249, 0.287]</r> to {the bar for 2001}<r>[0.143, 0.063, 0.173, 0.287]</r>, the difference in height is small. Progressing to {the bar for 2004 CO2 Emissions}<r>[0.371, 0.044, 0.402, 0.287]</r>, there's a notable jump from {the bar for 2003}<r>[0.295, 0.056, 0.326, 0.287]</r>, suggesting a greater increase. This process is repeated for each year until \"2011\" {bar for 2011 CO2 Emissions}<r>[0.905, 0.044, 0.935, 0.287]</r>. After examining all year-to-year changes, the year with the highest rise can be identified.\nBased on the above thought, the answer is:\n2004"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "AGoR4Lle5Jda5BBd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data from the bar chart comparing 'Substantial Increase' and 'Erratic Increase' over time into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the {subplot_2}<r>[0.103, 0.672, 0.975, 0.942]</r> which includes the comparison of 'Substantial Increase' and 'Erratic Increase', I first observe the {xticks}<r>[0.143, 0.951, 0.201, 0.965]</r> and <r>[0.877, 0.951, 0.935, 0.965]</r> which correspond to the '2008' and '2010' labels at the bottom of the chart. Then I identify the corresponding bars for 'Substantial Increase' marked by {red color}<r>[0.124, 0.685, 0.170, 0.695]</r> and 'Erratic Increase' marked by {blue color}<r>[0.124, 0.706, 0.170, 0.716]</r>. Using the height of {bars}<r>[0.143, 0.761, 0.202, 0.942]</r> and <r>[0.877, 0.685, 0.935, 0.942]</r> for 'Substantial Increase' and {bars}<r>[0.143, 0.799, 0.202, 0.942]</r> and <r>[0.877, 0.758, 0.935, 0.942]</r> for 'Erratic Increase', I can extract the rate of growth for the years 2008 and 2010. These include '30.50 -> 50.20' for 'Substantial Increase' and '27.80 -> 35.80' for 'Erratic Increase', allowing me to tabulate the data.\nBased on the above thought, the answer is:\nYear,Substantial Increase,Erratic Increase\n2008,35.20,27.80\n2010,50.20,35.80"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "QUXht7dPpSQFOXkF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich growth trend category showed more stability over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine stability, I look for smaller changes in growth rates over time for each category. By examining the height differences between the years 2008 and 2010 for each category, I am looking for the ones with less variation. Starting with {Persistent Growth}<r>[0.124, 0.029, 0.170, 0.038]</r>, I can see that there is a clear increase from the bar heights {2008}<r>[0.143, 0.197, 0.202, 0.285]</r> to {2010}<r>[0.877, 0.028, 0.935, 0.285]</r>. However, when I analyze {Marginal Improvement}<r>[0.124, 0.378, 0.170, 0.388]</r>, the increase in the bar heights from {2008}<r>[0.143, 0.491, 0.202, 0.613]</r> to {2010}<r>[0.877, 0.480, 0.935, 0.613]</r> is much smaller, indicating more stability. Categories like {Abrupt Increase}<r>[0.124, 0.357, 0.170, 0.367]</r> and {Erratic Increase}<r>[0.124, 0.706, 0.170, 0.716]</r> are ruled out due to the visible higher variation in their growth rates.\nBased on the above thought, the answer is:\nMarginal Improvement. The 'Marginal Improvement' category exhibited more stability over the period as it showed a smaller change in growth rate compared to the other categories."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "yrn7Ch44ezo1fUJ4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in 'Persistent Growth' from 2008 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in 'Persistent Growth' between the years 2008 and 2010, I will reference the bars for 'Persistent Growth' identified by the {blue color}<r>[0.124, 0.029, 0.170, 0.038]</r>. First, locating the bars for the years {2008}<r>[0.143, 0.197, 0.202, 0.285]</r> and {2010}<r>[0.877, 0.028, 0.935, 0.285]</r>, I can see that the {initial value}<r>[0.143, 0.197, 0.202, 0.285]</r> is 10.5% and the {final value}<r>[0.877, 0.028, 0.935, 0.285]</r> is 30.5%. The percentage increase can be calculated by the formula ((final - initial) / initial) * 100.\nBased on the above thought, the answer is:\n190.476%"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "nnyXOmgkIamkoKUc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the rates of technological breakthroughs and innovation introduction for each year as presented in the bar charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the two subplots within the chart, I see that technological breakthroughs are shown in subplot_0 with {the bars}<r>[0.161, 0.434, 0.214, 0.446]</r> for {the year 2008}<r>[0.093, 0.456, 0.151, 0.470]</r>, extending to {the 2020 bar}<r>[0.884, 0.036, 0.936, 0.446]</r>. Each bar represents the growth rate for the respective year. Similarly, innovation introduction rates are shown in subplot_1 with {the bars}<r>[0.161, 0.726, 0.214, 0.780]</r> for {the year 2008}<r>[0.093, 0.951, 0.151, 0.965]</r>, continuing to {the 2020 bar}<r>[0.884, 0.694, 0.936, 0.726]</r>. To construct a table, the year labels and bar heights are compared against the {y-axis labels}<r>[0.058, 0.482, 0.106, 0.496]</r> and <r>[0.058, 1.044, 0.106, 1.058]</r> to determine the growth rate percentages for both the technological breakthroughs and innovation introduction.\nBased on the above thought, the answer is:\nYear,Technological Breakthrough,Innovation Introduction\n2009,3,-5\n2010,7,-6\n2011,12,-9\n2012,22,-15\n2013,17,-7\n2014,35,-12\n2015,50,-14\n2016,38,-9\n2017,68,-4\n2018,80,-6\n2019,92,-2\n2020,100,3"
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "iMMyjRDjBzTPkwqr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart contained more variance in its growth rates, and what conclusions can be drawn from that?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine variance, I need to assess the variability in the heights of the bars representing each year. The chart for {technological breakthroughs is in subplot_0}<r>[0.123, 0.015, 0.975, 0.446]</r> shows growth with minor fluctuations, seen in the bar heights like {the bar for year 2013}<r>[0.424, 0.377, 0.477, 0.446]</r> being smaller than its preceding year {2012}<r>[0.358, 0.356, 0.411, 0.446]</r>. Conversely, the chart for {innovation introduction in subplot_1}<r>[0.123, 0.510, 0.975, 0.942]</r> demonstrates a major variance, as evidenced by significant fluctuations like the sizeable drop from {the year 2011 bar}<r>[0.358, 0.726, 0.411, 0.888]</r> to the subsequent rise in {2020}<r>[0.884, 0.694, 0.936, 0.726]</r>. While technological breakthroughs exhibit steady growth with some exceptions, innovation introduction rates fluctuate greatly, suggesting that breakthroughs faced steadier progress whereas the introduction of innovations saw inconsistent shifts over the same period.\nBased on the above thought, the answer is:\nThe chart for innovation introduction contained more variance. This suggests that while technological breakthroughs were steadily increasing, the rate at which innovations were introduced to the market was highly erratic, indicating possible challenges in transitioning from breakthroughs to practical, market-ready innovations."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "ECemmXtrsBlFU96C",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the observed period, how many years saw a growth in technological breakthroughs but a decline in innovation introduction?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am analyzing each year's growth rate by looking at the bars in subplot_0 for {technological breakthroughs}<r>[0.161, 0.434, 0.214, 0.446]</r> and subplot_1 for {innovation introduction}<r>[0.161, 0.726, 0.214, 0.780]</r>. The years where technological breakthrough rates are above the {0% line}<r>[0.123, 0.446]</r> while innovation introduction rates are below the {0% line}<r>[0.123, 0.942]</r> would indicate the described condition. I proceed to count these occurrences for each year.\nBased on the above thought, the answer is:\n10"
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "kDBFULl5QWpe4Eih",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented by the stacked bar chart into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The x-axis represents the different project phases {Conceptualization}[0.137, 0.919, 0.262, 0.942], {Development}[0.372, 0.919, 0.465, 0.942], {Implementation}[0.582, 0.919, 0.692, 0.942] and {Evaluation}[0.819, 0.919, 0.893, 0.942]. Each phase has a bar stacked with 4 segments, each denoting a measure of project evaluation and management according to the {legend}[0.078, 0.074, 0.254, 0.220]. The data values are represented by the height of the bar segments. To determine specific values, I'll analyze the height of the segments and the corresponding value annotations. For instance, the segment representing the {Lightbulb Moment} in the {Conceptualization} phase has a height corresponding to 25, as confirmed by {the value annotation \"25\"}[0.191, 0.863, 0.208, 0.886].\nBased on the above thought, the answer is:\nProject Phase,Lightbulb Moment,R&D Spend,User Acceptance,Market Response\nConceptualization,25,33,40,50\nDevelopment,50,55,85,65\nImplementation,84,43,105,90\nEvaluation,20,110,50,171"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "qjkqL0qQDlTjqFyq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, during which project phase does the 'R&D Spend' seem to have been most effectively used, given the corresponding 'Market Response'?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the chart, {R&D Spend}[0.122, 0.118, 0.201, 0.141] and {Market Response}[0.122, 0.188, 0.241, 0.211] are two measure segments that we need to focus on. I will analyze the segment heights and value annotations to compare the performance during different project phases. For {Conceptualization}[0.112, 0.846, 0.287, 0.903] and {Development}[0.331, 0.789, 0.506, 0.903], both R&D Spend and Market Response have gradually risen, which shows effective R&D spending. However, during {Implementation}[0.550, 0.711, 0.725, 0.903], R&D Spend has a decrease, but Market Response continues to grow, which might indicate a good return on previous spending. In the last phase, {Evaluation}[0.768, 0.857, 0.943, 0.903], both R&D Spend and Market Response skyrocket, showing a high spending and a matching high market response.\nBased on the above thought, the answer is:\nThe Implementation phase. Although the R&D spending dropped during this phase, the market response continues to grow, indicating that the spending in the previous phases was effectively used to produce results."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "hoPL9GI8nQWAE4nt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the result of subtracting the Lightbulb Moment's value of the Implementation phase from that of the Development phase?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to locate the corresponding bars of the Lightbulb Moment for the Development and Implementation phases. The bar for the {Development} phase [0.331, 0.789, 0.506, 0.903] has a value as per the annotation {50}[0.410, 0.834, 0.427, 0.858]. Similarly, the bar for the {Implementation} phase [0.550, 0.711, 0.725, 0.903] has a value of {84}[0.628, 0.795, 0.646, 0.819]. Thus, to answer the question, we subtract the value of the Lightbulb Moment in the Implementation phase from its value in the Development phase.\nBased on the above thought, the answer is:\n34"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "kd2aXxr1ueo7rLhm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from the stacked bar chart, can you prepare a CSV data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, each stack on the chart represents a separate category of AI and ML tools application. The blue and light blue bars represent the 'Deep Learning' and 'Machine Learning' trends respectively, as per {the legend}[0.805, 0.074, 0.978, 0.151]. These bars are segmented by bars for each category: {Computer Vision}[0.112, 0.521, 0.251, 0.903], {Natural Language Processing}[0.285, 0.673, 0.424, 0.737], {Statistical Analysis}[0.459, 0.665, 0.597, 0.805], {Mathematical Modelling}[0.632, 0.102, 0.770, 0.385], and {Anomaly detection}[0.805, 0.514, 0.943, 0.748]. The layers of the bars represent the usage of both tools in that sector. The CSV data table can be generated by identifying the specific values labeled within each bar corresponding to Deep Learning and Machine Learning.\nBased on the above thought, the answer is:\nApplications, Deep Learning, Machine Learning\nComputer Vision, 68, 33\nNatural Language Processing, 44, 17\nStatistical Analysis, 26, 37\nMathematical Modelling, 137, 75\nAnomaly Detection, 41, 62"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "O84dOhnaDwqnOyC4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which tool appears to have a broader range of applications in AI & ML and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the bar segments corresponding to {Deep Learning}[0.805, 0.074, 0.978, 0.151] and {Machine Learning}[0.805, 0.074, 0.978, 0.151], we can visually establish which tool displays a wider range of applications. Observing the Statistical Analysis category, the blue bar (Deep Learning) extends from the base (Y-axis) to 26, and for Machine Learning, the light blue segment begins from 26 and extends to 63 {Total segment}[0.459, 0.665, 0.597, 0.805]. The same analysis is carried out on other categories, such as Computer Vision [0.112, 0.521, 0.251, 0.903], Natural Language Processing [0.285, 0.673, 0.424, 0.737], Mathematical Modelling [0.632, 0.102, 0.770, 0.385], and Anomaly Detection [0.805, 0.514, 0.943, 0.748]. We find that Machine Learning appears to be widely used across most applications, especially evident in Anomaly Detection, where the difference in usage is significantly higher than Deep Learning.\nBased on the above thought, the answer is:\nMachine Learning. It shows consistent and broad application across the AI & ML fields, and significantly higher usage in Anomaly Detection compares to Deep Learning."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "VC5Pz5GyAOAZIklK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which AI & ML application, the difference between the usage of Deep Learning and Machine Learning is the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the minimal difference between the use of Deep Learning and Machine Learning requires a comparison of the height differences (usage difference) between each procedure's bars for each category. Starting from Computer Vision, I'm observing the usage difference between {Deep Learning and Machine Learning}[0.112, 0.646, 0.251, 0.903]. Same way, I compare for Natural Language Processing[0.285, 0.673, 0.424, 0.737], Statistical Analysis[0.459, 0.665, 0.597, 0.805], Mathematical Modelling[0.632, 0.102, 0.770, 0.385] and Anomaly Detection[0.805, 0.514, 0.943, 0.748]. Upon measuring and comparing the usage differences from each AI & ML tool, it appears that the category with the smallest difference between the usage of Deep Learning and Machine Learning is Natural Language Processing.\nBased on the above thought, the answer is:\nNatural Language Processing."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "fC9br3tr1pgwp2SS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart that you're currently looking at, could you please convert the chart data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By interpreting the content of {the chart title}<r>[0.279, 0.025, 0.776, 0.055]</r> which is \"Stacked Bar Chart of Social Inequality and Rights Protection\" and using the values represented by {the y-axis tick labels}<r>[{\"0\"}[0.052, 0.894, 0.061, 0.917], {\"20\"}[0.043, 0.779, 0.061, 0.802], {\"40\"}[0.043, 0.663, 0.061, 0.687], {\"60\"}[0.043, 0.548, 0.061, 0.571], {\"80\"}[0.043, 0.433, 0.061, 0.456], {\"100\"}[0.035, 0.318, 0.061, 0.341], {\"120\"}[0.035, 0.203, 0.061, 0.226], {\"140\"}[0.035, 0.087, 0.061, 0.111], {\"160\"}[0.034, -0.028, 0.061, -0.005]]</r>, we can see that the vertical axis represents the values. Looking at {the x-axis tick labels}<r>[{\"Right Protection\"}[0.144, 0.919, 0.255, 0.942], {\"Economic Inequality\"}[0.348, 0.919, 0.489, 0.942], {\"Social Inequality\"}[0.580, 0.919, 0.695, 0.942], {\"Civil Rights\"}[0.817, 0.919, 0.895, 0.942]]</r> and we can tell that the horizontal axis represents different categories. Each category has two stacks in their bar, respectively {Routine Monitoring}<r>[{\"Routine Monitoring\"}[0.122, 0.083, 0.254, 0.106]]</r> and {Reinforcement}<r>[{\"Reinforcement\"}[0.122, 0.118, 0.224, 0.141]]</r>. To obtain these values, I need to calculate the sum of the heights of each stack. For example, under the category of Right Protection, Routine Monitoring and Reinforcement are represented by {two segments of bars}<r>[{\"Routine Monitoring\"}[0.112, 0.701, 0.287, 0.903], {\"Reinforcement\"}[0.112, 0.413, 0.287, 0.701]]</r> which sum up to \"35 + 50 =85\".\nBased on the above thought, the answer is:\nCategories,Routine Monitoring,Reinforcement\nRight Protection,35,50\nEconomic Inequality,45,30\nSocial Inequality,23,35\nCivil Rights,67,72"
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "m2DRQdRXcnvEgah3",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the chart, which measure seems to be more effective in addressing Social Inequality and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the category of Social Inequality, we have two measures represented by {two segments of bars}<r>[{\"Routine Monitoring\"}[0.550, 0.770, 0.725, 0.903], {\"Reinforcement\"}[0.550, 0.569, 0.725, 0.770]]</r>. The {Routine Monitoring}<r>[0.550, 0.770, 0.725, 0.903]</r> represented by the top segment has a value of 23. On the other side, the {Reinforcement}<r>[0.550, 0.569, 0.725, 0.770]</r> represented by the bottom segment has a higher value of 35. Since a higher value indicates more effectiveness in addressing social inequality, it seems that Reinforcement is more effective than Routine Monitoring in this category.\nBased on the above thought, the answer is:\nReinforcement. Since it has a higher value, it appears to be more effective in addressing Social Inequality."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "zsnzMdW546G4Sv6h",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total value spent on Civil Rights?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both {Routine Monitoring}<r>[0.848, 0.698, 0.865, 0.722]</r> and {Reinforcement}<r>[0.848, 0.298, 0.865, 0.321]</r> involve expenses on Civil Rights. The value of expenses by Routine Monitoring on Civil rights is 67, and that of Reinforcement on Civil rights is 72. I will add these two values together to get the total value spent on Civil Rights.\nBased on the above thought, the answer is:\n139."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "kbgYBdihtY12ZWTp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you summarize the data shown in the stacked bar chart as a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The stacked bar chart in front of me is indicating five categories of Entrepreneurial actions {\"Innovation\"}[0.145, 0.919, 0.218, 0.942], {\"Experiment\"}[0.315, 0.919, 0.395, 0.942], {\"Exploit\"}[0.504, 0.919, 0.552, 0.942], {\"Expand\"}[0.675, 0.919, 0.727, 0.942], {\"Exceed\"}[0.849, 0.919, 0.899, 0.942] against two predictive factors named {\"Motivation\"}[0.895, 0.083, 0.968, 0.106] and {\"Risk-taking\"}[0.895, 0.118, 0.972, 0.141] as per the legend provided. By looking at each bar and the text on it, I can extract the values. For example, the value of Motivation for Innovation category can be deduced from the height and the annotation {30}[0.173, 0.805, 0.190, 0.829] from the bar with coordinates [0.112, 0.731, 0.251, 0.903].\nBased on the above thought, the answer is:\nCategories, Motivation, Risk-taking\nInnovation, 30, 70\nExperiment, 50, 50\nExploit, 80, 60\nExpand, 70, 40\nExceed, 90, 20"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "ukIqsF3V89chcgzE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, how would you explain the trends in entrepreneurial actions with regard to motivation and risk-taking?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, it is clear that \"Motivation\" and \"Risk-taking\" have contrasting trends across the five categories of entrepreneurial actions. The bars corresponding to \"Motivation\" {Motivation bars}<{\"Innovation\"}[0.112, 0.731, 0.251, 0.903]; {\"Experiment\"}[0.285, 0.617, 0.424, 0.903]; {\"Exploit\"}[0.459, 0.445, 0.597, 0.903]; {\"Expand\"}[0.632, 0.503, 0.770, 0.903]; {\"Exceed\"}[0.805, 0.388, 0.943, 0.903]>, show a slight increase with a dip in the \"Expand\" category. This could suggest that motivation to innovate and exploit entrepreneurial opportunities rises over time, notwithstanding the slight dip in motivation to expand. On the contrary, bars belonging to \"Risk-taking\" {Risk-taking bars}<{\"Innovation\"}[0.112, 0.331, 0.251, 0.731]; {\"Experiment\"}[0.285, 0.331, 0.424, 0.617]; {\"Exploit\"}[0.459, 0.102, 0.597, 0.445]; {\"Expand\"}[0.632, 0.274, 0.770, 0.503]; {\"Exceed\"}[0.805, 0.274, 0.943, 0.388]>, follow a decreasing trend. This could signify that as entrepreneurial actions progress from \"Innovation\" to \"Exceed\", the inclination to take risks decreases.\nBased on the above thought, the answer is:\nAcross the five entrepreneurial actions, \"Motivation\" generally increases and \"Risk-taking\" generally decreases. Exceptionally, there's a slight dip in Motivation in the \"Expand\" category, which is to be worthy of attention. However, the general trends suggest that as entrepreneurial actions evolve, they are characterised by increased motivation and reduced risk-taking."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "olsAsC6ahTjQ72Tc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which category shows the smallest difference between \"Motivation\" and \"Risk-taking\" values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the category with the smallest difference between \"Motivation\" and \"Risk-taking\", I should subtract the two values from each other and find the smallest difference. For instance, the difference in \"Innovation\" category values is observed by calculating the difference between {\"Motivation\"}<annotation>[30][0.173, 0.805, 0.190, 0.829]</annotation> and {\"Risk-taking\"}<annotation>[70][0.173, 0.519, 0.190, 0.543]</annotation>. This process should be repeated for all five categories and the one with the smallest difference can be identified.\nBased on the above thought, the answer is:\nExperiment."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "z5Nq4nuREVYmN0kO",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV format data table for this Stacked Bar Chart describing Design Quality and the Level of Creativity?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can create the CSV data table by extracting values from the bars segmented for 'Design Quality' and 'Creativity Levels' for each parameter. For example, under the parameter 'Brainstorming Use', there are two segments for the bars respectively {Design Quality}<r>[0.112, 0.600, 0.287, 0.903]</r> with value \"70\" and {Creativity Levels}<r>[0.112, 0.340, 0.287, 0.600]</r> with value \"60\". Therefore, for Brainstorming Use, Design Quality is 70 and Creativity Level is 60. Similarly other parameters can be evaluated.\nBased on the above thought, the answer is:\nInnovation Parameters, Design Quality, Creativity Levels\nBrainstorming Use, 70, 60\nDesign Thinking Process, 80, 85\nPrototyping and Simulation, 90, 95\nCross-Disciplinary Collaboration, 70, 80"
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "mQs8qklhBhXgrLpl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which innovation parameter seems to be most effective for enhancing the creativity levels among all?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the segment colours according to the legend<r>[0.811, 0.074, 0.977, 0.151]</r> ('Design Quality' as '#097138' and 'Creativity Levels' as '#74c476'), the 'Creativity Levels', represented by the lighter segment of each bar, can be analyzed. Looking at the Creativity Levels across the four parameters: \n- The segment for Creativity Levels under Brainstorming Use<r>[0.112, 0.340, 0.287, 0.600]</r> has the lower half filled, suggesting moderate levels of creativity.\n- The Creativity Levels under Design Thinking Process<r>[0.331, 0.189, 0.505, 0.557]</r> fills most of the top half of the bar, indicating higher creativity levels than Brainstorming Use. \n- The Creativity Levels in Prototyping and Simulation<r>[0.549, 0.102, 0.724, 0.513]</r> fill an even larger portion of the bar, indicating higher creativity levels than the previous two parameters.\n- The Creativity Levels under Cross-Disciplinary Collaboration<r>[0.767, 0.254, 0.942, 0.600]</r> fills most of the bar but is lower than that for Prototyping and Simulation.\nSo, the 'Prototyping and Simulation' parameter seems to have been most effective in enhancing creativity levels.\nBased on the above thought, the answer is:\n'Prototyping and Simulation' - It shows the highest level of creativity among all the parameters according to the chart."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "nbosqn2GigmvSVve",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which innovation parameter had the highest combined value of Design Quality and Creativity Level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the innovation parameter with the highest combined value of Design Quality and Creativity Level, I can look at where the top edge of the highest bar is. In this chart, that is represented by the top edge of the Creativity Level segment under each parameter. From left to right:\n- For Brainstorming use, the combined value is at the top edge of the corresponding Creativity Level segment<r>[0.112, 0.340, 0.287, 0.600]</r>, which is positioned near the y-axis label \"125\".\n- For Design Thinking Process, the top edge of the Creativity Level segment<r>[0.331, 0.189, 0.505, 0.557]</r> is near the y-axis label \"175\".\n- The Prototyping and Simulation parameter Creativity Level segment<r>[0.549, 0.102, 0.724, 0.513]</r> top edge is slightly above the y-axis label \"175\".\n- Cross-Disciplinary Collaboration Creativity Level segment<r>[0.767, 0.254, 0.942, 0.600]</r> top edge is above the y-axis label \"150\".\nTherefore, the Prototyping and Simulation parameter had the highest combined value of Design Quality and Creativity Level.\nBased on the above thought, the answer is:\nPrototyping and Simulation"
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "1exsTkyIzAAD6mFm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the stacked bar chart you observed, can you arrange the data in a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's title {\"Stacked Bar Chart of Human Life and Living Environment Data\"}[0.268, 0.025, 0.787, 0.055] and the x-axis tick labels {[{\"Population Growth\"}[0.136, 0.919, 0.264, 0.942]; {\"Green Areas\"}[0.376, 0.919, 0.461, 0.942]; {\"Urbanization level\"}[0.574, 0.919, 0.700, 0.942]; {\"Carbon Footprint\"}[0.797, 0.919, 0.915, 0.942]],} I understand that these categories are compared on the x-axis. The y-axis {[{0}[0.052, 0.894, 0.061, 0.917] to {200}[0.035, 0.004, 0.061, 0.027]]} represents the corresponding values for each category in numerical terms. There are two layers for each data point in the categories according to {the legend}[0.078, 0.074, 0.262, 0.151] where {Dark blue bars}<{\"Human Life\"}[0.122, 0.083, 0.202, 0.106]> and {light blue bars}<{\"Living Environment\"}[0.122, 0.118, 0.256, 0.141]> represent different data layers. The values for both Human Life and Living Environment are needed for each category that can be determined by the height of each bar and the value labels on them. For instance, for \"Population Growth\", {the dark blue bar}[0.112, 0.680, 0.287, 0.903] is at the 50 position, marked by {a value label \"50\"}[0.191, 0.780, 0.208, 0.803] and {the light blue bar}[0.112, 0.391, 0.287, 0.680] constitutes the remaining 65, marked by {a value label \"65\"}[0.191, 0.524, 0.209, 0.548]. The CSV table therefore would contain these values for all categories and for each Human Life and Living Environment.\nBased on the above thought, the answer is:\nCategories, Human Life, Living Environment\nPopulation Growth, 50, 65\nGreen Areas, 30, 55\nUrbanization level, 75, 45\nCarbon Footprint, 95, 85"
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "EAyBUnVaxlbMhPev",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which aspect of human life and living environment require more conscious efforts for improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, the aspects requiring more conscious efforts for improvement can be determined by the larger value in each category. Looking at {Population Growth bar group}[0.112, 0.391, 0.287, 0.903], we can see that Living Environment needs greater improvement as it is higher with a value of 65 in comparison with Human Life at 50. Next, examining {Green Areas bar group}[0.331, 0.525, 0.506, 0.769] shows the same thing, where Living Environment is higher at 55 and Human Life at 30. On the contrary, {Urbanization Level bar group}[0.550, 0.369, 0.725, 0.569] and {Carbon Footprint bar group}[0.768, 0.102, 0.943, 0.480], indicate that Human Life needs more improvement as they are higher with values of 75 and 95 respectively than Living Environment with 45 and 85. Summing up these values gives an understanding that Human Life might require more collective efforts overall for improvement as it has greater needs in more categories than Living Environment.\nBased on the above thought, the answer is:\nHuman Life. This is because it displays higher values in two fundamental categories (Urbanization Level and Carbon Footprint) when compared with the living environment, which indicates more improvement is needed."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "hVxGQTUrOyWYHB5R",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the least total value when considering both 'Human Life' and 'Living Environment'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the total heights of all the columns in the chart, which are determined by the sum of the human life bars and living environment bars. Firstly, {Population Growth bar group}<[{\"Human Life Bar\"}[0.112, 0.680, 0.287, 0.903]; {\"Living Environment Bar\"}[0.112, 0.391, 0.287, 0.680]]> has a total value of 115. Next, {Green Areas bar group}<[{\"Human Life Bar\"}[0.331, 0.769, 0.506, 0.903]; {\"Living Environment Bar\"}[0.331, 0.525, 0.506, 0.769]]> has a total value of 85. Similarly, Urbanization level totals 120 as shown in {Urbanization Level bar group}<[{\"Human Life Bar\"}[0.550, 0.569, 0.725, 0.903]; {\"Living Environment Bar\"}[0.550, 0.369, 0.725, 0.569]]> and lastly, {Carbon Footprint bar group}<[{\"Human Life Bar\"}[0.768, 0.480, 0.943, 0.903]; {\"Living Environment Bar\"}[0.768, 0.102, 0.943, 0.480]]> totals 180. Hence amongst all these, Green Areas category has the least total value.\nBased on the above thought, the answer is:\nGreen Areas."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "F2nycHfRe3rQcz1F",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the stacked bar chart you observed and present it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the given chart, I can recognize the title {\"Stacked Bar Chart of Social Welfare\"}[0.381, 0.025, 0.675, 0.055] and understand that the chart presents information about different categories of social welfare. From {the x-axis tick labels}[{\"Schooling\"}[0.166, 0.919, 0.233, 0.942]; {\"Healthcare\"}[0.381, 0.919, 0.456, 0.942]; {\"Food Stamps\"}[0.592, 0.919, 0.682, 0.942]; {\"Housing\"}[0.828, 0.919, 0.884, 0.942]], it's clear that these are the categories of public services. The y-axis is {the values}[{\"0\"}[0.052, 0.894, 0.061, 0.917]; {\"20\"}[0.043, 0.734, 0.061, 0.757]; {\"40\"}[0.043, 0.573, 0.061, 0.597]; {\"60\"}[0.043, 0.413, 0.061, 0.437]; {\"80\"}[0.043, 0.253, 0.061, 0.277]; {\"100\"}[0.035, 0.093, 0.061, 0.116]; {\"120\"}[0.035, -0.067, 0.061, -0.044]] being used to represent the count of people in each trend. The {legend}<r>[0.857, 0.074, 0.978, 0.151]</r> shows that the two different segments of each bar represent {Expansion}<r>[0.901, 0.083, 0.972, 0.106]</r> and {Decline}<r>[0.901, 0.118, 0.953, 0.141]</r> segments. I can identify the count for expansion and decline in each category by the height of each segment and the texts embedded in the segment. For instance, the {segment for Expansion in Schooling}<r>[0.112, 0.422, 0.287, 0.903]</r> has a {text of \"60\"}<r>[0.191, 0.651, 0.209, 0.674]</r> aligned with it. By adopting the same approach to others, I can present the information in a table.\nBased on the above thought, the answer is:\nCategories,Expansion,Decline\nSchooling,60,40\nHealthcare,70,30\nFood Stamps,50,50\nHousing,40,60"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "pS7TQxXtThC80EcP",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the visualization you are observing, which type of services are preferably growing and which are unfortunately declining?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing from the stacked bar chart, we can observe that the {Expansion bars}<r>[0.901, 0.083, 0.972, 0.106]</r> are representing the growing trend of services. The services that are showing growth are {Schooling}<r>[0.112, 0.422, 0.287, 0.903]</r> and {Healthcare}<r>[0.331, 0.342, 0.506, 0.903]</r>, which have higher values in the \"Expansion\" segment than in the \"Decline\" segment. On the other hand, the {Decline bars}<r>[0.901, 0.118, 0.953, 0.141]</r> are representing the services that are unfortunately declining. The services that are showing decline are {Food Stamps}<r>[0.550, 0.102, 0.725, 0.503]</r> and {Housing}<r>[0.768, 0.102, 0.943, 0.583]</r>, which have higher values in the \"Decline\" segment than in the \"Expansion\" segment.\nBased on the above thought, the answer is:\nSchooling and Healthcare are the services that are growing, while Food Stamps and Housing are unfortunately declining."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "omnsCp1gz2EkwPsx",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social welfare category has the smallest difference in usage trends between Expansion and Decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the category with the smallest difference, I will observe and compare the height difference between the Expansion and Decline segments in each bar. For example, the first pair, {the \"Expansion\" section for \"Schooling\"}<r>[0.112, 0.422, 0.287, 0.903]</r> and {the \"Decline\" section for \"Schooling\"}<r>[0.112, 0.102, 0.287, 0.422]</r>, the difference between 60 and 40 is 20. By observing and comparing the difference in all categories in the same manner, I can see that the {\"Food Stamps\"}<r>[0.550, 0.102, 0.725, 0.903]</r> category has the smallest difference, their values are both 50, so the difference is 0.\nBased on the above thought, the answer is:\nFood Stamps."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "HDCedPvTzkmNP7KA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the market share percentages from the stacked bar chart into a structured table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stacked bar chart starting with the x-axis labels for years which range from {2015}<r>[0.152, 0.919, 0.187, 0.942]</r> to {2020}<r>[0.869, 0.919, 0.904, 0.942]</r>, the y-axis labels indicate percentages from {0%}<r>[0.052, 0.894, 0.061, 0.917]</r> to {120%}<r>[0.035, 0.028, 0.061, 0.052]</r>, showing the total market shares stacked for each year. The companies are differentiated by the colors in the bars, according to the legend: {Apple}<r>[0.083, 0.085, 0.111, 0.101]</r>, {Samsung}<r>[0.083, 0.120, 0.111, 0.136]</r>, {Google}<r>[0.083, 0.155, 0.111, 0.171]</r>, {Microsoft}<r>[0.083, 0.190, 0.111, 0.206]</r>, and {Others}<r>[0.083, 0.225, 0.111, 0.241]</r>. Each bar segment's height, such as {Apple's market share for 2015}<r>[0.112, 0.795, 0.227, 0.903]</r>, corresponds to the market share percentage indicated. To generate the CSV table, I will compile each company's market share per year as displayed in the chart.\nBased on the above thought, the answer is:\nYear,Apple,Samsung,Google,Microsoft,Others\n2015,15,30,10,5,40\n2016,20,35,15,7,23\n2017,22,40,20,8,10\n2018,24,42,30,10,5\n2019,21,40,25,8,6\n2020,20,43,23,7,7"
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "IrX7ygnLNArkzIYM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has shown the most notable decline in market share over the period from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "A notable decline in market share will be evident by the shrinking size of the color segment for the company across the bars from {2015}<r>[0.152, 0.919, 0.187, 0.942]</r> to {2020}<r>[0.869, 0.919, 0.904, 0.942]</r>. Looking at the segments for {Others in 2015}<r>[0.112, 0.182, 0.227, 0.470]</r> showing 40% down to {Others in 2020}<r>[0.829, 0.182, 0.943, 0.232]</r> showing 7%, there is a dramatic decrease. This is the most significant reduction when compared to other companies like {Apple}<r>[0.083, 0.085, 0.111, 0.101]</r>, {Samsung}<r>[0.083, 0.120, 0.111, 0.136]</r>, {Google}<r>[0.083, 0.155, 0.111, 0.171]</r>, and {Microsoft}<r>[0.083, 0.190, 0.111, 0.206]</r> over the same time frame.\nBased on the above thought, the answer is:\nThe \"Others\" category. It experienced a sharp decline from 40% in 2015 to 7% in 2020, drastically reducing its portion in the market share."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "0ZJipxX5nMMEZNta",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total market share percentage for Google from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total market share percentage for Google over the given years, I will look at the segments of bars representing Google, starting from {Google in 2015}<r>[0.112, 0.506, 0.227, 0.578]</r> with 10%, and continue with the following years: {Google in 2016}<r>[0.256, 0.398, 0.370, 0.506]</r> has 15%, {Google in 2017}<r>[0.399, 0.311, 0.513, 0.456]</r> has 20%, {Google in 2018}<r>[0.542, 0.210, 0.657, 0.427]</r> has 30%, {Google in 2019}<r>[0.685, 0.283, 0.800, 0.463]</r> has 25%, and finally, {Google in 2020}<r>[0.829, 0.283, 0.943, 0.448]</r> has 23%. Summing these values gives Google's combined market share over the six years.\nBased on the above thought, the answer is:\n123"
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "mN9THKi4Ov09s4yS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data from the stacked bar chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a CSV table from the bar chart, I need to label the data categories and values. The title is {Stacked Bar Chart of Scientific Research and Technological Innovation}[0.242, 0.025, 0.823, 0.055]. The x-axis represents different research fields, indicated by {the x-axis tick labels}[{\"Physics Research\"}[0.148, 0.919, 0.267, 0.942]; {\"Chemistry Research\"}[0.354, 0.919, 0.493, 0.942]; {\"Biology Research\"}[0.581, 0.919, 0.700, 0.942]; {\"Robotics Innovation\"}[0.789, 0.919, 0.926, 0.942]]. The y-axis represents counts, ranging from {0}[0.061, 0.894, 0.070, 0.917] to {8000}[0.035, -0.035, 0.070, -0.011]. The {legend}[0.086, 0.074, 0.254, 0.151] indicates that the green bars represent the papers published and the orange ones represent citations received. Therefore, for each research field, we have values for both papers published and citations, which can be extracted from the bars and text values within {the bars}[{bars in first group}[0.121, 0.856, 0.294, 0.903; 0.337, 0.851, 0.511, 0.903; 0.554, 0.845, 0.727, 0.903; 0.771, 0.833, 0.944, 0.903]; {bars in second group}[0.121, 0.276, 0.294, 0.856; 0.337, 0.363, 0.511, 0.851; 0.554, 0.195, 0.727, 0.845; 0.771, 0.102, 0.944, 0.833]].\nBased on the above thought, the answer is:\nResearch Field,Papers Published,Citations\nPhysics Research,400,5000\nChemistry Research,450,4200\nBiology Research,500,5600\nRobotics Innovation,600,6300"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "j9PrJSexTBykngdb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, in which field do the academic works seem to have the most influence? And how did you reach this conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "The influence of academic works is typically indicated by the number of citations their papers have received. In the chart, {Citations}[0.086, 0.074, 0.254, 0.151] are represented by orange bars. Looking at their distributions in different fields -  {Physics Research}[0.148, 0.919, 0.267, 0.942], {Chemistry Research}[0.354, 0.919, 0.493, 0.942], {Biology Research}[0.581, 0.919, 0.700, 0.942], and {Robotics Innovation}[0.789, 0.919, 0.926, 0.942] depicted along the x-axis, we can estimate which field's works have the most influence based on the height of the orange bars (the count of citations). The tallest orange bar corresponds to the {Robotics Innovation} field[0.771, 0.102, 0.944, 0.833], indicating the highest count of citations in that area. This suggests that works in the Robotics Innovation field are possibly the most influential.\nBased on the above thought, the answer is:\nThe academic works in the Robotics Innovation field seem to have the most influence, as they have received the most citations, a common measure of scholarly impact."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "ddgSEAsaa38XBJ1S",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, which research field has the highest number of citations?"
+            },
+            {
+                "from": "gpt",
+                "value": "The citations are represented by the orange bars of the stacked bar chart, with indications located within the bar areas showing the exact number of citations({'Citations'} plot labeled in {the legend}[0.086, 0.074, 0.254, 0.151]). The fields are placed along the x-axis and indicated by their tick labels {Physics Research}[0.148, 0.919, 0.267, 0.942], {Chemistry Research}[0.354, 0.919, 0.493, 0.942], {Biology Research}[0.581, 0.919, 0.700, 0.942], and {Robotics Innovation}[0.789, 0.919, 0.926, 0.942]. I can visually compare the bar heights of different research fields to find out which one has the highest count of citations. The tallest orange bar corresponds to the field of {Robotics Innovation} with coordinates [0.771, 0.102, 0.944, 0.833], and hence, Robotics Innovation has the highest number of citations.\nBased on the above thought, the answer is:\nRobotics Innovation"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "5gwteX9jCDePtiUv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, please translate the data into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the stacked bar chart's subtitle {\"Stacked Bar Chart of Social Networking\"}<r>[0.365, 0.025, 0.691, 0.055]</r> and the x-axis labels <r>[\"Continuous Decrease\"[0.223, 0.919, 0.371, 0.942]; \"Steep Decline\"[0.711, 0.919, 0.807, 0.942]]</r>, I can see that the x-axis represents different trends. The y-axis values range from {0}<r>[0.052, 0.894, 0.061, 0.917]</r> to {400}<r>[0.035, -0.048, 0.061, -0.025]</r>. There are four stacked bars representing different social platforms according to {the legend}<r>[0.857, 0.074, 0.978, 0.220]</r>. To translate this data into a CSV format, I would record the user counts found in the stack bars text annotations under each trend for the four social platforms. For instance, under \"Continuous Decrease\", the user count for Facebook is seen at {\"100\"}[0.284, 0.773, 0.310, 0.797]. The count for Twitter is notified at {\"90\"}[0.288, 0.550, 0.306, 0.573] under the same trend category.\nBased on the above thought, the answer is:\nTrends,Facebook,Twitter,Instagram,LinkedIn\nContinuous Decrease,100,90,80,70\nSteep Decline,80,60,40,20"
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "RXNvnA37tPN6Bdr6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social networking platform had the biggest fall in users across both trends and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive comparison of the heights of the bars representing each social networking platform under both trend categories. Under \"Continuous Decrease\", we can observe from the rectangles and text annotations that Facebook, Twitter, Instagram, LinkedIn lost 100-70, 90-80, 80-70, 70-70 = 30, 10, 10, 0 users respectively. Under \"Steep Decline\", we can observe that Facebook, Twitter, Instagram, LinkedIn lost 80-80, 60-60, 40-40, 20-20 = 0, 0, 0, 0 users respectively. By summing the numbers of lost users in both trends, we see that Facebook lost the most users.\nBased on the above thought, the answer is:\nFacebook. Because it lost a total of 30 users across both trends, which is the most significant loss if we compare it with other social networking platforms."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "OghnujmxINzj4rOX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder which trend did Facebook lose more users?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will compare the height of the Facebook bars under the two trend categories. The bar representing Facebook's user loss under \"Continuous Decrease\" is located at [0.112, 0.667, 0.482, 0.903], and the annotation {\"100\"}[0.284, 0.773, 0.310, 0.797] places Facebook's initial user count at 100 for this trend. However, under \"Steep Decline\", the corresponding bar is at [0.574, 0.714, 0.943, 0.903], and from the annotation {\"80\"}[0.750, 0.797, 0.768, 0.820], we can see Facebook's initial user count dropped to 80. So, Facebook lost more users under the \"Continuous Decrease\" trend.\nBased on the above thought, the answer is:\nContinuous Decrease."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "BqOY9VxilnrZtO3k",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table that reflects the data represented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let me translate the stacked bars into a CSV table. The bars are stacked into two groups, one for each scenario, as shown in the x-axis labels: {Policy Initiatives}[0.241, 0.919, 0.353, 0.942] and {Political Unrest}[0.707, 0.919, 0.810, 0.942]. Each group is divided into four sections, each labeled by {the legend}[0.078, 0.074, 0.304, 0.220] as: 'Increase in National Debt', 'Consumer Price Index', 'Tax Revenue', and 'Government Spending'. The values of each sector correspond to the y-axis values which range from {0}[0.052, 0.894, 0.061, 0.917] to {350}[0.035, -0.025, 0.061, -0.002]. Taking the 'Policy Initiatives' stack as an example, the 'Increase in National Debt' bar at the bottom {[0.112, 0.732, 0.482, 0.903]} is the largest, thus indicating a value of 65. The pattern repeats with the 'Consumer Price Index', 'Tax Revenue', and ' Government Spending' bars, with respective values of 70, 75, and 50 displaying a sequential stack pattern.\nBased on the above thought, the answer is:\nScenarios,Increase in National Debt,Consumer Price Index,Tax Revenue,Government Spending\nPolicy Initiatives,65,70,75,50\nPolitical Unrest,70,90,85,60"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "dzpWpykFke6roGyT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which scenario requires the government to spend more? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "This question is asking for the scenario where 'Government Spending' is highest. 'Government Spending' is represented by the top-most section of each stack. Comparing {Government Spending bar in the 'Policy Initiatives'}[0.112, 0.220, 0.482, 0.352] to {Government Spending bar in the 'Political Unrest'}[0.574, 0.102, 0.943, 0.260], the latter is visibly smaller than the former, and this is confirmed by the values {50}[0.288, 0.274, 0.306, 0.298] and {60}[0.750, 0.169, 0.768, 0.193] respectively. So, the government spends more during 'Policy Initiatives'.\nBased on the above thought, the answer is:\nPolicy Initiatives, because the 'Government Spending' section during 'Policy Initiatives' is larger than that during 'Political Unrest'."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "VW31md3jGVepOXEl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to 'Policy Initiatives', how much more increase in the 'National Debt' is expected during 'Political Unrest'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the 'Increase in National Debt' section of both the 'Policy Initiatives' and 'Political Unrest' stacks. This is represented by the bottom section of each group of bars {Policy Initiatives National Debt Bar}[0.112, 0.732, 0.482, 0.903] and {Political Unrest National Debt Bar}[0.574, 0.719, 0.943, 0.903]. The value annotation on top tells me the height of 'Increase in National Debt' in each scenario. For 'Policy Initiative', the value is {65}[0.288, 0.806, 0.306, 0.829] and for 'Political Unrest', it is {70}[0.750, 0.799, 0.767, 0.823]. The difference can be calculated by subtracting the 'Policy Initiative' value from the 'Political Unrest' value.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "BwyC0PiuuqecgXJc",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you've analyzed, can you convert the data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the top of the chart from the coordinates segements includes {\"Progress Chart of AI and ML Concepts\"}[0.356, 0.083, 0.669, 0.113]. The x-axis labels or categories include {Neural Networks, Deep Learning, Machine Learning, Robotics, Speech Recognition} and were located using the coordinates [0.161, 0.906, 0.277, 0.930] till [0.737, 0.906, 0.875, 0.930] respectively. The y-axis labels denote the prominence of these concepts. The concepts are divided in two trends: {Trend 2015}<r>[0.810, 0.141, 0.887, 0.164]</r> and {Trend 2020}<r>[0.810, 0.176, 0.887, 0.199]</r>. I can use the height of the bars to determine the values, for example, the prominence of Neural Networks in 2015 from the code {\"Trend 2015\"}[0.810, 0.141, 0.887, 0.164] and the corresponding bar located at [0.160, 0.680, 0.278, 0.890] was 50. This process will be repeated for each category under both trends.\nBased on the above thought, the answer is:\nConcepts,Trend 2015,Trend 2020\nNeural Networks,50,35\nDeep Learning,75,100\nMachine Learning,65,90\nRobotics,40,55\nSpeech Recognition,55,50"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "jfrN3r4VrPFvPvYO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's data, which concept in AI has seen consistent popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the gap of both bars (Trend 2015 and Trend 2020) for each AI concept. The closer the two values for a specific concept, the more consistent its popularity between 2015 to 2020. After analyzing the bars, it appears that the bar heights for 'Robotics' {\"Trend 2015\"}<r>[0.601, 0.722, 0.718, 0.890]</r> and {\"Trend 2020\"}<r>[0.601, 0.492, 0.718, 0.722]</r> have a relatively smaller gap compared to the other categories. Thus, it seems like the concept of Robotics has retained consistent popularity over the years.\nBased on the above thought, the answer is:\nRobotics, it has shown consistent popularity from 2015 to 2020."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "gM7uosYtwuMXG5Zj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field of AI has shown the most growth from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract this information, I have to compare the difference between the heights of bars in the year 2015 and 2020 for every concept. Looking at {'Neural Networks'}<r>[0.160, 0.680, 0.278, 0.890]</r> and its corresponding bar in 2020>r>[0.160, 0.534, 0.278, 0.680]</r>, the difference is -15. Going through the rest of the concepts, I found that {'Deep Learning'}<r>[0.307, 0.576, 0.424, 0.890]</r> and its 2020's counterpart <r>[0.307, 0.157, 0.424, 0.576]</r> has a difference of +25. With this in mind, the field with the most growth between 2015 to 2020 is Deep Learning.\nBased on the above thought, the answer is:\nDeep Learning."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "9qug5BJMPvLbSHo3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly convert the stacked bar chart information into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the chart title located at {'Stacked Bar Chart of Human Life and Living Environment'}<r>[0.291, 0.025, 0.765, 0.055]</r> and the x-axis{'Indexes'}<r>[0.501, 0.952, 0.555, 0.975]</r> and y-axis labels {\"Indicators' values\"}<r>[0.015, 0.381, 0.029, 0.584]</r>, each category on the x-axis is associated with stacked bars representing the City Quality of Life Index and People Satisfaction Index. The number on top of each bar indicates the values of the corresponding indicator for both Indexes. For instance, the stacked bar at {the economic indicator}<r>[0.201, 0.265, 0.287, 0.903]</r> reads 70 on top, which belongs to the City Quality of Life Index and 75 which belongs to the People Satisfaction Index. By extracting these values for each indicator, a CSV can be created.\nBased on the above thought, the answer is:\nIndicators,City Quality of Life Index,People Satisfaction Index\nEconomic Indicator,70,75\nSocial Indicator,65,85\nEnvironmental Indicator,86,78\nHealth Indicator,90,92"
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "usJnxYs8LKEbtFg0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which Index has overall better values across all the indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will analyze the height of the stack corresponding to each Index for each Indicator category. For example, in the case of the Economic Indicator, the values for the City Quality of Life Index and the People Satisfaction Index are 70 and 75, respectively {Economic Indicator}<r>[0.201, 0.265, 0.287, 0.903]</r>. By comparing these values for all categories {City Quality of Life Index}<r>[0.112, 0.595, 0.287, 0.903];[0.331, 0.617, 0.506, 0.903];[0.550, 0.525, 0.725, 0.903];[0.768, 0.507, 0.943, 0.903]</r> and {People Satisfaction Index}<r>[0.112, 0.265, 0.287, 0.595];[0.331, 0.243, 0.506, 0.617];[0.550, 0.181, 0.725, 0.525];[0.768, 0.102, 0.943, 0.507]</r>, I can determine which Index generally achieves better values across all indicators.\nBased on the above thought, the answer is:\nPeople Satisfaction Index. This Index generally achieves better values across all indicators compared to the City Quality of Life Index."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "ZIXqpHC5apBGE2hK",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Indicator has the largest total values among the City Quality of Life Index and People Satisfaction Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I will gather the total value of both the City Quality of Life Index and People Satisfaction Index for each Indicator. Using the coordinates of each bar and the value listed on top of the bars, I can establish that, for instance, the total value for the Economic Indicator is 70 (City Quality of Life Index) + 75 (People Satisfaction Index){Economic Indicator}<r>[0.201, 0.265, 0.287, 0.903]</r>. Applying this to every Indicator, I can compare these totals.\nBased on the above thought, the answer is:\nHealth Indicator."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "1lzvd588vICPuLvV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you observed, could you please convert the viewership data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the information from the X-axis {Streaming Platforms label}[0.461, 0.952, 0.603, 0.975] and bars, it is clear that this axis represents the different streaming platforms. The Y-axis represents viewership in millions, as indicated by the {ylabel}[0.015, 0.348, 0.029, 0.617]. The viewership for each streaming platform is split into two sections according to the {legend}[0.893, 0.074, 0.978, 0.151], {2019 viewership}[0.937, 0.083, 0.973, 0.106] and {2020 viewership}[0.937, 0.118, 0.972, 0.141]. Thus, to create a CSV table, the platform names from the x-axis are required, as well as the two viewership values corresponding to each platform from the bars. For example, for Netflix the viewership figures come from {the 2019 bar}[0.121, 0.567, 0.294, 0.903] and {the 2020 bar}[0.121, 0.148, 0.294, 0.567], which can be read as approximately 550 million and 685 million respectively.\nBased on the above thought, the answer is:\nStreaming_Platform,2019_Viewership,2020_Viewership\nNetflix,550,685\nHBO,620,690\nAmazon_Prime,220,300\nDisney+,130,230"
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "QOdzoyKPl2vJ9UCZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which streaming platform showed the most significant year-on-year growth from 2019 to 2020? And provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comparison of the growth rates of the four platforms from 2019 and 2020. Growth rate can be calculated as the increase in viewership from 2019 to 2020 divided by the viewership for 2019. Using Netflix as an example, the growth rate would be (685-550)/550, based on the {orange bar for 2020}[0.121, 0.567, 0.294, 0.903] and the {blue bar for 2019}[0.121, 0.148, 0.294, 0.567] which represent the 2020 and 2019 viewership values respectively. The same calculation can be done for the other platforms using the values from their respective pairs of bars. Upon calculation, we see that {Disney+'s bars}[0.771, 0.823, 0.944, 0.903]; [0.771, 0.683, 0.944, 0.823], which indicate viewership values of about 130 million and 230 million for 2019 and 2020 respectively, yield the largest growth rate, indicating the most significant growth.\nBased on the above thought, the answer is:\nDisney+. This is calculated by comparing viewership growth rates from 2019 to 2020 of all platforms. Disney+'s increase in viewership represents a growth rate of nearly 77%, the highest among the platforms."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "IOJPVN7NpH9sC3q7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich streaming platform had the greatest increase in viewership from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to determine the viewership difference for each platform between 2019 and 2020. This can be obtained by subtracting the 2019 viewership from the 2020 viewership for each platform. For example, for Netflix, I would subtract the {2019 value}[0.121, 0.567, 0.294, 0.903] of about 550 million from the {2020 value}[0.121, 0.148, 0.294, 0.567] of about 685 million to get approximately 135 million. Following similar procedures for HBO, Amazon Prime and Disney+, finally Disney+ has such {two bars}[0.771, 0.823, 0.944, 0.903]; [0.771, 0.683, 0.944, 0.823] indicates that it had the greatest increase of approximately 100 million.\nBased on the above thought, the answer is:\nDisney+"
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "Knf1q2D0SHQBsj6A",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart for government policy effectiveness, can you convert the dataset into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, let's start by looking at the x-axis ticks: {Subdued Expansion}<r>[0.101, 0.919, 0.238, 0.942]</r>, {Gradual Increase}<r>[0.254, 0.919, 0.372, 0.942]</r>, {Substantial Increase}<r>[0.386, 0.919, 0.527, 0.942]</r>, {Rapid Expansion}<r>[0.542, 0.919, 0.657, 0.942]</r>, {Unstable Behavior}<r>[0.679, 0.919, 0.807, 0.942]</r>, and {Sharp Decrease}<r>[0.831, 0.919, 0.941, 0.942]</r>, which represent different government policies. The y-axis represents the impacts, which range from 0 to 200. To create the CSV table, we need to consider the four different stacks within each bar which signify impacts on Economy, Health, Environment, and Tourism, as shown in the {legend}<r>[0.839, 0.074, 0.978, 0.220]</r>. Thus, for each policy, we need to estimate the impact on these four sectors based on the length of the corresponding section in the bar. For example, let's take the policy {Subdued Expansion}: starting from the lowest stack to the top, the impact on Economy is 70 (from the annotation inside the bar), Health is 30, Environment is 45, and Tourism is 30. Applying this method to all bars, we can create the following CSV structure.\nBased on the above thought, the answer is:\nPolicies,Economy,Health,Environment,Tourism\nSubdued Expansion,70,30,45,30\nGradual Increase,50,24,32,45\nSubstantial Increase,30,15,25,40\nRapid Expansion,60,80,65,60\nUnstable Behavior,45,60,20,75\nSharp Decrease,20,70,35,20"
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "lBIpIJIsAA6mP4i7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which sector seems to benefit the most from government policies overall and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector that benefits the most from all policies, I need to consider the combined length of the same-colored sections in all bars. Looking at the colors assigned to each sector in the {legend}<r>[0.839, 0.074, 0.978, 0.220]</r>, I can see the darkest color, representing {Economy}<r>[0.884, 0.083, 0.948, 0.106]</r> seems to be taking a large chunk in all the bars. Across all policy bars, the blue sections of the bars are noticeably longer for the majority of policies. Hence, it seems that the Economy sector benefits the most from the government policies.\nBased on the above thought, the answer is:\nThe Economy sector, because it has the longest combined section length in the bars, indicating it benefits the most across all government policies."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "BqVsiIokodsGvRkA",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy has the highest cumulative impact across all sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the policy with the highest cumulative impact, I need to consider the total length of each bar. That represents the sum of the impacts in Economy, Health, Environment, and Tourism sectors. {Rapid Expansion}<r>[0.542, 0.102, 0.657, 0.903]</r> is the tallest bar with a height reaching up to 265 as per the y-ticks that means it has the highest cumulative impact across all sectors.\nBased on the above thought, the answer is:\nRapid Expansion."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "fVD1E9NLFeobQFR9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you have observe, can you convert the data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a CSV formatted table, I first look at the y-tick labels to determine the categories, which are {GDP}[0.077, 0.852, 0.103, 0.891], {Unemployment rate}[0.012, 0.705, 0.103, 0.840], {Inflation rate}[0.042, 0.557, 0.103, 0.649], {Retail sales}[0.048, 0.409, 0.103, 0.491], {Trade balance}[0.037, 0.261, 0.103, 0.360], and {Interest rates}[0.040, 0.114, 0.103, 0.208]. I also notice that the x-tick labels represent the corresponding values based on the length of {the blue bars}, which shows the values from {0}[0.103, 0.939, 0.111, 0.957] to {40000}[0.951, 0.939, 0.988, 0.957]. Now we need to extract the lengths of {the blue bars}<r>[0.107, 0.800, 0.876, 0.918]</r> corresponding to each category using the boundary coordinates and the x-tick labels to convert it into actual values.\nBased on the above thought, the answer is:\nIndicators,Values\nGDP,35689\nUnemployment rate,14.2\nInflation rate,8.3\nRetail sales,25648\nTrade balance,1724\nInterest rates,0.5"
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "iMmKC4JigeV2yaDu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which economic indicator has the worst performance?  And can you explain why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires me to compare the lengths of {the blue bars} of each economic indicator. Based on the length and the x-axis labels, GDP has the highest value around 35689 which is generally a good performance; Unemployment Rate and Inflation Rate are around 14.2 and 8.3 respectively, which are not ideal as lower values are prefered here; Retail Sales has a decent performance with the value about 25648, higher value is generally better; Trade Balance reaches to 1724 which is good as we prefer a positive balance; Interest Rate is around 0.5 which can be considered as normal. Therefore, compared to the others, the Unemployment Rate performs the worst given its relatively high value although lower values are preferred.\nBased on the above thought, the answer is:\nUnemployment Rate. Because it has a high value compared to the normal range and unemployment rate is an indicator where lower values are more favorable."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "H92tpTkRC8yixDtd",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the value difference between the Employment rate and the Inflation rate according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the Employment rate and the Inflation rate, first locate the corresponding horizontal bars. {The Unemployment Rate bar}[0.107, 0.652, 0.224, 0.771] extends to around 14.2 on the x-axis, while {The Inflation Rate bar}[0.107, 0.505, 0.189, 0.623] extends to around 8.3 on the x-axis. Take the larger one and subtract the smaller one to calculate the difference.\nBased on the above thought, the answer is:\n5.9"
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "d1kKNcRcf1VNfRbU",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV table as per the periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, from the chart, I see that the {x-axis}<r>[0.488, 0.964, 0.569, 0.981]</r> represents the \"Index Value\" and the {y-axis}<r>[0.015, 0.871, 0.072, 0.204]</r> represents different \"Periods\" from 1-8. The plotted bars signify the two indices: namely \"{Creativity Index}\"<r>[0.795, 0.062, 0.906, 0.080]</r> and \"{Design Innovation Index}\"<r>[0.795, 0.088, 0.964, 0.106]</r> according to the legend. Each period has two bars adjacent to each other. For example, {Period 1}<r>[0.015, 0.871, 0.072, 0.889]</r> has bars represented by <r>[0.081, 0.849, 0.148, 0.878]</r> and <r>[0.081, 0.878, 0.202, 0.908]</r> for Creativity and Design Innovation Indices respectively. The width of the bar represents the index value, which could be calculated proportionally to the width of the x-axis. Using similar process for other periods, I can extract data from the chart and subsequently arrange it for the CSV table.\nBased on the above thought, the answer is:\nPeriods,Creativity Index,Design Innovation Index\nPeriod 1,0.75,1.35\nPeriod 2,2.45,0.74\nPeriod 3,4.32,1.95\nPeriod 4,5.35,1.24\nPeriod 5,3.80,4.20\nPeriod 6,6.55,2.80\nPeriod 7,7.60,3.95\nPeriod 8,8.01,4.68"
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "WX9KzmF9eHsDUJtI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analysing the chart, which index depicts a more consistent trend over the progressing periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I observe that the {Creativity index bars}<r>[0.081, 0.849, 0.798, 0.193]</r> have quite a steady increment despite a few fluctuations. In contrast, {Design Innovation Index bars}<r>[0.081, 0.878, 0.500, 0.223]</r> present a pattern with much more abrupt changes over the periods, for example a peak value of 4.20 achieved at period 5 against the regular slow progression. Hence, it could be reasoned that the Creativity Index maintains a more consistent trend compared to Design Innovation Index.\nBased on the above thought, the answer is:\nCreativity Index shows a more consistent trend."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "ix73ROTTJxsp6b3M",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich period has the highest Creativity Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at each Creativity Index bar for all periods. For example, {the Creativity Index bar for Period 1}<r>[0.081, 0.849, 0.148, 0.878]</r> represents a Creativity Index of 0.75. By comparing it with all the other Creativity Index bars that are associated with different periods such as {Period 2}<r>[0.081, 0.751, 0.301, 0.780]</r>, {Period 3}<r>[0.081, 0.653, 0.468, 0.683]</r> and so on, I can determine that {the Creativity Index bar for Period 8}<r>[0.081, 0.164, 0.798, 0.193]</r> has the most significant x-coordinate, signifying the highest value of 8.01.\nBased on the above thought, the answer is:\nPeriod 8."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "vB3BIFixqEAKMWy2",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom what you observed, could you convert the bar chart data to CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title that shows \"Region Migration\" I can see that the y-axis represents the different regions which include { North America }<r>[0.019, 0.839, 0.145, 0.867]</r>, { Europe }<r>[0.083, 0.687, 0.145, 0.715]</r>, { Asia }<r>[0.108, 0.535, 0.145, 0.563]</r>, { Africa }<r>[0.095, 0.383, 0.145, 0.411]</r> and { Australia }<r>[0.068, 0.231, 0.145, 0.259]</r> . The x-axis displays the scale rounding from the minimum of 0 to the maximum of 24. From the sublegend <r>[0.350, 0.034, 0.788, 0.084]</r>, it is clear that there are two types of movements: urban  <r>[0.405, 0.045, 0.555, 0.073]</r> and rural <r>[0.639, 0.045, 0.781, 0.073]</r>. The length of the bars shows the proportion of urban and rural movement in each region. For example, for North America, the length of the {Urban Movement bar}<r>[0.157, 0.812, 0.498, 0.888]</r> corresponds to 10.2 and the length of {Rural Movement bar}<r>[0.498, 0.812, 0.661, 0.888]</r> corresponds to 4.9. Similarly, I figured out the rest of the values from the length of the bars for each respective regions.\nBased on the above thought, the answer is:\nRegion, Urban Movement, Rural Movement\nNorth America, 10.2, 4.9\nEurope, 7.1, 5.3\nAsia, 14.3, 5.9\nAfrica, 18.7, 4.8\nAustralia, 2.5, 2.3"
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "RFpzsUOZYebVWXXV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, which region has the highest balance between urban and rural migration, and why would you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The balance between urban and rural migration in a region implies that the population migrating within urban spots and rural spots are almost equal. To find out this, I need to observe the bar sizes, where the lengths of the Urban Movement and Rural Movement bars almost the same. This is {Australia}<r>[0.068, 0.231, 0.145, 0.259]</r>. The respective bars {[0.157, 0.204, 0.240, 0.280]; [0.240, 0.204, 0.317, 0.280]}  show that the urban instigated population movement accounts for ~2.5 and the rural movement accounts for ~2.3. The difference between these values is minimal compared to other regions indicating a balance of population migration between urban and rural areas.\nBased on the above thought, the answer is:\nAustralia. It's due to the fact they have the minimal difference between urban and rural population movement compared to other regions."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "oy9RMTRd2RayM8Wi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Urban Movement and Rural Movement in Asia?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the bar lengths, I can determine the values. First, I observe {The Urban Movement bar for Asia}<r>[0.157, 0.508, 0.635, 0.584]</r>, which approximately lines up at the 14.3 position on the x-axis. Then I observe {The Rural Movement bar for Asia}<r>[0.635, 0.508, 0.832, 0.584]</r>, approximately at the 20.2 position on the x-axis. Now, to calculate the difference between these two values, I subtract the lesser value from the larger value (20.2 - 14.3).\nBased on the above thought, the answer is:\n5.9"
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "OZhXo3GAmOiLN582",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the detailed data utilization for \"Cloud Storage\" by all sectors as recorded in the horizontal stacked bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the given structured chart data, to extract the \"Cloud Storage\" information, I observe the horizontal bars for {Cloud Storage}<r>[0.222, 0.298, 0.368, 0.364]</r>. Each horizontal bar corresponds to one sector, showing how much of the \"Cloud Storage\" stage is utilized proportionally. I need to look at the x-range values at the end of each corresponding {bar}<r>[x0, y0, x1, y1]</r> to see the exact usage percentage. For {Retail}<r>[0.222, 0.298, 0.286, 0.364]</r>, the x-range starts at 0.00 and ends at 4.70 suggesting a usage of 4.70%. The same process is repeated for each sector to fill in the table with correct values for \"Cloud Storage\" data utilization.\nBased on the above thought, the answer is:\nSector,Cloud Storage\nRetail,5.7\nTransport,7.2\nEducation,3.2\nHealthcare,8.9\nFinance,5.1\nGovernment,9.7"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "KIv0tukYWd18rb3v",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has integrated Big Data the most evenly across all stages, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the most even distribution of Big Data utilization across all stages, I examine the lengths of each color segment within the horizontal bars dedicated to different sectors and stages. The {sector bars}<r>[x0, 0.168, x1, yt]</r> extend from \"Big Data from IoT\" to \"AI with Big Data\". The most even distribution would be indicated by bars of comparable lengths across all stages. For example, the {Retail bar}<r>[0.222, 0.168, 0.319, 0.233]</r> shows a significant decline, which is steeper than other sectors'. By visually comparing the length of colored segments for each sector, I can determine that {Government}<r>[0.222, 0.821, 0.463, 0.887]</r> shows less variance in the lengths from {IoT}<r>[0.222, 0.168, 0.945, 0.233]</r> to {AI}<r>[0.405, 0.821, 0.463, 0.887]</r>, suggesting a relatively even distribution compared to all others.\nBased on the above thought, the answer is:\nThe Government sector. Its horizontal bar segments from Big Data through IoT to AI with Big Data show the least variance in length, indicating a more even integration of Big Data across all stages."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "5IRgUDHAOhlc4nFM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the \"Finance\" sector contribute to the total utilization percentage for \"Big Data from IoT\" compared to other sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the contribution of the Finance sector to \"Big Data from IoT\" utilization, I look at the structured information regarding each horizontal bar that corresponds to this stage. The {Finance} sector bar for \"Big Data from IoT\" is represented as <r>[0.222, 0.168, 0.759, 0.233]</r>. The x-range provided (0.00 -> 47.50) indicates the percentage usage by {Finance}<r>[0.222, 0.168, 0.759, 0.233]</r>. Adding up similar values for each sector gives the total: {Retail}<r>[0.222, 0.168, 0.319, 0.233]</r> contributes 8.6%, {Transport}<r>[0.222, 0.168, 0.438, 0.233]</r> contributes 10.5%, {Education}<r>[0.222, 0.168, 0.499, 0.233]</r> contributes 5.4%, {Healthcare}<r>[0.222, 0.168, 0.672, 0.233]</r> contributes 15.3%, {Finance}<r>[0.222, 0.168, 0.759, 0.233]</r> contributes 7.7%, and {Government}<r>[0.222, 0.168, 0.945, 0.233]</r> contributes 16.4%. The percentages should then be calculated individually to derive {Finance}'s contribution.\nBased on the above thought, the answer is:\n15.76%"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "0Dy6clZtR0n2YHWz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data presented in the bar chart to a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of the chart represents the number of studies as indicated by {\"Number of Studies\"}[0.517, 0.942, 0.679, 0.970] on the x-axis. The y-axis represents different research fields, as indicated by the labels {\"Physics\"}[0.161, 0.134, 0.225, 0.162], {\"Chemistry\"}[0.136, 0.268, 0.225, 0.296], {\"Biology\"}[0.161, 0.401, 0.225, 0.429], {\"Computer Science\"}[0.066, 0.535, 0.225, 0.563], {\"Artificial Intelligence\"}[0.047, 0.669, 0.225, 0.697], and {\"Quantum Computing\"}[0.043, 0.802, 0.225, 0.830]. I need to check the width of each bar for each research field to obtain the research details. For instance, the bar representing Physics is located at [0.237, 0.111, 0.839, 0.178] with the X-coordinate of the right end around 0.839. Scaling this to the tick marks on the x-axis, we can see that the number of studies is approximately 5000.\nBased on the above thought, the answer is:\nResearch Field, Studies Published\nPhysics, 5000\nChemistry, 4580\nBiology, 5100\nComputer Science, 3600\nArtificial Intelligence, 5700\nQuantum Computing, 2600"
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "0HtS6dvBUl6oNdLh",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, could you identify a trend or make a conclusion about these research fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number of studies published in each field is represented by the length of the bars. From the {\"Physics\"}[0.237, 0.111, 0.839, 0.178], {\"Chemistry\"}[0.237, 0.245, 0.788, 0.312], {\"Biology\"}[0.237, 0.379, 0.851, 0.446], {\"Computer Science\"}[0.237, 0.512, 0.670, 0.579], {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713], and {\"Quantum Computing\"}[0.237, 0.780, 0.550, 0.847] bars we observe that the Artificial Intelligence field has seen the highest amount of published studies, indicating increased interest and research activity. While Quantum Computing is an emerging field with fewer studies published, it shows great potential and is rapidly growing.\nBased on the above thought, the answer is:\nThere is a rising interest in the field of Artificial Intelligence which has the most number of published studies, whereas Quantum Computing, while currently having fewer studies, seems a potential area of interest in scientific research due to its rapid growth."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "bOKmUfRkyyAYZXLN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research field has the highest number of studies published according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number of studies published in each research field is represented by the length of the bars on the x-axis. The longest bar indicates the research field with the most studies published. By comparing the length of the bars {\"Physics\"}[0.237, 0.111, 0.839, 0.178], {\"Chemistry\"}[0.237, 0.245, 0.788, 0.312], {\"Biology\"}[0.237, 0.379, 0.851, 0.446], {\"Computer Science\"}[0.237, 0.512, 0.670, 0.579], {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713], and {\"Quantum Computing\"}[0.237, 0.780, 0.550, 0.847], we find that the bar for {\"Artificial Intelligence\"}[0.237, 0.646, 0.923, 0.713] is the longest, indicating that it has the highest number of published studies.\nBased on the above thought, the answer is:\nArtificial Intelligence"
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "DOBvNIlYFkTxm1jh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please provide a structured breakdown of the economic data shown on the bar chart with the categories and socio-economic factors in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the information given by the chart legend located at [0.291, 0.879, 1.026, 0.971], there are six categories: {Fluctuating Decline}[0.347, 0.890, 0.498, 0.918], {Gradual Downturn}[0.347, 0.932, 0.506, 0.960], {Marginal Improvement}[0.589, 0.890, 0.787, 0.918], {Inexorable Rise}[0.589, 0.932, 0.722, 0.960], {Stable Growth}[0.870, 0.890, 0.994, 0.918], {Declining Pattern}[0.870, 0.932, 1.019, 0.960]. We also have 5 different socio-economical factors as shown on the y-axis: {Online Education Participation}[0.062, 0.738, 0.324, 0.766], {Telecommunication Advancements}[0.019, 0.577, 0.324, 0.605], {Alternative Energy Consumption}[0.042, 0.417, 0.324, 0.445], {AI Adoption}[0.224, 0.256, 0.324, 0.284], {Smart Home Usage}[0.155, 0.095, 0.324, 0.123]. The value for each category in each socio-economic factor can be estimated by the length of the corresponding bar on the x-axis and comparing it with the scale from {0}[0.331, 0.845, 0.342, 0.873] to {50}[0.670, 0.845, 0.692, 0.873].\nBased on the above thought, the answer is:\nCategories,SmartHomeUsage,AIAdoption,AltEnergyConsumption,TeleCommunicationAdvancements,OnlineEducationParticipation\nFluctuating Decline,8.7,6.5,7.8,15.8,10.5\nGradual Downturn,7.3,5.2,7.2,10.1,9.5\nMarginal Improvement,8.7,8.0,7.9,17.0,11.1\nInexorable Rise,9.8,9.2,8.5,18.8,12.0\nStable Growth,8.5,8.6,7.7,17.1,11.3\nDeclining Pattern,7.0,5.1,6.2,10.2,8.6"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "ahURSwE5EODBxISv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which category scenarios can we expect stability in AI Adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to focus on the 'AI Adoption'[0.224, 0.256, 0.324, 0.284] rows depicted by the bars on the y-axis. The six bars that correspond to the six existing categories in this row are at coordinates [0.381, 0.227, 0.417, 0.307][0.417, 0.227, 0.472, 0.307][0.472, 0.227, 0.536, 0.307][0.536, 0.227, 0.595, 0.307][0.595, 0.227, 0.630, 0.307][0.630, 0.227, 0.665, 0.307]. Based on the length of these bars, the 'AI Adoption' scenario does not show drastic fluctuations for the category scenarios {Stable Growth}[0.870, 0.890, 0.994, 0.918] and {Marginal improvement}[0.589, 0.890, 0.787, 0.918]. So they depict relative stability.\nBased on the above thought, the answer is:\nStable Growth and Marginal improvement scenarios indicate stability in AI Adoption."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "5l8R3bcfvmOPRlVC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category indicates the highest increase in TeleCommunication Advancements?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to focus on the TeleCommunication Advancements rows depicted by the bars on the y-axis at coordinates [0.019, 0.577, 0.324, 0.605]. The six bars that correspond to the six categories in this row are at coordinates [0.445, 0.548, 0.515, 0.629],[0.515, 0.548, 0.632, 0.629],[0.632, 0.548, 0.762, 0.629],[0.762, 0.548, 0.880, 0.629],[0.880, 0.548, 0.951, 0.629]. By comparing these bars' length, we can see that the longest bar, indicating the highest value, is for the category {Inexorable Rise}[0.589, 0.932, 0.722, 0.960].\nBased on the above thought, the answer is:\nInexorable Rise."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "YSf76oyI6N1mK03J",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart received, could you put the data it represents into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the six labels visible on the Y-axis indicating {\"Internet Users\"}<r>[0.147, 0.149, 0.270, 0.177]</r>, {\"Podcast Listeners\"}<r>[0.118, 0.265, 0.270, 0.293]</r>, {\"Online Ad Spend\"}<r>[0.125, 0.381, 0.270, 0.409]</r>, {\"Social Media Engagement\"}<r>[0.044, 0.497, 0.270, 0.525]</r>, {\"Video Streaming Subscribers\"}<r>[0.019, 0.613, 0.270, 0.641]</r>, {\"Third-Party Content Creators\"}<r>[0.020, 0.729, 0.270, 0.757]</r>, and {\"OTT Platforms\"}<r>[0.147, 0.845, 0.270, 0.873]</r>, I can determine that there are seven different categories in the data. There are two bars grouped together for each category, with {\"2019 values\"}<r>[0.570, 0.035, 0.614, 0.063]</r> represented by the color \"#20b2aa\", and {\"2020 values\"}<r>[0.698, 0.035, 0.741, 0.063]</r> represented by the color \"#da70d6\", as indicated by {the chart's legend}<r>[0.515, 0.024, 0.748, 0.074]</r>. For each category, I need to decode the corresponding 2019 and 2020 values by using the width of the bars and the labels on the X-axis. For example, for \"Internet Users\", the widths of the {2019 bar}<r>[0.282, 0.131, 0.327, 0.189]</r> and the {2020 bar}<r>[0.327, 0.131, 0.577, 0.189]</r> correspond to the X-axis labels at around 22 and 120 respectively. Therefore, the 2019 value for \"Internet Users\" is 22 million and the 2020 value is 120 million.\nBased on the above thought, the answer is:\nCategory,2019,2020\nInternet Users,22,120\nPodcast Listeners,40,80\nOnline Ad Spend,35,130\nSocial Media Engagement,50,200\nVideo Streaming Subscribers,60,260\nThird-Party Content Creators,20,150\nOTT Platforms,15,70"
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "iSRuKgNCxBg9FvM2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, which category of new media and digital content has achieved the fastest growth in 2020 compared to 2019 and can explain what factors might contribute to its rapid growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the widths of the 2019 and 2020 bars for each category, it can be observed that the 'Video Streaming Subscribers' category has seen the most growth. The width of {the 2019 bar}<r>[0.282, 0.595, 0.407, 0.653]</r> and {the 2020 bar}<r>[0.407, 0.595, 0.948, 0.653]</r> correspond to the X-axis labels of approximately 60 and 260, indicating a growth difference of 200. This rapid growth can be attributed to several factors. The year 2020 saw a global pandemic, which could have led to more people staying at home and consequently more people subscribing to video streaming services. Besides, the substantial improvement and wide adoption of internet technology have made video streaming services more user-friendly and accessible, thus attracting a larger population of subscribers.\nBased on the above thought, the answer is:\nThe 'Video Streaming Subscribers' category has achieved the fastest growth. Possible factors for this growth include increased home time due to the global pandemic and the advancement and wider adoption of internet technology making these services more accessible."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "Jz8tlwAMTvVFmnPF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which category showed the most significant growth from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the category with the most significant growth from 2019 to 2020, I need to calculate the difference between the 2019 and 2020 bars for each category. Using the width of the bars and the labels on the X-axis, I can derive the value for each year. For example, in the 'Video Streaming Subscribers' category, the width of the {2019 bar}<r>[0.282, 0.595, 0.407, 0.653]</r> and the {2020 bar}<r>[0.407, 0.595, 0.948, 0.653]</r> correspond to the X-axis labels at around 60 and 260 respectively. So, the growth in this category is 260-60=200. By applying the same method to the rest of the categories, it can be concluded that the largest difference is in the 'Video Streaming Subscribers' category, with a growth of 200.\nBased on the above thought, the answer is:\nVideo Streaming Subscribers"
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "lIlonY0hO6kypDCI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe bar chart before me depicts Scientific Research and Technological Innovation in different nations. Could you help me represent this as a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at this bar chart, I see that the {x-axis}<r>[0.245, 0.923, 0.970, 0.942]</r> displays values ranging from {0}<r>[0.240, 0.942, 0.251, 0.970]</r> to {50}<r>[0.959, 0.942, 0.981, 0.970]</r>, which represent the value of scientific research or technological innovation. The {y-axis}<r>[0.245, 0.923, 0.245, 0.170]</r> contains labels for two categories {Scientific Research}<r>[0.069, 0.307, 0.233, 0.335]</r> and {Technological Innovation}<r>[0.019, 0.763, 0.233, 0.791]</r>. Also visible is a {legend}<r>[0.432, 0.034, 0.783, 0.126]</r> that identifies five nations- US, EU, China, India, and Brazil. They are associated with the bars corresponding to their data on the chart. For example, for US' Scientific Research the bar<r>[0.245, 0.204, 0.281, 0.432]</r> aligns with the value 2.5 and for Technological Innovation, the bar<r>[0.245, 0.660, 0.297, 0.888]</r> aligns with the value 3.6. This process repeats for each bar, enabling me to confidently construct a CSV table as per the data displayed in the chart.\nBased on the above thought, the answer is:\n,Nation,Scientific Research,Technological Innovation\n0,US,2.5,3.6\n1,EU,5.1,7.2\n2,China,1.1,1.5\n3,India,5.3,6.4\n4,Brazil,2.0,3.0"
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "DkSFcT7sy06Y6gA2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which country do you think is doing better in terms of progressing in Scientific Research and Technological Innovation, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires analysis of both Scientific Research and Technological Innovation data. Taking the first parameter, Scientific Research, the largest {bar}<r>[0.281, 0.204, 0.355, 0.432]</r> represents {EU}<r>[0.488, 0.087, 0.511, 0.115]</r>, indicating that the EU is leading in scientific research. On the other hand, for Technological Innovation, {India}<r>[0.595, 0.087, 0.637, 0.115]</r> is leading with the largest {bar}<r>[0.424, 0.660, 0.516, 0.888]</r>. Both categories are equally important so it is not possible to determine which country is performing better overall without a more in-depth analysis. However, from the chart, EU leads in Scientific Research and India in Technological Innovation.\nBased on the above thought, the answer is:\nFrom an overall high-level perspective, it's tough to determine as EU is leading in Scientific Research and India in Technological Innovation. Both categories play a critical role and the importance of one over the other depends on specific contexts and goals of these countries."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "eO5nwHzY3HbcYIGR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which nation shows the largest difference between Scientific Research and Technological Innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the {bars}<r>[0.245, 0.660, 0.560, 0.888]</r> representing each nation's Scientific Research and Technological Innovation, I can calculate the difference. For instance, in the case of the {US}<r>[0.488, 0.045, 0.511, 0.073]</r>, the bars are at {2.5}<r>[0.245, 0.204, 0.281, 0.432]</r> for Scientific Research, and {3.6}<r>[0.245, 0.660, 0.297, 0.888]</r> for Technological Innovation. I observe similar positions for EU, China, India, and Brazil respectively. By calculating the difference for each, I find that {India}<r>[0.595, 0.087, 0.637, 0.115]</r> has a difference of {1.1}<r>[0.371, 0.204, 0.448, 0.432], [0.424, 0.660, 0.516, 0.888]</r> which is the largest amongst all nations.\nBased on the above thought, the answer is:\nIndia"
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "CXBUWyrzBXNh10G9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart data into a CSV format considering each regional segment within the technology categories."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a horizontal bar chart showcasing technological development trends across regions. These regions are represented by different colors and divided into segments within each technology category. Taking {\"Internet Speed\"}<r>[0.061, 0.853, 0.175, 0.876]</r> as an example, the segments are divided into five categories, each of which represents a region {\"US\"}<r>[0.186, 0.827, 0.303, 0.897]</r>, {\"EU\"}<r>[0.303, 0.827, 0.487, 0.897]</r>, {\"China\"}<r>[0.487, 0.827, 0.748, 0.897]</r>, {\"India\"}<r>[0.748, 0.827, 0.897, 0.897]</r>, {\"Brazil\"}<r>[0.897, 0.827, 0.945, 0.897]</r>. The length of each segment gives us the value for the corresponding category and region. By applying the same process to all technology categories, a CSV table can be generated.\nBased on the above thought, the answer is:\nCategory,US,EU,China,India,Brazil\nInternet Speed,29.3,45.7,65.2,37.1,12.1\nArtificial Intelligence,8.2,12.4,16.4,18.3,14.1\nCybersecurity,22.9,30.2,43.1,33.5,28.7\nBig Data,16.4,21.3,28.7,31.4,24.1\nCloud Computing,12.6,18.5,25.4,23.3,15.2\n5G Connectivity,10.6,16.2,24.3,17.4,10.8"
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "zoLTYV2kJDaHxKej",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you identify the region that has the most balanced development across all technology categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the region with the most balanced development, we need to check the lengths of the segments representing each region inside all categories. By comparing the segment lengths for all regions within the categories - {\"US\"}<r>[0.478, 0.027, 0.499, 0.050]</r>, {\"EU\"}<r>[0.478, 0.062, 0.499, 0.085]</r>, {\"China\"}<r>[0.573, 0.027, 0.617, 0.050]</r>, {\"India\"}<r>[0.573, 0.062, 0.611, 0.085]</r>, {\"Brazil\"}<r>[0.691, 0.027, 0.735, 0.050]</r> - we can observe that EU has the most consistent segment lengths, indicating a balanced development across all categories.\nBased on the above thought, the answer is:\nEU. This region has the most balanced development across all technology categories as the length of the segments, representing EU in every category, is relatively consistent."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "BljWDHkui8nH9c3K",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category showed the highest development in the EU region?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the chart represent different categories and the length of the EU segments inside these bars, represented by {\"EU\"}<r>[0.573, 0.062, 0.611, 0.085]</r> in the legend, represents the EU's development within each category. Looking at the various EU segments - {\"Internet Speed\"}<r>[0.303, 0.827, 0.487, 0.897]</r>, {\"Artificial Intelligence\"}<r>[0.219, 0.688, 0.268, 0.757]</r>, {\"Cybersecurity\"}<r>[0.278, 0.548, 0.399, 0.618]</r>, {\"Big Data\"}<r>[0.252, 0.408, 0.337, 0.478]</r>, {\"Cloud Computing\"}<r>[0.236, 0.269, 0.311, 0.338]</r>, and {\"5G Connectivity\"}<r>[0.228, 0.129, 0.293, 0.199]</r>, we can calculate that the \"Internet Speed\" category has the longest length and thus highest development.\nBased on the above thought, the answer is:\nInternet Speed."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "QjzgFHejZBuw4yuK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table based on the observation of the bar chart depicting the total number of patents issued each year from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table, I will analyze the horizontal bars corresponding to the \"Patents Issued\" category. I notice that for each year, the horizontal bars extend from the y-axis and are color-coded to represent each year. Utilizing the {x-axis tick marks}<r>[0.270, 0.841, 1.102, 0.861]</r> and {the horizontal bars for Patents Issued}<r>[0.274, 0.283, 0.949, 0.340]</r>, I can estimate the length of each bar for respective years 2016 to 2020 to convert into numerical values as follows: for {2016}<r>[0.274, 0.283, 0.403, 0.340]</r>, the value is approximately 5610, for {2017}<r>[0.403, 0.283, 0.535, 0.340]</r>, the value is 5715, and so on.\nBased on the above thought, the answer is:\nYear,Patents Issued\n2016,5610\n2017,5715\n2018,5800\n2019,6000\n2020,6200"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "KS3YprjCKNqM6BEp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which area of the Medical and Biotechnology industry shows the least variation over the 5 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the variations in bar lengths for each category over the 5 years, I will identify the one with the least change. For example, {Pharmaceutical R&D Spent}<r>[0.274, 0.058, 0.294, 0.114]</r> shows a consistent increase. Observing the {bars representing Medical Devices Manufacturing Revenue}<r>[0.274, 0.171, 0.289, 0.227]</r>, which vary from a minimum of $120 billion to a maximum of $130 billion over the period, compared to the others, shows the least variation in terms of absolute value difference and visually presents the smallest changes in bar lengths year over year.\nBased on the above thought, the answer is:\nThe Medical Devices Manufacturing Revenue shows the least variation over the observed 5 years, as indicated by the small changes in bar lengths on the chart."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "rFFAa4OTRh1LM3Ti",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in the Private Investments in Biotech from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the length of the horizontal bars for Private Investments in Biotech, the actual values can be deciphered. Starting at {2016}<r>[0.274, 0.734, 0.275, 0.790]</r> with a value of $35 billion, and ending at {2020}<r>[0.278, 0.734, 0.279, 0.790]</r> with a value of $44 billion, I can calculate the percentage increase as (44 - 35) / 35 * 100.\nBased on the above thought, the answer is:\n25.71%"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "0i61G9lgNe1oq7wS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart displaying technological industry trends, can you provide a CSV formatted table of the data for 5G Coverage across the five markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table of 5G Coverage data represented in the chart, I will examine and extract the horizontal bar data for each market. First, the chart's horizontal bars for {5G Coverage}<r>[0.213, 0.204, 0.323, 0.257]</r> and <r>[0.323, 0.204, 0.451, 0.257]</r> and so on show the extent of growth for the US, China, EU, India, and Brazil. From the x-axis tick marks, I can see the bar ending points correspond to values. For {US}<r>[0.213, 0.204, 0.323, 0.257]</r>, it appears to end at 56.7, for {China}<r>[0.323, 0.204, 0.451, 0.257]</r> it extends to 75.8, and so on for the rest of the markets by addressing their respective rectangles.\nBased on the above thought, the answer is:\nMarket,5G Coverage\nUS,56.7\nChina,75.8\nEU,50.1\nIndia,48.0\nBrazil,25.6"
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "h9SxZTrO9T0jCjoC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market consistently places above average across all technological sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consistency above average can be determined by analyzing the horizontal bars for each market across all the technology sectors. I'll locate each market's bar for every sector using the coordinate information given for each bar start and end point. For example, {US bars}<r>[0.213, 0.836, 0.323, 0.888]</r> starting with Blockchain, {China bars}<r>[0.323, 0.836, 0.451, 0.888]</r> through to {India bars}<r>[0.548, 0.836, 0.631, 0.888]</r> and {Brazil bars}<r>[0.631, 0.836, 0.686, 0.888]</r>. With 'average' meaning the midpoint along the x-axis, one market's bars are consistently extending beyond the middle, indicating it is above average. Comparing all bars across all sectors, {China}<r>[0.323, 0.204, 0.451, 0.257]</r> shows a clear dominance with its bars extending farther than all other markets, which places it above the average in every sector.\nBased on the above thought, the answer is:\nChina consistently places above average across all technological sectors. Each of China's sector-specific bars extends beyond the midpoint on the x-axis of the chart, which delineates the average value."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "MTDudZ3WXqsEzJ3n",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market shows the least development in Robotics compared to others?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the market with the least development in Robotics, I will compare the lengths of the bar representing Robotics for each market\u2014beginning with the bar ends closest to the y-axis and ending at the recorded values on the x-axis. The corresponding bars' coordinates for {US}<r>[0.213, 0.730, 0.367, 0.783]</r>, {China}<r>[0.367, 0.730, 0.548, 0.783]</r>, {EU}<r>[0.548, 0.730, 0.690, 0.783]</r>, {India}<r>[0.690, 0.730, 0.813, 0.783]</r>, and {Brazil}<r>[0.813, 0.730, 0.912, 0.783]</r> are assessed, with Brazil displaying the shortest bar, indicating the lowest development values.\nBased on the above thought, the answer is:\nBrazil."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "x2VhWlLgUvl8pFQl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data you observed from the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical category labels on the y-axis of the chart include six types of gastronomy related activities namely: {Local Product Consumption}<r>[0.113, 0.189, 0.303, 0.217]</r>, {Local Cuisine Uniqueness}<r>[0.127, 0.320, 0.303, 0.348]</r>, {Gourmet Festival Attendance}<r>[0.100, 0.451, 0.303, 0.479]</r>, {Michelin Starred Restaurant}<r>[0.110, 0.581, 0.303, 0.609]</r>, {Number_of_Gastronomy_Books}<r>[0.111, 0.712, 0.303, 0.740]</r>, and {Number_of_TV_Shows_Talk_About_Gastronomy}<r>[0.015, 0.843, 0.303, 0.871]</r>. \n\nThe data for each category, per continent, is represented by a series of horizontal bars each having a unique color based on the continent. For example, the {blue bar}<r>[0.313, 0.168, 0.399, 0.233]</r> represents the Local Product Consumption of America, and I can estimate its value by comparing the position of its end with the values in the x-axis. \n\nLooking at {the legend}<r>[0.473, 0.027, 0.816, 0.119]</r>, there are 5 different categories represented by unique colors viz, {America}<r>[0.518, 0.038, 0.575, 0.066]</r>, {Europe}<r>[0.518, 0.080, 0.567, 0.108]</r>, {Asia}<r>[0.642, 0.038, 0.671, 0.066]</r>, {Africa}<r>[0.642, 0.080, 0.682, 0.108]</r>, and {Australia}<r>[0.748, 0.038, 0.810, 0.066]</r>. \n\nI can then compile each category data for each continent and create a CSV representation.\nBased on the above thought, the answer is:\nCuisine,Local Product Consumption,Local Cuisine Uniqueness,Gourmet Festival Attendance,Michelin Starred Restaurant,num_of_Gastronomy_Books,num_of_TVShows_TalkAbout_Gastronomy\nAmerica,6.5,7.6,5.2,4.4,3.8,6.0\nEurope,8.7,8.3,6.7,5.8,5.2,7.6\nAsia,9.2,9.0,7.6,6.7,6.6,9.0\nAfrica,5.2,6.6,3.5,1.5,1.2,3.0\nAustralia,6.8,6.0,4.3,2.5,2.0,3.8"
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "O5QYVUkWlPSgUr2P",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which region has the most potential in terms of Gastronomy and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents the level of Gastronomy across all regions, defined by the length of the bars. The higher the bar length for a certain region, the higher the level of Gastronomy in that region across the categories.\nI can observe that {Asia}<r>[0.642, 0.038, 0.671, 0.066]</r> has consistently high values across all categories which are noticeably higher compared to other regions. Based on this, it can be concluded that Asia has high potential owing to the high numbers across the board, notably the {Number of TV Shows Talking About Gastronomy} <r>[0.493, 0.821, 0.613, 0.887]</r> and the {Number of Gastronomy Books} <r>[0.432, 0.691, 0.520, 0.756]</r>. It's apparent that Gastronomy is thriving in Asia in terms of content production and the adoption of local foods and customs.\nBased on the above thought, the answer is:\nAsia shows the most potential. With high levels across all categories, specifically Number of TV Shows talking about gastronomy and Number of Gastronomy books, it signifies an interest and a thriving environment for gastronomy."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "Kcp2uYHLflD8lKKF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIrispect to which cuisine category, does Asia show the highest difference in score compared to America?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the category with the largest difference in score between Asia and America, I need to compare the lengths of the corresponding bars for each category. The categories from top to bottom on the y-axis are {num_of_TVShows_TalkAbout_Gastronomy}<r>[0.015, 0.843, 0.303, 0.871]</r>, {num_of_Gastronomy_Books}<r>[0.111, 0.712, 0.303, 0.740]</r>, {Michelin Starred Restaurant}<r>[0.110, 0.581, 0.303, 0.609]</r>, {Gourmet Festival Attendance}<r>[0.100, 0.451, 0.303, 0.479]</r>, {Local Cuisine Uniqueness}<r>[0.127, 0.320, 0.303, 0.348]</r>, and {Local Product Consumption}<r>[0.113, 0.189, 0.303, 0.217]</r>. By examining each pair of bars horizontally, such as the bars length at {num_of_TVShows_TalkAbout_Gastronomy for America and Asia}<r>[0.313, 0.821, 0.392, 0.887]</r> <r>[0.493, 0.821, 0.613, 0.887]</r>, it can be concluded that {Local Cuisine Uniqueness}<r>[0.127, 0.320, 0.303, 0.348]</r>, with bars at <r>[0.313, 0.298, 0.414, 0.364]</r> for America and <r>[0.524, 0.298, 0.643, 0.364]</r> for Asia, shows the largest difference.\nBased on the above thought, the answer is:\nLocal Cuisine Uniqueness"
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "zwvy1Ky1HJd30JfD",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you sort the emerging markets and consumer behavior data in a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the information from the chart, including the bar positions and the associated market labels, I can create a CSV data table. From examining the chart, I notice the labels {'US'}<[0.472, 0.045, 0.495, 0.073]>, {'EU'}<[0.472, 0.087, 0.495, 0.115]>, {'China'}<[0.578, 0.045, 0.628, 0.073]>, {'India'}<[0.578, 0.087, 0.621, 0.115]>, and {'Brazil'}<[0.711, 0.045, 0.760, 0.073]> on the legend and the corresponding color coded bars {'US'}<[0.202, 0.660, 0.346, 0.888], [0.202, 0.204, 0.346, 0.432]>, {'EU'}<[0.346, 0.660, 0.476, 0.888], [0.346, 0.204, 0.476, 0.432]>, {'China'}<[0.476, 0.660, 0.664, 0.888], [0.476, 0.204, 0.664, 0.432]>, {'India'}<[0.664, 0.660, 0.828, 0.888], [0.664, 0.204, 0.828, 0.432]> and {'Brazil'}<[0.828, 0.660, 0.944, 0.888], [0.828, 0.204, 0.944, 0.432]>. The horizontal bar length determines the numerical value for an emerging market, with '0' at the far left side {'0'}<[0.197, 0.942, 0.207, 0.970]> and '55' on the far right {'55'}<[0.379, 0.942, 0.401, 0.970]>. Thus, by matching the bars' color code bars with the corresponding market names in the legend, then measuring the length of the bars, the CSV format of the data can be generated.\nBased on the above thought, the answer is:\nEmerging Markets,Consumer Behavior\nUS,42\nEU,38\nChina,55\nIndia,48\nBrazil,34"
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "E8ssU7wPbSz5kdAr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed bar chart, what is the overall trend of consumer behavior across the emerging markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To address this question, I must study the size and direction of the bars across different markets. Looking at the five bars in the chart, including those found at<br>{'US'}<[0.202, 0.660, 0.346, 0.888]>,<br>{'EU'}<[0.346, 0.660, 0.476, 0.888]>,<br>{'China'}<[0.476, 0.660, 0.664, 0.888]>,<br>{'India'}<[0.664, 0.660, 0.828, 0.888]>, and<br>{'Brazil'}<[0.828, 0.660, 0.944, 0.888]>, it is clear each bar represents a different length, which parallels different consumer behavior indices. The bars representing China and India extend quite far, indicating strong consumer behavior in those markets. The bars for US show a moderate level of consumer behavior, while EU and Brazil represent the least. Therefore, the dominant trend suggests a higher consumer behavior index in Asian markets- specifically China and India.\nBased on the above thought, the answer is:\nThe overall trend displays strong consumer behavior in the Asian markets, particularly in China and India, followed by more moderate levels in the US. The European and Brazilian markets seem to show weaker consumer behavior."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "ift3it07PyKcrk6f",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat emerging market has the most significant consumer behavior index as presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the markets with the highest consumer behavior index, I need to find the bar extending furthest to the right in the chart. In this case, it is the bar positioned at {'China'}<[0.476, 0.660, 0.664, 0.888]>. By comparing its length to the x-axis index I see it extends almost to '55' {[0.379, 0.942, 0.401, 0.970]}. As such, China corroborates with the raw data with the highest consumer behavior index.\nBased on the above thought, the answer is:\nChina."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "aP0equ074BFGoRYx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data you observed from this bar chart about Education Resource Distribution and Utilization into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this chart into a CSV table, we need to identify the x-axis and y-axis and the segmentation in each bar that shows different trends. The y-axis shows {different categories}[{\"Online Education\"}[0.068, 0.224, 0.216, 0.252]; {\"Classroom Education\"}[0.033, 0.349, 0.216, 0.377]; {\"Homework Assistance\"}[0.025, 0.473, 0.216, 0.501]; {\"Student-Teacher Ratio\"}[0.025, 0.598, 0.216, 0.626]; {\"Education Expenditure\"}[0.019, 0.722, 0.216, 0.750]; {\"College Education\"}[0.059, 0.846, 0.216, 0.874]], while the x-axis shows the quantity for each category and trend. The legend {\"sublegend\"}[0.238, 0.034, 0.971, 0.126] has different colors for trends. For each category, there are five bars with different colors. \nFor instance, let's take the category {\"Online Education\"}[0.068, 0.224, 0.216, 0.252]. There are five bars each representing a different trend. The leftmost bar {\"Continuous decline\"}[0.228, 0.204, 0.365, 0.266] is colored in red and ends around 9.2 on the x-axis. Similarly, the second bar {\"Marginal improvement\"}[0.365, 0.204, 0.445, 0.266] is colored in yellow, and ends around 5.4. I can continue this for all other bars under Online Education, and then repeat with the same method for all other categories.\nBased on the above thought, the answer is:\nCategories,Continuous decline,Marginal improvement,Slight decrease,Slow growth,Inconsistent decline\nOnline Education,9.2,5.4,7.8,4.1,2.2\nClassroom Education,12.1,7.5,10.1,6.5,3.8\nHomework Assistance,7.4,6.3,6.2,7.1,1.5\nStudent-Teacher Ratio,13.6,7.2,8.9,6.2,4.5\nEducation Expenditure,15.7,8.7,10.7,8.1,5.1\nCollege Education,8.2,7.1,6.9,5.9,3.1"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "b4geqhQK0Y1x9U7M",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret the trend of 'Slow growth' across all categories in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the 'Slow growth' trend across all categories, I need to analyze the length of the bar representing 'slow growth' for each category. The category with the highest quantity would have the best slow growth trend.\nTaking a look at the 'Slow growth' trend (represented in green bars), the quantity for each category are as follows: {\"Online Education\"}[0.561, 0.204, 0.622, 0.266] is about 4.1, {\"Classroom Education\"}[0.669, 0.329, 0.766, 0.391] is about 6.5, {\"Homework Assistance\"}[0.629, 0.453, 0.654, 0.515] is about 7.1, {\"Student-Teacher Ratio\"}[0.669, 0.577, 0.761, 0.640] is about 6.2, {\"Education Expenditure\"}[0.749, 0.702, 0.870, 0.764] is about 8.1, and finally, {\"College Education\"}[0.558, 0.826, 0.645,0.888] has around 5.9. \nBased on this, 'Education Expenditure' category shows the greatest 'Slow growth' trend. And, 'Online Education', despite its prominence, shows the lowest 'Slow growth' trend which could imply a concern in shifting learning modes.\nBased on the above thought, the answer is:\nThe trend 'Slow growth' is fairly diverse across different categories. 'Education Expenditure' shows the most significant slow growth with a quantity of about 8.1. It implies that the investment in education has been continuously increasing, but at a slow rate. In contrast, 'Online Education' shows the least slow growth with a quantity of about 4.1, suggesting that the online education sector has much potential for further expansion. However, all sectors indicated a slow, continued growth, stating an overall improving state in education efforts."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "1wB50VCH1dzaujgv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the bar chart's depiction, which category does receive the least assistance in the 'marginal improvement' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to locate each category for the trend of 'Marginal improvement'. Then, by comparing the bar lengths of all the categories, I can find the one with the shortest length which receives the least assistance.\nIn the 'Marginal improvement' trend, the height of {\"Online Education\"}[0.365, 0.204, 0.445, 0.266] is around 5.4, {\"Classroom Education\"}[0.460, 0.329, 0.519, 0.391] is around 7.5, {\"Homework Assistance\"}[0.382, 0.453, 0.477, 0.515] is about 6.3, {\"Student-Teacher Ratio\"}[0.486, 0.577, 0.583, 0.640] is around 7.2, {\"Education Expenditure\"}[0.542, 0.702, 0.672, 0.764] is approximately 8.7 and {\"College Education\"}[0.492, 0.826, 0.583, 0.888] is about 7.1. After comparison, the segment for 'Online Education' has the shortest length in the 'Marginal improvement' category making it receive the least assistance.\nBased on the above thought, the answer is:\nOnline Education"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "1E8fjtUN4JVSN6wj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart you're seeing, can you extract the data and present it in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents ten different economic statistics on the y-axis, with corresponding trend values on the x-axis. Taking the y-axis, I'll collect each y-label from the bar coordinates. For instance, {the bar for \"Gross Domestic Product (GDP)\"}<r>[0.319, 0.818, 0.894, 0.886]</r> shows the name of the statistic and has a text annotation {\"8.3\"}<r>[0.894, 0.843, 0.927, 0.861]</r> at the end, signifying that the trend value for \"Gross Domestic Product (GDP)\" is 8.3. I will repeat this process for all the bars in the chart.\nBased on the above thought, the answer is:\nStatistic,Trend\nGross Domestic Product (GDP),8.3\nSpending,6.7\nEmployment,9.1\nInflation,5.6\nInterest Rates,2.8\nIncome,7.5\nExports,3.4\nImports,4.8\nFactory Orders,7.2\nUnemployment,1.2"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "8Onga4qs167HzosZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend values, what can you infer about the relative performance of the economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The length of the bars in the chart provides an idea about the trend of different economic indicators. For instance, the {bar for \"Unemployment\"}<r>[0.319, 0.060, 0.402, 0.127]</r> is very short, indicating that unemployment rates (which are unfavorable when high) are low. Conversely, the {bar for \"Employment\"}<r>[0.319, 0.650, 0.950, 0.717]</r> is significantly long, indicating that the employment rates (which are favorable when high) are high. Also, the high value depicted by the {bar for \"Gross Domestic Product (GDP)\"}<r>[0.319, 0.818, 0.894, 0.886]</r> signifies an increasing economy. Thus, these trends suggest that the economic performance is quite positive and strong in terms of employment and GDP, although there might be areas that require further improvement.\nBased on the above thought, the answer is:\nThe economy is doing well overall, especially in terms of low unemployment and high employment. The Gross Domestic Product is also showing a positive trend, which suggests economic growth. However, some indicators such as Interest Rates may need attention to boost the economic performance further."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "462BJAsZ25bJgeNW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator has the highest trend value according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which economic indicator has the highest trend value, I can compare the length of the bars. For example, {the bar for \"Gross Domestic Product (GDP)\"}<r>[0.319, 0.818, 0.894, 0.886]</r> extends up to an x-coordinate of 0.894. But if I examine {the bar for \"Employment\"}<r>[0.319, 0.650, 0.950, 0.717]</r>, it goes up to x=0.950, which suggests that Employment has a higher trend value.\nBased on the above thought, the answer is:\nEmployment."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "BKkk3pYbMfhahSzm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, can you construct a table displaying the Innovation Indicators and their corresponding percentages in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The input data consists of various Innovation Indicators mentioned on the y-axis {yticks}[{\"Patents\"}[0.196, 0.824, 0.248, 0.848]; {\"Scientific Publications\"}[0.097, 0.746, 0.248, 0.770]; {\"R&D Personnel\"}[0.145, 0.668, 0.248, 0.692]; {\"Startup Creation\"}[0.133, 0.590, 0.248, 0.614]; {\"Venture Capital Investment\"}[0.058, 0.512, 0.248, 0.536]; {\"Collaboration in Innovation\"}[0.060, 0.434, 0.248, 0.458]; {\"Knowledge Transfer\"}[0.112, 0.356, 0.248, 0.380]; {\"Business Expenditures on R&D\"}[0.035, 0.278, 0.248, 0.302]; {\"Entrepreneurial Culture\"}[0.085, 0.200, 0.248, 0.224]; {\"Technological Infrastructure\"}[0.055, 0.122, 0.248, 0.146]] and their corresponding percentages in the {bars_of_group_0} [{'percentage for Patents'}[0.687, 0.822, 0.722, 0.845], {'percentage for Scientific Publications'}[0.467, 0.744, 0.502, 0.767], {'percentage for R&D Personnel'}[0.783, 0.666, 0.818, 0.689], {'percentage for Startup Creation'}[0.812, 0.588, 0.848, 0.611], {'percentage for Venture Capital Investment'}[0.669, 0.510, 0.705, 0.533], {'percentage for Collaboration in Innovation'}[0.538, 0.432, 0.573, 0.455], {'percentage for Knowledge Transfer'}[732, 0.354, 0.776, 0.377], {'percentage for Business Expenditures on R&D'}[0.944, 0.276, 0.988, 0.299], {'percentage for Entrepreneurial Culture'}[0.604, 0.198, 0.639, 0.221], and {'percentage for Technological Infrastructure'}[0.419, 0.120, 0.454, 0.143]]. I can extract these values and construct the CSV data using the labels denoting the innovation indicators as rows and the percentages as the matching values.\nBased on the above thought, the answer is:\nInnovation Indicator, Percentage (%)\nPatents,7.2\nScientific Publications,3.5\nR&D Personnel,8.8\nStartup Creation,9.3\nVenture Capital Investment,6.9\nCollaboration in Innovation,4.7\nKnowledge Transfer,8.1\nBusiness Expenditures on R&D,11.5\nEntrepreneurial Culture,5.8\nTechnological Infrastructure,2.7"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "VM5VYPqCl3B9nOHU",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the displayed chart, which area in the entrepreneurship ecosystem needs the most attention and improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "The areas needing the most attention in the entrepreneurship ecosystem would be the ones with lower percentages, as these percentages represent their contribution to the ecosystem. By examining the {bars_of_group_0} corresponding to the y-labels, the smallest bar corresponds to 'Technological Infrastructure', which is placed at {'Technological Infrastructure'}[0.055, 0.122, 0.248, 0.146] with its length indicating a percentage of {'2.7%'}[0.419, 0.120, 0.454, 0.143]. Therefore, 'Technological Infrastructure' is the area that requires the most improvement.\nBased on the above thought, the answer is:\nTechnological Infrastructure. As the percentage indicates that it contributes only 2.7% to the Entrepreneurship and Innovation Ecosystem, which is the smallest proportion as compared to all other Innovation Indicators."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "AxGT64OywPE4oJLt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the highest contribution towards the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the bar with the longest x-axis value. Looking at all the {labels with percentages}, I can see that the longest one is {'Business Expenditures on R&D'}[0.944, 0.276, 0.988, 0.299] which shows '11.5%', and this is the indicator with the highest contribution to the Entrepreneurship and Innovation Ecosystem.\nBased on the above thought, the answer is:\n11.5%"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "epWBN11SW7q3oNaj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the perceived data from the horizontal bar chart into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "Following the labels on the Y-Axis which represents various social issues in order from \"{Climate Change}\"<r>[0.080, 0.844, 0.191, 0.861]</r> to \"{Human Rights}\"<r>[0.093, 0.116, 0.191, 0.134]</r>, we can observe that there are 9 social issues being discussed in the data. In the horizontal bar chart, each bar's length corresponds to the frequency of discussion on the respective issue which I can observe from the X-Axis range from \"{0}\"<r>[0.196, 0.939, 0.205, 0.957]</r> to \"{10}\"<r>[1.001, 0.939, 1.019, 0.957]</r>. The frequency percentage for each issue can be derived as per the length of respective bars associated with the social issues. For instance, the bar of \"{Climate Change}\"<r>[0.201, 0.814, 0.565, 0.887]</r> occupies an area from 0 to around 4.5 implying a frequency of 4.5% against climate change. I would follow similar analysis for all issues to extract the frequency data.\nBased on the above thought, the answer is:\nSocial_Issue,Frequency(%)\nClimate_Change,4.5\nWaste_Management,5.2\nFair_Trade,5.7\nSustainable_Packaging,6.3\nRenewable_Energies,6.8\nDiversity_Inclusion,7.0\nGender_Equality,7.6\nAnimal_Welfare,8.5\nHuman_Rights,9.2"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "0lRQd749ewnSFQqx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you interpret from the distribution of frequency pertaining to discussions on different social issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution of frequencies against all social issues indicates that the discussion on social issues gradually intensifies from \"{Climate Change}\"<r>[0.201, 0.814, 0.565, 0.887]</r> to \"{Human Rights}\"<r>[0.201, 0.087, 0.945, 0.159]</r>. This can be observed from the increase in bar lengths for each issue from Climate Change (4.5%) to Human Rights (9.2%). For instance, issues such as \"{Gender_Equality}\"<r>[0.201, 0.269, 0.816, 0.341]</r> and \"{Animal_Welfare}\"<r>[0.201, 0.178, 0.889, 0.250]</r> occupy more space on the X-Axis, indicating a higher discussion frequency about them as compared to issues like Climate Change and \"{Waste_Management}\"<r>[0.201, 0.723, 0.622, 0.796]</r>. This could mean that while all issues are important, some issues including Human Rights and Animal Welfare might currently be more pressing or in-focus in discussions around social responsibility and sustainable business than others.\nBased on the above thought, the answer is:\nThe distribution of frequencies suggests growing importance on issues related to Human Rights, Animal Welfare and Gender Equality over other issues. However, given the nature and importance of all these issues, it also suggests a need for more balanced discussions across all issues rather than a few specific ones."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "i31FapBDADaEfByn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest percentage difference between consecutive social issues discussed in terms of frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question is about the difference in frequency percentages between every two successive social issues. Starting from the first issue \"{Climate Change}\"<r>[0.201, 0.814, 0.565, 0.887]</r>, with a frequency of 4.5%, followed by \"{Waste Management}\"<r>[0.201, 0.723, 0.622, 0.796]</r> that shows a difference of 0.7% (5.2-4.5) and so on. By comparing the differences between consecutive issues one by one, we can find that the difference between \"{Gender Equality}\"<r>[0.201, 0.269, 0.816, 0.341]</r> (7.6%) and \"{Animal Welfare}\"<r>[0.201, 0.178, 0.889, 0.250]</r> (8.5%) is 0.9%, which is the largest difference among all consecutively mentioned social issues.\nBased on the above thought, the answer is:\n0.9%"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "gywDQezKOKxuqUuP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar graph figure you see, can you input the data into a structured table using CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "I comprehend that the chart represents the main causes of US deaths and their percentile contribution to the total deaths. It is possible to ascertain information using different elements of the chart. For instance, the title of the chart {\"Main Causes of Death in US Population\"}[0.455, 0.019, 0.858, 0.041] clearly describes what the chart is about. The x-axis label {\"Percentage of Total Deaths (%)\"}[0.522, 0.964, 0.791, 0.981] represents the percentage contribution to the total deaths, and the y-axis label {\"Cause\"}[0.019, 0.460, 0.036, 0.514] displays the different causes of death. Different bars in the chart represent different causes of death like {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887], {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], {\"Accidents\"}[0.355, 0.658, 0.512, 0.724] and so on. Lastly, the values on top of each bar show the corresponding percentage value, for instance \" 23.1%\"[0.929, 0.846, 0.989, 0.863] represents the percentage of total deaths caused by Heart Disease.\nBased on the above thought, the answer is:\nDisease,Percentage\nHeart Disease,23.1\nCancer,22.5\nAccidents,6.3\nChronic lower respiratory diseases,5.6\nStroke,5.1\nAlzheimer's disease,4.5\nDiabetes,3.3\nPneumonia,2.6\nKidney Disease,1.8\nSuicide,1.7"
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "qcvqCx8lZLp2jm1l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were going to reduce death rates based on the chart data, which two causes would you focus on first?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I were going to reduce death rates based on this chart, I would concentrate on lowering the rates of the two causes of death with the highest percentages. These are {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887] and {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], which are responsible for \"23.1%\"[0.929, 0.846, 0.989, 0.863] and \"22.5%\"[0.914, 0.764, 0.974, 0.782] of total deaths respectively. This strategy would have the largest impact on total death rates, as these two categories together make up almost half of all deaths.\nBased on the above thought, the answer is:\nI would focus on Heart Disease and Cancer as they are the leading causes of death and therefore addressing these two would have the biggest impact on the total death rate."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "EOcT1wBLXNFaGPyR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the difference between the percentage values of the two leading causes of death?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the data from {\"Heart Disease\"}[0.355, 0.822, 0.929, 0.887] with that of {\"Cancer\"}[0.355, 0.740, 0.914, 0.805], it's visible that Heart Disease leads with \"23.1%\"[0.929, 0.846, 0.989, 0.863] and Cancer follows with \"22.5%\"[0.914, 0.764, 0.974, 0.782]. So, the difference between them can be calculated.\nBased on the above thought, the answer is:\n0.6"
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "XA4M7ucnbZeLA5Jm",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title saying {\"Internet and Communication Services\"}<r>[0.019, 0.309, 0.036, 0.637]</r>, I can infer that different internet services are being measured. The y-axis is labeled with different internet and communication services such as {e-Library}<r>[0.146, 0.087, 0.224, 0.104]</r>. The x-axis is labeled {\"Utilization rate (%)\"}<r>[0.523, 0.964, 0.687, 0.981]</r>, which means the bars represent the percentage of how widely each service is used. The length of each bar also corresponds to the actual percentage value that is annotated at the end of each bar. For example, the {e-Library bar}<r>[0.236, 0.060, 0.447, 0.127]</r> ends around the number 6 on the x-axis, and there is an accompanying label of {\"6%\"}<r>[0.447, 0.085, 0.480, 0.102]</r> at the end of the bar, so the utilization rate of e-Library is 6.\nBased on the above thought, the answer is:\nServices,Utilization rate (%)\nInternet Applications,20\nChat Room,20\nEmail,19\ne-Business,17\nVoIP,17\nInternet Games,12\nVideo Conferencing,11\nInternet TV,10\nTelecommuting,8\ne-Library,6"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "00njyyDg6s3zL4yN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you infer which services are more often used by people and thus might be vital to them?"
+            },
+            {
+                "from": "gpt",
+                "value": "When I look at this bar chart, I can infer from the chart that the longer the bar of a service, the higher the adoption rate, which might indicate its relative importance to users. According to the bar lengths, {Internet Applications}<r>[0.236, 0.818, 0.939, 0.886]</r> and {Chat Room}<r>[0.236, 0.734, 0.939, 0.802]</r> bars extend further along the x-axis, touching the 20 level, which suggests that these two services are used most frequently by people. Conversely, services like {e-Library}<r>[0.236, 0.060, 0.447, 0.127]</r> and {Telecommuting}<r>[0.236, 0.144, 0.517, 0.212]</r> have bars ending at lower values on the x-axis, indicating that these services are less frequently used by people.\nBased on the above thought, the answer is:\nThe services that are more often used by people and thus might be vital to them are Internet Applications and Chat Room, as they have the highest usage rates according to the chart. Conversely, services like e-Library and Telecommuting have lower adoption rates."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "kIpNA9uNClu94j5z",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service has the highest usage rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the service with the highest usage rate, I need to locate the bar with the highest value on the x-axis. Moving from bottom to top, I see the initial bar for {e-Library}<r>[0.236, 0.060, 0.447, 0.127]</r> ends at 6, next is {Telecommuting}<r>[0.236, 0.144, 0.517, 0.212]</r> at 8, and so on. The longest bars are for {Internet Applications}<r>[0.236, 0.818, 0.939, 0.886]</r> and {Chat Room}<r>[0.236, 0.734, 0.939, 0.802]</r>, both ending around the 20 level on the x-axis.\nBased on the above thought, the answer is:\nInternet Applications and Chat Room."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "g57TEvZdkqKFLjPC",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, extract and present the sales data in table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart lists the sales of various industries in thousands of dollars. The industries are plotted along the y-axis and are represented by rectangles whose x-coordinates represent sales data. For example, the sales data for the {industry: 'Information Technology'}<r>[0.301, 0.811, 0.831, 0.886]</r> can be extracted to be 25000 by correlating the end x-coordinate of the rectangle to the 'Sales' label on the x-axis. Repeating this operation for each rectangle in the bars list will generate table data for all the industries.\nBased on the above thought, the answer is:\nIndustry, Sales\nInformation Technology, 25000\nBiotechnology, 22000\nSemiconductors, 20000\nSoftware, 30000\nCommunications Equipment, 18000\nTelecommunications, 21000\nMedia, 23000\nPharmaceuticals, 27000\nData processing, 28000"
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "cnaxe40QQ2ohVDSM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which industry may need more investment based on sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Typically, industries with lower sales may need more investment for improvement. By looking at the bar chart, it is evident that the industry with the lowest sales volume, represented by the shortest bar, is {'Communications Equipment'}<r>[0.301, 0.435, 0.682, 0.510]</r>. Its end x-coordinate relates to a 'Sales' value of 18000 which is lower than other industries in the chart. Therefore, the 'Communications Equipment' industry may need more investment.\nBased on the above thought, the answer is:\nCommunications Equipment."
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "CxnktMoEozfPawDz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the highest sales volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "Through the bar chart, I can detect sales data by examining the x-coordinates of each rectangle in the bar list. The industry with the highest sales overflows from the x-plot that the rectangle with longest length. That is the bar of {'Software'}<r>[0.301, 0.529, 0.937, 0.604]</r> reached the most right position in the x-axis which corresponds to about 30000 in 'Sales'. Therefore, the industry with the highest sales volume is 'Software'.\nBased on the above thought, the answer is:\nSoftware"
+            }
+        ],
+        "id_raw": 815
+    },
+    {
+        "id": "khh85OKGueD869yZ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn a CSV format table, can you extract and display the data regarding the probability of future scenarios depicted in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on what I can notice on the chart, the title {\"Probability of Future Scenarios\"}[0.507, 0.015, 0.760, 0.033] signifies what the chart is about. The y-axis ticks are labeled with future scenarios and the x-axis ticks with probabilities. By calculating the length of each bar, I can precisely determine the probability associated with each scenario. For instance, the length of {the bar for \"Telework\"}[0.281, 0.826, 0.951, 0.901] equals to 25.0 according to the annotation {\"25.0\"}[0.951, 0.856, 0.982, 0.870] placed at the end of this bar. Thus, the probability for \"Telework\" is 25.0.\nBased on the above thought, the answer is:\nScenario,Probability(%)\nTelework,25.0\nE-learning,22.8\nVirtual Reality Entertainment,20.6\nAutomated Transport,18.3\nArtificial Intelligence in Household,14.2\nRemote Healthcare,12.0\nRobot Companions,8.7\nTime Travel,3.5\nLiving on Mars,2.1"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "OO9ipWBY810Boee1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the probabilities represented in the chart, which future scenarios do you think people believe are most likely to happen?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the chart, the bars with the longest lengths represent the scenarios that people think are most likely to happen. The bar for {Telework}[0.281, 0.826, 0.951, 0.901] has an approximate length of 25.0, the longest of all, indicating that it's considered the most probable scenario. Following that, the bar for {E-Learning}[0.281, 0.732, 0.893, 0.807] has the second-longest length, approximate 22.8, leading us to believe it's the next most probable scenario. Lastly, the third most probable scenario is {Virtual Reality Entertainment}[0.281, 0.639, 0.834, 0.714], with an approximate length of 20.6.\nBased on the above thought, the answer is:\nPeople believe that 'Telework', 'E-learning', and 'Virtual Reality Entertainment' are the most probable future scenarios."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "PKs0vcvq1K4nYsq3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data represented in the bar chart, what is the probability difference between the highest probable and lowest probable future scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the lengths of all the bars. The longest bar is representing \"Telework\" - {[0.281, 0.826, 0.951, 0.901]} with a value of 25.0 % according to the annotation {\"25.0\"}[0.951, 0.856, 0.982, 0.870]. The shortest bar corresponds to \"Living on Mars\" - {[0.281, 0.078, 0.338, 0.153]} with a value of 2.1 % according to the annotation {\"2.1\"}[0.338, 0.109, 0.360, 0.123]. Subtracting the shortest scenario's probability from the longest one would give me the difference.\nBased on the above thought, the answer is:\n22.9%"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "R0gSDCXDhIHhsiW4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you lay out the data from the horizontal bar chart into a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 10 bars on the chart, each representing a different social element with a particular value attached to it. Each value can be found beside the corresponding bar. For example, the {\"Number of Friends\"}<r>[0.102, 0.848, 0.262, 0.865]</r> bar begins near the y-axis and extends to a point that corresponds to a value of 20, which is confirmed by the associated text {\" 20\"}<r>[0.723, 0.846, 0.751, 0.863]</r>. Similarly, the {\"Number of Active Friends\"}<r>[0.043, 0.766, 0.262, 0.783]</r> bar extends to a corresponding value of 15.5 and so on. Accordingly, I can construct the CSV table.\nBased on the above thought, the answer is:\nSocial Element,Value\nNumber of Friends,20\nNumber of Active Friends,15.5\nMessages Sent,25.7\nMessages Received,28.6\nVideo Calls,30\nText Conversations,18.3\nGroup Chats Participated,22.2\nSocial Events Attended,14.4\nRelationship Initiation,9.6\nRelationship Termination,6.1"
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "DUMDpn3sSI4U4sFv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social element, from the ones depicted in the chart, is not being adequately addressed and needs more attention?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart from bottom to top, I am reasoning the inverse length of each bar with its corresponding social element. The bar with the least length corresponds to the aspect that is being the least represented and hence, requires more focus. I see the bar representing {\"Relationship Termination\"}<r>[0.048, 0.112, 0.262, 0.130]</r> ends before the 10 mark on the x-axis, and the corresponding value is {\" 6.1\"}<r>[0.411, 0.111, 0.444, 0.128]</r>. I also cross-verified with other bars, all the other bars extend beyond the 10 mark. The \"Relationship Termination\" is not being adequately represented and may need more attention.\nBased on the above thought, the answer is:\nRelationship Termination."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "CYWOc6KzyeitlIGF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value represented by the social elements, according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the highest value, my aim is to identify the social element bar that extends the furthest to the right. The {\"Video Calls\"}<r>[0.166, 0.521, 0.262, 0.538]</r> bar reaches the furthest, going beyond the 25 mark along the x-axis. With the value text {\" 30\"}<r>[0.948, 0.519, 0.975, 0.536]</r>, I determine that \"Video Calls\" has the highest value, and it is 30.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "0rPndQ2ZZnCgyXHM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data represented by the bar chart into a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal bars on the chart represent various economic indicators, with their lengths proportional to the respective indicator's value. Looking at {GDP}<r>[0.321, 0.811, 0.656, 0.886]</r>, the corresponding numerical value shows {\"4.1\"}<r>[0.656, 0.840, 0.684, 0.857]</r>. Similarly, we can observe for {Inflation}<r>[0.321, 0.717, 0.501, 0.792]</r> with {\"2.2\"}<r>[0.501, 0.746, 0.528, 0.763]</r> and so on for all the indicators including Unemployment, Interest Rates, Stock Market, Trade Balance, Currency, Housing, and Consumer Confidence. Now, by using the y-axis labels and corresponding annotations, we can tabulate these visual data into a CSV format.\nBased on the above thought, the answer is:\nIndicator,Value\nGDP,4.1\nInflation,2.2\nUnemployment,6.1\nInterest Rates,0.25\nStock Market,7.6\nTrade Balance,-0.5\nCurrency,3.4\nHousing,1.8\nConsumer Confidence,1.2"
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "LYPC7AiheJWwSPX5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, which economic indicator appears to be in a promising state, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart requires consideration of both, the type of indicator and associated values. Positive indicators like {GDP}<r>[0.321, 0.811, 0.656, 0.886]</r>, {Stock Market}<r>[0.321, 0.435, 0.943, 0.510]</r>, and {Housing}<r>[0.321, 0.154, 0.468, 0.229]</r> are desirable when they are high. On the other hand, lower values of negative indicators, such as {Unemployment}<r>[0.321, 0.623, 0.820, 0.698]</r> and {Inflation}<r>[0.321, 0.717, 0.501, 0.792]</r>, signify a promising state. Looking at these in specific, the Stock Market with a score of {\"7.6\"}<r>[0.943, 0.464, 0.970, 0.482]</r> performs significantly well among the positive indicators, and Unemployment is fairly controlled with a score of {\"6.1\"}<r>[0.820, 0.652, 0.848, 0.669]</r> among the negative indicators.\nBased on the above thought, the answer is:\nThe Stock Market appears to be in a promising state, given its high value of 7.6, being consistent with the positive nature of the indicator. A robust stock market typically signifies healthy investor confidence and economic growth."
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "bOxvs66ggHYZNLIN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator has shown the highest value, as per the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the highest value from the graph, we need to compare the lengths of horizontal bars under each category. {GDP}<r>[0.321, 0.811, 0.656, 0.886]</r> extends up to 4.1 as per annotation {\"4.1\"}<r>[0.656, 0.840, 0.684, 0.857]</r>, {Unemployment}<r>[0.321, 0.623, 0.820, 0.698]</r> extends up to 6.1, and {Stock Market}<r>[0.321, 0.435, 0.943, 0.510]</r> extends up to 7.6 as indicated by {\"7.6\"}<r>[0.943, 0.464, 0.970, 0.482]</r>. Similarly for other indicators. By comparing these, we can find the highest value.\nBased on the above thought, the answer is:\nStock Market"
+            }
+        ],
+        "id_raw": 818
+    },
+    {
+        "id": "UTB0Dl03ySH6Tvbk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information you've observed on the horizontal bar chart, can you convert this data into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart comprises of a horizontal bar plot with differing lengths representing different percentages associated with several activities. From the {xlabel}[0.504, 0.964, 0.637, 0.981] and the {ylabel}[0.019, 0.440, 0.036, 0.506], I understand that the x-axis represents 'Percentage (%)' and y-axis represents 'Activity'. The activities ranging from 'Exercise' to 'Doing_Nothing' are listed on the Y-axis as indicated by the {yticks}<r>[{\"Exercise\"}[0.099, 0.845, 0.171, 0.863]; {\"Doing_Nothing\"}[0.043, 0.087, 0.171, 0.105]]</r>. The percentage corresponding to these activities, can be obtained from the information embedded within each {bar}<r>[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]]</r> and by reading the value from the {text_coordinates}<r>[{\"Exercise\"}[0.921, 0.843, 0.981, 0.861]; {\"Doing_Nothing\"}[0.258, 0.085, 0.307, 0.102]]</r>.\nBased on the above thought, the answer is:\nActivity,Percentage (%)\nExercise,32.5\nSocializing,28.0\nReading,22.1\nGaming,17.8\nCooking,14.4\nTraveling,12.0\nWorking,10.4\nStudying,8.5\nWatching_TV,4.6\nDoing_Nothing,3.3"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "GdoXUVVF5eXpbj65",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a broad analysis of the general lifestyle trends based on the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a depiction of the lifestyle trends of individuals based on percentage participation in diverse activities. Leveraging the information from the {bars}<r>[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Socializing\"}[0.183, 0.734, 0.819, 0.802]; {\"Watching_TV\"}[0.183, 0.144, 0.288, 0.212]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]]</r> it's clear that activities like 'Exercise' and 'Socializing' have the highest percentages, signifying high engagement levels and an inclination towards health and social activities. Intermediate level activities include 'Reading', 'Gaming',  and 'Cooking' which signify a balanced engagement in solitary activities. Lower level activities such as 'Watching_TV' and 'Doing_Nothing' depict a lower percentage of engagement indicating a lesser inclination towards passive activities. The exception in this trend is 'Working', which is surprisingly less engaged-in as compared to 'Traveling' and 'Cooking', indicating a possible trend towards work-life balance or predominance of non-working population in the survey.\nBased on the above thought, the answer is:\nThe general lifestyle trends indicate a high engagement in physical activities and socializing, a balanced engagement in solitary pursuits like reading, gaming, and cooking, and a lower inclination towards passive activities such as watching TV and doing nothing. There is also a surprising lesser engagement in work compared to traveling and cooking, suggesting a trend towards work-life balance or a predominance of non-working population in the survey."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "vYFMpOrqZKW0jnqW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has the lowest participation percentage according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to examine the length of each horizontal bar, which represents the corresponding participation percentage for each activity. Visually comparing the {bars}<r>[{\"Exercise\"}[0.183, 0.818, 0.921, 0.886]; {\"Doing_Nothing\"}[0.183, 0.060, 0.258, 0.127]]</r>,  I see that the smallest bar corresponds with the 'Doing_Nothing' activity at the bottom of the graph. Additionally, this is supported by observing the {text_coordinates}<r>[{\"Doing_Nothing\"}[0.258, 0.085, 0.307, 0.102]]</r> which shows a value of 3.3%. Hence, the activity with the lowest participation percentage is 'Doing_Nothing'.\nBased on the above thought, the answer is:\nDoing Nothing."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "yKBiySC8EmeQikzi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the chart's title {\"Investment Trends in Venture Capital Markets\"}[0.394, 0.015, 0.773, 0.033], and based on the x-axis labels ranging from 0 to 900 and the y-axis labels, it seems that the x-axis represents the millon dollars investment made in different industries. Each bar in the chart corresponds to a specific investment amount on the x-axis for a certain industry on the y-axis. For example, the bar for {the industry \"Fintech\"}[0.122, 0.862, 0.172, 0.876] extends to approximately 780 on the x-axis. Thereby, by checking each bar's coordinate and its corresponding label, we can succesfully convert the chart data into a CSV table.\nBased on the above thought, the answer is:\nIndustry,Investment_Million_Dollars\nFintech,780\nAI/ML,600\nHealthCare,550\nEcommerce,500\nMobility,375\nEnterprise Software,320\nManufacturing,280\nEducation,230\nAR/VR,130\nGaming,80"
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "9vj5SQ0BCoIVA34t",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industries seem to be the most and the least attractive for investors, according to the bar chart? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the bar lengths. Bars with greater lengths {like Fintech}<r>[0.182, 0.833, 0.947, 0.901]</r> and {AI/ML}<r>[0.182, 0.750, 0.770, 0.817]</r> suggest more investment, meaning that these industries are more attractive for investors. Fintech has the greatest investment, around 780 million dollars, suggesting it is the most attractive industry for investors. Conversely, shorter bars {like AR/VR}<r>[0.182, 0.162, 0.309, 0.229]</r> and {Gaming}<r>[0.182, 0.078, 0.260, 0.146]</r> indicate less investment. Among all, Gaming is with the smallest investment, around 80 million dollars, which suggests it to be the least attractive industry for investors.\nBased on the above thought, the answer is:\nThe most attractive industry for investors seems to be Fintech due to the highest investment it receives, and the least attractive seems to be Gaming as it receives the lowest investment."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "xUwGWZhDIhnULgFi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has received the least amount of investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart, I need to identify which bar is the shortest, indicating the smallest investment. Starting from the top bar, which represents {the Fintech industry}[0.122, 0.862, 0.172, 0.876] and has an investment of about 780 million dollars, and moving downwards, I can see that the bar's length gradually decreases until I get to the very last bar, representing {the Gaming industry}[0.117, 0.106, 0.172, 0.120]. The length of this bar indicates that the investment was about 80 million dollars, which seems to be the lowest investment of all industries.\nBased on the above thought, the answer is:\nGaming"
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "fpJ5SwkqRAFAxlxr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract and layout the provided data in a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, I can see that each data point represents an office location and its corresponding visitation percentage. The location of each office is marked on the y-axis, and the corresponding visitation rate is displayed as the length of a horizontal bar starting from the left side of the chart and extending to the right. For instance, the {Shanghai Office}<r>[0.185, 0.831, 0.936, 0.900]</r> has the longest bar ending at approximately 75.6%, which matches with the text {75.6%}<r>[0.936, 0.858, 0.984, 0.872]</r> to the right side of the bar. Following the same process, I will convert all the office locations and their corresponding visitation percentage into a structured CSV format.\nBased on the above thought, the answer is:\nLocation,Visitation(%)\nShanghai Office,75.6\nBeijing Office,62.4\nTokyo Office,48.2\nSeoul Office,44.3\nSingapore Office,37.5\nBangkok Office,32.8\nJakarta Office,29.1\nManila Office,26.3\nKuala Lumpur Office,22.5\nHanoi Office,15.9"
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "XpFHc2z2TCp8PzNr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which office has shown a significant outlier in terms of visitation percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify an outlier, we need to assess how significantly any given bar deviates from the other bars in the chart. Visually, the {Beijing Office}<r>[0.185, 0.745, 0.805, 0.814]</r> shows as an outlier with its visitation rate significantly higher than those offices ranked after it, and fairly comparable to the {Shanghai Office}<r>[0.185, 0.831, 0.936, 0.900]</r> which has the highest visitation. This discrepancy in visitation among similar ranked offices suggests that the Beijing Office could be an outlier in terms of the visitation pattern observed here.\nBased on the above thought, the answer is:\nBeijing Office."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "uVChs9GuamwDgsjC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in visitation between the most visited and the least visited office?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to identify the most visited office and the least visited office and then compute the difference between their visitation percentages. The {bar for the Shanghai Office}<r>[0.185, 0.831, 0.936, 0.900]</r> extends all the way to the level marked as 75.6% while the {bar for the Hanoi Office}<r>[0.185, 0.057, 0.343, 0.126]</r> ends close to the level marked as 15.9%. Extracting these values from the bars, I can compute the difference between these two percentages.\nBased on the above thought, the answer is:\n59.7%"
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "jiymWBVJ0WEj7AxW",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data showed in the chart in the format of a table, with each row contains an investment strategy and the corresponding return rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {ylabel}<r>[0.019, 0.398, 0.036, 0.576]</r> and {xticks}<r>[{\"label\": \"0\", \"label_coordinates\": \"[0.223, 0.939, 0.234, 0.957]\", \"tick_coordinates\": \"[0.229, 0.927]\"}, {\"label\": \"2\", \"label_coordinates\": \"[0.333, 0.939, 0.344, 0.957]\", \"tick_coordinates\": \"[0.338, 0.927]\"}, {\"label\": \"4\", \"label_coordinates\": \"[0.442, 0.939, 0.453, 0.957]\", \"tick_coordinates\": \"[0.447, 0.927]\"}, {\"label\": \"6\", \"label_coordinates\": \"[0.551, 0.939, 0.562, 0.957]\", \"tick_coordinates\": \"[0.557, 0.927]\"}, {\"label\": \"8\", \"label_coordinates\": \"[0.661, 0.939, 0.672, 0.957]\", \"tick_coordinates\": \"[0.666, 0.927]\"}, {\"label\": \"10\", \"label_coordinates\": \"[0.764, 0.939, 0.787, 0.957]\", \"tick_coordinates\": \"[0.776, 0.927]\"}, {\"label\": \"12\", \"label_coordinates\": \"[0.874, 0.939, 0.896, 0.957]\", \"tick_coordinates\": \"[0.885, 0.927]\"}, {\"label\": \"14\", \"label_coordinates\": \"[0.983, 0.939, 1.005, 0.957]\", \"tick_coordinates\": \"[0.994, 0.927]\"}]</r>, I know the vertical axis represents the investment strategies and the horizontal axis represents the return rates. The {bar chart}<r>[{\"bar_coordinates\": \"[0.229, 0.822, 0.923, 0.887]\", \"text_coordinates\": \"[0.923, 0.846, 0.983, 0.863]\", \"text_params\": {\"s\": \" 12.7%\", \"color\": \"black\"}, \"y_label\": \"High Risk\", \"x_range\": \"0.00 -> 12.70\"}, {\"bar_coordinates\": \"[0.229, 0.740, 0.699, 0.805]\", \"text_coordinates\": \"[0.699, 0.764, 0.748, 0.782]\", \"text_params\": {\"s\": \" 8.6%\", \"color\": \"black\"}, \"y_label\": \"Low Risk\", \"x_range\": \"0.00 -> 8.60\"}, {\"bar_coordinates\": \"[0.229, 0.658, 0.573, 0.724]\", \"text_coordinates\": \"[0.573, 0.682, 0.623, 0.700]\", \"text_params\": {\"s\": \" 6.3%\", \"color\": \"black\"}, \"y_label\": \"Hedging\", \"x_range\": \"0.00 -> 6.30\"}, {\"bar_coordinates\": \"[0.229, 0.577, 0.524, 0.642]\", \"text_coordinates\": \"[0.524, 0.601, 0.573, 0.618]\", \"text_params\": {\"s\": \" 5.4%\", \"color\": \"black\"}, \"y_label\": \"Diversification\", \"x_range\": \"0.00 -> 5.40\"}, {\"bar_coordinates\": \"[0.229, 0.495, 0.672, 0.560]\", \"text_coordinates\": \"[0.672, 0.519, 0.721, 0.536]\", \"text_params\": {\"s\": \" 8.1%\", \"color\": \"black\"}, \"y_label\": \"Growth Investing\", \"x_range\": \"0.00 -> 8.10\"}, {\"bar_coordinates\": \"[0.229, 0.413, 0.661, 0.479]\", \"text_coordinates\": \"[0.661, 0.437, 0.710, 0.455]\", \"text_params\": {\"s\": \" 7.9%\", \"color\": \"black\"}, \"y_label\": \"Value Investing\", \"x_range\": \"0.00 -> 7.90\"}, {\"bar_coordinates\": \"[0.229, 0.332, 0.726, 0.397]\", \"text_coordinates\": \"[0.726, 0.356, 0.776, 0.373]\", \"text_params\": {\"s\": \" 9.1%\", \"color\": \"black\"}, \"y_label\": \"Income Investing\", \"x_range\": \"0.00 -> 9.10\"}, {\"bar_coordinates\": \"[0.229, 0.250, 0.644, 0.315]\", \"text_coordinates\": \"[0.644, 0.274, 0.694, 0.291]\", \"text_params\": {\"s\": \" 7.6%\", \"color\": \"black\"}, \"y_label\": \"Small-Cap Investing\", \"x_range\": \"0.00 -> 7.60\"}, {\"bar_coordinates\": \"[0.229, 0.168, 0.519, 0.234]\", \"text_coordinates\": \"[0.519, 0.192, 0.568, 0.210]\", \"text_params\": {\"s\": \" 5.3%\", \"color\": \"black\"}, \"y_label\": \"Market Timing\", \"x_range\": \"0.00 -> 5.30\"}, {\"bar_coordinates\": \"[0.229, 0.087, 0.431, 0.152]\", \"text_coordinates\": \"[0.431, 0.111, 0.480, 0.128]\", \"text_params\": {\"s\": \" 3.7%\", \"color\": \"black\"}, \"y_label\": \"Bond Investing\", \"x_range\": \"0.00 -> 3.70\"}]</r> contains the exact return rates of each investment strategy. By using the labels and data of each bar in the chart, I can convert the chart into a table.\nBased on the above thought, the answer is:\nInvestment Strategy,Return Rate(%)\nHigh Risk,12.7\nLow Risk,8.6\nHedging,6.3\nDiversification,5.4\nGrowth Investing,8.1\nValue Investing,7.9\nIncome Investing,9.1\nSmall-Cap Investing,7.6\nMarket Timing,5.3\nBond Investing,3.7"
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "5UMZ3C5nF3lqrFdf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you reconstruct the data displayed in the bar chart into a CSV format file?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the title}[0.556, 0.008, 0.556, 0.008] at the top of the chart, and x-axis tick labels from {0}[0.139, 0.939, 0.150, 0.957] to {10}[1.036, 0.939, 1.058, 0.957], as well as the y-axis tick labels from {\"Facebook\"}[0.050, 0.845, 0.132, 0.863] to {\"Tumblr\"}[0.073, 0.087, 0.132, 0.104], I can conclude that the y-axis represents different social networks and the x-axis represents their respective engagement performances. The values can be obtained by observing the lengths of the horizontal bars and the textual annotations at the end of each bar. For instance, the bar for Facebook from [0.144, 0.818, 0.930, 0.886] ends roughly at the position of 8.7 on the x-axis, and there is also a text annotation \"8.7%\" at the end of the bar [0.930, 0.843, 0.979, 0.861], so the performance of Facebook is 8.7%.\nBased on the above thought, the answer is:\nSocial_Network,Performance\nFacebook,8.7\nTwitter,7.5\nInstagram,7.3\nLinkedIn,6.8\nSnapchat,6.5\nReddit,5.0\nPinterest,4.3\nWhatsApp,4.1\nWeChat,4.0\nTumblr,2.5"
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "ojKz2azvC31M8Piq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social network is winning at user engagement and which one is losing? Provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires interpreting the chart and comparing the lengths of the bars associated with each social network. Identifying the social network with the best user engagement is straightforward as it has the longest bar. In this case, {Facebook}[0.932, 0.881, 0.981, 0.899] stands out because it has the longest bar, almost spanning the length of the x-axis, indicating that it has the highest performance. On the other hand, {Tumblr}[0.366, 0.062, 0.415, 0.080] has the shortest bar, indicating the least user engagement among all the social networks listed.\nBased on the above thought, the answer is:\nFacebook is winning at user engagement because it has the longest bar, indicating the highest performance. Conversely, Tumblr is losing as it has the shortest bar, indicating the lowest performance."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "rx2MSsTtyLxfcAKt",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social network has the lowest performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the social network with the least performance, I need to compare the length of all the bars. As the smaller the bar length, the lower the performance. Looking from top to bottom, first is {Facebook}[0.144, 0.818, 0.930, 0.886] with a length roughly at 8.7, then {Twitter}[0.144, 0.734, 0.821, 0.802] roughly at 7.5, and so on until {Tumblr}[0.144, 0.060, 0.370, 0.127], which has a length roughly at 2.5 on the x-axis, and thus has the minimum performance among all social networks.\nBased on the above thought, the answer is:\nTumblr"
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "TCVFHwapP6zK6Hij",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart given, can you create a table in CSV format that reflects the share percentages of different platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart, the `Platforms` are placed on the y-axis which is apparent from labels present at the {left of the chart} <r>[0.040, 0.848, 0.106, 0.865]; [0.035, 0.766, 0.106, 0.783]; [0.040, 0.658, 0.106, 0.702]; [0.047, 0.603, 0.106, 0.620]; [0.059, 0.521, 0.106, 0.538]; [0.061, 0.439, 0.106, 0.457]; [0.035, 0.358, 0.106, 0.375]; [0.042, 0.276, 0.106, 0.293]; [0.063, 0.194, 0.106, 0.212]; [0.067, 0.112, 0.106, 0.130]</r>. Similarly, `shares per USER (%)` can be derived from the X-axis labeled at the {bottom} <r>[0.471, 0.964, 0.609, 0.981]</r>. The bars represent the share of different platforms like `Facebook, Instagram, Snapchat, LinkedIn, Twitter, Reddit, WhatsApp, Telegram, TikTok, Email`. The length of each bar from the y-axis to the right represents the `Shares per USER (%)`. CSV format can be derived by making a column for each variable.\nBased on the above thought, the answer is:\nPlatform, Shares per user (%)\nFacebook, 62.5\nInstagram, 67.25\nSnapchat, 54\nLinkedIn, 29.5\nTwitter, 42.25\nReddit, 35.5\nWhatsApp, 59\nTelegram, 29.75\nTikTok, 51.25\nEmail, 22.75"
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "rubyMlpeY3DkC7GJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the user sharing behavior across different social media platforms based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart with bars representing the sharing rate, we can get insights on the sharing behavior for different platforms. For instance, the {bar for Instagram}<r>[0.115, 0.740, 0.924, 0.805]</r> is the longest, which means it has the most content sharing. More traditional communication methods like {Email}<r>[0.115, 0.087, 0.389, 0.152]</r> and {LinkedIn}<r>[0.115, 0.577, 0.470, 0.642]</r> have shorter bars, indicating lesser sharing rates. So it can be inferred that users tend to share more content on social media platforms over traditional ones.\nBased on the above thought, the answer is:\nThe bar chart represents that social platforms like Instagram, Facebook and WhatsApp have higher shares per user, which indicates users are more active and engaging on these platforms. In contrast, platforms like LinkedIn, Telegram and Email have lesser sharing rates which suggest that these platforms could be used more for professional and more specific communications rather than frequent content sharing."
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "NAJ8UVw8hoBXcSUr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has the least sharing rate based on my observation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the platform with the least share rate, I need to compare the lengths of all the bars. The smallest bar on the chart is {the bar at the bottom of the chart}<r>[0.115, 0.087, 0.389, 0.152]</r> which refers to {the label \"Email\"}<r>[0.067, 0.112, 0.106, 0.130]</r> with a share rate of 22.75%.\nBased on the above thought, the answer is:\nEmail"
+            }
+        ],
+        "id_raw": 824
+    },
+    {
+        "id": "M8npdWGr53YGsyLF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV formatted table with the volume of items transported by airways for each of the seven years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart with the title {\"Transportation and Logistics Industry Trends Over Time\"}[0.214, 0.020, 0.786, 0.050] includes a subplot for airways {\"Airways\"}[0.493, 0.672, 0.575, 0.702], which has horizontal bars representing volumes for each year. To extract the raw data, I analyzed the length of the bars and correlated them to the x-axis labels showing the volume (in thousands). The y-axis shows each year represented by labeled ticks. For instance, the bar for Year 1 ends at [0.874] on the x-axis, which corresponds to a volume of 900 {\"Year 1\"}[0.043, 0.719, 0.096, 0.742]<r>[0.109, 0.718, 0.874, 0.738]</r>. Repeating this process for all years gives the full data set.\nBased on the above thought, the answer is:\nYear,Volume\nYear 1,900\nYear 2,670\nYear 3,800\nYear 4,500\nYear 5,730\nYear 6,130\nYear 7,620"
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "7dOZZFsdOVlDuPPN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich mode of transportation showed the most consistent growth over the seven years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess consistency in growth, I am observing all bars within the subplots for each mode of transportation: {Shipping}<r>[0.109, 0.117, 0.959, 0.311]</r>, {Rail}<r>[0.109, 0.413, 0.959, 0.607]</r>, and {Airways}<r>[0.109, 0.709, 0.959, 0.903]</r>. For consistent growth, we're looking for a regular increase in the length of the bars from year to year without significant fluctuations. By analyzing the bar charts, it's clear that {Airways}<r>[0.109, 0.709, 0.959, 0.903]</r> depicted the fewest deviations in bar length increase, except for the decrease visible in the 6th year. Since the datasets for Shipping and Rail show a larger variance including noticeable downturns in volumes in some years, it is evident that Airways demonstrated the most consistent growth despite its Year 6 drop.\nBased on the above thought, the answer is:\nAirways showed the most consistent growth over the seven years, with the exception of a drop in Year 6. However, its general trend appears steadier compared to the other modes of transportation."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "uXSd4d4HsjyWYHW4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the lowest volume transported by rail over the seven-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the lowest volume transported by rail, I am examining each bar in the subplot for rail {Rail}<r>[0.515, 0.376, 0.553, 0.406]</r>, and cross-referencing the end of each horizontal bar with the volume marks on the x-axis. By visually analyzing the bars, I identified that the shortest one corresponds to Year 6, with the end located approximately at [0.279] on the x-axis {Year 6}<r>[0.109, 0.552, 0.279, 0.572]</r>, which represents a volume of 200 according to the x-axis labels.\nBased on the above thought, the answer is:\n200"
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "Ai3iNQTHrxqGk9sF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the investments in each technology from the years 2021 to 2025 into a CSV format table using the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I examine each bar chart corresponding to the respective year. For the 2021 bar chart visible in the area {axes}<r>[0.213, 0.062, 0.981, 0.181]</r>, I note that there are three bars corresponding to Blockchain, AI, and Quantum Computing. I read the investment counts from the lengths of the bars for Blockchain {Blockchain 2021 bar}<r>[0.213, 0.068, 0.823, 0.098]</r>, AI {AI 2021 bar}<r>[0.213, 0.106, 0.701, 0.137]</r>, and Quantum Computing {Quantum Computing 2021 bar}<r>[0.213, 0.144, 0.945, 0.175]</r>. I continue this process for each subsequent year: 2022 in {axes}<r>[0.213, 0.251, 0.981, 0.369]</r>, 2023 in {axes}<r>[0.213, 0.440, 0.981, 0.558]</r>, 2024 in {axes}<r>[0.213, 0.628, 0.981, 0.747]</r>, and 2025 in {axes}<r>[0.213, 0.817, 0.981, 0.935]</r>, extracting the investment data accordingly.\nBased on the above thought, the answer is:\nYear,Blockchain,AI,Quantum Computing\n2021,500,400,600\n2022,480,420,580\n2023,460,430,570\n2024,430,440,500\n2025,400,450,480"
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "xJQ2hAxgvOCabqlF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the progression observed in the chart, which technology shows the most consistent upward trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the trend for each technology by locating the bars for Blockchain {Blockchain bars}<r>[0.213, 0.068, 0.823, 0.098]; [0.213, 0.256, 0.798, 0.287]; [0.213, 0.445, 0.774, 0.476]; [0.213, 0.634, 0.737, 0.664]; [0.213, 0.822, 0.701, 0.853]</r>, AI {AI bars}<r>[0.213, 0.106, 0.701, 0.137]; [0.213, 0.295, 0.725, 0.326]; [0.213, 0.483, 0.737, 0.514]; [0.213, 0.672, 0.749, 0.703]; [0.213, 0.861, 0.762, 0.892]</r>, and Quantum Computing {Quantum Computing bars}<r>[0.213, 0.144, 0.945, 0.175]; [0.213, 0.333, 0.920, 0.364]; [0.213, 0.522, 0.908, 0.553]; [0.213, 0.711, 0.823, 0.741]; [0.213, 0.899, 0.798, 0.930]</r>. Blockchain shows a declining trend, while Quantum Computing fluctuates. AI, however, indicates a minor increase from 2021 to 2025. This is deduced by comparing the relative lengths of the bars for each technology across the years, which reflect investment values.\nBased on the above thought, the answer is:\nAI shows the most consistent upward trend over the years. This positive trajectory is identified by the slight incremental growth in the length of the AI bars each year, despite the overall variances seen in the other technologies."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "HzDIERwPy596yy3z",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average yearly investment value for Quantum Computing over the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average investment value for Quantum Computing, I will sum the investment values from each year, read from the length of {Quantum Computing bars}<r>[0.213, 0.144, 0.945, 0.175]; [0.213, 0.333, 0.920, 0.364]; [0.213, 0.522, 0.908, 0.553]; [0.213, 0.711, 0.823, 0.741]; [0.213, 0.899, 0.798, 0.930]</r> and divide by the total number of years, which is 5. This yields the values 600, 580, 570, 500, and 480 for each respective year.\nBased on the above thought, the answer is:\n546"
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "13SljagbxTtnbUis",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data represented in the bar chart into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on what I have observed in the chart, I can see that it is mainly about the progress in Environmental Protection and Sustainable Development. Now I will start to convert the chart into a CSV format table. The x-axis corresponds to the year displayed from {2015}<r>[0.098, 0.939, 0.133, 0.957]</r> to {2020}<r>[0.718, 0.939, 0.753, 0.957]</r>, and the y-axis value ranges from {0}<r>[0.052, 0.920, 0.061, 0.938]</r> to {250}<r>[0.035, 0.040, 0.061, 0.057]</r>. The vertical bars represent the development of four different areas. The {legend}<r>[0.787, 0.055, 0.985, 0.165]</r> tells us that the blue color represents 'Carbon Footprint', the light green 'Forestation', the orange 'Green Energy', and the light orange 'Wildlife Conservation'. The length of these bars in the image corresponds to the value on the y-axis. For instance, the {Carbon Footprint in 2015}<r>[0.103, 0.575, 0.128, 0.927]</r> is about 100, by reading the y-axis based on the height of the bar.\nBased on the above thought, the answer is:\nYear,CarbonFootprint,Forestation,GreenEnergy,WildlifeConservation\n2015,100,15,22,35\n2016,95,20,25,33\n2017,90,25,30,32\n2018,92,30,35,30\n2019,85,34,45,31\n2020,70,42,60,28"
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "YNrxNnXn4Cq8O4X6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area had the least positive change over the years? Please explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the improvements in the four areas, I notice that the vertical bars that correspond to the different years show the different performances of each area. Let's look at the heights for each area from 2015 to 2020. From the {Carbon Footprint in 2015}<r>[0.103, 0.575, 0.128, 0.927]</r> to the {Carbon Footprint in 2020}<r>[0.723, 0.681, 0.748, 0.927]</r>, represented by the blue sections of the bars, we see a significant decrease in Carbon Footprint. In contrast, the heights of the light green sections, representing Forestation, show a noticeable increase from the {Forestation in 2015}<r>[0.103, 0.522, 0.128, 0.575]</r> to the {Forestation in 2020}<r>[0.723, 0.533, 0.748, 0.681]</r>. The increase in the size of the colored sections representing Green Energy from the {Green Energy section for 2015}<r>[0.103, 0.445, 0.128, 0.522]</r> to the {Green Energy section for 2020}<r>[0.723, 0.321, 0.748, 0.533]</r> is also significant. Looking at Wildlife Conservation, the level of the light orange colored sections depicting it shows an irregular fluctuating trend from {the one in 2015}<r>[0.103, 0.321, 0.128, 0.445]</r> to {the one in 2020}<r>[0.723, 0.223, 0.748, 0.321]</r>. The overall trend of Wildlife Conservation isn't clear if it's an improvement or a decrease, and the apparent lack of steady improvement can be seen as having the least positive change over the years.\nBased on the above thought, the answer is:\nWildlife Conservation. It does not show a consistent improvement as seen in the other three areas over the years."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "SWPbom5lymFGASpR",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Green Energy see its biggest increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to observe the height of the orange section of the bars, representing Green Energy, across different years. Comparing the height of the {Green Energy section for 2015}<r>[0.103, 0.445, 0.128, 0.522]</r> and the subsequent {Green Energy section for 2016}<r>[0.227, 0.434, 0.252, 0.522]</r>, I find that there's a slight increase. Continuously comparing the height differences between subsequent years, I find the biggest jump from the {Green Energy section for 2019}<r>[0.599, 0.350, 0.624, 0.508]</r> to the {Green Energy section for 2020}<r>[0.723, 0.321, 0.748, 0.533]</r>. So, the biggest increase in Green Energy came in the year 2020.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "NxTiG2foIOEeIWgj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a structured table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data from the bar chart can be compiled into a CSV table by extracting the values represented by each bar in every group. For example, in the year {\"2000\"}<r>[0.140, 0.939, 0.175, 0.957]</r>, the horizontal coordinator ranges of the groups are {\"Wind Turbine\"}<r>[0.103, 0.560, 0.130, 0.927]</r>, {\"Solar Cell\"}<r>[0.130, 0.780, 0.157, 0.927]</r>, {\"Hydro Power\"}<r>[0.157, 0.707, 0.184, 0.927]</r>, and {\"Thermal Power\"}<r>[0.184, 0.634, 0.211, 0.927]</r>. The percentage text on the left of each bar indicates the progress of each technology. From the text {\"50%\"}<r>[0.101, 0.510, 0.132, 0.527]</r>, {\"20%\"}<r>[0.264, 0.657, 0.295, 0.674]</r>, {\"30%\"}<r>[0.426, 0.657, 0.457, 0.674]</r>, and {\"40%\"}<r>[0.589, 0.363, 0.620, 0.381]</r> we can tell that in 2000 the progress of Wind Turbine is 50%, the Solar Cell is 20%, the Hydro Power is 30%, and the Thermal Power is 40%. Repeat this process for following years.\nBased on the above thought, the answer is:\nYear, Wind Turbine, Solar Cell, Hydro Power, Thermal Power \n2000,50%,20%,30%,40%\n2004,40%,30%,30%,50%\n2008,30%,40%,30%,60%\n2012,20%,60%,20%,70%\n2016,10%,80%,20%,90%"
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "wkHz6R2SM3NrRyMJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which clean energy technology developed the most rapidly over the period 2000-2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's x-axis represents the years, which range from {\"2000\"}<r>[0.140, 0.939, 0.175, 0.957]</r> to {\"2016\"}<r>[0.681, 0.939, 0.717, 0.957]</r>. The bar heights and corresponding percentage values allow us to compare the development progress made by each clean energy technology over this period. It is clear to see that the \"Solar Cell\" technology has experienced the most substantial change, starting at 20%<{\"20%\"}<r>[0.264, 0.657, 0.295, 0.674]</r> in the year 2000 and soaring to 80%<{\"80%\"}<r>[0.399, 0.583, 0.430, 0.601]</r> by 2016. In comparison, other clean energy technologies, such as \"Wind Turbine\", \"Hydro Power\", and \"Thermal Power\", have either seen less significant increases or, in the case of \"Wind Turbine\", have actually decreased over the same time period. Therefore, \"Solar Cell\" technology has developed the most rapidly over the period 2000-2016.\nBased on the above thought, the answer is:\nSolar Cell technology."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "5P0iYrh6mk6PiJhc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which year did the Solar Cell technology made the most progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, we can observe year by year, the progress of the Solar Cell has increased. Its progress can be detected by the bar height and the number on its left. In 2000<{\"2000\"}[0.140, 0.939, 0.175, 0.957]>, the progress of Solar Cell was 20%,<{\"20%\"}[0.264, 0.657, 0.295, 0.674]>. Then each year, the Solar Cell increase its progress, until 2016<{\"2016\"}[0.681, 0.939, 0.717, 0.957]>, where its progress reached its peak with 80% <{\"80%\"}[0.399, 0.583, 0.430, 0.601]>. So in 2016, the progress of Solar Cell is the most significant.\nBased on the above thought, the answer is:\n2016"
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "i7eWgWQaMknbVz5V",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the quarterly sales data for Apple, Google, Amazon and Microsoft from the bar chart into a structured CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reading the {quarter labels}<r>[0.138, 0.939, 0.158, 0.957]; [0.359, 0.939, 0.379, 0.957]; [0.580, 0.939, 0.600, 0.957]; [0.801, 0.939, 0.820, 0.957]</r> on the x-axis and the legend entries {for the different companies}<r>[0.909, 0.062, 0.949, 0.080]; [0.909, 0.088, 0.958, 0.106]; [0.909, 0.115, 0.965, 0.132]; [0.909, 0.141, 0.972, 0.158]</r>, I can see that the quartiles represent the x-axis and company represents the entries of the legend. The y-axis values are corresponding to sales data plotted by the bars. Given that, sales data can be extracted from the height of bars corresponding to each company for each quarter. For example, {Apple's Q1 sales bar}<r>[0.104, 0.193, 0.148, 0.927]</r> extends up to the tick mark for 50, representing a sales figure of 50 million for Apple in Q1. Extracting similarly for all companies across all quarters.\nBased on the above thought, the answer is:\nQuarter,Apple,Google,Amazon,Microsoft\nQ1,50,20,30,15\nQ2,48,25,26,20\nQ3,55,28,38,18\nQ4,45,27,35,25"
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "S7cc6bERN9XxTmaB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the sales trends observed in the bar chart, which company do you predict will have the highest sales growth in the next quarter? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To predict the company with the highest sales growth, I need to analyze the trend over the last four quarters by company. Looking at {Apple's sales bars}<r>[0.104, 0.193, 0.148, 0.927]; [0.325, 0.223, 0.369, 0.927]; [0.546, 0.120, 0.590, 0.927]; [0.766, 0.267, 0.811, 0.927]</r>, the trend seems relatively stable but overall a slight decrease. {Google's trend}<r>[0.148, 0.634, 0.192, 0.927]; [0.369, 0.560, 0.413, 0.927]; [0.590, 0.516, 0.634, 0.927]; [0.811, 0.531, 0.855, 0.927]</r> shows a steady increase, with each quarter posting higher sales than the previous one. For {Amazon's sales bars}<r>[0.192, 0.487, 0.236, 0.927]; [0.413, 0.546, 0.457, 0.927]; [0.634, 0.370, 0.678, 0.927]; [0.855, 0.414, 0.899, 0.927]</r>, there is sharp rise in Q3 and Q4 indicating an increasing trend. {Microsoft's bars}<r>[0.236, 0.707, 0.281, 0.927]; [0.457, 0.634, 0.501, 0.927]; [0.678, 0.663, 0.722, 0.927]; [0.899, 0.560, 0.943, 0.927]</r> for the four quarters show a slow and steady increase, indicating a consistent growth in sales. But if we compared the absolute growth, the growth numbers from Google and Amazon are higher other two companies.\nBased on the above thought, the answer is:\nAmazon and Google are potential candidates, but based on recent acceleration, I predict Amazon will see the highest sales growth next quarter."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "udGMG8BmbjebkckV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had the highest average sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company had the highest average sales across all four quarters, I can summarize the height of the bars for each company and then divide this sum by four. This can be visualized by lining up the height of the bars in the four quarters for each company. For example, {Apple's sales bars}<r>[0.104, 0.193, 0.148, 0.927]; [0.325, 0.223, 0.369, 0.927]; [0.546, 0.120, 0.590, 0.927]; [0.766, 0.267, 0.811, 0.927]</r> have an average height of close to 50, which is already higher than the highest bar of other companies, which means that Apple has the highest average sales.\nBased on the above thought, the answer is:\nApple."
+            }
+        ],
+        "id_raw": 843
+    },
+    {
+        "id": "1wOig5RbHCzUGsKZ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the chart data into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from {the title}<r>[0.349, 0.025, 0.721, 0.055]</r> and the x-axis labels {Rural}<r>[0.297, 0.919, 0.342, 0.942]</r> and {Urban}<r>[0.800, 0.919, 0.853, 0.942]</r>, the x-axis represents different types of areas. According to {the legend}<r>[0.772, 0.074, 0.973, 0.220]</r>, the four color-coding bars each represent Life Expectancy, CO2 Emission, Water Usage, and Plastic Waste. The y-axis represents the values of these factors. I can get the values from each bar's height. For instance, the height of {the bar for \"Life Expectancy\" in Rural}<r>[0.129, 0.603, 0.205, 0.903]</r> is about 75 judging from its corresponding position on the y-axis.\nBased on the above thought, the answer is:\nArea,Life Expectancy,CO2 Emission,Water Usage,Plastic Waste\nRural,75,24,200,80\nUrban,68,50,150,115"
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "NsrCuXo0dSbfXAjg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observation can we make about the differences in environmental sustainability factors between rural and urban areas using this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The comparison of sustainability factors such as CO2 Emission, Water Usage, and Plastic Waste between rural and urban areas requires analysis of the corresponding bars. According to {the legend}<r>[0.772, 0.074, 0.973, 0.220]</r>, [{CO2 Emission}<r>[0.828, 0.118, 0.947, 0.141]</r>, {Water Usage}<r>[0.828, 0.153, 0.939, 0.176]</r>, {Plastic Waste}<r>[0.828, 0.188, 0.943, 0.211]</r>] are represented as yellow, red, and grey bars respectively. By comparing the heights of the bars for CO2 Emission [{rural}<r>[0.205, 0.807, 0.281, 0.903]</r>, {urban}<r>[0.712, 0.703, 0.788, 0.903]</r>], Water Usage [{rural}<r>[0.281, 0.102, 0.357, 0.903]</r>,{urban}<r>[0.788, 0.302, 0.865, 0.903]</r>] and Plastic Waste [{rural}<r>[0.357, 0.583, 0.433, 0.903]</r>, {urban}<r>[0.865, 0.443, 0.941, 0.903]</r>] it's clear that these factors tend to increase in the Urban setting compared to the Rural one, except for Water Usage which is seen to decrease. Here, a higher value indicates more usage or emission which is a negative mark on sustainability.\nBased on the above thought, the answer is:\nThe CO2 Emission and Plastic Waste are significantly higher in urban areas compared to rural ones, while the Water Usage is lower in urban areas, which implies urban lifestyle may pose more substantial environmental challenges in terms of CO2 emissions and plastic waste. Despite lower water consumption, urban areas may still need to embrace more sustainable practices."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "F9m9OqcgyAcyDcRV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in water usage between urban and rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the difference in water usage between urban and rural areas, I need to find the corresponding bars. According to {the legend}<r>[0.772, 0.074, 0.973, 0.220]</r>, {Water usage}<r>[0.828, 0.153, 0.939, 0.176]</r> is depicted as red bars. The height of the {Water Usage bar for Rural}<r>[0.281, 0.102, 0.357, 0.903]</r> is at 200 while the height of {Water Usage bar for Urban}<r>[0.788, 0.302, 0.865, 0.903]</r> is at 150 on the y-axis. The difference can then be calculated by subtracting the Urban value from the Rural value.\nBased on the above thought, the answer is:\n50"
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "iAZAMzbMXNUCb0AB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, can you convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing the chart, I find that there are two groups of bars. The first group of bars denotes the Living Population and is color coded in {blue}<r>[0.731, 0.083, 0.966, 0.106]</r> according to the legend. The second group of bars signifies the Living Quality and is color coded in {orange}<r>[0.731, 0.118, 0.851, 0.141]</r> according to the legend. The chart has five categories shown on the x-axis {\"Ancient\"}<r>[0.143, 0.919, 0.209, 0.942]</r>, {\"Medieval\"}<r>[0.319, 0.919, 0.397, 0.942]</r>, {\"Industrial\"}<r>[0.500, 0.919, 0.581, 0.942]</r>, {\"Modern\"}<r>[0.690, 0.919, 0.755, 0.942]</r>, and {\"Future AI\"}<r>[0.866, 0.919, 0.944, 0.942]</r>. For each category, a pair of bars, the blue bar represents the living population, the orange bar indicates the living quality for that period, as denoted by their heights on the y-axis. Thus, the data can be extracted as:\nBased on the above thought, the answer is:\nScenario, Living Population, Living Quality\nAncient, 1200, 20\nMedieval, 950, 45\nIndustrial, 750, 60\nModern, 580, 80\nFuture AI, 250, 90"
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "Ht6WdDIlsGIlm6DK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data and the chart, how has the transition from ancient to future societies affected living conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {blue}<r>[0.731, 0.083, 0.966, 0.106]</r> and {orange}<r>[0.731, 0.118, 0.851, 0.141]</r> bars in the chart, it can be seen that as society transitions from ancient to future AI stages, the living population decreases, suggesting a high level of AI integration and automation. Contrasting to the decrease in population, the living quality increases, depicted by the increasing heights of {orange bars}[0.176, 0.891, 0.212, 0.903] to [0.905, 0.849, 0.941, 0.903], indicating improved living conditions due to advancements in technology and healthcare. Hence, the transition has brought about a decrease in population, but an improvement in living conditions.\nBased on the above thought, the answer is:\nThe societal transition from the ancients to the future has brought about a decrease in the living population but an increase in the quality of living as represented by the decrease in the height of the blue bars and increase in height of the orange bars. This suggests that though the population has decreased probably due to a higher level of AI integration and automation, the living conditions have improved drastically due to advancements in technology and healthcare."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "JwaRQ0BhXddfEy5c",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which societal stage does the population decrease by a larger margin?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the height of the {blue bars}<r>[\"Living Population (millions)\"]</r> for each societal stage, the largest decrease in population seems to occur between {Industrial}<r>[0.504, 0.453, 0.540, 0.903]</r> and {Modern}<r>[0.686, 0.555, 0.722, 0.903]</r> society as the difference in their heights is the most significant compared to other stages.\nBased on the above thought, the answer is:\nBetween Industrial and Modern society."
+            }
+        ],
+        "id_raw": 846
+    },
+    {
+        "id": "LjmAiZBIN2azTSrE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured data table that represents the relevant details from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the subtitle, {\"Effect of Encouragement and Discouragement on Creativity and Design Innovation\"}[0.104, 0.025, 0.965, 0.055], and {xlabel}[0.462, 0.952, 0.608, 0.975] representing \"Design Elements\", I determined that the x-axis stands for two design elements: \"Encouragement\" and \"Discouragement\". The y-axis {ylabel}[0.019, 0.449, 0.036, 0.516], representing \"Effect\", ranges from 0 up to 100. Three aspects of design innovation: Imagination, Ongoing Discovery and Innovative Thinking, are assessed for both types of design elements based on their respective {legend}[0.739, 0.074, 0.973, 0.185]. For each aspect, there are two bars under two design elements, each holding value, corresponding to the y-axis specification. For example, the bar of Imagination under Encouragement{\"Imagination bar under 'encouragement'\"}[0.129, 0.307, 0.213, 0.903] represents the effect of 78. Using these details a CSV data table can be constructed.\nBased on the above thought, the answer is:\nDesign_Elements,Imagination,Ongoing_Discovery,Innovative_Thinking\nEncouragement,78,102,85\nDiscouragement,50,43,32"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "Mm4pqb5GWUnLk7f1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the chart, which attitude - Encouragement or Discouragement - seems more impactful on the different aspects of design skill? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the impact of either Encouragement or Discouragement on different aspects of design skill, we need to analyze the lengths of the bars representing these aspects for both Encouragement and Discouragement. Starting with Imagination, the bar {\"Imagination bar under 'Encouragement'\"}[0.129, 0.307, 0.213, 0.903] representing Encouragement is longer than the bar {\"Imagination bar under 'Discouragement'\"}[0.689, 0.521, 0.773, 0.903] for Discouragement. The same trend is evident for the other aspects of Ongoing Discovery - {\"Ongoing Discovery bar under 'Encouragement'\"}[0.213, 0.123, 0.297, 0.903] and {\"Ongoing Discovery bar under 'Discouragement'\"}[0.773, 0.574, 0.857, 0.903], and Innovative Thinking - {\"Innovative Thinking bar under 'Encouragement'\"}[0.297, 0.253, 0.381, 0.903] and {\"Innovative Thinking bar under 'Discouragement'\"}[0.857, 0.658, 0.941, 0.903]. In all aspects, the bars for Encouragement are longer, indicating a stronger positive impact on these aspects as compared to Discouragement.\nBased on the above thought, the answer is:\nEncouragement is more impactful. As evident from the chart, for all three aspects - Imagination, Ongoing Discovery, and Innovative Thinking - the effect due to Encouragement is greater than the effect due to Discouragement."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "PQtVKzEt7shf0T4Z",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which aspect of design skill shows maximum difference between the effects of encouragement and discouragement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the aspect of design that shows maximum difference between the effect of encouragement and discouragement, we would need to compare the length of bars under Encouragement with bars under Discouragement for each aspect. For example, the bar for Encouragement under Imagination{\"Imagination bar under 'Encouragement'\"}[0.129, 0.307, 0.213, 0.903], has the length corresponding to effect of 78 whereas, bar for Discouragement {\"Imagination bar under 'Discouragement'\"}[0.689, 0.521, 0.773, 0.903] has an effect of 50. The difference is 28. Continuing in similar way for rest of the aspects, it can be seen that \"Ongoing Discovery\", with bars at {\"Ongoing Discovery bar under 'Encouragement'\"}[0.213, 0.123, 0.297, 0.903] and {\"Ongoing Discovery bar under 'Discouragement'\"}[0.773, 0.574, 0.857, 0.903], shows the maximum difference of 59.\nBased on the above thought, the answer is:\nOngoing Discovery."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "PaLGUywrJaSKnFM4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar chart data you observed into an organized structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see chart information like  {Creativity Type}[0.034, 0.785, 0.050, 0.805], represented on the x-axis, with 18 different labels each corresponding to a bar. Based on  {Innovation Design}[0.028, 0.564, 0.050, 0.584] marked on the y-axis, these bars show the percentage values related to each 'Creativity Type', which range between {0%}[0.034, 0.785, 0.050, 0.805] and {60%}[0.028, 0.124, 0.050, 0.144]. For instance, the bar for {Idea Generation}[0.054, 0.806, 0.118, 0.934] has {a length}[0.099, 0.401, 0.137, 0.793] roughly between 30% and 40%. Given that there isn't an exact annotation of the value, and we use raw data for confirmation instead. We find the 'Idea Generation' creativity type has a score of 35.5%. Likewise, we can interpret each bar's length to sift the corresponding percentages for all creativity types. We have the information we need to create a CSV table.\nBased on the above thought, the answer is:\nCreativity Type,Innovation Design\nIdea Generation,35.5\nConcept Development,25.9\nProduct Development,63.2\nStrategy Creation,50.3\nVisionary Thinking,40.7\nProblem Solving,45.5\nCollaborative Thinking,55.9\nPlatform Designing,47.2\nModel Building,28.6\nPrototyping,60.4\nExperimentation,39.7\nRisk-taking,35.9\nDivergent Thinking,54.8\nAesthetic Sensibility,62.3\nArtistic Expression,47.9\nStorytelling,30.6\nPattern Recognition,42.7\nSystem Thinking,29.9"
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "4gL0XgtOWMlbeRTV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, please explain which types of creativity are more effective in terms of innovation design?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer his question, one must compare the lengths of all the bars within the chart. The longer the bar, the more effective that type of creativity is in terms of innovation design. It's apparent that {Product Development}[0.194, 0.096, 0.232, 0.793], {Collaborative Thinking}[0.385, 0.177, 0.423, 0.793] and {Aesthetic Sensibility}[0.718, 0.106, 0.756, 0.793] have longer bars compared to others which means their corresponding 'Innovation Design' percentages are higher indicating that these types are more effective in driving innovation design. On the other hand, {Concept Development}[0.146, 0.507, 0.185, 0.793], {Model Building}[0.480, 0.478, 0.518, 0.793] and {System Thinking}[0.909, 0.463, 0.947, 0.793] have shorter bars implying that these types are less effective in terms of innovation design.\nBased on the above thought, the answer is:\nProduct Development, Collaborative Thinking and Aesthetic Sensibility are the creativity types which are more effective in terms of innovation design. This is because these types have higher 'Innovation Design' percentages."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "zS3MjgvJvenf0hSk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich creativity type has the lowest score of innovation design in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the creativity type with the lowest innovation design score, I need to compare the lengths of all the bars on the chart. From left to right, {the first bar}[0.099, 0.401, 0.137, 0.793] corresponding to 'Idea Generation' has a quite long length, definitely not the shortest. {The second bar}[0.146, 0.507, 0.185, 0.793] corresponding to 'Concept Development' is shorter than the first one but it's not the shortest either. Then, as I continue on, some bars are much shorter than previous ones, such as {'System Thinking'}[0.862, 0.806, 0.928, 0.937]. The bar[0.909, 0.463, 0.947, 0.793] for 'System Thinking' is shorter than all the previous ones. After comparing all bars, it's clear 'System Thinking' has the shortest bar, making the lowest score.\nBased on the above thought, the answer is:\nSystem Thinking."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "o8NbOVa8g2S2A9Xd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a CSV table based on the stacked bar chart you see."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart consists of various categories, or energy sources, which are outlined by the {x-axis labels}<{\"Fossil Fuels\"}[0.122, 0.761, 0.203, 0.924]; {\"Nuclear Power\"}[0.204, 0.761, 0.305, 0.963]; {\"Hydro\"}[0.322, 0.761, 0.371, 0.859]; {\"Renewables\"}[0.396, 0.761, 0.482, 0.933]; {\"Biomass\"}[0.499, 0.761, 0.563, 0.889]; {\"Coal\"}[0.603, 0.761, 0.643, 0.840]; {\"Natural gas\"}[0.673, 0.761, 0.757, 0.928]; {\"Oil\"}[0.792, 0.761, 0.821, 0.819]>. The y-axis, whose labels range from {0%}[0.041, 0.723, 0.068, 0.758] to {100%}[0.019, 0.122, 0.068, 0.157], represents the energy usage percentage of these sources. Each energy source bar is divided into segments representing three different years: Year1, Year2, Year3. These years are also signified in {the legend}[0.345, 0.063, 0.717, 0.126] with different colors for each year. For example, for {Fossil Fuels}[0.163, 0.737], Year1 is represented by {blue}[0.121, 0.523, 0.204, 0.737], Year2 by {purple}[0.121, 0.431, 0.204, 0.523], and Year3 by {orange}[0.121, 0.297, 0.204, 0.431]. I will need to calculate the height from each segment to the x-axis to determine the energy usage for each year and source.\nBased on the above thought, the answer is:\nSource,Year1,Year2,Year3\nFossil Fuels,35.5,15.4,22.3\nNuclear Power,25.4,30.2,22.6\nHydro,21.1,27.5,26.7\nRenewables,5.7,8.3,11.4\nBiomass,2.7,4.5,6.3\nCoal,50.6,32.3,18\nNatural gas,16.5,14.4,21.4\nOil,7.8,6.9,15.4"
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "vbkZ6YhFuPrjAwXF",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat general trend can you discern for energy sources from Year1 to Year3 according to the stacked bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern the trend, I need to examine the bars of each energy source from Year1 to Year3. For example, the bars for {Fossil Fuels}[0.121, 0.297, 0.204, 0.737] and {Coal}[0.581, 0.130, 0.664, 0.737] decrease from Year1 to Year3, whilst those for {Renewables}[0.397, 0.584, 0.480, 0.737] and {Biomass}[0.489, 0.656, 0.572, 0.694] increase. {Nuclear Power}[0.213, 0.267, 0.296, 0.737] and {Hydro}[0.305, 0.284, 0.388, 0.445] show increments followed by reduction. {Natural Gas}[0.673, 0.422, 0.756, 0.737] and {Oil}[0.765, 0.556, 0.848, 0.649] show consistent increase from Year1 to Year3. The overall trend indicates a decrease in the use of fossil fuels and an increase in renewables.\nBased on the above thought, the answer is:\nThe usage of Fossil Fuels and Coal notably declines while the usage of Renewables and Biomass noticeably increases. The usage of Nuclear Power and Hydro generally strays a little but ends up in near-unchanged positions while Natural Gas and Oil show steady increase. This suggests a general shift towards more sustainable energy sources over the three years."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "cXrKugBVR6jv2wRZ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage change in usage of coal from Year1 to Year3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change, I can calculate the difference in percentage usage of coal from Year1 to Year3. The Year1 usage can be determined from {the blue bar}[0.581, 0.433, 0.664, 0.737], which corresponds to 50.6%. The Year3 usage can be determined from {the orange bar}[0.581, 0.130, 0.664, 0.238], which corresponds to 18%. The percentage change equals the difference between the Year3 and Year1 usage divided by the Year1 usage, multiplied by 100%.\nBased on the above thought, the answer is:\n64.42%"
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "8A21BmMk7aGjWvqr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the stacked bar chart data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart does not have a visible title, but it clearly represents the distribution of different personality traits - Introversion, Extroversion and Ambiversion - across various social networking platforms. Based on the {x-axis}[{\"Facebook\"}[0.088, 0.812, 0.158, 0.953]; {\"Instagram\"}[0.166, 0.812, 0.241, 0.962]; {\"Twitter\"}[0.270, 0.812, 0.324, 0.920]; {\"LinkedIn\"}[0.343, 0.812, 0.407, 0.940]; {\"Snapchat\"}[0.419, 0.812, 0.489, 0.953]; {\"Reddit\"}[0.520, 0.812, 0.572, 0.916]; {\"WhatsApp\"}[0.580, 0.812, 0.655, 0.963]; {\"Zoom\"}[0.690, 0.812, 0.738, 0.908]; {\"TikTok\"}[0.770, 0.812, 0.820, 0.913]; {\"Skype\"}[0.854, 0.812, 0.903, 0.911]] we can see 10 different social media platforms that form the categories. The {y-axis}[{\"0%\"}[0.041, 0.774, 0.068, 0.809]; {\"20%\"}[0.030, 0.644, 0.068, 0.679]; {\"40%\"}[0.030, 0.513, 0.068, 0.548]; {\"60%\"}[0.030, 0.383, 0.068, 0.418]; {\"80%\"}[0.030, 0.252, 0.068, 0.287]; {\"100%\"}[0.019, 0.122, 0.068, 0.157]] ranges from \"0%\" to \"100%\", representing the percentage of each personality trait. The proportions of Introversion, Extroversion, and Ambiversion for each platform are represented by the respective rows of stacked bars. The position of the color split points correlates with the proportion relative to the y-axis. Therefore, I can determine the values for the CSV data by comparing the height of each colour split with the y-axis values. For example, for Facebook, {the green bar}<r>[0.121, 0.557, 0.196, 0.788]</r> represents Introversion and spans from 0% to 35.34%. {The gold bar}<r>[0.121, 0.261, 0.196, 0.557]</r> above it represents Extroversion and spans from 35.34% to 80.85%. {The blue bar}<r>[0.121, 0.136, 0.196, 0.261]</r> at the top represents Ambiversion and spans from 80.85% to 100%.\nBased on the above thought, the answer is:\nSocial Media,Introversion,Extroversion,Ambiversion\nFacebook,35.34,45.51,19.15\nInstagram,30.68,49.94,19.38\nTwitter,24.78,55.12,19.31\nLinkedIn,40.20,41.78,18.02\nSnapchat,22.55,57.99,19.46\nReddit,46.72,33.96,19.32\nWhatsApp,33.39,47.29,19.31\nZoom,42.23,38.64,19.13\nTikTok,20.97,59.68,19.35\nSkype,39.12,41.62,19.26"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "QwhJ8MDbq0oxswUY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the distribution of personality traits on social media platforms, can you predict the environment of various platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the relative prevalence of different personality traits across a range of social networking platforms. Each platform is represented by a stack of bars consisting of three segments: {Introversion}<r>[0.121, 0.557, 0.196, 0.788]</r> (represented by green), {Extroversion}<r>[0.121, 0.261, 0.196, 0.557]</r> (represented by gold), and {Ambiversion}<r>[0.121, 0.136, 0.196, 0.261]</r> (represented by blue). By analyzing the heights of these segments, we can make inferences about the personality profiles that likely dominate each platform. For example, {Reddit}<r>[0.535, 0.483, 0.609, 0.788]</r> appears to have a higher percentage of Introversion than any other platform. At the same time, {TikTok}<r>[0.783, 0.651, 0.858, 0.788]</r> has the highest percentage of Extroversion. Therefore, one can infer that Reddit tends to foster more reserved, introspective discussions, whereas TikTok likely caters to a more vibrant and extroverted crowd. Platforms like LinkedIn and Zoom have balanced distributions between Introverts and Extroverts and may be suitable for users of varying personalities. The majority of the platforms, irrespective of the prevailing personality trend, seem to maintain around ~19% of Ambiverts - a balance between Introversion and Extroversion.\nBased on the above thought, the answer is:\nYes, the Reddit platform may cater more to introverted personalities. In contrast, TikTok attracts a larger proportion of extroverts. LinkedIn and Zoom appear to be balanced platforms that cater to both introverted and extroverted users. Irrespective of the dominant personality trait, most platforms maintain a consistent distribution of ambiverts around ~19%."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "r52OujOHrNrEbH3l",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social networking platform has the highest number of Ambiverts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the respective blue bar segments for all the platforms. I can check the top edge coordinates of each blue segment to estimate the value more accurately based on the Y-axis. Starting from the left, {Facebook}<r>[0.121, 0.136, 0.196, 0.261]</r> has the height about 19.15%; {Instagram}'s<r>[0.204, 0.136, 0.278, 0.262]</r> height is also similar at approximately 19.38%; {Twitter}<r>[0.287, 0.136, 0.361, 0.262]</r> -19.31%; {LinkedIn}<r>[0.369, 0.136, 0.444, 0.253]</r> - 18.02%; {Snapchat}<r>[0.452, 0.136, 0.527, 0.263]</r> - 19.46%; Reddit<r>[0.535, 0.136, 0.609, 0.262]</r> - 19.32%; WhatsApp<r>[0.618, 0.136, 0.692, 0.262]</r> - 19.31%; Zoom<r>[0.700, 0.136, 0.775, 0.260]</r> - 19.13%; {TikTok}<r>[0.783, 0.136, 0.858, 0.262]</r> - 19.35%; Skype<r>[0.866, 0.136, 0.940, 0.261]</r> - 19.26%. Hence, among all these social network platforms, Snapchat contains the highest proportion of Ambiverts.\nBased on the above thought, the answer is:\nSnapchat."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "ay594TtV6r2opN9t",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table that captures the information presented in this bar graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param2\"}[0.204, 0.568, 0.278, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]] in the chart, these represent different cultural paradigms across regions. The x-axis of the chart contains labels which represent the {regions}[0.127, 0.823, 0.190, 0.947], while the height of the bars represents the percentage for each paradigm. By considering these two piece of information, we can create a CSV table.\nBased on the above thought, the answer is:\nRegion,Param1,Param2,Param3\nRegion1,30.71,29.48,39.81\nRegion2,31.85,37.94,30.21\nRegion3,27.92,52.61,19.47\nRegion4,32.81,30.68,36.51\nRegion5,28.37,38.97,32.66\nRegion6,39.67,19.33,41.00\nRegion7,26.48,34.12,39.40\nRegion8,31.09,35.62,33.29\nRegion9,34.32,24.37,41.31\nRegion10,29.45,35.11,35.44"
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "phTuFpnvJ5jVGTLH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat cultural paradigm seems to be most prevalent in Region3, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to look at the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param2\"}[0.204, 0.568, 0.278, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]] corresponding to Region3. I can see that the tops of these bars mark the values for each parameter. The bar that reaches the highest point represents the most prevalent cultural paradigm. Upon observation, the second highest section which represents Param2, has the highest value among three parameters for Region3, indicating that Param2 is the most prevalent cultural paradigm in Region3.\nBased on the above thought, the answer is:\nParam2 is the most prevalent cultural paradigm in Region3, as its value is the highest among all three parameters in this region."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "SJBmI6K6j3aOAZFJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which region is the difference between Param1 and Param3 the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that Param1 and Param3 are represented by the lowest and highest sections in the {bars}[ {\"Param1\"}[0.121, 0.576, 0.196, 0.798]; {\"Param3\"}[0.287, 0.596, 0.361, 0.798]]. I should directly compare these two parameters by region to find out which one has the largest difference. By looking at the bars corresponding to each of the 10 regions, I notice that the largest difference in height between Param1 and Param3 occurs in Region6, represented by the {Bar of Region6}[0.535, 0.511, 0.609, 0.798]}.\nBased on the above thought, the answer is:\nRegion6"
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "DhGc7RUUWSDZBROE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by analyzing the X-axis, the seven categories in it are {\"Family_Size\"}[0.168, 0.634], {\"Number_of_rooms\"}[0.272, 0.634], {\"Home_Area\"}[0.375, 0.634], {\"Number_of_bathrooms\"}[0.479, 0.634], {\"Number_of_cars\"}[0.583, 0.634], {\"Public_Transport\"}[0.686, 0.634], {\"School_District\"}[0.790, 0.634] and {\"Proximity_to_work\"}[0.894, 0.634]. These categories have three options according to the {legend}[0.313, 0.078, 0.749, 0.141] represented by three different levels of opacity in the bars; {Option1}, {Option2} and {Option3}. Now, we calculate the values for each option's percentage for each category using the y-tick values range from {0%}[0.041, 0.620, 0.068, 0.655] to {100%}[0.019, 0.122, 0.068, 0.157], for example, {Family_Size, Option1}[0.121, 0.345, 0.215, 0.634] lies above {20%} and below {60%}, which can be interpreted as 58 considering the y-tick labels.\nBased on the above thought, the answer is:\nAttribute,Option1,Option2,Option3\nFamily_Size,58,45,40\nNumber_of_rooms,60,59,37\nHome_Area,72,55,44\nNumber_of_bathrooms,55,53,47\nNumber_of_cars,50,47,36\nPublic_Transport,45,44,34\nSchool_District,67,60,57\nProximity_to_work,70,49,40"
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "RlRLSekmBUqQlJlc",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of public transportation, what opinion does the public prefer more? Please elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "The preferences of community members regarding transportation are clearly depicted as three layers in the Public Transport category comprising of {Option1}[0.640, 0.410, 0.733, 0.634], {Option2}[0.640, 0.190, 0.733, 0.410] and {Option3}[0.640, 0.021, 0.733, 0.190]. Based on the chart, {Option1} has a length between {40%} and {80%}, {Option2} has a length between {20%} and {40%}, and {Option3} lies between {0%} and {20%}. Hence, the public members prefer {Option1} more than the other two options, {Option2} is the second most preferred, and the least preferred is {Option3}.\nBased on the above thought, the answer is:\nThe community members prefer Option1 the most when considering public transportation. This suggests that this option may be viewed as the most convenient, affordable or accessible among the given choices."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "NQjs0qIE9fU4HHyI",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of Option3 across all household living conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average value of {Option3}, I need to add the values of all {Option3} under different categories then divide it by the number of categories. The values can be figured out according to the relative lengths of {Option3} bars against the y-axis, ranging between {0%}[0.019, 0.122, 0.068, 0.157] and {100%}[0.041, 0.620, 0.068, 0.655]. For instance, {Family_Size, Option3}[0.121, -0.079, 0.215, 0.121] is higher than {40%} and lower than {80%}, so it represents 40. Continue the same operation to get the values of {Option3} for Number_of_rooms, Home_Area, Number_of_bathrooms, Number_of_cars, Public_Transport, School_District and Proximity_to_work. After that, sum up all the values of {Option3} and divide by 8, which is the total number of categories.\nBased on the above thought, the answer is:\n47.625"
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "yNmwd9QcVnGpNWDA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the dual bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the bar chart, I can see that it's a dual bar chart with two categories, AI Research and Automation Applications. These categories are indicated by two different patterns on the bars. Bars for AI Research have a light color pattern while bars for Automation Applications are filled in blue. Each year from 2010 to 2020 has two bars associated with it, one for each category. The height of the bars indicates the value of each category in that specific year. To generate the CSV table, I need to extract the values for AI Research and Automation Applications for each year. The AI Research bar for 2010 at coordinates [0.096, 0.648, 0.136, 0.901] has a value of 35, and the Automation Applications bar for the same year at coordinates [0.096, 0.504, 0.136, 0.648] has a value of 20. I follow a similar procedure for the remaining years.\nBased on the above thought, the answer is:\nYear,AI Research,Automation Applications\n2010,35,20\n2011,38,28\n2012,37,45\n2013,52,60\n2014,55,65\n2015,63,80\n2016,70,86\n2017,74,90\n2018,80,95\n2019,85,100\n2020,90,105"
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "azju062Snpb3pGRq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the trends in AI Research and Automation Applications based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The values derived from the bar chart suggest a steady upward trend for both AI Research and Automation Applications from the year 2010 to 2020. The increase in AI Research has been somewhat gradual with a noticeable jump in 2013 as represented by the higher bar at coordinates [0.339, 0.525, 0.379, 0.901]. On the other hand, Automation Applications have surged quite dramatically, as evidenced by the rapidly escalating bars for this category. Exceptionally, the bar at {2020}[0.905, -0.507, 0.945, 0.251] indicates an outlier data point for Automation Applications as it exceeds the 100% mark with a value of 105.\nBased on the above thought, the answer is:\nBoth AI Research and Automation Applications have shown a consistent increase over the years from 2010 to 2020. AI Research has been growing gradually while Automation Applications have witnessed a dramatic surge. The year 2020 is particularly notable for Automation Applications as it surpasses the 100% mark, indicating a substantial growth in this domain."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "P2F7Fr0FkHuFd4nk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the highest growth in Automation Applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest growth in Automation Applications, I must find the year where the bar for Automation Applications is the tallest. The bar representing Automation Applications in 2020 with coordinates [0.905, -0.507, 0.945, 0.251] is higher than any other bar for Automation Applications in any year, extending well beyond the 100% mark with a value of 105.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "z8lH4Gl4Ri3SaKn5",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the bar chart, could you generate a table in CSV format displaying the Quality, Availability, and Equity indices for each social welfare and public service category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on my analysis of the bar chart, the X-axis represents distinct categories of societal services-medical, environmental, transportational, etc which can be found at [{\"Public Safety\"}[0.123, 0.756, 0.196, 0.879], {\"Environment\"}[0.192, 0.756, 0.264, 0.877], {\"Transportation\"}[0.256, 0.756, 0.337, 0.891], {\"Health Services\"}[0.322, 0.756, 0.408, 0.900], {\"Social Services\"}[0.392, 0.756, 0.475, 0.895], {\"Education\"}[0.473, 0.756, 0.532, 0.854], {\"Public Library\"}[0.532, 0.756, 0.609, 0.884], {\"Local Council\"}[0.602, 0.756, 0.677, 0.881], {\"Welfare\"}[0.684, 0.756, 0.731, 0.835], {\"Parks\"}[0.758, 0.756, 0.794, 0.817], {\"Recreation Facilities\"}[0.791, 0.756, 0.899, 0.936], {\"Public Transport\"}[0.869, 0.756, 0.958, 0.903]]. The Y-axis on the other hand represents the percentage index, with markings at intervals of 20%, which can be seen at [{\"20%\"}[0.047, 0.609, 0.078, 0.632], {\"40%\"}[0.047, 0.486, 0.078, 0.510], {\"60%\"}[0.047, 0.364, 0.078, 0.388], {\"80%\"}[0.047, 0.242, 0.078, 0.265], {\"100%\"}[0.039, 0.120, 0.078, 0.143]]. Based on the {legend}[0.360, 0.049, 0.713, 0.091], white regions represent Quality, horizontal lines show Availability and diagonal lines convey Equity. I can discern the percentage values associated with each of these indices for a category by the {bars}[0.883, -0.794, 0.944, -0.317]. By undertaking this process systematically for each category, the data can be tabulated in a CSV format.\nBased on the above thought, the answer is:\nCategory,Quality,Availability,Equity\nPublic Safety,87,92,76\nEnvironment,91,95,83\nTransportation,94,98,90\nHealth Services,92,94,85\nSocial Services,88,92,78\nEducation,95,98,91\nPublic Library,91,93,84\nLocal Council,79,83,70\nWelfare,82,84,75\nParks,88,92,82\nRecreation Facilities,87,90,80\nPublic Transport,85,88,78"
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "xR58kW1KIqzBlzka",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which category of social welfare and public services needs significant improvements and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart categories requiring significant improvements are those with the lowest overall index. The y-values determine this, which can be established from the top of each bar including the ones for (Public Safety)[0.190, 0.740], (Environment)[0.259, 0.740], (Transportation)[0.327, 0.740], (Health Services)[0.396, 0.740], (Social Services)[0.464, 0.740], (Education)[0.533, 0.740], (Public Library)[0.602, 0.740], (Local Council)[0.670, 0.740], (Welfare)[0.739, 0.740], (Parks)[0.807, 0.740], (Recreation Facilities)[0.876, 0.740] and (Public Transport)[0.944, 0.740]. Upon analysis, it's evident that 'Local Council' has the lowest overall index, indicating a need for enhancement. Also, the outside white area representing 'Quality'[0.608, 0.257, 0.670, 0.740] is significantly less for 'Local Council', indicating poor quality of output. Hence, 'Local Council' likely requires substantial improvement.\nBased on the above thought, the answer is:\nThe 'Local Council' category needs significant improvement because it has the lowest overall index. The quality of services in this category is particularly low compared to others, indicating that improvements in this area could significantly increase the overall index."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "mFNwacXUIqgIszeS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart, which category has the highest total index value across Quality, Availability and Equity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond to this, I need to locate the top of each bar on the graph, which corresponds to the total index value of all three sub-categories. The exact location of the top of each bar can be found at (Public Safety)[0.190, 0.740], (Environment)[0.259, 0.740], (Transportation)[0.327, 0.740], (Health Services)[0.396, 0.740], (Social Services)[0.464, 0.740], (Education)[0.533, 0.740], (Public Library)[0.602, 0.740], (Local Council)[0.670, 0.740], (Welfare)[0.739, 0.740], (Parks)[0.807, 0.740], (Recreation Facilities)[0.876, 0.740] and (Public Transport)[0.944, 0.740]. Observing these y-values, I can conclude that the 'Education' category has the highest total index value.\nBased on the above thought, the answer is:\nEducation."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "OUXadUtDovqmAiJY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart you are observing, can you tabulate the sales trend for each product category in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is titled {\"Sales Trend Percentage\"}[0.3, 0.9, 0.7, 0.91] on the top, indicating that the sales trends are divided into five parts based on the colors in the legend box {legend} [0.275, 0.032, 0.774, 0.109]  i.e., 'Slow Decline', 'Inexorable Decline', 'Volatile Growth', 'Steady Increase', and 'Sharp Rise'. The x-axis corresponds to the product categories as indicated by the labels {\"Electronics\"}[0.151, 0.857, 0.215, 0.962], {\"Fashion\"}[0.330, 0.857, 0.377, 0.935], {\"Grocery\"}[0.500, 0.857, 0.549, 0.938] , {\"Automobiles\"}[0.660, 0.857, 0.731, 0.975], {\"Stationery\"}[0.836, 0.857, 0.897, 0.958]. To generate the table, I would extract the heights for the colored segments within each bar which corresponds to the exact percentage in each sales trend.\nBased on the above thought, the answer is:\nProduct Category, Slow Decline, Inexorable Decline, Volatile Growth, Steady Increase, Sharp Rise\nElectronics,92,80,9,88,96\nFashion,85,77,25,90,85\nGrocery,70,65,35,89,89\nAutomobiles,60,58,50,92,92\nStationery,55,53,48,95,95"
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "5oE0k9Ut3KSOEeCi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you are observing, which product category has shown the most fluctuation in sales trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires a careful comparison of the sizes of different segments in each bar. For instance, looking at the {\"Electronics\"}[0.151, 0.184, 0.215, 0.357] category, we see a relatively consistent sales trend with little fluctuation: the size of the \"Slow Decline\" and \"Sharp Rise\" segments are both considerably larger than the \"Volatile Growth\", \"Inexorable Decline\", and \"Steady Increase\" segments. This pattern repeats in other categories as well but looking at the {\"Automobiles\"}[0.660, 0.208, 0.731, 0.373] category, we see that the sizes of the segments appear to be more evenly distributed, suggesting a more fluctuating pattern of sales.\nBased on the above thought, the answer is:\nAutomobiles. The relatively equal distribution of the different sales trends in the \"Automobiles\" category suggests a more fluctuating sales trend compared to other categories."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "XVipn6dkEYQ3Q9KO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category has shown the highest percentage of steady increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the legend and color of the bar {\"legend\"} [0.275, 0.032, 0.774, 0.109] , the \"Steady Increase\" trend is represented by the 4th segment from the bottom in all the product categories. Now, looking at the 4th segment of all bars for the following categories {\"Electronics\"}[0.151, 0.357, 0.215, 0.515], {\"Fashion\"}[0.330, 0.342, 0.377, 0.504], {\"Grocery\"}[0.500, 0.375, 0.549, 0.535], {\"Automobiles\"}[0.660, 0.373, 0.731, 0.538], {\"Stationery\"}[0.836, 0.389, 0.897, 0.560], it can be inferred that Stationery category has shown the highest \"Steady Increase\" segment.\nBased on the above thought, the answer is:\nStationery."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "FNlWFclxrCIOgZBZ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a visual description of the frequency distributions of Tech Startups, Venture Capital, and Government Grants across different technology sectors in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the provided {stacked bar chart}<r>[0.054, 0.127, 0.988, 0.827]</r>, each bar is divided into segments that represent the distribution of funding by {Tech Startups}<r>[0.096, 0.766, 0.155, 0.827]</r>, {Venture Capital Coverage}<r>[0.096, 0.458, 0.155, 0.766]</r>, and {Government Grants}<r>[0.096, 0.127, 0.155, 0.458]</r> across various sectors like {Green Tech}<r>[0.099, 0.841, 0.153, 0.934]</r>, {Bio Tech}<r>[0.170, 0.841, 0.212, 0.913]</r>, and so on. The segments are scaled proportionally to the percentage distributions and are distinguished by colors. By analyzing the heights of these segments and their relationship to the {y-axis tick labels}<r>[0.027, 0.820, 0.045, 0.840]</r>, which represent percentages from 0% to 100%, we can convert the visual data into a table format.\nBased on the above thought, the answer is:\nSector,Tech Startups,Venture Capital,Government Grants\nGreen Tech,8.8,43.9,47.3\nBio Tech,32.6,51.3,16.1\nDigital Media,23.5,35.7,40.8\nFinTech,34.7,16.3,49.0\nFood Tech,20.0,61.6,18.4\nHealth Tech,15.6,31.1,53.3\ne-Commerce,34.1,34.8,31.1\nMaterials Science,45.9,27.0,27.1\nAI and Robotics,29.5,49.2,21.3\nSpace Tech,31.0,29.1,39.9\nAgri Tech,25.6,24.1,50.3\nOn-Demand,38.5,35.8,25.7\nAverage,30.0,35.6,34.4"
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "wbVqkCNXFxJRSbc7",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology sector appears most reliant on Government Grants for funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the {stacked bar chart}<r>[0.054, 0.127, 0.988, 0.827]</r>, I will look for the sector where the segment representing {Government Grants}<r>[0.096, 0.127, 0.155, 0.458]</r> is largest relative to the other sources of funding within that sector's bar. By examining the bar heights and comparing them, it is evident that the {Bio Tech sector}<r>[0.162, 0.240, 0.221, 0.599]</r> shows the smallest portion for {Government Grants}<r>[0.162, 0.127, 0.221, 0.240]</r> compared to {Tech Startups}<r>[0.162, 0.599, 0.221, 0.827]</r> and {Venture Capital Coverage}<r>[0.162, 0.458, 0.221, 0.599]</r>, indicating that it relies less on Government Grants than the other sectors. Conversely, {Green Tech}<r>[0.099, 0.841, 0.153, 0.934]</r> shows a larger portion of its funding coming from {Government Grants}<r>[0.096, 0.458, 0.155, 0.766]</r> relative to the other sources, which suggests high reliance on this type of funding.\nBased on the above thought, the answer is:\nGreen Tech. It appears most reliant on Government Grants as a significant majority of its funding, 47.3%, comes from this source compared to tech startups and venture capital."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "Fxt3ONkrwChPLz7F",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more Venture Capital Coverage funding does Bio Tech receive compared to Health Tech?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am looking at the portion of the bars that represent {Venture Capital Coverage funding}<r>[0.162, 0.240, 0.221, 0.599]</r> for the Bio Tech sector and {Health Tech}<r>[0.425, 0.500, 0.484, 0.718]</r>. By comparing the relative heights of these segments, respectively with the corresponding {y-axis percentage scale}<r>[0.020, 0.679, 0.045, 0.699]</r>, I can determine the difference. {Bio Tech}<r>[0.170, 0.841, 0.212, 0.913]</r> receives 51.3% Venture Capital Coverage, and {Health Tech}<r>[0.427, 0.841, 0.483, 0.937]</r> receives 31.1% Venture Capital Coverage.\nBased on the above thought, the answer is:\n20.2%"
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "gwOhAjf1L73oiiZt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the stack bar chart information into a CSV table structure that outlines the percentages of Recovery, Performance, and Decline for each industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the stack bar chart, I start from the {industry labels on the x-axis}<r>[0.104, 0.820, 0.942, 0.900]</r>. Each industry label corresponds to a stack bar composed of segments with different hatch patterns and edge colors, representing Recovery, Performance, and Decline. For instance, the Theatre's stack bar spans from {bottom of the stack bar}<r>[0.106, 0.365, 0.151, 0.804]</r> to {top of the stack bar}<r>[0.106, 0.129, 0.151, 0.230]</r>. By referencing the y-axis percentages, I can discern the values for Theatre as 65% for Recovery, 20% for Performance, and 15% for Decline. Repeating this process for all stack bars, I can construct the complete CSV table.\nBased on the above thought, the answer is:\nIndustry,Recovery,Performance,Decline\nTheatre,65,20,15\nArt Gallery,30,50,20\nMusic Production,45,35,20\nFilm Industry,80,10,10\nDance,57,30,13\nOpera,38,42,20\nDesign,64,19,17\nArchitecture,52,38,10\nFashion,72,18,10\nPhotography,30,60,10\nPoetry,40,30,30\nWriting,70,20,10\nMuseums,56,30,14\nFestivals,60,25,15\nComedy,15,50,35\nAnimation,75,15,10\nNovels,45,40,15"
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "edUNEyUIHKAvzObV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend analysis in the bar chart, which sector of Culture and Arts is most stable with a low decline percentage and yet shows significant overall growth? Explain your process of analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing for a stable sector with low decline and significant growth entails looking for an industry with a low {Decline trend segment}<r>[0.800, 0.129, 0.844, 0.365]</r> and high values in {Recovery and Performance trend segments}<r>[0.800, 0.365, 0.844, 0.804]; [0.800, 0.230, 0.844, 0.365]</r>. I examine each industry's bar segments from left to right, noting the proportions. For instance, {Film Industry}<r>[0.255, 0.264, 0.299, 0.804]</r> has a high Recovery percentage and very low Decline, while {Art Gallery}<r>[0.156, 0.264, 0.200, 0.804]</r> displays strong Performance but with a higher Decline segment. By comparing the uppermost segments representing Decline and considering those with the least height from the y-axis 100% line (lowest Decline), against the total height of the bar (overall growth), I can determine which industry fits this criterion.\nBased on the above thought, the answer is:\nThe Film Industry stands out as the most stable sector in Culture and Arts, with the least percentage of Decline and strong overall growth, as indicated by a sizable Recovery segment and a minimal Decline segment in its bar on the chart."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "qkPcOogGSCWi6JhW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the lowest total percentage for Recovery and Performance combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must calculate the sum of Recovery and Performance percentages for each industry by examining the stack bars representing each industry. Each bar is divided into three segments, such as {Recovery segment of 'Theatre'}<r>[0.106, 0.365, 0.151, 0.804]</r> and {Performance segment of 'Theatre'}<r>[0.106, 0.230, 0.151, 0.365]</r>. By successively observing and adding these values for each industry, I can ascertain which industry has the lowest combined Recovery and Performance percentage. Matching these calculations against the y-axis labels confirms the percentage values.\nBased on the above thought, the answer is:\nComedy"
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "7WiNODOtG9HUHHce",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the stacked bar chart, could you represent the risks and measures across various categories in the form of a tabular CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "After closely observing the chart, I discern that the x-axes are composed of the various categories in the financial industry. This is evident from the labels placed at {x-axis tick labels}[{\"Debt Management\"}[0.068, 0.759, 0.170, 0.928], {\"Equity Management\"}[0.203, 0.759, 0.312, 0.940], {\"Credit Risk Control\"}[0.352, 0.759, 0.454, 0.928], {\"Reputation Risk Control\"}[0.470, 0.759, 0.596, 0.968], {\"Financial Reporting\"}[0.634, 0.759, 0.737, 0.932], {\"Operational Risk Control\"}[0.749, 0.759, 0.879, 0.975]]. The y-axis contains the ranges of the various measures for each category, ranging from {0%}[0.033, 0.733, 0.055, 0.757] to {200%}[0.015, 0.262, 0.055, 0.285].\nEach stacked bar in each category represents three different measures - \"Regulation Compliance\", \"Operational Oversight\" and \"Market Risk Control\", spotted from the {legend}[0.768, 0.025, 0.978, 0.137]. The value for these measures can be obtained by viewing the heights of the bars for each section.\nBased on the above thought, the answer is:\nCategory,Regulation Compliance,Operational Oversight,Market Risk Control\nDebt Management,80,60,55\nEquity Management,60,75,68\nCredit Risk Control,70,80,90\nReputation Risk Control,85,80,75\nFinancial Reporting,75,85,80\nOperational Risk Control,90,70,60"
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "KBdytLmjoULnPqus",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the chart, which measure seems to have been given the highest priority across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be assessed by examining the thickness of the sections corresponding to each measure in the stacked bars for the various categories. For instance, \"Regulation Compliance\" may be noted by the color scheme dedicated to it in the {legend}[0.768, 0.025, 0.978, 0.137]. The respective annotations are \"Operational Oversight\" and \"Market Risk Control\". By assessing the thickness for each measure across the categories like {Debt Management}[0.106, 0.554, 0.234, 0.742], it appears that \"Regulation Compliance\" generally has a larger area in the bars than \"Operational Oversight\" or \"Market Risk Control\". A similar pattern is observed in other categories. Thus we can infer that \"Regulation Compliance\" has been given the highest priority across categories.\nBased on the above thought, the answer is:\nRegulation Compliance"
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "VbrWFXRzBKnoWlik",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest cumulative value across all measures?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the highest cumulative value, I need to measure the total height of the stacked bars in each category. For instance, the first stacked bar represents 'Debt Management', at a glance, it seems quite tall. The height of the bar representing \"Regulation Compliance\" {orange bar}[0.106, 0.554, 0.234, 0.742] is at the 80% mark; \"Operational Oversight\" {grey bar}[0.106, 0.412, 0.234, 0.554] is at 60% and \"Market Risk Control\" {white bar}[0.106, 0.283, 0.234, 0.412] is approximately at 55%. When these are summarized, the overall value for \"Debt Management\" is 195.\nThis process is repeated for all categories, including \"Equity Management\", \"Credit Risk Control\", \"Reputation Risk Control\", \"Financial Reporting\" and \"Operational Risk Control\". The category with the highest cumulative value is the one where the sum of the heights of all the partitions of the stack is the greatest.\nBased on the above thought, the answer is:\nCredit Risk Control"
+            }
+        ],
+        "id_raw": 868
+    },
+    {
+        "id": "PK67y8Lkb0WKYjXd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the bar chart data into a CSV data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe that the chart is a stacked bar chart with the x-axis representing two categories: {Artificial Intelligence}<r>[0.246, 0.686, 0.384, 0.963]</r> and {Machine Learning}<r>[0.686, 0.686, 0.807, 0.928]</r>, and the y-axis representing workload percentage from {0%}<r>[0.041, 0.648, 0.068, 0.683]</r> to {100%}<r>[0.019, 0.122, 0.068, 0.157]</r>. Each category contains three bars: {RegOpt}<r>[0.121, 0.241, 0.509, 0.662]</r> in blue, {BIOpt}<r>[0.121, 0.188, 0.509, 0.241]</r> in red, and {LoopOpt}<r>[0.121, 0.136, 0.509, 0.188]</r> in green, according to {the legend}<r>[0.323, 0.074, 0.739, 0.137]</r>. I can determine the weight of each bar by calculating the distance from the bottom of the bar to the y-axis' ticks. For example, for Artificial Intelligence, the height of {RegOpt bar}<r>[0.121, 0.241, 0.509, 0.662]</r> reaches 80% on the y-axis; the height of {BIOpt bar}<r>[0.121, 0.188, 0.509, 0.241]</r> adds another 10%, and the height of {LoopOpt bar}<r>[0.121, 0.136, 0.509, 0.188]</r> adds the rest 10%. Therefore, the weight of RegOpt, BIOpt, and LoopOpt for Artificial Intelligence is 80%, 10%, and 10% respectively.\nBased on the above thought, the answer is:\nCategory,RegOpt,BIOpt,LoopOpt\nArtificial Intelligence,80,10,10\nMachine Learning,20,60,20"
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "e6IpU1eHC3xec4wU",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering this chart, which optimization method should be the focus for future Machine Learning processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To decide which optimization method to focus on, it's important to look at the heights of the bars for each method within the {Machine Learning}<r>[0.686, 0.686, 0.807, 0.928]</r> category - {RegOpt}<r>[0.779, 0.728, 0.812, 0.967]</r>, {BIOpt}<r>[0.812, 0.472, 0.846, 0.728]</r>, and {LoopOpt}<r>[0.846, 0.399, 0.880, 0.472]</r>. The largest segment is BIOpt, which reaches {80%}<r>[0.030, 0.227, 0.068, 0.262]</r> on the y-axis, significantly larger than the other two methods. Therefore, focusing on BIOpt would cover the majority of the workload for Machine Learning processes.\nBased on the above thought, the answer is:\nBIOpt, as it represents the majority (80%) of the workload in Machine Learning processes."
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "XK5KoAAX808RvbLp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the workload difference between RegOpt and BIOpt for Machine Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find these percentages by comparing the bars for {RegOpt}<r>[0.779, 0.728, 0.812, 0.967]</r> and {BIOpt}<r>[0.812, 0.472, 0.846, 0.728]</r> for Machine Learning, according to {the legend}<r>[0.323, 0.074, 0.739, 0.137]</r>. The bar for RegOpt reaches {20%}<r>[0.030, 0.543, 0.068, 0.578]</r> on the y-axis, and the bar for BIOpt stretches beyond that point to the {80%}<r>[0.030, 0.227, 0.068, 0.262]</r> tick line. By subtracting the 20% represented by RegOpt from the 80% represented by BIOpt, I find that the workload of BIOpt is 60% more than that of RegOpt.\nBased on the above thought, the answer is:\n60%"
+            }
+        ],
+        "id_raw": 869
+    },
+    {
+        "id": "DOynnHpRdciIGmEN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data of different areas of healthcare and their device accuracy into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title placed in the chart, {\"Healthcare and Medical Technology\"}[0.322, 0.118, 0.689, 0.163] and the {x-axis' tick labels}[{\"Anesthesia\"}[0.158, 0.927, 0.254, 0.962]; {\"Radiology\"}[0.363, 0.927, 0.449, 0.962]; {\"Cardiology\"}[0.559, 0.927, 0.652, 0.962]; {\"Neurology\"}[0.761, 0.927, 0.849, 0.962]]; I can assert that the x-axis represents different areas in the healthcare and medical technology field. The y-axis tick labels represent the corresponding values, ranging from {0}[0.065, 0.889, 0.076, 0.924] to {100}[0.043, 0.160, 0.076, 0.195] percent. According to {the legend}[0.097, 0.046, 0.315, 0.161], it's possible for me to deduce that the {blue column bars}[0.126, 0.354, 0.179, 0.903; 0.326, 0.445, 0.379, 0.903; 0.526, 0.460, 0.579, 0.903; 0.725, 0.616, 0.778, 0.903] represent the accuracy of device1; the {dark blue column bars}[0.179, 0.401, 0.233, 0.903; 0.379, 0.501, 0.432, 0.903; 0.579, 0.513, 0.632, 0.903; 0.778, 0.666, 0.832, 0.903] represent the accuracy of device2. For each area, there are three bars representing the truthful and misleading recalls. The approximate values can be deduced from the top position of each bar, for example, in the Anesthesia area, the values for the two devices are 75.3 and 68.9 respectively, according to the {blue column bar}'s coordinates[0.126, 0.354, 0.179, 0.903; 0.179, 0.401, 0.233, 0.903] and their correspondent number {labeled on top}[0.134, 0.370, 0.172, 0.405; 0.240, 0.529, 0.278, 0.564].\nBased on the above thought, the answer is:\nArea,Device Accuracy1,Device Accuracy2,Misleading Information\nAnesthesia,75.3,68.9,30.2\nRadiology,62.9,55.1,35.2\nCardiology,60.8,53.5,38.6\nNeurology,39.4,32.6,28.2"
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "2P1mMoLHpFj6tXiw",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which healthcare area faces the most critical problem with misleading information from a device, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question needs to comprehensively analyze {the red bar}<r>[0.286, 0.174, 0.339, 0.614; 0.486, 0.174, 0.539, 0.687; 0.685, 0.174, 0.739, 0.737; 0.885, 0.174, 0.939, 0.585]</r> across four different areas. Firstly, Anesthesia's misleading information, from the value labeled as 30.2 on the top of its red bar in coordinates[r][0.240, 0.529, 0.278, 0.564], the tallest misleading recall happened in Cardiology, considering it reaches up to 38.6 based on coordinates of its top{r}[0.639, 0.651, 0.678, 0.686]. Also, comparing the rest of the red bars for Radiology and Neurology, their values are 35.2 and 28.2 respectively, which are shown with the coordinates <r>[0.440, 0.602, 0.478, 0.637; 0.839, 0.500, 0.877, 0.535]</r>, both lower than Cardiology. Therefore, Cardiology is the area that faces the most critical problem as per the given misleading information.\nBased on the above thought, the answer is:\nCardiology. Because the red column representing misleading information in the cardiology area reaches up to 38.6, which is the highest among all four healthcare areas according to the bar chart."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "0cZY8yJ2TSNlh6Ke",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich healthcare area experienced the largest drop in device accuracy from Device 1 to Device 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the healthcare area that experienced the largest drop in device accuracy from Device 1 to Device 2, I would need to compare the heights of the bars corresponding to Device 1 and Device 2 in each area. The bars corresponding to Device 1 and Device 2 for Anesthesia are {first set of bars}<r>[0.126, 0.354, 0.179, 0.903; 0.179, 0.401, 0.233, 0.903]</r>, for Radiology are {second set of bars}<r>[0.326, 0.445, 0.379, 0.903; 0.379, 0.501, 0.432, 0.903]</r>, for Cardiology are {third set of bars}<r>[0.526, 0.460, 0.579, 0.903; 0.579, 0.513, 0.632, 0.903]</r>, and for Neurology are {fourth set of bars}<r>[0.725, 0.616, 0.778, 0.903; 0.778, 0.666, 0.832, 0.903]</r>. By comparing the heights of these bars, I see that the area of Cardiology shows the largest difference between the heights of bars corresponding to Device 1 and Device 2.\nBased on the above thought, the answer is:\nCardiology."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "HwrMeOAsdbSEda0e",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you abstract the data of 'AI Models Recall' and 'Advanced Automation Recall' in different models into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I see a series of 8 category labels--one for each model--along the x-axis, ranging from {Model 1}<r>[0.095, 0.942, 0.150, 0.970]</r>, {Model 2}<r>[0.197, 0.942, 0.252, 0.970]</r> to {Model 8}<r>[0.810, 0.942, 0.865, 0.970]</r>. Then there are two groups of bars according to different recalls for each model. The blue bars are labeled 'AI Models Recall', such as {Model 1}<r>[0.102, 0.101, 0.143, 0.499]</r>, {Model 2}<r>[0.204, 0.255, 0.245, 0.499]</r> and so on; and the pink bars are labeled 'Advanced Automation Recall', such as {Model 1}<r>[0.143, 0.499, 0.183, 0.701]</r>, {Model 2}<r>[0.245, 0.499, 0.286, 0.839]</r> and so on. I retrieve the corresponding values for each model and kind of recall according to the height of the bars and their value annotations at the top.\nBased on the above thought, the answer is:\nModel,AI Models Recall (%),Advanced Automation Recall (%)\nModel 1,56.3,23.9\nModel 2,34.5,40.2\nModel 3,27.8,38.8\nModel 4,18.4,12.4\nModel 5,33,45\nModel 6,37,24\nModel 7,43.5,33\nModel 8,30.1,22"
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "do2XibOdq7GF3ryk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the data, what can you infer about the trend between AI Models and Advanced Automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing all labeled bars of {AI Models}<r>[0.102, 0.101, 0.143, 0.499]; [0.204, 0.255, 0.245, 0.499]; [0.306, 0.302, 0.347, 0.499]; [0.408, 0.369, 0.449, 0.499]; [0.510, 0.265, 0.551, 0.499]; [0.612, 0.237, 0.653, 0.499]; [0.715, 0.191, 0.755, 0.499]; [0.817, 0.286, 0.858, 0.499]</r> and all labeled bars of {Advanced Automation}<r>[0.143, 0.499, 0.183, 0.701]; [0.245, 0.499, 0.286, 0.839]; [0.347, 0.499, 0.388, 0.828]; [0.449, 0.499, 0.490, 0.604]; [0.551, 0.499, 0.592, 0.880]; [0.653, 0.499, 0.694, 0.702]; [0.755, 0.499, 0.796, 0.778]; [0.858, 0.499, 0.898, 0.685]</r>, I notice that these two types of recalls for each model seem not explicitly correlated as the AI Models Recall is not proportionally grow with Advanced Automation Recall. However, it is noticeable that Model 5 presents an outlier value for Advanced Automation.\nBased on the above thought, the answer is:\nThe trend between AI Models and Advanced Automation seems inconsistent, as shown by the bars for each model. There's not an obvious proportionality between the AI Models Recall and the Advanced Automation Recall. Interestingly, the Advanced Automation for 'Model 5' is significantly higher than other models, which displays as an outlier."
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "m4ujddqUoCZd67JW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent does AI Model recall exceed Advanced Automation recall in Model 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out how much the AI Model recall exceeds the Advanced Automation recall in {Model 1}<r>[0.095, 0.942, 0.150, 0.970]</r>, I need to read off the two groups of bars respectively. The {blue bar}<r>[0.102, 0.101, 0.143, 0.499]</r> with a value annotation of {56.3}<r>[0.107, 0.100, 0.138, 0.128]</r> represents the 'AI Models Recall' of 'Model 1'. The {pink bar}<r>[0.143, 0.499, 0.183, 0.701]</r> with a value annotation of {23.9}<r>[0.148, 0.662, 0.178, 0.690]</r> represents the 'Advanced Automation Recall' of 'Model 1'. Based on these data, I can calculate how much the former exceeds the latter.\nBased on the above thought, the answer is:\n32.4%"
+            }
+        ],
+        "id_raw": 871
+    },
+    {
+        "id": "gZPaU1NAm7PvSK8P",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chat you're observing, can you convert the data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart seems to demonstrate the impact of various media outlets on public opinion, rated by influence score along the y-axis{\"Influence Score\"}[0.019, 0.462, 0.036, 0.641], with four media outlets detailed on the x-axis{\"CNN\"}[0.171, 0.952, 0.209, 0.975]; {\"FOX News\"}[0.377, 0.952, 0.463, 0.975]; {\"BBC\"}[0.632, 0.952, 0.668, 0.975]; {\"Al Jazeera\"}[0.836, 0.952, 0.923, 0.975], and two colours of bars for each outlet {\"Positive Influence\"}[0.153, 0.035, 0.305, 0.058]; {\"Negative Influence\"}[0.153, 0.070, 0.316, 0.093]. The values can be inferred from the height of the bars, for instance, for CNN, the 'Positive Influence' bar {\"Positive Influence bar for CNN\"}[0.129, 0.238, 0.190, 0.647] indicates approximately 85.4 influence score, and similarly the 'Negative Influence' bar {\"Negative Influence bar for CNN\"}[0.190, 0.333, 0.251, 0.647] indicates about 65.5 influence score. Once the values for each media outlet are discerned, the information can be compiled into the CSV table.\nBased on the above thought, the answer is:\nMedia Outlet,Positive Influence,Negative Influence\nCNN,85.4,65.5\nFOX News,65.6,83.7\nBBC,90.2,54.5\nAl Jazeera,63.7,80.1"
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "FszoFPVp48zlFDcE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which media outlet seems to be striking a better balance in terms of positive and negative influence in guiding public opinion, and can you explain your reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce the balance between positive and negative influence of each media outlet, we evaluate the similarity in heights of the positive and negative influence bars. After examining, the bars of BBC in both colors{\"Positive Influence bar for BBC\"}[0.588, 0.214, 0.650, 0.647] and {\"Negative Influence bar for BBC\"}[0.650, 0.386, 0.711, 0.647], have the least difference between their heights. This implies that BBC has the most balanced impact in guiding public opinion, as it is neither excessively positive, nor excessively negative.\nBased on the above thought, the answer is:\nBBC. This is because the balance between positive and negative influence appears to be relatively equitised for the BBC, as indicated by a minor difference in their heights. Hence, it indicates a balanced approach to guiding public opinion."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "czJGCriYiwntwbNM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media outlet has the highest negative influence score according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the media outlet with the highest negative influence, we look at the height of the negative influence graphical bars{\"Negative Influence\"}[0.153, 0.070, 0.316, 0.093]. We can see that the highest bar corresponds to FOX News{\"bar of FOX News\"}[0.420, 0.246, 0.481, 0.647]. Therefore, FOX News has the highest negative influence.\nBased on the above thought, the answer is:\nFOX News."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "As1cVJ0vtdRb8qUa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and present the percentage values of Economic Development and Social Consciousness as a table from the chart you're analyzing?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the x-axis labels}<r>[0.113, 0.927, 0.873, 0.962]</r> I see they represent the 11 data points over time from '0' through '10'. For 'Economic Development', I observe a series of {blue bars}<r>[0.104, 0.443, 0.877, 0.903]</r> with values labeled above each bar, e.g., {the first blue bar's value}[0.091, 0.415, 0.146, 0.450], and for 'Social Consciousness', there are {green bars}<r>[0.134, 0.525, 0.907, 0.903]</r> with their respective values. I use these values and create a structured table with each bar's corresponding data point and percentage.\nBased on the above thought, the answer is:\nData Point,Economic Development %,Social Consciousness %\n0,65.7,30.1\n1,70.1,35.2\n2,60.4,38.7\n3,69.3,34.0\n4,65.8,35.6\n5,67.9,39.2\n6,74.9,37.1\n7,85.1,43.2\n8,91.3,45.7\n9,95.8,49.9\n10,99.1,55.3"
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "ydFrqntyZXEyxr2r",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the course of the data points, does the pace of change in 'Economic Development' consistently exceed that of 'Social Consciousness'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the pace of change, I need to evaluate the trend of both Economic Development and Social Consciousness over the timeline. I observe each {set of bars}<r>[0.104, 0.443, 0.877, 0.903]</r> <r>[0.134, 0.525, 0.907, 0.903]</r>, noting that Economic Development starts at 65.7 and ends at 99.1, while Social Consciousness starts at 30.1 and ends at 55.3. I examine the labels of each bar finding the rise in percentages. The sharp rise in Economic Development particularly at the {7th and 8th points}<r>[0.624, 0.307, 0.654, 0.903]</r> <r>[0.699, 0.264, 0.728, 0.903]</r> suggests there were times where Economic Development's pace exceeded the comparatively smooth but slower upward trend of Social Consciousness.\nBased on the above thought, the answer is:\nWhile both 'Economic Development' and 'Social Consciousness' show an overall upward trend, there are instances, particularly between the 7th and 8th data points, where 'Economic Development' demonstrates a much faster pace of change in comparison to 'Social Consciousness'. Hence, the pace of change in 'Economic Development' does occasionally exceed that of 'Social Consciousness'."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "yNldWSmKdsNFckoc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth in Social Consciousness from data point '0' to data point '10'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage growth, I look at the chart and locate the values for Social Consciousness at {data point '0'}<r>[0.134, 0.525, 0.163, 0.903]</r> and {data point '10'}<r>[0.877, 0.209, 0.907, 0.903]</r> which are 30.1% and 55.3% respectively. The percentage growth calculation involves subtracting the initial value from the final value and then dividing by the initial value. That is (55.3 - 30.1) / 30.1.\nBased on the above thought, the answer is:\n83.7%"
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "6c1z0xjXtW0t9JHf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you have analyzed, please present the sales volumes of the different products in a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From {the title of the chart}<r>[0.317, 0.118, 0.742, 0.163]</r> and {the y-axis label 'Sales Volumes'}<r>[0.019, 0.414, 0.036, 0.664]</r> I can see that the y-axis represents the sales volumes. The x-axis labels indicate the names of different products, which include {LLaMA}<r>[0.138, 0.927, 0.195, 0.962]</r>, {Vicuna}<r>[0.379, 0.927, 0.438, 0.962]</r>, {Alpaca}<r>[0.621, 0.927, 0.680, 0.962]</r>, and {WizardLM}<r>[0.850, 0.927, 0.934, 0.962]</r>. The sales volumes for these products can be found by checking the height of the bars and the label on top of them, such as {bar for LLaMA}<r>[0.118, 0.407, 0.215, 0.903]</r>.\nBased on the above thought, the answer is:\nProduct,Sales Volume\nLLaMA,50\nVicuna,45\nAlpaca,54\nWizardLM,70"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "OZ5bGerMdYYI8CqE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you describe the sales trend among these products?"
+            },
+            {
+                "from": "gpt",
+                "value": "The heights of the bars represent sales volumes for each product are sequentially displayed as [LLaMA]<r>[0.138, 0.927, 0.195, 0.962]</r>, [Vicuna]<r>[0.379, 0.927, 0.438, 0.962]</r>, [Alpaca]<r>[0.621, 0.927, 0.680, 0.962]</r>, and [WizardLM]<r>[0.850, 0.927, 0.934, 0.962]</r>. The sales volumes are shown on top of each bar as 50, 45, 54, and 70 respectively. Thus we can observe a general upward trend in the sales volumes from LLaMA (50) to Alpaca (54) and WizardLM (70) with a slight decrease at Vicuna (45).\nBased on the above thought, the answer is:\nThe sales trend among the products is generally upward, with a slight dip for Vicuna. Thus, it can be observed that the total sales are increasing over time from LLaMA to WizardLM, with Vicuna being an exception."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "YcdnBM6HfnPMb28k",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product has the largest sales volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the product with the largest sales volume from the highest bar on the chart. If we look at the heights of the bars and the label flying on top of them, we can see that {WizardLM bar}<r>[0.843, 0.209, 0.940, 0.903]</r> with its label <r>[0.881, 0.231, 0.903, 0.266]</r> is the highest bar among the four products, thus it suggests that the sales volume is the largest.\nBased on the above thought, the answer is:\nWizardLM"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "EbudF8Ace6ltx7ow",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, could you kindly format the chart data into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To dissect this chart, we should first identify the significant elements. One such critical element is the {subtitle}<r>[0.386, 0.118, 0.649, 0.163]</r>, informs the chart shows an overview of Statistical Data. The x-axis, which doesn't explicitly carry any label, denotes 2 points in time: {Initial}<r>[0.352, 0.927, 0.401, 0.962]</r>, and {End}<r>[0.829, 0.927, 0.862, 0.962]</r>. On the y-axis, the {values}<r>[0.030, 0.525, 0.041, 0.560]; [0.019, 0.403, 0.041, 0.438]; [0.019, 0.282, 0.041, 0.317]; [0.019, 0.160, 0.041, 0.195]</r>, ranging from 0 to 60, denote the value of statistical fields. The {legend}<r>[0.062, 0.046, 0.324, 0.161]</r> describes the vivid relationship in our data comparing two Statistical Data fields: {Trend Analysis}<r>[0.117, 0.060, 0.242, 0.095]</r> and {Variance Measurement}<r>[0.117, 0.112, 0.317, 0.147]</r>. The initial and end values for these parameters are displayed on the chart via bars in different colors with their respective values. By analyzing these components, we can tabulate the data accordingly.\nBased on the above thought, the answer is:\nStatistical Field, Initial Values, End Values\nTrend Analysis, 45, 55\nVariance Measurement, 25, 50"
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "oCTDhPsVj4gbZNbT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data provided, can we draw any conclusions about the performance or trends of the two statistical fields over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {blue bars}<r>[0.095, 0.265, 0.283, 0.539]; [0.564, 0.205, 0.752, 0.539]</r> allocated to {Trend Analysis}<r>[0.117, 0.060, 0.242, 0.095]</r> and comparing their {annotated quantities}<r>[0.178, 0.226, 0.200, 0.261]; [0.366, 0.347, 0.387, 0.382]</r>, it's evident this field saw a slight increase from 45 to 55 points. Thus it can be concluded the trend here is \"Abrupt Increase\". However, upon looking at {Variance Measurement}<r>[0.117, 0.112, 0.317, 0.147]</r> represented by {green bars}<r>[0.283, 0.387, 0.470, 0.539]; [0.752, 0.235, 0.939, 0.539]</r> and focusing on their {values}<r>[0.647, 0.165, 0.669, 0.200]; [0.834, 0.195, 0.856, 0.230]</r>, it's clear that this field made a dramatic jump from 25 to 50 points. This characteristic indicates volatility or an \"Instable Increase\" in this statistical field data.\nBased on the above thought, the answer is:\nYes, we can conclude that the Trend Analysis saw an Abrupt Increase going from 45 to 55, while Variance Measurement went through an Instable Increase with its values rising dramatically from 25 to 50."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "GcXinBlhfeHcfa4N",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which Statistical Field experienced the greatest increase, and its precise numerical rise?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart comparison provides us the insight to measure the exact increase each Statistical Field experienced. For the field {Trend Analysis}<r>[0.117, 0.060, 0.242, 0.095]</r> - the increase is derived from the final value {55}<r>[0.366, 0.347, 0.387, 0.382]</r> and the initial value {45}<r>[0.178, 0.226, 0.200, 0.261]</r>, resulting in an increase of 10 points. Similarly, comparing the field {Variance Measurement}<r>[0.117, 0.112, 0.317, 0.147]</r>, we see an increase from {25}<r>[0.647, 0.165, 0.669, 0.200]</r> to {50}<r>[0.834, 0.195, 0.856, 0.230]</r>, resulting in a much larger increase of 25 points.\nBased on the above thought, the answer is:\nVariance Measurement experienced the greatest increase, precisely 25 points."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "ZUafP0XMrFoegM1H",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart information into a structured data tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart has a title {\"Trends in Media and Entertainment Industry\"}[0.272, 0.053, 0.728, 0.083] at the top. The x-axis labels are {\"Traditional\"}[0.151, 0.952, 0.242, 0.975], {\"Streaming\"}[0.354, 0.952, 0.444, 0.975], {\"Social\"}[0.575, 0.952, 0.627, 0.975], and {\"Interactive\"}[0.757, 0.952, 0.850, 0.975]. There are two y-axes. The left y-axis represents Media Growth (%) ranging from 0 to 15. The right y-axis represents Entertainment Decline (%) ranging from -10 to 0. For each type of media, there are two bars. The bars for Media Growth have the color of blue, labeled as {\"Media Growth\"}[0.142, 0.027, 0.262, 0.050]. The bars of Entertainment Decline have the color of pink, labeled as {\"Entertainment Decline\"}[0.711, 0.027, 0.907, 0.050]. The value of each bar can be determined by its height and the corresponding value annotation above or below it. For example, the height of the blue bar under \"Traditional\" Media [0.116, 0.637, 0.197, 0.935] corresponds to a value annotation of \"5.3\" [0.143, 0.675, 0.170, 0.698]. Therefore, Media Growth of \"Traditional\" Media equals to 5.3%.\nBased on the above thought, the answer is:\nMedia Type,Media Growth,Entertainment Decline\nTraditional,5.3,-3.2\nStreaming,11.4,-7.5\nSocial,9.2,-5.8\nInteractive,3.8,-2.7"
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "5e710IYMEJYDd9dn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does Streaming Media compare with other types of media in terms of growth in the Media sector and the decline in the Entertainment sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, I can see that Streaming Media has the highest growth in the Media sector, represented by {\"the blue bar\"}[0.318, 0.293, 0.399, 0.935] and {\"the value annotation '11.4'\"}[0.339, 0.331, 0.378, 0.355]. However, Streaming Media also has the largest decline in the Entertainment sector, represented by {\"the pink bar\"}[0.399, 0.090, 0.480, 0.724] and {\"the value annotation '-7.5'\"}[0.426, 0.664, 0.453, 0.687]. Compared to the other three types of media, Traditional, Social, and Interactive, Streaming Media stands out in both sectors, but in opposite ways.\nBased on the above thought, the answer is:\nStreaming Media has the highest growth in the Media sector, but also has the largest decline in the Entertainment sector, showing a large gap between the two sectors."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "Kf4xfdyQBphH4ACU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of media showed the largest growth in the media sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the height of the blue bars and the value annotations, I can deduce the growth rates for each media type. The coordinates of the value annotations are: {\"5.3\"}[0.143, 0.675, 0.170, 0.698], {\"11.4\"}[0.339, 0.331, 0.378, 0.355], {\"9.2\"}[0.547, 0.455, 0.574, 0.479], and {\"3.8\"}[0.749, 0.759, 0.777, 0.783]. The numbers correspond to the media types from left to right, that is, \"Traditional\", \"Streaming\", \"Social\", and \"Interactive\". The largest number is \"11.4\" associated with \"Streaming\" media.\nBased on the above thought, the answer is:\nStreaming."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "HTqSK821HGC27sx8",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart observations including 'Effective Management' and 'Adaptable Leadership' scores for each organization into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting from my interpretation of the x-axis, I note that it represents the different organizations including {SoftCorp}[0.174, 0.927, 0.249, 0.962], {FlexCorp}[0.406, 0.927, 0.482, 0.962], {DynamicInc}[0.626, 0.927, 0.728, 0.962], and {SteadFastLLC}[0.851, 0.927, 0.968, 0.962]. The y-axis using tick markers represents the score value, ranging from {0}[0.054, 0.889, 0.065, 0.924] to {80}[0.043, 0.104, 0.065, 0.139]. Focusing on the bars of different colors, the {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> represent the score for 'Effective Management' while the {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]> are indicative of the score for 'Adaptable Leadership'. By matching up the heights of the bars to the corresponding y-values, I can obtain the exact scores for each category. As an example, the first blue bar {SoftCorp Effective Management}[0.118, 0.209, 0.180, 0.903] aligns approximately with the 70 marker on the y-axis. This process is repeated to extract scores for all categories and compile the structured table.\nBased on the above thought, the answer is:\nOrganization,Effective Management,Adaptable Leadership\nSoftCorp,70.7,61.2\nFlexCorp,65.5,49.1\nDynamicInc,46.5,35.4\nSteadFastLLC,42.1,28.8"
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "xPwrMfAHhJgaEEgO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your interpretation of the bar chart, can you identify which area\u2014'Effective Management' or 'Adaptable Leadership'\u2014 organizations are generally performing better in?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area organizations are generally performing better in, I need to look at the average heights of the {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> versus the {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]>. On average, it appears that the blue bars are higher than the green bars, suggesting higher scores for Effective Management. Thus, organizations are generally performing better in the area of Effective Management compared to Adaptable Leadership.\nBased on the above thought, the answer is:\nOrganizations are generally performing better in the area of 'Effective Management'."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "5jC3dx92GZTqH2NV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich organization has the highest absolute difference between their 'Effective Management' and 'Adaptable Leadership' scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the organization with the largest absolute score difference between 'Effective Management' and 'Adaptable Leadership', the respective bar heights of {blue bars}<{\"Effective Management\"}[0.142, 0.060, 0.338, 0.095]> and {green bars}<{\"Adaptable Leadership\"}[0.142, 0.112, 0.331, 0.147]> for each organization need to be compared. The absolute differences are computed for each organization as follows: for {SoftCorp}[0.174, 0.927, 0.249, 0.962], the difference is calculated by noting the heights of the blue bar[0.118, 0.209, 0.180, 0.903] and the green bar[0.180, 0.302, 0.242, 0.903], and so forth for each organization. The organization with the largest such difference is identified as the one with the highest absolute difference.\nBased on the above thought, the answer is:\nSoftCorp"
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "Vucqgvf2olutJjWo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please create a table structure from the bar chart data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays information on two separate trends represented by different colors, namely {Global trade}<r>[0.113, 0.045, 0.200, 0.073]</r> indicated by a blue color, and {Supply Chain Management}<r>[0.113, 0.087, 0.301, 0.115]</r> indicated by a green color. The X-axis enumerates the names of the continents, ranging from {Asia} <r>[0.130, 0.942, 0.159, 0.970]</r> to {Australia}<r>[0.872, 0.942, 0.933, 0.970]</r>. The Y-axis indicates the trade values from {0}<r>[0.043, 0.570, 0.052, 0.598]</r> to {60}<r>[0.035, 0.159, 0.052, 0.187]</r>. To create the CSV table, I need to derive the values of Global Trade and Supply Chain Management for each continent. We can refer to the heights of the bars and the annotations on each bar. For instance, the height of the {Global Trade bar for Asia}<r>[0.104, 0.269, 0.144, 0.581]</r> reaches approximately the 45.6 position on the Y-axis, which corresponds to the {annotation \"45.6\"}<r>[0.109, 0.288, 0.139, 0.316]</r> on the bar.\nBased on the above thought, the answer is:\nContinents,Global Trade,Supply Chain Management\nAsia,45.6,38.7\nNorth America,39.4,49.3\nEurope,34.7,42.5\nAfrica,27.6,35.1\nSouth America,29.5,33.8\nAustralia,15.2, -31.6"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "LMIkaAmQ0v9Js5Dr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, can you identify any significant trends or outlier in either Global Trade or Supply Chain Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar values on the chart, {Global Trade}<r>[0.113, 0.045, 0.200, 0.073]</r> seems to be higher in general than {Supply Chain Management}<r>[0.113, 0.087, 0.301, 0.115]</r> for the same continent. The general trend is that both Global Trade and Supply Chain Management show lower values for Australia than for the other continents. Additionally, a remarkable outlier is present in {Supply Chain Management for Australia}<r>[0.903, 0.581, 0.943, 0.798]</r>, which displays a negative value label, {\"-31.8\"}<r>[0.905, 0.735, 0.941, 0.763]</r>, while all others are positive.\nBased on the above thought, the answer is:\nYes, the general trend shows lower values in both Global Trade and Supply Chain Management for Australia in comparison to other continents. The significant outlier is the negative value in Supply Chain Management for Australia."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "Wxryi0de1BiV1loM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich continent has the highest combined value of Global Trade and Supply Chain Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to add the trade values of Global Trade and Supply Chain Management for each continent and compare them. For example, {Asia}<r>[0.130, 0.942, 0.159, 0.970]</r> has a total trade value from {Global Trade}<r>[0.104, 0.269, 0.144, 0.581]</r> and {Supply Chain Management}<r>[0.144, 0.316, 0.185, 0.581]</r> of 45.6 + 38.7 = 84.3. By calculating and then comparing these total values for all continents, the continent with the highest combined trade value can be identified.\nBased on the above thought, the answer is:\nNorth America."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "vEv1IEm2SOyqZCSt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you saw, could you generate a data table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on what I noticed, there are two bars for each segment of the x-axis, which are labelled as {Value1}<r>[0.167, 0.927, 0.226, 0.962]</r>, {Value2}<r>[0.471, 0.927, 0.529, 0.962]</r>, and {Value3}<r>[0.774, 0.927, 0.833, 0.962]</r>. These labels represent the financial periods. The heights of the bars for each period represent the financial performance of {Company A}<r>[0.019, 0.398, 0.036, 0.598]</r> and {Company B}<r>[0.964, 0.398, 0.981, 0.599]</r>. For CompanyA, the respective heights at Value1<r>[0.116, 0.325, 0.197, 0.903]</r>, Value2<r>[0.419, 0.498, 0.500, 0.903]</r>, and Value3<r>[0.722, 0.614, 0.803, 0.903]</r> are 50, 35, and 25. Similarly, for CompanyB, the corresponding heights at Value1<r>[0.197, 0.209, 0.278, 0.903]</r>, Value2<r>[0.500, 0.383, 0.581, 0.903]</r>, and Value3<r>[0.803, 0.440, 0.884, 0.903]</r> are 60, 45, and 40.\nBased on the above thought, the answer is:\nCompany,Value1,Value2,Value3\nCompanyA,50,35,25\nCompanyB,60,45,40"
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "YLgvNa2ClEgShA6J",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the financial growth trends for CompanyA and CompanyB?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar heights for each financial period, I can see different trends for the two companies. {CompanyA's bars}<r>[0.116, 0.325, 0.197, 0.903]; [0.419, 0.498, 0.500, 0.903]; [0.722, 0.614, 0.803, 0.903]</r> show a decrease from Value1 to Value3, indicating a downward trend in financial performance. In contrast, {CompanyB's bars}<r>[0.197, 0.209, 0.278, 0.903]; [0.500, 0.383, 0.581, 0.903]; [0.803, 0.440, 0.884, 0.903]</r> also show a decrease from Value1 to Value3, but the overall values are higher than those of CompanyA, indicating a more positive financial performance despite the downward trend.\nBased on the above thought, the answer is:\nBoth companies show a downward trend in financial performance from Value1 to Value3. However, CompanyB has higher overall values for each financial period, indicating a more positive financial performance despite the downward trend."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "vIgdVUth5uBQ0HoO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest financial performance in the Value1 period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars representing the Value1 period for both companies are at {CompanyA Value1}<r>[0.116, 0.325, 0.197, 0.903]</r> and {CompanyB Value1}<r>[0.197, 0.209, 0.278, 0.903]</r>. By comparing the heights of these two bars, it's clear that CompanyB's bar is taller, indicating a higher financial performance in the Value1 period.\nBased on the above thought, the answer is:\nCompanyB"
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "YkvtzK8x20uVaGIV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract data from your analysis of the bar chart and structure it in a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides information on three different attributes; \"BlueChip\", \"Gold ETFs\", and \"Market Manipulation\" over a period of four weeks represented on the x-axis. The height of the bars represent the market performance in each week, which I can infer from the labels present on the y-axis. For instance, In \"Week1\" the height of the {blue bar}<r>[0.115, 0.250, 0.168, 0.903]</r> indicates a market performance of 48% for the \"BlueChip\" security. Using the same method, the data for the other weeks and securities can be obtained. This will form a table as follows.\nBased on the above thought, the answer is:\nSecurity,Week1,Week2,Week3,Week4\nBlueChip,48,45,51,47\nGold ETFs,38,31,40,29\nMarket Manipulation,45,48,37,52"
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "P8DOvCXRzmvsC1Se",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the market manipulation activities observed in the chart over the 4 weeks period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the performance of market manipulation activities is represented in the chart with pink bars. At a glance, we can observe substantial fluctuations in these activities over the weeks. Specifically, in {Week1}<r>[0.221, 0.174, 0.274, 0.775]</r> we see the market manipulation at 45%. In {Week2}<r>[0.420, 0.174, 0.473, 0.815]</r>, there is an increase to 48% indicating heightened manipulation. In {Week3}<r>[0.619, 0.174, 0.672, 0.668]</r>, we see a decline to 37% which could indicate improved regulation or less volatile market conditions. Finally, in {Week4}<r>[0.817, 0.174, 0.870, 0.868]</r> there is a significant increase to 52%, the highest in the period under review, which could indicate increased instability and risk in the market.\nBased on the above thought, the answer is:\nYes, the market manipulation activities fluctuated substantially over the four weeks. It was 45% in Week1, increased to 48% in Week2, decreased to 37% in Week3 and then significantly increased to 52% in Week4, indicating great volatility and increased risk."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "I9vGtYtcrx180a6W",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich week showed the highest market performance for the BlueChip security?"
+            },
+            {
+                "from": "gpt",
+                "value": "The weekly performance data for the BlueChip security are represented by the set of bluish bars. Looking at the {bar for week1}<r>[0.115, 0.250, 0.168, 0.903]</r>, with the label \"48\" at the top, it shows a performance of 48%, which we will use as a starting point for comparison. Following the same method, we see that the weekly performance for weeks 2, 3, and 4 are 45%, 51%, and 47% respectively. Therefore, we can see from bar heights that the highest performance for the BlueChip security was reached in week 3 as indicated by the {bar for Week3}<r>[0.513, 0.209, 0.566, 0.903]</r>.\nBased on the above thought, the answer is:\nWeek 3."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "uqr9m8C6JMfNBQbG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me convert the data related to international trade growth and globalization in the bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {\"International Trade and Globalization Trends\"}[0.271, 0.118, 0.729, 0.163] and the x-axis tick labels [{\"USA\"}[0.179, 0.927, 0.215, 0.962]; {\"China\"}[0.374, 0.927, 0.424, 0.962]; {\"UK\"}[0.589, 0.927, 0.613, 0.962]; {\"India\"}[0.782, 0.927, 0.825, 0.962]], it's clear that the x-axis represents different countries. The y-axis tick labels display corresponding values ranging from {0}[0.054, 0.525, 0.065, 0.560] to {60}[0.043, 0.160, 0.065, 0.195]. For each country, there are two bars representing International Trade and Globalization respectively according to {the legend}[0.086, 0.046, 0.313, 0.108] and [0.738, 0.046, 0.914, 0.108]. Therefore, to generate the CSV table, I need to extract the values for both categories in each country. I can check the height of the bars and the value annotations on top of the bars to find these values. For instance, {the bar for USA}[0.116, 0.257, 0.170, 0.539] in International Trade is annotated with 46.3 and {the bar for USA}[0.224, 0.539, 0.278, 0.758] in Globalization is annotated with 30.1.\nBased on the above thought, the answer is:\nCountry,International Trade,Globalization\nUSA,46.3,30.1\nChina,56.9,34\nUK,53.8,43.7\nIndia,19.4,20"
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "thxLNJPBCRaBqdi0",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your inspection of the chart, which country shows the most balanced growth between international trade and globalization? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The question asks which country exhibits the most balanced growth between international trade and globalization. By assessing both sets of bars, I need to consider which country has similar values for both International Trade and Globalization. For {\"USA\"}[0.179, 0.927, 0.215, 0.962], the {International Trade bar}[0.116, 0.257, 0.170, 0.539] and {Globalization bar}[0.224, 0.539, 0.278, 0.758] show distinct heights; similarly, this is the case for {\"UK\"}[0.589, 0.927, 0.613, 0.962] bars {[0.520, 0.212, 0.574, 0.539],[0.628, 0.539, 0.682, 0.857]} and {\"India\"}[0.782, 0.927, 0.825, 0.962] bars {[0.723, 0.421, 0.776, 0.539],[0.830, 0.539, 0.884, 0.684]}. However, the {International Trade bar}[0.318, 0.193, 0.372, 0.539] and {Globalization bar}[0.426, 0.539, 0.480, 0.787] of {\"China\"}[0.374, 0.927, 0.424, 0.962] are both quite high and very close in value (56.9 for trade and 34 for globalization).\nBased on the above thought, the answer is:\nChina. Although its International Trade value is greater than its Globalization index, the two values are more balanced when compared to the other countries, which have a wider disparity between their International Trade and Globalization values."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "olFd8wkodoOusVv2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has the highest globalization index according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the country with the highest globalization index, I need to take a look at the bars on the right side of every pair in every country. These bars represent the globalization index. Starting from {\"USA's globalization bar\"}[0.224, 0.539, 0.278, 0.758] moving right to {\"China's globalization bar\"}[0.426, 0.539, 0.480, 0.787], {\"UK's globalization bar\"}[0.628, 0.539, 0.682, 0.857] and then ending at {\"India's globalization bar\"}[0.830, 0.539, 0.884, 0.684], the y coordinates inform me that the normalization index for these countries is 30.1, 34, 43.7, and 20 respectively. The UK's bar is the highest among the four.\nBased on the above thought, the answer is:\nUK"
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "EBeuytTUabUvEoru",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart about healthy habits into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart with title {\"Healthy Habits: Healthy vs Freelance Lifestyles\"}[0.328, 0.025, 0.719, 0.055], I notice five categories of healthy habits on the x-axis: {Morning Run}[0.142, 0.811, 0.214, 0.931], {Healthy Eating}[0.322, 0.811, 0.404, 0.949], {Regular Checkups}[0.499, 0.811, 0.597, 0.975], {Limited Alcohol}[0.691, 0.811, 0.776, 0.954], and {Avoid Smoking}[0.877, 0.811, 0.960, 0.949]. Depending on the {legend}[0.798, 0.074, 0.978, 0.151] of the chart, the graph represents two different lifestyles: {Healthy Lifestyle}[0.843, 0.083, 0.959, 0.106] and {Freelance Lifestyle}[0.843, 0.118, 0.972, 0.141], each represented by different colored bars. I need to extract the values from the bars and their annotations to create a CSV table. For example, for the category \"Morning Run\", {the blue bar}[0.104, 0.209, 0.153, 0.795] and {the orange bar}[0.153, 0.499, 0.203, 0.795] represent a value of 53.1 and 26.8 respectively for \"Healthy Lifestyle\" and \"Freelance Lifestyle\".\nBased on the above thought, the answer is:\nHealthy Habits,Healthy Lifestyle,Freelance Lifestyle\nMorning Run,53.1,26.8\nHealthy Eating,63.2,37.9\nRegular Checkups,50.3,28.4\nLimited Alcohol,55.9,31.7\nAvoid Smoking,41.3,23.5"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "VA84qTJJKYkERaLS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the general trend shown in the chart regarding the habits and lifestyles?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart outlines the comparison between a healthy lifestyle and a freelance lifestyle across five different healthy habits: {Morning Run}[0.142, 0.811, 0.214, 0.931], {Healthy Eating}[0.322, 0.811, 0.404, 0.949], {Regular Checkups}[0.499, 0.811, 0.597, 0.975], {Limited Alcohol}[0.691, 0.811, 0.776, 0.954], and {Avoid Smoking}[0.877, 0.811, 0.960, 0.949]. Looking at the bars for each habit, such as those for Morning Run {healthy lifestyle}[0.104, 0.209, 0.153, 0.795] and {freelance lifestyle}[0.153, 0.499, 0.203, 0.795], and continuing this trend for the rest of the habits, it's clear that across all habits, a higher percentage of people following a healthy lifestyle adhere to these habits than those follow a freelance lifestyle. This trend implies that people who lead a healthy lifestyle are more likely to engage in these healthy habits than those who live a freelance lifestyle.\nBased on the above thought, the answer is:\nAcross all healthy habits (Morning Run, Healthy Eating, Regular Checkups, Limited Alcohol, and Avoid Smoking), a higher proportion of people living a Healthy Lifestyle adhere to these habits than those living a Freelance Lifestyle."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "MXw7PQaaTNXnmKJh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich healthy habit has the largest percentage difference between the Healthy Lifestyle and the Freelance Lifestyle?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the healthy habit with the largest percentage difference between the Healthy Lifestyle and the Freelance Lifestyle, I will need to calculate the difference for each habit. By observing the bar pairs for each habit, such as {Morning Run}[0.104, 0.209, 0.153, 0.795] and [0.153, 0.499, 0.203, 0.795], I can see that the difference is around 26.3%. By continuing this process for {Healthy Eating}[0.289, 0.097, 0.338, 0.795] and [0.338, 0.377, 0.388, 0.795]; {Regular Checkups}[0.474, 0.240, 0.523, 0.795] and [0.523, 0.481, 0.573, 0.795]; {Limited Alcohol}[0.659, 0.178, 0.709, 0.795] and [0.709, 0.445, 0.758, 0.795] ; and {Avoid Smoking}[0.844, 0.339, 0.894, 0.795] and [0.894, 0.536, 0.943, 0.795], it's clear that the largest difference is for Healthy Eating, which contains a difference of about 25.3%.\nBased on the above thought, the answer is:\nHealthy Eating"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "NuD6zt1KRcHaf6aH",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you have seen, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is titled \"Career and Workplace Development Trends\"[0.304, 0.037, 0.755, 0.083] implying its representation of certain career paths and their associated trends in the workplace. The x-axis labels, observed at {\"Software Engineer\"}[0.182, 0.927, 0.341, 0.962] and {\"Data Analyst\"}[0.741, 0.927, 0.853, 0.962] specify two different career paths. The units for y-axis are in percentage (%) [0.019, 0.365, 0.036, 0.632] indicating measurement of job trends. There are two groups of bars specified in {the legend}[0.086, 0.111, 0.252, 0.226], representing \"Job Stability\"[0.093, 0.128, 0.128, 0.152] and \"Job Growth\"[0.093, 0.180, 0.128, 0.204]. For each career path, I could infer values from both trends by analysing the height of each bar and reading the annotations at the top of the bars. For instance, the bar at [0.118, 0.281, 0.261, 0.903] annotated with \"61.5\"[0.171, 0.238, 0.209, 0.273] specifies the Job Stability of Software Engineers.\nBased on the above thought, the answer is:\nCareer Path,Job Stability,Job Growth\nSoftware Engineer,61.5,75.3\nData Analyst,50.2,64.7"
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "zEowVhKI8Kp43Xix",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which profession has a higher Job Growth percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the Job Growth values of Software Engineer and Data Analyst. The Job Growth values can be obtained from the height of the corresponding bars. Looking at the chart, the bar \"Job Growth for Software Engineer\" is located at [0.261, 0.141, 0.404, 0.903] while that for \"Job Growth for Data Analyst\" is at [0.797, 0.248, 0.940, 0.903]. The annotated values for each are \"75.3\"[0.314, 0.098, 0.352, 0.133] and \"64.7\"[0.849, 0.206, 0.888, 0.241] percent respectively after checking the annotations above the bars. Hence the Job Growth is higher for Software Engineer.\nBased on the above thought, the answer is:\nSoftware Engineer."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "zjUd3HQSZQNi05Xv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the grouped bar chart, can you turn the represented data into a tabular CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Transportation and Logistics Industry Trends\"}<r>[0.295, 0.080, 0.753, 0.125]</r> indicates that the chart is about different types of freights over the years 2019, 2020 and 2021. There are 4 groups of bars identified by different colors according to {the legend}<r>[0.076, 0.038, 0.248, 0.258]</r>, from which I know {\"Road Freight\"}<r>[0.132, 0.052, 0.241, 0.087]</r>, {\"Air Freight\"}<r>[0.132, 0.104, 0.221, 0.139]</r>, {\"Sea Freight\"}<r>[0.132, 0.156, 0.230, 0.191]</r> and {\"Rail Freight\"}<r>[0.132, 0.209, 0.229, 0.244]</r>. Each of these groups has 3 bars representing data for 3 years: 2019, 2020, and 2021 indicated by the {labels on the x-axis}<r>[{\"2019\"}[0.206, 0.927, 0.250, 0.962]; {\"2020\"}[0.502, 0.927, 0.546, 0.962]; {\"2021\"}[0.799, 0.927, 0.843, 0.962]]</r>. The numerical data for each bar can be obtained from the text annotations shown at the end of each bar, such as {\"78.3\"}<r>[0.119, 0.138, 0.158, 0.173]</r> for Road Freight in 2019. Hence, it is possible to compile this data into a CSV format.\nBased on the above thought, the answer is:\nTransport Type,2019,2020,2021\nRoad Freight,78.3,56.2,68.2\nAir Freight,45.6,30.3,41.6\nSea Freight, 58.5, 42.1, 50.9\nRail Freight, 36.7, 40.2, 42.8"
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "DV6SlelYdcbeDFnm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn general, what is the trend seen in the logistics and transportation industry from 2019 to 2021, as depicted in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart includes data for four different types of transportation: Road Freight, Air Freight, Sea Freight, and Rail Freight. Each type of transportation shows three bars for three different years: {\"2019\"}<r>[0.206, 0.927, 0.250, 0.962]</r>, {\"2020\"}<r>[0.502, 0.927, 0.546, 0.962]</r>, and {\"2021\"}<r>[0.799, 0.927, 0.843, 0.962]</r>. Comparing the bars' heights in the bar chart for each year across all transport types, I see a significant decline from 2019 to 2020, which can be observed from degree of lowering of the bars in 2020 compared to the bars in 2019 such as for {\"Road Freight\"}<r>[0.109, 0.171, 0.168, 0.570]</r> and <r>[0.406, 0.283, 0.465, 0.570]</r>. The industry then started to recover in 2021, as seen in the rising of the bars in 2021 compared to 2020, for example {\"Road Freight\"}<r>[0.702, 0.222, 0.762, 0.570]</r>. It can be inferred that the logistics and transportation industry probably faced a significant challenge such as the COVID-19 pandemic in 2020, and then started to recover in 2021.\nBased on the above thought, the answer is:\nThe general trend from 2019 to 2021 in the logistics and transportation industry shows a significant decline from 2019 to 2020, followed by a recovery in 2021."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "aP08rQOjarqhIok5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong all transportation types, which one experienced the least decline in 2020 from its 2019 numbers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which type of freight experienced the least decline in 2020 from 2019, I need to compare the differences between the heights of the bars of each group for 2019 and 2020. The bars for 2019 and 2020 for {\"Road Freight\"}<r>[0.109, 0.171, 0.168, 0.570]</r> and <r>[0.406, 0.283, 0.465, 0.570]</r>, the bars for 2019 and 2020 for {\"Air Freight\"}<r>[0.168, 0.337, 0.228, 0.570]</r> and <r>[0.465, 0.415, 0.524, 0.570]</r>, the bars for 2019 and 2020 for {\"Sea Freight\"}<r>[0.228, 0.570, 0.287, 0.868]</r> and <r>[0.524, 0.570, 0.584, 0.785]</r>, and the bars for 2019 and 2020 for {\"Rail Freight\"}<r>[0.287, 0.570, 0.346, 0.757]</r> and <r>[0.584, 0.570, 0.643, 0.775]</r>. Among them, the difference between Rail Freight from 2019 to 2020, based on the bar heights <r>[-36.7, -40.2]</r> and the number annotations, is the smallest.\nBased on the above thought, the answer is:\nRail Freight."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "76YMr0u9PZl5dcd1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a structured CSV table based on your observations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart title {\"Trends in New Media and Digital Content Creation\"} It's observed that this bar chart represents trend scales for different types of new media and digital content creation under two scenarios: \"Rapid Recovery\" and \"Accelerating Decline\". Looking at the labels on the x-axis, we can see the four categories: {Podcast Subscribers}[0.116, 0.919, 0.255, 0.942], {Online Webinars}[0.355, 0.919, 0.469, 0.942], {Virtual Reality Content}[0.559, 0.919, 0.717, 0.942], and {Live Streaming}[0.812, 0.919, 0.916, 0.942]. Based on the y-axis range that varies from -5 to 5, we can determine the trend scale value for each category from the lengths of the bars. To create a structured CSV table, we look at each bar's height under both categories. For example, the height of the bar for {Podcast Subscribers in Rapid Recovery}[0.107, 0.196, 0.186, 0.483] is around 3, and in {Accelerating Decline}[0.186, 0.483, 0.265, 0.865] is around -4. Similar calculations are done for all bars in all categories to extract the values.\nBased on the above thought, the answer is:\nIndicator,Rapid Recovery,Accelerating Decline\nPodcast Subscribers,3,-4\nOnline Webinars,-2,0\nVirtual Reality Content,-1,4\nLive Streaming,2,-3"
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "BBRcXNjQk9lMMXtn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which new media or digital content creation trend is suffering the most decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the category suffering the most decline, it is necessary to observe each category's negative trend scale values. This can be done by evaluating the lengths of the negative bars representing \"Accelerating Decline\". For example, {Podcast Subscribers under Accelerating Decline}[0.186, 0.483, 0.265, 0.865] has a trend scale of -4. Similarly analysis is done for other categories. The category with the largest negative value is the one suffering the most decline. Upon making these evaluations, it's evident that both Podcast Subscribers and Live Streaming show the greatest decline, both having a trend scale of -4.\nBased on the above thought, the answer is:\nPodcast Subscribers and Live Streaming"
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "MGuCcU7Q5UTtQDnG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category is the difference between \"Rapid Recovery\" and \"Accelerating Decline\" highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question accurately, I would need to evaluate the difference in trend scale values between \"Rapid Recovery\" and \"Accelerating Decline\" across all categories. This can be done by looking at the lengths of the corresponding bars for each category and calculating their differences. For instance, in {Podcast Subscribers}, we have {Rapid Recovery bar}[0.107, 0.196, 0.186, 0.483] at 3, and {Accelerating Decline bar}[0.186, 0.483, 0.265, 0.865] at -4. The absolute difference is 7. By conducting a similar exercise across all categories, it can be determined that Podcast Subscribers have the highest difference between Rapid Recovery and Accelerating Decline.\nBased on the above thought, the answer is:\nPodcast Subscribers"
+            }
+        ],
+        "id_raw": 885
+    },
+    {
+        "id": "kuMY1bLi2Gr38a05",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you visualized, can you convert the data into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, the x-axis {categories (Type 1 - Type 4)}<r>[0.143, 0.903, 0.189, 0.931]; [0.379, 0.903, 0.424, 0.931]; [0.614, 0.903, 0.659, 0.931]; [0.849, 0.903, 0.895, 0.931]</r> represent the different types of fuel. The y-axis values range from {-2}<r>[0.023, 0.946, 0.043, 0.974]</r> to {10}<r>[0.026, -0.069, 0.043, -0.041]</r>, and the bars represent the scores awarded to each technology, with each technology being represented by a unique color according to {the legend}<r>[0.060, 0.089, 0.238, 0.264]</r>. To construct the table, I need to examine the height of each bar according to their y-coordinates, which eventually gives us the score for that technology in the respective fuel type category.\nBased on the above thought, the answer is:\nFuel Types,Hybrid Technology,Diesel Technology,Gasoline Technology\nType 1,0.5,0.8,1.4\nType 2,4,6,7\nType 3,7,6,5\nType 4,8,6,7"
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "L8hI9LZg0yFB9dol",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend shown by the graph, which technology has shown the most consistent performance across different fuel types?"
+            },
+            {
+                "from": "gpt",
+                "value": "By tracking the height of each bar representing the different fuel types, I can determine that the technology that has been most consistent across various fuel types. Specifically, observing the height of the bars at {Hybrid technology}<r>[0.096, 0.745, 0.143, 0.788]; [0.331, 0.449, 0.378, 0.788]; [0.566, 0.196, 0.613, 0.788]; [0.801, 0.111, 0.849, 0.788]</r>, {Diesel technology}<r>[0.143, 0.720, 0.190, 0.788]; [0.378, 0.280, 0.425, 0.788]; [0.613, 0.280, 0.660, 0.788]; [0.849, 0.280, 0.896, 0.788]</r>, and {Gasoline technology}<r>[0.190, 0.669, 0.237, 0.788]; [0.425, 0.196, 0.472, 0.788]; [0.660, 0.365, 0.707, 0.788]; [0.896, 0.449, 0.943, 0.788]</r>, I can see changes within each set. The Fuel Type ratings for Diesel Technology exhibit the least fluctuation around a generally steady value, making it the most consistent technology across different fuel types.\nBased on the above thought, the answer is:\nDiesel Technology"
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "xiNwoQOlytR6FhkN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which fuel type has the highest total score across all technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the fuel type with the highest total score, I combine all scores in each category. By comparing height of the bars of {Hybrid Technology}<r>[0.096, 0.745, 0.143, 0.788]; [0.331, 0.449, 0.378, 0.788]; [0.566, 0.196, 0.613, 0.788]; [0.801, 0.111, 0.849, 0.788]</r>, {Diesel Tech}<r>[0.143, 0.720, 0.190, 0.788]; [0.378, 0.280, 0.425, 0.788]; [0.613, 0.280, 0.660, 0.788]; [0.849, 0.280, 0.896, 0.788]</r> and {Gasoline Tech}<r>[0.190, 0.669, 0.237, 0.788]; [0.425, 0.196, 0.472, 0.788]; [0.660, 0.365, 0.707, 0.788]; [0.896, 0.449, 0.943, 0.788]</r> in each category, we can see that Type 4 has the highest total score.\nBased on the above thought, the answer is:\nType 4"
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "6Of1PP38TtSWDsHP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the x-axis, it contains 4 categories which are {Artificial Intelligence}<r>[0.086, 0.903, 0.228, 0.931]</r>, {Machine Learning}<r>[0.341, 0.903, 0.464, 0.931]</r>, {Cloud-based Learning}<r>[0.573, 0.903, 0.724, 0.931]</r> and {VR/MR Technology}<r>[0.830, 0.903, 0.958, 0.931]</r>. And the y-axis range is from {0}<r>[0.035, 0.872, 0.057, 0.900]</r> to {4.5}<r>[0.035, 0.006, 0.057, 0.034]</r>. From the {legend}<r>[0.073, 0.089, 0.287, 0.181]</r>, two color bars are used to represent two trends which are {EdTech Adoption}<r>[0.118, 0.100, 0.233, 0.128]</r> and {Online Learning Growth}<r>[0.118, 0.142, 0.282, 0.170]</r>. Therefore, to generate the CSV table, I need to extract the value of each bar using both colors under each category. For example, the height of {EdTech Adoption bar}<r>[0.108, 0.595, 0.157, 0.883]</r> under the Artificial Intelligence category is around 1.5 while the height of {Online Learning Growth bar}<r>[0.157, 0.498, 0.206, 0.883]</r> under the same category is about 2.\nBased on the above thought, the answer is:\nFeatures,EdTech Adoption,Online Learning Growth\nArtificial Intelligence,1.5,2\nMachine Learning,2,2.5\nCloud-based Learning,2.5,3\nVR/MR Technology,3,4"
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "bbDWxa8m6nGZl9uD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the perspective of the future development of online education, which technology do you think will receive the most attention? And please explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive analysis of the height of bars which represent the {Online Learning Growth}<r>[0.118, 0.142, 0.282, 0.170]</r> in each category. {VR/MR Technology}<r>[0.830, 0.903, 0.958, 0.931]</r> has {the highest bar}<r>[0.894, 0.113, 0.943, 0.883]</r> in Online Learning Growth, indicating it will expectedly receive the most attention in the future. Such growth can be attributed to the immersive and interactive experiences it brings to online education. This technology can transform the way we learn and teach, making knowledge transfer more efficient and effective.\nBased on the above thought, the answer is:\nVR/MR Technology. From the chart, it's evident that the adoption of VR/MR Technology in online learning is gaining remarkable growth compared to other technologies which signifies its importance and attention in the upcoming future."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "4uNE3FVHtzek0Rpz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich feature has the smallest difference between EdTech Adoption and Online Learning Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height difference between the two bars which represent {EdTech Adoption} and {Online Learning Growth} for each feature. For example, for the feature of {Artificial Intelligence}<r>[0.086, 0.903, 0.228, 0.931]</r>, the heights of the bars {EdTech Adoption}<r>[0.108, 0.595, 0.157, 0.883]</r> and {Online Learning Growth}<r>[0.157, 0.498, 0.206, 0.883]</r> are 1.5 and 2 respectively, giving a difference of 0.5. Similarly, for {Machine Learning}<r>[0.341, 0.903, 0.464, 0.931]</r>, the difference between the heights of the two bars {EdTech Adoption}<r>[0.354, 0.498, 0.403, 0.883]</r> and {Online Learning Growth}<r>[0.403, 0.402, 0.452, 0.883]</r> is 0.5 as well. After doing this for all the four features, it can be seen that the feature with the smallest difference between the value of EdTech Adoption and Online Learning Growth is {Artificial Intelligence}<r>[0.086, 0.903, 0.228, 0.931]</r> and {Machine Learning}<r>[0.341, 0.903, 0.464, 0.931]</r>.\nBased on the above thought, the answer is:\nArtificial Intelligence, Machine Learning"
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "TVixQFwGN73SKFXb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a CSV format table from the bar chart you are observing."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV table, I need to get the value of sales for each product in each quarter firstly. The chart shows 4 quarters from {Q1}<r>[0.172, 0.903, 0.191, 0.931]</r> to {Q4}<r>[0.864, 0.903, 0.884, 0.931]</r> on the x-axis while shows a products' sales represented by bars with different colors according to {the legend}<r>[0.881, 0.089, 0.978, 0.264]</r>. There are four products: {iPhone}<r>[0.926, 0.100, 0.972, 0.128]</r>, {iPad}<r>[0.926, 0.142, 0.955, 0.170]</r>, {Mac}<r>[0.926, 0.183, 0.954, 0.211]</r>, and {Watch}<r>[0.926, 0.225, 0.969, 0.253]</r>. For iPhone's sales in Q1, the value is represented by the height of {the blue bar}<r>[0.112, 0.421, 0.147, 0.883]</r>, which can be calculated as 120,000 units based on the y-axis. Similarly, the rest of the sales can be obtained by referencing the heights of the bars corresponding to each product in each quarter.\nBased on the above thought, the answer is:\nProduct,Q1,Q2,Q3,Q4\niPhone,120,130,150,200\niPad,100,90,85,80\nMac,50,70,75,100\nWatch,30,50,70,100"
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "dUBjFmOqocLLRb3D",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product's sales is the most stable throughout the four quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the product with the most stable sales, we need to compare the fluctuation of product sales from Q1 to Q4 for each product. The fluctuation is represented by the changes in the bar heights for each product. For example, for {iPhone}<r>[0.926, 0.100, 0.972, 0.128]</r>, its sales from Q1 to Q4 have been increasing, which indicates strong growth, but also more fluctuations. {iPad}<r>[0.926, 0.142, 0.955, 0.170]</r> sales have been declining, which indicates that iPad has fluctuated sales, too. {Mac}<r>[0.926, 0.183, 0.954, 0.211]</r> sales also show constant growth, meaning less stability. Lastly, {Watch}<r>[0.926, 0.225, 0.969, 0.253]</r> sales shows an even and steady increase, which indicates the most stability.\nBased on the above thought, the answer is:\nWatch. Despite the fact that its sales are the lowest among the four products, its quartal sales performance shows the steadiest growth throughout the year, indicating the most stability."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "FV4q4zpI23GfxGj5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product has the greatest increase in sales from Q1 to Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which product had the greatest sales increase from Q1 to Q4, I need to compare the initial and final quarter sales of each product. This can be done by looking at the height of each bar corresponding to each product in the Q1 and Q4 quarters. For iPhone, {the bar for Q1 sales}<r>[0.112, 0.421, 0.147, 0.883]</r> is approximately 120 and {the bar for Q4 sales}<r>[0.805, 0.113, 0.840, 0.883]</r> is 200, indicating an increase of 80; for the other products, I can similarly get the sales increase. After comparing the increase of each product, it can be seen that the iPhone has the greatest sales increase.\nBased on the above thought, the answer is:\niPhone."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "6RpIF5JYM8psqvYw",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease decipher the information in the chart and arrange it in a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart title {\"Human Health and Disease Management\"}[0.357, 0.030, 0.699, 0.066], it is a statistical analysis relating to disease management in different management areas which are located on the x-axis {\"Management Areas\"}[0.460, 0.942, 0.595, 0.970]. Specifically , this list includes {\"Minor Illness\"}[0.118, 0.903, 0.204, 0.931], {\"Chronic Disease\"}[0.350, 0.903, 0.461, 0.931], {\"Accident & Injury\"}[0.591, 0.903, 0.710, 0.931] and {\"Mental Health\"}[0.846, 0.903, 0.943, 0.931]. The y-axis provides efficiency scores {\"Efficiency Score\"}[0.015, 0.369, 0.029, 0.590]. According to the legend {\"Efficient Care Continuum\", \"Personalized Care\"}[0.416, 0.089, 0.640, 0.181], the chart analyzes 2 indicators for health and disease management: Efficient care continuum and Personalized care. Their values can be deciphered from the height of the bars corresponding to each indicator and each management area. For example, for Minor Illness management, the score of Efficient care continuum is obtained from the height of the first bar which reaches about 70 {[0.112, 0.344, 0.161, 0.883]}, and that of Personalized care is obtained from 100, as deduced from the second bar's height {[0.161, 0.113, 0.210, 0.883]}. Similarly, for the other aspects, the scores of each indicator can be obtained. Following this manner, I can construct the table format needed.\nBased on the above thought, the answer is:\nManagement Areas,Efficient care continuum,Personalized care\nMinor Illness Management, 70,100\nChronic Disease Management, 90,75\nAccident & Injury Prevention, 85,80\nMental Health Therapy, 60,95"
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "Qr8gD0dzPPq4ctTn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the efficiency of disease management based on the information in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides lots of useful insights regarding the efficiency of disease management. For the management performance of {\"Efficient Care Continuum\"}[0.460, 0.100, 0.634, 0.128] and {\"Personalized Care\"}[0.460, 0.142, 0.585, 0.170], one can conclude that the efficiency of the latter is generally higher in all four aspects: {\"Minor Illness\"}[0.118, 0.903, 0.204, 0.931], {\"Chronic Disease\"}[0.350, 0.903, 0.461, 0.931], {\"Accident & Injury\"}[0.591, 0.903, 0.710, 0.931]and {\"Mental Health\"}[0.846, 0.903, 0.943, 0.931]. According to the legend, the corresponding bars {[0.161, 0.113, 0.210, 0.883], [0.406, 0.306, 0.454, 0.883], [0.650, 0.267, 0.699, 0.883], [0.895, 0.152, 0.943, 0.883]} of 'Personalized Care' are evidently higher than that of 'Efficient Care Continuum' {[0.112, 0.344, 0.161, 0.883], [0.357, 0.190, 0.406, 0.883], [0.601, 0.229, 0.650, 0.883], [0.846, 0.421, 0.895, 0.883]}. Therefore, it seems that personalized treatment may be a good development direction and worth further research and investment.\nBased on the above thought, the answer is:\nPersonalized care may provide more efficient disease management in the areas of Minor Illness, Chronic Disease, Accident & Injury, and Mental Health, as it performs better than Efficient Care Continuum in these aspects. This suggests that personalized treatment could be a promising way forward in improving disease management efficiency."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "S24vkc4tH35bJxLg",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which management area is the improvement in efficiency score from 'Efficient care continuum' to 'Personalized care' the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I will compare the bar heights in each of the four management areas from 'Efficient care continuum' to 'Personalized care'. The pairs of bars to be compared are as follows: {\"Minor Illness Management\"}[0.112, 0.344, 0.161, 0.883, 0.161, 0.113, 0.210, 0.883], {\"Chronic Disease Management\"}[0.357, 0.190, 0.406, 0.883, 0.406, 0.306, 0.454, 0.883], {\"Accident & Injury Prevention\"}[0.601, 0.229, 0.650, 0.883, 0.650, 0.267, 0.699, 0.883], {\"Mental Health Therapy\"}[0.846, 0.421, 0.895, 0.883, 0.895, 0.152, 0.943, 0.883]. The difference of score in each management area can be calculated by subtracting the value of 'Efficient care continuum' from the value of 'Personalized care'. For instance, in the case of 'Minor Illness', the improvement in score would be 100 - 70 = 30. From my calculations the most substantial improvement in efficiency score is seen in 'Mental Health Therapy' with an increase of 35 units.\nBased on the above thought, the answer is:\nMental Health Therapy"
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "IukzXXsOUulUt50V",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you are observing, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the bar chart is {\"Culture and Arts Industry Development\"}[0.360, 0.030, 0.687, 0.066], and there are four major categories along the x-axis, which represent different sectors in the Culture and Arts Industry: {\"Visual Arts\"}[0.116, 0.903, 0.190, 0.931], {\"Performing Arts\"}[0.346, 0.903, 0.454, 0.931], {\"Sculpture\"}[0.614, 0.903, 0.680, 0.931], and {\"Writing\"}[0.869, 0.903, 0.919, 0.931]. The y-axis represents the development score, ranging from {0}[0.043, 0.872, 0.052, 0.900] to {80}[0.035, 0.051, 0.052, 0.079]. According to {the legend}[0.853, 0.089, 0.978, 0.181], the {blue bars}<{\"Developed\"}[0.898, 0.100, 0.972, 0.128]> represent scores of the developed sectors while the {orange bars}<{\"Emerging\"}[0.898, 0.142, 0.964, 0.170]> represent the emerging sectors. The score for each sector is calculated from the height of the bars. From this, I can construct a CSV format data representation.\nBased on the above thought, the answer is:\nCulture Aspects, Developed, Emerging\nVisual Arts, 75, 55\nPerforming Arts, 68, 46\nSculpture, 67, 35\nWriting, 49, 68"
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "CJWKUli2APe4I207",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the development in the Culture and Arts Industry based on the data presented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question focuses on the comprehensive understanding of the bar chart titled as {\"Culture and Arts Industry Development\"}[0.360, 0.030, 0.687, 0.066] which displays the development scores in the sectors of visual arts, performing arts, sculpture, and writing. The analysis can be done by comparing the {blue bars}<{\"Developed\"}[0.898, 0.100, 0.972, 0.128]> and the {orange bars}<{\"Emerging\"}[0.898, 0.142, 0.964, 0.170]>. We can see that in most cases except {'Writing'}[0.869, 0.903, 0.919, 0.931], the Emerging sector shows lower scores as compared to Developed sectors. This could imply that these areas may be less prevalent or less developed in the Emerging sectors. However, the Emerging score is significantly higher in the {Writing} sector [0.894, 0.185, 0.943, 0.883], suggesting it has exceeded the Development score [0.844, 0.380, 0.894, 0.883], indicating a tremendous growth and attention in this particular field.\nBased on the above thought, the answer is:\nVisual arts, performing arts, and sculpture are better developed in the Developed sectors compared to the Emerging sectors, suggesting these sectors are relatively mature and have received more attention and investment. However, in the writing sector, the Emerging sector scores higher than Developed sectors, indicating a rapid development and potentially a shift in focus towards writing, possibly due to the rise of digital media making it more accessible and popular."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "hFZWOZzDZGzkkBlj",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Culture Aspect has the most significant development in the Emerging sectors as compared to Developed sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "We are looking for the category in which the height of the orange bar, representing the \"#Emerging\" sector, is higher than the blue bar, representing the \"#Developed\" sector. By observing the bars in each category, we find the pairs of bars in the category {\"Visual Arts\"}[<{\"Developed\"}[0.104, 0.113, 0.153, 0.883]; {\"Emerging\"}[0.153, 0.319, 0.203, 0.883]>], the category {\"Performing Arts\"}[<{\"Developed\"}[0.351, 0.185, 0.400, 0.883]; {\"Emerging\"}[0.400, 0.411, 0.449, 0.883]>], the category {\"Sculpture\"}[<{\"Developed\"}[0.597, 0.195, 0.647, 0.883]; {\"Emerging\"}[0.647, 0.524, 0.696, 0.883]>] and the category {\"Writing\"}[<{\"Developed\"}[0.844, 0.380, 0.894, 0.883]; {\"Emerging\"}[0.894, 0.185, 0.943, 0.883]>]. It can be observed that in the \"Writing\" category, the {\"Emerging\"}[0.894, 0.185, 0.943, 0.883] bar is visibly higher than the {\"Developed\"}[0.844, 0.380, 0.894, 0.883] bar, hence showing the most significant development in the Emerging sectors as compared to Developed sectors.\nBased on the above thought, the answer is:\nWriting"
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "1E6WsendRqi7k3MQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you summarize the performance scores data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title {\"Social Responsibility and Sustainable Business\"}[0.326, 0.030, 0.713, 0.066] gives us the idea that the performance scores of CSR and Sustainable development strategies in Default and HAG are shown. The tick labels of the x-axis {\"2019\", \"2020\", \"2021\", \"2022\", \"2023\"}[{\"2019\"}[0.148, 0.903, 0.184, 0.931]; {\"2020\"}[0.325, 0.903, 0.360, 0.931]; {\"2021\"}[0.502, 0.903, 0.537, 0.931]; {\"2022\"}[0.678, 0.903, 0.713, 0.931]; {\"2023\"}[0.855, 0.903, 0.890, 0.931]] represent the years. The y-axis tick labels ranging from {0}[0.035, 0.872, 0.043, 0.900] to {7}[0.035, -0.026, 0.043, 0.002] represent the performance scores. According to {the sublegend}[0.060, 0.089, 0.247, 0.264], each color stands for a different sub-category.\nEach year has a cluster of four bars, such as {2019}[0.096, 0.242, 0.237, 0.883]. The heights of the bars represent values, and the positions of the bars match up with the y-axis tick labels. Let's take 2019 performance scores as an example: the heights of the bars {\"CSR-Default\"}[0.096, 0.242, 0.131, 0.883], {\"CSR-HAG\"}[0.131, 0.370, 0.166, 0.883], {\"Sustainable-Default\"}[0.166, 0.370, 0.201, 0.883] and {\"Sustainable-HAG\"}[0.201, 0.498, 0.237, 0.883] are corresponding to scores 5, 4, 4, 3 respectively. Similarly, we can decode the data from 2020 to 2023.\nBased on the above thought, the answer is:\nYear,CSR-Default,CSR-HAG,Sustainable-Default,Sustainable-HAG\n2019,5,4,4,3\n2020,4,5,3.5,4\n2021,3,5.5,3,5\n2022,2,5.5,2.5,5\n2023,2,6,2.5,5.5"
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "RrSvRv5IxiIk0kpf",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which strategy shows constant growth in performance scores through the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which strategy shows constant growth, I should examine the trend of the bars' heights for each strategy from 2019 to 2023. Let's scrutinize the bars representing each sub-category, such as {CSR-HAG}[{\"2019\"}[0.131, 0.370, 0.166, 0.883]; {\"2020\"}[0.307, 0.242, 0.343, 0.883]; {\"2021\"}[0.484, 0.177, 0.519, 0.883]; {\"2022\"}[0.660, 0.177, 0.696, 0.883]; {\"2023\"}[0.837, 0.113, 0.872, 0.883]]. Similarly, we can analyze CSR-Default, Sustainable-Default, and Sustainable-HAG. By looking at the bar heights and comparing them across years, it can be noted that the 'CSR-HAG' strategy shows a constant upward trend across the years.\nBased on the above thought, the answer is:\nThe 'CSR-HAG' strategy shows constant growth in performance scores from 2019 to 2023."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "txi3ZlZpIRUDum12",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year did the 'CSR-HAG' score the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the answer by comparing the heights of the bars representing 'CSR-HAG' scores across different years. Let's look at the second bar in each year: they are {\"2019\"}[0.131, 0.370, 0.166, 0.883], {\"2020\"}[0.307, 0.242, 0.343, 0.883], and so forth. The heights of the bars correspond to the y-axis tick labels {\"0\"}[0.035, 0.872, 0.043, 0.900] to {\"7\"}[0.035, -0.026, 0.043, 0.002], allowing me to read the score for each year. By this comparison, 'CSR-HAG' gets the lowest score in {2019}[0.131, 0.370, 0.166, 0.883].\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "Gn3bu8WdAhX3Ixv1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the raw data of the product sales trend for the first half of the year according to the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Product Sales Trend} [0.455, 0.030, 0.618, 0.066] and {the x-axis labels} [Cosmeceuticals [0.169, 0.903, 0.277, 0.931], Natural Products [0.374, 0.903, 0.490, 0.931], Superfoods [0.602, 0.903, 0.680, 0.931], Battery [0.824, 0.903, 0.876, 0.931]], it is evident that the x-axis represents different product categories. The y-axis labels show the sales values that start from 0 and go up to 60000. The colored bars represent sales in different months. For example, {the blue bar for Cosmeceuticals} [0.129, 0.340, 0.160, 0.883] reaches approximately the 36000 position on the y-axis, indicating that the sales for Cosmeceuticals in January were around $36000. Using the heights of the bars, I can derive the sales for each product and month, enabling the creation of a CSV table.\nBased on the above thought, the answer is:\nProduct,Jan,Feb,Mar,Apr,May,Jun\nCosmeceuticals,36000,33800,36750,37250,38000,40000\nNatural Products,42250,44000,45800,47500,49250,51000\nSuperfoods,48500,45500,43000,40000,37500,35500\nBattery,47500,49250,45500,45500,47500,49250"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "BvBoeNUqeL6qxYHX",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category has shown a predictable growth over the six months considering the sales trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the height of the bars representing sales for each product over the months Jan to Jun. {Cosmeceuticals bars} [{'Jan'}[0.129, 0.340, 0.160, 0.883], {'Feb'}[0.160, 0.373, 0.192, 0.883], {'Mar'}[0.192, 0.328, 0.223, 0.883], {'Apr'}[0.223, 0.321, 0.254, 0.883], {'May'}[0.254, 0.310, 0.286, 0.883], {'Jun'}[0.286, 0.279, 0.317, 0.883]] show a slight but consistent growth. {Natural Products bars} fluctuate slightly but show a strong and stable rise, indicating a positive and predictable growth. The bars for {Superfoods} and {Battery} do not depict a predictable growth as their height decreases or fluctuates inconsistently. Therefore, based on the bar chart, Natural Products have shown a predictable and positive growth over the first six months of the year.\nBased on the above thought, the answer is:\nNatural Products."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "U27yVDVaekv7cLA5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows the highest sale volume in June?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the product with the highest June sale, I hunt for the rectangle bar that reaches the highest on the y-axis in each group, all of them are represented by a brown color. Starting from the left, {the Cosmeceuticals bar} [0.286, 0.279, 0.317, 0.883] hits around the 40000 mark, {the Natural Products bar} [0.495, 0.113, 0.526, 0.883] touches approximately the 51000 mark, {the Superfoods bar} [0.704, 0.347, 0.735, 0.883] touches around the 35500 mark, and finally, {the Battery bar} [0.913, 0.140, 0.944, 0.883] hits near the 49250 mark. Therefore, 'Natural Products' achieves the highest sale in June.\nBased on the above thought, the answer is:\nNatural Products."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "4nTXj2bFy1aYMEMx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data presented in the bar chart into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart primarily represents four categories of media types on the x-axis {\"Media Type 1\"}[0.145, 0.903, 0.237, 0.931], {\"Media Type 2\"}[0.370, 0.903, 0.461, 0.931], {\"Media Type 3\"}[0.594, 0.903, 0.686, 0.931], and {\"Media Type 4\"}[0.819, 0.903, 0.910, 0.931] and their corresponding influence score values on the y-axis, ranging from {0}[0.052, 0.872, 0.061, 0.900] to {250}[0.035, -0.003, 0.061, 0.025]. The interpretation of each bar is given by {the legend}[0.841, 0.089, 0.978, 0.181] which states that the entries {\"Local News\"}[0.885, 0.100, 0.963, 0.128] and {\"Social Media\"}[0.885, 0.142, 0.972, 0.170] have different colors. Therefore, I can determine the influence scores for each media type in both categories using the height of the bars. For example, the height of {\"the first bar in Local News\"}[0.112, 0.463, 0.191, 0.883] corresponds to an influence score of about 120, which matches the start of the y-range text {\"0.00 -> 120.00\"} on the bar itself.\nBased on the above thought, the answer is:\nMedia Type,Local News,Social Media\nMedia Type 1,120,220\nMedia Type 2,150,130\nMedia Type 3,80,100\nMedia Type 4,130,190"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "qW082OVZXTTA78AY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which type of news, Local or Social media, do you think has a more significant influence on the audience overall? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires me to compare the overall influence of Local News and Social Media. By examining {the legend entries}[0.841, 0.089, 0.978, 0.181], the colors indicate the categories. For comparison, I shall compare the bars from {\"Local News\"}[0.885, 0.100, 0.963, 0.128] and {\"Social Media\"}[0.885, 0.142, 0.972, 0.170] in each media type. In detail, for {\"Media Type 1\"}[0.145, 0.903, 0.237, 0.931], the bar for Social Media seems much taller than that for Local News. Similarly, in {\"Media Type 2\"}[0.370, 0.903, 0.461, 0.931], although the bar for Local News is taller, the difference is not as substantial. In {\"Media Type 3\"}[0.594, 0.903, 0.686, 0.931] and {\"Media Type 4\"}[0.819, 0.903, 0.910, 0.931], the bars for Social Media are again taller. Summarizing these observations, Social Media appear to have a more significant influence on audiences overall.\nBased on the above thought, the answer is:\nSocial Media. Because in three of out four media types, Social Media has a higher influence score than Local News. It suggests that Social Media tends to have a more significant influence on the audience overall."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "GnCtI3lJrJSneOQ9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media type has the highest difference in influence score between Local News and Social Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which media type has the highest difference in influence score, I will need to calculate the absolute difference between the heights of the corresponding bars in the \"Local News\" and \"Social Media\" categories. Looking at all four sets of bars, it appears that {the first set}[{Local News}[0.112, 0.463, 0.191, 0.883], {Social Media}[0.191, 0.113, 0.269, 0.883]] (representing \"Media Type 1\") has the largest difference. This would be consistent with the observation that {the bar for \"Local News\"}[0.112, 0.463, 0.191, 0.883]  is around the middle of the y-axis, while {the bar for \"Social Media\"}[0.191, 0.113, 0.269, 0.883] is almost reaching the top, indicating a significant difference in the influence score.\nBased on the above thought, the answer is:\nMedia Type 1"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "iFbFz9qG7RpQwpHh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the total development scores for each technology from the bar chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, observing the chart, I identify the years on {the x-axis labels}<r>[0.099, 0.939, 0.124, 0.957]; [0.180, 0.939, 0.205, 0.957]; [0.261, 0.939, 0.286, 0.957]; [0.342, 0.939, 0.367, 0.957]; [0.423, 0.939, 0.448, 0.957]; [0.504, 0.939, 0.529, 0.957]; [0.585, 0.939, 0.610, 0.957]; [0.666, 0.939, 0.691, 0.957]; [0.747, 0.939, 0.772, 0.957]; [0.828, 0.939, 0.854, 0.957]; [0.910, 0.939, 0.935, 0.957]</r>, which range from 2010 to 2020. The bars corresponding to each technology are marked as {Artificial Intelligence bars}<r>[0.087, 0.669, 0.099, 0.814]; [0.168, 0.596, 0.180, 0.814]; [0.249, 0.451, 0.261, 0.814]; ...</r> for Artificial Intelligence, {Quantum Computing bars}<r>[0.099, 0.814, 0.111, 0.814]; [0.180, 0.669, 0.193, 0.814]; [0.261, 0.596, 0.274, 0.814]; ...</r> for Quantum Computing, {5G bars}<r>[0.111, 0.814, 0.124, 0.887]; [0.193, 0.814, 0.205, 0.887]; [0.274, 0.814, 0.286, 0.851]; ...</r> for 5G, and {Data Science bars}<r>[0.124, 0.742, 0.136, 0.814]; [0.205, 0.705, 0.217, 0.814]; [0.286, 0.669, 0.298, 0.814]; ...</r> for Data Science. By assessing the height of each bar and referencing the {y-axis labels}<r>[0.023, 0.953, 0.037, 0.971]; [0.031, 0.807, 0.037, 0.825]; [0.031, 0.662, 0.037, 0.679]; ...</r>, I can calculate the scores for each year and summarize them into a table.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,5G,Data Science,Total\n2010,2,0,-1,1,2\n2011,3,2,-1,1.5,5.5\n2012,5,3,-0.5,2,9.5\n2013,5,4,0,2.5,11.5\n2014,4,6,0.5,3,13.5\n2015,3,7,0.5,2.5,13\n2016,5,8,1,3,17\n2017,6,8,2,3.5,19.5\n2018,4.5,6,2,3,15.5\n2019,5,9,3,3.5,20.5\n2020,7,10,2.5,4,23.5"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "9nl8dIE7wLeFagHM",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the most stable positive development over the 2010 to 2020 period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine stability and positivity of development, I must analyze the growth pattern of each sector over the years. Observing the {Artificial Intelligence bars}<r>[0.087, 0.669, 0.099, 0.814]; ... [0.898, 0.305, 0.910, 0.814]</r> where there are several ups and downs, it indicates erratic growth. For {Quantum Computing}<r>[0.099, 0.814, 0.111, 0.814]; ... [0.910, 0.087, 0.922, 0.814]</r>, the growth seems moderate but consistent. {5G}<r>[0.111, 0.814, 0.124, 0.887]; ... [0.922, 0.632, 0.934, 0.814]</r> started from negative but shows stable growth throughout. {Data Science}<r>[0.124, 0.742, 0.136, 0.814]; ... [0.934, 0.523, 0.946, 0.814]</r> shows a consistent positive slope without negative years. Evaluating the bars related to each sector, with special attention given to the absence of negative bars, steady height increase, and absence of sharp jumps or dips, I can interpret the overall trend stability and positivity.\nBased on the above thought, the answer is:\nData Science. This sector not only displayed a consistent positive development score through all the years without ever dipping below zero but also showed an upward trend, which indicates stable positive growth."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "7bHvAukN57IZYk7K",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average score of Data Science across all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the chart, I can obtain the value for Data Science in each year by referencing the heights of the {Data Science bars}<r>[0.124, 0.742, 0.136, 0.814]; [0.205, 0.705, 0.217, 0.814]; [0.286, 0.669, 0.298, 0.814]; ... [0.934, 0.523, 0.946, 0.814]</r>. To calculate the average, I sum up the scores provided for each year from the chart and divide by the number of years which is 11, from 2010 to 2020.\nBased on the above thought, the answer is:\n2.77"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "Tv7ovqLO3m519eDT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me translate the bar chart information you observed into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the information I observed from the bar chart, we have both {Sustained Growth and Rapid Expansion}[0.060, 0.089, 0.234, 0.181] trends for four different educational technologies. These technologies are represented on the x-axis as {Online Classes}[0.095, 0.903, 0.196, 0.931], {MOOCs}[0.369, 0.903, 0.420, 0.931], {Webinars}[0.611, 0.903, 0.676, 0.931], and {Virtual Reality}[0.844, 0.903, 0.942, 0.931]. The y-axis, labelled 'Growth Units'[0.015, 0.389, 0.029, 0.569], represents the growth in terms of units. The green and orange bars represent the units of growth for Sustained Growth and Rapid Expansion trends, respectively. For example, looking at Online Classes, the green bar for Sustained Growth has a height at approximately {2 units}[0.096, 0.702, 0.145, 0.883] and the orange bar for Rapid Expansion is at {1 unit}[0.145, 0.793, 0.195, 0.883]. Using this process, I can collect the data from all the bars.\nBased on the above thought, the answer is:\nPlatforms,Sustained Growth,Rapid Expansion\nOnline Classes,2.0,1.0\nMOOCs,3.0,3.5\nWebinars,3.5,6.0\nVirtual Reality,4.0,8.5"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "ChPF2pxSF0ms1FGA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, which growth trend would you say is more beneficial for the advancement of educational technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question calls for an evaluation of the merits of the {Sustained Growth}[0.105, 0.100, 0.228, 0.128] and {Rapid Expansion}[0.105, 0.142, 0.220, 0.170] trends. Looking at the individual bars for each trend per technology, I see that in Online Classes, the Sustained Growth value is {higher}[0.096, 0.702, 0.145, 0.883] than the Rapid Expansion value{1.0}[0.145, 0.793, 0.195, 0.883]. However, in MOOCs, both growth trends are {the same}[0.345, 0.612, 0.394, 0.883; 0.394, 0.566, 0.444, 0.883]. In both Webinars and Virtual Reality, the Rapid Expansion trend values are {greater}[0.644, 0.340, 0.693, 0.883; 0.893, 0.113, 0.943, 0.883] than their Sustained Growth counterparts. It seems that while the Sustained Growth trend offers stable and reliable growth, the Rapid Expansion trend has the capacity for considerable leaps.\nBased on the above thought, the answer is:\nRapid Expansion. While Sustained Growth offers guaranteed steady progress, Rapid Expansion provides the potential for significant advancements that can outpace the steady pace of Sustained Growth in technologies like Webinars and Virtual Reality."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "iaDjirhlyVzno7w7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which educational technology did the Rapid Expansion trend exhibit the most growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine where the Rapid Expansion trend exhibit the most growth, I need to compare the height of the orange bars across all four educational technologies. {The bar for Online Classes}[0.145, 0.793, 0.195, 0.883] is approximately at 1 unit on the y-axis. {The MOOCs bar}[0.394, 0.566, 0.444, 0.883] is approximately at 3.5 units on the y-axis. {The Webinars bar}[0.644, 0.340, 0.693, 0.883] is approximately at 6 units, and finally, {the bar for Virtual Reality}[0.893, 0.113, 0.943, 0.883] is the highest, approximately at 8.5 units. This suggests that the Rapid Expansion trend exhibited the most growth in Virtual Reality.\nBased on the above thought, the answer is:\nVirtual Reality."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "zTOjgaGJfFJDtBIL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you turn the observations into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the x-axis tick labels, it can be seen that each area corresponds to two bars, one representing Traditional Media and the other Digital Media. The {x-axis labels}<r>[0.172, 0.903, 0.200, 0.931]; [0.358, 0.903, 0.466, 0.931]; [0.579, 0.903, 0.697, 0.931]; [0.811, 0.903, 0.917, 0.931]</r>shows the different areas namely SEO, Content Editing, Video Production, and Graphic Design. According to {the legend}<r>[0.072, 0.089, 0.241, 0.181]</r>, {the red bars} <r>\"Traditional Media\"[0.077, 0.102, 0.105, 0.122]</r> represent Traditional Media, and {the blue bars}<r>\"Digital Media\"[0.077, 0.144, 0.105, 0.164]</r> represent Digital Media. For instance, the height of {the red bar for SEO}<r>[0.107, 0.380, 0.186, 0.524]</r> is at the 0.8 position, so the score for Traditional Media in the SEO category is 0.8. Similarly, I can extract the values for other categories and for Digital Media.\nBased on the above thought, the answer is:\nType,SEO,Content_Editing,Video_Production,Graphic_Design\nTraditional_Media,0.8,1.1,-1.4,1.5\nDigital_Media,1.2,1.6,-1.8,2.3"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "6vZQZih7jgDRb53l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what would you say about the position of traditional media and digital media in different dimensions of new media and digital content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart, we can see that both Traditional Media and Digital Media have positive scores in SEO, Content Editing, and Graphic Design represented by {bars}<r>[0.107, 0.380, 0.186, 0.524]; [0.333, 0.327, 0.412, 0.524]; [0.785, 0.255, 0.864, 0.524]; [0.186, 0.309, 0.265, 0.524]; [0.412, 0.237, 0.491, 0.524]; [0.864, 0.111, 0.943, 0.524]</r>; and negative scores in Video Production, shown by {bars}<r>[0.559, 0.524, 0.638, 0.775]; [0.638, 0.524, 0.717, 0.847]</r>. The general pattern, therefore, indicates a more positive reception or success in the former three fields and less in Video Production for both types of media. However, the digital media, with the unique color of {blue bars}<r>\"Digital Media\"[0.077, 0.144, 0.105, 0.164]</r>, consistently outperforms traditional media in all areas.\nBased on the above thought, the answer is:\nBoth Traditional Media and Digital Media perform well in SEO, Content Editing, and Graphic Design, albeit with Digital Media performing slightly better. However, both media types underperform in Video Production, with the decline being especially sharp for Digital Media. This suggests that while Digital Media is more successful in most areas, both types of media might need to address some challenges in Video Production."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "p33JroDRt8AZ5l40",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media type has more score in SEO?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the heights of the Traditional and Digital Media bars in the SEO category. From the coordinate data, we can see that {the red bar for SEO}<r>[0.107, 0.380, 0.186, 0.524]</r> representing Traditional Media reaches a height of about 0.8, while {the blue bar for SEO}<r>[0.186, 0.309, 0.265, 0.524]</r> representing Digital Media reaches a height of about 1.2. So, Digital Media has more score in the SEO category.\nBased on the above thought, the answer is:\nDigital Media"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "8K91qQrLt0BpB0tG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you have observed, could you please convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "sure, given the title at the top of the chart {\"Research Quantity in Different Academic Fields\"}[0.328, 0.030, 0.719, 0.066] and the x-axis labels {\"Humanities\"}[0.153, 0.883], {\"Natural_Sciences\"}[0.400, 0.883], {\"Formal_Sciences\"}[0.647, 0.883], {\"Social_Sciences\"}[0.894, 0.883], I infer that these are the academic fields being compared. The y-axis represents research quantities from 0 to 35. We have two sets of bars according to {the legend}[0.802, 0.089, 0.978, 0.181], {the 'Bachelors_Degree' bars}<{\"BLUE\"}[0.808, 0.102, 0.835, 0.122]> and {the 'PhD' bars}<{\"ORANGE\"}[0.808, 0.144, 0.835, 0.164]>, for each academic field. I will approximate the values by looking at the height of the bars. For instance, for the academic field 'Humanities', the {Bachelors_Degree bar}[0.104, 0.370, 0.153, 0.883] is at approximately 20 units on the y-axis, while the {PhD bar}[0.153, 0.498, 0.203, 0.883] is at approximately 15 units. Therefore, I can generate a CSV table converting this chart into tabular data.\nBased on the above thought, the answer is:\nAcademic_fields, Bachelors_Degree, PhD\nHumanities, 20, 15\nNatural_Sciences, 25, 22\nFormal_Sciences, 30, 28\nSocial_Sciences, 27, 23"
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "MOVkB0vqRjinZTks",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be gained relating to research quantity in different academic fields and educational levels, from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides a good comparison of research quantity across different academic fields such as {the \"Humanities\"}<[0.114, 0.903, 0.193, 0.931]>, {the \"Natural_Sciences\"}<[0.340, 0.903, 0.460, 0.931]>, the {the \"Formal_Sciences\"}[0.589, 0.903, 0.704, 0.931], and the {the \"Social_Sciences\"}[0.839, 0.903, 0.948, 0.931] for Bachelor's degree holders and PhDs. By analyzing the bar heights, we can see bachelors have conducted more research in all fields when compared to individuals with PhDs. However, the pattern of research quantity across different fields is similar for both education levels, with an overall increase from Humanities to Formal Sciences, and then decreasing in the Social Sciences field. The notable exception is a significant drop in Humanities for PhDs when compared to Bachelor's degree holders, possibly indicating a shift towards more empirical sciences.\nBased on the above thought, the answer is:\nThe pattern of research quantity is similar for both education levels showing humanities as an exception. PhD holders focus more on empirical sciences and bachelor's degree holders conduct more research."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "205orYBgbVfbMGrn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic field has the smallest difference in research quantity between Bachelors_Degree and PhD?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out which academic field has the smallest difference between the numbers of research of Bachelors and PhDs, I need to compare the bar heights of Bachelors and PhDs for each academic field respectively.  By referencing the y-axis where the bar tops align, the differences in 'Humanities' is [20-15=5], in 'Natural_Sciences' is [25-22=3], in 'Formal_Sciences' is [30-28=2], and in 'Social_Sciences' is [27-23=4]. Out of these differences, 2 is the smallest, which corresponds to the 'Formal_Sciences' academic field.\nBased on the above thought, the answer is:\nFormal Sciences"
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "TBYeyd3Hx35p858b",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a CSV table from the grouped bar chart showing the sales data for each laptop model per month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sales data presented in the grouped bar chart, I am processing the visual information. Starting with {Apple Macbook}<r>[0.093, 0.564, 0.108, 0.917]</r>, I notice bars corresponding to each month with the top end indicating sales figures. By examining their height relative to {the y-axis tick labels}<r>[0.050, 0.917, 0.044, 0.929]</r>, it's feasible to extract their corresponding values. Parallelly, I process the monthly sales data for {Samsung Chromebook}<r>[0.108, 0.670, 0.122, 0.917]</r>, {Lenovo Thinkpad}<r>[0.122, 0.682, 0.137, 0.917]</r>, and {Dell XPS}<r>[0.137, 0.329, 0.151, 0.917]</r>. Each group of bars is analyzed in relation to the {x-axis tick labels}<r>[0.103, 0.931, 0.141, 0.951]</r> to determine the month they represent.\nBased on the above thought, the answer is:\nMonth,Apple_Macbook,Samsung_Chromebook,Lenovo_Thinkpad,Dell_XPS\nJanuary,150,105,100,250\nFebruary,140,110,120,220\nMarch,130,115,110,215\nApril,125,120,115,300\nMay,120,150,110,350\nJune,110,145,105,330\nJuly,100,140,120,320\nAugust,90,130,140,320\nSeptember,80,125,150,310\nOctober,75,120,160,305\nNovember,70,110,170,300\nDecember,60,100,180,295"
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "FmhKzpzCkCsJKlV4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the data, which laptop model shows a significant increase in sales at any point during the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify a significant sales increase, I examine the height of each bar for every laptop brand throughout the monthly groupings. I observe the {Dell XPS bars}<r>[0.137, 0.329, 0.151, 0.917]</r>, particularly in April and May. For these months, Dell XPS bars show a substantial increase in height compared to the previous months. This indicates a notable rise in sales figures. In April, the bar starts around y0.329, contrasting sharply with March's bar, which starts around y0.412. A similarly significant increase is visible for May, as the top of the bar is even higher, starting around y0.094.\nBased on the above thought, the answer is:\nThe Dell XPS model shows a significant increase in sales, particularly in the months of April and May, as indicated by the taller bars in the chart for these months when compared to others."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "9b0OxQWhCyodQEYV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales figure for Lenovo Thinkpad over the 12-month period?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate the total sales figure for {Lenovo Thinkpad}<r>[0.122, 0.682, 0.137, 0.917]</r>, I will sum the sales numbers represented by the heights of each individual bar corresponding to the months January through December. These sales figures are indicated by the space between the bottom and top of each bar as compared to the {sales indicators on the y-axis}<r>[0.025, 0.087, 0.044, 0.107]</r>. For instance, for January, the bar extends from approximately y0.682 to the baseline at y0.917, which corresponds to the sales figure of 100. I will perform a similar analysis for each subsequent month.\nBased on the above thought, the answer is:\n1420"
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "hYsF66w8yqECQxIq",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a structured table including all indexes and their corresponding performance for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing {the bar chart}<r>[0.050, 0.047, 0.988, 0.874]</r> representing the performance of various market indexes, I can identify four different sets of bars for each year, each set corresponding to an index. The labels on the x-axis {years}<r>[0.105, 0.881, 0.933, 0.898]</r> range from 2007 to 2019. The y-axis has performance values which range from {\u22123}<r>[0.029, 0.868, 0.046, 0.885]</r> to {5}<r>[0.039, 0.040, 0.046, 0.057]</r>. Each index is represented by a unique color, as described in {the legend}<r>[0.256, 0.959, 0.781, 0.990]</r>. The bars corresponding to the SPX-Default index, for example, range from coordinates <r>[0.093, 0.430, 0.106, 0.564]</r> to <r>[0.892, 0.140, 0.905, 0.564]</r>. By noting the heights of the bars and their positions in relation to the y-axis, I can extract the individual performance values for each index in each year.\nBased on the above thought, the answer is:\nYear,SPX-Default,DOW-Default,NASDAQ-Default,CRSP-Default\n2007,1.3,1.0,1.1,1.2\n2008,-1.2,-1.4,-1.3,-1.5\n2009,-2.0,-1.9,-2.1,-2.2\n2010,0.8,0.6,0.9,1.0\n2011,1.2,1.3,1.4,1.1\n2012,1.9,1.7,2.0,1.8\n2013,2.4,2.6,2.5,2.3\n2014,2.8,3.0,2.9,2.7\n2015,3.2,3.1,3.3,3.0\n2016,3.6,3.5,3.7,3.4\n2017,4.5,4.3,4.4,4.1\n2018,3.8,3.6,3.7,3.4\n2019,4.1,4.2,4.3,4.0"
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "uLmVyu9qhPy4foGa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index had the most negative impact during the financial crisis of 2008 as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the index most negatively impacted by the financial crisis in 2008, I need to observe the bar chart's representation of index performance for that specific year. I locate {the set of bars for 2008}<r>[0.159, 0.564, 0.186, 0.709]</r> to <r>[0.199, 0.564, 0.212, 0.719]</r> and identify the one with the lowest endpoint relative to the y-axis, which indicates the most significant decrease. The {CRSP-Default label}<r>[0.700, 0.965, 0.777, 0.983]</r> and its corresponding bar <r>[0.199, 0.564, 0.212, 0.719]</r> show the lowest point, indicating the largest negative performance value.\nBased on the above thought, the answer is:\nThe CRSP-Default index had the most negative impact during the financial crisis of 2008, as it is represented by the bar with the lowest endpoint for that year."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "l38BR3buR5pBqWke",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average performance of the SPX-Default index over the years presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average performance of the SPX-Default index, I need to identify all the representing bars for SPX-Default across the years on the chart. Examining {the SPX-Default bars}<r>[0.093, 0.430, 0.106, 0.564]</r> to <r>[0.892, 0.140, 0.905, 0.564]</r>, I note the values for each year. Adding all the values from years 2007 to 2019 and then dividing by the total number of years (13) will give me the average. The values are 1.3, -1.2, -2.0, 0.8, 1.2, 1.9, 2.4, 2.8, 3.2, 3.6, 4.5, 3.8, and 4.1.\nBased on the above thought, the answer is:\n2.1"
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "WH8ul456frbBKzHS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the figure, the x-axis is represented by technology areas provided by the labels - {Quantum}<r>[0.128, 0.939, 0.182, 0.957]</r>, {Robotics}<r>[0.370, 0.939, 0.419, 0.957]</r>,{Genomics}<r>[0.605, 0.939, 0.662, 0.957]</r>, and {CyberSecurity}<r>[0.832, 0.939, 0.914, 0.957]</r>. The y-axis represents the percentage increases, the scale for which ranges from {0}<r>[0.029, 0.926, 0.036, 0.944]</r> to {7}<r>[0.029, -0.028, 0.036, -0.011]</r>. Each technology area has three bars colored in accordance with {the legend}<r>[0.830, 0.055, 0.982, 0.139]</r>: {TechArea}<r>[0.867, 0.062, 0.920, 0.080]</r>,{ResearchGrowth}<r>[0.867, 0.088, 0.962, 0.106]</r> and {InnovationIncrease}<r>[0.867, 0.115, 0.977, 0.132]</r>. By noting the height of each bar, the corresponding values can be discerned. For example, for {Quantum ResearchGrowth}<r>[0.131, 0.524, 0.179, 0.933]</r> the bar reaches approximately to the 3 mark on the y-axis, thus its value is 3.\nBased on the above thought, the answer is:\nTechArea,ResearchGrowth, InnovationIncrease\nQuantum,1.5,3\nRobotics,3,4\nGenomics,2.5,5\nCyberSecurity,4,4.5"
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "LXRVkIYONASyLUEe",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, which technology area should receive more investment and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to comprehensively compare the 'ResearchGrowth'-{second color}<r>[0.867, 0.088, 0.962, 0.106]</r> and 'InnovationIncrease'-{third color}<r>[0.867, 0.115, 0.977, 0.132]</r> in each technology area. Generally, areas with high 'ResearchGrowth' are ripe for innovation, and areas where 'InnovationIncrease' has far outpaced 'ResearchGrowth' are likely to have a lot of untapped potential. The largest difference between 'ResearchGrowth' and 'InnovationIncrease' occurs in the {CyberSecurity}<r>[0.832, 0.939, 0.914, 0.957]</r> area where {ResearchGrowth}<r>[0.849, 0.115, 0.897, 0.933]</r> is strikingly lower than {InnovationIncrease}<r>[0.897, 0.797, 0.944, 0.933]</r>. Therefore, it might be beneficial to increase investment in 'CyberSecurity' research to maximize innovation potential.\nBased on the above thought, the answer is:\nCyberSecurity. This is because its 'InnovationIncrease' significantly surpasses the 'ResearchGrowth', indicating there could be substantial untapped potential in this area."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "OE27qVPMVtcef6T1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology area has the highest growth in innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I have to find the bar representing the highest 'Innovation Increase'. The bars for 'Innovation Increase' are identified by {the third color}<r>[0.867, 0.115, 0.977, 0.132]</r> in the legend. As the y-axis represents the percentage increase, I scanned each technology area by comparing the height of the corresponding 'Innovation Increase' bars, namely: {Quantum}<r>[0.179, 0.729, 0.227, 0.933]</r>, {Robotics}<r>[0.418, 0.660, 0.466, 0.933]</r>, {Genomics}<r>[0.657, 0.592, 0.705, 0.933]</r>, and {CyberSecurity}<r>[0.897, 0.797, 0.944, 0.933]</r>. After analysis, {Genomics}<r>[0.657, 0.592, 0.705, 0.933]</r> has the highest bar and therefore the highest growth in innovation.\nBased on the above thought, the answer is:\nGenomics."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "fsdTailTo4UTDVaE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information represented in the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays data on the investment or interest level for different areas of healthcare and medical technology between the years 2016 and 2020. According to the x-axis labels that I observed {{\"2016\"}[0.160, 0.880, 0.195, 0.900]; {\"2017\"}[0.334, 0.880, 0.369, 0.900]; {\"2018\"}[0.508, 0.880, 0.543, 0.900]; {\"2019\"}[0.682, 0.880, 0.717, 0.900]; {\"2020\"}[0.856, 0.880, 0.891, 0.900]}, these represent the years under consideration. The y-axis labels indicate the level of investment or interest, ranging from 0 (observed at {{\"0\"}[0.052, 0.865, 0.061, 0.885]}) to 100 (observed at {{\"100\"}[0.035, 0.045, 0.061, 0.065]}). The chart's subtitle {\"Healthcare and Medical Technology\"}[0.378, 0.021, 0.672, 0.047] indicates the fields of interest. The bars represented in four different colors correspond to specific healthcare and technology categories. The corresponding legend at the bottom, {\"Telemedicine\"}[0.288, 0.958, 0.378, 0.978]; {\"Health Apps\"}[0.445, 0.958, 0.529, 0.978]; {\"EHR\"}[0.596, 0.958, 0.625, 0.978]; and {\"AI in Healthcare\"}[0.691, 0.958, 0.802, 0.978] shows how the bars map to specific healthcare categories. To create the CSV table, I need to extract values for each category for each year from the chart. I will use the height of the bars to determine these values.\nBased on the above thought, the answer is:\nYear,Telemedicine,HealthApps,EHR,AIinHealthcare\n2016,30,35,20,15\n2017,40,47,25,22\n2018,55,38,30,35\n2019,50,60,40,30\n2020,75,65,45,40"
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "mHNVgQFgIwGncQbQ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trend of each technology field, which technology would you say is currently drawing more investment or interest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the bars representing each technology in 2020, as they would provide the most recent data on investment or interest levels. The relevant coordinates for each technology in 2020 are {\"2020 Telemedicine\"}[0.804, 0.258, 0.839, 0.873], {\"2020 Health Apps\"}[0.839, 0.340, 0.874, 0.873], {\"2020 EHR\"}[0.874, 0.504, 0.908, 0.873], and {\"2020 AI in Healthcare\"}[0.908, 0.545, 0.943, 0.873]. By considering the height of these bars, I can observe that the \"Telemedicine\" bar for 2020 reaches the highest point on the y-axis, indicating that it currently attracts the most investment or interest.\nBased on the above thought, the answer is:\nTelemedicine, as it has the highest bar for the year 2020, meaning it is currently attracting the most investment or interest."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "1UkvWv35Mc9TSnzU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology had the highest increase in investment or interest from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the height of each technology's bars in 2016 and 2020, I can determine which technology saw the greatest increase in investment or interest. The bars for 2016 can be found at these coordinates {\"2016 Telemedicine\"}[0.108, 0.627, 0.142, 0.873], {\"2016 Health Apps\"}[0.142, 0.586, 0.177, 0.873], {\"2016 EHR\"}[0.177, 0.709, 0.212, 0.873], and {\"2016 AI in Healthcare\"}[0.212, 0.750, 0.247, 0.873]. The equivalent coordinates for 2020 are {\"2020 Telemedicine\"}[0.804, 0.258, 0.839, 0.873], {\"2020 Health Apps\"}[0.839, 0.340, 0.874, 0.873], {\"2020 EHR\"}[0.874, 0.504, 0.908, 0.873], and {\"2020 AI in Healthcare\"}[0.908, 0.545, 0.943, 0.873]. By comparing the bar heights from both years, it can be observed that the bar for \"Telemedicine\" has the highest increase in height.\nBased on the above thought, the answer is:\nTelemedicine."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "RBIwyauJZu0x1n9E",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart's data into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, the chart provides indicators for years at the bottom represented on the x-axis. These are {2025}<r>[0.177, 0.880, 0.212, 0.900]</r>, {2030}<r>[0.395, 0.880, 0.430, 0.900]</r>, {2035}<r>[0.613, 0.880, 0.648, 0.900]</r>, and {2040}<r>[0.830, 0.880, 0.865, 0.900]</r>. These years will be the columns of our CSV table along with the sectors. We have 4 sectors labeled at {legend}<r>[0.125, 0.950, 0.917, 0.986]</r>. These are Automobile Production, Oil Production, Renewable Energy Production, and AI Adoption. The y-axis indicates the projected value. I can identify the value for each sector in each year by observing the height of the corresponding bars in the graph. For example, the bar representing Automobile Production in 2025<r>[0.107, 0.217, 0.151, 0.873]</r> extends to approximately 120 on the y-axis.\nBased on the above thought, the answer is:\nSector,Years,2025,2030,2035,2040\nAutomobile_Production,120,115,110,100\nOil_Production,85,80,75,60\nRenewable_Energy_Production,50,70,90,120\nAI_Adoption,20,40,60,100"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "RxlMrpbonimsehKn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the trend and predict which sector will show the most growth in the future."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trend, we can compare the growth in each sector across the years. The bars representing each sector are as follows: {Automobile Production}<r>[0.107, 0.217, 0.151, 0.873],[0.325, 0.245, 0.369, 0.873], [0.543, 0.272, 0.586, 0.873], [0.761, 0.327, 0.804, 0.873]</r>, {Oil Production}<r>[0.151, 0.409, 0.194, 0.873],[0.369, 0.436, 0.412, 0.873], [0.586, 0.463, 0.630, 0.873], [0.804, 0.545, 0.848, 0.873]</r>, {Renewable Energy Production}<r>[0.194, 0.600, 0.238, 0.873],[0.412, 0.490, 0.456, 0.873], [0.630, 0.381, 0.674, 0.873], [0.848, 0.217, 0.891, 0.873]</r>, and {AI Adoption}<r>[0.238, 0.764, 0.281, 0.873],[0.456, 0.654, 0.499, 0.873], [0.674, 0.545, 0.717, 0.873], [0.891, 0.327, 0.935, 0.873]</r>. From these bars, it is clear that the height of bars representing AI adoption increases dramatically from 2025 to 2040, symbolizing significant growth. Therefore, the most growth is predicted in the AI Adoption sector.\nBased on the above thought, the answer is:\nThe AI Adoption sector is projected to show the most growth in the future."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "LT4ZmoA9fHE0eyFv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector is projected to have the highest value in 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which sector is projected to have the highest value in 2040, I need to compare the height of the bars for the year 2040. The bars for 2040 are {Automobile Production}<r>[0.761, 0.327, 0.804, 0.873]</r>, {Oil Production}<r>[0.804, 0.545, 0.848, 0.873]</r>, {Renewable Energy Production}<r>[0.848, 0.217, 0.891, 0.873]</r>, and {AI Adoption}<r>[0.891, 0.327, 0.935, 0.873]</r>. Comparing these bars, the {AI Adoption}<r>[0.891, 0.327, 0.935, 0.873]</r> bar is the highest, reaching to value 100 on the y-axis.\nBased on the above thought, the answer is:\nAI Adoption"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "qP72x17ToWM92CDg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart, could you create a CSV format table from it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis represents different companies which include {Goldman Sachs}[0.111, 0.818, 0.169, 0.942], {J.P. Morgan}[0.270, 0.818, 0.312, 0.909], {Morgan Stanley}[0.413, 0.818, 0.471, 0.942], {Bank Of America}[0.562, 0.818, 0.623, 0.951], {Wells Fargo}[0.721, 0.818, 0.765, 0.912], and {Citigroup}[0.876, 0.818, 0.912, 0.896]. The y-axis represents scores for each of them that ranging from {0}[0.023, 0.796, 0.038, 0.816] to {3.5}[0.023, -0.011, 0.038, 0.009]. Each company has two bars, for 'Economic Affairs' and 'Risk Control' according to {legend}[0.875, 0.063, 0.985, 0.129]. {The blue bars}[0.904, 0.071, 0.982, 0.091] represent scores in 'Economic Affairs' and {the green bars}[0.904, 0.101, 0.960, 0.121] represent scores in 'Risk Control'. By analysing the height of the bars and corresponding to the y-axis, we are able to estimate the scores for 'Economic Affairs' and 'Risk Control' for each company.\nBased on the above thought, the answer is:\nCompany,Economic Affairs,Risk Control\nGoldman Sachs,1.7,2.3\nJ.P. Morgan,2.5,3.1\nMorgan Stanley,1.5,2.2\nBank Of America,2.1,1.9\nWells Fargo,1.9,1.5\nCitigroup,1.4,2.0"
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "6mOMFtf3jZzJ2KL1",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which company is the most balanced when it comes to managing Economic Affairs and Risk Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height difference between the {blue bar}[0.904, 0.071, 0.982, 0.091] and the {green bar}[0.904, 0.101, 0.960, 0.121] for each company as it represents the difference between the scores in Economic Affairs and Risk Control. The company with the smallest height difference will demonstrate the most balance between the two sectors. From the comparison, it can be seen that {Bank Of America}[0.540, 0.320, 0.593, 0.804] and [0.593, 0.366, 0.645, 0.804] has the smallest height difference among all companies, indicating a balance between managing Economic Affairs and Risk Control.\nBased on the above thought, the answer is:\nBank Of America. As it shows the smallest difference in scores between Economic Affairs and Risk Control according to the bar chart."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "O9j0hjplno79wPpH",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest score in Risk Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the company with the highest Risk Control score, I have to compare the height of the {green bars}[0.904, 0.101, 0.960, 0.121] for each company. Starting from Goldman Sachs, the height of its green bar is [0.140, 0.274, 0.193, 0.804] which corresponds to a score of 2.3. Repeating this process for all companies, I can see that the green bar for {J.P. Morgan}[0.291, 0.089, 0.344, 0.804] rises above the 3 score line on the y-axis, which is the highest among all companies.\nBased on the above thought, the answer is:\nJ.P. Morgan."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "bKAZP3jKp8mgbnBi",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar chart data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Definitely. I have the title {\"Entrepreneurship and Innovation Ecosystem\"}[0.337, 0.021, 0.705, 0.047] at the top of the chart and I can see that we have two sets of data representing \"Community Engagement\" and \"R&D Investment\". The X-axis represents the years {2018}[0.131, 0.880, 0.166, 0.900], {2019}[0.379, 0.880, 0.415, 0.900], {2020}[0.628, 0.880, 0.663, 0.900], {2021}[0.876, 0.880, 0.911, 0.900]. The Y-axis represents scores starting from {0}[0.043, 0.865, 0.052, 0.885] going up to {45}[0.035, 0.045, 0.052, 0.065], linked to each respective year. According to the legend with {Community Engagement}[0.325, 0.960, 0.352, 0.974] and {R&D Investment}[0.565, 0.960, 0.592, 0.974] bars, I can see the performance of each category over the years. The scores for each year can be estimated from the height of the bars. For instance, in the year 2018, the score for Community Engagement is roughly 5, as per the height of the bar at [0.099, 0.782, 0.149, 0.873], and for R&D Investment is approximately 30 observing the bar height at [0.149, 0.327, 0.198, 0.873]. I can go on and do the same for the remaining years.\nBased on the above thought, the answer is:\nYear,Community Engagement,R&D Investment\n2018,5,30\n2019,12,35\n2020,25,37\n2021,40,40"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "0J0jZ2LCx3khj83V",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what conclusion can you draw about the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {bar chart}[0.057, 0.053, 0.985, 0.873], the chart shows data over a period of time for {Community Engagement}[0.325, 0.960, 0.352, 0.974] and {R&D Investment}[0.565, 0.960, 0.592, 0.974]. Observing the bars' heights, it's clear that both had seen increments over the years, with the bar heights representing Community Engagement starting at [0.099, 0.782, 0.149, 0.873] in 2018 and reaching [0.844, 0.144, 0.893, 0.873] in 2021, and the bar heights for R&D Investment starting at [0.149, 0.327, 0.198, 0.873] in 2018 and reaching [0.893, 0.144, 0.943, 0.873] in 2021. Community Engagement experienced a more significant increase, going from 5 to 40, while R&D investments increased at a slower rate, going from 30 to 40.\nBased on the above thought, the answer is:\nWe can conclude that both Community Engagement and R&D Investment in the Entrepreneurship and Innovation Ecosystem have increased over the period from 2018 to 2021. The most notable improvement has been seen in the area of Community Engagement, signalling an active involvement of the community in entrepreneurship activities. R&D Investments also increased, but at a steadier pace, indicating a consistent dedication of resources towards research and development in this field."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "johcmT2b2VAeS10Y",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the largest increment over the data timeline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll look at the change in bar heights from 2018 to 2021 for both categories. For {Community Engagement}[0.325, 0.960, 0.352, 0.974], the bar for 2018 is here [0.099, 0.782, 0.149, 0.873] and for 2021 is here [0.844, 0.144, 0.893, 0.873]. Looking at these two bars, it appears that there was a significant increase. Now, let's look at {R&D Investment}[0.565, 0.960, 0.592, 0.974]. The bar for 2018 is here [0.149, 0.327, 0.198, 0.873] and for 2021 at these coordinates [0.893, 0.144, 0.943, 0.873]. So, the increment in the R&D Investment category appears to be smaller compared to the Community Engagement category.\nBased on the above thought, the answer is:\nCommunity Engagement."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "B7I3LFzGCCaPJGy9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the provided bar chart, can you convert the data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the provided JSON data, it is indicated that the x-axis represents a set of years with tick labels spanning {\"2010\"}[0.140, 0.880, 0.175, 0.900], {\"2015\"}[0.285, 0.880, 0.320, 0.900], {\"2020\"}[0.431, 0.880, 0.466, 0.900], {\"2025\"}[0.576, 0.880, 0.611, 0.900], {\"2030\"}[0.722, 0.880, 0.757, 0.900] and {\"2035\"}[0.867, 0.880, 0.902, 0.900]. These years correspond to different bars each presenting data pertaining to \"Planning Strategy\", \"Pollution Control\", \"Sustainable Energy\", and \"Wildlife Conservation\" as provided in {legend}[0.136, 0.950, 0.906, 0.986]. The y-axis represents the progress score in these key areas, with values ranging from 0 to 60. Following the color coding and bars length, we can generate a CSV table through extracting the upper edge of each bar and mapping it onto the y-axis.\nBased on the above thought, the answer is:\nYear,Planning_Strategy,Pollution_Control,Sustainable_Energy,Wildlife_Conservation\n2010,23,17,30,25\n2015,25,19,33,20\n2020,29,25,40,15\n2025,35,35,45,11\n2030,45,38,56,14\n2035,43,38,55,20"
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "36Bbd8VgqgRoDTOJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the progress trend of \"Wildlife Conservation\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bars representing \"Wildlife Conservation\" {brown bar}[0.186, 0.531, 0.216, 0.873]; [0.332, 0.600, 0.361, 0.873]; [0.477, 0.668, 0.506, 0.873]; [0.623, 0.723, 0.652, 0.873]; [0.768, 0.682, 0.797, 0.873]; and [0.914, 0.600, 0.943, 0.873] for the years 2010, 2015, 2020, 2025, 2030 and 2035 respectively, it's evident that efforts in this area initially showed a decline till 2025, but post-2025 there was a considerable increase in progress scores. This might suggest a delayed response in recognizing the critical importance of wildlife conservation which was eventually rectified post 2025, leading to enhanced efforts in this key area of sustainable development.\nBased on the above thought, the answer is:\nThe progress trend of \"Wildlife Conservation\" indicates a delayed response in addressing the critical need of wildlife conservation. While there was a decline in effort till 2025, there was a substantial increase post-2025, highlighting an enhanced recognition of the importance of wildlife conservation in sustainable development."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "DWHcqYQCVwndNELw",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which mitigation strategy shows the highest progress score in 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look into the bar heights in 2030. Judging by the bar lengths, for {\"2030\"}[0.722, 0.880, 0.757, 0.900], the bars representing \"Planning Strategy\" {green bar}[0.681, 0.258, 0.710, 0.873], \"Pollution Control\" {yellow bar}[0.710, 0.354, 0.739, 0.873], and \"Sustainable Energy\" {blue bar}[0.739, 0.108, 0.768, 0.873] are quite close to each other, however, the \"Sustainable Energy\" bar is the longest among them.\nBased on the above thought, the answer is:\nSustainable Energy."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "d1G6f2eQ1VfXlxOr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data in CSV format presented in this Grouped Bar Plot titled \"Health Status by Age Group\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The chart title \"Health Status by Age Group\"}<r>[0.425, 0.019, 0.618, 0.041]</r> suggests that the data deals with the health status of different age groups. The {Age groups}<r>[{\"20-30\"}[0.136, 0.881, 0.169, 0.898]; {\"30-40\"}[0.320, 0.881, 0.353, 0.898]; {\"40-50\"}[0.505, 0.881, 0.538, 0.898]; {\"50-60\"}[0.689, 0.881, 0.722, 0.898]; {\"60 and above\"}[0.850, 0.881, 0.930, 0.898]]</r> used on the x-axis suggest the different categories for our table rows. The categories along the Y-axis are percentages ranging from {0}<r>[0.043, 0.868, 0.051, 0.885]</r> to {100}<r>[0.029, 0.040, 0.051, 0.057]</r> and will be the values in our table. The legends provide the columns we should consider in our table: {\"Healthy Lifestyle\"}<r>[0.258, 0.965, 0.356, 0.983]</r>, {\"Sedentary Lifestyle\"}<r>[0.411, 0.965, 0.523, 0.983]</r>, {\"Chronic Disease\"}<r>[0.579, 0.965, 0.672, 0.983]</r>, and {\"Severe Disease\"}<r>[0.727, 0.965, 0.816, 0.983]</r>. Each bar represents the value for corresponding age group and category. For instance, looking at the \"20-30\" category and the \"Healthy Lifestyle\" category, {the green bar}<r>[0.097, 0.212, 0.125, 0.874]</r> represents the percentage of people within this intersection; judging by its height and the corresponding Y-axis, we can see this value is around 80.\nBased on the above thought, the answer is:\nAge Group,Healthy Lifestyle,Sedentary Lifestyle,Chronic Disease,Severe Disease\n20-30,80,20,10,5\n30-40,70,25,20,10\n40-50,60,30,30,15\n50-60,55,40,35,20\n60 and above,50,45,40,25"
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "uk3rOLkVY4rEMtVx",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the data represented in the chart, which age group seems to face the biggest health challenges?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which age group faces the biggest health challenges, I must analyze the data for each age group. The height of bars in each group corresponds to the percentage of people in that category. In the context of health challenges, Sedentary Lifestyle, Chronic Disease, and Severe Disease categories suggest undesired health paradoxes. Thus, the age group with high percentages in these three categories faces the biggest health challenges. Let\u2019s assess that using the chart. For the age group {\"60 and above\"}<r>[0.850, 0.881, 0.930, 0.898]</r>, I notice that {the orange colored bar}<r>[0.862, 0.502, 0.890, 0.874]</r> representing Sedentary Lifestyle marks around 45%, {the red colored bar}<r>[0.890, 0.543, 0.917, 0.874]</r> for Chronic Disease around 40% and {the pink colored bar}<r>[0.917, 0.667, 0.945, 0.874]</r> for Severe Disease around 25%. These percentages are high compared to the other groups; hence people over 60 face the biggest health challenges.\nBased on the above thought, the answer is:\nAge Group 60 and above."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "wqMpeFUCt4oAeyop",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between healthy lifestyle and sedentary lifestyle for the age group 50-60?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to compare the two coloured bars for the age group, \"50-60\". The height of the {green bar}<r>[0.650, 0.419, 0.678, 0.874]</r> represents the percentage of the healthy lifestyle for this group, which appears to be 55% according to the Y-axis. Similarly, the height of the {orange bar}<r>[0.678, 0.543, 0.705, 0.874]</r> represents the percentage of the sedentary lifestyle, which appears to be around 40%. The difference between these two values is what the question is asking for.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "RokeGCFzpB1fSZOF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you process the available data to generate a CSV format table that displays the variation in socia-cultural values from 1980 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart's {title}<r>[0.391, 0.021, 0.667, 0.047]</r>, this chart demonstrates the change in certain socio-cultural values from 1980 to 2020. The x-axis denotes the years as illustrated by {the x-axis labels}<r>[\"1980\"[0.293, 0.880, 0.329, 0.900]; \"2020\"[0.730, 0.880, 0.765, 0.900]]</r>, and the y-axis represents the scores ranging from -3.5 to 0 as depicted by {the y-axis labels}<r>[\"\u22123.5\"[0.035, 0.865, 0.068, 0.885]; \"\u22123.0\"[0.035, 0.748, 0.068, 0.768]; \"\u22122.5\"[0.035, 0.631, 0.068, 0.651]; \"\u22122.0\"[0.035, 0.514, 0.068, 0.534]; \"\u22121.5\"[0.035, 0.397, 0.068, 0.417]; \"\u22121.0\"[0.035, 0.280, 0.068, 0.300]; \"\u22120.5\"[0.035, 0.163, 0.068, 0.183]; \"0.0\"[0.046, 0.045, 0.068, 0.065]]</r>.The significant socio-cultural values: Globalization, Environmental Awareness, and Information Age are represented by individual colored bars as indicated by {the legend}<r>[0.246, 0.950, 0.812, 0.986]</r>. The length of these bars corresponds to the scores of these socio-cultural values. For instance, {the blue bar for \"Globalization\"}<r>[0.114, 0.053, 0.245, 0.405]</r> in 1980 extends to nearly -1.5 on the y-axis, thus indicating the score as -1.5 as per the raw data.\nBased on the above thought, the answer is:\nYear,Globalization,Environmental Awareness,Information Age\n1980,-1.5,-3,-0.5\n2020,-1,-2.8,-0.7"
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "IuXi2QGxhx4abIyI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the chart, which socio-cultural value has become more influential in 2020 compared to 1980, and why do you infer it this way?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the analysis of the dimensions embodied in {the chart}<r>[0.073, 0.053, 0.985, 0.873]</r>, it can be glimpsed that the value of all three socio-cultural constants has increased from 1980 to 2020, depicted through a shorter length of the bars in 2020 relative to 1980. However, most notably, {Globalization}<r>[\"1980\"[0.114, 0.053, 0.245, 0.405]; \"2020\"[0.551, 0.053, 0.682, 0.288]]</r> has undergone the most extraordinary change from -1.5 to -1. This means that despite retaining a negative score, Globalization has increased in influence drastically. Since a more negative score on the y-axis suggests a stronger degree of influence of that value, Globalization having less negative scores means it has become more influential in 2020 compared to 1980.\nBased on the above thought, the answer is:\nGlobalization has become more influential in 2020 compared to 1980 because it reflects the most significant increase in score, indicating a rising level of influence."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "FVQ29YM7AjaY9ROL",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of the chart, which socio-cultural value has witnessed the most significant change from 1980 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To resolve this, it is crucial to observe and analyze the difference in the lengths of the bars, standing for each value from 1980 to 2020. The change can be computed as the difference in scores of {Globalization}<r>[\"1980\"[0.114, 0.053, 0.245, 0.405]; \"2020\"[0.551, 0.053, 0.682, 0.288]]</r>, {Environmental Awareness}<r>[\"1980\"[0.245, 0.053, 0.376, 0.756]; \"2020\"[0.682, 0.053, 0.813, 0.709]]</r>, and {Information Age}<r>[\"1980\"[0.376, 0.053, 0.507, 0.170]; \"2020\"[0.813, 0.053, 0.944, 0.217]]</r>. Given that the scores for Globalization changes from -1.5 to -1, the difference is 0.5; for Environmental Awareness, it transitions from -3 to -2.8, so the difference is 0.2; finally, for Information Age, transforming from -0.5 to -0.7 gives a difference of 0.2 as well. Hence, {Globalization} has experienced the most profound change.\nBased on the above thought, the answer is:\nGlobalization."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "7VXHGwmRlqXy9VZG",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart data about the development of human civilization through different eras into CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a CSV table from the chart, I first identify the categories and variables. The x-axis describes the {different eras}[{\"Bronze Age\"}[0.130, 0.931, 0.210, 0.951]; {\"Iron Age\"}[0.321, 0.931, 0.379, 0.951]; {\"Middle Ages\"}[0.488, 0.931, 0.572, 0.951]; {\"Industrial Revolution\"}[0.638, 0.931, 0.781, 0.951]; {\"Contemporary Era\"}[0.826, 0.931, 0.954, 0.951]], which are 5 in total. The y-axis describes the scores or counts of four indicators, namely {Life Expectancy}[0.087, 0.073, 0.115, 0.087], {Human Population}[0.087, 0.103, 0.115, 0.117], {Technology Index}[0.087, 0.133, 0.115, 0.147], and {Culture Index}[0.087, 0.163, 0.115, 0.177], according to {the legend}[0.081, 0.063, 0.259, 0.189]. Each indicator is represented by stacked bars of different colors. To obtain the values, I have to analyze the height of each bar. For example, the height of {the Life Expectancy bar in the Bronze Age}[0.116, 0.880, 0.143, 0.884] represents its score/count, which is 35.\nBased on the above thought, the answer is:\nEra,Life_Expectancy,Human_Population,Technology_Index,Culture_Index\nBronze Age,35,7.2,-2.7,-1.9\nIron Age,50,15,-2.3,-1.1\nMiddle Ages,33,100,-1.5,-0.5\nIndustrial Revolution,50,700,-0.8,0.1\nContemporary Era,70,7600,-0.2,1.5"
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "AYPXxiS816Ek5B8u",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how has human civilization developed over the different eras?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at this bar chart, it appears that human civilization has developed significantly over time. The {Life Expectancy}[0.087, 0.073, 0.115, 0.087] started at around 35 in the Bronze Age, dipped to 33 in the Middle Ages, before climbing to 70 by the Contemporary Era. {Human Population}[0.087, 0.103, 0.115, 0.117] has grown massively, from just 7.2 million in the Bronze Age to a staggering 7600 million in the Contemporary Era. On the other hand, the {Technology Index}[0.087, 0.133, 0.115, 0.147] initially had negative values, implying a lack of technological advances but has progressively moved towards positivity, reflecting the growth in technologies. Similarly, the {Culture Index}[0.087, 0.163, 0.115, 0.177] also moved from negative to positive values, suggesting cultural development. The negative values in these indices during the Bronze Age indicate their rudimentary beginnings. The increase in positive values over time reflects the path of human development.\nBased on the above thought, the answer is:\nHuman civilization has seen significant development over different eras. Life Expectancy has generally increased despite a temporary regression during the Middle Ages, reflecting improvements in healthcare and living conditions. The Human Population has witnessed explosive growth, indicating human's ability to adapt and survive in varying circumstances. Furthermore, Technology and Culture Index show a clear upward trend from their negative beginnings, reflecting the advancements and progress in technology and cultural aspects of human civilization."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "bKp4gPfEXuVptQXF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which Era is the Life Expectancy highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the height of the Life Expectancy bars across different eras. These are displayed as green bars. Looking at these bars, {the bar in the Contemporary Era}[0.836, 0.877, 0.863, 0.884] is the highest compared to all other eras. It reaches closer to the value of 70 on the vertical axis.\nBased on the above thought, the answer is:\nContemporary Era."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "JEkhLPdT5tP6GfWj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data shown in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the bar chart represents trends in the education and training industry over a period of 5 years, from 2015 to 2019. The categories represented include {Online Education}<r>[0.227, 0.965, 0.326, 0.983]</r>, {Traditional Education}<r>[0.381, 0.965, 0.503, 0.983]</r>, {Education Technology}<r>[0.559, 0.965, 0.685, 0.983]</r>, and {Graduate Employment}<r>[0.740, 0.965, 0.872, 0.983]</r> as given in the legend. For each category, there are five bars each representing a year from 2015 to 2019. The values for these categories can be extracted based on the height of the bars on the y-axis, which denotes the 'Number of Individuals (in thousands)'. For instance, in the category of Online Education for the year 2015, the bar<r>[0.120, 0.047, 0.147, 0.476]</r> reaches approximately to -7000 on the y-axis. By following the same method, the values for each of the categories throughout the years can be extracted.\nBased on the above thought, the answer is:\nYear,Online Education,Traditional Education,Education Technology,Graduate Employment\n2015,-7000,-13000,-500,-2500\n2016,-7300,-12800,-600,-2300\n2017,-7600,-12500,-750,-2400\n2018,-7900,-12000,-850,-2350\n2019,-8200,-11500,-950,-2000"
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "JlePtMADbo0MP9jc",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends you're seeing in the chart, which category would you say is having the most negative trend over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires me to analyze trend changes over the years in each category. Looking at the bar heights of {Online Education}<r>[0.227, 0.965, 0.326, 0.983]</r>, {Traditional Education}<r>[0.381, 0.965, 0.503, 0.983]</r>, {Education Technology}<r>[0.559, 0.965, 0.685, 0.983]</r>, and {Graduate Employment}<r>[0.740, 0.965, 0.872, 0.983]</r>, it's clear that all categories are showing a downward trend over the years. However, Traditional Education category shows the least decline as the bars are getting taller each year from 2015 to 2019. In contrast, the {Education Technology}<r>[0.559, 0.965, 0.685, 0.983]</r> seems to have the most negative trend. The decrease in its enrollment was not only continuous but also the slope of descent is the steepest, particularly the bars from 2017 to 2019<r>[0.533, 0.047, 0.560, 0.093; 0.713, 0.047, 0.740, 0.099; 0.892, 0.047, 0.919, 0.105]</r> show the most significant drops compared to previous years.\nBased on the above thought, the answer is:\nThe Education Technology category is showing the most negative trend since it consistently exhibits a more significant drop each year compared to the other categories."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "Pew7NFCVnL3T3qbb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the number of individuals in Online Education the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the height of the bars representing {Online Education}<r>[0.227, 0.965, 0.326, 0.983]</r> across the different years given on the x-axis. According to the bars, the number of individuals in Online Education decreased consecutively each year. Therefore, the lowest value is represented by the bar in {2019}<r>[0.838, 0.047, 0.865, 0.549]</r> which reaches approximately -8200 on the y-axis which is the lowest point.\nBased on the above thought, the answer is:\n2019."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "NB8KuQgEK7NCwoWy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data into a table format based on your observations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, it's evident from the labels and coordinating elements that it's about the tech industry growth over the years from 2017 to 2020 for Apple and Google in the sectors of Hardware and Software. The year labels are placed on the x-axis <r>[0.178, 0.803, 0.873, 0.823]</r> and the score labels are placed on the y-axis <r>[0.043, 0.788, 0.061, 0.065]</r>. Based on the legend <r>[0.357, 0.916, 0.572, 0.960]</r>, the blue bars represent Google Hardware, the light blue bars represent Google Software, the orange bars represent Apple Hardware and the light orange bars represent Apple Software. By noticing the height of the bars, we can determine the scores in each sector for every specific year.\nBased on the above thought, the answer is:\nYear,Google_Hardware,Google_Software,Apple_Hardware,Apple_Software\n2017,30,50,60,70\n2018,40,55,70,75\n2019,50,60,80,80\n2020,60,65,85,85"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "pCNbAnHErcBtZWpW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze and compare the overall growth and progress of Google and Apple over the four years presented according to the data reflected in the bar chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart highlights the growth of Google and Apple over four years, separated by hardware and software sectors. According to the bars, Google's hardware sector <r>[0.108, 0.573, 0.152, 0.796]</r> has had a linear growth starting from a score of 30 in 2017 to 60 in 2020. Its software sector <r>[0.152, 0.425, 0.196, 0.796]</r> has also shown a steady growth from 50 in 2017 to 65 in 2020. Together, Google sectors have shown a steady upward trend over the years. As for Apple, their hardware sector <r>[0.196, 0.350, 0.240, 0.796]</r> has grown from a score of 60 in 2017 to 85 in 2020, and their software sector <r>[0.240, 0.276, 0.283, 0.796]</r> has improved from 70 to 85 in the same timeframe. Overall, Apple sectors have shown a greater growth compared to Google. However, both companies have indeed shown significant progress.\nBased on the above thought, the answer is:\nBoth companies have seen positive growth throughout the four years. Though Google has shown linear growth in both their sectors, Apple has shown greater progress in the same timeframe. Apple's growth rate in both sectors is higher than Google's."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "FNWAadRDNLljE0Yn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year showed the highest combined score for Google's hardware and software sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll have to calculate the total scores for Google's Hardware and Software sectors each year. By examining the heights of the bars representing Google's sectors, I see that bar heights for Google in 2020 <r>[0.767, 0.350, 0.811, 0.796]</r> and <r>[0.811, 0.313, 0.855, 0.796]</r> are the maximum, implying that their total score is the highest in 2020.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "dTrx3WA53t2NKuDE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the chart data by month for the category 'Delivery' in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the bar data for the category labeled 'Delivery', recognizing its corresponding color from {the legend}<r>[0.652, 0.960, 0.680, 0.974]</r>. As I move along the x-axis, each group of bars corresponds to a month starting with January at <r>[0.142, 0.764, 0.156, 0.873]</r> and ending with December at <r>[0.929, 0.436, 0.943, 0.873]</r>. Each bar within these coordinates provides the count for 'Delivery' as labeled with text coordinates above the bars. Extracting these values based upon their locations on the y-axis allows me to construct a CSV formatted table representing 'Delivery' data by month.\nBased on the above thought, the answer is:\nMonth,Delivery\nJanuary,4\nFebruary,7\nMarch,11\nApril,10\nMay,8\nJune,13\nJuly,8\nAugust,12\nSeptember,14\nOctober,15\nNovember,12\nDecember,16"
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "ippHZvxOXsM8XNs2",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are your observations about the trend in the number of orders from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the 'Order' bars, represented by the color in {the legend}<r>[0.546, 0.960, 0.574, 0.974]</r>, starting with January at <r>[0.128, 0.627, 0.142, 0.873]</r>, there is a progressive increase towards March, visible at <r>[0.271, 0.190, 0.285, 0.873]</r>, followed by a drop in April at <r>[0.342, 0.327, 0.357, 0.873]</r>, and a fluctuating pattern thereafter until reaching the peak in December at <r>[0.914, 0.108, 0.929, 0.873]</r>. Overall, despite fluctuations, there's an observable upward trend in the number of orders throughout the year.\nBased on the above thought, the answer is:\nThe trend in the number of orders shows an initial increase reaching a high in March followed by a drop in April. Post-April, the trend is irregular, with numbers fluctuating before peaking in December. While the monthly values exhibit some inconsistency, the overall direction is upward, indicating an increase in order activity throughout the year."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "j3gIwN9LNytykU6j",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many total complaints are there across all months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total number of complaints, I must add the value at the top of each bar pertaining to complaints, identified by the color specified in {the legend}<r>[0.408, 0.960, 0.436, 0.974]</r>. Starting from January with the bar at <r>[0.114, 0.791, 0.128, 0.873]</r> through December at <r>[0.900, 0.490, 0.914, 0.873]</r>, I sum the numerical values displayed atop each bar.\nBased on the above thought, the answer is:\n109"
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "NBQS7dD76yntBmwp",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the group bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see four product categories: {Mobile}[0.328, 0.950, 0.374, 0.970], {Electronics}[0.440, 0.950, 0.516, 0.970], {Furniture}[0.583, 0.950, 0.645, 0.970], and {Supplies}[0.712, 0.950, 0.770, 0.970] from the chart legend. For every category, there are four data points, which correspond to four quarters, which I can find on the {x-axis tick labels}[{\"Q1\"}[0.193, 0.824, 0.213, 0.844]; {\"Q2\"}[0.411, 0.824, 0.431, 0.844]; {\"Q3\"}[0.629, 0.824, 0.648, 0.844]; {\"Q4\"}[0.847, 0.824, 0.866, 0.844]]. The way to locate these data points is based on the different colors of the group bars represented by the rectangles of different colors. Heights of rectangles represent profits, such as the height of {the first bar of the Mobile category}[0.116, 0.531, 0.159, 1.104] stands for $3000 profit in thousands of dollars on the {Q1 point}[0.203, 0.817]. From these observations, I can convert the chart into a CSV format table.\nBased on the above thought, the answer is:\nCategory, Q1, Q2, Q3, Q4\nMobile, 3000, 3500, 3200, 4000\nElectronics, 4800, 4300, 5000, 5300\nFurniture, 2500, 2900, 2600, 2800\nSupplies, 1900, 2100, 2200, 1800"
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "oT60fdwtCOvgAH7c",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product category has shown a consistent rise in profits throughout the quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, we can see that the profit changes of each category are represented by the change of bar heights from the first to the fourth quarter. So, we need to compare the heights of each category's bars. We can see that the height of the {Electronics category}'s bar increases from {Q1}[0.334, 0.435, 0.377, 1.104] to {Q2}[0.377, 0.283, 0.421, 1.104], {Q3}[0.595, 0.149, 0.639, 1.104] and then to {Q4}[0.813, 0.092, 0.856, 1.104]. Thus, it appears that the \"Electronics\" category has shown a consistent rise in profits throughout the quarters.\nBased on the above thought, the answer is:\nElectronics"
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "hY5gdussfXrMqbVV",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum profit earned by Mobile in any quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to look at the height of each rectangle bar that represents the Mobile category. The y-coordinate of the {Mobile category}[0.116, 0.531, 0.159, 1.104] in Q1 is 0.531, corresponding to 3000 on the Y-axis tick labels. Then I analyze the other rectangles' profit values in [\"Q2, Q3, Q4\"], and find that the profit of the {Mobile category in Q4}[0.769, 0.340, 0.813, 1.104] has the y-coordinate at the highest position, which is 0.340, equal to 4000 in thousands of dollars.\nBased on the above thought, the answer is:\n4000"
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "jeK7MiQG1UlhjF9R",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart you see, can you convert the task numbers and completion times of the three types of Big Data Integration into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the title {\"Big Data Integration Overview\"}[0.395, 0.021, 0.647, 0.047] at the top of the chart and the x-axis which represents different types of data integration, {\"Full Integration\"}[0.117, 0.931, 0.222, 0.951], {\"Partial Integration\"}[0.459, 0.931, 0.584, 0.951], and {\"Manual Integration\"}[0.807, 0.931, 0.938, 0.951]. The y-axis represents the corresponding values, which span from {0}[0.043, 0.916, 0.052, 0.936] to {70}[0.035, 0.045, 0.052, 0.065]. For each data integration type, there are two bars reflecting specific values given in the legend which includes {\"Number of Tasks\"}[0.801, 0.071, 0.916, 0.091] and {\"Time to Complete (days)\"}[0.801, 0.101, 0.973, 0.121]. The CSV table can be generated by gathering the values for each category by observing the height of the bars e.g., the value of {\"Number of Tasks for Full Integration\"}[0.099, 0.489, 0.170, 0.924] can be seen to be around the 35 mark of the y-axis while its {\"Time to Complete\"}[0.170, 0.737, 0.240, 0.924] is around the 15 mark.\nBased on the above thought, the answer is:\nIntegration Type,Number of Tasks,Time to Complete (days)\nFull Integration,35,15\nPartial Integration,55,30\nManual Integration,20,60"
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "Ts20AngLFYcWUstA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDrawing from the chart, can you suggest the most efficient method of Big Data Integration and provide a brief justification?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency in terms of Big Data Integration can be measured as achieving the most number of tasks in the shortest time. By looking at the bar chart, you can clearly see that {\"Number of Tasks for Full Integration\"}[0.099, 0.489, 0.170, 0.924] is superior, at 35 tasks, and the {\"Time to Complete for Full Integration\"}[0.170, 0.737, 0.240, 0.924] only takes 15 days, which shows a balance between a substantial number of tasks and a shorter completion time. In contrast, the task count and completion time for both {\"Partial Integration\"}[0.459, 0.931, 0.584, 0.951] and {\"Manual Integration\"}[0.807, 0.931, 0.938, 0.951] are less optimal, either needing longer completion time or achieving fewer tasks within the stated timeframe.\nBased on the above thought, the answer is:\nFull Integration method is the most efficient. It completes a substantial number of tasks (35) in the shortest time (15 days) compared to the other methods."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "asFk93D6B68dI8ex",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_63_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the three methods, which one completes tasks at the quickest average pace? In other words, calculate the days per task for each method and find the least value."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the quickest average task pace, I need to calculate the days per task, which is given by the value of the {\"Time to Complete (days)\"}[0.801, 0.101, 0.973, 0.121] divided by the {\"Number of Tasks\"}[0.801, 0.071, 0.916, 0.091] for each integration type. For instance, {'Full Integration'}[0.117, 0.931, 0.222, 0.951] has a bar height of {15 days}[0.170, 0.737, 0.240, 0.924] for task completion time and {35 tasks}[0.099, 0.489, 0.170, 0.924] for number of tasks, and so the calculation would be 15/35. Applying the same method for Partial Integration {[0.459, 0.931, 0.584, 0.951]} and Manual Integration {[0.807, 0.931, 0.938, 0.951]}. I can then compare the days per task for each integration type and find the least value.\nBased on the above thought, the answer is:\nFull Integration."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "h2XOjhSMxlm1ZwgQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, can you convert the sales data of each product into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I notice that the x-axis represents the months of the year, with months {January}[0.164, 0.879, 0.230, 0.914] through {May}[0.855, 0.879, 0.891, 0.914] labeled. The y-axis, ranging from {0.0}[0.065, 0.841, 0.076, 0.876] to {900}[0.043, 0.025, 0.076, 0.060], signifies the sales data. There are five different product groups color-coded on the chart according to {the legend}[0.036, 0.037, 0.048, 0.083], such as Apple, Banana, Cherry, Durian, and Eggplant. The sales data for each product from January to May can be determined by analyzing the height of each bar. For example, for {Apple in January}[0.129, 0.492, 0.264, 0.854], its bar height corresponds to 400 according to the y-axis.\nBased on the above thought, the answer is:\nProduct,January,February,March,April,May\nApple,400,450,500,600,700\nBanana,800,750,700,750,800\nCherry,700,650,600,550,500\nDurian,300,350,400,450,500\nEggplant,500,550,600,550,500"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "e8nEWOlWUtAXQVCm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product(s) demonstrated a consistent increase in sales over the five-month period based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to observe the trend of sales for each product. If the bars corresponding to each month gradually increase in height, it means the sales for that product have persistently risen. I can see that {Apple}[{'January'}[0.129, 0.492, 0.264, 0.854]; {'February'}[0.298, 0.447, 0.433, 0.854]; {'March'}[0.467, 0.401, 0.602, 0.854]; {'April'}[0.636, 0.311, 0.772, 0.854]; {'May'}[0.805, 0.220, 0.941, 0.854]] and {Durian}[{'January'}[0.129, 0.583, 0.264, 0.854]; {'February'}[0.298, 0.537, 0.433, 0.854]; {'March'}[0.467, 0.492, 0.602, 0.854]; {'April'}[0.636, 0.447, 0.772, 0.854]; {'May'}[0.805, 0.401, 0.941, 0.854]] have rising bars for sales from January to May, implying an unbroken sales growth.\nBased on the above thought, the answer is:\nApple and Durian"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "8QeofuJlLlXOmSyc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product had the highest sales in March considering the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the product with the highest sales in March, I need to observe the heights of the {bars for March}[0.467, 0.401, 0.602, 0.854] for each product. Comparing the heights for Cherry, Durian, Banana, Apple, and Eggplant, I can see that the tallest bar belongs to the Banana which has the sales amount reaching up to 700.\nBased on the above thought, the answer is:\nBanana"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "YuWcosKQJ1nTjDeu",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented in the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing the chart's title {\"Innovation and Intellectual Property in Tech Sectors\"}[0.424, 0.021, 0.710, 0.047], one can decipher that there are different types of technologies displayed on the y-axis listed as {'ytick labels'}[{'Robotic Technology'}[0.048, 0.823, 0.138, 0.843], {'AI Technology'}[0.074, 0.708, 0.138, 0.728], {'BlockChain'}[0.086, 0.593, 0.138, 0.613], {'Wireless Communication'}[0.023, 0.477, 0.138, 0.497], {'Quantum Computing'}[0.041, 0.362, 0.138, 0.382], {'BioTech'}[0.102, 0.246, 0.138, 0.266], {'Cybersecurity'}[0.073, 0.131, 0.138, 0.151]]. Each technology has a corresponding horizontal bar illustrating the number of patents they hold. For instance, the bar for {'Robotic Technology'}[0.144, 0.785, 0.579, 0.877] shows it holds around 2700 patents. Following the same method, one can construct the CSV data by pairing each technology sector with its number of patents.\nBased on the above thought, the answer is:\nTechnology,Number of Patents\nRobotic Technology,2700\nAI Technology,3200\nBlockChain,2200\nWireless Communication,5000\nQuantum Computing,1300\nBioTech,2900\nCybersecurity,4600"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "If8OmaWHIkVtxC8F",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, which sector do you think prioritizes innovation and intellectual property?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, {'Wireless Communication'}[0.144, 0.439, 0.950, 0.531] has the longest bar, which means it has the highest number of patents. The number of patents in a sector indicates the amount of innovation and intellectual property in that area. Having many patents signifies that this sector has spent a significant amount of time and resources on research and development activities, leading to the creation of new technologies or processes. Therefore, it can be inferred that the Wireless Communication sector prioritizes innovation and intellectual property the most among the listed sectors.\nBased on the above thought, the answer is:\nWireless Communication"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "DVJQws8kyCtdTxty",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find out which sector has the most patents based on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which sector has the most patents, one must look at the lengths of the bars corresponding to each technology sector. By observing each bar, one can see that the bar for {'Wireless Communication'}[0.144, 0.439, 0.950, 0.531] is the longest amongst all representing around 5000 patents, concluded based on its coordinates and the scale given on x-axis{'xticks labels'}[{'5000'}[0.938, 0.931, 0.961, 0.951]]. Thus, the Wireless Communication sector has the most patents.\nBased on the above thought, the answer is:\nWireless Communication"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "MtTHmhZ7OZwklGI6",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the bar chart, could you convert the given data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is {\"Internal Business Process Analysis\"}[0.381, 0.030, 0.666, 0.066] indicating that the data is related to various internal processes. These are represented on the {x-axis}[0.497, 0.942, 0.550, 0.970], which specifically are 'Product Development', 'Marketing', 'Sales', 'Management', 'Customer Service', 'Manufacturing' and 'Financial'. The y-axis shows the 'Effectiveness' ranging from {0}[0.043, 0.872, 0.052, 0.900] to {100}[0.026, 0.035, 0.052, 0.063]. The values for each process can be inferred from the height of the bars, for instance, {the bar for 'Product Development'}[0.104, 0.172, 0.203, 0.883] approximately aligns with 85 on the y-axis, hence, the effectiveness for 'Product Development' is 85. By extrapolating this method to remaining bars, we can convert the chart data into a CSV format table.\nBased on the above thought, the answer is:\nProcess,Effectiveness\nProduct Development,85\nMarketing,90\nSales,78\nManagement,88\nCustomer Service,92\nManufacturing,80\nFinancial,89"
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "KstrQDWLdU68d8RA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you suggest which process requires immediate attention in terms of effectiveness and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains a set of bars{bars_of_group_0}, each representing a different process. Effectiveness is calculated on a scale from 0 to 100, where 100 signifies the highest effectiveness. {The 'Sales' bar}[0.351, 0.230, 0.449, 0.883] is the shortest among all, indicating that it has the lowest effectiveness of 78. Similarly, 'Product Development' and 'Manufacturing' have lower effectiveness ratings, closer to 'Sales'. Comparing these ratings with the higher ones (viz. 'Customer Service' and 'Marketing'), it seems clear that these low-scoring areas, and especially 'Sales', might require immediate attention in order to improve the overall effectiveness of business processes.\nBased on the above thought, the answer is:\nThe 'Sales' process requires immediate attention because it has the lowest effectiveness rating of 78, which is significantly lower compared to other processes like 'Customer Service' and 'Marketing'."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "4iXZ0XMIecSt16Wl",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given chart, which process has the least effectiveness rating?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question I need to evaluate the bars on the chart which reflect the effectiveness of each process. Looking at the bar chart, {the bar for 'Sales'}[0.351, 0.230, 0.449, 0.883] seems to have the smallest height, indicating that 'Sales' has the smallest value of all processes. Comparing this with other bars, it can be confirmed that 'Sales' indeed has the lowest effectiveness rating.\nBased on the above thought, the answer is:\nSales"
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "V0qBEOi4RjULhAY1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the time series data you see on this bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Time Series Data Comparison Over Years\"}[0.348, 0.030, 0.690, 0.066] at the top and the {x-axis tick labels}[{\"Slow expansion\"}[0.105, 0.903, 0.213, 0.931]; {\"Unstable behavior\"}[0.275, 0.903, 0.402, 0.931]; {\"Slight increase\"}[0.468, 0.903, 0.570, 0.931]; {\"Inconsistent decline\"}[0.630, 0.903, 0.769, 0.931]; {\"Downward trend\"}[0.822, 0.903, 0.937, 0.931]], it is clear that the x-axis represents different trends. The y-axis tick labels represent values, ranging from {0}[0.035, 0.872, 0.043, 0.900] to {9}[0.035, 0.037, 0.043, 0.065]. For each trend, there are two bars that represent different years as indicated by {the legend}[0.893, 0.089, 0.978, 0.181] - {the light blue bars}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]> represent the year 2000, and {the dark blue bars}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]> represent the year 2020. The height of the bars and the labels at the y-axis visually indicate the corresponding values for each trend and year.\nBased on the above thought, the answer is:\nTrends,2000,2020\nSlow expansion,4.9,8.3\nUnstable behavior,6.1,6.8\nSlight increase,3.2,7.5\nInconsistent decline,7.1,4.6\nDownward trend,8.3,6.1"
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "x3YuJODYFmx8jBGv",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed chart, which trend are we seeing an improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, we should identify which trends show an increase over the twenty-year period. That would be inferred from comparing {the bars representing the year 2000}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]> and {the bars representing the year 2020}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]>. By Observation, we could say \"Slow expansion\": {the bar of year 2000}[0.096, 0.429, 0.159, 0.883] appears shorter than {the bar of year 2020}[0.159, 0.113, 0.222, 0.883]; \"Slight increase\": {the bar of year 2000}[0.456, 0.586, 0.519, 0.883] appears shorter than {the bar of year 2020}[0.519, 0.187, 0.582, 0.883] indicating an upward pattern over the twenty years. Thus, these trends can be interpreted as improvements.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that the trends \"Slow expansion\" and \"Slight increase\" have shown significant improvement over the last twenty years."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "Vc9ESiGHcjx7lcz5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the year 2000 and 2020, which trend shows the highest increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the increase in each trend by subtracting the value of the year 2000, represented by {the light blue bars}<{\"2000\"}[0.937, 0.100, 0.972, 0.128]>, from the value of the year 2020, represented by {the dark blue bars}<{\"2020\"}[0.937, 0.142, 0.972, 0.170]>. The trend that shows the highest increase is that with the maximum difference between the heights of the two bars. For each trend, I locate the two bars and estimate their heights by comparing with the y-axis labels. For example, for \"Slow expansion\": {the bar of year 2000}[0.096, 0.429, 0.159, 0.883] with an estimated height of 4.9 and {the bar of year 2020}[0.159, 0.113, 0.222, 0.883] with an estimated height of 8.3. The difference is 8.3 - 4.9 = 3.4. I apply the same process for the other trends and find out that \"Slight increase\" trend has the highest increase value of 4.3.\nBased on the above thought, the answer is:\nSlight increase"
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "t5I5bKDwOEOmVGtL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you observed, please organize the data into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From {the title, \"Market Research of BMW Vehicle Series\"}<r>[0.366, 0.025, 0.694, 0.055]</r>, it's clear that this chart depicts data related to BMW's vehicle series market shares. The x-axis, where {the labels are the names of \"BMW Series\"}<r>[0.482, 0.952, 0.578, 0.975]</r>, act as the primary categories, and the percentages on the y-axis represent the corresponding values, which ranges from {0.0}<r>[0.043, 0.894, 0.065, 0.917]</r> to {20.0}<r>[0.035, 0.051, 0.065, 0.074]</r>. The height of each bar represents the market share of the corresponding BMW series. For example, the bar for the {1 Series}<r>[0.116, 0.397, 0.214, 0.903]</r> reaches up to around the 12-spot on the y-axis, implying that the 1 series has a market share of 12%. This process can be repeated for each BMW series.\nBased on the above thought, the answer is:\nBMW Series,Market Share(%)\n1 Series,12.0\n3 Series,16.0\n5 Series,19.0\n7 Series,9.0\nx Series,15.0\nZ Series,14.0\ni Series,15.0"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "AXIiZ9J1qVOOYmkg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you draw from this chart about BMW's market strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bar chart provides market share data for different BMW vehicle series. From {title}<r>[0.366, 0.025, 0.694, 0.055]</r>, it seems that the strategy is distributed across multiple series rather than focusing on one model. This is evident from the fact that all models except {7 Series}<r>[0.481, 0.524, 0.579, 0.903]</r> have market shares close to 15%. The {7 Series}<r>[0.481, 0.524, 0.579, 0.903]</r> has lower market shares indicating it may be a luxury or niche offering. The {5 Series}<r>[0.360, 0.102, 0.457, 0.903]</r> has the highest share, suggesting that it may be the brand\u2019s popular or flagship model. Overall, the diversity in the market share of different models indicates a broad target market.\nBased on the above thought, the answer is:\nBMW's market strategy is diversified rather than focused. While the 5 Series appears to be the primary and popular model, the company also maintains a variety of other series to cater to different market segments. The 7 Series, with its somewhat lower market share, could be a niche or luxury offering."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "pirJyv0c88g3YS9P",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich BMW Series has the highest market share according to the Chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to identify the tallest bar in the chart. By examining each of the bars, I can see that the {5 Series}<r>[0.360, 0.102, 0.457, 0.903]</r> bar is reaching highest on the y-axis. Therefore, the BMW 5 Series has the highest market share.\nBased on the above thought, the answer is:\n5 Series."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "IWZuQj43CB2DCeG4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, could you extract the displayed data and present it in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is showing the conditions of different family members based on a bar chart. The family members {\"Dad\"}[0.157, 0.879, 0.193, 0.914], {\"Mom\"}[0.296, 0.879, 0.338, 0.914], {\"Brother\"}[0.426, 0.879, 0.491, 0.914], {\"Sister\"}[0.575, 0.879, 0.625, 0.914], {\"Pet\"}[0.728, 0.879, 0.755, 0.914] and {\"Carer\"}[0.860, 0.879, 0.907, 0.914] are identified on the x-axis, and their associated conditions are represented on the y-axis. The conditions values can be extracted from the height of each bar, which correlate with the percentages on the y-axis. For example, the bar corresponding to {\"Dad\"}[0.118, 0.210, 0.232, 0.854] reaches up to approximately 80 on the y-axis. Therefore, the conditions of \"Dad\" is 80.\nBased on the above thought, the answer is:\nfamily_members, conditions\nDad, 80\nMom, 82\nBrother, 90\nSister, 85\nPet, 90\nCarer, 70"
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "F8YtMmcNyDMDdDrL",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes this chart indicate any need for an intervention? State your reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, there might be a need for intervention. The observation is made based on the following comparisons. The bar chart shows living conditions of all the family members including a Pet and a Carer. Whilst all family members and the pet have a decent living condition score above 80%, the {Carer}[0.827, 0.291, 0.940, 0.854] on the far right is at 70%, which is quite low compared to others. As the Carer might be taking care of all family members including the pet, their relatively lower living condition might have an effect on the services they provide, hence intervention might be necessary.\nBased on the above thought, the answer is:\nYes, an intervention might be needed to improve the living conditions of the Carer, as their conditions are notably lower compared to all other family members and the pet. A low live condition of the Carer might negatively impact the care being provided to the family members."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "8FIEwoYlPElVIj9u",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich family member has the lowest living conditions percentage according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the heights of the bars corresponding to each family member, I can find out who has the lowest living conditions percentage. Starting from the left, the height of the bar representing {Dad}[0.118, 0.210, 0.232, 0.854] is 80, for {Mom}[0.260, 0.194, 0.373, 0.854] it's 82, {Brother}[0.402, 0.130, 0.515, 0.854] it's 90, {Sister}[0.543, 0.170, 0.657, 0.854] it's 85, {Pet}[0.685, 0.130, 0.798, 0.854] it's 90 and finally, the height of the bar representing {Carer}[0.827, 0.291, 0.940, 0.854] is 70, which is lower than all the rest, indicating that the Carer has the lowest living conditions.\nBased on the above thought, the answer is:\nCarer."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "o3Jys53ViZGW480H",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the bar chart into a spreadsheet format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can. Based on the chart, I can observe that the x-axis represent various segments within the Education and Training industry, and is labeled 'Sector' as seen in the xlabel [{\"Sector\"}[0.501, 0.942, 0.546, 0.970]]. These sectors include 'Software Development', 'Instructional Design', 'E-Learning', 'Corporate Training', 'Language Learning', and 'Career Counseling', as read from the xticks category. The y-axis correspondingly represents a hypothetical measure of 'Industry Bloom'[0.015, 0.374, 0.029, 0.584] and varies between 0 to 35 as read from the yticks category. Each bar in the chart depicts a specific sector, and the height of the bar indicates the level of Industry Bloom. The data for each sector is extracted by comparing the coordinates of each bar with the y-axis ticks. For instance, the bar corresponding to 'Software Development'[0.104, 0.402, 0.220, 0.883] extends up to 20 on the y-axis, indicating an Industry Bloom level of 20 for this sector. Following similar methodology for other sectors, the data can be tabulated into a CSV format.\nBased on the above thought, the answer is:\nSector,Industry Bloom\nSoftware Development,20\nInstructional Design,25\nE-Learning,32\nCorporate Training,21\nLanguage Learning,19\nCareer Counseling,12"
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "97Q9Zw9fqfq1tdpa",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, what conclusion can you draw about the Industry Bloom trends in the Education and Training industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart indicates various trends in the Education and Training industry. Judging from the bar heights, it appears that 'E-Learning'[0.393, 0.113, 0.509, 0.883] is the sector experiencing the most growth, or \"industry bloom,\" with a measurement of 32. On the other hand, 'Career Counseling'[0.827, 0.595, 0.943, 0.883] presents less growth with a low value of 12. 'Software Development'[0.104, 0.402, 0.220, 0.883] and 'Corporate Training'[0.538, 0.378, 0.654, 0.883] have similar and modest levels of bloom around 20, while 'Instructional Design'[0.249, 0.282, 0.364, 0.883] shows a higher bloom of 25. 'Language Learning'[0.683, 0.426, 0.798, 0.883] has a level slightly lower at 19. Therefore, it seems like growth or industry bloom is largely concentrated in 'E-Learning', suggesting a possible trend towards online learning solutions, while areas like 'Career Counseling' may be experiencing less growth or focus in the Education and Training industry.\nBased on the above thought, the answer is:\nThe 'E-Learning' sector is experiencing the highest growth in the Education and Training industry, suggesting a trend towards online learning solutions. In contrast, 'Career Counseling' has the lowest growth indicating less focus on this area in the industry."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "LvH5nZzroDz59Rn3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which sector has the highest industry bloom?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the highest industry bloom, I need to compare the heights of different bars. Each bar corresponds to a different sector. By inspecting the coordinates of each bar and comparing their heights (which represent the Industry Bloom level), it is clear that the bar for 'E-Learning'[0.393, 0.113, 0.509, 0.883] is the highest. It extends up to 32 on the y-axis, while other bars such as 'Software Development'[0.104, 0.402, 0.220, 0.883] or 'Instructional Design'[0.249, 0.282, 0.364, 0.883] do not reach this height. Therefore, 'E-Learning' is the sector with the highest industry bloom.\nBased on the above thought, the answer is:\nE-Learning."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "7zjKius7fZeeZPIa",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease transform the data demonstrated on the bar chart into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Taking note of the chart title {\"Content Views on Different Social Media Platforms\"}[0.290, 0.037, 0.812, 0.083] at the top, and the labels of the {X-axis}[{\"Facebook\"}[0.184, 0.879, 0.266, 0.914], {\"Instagram\"}[0.344, 0.879, 0.433, 0.914], {\"Twitter\"}[0.522, 0.879, 0.580, 0.914], {\"YouTube\"}[0.679, 0.879, 0.749, 0.914], {\"LinkedIn\"}[0.841, 0.879, 0.913, 0.914]], the vertical distribution of numbers on the {Y-axis} indicates the views in hundreds of thousands. For each platform, there is a single bar denoting its associated content views. These bars' heights and their respective {labels}[{\"Facebook\"}[0.160, 0.492, 0.291, 0.854], {\"Instagram\"}[0.323, 0.347, 0.453, 0.854], {\"Twitter\"}[0.486, 0.564, 0.616, 0.854], {\"YouTube\"}[0.649, 0.130, 0.779, 0.854], {\"LinkedIn\"}[0.812, 0.637, 0.942, 0.854]] can be used to determine their corresponding values in the CSV table. For instance, the bar associated with Facebook extends to a {label of \"250000\"}[0.098, 0.696, 0.109, 0.731], signifying its value for the metric \"Views\". This process will be followed for all the platforms to generate the CSV table.\nBased on the above thought, the answer is:\nPlatform,Views\nFacebook,250000\nInstagram,350000\nTwitter,200000\nYouTube,500000\nLinkedIn,150000"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "92NKHBxBIRxT4px9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graphical representation, which platform would you categorize as the most influential in dispersing content based on views? Provide a detailed explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most influential platform, I need to find the highest bar on the chart as it signifies the highest number of content views. Starting from the left, I see the bars for {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]. Among all these, the {YouTube bar}[0.649, 0.130, 0.779, 0.854] stands out as the tallest, indicating the maximum content views. Therefore, based on the number of content views depicted in the bar chart, YouTube seems to be the most influential platform for content dispersion.\nBased on the above thought, the answer is:\nYouTube, as it has the highest number of content views among all the platforms present in the graph, making it the most influential in dispersing content."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "ZzWhhMzlltjpMqSb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform drives the least number of content views according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the platform with the least views, I would compare the heights of all the bars in the chart visually. The bar with the smallest height will indicate the platform with the least views. Starting from the left, we observe the bars for {Facebook}[0.160, 0.492, 0.291, 0.854], {Instagram}[0.323, 0.347, 0.453, 0.854], {Twitter}[0.486, 0.564, 0.616, 0.854], {YouTube}[0.649, 0.130, 0.779, 0.854], and {LinkedIn}[0.812, 0.637, 0.942, 0.854]. Visually, the bar for LinkedIn is the shortest among all bars representing the least views.\nBased on the above thought, the answer is:\nLinkedIn"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "Vc8hheP4muNWhKMT",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table from the chart's data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a CSV table of the data from the bar chart, we'll first have to understand what the x and y-axis represent. Looking at {the x-axis label}[0.489, 0.942, 0.558, 0.970], it represents different business processes. The y-axis, according to {the y-axis label}[0.015, 0.295, 0.029, 0.663], represents the time consumed by each process. With these roles identified, we can now analyze the height of each bar to determine the time consumption for each process. For example, the {bar for \"Order Processing\"}[0.104, 0.428, 0.244, 0.883] reaches up to more than half of the y-axis, indicating a high time consumption. By checking the y-axis scales, we can determine that the time consumed here is approximately \"15.2 hours\". By proceeding this way for all the bars, we can extract the data for our CSV table.\nBased on the above thought, the answer is:\nProcesses,Time Consumption\nOrder Processing,15.2\nInventory Management,10.8\nDelivery,25.7\nCustomer Service,19.5\nQuality Assurance,9.9"
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "wrUHAdHQc7bBtVCg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which process needs improvement in terms of reducing time consumption? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider which process has the highest time consumed. In this chart, that is represented by the length of the bars. The bar for {Delivery}[0.454, 0.113, 0.593, 0.883] has the highest reach on the y-axis, indicating that it takes the longest time. As such, it would be the most obvious candidate for time consumption reduction improvements. Furthermore, it outliers the rest of the bars significantly, which also suggests a potential issue in this process.\nBased on the above thought, the answer is:\nThe Delivery process needs improvement in terms of reducing time consumption. It takes far more time than any other process, indicating a high level of inefficiency. Reducing the time it takes to deliver could significantly streamline the company's operations."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "pJZMnsWWmAoi9Wpz",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process has the longest time consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to look at the bar chart and determine which of the five processes has the longest corresponding bar. Observing the bars, I see that {the bar for \"Delivery\"}[0.454, 0.113, 0.593, 0.883] is significantly longer than the others, indicating that it represents the process with the highest time consumption.\nBased on the above thought, the answer is:\nDelivery."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "bVmFmbHNPnBAQZs1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the given bar chart data on global environmental changes from 1980 to 2015 into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create a table from this chart, I can take the data directly from the x-axis {'Year'}<r>[0.522, 0.942, 0.551, 0.970]</r> and y-axis {'CO2 Emission and Deforestation'}<r>[0.015, 0.004, 0.029, 0.955]</r>. The bars represent the emissions and deforestation percentage each year from the x-axis tick labels <r>[\"1980\", \"1985\", \"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"]['coordinates']</r>, and their respective height/location represents the value on the y-axis. By referencing the y-axis and the position of each bar, I can determine the respective amounts for CO2 emissions in million metric tons and deforestation in percentage of forest area. We have two sets of bars: one for CO2 emissions<{'bars_of_group_0'}[{'bar_coordinates': '[0.171, 0.268, 0.213, 0.883]'}...{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}]> and one for deforestation<{'bars_of_group_1'}[{'bar_coordinates': '[0.129, 0.883, 0.171, 0.883]'}...{'bar_coordinates': '[0.861, 0.883, 0.902, 0.883]'}]>. The location of each bar vertically represents the percentage for that particular year.\nBased on the above thought, the answer is:\nYear,CO2 Emission (Milion Metric Tons),Deforestation (% of Forest Area)\n1980,20698,1.3\n1985,21497,1.6\n1990,22147,1.8\n1995,22734,2.0\n2000,23155,2.2\n2005,23961,2.5\n2010,24836,2.9\n2015,25904,3.4"
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "01udu5qx9avRN3Ip",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what are the environmental implications for the trend of CO2 emissions and deforestation percentage from 1980 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart delineates two primary trends: CO2 emissions and deforestation percentage. The bar graph representing {CO2 emissions}<r>[0.171, 0.268, 0.213, 0.883]...[0.902, 0.113, 0.944, 0.883]</r>, indicated in the legend, shows a clear upward trend from 1980 to 2015. This tells us that the release of CO2 into the atmosphere has been increasing consistently over these years. The second bar graph representing {deforestation percentage}<r>[0.129, 0.883, 0.171, 0.883]...[0.861, 0.883, 0.902, 0.883]</r> shows an almost flat trend. This could suggest that while total forest area may be decreasing, it's not doing so at an exceptionally accelerated rate. It is essential to note that the rising CO2 emission trend is a significant factor contributing to global warming and climate change.\nBased on the above thought, the answer is:\nThere is a continuous increase in CO2 emissions from 1980 to 2015, contributing to global warming and climate change. Simultaneously, the deforestation percentage has not seen a drastically steep increase during the same period. This could indicate some level of conservation efforts, but the fact that it is not decreasing is still a cause for concern for biodiversity and natural carbon sinks."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "Kij9Z2LP1wahvIaF",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year with the highest CO2 emission according to the data from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on each year's bar for CO2 emission<{'bars_of_group_0'}[{'bar_coordinates': '[0.171, 0.268, 0.213, 0.883]'}...{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}]>, I can tell that the bar with the highest value indicates the year with the highest CO2 emission. By comparing all these bars' heights, I can see that the bar representing the year 2015<{'bar_coordinates': '[0.902, 0.113, 0.944, 0.883]'}> has the largest height among all. It is located higher on the y-axis '{CO2 Emissions / Deforestation}'<r>[0.015, 0.004, 0.029, 0.955]</r> compared to other years.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "vq4KGYEzrYkNArRv",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data you observed in the bar chart into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my analysis, I observe that this is a bar chart with the title {\"Greenhouse Gas Emissions by Country\"}[0.396, 0.025, 0.665, 0.055]. Each bar on the chart represents a different country as depicted by the x-axis labels or tick marks {\"USA\"}[0.160, 0.919, 0.184, 0.942], {\"China\"}[0.299, 0.919, 0.332, 0.942], {\"India\"}[0.445, 0.919, 0.473, 0.942], {\"Russia\"}[0.584, 0.919, 0.621, 0.942], {\"Japan\"}[0.729, 0.919, 0.762, 0.942], and {\"Germany\"}[0.862, 0.919, 0.915, 0.942]. On the y-axis are values labeled from {0}[0.058, 0.894, 0.065, 0.917] to {14,000}[0.029, -0.059, 0.065, -0.036], indicating the greenhouse gas emissions in million tons for each country. Each bar's height {\"USA\"}[0.115, 0.471, 0.230, 0.903], {\"China\"}[0.258, 0.102, 0.373, 0.903], {\"India\"}[0.402, 0.736, 0.516, 0.903], {\"Russia\"}[0.545, 0.731, 0.659, 0.903], {\"Japan\"}[0.688, 0.816, 0.803, 0.903], {\"Germany\"}[0.831, 0.850, 0.946, 0.903], indicates the emissions amounts. For converting the bar chart to a CSV table, each bar's country with corresponding greenhouse gas emission will be transformed into a row in the table.\nBased on the above thought, the answer is:\nCountry,Greenhouse Gas Emissions (million tons)\nUSA,6341\nChina,11767\nIndia,2453\nRussia,2532\nJapan,1272\nGermany,776"
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "ZUhyg9tb9RyA9vEA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which country is most responsible for global warming from the perspective of greenhouse gas emissions, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I look at the height of each bar representing the amount of greenhouse gas emissions by each country. The bar representing {China}[0.258, 0.102, 0.373, 0.903] is significantly higher compared to others, meaning that China emits the most greenhouse gases. The larger the amount of greenhouse gases emitted by a country, the greater its contribution to global warming is, assuming all other factors are equal. Hence, China holds the most responsibility for global warming in terms of greenhouse gas emissions.\nBased on the above thought, the answer is:\nChina. Because it emits the most greenhouse gases among the countries shown in the chart."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "TPgOBS0dRIryV8V4",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more greenhouse gas does China emit compared to the USA in terms of million tons?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the difference in greenhouse gas emissions between China and the USA, I must compare the relative heights of the bars representing each. The rectangle {USA}[0.115, 0.471, 0.230, 0.903] implies the USA emits about 6341 million tons of greenhouse gases. Meanwhile, the rectangle {China}[0.258, 0.102, 0.373, 0.903] shows that China emits about 11767 million tons. The difference in emissions is determined by subtracting the emission of the USA from China's emission.\nBased on the above thought, the answer is:\n5426 million tons"
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "DGPfxBu1sUUHXPVQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart's data, can you format the information into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can see that the bar chart's title is \"Efficiency Improvement Potential Assessment\", positioned at [0.291, 0.037, 0.767, 0.083]. The chart is organized into {five bars}<r>[0.118, 0.415, 0.255, 0.854]; [0.290, 0.525, 0.427, 0.854]; [0.461, 0.239, 0.598, 0.854]; [0.632, 0.635, 0.769, 0.854]; [0.803, 0.130, 0.940, 0.854]</r>, each representing a unique process in the company's operations. In specific, they are {Manufacturing}<r>[0.124, 0.879, 0.250, 0.914]</r>, {Distribution}<r>[0.307, 0.879, 0.409, 0.914]</r>, {Logistics}<r>[0.492, 0.879, 0.567, 0.914]</r>, {Management}<r>[0.643, 0.879, 0.758, 0.914]</r>, and {Sales}<r>[0.849, 0.879, 0.895, 0.914]</r>. The height of each bar indicates the efficiency improvement potential for each process. For example, the height of the Manufacturing bar indicates a potential efficiency improvement of 20%, while the Sales bar indicates a potential increase of 33%.\nBased on the above thought, the answer is:\nProcesses,Efficiency_Improvement_Potential(%)\nManufacturing,20\nDistribution,15\nLogistics,28\nManagement,10\nSales,33"
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "oPxjfzsmNFNuHivV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the organization's strategic planning, which process do you think should be given priority for improvement and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart, the organization's performance enhancement should focus on the process with the highest efficiency improvement potential. By examining the {Sales bar}<r>[0.803, 0.130, 0.940, 0.854]</r> particularly stands out due to its significantly higher potential for efficiency improvement compared to other processes such as {Manufacturing}<r>[0.118, 0.415, 0.255, 0.854]</r>, {Distribution}<r>[0.290, 0.525, 0.427, 0.854]</r>, {Logistics}<r>[0.461, 0.239, 0.598, 0.854]</r>, and {Management}<r>[0.632, 0.635, 0.769, 0.854]</r>. This suggests that investing resources in improving the Sales process would yield the highest return in terms of improving overall operational efficiency.\nBased on the above thought, the answer is:\nThe Sales process should be prioritized for improvement since it has the highest potential for efficiency enhancement."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "vClPrOyzfb12oEwJ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which process has the highest efficiency improvement potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows five different processes on the x-axis, with the corresponding efficiency improvement potential (%) indicated by the heights of the bars on the y-axis. By comparing the heights of all the bars like {Manufacturing}<r>[0.118, 0.415, 0.255, 0.854]</r>, {Distribution}<r>[0.290, 0.525, 0.427, 0.854]</r>, {Logistics}<r>[0.461, 0.239, 0.598, 0.854]</r>, {Management}<r>[0.632, 0.635, 0.769, 0.854]</r>, and {Sales}<r>[0.803, 0.130, 0.940, 0.854]</r>, I can see that the highest bar corresponds to the Sales process. This suggests that Sales has the highest efficiency improvement potential.\nBased on the above thought, the answer is:\nSales"
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "KIsTrPQAxhzTBcd1",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the ecological environment sustainability assessment shown on the bar chart into a structured table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the graph with the title {\"Ecological Environment Sustainability Assessment\"}<r>[0.268, 0.037, 0.790, 0.083]</r>, I can see that the X axis, marked as {the 'Country' label}<r>[0.495, 0.927, 0.564, 0.963]</r>, and the Y axis, marked as {the 'Environment Score' label}<r>[0.019, 0.309, 0.036, 0.639]</r>, have the countries and their environment points respectively. I can read the corresponding values from the green bars and generate the CSV based on these information. For instance, the USA is represented by {a green bar}<r>[0.118, 0.180, 0.232, 0.854]</r> with an approximate height aligning with the y-axis 80, so it's environment point is around 78.6.\nBased on the above thought, the answer is:\nCountry,Environment_Points\nUSA,78.6\nGermany,84.5\nChina,62.3\nIndia,60.8\nBrazil,76.2\nAustralia,82.1"
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "Cq4DL9u0Ni2I8L9w",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs any of the countries treated as an outlier in terms of the environmental sustainability score? Provide reasons for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "An outlier is a data element that significantly differs from other observations. In the context of this bar chart, if a country's environmental sustainability score is significantly lower or higher than the scores of most other countries, it would be considered an outlier. Looking at the distinct bars such as {USA}<r>[0.118, 0.180, 0.232, 0.854]</r>, {Germany}<r>[0.260, 0.130, 0.373, 0.854]</r>, {Brazil}<r>[0.685, 0.201, 0.798, 0.854]</r> and {Australia}<r>[0.827, 0.150, 0.940, 0.854]</r>, their environment points don't deviate much as they are all above approximately 75 with a narrow range of less than 10 points. However, comparing {China}<r>[0.402, 0.320, 0.515, 0.854]</r>, which has a score of 62.3, to these four countries and to the entire data set, the score of China deviates significantly from the others. Thus, we might consider China as an outlier in terms of the environment sustainability score.\nBased on the above thought, the answer is:\nYes, China is an outlier. This is because its environmental sustainability score of 62.3 is significantly lower than the scores of the other countries, which are all above approximately 75, causing a large deviation."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "R3jHLI7h5lUi5M2d",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the environment points difference between the country with the highest and lowest scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between highest and lowest scores, I should identify the bars with highest and lowest heights. {The Germany bar}<r>[0.260, 0.130, 0.373, 0.854]</r> extends to the highest position on the Y-axis at 84.5, indicating Germany has the highest score. On the other hand, {the India bar}<r>[0.543, 0.333, 0.657, 0.854]</r> extends to the lowest position on the Y-axis at around 60.8, thus representing the lowest score. The point difference between those bar heights shows the required answer.\nBased on the above thought, the answer is:\n23.7"
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "BfNNRunXj4BiLDh4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the information on the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can accomplish that. This chart coveres different types of investments. From {the subtitle}<r>[0.372, 0.025, 0.675, 0.055]</r> and {the x-axis tick labels}<r>[{\"Bonds\"}[0.152, 0.919, 0.195, 0.942]; {\"Stocks\"}[0.326, 0.919, 0.371, 0.942]; {\"Real Estates\"}[0.481, 0.919, 0.566, 0.942]; {\"Commodities\"}[0.653, 0.919, 0.744, 0.942]; {\"Cryptocurrencies\"}[0.814, 0.919, 0.932, 0.942]]</r>, I can infer that the x-axis represents different investment vehicles while {the y-axis}<r>[0.015, 0.390, 0.029, 0.575]</r> represents the return rate in percentage. I can infer the return rate by the height of each bar. For instance, {the bar representing the Bonds}<r>[0.104, 0.717, 0.244, 0.903]</r> about 3.5%, which is confirmed by the y-axis label nearby. Taking similar steps for the rest of the bars produces a CSV table.\nBased on the above thought, the answer is:\nInvestment Vehicles, Return Rate (%)\nBonds,3.5\nStocks,8.3\nReal Estates,6.2\nCommodities,2.7\nCryptocurrencies,15.1"
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "noSD9mhb95T2SQwT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an investor wants steady returns and avoids high fluctuations, which investment vehicle would you recommend based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "If stability is a priority, the investor has to look for investments that have moderate but relatively consistent returns. According to the chart, {the bonds bar}<r>[0.104, 0.717, 0.244, 0.903]</r> depicts a stable growth of 3.5%. While it doesn't offer the highest return, it does promise less volatility than something like Cryptocurrencies, which, based on {the bar for Cryptocurrencies}<r>[0.803, 0.102, 0.943, 0.903]</r>, can yield a high return of 15.1% but is also highly unpredictable. Thus, considering the stable growth and lower risks, I would recommend bonds as an investment vehicle for the investor.\nBased on the above thought, the answer is:\nBonds."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "TD5EsQlhB2tEB2dG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_64_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat investment vehicle presents the highest return as illustrated by the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which investment vehicle has the highest return, I need to compare the height of each bar, which indicates the return rate. Observing the graph, it's clear that {the Cryptocurrencies bar}<r>[0.803, 0.102, 0.943, 0.903]</r> is the tallest bar among the five, rising to about 15.1% according to the scale on y-axis. Therefore, it has the highest return rate.\nBased on the above thought, the answer is:\nCryptocurrencies."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "jj2gjrxNsJ5bwMHS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table displaying the annual return rates for Stock, Bond, and Real Estate Investments from 2020 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have analyzed the chart and identified three subplots, each representing a different investment type with their respective return rates over years. By referring to {the coordinates for Stock Investment bars}<r>[0.098, 0.211, 0.160, 0.297]</r> through <r>[0.882, 0.032, 0.945, 0.297]</r> and their corresponding years from the x-axis labels, I can extract the return rates for Stock Investment. A similar approach is taken for {Bond Investment bars}<r>[0.098, 0.568, 0.160, 0.612]</r> through <r>[0.882, 0.550, 0.945, 0.612]</r> and {Real Estate Investment bars}<r>[0.098, 0.827, 0.160, 0.927]</r> through <r>[0.882, 0.749, 0.945, 0.927]</r> to gather the corresponding return rates for those investments as well.\nBased on the above thought, the answer is:\nYear,Stock Investment,Bond Investment,Real Estate Investment\n2020,0.12,0.05,0.18\n2021,0.15,0.06,0.19\n2022,0.16,0.06,0.20\n2023,0.19,0.07,0.21\n2024,0.23,0.08,0.25\n2025,0.27,0.09,0.23\n2026,0.30,0.10,0.25\n2027,0.32,0.11,0.27\n2028,0.33,0.09,0.28\n2029,0.35,0.08,0.30\n2030,0.37,0.07,0.32"
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "jTVKkTAvLT9oHCNV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the trend observed in Bond Investment returns after 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the bond investment returns after 2026, I have to observe the series of bars representing these years within the Bond Investment subplot. For the years 2027 <r>[0.647, 0.515, 0.710, 0.612]</r>, 2028 <r>[0.725, 0.533, 0.788, 0.612]</r>, 2029 <r>[0.804, 0.542, 0.867, 0.612]</r>, and 2030 <r>[0.882, 0.550, 0.945, 0.612]</r>, it's clear that the bars' tops are descending, albeit not steadily. While the return rate for 2027 is at its peak for the discussed range, there is a slight drop in 2028 and 2029 before rising again in 2030. This fluctuation suggests an erratic decrease with some recovery rather than a steady downturn.\nBased on the above thought, the answer is:\nThe Bond Investment returns after 2026 exhibit a declining trend with fluctuations, including a recovery in 2030. Despite this recovery, the returns do not reach the peak observed in 2027 within the discussed timeframe."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "nbhT1XDOmJNZr62U",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the return rate for Real Estate Investment in the year 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the return rate for Real Estate Investment in the year 2027, I will locate the bar corresponding to 2027 on the Real Estate Investment subplot. The bar is identified within {the coordinates}<r>[0.647, 0.777, 0.710, 0.927]</r> and by checking the y-axis tick labels from earlier years, we can extrapolate the return rate referring to the y-ticks <r>[0.055, 0.699]</r> for 0.2 and <r>[0.055, 0.642]</r> for 0.3. The bar's top aligns with the 0.27 return rate based on its position relative to the y-axis ticks.\nBased on the above thought, the answer is:\n0.27"
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "HwhTrtfcE8W0fRfN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the Duration of Reading data from the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bottom bar chart, {the ylabel}<r>[0.015, 0.023, 0.029, 0.255]</r> indicates that this chart represents 'Duration of Reading'. The social networks are labeled on the x-axis as {xticks}<r>[0.150, 0.808]</r> to <r>[0.910, 0.808]</r>, including Facebook, Twitter, and others. The corresponding bars for each social network such as {Facebook's bar}<r>[0.116, 0.036, 0.184, 0.254]</r>, {Twitter's bar}<r>[0.201, 0.109, 0.268, 0.254]</r>, and so on, represent the reading duration, which can be converted to exact values using the y-range labels like {0.00}<r>[0.075, 0.254]</r> and {0.90}<r>[0.075, 0.012]</r>. By comparing the heights of the bars against these y-range labels, the duration of reading can be deducted for each social network. The data will then be formatted into the requested CSV structure.\nBased on the above thought, the answer is:\nSocial Networks,Duration of Reading\nFacebook,0.9\nTwitter,0.6\nInstagram,0.85\nSnapchat,0.8\nLinkedIn,0.7\nReddit,0.65\nWhatsApp,0.9\nWeChat,0.75\nTikTok,0.5\nPinterest,0.55"
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "1tSmR9wLUhOVjDUH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social network from the chart seems to balance online and offline interactions most equally?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the balance between online and offline interactions requires analyzing the middle and bottom sub-charts. I will observe the height of {bars indicating \"Online Interaction\"}<r>[0.116, 0.326, 0.184, 0.531]</r> and {bars indicating \"Offline Interaction\"}<r>[0.116, 0.648, 0.184, 0.808]</r> for all social platforms. For instance, if {Facebook\u2019s bar for online}<r>[0.116, 0.326, 0.184, 0.531]</r> is close to {Facebook's bar for offline}<r>[0.116, 0.648, 0.184, 0.808]</r>, it indicates a balance. I should repeat this comparison for each social network and identify which one has the closest values between the two interactions. This will require a visual estimate based on the bar heights in relation to the y-axis ticks.\nBased on the above thought, the answer is:\nWhatsApp. Balance is inferred from the similar heights of {WhatsApp's online bar}<r>[0.623, 0.313, 0.690, 0.531]</r> and {offline bar}<r>[0.623, 0.625, 0.690, 0.808]</r>, indicating both interaction types are near equal values around 0.8."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "62aV6iFipOL801ju",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of Offline Interaction among the social networks illustrated on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average value of Offline Interaction, I will identify the third sub-chart by referencing {the ylabel for \"Offline Interaction\"}<r>[0.024, 0.589, 0.038, 0.798]</r>. Each bar, such as {Facebook's bar}<r>[0.116, 0.648, 0.184, 0.808]</r> and {Twitter's bar}<r>[0.201, 0.716, 0.268, 0.808]</r>, represents a social network's Offline Interaction value. Using the ytick measurements {0.0}<r>[0.075, 0.808]</r> to {1.0}<r>[0.075, 0.579]</r>, I will calculate the values by approximating the top of each bar's position relative to the labeled y-values. The values will then be summed and divided by the number of social networks, which is 10, to find the average value of Offline Interaction.\nBased on the above thought, the answer is:\n0.65"
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "RqMbSId90bXrO7vQ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCreate a table to segment and showcase the change in appreciation rates for each of the three cuisines over the 10-year period as observed on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar charts, we observe that the first subplot {Italian cuisine appreciation}<r>[0.075, 0.025, 0.985, 0.292]</r> shows bars descending in height from left to right, indicating a decline in appreciation rates from the first to the tenth year. The x-axis represents the years ranked 1 through 10, and the y-axis has ratings ranging from 0 to 1. Each bar's upper edge aligns with its corresponding value on the y-axis. The same process can be applied to the subplots for {French}<r>[0.075, 0.331, 0.985, 0.597]</r> and {Mexican cuisine}<r>[0.075, 0.636, 0.985, 0.903]</r> appreciation, with each bar on these charts also showing a slight decline from left to right. By reading off the coordinates for each bar, we can construct a structured table reflecting the appreciation rates over a 10-year period for each cuisine.\nBased on the above thought, the answer is:\nYear,Italian,French,Mexican\n1,0.95,0.85,0.75\n2,0.93,0.82,0.68\n3,0.91,0.79,0.61\n4,0.88,0.76,0.54\n5,0.85,0.73,0.47\n6,0.82,0.70,0.40\n7,0.79,0.67,0.33\n8,0.76,0.64,0.26\n9,0.73,0.61,0.19\n10,0.70,0.58,0.12"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "LP7rfPwBooPwbJLr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the average appreciation rate of Mexican cuisine over the 10-year period compare with the overall decreasing trend observed in the other two cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To perform this comparison, I need to first determine the trend for each of the cuisines. Observing the {Italian cuisine plot}<r>[0.075, 0.025, 0.985, 0.292]</r>, I note the bars decrease in height steadily, reflecting a declining trend. Similarly, in the {French cuisine plot}<r>[0.075, 0.331, 0.985, 0.597]</r>, the bars also show a decreasing trend yet not as pronounced as the Italian. In the {Mexican cuisine plot}<r>[0.075, 0.636, 0.985, 0.903]</r>, I see a sharp decline with more significant decreases each year, demonstrated by the bars' height from left to right. By comparing the steepness of the decline in the bar heights across all three plots, I am able to infer the comparative trends.\nBased on the above thought, the answer is:\nMexican cuisine shows a more persistent and remarkable decline in appreciation when compared with the Italian and French cuisines. While all three cuisines exhibit a negative trend over the 10-year period, the decline in Mexican cuisine is steeper, as seen in the diminished bar heights for successive years."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "cr2ewuvJuG8LACQU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAcross all three cuisines, which cuisine had the highest average appreciation rate over the 10-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest average appreciation rate, I will analyze the area of the bars representing each cuisine's appreciation over the 10 years. I will add the {heights of the bars for Italian cuisine}<r>[0.116, 0.038, 0.184, 0.292]; [0.201, 0.043, 0.268, 0.292]; ... [0.876, 0.104, 0.944, 0.292]</r> from their respective y-axis values and calculate the average. The same methodology will be applied to the {French cuisine}<r>[0.116, 0.343, 0.184, 0.597]; ... [0.876, 0.424, 0.944, 0.597]</r> and {Mexican cuisine bars}<r>[0.116, 0.721, 0.184, 0.903]; ... [0.876, 0.874, 0.944, 0.903]</r>. After comparing the results, the cuisine with the highest summed value divided by 10 will present the highest average appreciation rate.\nBased on the above thought, the answer is:\nItalian cuisine."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "IGkaFKKxylVhMFIP",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you structure the forecast trend data of the three movie genres from 2020 to 2029 into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the forecast trend data into a CSV table, I've analyzed the bar charts for each genre. The first sub-chart represents Genre_1 with {blue bars}<r>[0.108, 0.195, 0.176, 0.289]</r> starting from 2020 at 0.6 and increasing progressively to 1.6 in 2029. The second sub-chart for Genre_2 is indicated by {red bars}<r>[0.108, 0.344, 0.176, 0.527]</r> beginning at 0.8, rising to a peak, and then declining to -0.25. The third sub-chart for Genre_3 with {purple bars}<r>[0.108, 0.779, 0.176, 0.815]</r> shows variations beginning at 0.4 and rising to 1.4 by the end of the period. I used the y-axis values and compared the relative heights of each bar corresponding to each year on the x-axis to determine the popularity figures.\nBased on the above thought, the answer is:\nYear,Genre_1,Genre_2,Genre_3\n2020,0.6,0.8,0.4\n2021,0.8,0.6,0.9\n2022,1.2,0.4,0.85\n2023,1.15,0.37,0.88\n2024,1.25,0.25,1.0\n2025,1.35,0.15,1.05\n2026,1.45,0.05,1.15\n2027,1.4,-0.05,1.25\n2028,1.5,-0.15,1.3\n2029,1.6,-0.25,1.4"
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "CZ6GKc4SjphWOI73",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich movie genre shows the most consistent growth over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess consistency in growth, I must review the trends represented by the bars in each sub-chart. Genre_1 has {blue bars}<r>[0.108, 0.195, 0.176, 0.289]</r> that show incremental growth each year from 2020 through 2029 without any decreases. Meanwhile, Genre_2 and Genre_3, represented by {red bars}<r>[0.108, 0.344, 0.176, 0.527]</r> and {purple bars}<r>[0.108, 0.779, 0.176, 0.815]</r> respectively, exhibit fluctuations and not steady growth. After comparing the height of the bars year over year, it is clear which genre provides consistent growth.\nBased on the above thought, the answer is:\nGenre_1 shows the most consistent growth over the period, with steady increases from 2020 to 2029 without any years of decline."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "c4vEKICfx0RtMQ5f",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Genre_3, what was the maximum popularity value shown, and in which year did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will locate the tallest {purple bar}<r>[0.875, 0.691, 0.943, 0.815]</r> in the Genre_3 sub-chart to determine the maximum popularity value. By observing the heights of each bar and referencing the y-axis, which has increments denoting popularity values, the tallest bar indicates the highest popularity. The year labels beneath each bar show that this peak occurs in the final year of the data set.\nBased on the above thought, the answer is:\n1.4 in 2029."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "cp3hJLJd9B9N0JGb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the profits over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "After examining the bar chart for profits from {subplot_2}<r>[0.097, 0.642, 0.985, 0.903]</r> with the years labeled on the x-axis from {x-ticks}<r>[0.170, 0.903]</r> to {r}[0.912, 0.903]</r> and profit values on the y-axis from \"0\" at {y-tick}<r>[0.078, 0.894, 0.087, 0.917]</r> to \"120000\" at {y-tick}<r>[0.035, 0.633, 0.087, 0.657]</r>, I see colored bars representing annual profit. Each bar, like {the bar for 2011}<r>[0.137, 0.859, 0.203, 0.903]</r>, corresponds to a profit value, labeled as \"0.00 -> 20000.00\" in the dataset. Repeating this process for each year allows for the creation of a table.\nBased on the above thought, the answer is:\nYear,Profit\n2011,20000\n2012,19500\n2013,22200\n2014,25000\n2015,27500\n2016,28000\n2017,29000\n2018,31500\n2019,34000\n2020,36000"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "na1R0Z8pYL5YkKxl",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year experienced the highest growth in profits when compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the highest profit growth, I need to compare the difference in heights of adjacent bars in the profit chart {subplot_2}<r>[0.097, 0.642, 0.985, 0.903]</r>. For example, the difference between {2011}<r>[0.137, 0.859, 0.203, 0.903]</r> and {2012}<r>[0.220, 0.861, 0.286, 0.903]</r> and so on through to {2019}<r>[0.796, 0.829, 0.862, 0.903]</r> and {2020}<r>[0.879, 0.825, 0.945, 0.903]</r>. By analyzing the positive changes in profit bars' heights and comparing these changes year over year, the largest growth can be determined.\nBased on the above thought, the answer is:\nBetween the years 2019 and 2020, as the profit bars for {2019}<r>[0.796, 0.829, 0.862, 0.903]</r> and {2020}<r>[0.879, 0.825, 0.945, 0.903]</r> show the highest difference in height, indicating the largest growth in profit."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "DCfXBpAtsdU5zrdi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the revenue increase from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in revenue, I look at the first bar {the bar for 2011}<r>[0.137, 0.110, 0.203, 0.285]</r>, which has a value of \"0.00 -> 75000.00\" as per the dataset, and compare it against {the bar for 2020}<r>[0.879, 0.037, 0.945, 0.285]</r> with a value of \"0.00 -> 106000.00\". Subtracting the 2011 revenue from the 2020 revenue yields the total increase.\nBased on the above thought, the answer is:\n31000"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "oq9VlbH0IG9J77SH",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the information depicted in the bar chart into a structured table, using a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a three-panel bar chart, each panel representing a different measure, which is shown by the y-label of each subplot: {Efficiency}<r>[0.024, 0.102, 0.038, 0.214]</r>, {Reliability}<r>[0.024, 0.407, 0.038, 0.521]</r> and {Diversity}<r>[0.024, 0.718, 0.038, 0.821]</r>. Furthermore, each panel depicts information pertaining to two different areas of business, as can be seen from the x-axis of the Diversity subplot: {Global Trade}<r>[0.257, 0.919, 0.344, 0.942]</r> and {Supply Chain Management}<r>[0.666, 0.919, 0.854, 0.942]</r>. Appropriate values for each combination of Business Area and Measure can be found by looking at the height of the bar. For example, the bar for Global Trade in the Efficiency subplot <r>[0.116, 0.041, 0.484, 0.292]</r> stretches up to approximately 0.87 on the y-axis <r>[0.043, 0.282, 0.065, 0.306]</r>. Thus, the efficiency of Global Trade is 0.87.\nBased on the above thought, the answer is:\nBusiness Area, Measure1 (Efficiency), Measure2 (Reliability), Measure3 (Diversity)\nGlobal Trade, 0.87, 0.43, 0.67\nSupply Chain Management, 0.88, 0.65, 0.79"
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "OOQie5sFKya2N8MT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area of business would you consider more stable across all measures: Efficiency, Reliability, and Diversity?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires assessing the variability in the ratings across diverse measures rather than just the net total. Looking at the Efficiency subplot, we can see that Global Trade and Supply Chain Management have similar ratings, {Global Trade}<r>[0.116, 0.041, 0.484, 0.292]</r> is at 0.87 and {Supply Chain Management}<r>[0.576, 0.038, 0.944, 0.292]</r> is at 0.88. However, in the Reliability subplot, {Supply Chain Management}<r>[0.576, 0.343, 0.944, 0.597]</r> has a noticeably higher rating at 0.65 than {Global Trade}<r>[0.116, 0.429, 0.484, 0.597]</r> which is at 0.43. Lastly, in the Diversity subplot, the gap in rating increases further; {Supply Chain Management}<r>[0.576, 0.649, 0.944, 0.903]</r> is at 0.79, while {Global Trade}<r>[0.116, 0.688, 0.484, 0.903]</r> falls at 0.67. Hence, Global Trade shows less variation across measures.\nBased on the above thought, the answer is:\nGlobal Trade is more stable across all measures."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "JF559eCwr6ouvRL9",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which area of business has better overall ratings in all three measures: Efficiency, Reliability, and Diversity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to analyze the subplots for each of the measures and compare the heights of the bars for Global Trade and Supply Chain Management. For Efficiency, the {Global Trade bar}<r>[0.116, 0.041, 0.484, 0.292]</r> reaches approximately 0.87, while the {Supply Chain Management bar}<r>[0.576, 0.038, 0.944, 0.292]</r> reaches approximately 0.88. For Reliability, the {Global Trade bar}<r>[0.116, 0.429, 0.484, 0.597]</r> reaches approximately 0.43, while the {Supply Chain Management bar}<r>[0.576, 0.343, 0.944, 0.597]</r> reaches approximately 0.65. And for Diversity, the {Global Trade bar}<r>[0.116, 0.688, 0.484, 0.903]</r> reaches approximately 0.67, while the {Supply Chain Management bar}<r>[0.576, 0.649, 0.944, 0.903]</r> reaches approximately 0.79. Adding together all the measures for each area of business, Global Trade totals roughly 1.97, and Supply Chain Management totals roughly 2.32.\nBased on the above thought, the answer is:\nSupply Chain Management."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "03gKZUH0Lgj4FuuM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the chart data showing charitable donations per year into a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the chart with {the y-axis label \"Charitable donations\"}<r>[0.015, 0.034, 0.029, 0.276]</r>, I see a list of bars with their heights corresponding to the value in dollars for each year from {2010}<r>[0.121, 0.065, 0.188, 0.285]</r> to {2019}<r>[0.877, 0.037, 0.944, 0.285]</r>. Each bar is plotted on the x-axis labeled with the specified year represented by the horizontal position of the bar and the dollar amount which is proportional to the bar's height. These heights determine the charitable donation values for their respective years. I will extract these values to create the table.\nBased on the above thought, the answer is:\nYear,Charitable Donations\n2010,8000\n2011,8050\n2012,8100\n2013,8120\n2014,8200\n2015,8300\n2016,8400\n2017,8500\n2018,8500\n2019,9000"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "Cm9vIH7rOGKuNfzt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHas there been consistent growth in social services from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the sizes of the bars in the subplot dedicated to social services, indicated by {the ylabel}<r>[0.015, 0.379, 0.029, 0.549]</r>. Noting each year's bar from {2010}<r>[0.121, 0.362, 0.188, 0.594]</r> to {2019}<r>[0.877, 0.352, 0.944, 0.594]</r>, some bars are taller while others are shorter compared to the previous year, depicting both periods of increase and decrease in the value. For instance, the bar representing 2016 <r>[0.625, 0.348, 0.692, 0.594]</r> is shorter than the one representing 2015 <r>[0.541, 0.346, 0.608, 0.594]</r>, indicating a drop in social services spending. The fluctuating bar heights over the years show that there has not been consistent growth in social services spending between 2010 and 2019.\nBased on the above thought, the answer is:\nNo, the social services spending has seen both increases and decreases throughout the years, indicating inconsistent growth."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "PEPRtieOpgnYrbyY",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relative growth in volunteer hours from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "By the {y-axis label \"Volunteer hours\"}<r>[0.015, 0.681, 0.029, 0.864]</r> and the bars representing 2010 and 2015 located at <r>[0.121, 0.903, 0.188, 0.903]</r> and <r>[0.541, 0.903, 0.608, 0.903]</r> respectively, I can see that volunteer hours grew from 2 to 2.6. To calculate the relative growth, I find the difference between the two values and divide by the original value from 2010 (2.6 hours - 2 hours) / 2 hours.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "E3H1mGvise9eMkfj",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data from the efficiency bar chart into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart for efficiency, {the bars of group_0}<[0.116, 0.158, 0.184, 0.292]> display individual data points for the AI algorithms. Each bar's height corresponds to its efficiency value. For instance, {the first bar}<[0.116, 0.158, 0.184, 0.292]> relates to the Neural Network with an efficiency range of 0.00 to 0.50. The bar coordinates and their respective efficiency are noted for all ten AI algorithms displayed on the x-axis, marked by their labeled positions from 1 to 10.\nBased on the above thought, the answer is:\nAlgorithm, Efficiency\nNeural Network, 0.5\nSupport Vector Machine, 0.7\nDecision Tree, 0.75\nNaive Bayes, 0.82\nDeep Learning, 0.8\nRandom Forest, 0.88\nGradient Boosting, 0.92\nLogistic Regression, 0.74\nK-Nearest Neighbors, 0.79\nXGBoost, 0.95"
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "4Nztyxl9bGxQ2T6L",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI algorithm appears to be most consistently reliable across efficiency, performance, and reliability, as per the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must evaluate the overall consistency of each AI algorithm across the three measures shown in the sub-charts. Examining the bars corresponding to each AI algorithm, {XGBoost}<[0.876, 0.038, 0.944, 0.292]> shows the highest values in all three sub-charts with {efficiency}<[0.876, 0.038, 0.944, 0.292]>, {performance}<[0.876, 0.343, 0.944, 0.597]>, and {reliability}<[0.876, 0.765, 0.944, 0.903]>. Each sub-chart indicates that XGBoost has the maximum valued bars, suggesting high and consistent ratings across all three dimensions.\nBased on the above thought, the answer is:\nXGBoost appears to be the most consistently reliable AI algorithm as it demonstrates top performance in efficiency, performance, and reliability on all counts, evident from its visually tallest bars in each respective sub-chart."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "ycdMvVobjO4ehzZT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average efficiency of the AI algorithms as indicated by the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average efficiency, I analyze the individual efficiency values shown by the heights of the bars in the first sub-chart. The efficiencies are {0.5}<[0.116, 0.158, 0.184, 0.292]>, {0.7}<[0.201, 0.104, 0.268, 0.292]>, {0.75}<[0.285, 0.091, 0.353, 0.292]> and so on till the tenth algorithm, which are duly noted at {0.95}<[0.876, 0.038, 0.944, 0.292]>. Summing these up and dividing by the count (10), we get the average efficiency.\nBased on the above thought, the answer is:\n0.78"
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "SrZMSs8a1HVgnlce",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar charts you've observed, can you convert the data into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the visual data into a tabular format, I need to extract the measurements for each stage of Big Data Integration from each of the three bar graphs. First, let's look at the top subplot at location {'subplot_0'}[0.075, 0.025, 0.985, 0.292]. Here, the y-axis {'ylabel'}[0.024, 0.105, 0.038, 0.211] tells us that it represents the Accuracy of the measurements. And if we observe carefully, each bar in the subplot represents the accuracy of a corresponding stage. For instance, {\"Size of data\"}[{'bar_coordinates'}[0.116, 0.151, 0.230, 0.292]] shows the accuracy of the \"Size of data\" stage is 0.5, judging from its height and the y-axis values. We will repeat this process for the remaining 5 different stages in this subplot. Next, we will look at the middle subplot {'subplot_1'}[0.075, 0.331, 0.985, 0.597]. Following a similar process, we can extract Recall of the measurements for each stage. For instance, the bar for \"Size of data\" {[{'bar_coordinates'}[0.116, 0.525, 0.230, 0.597]]} shows a recall value of 0.2. Let's repeat the same for the remaining stages. Finally, we'll look at the bottom subplot {'subplot_2'}[0.075, 0.636, 0.985, 0.903], and following the same process, we can extract the Precision of the measurements for each stage. For example, \"Size of data\" {[{'bar_coordinates'}[0.116, 0.709, 0.230, 0.903]]} displays a precision value of 0.8. The same will be done for the other stages.\nBased on the above thought, the answer is:\nStages, Accuracy, Recall, Precision\nSize of data, 0.5, 0.2, 0.8\nData processing, 0.67, 0.45, 0.54\nData storage, 0.73, 0.53, 0.61\nData retrieval, 0.6, 0.4, 0.9\nData analysis, 0.9, 0.7, 0.75\nData integration, 0.8, 0.63, 0.85"
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "c236ZoyGpLJofU5l",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you suggest a stage in Big Data Integration which might need some improvement based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To suggest a stage that needs improvement, we need to take all three subplots into account and look for a stage that has lower values in at least two metrics. From the top subplot \"Accuracy\" {'subplot_0'}[0.075, 0.025, 0.985, 0.292], {\"Data processing\"}[{'bar_coordinates'}[0.259, 0.103, 0.373, 0.292]] has a quite low bar, representing a low Accuracy. In the middle subplot \"Recall\" {'subplot_1'}[0.075, 0.331, 0.985, 0.597], Data processing {[{'bar_coordinates'}[0.259, 0.434, 0.373, 0.597]]} again has a relatively low score. In the case of the bottom subplot \"Precision\" {'subplot_2'}[0.075, 0.636, 0.985, 0.903], it also shows an average level of Precision. Since the \"Data processing\" stage has lower values in two metrics and an average score in the third one, it could be suggested for improvement.\nBased on the above thought, the answer is:\nThe stage in Big Data Integration that might need some improvement is Data processing, as it has lower values in both Accuracy and Recall and an average Precision score."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "QS78Mj8VJuTf4nDD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stage in Big Data Integration achieved the highest Precision according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which stage achieved the highest precision, we need to look at the bottom subplot {'subplot_2'}[0.075, 0.636, 0.985, 0.903], which represents Precision values. By comparing heights of all bars, we can notice that the highest one belongs to \"Data retrieval\". This conclusion can be made by noticing that the bar at location {'Data retrieval'}[{'bar_coordinates'}[0.544, 0.685, 0.658, 0.903]] reaches closest to the maximum value of Precision, which is 1 based on the y-axis labels.\nBased on the above thought, the answer is:\nData retrieval."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "DxJRhAryUsX4LA4c",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the chart data into a structured table for the efficiency percentages across all three quarters for HR?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I note that {the y-axis label \"Efficiency\"}<r>[0.024, 0.015, 0.038, 0.295]</r> and {x-axis labels}[{label '1'}<r>[0.146,0.919,0.154,0.942]</r> to {label '10'}<r>[0.901, 0.919, 0.919, 0.942]</r>] represent the quarters and processes respectively. The third process, which is HR, has bars in three different sections of the chart. The efficiency for HR in the first quarter is represented by the bar located at {HR first quarter}<r>[0.285, 0.051, 0.353, 0.285]</r> with a y-range representing 0.85 efficiency, in the second quarter located at {HR second quarter}<r>[0.285, 0.392, 0.353, 0.594]</r> with a y-range representing 0.74 efficiency, and in the third quarter located at {HR third quarter}<r>[0.285, 0.701, 0.353, 0.903]</r> indicating an efficiency of 0.91.\nBased on the above thought, the answer is:\nProcess,First Quarter,Second Quarter,Third Quarter\nHR,0.85,0.74,0.91"
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "AVC1xJLTKx8LnHcn",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter demonstrates the most consistent performance across all processes, and why do you come to this conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the variation in bar heights within each quarter's section to evaluate consistency. The first quarter's bars are at [0.075, 0.025, 0.985, 0.285], and they show varied heights, indicating fluctuating efficiencies across different processes. The second quarter's bars at [0.075, 0.334, 0.985, 0.594] show less variation, implying more consistency, but still have notable differences like for HR at {HR second quarter}<r>[0.285, 0.392, 0.353, 0.594]</r>. The third quarter's bars at [0.075, 0.642, 0.985, 0.903] display similar heights, particularly among Finance at {Finance third quarter}<r>[0.623, 0.675, 0.690, 0.903]</r> and Marketing at {Marketing third quarter}<r>[0.538, 0.708, 0.606, 0.903]</r>, illustrating the most consistent performance.\nBased on the above thought, the answer is:\nThe third quarter shows the most consistent performance because the bar heights are similar across different processes, indicating that efficiency levels are more even."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "boSGaawhql0pUIez",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in efficiency for the Finance process from the first to the third quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must locate the Finance process in the first and third quarters and calculate the increase. The Finance bar for the first quarter is at {Finance first quarter}<r>[0.623, 0.043, 0.690, 0.285]</r> with an efficiency of 0.88, and for the third quarter at {Finance third quarter}<r>[0.623, 0.675, 0.690, 0.903]</r> with an efficiency of 0.95. The calculation for percentage increase is then (0.95 - 0.88) / 0.88 * 100%.\nBased on the above thought, the answer is:\n7.95%"
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "O0M7BpblCWeyc9Q0",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide me with a CSV table from the presented bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I need to convert the bars into numerical data. Let's start with the {Local Government funding}[0.066, 0.031, 0.985, 0.448] bar chart. Starting from the left, each bar from the first bar at {bar 1}[0.108, 0.076, 0.176, 0.448] to the last bar at {bar 10}[0.875, 0.051, 0.943, 0.448], each represents a sequence of fund allocation with the increase gradually from 0.9 to 0.96. Then, moving on to the {Community Development}[0.066, 0.487, 0.985, 0.903] bar chart, the same procedure can be applied. The first bar at {bar 1}[0.108, 0.562, 0.176, 0.903] to the last bar {bar 10}[0.875, 0.540, 0.943, 0.903], depict a trend of increase from 0.9 to 0.96. The only exception is at sequences 5 and 6, where Community Development funding remains constant.\nBased on the above thought, the answer is:\nLocal Government,Community Development\n1,0.9\n2,0.85\n3,0.82\n4,0.79\n5,0.78\n6,0.78\n7,0.82\n8,0.86\n9,0.91\n10,0.96"
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "Sn2Yxk62fMzfoW4q",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what sequence does the funding for community development start to increase again after reaching the lowest point?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart for {Community Development}[0.066, 0.487, 0.985, 0.903] shows a decreasing trend at the beginning. Looking at the height of the bars from the first bar at {bar 1}[0.108, 0.562, 0.176, 0.903] to the fifth bar at {bar 5}[0.449, 0.612, 0.517, 0.903], the height of the bars decrease which indicates the funding decreases from 0.9 to 0.78. The sixth bar at {bar 6}[0.534, 0.608, 0.602, 0.903] maintains at the same height, indicating that the funding remains at 0.78. It is the seventh bar at {bar 7}[0.619, 0.596, 0.688, 0.903] where the bar starts to increase in height, indicating the increase of funding from 0.78 to 0.82. Therefore, the funding for community development starts to increase again at sequence 7.\nBased on the above thought, the answer is:\n7"
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "qrV5sBxA8LE7DRLf",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a structured CSV format table from the bar chart data for the GDP growth rate over the years 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the first subplot which is dedicated to the GDP growth rate, and examining the bars representing each year from left to right; for instance, {the first bar}<r>[0.116, 0.183, 0.184, 0.292]</r> corresponds to the year 2000 with a growth rate ranging from {0.00}<r>[0.043, 0.282, 0.065, 0.306]</r> to {0.30}<r>[0.116, 0.183]</r>. By analyzing each bar's top and bottom coordinates relative to the y-axis labels, I can determine the GDP growth rate for each year. This process is repeated for each subsequent bar in the chart.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate\n2000,0.3\n2001,0.4\n2002,0.5\n2003,0.6\n2004,0.7\n2005,0.6\n2006,0.65\n2007,0.7\n2008,0.6\n2009,0.45"
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "iLWWUwDbFedewMHZ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the overall trend of inflation rate based on the bar chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Focusing on the second subplot, which depicts the inflation rates, I begin by looking at the first year, {2000}<r>[0.133, 0.919, 0.168, 0.942]</r>, with its corresponding bar reaching just below {0.5}<r>[0.043, 0.475, 0.065, 0.499]</r>. Surveying the bars chronologically, I notice fluctuations in the inflation rate, as some years, like {2003}<r>[0.386, 0.919, 0.421, 0.942]</r>, show higher rates approaching {0.9}<r>[0.043, 0.362, 0.065, 0.386]</r>. However, after {2004}<r>[0.470, 0.919, 0.505, 0.942]</r>, there's a visible downturn with a decrease in bar height, eventually leading to a gradual decline until it reaches a low in {2009}<r>[0.892, 0.919, 0.927, 0.942]</r>, where the bar height is just marginally higher than the starting point in {2000}<r>[0.116, 0.456, 0.184, 0.597]</r>. This pattern indicates an erratic but overall downward trend in inflation after 2004, followed by some stabilization at rates just below {0.5}<r>[0.043, 0.475, 0.065, 0.499]</r>.\nBased on the above thought, the answer is:\nInflation rate showed an erratic behavior with peaks around 2003 and 2004; however, after 2004, the trend was a downward trajectory, ending the decade at a lower rate almost similar to that of the year 2000."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "g6TcF7lx9JK0fkfY",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the unemployment rate in 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the unemployment rate in 2008, I will assess the third subplot dedicated to unemployment rates. Locating the specific bar representing the year {2008}<r>[0.808, 0.919, 0.843, 0.942]</r>, I then examine the top of the bar at <r>[0.792, 0.721]</r> and compare it to the y-axis labels, finding that the top aligns with the label {0.75}<r>[0.043, 0.700, 0.065, 0.723]</r>, which indicates an unemployment rate of 0.75.\nBased on the above thought, the answer is:\n0.75"
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "MvvK1fRhiT8plwWt",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTurn the data on \"Internet Users\" from the bar chart into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar chart specifically for {Internet Users}<r>[0.066, 0.025, 0.985, 0.292]</r> by examining the {bars}<r>[0.108, 0.228, 0.176, 0.292]</r>; <r>[0.193, 0.196, 0.261, 0.292]</r>; ..., I observe a consistent ascending trend. Starting with the year 2010, a {bar}<r>[0.108, 0.228, 0.176, 0.292]</r> presents a value range that starts from 0 indicated by the coordinate base at <r>[0.108, 0.292]</r> and rises to approximately 0.3 based on the top of the bar at <r>[0.108, 0.228]</r>. Proceeding year by year, the top of each bar indicates the higher range of Internet Users for the corresponding year. I shall convert the heights of these bars into a structured table.\nBased on the above thought, the answer is:\nYear,Internet Users\n2010,0.3\n2011,0.45\n2012,0.5\n2013,0.55\n2014,0.7\n2015,0.85\n2016,0.9\n2017,0.95\n2018,1.0\n2019,1.2"
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "L1IB9NmE02fNUekq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any indicator among \"Internet Users,\" \"Mobility Rate,\" or \"Political Protests\" that has demonstrated a consistent decline or increase over the period shown?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will review the trends observed in the bars for {Internet Users}<r>[0.066, 0.025, 0.985, 0.292]</r>, {Mobility Rate}<r>[0.066, 0.331, 0.985, 0.597]</r> and {Political Protests}<r>[0.066, 0.636, 0.985, 0.903]</r>. For a consistent increase, the bars' tops should move upwards progressively, and for a consistent decrease, they should move down year after year. By examining the heights of bars, such as <r>[0.108, 0.228, 0.176, 0.292]</r> for 2010 and progressing to <r>[0.875, 0.038, 0.943, 0.292]</r> for 2019, I can determine the trend for \"Internet Users.\" The other indicators' bars are analyzed similarly.\nBased on the above thought, the answer is:\n\"Internet Users\" has shown a consistent increase over the period, with the top of the bars rising higher each year from {2010}<r>[0.108, 0.228, 0.176, 0.292]</r> to {2019}<r>[0.875, 0.038, 0.943, 0.292]</r>. Contrarily, \"Political Protests\" demonstrated an overall decline, especially noticeable with low peaks in {2018}<r>[0.790, 0.882, 0.858, 0.903]</r> and {2019}<r>[0.875, 0.872, 0.943, 0.903]</r>. \"Mobility Rate\" fluctuated and does not show a consistent trend."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "4Z7KRBMdNPVX1EXk",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many times did the value of \"Political Protests\" drop below 0.20 between 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the number of times the \"Political Protests\" dropped below 0.20, I will analyze each {bar}<r>[0.108, 0.813, 0.176, 0.903]</r>; <r>[0.193, 0.825, 0.261, 0.903]</r>; ... on the bar chart dedicated to {Political Protests}<r>[0.066, 0.636, 0.985, 0.903]</r>. A lower bar top compared to {0.2 tick mark on the y-axis}<r>[0.035, 0.853, 0.057, 0.876]</r> will indicate the value is less than 0.20. Each respective year-wise bar is analyzed and compared against this benchmark to count the occurrences.\nBased on the above thought, the answer is:\nThree times."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "SrOWlSpm58wv8GRs",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the efficiency values of the three products for series 1 to 5 into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analysis of the bar chart, particularly the three subplots, I can see that for each product series there are corresponding bars for products 1 to 3 representing their respective efficiency values. The first subplot corresponds to the efficiency of {Product 1}<r>[0.075, 0.025, 0.985, 0.292]</r> with {bars}<r>[0.116, 0.108, 0.184, 0.292]; [0.201, 0.103, 0.268, 0.292]; [0.285, 0.091, 0.353, 0.292]; [0.370, 0.088, 0.437, 0.292]; [0.454, 0.080, 0.522, 0.292]</r>. The values of these bars can be derived from their height in relation to the {y-ticks}<r>[0.043, 0.000, 0.065, 0.024]</r>. The second and third subplots follow the same structure for {Product 2}<r>[0.075, 0.331, 0.985, 0.597]</r> and {Product 3}<r>[0.075, 0.636, 0.985, 0.903]</r>, respectively. For instance, the first bar of Product 1 located at <r>[0.116, 0.108, 0.184, 0.292]</r> corresponds to an efficiency value of 0.65, as indicated by the bar's top reaching just above the 0.6 mark on the y-axis.\nBased on the above thought, the answer is:\nProduct Series,Product 1,Product 2,Product 3\n1,0.65,0.73,0.89\n2,0.67,0.75,0.85\n3,0.71,0.67,0.93\n4,0.72,0.64,0.91\n5,0.75,0.62,0.96"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "Yg2CiUfYTkm7DHPi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows the steadiest increase in efficiency over the series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which product shows the most consistent rise in efficiency, it is essential to examine the slope of the bars representing efficiency across all series for each product. Starting with {Product 1}<r>[0.075, 0.025, 0.985, 0.292]</r>, the bars gradually increase in height from the first series located at <r>[0.116, 0.108, 0.184, 0.292]</r> to the tenth series located at <r>[0.876, 0.038, 0.944, 0.292]</r>, indicating a steady increase. Next, evaluating {Product 2}<r>[0.075, 0.331, 0.985, 0.597]</r>, I notice a slight decrease in efficiency from the bar located at <r>[0.538, 0.404, 0.606, 0.597]</r> to the one at <r>[0.623, 0.413, 0.690, 0.597]</r>, which implies a less stable progression. Lastly, {Product 3}<r>[0.075, 0.636, 0.985, 0.903]</r> also shows larger fluctuations, particularly a significant peak at the bar located at <r>[0.538, 0.665, 0.606, 0.903]</r>. This analysis allows me to conclude that Product 1 has the most gradual and steady increase in efficiency.\nBased on the above thought, the answer is:\nProduct 1 shows the steadiest increase in efficiency. This is evident by the progressive and consistent rise of the bars in its respective subplot, indicating minimal fluctuations in its growth throughout the series."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "VOb1N7zvD5xYuDw2",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_65_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum efficiency recorded for Product 3 across all series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the maximum efficiency value for {Product 3}<r>[0.075, 0.636, 0.985, 0.903]</r>, I scan all the bars corresponding to Product 3 in series 1 through 10. The vertical height of each bar linked to Product 3 on the y-axis tells us its efficiency value. On careful observation, I notice that the bar for series 6, located at <r>[0.538, 0.665, 0.606, 0.903]</r>, reaches the highest point just at the level of the y-axis' maximum value, which corresponds to an efficiency of 1.0.\nBased on the above thought, the answer is:\n1.0"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "v51zkKl4K5EREC75",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the information in the visual data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {study of the legend}<r>[0.078, 0.074, 0.273, 0.325]</r>, I can see that each color represents one technological industry. Combining this information with the data represented by individual bars in the chart, we can figure out the development level each industry reached in each particular year. For example, the {blue rectangle}<r>[0.112, 0.535, 0.133, 0.935]</r> represents the 'Artificial Intelligence (AI)' industry in the year 2015, and according to the y-axis, its development level was about 55. Each industry has four bars, each located in a different x-axis category, which corresponds to the years 2015, 2020, 2025, and 2030 respectively. By examining the height of each bar and cross-verifying with the y-axis, I can determine the development level of each industry in each year.\nBased on the above thought, the answer is:\nYear,AI,Cloud Computing,Quantum Computing,Mobile Technology,IoT,AR,VR\n2015,55,60,15,45,30,25,10\n2020,80,75,55,70,85,30,15\n2025,110,90,70,75,100,40,20\n2030,90,100,85,80,105,50,30"
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "8Gi1ZsRcSmnBv7Gg",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn your opinion, which technological industry will be the most developed by the year 2030 and why do you say this?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question asks me to identify the most developed technological industry by 2030, which can be determined by the height of the bars in the 2030 category. In this case, examining the {height of bars}<r>[0.799, 0.281, 0.820, 0.935];[0.820, 0.208, 0.840, 0.935];[0.840, 0.317, 0.861, 0.935];[0.861, 0.353, 0.882, 0.935];[0.882, 0.171, 0.902, 0.935];[0.902, 0.572, 0.923, 0.935];[0.923, 0.717, 0.943, 0.935]</r> in the x-axis 2030 category, I found that the IoT sector bar was the tallest, indicating the IoT will be the most developed sector by 2030.\nBased on the above thought, the answer is:\nThe Internet of Things (IoT) sector will be the most developed by 2030 according to the chart. This is indicated by the height of its corresponding bar in the 2030 category, which is visibly higher than all other sectors' bars."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "NWZaIjWIwp9f19Tv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technological sector has the highest developmental growth from 2015 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the technological sector that has the highest developmental growth from 2015 to 2030, I need to calculate the difference between the development levels in 2030 and 2015 for each sector. For example, for AI, I need to compare the heights of the bars {\"in 2015\"}<r>[0.112, 0.535, 0.133, 0.935]</r> and {\"in 2030\"}<r>[0.799, 0.281, 0.820, 0.935]</r>. The same method can be applied to Cloud Computing with bars {\"in 2015\"}<r>[0.133, 0.499, 0.153, 0.935]</r> and {\"in 2030\"}<r>[0.820, 0.208, 0.840, 0.935]</r>. After the differences are calculated for all sectors, the largest one is chosen. Through my observations, I find that the IoT sector represented by bars {\"in 2015\"}<r>[0.195, 0.717, 0.215, 0.935]</r> and {\"in 2030\"}<r>[0.882, 0.171, 0.902, 0.935]</r> has the most significant growth from about 30 to 105, which amounts to a 75 point increase.\nBased on the above thought, the answer is:\nInternet of Things (IoT)."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "amKe2TQyQZhVPzV9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you generate a CSV table to detail the market share percentage of each game from 2016 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title at the top, {Game Development in the Entertainment Industry}[0.314, 0.025, 0.733, 0.055], it is clear that the chart is about popular games from 2016 to 2022. For each game, there are four bars with different colors. The bars are grouped by the game names located on the x-axis, with labels such as {\"WoW\"}[0.163, 0.952, 0.198, 0.975] and {\"LoL\"}[0.315, 0.952, 0.338, 0.975]. According to the colored lyrics located at {the legend}[0.893, 0.074, 0.978, 0.220], the colors of the bars indicate different years. Each bar's height corresponds to its game's market share for that year. \nTo generate the CSV table, I will extract the height of the bars for each game in each year. For instance, with {the bar for WoW in 2016}<r>[0.104, 0.312, 0.126, 0.935]</r>, I estimate the market share to be approximately 60%. I do the same for each game in each year.\nBased on the above thought, the answer is:\nGame,2016,2018,2020,2022\nWoW,60,50,40,35\nLoL,55,65,75,80\nDOTA,50,55,60,65\nPUBG,5,15,25,45\nFortnite,10,15,40,40\nOverwatch,45,45,45,40"
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "90bN2YBOeYor0ekd",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy 2022, what is the growth rate of the \"PUBG\" market share from 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rate, I first need to find the market shares of PUBG in both 2016 and 2022. Looking at {the 2016 bar for PUBG}<r>[0.542, 0.883, 0.564, 0.935]</r>, it stands at around 5%, and in 2022 {the 2022 bar for PUBG}<r>[0.629, 0.468, 0.651, 0.935]</r> stands at around 45%. Using the formula (End Value - Start Value)/ Start Value* 100, I can calculate the growth rate.\nBased on the above thought, the answer is:\n800%"
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "AFyCW9giq6QDv341",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your inspection of the bar chart, can you convert the depicted data into a table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I note that the x-axis labels represent the different stages of the business value chain which include: {R&D}[0.149, 0.942, 0.193, 0.970]; {Raw Materials}[0.222, 0.942, 0.362, 0.970]; {Product Design}[0.338, 0.942, 0.488, 0.970]; {Manufacturing}[0.461, 0.942, 0.606, 0.970]; {Distribution}[0.596, 0.942, 0.712, 0.970]; {Sales}[0.749, 0.942, 0.801, 0.970] and {After Sales}[0.842, 0.942, 0.949, 0.970]. Each stage features two corresponding bars: one for {Value Creation}<r>[0.162, 0.100, 0.307, 0.128], represented by the blue color</r>, and another for {Value Erosion}<r>[0.162, 0.142, 0.295, 0.170], represented by the red color</r>. I can determine the specific value at each stage by examining the height of the corresponding bars. For example, for the stage R&D, the blue bar measures approximately 38 units, as indicated by the height of {the bar}[0.129, 0.462, 0.171, 0.923] and the red bar measures approximately 12 units, as indicated by the height of {the bar}[0.171, 0.777, 0.213, 0.923]. Therefore, I can convert this information into table form.\nBased on the above thought, the answer is:\nValue Chain Element,Value Creation,Value Erosion\nR&D,38,12\nRaw Materials,42,10\nProduct Design,45,10\nManufacturing,50,8\nDistribution,55,6\nSales,60,5\nAfter Sales,65,3"
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "9ekC0CbAFwJWAusk",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you deduce at which stage of the value chain the business is most successful at minimizing Value Erosion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the stage where Value Erosion is the lowest. The Value Erosion bars are represented by {red}<r>[0.106, 0.144, 0.146, 0.164]</r> on the chart. By tracking the red bar heights across various stages such as {R&D}<r>[0.171, 0.777, 0.213, 0.923]</r>; {Raw Materials}<r>[0.292, 0.801, 0.334, 0.923]</r>; {Product Design}<r>[0.413, 0.801, 0.455, 0.923]</r>; {Manufacturing}<r>[0.534, 0.826, 0.576, 0.923]</r>; {Distribution}<r>[0.654, 0.850, 0.697, 0.923]</r>; {Sales}<r>[0.775, 0.862, 0.817, 0.923]</r>; {After Sales}<r>[0.896, 0.886, 0.938, 0.923]</r>, I can determine that the business is most successful at minimizing Value Erosion in the After Sales Services stage, as this stage has the smallest Value Erosion bar.\nBased on the above thought, the answer is:\nAfter Sales Services."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "0ojSX4MCBqH2ZKWS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which value chain stage is the difference between Value Creation and Value Erosion the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the answer, I need to locate where the value difference between Value Creation and Value Erosion is at its smallest. To do this, I need to compare the heights of two corresponding bars at each stage. After comparing the bars from various stages such as {R&D}<r>[0.129, 0.462, 0.171, 0.923], [0.171, 0.777, 0.213, 0.923] </r>; {Raw Materials}<r>[0.250, 0.414, 0.292, 0.923], [0.292, 0.801, 0.334, 0.923]</r>; {Product Design}<r>[0.370, 0.377, 0.413, 0.923], [0.413, 0.801, 0.455, 0.923]</r>; {Manufacturing}<r>[0.491, 0.317, 0.534, 0.923], [0.534, 0.826, 0.576, 0.923]</r>; {Distribution}<r>[0.612, 0.256, 0.654, 0.923], [0.654, 0.850, 0.697, 0.923]</r>; {Sales}<r>[0.733, 0.196, 0.775, 0.923], [0.775, 0.862, 0.817, 0.923]</r>; {After Sales}<r>[0.854, 0.135, 0.896, 0.923], [0.896, 0.886, 0.938, 0.923]</r>. From this comparison, I can see the smallest difference (of 2 units) is at the {Sales} stage.\nBased on the above thought, the answer is:\nSales."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "iUqAXcJqsKtnNxRl",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart's data representing various AI model's performance over four different sequences into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the dataset's title {\"AI Models Performance Over Sequences\"}[0.296, 0.030, 0.770, 0.066], I can infer that the x-axis represents different AI models and the y-axis represents their corresponding performance scores. Some of the AI models noted are {\"Perceptron\"}[0.129, 0.942, 0.236, 0.970], {\"SVM\"}[0.301, 0.942, 0.344, 0.970], {\"KNN\"}[0.442, 0.942, 0.484, 0.970], {\"NaiveBayes\"}[0.545, 0.942, 0.661, 0.970], {\"DecisionTree\"}[0.680, 0.942, 0.806, 0.970], and {\"GradientBoosting\"}[0.797, 0.942, 0.970, 0.970]. By measuring the heights of the bars themselves, I can estimate the performance scores for each model. For instance, the height of {\"Perceptron\"} bar sequence 1 [0.140, 0.287, 0.161, 0.923] is approximately at the 75 position on the y-axis, so the value of Seq-1 for the Perceptron model is 75.\nAnd we can follow the same pattern for the other bars as well.\nBased on the above thought, the answer is:\nAI_Models,Seq-1,Seq-2,Seq-3,Seq-4\nPerceptron,75,80,85,70\nSVM,65,70,90,95\nKNN,60,70,80,55\nNaiveBayes,70,60,90,85\nDecisionTree,75,85,95,70\nRandomForest,80,95,90,80\nGradientBoosting,90,95,85,75"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "nwTpjG9ahS9n8c63",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which AI model has seen a decrease in performance from Seq-1 to Seq-4?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can answer this question by observing the heights of the bars representing Seq-1 and Seq-4 for each AI model. For instance, in the case of {\"Perceptron\"} [0.140, 0.287, 0.161, 0.923] [0.203, 0.329, 0.224, 0.923], the performance appears to decrease from 75 in Seq-1 to 70 in Seq-4. Similarly, for {\"KNN\"} [0.421, 0.414, 0.442, 0.923] [0.484, 0.456, 0.505, 0.923], the performance goes from 60 in Seq-1 to 55 in Seq-4. Thus, both these models show a decrease in performance between Seq-1 and Seq-4.\nBased on the above thought, the answer is:\nPerceptron and KNN"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "zCMJBBNop4aTtw4q",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI model performs best in Seq-2 based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the best-performing AI model in Seq-2, I need to compare the heights of the second bar in each group. Looking from left to right, I examine the bars representing each AI model in sequence 2. For instance, the second bar for the {\"Perceptron\"} model [0.161, 0.244, 0.182, 0.923] represents score 80, and the second bar for the {\"SVM\"} model [0.301, 0.329, 0.323, 0.923] represents score 70. Continuing in this manner, I can find that the {\"GradientBoosting\"} [0.862, 0.117, 0.884, 0.923] model has the tallest second bar, which corresponds to a score of 95. This is the highest among the AI models investigated.\nBased on the above thought, the answer is:\nGradientBoosting"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "HOPmV8bpEdvLDOt4",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease structure the data from the bar chart into a CSV file."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data in the chart, each year from 2000 to 2020 has data for startups founded, patents filed, investments received, and funds raised which are marked with distinct colors according to {the legend}<r>[0.123, 0.089, 0.413, 0.264]</r>. To form a CSV table, I'll need to denote each data group for every year. For example, in 2000, the bar that corresponds to startups founded reaches up to approximately 55 on the y-axis. Then, the bar that stands for patents filed extends up to around 120. The bar for investments received measures about 150, and the bar for funds raised is nearly non-visible, around 5 million USD. Similar steps will be followed for subsequent years.\nBased on the above thought, the answer is:\nYear,Startups Founded,Patents Filed,Investments Received,Funds Raised\n2000,55,120,150,5000000\n2005,150,200,300,12000000\n2010,450,560,650,40000000\n2015,600,700,850,55000000\n2020,650,780,900,60000000"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "FugqN2KChZCP80zD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you infer about investments and entrepreneurial trends based on the bar chart trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, there is a significant increase in all four parameters - Startups Founded, Patents Filed, Investments Received, and Funds Raised - over the 20-year period. The chart suggests that the entrepreneurial sector was becoming more active and attracting more investment. For instance, looking at the bars for {startups founded}<r>[{\"2000\"}[0.153, 0.876, 0.178, 0.923]; {\"2005\"}[0.324, 0.795, 0.349, 0.923]; {\"2010\"}[0.495, 0.541, 0.520, 0.923]; {\"2015\"}[0.666, 0.414, 0.691, 0.923]; {\"2020\"}[0.837, 0.371, 0.862, 0.923]]</r>, these bars show that the number of startups founded increased rapidly from 2000 to 2015 with a slight decrease in 2020. This indicates the booming startup culture and entrepreneurial spirit during this period. Additionally, {the investments received}<r>[{\"2000\"}[0.204, 0.795, 0.230, 0.923]; {\"2005\"}[0.375, 0.668, 0.401, 0.923]; {\"2010\"}[0.546, 0.371, 0.572, 0.923]; {\"2015\"}[0.717, 0.202, 0.743, 0.923]; {\"2020\"}[0.888, 0.159, 0.914, 0.923]]</r> and {the funds raised}<r>[{\"2000\"}[0.230, 0.918, 0.255, 0.923]; {\"2005\"}[0.401, 0.912, 0.426, 0.923]; {\"2010\"}[0.572, 0.889, 0.597, 0.923]; {\"2015\"}[0.743, 0.876, 0.768, 0.923]; {\"2020\"}[0.914, 0.872, 0.939, 0.923]]</r> also increased over time, signaling growing investor confidence and mobilization of funds into the sector despite the varying number of startups in the latter years.\nBased on the above thought, the answer is:\nThe bar chart suggests a positive trend in entrepreneurial spirit and investments over the period from 2000 to 2020. The increase in startups founded, patents filed, investments received, and funds raised suggests that the startup culture boomed during this period, attracting more investments. The slight decline in the number of startups in 2020 didn't deter the growth of investments, suggesting that the industry continued to have a positive outlook and investor confidence remained high."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "ok2dwUGL3W4HA3F6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year were the number of startups founded the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest number of startups, I'll need to compare the heights of the {bars representing startups}<r>[{\"2000\"}[0.153, 0.876, 0.178, 0.923]; {\"2005\"}[0.324, 0.795, 0.349, 0.923]; {\"2010\"}[0.495, 0.541, 0.520, 0.923]; {\"2015\"}[0.666, 0.414, 0.691, 0.923]; {\"2020\"}[0.837, 0.371, 0.862, 0.923]]</r>. Looking at the height of these bars, I see that the highest bar is for the year 2015. Therefore, the number of startups founded was the highest in that year.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "4rPMIRnbsnbUXGGk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you read the bar chart and convert the data into a CSV format data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Cuisine and Gastronomy Culture Analysis}[0.356, 0.021, 0.700, 0.047] and {the x-axis tick labels}[{\"Mediterranean\"}[0.116, 0.959, 0.217, 0.979]; {\"Asian\"}[0.328, 0.959, 0.366, 0.979]; {\"Anglo Saxon\"}[0.485, 0.959, 0.571, 0.979]; {\"Latin America\"}[0.660, 0.959, 0.757, 0.979]; {\"African\"}[0.865, 0.959, 0.914, 0.979]], I can see that the x-axis represents different Cuisines. The y-axis tick labels represent the corresponding values, ranging from {0}[0.052, 0.937, 0.061, 0.957] to {100}[0.035, 0.045, 0.061, 0.065]. For each Cuisine, there are three bars which represent Rating, Popularity, and Diversity respectively. According to {the legend}[0.858, 0.063, 0.978, 0.159], {the red bars}<r>[{\"Rating\"}[0.903, 0.071, 0.948, 0.091]]</r> represent Rating, the {blue bars}<r>[{\"Popularity\"}[0.903, 0.101, 0.972, 0.121]]</r> denote Popularity, and the {green bars}<r>[{\"Diversity\"}[0.903, 0.131, 0.964, 0.151]]</r> denote Diversity. I need to extract all values of each bar, reading from the left. For example, the height of the {red bar for \"Mediterranean\"}[0.112, 0.187, 0.148, 0.945] is approximately at the 85 position, so I infer that the Rating for the Mediterranean is 85.\nBased on the above thought, the answer is:\nCuisine,Rating,Popularity,Diversity\nMediterranean,85,90,75\nAsian,80,85,88\nAnglo Saxon,60,65,70\nLatin America,75,70,85\nAfrican,55,52,58"
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "EkkJhbXX5NnOUGPB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you rank these cuisines based on their popularity according to this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Popularity of each cuisine is represented by the {blue bars}<r>[{\"handle\": {\"color\": \"#b2d8f7\"}}]</r> in each group. To rank the cuisines, I need to compare the heights of these blue bars. The higher the bar, the more popular the cuisine. For example, the height of {the blue bar for Mediterranean}<r>[0.148, 0.142, 0.184, 0.945]</r> corresponds to a score of 90, which is the highest among all the cuisines, so Mediterranean is the most popular one. Similarly, I can find that Asian is the second most popular cuisine with a score of 85 from {the blue bar for Asian}<r>[0.329, 0.187, 0.365, 0.945]</r>, and so on.\nBased on the above thought, the answer is:\nMediterranean, Asian, Latin America, Anglo Saxon, African."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "qCnt2wes3nGxmVTX",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has the highest sum of rating, popularity and diversity scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to measure the score of each cuisine by adding the score positions of the three bars in each group (Rating, Popularity and Diversity). For example, {the group representing Mediterranean cuisine}<r>[0.112, 0.142, 0.221, 0.945]</r> consists of a {red bar}[0.112, 0.187, 0.148, 0.945], a {blue bar}[0.148, 0.142, 0.184, 0.945] and a {green bar}[0.184, 0.276, 0.221, 0.945], with the positions of their tops corresponding to 85, 90 and 75 respectively, so the total score of the Mediterranean is 85 + 90 + 75 = 250. I repeat this calculation for each of the other cuisines {Asian}<r>[0.293, 0.187, 0.401, 0.945]</r>, {Anglo Saxon}<r>[0.474, 0.365, 0.582, 0.945]</r>, {Latin America}<r>[0.654, 0.187, 0.763, 0.945]</r> and {African}<r>[0.835, 0.428, 0.943, 0.945]</r> to find out the cuisine with the highest total score.\nBased on the above thought, the answer is:\nMediterranean."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "jSJXpSg5u6kcAJip",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV table showing the performance of the Information Security sector from 2011 to 2020 as depicted in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, which displays the trends in technology sectors over several years, I focus on the bars representing the Information Security sector. These bars are assigned a green color, with relevant coordinates provided for each bar. Based on the chart's structure, the years 2011 to 2020 are indicated on the x-axis, with coordinates provided for each year label. The y-axis denotes the performance metric, with each tick labeled accordingly. To create the CSV table, I will extract the information relating to the Information Security sector by translating the bar height in relation to the y-axis tick labels, which indicate performance values. The coordinates represent the bottom and top of each green bar for years 2011 to 2020 as follows: {bar 2011}<r>[0.130, 0.722, 0.143, 0.945]</r>, {bar 2012}<r>[0.216, 0.700, 0.229, 0.945]</r>, through to {bar 2020}<r>[0.904, 0.499, 0.917, 0.945]</r>.\nBased on the above thought, the answer is:\nYear,Information Security Performance\n2011,20\n2012,22\n2013,24\n2014,26\n2015,28\n2016,30\n2017,32\n2018,34\n2019,36\n2020,40"
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "mAxAy59NygZyNgjH",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart data, which sector has shown the most inconsistent growth pattern between the years 2011 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the most inconsistent growth pattern, I will analyze the year-over-year changes in the height of bars for each sector. By examining the {Computer Manufacturing}<r>[0.104, 0.610, 0.117, 0.945]</r>, {Software Development}<r>[0.117, 0.443, 0.130, 0.945]</r>, {Information Security}<r>[0.130, 0.722, 0.143, 0.945]</r>, {Data Analysis}<r>[0.143, 0.555, 0.156, 0.945]</r>, and {AI Research}<r>[0.156, 0.666, 0.168, 0.945]</r>, I observe the intervals between the bars within each sector. Notable fluctuations such as the surge in {AI Research}<r>[0.672, 0.298, 0.685, 0.945]</r> in 2017 indicate inconsistency. This sector\u2019s performance jumps sharply compared to the gradual increase in the prior years, suggesting it has the most inconsistent growth pattern.\nBased on the above thought, the answer is:\nAI Research. The AI Research sector displays a sudden significant increase in performance in 2017, making it the most inconsistent compared to the other sectors, which exhibit more gradual trends."
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "fwGq1fTN9jxSB5vm",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual growth rate for the AI Research sector between 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average annual growth rate for the AI Research sector, I first locate the bars corresponding to this sector, denoted by a purple color. I then note the performance value at the top of the bars for each year from 2011 to 2020, given by the y-range attribute in the JSON: {2011}<r>[0.156, 0.666, 0.168, 0.945]</r> with a value of 25, through to {2020}<r>[0.930, 0.165, 0.943, 0.945]</r> with a value of 70. With these values, I compute the average annual growth rate using the formula [(final value / initial value) ^ (1 / number of years) - 1], where 'final value' is 70, 'initial value' is 25, and 'number of years' is 9.\nBased on the above thought, the answer is:\n11.07%"
+            }
+        ],
+        "id_raw": 982
+    },
+    {
+        "id": "9FORkjvAH4zNxzRy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the bar chart data, can you convert this into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {chart title \"Future Energy and Resource Allocation\"}<r>[0.310, 0.030, 0.769, 0.066]</r>, I can discern that the data talks about resource allocation percentages across distinctive energy sectors. The {x-axis}<r>[0.540, 0.981, 0.540, 0.981]</r> represents the various energy sectors: {Solar}<r>[0.148, 0.942, 0.198, 0.970]</r>, {Wind}<r>[0.331, 0.942, 0.381, 0.970]</r>, {Geothermal}<r>[0.481, 0.942, 0.598, 0.970]</r>, {Hydro}<r>[0.694, 0.942, 0.753, 0.970]</r>, null and {Nuclear}<r>[0.868, 0.942, 0.945, 0.970]</r>. \nThe Y Axis provides a range from {0%}<r>[0.075, 0.912, 0.087, 0.940]</r>, up to {100%}<r>[0.006, 0.064, 0.033, 0.092]</r>, and each corresponding vertical bar height gives an indication of each energy sector's resource allocation, which can be compared with the y-axis ticks to get its value. For instance, {the bar representative of Solar's allocation}<r>[0.141, 0.626, 0.205, 0.923]</r> clearly reaches upwards to about 35% on the y-axis. This data can be transcribed into a CSV format based on these observations.\nBased on the above thought, the answer is:\nEnergies, Resource Allocation\nSolar, 35\nWind, 25\nGeothermal, 40\nHydro, 20\nNuclear, 60"
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "7qc42s9gS27iDMPy",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you infer about the future allocation of resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart titled {Future Energy and Resource Allocation}<r>[0.310, 0.030, 0.769, 0.066]</r> depicts a distinct trend in resource allocation for different energy sectors. Examining the bars for each sector <r>[0.141, 0.626, 0.205, 0.923]; [0.324, 0.711, 0.388, 0.923];  [0.508, 0.583, 0.572, 0.923]; [0.691, 0.753, 0.755, 0.923]; [0.874, 0.414, 0.939, 0.923]</r>, I can infer that the graphic indicates a higher future priority for nuclear energy, given its highest allocation at 60%, followed by Geothermal having a reasonable focus with a 40% allocation. Conversely, the Hydro sector, with the lowest allocation of 20%, seems to be of lesser priority in future strategies based on these numbers.\nBased on the above thought, the answer is:\nIn the future, the majority of resources are expected to be allocated to the Nuclear sector, with a significant consideration for the Geothermal sector. However, allocation for the Hydro sector seems to be a lower priority based on current trends."
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "d4uTPWkOZxJ7nhM3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat energy sector consumes the second largest resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the {heights of the bars}, <r>[{Solar bar}[0.141, 0.626, 0.205, 0.923]; {Wind bar}[0.324, 0.711, 0.388, 0.923]; {Geothermal bar}[0.508, 0.583, 0.572, 0.923]; {Hydro bar}[0.691, 0.753, 0.755, 0.923]; {Nuclear bar}[0.874, 0.414, 0.939, 0.923]]</r> for each sector, I can identify that the {Nuclear's bar}<r>[0.874, 0.414, 0.939, 0.923]</r> is the tallest reaching the y-axis value of 60 representing the greatest resource allocation. The next tallest bar right after it is {the Geothermal's}<r>[0.508, 0.583, 0.572, 0.923]</r>, indicating that it has the second-largest resource allocation.\nBased on the above thought, the answer is:\nGeothermal"
+            }
+        ],
+        "id_raw": 983
+    },
+    {
+        "id": "9XG50K4taiY9ggJV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the bar chart, can you organize the utilization data for the educational resources in each series into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the information provided by the x-axis, it is clear that we have several categories for the nature of the educational resources, as found in {\"Tutorials\"}[0.145, 0.959, 0.203, 0.979], {\"Videos\"}[0.328, 0.959, 0.374, 0.979], {\"Exercises\"}[0.495, 0.959, 0.560, 0.979], {\"RealCases\"}[0.669, 0.959, 0.740, 0.979], and {\"SampleCodes\"}[0.834, 0.959, 0.929, 0.979]. The y-axis gives us numerical values to assign for the utilization rate of each resource in the two series, as indicated in the ranges from {0}[0.052, 0.937, 0.061, 0.957] to {100}[0.035, 0.045, 0.061, 0.065]. According to {the legend}[0.078, 0.063, 0.309, 0.129], {the light green bars}<{\"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091]> express the declining utilization trend, while {the light blue bars}<{\"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121]> express the continuous increase in utilization. From these details, the utilization rates for both trends can be extracted per each educational resource, referring to the height of the bars. For instance, the height of {the light green bar for \"Tutorials\"}[0.112, 0.383, 0.174, 0.945] is about 63 units, and the height of {the light blue bar for \"Tutorials\"}[0.174, 0.633, 0.236, 0.945] is about 35 units in comparison, to correspond with the data for the two series. All the values must follow the same extraction method to complete the table accordingly.\nBased on the above thought, the answer is:\nResources,FluctuatingDeclineSeries,ContinuousRiseSeries\nTutorials,63,35\nVideos,58,45\nExercises,50,55\nRealCases,46,65\nSampleCodes,40,75"
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "CppMF3NucbyU1r2K",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReflecting on the bar chart, what insights emerge about the trends in educational resource utilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart depicts two distinct trends of resource utilization: one fluctuating decline as represented by {series 1 bars}<{\"Fluctuating Decline Series\"}[0.122, 0.071, 0.303, 0.091]> and one continuous rise represented by {series 2 bars}<{\"Continuous Rise Series\"}[0.122, 0.101, 0.281, 0.121]>. Looking at each resource, the \"Fluctuating Decline Series\" starts with a high utilization for \"Tutorials\"[0.112, 0.383, 0.174, 0.945] at 63%, then continuously drops reaching 40% at {sample codes}[0.820, 0.588, 0.882, 0.945] with minor ups and downs in-between. On the other hand, the \"Continuous Rise Series\" starts low for \"Tutorials\"[0.174, 0.633, 0.236, 0.945] and sees a steady rise, reaching 75% at {sample codes}[0.882, 0.276, 0.943, 0.945]. Correlations seem to appear between the complexities of resources (as we move from tutorials to sample codes) and trends in learning behaviours. Learners may prefer hands-on and practical resource types such as RealCases and SampleCodes. The rising trend in such resources seems encouraging, as students are relying more on these resources over time.\nBased on the above thought, the answer is:\nOver time, learners seem to prefer using hands-on, practical resources and reduce their reliance on basic instructional content. The decline in utilization of traditional learning resources (such as tutorials and videos) and the increase in utilization of more practical learning resources (such as RealCases and SampleCodes) suggest a shift towards more application-oriented learning styles. This is a positive development, as it shows learners are willing to engage with more complex educational content over time."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "cEu8poTDC8V15Z8i",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the bar chart, which type of educational resource had the largest increase in utilization from the Fluctuating Decline Series to the Continuous Rise Series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the educational resource with the greatest increase in utilization set between the Fluctuating Decline Series and the Continuous Rise Series, we need to compare the differences in the bar heights across all available resources. Starting with {\"Tutorials\"}<FluctuatingDeclineSeries>[0.112, 0.383, 0.174, 0.945] and <ContinuousRiseSeries>[0.174, 0.633, 0.236, 0.945] the difference is 28 units. The comparison must proceed with {\"Videos\"}, {\"Exercises\"}, {\"RealCases\"}, and {\"SampleCodes\"} to check for possible larger differences in the chart. Particularly while reviewing the category of {RealCases}[0.669, 0.959, 0.740, 0.979], {the associated bars}[{FluctuatingDeclineSeries}[0.643, 0.535, 0.705, 0.945]; {ContinuousRiseSeries}[0.705, 0.365, 0.767, 0.945]] show larger values of differences, reaching to about about 19. This would be considered the largest increase amongst the computed differences through the chart.\nBased on the above thought, the answer is:\nRealCases."
+            }
+        ],
+        "id_raw": 984
+    },
+    {
+        "id": "1WtenYMDfFG3XccA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give us a table capturing the information from the bar chart showing different investment strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the setup, we see that the x-axis represents different investment strategies, which features {six models}<r>[0.131, 0.942, 0.226, 0.970]</r>,<r>[0.254, 0.942, 0.387, 0.970]</r>,<r>[0.405, 0.942, 0.520, 0.970]</r>,<r>[0.538, 0.942, 0.671, 0.970]</r>,<r>[0.658, 0.942, 0.835, 0.970]</r>,<r>[0.820, 0.942, 0.957, 0.970]</r>. The y-axis represents the level of return rate and Risk level, which ranges from {0}<r>[0.062, 0.912, 0.075, 0.940]</r> to {40}<r>[0.050, 0.064, 0.075, 0.092]</r>. From the {legend}<r>[0.098, 0.089, 0.287, 0.181]</r>, we know that there are two types of bars: The green ones represent the 'Return Rate' and the blue ones represent the 'Risk Level'. The height of the bars indicates the value according to the y-axis. Based on this, I can generate a CSV table using the height of each bar. For instance, the height of the {'Return Rate' bar under 'Blue Chip'}<r>[0.129, 0.711, 0.179, 0.923]</r> is around 10, and the height of the {'Risk Level' bar under 'Blue Chip'}<r>[0.179, 0.774, 0.228, 0.923]</r> is around 7. So the 'Return Rate' and 'Risk Level' for 'Blue Chip' are 10 and 7 respectively.\nBased on the above thought, the answer is:\nModels,Return Rate,Risk Level\nBlue Chip,10,7\nGrowth Stock,20,25\nValue Stock,15,12\nIncome Stock,12,4\nSpeculative Stock,18,30\nCyclical Stock,8,20"
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "cxCJjkAkaN5yW6xD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock would an investor seeking high returns but wanting to avoid extreme risk likely choose to invest in, and why is this the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I'm looking for stocks that have a high return rate and a relatively low-risk level. So I will compare all the stocks, starting with the 'Blue Chip'{bar1}<r>[0.129, 0.711, 0.179, 0.923]</r>.\n{bar2}<r>[0.179, 0.774, 0.228, 0.923]</r> where we get a Return Rate of 10 and Risk Level of 7, so this strategy does not provide a high return. Continuing with the other stocks till I get to 'Income Stock'{bar1}<r>[0.555, 0.668, 0.604, 0.923]</r>,{bar2}<r>[0.604, 0.838, 0.654, 0.923]</r>, we see it has a return of 12 with a very low-risk level of 4. It provides a balance between return and risk. Therefore, considering this balance, an investor seeking high returns but wanting to avoid extreme risk would choose this stock.\nBased on the above thought, the answer is:\nThe investor would likely choose to invest in 'Income Stock' because it provides a balance between a high return rate and a lower risk level when compared to other stocks."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "BbqDMpSPCAXrkXW7",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock provides the highest return relative to its associated risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which stock provides the highest return relative to its associated risk, I'll need to compute the ratio between the return rate and risk level for each stock. Using the bar heights as a visual representation, we can determine this: for instance, for 'Blue Chip', the {'Return Rate'}<r>[0.129, 0.711, 0.179, 0.923]</r> is 10, and the {'Risk Level'}<r>[0.179, 0.774, 0.228, 0.923]</r> is 7, giving a ratio of 10/7. Similar calculations can be done for the 'Growth Stock', 'Value Stock', 'Income Stock', 'Speculative Stock', and 'Cyclical Stock'. Comparing these ratios will provide the answer.\nBased on the above thought, the answer is:\nIncome Stock."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "FHcVxXm30S0Ky4Tx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observations, please convert the funds for different charity activities into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {subtitle of the chart}<r>[0.371, 0.021, 0.685, 0.047]</r>, I understand that the chart focuses on the funding analysis for different charity activities. The x-axis contains the names of different activities, represented by {the x-axis tick labels}<r>[{\"Canister_Collection\"}[0.103, 0.958, 0.236, 0.979]; {\"Church_Opening\"}[0.256, 0.958, 0.370, 0.979]; {\"Social_Service\"}[0.406, 0.958, 0.506, 0.979]; {\"Coastal_Cleanup\"}[0.541, 0.958, 0.658, 0.979]; {\"Book_Sale\"}[0.707, 0.958, 0.779, 0.979]; {\"Food_Drive\"}[0.847, 0.958, 0.925, 0.979]]</r>. The y-axis represents the funding amount, based on the range provided by {the labels of y-axis tick}<r>[{\"0\"}[0.052, 0.937, 0.061, 0.957]; {\"100\"}[0.035, 0.045, 0.061, 0.065]]</r>. Each activity has four colored bars corresponding to different funding sources, represented by {the legend}<r>[0.078, 0.063, 0.251, 0.189]</r>. To generate the CSV table, I need to extract the values each bar stands for. For example, {the bar for \"Canister_Collection\" from \"Local Giving\"}<r>[0.112, 0.499, 0.141, 0.944]</r> has a height corresponding to approximately 50 according to the y-axis range. Hence, the value of \"Local Giving\" for the \"Canister_Collection\" activity is 50.\nBased on the above thought, the answer is:\nActivities,Local_Giving,Foreign_Aid,Public_Funding,Private_Donations\nCanister_Collection,50,65,35,70\nChurch_Opening,40,55,60,50\nSocial_Service,85,90,75,80\nCoastal_Cleanup,10,15,20,30\nBook_Sale,55,60,45,40\nFood_Drive,35,40,25,32"
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "sXEJuwNW3hAQJG6N",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which funding source is considered more reliable for the charity activities, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the height of the bars representing each funding source across different activities, one can evaluate their reliability.  Specifically, {Local Giving}<r>[0.122, 0.071, 0.207, 0.091]</r> has funded around 50 to 85 funds except for Coastal Cleanup which might be the sporadic funding requirements. {Foreign Aid}<r>[0.122, 0.101, 0.200, 0.121]</r> has funded around 60 to 90 all the time and {Public Funding}<r>[0.122, 0.131, 0.223, 0.151]</r> changed drastically from 35 to 75. Lastly, {Private Donations}<r>[0.122, 0.161, 0.245, 0.181]</r> performed more stable in most categories, but with a lower amount, around 30 to 70.\nBased on the above thought, the answer is:\nConsidering the stability and amount of funds, Foreign Aid seems to be the most reliable funding source because it consistently provides a relatively high amount of resources."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "PFem9vhmFeTAfCrc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity receives the least amount of total funds?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which activity receives the least total funds, I need to sum up the funds from different sources for each activity. Looking from left to right, the first activity is {Canister_Collection}[0.103, 0.958, 0.236, 0.979] with bars located at [0.112, 0.499, 0.141, 0.944], [0.141, 0.365, 0.170, 0.944], [0.170, 0.633, 0.198, 0.944] and [0.198, 0.321, 0.227, 0.944], the heights which correspond to the funds are about 50, 65, 35 and 70 respectively and the sum is 220. Continuing for other activities, it turns out that the {Coastal_Cleanup}[0.541, 0.958, 0.658, 0.979], with bars located at [0.542, 0.855, 0.571, 0.944], [0.571, 0.811, 0.599, 0.944] , [0.599, 0.766, 0.628, 0.944] and [0.628, 0.677, 0.657, 0.944], has the smallest total amount of around 75.\nBased on the above thought, the answer is:\nCoastal Cleanup."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "gj6XYexHitSF5EDd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the career and workplace development trends into csv format using raw data for each category between 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the chart data into CSV format, I need to take into account the categories represented on the X-axis {\"Professional Training\"}[0.148, 0.774, 0.162, 0.979], {\"Skill Acquisition\"}[0.296, 0.774, 0.310, 0.930], {\"Networking\"}[0.443, 0.774, 0.457, 0.888], {\"Entrepreneurship\"}[0.590, 0.774, 0.604, 0.946], {\"Mentoring\"}[0.737, 0.774, 0.751, 0.875], {\"Career Counseling\"}[0.885, 0.774, 0.899, 0.957]. Then I'll look at the two sets of bars, each representing the data for a particular year. According to {the legend}[0.069, 0.063, 0.154, 0.129], {the light blue bars}<{\"2019\"}[0.113, 0.071, 0.149, 0.091]> correspond to the year 2019 and {the dark red bars}<{\"2020\"}[0.113, 0.101, 0.148, 0.121]> correspond to 2020. The height of each bar represents the value for each category in each year, and can be determined using the Y-axis {\"y_range\"} values ranging from {0}[0.043, 0.753, 0.052, 0.773] to {90}[0.035, -0.024, 0.052, -0.004]. The CSV table will comprise columns for each category and rows for each year.\nBased on the above thought, the answer is:\nYear, Professional Training, Skill Acquisition, Networking, Entrepreneurship, Mentoring, Career Counseling\n2019, 60, 35, 48, 53, 67, 72\n2020, 64, 32, 50, 58, 70, 78"
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "nxAPSGcEQvukev5O",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the visible trends from 2019 to 2020, which aspect of professional development appears to be becoming less popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze and compare the heights of the pairs of bars representing {2019}<[\"2019\"][0.113, 0.071, 0.149, 0.091]> and {2020}<[\"2020\"][0.113, 0.101, 0.148, 0.121]> for the categories {\"Professional Training\"}([0.104, 0.242, 0.155, 0.761], [0.155, 0.208, 0.207, 0.761]), {\"Skill Acquisition\"}([0.251, 0.458, 0.303, 0.761], [0.303, 0.484, 0.354, 0.761]), {\"Networking\"}([0.398, 0.346, 0.450, 0.761], [0.450, 0.329, 0.501, 0.761]), {\"Entrepreneurship\"}([0.546, 0.303, 0.597, 0.761], [0.597, 0.260, 0.649, 0.761]), {\"Mentoring\"}([0.693, 0.182, 0.744, 0.761], [0.744, 0.156, 0.796, 0.761]), and {\"Career Counseling\"}([0.840, 0.139, 0.892, 0.761], [0.892, 0.087, 0.943, 0.761]). The only aspect of professional development which saw a decrease in its bar height from 2019 to 2020 was 'Skill Acquisition'.\nBased on the above thought, the answer is:\nSkill Acquisition. This category has shown a decrease in the presented activity level between 2019 and 2020."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "pVhuzdic7D16S5XO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, in which category was the highest level of career and workplace development activity observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, one needs to compare the heights of the {light blue bars}<{\"2019\"}[0.113, 0.071, 0.149, 0.091]> representing categories {\"Professional Training\"}[0.104, 0.242, 0.155, 0.761], {\"Skill Acquisition\"}[0.251, 0.458, 0.303, 0.761], {\"Networking\"}[0.398, 0.346, 0.450, 0.761], {\"Entrepreneurship\"}[0.546, 0.303, 0.597, 0.761], {\"Mentoring\"}[0.693, 0.182, 0.744, 0.761], and {\"Career Counseling\"}[0.840, 0.139, 0.892, 0.761] in 2019. The bar with the greatest height indicates the highest level of activity. This can be verified by comparing the heights of the bars which are judged in accordance to the Y-axis {\"y_range\"} values. After the comparison, we find that the one corresponds to 'Career Counseling' is the tallest.\nBased on the above thought, the answer is:\nCareer Counseling."
+            }
+        ],
+        "id_raw": 987
+    },
+    {
+        "id": "Tqll871AoVnDDJ8Q",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the observed bar chart, could you please generate a CSV format table for each type of technology and the activities in technological innovation and intellectual property?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {\"Tech Innovation & Intellectual Property\"}[0.304, 0.030, 0.763, 0.066], it can be observed that the x-axis represents the types of technology which are {VR}[0.169, 0.942, 0.197, 0.970], {AR}[0.314, 0.942, 0.341, 0.970], {Blockchain}[0.418, 0.942, 0.526, 0.970], {AI}[0.607, 0.942, 0.626, 0.970], {Robotics}[0.719, 0.942, 0.803, 0.970], {IoT}[0.891, 0.942, 0.921, 0.970] and y-axis represents the count ranges from {0}[0.062, 0.912, 0.075, 0.940] to {100}[0.037, 0.014, 0.075, 0.042]. The legend shows four categories: {Innovation}[0.162, 0.100, 0.267, 0.128], {Patents Infrienged}[0.162, 0.142, 0.343, 0.170], {Failed Patents}[0.162, 0.183, 0.300, 0.211] and {Abandoned Patents}[0.162, 0.225, 0.355, 0.253]. I can generate the CSV table by taking these into account and matching the bar heights to the count on the y-axis.\nBased on the above thought, the answer is:\nTechType,Innovation,PatentsInfrienged,FailedPatents,AbandonedPatents\nVR,15,35,5,10\nAR,22,45,8,12\nBlockchain,25,60,10,14\nAI,30,70,12,16\nRobotics,35,80,15,20\nIoT,40,90,18,25"
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "eykq4lB1XO1S6ND5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the overall trends in the chart, where should a technology company invest for most intellectual property rights and highest innovation chance?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to look at multiple things including highest Innovation score and lowest counts of patents that have been infringed, failed and abandoned. Looking at the chart, it can be seen that most innovations are happening in {IoT}[0.851, 0.564, 0.873, 0.923]. Technology companies should also avoid areas where many patents have been infringed and this is highest in {IoT}[0.873, 0.115, 0.895, 0.923], but also quite high in {Robotics}[0.729, 0.205, 0.750, 0.923]. Thus, considering between the lowest Failed Patents and Abandoned Patents, {VR}[0.172, 0.878, 0.194, 0.923; 0.194, 0.833, 0.216, 0.923] and {AR}[0.317, 0.851, 0.338, 0.923; 0.338, 0.815, 0.360, 0.923] have the lowest counts. Therefore, the safest bet might be VR or AR. It is observed that IoT has a substantial amount of innovation, though it also comes with a high risk of patent infringement, failed patents and abandoned patents.\nBased on the above thought, the answer is:\nA company looking for highest intellectual property rights and innovation prospects should consider investing in VR or AR."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "DN9zD1WrW6Lm0Ck1",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological category has the highest accumulated score across all activities of technological innovation and intellectual properties?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the technology category with the highest accumulated score, I have to assign the height of each bar to its accurate technology, and add up all the counts under each technology. For instance, in the VR category, {the innovation bar height}[0.129, 0.788, 0.151, 0.923] interprets to 15, {the patents infringed bar height}[0.151, 0.609, 0.172, 0.923] interprets to 35, {the failed patents bar height}[0.172, 0.878, 0.194, 0.923] interprets to 5, and {the abandoned patents bar height}[0.194, 0.833, 0.216, 0.923] interprets to 10. Then moving across and performing the same analysis for AR, Blockchain, AI, Robotics and IoT. Then totaling up the cumulative counts recorded for each technology.\nBased on the above thought, the answer is:\nIoT."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "rzRl17pphBCYhF7w",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please construct a CSV table from the data you've observed in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart titled {Big Data Integration Overview}<r>[0.363, 0.025, 0.678, 0.055]</r>, I can see that each bar represents a specific technology. The x-axis labels are the names of each technology and include {Cloudera}<r>[0.131, 0.952, 0.209, 0.975]</r>, {Hortonworks}<r>[0.255, 0.952, 0.365, 0.975]</r>, {MapR}<r>[0.426, 0.952, 0.475, 0.975]</r>, {AWS-Glue}<r>[0.548, 0.952, 0.633, 0.975]</r>, {Azure Data Factory}<r>[0.648, 0.952, 0.814, 0.975]</r>, and {Google Cloud Dataprep}<r>[0.769, 0.952, 0.973, 0.975]</r>. Notice that for each technology, there are three bars of different colors, representing different types of data: unstructured data, semi-structured data, and structured data according to {the legend}<r>[0.097, 0.074, 0.348, 0.185]</r>. The y-axis indicates the efficiency of handling each type of data, ranging from {0}<r>[0.065, 0.926, 0.076, 0.950]</r> to {100}<r>[0.043, 0.132, 0.076, 0.156]</r>. Therefore, I would need to extract the height of each colored bar under each technology in order to build the table.\nBased on the above thought, the answer is:\nTechnologies,Unstructured Data,Semi-Structured Data,Structured Data\nCloudera,60,80,45\nHortonworks,40,60,50\nMapR,55,75,60\nAWS-Glue,80,95,72\nAzure Data Factory,75,88,65\nGoogle Cloud Dataprep,85,100,77"
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "HpzmRX9DxRHgIch6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology appears to be the most efficient across all types of data and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the most efficient technology, I must compare the overall performance of the six technologies across all types of data. This means analyzing the three bars under each technology. The bars include unstructured data, semi-structured data, and structured data, according to the chart's {legend}<r>[0.097, 0.074, 0.348, 0.185]</r>. By comparing the height of these bars, which represent efficiency, I can assess each technology's performance. From left to right, the first group of bars correlate with {Cloudera}<r>[0.131, 0.952, 0.209, 0.975]</r>, the second group with {Hortonworks}<r>[0.255, 0.952, 0.365, 0.975]</r>, and so on. The last group of bars, which correlate with {Google Cloud Dataprep}<r>[0.769, 0.952, 0.973, 0.975]</r>, seem to have the highest average height, indicating the highest overall efficiency across all types of data, thereby making it the most efficient.\nBased on the above thought, the answer is:\nGoogle Cloud Dataprep. This technology has the highest average efficiency across all types of data as visualized on the bar chart."
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "ML6tc9drICvY6qRC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_66_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest efficiency a technology has achieved in handling semi-structured data according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the orange bars which represent the {Semi-Structured Data}<r>[0.153, 0.118, 0.341, 0.141]</r> according to the legend. Each bar is aligned with a specified technology on the x-axis. By examining the maximum height reached by the orange bars, I can reach a conclusion. The highest orange bar corresponds to the technology {Google Cloud Dataprep}<r>[0.857, 0.142, 0.885, 0.935]</r>, which reaches almost the top of the y-axis, indicating an efficiency level of 100%.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 989
+    },
+    {
+        "id": "9ofQw4JX6vuR6lJV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a CSV format table based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart presents the changing values of five different digital currencies across three periods. The currencies include {Bitcoin}[0.129, 0.039, 0.177, 0.059], {Ethereum}[0.129, 0.069, 0.197, 0.089], {Ripple}[0.129, 0.099, 0.173, 0.119], {Stellar}[0.129, 0.129, 0.174, 0.149], and {Tether}[0.129, 0.159, 0.172, 0.179]. The change in the value of the currencies over time can be obtained by the relative heights of the bars, noting that the y-axis ticks are in the form of \u201cChange in Value\u201d. Let\u2019s consider Bitcoin: for the first period, the corresponding bar is located at {[0.166, 0.674, 0.191, 0.674]}, which means there's no change from its initial value. For the second period, the bar is significantly higher, spanning from 0 to 0.2 in value as indicated by the bar's coordinates {[0.416, 0.521, 0.441, 0.674]}. For the third period, the change in value is smaller, being 0.05, and for the fourth period, it's 0.03, as deduced from the coordinates of the respective bars. The same process can be done for the other currencies.\nBased on the above thought, the answer is:\nPeriod,Bitcoin,Ethereum,Ripple,Stellar,Tether\n0,0.0,0.0,0.0,0.0,0.0\n1,0.2,0.6,-0.1,0.02,-0.1\n2,0.05,0.7,-0.2,0.03,0.1\n3,0.03,0.8,-0.3,0.04,-0.05"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "D9YZ8efiYU19a6iV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, which currency would you recommend an investor who wants a steady growth investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the analysis of the bar chart, each currency shows a different growth trend over time. Let's analyze them one by one. Starting with {Bitcoin}, the bars of Bitcoin, as per their coordinates, show inconsistent changes over time. The second currency we consider is {Ethereum}, which exhibits positive growth consistently over all periods. {Ripple}, on the other hand, shows a steady decline in its values over time. {Stellar} exhibits a very small but steady increase throughout the periods, indicating minimal growth. Lastly, {Tether} demonstrates inconsistent changes, showcasing the fluctuating nature of its value. Based on all this, if an investor wants steady growth, Ethereum seems to be a good choice as it has shown consistent positive growth over the periods.\nBased on the above thought, the answer is:\nEthereum. Ethereum has consistently demonstrated positive growth over the periods, which makes it a sound choice for investors seeking steady growth."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "jMmqyzFPhQHQaLwG",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which cryptocurrency has shown the greatest positive change in value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the cryptocurrency with the highest positive change, I need to compare the heights of the highest bars for each cryptocurrency. Starting from Bitcoin, the highest bar {[0.416, 0.521, 0.441, 0.674]} corresponds to a change of 0.2. Ethereum's highest bar reaches {[0.943, 0.063, 0.968, 0.674]}, indicating a change of 0.8. Ripple's bar doesn't show a positive change since its maximum location is at the baseline. Stellar's maximum bar {[0.993, 0.643, 1.018, 0.674]} shows a change of 0.04. For Tether, the maximum change, 0.1, is indicated by the bar {[0.767, 0.598, 0.793, 0.674]}. Comparing these, Ethereum has the highest bar, thus the greatest positive change.\nBased on the above thought, the answer is:\nEthereum"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "YzKdUFc3lp04aQsM",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the bar chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's legend, {the legend}<r>[0.086, 0.037, 0.237, 0.218]</r>, I recognize that there are five groups, each representing a social media platform: Facebook, Twitter, Instagram, LinkedIn, and Snapchat. {The x-axis}<r>{\"score_1\"}[0.044, 0.952, 0.111, 0.975]; {\"score_2\"}[0.335, 0.952, 0.401, 0.975]; {\"score_3\"}[0.625, 0.952, 0.691, 0.975]; {\"score_4\"}[0.915, 0.952, 0.981, 0.975]</r> contains the labels for four different scores. The chart uses different colored bars to represent user engagement scores for the platforms, and the height of each bar signifies the corresponding numerical score. Looking at the bars for each group across different scores, I can perceive the corresponding user engagement scores for each social media platform at each score interval.\nBased on the above thought, the answer is:\nscores,Facebook,Twitter,Instagram,LinkedIn,Snapchat\nscore_1,5,3,8,2,7\nscore_2,15,10,20,15,14\nscore_3,20,25,35,28,38\nscore_4,75,83,68,70,78"
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "EJlAdlCZEMOrNarD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, what is the trend of user engagement on these social media platforms over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires analyzing the height increment of each group's bars from {score_1}<r>[\"score_1\" label][0.044, 0.952, 0.111, 0.975]</r>, {score_2}<r>[\"score_2\" label][0.335, 0.952, 0.401, 0.975]</r>, {score_3}<r>[\"score_3\" label][0.625, 0.952, 0.691, 0.975]</r> to {score_4}<r>[\"score_4\" label][0.915, 0.952, 0.981, 0.975]</r>. Based on the respective heights of the bars, I can infer that there is a general increase in user engagement scores across all platforms over time. All the bars of the final score, which I assume to be the latest score, {score_4}<r>[\"score_4\" label][0.915, 0.952, 0.981, 0.975]</r>, are higher than the previous ones, which indicates an overall increase in user engagement over time.\nBased on the above thought, the answer is:\nOver time, there is a general increasing trend of user engagement on all these social media platforms tested."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "JGUGV4LRLw5irO2E",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform had the highest user engagement score at the second score interval?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the height of the bars corresponding to the second score interval for each social media platform. {score_2}<r>[\"score_2\" label][0.335, 0.952, 0.401, 0.975]</r>. Looking at the bars under the label 'score_2', I observe that the highest bar is for Instagram {Instagram bar for score_2}<r>[0.208, 0.727, 0.237, 0.935]</r>. Therefore, Instagram had the highest user engagement score at the second score interval.\nBased on the above thought, the answer is:\nInstagram."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "PDmKftCqP52tM0Yw",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the energy consumption values from the bar chart and create a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I can see five different energy resources: {'Coal'}<r>[0.174, 0.055, 0.218, 0.083]</r>, {'Natural Gas'}<r>[0.174, 0.097, 0.292, 0.125]</r>, {'Renewable'}<r>[0.174, 0.139, 0.283, 0.167]</r>, {'Nuclear'}<r>[0.174, 0.181, 0.251, 0.209]</r>, and {'Oil'}<r>[0.174, 0.223, 0.201, 0.251]</r>. For each resource, there are four bars at {'2020'}<r>[0.076, 0.942, 0.126, 0.970]</r>, {'2030'}<r>[0.360, 0.942, 0.410, 0.970]</r>, {'2040'}<r>[0.644, 0.942, 0.694, 0.970]</r>, and {'2050'}<r>[0.929, 0.942, 0.979, 0.970]</r>. The height of each bar on the 'Y' axis represents the consumption of the respective resources. For example, for Coal in 2020, the bar height extends up to 100 mark shown at <r>[0.075,0.912,0.087,0.940]</r> on y-axis. Following a similar approach, I can extract the consumption values for all resources for all years.\nBased on the above thought, the answer is:\nResource,2020,2030,2040,2050\nCoal,100,90,75,50\nNatural Gas,120,140,130,150\nRenewable,60,90,150,200\nNuclear,80,85,90,95\nOil,90,85,75,65"
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "xWeq8mg8eDWLxkYS",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the future of energy resource consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the height of the bars, it is apparent that the usage of renewable energy {Renewable}<r>[0.174, 0.139, 0.283, 0.167]</r> is expected to increase significantly over the years. The increase is highlighted by the bars at <r>[0.229, 0.540, 0.257, 0.923]</r>, <r>[0.513, 0.285, 0.541, 0.923]</r>, and <r>[0.797, 0.073, 0.826, 0.923]</r>. At the same time, the consumption of traditional energy resources like Coal <{Coal}[0.174, 0.055, 0.218, 0.083]> and Oil <{Oil}[0.174, 0.223, 0.201, 0.251]> are projected to decline, as indicated by the shrinking bars, for example, <r>[0.456, 0.604, 0.485, 0.923]</r> and <r>[0.854, 0.646, 0.883, 0.923]</r>. This trend suggests a positive shift towards clean and renewable energy in the future.\nBased on the above thought, the answer is:\nThe energy consumption in the future is shifting significantly towards renewable resources, while the consumption of traditional energy resources like coal and oil is on the decline. This indicates a positive movement towards sustainability and environmental consciousness."
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "0bhOIns3RK86lNDU",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year is the total energy consumption projected to be the highest among the five resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the total bar heights of the five energy resources in each of the four years. For year 2020 the total is calculated by summing heights of bars at <r>[-0.112, 0.498, -0.084, 0.923]</r>, <r>[-0.084, 0.413, -0.055, 0.923]</r>, <r>[-0.055, 0.668, -0.027, 0.923]</r>, <r>[-0.027, 0.583, 0.001, 0.923]</r>, <r>[0.001, 0.540, 0.030, 0.923]</r>. Following same approach for other years it is clear that the highest total energy consumption is expected in year 2050 with the total height of bars at series of coordinates - <r>[0.740, 0.710, 0.769, 0.923]</r>,<r>[0.769, 0.285, 0.797, 0.923]</r>,<r>[0.797, 0.073, 0.826, 0.923]</r>,<r>[0.826, 0.519, 0.854, 0.923]</r>,<r>[0.854, 0.646, 0.883, 0.923]</r>.\nBased on the above thought, the answer is:\n2050"
+            }
+        ],
+        "id_raw": 994
+    },
+    {
+        "id": "Q4jfX0mwUPs18ALE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV format table based on the data shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, starting from the {legend}<r>[0.411, 0.028, 0.636, 0.164]</r>, I know that this chart represents five different global trends from 2020-2040, including 'Inflation rates', 'Poverty rates', 'Population increase', 'Carbon emission', and 'Renewable energy usage'. Each type of data is displayed as a separate bar graph with time stamps {2020, 2025, 2030, 2040}<r>[0.114, 0.964, 0.149, 0.981]; [0.329, 0.964, 0.364, 0.981]; [0.544, 0.964, 0.579, 0.981]; [0.758, 0.964, 0.793, 0.981]</r>. I will organize this data into a CSV table with the timestamps as column headers and each global trend as a row.\nBased on the above thought, the answer is:\ntime,Inflation rates,Poverty rates,Population increase,Carbon emission,Renewable energy usage\n2020,2.3,14.5,1.1,33.2,17.5\n2025,2.1,13.7,0.8,32.1,19.8\n2030,3.1,11.2,0.5,29.3,27.1\n2040,2.7,13.8,0.4,27.4,33.6"
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "Anpn0wxF9pxzrwsB",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data represented in this bar chart, do you believe efforts to decrease carbon emissions and increase renewable energy usage are working? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to focus on the bars related to 'Carbon emission' and 'Renewable energy usage'. For 'Carbon emission'<r>[0.099, 0.074, 0.132, 0.952]; [0.314, 0.104, 0.346, 0.952]; [0.529, 0.178, 0.561, 0.952]; [0.743, 0.228, 0.776, 0.952]</r>, the heights of the bars decrease consistently from 2020 to 2040, indicating the amount of carbon emissions has been steadily declining during this period. On the contrary, 'Renewable energy usage'<r>[0.132, 0.489, 0.164, 0.952]; [0.346, 0.428, 0.379, 0.952]; [0.561, 0.236, 0.593, 0.952]; [0.776, 0.064, 0.808, 0.952]</r> demonstrates a consistent rise, particularly between 2025 and 2030 there is a significant increase. This shows that the amount of renewable energy usage has continuously been on the rise. Combining these findings, it seems that efforts to reduce carbon emissions and increase renewable energy usage are indeed working.\nBased on the above thought, the answer is:\nYes, the efforts are working. The data shows a significant decrease in carbon emissions and a notable increase in renewable energy usage over time."
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "QEK2pIcsDREG28z3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what year has the carbon emissions been the lowest according to the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the {bars representing Carbon Emission}<r>[0.099, 0.074, 0.132, 0.952]; [0.314, 0.104, 0.346, 0.952]; [0.529, 0.178, 0.561, 0.952]; [0.743, 0.228, 0.776, 0.952]</r>. According to the graph, the last bar which corresponds to the year 2040 has the lowest value of all bars and thus represents the lowest carbon emission level.\nBased on the above thought, the answer is:\n2040"
+            }
+        ],
+        "id_raw": 995
+    },
+    {
+        "id": "BNNcYztAaexp6oHA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the progress of the 'Digital Diagnostics' and 'Telemedicine' sectors as shown in your observed chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a bar chart tracing the progress of two key healthcare technology sectors across 12 years. The horizontal axis represents the years from 2008 to 2019 as indicated by {the x-axis tick labels}[{\"2008\"}[0.137, 0.959, 0.172, 0.979]; {\"2009\"}[0.207, 0.959, 0.242, 0.979]; so on till {\"2019\"}[0.912, 0.959, 0.947, 0.979]]. The vertical axis represents the scores from 0 to 120, as denoted by {the y-axis tick labels}[{\"0\"}[0.052, 0.937, 0.061, 0.957]; {\"20\"}[0.043, 0.761, 0.061, 0.781]; so on till {\"120\"}[0.035, -0.118, 0.061, -0.098]]. There are two categories of bars, distinguished by color, as indicated by {the legend}[0.078, 0.031, 0.258, 0.097]. The blue bars {Digital Diagnostics}<r>{\"Digital Diagnostics\"}[0.122, 0.039, 0.252, 0.059]<r>represent one sector and the orange bars {Telemedicine} represent the other sector<r>{\"Telemedicine\"}[0.122, 0.069, 0.213, 0.089]<r>. To create the table, I need to identify the score for each sector in a given year, as indicated by the respective bar's height. For instance, in 2008, the blue bar{\"Digital Diagnostics\"}[0.112, 0.804, 0.140, 0.945] corresponds to a score of 16 whereas the orange bar{\"Telemedicine\"}[0.140, 0.681, 0.169, 0.945] corresponds to a score of 30. I will repeat this exercise for every year to capture all data.\nBased on the above thought, the answer is:\nYear,Digital Diagnostics,Telemedicine\n2008,16,30\n2009,28,64\n2010,30,70\n2011,35,80\n2012,40,85\n2013,43,88\n2014,45,90\n2015,40,92\n2016,38,95\n2017,30,97\n2018,25,99\n2019,21,100"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "hCvIBodCkYsdCssb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how do the trends depicted by the 'Telemedicine' and 'Digital Diagnostics' differ over the years 2008 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart depicts the progression of two healthcare technology sectors, namely {'Digital Diagnostics'}<r>[0.122, 0.039, 0.252, 0.059]</r> and {'Telemedicine'}<r>[0.122, 0.069, 0.213, 0.089]</r> over a period of 12 years, from {'2008'}<r>[0.137, 0.959, 0.172, 0.979]</r> to {'2019'}<r>[0.912, 0.959, 0.947, 0.979]</r>. The bars corresponding to these sectors represent their respective scores in a particular year. Looking at the height of these bars over the years gives us an insight into their trends. The Digital Diagnostics sector, represented by the sequence of blue bars, exhibits a decreasing trend over the years, with its highest value in {'2014'}<r>[0.560, 0.959, 0.595, 0.979]</r> and the smallest towards {'2019'}<r>[0.912, 0.959, 0.947, 0.979]</r>. On the other hand, the Telemedicine sector, symbolized by the orange bars, shows a steady increase in values from {'2008'}<r>[0.137, 0.959, 0.172, 0.979]</r>  to {'2019'}<r>[0.912, 0.959, 0.947, 0.979]</r>, indicating an upward trend in its progress over the years.\nBased on the above thought, the answer is:\nThe 'Digital Diagnostics' sector shows a broadly decreasing trend over the years with its scores reducing from 45 in 2014 to 21 in 2019. On the other hand, the 'Telemedicine' sector has shown a consistent upward trend with scores increasing incrementally from 30 in 2008 to 100 in 2019. Hence, the growth and prominence of 'Telemedicine' in the healthcare sector is increasing while 'Digital Diagnostics' seems to be declining towards the end of the observed period."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "6r2nIXE1BJ4L9SEa",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did 'Telemedicine' score the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year in which Telemedicine recorded its highest score, I need to identify the tallest orange bar on the chart. Looking through the chart, I find that the tallest orange bar is for the year 2019{\"Telemedicine's bar for 2019\"}[0.915, 0.065, 0.943, 0.945]. When I look at its height in relation to the y-axis values{\"yticks\"}[0.043, 0.585, 0.061, 0.605], it is evident that the bar touches the y-axis at 100. Therefore, 2019 is the year when Telemedicine scored the highest value of 100.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "3kF7Fnr9zsaGMCxs",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the visualized bar chart, can you convert the depicted data into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the information in the chart, I can see that the x-axis refers to different score intervals represented by {xticklabels}[{0}[0.170, 0.952, 0.181, 0.975];{20}[0.430, 0.952, 0.451, 0.975];{40}[0.694, 0.952, 0.716, 0.975];{60}[0.959, 0.952, 0.981, 0.975]]. The y-axis, labeled as {\"Scores\"}[0.019, 0.442, 0.036, 0.519], indicates the corresponding values spanning from {0}[0.043, 0.926, 0.071, 0.950] to {1}[0.043, 0.059, 0.071, 0.083]. Meanwhile, {the legend}[0.440, 0.037, 0.613, 0.218] provides the categories illustrated by different colors in the bar chart. The height of each bar can be used to extract the scores for different categories at each score interval. For instance, the colored bar representative of {Running}[0.447, 0.048, 0.482, 0.064] has a height approximately at the 0.7 position at score interval {60}[0.959, 0.952, 0.981, 0.975], allowing me to confirm the value of Running at a score of 60 to be 0.7. Processing the values based on the above reasoning, we can construct a CSV table.\nBased on the above thought, the answer is:\nScore Type,Running,Yoga,Swimming,Weightlifting,Boxing\n0,0,0.6,0.7,0.4,0.6\n20,0.2,0.9,0.5,0.3,0.2\n40,0.4,0.8,0.6,0.6,0.7\n60,0.7,1,0.7,0.8,0.9"
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "iunTUeyzKReOWMxV",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which fitness workout seemed to perform the best across all score intervals and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which fitness workout performed the best, I should concentrate on the one that consistently generated the highest scores across all score intervals. By comparing the bar heights in each score interval, such as at {Score 0}[0.170, 0.952, 0.181, 0.975], {Score 20}[0.430, 0.952, 0.451, 0.975], {Score 40}[0.694, 0.952, 0.716, 0.975], and {Score 60}[0.959, 0.952, 0.981, 0.975], and reviewing {the legend}[0.440, 0.037, 0.613, 0.218], I notice that the {Yoga}[0.447, 0.083, 0.482, 0.099] category consistently has some of the tallest bars across the intervals. This suggests that Yoga has achieved the highest scores among the workouts overall.\nBased on the above thought, the answer is:\nYoga. Because it consistently scores among the highest across all intervals."
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "qxta7KjbUAxOLn4A",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which workout showed the highest increase in score between the intervals of Score Type 20 and 40?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which workout had the biggest score increase from Score Type 20 to 40, I have to compare the score difference for each workout from {20}[0.430, 0.952, 0.451, 0.975] to {40}[0.694, 0.952, 0.716, 0.975] bars. The score of {Running}[0.447, 0.048, 0.482, 0.064] from 20 interval bar[{2nd}[0.142, 0.762, 0.182, 0.935]] to 40 intervals[{3rd}[0.407, 0.589, 0.447, 0.935]] is grown by about .2. Similarly, other workouts like {Yoga}[0.447, 0.083, 0.482, 0.099], {Swimming}[0.447, 0.118, 0.482, 0.134], {Weightlifting}[0.447, 0.153, 0.482, 0.169], {Boxing}[0.447, 0.188, 0.482, 0.204] have individually different score increases between score intervals 20 and 40 by examining their respective bar heights at those intervals.\nBased on the above thought, the answer is:\nYoga"
+            }
+        ],
+        "id_raw": 997
+    },
+    {
+        "id": "H4HvMz0yykkoXkEr",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the performance evaluation of the Biotech companies mentioned in the chart at the respective time intervals? Could you provide the readings in a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the normalized performance evaluation of five pharmaceutical companies. They are indicated with distinctive colors and are labeled in {the legend}[0.105, 0.044, 0.359, 0.262]. The companies are Pfizer (denoted by blue), BioNTech (denoted by light blue), Moderna (denoted by beige), Novavax (denoted by deep blue), and Johnson & Johnson (denoted by navy blue).\nThis bar chart displays the performance scores of these companies at specific intervals of time which are denoted on the x-axis labeled as '0', '20', '40', and '60'. For each time period, the y-axis is used to denote the normalized performance score, labeled from '0.0'[0.050, 0.912, 0.081, 0.940] up to '1.2'[0.049, -0.016, 0.081, 0.012].\nI can see each bar for each company, and the height of the bars, and compute the score. For instance, in bar set labeled '20', Pfizer's score <r>[0.284, 0.536, 0.309, 0.923]</r> is '0.5', BioNTech's score <r>[0.309, 0.382, 0.335, 0.923]</r> is '0.7', Moderna's score <r>[0.335, 0.459, 0.360, 0.923]</r> is '0.6', Novavax's score <r>[0.360, 0.768, 0.385, 0.923]</r> is '0.2', and Johnson & Johnson's score <r>[0.385, 0.613, 0.410, 0.923]</r> is '0.4'.\nSimilarly, I can extract scores for other time intervals and map them to the relevant companies. Let's transform these numerical scores into a CSV format.\nBased on the above thought, the answer is:\nTime, Pfizer, BioNTech, Moderna, Novavax, Johnson & Johnson\n0,0,0,0,0,0\n20,0.5,0.7,0.6,0.2,0.4\n40,0.3,0.5,0.4,0.15,0.25\n60,0.9,1.1,1,0.45,0.75"
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "a1vUhknLFIl5a081",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the performance evaluation, which pharmaceutical firm has consistently underperformed compared to others?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires a comprehensive analysis of the entire bar chart. A company that has underperformed consistently would have shorter bars (lower scores) compared to the others at each time interval.\nInspecting the chart, it becomes evident that the company denoted by the deep-blue color, Novavax as per the {legend}[0.105, 0.044, 0.359, 0.262], consistently has bars that are shorter than those of the other companies. The bars for Novavax at time intervals '0', '20', '40', and '60' are {bar_1}[0.107, 0.923, 0.133, 0.923], {bar_2}[0.360, 0.768, 0.385, 0.923], {bar_3}[0.612, 0.807, 0.638, 0.923], and {bar_4}[0.865, 0.575, 0.890, 0.923] respectively. Comparing these bars to the corresponding bars of the other companies at each time interval, it's clear that Novavax generally has the lowest performance scores.\nBased on the above thought, the answer is:\nBased on the chart, Novavax is the pharmaceutical firm that has underperformed consistently compared to the other firms."
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "HkrYb9vLV5CMw6Mr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has shown the greatest increase in performance score from time '0' to time '60'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the score at '0' and at '60' for all companies. In the bars labeled '0' and '60', the respective score for each company can be calculated. Looking at the time '0', all companies {Pfizer, BioNTech, Moderna, Novavax, and Johnson & Johnson} bars <r>[0.032, 0.923, 0.057, 0.923], [0.057, 0.923, 0.082, 0.923], [0.082, 0.923, 0.107, 0.923], [0.107, 0.923, 0.133, 0.923], [0.133, 0.923, 0.158, 0.923]</r> started with a score of '0'. At time '60', {Pfizer, BioNTech, Moderna, Novavax, and Johnson & Johnson} bars <r>[0.789, 0.227, 0.814, 0.923], [0.814, 0.073, 0.840, 0.923], [0.840, 0.150, 0.865, 0.923], [0.865, 0.575, 0.890, 0.923], [0.890, 0.343, 0.915, 0.923]</r> showed scores of '0.9', '1.1', '1', '0.45', and '0.75' respectively. Subtracting the initial value from the final, we can see that BioNTech has shown the highest increase of '1.1'.\nBased on the above thought, the answer is:\nBioNTech"
+            }
+        ],
+        "id_raw": 998
+    },
+    {
+        "id": "I3de3QvPQEctdCEI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you translate the observed scores of 'Community Development' and 'Urban Planning' over time into a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows values for two categories \u2013 'Community Development' and 'Urban Planning' \u2013 over four periods of time ('0', '20', '40', '60'). These are displayed on {the x-axis}<r>[0.261, 0.942, 0.957, 0.970]</r>. The y-axis <r>[0.050, 0.912, 0.062, -0.101]</r> represents the scores, which range from 0 to 5. I can interpret the data from the chart into a structured table by examining {the height of the bars}<r>['[0.149, 0.125, 0.228, 0.923]', '[0.375, 0.333, 0.454, 0.923]', '[0.601, 0.610, 0.680, 0.923]', '[0.826, 0.853, 0.905, 0.923]']</r> and <r>['[0.228, 0.073, 0.307, 0.923]', '[0.454, 0.246, 0.533, 0.923]', '[0.680, 0.489, 0.759, 0.923]', '[0.905, 0.714, 0.984, 0.923]']</r> for each period, which according to {the legend}<r>[0.643, 0.044, 0.969, 0.136]</r> represent the scores for 'Community Development' and 'Urban Planning' respectively.\nBased on the above thought, the answer is:\nPeriod,Community Development,Urban Planning\n0,4.6,4.9\n20,3.4,3.9\n40,1.8,2.5\n60,0.4,1.2"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "uYV5kW00TBRngMHb",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which trend - 'Community Development' or 'Urban Planning' - declines more drastically over the periods observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires an evaluation of the degree of decline in the scores for the two trends 'Community Development' and 'Urban Planning', represented by {dark blue}<r>['[0.149, 0.125, 0.228, 0.923]', '[0.375, 0.333, 0.454, 0.923]', '[0.601, 0.610, 0.680, 0.923]', '[0.826, 0.853, 0.905, 0.923]']</r> and {light blue}<r>['[0.228, 0.073, 0.307, 0.923]', '[0.454, 0.246, 0.533, 0.923]', '[0.680, 0.489, 0.759, 0.923]', '[0.905, 0.714, 0.984, 0.923]']</r> bars respectively. Observing the heights of the bars for both trends over the four periods, it's evident that while both trends decrease over time, the 'Community Development' trend goes from a high of 4.6 at the period of 0 to a mere 0.4 by the period of 60 \u2013 a sharp decrease. In contrast, the 'Urban Planning' trend decreases at a slower rate, moving from 4.9 to 1.2 over the same time frame.\nBased on the above thought, the answer is:\n'Community Development'. This trend demonstrates a more drastic decrease in scores than 'Urban Planning' over the observed periods."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "4jsijNDTBAtNJzeW",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which period was the score difference between 'Community Development' and 'Urban Planning' the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the time period with the smallest score difference, I need to examine the difference in bar heights at each period. For the time period of 0, the score difference between 'Community Development' and 'Urban Planning' is found by comparing the heights of {the first pair of bars}<r>[0.149, 0.125, 0.228, 0.923], [0.228, 0.073, 0.307, 0.923]</r>. These procedure has to be repeated for the other periods as well, with comparison of bar heights at {20}<r>[0.375, 0.333, 0.454, 0.923], [0.454, 0.246, 0.533, 0.923]</r>, at {40}<r>[0.601, 0.610, 0.680, 0.923], [0.680, 0.489, 0.759, 0.923]</r> and at {60}<r>[0.826, 0.853, 0.905, 0.923], [0.905, 0.714, 0.984, 0.923]</r>. After checking the differences in the four pairs of bars, the smallest difference is seen at the period of 60.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "5B9DEqzeFiGHBXro",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract all the scores for each social media platform and time period and present this information in a properly structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the structure of bar chart, it seems that it represents the performance of different social media platforms in different time periods. I see the title labeled as {'Scores'} given on the y-axis<ylabel>[0.021, 0.430, 0.041, 0.522]</ylabel>, which indicates the metric of the scores represented. There are 5 groups of bars, each related to a specific social media platform, indicated by the {Social Media Legend} located at <sublegend>[0.105, 0.044, 0.277, 0.262]</sublegend>. From the x-axis, I can confirm the three different time periods { '20', '40', '60' }. Using the chart's legend to identify the social media platforms and the height of the bars to determine the scores, I can extract the data for the respective time period. The values were determined by the height of the bars, such as the {bar of facebook for '20'}[0.167, 0.734, 0.196, 0.923], for example, This bar stretches from 0.734 to 0.923 on the y-axis, which corresponds to a score of 0.2 according to the y-axis labels.\nBased on the above thought, the answer is:\nPlatforms,20,40,60\nFacebook,0.2,0.5,0.7\nInstagram,0.4,0.3,0.7\nTwitter,0.6,0.8,0.5\nSnapchat,0.1,0.2,0.4\nWhatsApp,0.5,0.7,0.9"
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "q5ONmCJ4zixKrZVU",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which social media platform seems to be consistently growing in popularity across all stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the trends in the bar heights for each social media platform across all time periods. For example, {Facebook}, indicated by the {white bars}<bars_of_group_0>[{\"bar_coordinates\": \"[-0.123, 0.923, -0.094, 0.923]\"}, {\"bar_coordinates\": \"[0.167, 0.734, 0.196, 0.923]\"}, {\"bar_coordinates\": \"[0.458, 0.450, 0.487, 0.923]\"}, {\"bar_coordinates\": \"[0.748, 0.261, 0.777, 0.923]\"}]</bars_of_group_0>, appears to be growing at each consecutive measurement point \u2014 with its score rising from 0.2 to 0.5 to 0.7 across the time periods of '20', '40', and '60' respectively. A similar analysis can be performed for Instagram, Twitter, Snapchat, and WhatsApp. By doing so, we can see that Facebook and WhatsApp both show consistent growth, but the growth rate of Facebook (from 0.2 to 0.7, a 0.5 increase) is higher than WhatsApp (from 0.5 to 0.9, a 0.4 increase).\nBased on the above thought, the answer is:\nFacebook, as its score continuously increases throughout the periods and it has the highest growth rate."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "qHUHrkD2l379cSaq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform had the most growth from the first to the last time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth in scores from the first to last time period can be determined by subtracting the score at {\"20\"}[0.167, 0.734, 0.196, 0.923] from the score at {\"60\"}[0.748, 0.261, 0.777, 0.923]. For each platform, I can calculate this difference using the respective bar heights. If we analyze {WhatsApp} for instance, the height of the bar in the first time period[0.283, 0.450, 0.313, 0.923] corresponds to a score of 0.5, and the height of the bar in the last time period[0.864, 0.073, 0.893, 0.923] is a score of 0.9. The difference would be 0.9 - 0.5 = 0.4. Doing similar calculations for Facebook, Instagram, Twitter, and Snapchat, it's clear that the largest growth was experienced by WhatsApp.\nBased on the above thought, the answer is:\nWhatsApp."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "pYDBKQM4fbv4tCBS",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the bar chart data of production efficiency you observed for Samsung S20 and iPhone 12 into a Table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "At the x axis, I observe four different labels at {'Continuous decrease'}<r>[0.021, 0.942, 0.230, 0.970]</r>,{'Sudden increase'}<r>[0.287, 0.942, 0.452, 0.970]</r>,{'Sustained growth'}<r>[0.526, 0.942, 0.700, 0.970]</r> and {'Outliers'}<r>[0.818, 0.942, 0.895, 0.970]</r>, which certifies varied categories. The y-axis, on the other hand, shows score ranges from {'0.0'}<r>[0.080, 0.912, 0.112, 0.940]</r> to {'1.4'}<r>[0.080, -0.080, 0.112, -0.052]</r>. To generate the CSV table, the value of each level for both products need to be determined. This can be worked out by correlating the height of each bar within the coordinates of each category. For instance, the height of {'Samsung S20 Continuous decrease'}<r>[0.065, 0.781, 0.089, 0.923]</r> is about 0.2 while that of {'iPhone 12 Continuous decrease'}<r>[0.089, 0.604, 0.113, 0.923]</r> is about 0.45. This can be followed suit for \"Sudden increase\", \"Sustained growth\" and \"Outliers\".\nBased on the above thought, the answer is:\nProduct,Continuous decrease,Sudden increase,Sustained growth,Outliers\nSamsung S20,0.2,0.6,0.75,1.2\niPhone 12,0.45,0.1,0.6,0.8"
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "L8NcfJPwR8sS9jVA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the overall differences in the production efficiency patterns between the Samsung S20 and the iPhone 12 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the overall differences in the production efficiency patterns between Samsung S20 and iPhone 12, I would consider both the height and position of their respective bars across all four trends. Starting with 'Continuous decrease', the bar for iPhone 12 at <r>[0.089, 0.604, 0.113, 0.923]</r> is taller than that of Samsung S20 at <r>[0.065, 0.781, 0.089, 0.923]</r>, suggesting the Samsung S20 has a lower score here and therefore performs better. However, in the 'Sudden increase' trend, the Samsung S20 bar is found at <r>[0.308, 0.498, 0.333, 0.923]</r> which is lower than the iPhone 12 bar at <r>[0.333, 0.852, 0.357, 0.923]</r>, revealing that Samsung S20 performs worse here. For the 'Sustained growth' trend, both products appear to perform similarly with almost the same height bars. In the final 'Outliers' category, the Samsung S20 is found at <r>[0.796, 0.073, 0.820, 0.923]</r> and is higher than the iPhone 12 at <r>[0.820, 0.356, 0.845, 0.923]</r>, indicating that Samsung S20 has a higher score for outliers, implying a worse performance.\nBased on the above thought, the answer is:\nThe Samsung S20 performs better in the 'Continuous decrease' trend, and worse in the 'Sudden increase' and 'Outliers' trends. However, in the 'Sustained growth' trend, both the Samsung S20 and iPhone 12 perform at a comparable level. This shows that while both phones have varied efficiencies in different trends, they seem to perform similarly under sustained growth scenarios."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "MLJYqCNMgVkDtquh",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of efficiency trend does the Samsung S20 perform notably worse than the iPhone 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the efficiency trend where Samsung S20 performs notably worse than iPhone 12, I must compare the heights of the paired bars under each trend. For instance, in the 'Continuous decrease' category, the Samsung S20 bar at <r>[0.065, 0.781, 0.089, 0.923]</r> is notably lower than the iPhone 12 bar at <r>[0.089, 0.604, 0.113, 0.923]</r>. Similar comparisons can also be made for the bars under the other trends such as 'Sudden increase', 'Sustained growth', and 'Outliers'. Upon comparing these, it is noticeable that the bar heights for Samsung S20 under the 'Sudden increase' trend located at <r>[0.308, 0.498, 0.333, 0.923]</r> shows that the Samsung S20 performs notably worse than the iPhone 12; which has its bar at <r>[0.333, 0.852, 0.357, 0.923]</r>.\nBased on the above thought, the answer is:\nSudden increase"
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "k9yzqqMrYOSDRbrz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the parameters of the two regions from the bar chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bar chart, I see that the parameters include 'Income Equality', 'Healthcare Quality', 'Climate Policy', 'Education Budget', and 'R&D Investment'. These parameters are represented by the x-axis labels. I will use the bar names on the x-axis {\"Income Equality\"}<r>[0.044, 0.833, 0.133, 0.960]</r>, {\"Healthcare Quality\"}<r>[0.207, 0.833, 0.309, 0.979]</r>, {\"Climate Policy\"}<r>[0.407, 0.833, 0.486, 0.946]</r>, {\"Education Budget\"}<r>[0.565, 0.833, 0.663, 0.972]</r>, {\"R&D Investment\"}<r>[0.749, 0.833, 0.839, 0.962]</r> as the parameters. The two sets of bars under each parameter correspond to two regions or eras, labeled as {\"Region 1\"}<r>[0.912, 0.039, 0.972, 0.059]</r> and {\"Region 2\"}<r>[0.912, 0.069, 0.972, 0.089]</r> in the legend. The y coordinate of the top of each bar indicates the value of the parameter for the corresponding region. For example, {the top of the bar for Income Equality in Region 1}<r>[0.071, 0.650, 0.133, 0.819]</r> is about at 0.2, so the value is 0.2. I'll repeat this for all parameters and regions to construct the table.\nBased on the above thought, the answer is:\nParameter,Region 1,Region 2\nIncome Equality,0.2,0.7\nHealthcare Quality,0.8,0.9\nClimate Policy,0.3,0.7\nEducation Budget,0.45,0.8\nR&D Investment,0.1,0.4"
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "CR0ffs0uJ4ivQ3O5",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you infer from the chart data about which region or era has a better socioeconomic and policy making status? Kindly give reasons for your conclusion."
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about the socioeconomic and policy making status, let's compare the parameters for each region. Here, {\"Income Equality\"}<r>[0.044, 0.833, 0.133, 0.960]</r>, {\"Healthcare Quality\"}<r>[0.207, 0.833, 0.309, 0.979]</r>, {\"Climate Policy\"}<r>[0.407, 0.833, 0.486, 0.946]</r>, {\"Education Budget\"}<r>[0.565, 0.833, 0.663, 0.972]</r>, and {\"R&D Investment\"}<r>[0.749, 0.833, 0.839, 0.962]</r> are the parameters to be considered. In the case of each parameter, the height of the bars can be directly compared. The heights of the bars for 'Region 2' are either equal to or higher than their corresponding 'Region 1' bars. This suggests that in terms of all parameters considered, Region 2 has the same or better conditions than Region 1.\nBased on the above thought, the answer is:\nRegion 2 seems to have a better socioeconomic and policy making status because it shows either the same or a higher value in all parameters considered compared to Region 1."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "I5iwDoyfOhACJvMu",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increase in Climate Policy value is observed from Region 1 to Region 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in 'Climate Policy', I'll examine the bars corresponding to 'Climate Policy'. The bars for 'Climate Policy' in Region 1 and Region 2 are {\"[0.424, 0.566, 0.486, 0.819]\"}<r>[0.424, 0.566, 0.486, 0.819]</r> and {\"[0.486, 0.228, 0.548, 0.819]\"}<r>[0.486, 0.228, 0.548, 0.819]</r> respectively. Based on the y coordinates of the bar tops, the 'Climate Policy' value for Region 1 is approximately 0.3 and for Region 2, it is approximately 0.7. Subtracting the Region 1 value from the Region 2 value will give the increase.\nBased on the above thought, the answer is:\n0.4"
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "x2OnkOEw99l65hok",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTaking into account the bar chart you've observed, can you convert the raw data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's x-axis labels {\"2012\"}[0.061, 0.952, 0.105, 0.975], {\"2016\"}[0.316, 0.952, 0.360, 0.975], {\"2020\"}[0.571, 0.952, 0.614, 0.975], {\"2024\"}[0.826, 0.952, 0.869, 0.975], it appears to be displaying data over time \u2013 spanning from 2012 to 2024. The y-axis {\"Population (In Millions)\"}[0.019, 0.349, 0.036, 0.611] indicates the volume of population across these years for different regions. Our key comparators are two regions: {Asia}<r>[0.905, 0.046, 0.941, 0.069]</r> and {Europe}<r>[0.905, 0.081, 0.966, 0.104]</r> which are distinguished by different colored bars. The bar heights approximate the population in millions, so for instance, for 2012 the {Asia bar}<r>[-0.006, 0.321, 0.083, 0.935]</r> represents 850,000 and the {Europe bar}<r>[0.083, 0.068, 0.172, 0.935]</r> denotes 1,200,000 individuals.\nBased on the above thought, the answer is:\nRegion,2012,2016,2020,2024\nAsia,850000,950000,1050000,1150000\nEurope,1200000,1150000,1120000,1080000"
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "QMQ4H17eZIg37I8R",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's data, what can you infer about the population migration and mobility patterns in the two regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart visually represents population migration and mobility trends for two regions: {Asia}<r>[0.905, 0.046, 0.941, 0.069]</r> and {Europe}<r>[0.905, 0.081, 0.966, 0.104]</r>. The {Asia bars}<r>[-0.006, 0.321, 0.083, 0.935]; [0.249, 0.249, 0.338, 0.935]; [0.503, 0.177, 0.593, 0.935]; [0.758, 0.104, 0.847, 0.935]</r> consistently become taller over the years, denoting an increasing trend in population migration and mobility. On the contrary, the {Europe bars}<r>[0.083, 0.068, 0.172, 0.935]; [0.338, 0.104, 0.427, 0.935]; [0.593, 0.126, 0.682, 0.935]; [0.847, 0.155, 0.937, 0.935]</r> show a downwards trend, suggesting a decreasing trend in population migration and mobility over the same period. These trends suggest increasing population mobility in Asia and potentially factors like job opportunities or economic growth could be driving this trend. Conversely, the downwards trend in Europe could hint towards declining job opportunities, economic slowdown, or possibly tighter immigration controls.\nBased on the above thought, the answer is:\nThe population migration and mobility pattern in Asia appears to be increasing, possibly indicating growing economic opportunities or appealing living conditions in the region. In contrast, European mobility trends are declining, potentially due to fewer available opportunities, stricter immigration policies, or diminishing appeal."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "1OP94uPLyWpJYAR3",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the period in the chart, which region has the largest population increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To pinpoint the region with the largest population increase, firstly, we need to look at the {Asia bars}<r>[-0.006, 0.321, 0.083, 0.935]; [0.249, 0.249, 0.338, 0.935]; [0.503, 0.177, 0.593, 0.935]; [0.758, 0.104, 0.847, 0.935]</r> and {Europe bars}<r>[0.083, 0.068, 0.172, 0.935]; [0.338, 0.104, 0.427, 0.935]; [0.593, 0.126, 0.682, 0.935]; [0.847, 0.155, 0.937, 0.935]</r> for the years 2012 to 2024, respectively. The first Asia bar shows 850,000 while the last indicates 1,150,000 \u2013 an increase of 300,000. For Europe, the first bar represents 1,200,000 while the last portrays 1,080,000, indicating a decrease of 120,000 over the period. So, Asia experiences the most significant increase in population during these years.\nBased on the above thought, the answer is:\nAsia."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "f0bUgI2lOqzJIn91",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart into a table format for easier reference?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart, it's clear we have four risk categories on the {x-axis}<r>[0.095, 0.923, 0.960, 0.923]</r>: {High Risk}<r>[0.328, 0.942, 0.420, 0.970]</r>, {Medium Risk}<r>[0.590, 0.942, 0.716, 0.970]</r>, {Low Risk}<r>[0.889, 0.942, 0.976, 0.970]</r> as well as labels for the Initial Risk  and Residual Risk having separate bars in each Risk category as determined from {the legend}<r>[0.748, 0.044, 0.950, 0.136]</r>. The vertical, or {y-axis}, represent the proportions from {0.0}<r>[0.050, 0.912, 0.081, 0.940]</r> to {0.7}<r>[0.050, -0.080, 0.081, -0.052]</r>. The height of each bar corresponds to the proportion in its respective risk category. Using this information, I will be able to convert this into a table format.\nBased on the above thought, the answer is:\nRisk_Level,High_Risk,Medium_Risk,Low_Risk,Very_Low_Risk\nInitial_Risk,0.6,0.2,0.1,0\nResidual_Risk,0.2,0.5,0.3,0"
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "eGLBmTmXsS1koO5U",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how effective would you say the risk mitigation strategies are?"
+            },
+            {
+                "from": "gpt",
+                "value": "The effectiveness of the risk mitigation strategies can be judged on the basis of how the distribution of risk changes from the Initial Risk stage to the Residual Risk stage. Considering the highest risk category, {High Risk}<r>[0.164, 0.073, 0.248, 0.923]</r> for the initial stage and <r>[0.248, 0.639, 0.332, 0.923]</r> for the residual stage, we can see that it has decreased significantly. The bar for {Medium Risk}<r>[0.444, 0.639, 0.528, 0.923]</r> in the initial stage is also notably taller than the one for the residual stage <r>[0.528, 0.214, 0.611, 0.923]</r>. Most importantly, {low risk}<r>[0.723, 0.781, 0.807, 0.923]</r> has increased in the residual stage <r>[0.807, 0.498, 0.891, 0.923]</r>. This shows that risks have been effectively managed and reduced by the strategies implemented.\nBased on the above thought, the answer is:\nThe risk mitigation strategies are very effective, as indicated by the significant reduction in high and medium risk categories and the increase in lower risk categories from the Initial Risk to the Residual Risk stages."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "lpqdHcKyZ65wnxjN",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_67_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which risk level did the largest reduction in proportion occur from the Initial Risk stage to the Residual Risk stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm comparing the initial and residual risk for each risk category {High Risk}<r>[0.164, 0.073, 0.248, 0.923]</r> and <r>[0.248, 0.639, 0.332, 0.923]</r>, {Medium Risk}<r>[0.444, 0.639, 0.528, 0.923]</r> and <r>[0.528, 0.214, 0.611, 0.923]</r>, and {Low Risk}<r>[0.723, 0.781, 0.807, 0.923]</r> and <r>[0.807, 0.498, 0.891, 0.923]</r>. After examining the lengths of these bars, it is apparent that the greatest reduction occurs under the High Risk category where we moved from a proportion of 0.6 to 0.2 between the Initial Risk and Residual Risk stages.\nBased on the above thought, the answer is:\nHigh Risk."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "BljCP0iinekNKSOF",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed bar chart, turn the data for \"Emerging Markets and Consumer Behavior\" into a structured CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart {Emerging Markets and Consumer Behavior}<r>[0.394, 0.019, 0.750, 0.041]</r> and the labels on the y-axis referring to different market sectors, such as {Electronics}<r>[0.074, 0.844, 0.149, 0.861]</r>, indicate different Market names. The x-axis displays the Growth (%) from range of {0%}<r>[0.155, 0.939, 0.164, 0.957]</r> to {30%}<r>[0.858, 0.939, 0.876, 0.957]</r>, denoting the corresponding growth rates for respective sectors. The length of each bar, such as the bar for {Electronics}<r>[0.159, 0.814, 0.733, 0.887]</r>, reflects growth percentage for that market sector. To construct a CSV file, the market sectors will be extracted with their corresponding growth rates. This is done by analysing the length of each bar along the chart's x-axis.\nBased on the above thought, the answer is:\nMarket,Growth (%)\nElectronics,24.3\nTextiles,18.5\nPharmaceuticals,11.7\nFootwears,14.9\nBeverages,17.4\nSoftware,29.2\nAutomobile,15.6\nCosmetics,13.8\nToys,12.3"
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "r8wUBamyyTqXzWmi",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's representation, which sectors are doing well and which are not? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart {Emerging Markets and Consumer Behavior}<r>[0.394, 0.019, 0.750, 0.041]</r> provides details about the percentage growth of various market sectors. The length of the bars represent the growth rate, with longer bars indicating higher growth. For instance, {Software}<r>[0.159, 0.360, 0.848, 0.432]</r> and {Electronics}<r>[0.159, 0.814, 0.733, 0.887]</r> sectors are doing well because they have long bars representing high growth rates of 29.2% and 24.3% respectively. On the other hand, sectors such as {Pharmaceuticals}<r>[0.159, 0.632, 0.435, 0.705]</r>, {Cosmetics}<r>[0.159, 0.178, 0.485, 0.250]</r> and {Toys}<r>[0.159, 0.087, 0.449, 0.159]</r> are not doing as well as they have short bars representing less growth percentages of 11.7%, 13.8% and 12.3% respectively.\nBased on the above thought, the answer is:\nThe sectors doing well are Software and Electronics, as they have high growth percentages of 29.2% and 24.3% respectively. This is reflected by their long bars. On the contrary, Pharmaceuticals, Cosmetics and Toys sectors are not doing well with less growth percentages of 11.7%, 13.8% and 12.3% respectively, reflected by their short bars."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "ffoQgnYW6mK1g2G6",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which market sector has the highest growth been observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the market sector with the highest growth, I need to compare the lengths of all bars. The longest bar represents the highest growth. From the bars representing {Electronics}<r>[0.159, 0.814, 0.733, 0.887]</r>, {Textiles}<r>[0.159, 0.723, 0.596, 0.796]</r> up to {Toys}<r>[0.159, 0.087, 0.449, 0.159]</r>, the longest bar is that of {Software}<r>[0.159, 0.360, 0.848, 0.432]</r> market sector. Hence, it can deduced that Software sector has the highest growth.\nBased on the above thought, the answer is:\nSoftware."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "JnD0Remn93ESKWnL",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information displayed on the bar chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I see three categories depicted by their respective horizontal bars. The categories are {\"Rural\"}<r>[0.082, 0.761, 0.127, 0.779]</r>, {\"Suburban\"}<r>[0.043, 0.466, 0.127, 0.484]</r>, and {\"Urban\"}<r>[0.074, 0.171, 0.127, 0.189]</r>. Each bar represents the development index value for its respective category. The x-axis labels range from {0}<r>[0.134, 0.939, 0.145, 0.957]</r> to {10}<r>[0.959, 0.939, 0.981, 0.957]</r> which indicates the range of development index. By observing the bar for each category, and comparing its ending point to the development index labels on the x-axis, I can find the development index value for each category. The bar for the \"Rural\" category<r>[0.139, 0.650, 0.264, 0.886]</r> end just above the 1 mark on the x-axis. The \"Suburban\" bar<r>[0.139, 0.355, 0.455, 0.591]</r> ends near the 4 mark, and the \"Urban\" bar<r>[0.139, 0.060, 0.738, 0.296]</r> ends just before the 7 mark. On this basis, I am able to structure the data presented in the chart into CSV format.\nBased on the above thought, the answer is:\nAreas,Development_Index\nRural,1.5\nSuburban,3.8\nUrban,7.2"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "7S6LrYQo0CN8pSrm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, how can you describe the general trend of development index among these areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the lengths of the bars representing {\"Rural\"}<r>[0.082, 0.761, 0.127, 0.779]</r>, {\"Suburban\"}<r>[0.043, 0.466, 0.127, 0.484]</r>, and {\"Urban\"}<r>[0.074, 0.171, 0.127, 0.189]</r> areas, I notice that the lengths increase from \"Rural\" to \"Urban\". The \"Rural\" bar<r>[0.139, 0.650, 0.264, 0.886]</r> extends to the 1.5 mark on the x-axis<r>[0.222, 0.927]</r>, which indicates a lower development index value. The \"Suburban\" bar<r>[0.139, 0.355, 0.455, 0.591]</r> extends to the 3.8 mark on the x-axis<r>[0.472, 0.927]</r>, showing a higher development index value than \"Rural\". And finally, the \"Urban\" bar<r>[0.139, 0.060, 0.738, 0.296]</r> extends the furthest to the 7.2 mark on the x-axis<r>[0.721, 0.927]</r>, indicating the highest development index value among the three. This suggests a general trend of increasing development index from \"Rural\" to \"Urban\" areas.\nBased on the above thought, the answer is:\nThe general trend of development index increases from Rural to Urban areas."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "IqexGsHwggFVZ6qM",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest development index based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the highest development index, I need to compare the bars for each category. {\"Rural\"}<r>[0.082, 0.761, 0.127, 0.779]</r> ends at the 1.5 mark<r>[0.222, 0.927]</r>, {\"Suburban\"}<r>[0.043, 0.466, 0.127, 0.484]</r> ends at the 3.8 mark<r>[0.472, 0.927]</r>, and {\"Urban\"}<r>[0.074, 0.171, 0.127, 0.189]</r> ends at the 7.2 mark<r>[0.721, 0.927]</r>. The \"Urban\" bar extends the farthest, indicating that it has the highest development index value.\nBased on the above thought, the answer is:\nUrban."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "XNStHGN36g1DB9bd",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the chart and create a table listing each industry and its corresponding development trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {chart title}[0.383, 0.019, 0.835, 0.041], I can see that this chart is about Technological Industry Development Trends. The {y-axis}[0.019, 0.451, 0.036, 0.523] represents different industries, and the {x-axis}[0.524, 0.964, 0.694, 0.981] represents the corresponding development trend. For each industry, there is a horizontal bar showing its development trend. I can calculate the trend value by comparing the length of this bar with the x-axis range. For instance, the {bar for \"AI\"}[0.237, 0.822, 0.670, 0.887] corresponds approximately to the x-position 3.2. Therefore, the development trend for AI is approximately 3.2.\nBased on the above thought, the answer is:\nIndustry,Development Trend\nAI,3.2\nIoT,2.9\nAutomation,4.5\nCloud Computing,4.8\nBlockchain,1.9\nAR/VR,1.7\nCybersecurity,3.5\nData Science,5.0\n5G Technology,3.0\nQuantum Computing,0.8"
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "0QhW70btRkP3T90O",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be deduced about the development trend of Quantum Computing as compared to AI, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, {the bar for \"Quantum Computing\"}[0.237, 0.087, 0.345, 0.152] and {the bar for \"AI\"}[0.237, 0.822, 0.670, 0.887] can be used to compare the development trends. The length of the bar for Quantum Computing is noticeably shorter than the one for AI, indicating that Quantum Computing has a lower development trend. This suggests that, while both are emerging technology industries, AI currently shows more development and advancement than Quantum Computing.\nBased on the above thought, the answer is:\nThe development trend of Quantum Computing is lower compared to AI. This suggests that AI has seen significant advancements and developments compared to Quantum Computing, which is still an emerging field."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "k61F9Ew5QJnRV7sn",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the highest development trend in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the industry with the highest development trend, I need to compare the lengths of the bars. The longest bar represents the highest development trend. Observing the chart, I can see that {the bar for \"Data Science\"}[0.237, 0.250, 0.914, 0.315] is the longest, extending to the 5.0 position on the x-axis, and therefore has the highest development trend.\nBased on the above thought, the answer is:\nData Science."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "GJ1QQpe1TvjwyVvV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the bar chart data into a tabular form."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart provided lists various categories on {the y-axis}[0.019, 0.411, 0.036, 0.517] and their frequency on the {x-axis}[0.325, 0.919, 0.336, 0.942]. The categories include {Hotel Bookings}[0.331, 0.769, 0.651, 0.863], {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], {Tour Package Purchases}[0.331, 0.534, 0.544, 0.628], {Airport Transfers}[0.331, 0.417, 0.810, 0.511], {Spa Appointments}[0.331, 0.300, 0.491, 0.394], {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276], and {Gym Memberships}[0.331, 0.065, 0.384, 0.159]. We evaluate the frequency of each category by the length of its respective bar on the x-axis. For instance, the bar for Entertainment Ticket Purchases ends near the {20 on the x-axis}[0.853, 0.919, 0.875, 0.942].\nBased on the above thought, the answer is:\nCategory,Frequency\nHotel Bookings,12\nRestaurant Reservations,15\nTour Package Purchases,8\nAirport Transfers,18\nSpa Appointments,6\nEntertainment Ticket Purchases,20\nGym Memberships,2"
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "AtBqliosUDlItpKt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of planning business strategies, what insights does this chart provide for the Tourism and Hospitality industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart illustrates the frequency of different categories in the Tourism and Hospitality industry. From the longer bars for {Restaurant Reservations}[0.331, 0.652, 0.731, 0.746], {Airport Transfers}[0.331, 0.417, 0.810, 0.511] and {Entertainment Ticket Purchases}[0.331, 0.182, 0.864, 0.276], it can be inferred that these services are highly demanded, hence, businesses may seek to strengthen such sectors. With the shortest bar representing {Gym memberships}[0.331, 0.065, 0.384, 0.159] near the {2 mark}[0.325, 0.919, 0.336, 0.942] on the x-axis, it is suggested that this aspect of the industry is less popular among consumers, prompting a need for businesses to either innovate their offerings in this sector or divert resources to more profitable sectors.\nBased on the above thought, the answer is:\nEssential insights include high demand for Restaurant Reservations, Airport Transfers, and Entertainment Ticket Purchases, suggesting these areas as potential growth sectors. The less popular Gym Memberships could indicate a need for business innovation or resource reallocation."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "kOZ6ukGAy25qBvBy",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the highest frequency in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I compare each bar length on the x-axis and observe the {Entertainment Ticket Purchases bar}[0.331, 0.182, 0.864, 0.276] extends the furthest on the x-axis, reaching almost up to the {20 mark}[0.853, 0.919, 0.875, 0.942]. Thus, Entertainment Ticket Purchases have the highest frequency.\nBased on the above thought, the answer is:\nEntertainment Ticket Purchases."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "2KBm7BNQhDojXs9p",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the data from the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The graph is a horizontal bar chart with 'Machine Learning Areas' on the y-axis and 'Frequency (%)' on the x-axis. The chart title {\"Popularity of Machine Learning Areas\"}[0.424, 0.019, 0.810, 0.041] suggests that the x-axis values indicate the popularity of each Machine Learning Area. According to the y-axis labels, these areas include 'Supervised Learning', 'Unsupervised Learning', 'Reinforcement Learning', 'Deep Learning', 'Neural Networks', 'Prediction Modeling', 'Classification', 'Regression', 'Clustering', and 'Anomaly Detection'. Correspondingly, the x-axis labels range from {0}[0.258, 0.939, 0.269, 0.957] to {25}[0.959, 0.939, 0.981, 0.957]. I can determine the frequency for each Machine Learning Areas by analyzing the length of the bars. For example, the bar for {Supervised Learning}[0.263, 0.822, 0.687, 0.887] extends to approximately the 15 position on the x-axis, so the frequency of 'Supervised Learning' is about 15%.\nBased on the above thought, the answer is:\nMachine Learning Areas,Frequency (%)\nSupervised Learning,15\nUnsupervised Learning,10\nReinforcement Learning,5\nDeep Learning,20\nNeural Networks,22\nPrediction Modeling,13\nClassification,18\nRegression,16\nClustering,11\nAnomaly Detection,9"
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "r5BOXYXw0Cx6jBp6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, can you provide a brief overview of the popularity trends in the different Machine Learning areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the bar chart titled {\"Popularity of Machine Learning Areas\"}[0.424, 0.019, 0.810, 0.041], it can be analyzed that areas such as 'Supervised Learning', 'Deep Learning', 'Neural Networks', 'Classification', and 'Regression' have higher frequencies, which are represented by longer bars such as {[0.263, 0.822, 0.687, 0.887] \"Supervised Learning\"}[0.263, 0.822, 0.687, 0.887], {[0.263, 0.577, 0.829, 0.642] \"Deep Learning\"}[0.263, 0.577, 0.829, 0.642], and {[0.263, 0.495, 0.885, 0.560] \"Neural Networks\"}[0.263, 0.495, 0.885, 0.560]. This indicates that these areas are more commonly used in AI applications. On the contrary, areas like 'Reinforcement Learning' and 'Anomaly Detection', represented by smaller bars like {[0.263, 0.658, 0.405, 0.724] \"Reinforcement Learning\"}[0.263, 0.658, 0.405, 0.724] and {[0.263, 0.087, 0.518, 0.152] \"Anomaly Detection\"}[0.263, 0.087, 0.518, 0.152], are less frequently employed, which likely speaks to the complexity or specific use cases of these techniques.\nBased on the above thought, the answer is:\nAccording to the chart, the most popular areas in Machine Learning are 'Neural Networks', 'Deep Learning', 'Classification', 'Supervised Learning', and 'Regression' due to their relatively higher frequency of usage in AI applications. At the same time, 'Reinforcement Learning' and 'Anomaly Detection' appear less popular, likely reflecting the complexity or specialized nature of these techniques."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "BqVYCgZT6bcFVIAo",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat Machine Learning Area is the most popular according to the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the most popular Machine Learning Area, I need to identify the bar with the longest length. Noting the lengths of each bar, the bar corresponding to {Neural Networks}[0.263, 0.495, 0.885, 0.560] appears to extend the farthest on the x-axis, with a frequency close to the 22 mark on the x-axis. Thus the 'Neural Networks' area appears to have the highest popularity in terms of frequency.\nBased on the above thought, the answer is:\nNeural Networks"
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "1KJcAh20t3wtQ5tY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured CSV table according to the bar chart you noticed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noticing the chart title as {\"New Energy and Clean Technology Growth Rate Survey\"}[0.347, 0.085, 0.689, 0.115] and x-axis, I found that the x-axis represents different technologies. The y-axis tick labels represent the corresponding growth rates, ranging from {0}[0.108, 0.881, 0.116, 0.904] to {8}[0.099, 0.107, 0.116, 0.130]. The bars on the chart represent the growth rates of these technologies. Each technology's growth rate is read from the right end of each bar. For instance, the growth rate of {Fusion Energy}[0.068, 0.841, 0.241, 0.861] is 7.7%, read from {the length of the bar}[0.247, 0.060, 0.912, 0.127].\nBased on the above thought, the answer is:\nTechnology,Growth Rate (%)\nSolar Energy,2.1\nWind Energy,2.7\nBiomass,3.0\nHydro Power,3.5\nCarbon Capture,3.8\nEnergy Storage,4.7\nSmart Grid,5.0\nGeothermal Energy,5.8\nWave and Tidal Power,6.0\nFusion Energy,7.7"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "Sf4Xk3d3Ub5NYhV6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the standpoint of this chart, which areas in the field of New Energy and Clean Technology Development are the most promising?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the length of the bar in the chart, it can be inferred that the longer the bar, the higher the growth rate, which means the more promising the technology is. Specifically, from the comparison of {Fusion Energy bar}[0.247, 0.060, 0.912, 0.127], {Wave and Tidal Power bar}[0.247, 0.144, 0.765, 0.212], and {Geothermal Energy bar}[0.247, 0.229, 0.748, 0.296], these are the three longest bars in the figure. It can be concluded that Fusion Energy, Wave and Tidal Power, and Geothermal Energy are the three most promising technology areas.\nBased on the above thought, the answer is:\nFusion Energy, Wave and Tidal Power, and Geothermal Energy."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "vqW16sJQZ45V6D8H",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has the highest growth rate in the New Energy and Clean Technology sectors according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the technology with the highest growth rate, I need to identify the longest bar in the chart. By reading the chart from top to bottom, {the bar representing Fusion Energy}[0.247, 0.060, 0.912, 0.127] is the longest, extending all the way to the position marking 7.7% on the x-axis. Therefore, Fusion Energy has the highest growth rate.\nBased on the above thought, the answer is:\nFusion Energy"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "EsOLdwTK19Z5nEGV",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the bar chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "If we consider the way this horizontal bar chart is set up, the 'Categories' or 'Companies' are the Y-Axis labels, which include {\"Apple\"}<r>[0.074, 0.839, 0.124, 0.857]</r>, {\"Samsung\"}<r>[0.043, 0.737, 0.124, 0.754]</r>, {\"Huawei\"}<r>[0.059, 0.634, 0.124, 0.651]</r>, {\"Xiaomi\"}<r>[0.064, 0.531, 0.124, 0.549]</r>, {\"OPPO\"}<r>[0.076, 0.429, 0.124, 0.446]</r>, {\"Vivo\"}<r>[0.086, 0.326, 0.124, 0.344]</r>, {\"Motorola\"}<r>[0.047, 0.223, 0.124, 0.241]</r>, and {\"Google\"}<r>[0.062, 0.121, 0.124, 0.138]</r>. Meanwhile, the X-Axis labels include {0}<r>[0.130, 0.939, 0.141, 0.957]</r>, {5}<r>[0.236, 0.939, 0.247, 0.957]</r>, {10}<r>[0.336, 0.939, 0.358, 0.957]</r>, {15}<r>[0.442, 0.939, 0.464, 0.957]</r>, {20}<r>[0.548, 0.939, 0.569, 0.957]</r>, {25}<r>[0.653, 0.939, 0.675, 0.957]</r>, {30}<r>[0.759, 0.939, 0.781, 0.957]</r>, {35}<r>[0.865, 0.939, 0.887, 0.957]</r> which represent the market shares in percentage of these companies. If I consider these coordinates in selecting the Company names and the corresponding Market Shares and put them in the correct format, I can create the CSV based on this bar chart.\nBased on the above thought, the answer is:\nCompany,Market Share\nApple,34\nSamsung,29\nHuawei,20\nXiaomi,9\nOPPO,4\nVivo,3.1\nMotorola,0.65\nGoogle,0.25"
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "tnpu3elvdzWv8pFE",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the displayed chart, what remarks can you make about the smartphone market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, it's clear that the market share in the smartphone industry is dominated by the major players {\"Apple\"}<r>[0.136, 0.805, 0.854, 0.887]</r>, {\"Samsung\"}<r>[0.136, 0.702, 0.749, 0.785]</r>, and {\"Huawei\"}<r>[0.136, 0.600, 0.559, 0.682]</r>. Apple occupies the leading position, with a market share of approximately {\"34%\"}<r>[0.136, 0.805, 0.854, 0.887]</r>. On the other hand, companies such as {\"Motorola\"}<r>[0.136, 0.189, 0.150, 0.271]</r> and {\"Google\"}<r>[0.136, 0.087, 0.141, 0.169]</r> maintain a relatively small portion of the market share, under 1%. The smartphone market competition is fierce among the top three players, while other brands hold significantly less market proportion.\nBased on the above thought, the answer is:\nThe smartphone market is primarily dominated by Apple, Samsung, and Huawei. Apple holds the leading position with the highest market share. Other brands like Motorola and Google hold a significantly smaller portion of the market, indicating a wide disparity among the smartphone manufacturers in the global market."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "22PrvlHMwkaFoPlC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which company has the highest market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the company with the highest market share, I'll look for the longest bar in the bar chart. By observing the bar chart, it is clear that the longest bar corresponds to {\"Apple\"}<r>[0.136, 0.805, 0.854, 0.887]</r>. Therefore, it would be safe to conclude that Apple has the highest market share.\nBased on the above thought, the answer is:\nApple."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "uzKod4HUI29JblJY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, please summarise the information in a CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal bars in the plot represent the funding distribution for multiple categories of social welfare and charity activities. These categories can be read from the labels on the {y-axis}[{\"Educational Aid\"}[0.100, 0.839, 0.236, 0.857]; {\"Food Distribution\"}[0.087, 0.737, 0.236, 0.754]; {\"Healthcare Assistance\"}[0.043, 0.634, 0.236, 0.651]; {\"Housing Aid\"}[0.132, 0.531, 0.236, 0.549]; {\"Disaster Relief\"}[0.110, 0.429, 0.236, 0.446]; {\"Research & Advocacy\"}[0.048, 0.326, 0.236, 0.344]; {\"Animal Welfare\"}[0.104, 0.223, 0.236, 0.241]; {\"Environmental Issues\"}[0.051, 0.121, 0.236, 0.138]]. The distribution values for each category are deduced from the {x-axis}[{\"0\"}[0.242, 0.939, 0.253, 0.957]; {\"5\"}[0.347, 0.939, 0.358, 0.957]; {\"10\"}[0.446, 0.939, 0.468, 0.957]; {\"15\"}[0.551, 0.939, 0.573, 0.957]; {\"20\"}[0.656, 0.939, 0.678, 0.957]; {\"25\"}[0.761, 0.939, 0.783, 0.957]; {\"30\"}[0.866, 0.939, 0.887, 0.957]] using the end points of the bars in each category [{\"bar\"}[0.248, 0.805, 0.585, 0.887]; {\"bar\"}[0.248, 0.702, 0.680, 0.785]; {\"bar\"}[0.248, 0.600, 0.893, 0.682]; {\"bar\"}[0.248, 0.497, 0.514, 0.579]; {\"bar\"}[0.248, 0.395, 0.455, 0.477]; {\"bar\"}[0.248, 0.292, 0.422, 0.374]; {\"bar\"}[0.248, 0.189, 0.260, 0.271]; {\"bar\"}[0.248, 0.087, 0.269, 0.169]]. Therefore, to structure a CSV table, I can record the name of each category along with its corresponding value read from the x-axis.\nBased on the above thought, the answer is:\nCategory,Percentage\nEducational Aid,16.1\nFood Distribution,20.6\nHealthcare Assistance,30.8\nHousing Aid,12.7\nDisaster Relief,9.9\nResearch & Advocacy,8.3\nAnimal Welfare,0.6\nEnvironmental Issues,1.0"
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "aue9NlX3qo3jG5ci",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences about spending priorities related to social welfare activities can be drawn from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the chart, the vertical length of each horizontal {bar}[0.248, 0.805, 0.585, 0.887, 'Educational Aid'.. 'Environmental Issues'][0.248, 0.087, 0.269, 0.169] in comparison to the others, indicates a spending priority. 'Healthcare Assistance', which has the longest bar, indicates that it receives the highest amount of funding. This suggests that within the sectors of social welfare, the highest priority is given to healthcare. Conversely, 'Animal Welfare' and 'Environmental Issues' have the shortest bars, indicating they receive least funding, showing that these areas are lower priority. Overall, the chart indicates that social welfare spending tends to prioritize human-related aid and services, while environmental causes receive less priority.\nBased on the above thought, the answer is:\nThe chart suggests that in the context of social welfare, the spending priorities are dominated by human-related aid such as Healthcare and Food distribution, while less emphasis is placed on issues like Animal Welfare and Environmental Concerns."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "LjfHpfejvxmHCoL5",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what category received the lowest funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which category received the lowest funding, I have to examine the {horizontal bars}[{\"bar\"}[0.248, 0.805, 0.585, 0.887]; {\"bar\"}[0.248, 0.702, 0.680, 0.785]; {\"bar\"}[0.248, 0.600, 0.893, 0.682]; {\"bar\"}[0.248, 0.497, 0.514, 0.579]; {\"bar\"}[0.248, 0.395, 0.455, 0.477]; {\"bar\"}[0.248, 0.292, 0.422, 0.374]; {\"bar\"}[0.248, 0.189, 0.260, 0.271]; {\"bar\"}[0.248, 0.087, 0.269, 0.169]] for each category and locate the one that extends least towards the right. After considering each of the bars, I can observe that the horizontal bar for 'Environmental Issues'{<one_bar>[0.248, 0.087, 0.269, 0.169]} extends to approximately 1% on the x-axis, being the shortest bar. Therefore, the 'Environmental Issues' category has the least funding.\nBased on the above thought, the answer is:\nEnvironmental Issues"
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "5TRi26iJWCCvkQ2J",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the bar chart data into a CSV format for each type of internet usage with its respective frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data into a CSV format, I can use the y-axis labels as the types of internet usage and the lengths of their respective bars as the frequency. The y-axis labels can be found at {\"Mobile Internet Users\"}[0.058, 0.851, 0.243, 0.868], {\"Desktop Internet Users\"}[0.043, 0.781, 0.243, 0.799], {\"E-commerce Growth\"}[0.066, 0.711, 0.243, 0.729], and so on, up to {\"Cryptocurrency\"} [0.109, 0.081, 0.243, 0.099]. The lengths of the respective bars range from {Mobile Internet Users'} [0.255, 0.830, 0.554, 0.886] indicating a frequency of 3.5, to {Cryptocurrency}[0.255, 0.060, 0.614, 0.116] indicating a frequency of 4.2. I'll match each type of internet usage with its corresponding frequency to create the CSV data.\nBased on the above thought, the answer is:\nType of Internet Usage,Frequency\nMobile Internet Users,3.5\nDesktop Internet Users,2.6\nE-commerce Growth,7.3\nEmail Usage,5.0\nSocial Media Usage,6.6\nVideo Streaming,7.9\nOnline Gaming,4.3\nVideo Conferencing,7.2\nOnline Education,6.1\nVirtual Reality,2.8\nInternet of Things,4.7\nCryptocurrency,4.2"
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "f3eky6Ql9OTTbgZY",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom examining the chart, which areas of internet usage do you think are trending upward based on their high frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the overall observation of the chart, it's evident that the types of internet usage that have bars extending towards the higher numbers on the x-axis [8 tick mark - 0.933, 0.939, 0.944, 0.957] are trending upward due to their high frequency. Specifically, these are {Video Streaming}[0.255, 0.480, 0.930, 0.536], {Video Conferencing}[0.255, 0.340, 0.870, 0.396], and {E-commerce Growth}[0.255, 0.690, 0.879, 0.746], with all their bars reaching a frequency of over 7. This suggests that these types of internet usage are experiencing rapid growth or increased usage.\nBased on the above thought, the answer is:\nVideo Streaming, Video Conferencing, and E-commerce Growth are the areas of internet usage that appear to be trending upward based on their high frequency."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "C2UmpzsjZhqp8PMT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the bar lengths in the chart, which type of internet usage has the highest frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the type of internet usage with the highest frequency, I need to look for the longest bar within the chart. I can see that {the bar corresponding to Video Streaming}[0.255, 0.480, 0.930, 0.536] is the longest, reaching towards the {8 tick mark} on the x-axis [0.933, 0.939, 0.944, 0.957], thereby indicating the highest frequency among all the categories.\nBased on the above thought, the answer is:\nVideo Streaming."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "LGKOTa59by0PrcQR",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you convert the visual representation into a table which details the item categories and their respective values?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart titled {\"Statistical Data Overview\"}[0.495, 0.015, 0.705, 0.033] depicts the instances of different categories of items denoted by bars. Each bar corresponds to the number of instances of an item category. The item categories are represented on the Y-axis with the names {\"High-quality Automobile\"}[0.035, 0.858, 0.205, 0.872], {\"Mid-Quality Automobile\"}[0.042, 0.764, 0.205, 0.778], and so on. The instance count is reflected in the length of the bars on the X-axis ranging from {0}[0.211, 0.951, 0.219, 0.965] to roughly {15}[0.886, 0.951, 0.903, 0.965]. For example, the bar for \"High-Quality Automobile\" extends to 15.2, signifying 15.2 instances of High-Quality Automobiles represented by the bar at [0.215, 0.826, 0.903, 0.901]. Using this information, I can then construct a table.\nBased on the above thought, the answer is:\nItems,Instances\nHigh-Quality Automobile,15.20\nMid-Quality Automobile,12.40\nLow-Quality Automobile,10.50\nHigh-Quality Electronics,14.60\nMid-Quality Electronics,11.20\nLow-Quality Electronics,9.80\nHigh-Quality Clothing,14.40\nMid-Quality Clothing,12.20\nLow-Quality Clothing,10.30"
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "wfrVIUKab0lnxQvz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom a quality perspective, can you interpret the distribution of all the items?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, we notice that the bars for each major category (Automobile, Electronics, and Clothing) are grouped into three quality types: High, Mid, and Low. Each major category has a bar representing each of the quality types such as {\"High-Quality Automobile\"}[0.215, 0.826, 0.903, 0.901], {\"Mid-Quality Automobile\"}[0.215, 0.732, 0.777, 0.807] and so on. Generally, the High-Quality items have the longest bars, Mid-Quality items have medium-length bars, and Low-Quality items have the shortest bars. This indicates that High-Quality versions of items are more prevalent, followed by Mid-quality and then Low-Quality items. However, an exception to this trend is seen in the clothing category where the {\"Mid-Quality Clothing\"}[0.215, 0.172, 0.768, 0.247] bar is longer than the {\"Mid-Quality Electronics\"}[0.215, 0.452, 0.722, 0.527] bar.\nBased on the above thought, the answer is:\nFrom a quality perspective, it looks like High-Quality items generally have the most instances, followed by Mid-quality and then Low-Quality items. However, there is an exception in the Mid-Quality category where Clothing has more instances than Electronics."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "G7pc4BU71EBoE0vO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the least number of instances according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the category with the least instances by comparing the length of each bar. The shortest bar represents the category with the least instances. By looking at all the bars such as {\"High-Quality Automobile\"}[0.215, 0.826, 0.903, 0.901], {\"Mid-Quality Automobile\"}[0.215, 0.732, 0.777, 0.807] and so forth, I notice that the shortest bar is {\"Low-Quality Electronics\"}[0.215, 0.359, 0.659, 0.433], which signifies that the \"Low-Quality Electronics\" category has the least instances.\nBased on the above thought, the answer is:\nLow-Quality Electronics"
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "QBBjrs08ExtAtoPE",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please generate a table from the chart data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I can see that the y-axis has listed various industries {\"Biotechnology\"}[0.097, 0.855, 0.221, 0.873], {\"Cryptocurrency\"}[0.087, 0.795, 0.221, 0.813] etc. and the x-axis presents the trend score {\"0\"}[0.228, 0.939, 0.239, 0.957],{\"1\"}[0.307, 0.939, 0.318, 0.957] and so on till 9. Each industry {e.g., \"Biotechnology\"}[0.233, 0.838, 0.643, 0.886] has a horizontal bar representing a specific trend score. Value can be determined from the x-axis corresponding to the bar length. To convert the chart to a CSV format, the industry names would form the first column and the corresponding trend scores would form the second column.\nBased on the above thought, the answer is:\nIndustry,Trend\nBiotechnology,5.2\nCryptocurrency,7.7\nArtificial Intelligence,6.9\nVirtual Reality, 3.6\nFintech, 7.3\nData Science,8.2\nBlockchain, 4.9\nRobotics,5.7\nCloud Computing, 6.1\nInternet of Things,4.4\nBig Data,8.5\nCybersecurity,6.1\nNanotechnology,2.5\nAugmented Reality,4.0"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "LA7fbjR2ki3UwbtW",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the provided trends, which industry shows the most significant growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be determined by analysing the lengths of different horizontal bars representing different industries. The bar with the maximum length {\"Data Science\"}[0.233, 0.539, 0.879, 0.587] represents the industry with equal trend value on x-axis and consequently the most significant growth.\nBased on the above thought, the answer is:\nData Science shows the most significant growth."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "Nte43WYl1174JfHb",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the mean trend value across all industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the mean, I need to take into account all the industries represented by the bars of the chart such as {Biotechnology}[0.233, 0.838, 0.643, 0.886], {Cryptocurrency}[0.233, 0.778, 0.840, 0.826], and append corresponding x-axis values to obtain trend values. Adding all those individual trend values and then divide by the total number of industries represented in the chart.\nBased on the above thought, the answer is:\n5.6"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "oTI9sUB9gqVbWdVb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a CSV format table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that the chart has vertical labelled bars to represent {'Political Trends'}[0.019, 0.408, 0.036, 0.537] such as Increasing Partisanship, Political Disaffection, Electoral Volatility etc. The length of the bars corresponds to the Frequency % labelled on {'x-axis'}[0.556, 0.964, 0.681, 0.981]. To generate a CSV table, I would extract the length of the bars which corresponds to the Frequency of the which specific Political Trend. For example, the bar for {Increasing Partisanship}<r>[0.256, 0.825, 0.691, 0.886]</r> is projected to approximately the 30 mark on the x-axis, so the frequency for \"Increasing Partisanship\" is 30.\nBased on the above thought, the answer is:\nPolitical_Trend,Frequency\nIncreasing Partisanship,30\nPolitical Disaffection, 24\nElectoral Volatility,14\nPolitical Polarization,37\nSocial Movements,26\nDigital Politics,41\nPolitical Scandal,32\nIdentity Politics,28\nPopulism,33\nAlternative Media,35\nPolitical Satire,19"
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "6icZiBVaulwDmqdJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect of Political Trend requires more attention as a social issue based on its frequency in the society?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look for the trend that is most frequently occurring, as it is likely to require the most attention based on these occurrences. Observing the bars, I see that the longest bar is {Digital Politics}<r>[0.256, 0.442, 0.851, 0.504]</r> touching the position of 40. Hence, 'Digital Politics' is the most recorded trend indicating that it needs the most attention among these political trends.\nBased on the above thought, the answer is:\n\"Digital Politics\" requires more attention as a social issue, due to its highest recorded frequency."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "PZqaDh7cSNvJIRCY",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum frequency noticed in the chart, and under which Political Trend is it occurring?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look for the longest bar in the chart, which represents the highest frequency. I detect that the bar named {Digital Politics}<r>[0.256, 0.442, 0.851, 0.504]</r> is longer than all the others, reaching almost to the position of 40 on the x-axis. So, 'Digital Politics' is associated with the highest frequency.\nBased on the above thought, the answer is:\n\"Digital Politics\" has the maximum frequency of 41."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "rPc6INwqGhjdpFSo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart depicting the frequency of various technology innovations into a CSV-formatted table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the chart title {\"Education and Training Technology Innovations\"}[0.427, 0.015, 0.817, 0.033] gives me an insight into what to expect on the chart. From {the x-axis}[0.572, 0.971, 0.672, 0.985], we have the frequency of different technology innovations expressed in percentages. The {y-axis}[0.015, 0.451, 0.029, 0.528] is labeled with different technology innovations like \"Augmented Reality\", \"Virtual Reality\", \"Artificial Intelligence\", etc. The length of the bars which are examples {\"Augmented Reality\"}[0.264, 0.856, 0.312, 0.901], {\"Virtual Reality\"}[0.264, 0.801, 0.351, 0.845], represents the frequency of each technology innovation. Let me generate the CSV table from the chart.\nBased on the above thought, the answer is:\nTechnology,Frequency (%)\nAugmented Reality,0.6\nVirtual Reality,1.1\nArtificial Intelligence,1.3\nGame-Based Learning,1.7\nFlipped Classroom,2.0\nBring Your Own Device,2.1\nWearable Technology,2.7\nMobile Browsers,3.5\nLearning Management Systems,3.6\nCloud-Based Systems,4.1\nMassive Open Online Courses,4.8\nStudent Engagement Systems,5.8\nDigital Badges,8.0\nBig Data,6.1\nLearning Analytics,8.2"
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "wNpqONUygzYP7srK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data on the chart, which technology innovation would you recommend to a beginner in the EdTech industry considering the integration frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "The frequency on the chart indicates the current acceptance and usage in the industry. Therefore, a high frequency suggests that the technology has been widely embraced and likely has a strong community and substantial resources for support. Upon close examination, the bar with the biggest length represents the technology with the highest frequency {\"Digital Badges\"}<r>[0.264, 0.190, 0.726, 0.234]</r>. This makes it a safer bet for someone starting in the EdTech industry.\nBased on the above thought, the answer is:\nDigital Badges would be a good start since it has the highest integration frequency indicating a wider acceptance in the industry."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "YPTIjO3PkfY7Enaf",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the least commonly incorporated technology innovation in education and training according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the bar lengths, I can find the least commonly used technology. Relatively shorter bars represent less frequent usage. So, I need to mainly look for the shortest bar. In this chart, the shortest bar is {\"Augmented Reality\"}<r>[0.264, 0.856, 0.312, 0.901]</r>.\nBased on the above thought, the answer is:\nAugmented Reality."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "sxoC8qgoVPxfxGBY",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the bar chart, can you provide the data representing different types of networks and relationships in the form of a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Frequency of Relationship Types\"}[0.229, 0.019, 0.981, 0.927] indicates this chart classifies different relationships and their frequencies. As per the markers {\"Social Network\"} [0.087, 0.822, 0.217, 0.840], {\"Personal Interaction\"} [0.043, 0.680, 0.217, 0.697], {\"Family Ties\"} [0.121, 0.537, 0.217, 0.555], {\"Work Relations\"} [0.087, 0.395, 0.217, 0.412], {\"Online Friendship\"} [0.066, 0.253, 0.217, 0.270], {\"Communities\"} [0.102, 0.110, 0.217, 0.128] on the y-axis, we can infer the respective type of relationship. The width of the individual bars such as {\"Social Network\"} [0.229, 0.772, 0.921, 0.886], {\"Personal Interaction\"} [0.229, 0.630, 0.740, 0.743], {\"Family Ties\"} [0.229, 0.487, 0.628, 0.601], {\"Work Relations\"} [0.229, 0.345, 0.537, 0.459], {\"Online Friendship\"} [0.229, 0.202, 0.402, 0.316], {\"Communities\"} [0.229, 0.060, 0.304, 0.174] highlights the frequency percentage for that relationship type. By using the markers on the X-axis, the width of the bars can be calculated to obtain numerical values according to each relationship category. For instance, the marker \"9\" [0.900, 0.939, 0.912, 0.957] corresponds to the end of bar \"Social Network\", indicating a value of 9.2. The CSV file can then be produced by compiling these values against respective categories.\nBased on the above thought, the answer is:\nRelationship,Frequency\nSocial Network,9.2\nPersonal Interaction,6.8\nFamily Ties,5.3\nWork Relations,4.1\nOnline Friendship,2.3\nCommunities,1"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "kXJ3EvtAmKJAyWoJ",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of relationships do people engage in most frequent? And please explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "From the observation of the bar chart,{\"Social Network\"}[0.087, 0.822, 0.217, 0.840] has the longest bar{\"Social Network Bar\"}[0.229, 0.772, 0.921, 0.886]. So, it is safe to say that Social Networks are the most frequent relationships that people engage in. This trend could potentially be due to the increase in accessibility and convenience of social networking platforms. These platforms allow the possibility to connect with a diverse group of people irrespective of geographic location.\nBased on the above thought, the answer is:\nSocial Network. Because with the widespread use of social media, people increasingly use social networks to communicate, keep up with friends, share their lives and connect around shared interests."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "7JFrkqMiG2TyXDbe",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich relationship type has the least frequency according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the relationship type with the least frequency, I can analyse the width of each bar in the chart. From the visual observation, {\"Communities\"}[0.102, 0.110, 0.217, 0.128] has the shortest bar {\"Communities Bar\"}[0.229, 0.060, 0.304, 0.174] representing the least frequency. Examining its length in relation to the x-axis labels indicates that it represents approximately 1 percent.\nBased on the above thought, the answer is:\nCommunities."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "qWbBnLwl0I72BkGb",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart you visualized, could you please generate the data in a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The visualization displays a horizontal bar chart of various categories in the knowledge economy and innovation industry. {The chart's title}<r>[0.329, 0.015, 0.843, 0.033]</r> indicates that the x-axis represents the frequency (%) of each category, while the y-axis lists the categories. The {blue bars}<r>[0.187, 0.833, 0.857, 0.146]</r> extending from the y-axis labels to the right represent the percentage of each category. By examining the length of each bar and correspondingly aligning it with the x-axis labels, the percentage frequency of each category can be determined. For instance, the {bar of \"Biotechnology\"}<r>[0.187, 0.833, 0.785, 0.901]</r> extends to roughly 7.5 on the x-axis, indicating that its frequency is 7.5%.\nBased on the above thought, the answer is:\nCategory,Frequency(%)\nBiotechnology,7.5\nSemiconductors,5.1\nInternet,4.2\nSoftware,6.7\nIT service,3.2\nDigital media,5.8\nNetwork security,2.0\nArtificial Intelligence,8.4\nTelecommunications,4.6\nE-commerce,7.3"
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "q5lB63ifChaYiepO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the variations in percentage frequencies, what industry knowledge would you suggest a new graduate to focus on?"
+            },
+            {
+                "from": "gpt",
+                "value": "A good measure of potential industry growth and hence better job prospects could be the current frequency of each category. This could suggest the industry's current demand and possible future trend. Looking at the data, the {Artificial Intelligence bar}<r>[0.187, 0.246, 0.857, 0.313]</r> is the longest, suggesting that it currently represents the largest proportion, which could be interpreted as a high demand. Additionally, the data provided also documents that Artificial Intelligence is showcasing abrupt growth. Therefore, it can be recommended for new graduates to focus on the field of Artificial Intelligence.\nBased on the above thought, the answer is:\nArtificial Intelligence due to its high frequency, suggesting a high demand and substantial growth."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "g09SSgYFSruKfvwv",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_68_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has the least frequent occurrence according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the industry with the least frequent occurrence, I need to find the shortest {blue bar}<r>[0.187, 0.833, 0.857, 0.146]</r>. By scanning the bars vertically from the top to the bottom, I find that the {Network security}<r>[0.187, 0.330, 0.346, 0.397]</r> industry has the shortest bar on the chart, thus making it the least frequent industry.\nBased on the above thought, the answer is:\nNetwork security."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "pNkkbMNdRmPimmef",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart contains information on seven categories, which are clearly labeled on the y-axis. These include {\"reinforcement_learning\"}<r>[0.025, 0.107, 0.295, 0.131]</r>, {\"neural_networks\"}<r>[0.103, 0.231, 0.295, 0.254]</r>, {\"regression\"}<r>[0.174, 0.354, 0.295, 0.377]</r>, {\"unsupervised_learning\"}<r>[0.033, 0.477, 0.295, 0.501]</r>, {\"convolutions\"}<r>[0.148, 0.601, 0.295, 0.624]</r>, {\"SVM\"}<r>[0.244, 0.724, 0.295, 0.748]</r> and {\"RNN\"}<r>[0.244, 0.848, 0.295, 0.871]</r>. The x-axis features percentage values for two types of data sources: synthetic data and human-authored data. These are represented by different colored bars. For instance, in the 'reinforcement_learning' category, synthetic data has a value of {'-5%'}<r>[0.520, 0.105, 0.557, 0.128]</r>, and human-authored data has a value of {'18%'}<r>[0.778, 0.475, 0.829, 0.498]</r>. Following this pattern for every category, I can generate the CSV table for this data.\nBased on the above thought, the answer is:\nCategory,Synthetic data, Human-authored data\nreinforcement_learning,-5,18\nneural_networks,-25,33\nregression,-15,20\nunsupervised_learning,-10,22\nconvolutions,-18,11\nSVM,-12,30\nRNN,-20,15"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "VHJ10e7ebb3oIQvz",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart indicate about the difference between synthetic and human-authored data in terms of AI and Machine Learning categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart allows for a comparative analysis between synthetic data and human-authored data across several AI and Machine Learning categories. Comparing the two types of data in terms of percentages, one can observe certain patterns. For instance, synthetic data show a fall in percentages from 'reinforcement_learning' to 'RNN', with a sharp drop in 'neural_networks'. On the other hand, human-authored data display an irregular growth trend, with the 'neural_networks' and 'SVM' categories showing the greatest occurrences. Particularly noticeable is the significant disparity between synthetic <{\"-25%\"}<r>[0.745, 0.105, 0.796, 0.128]</r> and human-authored data <{\"33%\"}<r>[0.868, 0.228, 0.919, 0.251]</r> in the 'neural_networks' category. Meanwhile, the least occurrence in synthetic data is also observed in 'neural_networks', whereas the least occurrence in human-authored data is in the 'convolutions' category.\nBased on the above thought, the answer is:\nThe chart suggests a discrepancy in the prevalence of different AI and Machine Learning categories between synthetic data and human-authored data, with some categories being more prevalent in synthetic data and others in human-authored data. Especially, there is a significant divergence in the 'neural_networks' category."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "twfVbVC2I90jEvJS",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum percentage difference between synthetic data and human-authored data across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum percentage difference, I need to compare the percentages of synthetic data and human-authored data in each category by subtracting the synthetic percentage from the human-authored percentage. For instance, for the category 'reinforcement_learning', synthetic data is <{\"-5%\"}<r>[0.520, 0.105, 0.557, 0.128]</r> and human-authored data is <{\"18%\"}<r>[0.778, 0.475, 0.829, 0.498]</r>, so the difference is 23. By calculating this difference for each category, I can conclude that the 'neural_networks' category, with synthetic data being <{\"-25%\"}<r>[0.745, 0.105, 0.796, 0.128]</r> and human-authored data being <{\"33%\"}<r>[0.868, 0.228, 0.919, 0.251]</r>, shows the maximum percentage difference of 58.\nBased on the above thought, the answer is:\n58%"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "XyKylIPBBBYggySh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart, could you please extract and structure the visual data in the form of a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, starting with the chart title at the top {\"Social Trends vs Political Trends\"}[0.346, 0.083, 0.679, 0.113], one can understand that there are two groups that have been represented on a horizontal bar chart. The {y-axis} represents the categories: {\"Dramatic surge\"}[0.074, 0.125, 0.253, 0.148], {\"Subdued expansion\"}[0.025, 0.295, 0.253, 0.318], {\"Rapid recovery\"}[0.078, 0.465, 0.253, 0.488], {\"Slow increase\"}[0.094, 0.635, 0.253, 0.658] and {\"Sluggish growth\"}[0.067, 0.805, 0.253, 0.828]. The x-axis represents the values corresponding to the categories. The {purple rectangles}<r>[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882]</r> to the left of the y-axis and the {pink rectangles}<r>[0.544, 0.066, 0.818, 0.202], [0.544, 0.236, 0.661, 0.372], [0.544, 0.406, 0.897, 0.542], [0.544, 0.576, 0.700, 0.712], [0.544, 0.746, 0.622, 0.882]</r> to the right of the y-axis represent the values of the two trends. Using this information, I can construct a CSV table showing the values of Social Trends and Political Trends for each category.\nBased on the above thought, the answer is:\nCategory,Political Trends,Social Trends\nDramatic surge,-35,35\nSubdued expansion,-5,15\nRapid recovery,-20,45\nSlow increase,-10,20\nSluggish growth,-15,10"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "Nkw6CzdQMUzcFIK8",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserve the chart and tell us which segment, social or political, exhibited a better trend across all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is split into two segments: {Social Trends}<r>[0.544, 0.066, 0.818, 0.202], [0.544, 0.236, 0.661, 0.372], [0.544, 0.406, 0.897, 0.542], [0.544, 0.576, 0.700, 0.712], [0.544, 0.746, 0.622, 0.882]</r> and {Political Trends}<r>[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882]</r>. Clearly, the bar lengths for all categories in the Social Trends are positive, projecting an overall positive trend. However, the bars under Political Trends are all negative, indicating a general contraction across categories. So, comparing the two segments of the chart, Social Trends exhibit a better, more positive trend across all categories.\nBased on the above thought, the answer is:\nSocial Trends, because they show growth and expansion in all categories, contrary to Political Trends which show contraction in each category."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "9sEPO0aneoUfEnSq",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category under Political Trends has shown the least contraction?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {negative horizontal bars}<r>[0.348, 0.066, 0.544, 0.202], [0.504, 0.236, 0.544, 0.372], [0.387, 0.406, 0.544, 0.542], [0.465, 0.576, 0.544, 0.712], [0.426, 0.746, 0.544, 0.882]</r> on the chart aligned with the {labels of the categories}<r>[0.074, 0.125, 0.253, 0.148], [0.025, 0.295, 0.253, 0.318], [0.078, 0.465, 0.253, 0.488], [0.094, 0.635, 0.253, 0.658], [0.067, 0.805, 0.253, 0.828]</r> on the y-axis, I deduce that each bar represents the level of regression in each category of Political Trends. Although all categories show a decrease, the category that shows the least contraction is represented by the shortest negative bar. Visually, the {Subdued expansion bar}<r>[0.504, 0.236, 0.544, 0.372]</r> is the shortest among all bars.\nBased on the above thought, the answer is:\nSubdued expansion"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "4iiaP0AHm04WinGB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the provided data and the bounding boxes around the y-axis tick labels, like {\"Dramatic surge\"}[0.095, 0.107, 0.274, 0.131] and {\"Rapid decline\"}[0.117, 0.231, 0.274, 0.254], it seems the y-axis lists different investment trends. The x-axis seems to depict the Return Rate in Private Equity and Public Equity Investments based on the label color of the bars, indicated by {The legend}<desc></desc>, with Private Equity Investments showing as {purple bars}<{\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}> and Public Equity Investments showing as {pink bars}<{\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}>. Each trend has corresponding bars that depict their respective return rates for each investment type. For example, the left bar under \"Dramatic surge\" trend {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"} which is -8%, and the one on the right {\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"} is 16%.\nBased on the above thought, the answer is:\nInvestment Type,Private Equity Investment,Public Equity Investment\nDramatic surge,-8,16\nRapid decline,-31,28\nPronounced decrease,-24,11\nConsistent expansion,-7,5\nUpward trajectory,-10,15\nRapid recovery,-10,16\nPersistent growth,-10,9"
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "5AecpLNM01YWXeyK",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which investment trend receives a better return rate on Public Equity Investments as compared to Private Equity Investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which investment trend has a better return rate for Public Equity Investments compared to the Private ones, I need to compare the lengths of the two bars associated with each investment trend. For instance, if in an investment trend the pink bar {\"bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}, which denotes Public Equity Investments, extends further to the right from the center compared to the purple bar {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, which denotes Private Equity Investments, then the Public Equity Investment outperforms the Private Equity Investment. For example, in the \"Dramatic surge\" trend, the Public Equity Investments rate {\"Public Equity bar_coordinates\": \"[0.589, 0.067, 0.705, 0.166]\"}, which is +16%, is greater than the Private Equity Investments rate {\"Private Equity bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, which is -8%. All the bar lengths for each trend need to be compared in a similar fashion.\nBased on the above thought, the answer is:\nAll investment trends have a better return rate on Public Equity Investments as compared to Private Equity Investments. This is due to each investment trend having a longer pink bar (Public Equity Investments) extending to the right from the center in comparison to the purple bar (Private Equity Investments)."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "F7OG28FcVX574uTT",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest positive return rate for Private Equity investments according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest positive return rate for Private Equity investments, I need to identify the longest bar extending to the right from the center. Since all the bars associated with Private Equity investments are negative as indicated by their left extension from the center like in the bar {\"bar_coordinates\": \"[0.530, 0.067, 0.589, 0.166]\"}, it means that there is no positive return rate for Private Equity investments in all the investment trends.\nBased on the above thought, the answer is:\nThere is no positive return rate for Private Equity investments."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "jQHniqx7BXeWnRvI",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data you analyzed; please convert the data into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a {bar chart}\n\n[0.157, 0.025, 0.985, 0.948] with two data groups namely {Policy Data}[0.216, 0.067, 0.420, 0.906] and {Effectiveness Evaluation Data}[0.420, 0.067, 0.927, 0.906] spanning 7 different categories on the y-axis: 'Public Health', 'Economic Policy', 'Infrastructure', 'Education', 'Defense', 'Energy', and 'Governments Ethics'.\nThe values of Policy data range from -35 to -11 and Effectiveness Evaluation Data ranges from 30 to 87 with every bar pair representing a category. For example, for Public Health, the Policy data{bar}[0.216, 0.067, 0.420, 0.166]\nshows -35 and Effectiveness Evaluation Data{bar}[0.420, 0.067, 0.927, 0.166] shows 87. It's the same for every other category, where the values together form the data for that category.\nBased on the above thought, the answer is:\nCategories, Policy Data, Effectiveness Evaluation Data\nPublic Health, -35, 87\nEconomic Policy, -22, 30\nInfrastructure, -11, 67\nEducation, -19, 75\nDefense, -14, 55\nEnergy, -17, 45\nGovernment Ethics, -21, 80"
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "9naAo1rYiwY1jkxT",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which policy area is having a strong impact despite a negative policy data trend, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires analyzing both the policy data group, {Policy Data}[0.216, 0.067, 0.420, 0.906], and the effectiveness evaluation data group, {Effectiveness Evaluation Data}[0.420, 0.067, 0.927, 0.906]. Specifically, we need to find categories where policy data is declining (negative), but the effectiveness evaluation data is high. After analyzing each category, I found that the category of 'Public Health', {Public Health bars}[{Policy data bar}[0.216, 0.067, 0.420, 0.166]; {Effectiveness evaluation data bar}[0.420, 0.067, 0.927, 0.166]], fits this condition the best. Despite a significant decline of -35% in policy data, it has a high effectiveness evaluation of 87%, which suggests that the impact of policy actions in Public Health is powerful.\nBased on the above thought, the answer is:\nPublic Health, because even though there's a significant decline in policy data, the effectiveness evaluation is high, indicating the substantial impact of these policies."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "JMPOlDSHju2qOnYi",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which category is the policy data above -20 percent?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the bars corresponding to the policy data group {Policy Data}[0.216, 0.067, 0.420, 0.906]. The policy data for the seven categories are represented by these bars, and their lengths indicate the values. By checking each bar one by one, it indicates that the policy data for category 'Infrastructure', {Infrastructure bar}[0.355, 0.314, 0.420, 0.413], is above -20 percent as its length notably smaller than other bars of the group.\nBased on the above thought, the answer is:\nInfrastructure."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "hlWXxS8ToJLKlVvo",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to generate a structured table that captures the data shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can do that. The applications of AI are listed along {the y-axis}[{'label': 'Speech Recognition', 'label_coordinates': '[0.060, 0.107, 0.289, 0.131]', 'tick_coordinates': '[0.305, 0.116]'}, {'label': 'Autonomous Driving', 'label_coordinates': '[0.052, 0.231, 0.289, 0.254]', 'tick_coordinates': '[0.305, 0.240]'}, {'label': 'Medical Diagnosis', 'label_coordinates': '[0.080, 0.354, 0.289, 0.377]', 'tick_coordinates': '[0.305, 0.363]'}, {'label': 'Supply Chain', 'label_coordinates': '[0.136, 0.477, 0.289, 0.501]', 'tick_coordinates': '[0.305, 0.487]'}, {'label': 'Advertisement', 'label_coordinates': '[0.119, 0.601, 0.289, 0.624]', 'tick_coordinates': '[0.305, 0.610]'}, {'label': 'Smart Homes', 'label_coordinates': '[0.131, 0.724, 0.289, 0.748]', 'tick_coordinates': '[0.305, 0.733]'}, {'label': 'Customer Service Bots', 'label_coordinates': '[0.025, 0.848, 0.289, 0.871]', 'tick_coordinates': '[0.305, 0.857]'}]. The bar chart is divided into two groups - one representing synthetic data and the other representing real use cases. Synthetic data is represented by {bars with color '#d4a3da'}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}]. Real use cases data is represented by {bars with color '#75147c'}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '81%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}]. The length of the bars and the percentages with the bars represent the scores. Therefore, by recognizing and extracting values from the lengths of these bars in both categories, I can construct a table that accurately captures the data displayed on the chart.\nBased on the above thought, the answer is:\nAI Applications,Synthetic Data Scores,Real Use Case Scores\nSpeech Recognition,82,95\nAutonomous Driving,63,78\nMedical Diagnosis,56,67\nSupply Chain,73,81\nAdvertisement,72,85\nSmart Homes,57,69\nCustomer Service Bots,66,77"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "mFm6H1oJUSFp5i0L",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComment on the practicality and credibility of synthetic data based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "As displayed in the chart, the synthetic data scores {synthetic data scores}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}] are consistently lower than the real use case scores [{real use case scores}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '95%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}]. This gap may suggest that synthetic data, while indeed useful for various AI applications, may not capture all the intricate characteristics and subtle complexities inherent in real use case data. Whether it's for Speech Recognition or Customer Service Bots, there exists a clear discrepancy between the scores associated with the synthetic data and their real use case counterparts. This may potentially undermine the use of synthetic data for these applications. However, it's worth noting the relatively close scores for Supply Chain application, suggesting that in some cases, synthetic data may be a reasonably reliable representation of the real-world data.\nBased on the above thought, the answer is:\nSynthetic data, while useful, often fall short in capturing the intricacies and overall essence of real use case data across numerous AI applications. However, in some special cases, like Supply chain management, synthetic data can closely mirror the real-world data, implying its potential in such domains."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "cZx73hW8kC2AiLPp",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI application has the smallest difference between the real use case scores and the synthetic data scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will compare the score difference between the synthetic data and the real use cases for each AI application. The calculation will be done by subtracting {synthetic data scores}[{'bar_coordinates': '[0.338, 0.067, 0.612, 0.166]', 'text_coordinates': '[0.287, 0.105, 0.338, 0.128]', 'text_params': {'s': '82%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '-82.00 -> 0.00'}, {...}, {'bar_coordinates': '[0.392, 0.808, 0.612, 0.906]', 'text_coordinates': '[0.317, 0.475, 0.368, 0.498]', 'text_params': {'s': '73%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '-66.00 -> 0.00'}] from {real use case scores}[{'bar_coordinates': '[0.612, 0.067, 0.930, 0.166]', 'text_coordinates': '[0.883, 0.475, 0.934, 0.498]', 'text_params': {'s': '95%', 'color': 'black'}, 'y_label': 'Speech Recognition', 'x_range': '0.00 -> 95.00'}, {...}, {'bar_coordinates': '[0.612, 0.808, 0.870, 0.906]', 'text_coordinates': '[0.870, 0.845, 0.921, 0.869]', 'text_params': {'s': '77%', 'color': 'black'}, 'y_label': 'Customer Service Bots', 'x_range': '0.00 -> 77.00'}] one by one, and then I'll find the AI application with the smallest difference.\nBased on the above thought, the answer is:\nSupply Chain"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "YOUnhxiFQf1zHcXy",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table that would illustrate the change in participation for sports activities represented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the bar chart, the categories are listed from the {top}[{\"Basketball\"}[0.051, 0.872, 0.172, 0.895]] to the {bottom}[{\"Running\"}[0.077, 0.097, 0.172, 0.121]], these are sports activities. We have two trends: {the first one, Traditional, shown in pink}<{\"Traditional\"}[0.347, 0.068, 0.556, 0.145]> and {the second one, Gym, displayed in purple}<{\"Gym\"}[0.556, 0.068, 0.806, 0.145]>. Each sports activity has two bars representing the change in participation for traditional and gym-based settings respectively, for example, for {Basketball}[0.430, 0.842, 0.556, 0.920] and [0.556, 0.842, 0.744, 0.920]. The percentages shown above each bar like {\"45%\"}[0.744, 0.869, 0.795, 0.893] and {\"30%\"}[0.379, 0.869, 0.430, 0.893] show the changes. Based on all these observations, I can generate the CSV table.\nBased on the above thought, the answer is:\nSport,Traditional,Gym\nRunning,-50,60\nSwimming,-20,30\nCycling,-35,50\nYoga,-15,20\nWeightlifting,-60,75\nCrossfit,-65,88\nBoxing,-78,79\nGolf,-12,14\nBasketball,-30,45"
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "LN6ugoIMoHYdJgYr",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, do gym-based activities seem to be on the rise compared to traditional ones over time? Please explain why you think so."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will observe the comparison between {Traditional}[0.347, 0.068, 0.556, 0.145] and {Gym}[0.556, 0.068, 0.806, 0.145] for each sport. Specifically, I observe that for each sport from {Running}[{\"Traditional\"}[0.347, 0.068, 0.556, 0.145]; {\"Gym\"}[0.556, 0.068, 0.806, 0.145]] to {Basketball}[{\"Traditional\"}[0.430, 0.842, 0.556, 0.920]; {\"Gym\"}[0.556, 0.842, 0.744, 0.920]], the {Traditional bars} are to the left of the center line, indicating a decrease in participation, and the {Gym bars} are to the right of the center line, indicating an increase in participation. This trend can be observed for all sports activities, with the Gym bars always showing an increase in participation.\nBased on the above thought, the answer is:\nYes. The bar chart clearly shows a common trend that gym-based activities are on the rise for all sports activities from Running to Basketball, whereas traditional ones are witnessing a decline."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "48NYaMYgi9sltXkE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sport has shown the maximum change in gym-based settings compared to traditional settings over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will examine the differences between the lengths of Traditional and Gym bars for each sport. Taking {Crossfit} as an example, the two bars representing Traditional and Gym changes are {bar 1}[0.284, 0.552, 0.556, 0.629] and {bar 2}[0.556, 0.552, 0.923, 0.629] respectively, with a difference between them. By comparing the differences of all sports, it is clear that {Crossfit} has the most substantial difference between Traditional and Gym settings.\nBased on the above thought, the answer is:\nCrossfit"
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "vVLu0TVSAETDkbfN",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented in the bar chart into a table in CSV format for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The chart indicates the performance of 'SynRobo-Investment' and 'Human-Investment' in different categories. The categories include 'Risk Management', 'Financial Planning', 'Wealth Management', 'Asset Allocation', 'Investment Strategy', 'Cash Flow', and 'Retirement Planning'. The data from 'SynRobo-Investment' is represented by pastel-colored bars {pastel-colored bars}[0.446, 0.067, 0.583, 0.166; 0.473, 0.190, 0.583, 0.289; 0.373, 0.314, 0.583, 0.413; 0.409, 0.437, 0.583, 0.536; 0.437, 0.561, 0.583, 0.659; 0.400, 0.684, 0.583, 0.783; 0.418, 0.808, 0.583, 0.906] and 'Human-Investment' data is represented by bold-colored bars {bold-colored bars}[0.583, 0.067, 0.802, 0.166; 0.583, 0.190, 0.774, 0.289; 0.583, 0.314, 0.875, 0.413; 0.583, 0.437, 0.838, 0.536; 0.583, 0.561, 0.783, 0.659; 0.583, 0.684, 0.884, 0.783; 0.583, 0.808, 0.856, 0.906]. I will determine the values by examining the width of the bars and the text labels on them. I will now start transcribing this data into a CSV format.\nBased on the above thought, the answer is:\nASX Stock, SynRobo-Investment, Human-Investment\nRisk Management, -15, 24\nFinancial Planning, -12, 21\nWealth Management, -23, 32\nAsset Allocation, -19, 28\nInvestment Strategy, -16, 22\nCash Flow, -20, 33\nRetirement Planning, -18, 30"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "al3doeT0mdWh2XTN",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of investment is considered safer regarding 'Financial Planning'? Please explain why you think so."
+            },
+            {
+                "from": "gpt",
+                "value": "The pastel-colored bar {pastel-colored bar}[0.473, 0.190, 0.583, 0.289] represents 'SynRobo-Investment' performance on 'Financial Planning', and the bold-colored bar {bold-colored bar}[0.583, 0.190, 0.774, 0.289] represents 'Human-Investment' on the same category. The 'Human-Investment' bar is located on the right side of the 0 point, indicating a positive result, while the 'SynRobo-Investment' bar is on the left, indicating loss. Considering that safety in financial planning refers to lower risk and positive returns, it seems 'Human-Investment' is the safer option for 'Financial Planning' based on this chart.\nBased on the above thought, the answer is:\nHuman-Investment. Because it presents a positive result in the 'Financial Planning' category while 'SynRobo-Investment' shows a loss in the same category."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "RbU6UYFCCGHWfC4n",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUnder which category is the 'SynRobo-Investment' showing the highest loss?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine where 'SynRobo-Investment' suffered the most loss, I need to find out the widest bar of the pastel-colored bars representing 'SynRobo-Investment'. By looking at their coordinates {pastel-colored bars}[0.446, 0.067, 0.583, 0.166; 0.473, 0.190, 0.583, 0.289; 0.373, 0.314, 0.583, 0.413; 0.409, 0.437, 0.583, 0.536; 0.437, 0.561, 0.583, 0.659; 0.400, 0.684, 0.583, 0.783; 0.418, 0.808, 0.583, 0.906], I can see that the bar associated with 'Wealth Management'[0.373, 0.314, 0.583, 0.413] is the widest one, indicating the highest loss for 'SynRobo-Investment'.\nBased on the above thought, the answer is:\nWealth Management"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "Rgc088NHhu7NSfP9",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease organize the chart data into a structured table using a csv format."
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, the {y-axis tick labels}<r>[0.096, 0.102, 0.292, 0.125]; [0.106, 0.210, 0.292, 0.233]; [0.101, 0.319, 0.292, 0.342]; [0.134, 0.427, 0.292, 0.450]; [0.094, 0.536, 0.292, 0.559]; [0.054, 0.644, 0.292, 0.668]; [0.056, 0.753, 0.292, 0.776]; [0.074, 0.861, 0.292, 0.885]</r> tell us that the categories are 'Domestic Robots', 'Climate Change', 'Self-driving Cars', 'Smart Homes', 'Cryptocurrencies', 'Artificial Intelligence', 'Genetic Engineering' and 'VR - Virtual Reality' respectively. Meanwhile, there are two sets of bars for each category, one representing {synthetic data}<r>[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914]</r> and the other representing {human-authored data}<r>[0.252, 0.067, 0.586, 0.154]; [0.252, 0.176, 0.530, 0.263]; [0.252, 0.284, 0.808, 0.371]; [0.252, 0.393, 0.697, 0.480]; [0.252, 0.501, 0.475, 0.588]; [0.252, 0.610, 0.864, 0.697]; [0.252, 0.718, 0.363, 0.805]; [0.252, 0.827, 0.641, 0.914]</r>. The values of the bars can be fetched from the text labels attached to the bars. For example, {the label of the first bar of the first row}<r>[0.423, 0.099, 0.475, 0.122]</r> tells us that the synthetic data value for 'Domestic Robots' is '20%'. Similar steps should be taken for each category and each bar.\nBased on the above thought, the answer is:\ncategories,synthetic_data,human_authored_data\nDomestic Robots,20%,30%\nClimate Change,45%,25%\nSelf-driving Cars,35%,50%\nSmart Homes,15%,40%\nCryptocurrencies,30%,20%\nArtificial Intelligence,40%,55%\nGenetic Engineering,50%,10%\nVR - Virtual Reality,15%,35%"
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "r5iVINN73sA5FwU6",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there significant differences between the synthetic model and human predictions for the impact of future societal and living scenarios? Please provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the {synthetic data}<r>[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914]</r> with the {human-authored data}<r>[0.252, 0.067, 0.586, 0.154]; [0.252, 0.176, 0.530, 0.263]; [0.252, 0.284, 0.808, 0.371]; [0.252, 0.393, 0.697, 0.480]; [0.252, 0.501, 0.475, 0.588]; [0.252, 0.610, 0.864, 0.697]; [0.252, 0.718, 0.363, 0.805]; [0.252, 0.827, 0.641, 0.914]</r>. There are significant differences observed in several categories. For example, for 'Genetic Engineering' the synthetic model shows a far greater impact (50%) compared to the human prediction (10%). For 'Artificial Intelligence', the human prediction (55%) is significantly higher than the synthetic prediction (40%). In some categories such as 'Climate Change', both the synthetic model (45%) and humans (25%) predict a notable impact but the synthetic estimate is considerably higher. Hence, there are some differences between the predictions of the synthetic model and humans.\nBased on the above thought, the answer is:\nYes, there are significant differences. For some categories such as 'Genetic Engineering' and 'Climate Change', the synthetic model predicts a notably higher impact compared to humans. In contrast, for 'Artificial Intelligence', humans foresee a larger impact than what the synthetic model suggests."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "ygrRSRJO3ifRpmPy",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which future societal and living scenario is predicted to have the greatest impact from the synthetic model?"
+            },
+            {
+                "from": "gpt",
+                "value": "The scenario with the greatest impact will be the one with the highest value for the {synthetic data}<r>[0.252, 0.067, 0.475, 0.154]; [0.252, 0.176, 0.753, 0.263]; [0.252, 0.284, 0.641, 0.371]; [0.252, 0.393, 0.419, 0.480]; [0.252, 0.501, 0.586, 0.588]; [0.252, 0.610, 0.697, 0.697]; [0.252, 0.718, 0.808, 0.805]; [0.252, 0.827, 0.419, 0.914]</r>. By comparing the bar lengths and their corresponding labels, I observe that the value for {Genetic Engineering}<r>[0.252, 0.718, 0.808, 0.805]</r> is 50%, which appears to be the largest among all scenarios.\nBased on the above thought, the answer is:\nGenetic Engineering."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "4TIBDWB4RVLcwZhA",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data represented by the bar chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The chart title is not explicitly mentioned, but considering the context of this chart, it is concerned with the declining trend in percentage of teams following various leadership styles in a corporate organization. The y-axis lists the eight leadership styles, for example {Transformational Leadership}<r>[0.019, 0.099, 0.266, 0.116]</r>. For each leadership style, there is a colored bar on the left-hand side of the y-axis. The length of each bar represents the respective decline in percentage for that leadership style, as also mentioned in labels above each bar for example {one such label saying '18%'}<r>[0.575, 0.097, 0.614, 0.114]</r>. Taking these into account, we can structure the CSV data as follows:\nBased on the above thought, the answer is:\nLeadership Style,Decline of Business Teams (%)\nTransformational Leadership,18\nAuthentic Leadership,24\nParticipative Leadership,21\nLaissez-faire Leadership,12\nTransactional Leadership,9\nCharismatic Leadership,16\nService Leadership,22\nCoaching Leadership,20"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "4ttkwwoAQuroCg2V",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you infer a trend about the state of leadership styles in the organization from the perspective of team adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, the chart is primarily showing us a downward or negative trend in the adoption of various leadership styles. Each bar, this one for {Transactional Leadership}<r>[0.758, 0.502, 0.901, 0.590]</r> for instance, extending to the left from the y-axis, indicates a decline in the number of teams demonstrating that leadership style. This trend is reinforced by the percentages illustrated above each bar like this one indicating {9%}<r>[0.730, 0.537, 0.758, 0.554]</r> in the case of Transactional Leadership. This visually suggests that fewer and fewer teams in the organization are showing different leadership styles over time.\nBased on the above thought, the answer is:\nThe trend of leadership styles in the corporate organization is on a decline. This is shown by the negative percentages for all leadership styles, indicating less number of teams are demonstrating these leadership styles over the course of time."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "NnLieVUUhSghBobr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich leadership style shows the highest percentage decline in business teams adopting it?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to see which bar extends the furthest to the left on the chart. Each case is represented by a color-coded bar, denoting the percentage decline for that leadership style. By comparing these, I can see that the bar for {Authentic Leadership}<r>[0.518, 0.172, 0.901, 0.260]</r> is the longest. This implies a higher percentage decline in teams adopting Authentic Leadership, which is also confirmed by the percentage label of {24%}<r>[0.480, 0.207, 0.518, 0.224]</r>.\nBased on the above thought, the answer is:\nAuthentic Leadership"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "DOriQD32bYauoxuk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert all the recent trend information across different sectors of the Transportation and Logistics industry from the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given chart, it is evident that the y labels represent the various sectors of the industry {\"Air Freight Volume\"}<r>[0.122, 0.839, 0.281, 0.857]</r>, {\"Ocean Freight Volume\"}<r>[0.090, 0.737, 0.281, 0.754]</r>, {\"Last Mile Delivery\"}<r>[0.126, 0.634, 0.281, 0.651]</r>, {\"Wholesale and Distribution\"}<r>[0.047, 0.531, 0.281, 0.549]</r>, {\"Retail and ECommerce\"}<r>[0.083, 0.429, 0.281, 0.446]</r>, {\"Manufacturing and Production\"}<r>[0.019, 0.326, 0.281, 0.344]</r>, {\"3PL/4PL Services\"}<r>[0.135, 0.223, 0.281, 0.241]</r> and {\"Transportation Management\"}<r>[0.035, 0.121, 0.281, 0.138]</r>. The x-axis represents the activity trend in percentage between Benchmark Trends denoted by the {red bars}<r>['Benchmark Trends'][0.518, 0.087, 0.669, 0.887]</r> and the recently Surveyed Trends shown by the {green bars}<r>['Surveyed Trends'][0.669, 0.087, 0.919, 0.887]</r>. The values can be extracted by locating the edge of each bar along the x-axis, values range from -30 to 30. Each sector has two bars corresponding to the two trends. So, I should collect these details for constructing the CSV table.\nBased on the above thought, the answer is:\nSectors,Benchmark Trends (%),Surveyed Trends (%)\nAir Freight Volume,-12,6\nOcean Freight Volume,-25,15\nLast Mile Delivery,-18,6\nWholesale and Distribution,7,-9\nRetail and ECommerce,10,-7\nManufacturing and Production,-20,14\n3PL/4PL Services,-13,9\nTransportation Management,-15,20"
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "DbIMUwlAZg0WaqfI",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which sector do you think is most negatively impacted? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most negatively impacted sector would be the one where the Surveyed Trends portray a large negative deviation from the Benchmark Trends. I can see that {\"Ocean Freight Volume\"}<r>[0.090, 0.737, 0.281, 0.754]</r> shows a large negative value for {Surveyed Trends}<r>[0.669, 0.702, 0.856, 0.785]</r> and a negative {Benchmark Trends}<r>[0.356, 0.702, 0.669, 0.785]</r> value as well, indicating a decrease in activity. And the difference between the Benchmark Trends and the Surveyed Trends for this sector is also the greatest, meaning that this sector not only performed badly, its performance also deteriorated more than any other sector compared to the benchmark.\nBased on the above thought, the answer is:\nThe Ocean Freight Volume sector is the most negatively impacted because it has the most substantial negative deviation in the Surveyed Trends compared to the Benchmark Trends."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "rv5yPssFeHEQtweD",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which sector shows the most positive deviation in Surveyed Trends compared to the Benchmark Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look for the sector in which the Surveyed Trend is greater than the Benchmark Trend by the most significant margin. Upon scanning the chart, I can see that the {bar for Transportation Management}<r>[0.669, 0.087, 0.919, 0.169]</r> in Surveyed Trends have the highest value on the positive side compared to its corresponding {bar in Benchmark Trends}<r>[0.481, 0.087, 0.669, 0.169]</r>, indicating that it has the most positive deviation in Surveyed Trends compared to Benchmark Trends.\nBased on the above thought, the answer is:\nTransportation Management"
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "3zY4ITKjZDqTwtc3",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data in the bar chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels represent different types of media which include {Newspapers}<r>[0.029, 0.107, 0.171, 0.131]</r>, {TV}<r>[0.141, 0.231, 0.171, 0.254]</r>, {Social Media}<r>[0.025, 0.354, 0.171, 0.377]</r>, {Blogs}<r>[0.107, 0.477, 0.171, 0.501]</r>, {Podcasts}<r>[0.069, 0.601, 0.171, 0.624]</r>, {Forums}<r>[0.086, 0.724, 0.171, 0.748]</r> and {Memes}<r>[0.088, 0.848, 0.171, 0.871]</r>. To create the table, I need to interpret the bar locations on the x-axis. The x-axis ranges from -25 to 40 which signifies the percentage of influence. For instance, the {Newspapers} category for synthetic data is represented by a bar from 0 to -15<r>[0.370, 0.067, 0.514, 0.166]</r> and human-related data is represented by a bar from 0 to 25<r>[0.514, 0.067, 0.754, 0.166]</r>. Following this method for every category, I can create a CSV table from the data.\nBased on the above thought, the answer is:\nMedia Type,Synthetic Data,Human Data\nNewspapers,-15,25\nTV,-24,38\nSocial Media,-21,16\nBlogs,-10,5\nPodcasts,-5,12\nForums,-9,20\nMemes,-6,8"
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "jKxY0oJRGUaEsKm4",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about the impact of synthetic data and human data on different media types?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpreting the bar chart, it is clear that the impact of synthetic and human data varies across different media types. The color-coded bars clearly distinguish between the two types of data for each media, with one color for {synthetic data}<r>0.836, 0.141, 0.888, 0.164]</r> and another for {human data}<r>0.836, 0.176, 0.888, 0.199]</r>. Each media type shows a unique relationship between synthetic and human data. For example, {TV}<r>[0.141, 0.231, 0.171, 0.254]</r>shows a negative influence from synthetic data and a high positive influence from human data, indicating a shift in trust towards human-created content in this sector. On the other hand, {Podcasts}<r>[0.069, 0.601, 0.171, 0.624]</r> show minimal impact from synthetic data and a moderate increase in impact from human data. This could be due to the nature of Podcasts being highly personal and requiring human touch. Looking at all the media types, it is clear that human data has more influence than synthetic data in all areas, suggesting a wider trust in human-generated content.\nBased on the above thought, the answer is:\nEach media type has a unique response to synthetic versus human influence. Generally, human-authored data has more impact on all categories, reflecting the wider trust in human-generated content. Notably, TV experiences the most considerable shift from synthetic to human influence, possibly due to increased trust in human-authored content. By contrast, Podcasts display minimal impact from synthetic influence and a modest rise in human influence, revealing a preference for the personal touch in this medium."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "3Xo34cIV63VYN231",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media type has the highest difference between synthetic data and human data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the media type with the highest difference between synthetic data and human data, I need to compare the bar lengths in each such pair. By visual inspection of the bars, I notice that {TV for synthetic data}<r>[0.283, 0.190, 0.514, 0.289]</r> and {TV for human data}<r>[0.514, 0.190, 0.879, 0.289]</r> have the longest combined length, implying the highest difference in values. This pair presents a difference of 38 (human data) - (-24) (synthetic data) = 62.\nBased on the above thought, the answer is:\nTV"
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "ATCMvntdCvo0qNgh",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the information about outsourced and internal handling processes in different departments of a business and represent it in structured table format (CSV)?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart, the y-axis represents specific departments of a business, from {\"Customer inquiries\"}<r>[0.041, 0.104, 0.205, 0.122]</r>, {\"Product development\"}<r>[0.019, 0.230, 0.205, 0.247]</r> to {\"After-sales service\"}<r>[0.046, 0.855, 0.205, 0.873]</r>. The bars extending towards the left denote the outsourced processes, and towards the right are internal handling processes. Blue bars represent outsourced processes, while the light blue bars represent internal handling processes according to {the legend section}<r>[0.7199cf: Internal Handling, 0.98abc5: Outsourced]</r>. Now, to create the CSV format, I examine each department. For example, for \"Customer inquiries\", the outsourced bar's coordinates are [0.462, 0.061, 0.556, 0.161] and the internal handling bar's coordinates are [0.556, 0.061, 0.707, 0.161], indicating a performance of -25% for outsourcing and 40% for internal handling respectively.\nBased on the above thought, the answer is:\nDepartment,Outsourced,Internal_Handling\nCustomer inquiries,-25,40\nProduct development,-55,70\nPurchasing,-20,30\nManufacturing,-80,100\nQuality control,-50,65\nSales,-75,90\nAfter-sales service,-35,50"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "fueCUPZyLIPp2Kv9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in which process do you think the company should definitely consider internal handling instead of outsourcing? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "For this analysis, we examine where the difference between {internal handling}<r>[#7199cf]</r> and {outsourced}<r>[#98abc5]</r> is most significant. For {manufacturing}<r>[0.079, 0.480, 0.205, 0.497]</r>, the extremities of the positive bar is at {100%}<r>[0.556, 0.437, 0.933, 0.537]</r> and of the negative bar is at {-80%}<r>[0.255, 0.437, 0.556, 0.537]</r>. This indicates a striking contrast between the effectiveness of internal handling versus outsourcing, with internal handling far outperforming outsourcing. This massive difference suggests that the company would substantially benefit from internal handling for the manufacturing process.\nBased on the above thought, the answer is:\nManufacturing. As per the chart, the efficiency of internal handling in manufacturing is 100% as compared to -80% for outsourced. This significant gap suggests that the company is significantly more efficient when handling manufacturing processes internally."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "LU2Y8YmueFgcuw4I",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which business process does outsourcing exhibit the least effectiveness as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which business process has the least effectiveness in terms of outsourcing, I need to inspect the size of each {bar representing outsourced process}<r>[#98abc5]</r> relative to the x-axis coordinate 0. Examining them, I can observe the longest bar representing outsourcing fall under the label {\"Manufacturing\"}<r>[0.079, 0.480, 0.205, 0.497]</r>. It extends from 0 to approximately {-80}<r>[0.255, 0.437, 0.556, 0.537]</r> on the negative x-axis, which makes it the least effective process when outsourced.\nBased on the above thought, the answer is:\nManufacturing"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "gWxIL6DayICww8gg",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you parse the chart data and convert it into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart compares the efforts invested in synthetic and human-driven projects throughout various stages of an Innovation Project Evaluation and Management cycle. The major categories are {'Project Initialization'}<r>[0.019, 0.104, 0.190, 0.122]</r>, {'Ideation'}<r>[0.119, 0.229, 0.190, 0.246]</r>, {'Design'}<r>[0.130, 0.354, 0.190, 0.371]</r>, {'Development'}<r>[0.073, 0.478, 0.190, 0.496]</r>, {'Testing'}<r>[0.129, 0.603, 0.190, 0.621]</r>, {'Deployment'}<r>[0.084, 0.728, 0.190, 0.745]</r>, and {'Review and Control'}<r>[0.022, 0.852, 0.190, 0.870]</r>. For each stage, there are two bars indicating synthetic and human efforts. According to {the legend}, {the teal bars}<r>\"synthetic data\"[0.393, 0.061, 0.488, 0.161]</r> represent efforts by synthetic projects, and {the purple bars}<r>\"human-authored data\"[0.488, 0.061, 0.536, 0.161]</r> represent efforts by human-driven projects. The values corresponding to the bar lengths indicate the percentage of effort. Using this information, a structured table can be created.\nBased on the above thought, the answer is:\nProject Stage,Synthetic Effort (%),Human Effort (%)\nProject Initialization,20,-10\nIdeation,45,-30\nDesign,10,-50\nDevelopment,25,-70\nTesting,40,-90\nDeployment,50,-80\nReview and Control,35,-60"
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "jcNWCj0j92DoOkex",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you infer about the allocation of effort between synthetic and human-authored projects across all stages of the Innovation Project Evaluation and Management cycle?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, it seems that the allocation of effort between synthetic and human-authored efforts varies across different stages of the Innovation Project Evaluation and Management cycle. In general, human-authored projects tend to give greater effort during the 'Design', 'Development', 'Testing', and 'Deployment' stages, represented by the bars <r>[0.488, 0.311, 0.726, 0.410]</r>, <r>[0.488, 0.435, 0.822, 0.535]</r>, <r>[0.488, 0.560, 0.917, 0.660]</r>, and <r>[0.488, 0.685, 0.869, 0.784]</r>. On the other hand, synthetic projects seem to prioritize the 'Ideation', 'Deployment', and 'Review and Control' stages, with bars <r>[0.274, 0.186, 0.488, 0.286]</r>, <r>[0.250, 0.685, 0.488, 0.784]</r>, and <r>[0.321, 0.809, 0.488, 0.909]</r>. This could suggest that synthetic and human-authored projects focus on different aspects within the Innovation Project Evaluation and Management cycle.\nBased on the above thought, the answer is:\nBased on the chart, it appears that synthetic projects focus more on the 'Ideation', 'Deployment', and 'Review and Control' stages, while human-authored projects prioritize the 'Design', 'Development', 'Testing', and 'Deployment' stages of an Innovation Project Evaluation and Management cycle."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "6ZFhXtQhWtKCWvGC",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in effort percentages between human and synthetic during the 'Development' stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I can examine the 'Development' category in the chart, which includes {'synthetic bar'}<r>[0.369, 0.435, 0.488, 0.535]</r> and {'human bar'}<r>[0.488, 0.435, 0.822, 0.535]</r>. The lengths of these bars represent the percentages of effort made by synthetic and human projects, respectively. The synthetic project invested 25% effort, and the human project invested 70% (as indicated by the negative sign, it actually means to the left of the y-axis). The difference can be calculated by subtracting 25 from 70.\nBased on the above thought, the answer is:\n45%"
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "g4SadXzfM0QEFVzu",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help convert the bar chart data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. From the y-axis labels on the left side {y-ticks labels}[{\"Infrastructural Development\"}[0.015, 0.103, 0.212, 0.117]; {\"Educational Facilities\"}[0.067, 0.229, 0.212, 0.243]; {\"Healthcare Facilities\"}[0.072, 0.355, 0.212, 0.369]; {\"Agriculture\"}[0.136, 0.481, 0.212, 0.495]; {\"Technological Advancement\"}[0.018, 0.607, 0.212, 0.621]; {\"Employment Opportunities\"}[0.025, 0.733, 0.212, 0.747]; {\"Transportation\"}[0.112, 0.859, 0.212, 0.873]], these are the sectors of development. There are two groups of colored bars, each representing a different setting. The group on the left-side of the x-axis is purple with {label \"Rural Growth\"}[0.308, 0.058, 0.653, 0.159] and on the right-side is blue with {label \"Urban Growth\"}[0.653, 0.058, 0.905, 0.159]. For every sector labeled on the y-axis, there is a pair of bars from both groups. The x-value of each bar represents the growth percentage in its respective setting. I will extract the growth percentage for both settings for all sectors and format the extracted data into a CSV format.\nBased on the above thought, the answer is:\nSector,Rural Growth, Urban Growth\nInfrastructural Development,-52,38\nEducational Facilities,-25,15\nHealthcare Facilities,-35,10\nAgriculture,-40,25\nTechnological Advancement,-55,30\nEmployment Opportunities,-30,40\nTransportation,-45,35"
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "bcLXU3cs5xc6cSsm",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, in which sector has the most balanced development between the rural and urban areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the most balanced development, I need to compare the length of the purple and blue bars of each sector because their lengths correspond to the growth in the rural and urban areas, respectively. The sector where the lengths of the two bars are closest will have the most balanced development. For example, for the {Infrastructural Development sector}[{\"Rural Growth\"}[0.308, 0.058, 0.653, 0.159]; {\"Urban Growth\"}[0.653, 0.058, 0.905, 0.159]], the difference between the two bars is large, so the development is not balanced. The comparison process continues for all sectors until I find the sector with the most balanced development.\nBased on the above thought, the answer is:\nHealthcare Facilities. Because in this sector, the growth percentages of the rural area and the urban area are the closest to each other."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "s92VuIeC9ZAP3V25",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_69_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which factor, the growth difference between rural area and urban area is the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the differences between rural and urban developments, I'll find the absolute value differences between the corresponding bars in the two groups for each sector. Starting with Infrastructural Development at the bottom, the purple bar at {coordinates}[0.308, 0.058, 0.653, 0.159] spans roughly from -52 to 0 on the x-axis, while the corresponding blue bar at {coordinates}[0.653, 0.058, 0.905, 0.159] spans from 0 to 38. The difference is 90. Similarly, I'll compare the differences for all other factors. The sector with the smallest difference will be the one I'm looking for.\nBased on the above thought, the answer is:\nHealthcare Facilities"
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "c7GgibmGwFcugQkw",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data showing improvements in symptom scores across medical specialties into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon inspection of the bar chart, the y-axis is labeled with medical specialties such as {Cardiology}<r>[0.031, 0.426, 0.105, 0.440]</r>, {Neurology}<r>[0.034, 0.309, 0.105, 0.323]</r>, {Dermatology}<r>[0.015, 0.192, 0.105, 0.206]</r>, and {Urology}<r>[0.052, 0.075, 0.105, 0.089]</r>. Each specialty has bars representing different constraint levels, each with a unique color as specified in {the legend}<r>[0.117, 0.516, 0.309, 0.604]</r>. The four bars within each group correspond to improvements in scores for 3, 5, 7, and 10 constraint words respectively. The individual scores are inscribed above each colored bar, allowing for precise data extraction.\nBased on the above thought, the answer is:\nSpecialty,3 Constraint Words,5 Constraint Words,7 Constraint Words,10 Constraint Words\nCardiology,-0.9,-0.5,-2.1,-0.7\nNeurology,-0.2,0.1,-1.7,-0.3\nDermatology,-2.1,-1.4,-1.9,-1.2\nUrology,-0.7,-2.0,-2.2,-1.8"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "bjbMokx7XXed9vXA",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the effect of increasing constraint words on symptom improvement in Dermatology from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the effect of increasing constraint words on symptom improvement in the Dermatology field, I need to evaluate the lengths and positions of the bars related to {Dermatology}<r>[0.015, 0.192, 0.105, 0.206]</r>. Observing the decrease or increase in value inscribed at the top of each bar for 3, 5, 7, and 10 constraint words, which are -2.1, -1.4, -1.9, and -1.2 respectively. A further insight is gained by reading the positions of these inscriptions along the x-axis, which reflect the actual value changes.\nBased on the above thought, the answer is:\nThe improvement in symptom scores in Dermatology tends to fluctuate as the number of constraint words increases. While there is a general negative trend, showing improvement, the rate of improvement is inconsistent and even reverses between 5 and 7 constraint words, before improving again at 10 constraint words."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "EhO6hpqfKyJqVMHO",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many medical specialties showed improvement across all four constraint levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out how many specialties showed improvement across all four constraint levels, I need to examine the values above each bar for the specialties listed on the y-axis. The improvements in symptom scores are indicated by negative values, as positive values would indicate worsening conditions. For each medical specialty such as {Cardiology}<r>[0.031, 0.426, 0.105, 0.440]</r>, {Neurology}<r>[0.034, 0.309, 0.105, 0.323]</r>, {Dermatology}<r>[0.015, 0.192, 0.105, 0.206]</r>, and {Urology}<r>[0.052, 0.075, 0.105, 0.089]</r>, I observe the bar labels to check if all values are negative.\nBased on the above thought, the answer is:\nFour"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "KxyrRB5HNN6Bzatk",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data in the visualization into a CSV table, using the coordinates to determine the category and values for each bar?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart compares {Graphene Research}<r>[0.015, 0.347, 0.151, 0.364]</r> and {AI Technology}<r>[0.056, 0.155, 0.151, 0.172]</r> in four terms, which are {3-Year Average}<r>[0.208, 0.527, 0.311, 0.544]</r>, {5-Year Average}<r>[0.208, 0.553, 0.311, 0.570]</r>, {7-Year Average}<r>[0.208, 0.579, 0.311, 0.597]</r> and {10-Year Average}<r>[0.208, 0.605, 0.320, 0.623]</r> respectively. The value for each data point is represented by the x-coordinate of each bar since the x-axis starts at 0 and extends outwards negatively. By using the located start of each bar as well as the x-axis ticks, I can calculate the value for each data point and then compile all given information into a CSV table.\nBased on the above thought, the answer is:\nItem,3-Year Average,5-Year Average,7-Year Average,10-Year Average\nGraphene Research,-1.4,-0.9,-2.3,-2.7\nAI Technology,-0.9,-1.3,-2.0,-2.4"
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "7OV3aJ5GDJGPObpu",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of R&D budget change over different time periods for AI Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires examination of the four bars representing AI Technology. Observing from the {3-Year Average}<r>[0.764, 0.674, 0.974, 0.722]</r> to the {10-Year Average}<r>[0.413, 0.530, 0.974, 0.578]</r> bars in order, the bars lengthen as the time period increases. This suggests that the R&D budget for AI Technology is decreasing at a quicker rate over larger time frames. Therefore, the trend of R&D budget change over different time periods for AI Technology is that the longer the average period, the greater the decrease in budget.\nBased on the above thought, the answer is:\nThe longer the average period, the greater the decrease in the R&D budget for AI Technology."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "urwbRz8cNiXCSexc",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research field had the biggest decrease in R&D budget over the 10-year average?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which research field had a bigger budget decrease in the 10-year average, I can compare the length of their respective bars. {Graphene Research}<r>[0.343, 0.722, 0.974, 0.770]</r> has a bar reaching to about the -2.7 point on the x-axis, while the bar for {AI Technology}<r>[0.413, 0.530, 0.974, 0.578]</r> reaches to about the -2.4 point on the x-axis. Hence, the budget decrease for Graphene Research is larger than AI Technology over a 10-year average.\nBased on the above thought, the answer is:\nGraphene Research"
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "ayvb5xObPT0D4AkK",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data from the bar plots in csv format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can convert the raw data into a CSV format by structuring it into rows and columns, with each row representing a location (Urban or Rural) and each column representing a trend. In this case, the first column would contain the regions, and the following columns would contain the scores for the trends of Gradual Recovery, Sporadic Growth, Marginal Decrease, and Gradual Downturn respectively. The score for each trend and each location can be obtained from {the length of the corresponding bars}[{Gradual Recovery Urban bar}[0.523, 0.871, 0.690, 0.914]; {Gradual Recovery Rural bar}[0.523, 0.658, 0.653, 0.701]; {Sporadic Growth Urban bar}[0.523, 0.829, 0.782, 0.871]; {Sporadic Growth Rural bar}[0.523, 0.616, 0.763, 0.658]; {Marginal Decrease Urban bar}[0.450, 0.786, 0.523, 0.829]; {Marginal Decrease Rural bar}[0.394, 0.573, 0.523, 0.616]; {Gradual Downturn Urban bar}[0.154, 0.743, 0.523, 0.786]; {Gradual Downturn Rural bar}[0.191, 0.530, 0.523, 0.573]]. By looking at the x-axis ticks, I can see that they range from -2.0 to 2.0, which represents the score. I can estimate the score of each bar by comparing its length to this scale. For instance, the Urban bar for Gradual Recovery extends from 0.0 up to approximately 0.9 on the axis, indicating a score of 0.9. A similar process can be followed for every bar to extract the scores for all trends and regions, and these scores can then be structured into the CSV format.\nBased on the above thought, the answer is:\nRegion,Gradual Recovery,Sporadic Growth,Marginal Decrease,Gradual Downturn\nUrban,0.9,1.4,-0.4,-2\nRural,0.7,1.3,-0.7,-1.8"
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "Q1yWUKOR1vfR05tq",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar plots, in which trend is the development difference between Urban and Rural regions the least noticeable?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the trend with the smallest development difference between Urban and Rural regions, I need to compare the lengths of the Urban and Rural bars for each trend. First, looking to {the subplot of Gradual Recovery}[{\"Urban\"}[0.523, 0.871, 0.690, 0.914], {\"Rural\"}[0.523, 0.658, 0.653, 0.701]], I can see that the length difference is about 0.2. Similarly, considering {the subplot of Sporadic Growth}[{\"Urban\"}[0.523, 0.829, 0.782, 0.871], {\"Rural\"}[0.523, 0.616, 0.763, 0.658]], the difference is roughly 0.1. Checking {the Marginal Decrease subplot}[{\"Urban\"}[0.450, 0.786, 0.523, 0.829], {\"Rural\"}[0.394, 0.573, 0.523, 0.616]], the difference is approximately 0.3, and finally, for {the Gradual Downturn subplot}[{\"Urban\"}[0.154, 0.743, 0.523, 0.786], {\"Rural\"}[0.191, 0.530, 0.523, 0.573]], the difference is around 0.2. Through these comparisons, it is evident that the Sporadic Growth trend has the smallest development difference between the Urban and Rural regions.\nBased on the above thought, the answer is:\nSporadic Growth. Because the difference in development scores between Urban and Rural regions is the least in the Sporadic Growth trend."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "we5h0FtiiTwxuQyo",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat score did the Rural region get in the Sporadic Growth trend according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the Sporadic Growth trend in the Rural region, I need to observe the corresponding bar, which is {the green bar under Rural on subplot_1}[0.523, 0.616, 0.763, 0.658]. By looking at the x-axis marks from {0.0}[0.523, 0.939, 0.534, 0.957] to {1.5}[0.789, 0.939, 0.811, 0.957], I can see that the length of the bar shows that the score is about 1.3, which happens to be where the endpoint of the Rural Sporadic Growth green bar exists.\nBased on the above thought, the answer is:\n1.3"
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "auCi8JeFfAh8G60h",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table that captures the emerging technology trends according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data visually represented in the chart, there are five different technologies specified on {the y-axis}[{\"AI\"}[0.122, 0.462, 0.136, 0.479], {\"Blockchain\"}[0.060, 0.356, 0.136, 0.374], {\"Cloud Computing\"}[0.015, 0.251, 0.136, 0.268], {\"5G\"}[0.116, 0.145, 0.136, 0.163], {\"IoT\"}[0.115, 0.040, 0.136, 0.057]]. Each technology has two bars representing two different years, {2020}[{\"Trend_2020\"}[0.892, 0.527, 0.972, 0.544]] and {2021}[{\"Trend_2021\"}[0.892, 0.553, 0.973, 0.571]], as specified in {the legend}[0.848, 0.520, 0.978, 0.578].\nI can measure the lengths of each bar to determine their corresponding values. For instance, for AI, {the 2020 bar}[0.563, 0.885, 0.647, 0.914] extends from the centerline to about 0.8 on the {x-axis}[0.563], and {the 2021 bar}[0.563, 0.857, 0.932, 0.885] extends even further to about 3.5. By similarly measuring the lengths of the bars for the remaining technologies, I can generate the CSV table.\nBased on the above thought, the answer is:\nTechnology,Trend_2020,Trend_2021\nAI,0.8,3.5\nBlockchain,-2.5,1.4\nCloud Computing,2.8,3.7\n5G,0.8,3.3\nIoT,-3.0,0.8"
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "LFOY6WzTWjbWZNeC",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emerging technology, based on the bar chart, witnessed the most significant improvement from 2020 to 2021 and why do you come to this conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which technology witnessed the most substantial improvement from {2020}[{\"Trend_2020\"}[0.892, 0.527, 0.972, 0.544]] to {2021}[{\"Trend_2021\"}[0.892, 0.553, 0.973, 0.571]], I need to look at the changes in bar lengths for each technology from 2020 to 2021. Specifically, I can compare the difference in lengths of the bars representing 2020 and 2021 for each technology. This will help me determine which technology had the biggest jump.\nFor instance, {the AI bar}[0.563, 0.885, 0.647, 0.914] seemed to have one of the longest lengths, corresponding to a value of 0.8 in 2020, and increased to a length symbolizing a value of 3.5 in 2021. The change from 0.8 to 3.5 is significant, amounting to a difference of 2.7. Now, I can compare this difference to those of the other technologies to determine which technology saw the greatest improvement. Based on the analysis, I can conclude that the {IoT bar}[[\"Trend_2020 bar\"}[0.246, 0.559, 0.563, 0.587], {\"Trend_2021 bar\"}[0.563, 0.530, 0.647, 0.559]] has the biggest difference, from -3 in 2020 to 0.8 in 2021, with a difference of 3.8, which is the highest among all technologies.\nBased on the above thought, the answer is:\nIoT. Since it has the most substantial improvement in trend scores, from -3 in 2020 to 0.8 in 2021, a net positive change of 3.8."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "hunAwfceq1b3VWcr",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology had the greatest trend score in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the technology with the greatest trend score in 2021, I must compare the lengths of {the 2021 bars}[{\"AI\"}[0.563, 0.857, 0.932, 0.885], {\"Blockchain\"}[0.563, 0.775, 0.710, 0.804], {\"Cloud Computing\"}[0.563, 0.694, 0.953, 0.722], {\"5G\"}[0.563, 0.612, 0.911, 0.641], {\"IoT\"}[0.563, 0.530, 0.647, 0.559]]. Upon examination, it's clear that {the Cloud Computing bar}[0.563, 0.694, 0.953, 0.722] extends to the furthest point on the {x-axis}[0.932], indicating that Cloud Computing had the greatest trend score in 2021.\nBased on the above thought, the answer is:\nCloud Computing"
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "9tshltoHr10RdMKx",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a structured table capturing the data in the bar chart on the trends in urban and rural development?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title in the subplot {Rural and Urban Development Comparison}<r>[0.392, 0.019, 0.749, 0.041]</r>, I can identify that key categories are Urban Development and Rural Development from the subplot's legend. Each of these categories has four different trends: {Steep Decline}<r>[0.066, 0.423, 0.163, 0.440]</r>, {Insignificant Increase}<r>[0.015, 0.310, 0.163, 0.327]</r>, {Sudden Decrease}<r>[0.041, 0.197, 0.163, 0.214]</r>, and {Persistent Decline}<r>[0.038, 0.084, 0.163, 0.101]</r>. The x-axis labels from {\u22123.5}<r>[0.252, 0.463, 0.285, 0.480]</r> to {0.0}<r>[0.963, 0.463, 0.985, 0.480]</r> represent score values for each trend. For Urban Development the score values for various trends can be inferred by the length of bars and their endpoints' positions. For instance, {the bar}<r>[0.510, 0.421, 0.974, 0.438]</r> for Steep Decline indicates a score of -2.3, while {the bar}<r>[0.631, 0.308, 0.974, 0.325]</r> for Insignificant Increase indicates a score of -1.7. Similar steps are followed for assessing values under Rural Development.\nBased on the above thought, the answer is:\nCategory,Trend,Score\nUrban Development,Steep Decline,-2.3\nUrban Development,Insignificant Increase,-1.7\nUrban Development,Sudden Decrease,-3.0\nUrban Development,Persistent Decline,-2.5\nRural Development,Insignificant Increase,-2.5\nRural Development,Steep Decline,-2.0\nRural Development,Sudden Decrease,-1.8\nRural Development,Persistent Decline,-2.3"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "E79nCQyi7kPaZrsO",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, do the trends of urban or rural development appear to be more progressive or improving over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar lengths extending from 0 towards -3.5 indicate the intensity of each trend. If the bar is closer to the 0 mark, the trend is less steep. The terms 'steep decline', 'insignificant increase', 'sudden decrease', and 'persistent decline' indicate negative trends. The bars for {Urban Development}<r>[0.510, 0.421, 0.974, 0.438]; [0.631, 0.308, 0.974, 0.325]; [0.369, 0.195, 0.974, 0.212]; [0.470, 0.082, 0.974, 0.099]</r> and {Rural Development}<r>[0.571, 0.404, 0.974, 0.421]; [0.470, 0.291, 0.974, 0.308]; [0.611, 0.178, 0.974, 0.195]; [0.510, 0.065, 0.974, 0.082]</r> point to the left (negative x direction), indicating a decline or decrease. By comparing the bar lengths, I can see that the bars associated with Rural Development appear closer to 0, indicating lower intensity of negativity as opposed to Urban Development.\nBased on the above thought, the answer is:\nRural Development appears to be more progressive or improving over time as compared to Urban Development, as its bar lengths indicate lower levels of negative impacts."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "EAcCOAASp37ODaeQ",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich development trend has the least negative impact or highest score for Urban development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the bar lengths for various trends under Urban Development from the sub-legend, we can observe that the least negative or highest score is when the bar is closer to the 0 mark on the x-axis. We can see that {the bar}<r>[0.631, 0.308, 0.974, 0.325]</r> for \"Insignificant Increase\" stops at the most rightward point towards zero among all trends.\nBased on the above thought, the answer is:\nInsignificant Increase"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "d3bS88xmglM4amSJ",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease transform the bar chart data on societal change into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows the impacts of several trends on two categories of societal change. The x-axis represents the effect from {\u22123.5}[0.261, 0.939, 0.294, 0.957] to {0.0}[0.963, 0.939, 0.985, 0.957] and the y-axis lists the categories: {Social Cultural Change}[0.015, 0.818, 0.173, 0.836] and {Value Change}[0.077, 0.598, 0.173, 0.616]. The lengths and labels on the bars in each category represent the impact of four different growth trends ({Erratic Growth}[0.229, 0.513, 0.330, 0.530], {Marginal Growth}[0.229, 0.539, 0.344, 0.556], {Moderate Decline}[0.229, 0.565, 0.352, 0.583], and {Sporadic Growth}[0.229, 0.591, 0.345, 0.609]). To create the CSV table, I'll take each combination of category and trend, then use the label on the bar itself to find the numerical impact value. For instance, the bar for {Erratic Growth in Social Cultural Change}[0.337, 0.869, 0.974, 0.913] has a label \"{-3.2}\"[0.324, 0.883, 0.351, 0.900], which is the impact level.\nBased on the above thought, the answer is:\nCategory,Erratic Growth,Marginal Growth,Moderate Decline,Sporadic Growth\nSocial Cultural Change,-3.2,-1.4,-0.8,-2.7\nValue Change,-2.9,-2.1,-1.6,-2.3"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "LnK41vrqeeKqpPsp",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the impact of erratic growth compare to moderate decline on social cultural change and value change according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires comparison of impact values of erratic growth and moderate decline on both categories. For the 'Social Cultural Change' category, {erratic growth}[0.324, 0.883, 0.351, 0.900] has the impact of -3.2 and {moderate decline}[0.383, 0.662, 0.410, 0.680] gives an impact of -0.8. Comparatively, erratic growth leads to a more adverse effect than moderate decline. The same must be done for the 'Value Change' category. For {erratic growth}[0.682, 0.839, 0.709, 0.856] the impact is -2.9, whereas {moderate decline}[0.543, 0.618, 0.570, 0.636] leads to -1.6, again indicating a worse effect of erratic growth.\nBased on the above thought, the answer is:\nThe impact of erratic growth on both 'Social Cultural Change' and 'Value Change' is more adverse than moderate decline. Specifically, for 'Social Cultural Change', erratic growth scores -3.2 while moderate decline scores -0.8. On 'Value Change', erratic growth scores -2.9, and moderate decline generates a score of -1.6. Therefore, erratic growth has a more significant negative impact in both categories compared to the moderate decline."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "pPkIIz03ewouiVSE",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factor has the most marginal growth on 'Value Change'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this task, I need to find the bar representing marginal growth in the 'Value Change' category and extract the value from this bar. The suitable bar is located at coordinates [0.556, 0.605, 0.974, 0.649] and the label of the bar {-2.1}[0.423, 0.750, 0.450, 0.768] gives away the value of marginal growth on 'Value Change'. The value is negative which represents the negative impact but since the scale ranges from -3.5 to -0.5, -2.1 represents a moderate level of impact.\nBased on the above thought, the answer is:\n-2.1"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "Reo8SL0gsn71L47Q",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, transform the data detailing the Critical and Recovered Cases for Various Diseases."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the title}<r>[0.144, 0.019, 0.560, 0.041]</r> of the bar chart, I can infer that the chart displays the measurements of critical and recovered cases of various diseases. Each disease is represented as a separate bar on the y-axis, with the corresponding bar length on the x-axis representing the respective score of each disease. Looking at {the labels on x-axis for the lower chart}<r>[\"\u22123.5\", \"\u22123.0\", \"\u22122.5\", \"\u22122.0\", \"\u22121.5\", \"\u22121.0\", \"\u22120.5\", \"0.0\"]</r>, the x-axis ranges from -3.5 to 0.0, representing the score of recovered cases. Similarly, the bars on the upper chart represent critical cases, with the same range on the x-axis. The diseases represented on the y-axis include Heart Stroke, Influenza, COPD, and Diabetes {y-axis labels}<r>[0.015, 0.409, 0.102, 0.427], [0.039, 0.300, 0.102, 0.317], [0.063, 0.190, 0.102, 0.207], [0.041, 0.557, 0.102, 0.575]</r>. By observing the length of each bar and the corresponding label, I can convert these values into a CSV format table.\nBased on the above thought, the answer is:\nType,Critical Cases,Recovered Cases\nHeart Stroke,-1.8,-0.5\nInfluenza,2.0,-2.3\nCOPD,-3.0,-1.2\nDiabetes,1.5,-1.5"
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "f098FT8HgP0JscA9",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you infer which disease is experiencing the worst situation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer from the chart, we need to analyze both {the Critical Cases bar chart}<r>[0.107, 0.523, 0.597, 0.933]</r> and {the Recovered Cases bar chart}<r>[0.107, 0.047, 0.597, 0.457]</r>. For a disease to be in the worst situation, it would need to have a high number of critical cases and a low number of recovered cases. From the upper chart, {the COPD bar}<r>[0.230, 0.175, 0.597, 0.219]</r> is the longest, which implies that it has the highest critical cases. Meanwhile, looking at the lower chart, {the COPD bar}<r>[0.450, 0.652, 0.597, 0.695]</r> is not the shortest but has a significantly low score, which means the number of recovered cases is low. Therefore, among the four diseases, COPD can be considered experiencing the worst situation.\nBased on the above thought, the answer is:\nCOPD. As it has the highest number of critical cases and relatively low number of recovered cases."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "VSM2elRjdkeLZTIa",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest score for the Recovered Cases in the four diseases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the lowest score for the recovered diseases, I need to look at the second chart where recovered cases are plotted. Thus, considering {the bars}<r>[0.536, 0.871, 0.597, 0.915], [0.316, 0.761, 0.597, 0.805], [0.450, 0.652, 0.597, 0.695], [0.414, 0.542, 0.597, 0.586]</r> of each disease from the second chart, the score for each recovered case can be calculated by the position of the end of the bars on the x-axis, comparing the lengths. The lowest score is represented by the shortest bar, which corresponds to the disease Influenza {shortest bar}<r>[0.316, 0.761, 0.597, 0.805]</r>.\nBased on the above thought, the answer is:\n-2.3 for Influenza."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "6vGgywqFRjj3LkBB",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values from this bar chart and represent them in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart features two subplots, one labeled {Ratings}<r>[0.498, 0.019, 0.561, 0.041]</r> and the other labeled {Interest}<r>[0.497, 0.509, 0.562, 0.532]</r>. Each subplot has two bars representing the cuisines Japanese and Italian. For the first subplot, {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] extends to roughly the 9.5 position on the x-axis and {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] extends to approximately 8.2. Similarly, in the Interest subplot, {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] extends to about 6.2 on the x-axis and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] extends to approximately 4.9. Hence, the CSV representation will be as shown below.\nBased on the above thought, the answer is:\nCuisine,Rating,Interest\nJapanese,9.5,6.2\nItalian,8.2,4.9"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "4saQ58vifPq1mZqD",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the values presented in the chart, which cuisine would you suggest to someone more interested in high-rated and popular food?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to interpret the chart's information on the popularity and ratings of both cuisines. The popularity can be gleaned from the Interest values represented by {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] which roughly equals 6.2 and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] which is approximately 4.9. The Rating values can be understood from {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] whose value is 9.5 and {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] which is roughly 8.2. Combining, these data reveals that Japanese cuisine has a higher Rating and Interest value compared to Italian cuisine, signifying that it is more popular and higher-rated.\nBased on the above thought, the answer is:\nJapanese, because it has higher rating and interest, indicating it is better-rated and of more interest to people."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "p3b37S91MEUb36EB",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has a higher overall score when the scores for both Rating and Interest are combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score for each cuisine is the sum of its rating and interest values. For Japanese cuisine, this includes {the Rating bar for Japanese}[0.083, 0.265, 0.932, 0.425] which is around 9.5 and {the Interest bar for Japanese}[0.083, 0.755, 0.637, 0.915] which extends to approximately 6.2. For Italian cuisine, the values to calculate are from {the Rating bar for Italian}[0.083, 0.065, 0.815, 0.225] which is roughly 8.2 and {the Interest bar for Italian}[0.083, 0.555, 0.521, 0.715] which extends to approximately 4.9. By adding these together, I can conclude which cuisine has the highest combined score.\nBased on the above thought, the answer is:\nJapanese"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "v7yBLhNsWtPHmpjz",
+        "Chart_type": "bar",
+        "QA_type": "Chart2table",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data of both rows in the bar chart regarding local government and community development into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the ylabel at the left side of the chart {\"Governance Structure\"}[0.054, 0.417, 0.206, 0.435], {\"Population Engagement\"}[0.040, 0.348, 0.206, 0.366], {\"Decentralization Measures\"}[0.022, 0.279, 0.206, 0.297], {\"Public Service Provision\"}[0.043, 0.210, 0.206, 0.228], {\"Local Government Stronger\"}[0.015, 0.141, 0.206, 0.159], {\"Community Insights Better\"}[0.020, 0.072, 0.206, 0.090], I can see that the y-axis represent different categories. The x-axis labels represent the corresponding scores, ranging from -3.5 to 0. The lengths of the bars represent the scores for each category. For the first row, {the orange bars}<{\"Row1 bars\"}[{'bar1'}[0.974, 0.410, 1.241, 0.438];{'bar2'}[0.974, 0.341, 1.413, 0.369];{'bar3'}[0.974, 0.272, 1.069, 0.300];{'bar4'}[0.974, 0.203, 1.203, 0.231];{'bar5'}[0.898, 0.134, 0.974, 0.162];{'bar6'}[0.764, 0.065, 0.974, 0.093]]> indicate the scores for category in the first row. The scores for the category in the second row are given by the lengths of {the green bars}<{\"Row2 bars\"}[{'bar1'}[0.593, 0.887, 0.812, 0.901];{'bar2'}[0.593, 0.818, 0.879, 0.832];{'bar3'}[0.593, 0.749, 0.745, 0.763];{'bar4'}[0.593, 0.680, 0.679, 0.694];{'bar5'}[0.497, 0.611, 0.593, 0.625];{'bar6'}[0.469, 0.542, 0.593, 0.556]]> of the second plot. By mapping the right ends of the bars to the x-axis values, I can retrieve the scores for each category.\nBased on the above thought, the answer is:\nCategory,Row1,Row2\nGovernance Structure,1.4,2.3\nPopulation Engagement,2.3,3.0\nDecentralization Measures,0.5,1.6\nPublic Service Provision,1.2,0.9\nLocal Government Stronger,-0.4,-1.0\nCommunity Insights Better,-1.1,-1.3"
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "C7fzwEhL2YSNCCCt",
+        "Chart_type": "bar",
+        "QA_type": "Open QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, do you believe that the local government initiatives in Row 2 are more beneficial to the community development in comparison with the initiatives in Row 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires a comprehensive analysis of the comparisons of {each pair of orange and green bars}<{\"Row1 and Row2 bars\"}[{'pair1'}[0.974, 0.410, 1.241, 0.438, 0.593, 0.887, 0.812, 0.901];{'pair2'}[0.974, 0.341, 1.413, 0.369, 0.593, 0.818, 0.879, 0.832];{'pair3'}[0.974, 0.272, 1.069, 0.300, 0.593, 0.749, 0.745, 0.763];{'pair4'}[0.974, 0.203, 1.203, 0.231, 0.593, 0.680, 0.679, 0.694];{'pair5'}[0.898, 0.134, 0.974, 0.162, 0.497, 0.611, 0.593, 0.625];{'pair6'}[0.764, 0.065, 0.974, 0.093, 0.469, 0.542, 0.593, 0.556]]>. Notably, the green bar of 'Decentralization Measures' {the third bar of Row2}[0.593, 0.749, 0.745, 0.763] extends far beyond the corresponding orange bar of Row1 {the third bar of Row1}[0.974, 0.272, 1.069, 0.300]. 'Population Engagement' {the second bar of Row2}[0.593, 0.818, 0.879, 0.832] and 'Public Service Provision' {the fourth bar of Row2}[0.593, 0.680, 0.679, 0.694] also shows greater values for Row2 as compared to Row1. While 'Local Government Stronger' and 'Community Insights Better' have seen some declines, as shown by their shorter bars in Row2 {the fifth and sixth bars of Row2}[0.497, 0.611, 0.593, 0.625, 0.469, 0.542, 0.593, 0.556] as compared to the corresponding bars in Row1 {the fifth and sixth bars of Row1}[0.898, 0.134, 0.974, 0.162, 0.764, 0.065, 0.974, 0.093], these declines seem to be not as drastic as the improvements.\nBased on the above thought, the answer is:\nYes. Although there are some declines in the metrics 'Local Government Stronger' and 'Community Insights Better', the overall initiatives in Row 2 seem to show more noticeable improvements, especially in the 'Decentralization Measures', 'Population Engagement', and 'Public Service Provision' categories, which could be significant for community development."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "08c53UA90vxlx25q",
+        "Chart_type": "bar",
+        "QA_type": "Numerical QA",
+        "image": "png_bar/bar_71_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric has seen the most improvement from Row 1 to Row 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the metric that has seen the most improvement from Row1 to Row2, I need to compare the scores of each metric for both rows. {The orange bars}<{\"Row1 bars\"}[{'bar1'}[0.974, 0.410, 1.241, 0.438];{'bar2'}[0.974, 0.341, 1.413, 0.369];{'bar3'}[0.974, 0.272, 1.069, 0.300];{'bar4'}[0.974, 0.203, 1.203, 0.231];{'bar5'}[0.898, 0.134, 0.974, 0.162];{'bar6'}[0.764, 0.065, 0.974, 0.093]]> represent the scores of metrics for Row1 while {the green bars}<{\"Row2 bars\"}[{'bar1'}[0.593, 0.887, 0.812, 0.901];{'bar2'}[0.593, 0.818, 0.879, 0.832];{'bar3'}[0.593, 0.749, 0.745, 0.763];{'bar4'}[0.593, 0.680, 0.679, 0.694];{'bar5'}[0.497, 0.611, 0.593, 0.625];{'bar6'}[0.469, 0.542, 0.593, 0.556]]> represent the scores of metrics for Row2. By subtracting the right end of the orange bar from the right end of the green bar, I get improvement (or decline) in score from Row1 to Row2 for each metric. After comparing all metrics, I find that \"Decentralization Measures\" has seen the most improvement as this metric's bar in Row2 {the third green bar}[0.593, 0.749, 0.745, 0.763] extends far beyond that in Row1 {the third orange bar}[0.974, 0.272, 1.069, 0.300].\nBased on the above thought, the answer is:\nDecentralization Measures."
+            }
+        ],
+        "id_raw": 1064
+    }
+]
\ No newline at end of file
diff --git a/data/final_0721_line.json b/data/final_0721_line.json
new file mode 100644
index 0000000000000000000000000000000000000000..2bfcf63fadff9fc2d45bcec9372e9a20238e382d
--- /dev/null
+++ b/data/final_0721_line.json
@@ -0,0 +1,200075 @@
+[
+    {
+        "id": "YoEVLhBBsQnvRmqe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.015, 0.372, 0.034, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "nKaPh882qRLM1TAh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.753, 0.478, 0.985, 0.664]"
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "jhYs4EIv5Qmu1CmD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.385, 0.943, 0.426, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "FCrJryXykNggHxvL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Rural_Population'}[[0.067, 0.705, 0.744, 0.791]] above or below {the line for 'Urban_Infrastructure'}[[0.067, 0.206, 0.744, 0.550]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "vi44sq2rPQT20isX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFind the coordinates for the point for 'Rural_Infrastructure' and 'Urban_Infrastructure' in Year 2001. Which of the two is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Rural_Infrastructure}[0.135, 0.705], {Urban_Infrastructure}[0.135, 0.533]. Rural_Infrastructure is higher."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "cBq48VAVGoyu1n8D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Rural_Population' in 2010}[0.744, 0.791] above or below {the point for 'Urban_Population' in 2010}[0.744, 0.413]"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "wm93ABskFnsYGE1a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found in [0.753, 0.478, 0.985, 0.664]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Rural_Population}[0.806, 0.489, 0.944, 0.520], {Urban_Population}[0.806, 0.534, 0.952, 0.564], {Rural_Infrastructure}[0.806, 0.578, 0.971, 0.608], and {Urban_Infrastructure}[0.806, 0.622, 0.978, 0.653], each color-coded."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "BaqN815lNCSpP4Yp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located in the region [0.067, 0.034, 0.744, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four overlapping lines, each representing a data series (Rural_Population, Urban_Population, Rural_Infrastructure, Urban_Infrastructure), and the associated data points plotted on the chart."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "fv3QkGrMFsfyoUXc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of the region between coordinates [0.067, 0.722] and [0.744, 0.791]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists mainly of the line named 'Rural_Population' plotted using blue stars showing changes from Year 2000 to 2010."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "bpoWm8DU6iXpwaCc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.144, 0.034, 0.785]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Use and Resource Consumption."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "ZUy2O1SDWmrR58aK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the line representing 'Renewable Energy' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.175, 0.161, 0.911, 0.893]"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "6wTKhghzajWbkReN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point}<r>[0.911, 0.161]</r> stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data of 'Renewable Energy' in 2021."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "W9kyPYjJYzN34JHi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Coal'}<r>[0.175, 0.537, 0.911, 0.624]</r> predominantly above or below {the line for 'Oil'}<r>[0.175, 0.467, 0.911, 0.628]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "6FZRJzdq520RCR3K",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Renewable Energy'}<r>[0.175, 0.161, 0.911, 0.893]</r> have a higher or lower ending point compared to {the line for 'Nuclear Energy'}<r>[0.175, 0.686, 0.911, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "WeiDIds9vbW3ZHaX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 1971, is the use of {Natural Gas}<r>[0.175, 0.679]</r> more or less than {Oil}<r>[0.175, 0.628]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "ysV7GvWaJYuE4XRE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.110, 0.048, 0.325, 0.320]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the chart's legend, listing the different energy sources: Coal, Oil, Natural Gas, Nuclear Energy, Hydro Power, Renewable Energy \u2013 each with distinct markers."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "P3LKfGmp5Rb7Bji9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.523, 0.943, 0.564, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label 'Year' for the x-axis."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "b6ikWZgv9mDQv79I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region <r>[0.894, 0.911, 0.929, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an x-axis tick label '2021' within this region."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "QESVi2NFqZdjeoMq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.082, 0.377, 0.098, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label, which represents 'Observation Value'."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "QqsrgTfMVv9nCyQE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the x-axis tick label present at the coordinates [0.287, 0.904, 0.316, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label at this coordinate is '2002'."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "IBSQWxnUDXfRlTjR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle mark in the legend, representing 'Consistent Expansion'}<r>[0.134, 0.147, 0.169, 0.159]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle mark color is #1f77b4."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "k8DAWcR7qaiiXArF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Consistent Expansion'}<r>[0.160, 0.237, 0.865, 0.649]</r> have a higher or lower starting point compared to {the line for 'Gradual Recovery'}<r>[0.160, 0.155, 0.865, 0.546]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "3tJwzglTRB25I53R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line ends at a higher value in 2010, {Fluctuating Growth}<r>[0.160, 0.155, 0.865, 0.608]</r> or {Slight Decrease}<r>[0.160, 0.505, 0.865, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fluctuating Growth."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "Y5Jeld10uYGDSzra",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Gradual Recovery'}<r>[0.160, 0.155, 0.865, 0.546]</r> above or below {the line for 'Fluctuating Growth'}<r>[0.160, 0.155, 0.865, 0.608]</r> at 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "179sCRZtFz9Jkseb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the region [0.132, 0.132, 0.331, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart, containing handles and labels for 'Consistent Expansion', 'Gradual Recovery', 'Fluctuating Growth', and 'Slight Decrease', each associated with a colored handle."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "CNQBw71MhWyfEaen",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the contents located at these coordinates [0.495, 0.932, 0.530, 0.959]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label, which displays 'Year'."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "EcIOL3GFrVoQPG4b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region between [0.1, 0.127, 0.3, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '40' is located here, at {coordinates}<r>[0.102, 0.127, 0.117, 0.147]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "agDPI4XdtcT6WWK5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.744, 0.533, 0.909, 0.563]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend item \"Water Quality Index\"."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "CkA9KuhhQxUKNpBO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {the x-coordinate}[0.880, 0.911, 0.897, 0.934] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "P7LCCsGV2kdyzFun",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the \"Natural Areas %\" line}<r>[0.083, 1.562, 0.888, 1.606]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "U4ZyHNyaqOT7Ls5T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Air Pollution Particles Index'}[0.083, 0.034, 0.888, 0.799] higher or lower than {the 'Natural Areas %'}[0.083, 1.562, 0.888, 1.606] at x=60?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "O4e1R00t9EGQ3svA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the \"Life Expectancy at Birth\" values}<r>[0.083, 0.241, 0.888, 0.262]</r> generally increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "1VQPUkntSFuQKX5J",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is represented by the {color \"#1f77b4\"}<r>[0.692, 0.494, 0.736, 0.512]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Expectancy at Birth."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "1XtQsyqFxB7cjVWh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.691, 0.478, 0.977, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It includes four indicators: \"Life Expectancy at Birth\", \"Water Quality Index\", \"Air Pollution Particles Index\", and \"Natural Areas %\". Each indicator is represented with a unique color and marker."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "5pnDEy2I7Z977sgw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.040, 0.121, 0.057, 0.144]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a y-axis label '85'."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "dP2uxMx9SetlbHJ4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.244, 0.187, 0.888, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains points from the Water Quality Index line with values for x=20 to x=60."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "T7FSF9J8UGQHgMuS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is expressed by the object with coordinates [0.454, 0.943, 0.496, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "SJeHBmb7BB8Cs4sj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the second Y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.049, 0.790, 0.092, 0.813]"
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "kh0N1Dav7x7Da3xd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the second line graph}<r>[0.177, 0.536, 0.773, 0.632]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Asia."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "ZQe3usqvrDAo4x1q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at year 2002 of the Africa line graph} [0.326, 0.818] higher or lower than {the corresponding point at year 2002 on the Asia line graph} [0.326, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "uQk76FkZHMXoZHZZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Africa'} [[0.177, 0.823], [0.251, 0.822] , [0.326, 0.818], [0.400, 0.816], [0.475, 0.808], [0.550, 0.804], [0.624, 0.794], [0.699, 0.785], [0.773, 0.795]] generally above or below {the line for 'America'} [[0.177, 0.751], [0.251, 0.747] , [0.326, 0.785], [0.400, 0.761], [0.475, 0.751], [0.550, 0.665], [0.624, 0.646], [0.699, 0.574], [0.773, 0.636]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "d0Alx6l43vdou5mW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2007, which of the continents had a higher market value, Africa or America?"
+            },
+            {
+                "from": "gpt",
+                "value": "America."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "rU9LD1iuVx5ekkhV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.856, 0.478, 0.985, 0.662]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, with labels for the line graphs of 'Global', 'Asia', 'Africa', and 'America'. Each of these labels has a colour-coded symbol associated with it."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "tUdgz72LzyGeqdy3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area [0.049, 0.694, 0.092, 0.718] of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis tick label '40000' is located in this area."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "aQHei6oiEHHlL4yK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region denoted by [0.159, 0.911, 0.194, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first x-axis tick label '2000'."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "V51zcLMiiBCfdMEY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.028, 0.279, 0.047, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is a Y-axis tick label \"120\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "hb3D5JE8q4ppas1U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"Year\" based on its positioning {at}<r>[0.507, 0.951, 0.536, 0.979]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "9389IjQxCz6pC6ww",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the first line plotted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the first line plotted is #1f77b4 which was for {New Drug Approvals}<r>[0.119, 0.754, 0.924, 0.832]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "5kbL88nCwqChrvaF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line represented by the markers 'o' and 'x' is on the top at the x-axis label 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represented by the marker 'o', {New Drug Approvals}<r>[0.441, 0.806]</r>, is on top of the line represented by the marker 'x', {Patents Filed}<r>[0.441, -4.023]</r>, at the x-axis label 2010."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "Vu1MaV4uEiTgzLaD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest point on the line representing Biotech Investment {coordinates}<r>[0.119, 0.102, 0.924, 0.753]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest point on the line is at {2000}<r>[0.119, 0.753]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "nTHZkYIgTTqHj8Xr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the New Drug Approvals line}<r>[0.119, 0.754, 0.924, 0.832]</r> above or below the {Biotech Investment line}<r>[0.119, 0.102, 0.924, 0.753]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "8KWCYKBBnjSFADyr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.060, 0.033, 0.313, 0.191]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. The items listed include {New Drug Approvals}<r>[0.098, 0.043, 0.220, 0.069]</r>, {Patents Filed}<r>[0.098, 0.080, 0.174, 0.106]</r>, {Market Size}<r>[0.098, 0.118, 0.261, 0.144]</r>, and {Biotech Investment}<r>[0.098, 0.156, 0.308, 0.181]</r>. Each of these items is associated with a unique color and marker."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "vGv3hUc2NEdfvmVI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the coordinates [0.106, 0.923, 0.937, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains tick labels of the x-axis for the years 2000 at {coordinates}<r>[0.106, 0.923, 0.131, 0.943]</r>, 2005 at {coordinates}<r>[0.267, 0.923, 0.292, 0.943]</r>, 2010 at {coordinates}<r>[0.428, 0.923, 0.453, 0.943]</r>, 2015 at {coordinates}<r>[0.589, 0.923, 0.614, 0.943]</r>, 2020 at {coordinates}<r>[0.750, 0.923, 0.775, 0.943]</r>, and 2025 at {coordinates}<r>[0.912, 0.923, 0.937, 0.943]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "Sv1wCf7IQF3jgA4g",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you portray the details seen within the coordinates [0.098, 0.043, 0.308, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangular region encapsulates all the text labels attached to the legend handles. These labels include {New Drug Approvals}<r>[0.098, 0.043, 0.220, 0.069]</r>, {Patents Filed}<r>[0.098, 0.080, 0.174, 0.106]</r>, {Market Size}<r>[0.098, 0.118, 0.261, 0.144]</r>, and {Biotech Investment}<r>[0.098, 0.156, 0.308, 0.181]</r>."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "xsnWuSGWyM87dFJr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the line labeled 'Modern'}<r>[0.130, 0.158, 0.942, 0.381]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#008000."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "2l3fwn4YQSpSdtdI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value associated with the second tick on {the y-axis}<r>[0.015, 0.394, 0.034, 0.539]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "$10^{-5}$."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "kxGDmy6Jv8Z81XyS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the point coordinates} at <r>[0.444, 0.299]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point on the line labeled 'Modern'."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "l4IL0ib2JJuRrv2v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the traditional line point at '30'}<r>[0.628, 0.470]</r> higher or lower than {the modern line point at '30'}<r>[0.628, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "VIizc0z75iskI0ds",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line begins at a higher position on the chart, {traditional}<r>[0.130, 0.723, 0.942, 0.381]</r> or {modern}<r>[0.130, 0.381, 0.942, 0.158]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "j2TSYN3EhvxAkTNk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point of the Traditional line at x = 40}<r>[0.758, 0.432]</r> occur above or below {the Modern line's starting position}<r>[0.130, 0.381]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "Bxn1pfdKZiXA94xA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region <r>[0.015, 0.394, 0.139, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label 'Precision' styled in black color at <r>[0.015, 0.394, 0.034, 0.539]</r> and several y-axis tick labels with tick coordinates ranging from <r>[0.083, 0.895]</r> to <r>[0.083, 0.038]</r>."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "C5sVMvvbmoaSsRT9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located at <r>[0.830, 0.480, 0.977, 0.576]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the chart's legend, which includes 'Traditional' with coordinates <r>[0.884, 0.491, 0.970, 0.521]</r> and 'Modern' with coordinates <r>[0.884, 0.535, 0.946, 0.565]</r>, each associated with color markers <r>[0.831, 0.496, 0.876, 0.514]</r> and <r>[0.831, 0.540, 0.876, 0.558]</r> respectively."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "91vpWF3TC7ZkznSG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included in the region <r>[0.122, 0.911, 0.951, 0.934]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses the x-axis labels from '10' at <r>[0.122, 0.911, 0.139, 0.934]</r> up to '60' at <r>[0.933, 0.911, 0.951, 0.934]</r>, each associated with their bottom tick coordinates from <r>[0.130, 0.895]</r> to <r>[0.942, 0.895]</r>."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "PSUO5YHeYjHZzXa2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the box with coordinates [0.892, 0.547, 0.965, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Unstable' in the legend."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "QWneA1xVvS2eWaxF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the y-axis label is at the coordinates [0.015, 0.247, 0.034, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Values (log scale)."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "fhGddhqQaYZ0yMXH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.050, 0.772, 0.128, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Activity_Level."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "jda2hUSH8xMrMZcI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Erratic'}[0.537, 0.343] higher or lower than {the point for 'Standard'}[0.537, 0.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "vDGstNylwKLr9zs2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt Social_Interaction, is {the line for 'Decrease'}[0.537, 0.261] above or below {the line for 'Unstable'}[0.537, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "3Wl0NpjRVAO5YFgx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at Activity_Level, {the line for 'Decrease'}[0.089, 0.263] or {the line for 'Standard'}[0.089, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "XUY9n40aEo9vcjWw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.839, 0.404, 0.977, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart legend is located within these coordinates. It contains four entries: {Standard}[0.892, 0.415, 0.967, 0.445], {Erratic}[0.892, 0.459, 0.946, 0.489], {Decrease}[0.892, 0.503, 0.970, 0.533], and {Unstable}[0.892, 0.547, 0.965, 0.577], each with an associated marker."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "rePT11G5mNThbcLv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the region at [0.040, 0.293, 0.079, 0.324]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label for the value of {10^-6}[0.040, 0.293, 0.079, 0.324]."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "WxBMUw2207flcui8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.040, 0.181, 0.079, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label for {10^-4}[0.040, 0.181, 0.079, 0.212] is located within this area."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "GIw04bmXzyStj7MA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the feature represented by {the handle colored '#1f77b4'}<r>[0.815, 0.494, 0.860, 0.512]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "9boWb2ieZPtvDryd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value displayed by {the point} at <r>[0.465, 0.670]</r> on the 'Hypothetical' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.569."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "82TNOamM0B98XcKl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the legend item} with label at <r>[0.868, 0.621, 0.973, 0.651]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hypothetical."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "oyjO8OaLb7XIV7eb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Standard' line point} at <r>[0.202, 0.260]</r> have higher or lower value than {the 'Constrained' line point} at <r>[0.202, 0.772]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "kT52rHy6oQwx5hqh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Experimental' line point} at <r>[0.596, 0.603]</r> above or below {the 'Hypothetical' line point} at <r>[0.596, 0.694]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "g9jAQZhxeHUSnVFG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {the x=30 point}, 'Standard' or 'Experimental'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "aZ4OlIHcnZYTsnpm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region <r>[0.814, 0.478, 0.985, 0.662]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, including labels and corresponding handles for 'Standard', 'Constrained', 'Experimental', and 'Hypothetical'."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "WOxOn9j0k31pokGV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.058, 0.025, 0.048, 0.909]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical area includes the y-axis tick labels ranging from '0' to '6'."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "d8yASj4DgIxcLieE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area <r>[0.465, 0.481, 0.465, 0.670]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, points from the 'Standard', 'Experimental', and 'Hypothetical' lines intersect the x=40 tick label."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "DkO70zJNve1DtALT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the volume of transactions in {Regular Cryptocurrency}<r>[0.109, 0.093, 0.943, 0.790]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue line with circle markers."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "K2MsQy80mag65GZ8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.303, 0.034, 0.667]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transactions Volume (billions)."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "r3D9reotBMAMpVfg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year represented by {the x-axis tick}<r>[0.759, 0.933, 0.794, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "xgJfmMKChcOwEB8U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trendline has a higher value in 2017, the {Regular Cryptocurrency line}<r>[0.109, 0.093, 0.943, 0.790]</r> or the {Blockchain Transactions line}<r>[0.109, 0.494, 0.943, 0.572]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain Transactions line."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "m06m3QfpPzSXlqS7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Stablecoin}<r>[0.109, 0.441, 0.943, 0.877]</r> in 2020 higher or lower than its value in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "a4MZAdmIeZ8iiNQ2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of {Traditional Currency}<r>[0.109, 0.842, 0.943, 0.877]</r> from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "EQ5GylebIwuQlbOo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are located in the region with coordinates [0.067, 0.049, 0.985, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main chart area, which includes four trend lines representing different types of currency, each marked with different color and line style. The labels for the year are located along the x-axis, and the labels for the transactions volume are located along the y-axis."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "yjZoHL36R83SFDZU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region with coordinates [0.074, 0.058, 0.290, 0.168]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region defines the legend of the chart, which contains 4 items, each representing a type of currency trend: {Regular Cryptocurrency}<r>[0.118, 0.065, 0.283, 0.082]</r>, {Stablecoin}<r>[0.118, 0.091, 0.191, 0.108]</r>, {Traditional Currency}<r>[0.118, 0.117, 0.258, 0.135]</r>, and {Blockchain Transactions}<r>[0.118, 0.143, 0.285, 0.161]</r>."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "gMnAMIGoY2sFFgEJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.067, 0.049, 0.949, 0.973]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire chart, including all of the labels, trend lines, axes, and the legend."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "DbxQDwASFfoWYDPi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.015, 0.289, 0.034, 0.626] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "Up8nfD0WS9oXM1hM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find {age 40} in the x-axis tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.334, 0.907, 0.351, 0.930]."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "kmsh5ygzUoM2pwdi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item at [0.753, 0.619, 0.797, 0.637] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aging."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "BMvW4WQ0jrMrCFgM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is at a higher point in the graph at age 80, {Preventable Stable}[0.575, 0.757] or {Preventable Growing}[0.575, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Preventable Stable."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "AwCarS9GAc3qme2C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the age of 120, is the {Eradicated} line[0.711, 0.060] above or below the {Aging} line[0.711, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "DCQXghNVc864YOug",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt age 100, is {Preventable Growing}[0.650, 0.335] above or below {Preventable Stable}[0.650, 0.661]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "hVSigp4XK1Oqn7oM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content found in the region [0.752, 0.472, 0.985, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the four lines: {Eradicated}[0.753, 0.487], {Preventable Growing}[0.753, 0.531], {Preventable Stable}[0.753, 0.575], {Aging}[0.753, 0.619]."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "w8KQFL0TVmcyPe84",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area spanning [0.357, 0.943, 0.461, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the x-axis label \"Age Group\"."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "6QoiY5gCAWHgddkf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region [0.101, 0.907, 0.119, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label for the age group '20'."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "FM5SqByvM2Dm8sts",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.817, 0.059, 0.935, 0.089] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's legend label, Stable Growth."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "GSZVXOCAPBhRN4yZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel} at [0.498, 0.943, 0.559, 0.975] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "zEuBBERTyDdtl9JF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.079, 0.142] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first data point for the \"Stable Growth\"."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "7tzsDRtAMIVg9QT3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x marker representing 'Erratic Decline'}[0.154, 0.397] above or below {the o marker representing 'Stable Growth'}[0.154, 0.166] in February?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "zTDbICT3HtZfRNDL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the triangle marker for 'Unstable Behavior'}[0.683, 0.325] above or below the {square marker for 'Minimal Change'}[0.683, 1.133] in September?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "3d7naDrvC7PGTLfw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn October, which shows a higher value, {Stable Growth}[0.758, 0.362] or {Erratic Decline}[0.758, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Erratic Decline."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "1bWWfLETxCEdlVrZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within region [0.764, 0.048, 0.977, 0.232]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the area of the legend which includes the labels 'Stable Growth', 'Erratic Decline', 'Minimal Change', 'Unstable Behavior' along with their corresponding color markers."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "WS8rBsGXC2F3I3lw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.817, 0.191, 0.970, 0.221]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the last text in the legend, the label 'Unstable Behavior'."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "rXYvMEbbu7yqRXcE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nMention the objects within [0.365, 0.911, 0.395, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis tick label for 'May'."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "gc9ywe1Htx4d4aOD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.271, 0.034, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis title."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "DeaHLMlBy3LVhZOs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the last item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.736, 0.621, 0.780, 0.639]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "boivaZi9U5Wa929b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data value does {the x-axis label}<r>[0.933, 0.911, 0.951, 0.934]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "Eyw7t4Lnj72RV37f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Anxiety Level at N=60'}<r>[0.942, 0.359]</r> higher or lower than {the point for 'Self-esteem Level at N=60'}<r>[0.942, 0.359]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "4KwB1WRyqCYzSYO5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Depression Level'}<r>[0.136, 0.226, 0.942, 0.226]</r> above or below the line for {the line for 'Self-esteem Level'}<r>[0.136, 0.159, 0.942, 0.359]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "vHmjhUU1TLn0UNht",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich variable shows a higher value for N=30, Anxiety Level or Life Satisfaction Level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Anxiety Level."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "VQ2nyxXq0enzAz2V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.735, 0.474, 0.977, 0.658]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart, which includes four variables - {Anxiety Level}<r>[0.788, 0.485, 0.900, 0.515]</r>, {Depression Level}<r>[0.788, 0.529, 0.930, 0.559]</r>, {Self-esteem Level}<r>[0.788, 0.573, 0.936, 0.603]</r> and {Life Satisfaction Level}<r>[0.788, 0.616, 0.970, 0.646]</r>, each labeled with a distinctive marker and color."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "oluy70F5oeGBGMzl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.136, 0.025, 0.942, 0.359]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the trend lines for 'Anxiety Level', 'Depression Level', 'Self-esteem Level' and 'Life Satisfaction Level' against the variable 'N' ranging from 10 to 60."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "weFFQCJsSkhXPWxV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you see within coordinates [0.136, 0.159, 0.942, 0.359]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this bounding box, we have points from the trend lines of three variables: 'Anxiety Level', 'Self-esteem Level', and 'Life Satisfaction Level', ranging from 'N'=10 to 'N'=60."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "Xs8h5u1vXVj43hX5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the handle at coordinates [0.885, 0.041, 0.922, 0.055] in the 'sublegend'?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the handle representing the color for 'Stock A'."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "x99xGGOyctwdIIEQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label at [0.012, 0.396, 0.028, 0.544] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label indicates 'Stock Prices'."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "Llxkooxg7PwNqGwQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.351, 0.933, 0.358, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "wqrv7UtOpMix0opa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Stock C'}[0.739, 0.438] above or below {the point for 'Stock A'}[0.739, 0.249] in the 11th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "eayvAkK3KAUC5ij4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the tenth month, is {the point for 'Stock B'}[0.675, 0.154] above or below {the point for 'Stock D'}[0.675, 0.722]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "WRAC1jsyoyxXG0OM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock has a higher value in the last month, {Stock A}[0.803, 0.217] or {Stock B}[0.803, 0.060]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock B."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "C76Dj5U4Xbv5cCrh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.884, 0.029, 0.988, 0.167]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart with indicators of four different stocks: {Stock A}[0.885, 0.041, 0.922, 0.055], {Stock B}[0.885, 0.074, 0.922, 0.088], {Stock C}[0.885, 0.107, 0.922, 0.120], and {Stock D}[0.885, 0.140, 0.922, 0.153]. Each indicator includes a color handle and a label representing a specific stock."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "Lu21CXCVqj8raUDd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.098, 0.217, 0.803, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line representing the 'Stock A' trend across the months. Its points range from month 1 to month 12, showing a mostly increasing trend."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "hp3oqL5kMhwrJstW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.033, 0.053, 0.055, 0.070]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains only one object. This is the Y-axis label '180'."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "5BmcW10LJU7oKxwe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.492, 0.957, 0.557, 0.981]"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "jtHTiI8CzAKmfab9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis} at [0.012, 0.381, 0.028, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity (%)."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "ngv5FwHFvOyxwEaZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identify of the object at [-1081.205, 0.113, 0.945, 0.530] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Standard line."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "EO5J5oltW7X4QnLB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine popularity is higher in France {Standard}[[-1081.205, 0.530]] or {Vegetarian Popularity}[-1081.205, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vegetarian Popularity."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "y3TVg6PWhgHRtvmI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn China, is {Standard}[0.430, 0.295] higher than {Street Food Popularity}[0.430, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "58Ki4tn1M0O0oOoF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn Thailand, is {Fine Dining Popularity}[0.945, 0.309] higher or lower than {Vegetarian Popularity}[0.945, 0.146]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "Dp8bIsiOQqpIQJzc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.777, 0.029, 0.981, 0.167] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, representing different line'd color keys such as Standard [0.822, 0.038, 0.885, 0.060], Vegetarian Popularity [0.822, 0.070, 0.970, 0.093], Street Food Popularity [0.822, 0.103, 0.975, 0.126] and Fine Dining Popularity[0.822, 0.136, 0.973, 0.159]."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "WXSc9bMp3rxdTA9L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.062, 0.019, 0.987, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main plotting area of the line chart, depicting the popularity of different cuisines of different countries It includes data points for various cuisine popularity categories such as Standard, Vegetarian Popularity, Street Food Popularity, and Fine Dining Popularity."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "oJaP03EVFhS8TuR7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [-0.991, 0.927, -0.963, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '$\\\\mathdefault{10^{-1}}$' is located within these coordinates."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "5jeW0Ke3kblYnG83",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.015, 0.415, 0.034, 0.504]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "dYESMqtHRoHISNbN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the second item of the legend with the coordinates [0.760, 0.423, 0.978, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inconsistent Development."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "jtQKEpdP50KMz0oK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the Y-axis show at the point [0.085, 0.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1200."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "XPXPLEQ8lZz5G5ZO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the {Inconsistent Development line}<r>[0.136, 0.262, 0.647, 0.361]</r>, is the {Rapid Expansion line}<r>[0.136, 0.065, 0.647, 0.756]</r> above or below?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "t5we2MMspgC7SAWW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {Inconsistent Development}<r>[0.136, 0.262, 0.647, 0.361]</r> greater or less than the values for {Inexorable Decline}<r>[0.136, 0.559, 0.647, 0.756]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "z25BbXvYnJibfPFS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, does the {Inexorable Decline line}<r>[0.136, 0.559, 0.647, 0.756]</r> have a higher or lower value than the {Rapid Expansion line}<r>[0.136, 0.065, 0.647, 0.756]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "7hmxWwwghoBQoNAY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.707, 0.368, 0.985, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart, showing four items namely {Inexorable Decline}<r>[0.760, 0.379, 0.915, 0.409]</r>, {Inconsistent Development}<r>[0.760, 0.423, 0.978, 0.453]</r>, {Rapid Expansion}<r>[0.760, 0.467, 0.898, 0.497]</r>, and {Dramatic Decrease}<r>[0.760, 0.511, 0.920, 0.541]</r>, each with an associated color marker located to their left respectively."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "mgZ5L6H4RO3GNQip",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the area of interest [0.371, 0.943, 0.412, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label \"Year\" is located within these coordinates."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "YHdeI7FVGcB56nIC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the space [0.040, 0.549, 0.075, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the Y-axis label '1000'."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "s8YuEJYvStXU0zfW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.369, 0.034, 0.551] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "Ah4A1eG8PBq02Oig",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the line marker} at [0.837, 0.621, 0.882, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frightened."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "0U0hjSKno4Dr5Dga",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.717, 0.911, 0.735, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "L1qPrpybH1xpjBsc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Relaxed'}[0.793, 0.453] above or below {the point of 'Anxious'}[0.793, 0.433] at 'N = 60'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "2OMAzKmYnKqllnVM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emotion experienced a decrease from 'N = 10' to 'N = 60', 'Happy' or 'Frightened'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frightened."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "6A87lEQytdwU7mjC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Anxious' and 'Happy' lines, which one is generally higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Happy."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "WrJhHEfYUddiS0Iz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.836, 0.474, 0.985, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend listing the 'Happy', 'Anxious', 'Relaxed', and 'Frightened' trends, complete with their respective colored markers: {Happy}[0.890, 0.485, 0.943, 0.515], {Anxious}[0.890, 0.529, 0.955, 0.559], {Relaxed}[0.890, 0.573, 0.956, 0.603], and {Frightened}[0.890, 0.616, 0.978, 0.646]."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "EfoJQFfvLoO55rdf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items between these coordinates [0.128, 0.165, 0.793, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, this area demonstrates the 'Frightened' trend line. It is represented by triangle markers and includes points corresponding to the values at different 'N' levels."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "K6RTgP1fKZR0Zqqo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by [0.446, 0.943, 0.465, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the x-axis label, which reads 'N'."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "BNhuN6sahfuIqBzs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the graph at coordinates [0.383, 0.943, 0.494, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Parameters."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "tZ3qy6xj4xBGhz26",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.868, 0.533, 0.968, 0.563]."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "svW45LSfatzhVBQt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.040, 0.541, 0.062, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "WHBQeKKiCSfGS5MW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Standard line point for 'ResponseTime'}[0.773, 0.464] higher or lower than {the Constrained line point for 'ResponseTime'}[0.773, 0.765] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "B5P3A2H0TldzK1Md",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Experimental'}[0.105, 0.206, 0.773, 0.636] have a positive or negative slope compared to {the line for 'Hypothetical'}[0.105, 0.077, 0.773, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "ocskAImHhHpQFztO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parameter has a higher value for Constrained line, 'NoOfQueries' or 'ResponseTime'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'NoOfQueries'."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "zQmERJiOh6We2kYm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.814, 0.478, 0.985, 0.662]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Standard}[0.868, 0.489, 0.943, 0.519], {Constrained}[0.868, 0.533, 0.968, 0.563], {Experimental}[0.868, 0.577, 0.978, 0.607] and {Hypothetical}[0.868, 0.621, 0.973, 0.651]. Each items is paired with a color coded marker."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "zsmKsWKWhbRGL6rL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.071, 0.034, 0.806, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart where 4 lines named Standard, Constrained, Experimental, and Hypothetical are plotted against two parameters named NoOfQueries and ResponseTime. The visible grids, x-axis, and y-axis labels are also within this area."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "Migwk62nVRfLM4kb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.105, 0.077, 0.773, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 objects: a point on the {Standard line}[0.773, 0.464], a point on the {Constrained line}[0.773, 0.765], a point on the {Experimental line}[0.773, 0.636], and a point on the {Hypothetical line}[0.773, 0.679]."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "8xxo6P31sfwEYl10",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.228, 0.034, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Patents."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "g3CwX1Krx0CAljht",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the fifth x-axis label}<r>[0.569, 0.750, 0.625, 0.842]</r> referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electrical."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "hmnRAmtdYfeDaGVX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point on the line chart}<r>[0.713, 0.236]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of patents in the 'Space' field."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "UxM75p28A03t9iQP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 'Mechanical'}[0.481, 0.630] above or below {the point 'Electrical'}[0.597, 0.558] on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "F6YUqMws1WoRlD1i",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point 'NanoTech'}[0.365, 0.487] and {the point 'Electronics'}[0.944, 0.057], which one has a higher number of patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electronics."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "jkBBUp0MyLjqFkr1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research field has more patents, {Point 'BioTech'}[0.250, 0.701] or {Point 'Artificial Intelligence'}[0.829, 0.343]?"
+            },
+            {
+                "from": "gpt",
+                "value": "BioTech."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "Png1VjDLWT4OFFBO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in <r>[0.101, 0.039, 0.317, 0.091]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the line chart with 'Number of Patents' written in coordinates {text_coordinates}<r>[0.155, 0.050, 0.310, 0.080]</r>. It is coupled with a colored marker."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "MDXkaC5hsG1l8rRi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region <r>[0.449, 0.750, 0.514, 0.858]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the 'Mechanical' label on the x-axis."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "Ifcaplz1xW0EpKTc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the area <r>[0.769, 0.738, 0.888, 0.935]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Artificial Intelligence' is located within this region."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "XK5SI09ciQIXqjf7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for the line representing 'Run 1' in the subplot titled 'Minimal decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "P8dVIvn66pmSsMVJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the y-axis label} in the 'Marginal decline' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.330, 0.109, 0.345, 0.312]."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "Sxb38K6H2BVWJgRL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the legend item 'Run 2'}<r>[0.401, 0.113, 0.444, 0.136]</r> in 'Marginal decline' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "#f08080."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "VmUKoPICVuqUQUcq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value labeled '72'}<r>[0.080, 0.176]</r> higher or lower than {the value labeled '56'}<r>[0.080, 0.344]</r> in 'Minimal decline' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "we41X0Ky3cycRvGt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first point}<r>[0.735, 0.344]</r> have a higher or lower value than {the fifth point}<r>[0.955, 0.122]</r> on 'Run 1' line in 'Downward trend' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "E6NRF27OFwUAouuY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents a higher value at {the coordinate}[0.462, 0.233] in 'Marginal decline' subplot, #dd5145 or #f08080?"
+            },
+            {
+                "from": "gpt",
+                "value": "#f08080."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "cCW0UyyBcuytEHzs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects included in the region [0.407, 0.358, 0.628, 0.358]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels of the 'Marginal decline' subplot, which include 'Phase 2', 'Phase 3', 'Phase 4', 'Phase 5' with their respective tick marks positioned lower than other items caught in the top view."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "MXAj8hSMpPo7nb3e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.120, 0.083, 0.160, 0.136] reveal?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the legend information for 'Run 1' and 'Run 2' in the 'Gradual decline' subplot including their labels and corresponding handles colored in #67a9cf and #dd5145 respectively."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "vxSAuTObybx5csUm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within [0.730, 0.064, 0.966, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for 'Downward trend' subplot displaying two runs represented by handles colored in #faa74b and #8034a0."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "q7eJjs3skegl53kC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.024, 0.143, 0.038, 0.300] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "36zaN9iLPfymTl1L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at <r>[0.073, 0.397, 0.108, 0.455]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "zesHT0pYeNg0VZkA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at <r>[0.073, 0.884, 0.108, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label for the year 2010."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "HsLZ7EjEuZQp5K89",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher, {the point for 'Run 1'}<r>[0.237, 0.138]</r> in the first subplot or {the point for 'Run 2'}<r>[0.200, 0.357]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Run 1."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "MIH2Svu6HUSr5zIu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Y-axis label}<r>[0.024, 0.143, 0.038, 0.300]</r> to the left of {the X-axis label}<r>[0.186, 0.464, 0.215, 0.488]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "KP8kBu9djdZVuuku",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 'Year 2014' in Run 1}<r>[0.894, 0.115]</r> in the third subplot have a higher value than {the point for 'Year 2015' in Run 1}<r>[0.930, 0.112]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "tP2M4EePiuHwDVH8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within <r>[0.450, 0.025, 0.607, 0.055]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the subplot title, specifically \"Category Y Growth,\" with the text in black."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "KCp4Lb2AmKKpoByd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed by <r>[0.188, 0.561, 0.213, 0.616]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a part of the legend in the fourth subplot, containing 'Run 1' and 'Run 2' labels and markers colored in #67a9cf and #dd5145 respectively."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "ur2PjzeeqDjM04om",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is present within <r>[0.450, 0.512, 0.608, 0.952]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This larger region includes axes labeled 'Year' and 'Trade Volume', numerical axis markers for years from 2010 to 2016, and y-axis values from \u2212250 up to 1500, along with the plot lines for 'Run 1' and 'Run 2' depicting trade volume growth in Category N."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "baWFZRRKBOwU6pop",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {Run 1 in the legend for Household Goods}<r>[0.785, 0.083, 0.825, 0.104]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "l3KqVS4fTtG699sR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the x-axis}<r>[0.835, 0.464, 0.872, 0.487]</r> in the Luxe Items sales chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Runs."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "zvO7iP4gj2Bw0oET",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis label}<r>[0.704, 0.556, 0.723, 0.580]</r> closest to Run 2 in the Luxe Items subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "33."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "iufpX1E2H0FgbnMG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the marker for Run 1 at point 1}<r>[0.412, 0.849]</r> higher or lower than {the marker for Run 2 at point 1}<r>[0.412, 0.849]</r> in the Necessities Sales subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "3WH4hj0cqqcoDVu6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich run marker is colored pink for {Lux Items}<r>[0.785, 0.570, 0.825, 0.592]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Run 1."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "Epxisha6Jb1CAfiT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value represented by the first tick mark on the y-axis}<r>[0.401, 0.905]</r> greater than or less than {the value represented by the second tick mark on the same axis}<r>[0.401, 0.849]</r> in the Necessities subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "qq7GI4d9iko3IQ2n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounds <r>[0.069, 0.064, 0.307, 0.375]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses the axes for the Books Sales subplot, displaying a light pink background, grids in gray dashed lines, and labels for sales and runs."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "jHF2BjufAlwJ5iLa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are located within the region <r>[0.059, 0.879, 0.318, 0.942]</r> in the Accessories Sales subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis tick labels 'Run 1' and 'Run 2', specially formatted with a 45-degree rotation in black color."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "gctz2YLWxugYLX6w",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be described in the area <r>[0.734, 0.551, 0.973, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the axes of the Luxe Items Sales subplot showing a light pink backdrop, gray dashed line grids, vertical sales labels, horizontal runs labels, and coordinate points for two sales runs."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "Aebi5CqR9fSujxAx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.348, 0.125, 0.362, 0.278] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "fLjqcZEoQqRSEgiL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.117, 0.584, 0.188, 0.610] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market label text."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "n2VUPMsXW0ovz7t2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the coordinates [0.081, 0.327], what specific element can be found?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "tpVL8Dv3hv6XrhTR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 'Period 1'}[0.081, 0.327] higher on the graph than {the point at 'Period 2'}[0.960, 0.573] in the {Cryptocurrency sub-plot}<r>[0.736, 0.562, 0.970, 0.812]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "wYVPIWIPkshjl1dI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, is the value at {Period 1}[0.414, 0.573] in the {Commodities sub-plot}<r>[0.403, 0.562, 0.638, 0.812]</r> higher than the value at {Period 2}[0.627, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "2QBY8Qnx6reicvQY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is positioned lower, the end point of the {Forex Market line}<r>[0.081, 0.088, 0.294, 0.315]</r> or the {Bond Market line}<r>[0.747, 0.088, 0.960, 0.315]</r> at 'Period 2'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bond Market."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "WS19H4l3YYrUNPHA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.722, 0.831, 0.985, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains x-axis tick labels for Period 1 and Period 2, located at {72.2% width, 83.1% height, 77.2% width, 93.1% height} and {93.5% width, 83.1% height, 98.5% width, 93.1% height} respectively in the Cryptocurrency sub-plot, aligned at a 45 degree angle."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "L94b0rQ4a8SSTABf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.067, 0.077, 0.295, 0.327]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section of the chart holds the area that plots the 'Forex Market' assets with specific colors assigned to the plotted lines indicated with corresponding points for 'Period 1' and 'Period 2'."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "GerFSfyQCglnooNf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the specifications of region [0.776, 0.574, 0.885, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the legend for the Cryptocurrency subplot, including the label and color handle for market labeling."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "kpb0RFbj3uCvmxhU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by {the color in the handle area}<r>[0.739, 0.568, 0.782, 0.591]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents 'Phase 1' of the 3D printing growth segment."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "mXCSM9VAayhAsafv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis does the text at {coordinates}<r>[0.366, 0.556, 0.396, 0.579]</r> belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis in the Immunotherapy Growth segment."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "vAKgBs7IVyv8n6ff",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the point at {coordinates}<r>[0.956, 0.832]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It marks the second phase of investment growth in 3D printing, with a value of 95."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "vdSHfNAtSRMSBJh9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-tick label '85'}<r>[0.704, 0.268, 0.723, 0.292]</r> higher or lower than {the y-tick label '90'}<r>[0.704, 0.202, 0.723, 0.225]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "1Dx0fk0KuwwjoOgn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the endpoint of line 'Phase 2'}<r>[0.301, 0.565]</r> have a higher or lower investment value compared to {the second phase}'s endpoint<r>[0.628, 0.565]</r> in the Immunotherapy Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "drGWkT5fGrSUDJgf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater at {the label 'Phase 1'<r>[0.458, 0.570, 0.513, 0.592]</r>, the value at the initial point<r>[0.417, 0.832]</r> or at the final point<r>[0.628, 0.565]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initial point."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "knwimoA8dPKPG4GB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region <r>[0.390, 0.862, 0.655, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the x-axis tick label 'Phase 1'}<r>[0.390, 0.862, 0.444, 0.942]</r> and {the tick label 'Phase 2'}<r>[0.602, 0.862, 0.655, 0.942]</r> in the Immunotherapy Growth subplot."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "Kp42YUjNdmsFWCMF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is within the bounding rectangle at <r>[0.039, 0.556, 0.068, 0.686]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It incorporates several y-axis labels, indicating the investment values ranging from 140 to 150 in the Medical Imaging Growth subplot."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "IxDG83RGJYjIqLu0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat comprises the area within <r>[0.718, 0.551, 0.983, 0.845]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area involves the entire plotting region for 3D Printing Growth, including y-axis labels for values 94 to 101, x-axis labels 'Phase 1' and 'Phase 2', and the corresponding data points and line plot."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "kvpdrPxxtkAMnzx8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the line representing 'Run 1'}<r>[0.080, 0.077]</r> in the subplot labeled 'Web Design'?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "s9QChryFikXI3p6c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what {the label at point 5 on the y-axis}<r>[0.038, 0.068, 0.058, 0.091]</r> represents in the subplot for 'Video Production'?"
+            },
+            {
+                "from": "gpt",
+                "value": "25."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "3q0zMGbTcVRldwQU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker at point 5}<r>[0.628, 0.344]</r> correspond to on the line chart in the subplot for 'Social Media Marketing'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Run 1."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "WkFMdINXApH10wAb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {phase 5}<r>[0.628, 0.358]</r>, the line labeled 'Run 1' or 'Run 2' in the 'SEO' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Run 1."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "xhzcMI828qSOkqcQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the performance at phase 1}<r>[0.408, 0.358]</r> on 'Run 1' above or below {the performance at phase 1}<r>[0.408, 0.311]</r> on 'Run 2' in the 'Social Media Marketing' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "DyPSzHAS8vuwQoVs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first y-axis label}<r>[0.069, 0.399]</r> in 'Web Design' lower or higher than {the second y-axis label}<r>[0.069, 0.349]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "kPuYb2D8dtNJhnwr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in region <r>[0.047, 0.065, 0.054, 0.420]</r> on the subplot for 'Content Writing'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick marks and labels ranging from 35 to 60."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "TK3yVsB3fRXMgH20",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the segment <r>[0.668, 0.063, 0.691, 0.360]</r> in the subplot titled 'SEO'?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains vertical lines of the grid, labels for phases 1 through 5 on the x-axis, and part of the y-axis label 'Performance Level'."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "wRdZGHAGKPBLJQzs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features exist within the boundaries <r>[0.076, 0.277, 0.166, 0.347]</r> in the 'Web Design' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend for 'Web Design' with items labeled 'Run 1' and 'Run 2', each paired with a color-coded marker."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "5XQcHfRT9XuoMkga",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the handle with color '#1f77b4'}<r>[0.162, 0.074, 0.199, 0.088]</r> represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Stability Index."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "LEvwduxhcf3hso3a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the x-axis tick at}<r>[0.498, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010.0."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "y1mnThXkdK4BthGJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker shape 'o'}<r>[0.101, 0.182]</r> indicate on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "A data point."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "gcAuzBl1vvsMmvCH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is represented by the color {the line with marker 'x'}<r>[0.101, 0.263]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Government Approval Rating."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "WF27rAaCtoYhsgf3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line color '#2ca02c'}<r>[0.101, 0.870]</r> found above or below {the line color '#1f77b4'}<r>[0.894, 0.368]</r> at the far right of the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "YLAyh7KQzwCI7BHY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-axis label 'Maintains Value'}<r>[0.015, 0.385, 0.029, 0.627]</r> apply to {the 0.6 y-axis tick label}<r>[0.061, 0.425]</r> or {the 0.8 y-axis tick label}<r>[0.061, 0.263]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.6."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "sNZSfQQZodwWXWVJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the bottom area of the chart covering <r>[0.061, 0.952, 0.934, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the x-axis label 'Year'}<r>[0.483, 0.952, 0.512, 0.975]</r> centered in the area, styled in black color."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "i0wppOqMTwIxnQEk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat attributes are depicted in the region spanned by <r>[0.161, 0.062, 0.839, 0.103]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the chart, including items for {Social Stability Index}<r>[0.206, 0.071, 0.351, 0.094]</r>, {Government Approval Rating}<r>[0.417, 0.071, 0.620, 0.094]</r>, and {Radicalization Trends}<r>[0.686, 0.071, 0.833, 0.094]</r> along with their corresponding colored handles."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "vnlFYlmHNCumV8kp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within the bounds <r>[0.933, 0.101, 0.982, 0.911]</r> on the right of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds {the y-axis label 'Government Approval Rating'}<r>[0.971, 0.337, 0.985, 0.674]</r> which tracks the approval ratings over years."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "NxBlxFFcoTfvthWw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color {of the line representing 'Carbon Footprints'}<r>[0.160, 0.155, 0.865, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "3CTfV1wZXiWkJ2VR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the line correspondent to 'Energy Efficiency'}<r>[0.160, 0.186, 0.865, 0.540]</r> styled?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solid line with circles."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "cRM9EC3D6gw2tVIs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker type for {the line indicating 'Renewable Sources'}<r>[0.160, 0.155, 0.865, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Triangles."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "CCbsLdNOJhoUXlY0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the level at point {x=10} for {Renewable Sources}<r>[0.436, 0.442]</r> higher than {Energy Efficiency}<r>[0.436, 0.391]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "12nZV7dHqf1ihUdB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line for {Energy Efficiency}<r>[0.160, 0.186, 0.865, 0.540]</r> at point {x=13} have a lower value compared to {Carbon Footprints}<r>[0.528, 0.658]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "10nNAHnupTWQ1WyG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween points x=5 and x=10, which line increases more, {Renewable Sources}<r>[0.283, 0.557]</r><r>[0.436, 0.442]</r> or {Energy Efficiency}<r>[0.283, 0.477]</r><r>[0.436, 0.391]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Sources."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "ms6QmQNksSv9tlV2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.160, 0.140, 0.865, 0.885]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three lines representing 'Energy Efficiency', 'Carbon Footprints', and 'Renewable Sources' with varied colors and marker styles. Each line displays individual points from weeks 1 to 24 plotted to show respective growth trends."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "gGQMrz3ZCpW2QwkN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.930, 0.031, 0.972, 0.972]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays the y-axis ticks ranging from 0.6 to 2.2 on the subplot showing 'Renewable Sources', along with corresponding y-axis labels."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Chu3prgDdXIBALgm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is displayed within <r>[0.125, 0.906, 1.057, 0.930]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis tick labels from '-5' through '30', aligning with the weeks plotted on both subplots for various data lines."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Wpr0ugULRNhbEspy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you indicate the type of the first marker found in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker is a circle {marker}<r>[0.132, 0.138, 0.162, 0.149]</r>."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "06JKhSzp4yh8qTfB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is 'Market %' {ylabel}<r>[0.082, 0.464, 0.094, 0.546]</r>."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "l7AFHFUccxLwCRbT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point {line_1, point 4}<r>[0.271, 0.742]</r> on 'Market Growth' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the data point is 0.22."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "vIm6wcvrDQJgVnv3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point labeled '10'}[0.494,0.522] for 'Market Share' line higher or lower than the {data point labeled '10'}[0.494, 0.262] for 'Market Saturation' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "FLJabxvBPgEYUw5m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of the {point 5}[0.309, 0.455] on 'Market Share' line higher or lower than the value of {point 5}[0.309, 0.702] on 'Market Growth' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "wGZgTK4uc3lpNE4N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell if the trend for 'Market Growth'{line_1}<r>[0.160, 0.155, 0.865, 0.855]</r> or 'Market Saturation' {line_2}<r>[0.160, 0.242, 0.865, 0.315]</r> is generally higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Growth."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "kLs6D0aVRBqiqYQ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects found in the area [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main plot of the chart, where three different line graphs are presented representing 'Market Share', 'Market Growth', and 'Market Saturation'. These lines are represented with different markers and line types."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "z1hic4y0SMLWdCWe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.131, 0.129, 0.277, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a legend which has three items: {Market Share}<r>[0.168, 0.136, 0.245, 0.153]</r> represented by a blue circle, {Market Growth}<r>[0.168, 0.162, 0.253, 0.179]</r> represented by a red X, and {Market Saturation}<r>[0.168, 0.188, 0.272, 0.205]</r> represented by a green triangle."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "lhriDtE6nBdWDWyg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.089, 1.015, 0.117, 1.032]?"
+            },
+            {
+                "from": "gpt",
+                "value": "An out of bounds Y-axis label is found here, indicating a value of '\u22120.2'."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "HP8rbiYGZEWGl8Fn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn this chart, what does the line with {coordinates}<r>[0.160, 0.155, 0.865, 0.282]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Service Spending."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "S3twmBRXTIhLlwoC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Unemployment Rate}<r>[0.160, 0.782, 0.865, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "4Rx99XT1ZwzCHt3Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis label}<r>[0.074, 0.398, 0.088, 0.612]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Values."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "c1BKOE5rSC2KM1wE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt month 4, was the {Infrastructure Spending}<r>[0.352, 0.228]</r> higher or lower than the {Social Service Spending}<r>[0.352, 0.210]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "4BOz2gnlIMPaRCv9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt month 11, is the {Infrastructure Spending}<r>[0.801, 0.391]</r> above or below the {Unemployment Rate}<r>[0.801, 0.791]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "6XRxc3ya6QOzMfaN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at month 2, {Infrastructure Spending}<r>[0.224, 0.237]</r> or {Unemployment Rate}<r>[0.224, 0.846]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unemployment Rate."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "plFyZQbUpi8yk1os",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in area <r>[0.184, 0.940, 0.841, 0.982]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Infrastructure Spending}[0.228, 0.949, 0.393, 0.973] with a blue marker, {Social Service Spending}[0.460, 0.949, 0.626, 0.973] with a red marker, and {Unemployment Rate}[0.693, 0.949, 0.836, 0.973] with a green marker."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "0dhu9IXNN6Jrt7uG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area <r>[0.093, 0.928, 0.115, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0.0' is located here."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "kQgtQRNHLfmKlSOJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in area <r>[0.125, 0.120, 0.900, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main plot of the chart, which includes 3 line graphs that represent Infrastructure Spending, Social Service Spending, and Unemployment Rate over a 12 month period."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "SscLxgXQPiUxI6lK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.012, 0.422, 0.024, 0.593]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Knowledge Score."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Q7AmUKxhxpMMPEYx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used for {the line representing 'Skills Score'}<r>[0.079, 0.286, 0.893, 0.626]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "SpbaAcyJhdAttgpI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used on {the 'Innovation Factor' line}<r>[0.079, 0.383, 0.893, 0.785]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Triangle."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "PlmXFI3KYYRStwgi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at week 15}<r>[0.472, 0.473]</r> for 'Skills Score' higher or lower than {the point at week 15}<r>[0.584, 0.522]</r> for 'Innovation Factor'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "w6zr5f9lwwvIpbaw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the last point on 'Knowledge Score' line}<r>[0.893, 0.646]</r> finish above or below {the last point on 'Innovation Factor'}<r>[0.893, 0.383]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "JEqexHAAp9f96Z8F",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a higher point on the chart, {the 'Skills Score' line}<r>[0.079, 0.286]</r> or {the 'Knowledge Score' line}<r>[0.079, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Skills Score."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "HhRA4nIAaBezRhFp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within region <r>[0.107, 0.276, 0.279, 0.436]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains data points for weeks 2, 3, and 4, respectively located at {coordinates}[0.107, 0.314], {coordinates}[0.135, 0.328], {coordinates}[0.163, 0.341] for the 'Knowledge Score' line. Each point is marked with a blue circle."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "UokKIF8dUQIKbTZu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the region <r>[0.668, 0.563, 0.809, 0.633]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains data points for weeks 22, 23, and 24 on the 'Innovation Factor' line represented by green triangles at their respective positions {coordinates}[0.668, 0.494], {coordinates}[0.696, 0.480], {coordinates}[0.724, 0.466]."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "yexYdeFuzeVpd1Gt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region <r>[0.332, 0.600, 0.584, 0.640]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains points from weeks 10, 11, 12 on the lines for 'Skills Score' and 'Innovation Factor'. The points are marked with red crosses and green triangles located at {coordinates}[0.556, 0.508], {coordinates}[0.528, 0.494] respectively."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "gKw9P9qlvH2BFSCS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the marker} at [0.280, 0.502, 0.280, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "0rhkP6XxyI3aT7Ua",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the x-axis} in the dataset located at [0.480, 0.952, 0.524, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Period."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "WnbfMQbDxe5Nq9FA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of {the line series representing Supply Capacity} identified by [0.212, 0.489, 0.349, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a dashed red line marked with 'x'."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "2bdYXJFrpUrlHlPw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '18'}[0.690, 0.471] above or below {the point labeled '12'}[0.485, 0.508] on the 'Trade Volume' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "mslqHj3DHdxWMhbj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Growth Rate'}[[0.110, 0.757], [0.894, 0.137]] or {the line for 'Supply Capacity'}[[0.110, 0.409], [0.144, 0.378]] yield higher value at period 7?"
+            },
+            {
+                "from": "gpt",
+                "value": "Supply Capacity."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "ExrOH2lXUGYmcDNQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich endpoint is closer to the bottom of the chart, {the ending point of 'Trade Volume'}[0.894, 0.594] or {the ending point of 'Supply Capacity'}[0.894, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trade Volume."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "1to3ayRhf2biMf1Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.943, 0.748, 0.965, 0.917]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes y-axis labels for 'Growth Rate' displaying values from 0.2 to 1.0, with each label aligned closely to corresponding ticks."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "4DySyRyXRuseJB0Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is presented in [0.110, 0.777, 0.417, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers parts of the 'Growth Rate' line graph, specifically the points from periods 1 to 11, showing a gradual increase in growth rate."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "p7AUHt6huBYLYlNi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.110, 0.529, 0.758, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line graph for 'Supply Capacity', showing a generally decreasing trend from 1 to 19 period with markers indicated by 'x'."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "IOoU5L1ZXOwp7e8s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the legend item for 'Performance'}<r>[0.350, 0.071, 0.438, 0.094]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "ildv0ZNbpKQLm2gE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the x-axis tick labeled '5.0'}<r>[0.261, 0.919, 0.283, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.272, 0.903]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "rZ8BMV7ISlSlVoqx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text written at {coordinates}<r>[0.500, 0.087, 0.500, 0.087]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Geographic Spatial Data Over Weeks."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "WeR6JSg7p8m5StJl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Efficiency line's point at 'Week 10'}<r>[0.479, 0.470]</r> above or below {the Performance line's point at 'Week 10'}<r>[0.479, 0.446]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "movqVYpieq69ATm3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 'Week 1' in Performance}<r>[0.106, 0.502]</r> have a higher or lower value than {the point for 'Week 1' in Efficiency}<r>[0.106, 0.422]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "aTvYn6P1zHxOTGF5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher ending value, {the line for 'Performance'}<r>[0.894, 0.438]</r> or {the line for 'Efficiency'}<r>[0.894, 0.406]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "OjAuTMH7ScZWTwMG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual elements in the region <r>[0.642, 0.616, 0.706, 0.677]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains high-value points for both 'Performance' and 'Efficiency' and is colored blue and red respectively, showing significant values in the later weeks."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "mMQr9AR1r6pcRtMa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area <r>[0.500, 0.069, 0.860, 0.133]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes part of the legend for 'Efficiency' in red and 'Growth' in green, with associated text labels and color markers."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "MkNWReh1uJlODzA7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the sector <r>[0.355, 0.438, 0.396, 0.486]</r> on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This sector captures a point where the 'Efficiency' line shows a decrease, marked by a square red marker, indicating a dip in efficiency levels around Week 8."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "58NuZtgs2FjoPtYR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the object at [0.101, 0.337, 0.899, 0.708] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance line graph."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "5mL7N4cqMrwfZXaJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.477, 0.952, 0.523, 0.975] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "PQGlXpPBwc10TRfX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.943, 0.919, 0.974, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label '22.5'."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "PEBAh7eSDSn1QZwF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the point}[0.500, 0.621] and {the point}[0.540, 0.641] on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "Ty6Y4bNTR9ltGcxW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line with color #1f77b4}[0.101, 0.337, 0.899, 0.708] represent a higher value at week 1 than {the line with color #d62728}[0.101, 0.411, 0.899, 0.742]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "xVkyiaogDhmSeqOs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis label '0.0'}[0.035, 0.902, 0.057, 0.925] below {the y-axis label '1.2'}[0.943, 0.092, 0.965, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "5Gigt5p2WTof6GYf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the coordinates [0.101, 0.337, 0.899, 0.708]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a blue line graph representing performance data across weeks, with points marked from week 1 to week 21, decreasing in value from initial to final."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "s2WeImCQQhF7uiE8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.061, 0.101, 0.939, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of green-colored labels indicating weeks from 0 to 22.5 along the x-axis, and nine blue and red line graphs representing various statistical data points across the weeks."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "CLMmzSMuXIEXQxDj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region with coordinates [0.943, 0.902, 0.965, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis labels for efficiency, ranging from 0.0 to 1.2, located along the y-axis on the right side, with corresponding tick marks."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "Nx5UZwpmCsDYHS08",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {title of the chart} at [0.365, 0.023, 0.635, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Sales Trend Observation."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "YuUg33OzaVzrFIt2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the marker for 'Revenue' in the legend}<r>[0.440, 0.074, 0.476, 0.088]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "zbi2hP2rSv3q5BuY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line with color #d62728}<r>[0.113, 0.138, 0.887, 0.874]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "gkuJ3MaZGkRibqXo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Sales' in Q4}<r>[0.887, 0.276]</r> higher or lower on the chart than {the point for 'Revenue' in Q4}<r>[0.887, 0.138]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "uQznC7sSZwneSrjI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the 'Sales' point in Q1}<r>[0.113, 0.736]</r> and {the 'Revenue' point in Q1}<r>[0.113, 0.874]</r> in terms of their positions on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "QsqYMpD3GZl1Vi4U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Sales line}<r>[0.113, 0.138, 0.887, 0.736]</r> generally above or below the {Revenue line}<r>[0.113, 0.138, 0.887, 0.874]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "PTNA5xIX80v6O205",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the area within [0.335, 0.062, 0.665, 0.103]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. The legend items: {Sales}<r>[0.379, 0.071, 0.416, 0.094]</r>, {Revenue}<r>[0.483, 0.071, 0.543, 0.094]</r>, and {Growth}<r>[0.609, 0.071, 0.660, 0.094]</r>, each have different colored markers."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "Nban15qiv27xOTr5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area at [0.075, 0.101, 0.925, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The space within these coordinates primarily consists of the line plots representing 'Sales', 'Revenue' and 'Growth' each differentiated by a unique color and linestyle."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "1d6nlJKaY0QTJD40",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.075, 0.101, 0.113, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick labels (0, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000) and a part of the line plot representing 'Sales' are located within this region. The color of the y-axis tick labels is blue and the color of 'Sales' line is #1f77b4."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "rEUbbBn0MdJvBEvx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the handle for the legend item labeled 'Market_Entropy'}<r>[0.237, 0.068, 0.273, 0.079]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "C9Mdh5pVfXBtXTL9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.438, 0.029, 0.574]</r> say?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market_Entropy."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "0U0faJo5hXlItfyj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text at the very top}<r>[0.195, 0.015, 0.805, 0.038]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Detailed Emerging Markets and Consumer Behavior Analysis Over Weeks."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "1LgBY99TdK7qdiMi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {point 5 on the Market_Growth line}<r>[0.223, 0.646]</r> higher than {point 5 on the Market_Knowledge line}<r>[0.223, 0.506]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "Xz0yEZnY8fZEziiQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of {Market_Entropy at point 20}<r>[0.661, 0.436]</r> exceed that of {Market_Growth at point 20}<r>[0.661, 0.380]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "7PGmBfkMN2kv6zkp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth of {Market_Knowledge from point 24 to point 28}<r>[0.777, 0.155], [0.894, 0.085]</r> steeper than that of {Market_Growth from point 24 to point 28}<r>[0.777, 0.436], [0.894, 0.415]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "2yZBku9shGtVhYzc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present in the region <r>[0.254, 0.055, 0.654, 0.096]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with labels and colored markers for 'Market_Entropy', 'Market_Knowledge', and 'Market_Growth'."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "vVvyLQxvCb6lKj4O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details in the axis region <r>[0.015, 0.438, 0.029, 0.574]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis labeled 'Market_Entropy' with tick labels ranging from 0.0 to 1.2, styled in blue."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "2ixuPaf7iQJSs6Fz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located in the area <r>[0.285, 0.227, 0.531, 0.261]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes points and annotations for Market_Growth and Market_Knowledge for weeks around 14 to 20, displaying a slight increase and decrease in both cases."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "63CnxIMaz2tV5qCe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat function does {the object}<r>[0.454, 0.062, 0.546, 0.103]</r> serve in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "Kd8HRFlhC6epo6ht",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the Urban line}<r>[0.097, 0.154, 0.916, 0.432]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urbanization Indicator."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "aYKmOsPBuyfFfTh1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.343, 0.023, 0.700, 0.053]</r> narrate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, labeled as 'Rural and Urban Development Comparison'."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "5vOAFrMBCRbO414j",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the {points on the Urban line}<r>[0.097, 0.154, 0.916, 0.432]</r>, does the value increase or decrease between point 1 and point 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "RDKr7XHEGFoUHkId",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {point 3}<r>[0.157, 0.379]</r> and {point 4}<r>[0.188, 0.389]</r> in the Urban line, was there an increase or decrease in urbanization indicator?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "kewRCiPbOACCZEdT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {point 18}<r>[0.612, 0.170]</r> and {point 19}<r>[0.643, 0.422]</r> on the Urbanization Indicator line, which one has the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 18."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "3KPJBhRo7J4M2bda",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents found in the area <r>[0.066, 0.101, 0.976, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the line chart comparing rural and urban development, with x-axis representing weeks and y-axis representing the Urbanization Indicator. The values range from point 1 to point 28 along x-axis and from 0.0 to 1.6 along y-axis."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "HYCiGO2LZZccJudo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the objects within the region <r>[0.498, 0.071, 0.541, 0.094]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label 'Urban' in the legend, paired with a marker in the color '#1f77b4'."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "HoP9WDXWF9y9KZQo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area <r>[0.015, 0.373, 0.029, 0.631]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label indicating 'Urbanization Indicator' is located in this area, and it's styled in the color 'tab:blue' with a rotation of 90.0 degrees."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "QBYpAJ0Zyh5quSVv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker at}[0.474, 0.271] signify in the corporate earnings subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.2."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "lIhTdoh7wNi9Pzgs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {coordinates}[0.581, 0.179] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "UvjarL6wIsGOkWns",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is represented by {the square marker}[0.688, 0.715]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.5."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "HLrN7pjZqOtMaOVI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.528, 0.492] above or below {the 2.0 mark}[0.528, 0.782] on the GDP subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "vZ7drN8sO7aMPoka",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {x=4}[0.367, 0.179], corporate earnings or GDP?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporate earnings."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "nwUhRdNz29HzexRJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the marker}[0.528, 0.774] match or exceed {the 2.0 inflation target}[0.528, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exceed."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "4QMAZ63db2Hitekt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within [0.140, 0.150, 0.290, 0.320]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the corporate earnings subplot's x-axis label displaying 'Time' and y-axis label displaying 'Value' with {coordinates for x-label}[0.420, 0.327, 0.420, 0.327] and {coordinates for y-label}[0.040, 0.158, 0.063, 0.200]."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "LLxWjmAcOYxvO4KK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.400, 0.175, 0.700, 0.200]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the line representing corporate earnings along with markers and annotations for values such as 1.0 at {the coordinates}[0.367, 0.179] and 1.1 at {the coordinates}[0.528, 0.168]."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "60ieIL2TvDw6Dfcq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content of [0.600, 0.700, 0.900, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the sublegend for the 'Inflation Over Time'subplot, containing the label 'Inflation' colored blue at {coordinates for label}[0.806, 0.812, 0.901, 0.828] and the legend handle at {coordinates for handle}[0.735, 0.815, 0.795, 0.824]."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "M2pbUjx98hqBbNdo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the object with these coordinates [0.096, 0.305, 0.154, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the X-axis label indicating the year 2010."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "sSyPfdJTZT6IwQH4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.621, 0.113, 0.790, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a sublegend item label that reads 'Export Volume'."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "C8nFPiDI0AvFhK8e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is found within the boundary box [0.424, 0.946, 0.483, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a X-axis label representing the year 2014."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "gcTOm4smFxOG7ioR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point}[0.618, 0.168] from the Export Volume line above or below the corresponding {data point}[0.618, 0.477] on the Import Volume line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "cSx6pbmTqhK98QdI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the Export Volume line, is the data point in 2015 {at}[0.535, 0.156] higher or lower than that of 2020 {at}[0.946, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "cQdZDVCiJUNN77c7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the Import Volume line, is the point for 2019 {at}[0.864, 0.423] above or below the point for 2020 {at}[0.946, 0.477]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "CYX3ZDvOGCTVWbE8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.547, 0.106, 0.800, 0.134]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the sublegend of the first subplot indicating 'Export Volume'. The text of the sublegend is accompanied by a green square icon."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "N7TNz1tlhIgEhuTz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the space [0.096, 0.305, 0.975, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area spans across several X-axis labels stating various years: {2010}[0.096, 0.305, 0.154, 0.321], {2012}[0.260, 0.305, 0.318, 0.321], {2014}[0.424, 0.305, 0.483, 0.321], {2016}[0.588, 0.305, 0.647, 0.321], {2018}[0.752, 0.305, 0.811, 0.321] and {2020}[0.917, 0.305, 0.975, 0.321]."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "ivNOf7VdB8Ib0q6B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within coordinates [0.094, 0.757, 0.109, 0.772]."
+            },
+            {
+                "from": "gpt",
+                "value": "This box contains a value from the Y-axis tick label, specifically, it is the label for the value '1.25'."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "de5Stqco65Q0ZD0k",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-label represent with coordinates [0.494, 0.640, 0.592, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Platform."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "CcUojHzTjY1FJHEM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate does the title \"Emojis Sent Over 60 Days\" have?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.363, 0.680, 0.723, 0.700]"
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "b4WYD8Dj7bQojA0C",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle of the legend item 'Emojis Sent'} <r>[0.435, 0.713, 0.495, 0.722]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "red."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "qqhFm8kdaJ1UR3WO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for 'LinkedIn'}<r>[0.936, 0.817]</r> above or below the {point for 'Snapchat'}<r>[0.621, 0.727]</r> on the 'Emojis Sent' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "cnP1Tig2PSOH0HmO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Instagram}<r>[0.452, 0.555, 0.476, 0.634]</r> have more or less messages sent than {Twitter}<r>[0.767, 0.555, 0.790, 0.607]</r> based on the line for 'Messages Sent'?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "Z51nFkor823PHm8H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has a higher emojis sent value at {point 5}<r>[0.936, 0.817]</r>, Facebook or LinkedIn?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "u4YOXy0jhEwuhdLc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.110, 0.064, 0.975, 0.217]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a chart showing the active days on different platforms over 60 days, with the x-axis representing the platforms and the y-axis representing active days. {The line with the green color}<r>[0.150, 0.071, 0.936, 0.210]</r> represents the trend of 'Days Active'."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "eCKsWSDD4WCdAiJM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.415, 0.377, 0.671, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the legend}<r>[0.489, 0.383, 0.661, 0.398]</r> for the chart that represents 'Messages Sent' with a blue colored marker."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "zPZUnjpWJuHBN6oa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.110, 0.720, 0.975, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a chart showing the number of emojis sent on different platforms over 60's days. It contains {the x-axis tick labels}<r>[0.138, 0.883, 0.161, 0.956], [0.295, 0.883, 0.318, 0.961], [0.452, 0.883, 0.476, 0.962], [0.610, 0.883, 0.633, 0.956], [0.767, 0.883, 0.790, 0.935], [0.924, 0.883, 0.947, 0.948]</r>, {the line for 'Emojis Sent'}<r>[0.150, 0.727, 0.936, 0.865]</r>, and an x-label and y-label indicating 'Platform' and 'Emojis' respectively."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "DvBJKJlLfsqJ2pxa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Drug Development}<r>[0.084, 0.054, 0.981, 0.284]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "Ej9NgkXZCenPnqeB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the label displayed at {coordinates}<r>[0.891, 0.312, 0.913, 0.324]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "hbZ2jVELwyBR1o3L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the orientation of the y-axis labels seen in the {rectangle}<r>[0.044, 0.049, 0.072, 0.311]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vertical."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "LAVDRD5DZkOAhfTc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point}<r>[0.712, 0.104]</r> for 2018 in Drug Development rest above or below the label '3.0' at {coordinates}<r>[0.044, 0.099, 0.072, 0.111]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "bnqThC6Fi4YXu8bd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {latest y-axis label}<r>[0.033, 0.697, 0.072, 0.708]</r> higher in value compared to {the first y-axis label}<r>[0.019, 0.947, 0.072, 0.959]</r> in Biotech Investment subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "Gt3BEnP3bAsEgsZS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the intervals between the years {2014}<r>[0.353, 0.312, 0.375, 0.324]</r> and {2016}<r>[0.510, 0.312, 0.555, 0.324]</r> smaller than between {2018}<r>[0.690, 0.312, 0.734, 0.324]</r> and {2020}<r>[0.870, 0.312, 0.913, 0.324]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "2jHRTGz9GTJuMFNP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual items between the coordinates [0.084, 0.054, 0.981, 0.304]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a line graph for Drug Development labeled as 'Trends in Drug Development' with points representing data from 2011 to 2021. The colors include a green line through {various points}<r>[0.084, 0.054, 0.981, 0.284]</r> and x-axis labels spaced at intervals corresponding to years."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "NOfegOmEj3SUkadI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what occupies the region from [0.044, 0.049, 0.981, 0.311]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mostly comprises y-axis labels and corresponding ticks. The labels, oriented vertically, represent values incrementally from 1.0 to 3.5 across three subplots."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "S7t7COHMwVKAXub3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the boundaries of [0.044, 0.049, 1.093, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large area includes all components of the three subplots; Drug Development, Vaccine Research, and Biotech Investment. It includes x-axis labels for years, y-axis labels for values, and lines representing various data trends in each sector. There are also subplot titles placed near the top of each subplot region."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "BsJWMP1AlqeRG2A2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.485, 0.078, 0.665, 0.094] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sublegend label."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "q339gYLwsye7CQWv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich mark can be found at [0.459, 0.156, 0.459, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point of the line."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "G2xvxqoyaJE9aKRi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.543, 0.327, 0.543, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "Q9CDoFetGaW7hmOX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.208, 0.202] above or below {the point}[0.208, 0.426]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "mijvOTsUDSfGGbZZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at [0.375, 0.171]} lie higher or lower than {the point at [0.375, 0.468]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "wyN9B6l1O572AI8E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is lower: {the y-coordinate of line point}[0.793, 0.102] or {the y-coordinate of line point}[0.793, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.793, 0.102."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "UpVm4Phl7S0MP6qa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area ranging from [0.072, 0.058, 0.109, 0.304]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all y-axis labels for the first subplot, visually represented as textual labels arranged vertically."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "LX1gAVG1QMWMRQF8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat significant elements are located within the coordinates [0.118, 0.288, 0.975, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes x-axis labels for all subplots, encompassing different years labeled from '0' to '10', distributed horizontally across the bottom of each subplot."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "YoHs4Vof3qogL2QV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found in the area from [0.118, 0.016, 0.975, 0.036]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the subtitle \"Slow Expansion Over Time,\" styled in black text centered along the x-axis of the first subplot."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "llVxHfRrfF3bbXrd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing Social Development}<r>[0.130, 0.406, 0.946, 0.602]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "rCfWK4c64t4nmu5Z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the shape of markers used in {the line for Environmental Protection}<r>[0.130, 0.744, 0.946, 0.920]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "nzvdUj75zf4NMKp4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's data point is located at {coordinates}<r>[0.864, 0.431]</r> in the Social Development subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "SkP9RthjDXoTygd0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-tick label '3'}<r>[0.099, 0.196, 0.114, 0.212]</r> higher or lower than {the y-tick label '5'}<r>[0.099, 0.104, 0.114, 0.120]</r> on the Y-axis of the Infrastructure subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "vr9toF3CdEJ1542Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the x-tick for 2012}<r>[0.293, 0.615]</r> align above {the x-tick for 2014}<r>[0.456, 0.615]</r> on the Social Development subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "JCuNDBAlRgFgmCCz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the marker at {2015}<r>[0.538, 0.569]</r> on the Social Development line above or below the marker at {2016}<r>[0.619, 0.514]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "80qO7DEq3iBbqd3m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements between the coordinates [0.101, 0.305, 0.198, 0.321]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis label '2010' {at}<r>[0.101, 0.305, 0.159, 0.321]</r>."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "tjrVX3OfCtXJMotp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the box [0.530, 0.948, 0.947, 0.990]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Year' for the Environmental Protection subplot is located in this region."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "Dnc7nw5nl5NpEc5j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the area defined by [0.130, 0.294, 0.270, 0.330]."
+            },
+            {
+                "from": "gpt",
+                "value": "The start of the x-axis, including the first y-tick {label}<r>[0.099, 0.288, 0.114, 0.304]</r> and the first x-tick {label '2010'}<r>[0.101, 0.305, 0.159, 0.321]</r>, is situated in this area."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "cyICnjJnqtvP4H4y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary Y-axis label for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population (%)."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "4Rv4VVVSggzr40ut",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the handle of the old population's legend item}<r>[0.550, 0.687, 0.610, 0.696]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "JqJ6LWgMJLZWRLHV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {these coordinates}<r>[0.511, 0.968, 0.560, 0.983]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "nwANAIZxcjjzI4uz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the marker at point '2015' for Young Population}<r>[0.968, 0.289]</r> above or below {the marker at point '2015' for Middle-Aged Population}<r>[0.968, 0.407]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "JVBJtS7GjZYzcgyD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label color of the x-axis ticks in the last subplot}<r>[0.012, 0.946, 1.059, 0.962]</r> have the same color as {the label of the Middle-Age subplot's ylabel}<r>[0.025, 0.443, 0.048, 0.556]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "LuroDeDkA8y3TXxH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at year '2009', {Middle-Aged Population}<r>[0.597, 0.422]</r> or {Old Population}<r>[0.597, 0.870]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Middle-Aged Population."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "AtYS0N7oi1Uz00ay",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within {this region}<r>[0.621, 0.364, 0.894, 0.380]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the text for the 'Middle-Aged Population' label in blue color."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "CqKoZtaFhEBXhkCB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area covered by <r>[0.550, 0.046, 0.610, 0.055]</r> and <r>[0.550, 0.687, 0.610, 0.696]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions contain the handles for the legend items of Young Population and Old Population, colored green and red respectively."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "7CB9wV4MbL6k6b3R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within {the rectangle}<r>[0.058, 0.609, 0.087, 0.624]</r> and <r>[0.058, 0.091, 0.087, 0.106]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the y-axis tick labels '10' and '60' of the Middle-Aged and Old Population charts, displayed in black."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "XY06K0wBOVDOKqNi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the color at coordinates [0.315, 0.425, 0.351, 0.433] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Steady Recovery."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "PpwLBAQjPOqv2p5t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the data for Month 6 labeled in the subplot for Substantial Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "300."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "WndFpk0AkzvCh73e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at {the coordinates}[0.988, 0.932, 0.488, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rapid Expansion handle."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "bQAq8ms4cTtoekqb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick mark for Month 2}[0.112, 0.459] aligned horizontally with {the tick mark for Month 12}[0.488, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "KegxfH3eE33vp0Gb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot has a higher sales number at Month 12, {Steady Recovery}[0.488, 0.063] or {Substantial Growth}[0.976, 0.064]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Substantial Growth."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "8tfs4my18PCZ6YnM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the month label '4'}[0.186, 0.459] appear in the same position in the two different subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "l6UhWxAqMc9xG69q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the annotations found between [0.438, 0.449, 0.570, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels '8' and '10' in the first subplot, marking the respective months in the sales trend graph."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "opK6h3mzbEDjCMSQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.765, 0.449, 0.976, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitles \"Rapid Expansion: Sales Trend\" and \"Unpredictable Decline: Sales Trend\" for the corresponding subplots, with text colored in black."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "0btWnjj2XYFLz0gF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are positioned within the coordinates [0.640, 0.417, 0.969, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item for Substantial Growth including its color handle and label, as well as the subtitle and y-label for the respective subplot."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "Pr6jJMb6zTUgSOVb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} for the subplot with given coordinates [0.044, 0.479, 0.067, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "DR2Qpv4sogNTyxJN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used to plot {the line for 'Periodic Fluctuations'} within [0.130, 0.423, 0.960, 0.538]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#0000ff)"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "ahZ01nPjYJs7bIPY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} for the subplot with these coordinates [0.517, 0.968, 0.573, 0.983]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "MtzFaf2UGVPEGXDv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the y-values for {the 'Periodic Fluctuations'}[0.130, 0.423, 0.960, 0.538] typically higher or lower than for {the 'Sudden Decrease'}[0.130, 0.122, 0.960, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "BY9YPEb0K7SDVIWE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the subplot labeled 'Volatile movement over time'}[0.344, 0.657, 0.746, 0.677] appear above or below {the subplot labeled 'Periodic fluctuations over time'}[0.333, 0.337, 0.757, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "BRakBKiIqCCPiIuq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the plots, does {the 'Volatile Movement'}[0.130, 0.763, 0.960, 0.859] start at a higher or lower y-value than {the 'Sudden Decrease'}[0.130, 0.122, 0.960, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "o6r0UOleIi0FLYHF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region that ranges from [0.344, 0.657, 0.746, 0.677]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the title of the subplot, which reads \"Volatile movement over time\". It's set in black font."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "KnDIVThCyxfwMGCV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what's located within this area: [0.077, 0.609, 0.113, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label for the 'Periodic Fluctuations' subplot, which is marked as \"0.7\"."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "KeMAs9zhFTNvW7kV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region bounded by [0.946, 0.305, 0.975, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area there is an x-axis tick label '10' in the 'Sudden Decrease' subplot, styled in black and a normal font weight."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "s6N8nCEdTLva6fyX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the object at [0.282, 0.569, 0.569, 0.569] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a point on the 'Venture Capital' line."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "I3RFe7l4oAtVpTom",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.274, 0.337, 0.805, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subtitle saying 'Venture Capital Investment Over Time'."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "WEnQBS9dxbfv6cml",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please inform me what the object at coordinates [0.905, 0.015, 0.917, 0.027] is in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label '2018'."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "3pvnfWXQe70cOmW8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the time point of 2014, which trend has a higher investment amount, {the line for 'Venture Capital'}[0.711, 0.487] or {the line for 'Private Equity'}[0.711, 0.124]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Private Equity."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "UXCUGpatG4zxgcE0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 2009, was {the 'Venture Capital' investment point}[0.282, 0.569] higher or lower than {the 'Private Equity' investment point}[0.282, 0.233]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "vHMUgfHSf1wfvNra",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Angel Investment' point}[0.882, 0.763] in 2016 above or below {the 'Private Equity' point}[0.882, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "DmylNJIVEjf5gY0A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is inside the area bounded by [0.111, 0.385, 0.968, 0.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this area, there is a plot line representing 'Venture Capital' investment over time. There is also a subtitle 'Venture Capital Investment Over Time', an x-axis with year labels running from 2006 to 2018. The y-axis represents 'Investment (in Trillions)' with labels ranging from 0.4 to 1.9."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "JsLU1YYi5sv5wjyp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects inside the area [0.111, 0.705, 0.968, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a plot line showing the trend of 'Angel Investment' over time, year labels on the x-axis from 2006 to 2018, and labels on the y-axis representing 'Investment (in Trillions)', ranging from 0.5 to 1.5. There is also a subtitle stating 'Angel Investment Over Time'."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "UL4sr62RMa0i3Wga",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the region from [0.111, 0.064, 0.968, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a line graph tracking 'Private Equity' investments over a period labeled on the x-axis from year 2006 to year 2018. The y-axis label is 'Investment (in Trillions)', ranging from 1.5 to 3.5. The subplot's title 'Private Equity Investment Over Time' is also located here."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "Nd69OCaPifz2GvVN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used at the point with coordinates [0.401, 0.205]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "xVW7OMf0s9qBAEmF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line at {point coordinates}[0.554, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "kOfqqKuzOcCcFDnl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with {tick mark}[0.937, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "12."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "VCHb1Vv2iEfi8NVJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich portfolio at {week 10}[0.937, 0.716] has the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Portfolio 3."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "uEpcLdkruba0ykTP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {Portfolio 1 at week 6}[0.630, 0.101] higher or lower than {Portfolio 3 at week 6}[0.630, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "ti8eEgqpqyxNvVTi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents {Portfolio 2's line}[0.171, 0.395] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "7r03OxShpiCf3wOL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region <r>[0.133, 0.064, 0.975, 0.294]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the graph for 'Slow Recovery (Portfolio 1)', displaying markers at each week with corresponding values, characterized by a greenish line progressing through the points."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "KxpCxmGvwvYQFyX9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in <r>[0.451, 0.037, 0.656, 0.065]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend entry for 'Portfolio_1' with a green color indicator beside the label text."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "JBMbZCy46ZNhYDHK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region <r>[0.025, 0.479, 0.049, 0.521]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label 'Value' for the subplot 'Continuous Decrease (Portfolio 2)', oriented vertically."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "xOLS8QKPazt3b40f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the year at {the x-axis tick} at [0.935, 0.294, 0.935, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "33TIUWz5eA9Jzyjc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented by coordinates [0.092, 0.064, 0.975, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "dwfwK8TYWOZBJjkF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color indicates the GDP Growth trend in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "llhztr5gMK5Aymv8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Unemployment data points}<r>[0.453, 0.454]</r> higher in the graph than the {GDP Growth data points}<r>[0.453, 0.747]</r> in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "F51zapTYDveXKIwV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {year label}<r>[0.214, 0.946]</r> closer to the {label for GDP Growth}<r>[0.178, 0.719]</r> or {label for Inflation}<r>[0.178, 0.078]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "eeeLAIGUzxa9iIsh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a larger value in 2013, {Unemployment}<r>[0.534, 0.471]</r> or {GDP Growth}<r>[0.534, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "XCDVNP7jvnlJMfNU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.040, 0.212, 0.076, 0.228]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the y-axis label '0.1' for inflation rate, described in black color and 0-degree rotation."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "LZQLF1njUhSXrcri",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are prevalent in the region [0.534, 0.327, 0.614, 0.415]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis label of GDP growth subplot, placed centrally, and the y-axis label of unemployment rate subplot, styled vertically."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "qqZ6Nc089lZNQCqv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.534, 0.946, 0.614, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the year label '2014' on the x-axis of the GDP Growth chart, written in black with no rotation."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "yRb6dTE0GGUKZob0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the meaning of the label at the position [0.345, 0.655, 0.689, 0.667]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"New Business Ownership Rate Over Time\"."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "TeVZmhljAaKndxw8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the position [0.806, 0.316, 0.884, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend item labeled \"Insignificant Increase\"."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "m98u3p1nWqw99hun",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies at coordinates [0.015, 0.471, 0.029, 0.504] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of subplot_1 which states \"Rate %\"."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "izReSMx28IYBq5ck",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2011 point on 'Established Business Ownership Rate'}[0.156, 0.482] higher or lower than {the 2011 point on 'New Business Ownership Rate'}[0.156, 0.875] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "vmoIPc8XOYq361jA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'New Business Ownership Rate' line}'s last point[0.968, 0.818] closer to the top of the figure or to the bottom?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bottom."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "tMQKUzSKhTQ16g1Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the values at {2020 point on 'Established Business Ownership Rate'}[0.968, 0.402] and {2020 point on 'New Business Ownership Rate'}[0.968, 0.818], which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Established Business Ownership Rate'"
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "vxwvbgbocvmaATmb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents of this region: [0.118, 0.684, 0.328, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend item, {New Business Ownership Rate}[0.118, 0.684, 0.328, 0.693], in subplot_2."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "bIJyEHw3OkyoEvg4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is contained within the region [0.229, 0.968, 0.264, 0.977]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the X-axis tick label {Year 2012}[0.229, 0.968, 0.264, 0.977] in subplot_2."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "tNl7V5lhSVF1vGwc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area from [0.118, 0.353, 0.377, 0.362]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend item for {Established Business Ownership Rate}[0.118, 0.353, 0.377, 0.362] in subplot_1."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "pU0bjKtcyNHlumjo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the line for 'Farm Profit'}<r>[0.147, 0.077, 0.968, 0.261]</r> in terms of color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "0ET241mbjExI9A1Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the x-axis tick}<r>[0.229, 0.294]</r> corresponding to the year 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "2006."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "I94qyWwWFiri9CWu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the highest point on the line for 'Agricultural Imports'}<r>[0.968, 0.720]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.968, 0.720]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "C6CdyUNyd5qwRFP3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line point for 2010}[0.558, 0.163] above or below {the line point for 2008}[0.393, 0.150] in 'Farm Profit' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "NIiObGI5Ob2M8UrD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the initial point}[0.147, 0.615] for 'Food Sales' start higher or lower than {the final point}[0.968, 0.406] in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "Pt7FJQRZ5y6BBVZy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the mark for 2009}[0.476, 0.843] on 'Agricultural Imports' horizontal line higher or lower than {the mark for 2015}[0.968, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "Ca49zBMK6G8GHvVa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.036, 0.305, 0.080, 0.321]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2004' is located here, displayed in black color."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "FhnnlJQZzZvzMS7l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.025, 0.135, 0.048, 0.223]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label 'Amount ($)' for the 'Farm Profit Over Years' chart, styled in black and rotated 90 degrees."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "ZYyY5zZKB5luiwL0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.147, 0.385, 0.968, 0.615]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for 'Food Sales' with points marking sales from 2005 to 2015, as well as corresponding y-axis labels ranging from 20000 to 30000 dollars."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "bBShGIF4oEQ3krmB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the dominant colors of {the markers for the line representing Scientific Research}<r>[0.079, 0.736, 0.128, 0.745]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "XbCVcRmiHWsq9vTL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis tick label corresponds to {the point at year 5 for Scientific Research}<r>[0.526, 0.147]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "xhpBq7oHTAk8k9nh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-coordinate for {the highest value point on the Technological Innovation line}<r>[0.884, 0.409]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "9."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "EJFLPmv4R3d0431z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the highest point on the Scientific Research line}<r>[0.884, 0.121]</r> higher than {the lowest point on the Technological Innovation line}<r>[0.884, 0.409]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "LPDPU4oftMSpusla",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is higher, {the point at year 4 for Technological Innovation}<r>[0.437, 0.601]</r> or {the point at year 6 for Scientific Research}<r>[0.616, 0.135]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Scientific Research."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "PIcr6EaWw6QFr1tn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the Scientific Research line}<r>[0.079, 0.299]</r> higher than {the first point on the Technological Innovation line}<r>[0.079, 0.612]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "T9rjFl5e7nsgb6iH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area with coordinates [0.039, 0.313, 0.068, 0.362]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several y-axis tick labels for Technological Innovation, specifically the labels for \u2018300\u2019 and \u2018400\u2019."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "73nsMdvUFdeRHdzv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present in [0.530, 0.312, 0.620, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains x-axis tick labels '6' and '8' for the subplots presenting timelines for Scientific Research and Technological Innovation."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "yuOE8TpVylx1LZqI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the region [0.136, 0.734, 0.282, 0.772]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text labels for the legends specifying 'Scientific Research' and 'Technological Innovation' with associated markers in green and blue."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "zMLj1i9uZULwDp66",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the shape that is coloured and placed at {coordinates}[0.401, 0.691, 0.447, 0.691] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a horizontal line."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "C3ySCjoAedPO8zTI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {y-axis tick}[0.114, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "-1.5."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "HhWXk4RHpXEOK8f4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat axis is associated with {the label}<r>[0.517, 0.968, 0.573, 0.983]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "NvbAYOx50bEhLHS2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Fossil fuel Emission'}[0.130, 0.742, 0.960, 0.908] start at a higher or lower value than {the line for 'Population Change'}[0.130, 0.408, 0.960, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "bZFVCS1Hu1ZwxZFV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {coordinate for '8th point'}[0.794, 0.779] on the Fossil fuel Emission trend move along the positive or negative Y direction compared to {coordinate for '7th point'}[0.711, 0.797]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "lU9LpIpGUoLiKX2L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the trend for 'Yield' and 'Population Change', which shows a positive slope between the 1st and 2nd points?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population Change."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "kLTfcGxB1N4J5EeK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region bounded by the coordinates [0.394, 0.016, 0.696, 0.036]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle \"Yield Trend Over Time\" in black."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "6IgGJfNPVMA0Zvd4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.130, 0.408, 0.960, 0.592] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the chart data for 'Population Change Over Time' with points plotted in red."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "y450XR4it7wdA7eV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of the subplot area [0.130, 0.064, 0.960, 0.294]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area illustrates 'Yield' trend according to time with the line drawn in green color connected vertically from the top left to the bottom right of this region."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "iAcHl3t0E40HNWkv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at coordinates [0.082, 0.630, 0.091, 0.642] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label \"0\" for subplot 1."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "bWYmhD41I7h8ocsG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.046, 0.421, 0.077, 0.433] indicate in the third plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label \"1.00\"."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "QVM9WVtfRn50kaRO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element is located at [0.035, 0.459, 0.029, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the second subplot."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "NN26uqTzdanbY9we",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label '0'}[0.082, 0.301, 0.091, 0.313] higher or lower than {the label '2'}[0.260, 0.301, 0.269, 0.313] in terms of coordinates on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "VAHzFTDnxiIXem9v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick at [0.087, 0.293]} within the bounds of {the line for 'Security Risk'}[[0.087, 0.063, 0.976, 0.284]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "x3IRk9sEB06H84oS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, does {the Y-axis label '1.4'}[0.055, 0.166, 0.077, 0.178] appear visually above {the Y-axis label '1.6'}[0.055, 0.131, 0.077, 0.143]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "UgkFdZYtDvZT2abu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found between the coordinates [0.376, 0.641, 0.589, 0.717]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses parts of the upper body of the chart's second subplot including the higher financial risk values and a subsection of the plotted financial risk line."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "4sTujBXRYUltQLP6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.087, 0.031, 0.250, 0.293]."
+            },
+            {
+                "from": "gpt",
+                "value": "The lower left corner of the first subplot; includes the x-axis from labels '0' to '2', the starting section of the security risk line, and corresponding grid lines."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "1L86I99NAYK1Caws",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.441, 0.636, 0.620, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the midsection of the x-axis and y-axis of the second subplot, showing part of the x-axis tick labels ('4', '6'), part of the financial risk line, and a fraction of the y-axis with its grid lines."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "Rp2c9yOOvigeKK2r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled '2022' located at {coordinates}[0.416, 0.626, 0.474, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "S1nls0NM60qPc6C7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object has the coordinates [0.058, 0.196, 0.094, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '1.2' Y-axis tick label of the first subplot."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "jAUA8VQTaC6vpw3M",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the main object at {coordinates}[0.025, 0.787, 0.049, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Progress' Y-axis label of the last subplot."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "h4qajQBeRpAip9GC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value in 2024 for 'Cloud Computing'}[0.779, 0.820] larger than the {value in 2020 of 'AI'}[0.111, 0.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "9O3QMO3hHBb9PYbF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at the {point in 2023}, the 'AI' line [0.612, 0.534] or the 'Blockchain' line [0.612, 0.248]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "4zC3NYMoEVeZ0dBL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of 'Blockchain' in 2025 [0.946, 0.156] lower than its value in 2021 [0.278, 0.202]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "dFtaNk4ftxHbytjH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the region [0.540, 0.358, 0.646, 0.386]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot, which indicates 'AI' with a red 'x' marker."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "bruxIk1rvtrxj6LI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be found in the area defined by [0.220, 0.657, 0.836, 0.677]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the title for the third subplot, which is 'Cloud Computing Growth from 2020 to 2025'."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "iEJ0iMK6r9hBWqnB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.540, 0.678, 0.824, 0.706] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend for the third subplot: 'Cloud Computing' with a blue square marker."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "B9ICv6azdXIY2GVv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.495, 0.855, 0.554, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "GV4sw0KB5clHBoTh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.114, 0.169, 0.935, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A line graph."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "kVgMGQ0OSIFYE2ds",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.069, 0.276, 0.104, 0.346]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "TLXW9xGGHimebjNy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2018'}[0.771, 0.318] above or below {the point for year '2019'}[0.853, 0.249] in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "Z6ulVtD1ifxkP4u2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2013'}[0.360, 0.664] higher or lower than {the point for year '2014'}[0.442, 0.631] in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "wCodCVHOQ6pheAJH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has a higher CO2 reduction, {the point for 2016}[0.607, 0.459] or {the point for 2020}[0.935, 0.169]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "C2eKgTdkaAMLPKAz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.069, 0.114, 0.104, 0.734]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick labels marked with values ranging from 10 to 65."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "NHUpViI1Kbl2YOt0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.114, 0.169, 0.935, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises a line graph representing the CO2 Reduction percentage from the year 2010 to 2020."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "UEjylZrVZ0AJXkhQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.069, 0.757, 0.970, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 11 objects. These are the x-axis tick labels representing the years 2010 to 2020. For instance, the labels '2010' and '2020' are located at {coordinates}[0.079, 0.757, 0.149, 0.827] and {coordinates}[0.900, 0.757, 0.970, 0.827], respectively."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "Z0G8DTEO2zgFu4Aw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vehicle Sales."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "FSrOI8EVPWeWkwmm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the point labeled '2005'}<r> [0.535, 0.239] </r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "74,000."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "XG4RwRi73fiZyZVH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point} <r>[0.280, 0.656]</r>represent on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2002."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "NZfpAUdBWZRtlucq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '2009'}<r>[0.874, 0.352]</r> selling more or less than {the point for '2002'}<r>[0.280, 0.656]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "WsWL7YmRWdHXyVmV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of sales, does {the point for '2001'}<r>[0.195, 0.732]</r>perform better or worse than {the point for '2007'}<r>[0.705, 0.371]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "xbYytaXO7YfzWuqR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the points, did the sales increase or decrease from {the point for '2008'}<r>[0.789, 0.694]</r> to {the point for '2010'}<r>[0.959, 0.087]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "SR0MXZntGCmhhUYN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region <r>[0.513, 0.903, 0.557, 0.931]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label '2005'."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "iCUFgf2gP3HWZXFe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region <r>[0.019, 0.366, 0.036, 0.547]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label 'Vehicle Sales' written in black and rotated by 90 degrees."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "DvRF3TsSlsGR8r8q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.600, 0.422, 0.639, 0.442]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the numerical label '62000' associated with the year 2006."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "qmwBV4jrZH421AGV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object of {xlabel}<r>[0.500, 0.855, 0.602, 0.925]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gender."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "alDfo7hVuLBDUbc3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis tick}<r>[0.132, 0.733]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "16."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "RF9VQmcIQhqYTVIr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {line_0}<r>[0.342, 0.184, 0.761, 0.655]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Average Income and Population Indicators for each Gender."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "ObanhKsMw3XiMZIn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the gender point at {coordinates}<r>[0.761, 0.184]</r>, does it map to a higher or lower value on the y-axis than the point at {coordinates}<r>[0.342, 0.655]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "0hG1Dri4Tmk2kwwL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Females}<r>[0.284, 0.757, 0.399, 0.827]</r> and {Males}<r>[0.720, 0.757, 0.801, 0.827]</r>, which has a higher indicator value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Females."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "K5GaiQhGfE15RUFl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-value of {Females}<r>[0.342, 0.655]</r> on the line greater than the y-value of {y-tick '30'}<r>[0.087, 0.637, 0.122, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "q7XCItMNeWywc1aT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific features are included in the region from {coordinates}<r>[0.284, 0.757, 0.399, 0.827]</r> to {coordinates}<r>[0.720, 0.757, 0.801, 0.827]</r> along the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two points on the line. The point \"Females\" is located at the coordinates {Females}<r>[0.284, 0.757, 0.399, 0.827]</r> on the left and the point \"Males\" at the coordinates {Males}<r>[0.720, 0.757, 0.801, 0.827]</r> on the right side of the x-axis."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "phvahey074ooXvRU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the {coordinate}<r>[0.342, 0.184, 0.761, 0.655]</r> area."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a line chart, representing the Average Income and Population Indicators for each Gender. The line connects two points, one representing 'Females' and the other representing 'Males'."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "bvtUYh5ZUlZMZ7Qx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present in the vertical area from {coordinates}<r>[0.087, 0.539, 0.122, 0.609]</r> to {coordinates}<r>[0.087, 0.647, 0.122, 0.717]</r> of the y-axis tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes several y-axis tick labels. Specifically, it includes tick labels from '50' to '28'."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "kfW9UAIhXczHJ5xf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label represented by the coordinates [0.054, 0.282, 0.078, 0.728] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "Gb55iLYLnD2lw8BT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis tick is located at coordinates [0.627, 0.906, 0.686, 0.953]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2048."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "1RGjhDZzRivgRvkT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you inform me about what {the data point}[0.763, 0.213] represents on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point represents 4096 friends count and an engagement index of 15.3."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "VFn6yPZjkmcs0QRv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of engagement index at {the data point}[0.900, 0.164] higher or lower than at {the data point}[0.869, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "tsu6eagwo2eor5Wo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the engagement index increasing or decreasing between {the data point}[0.550, 0.362] and {the data point}[0.657, 0.389]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "41Wu8ou2TWQ946GK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in engagement index from {the point at 128 friends count}[0.231, 0.775] to {the point at 512 friends count}[0.444, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "y13ijsQGX2QR8OM7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information contained within the range [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted line chart which bellows data points of friends count against engagement index, starting from 64 to 10000 friends."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "MPY97NhxYdhNqZ7i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found in the range [0.087, 0.322, 0.117, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a Y-axis tick label indicating {13}[0.088, 0.322, 0.117, 0.368]."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "vh0rur74qYSyYcln",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we find in the region [0.209, 0.906, 0.253, 0.953]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an X-axis label for the friends count {128}[0.209, 0.906, 0.253, 0.953]."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "UjIX67wnI8DcvIcZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "CO2 Emissions (Gt)."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "NuAvRFNho8LLiaJQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the x-axis label located on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.496, 0.927, 0.545, 0.963]."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "f3wnObzahfVAP9OV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the point with coordinates [0.903, 0.170] on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value for CO2 Emissions (Gt) in the year 2019."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "kiMFWtOMQLwzZ6zF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for CO2 emissions {in 2003}[0.223, 0.568] higher or lower than {in 2005}[0.308, 0.460]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "6aW5RiGLigGxzvl0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a decline or increase in CO2 emissions from {2016}[0.776, 0.195] to {2018}[0.861, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decline."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "Qyk339mizDFl5s7f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point has a higher value: {the data point in 2008}[0.435, 0.282] or {the data point in 2015}[0.733, 0.191]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point in 2008."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "WM5my2G7hNwr969p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.491, 0.879, 0.550, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the year '2010' from the x-axis labels."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "j89MvWzxNBEqjSmp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.058, 0.438, 0.087, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label '30', representing a CO2 emission level of 30 Gt."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "s3QJC6CpHw4O27t5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.095, 0.158, 0.903, 0.672] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire line graph, showing the annual CO2 emissions from 2000 to 2019."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "HUYtTL0MdXhtAwqy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label represent at coordinates [0.015, 0.280, 0.029, 0.602] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, which is 'Intrusion Detection (%)'."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "BIPYWQaIkCFxmoz6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the data point} at [0.511, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "98."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "2C3Cgi5ChztEn57S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.494, 0.942, 0.531, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Years'."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "AQY9AYWMbRrpapaP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn reference to {the data point}<r>[0.059, 0.687]</r> and {the data point}<r>[0.169, 0.276]</r>, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point at [0.169, 0.276]."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "aNa78tiBrJCO2s8N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point}<r>[0.059, 0.687]</r> above or below {the data point}<r>[0.112, 0.249]</r> on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "NYvHXbDAep4Xa3me",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had a higher intrusion detection percentage, the year at {point}<r>[0.062, 0.660]</r> or the year at {point}<r>[0.070, 0.496]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year at point [0.062, 0.660]."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "qLcbQp4yUISh03ne",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.041, 0.861, 0.985, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains x-axis labels representing the years from 2012 to 2020."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "eeWcjI2jwP3MWiVE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.035, 0.156, 0.052, 0.869]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains y-axis tick labels representing intrusion detection percentages from 70 to 95."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "tfjWZ7PVd2AXPrXf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the area [0.059, 0.085, 0.968, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the plot of the line chart, showing the trend of intrusion detection percentages over the years."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "sL7iC7s9r7Yj12Hr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.021, 0.298, 0.041, 0.513] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "aINIPKnS6hPIJw5z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the label for the \"Education\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.142, 0.796, 0.226, 0.881]."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "zGUqJ8EbdMtuRUHO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}[0.859, 0.491, 0.884, 0.511] related to \"Waste Management\" in the graph indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the satisfaction level, which is 77."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "091zZxu5NThzkhem",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the \"Security\" category}[0.286, 0.482] higher or lower than {the point for the \"Roads\" category}[0.594, 0.559]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "ZztCMZbIUCxLZEBH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point for \"Public Transport\"}[0.696, 0.405] and {the point for \"Roads\"}[0.594, 0.559], which category has a higher satisfaction level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Roads."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "XznMzyu4JXfk7zo5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the satisfaction level of the \"Education\" category}[0.184, 0.405] more or less than {the \"Housing\" category}[0.389, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "kqg3tGPNyqHl67eh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the coordinates [0.081, 0.021, 0.901, 0.790]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main plot area of the line graph, which includes a line graph, tick marks on the x and y-axes, labels for both axes, as well as labels for each data point in the plot."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "kJaHvH1FD9fZUHfD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region [0.142, 0.796, 0.226, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for the \"Education\" category."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "crQE0i9f847hPdAs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region defined by [0.081, 0.137, 0.901, 0.559]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the line graph's path, from the point of \"Healthcare\" to the point of \"Waste Management,\" demonstrating the variations in satisfaction levels across various categories."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "JWILc641rE9D6UQC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.457, 0.927, 0.580, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label \"Age Group\"."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "Wv4BckoABMeH0lqv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.025, 0.364, 0.048, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label \"Stress Level\"."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "3Oz755s9AmQXDxbU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.095, 0.093, 0.942, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The overall line graph with data points."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "0R74nYrqn7KIkDqB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {stress level for the age group '30-40'}[0.236, 0.173] higher or lower than the {stress level for the age group '20-30'}[0.095, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "edn8DQT8ezv5BIZg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {stress level of the age group '70-80'}[0.800, 0.494] compare to the {stress level of the age group '60-70'}[0.659, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "qkLiFn5d4j1WKkCa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {stress level at the age group '40-50'}[0.377, 0.141] above or below the {stress level at the age group '50-60'}[0.518, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "aW2sEo7EBVkOKHqX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area enclosed by [0.062, 0.879, 0.975, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region containing the X-axis labels denoting different age groups: {20-30}[0.062, 0.879, 0.128, 0.914], {30-40}[0.203, 0.879, 0.269, 0.914], {40-50}[0.344, 0.879, 0.410, 0.914], {50-60}[0.485, 0.879, 0.552, 0.914], {60-70}[0.626, 0.879, 0.693, 0.914], {70-80}[0.767, 0.879, 0.834, 0.914] and {80-90}[0.908, 0.879, 0.975, 0.914]."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "f5tJg3tqL1KrAHEF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the space between coordinates [0.058, 0.201, 0.087, 0.381]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is comprised of the Y-axis tick labels {30}[0.058, 0.780, 0.087, 0.815], {40}[0.058, 0.635, 0.087, 0.670], {50}[0.058, 0.491, 0.087, 0.526], {60}[0.058, 0.346, 0.087, 0.381], {70}[0.058, 0.201, 0.087, 0.236] signifying different stress levels."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "VJuXheFRTgK8oUv5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the region [0.077, 0.038, 0.959, 0.747]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes coordinate points and corresponding stress levels for the age groups: {52.5 for '20-30'}[0.077, 0.413, 0.113, 0.438], {72.9 for '30-40'}[0.218, 0.118, 0.254, 0.143], {75.1 for '40-50'}[0.359, 0.086, 0.395, 0.111], {78.4 for '50-60'}[0.500, 0.038, 0.536, 0.063], {63.2 for '60-70'}[0.641, 0.258, 0.677, 0.283], {50.7 for '70-80'}[0.782, 0.439, 0.819, 0.464], and {31.2 for '80-90'}[0.924, 0.722, 0.959, 0.747]. These elements are connected by the line graph outlining the stress level by age group."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "skTZAtSuC6h6X2qC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeled object that corresponds to [0.060, 0.095, 0.968, 0.867] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate line graph."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "INcBthFt6S1ggFvq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {the coordinate} [0.806, 0.400] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.5."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "FLh3b8NvnHxnn2iv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.015, 0.344, 0.029, 0.592] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate (%)."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "Lmwz1ttsrHN4nKLo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the GDP growth rate for the {year 2014} [0.695, 0.354] lower or higher than the GDP growth rate for {year 2016} [0.786, 0.436]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "hB3euPfst9TuC8RS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line graph start at {the point} [0.060, 0.310] or {the point} [0.968, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It starts at [0.060, 0.310]."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "HKL1fsRMj70p54EM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the GDP growth rate for {year 2009} [0.470, 0.735] positive or negative?"
+            },
+            {
+                "from": "gpt",
+                "value": "Negative."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "MK97AwICHoL3gXOW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.035, 0.206, 0.055, 0.229]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '6' is located here."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "6Napp2q6jbzn5I6V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details of the area [0.042, 0.331, 0.350, 0.356]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels for the years 2000, 2002, 2004, and 2006."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "WBcuAM3uPXYFhgFd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the space [0.349, 0.735, 0.968, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region are the points from year 2008 to year 2020 on the GDP growth rate line graph. Each point is labeled with the corresponding GDP growth rate for the year."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "HPT4HouOQix7mVRd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the position [0.678, 0.164, 0.694, 0.184] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text label '80'."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "1Se9trUZqjcSrkgf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is at [0.112, 0.878] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "7NmgNGQh6bIqJe9R",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.858, 0.888, 0.976, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label for 'Water Conservation Programs'."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "CuKlDAqm12Y0Pvqg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the point}[0.571, 0.256] higher than {the point}[0.456, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "M9NeHWu0DWVEEbO5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the percentages at {coordinates for text label '85'}[0.793, 0.108, 0.809, 0.128] higher than those at {coordinates for text label '40'}[0.908, 0.616, 0.924, 0.636]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "YuSTnRt8JQ7PqiUO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point with a label of '75'}[0.563, 0.221, 0.579, 0.241] higher on the chart than {the point with a label of '50'}[0.449, 0.503, 0.464, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "fPQ2PLeUGBSLGPwK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the area [0.043, 0.502, 0.065, 0.575]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis tick labels '50'[0.043, 0.528, 0.065, 0.556] and '60'[0.043, 0.415, 0.065, 0.443], both marked in black against a pale background."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "VZ2dP8x7TzkTYtBj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the region [0.514, 0.888, 0.629, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the x-axis label {Community Health Programs}[0.514, 0.888, 0.629, 0.931], paired with a bottom tick at coordinates [0.571, 0.878]."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "o7VR9JTDacuAFqPW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section from [0.856, 0.878, 0.976, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the x-axis tick label for 'Water Conservation Programs'}[0.856, 0.888, 0.976, 0.931] and its corresponding x-axis tick at {coordinates}[0.916, 0.878]."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "m2MEm9DRwgdlzjAj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.496, 0.940, 0.611, 0.969]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age Group."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "UlkPQhNS3iqhTrpI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the data point} at [0.856, 0.788] represent in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "10000 User Count in the 65+ Age Group."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "iZk5jwHUCYI4BlJd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by {the y-axis tick label}<r>[0.054, 0.776, 0.123, 0.806]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "10000."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "TjKCGf00upXxrhoh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the user count for {25-34 <r>[0.372, 0.183]</r>} age group higher or lower than {18-24 <r>[0.251, 0.283]</r>} age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "VkJssYN2ZR182Zg1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the age group {35-44 <r>[0.493, 0.384]</r>} having more or fewer users than the age group {45-54 <r>[0.614, 0.536]</r>}?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "73H3gwSPhDGcFL7l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing age groups, does {55-64 <r>[0.735, 0.637]</r>} have the higher user quantity than{65+ <r>[0.856, 0.788]</r>}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "xSV77Pqgbg6KhFpv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present in the range [0.251, 0.183, 0.372, 0.283]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there's the data point {25-34}<r>[0.372, 0.183]</r> which is labeled with 22000 users and is the highest point in the chart."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "rzCxx53jK4xE5LUR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow about the content in the region [0.833, 0.899, 0.879, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label {65+}<r>[0.833, 0.899, 0.879, 0.928]</r>."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "Y6Ut03oeHEqlSQwT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the range [0.735, 0.637, 0.856, 0.788]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the last two data points of the line chart. The first one is {55-64}<r>[0.735, 0.637]</r> with 13000 users, and the second one is {65+}<r>[0.856, 0.788]</r> with 10000 users."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "1TNAfEZrnFIM5atQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {coordinates} [0.711, 0.849, 0.747, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label 'Cisco'."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "H2HVsSyz9PEZBB7A",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object located at {coordinates} [0.362, 0.541]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the point corresponding to 'Facebook' on the line graph."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "oRu9F8bRvX7dXgYv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at coordinates [0.015, 0.254, 0.029, 0.613] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Net Profit (in billions of dollars)'."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "1e6vQUN33qxAZFmV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point for Apple}<r>[0.112, 0.573]</r> above or below the {data point for Google}<r>[0.195, 0.128]</r> on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "Lpj3WLtw9shdBSmX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Google}<r>[0.195, 0.128]</r> or {Oracle}<r>[0.611, 0.175]</r> have a higher net profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "lNqRAnDMv7087GbY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point representing IBM}<r>[0.528, 0.557]</r> to the left or right of the {data point representing HP}<r>[0.777, 0.446]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "3cV4uxbgpIXy9Jsd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's in the area with coordinates [0.845, 0.825, 0.975, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels 'Dell' and 'Intel' at {coordinates} [0.846, 0.849, 0.875, 0.897] and {coordinates} [0.928, 0.849, 0.959, 0.901] respectively."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "3PXn6stoQ2pKNERb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.693, 0.100, 0.783, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the data point {for Cisco on the line graph}<r>[0.694, 0.609]</r>, the x-axis label {Cisco}<r>[0.676, 0.849, 0.712, 0.909]</r>, and the y-axis tick label {70}<r>[0.043, 0.424, 0.061, 0.448]</r>."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "cfYhQclUvBDe4q2h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.240, 0.500, 0.400, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data point for {Microsoft}<r>[0.278, 0.549]</r> and the x-axis label {Microsoft}<r>[0.251, 0.849, 0.306, 0.940]</r>."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "gplJ0004OlR7KupU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label in the chart at coordinates [0.314, 0.025, 0.737, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, which is \"Market Share of Smartphone Companies\"."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "aNtXQYUaoM2Iurjc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the label}[0.899, 0.843, 0.943, 0.918] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Google\"."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "xBBNyz0PwvLXOfjR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label}[0.039, 0.342, 0.056, 0.365] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"10\"."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "DbQrJpJrg24IgYQt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Samsung'}[0.184, 0.099] higher or lower than {the point for 'Huawei'}[0.260, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "hPE5hkWZubzMpvsS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the line graph, is {the point for 'LG'}[0.715, 0.722] placed at a higher or lower position than {the point for 'Motorola'}[0.791, 0.727]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "kCshjpkChOmtQrAf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Apple'}[0.108, 0.622] above or below {the point for 'OnePlus'}[0.867, 0.770]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "7o5HvhgULger3v19",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the coordinates [0.066, 0.066, 0.985, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plot of smartphone companies' market share. The line graph within this area depicts the market share percentages of the companies Apple, Samsung, Huawei, Xiaomi, Vivo, Oppo, Realme, Lenovo, LG, Motorola, OnePlus and Google."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "HYoIM6XcpbRA9C23",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.070, 0.843, 0.943, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains x-axis labels, specifically the names of the smartphone companies being compared in the chart. They are {Apple}[0.070, 0.843, 0.108, 0.907], {Samsung}[0.128, 0.843, 0.184, 0.936], {Huawei}[0.213, 0.843, 0.260, 0.921], {Xiaomi}[0.292, 0.843, 0.336, 0.916], {Vivo}[0.380, 0.843, 0.412, 0.895], {Oppo}[0.451, 0.843, 0.487, 0.904], {Realme}[0.517, 0.843, 0.563, 0.921], {Lenovo}[0.594, 0.843, 0.639, 0.919], {LG}[0.692, 0.843, 0.715, 0.882], {Motorola}[0.738, 0.843, 0.791, 0.932], {OnePlus}[0.818, 0.843, 0.867, 0.926] and {Google}[0.899, 0.843, 0.943, 0.918]."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "uxvvGITCpD3Oh9Ww",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.039, 0.152, 0.056, 0.175] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label {14}[0.039, 0.152, 0.056, 0.175]."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "JBpaEcsBJX2uawvY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element labeled at coordinates [0.021, 0.341, 0.041, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis (Market Share (%))."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "8ClCz2mg4WwLyhJ8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line with coordinates spanning from [0.081, 0.190] to [0.954, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#4c72b0)."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "noFkVTcmsxwS9iWf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the first tick on the x-axis}<r>[0.056, 0.903, 0.107, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "1CsiFXaWDzuyPCap",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of the line with coordinates starting from [0.081, 0.807] and ending at [0.954, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "w5wYphZUpFcHkWIs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year has a higher Market Share, {2018} at <r>[0.331, 0.431]</r> or {2019} at <r>[0.455, 0.237]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "55o7SGjd30KezyVQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point on the line plot has a higher y-coordinate, the point at <r>[0.331, 0.431]</r> or the point at <r>[0.061, 0.763]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at [0.061, 0.763]."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "lGStZ1DYhhkCGAQr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area located at [0.050, 0.364, 0.075, 0.392]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label '42', horizontally oriented and presented in black color."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "CXYXXWjMD8fq6Nm4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the section of the line plotted between coordinates [0.704, 0.250] and [0.954, 0.815]."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment of the line shows a negative trend from the year 2020 to 2022, with the market share decreasing from 44.9% to 31.8%."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "SH7Fun89IQwCnvx3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.081, 0.030, 0.954, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the main plot area of the graph. It includes a blue line plot representing market share from 2015 to 2022, with respective data points labeled with their values. The grid is visible with white lines."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "fW076Mi3sgC5kaBg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThere is an object in the chart at these coordinates [0.387, 0.855, 0.593, 0.925]. Can you tell me what it is?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "VnmK6ZqXweG8ogFJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the coordinates [0.157, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point for 'Chatbot'."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "9CrnDAOWLodLNMhw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.069, 0.238, 0.104, 0.308]?"
+            },
+            {
+                "from": "gpt",
+                "value": "85."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "Oj6Bz11KtBUnJGqf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the value of {the 'Speech Recognition' point}[0.324, 0.328] compare to the value of {the 'Chatbot' point}[0.157, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "YZ9KzLbIwjDulyXW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value of 'Autonomous Vehicles'}[0.824, 0.202] greater or lesser than {the value of 'Image Recognition'}[0.657, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "j5aiFq50PitUxKon",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich application has a lower success rate, {Autonomous Vehicles}[0.824, 0.202] or {Natural Language Processing}[0.490, 0.430]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Autonomous Vehicles."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "OoOXhZ2EBUHQkTYi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.187, 0.757, 0.461, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for 'Speech Recognition' is found here."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "fC9gskrGmeKl3dN3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with coordinates [0.069, -0.149, 0.058, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, 'Successful Implementation (%)', is found in this area."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "Hg3QEJAcCxm87X8U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.145, 0.472, 0.336, 0.269]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the data points for 'Chatbot' and 'Speech Recognition', with their respective values '60' and '80' labeled."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "aa1TjxlHu8eVQCGi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of label is attached to {this object}<r>[0.015, 0.229, 0.029, 0.684]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "XBveXsY5j4D0rGJD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label located at {this coordinate}<r>[0.509, 0.942, 0.538, 0.970]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "CerO87mMpK1dg3lR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value indicated by {this label}<r>[0.103, 0.724, 0.128, 0.744]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "81.2."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "hVsgjQFeDKcqz4hK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for 2009}[0.326, 0.693] higher or lower than the {point for 2010}[0.392, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "CH9y4S67oq0JF1ts",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Innovation Confidence Score for 2014}[0.655, 0.375] exceed the {Innovation Confidence Score for 2012}[0.523, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "nkDL3oeKwBQXN38a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the respective Innovation Confidence Scores in {2006}[0.128, 0.759] and {2007}[0.194, 0.639] increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "FgYnPrlTULSqrqJZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the region between {coordinates}<r>[0.062, 0.174, 0.985, 0.856]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a line chart that plots the 'Innovation Confidence Score (%)' from the year 2005 up to 2019. The data points are plotted in blue and are connected by a blue line. The innovation confidence score seems to be generally increasing over these years."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "NBcDuBUgK4dZScbS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the area bounded by these {coordinates}<r>[0.035, 0.213, 0.052, 0.241]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-tick label '95' is represented in this area."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "xchXSDfcD8HHYalc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information contained within the {coordinates}<r>[0.235, 0.480, 0.260, 0.500]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value '87.5' corresponding to the year 2008 of 'Innovation Confidence Score (%)' is located."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "ZyJw2tDdOmvW34VY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart according to the JSON information and its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Size of Training Data (No. of Data Points)\" and its coordinates are {X-axis label}<r>[0.306, 0.927, 0.779, 0.963]</r>."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "dXJhH4UwVCJ9A7sC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.530, 0.383] in the JSON structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "That coordinate is a point on the data line, specifically the point corresponding to the x value of '32' and y value '87.6' in the given data."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "z5pkZYlSUK5v5s13",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the JSON data, what is the coordinate of the y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate of the y-axis label is {y-axis label}<r>[0.025, 0.258, 0.048, 0.648]</r>."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "8PeuXL3l1TnX7MPi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the JSON structure, is the data point for x='8' higher or lower than the data point for x='4' on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "tzb2ie93PszPJFZq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the coordinates in the JSON structure, is the data point at x='16' closer to the x='8' point or the x='32' point?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x='32' point."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "RQDi2HuyCBtX0hRk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the JSON structure, which value for x has a higher corresponding y value, x='256' or x='128'?"
+            },
+            {
+                "from": "gpt",
+                "value": "x='256'."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "LSUIgdlHda3HQWqy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content present in the region mentioned by the coordinates [0.171, 0.129, 0.951, 0.849] in the JSON data."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a line graph which represents the AI model accuracy based on the size of the training data set, with points plotted at specific data points with labelled y-axis values."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "B4dZBXK0XBoF0O7T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.058, 0.037, 0.102, 0.073] as per the JSON structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '100'."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "lxs8hOXNp0ur5Rbz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.215, 0.855, 0.228, 0.914] based on the JSON structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label '$2^{2}$'."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "PWFBAfUzmxpcNQ1O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.029, 0.722, 0.136, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label of household_incomes."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "2zwf7wH4kVZd5ptb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item of information does the coordinate [0.650, 0.454] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a data point for the interest_rate."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "9SaGcd1Msv1eaRv4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the label located at [0.015, 0.299, 0.029, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, indicating Metric Value in the plot."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "zv9tTi0gSMSSXatx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point representing GDP_growth at [0.356, 0.454] higher or lower than the data point of consumer_price_index at [0.577, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "400e5zi9r6J5GYtN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point representing household_incomes at [0.136, 0.058] to the left or to the right of the data point for salary_increase at [0.209, 0.420]?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the left."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "31RD1nH3HkMJ1m7B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to business_profit at [0.871, 0.398], is the data point for trade_balance at [0.945, 0.683] above or below?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "D2uvObMkZHe8xDo5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the text located at [0.470, 0.952, 0.610, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the xlabel or title for the X-axis, which is labeled as 'Economic Indicators'."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "SwCWPwcficI1J8vv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.678, 0.722, 0.797, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a label on the x-axis: personal_savings_rate."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "n02dX6cuWo2xo1Y8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.400, 0.722, 0.503, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is an x-axis tick label representing the economic indicator 'real_estate_pricing'."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "RLpcDoJ89Ei77siJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.030, 0.112, 0.058, 0.671] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "AeUhlpWPPXerNrvj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the last tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.900, 0.757, 0.970, 0.827]"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "7RfW3Lir1Zd1A1d4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tick label on the y-axis}<r>[0.069, 0.364, 0.105, 0.434]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "CWa678wQgKfe2ClL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {the year 2014}<r>[0.292, 0.757, 0.362, 0.827]</r>, was food production higher or lower compared to {the year 2012}<r>[0.089, 0.757, 0.159, 0.827]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "L9kkJrZpU0YxE8bB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the food production in {the year 2019}<r>[0.798, 0.757, 0.869, 0.827]</r> higher or lower than in {the year 2018}<r>[0.697, 0.757, 0.768, 0.827]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "RjGyMUJpZyjItUMa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the food production in {the year 2015}<r>[0.393, 0.757, 0.463, 0.827]</r> and {the year 2017}<r>[0.596, 0.757, 0.666, 0.827]</r>, which year had a lower food production?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "PNFeNRmegzJMzjKz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the coordinate area [0.500, 0.855, 0.559, 0.925]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label \"Year\", styled in a black color."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "TOd2wUv8FmXt8aj5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.069, 0.111, 0.105, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label '94', colored in black."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "CCOoYnc6bBNvKp7e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the coordinates [0.732, 0.238, 0.760, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The food production point for the year 2018 is located here, labeled with '92.43'."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "JKTLYBPkqxucGmQy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.043, 0.152, 0.065, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label \"90\"."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "SUfPTFf6j121XyPu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at these coordinates [0.113, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for \"Continuity of Culture (CC)."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "IUoEZwfELJS40ydE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} at [0.415, 0.942, 0.638, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social and Cultural Issues."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "97u24yuPhbLRukBK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {point for 'CC'}[0.113, 0.766] above or below {the point for 'TD'}[0.231, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "05qO6muRjOuGANiX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {point for 'HR'}[0.704, 0.150] and {point for 'N'}[0.822, 0.445], which one has higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point for 'N'."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "XfMmKQ7UKqO94qoh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher value at the 3rd point, {CC}[0.113, 0.766] or {TD}[0.231, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "CC."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "MYSKROG4adPicSfy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.043, 0.152, 0.065, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis tick label '90'."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "vC9UGsN3aiu5J8cn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease give me details about the area [0.100, 0.903, 0.125, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label for 'CC'."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "t5PHS6TYLfyObyUL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the coordinates [0.567, 0.903, 0.603, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the x-axis tick label 'CC2'."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "3BL6VbqRBMKbAiJS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.092, 0.635, 0.120, 0.668] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label \"60\"."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "ALhEhfByePUxglqn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.577, 0.809, 0.657, 0.842] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label \"Rebirth\"."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "fJnQFCRD4McLicDX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title of the subplot}<r>[0.460, 0.852, 0.577, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Lifecycle."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "nvd9EGDT3KDZX5Zv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {point 'Stagnation'}<r>[0.420, 0.511]</r> and {point 'Decline'}<r>[0.519, 0.725]</r> in terms of the 'Startup Success' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Stagnation' point is higher than the 'Decline' point on the 'Startup Success' line."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "S5ZAsz2boR9q0j0c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {point 'Rebirth'}<r>[0.617, 0.343]</r>, where is {point 'Transformation'}<r>[0.715, 0.374]</r> located on the 'Startup Success' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "29n6MdmkLshgFVfE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point on the 'Startup Success' line is lower, {point 'Rebirth'}<r>[0.617, 0.343]</r> or {point 'Reinvention'}<r>[0.814, 0.160]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Reinvention'."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "3Vyum1YoahLnn0Fa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what occupies the region [0.149, 0.809, 0.298, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is an x-axis label of 'Mature Stage'."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "lDgke0MMTGOMfvhw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region [0.389, 0.910, 0.648, 0.973]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the sub-legend titled 'Startup Success' along with its associated color marker."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "C776akcP5YkqX8U9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.092, 0.635, 0.120, 0.668] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label '60'."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "TLP0iVSsHNQFV6Ri",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat attribute does the y-axis label at [0.017, 0.143, 0.048, 0.765] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance of Integration."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "pCeg8zFL8gjfSmT7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the marker of 'API Integration'}<r>[0.688, 0.759, 0.728, 0.773]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ca02c."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "ysvBXJuj4EQI6yaJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.407, 0.928, 0.673, 0.975]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Integration Steps."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "BOayx8BXUs7NT4UZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Database Integration'}<r>[0.112, 0.080, 0.968, 0.662]</r> above or below {the line for 'Data Lake Integration'}<r>[0.112, 0.205, 0.968, 0.495]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "AjD0ebwPzH5pb5h0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line is higher at point '5', {the line for 'Data Warehouse Integration'}<r>[0.492, 0.516]</r> or {the line for 'API Integration'}<r>[0.492, 0.350]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Warehouse Integration."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "n7AISxookL5z3e2y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Integration has the highest performance at step '10', {Database Integration}<r>[0.968, 0.080]</r> or {Data Lake Integration}<r>[0.968, 0.205]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Database Integration."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "4sUPkV0NkQ2WbVQN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects found within rectangle [0.736, 0.721, 0.954, 0.853]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend description for 'Data Warehouse Integration' and 'Data Lake Integration' along with their respective colored markers."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "CVieCYeN5jQrhFhK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in rectangle [0.054, 0.025, 0.112, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick labels ranging from 70 to 110, indicating the performance of integration."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "0J6LPDRpy5jdeBQi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area [0.687, 0.711, 0.960, 0.858]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend which includes names of the integration types 'Database Integration', 'API Integration', 'Data Warehouse Integration', and 'Data Lake Integration', each paired with a different colored marker."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "mSEas14Y2lx8f2MJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the indication of the color {#008000}[0.101, 0.085, 0.137, 0.097] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthy Lifestyle."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "ILSOmvB7cNzFKaky",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the Y-axis located at [0.015, 0.137, 0.035, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage of Engagement (%)."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "u3wediJmPAeDppWh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the marking at coordinate [0.813, 0.257] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reading Hours for Healthy Lifestyle."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "Vsn9x3xI481OJtUD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {Outdoor Activities}[0.196, 0.270], which lifestyle is more popular, {Healthy Lifestyle}[0.196, 0.270] or {Sedentary Lifestyle}[0.196, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthy Lifestyle."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "mVkR1ZcCFKK8bTBv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point representing {Meditation[0.299, 0.514] in Sedentary Lifestyle} above or below the point representing {Meditation[0.299, 0.199] in Healthy Lifestyle}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "FHBJF6FS85EyQLvV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lifestyle has a higher percentage in {Reading}[0.813, 0.385], Healthy Lifestyle or Sedentary Lifestyle?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sedentary Lifestyle."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "3agXQToxS3fY95Tj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.250, 0.720, 0.348, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Meditation' label of the x-axis, which is displayed in black color and is horizontally aligned."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "T9JmQUj4fNqrVzmd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.093, 0.302, 0.916, 0.610]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data points for the Healthy Lifestyle and Sedentary Lifestyle, plotted as lines with distinct markers for each datapoint. Healthy Lifestyle is plotted as a green line with circular markers, while Sedentary Lifestyle is plotted as a red dashed line with square markers."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "2uNtQD94uhVytuy0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.196, 0.270, 0.196, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the comparison of a Healthy Lifestyle and a Sedentary Lifestyle for the Outdoor Activities. The point for the Healthy Lifestyle is above the point for the Sedentary Lifestyle."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "bP0Whj3CvvK5PdFC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main label in coordinates [0.017, 0.373, 0.039, 0.494] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "o5Ft3GlmQDXpzaMa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third tick label on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.308, 0.809, 0.324, 0.842]"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "0YzzLojTQ7eiAeMQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the {coordinates}<r>[0.223, 0.556]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point of 'Population' line corresponding to 'Year 2'."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "K435osIuRwyreR4o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point {5 on the 'Population'}<r>[0.502, 0.409]</r> higher or lower than the point {5 on the 'GDP'}<r>[0.502, 0.670]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "TWBURldADpJRu5RE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, either {Population}<r>[0.129, 0.147, 0.968, 0.638]</r> or {GDP}<r>[0.129, 0.311, 0.968, 0.785]</r>, generally has higher values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "jkqR077zPPlRreSd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {8th point on the x-axis}<r>[0.781, 0.301]</r>, does the 'Population' line or the 'GDP' line have a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Population'."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "21NT4FVsfH3hjAb5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.353, 0.903, 0.744, 0.966]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. The legend includes {Population}<r>[0.432, 0.918, 0.562, 0.952]</r> and {GDP}<r>[0.681, 0.918, 0.734, 0.952]</r> each paired with a color marker."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "GwcZmNJNnCaK8J3f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area defined by the coordinates [0.045, 0.297, 0.124, 0.331]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '30000' is located in this region."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "iQjj8PcekhP8dAOe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are located within [0.401, 0.809, 0.510, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '4' tick label on the x-axis and corresponding tick marker are located within this region."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "uPYcJaac4gXGRv7G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.417, 0.918, 0.619, 0.946]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis label."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "O9AIkBDLbq74zIuo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}[0.574, 0.961, 0.695, 0.981] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Efficiency."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "m7TpN5CLDsiPvvhi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.035, 0.652, 0.083, 0.680]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "EJaZXXgc6yRzXxTr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the efficiency level \"Very High\", {Material Efficiency line's point}[0.804, 0.344] or {Energy Efficiency line's point}[0.804, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Material Efficiency."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "ymv0tm7ov8AV3CjQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the efficiency level \"Medium\", does the {Material Efficiency line's point}[0.374, 0.663] have a greater value than the {Energy Efficiency line's point}[0.374, 0.600]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "N94phPFCgidbS70N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Energy Efficiency point}[0.947, 0.108] at the \"Outlier\" level below the {Material Efficiency point}[0.947, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "Yhl7Etsm7fadPv2X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within coordinates [0.335, 0.953, 0.700, 0.989]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart, with two items. The first item is {Material Efficiency}[0.379, 0.961, 0.508, 0.981] with its signifying marker, and the second one is {Energy Efficiency}[0.574, 0.961, 0.695, 0.981] with its identifying marker."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "MPVwTPep9XLG6Lyc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details at [0.055, 0.021, 0.083, 0.050]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a y-axis tick label of '100'."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "Rccm8bTNR5iXJbxg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.088, 0.820, 0.947, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the plot of Material Efficiency with a line and its points, leading from 'Low' to 'Outlier' levels of efficiency."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "CNU8oaKqA40N5mJq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.050, 0.907, 0.127, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '1970'."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "4MXRFdwqMWbMl2tX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at {coordinates} [0.057, 0.946, 0.098, 0.974]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '400'."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "9sentEAdSQoVgFSn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the line} at [0.107, 0.475, 0.957, 0.732] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural Population."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "8DpNaggWu3GO85Kl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 1980 on the 'Rural Population' line}[0.277, 0.581] above or below {the point for 1980 on the 'Urban Population' line}[0.277, 0.719]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "Cr8k1DdZbZfyio8f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2020 on the 'Rural Population' line}[0.957, 0.732] below or above {the point for 2020 on the 'Urban Population' line}[0.957, 0.138]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "r39KlKiFOzz2taE5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend shows a higher population in 2000, the Rural or Urban population?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Population."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "qXmhOtMfDfpJODtB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are found at the position [0.118, 0.076, 0.390, 0.175]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend entries: {Rural Population}[0.189, 0.089, 0.371, 0.118] and {Urban Population}[0.189, 0.133, 0.381, 0.162]. Each accompanied by a color-coded marker."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "wSPcrNbBDIfEi5cm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content and elements present in the area [0.107, 0.060, 0.957, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart where the 'Rural Population' and 'Urban Population' trends are plotted."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "jGMEW28ZBLeHzlB4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.050, 0.900, 0.120, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the X-axis tick label '1970' at {coordinates}[0.079, 0.913, 0.135, 0.942] and the Y-axis tick label '400' at {coordinates}[0.055, 0.888, 0.097, 0.917]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "s2Aukfu5Hid4u5TO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object with coordinates [0.017, 0.305, 0.039, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "qWk6T0ZX6neQ35kU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fifth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.386, 0.809, 0.437, 0.842]"
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "vuEbgPUHT59TW5Qt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the x-axis tick} at [0.231, 0.809, 0.278, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mar."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "UnyaPK2jAmwHitZc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled 'Jun'}[0.489, 0.579] above or below {the point labeled 'Apr'}[0.333, 0.801] on the 'Bstock' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "5ZVJrCNQByAexAfh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Astock'}[[0.098, 0.247, 0.959, 0.801]] generally above or below {the line for 'Bstock'}[[0.098, 0.136, 0.959, 0.801]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "ba4snWiLeFtMuDki",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock has a higher value at the {point labeled 'Dec'}[0.959, 0.247]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Astock."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "ggfsWGQWGGCkN9bB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.342, 0.914, 0.715, 0.977]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area hosts the chart's legend with two entries: {Astock}[0.421, 0.928, 0.504, 0.962] and {Bstock}[0.622, 0.928, 0.705, 0.962], each associated with a colored marker."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "730Xk4SW8q2dRkJq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.098, 0.801, 0.959, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis tick labels representing each month from 'Jan' to 'Dec' spread across the entire width of the chart."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "Zj6YbYKLP9nOzEER",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.079, 0.809, 0.198, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The x-axis tick labels 'Jan' and 'Feb' are located at {coordinates}[0.079, 0.809, 0.117, 0.842] and {coordinates}[0.154, 0.809, 0.198, 0.842], respectively."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "ZvySgWmRjj2r6Ppk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by these coordinates - [0.080, 0.809, 0.115, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Q1'."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "bMaHgmzKaHNiILrE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for 'Green Spaces Development'}<r>[0.098, 0.314, 0.966, 0.558]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #1f77b4."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "mFNZifArUDTwEkse",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at {the legend}<r>[0.210, 0.865, 0.555, 0.898]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Spaces Development."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "g8Sdq4Y5iLKa5a98",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line is higher at point 'Q4', {the line for 'Green Spaces Development'}<r>[0.966, 0.314]</r> or {the line for 'Public Health Infrastructure'}<r>[0.966, 0.168]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Green Spaces Development'."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "exVhA3kJiFSt5IAL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Public Health Infrastructure'}<r>[0.387, 0.265]</r> higher or lower than {the point for 'Green Spaces Development'}<r>[0.387, 0.509]</r> in Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "nyh5Wjwi7KR3zcoN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project line had the highest completion rate in Q1, {the line for 'Green Spaces Development'}<r>[0.098, 0.558]</r>, {the line for 'Public Health Infrastructure'}<r>[0.098, 0.314]</r>, or {the line for 'Education Programs'}<r>[0.098, 0.679]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Education Programs'."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "YPbeeFmfUb8xkPei",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents bounded by the coordinates [0.210, 0.865, 0.555, 0.898]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the text for the legend item 'Green Spaces Development' with a color marker to its left."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "oXuZScD0GgCzMhrx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat am I seeing in the region [0.098, 0.679, 0.966, 0.801]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line graph for 'Education Programs', shown as a dashed line with triangle markers at the points. The line starts at point 'Q1' {at 75%}<r>[0.098, 0.801]</r> and ends at point 'Q4' {at 72%}<r>[0.966, 0.752]</r>."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "6VUXz5lvfnk944Nl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within [0.061, 0.301, 0.092, 0.334]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label '90', denoting the 90% completion rate."
+            }
+        ],
+        "id_raw": 107
+    },
+    {
+        "id": "Csg2b4n4RHSDsNQL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.012, 0.440, 0.029, 0.527] indicate on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "lTY8wYDZX3BCOpqH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the subplot's second point on the 'CO2 ppm' line at [0.106, 0.179]?"
+            },
+            {
+                "from": "gpt",
+                "value": "357."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "Bxt90ZlRfLRbOKsa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at coordinates [0.957, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-tick for the year 2017."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "dVJ46EpF8JwoorTQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {CO2 ppm}<r>[0.763, 0.096]</r> in the year 2011 higher or lower than the {Temperature Change (\u00b0C)}<r>[0.763, 0.905]</r> in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "P3dBES32klVF1FmJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the last values on the lines, which is greater, {CO2 ppm}<r>[0.988, 0.060]</r> or {Temperature Change (\u00b0C)}<r>[0.988, 0.905]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "CO2 ppm."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "vxLAWfGoZKP73b0g",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at the {Temperature Change (\u00b0C) line}<r>[0.563, 0.905]</r> greater than the {CO2 ppm line}<r>[0.563, 0.122]</r> at the same point?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "HTIrDhGfpvj8oPnO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area determined by [0.081, 0.074, 0.347, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the graph's legend, listing two items: {CO2 ppm}[0.133, 0.085, 0.209, 0.113] and {Temperature Change (\u00b0C)}[0.133, 0.127, 0.341, 0.154]. Each label has a corresponding colored marker."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "whmvguEJ8x2E2pUp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can you provide about the area [0.034, 0.080, 0.069, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '400' from the Y-tick labels is located in this area."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "2yUSF7FLZtV5KckL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the items located in [0.046, 0.793, 0.069, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a Y-tick labels at this region, labeled '50'."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "NWA3JQfLkvVxS7g8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of mark at point [0.236, 0.439] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle marker for line chart."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "2zc0mxiB9moJ8TbH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat online tool does the x-axis label refer to at [0.187, 0.753, 0.236, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Zoom."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "JHgsFrCrkFASMauR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the {y-axis tick label} indicate at coordinates [0.116, 0.224, 0.139, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "85."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "7z6QUjdRu7d4ydI7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at the 'Udemy' position, {Student Engagement}[0.422, 0.337] or {Learning Efficiency}[0.422, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Student Engagement."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "nA1MyRNmnzht3ibA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Learning Efficiency of {Lynda}[0.701, 0.337] less or more than the one of {BrainPOP}[0.794, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "Mf2qorPDpOn13sFY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value relationship between the engagement level of {Google Classroom}[0.143, 0.439] and the efficiency of {Reading Eggs}[0.981, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google Classroom has higher Student Engagement."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "Sr3iMAstYZQD8RKx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.143, 0.029, 0.981, 0.747]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the primary chart content with the x-axes being different online educational tools (e.g. Google Classroom, Zoom, etc.) and y-axes being percentage values. The area also contains two lines indicating 'Student Engagement' and 'Learning Efficiency' across the different tools."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "xdxoG3u5jDUzY7t6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.094, 0.281, 0.111, 0.495] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the Y-axis, which is 'Percentage (%)'."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "5kl8zc8V8MWginld",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.297, 0.902, 0.826, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend, containing two items: 'Student Engagement' and 'Learning Efficiency', each paired with a respective colored symbol."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "OvswewTtbbsdefZM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.015, 0.316, 0.033, 0.637] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "ExzZiMwyvffC4Lvb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of {the fifth x-axis tick label}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.495, 0.906, 0.538, 0.936]"
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "NxKVwyDm18Y1z6hm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.039, 0.534, 0.060, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "65."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "Le70GNZmjrE5FuWy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Biotechnology Pharmacists' in year 2002}[0.159, 0.697] above or below {the point of 'Medical Equipment Manufacturers' in the same year}[0.159, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "qu7e7WwGUbLLviy3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {trend of 'Biotechnology Pharmacists'}[0.069, 0.146, 0.964, 0.752] intersect with the {trend of 'Medical Equipment Manufacturers'}[0.069, 0.477, 0.964, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "T7joMnDtKqTgLwfy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has a higher value in 2017, {Biotechnology Pharmacists}[0.830, 0.229] or {Medical Equipment Manufacturers}[0.830, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotechnology Pharmacists."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "AE38WVg78n7ygPi0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.306, 0.078, 0.956, 0.130]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area. It contains two items: {Biotechnology Pharmacists}[0.360, 0.089, 0.585, 0.119] with a color indication of '#1f77b4'{at coordinate}<r>[0.308, 0.093, 0.352, 0.112]</r>, and {Medical Equipment Manufacturers}[0.665, 0.089, 0.949, 0.119] with a color indication of '#2ca02c'{at coordinate}<r>[0.613, 0.093, 0.657, 0.112]</r>."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "7m9EtXRUOTy497p2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.069, 0.064, 0.964, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area is located here, showing the trends of 'Biotechnology Pharmacists' and 'Medical Equipment Manufacturers' over the years 2000 to 2020. The x-axis represents the years and the y-axis represents the percentage growth of each industry."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "P4CRJ3FP1mpL8teb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.093, 0.093, 0.657, 0.112]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the color indicators for the two legend items: {Biotechnology Pharmacists}<r>[0.308, 0.093, 0.352, 0.112]</r> and {the Medical Equipment Manufacturers}<r>[0.613, 0.093, 0.657, 0.112]</r>."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "aI02eZKyu3KwHy07",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label in the chart signify, located at [0.015, 0.326, 0.034, 0.606]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis represents 'Temperature (\u00b0C)'."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "bclDyLm3y7r1aCAi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the first x-axis tick label at [0.057, 0.902, 0.089, 0.934] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Fall' temperature season."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "RuuyEZisQ4HGwtIC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the first item in the legend, found at [0.080, 0.059, 0.119, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line labeled 'Average Temp' and its color is blue."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "B0G3EsnQF9QztSoe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich temperature is higher in the 'Summer' season, {the Average Temp}[0.520, 0.086] or {the Minimal Temp}[0.520, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Temp."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "bGYoPmHWH0pKUzGx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Winter' season, is {the Average Temp}[0.222, 0.723] above or below {the Minimal Temp}[0.222, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "2QLjmAZXfGQw5CAg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Fall' and 'Spring' seasons, is the {Average Temp at Fall}[0.073, 0.478] higher or lower than the {Average Temp at Spring}[0.371, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "Uggm8JFLEzAgcr6j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.080, 0.049, 0.229, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend with two items: {Average Temp}[0.125, 0.058, 0.223, 0.082] represented by a blue line, and {Minimal Temp}[0.125, 0.093, 0.221, 0.117], represented by a red line."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "k0cyyFmw9HarpVrA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.420, 0.943, 0.620, 0.975] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label 'Temperature Season'."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "wvXrryh3CEXKpY69",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.073, 0.086, 0.966, 0.723]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the line representing 'Average Temp', which has marked points corresponding to different seasons: Fall, Winter, Spring, Summer, Monsoon, Dry, and Wet. The line color is blue and it has a dashed style."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "LCWNeSBNDYl6cVwN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.017, 0.398, 0.037, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "qaAoIILCivCK81Bf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.170, 0.936, 0.231, 0.964]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Green Intelligence' color marker."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "LFje412rzmMpo45G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.542, 0.936, 0.842, 0.968]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Environmental Philanthropy' label."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "MjK7LhsdhfHKPvkP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line at the 'Retail' marker [0.190, 0.845, 0.216, 0.875] is higher, 'Green Intelligence' or 'Environmental Philanthropy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Green Intelligence'."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "AqpKXNQY0xkKrYeg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 'Healthcare' point, is {the 'Green Intelligence' line}[0.510, 0.357] above or below {the 'Environmental Philanthropy' line}[0.510, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "CNMq2jn2JIbWtrA8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point 'Automotive'[0.617, 0.581], is the 'Environmental Philanthropy' line above or below the 'Green Intelligence' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "5bEhSyy76s121Uid",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in area [0.170, 0.924, 0.851, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region seems to be the {legend area}<r>[0.170, 0.924, 0.851, 0.981]</r>. It contains two entries. One for 'Green Intelligence' with a blue color symbol and other for 'Environmental Philanthropy' with a green color symbol."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "VZjFC0JBQMZ5QI6a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the region between [0.055, 0.025, 0.078, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick labels '95' and '100'."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "3eb0jBSPcX2G39Iv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the region [0.460, 0.845, 0.561, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Healthcare' label on the x-axis."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "UzcTTysE63xkrCvw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Time Period}<r>[0.462, 0.952, 0.553, 0.975]</r>"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "5VQznYdDmp1i21Z1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is applied to {the point on the \"Recorded History Events (thousands)\" line}<r>[0.424, 0.741]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Middle Ages."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "roJdynkVhMMpnxb6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in this chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Count}<r>[0.017, 0.370, 0.032, 0.439]</r>"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "E8boOG7kmTe7epjv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the \"Human Population (millions) line\"}<r>[0.088, 0.081, 0.927, 0.745]</r> and {the \"Recorded History Events (thousands) line\"}<r>[0.088, 0.660, 0.927, 0.745]</r>, which line has a higher point for the \"Industrial Age\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human Population (millions) line."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "NzHqYGIU2StMu3gB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line falls below at the \"Post-Industrial Age\", is it {the \"Human Population (millions) line\"}<r>[0.088, 0.081, 0.927, 0.745]</r> or {the \"Recorded History Events (thousands) line\"}<r>[0.088, 0.660, 0.927, 0.745]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recorded History Events (thousands) line."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "L17FHj2OpOQnjrnw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {first point of the \"Recorded History Events (thousands)\" line}<r>[0.088, 0.745]</r> lower than the {first point of the \"Human Population (millions)\" line}<r>[0.088, 0.745]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "CrejyfOsOOYllqWs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the rectangle [0.096, 0.075, 0.438, 0.152]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the chart legend is located, with two items: {Human Population (millions)}<r>[0.145, 0.085, 0.363, 0.108]</r> and {Recorded History Events (thousands)}<r>[0.145, 0.120, 0.432, 0.143]</r>. Each label is preceded by a colored marker."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "OJqrvogL3EOkHMxx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within these coordinates: [0.350, 0.650, 0.500, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two line data points for {Middle Ages}[0.385, 0.762, 0.462, 0.877] on both lines, with a y-axis label {1000}[0.038, 0.651, 0.077, 0.674] and grid lines."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "CWKuOcGtjW3bbgD8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements occupy the region [0.850, 0.650, 1.00, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the last x-axis tick label {Post-Industrial Age}[0.870, 0.762, 0.983, 0.932], the last points on both lines {Human Population (millions)}<r>[0.927, 0.081]</r> and {Recorded History Events (thousands)}<r>[0.927, 0.660]</r>, and the right-side y-axis grid lines."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "eNXS37pkeqcrsho4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label '55' in y-axis tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.061, 0.878, 0.092, 0.911]"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "T8m4BGe3RxKxMDyK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "lpoI9Yb6Ib2DVxa8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith respect to {the fifth index point}[0.536, 0.891], what is the percentage at the corresponding {point of the 'carbon emission'}[0.536, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "77."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "Zc5hGmeHqIXjFneo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Carbon Emissions' line point at index '3'}[0.426, 0.550] above or below the {'Volunteers' line point at index '3'}[0.426, 0.362]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "FsWHuIHtMcRq0luj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Carbon Emissions' line's starting point}[0.098, 0.124] and {its ending point}[0.974, 0.874], does it trend upward or downward?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downward."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "o9CbdI9VW7pqZPNU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {index '4' point on the 'Volunteers' line}[0.536, 0.294] with {index '3' point on the same line}[0.426, 0.362], does it trend upward or downward?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "52km8HuEnjJaPW2x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.732, 0.772, 0.952, 0.858]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend text for 'Carbon Emissions' and 'Volunteers'."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "POzVXXKwl5IB3w6x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is held within the region [0.017, 0.322, 0.092, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, displaying 'Percentage (%)'."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "lyg9ft8zu0ypldGw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the region [0.309, 0.899, 0.544, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space includes x-axis tick labels for index points '2' and '4'."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "eAgvoFRnJP4Pl5JL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.088, 0.674] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2012 data point for the Online Training line."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "JnXwUC7AangHlTSc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.209, 0.035, 0.630]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index Score out of 100."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "NYR6IrWMfp7JrOdy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data point does the coordinate [0.957, 0.801] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2021 data point for the Classroom Training line."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "pRY7xH9kgPRwAeps",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '2017'}[0.571, 0.356] for Online Training line above or below {the point for '2017'}[0.571, 0.610] for Classroom Training line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "5DFKEwN0mkHbqyFK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '2018'}[0.667, 0.267] for Online Training line above or below {the point for '2018'}[0.667, 0.674] for Classroom Training line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "fAa3GmgMKzz81A3a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the Classroom Training and Online Training lines, which one has a higher index score in {2016}[0.474, 0.483] and [0.474, 0.420] respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Training."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "t75OKRx98hf664OE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.240, 0.910, 0.805, 0.973]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend. It provides information about the two lines plotted on the chart: {Online Training}[0.311, 0.925, 0.478, 0.958] and {Classroom Training}[0.585, 0.925, 0.796, 0.958], each with a color marker preceding the label."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "q6VOQAKvG25IuwpG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located within the area [0.088, 0.102, 0.957, 0.801]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the plotted data points and lines for Online Training and Classroom training from 2012 to 2021. The coordinates of lines span the entire x-axis and y-axis within this region."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "s0Nun341K1OQvaUQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.499, 0.852, 0.546, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Year' is located within these coordinates."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "nPS31hVXf2WjioCx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.176, 0.139, 0.290, 0.161] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Package Volume label."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "HaPeWz70wSNalNef",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker}[0.133, 0.142, 0.170, 0.156] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Package Volume."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "ekYWknEzfPxFJhCn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.080, 0.692, 0.117, 0.710]?"
+            },
+            {
+                "from": "gpt",
+                "value": "26300."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "SQ1eyePjdyAuBFpz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the Freight Revenue line}[0.125, 0.476] above or below {the first point on the Package Volume line}[0.125, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "1F1zwQIItMEGCVig",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Package Volume'}[0.125, 0.863, 0.900, 0.888] intersect with {the line for 'Freight Revenue'}[0.125, 0.129, 0.900, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "Nawc9NWKVLwMOn7h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label had a higher end value in 2020, {Package Volume}[0.900, 0.863] or {Freight Revenue}[0.900, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Freight Revenue."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "YwbZqI8x9MId7Pzf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.176, 0.130, 0.300, 0.210]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend subtitles and markers for {Package Volume}[0.176, 0.139, 0.290, 0.161] and {Freight Revenue}[0.176, 0.172, 0.289, 0.194] are located here, along with their corresponding color markers."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "P5AT5HLxyzod77Tw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are in the region [0.080, 0.400, 0.200, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several {y-axis labels}[0.080, 0.466, 0.117, 0.484] and [0.080, 0.883, 0.117, 0.901], {y-axis label}[0.080, 0.710, 0.117, 0.727] and {ticks for y-axis scaling}[0.125, 0.716], [0.125, 0.456]."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "9QxQjJ97EuYFoNNL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the section between [0.450, 0.400, 0.900, 0.920] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the right half of the plot where you can see plot points for both {Package Volume line}[0.474, 0.877, 0.512, 0.879] and {Freight Revenue line}[0.745, 0.259, 0.784, 0.233]."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "ucPcW68AZpvu7Ga1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.051, 0.809, 0.145, 0.842]"
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "Q4nhJTrlg0Ub0S7L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label at {coordinates}<r>[0.602, 0.925, 0.721, 0.958]</r> in the sub legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product B."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "41Hd2TdtZ0AUm7At",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object identified using {coordinates}<r>[0.017, 0.370, 0.039, 0.469]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "HiaMZWTkt3uKUgSX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for May in {the Product A line}<r>[0.397, 0.310]</r> higher or lower than the point for May in {the Product B line}<r>[0.397, 0.525]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "275UwPAIoWwkMUby",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which point does {the Product A line}<r>[0.079, 0.601, 0.919, 0.420]</r> overlap with {the Product B line}<r>[0.098, 0.396, 0.919, 0.801]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "January."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "69sBCJotANIHoX0G",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has a higher sales in the end of the year, {Product A}<r>[0.919, 0.081]</r> or {Product B}<r>[0.919, 0.801]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product B."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "q9biSAel80SF4h2j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in {coordinates}<r>[0.286, 0.910, 0.731, 0.973]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend area with two items: {Product A}<r>[0.365, 0.925, 0.484, 0.958]</r> and {Product B}<r>[0.602, 0.925, 0.721, 0.958]</r>, marked with square and circle respectively."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "GCPcbkk50yn6Ntdw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information given within {coordinates}<r>[0.045, 0.406, 0.092, 0.440]</r> and {coordinates}<r>[0.045, 0.311, 0.092, 0.344]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are y-axis labels \"100\" and \"120\" respectively."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "ECduQrCU7VacjUXm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the bounding box <r>[0.324, 0.491, 0.471, 0.563]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items inside the bounding box, an April data point from the {Product A line}<r>[0.322, 0.343]</r> and a June data point from the {Product B line}<r>[0.471, 0.563]</r>."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "BzwKbXLRxnhNBBAQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the first line}<r>[0.103, 0.187, 0.950, 0.280]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line color is '#0c5da5'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "0c3IcK2xtC8aD17I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the last vertical tick}<r>[0.050, 0.051, 0.088, 0.083]</r> in the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is '1.0'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "9LEsxYz5PvVzFTXc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the {object}<r>[0.135, 0.025, 0.918, 0.057]</r> just below the subplot box?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the chart subtitle 'Automotive and Transportation Market Preference (2015 - 2020)'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "vf5RFZzyY8q4CCjY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point corresponding to 2017 in the 'Public_Transportation' line}<r>[0.442, 0.187]</r> above or below {the point corresponding to 2017 in the 'Automobiles' line}<r>[0.442, 0.203]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "uZX506d2QKOeWiYo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Bicycles' line at 2020}<r>[0.950, 0.334]</r> have a lower or higher value than {the 'Public_Transportation' line at 2020}<r>[0.950, 0.280]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "jeFAXiYQ8q0UbUZm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of preference, is {2015 for 'Automobiles'}<r>[0.103, 0.141]</r> higher or lower than {2015 for 'Bicycles'}<r>[0.103, 0.681]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "jrUzO41DmEQFqgMz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the region <r>[0.050, 0.745, 0.088, 0.777]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object within these coordinates is the y-axis tick label '0.1'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "AnxsU3Szxqnzohhc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region <r>[0.238, 0.934, 0.460, 0.964]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has the legend label of 'Public_Transportation'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "mUsI7jdO3dPCnn1n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.171, 0.923, 0.882, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legends for 'Public_Transportation', 'Automobiles', and 'Bicycles'. Their respective colors are '#0c5da5', '#ff9500', and '#00b945'."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "U7fX27pIOrRSPd7y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the marker with handle at coordinates [0.650, 0.791, 0.703, 0.804] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "Avg Temperature (\u00b0C)"
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "U15cmbi4ka2XmoLS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.034, 0.837, 0.050, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "DswhXLKzy8aV0aMd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.714, 0.828, 0.901, 0.858] mention?"
+            },
+            {
+                "from": "gpt",
+                "value": "Avg Snowfall (cm)."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "VKUswDamKCV3T0Zd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Avg Temperature (\u00b0C)}<[0.714, 0.784, 0.938, 0.814]> higher or lower in Bari compared to Aosta?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "xyzm1Esp3pLxemDq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Avg Snowfall (cm)}<[0.714, 0.828, 0.901, 0.858]> more in Torino or Bologna?"
+            },
+            {
+                "from": "gpt",
+                "value": "Torino."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "fwvLTfPHqLP7nUGZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes Aosta have a lower {Avg Temperature (\u00b0C)}<[0.714, 0.784, 0.938, 0.814]> or {Avg Snowfall (cm)}<[0.714, 0.828, 0.901, 0.858]>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Avg Temperature (\u00b0C)."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "VGQe2sL2LaarWYx6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.019, 0.051, 0.050, 0.083]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick label '40'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "6NPQljvAHwm9gVmX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within [0.647, 0.773, 0.947, 0.869] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend with labels for Avg Temperature (\u00b0C) at {coordinates}[0.714, 0.784, 0.938, 0.814] and Avg Snowfall (cm) at {coordinates}[0.714, 0.828, 0.901, 0.858]."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "qVXZ73iZgSc9Y9fR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content of the area [0.319, 0.025, 0.702, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the title of the chart, \"Yearly Averages in Italian Cities\"."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "gRtZ9Q7UALwjjnSZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.056, 0.697, 0.112, 0.790] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Strategy \"High Risk\"."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "IYiC4H44OMrm3VOp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label {ylabel} at [0.015, 0.312, 0.033, 0.432] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Earnings."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "VOrk0v4v69ibjrTG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the point shown at {coordinates}[0.263, 0.311] on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.2."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "gBClZbnGMuSVgGzy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the earnings at [0.112, 0.557] and [0.188, 0.434], which investment strategy shows higher earnings, {High Risk} or {Moderate Risk}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Moderate Risk."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "4tGNgnKwF46c5gpF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the earnings value at {Bonds}[0.641, 0.156] higher or lower than {Mutual Fund}[0.490, 0.218]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "OvlVaSrji4knyrbD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the earnings for the {Commodities}[0.943, 0.212] greater or less than {Options}[0.868, 0.419]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "e1Smj2RNsHlHfyT0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present within [0.351, 0.697, 0.490, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we have two investment strategy labels: {Index Fund}[0.351, 0.697, 0.414, 0.803] and {Mutual Fund}[0.420, 0.697, 0.490, 0.814]."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "pelPsAsJBYU5k6wv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the space within the coordinates [0.112, 0.135, 0.943, 0.557]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these boundaries, we can see a line graph representing the earnings of each Investment Strategy. The points for each strategy are represented along the line marked with 'o' markers."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "Gc3kowsJ8QisPfCm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the region bounded by [0.707, 0.697, 0.868, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, we can see two x-axis labels referring to {Cryptocurrency}[0.707, 0.697, 0.792, 0.840] and {Options}[0.820, 0.697, 0.868, 0.777]."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "zvmFcTUA7twopB4J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label of the y-axis is?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Metrics."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "03S4UUp66mqInKcM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the x-tick label located at [0.164, 0.851, 0.226, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2011."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "onpX9qae922S3FUG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line labeled 'Theater', with coordinates '[0.100, 0.334, 0.950, 0.642]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff9500."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "ysMrAl1twXpa83Xc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point of '2017', is the line representing 'BoxOffice' higher or lower than the line representing 'Awards'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "EbJKvYNdyRvbJCip",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'BoxOffice' trend higher or lower than 'Theater' for the year '2010'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "i6G1DAEYZGukuovG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, did the 'Theater' perform better or worse than the 'Awards' in '2012'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Better."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "v0qOaz3FgTJ9M6cK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the area of [0.260, 0.923, 0.791, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area presents the legend of the chart. Three items are displayed, 'BoxOffice' with color '#0c5da5', 'Theater' with color '#ff9500', and 'Awards' with color '#00b945'."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "dMoBn5KwYelCTOnm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region bounded by [0.070, 0.851, 0.981, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick labels for the data years from '2010' to '2019'."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "YwLuUTV2ohOkWf6O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the figure region [0.050, 0.206, 0.088, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick label '1.0'."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "KvI8JdgBtAgq3sPr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with the coordinates [0.367, 0.953, 0.398, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label for 'Coal'."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "f91wAo7ZKEd46stO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.510, 0.881, 0.551, 0.905]"
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "4OaryFyQ39lIMZHr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-tick label}<r>[0.040, 0.523, 0.089, 0.547]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "4500."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "211EBT3TPnavhmu1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Coal' line}<r>[0.101, 0.078, 0.960, 0.351]</r> upward or downward trend in general?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "SHwfOKQfqd5mA18J",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point in 'Oil'}<r>[0.869, 0.775]</r> higher or lower than {the point in 'Natural Gas'}<r>[0.869, 0.503] </r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "d0ai8v2KCghRsFUI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource has a higher value at {close to mid-point of 2009}<r>[0.840, 0.851, 0.889, 0.874]</r>, Coal or Natural Gas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "IukJU4IUHCZktSgT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.418, 0.960, 0.448, 0.978]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label for 'Coal' in the chart."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "UnUrG59QDaGSkkym",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.557, 0.963, 0.593, 0.973]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the colored handle for the 'Natural Gas' legend."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "Q2A4PuSUmPXjbIjD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.363, 0.851, 0.412, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label '2004'."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "hTUGsFfHrK1HC6lL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.108, 0.064, 0.951, 0.835] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main axes of the chart where the data is plotted."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "TzFUzFwWFBUZ1eQI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element described by the coordinates [0.188, 0.934, 0.381, 0.964]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is part of the legend, labeling the line for 'High-tech startups'."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "SGtqWSWFjKIzJ7uu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at [0.019, 0.363, 0.043, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the y-axis, indicating 'Rates in %'."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "NwOxPhK8E7cptwbG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we compare the point for 'Attr5' in {the line for 'High-tech startups'}[0.589, 0.699] and {the line for 'Government support'}[0.589, 0.174], which one is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for 'Attr5' in the line for 'High-tech startups' is higher."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "N1IdHQ3akSVNBosM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 'Attr1' in the 'Success rate' line}[0.108, 0.202] appear above or below {the point for 'Attr1' in the 'Government support' line}[0.108, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "CrkDBeST9pdEIIvo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 'Attr8' in the 'High-tech startups' line}[0.951, 0.598] lie above or below {the point for 'Attr8' in the 'Success rate' line}[0.951, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "yWgejConkHpYHNDR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found within the coordinates [0.121, 0.923, 0.937, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart's legend, indicating the color key and category for 'High-tech startups', 'Success rate', and 'Government support'."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "44yxgjsmsDXaoAGv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the region defined by [0.065, 0.051, 0.096, 0.083]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '100'."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "ri1Ry46fDkZR97SI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.049, 0.051, 0.096, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '100'."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "deWkKAPjPEeQ3wpq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text} located at [0.308, 0.954, 0.487, 0.977] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"Global Trade Demand\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "uuYqjbI3SPqxIge2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.248, 0.034, 0.638]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "Demand and Supply (in billions)."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "I0XSAoRc6BFn70EV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does {the x-axis label} at coordinates [0.856, 0.852, 0.898, 0.875] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019"
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "XtrDyi76vzoe9Myu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2010 in Global Trade Demand}<r>[0.093, 0.706]</r> above or below {the point for the same year in Global Supply Management}<r>[0.093, 0.732]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "cwyIK2IoX1iFY3nr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values at the 2017 mark for both {Global Trade Demand}<r>[0.703, 0.278]</r> and {Global Supply Management}<r>[0.703, 0.311]</r> equal or different?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "x3y1pIpmN5kKB8gP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point for 2020 in Global Trade Demand}<r>[0.964, 0.127]</r> and {the point for the same year in Global Supply Management}<r>[0.964, 0.114]</r>, which one registers a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Supply Management."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "sFDoj7KG5WcK0nYF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by the coordinates [0.254, 0.946, 0.803, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the line plot. It contains two entries: {Global Trade Demand} categorized with color #0c5da5 at coordinates <r>[0.308, 0.954, 0.487, 0.977]</r> and {Global Supply Management} categorized with color #ff9500 at coordinates <r>[0.567, 0.954, 0.796, 0.977]</r>."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "8Yp7Gw9CzbMYHIsU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be found in the region with coordinates [0.093, 0.048, 0.964, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the main content of the line plot, including the plotted lines representing the global trade demand and global supply management, as well as all labels, y-ticks, and x-ticks."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "Nahow7rTeL3QtPLI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects placed at [0.508, 0.852, 0.550, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, it contains one object which is the {x-axis label}<r>[0.508, 0.852, 0.550, 0.875]</r> representing the year \"2015\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "OhS2Zy1Ym6rwBX4W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.348, 0.043, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Growth Rate\"."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "6z4pc06Pi2pAd063",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bottom right coordinate of the third legend item, \"5G\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.330, 0.964]."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "vbq9EjqpyexNtMrg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the trend line for {Blockchain}<r>[0.110, 0.481, 0.950, 0.794]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#800080 (purple)."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "Fd9ydevpohq9mDib",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher at the end of the year 2023, {AI}<r>[0.614, 0.489]</r> or {Cloud computing}<r>[0.614, 0.511]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cloud computing."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "YPXwaQEg3ENg255f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend line for {AR}<r>[0.950, 0.720]</r> above or below the trend line for {5G}<r>[0.950, 0.138]</r> in the final year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "TKdUtGR2Obe33c9t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line has a steeper growth, {Blockchain}<r>[0.782, 0.571, 0.950, 0.481]</r> or {VR}<r>[0.782, 0.750, 0.950, 0.653]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "8HdjgySDB2uLVpD8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the detailed content in the area <r>[0.049, 0.051, 0.096, 0.083]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a y-axis tick label with the value '100'."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "mA9gZn0b9W6aiRHn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the information within <r>[0.577, 0.890, 0.689, 0.920]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text for 'Blockchain', colored with a unique color (purple) for its corresponding line plot."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "9S3Wwi0TS0eAulv7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the data within the region <r>[0.751, 0.855, 0.813, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis tick label \"2024\"."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "oMMDvZsSqxvokIEn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at the coordinates [0.165, 0.934, 0.335, 0.964]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label of the 'Heuristic Search' algorithm in the legend."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "AlA0Q3DmiC48pzNv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label represent at the coordinates [0.019, 0.276, 0.043, 0.622]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The term 'Achievable Efficiency' is represented at these coordinates."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "vUalM1bKIOFahkIs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by {the x-axis tick label} at [0.362, 0.851, 0.401, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 0.5."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "HblCAvjl0oE9z3w7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Heuristic Search' efficiency increase or decrease from the point at [0.663, 0.192] to the point at [0.944, 0.150]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "D4kEZkLvensPGwGO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the efficiency of the {Genetic Algorithm}[0.804, 0.235, 0.944, 0.107] less or more than the efficiency of the {Simulated Annealing}[0.663, 0.235, 0.944, 0.150] at the highest level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "KzoQpfjS7LT7HjvD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich algorithm represents the greater efficiency at {1.5}[0.944, 0.150, 0.964, 0.407], Genetic Algorithm or Simulated Annealing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Simulated Annealing."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "M5ZkZb7Lk21Oz90W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.098, 0.923, 0.947, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, including labels for 'Heuristic Search', 'Genetic Algorithm', and 'Simulated Annealing', each accompanied by a colored handle."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "7mTdn9uCjnHnQpVL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.421, 0.892, 0.624, 0.924]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis label with the term 'Efficiency Levels'. It's written in black text."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "bREQAIbtF1R6in3Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items can be found within the area from [0.050, 0.051, 0.088, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At these coordinates, the y-axis tick label '1.0' is found."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "B7O6YtgZAKXv0ztV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at [0.564, 0.917, 0.586, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label for the year 2011."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "h8L50Zf9fUUIrqRm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'ICU Beds per 100k'}<r>[0.106, 0.102, 0.940, 0.686]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#0c5da5."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "0rFqI2pRpRnwyUxT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the coordinate [0.314, 0.901] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tick mark for the year 2005."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "iRUNQ8lvUqljPl7P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value at year 2005}<r>[0.314, 0.663]</r> greater than {the value at year 2004}<r>[0.272, 0.670]</r> for 'ICU Beds per 100k'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "rhlHv8jvRuR4asA1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {line 'Telemedicine Service' value in 2015}<r>[0.731, 0.852]</r> higher than {line 'Rapid Tests in Mill.' value in 2015}<r>[0.731, 0.860]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "rY3o7pul1Vb7KFjp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the year marker 2020}<r>[0.940, 0.901]</r> have a higher y-point for 'ICU Beds per 100k' compared to 'Telemedicine Service'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "N0tceV2vodxqCTVP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features in region [0.064, 0.840, 0.124, 0.960]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains y-axis labels ranging from 500 to 0, formatted in black color and not rotated."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "gpijy9Ly4AihI5qW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears in the region [0.041, 0.075, 0.369, 0.218]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes legend items, specifically {ICU Beds per 100k}<r>[0.141, 0.089, 0.334, 0.119]</r>, along with corresponding color handles."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "2r70ANac93mgdwS9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide information about the elements in [0.522, 0.917, 0.544, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2010' is located here, printed in black color in a tilted manner."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "QpKjQbUwaXrxdSYi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.015, 0.375, 0.034, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "oZdg2Y0KgiDovnDe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.052, 0.927, 0.101, 0.951]"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "fTIBrXl4LfOZdQZ1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.040, 0.903, 0.064, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "IjnlwEALVZd4aKbr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2001'}[0.165, 0.718] above or below {the point for year '2000'}[0.076, 0.696] on the 'Company A' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "3EgyLIV3uduNDWTD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt year '2003', is {the line for 'Company A'}[0.341, 0.588] above or below {the line for 'Company B'}[0.341, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "7qZFUmygoSh0CF2m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has a higher market share at {the point for year '2010'}[0.960, 0.265], Company A or Company B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company A."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "7rQukqMhcXDInUjO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.138, 0.068, 0.233, 0.156]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the three lines: {Company A}[0.138, 0.134, 0.233, 0.156], {Company B}[0.138, 0.101, 0.233, 0.123], and {Company C}[0.138, 0.068, 0.233, 0.090], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "dc09H3MFYtUVuxn1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.040, 0.687, 0.064, 0.711]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label: {20}[0.040, 0.687, 0.064, 0.711]."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "Faa9XQ0AT9pGJGUC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.040, 0.903, 0.101, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The y-axis tick label '10' is at these {coordinates}[0.040, 0.903, 0.064, 0.926] and the x-axis tick label '2000' is located at these {coordinates}[0.052, 0.927, 0.101, 0.951]."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "2LcJluLqnf6NIoUj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {the line_1}<r>[0.074, 0.681, 0.960, 0.725]</r> in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the trend line representing Alcohol."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "BsDzVk2E1aADMV6a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle at {the legend}<r>[0.604, 0.940, 0.646, 0.954]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00b945."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "hDIuoD2ICz7qSg7B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis tick at {coordinates}<r>[0.040, 0.822, 0.064, 0.854]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "15."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "HdW5Qifxz2luKOWB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Smoking point in 2005}<r>[0.296, 0.615]</r> higher or lower than {the Obesity point in 2005}<r>[0.296, 0.417]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "6rCdML4NXa3hxn3n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Alcohol point in 2010}<r>[0.517, 0.681]</r> located at the same level as {the Smoking point in 2010}<r>[0.517, 0.637]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "Sy556fyazeRThrhC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend increases faster in 2020, {Obesity}<r>[0.960, 0.153]</r>or {Smoking}<r>[0.960, 0.703]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Obesity."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "6b0kqj9NSwkVkabO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area at <r>[0.074, 0.066, 0.960, 0.835]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the main plot of the chart. It marks the year (x-axis) against the percentage of population (y-axis) for three trend lines representing Smoking, Alcohol, and Obesity over time."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "wQWL3teSCAzGLiO9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at <r>[0.040, 0.822, 0.064, 0.854]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "A single y-axis tick mark with the label '15' is located here."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "6HfRneH9b4gqqfId",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area at <r>[0.074, 0.571, 0.960, 0.703]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the trend line for Smoking, which is plotted against the year, as well as five data points (2000, 2005, 2010, 2015, 2020)."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "bDckuR9kdiyrdQGd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {Metric Values}<r>[0.019, 0.341, 0.043, 0.558]</r>."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "FfrCdzVyVIAHw0af",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Population Growth line}<r>[0.103, 0.144, 0.950, 0.546]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the Population Growth line is #ff7f0e."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "C7n2XUZLvqO0NXMu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {text label} at [0.744, 0.934, 0.910, 0.964] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Climate Change\"."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "VzX2ozC3mIHv9C2V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Year 2015}[0.574, 0.739] from the 'Climate Change' line higher or lower than {the point for same Year}[0.574, 0.257] from the 'Population Growth' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "9lOqtbvqJVhy0tzu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Year 2016}[0.668, 0.747] from the 'Climate Change' line higher or lower than {the point for Year 2017}[0.762, 0.753] on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "Pr1A0ar5y1ueAsfp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric sees a more drastic fall from {Year 2011}[0.197, 0.724] to {Year 2012}[0.291, 0.729], 'Desertification' or 'Climate Change'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Desertification."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "wpYCm4C5NM0UQGgv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.135, 0.923, 0.918, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the legend of the chart, listing three data series: {Desertification} represented by a line color of #1f77b4, {Population Growth} represented by a line color of #ff7f0e, and {Climate Change}[0.744, 0.934, 0.910, 0.964] represented by a line color of #2ca02c."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "duZ8IXdaufHPkIyY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.543, 0.855, 0.605, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a x-axis tick for the {year 2015}[0.543, 0.855, 0.605, 0.886]."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "IheHnLGuLC98hHmt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.390, 0.941, 0.442, 0.955]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the color marker of the legend for 'Population Growth', with a representation color of #ff7f0e."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "IQxYqer94YLHAAS3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of {the line chart object with its points coordinates from \"[0.103, 0.163]\" to \"[0.974, 0.800]\"}<r>[0.103, 0.163, 0.974, 0.800]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "ANN."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "wtRh8ilHgthLNloo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.385, 0.855, 0.401, 0.886]"
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "7lPvjL9SeyhMjgQu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the line representing the SVM model}<r>[0.435, 0.941, 0.487, 0.955]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff9500."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "aO0HUSlKd8DtJzbd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich model {line_1}[0.103, 0.099, 0.974, 0.375] or {line_0}[0.103, 0.163, 0.974, 0.800] performs better on the 7th dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "SVM"
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "JZaCVZuAq8GLdlaL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the SVM model}[0.393, 0.163] performance better or worse than {the ANN model}[0.393, 0.375] on the third dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "Better."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "JEFcoTYKO0GTQYQs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Decision Tree model}[0.103, 0.481, 0.974, 0.694] and {the ANN model}[0.103, 0.163, 0.974, 0.800], which one has a higher accuracy on the first dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "ANN."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "bNh1INV6aKUMCbOa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.286, 0.923, 0.790, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend explaining the models, including {ANN}[0.353, 0.934, 0.398, 0.964] represented by a line in blue, {SVM}[0.498, 0.934, 0.543, 0.964] represented by a line in orange, and {Decision Tree}[0.643, 0.934, 0.782, 0.964] represented by a line in green."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "RBzkAo881tTMdtZB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.049, 0.150, 0.088, 0.182]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label of the y-axis tick, which is \"0.9\"."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "dqqn4sRLEpP9fS8L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.530, 0.855, 0.546, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number \"4\" which refers to the 4th dataset on the x-axis is located at this coordinate."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "W0UJF1KqSc4A7QSi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.019, 0.404, 0.043, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "fkXKXePb47ayBrl5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text representing the 'R&D Funding Rate'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.751, 0.934, 0.940, 0.964]"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Y94aaAMRZUmEah3X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the {coordinates}<r>[0.872, 0.851, 0.933, 0.883]</r> refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label for the year 2020."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "88TaP68QQhYaxWRa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Startup Rate line}[[0.139, 0.321, 0.902, 0.591]] generally above or below {the R&D Funding Rate line}[[0.139, 0.269, 0.902, 0.604]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "m25og96BzPwL4VUo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFollowed the given coordinates, is {the point for the year '2018'}[0.711, 0.385] on the Startup Rate line above or below {the point for the year '2018'}[0.711, 0.205] on the Investment Funding Rate line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Myr1Pf3EwlE3d1Fu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich rate had the higher value in the year 2018, Startup Rate or R&D Funding Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "R&D Funding Rate."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "l5rJ0MLbAt83Zb4R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area [0.202, 0.025, 0.839, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the subtitle of the chart, 'Entrepreneurship and Innovation Ecosystem Metrics', is located."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "hwE5V8x0lp9dUunh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area [0.092, 0.923, 0.949, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend including the labels: {Startup Rate}[0.159, 0.934, 0.290, 0.964], {Investment Funding Rate}[0.390, 0.934, 0.651, 0.964], and {R&D Funding Rate}[0.751, 0.934, 0.940, 0.964]. Each label has a corresponding color marker."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "sJxydWJWQPzG64sH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in [0.488, 0.892, 0.553, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the x-axis, specifically \"Years\", is located in this area."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "ohlXzD9ZpmM93GNg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type indicated in the JSON structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "line"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "hEW3B2ye154AbFs3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at [0.054, 0.906, 0.089, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "y-axis tick label."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "pQkAAfknxQoT6e5p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label represent at coordinates [0.023, 0.286, 0.045, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gaming Users (Billions)"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "6uGGOiSFiffbhj62",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Gaming Revenue point in 2019}[0.735, 0.397] higher or lower than the {Gaming Revenue point in 2018}[0.616, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "DYXTmYsKiMrB3WIO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Gaming Users graph} [0.139, 0.070, 0.854, 0.840] show an increase or decrease in gaming users from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "GCKBpTJ6vWyCwZOE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in the year 2016 - {Gaming Users}[0.378, 0.647] or {Gaming Revenue}[0.378, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gaming Users"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "AgO4czQLt9ukFk4a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content in area [0.112, 0.899, 0.167, 0.928] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2014' is situated here."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "P47mTQUgzM1L98Ky",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements in the region [0.104, 0.31, 0.889, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's plot area with data points, lines representing Gaming Users and Gaming Revenues over the years 2014 to 2020 alongside the x-axis labels and grid lines."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "ipoovLiN2S1YQHLl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.955, 0.294, 0.977, 0.616] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 'Revenue (in billions $)' is located here."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "bYA47l9VbP5O97lY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel}<r>[0.015, 0.261, 0.034, 0.637]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of participants."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "pFDgtJn9tEAA1yx7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {year}<r>[0.273, 0.849, 0.315, 0.879]</r> is found on the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "cvS4Fw9M5fv2hqCg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {color of the line}<r>[0.593, 0.945, 0.629, 0.959]</r> representing 'Sports' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00b945."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "moF7vXGatZZlx5a4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is above at year {2018}[0.741, 0.622, 0.741, 0.142]: 'Routine', 'Sports', or 'Workout'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sports."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "Otwz6vpKDhkuYjfO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year {2016}[0.517, 0.660, 0.517, 0.181], was the 'Workout' trend higher or lower than the 'Routine' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "nPF7chP6pBhsaAao",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value of 'Sports'}<r>[0.294, 0.219]</r> at 2014 higher or lower than the {value of 'Workout'}<r>[0.294, 0.698]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "si9UqW7alZrMAGqv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region [0.348, 0.933, 0.686, 0.974]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It lists 'Workout', 'Routine', and 'Sports' labeled respectively in colors #0c5da5, #ff9500, and #00b945."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "oKHU93DEAMwYUsAU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the contents within [0.040, 0.533, 0.061, 0.563]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a y-axis tick label, specifically the '50' mark."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "WWDvlF7R4wuUqKyJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the region [0.161, 0.849, 0.203, 0.879]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an x-axis tick, particularly marking the year '2013'."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "5zKkIBfgewQelBcd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.121, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point on the 'Population Growth' line."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "9tBANSTNXrdAWiEY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at coordinates [0.514, 0.952, 0.550, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "Ay0tlKMkMeCEC9nk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.131, 0.116, 0.231, 0.139] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Sea Level Rise' in the legend."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "x9Vf4oQUxtbOTTnh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the point on the 'Population Growth' line}[0.807, 0.862] or {the point on the 'Sea Level Rise' line}[0.807, 0.766] for the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Population Growth' point."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "8vZyfeIqTw6NwzOi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of line placement on the chart, is {the 'CO2 Emission' line}[0.121, 0.065, 0.944, 0.351] above or below {the 'Sea Level Rise' line}[0.121, 0.763, 0.944, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "z9QuwEQuFntObfh2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the 'CO2 Emission' line} at [0.944, 0.065] higher or lower than {the point on the 'Population Growth' line} at [0.944, 0.862] for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "NMKisPt4Xa865ay4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located within the coordinates [0.032, -0.030, 0.070, -0.007]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a y-axis tick label '3500'."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "4WdnTSxOBPericMk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.086, 0.037, 0.264, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend of the chart, including 'Population Growth', 'CO2 Emission', and 'Sea Level Rise' is located in this region with respective colored markers."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "9rvYxtdIv8ODAyZf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.789, 0.919, 0.824, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label '2015'."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "nfIkzPzTYKhHR0lA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the {coordinates} [0.987, 0.884, 0.971, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Period 5'."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "ZOEM2tyOzYNmUpbf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the {coordinates} [0.101, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point from the Series 3."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "nDt8RtbkNLLaSydI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.532, 0.300, 0.576, 0.360] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '1.0'."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "OjYyNWAcPXpP4cx2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Series 1 point} at [0.337, 0.404, 0.340, 0.468] higher or lower than the {Series 2 point} at [0.337, 0.468, 0.340, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "BvAEqrdKOwieYQI0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Series has a higher value at {Period3} [0.534, 0.092, 0.556, 0.465], Series 1 or Series 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series 1."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "4e94Et92aeo0bdF7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is the {Series 1 point} at [0.534, 0.084, 0.537, 0.116] above or below the {Series 3 point} at [0.534, 0.301, 0.537, 0.404]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "3l3clNOJpmIrbZtL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.301, 0.879, 0.578, 0.914] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains x-axis labels for Period 3 and 4 which are located at {Period 3}[0.490, 0.879, 0.577, 0.914] and {Period 4}[0.687, 0.879, 0.774, 0.914] respectively."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "p8AoUCaCfQR7NHxw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area [0.101, 0.116, 0.579, 0.737] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one point each from Series 1, Series 2, and Series 3 for Period 3. These are {Series 1 Point}[0.534, 0.092, 0.537, 0.116], {Series 2 Point}[0.534, 0.214, 0.537, 0.237], {Series 3 Point}[0.534, 0.301, 0.537, 0.404]."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "akEqkRZnumTCfXqb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you shed light on the region in the chart that contains coordinates [0.700, 0.884, 0.971, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains x-axis labels for Period 4 and 5 which are coincidently at {Period 4}[0.687, 0.879, 0.774, 0.914] and {Period 5}[0.884, 0.879, 0.971, 0.914] respectively."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "LNYoOiIPd4xFZTna",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "5uhWZ2cFQYIchiB7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.485, 0.927, 0.569, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label which is 'Months'."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "WBlfCgPL6GLxV1TJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-coordinate of the point}[0.268, 0.782] for the Moisture line?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.72."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "SdZipc9VyEzkoI1U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which month does {the 'Temperature' point}[0.638, 0.078] have a higher value than {the 'Moisture' point}[0.638, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "August."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "K6m3L58z7W9Paril",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Temperature' data point in the 6th month}[0.490, 0.093] higher or lower than {the 'Air Quality' data point in the 6th month}[0.490, 0.708]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "6VyAQLm9zoevgPx8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the 'Moisture' point in the 8th month}[0.638, 0.784], is {the 'Temperature' point in the 8th month}[0.638, 0.078] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "sXmk3MDWET0UOl9d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.772, 0.879, 0.801, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '10' is located here."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "xjMVMuEygROmOEpC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.120, 0.073, 0.934, 0.180]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line representing 'Temperature'. It shows 12 data points ranging from {the beginning point}[0.120, 0.107] to {the end point}[0.934, 0.180]."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "qnex9vGb5wE3rXf6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the region [0.268, 0.782, 0.342, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two points from the 'Moisture' line; corresponding to {the point in the 3rd month}[0.268, 0.782] and {the point in the 4th month}[0.342, 0.782]."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "gDo6oIdLjyIABeXA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual content at coordinates [0.117, 0.083, 0.156, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emo."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "0v3P2mh88Lq8TLcF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the line that represents {the label 'Emo'}<r>[0.095, 0.302, 0.939, 0.843]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "AP9a42NIdS5EAMMG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the x-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "FDGAQulPw1HD7GGg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend starts at a higher value in the year 2000, {Emo trend}<r>[0.095, 0.302]</r> or {Punk trend}<r>[0.095, 0.422]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emo."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "5GVGFKkzCkNKBBjg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Emo trend}<r>[0.095, 0.302, 0.939, 0.843]</r> end at a higher value in 2020 compared to {the Punk trend}<r>[0.095, 0.182, 0.939, 0.543]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "vFr5YuarzUlGQs4C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing Emo and Gothic subcultures, which one had more followers in the year 2005? Is it {Emo}<r>[0.306, 0.483]</r> or {Gothic}<r>[0.306, 0.302]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gothic."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "OPeClwAeUmL5ixFZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.062, 0.074, 0.180, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. Each label corresponds to a different trend line: {Emo}<r>[0.117, 0.083, 0.156, 0.106]</r> is associated with a blue line, {Punk}<r>[0.117, 0.118, 0.159, 0.141]</r> with a red line, and {Gothic}<r>[0.117, 0.153, 0.173, 0.176]</r> with an orange line."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "K2oqQyl2cEqVQGzY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.095, 0.302, 0.306, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the beginning portion of the Emo trend line, with data points representing the trend ratings for the years 2000 and 2005."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "KDpIWc8uVpQnuODd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region specified by the coordinates [0.917, 0.919, 0.939, 0.843]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the final data points of the Emo, Punk, and Gothic trend lines for the year 2020."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "Kby6Wis5NY0uW0Ch",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with coordinates [0.141, 0.069, 0.935, 0.562] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline Improvement."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "pkPnqWazwLceW0Pp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value on the y-axis at [0.025, 0.591, 0.062, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "a40ok2ztaEzlZv46",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the label of the x-axis present at [0.429, 0.927, 0.647, 0.963]."
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Process."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "4527TvHgmyIPchaO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {y-axis value represented at [0.025, 0.270, 0.062, 0.305]} higher or lower than the {y-axis value represented at [0.025, 0.162, 0.062, 0.197]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "Kehuv0HzbZTKFVKv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which marker point {of the 'Baseline Improvement' line}<r>[0.141, 0.069, 0.935, 0.562]</r> is the value higher: {at 40}[0.175, 0.433] or {at 90}[0.348, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "PzctvBe5YlUxw7RW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of 'Baseline Improvement' {at 220}[0.797, 0.155] exceed the {y-axis value represented at [0.025, 0.162, 0.062, 0.197]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "REP2247585Oo5VkQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the y-axis section from [0.025, 0.591, 0.062, 0.197]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section displays four labels on the y-axis with values '1.0', '1.5', '2.0', '2.5' and '3.0' from top to bottom."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "6deWxx1yydqZmmLN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.775, 0.879, 0.957, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two x-axis labels: '220' and '260'."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "0bKmoDwPAIfjcUzW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are contained within [0.161, 0.879, 0.190, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays the '40' label on the x-axis."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "hheorsB6ce6SaAtc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at [0.172, 0.107, 0.937, 0.547]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "OZ9TpYbQFTUsL1Qh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate for the tick label '1990'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.143, 0.879, 0.202, 0.914]."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "1v9mHrAZu770AUjE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line represented by {coordinates} [0.172, 0.387, 0.937, 0.707] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "CCekwrjeuNlqXJua",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Urban development line}<r>[0.172, 0.107, 0.937, 0.547]</r> generally above or below the {Rural development line}<r>[0.172, 0.387, 0.937, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "0do4JCUO35tJahgH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the {x-tick} labelled '2020', what is higher - {Urban}<r>[0.746, 0.267]</r> or {Rural}<r>[0.746, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "tinNvBdCMw7fvgRz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Rural development line}<r>[0.172, 0.387, 0.937, 0.707]</r> start at a higher or lower point than {Urban development line}<r>[0.172, 0.107, 0.937, 0.547]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "jMwqtQz3unDCBmHK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a description of the region [0.145, 0.055, 0.299, 0.170]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the graph, with labels for {Urban}<r>[0.219, 0.069, 0.290, 0.104]</r> and {Rural}<r>[0.219, 0.121, 0.279, 0.156]</r> along with their corresponding color markers."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "wolxBTS5uNIqNYJb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.524, 0.927, 0.585, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label for the x-axis, which is labeled as 'Years'."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "KZb1cJwGApJ9gIQ3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the coordinates [0.025, 0.312, 0.048, 0.545]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, which is 'Development', is located here in this region."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "kaf7lHJWDuE20YgO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located at [0.507, 0.942, 0.544, 0.970] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "oCBNL6S2aM4a4TZb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's the object at [0.126, 0.154, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color marker for 'Supply Chain Costs' in the legend."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "13QueNuPhhSa8jx6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at [0.664, 0.563] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the 'Intermodal Volumes' for the year 2015."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "liRVfOTuxpq0YdIK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the points at [0.526, 0.714] and [0.572, 0.687], which has a higher value for 'Truckload Tonnage'?"
+            },
+            {
+                "from": "gpt",
+                "value": "2013"
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "Prb4P53hhQaQIoM9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Supply Chain Costs' at [0.848, 0.134] higher or lower than its value at [0.756, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "L2Fv8GbPr2VmGXeH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in the year 2011, 'Truckload Tonnage' at [0.480, 0.726] or 'Intermodal Volumes' at [0.480, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Truckload Tonnage"
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "YMbr6KdbpjirqCp3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements present in the area [0.079, 0.055, 0.315, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the chart's legend. There are 3 items: 'Truckload Tonnage'[0.135, 0.066, 0.296, 0.094], 'Intermodal Volumes'[0.135, 0.108, 0.308, 0.136], and 'Supply Chain Costs'[0.135, 0.150, 0.302, 0.178], each associated a colored marker."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "j45nB1mJ4jokhqQK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the chart within the region [0.112, 0.598, 0.940, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line plot representing 'Truckload Tonnage' from the year 2003 to 2021 is located here, with specific points denoting the value for each year."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "Kwwa0hK7o3sXmM8l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.019, 0.494, 0.041, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label '20'."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "vmAoQEg833Aydt4s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart refer to with the coordinates [0.025, 0.169, 0.048, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "T5hOB8fYpE2uw4vI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth text in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.473, 0.879, 0.667, 0.914]."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "1kqS3MAAIuVfPBmF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.631, 0.879, 0.781, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Sciences."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "SmGe8aE66w5erfym",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 'Biotech'}[0.297, 0.455] on the line, fall above or below {the point for 'Life Sciences'}[0.706, 0.332]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "GWyDYmJ1IwO7VJ0B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for {Industry trends from 'Medical Services'}[0.570, 0.414] higher or lower than for {HealthCare Equipment}[0.433, 0.616]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "x8YsKx5gAXmKnKiz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Industry trend has a lower value at the point, Life Sciences or HealthCare Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Sciences."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "g2CzTtQksUkHDNhd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.413, 0.927, 0.590, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's x-label, which is \"Industry Trends\", which is in black color."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "zJH9u4c3DwyzLEOG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the section [0.302, 0.879, 0.565, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area consists of the x-axis tick label for the industry trend {HealthCare Equipment}[0.302, 0.879, 0.565, 0.914]."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "iElFNmiSEYK1jtea",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within [0.160, 0.095, 0.843, 0.616]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted data for the Industry Trends. The data includes the points for {Pharmaceuticals}[0.160, 0.580], {Biotech}[0.297, 0.455], {HealthCare Equipment}[0.433, 0.616], {Medical Services}[0.570, 0.414], {Life Sciences}[0.706, 0.332], and {HealthCare Technology}[0.843, 0.095] connected via a line."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "E9eScE4MsHOWrqS0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the object with coordinates [0.322, 0.879, 0.351, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, '50'."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "W3StnkDzTDkkHBmm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.935, 0.485] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point on Line 3."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "qbOdTfTr3hmgOmPK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.025, 0.130, 0.062, 0.165]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.8."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "LMi9b7AIO3mCnhlO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Line 1}<r>[0.137, 0.295, 0.935, 0.561]</r> or {Line 2}<r>[0.137, 0.144, 0.935, 0.561]</r> have a lower value at the 200th Gradient Step?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line 1."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "10AnLrC5RIHGpmEx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-value of Line 3}<r>[0.536, 0.182]</r> higher or lower than {the y-value of Line 1}<r>[0.536, 0.409]</r> in the 100th Gradient Step?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "Gm7oqQ2mPMnPuFVd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {y=1}<r>[0.097, 0.447]</r>, does Line 3 intersect the y-axis before or after {Line 1}[0.137, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "VBL61SoRFGjQkZSK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.025, 0.130, 0.062, 0.165]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label '1.8', represented in black color."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "ydkd0KL8QPGwAxFL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.025, 0.433, 0.062, 0.468]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '1.0', which is expressed in black color."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "2hmklOkvhLOEFQAL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.137, 0.144, 0.935, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three objects, {Line 1}<r>[0.137, 0.295, 0.935, 0.561]</r>, {Line 2}<r>[0.137, 0.144, 0.935, 0.561]</r>, and {Line 3}<r>[0.137, 0.182, 0.935, 0.485]</r>, which represent different trends in the chart."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "l8gE9upYz6yJc2cY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xlabel of the chart at coordinates [0.451, 0.952, 0.600, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Cultural Heritage\"."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "81VcOdqIb3OrLLqQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value of {the point in the 'Diversity Index' line} at [0.730, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-value is 0.6."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "rCELMGsBXu2xUof0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label} at [0.075, 0.919, 0.159, 0.942] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the cultural heritage 'Culture_A'."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "P3441pIUJOieHtu8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point {Culture_A}, is the { 'Diversity Index'}[0.117, 0.560] higher or lower than the {'Integration Index'}[0.117, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "7D388Rm7mnEnv073",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {Culture_B's Integration Index score}[0.321, 0.382] compare to its {Diversity Index score}[0.321, 0.471]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "kEv4QxKatTV1WBlx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index is higher for {Culture_D}, the {Diversity Index}[0.730, 0.337] or {Outliers}[0.730, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Outliers."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "t3jdIE2VwvWKMkzj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.075, 0.919, 0.976, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels for the chart. The labels are the names of five different cultural heritages, namely, {Culture_A}[0.075, 0.919, 0.159, 0.942], {Culture_B}[0.279, 0.919, 0.363, 0.942], {Culture_C}[0.483, 0.919, 0.567, 0.942], {Culture_D}[0.687, 0.919, 0.772, 0.942], and {Culture_E}[0.893, 0.919, 0.976, 0.942]."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "uGVN9ANoNlDgyK7T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.117, 0.071, 0.934, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of two trend lines, namely, {Diversity Index} line with coordinates [0.117, 0.249, 0.934, 0.560] and {Integration Index} line with coordinates [0.117, 0.071, 0.934, 0.382], each accompanied by a series of data points marking their respective values for various cultural heritages."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "wiiSXrcDXXfybBiU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region between coordinates [0.019, 0.862, 0.046, 0.085]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region primarily contains y-axis labels, specifically, the decimal values from '0.0' to '0.9' at each 10% interval. These labels indicate percentage values across the vertical axis of the chart."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "xi6ZylDS0ABOaCkb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in the plot with the label at [0.002, 0.389, 0.025, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis represents \"Values\"."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "5M9Yf6IQFB9RLyn5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the point marker {at [0.935, 0.812]}<r>[0.935, 0.812]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the point marker is orange, denoting 'Hotel Occupancy Rate'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "AS4FMvoa0G4H9KEm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the last x-axis label indicate at [0.906, 0.913, 0.964, 0.948]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is \"2020\"."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "6k4VanjddREq5xef",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Hotel Occupancy Rate'}<r>[0.129, 0.741, 0.935, 0.812]</r> above or below {the line for 'Restaurant Revenue'}<r>[0.129, 0.083, 0.935, 0.540]</r> in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "dDKa4RCpIqQ4FfR1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line ends at the higher point, {the line for 'Hotel Occupancy Rate'}<r>[0.129, 0.741, 0.935, 0.812]</r> or {the line for 'Tourist Arrival Number'}<r>[0.129, 0.688, 0.935, 0.800]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Tourist Arrival Number'."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "OG74dG34GLW497yM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the 2016 data, is the {Hotel Occupancy Rate}<r>[0.129, 0.741]</r> higher or lower than {Restaurant Revenue}<r>[0.129, 0.083]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "mb7TlAVwUJrvXy9V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.100, 0.362, 0.433, 0.416]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend labels for 'Hotel Occupancy Rate' and 'Restaurant Revenue', each paired with a point marker colored orange and blue respectively."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "nxM8ddhzkwoQVkA2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the region [0.507, 0.962, 0.556, 0.997]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Year' label for the x-axis is situated within this region."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "iKBqOIC80UFWNqWI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.935, 0.540, 0.935, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the end points of two lines, 'Restaurant Revenue' at {coordinates}[0.935, 0.540], and 'Tourist Arrival Number' at {coordinates}[0.935, 0.800]."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "xlQMogZW54Yag0rS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.461, 0.952, 0.607, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "bDwva7rHlK9zhs6O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first colored marker in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.832, 0.769, 0.878, 0.783]"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "gE3zXhfGcz8GK4ZN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label} at [0.019, 0.448, 0.057, 0.471]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.85"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "0y6rsvreT2UCRPtg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the behavior of 'Precision' at [0.127, 0.739] and 'Recall' at [0.127, 0.626] during the first processing step, which measure has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Recall'."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "3IgwQAEC6QNwBouT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the third step of processing with coordinates [0.534, 0.485] for Precision and [0.534, 0.372] for Recall, which measure experienced a higher increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recall."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "TZrlKfobbOv1weU4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the final point of 'Accuracy'}[0.941, 0.203] and {the final point of 'Recall'}[0.941, 0.147], which measure is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accuracy."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "F8Id99upPljFY6Yk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in area [0.831, 0.756, 0.973, 0.868]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes 'Accuracy', 'Precision', and 'Recall'. Each label is paired with a different color marker."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "8kXB9Q217EGwLVrz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.122, 0.919, 0.157, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label '0'."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "31YwTAGhYcQxHHx4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the area [0.737, 0.316, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two points on the lines: the final point of {the 'Accuracy' line}[0.941, 0.203] and the final point of {the 'Recall' line}[0.941, 0.147]. These represent the final readings for the two performance measures after 200 processing steps."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "NdoOrWMDe6J1Y6ZW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.025, 0.508, 0.054, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "B2ndRPXhL9VDVkQZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the color of {the Focus Level line} at [0.334, 0.567]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "v6sTXU4n8kfAORvT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate [0.319, 0.879, 0.349, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label \"50\"."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "o4nECKmqWwQu34An",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Anxiety Level at session 200}<r>[0.935, 0.433]</r> positioned above or below {the point for Focus Level at session 200}<r>[0.935, 0.373]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "nTbGaZlcQDf23JOd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is to the left on the y-axis, {Anxiety Level or Focus Level}<r>[0.094, 0.225]</r> at session 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Anxiety Level."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "R2dFPze2seXGQ4Tl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich dataset has a higher final session value, {Anxiety Level}<r>[0.935, 0.433]</r> or {Intrusive Thoughts}<r>[0.935, 0.076]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Intrusive Thoughts."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "CkNWBHGKAnog4yjm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounds [0.025, 0.806, 0.054, 0.841]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the y-axis tick label '30'}<r>[0.025, 0.806, 0.054, 0.841]</r> represented in black."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "N3e1Wa6cesr3ViHd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region [0.312,0.820, 0.556, 0.914] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes {x-axis tick label '100'}<r>[0.512, 0.879, 0.556, 0.914]</r> and {tick line for the '100' label}<r>[0.534, 0.820, 0.534, 0.879]</r>."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "AeQUZ2MKJtuqfKWi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bounded by coordinates [0.134, 0.373, 0.957, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes {x-axis labels}<r>[0.126, 0.879, 0.957, 0.914]</r> ranging from '0' to '200' and corresponding {tick lines}<r>[0.820, 0.820]</r> marked across the x-axis."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "puBbja5VKsq86j2B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of the chart with coordinates [0.108, 0.037, 0.975, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "2JR1rAq3SJ6yawUS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text(X-axis tick label) in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.228, 0.879, 0.243, 0.914]"
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "smjxbwJvqucVLDHi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.016, 0.429, 0.016, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ylabel is empty."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "LiEfzMlUw5WFmraa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2' on 'Sales 2017'}[0.235, 0.550] above or below {the point labeled '2' on 'Sales 2018'}[0.235, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "gqMYQfsCLTD7LC5n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Sales 2017'}[[0.148, 0.536, 0.936, 0.706]] generally above or below {the line for 'Sales 2019'}[[0.148, 0.082, 0.936, 0.479]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "j4Eg69417V1Z9o3F",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sale has a higher value at {point 7}[0.673, 0.110], Sales 2018 or Sales 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales 2019."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "BAiFRoUKrTRm7Udl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.477, 0.927, 0.606, 0.963]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the xlabel of the chart, \"Time Steps,\" in black color."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "8CWIbKXQokMBn8zF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.025, 0.096, 0.069, 0.131]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick label '250' in black color."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "Jam2QmUbOcjXtPo4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.148, 0.536, 0.936, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all ten data points from the 2017 sales data line chart."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "yrDTYDE4tak0IOow",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the main object at {coordinates}[0.395, 0.927, 0.681, 0.963] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "jXDg7CON7Bi6IT3p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what is plotted at the datapoint {coordinates}[0.935, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the value of Predictive Learning Algorithms at 200 Gradient steps."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "Z7L3nXIz1ZikhdC3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates}[0.025, 0.406, 0.062, 0.441] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the y-axis tick label '1.0'."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "RS2rcorhgoSim8bG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at 50 gradient steps, {EdTech AI Adoption}[0.339, 0.600] or {Predictive Learning Algorithms}[0.339, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech AI Adoption."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "pxqNCZRokJDVA1WV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {EdTech AI Adoption}[0.141, 0.744] value increase, decrease, or stay the same at 150 Gradient Steps {coordinates}[0.737, 0.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "XS9Ku6SJqQZ63nL0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Predictive Learning Algorithms} line[0.141, 0.239, 0.935, 0.492] above or below the {Distance Learning Platforms} line[0.141, 0.130, 0.935, 0.636] in the range from 0 to 200 Gradient Steps?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "shprN0dGKXDu6Pyx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the area with coordinates [0.101, 0.037, 0.975, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is primarily filled with the main chart area consisting of the gridlines, x and y-axis tick labels, and lines representing EdTech AI Adoption, Predictive Learning Algorithms, and Distance Learning Platforms."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "nU2MJdrVQonrxVGA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is specified within the region with bounding box coordinates [0.025, 0.044, 0.062, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "On the y-axis, the tick label '2.0' falls within these coordinates."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "cX8iwihjdVgJ8Y8x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are contained in the region [0.133, 0.879, 0.148, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label '0', marking the Gradient Steps' initial point."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "gSQjixEjxlgRclHt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the main graph item whose y-coordinate starts at 0.488?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpersonal Relationships."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Z9WsI6N0SSGi34Tu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis value of {the tick label}<r>[0.654, 0.838, 0.698, 0.885]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "15."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Bq24bUBlxzmnw98F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the item with coordinates [0.450, 0.903, 0.654, 0.950] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis Label."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "guc7zmsiRxNZ8edb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the x-axis value \"10\", does {the line 'Social Networks'}[0.178, 0.080, 0.427, 0.420, 0.676, 0.250, 0.925, 0.080] lie above or below {the line 'Interpersonal Relationships'}[0.178, 0.488, 0.427, 0.454, 0.676, 0.556, 0.925, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "g5lgi5y34p72BPTO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick label '4'}<r>[0.060, 0.605, 0.082, 0.652]</r> higher or lower than {the tick label '8'}<r>[0.060, 0.469, 0.082, 0.516]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Vrnn2GbMdQyDRHXd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line gets to lower value at x=20, Social Networks or Interpersonal Relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Networks."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "cgdd7h8GTL8Wk7Xd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is present in the region [0.141, 0.050, 0.963, 0.759]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotting area of the graph. It includes two line charts, one for 'Social Networks' and one for 'Interpersonal Relationships'. The X-axis with labels 5, 10, 15, and 20 and the Y-Axis with labels 0, 4, 8, 12, 16, and 20 are also included in this region. The plotting grid is visible with a light gray color."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "CgK7I1oP9xhJYxBK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects at the region from [0.676, 0.556, 0.925, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for the trend of 'Interpersonal Relationships' from the x-axis value of 15 to 20 is present here."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "nJN9yxrANetGEcBh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the region [0.141, 0.352, 0.082, 0.380] understanding from the JSON data?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label '12' located within these coordinates."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "JllIAJU1nzUBehTE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text associated with the y-coordinate located at [0.012, 0.452, 0.012, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "VydyU0RYug3hhN5u",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label does this set of coordinates [0.399, 0.880] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "RI5HwrfbHDXBff46",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at {coordinates} [0.625, 0.919, 0.706, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "tYzo2ORQWrVlf6VC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Facebook's followers}[0.666, 0.193] greater or lesser than {Twitter's followers}[0.399, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "rVi4u49Db7N4bTZH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher engagement rate at x-coordinate 0.399, {Twitter}[0.399, 0.841] or {Website}[0.132, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "lOwUcpAMsRtSWTlo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform's bounce rate is higher at x={point 0.666}, {Facebook's bounce rate}[0.666, 0.825] or {Instagram's bounce rate}[0.932, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "ufq9cuCaMKIwS14f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.092, 0.025, 0.972, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot of the chart. It incorporates graphs for Followers, Engagement rate and Bounce rate for Website, Twitter, Facebook and Instagram."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "qC2aBL2EiVzAHyhO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.888, 0.919, 0.976, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area has the X-axis category label 'Instagram'."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "p8bjcp8lUeLvTc4N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what lies between [0.052, 0.832, 0.063, 0.855]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is the Y-axis label '0' in this area."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "SnMB2wQe6ibJyGHd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the x-axis tick} at [0.032, 0.789] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "LNcBj8Z3xTPpqpBj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the {Ethereum subtitle}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.320, 0.037, 0.383, 0.085]."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "h8UEoLtn5N1YxznT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat crypto currency's subplot includes {the axes} with the coordinates [0.964, 0.096, 0.983, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stellar."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "RaOK2EL9uFjlSj2D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the Bitcoin subplot, is {the point on 'pred_length=96'}[0.032, 0.351] above or below {the point on 'pred_length=192'} at [0.035, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "gRzz8HD15AVFJQNa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Ethereum subplot, are {the x-axis ticks}[0.339, 0.813, 0.345, 0.848] further right or left than {the y-axis ticks} at [0.321, 0.775, 0.336, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "Un9w5nvYvGeUwo7k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {4th point on 'pred_length=336'}[0.049, 0.575] on the Ripple subplot higher or lower than {the 1st point on 'pred_length=720'} at [0.032, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "FdzHQjMAt3ZzCRDO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.949, 0.037, 0.994, 0.085]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle \"Stellar\" in black font and a no rotation setting."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "GDbyfaTWCy26jW80",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.342, 0.096, 0.361, 0.789] in the second subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the axes for the Ethereum subplot in white facecolor."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "8Qlt8prKlBqq78Xn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.010, -0.026, 0.025, 0.009]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one object: the y-axis tick label '0.6' is located in these coordinates."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "MZH4oPCzMycQl3ht",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.010, 0.149, 0.023, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "EjMFOLB54G6enzMQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.510, 0.857, 0.533, 0.892]"
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "aEfC5gcj5mUahkwL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the legend item} at [0.926, 0.889, 0.978, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Over 45 age group."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "NIjBTHSeSgMbpTHk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line graph, does {the '25 - 34' age group} at <r>[0.096, 0.760, 0.947, 0.515]</r> have a higher or lower population in 2000 compared to {the 'Under 25' age group} at <r>[0.096, 0.548, 0.947,0.327]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "gCSH1JrteSnbfLYY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year '2020'} on {the 'Under 25' line}<r>[0.096, 0.548, 0.947,0.327]</r> above or below {the point for the same year} on {the 'Over 45' line}<r>[0.096, 0.129, 0.947, 0.539]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "fTA3zhopOBDjfmJk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich age group has a higher population in 2040, {35 - 44}<r>[0.096, 0.242, 0.947, 0.621]</r> or {25 - 34}<r>[0.096, 0.760, 0.947, 0.515]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "25 - 34."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "kuaOSbhGcCkSkfIr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within these coordinates [0.584, 0.889, 0.921, 0.936]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a part of the chart's legend. The labels 'Under 25' and '25 - 34' reside here, each of them is followed by a colored handle representing their line color on the chart."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "H72AVlTuz0o1r81Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe what is within the area [0.096, 0.242, 0.947, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area consists of the line graph representing the '35 - 44' age group. The line graph contains markers at fixed intervals representing data points across the years. Their coordinates represent the population for that year."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "xmtfcYTxTGHGyXCN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information lies in the box with [0.084, 0.857, 0.959, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label information lies here, showing the different years that include '2000', '2010', '2020', '2030', '2040'."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "W11plYQn9spXAfoy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text that is at the coordinate [0.012, 0.030, 0.072, 0.066] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Transportation\"."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "FYFwuEFDRCljIVqQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the element located at [0.007, 0.243, 0.017, 0.628] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the y-axis label or title which is {Sector Performance}<r>[0.007, 0.243, 0.017, 0.628]</r>."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "uOmCo3H6k0Aa5ypr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the value of y-axis tick} at coordinate [0.020, 0.545, 0.035, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 0.14."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "29whc922uan4Gm4A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {point 1 about Transportation sector performance}<r>[0.040, 0.676]</r> or {point 2 about Transportation sector performance}<r>[0.041, 0.616]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 1 is higher."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "w6Tz30bap24YWOh0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRelative to {the value at time index 512 for the Financials sector}<r>[0.359, 0.075]</r> is the {value at time index 720}<r>[0.360, 0.135]</r> higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "pIb7Po1fNEnYDdad",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the value for Information Technology at instance 96}<r>[0.672, 0.435]</r> and {the value for Information Technology at instance 336}<r>[0.674, 0.496]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value at instance 336 is lower."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "awK0L8QWUfVBiEgJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found at region coordinates [0.988, 0.255, 0.992, 0.556]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the {line of data points representing the Industrials' sector performance over five time steps}<r>[0.988, 0.255, 0.992, 0.556]</r>."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "FAMHRWL1SZ4RkF6J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region defined by [0.356, 0.075, 0.361, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a subplot for the Financials sector, it includes the line graph {coordinates} [0.356, 0.075, 0.361, 0.315] and associated x and y-axis labels and values."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "7aq7hIsTjpqtwdp6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.672, 0.075, 0.677, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a chart or subplot for the 'Information Technology' sector, showing performance levels across five points in time {points for Information Technology}<r>[0.672, 0.375, 0.676, 0.616]</r>."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "gpZfN853vfPvobwk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for 'Career_Skill_Demand'}<r>[0.134, 0.530, 0.673, 0.701]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#4c92c3."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "l3RRckLHRrYPGA8N",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis tick at}<r>[0.094, 0.487, 0.125, 0.510]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.44."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "tq6lTZMKq7XECGmN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat shape is {the marker on the line for 'Job Market Trends'}<r>[0.134, 0.530, 0.673, 0.787]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Star."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "EgWEbZyiJE44jQSS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line point for 2020 in 'Career Tech Advance'}[0.673, 0.223] higher than {the line point for 2020 in 'Workplace Automation'}[0.673, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "WI3ayBlNA7JMlXH7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has a higher {y-axis placement} between 2009<r>[0.179, 0.616, 0.179, 0.787]</r> and 2013<r>[0.359, 0.530, 0.359, 0.787]</r> for the 'Career_Skill_Demand' study?"
+            },
+            {
+                "from": "gpt",
+                "value": "2013."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "RA8ct1yK298QpaS9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line point from 2019 under 'Workplace Automation'}[0.629, 0.360] fall above or below {the line point from 2019 under 'Career Tech Advance'}[0.629, 0.257]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "5wYupqfwSG7nIsi2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many different studies are highlighted in {the legend area}<r>[0.682, 0.203, 0.931, 0.431]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are four studies listed; each paired with a unique color & marker style."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "yRhqFA3lldL61Sgx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located within <r>[0.359, 0.530, 0.494, 0.601]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains points from 'Career Tech Advance' and 'Workplace Automation' studies from 2013 to 2016, included with respective markers on the chart."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "nmqibrB1Ggo915gD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the indicators found in <r>[0.004, 0.820, 0.691, 0.843]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes {x-axis labels for years}<r>[0.117, 0.820, 0.691, 0.843]</r> ranging from 2008 to 2020, marked clearly under the chart."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "abmiZ5FSkjwAFVit",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the first y-axis tick label on the Beige_Book_A subplot}<r>[0.026, 0.866, 0.052, 0.894]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "J0w66UDyEpdbrVXU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tick mark for the fifth Timestep on the Bond_Index_A subplot}<r>[0.382, 0.800]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.379, 0.820, 0.385, 0.848]."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "ilxBu3uKwHpQAvIm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker style of {the last data point in Economic_Growth_A}<r>[0.712, 0.110]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "4KKnApNp59KzATSW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point in Beige_Book_A}<r>[0.065, 0.658]</r> higher than {the first point in Bond_Index_A}<r>[0.329, 0.110]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "Z7J3eZD3GHJ9YhSI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-axis label of Bond_Index_A chart}<r>[0.274, 0.384, 0.286, 0.493]</r> appear to the left or right of {the labels of Beige_Book_A chart}<r>[0.010, 0.384, 0.022, 0.493]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the right."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "yLYeYz7a2XVLGAe9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is taller, {point 7 label in Economic_Growth_A}<r>[0.673, 0.417]</r> or {point 7 in Bond Index_A}<r>[0.408, 0.417]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic_Growth_A."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "ilGavochiQNs1ppn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.590, 0.820, 0.640, 0.850]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis tick labels '4' {coordinates}<r>[0.630, 0.820, 0.636, 0.848]</r> and '3' {coordinates}<r>[0.617, 0.820, 0.623, 0.848]</r> for the Economic_Growth_A chart."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "dqQC7lFUKdHxbeov",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.285, 0.362, 0.330, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label '0.024' {coordinates}<r>[0.290, 0.362, 0.316, 0.390]</r> alongside its corresponding tick {coordinates}<r>[0.323, 0.373]</r> for the Bond_Index_A chart."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "YZqQw4gOt9tE0sOW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.060, 0.700, 0.110, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis tick labels '0.022' {coordinates}<r>[0.026, 0.537, 0.052, 0.565]</r> and '0.024' {coordinates}<r>[0.026, 0.428, 0.052, 0.456]</r> for the Beige_Book_A subplot."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "4y1Ej8Kv3talmZC0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at coordinates [0.272, 0.083, 0.317, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle for Sport."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "6TV2uJKiwSxSy9b3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object located at [0.540, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "Y-tick coordinate."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "6oVKwHXUn5uKQ390",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.046, 0.919, 0.055, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label '0'."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "sA2ALc4z2KcOrohz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {point}[0.540, 0.252] on the chart for Leisure higher than {point}[0.540, 0.287] on the chart for Work?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "ckG41iQjzyQaCNGC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {point}[0.959, 0.137], the line for Leisure or the line for Work?"
+            },
+            {
+                "from": "gpt",
+                "value": "Leisure."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "tz0SWoaGCLqsvVEz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at {point}[0.272, 0.370] greater than the value at {point}[0.450, 0.137] in the Sport subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "vMPKBuGhjCe9RM8B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within the bounding box [0.540, 0.121, 0.979, 0.477]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the axes for Leisure with lines plotted indicating the performance over varying samples marked by {points}[0.560, 0.137, 0.959, 0.460] and the subtitle 'Leisure' located at {coordinates}[0.730, 0.083, 0.789, 0.113]."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "juXKEwc5MosqcgI5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.075, 0.547, 0.514, 0.903] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart for Hobby with MSE values plotted, distinct y-axis with labels, and the main title 'Hobby' marked at the top."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "kO8ZidRjpprkmyqn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the objects within the region [0.075, 0.121, 0.514, 0.477]."
+            },
+            {
+                "from": "gpt",
+                "value": "The plot area for Sport, inclusive of a line graph {line graph}[0.095, 0.137, 0.494, 0.460], y-axis and y-ticks {labels and ticks}[0.043, 0.369, 0.065, 0.493], and the subtitle 'Sport' located {location}[0.272, 0.083, 0.317, 0.113]."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "1tVKtjfgUZ3WdulU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.015, 0.025, 0.034, 0.179]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trends Value."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "0ihDmIM61ZviOD7U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the first legend handle}<r>[0.080, 0.037, 0.116, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#4c92c3."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "kL7KrQnLe24uNW8G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the x-axis tick at}<r>[0.201, 0.197, 0.210, 0.215]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "RHbEL16E6hspHUkf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {tick label}<r>[0.570, 0.197, 0.579, 0.215]</r> has a higher trend value, '5' or '4'?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "X7BCyJoHL8QHC4ry",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Technology in Education'}<r>[0.205, 0.174, 0.574, 0.172]</r> ascend or descend between points '2' and '6'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Descend."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "UMV2BFbcxL23jOXM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher at {the last point}<r>[0.943, 0.171]</r>, 'Technology in Education' or 'Online Learning'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "6IYrcU7Ul4iS77i6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within <r>[0.015, 0.025, 0.034, 0.179]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label \"Trends Value\" oriented vertically in black color."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "6lZd0IdsPf8dWVWT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.508, 0.222, 0.549, 0.245]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"Year\" in black text, positioned centrally below the chart."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "H03RXgodjhKt1C5s",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present in the rectangular area <r>[0.113, 0.185, 0.943, 0.215]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses all the x-axis tick marks and labels ranging from '1' to '10', aligned horizontally along the bottom of the chart."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "djA1y22k4MwuAG3c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.012, 0.288, 0.028, 0.672]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Degree of Globalization."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "xFSmEEydDaDdRS6E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the handle of the fourth item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.075, 0.240, 0.119, 0.264]."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "A7Bkol4ZBed8UEeB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Subplot line 'Moderate Increase'}<r>[0.250, 0.637, 0.984, 0.866]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "3fPX3e6qefiqK7kV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {point 4}, 'Moderate Increase' with point coordinates <r>[0.800, 0.723]</r> or 'Persistent Growth' with point coordinates <r>[0.800, 0.351]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Moderate Increase."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "5ia5eva4E4E9lmO6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Persistent Growth' trend at {point 2}<r>[0.434, 0.637]</r> and 'Marginal Improvement' trend at <r>[0.434, 0.752]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marginal Improvement."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "2zUpRXRd597vulvO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Persistent Growth' trend}<r>[0.250, 0.208, 0.984, 0.780]</r> intersect with {the 'Insignificant Increase' trend}<r>[0.250, 0.780, 0.984, 0.780]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "p47XGUQJSEkn2nL9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.075, 0.082, 0.317, 0.283]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend with 4 entries: 'Moderate Increase', 'Marginal Improvement', 'Insignificant Increase', and 'Persistent Growth'. Each entry has a corresponding handle with a unique color."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "HBpdIW0pfj9jnJYC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.477, 0.943, 0.573, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label 'Time Period' in black color."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "XzlzExYvZS0hHFGT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.063, 0.911, 0.988, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are all the x-axis labels, starting from '0' until '5' at the positions <r>[0.063, 0.911, 0.070, 0.934]</r>, <r>[0.246, 0.911, 0.254, 0.934]</r>, <r>[0.430, 0.911, 0.437, 0.934]</r>, <r>[0.613, 0.911, 0.621, 0.934]</r>, <r>[0.797, 0.911, 0.804, 0.934]</r>, and <r>[0.980, 0.911, 0.988, 0.934]</r> respectively."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "YYjzLfoM1hCeSMKo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is on the y-axis with coordinates [0.010, 0.448, 0.023, 0.551] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mean Squared Error (MSE)."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "DzPm17t2Rt83n2Hg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label for time_point 288 in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.389, 0.927, 0.406, 0.962]"
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "xJ3PU9lINAExSeO7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line} at [0.557, 0.774, 0.733, 0.839] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock 3 performance."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "jDYc3r7uCCSSNteh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value for 'Stock 1'}[0.238, 0.667] higher or lower than the {value for 'Stock 2'}[0.485, 0.709] at time point 480?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "eDZ4eZDT1mfqJgtA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the Mean Squared Error (MSE) of {Stock 3}[0.733, 0.839] and {Stock 4}[0.980, 0.257] at time_point 480?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock 3 has lower MSE."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "yajYSPQPFdEO3rMT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich MSE value is higher at the same time point 96, {Stock 1}[0.062, 0.500] or {Stock 4}[0.805, 0.160]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock 4."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "eWNu7EM5mLyqPiru",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what lies in the region [0.796, 0.096, 0.989, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subplot representing Stock 4's performance over time. The MSE values are on the y-axis and the corresponding time points are on the x-axis. A line graph is plotted to represent the MSE values at various time points."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "SAw2zVCtz1MFYqVD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of area [0.621, 0.037, 0.669, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label \"Stock 3\", which is the subtitle for the third subplot."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "2K4tr1nk52p59sxk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be seen within the region [0.916, 0.823, 0.984, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for subplot 3, specifying that the color '#de5253' corresponds to Stock 4."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "NwPXQ7qJ5i5ZvlId",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at this bounding box [0.145, 0.139, 0.224, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label of 'Land_Value'."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "OLbxxydHWtVR2FuY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label positioned at [0.015, 0.398, 0.034, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "ILUYNflT2saGfVqH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the first line at the left end point at [0.134, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Residential_Building'."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "k8VORZf2xH6oLM8Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the price of {Residential Buildings}[0.134, 0.575, 0.944, 0.663] higher or lower in 2011 than in 2005 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "pYaPOn5Ge8KTIDoe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at the time marker '2011', {Commercial_Building}[0.134, 0.647, 0.944, 0.701] or {Land_Value} [0.134, 0.068, 0.944, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Land_Value."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "URLF1cTMHO8vJw1t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Mortgage_Rate} values [0.134, 0.835, 0.944, 0.835] decreasing or increasing over the period 2000-2004?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "Uz6rjHDKwrhorsSV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.100, 0.044, 0.297, 0.221]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains subplot legend featuring labels for Residential_Building, Commercial_Building, Land_Value, and Mortgage_Rate, each accompanied by its unique colored line marker."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "NDq4q0CUfTV0u2qV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area from [0.134, 0.068, 0.944, 0.452]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The green dashed line representing the Land_Value from 2000 to 2011 is plotted in this region, with square markers indicating the annual values."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "1fr3zy0TJFcuplIc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the chart presents inside the box [0.134, 0.575, 0.944, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this box, we see a dashed line with dot markers, which maps the Residential_Building value across the years."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "dm7qiPy0H4UcW3Gr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.021, 0.342, 0.031, 0.687] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "gPJjJWNoe8Smnk0c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.086, 0.190, 0.118, 0.208]"
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "QmdRuMKDdhqEz7ez",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.296, 0.892, 0.326, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "RssAQ3eUYTWGvvtm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Factory 1'}[[0.102, 0.383], [0.311, 0.383], [0.520, 0.470], [0.729, 0.412], [0.937, 0.500]] generally above or below {the line for 'Factory 2'}[[0.102, 0.295], [0.311, 0.353], [0.520, 0.266], [0.729, 0.236], [0.937, 0.207]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "yR1fRFvrvUA9DQNH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Year 1' for Factory 1}[0.102, 0.441] above or below {the point for 'Year 1' for Factory 5}[0.102, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "AFA9xtxunFX714ex",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Factory has a higher efficiency at {Year 5}[0.937, 0.441], Factory 4 or Factory 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "Factory 4."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "k9qAarGNF0FTtRbS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.102, 0.207, 0.937, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly corresponds to the lines representing the manufacturing efficiency for each factory over the years. Each factory is represented by a unique line style and marker."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "hgcPU4l2cagLuvfe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.086, 0.164, 0.118, 0.182]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend item: {Factory 1}[0.086, 0.164, 0.118, 0.182] with its corresponding colored marker."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "kI7MItdcHip92O7Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.714, 0.892, 0.744, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object. The x-axis tick label 'Year 4' is located at {coordinates}[0.714, 0.892, 0.744, 0.916]."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "iAXiIHRry5YviNhD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.338, 0.025, 0.709, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, \"Media Influence & Public Opinion Guidance over Time\"."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "aF98LN6vdBoPtJoP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line at coordinates '[0.102, 0.131, 0.945, 0.722]' with a '--' style and '#4c92c3' color?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represents the \"Social Media Influence\" trend."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "FjWNlztCxXAdQ8Kg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for 'Election Public Opinion'}<r>[0.807, 0.849, 0.841, 0.872]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#ff993e'."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "abY8pqN5YPAa8MsQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend reaches a higher influence level faster according to the points at {coordinates for Social Media Influence at x=4}<r>[0.296, 0.585]</r> and {coordinates for Election Public Opinion at x=4}<r>[0.296, 0.722]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Social Media Influence' trend."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "o7LDHqkK06QKfmkO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Social Media Influence' at x=14}<r>[0.945, 0.131]</r> higher or lower than {the point of 'Election Public Opinion' at x=14}<r>[0.945, 0.267]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "hpemm91hPdxz6XBJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher influence level at x=2, is it {Social Media Influence}<r>[0.166, 0.676]</r> or {Election Public Opinion}<r>[0.166, 0.813]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Election Public Opinion."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "HU84nAU5JZKw1WJ9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within the region [0.808, 0.806, 0.982, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the legend for the chart is located. It includes entries for 'Social Media Influence' and 'Election Public Opinion', with associated color markers."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "OokOzwaUFakeKWEv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents at the coordinates [0.504, 0.943, 0.543, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label of the graph. The text there is 'Time'."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "VLdXb17dld9psiYL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is located within the coordinates [0.013, 0.372, 0.028, 0.617]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area assigns the y-axis label for the graph. It reads 'Influence Level'."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "X4SzIgziQbjweCfT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.252, 0.262, 0.266, 0.688]</r> indicate in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Effectiveness Evaluation."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "gkosZatYt2pR2Oug",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the last x-axis tick label}<r>[0.973, 0.879, 0.989, 0.914]</r> in the subplot for Climate Change?"
+            },
+            {
+                "from": "gpt",
+                "value": "720."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "WkggNCGgFe1e8lKz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at {the coordinates}<r>[0.675, 0.544]</r> in the Healthcare Reform subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point on the line."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "bHr82c4ACtLMyrxw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point marked at '512'}<r>[0.922, 0.303]</r> in the Climate Change subplot higher than {the point marked at '512'}<r>[0.675, 0.544]</r> in the Healthcare Reform subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "yJVH4MH4ORoPkRpn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-axis tick mark is closer to the bottom, {the one at '0.185'}<r>[0.010, 0.944]</r> or {the one at '0.148'}<r>[0.765, 0.921]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.148."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "IUupcio7hkOJxtyW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line labeled '720'}<r>[0.804, 0.130, 0.981, 0.820]</r> in Climate Change reach a higher point than {the line labeled '192'}<r>[0.309, 0.130, 0.486, 0.820]</r> in Gun Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "qnntdHepLU9zKPLW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within <r>[0.251, 0.879, 0.846, 0.914]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick labels with periods labeled '96', '192', '336', '512', '720' across four subplots with their respective coordinates clearly marked under each subplot."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "RcSkFxlgQcrpKf9t",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located in <r>[0.022, 0.262, 0.046, 0.688]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick labels including values '0.185', '0.190', '0.195', '0.200', '0.205', '0.210', '0.215', '0.220' corresponding to the subplot of Tax Reform."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "j4fMV866sZlZonuR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within <r>[0.116, 0.037, 0.941, 0.085]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Titles for each subplot including 'Tax Reform', 'Gun Control', 'Healthcare Reform', and 'Climate Change', each aligned with their respective subplot areas."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "IDFJB9JRKOoNFVIX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the vertical label correspond to on this chart at the specified coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The vertical label}<r>[0.015, 0.373, 0.029, 0.573]</r> corresponds to the \"Index (Base Year 2010)\"."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "bvagMsVP20MczWkN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what does the point represent at the coordinates [0.968, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This point represents the year 2030, and the index for 'Population' in the chart."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "gxY7uNG2tQQ2hgRS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the third item in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The third item in the legend is called 'Food Import', its color is green and its marker is 'x'. The coordinates are {this}<r>[0.122, 0.087, 0.206, 0.104]</r>."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "tdhaiCxzn348fn8K",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the graph, 'Food Production' with coordinates [0.071, 0.067, 0.968, 0.545] or 'Population' with coordinates [0.071, 0.679, 0.968, 0.832], starts at a higher index value in the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Food Production'."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "8k2lEUMXzQ83O6Xb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2030, is the index of 'Food Import' higher or lower than 'Population'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "uJkzdEiZolZCMvAt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween lines for 'Food Production' and 'Agricultural Export', which one has experienced more growth from 2010 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food Production."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "YxpaEbKis1uXIXbM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the area [0.035, 0.203, 0.061, 0.251]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two y-axis labels, '135' and '150', with their coordinates being {these}<r>[0.035, 0.203, 0.061, 0.221]</r> and {these}<r>[0.035, 0.060, 0.061, 0.077]</r> respectively."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "QbAuRQeWMUfdPjkx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be found in the area [0.035, -0.084, 0.061, 0.938]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the y-axis labels from '60' to '165'. The '165' label is slightly off the frame."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "Ebwtuontgkq6P1Hc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the object located in the region [0.078, 0.027, 0.258, 0.137]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It has four items representing 'Food Production', 'Agricultural Export', 'Food Import', and 'Population'. Each legend item has a different color and marker."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "smVWXG4GW7pndLKO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object with the label 'Cuisines'}<r>[0.497, 0.952, 0.546, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "zR5EB8uLDKBYGdLQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the y-axis label located on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.012, 0.299, 0.024, 0.628]</r>."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "5XKJ9oEl9Ale7OqJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.036, 0.747, 0.065, 0.771]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5000."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "tncgQOcYGVNtxscW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Italian Cuisine'}<r>[0.073, 0.610]</r> higher or lower than {the point for 'French Cuisine'}<r>[0.173, 0.376]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "sbb213R3fe2kS4PH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Mexican Cuisine'}<r>[0.273, 0.201]</r> above or below {the point for 'Indian Cuisine'}<r>[0.372, 0.259]</r>,"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "orkvUQP89wdC9Bc8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has a higher trend, 'Thai'<r>[0.571, 0.113]</r> or 'Vegan'<r>[0.970, 0.084]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Thai."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "5b2vbbAz5RwkN6N0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the box <r>[0.449, 0.919, 0.495, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Chinese' label of x-axis is located here."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "X7pFE05gssbHuyuR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the area <r>[0.073, 0.903, 0.065, 0.917]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a y-axis tick label of value '0'."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "aIS6r7nHlfEwUKoc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region <r>[0.828, 0.919, 0.912, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Mediterranean' cuisine label of x-axis is found here."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "f4m3rjczWwSZtfXF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinates [0.175, 0.030, 0.317, 0.053] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label for \"Waste Recycling\"."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "adJyy7xI4uAfVruO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.088, 0.154, 0.959, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line for \"Waste Recycling\"."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "RfoT65NBK58SXd4B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the {y-axis tick} located at [0.054, 0.736, 0.076, 0.759]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "1t74ZQumGYjTQNFf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value for 'Solar Energy'}[0.524, 0.588] in 2017 higher or lower than {the value for 'Waste Recycling'}[0.524, 0.272]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "Bq4C5flfr35sNIII",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label has a higher value in 2019, {Waste Recycling}[0.959, 0.154] or {Nuclear Energy}[0.959, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They have the name value."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "xljGbldIdIqGWIR4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value for the 'Nuclear Energy'}[0.741, 0.272] line at 2018 above or below {the 'Wind Energy'}[0.741, 0.193] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "q8VvefdeIYdLhAOZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.120, 0.020, 0.928, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend with four items: {Waste Recycling}[0.175, 0.030, 0.317, 0.053], {Solar Energy}[0.400, 0.030, 0.511, 0.053], {Wind Energy}[0.594, 0.030, 0.704, 0.053], and {Nuclear Energy}[0.788, 0.030, 0.921, 0.053]. Each label is next to a marker."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "Ygm3mDLKj7SSIEyi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.088, 0.154, 0.959, 0.390] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the 'Waste Recycling' line chart from the year 2015 to 2019."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "AJZj9XAGYpVALKVu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.719, 0.919, 0.764, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2018' is located within this area."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "A4F6IdUrP6ndiilD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the object} at [0.019, 0.367, 0.036, 0.570] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label named \"Funding (Millions)\"."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "1EKuYtYMVx1BD6fo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first line 'Education' in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.132, 0.058, 0.177, 0.072]."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "SlpGCQ31WRiYUhvs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label and its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is \"Year\" and its coordinates are [0.522, 0.952, 0.559, 0.975]."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "pVxDbAPtXDgob3vG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has higher initial value, {the line for 'Education'}[[0.121, 0.773, 0.959, 0.512]] or {the line for 'Healthcare'}[[0.121, 0.642, 0.959, 0.121]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "2la1shbVonjR1995",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Public Transport' line}[0.121, 0.425, 0.959, 0.729] or {the line for 'Environmental Protection'}[0.121, 0.382, 0.959, 0.859] end with a higher value in 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environmental Protection."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "Lgg0PG7XIBwlhr58",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAround the year 2020, was {funding for 'Healthcare'}[0.540, 0.382] higher or lower than {funding for 'Education'}[0.540, 0.599]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "OVCMkUsNA0ePbtkV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area [0.130, 0.046, 0.412, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart which includes markers and texts for 'Education', 'Healthcare', 'Public Transport', and 'Environmental Protection'."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "efZDNfsJOpzNOY7z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.121, 0.425, 0.959, 0.729]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains parts of three different lines, representing 'Education', 'Public Transport', and 'Environmental Protection', as well as their associated data points."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "HteUsdWE1whalJS7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.121, 0.642, 0.959, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing 'Education' from the years 2010 to 2030 is within these coordinates. In addition, a portion of the 'Healthcare' line is present."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "1wMh4u85H34of6pH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at [0.540, 0.020, 0.540, 0.020] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "N2m6pdwlrr2xiVeB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the line for 'Bonds'} at coordinates [0.099, 0.515, 0.976, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "DUtddWahAzQeFWuj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of {the y-axis'} text at [0.019, 0.402, 0.036, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vertical."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "aQx5oNpZ9eNK9p4x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend for 'Real Estate'}[0.440, 0.459] higher or lower than {the trend for 'Bonds'}[0.440, 0.621] in the year indexed by [0.440, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "KyJqD1DmwHCDbHqY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a lesser value at coordinates [0.586, 0.883], {the 'Stock Market'} or {the 'Gold Price'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gold Price."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "XVpzIzZK9pSolemu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for 2001 at the 'Real Estate'}[0.099, 0.317] above or below {the equivalent for 'Bonds'}[0.099, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "GqxQvf1NuD4FmfKi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the rectangle [0.205, 0.937, 0.870, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend referencing the financial datasets. It lists 'Gold Price', 'Stock Market', 'Real Estate', and 'Bonds' each with corresponding color symbols."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "vKAXbKO8gyP5QPBN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the zone [0.077, 0.874, 0.087, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the y-axis label, where '0' is displayed as the first label positioned vertically."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "EcmibZi0owrnOp0I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.519, 0.932, 0.556, 0.955]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section holds the x-axis label, which clearly states 'Year' in horizontal alignment."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "GOhmw05dfN66IQEN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.019, 0.332, 0.036, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "tz6vt1mkW6HH061A",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at the coordinates [0.283, 0.030, 0.355, 0.053] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for 'Exercise' in the legend."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "X8kwYOQ1iTUMSZn6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.959, 0.919, 0.981, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis tick label for '50'."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "kN7TPLu6KfyiENNk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Meditation line}[0.088, 0.282, 0.970, 0.365] end at a higher or lower point on the Y-axis than the {Exercise line}[0.088, 0.241, 0.970, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "TheEowcCFUG6BaRW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Journaling line}[0.088, 0.572, 0.970, 0.613] and {Trips line}[0.088, 0.737, 0.970, 0.820], which one starts from a lower point at x=30?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Trips' line."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "JlZKi5JnOhbdS9we",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Exercise line}[0.088, 0.241, 0.970, 0.323] and {Meditation line}[0.088, 0.282, 0.970, 0.365], which one shows a decrease in the Activity Engagement Score as the age increases from 30 to 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Exercise' line."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "cKLh1shlkZywRImW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what elements are found in the area defined by [0.227, 0.020, 0.831, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with four items: {Exercise}[0.283, 0.030, 0.355, 0.053], {Meditation}[0.438, 0.030, 0.531, 0.053], {Journaling}[0.614, 0.030, 0.701, 0.053], {Trips}[0.784, 0.030, 0.824, 0.053]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "6UmadAfmQ0FoaNwO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the bounding box at coordinates [0.077, 0.919, 0.099, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates exists a label on the X-axis that reads '30'."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "a3Eyr8OQqmmBpZTM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within coordinates [0.088, 0.241, 0.970, 0.323] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the line representing 'Exercise', starting at the point [0.088, 0.241] and ending at [0.970, 0.323], showing a decline in Activity Engagement Score as the age increases."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "FHjYzwsQWyqH0alv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.015, 0.391, 0.029, 0.537] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label which is \"Progress (%)\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "lmkygzp7WaughrJv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the object located at [0.950, 0.919, 0.985, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis tick label for the year \"2030\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "vX6WHBvhgqAbOOX3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Public Infrastructure}<r>[0.071, 0.135, 0.968, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line for Public Infrastructure is #1f77b4 (blue)."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "jAuGRjOmXk4pdAwX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2030, is {Progress in Public Infrastructure}<r>[0.968, 0.135]</r> higher or lower than {Progress in Residential Areas}<r>[0.968, 0.135]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "G7Qj8482hXOasP3t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Residential Areas}<r>[0.071, 0.135, 0.968, 0.793]</r> and {Commercial Areas}<r>[0.071, 0.244, 0.968, 0.683]</r>, which one shows higher progress in the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commercial Areas."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "wXQLSbkLjvUDHw0W",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the progress of {Public Infrastructure}<r>[0.071, 0.574]</r> and {Commercial Areas}<r>[0.071, 0.683]</r> in 2010, which one had a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commercial Areas."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "dIyOTaIdvKuUGuED",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.770, 0.745, 0.961, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the line charts. It includes labels for Public Infrastructure, Residential Areas, Commercial Areas, and Green Spaces along with their respective line color markers."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "ueo7vBcKaxebLL9P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.043, 0.345, 0.061, 0.368] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the y-axis tick label for the value \"90\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "M0JbK83smOlhLeCJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.950, 0.919, 0.985, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains an x-axis tick label. The label is \"2030\", indicating that this region of the chart represents the year 2030."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "jhQVOqfKu1NYBcDF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the set of coordinates [0.148, 0.141, 0.245, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label for \"AI Investment\"."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "AZSsMLvYuUVTIFqq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.975, 0.939, 0.985, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last x-axis tick label, which is \"2011\"."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "I3Qzcd8ewEFR6EUu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.809, 0.267] referring to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a point on the 'AI Research' line showing data for the year 2009."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "Z3F8Voyr8sijepDa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, {AI Research}[0.967, 0.212] or {AI Investment}[0.967, 0.377], has a higher value for the year 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Investment."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "WUUgcYsiNRiCC6yc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {AI Adoption} trend point for 2002 [0.255, 0.914] higher or lower than the {AI Salaries} trend point for the same year [0.255, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "yDSxriDEIqLow6IE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line was higher in 2006, {AI Research}[0.572, 0.377] or {AI Investment}[0.572, 0.762]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "MvjC9jtOvFjxdTX9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within these coordinates [0.097, 0.102, 0.967, 0.652] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures all 4 trend lines for 'AI Research', 'AI Adoption', 'AI Salaries', and 'AI Investment', showing their values from the year 2000 to 2011."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "f0hP6LBQI8zZLzAn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is characterized by [0.633, 0.939, 0.668, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the x-axis tick label for the year '2007'."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "4jAfYI2crBm11THC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.104, 0.055, 0.250, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend that details the four category labels: {AI Research}[0.148, 0.062, 0.230, 0.080], {AI Adoption}[0.148, 0.088, 0.228, 0.106], {AI Salaries}[0.148, 0.115, 0.221, 0.132], and {AI Investment}[0.148, 0.141, 0.245, 0.158], each paired with a unique color and marker."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "ht8ivtyXr3SZOxzg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.043, 0.066, 0.076, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label '100'."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "xpIB0Bu9aPvvETRB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location [0.280, 0.030, 0.387, 0.053] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label for 'Online Sales'."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "HcfS9lwoQUNmM7SI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.850, 0.919, 0.894, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "O8hvVbrpdFzBmNGO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {line for 'Online Sales'}[0.088, 0.158, 0.959, 0.489] trending higher or lower over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "zLW5oppm1UhdQamL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Offline Sales} [0.088, 0.323, 0.959, 0.696] and {Ecommerce Sales} [0.088, 0.365, 0.959, 0.820], which one has a steeper incline over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ecommerce Sales"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "byoLYCrjdPrgV3o4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for 'Online Sales' in 2009 {point_coordinates}[0.437, 0.381] higher or lower than the data point for 'Offline Sales' in the same year {point_coordinates}[0.437, 0.448]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "kU8o9TnwLGheTER1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the region [0.224, 0.020, 0.824, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, with labels for 'Online Sales' {text_coordinates}[0.280, 0.030, 0.387, 0.053], 'Offline Sales' {text_coordinates}[0.470, 0.030, 0.579, 0.053], and 'Ecommerce Sales' {text_coordinates}[0.663, 0.030, 0.817, 0.053]. Each of these has a corresponding color code."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "rRZapOWsaukpHOk2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found within the area marked by [0.066, 0.919, 0.110, 0.942] on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2005' is located within this area."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "wlsAPLxeIFZyWZQI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.019, 0.458, 0.036, 0.520]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label 'Sales'."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "pJoW0Nmh1UQT7s1z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-label} at [0.079, 0.455, 0.090, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Value."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "frRTIulhJtd2DiLd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the first x-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "cWMqOMqLBwzPZW2P",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for 'Industry 4.0'}<r>[0.168, 0.188, 0.237, 0.205]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ca02c."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "l1YJKD41kQFd4EJo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line representing 'Artificial Intelligence'}<r>[0.125, 0.158, 0.900, 0.852]</r> escalating or declining over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Escalating."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "K2dqpcgXmkH9eCNH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value represented by {the trend of 'Automation Processes'}<r>[0.125, 0.351, 0.900, 0.774]</r> in 2010 has increased or decreased by 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increased."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "qHiVlrnPOc3G0hpZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Internet of Things'}<r>[0.125, 0.235, 0.900, 0.813]</r> have a higher value in 2020 as compared to {the 'Industry 4.0'}<r>[0.125, 0.274, 0.900, 0.659]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "y4BAvbErdR0QEV72",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.168, 0.136, 0.287, 0.232]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items of 'Artificial Intelligence', 'Automation Processes', 'Industry 4.0', and 'Internet of Things', along with their respective color markers."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "SRYHdRXyGwpeFOie",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.125, 0.885, 0.915, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are x-axis tick labels, ranging from 2010 to 2020 with a differing amount of space between each label."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "w4LyK5AZlsaoh6l8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.365, 0.092, 0.660, 0.115]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle of the chart \"Trends in AI and Automation (2010 - 2020)\"."
+            }
+        ],
+        "id_raw": 189
+    },
+    {
+        "id": "hikO3sT6QCPDFCQo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.154, 0.204, 0.176, 0.227]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label \"25\"."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "qYcpOGuaV9s5NXLU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.806, 0.919, 0.817, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the X-axis label \"5\"."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "cH5X4DAkJufXonD5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label at [0.165, 0.756, 0.176, 0.779] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is \"5\"."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "z2UYk7P9xUtWaPnt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line of 'Industrial Development'}[0.188, 0.213, 0.812, 0.627] have a higher value at point 5 or does {the line of 'Technological Advancement'}[0.188, 0.379, 0.812, 0.765]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line of 'Industrial Development'."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "RTasTXuG4jFf4Bje",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second year, which development indicates a higher value: {the line of 'Agricultural Development'}[0.188, 0.130, 0.812, 0.351] or {the line of 'Industrial Development'}[0.188, 0.213, 0.812, 0.627]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line of 'Agricultural Development'."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "hXuxD2BlK9QQGnE9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the first point, is {the value of 'Industrial Development'}[0.188, 0.627] greater or smaller than {the value of 'War Incidences'}[0.188, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "k7gDVL2xhENnkX7o",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the area [0.154, 0.066, 0.176, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label \"30\" for the Y-axis."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "moBtogBaSV67CWac",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area [-0.055, 0.033, -0.010, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the handle color marker for the legend entry of 'Agricultural Development'. The color is '#1f77b4'."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "L81jNk0NbK3ISNKc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat description can you give for the area within [-0.057, 0.020, 1.057, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend including the items 'Agricultural Development', 'Industrial Development', 'Technological Advancement', and 'War Incidences', which all have different colors associated with them."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "4W9ldeo94KnrJIDW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content do you see at the {given points}<r>[0.959, 0.158]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the data for MOOCs Usage in the year 2020."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "LHy170v8RFR7jWfu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label of the chart with coordinates {coordinates}<r>[0.019, 0.286, 0.036, 0.692]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Increase in Usage / Investment (%)'."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "IKEivjtfhzqKSYzL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified within the chart at the {coordinates}<r>[0.765, 0.451, 0.783, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the data point for the EdTech Investment in the year 2019."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "NYoIMfIKk3A4Ziv3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {line of 'MOOCs Usage'}[0.088, 0.158, 0.959, 0.406] above or below the {line for 'University Online Education'}[0.088, 0.241, 0.959, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "Gxs3aHe0hVaSf2AZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher value in 2020, {MOOCs Usage}<r>[0.959, 0.158]</r> or {EdTech Investment}<r>[0.959, 0.282]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "MOOCs Usage."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "rt9WJlgfpXCLAZDG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a significant rise from the year 2015 to 2020, {University Online Education}<r>[0.088, 0.489, 0.959, 0.241]</r> or {EdTech Investment}<r>[0.088, 0.754, 0.959, 0.282]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech Investment."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "izUU37gTwkaRMzNv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the chart components in the {coordinates}<r>[0.244, 0.020, 0.804, 0.097]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a two-column legend with four items. The items are 'MOOCs Usage', 'K-12 Online Courses', 'University Online Education' and 'EdTech Investment' each with a different color marker."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "RvR0lpBbzLAoLhd9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are included in the {coordinates}<r>[0.788, 0.224, 0.808, 0.254]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a chart data point for the EdTech Investment line in the year 2020."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "TSQ3Vvz17eJLlRG9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the plot features located in the {coordinates}<r>[0.088, 0.158, 0.611, 0.406]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains part of two data series. Specifically, part of the MOOCs Usage distinguishable by circle markers and part of the University Online Education line marked by cross symbols, from the year 2015 to 2018."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "PFAEgrDpuBU4z4T6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element positioned at [0.019, 0.385, 0.036, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "pvDsrvecc4NX3HlU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the marker for 'Solar' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.674, 0.033, 0.719, 0.047]."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "RjWCDzcCDD8aMCjF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented by {the line for 'Coal'}[0.077, 0.199, 0.959, 0.385]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage of energy usage from 'Coal' between the years 2014 and 2019."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "qoSQTTkjVEHRrKpD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Natural Gas'}[0.077, 0.179, 0.959, 0.437] show an increase or decrease in energy usage over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "3jWzLWpg4Ej2CUFF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the final data point for 'Solar'[0.959, 0.748] on y-axis compare with the final data point for 'Oil'[0.959, 0.354]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "rbUD0oqOi6brcMqX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich had a higher energy usage percentage in 2014, Oil [0.077, 0.385] or Natural Gas [0.077, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Oil."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "ohwv0rycBZdVzLQP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the coordinates [0.258, 0.020, 0.779, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend items: {Oil}[0.313, 0.030, 0.336, 0.053], {Natural Gas}[0.420, 0.030, 0.523, 0.053], {Coal}[0.606, 0.030, 0.644, 0.053], and {Solar}[0.728, 0.030, 0.772, 0.053]. Each item is paired with a corresponding colored marker."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "QnRKWMjlRtI5S43V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within the region [0.055, 0.919, 0.981, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels '2014' [0.055, 0.919, 0.099, 0.942], '2015' [0.232, 0.919, 0.276, 0.942], '2016' [0.408, 0.919, 0.452, 0.942], '2017' [0.585, 0.919, 0.628, 0.942], '2018' [0.761, 0.919, 0.805, 0.942] and '2019' [0.937, 0.919, 0.981, 0.942] are included in this region."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "IIE3FL35Mzx9SR1m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region from [0.055, 0.066, 0.065, 0.710]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels '0' [0.054, 0.894, 0.065, 0.917], '20' [0.043, 0.687, 0.065, 0.710], '40' [0.043, 0.480, 0.065, 0.503], '60' [0.043, 0.273, 0.065, 0.296] are located in this region."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "zl9EVhEOFAtLNix6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred by the coordinate [0.019, 0.368, 0.036, 0.610] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "k3LBVVpS4C32JYvI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate information for the third tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.451, 0.919, 0.462, 0.942]."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "00trV9KQ2EvFLlbm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the tick label on the y-axis at the coordinates [0.043, 0.539, 0.098, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label is '30000'."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "wqApjyvZI57tQWmN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for 'Resources' at {point 2}[0.456, 0.217] higher or lower than at {point 3}[0.630, 0.785]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "PmApWW4DaMiuU0sS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWell, for 'Resources', is energy consumption at {point 1}[0.283, 0.383] greater or smaller than at {point 0}[0.110, 0.146]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "gxzECbkxC637o1Jt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data point for 'Resources' has lower energy consumption, {point 5}[0.976, 0.300] or {point 4}[0.803, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 4."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "3YDBueK8mHlWKVMr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what the area with coordinates [0.110, 0.075, 0.976, 0.903] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main plot area containing the line chart, with points marking 'Resources' energy consumption over 5 points."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "ZuLIasCMvDKVZ8Uz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's inside the coordinate [0.468, 0.020, 0.618, 0.062]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend {Resources}[0.523, 0.030, 0.611, 0.053] with a colored marker."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "BLdGMGzdMnWTFCxw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.087, 0.894, 0.098, 0.917] and [0.043, 0.066, 0.098, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These two areas contain the y-axis labels '0' at {coordinates}[0.087, 0.894, 0.098, 0.917] and '70000' at {coordinates}[0.043, 0.066, 0.098, 0.089], respectively."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "nsIBTM5I61qg6U1c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type depicted in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "FgO7gPfoOQHCUaAB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.015, 0.395, 0.029, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity Score."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "9fnequShmDpLaD9h",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by {coordinates} at [0.273, 0.030, 0.316, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Italian label text."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "p7OgmvV2lL3cIW8k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the sixth line point of the French line}[0.775, 0.241] above or below {the sixth line point of the Italian line}[0.391, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "tZR9CQJtdVgOIexT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for the year 2010 on the Japanese line}[0.327, 0.671] have a higher or lower score than {the same year on the Mexican line}[0.327, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "vPrYeAb2IiJa2sHr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line showcases a higher popularity score in 2016, {French}[0.711, 0.282] or {Chinese}[0.711, 0.406]?"
+            },
+            {
+                "from": "gpt",
+                "value": "French."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "lDz4CH6a9SgUBWAC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the bounding box [0.185, 0.909, 0.999, 0.944]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains multiple x-axis tick labels representing the years from 2008 to 2020."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "JFEvIq39NdaIDWmB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents can be found within the area [0.180, 0.713, 0.710, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Part of the lines for the Italian, French, Chinese, Japanese, and Mexican cuisine trends from the years 2009 to 2015 are found in this region."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "gB40DdZQfxLNAq1z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items located in the region [0.200, 0.019, 0.800, 0.060]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart is found in this area, listing each type of cuisine with their corresponding color coded handles."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "da0CwKrcJcDCxHnE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the set of coordinates [0.015, 0.419, 0.029, 0.555] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "3uPmsGrBFneTcder",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the x-axis tick label} for the year 2010 located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.497, 0.939, 0.532, 0.957]"
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "sozdAtQrPRVJJDwL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is referred to at these coordinates [0.261, 0.127, 0.439, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart that reads \"Energy Use and Resource Consumption over Years.\""
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "4MujfQEXIRQXJ8rU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy the end of the period reflected in the chart, are the values for {Hydrogen Fuel Cell Consumption}<r>[0.968, 0.751]</r> greater or less than the values for {Oil Consumption}<r>[0.968, 0.432]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "36oZbH3m0PZLuQkn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Coal Consumption}<r>[0.288, 0.179]</r> in 2005 to {Nature Gas Consumption}<r>[0.288, 0.762]</r> in the same year, which one had a higher consumption percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal Consumption."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "xuHpZNghWPV4OISH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the first point for {Nature Gas Consumption}<r>[0.062, 0.817]</r> and {Oil Consumption}<r>[0.062, 0.487]</r>, which had a higher consumption percentage in year 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Oil Consumption."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "K7nPjcKRQwBDPLii",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information within these coordinates, [0.069, 0.055, 0.346, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area serves as the chart legend. It presents the names of different energy types: {Coal Consumption}<r>[0.113, 0.062, 0.240, 0.080]</r>, {Oil Consumption}<r>[0.113, 0.088, 0.228, 0.106]</r>, {Nature Gas Consumption}<r>[0.113, 0.115, 0.288, 0.132]</r>, and {Hydrogen Fuel Cell Consumption}<r>[0.113, 0.141, 0.340, 0.158]</r>, each one paired with a different colored symbol."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "LZbpwYTFLYTZUm7P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within these coordinates, [0.062, 0.047, 0.968, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart. It depicts four lines representing the energy consumption of different energy types over the years. The X-axis represents years, ranging from 2000 to 2020, while Y-axis represents the percentage of consumption."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "MjNN1E57jhSKHssi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within these coordinates [0.062, 0.102, 0.968, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line for {Coal Consumption}<r>[0.062, 0.102, 0.968, 0.212]</r>, represented with a blue line and circle markers, showing the percentage every 5 years from 2000 to 2020."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "Bb2IJdYGM4pXbQCl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.066, 0.919, 0.110, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label '2010'."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "jef1Dx2zXQMLgsno",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {a line with color '#2ca02c'}<r>[0.088, 0.241, 0.959, 0.365]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line represents the trend of 'Nuclear' energy consumption."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "QDJ8HbxAEhPnLjHn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the marker located at [0.515, 0.033, 0.560, 0.047] signify in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This marker stands for 'Nuclear' in the legend."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "xVB1iyrY29Jn1CzG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {power source mark}<r>[0.326, 0.033, 0.371, 0.047]</r> given more representation compared to the {power source mark}<r>[0.515, 0.033, 0.560, 0.047]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "HFNV1OsmFDVwF5xu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre coordinates for year 2011 for {line 'Coal'} <r>[0.175, 0.282]</r> higher or lower than the {line 'Hydropower'}<r>[0.175, 0.290]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "ELewnomfM29Fih6S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source dips lower in 2016, 'Nuclear' or 'Coal'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Coal'."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "7jSL9Ucwm0JFgjmk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be found between the coordinates [0.203, 0.020, 0.845, 0.062]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with markers and labels denoting different power sources: {Coal}<r>[0.258, 0.030, 0.297, 0.053]</r>, {Hydropower}<r>[0.380, 0.030, 0.486, 0.053]</r>, {Nuclear}<r>[0.569, 0.030, 0.636, 0.053]</r>, and {Green Energy}<r>[0.719, 0.030, 0.838, 0.053]</r>."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "C1E81oLnAk5jUN39",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is between the coordinates [0.066, 0.919, 0.981, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the labels for x-axis representing different years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "k6mbqPVFmIUIfNB2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat key elements can be found in the region with coordinates [0.088, 0.241, 0.785, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a portion of each line representing 'Coal', 'Hydropower', 'Nuclear', and 'Green Energy' trends from 2010 to 2018."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "dcjGU8U9VnPOwqZk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis represents the {Adoption Rates (%)}<r>[0.031, 0.377, 0.048, 0.601]</r>."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "1ix3kCXLAc6ElGfs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.795, 0.033, 0.840, 0.047] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object indicates the color marker for the {Telemedicine}<r>[0.849, 0.030, 0.962, 0.053]</r> legend."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "RpLoNfZ13pLWF1vx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the technology being represented by the line with coordinates [0.233, 0.257, 0.767, 0.340]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The technology being represented is {Electronic Health Record}<r>[0.324, 0.030, 0.538, 0.053]</r>."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "oFbilkh35ytQgJKN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the adoption rate of the technology {Telemedicine}<r>[0.849, 0.030, 0.962, 0.053]</r> higher or lower at {20 Years}<r>[0.622, 0.919, 0.644, 0.942]</r> compared to its rate at {15 Years}<r>[0.489, 0.919, 0.511, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "WaLruE6NaNdXx9Dl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {AI in Radiography}<r>[0.087, 0.030, 0.241, 0.053]</r> and {AI in Diagnostics}<r>[0.621, 0.030, 0.765, 0.053]</r>, which one had a higher adoption rate at {10 Years}<r>[0.356, 0.919, 0.378, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI in Radiography."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "l8c0hWNefSSpnlem",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the adoption rate of {AI in Diagnostics}<r>[0.621, 0.030, 0.765, 0.053]</r> increasing or decreasing from {5 Years}<r>[0.228, 0.919, 0.239, 0.942]</r> to {25 Years}<r>[0.756, 0.919, 0.778, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "5ym4gDw6fBGB3OCs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the graph in the region [0.233, 0.133, 0.767, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four lines representing different technologies: {AI in Radiography}<r>[0.233, 0.133, 0.767, 0.257]</r>, {Electronic Health Record}<r>[0.233, 0.257, 0.767, 0.340]</r>, {AI in Diagnostics}<r>[0.233, 0.191, 0.767, 0.530]</r> and {Telemedicine}<r>[0.233, 0.166, 0.767, 0.655]</r>. Each of these lines includes data points for 5, 10, 15, 20, and 25 years."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "56FclKW6aOnSJyb4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.031, 0.020, 0.969, 0.062] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the chart's legend. Four different items are listed here: {AI in Radiography}<r>[0.087, 0.030, 0.241, 0.053]</r>, {Electronic Health Record}<r>[0.324, 0.030, 0.538, 0.053]</r>, {AI in Diagnostics}<r>[0.621, 0.030, 0.765, 0.053]</r>, and {Telemedicine}<r>[0.849, 0.030, 0.962, 0.053]</r>."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "yIOw08R7puaWOYD3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.066, 0.066, 0.088, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region denotes the y-axis labels and ticks. The labels are increments of 20, starting from {0}<r>[0.077, 0.894, 0.088, 0.917]</r> and going up to {100}<r>[0.055, 0.066, 0.088, 0.089]</r>."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "tt6Bk0o7sC5oZuiY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at the coordinates [0.522, 0.952, 0.559, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart's x-axis label."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "3CEKg285D9BZJKJR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the 'Manufacture' section of the legend in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.387, 0.030, 0.497, 0.053]"
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "1JDUwlNJjMd8xi6F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.043, 0.130, 0.109, 0.153]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "120000."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "nbP49ykT9EwDyHPT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'E-commerce'}<r>[0.697, 0.393]</r> at 2015 above or below {the point for 'Software development'}<r>[0.697, 0.393]</r> at 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "Vl4vVrsKQEzY4D8e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data points, is the {E-commerce}<r>[0.121, 0.215, 0.959, 0.584]</r> generally above or below {Manufacture}<r>[0.121, 0.457, 0.959, 0.635]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "liwvsaLCECRjyleG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector, {E-commerce}<r>[0.959, 0.215]</r> or {Robotics}<r>[0.959, 0.113]</r>, had higher value in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Robotics."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "mU7en8uXeElwcUX2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the region specified by [0.748, 0.004, 0.927, 0.046]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the last two entries of the legend: {Software development}<r>[0.780, 0.030, 0.777, 0.053]</r> and {Robotics}<r>[0.860, 0.030, 0.934, 0.053]</r>. Each entry is paired with its corresponding color marker."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "TL679YgIiTemFKvG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the area defined by [0.806, 0.033, 0.852, 0.047]."
+            },
+            {
+                "from": "gpt",
+                "value": "The space contains a colored marker that denotes the 'Robotics' line on the graph."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "s2suiEvONZa0WWS8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.803, 0.103, 0.965, 0.142]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects. The y-axis tick labels '116000' and '120000' are located at {coordinates}<r>[0.803, 0.104, 0.828, 0.127]</r> and {coordinates}<r>[0.855, 0.119, 0.880, 0.142]</r>, respectively."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "DWBEecEaVPwngdTm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the specific location [0.015, 0.354, 0.029, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "taaPCbdAWE4oBNBq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell the x-coordinate of the tick label for '2015'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.726, 0.931, 0.761, 0.951]"
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "5hj8ZOIUUoQc4BM6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line for {Middle-Income Households}<r>[0.071, 0.242, 0.968, 0.526]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "ATWjhPjpUd0XxMyc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Low-Income Households}<r>[0.071, 0.367, 0.968, 0.899]</r> line have a higher or lower value at the year 2020 than the {High-Income Households}<r>[0.071, 0.065, 0.968, 0.242]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "CIiWlhQtWbLhTryI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the year 2015 point on the {Middle-Income Households}<r>[0.743, 0.296]</r> line with the year 2015 point on the {Average Households}<r>[0.743, 0.313]</r> line. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Middle-Income Households."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "NtYk1Wn9B8BufnXf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for {High-Income Households}<r>[0.071, 0.065, 0.968, 0.242]</r> generally above or below the line for {Average Households}<r>[0.071, 0.260, 0.968, 0.562]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "qzYoOzEfaUYGUBHk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region [0.724, 0.039, 0.961, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the line chart, which include {Low-Income Households}<r>[0.768, 0.047, 0.936, 0.067]</r>, {Middle-Income Households}<r>[0.768, 0.077, 0.955, 0.097]</r>, {High-Income Households}<r>[0.768, 0.107, 0.941, 0.127]</r>, and {Average Households}<r>[0.768, 0.137, 0.910, 0.157]</r>, each paired with a different color."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "8J7FY3zDMlpHkLtU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with [0.043, 0.199, 0.061, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label with the value '90'."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "pyLHuy3jhabkW9NQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area [0.502, 0.931, 0.537, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2010' is within this region."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "ccxDnt5d94GKf847",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the x-axis tick} at [0.664, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pre-Final_Stage."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "7X2pebNdhjmreOFk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line label 'Brand B'}[0.900, 0.134, 0.966, 0.164] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#dc3545."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "PrEsGfk4g8cUp713",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ylabel in the bar chart with coordinates shown in [0.015, 0.377, 0.034, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "Y3Y6X0oGE5YHkdPF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Performance of BrandA at Middle_Stage}[0.388, 0.363] higher or lower than {Performance of BrandB at the same stage}[0.388, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "V3aTPkrHAiJHNGQX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the performance comparison between {Performance of Brand A at the Pre-Final_Stage}[0.664, 0.610] and {Performance of Brand B at the same Stage}[0.664, 0.671]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Brand A is higher."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "4MIqC5N2TQiSbGe2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the performance of {BrandA in the Final_Stage}[0.939, 0.733] more or less than the {Initial_Stage of the same brand}[0.113, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "eAqbVIuMfOA8vlUy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content presented in this coordinate space [0.846, 0.079, 0.972, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This space includes the legend of the chart. There are two items: {Brand A}[0.900, 0.091, 0.966, 0.121] and {Brand B}[0.900, 0.134, 0.966, 0.164]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "5e8PASAMc30oW8Qo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area covering the coordinates from [0.070, 0.911, 0.979, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis showing the stages. It includes the label for {Initial_Stage}[0.070, 0.911, 0.155, 0.934], {Middle_Stage}[0.342, 0.911, 0.435, 0.934], {Pre-Final_Stage}[0.610, 0.911, 0.717, 0.934] and {Final_Stage}[0.900, 0.911, 0.979, 0.934]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "48INO1fIrUy6gJul",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.040, 0.106, 0.062, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A y-axis tick label is located here, labelled {0.8}[0.040, 0.106, 0.062, 0.130]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "mTGFdBMcfchLJVzJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.015, 0.327, 0.034, 0.633] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is Y-axis label."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "vHCFGQpSCFX73ScB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinates of the second label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.893, 0.134, 0.943, 0.164]."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "xfPsSyJyPrcS5JBc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.476, 0.943, 0.580, 0.975]</r> named in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age Group."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "KFPMl4Xt2dY2t58q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the 'Millenials' line, does {the point at}[0.683, 0.182] have a higher or lower value than {the point at}[0.246, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "c40cDFwNnxFQy9GC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {line for 'Millenials'}[0.159, 0.148, 0.946, 0.729] generally above or below the {line for 'Gen Z'}[0.159, 0.231, 0.946, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "W0eD5Z7Dhwwr9PRH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {point 6}[0.596, 0.463], 'Gen Z' or 'Millenials'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Millenials."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "4Rikte2WR0ebjPgG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.840, 0.079, 0.977, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the lines in the chart. There are two entries: {Millenials}[0.893, 0.091, 0.970, 0.121] and {Gen Z}[0.893, 0.134, 0.943, 0.164], both paired with respective markers."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "XG1CK6SsIJN5axGk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.071, 0.895, 0.946, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis ticks at regular intervals, representing incremental age groups from 0.0 to 1.0."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "c6nQxsmR8BZ9DgLs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.159, 0.231, 0.946, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates contain two objects, the 'Millenials' and 'Gen Z' lines. The 'Millenials' line is generally above the 'Gen Z'."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "l6051D1iybRvyZiF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the main object at coordinates [0.015,0.377,0.034,0.583] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "2EXgsNiCEC5dj5wO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.060,0.911,0.082,0.934]."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "Bi4We7MWOj5D6rhx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value represented by {the data point}[0.159,0.314] from the AAPL line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.7."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "6xpxMBQsZdNUwycv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which stock {AAPL Stock}[0.159,0.314] or {MSFT Stock}[0.159,0.356] had better performance at the investment ratio of 0.1?"
+            },
+            {
+                "from": "gpt",
+                "value": "MSFT Stock."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "kfaxYQ5aurWgLGOD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point {line_0, point3}[0.596, 0.414], is AAPL's performance above or below {line_3, point3}[0.596, 0.206] of MSFT's performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "yT7gPAEJ43ejM05r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two stocks {AAPL Stock}[0.946,0.480] or {MSFT Stock}[0.946,0.148] had a higher performance at an investment ratio of 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "MSFT Stock."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "c33fOSIDAzLhbQSr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the entities appearing in the region: [0.080, 0.079, 0.235, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the graph, including {AAPL Stock}[0.133, 0.091, 0.227, 0.121] with a blue marker and {MSFT Stock}[0.133, 0.134, 0.229, 0.164] with a red marker."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "0SBcipMTkBR8dLVp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region: [0.071, 0.895, 0.957, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis of the chart along with its labels representing Investment Ratios, specifically value labels ranging from '0.0' to '1.0' at regular intervals."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "tqIlNaRyr7u7gjQo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region covered by [0.159, 0.148, 0.946, 0.356]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of line plots representing the performance of both AAPL and MSFT stocks at different investment ratios. Starting from the lower limit, we have data points for MSFT performance, followed by data points for AAPL performance."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "ELMBARanwjOWNPrg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line for 'Design Score'} located at [0.159, 0.231, 0.946, 0.853] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the representation of the Design Innovation Score against the Creativity index."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "Geu75AK7xCluuCJY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.282, 0.034, 0.678]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Design Innovation Score."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "faiwIbUaogVO8Vuq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {y-axis tick label}<r>[0.040, 0.554, 0.062, 0.577]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "oeU2Fl8YQTHLzkQj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Design Score point at '0.5'}[0.508, 0.439] higher or lower than the {Design Score point at '0.6'}[0.596, 0.563]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "CRB2pjnaSbpQQjxl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in location between {the point for '0.1' on the Design Score line}[0.159, 0.231] and {the point for '0.2' on the same line}[0.246, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for '0.2' is above and to the right of the point for '0.1'."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "PSsPm3GaKZBDEsDg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Design Score line point for '1.0'}[0.946, 0.853] above or below the {Design Score line point for '0.9'}[0.858, 0.770]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "2dtJ6FmSDTSmza1n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area given by these coordinates [0.807, 0.079, 0.977, 0.132]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the Design Score line with a label and a corresponding color marker."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "QDkfp5N5KwXvyJtq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within this region [0.071, 0.895, 0.957, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes. This area contains the x-axis tick labels and tick lines for different Creativity Index values ranging from 0.0 to 1.0."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "HT5nItsZTIjiqbUF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.034,0.222,0.071,0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of 4 y-axis tick labels with corresponding values '0.2', '0.4', '0.6' and '0.8'."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "SHyqHbRpGi6kTZ1x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.015, 0.364, 0.034, 0.596] denote in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, representing Public Opinion."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "dMsXDcCU5EZqmdSe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with coordinates [0.159, 0.306, 0.946, 0.480]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represents the trend in 'Scandal' data."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "AZbA74ISzwy8plZq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick on the X-axis at [0.246, 0.895] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a point on the X-axis labeled '0.2'."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "HAEmQytUy0xwCOhu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher at the point of x=0.8, is it 'Popularity' {line}[0.159, 0.148, 0.946, 0.422] or 'Scandal' {line}[0.159, 0.306, 0.946, 0.480]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Popularity'."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "9IbsVSqiB2syswwk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Popularity' trend}[0.596, 0.281] higher or lower than the corresponding point on the 'Scandal' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "3rIZaOV0kmfsNmwK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Scandal' {line}[0.159, 0.306, 0.946, 0.480] curve ascend or descend from x=0.1 to x=0.6?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascend."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "LdzIRzyOgpYxvgat",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.833, 0.079, 0.977, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the two data series: {Popularity}[0.887, 0.091, 0.970, 0.121], and {Scandal}[0.887, 0.134, 0.952, 0.164], each represented by a unique color."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "1usCpxQcjtL5TdP7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the region [0.071, 0.066, 0.985, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the area of the plot chart, including the X and Y axes, the 'Popularity' and 'Scandal' lines, and their data points."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "gQNtkax8YLZe3ADk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.276, 0.025, 0.780, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area serves as the location of the subtitle of the plot chart: \"Media Influence and Public Opinion Guidance\"."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "97sy2zaky0gqgaH2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to {coordinates}<r>[0.040, 0.979, 0.073, 1.002]</r> in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to the y-axis tick label '-0.3'."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "42KPfXjo1ie3mE17",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the third city}<r>[0.281, 0.911, 0.331, 0.934]</r> labeled along the x-axis in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The third city along the x-axis is 'Atlanta'."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "5paYCwBk1T6w8ast",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item 'Longitude Changes'}<r>[0.098, 0.148, 0.131, 0.148]</r> in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the 'Longitude Changes' line in the legend is '#dc3545' (a shade of red)."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "7jcRKzPp9eN9Ep2G",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Atlanta' on the 'Latitude Changes' line}<r>[0.306, 0.415]</r> above or below the corresponding point on the 'Longitude Changes' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "wHpV3WjuYjkJQO00",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the 'Longitude Changes' point for 'Denver'}<r>[0.671, 0.791]</r> located in relation to {the 'Latitude Changes' point for 'Denver'}<r>[0.671, 0.267]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "EJY9tR7iN55hyLSw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, 'Longitude Changes' or 'Latitude Changes', has a higher point for {the city 'New York'}<r>[0.124, 0.365]</r> and <r>[0.124, 0.578]</r> respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Longitude Changes."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "IEABcUxmj2XKQkYe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section of the line chart within the coordinates <r>[0.083, 0.066, 0.985, 0.895]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the entirety of the plotted data. Two line plots, representing 'Latitude Changes' and 'Longitude Changes' across various locations, are visible. The x-axis is labeled with the names of different cities, and the y-axis is labeled with a range of values from -0.3 to 0.3."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "h3RAhy0lFjcljWFh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the region defined by the coordinates <r>[0.091, 0.079, 0.311, 0.176]</r> on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's legend. It contains two items: 'Latitude Changes' with a teal color marker and 'Longitude Changes' with a red color marker."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "IzK4jmojZ8Ar5eD5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area defined by <r>[0.324, 0.025, 0.744, 0.058]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area occupied by the chart's subtitle, \"Geographic Spatial Data Visualization\", styled in black color."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "TjYMTsR3MmqF3vdk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is situated at [0.498, 0.943, 0.539, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "wAo9xBno2f98p8dH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {the specific point}[0.110, 0.267]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "GXThE9AcNEUcZ7mq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label occupies the space at [0.164, 0.520, 0.246, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Decline point label for 2001."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "S0RHdecRU2fpkLQQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point}[0.646, 0.141] higher or lower than {the base horizontal line}[0.110, 0.895, 0.914, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "6mJxy9c1oUkFD9fe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label '3'}[0.060, 0.056] correspond below or above {the point '-2'}[0.110, 0.658] on the vertical axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "12Ty7yWjEuZxdQUs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point of Population Growth}[0.110, 0.267] positioned above or below {the first point of Economic Decline}[0.110, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "izrmv9Ed2ooZA7YV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCharacterize the zone within [0.040, 0.056, 0.060, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis labels ranging from '3' to '\u22124', positioned vertically with black font color."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "yoY0GrEQ2SkPEh5H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.131, 0.091, 0.291, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section comprises labels for {Population Growth}[0.131, 0.091, 0.284, 0.121] and {Economic Decline}[0.131, 0.134, 0.279, 0.164], each associated with a respective color marker, outlined in {the region's coordinates}."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "NYswsC55KvlRswf5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the rectangle [0.217, 0.895, 0.753, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the x-axis with labeled years for {2002}[0.200, 0.911, 0.235, 0.934], {2004}[0.307, 0.911, 0.342, 0.934], and {2006}[0.414, 0.911, 0.449, 0.934], among others, all marked in black font color distributed evenly across the axis."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "eIKkg7sW8wSCooB4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies at the coordinates [0.816, 0.487, 0.970, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the data category 'Data Virtualization'."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "rY64G0qPvVtsA9pP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value of the point at {coordinates}[0.537, 0.706] in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.79."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "CP6VFQKWCQ3E9Rwh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data does the color code at {coordinates}[0.763, 0.535, 0.808, 0.554] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Governance."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "WnXe3uNJqhMcTmKz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, does {Data Integration}[0.944, 0.744] perform better than {Data Virtualization}[0.944, 0.631] in large data size?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "vAIkwO32aeI0wu3V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the medium data size category, is {Data Quality}[0.537, 0.179] higher or lower compared to {Data Integration}[0.537, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "FZzPrbw1yOQ9LDL5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the performance of {Data Governance}[0.537, 0.668] and {Data Virtualization}[0.537, 0.744] for medium data size, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Virtualization."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "sdqGKlszodJhZI5q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the coordinates [0.040, 0.094, 0.079, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label '0.950'."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "BizLF33PwIsGkz1m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area at [0.762, 0.388, 0.977, 0.572]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the chart. These include the legend for {Data Integration}[0.816, 0.399, 0.952, 0.429], {Data Quality}[0.816, 0.443, 0.920, 0.473], {Data Virtualization}[0.816, 0.487, 0.970, 0.517], and {Data Governance}[0.816, 0.531, 0.961, 0.561]. Each item is associated with a unique color marker."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "AtntgpweviabSvFt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.925, 0.911, 0.964, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object in this region is the x-axis label 'Large'."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "5zpgKjEhgezmB7CC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line with color '007bff'}<r>[0.113, 0.339, 0.943, 0.853]</r> in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Gaming Growth"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "p9SRleJz0MVIsNbs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.421, 0.034, 0.539]</r> refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "chseg5VLmUKAju4m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-tick label}<r>[0.926, 0.911, 0.961, 0.934]</r> mean in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "DBjdlKmOYv96nRXk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Mobile Gaming Growth'}<r>[0.943, 0.339]</r> above or below {the point for 'Console Gaming Growth'}<r>[0.943, 0.621]</r> in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "jX7AMk6yfJHSnhjf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich growth is greater in the year 2010, {Mobile Gaming Growth}<r>[0.113, 0.853]</r> or {Console Gaming Growth}<r>[0.113, 0.356]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Console Gaming Growth"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "zJY3fDXJ3ebIwXMd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the points for the year 2011, is {Console Gaming Growth}<r>[0.205, 0.385]</r> greater or smaller than {Mobile Gaming Growth}<r>[0.205, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "qxZkGBO3nVSzvN7v",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region <r>[0.715, 0.079, 0.977, 0.176]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. More specifically, it includes two items: {Mobile Gaming Growth}<r>[0.769, 0.091, 0.959, 0.121]</r> and {Console Gaming Growth}<r>[0.769, 0.134, 0.970, 0.164]</r>."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "epuqHGTKsHPNJZ6C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the points located in the region <r>[0.667, 0.477, 0.759, 0.623]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two points representing the growth rates in Mobile Gaming and Console Gaming in the year 2017."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "cHydmZMRjunm60B2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region <r>[0.508, 0.943, 0.549, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the label of the x-axis, and the chosen label is 'Year'."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "inCq05lsQtSPoL7L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.023, 0.376, 0.053, 0.634] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "BYxO5dbODRjBsUba",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of 'Data Scientist' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.695, 0.206, 0.877, 0.244]."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "sW1sVkgtFoFfh9KM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-tick label}<r>[0.780, 0.910, 0.950, 0.939]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Executive Level'."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "i43XwvqXldBQiRq6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the performance of {Data Analyst}<r>[0.160, 0.225, 0.865, 0.750]</r> higher or lower than {Data Scientist}<r>[0.160, 0.155, 0.865, 0.680]</r> at 'Entry Level' stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "j64fMWUElUEkdyjs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Data Analyst line}<r>[0.160, 0.225, 0.865, 0.750]</r> rising or declining compared to {the Data Scientist line}<r>[0.160, 0.155, 0.865, 0.680]</r> from 'Mid Level' to 'Senior Level' stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Declining."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "QU4o0b2o5xWMuUGB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data set has a higher performance at {the 'Executive Level' stage}<r>[0.865, 0.890]</r>, Data Analyst or Data Scientist?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Scientist."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "LZ3Gi3mEo3CXGEpd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.135, 0.069, 0.890, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart subtitle \"Career and Workplace Development Trends,\" in a default black color and non-rotated."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "1lZfalifPaGcLoTd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.612, 0.137, 0.887, 0.258]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It includes two items: {Data Analyst}<r>[0.695, 0.151, 0.862, 0.189]</r> and {Data Scientist}<r>[0.695, 0.206, 0.877, 0.244]</r>."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "YxskwurO681sv6RP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.446, 0.910, 0.792, 0.939]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates lie two x-tick labels, {Senior Level}<r>[0.446, 0.910, 0.579, 0.939]</r> and {Management Level}<r>[0.585, 0.910, 0.792, 0.939]</r>."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "B0GRC27WaC7XnEmN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of markers does {the line representing Environmental Quality} use according to the data at <r>[0.098, 0.372, 0.945, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "LyYvfNOD0brKX9wH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {x-axis labels} starting at <r>[0.084, 0.933, 0.113, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "NY4FULLKwgyi7dTm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the grid line} at <r>[0.056, 0.049, 0.987, 0.921]</r> look like in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed and grey."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "KTZm6yESz70bAKOA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, 'Environmental Quality' or 'Life Expectancy', starts higher in the year 2000 at {coordinates} <r>[0.098, 0.372]</r> and <r>[0.098, 0.171]</r> respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Expectancy."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "SAAbYjMDOAw6y1mV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the last marker for Life Expectancy}<r>[0.945, 0.171]</r> above or below {the last marker for Environmental Quality}<r>[0.945, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "LHdu5q4NCA4FLZXg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Environmental Quality' and 'Life Expectancy', which line has a smoother curve from {start}<r>[0.098, 0.372]</r> to {end}<r>[0.945, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Expectancy."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "3bYZ3TBiwzhUFzba",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual features found in the area <r>[0.040, 0.049, 0.048, 0.932]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick labels for values from 0 to 10, placed vertically and styled in black color."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "2brfDIdhLuzIWhwP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visual elements are present within the bounds <r>[0.063, 0.838, 0.267, 0.910]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend for the chart with labels 'Environmental Quality' in blue and 'Life Expectancy' in red, signifying each with their respective line colors on the plot."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "CAWtL9QA01VtQ9Kv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you outline the elements within the plot space <r>[0.056, 0.049, 0.987, 0.921]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This expansive region includes the main graph area with grey dashed grid lines, data lines for Environmental Quality and Life Expectancy, primary x and y axes with labels, and multiple data points across the 21-year timespan."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "SSXHv6KInO8jbhhf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {handle's color}<r>[0.081, 0.844, 0.125, 0.862]</r> in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#17a2b8."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "U0nLhwUPzDB1IYkH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label coordinate for the year '2015' on the x-ticks of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.557, 0.911, 0.592, 0.934]."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "uT8xxjpyGftBTN3p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {label for the line}<r>[0.133, 0.664, 0.266, 0.694]</r> in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Minimal Decline."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "OQ6I8e3IiR1Uhk9E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at the 2012 point, the {Minimal Decline line}<r>[0.113, 0.302, 0.943, 0.421]</r> or the {Gradual Increase line}<r>[0.113, 0.160, 0.943, 0.421]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gradual Increase."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "2nBjqX8OplBs94GG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the first point of {Gradual Increase}<r>[0.113, 0.421]</r> and the last point of {Minimal Decline}<r>[0.943, 0.421]</r>, which one is higher on the y-scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "First point of Gradual Increase."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "kQKCQ3B2KPBlMfH4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the color of {the marker for Minimal Decline}<r>[0.081, 0.668, 0.125, 0.687]</r> and the color of {the marker for Downward Trajectory}<r>[0.081, 0.800, 0.125, 0.819]</r> the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "pw60mB6tO89X1PT4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area with these coordinates [0.080, 0.653, 0.315, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend that includes labels and corresponding color markers for five different trends: Minimal Decline, Gradual Increase, Dramatic Decrease, Downward Trajectory, and Minimal Change."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "6laMNTub8CIRbHC5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of this {ylabel}<r>[0.015, 0.370, 0.034, 0.590]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This presents the y-axis label 'Global Trends' with the text color to be black and the label is in vertical alignment."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "i8opSAJzPbBxp7Rb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information placed within these coordinates [0.095, 0.911, 0.130, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the coordinates for the x-tick label '2010'."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "wPJlgghk9FREQLET",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the subtitle}<r>[0.321, 0.025, 0.736, 0.058]</r> say?"
+            },
+            {
+                "from": "gpt",
+                "value": "Venture Capital Markets Performance."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "wsiIzlZsH5LjqzRg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-label}<r>[0.015, 0.377, 0.034, 0.583]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "ElrGXkacauBji97d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second item in the legend}<r>[0.133, 0.134, 0.272, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series A Funding."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "wNx6g4VfAfjqXUIx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of {Seed Funding}<r>[0.113, 0.141, 0.943, 0.668]</r> or {Series A Funding}<r>[0.113, 0.412, 0.943, 0.774]</r> has a higher value in the Q3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seed Funding."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "tDhS7vcWy60zvquU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Q6, is the {Seed Funding}<r>[0.574, 0.261]</r> above or below the {Series A Funding}<r>[0.574, 0.774]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "6M9cy1fnpPQKfFQe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the performances of {Seed Funding}<r>[0.113, 0.141, 0.943, 0.668]</r> and {Series A Funding}<r>[0.113, 0.412, 0.943, 0.774]</r> compare in Q10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seed Funding is higher."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "dKPCCJznJRMqU4iB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the highlighted region from <r>[0.442, 0.943, 0.614, 0.975]</r> show?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the x-axis label, which is \"Investment Stage\"."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "BGywUVCgdXlyRUlT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the coordinates <r>[0.080, 0.079, 0.278, 0.176]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It distinguishes Seed Funding, indicated by a color '#007bff', and Series A Funding, indicated by a color '#dc3545'."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "KZfwObL7UQCIixVx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of this region <r>[0.390, 0.373, 0.482, 0.311]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a section of the Q4 and Q5 points in both Seed Funding and Series A Funding lines. Seed Funding shows a performance value of 0.83 and 0.9 while Series A Funding shows values of 0.68 and 0.62 for Q4 and Q5 respectively."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "74sEg81nrJiIeCTN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label in the chart with coordinates [0.446, 0.943, 0.593, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Stages."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "xz5jMyqu6wz8F8RM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the object}<r>[0.015, 0.435, 0.034, 0.525]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is the y-axis label, read as \"Value\"."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "2w93a0WxObqrFGxS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.699, 0.911, 0.739, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis tick label labeled as 'Retail'."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "Bgde7HzHc4ND4je3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the cost curve}[0.120, 0.276, 0.919, 0.594] lie above or below {the value curve}[0.120, 0.184, 0.919, 0.822] at the 'Retail' point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "ccCr9LuqY1aWuGh7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich marker on the distribution stage is higher {Cost}[0.519, 0.594] or {Value}[0.519, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "xDnd15gii5648d5g",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the costs at {procurement}<r>[0.120, 0.276]</r> higher or lower than the value at {manufacturing}<r>[0.320, 0.367]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "PFxMA1vm2pPYvWZx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content shown within the coordinates [0.305, 0.025, 0.733, 0.058] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content within these coordinates is the subplot title called 'Value Chain Visualization and Analysis'."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "HMHqkvkInsfVhZRJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside this region - [0.845, 0.079, 0.950, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend items: a blue marker labeled 'Cost' at {coordinates}[0.898, 0.091, 0.935, 0.121] and a red marker labeled 'Value' at {coordinates}[0.898, 0.134, 0.944, 0.164]."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "6aQ9q4RWjpf5GNDg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in this set of coordinates [0.040, 0.221, 0.070, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In these coordinates, the y-axis has a tick label of '25.0'."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "IkZP6W21AViviO9Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of element can be found at this location in the graph [0.015, 0.371, 0.034, 0.589]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "Xy7i8Ksje4kwyHWI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the fifth tick label on the x-axis located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.667, 0.895]"
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "JWjKmFJRvN0Wc8TX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point at these coordinates {point}[0.851, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.56."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "4rf9auCse3lSOnls",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing {the point labeled '2012'}[0.297, 0.480] and {the point labeled '2015'}[0.574, 0.563] on the line representing 'Rural Development', which year has a higher development value?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "YkxWBMU9Gih9LiXd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the value of 'Urban development' in 2010}[0.113, 0.729] compare with {the value of 'Urban development' in 2011}[0.205, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is lower."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "KJAb5IzJt1AytXUB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich development has a higher value in the year 2019, Rural or Urban development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "LdAS4PTTW9Jv2CXj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of what's contained within these coordinates [0.749, 0.079, 0.977, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangular region contains the legend that corresponds to the 'Rural Development' and 'Urban Development' lines. The 'Rural Development' label and its correlating blue circular marker are located in the top part of the region, while the 'Urban Development' label and its correlating red X-shaped marker are located in the lower part."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "Cb45LaZih2rk1tys",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found in the region [0.508, 0.943, 0.549, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Year' is found within this region."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "5ASqfilBRywOvMXY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can one see in the region [0.113, 0.397, 0.943, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large region contains two lines with data points resulting from 'Rural Development' and 'Urban Development'. The 'Rural Development' line shows a trend of decreasing development values over the years, and it is marked with blue circles. The 'Urban Development' line shows a general trend of increasing values from 2010 to 2019, and it is marked with red X shapes."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "b1vDZxRtKyLKmlPs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object correspond to the coordinates [0.015, 0.377, 0.034, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label which reads \"Performance\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "IlAddKgFUfcHVulf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.095, 0.911, 0.130, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is an x-axis tick label, which indicates the year \"2010\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "LqSIbz3NegnHLi87",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element corresponds to these coordinates [0.133, 0.091, 0.303, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to a legend item labeled \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "iUd5KyaimV9SxKwN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {Internet of things line}<r>[0.113, 0.687, 0.943, 0.853]</r> trend compared to the {Artificial Intelligence line}<r>[0.113, 0.066, 0.943, 0.729]</r> in 2019 (at x-tick '2019')?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "vyo3t9KMxZmB0Gan",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 4th point on Artificial Intelligence line}[0.390, 0.480] above or below {the 4th point on the Internet of Things line}[0.390, 0.785]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "EehpSDqXdIikeyBp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher performance in 2010, {Artificial Intelligence}[0.113, 0.729] or {Internet of Things}[0.113, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "Zzu6NWnYnWvJ1Pnj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.113, 0.687, 0.943, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses the line chart for 'Internet of Things' from the year 2010 to 2019, represented by 'X' markers on the line."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "JZBrk36iAL5e7uIK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area inside [0.133, 0.091, 0.303, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the \"Artificial Intelligence\" label from the legend, corresponding to one of the line charts in a blue color."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "aDJzcVtIVowdGE8G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.285, 0.025, 0.771, 0.058] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a text which is the subtitle of the chart \"Technological Industry Development Trends\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "DimKUbBwurt3kpYz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.025, 0.392, 0.048, 0.560] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the Y-axis label which represents 'Efficiency'."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "q2LdCm6hJ7Iz9YKF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the coordinates of the 'Week' label in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Week' label on the x-axis is located at the coordinates [0.518, 0.927, 0.582, 0.963]."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "hSU7HkbudAv2sasc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis tick label at the coordinates [0.522, 0.879, 0.551, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value '15'."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "BBLMRK4ACBwjZkic",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Material Efficiency}[0.164, 0.438, 0.936, 0.693] or {Machine Efficiency}[0.164, 0.260, 0.936, 0.438], is higher on {x=10}[0.403, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Material Efficiency."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "EIFLlXayB1UbY0FY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Machine Efficiency} line[0.164, 0.260, 0.936, 0.438] above or below the {Material Efficiency} line[0.164, 0.438, 0.936, 0.693] at x=20?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "y3Zbz5wuZAw1ORxY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich efficiency level had a higher value on week 15, Material Efficiency or Machine Efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Efficiency."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "rD6kUs9Srr9st2cl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content found within the area [0.664, 0.116, 0.963, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the chart legend with two items: {Material Efficiency}[0.738, 0.130, 0.952, 0.165] colored in cyan, and {Machine Efficiency}[0.738, 0.182, 0.954, 0.217] colored in salmon."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "YmEnfkFxNtmRZKQM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the area bounded by [0.111, 0.037, 0.989, 0.088]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, 'Manufacturing Production Efficiency Evaluation'."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "V2PfPFLY8RhFkCQ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within [0.789, 0.879, 0.818, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an x-axis tick label '25' located within these coordinates."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "OyB31p83XtplOC2K",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object on the chart with coordinates [0.516, 0.927, 0.591, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label representing 'Weeks'."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "TR2AWZrQRFcLVovc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is stated by the {ylabel}<r>[0.025, 0.425, 0.048, 0.528]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It states 'Count.'"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "oPopr3HCtgnP17MZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content inside this coordinate {sublegend item}<r>[0.218, 0.182, 0.355, 0.217]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to 'Interactions'."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "AkXGd5nG9NtOR9oS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for 'Users'}[0.454, 0.639] above or below the {point for 'Interactions'}[0.454, 0.612] at the same value in weeks?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "qpi2CZZkRHlctL6I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the final points, does the 'Users'<r>[0.908, 0.196]</r> line end above or below the 'Interactions'<r>[0.937, 0.178]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "6JoDda6iSo00ezlk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts with a higher y-value: the line for 'Users'<r>[0.171, 0.784]</r> or the line for 'Interactions'<r>[0.171, 0.802]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Interactions'."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "iol5ez1dQsZ4YL04",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is present in the region [0.000, 0.854, 1.008, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick labels, which range from '\u22125' to '30'."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "OeC8qUmGXJYkZxMU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area contained within the coordinates [0.144, 0.116, 0.365, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the subplot legend, indicating 'Users' with color '#00bfff' and 'Interactions' with color '#fa8072'."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "bABND5TikXENFs7F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of this area [0.169, 0.037, 0.939, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle of the chart, \"Media and Entertainment Industry Trends\"."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "FGI4QPr9H7TM1vcy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Staff Recruitment'}<r>[0.150, 0.864, 0.942, 0.865]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is coral."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "QEZwZGqoqfbkeKKH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the '2020' label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.920, 0.919, 0.964, 0.942]"
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "CJHYEWUPS7UihxGz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the count at '2018'}<r>[0.744, 0.147]</r> for 'Student Enrollment'?"
+            },
+            {
+                "from": "gpt",
+                "value": "28500."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "5xK98G0NegasVbWD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2017, is the value of {Student Enrollment}<r>[0.645, 0.159]</r> higher or lower than the value of {Staff Recruitment}<r>[0.645, 0.864]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "WwLLn3EjJkuSzgK0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts first in 2012, {Student Enrollment}<r>[0.150, 0.236]</r> or {Staff Recruitment}<r>[0.150, 0.864]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both start at the same time."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "ukKPIgDeVUz6JxlP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {2015}[0.447, 0.864], Student Enrollment or Staff Recruitment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Student Enrollment."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "KGzKl0z7NTk7gJkH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the area [0.119, 0.077, 0.349, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. Two objects are present: {Student Enrollment}<r>[0.174, 0.086, 0.343, 0.110]</r> with a light blue marker and {Staff Recruitment}<r>[0.174, 0.121, 0.328, 0.145]</r> with a coral marker."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "fUg1aRhXHlC9VbKv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is located in the region [0.043, 0.100, 0.098, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '30000' is located in this region."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "rKkiBtWqPOCoCzIk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the bounding box at [0.275, 0.025, 0.817, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart \"Education and Training Industry Trends\" is contained in this region."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "pH17ZMGUlS2AoCaW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.019, 0.447, 0.036, 0.522] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "FeFQE82MCx6dJ84H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second point on the 'Commodity Price' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.139, 0.829]"
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "7BsFmkjX6F8Bkl3b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label} at [0.131, 0.121, 0.337, 0.145] within the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Luxury Goods Spending."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "l5n72gHuNvixf0iK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Commodity Price} at [0.254, 0.689] greater or lesser than {Luxury Goods Spending} at [0.254, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "XoMII79BMLPdCnsc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '5'}[0.231, 0.723] on the 'Commodity Price' line above or below {the point labeled '5'}[0.231, 0.440] on the 'Luxury Goods Spending' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "nONZ9m2bXvfPTeqa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point [0.940, 0.422] on the chart, is the line for Commodity Pricing higher or lower than Luxury Goods Spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "gt20IpjpkZxr3hN3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within [0.075, 0.077, 0.344, 0.154] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The legend items: {Commodity Price}[0.131, 0.086, 0.279, 0.110], and {Luxury Goods Spending}[0.131, 0.121, 0.337, 0.145], each with their identified color markers."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "eoFu3Zj4kN43KCQE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.282, 0.025, 0.766, 0.058] area."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, \"Consumer Behavior Trend Analysis\"."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "1r2zh2GhE1dyr3RB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area bound by [0.696, 0.511, 0.740, 0.530]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis point '21' is positioned here on the line for 'Commodity Price'."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "3yaQ1dMFa1l95GFT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.025, 0.425, 0.048, 0.528] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "aZpNNTjBP3uFsJ8a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the attribute of the element located at [0.175, 0.135, 0.236, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color marker for 'Active Users' in the legend."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "ziIYA5NebC5AMEP3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.163, 0.037, 0.973, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subplot title - \"Active Users and Learning Hours Over Time\"."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "iik3IxZJe0pwmjAr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Active Users} coordinate [0.198, 0.591] greater or less than {Learning Hours} coordinate [0.198, 0.699] at month 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "8qPxJ2zxHuJSWLDf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Active Users} value at coordinate [0.938, 0.160] in Month 12 greater than {Learning Hours} value at coordinate [0.938, 0.402]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "qHrgOewBheoGHqD1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Active Users} and {Learning Hours} lines at timestamp {3}[0.333, 0.645], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Learning Hours."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "sV4OMEXM8VgKY0KJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.173, 0.116, 0.432, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the two lines {Active Users}[0.247, 0.130, 0.390, 0.165] and {Learning Hours}[0.247, 0.182, 0.423, 0.217], each paired with a color marker."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "hT8zVO0FD1mM9F3u",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region at [0.198, 0.160, 0.938, 0.699] describe?"
+            },
+            {
+                "from": "gpt",
+                "value": "It describes the {Active Users} trend line from Month 1 to 12, with each point representing the count of active users for the corresponding month."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "votZQC1yMOS01llw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.198, 0.402, 0.938, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region describes the trend line for {Learning Hours} over a span of 12 months, with each point on the line representing the count of learning hours in a specific month."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "0km56jjFJcsoGmmw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.218, 0.736, 0.369, 0.771] refer to on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the label \"Stock Market\" in the legend."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "YePS5IhqyJCdAwrY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates} [0.025, 0.429, 0.048, 0.524] mean?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label \"Value\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "N4mwLggqqVJvn5rB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis tick label at the position {location}<r>[0.658, 0.879, 0.687, 0.914]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"20\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "3L0scsk1hdH3j2zP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on January 15, {Stock Market}<r>[0.540, 0.683]</r> or {Bonds}<r>[0.540, 0.160]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Market."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "T726kpNisGaYeYcu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Stock Market}<r>[0.567, 0.692]</r> have a higher or lower value compared to its value at {previous point}<r>[0.540, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "zujknL9RHhi9faCo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends, is the {Bonds}<r>[0.884, 0.136]</r> value increasing or decreasing compared to its previous point at {Bonds}<r>[0.857, 0.138]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "dCHcD3OhbC5BsA5g",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in region [0.058, 0.824, 0.116, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis tick label \"1000\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "Twh3xvrv7LL8kmRt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region starting from [0.171, 0.133] to [0.937, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the path of the line representing the Bonds over the whole of January."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "1VYRgVlxeP8UsKp9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.311, 0.037, 0.797, 0.088] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart: \"Financial Data Exploration\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "rvcuKlNKGflhwClR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element present at coordinates [0.019, 0.436, 0.036, 0.526] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "6NXHUrdjuAeXIKAo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.174, 0.104, 0.350, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Avg Education Level\"."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "bwMUxtDPZJADYfB9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the tick label} at [0.128, 0.903, 0.172, 0.931] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 1990."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "ByJIcd58kDe448ID",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the point represented by the year 1995}[0.291, 0.847], the 'Avg Education Level' line or the 'Avg Income' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Avg Income'."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "tH2kwMOJUNkoblxm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {coordinates at year 1996}[0.319, 0.847] on the 'Avg Education Level' line above or below the corresponding point on the 'Avg Income' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "fMKaSWgpwB9LV17F",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher value at {the year 2001}[0.461, 0.847], 'Avg Education Level' or 'Avg Income'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Avg Income'."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "94poJmPHfzezl51s",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.119, 0.093, 0.357, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the legend of the chart, which includes \"Avg Education Level\" at {coordinates} [0.174, 0.104, 0.350, 0.132], and \"Avg Income\" at {coordinates} [0.174, 0.146, 0.276, 0.174]."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "uGtyU4XoOqaj8vWQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.043, 0.104, 0.098, 0.132]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises a y-axis tick label, displaying the value '60000'."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "9TPUmfRXUYCfubQR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within [0.150, 0.542, 0.942, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region are points on the 'Avg Education Level' line and the 'Avg Income' line, represented by years from 1990 to 2018."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "3BFTQt4THvl1bDw0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object [0.043, 0.475, 0.071, 0.498] on the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '1.0'."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "tHa3vHRgSSvo4Kt0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first x-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Area_1."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "f49kbnYaiUppZT1w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the line}[0.124, 0.104, 0.940, 0.865] on the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Demographic Influx."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "aBN4tkKDcRaIhvYH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to this chart, does {Area_24}[0.819, 0.234] have a higher demographic influx than {Area_18}[0.638, 0.365]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "8T5PvX1dgSU8y7k6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the positions on the y-axis, is {Area_9}[0.366, 0.745] having a higher or lower demographic influx compared to {Area_6}[0.275, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "bcDia4MOQbfVvrXD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart lines, does {Area_23}[0.789, 0.288] see more or less demographic influx than {Area_20}[0.698, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "odapnyRpzFje6wRt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is presented in the region [0.043, 0.149, 0.071, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '1.3' is present in this region."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "pEZjdJhMs4q90oNb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the region [0.094, 0.919, 0.154, 0.942] of the chart includes."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label representing {Area_1}[0.124, 0.810]."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "uHgSjRIYhhHuubMx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the object located at [0.396, 0.473] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This point corresponds to the demographic influx level of {Area_10}[0.396, 0.473] which is 1.01."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "OSlsQpmKhhCRvbW2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.025, 0.295, 0.048, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label which is \"Energy Consumption\"."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "j7PbJ7VQTjxTBKyl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the coordinates of the first tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.086, 0.879, 0.101, 0.914]."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "zFHc8GeV5O6qcc3X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.489, 0.927, 0.565, 0.963]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label \"Weeks\" on the x-axis."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "gt2dcSk9uoUMfWLY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line travels higher at {the 15th point}[0.613, 0.391], the 'Green Energy' line or the 'Non-Renewable Energy' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Energy."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "MyGjHNIKPfsh8JpD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Green Energy line's point at 12th week}[0.509, 0.477] above or below {the Non-Renewable Energy line's point at 12th week}[0.509, 0.445]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "bq9Dq9cQ17AWkynp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a lower value of consumption at {the 10th point}[0.440, 0.502], the 'Green Energy' or the 'Non-Renewable Energy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Energy."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "2E48wU6PzoCos29N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content located in [0.675, 0.736, 0.833, 0.771]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend item {Green Energy}<r>[0.675, 0.736, 0.833, 0.771]</r>, paired with a blue marker."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "L62n2cw4Ri51M6De",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found in the region [0.675, 0.788, 0.944, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend text {Non-Renewable Energy}<r>[0.675, 0.788, 0.944, 0.823]</r> with an associated red marker."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "x5ZxD7yjRu2axPQ6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.058, 0.819, 0.072, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '2'."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "DoNqscmt4Ugf8JuI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the subplot title} placed at [0.147, 0.037, 0.945, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical and Biotechnology Industry Trends."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "FNXW9MdNzxtaRylP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the {Y-axis label} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.025, 0.425, 0.048, 0.528]."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "6BcSZcnKlayhQ33Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the X-axis tick label} at [0.835, 0.879, 0.864, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "25."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "utK4N1NmlZ7xATIE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 15th point of 'New Drugs Development'}[0.561, 0.695] above or below {the 15th point of 'Biotech Patents'}[0.561, 0.333]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "Ef6oudHgGAM8zf8v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the 18th Time Step}[0.647, 0.727, 0.657, 0.741], which trend is higher: 'Biotech Patents' or 'New Drugs Development'?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Drugs Development."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "sFMS5TVYXYDcg9zD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher at {the 7th Time Step}[0.330, 0.716, 0.340, 0.730]: Biotech Patents or New Drugs Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotech Patents."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "3rhcAGx8emFY2eZi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region [0.129, 0.116, 0.502, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region containing the plot legend. It consists of two items, {Biotech Patents}[0.203, 0.130, 0.384, 0.165] with a color marker in '#00bfff', and {New Drugs Development}[0.203, 0.182, 0.493, 0.217] with a color marker in '#fa8072'."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "Y4WgHF0cA3FpYQO6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.118, 0.098, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main plot of the chart, including lines representing 'Biotech Patents' and 'New Drugs Development', along with all their points plotted against the Y-axis ('Count') and X-axis ('Time Steps')."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "dt6Qwtzd9jzaZEB4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area represented by the coordinates [0.118, 0.098, 0.975, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the primary chart area. It includes the plotting of two trends: {Biotech Patents}[0.157, 0.183, 0.936, 0.404] and {New Drugs Development}[0.157, 0.529, 0.936, 0.805]. Metaphorically, it illustrates the trends of patents and new drugs development in the medical and biotechnology industry over time."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "U2s8RCxHuD71pw6f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the elevated point of the line representing 'Free Textbook Distribution'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the line point}<r>[0.273, 0.829]</r> represents week 6 with 510 distributions."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "8kRBlzzKBGTQNwLu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the 'eLibrary Subscriptions' line represented in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is {purple}<r>[0.088, 0.125, 0.124, 0.138]</r>."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "ra0axuLHWQXMIxKC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label of {coordinates} [0.015, 0.444, 0.029, 0.525] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Counts\"."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "QInDiHbHmhqPKfTq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt school week 15, which resource has more counts: 'Free Textbook Distribution' or 'eLibrary Subscriptions'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The eLibrary Subscriptions}<r>[0.547, 0.294]</r> is higher than {the Free Textbook Distribution}<r>[0.547, 0.852]</r>."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "r6he6djxDw0hUMjD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line point of 'eLibrary Subscriptions'}<r>[0.944, 0.109]</r> above or below {the line point of 'Free Textbook Distribution'}<r>[0.944, 0.862]</r> at school week 28?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "qsK5y4YS2oimPhHj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 20}<r>[0.700, 0.223]</r> of 'eLibrary Subscriptions', does the resource count exceed the point at {week 20}<r>[0.700, 0.855]</r> of 'Free Textbook Distribution'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "Sr8FPJRz9eQr87nu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region <r>[0.121, 0.109, 0.944, 0.413]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the plots of 'eLibrary Subscriptions', drawn with a dashed line and star markers. The data points range from week 1 to 28 and show counts increasing from 2000 to 3050."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "Luq3QkECl0hqSnVe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within <r>[0.086, 0.077, 0.318, 0.154]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It involves two elements: {Free Textbook Distribution}<r>[0.131, 0.086, 0.312, 0.110]</r> marked with a green color and {eLibrary Subscriptions}<r>[0.131, 0.121, 0.286, 0.145]</r> marked with a purple color."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "zIAvblqn6U3yeDHk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the structure within the region <r>[0.270, 0.025, 0.795, 0.058]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart, which reads \"Education Resource Distribution and Utilization\"."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "PFYkMmvb9YuSkDOK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object position corresponds with [0.025, 0.420, 0.048, 0.533] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "gzSLAdopf660NIag",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn this chart, which x-axis tick label corresponds to [0.891, 0.879, 0.971, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020.0."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "Ip1IcHJRWFtdvXW1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the {last point on the 'Innovation Index' line}<r>[0.931, 0.148]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.931, 0.148]."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "2Npy51vbBXmRksY0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year {2006}[0.360, 0.573], is the {Startup Foundation line}<r>[0.360, 0.534]</r> above or below the {Innovation Index line}<r>[0.360, 0.534]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same level."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "nkMdkU2loFqJ1b4f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {first point of 'Startup Foundation'}[0.156, 0.804] higher or lower than the {first point of 'Innovation Index'}[0.156, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "cpNyVNgAM1gUbs97",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value for the year {2003}[0.238, 0.727], 'Startup Foundation' or 'Innovation Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Foundation."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "TWSqhFv1f6OeFXcF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area of [0.129, 0.116, 0.436, 0.231]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. It includes two items: {Startup Foundation}[0.203, 0.130, 0.426, 0.165] with a blue line color marker, and {Innovation Index}[0.203, 0.182, 0.397, 0.217] with a salmon line color marker."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "4wrji0C7dGY7yOa3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area [0.049, 0.037, 1.038, 0.088]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the subtitle of the chart, labeled as {Entrepreneurship and Innovation Ecosystem Analysis}<r>[0.049, 0.037, 1.038, 0.088]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "yXv1VhPalAi9mcyX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.483, 0.879, 0.564, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, the x-axis tick label for the {year 2010}<r>[0.483, 0.879, 0.564, 0.914]</r> is located."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "97nxN4gxMR0JEMpg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.019, 0.417, 0.036, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "P9TpVZFAup5B7vmc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for the {legend entry for 'Health Index'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.745, 0.086, 0.855, 0.110]"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "Ab3RNaYNdZXg6sIj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year of {the x-axis tick} located at [0.782, 0.919, 0.826, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "mAIZfARt0w7PZO4E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is highest at {the year 2000} point[0.396,0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health Index."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "EiWPWhx7oFMa5bOg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 1992, is {the Health Index}[0.178, 0.697] line above or below {the Living Environment Index}[0.178, 0.613] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "oVG9rG5wiw5Mgt3M",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Health Index} line[0.124, 0.315, 0.940, 0.724] generally above or below the {Living Environment Index} line[0.124, 0.245, 0.940, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "U75NJr8ttNdW8GBc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the contents of [0.083, 0.066, 0.981, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a line graph plot, representing the trends of 'Health Index' and 'Living Environment Index' over the years from 1990 to 2020. The Health Index line is colored in blue, while the Living Environment Index line is in red."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "Y4fG2ikEVw7goxux",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.689, 0.077, 0.973, 0.154] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the line graph. It includes two entries: {Health Index}[0.745, 0.086, 0.855, 0.110] with a blue line marker and {Living Environment Index}[0.745, 0.121, 0.966, 0.145] with a red 'x' marker."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "j8rzjln9uR6AfLkq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in [0.510, 0.919, 0.554, 0.942] area."
+            },
+            {
+                "from": "gpt",
+                "value": "This area only contains one item {2005 label}[0.510, 0.919, 0.554, 0.942] from the x-axis tick label."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "M421Jo7PaqhQOhoF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the chart's label}<r>[0.025, 0.420, 0.048, 0.533]</r> ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "i36GFM82RYvHMIMU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.836, 0.879, 0.865, 0.914] refer to on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '25'."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "zzgOF8Zj8q3AI2xO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item} at <r>[0.211, 0.182, 0.548, 0.217]</r> in Subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technology Entrepreneurship."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "YATeW7P27sfaSOGa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point '20'}[0.707, 0.715] for the Innovation curve above or below {the data point '20'}[0.707, 0.384] for the Technology Entrepreneurship curve?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "wKPDSfEMypiDoNaX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'week 1'}[0.164, 0.803] data point for Innovation higher or lower than {the 'week 1'}[0.164, 0.722] data point for Technology Entrepreneurship?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "OEUghjOez9tpZPNm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {point 17}[0.622, 0.311] for Technology Entrepreneurship has more or less value than {point 17}[0.622, 0.740] for Innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "Oy5z86vPXz1F6SxP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.211, 0.130, 0.548, 0.217]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are labels for two lines in the chart, the \"Innovation\" and \"Technology Entrepreneurship\" lines, each paired with a corresponding marker."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "oWtTuUdzdRmdEDqZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.164, 0.590, 0.936, 0.820]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains 28 data points and lines representing the \"Innovation\" trend over 28 weeks. Each data point represents the value of Innovation for a specific week."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "w3hc8JSYVxQCEoZy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what\u2019s inside the region [0.164, 0.133, 0.936, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains 28 data points and lines, representing the trend of \"Technology Entrepreneurship\" across 28 weeks. Each data point signifies the value of Technology Entrepreneurship for a given week."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "YmbsbaviHCzHPvbP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.025, 0.429, 0.048, 0.524] on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "X7vt29oUG3kcn6Qv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.121, 0.879, 0.135, 0.914]"
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "pmI3yfFv11rvkgyf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text} at [0.203, 0.736, 0.357, 0.771] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Overall Index\"."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "ZUv9286ZPMaBAd9N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point 16, is the value of {the Overall Index line}<r>[0.590, 0.629]</r> higher or lower than {the Air Quality Index line}<r>[0.590, 0.184]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "LA5FZP7xknfTD4lI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Overall Index line}[[0.157, 0.222], [0.936, 0.794]] generally above or below {the Air Quality Index line}[[0.157, 0.158], [0.936, 0.349]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "8GUVHeRJMb8uqWs7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index shows a constant value after the 20th week, {the Overall Index line}<r>[0.705, 0.794]</r> or {the Air Quality Index line}<r>[0.705, 0.158]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Quality Index."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "o2QsFvaMWxITtKGv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.058, 0.208, 0.102, 0.243]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label '100' for the Y-axis."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "T1CD0Yg1XkZXjM1i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.129, 0.722, 0.406, 0.837]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. The labels 'Overall Index' and 'Air Quality Index' are situated here. Each item in the legend is paired with a marker of its respective colour."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "5jbW2RKCugQf8C4b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.157, 0.222, 0.936, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot points and lines for the 'Overall Index' trend on the graph. The trend starts from the first week and ends at the 28th week."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "p3FSWNwLZRt8wp2X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.505, 0.952, 0.542, 0.975]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "cpsexHGDfbWnw0Z3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.184, 0.919, 0.228, 0.942]"
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "Z5CJztXNVnsGUczy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate consists of the fifth y-axis tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.043, 0.264, 0.054, 0.287]"
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "hwGsadNeB3ismzmV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point in 2012 for Artificial Intelligence}<r>[0.259, 0.696]</r> higher or lower than {the data point in 2012 for Robotics}<r>[0.259, 0.780]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "F1FooAJINijwsCeB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point in 2021 for Artificial Intelligence}<r>[0.940, 0.104]</r> located above or below  {the data point in 2021 for Robotics}<r>[0.940, 0.527]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "kdmRlxfbKVOkUafo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology had a higher development indicator in 2010, {Artificial Intelligence}<r>[0.108, 0.865]</r> or {Robotics}<r>[0.108, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "JuZxiUPeo24RZgDZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in [0.220, 0.025, 0.827, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart: \"Technological Industry Development Trends\"."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "r7wzsRKkFvZqF9bY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the content found between [0.075, 0.077, 0.316, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend with two items. {Artificial Intelligence}<r>[0.131, 0.086, 0.309, 0.110]</r> paired with an bright blue [<r>#00bfff</r>] marker and {Robotics}<r>[0.131, 0.121, 0.204, 0.145]</r> paired with a salmon [<r>#fa8072</r>] marker."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "9Sx0riGibKazcY9V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region from [0.259, 0.696] to [0.259, 0.780]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two data points from the year 2012, one each for {Artificial Intelligence}<r>[0.259, 0.696]</r> and {Robotics}<r>[0.259, 0.780]</r>."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "S5FctOrud5wb5Z9a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.134, 0.689, 0.941, 0.826]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend line for Asian Cuisine."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "sQvzzYyoxBH3vvkY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first x-axis tick text with coordinate [0.017, 0.895, 0.065, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "3.75."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "wXkZPTN7xkPqFafy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {color}[0.104, 0.090, 0.149, 0.104] of the Asian Cuisine Trend line in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sky blue(#00bfff)."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "8XKHYQ5eefQXtPhm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '2000'}[0.134, 0.689] for the Asian Cuisine Trend above or below {the point '2000'}[0.134, 0.767] for the European Cuisine Trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "vg6uVKwHiSxb1DzQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Asian Cuisine Trend label} [0.158, 0.086, 0.327, 0.110] in the legend above or below the {European Cuisine Trend label}[0.158, 0.121, 0.363, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "wR8vGrdR4rhJSRI2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, Asian Cuisine or European Cuisine, is higher at {year 2020}[0.941, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Asian Cuisine Trend."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "yS4eS4t2TnWZzLSj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the coordinates [0.102, 0.077, 0.369, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart with two items: the {Asian Cuisine Trend}[0.158, 0.086, 0.327, 0.110] in sky blue color and the {European Cuisine Trend}[0.158, 0.121, 0.363, 0.145] in coral color."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "rJcbzRfPzH9ind7y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within [0.134, 0.533, 0.941, 0.767]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the trend lines for both Asian and European Cuisines trends from the year 2000 to 2020. The Asian Cuisine trend is a solid line with circle markers while the European Cuisine trend is a dashed line with 'x' markers."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "2hBui437ZtAb3dSB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.043, 0.895, 0.081, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object, the y-axis tick, which is labeled as '3.75'."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "1BbufclPFAHkhMX6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.025, 0.273, 0.048, 0.680] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "cxK0ayHTh4R63xvq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'Facebook' label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.845, 0.130, 0.954, 0.165]."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "wSxamnS2YGwWxV6f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point represented by the coordinates [0.445, 0.482] on the line for Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point represents the user count of Facebook on the 11th week."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "JIfllJvDo3RwgKiv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Facebook data point at week 9}[0.388, 0.577] above or below {the Twitter data point at week 9}[0.388, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "I42fyGhgW2csA0jA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {the line for 'Facebook'}[0.157, 0.133, 0.936, 0.814] or {the line for 'Twitter'} [0.157, 0.613, 0.936, 0.820], reaches the highest point in user count?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "cPbTnneI2elr6PIS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform shows a higher user count at week 16: {Twitter}[0.590, 0.645] or {Facebook}[0.590, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "Wzfv1sECPFPjXfGh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.771, 0.116, 0.963, 0.231]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area involves the legend of the chart. It comprises two elements: {the 'Facebook' label}<r>[0.845, 0.130, 0.954, 0.165]</r> with a blue marker and {the 'Twitter' label}<r>[0.845, 0.182, 0.923, 0.217]</r> with an orange marker."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "Zn61qov8R4K8LYLB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.087, 0.860, 0.102, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {the first y-axis tick label '0'}<r>[0.087, 0.860, 0.102, 0.895]</r>."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "KpFsdHq3P520AJNy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.214, 0.784, 0.243, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two data points are located within this region. One represents the {3rd week Facebook user count}<r>[0.214, 0.784]</r> and the other indicates the {4th week Twitter user count}<r>[0.243, 0.802]</r>."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "QSXOB3AdNDdMXsIZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.024, 0.908, 0.045, 0.925] in the chart referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '1M'."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "BUFUFYQwD8GsDlGh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at [0.137, 0.044, 0.241, 0.069] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Part of the legend - specifically, the label 'Facebook'."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "UP12rdLgW202qIii",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object at the coordinates}<r>[0.015, 0.072, 0.045, 0.089]</r> representing on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '15M'."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "FBo5l0Ta4Qq6nsaS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point {2017}[0.435, 0.497], is {the 'Facebook' line above or below the 'Twitter' line} at the coordinates [0.435, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "z7tjKmBnWHaPzySG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Facebook' data point for 2015}[0.097, 0.735] greater or less than {the 'Twitter' data point for the same year'[0.097, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "djKMtuHIhqRs7Qiz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform has a higher user count in {the year 2020}[0.943, 0.078] - Facebook or Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "5aLa8jNVDUzRXCer",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area [0.065, 0.033, 0.250, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It includes two items: {Facebook}[0.137, 0.044, 0.241, 0.069] marked with color '#4267b2' and {Twitter}[0.137, 0.083, 0.212, 0.108] marked with color '#00acee'."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "2wNsR7TQctpA8Siu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.097, 0.753, 0.943, 0.867]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the line and points of the 'Twitter' data series over the years from 2015 to 2020."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "GbuSx1xkwEYiBTno",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are within [0.097, 0.078, 0.943, 0.735]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Facebook' data series, with points at years 2015 to 2020 as represented by their respective coordinates."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "AdXrb9LDXFWfZhhN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.010, 0.462, 0.010, 0.462]</r> positioned for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy consumption."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "8bmLORvCsv1WYJN0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the marker for 'Wind Energy Consumption'}<r>[0.082, 0.126, 0.141, 0.140]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "KZDUpeGaMXfY71xE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for {the last point on the 'Solar Energy Consumption' line}<r>[0.921, 0.337]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.921, 0.337]."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "ehn84F0C6B0EdIes",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the year 1996 for Solar Energy Consumption}[0.476, 0.632] higher than {the year 1996 for Wind Energy Consumption}[0.476, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "fICu6xzqcmeZOf8Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich marker is closest to {the year '2008' point on 'Wind Energy Consumption'}<r>[0.709, 0.152]</r>, Solar or Wind marker?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wind."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "2YBykvNmz7rCfJxN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the year 2014 on the 'Solar Energy Consumption' line}[0.824, 0.384] have a higher value than {the year 2014 on the 'Wind Energy Consumption' line}[0.824, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "X9s4PQ0uLC3hs9n6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is shown within the range <r>[0.650, 0.133, 0.920, 0.340]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the points on the 'Solar Energy Consumption' line for the years 2005 to 2019, showing values rising moderately."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "dMvGF0A7CRWiM7L9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is detailed within the plot area <r>[0.105, 0.034, 0.925, 0.492]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes both the 'Solar Energy Consumption' and 'Wind Energy Consumption' lines, exhibiting various data points for different years indicated by markers of blue for solar and orange for wind."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "2n5C5a1IGnOuCHcy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's happening in the section <r>[0.553, 0.050, 0.924, 0.350]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section shows the data points representing 'Wind Energy Consumption' from the year 2000 to 2019, marked by steady increases in values, especially notable from 2015 onwards."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "D5GwdQ5X0m0NZJdQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the coordinates [0.062, 0.380, 0.084, 0.630] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "sa85rI5Fqd6TeTcK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fourth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.329, 0.906, 0.368, 0.930]"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "7Ejm064bd47Et4xQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {year}[0.442, 0.890] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2006"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "q4z1SqJ0TL3QmpxP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '2006'}[0.442, 0.598] of the Pharmaceutical line higher or lower than {the point for '2006'}[0.442, 0.758] of the Biotech line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "DKPC3kHtreBcB6p5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Pharmaceutical line}[0.160, 0.155, 0.865, 0.718] increase or decrease from {year '2005'}[0.395, 0.557] to {year '2010'}[0.630, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "Erfff25vTQXbxFuu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry has a higher trend value in {2013}[0.771, 0.276, 0.771, 0.807], Biotech or Pharmaceutical?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pharmaceutical."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "4Vui5K4XRCFnTbTW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinates [0.134, 0.134, 0.344, 0.230]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. There are 2 items: {Pharmaceutical}[0.194, 0.145, 0.336, 0.175] with a blue marker, and {Biotech}[0.194, 0.189, 0.263, 0.219] with an orange marker."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "Jrmv9XL1kt3cSRdz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.160, 0.155, 0.865, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Pharmaceutical line with data points ranging from years 2000 to 2015, the line is blue in color."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "HQcmeUJeVqzGzM6Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region defined by [0.490, 0.939, 0.535, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Year' which indicates the x-axis of the chart."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "o07PmiOQojluTvJh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at {coordinates}<r>[0.017, 0.316, 0.038, 0.604]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "XLx7VYmytYRnMPwZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the coordinates of the {xtick} labelled '2010.0'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.301, 0.911, 0.355, 0.934]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "BqjLQbdVo7NOz7Jq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {line}<r>[0.124, 0.596, 0.942, 0.734]</r> in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physical Store Sales."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "MnseKNBvmNLzB25O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt year {2020}[0.738, 0.895], which sales were higher, Online or Physical Store?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "WzztldKw5UQeD1RZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for 'Online Sales'}<r>[0.942, 0.065]</r> at year 2025 above or below the {point for 'Physical Store Sales'}<r>[0.942, 0.734]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "37KxYZfMbtGZH7XQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the points at year {2015}, is {Online Sales}<r>[0.533, 0.402]</r> higher or lower than {Physical Store Sales}<r>[0.533, 0.666]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "EEXQjD3s1JEC4ZMv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with the coordinates [0.095, 0.044, 0.432, 0.158] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two items in the legend; 'Online Sales' with corresponding marker at [0.102, 0.067, 0.157, 0.077] and 'Physical Store Sales' with its marker at [0.100, 0.117, 0.159, 0.131]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "szJmDgNak0xolzKX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.916, 0.911, 0.969, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label '2025.0'."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "3SsR4WSIXCWUjIeT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the region from [0.301, 0.911, 0.764, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "The objects in this region are the x-axis labels '2010.0', '2012.5', '2015.0', '2017.5', '2020.0', '2022.5', and '2025.0'."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "hfVlT7JFiGK8rxQ5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point at}[0.944, 0.816] on the line represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sales of Condos in 2009."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "LLL64Ds073jHk4Ti",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of {the color handle}[0.127, 0.123, 0.185, 0.137] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Single-Family Home Sales."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "UE8Nq3eKv98JKYXs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-axis}[0.017, 0.413, 0.038, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "kDG4GXH4gRhmbbSi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing 2000 for 'Condo Sales'}[0.149, 0.843] above or below {the point representing 2000 for 'Single-Family Home Sales'}[0.149, 0.382] in the sales trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "QtsbSgRhMoFJqKjB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is plotted higher at {the year 2006}[0.679, 0.069]: Condo Sales or Single-Family Home Sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Single-Family Home Sales."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "4fOhiPQmBqI75qt5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the sales of Condos in 2008}[0.855, 0.640] more or less than {the sales of Single-Family homes in 2008}[0.855, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "XmsGgTNsZITzBhdZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within {region}[0.122, 0.049, 0.530, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. This includes 'Condo Sales' with its associated color marker and 'Single-Family Home Sales' with its associated color marker."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "fn37labk4u88erog",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in {the lower left part of the graph}[0.109, 0.031, 0.5, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lower left part of the graph consists of the beginning of the 'Condo Sales' and 'Single-Family Home Sales' trend lines. It also contains the labels and tick marks for early years on the x-axis and the lower limit values on the y-axis."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "cyA5nJluOJ87e0Sg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.2, 0.5, 0.8, 0.9]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the middle part of the plot, including the bulk of the trend lines for 'Condo Sales' and 'Single-Family Home Sales'. The section shows the fluctuating sales data from around the year 2002 to 2007."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "UmAUTAamSIKRxeef",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.017, 0.414, 0.038, 0.505] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, which in this case is {Users}<r>[0.017, 0.414, 0.038, 0.505]</r>."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "KctpvqFdyb8ZcV3n",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the item labeled 'Traditional Education' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.175, 0.058, 0.436, 0.092]."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "NeFiUMd64TMffI6V",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is displayed at {the x-axis label}<r>[0.511, 0.943, 0.557, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is 'Year'."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "g0MPu6AT7Yh9zvg1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the year 2002 point on 'Traditional Education' line}[0.242, 0.463] higher or lower than {the year 2002 point on 'Virtual Training' line}[0.242, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "iovB1Y8H5TCBj1aM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Traditional Education' line}<r>[0.125, 0.350, 0.942, 0.687]</r> or {the 'Virtual Training' line}<r>[0.125, 0.855, 0.942, 0.065]</r> end at a higher value in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "Virtual Training."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "fIAaMgtNIcDCEDNN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {the year 2012 point on 'Traditional Education' line}[0.826, 0.664] above or below the {the year 2012 point on 'Virtual Training' line}[0.826, 0.222]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "oL74G06MNbkORgum",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.096, 0.044, 0.446, 0.158]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the legend for the line chart. It includes two items: {Traditional Education}<r>[0.175, 0.058, 0.436, 0.092]</r> and {Virtual Training}<r>[0.175, 0.110, 0.362, 0.144]</r> with their respective color markers."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "M4VVHqOfWw3hgjvi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the plot in the region [0.125, 0.065, 0.942, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the entire plotting area of the line graph. It includes two lines: {the 'Traditional Education' line}<r>[0.125, 0.350, 0.942, 0.687]</r> and {the 'Virtual Training' line}<r>[0.125, 0.065, 0.942, 0.855]</r> each representing a different data series with their points plotted for different years."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "DbAYvTqoVMFBdiM8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.923, 0.911, 0.962, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specific area contains the right-most X-axis label, which represents the {year '2014'}<r>[0.923, 0.911, 0.962, 0.934]</r>."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "ll8CR48peRkbYvEa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary component at [0.017, 0.405, 0.038, 0.496]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "UCnpzN9VPBLXbJbS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.139, 0.068] in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the point on the 'Standard Policy' line with '1' as the x-axis value."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "jYxXkHFU2lX9gTTN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.942, -0.118] in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the point on the 'Traditional Education' line with the year '2000' as the x-axis value."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "K3Vivz3gKkw9nRjF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point of {the 'Standard Policy' curve}[0.212, 0.131] above or below the point of {the 'Extraordinary Policy' curve}[0.212, 0.193]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "bugP9YZDdoDd66u9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Standard Policy' point at coordinates '[0.309, 0.068]'} higher or lower than the {corresponding 'Extraordinary Policy' point at coordinates '[0.309, 0.317]'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "wpIUBqhX0mcYq2zj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {point on the 'Traditional Education' line at [0.943, -0.129]} and the {point on the 'Virtual Training' line at [0.943, -0.129]}, which line has a higher y-value?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Virtual Training'."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "A4Ks25HSNRt1nPDF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you illustrate the content within the range [0.111, 0.639, 0.460, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the legend elements. Each item has its identification text next to a marker of its corresponding color. The legend items are {Traditional Education}[0.190, 0.653, 0.451, 0.687], {Virtual Training}[0.190, 0.705, 0.377, 0.739], {Standard Policy}[0.190, 0.757, 0.382, 0.791], and {Extraordinary Policy}[0.190, 0.809, 0.438, 0.842]."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "j0cuCbJ627AEKlqL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the features found in the region [0.139, 0.068, 0.328, 0.379]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses points on the 'Standard Policy' and 'Extraordinary Policy' line graphs. The 'Standard Policy' points range from x=1 to x=6, with corresponding y-values. Similarly, the 'Extraordinary Policy' points also go from x=1 to x=6 but have different y-values."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "VanXv28LFIPN9DXL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is covered in the region [0.944, -0.130, 0.944, 0.007]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the line graph named '_child2' that seems to delineate a threshold or reference line in the plot, with the start point at [0.943, 0.006] and end point at [0.943, -0.119]."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "eujrHvV0k0yTEyYs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the xlabel with the coordinates [0.495, 0.943, 0.552, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Years'."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "oWRKIC13azpUnYbd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the handle in the Online Courses legend entry}<r>[0.698, 0.073, 0.754, 0.083]</r> have?"
+            },
+            {
+                "from": "gpt",
+                "value": "'#1f77b4'."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "M8YIn18xO9RKIWGA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.017, 0.855, 0.054, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'$10^{-2}$'."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "nXFMFX1XiC80dBPt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '2' for 'Online Courses'}<r>[0.429, 0.147]</r> higher or lower than {the point '2' for 'Online Degrees'}<r>[0.429, 0.364]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "maWFgYTA3ryjM1tt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at point '3', {the Online Courses line}<r>[0.618, 0.256]</r> or {the Online Degrees line}<r>[0.618, 0.473]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Degrees."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "94le9WgYzqxwzgMo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point '5', is the value for {Online Courses}<r>[0.857, 0.473]</r> greater than the value for {Online Degrees}<r>[0.857, 0.690]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "GeoUQr39kkIlCs2L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the content in the area [0.692, 0.049, 0.971, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two legend entries: {Online Courses}<r>[0.771, 0.064, 0.958, 0.098]</r> with a colored (#1f77b4) handle at [0.698, 0.073, 0.754, 0.083] and {Online Degrees}<r>[0.771, 0.116, 0.961, 0.149]</r> with a colored (#ff7f0e) handle at [0.697, 0.123, 0.755, 0.137]."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "Gi2D7iZAz2gFW3de",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the covered area from [0.106, 0.039, 0.942, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all the points plotted in the line for {Online Courses}<r>[0.106, 0.039, 0.942, 0.581]</r>."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "DaM19rJCbAFvrk95",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is happening in the region [0.106, 0.256, 0.942, 0.798]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers all points of {the Online Degrees line}<r>[0.106, 0.256, 0.942, 0.798]</r>."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "uWQUojGEMYZJJj0S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel}<r>[0.017, 0.318, 0.038, 0.601]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Value."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "qxThwMhIbHUVrUrV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at coordinates [0.195, 0.058, 0.370, 0.092] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup invest."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "QJxDx3qK0S3DHoyz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {x-tick label}<r>[0.932, 0.911, 0.954, 0.934]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q6."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "CZOIwXHAfz4QoNk0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 'Startup invest'}<r>[0.303, 0.639]</r> higher or lower than {point of 'Corporate invest'}<r>[0.303, 0.783]</r> for Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "XzmRMFR1KYh9L8QY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Corporate invest marker}<r>[0.121, 0.117, 0.180, 0.131]</r> located to the left or right of the {Startup invest marker}<r>[0.121, 0.065, 0.180, 0.079]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The markers are vertically aligned, neither to the left nor the right."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "Au66ptDcE08Hnz75",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the investment values for 'Startup invest'<r>[0.143, 0.065, 0.943, 0.783]</r> generally higher or lower than 'Corporate invest'<r>[0.143, 0.496, 0.943, 0.855]</r> over Q1-Q6?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "02QiTBM8JKSx6FIU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the bounding box [0.083, 0.918, 0.093, 0.941]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object, which is the y-axis tick label '0'."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "n8iZiP3ljEAEA4CF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.116, 0.044, 0.411, 0.158]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, which includes 2 items: {Startup invest} with a blue marker at <r>[0.195, 0.058, 0.370, 0.092]</r> and {Corporate invest} with an orange marker at <r>[0.195, 0.110, 0.402, 0.144]</r>."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "i0sI7XltsKXDK3Bv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.043, 0.630, 0.093, 0.654]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label \"20000\"."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "3AZUPfSRuZgx55ry",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label represent for the line in the color \"#ff7f0e\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Experienced Entrepreneurs"
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "p6bnmh0Cyly4zLvU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.938, 0.702] on the 'Novice Entrepreneurs' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point for the year 2010."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "rHLGZscMwODcSTCf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-tick label at {coordinates}[0.017, 0.858, 0.054, 0.889] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "$10^{-4}$."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "nzDJbxVCgMZwMkXh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho had a higher value in the year 2006, {Novice Entrepreneurs}[0.273, 0.448] or {Experienced Entrepreneurs}[0.273, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Novice Entrepreneurs."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "ARce7SyLGFczKmCd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHas the value for {Experienced Entrepreneurs} at [0.938, 0.870] increased or decreased compared to its value in 2005 at [0.106, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreased."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "YPg86JtvbqgxCwEz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for {Novice Entrepreneurs} at [0.605, 0.787] higher or lower than its value in the prior year at point [0.439, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "t05a2Cj1qXxoHKbM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the space of [0.044, 0.777, 0.054, 0.801]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the space where the y-axis tick label '1' is positioned."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "RgROWz60FKg6Gnbf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates of [0.620, 0.110, 0.957, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend text for the 'Experienced Entrepreneurs' marker."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "SeqYQ0SA7cuCCG3D",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects between [0.106, 0.448, 0.938, 0.787] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart contains the plot line for 'Novice Entrepreneurs'. It consists of several points corresponding to the years from 2005 to 2010."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "nGvSDM5wL3BXedpL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.017, 0.357, 0.054, 0.388]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis tick label - $10^{-1}$."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "6xnF2rR5gAVvNPxk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the item with coordinates [0.156, 0.071, 0.356, 0.105] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label for Digital Currency."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "5hC4sB4GH7TQgVpp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mark at [0.756, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a point on the line labeled 'Digital Currency'."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "nOdDqe45S1Adaf3R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the x-coordinate around 0.358, is {the line for 'Digital Currency'}[0.358, 0.718] above or below {the line for 'FinTech Application'}[0.358, 0.407]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "ukxLjRqT5wZ7BqRl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher y-value at x=5, {Digital Currency at point}[0.690, 0.739] or {FinTech Application at point}[0.690, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Currency."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "37So2alxJsqSDQXP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing Digital Currency}[0.756, 0.718] to the right or left of {the point representing FinTech Application}[0.756, 0.230]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they are aligned vertically on the x-axis."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "ILItmM9PH9dHP1Ft",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.495, 0.943, 0.553, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the x-axis, which reads \"Years\"."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "8qUGpwSttU1D5oK6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the objects within the coordinates [0.065, 0.038, 0.983, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region takes up the majority of the chart and contains the actual plotting area of the line graph, including x and y grid lines, two lines representing 'Digital Currency' and 'FinTech Application' respectively, and corresponding ticks and labels on both axes."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "yEdiPrFVSsgCQHJR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is at the boundaries [0.077, 0.057, 0.403, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend of the chart. It contains two entries: \"Digital Currency\" and \"FinTech Application\", each with their respective color markers."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "zost7913MbzaCTlM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value label of {the first point}[0.166, 0.065] on the 'Media Influence' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "120"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "LkGmf0vDEvLkRfNm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.765, 0.110, 0.942, 0.144] in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "2Uj8f1reireIsamB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis label}[0.017, 0.344, 0.038, 0.576]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "9BZPO0sdxVNieCYW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the line for 'Media Influence'[0.166, 0.065, 0.942, 0.443], and for 'Public Opinion'[0.125, 0.202, 0.223, 0.855], which line represents higher values at the start of their respective scales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Media Influence"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "weJR8FW1iArZqC71",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line terminates at a higher value, {Media Influence}[0.942, 0.443], or {Public Opinion}[0.223, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "kOORReXHLMd6LN6n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich points are at a higher value: {the points on 'Media Influence' line}[0.370, 0.236], or {the points on 'Public Opinion' line}[0.182, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "vO0rmnGS1h1j6Xrq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.686, 0.044, 0.971, 0.158]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, with two items: {Media Influence}[0.765, 0.058, 0.961, 0.092] and {Public Opinion}[0.765, 0.110, 0.942, 0.144]. Each item's label is colored to match its corresponding line on the chart."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "G2GkOi0vRTbcu1Uf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.166, 0.065, 0.942, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {the line representing 'Media Influence'}[0.166, 0.065, 0.942, 0.443]. The line is marked with circular points at various coordinates along the x-axis scale."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "7Q2uJryC1Wz873yT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.125, 0.202, 0.223, 0.855]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains {the line representing 'Public Opinion'}[0.125, 0.202, 0.223, 0.855]. The line is marked with x-shaped points along the scale."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "1Gj7SfIfyB5kHuSm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.017, 0.855, 0.054, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents a y-axis tick label."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "8u8AQYupV3uePLlD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of handle is associated with {the label 'Sporadic Grower'}<r>[0.448, 0.801, 0.654, 0.835]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a cross shaped handle."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "7ihUb2gKcWO3ludc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the label {Persistent Grower}<r>[0.448, 0.750, 0.669, 0.783]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the label 'Persistent Grower' is #1f77b4 (a deep blue color)."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "0yysJr5PLOotiEAZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Persistent Grower}<r>[0.106, 0.038, 0.942, 0.155]</r> or {Sporadic Grower}<r>[0.106, 0.038, 0.942, 0.764]</r>, reaches a higher Y-value at the point where X=50 on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sporadic Grower."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "c5y01PatbDuJFFpB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {6th point}<r>[0.942, 0.155]</r> of the Persistent Grower line above or below the {6th point}<r>[0.942, 0.764]</r> of the Sporadic Grower line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "C2TIRWBEXmmq1RQp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {3rd point}<r>[0.618, 0.066]</r> of Persistent Grower line, is it above or below the {3rd point}<r>[0.618, 0.246]</r> of Sporadic Grower line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "hbXskaV4AoBwKO7C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.369, 0.735, 0.678, 0.850]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the chart. There are two labels, {Persistent Grower}<r>[0.448, 0.750, 0.669, 0.783]</r> and {Sporadic Grower}<r>[0.448, 0.801, 0.654, 0.835]</r>, each with their respective colored handles, blue for Persistent Grower and orange for Sporadic Grower."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "In6y5tlJ4C3YMOHq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nEnumerate the items in the region [0.106, 0.038, 0.942, 0.764]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two lines in this region, the {line of Persistent Grower}<r>[0.106, 0.038, 0.942, 0.155]</r> and the {line of Sporadic Grower}<r>[0.106, 0.038, 0.942, 0.764]</r>, as well as a series of points associated with each line."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "9ArKRj6218FBpvpl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.065, 0.038, 0.983, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot of the chart with X and Y axis labels, tick lines and labels, gridlines, as well as two plotted lines: {Persistent Grower}<r>[0.106, 0.038, 0.942, 0.155]</r> and {Sporadic Grower}<r>[0.106, 0.038, 0.942, 0.764]</r>, including all the points on both lines."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "eJ9rzwMfPh9mHvJb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label associated with the bounding box [0.771, 0.103, 0.967, 0.133] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainable Business."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "Lx7iuoQxtNnsA86Z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label}<r>[0.017, 0.781, 0.041, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.2."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "kJyg1MCGkdIY4RCO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what is represented by the coordinates of the point [0.835, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of both 'Social Responsibility' and 'Sustainable Business' in year 2019."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "Hgg6fmN0J6U7r1nv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is the {2018 point of 'Social Responsibility'}<r>[0.729, 0.611]</r> above or below the corresponding {2018 point of 'Sustainable Business'}<r>[0.729, 0.790]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "QKAIHsYBo4SHee3L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category, {Social Responsibility}<r>[0.094, 0.223, 0.941, 0.686]</r> or {Sustainable Business}<r>[0.094, 0.318, 0.941, 0.790]</r>, decrease more dramatically from 2017 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainable Business."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "llODY5gbOEwP45g2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {Social Responsibility}<r>[0.835, 0.648]</r> higher or lower in 2019 compared to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "RhCWJHjR3AtsbXdu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the contents displayed in the chart between coordinates [0.627, 0.554, 0.941, 0.790]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the plot points for 'Social Responsibility' and 'Sustainable Business' from the years 2017, 2018, 2019 and 2020. While 'Social Responsibility' shows a decreasing trend, 'Sustainable Business' decreases abruptly from 2017 to 2018 and rose again from 2019 to 2020."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "fEdagi7EYPqB2dy7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details on the area [0.712, 0.048, 0.974, 0.144] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The area covers the chart legend which includes two items: {Social Responsibility}<r>[0.771, 0.059, 0.960, 0.089]</r> with a blue color label, and {Sustainable Business}<r>[0.771, 0.103, 0.967, 0.133]</r> with a orange color label."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "BptuMyxurqq1VqYf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is notable within the boundaries [0.094, 0.223, 0.412, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region we can observe points from 2012 up until 2015 for 'Social Responsibility' and 'Sustainable Business'. Both have shown a decreasing trend over these years."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "sCoayTe0Ltft0xIv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.017, 0.025, 0.045, 0.056]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "cYctQagtlJU8WfAy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {text inside sublegend}[0.823, 0.071, 0.961, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "VC_Series1."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "iafoWWwwUe4ttwDG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.601, 0.315] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point on the VC_Series2 line."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "9rvF5dnt8ufPOthR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is {the point at [0.260, 0.266]} part of, VC_Series1 or VC_Series2?"
+            },
+            {
+                "from": "gpt",
+                "value": "VC_Series1."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "grPK8Pz7xH02BGDv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at [0.601, 0.315]} higher or lower on the chart than the {point at [0.601, 0.251]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "VkDqLP0DpRVyeEeR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a larger value {at point [0.098, 0.303]}, VC_Series1 or VC_Series2?"
+            },
+            {
+                "from": "gpt",
+                "value": "VC_Series2."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "sXKR8iwAxi0Dw3uA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region represented by the coordinates [0.744, 0.057, 0.971, 0.175]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the legend items of the chart. It has labels for VC_Series1 at {coordinates}[0.823, 0.071, 0.961, 0.106] and VC_Series2 at {coordinates} [0.823, 0.125, 0.961, 0.160]."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "QfH4XZIC9lWhUbxZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area from [0.056, 0.038, 0.983, 0.868] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot of the chart showing the two lines: VC_Series1 and VC_Series2, with values plotted along the x and y axes, and it also contains tick labels for these axes."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "hXD8EQiBUcyizdru",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.098, 0.231, 0.941, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the line for VC_Series1 is plotted with individual data points represented as circles. A similar line for VC_Series2 is also intersecting this region but with markers as crosses."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "3q26ljaZAm2aPz7j",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.721, 0.414, 0.961, 0.447] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend text labeled as \"Student Enrollment\"."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "m5TfQ8bk1COZ2JcT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is located at coordinates [0.604, 0.797] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a 'x' mark on line chart depicting \"Tutor Recruitment\" data for the year 2018."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "b209HqPkna2WauoR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.017, 0.561, 0.046, 0.584] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's 100."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "DTsCQFV8wpqrCDEs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point on the 'Student Enrollment' line at 2018}[0.604, 0.111] higher or lower than the {point on the 'Tutor Recruitment' line at 2018}[0.604, 0.797] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "itDKlWnSygPuA0cR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2017, did {Student Enrollment}[0.436, 0.093] have a larger or smaller number than {Tutor Recruitment}[0.436, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "UOxv0PyDd7y1QxLP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2016, which line - {Student Enrollment}[0.267, 0.079] or {Tutor Recruitment}[0.267, 0.824] - is closer to the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Student Enrollment."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "dABJABiJjRKV3rKe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in this chart location [0.099, 0.752, 0.941, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the line for \"Tutor Recruitment\" with data from 2015 to 2020 represented as 'x' marks."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "IUpV7w0HjH2tq9dv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents located between coordinates [0.642, 0.399, 0.971, 0.514] in the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart that contains the legend. Two labels are located here: {Student Enrollment}[0.721, 0.414, 0.961, 0.447] plotted in blue color and {Tutor Recruitment}[0.721, 0.466, 0.943, 0.499] plotted in orange color."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "jrSH8SaPBKjqv22T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the coordinate area of [0.057, 0.025, 0.983, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large area comprises the entirety of the main chart area. It includes the x-axis from 2014 to 2021, the y-axis from 50 to 200, the 'Student Enrollment' line from 2015 to 2020 represented as circular points, the 'Tutor Recruitment' line from 2015 to 2020 represented as \u2018x\u2019 marks, and also a grey dotted line at 2018."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "SkSJ5h420gIAymaX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the object at [0.498, 0.943, 0.544, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the X-axis label displaying \"Year.\""
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "l176eDGhWXzu1xCQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the blue color line}[0.106, 0.470, 0.936, 0.851]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Waiting Time\"."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "6Yg1XauUCngN49aK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object {located at}[0.106, 0.067, 0.936, 0.284]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line representing \"Processing Time\""
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "ErYq7VwddLi4Hviw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the point of 2008, {Waiting Time}[0.106, 0.470, 0.936, 0.851] or {Processing Time}[0.106, 0.067, 0.936, 0.284]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Waiting Time."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "fxm68NqXYlIaIzJZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing {the plot at 2000}[0.106, 0.470] with {the plot at 2010}[0.936, 0.851] for Waiting Time, is the trend increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "20ujdvDOqHrpCrvz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Processing Time}[0.106, 0.067, 0.936, 0.284] higher or lower than {Waiting Time}[0.106, 0.470, 0.936, 0.851] at the point of 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "iTdWwDZvD5Hyxxiq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.077, 0.047, 0.366, 0.161] area correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the legend of the chart. It includes two items: {Waiting Time}[0.156, 0.061, 0.317, 0.095], represented with a blue color line, and {Processing Time}[0.156, 0.113, 0.357, 0.147], represented with an orange color line."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "SCmypmDHlfzCQS3Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area denoted by [0.106, 0.470, 0.936, 0.851]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart displaying the points of the \"Waiting Time\" line plot over different years."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "1KpuIDgqbOgbVqoi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.106, 0.067, 0.936, 0.284]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line plot for \"Processing Time\", plotted with marked points for different years."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "9GdTResRmlEBPAKZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object described by the coordinates [0.322, 0.034, 0.441, 0.066] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "JG8g8EvGQ7F34r0B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of {the object} at [0.050, 0.539, 0.061, 0.562] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '3'."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "OFqAqOZ18JVlM3SF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the 'Design Innovation' line in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.557, 0.034, 0.778, 0.066]."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "AJSVVJkT2fClqlwz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the Creativity line at '81'}[0.579, 0.721] higher or lower in value than {the point on the Design Innovation line at '81'}[0.579, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "39DGCbxyl0jZbsmB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Creativity line}[0.073, 0.663, 0.959, 0.866] and {the Design Innovation line}[0.073, 0.201, 0.959, 0.871], which ends at a lesser value on scale '2187'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "EUKmXUSnQOFaSCGW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the point of the Creativity line at '3'}[0.199, 0.779] and {the point of the Design Innovation line at '3'}[0.199, 0.548]. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Design Innovation."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "EttBVhSBXj5mgOHb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease elaborate on the area specified by [0.245, 0.021, 0.787, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, which includes the labels {Creativity}[0.322, 0.034, 0.441, 0.066] and {Design Innovation}[0.557, 0.034, 0.778, 0.066], along with their respective color markers."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "uSSsF2MwD3FzunfF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details in the region [0.073, 0.663, 0.959, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the 'Creativity' line of the chart, reflective of variable Creativity values on different scales from '1' to '2187'."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "hHZGBfjV9T8rt3ap",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is found within [0.050, 0.423, 0.061, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a Y-axis label, '4'."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "oz7QWZoLNRSRvCn7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest object in the chart at the coordinates [0.106, 0.349, 0.925, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line representing Investment."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "uKmzHfO8Hq2SiJqe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the forth tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.451, 0.911, 0.462, 0.934]"
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "LLeds18EiKb5yEVt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.775, 0.048, 0.824, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle for the 'Profit' trend in the legend."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "0594QHa9gYs742jZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 7th point of the 'Investment'}[0.808, 0.389] located above or below {the 7th point of the 'Profit'}[0.808, 0.440]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "lDavPjfsDxXZMSDc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Investment'}[0.106, 0.349, 0.925, 0.793] generally above or below {the line for 'Profit'}[0.106, 0.409, 0.925, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "utqItsgKv0N3PIrK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is at higher y-value at {point 4} [0.457], Investment or Profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "Profit."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "YOFlXujybwhOYXnR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.082, 0.48, 0.925, 0.93]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart graph, which includes 3 lines representing Investment, Risk, and Profit and their respective data points plotted in various positions."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "zodQcyyhKpHXP5u1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we have in the area [0.072, 0.021, 0.959, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Content in this area includes the legend indicating the labels and their corresponding colors for the 'Investment', 'Risk', and 'Profit' lines."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "iIrcMExlBZBRTjRO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of information can be observed in the region [0.451, 0.911, 0.462, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The forth x-axis label is in this region, which is the year '4' in the data set."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "GWI8r3vbfnlRdTJb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.019, 0.406, 0.043, 0.574] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "PZlyzk0Sr5yltJvk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first tick mark on the x-axis in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.051, 0.911, 0.095, 0.934]"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "1cBoche7UyYlL3qf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.139, 0.911, 0.183, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2001."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "1yV4vEInk3WIYDYV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2002'}[0.250, 0.672] on the Running line above or below {the point for year '2002'}[0.250, 0.440] on the Yoga line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "AybEYm4msPR8DywD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Running line}[[0.073, 0.298, 0.959, 0.763]] generally above or below {the Yoga line}[[0.073, 0.136, 0.959, 0.541]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "MYK9NOHVE9uoc9Ol",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has a higher frequency in 2010, Running or Yoga?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yoga."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "m7DcY4fYx01MgB8Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.484, 0.943, 0.548, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The section contains the x-axis label \"Years\", rendered in black color."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "Qs3T861eKlMeiYEp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.233, 0.021, 0.799, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Running}[0.311, 0.034, 0.410, 0.066], {Yoga}[0.527, 0.034, 0.583, 0.066], and {Cycling}[0.700, 0.034, 0.790, 0.066], each paired with a marker of corresponding color."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "vqeNAsctsXuulaIk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.671, 0.279, 0.981, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 y-axis tick labels '7' and '8' within this region. They are located at {coordinates}[0.671, 0.279, 0.682, 0.302] and {coordinates}[0.937, 0.279, 0.948, 0.302], respectively."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "aAEGw2nOrnrIlm0t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label}<r>[0.115, 0.034, 0.385, 0.066]</r> in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hotel Accommodation."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "PjxCALVhWFSOcSEt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point coordinate represented by 'Q4' in the 'Food Services' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.906, 0.591]."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "8Q2DuvPGeH3cIGI2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.023, 0.431, 0.047, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "FjF1GagCFqvC1WLr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Hotel Accommodation'}<r>[0.365, 0.591]</r> and {the 'Food Services'}<r>[0.365, 0.652]</r> lines in 'Q2', which one shows higher growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food Services."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "DXrZPZQvvIz4Ue6e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the 'Q3' results for {the 'Food Services'}<r>[0.635, 0.713]</r> and {the 'Travel Agents'}<r>[0.635, 0.571]</r> growing or declining compared to the 'Q2' results?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food Services is declining while Travel Agents is growing."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "6WBJ8JfML0i1ULfG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry shows a decline in 'Q4' compared to 'Q3' between {the 'Food Services'}<r>[0.906, 0.591]</r> and {the 'Travel Agents'}<r>[0.906, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Travel Agents."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "KLzBB8ZX5PyvXknG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area in [0.037, 0.021, 0.963, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the legend of the chart. It includes three items: {Hotel Accommodation}<r>[0.115, 0.034, 0.385, 0.066]</r>, {Food Services}<r>[0.502, 0.034, 0.671, 0.066]</r>, and {Travel Agents}<r>[0.788, 0.034, 0.953, 0.066]</r>, each accompanied by a color indicator."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "fBPlMWQwtPfv1KPN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.094, 0.086, 0.906, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart. Within this, three line plots represent the growth of 'Hotel Accommodation', 'Food Services', and 'Travel Agents' over four quarters. Each line plot has distinct coordinates indicating various growth values for each quarter."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "aDHu6885KEgGOsH2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in [0.453, 0.943, 0.547, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label, which reads 'Quarter'."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "9UvyR0niMfGdEYH5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinate [0.811, 0.055, 0.934, 0.078] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Schools."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "C2vY46LhXFomPdqS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.950, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last point on the 'Online Schools' line."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "Dal2wsMGfb3eX2Bz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at {the coordinates}<r>[0.196, 0.911, 0.240, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2011'."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "29NN4N75nQhQb3uJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Public Schools' line}[0.095, 0.120, 0.959, 0.464] above or below {the 'Private Schools' line}[0.095, 0.421, 0.959, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "kyGWZXuKTtCurSqW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the 'Online Schools'}[0.095, 0.486, 0.959, 0.808] have more or less students in 2010 compared to {the 'Private Schools'}[0.095, 0.421, 0.959, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "6cHVd5UKUQwmmnxe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Public Schools' line}[0.095, 0.120, 0.959, 0.464] and {the 'Online Schools' line}[0.095, 0.486, 0.959, 0.808], which one depicts an overall decrease in numbers from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Schools."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "QtIWhZVhaE5gsbI0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region from [0.095, 0.421, 0.959, 0.550]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Private Schools' line graph, which represents the number of students in private schools from 2010 to 2017. The line starts from the year 2010 with the label indicating 80 and ends at 2017 marking around 95."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "9jobVX2G6LGyeQcg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is located in the area from [0.755, 0.046, 0.951, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the graph is located within this region. It has three entries: {Public Schools}<r>[0.811, 0.055, 0.934, 0.078]</r>, {Private Schools}<r>[0.811, 0.090, 0.944, 0.113]</r>, and {Online Schools}<r>[0.811, 0.125, 0.939, 0.148]</r>, each paired with a colored line representing its respective line graph on the chart."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "mXbvDMs618KdCKn4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.095, 0.034, 0.959, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This regional box encapsulates the entire graph plot, including 3 line charts marked as 'Public Schools', 'Private Schools' and 'Online Schools'. There are eight x-axis ticks representing the years from 2010 to 2017 and nine y-axis ticks indicating counts from 0 to 200."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "MFIXQIzKN1s3mgP2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category in the chart does {the colored line with coordinates [0.073, 0.357, 0.959, 0.787]}<r>[0.073, 0.357, 0.959, 0.787]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tutoring."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "3f5jjrwWif3ErQ7s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the fifth x-axis tick label}<r>[0.494, 0.911, 0.538, 0.934]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2010."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "IQ8o6j5edNJRMDs5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the third line chart}<r>[0.073, 0.357, 0.959, 0.787]</r> in this data presentation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tutoring."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "gXrPXaTmz5kR0lxp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{The Classroom Learning line chart}<r>[0.073, 0.034, 0.959, 0.249]</r> records a drop after 2012. In comparison, does {the Online Learning line chart}<r>[0.073, 0.357, 0.959, 0.787]</r> show a similar or opposite trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Opposite."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "Jcv6zAUgPTzUnw8J",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing 2004 for the Tutoring category} [0.250, 0.411]<r> [0.250, 0.411]</r> higher or lower than {the point representing 2006 for the same category}<r>[0.339, 0.437]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "5A8sBq6utY25Cv4v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, did {the Quality Score in Classroom Learning}<r>[0.516, 0.088]</r> surpass the score in {Online Learning}<r>[0.516, 0.626]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "GormkkkMjkL7qJ0z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are enclosed in <r>[0.050, 0.885, 0.061, 0.909]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0' is located within these coordinates."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "5qNNFDycY0MHiluQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region defined by <r>[0.619, 0.050, 0.947, 0.204]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend, which includes the following items: {Online Learning}<r>[0.697, 0.063, 0.889, 0.095]</r>, {Classroom Learning}<r>[0.697, 0.111, 0.938, 0.143]</r>, and {Tutoring}<r>[0.697, 0.159, 0.796, 0.191]</r>, each marked with a distinctive color."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "oi1Sw64mXKpBRnHK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the contents within the bounds <r>[0.490, 0.943, 0.542, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Year' is located within these coordinates."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "x8IJpdLJnzranPJE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the {x-axis tick label} '4'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.844, 0.911, 0.855, 0.934]"
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "qXA59RfkbqxLTd4V",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the subtitle in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is black."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "sDqXTPacNOATiTRt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at this coordinate [0.089, 0.411, 0.976, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the line representing Optimization."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "lBZRxVX4cGARdpGV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for {Optimization}[0.089, 0.411, 0.976, 0.679] above or below the line for {Innovation}[0.089, 0.438, 0.976, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "eWjJH23k4DjIynSc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich strategy has a higher potential for Efficiency Improvement at {time point '4'}[0.844, 0.911, 0.855, 0.934], Optimization or Expansion?"
+            },
+            {
+                "from": "gpt",
+                "value": "Expansion."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "YWl01A6MvOa0WqdF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich strategy reaches its highest potential first, {Innovation}[0.089, 0.438, 0.976, 0.868] or {Expansion}[0.089, 0.303, 0.976, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "qaiaObs4NNapmw36",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within this region [0.220, 0.021, 0.844, 0.074]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the graph. It features labels for the strategies: {Optimization}[0.287, 0.032, 0.420, 0.062], {Innovation}[0.520, 0.032, 0.629, 0.062] and {Expansion}[0.729, 0.032, 0.836, 0.062]. Each label is paired with a corresponding line color."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "4ckcSrfN7LgxF5g0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained in the region [0.455, 0.911, 0.981, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labels '0.5', '1', '2', '4', and '8' are within this region."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "c0PCg7I8dsrgy44P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the coordinates [0.089, 0.088, 0.976, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main plot of the chart, specifically the lines representing the strategies Optimization, Innovation, and Expansion."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "i7eQ46MStFYWgWr3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element with coordinates [0.058, 0.084, 0.967, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main graph area."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "lwzYCPBOgArloHtj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.492, 0.957, 0.533, 0.981]"
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "4BctL0E7OeQzVzLa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.040, 0.914, 0.048, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "61akWNql1NHKU4yv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Hotels'}[[0.058, 0.419, 0.967, 0.712]] above or below {the line for 'Restaurants'}[[0.058, 0.293, 0.967, 0.586]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "CDcAKTtjDg466GvR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {the year 2000 point}[0.058, 0.921], Hotels or Restaurants?"
+            },
+            {
+                "from": "gpt",
+                "value": "Restaurants."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "7oNSLoiGrbj7cE3d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the revenue of {Travel Agencies}[0.058, 0.136, 0.967, 0.481] decrease or increase from the year 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "jNCpjIEdUmJUj0ZS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.219, 0.012, 0.807, 0.055]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Hotels}[0.281, 0.022, 0.343, 0.045], {Restaurants}[0.436, 0.022, 0.553, 0.045], and {Travel Agencies}[0.646, 0.022, 0.799, 0.045], each paired with a colored line marker."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "olZ8E1BImjWQ5bZI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the rectangle ranging from [0.404, 0.933, 0.439, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2004' is located within these coordinates."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "xuEQ8oGGwHTZQQcj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.058, 0.136, 0.967, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 lines representing Hotel, Restaurant, and Travel Agency revenues over the years 2000 to 2010."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "EEGDoBLEBULezgb5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.055, 0.116, 0.079, 0.880]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Measure (in billions of tons or million hectares)."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "eYYzXJenbXRZoFpC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the first legend item in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Annual CO2 Emissions."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "sWR27GBI2xUUoiKL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year, indicated by {the xtick label}<r>[0.663, 0.911, 0.707, 0.934]</r>, in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "FiEVjXGmHg4YREP2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {New Forests Area} line<r>[0.131, 0.181, 0.869, 0.668]</r> above or below the {Carbon Capture} line<r>[0.131, 0.838, 0.869, 0.883]</r> at point '2015'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "MtxelZ2KQEO7437Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich indicator has a higher value at the year '2020', {Annual CO2 Emissions}<r>[0.869, 0.829]</r> or {Carbon Capture}<r>[0.869, 0.838]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Capture."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "6MX0IdL6xX47aUnY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line trend is steeper, {New Forests Area}<r>[0.131, 0.181, 0.869, 0.668]</r> or {Annual CO2 Emissions}<r>[0.131, 0.829, 0.869, 0.838]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Forests Area."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "IhFEDLgmxI19nhIk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the area[r>0.001, 0.022, 0.999, 0.080</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart, which has three items: {Annual CO2 Emissions}<r>[0.078, 0.035, 0.351, 0.067]</r>, {Carbon Capture}<r>[0.468, 0.035, 0.660, 0.067]</r>, and {New Forests Area}<r>[0.777, 0.035, 0.990, 0.067]</r>, each represented by a different color."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "U3VzFxY4lf7tJm2y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the contents in the region [0.131, 0.181, 0.869, 0.668]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the lines representing different trends over years: {Annual CO2 Emissions}<r>[0.131, 0.829, 0.869, 0.838]</r>, {Carbon Capture}<r>[0.131, 0.838, 0.869, 0.883]</r>, and {New Forests Area}<r>[0.131, 0.181, 0.869, 0.668]</r>."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "U9MA7Xc3ngrTrpqw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what lies within the coordinates [0.131, 0.101, 0.869, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main plotting area of the chart. It includes three lines for different trends: {Annual CO2 Emissions}<r>[0.131, 0.829, 0.869, 0.838]</r>, {Carbon Capture}<r>[0.131, 0.838, 0.869, 0.883]</r> and {New Forests Area}<r>[0.131, 0.181, 0.869, 0.668]</r>, as well as the x and y-axis labels and ticks."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "hmDyZLUv82NSO5EN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the annotation at the coordinates [0.359, 0.034, 0.527, 0.066] represent in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the term \"AI Application\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "gSYOg6uQJsdqYPuI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label indicated by coordinate [0.471, 0.943, 0.604, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Time Scale\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "UlDVVX9rmW1QKNtc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label}<r>[0.049, 0.279, 0.088, 0.302]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "15.0."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "6gko15lxgt6O49mp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line plot, is {the point '2'}<r>[0.350, 0.733]</r> in the 'AI Application' line above {the point '2'}<r>[0.350, 0.773]</r> in the 'Automation' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "EcoUfJpAVdAMd4bp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to {the point '0'}<r>[0.100, 0.854]</r> on the 'Automation' line, where is {the point '0'}<r>[0.100, 0.895]</r> on the 'AI Application' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "I4PnDXvWiUUfskoY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBase on the line plot, is {the value of '4'}<r>[0.851, 0.814]</r> in 'AI Application' greater or smaller than {the value of '4'}<r>[0.851, 0.814]</r> in 'Automation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The values are equal."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "4Kfd8PYQbppU8MeP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.281, 0.021, 0.795, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. There are two entries: one for {AI Application}<r>[0.359, 0.034, 0.527, 0.066]</r> with a marker in blue color, and another for {Automation}<r>[0.643, 0.034, 0.785, 0.066]</r> with a marker in orange color."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "ae0OIkNPdkG6NjWP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.100, 0.248, 0.976, 0.895] describe?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the plot of the 'AI Application' line. It starts from point 0 to point 8 and shows an increase then a decrease in Performance Gain (%)."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "IZ9OfedVeOE4b9oO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.100, 0.450, 0.976, 0.874]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the graph line for 'Automation'. The line begins at 0 and increases to a peak at point 1, then it decreases and flattens out by point 8, representing changes in Performance Gain (%)."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "YsRff45ZmIUwzq7a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the item handle}<r>[0.111, 0.066, 0.150, 0.066]</r> related to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Income."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "5Q4Ic2i3PXCfkenv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xlabel of the chart with its coordinates [0.510, 0.951, 0.551, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "4r1QWwIO9E651asp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown on the y-axis at {coordinates}<r>[0.040, 0.608, 0.083, 0.628]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "20000."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "s5WRi8Q2QxUwHcb7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Income}<r>[0.181, 0.161]</r> greater or less than the value of {Housing Expenditure}<r>[0.181, 0.601]</r> in year 2001?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "oixylUqTdn31RVBK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Food Expenditure}<r>[0.2676, 0.756]</r> in year 2002 above or below {Housing Expenditure}<r>[0.268, 0.572]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "UQcOD73EDQ26urW6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger in year 2009, {Income}<r>[0.880, 0.073]</r> or {Food Expenditure}<r>[0.880, 0.704]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food Expenditure."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "MgWPJykYK79gA4x1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within {the legend coordinates}<r>[0.103, 0.043, 0.376, 0.175]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains three items: Income with a color indicator of #020122, Food Expenditure with a color indicator of #f05a28, and Housing Expenditure with a color indicator of #76b041."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "gUx7ymfv40IORFlL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region marked by [0.510, 0.951, 0.551, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The xlabel 'Year' is located in this region."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "hZMB9mKgJRQp6V2A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the region [0.093, 0.059, 0.967, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Income' line chart points ranging from the year 2000 to 2010."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "E2xGds79Y6Zx9WTx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the chart at these coordinates [0.023, 0.311, 0.046, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Gain (%)."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "QBP3lhpHtaEVsx7b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel} at [0.442, 0.943, 0.559, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Prospects."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "iunR725J1JxQ0QTK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the legend item}[0.257, 0.034, 0.280, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "C9RugTx5wkZTEVnb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line of {AI}[0.087, 0.248, 0.913, 0.490] generally above or below the line of {IoIT}[0.087, 0.167, 0.913, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "FtYBhRd52KSpO67v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Quantum Computing}[0.087, 0.328, 0.913, 0.814] higher than {AI}[0.087, 0.248, 0.913, 0.490] at the 'Minimal Change' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "SbQLsru2kHJn2PBP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the trend of {IoIT}[0.087, 0.167, 0.913, 0.652] move upwards or downwards across the categories from 'Minimal change' to 'Minimal decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downwards."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "TcNsRUcnTSo9Ltkp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.064, 0.238, 0.075, 0.262]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a y-axis tick label '8'."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "IeHMwBgxq1J3vNcX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.179, 0.021, 0.822, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes legend items: {AI}[0.257, 0.034, 0.280, 0.066], {IoIT}[0.397, 0.034, 0.441, 0.066], and {Quantum Computing}[0.558, 0.034, 0.812, 0.066], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "oTUMrmviMyHBOpsd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.278, 0.911, 0.448, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label 'Pronounced decline'."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "iAeGgGqoWQpGXnlZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the graph title}<r>[0.501, 0.943, 0.553, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "jncfPbOYLH60BVoZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Social Welfare'}<r>[0.095, 0.234, 0.959, 0.625]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#20a983."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "K91PwLOYu4Op99h0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of {the 'Healthcare' label}<r>[0.469, 0.034, 0.601, 0.066]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.469, 0.034, 0.601, 0.066]."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "iUqDT1yugjwwXmI6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label has a higher point at '2010', {the 'Healthcare' line}<r>[0.095, 0.187, 0.959, 0.591]</r> or {the 'Education' line}<r>[0.095, 0.153, 0.959, 0.558]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "ulQCPfw3AGJf9Qwg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing their positions at '2005', is {the 'Education' line}<r>[0.527, 0.342]</r> above or below {the 'Social Welfare' line}<r>[0.527, 0.423]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "8jHVE2cnqGWKlRFq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the '2001' marker, which line is higher, {the 'Healthcare' line}<r>[0.181, 0.490]</r> or {the 'Social Welfare' line}<r>[0.181, 0.578]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Welfare."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "Zh8rvxb267wi0qsL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe what is in the region <r>[0.153, 0.021, 0.901, 0.079]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend of the chart with three entries: {Education}[0.231, 0.034, 0.352, 0.066], {Healthcare}[0.469, 0.034, 0.601, 0.066] and {Social Welfare}[0.718, 0.034, 0.891, 0.066], each accompanied by a color marker."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "s8ox31N1zisUxHnk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area <r>[0.095, 0.086, 0.959, 0.895]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plot area of the chart, including the lines representing 'Education', 'Healthcare', and 'Social Welfare', as well as the x and y grid lines of the chart."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "02laMg1PLwOe0m1h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the components in the region <r>[0.095, 0.772, 0.082, 0.774]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a y-axis tick with a label '20'."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "erkuzfqML97Z0p9G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of text located at the coordinates [0.472, 0.957, 0.587, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the x-axis, which is \"Time Period\"."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "4WnPyFiXiGMvpp5s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the line representing \"Tutoring Platform Expansion\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green, specifically color code is #008000."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "1utswRGOyHymqYNu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.099, 0.088, 0.133, 0.088] in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a handle represented by a line in the color Red, specifically color code is #ff0000."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "YGHVAUIMI5yf1ViF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for \"Tutoring Platform Expansion\" at {point 0.2}[0.084, 0.423] higher or lower than the line for \"Online Course Enrollment\" at point 0.2?[0.084, 0.305]"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "ouIBMDoKGtA3QNS8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point x=3, which line is higher, \"Tutoring Platform Expansion\"({coordinates}[0.847, 0.581]) or \"Investment in EdTech\"({coordinates}[0.847, 0.920])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment in EdTech."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "v4OJZJEaPjE6WgSt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point x=2, is the \"Online Course Enrollment\" trend({coordinates}[0.593, 0.227]) higher or lower compared to \"Tutoring Platform Expansion\" trend({coordinates}[0.593, 0.509])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "rggQS7MsGri64GMH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.093, 0.036, 0.386, 0.141] in the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legends, including labels for \"Tutoring Platform Expansion\", \"Online Course Enrollment\" and \"Investment in EdTech\" along with their respective colored handle lines."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "rIblcS4wRD5ffEbe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area ranging from [0.040, 0.466, 0.075, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '2000' from the y-axis tick labels is located in this area."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "tduYpE2bTGljgmwc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found within the region [0.040, 0.019, 0.075, 0.036]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '4000' is in this region."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "JWSWaJqgldzVnrgj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the position [0.045, 0.396, 0.069, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "wjn9dNRbX9PuePXD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location [0.408, 0.034, 0.649, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend text for Water Quality Index."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "wN7pLpPS1mdCwV4Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the box with coordinates [0.500, 0.409]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for 2000 on the Soil Quality Index line."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "w3KLQItiL0ZPURjB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index is higher in 1990, {Air Quality Index}[0.310, 0.393] or {Water Quality Index}[0.310, 0.417]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Quality Index."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "ucLH3eVbwJFl86Gj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Data point for 1985}[0.216, 0.328] on the Soil Quality Index line higher or lower than the {Data point for 1990}[0.310, 0.361] on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "97xS8eUTTTwJTSxG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Soil Quality Index}[0.879, 0.571] in 2020 have a higher value than the {Air Quality Index}[0.879, 0.611] in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "TJ0m5Iawxkki7pNf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what is within the area defined by [0.011, 0.021, 0.989, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, with labels for Air Quality Index, Water Quality Index, and Soil Quality Index. Each label comes with a distinct color marker."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "2XpyLe9HsEblrezr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.474, 0.943, 0.526, 0.975] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the x-axis label, which is \"Year\"."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "FPwoIDcS97927kgT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.121, 0.369, 0.879, 0.506]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the Water Quality Index trend line, appeared from 1980 to 2020."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "y0t3Zq5bfkSBupUn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in these coordinates: [0.485, 0.943, 0.579, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, which is \"Quarter\"."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "0z3w1daKxe7rjk8i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the position {coordinates}<r>[0.291, 0.034, 0.384, 0.066]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Exports\"."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "Bt0pFQoSw5uCQeIN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at the coordinates {tick tick coordinates}<r>[0.969, 0.895]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labeled 'Q4'."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "7d93EcEK2JQND0TF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {Q1}[0.095, 0.895], {the Exports line}<r>[0.095, 0.201, 0.969, 0.617]</r> or {the Production line}<r>[0.095, 0.201, 0.969, 0.571]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Exports line."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "rbwsd2tRosnhDKbF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Imports line}<r>[0.095, 0.201, 0.969, 0.594]</r> and {the Production line}<r>[0.095, 0.201, 0.969, 0.571]</r>, which of them is lower at {Q2}[0.386, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Production line."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "0CdHfVI4mCCOUUl6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Production line}<r>[0.095, 0.201, 0.969, 0.571]</r> and {the Exports line}<r>[0.095, 0.201, 0.969, 0.617]</r>, which one peaks first?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Exports line."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "k5MtD7UvxjrLLcg1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.214, 0.021, 0.850, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart, listing 3 items: {Exports}<r>[0.291, 0.034, 0.384, 0.066]</r> with a blue marker, {Imports}<r>[0.500, 0.034, 0.594, 0.066]</r> with an orange marker, and {Production}<r>[0.711, 0.034, 0.840, 0.066]</r> with a green marker."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "96jXolmyGHPby7JI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.095, 0.201, 0.969, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Production' line graph tracing from Q1 to Q4 with multiple points, visualizing the trend in Volume changes."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "9ktdO8THDfsIfdVA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.071, 0.885, 0.082, 0.909]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick labeled '0'."
+            }
+        ],
+        "id_raw": 277
+    },
+    {
+        "id": "dxUGuWbhUNcBXjqt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the component located at [0.019, 0.311, 0.043, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "hP6FlAqeknoDp0my",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.177, 0.911, 0.226, 0.934]"
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "u1sVJHKzRSTeuuJG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label} at [0.050, 0.238, 0.061, 0.262]?"
+            },
+            {
+                "from": "gpt",
+                "value": "4."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "ARYhb7blBz1MHEWM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '2' on the 'Thoughtful design' line}[0.202, 0.571] above or below {the point '2' on the 'Rich form' line}[0.202, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "wBdEcDT7gOUqBFZj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Rich form' line}[0.073, 0.248, 0.976, 0.814] generally above or below {the 'Thoughtful design' line}[0.073, 0.409, 0.976, 0.733]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "O02sgREfiYWEcjkK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line segment has a higher value at {point '4'}[0.847, 0.700], Thoughtful design or Rich form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rich form."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "FpRVLnsKVe3bLTAR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.253, 0.021, 0.795, 0.079]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area. It contains labels of two line trends: {Thoughtful design}[0.331, 0.034, 0.551, 0.066] with a blue marker {at [0.263, 0.048, 0.312, 0.048]} and {Rich form}[0.668, 0.034, 0.785, 0.066] with an orange marker {at [0.600, 0.048, 0.649, 0.048]}."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "MLsXfivdn7Wk6LWZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details does the area [0.446, 0.911, 0.473, 0.934] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the x-axis tick label '0.5'."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "5ZPbHXmtTmtfSMwi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects falling within [0.073, 0.409, 0.976, 0.733]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the points from 'Thoughtful design' trend line ranging from x = 0 to x = 8 on the 'Detail' axis."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "LWRsVF3zyXjzIl5d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the y-axis label on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.019, 0.201, 0.043, 0.728]."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "qMwIuyLRWZNwTSqo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.804, 0.159, 0.959, 0.191]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the 'Solar Energy' legend."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "QJW4LN4JxzWGDiDJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinate [0.113, 0.911, 0.157, 0.934] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '2015'."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "6xE5I9No8PIub7GJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Wind Energy}[0.135, 0.593, 0.941, 0.808] at point '2015'[0.135, 0.808] higher or lower than that of {Solar Energy}[0.135, 0.748, 0.941, 0.808] at the same point?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are equal."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "bEMGLvYqRIQCSoSn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Fossil Fuels}[0.135, 0.206, 0.941, 0.473] and {Solar Energy}[0.135, 0.748, 0.941, 0.808], which energy source had a higher usage percentage in '2022'[0.919, 0.911, 0.963, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fossil Fuels."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "1JR48bXhrAQkjAjR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year labeled '2019'[0.573, 0.911, 0.618, 0.934] on the x-axis, is the data point for {Fossil Fuels}[0.135, 0.206, 0.941, 0.473] higher or lower than {Wind Energy}[0.135, 0.593, 0.941, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "wRl9HfB8Za6LBIgo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide detail about the contents within the coordinates [0.726, 0.050, 0.969, 0.204]."
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the legend details. It contains three items: {Fossil Fuels}[0.804, 0.063, 0.942, 0.095], {Wind Energy}[0.804, 0.111, 0.958, 0.143], and {Solar Energy}[0.804, 0.159, 0.959, 0.191]. Each item is represented with a colored marker."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "zkaTU1oVTC2RKm26",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements lying within the region bounded by [0.095, 0.034, 0.981, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "Many elements are located within these coordinates. On the x-axis, we have sequential years from '2015' to '2022'. On the y-axis, we observe percentage increments from '0' to '100'. This region also encapsulates three energy sources' data, tracked over these years as a line chart. The three lines represent the percentages of {Fossil Fuels}, {Wind Energy}, and {Solar Energy}."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "nWxNrn4l5Vhtvq66",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_14_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the contents of the area [0.113, 0.911, 0.963, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains eight ticks on the x-axis, each corresponding to a year from '2015' to '2022'."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "6HX8lVKk3yS5IOgN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented within the coordinates [0.051, 0.228, 0.103, 0.258] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '4000'."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "b061s2XkZA2hr4dq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented at [0.545, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A data point on the 'Communication Companies Revenue (Billions)' line for the year 2010."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "dlIS12MIyOGY1Gjg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {ytick label}<r>[0.051, 0.064, 0.103, 0.094]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'5000'."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "xMTTOuZcKyvUMHaU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the coordinate}[0.400, 0.721], is 'Internet Users (Millions)' more or less than the 'Communication Companies Revenue (Billions)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "33z8aJnkE6h5eaWy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the point}[0.255, 0.827] and {the point}[0.836, 0.129], do 'Internet Users (Millions)' trend upwards or downwards over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downwards."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "ufUE8hdoXUunUW8R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the y-axis label '3500'}<r>[0.051, 0.310, 0.103, 0.340]</r> and the point at {coordinates}[0.691, 0.380], is the 'Internet Users (Millions)' data for the year 2015 above or below 3500?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "jWsgSLB6NcLJf3Xy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the key elements within [0.120, 0.089, 0.675, 0.229]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart, specifically for the two lines represented. The legend title 'Indicators' is located, with the two indicators being 'Internet Users (Millions)' which is depicted in green and 'Communication Companies Revenue (Billions)' which is in an orange color."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "IjMAxDzZaCSvCOQp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.255, 0.865, 0.836, 0.884] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, you can find the trend line for 'Communication Companies Revenue (Billions)' going from the year 2000 to 2020, with markers for each year's data point represented with coordinates and shaped as circles."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "t885AJ3DwmcouiOL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me what's within [0.255, 0.129, 0.836, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space houses the trend line for 'Internet Users (Millions)'. You can see the progression from 2000 to 2020 with circular markers indicating the number of users for each of those years."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "nmrg4wKMw5fI3VOK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.698, 0.744, 0.748, 0.758] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend marker for Artificial Intelligence."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "6H9ztMIOjVOMnvlE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.758, 0.820, 0.864, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "uFVhOdLpP6oiRVUl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.023, 0.341, 0.052, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Projected Value."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "9HNwDzWoZrkg8Vcw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2023 of Artificial Intelligence}[0.237, 0.812] above or below {the point for 2023 of Big Data}[0.237, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "U34X5hTKhH0V2XEw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Big Data'}[[0.149, 0.834], [0.237, 0.669], [0.324, 0.724], [0.412, 0.702], [0.499, 0.657], [0.587, 0.691], [0.675, 0.713], [0.762, 0.746], [0.850, 0.779], [0.937, 0.801]] generally above or below {the line for 'Blockchain'}[[0.149, 0.724], [0.237, 0.702], [0.324, 0.613], [0.412, 0.558], [0.499, 0.447], [0.587, 0.337], [0.675, 0.282], [0.762, 0.226], [0.850, 0.171], [0.937, 0.116]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "NVJ53yfOWxE1UB4u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology shows a higher Projected Value at the year 2029, Artificial Intelligence or Blockchain?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "q5Je6SJm7bnwHJGC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.060, 0.104, 0.095, 0.133]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '1.0' is located here, in black color."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "HTxvA0zw12CWDxkF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.758, 0.780, 0.842, 0.807]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend text: {Big Data}[0.758, 0.780, 0.842, 0.807]."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "I3IhfX2uAmw4w20m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.758, 0.740, 0.959, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The legend texts for 'Artificial Intelligence' and 'Blockchain' are located at {coordinates}[0.758, 0.740, 0.959, 0.767] and {coordinates}[0.758, 0.820, 0.864, 0.847], respectively."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "PxoNT1H9jLxiJN65",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of {element}<r>[0.019, 0.262, 0.044, 0.714]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "1yNNwdgc2Kuay1ZS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat integer do the coordinates {element}<r>[0.292, 0.640]</r> represent in the line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "72."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "4mfgUZn6CKjga7rr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the image at position {element}<r>[0.779, 0.832]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019 point on the Community Development Budget line."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "J8omxXQOJOKVRcsn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, is {point 2020 on the Community Development Budget line}[0.941, 0.863] higher or lower than {point 2020 on the Local Government Support Budget line}[0.941, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "sdrwuyXmi1TnT0fL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Community Development Budget line}[[0.130, 0.592], [0.292, 0.640], [0.454, 0.704], [0.617, 0.752], [0.779, 0.832], [0.941, 0.863]] generally lie above or below the {Local Government Support Budget line}[[0.130, 0.512], [0.292, 0.480], [0.454, 0.369], [0.617, 0.321], [0.779, 0.225], [0.941, 0.113]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "ay3sCsZwX8UKEQ6z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the {Local Government Support Budget in 2017}[0.454, 0.369] in relation to the {Community Development Budget in 2018}[0.617, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "yTZzjXBU5y9y2H5e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content do we have in region {region}<r>[0.100, 0.089, 0.535, 0.229]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend of the chart. It includes two items: {Community Development Budget}[0.167, 0.144, 0.515, 0.174] and {Local Government Support Budget}[0.167, 0.188, 0.526, 0.218], each associated with a specific color."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "c6r0TI1yXWTBC4oQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in the area {region}<r>[0.292, 0.640, 0.454, 0.704]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes 2 points on the Community Development Budget line, corresponding to the years 2016 and 2017."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "KwSM5QM5bnmtBN6p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me more about the area enclosed by these coordinates {region}<r>[0.617, 0.321, 0.779, 0.225]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two points from the Local Government Support Budget line, representing data for the years 2018 and 2019. The trend shows a decline in budget."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "FUjLfk8JBHdoRHOt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third y-tick in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.083, 0.664]"
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "DW96GP1Syvbz8sak",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "Oz9S7XHT67LLfVoz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.223, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A data point from the line graph in both subplots."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "onVMU9b0qMpGB7m5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is the data point from 2010 located above or below the data point from 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "vkGGqfOHZ9JSV9sS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is {the y-axis tick label '7.0'}[0.044, 0.716, 0.071, 0.739] above or below {the y-axis tick label '7.2'}[0.044, 0.558, 0.071, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "hegzka6q2dl3fbpk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2000 and 2020, did the annual grain production increase or decrease according to {the line graph}[0.223, 0.089, 0.783, 0.864] in subplot one?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "HWSSvwqNfTPyFN8N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.201, 0.919, 0.805, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the x-axis labels representing years from 2000 to 2020. The label for the year 2000 is at {coordinates}[0.201, 0.919, 0.245, 0.942], the label for the year 2005 is at {coordinates}[0.341, 0.919, 0.385, 0.942], and so on until the year 2020 which is at {coordinates}[0.761, 0.919, 0.805, 0.942]."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "NrvZTpqPV0MSQolJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.044, 0.716, 0.071, 0.739]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label '7.0' from the first subplot."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "rMG5Aa6OMMfjuqZI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.083, 0.050, 0.923, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plot for both subplots including the axes, tick labels, line graphs and subtitles. The first subplot displays a blue line graph representing grain production and the second subplot displays a red line graph representing per capita meat consumption over the years."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "zhi4sNhKOrrzOdtv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the label of the x-axis in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the x-axis in the chart is \"Month\"."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "SBt8LSQgaT0y5jle",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information do the coordinates [0.055, 0.214, 0.110, 0.243] on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates point to the y-axis label \"1000\"."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "HkYVg6UAbcCzcZp2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the third item in the legend}<r>[0.746, 0.113, 0.980, 0.142]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the label 'Competitor Efficiency'."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "supY5CbPmyj3pVpC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the Platform Efficiency line}<r>[0.160, 0.155, 0.865, 0.772]</r> and {the Competitor Efficiency line}<r>[0.160, 0.438, 0.865, 0.855]</r>, which has a greater value in March?"
+            },
+            {
+                "from": "gpt",
+                "value": "Platform Efficiency."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "rTHqdiLdIVZZU3yV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the sequence of objects represented on the x-axis, does {the label 'Aug'}<r>[0.587, 0.910, 0.630, 0.939]</r> come before or after {the label 'Jun'}<r>[0.464, 0.910, 0.497, 0.939]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "AIJI7wb23dM12EHf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the {Online Enrollment line}<r>[0.160, 0.155, 0.865, 0.855]</r>, does the value in November (Nov) increase or decrease compared to the previous month (Oct)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "9e5Rzjbva3WriXyr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region marked by coordinates [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot area of the chart with three lines representing 'Online Enrollment', 'Platform Efficiency', and 'Competitor Efficiency'. The x-axis at the bottom lists months from January to December, and there are two y-axes on the left and right corresponding to different quantities."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "vz6X7Zl7G67p3TYa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region demarcated by [0.746, 0.026, 0.941, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the first item of the legend marking 'Online Enrollment' with a blue color line sign."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "YDl66zfnRUlSSHsp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area defined by the coordinates [0.915, 0.760, 0.950, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the y-axis label '0.6'."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "tYQb4OCvXfmH0kAv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.015, 0.346, 0.035, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "k2wVq1OjtseCijhM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.489, 0.956, 0.547, 0.981]"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "dOWbs0fXcmsSeh72",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis tick at [0.041, 0.278, 0.067, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.1."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "hwaehENMIdyyW9Qp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Civilization Progress'}[[0.072, 0.287, 0.964, 0.863]] above or below {the line for 'Technological advancements'}[[0.072, 0.114, 0.964, 0.892]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "OHz2jTA1HYRcj5jM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Civilization Progress} at [0.607, 0.402] higher or lower than {Technological advancements} at the same point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "BAY0H4hElJwGlwXy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what point do {the 'Civilization Progress' line}[0.786, 0.345] and {the 'Technological advancements' line}[0.786, 0.345] intersect?"
+            },
+            {
+                "from": "gpt",
+                "value": "At ['4000', '1.0']."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "0DtbVWxHkrzpMqY3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be located within coordinates [0.080, 0.067, 0.380, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend with two items: {Civilization Progress}[0.134, 0.075, 0.301, 0.098] with an orange color marker and {Technological advancements}[0.134, 0.108, 0.373, 0.131] with a black color marker."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "cAkHAvM99UAzQ7QU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be observed in the coordinates [0.072, 0.287, 0.964, 0.863]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates plot the {Civilization Progress line}, an orange line going upward as the years increase, with its points plotted using marker 'o'."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "wDoHpmSMYFgHyFdG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.072, 0.114, 0.964, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the {'Technological advancements' line}, a dotted line that increases as the years increase. No markers are used to plot its points."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "nFiLX7niuPMHP4uQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label for the year 2020 on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label for the year 2020}<r>[0.790, 0.902, 0.842, 0.932]</r>"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "qdt7YjGtwPwhSsIw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the data markers representing 'Grocery Sales'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "AuiDgu7DE5zB8GPQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "2dg0OOHsJ8UIlre4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, did the Grocery Sales increase or decrease from 2016 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "5LZSEdPmY0cOWk74",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the 'E-commerce Sales' at 2018 above or below the 'Grocery Sales'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "JSrlzERTTYVv5TZr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sales line, Grocery or E-commerce, is higher in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce Sales."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "F0jSzRhXabe4Kxl9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the given region [0.677, 0.740, 0.944, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the graph indicating 'Grocery Sales' and 'E-commerce Sales' with their respective line colors."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "99t1qwSjuUNkwTnM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe the content within bounding box [0.096, 0.902, 0.981, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encompass the x-axis labels, specifically the years from 2015 to 2021."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "JM3fdZrBasoqwotB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the contents of the region [0.064, 0.883, 0.117, 0.094]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures the y-axis labels representing the sales range from 3000 to 14000 in units."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "zVZTqWgTV05MZxAW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the blue line}<r>[0.160, 0.374, 0.865, 0.549]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend of productivity under Innovative Leadership over years."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "Ns6OV8R95EY8y8Jr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the dataset, what does the coordinate [0.395, 0.374] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the productivity level of Innovative Leadership in the year 2016."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "htw0UUZciANyWzPL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the location}<r>[0.601, 0.835, 0.831, 0.865]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Innovative Leadership'."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "43xEZDkpLf7mWElA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2016, is the productivity of {Conventional Management}<r>[0.395, 0.374]</r> higher or lower than that of {Innovative Leadership}<r>[0.395, 0.483]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "BXwx0qIJwviupn0p",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 2014 data point, which management type shows more productivity, {Conventional Management}<r>[0.160, 0.155]</r> or {Innovative Leadership}<r>[0.160, 0.374]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Conventional Management."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "GZmYYfTChqtwdcGS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing two points in 2020 on the chart, is {Conventional Management}'s productivity point<r>[0.865, 0.855]</r> positioned higher or lower than {Innovative Leadership}'s productivity point<r>[0.865, 0.374]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "bKvAuomv8XNJrGZ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements found within the region [0.535, 0.736, 0.890, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, detailing the color codes for two management types: {Conventional Management}<r>[0.601, 0.791, 0.881, 0.821]</r> in orange and {Innovative Leadership}<r>[0.601, 0.835, 0.831, 0.865]</r> in blue."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "LJL4uFEaiYae1ciD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region occupies the majority of the chart, showing the main plot area. It displays two varying productivity trends over the years 2014 to 2020, one for each type of management."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "uQbecT2dRMDduNxu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is encompassed at coordinates [0.202, 0.069, 0.823, 0.116]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's subtitle \"Productivity Comparison Over Years,\" which provides context to the data being plotted."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "VAHnWSavSZ6FVggi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.019, 0.200, 0.044, 0.770]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "jrK8dR1COwThHWCX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is represented by the coordinates [0.163, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a point on the 'Online Education Impact' line."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "IJK2ASdKNWF4KNfk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label}<r>[0.433, 0.902, 0.459, 0.932]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the number 40."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "yNReNlywhQ5Czmz1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on line 'Online Education Impact'}<r>[0.163, 0.621]</r> of higher value than {the point on line 'Online Education Impact'}<r>[0.111, 0.348]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "xcZ5jdsrgdndc62e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the tick '20' on the x-axis}<r>[0.261, 0.902, 0.288, 0.932]</r>, is {the tick '60' on the x-axis}<r>[0.605, 0.902, 0.631, 0.932]</r> positioned towards the left or right?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "kcB00eduI2d4JzSU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point on the line 'Online Education Impact'}<r>[0.704, 0.239]</r> represent a higher or lower Online Education Impact Index value than {the next point on the same line}<r>[0.833, 0.348]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "avlttKwN7EY7jzZB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you give some details on the area defined as [0.333, 0.942, 0.731, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the X-axis label \"Technology Innovation Index\", it is colored in black."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "RIIF1DUUa29cqKPw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by the coordinates [0.621, 0.784, 0.951, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area incorporates the legend for the subplot, with the title being \"Impact Index\". It also includes an item labeled as \"Online Education Impact\" with its corresponding handle in green color at the coordinates [0.625, 0.846, 0.677, 0.860]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "JkTUgRItQIFAqMRd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.163, 0.621, 0.833, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region primarily contains the line for 'Online Education Impact' represented in green color with 8 data points starting from coordinates [0.111, 0.239] to [0.833, 0.348]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "wO98t1dgeiHoQrE1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.167, 0.838, 0.418, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Household Size."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "ABOzXAiQGYYi196C",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.964, 0.360, 0.981, 0.609] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-label for subplot_1."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "lWnrIc1mSk9nwIGl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.296, -143027.565, 0.706, -49036.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line showing the change in Median Housing Price over time."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "EFxsFFwBDf0TAQ5Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the year 2020 value for Average Household Size}<r>[0.706, 0.856]</r> higher or lower than {the year 2010 value for Average Household Size}<r>[0.296, 0.113]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "R0VotbF9q3sd1IiA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Median Housing Price}<r>[0.296, -143027.565, 0.706, -49036.361]</r> correspondingly increase or decrease with decrease in {Average Household Size}<r>[0.296, 0.113, 0.706, 0.856]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "Zc78DkWVzq00F4wp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point {2015}<r>[0.501, 0.525]</r>, which is higher, the Average Household Size or the Median Housing Price?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Median Housing Price."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "pFQnGrOS2gQqFfFj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain what is plotted in the area [0.296, -143027.565, 0.706, -49036.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the details of the 'Median Housing Price': a line graph is plotted representing the fluctuations in the median housing price of families from 2010 to 2020. This line graph is displayed with a color of #202020 and a linestyle of '--', with no marker at the points."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "ySczRRjdNFX67QNs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content at location [0.101, 0.783, 0.427, 0.879]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the subplot_0, with the title being \"Average Household Size\". The handle for the legend is colored #ffa500 and placed at coordinates [0.104, 0.845, 0.156, 0.858]. The text \"Average Household Size\" is located at [0.167, 0.838, 0.418, 0.868]."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "feJMYdr14FxxYopq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's present in the area [0.090, 0.076, 0.912, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two subplots: subplot_0 and subplot_1. Subplot_0 displays the change in Average Household Size over the years, and subplot_1 represents the change in Median Housing Price over time. The title \"Family and Household Living Conditions over Time\" can also be found in this region."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "6ZHXuIVBvMMD2W4m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.417, 0.035, 0.552]</r> as seen in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "c5IhJhqWU5GJXd7t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label text of the third object in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bioengineering."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "6zxm9pJ79ipTDttd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value provided for {the x-axis tick label}<r>[0.259, 0.909, 0.294, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2022."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "illcZMqjMITtSWZc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line data, does the 'Artificial Intelligence' label represented by the line coordinates {line_0}<r>[0.110, 0.113, 0.943, 0.678]</r> show an overall increase or decrease over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "ChyO7JAXRNUdz3ZE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the lines' end coordinates, did Quantum Computing finish <r>[0.943, 0.323]</r> higher or lower than Bioengineering <r>[0.943, 0.533]</r> in the end year represented on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "7oDOpqUV87YlPOSJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the line for 'Artificial Intelligence', is the point represented by coordinate <r>[0.860, 0.210]</r> higher or lower than the point at <r>[0.776, 0.242]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "ETQsdkyzKXxl8rQM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's in the region <r>[0.076, 0.089, 0.310, 0.229]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend with 3 items: the labels 'Artificial Intelligence', 'Quantum Computing', and 'Bioengineering', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "oROiZhOAtWWGJexR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region marked by the bounding box <r>[0.068, 0.076, 0.985, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main plot area of the chart, including 3 trends: 'Artificial Intelligence', 'Quantum Computing', 'Bioengineering', and the x and y axis with their labels and ticks."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "Shfbgebkq7z08SIG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region <r>[0.110, 0.807, 0.943, 0.856]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the trend line for 'Bioengineering' as it develops over the years represented in the chart."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "JuUo6IY3p1XghgSI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is presented in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "HAPgEcnkiBB8sYsr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the color of the line labeled \"Bitcoin Price\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line labeled \"Bitcoin Price\" is #ffa500."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "sXMEgR3taGZlZBNv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.377, 0.044, 0.599]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Price of BTC."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "ud5c0Go8wCZDauX1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Bitcoin Price line}<r>[0.238, 0.113, 0.745, 0.863]</r> above or below the {Number of Transactions line}<r>[0.238, -3.290, 0.745, -1.512]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "ySMTG0U0JVbee2Y3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2019, is Bitcoin Price or Number of Transactions higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Transactions."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "JTGWR3xjpXwIEUPX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the chart has a steeper slope, {Bitcoin Price}<r>[0.238, 0.113, 0.745, 0.863]</r> or {Number of Transactions}<r>[0.238, -3.290, 0.745, -1.512]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Transactions."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "sWGWl4tAyqD003In",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area between <r>[0.112, 0.076, 0.871, 0.901]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area is located here, with two line charts showcasing Bitcoin Price and Number of Transactions. The area has a grey color ('#f5f5f5') and a white color ('#ffffff') background for the two line charts."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "1PoWrr1jAoRLhUZT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area <r>[0.668, 0.846, 0.990, 0.986]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. The legend items are: {Bitcoin Price with color #ffa500}<r>[0.735, 0.901, 0.863, 0.931]</r> and {Number of Transactions with color #202020}<r>[0.735, 0.945, 0.981, 0.975]</r>."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "J1xEpXwPouT07skC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area <r>[0.112, 0.076, 0.871, 0.901]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two line charts. One for depicting Bitcoin Price from 2017 to 2021, with its line colored as '#ffa500'. The other for showing the Number of Transactions from 2017 to 2021, with its line colored as '#202020'."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "N7QKn1KSwVFpEJ7n",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of values represented by the y-axis based on the tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range goes from 0.30 to 0.95."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "SM7RWR5yc4rIu5lJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the line with color '#ffa500'}[0.176, 0.193, 0.908, 0.485] plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency of Power Generation."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "9QEJILYbbvORkjCw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred to by the label at the coordinates [0.625, 0.795, 0.944, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency of Power Generation."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "k0HygmcRPppf1Y70",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {Efficiency of Power Generation}[0.176, 0.193, 0.908, 0.485] mostly above or below the {Efficiency of Power Transmission}[0.176, 0.204, 0.908, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "ow9x2XpPXZVVtLUU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Efficiency of Power Transmission line}[0.176, 0.204, 0.908, 0.286] show an increase or decrease over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "3rrPqB6iwAzojzTJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {Efficiency of Power Generation}[0.176, 0.193, 0.908, 0.485] at the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.9."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "tvJ1JDRHqUsDyAXY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned in the region [0.558, 0.740, 0.971, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart \"Energy and Resource Allocation\" is located here, with two items {Efficiency of Power Generation}[0.625, 0.795, 0.944, 0.825] and {Efficiency of Power Transmission}[0.625, 0.839, 0.963, 0.869], each with a color code."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "i1ntjytFhf1sOhVG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is found within the coordinate space [0.051, 0.298, 0.097, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0.80' is located here."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "ZT4NoixBpH4O2VM0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.081, 0.025, 1.003, 0.072]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the subplot title; it reads \"Trend of Efficiency in Energy and Resource Allocation\" in black font color."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "ScY8oPfeS1j6OUYn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.019, 0.376, 0.044, 0.592]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index Value."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "DHPNwHaKZXGARDNV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at the coordinates <r>[0.112, 0.089, 0.416, 0.186]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot Legend."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "4gaCraQWwzButkMN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the third xtick labeled as on the x-axis with coordinates <r>[0.279, 0.909, 0.323, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2008."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "31H37LJuAEy6guGl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2006, is the {Consumer Confidence}<r>[0.195, 0.391]</r> higher or lower than {Market Sentiment}<r>[0.195, 0.150]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "wcXZgNcvh6nFPTaD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the {Market Sentiment}<r>[0.941, 0.484]</r>, is the {Consumer Confidence}<r>[0.941, 0.484]</r> the same or different in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "D8A8LoHOHDfxauXz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2019, which line {Consumer Confidence}<r>[0.888, 0.150]</r> or {Market Sentiment}<r>[0.888, 0.206]</r> is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consumer Confidence."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "CJACHQFYHjsFyB6U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region <r>[0.112, 0.089, 0.416, 0.186]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains subplot legend with the items: {Consumer Confidence}<r>[0.178, 0.101, 0.407, 0.131]</r>, and {Market Sentiment}<r>[0.178, 0.144, 0.364, 0.174]</r>, each with a unique color marker."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "GVOxoGtlhXLcjCPt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region having coordinates <r>[0.919, 0.909, 0.963, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2020' is located within these coordinates."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "Z4oUUXJQKJ7txMGI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is enclosed in the region with coordinates <r>[0.051, 0.939, 0.089, 0.963]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '0.35' is located within these coordinates."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "ONTEqyI5bBLmCcxJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the objeto on the coordinates [0.019, 0.406, 0.044, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "laR30fpJih5CFN7i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the xposicion of the marker for the second legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.727, 0.853, 0.768, 0.853]"
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "qSvWhKa3qxW6aZiW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value associated with the x-axis tick label at position [0.298, 0.902, 0.311, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "eyUMhABFOFwMynsp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for the {line 'Rights Protection'}[0.378, 0.280] above or below the corresponding point for the {line 'Social Inequality'}[0.378, 0.690]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "vugMczHDeKJwPzRG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Social Inequality}[0.137, 0.212, 0.941, 0.894] or {Rights Protection}[0.137, 0.089, 0.941, 0.430], ends at a higher position for the last x-tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Inequality."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "ERy7XzCcRthAIFc3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a higher value at period 60, Social Inequality or Rights Protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rights Protection."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "E8SxQIps5IDNcguj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region of coordinates [0.122, 0.025, 0.955, 0.072]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart: \"Social Inequality vs Rights Protection Over Time\", which is displayed in black font."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "PXDOKnlOdCykkJbg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.718, 0.740, 0.971, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, which includes descriptions of the lines for \"Social Inequality\" and \"Rights Protection\", each associated with a uniquely colored marker."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "StC05sHN7WKawqPt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the region with corners at [0.096, 0.076, 0.096, 0.894] and [0.941, 0.076, 0.941, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot area of the chart, which includes the drawn lines for \"Social Inequality\" and \"Rights Protection\" and their respective data points."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "ybk9SVqj4eiblrnK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.019, 0.443, 0.044, 0.527] represent in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "4V3CGZd65ZYJ4VSJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the '2015' x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.640, 0.902, 0.693, 0.932]"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "sLcMd7y8WyIBiiHb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the object}<r>[0.745, 0.902, 0.798, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2017."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "wNS0nCx7jsv5Ir7s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Student Engagement line}<r>[0.142, 0.198, 0.929, 0.731]</r> or {Innovation in Teaching line}<r>[0.142, 0.403, 0.929, 0.812]</r>, is above the other at point '2010'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Student Engagement line."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "n3YJY1BLNr7Gk6qi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Student Engagement' trend}<r>[0.142, 0.198, 0.929, 0.731]</r> overall increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "0B4zvWTpScwwkxT8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, is the {Student Engagement}<r>[0.929, 0.198]</r> lower or higher than {Innovation in Teaching}<r>[0.929, 0.403]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "sFjnwQCFbos0qqG2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.663, 0.740, 0.971, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. It includes items for {Student Engagement}<r>[0.730, 0.795, 0.950, 0.825]</r> with an orange marker and {Innovation in Teaching}<r>[0.730, 0.839, 0.962, 0.869]</r> with a black marker."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "4QRpIN2XSbXMSvwg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.536, 0.902, 0.588, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a segment of the x-axis where the label {2013}<r>[0.536, 0.902, 0.588, 0.932]</r> is located."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "5vfp7M17VdHP92xM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.640, 0.902, 0.798, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The x-axis tick labels '2015' and '2017' are located at {coordinates}<r>[0.640, 0.902, 0.693, 0.932]</r> and {coordinates}<r>[0.745, 0.902, 0.798, 0.932]</r>, respectively."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "GYUZStqnHNOWuJAR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate {the x-axis label}[0.519, 0.956, 0.567, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "VNkF0jTLsxeycHVz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label}<r>[0.015, 0.315, 0.035, 0.656]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Change in Measurement."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "TC8uHMZHeXklSHuu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate of the fifth tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "tIKyAvk7cti57kzg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Global Temperature Change point at 2000}[0.212, 0.424] higher or lower than {the Sea Level Rise point at 2020}[0.654, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "FkUlC4lOhYH15NCy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the line indicating Global Temperature Change}<r>[0.212, 0.174, 0.875, 0.424]</r> have?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "2Zhn4uHVqc4gpo0a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Air Pollution Index at 2030}[0.875, -16.368] higher or lower than {the Sea Level Rise at 2005}[.322, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "7nkegIMA5pW88A6z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the rectangle <r>[0.109, 0.067, 0.401, 0.205]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area for the chart. It labels the \"Global Temperature Change\" with a red color indicator, \"Sea Level Rise\" with a green color indicator, and the \"Air Pollution Index\" with a blue color indicator."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "jwjNmmTeJ1UstAMi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.265, 0.019, 0.821, 0.054]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Global Trends and Changes Observation\" is found in this region."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "eXXMzIpFaz2f4rYD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the region <r>[0.212, 0.498, 0.875, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"--\" green line associated with the Sea Level Rise is present in this region. The line has square markers at points from year 2000 to 2030."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "KofxHGHt1JJ7KKIS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is {the element with coordinates}[0.019, 0.430, 0.044, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "3Mwa38utFJmbklBD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLocate the coordinates of {the 5th tick label on the x-axis}."
+            },
+            {
+                "from": "gpt",
+                "value": "[0.422, 0.902, 0.435, 0.932]"
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "Pf0OuBZ2VNrGohks",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.422, 0.902, 0.435, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "QeqnIpQqCY1ZRPuH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.221, 0.298]} on the 'Orders' line higher or lower than {the point at [0.221, 0.356]} on the 'Shipments' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "JlmBXtprM7uOHiT9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line has a higher value at the 11th point, {the 'Orders' line}<r>[0.843, 0.193]</r> or {the 'Shipments' line}<r>[0.843, 0.286]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Shipments."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "BU7ISkXHFoDmjpyD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {\"Inventory\"}<r>[0.152, 0.684, 0.912, 0.801]</r>, is it generally above or below {the 'Orders' line}<r>[0.152, 0.193, 0.912, 0.333]</r> and {the 'Shipments' line}<r>[0.152, 0.286, 0.912, 0.380]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "889l1Brhmt2hF0m7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the area [0.146, 0.025, 0.918, 0.072]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates the chart subtitle, \"Global Trade and Supply Chain Management\", in a black font and no rotation."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "VMwIuGM1vW4iQpcD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.051, 0.181, 0.077, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis tick label '60' is located within this region."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "mBtTjlL1kggXk1MI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's in the area [0.152, 0.193, 0.912, 0.801]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three lines: {Orders line}<r>[0.152, 0.193, 0.912, 0.333]</r>, {Shipments line}<r>[0.152, 0.286, 0.912, 0.380]</r>, and {Inventory line}<r>[0.152, 0.684, 0.912, 0.801]</r> each marked with different colors and point markers. Each line also includes multiple points that represent monthly data."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "ztC7zTVuuAOffRKy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the tick label '2023' in the subplot labeled 'Hydropower'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.542, 0.952, 0.586, 0.975]"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "PWrl0FLMXYHkg7Ol",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at coordinates [0.446, 0.464, 0.490, 0.487] in the subplot labeled 'Solar Power'?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label '2022'"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "HEt9G7NutETxou3g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-tick}<r>[0.554, 0.030, 0.578, 0.044]</r> in the subplot labeled 'Geothermal'?"
+            },
+            {
+                "from": "gpt",
+                "value": "700"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "Ixv4paf4f7MV9UbY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's data point in the 'Hydropower' subplot is at a lower position, {2020}[0.276, 0.743] or {2022}[0.468, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2022"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "nkBdFSULMzPkh5iy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {y-tick labeled '800'}<r>[0.554, 0.480, 0.578, 0.494]</r> in 'Geothermal' subplot higher or lower than the {y-tick labeled '800'}<r>[0.064, 0.567, 0.088, 0.581]</r> in 'Hydropower'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "xn7FfHRI5S1dFrOM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {first data point}<r>[0.083, 0.567]</r> in 'Hydropower' have a higher or lower y-coordinate than the {first data point}<r>[0.574, 0.918]</r> in 'Geothermal'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "Qdc30uhvwCK3BGJe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by the coordinates [0.064, 0.497, 0.052, 0.520] in the 'Solar Power' subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-tick label '500'."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "4ntZCYQPbwqUaeN5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region contained within the coordinates [0.456, 0.952, 0.499, 0.975] in the subplot labeled 'Geothermal'."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label '2017'."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "fJIapnjBp7lmBjjd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area bounded by [0.157, 0.464, 0.201, 0.487] in 'Wind Power'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label for the year '2019' is present."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "tCQ68A059hHIxhs3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the Y-axis label in the subplot featuring the Interest Rate over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label<r>[0.503, 0.319, 0.513, 0.651]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "KP0kEP211R5dzW8k",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the Y-axis in the GDP subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.077, 0.343, 0.087, 0.627]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "JTSgIHPGYUZmd4N7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the location of the gridline in the inflation subplot at 4.0%?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.353, 0.363]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "wQGoI3yPa4Gf6MYx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the GDP value in the year 2004 above or below that of 2005 on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below<r>[0.162, 0.765]</r><r>[0.170, 0.728]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "i1dOUJgm1gWrPdYc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's point is higher for inflation, 2016 or 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016<r>[0.459, 0.655]</r><r>[0.466, 0.680]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "cky0gUGGjEcIceNz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the bond yields for the years 2014 and 2015 in increasing order?"
+            },
+            {
+                "from": "gpt",
+                "value": "No<r>[0.855, 0.412]</r><r>[0.863, 0.485]</r>."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "dAZQIAfAaS5eR06R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is captured within the boundaries <r>[0.142, 0.456, 0.234, 0.567]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays a part of the line for GDP growth over several years. The specific points and their corresponding years are partially visible."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "hrO90NicGgu600uT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the plot area enclosed in <r>[0.653, 0.215, 0.783, 0.365]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a segment of the bond yield chart, showcasing a few yearly data points."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "U7nonltgffnQ6v8a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat entails the defined space <r>[0.291, 0.619, 0.410, 0.729]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows a portion of the Inflation subplot including a line graph that indicates annual inflation rates for several years."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "Ddf4WqA7DG8IPKwx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinate [0.515, 0.943, 0.555, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "QNOgtsqlIVijfCUR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the object} is at [0.765, 0.090, 0.801, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its icon representative for 'Cybersecurity spending' in the legend."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "ENfJeWQzddm486PQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.040, 0.459, 0.075, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2500."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "wKJFz76zzZrUzPbC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {line for 'Cybersecurity spending'}[[0.125, 0.863, 0.944, 0.865]] generally above or below {line for 'Data breaches'}[[0.125, 0.364, 0.944, 0.420]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "GY8fnIjKZ4gZNUiG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first point of {line for 'Cybersecurity spending'} [0.125, 0.865] located to the left or right of the first point of {line for 'Data breaches'} [0.125, 0.364] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they align vertically on the X-axis."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "TWnjpKCDgG7jsBYP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line ends at a higher point, {the line for 'Cybersecurity spending'}[0.944, 0.863] or {the line for 'Data breaches'}[0.944, 0.420]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cybersecurity spending."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "7Ys6PzMweWxWul0D",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the coordinate [0.764, 0.077, 0.978, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend, which includes two items. {First item} labeled 'Cybersecurity spending'<r>[0.808, 0.086, 0.973, 0.110]</r> with its corresponding blue marker at <r>[0.765, 0.090, 0.801, 0.104]</r>. The {second item} labeled 'Data breaches'<r>[0.808, 0.121, 0.909, 0.145]</r> is paired with its red marker at <r>[0.765, 0.125, 0.801, 0.138]</r>."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "AuyvihOGBGYjXoAh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.084, 0.066, 0.985, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the main plot area of the chart, where we can see two data series represented as lines with markers: {line for 'Cybersecurity spending'}<[0.125, 0.863, 0.944, 0.865]> and {line for 'Data breaches'}<[0.125, 0.364, 0.944, 0.420]>."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "f5YCLfVizGqITias",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are included within [0.231, 0.025, 0.839, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one object which is the {subplot title} \"Cybersecurity Spending and Data Breaches Over Time\"."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "VZG1ew0x1GoaCuGz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.013, 0.390, 0.033, 0.414] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "bc03mmVhcjYUDgui",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the sixth y-tick label in the chart for Social Sciences?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.547, 0.563, 0.568, 0.587]"
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "CkL20heSB9mHYCJt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.364, 0.894, 0.405, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2024."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "ccgfwqTjHr7YmEH8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2020'}[0.645, 0.152] above or below {the point labeled '2021'}[0.713, 0.214] for the Artificial Intelligence research output?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "UXQEXERxSElPv3tv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-tick label '80'}[0.547, 0.142, 0.568, 0.166] higher or lower than {the y-tick label '85'}[0.547, 0.081, 0.568, 0.104] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "td7IAQu14JJWSfw1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had a higher value at {point marked for 2023}[0.850, 0.226], Social Science or Artificial Intelligence?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Science."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "fvD8B5Qh759u1tY1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.041, 0.028, 0.453, 0.399]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis with labels from 2020 to 2024 {at 2020}[0.110, 0.411, 0.130, 0.435], {at 2024}[0.364, 0.411, 0.405, 0.435]; the y-axis labeled from 60 to 90 {at 60}[0.013, 0.390, 0.033, 0.414], {at 90}[0.013, 0.019, 0.033, 0.042]; and a line graph reflecting Natural Science Research Output with five data points spanned across the years."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "mZZhtUM1nrTHnOlx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.576, 0.511, 0.988, 0.882]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Social Science research output data plot including the x-axis with year labels from 2020 to 2024 {for 2020}[0.624, 0.894, 0.665, 0.918], {for 2024}[0.898, 0.894, 0.940, 0.918] and y-axis tick labels from 60 to 90 {at 60}[0.547, 0.872, 0.568, 0.896], {at 90}[0.547, 0.501, 0.568, 0.525]. The plot has a purple line showing the trend over the years with distinct data points."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "qx0r0LWxh5Acgu55",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.041, 0.511, 0.453, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for years 2020 to 2024 {at 2020}[0.089, 0.894, 0.130, 0.918], {at 2024}[0.364, 0.894, 0.405, 0.918] and y-axis tick labels from 60 to 90 {at 60}[0.013, 0.872, 0.033, 0.896], {at 90}[0.013, 0.501, 0.033, 0.525] can be found. Additionally, multiple line graphs representing Humanities research with each having distinct data points and colorations."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "DqKkcD08Wlu4cYsa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.526, 0.914, 0.560, 0.949] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "GlPyHQg0x3k78z9m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.091, 0.307, 0.117, 0.342]<r>[0.091, 0.307, 0.117, 0.342]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.75."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "NJTYI67cp8PrzXOL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.369, 0.062, 0.451, 0.109] represent in the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "WRTvytx4Dn0T00CQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Type4'}[0.857, 0.155] higher or lower than {the point for 'Type5'}[0.893, 0.455] on the 'Snapchat' plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "C6Gi49hBhC8amojf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values at {point for 'Type2'}[0.785, 0.622] above or below {point for 'Type3'}[0.821, 0.688] in the 'Snapchat' plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "BevlO7Ty9gPy4nt8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type has a higher value, {point for 'Type1'}[0.338, 0.855] or {point for 'Type5'}[0.482, 0.487] in the 'Instagram' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Type1."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "w3UQdEu3MIbtvAJq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements contained in the range [0.732, 0.914, 0.910, 0.949]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the x-axis labels for Types 1 through 5 for Snapchat: {Type1}[0.732, 0.914, 0.766, 0.949], {Type2}[0.768, 0.914, 0.802, 0.949], {Type3}[0.804, 0.914, 0.838, 0.949], {Type4}[0.840, 0.914, 0.874, 0.949], {Type5}[0.876, 0.914, 0.910, 0.949]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "bxLkkonaVBSis5WG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the section [0.297, 0.049, 0.499, 0.176] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains y-axis tick labels indicating values from 3.25 to 0.50 within the 'Instagram' subplot: {3.25}[0.297, 0.049, 0.323, 0.084], {3.00}[0.297, 0.141, 0.323, 0.176]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "1WcBICbUAZ3Da4W3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are in the space [0.526, 0.914, 0.704, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for Types 1 to 5 are in this zone for the 'Twitter' subplot with corresponding values: {Type1}[0.526, 0.914, 0.560, 0.949], {Type2}[0.562, 0.914, 0.596, 0.949], {Type3}[0.598, 0.914, 0.632, 0.949], {Type4}[0.634, 0.914, 0.668, 0.949], {Type5}[0.670, 0.914, 0.704, 0.949]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "rVbpaMIPdjtBueAV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates}<r>[0.215, 0.942, 0.224, 0.970]</r> signify in the context of the sales trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month 11."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "L7yeVB7sfq7n1PL5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the label shown at the x-axis position {coordinates}<r>[0.052, 0.942, 0.056, 0.970]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "QvM8YMmlKClcQQ51",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the label at {coordinates}<r>[0.508, 0.873, 0.517, 0.901]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "TEcmeYjzQnLVm99X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first point of Product A sales{point}<r>[0.036, 0.884]</r> higher than the first point of Product B sales{point}<r>[0.284, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "aDEjiGzQdtx5lOxe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month {coordinates}<r>[0.734, 0.884]</r> for Product C shows the lowest sales compared to {point}<r>[0.982, 0.884]</r> for Product D?"
+            },
+            {
+                "from": "gpt",
+                "value": "12."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "Pq86EQPKDq8cUVuB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the sales point at {coordinates}<r>[0.854, 0.923]</r> on Product D's plot above or below the line from the sales point {coordinates}<r>[0.872, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "tNxJKDhgQP9v8FWl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the plotted area between {the x-tick labels '2'[r>[0.054, 0.923], [0.302, 0.923]]}<r> and '3'{r>[0.073, 0.923], [0.321, 0.923]]}<r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area shows a positive sales trend for both Product A and Product B with upward slopes in their respective line plots."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "II2F58I2wXjfUaDL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section between the y-axis labels {100 and 120}<r>[0.026, 0.647] and [0.026, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this section, vertical lines indicating grid visibility can be seen overlaying a portion of the line plots demonstrating varying sales volumes."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "kJN9RsV6J7cUN2rE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual data presented between {Product A's first and second x-tick label}<r>[0.036, 0.923] to [0.054, 0.923]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This portion shows a steady increase from the first to the second sales data point on Product A's plot, indicating a rise in sales."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "eoZGchtWan0r7cei",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object does {the label}<r>[0.091, 0.280, 0.117, 0.297]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "CUC4i1lwQUclkq1D",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {coordinates}<r>[0.564, 0.281]</r> on the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "tUY0VHe6s9rexsEt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the feature at {the exact location}<r>[0.345, 0.203]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "lToaRmxfJvDmdxU4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at}<r>[0.345, 0.203]</r> higher in value than {the point at}<r>[0.374, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "AFHhtzAqX1ThCIth",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-value at}<r>[0.564, 0.640]</r> on the third subplot exceed that at {location}<r>[0.884, 0.690]</r> on the fourth subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "JzvDMspR9dtXkqPL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the scoring point}<r>[0.248, 0.668]</r> and {the point just before it}<r>[0.248, 0.612]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "k3G44hmMkMNuFAJK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the zone <r>[0.564, 0.583, 0.884, 0.757]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains several data points for the scores or growth rate from various semesters, plotted as blue circle markers, falling between the y-values of approximately 63.0 to 77.0."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "DrLKQyVuMPhLkpee",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area within <r>[0.141, 0.562, 0.461, 0.873]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region in the third subplot includes a series of points representing semester growth rates, plotted using markers without lines, with y-values ranging from about 64.0 to 81.0."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "6IJUchz9vSJIRZlQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide insights into the region marked by <r>[0.079, 0.621, 0.461, 0.920]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features tick labels for various semesters on the x-axis, each label undertexted with a semester year ranging from 'Fall-2010' to 'Spring-2016'."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "ymTQ0kqMhD2u5djO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.108, 0.917, 0.444, 0.967] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle for the first subplot \"(a) Application Analysis.\"."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "JDzhjpN1DgkIVRLt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.019, 0.152, 0.050, 0.199]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label '70' in the first subplot."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "w6JjWcfW84Ox3vw6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the blue line with circular markers}<r>[0.118, 0.170, 0.457, 0.503]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series1 data points in the Application Analysis subplot."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "FKjvuHZUlPIPmGTM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {Series1 represented by the blue line with circular markers}<r>[0.118, 0.170, 0.457, 0.503]</r> compare to {the grey line with underscore markers}<r>[0.118, -0.095, 0.457, 0.303]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "2boe51xw4VazVD2I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is {Series1 represented by the blue line with circular markers}<r>[0.609, 0.237, 0.947, 0.503]</r> trending higher or lower than {the grey line with underscore markers}<r>[0.609, 0.104, 0.947, 0.370]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "amJMNgpNxSXuBN6B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, which series is higher at {x coordinate 0.3}<r>[0.344, 0.170]</r>, {Series1 represented by the blue line with circular markers}<r>[0.118, 0.170, 0.457, 0.503]</r> or {the grey line with underscore markers}<r>[0.118, -0.095, 0.457, 0.303]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series1."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "7zziMx8HVfjYtSXP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the region from [0.552, 0.037, 0.981, 0.835]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot called \"Research Innovation\", showing the comparison of multiple series represented by different markers and lines along with X and Y axis labels and ticks."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "W3d9kGj4Q2mCMN5F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.597, 0.917, 0.937, 0.967]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle for the second subplot, \"(b) Research Innovation.\"."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "O0lEyZeu90KMA2Ey",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located between [0.609, 0.237, 0.947, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the blue line with circular markers representing Series1 data points in the Research Innovation subplot."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "3vKM6yhd1tWZPGQc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the vertical axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the vertical axis label} at [0.018, 0.445, 0.018, 0.445]."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "xTgep5AwOXsbUD1c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at [0.106, 0.445] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label 65."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "xSpkXjVBjazsPIqy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at coordinates [0.860, 0.920, 0.860, 0.920] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label in the subplot for 'Dramatic decrease.'"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "pRktP0uYhiDKnsdb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at {point 2}[0.353, 0.445] from the 'Fluctuating decline' subplot lower than {the y-axis 50 mark}[0.299, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "xOLueNtDrf525RnB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWere there more {line entries for the '3Q'} in ['Periodic fluctuations', 'Dramatic decrease'] subplots above {y-axis mark 75} than below it?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "k4545Ez0CLTkrqiD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {the lowest point}[0.940, 0.601] on the line for 'Dramatic decrease' in a different position compared to {the highest point}[0.800, -0.125] on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "5DtsSM1J6kf0Yaen",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart area from [0.753, 0.859, 0.988, 0.907]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels for '1st Quarter' to '4th Quarter' in the subplot for 'Dramatic decrease', shown in clear, black font."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "9r3sOjk4RTNmmmck",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included within the section [0.752, 0.056, 0.947, 0.116] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the annotations alongside the y-axis in the 'Dramatic decrease' subplot, displaying labels from '50' to '80' paired with ticks, all oriented vertically."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "dND2qxku8n7axNbh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the components found in the area from the x-axis to the first y-axis tick label for the subplot marked as 'Unstable behavior'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section includes the x-axis, labeled with '1st Quarter' to '4th Quarter', each paired with its respective tick mark along the axis, and {the first y-axis tick label}[0.052, 0.835] is labeled as '50'."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "jiMtWSl21YbuguI8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the line representing 'Public Trust in Government'}<r>[0.781, 0.486, 0.979, 0.700]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "KdppaUJrryCtB4Ks",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the last tick on the x-axis for the subplot 'Social Unrest Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "y4eBI2Oh3pLoU6x7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis tick at}<r>[0.509, 0.192, 0.518, 0.217]</r> represent on the 'Social Unrest Index' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "kJrgC78iu9nfKL09",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at <r>[0.427, 0.344]</r> from the subplot 'Freedom of Speech Index' plotted higher than the one at <r>[0.675, 0.439]</r> in the 'Social Unrest Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "g32aUVS0tAX8uBKc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the chart value at <r>[0.810, 0.676]</r> in 'Public Trust in Government' represent a greater value than the one represented by <r>[0.456, 0.320]</r> in 'Freedom of Speech Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "XeSCOFYaHSQgTrO9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is lower, the one at <r>[0.095, 0.652]</r> in 'Political Stability Index' or at <r>[0.618, 0.356]</r> in 'Social Unrest Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The one at <r>[0.095, 0.652]</r>."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "02lqtb5ZLsD161n7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features found within the coordinates [0.260, 0.088, 0.495, 0.130]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily includes x-axis labels for the subplot 'Freedom of Speech Index', ranging from 1985 to 2025 at approximate intervals of 5 years, set in black font."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "GkpJqRrOI8Kkc1jj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main elements in the region [0.752, 0.083, 0.989, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick labels for 'Public Trust in Government', with values from 30 to 100, and a line graph representing the index from 1985 to 2025."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "Rh8vGWcaGcPfCmXs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are visible in the region [0.006, 0.083, 0.246, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot contains the 'Political Stability Index' chart, which includes a line graph indicating the index from 1985 to 2020, supplemented by corresponding x-axis and y-axis labels."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "YoweX5lGzedYhZjF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.015, 0.537, 0.029, 0.730] in the chart's subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "UIwbbGqSr8X87jg3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the subtitle}<r>[0.177, 0.015, 0.177, 0.015]</r> of subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an empty string, indicating no subtitle."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "LahEdVVjpKkveMF5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the tick on the x-axis}<r>[0.823, 0.461]</r> in subplot_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '0.8'."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "w5S2TriHJAMGdbiJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick at '0.2'}<r>[0.742, 0.461]</r> in subplot_5 positioned to the left or right of {the tick at '0.4'}<r>[0.808, 0.461]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "kEWZVvvNPDc0skkj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-tick labeled '0.8'}<r>[0.361, 0.106, 0.376, 0.123]</r> in subplot_4 have a higher or lower numerical label than {the y-tick labeled '0.6'}<r>[0.361, 0.193, 0.376, 0.210]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "WnaSr8aBDC4mDcaJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater at {subplot_3's x-axis tick '0.4'}<r>[0.148, 0.952]</r> in comparison to {subplot_4's x-axis tick '0.2'}<r>[0.419, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "UAFpkIw1bwdeTe0S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within [0.361, 0.473, 0.653, 0.491] in subplot_1."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains x-axis tick labels '0.0' to '1.0', positioned sequentially from left to right."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "IlarHzZkuNuVTaK8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found between the area [0.507, 0.473, 0.837, 0.491] on the x-axes of all subplots combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "All the middle x-axis tick labels ranging from '0.4' to '0.6' are found here spread across subplots 1 to 5."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "1p8MQpj97GLPAKMi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section of the chart within [0.010, 0.019, 0.025, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick labels from '0.0' to '1.0' in the last subplot, arranged vertically from bottom to top."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "tm4fMKhlY0fXDCuf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at these coordinates [0.368, 0.062, 0.452, 0.109] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "wu147U4OODRC55A4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the label present at [0.802, 0.913, 0.841, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "5XDgcOLA1PlQXLlB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is described by the coordinates [0.282, 0.351, 0.297, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "hGrKtghUraEFUtBh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year is represented by {the point}[0.338, 0.245] on the Nano Tech graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "3tZ4ZyllXZueiJkW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label has a higher numerical value, the one at {the point}[0.687, 0.155] or {the point}[0.749, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "uJfGCi4R9R9Vbviw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the tech progress in 2005 for Blockchain and Nano Tech by evaluating {these coordinates}[0.579, 0.622] and {these coordinates}[0.374, 0.173]."
+            },
+            {
+                "from": "gpt",
+                "value": "Nano Tech."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "1UFWq0IWtudeIZ1Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area mentioned by [0.125, 0.120, 0.283, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the AI Tech graph axis along with a grid background, x and y-axis labels, and title with a white background."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "VP1In9xkfOBN39NU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents found within the bounds [0.536, 0.120, 0.694, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the Blockchain Tech graph including the axes, grid lines, and various data points plotted along the timeline from year 1990 to 2030, with markers indicating values."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "OGFDsy55mUBkJI91",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section [0.125, 0.914, 0.296, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of the x-axis with tick labels for the years 1990, 2000, and 2010 identifiable in black font."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "0L0865DIHYecMPhD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {coordinates}<r>[0.236, 0.253, 0.236, 0.253]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "fcJArj4qCCSDVNdp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data marker can be found at {the exact point}<r>[0.735, 0.243, 0.735, 0.243]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "lTuNlMdCcbVpbEg3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the element}<r>[0.283, 0.204, 0.283, 0.204]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "cigZkVDbfEPMRbD3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}<r>[0.236, 0.253]</r> higher or lower than {the point}<r>[0.423, 0.081]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "JdlKxXZ6R5jPP1T5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is to the left, {the y-axis tick label '72'}<r>[0.022, 0.311]</r> or {the y-axis tick label '76'}<r>[0.022, 0.297]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "So1QoypPmMAoHpKf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at a higher value on the y-axis, {the point}<r>[0.969, 0.057]</r> or {the point}<r>[0.922, 0.098]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "First."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "vOdZIs9jkkQc9c8E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the region <r>[0.549, 0.940, 0.969, 0.960]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The tenth point for Rakuten is plotted in this area, appearing at coordinates {0.969, 0.549}."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "8exWDGc3sGHBQzC2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area covered by <r>[0.049, 0.040, 0.470, 0.460]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Walmart plot's trend line connecting all the points from values at thresholds 0.1 through 1.0."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "K8SOXi47eJzaMZNn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contains the segment <r>[0.549, 0.530, 0.969, 0.961]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plotting area for Rakuten including a line graph representing different points and y-tick labels corresponding to values like '62', '64', and '66' and others."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "drtHFmC61aQOd6fD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the ylabel}<r>[0.064, 0.272, 0.076, 0.318]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "YuvyQixHhcG9VzFw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the marker color of {the tenth point on the line chart in the third subplot}<r>[0.429, 0.627, 0.429, 0.627]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "MPNbnNkORhp51uQW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.686, 0.512, 0.761, 0.535]</r> in the fourth subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Land Sales."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "G16SAZh3wJsvoMRy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 2008 in subplot 3}<r>[0.397, 0.721]</r> higher or lower than {the first point in the same subplot}<r>[0.141, 0.874]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "S37WmAfvC9lYo6ng",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label '50000'}<r>[0.080, 0.256, 0.117, 0.274]</r> for a higher or lower value compared to {the label '35000'}<r>[0.080, 0.495, 0.117, 0.512]</r> on the Y-axis in subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "NdqQwICejCidGEiJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the fourth tick on the x-axis in subplot 1}<r>[0.269, 0.470]</r> have a greater x-coordinate value than {the first x-axis tick in the same subplot}<r>[0.077, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "FxWySLjQjRU6mPYl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features seen in the region <r>[0.064, 0.092, 0.450, 0.318]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a Y-axis label 'Sales' styled in black and rotated 90 degrees, along with multiple Y-axis labels spanning values from 35000 to 55000."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "TunTQ6678jXn961k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are observed in the boundaries <r>[0.548, 0.092, 0.900, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The second subplot, which includes the subtitle 'Commercial Sales', Y-axis labels without specified values, and data points for annual commercial sales represented in blue line chart form."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "IDWvuMYV20FOcnUd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could one describe the segment located within <r>[0.125, 0.540, 0.477, 0.890]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This segment shows the third subplot graphing industrial sales over time, containing a line chart with colored points ranging in descending Y-axis positions as the years progress, also including sales figures pointed on Y-axis from 38000 upwards."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "GoNsqPAnQsukN5ln",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the data point for the year 2015 in the \"Poverty Alleviation\" plot}<r>[0.454, 0.330]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.77"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "1ttXLmzdDnnFR0Mz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first x-tick label in the \"Health Improvement\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.691, 0.909, 0.711, 0.945]"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "BgNifdesI0jSAny5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the fourth marker on \"Food Security\" line}<r>[0.184, 0.909, 0.204, 0.945]</r> represent in terms of value?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.84"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Yr6Pv3mwqkUjL9Rv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the \"Food Security\" plot}<r>[0.133, 0.855]</r> higher than {the first point on the \"Poverty Alleviation\" plot}<r>[0.335, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "zCP2LwRAiE2TeP5Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-tick label is closest to {the 2020 data point in \"Education Support\"}<r>[0.690, 0.155]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.680, 0.909, 0.700, 0.945]"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "MP4JLhMJHYD78xi4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 2015 endpoint of the \"Health Improvement\" line}<r>[0.854, 0.330]</r> exceed the corresponding y-axis tick value of '0.65'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Yh595kVSzrXKQmqd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of <r>[0.071, 0.075, 0.287, 0.111]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle area for \"Food Security\" displayed in black text, oriented horizontally."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "BQumoExKiPaxU8Dm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region <r>[0.291, 0.909, 0.515, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the first three x-tick labels of the \"Poverty Alleviation\" subplot, displaying values '0.68', '0.70', and '0.72'."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "TMZUecMtMuGQPlVH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are found in the area <r>[0.489, 0.909, 0.739, 0.945]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-tick labels ranging from '0.55' to '0.85' for the \"Education Support\" subplot."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Zz9ZeqfQrJHEvrHj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {subplot_0's x-label}<r>[0.506, 0.942, 0.565, 0.975]</r> refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-label refers to 'Year'."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "36h3pXfxxab41X9m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line labelled as {Microsoft}<r>[0.153, 0.092, 0.233, 0.116]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line labelled as 'Microsoft' is '#ff0000' (red)."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "VSYfiXIN1Q9uPAf7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value on y-axis for {line_3 labelled as 'Facebook'} at point '2007'<r>[0.247, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value on y-axis for 'Facebook' at point '2007' is 0.2."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "u5quVqFaIiIHCfr2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, which company shows the highest growth from 2005 to 2020 <r>[0.142, 0.079, 0.929, 0.861]</r> <r>[0.142, 0.696, 0.929, 0.849]</r> <r>[0.142, 0.611, 0.929, 0.872]</r> <r>[0.142, 0.420, 0.929, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "eiSQq9mlVtIw9vO2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the year 2005, which company between {Apple}<r>[0.142, 0.872]</r> and {Facebook}<r>[0.142, 0.886]</r> had the lower innovation output?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "VskVIc0x42MhAVPv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, did {Facebook}<r>[0.929, 0.420]</r> surpass {Microsoft}<r>[0.929, 0.696]</r> in innovation output?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "FMyghoGocY2XszGN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in [0.098, 0.048, 0.242, 0.195]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the chart, including {Google}<r>[0.153, 0.057, 0.215, 0.081]</r>, {Microsoft}<r>[0.153, 0.092, 0.233, 0.116]</r>, {Apple}<r>[0.153, 0.127, 0.203, 0.151]</r> and {Facebook}<r>[0.153, 0.162, 0.235, 0.186]</r>. Each label is paired with a colored marker."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "Jm4TK7BbKVO2XlQ8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe [0.142, 0.079, 0.929, 0.861]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area traces the timeline of \"Google's\" innovation output from the year 2005 to 2020. The line representing Google is blue in color."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "2f8InS9R6C48Icw1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.019, 0.293, 0.044, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label \"Innovation Output\" located in this region which indicates the degree of innovation of the included companies per year."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "n2jcV1gWRgYYcsug",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.015, 0.242, 0.034, 0.672] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "YGftrLwB1gNWtUsM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the set of coordinates [0.594, 0.893, 0.629, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for the year 2012."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "oas6zVQRB8p9zBiq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis tick at coordinates [0.344, 0.893, 0.380, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is the year 2006."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "Kjf1tPEdHHEoOlMG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {point for the year 2015}[0.736, 0.207] with the {point for the year 2010}[0.528, 0.416], which year has a higher 'Sea Level Change'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2010."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "we96kIZdoamcc89l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the point for the year {2005}[0.320, 0.499] and the point for the year {2018}[0.860, 0.138], which one has a higher sea level?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2005."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "k24B640X78hDaCjo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {point of the year 2003}[0.237, 0.457] and the {point of the year 2017}[0.819, 0.152], which one recorded a lower sea level change?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2017."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "yH1EOo8HYZ8w7xEE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the area defined by [0.040, 0.862, 0.062, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick label \"0.0\" for the 'Sea Level Change'."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "CFwgGAqvm9RCloGL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the region [0.077, 0.873, 0.899, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis tick labels for the years from 1998 to 2022, specifically {the year 2000}[0.095, 0.893, 0.130, 0.921], {the year 2002}[0.178, 0.893, 0.213, 0.921], {the year 2004}[0.261, 0.893, 0.296, 0.921], {the year 2006}[0.344, 0.893, 0.380, 0.921], {the year 2008}[0.428, 0.893, 0.463, 0.921], {the year 2010}[0.511, 0.893, 0.546, 0.921], {the year 2012}[0.594, 0.893, 0.629, 0.921], {the year 2014}[0.677, 0.893, 0.712, 0.921], {the year 2016}[0.760, 0.893, 0.795, 0.921], {the year 2018}[0.843, 0.893, 0.878, 0.921], and {the year 2020}[0.926, 0.893, 0.961, 0.921]."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "JArsAez1C2ltylV8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.528, 0.924, 0.549, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label \"Year\"."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "JvcQpFk1Ewk38XEP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found on the coordinates [0.853, 0.733, 0.977, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label for \"Virtual Integration\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "8EDWyV0aMV5kGQ5E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of data is represented by the object at [0.162, 0.617, 0.282, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item labeled \"Small Scale\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "nW2DkxbCoUFj3hoo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick} at the coordinates [0.040, -0.053, 0.073, -0.030]?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "Jmkehn5fWylqmLog",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher effectiveness in Small Scale, {Direct Method}[0.124, 0.094] or {Data Federation}[0.282, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Direct Method."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "2RjlsuZlEmjA6SK8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Direct Method'}[0.124, 0.094] for 'Big Scale' located higher or lower than its corresponding point in 'Small Scale'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "iZLjZQFjjfVynEeB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Big Scale, is the effectiveness of {Machine Learning}[0.441, 0.232] higher or lower than {Data Federation}[0.282, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "2ORBOGZfNAWsn8Ip",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.095, 0.606, 0.290, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend with two items representing the \"Small Scale\" and \"Big Scale\" lines, marked in red and blue respectively."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "AHyDbGJUxK0c4VRm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region demarcated by the coordinates [0.524, 0.733, 0.674, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for \"Multidimensional View\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "9IdjTkdOrIOVNGrY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the coordinates [0.124, 0.094, 0.915, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within those coordinates, one can find the plotted line for 'Small Scale', representing the effectiveness of various methods on a small scale."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "s8UIGL3u4Mem1BiP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the vertical axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Startups."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "GFMxcBC1KxrmYHiK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.519, 0.942, 0.578, 0.975]."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "NTEjPGfrDTuZT6eh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the tick located at [0.909, 0.886] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "ojPX0EcRSbLrVhKV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for 2011 above or below the data point for 2012 in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "1TrgQYHlfKiGs5wO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for 2013 above or below the data point for 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "bXmcS7Mwog48kCqp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the data point in 2020 have a higher or lower value than the data point in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "Om3Wf8VQ1qvlB9Q8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.188, 0.093, 0.909, 0.745]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line chart depicting the trends of the Number of Startups from 2010 to 2020. The line starts at the point corresponding to 5000 startups in 2010, peaks in 2019, and ends in 2020 with a downturn."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "OBRm8Br6rZQcbFYy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.115, 0.037, 0.981, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area. It houses the plotted line chart, which represents the Number of Startups between 2010 and 2020. X-axis labels indicate the years from 2010 to 2020, and the y-axis labels indicate the Number of Startups from 4000 to 7000."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "Fk0Pf9wZ0QdKzdSZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within [0.404, 0.745, 0.548, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two data points from the line chart. One is corresponding to 4500 startups in 2013, and the other one represents 5000 startups in 2015."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "qI71oDP0dHncsLb1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}[0.502, 0.942, 0.636, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Factors."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "qflyshHR7PI1Jv1o",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by {the line with coordinates '[0.153, 0.138, 0.985, 0.580]'}<r>[0.153, 0.138, 0.985, 0.580]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the percentage of high-risk for various factors."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "jABN2sEDqvFD4A1i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis label}[0.097, 0.107, 0.117, 0.579] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "High-Risk Percentage (%)."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "XuSNyRYnq1XJNFjZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the risk factor 'Financial Impact'}[0.211, 0.665, 0.319, 0.845] considered higher or lower risk than {the risk factor 'Reputational Damage'}[0.262, 0.665, 0.403, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "vKqFAeJUA0dq9dXd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {risk associated with 'Natural Disasters'}[0.704, 0.665, 0.819, 0.856] lower or higher than {risk associated with 'Human Error'}[0.648, 0.665, 0.735, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "lDvyt8ay7gM6E2LB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the risk factor 'Operational Impact'}[0.360, 0.665, 0.486, 0.875] hold a higher or lower percentage than {the risk factor 'Legal Consequences'}[0.435, 0.665, 0.569, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "8rpJG0G747ZbgwQK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.015, 0.665, 0.153, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label for 'Critical Infrastructure'."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "Gq9mYgh9zmEiGSKj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.704, 0.665, 0.819, 0.856] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label for 'Natural Disasters'."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "PXr89fwJ115mMH7e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the area [0.319, 0.263, 0.403, 0.303]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes two points on the line graph corresponding to 'Financial Impact' and 'Reputational Damage'."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "DFZED00Tykk9viIp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is placed at the top center of the chart according to the coordinates [0.423, 0.942, 0.648, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the xlabel, \"Investment Year\"."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "niaRh3trhvneYVGO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of value does the y-axis show according to the coordinates [0.019, 0.241, 0.044, 0.682]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ylabel shows the \"Amount (Billion Dollars)\"."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "YMBIq8YRcRzsMxIO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.794, 0.057, 0.966, 0.081] within the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"Investment Amount\"."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "DpU5fyNiiTiR3Hzd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates, is the 'Investment Amount' in 2006 [0.238, 0.792] more or less than the 'MV Amount' in the same year [0.238, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "BWXteB7kQdDgCh12",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the last year, 2020, is the 'Investment Amount'[0.932, 0.414] higher or lower than the 'MV Amount'[0.932, 0.131]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "DYB9C9h4LCZ9Hw6Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2008, considering the coordinates, did the 'Investment Amount' [0.337, 0.792] increase or decrease compared to the 'MV Amount'[0.337, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "wC77qM3J5oaGGIx6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region [0.738, 0.048, 0.973, 0.125]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart with two entries: {Investment Amount}[0.794, 0.057, 0.966, 0.081] and {MV Amount}[0.794, 0.092, 0.895, 0.116], each entry is associated with a color marker."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "3toKRbJz2swUrgw8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects situated in the region [0.089, 0.131, 0.932, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two lines representing the 'Investment Amount' and the 'MV Amount' from year 2004 to 2020. Each line is marked with points at intervals indicating the amount for each year."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "83H3QyA7bbYMs8ep",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.089, 0.037, 0.981, 0.886] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main chart area where the datasets 'Investment Amount' and 'MV Amount' are plotted as lines against the x-axis ('Investment Year') and y-axis ('Amount (Billion Dollars)')."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "bcNm70qbjSH5bjKa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found in the coordinates [0.041, 0.745, 0.051, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Second y-axis tick label."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "V36MwX262QH0bKaY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the location [0.041, 0.226, 0.051, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '5'."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "eTRYwqcDD8WbisAB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at coordinates [0.115, 0.062, 0.931, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line graph representing the Estimated Global Food Production from 2005 to 2020."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "JQ7PCM455o7lERQ6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas the Estimated Global Food Production higher in {the year 2011}[0.441, 0.598] or in {the year 2013}[0.550, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2011."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "24qulrxkDfhriN3l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas there a larger Estimated Global Food Production in the year labeled at {point}[0.550, 0.564] or in the year at {point}[0.768, 0.235]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The latter point, which represents the year 2017."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "iS52ruUghFvOE5at",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Estimated Global Food Production {point}[0.768, 0.235] greater or less than the one at {point}[0.659, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "4H0nHlVWQbNOM9x0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the location within the coordinates [0.278, 0.884, 0.300, 0.949]."
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2008 is located in this area on the x-axis."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "HXfgwrMLHFTfCwvo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the objects in the region [0.692, 0.896, 0.735, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for the year 2016."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "6z6QUSVIOS4Go9ea",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found between [0.387, 0.624] and [0.441, 0.598]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain two points on the line graph representing the Estimated Global Food Production in years 2010 and 2011 respectively."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "sod1TEIMxAPbcZaa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {object with the longest coordinates in length} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis data points."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "PyxJuImTlDQH8pj6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {chart legend item} at [0.881, 0.779, 0.920, 0.802] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "JRSM3VAZTsVRhlv7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is signified by {the line} [0.122, 0.374, 0.944, 0.855] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue from Video games."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "B2CKonKsY0qXvWRj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Box Office'}[0.122, 0.775, 0.944, 0.840] exhibit a higher or lower revenue trend compared to {the line for 'Music'}[0.122, 0.532, 0.944, 0.677]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "GY1tBKpk1LKlL9j4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich source saw a greater increase in revenue between 2007 and 2019, {Box Office}[0.122, 0.840, 0.944, 0.775] or {Video Games}[0.122, 0.855, 0.944, 0.374]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Games."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "sTUAfjWR5vhBJE9o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry\u2019s revenue is higher in 2018, {Books} [0.875, 0.214] or {Music} [0.875, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Books."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "fVX2EpWnCOMzwJkv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region having coordinates [0.836, 0.735, 0.978, 0.881]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend indicating the color and associated revenue source: {Box Office}[0.881, 0.744, 0.952, 0.767], {Music}[0.881, 0.779, 0.920, 0.802], {Books}[0.881, 0.814, 0.922, 0.837], {Video Games}[0.881, 0.849, 0.973, 0.872]."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "82GIsvTmXP8AVXad",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within coordinates [0.192, 0.025, 0.875, 0.065]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, \"Media & Entertainment Industry Revenue (2007-2019)\"."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "9kCsvaBScriKl8LK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region with coordinates [0.081, 0.893, 0.995, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis labels showing the years 2006 through to 2020 at regular intervals."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "ZInMz9Euxq6kDs9P",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.025, 0.373, 0.058, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "gHEP2KSJa5pBv4Qk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinates are used for the fifth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.583, 0.883, 0.654, 0.919]"
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "cwjIJp1qVrPv1qYl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the point with coordinates}[0.618, 0.852] on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Innovation score value in 2016."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "BSpfb94VQ1I5fYuO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2012, is the {Innovation Score}[0.333, 0.853] higher or lower than the {Community Health Score}[0.333, 0.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "glHSEe6zNWH5hp2x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2014, is the {Innovation Score}[0.476, 0.853] higher or lower than the {Community Health Score}[0.476, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "M0IH98RX6hXomFJj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Community Health Score}[0.904, 0.044] rise or fall from 2010 [0.190, 0.061] to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rises."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "vCARHxVRIWBfPvHK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the coordinates [0.131, 0.757, 0.503, 0.850]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend area containing labels for {Innovation Score}[0.205, 0.769, 0.399, 0.797] and {Community Health Score}[0.205, 0.810, 0.494, 0.838], both color-coded."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "ECNJnHIoU1QKvwg4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the verse [0.190, 0.044, 0.904, 0.061]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the {Community Health Score} line chart from 2010 to 2020, indicating an increasing trend over time."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "xBMiPZcsHf7CMB1t",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the space delineated by [0.155, 0.883, 0.939, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the x-axis labels spanning the years 2010 to 2020."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "8SI7mqJ5C4373WgW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at these coordinates: [0.091, 0.576, 0.108, 0.612]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for '1'."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "uAtlXV0YaH0A3dja",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object situated at [0.125, 0.889, 0.195, 0.920] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis tick label '2010'."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "wYnCo55oRhWLhBRA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the position [0.550, 0.883, 0.620, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2016' is present."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "c28kpywUPId3Zw0n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2015}[0.514, 0.235] higher on the chart than {the point for 2013}[0.373, 0.408]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "m2cuWMQsoopQWZ7n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing 2018}[0.727, 0.108] closer to the x-axis than {the 2012 point}[0.302, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "DfdRK9ErS5aYweHw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the y-axis, {point for 2019}[0.798, 0.135] or {point for 2016}[0.585, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for 2016 is higher."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "gSVMVX0c4ycRSiG1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements encapsulated within the area [0.160, 0.108, 0.869, 0.818]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area occupied by the line chart. It includes 11 points, each representing average growth rate data for the years 2010-2020. The line follows a mostly increasing trend, with values ranging from -1.5 to 6.3."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "UAtzOkTG3mwsOU22",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents present in the region defined by [0.125, 0.844, 0.904, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the x-axis of the chart with tick labels for the years 2010, 2012, 2014, 2016, 2018, and 2020."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "W4yhwbQDNrMUqY95",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.091, 0.030, 0.108, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis of the chart with tick labels ranging from -2 to 7, representing the average growth rate."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "HhlsZuLIxdpevLZx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quality of Relationship."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "sqthGSnTNQOt8b55",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the x-axis label coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.394, 0.930, 0.688, 0.970]."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "VD6bNoJyMVwypIKj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the third tick label on the x-axis located at [0.252, 0.883, 0.287, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "x6EgmCwnOGqv8GgI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '50' friends}[0.270, 0.601] above or below {the point at '60' friends}[0.304, 0.568] in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "DUy8terHVaPzlr4N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point at '90' friends}[0.406, 0.372] and {the point at '110' friends}[0.473, 0.765], which one shows higher quality of relationship?"
+            },
+            {
+                "from": "gpt",
+                "value": "90 friends."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "fJineTAjkyh3xDmS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the point for {80 friends}[0.372, 0.765] indicate a higher or lower quality of relationship compared to the point for {190 friends}[0.745, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "NeBtwO3EggIDYtIn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.068, 0.030, 0.111, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the y-axis ticks ranging from '6.0' to '8.5'. The labels are styled in black color."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "yTaI1ELz9jcZkUP5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.117, 0.883, 0.282, 0.919]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three labels on the x-axis. They are the labels for the numbers of friends: {10}[0.117, 0.883, 0.152, 0.919], {30}[0.185, 0.883, 0.220, 0.919], and {50}[0.252, 0.883, 0.287, 0.919]."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "WXJM9rzFCB9V4K3V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by the coordinates [0.134, 0.208, 0.948, 0.863]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encapsulates the line plot, which represents the trend of 'Quality of Relationship' with increasing 'Friends Number' from 10 to 250."
+            }
+        ],
+        "id_raw": 333
+    },
+    {
+        "id": "tET6pkrvQu0sUbjh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.440, 0.956, 0.638, 0.981] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "b75zgRrnCvVSqvww",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first x-axis tick label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.124, 0.764, 0.142, 0.932]"
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "8u7VF0qoczjvhlpk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {the plot line}<r>[0.133, 0.052, 0.944, 0.719]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the growth of population in various civilization periods."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "QFQAJPdq8zcLgrEv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the population in {Paleolithic Late}[0.241, 0.718] more or less than in {Paleolithic Middle}[0.187, 0.718]"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "iLId0jmaEAwTzGTG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {Bronze Age Early}<r>[0.512, 0.717]</r> higher than the point at {Neolithic Late}<r>[0.458, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "SdAz9Q9lHyAbMLIG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich civilization period had a lower population, {Medieval Early}<r>[0.728, 0.711]</r> or {Medieval Late}<r>[0.782, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medieval Early."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "mi240Xck29IU5zU6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.111, 0.019, 0.879, 0.752]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot of the line chart. It starts from the point representing 'Paleolithic_Early' and ends at the point representing 'Modern_Age', showing the growth of the population from nearly 1 million to 7800 million across different civilization periods."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "WU6YJPTlmzOwPqXY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.072, 0.710, 0.083, 0.732]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the '0' label on the Y-axis, representing zero population."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "pYbBb2SMzfxw9oDO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within [0.827, 0.764, 0.845, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is an x-axis tick label corresponding to the 'Renaissance' period."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "7ZCaVRgTtk6XSyJM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.019, 0.242, 0.044, 0.669] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, which indicates 'Attendance & Revenue'."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "2JIuTA6Ev1TFMQpq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.193, 0.094, 0.283, 0.124] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Revenue'."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "z2zD9W1wBRsq2CN3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the label located at coordinates [0.522, 0.902, 0.575, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year '2015'."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "ANtXJqmhjbmROrfk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line reaches a higher value in 2018, {the 'Attendance'}[0.785, 0.851] or {the 'Revenue'}[0.785, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Revenue'."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "oRnhARdItjbmB2CM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich shows a higher value in 2020, {the 'Attendance'}[0.942, 0.866] or {the 'Revenue'}[0.942, 0.430]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Attendance'."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "5f1lKRD5WJGcSW14",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2011, is {the 'Attendance'}[0.234, 0.859] value higher or lower than {the 'Revenue'}[0.234, 0.340]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "aI59BR2W6bWksI9E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the range [0.126, 0.039, 0.320, 0.135]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the chart's legend. It contains two labels, {the 'Attendance'}[0.193, 0.050, 0.312, 0.080] with its associated color marker at coordinates [0.134, 0.064, 0.176, 0.064], and {the 'Revenue'}[0.193, 0.094, 0.283, 0.124], with its respective color marker at coordinates [0.134, 0.108, 0.176, 0.108]."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "ivOzJ3JtMQMIJAFi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found within the box ranging from [0.515, 0.942, 0.585, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Years' is located in this region."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "iccSD19u4UpAg1mP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content included in the area from [0.207, 0.902, 0.260, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "A single object, the x-axis tick label for the year '2011', is located in this area."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "l8dXZH6tn9ZdpZIT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text labeled at coordinate [0.755, 0.846, 0.859, 0.869] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Periodic Spikes."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "Qom0vXgLlhzjTPBy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for 'Fluctuating Growth' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.755, 0.776, 0.887, 0.799]."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "LTFx2Tb99E3TgNRQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the entity at coordinates [0.160, 0.235, 0.865, 0.298]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the line for 'Rapid Recovery' in the line chart."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "xVurWs76bgcEGjY2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the {line for 'Rapid Recovery'}[0.160, 0.235, 0.865, 0.298] and the {line for 'Sharp Rise'}[0.160, 0.537, 0.865, 0.775] is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Sharp Rise'."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "xA8YyWH9fCeCSWKY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {10th x-axis label}[0.856, 0.906, 0.874, 0.930] to the right of the {5th x-axis label}[0.156, 0.906, 0.165, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "bMz4IGvAcOrkUVqq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {line for 'Fluctuating Growth'}[0.160, 0.497, 0.865, 0.537] have a higher value at the rightmost point, compared to the {line for 'Periodic Spikes'}[0.160, 0.378, 0.865, 0.457]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "hP5e6jMlELTf1nXr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region at [0.755, 0.741, 0.863, 0.764] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend text 'Rapid Recovery' labeled in the chart with a red color line marker to its left."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "QRg0fVup5vV353fD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside the region [0.160, 0.235, 0.865, 0.298]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the representation of the 'Rapid Recovery' feature, depicted as a red line in the chart, starting from 'x'=5 and ending at 'x'=30."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "KxPlVj54nkrOteP4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area located in coordinates [0.715, 0.846, 0.859, 0.869]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the legend item 'Periodic Spikes' along with its corresponding line color marker."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "VxkxPCoqeVG8S4dp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the yticks} located on the coordinates [0.068, 0.923, 0.120, 0.959], [0.068, 0.800, 0.120, 0.836], [0.068, 0.678, 0.120, 0.714] etc represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent interval values on the Y-axis which corresponds to the 'Engagement Trend' in this context."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "wlYsOd9MGubOATr8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {line_0} at coordinate [0.169, 0.030, 0.943, 0.863] represent in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the engagement trend over time."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "wVtomfdaTmI6YONI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.510, 0.930, 0.601, 0.970]</r> at the lower part of the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "sjxPj1CzdUgHxxbe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '810'}<r>[0.523, 0.055]</r> on the 'Engagement Trend' line higher or lower than {the point labeled '800'}<r>[0.491, 0.079]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "o2YmyFd1aHZkMoin",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of 'Engagement Trend', is value at {the point 6}<r>[0.169, 0.814]</r> greater or less than value at {the point 30}<r>[0.943, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "PsIoa6Sx3OE9jAV8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '730'} on this line chart closer to {the left boundary}<r>[0.136, 0.030]</r> or {the right boundary}<r>[0.975, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right boundary."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "7ZjWLo2RYH3XpwvI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the function of the region starting from [0.169, 0.030] and ending at [0.943, 0.863]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main plotting area of the line chart that displays the 'Engagement Trend' over 'Time'. The line within this area indicates the trend of engagement."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "7L9KtTdBGz5pyQc6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements of the chart within the coordinate range [0.510, 0.930, 0.601, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there's the label for the x-axis, which is 'Time'."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "TEN9f9FMgTG5kpu2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the area at [0.025, 0.237, 0.058, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is occupied by the label for the Y-axis, which is 'Engagement Trend'."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "EnhESHxP9pnfXle2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat component of the chart lies within the coordinates [0.079, 0.038, 0.223, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area for the plot's legend."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "XxPAW8aph86f4wsZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line for {Industry 1}<r>[0.085, 0.056, 0.119, 0.056]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line is blue."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "x3SC6VNBRzUUddUH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text contained within the element with coordinates [0.507, 0.957, 0.548, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is 'Year'."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "5oKPJiRUcTm7HSuM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2009, did {Industry 1}<r>[0.506, 0.751]</r> have higher or lower market share compared to {Industry 2}<r>[0.506, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "qf4FDGVaIIMbLvDA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 2000 data point for Industry 1}<r>[0.114, 0.757]</r> and {the 2000 data point for Industry 2}<r>[0.114, 0.624]</r> above or below the 78% mark on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "Pkkm5w1dPd8BKF8y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, between {Industry 1}<r>[0.114, 0.078, 0.941, 0.916]</r> and {Industry 2}<r>[0.114, 0.243, 0.941, 0.776]</r>, ends at a higher position in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industry 1."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "MIfY5yzjeykAZygx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what objects are located within the coordinates [0.615, 0.928, 0.658, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is the x-axis tick label '2012'."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "1Qzu93iR7xEOvmJz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.790, 0.928, 0.832, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the '2016' x-axis tick label."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "vM4lL2Uy1R0mu0hZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area with coordinates [0.040, 0.146, 0.061, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the '90' y-axis tick label."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "H5uJhJyCWYPMcvxo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.820, 0.043, 0.935, 0.069] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label {Slow Increase} in the legend."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "fZO1oaXGBpIaoIYi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at coordinates [0.015, 0.361, 0.035, 0.563] display?"
+            },
+            {
+                "from": "gpt",
+                "value": "It displays the label {Performance}."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "K1rjJ9KgFrPZQAWn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present at coordinates [0.774, 0.092, 0.807, 0.092]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color handle for the {Stable Growth} trend in the legend."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "664h7jndEwB4ACkG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Slow Increase} line [0.107, 0.311, 0.950, 0.538] above or below the {Abrupt Drop} line [0.107, 0.221, 0.950, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "NkGfB9zk5vZeJLx5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points of the {Irregular Variation} line [0.107, 0.062, 0.950, 0.294] generally increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "pspTgURe9yBrKnsS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-coordinate of the point at {1}[0.107, 0.402] for {Stable Growth} line higher or lower than the corresponding point of the {Irregular Variation} line [0.107, 0.086]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "vUt9QyA0oRYCVTBb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within coordinates [0.767, 0.033, 0.977, 0.191]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Slow Increase}[0.820, 0.043, 0.935, 0.069], {Stable Growth}[0.820, 0.080, 0.938, 0.106], {Abrupt Drop}[0.820, 0.118, 0.922, 0.144], and {Irregular Variation}[0.820, 0.156, 0.970, 0.181], each paired with a colored handle."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "yNi16lEPIFgy173W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region specified by [0.041, -0.056, 0.062, -0.031]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"70\" is present in this region."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "tzlVBoyVNkuNDpY0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region defined by [0.101, 0.916, 0.112, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the first x-axis tick label '1' is present."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "UWlvjg46D3HZORDg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.013, 0.346, 0.028, 0.614] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "reuQPuCV0RSRGv8O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fourth x-axis tick label in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.656, 0.912, 0.686, 0.936]."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "lOsP12Bmuq0nhqNY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {line}[0.572, 0.132, 0.968, 0.828] in the second subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Art Development."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "5T1ZutShyStZivGe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Science Development line}[0.078, 0.141, 0.474, 0.819] in the first subplot ascend or descend from 500 to 1500?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascend."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "MdG0q0qPDRyIcZ8m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {Art Development}[0.572, 0.132, 0.968, 0.828] in the second subplot begin at a lower point than {Science Development}[0.078, 0.141, 0.474, 0.819] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "8R69f514vaCnicYq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the period of 2500, is the {Art Development}[0.819, 0.223] level in the second subplot higher or lower than {Science Development}[0.326, 0.173] level in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "1JtKQjd7FS8jN3gg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.552, 0.064, 0.988, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot illustrating the Art Development Over Time with the x-axis labeled as 'Period' and y-axis labeled as 'Development Level'. The art development trend is represented by a line chart with corresponding points marked at various periods."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "ItDpBIdA1pHA4yIm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be found in the region [0.656, 0.025, 0.884, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle for the second subplot, which reads 'Art Development Over Time'."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "cRpD3uOMrVrGRQbA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present within the coordinates [0.841, 0.760, 0.982, 0.801]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend for the second subplot named, specifically the {Art Development line with the color indicator}[0.843, 0.773, 0.872, 0.785], and corresponding label situated at [0.878, 0.769, 0.977, 0.792]."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "Awy5qbJkzIpkIQg3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.017, 0.231, 0.037, 0.709]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Design Elements Value."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "qV9t2e15BX06AQbG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of line does {the line labeled 'Ideas'}<r>[0.093, 0.175, 0.384, 0.810]</r> use in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solid line with circle markers."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "tYQSCwfpneiGtAEA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the dataset 'Refinement (FT)'} in the second subplot's legend<r>[0.806, 0.238, 0.845, 0.256]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "UO6sZzVRbxFqRayo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at x=4}<r>[0.202, 0.485]</r> from the 'Originality' dataset higher than the corresponding point in {the 'Refinement'} dataset<r>[0.202, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "1oWh0uLWNat2THoM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich dataset has a higher final point, {the 'Ideas'} at <r>[0.384, 0.175]</r> or {the 'Execution (FT)'} at <r>[0.782, 0.130]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Execution (FT)."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "b3xOtZabP9V6mAXz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre value labels for x-tick '4' in both subplots aligned at the same vertical level, comparing {first subplot's x-tick label 4}<r>[0.202, 0.869, 0.207, 0.904]</r> and {second subplot's x-tick label 4}<r>[0.600, 0.869, 0.605, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "RHF0Vud704yboc5G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects visible in the chart area defined by <r>[0.491, 0.582, 0.782, 0.743]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region in the second subplot encompasses points on the lines for 'Ideas (FT)', 'Originality (FT)', and 'Refinement (FT)', where they are showing line segments interconnecting the points."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "CLc5lxhlvftanW0e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the graphical components located in the region <r>[0.078, 0.154, 0.398, 0.319]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the lower part of the y-axis including y-tick labels for '0.8' and '0.6', and part of the 'Ideas', 'Originality', 'Refinement', and 'Execution' lines starting phase in the first subplot."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "LuzpSkkRNSb5eawe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the bounds from <r>[0.076, 0.844, 0.420, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds all the x-axis tick labels ('0' to '10') from the first subplot, displaying the iteration steps of the chart."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "dow42wFUGbjVWjvJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object type at the location [0.067, 0.171, 0.475, 0.790]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a line for Old-Business in the first subplot."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "T1ScuqJphN4VBXOm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at {coordinates}<r>[0.257, 0.945, 0.285, 0.975]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "LbfQlslDlaJnhUo5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart element is located at {position}<r>[0.627, 0.238]</r> within the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "tkLsekeefUpbD9iH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the dataset at {starting point}<r>[0.442, 0.896]</r> indicate a higher value in {Old-Business}<r>[0.562, 0.170]</r> or {Streaming Platform}<r>[0.970, 0.783]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Old-Business."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "k4oJFFWZdN0GA4zp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point {label}<r>[0.310, 0.310]</r> on the Social Media line higher or lower than the data point {label}<r>[0.320, 0.310]</r> on the Old-Business line in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "vwzOh94fCKpZWdXf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is rising faster in the year 2015 between {Old-Business}<r>[0.884, 0.742]</r> and {Streaming-Platform}<r>[0.884, 0.173]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Streaming-Platform."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "aFfgqlE3Ff7RkuEZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the setup in the area [0.500, 0.800, 0.850, 0.950]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label {Years}<r>[0.753, 0.945, 0.780, 0.975]</r> and some tick labels for the years in the second subplot."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "kpSXjSUfTYGsna7T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.443, 0.080, 0.545, 0.175]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {x-tick labels}<r>[0.494, 0.912, 0.524, 0.936]</r> from the second subplot arranged in chronological increment from years 2017.5 to 2020.0."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "ZUt7qzapaC2dcacG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the elements found in the region [0.250, 0.200, 0.350, 0.300]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the {x-axis ticks}<r>[0.282, 0.896]</r> and the data points for {Old-Business}<r>[0.282, 0.673]</r> and {Social Media}<r>[0.282, 0.294]</r> around the year 2010 in the first subplot."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "TrVKAbDDrP2q5M6u",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.017, 0.349, 0.037, 0.591]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the first sub-chart."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "GYZQC77uoIZOHseT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the line}[0.322, 0.243, 0.458, 0.716] in the second sub-chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporate Social Responsibility vs Crime Against Humanity."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "Kf6sUEw06A0rZ0aq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.259, 0.037, 0.521, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the second sub-chart: 'SBP and GoB Evaluation'."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "VbpmFRxRNvls35Qj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, does {the point at Year 1997}[0.190, 0.243] have a higher or lower value than {the point at Year 1991}[0.100, 0.405] on the 'Corporate Social Responsibility vs Crime Against Humanity' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "iHdmSqYf7yLHF2Ih",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line of \u2018Corporate Social Responsibility vs Crime Against Humanity\u2019}[0.322, 0.243, 0.458, 0.716] start or end with a higher index value in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "End."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "5VW4dkbyduWxaygk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn first subplot, is {the 1995 point}[0.160, 0.276] on 'Corporate Social Responsibility vs Crime Against Humanity' line higher or lower than {the 1995 point}[0.160, 0.438] on 'Sustainable Business Practices vs Greening of Business' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "HKkZq4diWDlnnRey",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.472, 0.787, 0.983, 0.902]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend for the second sub-chart. The legend has 2 items: {Corporate Social Responsibility vs Crime Against Humanity}[0.522, 0.801, 0.977, 0.836] with a handle color of '#0a6ae1' and {Sustainable Business Practices vs Greening of Business}[0.522, 0.853, 0.949, 0.888] with a handle color of '#d75faa'."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "Pupce3LAdF3FkSFx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.315, 0.096, 0.465, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the second sub-chart titled 'SBP and GoB Evaluation' and its x and y axes with labels 'Year' and 'Index Value'. The x-axis has ticks labeled from '1985' to '2000' and the y-axis has ticks from '0.10' to '0.55'. There are two line plots with points ranging from '1990' to '1999'."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "AKjGk526WuuCd1gJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.078, 0.096, 0.227, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region has the first sub-chart titled 'CSR and CAH Evaluation' and its x and y axes with labels 'Year' and 'Index Value'. Similarly, there are four ticks on the x-axis from '1985' to '2000' and six ticks on the y-axis from '0.5' to '1.0'. Two line charts are plotted in this area showcasing trends of 'Corporate Social Responsibility vs Crime Against Humanity' and 'Sustainable Business Practices vs Greening of Business' from '1990' to '1999'."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "41smMJID788oDlhv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.439, 0.810] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a data point on the line plot representing final reading of Electricity Consumption."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "sxXpw5TzsNNjCTfg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the y-axis label in the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.508, 0.333, 0.528, 0.607]"
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "NxEMeUNPF9drrdop",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the point at {coordinates}[0.601, 0.130] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "The initial reading of Gas Consumption."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "1l3PAHMT5XNjWBxe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at the final reading, the {gas consumption}[0.931, 0.300] or the {water consumption}[0.931, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gas consumption."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "wEEHg463WdddjXsd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {initial reading of Gas Consumption}[0.601, 0.130] greater than the {initial reading of Electricity Consumption}[0.109, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "OvY8wJZGSAVmUgRY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the final points, did the {Electricity Consumption}[0.439, 0.810] decrease more than the {Water Consumption}[0.931, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "7yBubx6u2rKAO3lR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in region [0.584, 0.096, 0.948, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an subplot illustrating the decrease in Gas and Water Consumption over time. It includes line plots for both consumptions, having data points marked with coordinates, axes with labels and ticks, and a legend indicating the color code for each type of consumption."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "GwpmLhhumQxnWq5G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.093, 0.096, 0.456, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subplot showcasing the decrease in Electricity Consumption. The plot includes a line tracing the consumption from Initial Reading to Final Reading, Axes with labels and ticks, and a subtitle giving a brief summary of the plot."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "SNCK8eKGuMHjRPH5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.839, 0.113, 0.940, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend with labels and colored markers for Gas Consumption and Water Consumption. The coordinates for the labels are {'Gas'}[0.888, 0.127, 0.917, 0.162] and {'Water'}[0.888, 0.179, 0.934, 0.214]."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "uLiLzvmw1UvFvo9s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.513, 0.159, 0.533, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It the y-axis label of the second subplot."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "vKSBEGkcYXBNFeKw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fifth y-axis tick label coordinate in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.043, 0.539, 0.077, 0.574]."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "L2Hd9f3NGBY7Opme",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text}[0.670, 0.037, 0.888, 0.085] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series A Investment."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "0lj6cJlfgAbefhON",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, does the {point at time period 2}[0.181, 0.369] have a higher or lower y- value compared to the {point at time period 3}[0.218, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "sVvDaDOfmTByojMP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the first and second subplots, is the {y-axis label in the second subplot}[0.513, 0.159, 0.533, 0.782] to the left or right of the {y-axis label in the first subplot}[0.017, 0.159, 0.037, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "9zTo2ruaxd6ux0Se",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point for time period 0 in subplot 1}[0.106, 0.185] have a higher or lower value than the {point for time period 0 in subplot 2}[0.593, 0.175]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "GHm7n56tPj277X1G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described within the region of [0.043, 0.355, 0.077, 0.390] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 7th y-axis tick label which is '0.90' lies within this region for the first subplot."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "ce5IKWHDd6NmNZlh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.0575, 0.096, 0.983, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the second subplot including the axes, title 'Series A Investment', x/y labels, as well as the plotted line for 'Series A'. Y-ticks labels from '0.7' to '1.3' and x-ticks labels from '-2' to '12' are displayed."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "oxI2ZxWeDmAmOFNm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items can be found within the coordinates [0.106, 0.185, 0.478, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plot line for the 'Angel & Seed' data in subplot 1 is within theses coordinates. The line includes markers for time periods 0 through 10."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "nPrxmMmbHc7357mL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.091, 0.037, 0.475, 0.085] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "GPT-6 Technological Advancements."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "VzqOJeEL0AMre0PC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label on the y-axis with the coordinates [0.508, 0.272, 0.528, 0.668] on the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Advancement Rate."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "iXCJXY24ItpYlnNZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label near {the point}[0.648, 0.808] on the 'Upgraded' line of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year '2035'."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "d5mvPhy3SFczXIeK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labelled '2040'}[0.221, 0.385] on the 'Original' line of the first subplot above or below {the point labelled '2040'}[0.713, 0.380] on the 'Original' line of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "25B0dA8VLlVoRluy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the second plot, {Original}[0.588, 0.180, 0.961, 0.580] or {Upgraded}[0.588, 0.430, 0.961, 0.780] ends at a higher rate in 2060?"
+            },
+            {
+                "from": "gpt",
+                "value": "Original."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "EAB6eotMyb2PpF0h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {values for '2050'}[0.837, 0.290] on the 'Original' line of the second subplot higher or lower than the {values for '2040'}[0.713, 0.380]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "8e25gkHsCKMxKpTZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.840, 0.637, 0.972, 0.752]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend for the second plot. It includes two entries: {Original}[0.890, 0.651, 0.950, 0.686] and {Upgraded}[0.890, 0.703, 0.966, 0.738], displayed with their respective colors."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "S3JyOG9AlqrR3Q6A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.078, 0.096, 0.488, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the first subplot. It contains two lines, labeled as 'Original' and 'Upgraded', representing the advancement rate of 'GPT-6' and 'GPT-6 Upgraded' from the years 2030 to 2060."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "Xx7FYZJjKyPceHvd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in [0.585, 0.037, 0.964, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle \"AAI-1 Technological Advancements\" for the second subplot."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "Zu89KL5tgIK45Mst",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the object at coordinates [0.197, 0.030, 0.367, 0.068]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Decline System, a subtitle for the first subplot."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "lFdO9JrRid5O2QsB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates of [0.739, 0.934, 0.805, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-label of the second subplot, namely \"Iterations\"."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "TL2u4hRaUHJ8W0wf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of the object located at [0.576, 0.153, 0.968, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A line representing 'Fast Decline' for the second subplot."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "oal8G3nIEClce1IC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.282, 0.580]} on the 'Slow Decline' line above or below {the point at [0.331, 0.632]} on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "Mrl3yf470ZDlGPX1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is larger at Iteration 500, the {data point at [0.184, 0.424]} in the 'Slow Decline' system or the {data point at [0.674, 0.315]} in the 'Fast Decline' system?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point in the 'Slow Decline' system."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "PMKZcG7Cs9FX4GN9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe {line for 'Fast Decline'}[0.576, 0.153, 0.968, 0.799] on the right has an upward or downward trend compared to {the line for 'Slow Decline'}[0.086, 0.165, 0.478, 0.787] on the left?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downward."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "X2vYxwIM3OD11OhT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the bounding box [0.556, 0.077, 0.988, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot. It includes an x-label of \"Iterations\" at {coords}[0.739, 0.934, 0.805, 0.970], a y-label of \"Relative Performance\" at {coords}[0.510, 0.298, 0.525, 0.654], and a line chart representing the 'Fast Decline' at {coords}[0.576, 0.153, 0.968, 0.799]. The subplot has its own set of x-axis and y-axis tick labels."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "eHMtsmXbqsACRkY3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.066, 0.077, 0.497, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first subplot of the chart is in this region, described as 'Slow Decline System'. The chart includes x and y-axis labels, representing \"Iterations\" and \"Relative Performance\" respectively, along with x-axis and y-axis value labels, and a line chart showing the 'Slow Decline' trend."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "P8m43qIbn3loV7OY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.870, 0.809, 0.982, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend for the 'Fast Decline' line of the second subplot with a colored marker and label at {coords}[0.907, 0.821, 0.977, 0.849]."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "iLwvXGMn7JRlgmO7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first object located at [0.568, 0.446] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the data point {'0'}<r>[0.568, 0.446]</r> of the Total label in the \"Connections over Time\" subplot."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "u7PZNg91ic5rt5Lw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label corresponds to the y-axis tick at [0.522, 0.819, 0.541, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '200'."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "fjniiukZMumNk8nB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is located within these coordinates [0.548, 0.064, 0.989, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a line chart."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "lhO1Fv4wm6UzmOPW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is the point located at [0.819, 0.675] on?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's on the \"Active\" line."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "UvZjqd7O63Ssi5wA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Total' line point}<r>[0.969, 0.140]</r> above or below {the 'Active' line point}<r>[0.969, 0.583]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "flYxepXYXn2gJedb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 3}<r>[0.224, 0.331]</r>, is the 'Total' line's value higher or lower than the 'Active' line's value at {point 3}<r>[0.224, 0.583]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "3w6rxZVw3OCiPV69",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.053, 0.064, 0.495, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents a subplot of the chart showing the 'Friends over Time' trend. It contains a line chart with two lines - 'Total' and 'Active'. Different quarters are plotted on the x-axis, and the count of friends is displayed on the y-axis."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "ta5FLf3ES0yUveYb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.689, 0.025, 0.849, 0.057]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle for the subplot - \"Connections over Time.\""
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "p88bi10BDrrlRBfS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.548, 0.064, 0.989, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Connections over Time' subplot, which includes a line chart showing 'Total' and 'Active' connections over time, an x-axis labeled 'Quarters', a y-axis labeled 'Count', and a legend showing line colors for 'Total' and 'Active'."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "WyPRMEUVFvwvbSle",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.017, 0.415, 0.037, 0.525] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "V8m6363bPuxP7SUl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.750, 0.917, 0.808, 0.963]</r> in the 2nd subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "bx8ViHTtjmF8ZTgs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.881, 0.689, 0.976, 0.752] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend for the 2020 sales trend in the 2nd subplot."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "d0FgmWxGV8c0s0kc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data point in 2020 sales trend line is higher, the point at {month 6}<r>[0.762, 0.432]</r> or the one at {month 3}<r>[0.661, 0.735]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month 6."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "zOkq0NMXx7f2H6Kk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {month 2} in the 2020 sales trend <r>[0.627, 0.583]</r> higher or lower than the point at {month 5}<r>[0.728, 0.644]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "a9N6dGrBvygfIOQR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 2019 sales trend, does the sales increase or decrease from {month 3}<r>[0.169, 0.727]</r> to {month 4}<r>[0.203, 0.644]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "4JhT8mg1nZ16l2ea",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region with coordinates [0.575, 0.096, 0.983, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart for the 2020 sales trend, represented by a line graph. It includes an x-label 'Month', a y-label 'Sales', several x and y ticks with their labels, and a legend."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "1HoIPnaX0pgUO6sI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region marked by [0.083, 0.096, 0.492, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the 2019 sales trend is plotted with a line graph. It includes the x-label 'Month', the y-label 'Sales', and several x and y ticks with labels."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "6AnOQ8CXUH58NmSx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.881, 0.689, 0.976, 0.752] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend for the 2020 sales trend line graph."
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "9B6unQ24ZI21slUz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at [0.017, 0.315, 0.037, 0.625] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the first subplot."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "XE3N2q7CRCsnKtFa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the {legend label 'YieldCurve'} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.887, 0.703, 0.969, 0.738]"
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "XsT0OJDEK4bJhzOM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the year label '2010'} on the x-axis at [0.134, 0.869, 0.173, 0.904] indicated?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "QizqCf9TQfAxzL3E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {YieldCurve point for the year 2016}[0.822, 0.318] above or below the {BlueChip point for the same year}[0.324, 0.345]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "WYpOAvM6NadDFEGP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, BlueChip or YieldCurve, shows a higher value for {the year 2018} pointing at [0.879, 0.251] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "YieldCurve."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "VFZri7Ec8sKkRu4I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {BlueChip point for the year 2009}[0.125, 0.774] above or below the {BlueChip point for the year 2010}[0.154, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "pBikofLWfoT2RyUV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you elaborate on what's found in the coordinates [0.576, 0.096, 0.983, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot labeled \"Yield Curve Trend\". The YieldCurve line graph resides in this region, represented with the color #d75faa. Additionally, the y-axis label 'Indicator Value' and the x-axis label 'Year' are placed here, including the corresponding tick labels and marks."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "I7HVpalVLKXnHTPq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are included in the region defined by [0.078, 0.096, 0.485, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses the first subplot labeled \"Blue Chip Stock Trend\", which includes the BlueChip line graph, the y-axis label 'Indicator Value', and x-axis label 'Year' along with related tick labels and marks."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "9ZyM7qSUYXER0f2W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region outlined by coordinates [0.838, 0.689, 0.976, 0.752]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend of the second subplot, where the label 'YieldCurve' is found with the associated color marker, colored #d75faa."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "ch6LZS5wTGSnRECc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label located at [0.017, 0.259, 0.037, 0.681] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Attack Success Rate."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "DvTeuJC94rn1xQZG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.190, 0.844]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "pDovp78VCMlEDQ8y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line does the legend with coordinates [0.815, 0.637, 0.972, 0.752] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fine-tuned."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "AlQWQG1oFHrKud7D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point [0.682, 0.305], is the 'Conventional' line above or below the 'Fine-tuned' line at [0.682, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "x3s4ojqr6PPs2CVI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the fifth point on the 'Conventional' line}[0.775, 0.214] higher or lower than the fifth point on the 'Fine-tuned' line at [0.775, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "qBlS2wNBspGa1bdy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, 'Conventional' at [0.470, 0.173] or 'Fine-tuned' at [0.470, 0.366], has a higher value at 2000 iterations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fine-tuned."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "IMV1nTtEFREUKG6c",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the object at the coordinates [0.693, 0.037, 0.856, 0.085]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the second sub-plot. It reads \"AES Evaluation\" and is displayed in black color."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "pcfQynsd5HWZkppo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.815, 0.637, 0.972, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the second subplot. It includes {Conventional}[0.864, 0.651, 0.966, 0.686] and {Fine-tuned}[0.864, 0.703, 0.946, 0.738]. Each legend item is matched with a colored marker."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "uKMauAzNpbJk2HA7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.588, 0.165, 0.961, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the 'Conventional' line in the second subplot. It contains several data points representing different 'Iterations' and corresponding 'Attack Success Rate'."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "1zhqut3kNUueEITp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.017, 0.332, 0.037, 0.608] on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label represents the 'Success Rate'."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "kG65BsBkufa3y6zC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate corresponds to the number '1500' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.243, 0.287, 0.287, 0.313]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "2nKvrun0AcqCM2A2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content marked at {coordinates} [0.863, 0.703, 0.966, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend for 'AR Education'."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "ctOHkWiSz47GS3p6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the success rate of {AR Education}<r>[0.643, 0.439]</r> higher or lower than that of {VR Education}<r>[0.152, 0.313]</r> for 500 students?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "m9eXViwDDfX9AhzN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the success rate of {VR Education} <r>[0.470, 0.679]</r> at 4000 students higher or lower than that of {AR Education} <r>[0.962, 0.192]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "dkRdd0O9yHct6FQq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher success rate at 3000 students, {AR Education}<r>[0.871, 0.212]</r> or {VR Education}<r>[0.379, 0.549]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR Education."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "V443LFmrUHkxIpYP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.508, 0.332, 0.528, 0.608]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the y-axis label of the subplot chart on the right, which represents the 'Success Rate' of 'AR Education'. The label is stylized with black color and a rotation of 90.0 degrees."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "sjzQThuxiqJtEe3T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the area specified by the coordinates [0.814, 0.689, 0.972, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the chart legend. It contains the label for 'AR Education' <r>[0.863, 0.703, 0.966, 0.738]</r>, which is paired with a colored marker <r>[0.816, 0.710, 0.854, 0.727]</r>."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "WUrEVVJUpICaUr45",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.579, 0.096, 0.980, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the right subplot of the figure. It covers the area from the x and y-axis labels to the data line representing 'AR Education'. The x-axis labels range from '\u22121000' to '5000' representing the 'Number of Students', and the y-axis labels range from '0.55' to '0.95', representing the 'Success Rate'. The line for 'AR Education' has been plotted with color '#d75faa' using a marker of 'o'."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "S2KjWxwbumOTSXaE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the right axis in the chart with coordinates [0.584, 0.096, 0.980, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The right axis represents the Trend of Progressivism Index."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "1pbl5YlxPp4E7mwr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label for the year 2005 on the right half of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.673, 0.869, 0.712, 0.904]"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "A6Ei4tZWCzyXuU24",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.503, 0.349, 0.523, 0.591] on the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index Value."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "KtT7f00RxrGinckT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {index value '0.55'} at [0.196, 0.470] higher or lower than the {index value '0.6'} at [0.286, 0.130] for the Conservatism Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "SJiYlAtIYtckuWVr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, did the Progressivism Index {[0.962, 0.130]} score higher or lower than the Conservatism Index {[0.465, 0.334]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "14rdepElrZ9IqwH9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point representing the year 2015}[0.872, 0.300] higher or lower than the {point representing the year 2010}[0.782, 0.470] in the Progressivism Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "cWFAPsDdQdghe38Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region with coordinates [0.131, 0.037, 0.440, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the left subplot, which is \"Trend of Conservatism Index\", styled in black color."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "BziMQFJryA8JrifE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area with the coordinates [0.584, 0.096, 0.980, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area describes the right subplot of the figure, it represents the trend of \"Progressivism Index\" over years with a line chart."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "XcEos9GNYq9uTbxQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region [0.627, 0.037, 0.937, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Trend of Progressivism Index\" is displayed in this region, it's styled in black color and does not have any rotation."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "OyYrGOf8P25kkOK4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.017, 0.422, 0.037, 0.518] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "7JlKtNaoai59jgTV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xtick at coordinates [0.269, 0.869, 0.308, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"2014\"."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "ODNVJW7p89xDP87D",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line} at [0.598, 0.130, 0.962, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic growth rate."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "3agbdD0ufXP1Ko61",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data line is {the point}[0.598, 0.595] part of, Unemployment or Economic Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Growth."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "rHV25bpCKzUzNMNc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at coordinate [0.901, 0.768]} on the unemployment line higher or lower than the {point at coordinate [0.901, 0.229]} on the economic growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "4rCfXKA0wKkRz84R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, which rate is higher, {Unemployment rate at [0.470, 0.364]} or {Economic Growth rate at [0.962, 0.744]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Growth rate."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "fFxWPsTBkc80FZr8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.026, 0.869, 0.065, 0.904]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a tick label on the x-axis, reading \"2006\"."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "NRsxKy6IMVi0n57l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.798, 0.689, 0.972, 0.752]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend identifying the Unemployment line with a distinct color at {coordinates}[0.800, 0.710, 0.839, 0.727]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "qPsfJWG98cRdWXHx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.658, 0.711, 0.689, 0.644]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The x-axis tick label \"2010\", and a point from the Economic Growth line quantifying the growth rate for that year."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "bAmbjjGjjo4JqGkz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item with coordinates [0.826, 0.703, 0.969, 0.738] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Density (log scale)."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "3mjzgzIXNZDBbZYc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the top left coordinates of the distance value of 400 on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.274, 0.869]."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "fZHTKS3DKE4wPOOy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {y-axis tick} at [0.043, 0.116, 0.072, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "550."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "DnBjfgsoEbr2brBJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAlong the 'Density' line, is the point at distance 200 from the city center higher or lower than the point at distance 800 from the city center?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "PLxdG9qCSwYDQ1T7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the 'Density (log scale)' line, is the density value at distance 100 from the city center higher or lower than that at distance 700 from the city center?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "smSaT5um5e1ROnYr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the 'Density' line, does the point at distance 100 have a higher or lower population density than the point at distance 400?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "MThSvOxT9DyJfckM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region with coordinates [0.776, 0.689, 0.976, 0.752]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the 'Density (log scale)' line. The legend item is colored in '#d75faa' and is accompanied by a marker of the same color."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "aM2Hy7pD7O17EyYU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.097, 0.869, 0.106, 0.904]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region is the label '0' from the x-axis representing the distance from the city center."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "AEJZFL0oA1IMJuos",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within [0.043, 0.116, 0.072, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a y-axis tick label indicating a population density of '550'."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "Ez2R3eK5ekY79PJ3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.053, 0.221, 0.072, 0.256] on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "skNrIMT3UsxuC2WT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at {coordinates}[0.273, 0.844] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tick mark of '6' for the Time (Month)."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "vu3S9IW27oPBGSX5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line marker}[0.753, 0.710, 0.791, 0.727]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "a2MsVoOJMcNTpcHU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {point 5} for Rate of Social Interaction{[0.238, 0.360]} or Self-satisfaction Index{[0.725, 0.598]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Self-satisfaction Index."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "jtsOWFFiAFX7envB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the term '12'}[0.478, 0.844] on the x-axis of Rate of Social Interaction subplot to the left or right of {the term '8'}[0.828, 0.844] on the Self-satisfaction Index subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "K5MvNCx2lFdBYPa9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values at {point 3}[0.170, 0.214] for the Rate of Social Interaction higher or lower than at {point 3}[0.657, 0.683] for the Self-satisfaction Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "nNDQebhfTgfALFKm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.500, 0.080, 0.800, 0.200]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subplot title for the chart \"Self-satisfaction Index Over Time\" styled in black text."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "fliAZQHiS3Wi0cWc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features within the area [0.040, 0.840, 0.090, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes multiple y-axis tick labels such as '90', '80', '70' along the left side of the Rate of Social Interaction chart, all styled in black."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "av214pDtREpIpenA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space between [0.200, 0.800, 0.900, 0.950] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section hosts the x-axis labels of 'Time (Month)' for both subplots, colored in black and ranging from values '0' to '12' for the Rate of Social Interaction and '0' to '14' for the Self-satisfaction Index."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "ik9h1KuM578G85XA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at coordinates [0.015, 0.285, 0.033, 0.667] in the subplot_0 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Participants."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "htbXj7C2uC3ujEea",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information at [0.640, 0.102, 0.685, 0.130] in the subplot_1 legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "WhyQiKCn8CsR7KzE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the line plot at these coordinates [0.119, 0.769, 0.479, 0.839] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "In-person."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "bC4ZKm0VrWO4VDsy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, is the {plot labeled 'Online'}<r>[0.119, 0.113, 0.479, 0.839]</r> above or below the {plot labeled 'In-person'}<r>[0.119, 0.769, 0.479, 0.839]</r> in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "SoidcCrIcTWvMB4c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, which line plot is higher at year 2015: {Online certificates}<r>[0.727, 0.699]</r> or {In-person certificates}<r>[0.727, 0.425]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online certificates."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "LkMmeRXTOUEynF3D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {subplot_0 legend item 'Online'}<r>[0.640, 0.102, 0.685, 0.130]</r> plotted with the same color as the {subplot_1 legend item 'Online'}<r>[0.640, 0.102, 0.685, 0.130]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "El9opfDqHAYZvIYX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region with coordinates [0.596, 0.091, 0.711, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for subplot_1. It includes two entries labeled \"Online\" and \"In-person\", each paired with a colored marker."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "HyhzdTi3HaLHvSyG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information can I find in box [0.589, 0.077, 0.985, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot showing the comparison of \"Online vs In-Person Certificates Issued\" over various years. It includes two line plots labeled \"Online\" and \"In-person\", an x-axis labeled \"Year\", and a y-axis labeled \"Number of Certificates Issued\"."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "wxYmmNPlr0DoMKTy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.261, 0.895, 0.297, 0.923] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label '2016'."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "aARyMk9oawt3F2qm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labelled at point [0.146, 0.087, 0.206, 0.108] according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "UKjaqVks0X0G0L1a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis represent given its label coordinates [0.513, 0.927, 0.574, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "ejbgPnoDRlQIoapR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the y-axis label with coordinates [0.025, 0.169, 0.048, 0.736], what does the y-axis represent in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population and Technology Level."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "mn67UVKcCmeSysRQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to their positions, does the {Population line}[0.170, 0.112, 0.917, 0.853] start before or after the {Technology line}[0.170, 0.372, 0.917, 0.854] in terms of Years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both lines start at the same time."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "GdnVJrw8rzR2M2Ty",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Population point}[0.668, 0.427] at 6000 Years above or below the {Technology point}[0.668, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "QldSYaKJposXN24e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 0 and 2500 Years, does the {Population line}[0.170, 0.112, 0.917, 0.853] increase at a faster or slower rate than the {Technology line}[0.170, 0.372, 0.917, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slower."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "rIiZ9muL6J39LpSi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region represented by [0.144, 0.069, 0.357, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart which consists of two items - {Population}[0.146, 0.087, 0.206, 0.108] and {Technology}[0.146, 0.140, 0.206, 0.161], each with corresponding color markers."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "X9BnYgFmNlHXzfLx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is presented in the section with coordinates [0.132, 0.051, 0.954, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main area of the plot, which depicts two lines - {Population line}[0.170, 0.112, 0.917, 0.853] and {Technology line}[0.170, 0.372, 0.917, 0.854], representing population and technology level over the years, respectively."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "TT0SrBDjKh6guH01",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's described in the area marked by [0.172, 0.879, 0.250, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label '{-5000}'[0.172, 0.879, 0.250, 0.914], representing the year -5000"
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "yIiTY0FI1Z5V6Aeg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the object located at coordinates [0.669, 0.121, 0.824, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for Dropout Rate in the legend."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "oQfOuJHN5aqs8Iuf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data at the point {coordinates}[0.671, 0.179]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents data for the year 2018 where the Student Enrollment Rate was 75%."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "Ru05u8dwL3JaiMSp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label that exists at [0.509, 0.927, 0.570, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Years'."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "Jw0RC7wN4K4ZMJI5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the point}[0.319, 0.106], {the Student Enrollment Rate line}<r>[0.143, 0.075, 0.935, 0.232]</r> or {the Dropout Rate line}<r>[0.143, 0.650, 0.935, 0.817]</r> ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Student Enrollment Rate line."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "MvZJeVQagIyk3HZf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for Student Enrollment Rate and Dropout Rate at {point coordinates}[0.935, 0.232] and [0.935, 0.650] respectively, increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "4DfCFfNqSkDT6Pgj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the last data point, does the {the Student Enrollment Rate line}<r>[0.935, 0.232]</r> have a higher or lower value than the {Dropout Rate line}<r>[0.935, 0.650]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "CGixBDCKvBipIAZw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area bounded by [0.595, 0.055, 0.963, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the chart, which includes labels for 'Student Enrollment Rate' and 'Dropout Rate', each with their respective markers."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "3rKoZm8uw9U6HPzX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details within the coordinates [0.103, 0.038, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main plotting area of the chart. It contains two lines, one for the 'Student Enrollment Rate' and the other for the 'Dropout Rate'. Each line has ten points corresponding to the years from 2012 to 2021. The x-axis and y-axis labels, tick labels and gridlines are also within this area."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "FY0BysiJYWC08HKY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content between coordinates [0.058, 0.009, 0.087, 0.044]?"
+            },
+            {
+                "from": "gpt",
+                "value": "the y-axis tick label '90'"
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "T9yTrJlxrmLiivhj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.842, 0.879, 0.857, 0.914]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "TEXwTjU1EqLglDVb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.665, 0.069, 0.963, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "wmgdFd8RnptbuYOU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.072, 0.573, 0.102, 0.608]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "xMaMzhsRfbhMvgGt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Intrusion Attempts point at 5 months}<r>[0.590, 0.419]</r> higher or lower than the {Data Breaches point at 5 months}<r>[0.590, 0.687]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "iiTMMmeysbY9uLi8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor point 2, does {Intrusion Attempts}<r>[0.330, 0.319]</r> have more instances than {Data Breaches}<r>[0.330, 0.620]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "cGjYP9sYOJdKCPEA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point 9, which has more instances {Intrusion Attempts}<r>[0.936, 0.587]</r> or {Data Breaches}<r>[0.936, 0.741]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Breaches."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "XtlAXQ3BbhwI7iRf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content found in the region [0.665, 0.069, 0.963, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend with two items: {Intrusion Attempts}<r>[0.739, 0.083, 0.954, 0.118]</r> and {Data Breaches}<r>[0.739, 0.135, 0.909, 0.170]</r>.  Each legend item is accompanied by a color marker."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "zWuG35jUOoczvHfT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.025, 0.370, 0.048, 0.535] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the Y-axis label, which says {Instances}<r>[0.025, 0.370, 0.048, 0.535]</r>."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "uRwn2Esvb7PsQl1H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.149, 0.879, 0.164, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object in this area. It is the x-axis tick label indicating the '0' time point."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "97l6itWN5wmAk8RO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is {Values}<r>[0.025, 0.389, 0.048, 0.502]</r>."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "7OUHVJLPDywT44Ju",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is present at the coordinates [0.591, 0.879, 0.649, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label present at these coordinates is {the year '2014'}<r>[0.591, 0.879, 0.649, 0.914]</r>."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "jYsVvoKblZoZDCIK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element in the legend is located at coordinates [0.463, 0.683, 0.661, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the {Real GDP Growth item}<r>[0.463, 0.683, 0.661, 0.718]</r>."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "NLewhkhDw8VDyX4u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2008, was {the Real GDP Growth}[0.305, 0.524] higher or lower than {the Earnings Per Share}[0.305, 0.265]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "Y77gFHHNSQSd8Oqr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the end points, did {Dividend Per Share}[0.936, 0.170] increase or decrease more than {the Real GDP Growth}[0.936, 0.817] from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "8xmn4Yfir7lydoZJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2019, was {Earnings Per Share}[0.883, 0.109] higher or lower than {Dividend Per Share}[0.883, 0.204]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "TzSguMPzb4QUy7Od",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinates [0.108, 0.037, 0.975, 0.854]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main chart area which includes the plots for {Real GDP Growth}[0.147, 0.265, 0.936, 0.817], {Earnings Per Share}[0.147, 0.075, 0.936, 0.394], and {Dividend Per Share}[0.147, 0.170, 0.936, 0.411], along with x-axis and y-axis labels, and tick labels."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "Tl9wSg5BARl6GLp3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in the area [0.389, 0.669, 0.694, 0.837]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specific region includes the chart's {legend}<r>[0.389, 0.669, 0.694, 0.837]</r> detailing the meaning of each plot line color, including {Real GDP Growth}[0.463, 0.683, 0.661, 0.718], {Earnings Per Share}[0.463, 0.736, 0.682, 0.771], and {Dividend Per Share}[0.463, 0.788, 0.684, 0.823]."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "1GwfKfMdgv7CMe4y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found within the coordinates [0.381, 0.879, 0.439, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {year 2010}<r>[0.381, 0.879, 0.439, 0.914]</r> x-axis tick label is found within these coordinates."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "GCKRM2vnFdXlAHzX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the Y-axis at these coordinates [0.019, 0.305, 0.041, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Production (GWh)."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "Xx4JeriK7sMG5Qyk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at [0.794, 0.120, 0.966, 0.143] within the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hydro Conventional."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "chF5LCcYGBYznQ2D",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being depicted by {this line}<r>[0.144, 0.102, 0.941, 0.761]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hydro Conventional power production trend over time."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "LMkqLrvrsZKwlHQa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2035, is {hydro power energy production}<r>[0.587, 0.723]</r> higher or lower than the {hydro conventional energy production}<r>[0.587, 0.490]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "o1ahZIyzPHls4Gkc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a larger energy production in the year 2030, {hydro power}<r>[0.144, 0.179]</r> or {hydro conventional}<r>[0.144, 0.102]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hydro Conventional."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "c2A0msbdX6PD1yRn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {hydro power energy production}<r>[0.233, 0.296]</r> in 2031 lower or higher than that of 2030<r>[0.144, 0.179]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "lBFQdimMDWXmfEdO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.048, 0.073, 0.092, 0.096]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis tick label '8250'."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "hXq2MBg0MjIqIEfw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements contained in the area [0.738, 0.075, 0.973, 0.152]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend with two entries: {Hydro Power}<r>[0.794, 0.085, 0.904, 0.108]</r> marked in blue, and {Hydro Conventional}<r>[0.794, 0.120, 0.966, 0.143]</r> in orange."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "m7I1opTDawSwHUHm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained within the chart at this coordinate region [0.477, 0.912, 0.520, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis tick label '2034' is located in this region."
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "9EKW2u52fpj3lbwO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object identified by the coordinates [0.019, 0.409, 0.036, 0.529]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "a5SkWoTaYFMdTDEz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker associated with {Market Risk}<r>[0.088, 0.163, 0.133, 0.177]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "e0IGwogFJ7acP8Ju",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the points {line_1}<r>[0.118, 0.382, 0.940, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Compliance Risk."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "TCtemSQxW5Y6WPpk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Credit Risk' at Time 4}[0.484, 0.660] above or below {the point for 'Market Risk' at Time 4}[0.484, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "2qbIYXVfM2dAonoI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line representing 'Compliance Risk'}[0.118, 0.382, 0.940, 0.764] generally above or below {the line for 'Operational Risk'}[0.118, 0.469, 0.940, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "81hQAal5KT5z2rwj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Risk has a higher score at Time 9, {Operational Risk}[0.940, 0.469] or {Compliance Risk}[0.940, 0.382]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Operational Risk."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "iFsgdAYPGdOM5we5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.086, 0.046, 0.292, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the plots. It includes markers and corresponding labels for Operational Risk, Compliance Risk, Credit Risk, and Market Risk."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "uT200ApWq8usZl7P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within the coordinates [0.019, 0.409, 0.036, 0.529]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the vertical, y-axis label area, labeled as 'Risk Score'."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "EYtYMHjAnQL8j2Lo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the range [0.301, 0.573, 0.392, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the third point on the Operational Risk line and the third point on the Compliance Risk line."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "TosTMQluqtnGYjtq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the first tick on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.017, 0.919, 0.061, 0.942]"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "FNRMv0DtJ2vrfUvE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label}<r>[0.019, 0.406, 0.036, 0.522]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "EZMjhwyemq2wQ1Fm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point}[0.670, 0.247] on the 'Streetwear' line represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data for the year 2011 with a Popularity value of 835."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "DXhY6JCi68sIcWQr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the point}[0.309, 0.304], is the 'Streetwear' popularity higher or lower than  the 'Haute Couture' at {the point}[0.309, 0.490]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "jtzj6AALLmxRqTiZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point}[0.850, 0.226] on the 'Streetwear' trend line towards the end of the chart higher or lower than the {point}[0.850, 0.763] on the 'Haute Couture' trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "vycBEHHZxaSVwSIq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher value at the beginning of the data, {Haute Couture at point}[0.129, 0.077] or {Streetwear at point}[0.129, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Haute Couture."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "HmLbU5PHirEdVJec",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.783, 0.037, 0.973, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: two line labels, {Haute Couture}[0.839, 0.046, 0.966, 0.069] in blue and {Streetwear}[0.839, 0.081, 0.935, 0.104] in orange. Each label is paired with a color indicator."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "eIJJVDbm1wJLOlJk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area {here}[0.516, 0.952, 0.553, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the X-axis label, which is 'Year'."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "vH00r0n70TOPMBta",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region with the coordinates [0.558, 0.919, 0.602, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis tick label '2010' is found in this region."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "Tvh2QoKEO8yO3LZO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.772, 0.085, 0.932, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Employee Training\"."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "wf9JsFQNlU78121d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at the coordinates [0.019, 0.292, 0.036, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label, \"Employee Training & Satisfaction\"."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "Ayhtm9UvtOmq4dWK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object having coordinate [0.488, 0.952, 0.571, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "3wyNlxuJdITOL7ql",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point {[0.210, 0.623]} larger or smaller than the point {[0.665, 0.413]} on 'Employee Training' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smaller."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "bpyjskH0GDw3mHuV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value marked at coordinate {[0.577, 0.344]} on 'Employee Training' line higher or lower than the point labeled at coordinate {[0.666, 0.441]} on 'Employee Satisfaction' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "NNVjkynkElwDQ8C8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt coordinate {[0.940, 0.483]}, is 'Employee Training' or 'Employee Satisfaction' having higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Employee Satisfaction."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "F6jxGyIWTQ19OIdP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.716, 0.075, 0.973, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend detailing the labels and colors representing \"Employee Training\" at coordinate {handle}<r>[0.718, 0.088, 0.763, 0.102]</r> and \"Employee Satisfaction\" at coordinate {handle}<r>[0.718, 0.123, 0.763, 0.137]</r>."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "68WSpeURKmGdBbnJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the area defined by the coordinates [0.278, 0.025, 0.781, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot title \"Career & Workplace Development Trends\" is located within these coordinates."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "xv8C2rWSbwnWlDLn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is present in the region bounded by [0.118, 0.190, 0.940, 0.763]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line chart for \"Employee Training\". The plotted markers representing various iterations are present in this area along with their respective values."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "TzCsGILr8hQ4tlQF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label is for the object found at the coordinates, [0.840, 0.055, 0.878, 0.078] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tech."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "hqp7kKBOZqcropYf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the {ylabel} in the chart from the provided coordinates [0.019, 0.345, 0.041, 0.585]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share (%)."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "Gyl4h37Rz2DMKhLA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre you able to identify {the line labeled 'Tech'} from its starting coordinates [0.123, 0.322] to its ending coordinates [0.940, 0.753] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is a line graph representing the trend of Tech from 2005 to 2014."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "zOciSmifYEa4soFx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Tech' in 2007}<r>[0.305, 0.753]</r> higher or lower than {its point in 2008}<r>[0.396, 0.523]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "H00geqHFxl8mAsjH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison with {the point for 'Manufacturing' in 2010}<r>[0.577, 0.235]</r>, is {the point for 'Tech' in 2010}<r>[0.577, 0.379]</r> higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "ImVeFl7t8FxX1WP5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had a higher market share in 2014, {Tech}<r>[0.940, 0.322]</r> or {Manufacturing}<r>[0.940, 0.322]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They were equal."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "De8gUHtaG4ZSiVx7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the region [0.784, 0.046, 0.973, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area serves as the legend for the chart including two items: {Tech}<r>[0.840, 0.055, 0.878, 0.078]</r> indicated by the color '#1f77b4' and {Manufacturing}<r>[0.840, 0.090, 0.966, 0.113]</r> indicated by the color '#ff7f0e'."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "vkSQ0SO7gNO5pT4m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.048, 0.312, 0.070, 0.336] in the chart contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '30'."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "n9UKIGgGVrTpL0vd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the region [0.396, 0.523, 0.486, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures two data points for the year 2008: a lower {Tech}<r>[0.396, 0.523]</r> value and a higher {Manufacturing}<r>[0.396, 0.695]</r> value."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "4FaUqjiuvSa5A8tl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.122, 0.046, 0.299, 0.069] refer to in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Patent Growth."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "dviKzU5v7O7DpEiB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.510, 0.952, 0.546, 0.975] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Years\"."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "1t0rV4U241HhMcoF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object {at these coordinates}<r>[0.035, 0.179, 0.061, 0.202]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick label '225'."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "OlFzU3WKX3ng7Lht",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher {at year 2005}<r>[0.528, 0.372]</r>, the line representing 'Innovation Patent Growth' or 'Intellectual Property Cases'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Patent Growth."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "Aq3ohF9iQGyFCUHA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line representing {the 'Innovation Patent Growth'}<r>[0.112, 0.045, 0.943, 0.719]</r> cross above or below the line representing {the 'Intellectual Property Cases'}<r>[0.112, 0.045, 0.943, 0.903]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "sTtgslIlNLWzU5wk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line dips below the other at {the year 2000}<r>[0.389, 0.544]</r>, 'Innovation Patent Growth' or 'Intellectual Property Cases'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Intellectual Property Cases."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "tGfZU2vQ1c4GLBAQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.078, 0.037, 0.314, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart, listing out the two lines present in the chart: {Innovation Patent Growth}<r>[0.122, 0.046, 0.299, 0.069]</r> with a color marker at {coordinates}<r>[0.079, 0.049, 0.115, 0.063]</r> and {Intellectual Property Cases}<r>[0.122, 0.081, 0.308, 0.104]</r> with a color marker at {coordinates}<r>[0.079, 0.084, 0.115, 0.098]</r>."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "CwzHWDpzmdStEuD2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.372, 0.919, 0.407, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis tick label for the {year 2000}<r>[0.372, 0.919, 0.407, 0.942]</r>."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "7uvdohSVYoINp0Uf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region from [0.043, 0.894, 0.061, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick label for the {value 50}<r>[0.043, 0.894, 0.061, 0.917]</r>."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "mdmu0NT9IlsrhtVr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart at [0.109, 0.085, 0.182, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labelled 'Population' in the chart's legend."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "i15JKEFQdWYoXMbw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third x-axis tick label on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.186, 0.912, 0.234, 0.936]"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "zx1Kzd7GgnxhGlGE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel} at [0.015, 0.301, 0.033, 0.659] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for both Population and Inflation Rate."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "dkExdrETnDwalz98",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich series is represented by the line with coordinates [0.543, 0.709, 0.721, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation Rate."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "VHM3vW1TBWZYVYmD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {value of Population}[0.366, 0.223] in year 2006 exceed the {Inflation Rate}[0.366, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "zJvkjFWWgvSNnvRD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Population series}[0.065, 0.088, 0.102, 0.102] represented by the same color as the {Inflation Rate series}[0.065, 0.123, 0.102, 0.137]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "wg0OgdYZicoInB6P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content located in [0.065, 0.075, 0.207, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It includes entries for the Population and Inflation Rate trends, each paired with a distinct colored marker."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "1kM8SP59L02t3dXB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.282, 0.025, 0.760, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Population and Inflation Rate Trends (2000-2019)\", can be found in this area."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "9pLGVEGsBxrOV96O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is seen in the region [0.076, 0.912, 0.123, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is an x-axis tick label of '2000.0'."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "vGvccsMnbt3WB6rU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label {coordinate} at [0.023, 0.373, 0.045, 0.583] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Price ($)."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "rUlE73jUU3rSQDEf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the status of the object located at {coordinates}[0.146, 0.114, 0.854, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A line graph representing the Stock Price trend over Days."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "2DyLBnG3G54gr8lB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the tick label at coordinate {location}[0.769, 0.899, 0.782, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "9."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "ElN4EAYdKtp5mrae",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Day 6} [0.539, 0.899, 0.546, 0.928], is {the stock price} [0.539, 0.438] higher or lower than it was at {Day 1} [0.146, 0.899, 0.153, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "5BOZGMGBhOvDuRsd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the last Day, {Day 10} [0.840, 0.899, 0.868, 0.928], is the {Stock Price} [0.854, 0.114] higher or lower compared to {Day 5} [0.454, 0.899, 0.468, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "3u6pCBtOBZGuj55f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Stock Price' higher on {Day 5} [0.461, 0.519] or {Day 1} [0.139, 0.899, 0.153, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Day 5."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "KOZLgyHvR73AeN6k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present within the region [0.139, 0.899, 0.310, 0.928]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are four X-axis labels located within this region. They correspond to the days: '1', '2', '3', and '4' located at coordinates {location}[0.139, 0.899, 0.153, 0.928], {location}[0.218, 0.899, 0.232, 0.928], {location}[0.297, 0.899, 0.310, 0.928] and {location}[0.375, 0.899, 0.389, 0.928] respectively."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "ejKoQ2sJ7VSJgZDB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content present in the region [0.111, 0.078, 0.890, 0.879]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main region of the graph is here, comprising the data points indicating Stock Price trends over the course of several days including lines and tick labels. This also includes X and Y axis labels - 'Day' and 'Stock Price ($)'."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "ABk7SKJ2bpHHtSPk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.139, 0.899, 0.782, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the tick labels from '1' to '9' for the x-axis corresponding to Days."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "p5etOKe0maumQPW9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {this line}<r>[0.056, 0.239, 0.976, 0.471]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line represents the performance trend of 'Running'."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "LNOKc0DyfQkyOgem",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the text at {the coordinates}<r>[0.107, 0.108, 0.164, 0.136]</r> says?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is 'Running'."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "DICaHP7nbO8r4qz8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.015, 0.393, 0.034, 0.640] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the y-axis with the name 'Performance'."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "PggGFtUdwSiSw1t1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the performance trends of the activities {Running}<r>[0.056, 0.239, 0.976, 0.471]</r> and {Zumba}<r>[0.056, 0.568, 0.976, 0.704]</r> increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are increasing over time."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "k4bPvbQ5T2skKgkN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt month 10, is the performance of {Running}<r>[0.809, 0.276]</r> better or worse than {Zumba}<r>[0.809, 0.596]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Running performance is better."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "L3hjCjRvcPyeSXy4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich athletic activity is performed better at {the 12th month}<r>[0.976, 0.239]</r> for Running and <r>[0.976, 0.568]</r> for Zumba?"
+            },
+            {
+                "from": "gpt",
+                "value": "Running."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "raDotS4cy6KqKBFV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within this region: [0.063, 0.097, 0.286, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the legend, which contains the labels for the two lines on the chart, namely 'Running' with color code '#ff00ff' and 'Zumba' with color code '#ffd700'."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "StZS4lSQKWDIG285",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is present within this coordinate sector: [0.052, 0.893, 0.985, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels for the x-axis ticks from 1 to 12, representing the months of the year."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "pRAj4RZ1TgWw0vDf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you notice about the items inside the frame [0.056, 0.156, 0.976, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can see the plotted data points and trend lines of 'Running' and 'Zumba' along with the grid lines and y-axis labels. This is the main area of the chart."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "cOTD2Tcf6DuCc4fW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} at [0.480, 0.940, 0.533, 0.969] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "4zLkYj3A5pE9hAtA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the ylabel} at [0.955, 0.306, 0.977, 0.604] is in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unemployment Rate."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "nz9MxGCZxdBzYCEh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subplot title} at [0.225, -0.104, 0.788, -0.063]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Indicator Presentation."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "BDohaTJWiAminNy1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Unemployment Rate at '2012-10'}[0.346, 0.441] higher or lower than the {Unemployment Rate at '2013-03'}[0.668, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "lBNwseZBfiBTergy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Consumer Confidence Index at '2012-11'}[0.410, 0.370] have a higher or lower value than the {Consumer Confidence Index at '2012-07'}[0.153, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "YSPrOPe0jV7GVMxp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Consumer Confidence Index at '2013-06'}[0.861, 0.070] have a higher or lower value compared to its value at {Consumer Confidence Index at '2013-01'}[0.539, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "1KPgeH6lMS9tSLjJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area [0.117, 0.031, 0.896, 0.879] of subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "The first subplot contains the x-axis labels with time stamps from '2012-07' to '2013-06', and the line representing the Consumer Confidence Index which rises from 92.38 to 96.02 over this period."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "kUg20s3Ve6LwSanI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give me a brief description of the region [0.117, 0.031, 0.896, 0.879] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region in the second subplot contains x-axis labels showcasing the timeline from '2012-07' to '2013-06', along with a line representing the Unemployment Rate that fluctuates from 4.85 to 4.03 over this period."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ZIsVUHjeSMs0ucgK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.108, 0.899, 0.906, 0.928]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels that mark the timeline from '2012-07' to '2013-06' in both subplots."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "Ojlz51xqVLhfNzU9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.013, 0.477, 0.028, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "uAZBG2LJ4My0I2V0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.819, 0.092, 0.848, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a color handle in the legend corresponding to the 'Vanilla Sales Trend'."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "IPwS0FxolKCNDa31",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about {the x-axis tick}[0.153, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the Year 2010."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "Pkt5iIYyX8Jvvs1y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Year 2009} [0.061, 0.898], which had higher sales - {Vanilla}[0.061, 0.319] or {Mint}[0.061, 0.730]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vanilla."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "vee2WZLCLtw4bkjd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {Year 2019} [0.973, 0.898], was the Mint sales trend [0.973, 0.327] higher or lower than the Vanilla sales trend [0.973, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "aJjzV0bvGpgJhMrU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point labeled '52'} at [0.153, 0.713] above or below the {point labeled '97'} at [0.153, 0.336]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "pmGUKjMvF5sAMhpt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in area [0.817, 0.079, 0.967, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It includes two items: {Vanilla Sales Trend} with representative color handle at [0.819, 0.092, 0.848, 0.104] and {Mint Sales Trend} with color handle at [0.819, 0.127, 0.848, 0.139]."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "g0j19odrk32TBYIb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region spanning from [0.047, 0.911, 0.988, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the x-axis tick labels for the years from 2009 to 2019."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "N1nTGHxZLTD6Eaxb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region [0.033, 0.301, 0.055, 0.325]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-tick label of '100' is located within this region."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "bsHXvBzzEaTjWBCk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.446, 0.034, 0.587]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Growth."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "7nfD3Zb6sDTmFNBK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.496, 0.932, 0.537, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "sKh3FWVK6cyTYM1x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is on the {coordinates}<r>[0.116, 0.223, 0.247, 0.251]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label: 'Arts and Literature'."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "gv8Y6E5Feg6ILn0X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 1900 from the 'Science and Technology' line}<r>[0.366, 0.713]</r> higher or lower than {the point for the year 1900 from the 'Arts and Literature' line}<r>[0.366, 0.737]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "BhOFyTcBY0GM5UMU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is higher at {year 2050}, the {point from 'Science and Technology'}<r>[0.817, 0.395]</r> or {point from 'Arts and Literature'}<r>[0.817, 0.690]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Science and Technology."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "NYddXuuCRkSMzWie",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2100}, is the {Science and Technology line}<r>[0.967, 0.182]</r> above or below the {Arts and Literature line}<r>[0.967, 0.682]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "sC7Q08wqGSD3pHKj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within this region<r>[0.065, 0.182, 0.967, 0.826]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 'Science and Technology' line of the chart, with different points indicating the growth rate in different years."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "mdu02g3NIXbaOSzC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region<r>[0.116, 0.181, 0.283, 0.251]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Science and Technology' from the legend is in this area."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "jlAbFbSv1ZMUYn70",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the content of this region<r>[0.065, 0.682, 0.967, 0.748]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'Arts and Literature' line with different points that show growth in various years."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "tKSwfpqC3R1ytk56",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis in the coordinates [0.029, 0.284, 0.048, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Performance"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "xSGVI6wqbt0ueKAF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the x-axis in the coordinates [0.460, 0.932, 0.565, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Categories"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "3bzfasnSFsbUZHsM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.066, 0.145, 0.083, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "40"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "7g7PPlLU2mfZjrTC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Regression Model'}[0.091, 0.245] above or below {the point of 'AI Assistant'}[0.091, 0.403] at 'Consistent_expansion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "L3HmaWpIHbFBU5oz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Regression Model'}[0.934, 0.817] above or below {the point of 'AI Assistant'}[0.934, 0.332] at 'Erratic_decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "KaWGyNPGizKKAmGG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data type has a higher value at {Consistent_expansion}[0.091], Regression Model or AI Assistant?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Assistant."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "6jxX8jCpv4uJlcxl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within [0.228, 0.035, 0.796, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Education & Training Technology Innovation Trends,\" is located here."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "fg6UXnh3bizwVhJ7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe what's within these coordinates [0.607, 0.097, 0.927, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Regression Model}[0.652, 0.109, 0.774, 0.137] with a handle colored in '#1e90ff', and {AI Assistant}[0.840, 0.109, 0.921, 0.137] with a handle colored in '#800080'."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "kyVVWM2YAPDs1tD0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.091, 0.245, 0.934, 0.817]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains 2 lines representing the Regression Model's and the AI Assistant's trend from 'Consistent Expansion' to 'Erratic Decline'. The Regression Model's coordinates are [0.091, 0.245], [0.934, 0.817], and the AI Assistant's coordinates are [0.091, 0.403], [0.934, 0.332]."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "zJokTBApmuCaWEgJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.015, 0.361, 0.034, 0.672]</r> to the far left of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "NrciYO8H4ia2JZks",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the subtitle}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.295, 0.035, 0.749, 0.075]."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "TqOvh98eUDiXHDYB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {x-axis title} at position <r>[0.492, 0.932, 0.552, 0.970]</r> mention?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "hxNpYMT2mpx7wsBP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the third point of Stock A}<r>[0.233, 0.582]</r> higher or lower compared to {the third point of Stock B}<r>[0.233, 0.799]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "Gq1nnrWzk8nDniBU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Stock A point at Month 9}<r>[0.728, 0.293]</r> and {Stock B point at Month 9}<r>[0.728, 0.628]</r>, which has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock A."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "hAegCLRcBQ02cuwG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the sixth point of the Stock A}<r>[0.481, 0.418]</r> positioned above or below {the sixth point of the Stock B}<r>[0.481, 0.714]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "4YR96EjBHb5oBMBg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information in <r>[0.075, 0.097, 0.296, 0.148]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items, Stock A with a handle color of '#ff00ff' between coordinates [0.077, 0.114, 0.111, 0.128] and Stock B with a handle color of '#ffd700' between coordinates [0.196, 0.114, 0.230, 0.128]."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "zSqtqt20ymtqlOX2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area <r>[0.068, 0.156, 0.976, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line graph representing the 'Return Rate (%)' of 'Stock A' over 12 months."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "mif5ojIp2qgiiMfa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region <r>[0.068, 0.517, 0.976, 0.858]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line graph representing the 'Return Rate (%)' of 'Stock B' over 12 months."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "6B4U4mrNUXlZIKLZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.083, 0.893, 0.131, 0.921] on the x-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Year label \"2000.0\""
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "o2BN1i7Ic2blbVER",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.040, 0.384, 0.075, 0.412] in the y-axis section of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the your label \"4000\"."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "GKs51GaEpBtvWsmI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color according to the legend associated with {the data line}<r>[0.107, 0.844, 0.594, 0.845]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#ff00ff\" which corresponds to the indicator \"Life Expectancy\"."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "urlKsIv6dnvZfdoR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of Life Expectancy in 2000}<r>[0.107, 0.845]</r> greater or lesser than {the value of Life Expectancy in 2002}<r>[0.155, 0.845]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "m0ya0M5lQQqLY7PQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of Area Gross Product in 2000}<r>[0.107, 0.566]</r> above or below {the value of Area Gross Product in 2003}<r>[0.180, 0.515]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "ifsNhywS0ceTw8qL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has seen a greater increase from 2001 to 2002, {Life Expectancy}<r>[0.131, 0.845] to [0.155, 0.845]</r> or {Area Gross Product}<r>[0.131, 0.555] to [0.155, 0.532]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Expectancy."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "uRCxhbea08mCMces",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the coordinates range [0.627, 0.114, 0.780, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "In that region, the indicator {Life Expectancy}<r>[0.670, 0.108, 0.780, 0.136]</r> is present, which is paired with a color marker of \"#ff00ff\"."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "x13uqoLJ6X75EKfz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area [0.326, 0.893, 0.618, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for the Years 2010.0 to 2020.0 are located within that region."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "iXutkqrY9Pzy8yHn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounding box [0.107, 0.188, 0.594, 0.566]."
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing the 'Area Gross Product' from the years 2000 to 2020 is located here. It is marked by a line and triangles in the color \"#ffd700\"."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "Z7XoRXcNhRDJF37Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bottom-most point on the 'Personal Development' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Advanced Career}<r>[0.666, 0.100]</r>"
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "AP2VbH7Q40GBKqAO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the top-most y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "27.5"
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "pQa9GQ6IFLFkdG47",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label for the third stage}<r>[0.606, 0.919, 0.726, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Advanced Career'"
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "Jk9PztyzksgKVTMr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher point at {Retirement stage}<r>[0.940, 0.865]</r>, 'Personal Development' or 'Workplace Development'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Workplace Development."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "LEQtd1q39KmJ63Av",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 'Early Career'}<r>[0.116, 0.483]</r> on 'Personal Development' line higher or lower than the point on 'Workplace Development' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "hJkXBqE5IcLLS986",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {last tick label on the y-axis}<r>[0.035, -0.004, 0.065, 0.019]</r> below or above the first point on 'Workplace Development' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "vWEJw8HyQ7k9T2pA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area with coordinates [0.075, 0.062, 0.982, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plot area of the chart which includes both 'Personal Development' and 'Workplace Development' lines with their respective points, x-axis and y-axis with their labels and ticks, and a colored bg."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "4pkl7xfVOSQnT9cE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with coordinates [0.709, 0.793, 0.973, 0.889]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the plot. It consists of two items: {Personal Development}<r>[0.763, 0.804, 0.951, 0.834]</r> marked with a magenta-colored handle and {Workplace Development}<r>[0.763, 0.848, 0.967, 0.878]</r> marked with a gold-colored handle."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "3Lsu92QBYgvwCVnD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.043, 0.855, 0.065, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label at {coordinate}<r>[0.043, 0.855, 0.065, 0.879]</r>, indicating the value '5.0', resides within this area."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "it3KmmtDMIQR6KZM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at these coordinates [0.015, 0.251, 0.034, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label which is \"Emissions (Millions of Tons)\"."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "Lqh1u4qGdpZ2sOn0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the second coordinate in the x-axis ticks?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '2011' at {coordinates}<r>[0.135, 0.893, 0.170, 0.921]</r>."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "ZutqcRfC6VksmYVI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is at these coordinates [0.848, 0.108, 0.973, 0.136] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Emission Increase' is found at those coordinates."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "XDwzH3V4aA6NlxBl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '2' for 'Emission Increase'}<r>[0.228, 0.654]</r> higher or lower than {the point '2' for 'Emission Reduction'}<r>[0.228, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "lLL5Gw4fBFZyzz4l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is above at point '2015': {the line for 'Emission Reduction'}<r>[0.455, 0.473]</r> or {the line for 'Emission Increase'}<r>[0.455, 0.580]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Emission Increase'."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "VaQsPOGMHaXSrHq8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is below at point '2021': {the line for 'Emission Reduction'}<r>[0.909, 0.304]</r> or {the line for 'Emission Increase'}<r>[0.909, 0.493]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Emission Reduction'."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "KdlbAAykY5YNwifO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information in this region [0.602, 0.097, 0.978, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart which represents two labels: {Emission Reduction}<r>[0.646, 0.108, 0.781, 0.136]</r> and {Emission Increase}<r>[0.848, 0.108, 0.973, 0.136]</r>, each paired with a unique color marker."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "xmFyxwX6HEQhNh0S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located within [0.218, 0.035, 0.843, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, which is \"Environmental Protection and Sustainable Development\"."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "Ir2PpdynrCDAU06e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be identified in the region [0.076, 0.878, 0.929, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the X-axis labels for the years from 2010 until 2021."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "MdtG70n9hcLJmPrA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the visual element with coordinates [0.115, 0.159, 0.907, 0.873] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Main plotting area."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "TapN5mZNyYFUpNin",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at {coordinate}<r>[0.862, 0.680, 0.883, 0.700]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Marked Point For 'Fluctuating Decline' in 'Spark'."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "6Ugh6PTnFjtAG0Lj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented by {coordinates}<r>[0.062, 0.082, 0.103, 0.111]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "160"
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "wVeMalKAX2XUHP66",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn scenario 'Unpredictable Decline', which method has higher performance {Hadoop point}<r>[0.151, 0.427]</r> or {Spark point}<r>[0.151, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hadoop."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "AvFsGwotmfipeGvK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the Hadoop line {coordinates}<r>[0.151, 0.191, 0.871, 0.427]</r> generally have a higher or lower performance than Spark {coordinates}<r>[0.151, 0.683, 0.871, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "qJkU1VUu90Z4oqzS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which case is the Hadoop's performance lower, 'Sporadic Growth' {coordinate}<r>[0.511, 0.309]</r> or 'Fluctuating Decline' {coordinate}<r>[0.871, 0.191]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fluctuating Decline."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "ggzYSbml0nYEP4gn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects found in this region [0.567, 0.102, 0.896, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It has two items, Hadoop is represented by purple at {coordinates}<r>[0.570, 0.119, 0.624, 0.134]</r> with corresponding label at {coordinates}<r>[0.636, 0.114, 0.720, 0.143]</r>. Likewise, Spark is represented by gold at {coordinates}<r>[0.758, 0.119, 0.813, 0.134]</r> with the label at {coordinates}<r>[0.825, 0.114, 0.887, 0.143]</r>."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "hOFisd53NcFAXDFX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area defined by [0.041, 0.868, 0.109, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes the label '80' for the y-tick, situated at {coordinates}<r>[0.076, 0.868, 0.103, 0.897]</r>."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "QeG0Pn5tfAk0TDnl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.031, 0.888, 0.977, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains five x-axis labels, specifically 'Unpredictable Decline', 'Unpredictable Fluctuations', 'Sporadic Growth', 'Downward Trend', and 'Fluctuating Decline' located respectively at {coordinates}<r>[0.031, 0.888, 0.271, 0.918]</r> , {coordinates}<r>[0.185, 0.888, 0.477, 0.918]</r> , {coordinates}<r>[0.421, 0.888, 0.601, 0.918]</r> , {coordinates}<r>[0.600, 0.888, 0.782, 0.918]</r> and {coordinates}<r>[0.766, 0.888, 0.976, 0.918]</r>."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "JaTZvDYbrqvuogj8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.353, 0.034, 0.600]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "JAUx0beY8fezYD9m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the fourth tick label on the x-axis at {coordinates}<r>[0.591, 0.893, 0.627, 0.921]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2036."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "KtWxRnQWRhbOPmHq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the marker coordinate}<r>[0.825, 0.551, 0.859, 0.565]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wind Energy."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "6arpHXU6FlixW4NR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Hydropower line in 2040}<r>[0.968, 0.688]</r> higher or lower than {the Bioenergy line in 2040}<r>[0.968, 0.609]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "xPSIOgDOT9hC9vtj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher performance value in 2030, {Geothermal}<r>[0.071, 0.767]</r> or {Hydropower}<r>[0.071, 0.714]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Geothermal."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "ilZT5bXnF54h0l3I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the performance of {Solar Power in 2030}<r>[0.071, 0.291]</r> with {Wind Energy in 2030}<r>[0.071, 0.397]</r>. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wind Energy."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "TAAyBZuCgwqzG2AE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the bounding box <r>[0.823, 0.367, 0.961, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the various energy sources: {Hydropower}<r>[0.867, 0.378, 0.951, 0.406]</r>, {Solar Power}<r>[0.867, 0.420, 0.949, 0.448]</r>, {Geothermal}<r>[0.867, 0.462, 0.949, 0.490]</r>, {Bioenergy}<r>[0.867, 0.504, 0.937, 0.532]</r>, and {Wind Energy}<r>[0.867, 0.546, 0.955, 0.574]</r>. Each legend item is paired with a colored marker."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "lw5omgMXy2cQVTiU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the content within the region <r>[0.305, 0.030, 0.734, 0.070]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, \"Future Energy and Resource allocation\", is located within this area, displayed in black color."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "axHGgsLBvOg3tKn3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements within the region <r>[0.494, 0.932, 0.545, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the xlabel of the chart which is titled \"Years\"."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "j2ChrpHytk3OwbGQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation of {this specific data point on the line}[0.383, 0.582] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a data point for the 'Tech Stock' trend."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "fJ81QvLaPXQk8zwA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {this legend item}[0.279, 0.073, 0.313, 0.087] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color marker for the 'Tech Stock' trend."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "pGVHPp9eX2iyMagt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {this y-axis tick}[0.125, 0.780]?"
+            },
+            {
+                "from": "gpt",
+                "value": "4."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "Dt26jmyh5zAQ5dEL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Bank Stock' point at 0.4 Assets}[0.383, 0.604] higher or lower than {the 'Tech Stock' point at 0.4 Assets}[0.383, 0.582] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "MSREGHLUbMXpuCVz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Bank Stock' point at 0.7 Assets}[0.642, 0.505] above or below {the 'Tech Stock' point at 0.7 Assets}[0.642, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "EVSwTXI46YKL6TAU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock, 'Bank Stock' or 'Tech Stock', had higher performance at 1.0 Assets?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bank Stock."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "hYbyi5a4rFwbOKN9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.132, 0.057, 0.400, 0.107]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two legend items for the chart: {Bank Stock}[0.176, 0.068, 0.254, 0.096] and {Tech Stock}[0.321, 0.068, 0.395, 0.096], each paired with a color marker in purple and gold, respectively."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "aT7hT7OYe43cV8Fo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region between [0.125, 0.208, 0.900, 0.846]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line that represents the 'Tech Stock' trend. The line includes multiple data points, with markers indicating each observation from the dataset."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "h0hFB5yB7AGqT9GD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual content available in the region [0.125, 0.417, 0.900, 0.615]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line representing the 'Bank Stock' trend as per the dataset. The line includes several data points, with markers signifying each datapoint from the data."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "R0fFXJPukFDS96Tv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance. {the y-axis label}<r>[0.015, 0.393, 0.034, 0.640]</r>"
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "AiLtQ5HwRisleyu3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the last tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0. {the x-axis tick label}<r>[0.422, 0.893, 0.444, 0.921]</r>"
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "yTEEEzVj1tNOgntl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing 'AI Technology Performance'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange. {the line for 'AI Technology Performance'}<r>[0.069, 0.607, 0.433, 0.715]</r>"
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "l9ASwXH8cz41M6yq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Physician Technology Performance' line}<r>[0.069, 0.246, 0.433, 0.643]</r> generally trend upward or downward as the 'Healthcare Condition' increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "0M3OiIzL03fw6O5P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one reaches a higher performance value at healthcare condition value 0.91, {the 'Physician Technology Performance'}<r>[0.069, 0.246, 0.433, 0.643]</r> or {the 'AI Technology Performance'}<r>[0.069, 0.607, 0.433, 0.715]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physician Technology Performance."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "fQgVM3tjpsgxO0xl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the health condition value of 0.64, is {the 'Physician Technology Performance' line}<r>[0.069, 0.246, 0.433, 0.643]</r> above or below {the 'AI Technology Performance' line}<r>[0.069, 0.607, 0.433, 0.715]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "9G0FHlvLa3Le7fhD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within the region [0.069, 0.878, 0.433, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region are the 'Healthcare Condition' x-axis, tick labels ranging from 0.1 to 1.0, and the lines representing 'Physician Technology Performance' and 'AI Technology Performance'."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "VoHECQotk4rkEibb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the details of the figure in area [-0.010, 0.035, 0.513, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart: 'Healthcare & Medical Tech Performance Trends'. The text is black and it is not rotated."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "qtyndNtTHCBUjhqJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.440, 0.097, 0.985, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend of the chart, with two entries: 'Physician Technology Performance' colored purple and 'AI Technology Performance' colored orange, alongside their respective color markers."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "F33J34h6686bNqv1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart with coordinates [0.015, 0.461, 0.034, 0.568]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Index'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "wyW7ru4Rq9GFH2ZC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the text from the legend located at [0.127, 0.181, 0.198, 0.209]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's 'CSR Index'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "mo3xWltAptmfAQmz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the x-axis tick label at [0.950, 0.893, 0.985, 0.921] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year '2019'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "6SUIpjsOXUG0Vmxa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {CSR Index} line <r>[0.076, 0.467, 0.967, 0.634]</r> overall increase or decrease over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "9hqDd5kEBUHQrbE2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {CSR Index} point <r>[0.373, 0.538]</r> higher or lower than the {Sustainable Development Index} point <r>[0.373, 0.491]</r> in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "Qjm7J0JwaNOOiRig",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the last year depicted on the graph, does the {CSR Index}[0.967, 0.467] exceed the {Sustainable Development Index}[0.967, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "gHhxKXiwehbCIrGn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content at coordinates [0.076, 0.395, 0.967, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two lines. The first one is the Sustainable Development Index line, and the second one is CSR Index line. The lines depict the trend of these indices from 2010 to 2019."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "n3ivcdFpDLBSbmVe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information contained within the area [0.057, 0.384, 0.066, 0.412]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis label '100' within this area."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "TrfgNAXc0AyB3rfu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information presented in the area [0.048, 0.671, 0.066, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a y-axis label '40'."
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "DSKwzx4ysCCUXFvu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the y-axis label}[0.015, 0.409, 0.034, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Price Trend."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "D2puMdCSfHDz7e3E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the ninth mark located on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.723, 0.893, 0.732, 0.921]."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "evdlOCdKsVInmMle",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the y-axis tick label}[0.040, 0.454, 0.057, 0.482] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "45."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "bfyDNTK1I3osGH3P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Stock Price Trend' point labeled '2'}[0.148, 0.731] above or below {the 'Commodity Price Trend' point labeled '2'}[0.148, 0.406]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "RumF9AaPgspbq7e7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the last point of {the 'Stock Price Trend' line}[[0.976, 0.595]] higher or lower than the last point of {the 'Commodity Price Trend' line}[[0.976, 0.274]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "ruUpgUQZBtXwLu3R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend shows a greater value at the third point, Commodity Price Trend or Stock Price Trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commodity Price Trend."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "1Nq0EtxgqLJDAVo2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements located in the region [0.803, 0.108, 0.964, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the text for one legend entry: {Commodity Price Trend}[0.803, 0.108, 0.964, 0.136]."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "CEbH0yROgSMdE4dk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are there in the area [0.065, 0.595, 0.976, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line chart for the {Stock Price Trend}[0.065, 0.595, 0.976, 0.766] that includes various data points across time."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "jwIFBTw5llB9u78w",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the plot objects found within the region [0.065, 0.274, 0.976, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the line chart for the {Commodity Price Trend}[0.065, 0.274, 0.976, 0.459] with data points representing values over a series of months."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "PiXDDRCUYX6ANOcl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at [0.888, 0.125, 0.988, 0.153] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Hours."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "eheD1rdlJFazR0re",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat shape is oriented at coordinates [0.845, 0.130, 0.880, 0.144] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a marker line for the legend."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "mrCENwVFld7mwJQB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the ylabel at the left}[0.015, 0.374, 0.034, 0.655] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Training Hours."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "xxzfeRG5d097ObYu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point in 2010 in Training Hours line}[0.341, 0.754] have a higher or lower value than {the point in 2009 in Training Hours line}[0.294, 0.730]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "KbXpW9TSPfcL8j5e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point in 2006 on the Salary line}[0.152, 0.544] higher or lower than {the point in 2005 on the Salary line}[0.105, 0.574]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "34jR0wKmChkFGweF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Training Hours point in 2021}[0.860, 0.215] higher than or lower than {the Salary point in the same year}[0.860, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "a08Rv9o0V8WVV9dD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the coordinates [0.134, 0.814, 0.860, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the Training Hours line, with multiple data points, representing each year from 2006 to 2021."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "mjBnp1bm9tWBpfci",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents at area [0.843, 0.114, 0.993, 0.206]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend section, which includes the labels for \"Training Hours\" and \"Salary\", accompanied by their respective colored line markers."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "vbbmKY6sSF6v2s72",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.040, 0.264, 0.960, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a y-axis tick label reading '100000' on the right side of the chart."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "DptPuAWdV3UgSV7T",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.123, 0.159, 0.923, 0.868] seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subplot area of the plot."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "rgOxLrVFsycDd16I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first legend's text in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.281, 0.114, 0.535, 0.143]"
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "O8gp2ndZ91azRTCq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.062, 0.739, 0.108, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "-10"
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "3bDCDRJ1PKZuCcQq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of 'Pre Cultural Integration'} at 'Rituals'[0.283, 0.438] higher or lower than {the value of 'Post Cultural Integration'} at 'Rituals'[0.283, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "N6uG0JlRMM0oKWN0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {the 'Post Cultural Integration' value for 'Art'}[0.123, 0.159] in the line chart have a higher or lower value than its {corresponding 'Pre Cultural Integration' value}[0.123, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "ClHhId4rOXN4VX2H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Culture Aspect sees a greater increase from {Pre to Post Cultural Integration} at the fifth X-axis labeled 'Dance'[0.763, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Post Cultural Integration."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "cxth9H9eXCWTgIDj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.212, 0.102, 0.912, 0.155]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend area of the chart, providing color markers for the two series {Pre Cultural Integration} with coordinates [0.215, 0.120, 0.269, 0.134] in purple and {Post Cultural Integration} with coordinates [0.573, 0.120, 0.627, 0.134] in gold."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "W3BqMx6IVqVmGNs8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.123, 0.159, 0.923, 0.868]."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's main plot area is situated within these coordinates, featuring two line charts representing the 'Pre Cultural Integration' and 'Post Cultural Integration' series, with associated data points."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "1vjKD5xgEqM1QoEU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.123, 0.301, 0.923, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts the line chart for the series 'Pre Cultural Integration'. It comprises of data points for 'Culture Aspects' such as 'Art', 'Rituals', 'Architecture', 'Music', 'Dance' and 'Literature'."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "fc4RvV2YhSKI9hsC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart event corresponds to the coordinate [0.682, 0.423]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Farming."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "Hs8MUINeB00yO4Uz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis label} at the coordinate [0.043, 0.155, 0.071, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.9."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "r4Duwm81XJSC3RPO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reveal which color is associated with {the second legend}[0.147, 0.833, 0.351, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "1E6GER5T7zd36Pkp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the event {Park Maintenance}, is the fund allocation of 2020 at {the point}[0.233, 0.553] higher or lower than that of 2019 at {the point}[0.233, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "g9aWqGeo0ucJaaDy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of {Community Outreach}, does {point}[0.532, 0.190] of Fund Allocation in 2020 show a higher or lower fund allocation as compared to Fund Allocation in 2019 at {point}[0.532, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "jRWrNuY9bK9wW1m9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents the {line}[0.233, 0.242, 0.832, 0.423] corresponding to Fund Allocation in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "QyK2yOGqKuK4T812",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within [0.460, 0.698, 0.908, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the last two event labels on the x-axis, namely, 'Urban Farming' at {coordinates}[0.630, 0.698, 0.733, 0.836] and 'Digital Implementation' at {coordinates}[0.755, 0.698, 0.908, 0.904]."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "GPWLeffNjGUTHgBU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.092, 0.788, 0.358, 0.865] illustrate?"
+            },
+            {
+                "from": "gpt",
+                "value": "This {region} represents the legend of the line chart. It comprises two items, 'Fund Allocation in 2019' found at {coordinates}[0.147, 0.798, 0.351, 0.821] and 'Fund Allocation in 2020' at {coordinates}[0.147, 0.833, 0.351, 0.856], each associated with a colored marker."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "K6dDOZ4w1jAh0afU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be derived from [0.083, 0.034, 0.981, 0.682]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This {region} contains the main plot area of the line chart, displaying data for five events regarding their fund allocation in 2019 and 2020, as well as tick labels and a legend."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "PFcSsRXYdPJcfgYZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.184, 0.101, 0.414, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Market Share Growth' legend item."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "gWxGESfhv8zyXjPD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.777, 0.899, 0.832, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2027."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "gqaVO2Qs6Ce7o7Kr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.114, 0.146, 0.175, 0.169] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Color marker for 'Revenue Growth'."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "UVMvFPFmObJPyBwz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the points of 'Revenue Growth'}[0.143, 0.070, 0.937, 0.459] generally above or below {the points of 'Sales Growth'}[0.143, 0.498, 0.937, 0.809]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "RC5Y2BhRl00ITfz3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich growth line has a higher value for the year 2025, 'Sales Growth' or 'Market Share Growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Growth."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "IImUG1CM02T8Z9A6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Revenue Growth' point at 2026}[0.672, 0.070] above or below {the 'Market Share Growth' point at 2026}[0.672, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "Vpgb3ho2zcrIzSOu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.114, 0.046, 0.423, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It includes 'Sales Growth' with a blue marker{marker coordinates}[0.114, 0.059, 0.175, 0.082], 'Market Share Growth' with a red marker{marker coordinates}[0.114, 0.102, 0.175, 0.125] and 'Revenue Growth' with a green marker{marker coordinates}[0.114, 0.146, 0.175, 0.169]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "tPMpcbPElIcRplQZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within [0.248, 0.899, 0.435, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two x-axis tick labels, '2023'{label coordinates}[0.248, 0.899, 0.303, 0.928] and '2024'{label coordinates}[0.380, 0.899, 0.435, 0.928]."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "1E15HMuuaJO5J5ot",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.054, 0.525, 0.088, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label, '0.4'."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "M0xbgAYFDjBJTpCM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of the line described by these coordinates [0.152, 0.285, 0.942, 0.345] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line represents the Unemployment Rate trend over the years."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "4Z1ry8m4GVpCiTri",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is at the coordinates [0.415, 0.902, 0.446, 0.934] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis label marking the year '2004'."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "o1kQZYxlbFIAlroC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {this marker}[0.118, 0.055, 0.174, 0.082] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this marker corresponds to the 'Inflation Rate' line in the chart."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "k5h9ZIRddPXbMYQu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point in 2009, is the {Unemployment Rate}[0.854, 0.288] higher or lower than the {Inflation Rate}[0.854, 0.406]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "18KpMtjcoYcl67Do",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the {Stock Market Returns}[0.240, 0.669] in 2002 compare to the {Unemployment Rate}[0.240, 0.331]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Stock Market Returns are significantly lower."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "2lc2CGXImoXTyszE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of number representation, is the {y-axis label}[0.019, 0.391, 0.043, 0.532] 'Rate (%)' greater at the top or bottom end?"
+            },
+            {
+                "from": "gpt",
+                "value": "The top."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "Qv671EjnB4psWsbt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.122, 0.049, 0.369, 0.161] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes 'Inflation Rate', 'Unemployment Rate', and 'Stock Market Returns'. Each category is paired with a distinct marker color."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "6JdLob0u0A77hRog",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.823, 0.902, 0.885, 0.934] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label '2009'."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "pz1S3iUuStgYQYxV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of items in the area [0.152, 0.053, 0.942, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a line chart representing 'Stock Market Returns' across different years. It has points representing data for each year ranging from '2001' to '2010'."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "GfGvHFJRa3oysvGd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.812, 0.435, 0.928, 0.452] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label for \"Innovation Index\" in the legend."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "KqcZ3m9xNxnUfyTs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label '5' in the x-axis labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.588, 0.926, 0.601, 0.949]"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "qwGr0cMtxbtcN4wx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label} at [0.041, 0.818, 0.072, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'$0.55$'"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "tCns9eHocBmG45Ae",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Innovation Index'}[0.594, 0.292] above or below {the point for 'Business Environment'}[0.594, 0.612] at Time Period 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "0N0taOnEbKQ0LMWC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Business Environment'}[[0.210, 0.434], [0.338, 0.470], [0.466, 0.558], [0.594, 0.612]] generally above or below {the line for 'High-Tech Enterprises'}[[0.210, 0.736], [0.338, 0.647], [0.466, 0.612], [0.594, 0.505]] in the first half of the time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "LNxyERTN69O0lio5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index has a higher value at {Time Period 8}[0.973, 0.926, 0.985, 0.949], Innovation Index or High-Tech Enterprises?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Index."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "cGvds5DwtZPNc9ZR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.812, 0.435, 0.972, 0.511]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend descriptions and associated color markers for the line graph. The labels include \"Innovation Index\", \"Business Environment\", and \"High-Tech Enterprises\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "GeRkL2OAnvwuExE1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.076, 0.926, 0.601, 0.949]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels, representing Time Periods from '1' through '5'."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "ru1EVV5e0bGfbiS5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.204, 0.926, 0.344, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects - The x-axis tick labels '2' and '3' are located in these coordinates."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "7UEeqVOmF2O6jRF8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.019, 0.319, 0.043, 0.604] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "VEeqM3hUM0wCl48y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.496, 0.837, 0.551, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item marker for 'Nuclear Energy'."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "usz0Pvr03aPxwMle",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {x-axis label} at [0.937, 0.901, 0.981, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "kCXKTlx6lP74b1KQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 'Fossil Fuels'}[0.277, 0.187] greater or lesser than the value of {point of 'Nuclear Energy'}[0.277, 0.546] at the same time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "OsXSryjFt6JLIrN6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the last point of {the 'Renewable Energy' line}[0.959, 0.250] located above or below the last point of {the 'Fossil Fuels' line}[0.959, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "BiiW2vUam79AvufS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source has a higher allocation at time period {0.4}[0.448, 0.610], Renewable Energy or Nuclear Energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nuclear Energy."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "ICYO7OU4VvtVYsGP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.107, 0.039, 0.959, 0.885] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plot of trends in energy allocation over time. It has line graphs for 'Renewable Energy', 'Fossil Fuels', and 'Nuclear Energy'."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "S3wbEG0bWuMo2Nwm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within [0.074, 0.831, 0.695, 0.873]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the legends for the line plots. It includes {Renewable Energy}[0.129, 0.840, 0.291, 0.864], {Fossil Fuels}[0.374, 0.840, 0.471, 0.864], and {Nuclear Energy}[0.555, 0.840, 0.688, 0.864], each with a corresponding color marker."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "L4NvmZOa58kQkDUz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.107, 0.462, 0.789, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes portions of all three energy line graphs, specifically the mid-latter trends for 'Renewable Energy', 'Fossil Fuels', and 'Nuclear Energy'."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "sc9YHlqP5yhsMK38",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the Y-axis}<r>[0.019, 0.313, 0.043, 0.558]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reported Level."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "IMxcP6e0dKQM7Zvk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing Anxiety}<r>[0.107, 0.237, 0.959, 0.773]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "eYSgAYkE3a06h48Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-tick label for a tick} at <r>[0.049, 0.422, 0.094, 0.456]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "$3.0$."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "sfsV9v7oCTNm55YJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is located higher at {Symptom intensity: 0.7}[0.675, 0.654]: Anxiety or Depression?"
+            },
+            {
+                "from": "gpt",
+                "value": "Depression."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "Pp8pxTWrWFFgRScS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does {the line for Depression}[0.107, 0.039, 0.959, 0.515] cross {the y-axis at the level of 3.0} [0.107, 0.436]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At Symptom intensity of approximately 0.9."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "rI651JoUn5vEBRWJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Anxiety line}<r>[0.107, 0.237, 0.959, 0.773]</r> end higher or lower \nthan {the Depression line}<r>[0.107, 0.039, 0.959, 0.515]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "dlBsIhNpfuMnGPi4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.179, 0.849, 0.223, 0.882]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis tick label '0.2' in black color."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "aovRJgUaKGjjWolF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region <r>[0.115, 0.903, 0.275, 0.980]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the lines: {Anxiety}[0.112, 0.909, 0.237, 0.936] which is represented by a blue line and {Depression}[0.112, 0.943, 0.268, 0.971] which is represented by a green line."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "ZYPrWsyLB1EpvOK9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region <r>[0.484,0.849,0.508,0.882]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label '0.5'."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "TnCbHDeFS9jeWUnR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text contained within this coordinates [0.181, 0.950, 0.249, 0.973]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Savings."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "cT2oTLwcsCWOxBK5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the curve}<r>[0.116, 0.098, 0.959, 0.465]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Savings."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "ChGDAUzSmVsreKkV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is found at {the legend item}<r>[0.332, 0.950, 0.405, 0.973]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Expense."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "syMkfgUtXiqfMEkx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for {Expense}<r>[0.622, 0.722]</r> higher or lower than the point for {Revenue}<r>[0.622, 0.245]</r> at the 0.6 mark on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "09h4fyJt89xSGMQm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Savings line}<r>[0.959, 0.098]</r> intersect with the {Revenue line}<r>[0.959, 0.062]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "19A0ozVQ9ob255b9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt x-axis point 0.8, is {Revenue}<r>[0.791, 0.098]</r> higher or lower than {Savings}<r>[0.791, 0.172]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "PTo8JDkVKnabKXUl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.125, 0.941, 0.571, 0.983]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains legend items - {Savings}<r>[0.181, 0.950, 0.249, 0.973]</r>, {Expense}<r>[0.332, 0.950, 0.405, 0.973]</r>, and {Revenue}<r>[0.489, 0.950, 0.564, 0.973]</r>. Each legend item is accompanied by its line color."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "5sL2UBtonyNvn7NE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components of the area denoted by [0.263, 0.849, 0.307, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains {the x-axis tick label}<r>[0.263, 0.849, 0.307, 0.882]</r> indicating the value '0.2'."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "10Oql8FGyPKVFrFB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.431, 0.849, 0.476, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes {the x-axis tick label}<r>[0.431, 0.849, 0.476, 0.882]</r> with a value of '0.4'."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "4r0vbzB80kx09aAm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.015, 0.368, 0.033, 0.553] in the chart image?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis label."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "g4GxhNWe3CaTat2t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists at the coordinate [0.615, 0.117, 0.654, 0.135]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color marker for 'Population Growth' legend item."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "EFJEgLvE8KRXIZVM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the position [0.495, 0.912, 0.623, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label 'Contemporary Era'."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "KnmPZ4dL7W6xwQAH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Civilization Development Index' line}[0.095, 0.221, 0.559, 0.533] and {the 'Science and Technology Development Index' line}[0.095, 0.199, 0.559, 0.824], which line has a higher value at the 'Renaissance Era' point?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Science and Technology Development Index' line."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "QEzRDqoWmJFkV95v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the coordinate {position}[0.559, 0.170], which line is represented?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Population Growth' line."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "vlFEvvETL5dztmwZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Population Growth' data point at 'Contemporary Era'}[0.559, 0.170] higher or lower than {the 'Science and Technology Development Index' data point at 'Contemporary Era'}[0.559, 0.199]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "OQR6TgesZkTxW2Kz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.615, 0.037, 0.972, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed area is the legend of the chart. It contains three items: {Civilization Development Index}[0.659, 0.046, 0.876, 0.069], {Science and Technology Development Index}[0.659, 0.081, 0.966, 0.104], and {Population Growth}[0.659, 0.116, 0.787, 0.139] each coupled with a colored marker."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "S5irS6s7Dlqwc9NU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBriefly explain what is contained in the coordinates [0.095, 0.199, 0.559, 0.533]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates encompass the 'Civilization Development Index' line plot with its corresponding data points."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "3l4vwhRfRndwgFSx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region box [0.055, 0.912, 0.623, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis captions for the time periods: {Ancient Era} at [0.055, 0.912, 0.135, 0.936], {Medieval Era} at [0.166, 0.912, 0.256, 0.936], {Renaissance Era} at [0.270, 0.912, 0.384, 0.936], {Modern Era} at [0.403, 0.912, 0.483, 0.936], and {Contemporary Era} at [0.495, 0.912, 0.623, 0.936]."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "cu2OYtyfQK0c6DVg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label for 'Bitcoin'}<r>[0.181, 0.950, 0.240, 0.973]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line of Bitcoin Price."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "WLSFItnwV77XcBkr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.389, 0.043, 0.468]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Price."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "QcIwLBgyJiDGcjl9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-coordinate}<r>[0.627, 0.833]</r> for 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-coordinate represents the Year 2019."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "7Yb8o44lSSmi1OQl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Ethereum'}<r>[0.156, 0.756, 0.942, 0.795]</r> higher or lower than the line for 'Bitcoin'<r>[0.156, 0.062, 0.942, 0.779]</r> in its general direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "gum3TArsOvkqKKG8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the position of the {2018 data point}<r>[0.470, 0.622]</r> on the 'Bitcoin' line compare to that on the 'Ethereum' line<r>[0.470, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "ULlrjaozhL4P72ZI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is at a higher value at {2020}<r>[0.785, 0.833]</r>, 'Bitcoin' or 'Ethereum'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "2Sm98FU9jieZdXVN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects located inside the region <r>[0.156, 0.062, 0.942, 0.779]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses three line plots labeled Bitcoin, Ethereum, and Ripple, representing their respective price trends from 2016 to 2021."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "CY5hbw9pEMOyRsJ9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are housed in the region <r>[0.125, 0.941, 0.553, 0.983]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend of the chart with labels for Bitcoin, Ethereum, and Ripple, each paired with a colored marker."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "C31D6zoSB3RLaqJ7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.116, 0.025, 0.981, 0.833]</r> on the chart contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main chart axis with labeled tick marks for the Years on the x-axis and Prices on the y-axis. It also contains the actual data points and lines of Bitcoin, Ethereum, and Ripple."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "vZqI9Absl1SsF4A5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.019, 0.241, 0.043, 0.616] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "i4o8GCJIO2e5pbvY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the seventh coordinate of the X-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.759, 0.849, 0.803, 0.882]"
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "sbzsINYiVsT9RtY8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the y-axis tick label} at [0.051, 0.636, 0.112, 0.669] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.85."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "P6iPHwb6kTP7r7wt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Continuous integration performance'}[0.781, 0.355] higher or lower than {the point of 'Innovation potential'}[0.781, 0.245] at x=1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "qlAjAZi9952OVCkf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is at a higher position at x=0.4, {the 'Continuous integration performance'}[0.387, 0.209] line or {the 'Innovation potential'}[0.387, 0.098] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Continuous integration performance' line."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "ydEPUN13EqAx9YYn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the x-coordinate 0.2, which measure shows a higher performance, 'Continuous integration performance' or 'Project management quality'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Continuous integration performance'."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "2j2LYhstpqm5UsR2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the region [0.133, 0.941, 1.099, 0.983]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the location of the legend of the chart. It contains three items: {Continuous integration performance}[0.189, 0.950, 0.504, 0.973] with a blue marker, {Project management quality}[0.588, 0.950, 0.834, 0.973] with a green marker, and {Innovation potential}[0.918, 0.950, 1.093, 0.973] with a red marker."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "EIsm4Z0q0WSEq4mf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the area [0.124, 0.172, 0.781, 0.355]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data points and line for the 'Continuous integration performance' measure. The line starts from {point}[0.124, 0.209] with a value of 0.0, and ends at {point}[0.781, 0.355] with a value of 1.0."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "U3eHkUUr90d5C6tS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat types of objects are observed in the region [0.518, 0.209, 0.781, 0.355]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects in this area. They are {[0.518, 0.245], [0.650, 0.319], [0.781, 0.355]} representing the 4th, 5th, and 6th markers on the 'Continuous integration performance' line."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "TiMaicd2zMEdGUua",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell the color of {the line representing 'Tech Industry'}<r>[0.123, 0.164, 0.940, 0.534]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "xJX7a2xhZxTiJEHs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of y-axis at the coordinates <r>[0.019, 0.290, 0.041, 0.579]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share (%)."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "65Fv8VQVe0fjZ44G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.265, 0.810, 0.309, 0.838]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "N4f1QMue2NNb9OTu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing two points in 2020 on the 'Food Industry' and 'Tech Industry' lines, which industry had the higher market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food Industry."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "AA0tcTWiLvWBnoBZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does {the line for 'Tech Industry'}<r>[0.123, 0.164, 0.940, 0.534]</r> at the point in 2018 lie in relation to {the line for 'Food Industry'}<r>[0.123, 0.278, 0.940, 0.648]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "gIYKTwytK7GSlwbB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the position of {the line for 'Automotive Industry'}<r>[0.123, 0.591, 0.940, 0.762]</r> at the point in 2016 compare to the position of {the line for 'Food Industry'}<r>[0.123, 0.278, 0.940, 0.648]</r> at the same point in time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "1J7CX8ragn257GiY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the section with boundaries at <r>[0.290, 0.030, 0.774, 0.070]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the subtitle of the chart, \"Industry Market Share Comparison\", written in black."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "k6OimCqEgywaM9dU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the content within the region <r>[0.091, 0.911, 0.731, 0.961]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Legend of the chart with three industries: {Tech Industry}<r>[0.147, 0.922, 0.262, 0.950]</r>, {Automotive Industry}<r>[0.345, 0.922, 0.523, 0.950]</r>, and {Food Industry}<r>[0.606, 0.922, 0.724, 0.950]</r> marked by blue, red, and green markers, respectively."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "TJkhjGfE0IbS9zoH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements located within the region <r>[0.101, 0.810, 0.799, 0.838]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains x-axis tick labels representing the years from 2015 to 2019."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "oiSg0Y10V56SmBF1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object in the coordinates of [0.015, 0.407, 0.034, 0.513] represents in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "FdhPAcbjT5IFsRB2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at the coordinates [0.902, 0.081, 0.953, 0.104] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for Series2."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "IHJuqEFM3xtlpefQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point of Series1 in Trend type 'Gradual increase'} at the coordinates [0.383, 0.100]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 2.03."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "W9dI3SbHm1VymMfj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has higher value at 'Gradual Increase' {Series1}[0.383, 0.100] or {Series2}[0.383, 0.661]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series1."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "HIdv1oXLyq2nM2ih",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of Series3 for 'Unstable behavior'}[0.654, 0.345] located higher or lower than {the point of Series1 for 'Unstable behavior'}[0.654, 0.201]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "zv955PEbWCQsnFJR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values in {Series1}[0.112, 0.100, 0.925, 0.826] generally higher or lower than the values in {Series3}[0.112, 0.065, 0.925, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "3QnvhYbVQqmuYkKJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in the area given by [0.322, 0.880, 0.920, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains x-axis labels, including 'Persistent rise', 'Gradual increase', 'Unstable behavior', and 'Gradual decline'."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "gf3ZqSUnigV2RzMy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the space defined by [0.857, 0.037, 0.958, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the legend entries: {Series1}[0.902, 0.046, 0.953, 0.069] with a red marker, {Series2}[0.902, 0.081, 0.953, 0.104] with a blue marker, and {Series3}[0.902, 0.116, 0.953, 0.139] with a green marker."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "KxFuB5cD8VvhK5sy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the sector from [0.112, 0.065, 0.925, 0.826] encompasses?"
+            },
+            {
+                "from": "gpt",
+                "value": "The space includes the entire data plot, including all trend lines and data points from Series1, Series2, and Series3."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "KCS94MbTzluDL4Qj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled with 'Year' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the x-axis label}<r>[0.498, 0.836, 0.545, 0.865]</r>."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "TrE70Ih29IHicQFL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is described by the coordinates [0.346, 0.918, 0.462, 0.938]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the legend for 'DNA Sequencing'}<r>[0.346, 0.918, 0.462, 0.938]</r>."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "n1iIQBZJbXrPaiBC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.041, 0.561, 0.072, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'$0.6$'."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "fmNBHLrSQIo7NvpP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2023, did {AI Research}<r>[0.609, 0.248, 0.634, 0.275]</r> have a higher development scale than {Quantum Computing}<r>[0.609, 0.572, 0.634, 0.599]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "PA1qBCa3N9y1sWQu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {line representing DNA Sequencing}<r>[0.082, 0.226, 0.960, 0.528]</r> intersect with the {line representing Quantum Computing}<r>[0.082, 0.356, 0.960, 0.733]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "U8AEMcdyvtixokgv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2021 and 2022, did {AI Research}<r>[0.258, 0.464, 0.433, 0.323]</r> see a downward or upward trend in development scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "dhOowMgNXzSE6Sgx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.041, 0.777, 0.785, 0.828]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick labels for each year from 2020 to 2025, which are evenly spaced out and positioned below the main chart area."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "ESPUdBhpp15bU64R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.089, 0.911, 0.468, 0.976]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with three items: {AI Research}<r>[0.134, 0.918, 0.215, 0.938]</r>, {Quantum Computing}<r>[0.134, 0.948, 0.279, 0.968]</r>, and {DNA Sequencing}<r>[0.346, 0.918, 0.462, 0.938]</r>."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "0MAYctdbMutTBdWA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.409, 0.801, 0.634, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two x-axis tick labels, {2022}<r>[0.409, 0.801, 0.458, 0.828]</r> and {2023}<r>[0.584, 0.801, 0.634, 0.828]</r>."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "bJMWJEWmlVP41ck2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the coordinates [0.015, 0.255, 0.035, 0.564] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, which is 'Development Scale'."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "aIGPQ0LcFbqxhu86",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.233, 0.801, 0.282, 0.828] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis tick label, the label is '2021'."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "qZuPHJBQA0zlyyoM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the line}[0.082, 0.356, 0.960, 0.733] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is red, indicating the 'Quantum Computing' trend."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "tnPMMFPzAXEUJ8zm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies higher on the y-axis at 2022: {the 'AI Research' trend}[0.433, 0.323] or {the 'Quantum Computing' trend}[0.433, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "LCowUtiPHBldTGDV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, is the {trend for 'DNA Sequencing'}[0.609, 0.226] higher or lower than the {trend for 'Quantum Computing'}[0.609, 0.572] in 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "NEaFxZ7K16V1XhsQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt 2025, which has a greater value: {the 'Quantum Computing' trend}[0.960, 0.356] or {the 'DNA Sequencing' trend}[0.960, 0.377]?"
+            },
+            {
+                "from": "gpt",
+                "value": "DNA Sequencing."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "sbIQlILFXAQDLRNp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found within the region [0.089, 0.911, 0.468, 0.976]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with labels 'AI Research' and 'Quantum Computing' found at {coordinates}[0.134, 0.918, 0.215, 0.938] and {coordinates}[0.134, 0.948, 0.279, 0.968] respectively. Also, label 'DNA Sequencing' is found at {coordinates}[0.346, 0.918, 0.462, 0.938]. Each label is paired with a different color."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "9nIYYhcF1F3NsiwX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.082, 0.787, 0.960, 0.828] enclose?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the x-axis tick labels, from '2020' to '2025', spaced evenly along the x-axis."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "3cwbX1u7CqH6VCPD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.082, 0.140, 0.960, 0.787]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three line graphs, representing 'AI Research', 'Quantum Computing', and 'DNA Sequencing'. They have different colors and marker styles, and their coordinates represent their values from 2020 to 2025."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "j2lQIGNpAlWjXlTw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.098, -0.043, 0.115, -0.019] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label '60'."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "fx4F934uCexKgZBR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item at [0.176, 0.211, 0.277, 0.234] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music Industry."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "JTMqPBX3HUmQ2pnB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.160, 0.769, 0.865, 0.855] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing Movie Box Office revenue."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "Ib37X76MzMrLe73R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {year 2004 point of Video Game Revenue}<r>[0.301, 0.583]</r> higher or lower than the {year 2004 point of Music Industry}<r>[0.301, 0.613]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "NrtxsbhXF3TFTaQe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relation between the {year 2010 point of Video Game Revenue}<r>[0.512, 0.330]</r> and the {year 2010 point of Movie Box Office}<r>[0.512, 0.729]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Video Game Revenue is higher."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "R5JcPOfzXGWpxC6U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry had higher revenue in 2000, {Video Game Revenue}<r>[0.160, 0.615]</r> or {Music Industry}<r>[0.160, 0.530]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music Industry."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "8mDM6UkMKRsToWgF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.098, 0.112, 0.115, 0.135]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the Y-axis label '50'."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "RfUoafrXIIwReglp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists in the space [0.265, 0.700, 0.275, 0.715]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a point on the Movie Box Office revenue line for a specific year."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "Qd1xVPudoSX4wEps",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe all elements within [0.849, 0.865, 0.889, 0.930]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two elements within this region. One is the X-axis label '2020.0' located at {coordinates}[0.841, 0.906, 0.889, 0.930] and the other is the last data point for all three lines indicating the revenue of each industry in the year 2020."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "7LHU8fzjV7km6yvU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.015, 0.335, 0.034, 0.585] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the y-axis label, which is \"Number of Jobs\"."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "Y6AioLlPDcVKXBl9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.153, 0.781, 0.321, 0.804] in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the \"Technology-Related Jobs\"."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "oywYCscl8AGbStzj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point on the 'Public Sector Jobs' line}<r>[0.945, 0.167]</r> signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the number of 'Public Sector Jobs' in the year 2020, which is 93000."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "XQfTij2vu7ulw5zA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2020, is {the point for 'Education Worker Jobs'}<r>[0.945, 0.548]</r> above or below {the point for 'Public Sector Jobs'}<r>[0.945, 0.167]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "zD9vgEnwlEAvdbS8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2016, does {the point on the 'Technology-Related Jobs' line}<r>[0.303, 0.833]</r> have a higher or lower value than {the point on the 'Public Sector Jobs' line}<r>[0.303, 0.079]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "ZLsBR4Vq7NNMNs0V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the years 2018 and 2019, does the value of {the 'Education Worker Jobs' line}<r>[0.624, 0.526], [0.784, 0.533]</r> increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "DcGKw8DZkW0fu0EI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content located within the coordinates [0.109, 0.771, 0.327, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart's legend. It includes labels for 'Technology-Related Jobs', 'Education Worker Jobs', and 'Public Sector Jobs', each accompanied by a different color-coded marker."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "LfDEVgXN7dFzptln",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the content in the area [0.142, 0.738, 0.945, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area maps the trend of 'Technology-Related Jobs' from 2015 to 2020. The points represent the job numbers for each respective year."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "99GdibogKWefcWcM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region [0.142, 0.065, 0.945, 0.167] present?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the trend of 'Public Sector Jobs', with different points marking the number of jobs for each year from 2015 to 2020."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "DicPmhcAHehw2cPh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat the label of {the y-axis}<r>[0.015, 0.249, 0.034, 0.535]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subscription Rate."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "a9Z3ueNakOqlF084",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the last quarter label in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.681, 0.763, 0.783, 0.934]."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "qMdz0vjD9mRaxnsX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the legend for 'Walmart Plus'}<r>[0.824, 0.149, 0.934, 0.179]</r> associated with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green (<r>#008000</r>)."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "xJqJaSw3prqLXHrS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point of 'eBay Prime' for 'Q2_next_year'}<r>[0.322, 0.585]</r> higher or lower compared to {the 'Amazon Subscribe'}<r>[0.322, 0.463]</r> in same quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "0AVrF9Y8UooyNtG1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Amazon Subscribe'}<r>[0.117, 0.138, 0.732, 0.646]</r> generally above or below {the line for 'Walmart Plus'}<r>[0.117, 0.422, 0.732, 0.625]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "qA6xWXUpqcedN9Je",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subscriptions show a higher rate in 'Q1_next_year': eBay Prime or Walmart Plus?"
+            },
+            {
+                "from": "gpt",
+                "value": "eBay Prime."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "o9gWDPSZ584QWOGY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.771, 0.051, 0.985, 0.191]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It contains three labels with each paired with a colored marker. The labels are: {eBay Prime}<r>[0.824, 0.062, 0.919, 0.092]</r> (Blue marker), {Amazon Subscribe}<r>[0.824, 0.106, 0.978, 0.136]</r> (Red marker), and {Walmart Plus}<r>[0.824, 0.149, 0.934, 0.179]</r> (Green marker)."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "eI9HrOR2Da3FFC5E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.234, 0.763, 0.328, 0.919]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for {Q1_next_year}[0.234, 0.763, 0.328, 0.919] which is roughly in the middle of the x-axis."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "DisJs0i0f4cRuvOl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.516, 0.022, 0.588, 0.065]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This span appears to contain no objects, it is likely blank space on the chart."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "hWf3WpGo1DhSagkW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the bottom right corner, indicated by coordinates [0.881, 0.919, 0.916, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "pGxriH5SVkqOeRLl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the y-axis label}<r>[0.015, 0.386, 0.029, 0.579]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "tab:blue."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "shnGyr0DqmQJoCVo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the shape of {the marker associated with the 'Knowledge Economy Index'}<r>[0.070, 0.121, 0.106, 0.135]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "27B6P0nLUzMo7Gok",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '76'}[0.580, 0.413] above or below {the point labeled '77'}[0.381, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "TCXzyuTx3G5nd1Cz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the lines for {the 'Innovation Index'}[0.102, 0.100, 0.898, 0.865] and {the 'Knowledge Economy Index'}[0.102, 0.100, 0.898, 0.865] intersecting or separate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Separate."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "jFdliRtdKjwuEd2r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index had a higher value in the year 2017, {Innovation}<r>[0.779, 0.268]</r> or {Knowledge Economy}<r>[0.779, 0.205]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "KJuWulDYqX33Ujcb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.096, 0.919, 0.131, 0.942] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year label."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "WbWACMjA84VAoYRc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {coordinates}[0.307, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "23.3."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "JXBZya1NFG2wwKod",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the label 'CO2 Emissions'}[0.127, 0.118, 0.229, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "i7MwN5ZjcX3rrUY2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '1990'}[0.114, 0.865] higher than {the point at '1991'}[0.152, 0.806] on the CO2 Emissions plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "RkfW8vxPUlMgK7oU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the tick for '1996' on the CO2 emissions plot}[0.346, 0.903] occur before or after {the tick for '1995' on the Temperature plot}[0.307, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "ozeFSeVq0FHNkKZG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is closer to the bottom, {the tick labeled '2008'}[0.809, 0.903] or {the tick labeled '2010'}[0.886, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "sX7VjWRzzcPUdyQv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region bounded by [0.075, 0.915, 0.200, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year labels '1990', '1991', '1992' are within this region, each represented with {black text}[0.096, 0.919, 0.131, 0.942], {black text}[0.135, 0.919, 0.170, 0.942], {black text}[0.173, 0.919, 0.209, 0.942] respectively."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "Dglx1j8DNviZGkBc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located in the area [0.025, 0.855, 0.065, 0.991]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis tick labels for Global Temperature: {14.4}[0.035, 0.631, 0.065, 0.654], {14.5}[0.035, 0.518, 0.065, 0.542], {14.6}[0.035, 0.406, 0.065, 0.429] in blue color."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "z146xu4ImBLxD8bk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the area [0.089, 0.086, 0.325, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes legend items for {Average Global Temperature}[0.127, 0.083, 0.325, 0.106] and {CO2 Emissions}[0.127, 0.118, 0.229, 0.141], each associated with a color marker, blue and red respectively."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "rvVU99961dJwVpuV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.118, 0.865, 0.158, 0.829] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "i1xvk0mJYgUWsQpA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is located at [0.118, 0.865] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "HA1qZ5EUwcitSxNr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify {the x-axis label} located in bounds<r>[0.490, 0.952, 0.519, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "1kf7TjFYnPfvrNka",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the starting point}<r>[0.118, 0.865]</r> lower or higher in value compared to {the ending point}<r>[0.891, 0.100]</r> in the chart tracking 'Number of Games'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "zQdayNeGW6TulJH3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has more increasing trend, {line for 'Number of Games'}<r>[0.118, 0.865, 0.891, 0.100]</r> or {line for 'User Base'}<r>[0.118, 0.865, 0.891, 0.100]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "User Base"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "qMa8Jap02ls30vfj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the data point at 2001}<r>[0.157, 0.829]</r> belong to {the 'Number of Games' or 'User Base'} line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Games"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "UHk94wk1LY0IUrjP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details about the contents found in the region<r>[0.079, 0.062, 0.929, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily displays two line graphs one tracking the 'Number of Games' in blue with circle markers and the other tracking 'User Base' in red with square markers against years from 2000 to 2020."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "lr4DrzoSUGHTXCg5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are embedded within the area<r>[0.939, 0.528, 0.965, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'User Base' y-axis label colored in red as well as accompanying tick labels denoting user base in millions."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "uT7ASPPoBEG51ZIE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the section<r>[0.035, 0.713, 0.070, 0.737]</r> on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The area houses the y-axis label for 'Number of Games' in blue color along with numerical labels for 2000 games."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "lWHkmOVtVzbmNjbo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.265, 0.025, 0.744, 0.055] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "rzPj0PTxtDBAW1dv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what {the second label from the right in the x-axis}[0.840, 0.919, 0.875, 0.942] represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "bndJdrM1MuNZ9yLz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented by {the first tick on the y-axis}[0.044, 1.021, 0.061, 1.044]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "MDpnQ96HfB4gbBvp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the chart, {the tick at 2008}[0.401, 0.903] or {the tick at 2018}[0.816, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.401, 0.903]."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "KllmqRm7JJfXNpXB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the values}[0.733, 0.903] for 2016 on each axis align vertically in the plot area?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "mz7HKpWJldUEB5nf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the marker for the Stock Price Trend in 2014}[0.650, 0.431] above or below {the marker for Consumer Sentiment in 2014}[0.650, 0.415]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "HPo3kLQYB8E2WQT0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.100, 0.900, 0.220, 0.940]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis tick labels for the years 2001 to 2005, specifically {2001}[0.110, 0.919, 0.128, 0.942], {2002}[0.134, 0.919, 0.169, 0.942], {2003}[0.176, 0.919, 0.211, 0.942], {2004}[0.217, 0.919, 0.252, 0.942], and {2005}[0.259, 0.919, 0.294, 0.942]."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "eBEET9nEcPjEfnql",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the objects in the top left quadrant of the plotting area?"
+            },
+            {
+                "from": "gpt",
+                "value": "This quadrant features the y-axis labels and corresponding markers for values from 90 to 130, particularly emphasizing {90}[0.043, 0.650, 0.061, 0.673], {100}[0.035, 0.464, 0.061, 0.487], {110}[0.035, 0.279, 0.061, 0.302], and {120}[0.035, 0.093, 0.061, 0.116]."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "S3cfaDB69zSVJ2GJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is displayed near the bottom center of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labels for the years from 2010 to 2015 are found here; these include {2010}[0.466, 0.919, 0.501, 0.942], {2011}[0.508, 0.919, 0.543, 0.942], {2012}[0.549, 0.919, 0.584, 0.942], {2013}[0.591, 0.919, 0.626, 0.942], {2014}[0.632, 0.919, 0.667, 0.942], and {2015}[0.674, 0.919, 0.709, 0.942]."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "gIxTHSWqFGjuz5NV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.015, 0.400, 0.029, 0.565]</r> for the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Welfare."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "DhhXXknDV39duDEU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Public Services Budget}<r>[0.096, 0.121, 0.133, 0.135]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "BoK2vxRVlAMBv5Nm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the seventh data point in the social welfare line}<r>[0.580, 0.373]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "9005."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "MVXseuHHqFdg1MRa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2013, which budget was higher, {Social Welfare Budget}<r>[0.353, 0.526]</r> or {Public Services Budget}<r>[0.353, 0.766]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Services Budget."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "BksjCqShyppaK8hO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point in 2010 for Public Services Budget}<r>[0.126, 0.323]</r> higher or lower than {the data point in 2010 for Social Welfare Budget}<r>[0.126, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "dqEzJPPeDbt3V7O8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Social Welfare Budget}[0.883, 0.100] or {Public Services Budget}[0.883, 0.221] have a higher budget in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Services Budget."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "tPiQKwUqL9nSyCQW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region <r>[0.108, 0.919, 0.900, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes x-axis labels, representing the years from 2010 to 2020. The year labels are in black color and have no rotation."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "o2ySPzbKjyQCEL1L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.095, 0.074, 0.303, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is a legend describing two lines: {Social Welfare Budget}<r>[0.140, 0.083, 0.293, 0.106]</r>, which is colored in blue, and {Public Services Budget}<r>[0.140, 0.118, 0.298, 0.141]</r>, which is colored in red."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "5tjFm4Q9gIDeettN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region <r>[0.126, 0.100, 0.883, 0.865]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two lines representing: {Social Welfare Budget}<r>[0.126, 0.100, 0.883, 0.865]</r>, which is a solid line with circle markers, and {Public Services Budget}<r>[0.126, 0.100, 0.883, 0.865]</r>, which is a dashed line with square markers. Points on these lines represent annual budget amounts over the years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "HvPsxwXLOP89XIEX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the line chart given its coordinates [0.298, 0.025, 0.702, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Cultural and Value Change Over a Decade\"."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "qZtHe6fbo9vdkdtV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is located at coordinates [0.971, 0.403, 0.985, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label for the subplot_1 which is \"Value Change\"."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "EX1tld0U8zlw5NbG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick label at coordinates [0.244, 0.919, 0.279, 0.942] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year 2002 on the x-axis."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "NcAIOrvAF3YKQAlP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the value of {Cultural change over time line}<r>[0.102, 0.100, 0.898, 0.865]</r> and {Value change over time line}<r>[0.102, 0.100, 0.898, 0.865]</r>, which value is higher in 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cultural change."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "iDYpHfw5BFyarcZs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Cultural change over time line}<r>[0.102, 0.100, 0.898, 0.865]</r> above or below {the Value change over time line}<r>[0.102, 0.100, 0.898, 0.865]</r> at 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "4Kel3catzJ3Js4fO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Cultural change over time represented by {the line}<r>[0.102, 0.100, 0.898, 0.865]</r> experiencing an overall increase or decrease over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "vDIsimABAzqQZUz5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.084, 0.919, 0.186, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label '2000' for the x-axis tick mark."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "6Ee92mmZ8lAc6HdR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region [0.069, 0.074, 0.300, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the two lines in the chart, {Cultural change over time}<r>[0.113, 0.083, 0.295, 0.106]</r> with color #1f77b4 and {Value change over time}<r>[0.113, 0.118, 0.278, 0.141]</r> with color #d62728."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "AfZAzhZVuPVnRS19",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is in the region [0.971, 0.403, 0.985, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label for the subplot_1, 'Value Change', is located in this area."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "GQV7mnRFZTdmhDkg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.971, 0.340, 0.985, 0.625] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label in subplot_1."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "rFgDAwhEAQJFnHaE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at [0.922, 0.856, 0.957, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label in subplot_1 which is '7000'."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "I72FJWapxaTrizYy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content present at the coordinates [0.131, 0.083, 0.280, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend item stating 'Pharmaceutical Sales'."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "vVgL1pgzdO7m4LRb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Pharmaceutical Sales line point at '2'}[0.186, 0.359] higher or lower than the {Pharmaceutical Sales line point at '3'}[0.255, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "nHxfym44PBXlq5AA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Biotech Investment line point at '1'}[0.117, 0.100] and {Biotech Investment line point at '7'}[0.530, 0.865], which point is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point '7' is higher."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "16vA1HugTMkE3zve",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Biotech Investment line point at '5'}[0.392, 0.829] have a higher or lower y-value than the {Pharmaceutical Sales line point at '5'}[0.392, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "uzYMJilW4nDntsHN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.113, 0.919, 0.122, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a label of the x-axis, indicative of the month '1'."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "PVlgzG85uL2cgkWy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the coordinates [0.922, 0.974, 0.957, 0.997]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label '6500' for subplot_1."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "HCxYTVqYTK5iowex",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region [0.117, 0.100, 0.874, 0.865]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the data points of the Biotech Investment and Pharmaceutical Sales lines for subplot_0 and subplot_1."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "vVMjnlso8GOA0YSU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by {the line with a series of points} at coordinates [[0.119, 0.865], [0.205, 0.804], [0.292, 0.712], [0.379, 0.559], [0.465, 0.559], [0.552, 0.498], [0.639, 0.406], [0.726, 0.284], [0.812, 0.253], [0.899, 0.100]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represents the trend of 'Innovation' over the years."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "097NAKi4rCHB9k6A",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element of the plot can be found {at the coordinates}<r>[0.971, 0.421, 0.985, 0.544]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the right y-axis label."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "0cHB7GzEghsHr28w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}[0.242, 0.025, 0.776, 0.055] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the subtitle of the chart, \"Technological Innovation and Intellectual Property over 10 years.\"."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "Ci1vPPILhirh5F0N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, does the 'Number of Patents' in {2026}<r>[0.552, 0.291]</r> exceed the 'Number of Patents' in {2023}<r>[0.292, 0.578]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "4rizjeTr8e6768kK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Innovation' score in {2029}<r>[0.812, 0.253]</r> exceed the 'Innovation' score in {2025}<r>[0.465, 0.559]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "kTrcoRlcPj93312w",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the year '2025'}<r>[0.465, 0.919, 0.483, 0.942]</r> and {the year '2028'}<r>[0.708, 0.919, 0.743, 0.942]</r>, did 'Innovation' show a rising trend, constant or a declining trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rising trend."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "sHUoayCc0fePugf6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's contained in this region starting from top left {corner}[0.080, 0.062] to the bottom right {corner}[0.938, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the body of the plot, including two lines marking the trends of 'Innovation' and 'Number of Patents' across the years, along with tick labels on both the x-axis and y-axis."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "m1fSTA6gRlZFfpyR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the text item located at the coordinates {the coordinates}<r>[0.242, 0.025, 0.776, 0.055]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area has the chart's title, \"Technological Innovation and Intellectual Property over 10 years,\" in black."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "SIn6h4IXoufrxU6j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region starting from {this point}<r>[0.086, 0.074]</r> to {this point}<r>[0.266, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the sub-legend of the chart, describing the colors of 'Number of Patents' and 'Innovation' lines on the plot."
+            }
+        ],
+        "id_raw": 429
+    },
+    {
+        "id": "dgRhHfCw4weqmhAF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at [0.971, 0.340, 0.985, 0.625]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technological Innovation."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "3kpfZGjjOOWD8jJS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {marker}<r>[0.088, 0.086, 0.124, 0.100]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "AABzIgakqxYHZkn8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.250, 0.025, 0.750, 0.055] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "YoLGqw0KE0ztZz4v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.797, 0.540] above or below {the line for 'Yearly Patents'}[0.118, 0.100, 0.882, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "fEdafd1qGF3sQMJr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point for 'Yearly Technological Innovations'}[0.797, 0.540] and {the point for 'Yearly Patents'}[0.797, 0.244] at year 9 in the trend lines, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yearly Patents."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "kjEqFLAEkyR7X6AE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the change in {Yearly Technological Innovations line}[0.118, 0.100, 0.882, 0.865] from year 7 to 8 rising or declining?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rising."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "kBAe2ROzaLBOS7pp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.086, 0.074, 0.361, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the line chart, which includes two entries: {Yearly Patents}[0.131, 0.083, 0.228, 0.106], marked in blue color, and {Yearly Technological Innovations}[0.131, 0.118, 0.355, 0.141], marked in red color."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "Yh8ndMLGc5Lqy2QG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within [0.368, 0.919, 0.377, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a single text element, which is the x-axis tick label for '4'."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "GTcSSiRBZUdRc4y9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.079, 0.062, 0.921, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area of the chart, which contains all the lines for 'Yearly Patents' and 'Yearly Technological Innovations', the tick labels, and markers."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "UMTIVxvfLBYSqeJf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the line}[0.078, 0.087, 0.922, 0.887] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Innovation Index."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "3ncfzkSROFVpkKZz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the y-axis label}<r>[0.010, 0.417, 0.019, 0.557]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "tab:blue."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "MbyAuXX1WlkR0Blt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.446, 0.964, 0.554, 0.981]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Design Decor Elements."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "iAFp5eGbEDf2aQKm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the rating higher or lower for Layout than for Typography?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "dF0Bpoob81lgcO60",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which Decor Element is the Index Rating lower, {Color Choice}<r>[0.078, 0.087]</r> or {Style}<r>[0.547, 0.525]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Style."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "suyn9A7Ke2vxiGVx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the rating for Functionality}<r>[0.641, 0.887]</r> higher or lower than {the Innovation Index for Material Choice}<r>[0.735, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "gSsf7inNUKbb1h7P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.023, 1.001, 0.977, 1.019]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-tick label '0'."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "hOivPVioENP7WxFF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the area <r>[0.040, 0.055, 0.122, 0.113]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the legend items for Rating and Innovation Index, each paired with a color marker."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "u1s7aif1MX5Xyd8W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.891, 0.939, 0.954, 0.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Sustainability' is located within these coordinates."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "io9olR5rxJLDX0db",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the line representing 'Commercial Buildings'}<r>[0.125, 0.100, 0.866, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "LnRCGPydE30VmNeW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the tick mark}<r>[0.125, 0.903]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "v5cc66nVmPUgVlb9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rotation angle of {the label for the y-axis}<r>[0.015, 0.362, 0.029, 0.603]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "90.0."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "WLiB7roHDM1JdSyQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher data points at the year 2019, {line for 'Residential Buildings'}<r>[0.829, 0.172]</r> or {line for 'Commercial Buildings'}<r>[0.829, 0.127]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commercial Buildings."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "lxvngmCYjZ0GPtoa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the first tick}<r>[0.125, 0.903]</r> and {the last tick}<r>[0.866, 0.903]</r> on the x-axis for years 2000 and 2020 respectively located at the same vertical height?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "NZ40G88JEjZrxuPb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {the point on the 'Residential Buildings' line}<r>[0.348, 0.650]</r> from year 2006 reach a higher value than {the point from the previous year}<r>[0.310, 0.674]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "HLr3eFb1isaqqx3z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region covering the subplot area of {Residential Buildings}<r>[0.088, 0.062, 0.903, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot includes x-axis tick labels for years 2000 to 2020, y-axis tick labels from 40000 to 80000, and a blue colored line representing Residential Buildings data points from 2000 to 2020. There are marker symbols at each data point along the line."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "l3toM51u9YdxO9wt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found within the coordinates of {the legend area}<r>[0.095, 0.074, 0.296, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend which defines 'Residential Buildings' and 'Commercial Buildings', represented by a blue line with circle markers and a red line with square markers, respectively. Each legend entry includes a text label and a corresponding color marker."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "zEdFQDEumBHXD0rd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region defined by {the y-axis ticks}<r>[0.035, 0.020, 0.078, 0.998]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tick marks and labels indicating the numeric values representing the count of Residential Buildings, ranging from 40000 to 80000 in staggered positions along the y-axis."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "JyJpLjuM5PXk2kTn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text}[0.116, 0.083, 0.201, 0.106] represent in the lower left corner of the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the label 'GDP Growth'."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "K0zX1KmjPnapdC0K",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this graph given at {the coordinates}[0.328, 0.025, 0.675, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is 'American Economic Indicators 2009-2020'."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "spc0OpOkqqNaSyPl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the provided coordinates, what does the label at {the location} [0.971, 0.340, 0.985, 0.625] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label represents 'Unemployment Rate (%)'."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "4i6vB5JNhO326Q6S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater in the year 2020, the {GDP Growth} at [0.898, 0.865] or the {Unemployment Rate} at [0.898, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unemployment Rate."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "QVKsyYDoD2bdod96",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {GDP Growth rate} at [0.177, 0.136] higher or lower than the {Unemployment Rate} at [0.177, 0.137] in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "GV3GRiVkh30iWEle",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the first points of each line, was the {GDP Growth} at [0.104, 0.745] more or less than the {Unemployment Rate} at [0.104, 0.100] in 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "b8dCKgPbHiqLij99",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the area [0.072, 0.074, 0.265, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the legend with two entries: {GDP Growth}[0.116, 0.083, 0.201, 0.106] and {Unemployment Rate}[0.116, 0.118, 0.259, 0.141]. Each label is paired with a colored marker."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "BlCyKz1csHLH3wNF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can be inferred from the area specified by the coordinates [0.065, 0.100, 0.898, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line plots for both GDP Growth and Unemployment Rate from 2009 to 2020, with the points connected by lines of different styles."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "4IsbnNnucsZ7CtEx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we find within the region [0.159, 0.919, 0.194, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2010' can be found in this area."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "0ZHYkBvYAXZpbhHu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object with coordinates [0.943, 0.340, 0.985, 0.625]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label of the secondary chart, which is 'Unemployment Rate (%)."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "Cd4xMtMj92ynrcbg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth label in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.399, 0.919, 0.408, 0.942]"
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "Cfr0k6weExAOBNCA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.943, 0.855, 0.965, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "3.8"
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "e4H2necQCZIbLEEn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Unemployment rate at Month 1}[0.122, 0.100] higher or lower than {the GDP at Month 1}[0.122, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "10Olfi8pdui6YQMx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the GDP at Month 5}[0.403, 0.587] greater or less than {the GDP at Month 4}[0.333, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "Zofu3sem2z9Pczcj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher value at month 12, GDP or Unemployment Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "bvNMzHS2fhwq3qFF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content present within the coordinates [0.091, 0.074, 0.284, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the charts, including {GDP}[0.135, 0.083, 0.165, 0.106] which is shown in colour '#1f77b4' and {Unemployment Rate}[0.135, 0.118, 0.278, 0.141] shown in colour '#d62728'."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "KwgXizgYZlbora8j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within these coordinates [0.259, 0.919, 0.267, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is the {x-axis tick label '3'}[0.259, 0.919, 0.267, 0.942]."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "jdMacSsLX3Xc4wZ5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the box defined by [0.035, 0.396, 0.074, 0.420]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the {y-axis tick label '22.75'}[0.035, 0.396, 0.074, 0.420]."
+            }
+        ],
+        "id_raw": 434
+    },
+    {
+        "id": "Ya8OEmASFhw8ggaT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {y-axis label}<r>[0.015, 0.326, 0.029, 0.639]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population Growth Rate(%)."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "9oks334yIi2ELHFy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the {x-axis tick label} that corresponds to the month of August?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.598, 0.919, 0.607, 0.942]."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "3NneBZyo9XMCasOa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure text located at {coordinates}<r>[0.393, 0.025, 0.603, 0.055]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Statistical Data Overview."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "HPYeQ9PPIFHo8hkZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point in the Average Monthly Income line}<r>[0.742, 0.355]</r> (October) fall below or above the {point in the Population Growth Rate line}<r>[0.742, 0.319]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "T6ZaGQ61zvXUa7TA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {Population Growth Rate value at point 7}<r>[0.533, 0.428]</r> with the {Average Monthly Income value at point 7}<r>[0.533, 0.610]</r>, which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Monthly Income."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "zy4PPFmCFAebCzfB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {ending point for Population Growth line}<r>[0.882, 0.100]</r> below or above the {ending point for Average Monthly Income line}<r>[0.882, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "PX4QDaQQFnxd6YgI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements inside the box bounded by this rectangle: [0.082, 0.074, 0.304, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the two lines in the plot: {Population Growth Rate}[0.126, 0.083, 0.291, 0.106] and {Average Monthly Income}[0.126, 0.118, 0.299, 0.141]."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "HLq2SDfyN1qrtb6F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.249, 0.919, 0.258, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label '3'."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "xtXlMM5YZ6sd2Ibx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.971, 0.322, 0.985, 0.643]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the y-axis label that represents 'Average Monthly Income($)'."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "nYqEthw0r2WvRWY2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of tick label corresponds to the coordinates [0.043, 0.932, 0.070, 0.955]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "aP6Armhm4LpSuyHw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the plot title}'s coordinates located at the bottom-middle of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.273, 0.025, 0.718, 0.055]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "b4oGY7GWYt2y8Xrm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text for the first legend item of subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.131, 0.083, 0.245, 0.106]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "djGOYaP6PoYLGRw8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmongst the plotted lines, which one intersects with {the point}[0.307, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Annual Concerts."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "Osk2BR9NF5C55nQH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point indicating '2012'}[0.117, 0.330] part of {the line for 'Annual Concerts'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "GEJq0KKbMsNKCY1s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Annual Concerts'} have a higher or lower value at {point 2020}[0.874, 0.865] compared to {the line for 'Annual Sites Visits'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "KGnCs52ThX0RgsuE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region area in [0.080, 0.062, 0.912, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses all the primary elements of the chart, including all the lines representing 'Annual Concerts' and 'Annual Sites Visits', all the x-tick and y-tick labels, and the chart subtitle \"Culture and Arts Industry Development (2012 - 2020)\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "VxqL71MeprsFyvnV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region: [0.086, 0.074, 0.264, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for subplot_0 with two items: {Annual Concerts} located at [0.131, 0.083, 0.245, 0.106] and paired with a colored marker, and {Annual Sites Visits} located at [0.131, 0.118, 0.258, 0.141] and paired with a different colored marker."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "oLyRY76cFuXlIMS1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within [0.035, 0.015, 0.070, 0.038]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, you can find the y-axis tick label '1400'."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "prs26FxX22Ti4yPo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object in the chart at coordinates [0.015, 0.330, 0.029, 0.635]."
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for subplot_0."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "DYro8GhaLXzJsZ1i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {y-axis tick label} at [0.939, 0.695, 0.957, 0.718] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "Pi8qRO24Rglp9sgK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the text for the second legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.131, 0.118, 0.361, 0.141]"
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "EV8Ea7v5SSx4xC7I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at Month 5 for 'Online Student Enrollment'}[0.399, 0.511] higher or lower than the {point at Month 5 for 'Daily Average Video Call Minutes'}[0.399, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "qvBR8AkjYAuSgeRy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Daily Average Video Call Minutes' trend line start at a higher or lower point than the 'Online Student Enrollment' trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "R190lzAYRxcHp8gM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt Month 6, did the online student enrollment increase or decrease compared to Month 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "Xu4traT3IKVGhg7b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region with coordinates [0.079, 0.062, 0.929, 0.903] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main body of the chart, where both the 'Online Student Enrollment' and 'Daily Average Video Call Minutes' line charts are plotted."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "O6tbrmq04mDwwMiy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.131, 0.083, 0.314, 0.106]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the {legend item} for 'Online Student Enrollment', with a blue color marker."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "wuVe4jUlUqlWsLHx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be seen in the region [0.939, 0.292, 0.965, 0.316]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a red-colored y-axis tick label, showing the '110' marker for 'Daily Average Video Call Minutes'."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "tYptkWu85ItdpagZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.317, 0.919, 0.354, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1999."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "MEXJVqwLtTM5IWeU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.294, 0.029, 0.671]</r> for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential Energy Consumption."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "8scSxatkFU4fIBmD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the legend label 'Commercial'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{coordinate}<r>[0.113, 0.118, 0.197, 0.141]</r>."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "ps2MLmrg3AZYlmpm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2008}<r>[0.572, 0.276]</r> above or below {the point for the year 2009}<r>[0.598, 0.367]</r> for Residential Energy Consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "rB8FumIIvojH7PnW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {year '2014 point'}<r>[0.729, 0.326]</r>, Residental or Commercial Energy Consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "zq5AhAM81b0jB4JP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {label}<r>[0.113, 0.118, 0.197, 0.141]</r> corresponds to {the line}<r>[0.101, 0.100, 0.886, 0.865]</r> using a dashed line style and square markers for Commercial Energy Consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commercial."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "6kMyCcfG0QgqF5XI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region<r>[0.035, 0.055, 0.052, 0.925]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis labels for both subplots, specifically \"Residential Energy Consumption\" in a blue font from the top bounding box at 74 down to the bottom bounding box at 64, and \"Commercial Energy Consumption\" in red font from 70 down to 50.0."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "nwDRqSrKCPDX4blj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present between<r>[0.101, 0.903, 0.886, 0.919]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick labels ranging from '1990' to '2020', positioned below each tick on the x-axis."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "8CUxZVUc8QFRVUj8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the chart area<r>[0.069, 0.074, 0.202, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the legend of the subplot with two entries: 'Residential' with a blue color marker and 'Commercial' with a red color marker."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "12Ab6GTXtL7CKv2q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.119, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a data point on the 'ML Deployment' line graph."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "AZdh66CEgWXUZ7AX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-label for the coordinates [0.971, 0.266, 0.985, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is \"ML Deployment (per 1000 personnel)\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "fTLIpZgatJtVqE6A",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the red square marker with coordinates [0.088, 0.121, 0.124, 0.135] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'ML Deployment' trend line."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "SJtvPqQZoR4BO6oI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for the 'AI Research' on month 4, with coordinates [0.331, 0.865], above or below the AI Research value for month 5 with coordinates [0.402, 0.464]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "6V0WRM4IIw9jX8Rm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn month 7, is the 'ML Deployment' marker {coordinates}[0.544, 0.319] higher or lower than the 'AI Research' marker {coordinates}[0.544, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "lTcR356lOfPqJLQr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of position, does the 'ML Deployment' data point {coordinates}[0.190, 0.428] for the 2nd month sit above or below the 'AI Research' data point {coordinates}[0.190, 0.646] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "5OIF5KYJGDvra0lT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements found between the bounding box [0.080, 0.062, 0.938, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plotted graph, including both the 'AI Research' and 'ML Deployment' trend lines with their corresponding datapoints, the X-axis and Y-axis with their labels, all the tick marks and labels, and the legends with their respective labels and markers."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "kHs1eH8tNReFUQDc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements enclosed in the coordinates [0.086, 0.074, 0.246, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend with two entries: The first entry is for 'AI Research' with the corresponding blue circle marker {[0.088, 0.086, 0.124, 0.100]}. The second entry is for 'ML Deployment' with the corresponding red square marker {[0.088, 0.121, 0.124, 0.135]}."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "lpHAxbSbigNLLM32",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of what lies within the coordinates [0.080, 0.062, 0.938, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers most of the chart area and includes 'AI Research' and 'ML Deployment' trend lines along with their corresponding datapoints. The X-axis and Y-axis with their labels, ticks, and tick labels are also encompassed within this area along with the legends."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "TbmMzkPOpYwz8My2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at coordinates [0.250, 0.037, 0.817, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "JwicbalOZaHy2PQj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.025, 0.295, 0.055, 0.511]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the Y-axis."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "hF6U4fa4OXBQ8ol5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first label on the X-axis that is at [0.109, 0.734, 0.188, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Political\"."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "fxh1WhKeXMO0amvB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRelative to {the coordinate labeled 'Industrial'}[0.405, 0.203], is {the coordinate labeled 'Operational'}[0.534, 0.283] higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "D6BGb8UIdDXqqtFX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '80'}[0.148, 0.124] on the left or right of {the point labeled '75'}[0.277, 0.164]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "M7dbz7W4kFraPC8P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Compliance' coordinate}[0.790, 0.443] with {the 'Reputation' coordinate}[0.919, 0.682], which one indicates a higher risk score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Compliance."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "1SuUxQOCK67R74gu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section of the chart with coordinates [0.110, 0.096, 0.957, 0.710]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main charting area which contains the line graph plot. The line graph plot represents different risk types along the X-axis and their corresponding risk scores along the y-axis."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "RqCfPv0BxGwl79BU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.079, 0.748, 0.094, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label '0'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "h6QBIicJd1hJl1Z5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from [0.477, 0.734, 0.590, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the X-axis label 'Operational'."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "ExXX0scoNm6ORJfr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.021, 0.164, 0.047, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "zCsi6lXat0GvQsXO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates for the legend item labeled as \"Total Activities\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.681, 0.150, 0.824, 0.178]."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "OTBmxpQktRcuXuDg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is encapsulated in the region [0.165, 0.664, 0.298, 0.850] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label \"Watching Movies\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "x8zQm8tBJpRLc19A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-value for {Weekend Activities} at the point 'Traveling'[0.362, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "6.0."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "K4vfXUClb0HAisaY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Routine Activities}[0.100, 0.111, 0.886, 0.635] and {Total Activities}[0.100, 0.041, 0.886, 0.323] which has higher engagement during 'Partying'[0.493, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Total Activities."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "c4rtLpl2KPTBu4Sj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison to {Routine Activities}[0.100, 0.111, 0.886, 0.635] curve, does the {Weekend Activities}[0.100, 0.081, 0.886, 0.323] curve show more or less engagement during 'Going to Music Concerts'[0.886, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "js3QlIUK3zkE1ZtT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space encapsulated by [0.617, 0.055, 0.876, 0.189] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the sub-legend of the chart containing the labels and corresponding markers for 'Weekend Activities', 'Routine Activities' and 'Total Activities'."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "UgbFvctnXOObxFtc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me about the area from [0.704, 0.664, 0.807, 0.809]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart encapsulates the x-axis tick label for 'Volunteering'."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "QyuYDjZRM6TiRpog",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content visible around position [0.595, 0.664, 0.654, 0.747] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "At this position, you can find the x-axis tick label 'Sports'."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "dplftMXmVt1VRFok",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object do the coordinates [0.023, 0.251, 0.045, 0.659] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for subplot_0."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "UQkxeKPtU7Xi8MoO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what coordinates can we find the '2008' x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.325, 0.899, 0.380, 0.928]."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "w5sJaYHDUeINs05V",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the xlabel}['Years'] located in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "At [0.485, 0.940, 0.542, 0.969]."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "tKcXwojemNLo0Sq0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Year 2005}[0.159, 0.840] and {Year 2006}[0.224, 0.781], which year had a higher Graduation Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2005."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "btEGkMGAirCYEmZU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the line plot, did the Per Student Spending rise or fall from {2007}[0.288, 0.778] to {2008}[0.353, 0.717]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It rose."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "KDW6Q5MQYbmKZim9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Year 2013}[0.675, 0.193] and {Year 2014}[0.739, 0.101] which year reports a higher Per Student Spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2014."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "GEQXVLudhb2vlnCk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements present in the region [0.068, 0.899, 0.895, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the x-axis tick labels ranging from '2004' to '2016' for both subplots."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "suEqS85qOrkprG0j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.918, 0.829, 0.946, 0.858]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label '78' for subplot_1."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "cI7UijyqsosuVfLN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.159, 0.070, 0.868, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line plot for both Per Student Spending and Graduation Rate, covering the data points from Year 2005 to Year 2016."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "5l34kbYIFbG86WnF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.019, 0.151, 0.044, 0.767]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transaction Volume (Billion Dollars)."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "ccNs7wZt15x3Gkx3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-coordinate of {the third tick on x-axis}<r>[0.453, 0.909, 0.497, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.475, 0.893]."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "Ky1wt1OcBT32v7EX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line is represented by {the point at [0.728, 0.502]}<r>[0.728, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ethereum."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "nBUgMbyy56Ehjs50",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Ethereum in 2020}<r>[0.728, 0.502]</r> above or below {the point for Bitcoin in 2020}<r>[0.728, 0.329]</r> on the trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "PKqx9TCufwbbfwEE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cryptocurrency had a higher transaction volume {in 2017}, Bitcoin at <r>[0.349, 0.285]</r> or Ethereum at <r>[0.349, 0.589]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bitcoin."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "XEVaaUEgQZzlufqB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Bitcoin in 2018}<r>[0.475, 0.589]</r> located to the right or left of {the point for Ethereum in 2016}<r>[0.222, 0.763]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "dR9f7sbHhsqRTOiB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.794, 0.039, 0.971, 0.135]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the line graph. It includes two entries: {Bitcoin} <r>[0.861, 0.050, 0.932, 0.080]</r> marked with the color blue, and {Ethereum} <r>[0.861, 0.094, 0.962, 0.124]</r> marked with the color red."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "lpdwFLxVqNLBpB9x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area <r>[0.511, 0.942, 0.566, 0.975]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the x-axis label which is 'Year'."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "tkjjAxlL82SdRnVd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area <r>[0.222, 0.199, 0.855, 0.589]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the trend line for Bitcoin. It spans from the year 2016 to 2021, with varying transaction volumes marked at each year."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "tpIp72w2OA6s4FfY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.456, 0.917, 0.526, 0.963] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "wPYhFjYhvJsK3t3v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first tick label in the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.206, 0.869, 0.276, 0.904]"
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "MK4PWxPuRFo4xIJp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {the coordinates}<r>[0.158, 0.037, 0.824, 0.844]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plotting area."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "TtcuaWtTSdAj0AK3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for year 2020}[0.574, 0.268] in 'Temperature Increase (\u00b0C)' line rise or fall compared to {the point for year 2015}[0.491, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rise."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "pEpqtL0NImGUmw0Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2000 on Temperature Increase (\u00b0C) line}[0.241, 0.729] higher or lower than {the point for the year 2000 on CO2 Emission (billion ton) line}[0.241, 0.791]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "KJKsfNrHQuCMDBSM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a lower temperature increase or higher CO2 emission at {the point for the year 2025}[0.658, 0.176, 0.658, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher CO2 emission."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "B25Qo3LoUarBQcxn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area with coordinates [0.344, 0.383, 0.810, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend with labels for {Temperature Increase (\u00b0C)}<r>[0.433, 0.397, 0.799, 0.432]</r> and {CO2 Emission (billion ton)}<r>[0.433, 0.450, 0.791, 0.485]</r>."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "c0wxzryaKYy9IPUk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the objects within the region [0.206, 0.869, 0.276, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '2000' label on the X-axis is located in this region."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "PZZL6X6uf2Nry5h5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the area [0.158, 0.037, 0.824, 0.844] in detail?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main plot area of the chart, where the 'Temperature Increase (\u00b0C)' and 'CO2 Emission (billion ton)' data lines drawn."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "7NpWPwMGKAPwhj8e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object placed at coordinates [0.052, 0.298, 0.078, 0.712] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "F3zmPwhN5uEGWnjs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'Series5' label in the x-tick section?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.609, 0.909, 0.675, 1.002]"
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "4aeSuzEvf1wIWaEf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text} at [0.570, 0.388, 0.713, 0.416] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The deviation at Series5."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "R4fS74xJjYWznsEA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Series has higher Innovation Efficiency between {Series2}[0.254, 0.688] and {Series7}[0.900, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series7."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "NEB0wNYtsxqsDsc4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Series1}[0.125, 0.789] have higher or lower Innovation Efficiency than {Series4}[0.512, 0.464]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "v7BVihVOynDd7NRJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Series has the lowest deviation text, {Series3}[0.312, 0.596, 0.455, 0.624] or {Series5}[0.570, 0.388, 0.713, 0.416]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Series5."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "0DbMSBdPOSkakeSz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly describe the region occupying the coordinates [0.125, 0.206, 0.900, 0.789]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line denoting efficiency with each point on the line corresponding to a series. Each point has a label indicating its deviation, colored markers, and a solid line connecting them."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "y45ZOq6YrYjtohLP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within [0.221, 0.909, 0.287, 1.002]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the x-axis tick label for 'Series2' is visible. The rotation is 45 degrees and the text color is black."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "ACOV2s9dp7jJgDYP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.086, 0.879, 0.111, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '40' is found in this area. It is placed vertically (0 degrees rotation) and colored black."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "F5LhZmjnjQ4Z9XxZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at [0.521, 0.945, 0.565, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "vpYZxqAIsyluakPl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {yth-tick}<r>[0.918, 0.806, 0.919, 0.818]</r> shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2100."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "N0ypUt7xyszTsXlk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {first line}<r>[0.167, 0.806, 0.919, 0.896]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "PaVfylic7GzBp7Xu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {blue line for 'FinTech Investment'}<r>[0.167, 0.806, 0.919, 0.896]</r> reach higher or lower value than the {red line for 'Digital Currency Market Cap'}<r>[0.167, 0.075, 0.919, 0.896]</r> in year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "CCOh1QjMHqoGSCWw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what year does the {line for 'Digital Currency Market Cap'}<r>[0.668, 0.650]</r> exceed the {line for 'FinTech Investment'}<r>[0.668, 0.850]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2017."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "cr73puoYca0Rxf59",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a higher point in 2009, the {blue line for 'FinTech Investment'}<r>[0.167, 0.896]</r> or the {red line for 'Digital Currency Market Cap'}<r>[0.167, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They start at the same point."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "jwAasoYnh4fDlNP2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area [0.625, 0.055, 0.769, 0.107]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes points and part of line segment from the {red line for 'Digital Currency Market Cap'}<r>[0.668, 0.650]</r>. The points represent the values of 'Digital Currency Market Cap' in years 2016, 2017, and 2018."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "ySMoYwtPP9i42dFY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.113, 0.046, 0.421, 0.123] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the legend where two items are listed: {FinTech Investment}<r>[0.113, 0.058, 0.160, 0.072]</r> represented with a blue marker and {Digital Currency Market Cap}<r>[0.115, 0.093, 0.160, 0.107]</r> represented with a red marker."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "VGtMsE8kHHdjqxJo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.019, 0.392, 0.041, 0.539] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label 'Billions ($)' is located in this region."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "l7JzoS2nA8sjadDb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle object with color #0000ff at [0.696, 0.423, 0.747, 0.440] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cyber Security."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "nr0OmpubjPOFSThW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label for year '2016' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.592, 0.895, 0.642, 0.923]."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "nDCUhYNN4QInIgQ9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.021, 0.288, 0.047, 0.662]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity (1-10 scale)."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "60OfAcqu6aCH3TFZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2013'}[0.376, 0.719] on the 'Cyber Security' line above or below {the point for year '2013'}[0.376, 0.659] on the 'Artificial Intelligence' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "Hd60oFGEN642vD7V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology had a higher popularity in 2020, {Cyber Security}[0.938, 0.449] or {Artificial Intelligence}[0.938, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "gWe5721ffry723Q0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2018, did {Cyber Security}[0.778, 0.524] have a higher or lower popularity compared to {Virtual Reality}[0.778, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "tIbOemx1QgnBEWay",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.694, 0.408, 0.969, 0.542]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It represents three categories: {Cyber Security}<r>[0.757, 0.419, 0.904, 0.447]</r> depicted in color #0000ff, {Artificial Intelligence}<r>[0.757, 0.461, 0.961, 0.489]</r> depicted in color #ffa500, and {Virtual Reality}<r>[0.757, 0.503, 0.897, 0.531]</r> depicted in color #008000."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "NRZZorEVegIMWBEc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.913, 0.895, 0.963, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis label {2020}<r>[0.913, 0.895, 0.963, 0.923]</r>."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "uH6QfV0UC8v3FD3H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.215, 0.802, 0.295, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there are 2 points from the lines representing different technologies. The point {for Cyber Security in 2011}<r>[0.215, 0.802]</r> and the point {for Cyber Security in 2012}<r>[0.295, 0.764]</r>."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "JvEVtDrphfkty0VT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.019, -0.065, 0.041, 0.986] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "DxQeeNe6vKS9I6dL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the label '2040' on the x-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.735, 0.912, 0.778, 0.936]."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "uKq9dqShCLN7qPzq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point}[0.869, 0.087] denote on the 'CO2 Emissions' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2050 data."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "b7Dz29ahTLCbUSdx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2040 point on the 'Temperature Increase' line}<r>[0.757, 0.833]</r> higher or lower on the chart compared to {[the 2040 point on the 'CO2 Emissions' line]}<r>[0.757, 0.118]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "F6WcvsNs3IyxqqAa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is {the point with coordinates}[0.532, 0.846] on?"
+            },
+            {
+                "from": "gpt",
+                "value": "Temperature Increase."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "0nri87R2H42DvKqc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line with points at {coordinates}<r>[0.195, 0.825, 0.869, 0.874]</r> or {coordinates}<r>[0.195, 0.087, 0.869, 0.513]</r> represent a steeper increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "CO2 Emissions."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "cH6Vb1hDz4k29DOW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.091, 0.037, 0.343, 0.113] in detail?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend. It consists of two entries: {Temperature Increase}<r>[0.147, 0.046, 0.336, 0.069]</r> and {CO2 Emissions}<r>[0.147, 0.081, 0.275, 0.104]</r>, each accompanied by a color marker."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "Z19xNcQfw5F2uYK1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me more about the region [0.510, 0.945, 0.554, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label for the x-axis of the chart, specifically the term 'Year'."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "cjzXw1vagrgxbSLL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.195, 0.825, 0.869, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Temperature Increase' line spanning from 1990 to 2050 as evidenced by the seven points at various corresponding coordinates."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "BCxgbyCEriy2y3KM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time Spent Online (hours)."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "oXAVSPiJHK8oFTAr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what coordinate is the label '45-54' on the x-axis located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.456, 0.869, 0.522, 0.904]"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "xKza815aLJ5RSt79",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {this second line}[0.089, 0.074, 0.890, 0.808] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comment Frequency."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "nHiEtYDaZSOIwm9g",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Data point '45-54' for 'Time Spent Online'}[0.489, 0.578] higher or lower than {Data point '45-54' for 'Comment Frequency'}[0.489, 0.150]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "hSXa3bI1JIW0NBRJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the age group '65-74', which was higher, 'Time Spent Online' or 'Comment Frequency'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time Spent Online."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "jfSbx0hiah1EYP38",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Comment Frequency for age '25-34'}[0.222, 0.327] higher or lower than {Time Spent Online for the same age group} [0.222, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "v4LtmAIJxovspo7C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is in the area of [0.355, 0.740, 0.755, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encompass the data points for the Age Group '45-54' and '55-64' for both 'Time Spent Online' and 'Comment Frequency' line graphs."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "6ezTaYTNVoKE8ze0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.489, 0.017, 0.489, 0.017]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a single point that does not contain any visible content on the chart."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "rE8HrvXy6J3Bv16B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located within [0.489, 0.578, 0.623, 0.665]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space contains 'Time Spent Online' data points for '45-54' age group and '55-64' age group."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "IkQM5ZQVrAxOpeod",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.467, 0.945, 0.511, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "4dkyK1LniYmaAQef",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the sixth x-axis tick label} located in the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.709, 0.912, 0.753, 0.936]"
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "UXEqvFhYulH2gBXF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis tick} at [0.919, 0.370, 0.941, 0.393]?"
+            },
+            {
+                "from": "gpt",
+                "value": "60"
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "js4xiSheyVRjKeqH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {trade volume point for 2030}[0.386, 0.345] higher or lower than the {globalization index for 2030}[0.386, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "pmJ0oueDaSd1ykpz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {globalization index at 2000}[0.178, 0.418] and the {trade volume at 2000}[0.178, 0.705], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trade Volume."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "dwEl0lFKD6ti1o7w",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2100}, is the {trade volume}[0.869, 0.073] higher or lower than the {globalization index}[0.869, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "lhesrHDRaJLifliO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section [0.080, 0.046, 0.309, 0.123] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the chart's legend, which includes two items: {Trade Volume}[0.136, 0.055, 0.253, 0.078] and {Globalization Index}[0.136, 0.090, 0.303, 0.113]. Each item has a different color marker."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "KSxVK6TuMtpZQyRi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.018, 0.912, 0.062, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the year '1980'."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "vkpAy3ZWiTLV8EVy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.919, 0.715, 0.941, 0.738]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a y-axis tick label, specifically the number '20'."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "xyqhz9HDlvSH3Jif",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.096, 0.919, 0.132, 0.942] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "zIWfvQzhBX1OWMoF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.851, 0.215, 0.869, 0.239]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "Y1XeRcWrJ9zCpyMa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at [0.253, 0.225] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "wRl36HQfHmYsHGZR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label '2014'}[0.374, 0.919] to the left or right of {the label '2016'}[0.512, 0.919] on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "mx4CfcUzrhhomGKG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the y-axis tick labeled '30'}[0.851, 0.854] higher or lower than {the y-axis tick labeled '40'}[0.851, 0.056] in the subplot for average work hours?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "CdXBLUnFTJFMxkKc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the label {2016}[0.512, 0.919] appear before or after the label {2018}[0.651, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "JsT0H6Jb1Nbv4Z4a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the bounding box of [0.035, 0.011, 0.070, 0.921] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick labels ranging from '200' to '1200' in increments of 200, colored in red."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "yh6PKZxAt6Ps4TCm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features in the area with coordinates [0.096, 0.919, 0.963, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels for years from '2008' to '2022' at regular intervals."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "2LgI4qV7jAfJAl5d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained in the boundary [0.114, 0.065, 0.807, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Multiple data points plotting the trends of number of employees trained, average work hours, and workplace expansion strategy from 2010 to 2020."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "2qSQLnaRtOyHycEs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line}[0.220, 0.100, 0.855, 0.689] referring to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the change in 'Technological Progress' over years from 2022 to 2030."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "Ek4YvGRiXLWqq7kW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}[0.030, 0.221, 0.066, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technological Progress."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "ouOYnEVa6kXKWDXz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location [0.417, 0.394, 0.658, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a text labeling the 'Gradual recovery' point on the line."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "6djWKYwGHyFYBOXy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend point for 2028 above or below the trend point for 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "XwFuT5ue9LYhnMrA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Slow recovery'}[0.220, 0.689] higher or lower than {the point for 'Marginal growth'}[0.299, 0.596]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "CU0ud38f3paDxiLB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Slow growth'}[0.458, 0.410] more towards right or left compared to {the point for 'Upward trajectory'}[0.379, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "VpiWTfdiOVJm1tzr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the box [0.690, 0.131, 0.861, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box contains the label 'Slow growth' for the year 2029."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "EWjMlSJVDacWWXyK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.742, 0.038, 0.968, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the 'Marginal growth' label referring to the year 2030 on 'Technological Progress' line."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "w1VP7k8NgcjibBk4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what's in the zone [0.186, 0.534, 0.412, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains label 'Marginal growth' which specifies a point on the 'Technological Progress' line for the year 2023."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "diDnvADsayqXYTa9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the location [0.839, 0.896] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick for the year 2016."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "mryQ3L1uNdNHrVMu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.107, 0.184, 0.148, 0.201]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label for 'Futures'."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "Jj18G46myfsAIiSL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at the coordinate [0.107, 0.106, 0.133, 0.123]."
+            },
+            {
+                "from": "gpt",
+                "value": "ETFs label."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "KPSeLC43NX1qv4na",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {coordinate}[0.903, 0.684], 'Mutual Funds' or 'Forex'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Forex."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "uyruTpOBdVFegqnL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the results for 'Bonds' at {point}[0.259, 0.857] higher or lower than 'Futures' at {point}[0.259, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "95Kehf2IFRwHL1pH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment returns more at {coordinate}[0.517, 0.656], 'Stocks' or 'Options'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stocks."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "xOyUx4vNI8EEZ99G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.130, 0.123, 0.710, 0.201]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the year labels for 'Forex' with a color-coded marker ranging from years 2005 to 2014."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "XMJgl9NuJKoAfW1b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are included in the space [0.130, 0.502, 0.710, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains multiple points from {Options line}[0.452, 0.502] to {Futures line}[0.710, 0.571] representing the years 2010 to 2014, marked with several data labels and respective markers."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "XNv7IuiChkh52BQd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a summary of what is displayed in the area [0.048, 0.065, 0.985, 0.171]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the x-axis, including labels for years from 2004 to 2018, and the y-axis label with percentages incrementing in steps of 5%, from 10% to 55%."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "6ODYJt0Bgzic5wuZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element present at coordinates [0.122, 0.870, 0.954, 0.451] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This position corresponds to the line plot representing the {Market Index}<r>[0.122, 0.068, 0.954, 0.451]</r>."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "11ss4q6tyUTr88xi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is {Global Trend (%)}<r>[0.021, 0.278, 0.050, 0.624]</r>."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "rABBle2ZTWcs1Hu6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the \"Global GDP\" line located in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Global GDP\" line is plotted at {coordinates}<r>[0.122, 0.870, 0.954, 0.870]</r>."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "0snLYC9261TbfTOW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is closer to the x-axis, {the point in 2014 on the 'Global GDP'}<r>[0.455, 0.870]</r>, or {the point in 2014 on the 'Market Index'}<r>[0.455, 0.298]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point in 2014 on the \"Market Index\" is closer."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "QMhva7M81qDDmtJA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point in 2018 on 'Market Index'}<r>[0.787, 0.145]</r> shows higher or lower value from the {point in 2018 on 'Global GDP'}<r>[0.787, 0.870]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "kR9bmSETnsJEKR28",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is closer to the origin, the {first point on the 'Global GDP' line}<r>[0.122, 0.870]</r>, or the {first point on the 'Market Index' line}<r>[0.122, 0.451]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first point on the 'Market Index' line."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "y0mlzViQyzCEhj7I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present between the coordinates [0.122, 0.451, 0.954, 0.870]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses two lines. The {Global GDP line}<r>[0.122, 0.870, 0.954, 0.870]</r> and the {Market Index line}<r>[0.122, 0.068, 0.954, 0.451]</r>. Both lines are represented with different color codes."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "lvHOqDfrmXtValvk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.122, 0.030, 0.954, 0.871]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main plot area of a line graph. It contains two lines representing {Global GDP}<r>[0.122, 0.870, 0.954, 0.870]</r> and {Market Index}<r>[0.122, 0.068, 0.954, 0.451]</r>, with corresponding data points indicated."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "c1vILlHNzbb1IkAw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the space located within [0.122, 0.068, 0.954, 0.451]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart that plots the {Market Index line}<r>[0.122, 0.068, 0.954, 0.451]</r>. It maps each year's data with different positions along the line to show the performance of the market index over years."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "SPS3za28RfTYm7bg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis in the chart signify based on the label with coordinates [0.045, 0.276, 0.074, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Currency Performance (%)."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "aXCCf0sxAFUE0Ftd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the coordinates for the label of year 2019 on the x-axis."
+            },
+            {
+                "from": "gpt",
+                "value": "[0.602, 0.910, 0.657, 0.939]"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "YTLtADKiXcnT6fBn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tick label} at [0.082, 0.756, 0.110, 0.785] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "55."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "94zm6jkPpvXyOnR6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {year 2017}[0.395, 0.890], is {Bitcoin}[0.395, 0.505] performing above or below {Ethereum}[0.395, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "2QPej3evflvUlvk8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Ethereum} and {Ripple} at the {year 2020}[0.747, 0.890], which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ethereum."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "XGDpO4czhwm4FaeA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {Bitcoin}[0.865, 0.418], is the performance at {2020}[0.747, 0.663] higher or lower than in {2021}[0.865, 0.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "imdXTwBy2vA3Tk1y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content found within the coordinates [0.136, 0.134, 0.320, 0.274]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart indicating three digital currencies: Bitcoin - {Blue label}[0.138, 0.150, 0.194, 0.168], Ethereum - {Red label}[0.138, 0.194, 0.194, 0.211], and Ripple - {Green label}[0.138, 0.237, 0.194, 0.255]."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "167mErT79xdl7V6R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present within [0.160, 0.155, 0.865, 0.855]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are three line trends for Bitcoin, Ethereum, and Ripple plotted across various years from 2015-2021. Each trend is represented by a distinct color and marked with dots signifying yearly values."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "LRSvpnbXVAHvoZ2d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items found within the region defined by [0.144, 0.069, 0.881, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the graph, labeled as \"Digital Currency Performance Over The Years\"."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "O9ZLXP3BKpGbT6Re",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.220, 0.033, 0.635] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "1Vznkvta6AKXTS5s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'France' text in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.387, 0.795, 0.401, 0.888]."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "kKF6MEptPYqXmkyX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label}<r>[0.039, 0.264, 0.056, 0.292]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "XtkFSoYuqdSqIIHw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled 'USA'}<r>[0.788, 0.275]</r> higher or lower than {the point labeled 'India'}<r>[0.722, 0.609]</r> on the \"_child0\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "Mecd3gy6nKralZTr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled 'Germany'}<r>[0.525, 0.108]</r> to the left or right of {the point labeled 'UK'}<r>[0.460, 0.191]</r> on the \"_child0\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "HXsA3VgCwwszR723",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country has a higher International Trade Index at their respective point, India or Japan?"
+            },
+            {
+                "from": "gpt",
+                "value": "India."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "NiqfXYMT5IQtBaE3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.296, 0.030, 0.755, 0.070]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle of the chart, \"International Trade and Globalization Trends,\" styled in black color."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "Ivmes2r6NErAkG8r",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.128, 795, 0.920, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the labels for the x-axis. The labels are the names of various countries such as {Japan}[0.125, 0.795, 0.139, 0.873], {Australia}[0.190, 0.795, 0.204, 0.918], and {China}[0.912, 0.795, 0.926, 0.875]."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "A7wkbQnWzCtJHCaT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.066, 0.776, 0.132, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object. The y-axis tick label '40' is located at {coordinates}[0.039, 0.765, 0.056, 0.793]."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "SQ4Ttci0zJzADbfb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label{ylabel}<r>[0.015, 0.392, 0.035, 0.526]</r> on Y-axis text?"
+            },
+            {
+                "from": "gpt",
+                "value": "Metrics."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "ZgZvTYBwFJOCfkEy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {chart's title}<r>[0.512, 0.942, 0.559, 0.975]</r> say?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "m8g9hliYbE591vUJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the eighth {x-axis tick}<r>[0.777, 0.909, 0.812, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "jWfN0gAHZLZqH08a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {GDP Growth Point in 2000}<r>[0.103, 0.893]</r> higher or lower than the {Freight Volume Point in 2000}<r>[0.103, 0.384]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "qnej45tYvDvL23rz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the {Transit time point of 2001}<r>[0.146, 0.893]</r> and the {Transit time point of 2002}<r>[0.189, 0.893]</r>. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transit time point of 2001."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "QYWFLqgctfV8FQJv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Freight Volume Point in 2005}<r>[0.319, 0.141]</r> higher or lower than the {Freight Volume Point in 2006}<r>[0.362, 0.170]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "Xnb6D7c9Opa8Riok",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.103, 0.893, 0.924, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the line of GDP growth rate, with distinct data points plotted for each year between 2000 and 2019."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "uzdNFUAM4TfutrRh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.110, 0.037, 0.275, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend, including {GDP growth rate}[0.154, 0.046, 0.270, 0.069], {Freight Volume}[0.154, 0.081, 0.258, 0.104] and {Transit Time}[0.154, 0.116, 0.239, 0.139], each labeled with a unique color."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "Z3Ud1U5qX8TeOLRL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within [0.172, 0.893, 0.207, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is the GDP growth rate data point for the year 2002."
+            }
+        ],
+        "id_raw": 460
+    },
+    {
+        "id": "zBHN9iG38XxeBqr8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of line corresponds to {this handle}<r>[0.702, 0.128, 0.747, 0.142]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for \"Life Satisfaction\"."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "xaKtBW1JuvScSFKV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the y-axis label located in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.019, 0.394, 0.044, 0.533]"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "A1fY0PPWEFySpV1a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the tick label} on y-axis at <r>[0.062, 0.540, 0.084, 0.564]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "S9S9j1OSHcUT3wJr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Environmental Quality line}<r>[0.145, 0.163, 0.932, 0.721]</r> at age 40 higher or lower than the {Life Satisfaction line}<r>[0.145, 0.189, 0.932, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "NcvhnMtNRRJQDnxX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at age 90, {Environmental Quality line}<r>[0.145, 0.163, 0.932, 0.721]</r> or {Life Satisfaction line}<r>[0.145, 0.189, 0.932, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environmental Quality."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "GTr3fNaOjHyrNZRz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a decrease in value from age 80 to age 90, {Physical Health Average line}<r>[0.145, 0.163, 0.932, 0.635]</r> or {Life Satisfaction line}<r>[0.145, 0.189, 0.932, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "qrWa4mldzqaKWq6H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.096, 0.034, 0.981, 0.893]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area of the chart which includes X and Y axis grids, labels for 'Age' and 'Ratings' on x and y axes respectively. It also contains the lines for 'Life Satisfaction', 'Environmental Quality' and 'Physical Health Average', each with their corresponding points at every decade from 20 to 100."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "eKgqmZxiZGymTg0W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the area <r>[0.700, 0.046, 0.973, 0.157]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the three legend items: {Physical Health Average}[0.755, 0.055, 0.966, 0.078], {Environmental Quality}[0.755, 0.090, 0.950, 0.113], and {Life Satisfaction}[0.755, 0.125, 0.895, 0.148], each paired with a color-specific marker."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "4MEI6tJFlFF63D3q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region <r>[0.916, 0.909, 0.949, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label '100' is located in this area."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "fIC8L3pZ4Ee2WQdG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the object}<r>[0.025, 0.375, 0.058, 0.502]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rate."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "9in30dQQRnaNhMGp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third line present in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.163, 0.496, 0.932, 0.782]."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "ya5R0zdNRxyYsswA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line represented by {the legend item 'Greenhouse Emissions'}<r>[0.693, 0.121, 0.954, 0.156]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "qQ0nlKRuguiUzii8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Deforestation'}<r>[0.163, 0.095, 0.932, 0.496]</r> above or below {the line for 'Animal Extinction'}<r>[0.163, 0.496, 0.932, 0.782]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "zYdNjpIKV2fxMwYi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts first, {the line for 'Greenhouse Emissions'}<r>[0.163, 0.553, 0.932, 0.627]</r> or {the line for 'Deforestation'}<r>[0.163, 0.095, 0.932, 0.496]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both start at the same time."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "H9I2UIZ2knPp2ObD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend shows a higher rate in year 2003, {Deforestation}<r>[0.334, 0.095]</r> or {Greenhouse Emissions}<r>[0.334, 0.622]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deforestation."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "DAMsGw9u4OheYrhE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area <r>[0.305, 0.864, 0.363, 0.899]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label '2003'."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "jYrlqrokVKkd7A28",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area <r>[0.619, 0.055, 0.963, 0.222]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with labels for 'Deforestation' at <r>[0.693, 0.069, 0.851, 0.104]</r>, 'Greenhouse Emissions' at <r>[0.693, 0.121, 0.954, 0.156]</r>, and 'Animal Extinction' at <r>[0.693, 0.173, 0.896, 0.208]</r>."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "ws2HkSzCeg4KLVIB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the coordinates <r>[0.618, 0755, 0.708, 0.790]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis label '0.7'."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "m0tu4x6GbOex8MxT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text value of the coordinate [0.310, 0.909, 0.345, 0.932] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2007."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "UOx2bFWdzX1RTwhb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat denote the object at coordinate [0.089, 0.058, 0.125, 0.072] referenced in the legend section?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker for 'Internet Use'."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "rJ9ijxx4twmh5QmB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.041, 0.259, 0.071, 0.283] on y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-label '0.40'."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "jebZ2guVkhRlojtK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does {line for 'Mobile Use'}[0.163, 0.268, 0.903, 0.815] stand in relation to {line for 'Social Media Use'}[0.163, 0.581, 0.903, 0.877] at the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "a3kP17qG7EB7TPdm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {line for 'Internet Use'}[0.163, 0.112, 0.903, 0.815] or {line for 'Social Media Use'}[0.163, 0.581, 0.903, 0.877], is higher on the chart for the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Internet Use."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "GxmHOlCS0GtVvVcX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the line of 'Internet Use'}[0.163, 0.112, 0.903, 0.815] in relation to {the line of 'Mobile Use'}[0.163, 0.268, 0.903, 0.815] at point '[0.492, 0.503]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "2tEWH8QVaaTGRvAW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region enclosed by [0.081, 0.034, 0.985, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main plot area. It is white and contains the trends for 'Internet Use', 'Mobile Use', and 'Social Media Use' from 2005 to 2014. The x-axis represents years, and the y-axis represents the usage percentage."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "GNgit0ydXM6ocURa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region inside [0.088, 0.046, 0.256, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart showing the different lines for the 'Internet Use', 'Mobile Use', and 'Social Media Use' with their respective marker colors and labels."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "TL7uqcjUAXU8iHmu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located in the area [0.015, 0.296, 0.035, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the y-axis label 'Usage Percentage', rotated by 90 degree."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "8OHLGE6rcLxd5tJW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first x-axis tick label according to the available coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "2008."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "zQ2U5e0TplkQVkuK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object within [0.025, 0.218, 0.058, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "QBGlMKNhxfJhdwLG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data is represented by the series located at [0.174, 0.118, 0.933, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Media_Total."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "8ihxcuSko8j1fsi9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data points, did Media_Total or SocialMedia have a higher value in the year 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Media_Total."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "Z4xO201WGOdkzbcm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich had a lower value in 2019, SocialMedia or TraditionalMedia?"
+            },
+            {
+                "from": "gpt",
+                "value": "TraditionalMedia."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "kj5HekEo4XGoojWs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of trend direction, is the SocialMedia line generally ascending or descending between the given coordinates [0.174, 0.086, 0.933, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascending."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "3WawKXm9JJKs6YFQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can you provide about the region [0.74, 0.055, 0.963, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, with markers and labels for Media_Total, SocialMedia, and TraditionalMedia."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "rZSOSrEgYAaLauhj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the area spanned by [0.132, 0.037, 0.975, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This large region encompasses the primary graph area, including the x and y axes with their respective labels (\"Year\" and \"Media Influence\"), the axes' tick labels, and the data lines for Media_Total, SocialMedia, and TraditionalMedia."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "ZauEyPW4ohp12UUq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the coordinates [0.174, 0.118, 0.933, 0.294]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the \u2018Media_Total\u2019 trend line with variable data points from 2010 to 2019."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "tohlgpQC9r5B6nBR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name of the {object}<r>[0.836, 0.069, 0.945, 0.104]</r> in the figure's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Car Sales."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "7WIVbvIvejxtWQcR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {this y-axis tick label}<r>[0.068, 0.291, 0.112, 0.326]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "XmTc8NVMoNsJOPN6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does {this x-axis tick label}<r>[0.819, 0.864, 0.877, 0.899]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "7Zf2mnR0IWLuvPfN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Car Sales'}<r>[0.848, 0.225]</r> higher or lower than {the point of 'Bike Sales'}<r>[0.848, 0.481]</r> for the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "EvhiJ90fgDXwuKc6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the 'Car Sales' value}<r>[0.594, 0.144]</r> in 2015 to {the 'EV Sales' value}<r>[0.594, 0.625]</r>, which is greater?"
+            },
+            {
+                "from": "gpt",
+                "value": "Car Sales."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "76FRGozJ3qyru0Dx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2011, did {Car Sales}<r>[0.255, 0.198]</r> or {EV Sales}<r>[0.255, 0.706]</r> have higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Car Sales."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "i0yqiqzxzH102gTP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.762, 0.055, 0.963, 0.222]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the figure's legend with three items: {Car Sales}[0.836, 0.069, 0.945, 0.104], {Bike Sales}[0.836, 0.121, 0.954, 0.156], and {EV Sales}[0.836, 0.173, 0.935, 0.208]. Each item includes a colored marker symbol and a label naming the sales category it represents."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "nzOrvrkgHT6D58ts",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.128, 0.038, 0.975, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily encompasses the plotting area of the line chart. It includes three distinct lines relating to 'Car Sales', 'Bike Sales', and 'EV Sales', and features corresponding markers showing yearly data for each category from 2010 to 2019."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "F6ufSKkhLtPcchAU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items in the region [0.903, 0.864, 0.962, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the last tick label on the x-axis, correlating to the year '2019'."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "Bu5qD8YLLBLzOd1m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the axis name for the position set as [0.510, 0.945, 0.554, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "XpqPU8Otx7pHMJjT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label {point}[0.757, 0.660] on the line chart represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "Augmented Reality in 2025."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "32EVwWM4JWqBXtQl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label in the coordinates [0.019, 0.257, 0.041, 0.673] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Students (millions)."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "l1bzRqvwndbJKbQx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 2030}[0.869,0.173], does 'Online Learning' have a larger number of students than 'Augmented Reality'[0.869,0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "kuuRuUZGKPFj73em",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the On-campus Learning line}[0.195, 0.312, 0.869, 0.507] depict a decrease in the number of students from the point at {2000}[0.195, 0.854] to the point at {2020}[0.644, 0.423]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "e23s6YUw5bR1IJyR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {On-campus Learning}[0.195, 0.451] point higher than the {Online Learning}[0.195, 0.854] point at year 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "Z5Gvyy2p8R5SzfBO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the main area bordered by [0.082, 0.034, 0.981, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart, containing three line graphs representing 'On-campus Learning', 'Online Learning', and 'Augmented Reality'. The x-axis represents 'Year', ranging from 2000 to 2030. The y-axis represents 'Number of Students (millions)', ranging from 0 to 30."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "BnlIjrn3BXnoSkCy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.091, 0.046, 0.333, 0.157] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart, with three labels: {On-campus Learning}[0.147, 0.055, 0.326, 0.078], {Online Learning}[0.147, 0.090, 0.283, 0.113], and {Augmented Reality}[0.147, 0.125, 0.313, 0.148], each associated with a distinct color."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "ytNolNCiP10wTHHh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.048, 0.164, 0.070, 0.187]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the '25' y-axis label, representing 25 million students."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "Olb0DrMZbNiIMcK4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.068, 0.235, 0.104, 0.270] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "y-axis tick label '0.9'."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "Ch1NLeH2Szwtg0V7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second point in the 'Firewall Detection Accuracy' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.230, 0.445]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "y7WXfOKtWen36Ri3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.222, 0.864, 0.237, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "8iQATNNqtgamAgc3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for month '3'}[0.320, 0.524] of 'Two-Factor Authentication Accuracy' above or below {the point for month '3'}[0.320, 0.445] of 'Firewall Detection Accuracy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "xtqLgljq2rahLCGI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Secure Login Accuracy'}[[0.139, 0.130, 0.957, 0.347]] generally above or below {the line for 'Firewall Detection Accuracy'}[[0.139, 0.248, 0.957, 0.544]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "ru8q1BFsCgqmeIhh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich security measure has a higher accuracy at {month 10}[0.957, 0.248], Secure Login or Firewall Detection?"
+            },
+            {
+                "from": "gpt",
+                "value": "Secure Login."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "UvD23Ozbxb9Wc8b2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.540, 0.668, 0.803, 0.703]."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the label for \"Secure Login Accuracy\", which is accompanied by a blue marker."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "FjNYp3DtHPzqvHJO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.466, 0.654, 0.963, 0.822]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Secure Login Accuracy}[0.540, 0.668, 0.803, 0.703], {Two-Factor Authentication Accuracy}[0.540, 0.721, 0.954, 0.756], and {Firewall Detection Accuracy}[0.540, 0.773, 0.861, 0.808], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "gXoNFBM7hMEz8K7v",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.068, 0.432, 0.104, 0.467]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The y-axis tick label '0.8' is located within these coordinates."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "qMDBNhHqqGIZLFDw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart does this JSON structure represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "line chart."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "CESLRP84ZMsTYQAQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} located at [0.508, 0.912, 0.587, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "73YBg5de0cXSg8rb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.134, 0.178, 0.195, 0.199] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the colored marker corresponding to 'Market Expansion'."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "UTzzST7zC8mzRMcD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2002 in Prototype Success}[0.334, 0.387] higher or lower than {the same year's point in Patent Filings}[0.334, 0.402]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "sNUfF13LWFkmQ5tP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the year 2005, which line {Prototype Success}[0.548, 0.497] or {Market Expansion}[0.548, 0.271] has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Expansion."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "UnDTkoE3ozpVOYpS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category, {Prototype Success}[0.904, 0.628] or {Patent Filings}[0.904, 0.169], had higher values for the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Prototype Success."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "gHB2wv6t8MxOqrPs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained within the region [0.192, 0.169, 0.904, 0.511]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line plots for 'Patent Filings' and  'Prototype Success' from 2000-2010 and the points along each line representing yearly values."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "vlQw4eiOdsT87OJW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.134, 0.055, 0.427, 0.222]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the region where the chart's legend is located. It contains labels for 'Prototype Success', 'Patent Filings', 'Market Expansion' along with their respective color markers."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "VfjMj09ID09yISjK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.163, 0.864, 0.933, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis of the chart, with tick labels showing the years from 2000 to 2010."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "VjhW3tIVtKNmQy2g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.859, 0.055, 0.966, 0.078]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label of Electric cars in the legend."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "Gk8wgjRBgG7MFpd8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the position [0.019, 0.340, 0.044, 0.587] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "uQoPhdXZ4sllrzji",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the text at coordinates [0.811, 0.909, 0.855, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label \"2019\"."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "3F4bVWgSnACQHWeL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the 2015 point, the {Electric cars line}[0.165, 0.206, 0.907, 0.876] or the {Hybrid cars line}[0.165, 0.412, 0.907, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hybrid cars."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "zKwgYCVgvY7c16wo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point corresponding to the year 2012, is the {Diesel cars line}[0.165, 0.206, 0.907, 0.670] above or below {the Electric cars line}[0.165, 0.206, 0.907, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "nplcdzTVDvJBJGp8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one reaches higher in 2020, {the Electric cars line} [0.165, 0.206, 0.907, 0.876] or {the Hybrid cars line}[0.165, 0.412, 0.907, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electric cars."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "LMYEdLiFCOyr4UX1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area of [0.804, 0.046, 0.973, 0.157]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, which includes makers and labels for \"Electric cars\", \"Hybrid cars\", and \"Diesel cars\", represented by different colors."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "q2CTToDob1aOeW1x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.288, 0.711, 0.331, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels for the years 2010 through 2020, placed appropriately at their respective positions."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "JghPJxt5fXSuWHJ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within the coordinates [0.051, 0.025, 0.078, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts the y-axis labels specifying different market shares, ranging from 0.0 to 0.5."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "Ul33fkpjppA9ILwv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.065, 0.887, 0.074, 0.910]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label for the value '0'."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "byERWlxnurqfQwTc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled {Mobile Phone Users} with handle color '#ff7f0e'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color refers to the line in the chart representing the Mobile Phone Users."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "ZBlWLa5FIb8wwZ5G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point}[0.277, 0.831] within the boundary of 'Email Users' line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the number of Email Users in the year 2002."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "7cs9RcCOIA1cpCSh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the coordinates of the {Internet Users}<r>[0.148, 0.626, 0.921, 0.857]</r> above or below {Mobile Phone Users}<r>[0.148, 0.145, 0.921, 0.814]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "N76hAnRroYRZ3CPI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Email Users}<r>[0.148, 0.509, 0.921, 0.844]</r> line between {Internet Users}<r>[0.148, 0.626, 0.921, 0.857]</r> and {Mobile Phone Users}<r>[0.148, 0.145, 0.921, 0.814]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "bUMieDLT0tRJfmhP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich set of users has a higher value at the year 2012, {Mobile Phone Users}<r>[0.921, 0.145]</r> or {Email Users}<r>[0.921, 0.509]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Phone Users."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "XtOUNI1hQXuDwMON",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content and color of the line indicated by these coordinates [0.148, 0.626, 0.921, 0.857]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line representing 'Internet Users', colored blue and marked with circular points."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "fNZaDO0WcgDokSw0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the structure present within the coordinates [0.083, 0.034, 0.985, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates represent a complete graph with y-grid and x-grid lines. It includes the plotting of 'Internet Users', 'Mobile Phone Users', and 'Email Users'. It also has the x-axis label 'Year' and y-axis label 'Number of Users (in Millions)'."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "N34iaJpobp9SiToi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.090, 0.046, 0.276, 0.157]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, including the labels 'Internet Users', 'Mobile Phone Users', and 'Email Users', each with their corresponding colors."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "tosZ0puoHZsJAiBs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.408, 0.035, 0.510]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "sHRDgtDR9x0LfwjT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the point for the year 2009 in the 'Global Trade' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.466, 0.546]"
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "RLlYH9aXt4yRBFNN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the x-axis tick label at [0.924, 0.909, 0.959, 0.932] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "SSFv4OzNtPcrtU1O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2005 on the 'Global Trade' line}[0.293, 0.536] above or below {the point for the year 2005 on the 'Supply Chain Management' line}[0.293, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "veQDON3WRu60uw6t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Global Trade'}[[0.120, 0.507, 0.942, 0.652]] overall above or below {the line for 'Volatile Market'}[[0.120, 0.616, 0.942, 0.866]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "0UFfosgRJv7fSgLi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric has a higher value for the year 2008, Global Trade or Volatile Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Trade."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "dskAZvy42WGCtxQl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.741, 0.037, 0.978, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend titled \"Metrics\", which includes items: {Global Trade}[0.785, 0.081, 0.872, 0.104], {Supply Chain Management}[0.785, 0.116, 0.972, 0.139], and {Volatile Market}[0.785, 0.151, 0.888, 0.174], each paired with a color marker respectively."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "rSJgGvHt8Zpyblsr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.041, 0.112, 0.067, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis tick label for the value '200'."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "4T9ihofnfYAx4HOK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.405, 0.909, 0.440, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis tick label '2008' is located at this coordinate."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "OnXVLOGkSgo2DwLx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat symbol represents the \"Urban Population\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue circle."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "AWKg9auO1Vz2ePMY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.159, 0.778] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The starting point of {the Urban Population line}<r>[0.159, 0.456, 0.936, 0.778]</r>."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "N18fPU7mqd0ZT0Ex",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.068, 0.487, 0.104, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5.0."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "2n6Yq9j2P0ttNSYh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Urban Population marker}<r>[0.159, 0.456]</r> higher than {the Total Population marker}<r>[0.159, 0.381]</r> in the year 2000 on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "5zM0HRq35xBh1z8l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is earlier, {the x-tick labeled '2002'}<r>[0.208, 0.864, 0.266, 0.899]</r> or {the x-tick labeled '2010'}<r>[0.518, 0.864, 0.577, 0.899]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2002."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "ttzEJ0gT8PedeBON",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the value at the first Rural Population point}<r>[0.159, 0.668]</r> with {the first Urban Population point}<r>[0.159, 0.778]</r>, which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural Population."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "BDw7D5Ixmt6NAJEJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area located between [0.120, 0.726, 0.940, 0.864]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis and the axis labels for years spanning from 2000 to 2020, arranged from left to right."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "FFJ3U3uMPmFi1Z5y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region [0.058, 0.185, 0.974, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses all the line plots representing Urban, Rural, and Total Population trend lines, including various data points marked along the lines."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "f8ctK4TFQPbXMDx4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the region starting from [0.006, 0.123] to [0.415, 0.277]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the sublegend for the graph showing Urban, Rural, and Total Population, along with color-coded markers beside each label to indicate their corresponding lines in the plot."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "4i1FuYDD484t8zWP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.814, 0.747, 0.969, 0.775] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interactive Learning."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "Dms47YCzAl1TcVNz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the element located at [0.038, 0.039, 0.068, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '100'."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "Eq8KNJL9hJwWlwG1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at [0.079, 0.030, 0.983, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plot area of the graph."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "ki49xArCAA1UsnxP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Direct Learning line}[0.120, 0.232, 0.942, 0.446] above or below {the Video Learning line}[0.120, 0.050, 0.942, 0.351] in 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "C9JmLdn6Yfdgt2FB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of Interactive Learning in 2020}[0.942, 0.066] higher or lower than {the point of Direct Learning in the same year}[0.942, 0.446]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "db0UfweyhaSTcRVc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the point of Video Learning in 2007}[0.120, 0.351] with {the point of Interactive Learning in the same year}[0.120, 0.398]. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interactive Learning."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "X6LQDiCv2nqiRbSD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the boundaries [0.765, 0.694, 0.976, 0.828]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the graph's legend, which includes: {Direct Learning with a blue color}[0.766, 0.709, 0.807, 0.726, 0.814, 0.705, 0.933, 0.733], {Interactive Learning with a green color}[0.766, 0.751, 0.807, 0.768, 0.814, 0.747, 0.969, 0.775], and {Video Learning with an orange color}[0.766, 0.793, 0.807, 0.809, 0.814, 0.789, 0.930, 0.817]."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "w9mp3VTBbj27gcXN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects inside the box [0.017, 0.316, 0.032, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical area contains the y-axis label, which reads 'Success Rate (%)."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "cu1LabgKlI78GDcK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements in the region [0.533, 0.242, 0.625, 0.352]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of part of the 'Direct Learning' and 'Interactive Learning' lines chart, including several points and lines between the years 2012 to 2015."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "xRwal6TgwHeWWDTi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.721, 0.735, 0.966, 0.763] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Energy Adoption."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "DzEBIeGB1CpRVSJ5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label of line_2 depicted at coordinates [0.127, 0.332, 0.945, 0.456]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population Growth."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "i8O1ZHxTuohsrf9d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at [0.019, 0.435, 0.044, 0.477] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "%."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "PrU3AN6QBazm45Fr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '4'}[0.399, 0.612] on the 'Renewable Energy Adoption' trend line above or below {the point '4'}[0.399, 0.269] on the 'CO2 Emissions' trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "LSORXyKcf0ATvUjL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 3, is the value of {Renewable Energy Adoption}[0.309, 0.664] less than, equal to, or greater than the value of {Population Growth}[0.309, 0.404]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "vieFONyHFzX3j8z8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 6, did {CO2 Emissions}[0.581, 0.321] increase or decrease compared to {year 5}[0.490, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "crAmea04DVwjMw4p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.667, 0.739, 0.713, 0.755]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a marker or an icon for the 'Renewable Energy Adoption' trend line in the color '#1f77b4'."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "XbJxcCAfxStk9cA4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of region [0.121, 0.891, 0.132, 0.919]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label '1' for the x-axis tick."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "irHg988nkdJSIsbH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.051, 0.341, 0.078, 0.369] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the '0.6' label for the y-axis tick."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "1ZVq2MhPLYLQaf21",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.025, 0.280, 0.058, 0.621] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "Sml3kGglMzUsGEum",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the third item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.199, 0.139, 0.525, 0.167]."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "mLRWWmMz02OCepV3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeling of the {point}<r>[0.221, 0.798]</r> on the third line?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "uK9HSOZ5mYJnsHO9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 2015}<r>[0.221, 0.798]</r> on the 'AI Adoption in Health Sector' line above or below the {point of 2016}<r>[0.329, 0.766]</r> on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "BLBqYIBA9YHAj5jX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {percentage in 2015 for 'Rate of Digital Record Adoption'}<r>[0.221, 0.556]</r> higher or lower than the {percentage in 2015 for 'Increase in Medical Data Volume'}<r>[0.221, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "b3k9i19y2EYnXFQB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher percentage in 2021, 'Increase in Medical Data Volume' or 'AI Adoption in Health Sector'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase in Medical Data Volume."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "ioe3QMiZPqPiqKOj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.125, 0.044, 0.587, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend with three items: {Rate of Digital Record Adoption}<r>[0.199, 0.055, 0.563, 0.083]</r>, {Increase in Medical Data Volume}<r>[0.199, 0.097, 0.577, 0.125]</r>, and {AI Adoption in Health Sector}<r>[0.199, 0.139, 0.525, 0.167]</r>. Each item has a color marker associated with it."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "yVvY6aZsy5ZW0ish",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area enclosed by [0.515, 0.891, 0.573, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The country\u2019s population information for the year {2018}<r>[0.515, 0.891, 0.573, 0.919]</r> is enclosed in this area."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "Koch6UDAae6y8lKM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the boundary  [0.221, 0.114, 0.867, 0.661]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line 'Increase in Medical Data Volume' with its data points for each year from 2015 to 2021 is contained within the boundary."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "hdh3KfOCCDhfYZjo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.337, 0.029, 0.631]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Tourists ('000)"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "Th1CbKMx2NAvTbDa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the point coordinate {for year 2012}<r>[0.273, 0.661]</r> on the blue line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.273, 0.661]"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "i2GpdQxFo7wP6ksj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.492, 0.952, 0.521, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "wBQefqEHKnSEtKr5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year 2014}[0.429, 0.525] above or below {the point for year 2019}[0.817, 0.104] on the 'Tourism' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "SD1cy5upGr0VVkZ2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Year 2018 point}[0.740, 0.167] in the red line higher or lower than {the Year 2018 point}[0.740, 0.185] in the blue line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "UTXax0Bl5hRJAqhq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher point at {Year 2016}[0.584, 0.321], 'Tourism' or 'Facility Rating'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Tourism'."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "ugBe7R9MuHFrcRv4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.247, 0.025, 0.766, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Tourism and Hospitality Industry Data Analysis,\" is located here."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "IPDxstS9CTkDJKq6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.079, 0.066, 0.934, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the plot of the chart features two intersecting lines: {the 'Tourism' line}[0.118, 0.104, 0.895, 0.865] indicated in blue, and {the 'Facility Rating' line}[0.118, 0.104, 0.895, 0.865] colored red, representing two different data trends over time."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "kA0fYbZ6fKYgmJW7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.101, 0.919, 0.136, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis tick label '2010' is located at {coordinates}[0.101, 0.919, 0.136, 0.942]."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "euDQG60Sy41iivkg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label for x-axis}<r>[0.473, 0.942, 0.585, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Level."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "YTybsNtFj0fl3Ur3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what object is located at [0.979, 1.002, 0.063, 1.002]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a Y-axis label, specifically -0.5."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "2hFMspelq9CVCupG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item}'s text at <r>[0.124, 0.086, 0.198, 0.110]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Probability."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "imS0ErYNputjT9lu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Risk Level 'High' on 'Probability'}<r>[0.529, 0.723]</r> has a higher or a lower value than {Risk Level 'Medium' on 'Probability'}<r>[0.321, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "8aCsqXX4C9IANCt1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {Impact's value for 'Medium' risk level}<r>[0.321, 0.767]</r> compared to {Probability's value for 'Medium' risk level}<r>[0.321, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "dPPV0NColgYLAZdf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Risk Level 'Low' on 'Impact'}<r>[0.114, 0.833]</r> higher or lower than {Risk Level 'Low' on 'Probability'}<r>[0.114, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "9h48kkfdf0C6wX9I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide some details about the objects in the area marked as [0.514, 0.909, 0.545, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is an X-axis tick label with the text 'High'."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "rCq3ByfIouMiRYsB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.079, 0.077, 0.203, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes the texts for 'Probability' with {coordinates}<r>[0.124, 0.086, 0.198, 0.110]</r>, and 'Impact' with {coordinates}<r>[0.124, 0.121, 0.172, 0.145]</r>."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "JKQXilyaG23tC5g2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.334, 0.025, 0.723, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is one object: the subtitle of the chart, which reads \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "2oaoFV3ByxUhi1bM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object at the coordinates [0.164, 0.813] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2001."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "9LUB803OZflzivqd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-tick label at {the coordinates} <r>[0.512, 0.902, 0.555, 0.932]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2010."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "roYKJ3tk0UgxUcAh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is assigned to the y-axis label in the coordinates [0.015, 0.366, 0.035, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Count/Level."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "c0BVnstrs76cHdcn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the coordinate} <r>[0.883, 0.932, 0.963, 0.9323]</r>, which is greater, Integration_Level or Heritage_Count?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heritage_Count."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "hQWtMk479OAm5N6o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point}[0.739, 0.845] of the Integration_Level line fall below or above the point at {the Heritage_Count line}[0.739, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "emGSMqGbNywCq5rf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Integration_Level at year {2000}<r>[0.164, 0.813]</r> lower or higher than year {2010}<r>[0.533, 0.832]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "ntxkymbCARsrZhY9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.41, 0.773, 0.48, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the year 2007 along the x-axis and corresponding point of the Heritage_Count line."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "nM0LV4oZYprwL94Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.808, 0.077, 0.978, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed area consists of the legend for the line chart. It contains labels for Heritage_Count and Integration_Level along with their respective color codings."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "DHViUZvim9jFokXU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are contained within the region [0.923, 0.902, 0.965, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label '2020'."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "1PoKbriVjh1gtrSi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the top chart in this line diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Active Chats\""
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "tSjuZg8jmKsLA1ie",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of chart according to the chart_type attribute of the metadata?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "XTQnyuEHTbfK38XH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the ylabel {coordinates}[0.015, 0.391, 0.029, 0.537] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tab:blue."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "gVJYtVyoMwyAYzKI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the first subplot is higher at point {coordinates}[0.270, 0.065], '#d62728' or '#1f77b4'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'#d62728'."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "as1hnEbSsOLgATt8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point with coordinates {coordinates}[0.895, 0.420] on the line '#d62728' above or below the point at {coordinates}[0.895, 0.672] on the line '#1f77b4' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "MfEXIDGbWSPDOyDU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the line color of the higher line at the point {coordinates}[0.348, 0.597] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "'#d62728'."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "REH3NqXjJIgk8rpo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.035, 0.965, 0.065, 0.988] in the second subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"0.8\" can be found here, styled in tab:blue color."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "nG6vGZei8nXPZ5uL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.035, 0.836, 0.065, 0.860] on the y-axis in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label \"1.0\", styled in tab:blue color."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "OBV9oLuBW5jdT6nq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be found in region [0.971, 0.307, 0.985, 0.621] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Average Response Time (s)\" can be found here, styled in tab:blue color."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "KPNCfpJnZWq0le8x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label with coordinates [0.015, 0.117, 0.035, 0.841] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Heart Rate (beats per minute)."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "HnCT3HJaWKssfhgb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.636, 0.909, 0.645, 0.932]"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "hk5x7NFQa8OwT20l",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {7th point of the 'Active Adults' line} at [0.564, 0.650]?"
+            },
+            {
+                "from": "gpt",
+                "value": "79.7."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "EVTUtMck3ui3juQW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 3rd point on the 'Active Adults' line}[0.261, 0.855] higher or lower than {the 3rd point on the 'Sedentary Adults' line}[0.261, 0.267]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "KmmOHK4qqXzvnIEL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich group has a higher heart rate at the {tenth point} [0.792, 0.445] for 'Active Adults' and [0.792, 0.336] for 'Sedentary Adults'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Active Adults."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "lbWluxEZWZRcPt90",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Active Adults' line}[[0.110, 0.295, 0.943, 0.855]] generally above or below {the 'Sedentary Adults' line}[[0.110, 0.103, 0.943, 0.513]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "hphJAPXMS8cjIvad",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.853, 0.121, 0.973, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the label for Sedentary Adults, denoted with a color marker."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "A8vFQtNt9GLghoJE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.853, 0.086, 0.944, 0.110]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the label for Active Adults, along with a color marker."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "qWirTWIOf9SaKDKc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.413, 0.103, 0.489, 0.719]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 5th data points for both Active and Sedentary Adults represented on their respective lines."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "S4ruzxptYyxjdKka",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.512, 0.942, 0.560, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "fdfbhheuZZv45Ahx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the eighth text on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.583, 0.902, 0.625, 0.932]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "2I71mKtcmh3YP6Yy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value shown by {the y-axis tick-label} at [0.041, 0.837, 0.077, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "30%."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "6sIomsiQgpuxMobu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the year 1999, is {Industry 1's share}[0.740, 0.700] higher or lower than {Industry 2's share}[0.740, 0.252]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "nsrPH8VlCO7b4WiS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Industry 1 point for the year 2002}[0.944, 0.640] to the left or right of {the Industry 2 point for the year 2002}[0.944, 0.103]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "bvIKmFbwFp9hsIhy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry had a higher market share in 1996, Industry 1 at {coordinates}[0.536, 0.670] or Industry 2 at {coordinates}[0.536, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industry 1."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "oOArUwFm2fXQjrAU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is within the coordinates [0.342, 0.025, 0.730, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the chart: \"Industry Market Share Comparison\"."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "2xGt8052bMgKBGFX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area [0.094, 0.077, 0.214, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend, which includes {Industry 1}[0.138, 0.086, 0.209, 0.110] with its blue color marker at {coordinates} [0.096, 0.091, 0.131, 0.102], and {Industry 2}[0.138, 0.121, 0.209, 0.145] with its red color marker at {coordinates}[0.096, 0.126, 0.131, 0.137]."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "fM82nXsMkWN3QZet",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within [0.041, 0.091, 0.077, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a y-axis tick label '55%'."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "xnyFqv8Zw3KN0Tbb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label at the coordinate [0.814, 0.121, 0.954, 0.145] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter Engagement."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "AU3fpCnInil3wqFk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label at the point with coordinates [0.118, 0.154, 0.944, 0.682]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Facebook Engagement\"."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "YOWPvPvN8IrzeJHb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label}<r>[0.015, 0.306, 0.035, 0.652]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Engagement Level."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "nyRVtBEWrDitNJVA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Facebook Engagement} line <r>[0.118, 0.154, 0.944, 0.682]</r> have higher values than {Twitter Engagement} line <r>[0.118, 0.294, 0.944, 0.858]</r> in the early years (2011 - 2013)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "i3jMnLk1dFX9MhDt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2011 point on the Facebook Engagement line}<r>[0.118, 0.682]</r> above {the 2011 point on the Twitter Engagement line}<r>[0.118, 0.858]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "BBfJeIu9IRPghKDK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line drops more from 2018 to 2020: the {Facebook Engagement} line<r>[0.760, 0.154, 0.944, 0.277]</r> or the {Twitter Engagement} line<r>[0.760, 0.470, 0.944, 0.294]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook Engagement."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "bO6XXWVShtaN4sk9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region <r>[0.770, 0.077, 0.978, 0.154]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the two legend items are located: {Facebook Engagement} <r>[0.814, 0.086, 0.972, 0.110]</r> and {Twitter Engagement} <r>[0.814, 0.121, 0.954, 0.145]</r>, each with their individual color markers."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "Os86jmuH3EdkbmOV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region from <r>[0.100, 0.909, 0.136, 0.932]</r> to <r>[0.926, 0.909, 0.961, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the X-axis labels, representing the years from 2011 to 2020."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "G38keYvxDr9epZga",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nList objects within region <r>[0.041, 0.056, 0.067, 0.080]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one object, the y-axis tick label '135'."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "RYlEqzn1xiuQ862E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the object at coordinates [0.023, 0.319, 0.045, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label for the Air Quality Index (AQI)."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "8Botr8r6B02AhnY4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me the coordinates of the 4th x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are [0.385, 0.899, 0.440, 0.928]."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "zZ21KSBXqUCPspwd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the y-axis label}<r>[0.023, 0.319, 0.045, 0.642]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Air Quality Index (AQI)\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "8FBfWZdBZHFu5WdN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {point at year 2014}[0.412, 0.459] and {point at year 2020}[0.815, 0.118], which year has a higher AQI?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2014."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "XR2ZzzOiYNem3Cz0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {population in 2020}[0.815, 0.118] higher or lower than the {population in 2010}[0.144, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "64oJMEOlyk4yPeFd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn reference to the AQI Line, does the {point in 2012}[0.278, 0.225] have a higher value than the {point in 2013}[0.345, 0.352]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "79MzcIAmVZ8HOAQ7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the boundary [0.103, 0.031, 0.856, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle \"AQI and Population changes over the years\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "N4WZ62s8EHGXqkwa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area located at [0.111, 0.082, 0.848, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents both the plots of AQI and Population changes over the years."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "pVLQT7w24r8Mj9eR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.144, 0.118, 0.815, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the plots for AQI and Population over the years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "RaYecG4tsFW5tkpi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.507, 0.942, 0.555, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "ucZIMhYEZ81VW927",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the fifth tick on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "140."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "vqQtPr7T0zxFMWzq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.015, 0.283, 0.035, 0.675] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "pRYrRJQCHDevEFMs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value for {Medical Technology Spending}[0.118, 0.103, 0.944, 0.777] increase or decrease from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "WukdRSMfS1ZMaPVl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2005, did {Medical Technology Spending}[0.118, 0.777] or {Medical Research Spending}[0.118, 0.259] have a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical Research Spending."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "zcmYiaho3MlN1wwt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, was the {Medical Technology Spending}[0.944, 0.103] higher than or lower than the {Medical Research Spending}[0.944, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "c9pt8X7v379LIWzw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content located at [0.084, 0.077, 0.339, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the \"Medical Technology Spending\" with a blue marker and \"Medical Research Spending\" with a red marker."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "PugOwZ2oC58zqJ8P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found in the area [0.106, 0.893, 0.944, 0.932]"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains x-axis tick labels indicating years from '2005' to '2020'."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "beqO3AQfcAafyBBF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visible in the area [0.041, 0.042, 0.067, 0.999]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains y-axis tick labels representing 'Spending (in billions)' from '60' to '240'."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "yaT6gdoDpCBGYaMk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat serves as the label of the y-axis within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Trend\", as observed at these coordinates: {ylabel}<r>[0.015, 0.351, 0.035, 0.453]</r>."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "yJyQT0A334nI7bOV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the point at the coordinates [0.490, 0.300]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the trend score of \"Crop Tops\"."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "DVPUuABjbWn8CbcQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label in the bounding box [0.429, 0.942, 0.624, 0.975] represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label represents the \"Fashion Elements\"."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "BGeytmpJF3uj52NA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the trend scores for 'Skinny Jeans' higher or lower than that of 'Ripped Jeans'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "M3JQt69jp80DGwTq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trend scores, is 'Off Shoulder' more or less trendy than 'Flare Skirts'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "pkBekW3HsGXFKqgv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Leather Jackets' and 'Animal Prints', which fashion element is more in trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Leather Jackets'."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "Ef8o4ZXkVr8RimLt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region defined by this bounding box: [0.068, 0.066, 0.045, 0.739]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plotting area of the chart where the trends of the different fashion elements are showcased as data points connected by line."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "w2w54UMSExO0LJOV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what you see contained in this area: [0.429, 0.942, 0.624, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, \"Fashion Elements\", is located here. It's rendered in black and has no rotation."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "tCazrIatQLWkwaTi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region bounded by the coordinates [0.015, 0.351, 0.035, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis label which denotes the 'Trend' of different fashion elements."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "6keLvjD7Thn8ANau",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mean Attendance."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "MdSMHrV9rMMh4KiG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the legend item representing 'Local Winterfell'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.094, 0.161, 0.129, 0.172]"
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "3uhDvhLWXlxxfDao",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat entity is presented at [0.094, 0.126, 0.129, 0.137] in the visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle for the 'International King's Landing' legend item."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "GMFZJVnPoKHjhesy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the marker for {point 30}[0.944, 0.430] on the 'Local Winterfell' line plot above or below {point 30}[0.944, 0.743] on the 'International Winterfell' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "EJArpWKPAAtBrfLN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color for {the line representing 'Local King's Landing'}[0.126, 0.130, 0.944, 0.226] more similar to {the color for the handle representing 'Local Winterfell'}[0.094, 0.161, 0.129, 0.172] or {the color for the handle representing 'International Winterfell'}[0.094, 0.196, 0.129, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Local Winterfell."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "vleXp6uFd570dKFm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label appears farther right on the x-axis, {the label '15'}[0.453, 0.893] or {the label '25'}[0.781, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "25."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "FKQZo63BLaYv1F6U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area between [0.126, 0.743, 0.944, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'International King's Landing' line along with its respective plot points and markers shaped as squares."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "SNEtQEza0x7ZJmtV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the bounding box [0.126, 0.412, 0.944, 0.430]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Local Winterfell' line plot runs through this area, represented by a green line with triangle markers denoting various statistical points."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "Ayx99Dk8jgCBB88d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region from [0.041, 0.271, 0.076, 0.909] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels for numerical values ranging from 6000 to 9000 are located in this region."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "Gnj8kGMq9WgHbqFQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"International Trade and Globalization Trends\"<r>[0.288, 0.025, 0.780, 0.058]</r>."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "M0f8mo9JnGc0Ndt2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the color coordinates [0.091, 0.091, 0.126, 0.102]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the color marker for \"Trade Volume Index\" in the legend."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "uGVDwF6GMyBappXE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.041, 0.351, 0.072, 0.381]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label \"120\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "dmxd44OCpq3DiX5N",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher index value at {year '2008'}[0.451, 0.902, 0.472, 0.932], the Trade Volume Index or the Globalization Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Trade Volume Index."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "3sjWGDvhuj3l2EQu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2010, is the {Globalization Index}[0.534, 0.712] higher or lower than the {Trade Volume Index}[0.534, 0.100]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "ddwPSxyEAZW00laf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points corresponding to the year 2012 for {Trade Volume Index}[0.616, 0.144] and {Globalization Index}[0.616, 0.694] located above or below the 100-level label on the y-axis [0.041, 0.525, 0.072, 0.555]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "3dJsN9a1WUKSlGrj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.089, 0.077, 0.276, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. The two items of the legend are {Trade Volume Index}[0.133, 0.086, 0.270, 0.110] and {Globalization Index}[0.133, 0.121, 0.267, 0.145] with their respective color markers precisely beside them."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "CiuuCnmN4wlazlEE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at [0.041, 0.176, 0.072, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label \"140\" from the y-axis."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "yHDgIsoEl0osGtkP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents located at [0.594, 0.902, 0.637, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The contents located at this coordinate are the x-axis label \"2012\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "MihxTyTFV0itQDmM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label at the y-axis} at [0.015, 0.273, 0.035, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Usage Percentage (%)."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "n736gbDsoFrvOT29",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the fifth position on the x-axis, what are its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Field Studies, [0.891, 0.909, 0.979, 0.932]."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "XxjakOONpkrW1MWB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the blue point} at [0.935, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The usage percentage of Field Studies which is 63.8%."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "j9QiFucXKHIpkPz9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Books'}[0.109, 0.125] at higher or lower percentage than {the point for 'Online Materials'}[0.316, 0.339]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "IE0Aa7ZVdCry2AQ5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Experiments'}[0.729, 0.825] at higher or lower usage percentage than {the point for 'Peer Discussions'}[0.522, 0.510]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "yCk8BVsLc0IxnAQd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource type has a higher usage percentage at {the point} [0.522, 0.510], Books or Peer Discussions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Peer Discussions."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "FKUsaRx2VxyKtLwQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.259, 0.025, 0.785, 0.058] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle of the chart : \"Education Resource Distribution and Utilization\"."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "0OCZ0EapvqkW3rJ4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained in the area [0.808, 0.077, 0.969, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the chart. It includes the descriptor \"Resource Usage\" with {its indicator color symbol} [0.810, 0.091, 0.845, 0.102] in the color '#3498db'."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "weSzsI8WZZRe9V8a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is in the region [0.088, 0.909, 0.979, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are five objects in this region. They are x-axis labels for 'Books' [0.088, 0.909, 0.130, 0.932], 'Online Materials' [0.259, 0.909, 0.372, 0.932], 'Peer Discussions' [0.464, 0.909, 0.580, 0.932], 'Experiments' [0.685, 0.909, 0.772, 0.932] and 'Field Studies' [0.891, 0.909, 0.979, 0.932], respectively."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "1jI98VsQAkN5mCd5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label with coordinates [0.342, 0.090, 0.683, 0.115] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle of the chart, \"Rural and Urban Development Comparison\"."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "IqYayZNojzriL4Re",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {y-axis label}<r>[0.082, 0.387, 0.095, 0.623]</r> read as?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Development Level\"."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "XpHLYVo1li8XbSxw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.664, 0.902, 0.689, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis tick label representing \"2013\"."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "XKLBakZP9pq5iQzd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point representing year 2010 for 'Rural'}<r>[0.480, 0.486]</r> higher than the {point representing year 2010 for 'Urban'}<r>[0.480, 0.409]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "pljkG3k9IqKmG1yH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {line labeled 'Rural'}<r>[0.160, 0.312, 0.865, 0.794]</r> start or end on a higher value compared to the {line labeled 'Urban'}<r>[0.160, 0.216, 0.865, 0.698]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It starts on a higher value."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "sDdGa5G4MKcFFF55",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich development level is higher in 2016, {Urban}<r>[0.865, 0.216]</r> or {Rural}<r>[0.865, 0.794]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "O4OXQeryZPFxMogn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with coordinates [0.829, 0.129, 0.895, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a legend. This consists of two entries: {Rural}<r>[0.861, 0.136, 0.887, 0.153]</r> with a blue color marker and {Urban}<r>[0.861, 0.162, 0.891, 0.179]</r> with a red color marker."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "tAbuWouvHyVwUMUI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.096, 0.113, 0.480, 0.498]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains y-axis labels ranging from '70' to '90' with their associated tick marks on the y-axis. It specifically includes the labels '70', '75', '80', '85' and '90'."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "oL9lDUpHbFSZvGs6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you find within [0.276, 0.902, 0.813, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick labels for the years 2007 to 2015."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "nMK6YoasqsrF4c5K",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line labeled 'Process 1 Efficiency'}[0.128, 0.130, 0.944, 0.827] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line representing the performance trend of the internal business process 1."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "9IS0wvDkeVOaSRHv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.294, 0.902, 0.305, 0.932]"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "GBI6c0gZWsMs9heb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.386, 0.035, 0.566]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "9HLrUIpQG3zjYpQZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher at time 10, {the 'Process 1 Efficiency'}<r>[0.514, 0.311]</r> or {the 'Process 2 Efficiency'}<r>[0.514, 0.465]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Process 1 Efficiency."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "AMNF7lqzrTfHD0nZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Process 1 Efficiency'}<r>[0.858, 0.723]</r> above or below {the point for 'Process 2 Efficiency'}<r>[0.858, 0.207]</r> at time 18?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "tCOIaYByZuycCpMy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is higher at {time 3}[0.214, 0.336], Process 1 Efficiency or Process 2 Efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Process 2 Efficiency."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "HlleUZE71AR0D6A6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.345, 0.025, 0.727, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the sub-title of the chart, \"Internal Business Process Analysis,\" displayed in black color."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "UWnyq8U7aJdxrcLf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.791, 0.077, 0.978, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Process 1 Efficiency}[0.835, 0.086, 0.972, 0.110] and {Process 2 Efficiency}[0.835, 0.121, 0.972, 0.145], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "b7mcA0pqPhe9Qs1S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.541, 0.902, 0.912, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 objects: the x-axis tick labels '11', '13', '15', '19' at coordinates [0.547, 0.902, 0.568, 0.932], [0.633, 0.902, 0.654, 0.932], [0.719, 0.902, 0.740, 0.932], and [0.891, 0.902, 0.912, 0.932] respectively."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "lPKheuDVGcMeUXGl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the main object in the [0.069, 0.465, 0.083, 0.545] coordinates correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "uYVy0NMwrB3yDXtA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many items are in the legend, and what are their coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items. One is \"Interactions\"<r>[0.176, 0.141, 0.259, 0.164]</r> and the other is \"Relationships\"<r>[0.176, 0.176, 0.269, 0.199]</r>."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "gDdwYC4Li8jSWoar",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis label}<r>[0.495, 0.939, 0.529, 0.962]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "4IYx0yPgT7UBD9hd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAround the coordinate [0.264, 0.745], which value is greater, \"Interactions\" or \"Relationships\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interactions."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "4wkmcc990Lmtu00k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the \"Interactions\" point coordinates at {Time '1'}<r>[0.158, 0.824]</r> higher or lower than the \"Relationship\" point coordinates at {Time '1'}<r>[0.158, 0.856]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "5KFqpqMs9mO3KYbD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point of \"Relationships\"<r>[0.291, 0.774]</r> at Time '6' above or below the point of \"Interactions\"<r>[0.291, 0.729]</r> at the same time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "Uk0Dbf4KPLlRLHu7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the objects are in the region [0.529, 0.939, 0.098, 0.161]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label \"Time\", y-axis label \"Counts\", and the plot area which contains the line graphs for \"Interactions\" and \"Relationships\"."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "NewPyjwkXRAPTTm3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.125, 0.120, 0.900, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main chart area. It includes all plotted lines for \"Interactions\" and \"Relationships\" over time, together with the relevant x and y axes."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "QO8Idyjs279ZjUp4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be identified in the area of [0.264, 0.745, 0.370, 0.722]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects within this region. Both are data points in the line graph, one from the \"Interactions\" trend<r>[0.264, 0.745]</r> and the other from the \"Relationships\" trend<r>[0.370, 0.722]</r>."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "CynbLPo34QZPnMPY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.117, 0.861, 0.160, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first x-axis tick label, '2010'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "yZscP3fS5YtS7YY6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description for {the object}[0.660, 0.091, 0.694, 0.102] shown in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the marker for the line showing 'Freight Volume (million ton-kilometers)'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "W0OjeUnAuHQEJDx1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a title for the legend, if so, what are its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, there isn't a title for the legend."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "qDmE4B6PxZwJTOKR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for '2015' on the 'Freight Volume (million ton-kilometers)'}[0.542, 0.704] higher or lower than the point for '2012' on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "a2weFDUrQlnQCPRp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend reaches a higher value at {the point for '2020'}[0.945, 0.702 for 'Freight Volume (million ton-kilometers)', 0.945, 0.441 for 'Passenger Miles (million)'], 'Freight Volume (million ton-kilometers)' or 'Passenger Miles (million)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Freight Volume (million ton-kilometers)."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "4lnPIVk2YtvCFXRC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 'Passenger Miles (million)' for '2018'}[0.783, 0.436] lower or higher than the point for '2011' on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "LVR9GrASf481ruNZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the rectangle [0.657, 0.077, 0.978, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. Each label, 'Freight Volume (million ton-kilometers)' and 'Passenger Miles (million)', is alongside a marker that corresponds to the line color on the chart."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "ARZU8Z8FrZICGtyt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region located at [0.098, 0.066, 0.985, 0.845]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area. It includes two segmented line plots displaying the trends of 'Freight Volume (million ton-kilometers)' and 'Passenger Miles (million)', for different years. The x-axis is labeled with years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "OYzvX4E8CBPlioaD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region at coordinates [0.015, 0.121, 0.035, 0.790]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis label which is 'Million ton-kilometers / Million miles'."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "O8bnnvvF75l6deyP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point with label 'Phoenix'}<r>[0.471, 0.453]</r> on the January line?"
+            },
+            {
+                "from": "gpt",
+                "value": "19.4."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "LLwTIa2CzmiWM9df",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates <r>[0.025, 0.180, 0.041, 0.203]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '35'."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "6RLG6a353S7ecdhQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat temperature does {the point with label 'San Diego'}<r>[0.757, 0.348]</r> on the July line represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "25.6."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "PxczPlPbpdcn5DJT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city has a higher temperature in January, {Philadelphia}<r>[0.566, 0.728]</r> or {San Antonio}<r>[0.662, 0.524]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Philadelphia."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "6HLFcv1d4V1O5c6G",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Houston}<r>[0.376, 0.194]</r> have a higher or lower temperature in July than {San Antonio}<r>[0.662, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "CVcqann44r3TNCDt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the July temperature of {Los Angeles}<r>[0.186, 0.300]</r> higher or lower than that of {San Jose}<r>[0.947, 0.294]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "PScEqoG8Tn1QpIAH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.917, 0.058, 0.985, 0.115]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend. It contains two items: {January}<r>[0.947, 0.065, 0.982, 0.082]</r> represented by a blue handle and {July}<r>[0.947, 0.091, 0.963, 0.108]</r>, represented by a red handle."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "IkukJaPfVTaHijU3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.048, 0.434, 0.994, 0.785]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the January temperature line plot. It ranges from cities {New York City}<r>[0.090, 0.748]</r> to {San Jose}<r>[0.947, 0.532]</r>, with variable temperature points for each city."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "ILNHhOm4P3ddTLk5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.281, 0.289, 0.376, 0.785]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the January and July temperature data points for {Chicago} and {Houston}<r>[0.281, 0.785]</r> and <r>[0.376, 0.194]</r>, respectively."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "AGmVQqZZs0UIAJhs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title of the chart}[0.360, 0.025, 0.667, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Demographics (1980-2000)."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "AVAL1Q4iGKmomyqN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the fig_texts} at [0.015, 0.752, 0.033, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "v2FnfqM2WRX845vK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the X-Axis label} at [0.490, 0.942, 0.537, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "1mkMb9SPcy7LQ4RO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Population Growth Rate (%)'}[0.085, 0.849, 0.942, 0.855] generally above or below {the line for 'Life Expectancy (Years)'}[0.085, 0.103, 0.942, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "Puq0mAwvnPMQGwya",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 1980, was the {Population Growth Rate (%)}[0.085, 0.849] higher or lower than {Life Expectancy (Years)}[0.085, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "80dNdSiAf6vncJhm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line reaches a higher y-value at its end point, {Population Growth Rate (%)}[0.942, 0.855] or {Life Expectancy (Years)}[0.942, 0.103]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Life Expectancy (Years)."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "aB9EeJsaj0loU1Yd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.015, 0.050, 0.033, 0.074]"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate represents a Y-axis label '80'."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "M5oQ5mm2k9lDxH7l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.490, 0.942, 0.537, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label: {Year}[0.490, 0.942, 0.537, 0.975]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "wvDoWrpT5a0irH5A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.918, 0.909, 0.966, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis label '2000.0' is located within these coordinates."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "3WpqdG3gRKgn2EVR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the label at [0.019, 0.296, 0.043, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Player 1 annoyance level."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "XgZpWAD0bGl7i2Rl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.522, 0.918, 0.587, 0.943]"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "gKcAcsHpWaTyyebn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.049, 0.461, 0.072, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "6."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "HvbNDYUwSkabmFmI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Player 1 annoyance level}[0.116, 0.207, 0.884, 0.669] decrease or increase over days?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "qXjFP2dParVPTtre",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Player 2 annoyance level}[0.116, 0.075, 0.884, 0.867] increase or decrease toward {Day 3}[0.336, 0.075] from {Day 0}[0.116, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "ie585GTrrdDrx0SV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has a higher annoyance level at {Day_2}[0.336, 0.405, 0.336, 0.075], Player 1 or Player 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Player 1."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "OmQcFpxsAa1pBiZj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.723, 0.045, 0.910, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. The items are {Player 1}[0.801, 0.056, 0.900, 0.084] and {Player 2}[0.801, 0.097, 0.900, 0.125], each with a color handle indicating their line color on the graph."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "8cz6txFN45hKWLHu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.194, 0.918, 0.258, 0.943]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the x-axis tick label for {Day_1}[0.194, 0.918, 0.258, 0.943]."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "RrdFvXTFYEPqUC50",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.303, 0.918, 0.368, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis tick label 'Day_2' is located."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "3KfHWj8Pk28nIBVz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Product Success Rates}<r>[0.114, 0.266, 0.880, 0.803]</r> from the plotted data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green (#008000)."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "DobSmEBc3xqFzfmi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the shape used as marker for the {Employee Satisfaction Rates}<r>[0.114, -2.790, 0.880, -2.253]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "rA3TMK70O5HaBwvq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label with coordinates [0.015, 0.441, 0.034, 0.509] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rates."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "qzlmg6mPOkSNGC7q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Employee Satisfaction Rates}<r>[0.114, -2.790, 0.880, -2.253]</r> line have a higher or lower data point in 2014 than the {Product Success Rates}<r>[0.114, 0.266, 0.880, 0.803]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "dRfffyNUF2hSlwSh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the position of the {Risk Management Efficacy}<r>[0.114, 0.296, 0.880, 0.773]</r> line data point in 2013 as compared to the {Product Success Rates}<r>[0.114, 0.266, 0.880, 0.803]</r> line data point corresponding to the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "naEHsoMgFywL6mn8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does trend of {IP Registrations}<r>[0.114, 0.117, 0.880, 0.743]</r> compare to {Employee Satisfaction Rates}<r>[0.114, -2.790, 0.880, -2.253]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "T15U95EC3c1BiKev",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.076, 0.028, 0.919, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main chart with multiple graphical lines including 'Product Success Rates', 'Risk Management Efficacy', 'Employee Satisfaction Rates', and 'IP Registrations'. The x-axis and y-axis with respective labels 'Years' and 'Rates' are also included within this area."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "xRR4HSUZWpVeI3jU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents enclosed in the legend area [0.083, 0.036, 0.331, 0.146]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a color-coded legend. It contains representations for 'Product Success Rates', 'Risk Management Efficacy', 'Employee Satisfaction Rates', and 'IP Registrations'. The color codes used are green, purple, blue, and red respectively."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "L2vM9VoCay2pCPzf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.093, 0.928, 0.136, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the year '2013' label of the x-axis."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "oBpmLk873KXuIBSD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.019, 0.324, 0.043, 0.617] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "PVNlzk1eKFXIsf5E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth x-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.443, 0.928, 0.456, 0.951]"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "PbF75MP8uNtzHVJ1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.049, 0.462, 0.076, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "15."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "mSS7FNWzmWr2CmkL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {points labeled '1 and 2'}[0.123, 0.892] and [0.205, 0.862] increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "MLCCEfupbN1WQwsO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '4'}[0.450, 0.561] above or below {the point labeled '5'}[0.532, 0.440]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "tsuOJooScQTu476B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Creativity Level} at [0.286, 0.802] higher or lower than at [0.613, 0.320]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "vIOSJ3ZlQb8SHiQo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.082, 0.019, 0.981, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content in this region encompasses the entire line chart plot with the 'Creativity Level' and 'Design Innovation Level',which includes x and y axes with labels and ticks, and a line marked with different points of values ranging from 0 to 10 for 'Creativity Level' and 1 to 27 for 'Design Innovation Level'."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "DouAQJ1HFHPKSm6K",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.436, 0.958, 0.627, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label \"Creativity Level\"."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "MqMHKQDi8yDtcvGp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.123, 0.109, 0.940, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plot of the line chart, covering values of 'Creativity Level' from 0 to 10 and their corresponding 'Design Innovation Level' ranging from 1 to 27."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "vmS8IZaoHmmCcC8F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.326, 0.043, 0.606]</r> of subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity Score (%)."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "ecolR73ovgTCkGa9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the underlying value for {the Shape data point}<r>[0.245, 0.286]</r> on the Creativity Scores line?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "Pdw7KlMIrGG5zAp4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point}<r>[0.457, 0.622]</r> on the Innovation Scores line representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The innovation score for the Space design element."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "E8IDfcap6qmIInlc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the Value design element}<r>[0.775, 0.911]</r>, which score is higher, Creativity Score or Innovation Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity Score."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "zgZj3SaHGKkJLIqV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Form data point}<r>[0.563, 0.062]</r> for the Innovation Scores line higher or lower than {the Form data point}<r>[0.563, 0.062]</r> for the Creativity Scores line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "Y0Ykjhc2engG7Vfi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the Texture data point for Creativity Scores}<r>[0.351, 0.331]</r> and {the Texture data point for Innovation Scores}<r>[0.351, 0.248]</r> higher or lower than the average of their respective lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "Upegx007SJbTjILi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region <r>[0.102, 0.021, 0.918, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot for both Creativity Score and Innovation Score lines, with x-axis representing Design Elements and y-axes representing the scores in %."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "EYg5gtiHTnCqqfgf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region <r>[0.647, 0.033, 0.908, 0.116]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the two lines {Creativity Scores}<r>[0.647, 0.045, 0.706, 0.064]</r> and {Innovation Scores}<r>[0.647, 0.082, 0.706, 0.102]</r>."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "zFR7ajihFb1buu0L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the coordinates<span><r>[0.112, 0.918, 0.905, 0.943]</r></span>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the labels for the x-axis ticks representing the different Design Elements: Color, Shape, Texture, Space, Form, Line, Value, and Type."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "f2PtUSiRvoVLRnql",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the top of {the y-axis}<r>[0.015, 0.323, 0.034, 0.614]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate(%)."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "zYedqSyyvFjVdgen",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the color of {the handle for 'Inflation Rate(%)'}<r>[0.008, 0.061, 0.055, 0.081]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f00."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "XfEIqTkVcFrqxYgN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label at}<r>[0.686, 0.923, 0.721, 0.943]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "MSsvCpNGbzaDXwnu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the unemployment rate marker for 2008}<r>[0.106, 0.567]</r> higher or lower than {the GDP growth rate marker for the same year}<r>[0.106, 0.206]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "VubEPN1qjtSDCXcb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is earlier, {the x-tick '2010'}<r>[0.225, 0.910]</r> or {the x-tick '2012'}<r>[0.345, 0.910]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "9C87sXILHTWsuGwa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 2015 in the unemployment chart}<r>[0.524, 0.633]</r> have a higher or lower rate than {the point for 2016}<r>[0.584, 0.685]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "ivD3yQtcKeDQfpCi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.106, 0.068, 0.823, 0.870]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains line charts for GDP Growth Rate(%). It shows fluctuating data points for each year from 2008 to 2020, highlighted with a blue line and circular markers."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "sGOzCzY1QyoIlZEC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region <r>[0.008, 0.012, 0.261, 0.132]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart with entries for GDP Growth Rate(%), Inflation Rate(%), and Unemployment Rate(%). Each entry includes a text label alongside a colored handle representing the respective data series in the chart."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "D8Gzsx1BVxIc9NdQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in <r>[0.868, 0.022, 0.877, 0.996]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical region includes the y-axis ticks for the inflation rate, ranging from -1 to 5, each accompanied by their respective labels."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "nfcM8lC4EgogRH1U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the line represented by {the coordinates of 'AI Usage'}<r>[0.104, 0.216, 0.943, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "uQVblFrIaAHklFUh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.356, 0.019, 0.691, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot title."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "EMEXYEY4e55LCPnY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis located at this position <r>[0.015, 0.438, 0.029, 0.536]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "3XRET3PWVix20fyj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'AI Usage'}<r>[0.104, 0.216, 0.943, 0.887]</r> line above or below {the 'Automation'}<r>[0.104, 0.087, 0.943, 0.871]</r> line in 2004?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "FQN8qYSa43CuI1Gk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'AI Usage'}<r>[0.104, 0.216, 0.943, 0.887]</r> line or {the 'Automation'}<r>[0.104, 0.087, 0.943, 0.871]</r> line reach a higher point in the year 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automation."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "RC0Ee0uyPzr92qUG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points of {the 'AI Usage'}<r>[0.104, 0.216, 0.943, 0.887]</r> line in 2006 above or below the points of {the 'Automation'}<r>[0.104, 0.087, 0.943, 0.871]</r> line in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "oPgzDJkkGbnIyAUf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region located at these coordinates [0.069, 0.055, 0.200, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the subplot. Two items are listed: 'AI Usage' with blue marker and 'Automation' with orange marker."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "B1oddKp8wq9Z9jFY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details of the entities that lie within the region [0.104, 0.087, 0.943, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'AI Usage' line plotted on the graph with several points along the line for different years from 2000 to 2012."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "PLL8P6VBtlIVzQsq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the coordinates [0.505, 0.964, 0.542, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Years' is positioned within these coordinates."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "zHlywaECgd8Qykui",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {ylabel}[0.019, 0.309, 0.043, 0.624] of subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "#377eb8."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "Eo7ErBRh1TXoZJDU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {x tick mark}[0.423, 0.911] in subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "k4BXtr5xnuKLaexr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {marker}[0.595, 0.045, 0.654, 0.064] for 'Social Inequality Index' in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "#377eb8."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "06NVTWuUW9JPxYq1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for year 2012 on the {line}[0.116, 0.062, 0.884, 0.870] for 'Social Inequality Index' higher or lower than the point for year 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "AeZIzyVUWMosCQ0R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for year 2018 on the {line}[0.116, 0.062, 0.884, 0.870] for 'Rights Protection Index' located higher or lower compared to the point for year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "g58VuSMSx4M5YcHN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat change, on 'Rights Protection Index' line, can be kbserved from year 2011 to 2012, an increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "jxJ2LQDH0NYFkCiV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area between the coordinates [0.595, 0.033, 0.912, 0.116] in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend, with two items. 'Social Inequality Index' text is located within this area at {coordinates}[0.662, 0.043, 0.897, 0.069] paired with a colored handle marker at {coordinates}[0.595, 0.045, 0.654, 0.064]. 'Rights Protection Index' text is situated in this region at {coordinates}[0.662, 0.080, 0.903, 0.106] with its related colored marker at {coordinates}[0.595, 0.082, 0.654, 0.102]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "eXokIRQITLkERGuM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects contained in the region [0.078, 0.021, 0.922, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the plotting space for subplot 0 and subplot 1, each containing their respective lines, xticks, and yticks. They are sharing the same xticks but own distinctive yticks. Also, subplot 0 has a ylabel and a sublegend."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "ko1kZ0b8jQVuAZ81",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinates [0.958, 0.305, 0.981, 0.628] in subplot 1."
+            },
+            {
+                "from": "gpt",
+                "value": "The given coordinates host the ylabel of subplot 1, with the label text as 'Rights Protection Index'."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "XESwUODSMLieDNHI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.019, 0.207, 0.043, 0.735] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the first half of the graph showing Facebook & WhatsApp Users."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "IEK3TBWwvHI8OTXJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.094, 0.925, 0.138, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year '2016' on the x-axis."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "Q3SLaqnepx4swgH2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is represented by the marked handle {object} <r>[0.685, 0.083, 0.741, 0.103]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for Twitter Users, pink."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "b78qAGS807QNMEV4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{Line for Facebook Users} <r>[0.116, 0.090, 0.884, 0.386]</r> is showing a decline or increase in user numbers from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "n2Td9f89kcuH2z11",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {coordinate of Instagram Users in 2016} <r>[0.116, 0.647]</r> more than the {coordinate of Facebook Users in 2017} <r>[0.270, 0.304]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "ef0vs67Sn4IqZxaG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has more users in 2020 based on the coordinates, Facebook ({Facebook Users 2020} <r>[0.730, 0.178]</r>)  or Instagram ({Instagram Users 2020} <r>[0.730, 0.339]</r>)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "A2BZW1sbOOuPGkGR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.686, 0.042, 0.912, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the sublegend of the chart. It contains labels for 'Instagram Users' and 'Twitter Users', each paired with a colored marker representing their distinct line in the chart."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "rH2NckIVOBcjjKtC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are in the region [0.958, 0.226, 0.981, 0.716]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the second half of the Y-axis, indicating Instagram & Twitter Users (in billions)."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "FZteFQjbuqlMHYal",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is in the region [0.116, 0.090, 0.884, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the line plot for Facebook Users from the years 2016 to 2021, showing an increase in their numbers."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "LeOsnYHCUMsRlR9m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.116, 0.231] on the \"Peace Index\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value '75.0'."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "P8crDrUGugBtDKcb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {ylabel text}<r>[0.019, 0.432, 0.043, 0.508]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the ylabel text is '#377eb8'."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "AZVcqG7ZF72bdk6B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a blank label element located at [0.958, 0.352, 0.981, 0.587] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the ylabel of the second subplot \"Absolute Poverty Rate (%)\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "BgbsYvGBcVZ9Jdpa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {the '2014' xtick}<r>[0.457, 0.925, 0.484, 0.951]</r> on the chart, {the 'Peace Index'}[0.457, 0.118] or {the 'Social Equality Index'}[0.457, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Equality Index."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "IX5kVbL3jnf361Fv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2016, does the {Peace Index}[0.628, 0.062] have a higher or lower value than the {Absolute Poverty Rate}[0.628, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "00GRLc7NzMbdDKro",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Social Equality Index}[0.202, 0.872] in 2011 higher or lower than the {Absolute Poverty Rate}[0.202, 0.171] value in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "Wxx8wkUoMH0XenEP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects belong within the area with coordinates [0.078, 0.849, 0.798, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the 'Social Equality Index' line with its respective points plotted for each year from 2011 to 2019, with coordinates varying from around [0.116, 0.847] to [0.884, 0.842]."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "PhnuHJPYZyfMvks1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the area from [0.078, 0.021, 0.922, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two subplot areas, with each having their individual xticks, yticks, lines representing different indices and a label representing the name associated with the y-values. The first subplot represents \"Peace Index\" and \"Social Equality Index\" values from 2010 to 2019, while the second subplot represents the \"Absolute Poverty Rate\" in the same time period."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "6PclBCIBxCvazPbm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within [0.087, 0.031, 0.342, 0.127]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the legend for the first subplot with labels \"Peace Index\", \"Social Equality Index\", and \"Absolute Poverty Rate\" along with their respective colors representing each plot. The color markers are placed left of each index."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "q4i5o2TcWbwne2ms",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.958, 0.327, 0.981, 0.606]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label on the secondary Y-axis, reading \"Approval Rating (%)\"."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "ApBzATij50DLHObF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.685, 0.816, 0.849, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this is the legend text for \"Social Impact\"."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "7WkGcN9vGr7Zr98f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.100, 0.923, 0.143, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2012\"."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "jswGx3KJs9RoWdbK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compare the position of {the point '2016'}[0.500, 0.496] on the Policy Efficiency line and {the point '2016'}[0.500, 0.332] on the Approval Rating line? Which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point on the Policy Efficiency line is higher."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "VRZMwdHTJGNmF5I3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2019 point}[0.784, 0.511] in Policy Efficiency above or below the {2019 point}[0.784, 0.097] in Approval Rating?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "RcQBBHRh0qjh4nbE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt year 2020, which line has a higher value, {Policy Efficiency}[0.879, 0.481] or {Approval Rating}[0.879, 0.064]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy Efficiency"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "eOjkoNj96ARIg6c8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.607, 0.723, 0.904, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "It houses the legend, featuring classifications like {Policy Efficiency}[0.685, 0.734, 0.881, 0.762], {Economic Impact}[0.685, 0.775, 0.894, 0.803], {Social Impact}[0.685, 0.816, 0.849, 0.844] and {Approval Rating}[0.685, 0.857, 0.879, 0.885], with various colors corresponding to each trend."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "r50XOyJ2LWKimIbA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region [0.084, 0.023, 0.916, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily encompasses the main chart area, displaying both the line trends for aspects like Policy Efficiency, Economic Impact, Social Impact, and Approval Rating from the year 2012 to 2020 as well as the tick labels for the years on x-axis and the corresponding ratings or scores on Y-axis."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "E5CDIyUn6FW08MdL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.951, 0.923, 0.995, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This segment belongs to the x-axis and holds the label of the last tick mark, representing the year \"2021\"."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "5ChDgAafaDMMfJyT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label represent in the coordinates [0.019, 0.451, 0.036, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "Z1381sRCchAgBmfJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth year labeled on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.590, 0.919, 0.634, 0.942]"
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "5TneVnlZebvts8dL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point of 'Sustainable Practices'} at [0.940, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020,25."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "U2xbzz0xJHmNEEYA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Corporate Responsibility'}[0.119, 0.419, 0.940, 0.865] generally above or below {the line for 'Environmental Protection'}[0.119, 0.100, 0.940, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "NdMH84YKozNCElAp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Sustainable Practices'}[0.119, 0.228, 0.940, 0.674] cross {the line for 'Environmental Protection'}[0.119, 0.100, 0.940, 0.546] over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "SQNvuGr3x0dLjVSj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the y-value at {2019}[0.776, 0.903], which line is highest among Corporate Responsibility, Environmental Protection, and Sustainable Practices?"
+            },
+            {
+                "from": "gpt",
+                "value": "Environmental Protection."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "gWK8MHVh8eb4Q49F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.086, 0.074, 0.369, 0.185]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart containing the labels: {Corporate Responsibility}[0.142, 0.083, 0.354, 0.106],[0.088, 0.086, 0.133, 0.100], {Environmental Protection}[0.142, 0.118, 0.362, 0.141],[0.088, 0.121, 0.133, 0.135], and {Sustainable Practices}[0.142, 0.153, 0.327, 0.176],[0.088, 0.156, 0.133, 0.170], displayed with corresponding color handles indicating their representation on the chart."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "gRvWWJzrfshNfwYu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.238, 0.025, 0.821, 0.055] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart: \"Trends in Social Responsibility and Sustainable Business.\""
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "fW1fwCA1e0TDqvdl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.511, 0.952, 0.548, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Year' is located in this region."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "S2cBEgkbsiWBeR1b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at the coordinates [0.015, 0.369, 0.034, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label: 'Growth Rate (%)'."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "upIPcIg05oKjQT2R",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {graph line}[0.806, 0.357, 0.944, 0.245] in the chart representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the '5G' trend."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "Rg9GLRE3nSoaNV10",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend marker with coordinates [0.087, 0.042, 0.129, 0.053]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#377eb8' which represents 'Voice Recognition'."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "ngKhKe6PfPecpBYq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate of {Quantum Computing}[0.117, 0.516, 0.944, 0.877] higher or lower than {Blockchain}[0.117, 0.019, 0.944, 0.493] in the year 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "I23B2gXWnK7w0t7P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {5G}[0.117, 0.245, 0.944, 0.922] have a higher growth rate than {AI}[0.117, 0.086, 0.944, 0.425] in the year 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "7XHdC7uT9oT16tj0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend of {Voice Recognition}[0.117, 0.651, 0.944, 0.357] increasing or decreasing between the years 2018 and 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "BiRc90Qm2awipgWg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content included within the coordinates [0.050, 0.800, 0.071, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label \"50\"."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "3b3lrNf5DBbYwIye",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed by [0.076, 0.029, 0.318, 0.200]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, representing 'Voice Recognition', 'Quantum Computing', 'Blockchain', '5G', and 'AI', each labeled with a color-coded marker."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "xzauPmRTpV1iLc6f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.372, 0.928, 0.414, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for the year '2020'."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "XazF0cXXWqg9knLi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description provided in the area [0.958, 0.292, 0.981, 0.641]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Market Growth (%)."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "Yr39QCewqO1EehOQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data indicated by {the point}[0.884, 0.070] on the red dotted line?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Stock Market Growth (%) for the year 2019."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "QcaahDDAPK83aQAe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is referred to by the coordinates [0.645, 0.046, 0.900, 0.074]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the label 'Stock Market Growth'."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "0iaGPAarpGDk4e8c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {2000's GDP Growth point}[0.116, 0.062] and {2000's Stock Market Growth point}[0.116, 0.292], which one was higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Market Growth."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "upRg7szZKUIAexIo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWere market conditions better in {2018}[0.843, 0.487] or in {2015}[0.722, 0.405] according to the Stock Market Growth shown?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "Jf6zwoIYG9JzFfwr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas the Stock Market Growth more positive in {2009}[0.480, 0.134] or in {2014}[0.682, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "sYEJjjZDqct0t3bK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown with the label at [0.116, 0.292, 0.884, 0.870] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the 'Stock Market Growth' line representing the actual percentage growth per year from 2000 to 2019."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "mdW84MbJsh6ZteWQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.078, 0.021, 0.922, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the entire span of the chart, including the lines representing 'GDP Growth' and 'Stock Market Growth', the x-axis labeled as 'Years' and y-axes labeled as 'GDP Growth (%)' and 'Stock Market Growth (%)'."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "eJt03kg7o1Zp9G0l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide within the coordinates [0.567, 0.035, 0.910, 0.085]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates define the chart legend, specifying {the label 'Stock Market Growth'}[0.645, 0.046, 0.900, 0.074] alongside its associated red-colored marker."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "6SqbFoHVtj8C2qKo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second line}[0.140, 0.556, 0.860, 0.830] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Integration."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "Z5AqwJaFI1W3A94l",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at {the location}[0.185, 0.188, 0.355, 0.217] in the sublegend of subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tech Revolution."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "yPelnBhgb5l7f3Bm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the tick label}[0.592, 0.888, 0.647, 0.918] on the x-axis in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "bJsPsQPfiMZoEdbU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, does the line for {Economic Integration}[0.140, 0.556, 0.860, 0.830] start at a higher or lower point than the line for {Global Trade Volume}[0.140, 0.069, 0.860, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "iytIlz0Di5lrTSJc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, is the beginning of the {Tech Revolution} line[0.140, 0.521, 0.860, 0.830] above or below the {Market Globalization} line[0.140, 0.069, 0.860, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "anNucleQVkDxU3vy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, does the {Tech Revolution} line[0.140, 0.521, 0.860, 0.830] end at a higher or lower point than where it started?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "jsVanBeFcB14EreO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.233, 0.888, 0.408, 0.918] of subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two X-axis tick labels: '1995' and '2000', located at {coordinates}[0.233, 0.888, 0.288, 0.918] and {coordinates}[0.353, 0.888, 0.408, 0.918] respectively."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "wzycZngUmkqq3xxb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what's in the region [0.062, 0.819, 0.938, 0.848] on subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the Y-axis tick label '5' at {coordinates}[0.911, 0.819, 0.924, 0.848] and a portion of the {Tech Revolution} line."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "e6DIVdqIoJb3FxhW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.115, 0.046, 0.425, 0.229] of subplot_1."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for each line plot in subplot_1: {Global Trade Volume}[0.185, 0.057, 0.408, 0.086], {Economic Integration}[0.185, 0.101, 0.416, 0.130], {Market Globalization}[0.185, 0.145, 0.409, 0.174], and {Tech Revolution}[0.185, 0.188, 0.355, 0.217], each paired with a colored line marker."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "MM7Jy1ICoftnTCB7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {ylabel}<r>[0.019, 0.394, 0.043, 0.549]</r> in subplot_0 refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ylabel refers to the CSR Rating."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "Qz5j2OR4WDTEYWrj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color denotation of the {legend}<r>[0.482, 0.058, 0.547, 0.078]</r> in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color denotes the Sustainable Business Trend."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "srWR39mVmwJlHZR9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {xticks}<r>[0.898, 0.918, 0.930, 0.943]</r> in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"7.0\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "wog3WkrNZ7UrnzSE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {CSR Trend}<r>[0.116, 0.075, 0.874, 0.911]</r> in subplot_0 tend to increase or decrease as the CSR Rating increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CSR Trend appears to decrease as the CSR Rating increases."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "zYWhcSMAbbBqIEbk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the marker style of {Sustainable Business Trend}<r>[0.116, 0.031, 0.874, 0.911]</r> in subplot_1 circle or triangle?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker style is a triangle."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "L17YpcfZeITYi8hZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the maximum point labeled values of {CSR Trend}<r>[0.116, 0.075, 0.874, 0.911]</r> and {Sustainable Business Trend}<r>[0.116, 0.031, 0.874, 0.911]</r>. Which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "CSR Trend has a higher maximum value."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "J2pk5UBt56p69K5g",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.078, 0.031, 0.912, 0.911] for subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a line graph representing the CSR Trend, with x-axis tick labels ranging from \"2.5\" to \"7.0\" and y-axis tick labels from \"2\" to \"7\". Also, it includes an x-label titled 'CSR Rating' along with a y-label 'CSR Rating', and a legend item 'CSR Trend' towards the lower left corner."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "JeU2ADy0WBoemJwt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in [0.078, 0.031, 0.912, 0.911] for subplot_1."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a line graph that represents the Sustainable Business Trend, with x-axis tick labels ranging from \"2.5\" to \"7.0\" and y-axis ranging from \"14\" to \"20\". Also, it includes a y-label titled 'Sustainable Business Rating' along with a legend item 'Sustainable Business Trend' towards the middle, lower part of the subplot."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "7HzHFcl71yDk66FH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.918, 0.925, 0.930, 0.945] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '7.0' for CSR Trend located at {coordinates}[0.898, 0.918, 0.930, 0.943] is found in this region."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "weF1R8JSmZuyqD50",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.019, 0.253, 0.043, 0.678] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "y-axis label."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "WnfenZvedaZhwVGr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me the coordinates of the third item's label in the legend."
+            },
+            {
+                "from": "gpt",
+                "value": "[0.173, 0.128, 0.326, 0.156]"
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "BLkCgQWXgl8bc4WI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find the {x-label's tick} with coordinates [0.429, 0.923, 0.473, 0.943] and tell me what it is?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label '2017'."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "VoTLNvELN7wqn8bi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the year 2018, did {Cardiovascular Diseases} have a higher or lower count than {Diabetes} in my plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "aKzdmuhahhIFihR3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2015}[0.124, 0.243] data point for Cardiovascular Diseases higher or lower than the Lung Cancer data point in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "MvlDIjfECbhc8I7d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the lines for {Cardiovascular Diseases} and {Lung Cancer}, which one has a higher data point value for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cardiovascular Diseases."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "98P6WciXOFplzwTM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the chart for the region with coordinates [0.096, 0.035, 0.495, 0.167]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend for your chart, with labels for Cardiovascular Diseases, Diabetes, and Lung Cancer, each paired with a color representing them in the chart."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "d1bEL8BLxBOgkRN4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area restricted by these coordinates [0.124, 0.110, 0.940, 0.243]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a line graph representing the trend of Cardiovascular Diseases from 2015 to 2020."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "eIv3WNfgbVWxJUlp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region from [0.124, 0.243, 0.940, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is dedicated to the line graph showing Lung Cancer trends from 2015 to 2020."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "rj0Emu0n2vcJm9rY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.019, 0.353, 0.043, 0.589]</r> of the first subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Programming(%)."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "T0LgiQSOsmsmHzVV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the marker at {coordinates}<r>[0.692, 0.597]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point of Programming trend in year 2018."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "HKTRGNeXXNA8sZAo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Year corresponding to the x-axis tick label at {coordinates}<r>[0.404, 0.911]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "tKsuhjI8xRJTaT3t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, does {Leadership trend}<r>[0.788, 0.383]</r> have a higher or lower value compared to {Team Building trend}<r>[0.788, 0.107]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "DeObI2Fa62VpkprT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is the data point {in 2015}<r>[0.404, 0.383]</r> for Programming Trend above or below {the point in 2016}<r>[0.500, 0.471]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "tBDveweilC7CQgfL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is {Leadership trend}<r>[0.115, 0.028]</r> generally above or below {Team Building trend}<r>[0.117, 0.056]</r> in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "8xIL8u81PUbgZaDz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside this region <r>[0.656, 0.056, 0.863, 0.084]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a text part of the subplot's legend indicating 'Leadership trend'."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "PFQYhm4BnZDrk7W5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the area <r>[0.656, 0.097, 0.900, 0.125]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a text part of the subplot's legend representing 'Team Building trend'."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "oIBRMKFIUpcCibHx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the area [0.116, 0.283, 0.884, 0.785]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a line representing Programming Trend with points marking every year from 2012 to 2020 in the first subplot."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "KEeByxXHpVkEbeOH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented by {the entire plot}<r>[0.0, 0.0, 1.0, 1.0]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "6uIDYt1NDtjqE4U2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing Instagram}<r>[0.403, 0.183, 0.634, 0.706]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "uA0aReZdA4ehlh1q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat marker symbol is used for {the LinkedIn growth trend line}<r>[0.732, 0.611, 0.964, 0.849]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "x."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "p2SdfKSBNyxvmAlg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {Facebook growth point}<r>[0.073, 0.658]</r> corresponds to the first month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 1."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "KlPllM1Mbk0gQO3C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the Twitter line}<r>[0.732, 0.777]</r> at a higher growth percentage compared to {the first point on the LinkedIn line}<r>[0.732, 0.849]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "KS9C8NF9Yke1QVVh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label at position}<r>[0.283, 0.912, 0.289, 0.936]</r> denote the eighth month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "mjlEo0KfgtLOfC2q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Microsoft Corp.}'s stock price trend in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ecc71"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "qn9bt4tmfJ7MlfeF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker style for {Amazon Inc.}'s line on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "x"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "tDg2fEve81pcmPby",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is found at coordinates {year label for 2017}<r>[0.885, 0.825, 0.924, 0.871]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2017"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "I4DqAkDVqNcHloBT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at the year marked by {2019}<r>[0.924, 0.825, 0.963, 0.871]</r>, {Microsoft Corp.}<r>[0.440, 0.753, 0.636, 0.787]</r> or {Amazon Inc.}<r>[0.748, 0.128, 0.983, 0.792]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Amazon Inc."
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "DBfBFuKrexRfzE7m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the stock value for {Apple Inc.} at the {2015}<r>[0.190, 0.825, 0.230, 0.871]</r> tick mark above or below 150?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "gDF2UkegXipnRZul",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does {Amazon Inc.}'s graph cross the 2500 stock price mark?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "My1XwYLx6e8tJcWR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the area [0.151, 0.183, 0.237, 0.737]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label \"Stock Price (in USD)\" for the second subplot, showing Google Inc. and Microsoft Corp. stock prices."
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "C4ruzdfhePeaxuWo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.420, 0.128, 0.600, 0.304]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot, specifying color-coded line representations for Google Inc. and Microsoft Corp."
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "p3gb2hRy3MZ1VReW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented between [0.093, 0.792, 0.327, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the x-axis tracking years from 2010 to 2020 for Apple Inc.'s stock price subplot."
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "a32BeN1tGQR2Hf1m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Production (in 1000 metric tons)."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "TPc3YQ3ic5i6kMy1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the handle} for the legend marked 'Meat Production'?"
+            },
+            {
+                "from": "gpt",
+                "value": "#f39c12<r>[0.434, 0.260, 0.465, 0.260]</r>."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "AHtXBH5C4D6x7OeW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the x-axis tick labeled '2020'}{r>[0.935, 0.825, 0.973, 0.871]</r> on the last subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "0ArLq1ttmRr3rTTt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at '2010' for Cereal Production Line}[0.240, 0.369] have a higher or lower coordinate than {the point at '2010' for Dairy Production Line}[0.895, 0.490] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "Gnz2y4phynHQHU1B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the labels for the y-axis tick '2000'}{r>[0.371, 0.351, 0.410, 0.398]</r> in the second subplot at the same height as {the labels for the y-axis tick '2000'}{r>[0.699, 0.351, 0.737, 0.398]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "72JAZpYS6QfwO2CH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher y-coordinate in 1995, {Vegetable Production Line}[0.479, 0.651] or {Meat Production Line}[0.479, 0.672]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Meat Production Line<r>[0.479, 0.672]</r>."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "iJHcgigcvPVDj9FH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region {bounded by axes lines}<r>[0.093, 0.128, 0.328, 0.792]</r> in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays two line charts, representing Cereal and Fruit Production metrics. X-axis labels range from '1990' to '2020', y-axis shows production volume from '750' to '2750' metric tons."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "RMHCRkgcaxx9XA88",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the chart region {bounded}<r>[0.420, 0.128, 0.656, 0.792]</r> of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two line charts indicating vegetable and meat production over years from 1990 to 2020 as well as x and y axes with corresponding tick marks and labels."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "rNZbShDWgfO7INou",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the contents within the chart area {r>[0.748, 0.128, 0.983, 0.792]</r> for the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart details the dairy production line showcasing the increase in production from the year '1990' to '2020'. It includes the x-axis with year intervals and the y-axis with production values, both featuring their respective ticks and labels."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "dsz4FMu4l8WgkEzi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at [0.058, 0.895, 0.064, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "632M4Gn85sSyzTEI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the 'Infrastructure' category}<r>[0.756, 0.108, 0.819, 0.136]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "vLFlyoUyuXZK8GzZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.058, 0.895, 0.064, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "yF3LD6snLFmnghtm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first data point for 'Policy'}<r>[0.721, 0.875]</r> higher than {the first data point for 'Infrastructure'}<r>[0.721, 0.840]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "JPh8mhlmf8ZQS16T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher data point at {x = 5}<r>[0.838, 0.875]</r>, 'Policy' or 'Infrastructure'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "qapmlRh5fRAA8yam",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '6000'}<r>[0.421, 0.375]</r> above or below {the point labeled '8000'}<r>[0.450, 0.208]</r> for 'Startups'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "yjRd7hUIrJG0gUje",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained within [0.361, 0.041, 0.655, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area spans an entire subplot which includes axes labels for 'Time' and 'Values', marked data points for the categories 'Startups' and 'Research', with corresponding lines and markers, and tick labels."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "fXWv3rxmkPCkFJVl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in [0.690, 0.030, 0.984, 0.198]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts y-axis labels for values 0 to 8000 along with corresponding grid lines, and parts of visual elements like lines and plus markers representing the 'Infrastructure' category."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "xOrg78L7jJZwvqYE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.058, 0.863, 0.384, 0.926] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes several x-axis tick labels from '1' to '4', as well as the y-axis label '0'."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "AGCHGL7pQVn1eOww",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in plot 3 according to the coordinates [0.671, 0.317, 0.686, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Progress Index."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "GmQcMxEFQIuaIeLF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the fourth marker on the x-axis}<r>[0.575, 0.869, 0.589, 0.904]</r> in the second plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "1R1wEu1zhZ6UcqJr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Emails'}<r>[0.728, 0.395, 0.972, 0.716]</r> according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ecc71."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "XoV99fwmPnc17p7S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the progress of 'Emails'}<r>[0.728, 0.395, 0.972, 0.716]</r> higher or lower than {the progress of 'Social Media'}<r>[0.728, 0.481, 0.972, 0.737]</r> at the 80th Decomposition IO Norm?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "FubP5qdkgYXg4v5y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the points for 'Desktops'}<r>[0.399, 0.310, 0.643, 0.673]</r> above or below {the points for 'Smartphones'}<r>[0.070, 0.181, 0.314, 0.630]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "HMrKmXZ3n2dP2LEp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Social Media'}<r>[0.728, 0.481, 0.972, 0.737]</r> have a steeper slope than {the line representing 'Emails'}<r>[0.728, 0.395, 0.972, 0.716]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "LGxk2bAqPJT3xls8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content inside this bounding box [0.101, 0.037, 0.284, 0.085]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the title of the first subplot, which is \"Smartphones Progress\", written in black color."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "wmgkIFWXhlKn9pne",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the bounding box [0.205, 0.764, 0.323, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box contains the legend for the first subplot. It represents {Smartphones}<r>[0.242, 0.778, 0.319, 0.813]</r> with a colored marker at these coordinates <r>[0.203, 0.776, 0.238, 0.811]</r>, colored in red."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "fyovddLl4GC82JLp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the coordinates [0.867, 0.712, 0.982, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area captures the legend for the third subplot. It includes two categories: {Emails}<r>[0.904, 0.726, 0.942, 0.761]</r> represented by a green marker at <r>[0.866, 0.724, 0.901, 0.758]</r> and {Social Media}<r>[0.904, 0.778, 0.977, 0.813]</r> represented with an orange marker at <r>[0.866, 0.776, 0.901, 0.811]</r>."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "sPPy4AplM4iA8HUh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred to by the text with coordinates [0.400, 0.025, 0.685, 0.057] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart: 'Policy Rate vs KPI'."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "1wwxeoDFMUKtI0cL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.848, 0.085, 0.954, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an item in the legend denoted as 'KPI Trend'."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "JYXL1b6f3WpAkQdH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.917, 0.912, 0.954, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is an x-axis tick label with the value '2.5'."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "1rpSNsEjSsHIAjAu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points on the {KPI Trend line}<r>[0.149, 0.147, 0.936, 0.813]</r> generally increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing and then increasing again."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "0E05wpcsJAf1KgVk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {KPI value at '1.0'}<r>[0.346, 0.397]</r> higher or lower than the {KPI value at '0.5'}<r>[0.149, 0.813]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "3ukh4GKlyfc0mNt9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {KPI point at '2.0'}<r>[0.739, 0.147]</r> right or left of the {KPI point at '2.5'}<r>[0.936, 0.314]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "9YA4m3x5kno21UM3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.774, 0.075, 0.963, 0.117]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend denoted by the title 'KPI Trend' in black, with a colored handle signifying the color code used for the line in the chart."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "YZGTGrS15tJVAjw1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.149, 0.147, 0.936, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted points of the 'KPI Trend' line."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "IkWlIAS2KpDBofUL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.064, 0.138, 0.094, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a y-axis label '58' with a tick corresponding to it."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "99sUCWsm1dTN7xiZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.178, 0.890, 0.223, 0.950] in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "ToEmUMumiwqEb8Kc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the Y-axis label}<r>[0.017, 0.307, 0.037, 0.613]</r> in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Stock Price\"."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "mC3PEzErltvOhkZo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the 80th x-axis tick label in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.620, 0.825, 0.640, 0.871]"
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "HBbYQWX1NrzBtIA2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the fourth point of 'Steady Stock'}<r>[0.251, 0.371]</r> higher than {the first point of 'Decreasing Stock'}<r>[0.754, 0.349]</r> in terms of the stock price?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "ukuJLxUcHT9SDSJ9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing in subplot_2, does {the 'Erratic Stock' line at point 40}<r>[0.856, 0.515]</r> have a stock price greater than {the 'Decreasing Stock' line at point 40}<r>[0.856, 0.515]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "YXqTVQNDHdKKaeCi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot's stock line has a higher value at time 60, {the 'Volatile Stock'}<r>[0.579, 0.405]</r> in subplot_1 or {the 'Steady Stock'}<r>[0.251, 0.371]</r> in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Volatile Stock\" in subplot_1."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "3mIjuHEVTnZtJVse",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.163, 0.686, 0.320, 0.769] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of subplot_0 that contains the label '{Steady Stock}'<r>[0.212, 0.704, 0.314, 0.751]</r> paired with a colored marker."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "EcUKkPahipacb0L6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content inside [0.673, 0.307, 0.693, 0.613] in subplot_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the y-axis label of subplot_2 which is '{Stock Price}'<r>[0.672, 0.307, 0.692, 0.613]</r>."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "CF6C1MJuGI3Rmi5S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within region [0.749, 0.825, 0.759, 0.871] in subplot_2."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label '0' with its {coordinates}<r>[0.749, 0.825, 0.759, 0.871]</r>."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "tBF43TL2UGm6VVSh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.017, 0.384, 0.037, 0.536] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the Climate Change Index subplot."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "XLevQdUEZZV18252",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the coordinates of the Year label for the Sea Level subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.838, 0.890, 0.877, 0.950]"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "yUEz4ZIasSzgE2F4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the 1990 x-axis tick label} at [0.733, 0.825, 0.772, 0.871] referring to in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 1990."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "5RWT2te61j6jbq2o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing the year 2000 in the Climate Change Index subplot}[0.167, 0.294] higher or lower than {the point representing the year 2010 in the same subplot}[0.237, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "Ri8jBn5CbAwz2Tx0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Sea Level' line}[0.753, 0.756, 0.963, 0.765] have a higher or lower ending point than {the 'Climate Change Index' line}[0.097, 0.239, 0.308, 0.355]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "V21tXzO16ofcHfcM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me whether the {the 'Air Quality' line start point}[0.425, 0.349] is above or below its endpoint {the 'Air Quality' line end point}[0.635, 0.671]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "kKHfVK1GuWCz35Q4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements present in the region [0.739, 0.128, 0.977, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a line plot which represents the sea level change over time. It has coordinates for x and y axes labels, x and y ticks, and an individual line plot with markers at specific years."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "RQHN8FQOCj85CHlE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.411, 0.128, 0.650, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the subplot for 'Air Quality', comprising x and y-axis labels, tick marks for both axes, and a dashed line plot showing the change in Air Quality over the years."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "CEVGvjBuUrQwZlFR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.083, 0.128, 0.322, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'Climate Change Index' subplot. The elements within this region include x and y axes labels, tick labels for both the axes, the subplot title, and a line representing the Climate Change Index over the years."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "mxEVu9XWTwJBFilr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label refer to at these coordinates [0.053, 0.776, 0.077, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, '0.4'."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "2Hvr9V3theLdiPJC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the 4th label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates are [0.580, 0.824, 0.601, 0.870]"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "txJMeaQwOlboW9KR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at this point {the marker}[0.969, 0.701, 0.909, 0.748] in the Biotech subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend marker."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "upaCzntngGjdppI6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at TRL 60 for ICT}[0.262, 0.325] above or below {the point at TRL 60 for Nanotech}[0.262, 0.208] in ICT & Nanotech subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "0LTKaZcqxwjWS1Ih",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the ICT line}[0.429, 0.325, 0.643, 0.525] generally above or below {the Nanotech line}[0.429, 0.160, 0.643, 0.458] in ICT & Nanotech subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "h16gCLYRdWC0NmpO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology level at {TRL 40}[0.207, 0.420] has a higher Innovation Success, Advanced Manufacturing or Cleantech?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cleantech."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "sENMRNpOpHachmel",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items located in the region [0.864, 0.688, 0.977, 0.769]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is only one legend item: {Biotech}[0.909, 0.706, 0.969, 0.753], with a colored marker associated with it."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "0Afg8cWqaNwL6ZS6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details of the area [0.147, 0.819, 0.166, 0.870]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes one x-axis label: {20}[0.148, 0.824, 0f.167, 0.870] in the first subplot (Advanced Manufacturing & Cleantech)."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "bRhQcIvzrUuiqIMB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.677, 0.132, 1.011, 0.195]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects: {the ICT label}[0.683, 0.165, 0.707, 0.209], {the & label}[0.781, 0.166, 0.797, 0.209], and {the Nanotech label}[0.981, 0.166, 1.058, 0.209] in the second subplot's legend (ICT & Nanotech)."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "zzWzRbtafw2zYgo0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with {the line representing 'Product Sales'}<r>[0.763, 0.290, 0.941, 0.571]</r> in the 'Cloud Technology' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "ZJEq7runwo9xYCdo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the linestyle used for {the line representing 'Investment'}<r>[0.106, 0.511, 0.284, 0.641]</r> in the 'AI Research' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solid."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "vJHQmAgd6xb813e9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the shape of the marker for {the line labeled 'Product Sales'}<r>[0.435, 0.576, 0.612, 0.770]</r> in the 'CyberSecurity' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "Wj24AFAev1b7ckrY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a lower y-coordinate at x = 2020 between {the 'Investment'}<r>[0.763, 0.258, 0.941, 0.582]</r> and {the 'Product Sales'}<r>[0.763, 0.290, 0.941, 0.571]</r> in 'Cloud Technology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "Qkfos4NqnVqAt41Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of height at x = 2019, which dataset is displayed at a higher position on {the graph}: {the 'Investment Sales'}<r>[0.763, 0.258, 0.941, 0.582]</r> or {the 'Product Sales'}<r>[0.763, 0.290, 0.941, 0.571]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Sales."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "ONTrb3chwuXalV6b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the initial values [0.106], which line begins higher on the plot for 'AI Research', {the 'Investment'}<r>[0.106, 0.511, 0.284, 0.641]</r> or {the 'Product Sales'}<r>[0.106, 0.490, 0.284, 0.630]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product Sales."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "BwPEK9eQwzQloQVp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed by [0.390, 0.086, 0.511, 0.155]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend for 'CyberSecurity' with handles colored in orange and green representing 'Investment' and 'Product Sales' respectively."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "bYatBxTJWO8S6JbW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.343, 0.278, 0.357, 0.680]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the y-axis of the 'CyberSecurity' subplot, featuring labels for Investment/Sales values."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "pq8ETwl4TDC9EQHh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section spanned by [0.180, 0.942, 0.210, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This narrow region holds the x-axis label of the 'Year' for the 'AI Research' subplot, presented in black."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "xXbS753z5TjuEqhi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.098, 0.890, 0.318, 0.950]</r> in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decomposition IO Norm."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "OoVxyH4QbuVhwlXT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does {the marker}<r>[0.155, 0.701, 0.202, 0.748]</r> in subplot_0 correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "BigData (10k)."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "eZgXK9NpWAqBgJ1N",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of {the point}[0.101, 0.194] on the line in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents that when the Decomposition IO Norm is 0, the accuracy of BigData (10k) is 0.78."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "AC9MomwrOD7IIcXU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween BigData (5k) represented by {the line}<r>[0.757, 0.227, 0.970, 0.493]</r> and Hive (5k) represented by {the line}<r>[0.757, 0.360, 0.970, 0.659]</r>, which one has a higher accuracy at Decomposition IO Norm of 40 in subplot_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "BigData (5k)."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "c8tTtLiffvkV746u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {BigData (10k) at point '40'}[0.208, 0.394] in subplot_0, is {Hive (10k) at point '40'}[0.536, 0.527] in subplot_1 higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "tUDlxrUJmz03YRHm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the accuracy of {BigData (10k) at point '80'}[0.314, 0.593] in subplot_0 higher or lower than {Hive (5K) at point '80'}[0.970, 0.360] in subplot_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "TyfaUahgDbgXRZpp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area <r>[0.822, 0.616, 0.976, 0.769]</r> in subplot_2 of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for BigData (5k) and Hive (5k) which are colored and styled with square and cross markers respectively. Their corresponding colors are green and orange."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "vIS7YvOtHFFiHN6q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the area <r>[0.822, 0.616, 0.976, 0.769]</r> in subplot_2."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two lines of legend. BigData (5k), marked with a green square, and Hive (5k), marked with an orange cross."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "icXZqwB2wURC5gpM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is featured in the <r>[0.763, 0.050, 0.963, 0.113]</r> area of subplot_2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot_2's subtitle, \"BigData & Hive 5K,\" populates this area."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "mhfImqZqL995geKx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the coordinates [0.810, 0.890, 0.903, 0.950] in the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the coordinates for the x-axis title \"Risk Level\" for the 'High Risk' subplot."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "VqDJKlBOzgQ8Yb0E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second point on the Risk Management Efficiency line in the 'Low Risk' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.201, 0.645]"
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "JnCyjAMvmCzgSp0Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for subplot_2 with the coordinates [0.672, 0.326, 0.692, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "4FfJcIVZbIXcYOIA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at '100'}[0.968, 0.541] in {line_0 of subplot_2} above or below the {point at '100'}[0.968, 0.571] in {line_1 of subplot_2}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "haiRA4mT1equ4bdV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the highest value on the y-axis for the subplot with coordinates '[0.734, 0.128, 0.979, 0.792]' match with the point at '0' on {line_0} in that subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "ZJwQxGNK6BSmzaBg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subplot with coordinates '[0.406, 0.128, 0.651, 0.792]', which label is associated with the 'o' marker and line color '#3498db'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Management Efficiency."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "QSrgh51jtN1BXVpC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's included in the region [0.806, 0.050, 0.907, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the 'High Risk' subplot is located here with font color black and zero rotation."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "fuF9UN4l1bbXDB6E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region located at [0.698, 0.616, 0.971, 0.769]."
+            },
+            {
+                "from": "gpt",
+                "value": "Two labels: {Risk Management Efficiency}[0.747, 0.634, 0.965, 0.681] and {Risk Assessment Efficiency}[0.747, 0.704, 0.955, 0.751] in the legend of the subplot 'High Risk'. Each label is paired with a color handle for visual representation."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "T7zEheFm9H5HwZcu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region located at [0.734, 0.128, 0.979, 0.792] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area occupied by the 'High Risk' subplot. It includes the title 'High Risk', risk levels as x-axis labels, 'Efficiency' as the y-axis label, and various line series."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "6N2B092Phzv7iQuj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle for the plot at coordinates [0.642, 0.050, 1.052, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Robotics and IoT Development Trends\"."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "mkVpUUy5zlIzdw9u",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what coordinate is the label '2010' on the x-axis in the first subplot located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.144, 0.825, 0.183, 0.871]."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "3vXI03HfkYVKUOjb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the tick on the Y-Axis}<r>[0.043, 0.442, 0.062, 0.488]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "30."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "UGMdPxwRUdZR8Vz7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'AI'} trend at [0.300, 0.175] higher or lower than {the 'Blockchain'} trend at [0.619, 0.460]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "tPqPk3ygKX6BVd2B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {marker for AI in subplot_0}<r>[0.078, 0.155, 0.104, 0.178]</r> the same color as the {marker for IoT in subplot_2}<r>[0.716, 0.199, 0.742, 0.222]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "nF9F76BHoqoqtGWo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {line for IoT}<r>[0.757, 0.318, 0.938, 0.745]</r> climbing steeper than the {line for Robotics}<r>[0.757, 0.318, 0.938, 0.697]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "VV7vDwTWl0aDE89q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart content within [0.392, 0.128, 0.664, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot showing the \"Blockchain and AR/VR Development Trends\". The region includes the X-axis and Y-axis, labeled 'Years' and varied numerical values respectively. There are lines depicting the development trends for Blockchain and AR/VR."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "wBKmlYZuUDHLG16G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.644, 0.824, 0.961, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the X-axis labels for the Year in the third subplot. The labels are for the years from 2010 to 2025."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "MQtK6wOOwVJTqSsN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.397, 0.142, 0.481, 0.238]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot. The legend shows two entries, one for Blockchain and one for AR/VR, each with their respective color-coded markers."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "2MOCbTQo7H5syER8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the first data point of the 'Homeless Rate' line plot in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{0}<r>[0.743, 0.762]</r>"
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "58ZmYHfPurqmJEfS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visible string is at {coordinates}<r>[0.903, 0.825, 0.922, 0.871]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "60"
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "mR4aBVL9Locubf7c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart lays under these coordinates [0.073, 0.128, 0.328, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "First subplot"
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "3qSkGc46RBz6QDuJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'High Income Households' line plot at coordinates [0.846, 0.825, 0.866, 0.871] above or below the 'Single Parent Households' line plot at the same x-value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "QGLfh5Tf8ZAZhZvz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt x-value '80', does the 'Poverty Rate' or 'Foreigner Households' have a higher y-value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Poverty Rate."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "IAVXEZc2Gbpq6wM8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point '0'} of Homeless Rate [0.743, 0.762] higher or lower than {the point '20'} of Homeless Rate [0.799, 0.765] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "KzsbOIB141CQtm7g",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within this region [<r>0.729, 0.128, 0.983, 0.792]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the third subplot. It's a line plot representing 'Homeless Rate'. The x-axis label is 'Decomposition IO Norm' and the y-axis label is 'Percentage'. The tick values for x-axis are '0', '20', '40', '60', '80' while those for the y-axis are '0', '2', '4', '6', '8', '10', '12'."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "Neyoa4bPq03WUyhf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the bounded box [0.401, 0.128, 0.656, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the second subplot of the chart. It consists of two line plots for 'Single Parent Households' and 'High Income Households' both plotted against 'Decomposition IO Norm'. The y-axis is labeled as 'Percentage'."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "OOaowOmRfsHvYyoX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.337, 0.170, 0.727, 0.862] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes part of the first subplot and the entire second subplot. The first subplot's line graph for 'Foreigner Households' and 'Poverty Rate' is cutoff, whereas the second subplot's line graph for 'Single Parent Households' and 'High Income Households' is fully visible."
+            }
+        ],
+        "id_raw": 534
+    },
+    {
+        "id": "qsaSGqarkAGHpZMQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the x-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "P5FkNEY2g9pGdMt3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the line labeled 'LYRAA'}<r>[0.765, 0.197, 0.962, 0.540]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "Q5deB0na4ktYqVwk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label shown in {subplot_3}<r>[0.743, 0.128, 0.983, 0.792]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Effectiveness."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "c9IEvN7WD7GUltVy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend for 'ALBA'}<r>[0.110, 0.197, 0.306, 0.724]</r> increasing or decreasing from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "9T8xZAXIUtE91Fog",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a lower start point at the year 2006, {the LYRAA trend}<r>[0.765, 0.265]</r> or {the MAPLE trend}<r>[0.765, 0.449]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "MAPLE."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "ccIAG5kq58ruoR3z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {label \"IOTA\" from the legend}<r>[0.606, 0.704, 0.642, 0.751]</r> match the {marker color}<r>[0.555, 0.701, 0.602, 0.748]</r> in its corresponding subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "cDKoTesT7J4UwiCB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.088, 0.128, 0.328, 0.792]</r> display in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows a line graph representing the growth of 'ALBA', with points marked for each year along the y-axis labeled 'Effectiveness' and the x-axis marked by years."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "swgKwaodyEDLg3Tt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements located in the region <r>[0.416, 0.128, 0.656, 0.792]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the 'IOTA Growth' subplot, illustrating its performance over years with a dotted line, and marked measurements along the y-axis denoting 'Effectiveness', similar to x-axis labels for each year."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "QePPjkRrTfR1V4ti",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart region <r>[0.743, 0.128, 0.983, 0.792]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment of the chart depicts the combined decline of 'LYRAA & MAPLE' shown by two distinct line styles for each in varied colors, with corresponding x and y-axis labels for 'Year' and 'Effectiveness' respectively."
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "olMXOZuG0U0VdNHb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the marker at {coordinates}<r>[0.106, 0.416]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Type_A Expenditure for 2009."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "xhzSa81Lb67pcZVA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the Type_B Expenditure as seen at {the handle}<r>[0.080, 0.200, 0.109, 0.223]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "jcD8K2KgIZKqdI96",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {the line for Type_C Expenditure}<r>[0.434, 0.588, 0.624, 0.774]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "3PyE6WiGmBMJ8hBu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {data point '2010' for Type_D Expenditure}<r>[0.793, 0.685]</r> above or below {data point '2015' for Type_D Expenditure}<r>[0.952, 0.544]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "RtRwN87omn7cj3cq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich expenditure type has a higher expenditure in 2011, {Type_D}<r>[0.825, 0.678]</r> or {Type_E}<r>[0.825, 0.281]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Type_E."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "BqCUEpKb9sxm5pyt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first tick label '2009' for Type E Expenditure}<r>[0.742, 0.851, 0.781, 0.898]</r> align with {the first point of Type E Expenditure}<r>[0.761, 0.351]</r> on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "Z7MBbU0ppzpvE5Yb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the subset area <r>[0.000, 0.312, 0.392, 0.956]</r> for subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes elements like {'xlabel for years'}<r>[0.185, 0.917, 0.217, 0.950]</r>, {'ylabel for expenditure in $'}<r>[0.017, 0.314, 0.028, 0.620]</r>, x-axis and y-axis numerical labels, and a line graph depicting Type A and B Expenditures with respective data points and a small legend displaying their color codes."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "1gzhf3McMHEgUUQO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content present in the area <r>[0.678, 0.050, 0.983, 0.974]</r> for subplot 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section encompasses {'the subtitle labeling Type D and E Expenditure'}<r>[0.757, 0.050, 0.956, 0.097]</r>, {'xlabel and ylabel for years and expenditure'}<r>[0.840, 0.917, 0.873, 0.950]</r><r>[0.672, 0.314, 0.683, 0.620]</r>, the associated x-axis and y-axis tick labels and two line graphs showing the expenditures for Types D and E, along with their legend entries."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "qCq2TxqU1gH1K6zp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained in the region <r>[0.074, 0.114, 0.656, 0.819]</r> that spans two subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes {'the axes of subplot 0 and 1'}<r>[0.074, 0.114, 0.328, 0.819]</r><r>[0.402, 0.114, 0.656, 0.819]</r> which cover the data visualizations for Type A, B, and C expenditures, their x and y axes labels, tick marks, and a part of their legends."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "nh3bCUtWaVtSTox9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.011, 0.422, 0.023, 0.530] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "cQ8Yy2uo6TBXRYxc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second x-axis tick label, which is also placed at [0.107, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data_Analysis."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "XKfOm2QARg159Wq8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.045, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point representing the value of 'Network_Length' in the 'Marginal Growth' trend."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "85Cpn6eYJyvTluMx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the points {Network_Length on Marginal Growth}[0.048, 0.839] and {Data_Analysis on Marginal Growth}[0.107, 0.113] which one has the higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Network_Length."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "FaspuCVrMY0Iq1bq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the 'Minimal Change' trend, is the value for {Data_Quality}[0.706, 0.234] higher or lower than the value for {Data_Variables}[0.765, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "QdST6mVcHDCbNyiE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {Network_Length on Marginal Growth}[0.048, 0.839] and {Data_Quality on Minimal Change}[0.706, 0.234], which one has a greater value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Network_Length."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "Bxq6gLifPAjxeozy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinate [0.150, 0.093, 0.281, 0.156]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a legend item labeled 'Marginal Growth', associated with a marker colored in '#e74c3c' located at {coordinates}[0.149, 0.109, 0.181, 0.137]."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "lqwRKwmpN35kPThP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between [0.374, 0.077, 0.615, 0.875] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subplot with the title 'Data Preprocessing Overview'. This region contains x and y-axis labels 'Categories' and 'Values' respectively, a line plotting for the trend 'Minimal Change', and x/y-axis tick labels."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "7AWy4dS92KDKMVDF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area from [0.669, 0.422, 0.681, 0.530]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see a y-axis label 'Values' in this area."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "YniZK28RgHqh8E5J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.159, 0.822, 0.192, 0.842] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Handle color for 'Public Trust in Government'."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "ssUbWZFz9RNUTVKP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the main element at [0.559, 0.828, 0.582, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "Handle color for 'Trust in Media'."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "mUMKW6EdlpMzTUnF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.837, 0.822, 0.982, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label text for 'Public Approval of Government'."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "dnX4W3eEIZUkhS08",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher start point on their respective y-axis, {the trend line marked 'Public Trust in Government'}[0.082, 0.512] or {the trend line marked 'Trust in Media'}[0.412, 0.469]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trust in Media."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "XGrcFVNjLPcimhVE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the end points of {the 'Political Engagement' trend}[0.955, 0.353] with {the 'Public Approval of Government' trend}[0.955, 0.440] on the y-axis. Which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Political Engagement."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "MSraPEutbEAheyju",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the start point of {the line 'Public Trust in Government'}[0.082, 0.512] higher or lower than the end point of the same line[0.295, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "kBQ5WzjTznZRu4ZB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is within the bounds [0.777, 0.030, 0.897, 0.068]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle, \"Political Trends.\""
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "f8XUXGGa9hmALQKa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the significance of the elements found between [0.808, 0.769, 0.985, 0.862]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the sublegend for the plot, including items for 'Political Engagement' and 'Public Approval of Government', each with their respective color indications."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "NKu26P2RjBW4RlJV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.157, 0.811, 0.325, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the sublegend area displaying the 'Public Trust in Government' along with its color handle used in the line graph."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "zzfqlP96Z9Uw42Ng",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is represented by the line with coordinates '[0.179, 0.352, 0.932, 0.733]' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The performance of the Base Model."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "JByFBuFy2Omw8mh6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label} at '[0.529, 0.838, 0.582, 0.885]' represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Epoch 100."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "rgjm3vuViqfjFh3H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the object with the coordinates '[0.141, 0.124, 0.970, 0.806]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "IXsyBGXySlXC7Sou",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Training Epoch 80}[0.480, 0.461], which model has a higher accuracy - the Base Model or the Improved Model?"
+            },
+            {
+                "from": "gpt",
+                "value": "Improved Model."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "qiJbpA57t7aH01XU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Training Epoch 150}[0.744, 0.206], is the accuracy of the {Base Model} higher or lower than the Improved Model?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "OdfCo7PtPa7YEqMZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Base Model}[0.932, 0.733] and {Improved Model}[0.932, 0.179] performances closer together or further apart at {Training Epoch 200}[0.932, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Further apart."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "Vj1OSMCwkuMSAslP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is inside the area, specifically '[0.155, 0.629, 0.479, 0.783]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend. It has entries for the Base Model [0.159, 0.656, 0.229, 0.680] and the Improved Model [0.159, 0.726, 0.229, 0.749] respectively."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "PtEHMkpWk1pxEEbr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within '[0.312, 0.838, 0.385, 0.885]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the x-axis tick '75' is located here."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "KH5UL1IYKTgIKkV6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the area '[0.231, 0.050, 0.881, 0.110]'"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, which is \"Model Accuracy across Training Epochs\"."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "H75lJF8ux4peLYJi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at coordinates [0.030, 0.402, 0.058, 0.529] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value"
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "WtYeunxeO53eMQVU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle of the Social Networks legend}[0.482, 0.245, 0.551, 0.268]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffa500"
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "SVz9byKEo6mBjkOL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.069, 0.242, 0.131, 0.289]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.70"
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "7QzMYRyOwVf6V8iE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at time 0.0 for 'Interpersonal Relationships'}[0.187, 0.261] above or below {the point at time 1.0526315789473684 for 'Interpersonal Relationships'}[0.227, 0.288]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "rJDBmHY7EBsoyCGq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at time 0.0 for 'Social Networks'}[0.187, 0.397] to the left or right of {the point at time 0.0 for 'Interpersonal Relationships'}[0.187, 0.261]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they are vertically aligned."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "HSeOlREuUBajvCq5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt time 15.789473684210526, which line, 'Interpersonal Relationships' or 'Social Networks', is at a higher value {Interpersonal Relationships point}[0.776, 0.533] or {Social Networks point}[0.776, 0.397]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpersonal Relationships."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "Z7wIszvAl9QeiAwR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content contained in the box with coordinates [0.478, 0.148, 0.956, 0.301]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legends for the lines. The first item is the line labeled 'Interpersonal Relationships', represented by a green marker at {coordinates}[0.482, 0.175, 0.551, 0.198]. The label of 'Interpersonal Relationships' is found at {coordinates}[0.567, 0.166, 0.945, 0.213]. The second item is the line labeled 'Social Networks', represented by an orange marker at {coordinates}[0.482, 0.245, 0.551, 0.268]. The label of 'Social Networks' is found at {coordinates}[0.567, 0.236, 0.788, 0.283]."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "DJqmTuRbWZ1OmSLN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.079, 0.050, 1.041, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart\u2019s subtitle 'Trends in Interpersonal Relationships and Social Networks' appearing in black color with no rotation."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "sBydzJrf2qQQUFNC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels and colors of the elements located in the box [0.165, 0.838, 0.302, 0.885]"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two x-axis labels located in this region: '0.0' at {[0.165, 0.838, 0.209, 0.885]}, and '2.5' at {[0.259, 0.838, 0.302, 0.885]}. Both labels are rendered in black."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "EZDDd7epqQqli9ay",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.015, 0.295, 0.029, 0.663] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "bqvzGNFXG70gJfME",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the tick} at [0.831, 0.903, 0.866, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "bgFzCv0tN7Xp9Dwa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of  {the line representing 'Company A'}<r>[0.096, 0.191, 0.943, 0.847]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green (#008000)."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "Mtp0gWgxx5gIpPou",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Company B'}<r>[0.096, 0.367, 0.943, 0.751]</r> generally above or below {the line for 'Company A'}<r>[0.096, 0.191, 0.943, 0.847]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "IZCqUF8SYVHiHqgu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Company C'}<r>[0.096, 0.111, 0.943, 0.431]</r> experience an increase or decrease between 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "jNzyYNKz6t3KGxPm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has a higher Social Responsibility Index in 2017, Company A or Company B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Company A."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "hYLf6RBI62UYHfIO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the region [0.060, 0.089, 0.190, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the lines. It includes the companies: {Company A}<r>[0.105, 0.100, 0.185, 0.128]</r> with green as its line color, {Company B}<r>[0.105, 0.142, 0.185, 0.170]</r> with red as its line color, and {Company C}<r>[0.105, 0.183, 0.185, 0.211]</r> with blue as its line color."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "9UyhJUTpA4G2U71j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.266, 0.903, 0.301, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label '2012'."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "HpupJGRadKH2M703",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found within the coordinates [0.096, 0.191, 0.943, 0.847]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the Social Responsibility Index trend line for {Company A}<r>[0.096, 0.191, 0.943, 0.847]</r>, which shows the company's performance from 2010 to 2019."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "xMne6VtUeffFoaHA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.015, 0.427, 0.029, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Value."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "l0C7ev2lUPDqa1wF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the xlabel?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.504, 0.959, 0.547, 0.979]"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "xoaD8TOLVMksAafg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.846, 0.063, 0.978, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "5ExWH4pmHSFisdxo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point 'January'}[0.108, 0.701] for 'Yoga Trends' higher or lower in value than the {point 'January'}[0.108, 0.399] for 'Gym Trends'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "NLsxAfG9EYvKrWf5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the 'Yoga Trends'}[0.943, 0.830] located in relation to {the 'Gym Trends'}[0.943, 0.312] in December?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "0oBw6llZmRrSClhr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the Gym Trends higher or lower in value at {July}[0.640, 0.796] compared to the Yoga Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "NPv3RmtwJP8VWl8M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.846, 0.063, 0.978, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. The legend is made up of two entries: {Yoga Trends}[0.890, 0.071, 0.972, 0.091] marked in color #00bfff and {Gym Trends}[0.890, 0.101, 0.972, 0.121] marked in color #b22222."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "D3MSpPum6in7M8f9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.104, 0.931, 0.728, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we have the X-axis tick labels. These tick labels indicate the months of the year from January to December."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "rjnbEpHwLhq4Y4qe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be found within [0.035, 0.909, 0.205, 0.929]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, the first two Y-axis ticks are present. They correspond to the values of {0.0}[0.035, 0.909, 0.057, 0.929] and {0.5}[0.035, 0.801, 0.057, 0.821]."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "eETZreaV9jnrngqw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.030, 0.325, 0.058, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "qSs9ktG4pSW8G8lu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.308, 0.838, 0.378, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "ZEZA0EDwxxBbVo2c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} located at [0.087, 0.651, 0.139, 0.698]?"
+            },
+            {
+                "from": "gpt",
+                "value": "200."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "XQblierVl1MbyFms",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2013'}[0.417, 0.458] in the 'Startups Registered' line above or below {the point labeled '2013'}[0.417, 0.760] in the 'Innovation Index' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "Xn7bZV05SC0SbD4Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2015'}[0.564, 0.397] in the 'Startups Registered' line higher or lower than {the point labeled '2014'}[0.491, 0.428] in the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "IW63fI4WLmrdW5Mm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher value at {the point labeled '2020'}[0.933, 0.187], Startups Registered or Innovation Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startups Registered."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "1HNzAyXcMrHAXRUC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.156, 0.110, 1, 0.806]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main graph showing two trend lines. {The 'Startups Registered'}[0.196, 0.187, 0.933, 0.571] line is in light blue, and {the 'Innovation Index'}[0.196, 0.748, 0.933, 0.766] line is in light grey. Both lines are marked with markers at each year from 2010 to 2020."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "giN7SllvCIK2fUgc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.262, 0.166, 0.537, 0.213]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses {the title of the first line in the graph, 'Startups Registered'}[0.262, 0.166, 0.537, 0.213], placed next to a light blue symbol representing the line color in the chart."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "pBHqRFZv5JMo3LU0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.087, 0.050, 1.041, 0.110]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region features the subtitle \"Entrepreneurship and Innovation Ecosystem (2010-2020)\" rendered in black color."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "L21BbaDUY5juoj26",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe {the Y-axis label}<r>[0.021, 0.377, 0.041, 0.581]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label is \"Adoption Stats\"."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "X3YhsM1njmd7jYCx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.175, 0.100, 0.354, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the 'Traditional EdTech' text in the legend."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "GQWzgjtimFUJ9HLP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the seventh x-axis label}<r>[0.707, 0.903, 0.751, 0.931]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the number '15.0'."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "5qzgihfkevlsBbfZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which week do {Traditional EdTech}<r>[0.270, 0.390, 0.450, 0.385]</r> and {Innovative EdTech}<r>[0.270, 0.460, 0.450, 0.455]</r> have the same adoption stats?"
+            },
+            {
+                "from": "gpt",
+                "value": "Week 1."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "qULiSbz3DUMMd88n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Adoption Stat for Innovative EdTech}<r>[0.771, 0.209]</r> higher or lower than {Adoption Stat for Traditional EdTech}<r>[0.771, 0.763]</r> at week 16?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "uKcJaTEioZEtSxen",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {Innovative EdTech}<r>[0.435, 0.425]</r> compare with the {Traditional EdTech}<r>[0.435, 0.683]</r> at week 8?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is greater."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "RVfSbiHeu1Tw54Z3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area marked by the coordinates [0.111, 0.089, 0.362, 0.181] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the labels for 'Traditional EdTech' and 'Innovative EdTech', along with color markers for each in a sublegend section."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "eTLLFyFo6IIHWpgc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by [0.141, 0.614, 0.939, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 'Traditional EdTech' line plot with plotted points, showing the adoption trends over 20 weeks."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "iZuPpGQSLwTLV05m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the attributes observed in the region [0.348, 0.030, 0.732, 0.066]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle for the chart, stating \"EdTech Adoption Over 20 Weeks\"."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "4hstCuStdqWROKiZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.181, 0.142, 0.460, 0.170] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for \"Innovation Ecosystem Index\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "Oqrv78JXnJIFZDr4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the {y-axis label}<r>[0.021, 0.398, 0.041, 0.560]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's \"Index Value\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "iH1PMQw3oxJ3F0PN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis tick label} at <r>[0.650, 0.903, 0.700, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "SJroBXjDprvwJKI5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2018, is the {Global Entrepreneurship Index} point<r>[0.851, 0.165]</r> higher or lower in value than the {Innovation Ecosystem Index} point<r>[0.851, 0.218]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "uCh68N0upKP78fNM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2017 on the Global Entrepreneurship Index line}<r>[0.763, 0.218]</r> higher or lower than {the point for the year 2017 on the Innovation Ecosystem Index line}<r>[0.763, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "BFHMVv7ArtKULgSj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first Index to reach a value of 0.8, {Global Entrepreneurship Index} or {Innovation Ecosystem Index}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Entrepreneurship Index."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "yete5oMD3y4AL6TG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the area located at [0.117, 0.089, 0.491, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the sublegend of the chart. There are two items: {Global Entrepreneurship Index} with coordinates <r>[0.181, 0.100, 0.483, 0.128]</r> and handle color blue, and {Innovation Ecosystem Index} with coordinates <r>[0.181, 0.142, 0.460, 0.170]</r> and handle color green."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "rn31oMH8TECvoWh2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.474, 0.903, 0.524, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label {2014}<r>[0.474, 0.903, 0.524, 0.931]</r> is situated here."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "pYyM8ob52xzzGAYH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents can be found within [0.147, 0.129, 0.939, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two line graphs. The {Global Entrepreneurship Index} line with coordinates <r>[0.147, 0.129, 0.939, 0.650]</r> and color blue, and the {Innovation Ecosystem Index} line with coordinates <r>[0.147, 0.165, 0.939, 0.758]</r> and color green."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "yOl30Eux7XCIfCGQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the y-axis label in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Scores\" is located at {coordinates}<r>[0.030, 0.388, 0.058, 0.542]</r>."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "bg5vqecUeGq62l7I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at <r>[0.069, 0.702, 0.113, 0.749]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick labeled '0.6'."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "VHfNAnPBaKA4NY7k",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plot found at <r>[0.167, 0.210, 0.842, 0.550]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line plot for the 'Base Model'."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "fHqTPwDk6vgQEfBo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two plots, {Base Model}<r>[0.167, 0.210, 0.842, 0.550]</r> or {Our Model}<r>[0.167, 0.261, 0.842, 0.584]</r>, has a higher score on the 'Time per Unit' factor?"
+            },
+            {
+                "from": "gpt",
+                "value": "Base Model."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "p3pyMCQF1XxksFHT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 'Fraction Rejected' factor, are the scores of {Our Model}<r>[0.167, 0.261]</r> above or below {Base Model}<r>[0.167, 0.210]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "CFSr6jdVNFqrw9ME",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the 'Scrap Rate' factor, is the score of {Our Model}<r>[0.617, 0.584]</r> greater than or less than the {Base Model}'s score<r>[0.617, 0.550]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "cvWNhnEisSUFbnkZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region <r>[0.112, 0.050, 0.896, 0.110]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the graph, 'Manufacturing Production Efficiency Evaluation', is within this region."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "s4ctg0CLsGouD7hj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region <r>[0.601, 0.148, 0.861, 0.301]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the two line plots, with labels for the 'Base Model' and 'Our Model', each associated with a color marker."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "ZINho1M6a02AdzZv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found between the coordinates <r>[0.454, 0.903, 0.554, 0.950]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Factors\" is located between these coordinates."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "ejqeNMv3FJgi7Zdx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.095, 0.060, 0.902, 0.908]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart area."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "9XqseXOIjhI12MgN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year indicated by {x-axis tick label}<r>[0.109, 0.919, 0.153, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "dRGK7Qbe7AYvRsOX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category does the {line}<r>[0.131, 0.100, 0.869, 0.865]</r> belong to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Footprint."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "S6ZiQZ66IeITR4sL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Corporate Giving'}<r>[0.131, 0.865, 0.213, 0.769]</r> generally increasing or decreasing over time in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "fl8jTQhJfKberQKC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at the year 2014, {Corporate Giving}<r>[0.459, 0.530]</r> or {Carbon Footprint}<r>[0.459, 0.394]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporate Giving."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "x3u2whMxmnkz2v5S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2011}<r>[0.213]</r>, is Corporate Giving or Carbon Footprint higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporate Giving."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "W2rvHtwwRkno3nRK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area with coordinates [0.258, 0.025, 0.742, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart \"Social Responsibility and Sustainable Business\"."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "BnOUBOFPgP8pgnAl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.094, 0.062, 0.906, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area. It contains two lines plotting: {Corporate Giving}<r>[0.131, 0.865, 0.213, 0.769]</r> and {Carbon Footprint}<r>[0.131, 0.100, 0.213, 0.218]</r>. Also, x-axis tick labels indicating years and y-axis tick labels indicating values."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "2n6ta7yzVdd0t8hV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by [0.019, 0.275, 0.036, 0.690]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the y-axis label \"Corporate Giving / Carbon Footprint\" is located."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "aLecux932oJdN856",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.030, 0.309, 0.058, 0.621] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "K6u0kZ1GcQeoUtke",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.763, 0.166, 0.945, 0.213]."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "BBbvMVcuo5h832XV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.916, 0.838, 0.951, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "12."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "fkW4l6QJyIDVNvjJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Our Product'}[0.866, 0.707] above or below {the point of 'Base Product'}[0.866, 0.654] in terms of sales unit at the 11th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "dZX3z4CD2ahbx3Ra",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Our Product'}[[0.196, 0.200, 0.933, 0.745]] generally above or below {the line for 'Base Product'}[[0.196, 0.200, 0.933, 0.692]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "27LNKhjEycEPOKGT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product has a higher sales unit at the {end of the 12th month}[0.933, 0.692, 0.933, 0.745], Our product or Base product?"
+            },
+            {
+                "from": "gpt",
+                "value": "Our product."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "qATMnFNKNsZeMx5u",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.295, 0.050, 0.834, 0.110]."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Product Sales Trend Observation,\" is located here, styled in black color."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "0axKETff30Lj8ZQv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.674, 0.148, 0.956, 0.301]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Base Product}[0.763, 0.166, 0.945, 0.213] and {Our Product}[0.763, 0.236, 0.928, 0.283], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "isMGGg7DtLbn3XuT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.781, 0.838, 0.817, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis tick label '10' is located at {coordinates}[0.781, 0.838, 0.817, 0.885]."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "Y4uQNe3CfnZNiVrB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}[0.509, 0.942, 0.555, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "xt1xiYQhPzvu49uF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the graph title from the given coordinates [0.328, 0.030, 0.736, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's \"Culture and Arts Industry Development\"."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "x2uBTOhksTZceGd0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.748, 0.147, 0.791, 0.161] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color marker for 'Trendy Support'."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "us3gnCCkK5Z6IpGM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2012 marked at coordinate [0.305, 0.883], which line represents a higher Level of Support - {Traditional Support}[0.305, 0.645] or {Trendy Support}[0.305, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Support."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "FP4BQGdvcrHJ72c6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich shows a greater increase in support from 2010 at coordinate [0.124, 0.883] to 2011 at coordinate [0.214, 0.883], {Tradition Support}[0.124, 0.585, 0.214, 0.620] or {Trendy Support}[0.124, 0.713, 0.214, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trendy Support."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "FHVQfgiZfjEMqnLY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Traditional Support} line[0.124, 0.543, 0.940, 0.679] rise or fall overall from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rise."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "ePGpVnT6RxdMIwtI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the [0.745, 0.089, 0.973, 0.181] region."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart. It includes the labels for 'Traditional Support' {marker}[0.748, 0.105, 0.791, 0.119] and 'Trendy Support' {marker}[0.748, 0.147, 0.791, 0.161], each with color markers."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "ASZblF5SK5YqtWMa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.396, 0.679, 0.487, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a part of the {Traditional Support} line[0.396, 0.679 into 0.487, 0.628], indicating the decline in support between 2013 and 2014."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "PxmqEipSZR7r6U8G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.759, 0.245, 0.850, 0.202]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a segment of the {Trendy Support} line[0.759, 0.245, 0.850, 0.202], representing the increased support from 2017 to 2018."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "BhQo3jeW8ZTLX8pt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element lies within these coordinates: [0.108, 0.200, 0.943, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing Global Average Temperature."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "SPMSirBFLumyjxzC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text within the box with these coordinates: [0.774, 0.859, 0.890, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Sea Level."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "FLVDgHvb7ajje7Cl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label, located at [0.507, 0.952, 0.544, 0.975], reads as?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "e5aY40ksUkUtALfq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the point in 'Global Average Temperature'} at [0.630, 0.353] and {the point in 'Global Sea Level'} at [0.630, 0.773], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Sea Level."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "f7mnJKPAaUNwRQGa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point in 'Global Average Temperature'} at [0.734, 0.276] higher or lower than {the point in 'Global Sea Level'} at [0.734, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "cNSWc44UFzdQTYOx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a higher value at the year 2000 - {Global Average Temperature}[0.526, 0.452] or {Global Sea Level}[0.526, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Sea Level."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "bTjYSI0F9cxiM7HA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide information on what is present within the region [0.066, 0.200, 0.985, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two lines. One is representing the Global Average Temperature with points ranging from 1980 to 2020. The other one illustrates the Global Sea Level in the same year range."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "5dA1VfUvIbSDmUIT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what the area [0.729, 0.815, 0.978, 0.891] includes."
+            },
+            {
+                "from": "gpt",
+                "value": "The area incorporates the legend for the chart, including 'Global Average Temperature' with its corresponding color marker, and 'Global Sea Level' with its color marker."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "YsFe4uy0k84550hx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within [0.066, 0.062, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the full graph plot, including the x and y-axes, their labels, the coordinates, and the plot area itself. The region also covers the lines for 'Global Average Temperature' and 'Global Sea Level', as well as their respective cordinates."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "We5J01oSm8QZe3cM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the top left corner coordinates [0.373, 0.025, 0.674, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Financial System and Policies."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "ecNuQpn8JH2kDQ39",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the coordinate {line}[0.104, 0.124, 0.943, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "0YGj43Re0Z2s3r3N",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the legend item}[0.113, 0.083, 0.192, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global GDP."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "a1RzqWVm49IKLbDg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich crosses the Y-axis first: {Global GDP}[0.104, 0.124, 0.943, 0.499] or {Global Inflation Rate}[0.104, 0.804, 0.943, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global GDP."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "34UXQWrkTyevvVt3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater in the year 2006: {Global GDP}[0.104, 0.124, 0.943, 0.499] or {Global Inflation Rate}[0.104, 0.804, 0.943, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global GDP."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "0OTuuZa0lx0DMd5b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Inflation Rate}[0.104, 0.804, 0.943, 0.867] rise or fall from 2002 to 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rise."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "VOccb3L4sJwOa9pR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of cuisine does the corresponding object at coordinates [0.162, 0.100, 0.302, 0.128] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Italian Cuisine."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "ARLTn7JJmnJA0BC8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.021, 0.367, 0.041, 0.591]</r> tell us about the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Popularity Scale."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "mThcrLNwtTOOmCd5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend does the blue line represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the trend of Italian Cuisine."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "TEc4216KuFGx00Ja",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of Italian Cuisine at 2005}<r>[0.635, 0.571]</r> higher or lower than {the point of Greek Cuisine at the same year}<r>[0.635, 0.329]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "hy3RUcV9wKVszwC2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHas {Italian cuisine}<r>[0.162, 0.100, 0.302, 0.128]</r> grown more popular than {Greek cuisine}<r>[0.162, 0.142, 0.300, 0.170]</r> over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "KLwM7XxbX3l9BJJW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has a higher popularity scale at {year 2020}<r>[0.938, 0.903, 0.963, 0.931]</r>, Italian or Greek?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greek."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "5IPy43ZOZUVBcIAq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.104, 0.903, 0.154, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis tick label '1980'."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "IPrz8dzTasymZvHS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.205, 0.903, 0.255, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the x-axis tick label for the year '1985'."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "MIQmnUEBy8W4vo2J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the area [0.711, 0.903, 0.761, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an x-axis tick label for the year '2010'."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "ioBAuekvlNOBk4mF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the colored rectangle located at {coordinates}<r>[0.090, 0.056, 0.139, 0.078]</r> in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the colored marker for the \"Western Cuisine Trend\" line in the chart."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "zJ2hMxfmY8huVyjR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object at coordinates {coordinates}<r>[0.506, 0.934, 0.557, 0.970]</r> of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the x-axis which is 'Time'."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "meDMTgg3H5lFB1Ve",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line does this {point with coordinates}<r>[0.850, 0.135]</r> belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point belongs to the 'Asian Cuisine Trend' line."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "G6TCH9tSQ35ahwuM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {time point 2}<r>[0.214, 0.681]</r>, is the 'Western Cuisine Trend' value above or below 'Asian Cuisine Trend' value at {points}<r>[0.214, 0.383]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "fluApstgk7ntlHTx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Western Cuisine Trend' at {point}<r>[0.850, 0.251]</r> have a higher or lower value than the 'Asian Cuisine Trend' at {point}<r>[0.850, 0.135]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "HH1IDFU2wsaXsz0k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line segment does this {point}<r>[0.759, 0.317]</r> belong to, 'Western Cuisine Trend' or 'Asian Cuisine Trend'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Western Cuisine Trend'."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "WeebJ9mscr8BKge6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the coordinates <r>[0.123, 0.069, 0.940, 0.433]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the 'Asian Cuisine Trend' line with square markers. The line is colored blue and follows a dashed pattern."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "tsSvDQQ7qbD2Qt7t",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the box defined by <r>[0.090, 0.044, 0.346, 0.136]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the legend box. Inside it, we have two labels and their corresponding color markers. The labels are 'Western Cuisine Trend' at {coordinates}<r>[0.146, 0.055, 0.339, 0.083]</r> and 'Asian Cuisine Trend' at {coordinates}<r>[0.146, 0.097, 0.315, 0.125]</r>. The color markers for each label are found at {coordinates} <r>[0.090, 0.056, 0.139, 0.078]</r> for 'Western Cuisine Trend' and at {coordinates}<r>[0.090, 0.098, 0.139, 0.120]</r> for 'Asian Cuisine Trend'."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "EKRLcml9s1redfV4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are encompassed within the area defined by coordinates <r>[0.123, 0.185, 0.940, 0.846]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes all the individual plot points and the entire line for the 'Western Cuisine Trend', which is drawn with a solid pattern and is decorated with circle-shaped markers."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "URmPvTpthTOWpdvh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend {subplot_0's sublegend item with color '#0392fb'}<r>[0.750, 0.098, 0.798, 0.120]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Midstream."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "t38w8MA7QLnS8iQZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Phase Value."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "jvewCffZYshZxZQQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value Chain Phase."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "zNvPfrY8MC7shjcu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point on the \"Upstream\" line at {xlabel 'Significant decrease'}<r>[0.547, 0.895, 0.723, 0.923]</r> above or below the threshold line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "1GLSscpj6iHTlzp7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a greater value at {xlabel 'Sharp rise'}<r>[0.334, 0.895, 0.422, 0.923]</r>, Upstream or Midstream?"
+            },
+            {
+                "from": "gpt",
+                "value": "Midstream."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "jqv7ActeuvHEdAEi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which stage does the line \"Downstream\" cross the threshold line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Abrupt increase."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "hHDAHtWBEvNV5Pce",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items in the legend located at <r>[0.750, 0.044, 0.922, 0.220]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains four items: {Upstream}[0.805, 0.055, 0.890, 0.083] with a handle color of '#ff8b26', {Midstream}[0.805, 0.097, 0.897, 0.125] with a handle color of '#0392fb', {Downstream}[0.805, 0.139, 0.915, 0.167] with a handle color of '#a3a101', and {Threshold}[0.805, 0.181, 0.891, 0.209] with a handle color of '#ff0000'."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "Y0En0IugHRY1vcTf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the y-axis labels located at <r>[0.048, 0.058, 0.076, 0.086]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The label here is '1.2', indicating a 1.2 score of Phase Value at this point on the y-axis. It is represented in black color."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "1V8rvQIjs0unN4xR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region <r>[0.082, 0.030, 0.931, 0.885]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the primary area of the chart, showcasing line charts of \"Upstream,\" \"Midstream,\" \"Downstream,\" and the 'Threshold'. Included here are x-axis and y-axis with their respective labels and tick marks showing different stages of the Value Chain Phase and their corresponding Phase Values."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "A1iOdRsYURnvkDok",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by the object with coordinates [0.838, 0.021, 0.988, 0.044] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label for Average Temperature in the legend."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "HecmajtT9tVoWqAl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.793, 0.057, 0.832, 0.075] refer to in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the handle for 'Average Rainfall' in the legend."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "s3W6bIDZWS35EuCB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel}<r>[0.015, 0.281, 0.033, 0.642]</r> in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the Y-axis, representing 'Average Temperature (\u00b0C)'."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "OeVwJelxMZ94E1BS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {coordinates}<r>[0.662, 0.277]</r> for 'Average Temperature' higher or lower than the point at {coordinates}<r>[0.502, 0.699]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "YleCnwkYqztgnU5o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Average Rainfall' point}<r>[0.662, 0.512]</r> and {the 'Average Temperature' point}<r>[0.878, 0.067]</r>, which one is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Average Rainfall' point is higher."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "cvaPCUcnlGWFZ2M5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Average Rainfall' at {point}<r>[0.878, 0.188]</r> higher or lower than the 'Average Temperature' at {point}<r>[0.105, 0.857]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "LiNTvJMYiyxzqG0P",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with the coordinates [0.793, 0.012, 0.993, 0.088]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the area that contains the chart's legend. There are two entries: 'Average Temperature' with a color marker '#ff8b26' at [0.793, 0.022, 0.832, 0.040], and 'Average Rainfall' with color marker '#0392fb' at [0.793, 0.057, 0.832, 0.075]."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "GawptYp9yDKr2qyU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the area defined by the coordinates [0.066, 0.027, 0.916, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area where the chart is plotted. It contains all the lines showing average temperature and rainfall, labels, markers, and tick labels for the X and Y axes."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "LfMMHJ10vpt8cKqU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area covered by the coordinates [0.966, 0.572, 1.0, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label for the second subplot 'Average Rainfall (mm)'."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "JKiKoH36IBAj0oyO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label corresponding to the coordinates [0.505, 0.945, 0.541, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "oqbaLVLD5qcPS0v5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of Industry 2's line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue, with the RGB value #0000ff."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "xdaqd00hN7zFN78e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.015, 0.331, 0.033, 0.598]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label indicating 'Market Share 1 (%)'."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "RUIU88jh8UTXP02T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry's market share was higher in 2013, Industry 1 or Industry 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industry 2."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "4EtBC0izcrh0ztZG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid Industry 1 have a higher market share in 2020 than in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "m8cJeQLRE7rjZ7EH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, were {Industry 1's market share}<r>[0.838, 0.447]</r> greater or lesser than {Industry 2's market share}<r>[0.838, 0.456]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "8UzdBObwEzfYW8X4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is happening in the area [0.523, 0.712, 0.733, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains points representing the market share of Industry 1 from the year 2016 to 2018. The market share is increasing over time in this period."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "o4b7gzEhnf9K0aFC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found within [0.208, 0.103, 0.418, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures the market share of Industry 2 between 2013 and 2015. A slight decline in market share is observable over this period."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "6fE56Z7ytMiiGXBb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside [0.858, 0.037, 0.978, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this box is the chart subplot's legend with two entries \u2013 Industry 1 and Industry 2, each with their associated colors."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "bomiHehElbtMiqaS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis} at [0.019, 0.287, 0.041, 0.628] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Trade Volume."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "EBnsBPUCfjzWJgJh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the location of {the xlabel} 'Month'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.467, 0.934, 0.532, 0.970]."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "4wfrifjs9q9001Zl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of this object on the x-axis: {xlabel}<r>[0.727, 0.895, 0.749, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "rz3XuYG2QS4O374y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Global Trade Volume'} at [0.193, 0.743] higher or lower than {the point for 'Supply Chain Risk'} at [0.193, 0.458]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "5OTOtc5R0QvCS2aY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the point of {the 'Global Trade Volume'} at [0.329, 0.743] with {the 'Supply Chain Risk'} at [0.329, 0.069], which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Trade Volume."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "0sEFY3oExwWPcHEy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Supply Chain Risk'} at [0.670, 0.652] located above or below {the point for 'Global Trade Volume'} at [0.670, 0.587]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "Z1qRE62RrPeXmJ2a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within [0.662, 0.044, 0.903, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area encompasses the legends of the two line graphs. The first one, {Global Trade Volume}<r>[0.718, 0.055, 0.897, 0.083]</r>, is paired with an orange marker. The second, {Supply Chain Risk}<r>[0.718, 0.097, 0.874, 0.125]</r>, is associated with a blue marker."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "iPQOga3AMSayn58y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.087, 0.030, 0.912, 0.885] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire chart plot. It includes all points, lines, axes, labels, marks, and grid lines of the two line charts, 'Global Trade Volume' and 'Supply Chain Risk'."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "SclAasqGn8Epm2fD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.924, 0.252, 0.952, 0.280]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label '5.0' for the 'Supply Chain Risk' chart."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "RIyQHQwAi6M1sOHB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis for the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Startups - Sector A."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "OTHPPzjcH66V4Z19",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the x-tick for '2016'} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.264, 0.895, 0.308, 0.923]."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "V94uFLlr9lplXnsN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at these coordinates [0.048, 0.991, 0.076, 1.019]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A y-axis tick label '1.5'."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "xdgEud3GKOT253BW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Startups - Sector B data point for the year 2020}[0.940, 0.069] higher or lower than {the corresponding Startups - Sector A data point}[0.940, 0.406]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "tMhDk7X76Y5UP46b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2017, is the value of {Startups - Sector A}[0.450, 0.509] greater or less than the {Startups - Sector B}[0.450, 0.587]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "Wlw3Pd9mbu4CzLsz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the year 2019, which startup sector had a higher number, {Startups - Sector A}[0.777, 0.121] or {Startups - Sector B}[0.777, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startups - Sector B."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "1CRffuv4DUwKdNdk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the area [0.048, 0.991, 0.076, 1.019]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A y-axis tick label '1.5' is displayed in this area."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "2Te1Rl7sZYHqWbuO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.286, 0.769, 0.940, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the data line for \"Startups - Sector B\". Markers for the line are represented by squares. The line is dashed and colored \"#0392fb\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "RfK9BKRcPnV292wr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located within these coordinates [0.286, 0.380, 0.940, 0.406]."
+            },
+            {
+                "from": "gpt",
+                "value": "The data line for \"Startups - Sector A\" is located in this area. The data points are represented by circles. The line is connected with a solid line and colored \"#ff8b26\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "74kyzfH0kxEPg53F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.019, 0.326, 0.041, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the subplot depicting \"Startup Growth\"."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "vUgsZUskcpjfmL4e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the innovation index point}<r>[0.375, 0.666]</r> in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5"
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "jh5oWh11vwRT51Pp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for \"VC Funding\"}<r>[0.02ad24]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "qiiEcX4K6yE1yaL9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2019, which index is higher, {Innovation Index}<r>[0.625, 0.410]</r> or {VC Funding}<r>[0.625, 0.282]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "VC Funding."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "cHKYjcGafRZQi4AI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, did {Startup Growth}<r>[0.750, 0.410]</r> increase or decrease compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "BQLem4sN7OnHvkkr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing 2017 and 2021, did {the VC Funding}<r>[0.875, 0.325]</r> increase, decrease, or stay the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "iBAiSegMUyo5dvJ3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the regional coordinates [0.097, 0.044, 0.304, 0.178]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the figure's legend, which includes the labels: {Startup Growth}<r>[0.152, 0.055, 0.285, 0.083]</r>, {Innovation Index}<r>[0.152, 0.097, 0.297, 0.125]</r>, and {VC Funding}<r>[0.152, 0.139, 0.251, 0.167]</r>, each marked with a different color."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "AMvrg15LapXysub1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.088, 0.030, 0.912, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two subplot line graphs showcasing \"Startup Growth\" and \"Innovation Index\" versus \"VC Funding\". The graphs contain x-axis labels for the years 2015 through 2021 and y-axis labels for relevant index values. Both graphs include line markers for respective indices."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "7vetHtejgK90MeXQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.125, 0.068, 0.875, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a line graph of VC Funding from the years 2015 through 2021. The graph includes data points for each year, with markers denoting the funding amounts."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "oV9J5XWz2vnqQYj0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being displayed at the coordinate [0.041, 0.431, 0.059, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label 'Return Rate' is displayed."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "AG6sj8vcRuLyBugA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFind the coordinate of the subtitle of the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "[0.566, 0.011, 0.566, 0.011]"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "RZXE5B7Spo72VE1v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.244, 0.896, 0.299, 0.920] in subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '20000'."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "03HisuKQ3D77r44q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '50000' for Fixed Rateturn}<r>[0.524, 0.217]</r> above or below {the point at '50000' for Random Rate}<r>[0.524, 0.217]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same level."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "l8CcBsEwMpLz6LkE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Fixed Rateturn}<r>[0.188, 0.042, 0.943, 0.392]</r> start lower than the {Random Rate}<r>[0.188, 0.217, 0.943, 0.392]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, they start at the same level."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "2iUuoKToBReyJEyj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which point does {Random Rate}<r>[0.188, 0.217, 0.943, 0.392]</r> cross {Fixed Rateturn}<r>[0.188, 0.042, 0.943, 0.392]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "At point '50000'."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "ztm1Uy5vqb5EiMS7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within the coordinates [0.150, 0.495, 0.981, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains Subplot 1. It contains multiple 'X' and 'Y' axis ticks and labels. The 'Y' axis label says 'Predictive Analysis Return'. There is a line chart inside which represents 'Predictive' analysis data points."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "53pBfqccmGQ8mVt3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are within the region [0.150, 0.025, 0.981, 0.410]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes Subplot 0, which carries the label of 'Fixed vs Random Return' on the Y-axis. It contains two line charts, one for 'Fixed Rateturn' and the other for 'Random Rate', with markers at data points '10000', '20000', '50000', and '100000'."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "wBMeCyAxAaBE2MJ9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.453, 0.430, 0.678, 0.472] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the legend for Subplot 1, containing one item labeled 'Predictive Analysis'."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "4JnRoHSusrYUkf0z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the chart, what does the label at position [0.086, 0.213, 0.100, 0.322] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "1Q8HC5fZQDvdQlGk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the 7th x-axis tick label on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.776, 0.451]."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "i1EZToTcnt9pBOZ1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by {the legend at}[0.429, 0.031, 0.488, 0.054]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "cKL5ytVT5l1cPS3x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trend lines for Baseline and SoftMoE, which trend line sits higher at {the 16 user point}[0.555, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "fH6WSLqICpUVYXsD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Baseline point at}[0.947, 0.833] sit above or below {the Top1-MoE point at}[0.947, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "qchGObXWWbbXBthj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the third tick on the x-axis, is {the SoftmoE line}[0.261, 0.555] above or below {the Top1-MoE line}[0.261, 0.778]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "oCEknFNrwptscOrA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.385, 0.021, 0.750, 0.063] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend which includes Baseline, SoftMoE, and Top1-MoE line markers."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "A7Ilv4n4QYhEYKvc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what can be seen in the region spanning [0.150, 0.482, 0.985, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is a subplot showing data for Twitter user counts. It includes y-ticks ranging from '4000' to '4800', x-ticks from '\u22125' to '35', and lines representing Baseline, SoftMoE, and Top1-MoE trends."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "wPWgQOPYOm8QhhUv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.386, 0.034, 0.422, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a color marker for the Baseline line in the legend."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "zFzhIi0nFX6l82mI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.532, 0.938, 0.568, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "RKuBNsAtCLEYG95r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the tenth tick on the x-axis}<r>[0.943, 0.880]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.943, 0.880]"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "6JgsBJf4fJJupT6n",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label in [0.091, 0.585, 0.109, 0.785] represent in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Single Parent (%)"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "Ip53W4ceL2qBPazl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point in 2000 for {the 'Rural'}<r>[0.188, 0.700]</r> line of the second subplot higher than the 'Urban' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "38tgxzbKHp24WTNz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does {the 'Urban'}<r>[0.566, 0.760]</r> line stand in comparison to {the 'Rural'}<r>[0.566, 0.640]</r> line in 2010 on the percentage of single parents graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "ADacaOVZ57JmpnAn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the last data point of 2020, which line is higher in the second subplot, {the 'Urban'}<r>[0.943, 0.640]</r> or {the 'Rural'}<r>[0.943, 0.550]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "4YWz0RlLU5kCAlls",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is depicted in the area from [0.188, 0.640, 0.943, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the 'Urban' line of the second subplot, which shows the change in the percentage of single parents from 2000 to 2020."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "gJavmvjgRR4bkhv0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.188, 0.550, 0.943, 0.700] within the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the 'Rural' line of the second subplot is plotted, indicating the percentage of single parents from the years 2000 to 2020."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "s9kUzCH92AhDm2Iz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any key features within the region [0.110, 0.131, 0.138, 0.154] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the y-axis tick label '3.0' for the 'Family Size' plot is located within this region."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "hEB68qkEXLNUwHDs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text element located at [0.041, 0.409, 0.059, 0.591]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the second subplot, 'Patent Expenses (in millions)'."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "eBjbbhshOStDOvis",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the point representing number of patents '7' on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.836, 0.112]"
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "O6kgJLgWROBFAFm4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of {the x-axis tick}[0.620, 0.880] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "omwEfxeySBlnwChm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the point for number of patents '5' have a higher or lower innovation score than the point for number of patents '6'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "MQjDu931w8TY507T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the x-axis tick '3' to the left or right of the x-axis tick '2' in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "kaWyn4FOKzkbhsSy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the innovation score for number of patents '4' higher or lower than the score for number of patents '3'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "1NIv58DbnrzPJVqS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the box [0.469, 0.938, 0.631, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart title 'Number of Patents', set in black color."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "IEf44n903FoYCBBo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.150, 0.031, 0.981, 0.413]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the first subplot which is a line graph showing 'Innovation Score' as y-axis label with y-axis tick labels like '3', '4', '5', '6', '7', '8'."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "yqRyqBn1g17FhQ9S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.150, 0.499, 0.981, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot displaying a line graph showing 'Patent Expenses (in millions)' as y-axis label with y-axis tick labels like '1.0', '1.5', '2.0', '2.5', '3.0', '3.5', '4.0', '4.5' and an x-axis with tick labels ranging from '0' to '9'."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "pE8tcPllXRU9k05q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.041, 0.418, 0.059, 0.582]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transportation Volume."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "wbb9lbILcX2qCqCg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the x-axis label}<r>[0.543, 0.412, 0.588, 0.435]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "b0Jw34wOyfYSqHqL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the marker style} for {the line representing Aviation Transport}<r>[0.188, 0.570, 0.943, 0.821]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "z2EKZ6aULx09BE1w",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the Truck Transport line}<r>[0.188, 0.266]</r> above or below {the first point on the Marine Transport line}<r>[0.188, 0.340]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "NhuhxCiDnT5jMEFK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 5th point of Marine Transport}<r>[0.524, 0.266]</r> and {the 5th point of Logistics Service}<r>[0.524, 0.674]</r>, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Logistics Service."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "iy8Tkt6nmf6JPGDy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the last point on Marine Transport}<r>[0.943, 0.172]</r> higher than {the last point on Aviation Transport}<r>[0.943, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "7LsZEvbhO84h8Nag",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.069, 0.079, 0.087, 0.340]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the y-axis labels for Transportation Volume, with all labels rotated 90 degrees and styled in black."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "XCtzogF55ejqwSXT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.342, 0.449, 0.789, 0.490]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains sublegend items {Aviation Transport}<r>[0.397, 0.458, 0.555, 0.481]</r> and {Logistics Service}<r>[0.638, 0.458, 0.783, 0.481]</r>, where each label is paired with a color representation but no legend keys are visible."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "p3VgEnMJN8SCYvC1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.188, 0.079, 0.943, 0.228]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {line for Goods Delivery}<r>[0.188, 0.079, 0.943, 0.228]</r>, showing points from the first to the last year depicted by circle markers sequentially placed along this straight line. The end points are {year 1}<r>[0.188, 0.228]</r> and {year 10}<r>[0.943, 0.079]</r>."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "hrvLOVSJOIInpaor",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.188, 0.319] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a data point on the Baseline line."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "psXVZlXcyiZL1Dmk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exactly is represented by {the object} on the left end of {the line} in [0.188, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the data point that represents the Success Score for SoftMoE when the Number of Patients is 500."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "YvEtZfi2QPpBm3xA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can we find at [0.471, 0.445, 0.542, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The word \"SoftMoE\" as a legend item."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "b6WSI1u80yNA6hd7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Baseline' score of 500 patients at {coordinate1}[0.188, 0.319] with the 'SoftMoE' score of 500 patients at {coordinate2}[0.188, 0.823], which technique is more successful?"
+            },
+            {
+                "from": "gpt",
+                "value": "SoftMoE."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "N5yolqL761tEFeCh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt [0.692, 0.300], is the Baseline score less than, equal to, or greater than the SoftMoE score at [0.692, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "yqFzYg0vsr6f7nO6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Top1-MoE' score at [0.943, 0.500] higher or lower than the Baseline score at [0.943, 0.129] when the Number of Patients is 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "oPuZP0Vy7q55oGjm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found in the region [0.091, 0.175, 0.109, 0.273]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label \"Baseline\" for the subplot."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "BJ3kZNh2SOyTS7cR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the area [0.416, 0.436, 0.716, 0.477]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area hosts the legend for the subplot, with the labels: {SoftMoE}[0.471, 0.445, 0.542, 0.468] and {Top1-MoE}[0.626, 0.445, 0.709, 0.468], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "HWdHvwU3Sb92wrlz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside the region [0.116, 0.025, 0.138, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, we find the y-axis tick label '95' for the 'SoftMoE' subplot."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "dlEd6e1UC7RU92Tf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.041, 0.353, 0.059, 0.647] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label of the top subplot."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "YENE1mSf1ChV6eYS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-value of the point located at [0.374, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Strategy 1."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "xIoFSalt12qsrMef",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the line}[0.187, 0.513, 0.934, 0.595] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Strategy 2."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "5aCR5kmUtyE86qRl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the top subplot, are the points for Strategy 1 plotted higher or lower than those of Strategy 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "2JrA0fNgIC5ObDRf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the second subplot, is the line of Strategy 1 plotted above or below the line of Strategy 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "DbKYDreb0BKBWdmi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the top subplot, between Baseline and Strategy 3, whose points are plotted higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "Y0Mo6EP5a0npomCA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is situated in the region [0.075, 0.548, 0.093, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label for the second subplot, \"Policy B & C (in Millions)\"."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "78Qco1DaLUSETEr6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the objects within [0.888, 0.896, 0.979, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label for the last strategy, Strategy 4, in the bottom subplot."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "yUqPU904TQZatxWl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.150, 0.495, 0.971, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the second subplot displaying the line graphs of Strategy 1 and Strategy 2 for Policy B & C."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "biT96cspKMxNtFpL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.463, 0.942, 0.537, 0.965] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart's title \"Methods\"."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "BApU7nBg8QWXU7SL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bottom most ytick label on the first subplot (Innovative Ideas)?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"2.4\" located at [0.111, 0.098, 0.138, 0.121]."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "b1NmjzDp7fzZJPJ7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the line plot found in the second subplot {at these coordinates}</r>[0.184, 0.550, 0.866, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green (#008000)."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "UYLH9XM0IAoT9IiN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Method Score, Method 1 or Method 2, achieved a higher score for 'Innovative Ideas' based on the coordinates of {Method 1} at <r>[0.184, 0.450]</r> and {Method 2} at <r>[0.525, 0.136]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Method 2."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "fLO9rYrsbJcvsTP2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for 'Innovative Ideas' of {Method 1}<r>[0.184, 0.450]</r> higher or lower than the score of 'Design Quality' of {Method 1}<r>[0.184, 0.864]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "qV6nBuLUG6uOprI5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Design Quality' scores, which of {method 2}<r>[0.525, 0.550]</r> or {method 3}<r>[0.866, 0.676]</r> scored better?"
+            },
+            {
+                "from": "gpt",
+                "value": "Method 3."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "Rch2HdBfKgObzgjm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.145, 0.896, 0.905, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of the x-label of the second subplot which contains 3 method labels: {Method 1}[0.145, 0.896, 0.223, 0.920], {Method 2} [0.486, 0.896, 0.564, 0.920], and {Method 3} [0.827, 0.896, 0.905, 0.920]."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "XRPm1hEk6PorUhxz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area spanning [0.111, 0.098, 0.138, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the y-axis tick labels from 2.4 (lowermost) to 3.75 (uppermost) for the second subplot, 'Design Quality'."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "uw1ohkY8v64ZU3hS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects present within the coordinates [0.150, 0.120, 0.900, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the first subplot, containing a line chart for 'Innovative Ideas' with three points at {Method 1}[0.184, 0.450], {Method 2}[0.525, 0.136] and {Method 3}[0.866, 0.278], with corresponding y-axis ticks labels ranging from 2.4 to 1.0."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "DukMuYZd2XT3H98i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-axis}<r>[0.081, 0.513, 0.098, 0.862]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Breaches & Online Fraud"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "eFSKsMt8MfzHtLLE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the line representing 'Data Breaches' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.188, 0.611, 0.943, 0.718]"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "RHjvXmW2O11Dsn4F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.105, 0.640, 0.138, 0.663] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "350"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "5GVJn2FUNoWOqhnJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point for 'Data Breaches'}[0.188, 0.649] higher or lower than {the first point for 'Online Fraud'}[0.188, 0.572] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "sCEB3tcBKnoEcXP8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Data Breaches'}[0.188, 0.611, 0.943, 0.718] above or below {the line for 'Online Fraud'}[0.188, 0.572, 0.943, 0.634] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "xBYvOiamueBtWUAz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich incident has a higher count at {point 4}[0.943, 0.634], Data Breaches or Online Fraud?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Breaches."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "0uJdla83ecAqo22k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.374, 0.430, 0.757, 0.472]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subplot legend items: {Data Breaches}[0.429, 0.439, 0.557, 0.463] and {Online Fraud}[0.640, 0.439, 0.750, 0.463], each with an associated marker color."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "rXTJKO9j3GqUiCyd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.105, 0.794, 0.138, 0.817]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label {250} in the second subplot."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "X8ecc9EgGzYTO70A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.188, 0.611, 0.943, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of {the line for 'Data Breaches'} in the second subplot, which spans points at 1, 2, 3 and 4 on the x-axis."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "WUZExLyDq0IrjveF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.477, 0.942, 0.523, 0.965] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text indicating 'Years'."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "9Vx0jDc4gwhNgIyY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the bar of 'Year 2018 under Unpredictable decline'} at [0.130, 0.181, 0.190, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a data bar representing the value 60."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "ZFS79hsaHdNADs3g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at these coordinates [0.091, 0.659, 0.113, 0.682] located in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a y-axis tick label indicating '70'."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "tYYLKRA9XO8yYfSh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Year 2019 under Oscillating trend'}[0.395, 0.295] above or below {the point for 'Year 2019 under Unpredictable decline'}[0.395, 0.250]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "wvuQVC8cvRI5eoku",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value for 'Year 2020 under Substantial decrease'}[0.630, 0.696] higher or lower than {the value for 'Year 2020 under Sustained decline'}[0.630, 0.640]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "JfY4qTqfimSspdmn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {Year 2021}[0.865, 0.181, 0.865, 0.687], which trend has a higher value, Oscillating trend or Accelerated growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accelerated growth."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "rqAtDWaHMT0u7nIU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.145, 0.482, 0.880, 0.523]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Substantial decrease}[0.201, 0.491, 0.383, 0.514], {Sustained decline}[0.466, 0.491, 0.620, 0.514], and {Accelerated growth}[0.704, 0.491, 0.873, 0.514], each with their respective colored markers."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "0I72tc7ZJNF8wWPj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.125, 0.540, 0.900, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a line plot graph with three lines representing 'Substantial decrease', 'Sustained decline', and 'Accelerated growth' with data points for the years 2018 to 2021. The x-axis displays the years and the y-axis displays the scores."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "B4iLy6cJld7E01yW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.273, 0.062, 0.752, 0.103]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend area for first subplot. It contains legend items: {Oscillating trend}[0.329, 0.071, 0.472, 0.094] and {Unpredictable decline}[0.555, 0.071, 0.745, 0.094], each with their respective colored markers."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "ilsYc01aqzGixmsF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.086, 0.018, 0.103, 0.568] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Established Democracies & Young Democracies."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "gy5BuJIDIamusBBB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth x-axis tick in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.563, 0.880]"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "EDeQkSDj8ckQsQmG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {the point}[0.411, 0.326] on the line in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "4.9"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "f4O6191yEHnqeuTt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend of {established democracies}[0.184, 0.147, 0.866, 0.416] in the first subplot going up or down?"
+            },
+            {
+                "from": "gpt",
+                "value": "Up."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "i2AUaziiwaIKrqnR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the y-axis at {x coordinate 9}[0.639, 0.192, 0.639, 0.394] in the first subplot, established democracies or young democracies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Established democracies."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "lQgFseS3UDYPCDR3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is larger at {x coordinate 6}[0.411, 0.326, 0.411, 0.693], established democracies in the first subplot or autocracies in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Established democracies."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "q7zCDYBZzESJDstt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.086, 0.639, 0.103, 0.775] in the second subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label. The y-axis label is \"Autocracies\" stylized with black font color and rotated 90 degrees."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "4o1sfjuXbV3kGGFT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.150, 0.120, 0.900, 0.465] in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the two lines representing the trends of established democracies and young democracies. The trend line for established democracies is colored in #3178bd and the trend line for young democracies is colored in #469c76. Both trend lines are marked with circles at each data point."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "zLGO4OFJc1sSOg5G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.263, 0.880, 0.603, 0.920] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are three objects here which correspond to x-axis labels '4', '6', and '8'."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "YAEm1XtsTy3bDPQB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.069, 0.100, 0.087, 0.334] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of first subplot."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "gSzikQgkSYK0ZYTI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the last element in the 'Export' line of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.943, 0.102]"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "Z5iOe5NiI8o1QbP9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year at {the x-axis tick label}<r>[0.922, 0.896, 0.965, 0.920]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "BE0aiVye0wJFMXva",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'CPI'}<r>[0.188, 0.389, 0.943, 0.392]</r> generally above or below {the line for 'Export'}<r>[0.188, 0.094, 0.943, 0.145]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "KzxYBO6OvfTpAtSv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTowards the end of 2020, does the 'Export'<r>[0.943, 0.102]</r> or 'Import'<r>[0.943, 0.042]</r> line have a higher value in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Export."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "vlspXuQvHy9ofUPe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is {the point for the year '2015'}<r>[0.566, 0.759]</r> on the 'Unemployment_rate' line above or below the {point for the year '2020'}<r>[0.943, 0.633]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "DFKbWxN3m2KTZ70x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.446, 0.430, 0.686, 0.472] of the second subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the sublegend items: {Unemployment_rate}<r>[0.501, 0.439, 0.679, 0.463]</r> which is paired with a colored marker."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "5MAUDictK7hora2U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.150, 0.401, 0.350, 0.430] in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains three X-axis tick labels '2008', '2010', and '2012' without explicitly given coordinates. It also includes the start of the 'CPI', 'Export', and 'Import' lines."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "aCXJ7VYGVOlFfjre",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.150, 0.495, 0.981, 0.880] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main elements within these coordinates are the axes of the plot, x-grid, y-grid, and the 'Unemployment_rate' line. It also contains the X and Y axis labels and tick labels."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "mQ1qNJaIFTOt5zpn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.124, 0.057] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a datapoint on the 'Security Risk' line."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "uNLSv3oUt3YOjRHd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.919, 0.583] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label '8'."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "dUM1dyTMLntYAfHT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the point [0.019, 0.161, 0.036, 0.311]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the 'Security Risk' y-axis label in the first subplot."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "iUDOwQUGlujHNYkv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Legal Risk' at timestep 5}<r>[0.487, 0.769]</r> higher or lower than {the point for 'Financial Risk' at timestep 5}<r>[0.487, 0.688]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "GBuiyT1wJs94e3tm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is {the point at coordinates [0.940, 0.415]}<r>[0.940, 0.415]</r> on, 'Legal Risk' or 'Security Risk'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Security Risk."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "P84yKvERsMv0TE3K",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk is at a higher level at {point at timestep 3}<r>[0.305, 0.137]</r>, 'Security Risk' or 'Legal Risk'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legal Risk."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "GyPOdZH4DUyE6Vhy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within coordinates [0.209, 0.919, 0.583, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the section of x-axis labels from '2' to '6'."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "hvgZdKrQh7T5OP4T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area specified by the coordinates [0.432, 0.501, 0.951, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains datapoints on both the 'Legal Risk' and 'Financial Risk' lines between the time periods of '4' to '10'."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "RE3ZrdXmQRxpbKrE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the section [0.043, 0.379, 0.071, 0.403]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the '2.5' y-axis label of the first subplot."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "owrzT5KufZ4YY6WU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label with coordinates [0.527, 0.938, 0.573, 0.962] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label, which is \"Years\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "qqIljPW3HaTOPpB9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me the coordinates of the 4th x-axis tick in Subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.636, 0.880]"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "wafoS9M61P3HVUCh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.086, 0.454, 0.103, 0.914]</r> in the lower chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Number of Patent Citations (in 10,000s)\""
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "tn3bDhg5gx20jrmP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Number of Patents}<r>[0.150, 0.129, 0.959, 0.391]</r> in 2014 higher or lower than its value in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "MMhsoRT4xzmS3ppA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2015, does {Number of Patent Citations}<r>[0.150, 0.586, 0.959, 0.782]</r> have a higher or lower value compared to {Number of Patents}<r>[0.150, 0.129, 0.959, 0.391]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "G7hvWjyqKAGpgAOo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the 'Number of Patent Citations' values increasing or decreasing from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "esREI5VxEPNKOHC2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with coordinates [0.150, 0.487, 0.959, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot, showing a line plot titled 'Number of Patent Citations (in 10,000s)', with values ranging from 5.0 to 6.0. The x-axis has labels for the years from 2010 to 2015."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "eNpROjuVbfuIXeyX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.111, 0.120, 0.138, 0.143]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a label on the y-axis. The label is \"3.0\" and is positioned towards the lower part of the axis."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "9aFjASbch8rTifQu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's in the area with coordinates [0.774, 0.896, 0.820, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains an x-axis label in Subplot 1. The label is \"2014\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "uF9IhJvuBowCDDt8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object located at coordinates [0.059, 0.086, 0.076, 0.252]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label of the first subplot, labeled as 'Base Capacity'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "wkJ7xpnK5JQonH0u",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.883, 0.612, 1.028, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no chart element at these coordinates."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "ScnjZirH0oCWK6n4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year represented by the x-axis tick located at coordinates [0.943, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year is 2024.0."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "LmK6tS527NZxgtwR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {coordinates}[0.188, 0.773] in subplot_2 higher or lower compared to the point at {coordinates}[0.377, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "SSGPzwrkOsJuuim2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the plotted points for 'College' generally above or below the points for 'Graduate School'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "O4KSsVIkWsM0ecEn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data for 'High School' at {coordinates}[0.188, 0.527] in subplot_1 higher or lower than the 'Base Capacity' at {coordinates}[0.188, 0.103] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "YQIlvsmF9yCF1Gjc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.150, 0.342, 0.981, 0.585]."
+            },
+            {
+                "from": "gpt",
+                "value": "This describes the second subplot which displays data for 'High School'. It includes a linear chart line, x and y grids, y-axis label, x and y tick marks with labels, and the legend for 'High School'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "zYGvgNPdAmTo8qBt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.150, 0.637, 0.981, 0.880] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the third subplot in the figure which represents data for 'Post High School' categories 'College' and 'Graduate School'. Among other things, it includes a the data lines for 'College' and 'Graduate School' represented with marker 'o', labels on the y-axis, tick marks on the x and y axes, and a legend for 'College' and 'Graduate School'."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "WjRUQRRBJhU42Gh8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.188, 0.773, 0.943, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the data line on the third subplot for 'College', with plotted lines and 'o' markers located at various points along the line."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "Lzjx3oiRyIz7Frzs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of efficiency is represented by the y-axis at [0.091, 0.066, 0.109, 0.369]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Operational Efficiency."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "FCPpgONH5IEllPHE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific point does these coordinates [0.188, 0.255] refer to in the line diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the point on the 'Baseline' line when the number of employees is 20 and the Operational Efficiency is 80%."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "BZnBOLZDmdnR9EhD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the third y-axis label in subplot_1 at [0.116, 0.769, 0.138, 0.792] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This label refers to the value '65' on the y-axis."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "nbDmMq6qza6Iq9HD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher y-value at x = 50, is it {Method A line's point}[0.362, 0.230] or the {Method B line's point}[0.362, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Method A."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "A5GovtT8Dbi53aoP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the {the 'Method A' point at x=20}[0.188, 0.192] and the {the 'Method B' point at x=150}[0.943, 0.167], which point is higher on the scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Method A' point at x=20."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "84HFnfu86ub29qL9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {Method A}[0.188, 0.658, 0.943, 0.742] and {Method B}[0.188, 0.537, 0.943, 0.646], which one generally has a higher y-coordinate value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Method A."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "WuqEMBdXg7S1YF26",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained in the coordinates [0.332, 0.430, 0.800, 0.472]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of subplot_1, which has the labels 'Baseline', 'Method A' and 'Method B', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "MMTGWHqHbtV5jfNO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.150, 0.495, 0.981, 0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire subplot_1, which demonstrates 'Functional Efficiency' with y-axis labels ranging from '55' to '90', x-axis labels that represent 'Number of Employees', and three line graphs that represent 'Baseline', 'Method A', and 'Method B'."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "mxznhem1N5LqiUF3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region defined by [0.150, 0.025, 0.981, 0.410] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains subplot_0 that represents 'Operational Efficiency' on the y-axis, 'Number of Employees' on the x-axis, and three line graphs for 'Baseline', 'Method A', and 'Method B'."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "nz0O9Cf17umqHCIb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.069, 0.091, 0.087, 0.354] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Internet Users (Million)."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "u4M3QJD7guNX2gkL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.566, 0.020, 0.566, 0.020] in the subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot's subtitle."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "EYuHcNs4iZxVBAIG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fourth marker on the line in subplot 1 at the coordinates [0.755, 0.663] indicating?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number of Mobile Users (Million) in 2015."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "1Vg3myOWWKwJ3FT2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot 0, is the point at {4th year}[0.566, 0.259] higher or lower than the point at {the 2nd year}[0.377, 0.333]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "lkt6jGqUYq6oIZXQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Internet Users (Million) in the {year 2005}[0.377, 0.333] higher or lower than in the {year 2010}[0.566, 0.259]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "hjAss8GvP14C2Dk1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot 1, which year has more Mobile Users (Million), {the year 2000}[0.188, 0.873] or {the year 2005}[0.377, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2000."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "cgvkVr8lMVHNCqCK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.150, 0.503, 0.981, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains subplot 1 which includes the graph for Mobile Users (Million) over different years and the y-axis labels ranging from 0 to 7000."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "JBZqG6MBcPBDABSv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.150, 0.034, 0.981, 0.411]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains subplot 0 which includes the plot for Internet Users (Million) over different years and the y-axis labels ranging from 0 to 5000."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "KJZt2sK2XxSZApnR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.535, 0.896, 0.596, 0.920]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the X-axis tick label \"2010.0\" and its tick on the X-axis in subplot 1."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "fxA0vsj8rLMPSu5E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent according to the coordinate information [0.019, 0.202, 0.036, 0.324]?"
+            },
+            {
+                "from": "gpt",
+                "value": "User score."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "4eOH6uKsJdnvF62G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of {the blue line}[0.124, 0.224, 0.940, 0.382] referring to in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Household Robots."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "RToeQ4lfhX2E5gNW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at this coordinate [0.729, 0.659] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a point on the line representing the performance of the 'Smart Home' category in the second subplot."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "gOROkLEH7fgA3Pqh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 4}[0.396, 0.342], is the score of {Household Robots} higher or lower than in {point 3}[0.305, 0.224]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "H0x33kvIdFBOPtTy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one reaches the maximum value faster; {Smart Home}[0.124, 0.620, 0.940, 0.659] or {AI Services}[0.124, 0.596, 0.940, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Services."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "wWJ5mDNpuOQOujnq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the timespan, does the {Household Robots}[0.124, 0.224, 0.940, 0.382] perform better or worse than the {Android Assistants}[0.124, 0.171, 0.940, 0.303] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "71CPWf5aYCznZR5Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content located within [0.596, 0.028, 0.756, 0.051]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area of the chart contains the legend label for {Android Assistants}[0.596, 0.028, 0.756, 0.051]."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "TebeU3mx5H3RMB5h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.357, 0.493, 0.707, 0.535]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label {Smart Home}[0.412, 0.502, 0.522, 0.526] and {AI Services}[0.605, 0.502, 0.700, 0.526] in the legend of second subplot, each associated with a different color."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "tHrBsVZiuMlJmNWL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.209, 0.952, 0.220, 0.975] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to an x-axis label of '2' in the second subplot."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "waNQO4ds5iRRvNSV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.012, 0.484, 0.029, 0.582] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis title."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "PVSKTOJWX10qw8Zd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is located at [0.079, 0.367, 0.958, 0.643]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart representing Mobiles sales."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "t76Z0HVq6H0WoKky",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.484, 0.942, 0.552, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Months."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "E97y6udjn7EZRdwI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Laptops'}[0.079, 0.275, 0.958, 0.533] generally above or below {the line for 'TVs'}[0.079, 0.202, 0.958, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "QcGwNOAhCMuwPSoR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows a higher value in October, {Mobiles}[0.798, 0.404] or {Headphones}[0.798, 0.570]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Headphones."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "ovuRc9XomtQ5mf37",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich representation uses a '--' linestyle, {Mobiles}[0.079, 0.367, 0.958, 0.643] or {Headphones}[0.079, 0.496, 0.958, 0.791]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobiles."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "NoHfZmWo3k5j9drO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.394, 0.128, 0.643, 0.158]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart subtitle, \"Consumer Behaviour Trend Analysis\", in black color."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "0qBEWR2fYgbEGXWq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.464, 0.032, 0.951, 0.117]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the graph. It includes four items: Mobiles, Laptops, TVs, and Headphones. Each element is represented with a different color and specific marker."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "9kJZEB8FakCQfv12",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.464, 0.032, 0.951, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items: {Mobiles}[0.517, 0.060, 0.570, 0.090] with color #2171b5, {Laptops}[0.637, 0.060, 0.691, 0.090] with color #6baed6, {TVs}[0.758, 0.060, 0.783, 0.090] with color #42cc46, and {Headphones}[0.850, 0.060, 0.937, 0.090] with color #9dfa9f."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "9aBNxrhC1XhoiWDB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.034, 0.245, 0.048, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label \"70\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "Oc5RkVtxiTz3tVCY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.034, 0.913, 0.048, 0.930] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis tick label \"30\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "fondgzdR6Jz1yUmL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend}<r>[0.671, 0.012, 0.966, 0.076]</r> represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend denotes different line styles and their corresponding color according to the markers used in the chart."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "KgumKHB70aPb4E8Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt year 2020, which color represents a higher indicator? \n{line_0 color} <r>[0.973, 0.394]</r> or {line_3 color} <r>[0.973, 0.306]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line with marker color '#2171b5'."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "yjbt1OCmRQTobfcb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line style '--'}<r>[0.973, 0.806]</r> performance better or worse than {the line style '-'}<r>[0.973, 0.306]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "sYftBHtI7twDqQL6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line style '-.'}<r>[0.973, 0.546]</r> show an overall increase or decrease over the years compared to {the line style ':'}<r>[0.973, 0.391]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "wBxIRx0gOucFAiVu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the region [0.551, 0.932, 0.580, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the X-axis tick label indicating the year 2016."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "Mt4RwZ6B8EasmWVm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is included within the area [0.671, 0.012, 0.966, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend which helps explain the styling of the lines used in the chart. Specifically, it includes four items with different line styles and colors."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "irkC42oDJNQJuDyt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is in the area [0.034, 0.078, 0.048, 0.096]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Y-axis tick label \"80\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "l8W4CUKcm4eL03Sc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label indicate at coordinates [0.092, 0.909, 0.127, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2016."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "TuJqnfpKTretfXHZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle of {Online Learning's} legend<r>[0.783, 0.140, 0.830, 0.163]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "zoI8CShNW3OvPvfK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the axis label at the coordinates <r>[0.503, 0.942, 0.550, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "zrv8TDfX25FLXj8m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, did {Group Study}[0.110, 0.340, 0.943, 0.438] increase or decrease from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "1p1UOXRPZGNRJNHN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Individual Study}[0.110, 0.064, 0.943, 0.084] line above or below the {Group Study}[0.110, 0.340, 0.943, 0.438] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "OKjinyeWAWgJGeAY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas there a greater percentage of people engaged in {Group Study}[0.943, 0.438] or {Online Learning}[0.943, 0.634] in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Group Study."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "ks57sDvc7I2ocETi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates [0.783, 0.039, 0.977, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend for the chart, with four entries including 'Individual Study', 'Group Study', 'Online Learning', and 'Practical Work', each with a corresponding color marker."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "1TtFt92XaE15n2v8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be seen within the coordinates [0.068, 0.025, 0.985, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plot area of the chart. It contains y-axis from \u221210 to 60 in increment of 10, x-axis from 2016 to 2021 and lines representing four different study types: 'Individual Study', 'Group Study', 'Online Learning', and 'Practical Work'. Each of these lines has distinct styles and color as represented in the legend."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "JEyeDsUbNzvPSvte",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visual components are within the coordinates [0.759, 0.909, 0.794, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains an x-axis label '2020'."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "Um3OfSGLjLbI6bi5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.435, 0.021, 0.977, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "xEf5tCzXPiiLSSBi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {ylabel}[0.744, 0.362, 0.764, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Index."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "wiORzYyrMcuf656i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {[0.797, 0.088, 0.985, 0.901]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main plotting area or axes of the chart."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "1MuJaFy4qPk7VdOk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at \"Outliers\", {ICT line}[0.797, 0.332] or {Biotech line}[0.797, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotech."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "w6zKiUTTRIZx8swn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Startups line}[0.797, 0.765] and {the Patents line}[0.797, 0.224] at the point \"Outliers\", which one is lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startups."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "HaU2yPmVRnV7t4h0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the Biotech and ICT trends, is the {Biotech point}[0.609,0.413] or the {ICT point}[0.609, 0.224] higher in the \"Trends\" category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotech."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "hDVFXvJhawCntpRx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from [0.770, 0.079, 0.787, 0.102]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label \"90\"."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "QE3h1kKUA0QaLmjy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.609, 0.115, 0.797, 0.765]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all the points of the ICT, Biotech, Startups, and Patents trend lines for the \"Trends\" and \"Outliers\" categories."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "mmaYkUlSqUTTKIT9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.744, 0.250, 0.764, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a part of the y-axis label \"Average Index\"."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "KLxEmjK3D3tCNmOb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object indicated by {the Y-axis label}<r>[0.012, 0.301, 0.029, 0.625]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Value."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "3wOeH6hMVcMxa7Jy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.514, 0.942, 0.554, 0.975]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "nnl7zSUOn1enawaO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the tick mark for the year '2014'}<r>[0.740, 0.901]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.740, 0.901]"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "n2H5teGmUwXVurSx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Startup Investments' for 2008}[0.245, 0.701] higher than {the point for 'Real Estate' in the same year}[0.245, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "CJ4ZVV9srAvsvW6Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is closer to {the top of the chart}, {the 'Stock Market' point in 2013}[0.658, 0.213] or {the 'Bank Savings' point in 2016}[0.905, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Market."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "u6gem6osMEmAuoSq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend line for 'Bank Savings' in 2013}<r>[0.658, 0.369]</r> above or below {the 'Real Estate' line at the same point}<r>[0.658, 0.576]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "zYzvcwCaqALF3Fdt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region around <r>[0.163, 0.400, 0.905, 0.801]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the trend line for the 'Stock Market', marked in blue with circles, showing significant fluctuations."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "QLWAZefNZc2dHm6o",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region described by <r>[0.041, 0.266, 0.070, 0.915]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the y-axis tick labels ranging from 6000 to 11500, aligned vertically."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "n4BpiwnXVjnlEJcU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the segment annotated by <r>[0.089, 0.057, 0.273, 0.212]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the chart legend, including labels and symbols for 'Stock Market', 'Real Estate', 'Startup Investments', and 'Bank Savings' with respective colors and shapes."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "2zFTrrasVPkNXbrF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title at the coordinates [0.502, 0.942, 0.542, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Task\"."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "uW46qWP0AbZZXgwE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the color of the line labeled as 'Raw Materials'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line labeled 'Raw Materials' is '#6baed6'."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "ghqaY8q2RRaKNbWb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.012, 0.352, 0.029, 0.637]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is \"Percentage (%)\"."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "Xefm1ONUOiCMTHEL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Raw Materials' trend}<r>[0.064, 0.328, 0.980, 0.611]</r> show a generally increasing or decreasing trend from task 1 to task 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "5Cvjl548xLulZlwC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Production' trend}<r>[0.064, 0.702, 0.980, 0.833]</r> and {the 'Sales' trend}<r>[0.064, 0.599, 0.980, 0.777]</r>, which one shows a faster rate of increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Sales' trend."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "1epLHMUoubp6ulQv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage value of {the 'Stakeholders' trend}<r>[0.064, 0.266, 0.980, 0.395]</r> at the point with coordinates [0.166, 0.394]?"
+            },
+            {
+                "from": "gpt",
+                "value": "62.31%."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "JZo7LmRyraRyfwLZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in [0.282, 0.021, 0.973, 0.106]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the graph, including labels for 'Stakeholders', 'Raw Materials', 'Production', 'Logistics', and 'Sales', each associated with a unique colored marker."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "BUbe2AkdVhpNsT1u",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.064, 0.088, 0.980, 0.901]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the graph where all the trends for 'Stakeholders', 'Raw Materials', 'Production', 'Logistics', and 'Sales' are plotted in different lines with various colors and styles. The x-axis represents 'Tasks' and y-axis represents 'Percentage (%)'."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "gICZu7Ap3wlA0U6V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located between the coordinates [0.060, 0.909, 0.269, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we have the x-axis tick labels '1' and '2' placed at {coordinates}<r>[0.060, 0.909, 0.068, 0.932]</r> and {coordinates}<r>[0.162, 0.909, 0.169, 0.932]</r> respectively."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "dm4CiEGZq8hVr0GR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for 'Instagram'}<r>[0.918, 0.814, 0.977, 0.837]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "yslZ2GCZbj58xAJg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the y-axis tick label for '20'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.034, 0.684, 0.048, 0.708]"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "cFUvNJ1OrRLSQxGS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text} at [0.346, 0.025, 0.698, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Media Active Users: 2010-2019."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "rZxvEj6283DrFgKs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media portrayed by the legend has a higher number of monthly active users in 2019, {Twitter}<r>[0.945, 0.486]</r> or {LinkedIn}<r>[0.945, 0.449]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "SGvunx3rNfs49R2V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing Facebook's users in 2011}<r>[0.193, 0.391]</r> above or below {the point representing Twitter's users in 2011}<r>[0.193, 0.653]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "ljB91HyY2vsVfaKW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {LinkedIn's active user count}<r>[0.663, 0.493]</r> in 2016 compare to {Instagram's active user count}<r>[0.663, 0.728]</r> the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "XGlEPNFUfsqFOPaW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region defined by [0.178, 0.909, 0.866, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis of the graph labeled with the years from 2011 to 2018."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "5ynq8hwlEqALYNFN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area in [0.881, 0.735, 0.982, 0.881] comprise of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, featuring colorful markers corresponding to social media platforms like Facebook, Twitter, Instagram, and LinkedIn."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "SLHMzdmRhMsoIyG6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space [0.034, 0.143, 0.048, 0.888] entail?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis of the graph ranging from 0 to 80, representing Monthly Active Users (M) in millions."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "Eo4OVWo1XSxOgNlz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented at [0.434, 0.050, 0.483, 0.080]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label for 'Benefit'."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "k5vgWGql5wqGGD1s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the x-axis label within the coordinates [0.505, 0.942, 0.541, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "FQfCYOAJoXZt54yc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data does the mark at [0.197, 0.474] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Net Health Benefit at age 30."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "P5YDllK8MIGxBuLb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.654, 0.775] at age 65 associated with higher value {than the point}[0.458, 0.627] at age 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "WlNvmq48TVVXqJ4O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the plot lines, does {the Admin Cost line at}[0.654, 0.534] position higher than {the Net Health Benefit line}[0.654, 0.775] for age 65?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "TPShnRfLYN3CJk0D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich variable is indicated higher at {age 55}[0.523, 0.611], Admin Cost or Patient Fee?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patient Fee."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "gLqCIbRGyaZk69cY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.390, 0.039, 0.973, 0.091]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend with labels such as {Benefit}<r>[0.434, 0.050, 0.483, 0.080]</r>, {Patient Fee}<r>[0.550, 0.050, 0.627, 0.080]</r>, {Admin Cost}<r>[0.694, 0.050, 0.773, 0.080]</r>, and {Net Health Benefit}<r>[0.840, 0.050, 0.968, 0.080]</r> coupled with their colored markers respectively."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "o4ULHH3moJ0mlWZA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the space [0.034, 0.071, 0.058, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the y-axis label {Values}<r>[0.012, 0.402, 0.029, 0.524]</r> and y-axis tick labels such as {'\u221240'}<r>[0.034, 0.892, 0.058, 0.915]</r> down to {'110'}<r>[0.036, 0.071, 0.058, 0.094]</r>."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "K6A9r7rCh5JebbxP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the information between [0.434, 0.000, 0.896, 1.000]."
+            },
+            {
+                "from": "gpt",
+                "value": "This broad area includes multiple line plots of {Benefit}<r>[0.066, 0.189, 0.980, 0.271]</r>, {Patient Fee}<r>[0.066, 0.271, 0.980, 0.572]</r>, {Admin Cost}<r>[0.066, 0.463, 0.980, 0.638]</r>, and {Net Health Benefit}<r>[0.066, 0.370, 0.980, 0.863]</r>, identified at various points along their respective x-coordinates."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "1ACsEngd6c21QEOC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by {line_2}[0.087, 0.548, 0.698, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line graph indicating \"Legal Risk\""
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "FZ5hohzwHVz56UXc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at {coordinates} [0.788, 0.117, 0.974, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Persistent Market Changes\""
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "WIxgl2OReDCdRQZN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is at {coordinate} [0.034, 0.646, 0.048, 0.670]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label \"40\""
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "YV0tykF7gnCDf38C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the line indicators for \"Legal Risk\" {coordinates} [0.087, 0.548, 0.698, 0.631] and \"Natural Disasters\" {coordinates} [0.087, 0.774, 0.698, 0.856] in the \"Shrinking\" scenario, which risk is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legal Risk"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "7Zlj71QErC2CQUrS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the \"Persistent Market Changes\" line {coordinates}[0.087, 0.113, 0.698, 0.388] on top or below the \"Natural Disasters\" line {coordinates}[0.087, 0.774, 0.698, 0.856] at the \"Expanding\" scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "M1NkcUmCiEFAfFS3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the \"Major Regulatory Changes\" {coordinates} [0.087, 0.278, 0.698, 0.408] points generally higher or lower than the \"Legal Risk\" {coordinates} [0.087, 0.548, 0.698, 0.631] points?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "I19w7829NnHDRTL6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.736, 0.089, 0.987, 0.307]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend, with four entries: \"Persistent Market Changes\", \"Major Regulatory Changes\", \"Legal Risk\", and \"Natural Disasters\". Each entry has a unique color and style of line to represent it on the chart."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "0fCeJHosv57M3Gc6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the area [0.027, 0.008, 0.048, 0.031]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"100\" is located in this area."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "w5VLsUpvR5HArV8e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included within the region of coordinates [0.061, 0.909, 0.114, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Stagnant\" is located within this region."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "9BvnqucEjAIUfd9L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by these coordinates [0.013, 0.282, 0.029, 0.599]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "hm3RDDSNnHpkty44",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point corresponding to the coordinates [0.790, 0.201]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the value 1.68 in the Rapid Increase trend for the Energy sector."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "SvX0fcPsKAl6KtUi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart element do the following coordinates correspond to: [0.034, 0.431, 0.052, 0.454]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A y-tick label showing the value '1.0'."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "q8hOz6ikCtonOkUP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Health sector in the Steady Recovery line}[0.206, 0.651] higher or lower than {the point for the same sector in the Rapid Increase line}[0.206, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "QUugzLx8g5wgAzIe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-coordinate of {the point for Education sector in Steady Recovery line}[0.352, 0.581] greater or less than {the point in Rapid Increase line for the same sector}[0.352, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "vTv7KgNjFI3o0fmw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Finance sector, does {the Rapid Increase trend}[0.060, 0.644] have a higher or lower data point value than {the Steady Recovery trend}[0.060, 0.704]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "U3oz5shOiOfLP4qg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the specific range [0.577, 0.032, 0.929, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart, providing keys for \"Steady Recovery\" and \"Rapid Increase,\" each with their own particular color and shape marker."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "yc73CHLK0lOiQLso",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within these coordinates:[0.468, 0.942, 0.528, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the xlabel of the chart, which reads 'Sector'."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "Ls4qK432Vkbv3xQc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in this specific range: [0.324, 0.800, 0.380, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the x-tick label for the 'Education' sector."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "Y4sxBefBJf3XpvmA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.013, 0.343, 0.029, 0.638]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Scenario Values."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "HxqaskOhXjMyWUMC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'VR Technology'}<r>[0.064, 0.132, 0.973, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #6baed6."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "umwoVUJWaM0hw853",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point}[0.973, 0.401] on the 'Climate Change' line represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of Climate Change in the year 2100."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "VTEEcQZDjxbNyEBG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich scenario has a higher value in the year 2030 - {Urbanization}[0.178, 0.275] or {VR Technology}[0.178, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urbanization."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "oMgyiCgK7fY3s1B8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Climate Change'}<r>[0.064, 0.152, 0.973, 0.401]</r> decreasing or increasing over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "J2LNBnnvhppayeru",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {AI Adoption} at [0.746, 0.428] have a higher value than {VR Technology} at [0.746, 0.297] in the year 2080?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "rwmKuEuRfOwSfBIi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the area bounded by <r>[0.789, 0.022, 0.966, 0.239]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The plotted area consists of a legend, with labels 'Urbanization', 'VR Technology', 'AI Adoption', and 'Climate Change' each paired with a colored marker squared, diamond, and circular respectively."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "PMzYaOzqEGaqkkTM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe in the region from <r>[0.064, 0.132, 0.973, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area illustrates the plotted lines for the four scenarios, 'Urbanization', 'VR Technology', 'AI Adoption', and 'Climate Change' over the decades."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "fmJh1150dPxBndxf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the data represented by x-axis ticks and their coordinates within the region <r>[0.064, 0.893, 0.973, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region delineates the decade labels ranging from 2020 to 2100 on the x-axis, mapped sequentially at coordinates starting from [0.064, 0.893] ending at [0.973, 0.893]."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "AcFEeQZ9Rxw8T82u",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element of the graph at coordinates [0.978, 0.501, 0.993, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "V1FmrHKi4gAdQRcl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at these coordinates [0.843, 0.141, 0.933, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for {AI Adoption}<r>[0.843, 0.141, 0.933, 0.158]</r> in the legend."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "0gqLisjz9GYMEsz1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label of {the x-axis tick} at [0.843, 0.909, 0.872, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2090."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "B0PyG7WGc87aQZgA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the data on {AI Adoption}<r>[0.842, 0.137, 0.921, 0.167]</r> and {Urbanization}<r>[0.842, 0.049, 0.930, 0.079]</r> in 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Adoption is lower."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "rjZWqMXOP2UAyeb9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2060, is the scenario value for Urbanization represented by {the line for 'Urbanization'}[0.064, 0.179, 0.973, 0.297] greater or less than the scenario value for VR Technology represented by {the line for 'VR Technology'}[0.064, 0.132, 0.973, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "TPqfSc84FIfBHNEB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Climate Change}<r>[0.842, 0.181, 0.952, 0.211]</r> and {AI Adoption}<r>[0.842, 0.137, 0.921, 0.167]</r>, which scenario had a lower value in 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "Climate Change."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "i3KEjc0msh1ICCEy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the coordinates [0.789, 0.022, 0.966, 0.239]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the graph with labels and color markers representing four different scenarios: {Urbanization}[0.842, 0.049, 0.930, 0.079], {VR Technology}[0.842, 0.093, 0.943, 0.123], {AI Adoption}[0.842, 0.137, 0.921, 0.167], and {Climate Change}[0.842, 0.181, 0.952, 0.211]."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "FHldDp6JEYGkt9vp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.013, 0.343, 0.029, 0.638]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Y-axis label, which is titled {Scenario Values}<r>[0.013, 0.343, 0.029, 0.638]</r>."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "CB95vlcxYEHGQpYs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's inside the area defined by [0.958, 0.909, 0.988, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object - the last {X-axis tick label}<r>[0.958, 0.909, 0.988, 0.932]</r>, it is the year '2100'."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "FSRlAfgCxEOy06QT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.086, 0.397, 0.098, 0.613] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label which is \"Global Trend Value\"."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "jufxvA8lKmUt2MZi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is located at the coordinates [0.708, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to a data point on the graph representing the year 2018 for Global Temperature."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "8JgpNcvIw7JqWDGU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.102, 0.658, 0.117, 0.681]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "vpCgVo2k6rI7OXuK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value in 2020, {Global Temperature}<r>[0.865, 0.832]</r> or {Sea Level Rise}<r>[0.865, 0.667]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Temperature."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "PLUmdtd2md5R9VKN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2013, is the value for {Renewable Energy Consumption}<r>[0.317, 0.492]</r> higher or lower than the {Sea Level Rise}<r>[0.317, 0.803]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "syZmTidMxJKhY8af",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2014, which one is higher, {global temperature}<r>[0.395, 0.840]</r> or {renewable energy consumption}<r>[0.395, 0.470]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global temperature."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "rPYz3XwqNpfn3Lcr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.168, 0.211, 0.356, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend item: {Renewable Energy Consumption}[0.168, 0.211, 0.356, 0.234]. It is paired with a green square marker."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "C7z26pTMsPPefTYK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description for the region defined by [0.102, 0.283, 0.117, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-axis label for the value '30' for the Global Trend Value."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "SiSsU3pta8DMYDKr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.160, 0.832, 0.865, 0.842] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for the Global Temperature trend from 2011 to 2020."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "MVjumKCEnXFNuSc6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.091, 0.397, 0.108, 0.592] on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "GfQp4fYmlM36CfDn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the text field of the fourth legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.799, 0.048, 0.952, 0.078]."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "HAAPNxv4G3ZHP0Nq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year corresponds to the x-axis tick label positioned at [0.795, 0.909, 0.824, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2008."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "QkqjmqrvBTJQVA75",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point {in 2002}[0.320, 0.514] for 'Moving within the city' higher or lower than the point for the same year for 'Moving to another city'[0.320, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "zkXF1uEb3tBxVNwi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at 2000}[0.157, 0.527] on 'Moving within the city' line placed above or below the {first point}[0.157, 0.230] on the 'Not moving' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "ERexlb3Mi75JKuOG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {2006}[0.647, 0.495], 'Moving within the city' or 'Moving to another city'[0.647, 0.821]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Moving within the city'."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "yRqyno3VAJiBRtUA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects found in the area [0.053, 0.021, 0.966, 0.106]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend. The legend includes the items: 'Moving within the city' {with the associated marker in blue square}<r>[0.061, 0.051, 0.101, 0.074]</r>, 'Moving to another city' {with the associated marker in light-blue triangle}<r>[0.281, 0.051, 0.320, 0.074]</r>, 'Moving to another country' {with the associated marker in green diamond}<r>[0.504, 0.051, 0.543, 0.074]</r>, and another item labeled 'Moving within the city' {with the associated marker in light-green circle}<r>[0.754, 0.051, 0.794, 0.074]</r>."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "IdSWbENnDGgEU30R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.545, 0.942, 0.585, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the x-axis label 'Year' is located."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "wLVAy3bR47JbJIis",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nChoose an area and describe what it contains [0.112, 0.171, 0.149, 0.194]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encloses the y-axis tick label '55000'."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "oxF4Hqrp7GmurcjM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinate [0.013, 0.297, 0.029, 0.692] stand for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "K4DckLpJ031lYpL4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object marked at these coordinates [0.064, 0.781]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the first data point of the 'AI in Healthcare' line."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "PGaz3nB8Cc1MqsrG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the coordinate [0.468, 0.901] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the x-axis tick for the year 2026."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "tzfmN44vVIgJCUpG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line lies above at the 2026 comparison point: {AI in Healthcare}<r>[0.468, 0.627]</r> or {Telemedicine}<r>[0.468, 0.445]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI in Healthcare."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "Zg3nZ5bJRg08xh2U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy 2031, which line indicates greater projected growth: {Electronic Health Records}<r>[0.973, 0.373]</r> or {Precision Medicine}<r>[0.973, 0.446]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Precision Medicine."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "ORqZReeluXl96HrZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {AI in Healthcare}<r>[0.872, 0.286]</r> and {Electronic Health Records}<r>[0.872, 0.360]</r>, who had a higher projected growth in 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "Electronic Health Records."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "869N94OJbbDL8ctb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within [0.064, 0.161, 0.973, 0.525]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes four line graphs: {AI in Healthcare}[0.064, 0.161, 0.973, 0.781], {Telemedicine}[0.064, 0.332, 0.973, 0.525], {Electronic Health Records}[0.064, 0.169, 0.973, 0.373], and {Precision Medicine}[0.064, 0.234, 0.973, 0.446] that represent the projected growth in their respective sectors."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "ol3r8nm8KNKfAmc9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region with coordinates [0.193, 0.021, 0.966, 0.106]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the chart legend with labels {AI in Healthcare}<r>[0.245, 0.048, 0.356, 0.078]</r>, {Telemedicine}<r>[0.422, 0.048, 0.513, 0.078]</r>, {Electronic Health Records}<r>[0.580, 0.048, 0.757, 0.078]</r>, and {Precision Medicine}<r>[0.824, 0.048, 0.952, 0.078]</r>, each paired with a colored iconic representation."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "cMvlcycSPihOZKGF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects located within the region [0.064, 0.088, 0.973, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the whole plotting area of the line chart containing the line graphs, the tick labels on both the x and y-axis and their respective labels."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "tt2y6CZEm2pJaxB9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text associated with the coordinate} <r>[0.265, 0.049, 0.413, 0.079]</r> refer to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Executive Leadership."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "PdmEJST8zJQmZZRG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} <r>[0.502, 0.942, 0.542, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Task."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "70u1WoUTlPsdlXQF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point on the 'Business Analysis' line} <r>[0.980, 0.783]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "13.7."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "7oWvUXLduiVa8HF6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for 'Strategic Planning'}<r>[0.267, 0.356]</r> below or above the corresponding point in {the 'Executive Leadership' line}<r>[0.267, 0.219]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "ketp4JG7e1ZSK0to",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does {the point for 'Team Building'}<r>[0.675, 0.645]</r> lie in relation to {the point for 'Business Analysis'}<r>[0.675, 0.765]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "WIuQeX8XXO3AYVsy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Task 8 point for 'Executive Leadership'}<r>[0.777, 0.368]</r> indicate a higher or lower performance than the {Task 8 point for 'Strategic Planning'}<r>[0.777, 0.491]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "m8nEgnX4CdLKE3TD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the area with the bounding box <r>[0.212, 0.022, 0.973, 0.107]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart with colors and labels indicating the different lines: {Executive Leadership}<r>[0.220, 0.051, 0.260, 0.074]</r>, {Strategic Planning}<r>[0.435, 0.051, 0.474, 0.074]</r>, {Team Building}<r>[0.628, 0.051, 0.667, 0.074]</r>, and {Business Analysis}<r>[0.792, 0.051, 0.831, 0.074]</r>."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "Emff6ZXSPSqvCb4W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is located in the box <r>[0.064, 0.088, 0.980, 0.893]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the main chart plotting area with lines representing the different categories: {Executive Leadership}<r>[0.064, 0.126, 0.980, 0.424]</r>, {Strategic Planning}<r>[0.064, 0.286, 0.980, 0.519]</r>, {Team Building}<r>[0.064, 0.552, 0.980, 0.696]</r>, and {Business Analysis}<r>[0.064, 0.731, 0.980, 0.783]</r>. Also, the x-axis and y-axis with their respective labels and tick labels are located within this bounding box."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "Ng0PzHqdBqjqZUho",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the box defined by <r>[0.048, 0.884, 0.056, 0.907]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the label '0' from the y-axis tick labels of the chart."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "eb8Y9iS5uiVb8Asu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.344, 0.019, 0.741, 0.041] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the first subplot, \"Automobile Positive Emotion\"."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "xDTm8b3io6AAQ4Qa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the object found at coordinates [0.025, 0.620, 0.048, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label of the second subplot."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "Yg3NrqfsDjjenqlq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.827, 0.939, 0.864, 0.957] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '0.9' on the x-axis of the second subplot."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "w70HE14c7kLx8y3l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '0.5'}[0.500, 0.320] higher or lower than {the point at '0.7'}[0.672, 0.359] on the 'Automobile' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "Hlh0PVFzoqpaiPdB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, comparing {the point at '0.9'}[0.845, 0.693] and {the point at '1.0'}[0.932, 0.700], which one is higher on the 'Public Transportation' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at '1.0'."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "HNyBsuAWLCR86cOk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe line of 'Automobile Positive Emotion' at {Ratio_0.2}[0.240, 0.352], is it above or below the 'Public Transportation Negative Emotion' line at {Ratio_0.3}[0.327, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "FTNIQYdLuxBbjPyp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by [0.269, 0.509, 0.816, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle of the second subplot, \"Public Transportation Negative Emotion\"."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "NR7Lq2EocGKOK3ad",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within [0.122, 0.055, 0.338, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area has the legend for the first subplot. It includes {the label 'Automobile'}[0.196, 0.062, 0.328, 0.080] and its corresponding marker."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "HLMmP6SHlrw4lk34",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region defined by [0.135, 0.939, 0.172, 0.957] and [0.913, 0.939, 0.950, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions contain the x-axis tick labels '0.1' and '1.0', respectively, for the second subplot."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "pSYbEYXejly7AW5Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinate [0.218, 0.553, 0.277, 0.570] indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That text indicates '2021'."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "opWpF9mnku0Y1naY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.175, 0.905] in 'subplot_1' refer to on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates refer to the data point for the year 2021 in the first month (0)."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "7ovK8r3VW80gVymZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.025, 0.709, 0.048, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "hc6GODtYhEmBDTMp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the sales graph of 2021 start at a higher or lower point compared to 2020, considering respective starting points {2020}[0.175, 0.395] and {2021}[0.175, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "MCRuqrEl0d5YQLUW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing the final sales points of {2020}[0.933, 0.395] and {2021}[0.933, 0.545], which year ended with higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "4Kxwlqx1W7plzYIl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring the 5th month, comparing the sales levels of {2020}[0.596, 0.410] and {2021}[0.596, 0.710], which year had higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "EKuhczFel3hOS31V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.518, 0.509, 0.589, 0.532]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle for the second plot, stating the label '2021.'"
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "b1J4lBujdujsCTKN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.144, 0.546, 0.286, 0.577]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the sales chart of 2021. The text '2021' is present, with its corresponding marker."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "e8atLSVdPx5MGl1N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.167, 0.939, 0.182, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the 0th month on the x-axis of the sales chart for 2021."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "lxNTsP6sRUlV9LiK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the object}[0.417, 0.509, 0.669, 0.532] is in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of a subplot, which reads \"Dividends_Growth\"."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "ggZL74ocf9omuiZJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.189, 0.132, 0.896, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the line for 'Earnings_Yield' in the chart."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "LhtidiE7IGbVGdTb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the chart at {coordinates}[0.875, 0.939, 0.912, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '1.0' is located here."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "NL7hKaMN6c3Rz3PI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Dividends_Growth'}[0.896, 0.912] at a higher or lower value than {the point for 'Earnings_Yield'}[0.896, 0.398] at the same Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "YqlgNWeUqks9lCfc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Earnings_Yield'}[0.189, 0.132, 0.896, 0.398] experience more or less growth than {the line for 'Dividends_Growth'}[0.189, 0.553, 0.896, 0.912] across the Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "BmCgGWIpTHPpMyHr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the last x-axis tick in the {sublot_0}[0.110, 0.047, 0.975, 0.437] above or below the first x-axis tick in the {subplot_1}[0.110, 0.537, 0.975, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "olWwQzmgzPbJtCIU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what you see in the region [0.110, 0.537, 0.975, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot which is titled 'Dividends_Growth'. It includes a line for 'Dividends_Growth' which is marked by a '--' linestyle and 'o' marker. There are labels at the top and bottom, as well as a grid, and tick labels along the x and y axis."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "I1fllFHM8Ubld6Wa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the region between [0.110, 0.047, 0.975, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the first subplot titled 'Earnings_Yield'. The plot includes a line to represent 'Earnings_Yield' in blue color with a '--' linestyle and 'o' markers. It also has x and y axis labels and tick labels with a visible grid."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "UIblmjMWIIddWShZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects inside [0.485, 0.449, 0.522, 0.466] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, one of the x-axis tick labels '0.5' is located."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "WvnXBfUMYBNp92x4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of label is at {position} [0.478, 0.320, 0.586, 0.330]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "6mltiOSJxeNcqTtU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the x-axis label} at [0.478, 0.650, 0.586, 0.660]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "hsnu2HlGVlNPLP4b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are specified for {the ylabel} at [0.019, 0.422, 0.036, 0.563]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Ratio of Usage/Consumption' in black color and rotated 90 degrees."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "Uw5OZwGTh4TJsm7k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the point at [0.757, 0.445]} compare in value to {the point at [0.846, 0.450]} on the energy use line for Oil?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "UNC1xE3TKsmabS5b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {point}<r>[0.936, 0.070]</r> has a higher y value, from Energy Use or Resource Consumption lines for Coal?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Use."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "mEkYFgR05U5a2wqY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of these points, {at [0.846, 0.796]} or {at [0.846, 0.822]}, marks a higher level of resource consumption, in Renewable energy data?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.846, 0.822]."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "WtmmCeDudQzKh1Cy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.478, 0.979, 0.586, 0.989]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label which reads \"Time (Years)\" in black text is situated here."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "CN1MIYPQN8J7LXYg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.019, 0.092, 0.036, 0.233]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the y-axis label 'Ratio of Usage/Consumption' presented in black and rotated 90 degrees."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "wF6m34boYRFO1jdQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are observed within [0.712, 0.324, 0.966, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It comprises the legend for the subplot, with labels 'Energy Use' at {coordinates}[0.766, 0.365, 0.864, 0.375] and 'Resource Consumption' at {coordinates}[0.766, 0.380, 0.966, 0.390], associated with markers that are colored blue and green respectively."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "vX4bBNWymCH3NiRf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marks are used for the data points in the 'High Risk' series?"
+            },
+            {
+                "from": "gpt",
+                "value": "'o'."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "S6H04dWWzUfBD9nq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.461, 0.012, 0.607, 0.027]?"
+            },
+            {
+                "from": "gpt",
+                "value": "SPRisk-0.5."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "S4Z1LKaTNs04P73c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the line representing 'Low Risk'}<r>[0.110, 0.143, 0.957, 0.162]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ca02c."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "NXyUBbwh1WllqhRL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, 'High Risk' or 'Low Risk', starts at a higher point in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "High Risk."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "e6LiLDOIENYf6beR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data points for {the 'High Risk' series}<r>[0.110, 0.847, 0.957, 0.865]</r> above or below {the data points for the 'Low Risk' series}<r>[0.110, 0.884, 0.957, 0.903]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "R4r30EXxbL6qKICQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the start of {the 'Low Risk' line}<r>[0.110, 0.143]</r> or the start of {the 'High Risk' line}<r>[0.110, 0.087]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "High Risk."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "BkzLJZRsT9uKY9Xh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents and appearance in the region [0.110, 0.031, 0.957, 0.329]."
+            },
+            {
+                "from": "gpt",
+                "value": "Contains x-axis labeled '{Policy}'<r>[0.500, 0.318, 0.567, 0.329]</r>, y-axis labeled '{Performance Score}'<r>[0.025, 0.107, 0.048, 0.217]</r>, and multiple grid lines, along with the plot of two series 'High Risk' and 'Low Risk'."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "Ux9lBMG1paJeOKX7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within [0.110, 0.360, 0.957, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot includes axes labels 'Policy' at <r>[0.500, 0.647, 0.567, 0.658]</r> and 'Performance Score' at <r>[0.025, 0.437, 0.048, 0.546]</r>, x-axis tick labels ranging from '0.1' to '0.5', y-axis tick labels from '0.3' to '0.9', and the plotted data for two trend lines labeled 'High Risk' and 'Low Risk'."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "mhivAEVErd4j9RfO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are observable in the area [0.092, 0.301, 0.975, 0.313]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the x-axis tick labels from '0.1' to '0.5' for the first subplot along with its respective grid lines."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "nKABtzHXelXPPLkC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.025, 0.364, 0.048, 0.584]</r> read?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "RlEfFnelUBo9yuMU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented by the coordinates [0.211, 0.125, 0.347, 0.160] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "uVXPCEOiNnBFtc6R",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at the coordinates [0.512, 0.927, 0.588, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio r."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "og0CdzM6aYEWdtd0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point with x being 0.7}[0.678, 0.496] for the line representing 'AI Research' higher or lower than {the corresponding point}[0.678, 0.389] for the 'AI Healthcare'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "Mq0zi6MdLhKSgps9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'AI Research'}[0.168, 0.254, 0.933, 0.810] increase or decrease from the point {x being 0.5}[0.508, 0.630] to the point {x being 0.9}[0.848, 0.281]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "1zLauDEiwvFZdWNW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {x being 0.3}, {the line representing 'AI Research'}[0.338, 0.747] or {the line representing 'AI Healthcare'}[0.338, 0.630]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Research."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "r390dJoLq964OJ1f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are seen in the area with coordinates [0.137, 0.111, 0.376, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend with two items: {AI Research with a 'o' marker}[0.211, 0.125, 0.347, 0.160] in '#1f77b4' color, and {AI Healthcare with a '^' marker}[0.211, 0.177, 0.367, 0.212] in '#2ca02c' color."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "KTwE4EcDMpwhUDu1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the coordinates [0.125, 0.093, 0.975, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily comprises of the main body of the chart which includes two colorful lines representing 'AI Research' and 'AI Healthcare', the x-axis and y-axis with their respective tick labels ranging from 0.1-1.0 and 0.15-0.95 respectively, and the grid overlay."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "Qs9oq2EDgtrhqfRL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the region [0.501, 0.037, 0.599, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a subtitle that reads \"Ratio 1\" in black color."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "xn5wLNW3iNoDs03s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.025, 0.112, 0.048, 0.212] within subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "2umZ3MFsWo3JSnFr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the '2019' x-axis label in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.495, 0.630, 0.554, 0.642]"
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "xz9lgqz5aAm1MTWl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.058, 0.114, 0.087, 0.126] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "kx5wb1clYNV3kbxd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2018}[0.103, 0.118] in subplot_0 above or below {the point for 2019}[0.524, 0.075] for 'Ind-A'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "lDumfDEMc6DQf6kM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2018}[0.103, 0.491] in subplot_1 higher or lower than {the point for 2020}[0.946, 0.448] for 'Ind-B'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "7wN7lbz9XeIvcDLH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year in subplot_2 has a higher market share for 'Ind-C', 2018 at {point}[0.103, 0.908] or 2019 at {point}[0.524, 0.908]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "QZRBhO1dSMzD75Tt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.103, 0.360, 0.946, 0.622]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents subplot_1 showcasing the trend of 'Ind-B'. It includes markers for the years 2018-2020 on the x-axis, marked percentage values for the y-axis, and the corresponding line graph in green, indicating the change in 'Market Share (%)' for the respective years."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "NjlC6rMEKOs2AZFb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.058, 0.201, 0.087, 0.213] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '30' is located here."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "mty0JKBywxSPTBrR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the area [0.103, 0.689, 0.946, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents subplot_2 for 'Ind-C'. The objects within this area are the line graph representing market share percentages across 2018 to 2020, along with the x-axis labels of the three years and the y-axis labels representing percentage values."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "R3FhSJJ5O0uRGQqf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel text}[0.024, 0.118, 0.038, 0.208] on the first subplot indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yield in Million Tons."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "1v3QiBqHpthevbTX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart type is displayed at {subplot coordinates}[0.075, 0.025, 0.985, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "wXhVqhNNTeOwOyZb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year corresponds to {the x-axis tick}[0.116, 0.301] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "7c6gmTUFTqbpd8pD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on 'Yield Trend for SPGrocery-0.5'}[0.208, 0.215] higher or lower than {the last point on the same trend}[0.944, 0.037]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "feQ8f35OGHBwRzQB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the y-axis tick label at}[0.035, 0.963, 0.065, 0.972] and {the y-axis tick label at}[0.035, 0.783, 0.065, 0.793] from the same subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "v8fQfRifUdQOHUG5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first point on the line}[0.116, 0.619] align vertically above or below {the corresponding x-axis tick}[0.116, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "uWywhxOrK7hwCUfP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the elements within the region [0.075, 0.025, 0.985, 0.301]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot for 'Yield Trend for SPGrocery-0.5' including y-axis with labels {2.4 to 3.4}[0.043, 0.314, 0.065, 0.028] and x-axis marking the years from {2000 to 2009}[0.099, 0.308, 0.961, 0.317]."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "LZz5yp8vzu3GZtzb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content shown in the region [0.075, 0.355, 0.985, 0.631]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates the 'Yield Trend for SPGrocery-0.7' chart, featuring a y-axis with varied yield values from {2.50 to 2.90}[0.035, 0.637, 0.065, 0.342] and x-axis marked with years {2000 to 2009}[0.099, 0.638, 0.961, 0.647] associated with specific yield points along a line chart."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "bnsVzRWXmu17dUeG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain what's included in the area [0.075, 0.685, 0.985, 0.961]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, the chart titled 'Yield Trend for SPGrocery-0.9' is present, showcasing a ylabel {Yield in Million Tons}[0.015, 0.778, 0.029, 0.868] and an x-axis extending from {2000}[0.099, 0.968, 0.134, 0.977] through {2009}[0.926, 0.968, 0.961, 0.977], with the corresponding line graph plotting yield points throughout the years."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "Jrtiq8rZKM5EjIBe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of {marker}[0.857, 0.862, 0.893, 0.876] for Lifestyle B in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Triangle marker."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "2xfqzDYJVIjXohJP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with coordinates [0.116, 0.550, 0.944, 0.865] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lifestyle B."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "jnB6tDCgYyADmnxT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at the coordinates [0.476, 0.952, 0.585, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Activities."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "hisc2yHhQ1wgPYZo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the Social Activity labeled 'Stay at Home', is {Lifestyle A}[0.116, 0.550] higher or lower than {Lifestyle B}[0.116, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "rN5zbiXIUXAvQRcY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at 'Outdoor Activities', {Lifestyle A}[0.323, 0.325] or {Lifestyle B}[0.323, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lifestyle A."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "GK6qqTIoUJ3xci3b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the lifestyle activities at 'Online Socialisation', is {Lifestyle A}[0.530, 0.460] more represented than {Lifestyle B}[0.530, 0.730]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "yJJPteOs0vJzfvoZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.856, 0.815, 0.978, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart. It contains information about 'Lifestyle A' with a blue circular marker at {position}[0.857, 0.827, 0.893, 0.841], and 'Lifestyle B' with a green triangular marker at {position}[0.857, 0.862, 0.893, 0.876]."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "Viu9FvOYkkKzuzpd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the area [0.075, 0.062, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the line chart. It contains two lines representing 'Lifestyle A' and 'Lifestyle B'. The plot also includes x-axis labels indicating different social activities and y-axis labels showing time spent, with tick marks at regular intervals."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "np5OrHfGBbm3twuU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.035, 0.204, 0.065, 0.227]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a y-axis label of 17.5."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "XeStbrYqr8f6yiAo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.058, 0.079, 0.094, 0.091] indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis value."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "x9jjEgtePynlr3N1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at position [0.154, 0.293, 0.154, 0.293] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick mark."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "J22Sls2hnlBYI0Wc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.827, 0.630, 0.864, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "FqFDu6IuHOZcl8L4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.240, 0.267]} higher or lower than {the point at [0.240, 0.267]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "0wmu54vLzCGwGw8s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is above at {point}[0.927, 0.084] between RPiS and CNN lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "CNN."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "2LcyqvrNHCkGR2Vy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the slope direction from the second point to the third point of {line labeled 'RPiS'}[0.240, 0.251, 0.327, 0.230] with that of {line labeled 'CNN'}[0.240, 0.267, 0.327, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Steeper."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "Z5Wh3XrwkabHVTw4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in the coordinates [0.025, 0.053, 0.256, 0.110]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend box containing the items {RPiS}[0.196, 0.042, 0.247, 0.053] with a blue marker and {CNN}[0.196, 0.059, 0.247, 0.071] with a green marker are located here."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "yTPcFQ2d9SzTOTJN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the bounding box from [0.504, 0.976, 0.581, 0.988]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Ratio r\" styled in black color can be observed within this region."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "5zbBri4YIGnhkwse",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content between [0.110, 0.301, 0.975, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the x-axis, where labels ranging from '0.1' at [0.135, 0.301, 0.172, 0.313] to '1.0' at [0.913, 0.301, 0.950, 0.313] appear, with corresponding tick marks from {coordinates}[0.154, 0.293] to {coordinates}[0.932, 0.293]."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "TCwFkYwK26iGAAVp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.504, 0.318, 0.581, 0.329]</r> display in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio r."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "ozJkhsJRTy4nH7ba",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the fifth point on the PNA line}<r>[0.500, 0.132]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "aR35TK8KyUar4vAO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {coordinates}<r>[0.058, 0.114, 0.094, 0.126]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.8."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "EWJnP3b4JXRu3JfR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {point 0.5}<r>[0.500, 0.132]</r>, {PNA}<r>[0.500, 0.132]</r> or {GIN}<r>[0.500, 0.197]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "PNA."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "duztuAVYeGoafrF7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label '0.9'}<r>[0.827, 0.960, 0.864, 0.971]</r> represent a higher or lower value than {the label '0.2'}<r>[0.222, 0.960, 0.258, 0.971]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "S7UKDRbTckGOC2ME",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 0.9 tick mark on the x-axis}<r>[0.845, 0.951]</r> to the left or right of {the 0.3 tick mark}<r>[0.327, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "ETPwv0rIvZ07Di7K",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within <r>[0.122, 0.695, 0.252, 0.734]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot legend for \"SPMotif-0.9\" featuring {PNA text}<r>[0.196, 0.700, 0.243, 0.712]</r> and {GIN text}<r>[0.196, 0.717, 0.238, 0.729]</r> with respective color markers."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "X0iSh6jY6AFRWnSc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.110, 0.031, 0.975, 0.293]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It consists of a line chart with labels such as {PNA line}<r>[0.154, 0.132, 0.932, 0.180]</r> and {GIN line}<r>[0.154, 0.145, 0.932, 0.241]</r>, and includes both axes labels and tick marks."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "mzUA6o7MOSK4jWwj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in <r>[0.058, 0.070, 0.094, 0.082]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label {0.9}<r>[0.058, 0.070, 0.094, 0.082]</r>, colored in black."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "K2WSJHu4eQTUkdaE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of mark represents {the point at time '0.7'}<r>[0.672, 0.254]</r> in the chart for \"Hunter-gatherer Eras\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "tS33tBD2jcWgFd5L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Agricultural development'}<r>[0.154, 0.426, 0.932, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "9i6bSo1SE42jIMs7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the label at {coordinate}<r>[0.308, 0.301, 0.345, 0.313]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.3."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "pskorTCNVvczZIlT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the development value depicted at {the point 0.5}<r>[0.500, 0.539]</r> in 'Agricultural development' higher than at {the point 0.3}<r>[0.327, 0.206]</r> in 'Hunter-gatherer Eras'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "K6W0jQt2iRX4FgFs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the start or end have a higher development index for {the line 'Industrial Revolution'}<r>[0.154, 0.694, 0.932, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "End."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "Am3UR42smf60Rmeo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the mark '0.4'}<r>[0.413, 0.548]</r> and {the mark '0.8'}<r>[0.759, 0.469]</r> in 'Agricultural development', which is higher on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "ex5SRoi6yo3rT43W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the labels surrounding the area <r>[0.058, 0.247, 0.094, 0.300]</r> on the y-axis within the subplot 'Hunter-gatherer Eras' represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range consistently lists numerical development values from 0.4 at the top to 0.3 at the bottom."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "eCZyCn6jp8xV1FW7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in the horizontal span across <r>[0.385, 0.976, 0.700, 0.988]</r> at the bottom of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Time (from ancient to now)\" for the 'Industrial Revolution' subplot is located here."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "94FGlUYQg0AKSPeX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the section <r>[0.058, 0.618, 0.094, 0.629]</r> on the third subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a y-axis tick label displaying the value 0.3 in the subplot 'Agricultural development'."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "1u5mgtizWbiAoBm1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked by {the point}<r>[0.172, 0.273]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP for 2007."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "qLKUgsqDVwVVzMak",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at {coordinates}<r>[0.204, 0.050, 0.253, 0.064]</r> in the subplot area?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend text \"GDP\"."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "rKt9hzok7bLaGeZ9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the label of {the x-axis tick}<r>[0.172, 0.285]</r> in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "2007."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "5tiOcswgMRwUf3ah",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {year 2012's GDP point}<r>[0.439, 0.600]</r> positioned higher or lower than {year 2012's CPI point}<r>[0.439, 0.424]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "q3WYL5Ca97fhwQf9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is located farther to the right, {the last GDP point}<r>[0.921, 0.923]</r> or {the last CPI point}<r>[0.921, 0.722]</r> in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are at the same position."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "f5q6F3e8CKttQe9Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the line labeled 'GDP'}<r>[0.172, 0.283]</r> and {the line labeled 'CPI'}<r>[0.172, 0.095]</r> plotted in the same color?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "dpVIZggJ23PgTorE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCharacterize the area defined by <r>[0.118, 0.037, 0.975, 0.285]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The lower part of the chart displays two line graphs representing GDP (in blue) and CPI (in green), flavored with x-grid and y-grid lines, having tick labels marking the years from 2007 to 2011, and y-axis values ranging from 0 to 160, located in respective tick mark positions."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "IUAvfafZKH6ij9O4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region within <r>[0.118, 0.366, 0.975, 0.613]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This middle portion of the chart features the continuation of the GDP and CPI lines, now marking the years from 2012 to 2016, with y-axis values stretching up to 280, and colored lines (blue and green, similarly) continuing the map of the economic metrics' trends."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "spS1guWW343F2vDZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the graphic elements and details within the location <r>[0.118, 0.694, 0.975, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The upper section encompasses line graphs for GDP and CPI over the years 2017 through 2021, illustrated using blue and green dashes and markers respectively. It includes x-axis and y-axis indicators, both offering detailed labels of years and economic metric values up to 280."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "lp6zzhLLZeGUqZs6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the label at coordinates [0.058, 0.879, 0.094, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label for 0.9."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "UdixCYakxnm8b0h6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Innovation\" line as seen at {handle}[0.760, 0.363, 0.820, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "lf9FHdxeb1JFAVae",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a marker at the point [0.845, 0.432] on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "AwvWUfNFL0U6yXUU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line at [0.240, 0.313] positioned higher on the graph than the line at [0.240, 0.467] at the same x-coordinate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "mtF3AN4Q9JLEcpf4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis tick corresponds to {the point at}[0.500, 0.127] situated on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "0QYTp9cLpDB0YujN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line point at {coordinate}[0.327, 0.462] have a higher performance metric than the point at {coordinate}[0.327, 0.319]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "IGd75ebVoozWD6VY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area inside [0.758, 0.161, 0.963, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend title for the subplot, which is unlabeled yet includes a color-coded handle indicating the \"Innovation\" line style in blue."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "I26WxxGdyiS3Pgqm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained within the region [0.058, 0.879, 0.094, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a y-axis tick label, specifically the label '0.9', marked in black."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "5kQyYpIDfyvn2G7I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.135, 0.182, 0.950, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains x-axis tick labels comprising numerical values from '0.1' to '1.0', evenly spaced along the axis."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "bSRQ2aTPS2H8AtbT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} in the 'Interpersonal Relationships' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio r<r>[0.504, 0.647, 0.581, 0.658]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "mH696rCgM9SXB5t8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the color of {the point marker for 'Family'} in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728<r>[0.154, 0.786]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "a7FhIWydQhOir2TS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} for the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance<r>[0.025, 0.455, 0.048, 0.528]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "4KluB3FlP4HP3gbp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line plot begins its y-coordinate at a higher position, {the line labeled 'Friends'} or {the line labeled 'Facebook'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook<r>[0.154, 0.786]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "le9EQ2e36sUahG5x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-coordinate of the last tick on the y-axis}[0.058, 0.737, 0.094, 0.749] higher or lower than {the first point of 'Family' line}[0.154, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower<r>[0.110, 0.742]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "KeKQIQa0zlPBMPLq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich plot shows a higher performance at 0.8 ratio r, {Facebook or Family} in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Family<r>[0.759, 0.731]</r>."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "hhPCELJWQDHThzGb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the lower section of the 'Interpersonal Relationships' labeled area."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the axes labels 'Ratio r' and 'Performance' styled in black color, x-axis and y-axis tick labels like '0.1', '0.4', all in black color, and a visible grid with lines styled as dotted and colored '#b0b0b0'."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "svkRaCNtNSyM3jHm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the middle section indication in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures the subplot titled 'Comparison' along with labeled lines for 'Facebook', 'Twitter', 'Friends', and 'Family' in various colors and markers, including the y-axis with performance percentages."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "M6aMQ6ynzItAcUwe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region spanning the majority of the plot along the top and labeled 'Social Networks'?"
+            },
+            {
+                "from": "gpt",
+                "value": "It features grid lines, plotted line indicating data trend of 'Facebook' and 'Twitter', x-axis and y-axis markers and labels with 'Ratio r' and 'Performance' respectively, and colored differently marked data points showing a detailed trend over a specified range."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "2H7vE36ellacHsPC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.147, 0.886, 0.931, 0.886] in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line for Male in Home Activities."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "YBCDG2kcHzS77to7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the sixth x-axis tick mark}<r>[0.583, 0.293]</r> labeled in the Social Interact subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.6."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "iwenNrjo6CW8waIk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the colored marker}<r>[0.799, 0.372, 0.859, 0.379]</r> in the legend of the second subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Male."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "VJkPkOdqrHY5YIyW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point marked at '0.2' ratio}<r>[0.234, 0.188]</r> in the Home Activities subplot for males higher or lower than for females?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "hNyQdKHVL1ZsVqEM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich gender has a higher value for the last data point in the chart, {Male}<r>[0.931, 0.886]</r> or {Female}<r>[0.931, 0.834]</r> in the Home Activities subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Female."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "p1h6VkwXLfqJggNS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the initial point for the Outdoor Activities trend lower for {Male}<r>[0.147, 0.622]</r> compared to {Female}<r>[0.147, 0.557]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "10wERDrUzZi9Euor",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.025, 0.776, 0.087, 0.865] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label, which reads \"Percentage (%)\", for the Home Activities subplot, styled in black color and a 90\u00b0 rotation."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "Gg2mWBsVftxlLqNT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the segment [0.103, 0.951, 0.975, 0.976] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the x-axis for the Home Activities subplot, displaying labels for the ratios from '0.1' to '1.0', complete with corresponding tick marks, under a subtitle labeled \"Ratio over time\"."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "QmgzOC2Mdu1VU7V2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is illustrated in the area [0.796, 0.037, 0.963, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the Social Interact subplot, indicating the color-coded labels for Male and Female, associated with their respective colored handles. Male is represented with a blue line, while Female is indicated by a red line."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "q0Sb16dlyapL1EOf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates {the ylabel's label}<r>[0.015, 0.337, 0.034, 0.623]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Values in $million"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "ztAmzVIaH6fGKW53",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis tick label at {x-axis tick label}<r>[0.057, 0.911, 0.077, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q1"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "7TuV28r8nypq084d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies at coordinates [0.076, 0.095, 0.121, 0.113] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Color handle for Revenue"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "OmX7LejzH23ACCG6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Q3 Expense point}<r>[0.430, 0.547]</r> above or below {the Q3 Revenue point}<r>[0.430, 0.460]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "IdKtiayEvrC3SnnM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Revenue line}<r>[0.067, 0.132, 0.975, 0.560]</r> end at a higher or lower position than {the Expense line}<r>[0.067, 0.373, 0.975, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "ELW2iYDc7KS42GN0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at point Q3, {the Revenue value}<r>[0.430, 0.460]</r> or {the Net Profit value}<r>[0.430, 0.808]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Net Profit."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "cBdrlPyBaVOqu2dx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content at coordinates [0.075, 0.079, 0.214, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It includes the labels for Revenue, Expenses, and Net Profit, each paired with a marker of different color and shape."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "Zr1v7M7UwG84qNRn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.430, 0.460, 0.612, 0.393]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are points from the Revenue line from Q3 to Q4."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "QuPRdizkQE0YA2Uy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.076, 0.095, 0.121, 0.113]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a color handle for Revenue in the chart's legend where the color is sky blue."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "QKpnlnFcTsIMyBUF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.019, 0.236, 0.043, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "7bdimLWdb1cbxsqY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.095, 0.182, 0.138, 0.205]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle of the legend for Machine A."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "0AN1EmFEvVMMm9E5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text of {the x-tick label}<r>[0.239, 0.821, 0.277, 0.868]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.00."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "sfB0wTZhKIS3N7l4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {Machine A}<r>[0.084, 0.218, 0.957, 0.443]</r> perform better or worse over time, compared to its initial performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Better."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "oKNeOmEWQbfJGbnd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich machine had the highest efficiency at {time 10.00}<r>[0.932, 0.821, 0.981, 0.868]</r>, {Machine A}<r>[0.957, 0.218]</r>, {Machine B}<r>[0.957, 0.477]</r>, or {Machine C}<r>[0.957, 0.512]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine A."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "rB5Mj64sLxevyLap",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Machine B}<r>[0.084, 0.408, 0.957, 0.529]</r> and {Machine C}<r>[0.084, 0.477, 0.957, 0.599]</r>, which one showed a better efficiency trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine B."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "llqX5tSyMtkjCR6e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects found in this region [0.092, 0.154, 0.245, 0.378]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three legend items for three machines, {Machine A}<r>[0.148, 0.173, 0.238, 0.219]</r> with a orange square handle<r>[0.095, 0.182, 0.138, 0.205]</r>, {Machine B}<r>[0.148, 0.243, 0.238, 0.289]</r> with a green diamond handle<r>[0.095, 0.251, 0.138, 0.275]</r>, and {Machine C}<r>[0.148, 0.312, 0.238, 0.359]</r> with a blue square handle<r>[0.095, 0.321, 0.138, 0.344]</r>."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "5stR0d44wKY7l4FG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the content in coordinates [0.115, 0.050, 0.926, 0.117]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content here is the subtitle of the chart and says \"{Manufacturing Production Efficiency Evaluation Over Time}\"<r>[0.115, 0.050, 0.926, 0.117]</r>, which is colored black."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "QHllI5AgRZUPlRsk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within [0.084, 0.789, 0.271, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two x-axis tick labels with values '0.00' at coordinates {position}<r>[0.064, 0.821, 0.103, 0.868]</r> and '1.00' at coordinates {position}<r>[0.152, 0.821, 0.190, 0.868]</r> are within this region.."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "wrSY3mZvDkhl2Lbr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object stands at [0.507, 0.915, 0.558, 0.963] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "MpGhhkbK8vHDY4K4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.183, 0.136, 0.376, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label for Precipitation (mm)."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "DiBe4NsRKzJrE0cd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.837, 0.121] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point for the year 2010 belonging to the line representing 'Precipitation (mm)'."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "k5u8Tp6Dg3LdPdHR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the 2000 data point}[0.593, 0.841], 'Temperature Increase (\u00b0C)' or 'Sea Level Rise (cm)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither. Both are at the same level."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "VkHufm4gLGpLuIsm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {''}[0.183, 0.136, 0.376, 0.181] higher or lower than the {'Sea Level Rise (cm) label'}[0.183, 0.267, 0.388, 0.312]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "AHPM2SRN3U95IlM0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for 'Precipitation (mm)' at 2005 higher or lower than its preceding year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "B06RuLgCfopCOZky",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.116, 0.119, 0.465, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the graph. There are 3 items: {Precipitation (mm)}[0.183, 0.136, 0.376, 0.181], {Temperature Increase (\u00b0C)}[0.183, 0.202, 0.456, 0.247], and {Sea Level Rise (cm)}[0.183, 0.267, 0.388, 0.312]. Each item is accompanied by a colored symbol."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "dYYKt7cWlhN3Zv4e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items in the region [0.572, 0.866, 0.615, 0.901]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for the year 2000."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "vmtB0Zvj2Hb9Jt6J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area covering the coordinates [0.106, 0.099, 0.959, 0.310]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line data for 'Precipitation (mm)'. It covers different data points from the year 1980 to 2015."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "jGBcPJ3MSuAdupfr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item being represented inside the bounding box [0.859, 0.357, 0.933, 0.417] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the 'Steroid' item in the legend."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "4RDsdllH5kGAJNjj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the coordinate points of the y-axis label in the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates of the y-axis label are [0.019, 0.304, 0.043, 0.616]."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "OrlCorChVDZG1n84",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.970, 0.821, 0.981, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "kozRDc0nxicBdoo9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the plotted lines, is {the point for 'Steroid'}[0.593, 0.360] above or below {the point for 'Antibiotic'}[0.593, 0.503] at the same dose?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "RaNvqWl5hvb7QXcZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the dose labeled '4'}[0.466, 0.145], which treatment has a higher response, 'Antibiotic' at [0.466, 0.145] or 'Steroid' at [0.466, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both have the same response."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "ZMvUSu7gztGgPztF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Control' point at Dose_7}[0.848, 0.732] showing a higher or lower response than {the 'Steroid' point at dose_7}[0.848, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "5SJa9D3THVz7NG00",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content enclosed in [0.784, 0.157, 0.964, 0.437]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes 3 legend items: {Control with blue color}[0.859, 0.181, 0.934, 0.241], {Antibiotic with red color}[0.859, 0.269, 0.957, 0.329], and {Steroid with green color}[0.859, 0.357, 0.933, 0.417]. Each legend is paired with a colored marker--a circle, a square, and a triangle respectively--that corresponds with the color and marker style of each line plot."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "RTspqLbnOCj68Fak",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the objects located in the box [0.499, 0.887, 0.560, 0.950] in the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-label of the chart, which is 'Dose'."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "SRma25aaBmFPsXTs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the items inside the bounding box [0.333, 0.821, 0.344, 0.868] and [0.460, 0.821, 0.471, 0.868] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "These objects represent the x-axis tick labels for the chart, specifically {Dose_3}[0.333, 0.821, 0.344, 0.868] and {Dose_4}[0.460, 0.821, 0.471, 0.868]."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "r0BqPzkw8PwS1AyB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data does the tick label at [0.925, 0.893, 0.985, 0.921] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021-Q4."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "NmyuvyV4hUumBSf6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color used for {the line for 'Tour Agency'}[0.071, 0.238, 0.955, 0.436] in the plotted chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "2hAnqafiA05YMyZo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the orientation of {the label}<r>[0.015, 0.371, 0.034, 0.581]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vertical."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "JoS5b1UZMAUVebc0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the plotted lines, which industry has a higher data point for 2021-Q2, {the Hotel Industry} line[r][0.071, 0.556, 0.955, 0.754] or {the Air Travel} line[r][0.071, 0.118, 0.955, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Travel."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "7eXrF3spY6bbykdy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsider the data points for 2021-Q1, is {the Tour Agency} at [0.576, 0.357] higher or lower than {the Hotel Industry} at [0.576, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "LgW0QnR2FDOXJY6X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry at {the point}[0.198, 0.714] in the chart has lower income, Air Travel or Hotel Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hotel Industry."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "dtYC1XRPxEStFRwn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found within the coordinates [0.769, 0.095, 0.947, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart that contains the legend for the three plotted lines. It includes the labels for \"Hotel Industry\", \"Air Travel\", and \"Tour Agency\", each associated with a color, representing their corresponding lines in the chart."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "XGBQocOjIMuFVix8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.071, 0.079, 0.955, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the primary plot area of the line chart, displaying three lines, each representing a different sector in Tourism and Hospitality: {Hotel Industry}[0.071, 0.556, 0.955, 0.754], {Air Travel}[0.071, 0.118, 0.955, 0.476], {Tour Agency}[0.071, 0.238, 0.955, 0.436]. The x-axis and y-axis with their labels and ticks are also within this area."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "uSLTlaEzzcZs05Di",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetailed explanation required for the area demarcated by [0.283, 0.030, 0.743, 0.070]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the subtitle of the chart, which reads \"Tourism and Hospitality Industry Analysis\"."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "jZorgoQkTL4enrWv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description corresponding to the colored marker at the coordinates [0.610, 0.278, 0.666, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sea Level Rise (inches)."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "rmvncylMdpMa3aV0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.342, 0.282, 0.342, 0.282] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a point in the 'CO2 Emissions' line that represents the value for the year 2000."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "A7iKfgREZXxwJvHC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the title at coordinates [0.231, 0.050, 0.823, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, which is \"Climate Change Trends from 1990 to 2025\"."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "alWN9t5p8P0l23Zo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the data points for the year 2005 on the 'CO2 Emissions (ppm)' line [0.465, 0.268] and 'Temperature Change (\u00b0C)' line [0.465, 0.788]. Which one is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point on the 'Temperature Change (\u00b0C)' line is higher."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "BQwIYTQpKcet9tLk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the tick label '200' [0.050, 0.360, 0.082, 0.406] above or below the 'Temperature Change (\u00b0C)' line data point for the year 2020 [0.836, 0.787]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "bp04g42ueLlfNiTN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subtitles [0.676, 0.181, 0.899, 0.241], [0.676, 0.269, 0.916, 0.329] and [0.676, 0.357, 0.941, 0.417], which one is positioned at the top of the others?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle 'CO2 Emissions (ppm)' is positioned at the top."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "uSdwgxH4Mwvlelkf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of the region with these coordinates [0.609, 0.159, 0.949, 0.439]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend that classifies three different trends: {CO2 Emissions (ppm)}<r>[0.676, 0.181, 0.899, 0.241]</r>, {Sea Level Rise (inches)}<r>[0.676, 0.269, 0.916, 0.329]</r>, and {Temperature Change (\u00b0C)}<r>[0.676, 0.357, 0.941, 0.417]</r>, along with their respective colored markers."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "YyLbJhV1gWyMspM8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the area between [0.342, 0.282, 0.836, 0.787]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains part of three trend lines from the years 2000 to 2020: {CO2 Emissions (ppm)}<r>[0.342, 0.282, 0.836, 0.186]</r>, {Sea Level Rise (inches)}<r>[0.342, 0.784, 0.836, 0.784]</r> and {Temperature Change (\u00b0C)}<r>[0.342, 0.788, 0.836, 0.787]</r>."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "YuwvwGvoDT65b8WD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region from [0.095, 0.159, 0.959, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the area that represents the plotted information on the climate change trends from three parameters ('CO2 Emissions', 'Sea Level Rise', and 'Temperature Change') over the years from 1990 to 2025."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "v3IEFmiW7RkJKFTn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the given coordinates [0.019, 0.327, 0.043, 0.594] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label which is \"Latitude\"."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "Z6q7BGoAbOCyu7hl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.762, 0.181, 0.938, 0.241] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label of the chart's legend, denoted as \"Geographic Data\"."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "gtiwQ6AtDBuL7tjI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item can I find at these coordinates [0.467, 0.747] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a data point on the line representing Geographic Data, where Longitude is \"-0.165\" and Latitude is \"1.018\"."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "FdcJkxVB0fHma0CI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the position of {the point for Longitude '-1.934'}[0.100, 0.625] and {the point for Longitude '-1.387'}[0.222, 0.542], which point is higher on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The point for Longitude '-1.934'}[0.100, 0.625] is higher."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "7Eh5QkuBIhXLiQlY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Longitude '0.319'}[0.589, 0.768] located to the left or right of {the point for Longitude '0.921'}[0.712, 0.747]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "5z6p7BR10xzpUEZt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich geographic point is located further to the right, {the point for Longitude '1.674'}[0.834, 0.542] or {the point for Longitude '2.538'}[0.957, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for Longitude '2.538'."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "yGQWaP15MtUPyKXk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is found in this region [0.696, 0.159, 0.946, 0.263] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. The legend includes the label \"Geographic Data\" at {coordinates}[0.762, 0.181, 0.938, 0.241] with its corresponding color marker at {coordinates}[0.697, 0.190, 0.753, 0.227]."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "jtCL37vuI1cTlMCq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description for the content in the area [0.100, 0.172, 0.957, 0.768] of the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main graph of the chart. It consists of a line representing 'Geographic Data' with the plotted points for various longitudes and latitudes."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "Z3714C8XP7cKmMnv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the objects within the coordinates [0.195, 0.821, 0.495, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region accommodates two labels for x-axis ticks corresponding to the Longitude values '-1.387' at {coordinates}[0.195, 0.821, 0.250, 0.868] and '-0.165' at {coordinates}[0.439, 0.821, 0.495, 0.868]."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "DC8WQ75poMnd63el",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.851, 0.181, 0.949, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend text for 'Facebook'."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "sqrCNh7iT5lq2zUr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the object at location [0.206, 0.821, 0.217, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-tick label '1'."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "8VkULvIV7pnjy9JF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media's line does the handle with coordinates [0.786, 0.278, 0.842, 0.315] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "PqGh5BhjLtwiWeoP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the third point of the {Twitter line}<r>[0.339, 0.387]</r> higher on the y-axis than the third point of the {Facebook line}<r>[0.339, 0.239]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "DIzLkJztYAoeLaXQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the seventh points of both lines, which is higher, the {Instagram line}<r>[0.848, 0.348]</r> or the {Facebook line}<r>[0.848, 0.317]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "TwVWVdPFFO8q9coj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the first points of the {Facebook line}<r>[0.084, 0.170]</r> and the {Instagram line}<r>[0.084, 0.518]</r>, which is on the left of the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are on the same leftmost point on the x-axis."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "N0jTocx0DNHM8n5L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe the objects and their contents present in this area: [0.785, 0.159, 0.965, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend containing three social platforms: {Facebook}<r>[0.851, 0.181, 0.949, 0.241]</r> in blue, {Twitter}<r>[0.851, 0.269, 0.921, 0.329]</r> in red, and {Instagram}<r>[0.851, 0.357, 0.957, 0.417]</r> in green."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "XODhbSBS9wUkFS89",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can you gather about the area [0.318, 0.050, 0.741, 0.117] depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart named {Social Media Usage Over Time}<r>[0.318,0.050,0.741,0.117]</r> in black color."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "mpAEYmqwWa8aX9LK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observations, what can you tell me about the objects present in the area [0.084, 0.159, 0.976, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly contains the main plot including the timeline on the x-axis, percentage usage on the y-axis, and visual representation of usage stats for 3 social platforms: Facebook, Twitter, and Instagram along with their markers plotted along the timeline."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "VADdq9mOO445fZia",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within these coordinates [0.754, 0.159, 0.949, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "MlTAfy4STTQCoI5b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis label}<r>[0.019, 0.077, 0.043, 0.843]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Venture Capital Activity."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "tNhTggIiA1oO7Qd6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item in the legend located at [0.820, 0.269, 0.941, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fundraising."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "JurK6EjEdMdEu3D7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is the {point '2017'}[0.521, 0.350] a part of, Startup or Investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "B3Yq9dlXLqCtFvNQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Fundraising line}[0.084, 0.183, 0.959, 0.371] have a higher or lower value in 2019 than {the Investment line}[0.084, 0.142, 0.959, 0.277]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "mWTkwNMdPlUbkZkW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line type color is darker at {the caption}[0.754, 0.159, 0.949, 0.439], the one for Startup or the one for Investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "qsOCVk5S3Xl79XMy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the layout within [0.062, 0.821, 0.106, 0.868]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2013' is located here."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "n7E1lHFnrMTX7R9Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.223, 0.050, 0.820, 0.117] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Venture Capital Activity from 2013 to 2021,\" is placed within these coordinates."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "RvPonb10d6v7JNVZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.019, 0.077, 0.043, 0.843]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 'Venture Capital Activity' is displayed within these coordinates."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "QfeSJHU8BukmiYsj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.019, 0.092, 0.043, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "Xk20UAW1zIbKI09o",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{The '2010' x-axis label}<r>[0.051, 0.821, 0.095, 0.868]</r>, can you tell me what coordinates represent its tick on the axis grid?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.073, 0.789]"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "WMM9xKSAjhhnt4HI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information of the object drawn in the area defined by the coordinates [0.865, 0.357, 0.931, 0.417] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legened label for 'Cardio'."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "aqIunNhqvWp4IL6Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Yoga' trend line higher or lower than the 'Cycling' trend line at the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "ujTj1DfqcD7qqP4r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line among {the 'Yoga'}[0.073, 0.225, 0.959, 0.507], {the 'Cycling'}[0.073, 0.131, 0.959, 0.601], and {the 'Cardio'}[0.073, -0.104, 0.959, 0.648] trend lines reach the highest point in the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cardio."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "U2H9TfkZC3INSO1B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, is {the 'Yoga' label}[0.865, 0.181, 0.913, 0.241] above or below {the 'Cycling' label}[0.865, 0.269, 0.941, 0.329]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "KeHXSMK6ppw5CKHQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are visible in the area marked by coordinates [0.798, 0.159, 0.949, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the legend for the chart is placed. It consists of labels and icons for the categories 'Yoga', 'Cycling', and 'Cardio'."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "ISomRqqGyGBOh33x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content visible inside the coordinates [0.073, 0.131, 0.959, 0.789]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the main chart plotted with x and y axes. It displays three diverse trend lines each representing 'Yoga', 'Cycling', 'Cardio' for the years between 2010 and 2017, with year labels and tick-marks along the X-axis and numerical markers along the Y-axis."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "Al5tb4PldrUu4BkF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the coordinates [0.153, 0.050, 0.879, 0.117]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates contains the chart subtitle \"Fitness and Sports Health Trends from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "xyMrFSonWCNWtFGr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by {coordinates} at [0.019, 0.257, 0.043, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "VwM1EkZYJJBeUnO1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the item located at these [0.680, 0.357, 0.837, 0.417] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend label for 'Public Facilities'."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "gropO6oBgaK7loHx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label at [0.484, 0.887, 0.548, 0.950] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Years'."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "Nf47o2wXJAr2a5SH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2020 point of the line for 'Residential Development'}[0.579, 0.219] higher or lower than {the 2020 point of the line for 'Business Development'}[0.579, 0.394]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "E8dI8lhimlzQKUS9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Residential Development' line}[0.073, 0.219, 0.959, 0.701] end higher or lower than {the 'Business Development' line}[0.073, 0.394, 0.959, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "zpdu6ykT92aYbe4I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, 'Residential Development' or 'Business Development', has a higher value at {the '2025' x-ticks}[0.706, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential Development."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "yZmFDAzR7fcej9TG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the space located at [0.613, 0.159, 0.949, 0.439]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart legend. It contains labels for 'Residential Development' {coordinates}[0.680, 0.181, 0.941, 0.241], 'Business Development' {coordinates}[0.680, 0.269, 0.918, 0.329], and 'Public Facilities' {coordinates}[0.680, 0.357, 0.837, 0.417], each label is associated with a different color marker."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "VdN3dEmLxPpF7Nmj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area with coordinates [0.177, 0.821, 0.221, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis tick label, representing the year 2005."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "XhXdBLc9RIwwTrCg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the region [0.073, 0.219, 0.959, 0.701]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'Residential Development' line, which details the growth rate of residential development from 2000 to 2035 through a series of connected points."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "M45jn4H9UUmcCzim",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the object}<r>[0.019, -0.063, 0.043, 0.983]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trade Volume & Economic Index."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "ZFqLkZwEO3GZ66Xs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the text \"GDP Growth\"} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.800, 0.269, 0.926, 0.329]."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "zmtSVGluRoTXoQjo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-value that corresponds to {the object}<r>[0.266, 0.601]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "8oRSKGiM3WA4QiK1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at the point labeled '2010', {Trade Volume}<r>[0.405, 0.538]</r> or {GDP Growth}<r>[0.405, 0.789]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "UQKNiAsVoR2HnrxN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Trade Volume'}<r>[0.127, 0.162, 0.959, 0.648]</r> generally increasing or decreasing over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "No4G9tNWLuPEHU0L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value in 2050, {GDP Growth}<r>[0.959, 0.789]</r> or {Gini Index}<r>[0.959, 0.789]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "apgdyOihWKr8ZzVh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area <r>[0.733, 0.159, 0.949, 0.439]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the legend and contains three items: {Trade Volume}<r>[0.800, 0.181, 0.941, 0.241]</r> marked with the color '#4169e1', {GDP Growth}<r>[0.800, 0.269, 0.926, 0.329]</r> marked with the color '#dc143c', and {Gini Index}<r>[0.800, 0.357, 0.904, 0.417]</r> marked with the color '#32cd32'."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "unYp6h6z5fBM2hDJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area <r>[0.019, -0.063, 0.043, 0.983]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Trade Volume & Economic Index\" is within the mentioned region."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "QOxQtVvAuno57keg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content enclosed in the zone <r>[0.236, 0.050, 0.851, 0.117]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, \"International Trade and Globalization Trends\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "ybQQvNbyRXfJhOKE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first x-axis tick label with these coordinates [0.076, 0.893, 0.111, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "TLw5xk6OaxglrdXT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name of {the line graph}<r>[0.093, 0.611, 0.968, 0.677]</r> traced with a '--' linestyle and square styled marker?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wheat."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "fRefi0a5GNhEuNLa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of {the label}<r>[0.015, 0.054, 0.034, 0.898]</r> on the y-axis with color 'black' and rotation 90.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Production Quantity (thousands of bushels)."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "rDLqqJ51SLBnEAZQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which year did {the Corn production}<r>[0.093, 0.079, 0.968, 0.324]</r> and {the Wheat production}<r>[0.093, 0.611, 0.968, 0.677]</r> have almost the same y-axis position on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "wLaVVlYiXhM40qUy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison to {the Corn production}<r>[0.093, 0.079, 0.968, 0.324]</r>, did {the Barley Production}<r>[0.093, 0.783, 0.968, 0.815]</r> generally produce higher or lower results from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "8vboh5mvNoZZ6o6c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich crop has the production marker at the lowest position on the y-axis in 2020, {Corn}<r>[0.968, 0.079]</r>, {Wheat}<r>[0.968, 0.611]</r>, or {Barley}<r>[0.968, 0.815]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corn."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "xz819AJnF6R2DLCa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the bounding box [0.101, 0.095, 0.215, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bounding box corresponds to a legend with three entries. The first entry is for {Corn}[0.155, 0.109, 0.194, 0.145] and has a marker in '#6495ed' color. The second entry, for {Wheat}[0.155, 0.161, 0.209, 0.197], has a marker in '#f5deb3' color. And the third entry is for {Barley}[0.155, 0.214, 0.208, 0.250] with a marker in '#b8860b' color."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "6Z9e10E462DgIv4c",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide information on the area between [0.093, 0.079, 0.968, 0.324]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart includes the plot for {Corn}[0.093, 0.079, 0.968, 0.324] production from 2010 to 2020 in the food and agriculture industry, plotted as a solid line with color '#6495ed' and 'o' styled markers."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "GzKgphwJtJJBqxqs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the chart in the area with the following coordinates [0.093, 0.611, 0.968, 0.815]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area of the chart contains the graphical representation of {Barley}[0.093, 0.611, 0.968, 0.815] from 2010 to 2020 in the food and agriculture industry. The line is trace in a ':' style, the color used is '#b8860b' and '^' styled markers have been used to mark the points on the line."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "Joy0pN739QevZ3Cw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.119, 0.043, 0.801]</r> displayed in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantity (in millions)."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "ceR2WgdTpQWGwwht",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fifth tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.589, 0.789]"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "UlB1q79EURDJc2OG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend text}<r>[0.159, 0.312, 0.317, 0.359]</r> in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vegetables Export."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "jYtCNDkQ0vOglSrZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line representing {Grains Production}<r>[0.095, 0.620, 0.959, 0.699]</r> predominantly above or below the line representing {Fruits Imports}<r>[0.095, 0.577, 0.959, 0.718]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "9YiE8wNuwA7jcVE2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend exhibits a higher value at the year 2000, {Grains Production}[0.342, 0.651] or {Fruits Imports}<r>[0.342, 0.704]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fruits Imports."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "8kyEjIJH01VUPZsx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2025, is {Vegetables Export}<r>[0.959, 0.443]</r> higher or lower than {Grains Production}<r>[0.959, 0.699]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "Efbu5BNPu9iPaN5a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.258, 0.050, 0.796, 0.117]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle of the chart, \"Food and Agriculture Industry Analysis.\""
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "wRATSEPWFY7WgOLo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is included within [0.103, 0.154, 0.324, 0.378]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the legend items: {Grains Production}<r>[0.159, 0.173, 0.313, 0.219]</r>, {Fruits Imports}<r>[0.159, 0.243, 0.278, 0.289]</r>, and {Vegetables Export}<r>[0.159, 0.312, 0.317, 0.359]</r>, each paired with a distinct marker."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "EKkM3r3XqTHMhvxg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be located between [0.218, 0.641, 0.836, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes several data points from the {Grains Production}<r>[0.218, 0.641]</r> to <r>[0.836, 0.683]</r> line representing the years 1995 to 2020."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "82Dqojmdi7pC34h6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object between [0.878, 0.091, 0.932, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label 'Rural'."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "txezQcWvkUlE129t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the box defined by [0.939, 0.911, 0.981, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label '2020'."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "IFsDXYN39OOROGe2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the bounding box [0.019, 0.338, 0.043, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label \"Development (%)\" is located here."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "7ydeZQG2Tbz8faGW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering {the point in 2000 for 'Rural'}[0.073, 0.685] and {the point in 2000 for 'Urban'}[0.073, 0.563], which category had higher development in the year 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rural."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "HXaBc0p3G8XUYZWs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2010, the development rate of which area is greater, judging by {the point on the Rural line}[0.516, 0.205] and {the point on the Urban line}[0.516, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "jrUZI6IgWQNETWKT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has experienced a greater growth rate in the year 2020, when considering {the 'Rural' data point}[0.959, 0.170] vs {the 'Urban' data point}[0.959, 0.153]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "X3bjadM57mjVkLd9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide information about the objects residing within the region defined by [0.811, 0.079, 0.949, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. Two legends items are included here - {Rural}[0.878, 0.091, 0.932, 0.121] and {Urban}[0.878, 0.134, 0.941, 0.164] each with their respective color markers."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "Mdaj7ZVRs3uaEvdH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region defined by bounding box [0.273, 0.911, 0.316, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label, which represents the year '2005'."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "JZw2XF58RNDUlEkc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within region [0.049, 0.362, 0.061, 0.385]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, we can find the y-axis tick label representing the value '6'."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "FJentr9mnFq7FyA8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the signified content of {xlabel}<r>[0.398, 0.828, 0.466, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hours."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "dY1m4lnfXsJ59ab7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the handle of 'Twitter Usage'}<r>[0.754, 0.904, 0.815, 0.925]</r> have?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "epA6PBal1hc5c3lm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the numerical label at {yticks coordinate}<r>[0.025, 0.585, 0.054, 0.620]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "yUXhX9ar1fZtVGYA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on {Point 1 for Instagram Usage}<r>[0.062, 0.430]</r> and {Point 1 for Twitter Usage}<r>[0.062, 0.598]</r>, which platform is used more at hour 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "Qs8Kd9Dtgf8btet0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point 6, is {Instagram Usage}<r>[0.432, 0.329]</r> below or above {Twitter Usage}<r>[0.432, 0.413]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "sDjp4gSxXi8sqO9o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the last point of Instagram Usage}<r>[0.802, 0.279]</r> and {the last point of Twitter Usage}<r>[0.802, 0.295]</r>, which one has a higher usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "ReqombTcAaIrXBv3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area <r>[0.444, 0.885, 0.993, 0.948]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend. It has two items, {Instagram Usage}<r>[0.518, 0.899, 0.715, 0.934]</r> with a blue color marker, and {Twitter Usage}<r>[0.826, 0.899, 0.984, 0.934]</r> with an orange color marker."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "10mzVLpVtTcsujhn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in region <r>[0.062, 0.178, 0.802, 0.430]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows the line representing Instagram Usage, plotted as a sequence of datum points from hour 1 to 11."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "8TsioYKFqwMDQ6rU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region <r>[0.062, 0.211, 0.802, 0.598]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for Twitter usage over time, depicted as a series of points from hour 1 to 11."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "fJUkQjwEIG4wuz23",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {line at coordinates}<r>[0.142, 0.661, 0.234, 0.612]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a segment of the AI Trend line."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "RhNxsS5CVIfgi6yq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the {tick label}<r>[0.393, 0.879, 0.437, 0.903]</r> on X axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label is 2024."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "wlwsHyOttS4diXF4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology does the {legend item}<r>[0.415, 0.948, 0.485, 0.972]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item refers to the AI Trend."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "eyxTeIfgOSDFoTWH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology prediction, {AI Trend}<r>[0.052, 0.135, 0.959, 0.709]</r> or {VR Trend}<r>[0.052, 0.645, 0.959, 0.871]</r>, has a higher value for the year 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Trend."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "Am9ldRngjUSyaoOE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of AI Trend}<r>[0.415, 0.483]</r> in 2024 above or below {the point of VR Trend}<r>[0.415, 0.734]</r> in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "pdya4ymMCBK9TOy7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween AI Trend and VR Trend, which one starts lower in {2020}<r>[0.052, 0.709]</r> and <r>[0.052, 0.645]</r> respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "VR Trend."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "mHrrxFS4VqaHerHh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within this region <r>[0.359, 0.939, 0.652, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend with two items. The first one, {AI Trend}<r>[0.415, 0.948, 0.485, 0.972]</r>, is marked with the color '#1f77b4', and the second one, {VR Trend}<r>[0.568, 0.948, 0.645, 0.972]</r>, is marked with the color '#ff7f0e'."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "nab2IeHfiaxYOeaV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the area given by the coordinates <r>[0.052, 0.062, 0.959, 0.871]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main plot of the chart, containing the line graphs of the AI Trend and VR Trend data plotted over time."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "p7Q138QLmcGU1E6D",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you depict what's in the region <r>[0.734, 0.779, 0.869, 0.839]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region has points from the AI Trend and VR Trend lines for the year 2028. The AI Trend is plotted at a higher value compared to the VR trend for this year."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "LnfczhW6YFDPn0mK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinate {the label}<r>[0.488, 0.879, 0.532, 0.914]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "BwWZE2PYTKEjrryl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle with {the handle's coordinates}<r>[0.071, 0.128, 0.118, 0.152]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "pRy1Tur3DDrhTNcA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with the first point at {coordinates}<r>[0.061, 0.147]</r> and the last point at {coordinates}<r>[0.959, 0.858]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tesla Return%."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "PIyIfi1AjUxie65P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the line plots, does 'Tesla Return%' {line's points}<r>[0.061, 0.823], [0.959, 0.147]</r> generally have higher returns compared to 'AAPL Return%' {line's points}<r>[0.061, 0.818], [0.959, 0.768]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "jO7MLfhL40ZKRSXY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for 2016 on the 'Tesla Return%' line {point 2016}<r>[0.510, 0.805]</r> above or below 2016 point on the 'AAPL Return%' line {point 2016}<r>[0.510, 0.834]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "Gla19QqhJOiynLvu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what point does 'AAPL Return%' {points coordinates}<r>[0.847, 0.766], [0.959, 0.768]</r> surpass 'Tesla Return%' {points coordinates}<r>[0.847, 0.823], [0.959, 0.147]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "5ASVnbDR3h8GAJw9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within {region coordinates}<r>[0.125, 0.125, 0.249, 0.212]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region includes the legend labels for 'Tesla Return%' and 'AAPL Return%', and their corresponding color markers."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "pII6LZl65ApmpVtA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the area {area coordinates}<r>[0.264, 0.879, 0.308, 0.914]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label for '2014'."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "Vdpekx3kpUosf9N8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the information shown in the region {region}<r>[0.033, 0.758, 0.055, 0.793]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label '80'."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "6nlpJvM2RaJybyP0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type depicted in this figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "NvwDCbTyapfXKNTo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with color '#1f77b4' represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Count."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "YAiXAvy7HcRoPky3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of {the point labeled '2020'} for the 'innovation_index' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.946, 0.142]."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "uD8Gf8rpcodymoMF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the point labeled '2018'}[0.769, 0.190], is the value of 'innovation_index' lower or higher than the value of 'startup_count'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "HMZPbeBczF87Z7ms",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2010'} for the 'startup_count' line[0.062, 0.866] above or below {the point labeled '2010'} for the 'innovation_index' line[0.062, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "viw3eiBe7enFreYI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'innovation_index' line generally ascending or descending between {the point labeled '2012'}[0.239, 0.364] and {the point labeled '2020'}[0.946, 0.142]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascending."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "yIq9fUGZaAgGoHeV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects found in [0.062, 0.866, 0.946, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A line graph labeled 'startup_count' is present here with points spread across the years from 2010 to 2020, each connected by a blue colored line."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "xAz4oMYY9WzEbSdi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.062, 0.142, 0.946, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Another line graph, labeled 'innovation_index', is found within this region. It also has points spread across the years from 2010 to 2020, connected by an orange colored line."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "drG28iopQP1VbsMF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located at [0.197, 0.037, 0.811, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, 'Entrepreneurship and Innovation Ecosystem', is positioned in this region."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "4xXrvLDPV8TnTNPf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.015, 0.305, 0.029, 0.660] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "jvpBy1bP8nizWnpy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.124, 0.118, 0.936, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Workplace Development Trend graph line."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "FTNoboKk5W0gASoM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis tick label is located at coordinates [0.346, 0.919, 0.390, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the 'Senior' label."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "9FJ4eB38M8Iavc4H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point 'Management'}[0.449, 0.728] above or below the {point 'Senior'}[0.368, 0.679] on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "Iiqlnh849cByaSOD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 'Intermediate'}[0.206, 0.756] located to the left or right of the {point 'Experienced'}[0.287, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "aPCwXONJr4VEP1xK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point 'Health'}[0.692, 0.118] on the trend line have a higher or lower value than the {point 'Novice'}[0.124, 0.833] in the Workplace Development Trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "DQdYT5xwuynzTesJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.298, 0.025, 0.762, 0.055] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart, which is \"Workplace Development across Different Career Stages.\""
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "E0ffM8RHQuSXdTfT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.706, 0.074, 0.969, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend item, {Workplace Development Trend}[0.751, 0.083, 0.964, 0.106], with a color marker of blue at {coordinates}[0.708, 0.086, 0.744, 0.100]."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "qVTTza5U1s4b5eX5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region defined by [0.081, 0.053, 0.216, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the Y-axis and includes respective tick labels and marks. For instance, the label '0.004' at {coordinates}[0.035, 0.894, 0.074, 0.917] and '0.006' at {coordinates}[0.035, 0.754, 0.074, 0.777]."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "iKLBEpCZ4uVKT9qf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label with coordinates [0.015, 0.358, 0.029, 0.608] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label representing \"Energy Allocation (%)\"."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "AZakQJcnzJu98jES",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of x-axis tick with coordinates [0.407, 0.919, 0.442, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"2024\"."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "glnwUcfl7CNb7jDF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line plot {Biofuels}\\<r>[0.062, 0.716, 0.968, 0.903]\\</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the line plot is blue (#0000ff)."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "lLFn1JIOY9T4ak4t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for Hydroelectric at {Year 2021}<r>[0.152, 0.118]\\</r> higher or lower than the data point for Biofuels at {Year 2021}<r>[0.152, 0.753]\\</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "B85WodN2Wd51CGue",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn Year 2020, is the {Energy Allocation} for Biofuels <r>[0.035, 0.707, 0.052, 0.730]\\</r> less than that for Hydroelectric <r>[0.062, 0.156]\\</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "uxiI2oqOsl2Dd0g2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the energy allocation for {Biofuels}<r>[0.862, 0.083, 0.917, 0.106]\\</r> decrease or increase from 2020 to 2030 <r>[0.062, 0.716, 0.968, 0.903]\\</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreases."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "C1KVeoRDip2IaB3f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.818, 0.074, 0.961, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the graph. It includes the labels for Biofuels and Hydroelectric, each associated with a color marker of blue and green, respectively."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "52vmLNqTORG5XS1b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you elaborate what's inside [0.500, 0.952, 0.529, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label, which is \"Year\"."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "xKbS8oeUpxrsw4jN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sort of content lies within the area [0.354, 0.025, 0.675, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Future Energy and Resource Allocation\" is located within this area."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "kFulXmjFt72o4W2U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.025, 0.381, 0.048, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "RoDEYF04UtzlyyJL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.339, 0.879, 0.541, 0.914] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend for GDP Growth Rate."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "rYEIMSGGms50wixQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.916, 0.761, 0.975, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "puWGXv5ZyCVOBNp4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the GDP Growth Rate} at point [0.622, 0.144] higher or lower than {the Inflation Rate} at point [0.622, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "oZpKZTUZLLzeMgsy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Inflation Rate} at [0.784, 0.213] above or below {the point for GDP Growth Rate} at [0.784, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "Mv8cRTiybc7U2D53",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich rate is higher at the year '2014', GDP Growth Rate or Inflation Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "EfcFfpCusnfrmDLd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.265, 0.865, 0.817, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart describing two items: {GDP Growth Rate}[0.339, 0.879, 0.541, 0.914] with a blue color marker, and {Inflation Rate}[0.652, 0.879, 0.807, 0.914] with an orange color marker."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "uQKpiU7pmEWxIp9T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is present in the area [0.136, 0.093, 0.946, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the main plot of the chart displaying two line graphs. The first one represents the {GDP Growth Rate}[0.136, 0.110, 0.946, 0.695] and the second line represents the {Inflation Rate}[0.136, 0.196, 0.946, 0.244]."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "crzmJAABFDATXisX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained within [0.290, 0.037, 0.792, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, labeled \"Global Financial System and Policies\", is located at this region."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "TjijUiS75WQz9HLK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.016, 0.421, 0.016, 0.421] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "qGEo5NT0XGhgF0Zh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label '6' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.614, 0.761, 0.629, 0.796]."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "3bAlL1hbCN6Ry2l4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.016, 0.421, 0.016, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's empty, no label is given."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "GO67oXOJUWOP9YM0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Layer 2'}[0.283, 0.281] for 'Social Responsibility' above or below {the point for 'Layer 2'}[0.283, 0.636] for 'Sustainable Business'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "JqsXtl60Nr4fmX1l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Layer 5'}[0.537, 0.327] for 'Social Responsibility' to the left or right of {the point for 'Layer 5'}[0.537, 0.674] for 'Sustainable Business'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they align vertically."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "T6SbayG2tHxjgWhf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {Layer 6}[0.622, 0.356] for 'Social Responsibility' or {Layer 6}[0.622, 0.636] for 'Sustainable Business'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Responsibility."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "JSbT2O323QT1fpp5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.195, 0.865, 0.879, 0.928]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the lines representing 'Social Responsibility' at {coordinates}[0.198, 0.884, 0.258, 0.905] and 'Sustainable Business' at {coordinates}[0.549, 0.884, 0.609, 0.905], each with a corresponding color marker."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "j4nRa1Yn4YHJW2wZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.154, 0.037, 0.920, 0.083]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart: 'Social Responsibility & Sustainable Business Over Time', in black color."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "OxGDvmJhP91x5G3X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.945, 0.761, 0.975, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '10' is located here."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "aC0oEfidb84wJlRG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the x-axis tick} at [0.839, 0.763, 0.865, 0.791]?"
+            },
+            {
+                "from": "gpt",
+                "value": "16."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "4k0NWlW5a1pEVP89",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of line is represented by the {coordinates} [0.113, 0.195, 0.966, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity-Level."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "swzJpXGACaqJ5y5l",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-axis tick} at [0.050, 0.214, 0.106, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.070."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "3FnXIlC9GlyFgAZf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Creativity-Level} at [0.852, 0.451] higher or lower than {Design-Innovation-Level} at [0.852, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "4f9xOKBcn8jDSBTK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Creativity-Level point} at [0.625, 0.376] above or below the {Design-Innovation-Level point} at [0.625, 0.617]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "guOtdkk2fiCYMQQ9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich level is higher at Layer 4, {Creativity-Level} at [0.170, 0.542] or {Design-Innovation-Level} at [0.170, 0.708]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity-Level."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "bYnsQ2D04nDw9elh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.498, 0.763, 0.523, 0.791]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a tick label '10' on the x-axis."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "P8kNA0E7lprxKnsM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.322, 0.903, 0.477, 0.931] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text 'Creativity-Level', which is part of the chart legend and is associated with a line on the chart."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "HcAVVga6xzzHlZlE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the area [0.454, 0.557, 0.454, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area indicates the 9th points on both Creativity-Level and Design-Innovation-Level lines."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "zixm2qnQ8N6zAvn3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.483, 0.927, 0.548, 0.963]</r> indicating?"
+            },
+            {
+                "from": "gpt",
+                "value": "Layer."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "rhrEYHQyEOHQLoeh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title of the chart}<r>[0.259, 0.037, 0.772, 0.083]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Inequality vs Rights Protection."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "ieJQSfAq7iEgKPT5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of {the point}<r>[0.062, 0.577]</r> on the 'Social Inequality' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates that the Social Inequality at Layer 1 is 5.0."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "ac5A9vyvSX5ZlAeL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Layer 6}<r>[0.515, 0.721]</r>, is the Social Inequality score higher or lower than the Rights Protection score <r>[0.515, 0.287]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "sAVAJuFh1LO7pljW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing Layer 8 in the Rights Protection trend}<r>[0.696, 0.190]</r> above or below {the point representing Layer 8 in the Social Inequality trend}<r>[0.696, 0.789]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "1J7wEwFxH1IeAxp7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater at {Layer 2}, the Social Inequality score <r>[0.153, 0.596]</r> or the Rights Protection score <r>[0.153, 0.441]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rights Protection."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "PF53HSCO6b0z2JL5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within this area <r>[0.244, 0.625, 0.334, 0.673]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area creates a segment of the 'Social Inequality' trend line going from Layer 3 at the value of 4.5< r>[0.244, 0.625]</r> to Layer 4 at the value of 4.0< r>[0.334, 0.673]</r>."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "SuAt0UkbFy2A019S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content inside these coordinates <r>[0.224, 0.864, 0.807, 0.926]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend items: {Social Inequality}<r>[0.226, 0.882, 0.286, 0.903]</r> and {Rights Protection}<r>[0.528, 0.882, 0.588, 0.903]</r>, each with their corresponding color marker."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "coU35na1vXto4262",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease define the content in <r>[0.600, 0.878, 0.798, 0.913]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text label {Rights Protection}<r>[0.600, 0.878, 0.798, 0.913]</r>, which is one of the line labels in the plot's legend."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "1JHAeh11ayhhvNtM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.025, 0.419, 0.048, 0.541] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "2CKUwygPlJCuJ9ho",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found in these coordinates [0.594, 0.734, 0.934, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot legend."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "uD0nVHHdIwO8YB5E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on provided coordinates, what is indicated by [0.073, 0.879, 0.132, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label for the year 1980."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "hbgClWThzwDXFPeW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAround year 2030, between {Inequality Index line}<r>[0.946, 0.535]</r> and {Right Protection Index line}<r>[0.946, 0.149]</r>, which index has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inequality Index."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "NxYl6agvuZj9aKWH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the point}<r>[0.777, 0.248]</r>, is the Right Protection Index higher or lower compared to the {previous point}<r>[0.693, 0.270]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "wN9VjwgEDD7g3CJN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates of each point in 2000 for {Inequality Index line}<r>[0.440, 0.712]</r> and {Right Protection Index line}<r>[0.440, 0.336]</r>, which index has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inequality Index."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "wA0KTiG52RB6kPN2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description for the details within coordinates [0.594, 0.734, 0.934, 0.849]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the subplot. It lists two items: {Inequality Index}<r>[0.669, 0.748, 0.854, 0.783]</r> in blue and {Right Protection Index}<r>[0.669, 0.800, 0.925, 0.835]</r> in orange."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "lmnQ8ojcZtSLlXEt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within [0.411, 0.879, 0.469, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis tick label for the year '2000'."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "UoVTTdqjTmqMKrAe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section at [0.102, 0.535, 0.946, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section presents the line for the Inequality Index, with data points represented by blue circles marking the index values from the years 1980 to 2030."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "a4d6zUkGbVEym9Na",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at [0.019, 0.400, 0.036, 0.573] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Freight Volume."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "Tid639GOKranhEPg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the object at [0.093, 0.835, 0.138, 0.849] represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color marker for E-commerce Freight Volume in the legend."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "wex10NwJjpUNRfSV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the label of the x-axis tick mark at [0.586, 0.919, 0.631, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "h09YLsq0lixnaeDh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2016, which value is higher, {E-commerce Freight Volume}[0.609, 0.422] or {Heavy Industrial Transport Volume}[0.609, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heavy Industrial Transport Volume."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "QTheLto0CePGaqeb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year {2018}[0.762, 0.919, 0.806, 0.942], is the {E-commerce Freight Volume}[0.784, 0.252] higher or lower than {Heavy Industrial Transport Volume}[0.784, 0.661]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "a4VJZ7PP3g9LdhHr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the first point of the {E-commerce Freight Volume}[0.082, 0.861] and the last point of the {Heavy Industrial Transport Volume}[0.959, 0.711], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce Freight Volume."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "pCXaSdeNPh4zbDA2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area enclosed by the coordinates [0.091, 0.823, 0.452, 0.899]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart. It includes two entries: {E-commerce Freight Volume}[0.146, 0.832, 0.390, 0.855] and {Heavy Industrial Transport Volume}[0.146, 0.867, 0.445, 0.890], with their respective handle color markers at [0.093, 0.835, 0.138, 0.849] and [0.093, 0.870, 0.138, 0.884]."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "ngRJbOjFLE0MJhu1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area [0.292, 0.025, 0.750, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, the chart's subheading \"Transportation and Logistics Industry Trends\" is found."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "1xVLENAApIGusbI7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area defined by [0.082, 0.062, 0.959, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large area includes all of the plot's content. This includes the x and y axises, the plot title, all of the data points and lines corresponding to {E-commerce Freight Volume} and {Heavy Industrial Transport Volume}, as well as the legend."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "EgABpdQ28vmRERpP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at this coordinate range in the chart: [0.362, 0.919, 0.444, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for 'FMCG Sales' in the legend."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "41dIOMME9Za8h4rY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis described by this bounding box [0.015, 0.378, 0.029, 0.440]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Sales'."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "idwWRlMa8DnGDEKk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the x-axis tick at the coordinate [0.075, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '2000'."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "bjXyk5yjXstpFEZT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sales trend, {FMCG Sales}[0.075, 0.302, 0.967, 0.724] or {Consumer Discretionary Sales}[0.075, 0.113, 0.967, 0.362], experienced a larger decline from 2000 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "FMCG Sales."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "aAlnkiheTplL3yVJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the values for the year 2008, {FMCG Sales}[0.670, 0.488] or {Consumer Discretionary Sales}[0.670, 0.210], which one was higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consumer Discretionary Sales."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "6u87nzqofqWe2vom",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {FMCG Sales}[0.075, 0.302, 0.967, 0.724] generally higher or lower than {Consumer Discretionary Sales}[0.075, 0.113, 0.967, 0.362] throughout the years 2000 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "eFBmy8AYagPqSUeJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the following region: [0.317, 0.910, 0.725, 0.952]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. It includes labels for 'FMCG Sales' and 'Consumer Discretionary Sales', each paired with a color marker."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "TvVxScybf64jVhik",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area bounded by [0.075, 0.062, 0.967, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot of the chart, including two lines representing 'FMCG Sales' and 'Consumer Discretionary Sales', as well as the axes with their corresponding labels and ticks."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "DMOVH35x8q9VXv05",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be found in the area [0.510, 0.919, 0.719, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend item for 'Consumer Discretionary Sales', paired with a colored marker."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "1t2t4o3VOoIJVHMf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the chart corresponds to the Charity Fund?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The blue line}<r>[0.093, 0.108, 0.959, 0.447]</r>"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "3zDTD5SQtNVx7dx5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker for {Direct Aid}<r>[0.551, 0.923, 0.596, 0.937]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "tQ9xubcMOvXL8uhu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {the point}[0.286, 0.293] on the Charity Fund line?"
+            },
+            {
+                "from": "gpt",
+                "value": "7000"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "PF6Ys671fQszg3cZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2011 and 2020, which fund had more money in 2017, the Charity Fund or Direct Aid?"
+            },
+            {
+                "from": "gpt",
+                "value": "Charity Fund."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "bnJOndRp8kjnKd8l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Charity Fund line}<r>[0.093, 0.108, 0.959, 0.447]</r> higher or lower than the {Direct Aid line}<r>[0.093, 0.170, 0.959, 0.709]</r> in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "bt8hmr3U2kcVl0Uy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fund was allocating more money in 2014, the Charity Fund or Direct Aid?"
+            },
+            {
+                "from": "gpt",
+                "value": "Charity Fund."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "vMfR8JIKLADWE80b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in this region [0.355, 0.910, 0.697, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the chart legend. It includes two entries: {Charity Fund}<r>[0.411, 0.919, 0.521, 0.943]</r> represented by a blue marker and {Direct Aid}<r>[0.605, 0.919, 0.690, 0.943]</r> represented by an orange marker."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "W8XCeLhcpVbjEFWL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.093, 0.062, 0.959, 0.756]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot of the chart, including the X and Y axis, the line graphs representing the Charity Fund and Direct Aid, and the axis tickmarks for years and amount of money."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "sW2F7zK4G2EMTssK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.819, 0.123, 0.884, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the last two points of each line chart for the year 2020."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "fvfzDOVMqB80r6XU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.017, 0.328, 0.032, 0.500] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label, indicating \"User Growth\"."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "qwpFJSrPt6C2h3La",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item is located at [0.478, 0.903, 0.578, 0.931] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of \"Twitter Users\"."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "xfTlzk4urADcNfGA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the text {the x-axis tick label} at [0.800, 0.763, 0.839, 0.791]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2018."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "5MYpb62QCzZ2np11",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Twitter Users}<r>[0.243, 0.210, 0.964, 0.482]</r> growth line above or below the {Facebook Users}<r>[0.243, 0.256, 0.964, 0.617]</r> growth line at the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "MWnypDbmh8M1hiYE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat growth line is at higher value at {year 2019}<r>[0.892, 0.753]</r>, Twitter Users or Facebook Users?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter Users."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "U1eqKQIugRe5mRqi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2015}<r>[0.603, 0.753]</r>, does the line for Twitter Users or Facebook Users project a higher user growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter Users."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "9QqlJWSubgkTK4nZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area between [0.429, 0.892, 0.778, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, with labels and symbols for {Twitter Users}<r>[0.478, 0.903, 0.578, 0.931]</r> and {Facebook Users}<r>[0.652, 0.903, 0.772, 0.931]</r>. A color symbol is assigned to each user group."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "phUDRCiglDrmU5zP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found in the area [0.243, 0.075, 0.964, 0.753]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily includes the main plot of the chart. It contains two line plots representing the growth of Twitter and Facebook users. It also contains the Y-axis tick labels and the X-axis tick labels for each year from 2010 to 2020."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "wbFMNHNY9iaO0JCU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region from [0.485, 0.030, 0.722, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's subtitle: \"Social Media User Growth\"."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "Gj8BTlibaSGGWWRl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located within these coordinates [0.025, 0.311, 0.048, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "bQlVsvb5tMVKTm1d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.278, 0.884, 0.338, 0.905] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color handle for {Recursion Slice}<r>[0.278, 0.884, 0.338, 0.905]</r>."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "K19sy6EfGdf8Vmj1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is linked with the coordinates [0.946, 0.761, 0.975, 0.796] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {12}<r>[0.946, 0.761, 0.975, 0.796]</r>."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "qzJWB3nQJ2V0ZGzY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {point 12 of Recursion Slice}<r>[0.960, 0.146]</r> with {point 12 of Chained List}<r>[0.960, 0.251]</r>, which algorithm performed better in the 12th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recursion Slice."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "RL9FmiMP91iBWwS9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 1}<r>[0.103, 0.723]</r>, did Chained List outperform Recursion Slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "Jyxu8qSFWotppx6a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat performance was greater at {the 6th month point}<r>[0.492, 0.500]</r>, Recursion Slice or Chained List?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recursion Slice."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "w8Pc7dYW6lv2JPbu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the objects located around these coordinates [0.275, 0.865, 0.788, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the chart legend. Two legends are present: {Recursion Slice}<r>[0.349, 0.879, 0.524, 0.914]</r> with the color blue, and {Chained List}<r>[0.635, 0.879, 0.778, 0.914]</r> with the color orange, each associated with a color handle indicating their respective lines."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "y26RZPCv8i71CptU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area within the coordinates [0.349, 0.879, 0.524, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item labeled {Recursion Slice}<r>[0.349, 0.879, 0.524, 0.914]</r>."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "kU80pj4OLnAmiT4E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.635, 0.879, 0.778, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a legend item here labeled as {Chained List}<r>[0.635, 0.879, 0.778, 0.914]</r>."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "HymW4VOWMz7SMvNY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label}<r>[0.016, 0.154, 0.016, 0.154]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no text for the Y-axis label."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "6nAj6Q6C95G8GMUf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is presented at this coordinate <r>[0.302, 0.037, 0.750, 0.083]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Indicator Presentation."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "9dlsVPBYkfMOEol4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fourth tick label on the X-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label is 2016}<r>[0.581, 0.227, 0.639, 0.262]</r>."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "97yI3KNN5hpeKCDG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the GDP Growth Rate and Inflation Rate, which indicator had higher value in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "{GDP Growth Rate}<r>[0.526, 0.123]</r> was higher than {Inflation Rate}<r>[0.526, 0.177]</r>."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "IlcwUmQqAC0JBrG6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the line datapoints, is the value of Inflation Rate in year 2012 lesser or greater than its value in 2019."
+            },
+            {
+                "from": "gpt",
+                "value": "The value in 2012<r>[0.274, 0.142]</r> was greater than in 2019<r>[0.862, 0.175]</r>."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "CXrNxqy5h3tt0DX6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the performance of GDP Growth Rate between 2018 and 2020. Which year had a better performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The performance in 2018<r>[0.778, 0.161]</r> was better than 2020<r>[0.946, 0.332]</r>."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "N2zlz475kFGsYqqr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe any gui elements at the area <r>[0.250, 0.171, 0.802, 0.234]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend area and it has two entries: {Inflation Rate}<r>[0.324, 0.185, 0.480, 0.220]</r> with a blue marker and {GDP Growth Rate}<r>[0.591, 0.185, 0.793, 0.220]</r> with an orange marker."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "XhVO2WKu6a63I74C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the content placed within the coordinates <r>[0.502, 0.276, 0.551, 0.311]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Year' is positioned at these coordinates, indicating the x-axis label."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "z3N20RxNTJxoZs4X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinates <r>[0.025, 0.080, 0.098, 0.115]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object. The Y-axis tick label '3.50%' is located at these coordinates."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "fn9yw6FFOchDxBy4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.723, 0.125, 0.966, 0.160] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for 'Government Support'."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "NmTgWwzWGdhHWWIb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinate [0.454, 0.879, 0.484, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '16' on the x-axis."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "Tya57yIEuq1865lU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at {the position}<r>[0.651, 0.130, 0.711, 0.150]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color marker for 'Government Support' in the legend."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "TnP8iPWNwnGzXnVT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point for 'Government Support'}<r>[0.133, 0.564]</r> above or below the {data point for 'Private Investment'}<r>[0.133, 0.557]</r> in Grade 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "lQqxIH9BVX4Rr6mY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the {x-value}<r>14</r>, is the {y-value for 'Government support'}<r>[0.357, 0.654]</r> higher or lower than the {y-value for 'Private Investment'}<r>[0.357, 0.422]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "6mzxlS85Fs2kG2ej",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Government Support' and 'Private Investment', which has a higher value at {x-value}<r>19</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Private Investment'."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "Hs7lW6pkotlQYKLu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the range [0.649, 0.111, 0.975, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the legend items for 'Government Support' {located in the box}<r>[0.723, 0.125, 0.966, 0.160]</r> and 'Private Investment' {located in the box}<r>[0.723, 0.177, 0.942, 0.212]</r>, each associated with a respective color handle, the 'Private Investment' color handle {is at}<r>[0.651, 0.182, 0.711, 0.203]</r> and the 'Government Support' color handle {is at}<r>[0.651, 0.130, 0.711, 0.150]</r>."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "uXppcs3UfPAfvGxd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the coordinates [0.077, 0.544, 0.637, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region traces the path of the 'Government Support' line plot with multiple intersecting points, including such {points}<r>[0.077, 0.544]</r> and {points}<r>[0.637, 0.834]</r>."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "gqifnzYx6U9RnAeL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we see in the region defined by [0.077, 0.126, 0.637, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the 'Private Investment' line plot extending from {grade 9}<r>[0.077, 0.577]</r> to {grade 19}<r>[0.637, 0.126]</r>."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "x2NMwFfH0Lt80AWJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {this area}<r>[0.252, 0.025, 0.788, 0.055]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Support vs. Military Expenditure (2005-2015)"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "jrIVgV5wED7dnV9H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in {this point}<r>[0.702, 0.392]</r> of the Public Support line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2012 with Public Support value at 0.61."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "OcKb9sKt6bGbLHbW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the series labeled {Military Expenditure}<r>[0.507, 0.923, 0.552, 0.937]</r> in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "gTrK79wvPyrDbMoc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two lines, {Public Support}<r>[0.063, 0.392, 0.976, 0.582]</r>, or {Military Expenditure}<r>[0.063, 0.149, 0.976, 0.669]</r>, shows a higher measurement at the year 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Support"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "d3Lf7dya9zu0jB8o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Public Support line point}<r>[0.793, 0.513]</r> in 2013 above or below the {Military Expenditure line point}<r>[0.793, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "OpbqmytcNFPNQ3vx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 2007 {point}<r>[0.246, 0.426]</r> on the Public Support line higher or lower than the 2007 point on the Military Expenditure line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "AUYndPUKXkXj5UEu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in {this region}<r>[0.501, 0.796, 0.538, 0.820]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Year\" on the x-axis is shown in this region."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "QrA2IRaHsbSPlpb8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details for {this section}<r>[0.296, 0.910, 0.743, 0.952]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section comprises the chart legend, including {Public Support}<r>[0.352, 0.919, 0.477, 0.943]</r> denoted in blue color, and {Military Expenditure}<r>[0.561, 0.919, 0.736, 0.943]</r> depicted in orange color."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "9Vz0XbUT0rVEYbrZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within {these coordinates}<r>[0.063, 0.392, 0.976, 0.582]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the line representing the Public Support data from the years 2005 to 2015."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "kDq76nseQfVjLCqJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart according to the coordinates you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Ecological Environment Sustainability Assessment\" <r>[-0.255, 0.025, 0.444, 0.072]</r>."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "eGqkByKmc25y7ypY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many lines are there in the chart? Could you tell me the color of the first line?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 lines in the chart. The color of the first line is black."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "vuxaYFeJPJFJE0YA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis} label<r>[0.015, 0.382, 0.035, 0.578]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label represents the Magnitude."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "s5D7XZ4wz4p7GMWQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Human Population Growth' line}<r>[0.088, 0.863, 0.101, 0.871]</r> rise or fall across the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rises."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "UrayO8Gfhnv7p38Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Deforestation rate' line}<r>[0.088, 0.750, 0.101, 0.786]</r> above or below {the 'Invention of Sustainable Technologies' line}<r>[0.088, 0.864, 0.101, 0.881]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "gbCVReyr7LzXiGFU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'CO2 in atmosphere'}<r>[0.088, 0.156, 0.091, 0.156]</r> and {the 'Polar Ice Caps Melting Rate'}<r>[0.088, 0.868, 0.101, 0.873]</r>, which has a steeper slope?"
+            },
+            {
+                "from": "gpt",
+                "value": "Polar Ice Caps Melting Rate."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "PLFg26drkWpTVB3d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.096, 0.818, 1.746, 0.871]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area with 5 items, including the items CO2 in atmosphere, Deforestation rate, Polar Ice Caps Melting Rate, Human Population Growth, and Invention of Sustainable Technologies, each paired with a different colored marker."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "r0eOt6iDW0FeF4by",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area <r>[0.041, 0.423, 0.078, 0.454]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '250' is located in this area."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "Y9R6AOry3TjBBp2h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects within <r>[0.068, 0.901, 0.088, 0.933]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These objects are x-axis tick labels '3', '10', '15', '30' and '40'."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "DZtew3RbsHnFllKP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}[0.025, 0.199, 0.058, 0.620]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "5WUYKxnXgMRQ0bxO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of {the text labeled 'Music'} in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.572, 0.571, 0.651, 0.631]"
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "x7ChKMuPhojf8Z9H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}[0.068, 0.398, 0.119, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "NDWcwPTChSyDNqFs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is {the line for 'Theatre'}[0.164, 0.249, 0.733, 0.379] generally higher or lower than {the line for 'Music'}[0.164, 0.336, 0.733, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "uVAm51HiUj9L0hux",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point in 1990 for 'Photography'}[0.164, 0.379] higher or lower than {the point in 2010 for 'Photography'}[0.733, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "93CsCG1Vs0Bp71Bv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the year 2010 tick}[0.733, 0.769], 'Theatre' or 'Music'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "O9pWUkF1J9Up2Yjr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the region [0.149, 0.549, 1.124, 0.741]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, describing all the different lines represented in the chart. It includes the labels {GPT-Neo}[0.238, 0.571, 0.356, 0.631], {Theatre}[0.238, 0.659, 0.345, 0.719], {Music}[0.572, 0.571, 0.651, 0.631], {Visual Arts}[0.572, 0.659, 0.719, 0.719], {Photography}[0.936, 0.571, 1.112, 0.631], and {Dance}[0.936, 0.659, 1.024, 0.719]."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "Yzikb2VlyyaBYUsV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.164, 0.163, 0.733, 0.466]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes several line plots representing different art categories' growth. The lines included are {Theatre}[0.164, 0.249, 0.733, 0.379], {Music}[0.164, 0.249, 0.733, 0.336], {Visual Arts}[0.164, 0.163, 0.733, 0.206], {Photography}[0.164, 0.379, 0.733, 0.466] and {Dance}[0.164, 0.423, 0.733, 0.509]."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "WZZ8LkBbzrkBNwUl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.123, 0.801, 0.774, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the labels for the x axis ticks, which are the '1990' and '2010' year markers, respectively located at {coordinates}[0.123, 0.801, 0.205, 0.865] and {coordinates}[0.692, 0.801, 0.774, 0.865]."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "jboRuU4UXz5ZfQPI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.050, 0.535, 0.083, 0.565] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label 1.6."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "xYQwC5YAKP61PMmU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {line_1}[0.135, 0.334, 0.941, 0.589]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aviation."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "4KgLJFhn0yqkxCvo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the text}[0.815, 0.831, 0.867, 0.861] located in [0.752, 0.838, 0.804, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Road."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "gVcONuLeSrExin88",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Shipping'}[0.135, 0.122, 0.941, 0.334] have higher or lower Growth Rate than {the line for 'Road'}[0.135, 0.674, 0.941, 0.844] by the end of quarter 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "ilWF1Kf9CVzDLWgC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the third point on the 'Aviation' line}[0.672, 0.334] above or below the {third point on the 'Shipping' line}[0.672, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "H2o9RaPFoDKi0WNL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter had higher Growth Rate for {Aviation}, the {second quarter}[0.404, 0.461] or the {fourth quarter}[0.941, 0.504]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fourth quarter."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "7mXp1UQfIzcnxqGC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region at [0.815, 0.656, 0.906, 0.817]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the label for 'Shipping' with its respective color marker, and the label for 'Road' with its color marker."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "g6C5OGXOCo3URdlo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.050, 0.195, 0.083, 0.395]."
+            },
+            {
+                "from": "gpt",
+                "value": "These are the y-axis tick labels '2.0' and '2.4'."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "QkuLZS056FXtacHh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.135, 0.334, 0.941, 0.589]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are the line plotted for 'Aviation' and its corresponding point markers."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "zm2uM01oxHwVSULw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is displayed with the dimensions {figure size}<r>[10.0, 5.0]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "iZdDa4NoqulvboAz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line labeled {Facebook}<r>[0.119, 0.055, 0.185, 0.083]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "cOgvbspqFWvRCGiF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate corresponding to {the line for 'LinkedIn'}<r>[0.108, 0.406, 0.919, 0.833]</r> at the 'Active Users in Billions' point?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.833."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "HE4wpa2l8xryuvGr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Facebook}<r>[0.108, 0.551, 0.919, 0.776]</r> and {Twitter}<r>[0.108, 0.334, 0.919, 0.824]</r>, which one has the higher value at 'Growth Rate in %'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "T6VTCxYFvoRtB7Sm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate of {LinkedIn}<r>[0.919, 0.406]</r> higher or lower than that of {Instagram}<r>[0.919, 0.068]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "L6CiBpnW3PqiQZYZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has a higher number of 'Active Users in Billions', {Facebook}<r>[0.108, 0.776]</r> or {Instagram}<r>[0.108, 0.809]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "1Rd4FqThSkPj42qL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the details in the box {coordinates}<r>[0.068, 0.030, 0.960, 0.871]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's where the plot occurs. This area includes the x and y axes, with gridlines, data lines labeled as 'Facebook', 'Twitter', 'Instagram' and 'LinkedIn', as well as the x-axis labels 'Active Users in billions' and 'Growth Rate in %', and y-axis labels ranging from -5 to 35."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "R9nwgq0Qql6mXx9Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is included in the legend box with {coordinates}<r>[0.075, 0.044, 0.196, 0.220]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend contains 4 items. These items are the labels for each dataset: 'Facebook' with a red marker, 'Twitter' with a blue marker, 'Instagram' with a green marker, and 'LinkedIn' with a yellow marker."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "O6QngqmF2GwD5mqU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with these {coordinates}<r>[0.450, 0.930, 0.577, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label, which reads 'Parameters'."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "C8E52gd1pITqXQ9J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'Milk' text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.860, 0.396, 0.916, 0.456]"
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "PBmAgad8di2Lr2a0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.025, 0.022, 0.058, 0.797]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of units sold."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "3cAOOiYueLWk88Gi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.917, 0.801, 0.958, 0.865]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "18."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "BbuE8YrVI18HDbyQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Milk' sales line}<r>[0.183, 0.267, 0.937, 0.334]</r> above or below {the 'Water' sales line}<r>[0.183, 0.167, 0.937, 0.612]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "4wtHyrRWQlb4hcxZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 18}[0.937, 0.334], is 'Coffee' sales higher or lower than 'Milk' sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "wf7w3qPZwFhT0pjs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Tea' sales line}<r>[0.183, 0.201, 0.937, 0.468]</r> and {the 'Juice' sales line}<r>[0.183, 0.100, 0.937, 0.602]</r>, which one generally has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Juice."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "GkGPJvgUhvOr9LzK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.771, 0.286, 0.961, 0.741]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend for the chart with 5 entries: {Coffee}<r>[0.860, 0.308, 0.950, 0.368]</r>, {Milk}<r>[0.860, 0.396, 0.916, 0.456]</r>, {Tea}<r>[0.860, 0.484, 0.906, 0.544]</r>, {Juice}<r>[0.860, 0.571, 0.926, 0.631]</r>, and {Water}<r>[0.860, 0.659, 0.942, 0.719]</r>. Each label is accompanied by a color handle situated from [0.775, 0.321, 0.845, 0.349] to [0.775, 0.672, 0.845, 0.700], each in a unique color."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "rf3rvmHyEx1INtBO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.146, 0.050, 0.975, 0.769]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart, featuring a white background and gridlines in light grey. There are five line plots representing sales data for five different items: Coffee, Milk, Tea, Juice, and Water at two time points (6 and 18). The area also includes x-axis and y-axis labels, multiple tick labels, and their respective coordinates."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "PErwuhOs9XcMPXQ6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.183, 0.334, 0.937, 0.435]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects. These are two points representing sales of 'Coffee' at time 6 and time 18, located at {coordinates}[0.183, 0.435] and {coordinates}[0.937, 0.334], respectively."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "LAJZgq6zaJokiCTd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.329, 0.035, 0.563]</r> written on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "ArPPgmTRZT8aJsDz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth legend text?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.154, 0.808, 0.200, 0.836]."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "t98vbwxpWOMGG8lh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {legend item}<r>[0.154, 0.808, 0.200, 0.836]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Area E."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "jI8OR6NsPg33Ls1e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Area B'}<r>[0.143, 0.177, 0.945, 0.372]</r> generally above or below {the line for 'Area C'}<r>[0.143, 0.079, 0.945, 0.226]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "NiAIKgHklGjjp69P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Area D'} at 2000 on top of or below the {point of 'Area E'} at 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "ZcMyU6X9UdkXIVRJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area has a higher population in 2010, {Area A}<r>[0.945, 0.813]</r> or {Area E}<r>[0.945, 0.617]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Area E."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "6iE4GyJMHYD7zYgA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within <r>[0.110, 0.588, 0.272, 0.848]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with labels for 2000 Population, Area A, Area B, Area C, Area D, and Area E, each paired with a color marker."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "1i75SKLIjCLBdByS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.515, 0.930, 0.573, 0.970]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the label for the x-axis, which is \"Years\"."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "e32fqYdx0O15OTHO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present within the range <r>[0.119, 0.881, 0.970, 0.919]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "There are x-axis labels '2000' and '2010' found at {coordinates}[0.119, 0.881, 0.168, 0.919] and {coordinates}[0.920, 0.881, 0.970, 0.919] respectively within this range."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "qBw7aLpRCtSDVfV0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at the coordinates [0.019, 0.394, 0.044, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "S3l1g7XHGFFrMgiF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.508, 0.942, 0.567, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "ZEXCPQrHRyyPRXvk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the yticks label }<r>[0.066, 0.872, 0.082, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "OweFPVtdsNOO7MaF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2009, which trend had a higher growth, {telecom}<r>[0.171, 0.062, 0.256, 0.093]</r> or {hardware}<r>[0.171, 0.150, 0.271, 0.180]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hardware."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "ksChC3nM8VN4cbw7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the growth trend of {cloud services}<r>[0.371, 0.062, 0.525, 0.093]</r> increase or decrease from 2009 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "iu87r3EYRGJrODu7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth trend of {software}<r>[0.171, 0.106, 0.262, 0.136]</r> in 2019 higher or lower than the {internet services}<r>[0.371, 0.106, 0.548, 0.136]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "IsMkkpJn82pQRCMK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents located in the region [0.104, 0.051, 0.556, 0.191]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart with five entries including Telecom, Software, Hardware, Cloud Services, and Internet Services, each paired with a colored marker."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "K46KGlNnfkZ9WvKA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present in the area marked by coordinates [0.094, 0.037, 0.981, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the main area of the chart where the five lines representing Telecom, Software, Hardware, Cloud Services, and Internet Services growth over the years 2009 and 2019 are plotted."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "9G9aT124kYBFxFf9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content contained within the coordinates [0.134, 0.567, 0.941, 0.673]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a line plot within this area representing the growth trend of the Telecom industry from the year 2009 to 2019."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "ALNvT1B98H3WU1wm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-tick labeled at coordinates [0.153, 0.851, 0.192, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q1."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "B6sN2REwfPBpG5Rq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the legend item whose handle has the coordinates [0.822, 0.674, 0.870, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "CLnFb6KjjPEi5cfe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line marked with these coordinates [0.172, 0.629, 0.940, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart line for Amazon's earnings."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "O21gmzUEFbN2PwR1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReading from the diagram, is {the 'Google' line}[0.172, 0.498, 0.940, 0.613] over the {line labeled 'Facebook'}[0.172, 0.416, 0.940, 0.531] in Q3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "WtaVqckyau1mwrdV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the second quarter, is {Apple's line}[0.428, 0.218] higher or lower than {IBM's line}[0.428, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "My5ORWRksZrlYiUa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong {the 'IBM'}[0.940, 0.366] and {the 'Google'}[0.940, 0.498] lines, which has a higher value in Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "PID2ZKzdnzNDzKLN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with coordinates [0.021, 0.084, 0.050, 0.780]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ylabel of the chart is located in this area, labeled \"Earnings per Share (in $)\". The label is colored black and is rotated at 90 degrees."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "iVd0fs9bwOYsRQOK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found in the area [0.619, 0.657, 0.970, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These are the legend items in the chart: {Apple}<r>[0.676, 0.670, 0.727, 0.702]</r>, {IBM}<r>[0.676, 0.718, 0.709, 0.751]</r>, {Google}<r>[0.676, 0.766, 0.739, 0.799]</r>, {Facebook}<r>[0.878, 0.670, 0.963, 0.702]</r>, {Amazon}<r>[0.878, 0.718, 0.950, 0.751]</r> with corresponding color markers."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "w78QTpZk9QIlQMkL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects in the region with coordinates [0.134, 0.038, 0.979, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plot region of the line chart. It includes the x and y-axes ticks and labels, line plots for the companies: Apple, IBM, Google, Facebook, and Amazon each representing their earnings per quarter."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "trQPGwlvlvSZ1Uom",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element does the coordinate [0.019, 0.355, 0.043, 0.532] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the y-axis label."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "SZeBCBfYdlTD4fs5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label for the second point on the 'Knowledge Economy' line located on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate is [0.404, 0.172]."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "XPX9c20ZhaZdk7yk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.519, 0.766, 0.700, 0.801] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the 'Knowledge Economy' label in the legend."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "Nz7EWotWdRzBVpwI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point '3', is {the point on the 'Knowledge Economy' line}[0.135, 0.210] above or below {the point on the 'Innovation Industries' line}[0.135, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "5Yue1TYVFaRSRDNV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point '30', is {the point on the 'Innovation Industries' line}[0.672, 0.288] above or below {the point on the 'Knowledge Economy' line}[0.672, 0.366]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "LB5YvWN7nNz30dpc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at point '100', Knowledge Economy or Innovation Industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Industries."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "Bcj3IIFMgtlyww8C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.463, 0.752, 0.973, 0.814] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. There are two items: {Knowledge Economy}[0.519, 0.766, 0.700, 0.801] labeled with a blue marker, and {Innovation Industries}[0.783, 0.766, 0.966, 0.801] labeled with a green marker."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "6tlvG67ss4ln5FrG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content of the region [0.049, 0.659, 0.082, 0.704] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area for the label of the y-axis tick mark '0.2'."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "owB6r4PXUG8kW3AH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are included in the area [0.095, 0.055, 0.981, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region for the main plot of the chart, including the lines representing 'Knowledge Economy' and 'Innovation Industries', their respective points, as well as the x and y-axis labels and tick labels."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "CNdLF10vg5otMB1z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis title} located at [0.025, 0.116, 0.058, 0.703]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency Value"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "a10xRKfHIaFbOuHC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.174, 0.672, 0.244, 0.700] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item representing the 'Learned Meaning' efficiency level"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "w5C7DsqKc2p5ezta",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis item} at coordinates [0.881, 0.801, 0.974, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Theta"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "9OZiMfRoYmlIYNVr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the 'Efficiency Levels' axis: {Theta in line for 'Literal Meaning'}[0.927, 0.721] or {Theta in line for 'Learned Meaning'}[0.927, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Literal Meaning"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "GxnEuraEnVWe3NAv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Epsilon for 'Literal Meaning'}[0.743, 0.649] located higher or lower than {Epsilon for 'Learned Meaning'}[0.743, 0.529]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "LZRVyAqC5LXFvjID",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has greater efficiency in the Alpha category: Literal Meaning [0.193, 0.457] or Learned Meaning [0.193, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Learned Meaning"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "wfSGlD8hhCuthUWR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.170, 0.549, 0.509, 0.741]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It has two items: 'Literal Meaning' with a red marker at {coordinates}[0.174, 0.585, 0.244, 0.612], and 'Learned Meaning' with a blue marker at {coordinates}[0.174, 0.672, 0.244, 0.700]."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "ysEX7cpnPVKnUMeJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information in the region [0.068, 0.265, 0.140, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a label for the '1.00' value on the y-axis."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "T6hxeom5ijeu72CB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain the objects in the area [0.193, 0.170, 0.927, 0.721]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the plotted line for both 'Literal Meaning' and 'Learned Meaning' marking levels of efficiency across the 'Alpha', 'Beta', 'Delta', 'Epsilon' and 'Theta' efficiency levels."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "VNwNbrcNEBkV8ugP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Y-axis label}<r>[0.015, 0.392, 0.035, 0.514]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "iuIG6EyxSHmonjOj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the X-axis label}<r>[0.519, 0.930, 0.566, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "xYzN3pr7mqYCOfgp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.151, 0.112, 0.257, 0.140] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the line representing 'Patent Granted'."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "RFYIcg75Yo0fEzsf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {innovation index trend line}[0.140, 0.858, 0.945, 0.860] above or below the {patent expired trend line}[0.140, 0.691, 0.945, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "xOjwJDJvFhyvXHX7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line value is higher in the year 2020, the {patent granted line}[0.140, 0.453, 0.945, 0.644] or the {patent transferred line}[0.140, 0.725, 0.945, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patent transferred line."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "UGFBasIeLaK00V3v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {patent filed trend line}[0.140, 0.113, 0.945, 0.487] increasing or decreasing over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "8Q9uUkQIPa5xy605",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.106, 0.059, 0.284, 0.277]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, listing out each of the categories plotted on the graph. These categories include Patent Filed with a red line, Patent Granted with a blue line, Patent Expired with a green line, Innovation Index in yellow, and Patent Transferred in black."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "2b6h4RIxPoQILAyj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within the bounding box [0.920, 0.881, 0.969, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the year '2020' is located in this region."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "6wwesVuydpCF7Mf4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information contained in [0.140, 0.725, 0.945, 0.841]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area describes the line showing the trend of Patents Transferred from the year 2000 to 2020, which is represented by the black line and triangular markers."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "Na5brtZlVNhEKUek",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.012, 0.274, 0.029, 0.635]</r> associated with on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Measurement Units."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "LiZLM2qqJyQNhwOW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker represents {the dataset for 'CO2(ppm)'}<r>[0.621, 0.084, 0.651, 0.098]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "sjiUZxsHkqWSWTek",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the x-axis label}<r>[0.495, 0.942, 0.565, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.495, 0.942, 0.565, 0.975]."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "lDAw4UAE5QlO7a3A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2000 on the 'CO2(ppm)' graph}<r>[0.114, 0.127]</r> higher or lower than {the point for 2000 on 'Methane(ppb)' graph}<r>[0.114, -2.809]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "H73Tt2VAkMbqUqXc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Global temperature(Celsius)'}<r>[0.114, 0.853, 0.946, 0.857]</r> above or below {the line for 'CO2(ppm)'}<r>[0.114, 0.029, 0.946, 0.127]</r> at the 2010 mark?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "CmIPCvUiLKigAi5E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a higher point in 2000, {the 'Nitrogen Oxide(ppb)' line}<r>[0.114, 0.240]</r> or {the 'Global temperature(Celsius)' line}<r>[0.114, 0.853]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global temperature(Celsius)."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "l46HLweVsmQZiTse",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the data presented in the region <r>[0.073, 0.025, 0.988, 0.885]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses axes, grid lines, and lines representing trends for 'Global temperature(Celsius)', 'CO2(ppm)', 'Methane(ppb)', and 'Nitrogen Oxide(ppb)', each with distinct markers and colors."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "BmLTWlsM26tXj6uQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the area <r>[0.620, 0.037, 0.982, 0.113]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend titled 'key environmental metrics' with items labeled 'Global temperature(Celsius)', 'CO2(ppm)', 'Methane(ppb)', and 'Nitrogen Oxide(ppb)', each associated with a colored marker."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "UiHNUWaKN2stVq8R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is visualized between the coordinates <r>[0.073, 0.885, 0.988, 0.932]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the x-axis with labels for the years from 2000 to 2019, with corresponding tick marks at regular intervals, facilitating a chronological view of the data."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "NHtd6dmhKdmSPasH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is this one?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "9EfvW5KWQB7gayA0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the coordinates [0.553, 0.113, 0.678, 0.149]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend for SME Growth line."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "hQ6XiJMcY1PVKzsC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is plotted at {the point}[0.748, 0.363] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a data point for the \"Startup Registration\" line corresponding to the year 2016."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "KyNMt41fTTevdLxR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n'Foreign Investment Line' and 'Startup Registration' both have points at year 2013. Which one has higher value at that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Startup Registration."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "4ywvMNqFyjYGJEKb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {line for 'Patents Registered'}[0.164, 0.462, 0.942, 0.595] above or below the {line for 'SME Growth'}[0.164, 0.662, 0.942, 0.762]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "W0w0VEyM30IHyYE8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year of 2017, which trend has a higher value, Patents Registered or SME Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patents Registered."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "yanKif2DJtRhQ88E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in the coordinates [0.125, 0.030, 0.981, 0.861]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the full scope of the plotted lines. All data trends, including Foreign Investment, Startup Registration, Patents Registered, and SME Growth are located here."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "N9AHLfQFHxueih6n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information in the area [0.135, 0.047, 0.687, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend items including {Foreign Investment Line}[0.202, 0.060, 0.453, 0.096], {Startup Registration}[0.202, 0.113, 0.410, 0.149], {Patents Registered}[0.202, 0.165, 0.397, 0.201], and two times {SME Growth}[0.553, 0.060, 0.678, 0.096],[0.553, 0.113, 0.678, 0.149]. Each entry is paired with a colored marker."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "q9JbmXIRpKD223Wt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region [0.911, 0.881, 0.973, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label for the year '2017'."
+            }
+        ],
+        "id_raw": 692
+    },
+    {
+        "id": "VB683sOb68YXKXgG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.019, 0.392, 0.044, 0.514]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "LS4lX1w3Y8womU59",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.118, 0.881, 0.179, 0.919]."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "fUdUGZuT8v8lHibu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {the trend line for Infrastructure Projects}<r>[0.149, 0.208, 0.228, 0.208]</r> represent in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the changes in the trend of Infrastructure Projects over the years."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "IJRlgCGUhYHCpcdG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend line for Building Materials}[0.149, 0.233, 0.942, 0.453] above or below {the trend line for Residential Buildings}[0.149, 0.167, 0.942, 0.412]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "7HRNU5tbJ3yAmJXk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich appears first in the vertical direction, {the legend for Residential Buildings}<r>[0.186, 0.233, 0.402, 0.269]</r> or {the legend for Building Materials}<r>[0.186, 0.128, 0.372, 0.164]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Building Materials."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "084iYWhzJp5XHayJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend shows an increase in years from 2012 to 2015, {Real Estate Agencies}<r>[0.149, 0.167, 0.942, 0.372]</r> or {Commercial Buildings}<r>[0.149, 0.265, 0.942, 0.519]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "a4zbg2skXqzXDxte",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.119, 0.062, 0.431, 0.388]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the six different trend lines in the graph: Infrastructure Projects, Building Materials, Real Estate Agencies, Residential Buildings, Commercial Buildings, and Public Sector Buildings."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "3vWxqcipv9onvvst",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the area [0.149, 0.233, 0.942, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the trend line for Building Materials. It shows how the trend has changed from year 2009 to 2019."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "LBAOGdUbCJeksTn6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.081, 0.846, 0.097, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0' is located in this region."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "YmeBxX9STKdZsNw1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.234, 0.102, 0.300, 0.130]</r> indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "aMchm3ayQYF1fuNX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what the color of {the line associated with Twitter}<r>[0.116, 0.370, 0.944, 0.875]</r> is on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "0IiwNiMZgDOowBFo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the last x-axis tick label}<r>[0.871, 0.895, 0.906, 0.923]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "HYvNOv8T1Is9YEN8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Twitter line}<r>[0.116, 0.370, 0.944, 0.875]</r> end above or below {the Netflix line}<r>[0.116, 0.117, 0.944, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "ztjNMNxuSn6nzDOc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat point is higher on the y-axis, {the 2019 point on the Google line}<r>[0.833, 0.343]</r> or {the 2019 point on the Facebook line}<r>[0.833, 0.543]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "S22DwnBIIkfknby6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line crosses the sixth y-tick label, {Netflix}<r>[0.116, 0.117, 0.944, 0.875]</r> or {Twitter}<r>[0.116, 0.370, 0.944, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "iKIYkB5ndZd14odF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements located in <r>[0.126, 0.102, 0.300, 0.213]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains 'Google' and 'Facebook' text labels for the legend, along with their respective color handles."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "9X6YuLLMEsYj6M1F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within <r>[0.039, 0.066, 0.065, 0.892]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels ranging from '0' to '600', and tick marks corresponding to these labels."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "UnFqs5hGWDviO7SU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of <r>[0.098, 0.895, 0.189, 0.923]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labels for '2006' and '2007' including their corresponding tick marks."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "sRka0DfRWCvNa62Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object marked with the coordinates [0.065, 0.872, 0.077, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '0'."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "7KRBBgSaSdHvF3lt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the graphical entity located at [0.944, 0.837, 0.944, 0.837]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point representing the 'Electrical Vehicles' in year '2020'."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "Zu7AYQWeehyeg4g0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the coordinate range [0.567, 0.150, 0.682, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Two Wheelers' in the legend."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "EW8K81oZDrPqWRiP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value for 'Commercial Vehicles' in 2019}[0.740, 0.613] higher or lower than the {value for 'Passenger Vehicles' in 2019}[0.740, 0.274]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "nEBlESSVKsAfS2wl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point on 'Electric Vehicles' line in 2016}[0.128, 0.878] have higher or lower value in comparison with the {point on 'Two Wheelers' line in 2016}[0.128, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "77WsnsO2fjbPQPt4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, is the {point for 'Passenger Vehicles' in 2017}[0.332, 0.270] above or below the {Point for 'Passenger Vehicles' in 2020}[0.944, 0.305]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "Wdt34qjEQStOu7X7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the [0.514, 0.051, 0.977, 0.191] region encompasses?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the chart's legend with labels 'Passenger Vehicles', 'Commercial Vehicles', 'Two Wheelers', 'Three Wheelers', and 'Electrical Vehicles' along with their respective color markers."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "9p8kaORxm7CqdwfX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents located within the coordinates [0.041, 0.025, 0.077, 0.057]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the lowermost y-axis label '100'."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "ZuENbR8f4dlEq5d0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are present within the region defined by coordinates [0.715, 0.901, 0.765, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the '2019' x-axis label."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "c5IW2MjFRB5Q9Vcz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with label coordinates [0.823, 0.125, 0.880, 0.185] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"USA\" label in the legend."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "kR0DXS0s2DJXNX1x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {Germany's line} on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is at [0.971, 0.353, 0.975, 0.408]."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "dO40yRVL8zAsanJ8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.025, 0.155, 0.058, 0.690] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label, which represents '% GDP in R&D'."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "CGu0Dj6QyBUf47MD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the lines for {USA}[0.971, 0.165, 0.975, 0.290] and {Germany}[0.971, 0.353, 0.975, 0.408], which country had a higher % GDP in R&D in 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "USA."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "EljXcu09qMLJ9W4s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the % GDP in R&D for {Brazil}[0.971, 0.471, 0.975, 0.526] increase or decrease from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreased."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "mLfwoPlEOSk2pT5b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data point is lower on the chart, {China's data point in 2002}[0.971, 0.658] or {India's data point in 2002}[0.971, 0.547]?"
+            },
+            {
+                "from": "gpt",
+                "value": "India's data point in 2002."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "Jbcf3sToZ9pnh9lH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.734, 0.103, 0.961, 0.558]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart's legend, with labels for the USA, China, India, Germany, and Brazil, each paired with a unique color marker."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "KIM6fIsNQ5kZCyEG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat analysis is presented in the plot area with coordinates [0.125, 0.075, 0.975, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line chart showing the % GDP spent on R&D by the USA, China, India, Germany, and Brazil from 2000 to 2010."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "dBWSITP3P9HhebXd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the area [0.971, 0.165, 0.975, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It holds the line for USA, indicating the trend of % GDP spent on R&D over the years from 2000 to 2010."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "fe3lahL9YBRxxKsj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy is represented by the line located at these coordinates: [0.116, 0.225, 0.930, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Monetary Policy."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "Za3LMuRZlBxyP45f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at these coordinates: [0.137, 0.690, 0.268, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text 'Monetary Policy'."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "4bv1Qv7mmn6iN8Q0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ylabel at these coordinates: [0.015, 0.305, 0.035, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy Impact."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "cbP8yiA0AcT9F2KP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the coordinate point [0.930, 0.592], which policy has a greater impact, {Monetary Policy}[0.116, 0.225, 0.930, 0.592] or {Fiscal Policy}[0.116, 0.176, 0.930, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fiscal Policy."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "RPMiIPTyOYgjCDNS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Monetary Policy line}[0.116, 0.225, 0.930, 0.592] generally above or below the {Investment Policy line}[0.116, 0.306, 0.930, 0.617]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "ckrPcaOq62yzqJEL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy begins at a higher point on the y-axis at the 'Decade50' point, {Tax Policy}[0.116, 0.470, 0.930, 0.763] or {Trade Policy}[0.116, 0.421, 0.930, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tax Policy."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "7p6PGHYJUzwoseTk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located within [0.053, 0.846, 0.065, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a y-axis tick label '0'."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "d1IEdTjKBpfCj4GQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.083, 0.677, 0.502, 0.845]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with labels such as {Monetary Policy}[0.137, 0.690, 0.268, 0.726], {Fiscal Policy}[0.137, 0.743, 0.235, 0.779], {Trade Policy}[0.137, 0.795, 0.235, 0.831], {Tax Policy}[0.348, 0.690, 0.429, 0.726] and {Investment Policy}[0.348, 0.743, 0.496, 0.779]."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "Bml4mFVxJbGjZp8V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.041, 0.030, 0.065, 0.068]."
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label '10' within this region."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "frFxSZPnJMAxWlqF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.017, 0.390, 0.038, 0.520]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "7qXVD049eAWpHYXS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the marker at the coordinates: [0.079, 0.702, 0.125, 0.719]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker corresponds to the color representation of the 'Average Health Score' line in the plot legend."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "XTHxSxQCcj77YOo2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at [0.496, 0.932, 0.553, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "eCq7fm20lgZU0KsK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data series has a higher score value at {point 5}[0.479, 0.249], the 'Average Health Score' line or the 'Medical Consultations' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Health Score."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "6VQpnDk3PevUJrpc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the values of {the 'Exercise Frequency' line at point 3}[0.293, 0.426] and {the 'Stress Level' line at point 3}[0.293, 0.301]. Which one is higher on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stress Level."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "f6A69ploorhHb87k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 10 on the 'Disease Incidence' line}[0.942, 0.383] lower or higher in the chart than {the point 10 on the 'Exercise Frequency' line}[0.942, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "sCfcG38eRSissQy1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is displayed in the area [0.135, 0.694, 0.335, 0.730]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 'Average Health Score' legend text color-coded with a certain marker."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "2SkiOhtnxfH9fffZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.461, 0.747, 0.663, 0.783]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a colored marker and the corresponding label in the legend, which reads 'Medical Consultations'."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "s9rcLqj4FwvhP7Nr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is found in the region [0.044, 0.030, 0.056, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '8'."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "atgtxyhPfSCeNT4r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object identified by the coordinates [0.432, 0.062, 0.488, 0.080] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sublegend label for Solar Energy."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "WrDpar7qATE11OtT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the bounded area [0.510, 0.449, 0.524, 0.466] indicate in the subplot about Wind Energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label \"0.0\"."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "wkisJTd6Ixh7AkGi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the label positioned at the exact coordinates [0.517, 0.847, 0.536, 0.864] in the fourth subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label \"0.05\"."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "jkPRGNYxVORymroT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for {the x-axis tick label '1.0'}[0.475, 0.437] greater or lesser than {the x-axis tick label '1.2'}[0.566, 0.437] in the Solar Energy subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "bM2heA0yfrsrot8K",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the energy figures for Nuclear Power at {point 1}[0.563, 0.564] higher or lower compared to Hydroelectric Power at {point 1}[0.067, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "ogjaxWlSecd58bME",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend bends more steeply downward, {Solar Energy at point 5}[0.393, 0.115] or {Wind Energy at point 5}[0.889, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Energy."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "vAYQ6QhhQuRnNdSZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found within the bounding box [0.0, 0.449, 0.1, 0.466]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the first x-axis tick label '0.0' in the subplot for Solar Energy, specifically located at {the tick label for '0.0'}[0.015, 0.449, 0.029, 0.466]."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "zQfnGtwGa5I5LPJD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents lay within the region specified by [0.5, 0.6, 0.9, 0.65]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the plotting area for wind and nuclear energy showing trend lines and points, including the {point 4 of Wind Energy}[0.807, 0.156] and {point 5 of Nuclear Power}[0.889, 0.891]."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "KB0f0O9MiAX7y8tV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features or elements are encompassed in the section [0.2, 0.4, 0.3, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the y-axis label \"Energy Allocation\", the 5th y-axis tick labeled '0.4' for Solar Energy, and a segment of {Solar Energy's trend line}[0.287, 0.449, 0.301, 0.466]."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "OSpBZZjtGbgkAYkl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.523, 0.279, 0.548, 0.297] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "I1pty2tpY2yGd4oB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.576, 0.419, 0.654, 0.330] in the subplot for Upstream Manufacturing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "4PI5DAw7I23P2Eut",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.688, 0.019, 0.856, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "JVXic8kEsAOXMDGy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis tick labeled '1.90'}[0.530, 0.058, 0.548, 0.075] higher or lower than {the y-axis tick labeled '1.95'}[0.530, 0.013, 0.548, 0.031]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "D6DgkFtrx8WkuJjz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the point at '0'}[0.498, 0.437] or {the point at '6'}[0.968, 0.437] in the Downstream Distribution plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "0sb8He5uB5jFL2Z2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first tick label on the x-axis}[0.004, 0.927] to the left or right of {the last tick label on the x-axis}[1.046, 0.927] for the Downstream Distribution subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "IwjbAqlgClFPsWxV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.000, 0.439, 0.549, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses all the x-axis tick labels and their respective tick marks for the Midstream Packaging subplot."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "JcdkbS6nwlhMuIk5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the area [0.494, 0.040, 0.988, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all elements of the Upstream Manufacturing subplot including axes labels, subtitle, x and y tick labels, and data points."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "3yL9HTBtNdrxfEPc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region spanning from [0.063, 0.530, 0.494, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space includes the entirety of the Midstream Packaging subplot's axes, grid lines, labels, legend, and its line series data points."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "jChDmiAtXz0CBi9Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the point corresponding to 'Twitter' on the Facebook subplot}<r>[0.208, 0.290]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "XHtgSKL2708I5RKc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform corresponds to {the x-axis tick}<r>[0.703, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "b9wHRNwW2yBUVBXg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the count represented at {the LinkedIn point in the Instagram subplot}<r>[0.969, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "3893."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "nKJCshtFkBRo5XpT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Facebook data point in the first subplot}<r>[0.075, 0.262]</r> higher or lower than {the Facebook data point in the second subplot}<r>[0.570, 0.101]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "htYyRA3e6n6JIPLi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has more entries: {the Instagram point under 'Stable Engagement'}<r>[0.341, 0.848]</r> or {the Instagram point under 'Volatile Feedbacks'}<r>[0.836, 0.821]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stable Engagement."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "6XgxNkgaogCyCZX0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first subplot's zero y-tick label}<r>[0.055, 0.457]</r> at a higher position than {the third subplot's zero y-tick label}<r>[0.055, 0.947]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "RqpqMOjrUxBC2BJI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.522, 0.772, 0.987, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the LinkedIn subplot's x-grid, y-grid, axes, y-ticks (labeled 4000, 6000, 8000), x-tick labels (Facebook, Twitter, Instagram, LinkedIn), and a subtitle 'LinkedIn'."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "B8lWin437GLgw140",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section between <r>[0.022, 0.002, 0.049, 0.503]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This signifies the range of the y-ticks and corresponding labels on the left side of the first subplot, encompassing 0 to 12000."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "v3uQKUWGC1W4fTvM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the components found within the region <r>[0.318, 0.080, 0.363, 0.957]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses Instagram related x-tick labels and line points from all four subplots, indicating data and labels for Instagram across all data categories shown."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "ExDdPPyjC8FayVLp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the line chart with the color '#800080'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Procedure A."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "gi1rGrtG8Jzfezsh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the subplot with coordinates '[0.542, 0.047, 0.991, 0.927]'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "zBQfIEKoByLUut8I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item marked with the orange color ('#ffa500') in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Procedure B."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "jO3azgnbFhfbXY8V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the line chart for Manufacturing Procedure A, is the point at 'r' value of 0.64 above or below the point at 'r' value of 0.82?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "eouzFgUnHjjQnB2H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Manufacturing Procedure B, is the performance at 'r' value 0.82 higher or lower than at 'r' value 0.64?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "AYCYIC6IstgWltxD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, does Manufacturing Procedure A or B have a higher performance at 'r' value 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Procedure B."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "0wwynRHDBPetzWSf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.752, 0.964, 0.781, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"Ratio r\" for the x-axis of the chart showing Manufacturing Procedure B's performance by ratio 'r'."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "GXr6RhAYVRm2ykmw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.903, 0.055, 0.986, 0.087]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the legend for the Manufacting Procedure B's line chart. The color marked '#ffa500' represents Procedure B."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "QYTQ1oORVVAKXLXr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.563, 0.116, 0.970, 0.850]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart for Procedure B is located here, showing performance at different 'r' ratios. Points are marked at 0.1, 0.28, 0.4599, 0.64, 0.82 and 1.0 'r' values."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "gRbwszT7jwM0T7Kg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance<r>[0.009, 0.187, 0.018, 0.297]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "lCLllPoqLBuXJuhU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Interactive Media\" line in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange<r>[0.385, 0.091, 0.407, 0.101]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "1CNN6fIpJW2TM2d2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trends<r>[0.260, 0.964, 0.288, 0.981]</r>."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "3kQMituZrno55IAh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the measurement point 5 on the line for 'Video Content'}[0.072, 0.555] above or below {the measurement point 5 on the line for 'Interactive Media'}[0.072, 0.585]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "StOG0Nvz0c7jHSRN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Y-value for {x=1 on 'Video Content'}[0.072, 0.419] higher than for {x=1 on 'Interactive Media'}[0.072, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "SFdHrL7QrTx9Ghe0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes point {7 on the 'Video Content'}[0.568, 0.555] have a higher trend measurement than point {7 on 'Interactive Media'}[0.568, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "Ary0QM6EWkKqgEay",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.548, 0.537, 0.991, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a subplot titled \"Rapid Decline vs Unstable Behavior,\" with axes labeled 'Trends' and 'Performance' and a legend that includes the items 'Video Content' and 'Interactive Media' with their associated colors of purple and orange."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "ZNQdZ959oQI2Vjnr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.052, 0.537, 0.495, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a subplot highlighting 'Steady Decline vs Consistent Expansion'. It contains x-axis labeled by trends with tick labels from '5' to '6' and y-axis labeled by performance ranging from '0.0' to '0.8'. It features a legend itemizing 'Video Content' and 'Interactive Media' displayed with purple and orange markers respectively."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "XKb9QfuTb0PVKPf4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the lower half of the chart area [0.548, 0.049, 0.991, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a subplot titled \"Dramatic Decrease vs Minimal Decline\" along with its axes describing 'Trends' and 'Performance.' The x-axis holds tick labels '3' and '4' whereas the y-axis details levels from '0.675' to '0.900'. The subplot's legend showcases 'Video Content' and 'Interactive Media' with their respective colors and markers."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "npjuRdCDXld4VD3r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Urban Plan.\" series on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "DiGxwknnMHXjTOUT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.014, 0.706, 0.021, 0.765]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Measure."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "MnXCdZmE8FFqe1na",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker for 'Com. Dev.'}<r>[0.426, 0.544, 0.444, 0.553]</r> look like?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "QPsEbD2xXfddCb1x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {point 0.5 on 'Com. Dev.'}<r>[0.240, 0.222]</r> above or below {point 0.5 on 'Urban Plan.'}<r>[0.739, 0.174]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "q01BqXtSgRXIEoPe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher final point, {the 'Inexorable Rise'}<r>[0.473, 0.556]</r> series or {the 'Minimal Change'}<r>[0.972, 0.642]</r> series?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inexorable Rise."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "sIb49GOqr62ETqq8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the second point of 'Gradual Decline'}<r>[0.599, 0.115]</r> represent a higher or lower value than {the second point of 'Slow Growth'}<r>[0.101, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "0BWdi9fMBMW15X6B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements in the region<r>[0.553, 0.037, 0.993, 0.449]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot for 'Gradual Decline,' which includes axes with coordinate labels, grid lines, x-axis labels positioned at the bottom and y-axis labels along the left side, displaying two line graphs representing 'Com. Dev.' and 'Urban Plan.' with corresponding legends."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "nBduU8IOQe6JmcyX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you detail about the subplot in<r>[0.033, 0.530, 0.494, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot labeled 'Inexorable Rise' features a plot area with vertical and horizontal grid lines, axes titled with measurements and observations, ticks for data points, and lines ascending sharply representing two categories, all depicted against a white background."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "uGhWmKMUnEQ8a8pt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are visible in the area<r>[0.532, 0.530, 0.993, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a subplot identified as 'Minimal Change' containing a white plot surface, grid lines, numerical labels on both x and y axes, minimal fluctuating lines for two distinct series displayed with shaped point markers, and legend entries reflecting the series titles."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "Ye5eyEbfelnUyLbJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'Creativity' line in the subplot titled 'Graph-SST2'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "JSmrngc7oDpCSCUP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker shape used for the 'Design Innovation' line in the subplot titled 'SPMotif-0.5'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "zmY0cjmCKOECO2Ip",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat axis label is located at [0.752, 0.964, 0.781, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio r."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "WIV0RnM6nlG45vu0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {y-value of the first point on the 'Creativity' line}{r}[0.563, 0.823] higher than the {y-value of the last point on the 'Design Innovation' line}{r}[0.970, 0.571] in the 'SPMotif-0.5' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "GdyaWt23d2UTB8uF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher endpoint in the subplot titled 'MNIST-75SP', {the 'Creativity' line}{r}[0.475, 0.598] or {the 'Design Innovation' line}{r}[0.475, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Creativity."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "xTIdSlrTONlMu61i",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-value of the middle point on the 'Creativity' line}{r}[0.726, 0.868] exceed that of {the middle point on the 'Design Innovation' line}{r}[0.726, 0.850] in the subplot labeled 'Graph-SST2'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "lUdvtqXXzCVe7a4j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.047, 0.537, 0.495, 0.012]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart title \"MNIST-75SP\" styled in black color."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "wd4cHfY443mJG0s5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.542, 0.047, 0.991, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the axes, grid lines, and points for the subplot titled 'Graph-SST2'. Both 'Creativity' and 'Design Innovation' lines along with their respective data points and trend lines are present."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "Ag3nUqCCkxLvHDKw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region between [0.381, 0.546, 0.491, 0.604]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the subplot titled 'MNIST-75SP'. It includes labels for 'Creativity' and 'Design Innovation' with associated colored markers."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "v9zA1E113TAd1yU0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.018, 0.219, 0.026, 0.265]</r> for the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "Uw2Ror4YrbvvPxCr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used for {the line representing Product 2}<r>[0.561, 0.064, 0.970, 0.419]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "KSxFEfrYDT1vuXMK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month corresponds to {the x-axis tick}<r>[0.253, 0.937, 0.267, 0.954]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Jun."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "frC2h2gKHpLkYydD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for June sales}<r>[0.747, 0.242]</r> higher or lower than {the point for June}<r>[0.253, 0.768]</r> in the plotted line for Product 1 vs Product 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "n2bYDdxuchXaRFXr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the axis for Product 4}<r>[0.561, 0.639]</r> located above or below {the first point for Product 1}<r>[0.067, 0.419]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "YS4UjYOHM5t6Cb8B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher starting sales value, {Product 1}<r>[0.067, 0.419]</r> or {Product 2}<r>[0.561, 0.419]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product 2."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "vHFirJeCbF3MHoXM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in {the region}<r>[0.541, 0.047, 0.991, 0.437]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot chart showing sales data for Product 2, with a grid and axis for each month and various sales figures plotted as a colored line."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "Q4HynwY8YrtQtqJx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within {the region}<r>[0.047, 0.537, 0.497, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the third subplot displaying Product 3's monthly sales, marked by an axis for months, sales values on the y-axis, and a green line tracking the sales trend."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "lLPlW4EgH1Ur4aHG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within {the region}<r>[0.258, 0.473, 0.285, 0.491]</r> and {the region}<r>[0.752, 0.473, 0.779, 0.491]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas both contain the x-axis labels 'Month' for the sales charts of Product 1 and Product 2 respectively."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "xmFUUJVMBms0moXy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at {the y-axis}<r>[0.009, 0.374, 0.018, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage of Households."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "qp10N9s3tUolfK7Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the handle color} for the legend item 'PNA + ours' at <r>[0.415, 0.065, 0.438, 0.075]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "2Nod4pXnBC9UnNl0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-tick label value at {the y-tick}<r>[0.521, 0.643, 0.532, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "45."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "K1rKoQ4YbMRlMdAB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line 'PNA + ours' at point 5}<r>[0.250, 0.369]</r> above or below {the line 'GIN + ours' at point 5}<r>[0.742, 0.739]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "mVPXewID27D7csGS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the y-axis label}<r>[0.009, 0.374, 0.018, 0.600]</r> and {the y-axis label}<r>[0.509, 0.374, 0.518, 0.600]</r> have different rotations?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "cPM4wV8X8Hr0ToiG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {point 3}, 'PNA + ours' or 'GIN + GSAT'?"
+            },
+            {
+                "from": "gpt",
+                "value": "PNA + ours."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "EE8zsofTcHtXyo3n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within <r>[0.018, 0.930, 0.041, 0.957]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Includes two y-tick labels and their corresponding y-ticks, one labeled '62.5' at {coordinates}<r>[0.022, 0.150, 0.041, 0.168]</r> and the other labeled '65.0' at {coordinates}<r>[0.022, 0.033, 0.041, 0.050]</r>."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "qt5DBK62jqCiTc7s",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region <r>[0.559, 0.442, 0.970, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a set of points marked on the line labeled 'GIN + GSAT', including point coordinates at {coordinate}<r>[0.559, 0.857]</r>, {coordinate}<r>[0.970, 0.442]</r>, among others, representing a downward trend in data."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "WGwWrNm6ilOC68Zx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the chart area <r>[0.067, 0.134, 0.479, 0.840]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Contains the lines for 'PNA + ours' and 'GIN + ours' across multiple points, with markers indicating data points for 'Urban Households' over the time span."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "v7yVP5pU2ifRnus3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the line representing 'Erratic growth'}<r>[0.067, 0.064, 0.478, 0.400]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "T3wro9ZXOO6v6KkK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.261, 0.473, 0.284, 0.491]</r> in the second subplot read?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "BUsr3n1XSHotsfQd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure associated with {the label 'Marginal growth'}<r>[0.572, 0.631, 0.642, 0.649]</r> in the legend of the fourth subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "a colored marker."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "xVRk8xlVtka8nwQU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at a 'Ratio' of 0.55, {Erratic growth}<r>[0.272, 0.307]</r> or {Subdued decline}<r>[0.272, 0.344]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subdued decline."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "pGjAxqq4WYFYJTZW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the third subplot, which line's point at a ratio of 1.0 is closer to {the x-axis}<r>[0.261, 0.964, 0.284, 0.981]</r>, 'Erratic growth' or 'Inconsistent decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Erratic growth."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "wNSEpAi8PjdEBjH5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the second last x-tick}<r>[0.790, 0.449, 0.795, 0.466]</r> to {the last x-tick}<r>[0.963, 0.449, 0.977, 0.466]</r> in the second subplot, which has a higher corresponding y-coordinate for 'Marginal growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "last x-tick."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "fz0OKRh9zVEZesaz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the area <r>[0.050, 0.065, 0.200, 0.165]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the first subplot. It includes items for 'Erratic growth', 'Inconsistent decline', 'Subdued decline', and 'Marginal growth', each with a distinct colored marker."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "Sjgxzo7l4zqomk2q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content resides within the coordinates <r>[0.260, 0.440, 0.470, 0.460]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes part of the x-axis, displaying multiple x-tick labels such as '0.2', '0.4', '0.6', '0.8', '1.0', and their corresponding tick marks."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "ysLo1cczFp57GeUX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details about the area <r>[0.250, 0.500, 0.330, 0.530]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region displays the subtitle 'Atmospheric Emission' at the top of the third subplot in black font."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "YbBP8pnmDeK9RMaB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the value for the first y-axis tick}<r>[0.022, 0.464, 0.046, 0.480]</r> on the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "102.5."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "gvpHbqQQ1nGiYHbS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify {the value on the second x-axis tick mark}<r>[0.128, 0.454, 0.139, 0.470]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "QJ8F74THnYWHTnCV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with {the third x-axis tick mark}<r>[0.841, 0.454, 0.862, 0.470]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2022."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "GAtUVt6Bxw54fdaR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {point 4}<r>[0.254, 0.188]</r> higher or lower than {point 5}<r>[0.317, 0.060]</r> on the 'Anonymous Country' line in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "HLiXfkLr9GbJ9BiI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the '2020' tick mark}<r>[0.395, 0.444]</r> appear before or after {the '2021' tick mark}<r>[0.458, 0.444]</r> on the x-axis in subplot 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "WOVMsGaGYGkUeHU5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at {point 3}<r>[0.191, 0.771]</r> on the line chart for 'Russia' in subplot 3 above or below {point 4}<r>[0.254, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "Jo6mwvyOhFWgReOF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within <r>[0.052, 0.454, 0.330, 0.470]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis tick labels for the years 2020 to 2024 in subplot 0, with each label followed by corresponding tick marks {year 2020}<r>[0.065, 0.444]</r>, {year 2021}<r>[0.128, 0.444]</r>, {year 2022}<r>[0.191, 0.444]</r>, {year 2023}<r>[0.254, 0.444]</r>, {year 2024}<r>[0.317, 0.444]</r>."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "4t6owzokTnnxU2W7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the components found within <r>[0.682, 0.449, 0.713, 0.464]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis tick label '102.5' along with its corresponding tick mark {y-tick 102.5}<r>[0.713, 0.455]</r> in subplot 6."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "Y4kXU9stpOeOUiEc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what exists in <r>[0.348, 0.966, 0.377, 0.982]</r> in the last subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '100' and its y-tick at {tick location}<r>[0.383, 0.972]</r> in subplot 5."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "rc3n9JP4MueOGNm2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented at the point found at coordinates [0.439, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point label '11'."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "fDhp9G2mLUC0Fams",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the mark}[0.934, 0.406] on the line chart in subplot 1 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value 805000."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "vpT406fnJr1lRuIj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is found at the location identified by [0.544, 0.048]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '42000'."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "RJnLIqzqMsoVTJxb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {point}[0.403, 0.170], the plot value in subplot 0 or subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "subplot 1."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "aOt1PLroH6tiNJ0Z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value}[0.439, 0.799] in the subplot 2 higher than {the value}[0.934, 0.867] in subplot 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "1hsXjUqCSdOaeTpu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is closer to {the origin point}[0.039, 0.468], the x-axis label or {the y-axis label}[0.009, 0.232, 0.018, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis label."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "khuNNpesyfVQmmz0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.244, 0.301, 0.475, 0.315]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the x-axis labels for the subplot 0, specifically housing the labels from '0' to '10' along the measured time frame."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "coGFaPR57v4uYfsc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area extending from [0.530, 0.450, 0.971, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds various x-axis labels ranging from '4' to '12' in subplot 1 indicating time in months, aligned with the associated tick marks."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "H5xnbxgVQo6Gd08m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present inside the bounds [0.244, 0.970, 0.475, 0.991]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The described region contains the lower edge of the chart, displaying the x-axis label 'Time (months)' for subplot 2."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "HkoJmvsLMMQmWvZT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.022, 0.301, 0.035, 0.319] represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "PihOIY1ssdYFl5N6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the feature at [0.022, 0.168, 0.035, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "97KAox1g6OwtYDFM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me the color of the marker at [0.870, 0.143, 0.892, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "11gVLNODxwjUnoWl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the tick at point '0.6'}[0.041, 0.735] or {the tick at point '0.7'}[0.041, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.7."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "NnFNlB7rhuq1HFfe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line labeled 'AI and Machine Learning'}[0.516, 0.776] above or below {the line labeled 'Quantum Technology'}[0.516, 0.250] at the 'Minimal Change' position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "aqMPxsqLMGNfCd46",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompares the colors of {the handle at 'Steady Decline', AI and Machine Learning}[0.870, 0.660, 0.892, 0.671] and {the handle at 'Steady Decline', Quantum Technology}[0.870, 0.169, 0.892, 0.180]."
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "QTliPsATH29DspAM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.480, 0.932, 0.980, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes x-axis tick labels for 'Minimal Change', 'Incremental Growth', and 'Steady Decline' with their respective tick points {Minimal Change}[0.516, 0.927], {Incremental Growth}[0.732, 0.927], {Steady Decline}[0.947, 0.927]."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "46cidr2lDV1yEXQw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat crucial chart elements are within the area [0.480, 0.449, 0.984, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis labels for 'Minimal Change', 'Incremental Growth', and 'Steady Decline', clearly identifying the trends in Quantum Technology."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "cmN0U5LvHYQKIQCl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.22, 0.3, 0.30, 0.4] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area houses the y-axis tick label '0.2' and part of the grid line, helping visualize the scale of 'Performance' in the Quantum Technology chart."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "YVP4jSgGTifab4qw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located in [0.427, 0.509, 0.607, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Influence of CNN on Public Opinion."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "3WVJdHqauoWAVaRe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the x label and their coordinates on the second chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x label is \"Scenarios\",[0.496, 0.964, 0.538, 0.981]."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "cwU3s6kdWvyvgl1w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-Axis label described at {coordinates}[0.009, 0.162, 0.018, 0.321] in the first chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Influence Measure."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "RLXSqrep80mPbWfJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {BBC}[0.095, 0.098, 0.940, 0.391] higher or lower than {CNN}[0.095, 0.568, 0.940, 0.898] in the 'Steady increase' scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "p7jjAPbgiYCpCVvQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Rapid expansion' scenario, which media outlet, {BBC}[0.095, 0.098, 0.940, 0.391] or {CNN}[0.095, 0.568, 0.940, 0.898] has more influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "BBC."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "MKoXlkvPWM8MWaWG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the influence measure of {BBC}[0.095, 0.098, 0.940, 0.391] higher or lower than {CNN}[0.095, 0.568, 0.940, 0.898] in the 'Accelerating decline' scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "e9DaYvhyD6GUPEWN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located within the region [0.959, 0.555, 0.975, 0.570]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for CNN, which is represented with purple color."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "bIXt3Cd8vyaKUc0d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.764, 0.949, 0.833, 0.967]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 'Steady increase' label on the x-axis of the second chart."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "DBYOSUDASC4nYvGH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.033, 0.946, 0.046, 0.964]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an y-axis tick label '0.3' on the second chart."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "AsEPKDwxfa3nuwvQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label for the subplot showing data on Acid Rain pH?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time<r>[0.507, 0.473, 0.528, 0.491]</r>."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "Ktt4HKVwYabq1Wv1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'SuddenDrop' label in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e<r>[0.228, 0.091, 0.250, 0.101]</r>."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "EVLWkzrrFH19uwdW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the subplot with Surfaces Temperature data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value<r>[0.670, 0.218, 0.679, 0.265]</r>."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "cHoFfFRTEE38psOZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the line for 'MarinePollution'}<r>[0.439, 0.904]</r> higher than {the point for 'RiverContamination'}<r>[0.770, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "Vi2vx53tEVclvaU9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the y-grid line at}<r>[0.044, 0.230]</r> align with {the y-tick marked '60'}<r>[0.044, 0.230]</r> in the Atmospheric PM2.5 subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "bktZep76NRj5af13",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point on the TrendSteadying line}<r>[0.057, 0.909]</r> lower than {the last point on the same line}<r>[0.317, 0.905]</r> in the Atmospheric PM2.5 subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "cSfPGuxSTpUocjYl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.044, 0.537, 0.330, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot for Atmospheric PM2.5, which includes axis labels: Time (Horizontal), Value (Vertical), y-tick marks from \u221220 to 120, x-tick marks from \u22121 to 6, and two line plots named TrendSteadying and SuddenDrop with markers."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "uVGM2RxGduIYYZxC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.659, 0.927, 0.991, 0.957] contain in the RiverContamination subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays the x-axis tick labels ranging from -1 to 6 for the RiverContamination data, aligned along the bottom of the subplot."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "AFzxW9K6IzCKSgXm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are included in the area [0.705, 0.047, 0.991, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This space encompasses the Surfaces Temperature subplot, including y-tick marks from -20 to 120, x-axis labels for time, a y-label indicating value, and two line plots representing environmental trends."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "uNkh2xqpC8xgUn6O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object corresponding to {coordinates}[0.658, 0.116, 0.694, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "legend handle for \"Polynomial Trend (from Fuel Efficiency)\"."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "fw18ZN2hcwpwvDRw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {coordinates}[0.468, 0.819] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point for \"Fuel Efficiency\" at 1000 steps."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "GFvzH8qtqo2QFNJi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat feature is displayed at {coordinates}[0.263, 0.344, 0.274, 0.372]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text label for a point on the \"Energy Efficiency\" line graph."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "xVwzdEbjeSoaU9ZX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Energy Efficiency' at 500 steps}[0.263, 0.394] higher or lower than {the point for 'Fuel Efficiency' at the same step}[0.263, 0.606]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "K6USFnvqGG8vCthW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the starting point on the line for 'Energy Efficiency'}[0.099, 0.494] above or below {the starting point on the line for 'Fuel Efficiency'}[0.099, 0.556]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "XpzD2hR55vTiveUC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at the 900th step, {Fuel Efficiency}[0.427, 0.806] or {Overall Efficiency in subplot 1}[0.924, 0.753]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Overall Efficiency."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "XIcPtLWnyZvwDUQi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.087, 0.059, 0.257, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region showcases the legends {Energy Efficiency}[0.132, 0.070, 0.252, 0.098] with a {handle}[0.089, 0.074, 0.125, 0.090], colored orange, and {Fuel Efficiency}[0.132, 0.112, 0.232, 0.140] with a {handle}[0.089, 0.116, 0.125, 0.132], colored blue."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "UINkJsyKVoWtX27S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the coordinate space [0.577, 0.045, 0.983, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area presents a plot with a labeled y-axis 'Polynomial Value' and multiple x-axis text labels for step increments from 100 to 1000 paired with respective plots for labels {Overall Efficiency}[0.701, 0.070, 0.818, 0.098] and {Polynomial Trend (from Fuel Efficiency)}[0.701, 0.112, 0.971, 0.140]."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "pBtyInjAvpFjKW1q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHighlight the components in the rectangle [0.523, 0.081, 0.553, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis tick labels ranging from 0.45 to 0.85 for the second subplot, informing someone on various polynomial values plotted against steps."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "IQspZz5PihpBjpqB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.035, 0.532, 0.079, 0.560] indicate on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "48500."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "yBNJKLJzHTpYfbbo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is present at [0.154, 0.055, 0.342, 0.083] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public School Loan Amount."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "SaUvMl0J3ycfQgba",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the point at [0.242, 0.481] on the Public School Loan Amount line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2012 loan amount."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "rGGBEjwelwQWQzF5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Public School in 2013}[0.283, 0.329] higher than {the point for Government School in 2013}[0.283, 0.677]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "OQXbgugmaWhKXG0M",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the trend line for 'Number of Software Destroyed'} cover a larger range vertically compared to {the trend line for 'Number of Hurricanes'} within the subplot coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "FUsdyXrBcZBHBjBr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color marker represents a greater loan amount in 2018, {Public School}[0.486, 0.068] or {Government School}[0.486, 0.391]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public School."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "VZxohUc1A4tLFwvl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents and visual arrangement in [0.035, 0.256, 0.079, 0.370]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis labels {68500}[0.035, 0.256, 0.079, 0.284] and {63500}[0.035, 0.325, 0.079, 0.353], each displayed with black text and vertically spaced."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "6nQDFnNGxpLQtd0J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the coordinates [0.711, 0.097, 0.971, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the legend for the subplot, specifically containing the label for 'Number of Hurricanes' with it's corresponding colored marker indicator."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "BhtyVuGZVPWnZVBt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.582, 0.903, 0.617, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes {the x-axis label '2009'}[0.582, 0.903, 0.617, 0.931] on the subplot showing 'Number of Software Destroyed' and 'Number of Hurricanes'."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "iFE8MY48sDFPOWnR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle represent at [0.448, 0.681, 0.485, 0.698] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Procurement."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "MRfW7oEl31PFYoU9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.939, 0.903, 0.947, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label 5."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "vEZS8pbuomrghyUo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis tick label} at [0.035, 0.127, 0.057, 0.155] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.5."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "xsa3YK4XxubDyl28",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point from 'After-sale Service'}[0.533, 0.425] higher or lower than the {point from 'Procurement'} at the same x-axis position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "SUusWOYCpnBO0HmT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{At 'Quarter 5'}[0.944], is the {value of 'Supply Chain'}[0.944, 0.568] higher or lower than the {value of 'After-sale Service'}[0.944, 0.209]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "mkQrvyA0CJ6O1Szb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {x=2}, is the {point of 'Delivery'}[0.327, 0.712] higher or lower than the {point of 'Manufacturing'}[0.327, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "UNlApsSxHf2zykRE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the coordinates [0.447, 0.624, 0.618, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes labels for the lines 'Supply Chain', 'Procurement', 'Manufacturing', 'Delivery', and 'After-sale Service'. These labels are associated with different colored markers representing each line in the chart."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "TX1PX1oGeXatri90",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the attributes of the region [0.080, 0.030, 0.985, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the axes of the chart, where the historical data are plotted. It contains the gridlines along the x and y axes, the x-axis labeled as 'Quarters (x Phenomenon)', indicating the different quarters, the y-axis labeled as 'Efficiency', reflecting the efficiency metrics, and all the plotted lines representing 'Supply Chain', 'Procurement', 'Manufacturing', 'Delivery', and 'After-sale Service'."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "wOdc7sv6hzJe5Wiw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.448, 0.633, 0.613, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two parts of the legend, specifically the labels and icons for 'Supply Chain' and 'Procurement'. Each label is accompanied by a colored symbol."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "HHicC6me2OYJXAxF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'Stocks' line on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "KpVIGfkjQCqbxv5V",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the the y-axis label's text?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Value."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "IymN4h8h1bG58cqF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is applied to the x-axis at {the coordinate}[0.480, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "12."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "QUnt4IomGnMIaegh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at month 6 on the 'Stocks' line}[0.294, 0.109] higher than {the corresponding point on the 'Bonds' line}[0.294, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "anVmx3Wqy599XVpk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a point located lower at {month 10}[0.418, 0.276]."
+            },
+            {
+                "from": "gpt",
+                "value": "Stocks."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "NUCmSK5MTUXuNRdM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first 'Gold' line point}[0.593, 0.215] higher in value compared to {the first 'Bonds' line point}[0.108, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "Ry5O5HknEMgCSvbt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.520, 0.060, 0.780, 0.130]."
+            },
+            {
+                "from": "gpt",
+                "value": "Contains y-axis labels ranging from '95.0' to '100.0' along with their tick marks integrated into the plot; labels have black text."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "DCMVXo7dsww6No8Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.085, 0.030, 0.495, 0.858]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the primary plot area for the 'Investment Value' graph, showcasing data lines for 'Stocks' and 'Bonds', grids, and axis labels."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "JQmdGd5BKWhXlX2q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is encompassed within the area [0.576, 0.044, 0.847, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the subplot containing `Gold` and `Polynomial Trend (from Bonds)` items, described with respective color markers (green and magenta)."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "wzfoe1WIlGgZUsQ7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text within the coordinates [0.770, 0.942, 0.799, 0.970] in the right-hand chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label, \"Year\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "shCKpPOTg57fzyxI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located at the coordinates [0.145, 0.097, 0.191, 0.125] within the left chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "eabqLPdL4hktyXTq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {the y-axis} at [0.035, 0.421, 0.070, 0.449] in the left-hand plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "1500."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "VDiLO8XN5k17EEFK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the right-hand plot, is {the WhatsApp data point at 2020}[0.965, 0.058] higher or lower than {the Instagram data point at 2020}[0.965, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "YIG32CVLmhZHxNWx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the left-hand plot, do {the line representing Facebook}[0.111, 0.065, 0.473, 0.419] and {the line representing Twitter}[0.111, 0.760, 0.473, 0.766] intersect?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "pvoROKb6irLD0s09",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the user numbers for {Facebook in 2016}[0.111, 0.419] higher or lower than the user numbers for {Twitter in 2016}[0.111, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "ux0GbPlTTmWYhWCh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the indicators given in the keys located at the coordinates [0.093, 0.044, 0.216, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two social networks, Facebook and Twitter, represented by different colors and marker symbols. The Facebook key is represented with a color of '#4169e1' and a round marker. Twitter is represented with color '#2e8b57' and a square marker."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "NYZ3LFRdkSC5O4f4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the content in the area marked by [0.508, 0.337, 0.522, 0.549]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Y-Axis label, 'Users (Millions)', for the right-hand plot. The label is in a black color and is rotated at a 90 degree angle."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "5S5CAFvn2iwku2ef",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information lies between the bounding box of [0.586, 0.030, 0.984, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This box encompasses a plot featuring the change in user counts for both WhatsApp and Instagram from 2016 to 2020. The counts are measured in Millions and denoted on the y-axis, and the years are denoted on the x-axis. A key is also located in this bounding box, defining the color and marker for WhatsApp and Instagram."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "GREhd2elsPGqF1Oh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.010, 0.345, 0.019, 0.559] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the first subplot."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "nXiNtxNAeXrSMBUQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information represented by the line located at coordinates [0.075, 0.081, 0.456, 0.750]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Citation Counts of different Academic Fields."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "tzzi2fYAp64uKfto",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the tick label at coordinates [0.173, 0.903, 0.232, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The academic field Mathematics."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "bFyilu2eOo3wZqN0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the data point for 'Physics'}[0.245, 0.222] and {the data point for 'Mathematics'}[0.202, 0.152], which one has higher citation counts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mathematics."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "i0cXHX1sfmJY1Dlw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for 'Health Science'}[0.621, 0.596] in the second subplot higher or lower compared to {the data point for 'Statistics'}[0.578, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "NxxkHRTfoBphC3ke",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the data point for 'Biology'}[0.329, 0.257] in the first subplot have more or less citations than {the data point for 'Engineering'}[0.372, 0.081]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "sjGQ9QG5ttU50KkG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the section between coordinates [0.559, 0.048, 0.978, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the second subplot of the chart that represents the polynomial trend value of citation counts of different academic fields. The line plot shows both the outliers and polynomial trend."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "aGvin66DhSWElaQH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide some details about the region [0.862, 0.062, 0.974, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot. It includes a green symbol representing outliers and a purple symbol representing the polynomial trend."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "LYRkmjMvMAnoP911",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.056, 0.048, 0.475, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the first subplot of the chart, which is a line plot illustrating the citation counts across a range of academic fields."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "tHPQu6MTlKAqWeGn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, what is the label for the object with coordinates [0.149, 0.055, 0.427, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Population Density (Persons per sq. km)\"."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "fCfb108ZXqqRSez1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is labelled as \"Value\"."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "kDcsrsawceJk3E1Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text for {the x-axis tick label}<r>[0.662, 0.903, 0.697, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2021\"."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "mZRdhnBV3l9HurbL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point for the year '2023'}<r>[0.330, 0.803]</r> and {the point for the year '2024'}<r>[0.402, 0.794]</r>, which year has a higher 'Population Density (Persons per sq. km)' value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year '2023'."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "LbAjEBn4JifN5em8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year '2025'}<r>[0.473, 0.068]</r> above or below {the point for year '2024'}<r>[0.894, 0.403]</r> in their Renewable Energy Production values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "KqpfvC6RhMqQuWqF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor 'Renewable Energy Production (in PJ)', is the value for {year '2020'}<r>[0.116, 0.294]</r> greater than, equal to, or less than the value for {year '2021'}<r>[0.187, 0.272]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "8AdU2YoXxRg9VFBJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.527, 0.423, 0.567, 0.643]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two y-axis tick labels of the second subplot: {487.8}[0.527, 0.615, 0.567, 0.643] and {540.2}[0.527, 0.807, 0.567, 0.835]."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "KTwYQnLwM8XErEyb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with coordinates [0.527, 0.044, 0.977, 0.094]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend associated with the second subplot. It represents {the 'Polynomial Trend (from Renewable Energy Production)'}[0.593, 0.055, 0.971, 0.083] graph line."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "lP2DlZ155W2epFb2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.098, 0.303, 0.473, 0.818] of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the data points for the lines representing 'Population Density (Persons per sq. km)' and 'Renewable Energy Production (in PJ)'."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "8TtxOgX4YHOnPSz8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis} at [0.015, 0.383, 0.029, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Intensity."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "H1FAzrBeXk26A47L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Rights Protection'} with points at coordinates like [0.098, 0.065, 0.461, 0.737]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (Hex color code: #0000ff)."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "xIHpn4SOVz539yNO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {the line representing 'Rights Protection'} within the coordinates [0.098, 0.421, 0.461, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square (represented by 's' in matplotlib)."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "xEEabHSFikAyl6ld",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the trend of 'Social Inequality'}[0.098, 0.065, 0.461, 0.737] and {the trend of 'Rights Protection'}[0.098, 0.421, 0.461, 0.757], which one dips lowest at the 200 months mark?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Inequality."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "HMCqNAA8XsNUHRq3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the start point (0 months), does {the trend of 'Social Inequality'}[0.098, 0.065, 0.461, 0.737] start higher or lower than {the trend of 'Rights Protection'}[0.098, 0.421, 0.461, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "FCGt87gUPRjWhj4v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the line of 'Rights Protection'}[0.604, 0.068, 0.967, 0.800] and {the line of 'Polynomial Trend (from Rights Protection)'}[0.604, 0.105, 0.967, 0.818], which ends higher at 300 months?"
+            },
+            {
+                "from": "gpt",
+                "value": "Polynomial Trend (from Rights Protection)."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "MZJhKF7eflmIjY04",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area with coordinates [0.586, 0.030, 0.985, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, it represents a subplot that displays a polynomial trend of 'Rights Protection' over time. It includes a detailed x-axis labeled as 'Time (months)', and a y-axis labeled as 'Polynomial Trend of Rights Protection'. The plot includes two different lines, one for the exact data of 'Rights Protection' and the other representing the polynomial trend derived from it. A legend is present to distinguish between the two lines."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "6zkXuEe2OI6Yc7AB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area [0.087, 0.044, 0.256, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the subplot. Specifically, it includes labels and color markers for the 'Social Inequality' and 'Rights Protection' trends."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "i4qOmOq56FfXXmsA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.494, 0.184, 0.508, 0.702] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is the y-axis label for the second subplot of the chart, reading as 'Polynomial Trend of Rights Protection'. The label is placed vertically and colored in black."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "0ygjmqFXPFiz3xyS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis denote in the plot on the left side of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "On Fire Value"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "YEEjI4dwHcAqyITe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object residing in the coordinates [0.035, 0.192, 0.052, 0.220] in the left subplot of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "80"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "giA49tyOHovcD1wZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data label of the object located at [0.369, 0.031, 0.384, 0.059] in the left subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "95"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "GpR8xvnW8qY0fyPU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the left subplot of the chart, is {the point labeled '73'}[0.095, 0.261] above or below {the point labeled '32'}[0.188, 0.595] in the 'On Fire' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "4whdflFW5LDw8LfM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the right subplot, looking at the 'Ice Cool Trend' line, is {the point at the third time interval}[0.779, 0.689] higher or lower than {the point at the first time interval}[0.591, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "cSWfkPJSWDsjbT3p",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the right subplot of the chart, which trend value is higher at the 5th time interval: 'Ice Cool Trend' or 'Polynomial Trend (from Ice Cool)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Polynomial Trend (from Ice Cool)"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "oMi6lVMw4QzFoFYB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area in the coordinates [0.087, 0.063, 0.182, 0.114] in the left subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a colored marker and the term {On Fire}[0.127, 0.074, 0.176, 0.102] from the legend."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "yZEIGCwmQjScglXL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the content of the area in [0.729, 0.942, 0.828, 0.970] in the right subplot of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label {Time Intervals}[0.729, 0.942, 0.828, 0.970] of the second subplot is located in this area."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "yQ4fUcOHpDe55fJV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the area [0.573, 0.049, 0.985, 0.856] in the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is occupied by a labeled grid chart. The grid chart has x-ticks for 'Time Intervals' running 1 through 5 and y-ticks running from 0 to 140 marking the 'Ice Cool Value'. The chart indicates two lines - Ice Cool Trend and Polynomial Trend (from Ice Cool) along with their points marked at the time intervals and their respective trend values."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "EKS4o9poZIil3r2R",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis represent in the chart according to the label at [0.760, 0.942, 0.811, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Months."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "m50WbyjvWOVJCsOd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat product is represented by the line with coordinates [0.604, 0.068, 0.967, 0.818] in the second sub-plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product C."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "8AOjNVwfQLAZ22QM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.527, 0.932, 0.562, 0.960] on the y-axis of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "1500."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "BHZ7gu3vpDdNPtc6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Product A}[0.111, 0.643, 0.474, 0.818] and {Product B}[0.111, 0.068, 0.474, 0.393] in the first subplot, which product has declining sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product A"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "UBAC7Sd9dRufs25L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Product C}[0.604, 0.068, 0.967, 0.818] in the second subplot has higher sales in {Month 12}[0.958, 0.903, 0.976, 0.931] compared to {Month 1}[0.600, 0.903, 0.608, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "MeMcyfxH8Dio7wJu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Product A}[0.111, 0.643, 0.474, 0.818] and {Product C}[0.604, 0.068, 0.967, 0.818], which product shows more fluctuation in sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product C"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "zc4cu0u0vgsYyz8h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates range [0.527, 0.058, 0.562, 0.022]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the lower section of the y-axis on the second subplot containing labels '3250' and '3750'."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "IgslB3u1vwO0l7oH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen between the coordinates [0.278, 0.088, 0.849, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region signifies the legend items in the first subplot, which include the labels for 'Product A - Rapid Decline' and 'Product B - Gradual Improvement' with their respective color markers."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "3Ad0p7uSlPZPux3g",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.145, 0.055, 0.321, 0.083]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region in the first subplot contains a legend item indicating the color marker and label for 'Product A - Rapid Decline'."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "UmBRRhnN6iV1JklG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.015, 0.226, 0.029, 0.671]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "R&D Investment (in Billion USD)."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "1agu2DVyix5RFWSk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the line representing 'Revenue Increase' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.587, 0.066, 0.966, 0.556]"
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "QkPyjuk10U58mVPc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is signified by {the y-axis tick label} at <r>[0.527, 0.210, 0.545, 0.238]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "11."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "qR2ER8jm1Udq5HQz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt year 3, is the R&D investment higher or lower than the revenue increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "um7H37uFxusRylq9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {R&D Investment}[0.179, 0.345, 0.474, 0.093] higher or lower at year 2 compared to year 9?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "NSSw6yIsvUHNJdQJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the initial and final years, did the {Revenue Increase}[0.587, 0.066, 0.966, 0.556] trend increase or decrease over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "KHimnOM32jcsPnQu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region <r>[0.068, 0.041, 0.490, 0.860]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a plot representing the R&D investment over a period of 10 years. It includes a light-colored grid, axis labels mentioning Time Period (in Years) and R&D Investment (in Billion USD), multiple tick labels, and a line plot with circular markers representing the trend."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "jCPJcnIbaXNBEqzS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.555, 0.046, 0.993, 0.857]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a plot displaying the increase in revenue over ten years. Similar to the first, it has a light-colored grid, axis labels detailing Time Period (in Years) and Revenue Increase (in Billion USD), multiple tick labels, and a line plot, but with square markers for the data points."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "3yJZy8mibZ9nmNtr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the rectangle <r>[0.565, 0.048, 0.962, 0.580]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the right side of the graph showing 'Revenue Increase' is located. It represents the revenue increase trend with square markers over a period of 10 years, with associated x and y-axis labels and ticks."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "EBzCBnV63sv7Lo9X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the Charity Index in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "7Vtvjvrn5Fugqfb2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.015, 0.675, 0.029, 0.768]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Charity Index."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "kadNP7tmykNuqa9K",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the legend for the first subplot located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.087, 0.031, 0.232, 0.056]."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "Kz6ou6CCynTuUTY3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line point for 2017}[0.645, 0.570] higher than {the point for 2020}[0.869, 0.680] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "u9Cu50klT3juYgeD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is lower, {the welfare index for 2016}<r>[0.570, 0.159]</r> or {the charity index for 2016}<r>[0.570, 0.652]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Welfare Index."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "G8G7Z82MnPd3ESDs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the points for 2021 is higher on the chart, {the welfare index}<r>[0.944, 0.043]</r> or {the charity index}<r>[0.944, 0.735]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Welfare Index."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "bs3OAsiGyUL6uKH3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.121, 0.437, 0.794, 0.475]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily includes x-axis labels for years 2010 through 2019 in the first subplot, presented in black text, \"2010\" at [0.104, 0.461, 0.139, 0.475] through \"2019\" at [0.777, 0.461, 0.812, 0.475]."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "lvqQBtrs3GTxnB0w",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the area [0.676, 0.522, 0.978, 0.568]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains legends for the second subplot displaying the 'Charity Index' and 'Polynomial Trend (For Charity Index)' colored blue and green respectively with labels placed at [0.720, 0.527, 0.813, 0.541] and [0.720, 0.548, 0.972, 0.562]."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "GlHo2NzGZhClozLA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found within [0.080, 0.024, 0.985, 0.437]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the first subplot which includes an axes area for plotting, grid lines in gray, and labels indicating the 'Welfare Index' on the y-axis and 'Years' on the x-axis. The line for the 'Welfare Index' runs across this region."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "bPE4Ygk5xH9lKtXX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {Y-Axis}<r>[0.025, 0.386, 0.058, 0.529]</r> in the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ratio."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "eXi4cpHtKcGNjWux",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {X-Axis}<r>[0.382, 0.912, 0.692, 0.963]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Index."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "erKH3XqamWHMILUY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color of the {legend entry's label}<r>[0.210, 0.319, 0.518, 0.364]</r> represent on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Index 6.6."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "lMHNggSAi4VhhQoS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the line plot, does the point marked with {Innovation Index 1.2}<r>[0.167, -78290.554, 0.167, 0.815]</r> lie to the left or right of the line marked with {Innovation Index 2}<r>[0.281, -78290.554, 0.281, 0.815]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "QVeaEkbLrcIjrtFb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Start-Up Density line}<r>[0.138, 0.110, 0.935, 0.541]</r> generally above or below the {Government Support line}<r>[0.138, 0.760, 0.935, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "eOnOekajASREalHh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher, the {point indicating Start-Up Density}<r>[0.281, 0.110]</r> or {Government Support}<r>[0.281, 0.805]</r> at the innovation index of 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Government Support."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "XzDtLmJ8GJcmTd77",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present in the area specified by these coordinates: <r>[0.114, 0.098, 0.537, 0.517]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, which includes labels for Innovation Index 1, Innovation Index 1.2, Innovation Index 2, Innovation Index 6.6, Start-Up Density, and Government Support, each with their corresponding color markings."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "3oi3GAwSMzQVY9Kg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the location defined by <r>[0.138, -78290.554, 0.935, 0.815]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this range, there are multiple lines drawn to represent Innovation Index 1, Innovation Index 1.2, Innovation Index 2, and Innovation Index 6.6."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "DsOhruBNhO9Nix8Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.210, 0.386, 0.459, 0.431]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for 'Start-Up Density' in the legend section of the chart, along with its associated color marker."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "AmRBWZ5SiO0UzZY2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which area is {the y-axis label}<r>[0.021, 0.292, 0.041, 0.612]</r> located?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "ya1lqsVVxHRXTtR6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is at these coordinates [0.105, 0.044, 0.245, 0.220]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "HC3m0dANrm5FrPLQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the point}<r>[0.768, 0.784]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Natural gas."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "XoEWIIlKG4g1lTtF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource consumption is higher at the point with {coordinates}<r>[0.938, 0.066]</r>, Coal or Nuclear?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nuclear."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "6tVUGKhfN2UnYnb0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Solar'}[0.257, 0.695, 0.257, 0.874] steeper than {the line for 'Biofuel'}[0.500, 0.695, 0.500, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are of the same steepness."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "9w6r0zhryiLRb9ej",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource has a lower energy use, {the point labeled 'Wind'}[0.378, 0.874] or {the point labeled 'Coal'}[0.622, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Wind."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "K9Mj7Benf5XVYCrj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you descibe the data provided within {the region}<r>[0.168, 0.055, 0.237, 0.167]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the legend label 'Biofuel', associated with a color handle."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "80tlrHOCeWqv22bj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.480, 0.942, 0.593, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label 'Energy Use'."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "xI29KMNPxDncZjVt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.050, 0.145, 0.081, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label '4.0' located at these coordinates."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "u5FHhmQRmiseDofx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identifier of the object located at [0.446, 0.675, 0.948, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Maximum Performance Efficiency' sublegend label."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "1n69tGGYHmS8IsXq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label at [0.434, 0.913, 0.661, 0.963] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cost Quality."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "f6ptyv6ASJMAMM0Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the figure, as given by the forward-most text in {the title field}<r>[0.025, 0.113, 0.058, 0.752]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no title given in the provided coordinates."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "W5FPm96LInyrq4Jz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Best Cost' line}<r>[0.159, 0.075, 0.159, 1.957]</r> to the left or right of {the 'Average Cost' line}<r>[0.315, 0.075, 0.315, 1.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "1bpEZLrIFAXUbTKS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Best Cost' line}<r>[0.159, 0.075, 0.159, 1.957]</r> above or below {the 'Maximum Performance Efficiency' line}<r>[-308.406, 0.075, 2.334, 0.075]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is not applicable because the lines are vertical and horizontal, respectively."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "DRX9pI8qtE1CQevL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Average Cost' line}<r>[0.315, 0.075, 0.315, 1.957]</r> to the left or right of {the 'Worst Cost' line}<r>[0.781, 0.075, 0.781, 1.957]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "q3oZMXkiIYq2mBXF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [[0.446, 0.472, 0.588, 0.517], [0.446, 0.539, 0.645, 0.584], [0.446, 0.607, 0.608, 0.652], [0.446, 0.675, 0.948, 0.720], [0.446, 0.742, 0.778, 0.787]]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the labels of the sublegend: {Best Cost}<r>[0.446, 0.472, 0.588, 0.517]</r>, {Average Cost}<r>[0.446, 0.539, 0.645, 0.584]</r>, {Worst Cost}<r>[0.446, 0.607, 0.608, 0.652]</r>, {Maximum Performance Efficiency}<r>[0.446, 0.675, 0.948, 0.720]</r>, and {Value Chain Efficiency}<r>[0.446, 0.742, 0.778, 0.787]</r>."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "EWCpu9XDZUKBU4P5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.352, 0.747, 0.431, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the colored handle of the {Value Chain Efficiency}<r>[0.352, 0.747, 0.431, 0.775]</r> in the sublegend."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "x3emkEEXE9t5F11l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the coordinates [0.159, 0.075, 0.936, 0.640]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It outlines the coordinates of the 'Value Chain Efficiency' line chart."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "WCWz2oqDBhzhFbE3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with the marker 'o' and color '#0173b2' represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health management trend."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "PkUHy83LsWL0MHTR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Search window size ratio."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "Ydupchurl5SVLHU6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates, what does the entry with points {coordinates}[0.141, 0.290, 0.215, 0.310] in the legend key represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health management trend."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "2UulCvWHOcKclsLc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend for {Initiation of Diabetes trend}<r>[0.421, 0.670, 0.421, 0.828]</r> above or below the trend for {Abrupt drop in Diabetes trend}<r>[0.604, 0.670, 0.604, 0.828]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "umNcaXh0ShB5kciR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend reaches the highest value, {Expansion in Virus Spread}<r>[0.920, 0.670, 0.920, 0.828]</r> or {Health management trend}<r>[0.159, 0.196, 0.933, 0.670]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health management trend."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "abjnkMd4twuPwVVj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn this chart, is the point at {coordinates}[0.604, 0.828] for {Abrupt drop in Diabetes trend}<r>[0.604, 0.828]</r> greater or lesser than the point for {Expansion in Virus Spread}<r>[0.920, 0.828]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "5W9LbOOw2tVyv2Zs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.136, 0.060, 0.694, 0.344]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend entries for the chart's lines: {Initiation of Diabetes trend}<r>[0.148, 0.097, 0.208, 0.097]</r>, {Abrupt drop in Diabetes trend}<r>[0.148, 0.165, 0.208, 0.165]</r>, {Expansion in Virus Spread}<r>[0.148, 0.232, 0.208, 0.232]</r>, and {Health management trend}<r>[0.141, 0.290, 0.215, 0.310]</r>."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "VNP6NxoqqFlaEReK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.376, 0.913, 0.716, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label, which is \"Quantization error.\""
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "OlAcBKLGiivz4y3r",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located within the area bounds [0.120, 0.037, 0.972, 0.828]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the plot itself, including four plotted line trends styled with different colors and styles: the Initiation of Diabetes trend, the Abrupt drop in Diabetes trend, the Expansion in Virus Spread, and the Health management trend. It also includes the x-tick and y-tick labels."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "OAG3XVYe6JmekUu7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.015, 0.350, 0.034, 0.606] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "LheuowYzY28mgiXy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the third x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.261, 0.911, 0.296, 0.934]"
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "8scKGEkyK8Fr8Esx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what {the point on line 'Public Service Cost'} at [0.528, 0.304] indicates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The cost of Public Service in 2005."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "VsxylUugsGNVPFh0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Public Service Cost' in 2003}[0.362, 0.362] higher or lower than {the point for 'Basic Welfare Cost' in 2003}[0.362, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "WhRnJV5dnHAxGlEN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cost was higher in 2009, {Public Service Cost at point}[0.860, 0.158] or {Basic Welfare Cost at point}[0.860, 0.537]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Basic Welfare Cost."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "zRueoymBw0WouxD1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, did the {Public Service Cost}[0.943, 0.100] exceed the {Basic Welfare Cost}[0.943, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "hMFZ5Ezm1zrxziEs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.080, 0.077, 0.317, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend section. It consists of two items: {Public Service Cost}[0.138, 0.089, 0.310, 0.119], represented with a color code of '#1f77b4', and {Basic Welfare Cost}[0.138, 0.134, 0.306, 0.164], represented with a color code of '#ff7f0e'."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "SLs9FFEHHi3cHp4T",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.508, 0.943, 0.549, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label, which reads {\u201cYear\u201d}[0.508, 0.943, 0.549, 0.975]."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "LOw2TbizA7XqH820",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements exist within [0.261, 0.911, 0.795, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there are 4 x-axis tick labels for the years 2002{[0.261, 0.911, 0.296, 0.934]}, 2004{[0.428, 0.911, 0.463, 0.934]}, 2006{[0.594, 0.911, 0.629, 0.934]} and 2008{[0.760, 0.911, 0.795, 0.934]}."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "Rxkb3ubSwNsjveyu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label {at these coordinates}<r>[0.172, 0.912, 0.310, 0.936]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Simulation Platform."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "4xa5F3semUsBQD84",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for 'Before Pandemic'}<r>[0.110, 0.465, 0.898, 0.858]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "Gj6c5EPYuuM9FYq5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label}<r>[0.039, 0.849, 0.061, 0.873]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "Q9ZzZXTK9T0OWM3P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the points {for 'Gamification Tools'}<r>[0.635, 0.607, 0.635, 0.339]</r>, which has a higher 'Usage Ratio', 'Before Pandemic' or 'During Pandemic'?"
+            },
+            {
+                "from": "gpt",
+                "value": "During Pandemic."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "mrOsbpJDEOh0fJ3k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 'Before Pandemic' for 'Learning Analytics Tools'}<r>[0.898, 0.733]</r> above or below {the point 'During Pandemic' for 'Learning Analytics Tools'}<r>[0.898, 0.465]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "mdmWmGI7jPtVxOPb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'During Pandemic' for 'e-Reading'}<r>[0.372, 0.103]</r> higher or lower than {the point for 'During Pandemic' for 'Online Assessment Tools'}<r>[0.766, 0.103]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same level."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "VoEb6QdbK1RWwkgT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the visualization within the region <r>[0.110, 0.103, 0.898, 0.858]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two line graphs, one representing the usage ratio of EdTech modules before the pandemic and the other during the pandemic. Each module is represented by a unique point on the line."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "VkAuBLeY2Sl4NHkl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me more about the region <r>[0.079, 0.079, 0.280, 0.176]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend of the line chart. It includes two entries: 'Before Pandemic' with a blue line symbol and 'During Pandemic' with an orange line symbol."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "Q5uq48C9vefO4E10",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region  <r>[0.322, 0.025, 0.685, 0.058]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the graph titled 'Growth Trend of EdTech Modules'."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "ro9oTGoqhH4rOL7T",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with coordinates [0.653, 0.717, 0.653, 0.828] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment A."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "6p3H0zr9HwaQDlH1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what coordinate can we find the label 'Risk Return Trade-off'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.210, 0.295, 0.524, 0.340]."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "JpzHfTRbURnGEURk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.068, 0.259, 0.082, 0.294]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "cYG8ZAuRVns6cYeV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the point}[0.653, 0.828] on the 'Investment A' line, is it on the left or right side compared to {the point}[0.767, 0.828] on the 'Investment B' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "Sl9zmDeOoVPSBsPH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher risk factor at the 0.4 return ratio, {Investment A}[0.653, 0.828] or {Investment B}[0.767, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment B."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "uX1WR3PFpu6RbCEL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the point}[0.767, 0.051] on the 'Investment B' line, is it above or below {the point}[0.935, 0.051] on the 'Investment C' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same level."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "RNM8ALhmxX7lEHfc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in between the coordinates [0.099, 0.051, 0.975, 0.828]."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the entire chart, including the axes labels, the tick labels, the legend, and the plot lines for Investment A, B, C and Risk Return Trade-off."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "cfFZYo66M3SKuXD4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region enclosed by the coordinates [0.114, 0.074, 0.536, 0.358]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can find the legend of the chart: {Investment A}<r>[0.210, 0.092, 0.410, 0.137]</r>, {Investment B}<r>[0.210, 0.159, 0.410, 0.204]</r>, {Investment C}<r>[0.210, 0.227, 0.410, 0.272]</r>, and {Risk Return Trade-off}<r>[0.210, 0.295, 0.524, 0.340]</r>."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "zDT27YukO8zpHlCL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the portion defined by [0.537, 0.030, 0.537, 0.030] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the area where the subtitle of the chart should be located. However, in this particular chart, the subtitle is absent."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "o2iNh9htXR1YuLdr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel}<r>[0.023, 0.394, 0.045, 0.557]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "vuVjfTuTKQwmx3uN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the {Unemployment Rate marker}<r>[0.606, 0.050, 0.662, 0.050]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "UmXUBRoiY3yEp2w5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the value of {the x tick label}<r>[0.269, 0.940, 0.283, 0.969]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "W1iPmef1tWV6rcNv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Unemployment Rate'}[0.138, 0.072] line above or below {the 'Median Income'}[0.828, 0.072] at X =-1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "AiNiwtSGoobiIxDJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Median Income points}<r>[0.138, 0.879, 0.828, 0.072]</r> rising or falling overall?"
+            },
+            {
+                "from": "gpt",
+                "value": "Falling."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "hf356Ly8oXuIOr9T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at X=1, Unemployment Rate or Median Income?"
+            },
+            {
+                "from": "gpt",
+                "value": "Median Income."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "KXWiavzRl5kmxqmh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is presented in the region [0.685, 0.034, 0.975, 0.071]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text 'Unemployment Rate' from the chart legend, paired with a red marker."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "y0v5BcvKziAyiy8y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the contents within the coordinates [0.877, 0.262, 0.946, 0.291]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y label '81000' is located in this region."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "00SlmDDM9Wk3Sl1p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area of [0.104, 0.072, 0.828, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plotted lines for both 'Unemployment Rate' and 'Median Income' with their corresponding data points."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "fDI3Wf13oalm4OXV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the detail at [0.329, 0.913, 0.760, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "TwrXOoxrZ5y4UHFy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.025, 0.115, 0.058, 0.750]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Improvement Potential."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "55r7fAE4ywYaTgse",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is indicated at {the coordinates}<r>[0.153, 0.792, 0.153, 0.828]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline-Efficiency-100 line."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "JYOlHdqspXah8aKG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point at x=1}<r>[0.153, 0.828]</r>, is the value of Baseline-Efficiency-100 the same as {the Efficiency Improvement point}<r>[0.153, 0.109]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "iM1VqifhXnHZQwwj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point coordinates}<r>[0.936, 0.720]</r>, is Efficiency Improvement line above or below {the Baseline-Efficiency-1024 line}<r>[0.936, 0.037]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "Ln15e3qE7Fm6R5ZF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {point 6.6}<r>[0.936, 0.828]</r>, Baseline-Efficiency-1024 or Efficiency Improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline-Efficiency-1024."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "iUj2z7n68099Qf6N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements present within the [0.128, 0.060, 0.606, 0.411] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes descriptions for multiple trends: Baseline-Efficiency-100, Baseline-Efficiency-256, Baseline-Efficiency-512, Baseline-Efficiency-1024, and Efficiency Improvement."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "JHMJnxrZztrnMIat",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide information about the elements located at [0.153, 0.109, 0.936, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers part of the Efficiency Improvement line, which is marked with blue color and circular markers, and includes multiple points from x = 1.0 to x = 6.6."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "LFW7oip7p1OsnQr1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.225, 0.078, 0.575, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text label for the Baseline-Efficiency-100 trend."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "jsyPJ0buAPbNVqZx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text with coordinates [0.015, 0.389, 0.033, 0.579] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label \"Spending (in USD)\"."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "Tr1mXEPu7WlMbGzS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific data does the point at the coordinates [0.276, 0.500] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Spending of Public Schools."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "2715LBjDQPTeIKFw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.454, 0.959, 0.582, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Institution Type."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "jrIvUq5o2923FQxV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Graduate Schools'}[0.599, 0.249] above or below {the point for 'Training Centers'}[0.760, 0.365] on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "JTLGDwR9lQGQbmJi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the spending increase or decrease from {the point of 'Colleges'}[0.437, 0.529] to {the point of 'Graduate Schools'}[0.599, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "U9qQL6FYN0fbnAtC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the point for 'Private Schools'}[0.114, 0.882] and {the point for 'Online platforms'}[0.922, 0.086], which one represents a higher spending level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Private Schools."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "TkiCtaza1jZIjF78",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region with coordinates [0.081, 0.055, 0.401, 0.087]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend, which is labeled as \"Education and Training Industry Trends\", along with the respective color indicator."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "tZFrxQT4oWP4jRPi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the area with coordinates [0.226, 0.934, 0.325, 0.952]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label \"Public Schools\" is located in this specific region."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "AMi9N2ip4EIYBwPa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.114, 0.086, 0.922, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {line chart representing 'Education and Training Industry Trends'}<r>[0.114, 0.086, 0.922, 0.882]</r> occupies this area, with specific points marking the spending levels of different types of educational institutions."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "IEFIuUKr2lSBUuci",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle at coordinates [0.126, 0.165, 0.186, 0.165] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Technology."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "Lyue03QFjJGdc0jF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line associated with {the object with coordinates [0.210, 0.078, 0.537, 0.123]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d86810."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "uVdp9wXwAYH7J7Um",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat marks the point corresponding to the handle at [0.126, 0.097, 0.186, 0.097]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education Technology."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "5myIhtQ1fY1oD3dz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.935, 0.046]} above or below {the point at [0.138, 0.527]} on the 'empirical relat.' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "Di6fnTMYRkQP1mQn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which direction does {the 'Average (Education Technology)' line at coordinates [0.666, 0.750, 0.666, 0.839]} run relative to {the 'Empirical relat.' line at coordinates [0.138, 0.046, 0.935, 0.527]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Vertical."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "YQ3XgaqdjMWA24dW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Training Technology' line at coordinates [0.787, 0.750, 0.787, 0.839]} to the left or the right of {the 'Education Technology' line at coordinates [0.509, 0.750, 0.509, 0.839]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the right."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "CiQZNtGUnJLdfrlC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside [0.114, 0.060, 0.706, 0.411]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend items of the chart. The text and handle of each item are represented in pairs. The labels are 'Education Technology', 'Training Technology', 'Average (Education Technology)', 'Average (Training Technology)', and 'empirical relat.'. Each label is paired with a colored handle that matches the color of corresponding line in the chart."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "YoZ3hA2NJ8u7ml1f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in [0.138, 0.046, 0.935, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 'empirical relat.' line that represents the correlation between 'Time' and 'Innovation Index'. It begins from {an x-value of 3.3 and a y-value of 3.5}[0.138, 0.527] and ends at {an x-value of 7.6 and a y-value of 8.9}[0.935, 0.046]."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "KSJMKH2FMvIF65mx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be located within the region [0.509, 0.750, 0.509, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the 'Education Technology' line. It is a vertical line indicating that the time series of 'Education Technology' is constant at a value of 5.3."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "XdAOe9AHiE3sxDwV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.019, 0.289, 0.044, 0.638]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "5OH3Il3gIqIuDd71",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the benchmark for {Risk Moderation}[0.157, 0.061, 0.474, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "80iuZDOa1JyxK9dp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {Supply Chain Integration benchmark line}[0.940, 0.770, 0.940, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#314e73"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "C3ManJAzLNWxlJbH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the performance index of {Risk Moderation} at end of the analysis period {at x=5}[0.161, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "3.8"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "T6HTww49xo99jegm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Supply Chain Integration line}[0.115, 0.034, 0.161, 0.169] finish higher than the {Risk Moderation line}[0.115, 0.280, 0.161, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "IbDrTrr7ApG9yohB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Supply Chain Integration benchmark line}[0.940, 0.770, 0.940, 0.893] to the right of the {Risk Moderation benchmark line}[0.757, 0.770, 0.757, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "VEFv3oZxMmq2CeKN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.085, 0.049, 0.510, 0.238]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the benchmark indicators and actual performance lines of \"Risk Moderation\" and \"Supply Chain Integration\", each identified with a color."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "tm1PfNC9wj3byxpM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.446, 0.942, 0.610, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Time Period\" is located in this area, serving as the x-axis label."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "AqjXbKol82D4pgyQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe all objects found in the area [0.094, 0.164, 0.139, 0.209]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains markers for the lines on the chart: the markers for Risk Moderation and Supply Chain Integration are found here, colored #1738b2 and #7ccd44 respectively."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "PdLuYLBuvHY9CZw7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text on the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is 'Score' {ylabel}<r>[0.025, 0.385, 0.055, 0.511]</r>."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "ZMYwiBbO1wV4jiHa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.707, 0.083, 0.826, 0.118]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the chart, the text at that location is 'CSR Score'."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "QXUqTD3j7irsv2ZO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the marker associated with 'Economic' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker for 'Economic' is colored '#de8f05' {Economic}<r>[0.707, 0.187, 0.819, 0.222]</r>."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "lrz9ewEQXI8BtYEU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for 'CSR Score' generally above or below the line for 'Economic'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "sAgR2vK7tOwtvIyE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with color '#0173b2' represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainable Business."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "geK4etIu99NPLB7I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for 'CSR Score' at x=3.0 higher or lower than the point for 'Economic' at the same x-value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "fqj9Mdw1icU1r2SE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area between [0.249, 0.869, 0.408, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two labels on the x-axis, '0' and '1', located at the respective coordinates {0}<r>[0.132, 0.869, 0.146, 0.904]</r> and {1}<r>[0.219, 0.869, 0.234, 0.904]</r>."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "ctr8Xa8ohKSnjTni",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.633, 0.069, 0.960, 0.341]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the chart legend is observed. It holds the information on five different lines represented in the chart: CSR Score {CSR Score}<r>[0.707, 0.083, 0.826, 0.118]</r>, Environment {Environment}<r>[0.707, 0.135, 0.855, 0.170]</r>, Economic {Economic}<r>[0.707, 0.187, 0.819, 0.222]</r>, Employee Wellness {Employee Wellness}<r>[0.707, 0.240, 0.930, 0.275]</r>, and Sustainable Business {Sustainable Business}<r>[0.707, 0.292, 0.951, 0.327]</r>, each with a color marker."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "WGT6bFAo0x0v4nvj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area from [0.139, 0.148, 0.575, 0.393]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line plot for 'Sustainable Business' tracked over years 0.0 to 5.0. The y-coordinates of the points at each year indicate the scores for that year."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "D3c7jgmtdVuqoPXR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by {the label} at [0.210, 0.295, 0.461, 0.340]?"
+            },
+            {
+                "from": "gpt",
+                "value": "BioTech Startups."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "ROBESLLWzW916agX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item} at [0.126, 0.449, 0.186, 0.449]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#367ae9."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "eNUjAMGTGfdKY9Qh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label} at [0.068, 0.814, 0.082, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "hUpPRC3xSLOUo5lb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for High-Tech Startups}[0.654, 0.634, 0.654, 0.828] to the left or right of {the point for QuantumTech Startups}[0.935, 0.634, 0.935, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "Ew6qukiY4oST9kQx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for AI startups}[0.701, 0.634, 0.701, 0.828] above or below {the line for Empirical Recovery}[0.138, 0.168, 0.935, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "xfFsgfLdp4jhAQ22",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich startup has a higher y-coordinate at {point on the x-axis where x=100000}[0.841, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Innovation."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "30puD7XDlTfzl9Nu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within [0.210, 0.092, 0.536, 0.137]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label \"Traditional Innovation\" next to a colored marker."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "J3rt6pcNoZ40EBq3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.068, 0.037, 0.082, 0.073]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis tick label '4'."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "pQtIfmB0ilYOeTo9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.841, 0.051, 0.841, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two lines, line_0 representing 'Traditional Innovation' and line_6 representing 'Empirical Recovery'."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "QA4KwMg5G5UL82vB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at these coordinates [0.025, 0.221, 0.058, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "5poSCVMx102Lop1K",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at this coordinate [0.210, 0.295, 0.647, 0.340]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for \"Outdoor Recreational Spaces\"."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "mtf2orQKNzLn4ssl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.068, 0.259, 0.082, 0.294] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis tick label '5'."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "TJUD9nNsUqvbQpmM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point representing \"Social Aging Care\" at {coordinates}[0.872, 0.828] positioned higher or lower than the point for \"Outdoor Recreational Spaces\" at {coordinates}[0.825, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same level."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "TVkB6SOrNv38IwFi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Policy Trend'}[0.138, 0.096, 0.935, 0.711] mostly to the left or right of the {line for 'Public Transport Accessibility'}[0.682, 0.717, 0.682, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is mostly to the right."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "xqHtjLSHKgKK8YsI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher point at 25 units, \"Social Aging Care\" or \"Policy Trend\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both have the same value."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "FofITSIKHh7ee29l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the content in the region [0.349, 0.913, 0.724, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region for the X-axis label, which is \"Average Time Spent\". It uses black color font."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "008DxQyvGR7lO7t0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we find in the area [0.114, 0.074, 0.659, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 legend items in this area: {Social Aging Care}[0.210, 0.092, 0.474, 0.137], {Public Transport Accessibility}[0.210, 0.159, 0.645, 0.204], {Mental Health Services}[0.210, 0.227, 0.557, 0.272], {Outdoor Recreational Spaces}[0.210, 0.295, 0.647, 0.340], and {Policy Trend}[0.210, 0.362, 0.389, 0.407], each with a different color marker."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "kvRauMJh6GfGBC45",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the bounding box [0.138, 0.096, 0.935, 0.711]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for the \"Policy Trend\" from point 5 (represented as {'x': 5, 'y': 6.6}) to point 25 (represented as {'x': 25, 'y': 1.2}). The line is marked with a blue color and 'o' markers."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "oYXiGip5Vi5yYhYC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the {line_0}<r>[0.180, 0.109, 0.937, 0.821]</r> within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Model Performance."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "9QSPJZaYFEUUPifs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by {the x-axis label}<r>[0.460, 0.913, 0.658, 0.963]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "Error Ratio"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "IycMAt3eVzGhoX8H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.025, 0.183, 0.058, 0.682]</r> portray?"
+            },
+            {
+                "from": "gpt",
+                "value": "Model Complexity"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "nLHFpOWh8yiPyu2u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the second point}<r>[0.445, 0.792]</r> in the 'Model Performance' line have higher or lower value compared to {the first point}<r>[0.180, 0.821]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "43ZfTDkEMfPsgzd9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the fourth point}<r>[0.625, 0.720]</r> in the 'Model Performance' line above or below {the third point}<r>[0.559,0.756]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "hNMas08O7fBlAqe9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point in the 'Model Performance' line has lesser value, is it {the fifth point}<r>[0.823, 0.469]</r> or {the sixth point}<r>[0.937, 0.109]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sixth point"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "HH8fFeSmGmmykf6u",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements included within <r>[0.157, 0.060, 0.556, 0.141]</r> region?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shares the legend for the 'Model Performance' line, with its corresponding colored marker."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "N29z9OzqqTldO4M5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within this section of the chart <r>[0.142, 0.038, 0.975, 0.828]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The encompassed region contains the main plot of the chart, including the 'Model Performance' line and the respective x and y grids."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "wjzAJnWlMjoXqmsv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in this region <r>[0.068, 0.096, 0.126, 0.131]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, the '1000' y-axis tick label is located."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "hvzxGWBv0gGOUwUb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object referred to by the coordinate on the Y axis [0.025, -0.113, 0.058, 1.003]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "lWnD5e5L1SkBIjBP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.521, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point on 'Paid User Ratio' graph line."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "rZHN0QObj9IcDnyl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line} at [0.294, 0.164, 0.899, 0.614]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Paid User Ratio."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "9l8GY9jmhHXrDthr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point on 'Paid User Ratio'}[0.672, 0.389] have a more significant increase compared to {the point on 'Revenue Generated'}[0.824, 0.839] between Year 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "faEM6nEZfv3TuCdd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Revenue Generated'}[[0.294, 0.838, 0.899, 0.839]] located above or below {the line for 'Paid User Ratio'}[[0.294, 0.164, 0.899, 0.614]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "XaiW3TnO3vKD6vTU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend shows more variation, 'Paid User Ratio' or 'Revenue Generated'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Paid User Ratio."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "dxEgmmPbxW5snA6S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a detailed description of the objects contained within [0.157, 0.074, 0.566, 0.560]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the seriale of the chart, with each different year represented by a different line color. The years 2000, 2005, 2010, 2015, and 2020 are represented, as well as a legend entry for 'Paid User Ratio' and 'Revenue Generated'."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "YsP5Zqk6gjkuzivC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of the region [0.124, 0.864, 0.918, 0.899]."
+            },
+            {
+                "from": "gpt",
+                "value": "These are the labels on the x-axis identifying the types of game development scores from 0.0 to 1.2."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "Ynf68YAVbXl1PPQt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the area with the coordinates [0.294, 0.164, 0.899, 0.614]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line graph for the 'Paid User Ratio' from 0.2 game development scores to 1.0 showing a significant increase in the ratio."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "bjhoQh5PDPAaZJKd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.025, 0.403, 0.048, 0.542] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "f6yVXMWoPcljlWgv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the data point on the \"Steady Increase\" line} at [0.643, 0.414] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value of the \"Steady Increase\" for \"Solar Power\"."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "zbFcsADTdMXJ2viB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many categories on x-axis are there in the chart and what are their coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 categories. {AI}<r>[0.129, 0.919, 0.151, 0.942]</r>, {Smart Grid}<r>[0.329, 0.919, 0.455, 0.942]</r>, {Solar Power}<r>[0.575, 0.919, 0.712, 0.942]</r>, {Hydroelectric}<r>[0.818, 0.919, 0.972, 0.942]</r>."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "nZtB9FSzbWOYGX3U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point for \"Solar Power\"}<r>[0.643, 0.414]</r> on the \"Steady Increase\" line is above or below the {data point for \"Solar Power\"}<r>[0.643, 0.443]</r> on the \"Moderate Growth\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "kiIEX8ZJ20Cp0hHZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of \"AI\" technology, does \"Moderate Growth\" or \"Steady Increase\" have higher investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Steady Increase\"."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "aKe7LCCL51vUioMd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line ends higher on the Y-axis, {the line for \"Moderate Growth\"}<r>[0.140, 0.268, 0.895, 0.794]</r> or {the line for \"Steady Increase\"}<r>[0.140, 0.180, 0.895, 0.736]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Moderate Growth\"."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "ykJZFYPUfPYhDwZJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within coordinates [0.114, 0.046, 0.695, 0.157]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend titled 'New Energy and Clean Technology Development'. There are two lines mentioned in the legend, {Moderate growth}<r>[0.338, 0.090, 0.536, 0.113]</r> and {Steady Increase}<r>[0.338, 0.125, 0.524, 0.148]</r>. Both of these legend items are associated with different marker types."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "ibCLgWeCzsb0XQig",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.129, 0.919, 0.972, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains all the labels of x-axis: {AI}<r>[0.129, 0.919, 0.151, 0.942]</r>, {Smart Grid}<r>[0.329, 0.919, 0.455, 0.942]</r>, {Solar Power}<r>[0.575, 0.919, 0.712, 0.942]</r>, {Hydroelectric}<r>[0.818, 0.919, 0.972, 0.942]</r>"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "j8z0mGO2sY7JfSQU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in coordinates [0.058, 0.025, 0.094, 0.048]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the smallest y-axis tick label which is {0.0}<r>[0.058, 0.025, 0.094, 0.048]</r>."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "WKaOCsymgGZL9cHe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the tick label representing 'Weight Training' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.216, 0.919, 0.396, 0.942]."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "NephM0Ui9hB4VZ57",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the line} at [0.155, 0.253, 0.912, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fitness trend."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "bXX1h2Fr2THSXgjn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {position} [0.771, 0.125, 0.929, 0.148] in the 'Exercise Trends' legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sports Health."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "qWP1phu5rolaSiVg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Fitness' level higher or lower for 'HIIT' than it is for 'Yoga'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "RTDPMpujRwecixir",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at coordinate} [0.912, 0.867] above or below the {point at coordinate} [0.912, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "oks1fN2XuziWixLY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Sports Health' level for 'Pilates' at {coordinate} [0.761, 0.779] higher or lower than the 'Fitness' trend at {coordinate} [0.761, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "gfGwrjcBiknTJ20L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area bounded by [0.117, 0.034, 0.950, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the main body of the line chart plotting 'Fitness' and 'Sports Health' against various 'Types of Exercise'."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "94Ppy0CxmzXY1ng5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.697, 0.046, 0.938, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the legend titled 'Exercise Trends' with entries for 'Fitness' and 'Sports Health'."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "2xoJZCQ3xMxivq20",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the area [0.216, 0.919, 0.396, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the X-axis label 'Weight Training' is located."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "fY9CzWVCtB54WE7Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the line with the square points represents in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Conflict Index. The coordinates of one of its many data points are here: {Conflict Index Point}[0.102, 0.207]."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "142uDpHQVMr58bAe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value of this point on the Development Index line {Development Index Point}[0.267, 0.824]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is 0.6."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "vr99SFdCrWWPfh2t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat age does this x-axis tick label represent {the x-axis tick label}[0.544, 0.939, 0.650, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Middle Ages."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "FnEVvxYksxu5DlZY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Digital Age point on the Development Index line} [0.927, -0.301] higher or lower than {the Digital Age point on the Conflict Index line} [0.927, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "B8CaT6dM8UjLGfCs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Development Index line} [0.102, -0.301, 0.927, 0.897] show an overall increase or decrease from Stone Age to Digital Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "y93W28ZT47C13PyZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Middle Ages point on the Conflict Index line} [0.597, 0.461] at a higher or lower index than {the Middle Ages point on the Development Index line} [0.597, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "ixH4bGSaKcVkScek",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within the area [0.702, 0.939, 0.822, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "In the area specified, there's the x-axis tick label 'Industrial Age'."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "8zm4fqEFHVbtN4DK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visuals can be seen in the region [0.102, 0.207, 0.927, 0.897]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes most of the chart itself, therefore it contains several points and lines for both the {Development Index}[0.102, -0.301, 0.927, 0.897] and the {Conflict Index}[0.102, 0.207, 0.927, 0.788] plotted across different eras."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "POQTEiQCqyR7nqGG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe this particular region [0.727, 0.034, 0.960, 0.118]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart, having two items labeled as {Development Index}[0.783, 0.067, 0.953, 0.085] and {Conflict Index}[0.783, 0.094, 0.902, 0.111], and it has the title 'Human History' as well."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "p5B8kzRZSUWmsGXQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the following coordinates: [0.025, 0.367, 0.048, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "fIxgmUaNQnwI9UOE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.226, 0.832, 0.370, 0.855] in the legend stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gold Values."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "jePMdFcZeoBe6h8J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the number represented by the x-axis tick label at this coordinate [0.377, 0.919, 0.391, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "bgEsBVykB9Layow8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {0th time interval}<r>[0.163, 0.638]</r>, which investment strategy has a higher value, Gold or Stocks?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stocks."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "EHZ9QW91vEAOVIJn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {6th time interval}<r>[0.826, 0.639]</r> of Gold Values, is it below or above its starting value at {0th time interval}<r>[0.163, 0.638]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "luTBSLaNueBv7J8t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {3rd time interval}<r>[0.494, 0.461]</r> and the {7th time interval}<r>[0.936, 0.863]</r> of Stocks Values, which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "3rd time interval."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "C95vobm6bmPWDA5z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.136, 0.788, 0.411, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend. The legend includes two labels, {Gold Values}<r>[0.226, 0.832, 0.370, 0.855]</r> and {Stocks Values}<r>[0.226, 0.867, 0.385, 0.890]</r>, each paired with a colored marker icon."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "jSoz3vpDGLibtyZw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.163, 0.093, 0.936, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains two line charts. The one indicated by gold, cubic markers represents the trend of Gold Values while the one symbolized by blue, square markers represents the Stocks Values trend over time."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "VMyDWg1WMlLLKIWm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's inside the area [0.058, 0.766, 0.116, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label in this region. It is labeled '1000'."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "uoN4mYU220YyCr0D",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element found at the coordinates [0.025, 0.289, 0.048, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "slSjriFF1nTYcLuZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label located at [0.415, 0.952, 0.669, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years of Development."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "2fyAoRVIOSHkv10H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.149, 0.174, 0.936, 0.701] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line plotting the Sales data."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "z0wWoYJJGpdjKSw7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Sales line}[0.149, 0.174, 0.936, 0.701] higher or lower than the {Profits line}[0.149, 0.315, 0.936, 0.806] at the year 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "K0ZyyKtpP52LXrw8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Profits line}[0.149, 0.315, 0.936, 0.806] increasing or decreasing from the 5th year to the 10th year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "HdfqL1pSokhxGAPx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the Sales line}[0.149, 0.174, 0.936, 0.701] positioned compared to the {the Profits line}[0.149, 0.315, 0.936, 0.806] at the year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "O7yl3wptrLvrnf0Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you characterize the area within the coordinates [0.121, 0.046, 0.695, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend titled \"Entertainment & Gaming Industry Development\". Two legend items are present: \"Sales\", marked in light blue, and \"Profits\", marked in yellow."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "AfiuM4ZWlsmGhKWl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region with coordinates [0.415, 0.952, 0.669, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, \"Years of Development\", is situated within this area."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "SW4DQGPs1AVHOCW5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located within the coordinates [0.149, 0.174, 0.936, 0.701]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area predominantly houses the \"Sales\" line plot. The line indicates the sales revenue over different years."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "g3i6nXPvEZZj99ub",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker at {the point on the line for Rights Protection}<r>[0.581, 0.451]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#000000."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "Bik5tZnq8Um3mZsK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.025, 0.388, 0.048, 0.557]</r> indicate as a measure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Measure Value."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "GqbpQVKQSA5xGmfb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the fifth tick}<r>[0.645, 0.919, 0.660, 0.942]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "1w4NDDWHrJrdvqaF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at}<r>[0.511, 0.451]</r> higher or lower than {the point at}<r>[0.652, 0.341]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "mGVFEMwpuQVGrC5z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at}<r>[0.511, 0.451]</r> belong to the same trend as {the curve starting at}<r>[0.156, 0.188]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "hc6zADxbSuC7oH5W",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is further to the right on the x-axis, {the point at}<r>[0.298, 0.297]</r> or {the point at}<r>[0.440, 0.429]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.440, 0.429]."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "OWIWpPfEOKMRl8KC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in <r>[0.058, 0.025, 0.109, 0.048]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the y-axis tick labeled '2.00'."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "gt3K0rDP6Sp8zpdO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region <r>[0.156, 0.122, 0.936, 0.613]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains points from two distinct data trends, Rights Protection and another trend with no label, displayed with black markers on blue and red lines respectively."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "OvaOIsodLJW9jnNQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area <r>[0.058, 0.025, 0.951, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the entire scale of the x-axis and y-axis, containing all 12 x-axis ticks and 9 y-axis ticks, plotted against the data trends shown on the graph."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "csTvLLCsrRLUKhSr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.058, 0.902, 0.094, 0.925] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label \"0.0\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "wOzqLL9xlQE6sE2t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object {at this location}<r>[0.440, 0.952, 0.611, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Market Growth\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "Ufyi0z05cac09bUd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at {these coordinates}<r>[0.141, 0.379]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point representing the \"Strong Growth\" market share for \"Sporty\" automotive."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "yrIOFlJ3UQgv9t6a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship in position between {Sporty automotive at 'No Growth'}<r>[0.910, 0.616]</r> and {Luxury automotive at 'No Growth'}<r>[0.910, 0.793]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Sporty\" point is below the \"Luxury\" point."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "W84azy5C5Fp4wUtl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the market shares of {Sporty}<r>[0.141, 0.143]</r> higher or lower than that of {Luxury}<r>[0.141, 0.379]</r> in the \"Strong Growth\" segment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "AoMj8JkK8PUeqmW7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich automotive type has higher market share at {\"No Growth\"}<r>[0.910, 0.616]</r>, Sporty or Luxury?"
+            },
+            {
+                "from": "gpt",
+                "value": "Luxury."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "svhlLA3XoLLd2xRc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region [0.114, 0.788, 0.693, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend titled \"Automotive and Transportation Market Research\". There are two legend items: a blue-colored 'Sporty' and a red-colored 'Luxury'. They are located at {coordinates}<r>[0.397, 0.832, 0.473, 0.855]</r> and {coordinates}<r>[0.397, 0.867, 0.475, 0.890]</r> respectively."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "4DrcJW7zt3iAwDed",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region of [0.057, 0.919, 0.225, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label for 'Strong Growth'."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "InvXosBPudeqdQ06",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.058, 0.666, 0.094, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0.4' is located within this region."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "eFTE7I1rLI24PadS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker represents the values on the 'Post-Covid' data line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "MMWDxTuQIJ6Xi6jz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is found at the coordinate [0.455, 0.952, 0.588, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Indicator."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "Lxgo8OMcZxpaSGSo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.435, 0.029, 0.510]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Values."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "rrXCQrBPkSxmY2tp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 'Employment Rate' for Pre-Covid}<r>[0.107, 0.104]</r> higher or lower than {the point at 'Employment Rate' for Post-Covid}<r>[0.107, 0.148]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "PBCNqXxCDyp3Dubk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator shows a higher value in the Post-Covid era between {Interest Rate}<r>[0.660, 0.907]</r> and {Inflation Rate}<r>[0.384, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation Rate."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "8LOZTIU5PcmIqDED",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the points for 'GDP Growth' for Pre-Covid}<r>[0.936, 0.893]</r> and {the points for 'Employment Rate' for Post-Covid}<r>[0.107, 0.148]</r>, which is positioned lower on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "ruQEMcP9hIxWpyvA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found in [0.015, 0.435, 0.029, 0.510]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label, \"Values\", which is aligned vertically and displayed in black color."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "0ropaMRJIk4UZhwA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.107, 0.104, 0.107, 0.148] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the data points for 'Employment Rate' for both Pre-Covid and Post-Covid, positioned at {Pre-Covid}<r>[0.107, 0.104]</r> and {Post-Covid}<r>[0.107, 0.148]</r>."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "JDs7zQDubx0kKobo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements contained in the region defined by [0.836, 0.832, 0.909, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The key elements in this area are the legend items for Pre-Covid, marked with blue color, and Post-Covid, marked with red color. The legend texts are: {Pre-Covid}<r>[0.836, 0.832, 0.902, 0.855]</r> and {Post-Covid}<r>[0.836, 0.867, 0.909, 0.890]</r>."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "CZyevvtvjx68A0ZV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeling on the y-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Effectiveness\"{ylabel}<r>[0.025, 0.395, 0.048, 0.550]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "xdV70GcrWakVWSxU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the last event on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Book Drive\"{xticks label}<r>[0.845, 0.919, 0.973, 0.942]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "Nor78sJ7Zeamf8FB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the event that corresponds to the tick mark at [0.456, 0.911] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Fun Run\"."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "kpCgG7AJpZVqLfyR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Participation of Volunteer higher or lower than the Increase of Donation on 'Clothing Drive'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower. {Donation Increasement's point on 'Clothing Drive'}<r>[0.758, 0.166]</r> {Volunteer Participation's point on 'Clothing Drive'}<r>[0.758, 0.473]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "jOhZcGINZz7tuDS6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on 'Book Drive', Volunteer Participation or Donation Increasement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Donation Increasement. {Donation Increasement's point on 'Book Drive'}<r>[0.909, 0.253]</r> {Volunteer Participation's point on 'Book Drive'}<r>[0.909, 0.604]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "g2A2VjtdlpLxt4UD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher effectiveness in 'Fun Run', the increase in donations or the participation of volunteers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Volunteer Participation. {Volunteer Participation's point on 'Fun Run'}<r>[0.456, 0.341]</r> {Donation Increasement's point on 'Fun Run'}<r>[0.456, 0.253]</r>"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "zpdBC80mwkqDPNwj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is defined in the region [0.129, 0.788, 0.541, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend, titled 'Social Welfare & Charity Activities', with two items: 'Donation Increasement' and 'Volunteer Participation' along with their color identifiers."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "lEk6TnI9KutPIsxr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features present in the space [0.845, 0.919, 0.973, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the final point on the x-axis, labeled as 'Book Drive'."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "OdxWmR4vXGVXuZ9i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.025, 0.395, 0.048, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for the y-axis, which is labeled as 'Effectiveness'."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "pv2zSbQEZ2oGIA4d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label} at [0.477, 0.964, 0.581, 0.981] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Career Field."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "rg48pyVvF9Aohww8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label} at [0.019, 0.363, 0.036, 0.596] represent in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Career Development Index."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "b2qqI6KIYXWgeCg6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is referenced by the coordinates [0.843, 0.067, 0.956, 0.085] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Early Career."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "IkGMBvmtIySAR6kQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Tech' in 'Early Career'}[0.118, 0.337] higher or lower than {the point for 'Tech' in 'Mid Career'}[0.118, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "3mEYPZMr80rATNAg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Sports' in 'Early Career'}[0.940, 0.233] above or below {the point for 'Sports' in 'Mid Career'}[0.940, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "I0Ad6sIe3Egartgt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Early Career' stages, which one is higher on the chart: {Tech}[0.118, 0.337] or {Medicine}[0.323, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medicine."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "6KTejHyFXt0r0z4W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.043, 0.926, 0.071, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The numeric label '0.0' for the Career Development Index is represented in this region."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "M7M7jVPScHI3K2WW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the content in the region [0.735, 0.285, 0.940, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Mid Career' points for 'Arts' and 'Sports', which are represented with a marker style of square and the color green."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "MzCpe4gnpQrecUlW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the area [0.118, 0.233, 0.940, 0.544]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all plotted points representing 'Early Career' and 'Mid Career' stages for five different fields: 'Tech', 'Medicine', 'Education', 'Arts', and 'Sports'. Different career stages are marked with different color codes and different marker styles."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "7wnLAw1gC593R0JR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the given coordinates [0.279, 0.832, 0.450, 0.855] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Inexorable rise\" from the legend."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "nyu7oPs1MEgvnAtb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by these coordinates [0.154, 0.385] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a point on the line labeled \"Inexorable rise\" representing the value of \"Income inequality\"."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "342kZa3QxrGorYQT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.279, 0.867, 0.450, 0.890] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the \"Slight Increase\" label in the legend."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "jvBbxMoFYjavBoq0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 'Healthcare cost'}[0.646, 0.429] on the 'Inexorable rise' line higher or lower than the {point of 'Public spending'}[0.400, 0.560]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "1IP1a0u3f3nfttIK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the points on the lines for 'Inexorable rise' and 'Slight Increase', which line has a higher value for 'Income inequality'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Slight Increase'."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "QdQVezg4dhm7Dxnh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trendline values are higher at the 'Social welfare' category, 'Inexorable rise' or 'Slight Increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Slight Increase'."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "ysCxEC9fZCcae9pf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by [0.129, 0.788, 0.537, 0.899] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend titled \"Socioeconomic and Policy Making\". It includes two entries: {Inexorable rise}<r>[0.279, 0.832, 0.450, 0.855]</r> marked in black color, and {Slight Increase}<r>[0.279, 0.867, 0.450, 0.890]</r> marked in red color."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "rGU6LuzVLqeuw5iG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area marked by the coordinates [0.117, 0.034, 0.929, 0.911] on the plot."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encloses the entire plotted area consisting of x-axis labeled 'Categories', y-axis labeled 'Values', associated tick labels, and two plotted lines - 'Inexorable rise' and 'Slight Increase'. Each line contains four data points each representing a category."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "qlMGhqcDjPg2r477",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements contained within the coordinates [0.058, 0.025, 0.109, 0.048]."
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the y-axis tick label '2.00'."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "ybyhIiOEop460Ll2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.025, 0.441, 0.048, 0.504]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "HFpKRKl8H3b8dH86",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.929, 0.902, 0.975, 0.925]</r> on the y-axis of the secondary chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "-1.5."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "HW3T7ng9UcwugmKF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the point}<r>[0.866, 0.708]</r> on the primary line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stagnant Markets."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "SRTQvs5I7qcIsEaO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the error bars for 'Emerging Markets' higher or lower level than the point itself on the secondary chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "rZNZuePEOdhJSn4z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis label is situated further right, {Emerging Markets} at <r>[0.031, 0.919, 0.241, 0.942]</r> or {Stagnant Markets} at <r>[0.764, 0.919, 0.969, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stagnant Markets."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "qsYwFJm3U64WM9e3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {'Developed Markets'}[0.380, 0.204] and {'Frontier Markets'}[0.623, 0.367], which point on the primary line chart is located higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Developed Markets."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "Bwv0lZdqQx88LnXu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.100, 0.034, 0.903, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the entire chart, including both the primary and secondary line charts. It includes all markers, lines, labels, and tick markers."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "RCuLKj3n5u0dtQrw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region <r>[0.111, 0.046, 0.318, 0.123]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area is the legend for one of the subplot charts. There is one item in the legend, labeled {GP Growth}<r>[0.186, 0.090, 0.309, 0.113]</r>, along with the matching black square marker."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "e3TGaaVZLrWU84ta",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is found in <r>[0.431, 0.952, 0.572, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the xlabel for the plot, which is 'Market Type'."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "JxgP4zA7bHizwxh7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.505, 0.952, 0.550, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tool."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "dVIYnwS5XxgUyO9G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entry in the dataset is represented by the point having the coordinates {0.527, 0.166}?"
+            },
+            {
+                "from": "gpt",
+                "value": "MegaDrill from the optimization set."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "iUDCDFfX7E4SdcJQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the Y-axis label at these coordinates: <r>[0.025, 0.382, 0.048, 0.563]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency Level."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "CbogjrUqnA22RAiz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}<r>[0.334, 0.359]</r> of HyperDrill in 'Optimization' above or below the baseline point {the point}<r>[0.334, 0.429]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "LRe3ScTH8Z3ydJuo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the efficiency level for {the point 'MegaDrill'}<r>[0.527, 0.210]</r> in 'Optimization' higher or lower than in the baseline {the point 'MegaDrill'}<r>[0.527, 0.210]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "VpKpBOYHWEUMESVT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the point of {SuperDrill in Baseline}<r>[0.141, 0.341]</r> to {PowerDrill in Optimization}<r>[0.913, 0.227]</r>, which one has a higher efficiency level?"
+            },
+            {
+                "from": "gpt",
+                "value": "SuperDrill in Baseline."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "eW2PAqm0s62kJ0ES",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the chart within {coordinates}<r>[0.102, 0.034, 0.952, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region depicts a line chart showing the efficiency level of various tools, both in baseline and after optimization. It includes lines with markers representing both scenarios for each tool, i.e., SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill. Along with this, the y-axis labels from 0 to 1 representing the efficiency level, and the x-axis labels for the names of the tools are also present."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "9jCMocXw7hCiZHMH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area enclosed within the given coordinates <r>[0.084, 0.919, 0.972, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels for the tools: SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "ZWvLXJMdZY6eIp6R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the region <r>[0.141, 0.210, 0.913, 0.560]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the lines representing the baseline and optimization data for the tools (SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill) on the chart, with each tool's point denoted."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "kb4A9SLbDQq05wQM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line with square markers}<r>[0.120, 0.613, 0.903, 0.823]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "l0TyfStmDfezwagW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {Speculative under Investment Styles}<r>[0.836, 0.919, 0.971, 0.942]</r> in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fourth."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "d1DZAnoxVFyxh7me",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {the y-axis coordinate}<r>[0.058, 0.726, 0.072, 0.750]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "BSFv2iKomj63cVSx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one is higher {High Risk at Aggressive}<r>[0.381, 0.297]</r> or {Low Risk at Aggressive}<r>[0.381, 0.823]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Low Risk."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "Xl8XFlWQAMZNxvtf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {Risk Levels} in chart depicted by <r>[#000000]</r> or by color <r>[#ff0000]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff0000."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "D4xmbhIYcH4TQfYl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at the fourth point, {High Risk}<r>[0.903, 0.122]</r> or {Low Risk}<r>[0.903, 0.788]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Low Risk."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "n6J8mRLgIzuRfU1J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within <r>[0.317, 0.919, 0.444, 0.942]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label 'Aggressive' of the x-axis with black color, regular font style."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "2MZxBwfWiErf7uMD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located in the region <r>[0.092, 0.788, 0.291, 0.899]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend titled 'Finance', indicating the two types of Investment Styles: {High Risk}<r>[0.166, 0.832, 0.282, 0.855]</r> with black color marker, and {Low Risk}<r>[0.166, 0.867, 0.267, 0.890]</r> with a red color marker."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "7dIGZYLv1tV1CYDk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region <r>[0.120, 0.122, 0.903, 0.455]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a black line marked with circle points indicating 'High Risk' investment style from 'Conservative' to 'Speculative'. At each marked point, a horizontal line is created representing the variations."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "T7ZJsR1fsoKvAepB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.012, 0.367, 0.024, 0.569] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "78NQj6cQKIJAa0nv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis tick label '8'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.771, 0.919, 0.778, 0.942]"
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "BQlO41FWkH3SLV5J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend located at [0.057, 0.788, 0.241, 0.899] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Human Life Quality Over Time."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "hfcKQN5qusyYqTyq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] above or below {the Rural Life Quality line}[0.094, 0.427, 0.945, 0.557] at Time (x-axis) equal to 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "DiqraU8fyOl0IknR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] cross {the Rural Life Quality line}[0.094, 0.427, 0.945, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "apnyGVsuML2xR2gY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] at Time (x-axis) equal to 10 greater than or less than the {Rural Life Quality line}[0.094, 0.427, 0.945, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "32CaG8Ye2JlZRp3Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.094, 0.244, 0.945, 0.557]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses two lines, the Urban Life Quality line and Rural Life Quality line, ranging in time (x-axis) from 0 to 10 years. The values on the y-axis represent the Life Quality Index for both urban and rural areas."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "hehDe1aGWq83Uc76",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in the range [0.029, 0.075, 0.047, 0.098]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick label '1.4', which represents a Life Quality Index of 1.4."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "A514XmXtEyn5A92c",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within [0.179, 0.244, 0.945, 0.557]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two main objects found, these are points on the Urban Life Quality line and Rural Life Quality line tracing from Time (x-axis) equal to 1 to 10 years."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "immuqpwDtWPoWW9Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the object at coordinates [0.025, 0.363, 0.048, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "pqlR3Q2sAFRnK8fw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at coordinates [0.025, 0.363, 0.048, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Score."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "RxmfbdYqJR2i1kXN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.465, 0.952, 0.572, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, which is \"Channels\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "cesYgYDcO8W1405u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point for 'New Creation'}[0.140, 0.239] and {the point for 'Digital Content'}[0.140, 0.472], which has a higher value on 'Search Engine'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Digital Content."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "GVcds9AVN5NXARGL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {point}[0.897, 0.356] on the 'Social Media' channel, is 'New Creation' or 'Digital Content' higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Creation."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "k2ngepKxHTT0tQb3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {New Creation}[0.897, 0.356] above or below {Digital Content}[0.897, 0.297] for 'Social Media' channel?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "BEcvQaDrfZekeUkq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within the region [0.114, 0.787, 0.372, 0.899]."
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to the legend of the chart showing two items: {New Creation}[0.188, 0.832, 0.344, 0.855] and {Digital Content}[0.188, 0.867, 0.363, 0.890], each with a different color marker."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "yMLpb0dAzGJdV7mI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can I find in the area [0.140, 0.239, 0.897, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the data lines for both 'New Creation' and 'Digital Content' for the 'Search Engine' and 'Social Media' channels."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "ZxUjMfBoLaL6EgDv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the area [0.465, 0.952, 0.572, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the X-axis label, which is \"Channels\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "9jNoNTpoRPByBHGm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media type is at position [0.875, 0.939, 0.976, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Radio News."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "Yj9dHASMXDjlLXX5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the y-axis label for coordinates [0.019, 0.414, 0.036, 0.545]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Influence Level."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "gmkqUPHXPVCeBOjB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the text of the last entry in the legend at [0.716, 0.094, 0.840, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "ghkYmllPzGFYohgF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the influence level of {Social Media}[0.112, 0.135] higher than {News Websites}[0.315, 0.280] in Trust in Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "h0HKrDGbpEVVYTYz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a higher value on the y-axis, {TV News}[0.722, 0.588] or {Radio News}[0.925, 0.443], in terms of Trust in Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "TV News."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "eroGSKwn2FKzKPhS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Print Newspapers}'s influence level[0.519, 0.743] higher or lower than {Radio News}[0.925, 0.643] according to Public Opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "jEJJIO3BQqkEsaOs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.071, 0.026, 0.966, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all the information of the chart: the x-axis labeled with different Media Types, the y-axis demonstrating Influence Level, two lines each representing Trust in Media and Public Opinion with corresponding markers for each media type."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "Ow1MorYi7qVe3Q29",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area defined by [0.551, 0.034, 0.957, 0.118]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend which has the title \"Media Influence and Public Opinion Guidance\" with descriptions of each line: {Trust in Media}[0.716, 0.067, 0.837, 0.085] and {Public Opinion}[0.716, 0.094, 0.840, 0.111]."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "30WKgieynYHl5AHc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found in the region [0.543, 0.939, 0.595, 0.957]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-label for {Print Newspapers}[0.543, 0.939, 0.595, 0.957]."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "lkfZD5Vx2Xfz1tSD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of {the marker for 'E-Learning Platform'}<r>[inf, inf, -inf, -inf]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "kFLtm2V7tG8dbcXG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is {the y-value for 'Accelerated decline'}<r>[0.861, 0.736]</r> in line_3?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.4."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "O8V6vgEXPk3S5I0v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the ylabel}<r>[0.025, 0.325, 0.048, 0.620]</r> of the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation/Adoption Rate."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "0MKvusjCKgKWjONi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Accelerated decline'}<r>[0.861, 0.736]</r> in line_3 higher or lower than {the point for 'Abrupt decline'}<r>[0.152, 0.210]</r> in line_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "5m8qZRCogLEyoXJB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo both {the line for 'AI Innovation'}<r>[0.152, 0.210, 0.861, 0.604]</r> and {the line for 'E-Learning Platform'}<r>[0.152, 0.385, 0.861, 0.736]</r> show a decrease or an increase in trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "Rtwpj43IgIy1rdLb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBesides color, what is the difference between {the line for 'AI Innovation'}<r>[0.152, 0.210, 0.861, 0.604]</r> and {the line for 'E-Learning Platform'}<r>[0.152, 0.385, 0.861, 0.736]</r> based on their parameters?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marker style."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "dd8MufeP2HKEAywg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area <r>[0.334, 0.046, 0.884, 0.157]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart, titled as \"Education and Training Technology Innovation\". It contains two items, \"AI Innovation\" at {coordinates}[0.527, 0.090, 0.680, 0.113] and \"E-Learning Platform\" at {coordinates}[0.527, 0.125, 0.756, 0.148], along with their corresponding color marks."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "PrUedYomcGoYF0IP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's within the region <r>[0.117, 0.034, 0.896, 0.911]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the main plot region of the chart. It contains several lines representing the trends of \"AI Innovation\" and \"E-Learning Platform\", along with labeled ticks on both the x and y-axes."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "oaE7azJgoEL0chyb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the bounding box <r>[0.469, 0.952, 0.544, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the xlabel of the chart, which says \"Trends\"."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "ku1K7UCmLyLOIbB6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object corresponding to the coordinates [0.061, 0.919, 0.216, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label 'Remote Work'."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "XXlwJomeRcyOVpsd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the y-axis label}<r>[0.025, 0.382, 0.048, 0.554]</r> is?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Trend Intensity'."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "YPISGbPJm01NGYJC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked at the point [0.855, 0.261] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Career trend 'Continuous Learning'."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "qDDsxnN2N6KKt110",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend 'Digital Literacy' compare between 2020 and 2021? The point for 2020 is {the point}<r>[0.676, 0.320]</r> and for 2021 is {the point}<r>[0.676, 0.438]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It declined."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "N8x10bEFNoMHOhZb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich career trend had a higher intensity in 2020, {Remote Work}<r>[0.138, 0.202]</r> or {Continuous Learning}<r>[0.855, 0.379]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Remote Work."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "P8eFbJBW2YKSrimm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Green Jobs}<r>[0.317, 0.616]</r> and {Technological Advancement}<r>[0.497, 0.557]</r>, which career trend had more intensity in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green Jobs."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "r0twiCob0lql4G7R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.736, 0.919, 0.974, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last x-axis tick label 'Continuous Learning' is located within this region."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "Kbu6r63eiXw4JPZE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements included in the bounding box with coordinates [0.138, 0.084, 0.855, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the line and points representing the career trend intensities of 2021 for all careers, which are 'Remote Work', 'Green Jobs', 'Technological Advancement', 'Digital Literacy', 'Continuous Learning'."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "gglmSrD3atzZIdLj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what is within the box with coordinates [0.114, 0.788, 0.500, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The box contains a legend for the year 2020 and 2021 with their respective identifiers."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "V9dVxQUC8qmtCLXZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.025, 0.386, 0.048, 0.559] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "zNDSGGZQMuLVUUrJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.510, 0.952, 0.567, 0.975] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "vou6Cl5yJZIBorcP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {Y-axis tick}[0.058, 0.573, 0.094, 0.596]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.5"
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "0APD3y0Y2qX81d08",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 5th time point, does the {water quality indicator}[0.495, 0.341] have a higher or lower value than the {deforestation indicator}[0.495, 0.385]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "OXpMHwKXvb5JqqAn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 6th time point, is the {water quality indicator}[0.583, 0.582] placed above or below the {deforestation indicator}[0.583, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "XxC3GuZXm5AWgvJE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of the {water quality indicator}[0.935, 0.560] rising or falling between the 8th and 10th time points compared to the {deforestation indicator}[0.935, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rising."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "RWjvHrEwRhmpK8y9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region given by [0.142, 0.210, 0.935, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region describes the trend of Water Quality over time. It is represented by a line with circle markers."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "SmCHsCWIwheOBrBq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the range [0.142, 0.341, 0.935, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range represents the Deforestation over time. The trend is represented by a line with square markers."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "ZtfXhp8sQOnlAWyT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's within the coordinates [0.114, 0.788, 0.714, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart legend and it includes two items: {Water Quality}[0.363, 0.832, 0.529, 0.855] and {Deforestation}[0.363, 0.867, 0.521, 0.890]. Each is represented by different markers with respective colors."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "Mv3fg5PdzlofCvC4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the object at [0.858, 0.393, 0.952, 0.415] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Minimal Change."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "N7k67vjGqYyEBu2U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the y-axis label corresponding to the coordinates [0.035, 0.789, 0.061, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "110."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "MjyIemcTv8b7VLsK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sixth text in the x-axis tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "2080."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "86Vomf5WYn4mVFg5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Minimal Change}[0.112, 0.825, 0.943, 0.865] trend higher or lower than {Inexorable Rise}[0.112, 0.100, 0.943, 0.333] at year 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "xoh8jbTP2Y8HBwjP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you compare the trend value in 2070 between {Upward Trend}[0.112, 0.200, 0.943, 0.599] and {Subdued Expansion}[0.112, 0.300, 0.943, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward Trend is higher."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "uIUoUeApO7hWKuY8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Gradual Decline}[0.112, 0.200, 0.943, 0.665] trend increasing or decreasing between 2030 and 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "1Vp1mwxoE2ySFhIO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content and structure of the area [0.821, 0.386, 0.979, 0.579]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, with each trend line name accompanied by a colored marker, these include Minimal Change, Gradual Decline, Upward Trend, Accelerated Decline, Inexorable Rise, and Subdued Expansion."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "hDB4fuQA57vpYwhh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the object layout in the region [0.035, 0.324, 0.061, 0.347]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a y-value of '180' from the y-axis label."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "jwT3CUOpbHq92Swd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.372, 0.025, 0.684, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's subtitle, which reads \"Future Forecast and Trend Predictions.\""
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "mxxhtfCKKR4W6BaK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No. of Published Papers."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "V2SRy33yUas6EwNg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the current position of the fourth point in the {line series for 'Deep Learning'}<r>[0.687, 0.495]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015"
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "wtimpgwJuhoI3wiA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the line for 'IVF'}<r>[0.254, 0.083, 0.831, 0.527]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffa500"
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "AtXIObNo7Dj7H10d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two has more points at year 2020: {the line for 'IVF'}<r>[0.831, 0.083]</r> or {the line for 'Deep Learning'}<r>[0.831, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning"
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "VhBBWOboi2kWTg1h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {The paper published in 2010 according to the 'Nanotechnology' line}<r>[0.542, 0.479]</r> more or less than the {paper according to the 'Biotechnology' line}<r>[0.542, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "RYxzSXFNRPdzD7Op",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field has a higher publication rate according to the lines in 2015, {IVF}<r>[0.687, 0.178]</r>, or {Deep Learning}<r>[0.687, 0.495]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Deep Learning."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "rjmNZHa2K2wGSZ3q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what information is contained within these coordinates [0.724, 0.564, 0.767, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend identifier 'IVF' and its respective line color represented by a square colored with the hex value #ffa500."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "nmHNAPdtZDGCYz1O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease explain how the contents of the area enclosed within [0.064, 0.355, 0.094, 0.390] contribute to the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed area contains the y-axis label '30', indicating that this is the value mapped along the y-axis at this grid height."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "NO5nji4suQNyEZ8I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.254, 0.083, 0.831, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entirety of the 'IVF' line plot data, which includes points corresponding to the years 2000, 2005, 2010, 2015, and 2020."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "8vRDrzybsZSTeKjG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinate [0.015, 0.399, 0.033, 0.549] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "Dw3vIx8D1W6NY7ld",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.136, 0.143, 0.355, 0.166]"
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "SGvRvskwfd7i05r6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick} at coordinate [0.362, 0.934, 0.396, 0.952] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2013."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "JkI57tzO9mY3STc2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {point}[0.909, 0.841], line for 'Game Sales (in Millions)' or line for 'Game Releases'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Game Sales (in Millions)."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "FICmpfBYUYeqscV1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'New Gaming Platforms' at year 2019}[0.833, 0.919] higher or lower than {the point for 'New Gaming Technologies' at year 2019}[0.833, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "HFUh0fd87LKFto0U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line decreases more steeply between {year 2014}[0.454, 0.922] and {year 2015}[0.530, 0.922], line for 'Game Releases' or line for 'Game Sales (in Millions)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Game Releases."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "bcOlZTCklEfG0KvY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the region [0.075, 0.026, 0.509, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "This major portion of the chart contains multiple line graphs representing game releases, game sales, new gaming platforms and new gaming technologies plotted over various years from 2010 to 2020. The lines for each of these data series are represented by various markers and colors."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "2ZUfiQ02Zk3WxrJC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the zone [0.083, 0.036, 0.361, 0.174]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with labels 'Game Releases', 'Game Sales (in Millions)', 'New Gaming Platforms', and 'New Gaming Technologies'. Each label is accompanied by a unique color and marker."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "fXd6IOEKi22prLZj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.151, 0.735, 0.909, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes points from the 'Game Sales (in Millions)' and 'New Gaming Platforms' lines for the years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "11g9RcpH43KMDp69",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.057, 0.769, 0.107, 0.797] represent in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label indicating '4000'."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "1zjlHoCYtNdc0WUI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.120, 0.077, 0.901, 0.311], a line with points on the chart, represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Sushi trend line."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "saSoYp2hqHGtDCxo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item} at [0.136, 0.121, 0.195, 0.137]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sashimi."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "mg4IqM6taVIOE2JI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Sushi} at [0.901, 0.124] higher or lower than {the point for Sashimi} at [0.901, 0.358] for the trend category 'Rapid Recovery'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "U2t6alTbGd0GIthD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line, {Sushi} or {Tempura}, starts at a higher count, judging by the points at [0.120, 0.311] and [0.120, 0.780] respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tempura."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "eO9UIJXphmxghqH8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two points [0.706, 0.077] for {Sushi} and [0.706, 0.545] for {Tempura}, which has a higher count in the 'Rapid Expansion' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sushi."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "3t4bxRLarxBkuIMm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area between [0.132, 0.047, 0.321, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. There are entries for three categories: {Sushi}[0.136, 0.068, 0.195, 0.085], {Sashimi}[0.136, 0.121, 0.195, 0.137], and {Tempura}[0.136, 0.173, 0.195, 0.190]. Each entry is paired with a distinct color code."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "6s01gKFeSBLlnwgq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.120, 0.873, 0.901, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers all the labels for the x-axis replicating the trend categories: {Dramatic Decrease}[0.025, 0.893, 0.216, 0.921], {Fluctuating Growth}[0.221, 0.893, 0.410, 0.921], {Gradual Downturn}[0.420, 0.893, 0.602, 0.921], {Rapid Expansion}[0.623, 0.893, 0.788, 0.921], and {Rapid Recovery}[0.824, 0.893, 0.979, 0.921]."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "kHnklc367UR2gjrA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects situated within [0.120, 0.030, 0.901, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area hosts the main line chart with the trend lines for Sushi, Sashimi, and Tempura along with all the associated data points. It also includes the x and y-axes with their corresponding labels and ticks."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "NCMv8ROxkRa4s0A4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content denoted by the coordinates [0.015, 0.378, 0.033, 0.582] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label, displaying 'Revenue ($bn)'."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "udrWqbBELOxIB2hG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the text at these coordinates [0.922, 0.912, 0.958, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis tick label, displaying '2018'."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "fQVHVrL62zxg3mee",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the point}[0.129, 0.493] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the first data point for the 'Knowledge Economy' line corresponding to the year 2000."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "TAOI7NDcsL1CUTsl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the data lines of {Knowledge Economy}<r>[0.129, 0.493, 0.940, 0.829]</r> and {Internet Economy}<r>[0.129, 0.091, 0.940, 0.859]</r>, which one generally has higher revenues?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Internet Economy."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "CZAchLiS4XGRHqcL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for 'Knowledge Economy' in {2002}<r>[0.219, 0.766]</r> above or below the corresponding point of 'Smart Manufacturing' in {2002}<r>[0.219, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "Nhm1gkha6S7qQq17",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data lines of {Smart Manufacturing}<r>[0.129, 0.413, 0.940, 0.891]</r> and {Innovation Industries}<r>[0.129, 0.118, 0.940, 0.614]</r>, which one has higher revenue in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Industries."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "bwyjtDgrY7iP5dqO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the area [0.092, 0.078, 0.327, 0.262] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend representing four economic sectors: {Knowledge Economy}[0.145, 0.089, 0.318, 0.119], {Innovation Industries}[0.145, 0.133, 0.320, 0.163], {Smart Manufacturing}[0.145, 0.177, 0.321, 0.207], and {Internet Economy}[0.145, 0.221, 0.293, 0.251]. Each sector is represented by a different color and marker symbol."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "v2iY2ArBG9zfQE6F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within the region [0.291, 0.916, 0.958, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis tick labels of the years, ranging from {2004}[0.291, 0.912, 0.326, 0.936] to {2018}[0.922, 0.912, 0.958, 0.936]."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "olhO8EOCvsqxNHXB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents found within the coordinates, [0.039, 0.162, .074, 0.266]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are y-axis tick labels: {1500}[0.039, 0.243, 0.074, 0.266] and {1650}[0.039, 0.162, 0.074, 0.185], representing billions in revenue."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "4iFr89E5xV7xWfuH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line with color #ff4500}<r>[0.097, 0.321, 0.959, 0.730]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "%Change on Restaurant Revenue."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "vtxW3mvxrfDqSyjT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.048, 0.531, 0.085, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "-10."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "kuik1qzwDAQyNgI3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.174, 0.841, 0.475, 0.871] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "%Change on Airline Revenue."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "SxXEErsgs6qZokRo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2015 on the Hotel Occupancy line}[0.384, 0.155] higher or lower than {the point for 2016 on the Hotel Occupancy line}[0.480, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "ZQ4NOxdsau1lQfBI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher value at {point for the year 2020}[0.863, 0.867], %Change on Restaurant Revenue or %Change on Travel Agency Revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "StNL5q6NutcKuTgL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2014 on the '%Change on Restaurant Revenue' line}[0.289, 0.380] positioned higher or lower than {the point for the same year on the '%Change on Airline Revenue' line}[0.289, 0.463]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "sXUNRviybGBxeQE8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.107, 0.698, 0.563, 0.882]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Hotel Occupancy}[0.174, 0.710, 0.350, 0.740], {%Change on Restaurant Revenue}[0.174, 0.753, 0.523, 0.783], {%Change on Travel Agency Revenue}[0.174, 0.797, 0.554, 0.827], and {%Change on Airline Revenue}[0.174, 0.841, 0.475, 0.871], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "FaJ4ymIiXJVtGq0B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.260, 0.025, 0.797, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Tourism and Hospitality Industry Analysis.\""
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "r21858oOqsAZDhyl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.862, 0.474, 0.864, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a line segment with identifier _child4, whose color code is #000000 and whose linestyle is --."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "ZvvblslGXW2PYOTs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the coordinates [0.025, 0.102, 0.055, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Income/Expenses/Risk_Controller."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "uurprw8qEbp9FxhN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.513, 0.917, 0.586, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "qarpZLqrGONTfY5S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item} at [0.257, 0.695, 0.465, 0.741]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk_Controller."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "k0sJQTIa2tBPF9qm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Income line point '2020'}[0.946, 0.165] above or below {the Expenses line point '2020'}[0.946, 0.334]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "3RsGcemnWcrHtMUC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Income line point '2015'}[0.154, 0.278] higher or lower than {the Expenses line point '2015'}[0.154, 0.391]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "y1GuuQssd679nct9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Income line point '2020'}[0.946, 0.165] to the left or right of {the Income line point '2015'}[0.154, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "KYbw2GrTs3juQBWR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.257, 0.629, 0.389, 0.674]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text for the \"Expenses\" legend item, which is associated with a red color marker."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "0cnYJoblpVdsvHEb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.257, 0.695, 0.465, 0.741]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend item: {Risk_Controller}[0.257, 0.695, 0.465, 0.741], which is paired with a blue square marker."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "lllqntRG6HjbRr1o",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.787, 0.210, 0.946, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 points. They are the Income line points for the years 2019 and 2020, respectively. The 2019 point is located at {coordinates}[0.787, 0.210] and the 2020 point at {coordinates}[0.946, 0.165]."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "uLfD8dETTs8xi3qb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object mapped at the coordinate [0.015, 0.388, 0.033, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label which is Population."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "5Jdl7KMfPstPTLbr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify what {the colored marker}[0.832, 0.804, 0.873, 0.818] in the legend stands for?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the chart line for 'Boomers'."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "gwiAOccl9VA4yKSr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the X-axis label at the [0.490, 0.945, 0.578, 0.975] coordinates stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Age Group."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "lkYlo8h92WP7vpcy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {GenX's line}[0.212, 0.265, 0.856, 0.722] above or below {Boomers' line}[0.212, 0.047, 0.856, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "uU1R1mpOyRYiQHiO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher population count at age '60-70', {Millennials'}[0.727, 0.613] or {GenZ}[0.727, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Millennials."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "KkRS6h1Xb32tmLhx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the age group '30-40', does {GenX}[0.341, 0.635] or {Boomers}[0.341, 0.657] have a higher population?"
+            },
+            {
+                "from": "gpt",
+                "value": "Boomers."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "S8BoSWOc4BLgJKFO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area at the [0.829, 0.698, 0.977, 0.882] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "This rectangular area contains the chart legend with item labels: {Millennials}[0.882, 0.710, 0.970, 0.740], {GenX}[0.882, 0.753, 0.927, 0.783], {Boomers}[0.882, 0.797, 0.956, 0.827], and {GenZ}[0.882, 0.841, 0.927, 0.871]. Each label is accompanied by a matching colored handle specifying their respective colors."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "wtk0310ESn8BEhKg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the content in area [0.048, 0.865, 0.074, 0.889] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to the Y-axis label '200'."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "HKkB6nMCEJZl1w23",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the rectangular region [0.450, 0.912, 0.490, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an X-axis tick label representing the '40-50' age group."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "BApJGVKoNTBMnrfz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the seventh y-axis tick label contained in {the Y-axis label}<r>[0.019, 0.220, 0.041, 0.711]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "35."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "haTsqHyEhIncmB3r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {the legend's third item tag}<r>[0.159, 0.147, 0.308, 0.177]</r> show?"
+            },
+            {
+                "from": "gpt",
+                "value": "DNA Structure."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "0fRAe7Jxe1xjRQYi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second point on the \"Protein Analysis\" line}<r>[0.307, 0.276]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2016."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "WmpQeYbX6h5RFNDq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for {Protein Analysis}<r>[0.157, 0.207, 0.906, 0.551]</r> above or below the line for {DNA Structure}<r>[0.157, 0.207, 0.906, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "2Jo8HdqoSuS6FU9I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for \"Cell Membrane Research\" representing the year 2020}<r>[0.906, 0.172]</r> higher or lower than {the point for \"Anaerobic Digestion\" representing the year 2020}<r>[0.906, 0.276]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "kH0wznlOGuUrwoPe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research area had a higher degree of focus in the industry in the year 2015, {Protein Analysis}<r>[0.157, 0.207]</r>, or {DNA Structure}<r>[0.157, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Protein Analysis."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "jSoNZ6yge0klGXXN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region <r>[0.093, 0.048, 0.426, 0.232]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the graph, which include {Protein Analysis}<r>[0.159, 0.059, 0.325, 0.089]</r>, {Cell Membrane Research}<r>[0.159, 0.103, 0.418, 0.133]</r>, {DNA Structure}<r>[0.159, 0.147, 0.308, 0.177]</r>, and {Anaerobic Digestion}<r>[0.159, 0.191, 0.368, 0.221]</r>, each paired with a colored marker indicating the line color on the chart."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "vhdWy0V9Hw3mnGLR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the region <r>[0.135, 0.912, 0.179, 0.936]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there's an x-axis label showing the year 2015."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "Y8sCliJ9iIKZKdD7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region <r>[0.159, 0.191, 0.368, 0.221]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item for {Anaerobic Digestion}<r>[0.159, 0.191, 0.368, 0.221]</r> is contained within this region."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "hu1DWIdJr50ZAnrk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the seventh x-axis label according to the coordinates on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "1LGVWSdnnJVhgYnh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line labeled as {Global Trade Flow}<r>[0.616, 0.696, 0.861, 0.741]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffa500."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "UPl5VQ12wAhEcYOc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.025, 0.189, 0.055, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "7BsA4qI9nG96Jegm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs point 7 of the {Global Trade Flow}<r>[0.174, 0.117, 0.933, 0.712]</r> above or below point 7 of the {Supply Chain Disruption}<r>[0.174, 0.117, 0.933, 0.712]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "BGCAofPNTFeD3F8C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '40'}<r>[0.427, 0.448]</r> for 'Global Trade Flow' higher or lower than {the point at '40'}<r>[0.427, 0.448]</r> for 'Supply Chain Disruption'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "7wwmz7aUBiGJW3zX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {x=100}<r>[0.933, 0.712]</r>, 'Global Trade Flow' or 'Supply Chain Disruption'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "fj9vd2LPyimXWNyF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects present within the region [0.328, 0.869, 0.357, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label '30'."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "t456uCVAuOD3XoJQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.528, 0.679, 0.961, 0.823]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Global Trade Flow}<r>[0.616, 0.696, 0.861, 0.741]</r> with a color marker in #ffa500 and {Supply Chain Disruption}<r>[0.616, 0.762, 0.950, 0.807]<r/> with a color marker in  #ff4500."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "WV3rIoRoSNAzcRHQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are found within the coordinates [0.412, 0.869, 0.441, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the x-axis label '40'."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "PJ9NU29CaetUkrRp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.041, 0.517, 0.019, 0.441] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "qc7iPCvkyYt4VRhi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the legend for 'Accumulated Knowledge' located?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.115, 0.121, 0.160, 0.135]"
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "H05o9ej85Fh1btS3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by this {point}[0.939, 0.866] on the Social Equality line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2050."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "n8edpsEwqwgwRdYr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Accumulated Knowledge}[0.939, 0.096] higher than the {Human Population}[0.939, -3245074.638] in the year 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "C91wDVVhwH4DTtZg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the years 1950 and 2000, does the {Social Equality line}[0.522, 0.884, 0.731, 0.876] increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "aVg2a4gSoPfbzdz9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {Warfare Intensity}[0.522, 0.866] and {Social Equality}[0.522, 0.884] in 1950 close to each other?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "Dj96HuDlZ2XhzgGm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.113, 0.074, 0.390, 0.220] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It lists four essential factors of human history and civilization development: Human Population with a yellow handle, Accumulated Knowledge with a red handle, Warfare Intensity with a royal blue handle, and Social Equality with a dark blue handle."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "BSfGxOkEhwKHe8zh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.081, 0.053, 0.092, 0.910]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis tick labels: {0}[0.081, 0.887, 0.092, 0.910], {500}[0.059, 0.720, 0.092, 0.744], {1000}[0.048, 0.553, 0.092, 0.577], {1500}[0.048, 0.387, 0.092, 0.410], {2000}[0.048, 0.220, 0.092, 0.243], and {2500}[0.048, 0.053, 0.092, 0.076]."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "G60Y6IdfSLOe9hLk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within [0.522, 0.866, 0.731, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart corresponds to the lines of Warfare Intensity and Social Equality between the years 1950 and 2000. The lines show a slight increase in Warfare Intensity and a more marked increase in Social Equality."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "2AJacyaju9yWSREy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at [0.025, 0.205, 0.055, 0.691]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No. of Reforms/Projects."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "maGZmOHyXFv2xKcq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label located at [0.491, 0.917, 0.564, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "aIyS4C5JW1n5VRu3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the first legend item located at [0.693, 0.327, 0.843, 0.372]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "yIW2Mmhiqtuz0vaT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Healthcare' line}[0.110, 0.131, 0.946, 0.567] and {the 'Social Security' line}[0.110, 0.250, 0.946, 0.368], which one generally has higher values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Security."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "tkSjs9M61c3QfyVr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point {2019}[0.946, 0.686], is {the 'Public Transport' line}[0.528, 0.567] above or below the 'Public Education' line [0.946, 0.567]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "FNUnw9986UIhlbLh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2015, is the 'Healthcare' or the 'Public Education' department initiating more reforms/projects based on points {Healthcare[0.110, 0.448]} and {Public Education[0.110, 0.368]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "E0adMRhfZ7MRxlyG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.604, 0.310, 0.932, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located here including the labels {Healthcare}[0.693, 0.327, 0.843, 0.372], {Public Education}[0.693, 0.392, 0.921, 0.437], {Public Transport}[0.693, 0.458, 0.913, 0.503], {Social Security}[0.693, 0.524, 0.897, 0.569], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "ZnHxctRnx8afKKgY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a brief description of the area [0.498, 0.869, 0.557, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis year label '2017' is located in this area."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "aXmAnCavwn5ky3a7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within the region [0.079, 0.632, 0.094, 0.667]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '5' is located here."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "kC2oS72zmaGfDBRb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.025, 0.383, 0.055, 0.499]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "R31qRvSYwtukaRaf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the line for 'Start-Up Spirit'}<r>[0.132, 0.138, 0.946, 0.441]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#008000"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "JqwJUBYqpMwm3fy8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.509, 0.917, 0.568, 0.963]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "aIzeCUIeJYM5uBYK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line representing 'Disruption'}<r>[0.132, 0.401, 0.946, 0.764]</r> above or below {the line representing 'Large Corporations'}<r>[0.132, 0.078, 0.946, 0.401]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "8yTJPUDYKzpBr8te",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2005 on the 'Start-Up Spirit' line}<r>[0.335, 0.340]</r> higher or lower than {the point for the same year on the 'Innovation' line}<r>[0.335, 0.441]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "yQRuARSRd6W9puQS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is at a higher point in the year 2015, 'Disruption' or 'Large Corporations'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Disruption"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "MjMd1EHXIpumIMQl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region <r>[0.568, 0.548, 0.932, 0.823]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend for the chart, with labels for {Start-Up Spirit}<r>[0.656, 0.564, 0.853, 0.609]</r> in green, {Innovation}<r>[0.656, 0.630, 0.803, 0.675]</r> in orange, {Disruption}<r>[0.656, 0.696, 0.800, 0.741]</r> in red, and {Large Corporations}<r>[0.656, 0.762, 0.921, 0.807]</r> in blue. Each label is accompanied by a color marker."
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "Bo9L8Bk6Dlslvh8V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of <r>[0.064, 0.125, 0.115, 0.866]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick labels ranging from 0.20 to 0.55."
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "5KV9NRFUKqa7SMWl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area <r>[0.102, 0.869, 0.975, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area houses the x-axis labels, marking the years from 2000 to 2020."
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "QTYz3DMmgQ1mvhrS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.047, 0.419, 0.082, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "7000."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "0fWi7Rruf6wGeo6c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.099, 0.075, 0.319, 0.222]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot legend."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "18Go3VVKQ62kVHEY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker for {Trademark Filings}<r>[0.100, 0.158, 0.137, 0.172]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#0343df)."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "XbJ4BGh2U09ITDHL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for the year 2010 higher in {Patent Grants}[0.539, 0.584] or in {Trademark Filings}<r>[0.539, 0.532]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patent Grants."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "nnhdqMy02bK3gnkv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the trend which terminates at the higher point for the year 2020, {Patent Applications}<r>[0.836, 0.116]</r> or {Trademark Registrations}<r>[0.836, 0.428]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff028d (Trademark Registrations)."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "T8gzaINLCcWQFttR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trendline has more points above the y-axis tick label '6000'[0.047, 0.523, 0.082, 0.546], {Patent Applications}<r>[0.241, 0.116, 0.836, 0.636]</r> or {Patent Grants}<r>[0.241, 0.427, 0.836, 0.792]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patent Grants."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "n387KJZ1WMgWsYv6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the region [0.100, 0.075, 0.319, 0.222]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend with trend labels and their color markers. The labels are: {Patent Applications}<r>[0.144, 0.085, 0.278, 0.108]</r>, {Patent Grants}<r>[0.144, 0.120, 0.239, 0.143]</r>, {Trademark Filings}<r>[0.144, 0.155, 0.266, 0.178]</r>, and {Trademark Registrations}<r>[0.144, 0.189, 0.313, 0.213]</r> with their respective color markers adjacent to them."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "cKJ4xsnTDV24CQek",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements between coordinates [0.241, 0.116, 0.836, 0.636]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of various points representing the trend for Patent Applications. The values are plotted for the years 2000 to 2020. The trend is represented by a purple line."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "OSvQe6zilENffpfn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.039, 0.107, 0.082, 0.130]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label for the value '10000'."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "OBpSaNnhNEcllQu1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.116, 0.912, 0.125, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '1'."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "vYMFoZEHQbXbDoHa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the item found at these coordinates {item from the legend}<r>[0.489, 0.747, 0.614, 0.771]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is 'Process Efficiency'."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "97KDjm7A6YoEbZcO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the object at {coordinates}<r>[0.779, 0.306, 0.944, 0.245]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the 6th point or marker on the Process Efficiency line."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "i6DiM7iJ4Tcvirx8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 3rd point of {Process Efficiency}[0.450, 0.550] above or below the 3rd point of {Process Timing}[0.450, 0.408]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "lwUiBB5xt4hWgOyU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line marker is higher, the 4th point on the {Process Efficiency} line[0.614, 0.367] or the 4th on the {Process Timing}<r>[0.614, 0.591]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Process Timing line."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "HobHKgbbNiJMMVvl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 6th point, is {Process Efficiency}[0.944, 0.245] having a higher or lower value than {Approval Rate}<r>[0.944, 0.082]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "DPBeYK0QEJH92hdq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me what the area enclosed within [0.445, 0.738, 0.619, 0.885] houses."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend detailing the color coding for the lines. The legend includes Process Efficiency, Process Timing, Approval Rate, and Process Cost, each with a unique color and marker."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "DqeIJlPvicF2H0k2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.120, 0.245, 0.944, 0.408]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the line for Process Efficiency, marked with the color #1f77b4 and 'o' style markers at points representing the six quarters."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "Jh4O3XNEdwSYpxA3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.489, 0.782, 0.593, 0.806]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Process Timing' from the chart's legend is within this region."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "MfjL1xmH0JC1kpDc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {coordinates}<r>[0.215, 0.911, 0.224, 0.934]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "rcXWl6XFpcCdy9DD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the {legend}<r>[0.075, 0.039, 0.296, 0.223]</r> associate with the \"Medication Group\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue (#0000ff)."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "ACgYbGH4tJJ2XpYy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at {coordinates}<r>[0.040, 0.306, 0.057, 0.329]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "5AmGOq3oDxwLVU4f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first point of the {Control Group}<r>[0.220, 0.736, 0.832, 0.842]</r> line above or below the first point of the {Therapy Group}<r>[0.220, 0.447, 0.832, 0.736]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "1CtIFMW0rd4j5cZP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Therapy Group}<r>[0.220, 0.447, 0.832, 0.736]</r> and the {Combination Group}<r>[0.220, 0.078, 0.832, 0.578]</r>, which one reaches higher on the fifth day?"
+            },
+            {
+                "from": "gpt",
+                "value": "Combination Group."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "yWxs4J8wkItVq9kc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the second point in the {Medication Group}<r>[0.220, 0.420, 0.832, 0.789]</r> line above or below the second point in the {Control Group}<r>[0.220, 0.736, 0.832, 0.842]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "PfsVFgrHgWDB6zHP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region <r>[0.075, 0.039, 0.296, 0.223]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It includes the labels: {Control Group}<r>[0.128, 0.050, 0.244, 0.080]</r>, {Therapy Group}<r>[0.128, 0.094, 0.253, 0.124]</r>, {Medication Group}<r>[0.128, 0.138, 0.275, 0.168]</r>, and {Combination Group}<r>[0.128, 0.182, 0.290, 0.212]</r>. Each label is associated with a unique color represented by a geometric figure to the left of the label."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "mbp2hHroRGwIX9C0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.035, 0.290, 0.062, 0.330]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one y-axis tick label: {40}<r>[0.040, 0.306, 0.057, 0.329]</r>."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "KR9cXBpRiHdIPSj2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.675, 0.911, 0.683, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the fourth x-axis label, which is the number {4}<r>[0.675, 0.911, 0.683, 0.934]</r>."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "jF1BSrqCtBxTx123",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker for {Product_B}<r>[0.157, 0.212, 0.227, 0.233]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff4500."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "Qh1V4vGarTrjoF2q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the point coordinates of the 8th data for {Product_C}<r>[0.662, 0.642]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "670."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "3JySCbyK2iXF5UsN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the label at the location <r>[0.064, 0.133, 0.123, 0.168]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1700."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "OW27Y64c6hsUS9zw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product's sales trend line from {Product_A}[0.242, 0.136, 0.380, 0.181] and {Product_B}[0.242, 0.202, 0.380, 0.248] is showing an increase in the 11th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product_B."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "9cxwNrNNMwFvFnpQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Product_A}'s 8th data point <r>[0.662, 0.796]</r> higher than {Product_D}'s 8th data point <r>[0.662, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "NeWYuS61BgeEDOkV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the 7th month's sale of {Product_C}[0.587, 0.637] and {Product_D}[0.587, 0.483]. Which is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product_C."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "9YZ9jJQt3bL3owtE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is contained in the area <r>[0.153, 0.119, 0.394, 0.398]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend providing information about different product lines (Product_A, Product_B, Product_C, Product_D) along with their respective line colors."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "Wrmc6JBxbOgO4QEn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the region <r>[0.064, 0.229, 0.123, 0.264]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis tick label that reads '1500'."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "fvisHNSNPyORl8K8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region <r>[0.281, 0.869, 0.296, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an x-axis label representing the 3rd month."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "MIOfeD8UjMXxdsr0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.661, 0.229, 0.941, 0.265] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Suburbs Planning Contacts."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "6EZGday9eptl9gbT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.493, 0.934, 0.548, 0.970]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "KkAdmsP8VDka4e5B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.059, 0.864, 0.070, 0.892]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "3Gt7s4n1IVjQ9XM4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'CBD Construction'}[0.162, 0.637, 0.880, 0.852] above or below {the line for 'Suburbs Construction'}[0.162, 0.279, 0.880, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "x5ZhIsbTLPD6Tp41",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {point 2009}[0.880, 0.852], CBD Construction or Suburbs Construction?"
+            },
+            {
+                "from": "gpt",
+                "value": "CBD Construction."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "nfV4xSm1ZviQMGB4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {CBD Planning Contacts line}[0.162, 0.303, 0.880, 0.518] intersect with {the line for 'Suburbs Planning Contacts'}[0.162, 0.089, 0.880, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "bJxL3pV4mlIA5o9A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.595, 0.058, 0.949, 0.278]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with 4 items: {CBD Construction}[0.661, 0.071, 0.845, 0.107], {Suburbs Construction}[0.661, 0.124, 0.885, 0.160], {CBD Planning Contacts}[0.661, 0.176, 0.900, 0.212], and {Suburbs Planning Contacts}[0.661, 0.229, 0.941, 0.265], each with a marker in a distinct color."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "KuDGDlCulyQ9JFty",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.048, 0.030, 0.070, 0.892]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis labels from '0' to '35'. Their respective coordinates are '0' at {coordinates}[0.059, 0.864, 0.070, 0.892], '5' at {coordinates}[0.059, 0.745, 0.070, 0.773], '10' at {coordinates}[0.048, 0.626, 0.070, 0.654], '15' at {coordinates}[0.048, 0.507, 0.070, 0.535], '20' at {coordinates}[0.048, 0.388, 0.070, 0.416], '25' at {coordinates}[0.048, 0.268, 0.070, 0.296], '30' at {coordinates}[0.048, 0.149, 0.070, 0.177], and '35' at {coordinates}[0.048, 0.030, 0.070, 0.058]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "RYxTrR0VZ6fLknAs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.162, 0.089, 0.880, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 objects. They are the line plots for CBD Construction, Suburbs Construction, CBD Planning Contacts, and Suburbs Planning Contacts."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "hLB827oP1m7GpTqV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Temperature."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "NZCKewBucu3hl6db",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Latitude."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "WrIZKjJiIixHQyZx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with color code \"#679fce\" represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Temperate_Climate."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "ENNBMnJd7AUj8eXo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt latitude -50, is the temperature for {the line representing 'Temperate_Climate'}[0.310, 0.604] higher or lower than {the line representing 'Tropical_Climate'}[0.310, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "BusycQ41daCxShdq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] or {Tropical_Climate}[0.130, 0.231, 0.941, 0.604] approaches a higher temperature at latitude 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tropical_Climate."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "X4l60yWmjGw01f5y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] show a higher temperature at latitude 40 or latitude 60?"
+            },
+            {
+                "from": "gpt",
+                "value": "Latitude 40."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "oOgtnPoUrGyIcA3A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's contained in the coordinates [0.100, 0.079, 0.375, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, specifying the color and label for each of the two temperature data series: Temperate_Climate and Tropical_Climate."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "sbcumg7KjiWbVh0r",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease characterize the content in the area [0.089, 0.066, 0.981, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises the entirety of the chart plot, which includes the plotted lines representing Temperate_Climate and Tropical_Climate, their respective data points, and the grid of the chart."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "vzdHpUSqkCzzRqvF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is captured within [0.050, 0.056, 0.077, 0.080]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, you can find the y-axis tick label '2.0'."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "f6reS7esbY7EnVih",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.161, 0.091, 0.300, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Inflation Rate\" in the legend."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "zGPsNQBl742ZUpSR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-tick label at [-0.013, 0.911, 0.047, 0.934] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"1997.5\" on the x-axis."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "39jToHYtl6P0B1Bj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.855, 0.669, 0.940, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last point on the Inflation Rate line for the year 2019."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "lxDKVCgDLkFqmCU8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Inflation Rate for the year 2009}[0.511, 0.857] above or below {the point for Unemployment Rate for the year 2009}[0.511, 0.130]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "OxcGsJk4vyYtj2sQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point [0.726, 0.707], is {Inflation Rate}[0.726, 0.364] above or below {Unemployment Rate}[0.726, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "cXmV34G5Gey7MGxB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich rate is higher in the year 2000, {Inflation Rate}[0.124, 0.574] or {Unemployment Rate}[0.124, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation Rate."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "E5hsJbn4lOpu6t5l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in [0.095, 0.095, 0.151, 0.157]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a marker for the Inflation Rate in the legend with the color '#af7dfe'."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "RW1SL3lMVU3rDCHS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.507, 0.943, 0.558, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Year\" is here, described in black color."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "zEUuY68qmaAT1rzD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within [0.202, 0.911, 0.262, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis tick label '2002.5'."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "CC9RKY2dFD8Ve319",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is demonstrated by {line_1}<r>[0.130, 0.231, 0.941, 0.328]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Performance."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "1cuq4EgduakjZhkg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.019, 0.377, 0.043, 0.583]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "6nlUVqxvFIKhTUDg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the subtitle}<r>[0.218, 0.025, 0.852, 0.058]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainability Measures for CSR Commitment."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "rZsnE2TD7X0etStN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at 0.9 index, {Environmental Performance}<r>[0.130, 0.483, 0.941, 0.508]</r> or {Social Performance}<r>[0.130, 0.591, 0.941, 0.645]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Performance."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "xNjLnP0oFWi9EL8L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt index 0.7, is {Economic Performance}<r>[0.130, 0.231, 0.941, 0.328]</r> higher or lower than {Environmental Performance}<r>[0.130, 0.483, 0.941, 0.508]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "qRUsoE3lINosHXem",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Environmental Performance}<r>[0.130, 0.483, 0.941, 0.508]</r> higher or lower at index 1.0 compared to index 0.1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "5rxrBBHw5rSEQcLp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within [0.050, 0.056, 0.077, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick labels. The labels indicate the performance values ranging from 0.2 to 0.8 with an increment of 0.1."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "WnwNVwnnfy7Uw0sK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.116, 0.911, 0.954, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the X-axis tick labels that represent the CSR Index ranging from 0.1 to 1.0."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "XEgIQd9aJHyhWd1K",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the zone from [0.100, 0.079, 0.464, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the graph\u2019s legend with the labels: {Environmental Performance}<r>[0.166, 0.091, 0.456, 0.121]</r>, {Economic Performance}<r>[0.166, 0.134, 0.405, 0.164]</r>, and {Social Performance}<r>[0.166, 0.178, 0.366, 0.208]</r>."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "zfsu780AbiTyvHMr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.509, 0.943, 0.561, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "quwK6SE8mHRG8qzi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the marker for the second data point in the line graph for the \"Inovation Industries\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.332, 0.548]"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "DK2Km0Hgrpa0zyMX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the values represented by {the y-axis tick label} at [0.050, 0.810, 0.077, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "YfNULr4I9HbZgjzd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the third data point for \"Knowledge Economy\"}[0.535, 0.292] above or below {the third data point for \"Inovation Industries\"}[0.535, 0.518]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "IS7C7VJaRRlxFgsz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for \"Knowledge Economy\" in 2020}[0.941, 0.171] have higher or lower growth compared to {the point for \"Inovation Industries\" in 2020}[0.941, 0.292]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "DVVJgEFKcAFXZYMF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line on the chart has a higher value at the point labeled {2019}, \"Knowledge Economy\" or \"Inovation Industries\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Inovation Industries\"."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "LL6rvI7mwDR5nWpx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with coordinates [0.108, 0.911, 0.152, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis tick label \"2016\"."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "5rKCgvUd3sqOBQ4B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region from [0.100, 0.079, 0.391, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the chart's legend, which includes the labels {Knowledge Economy}[0.166, 0.091, 0.382, 0.121] and {Inovation Industries}[0.166, 0.134, 0.372, 0.164], each associated with a specific color."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "SCIJ9xT7cQ3ydacO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area [0.061, 0.025, 1.009, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Growth Comparison: Knowledge Economy and Innovation Industries,\" is positioned here."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "RFRsDVTaodDSt1bc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the coordinates [0.015, 0.426, 0.034, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "ZREtFUrKtTm5YUfs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first legend item in the subplots?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.861, 0.068, 0.970, 0.090]."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "GksYeveFwXegBhem",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich step does {the x-axis tick label}[0.108, 0.933, 0.117, 0.951] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Step number 1."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "2uxGtX2P4rcAnysJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at step 3 of 'Supply Chain'}[0.200, 0.147] higher or lower than {the point at step 3 of 'Fulfillment'}[0.200, 0.245]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "AUI2Ba1AtVqs7Lrc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Supply Chain'}[0.113, 0.093, 0.943, 0.507] generally above or below {the line for 'Fulfillment'}[0.113, 0.104, 0.943, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "onOt0C6QPOrbgxop",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process has a higher efficiency at {step 10}[0.506, 0.921], Supply Chain or Fulfillment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Supply Chain."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "FoIv652MzYd8yQ7J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the region [0.314, 0.019, 0.742, 0.044]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, \"Value Chain Visualization and Analysis\", styled in black color."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "ciCVM1kCNJbj44pY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with coordinates [0.808, 0.060, 0.977, 0.132]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two items in the area. These are the legend entries of the two plotted processes: {Supply Chain}[0.861, 0.068, 0.970, 0.090] with a {color marker}[0.809, 0.071, 0.853, 0.085] and {Fulfillment}[0.861, 0.101, 0.948, 0.123] paired with a different {color marker}[0.809, 0.104, 0.853, 0.118]."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "DuQ8zJIXNAWy33d7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within the region [0.113, 0.093, 0.943, 0.507]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two objects are present within this region. The lines representing the 'Supply Chain' and 'Fulfillment' processes extend within these coordinates, depicting changes in efficiency across different steps."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "IVovzIeSkoN8OEZc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at the coordinates [0.019, 0.373, 0.043, 0.587]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "mvsgIYcyA3qFbJK0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you let me know the coordinates of the third tick on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.270, 0.911, 0.314, 0.934]"
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "aInvyOX2awGpWzRq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the text}[0.513, 0.911, 0.557, 0.934] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2010."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "4PmhLVTbNioVyuuS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2002, is the {Electricity Consumption}[0.211, 0.607] higher or lower than the {Gas Consumption}[0.211, 0.740]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "bPTYkj7o8UcGVErU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2008, which consumption is higher, {Electricity Consumption}[0.454, 0.480] or {Gas Consumption}[0.454, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gas Consumption."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "iZeRFNP9QiPrC0mL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Gas Consumption}[0.616, 0.872] in the year 2012 greater than the {Electricity Consumption}[0.616, 0.392] in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "p6H7xmcV96Q4bVms",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the region [0.100, 0.079, 0.422, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It includes two items: {Electricity Consumption}[0.166, 0.091, 0.413, 0.121] marked with color '#af7dfe' and {Gas Consumption}[0.166, 0.134, 0.350, 0.164] marked with color '#feac60'."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "T1G7Bb79w1P2OzFQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is represented in the location [0.049, 0.222, 0.077, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label '1.2'."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "Fep9LrhnZ1M53WLR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the coordinates [0.432, 0.911, 0.476, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object located here, which is an x-axis label for '2008'."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "2GZPV5brzJKa4bEF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.509, 0.943, 0.561, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "D1FnvVpI8dVDdoN5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.101, 0.095, 0.156, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Colored marker in the legend."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "HYaFlZvgCfN8gLDR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the Y-tick} at the location [0.049, 0.575, 0.077, 0.598] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.5"
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "RDDvRi21qc8fxLEc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the trend of {Biomedicine Trends}[0.170, 0.273, 0.900, 0.729] surpass or fall behind {Medical Tech Biotech Trends}[0.170, 0.086, 0.900, 0.667] in the year indicated by the x-axis tick at [0.414, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Surpass."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "qn6mrJDPA6gEjlPH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point marked at {coordinates}[0.900, 0.273] on the 'Biomedicine Trends' line higher or lower than the corresponding point on the 'Medical Tech Biotech Trends' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "GFTmU0d6sncJs62Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend appears to grow faster across the years, {Medical Tech Biotech Trends}[0.170, 0.086, 0.900, 0.667] or {Biomedicine Trends}[0.170, 0.273, 0.900, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical Tech Biotech Trends."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "h3gRWmrWKm5ZfBiY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the region [0.100, 0.079, 0.466, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for two data series: {Medical Tech Biotech Trends}[0.166, 0.091, 0.458, 0.121] and {Biomedicine Trends}[0.166, 0.134, 0.368, 0.164], each coupled with its corresponding color marker."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "JlcgpoJMFR2workU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between coordinates [0.089, 0.066, 0.981, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily comprises the data visualization area. It includes two lines representing the trends of 'Medical Tech Biotech Trends' and 'Biomedicine Trends', along with the x and y axes, their respective tick labels, and grid lines."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "6l2hxddFhDFlApEy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are found within the region [0.170, 0.086, 0.900, 0.667]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains points and marker lines labelling the trend data for the series: 'Medical Tech Biotech Trends'."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "MTbU5navOk7Avhyu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.386, 0.043, 0.574]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index Value."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "8jAxtqNBhyZKrAuE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xAxis tick label}<r>[0.297, 0.911, 0.368, 0.934]</r> in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "America."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "Eldu7Aia4z3XLNPS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend item}<r>[0.733, 0.796, 0.962, 0.826]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consumer Confidence."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "cjAqDz5RA5Oxbm2X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Asia' in the 'Consumer Confidence' line}<r>[0.130, 0.713]</r> higher or lower than {the point for 'Asia' in the 'Economy Index' line}<r>[0.130, 0.536]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "QujyqtixS7fySgZH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the points for 'Europe' in the two lines, which has a higher value, 'Consumer Confidence' {the line for 'Consumer Confidence' at 'Europe'}<r>[0.535, 0.301]</r> or 'Economy Index' {the line for 'Economy Index' at 'Europe'}<r>[0.535, 0.360]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economy Index."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "VxhrCnrN9Tg6WGy2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'America' in 'Consumer Confidence'}<r>[0.332, 0.418]</r> higher or lower than {the point for 'America' in 'Economy Index'}<r>[0.332, 0.654]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "hFLNcnVscasLFnym",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the box <r>[0.666, 0.785, 0.971, 0.881]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend, indicating 'Consumer Confidence' with a dark purple color and 'Economy Index' with an orange color."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "Nc5LbLZ7fXJIsbvP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the box <r>[0.089, 0.066, 0.981, 0.895]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the main plotting area of the line chart depicting 'Consumer Confidence' and 'Economy Index' across different regions."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "RN6XwdTNnYQaWEwA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the area <r>[0.493, 0.943, 0.577, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to a label with the text 'Region'."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "8mFDhRZtQq3Fqn6b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.095, 0.066, 0.981, 0.895] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Main plot area."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "6AuR3KGTJuOtuaxV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.019, 0.253, 0.043, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label representing Production (Millions of Tons)."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "hR7AhM0U71JKc2He",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.269, 0.025, 0.807, 0.058] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the subtitle of the chart, 'Food and Agriculture Industry Analysis'."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "0aseFe4kI7GOmPj6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is the {Grain Production line}[0.135, 0.273, 0.941, 0.584] above or below the {Fruit Production line}[0.135, 0.604, 0.941, 0.739] in the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "N24HaXJBfHZZASXT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the {Grain Production line}<r>[0.135, 0.273, 0.941, 0.584]</r> and the {Fruit Production line}<r>[0.135, 0.604, 0.941, 0.739]</r>, which one shows a higher value in year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grain Production line."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "K4V1TJ435zr1Wn6y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2008, did the {Grain Production line}<r>[0.135, 0.273, 0.941, 0.584]</r> start above or below the {Fruit Production line}<r>[0.135, 0.604, 0.941, 0.739]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "s3mzUpmvRmdCv3mz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content lying within these coordinates: [0.105, 0.079, 0.353, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. The legend items specifically here are {Grain Production}<r>[0.172, 0.091, 0.345, 0.121]</r> and {Fruit Production}<r>[0.172, 0.134, 0.335, 0.164]</r>, each identified with a different color."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "w9hlVieUzPYdLfwj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the area [0.506, 0.943, 0.570, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label 'Years' is located within these coordinates."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "ZdRBN4w4xPH3iKmQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within [0.919, 0.911, 0.963, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a single object, the x-axis tick label '2017'."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "dr7Rw62eLEbwnPmq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.166, 0.091, 0.475, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for {Eco-Friendly Product Revenue}<r>[0.166, 0.091, 0.475, 0.121]</r>."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "Nx4JkuSoFi4t6OXv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.049, 0.375, 0.077, 0.399]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis tick label {1.0}<r>[0.049, 0.375, 0.077, 0.399]</r>."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "tJreduIfXKWkPFz5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.067, 0.911, 0.111, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis tick label {2012}<r>[0.067, 0.911, 0.111, 0.934]</r>."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "gTrzXpt6GuskAI8H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Eco-Friendly Product Revenue} line [0.089, 0.193, 0.959, 0.831] end ahead or behind of the {Social Responsibility Spend} line [0.089, 0.129, 0.959, 0.767] in terms of the Year axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ahead."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "PqEZ690d9rRqrrH1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher Spend/Revenue in 2017, {Eco-Friendly Product Revenue}[0.633, 0.512] or {Social Responsibility Spend}[0.633, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Responsibility Spend."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "s5EhKwE1F4ARNpRR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2018, is the {Social Responsibility Spend}<r>[0.742, 0.448]</r> more or less than the {Eco-Friendly Product Revenue}<r>[0.742, 0.384]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "EU3cbpsAnkvoKFMz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.100, 0.079, 0.484, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legends of two lines: The line {Eco-Friendly Product Revenue}<r>[0.166, 0.091, 0.475, 0.121]</r> and the line {Social Responsibility Spend}<r>[0.166, 0.134, 0.450, 0.164]</r>."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "udSKneDoULVkXe2d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the bounding box [0.150, 0.025, 0.899, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, \"Social Responsibility and Sustainable Business Trends\"."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "vMgv0Hpoa83zxOu8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the area [0.502, 0.911, 0.655, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes two x-axis tick labels: {2016}<r>[0.502, 0.911, 0.546, 0.934]</r> and {2017}<r>[0.611, 0.911, 0.655, 0.934]</r>."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "9ryUwdqbl9yppHsd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.019, 0.377, 0.043, 0.583] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "pE2UA9TbmmmhyZW6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the ninth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.927, 0.911, 0.954, 0.934]"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "ElKJIBTyINppKXRs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.166, 0.134, 0.358, 0.164] in the legend denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "IP Protection Level."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "P6RmduT0ZSLUEynb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is in a higher position, {the point on the R&D Performance line at x = 0.4}[0.400, 0.646] or {the point on the IP Protection Level line at x = 0.4}[0.400, 0.701]?"
+            },
+            {
+                "from": "gpt",
+                "value": "IP Protection Level."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "JtPDyuIgryTGGExN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point on the R&D Performance line at x = 0.7}[0.670, 0.265] and {the point on the IP Protection Level line at x = 0.7}[0.670, 0.773], which one has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "IP Protection Level."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "Vehut12J2WOcknN5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the R&D Performance line}[0.851, 0.154] at x = 0.9 above or below {the point on the IP Protection Level line}[0.851, 0.817] at the same x value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "wBSDow3jFxPEQGaS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the area [0.125, 0.025, 0.945, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Technological Innovation and Intellectual Property Analysis,\" is located here with parameters such as label, color and rotation."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "2ZROJxRss94NI3uW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.100, 0.079, 0.367, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, with the labels {R&D Performance}[0.166, 0.091, 0.351, 0.121] and {IP Protection Level}[0.166, 0.134, 0.358, 0.164]. Each label corresponds to a color indicated by its handle."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "Wimm7nsFr0CGC7XL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.927, 0.911, 0.954, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '1.0' is situated within these coordinates."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "Lahm9YncqaHwifxj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label at the lower left corner}<r>[0.019, 0.390, 0.043, 0.570]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock Price."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "2wNC3Tok69qA0RFR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year is represented by {the tick mark}<r>[0.255, 0.895]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2004."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "DCRZaDCdxvww0CWo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label content at the top center of the chart<r>[0.500, 0.943, 0.565, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "CULYCkpVPjfLiKNt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {the year 2007}<r>[0.384, 0.307]</r>, {WM Stock Price}<r>[0.384, 0.376]</r> or {EM Stock Price}<r>[0.384, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "EM Stock Price."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "NsHcyICC5rXJHPoA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the error line at 2020}<r>[0.941, 0.193]</r> represent a value greater than the {error line at 2001}<r>[0.126, 0.397]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "R1K7BxPT4FMrsDjX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for EM Stock in 2010}<r>[0.512, 0.618]</r> above {the data point for WM Stock in the same year}<r>[0.512, 0.411]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "rOmLFhW7KCwjUr9a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in the range [0.200, 0.300, 0.400, 0.600]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses multiple data points, including {EM and WM Stock Prices}<r>[0.255, 0.411]</r> for the year 2004, with each stock price marked by respective data points."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "xSb0gp04oh0W74D9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is encapsulated in the bounding box [0.540, 0.350, 0.750, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this bounded area lies data points for multiple years around 2014-2016 for both {EM and WM Stock Prices}<r>[0.683, 0.411]</r>, <r>[0.683, 0.480]</r>, visually charted on the line graphs."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "oJdWVKmNyeJ30T9A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCharacterize the section [0.820, 0.200, 0.950, 0.350]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the data points for the year {2020}<r>[0.941, 0.135]</r>, including values for both stock types and the error bars reflecting price uncertainties."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "v90DC0aPS618vXdA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at the coordinates [0.328, 0.025, 0.742, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Time Series Data Comparison\""
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "G4OkQTPALzuAchKA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.019, 0.377, 0.043, 0.583]"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "eXAq2w2yiMl5URzd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat numerical value does the x-axis label at coordinates [0.530, 0.911, 0.541, 0.934] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "5"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "h8Rjqd5jfFO5wyT0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point at {coordinates}[0.944, 0.462] from the \"Slow Expansion\" or \"Erratic Growth\" set?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Expansion"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "XTpHy11Uj9z7w94m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data set between \"Erratic Growth\" and \"Slow Expansion\" has higher values at the time point of 7, considering points at {coordinates}[0.697, 0.448] and {coordinates}[0.697, 0.469]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Expansion"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "EaxAdvxwJ0NlGcXB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data points for \"Erratic Growth\" at {coordinates}[0.616, 0.433] and \"Slow Expansion\" at {coordinates}[0.616, 0.484] above or below the y = 0.5 line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "WmW6HYsjxHBQ5O0k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within the coordinate area of [0.100, 0.079, 0.337, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items of the chart: {Erratic Growth}<r>[0.166, 0.091, 0.316, 0.121]</r> and {Slow Expansion}<r>[0.166, 0.134, 0.328, 0.164]</r>, each accompanied by a color marker."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "saGN9VsKtV6YGdsh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region defined by coordinates [0.089, 0.066, 0.981, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plot of the chart is located here, showing the time series data for \"Erratic Growth\" and \"Slow Expansion\"."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "aFU7F5ajtPXyTU66",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the area of [0.019, 0.377, 0.043, 0.583]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the y-axis, which is \"Performance\"."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "Cb1tkBqvPW3Du1oR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates}<r>[0.012, 0.452, 0.028, 0.518]</r> on the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "ETSu3c338K4etN7S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what is located at {coordinates}<r>[0.869, 0.068, 0.975, 0.090]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Speed Increase label."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "TsawAn8It4HFBdju",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {coordinates}<r>[0.869, 0.101, 0.942, 0.123]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Users."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "1LVzdJhD4P8DVhNz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year corresponds to {coordinates}<r>[0.189, 0.876]</r> for the point on the 'Speed Increase' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "2011."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "fXE9VREhtAWuOj8F",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the year at {coordinates}<r>[0.441, 0.878]</r> in the 'Speed Increase' line precede or follow the year at {coordinates}<r>[0.441, 0.349]</r> in the 'New Users' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Precede."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "rZWIGxeB2JPDD4rq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is denser at {coordinates}<r>[0.525, 0.876]</r>, labels from 'New Users' or 'Speed Increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Speed Increase."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "5l1evvr3wQ9cE4C0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the focal content in the range <r>[0.063, 0.049, 0.988, 0.921]</r> on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the main plotting area where multiple lines representing 'Speed Increase' and 'New Users' are displayed with respective points marked, against axes labeled with years and values."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "9G8l1sXAj8AqtVEv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region <r>[0.824, 0.060, 0.981, 0.132]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend section indicating the criteria 'Speed Increase' and 'New Users' with their color codes is present here."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "jqXQ15hdc76HR5nl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates <r>[0.105, 0.862, 0.945, 0.879]</r> on the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the line representing the 'Speed Increase', plotted with several defined points corresponding to different years."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "N1REN0FXvCtY8KEn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.019, 0.436, 0.043, 0.525]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Index."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "iHjzTYEzGYOWoIRx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label}<r>[0.954, 0.911, 0.981, 0.934]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.1."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "DCG65XQZsNKbboEt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker at {the last data point}<r>[0.888, 0.694]</r> in the plotted line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mustard yellow."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "3danxlrS7c3hmaMj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point}<r>[0.169, 0.740]</r> higher or lower than {the fifth point}<r>[0.488, 0.637]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "dFM3TBkdbXRfPY1n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line starting at {point one}<r>[0.169, 0.474]</r> end above or below {the final point}<r>[0.888, 0.580]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "iUMRyD5LzdASIxap",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater, the y-value at {beginning point}<r>[0.169, 0.740]</r> or at {midpoint}<r>[0.488, 0.603]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Beginning point."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "VvgbxTUNzqGJlXXH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between [0.076, 0.911, 0.249, 0.934] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the x-axis tick labels for {0.0}<r>[0.076, 0.911, 0.103, 0.934]</r> and {0.1}<r>[0.155, 0.911, 0.183, 0.934]</r>."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "RIuukb5G39E92jPz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe features found within [0.019, 0.436, 0.043, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label {Index}<r>[0.019, 0.436, 0.043, 0.525]</r> and several y-axis tick labels, for 0.1 to 0.9."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "lc5AkmaXsXnP2pZg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are displayed in [0.169, 0.409, 0.888, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several {x-axis tick labels}<r>[0.076, 0.911, 0.954, 0.934]</r>, {x-axis ticks}<r>[0.089, 0.895, 0.888, 0.895]</r>, and multiple data {points}<r>[0.169, 0.409, 0.888, 0.730]</r> plotted on a line."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "N961xhH7SoTCSfw4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.503, 0.943, 0.567, 0.975]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Years'."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "5d5Cz9jmvy1P39KA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with these coordinates represent <r>[0.130, 0.563, 0.941, 0.770]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the performance of 'Twitter' over the years."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "z8GlwF4rlMRenFdm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.019, 0.313, 0.043, 0.647]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Performance Metrics'."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "8HScznJDF4PorZ74",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2017}<r>[0.454, 0.911, 0.476, 0.934]</r>, is {Facebook's performance}<r>[0.454, 0.480]</r> higher than {Twitter's performance}<r>[0.454, 0.646]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "RV4lIAPB5frDW7d5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is {Facebook's performance point in 2020}<r>[0.941, 0.148]</r> above {Twitter's performance point in 2020}<r>[0.941, 0.770]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "PNUTxbI4dMNUxF97",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media had a higher performance in {2017}<r>[0.454, 0.911, 0.476, 0.934]</r>, Twitter or Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "94DNz0d71iuTepQp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in <r>[0.503, 0.943, 0.567, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label 'Years'."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "8uFF7Sc8l75mdbf8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents can be found within the area <r>[0.798, 0.079, 0.971, 0.176]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend indicating the line colors for 'Facebook' and 'Twitter'."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "kXxIwDvv2ZYx4uSM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region <r>[0.130, 0.895, 941, 0.770]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the performance over years for 'Facebook' and 'Twitter'. Information of the years is located at the x-axis in the form of x-axis tick labels, 'Facebook' performance is shown in purple line and 'Twitter' performance is shown in orange line."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "MW2nA6zhI5gzo02W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the sixth x-axis tick label represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "MNTrA3YinTooqMWy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.050, 0.609, 0.077, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "SeUdaJzHcTNulqXn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company does the line with these coordinates [0.130, 0.204, 0.941, 0.722] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "INTEL."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "5gm4fV5AfnBCU4Fj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the INTEL line}<r>[0.130, 0.204, 0.941, 0.722]</r> above or below {the AMD line}<r>[0.130, 0.238, 0.941, 0.584]</r> in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "UYGqxRQaSYIMPLG2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the performance of INTEL}<r>[0.130, 0.204, 0.941, 0.722]</r> greater or less than {the performance of AMD}<r>[0.130, 0.238, 0.941, 0.584]</r> in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "JZwBBDt55oiMKCft",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company had a higher performance in 2014, INTEL or AMD?"
+            },
+            {
+                "from": "gpt",
+                "value": "AMD."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "NMSKZrbWf2M5KDtw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give me a brief description of the content within [0.100, 0.079, 0.234, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the two plotted lines. `INTEL` is represented by a blue marker located at {coordinates}[inf, inf, -inf, -inf] and `AMD` is represented by a red marker with the {coordinates}[inf, inf, -inf, -inf]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "O0GXwClF6jhXkrtA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects contained within the region [0.198, 0.911, 0.602, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the x-axis labels for the years 2011 through 2015, positioned at {Coordinates}[0.198, 0.911, 0.242, 0.934], [0.288, 0.911, 0.332, 0.934], [0.378, 0.911, 0.422, 0.934], [0.468, 0.911, 0.512, 0.934] and [0.558, 0.911, 0.602, 0.934] respectively."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "Xo3Gypp8d1twQXIc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within [0.231, 0.025, 0.840, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart: 'Market Competition Analysis: INTEL vs AMD', which is colored black."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "IJDsM0SHlzifVrIy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the point at [0.512, 0.342] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Houston."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "ebe1RvIZBrXs6Wpx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with coordinates [0.160, 0.155, 0.865, 0.808] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average temperature of the year 2020 for various cities."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "KIE0eTZa6vyBPAXI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at {the coordinates}<r>[0.852, 0.187, 0.887, 0.215]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label for \"2021\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "Lf23fE7QM3nbizzH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the average temperature of {Houston}<r>[0.512, 0.342]</r> in 2021 higher or lower than its temperature in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "g4jwjv22UD8AkhZT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Chicago}<r>[0.395, 0.808]</r> and {Philadelphia}<r>[0.747, 0.668]</r> which city had a higher average temperature in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Philadelphia."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "WuXjkSKH1icGmfUJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the average temperatures of {New York in 2020}<r>[0.160, 0.762]</r> and {Philadelphia in 2021}<r>[0.747, 0.715]</r> compare?"
+            },
+            {
+                "from": "gpt",
+                "value": "New York had a lower average temperature."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "odk8tygaeCGTLjsU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible within the chart area bounded by the coordinates [0.160, 0.155, 0.865, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains lines representing average temperatures for various cities in 2020 and 2021. Individual points on these lines, representing various cities, are connected by the lines."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "4IIvGMSgYnYff3qC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the area with coordinates [0.125, 0.890, 0.865, 0.937]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis labels representing the cities of {New York}<r>[0.128, 0.909, 0.192, 0.937]</r>, {Los Angeles}<r>[0.236, 0.909, 0.319, 0.937]</r>, {Chicago}<r>[0.367, 0.909, 0.423, 0.937]</r>, {Houston}<r>[0.484, 0.909, 0.541, 0.937]</r>, {Phoenix}<r>[0.602, 0.909, 0.657, 0.937]</r>, {Philadelphia}<r>[0.705, 0.909, 0.790, 0.937]</r> and {San Antonio}<r>[0.823, 0.909, 0.907, 0.937]</r>."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "7mqVbB3QHkexmNf3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the bounding box with coordinates [0.852, 0.145, 0.887, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two legend labels: {2020}<r>[0.852, 0.145, 0.887, 0.173]</r> and {2021}<r>[0.852, 0.187, 0.887, 0.215]</r>."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "tC5lyiOWar4c5yAI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main element at the location [0.025, 0.139, 0.057, 0.766] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "SqwCZeay2cUorJ05",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fourth element in the title?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.500, 1.000, 0.500, 1.000]"
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "4QVE5oYMo8YkAktY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fifth {x-axis tick label}<r>[0.731, 0.895, 0.747, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "H8h8e12L5EFQ9qMg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point labeled 2}<r>[0.260, 0.199]</r> on the 'Knowledge Economy' line lower than the {point labeled 2}<r>[0.380, 0.332]</r> on the 'Innovation Industries' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "EwzTnPvH3gD09IWt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Knowledge Economy'}<r>[0.140, 0.130, 0.860, 0.645]</r> generally higher than the {line for 'Innovation Industries'}<r>[0.140, 0.151, 0.860, 0.754]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "n1ea1m69scMgoTok",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, 'Knowledge Economy' or 'Innovation Industries', does the point {at 5}<r>[0.740, 0.214]</r> show the highest value for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Knowledge Economy."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "kGcrw2KZLxmMuDoO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content lies within the region [0.904, 0.257, 0.934, 0.300]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the y-axis tick labels for the 'Innovation Industries' chart. The labels '22' and '21' are located at {coordinates}<r>[0.904, 0.137, 0.934, 0.179]</r> and {coordinates}<r>[0.904, 0.257, 0.934, 0.300]</r> respectively."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "iXdYkl2iVlvgHGNw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me about the content in the region [0.120, 0.049, 1.491, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend items: {Avg. Knowledge Economy Growth}<r>[0.224, 0.065, 0.772, 0.103]</r> and {Avg. Innovation Industries Growth}<r>[0.927, 0.065, 1.478, 0.103]</r>."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "ibtPtacaa69DHLkw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region between [0.140, 0.130, 0.860, 0.645]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highlighted region includes a line plot showing the 'Knowledge Economy' trend over the years. The data points plotted correspond to various years and their corresponding 'Knowledge Economy' growth statistics."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "tAJdCMduhdBQhxqA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label with coordinates [0.019, 0.247, 0.036, 0.681] for the top subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Agriculture, Manufacturing shares(%)."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "eJyzC6WM8EJ2LVVK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label for the bottom subplot located at [0.964, 0.335, 0.981, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Service, ITS shares(%)."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "5Hp2JEVs47ikj5AK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend line represented by the green circles at coordinates [0.116, 0.397, 0.884, 0.863] in the top subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Agriculture."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "ULlNWpbLXAmzKDgd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for {2014 for Agriculture}[0.308, 0.564] higher or lower than the {2013 mark for Manufacturing}[0.212, 0.098]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "7RGt8Fz9bmGY3GT0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2015 point for Service}[0.404, 0.254] higher or lower than the {2016 point for ITS}[0.500, 0.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "MDmNcFYVv8aIL2fH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point at {2019 for Manufacturing}[0.788, 0.331] above or below the {2019 point for Agriculture}[0.788, 0.830]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "3yM3cDHbFaAfeJOD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area with coordinates [0.077, 0.025, 0.923, 0.903] in the top subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the plotting area for the top subplot, and it contains two line series \u2013 {Agriculture}[0.116, 0.397, 0.884, 0.863] and {Manufacturing}[0.116, 0.065, 0.884, 0.397], represented by circles and squares, respectively, alongside their corresponding markers in green and red."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "KR81bN15AA1I36f1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.077, 0.025, 0.923, 0.903] in the bottom subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the plotting area for the bottom subplot and it contains two trend lines named {Service}[0.116, 0.065, 0.884, 0.417] and {ITS}[0.116, 0.809, 0.884, 0.863] that are tracked with blue and cyan triangles, respectively, along with their respective colored markers."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "Y7UAWMHRrWKZlLMU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.787, 0.037, 0.914, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend for the bottom subplot, including {Service}[0.843, 0.046, 0.907, 0.069] with a blue marker and {ITS}[0.843, 0.081, 0.869, 0.104] with a cyan marker."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "V7AZN2AacR2UYvn8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.943, 0.293, 0.975, 0.612]</r> on the right vertical side of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of 'Video Error Rate'."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "VXATEB2Xn2EGhlVR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat number does the {fifth x-axis tick}<r>[0.491, 0.885, 0.509, 0.921]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "6."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "OFbH65cJfDdxWRBV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the seventh x-axis tick}<r>[0.731, 0.885, 0.749, 0.921]</r> in the chart referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "p3oeABblffX05TUK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the second point}<r>[0.260, 0.172]</r> on the 'Video Error' line higher or lower compared to {the third point}<r>[0.380, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "mhOBVJsI6cmlHnbD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at the {last point}<r>[0.860, 0.874]</r>, the 'Audio Error' or 'Video Error' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Video Error."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "2X3mAD2gdMFFPfy8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Point 1 on the 'Audio Error' line}<r>[0.140, 0.030]</r> to the left or right of {Point 3 on the 'Video Error' line}<r>[0.380, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "PFWdnp0paJXKLEIZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.131, 0.885, 0.149, 0.921]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the number '3', which is a tick label on the x-axis."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "JvVymtAIhJR6bkZm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's in the area <r>[0.962, 0.885, 0.998, 0.921]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the '10' label on the X-axis, representing the digital tools number."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "563wJPZNTtr5UNTo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.904, 0.227, 0.934, 0.270]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the '18' label from the y-axis tick labels on the 'Video Error Rate' subplot."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "7e2MH75hOVKDrEj7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.019, 0.333, 0.036, 0.595] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for Investment Growth(%)."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "qcg6lUc0Fsyp6dAf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked by the point at coordinates [0.106, 0.299] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the Investment Growth(%) in the year 2012."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "dhG2IegUNdztFVyM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.964, 0.384, 0.981, 0.544] stand for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It stands for the Y-axis label for Risk Score(%)."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "yqIcR3pndVxGKGhC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2014, was the {Investment Growth}[0.281, 0.497] higher or lower than the {Risk Score}[0.281, 0.201]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "TAofKmfa9A01iSrj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Risk Score}[0.456, 0.815] for 2016 represented above or below the {Investment Growth}[0.456, 0.244] for the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "bhGRR8yaq6oB9yXD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the {Investment Growth}[0.631, 0.157] and {Risk Score}[0.631, 0.288] in 2018, which one was higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Score."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "koUwrqCvN76l4iEX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.077, 0.049, 0.297, 0.104]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the {legend labels}[0.131, 0.046, 0.297, 0.069 and 0.077, 0.084, 0.122, 0.098] for Investment Growth and Risk Score, each associated with a colored marker according to their respective plot line colors."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "95Q1AUBHLIA56lPF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the area [0.434, 0.919, 0.478, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the {X-axis tick label}[0.434, 0.919, 0.478, 0.942] representing the year 2016."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "WZGIZFoNus9EjFMy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within the coordinates [0.259, 0.919, 0.303, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there exists the {label '2014'}[0.259, 0.919, 0.303, 0.942] on the x-axis of the graph."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "HQRLUSLNNC2BMVMf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line at coordinates [0.098, 0.060, 0.902, 0.887] with color '#00bfff'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Growth."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "A2kNSPvYCnV6sBze",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis tick for the year '2016'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.688, 0.928]"
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "wxKXYcFgDMPeccaT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.015, 0.365, 0.033, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Growth(%)."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "KAe0GBJUIi3pATJz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'New Businesses Started' in 2012}[0.473, 0.413] above or below {the point for 'Economic Growth' in 2012}[0.473, 0.279] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "eByNkJpVB4ZFRw0t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of {Economic Growth}[0.795, 0.180] in 2018 exceed the value of {New Businesses Started}[0.795, 0.060]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "KFMtz6v32gexYf3V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend showed a higher percentage in 2007, {Economic Growth}[0.205, 0.074] or {New Businesses Started}[0.205, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Businesses Started."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "yE7ifqDmSEhBgRB1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.134, 0.934, 0.169, 0.958]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label '2006' of the chart."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "tCRyLc7Ha78LUNdk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.064, 0.027, 0.281, 0.085]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Economic Growth}[0.109, 0.034, 0.231, 0.052] marked in deepskyblue, and {New Businesses Started}[0.109, 0.061, 0.276, 0.078] marked in coral."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "TMCM4eTNUaKpFhjG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.098, 0.060, 0.902, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are data points representing each year from 2005 to 2020 for the Economic Growth line."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "Dxdcza8eiJtLkkDz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates at [0.052, 0.379, 0.074, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "kHIp4WUxTX3Mt1qm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth label text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.781, 0.835, 0.858, 0.864]"
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "y0s13RBFsQ3g1jDg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the numerical value of {the y-axis tick label}<r>[0.082, 0.519, 0.110, 0.548]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "wBEPN7eJROH2h4CY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2014, is {the 'Microsoft' line}<r>[0.473, 0.201, 0.769, 0.717]</r> higher or lower than {the 'Google' line}<r>[0.473, 0.662, 0.769, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "Vm1P079GADiRqFhY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the years 2010 and 2012, does {the 'Apple' line}<r>[0.160, 0.855, 0.317, 0.696]</r> show a rising or falling trend compared to {the 'Amazon' line}<r>[0.160, 0.388, 0.317, 0.334]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rising."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "1lO9XsN2negtDpVt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, does {the 'Microsoft' line}<r>[0.865, 0.275]</r> have a higher or lower value than {the 'Google' line}<r>[0.865, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "AOrpeYr2AgHUx1r1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the area with coordinates [0.781, 0.704, 0.880, 0.733]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend item for {Microsoft}<r>[0.781, 0.704, 0.880, 0.733]</r>."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "PPErOt66Jd49GBXQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.133, 0.910, 0.188, 0.939]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {the x-axis tick label '2010'}<r>[0.133, 0.910, 0.188, 0.939]</r>."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "Fe8OeZKaxqIBonRd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is features the region [0.829, 0.815, 0.865, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the {2018 x-axis label}<r>[0.759, 0.910, 0.814, 0.939]</r> and the {Amazon plot point}<r>[0.786, 0.246]</r>."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "zymepUOMcPtr2iTD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the location of the ylabel \"Stock Value\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.065, 0.436, 0.083, 0.574]"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "wjWAb9ocCdkBjFH4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the plot line associated with {Stock C}<r>[0.160, 0.802, 0.865, 0.868]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2ca02c"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "cGQ8jSyJkEvj2FaN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.495, 0.931, 0.530, 0.956]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "qOGeGsYVyS9JYcMu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {StockA}<r>[0.160, 0.186, 0.865, 0.340]</r> maintaining a higher or lower value trend compared to {StockC}<r>[0.160, 0.802, 0.865, 0.868]</r> over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "J7OFfcw8xwjDg8L0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy the year 2017, did {StockB}<r>[0.160, 0.208, 0.865, 0.364]</r> have a higher or lower stock value than {BondA}<r>[0.160, 0.216, 0.865, 0.409]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "Pk6vgnJ5xEMiILHn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock showed a greater decline from 2018 to 2019, {StockA}<r>[0.583, 0.252, 0.724, 0.296]</r> or {StockB}<r>[0.583, 0.287, 0.724, 0.318]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "StockA"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "KgBR4koS66WCef30",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.704, 0.136, 0.889, 0.155] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It has the text label 'Bond A' with the color #d62728 associated with it, indicating that it represents the data for 'Bond A' on the chart."
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "iVKNRGuhUMkY4uT7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.171, 0.191, 0.219, 0.210] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend text 'Stock C' and is associated with a color marker #2ca02c which represents 'Stock C' on the chart."
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "nPPATvhU1vPQIGlY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's located in the region [0.089, 0.220, 0.120, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for y-tick 300 on the left side of the chart, formatting the y-axis."
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "8FMDgOPxjrTwyGbA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the main y-axis label}<r>[0.021, 0.340, 0.049, 0.567]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cargo Volume."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "LApreGvcdnMjZH0l",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis tick label at <r>[0.428, 0.890, 0.489, 0.920]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2014."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "zgXqqCTyX34Gqq8Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object represented by coordinates <r>[0.951, 0.343, 0.979, 0.563]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trade Volume, which is the y-axis label of the second plot."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "PUfc4GwGIayeGzbs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line trend, between 'Cargo Volume' and 'Trade Volume,' is higher at <r>[0.790, 0.239]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cargo Volume."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "Ezak5Er7gJttjQzF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the 'Cargo Volume' line at year 2015}<r>[0.541, 0.646]</r> higher or lower than {the point on the 'Trade Volume' line at the same year}<r>[0.541, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "UbizdgBWJoaVZSqT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the end point of {the 'Cargo Volume' line}<r>[0.873, 0.239]</r> to the end point of {the 'Trade Volume' line}<r>[0.873, 0.196]</r>. Which one is at a higher position?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cargo Volume."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "mLok5UDRxErnZMoj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region between <r>[0.089, 0.881, 0.987, 0.920]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the x-axis tick labels representing the years from 2008 to 2020."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "3Y6cxF6lCmhrhRmt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside region <r>[0.089, 0.870, 0.943, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-tick labels for the second line plot, representing the 'Trade Volume,' ranging from 100000 to 190000."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "25lKnLqioNL9latX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region <r>[0.127, 0.196, 0.873, 0.881]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two lines representing the 'Cargo Volume' and 'Trade Volume' from 2010 to 2019."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "k0ymKZGHdRmHBF0w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary color indicated by {the handle for 'Rural'}<r>[0.544, 0.747, 0.632, 0.775]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "5g9wHZixoJSHnsMv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the vertical axis label}<r>[0.025, 0.266, 0.057, 0.653]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban development."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "3Nmf1dJlIuXWOekL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the point at}<r>[0.860, 0.501]</r> for the 'Rural' line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "v9OK2iLM8dDmiSwq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Average urban' line point at 1995}<r>[0.638, 0.293]</r> higher than {the 'Urban' line point at 1995}<r>[0.638, 0.546]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "Ixq1qbcYjJCr9KE0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is labeled higher on the plot, {the tick for the year 2000}<r>[0.694, 0.875]</r> or {the tick for the year 2010}<r>[0.805, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "3aOAEGZ4rhdBAelL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Urban' value in 1985}<r>[0.528, 0.418]</r> surpass {the 'Average urban' value in 1985}<r>[0.528, 0.601]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "urcDvo0a9QU0tH2z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is visualized in the region <r>[0.140, 0.148, 0.860, 0.750]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region exhibits the plot for 'Urban' development from 1950 to 2015 with markers indicating individual data points, represented with a blue line and circle markers."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "RPJHzqFhAUaMONnS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within <r>[0.465, 0.932, 0.534, 0.970]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label stating 'Year' is located here in black text."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "C5kYolCVCFWtDtHq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area <r>[0.104, 0.044, 0.896, 0.875]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot contains axes, lines representing 'Urban' and 'Average urban' trends, tick labels for years and urban development values, along with the background marked in white."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "JBSVjA7f3bGvmLvH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of chart is displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "L5Seb2HiQ4TxTxum",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.943, 0.224, 0.975, 0.681]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the second subplot."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "SojPfUPLl0viYfWK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.860, 0.734] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point corresponding to the year 2021 for eCommerce AI data in the second subplot."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "rVFODcDrExDge4RA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at '2020'}[0.740, 0.664] on the eCommerce AI line above or below the point of the same year on the Self Driving AI line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "E1Mg1Pu7RYWIqLjz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line is higher at the {point '2015'}[0.140, 0.805]; is it Self Driving AI or eCommerce AI?"
+            },
+            {
+                "from": "gpt",
+                "value": "eCommerce AI."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "3xeovf7dWDhb8Fyg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is higher at {Year '2019'}[0.620, 0.382] on the Self Driving AI line or eCommerce AI line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Self Driving AI."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "sHrYVHwJl8cMTTum",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area that ranges from [0.500, 0.523] to [0.620, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two points. The '2018' point for eCommerce AI line is located at [0.500, 0.523] and the '2019' point for the same line is at [0.620, 0.593]."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "LcGgAbNikxwuEE6C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.904, 0.298, 0.934, 0.361] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label '110' for eCommerce AI."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "JWb7KxpoO09TxF76",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the region [0.105, 0.885, 0.175, 0.921] holds?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains '2015' label for x-axis ticks in the Self Driving AI line."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "uNZCQFs32Xfk5hN1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is at the coordinate [0.023, 0.307, 0.052, 0.595] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label for 'GDP Change (%)'"
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "yNNyT9zl425b8BhI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the object at the coordinates [0.437, 0.031, 0.682, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That represents the legend item 'Market Growth (%)'."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "EgjxYHzBohDt5jCW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.107, 0.031, 0.911, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plotting area of each subplot."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "f4QPLr2HLUEp1oBd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the year 2014, does {GDP Change (%) trend}[0.436, 0.069] has higher or lower value than {Market Growth trend}[0.436, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "ucOQfwNP5KFlV5bU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at 2010 for {GDP Change (%) trend}[0.144, 0.352] above or below {Market Growth trend}[0.144, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "Ko1X6T2eGsGYfRzf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2017, is the {Market Growth trend}[0.655, 0.069] above or below the {GDP Change (%) trend}[0.655, 0.188]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "qmSaYdVW2CZg27H1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the area bounded by [0.013, 0.017, 0.692, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the legend of the graph, including two items: {GDP Change (%)}[0.096, 0.031, 0.312, 0.069] and {Market Growth (%)}[0.437, 0.031, 0.682, 0.069], each paired with a specific color marker."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "ny7UELkmWi8LApru",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.107, 0.031, 0.911, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main plotting area of the chart, showing the line graphs of both GDP Change (%) and Market Growth (%) over the years from 2010 to 2020."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "OXCQoYIrawdhTZtC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region specified by the coordinates [0.926, -0.063, 0.953, -0.034]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label '10' for the y-axis of the subplot showing the Market Growth (%) trend."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "GdRYBV9JxuZ9JPzi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.966, 0.218, 0.985, 0.715]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of subplot_1."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "Had6aypa8kmLdFBx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line for \"Farm Civilization\"}[0.102, 0.151, 0.898, 0.724]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f00."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "4KC4atshJBdjK7lQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 1973 data point in \"Farm Civilization\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.841, 0.701]."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "Ge6YvSdTDv3txKzM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in 1974, {the point \"Industrial Civilization\"}[0.898, 0.037] or {the point \"Farm Civilization\"}[0.898, 0.724]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Farm Civilization."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "cA0uA2CNq8MTo3j5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 1972 data point for \"Farm Civilization\"}[0.784, 0.667] below or above {the 1972 data point for \"Industrial Civilization\"}[0.784, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "bFeKKjqv7yeaOU5W",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {1962 data point for \"Farm Civilization\"}[0.216, 0.243] higher or lower than the {1962 data point for \"Industrial Civilization\"}[0.216, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "b5z6Qvxl0oSbGmXC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.966, 0.218, 0.985, 0.715]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the Y-axis label \"Farm Civilization Development\" aligned vertically and styled with the color code '#ff7f00'."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "zYzS8ZZSS8SCTaFQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.102, 0.037, 0.159, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two objects. The data points for the year 1960 in the lines for {Industrial Civilization}[0.102, 0.638] and {Farm Civilization}[0.102, 0.151] are located within this region."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "lAJUbhmH76Oul2Ta",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is happening in the region [0.425, 0.912, 0.461, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label of \"1966\"."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "dzYC42wXwXvYor9B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.023, 0.217, 0.053, 0.684] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for subplot 0."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "tqm7qPi7155fxCYj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the legend for 'Cinema goers percentage' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's no dedicated legend for specific lines in the chart."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "Q9bNBdXL9UfpqaGO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value do {the x-axis tick label} at [0.250, 0.880, 0.316, 0.918] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "lABMTeGK0k8gXUlr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point in 2010} at [0.139, 0.139] in subplot 0, and {the first point in subplot 1}[0.139, 0.803], which one has a higher percentage value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point in subplot 1."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "jXGLkczDA7zEYe44",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'TV viewers percentage'}[0.139, 0.069, 0.861, 0.832] intersect with {the line for 'Cinema goers percentage'}[0.139, 0.069, 0.861, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "Qce431df0P8ThZq2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {x-axis tick label labelled '2020'}[0.834, 0.890, 0.889, 0.919] to the right or left of {the x-axis tick label labelled '2018'}[0.689, 0.890, 0.744, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "1CCaPeLPiN1D2KT3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.102, 0.870, 0.277, 0.900]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the topmost portions of the 'TV viewers percentage' and 'Cinema goers percentage' lines for their earliest data points."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "YexaOfdHmZepnHaH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents does region [0.500, 0.509, 0.570, 0.583] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a point from the 'Cinema goers percentage' line for the year 2016."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "jhZhSfn5QCIzIVkx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents around [0.947, 0.188, 0.977, 0.713]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the y-axis label of subplot 1 marking 'Cinema goers percentage'."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "69vePF1F4pE9iA1h",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label mentioned at the coordinates [0.466, 0.932, 0.534, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "lVdg87zDLdA3si78",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.943, 0.273, 0.975, 0.632] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "6M9zeKR2Yv0Aah5c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text annotation associated with {the line handle}<r>[0.122, 0.068, 0.210, 0.095]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Heart Disease."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "zyfo0ItRT3CTzcP6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two lines represented by {the blue line}<r>[0.140, 0.068, 0.860, 0.837]</r> and {the orange line}<r>[0.140, 0.837, 0.860, 0.068]</r> has a descending trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue line."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "wInH0q2YvOS5cWxf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Diabetes Mortality line}<r>[0.780, 0.129, 0.860, 0.068]</r> above or below the {Heart Disease Mortality line}<r>[0.780, 0.740, 0.860, 0.837]</r> on the Year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "nOIhBcABtffrHN37",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is represented by the {marker}<r>[0.505, 0.068, 0.593, 0.095]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Diabetes."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "JkldS8AB6tcntaJv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the area [0.120, 0.049, 0.765, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend items: {Heart Disease with a blue handle}<r>[0.224, 0.065, 0.452, 0.103]</r>, {Diabetes with an orange handle}<r>[0.608, 0.065, 0.752, 0.103]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "na86IilqYoPBYzNS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section within [0.271, 0.895, 0.329, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains an x-axis label which is {the year '2012'}<r>[0.271, 0.895, 0.329, 0.923]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "9K7PKbsTEPJqPpdU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.745, 0.895, 0.809, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains an x-axis label representing {the year '2018'}<r>[0.745, 0.895, 0.809, 0.923]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "zauw3c9OcYdw62W1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the \"Unemployment Rate\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f00<r>[0.075, 0.050, 0.111, 0.062]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "Jmu10LQucr0AdKHo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the y-axis label} for \"Unemployment Rate (%)?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.015, 0.298, 0.034, 0.641]<r>[0.015, 0.298, 0.034, 0.641]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "RLVBoctRpTRaKCLO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has {the coordinate}[0.697, 0.264] associated with a data point?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Income Inequality\"<r>[0.697, 0.264]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "AK8h6pHSXr3uV2ml",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at} [0.303, 0.716] higher or lower than {the point at} [0.382, 0.657] on the chart labeled \"Income Inequality\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher<r>[0.303, 0.716]</r> <r>[0.382, 0.657]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "WfBGLcugoZZcYJ8x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the label at {coordinate} [0.185, 0.833] signify a higher or lower value compared to the label at [0.539, 0.387] for the 'Income Inequality' graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher<r>[0.185, 0.833]</r> <r>[0.539, 0.387]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "RKRh5wLzBtwST0G1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a lower point coordinate in 2009, \"Unemployment Rate\" or \"Income Inequality\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unemployment Rate<r>[0.461, 0.080]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "MmrMVubMtSdnOXt0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is found within the chart area [0.067, 0.029, 0.933, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart area contains multiple data points representing both \"Unemployment Rate\" and \"Income Inequality\" with x-axis for 'Years' and y-axes for 'Unemployment Rate (%)' and 'Income Inequality Index'."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "gfS0t3uQrTAue0Tw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the region [0.500, 0.017, 0.500, 0.017] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly holds the subtitle of the chart but it is empty, indicating no subtitle is set there."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "TNWNq1kYbIvayzzV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the coordinates [0.106, 0.054, 0.894, 0.822]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a line graph plotting the Unemployment Rate in various years, marked with orange circles representing each data point for respective years."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "CqSEfiI1AQqfmI3s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object at [0.066, 0.439, 0.096, 0.460] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 6."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "lI6JRBfg5yWpocR4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {object}<r>[0.066, 0.861, 0.096, 0.882]</r> in the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 3."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "RwZCXrWMfqIWaUlQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.904, 0.257, 0.934, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a y-axis tick label of '80' in the secondary plot."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "iVRZnpph75wd5GWq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point labeled '2'}[0.220, 0.388] of Experimental group greater than the value of the fifth point in the {Control group}<r>[0.860, 2.046]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "VnOt9QJ6qTD3hjke",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first point coordinate of {Experimental line}<r>[0.140, 0.262]</r> above or below {Control line point}<r>[0.540, 2.046]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "SnnlRn99kn8KXn0H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher label in the y-axis, the point at [0.220, 0.388] or the point at [0.540, 2.046]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at [0.540, 2.046]."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "dqR3ZYXPqgj2nOaU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area [0.904, 0.137, 0.934, 0.179]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a tick label on the y-axis of the subplot. The label reads '85' and it's colored orange indicating the productivity level in the Control group."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "9M43rXX8Oa1Yfzdu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.541, 0.0, 0.860, 2.0]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the Control group data in orange, represented by a line plot with upward pointing markers at points with {coordinates}[0.540, 2.046], [0.620, 2.046], [0.700, 2.046], [0.780, 2.046], and [0.860, 2.046]."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "qxVcYTKnIz5ttHAE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.120, 0.049, 0.436, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's subtitle with two legend items. One for 'Experimental' group represented in black at {coordinates}[0.133, 0.081, 0.198, 0.081] and the other for 'Control' group also represented in black at {coordinates}[0.309, 0.081, 0.374, 0.081]."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "kvmX3lS4Cg9on5kj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.146, 0.840] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The starting point of the Online Shopping line."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "ZBMAdCBWhH6qmisK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.947, 0.278, 0.977, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for subplot_1"
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "gSa7nbhXqkZAqt1S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified at {these coordinates}<r>[0.062, 0.683, 0.103, 0.712]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '100'."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "CT293v5zwdBaJG3l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Online Shopping}<r>[0.854, 0.070]</r> greater than the value of {In-store Shopping}<r>[0.854, 0.840]</r> for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "Ec8DuwnWln5YMZH9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {In-store Shopping}<r>[0.358, 0.437]</r> for 2013 less or more than that of {Online Shopping}<r>[0.358, 0.707]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "QwXBzdOefqSLXO2J",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Y-axis tick label '100'<r>[0.062, 0.683, 0.103, 0.712]</r> greater or less than the Y-axis tick label '120'<r>[0.062, 0.537, 0.103, 0.567]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "efJEouIrFpCal92n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found from [0.146, 0.070] to [0.854, 0.840]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line plot for Online Shopping from 2010 to 2020, indicating the annual values."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "8bnTHsRjSQBLYK9F",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.146, 0.070, 0.854, 0.840]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region portrays the data trends of both Online Shopping and In-store Shopping from 2010 to 2020. The Online Shopping has an increasing trend while In-store Shopping exhibits a slow decrease."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "VJw8gCjK8r1hGthk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you observe in the region [0.111, 0.878, 0.889, 0.031]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region presents the data chart for two lines which represent Online Shopping and In-store Shopping. The X-axis is labelled with years from 2008 to 2022, and the Y-axis represents the measurements for Online Shopping and In-store Shopping."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "3vHSyKiBoLhWIWfP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.708, 0.413, 0.962, 0.453] in the chart refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Coal Consumption\" in the legend."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "B6GgF0XLRpH50B8y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object has the coordinate [0.449, 0.945, 0.488, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "DbTyUkuS7BNIwm7q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis tick label} at [0.027, 0.883, 0.056, 0.906] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value '750'."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "qsRWcEZmZu0MqZz3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Coal Consumption'}[0.103, 0.288] above or below {the point of 'Oil Consumption'}[0.103, 0.808] in the year 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "BixgmYxcSxdsCBur",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2020, is {the 'Coal Consumption'}[0.835, 0.369] above or below {the 'Oil Consumption'}[0.835, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "mjdpdl3ky9BCUW6d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at the year 2012, Coal Consumption or Oil Consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal Consumption."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "w3gKdnWZ8ZG8ftGq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the coordinate [0.620, 0.396, 0.973, 0.530]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart legend with two items: {Coal Consumption}[0.708, 0.413, 0.962, 0.453] represented in red color and {Oil Consumption}[0.708, 0.474, 0.939, 0.514] represented in green color."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "cDxe8ngeviVAYoFF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within the coordinates [0.103, 0.107, 0.835, 0.369] relate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the line of \"Coal Consumption\" from 2008 to 2020."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "8Jyg4CsoaOJxIoud",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within [0.103, 0.482, 0.835, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the line for \"Oil Consumption\" with individual points for the years 2008, 2012, 2016, and 2020."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "oVq6Ja5XoxgkK8Gj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at the coordinates [0.161, 0.224, 0.322, 0.256] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Buying Intention."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "GRbh9KzBTrUCUzlT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.129, 0.912, 0.156, 0.936] correspond to on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2006."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "hgnatrYX8CoKToYS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.015, 0.256, 0.033, 0.280]?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "5PqiLDef0txTR5wQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of y-values, does {the point for the year 2013 for 'Average Hours Spent'}[0.514, 0.486] plot above or below {the point for the year 2013 for 'Buying Intention'}[0.514, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "0qeT8ZuNH6QMNiBm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trendline begins higher on the y-axis, the {Average Hours Spent} line at [0.085, 0.739] or the {Buying Intention} line at [0.085, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Hours Spent."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "buXvuirPpL0kEbeU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trendline has a greater y-value at {the endpoint}[0.942] on the x-axis, 'Average Hours Spent' or 'Buying Intention'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Buying Intention."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "rkOumrgxxEQmyWBS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the bounding box [0.099, 0.164, 0.375, 0.269]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the plot. It includes two trendlines: {Average Hours Spent}[0.161, 0.177, 0.368, 0.208] and {Buying Intention}[0.161, 0.224, 0.322, 0.256], paired with respective markers."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "EnkeFpX09PjuDi5f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area defined by coordinates [0.165, 0.025, 0.863, 0.072]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the plot, \"Entertainment and Gaming Industry Development\", is located in this area."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "Hz0OM2IuPBJLxDmP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is represented within the region [0.492, 0.945, 0.536, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label, which is \"Years\"."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "meh1BPrcUxq6x6dz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is located at these coordinates [0.124, 0.064, 0.924, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the line representing the 'Knowledge Economy' in the chart."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "8RK9UhfBJHz4vGIE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the figure at the coordinates [0.017, 0.334, 0.039, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Value (in USD)' which is the Y-Axis label of the chart."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "YM5GsIPvM8IqrcX1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the marker at [0.683, 0.468, 0.723, 0.482] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker represents the 'Innovation Economy' trend in the legend."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "PQF1wdG62Mm7ar4d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Software' on the 'Knowledge Economy' line}[0.444, 0.492] higher or lower than {the point for 'Software' on the 'Innovation Economy' line}[0.444, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "bd0oEUqngV9dOWKN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the coordinates for {the 'Innovation Economy' line}[0.124, 0.327, 0.924, 0.853] generally above or below {the 'Knowledge Economy' line}[0.124, 0.064, 0.924, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "FDwg9JB7DoHIvYDM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the point for 'Biotechnology' in 'Innovation Economy'}[0.924, 0.769] in relation to {the point for 'Biotechnology' in 'Knowledge Economy'}[0.924, 0.480]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "jvD4rEfr2YCeHW3x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area defined by the coordinates [0.681, 0.421, 0.897, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart's legend. The 'Knowledge Economy' legend item is located at {coordinates}[0.731, 0.430, 0.891, 0.453] with a color marker at {[0.684, 0.435, 0.721, 0.445]}, and the 'Innovation Economy' legend item is at {coordinates}[0.731, 0.465, 0.888, 0.488], with a marker at {[0.683, 0.468, 0.723, 0.482]}."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "bXB2OxTL7lAPNpH6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the region [0.045, 0.029, 0.074, 0.053]."
+            },
+            {
+                "from": "gpt",
+                "value": "The '60k' y-axis tick-label is located within this region at {[0.045, 0.029, 0.074, 0.053]}."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "mqoi4w9SUGb4uxK0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is concentrated in the region [0.055, 0.547, 0.074, 0.570]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '5k' y-axis tick-label is stationed in this area at {coordinates}[0.055, 0.547, 0.074, 0.570]."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "Jn7hBzgAC58id5NX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is located at the coordinates [0.024, 0.157, 0.061, 0.188] in the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "$10^{-3}$."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "BmZt2sTGOmry6zTc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what {the tick with the label 'Finished Products'} is using its coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its coordinates are <r>[0.513, 0.896]</r> on the x-axis."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "tEqCm1vJBSilFZFV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does {the legend item}<r>[0.766, 0.478, 0.934, 0.518]</r> stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Constrained."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "EWj5oke5nEoamApD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing their positions, is {the point representing 'Quality Check' on 'Standard'}<r>[0.112, 0.434]</r> above or below than {the point representing 'Quality Check' on 'Constrained'}<r>[0.112, 0.724]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "531KmXvVfh4QeD1z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt 'Finished Products', which line has a higher y value, {the 'Standard'}<r>[0.513, 0.322]</r> or {the 'Constrained'}<r>[0.513, 0.733]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Constrained."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "MHAZwXK0YJMB9eGd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher in the figure, {the 'Standard'} line at 'Product Lifetime'<r>[0.915, 0.302]</r> or the {the 'Constrained'} line at 'Quality Check'<r>[0.112, 0.724]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quality Check on the Constrained line."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "S8iDSw96xDupokOR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the box region <r>[0.678, 0.400, 0.945, 0.534]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart with two items: {Standard}<r>[0.766, 0.417, 0.893, 0.457]</r> and {Constrained}<r>[0.766, 0.478, 0.934, 0.518]</r>, each with their respective color indicator."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "1wlL0ANjgfFdoNB4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the chart area enclosed by <r>[0.071, 0.038, 0.955, 0.896]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area where the 'Standard' and 'Constrained' lines are plotted representing 'Quality Check', 'Finished Products', and 'Product Lifetime'. The x and y-axis are also part of this area."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "I9mx85pRBQaB3la2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you detail what is in the area from <r>[0.446, 0.912, 0.581, 0.936]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Finished Products' of the x-axis is located within this area."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "9zW09G82GrMuttH5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label at these coordinates [0.921, 0.908, 0.956, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "PhzBsRzb7Aur9Wc5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with the coordinates [0.933, 0.128, 0.943, 0.428] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Imports."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "aPmwUc7TrX8RGG9H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year is represented by the x-axis tick label at [0.915, 0.908, 0.950, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "WbLj0lFv1Tgy3rPT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Imports line}[0.933, 0.128, 0.943, 0.428] have a higher or lower final point than the {Exports line}[0.933, 0.043, 0.943, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "vLpJdapv7q6q4xwG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year represented by the tick label at [0.921, 0.908, 0.956, 0.932], which is greater: Imports or Exports?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exports."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "cV8ZB6uma5Ccl2lx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Exports line}[0.933, 0.043, 0.943, 0.442] steadily increasing or decreasing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "uahZKIhJZH1qlNqx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region located at [0.080, 0.039, 0.204, 0.135]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's subplot legend. The two items, {Imports}[0.133, 0.050, 0.197, 0.080] and {Exports}[0.133, 0.094, 0.196, 0.124], are both indicated, each with respective colored markers."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "Ve6Hiam612uQ94ps",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what the region [0.933, 0.128, 0.943, 0.428] on the chart indicates."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the Imports line in the chart. It depicts changes of import values over time, indicated by vertically aligned points on the line."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "Ma1KA343zNC7aFwg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what the region [0.015, 0.033, 0.062, 0.057] depicts on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.015, 0.033, 0.062, 0.057] includes only the y-axis label \"1e+11\" that represents the highest value on the y-axis."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "JCnP5t7ma9kiMG6z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.440, 0.945, 0.479, 0.975] represent in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "Tw0exjyTlGYWkM1Z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label contained within the coordinates [0.694, 0.474, 0.972, 0.514] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Medicine."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "NMZJDbZYHz7Qd4ek",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the category of the line given by {line_1}<r>[0.107, 0.443, 0.812, 0.851]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Medicine."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "ou4aqsZfTpy8G6Ju",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2020 under Advanced Medicine}<r>[0.812, 0.592]</r> higher or lower on the chart compared to {the point for the year 2020 under Traditional Medicine}<r>[0.812, 0.851]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "EaDoVlqEZRsS9WdN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, does {Advanced Medicine}<r>[0.343, 0.361]</r> or {Traditional Medicine}<r>[0.343, 0.497]</r> have a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Advanced Medicine."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "Jyr1DfsBxnV1JSJj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the points for the year 2015 for {Advanced Medicine}<r>[0.578, 0.484]</r> and {Traditional Medicine}<r>[0.578, 0.592]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Advanced Medicine is lower."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "209Ek9wboGSz5mYt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.534, 0.945, 0.547, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "N."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "hWkveDbtrmQJpvTW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text displayed on the {first item in the legend}<r>[0.201, 0.038, 0.388, 0.078]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identity Theft."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "e65pAP4Of0uf8i7b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {y-axis tick label}<r>[0.050, 0.434, 0.087, 0.465]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "$10^{-8}$."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "v3U6wu0r79fjdSrI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich curve reaches a higher value, {Identity Theft}<r>[0.138, 0.085, 0.943, 0.437]</r> or {Data Breach}<r>[0.138, 0.514, 0.943, 0.678]</r>, at N=4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Breach."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "yAKcw5R3oWzw0LvO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Identity Theft line}<r>[0.138, 0.085, 0.943, 0.437]</r> above or below the {Data Breach line}<r>[0.138, 0.514, 0.943, 0.678]</r> at N=12?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "5eg19BSn74LVOtcf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the N axis, is the {first plotted point of Identity Theft line}<r>[0.138, 0.437]</r> to the left or right of the {first plotted point of Data Breach line}<r>[0.138, 0.678]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same point on the x-axis."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "oU9BNdQSwnP3pdkQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included in the region <r>[0.098, 0.043, 0.983, 0.889]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the graph itself, both Identity theft and Data Breach lines are presented with their markers. It also contains the X and Y-axis with their labels and ticks. The grid lines of Y-axis are visible."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "mQkY7246uVrLIfa1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area in <r>[0.112, 0.021, 0.399, 0.155]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend, including two items: {Identity Theft}<r>[0.201, 0.038, 0.388, 0.078]</r> with a blue marker, and {Data Breach}<r>[0.201, 0.099, 0.373, 0.139]</r> with an orange marker."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "jmCJbkxSRFYHMD4v",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.138, 0.085, 0.943, 0.437]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the Identity Theft line with its markers on the plot."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "95Yzppy2Il13mqNl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease identify the object situated at coordinates [0.501, 0.945, 0.554, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "hPZi8UIhv26Jgsmt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the co-ordinate of the second label on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.043, 0.167, 0.087, 0.191]"
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "y7Nn3zIgmsRNYCNN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the tick label}<r>[0.904, 0.905, 0.932, 0.936]</r> on the x-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "10^1"
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "UCDArfLXHeiEqbVc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2'}<r>[0.372, 0.269]</r> on the 'Standard' line higher or lower than {the point labeled '2'}<r>[0.372, 0.412]</r> on the 'Constrained' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "HEUrEFDldOI40cBO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has a higher value at {point 3}<r>[0.510, 0.105]</r>, Standard or Constrained?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "FkylFqmRhbUPj4Ft",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is {the point labeled '5'}<r>[0.683, 0.746]</r> a part of, Standard or Constrained?"
+            },
+            {
+                "from": "gpt",
+                "value": "Constrained."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "Y3JUPJcYLK1CCsrR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content placed in the region [0.688, 0.394, 0.956, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two legend items within this region: {Standard}<r>[0.776, 0.411, 0.903, 0.451]</r> and {Constrained}<r>[0.776, 0.472, 0.944, 0.512]</r>. Both items have colored markers associated with them in the scene."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "l0MM8jssRhITwIvF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can I find in this region [0.098, 0.034, 0.957, 0.889]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main plotting area for the graph is contained within this region. It includes the graph lines for both 'Standard' and 'Constrained' conditions. The region also contains the x-axis and y-axis which have multiple labeled tick marks."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "n5jePFZZt3Flh1xj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items located at [0.137, 0.105, 0.683, 0.461]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains points from the 'Standard' line graph. Five points are plotted here, corresponding to entities with values ranging from 1e-08 to 1e-05."
+            }
+        ],
+        "id_raw": 850
+    },
+    {
+        "id": "TFpUd6GjTixmtvLQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of the chart with the coordinate of [0.062, 0.064, 0.983, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line chart."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "w3D5e0EsdZKZUEMh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the label for {the 'Traditional Education'}<r>[0.602, 0.107, 0.830, 0.139]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the plot legend."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "tMIMJ27Feto8YdC0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point on the 'Online Learning' line}<r>[0.187, 0.598]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number of students engaged in Online Learning in 2011."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "ty8lDh3NL0X8za2v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for {2010 in 'Traditional Education'}<r>[0.104, 0.098]</r> higher than {2010 in 'Online Learning'}<r>[0.104, 0.790]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "yCv0DpjFnVqBWCPZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the number of students in {Traditional Education}<r>[0.941, 0.127]</r> or {Online Learning}<r>[0.941, 0.190]</r> increase more in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "WkkP2NQDrcJSQNq4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for {Year 2018 in 'Traditional Education'}<r>[0.774, 0.125]</r> lower or higher than the point for {Year 2018 in 'Online Learning'}<r>[0.774, 0.293]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "cTfJQR8Jrka6hGHp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.533, 0.094, 0.839, 0.200]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. There are two entries, {Traditional Education}<r>[0.602, 0.107, 0.830, 0.139]</r> marked with a blue color, and {Online Learning}<r>[0.602, 0.155, 0.773, 0.187]</r> marked with an orange color."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "Dw0YRpQJsb7sJSLI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.062, 0.064, 0.983, 0.896] cover in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire chart plot including the axes, the lines representing 'Traditional Education' and 'Online Learning' and their respective data points over the years."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "hVHmfQXm6TSqEqEl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within [0.922, 0.912, 0.961, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '2020' of the x-axis is located within these coordinates."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "XRWLRZJXXrWGeBft",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinate [0.732, 0.427, 0.955, 0.467]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Existing System\""
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "w84loCtfbEJL6s18",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the color of {the line with handle}[0.650, 0.498, 0.714, 0.511] used in sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e"
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "bi6VTrn9duxJw7Wy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the y-axis label}<r>[0.017, 0.410, 0.037, 0.544]</r> indicates?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency"
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "UjRGUPexkhzjgCmE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for the 'Potential System' at stage 10}<r>[0.413, 0.768]</r> higher or lower on the chart than the {point for the 'Existing System' at stage 10}<r>[0.413, 0.435]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "Rt7NMT64yAd8rJfy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {last point on the 'Existing System' line}<r>[0.855, 0.103]</r> at the same level as the {last point on the 'Potential System' line}<r>[0.855, 0.602]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "ZHQ4Nu9VJSQIyk7i",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at the 15th stage, the 'Existing System' or the 'Potential System'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Potential System."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "8MdUux8kZoES1WYp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.643, 0.410, 0.977, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This includes the legend for the chart, with two entries: {The Existing System}[0.732, 0.427, 0.955, 0.467] represented with color code #1f77b4, and {The Potential System}[0.732, 0.487, 0.966, 0.527] represented with color code #ff7f0e."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "F0klHgKkHGCpmSzU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.229, 0.025, 0.759, 0.058]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle of the chart, which is \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "jFRSQFIjOEXlq6FF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.396, 0.945, 0.592, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the x-axis, which is \"Stage of Deployment\"."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "kaRYvD4Zi4pfHlte",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the point on the line of Artificial intelligence at [0.777, 0.162]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 2026."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "Fah7KoPwdKYMR8CL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at the coordinates [0.112, 0.080, 0.233, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend for the \"Artificial intelligence\" line."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "3DuARkBc3Y9Qy9jt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the data chart at coordinates of [0.341, 0.025, 0.714, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, labeled \"Future Technological Development Prospects.\""
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "UrTUV8OXOHj6SLJ4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for 'Artificial intelligence' at [0.611, 0.213] above or below the point for 'Internet of Things' at [0.611, 0.140]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "rbTGdqOg8fvsdr5T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for 'Artificial intelligence'[0.111, 0.100, 0.943, 0.420] above or below the line for 'Internet of Things'[0.111, 0.119, 0.943, 0.865] in general?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "hvUyN8dHKzhLhicb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research area has more publications in the year {2024}[0.444, 0.243]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial intelligence."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "St8uh5Pl5tUpcBXx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the regional box [0.075, 0.072, 0.237, 0.140]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are the legends of two lines: {Artificial intelligence}[0.112, 0.080, 0.233, 0.101] and {Internet of Things}[0.112, 0.111, 0.216, 0.133]."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "Uf3KgIDVjxZXKPV4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.513, 0.952, 0.542, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label \"Year\"."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "XspJZvqTMYlZPrGR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.611, 0.140, 0.943, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, there are two points on the lines for 'Artificial intelligence' and 'Internet of Things' representing the years {2025}[0.611, 0.213] and {2026}[0.777, 0.162] for 'Artificial intelligence' and {2025}[0.611, 0.140] and {2027}[0.943, 0.749] for 'Internet of Things'."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "hmRtnPomkIp7iGf9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.578, 0.408, 0.984, 0.448] referred to in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech Investment (Billion $)"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "pjHioc64xock2lfm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.578, 0.469, 1.021, 0.509] according to the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Innovative Programs"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "krduv3gOXt2CqHAm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented at the coordinates [0.616, 0.645]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents data point for the year 2020 in the line 'EdTech Investment (Billion $)'."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "Wypu8VJ2oogPoGYB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line trend has a higher point at the year 2015, is it 'EdTech Investment (Billion $)'[0.464, 0.690] or 'Number of Innovative Programs'[0.464, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech Investment (Billion $)"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "cPL51DCNCqqN6lMI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2011, what chart trend was higher: {EdTech Investment (Billion $)}[0.312, 0.803] or {Number of Innovative Programs}[0.312, 0.169]?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech Investment (Billion $)"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "vgXTDCQZIq9yKHix",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher on the chart for the year 2016: {EdTech Investment (Billion $)}[0.502, 0.681] or {Number of Innovative Programs}[0.502, 0.095]?"
+            },
+            {
+                "from": "gpt",
+                "value": "EdTech Investment (Billion $)."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "PCbnxJ2kLZGUT4Jq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the coordinates [0.084, 0.625, 0.654, 0.853]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line trend for 'EdTech Investment (Billion $)', represented as a blue line with circular markers. The values range from 2005[0.084, 0.853] to 2020[0.654, 0.625]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "xUaZfbzr2aS1uskb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.084, 0.064, 0.654, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line trend for 'Number of Innovative Programs' plotted with orange line and \u2018x\u2019 markers. The values range from 2005[0.084, 0.213] to 2020[0.654, 0.064]."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "G3EKwi8rIQlS6Min",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the coordinates [0.084, 0.064, 0.654, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses both the line trend for 'EdTech Investment (Billion $)' and 'Number of Innovative Programs'. Different years from 2005 to 2020 are along the x-axis and their respective values along the y-axis."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "rJ4ATK4WoXajlGd6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being indicated by the label at coordinates [0.719, 0.476, 0.933, 0.516] included in the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's indicating the 'Order Accuracy' series."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "g6MIYJlw9gjroiy0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at the coordinates [0.839, 0.091] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last data point of the 'Inventory Velocity' line."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "vY78a9oAQZP2MD0J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of {the text label}<r>[0.463, 0.945, 0.502, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the x-axis, representing \"Year\"."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "ii7UJjJ4P3IETCdE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2018, did the 'Inventory Velocity' or 'Order Accuracy' have a higher value according to the points at {coordinates}[0.839, 0.091] and {coordinates}[0.839, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Order Accuracy'."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "UkqFwqnMebX2Pssf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data points of 'Order Accuracy' line above or below the 'Inventory Velocity' line at the points {2012, Order Accuracy}[0.305, 0.596] and {2012, Inventory Velocity}[0.305, 0.405]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "hNK1kZXY0lsHIB2r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2014, how do the locations of 'Inventory Velocity' and 'Order Accuracy' at {points}[0.483, 0.308] and {points}[0.483, 0.552] relative to one another?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Inventory Velocity' point is below the 'Order Accuracy' point."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "Fl45bPI7hvYgDz7f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the content within the coordinates [0.630, 0.398, 0.980, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plot legend. It provides the color coding information for the two lines: 'Inventory Velocity' which is marked in blue color at {coordinates}[0.638, 0.427, 0.700, 0.436] and 'Order Accuracy' marked in orange color at {coordinates}[0.636, 0.486, 0.701, 0.500]."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "RCa1S069NBQ6SuXN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.126, 0.439, 0.839, 0.644]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for 'Order Accuracy' from the year 2010 to the year 2018. The line has an upward trajectory indicating an increase in order accuracy over these years."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "1syLaL3EsVIrHKa8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.126, 0.091, 0.839, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unlike the order accuracy line, the 'Inventory Velocity' line is highlighted in this area from the beginning of the year 2010 to the ending of the year 2018."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "B8m10XIu7LzsAbE9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line labeled 'Air Quality Index'}<r>[0.937, 0.246, 0.941, 0.558]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #1f77b4, a shade of blue."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "fnHvyZKiq1OMDWmp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the X axis represent in this chart according to the coordinate [0.501, 0.945, 0.540, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X axis represents year."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "QYrEGWIJ2vnmBGi2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is represented at coordinate [0.069, 0.848, 0.115, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color handle for the 'Ocean Acidification Index' in the legend."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "6bAJGksotCE8cHXe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the values in 2020, is the {Air Quality Index}[0.941, 0.558] higher or lower than the {Ocean Acidification Index}[0.941, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "KCiSWkCMZ6IgV8RE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the coordinates [0.937, 0.363, 0.941, 0.558] and [0.937, 0.363, 0.941, 0.607], does the Air Quality Index or the Ocean Acidification Index start at a higher value in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "Air Quality Index."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "8JtUbp2twJN0xIPM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Air Quality Index line}[0.937, 0.246, 0.941, 0.558] intersect with the {Ocean Acidification Index line}[0.937, 0.363, 0.941, 0.607] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "MBShWRoHynYQvhId",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in area [0.066, 0.786, 0.367, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the chart's legend, including the labels and color handles for 'Air Quality Index' and 'Ocean Acidification Index'."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "90EPHhLdBlqVgHBU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.127, 0.797, 0.279, 0.827] contain in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the text for the 'Air Quality Index' item in the legend."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "rcxMvQS4cAt9jVpd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region with coordinates [0.027, 0.402, 0.046, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A Y-axis tick label '80' is located in this region."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "hb3IV3nhEORF1AWh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label shown at these coordinates, [0.500, 0.945, 0.539, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at these coordinates is \"Year\", which is the x-axis label."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "blcMAKFJys88SM82",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart color for the {Venture Capital line}<r>[0.098, 0.238, 0.941, 0.745]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the Venture Capital line is '#ff7f0e'."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "qMevuAZUgc2MRHKH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item found at {coordinates}<r>[0.343, 0.472, 0.389, 0.486]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color marker for 'Venture Capital (in billions)'."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "KPNnBSbrHLvVstUm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Total Investment' higher or lower than the 'Venture Capital' in 2018 judged from the points {2018, Total Investment}<r>[0.848, 0.081]</r> and {2018, Venture Capital}<r>[0.848, 0.258]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "WpABo2FxB3hEkCYz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the total investment in 2016}<r>[0.660, 0.133]</r> exceed {the total investment in 2017}<r>[0.754, 0.111]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "HkgCGxG2WY2yqk1m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relative position of {Venture Capital in 2014}<r>[0.473, 0.405]</r> compared to {Venture Capital in 2015}<r>[0.567, 0.333]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "g5OL2ILjoMmhsoYk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the chart title in the area [0.519, 0.011, 0.519, 0.011]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area appears to be empty, with no chart title present."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "aKxxlUCSet20oyGm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects located within the region [0.340, 0.411, 0.668, 0.507]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart. Two items are included: {Total Investment (in billions)}<r>[0.399, 0.422, 0.660, 0.452]</r> and {Venture Capital (in billions)}<r>[0.399, 0.466, 0.649, 0.496]</r>. Each one is associated with a specific color represented by a colored marker."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "FtPavurZ5jTHwJ7o",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's visible inside the bounding box [0.098, 0.081, 0.941, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Located within these coordinates are the plotted points and connecting line for 'Total Investment (in billions)'. Each point represents the investment for a specific year."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "PHiQlJSOWrWO8HIh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the point with coordinates [0.532, 0.513] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a point on the 'Educational Services' line representing the year 2010."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "8YbAfhfGuWkNtoCq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified at the coordinates [0.510, 0.945, 0.549, 0.975] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the place where the x-axis label, 'Year', is located."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "g2jcFGACQaaj2Vzr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at [0.017, 0.327, 0.037, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label, \"Investment (in $M)\", is situated here."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "uzYE0RL5LrWWOAdu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, is {the Educational Services data point for 2000}[0.328, 0.683] higher or lower than {the Public Health Services data point for the same year}[0.328, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "sB77Bm3Z0Z7t2KAu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Education Services line}<r>[0.122, 0.174, 0.937, 0.853]</r> generally higher or lower in the chart than {the Public Health Services line}<r>[0.122, 0.064, 0.937, 0.628]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "pCkFTYoGSlaSbUJU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service has a higher Investment in {year 2030}[0.937, 0.174] for Education Services and [0.937, 0.064] for Public Health Services?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Health Services."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "JsIYFDm5AU3I44PG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.697, 0.411, 0.969, 0.507]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of the legend for the chart. It contains two items: 'Educational Services' with a blue circle symbol located at {coordinates}[0.701, 0.431, 0.745, 0.440] and 'Public Health Services' with an orange cross symbol located at {coordinates}[0.700, 0.472, 0.746, 0.486]."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "TiOIVOa8hXmqPAyY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you detail the area demarcated by [0.122, 0.064, 0.937, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range contains the 'Public Health Services' line graph. The line starts at the left edge with a point representing the year 1990 and concludes on the right edge with a point for the year 2030. The line generally moves upwards as time progresses, indicating an increase in investment."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "EVkcEnlyjJEUpNWJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information within [0.082, 0.025, 0.978, 0.892]."
+            },
+            {
+                "from": "gpt",
+                "value": "This large region encompasses the primary plot of the graph. It holds the two line graphs for 'Educational Services' and 'Public Health Services' along with the x-axis (Year) and y-axis (Investment in $M), as well as the corresponding labels, tick labels, and grid lines."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "lOCGXQQRUX0QSyLF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the horizontal axis}<r>[0.497, 0.945, 0.536, 0.975]</r> in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "IpavEKcHvKPLnZP8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure located at the coordinates [0.098, 0.064, 0.936, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a line graph representing the term 'Energy'."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "fBVzhyYeh4Mhln4l",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the legend labels}<r>[0.776, 0.408, 0.874, 0.448]</r> and <r>[0.776, 0.469, 0.919, 0.509]</r> represent in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Energy' and 'Resources', respectively."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "yyQR164SaoAHezVb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at {the 'Energy' line}<r>[0.098, 0.064, 0.936, 0.853]</r>, is the point representing the year 2014 above or below the point representing 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "MlPAR6tAOuH7ImLc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Energy' line}<r>[0.098, 0.064, 0.936, 0.853]</r> above or below {the 'Resources' line}<r>[0.098, 0.324, 0.936, 0.808]</r> in the year of 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "dNlie8qhzGvRcpqY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at the year 2010, {Energy}<r>[0.378, 0.476]</r> or {Resources}<r>[0.378, 0.640]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Resources."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "9yTfyR2PAT0CK7We",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area <r>[0.687, 0.392, 0.930, 0.526]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes two items labeled 'Energy' and 'Resources', each accompanied by a corresponding symbol."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "0KwkX2WKbIB52auI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the area with coordinates <r>[0.010, -0.528, 0.045, -0.497]</r> and <r>[0.017, -0.170, 0.045, -0.139]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "These areas contain the '10^10' and '10^9' labels for the vertical axis respectively."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "GDmwwBS1m8twfhmG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects enclosed by the coordinates between <r>[0.098, 0.064, 0.936, 0.853]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily encapsulates a line graph labeled as 'Energy'. The graph plots data from the years 2005 to 2020."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "M1ZUGa3fCL9f0smS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the marker for the \"Carbon Sequestration\" data series?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "lxSAYveuIezh2IdH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for the \"Plastic Reduction\" line on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Star."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "QK6nTWd0jaGCtxBh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line labeled 'Afforestation'}<r>[0.093, 0.139, 0.738, 0.286]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "CbeeHNdb1cQ4dpm8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-coordinate for point 5 on 'Carbon Sequestration'}[0.229, 0.356] higher or lower than {the y-coordinate for point 5 on 'Plastic Reduction'}[0.229, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "wh9w9VdXWJjlqx5l",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a lower y-coordinate at year 15, {Regeneration}<r>[0.568, 0.186]</r> or {Afforestation}<r>[0.568, 0.178]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Afforestation."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "7OXfN8uonkukEEDV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the starting point of {Carbon Sequestration}<r>[0.093, 0.433]</r> above or below the starting point of {Plastic Reduction}<r>[0.093, 0.763]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "37YTr0suxrP8uFgB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the coordinate region [0.763, 0.040, 0.985, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend with labels for the lines such as Regeneration, Afforestation, Carbon Sequestration, and Plastic Reduction along with their corresponding color markers."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "wMqSa7D5DE7aRRtf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region bounded by coordinates [0.076, 0.029, 0.755, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main plot area containing multiple lines representing different data series such as Regeneration, Afforestation, Carbon Sequestration, and Plastic Reduction, marked with various colored markers and styles."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "asyv0tCQCXEEGD0L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region between [0.093, 0.066, 0.738, 0.763]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the data points for the lines representing Carbon Sequestration and Plastic Reduction as they progress over the years."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "tAjfKfb3gEpxPw0i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object at [0.015, 0.397, 0.029, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "KL8FhnGXGKtaoDE6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {Digital Theft's fifth point}<r>[0.474, 0.107]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A data point in 2015 for the 'Digital Theft' trend."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "wDj97K2OIqhm58aJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.127, 0.931, 0.162, 0.951]."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "K1mLzOdfyVNxQwsz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2019' for 'Data Breach'}<r>[0.803, 0.474]</r> above or below {the point labeled '2019' for 'Privacy Invasion'}<r>[0.803, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "IYYHP31t2utYPLvh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Privacy Invasion' trend line}<r>[0.144, 0.536, 0.885, 0.666]</r> above or below {the 'Cyber Attacks' trend line}<r>[0.144, 0.162, 0.885, 0.350]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "Z0BdJ7XVcWauoHy0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point in 2012 for 'Digital Theft'}<r>[0.227, 0.081]</r> at a higher or lower position than {the point in 2012 for 'Data Breach'}<r>[0.227, 0.323]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "ah6hmg9v64vkzWL1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region at [0.462, 0.891, 0.553, 0.917]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the x-axis label 'Year', placed centrally within this region, with black color."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "8eIsXuPZegxYc6JF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within [0.815, 0.038, 0.961, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart with items {Digital Theft}<r>[0.855, 0.045, 0.932, 0.064]</r>, {Cyber Attacks}<r>[0.855, 0.073, 0.942, 0.091]</r>, {Data Breach}<r>[0.855, 0.100, 0.933, 0.119]</r>, and {Privacy Invasion}<r>[0.855, 0.128, 0.956, 0.146]</r>. Each legend item is accompanied by a colored marker."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "I0QiiyxvQSnTbp4G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.538, 0.931, 0.903, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects: the x-axis labels '2016'<r>[0.538, 0.931, 0.573, 0.951]</r>, '2017'<r>[0.621, 0.931, 0.656, 0.951]</r> and '2020'<r>[0.868, 0.931, 0.903, 0.951]</r>."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "gbrvvmdQRua7YEy9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object located at [0.019, 0.347, 0.044, 0.576] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "bhUQmXtVMaCcWkRI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the third tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.315, 0.902, 0.329, 0.932]."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "aQcUiFUlvppEIOa0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} specified by coordinates [0.491, 0.902, 0.504, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "AdBp9yeVpQB5R4rz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point from 'Protest Activity'}[0.410, 0.750] of a higher value compared to {the data point from 'Political Stability'}[0.410, 0.275] in year 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "2I0THsUFFeqee4ji",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the trend line for 'Public Opinion'}[[0.146, 0.079, 0.937, 0.359]] lie above or below {the trend line for 'Protest Activity'}[[0.146, 0.546, 0.937, 0.776]] throughout the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "HccBBggZGsacXsZZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric shows a higher value during the 5th year, the 'Policy Changes' or 'Public Opinion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Opinion."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "otkIN01Vp1QnoPr6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region [0.745, 0.049, 0.972, 0.208]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the legend of the chart. It contains labels for 'Protest Activity', 'Political Stability', 'Policy Changes', and 'Public Opinion', each associated with a color-coded marker."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "WJIUvsOd8MQOKiaq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.077, 0.875, 0.090, 0.905]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the first tick label '0' on the y-axis."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "X1qDxYqGjhNsdzNW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within the region [0.322, 0.130, 0.937, 0.359]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes parts of the trend lines for 'Policy Changes' and 'Public Opinion'. It also includes the respective data points from year 3 up to year 10 for these trends."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "VHFt3luGx4r4Ik3g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker for the \"Diet\" line in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "mJq1Gz7uHdaBlk6e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for 'Social_Network'}<r>[0.866, 0.151, 0.973, 0.174]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "7EVnpw1e2Vk86lLJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker shape used for {the Sleep data line}<r>[0.099, 0.226, 0.943, 0.871]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Diamond."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "8WrwUOVlvQJ5jaxw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at the year 2019, {the line for 'Exercise'}[0.901, 0.656] or {the line for 'Social_Network'}[0.901, 0.763]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exercise."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "u7fH6yNJzwfZHrwC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point for 'Diet' in 2003}[0.226, 0.387] fall above or below {the point for 'Social_Network' in the same year}[0.226, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "unYhlAIPNtQkolGZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Exercise' at 2010}[0.521, 0.522] closer in value to {the final point for 'Sleep' in 2020}[0.943, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "emzOpxM82ADoCsWx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents and visual styles in the region [0.822, 0.037, 0.978, 0.183]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. Items include 'Exercise' with a blue square marker, 'Diet' with an orange triangle-down marker, 'Sleep' with a green diamond marker, and 'Social_Network' with a red circle marker."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "L8HHRE53VLFE8Ftl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat types of data and how are they represented in the region [0.099, 0.065, 0.943, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains four line graphs representing different lifestyle categories. Exercise is depicted with blue squares, Diet with orange triangles, Sleep with green diamonds, and Social Network with red circles. Each line graph shows their respective data points across years from 2000 to 2020."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "emXkGY5YJNq1rN2y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is conveyed in the area [0.057, 0.025, 0.985, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area spans the main chart area displaying line graphs for Exercise, Diet, Sleep, and Social_Network, ranging from the year 2000 to 2020. Important details like grid lines for both x and y-axis, corresponding tick marks, and data point markers for each category are visible. The y-axis is labeled 'Lifestyle Categories'."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "7Ej6Yg36WLR2cTJJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at {coordinates}<r>[0.015, 0.331, 0.035, 0.617]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Metrics."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "xAjYqWh4Oh2Wygw4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at {coordinates}<r>[0.874, 0.885, 0.896, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "HR label in the legend."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "kC5Y2JVCh8DQJRbD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease identify the object at {coordinates}<r>[0.367, 0.927, 0.377, 0.949]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labeled '7'."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "OCu4rxhk1PN5EMvv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-axis tick label value is higher, the one at {coordinates}<r>[0.051, 0.912, 0.072, 0.934]</r> or at {coordinates}<r>[0.051, 0.168, 0.072, 0.190]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "qmbBKzprReyAhQjz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the point coordinates of the 'HR' line at {point 14}<r>[0.690, 0.494]</r> higher or lower in value than those of the 'Optimization' line at {point 14}<r>[0.690, 0.215]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "Ypr9xXZXUBY42M4m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater x-coordinate in the fifth point, the line labeled 'Innovation' at {coordinates}<r>[0.281, 0.385]</r> or the line labeled 'HR' at {coordinates}<r>[0.281, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The same."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "dMgs0skUrj03hYTi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the components within {region}<r>[0.826, 0.802, 0.954, 0.904]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes text labels 'Innovation', 'Strategy', 'Optimization', and 'HR' along with their respective color handles in the chart legend."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "LihW80E19GW6bNML",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the bounded region {region}<r>[0.374, 0.282, 0.785, 0.770]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This selected region encompasses the lines representing 'Strategy' and 'Optimization', showing various points marking their respective values."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "iSZf7vNszeHB0sAg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide details about the contents located within {region}<r>[0.787, 0.317, 0.982, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes parts of the lines and labels corresponding to the last few tasks, specifically from tasks 16 to 20, showing their values for each performance metric."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "t7CJzPfkDSqfJqbi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend item for '5G Infrastructure'}<r>[0.108, 0.177, 0.157, 0.191]</r> have on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "NovfjjMiWvB3MhgB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker shape used for {the 2008 point on the 'Fiber Optic Connections'} line<r>[0.434, 0.831]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Diamond."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "aJ5syfGlLV28Q7A3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data value is indicated by {the y-axis label}<r>[0.019, 0.306, 0.044, 0.625]</r> at the middle position in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "x3Wwnf5naWDb3QuI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '2001'}[0.139, 0.894] higher or lower than {the point for '2020'}[0.939, 0.122] on the 'Mobile Users' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "FuDJiDm8PGzE7vgb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is further right, {the last point of 'Internet Users'}[0.939, 0.465] or {the last point of 'Fiber Optic Connections'}[0.939, 0.732] on their respective lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "QkFBMLk2rVRzqInT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first data point for '5G Infrastructure'}[0.139, 0.894] positioned on the same level as {the last data point for '5G Infrastructure'}[0.939, 0.704] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "n4KGkboSnK9QKVsZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents between the coordinates [0.096, 0.894, 0.948, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains x-axis labels for the years ranging from 2001 to 2020, marking the beginning and end of the dataset visualized in the chart, formatted in grey text."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "6vsUM4sHekTGGU2C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components observed within the rectangle specified by [0.112, 0.060, 0.395, 0.198]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains legend items for 'Mobile Users', 'Internet Users', 'Fiber Optic Connections', and '5G Infrastructure', each paired with their respective color markers."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "rt22Us1bPY0R1y4G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details about the area spanning [0.019, 0.306, 0.090, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains y-axis labels marking values from 0 to 450 in intervals, styled in dark grey color, aligning vertically along the left margin of the chart."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "eP01iFK0z9u0D1H2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} in the area [0.015, 0.172, 0.035, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Career and Workplace Development Trends."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "xTo1HPNw1URSMB2M",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the trend line}[0.105, 0.578, 0.962, 0.771]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seniors."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "Q2mEDAmXww5rhWEO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at [0.504, 0.956, 0.563, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "nhb7QcmX2hXLyDmx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Seniors' at year 13}[0.647, 0.771] in higher position compared to {the point for 'Adults' at year 13}[0.647, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "d25I5f8IzA4JT6CN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Teenagers' at year 6}[0.331, 0.414] higher or lower than {the point for 'Teenagers' at year 5}[0.285, 0.474]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "H9mSA8HxfCTV6ygY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich group had a higher development trend at {year 16}[0.782, 0.355] on the chart, 'Teenagers' or 'Adults'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adults."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "uc842YuG4baerLYL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the content in [0.844, 0.819, 0.977, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region presents the legend of the plot. The labels {Teenagers}[0.893, 0.827, 0.971, 0.846], {Adults}[0.893, 0.855, 0.941, 0.874], and {Seniors}[0.893, 0.884, 0.950, 0.902] are included, accompanied by colored markers."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "TZSpHPRGJmaU88mu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.234, 0.926, 0.247, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '4' is located within these coordinates."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "qDHaGHrK5nBBiWVT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.105, 0.578, 0.962, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the trend line for 'Seniors'. There are data points for each year, starting from year 1 through year 20. Each data point shows the respective value for 'Seniors' per year."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "tSd50Jczq6uV09Iu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.019, 0.321, 0.044, 0.610] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label named \"Usage Rate (%)\"."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "0NCxXICNsCLYmReL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the text located at the coordinates [0.670, 0.060, 0.839, 0.085] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Machine Learning\"."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "RvzoQp67YSw0rBoq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick label at the coordinates [0.714, 0.902, 0.767, 0.932] on the x-axis indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the year \"2019\"."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "oAUc5aTed6MbVuPT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {line marked as 'Machine Learning'}<r>[0.096, 0.379, 0.955, 0.550]</r> line have a higher or lower value at 2016 compared to the {line marked as 'Deep Learning'}<r>[0.096, 0.372, 0.955, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "WzYYdskuF2L5qCw4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relative position of {the 'Robotic Process Automation'}<r>[0.096, 0.240, 0.955, 0.416]</r> line at 2020 compared to the {line labeled 'Machine Learning'}<r>[0.095, 0.379, 0.955, 0.55]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "ePoogApGg3ZqOUGy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, {Machine Learning}<r>[0.096, 0.379, 0.955, 0.550]</r> or {Automation}<r>[0.096, 0.182, 0.955, 0.337]</r>, has a higher value at year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automation."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "S9QtP0BywKDSVLkH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the coordinates [0.096, 0.037, 0.955, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains four line plots representing the trends of different technologies ('Machine Learning', 'Deep Learning', 'Automation', 'Robotic Process Automation') from 2016 to 2020 on a grid, with x-ticks representing years, and y-ticks representing the usage rate in percentage."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "Qye6SufPJVDeb96d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from coordinates [0.609, 0.049, 0.945, 0.208]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart that comprises keys for the four different trends. Each trend is denoted by a different color and unique marker symbol."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "I6AsAoDYqG8OnmDX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within the coordinates [0.096, 0.379, 0.955, 0.550] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the 'Machine Learning' line trend. It starts from year 2016 with a usage rate approximately at 40% and ends at year 2020 with the usage rate slightly above 60%."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "JKyG02Vo54xbRZn2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object at coordinates [0.509, 0.953, 0.555, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "J0Ib3iVIdqtfuaYe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the marker color} at [0.771, 0.077, 0.807, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "66MrzHKDpbmW7LKq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.457, 0.021, 0.607, 0.050]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "A2JHTXzy7JntuQXl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for week 3}[0.207, 0.875] positioned higher or lower than {the point for week 5}[0.293, 0.872] on the 'Temperature' graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "h2ir0N3XmijnQKVF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the blue marker}[0.771, 0.107, 0.807, 0.119] represent a higher or lower value compared to {the red marker}[0.771, 0.167, 0.807, 0.179] on the 'Public Transport Usage' line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "pjDQ0y3n5VI9H4E0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first week point}[0.120, 0.525] on the 'Grocery Purchases' line above or below {the tenth week point}[0.510, 0.485]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "CUoXV1If18ILYnjZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounds [0.434, 0.925, 0.604, 0.945]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels for weeks 9 and 10 at coordinates {week 9}[0.462, 0.925, 0.471, 0.945] and {week 10}[0.501, 0.925, 0.519, 0.945]."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "CliCkT33T66EkvK7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.100, 0.492, 0.200, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes {the first three points on the 'Hospital Admissions' line} at coordinates [0.120, 0.859], [0.163, 0.858], and [0.207, 0.856]."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "lpiLhZYaIAkGctjn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the significant objects found within the area [0.110, 0.100, 0.300, 0.500]."
+            },
+            {
+                "from": "gpt",
+                "value": "This sector encases two lines; {the 'Grocery Purchases'} from weeks 1 to 3 seen at coordinates [0.120, 0.525], [0.163, 0.552], [0.207, 0.545] and the {beginning of the 'Public Transport Usage'} line from weeks 1 to 3 positioned at coordinates [0.120, 0.195], [0.163, 0.128], [0.207, 0.095]."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "drrk3X90Eg2tEW2N",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the marker at {this position}<r>[0.815, 0.100, 0.865, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pattern B."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "iQojxiBN64Wk3lSp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is at {the coordinate}<r>[0.340, 0.165, 0.340, 0.165]</r> in the plotted line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "VSoLGwLwTVpzlfCM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {this marker}<r>[0.826, 0.654, 0.826, 0.654]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pattern B."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "qzHUl2c8SMrGS7K6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value at {point 8}<r>[0.428, 0.337, 0.428, 0.337]</r> for Pattern B above or below the value at {point 8}<r>[0.428, 0.380, 0.428, 0.380]</r> for Pattern D?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "5kZ5ilrNNWAFXSdS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pattern has a higher value at the 15th point between {Pattern A}<r>[0.738, 0.423, 0.738, 0.423]</r> and {Pattern D}<r>[0.738, 0.551, 0.738, 0.551]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pattern A."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "rmOrFoERp6nyKGUc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Pattern C}<r>[0.959, 0.637, 0.959, 0.637]</r> marker at point 20 below {Pattern D}<r>[0.959, 0.680, 0.959, 0.680]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "MNrfiwKKRcBoBrMq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within {this region}<r>[0.112, 0.902, 0.972, 0.932]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of x-axis labels from '1' to '20', each associated with a single tick along the bottom of the plot."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "YQfv3w2scWmvmd7H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area {between these coordinates}<r>[0.096, 0.025, 0.874, 0.198]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses multiple elements of interest, including y-axis labels from '0' to '100' scaled every 10 units, and a legend detailing four patterns each identified by different colors and markers."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "lw04KcXMfD7uSIOX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within {the space}<r>[0.874, 0.060, 0.962, 0.198]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend describing Pattern A, Pattern B, Pattern C, and Pattern D, detailed with associated color and marker shape symbols."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "qG3gBz4pF5TuGQ5s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker shape of {the line labeled 'Biotechnology'}<r>[0.118, 0.380, 0.959, 0.560]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "v."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "RTCUfHAhSpbOaW28",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used for {the line for 'Digital Media'}<r>[0.118, 0.062, 0.959, 0.594]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#de9ed6."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "bmAcWD2AxgX86ayB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ylabel of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Performance (%)."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "M493OnynS7unPxAq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at step 10 for Nanotechnology}<r>[0.517, 0.354]</r> above or below {the point at step 10 for Digital Media}<r>[0.517, 0.311]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "kXxWVbRpcqNKFC9i",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher value at step 20, {Digital Media}<r>[0.959, 0.062]</r> or {Information Technology (IT)}<r>[0.959, 0.037]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Information Technology (IT)."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "h7slTNA4mJuSsdr5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the trend for Nanotechnology} start or end higher than {the trend for Biotechnology}<r>[0.118, 0.380, 0.959, 0.560]</r> at step 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "qpIhs0STxzSvDt3A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content and layout in the area <r>[0.096, 0.025, 0.972, 0.055]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label '100' at {coordinates}[0.051, 0.025, 0.090, 0.055]. It's styled in dark grey font color."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "FMivivY7KT0E5Rzu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visual elements are contained within <r>[0.112, 0.902, 0.130, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes the x-axis_tick label '1' at {coordinates}[0.112, 0.902, 0.125, 0.932]."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "jysjWDCiCA4fmdkW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area <r>[0.064, 0.025, 0.090, 0.055]</r> include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-tick label '100' is situated within this space, rendered in dark grey font color."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Etxg154R14hMENvR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {GradientBoost}[0.670, 0.229, 0.708, 0.229]?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "9vJyVh2wW3qMmgie",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat describes {the label 'Task'} at coordinates [0.486, 0.931, 0.539, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis label."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "fbBCbtL0IZlbMY06",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the linestyle of {the DeepLearning line}[0.160, 0.212, 0.865, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "hK9ODCjwoBBJfIKP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label has a higher y-coordinate, {DeepLearning}[0.160, 0.212] or {PyTorch}[0.160, 0.426]?"
+            },
+            {
+                "from": "gpt",
+                "value": "DeepLearning."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "KwQGIHUCS90hFdZD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {line for ConvolutionalNN}[0.197, 0.292] above or below {line for GradientBoost}[0.234, 0.557] in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "u27zvW1KT6vLgc88",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {20th tick label}[0.854, 0.898, 0.876, 0.921] align with {the highest y-coordinate of line for GradientBoost}[0.865, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "czSw9SlwyahjVN5R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.160, 0.212, 0.865, 0.309]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes points from labels {DeepLearning}<r>[0.160, 0.212, 0.865, 0.580]</r> and {ConvolutionalNN}<r>[0.160, 0.215, 0.865, 0.309]</r>. The points for {ConvolutionalNN} are generally above the points for {DeepLearning} in this area."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "L3GpjqQYbWOOEy6E",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified in the region [0.119, 0.079, 0.906, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart {AI & ML Models Task Performance Efficiency Comparison}<r>[0.119, 0.079, 0.906, 0.113]</r> can be found here."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "iY55SE9dI81NgX9f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.655, 0.133, 0.890, 0.291]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segment includes labels {GradientBoost}<r>[0.723, 0.218, 0.861, 0.243]</r> and {PyTorch}<r>[0.723, 0.256, 0.797, 0.281]</r> including their corresponding color handles, giving details about the legend items."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "5dzA4s7lOkQgQDgW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.207, 0.902, 0.214, 0.932] on the x-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "3."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "LPDzR6JpzhmIpH3y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the legend item at [0.720, 0.179, 0.767, 0.188]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#de9ed6."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "2vIDXna2iW0YcUaC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the engagement percentage at the y-axis tick label located at [0.064, 0.282, 0.090, 0.312]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "EraCSoPRoPyKEw5L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the point at {Healthy Lifestyle}<r>[0.163, 0.320]</r> or {Unhealthy Lifestyle}<r>[0.163, 0.182]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Unhealthy Lifestyle."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "v3IzYIPKDgWSthct",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the label '12' closer to {the label '13'}<r>[0.636, 0.902, 0.663, 0.932]</r> or {the label '11'}<r>[0.548, 0.902, 0.574, 0.932]</r> in terms of x-position?"
+            },
+            {
+                "from": "gpt",
+                "value": "13."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "7AgNhasqcM0gl5pq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points displayed at {Fitness Training}<r>[0.472, 0.097]</r> and {Healthy Lifestyle}<r>[0.472, 0.414]</r> at the same x-axis value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "qPFGtHyGmwLGyUTn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.500, 0.871, 0.580, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Activity' is displayed here, marked in a dark grey color {.15}."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "yvNQJ0DBRDBFUoRH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.064, 0.111, 0.090, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '90' is located in this area."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "cHQn0Pz6a2RWI678",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are present within the region [0.725, 0.085, 0.798, 0.198]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays the legend item for {Fitness Training}<r>[0.779, 0.173, 0.927, 0.198]</r> with its corresponding color marker at {coordinates}<r>[0.722, 0.179, 0.767, 0.188]</r>."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "4a3T6PHXTwdiTV9U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information on the y-axis coordinate [0.019, 0.273, 0.044, 0.535]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity (%)."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "QRBGYvGOU4Uid0e3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at coordinate [0.844, 0.741, 0.978, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Private Tutorials."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "aON8kBCZsH8HelKI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you find at the following coordinates [0.167, 0.534, 0.226, 0.559] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The term \"Year 1\"."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "qhHYpFNgyU4xkpzN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the point \"Private Tutorials\"}[0.911, 0.309], the line for \"Year 3\"[0.135, 0.172, 0.911, 0.237] or the line for \"Year 5\"[0.135, 0.087, 0.911, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 5."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "GEQQKb52SUmW0wJy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for \"Online Courses\" in 'Year 1'[0.652, 0.269] higher or lower than in 'Year 3'[0.652, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "uoCh1lITuceUGdVz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for \"Library Books\"}[0.394, 0.087] on the 'Year 5' line lie above or below {the point at \"Online Courses\"}[0.652, 0.204]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "ExyP0YfO4Hv2M9Pi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area enclosed by the following coordinates [0.096, 0.076, 0.950, 0.733]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the main plot area of the chart with grid lines. The chart shows trends for different types of educational resources over five years, depicted by different color-coded lines."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "ig0VivwZARin3FIu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within [0.106, 0.524, 0.234, 0.720]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend with labels and color markers for 'Year 1' to 'Year 5'."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "JtbMTs3xZ58dyJhq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within these coordinates [0.588, 0.741, 0.716, 0.910]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label for the x-axis tick, specifically \"Online Courses\"."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "WIcHKSXoF2OIWaBX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.051, 0.296, 0.077, 0.326] on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "14."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "r7urzoFhWOOqih1v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend item for 'Government'}<r>[0.823, 0.161, 0.943, 0.186]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#800080."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "3msOQUk3d2OVYmqo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of tick does {the x-axis tick label} at [0.914, 0.902, 0.967, 0.932] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2020."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "l9zSis1TmuvUPmEI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line is closer to {the y-axis label '10'}<r>[0.051, 0.521, 0.077, 0.551]</r>, {the 'Residential'}<r>[0.124, 0.296, 0.940, 0.753]</r> trend or {the 'Housing Starts'}<r>[0.124, 0.065, 0.940, 0.516]</r> trend at year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "dJQqAJbVgxJCJU1I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the 'Commercial'}<r>[0.124, 0.680, 0.940, 0.855]</r> trend in year 2012, is {the 'Government'}<r>[0.124, 0.589, 0.940, 0.725]</r> trend higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "Zyfp5hH0KopAzES3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point {the x-axis label '2018'}<r>[0.733, 0.902, 0.785, 0.932]</r> which line has a higher value, the {line for 'Residential'}<r>[0.124, 0.296, 0.940, 0.753]</r> or the {line for 'Housing Starts'}<r>[0.124, 0.065, 0.940, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Residential."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "qEAkXrf0IHp5fkfX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region located at [0.883, 0.048, 0.989, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two legend items: {label 'Commercial'}<r>[0.823, 0.123, 0.938, 0.148]</r> with its associated red marker, and {label 'Government'}<r>[0.823, 0.161, 0.943, 0.186]</r> with its associated purple marker."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "mKTjAs56LiZkLER2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.015, 0.902, 0.067, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the year '2010' is located here."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "d6R4hEWTHoOKzNzj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.124, 0.065, 0.940, 0.853]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area covers several objects related to the data lines. It includes the {line for 'Residential'}<r>[0.124, 0.296, 0.940, 0.753]</r>, the {line for 'Housing Starts'}<r>[0.124, 0.065, 0.940, 0.516]</r>, the {line for 'Commercial'}<r>[0.124, 0.680, 0.940, 0.855]</r>, and the {line for 'Government'}<r>[0.124, 0.589, 0.940, 0.725]</r>. All these lines represent different categories of building permits trends from the year 2011 to 2020."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "7xJ4ARSnKPsJ5DKC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.051, 0.319, 0.065, 0.347] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label for the value '70'."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "2Zln8reimk0zXl5r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label on the x-axis at [0.113, 0.764, 0.185, 0.939] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Wind Energy'"
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "g687rca0kCgfnMrI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with the coordinates [0.582, 0.194, 0.968, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2023 projections for development progress in various energy sectors."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "Jk80v1loH4K2aV2X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the 2022 Observations, is the development progress of {Hydro Energy}[0.282, 0.160] higher than the {Nuclear Energy}[0.378, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "LAsYKWIeahtTg8oi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of 2023 Projections, is the development progress of {Solar Energy}[0.582, 0.381] lower than {Wind Energy}[0.679, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "7GPi1RnzcQ0bw0ri",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to 2022 Observations, does {Solar Energy}[0.089, 0.500] have a higher development progress than {Fusion Energy}[0.474, 0.670]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "whJdOC3utHdoya9X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is displayed in the region defined by [0.563, 0.075, 0.988, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the subplot titled 'Projected Development Progress.' It includes the 2023 Projections of development progress for various energy sectors, such as Solar Energy, Wind Energy, Hydro Energy, Nuclear Energy, and Fusion Energy."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "8uh0jVWScXbZEdGD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present in the region from [0.069, 0.075, 0.494, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the subplot that presents the 'Observed Development Progress' in 2022. It contains development progress data of Solar Energy, Wind Energy, Hydro Energy, Nuclear Energy, and Fusion Energy."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "LPMftenhdB40XhYs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area defined by [0.570, 0.091, 0.736, 0.154]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend item {2023 Projections}[0.614, 0.105, 0.730, 0.141], indicating the orange lines in the second subplot signify the 2023 Projections."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "eCGoUCMf6g8utE9q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line plotted}[0.558, 0.235, 0.967, 0.525] in the right subplot chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Cultural and Value Change in 2023."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "mTze1QPmXXMSJrsb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate represents the 'Power Distance' in the 2022 value chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.272, 0.718]."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "OSAUdQsqlMqWYh3z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 'Masculinity' x-tick label in the 2023 chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.776, 0.728, 0.831, 0.858]."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "JAdqtc4f9qb068bW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the plotted value of 'Indulgence' increase or decrease from 2022 {coordinate}[0.477, 0.396] to 2023 {coordinate}[0.967, 0.300]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "dnvSPDIS3lA5BYdy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the plotted values is greater in the cultural value change for 2022, 'Power Distance'[0.272, 0.461] or 'Indulgence'[0.477, 0.396]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Power Distance'."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "7iieF9W22KMxiF4W",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the plotted 'Masculinity' value[0.341, 0.332] in 2022 higher or lower than the '70' y-tick value[0.043, 0.413]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "3voZprJezjEp9umv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.628, 0.030, 0.897, 0.066]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the chart title 'Social Cultural and Value Change 2023' in black color."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "8Pk8KxSR1YKtltBf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the region [0.048, 0.075, 0.497, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 2022 line chart of Social Cultural and Value Change, with labels 'Tradition', 'Individualism', 'Uncertainty Avoidance', 'Power Distance', 'Masculinity', 'Long-term Orientation', 'Indulgence', and y-axis label 'Values'."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "MeKfschcK4IlGiR0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.201, 0.728, 0.272, 0.899]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-tick label 'Power Distance' for the line chart representing cultural value change in the year 2022."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "1R0RAktPLo5estxI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label {the y-axis handle} at [0.013, 0.219, 0.029, 0.600]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluacion Score"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "0MdRrjFIi6ipq6or",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-coordinate of the starting point of {the line in legend[0.059, 0.612, 0.222, 0.728] labeled 'Innovation 2022' with color #3d89be}?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.062"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "R2PybKCKZKTbsYgs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at this set of coordinates [0.178, 0.030, 0.381, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the left-side plot, labeled 'Innovation Project Evaluation'"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "FY0L7gZq7nRb6ABV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the right chart rises more steeply, the {line for 'Management 2022'}[0.554, 0.298, 0.967, 0.700] or the {line for 'Management 2023'}[0.554, 0.253, 0.967, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Management 2022"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "OwBWUuvzaK1WEW2R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the position {SEAL's point in 'Management 2023'}[0.898, 0.343] higher or lower than {SEAL's point in 'Innovation 2023'}[0.417, 0.387]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "YDyA74GHeZIBI590",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the right chart starts at a higher point, {the line for 'Management 2022'}[0.554, 0.298, 0.967, 0.700] or {the line for 'Management 2023'}[0.554, 0.253, 0.967, 0.610]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Management 2022"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "bUrf2ri85KWElMmY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features of the objects within this region: [0.258, 0.754, 0.348, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label \"NeoGNN-BUDDY\" for the left chart. The label is inclined at an angle of 45 degrees."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "LjMahKQ83Sv3qdNs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content lies in the rectangular area [0.540, 0.612, 0.721, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of the legend of the right-hand chart, specifically the text labels and corresponding colored markers for \"Management 2022\" and \"Management 2023\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "EVQ4G9WloauCFAky",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.524, 0.754, 0.554, 0.827]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains 'MLP' which is the first x-axis label of the right-side plot."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "RSEO3ZeUY1czmfrp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is displayed at the y-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Metrics."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "1IzM9kRZhF0QmWyR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the last x-axis tick mark on the left subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "PMPS."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "6j58i1Y2CaMHOXbf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company is represented by the point at coordinates [0.190, 0.232]?"
+            },
+            {
+                "from": "gpt",
+                "value": "LEAP."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "3uajAeH1A1Vw93Hw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'LEAP'}[0.190, 0.232] higher or lower than {the point for 'DHRM'}[0.249, 0.705] in 2022's results?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "mMrdARafJyXqIGY7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first subplot's Year 2022 label}[0.104, 0.105, 0.173, 0.141] contain more or less data points than {the second subplot's Year 2023 label}[0.588, 0.105, 0.657, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "StkrrHXVrXwELjxh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich x-axis tick {label}[0.328, 0.872, 0.366, 0.963] is positioned closest to the beginning of the x-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "DRSB."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "5CCnKqBqo9YQIVQp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content between the y-axis and the first y-axis tick label in subplot 1 for the year 2023."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains no visual elements; it serves as padding between the y-axis and the first y-axis tick label."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "LWwL3wiTaryY0VGf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region bounded by [0.013, 0.239, 0.029, 0.698]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the y-axis of the chart where {the label 'Performance Metrics'}[0.013, 0.239, 0.029, 0.698] is displayed vertically."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "yCiLgk8yf1BMVUiv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the components found within the coordinate bounds [0.520, 0.872, 0.791, 0.960]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes x-axis tick labels for FSRO, ACME, LEAP, DHRM, and BLP in the 2023 subplot, specifically labels for FSRO, ACME, LEAP, DHRM, and BLP are positioned at respective coordinates [0.520, 0.872, 0.557, 0.960], [0.577, 0.872, 0.616, 0.964], [0.639, 0.872, 0.674, 0.956], [0.692, 0.872, 0.733, 0.970], and [0.763, 0.872, 0.791, 0.941]."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "hY5MQdo5Dh9NP4O3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}[0.034, 0.210, 0.048, 0.238] on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "ZbO0PnfkgF9Vh0MH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the year 2022 in {the legend}<r>[0.104, 0.689, 0.139, 0.725]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "tDNBkue405TfNNTN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine type is associated with {the x-axis tick mark}<r>[0.243, 0.808]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mexican."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "d8IF63aMmLrW5uJB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Moroccan in 2022}<r>[0.470, 0.456]</r> higher than {the point for Indian in 2022}<r>[0.300, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "U0unPTufqlnxPwUB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year shows a higher rating for {the \"Vegan\" cuisine}<r>[0.740, 0.603]</r>, 2022 or 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "2023."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "oIQFnV6vR47zvkPM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the ratings for {the \"Paleo\" cuisine in 2022}<r>[0.968, 0.632]</r> greater or lower compared to {the \"Gluten_free\" cuisine in 2022}<r>[0.911, 0.573]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "ayFuaITN0qqefrtG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within [0.570, 0.544, 0.968, 0.691]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes various points representing the 2022 ratings for Gastronomy cultures such as {Farm-to-table}<r>[0.570, 0.661]</r>, {Small_plate}<r>[0.627, 0.691]</r>, {Vegetarian}<r>[0.684, 0.632]</r>, among others, using a square marker."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "Fl3KR0j8DPNrKTNp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.557, 0.676, 0.642, 0.791] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for Gastronomy Culture Ratings with items for the years {2022}<r>[0.601, 0.689, 0.637, 0.725]</r> and {2023}<r>[0.601, 0.742, 0.637, 0.778]</r>, represented in orange and dark orange colors respectively."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "khpZrifgTRnbLfSU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included in the region [0.034, 0.064, 0.048, 0.825]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section displays the y-axis with tick labels ranging from {95}<r>[0.034, 0.064, 0.048, 0.092]</r> to {70}<r>[0.034, 0.797, 0.048, 0.825]</r>, marked in a dark color with no rotation."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "3HIHuNmfIYJB9F2p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart in subplot_0 with coordinates [0.162, 0.030, 0.410, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Baseline Trend in Emerging Markets."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "qTSsZ0LEJJhQKATD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the content of the object found at coordinates [0.059, 0.064, 0.074, 0.092]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-Axis tick label '80'"
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "vbfXBrMOqUTUd1Fg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, what is the label for the line at coordinates [0.578, 0.091, 0.715, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot Legend."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "qRc6RpNLnSHbcpUk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, is the {point for 'Emerging-Europe'}<r>[0.191, 0.243]</r> above or below {the point for 'Latin-America'}<r>[0.286, 0.326]</r> in Baseline 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "CopJg0FosN78253v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, are the values of {Trend1 2022}<r>[0.590, 0.285, 0.969, 0.431]</r> on average higher or lower than those of {Trend1 2023}<r>[0.590, 0.222, 0.969, 0.326]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "lMGpYHuIgQAIoIOy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, does {Emerging-Europe}<r>[0.191, 0.243]</r> have a higher or lower Consumer Index value than {Sub-Saharan Africa}<r>[0.475, 0.368]</r> in Baseline 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "NDAn9CG0YsfJGz1s",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide the information within [0.230, 0.609, 0.380, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the x-axis tick label 'Middle-East and North Africa'."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "F5skigC5FLnV4XwC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within region [0.080, 0.467, 0.233, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subplot legend for Baseline 2022 and Baseline 2023, each paired with a different colored handle, blue and cyan respectively."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "EpOf1W9k43vfpxOe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region [0.572, 0.091, 0.715, 0.207]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend items for Trend1 2022 and Trend1 2023, each paired with a colored handle."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "1pHIpTMg06qyc3Yp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}[0.261, 0.954, 0.285, 0.975] for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "hKYEvBTjTloySY2z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the line labeled 'Child Labor'}[0.058, 0.425, 0.487, 0.877] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "pVYw6hbgljjezsZx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can the label '2030' for {the x-axis}[0.475, 0.924, 0.499, 0.945] be found in terms of coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.475, 0.924, 0.499, 0.945]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "val8h5f12zKs0jeo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value for the year 2023 from the line 'Migration Rights'}[0.635, 0.676] higher than the {value for the year 2022 from the same line}[0.588, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "n9TeYV4USUJGC9LU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the handle for 'Global Issues'}[0.043, 0.082, 0.067, 0.096] have the same color as {the handle for 'Child Labor'}[0.043, 0.179, 0.067, 0.193] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "2U6p5NzlGtdxzLjC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is further to the right, {the start of the line for 'Health Rights'}[0.540, 0.174] or {the start of the line for 'Free Education'}[0.540, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health Rights."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "PhS6tKGnMSEsKbxq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features located within the bounded box [0.022, 0.166, 0.034, 0.312]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis tick labels '90' and '85' at {coordinates}[0.022, 0.166, 0.034, 0.186] and {coordinates}[0.022, 0.291, 0.034, 0.312], respectively."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "vKMhB03ttGu8IYyH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.058, 0.174, 0.154, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes multiple point coordinates for 'Global Issues' from {years 2021 to 2023}[0.058, 0.827, 0.154, 0.626]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "n9fXs52vMJYAiKzA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements found between the coordinates [0.540, 0.174, 0.969, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entirety of the 'Health Rights' line from years 2021 to 2030, starting from the highest point at {coordinates}[0.540, 0.726] to the lowest at {coordinates}[0.969, 0.174]."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "aSVa9YrwP3Ga3awI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.188, 0.942, 0.261, 0.970] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for {Smart Home}<r>[0.188, 0.942, 0.261, 0.970]</r> in the x-axis."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "TweOG1XYcHyHsX4h",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell where is the {label for 'Status in 2024'} located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Status in 2024' is located at coordinates [0.691, 0.030, 0.792, 0.066]."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "WuGaqqBPB9w2HztS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual information in the figure at location [0.236, 0.942, 0.329, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for 'Agriculture Tech' is found at these coordinates."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "ZgceNo0TlDpyMqS6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {Green Energy} in 2023 located at [0.456, 0.932] compared to its value in 2024 at [0.944, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increased."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "LHhcKWhRTgNK6Lfk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the year 2023's {City Life} at [0.051, 0.932] and the year 2024's {City Life} at [0.540, 0.932], how did the value change over a year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increased."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "3t3pOaXNDP3wcfPx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell if {Online Work} at [0.109, 0.932] performed better or worse in 2023 than in 2024 at [0.597, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "PkNjoHOte61Pg1Jn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region from [0.354, 0.942, 0.495, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains labels of two x-axis categories from 2023's data: 'Education Tech' and 'Green Energy', positioned respectively at coordinates [0.354, 0.942, 0.441, 0.970] and [0.416, 0.942, 0.495, 0.970]."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "thezpfgrx6OaYPS5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items placed in the space [0.674, 0.942, 0.870, 0.970] please."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two x-axis categories from the data of 2024: 'Smart Home' and 'Agriculture Tech', at the following locations respectively: [0.674, 0.942, 0.749, 0.970] and [0.724, 0.942, 0.817, 0.970]."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "du61ZV48KlglTxtY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what is in the area between [0.008, 0.280, 0.031, 0.291]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location for the y-axis label '85' for the data of 2023."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "tNK8oc9yktTheXF1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label in the chart represented at [0.012, 0.327, 0.029, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Profits ($M)."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "CPDxYBt3unTtJDyE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the xtick label at [0.095, 0.812, 0.133, 0.889] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Retail."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "vkhaCXGWu3XRdFYa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the subplot at [0.157, 0.025, 0.406, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Profit Trends in Trade and SCM 2022."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "XGRmsqzuDPigb3mc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {profit of Logistic}[0.371, 0.359] more than the {profit of Consulting}[0.430, 0.270] in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "6mmq6YBFiJlaLwPL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Software}[0.490, 0.211] have a higher profit than {Retail}[0.133, 0.448] in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "PdlTy8R5FdzlweZb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {profit of Manufacturing}[0.252, 0.685] compare to the {profit of Wholesale}[0.192, 0.567] in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "6IlZc7dKs6Ich8T9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in [0.059, 0.738, 0.193, 0.790]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the legend for the 2022 profits. It's colored in '#3d89be'."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "2woKfZK2nuZFgzK4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.073, 0.211, 0.490, 0.656] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the line chart displaying the 2022 profits across different sectors from 'All' to 'Software'."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "xPAaiyZ8gEiwFkuR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the region [0.530, 0.062, 0.988, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region displays the sub-chart for \"Profit Trends in Trade and SCM 2023\". It includes line points for profits of different sectors such as 'All', 'Retail', 'Wholesale', through to 'Software'."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "lCClS1WetHsdb2Cy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.022, 0.130, 0.039, 0.640]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label in the first subplot."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "Se1rI7grYBJ7hA3L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second item text in the legend of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.125, 0.157, 0.220, 0.193]"
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "6WTCBqMiG7g0KXwk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the X-axis label} at [0.366, 0.705, 0.475, 0.966] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Relative Vigor Index\" method."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "7yuDVwoXYzFrQZtp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the \"USD-JPY 2022\" line}[0.092, 0.199, 0.475, 0.397] showing a higher or lower result than {the \"USD-JPY 2023\" line}[0.092, 0.099, 0.475, 0.372] at the \"Momentum\" method?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "K21o7BJ98S4W41mI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the \"USD-JPY 2022\" line}[0.092, 0.199, 0.475, 0.397] and {the \"USD-JPY 2023\" line}[0.092, 0.099, 0.475, 0.372] intersect?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "MkyvPJiwU2GWFUAT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method, \"Buy-and-Hold\" or \"MACD\", has a higher value for \"USD-JPY 2023\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "MACD."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "JAgSsX0R9I7MaLoC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.366, 0.705, 0.475, 0.966]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the X-axis label for the method \"Relative Vigor Index\"."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "ebcT2iwmJtm0Y1mw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.080, 0.091, 0.226, 0.207]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area in the first subplot represents the legend of two lines: {USD-JPY 2022}<r>[0.125, 0.105, 0.220, 0.141]</r> with color blue and {USD-JPY 2023}<r>[0.125, 0.157, 0.220, 0.193]</r> with color light blue."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "cPxEJchyo2xfsO6M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.509, 0.705, 0.968, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 objects in the area within the X-axis labels 'MACD', 'Stochastic Oscillator', and 'Relative Vigor Index' at points {coordinate}[0.819, 0.705, 0.859, 0.801], {coordinate}[0.803, 0.705, 0.914, 0.970], and {coordinate}[0.860, 0.705, 0.968, 0.966] respectively."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "JiVX9UVB5ToFuJAh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the line with the highest data values near {the coordinate}[0.967, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Orange."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "wcMRWempiwMuUi5J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {fourth y-axis tick label}<r>[0.671, 0.609, 0.694, 0.637]</r> from the left in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "110."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "htL2BHzGYai5JtEJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is present at {the last visible point}<r>[0.967, 0.105]</r> in the subplot of AI Outliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Square."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "KDsmZ3aQ8xd0Fhhd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the point on Case5}<r>[0.311, 0.396]</r> in AI Applications above or below {the point on Case7}<r>[0.427, 0.250]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "azf0GhWTX8eJT3AG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Case8}<r>[0.485, 0.135]</r> from the AI Applications line have a higher value than {Case1}<r>[0.563, 0.347]</r> of AI Outliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "bZxDOSDVhCP1tO3O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {Case3}<r>[0.637, 0.166]</r>, AI Applications or AI Outliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Outliers."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "jgV3Z7tOt33F6xg6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.680, 0.345, 0.810, 0.400]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the point labeled {Case7}[0.909, 0.135] from the AI Outliers Trends plot, highlighted as a higher point with an orange color and square marker."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "AcBOVkvdoDLOlg3p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.030, 0.780, 0.103, 0.960]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features part of the y-axis with tick labels '70'<r>[0.056, 0.851]</r> and '80'<r>[0.056, 0.791]</r> and the corresponding gridlines enhancing the visibility of the charted data."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "dbf8q5PEcxVs5gpI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.300, 0.320, 0.400, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two points. One at {Case6}<r>[0.369, 0.329]</r> from AI Applications Trends, showing a high-value measurement and the second at {Case5}<r>[0.311, 0.420]</r> showing increased trends on a blue line and also being significant."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "kXy1J8k7N5JNnkiv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinate [0.264, 0.030, 0.299, 0.066] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot subtitle."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "tQAjwwEIxWCYtNJK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object {ylabel}[0.012, 0.396, 0.029, 0.514] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Sales'."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "wsKHoRVqOKxZCZo4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line_0} in [0.564, 0.138, 0.967, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#de3163 (some shade of red)."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "jFeDF9IrFfQIEq5I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'iPhone' point in 2020}[0.080, 0.328] and {the 'iPhone' point in 2021}[0.564, 0.138], which year had more sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "GneNmR2TrQH1InO0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'iPad' in 2021}[0.665,0.455] higher or lower than {the point for 'iPad' in 2020}[0.181,0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "dyyBBXAJF2TzAArH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product had a higher sales in 2020, {Mac}[0.282,0.455] or {Services}[0.483, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Services."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "RKLKxvjXcn0TBV7I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the coordinate area [0.060, 0.075, 0.504, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of subplot_0, including the 2020 sales data for iPhone, iPad, Mac, Watch, and Services. It also contains the xticks and yticks of the plot, as well as three lines indicating the actual sales figures."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "2s473tPyg5WyIpEQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.544, 0.075, 0.988, 0.835]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses subplot_1, which contains the 2021 sales data for iPhone, iPad, Mac, Watch, and Services. It also includes the xticks and yticks of the plot, and three separate lines indicating the actual sales figures."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "UUd1WlzoYNOswz9i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.748, 0.030, 0.783, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot subtitle \"2021\" is in this area."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "yWcUq3y7qUfheJFU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.330, 0.030, 0.718, 0.066] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "LVd6zdCBNLK34Zss",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel} at [0.015, 0.234, 0.035, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "mJXq6K0Cjw3VwXtl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.554, 0.685, 0.664, 0.905]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label for \"Computer Vision\"."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "lYIqzOOf7GtQIjAW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Business AI' in AI Case 1}[0.850, 0.355] above or below {the point for 'Business AI' in AI Case 2}[0.850, 0.297]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "lwC4HJGcYydRrz0E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing AI Case 1 and AI Case 2, which one has the higher value for \"Cybersecurity AI\": {AI Case 1}[0.943, 0.219] or {AI Case 2}[0.943, 0.102]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Case 2."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "wmtnftWyIB8gNUxx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is plotted higher for \"Computer Vision\", {AI Case 1}[0.664, 0.394] or {AI Case 2}[0.664, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Case 2."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "QmP1exRDe1NUP8Ur",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content inside [0.844, 0.091, 0.977, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend for 'AI Case 1' located at {coordinates}[0.897, 0.105, 0.970, 0.141] and 'AI Case 2' located at {coordinates}[0.897, 0.157, 0.970, 0.193]."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "jbqJModNbUdt20hy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.360, 0.685, 0.477, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content falls under the x-axis tick label, which refers to the category of AI applications named \"Natural Language.\""
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "QJPuzRzzjC1QlZov",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.041, 0.169, 0.058, 0.197]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the y-axis tick label '80'."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "R3FahzfexEpIHHXV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.282, 0.981, 0.282, 0.981]</r> of subplot_0 on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no xlabel for subplot_0."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "N9F8Ewf56xDVMXaM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the { ylabel for subplot_0 }<r>[0.013, 0.321, 0.029, 0.678]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the ylabel for subplot_0 is \"Indicator Values\"."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "FJPDrZcWtyRlnKZC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is represented in { line_0 }<r>[0.073, 0.147, 0.490, 0.851]</r> in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line_0 represents the Logistics Efficiency Trend from the year 2022 to 2029."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "u7DfXuBprwZ8xWcW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_1, is the {2023 value of 'Transportation Efficiency'}<r>[0.608, 0.536]</r> greater or less than the {2022 value}<r>[0.548, 0.657]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "p0J306kLZJzWB4qk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, do the {2025 value for 'Logistics Efficiency'}<r>[0.252, 0.851]</r> and the {2026 value}<r>[0.312, 0.317]</r> show an upward or downward trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downward."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "OcIpQx1PFgPJ2EzR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {value of 'Logistics Efficiency'}<r>[0.133, 0.609]</r> in 2023 with its {value}<r>[0.252, 0.851]</r> in 2025, is there an increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "N0vjh4RPSlVlo1gL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section within the coordinates [0.527, 0.075, 0.986, 0.924] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section represents the subplot_1 in the chart which plots the trend of 'Transportation Efficiency' from 2022 to 2029. It includes x-grid, y-grid, title, and data points which are represented as an orange line."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "C8fxXEJQsdhov2E1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the area defined by coordinates [0.323, 0.845, 0.504, 0.908] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend is located in this area, labeled as \"Logistics Efficiency\" with a blue marker."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "ST9CrtsotzLHYJrX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region from [0.777, 0.845, 0.979, 0.908] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend term 'Transportation Impact' associated with an orange marker."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "jivUF4NMcfv0A11T",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the secondary object with coordinates [0.527, 0.075, 0.986, 0.924] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plot for 'Lifestyle Trends'."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "4urdYpa1Hx9i97pn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label '2023' in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.473, 0.934, 0.508, 0.970]"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "zhBK9Va9eQxJr6R3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.104, 0.105, 0.213, 0.141]</r> indicate in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Activities."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "IQKw93XwqjEstJW7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of {Social Activities}<r>[0.073, 0.287, 0.490, 0.712]</r> increase or decrease from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "WOGhDZmI7gcGlnTK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend for {Lifestyle}<r>[0.548, 0.754, 0.965, 0.797]</r> increasing or decreasing between 2022 and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "UnvEdIVzaxmGp7No",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has a higher percentage in 2023: {Social Activities}<r>[0.490, 0.287]</r> or {Lifestyle}<r>[0.965, 0.754]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Social Activities."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "a61hcarPtzrg7Ely",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.522, 0.499, 0.810, 0.666]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subplot for 'Lifestyle Trends'. It includes value points for the years 2022 and 2023 with a line joining them indicating the trend."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "DPymx6KUsx5vsG7O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.104, 0.105, 0.213, 0.141]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents a single entry in the legend. It is labeled 'Social Activities' and is associated with a colored handle."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "sulCXw42KOoK55kr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.053, 0.075, 0.511, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the other subplot, 'Social Activities Trends', which includes x-axis and y-axis labels, tick labels, the plot line, and data points for the years 2022 and 2023."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "oJYcr0BffGrzecQt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is signified by the text at the coordinates [0.061, 0.719, 0.151, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the x-axis label \"Random Forests\"."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "x9j9JhSTK3qvffbv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.367, 0.233] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a point on the line for ANN in task 1 performance."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "Hnay3VbcT5nb5hv5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.758, 0.030, 0.801, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the sub-title of the second subplot, which is \"Task 2\"."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "OscubnXol1CMLII7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'SVM' in Task 1}[0.205, 0.297] above or below {the point for 'KNN' in Task 1}[0.259, 0.455]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "ntYQpf4wE1MxGNXx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot for Task 2, is {the point for 'ANN'}[0.860, 0.233] higher or lower than {the point for 'SVM'}[0.698, 0.471]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "9veJcugiYHoHRS4g",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich model has a higher accuracy in Task 2, {Random Forests}[0.644, 0.360] or {RNN}[0.969, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "ANN."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "4EZtKvbGpfWiUF0X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is contained within the coordinates [0.509, 0.719, 0.969, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all x-axis labels for the second plot, those are Decision Trees, Random Forests, SVM, KNN, Logistic Regression, ANN, CNN and RNN."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "khnLqr3rAuqDjuul",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content does the area defined by [0.571, 0.075, 0.987, 0.709] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the plotting area of the second subplot which presents the performance of the AI & ML models for 'Task 2'. We have 8 models represented on the x-axis, and the y-axis represents the performance accuracy. Each model has a line plot point representing its accuracy."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "VYZNT1VAj5fjYTlS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.078, 0.075, 0.494, 0.709]."
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates encompass the first subplot which displays the performance of AI & ML models for 'Task 1'. It includes the line plots for various models along with their Y-axis 'Accuracy' and X-axis labels for different models 'Decision Trees', 'Random Forests', 'SVM', 'KNN', 'Logistic Regression', 'ANN', 'CNN', and 'RNN'."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "WM9sLt1VL8XF7Fbj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the object with coordinates [0.034, 0.792, 0.056, 0.820] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '450'."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "R9RErdMfaekpjZwR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled as 'Hydro' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{X-axis tick label}<r>[0.040, 0.813, 0.080, 0.908]</r>."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "FDj5YCZXYNVIAhAc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's the color of {the legend marker}<r>[0.551, 0.745, 0.586, 0.762]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "hTicqv0ShpVjkJ3D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, is {the Coal energy production point 2030}<r>[0.967, 0.667]</r> higher or lower than {the NaturalGas energy production point 2030}<r>[0.832, 0.384]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "kNuUbUUHN6OT9njI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Solar energy production point in 2025}<r>[0.148, 0.306]</r> positioned lower or higher in the chart than {the Solar energy production point in 2030}<r>[0.630, 0.242]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "lAbGOOi69TcVXL2x",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source has the lowest production in 2030, {Nuclear}<r>[0.765, 0.175]</r> or {Wind}<r>[0.697, 0.769]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nuclear."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "wnn0xG4LorIBmUIC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located within the bounding box [0.111, 0.737, 0.251, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label 'Energy Source 2025' for the first subplot."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "fbJJ596X9Q9XwDWU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region at [0.215, 0.794, 0.485, 0.690]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area marks the Energy Production points on the 2025 chart for Wind, Nuclear, NaturalGas, Oil, and Coal."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "ZX6EZKOCAjVp6qlN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does [0.682, 0.030, 0.848, 0.066] region contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subplot title 'Energy Production 2030'."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "m5nHT2uAfLeAOdMX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.030, 0.272, 0.046, 0.557] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the subplot on the left."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "7E7dxKp3GXjrAQPt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.121, 0.157, 0.708, 0.193]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for \"Policy B 2021\" in the legend of the second subplot."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "1LRTNecprXiJmw3v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.051, 0.249, 0.066, 0.277]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "A3wvt77cXa5yt2Ez",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score of {Policy A 2021 for 'Taxation'}<r>[0.474, 0.383]</r> lower or higher than {Policy B 2020 for 'Taxation'}<r>[0.968, 0.569]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "sND78zqfKvOMr6SS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point {Policy B 2020 for 'Education'}<r>[0.748, 0.383]</r> located higher or lower than {Policy B 2021 for 'Education'}<r>[0.748, 0.322]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "PiwmkCltmeyMm2Mn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Year 2020, did {Policy B for 'Environment'}<r>[0.583, 0.445]</r> have a higher impact level than {Policy A for Transportation'}<r>[0.364, 0.322]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "ugW8QIzQG2qAYTbf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in the region [0.058, 0.764, 0.144, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the {x-axis label 'Unemployment'}<r>[0.058, 0.764, 0.144, 0.970]</r> for the subplot on the left."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "shKiByjJjNgMlxHt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents found in the area defined by [0.570, 0.091, 0.714, 0.207]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the subplot on the right with labels {Policy B 2020}<r>[0.615, 0.105, 0.708, 0.141]</r> and {Policy B 2021}<r>[0.615, 0.157, 0.708, 0.193]</r>."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "ISuzUrAC443XmP6m",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region defined by the coordinates [0.089, 0.136, 0.474, 0.445]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the line for {Policy A 2020}<r>[0.089, 0.136, 0.474, 0.445]</r> in the left subplot. Specifically, it includes the points from \"Environment\" to \"Taxation\". Each point corresponds to a value of the 'Policy A 2020'."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "c6lgWzbCgPXGSqSI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title for the first subplot within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "New Media methods Results in 2022."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "UOhBdb2EfUXDWoHz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the line representing 'New Media methods 2022' in subplot 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "#3d89be."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "WCPzJv3of53UHJxk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element lies at the coordinates [0.093, 0.488, 0.107, 0.516] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"20\" label in y-axis tick labels."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "wxyDiTDGV9yBL2lt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is the data point 'SEO Optimization'[0.245, 0.312] above or below 'Influencer Marketing'[0.418, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "mBkRvd0agt0ggX1v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, is the datapoint for 'AI Content Generator'[0.623, 0.533] higher or lower than 'Influencer Marketing'[0.912, 0.312]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "Fm5lwax18DvK7Lzg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, how does the value of 'SEO Optimization'[0.738, 0.448] compare to 'Email Marketing'[0.796, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is lower."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "bovfI4udL8X3D5hY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates [0.611, 0.090, 0.844, 0.155] in subplot 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot legend labeling the 'New Media methods 2023' line in a particular color."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "ZMyq5MjNoW6jtO9B",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within coordinates [0.508, 0.667, 0.971, 0.959] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels for the categories in New Media methods Results for 2023, such as 'AI Content Generator', 'Social Media Platforms', 'SEO Optimization', 'Email Marketing', 'VR/AR Content', 'Influencer Marketing', and 'Video Marketing'."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "yiSD6C9NK7HC2RFE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the coordinates [0.603, 0.074, 0.989, 0.670]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire second subplot, which represents the 'New Media methods Results in 2023' with a line and marked points for 'AI Content Generator', 'Social Media Platforms', 'SEO Optimization', 'Email Marketing', 'VR/AR Content', 'Influencer Marketing', and 'Video Marketing'."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "N0UuHFoFYc8wZfC9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the y-axis tick mark}[0.083, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "62000."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "GuUn8GPxC9sq8JR2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the purpose of the graphical object located at}[0.906, 0.184]?"
+            },
+            {
+                "from": "gpt",
+                "value": "To display the point value for '24 hours' on the 'User Engagement' line chart."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "FTXxUW8z3MElxMht",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the last point of the 'User Engagement' line path}[0.941, 0.152] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "62500."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "Bh3RpKoHCg54xqU3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '10:00'}[0.465, 0.279] positioned higher or lower than {the point labeled '12:00'}[0.533, 0.120] in the 'User Engagement' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "9TnEU8LiRBHp5sPX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher count at {point 14}[0.601, 0.630], the 'Ad Views' or 'User Engagement' lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ad Views."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "SK0mtWaRuYGk9SYK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the marker at {location}[0.669, 0.152] on the 'User Engagement' higher or lower relative to the marker at {coordinate}[0.669, 0.632] on the 'Ad Views'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "MlmhKpjwfejJCdxy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.065, 0.302, 0.131, 0.477]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains a part of the {y-axis}[0.015, 0.222, 0.029, 0.270] for 'User Engagement' and part of the {grid line}[0.083, 0.037, 0.982, 0.454], which appears along the y-axis scale."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "WCe5lQhBYg4B1vYm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the range [0.800, 0.250, 0.980, 0.400]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the {last section of the 'User Engagement' line}[0.941, 0.152] and the {x-tick near label \"20\"}[0.805, 0.454]."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "ubdSKO8Q3TGoggyd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.450, 0.890, 0.550, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the lower half of the {x-axis labels}[0.488, 0.971, 0.578, 0.985] on the 'Ad Views' chart."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "clZL0evazMxs86Zy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label}[0.464, 0.946, 0.613, 0.960] corresponding to the x-axis in the lower subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Hours)."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "ZiHFt3GVpX01Q9W1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the marker color}[0.408, 0.980, 0.454, 0.980] for the Online Education line?"
+            },
+            {
+                "from": "gpt",
+                "value": "#dc143c."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "zhGzbQH2yni1XZv7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}[0.025, 0.679, 0.048, 0.772] in the lower subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Activity Level."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "pUiPXXdM32odDqjm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the x-axis tick labeled '10'}[0.466, 0.922] align horizontally with {the point at time 10}[0.466, 0.763] in Online Education subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "o4IWk2a1ZT1CzSF5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point with coordinates }[0.321, 0.345] above or below {the point with coordinates }[0.576, 0.382] in the Traditional Education plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "PVGEWJeIa5nBMn9S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-axis tick labeled '3.0'}[0.102, 0.586] located higher than {the y-axis tick labeled '2.5'}[0.102, 0.642] in the Online Education chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "60FZ4f4P8prcahcr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.398, 0.968, 0.679, 0.993]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for Online Education, with the text \"Online Education\" and a corresponding line styled in red."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "KXexAQqztweWsvUo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what is located in [0.058, 0.200, 0.094, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This longitudinal slice includes all the y-axis tick labels ranging from '0.0' to '2.0' in the Online Education subplot."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "AF6tjOGQ2YL9uNbY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what can be observed in [0.095, 0.434, 0.481, 0.448]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the x-axis tick labels '0', '5', and '10' along the bottom of the Traditional Education chart."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "ZvJGeDDVFbVJmZq0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plot located at these coordinates [0.106, 0.037, 0.954, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This plot is a line chart representing the number of 'Patents Applied Over the Years'."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "BqL6MoOcdWEhHJXT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at these coordinates [0.068, 0.613, 0.100, 0.627] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis tick label for the 'Research Grants Received Over the Years' plot with a value of '2.0'."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "pwE0qqYrbabS6Oyz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the position [0.050, 0.091, 0.100, 0.105]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a label on the y-axis of the 'Patents Applied Over the Years' line chart with a value of '4000'."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "MAOwY3HLneBHjwLp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point in 2012}[0.276, 0.285] for 'Patents Applied' higher or lower than the {point in 2013}[0.361, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "3ltX8OQSRQUlLokQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the {value in 2011}[0.191, 0.773] for 'Research Grants Received', is the {value in 2020}[0.954, 0.839] for 'Research Grants Received' higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "oktSk8ybsQbLyuFF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {value for 'Patents Applied'} at [0.445, 0.255] in 2014 surpass the {value for 'Research Grants Received'} at [0.445, 0.817] in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "txSVQ3rGgHIeOPFF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give me a summary of the information contained within the area defined by these coordinates [0.106, 0.530, 0.954, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area plots the line chart for 'Research Grants Received Over the Years'. It has an x-axis labeled with years ranging from '2010' to '2020' and a y-axis labeled with amounts from '1.4' to '2.0'."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "NK8OWxGzVVobiatE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area [0.201, 0.468, 0.428, 0.493]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is a legend specifying that the '--' line in the color '#ff7f50' represents the 'Patents Applied' data series."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "vRMvXyb0fX4ZNcFJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat visual elements are included in the area [0.284, 0.508, 0.777, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the 'Research Grants Received Over the Years' line chart is located in this area. The subtitle is styled in color '.15' and has no rotation."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "MlBM9Xga4BICyN5E",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at {the coordinates}<r>[0.351, 0.479, 0.400, 0.493]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "nFYneb6DRSE3259X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label placed at {this specific point}<r>[0.642, 0.122]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "mktqNH7ZnoHIbnNI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the attribute of {the color handle}<r>[0.663, 0.236, 0.709, 0.236]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "ssTiRKLdBJFsr4Ez",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label '2000 at point}'<r>[0.287, 0.454]</r> come before or after {the label '2010 at point}'<r>[0.464, 0.454]</r> chronologically on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "BhPuKijld5OfEUEi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-tick '2'}<r>[0.072, 0.340, 0.102, 0.354]</r> above or below {the y-tick '4'}<r>[0.072, 0.232, 0.102, 0.246]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "IED6pEfCKNfF2UPa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the point at '2007'}[r][0.411, 0.153]</r> or {the point at '2010'}[r][0.464, 0.151]</r> on the Trade Volume line?"
+            },
+            {
+                "from": "gpt",
+                "value": "2007."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "t1m3xieZE3QRE1eF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region marked by [0.087, 0.015, 0.102, 0.029]."
+            },
+            {
+                "from": "gpt",
+                "value": "The bottom y-axis tick with a label of '8' showing the lowest part of the scale for the Globalization Index in a rotated font."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "es6nZQYysu1j9sod",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOutline what is contained between [0.080, 0.459, 0.139, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '1990' initiating the timeline in black colored text in a horizontal orientation."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "4xdFguXdFNWWDr5J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is represented in the bounding box [0.663, 0.225, 0.975, 0.250]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend indicating {Global Trade Volume}[0.727, 0.230, 0.966, 0.244] with a corresponding green line indicator {color handle}[0.663, 0.236, 0.709, 0.236] beside the label text."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "C1rAo6DXXBE8VX2Z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the x-axis label for the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The x-axis label for the second subplot}<r>[0.499, 0.971, 0.528, 0.985]</r>"
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "uBsc2eJtOdWh74Fl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the set of points in {line_0}<r>[0.066, 0.555, 0.961, 0.913]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the growth of the Game Industry."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "0wBHo5yuXFF3fC2d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found with coordinates [0.846, 0.878, 0.948, 0.892] in the sublegend of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Game Industry'."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "wO7J2vzp70bsMzOM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the revenue points in 2001 for the Movie and Game industry. Which one had higher revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Game Industry."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "YF99FyFsgNQkuPT9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the Movie Industry point at year 2002 {coordinates}[0.155, 0.412] have higher or lower revenue than the Point at the year 2001 {coordinates}[0.111, 0.437]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "aMv3IE92tLhvi7XF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the revenue of the Game Industry at year 2007 [0.379, 0.742] more than the revenue in 2006 [0.334, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "gTINPuuxPI0zu5Ux",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat aspect of the chart is described in [0.419, 0.508, 0.608, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a subtitle which is specified as 'Game Industry Growth'."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "bZNk1zJVbB3tGLa8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the content within the region [0.801, 0.873, 0.954, 0.898] of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two items: the color handle of Game Industry and the corresponding text label."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "Er1xEGhGJ52sy9nx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region between [0.737, 0.647] and [0.871, 0.580] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Game Industry's line between 2015 and 2018 is found within these coordinates. It indicates growth from 460 to 540 Million in Revenue."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "4kzugPg3944QczBX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the x-axis label}[0.460, 0.479, 0.610, 0.493] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grey."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "Pubo1kcPLurACXs3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the linestyle of {Investment 2 Return Rate line}[0.110, 0.572, 0.926, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "NP3ti1ufq8GTr41b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the shape of {the marker for the second subplot's legend}[0.131, 0.548, 0.177, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "nJygCZ8G0HoKXegM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 5 hours}[0.280, 0.454] above or below {the point at 10 hours}[0.450, 0.412] for Investment 1 Return Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "IjAwBSt8PfZqnvxW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 90% ylabel}[0.058, 0.365, 0.102, 0.379] come before or after {the 130% ylabel}[0.058, 0.240, 0.102, 0.254] in order of their numeric value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "vcwtybnyKQ3pD5If",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher end value, {Investment 1 line at 19 hours}[0.756, 0.454] or {Investment 2 line at 19 hours}[0.756, 0.676]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment 2."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "Q9cq0YC6zAapbOTo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.103, 0.459, 0.975, 0.473]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains x-axis tick labels for both subplots, ranging from '0' to '25' hours in increments of 5 hours for each tick, labeled in grey color."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "xOckRXB09RKf6g0f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in [0.110, 0.037, 0.960, 0.454]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the subplot displaying Investment 1 Return Rate, inclusive of {line graph}[0.110, 0.079, 0.926, 0.454], {y-axis labels}[0.058, 0.074, 0.102, 0.463], {x-axis and its labels}[0.103, 0.459, 0.975, 0.473], and a {legend}[0.121, 0.044, 0.502, 0.069], all rendered with a light grey grid background."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "x7HmPLLcpH6GyxsT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is shown in [0.025, 0.683, 0.048, 0.794]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes y-axis labels for the second subplot, with labels for return rates ranging from 110% to 270%, displayed vertically oriented in grey color."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "AgfD1tvzBmu5eAc0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.032, 0.174, 0.056, 0.293]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emissions (Units)."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "5QYcaYVswXK8LxOB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to {the line indicating average greenhouse gas emissions per hour}<r>[0.440, 0.481, 0.954, 0.495]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#228b22."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "o6Gub8PjIXWhjH9d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the value on the x-axis}<r>[0.999, 0.434, 1.026, 0.448]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "25."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "DOVTPkZXXyAj28hj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {the value at point 0}<r>[0.102, 0.299]</r> on the emissions line or {the value at point 24}<r>[0.975, 0.549]</r> on the recycling line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emissions."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "0UOwknqmB28Rf3JN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point shown at 0 hours}<r>[0.102, 0.922]</r> in recycling rates above or below {the point at 12 hours}<r>[0.539, 0.640]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "hBTYalSkNjhWISpf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to {the higher y-tick}<r>[0.102, 0.037]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "30."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "OSimmaXEnezdVdQy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements contained in <r>[0.375, 0.488, 0.954, 0.495]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area for 'Average greenhouse gas emissions per hour' with a green handle at {points}[0.375, 0.488, 0.421, 0.488] and text in {points}[0.440, 0.481, 0.954, 0.495]."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "nhyx6XcIhmDl5q4q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located in <r>[0.275, 0.508, 0.725, 0.526]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle 'Recycling Rates Over Time' is located in this region, and it describes the second subplot of the chart."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "SNgmCcEmyjgwStEH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area <r>[0.080, 0.163, 0.094, 0.373]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick labels '20', '15', '10', and '5' for the emissions unit are included in this region."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "tcqpy7PaLOrEqNud",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinates [0.242, 0.361] in the subplot_0 of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a data point on the \"Cultural Development\" line at the time 2000 years."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "LW1rI8mD91fG4TrN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {scientific development line}<r>[0.061, 0.576, 0.963, 0.947]</r> in subplot_1 of the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "6FFi4hbheYE0ydCB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element found at {coordinates} [0.242, 0.872] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a point on the \"Scientific Development\" line at the time 2000 years."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "4eKrhMeoXTaRELWO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Cultural development line}[0.061, 0.454, 0.963, 0.195] growth faster or slower than the {Scientific Development line}[0.061, 0.576, 0.963, 0.947] over the same time period in the fig?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slower."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "xLeiRAcrIoYxbiEv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt time point '2000', is the development level of {Cultural Development line's point}[0.242, 0.361] higher or lower than that of {Scientific Development line's point}[0.242, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "dSyyMCjJr8MeECn4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Cultural Development line}[0.061, 0.195, 0.963, 0.454] have a higher or lower starting point than the {Scientific Development line}[0.061, 0.576, 0.963, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "mbLbsB9MQbWzuDE3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.330, 0.508, 0.695, 0.526]."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Scientific Development over Human History\" for subplot_1 is located in this region."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "Drl8Gf7znssynvl4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region at [0.745, 0.915, 0.956, 0.940] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the \"Scientific Development\" line, which is associated with the color green."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "uBxlwKIkYEen9siY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.753, 0.422, 0.956, 0.447] in subplot_0 contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for subplot_0, specifically for the \"Cultural Development\" line which is associated with the color dark blue."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "JlCid88NkUHPNgUq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the coordinates [0.025, 0.167, 0.048, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the subplot_0."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "jI6z937CvMng8tVC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first icon in the legend of the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.549, 0.972, 0.595, 0.972]"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "PTB1Ib9L9RhOWNqc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label present at the coordinates [0.776, 0.966, 0.969, 0.980]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Remote Work."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "YCwFBSBGYAYNu7YS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at the 10th hour under Remote Work}[0.441, 0.685] higher or lower than {the tenth point under In-Office Work}[0.441, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "IGzIajyvMG1RPsAQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {activity level at the end of the day for Remote Work}[0.926, 0.544] compare to {the starting activity level for In-Office Work}[0.095, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "YXJuJVi89H1qwE5n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {label coordinates for \"5\" on the X-axis}[0.261, 0.395, 0.275, 0.409] of the subplot_0 above or below the {label coordinates for \"25\" on the X-axis}[0.946, 0.888, 0.975, 0.902] of subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "2dY7yLc4XfUokzno",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region [0.053, 0.244, 0.102, 0.409]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, we have the tick labels for Y-axis from '10' to '30' for subplot_0."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "nDCreonbtE8rmu37",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region represented by coordinates [0.054, 0.670, 0.200, 0.760]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section includes a part of y-axis label of subplot_1."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "jnk8HRfJ6zWR7wsd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region marked by coordinates [0.095, 0.390, 0.926, 0.390]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the complete line graph of the 'In-Office Work' trend from the beginning till the 24th hour."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "hb6eNPy02hG9nZD8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.466, 0.946, 0.604, 0.960]</r> of the second sub plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hour of Day."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "SEmr0mcaXRTUwmYW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at {coordinates}<r>[0.476, 0.481, 0.658, 0.495]</r> in the legend of subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard Living."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "85V8KNsxuAZmSiFd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the top y-axis label in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "90."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "XQhz6hLubIymzHW4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at hour 10}<r>[0.462, 0.681]</r> of Innovative Living above or below {the point at hour 10}<r>[0.462, 0.230]</r> of Standard Living?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "rTVdBGaZyuObmCFL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Standard Living'}<r>[0.095, 0.116, 0.975, 0.369]</r> have a higher or lower end value than the start for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "LESiqur8UWo558yy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point for hour 15 in Standard Living above or below the equivalent point for Innovative Living?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "ebUgpj1LluC1KpYK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.025, 0.676, 0.048, 0.776]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label for the second subplot, which is \"Level of Living\"."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "LHf2icC7zPjYvUyf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region <r>[0.466, 0.946, 0.604, 0.960]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specific area contains the x-label of the second subplot which is \"Hour of Day\"."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "AQxxfB2aUPVWRLSD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region <r>[0.394, 0.968, 0.676, 0.993]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend title for subplot_1 but the title is just an empty string, and the label {Innovative Living}<r>[0.468, 0.974, 0.666, 0.988]</r>. The color corresponding to {Innovative Living}<r>[0.404, 0.980, 0.450, 0.980]</r> in the legend is in a purplish (#800080) color."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "Uwt5XzhBpQF74UfI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the Rural Migration line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1e90ff."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "p1EQXF8x7rjkdwGt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label belongs to the y-axis tick at coordinates [0.058, 0.358, 0.114, 0.372]?"
+            },
+            {
+                "from": "gpt",
+                "value": "-0.3."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "NxWQuSA92YVRRax6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the x-axes tick}<r>[0.443, 0.395, 0.472, 0.409]</r> in the Rural Migration graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "Zv40W16VVg30PLeD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the last point on the Rural Migration line}<r>[0.960, 0.305]</r> higher or lower than {the last point on the Urban Migration line}<r>[0.960, 0.539]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "VeQx4pltcgBsE6DX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the value of {point 10 on Urban Migration line}<r>[0.457, 0.640]</r> to {point 10 on Rural Migration line}<r>[0.457, 0.356]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "By5FH2r6D2acaBki",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the subtitle for Rural Migration}<r>[0.393, 0.015, 0.689, 0.033]</r> have a higher veritical position than {the subtitle for Urban Migration}<r>[0.387, 0.508, 0.695, 0.526]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "LYEZDLQjbhbci3ev",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within <r>[0.025, 0.037, 0.114, 0.429]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the y-axis label 'Migration Trend'}<[0.025, 0.160, 0.048, 0.268]> and several {y-axis tick labels}<[0.058, 0.385, 0.114, 0.399]>."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "gBzoLgNAOOfhXO1f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the chart elements within the region <r>[0.114, 0.385, 0.175, 0.425]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Several {x-axis tick values} lie here, including '0' and '5' at {coordinates}<r>[0.114, 0.395, 0.129, 0.409]</r> and <r>[0.282, 0.395, 0.297, 0.409]</r> respectively."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "cNwss9aJzTDvqBdJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be described in the area <r>[0.122, 0.301, 0.167, 0.409]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the start of two migration lines {Rural Migration}<r>[0.122, 0.305, 0.167, 0.382]</r> and {Urban Migration}<r>[0.122, 0.539, 0.167, 0.617]</r> starting points, with the x-axes just below them."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "gpIzM4RG47Z1qLGC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart found within the coordinates [0.345, 0.508, 0.696, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Progression of Innovation\""
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "e9jre7YbayxN8wuO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at [0.688, 0.422, 0.949, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend with the label \"Self-confidence\""
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "P1jHEzPQ7N8fPyrH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the axis label at [0.025, 0.172, 0.048, 0.319] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Self-confidence Level"
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "LcOGe95KPVb10UTP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point corresponding to time 10.0}[0.468, 0.401] above or below {the point corresponding to time 20.0}[0.784, 0.329] on the Self-confidence line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "z0fVXbrRI7eq4vnZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the label for '2.0' on the y-axis}[0.058, 0.330, 0.072, 0.344] appear above or below {the label for '4.0' on the y-axis}[0.058, 0.210, 0.072, 0.224] for the Innovation chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "pBz9YVmGfpGOwa69",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {line trend labeled 'Self-confidence'}[0.080, 0.299, 0.925, 0.436] positioned higher than {the line trend labeled 'Innovation'}[0.080, 0.542, 0.925, 0.798] at the point labeled '0.0' (coordinate check)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below"
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "8t3t9tPUyvVeXQcc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the bottom left section of the chart area coordinated in [0.080, 0.037, 0.230, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the y-axis of the Self-confidence chart with labels ranging from '0' to '7', each represented individually, starting from {label '0'}[0.058, 0.449, 0.072, 0.463] to {label '7'}[0.058, 0.032, 0.072, 0.046]."
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "MfyhJxJ2yOjWKqF2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can we gather from the middle region [0.200, 0.400, 0.600, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses multiple trends shown by lines marking the growth of Self-confidence and Innovation. Points labeled '5.0' and '10.0' for Self-confidence are distinctly visible at {coordinates}[0.256, 0.433] and {coordinates}[0.432, 0.406], respectively."
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "F1iRvMUC0NDlsUwM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the components within the coordinates [0.446, 0.479, 0.595, 0.493]."
+            },
+            {
+                "from": "gpt",
+                "value": "This narrow region holds the x-axis label for the upper chart about the Time (Hours) scale, indicating progression in hours from start to end."
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "pGzOweRY59Kmcu3g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subplot title}<r>[0.212, 0.508, 0.858, 0.526]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Clean Technology Development Over 24 Hours"
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "QCzVLsAbtEZTodQo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the tick mark corresponding to '100' on the y-axis of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.110, 0.335]"
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "a1Apo3qOcKqpWWby",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'New Energy Development'}<r>[0.110, 0.317, 0.926, 0.365]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green - #008000"
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "e4OGdAjvXawlvPBF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'New Energy Development' data, is the level at {hour 10}<r>[0.450, 0.335]</r> higher or lower than the level at {hour 0}<r>[0.110, 0.335]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "oEHWCVSWDHWymBWG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 'Clean Technology Development', is the development level at {hour 24}<r>[0.926, 0.554]</r> higher or lower than the level at {hour 0}<r>[0.110, 0.708]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "x9xzQIP9jEbX8sRq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {hour 12}<r>[0.518, 0.333]</r> in 'New Energy Development', is the development level higher or lower than that at {hour 12}<r>[0.518, 0.637]</r> in 'Clean Technology Development'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "Cyr0n4YvhIdCnvtU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the bounding box [0.110, 0.530, 0.960, 0.947]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot of the line chart, titled \"Clean Technology Development Over 24 Hours\". It depicts changes in clean technology development over 24 hours using a dashed line colored blue. The x-axis represents time in hours, ranging from 0 to 25 hours, while the y-axis represents the development level, ranging from 0 to 350. Both axes are labeled and have tick marks indicating varying levels and times."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "x9FSwZtuZXQwICwX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within this bounding box [0.110, 0.037, 0.960, 0.454]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the first subplot of the line chart titled \"New Energy Development Over 24 Hours\". The plot displays the variation in new energy development over a period of 24 hours using a solid green line. The x-axis and y-axis denote time (Hours) and Development Level respectively. Both the axes are suitably labeled and contain appropriate tick marks signifying different levels of time and development."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "AdWJ1Svq1TXqugSM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents can be found inside the bounding box [0.058, 0.032, 0.102, 0.046]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there is a y-axis tick label indicating '350' for the second subplot."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "q3GUTijudvinhMQO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label associated with the y-axis at coordinates [0.025, 0.653, 0.048, 0.798]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Value ($)."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "8a6XrIyljlAOuBTN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the subplot titled \"Standard Investment Strategy\", what coordinates are associated with the line representing the Standard Strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.124, 0.326, 0.927, 0.429]."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "oCxSKPhw4StvXYFo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label located at [0.468, 0.946, 0.617, 0.960] in 'Innovative Investment Strategy' sub-plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Hours)."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "MxqP3MXDtmvuwihT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point 4}[0.258, 0.828] and {the point 5}[0.291, 0.796] for 'Innovative Strategy', is the investment value increasing or decreasing over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "ix1gUFXxYTEp66aN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point}[0.124, 0.922] of 'Innovative Strategy' line higher or lower than {the last point}[0.927, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "7hQz41Iub9qj5REB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {15th point of 'Innovative Strategy'}[0.626, 0.702] higher or lower than the {17th point of 'Innovative Strategy'}[0.693, 0.640]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "UULiFZJiWOXqDH1S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you elaborate on the contents within the region [0.124, 0.530, 0.960, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line graph for the \"Innovative Strategy\", the x and y axis, as well as their labels and tick labels. Also, the subtitle, 'Innovative Investment Strategy', is located in this region."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "G3DabjPtLo9ia208",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the coordinates [0.323, 0.508, 0.762, 0.526]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle for the 'Innovative Investment Strategy' subplot."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "zb8rlapDtFehE6Ba",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.394, 0.968, 0.698, 0.993]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subplot legend, specifically, the color handle and text for the 'Innovative Strategy'."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "PkGgAKCcQXHs0I53",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information represented by the line plotted within {coordinates}[0.124, 0.121, 0.927, 0.454] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line represents the Global Trade Volume over time."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "NqdopSWcfpxcPEYW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the line within {coordinates}[0.124, 0.613, 0.927, 0.947] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line illustrates the Supply Chain Efficiency over time."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "yjDGl5T54CXMvMo2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at the location {coordinates}[0.324, 0.015, 0.761, 0.033]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the first subplot: \"Global Trade Volume Over Time\"."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "4mHRv8Onmqg3WYwx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of Supply Chain Efficiency at {coordinates}[0.726, 0.613] higher or lower than its value at {coordinates}[0.659, 0.634]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "mXjIEhEZGrEWWW64",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the two points on the Global Trade Volume line at {coordinates}[0.425, 0.121] and {coordinates}[0.927, 0.121], which point reflects a higher trade volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at [0.425, 0.121]."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "aRJphUnaZSkHmobI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Supply Chain Efficiency at the first point {coordinates}[0.124, 0.947] higher or lower than its value at {coordinates}[0.927, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "zH1RToiy5556su2y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region from {coordinates}[0.854, 0.469, 0.999, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label \"Time (Months)\" for the first subplot."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "0OFNdVxMoPHm82g8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region from {coordinates}[0.594, 0.537, 0.949, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend for the second subplot. It describes the line color for \"Supply Chain Efficiency\"."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "UlGXSZv1165TUgDN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what is shown in the region {coordinates}[0.304, 0.508, 0.781, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle of the second subplot: \"Supply Chain Efficiency Over Time\"."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "eSjvGgToSInmz2lP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object in [0.025, 0.194, 0.048, 0.273] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the chart 'Standard Energy Use'."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "oGgXEgCRJbLaJZUa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the object within these coordinates [0.122, 0.037, 0.960, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart area for 'Standard Energy Use'."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "KlW7OzDBaVeyX65J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the line}[0.122, 0.549, 0.927, 0.791] in subplot_1 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Innovative Energy Use' data line."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "tKVAuqgu5btlB9V5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {point '2' at [0.189, 0.364]} and {point '9' at [0.424, 0.201]}, which point from the 'Standard Energy Use' line has a lower value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point '9'."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "tnZxE3mvv175umoL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\n{The point at [0.726, 0.634]} and {the point at [0.793, 0.608]}, which one on the 'Innovative Energy Use' line has the higher energy use?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at [0.726, 0.634]."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "z8yjW0ELdIaTX1nk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label 'Standard Energy Use'}[0.451, 0.481, 0.696, 0.495] in subplot_0 above or below {the label 'Innovative Energy Use'}[0.443, 0.974, 0.704, 0.988]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "tXv42l1mK4brT35j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the layout within the coordinates [0.369, 0.968, 0.713, 0.993]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area for the 'Innovative Energy Use' chart, containing a colored marker and the corresponding label for 'Innovative Energy Use'."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "W8G6H0VfetR6mpIH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region of [0.122, 0.549, 0.927, 0.791]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data line for 'Innovative Energy Use', with several data points representing various energy use values at different times."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "FJWJs6oTwQy2uK85",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area bound by [0.025, 0.687, 0.048, 0.765] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a Y-axis label, denoting 'Energy Use' for the 'Innovative Energy Use' chart."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "jmKNn1iWBFP3djjs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of label is situated at these coordinates [0.025, 0.678, 0.048, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "1BIqAAuh5Q5NQaDn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the line representing 'Market Share Traditional'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.095, 0.271, 0.946, 0.379]."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "7khC5BkDczt2lpD7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-ticks located at [0.633, 0.951, 0.692, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "IHmX6dmV9E7wD4YT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Market Share Traditional}[0.095, 0.271, 0.946, 0.379] or {Market Share Innovative}[0.095, 0.572, 0.946, 0.863], shows a more significant decline in the period represented in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share Traditional."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "bf6R4M5QQuAxXTfA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of Market Share, Traditional or Innovative, marks more than 30% at the end of the period as per the data represented by the line {Market Share Innovative}[0.095, 0.572, 0.946, 0.863]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share Innovative."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "zag9SGTQOmOkmoxH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Market Share Traditional}[0.095, 0.271, 0.946, 0.379] point at 2010 closer to 20% or 25%?"
+            },
+            {
+                "from": "gpt",
+                "value": "20%."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "6XPHrHMOr3Wilo7d",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within this area [0.568, 0.537, 0.934, 0.562]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the graph which includes the item {Market Share Innovative}[0.642, 0.542, 0.925, 0.556]. The legend box has its color #eaeaf2 and arranges items in one column. The color of the Market Share Innovative is #228b22."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "um4yKKUe6DJRp2eB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.566, 0.951, 0.975, 0.965]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label of the last x-axis tick which represents the year '2020'."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "4gqfVUfcT5t1zxPi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects exist in the region from [0.490, 0.971, 0.551, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label \"Years\"."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "NfJaG5vvr19q7GDW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line plotted} for 'Standard Sales'<r>[0.066, 0.294, 0.940, 0.385]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1e90ff."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "AmRi58xZBpKl399g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the upper subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales (units)."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "q2TW3yDOf3Y6xEai",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {x-axis tick label} at [0.785, 0.951, 0.803, 0.965] in the lower subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "dCwiqrjLKIssxrDB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for Standard Sales}[0.139, 0.374] above or below the {point for Standard Sales}[0.175, 0.367] in the time series?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "mUlgdBQgIM6Yop5e",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the 'Standard Sales' above or below 300 units at {the 12th time point}[0.503, 0.336]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "z4YGnZtd6Vlhq4QT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre 'Innovative Sales' increasing or decreasing between {the 20th and 21st time points}[0.794, 0.637, 0.831, 0.627]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "CQEVZ2n3xwtHWSos",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.065, 0.294, 0.940, 0.385] in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the line plot for 'Standard Sales'. The line represents sales over time, ranging from 200 to 330 units. It is colored in '#1e90ff', indicating the sales of standard vehicles."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "lRe7uht1jQshEUde",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.316, 0.508, 0.725, 0.526]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle \"Automotive Market Research - Innovative Vehicle\". The text is presented in black ('.15' color), and is not rotated."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "p3IROfJYUJyVwqEy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.785, 0.951, 0.976, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is 1 object. The x-axis tick label '20' is located at {coordinates}[0.785, 0.951, 0.803, 0.965]."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "n6SJRQoK10qKA2lF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.043, 0.766, 0.054, 0.789] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a y-axis tick label in subplot_0."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "ptmw6u65ASufUo05",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text belongs to the x-axis label located at [0.473, 0.952, 0.510, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Year\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "vr3O8E9gGRg1AzRz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at the position [0.756, 0.0.919, 0.816, 0.942] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis tick label representing the year \"2017.5\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "bv8a3zQfXScbowC0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the points for the years 2000, is the point for 'Rural Development' higher or lower than that of 'Urban Development'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Urban Development' has a higher value. The coordinates of the 'Rural Development' point are [0.100, 0.871] and the coordinates for the 'Urban Development' point are [0.100, 0.815]."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "xldDiLRNfYwYxQ9M",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2002, did 'Rural Development' increase or decrease compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "It increased. The coordinates of the 'Rural Development' points for 2001 and 2002 are [0.139, 0.852] and [0.178, 0.813] respectively."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "x4Wi5y2oqgjS80d5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing the 'Urban Development' values, which is greater between the years 2008 and 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2008 is greater. It's coordinate is [0.413, 0.537], while the coordinates of 2009 is [0.453, 0.565]."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "M4MSbTwoO0qcG2xy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region with coordinates [0.756, 0.919, 0.816, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains an x-axis tick label that represents the year \"2017.5\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "caUMEMVkAdWbaCdU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the significant features found within the region of [0.964, 0.351, 0.981, 0.585]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label for the subplot_1 section of the chart, denoted as 'Urban Development'."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "iXf7p3RXRe2x9MgY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area defined by [0.492, 0.919, 0.522, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A label that indicates the year \"2010.0\" on the x-axis is located in this area."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "vrgMJAQwIdchuYdY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.157, 0.086, 0.233, 0.110] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue legend label."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "H0fAQQ3bA7mMtDiD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the object}<r>[0.283, 0.862, 0.385, 0.862]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cost marker."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "Y6RLf4BkFI8CAfXw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label}<r>[0.087, 0.714, 0.087, 0.725]</r> indicating?"
+            },
+            {
+                "from": "gpt",
+                "value": "5000."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "3VXcxlmLexebSII6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.870, 0.911] for the Revenue line higher or lower than {the point}[0.870, 0.911] for the Cost line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "kJAgHWrIn45WLCuw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {point 20}[0.615, 0.287], Revenue or Cost?"
+            },
+            {
+                "from": "gpt",
+                "value": "Revenue."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "CgODCzYUmQZ0yBks",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the revenue point at 15}[0.487, 0.330] higher than {the cost point at 15}[0.487, 0.875] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "yDjeQDYvzCUOBVQR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.100, 0.911, 0.870, 0.939]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis with labels ranging from -5 to 35 at intervals of 5, styled in dark text."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "tW217Azs5wOqPnYu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.093, 0.066, 0.907, 0.266]."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart includes the lower values of the revenue line starting from approximately 4000 and includes points 1 to 10, displayed as blue circles."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "lmLpKAHnZgVUeFSl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.040, 0.790, 0.200, 0.925] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis labels for Revenue from 4500 to 8000, positioned vertically."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "V7emN5d1UCmtKGBA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate [0.229, 0.647] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A point on the Cloud Computing line."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "RX1pxPi6o2Z7Os43",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.071, 0.957] represent in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '0.9'."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "ezHo2IDydtPWJazi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat feature is at [0.774, 0.875] in the data visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is empty space."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "qFlMUfiIYyNQIPNe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point}[0.115,0.873] higher or lower than {the point}[0.144,0.801] on the Artificial Intelligence line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "0AsJthYGWufe7qlT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at [0.457,0.209]} belong to the Artificial Intelligence trend or the Cloud Computing trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "O3p6Zex99tJLM7jm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.799, 0.698]} situated above or below {the point at [0.856, 0.739]} in the trend line for Cloud Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "1PBfQRsZMYwnhduB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bounded by [0.000, 0.850, 0.500, 0.950]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's x-axis, labeled as 'Weeks' in a light gray color, and the right section is primarily empty except for grid lines."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "sAq1irP768sIzbGv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what's within the region [0.350, 0.000, 0.650, 1.000]."
+            },
+            {
+                "from": "gpt",
+                "value": "The central area of the chart contains many intersecting lines and points from the data trends Artificial Intelligence and Cloud Computing, marked by circles and x-markers respectively, with varying y-values."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "RdfNlXnKKzLhYINT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the visual elements found between the coordinates [0.010, 0.300, 0.980, 0.320]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This narrow horizontal band mainly features lines from both Artificial Intelligence and Cloud Computing trends, crossing this region at varied elevations along the chart."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "BeL7D7ESZKqClPam",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line for 'Artificial Intelligence'}[0.110, 0.226, 0.890, 0.810] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a line plot showing the trend of Artificial Intelligence."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "HMG1cq5RhOVX8AiX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third point in the line for 'Virtual Reality'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.283, 0.632]"
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "8giB66wKdNKSXz60",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title of the chart}[0.200, 0.025, 0.800, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emerging Technologies and Trends Outlook."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "uShx1oANm0DDRUpf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy the end of 2020, was Artificial Intelligence more or less popular than Virtual Reality based on {their points at 2020}[0.890, 0.226], [0.890, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More Popular."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "ecsOErYE3DXMyR21",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas {the popularity of Artificial Intelligence}[0.110, 0.226, 0.890, 0.810] higher or lower in 2015 than {the popularity of Virtual Reality}[0.110, 0.615, 0.890, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "sxDHjAboE87Jg6fo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the second point in Artificial Intelligence line}[0.197, 0.784] with {the first point in Virtual Reality line}[0.110, 0.717], which was higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "q7GGqS3npgLUNx14",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what all elements are in the region [0.080, 0.077, 0.321, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for Artificial Intelligence which is represented by a blue color and a circle marker."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "KlyJIHVu6kbFF5r1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive a brief detail about the items present in the region [0.735, 0.077, 0.920, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the legend for Virtual Reality which is denoted by a red color and a cross marker."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "XHBbYJBRzHXGHm6N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's present in the region [0.370, 0.604, 0.457, 0.615] and [0.543, 0.497, 0.630, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains 2 line points for Artificial Intelligence for the years 2014 and 2016 respectively."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "6yxeR6O7nIwcML3O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart at the coordinates [0.964, 0.330, 0.981, 0.646]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the Y-axis label for the second subplot, named 'Data Science Development'."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "Zp2jhe3hWnZOaS6I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the 4th point on the Cloud Computing line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.494, 0.386]"
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "YpfjazIXY78JmouV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.346, 0.919, 0.390, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2017."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "dagDGJ2s8cbXZNzL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 3rd point for Data Science [0.368, 0.392] with the 3rd point for Cloud Computing [0.368, 0.590], which point is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Cloud Computing point."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "FJEQekljQcohIUm3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first Data Science development point [0.115, 0.873] higher or lower than the first Cloud Computing development point [0.115, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "Nj5ZwAC8bB7YoHcy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the 5th points for both the Cloud Computing line [0.621, 0.347] and the Data Science line [0.621, 0.200], which line has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cloud Computing."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "vmTFoWj3WLMeTrf1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.726, 0.077, 0.903, 0.119] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the plot \"Data Science\" with the handle colored in red and associated text. The legend item is specifically located at {coordinates}[0.782, 0.086, 0.896, 0.110]."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "XPrEWlcoTBYOUkoD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.043, 0.959, 0.956, 0.983]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the label '1.00' on the y-axis for the 'Data Science Development' line, specifically located at {coordinates}[0.918, 0.959, 0.956, 0.983]."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "oNRZHywiQyeeKlwR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.077, 0.066, 0.912, 0.911] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot area for the chart. It envelops both the 'Cloud Computing' and 'Data Science' development trend lines along with their corresponding points, the x and y axes, and gridlines."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "ZMvTGGYCHUfHMPyn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what the object {the line for Classification 'Recurrent Neural Network'}<r>[0.720, 0.100]</r> represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "Precision for Recurrent Neural Network."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "3qRiEpj78B4CMyAn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the set of coordinates <r>[0.964, 0.433, 0.981, 0.503]</r> correspond to in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label for Recall."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "1WnhV6BSA3EfkYsa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.503, 1.000, 0.503, 1.000]</r> for the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's blank, the xlabel doesn't contain any text."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "8mH1y3MKnWT5H23A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Precision for 'Recurrent Neural Network'}<r>[0.720, 0.100]</r> higher or lower than {the recall for the same 'Recurrent Neural Network'}<r>[0.720, 0.830]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "EuUsB60mXCIxEb7P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the point representing Precision for 'Support Vector Machine'}<r>[0.865, 0.871]</r>, where does {the point representing the Recall for 'Support Vector Machine'}<r>[0.865, 0.871]</r> lie?"
+            },
+            {
+                "from": "gpt",
+                "value": "At the same place."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "bnl4XoT6xeJ1iK30",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the Precision values for {the 'Decision Tree'}<r>[0.431, 0.556]</r> greater or less than its Recall values {in subplot_1}<r>[0.431, 0.186]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "PB77C8zNQSVYDmtz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the area with coordinates <r>[0.020, 0.919, 0.969, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis labels: {Robotic Process Automation}[0.020, 0.919, 0.262, 0.942], {Neural Network}[0.218, 0.919, 0.354, 0.942], {Decision Tree}[0.373, 0.919, 0.488, 0.942], {Convolutional Neural Network}[0.445, 0.919, 0.706, 0.942], {Recurrent Neural Network}[0.608, 0.919, 0.833, 0.942] and  {Support Vector Machine}[0.761, 0.919, 0.969, 0.942]."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "w1MmhvjinqE0p1XQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region marked by the coordinates <r>[0.907, -0.024, 0.957, -0.001]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label '0.900' for the factor Recall."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "P4oaGrFlHh4YvKPV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is available in the region defined by <r>[0.105, 0.025, 0.901, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the chart or plot area with all the data points, gridlines and labels for both Precision and Recall measurements."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "DHfQ7A0N1BetnjJE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label} at [0.396, 0.296] on the 'Enrollment Rate' line represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 5th quarter."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "dnEOkWoCvDxQDghE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the annotation for {the point} at [0.258, 0.450] on the \"Enrollment Rate\" graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "9ilYilNRN1k1YW3z",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label with coordinates [0.019, 0.395, 0.036, 0.581] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Enrollment Rate."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "qUykI8gSeJPTYlDu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point on the Dropout Rate line} at [0.465, 0.826] occur earlier or later in the same year as the {point on the Enrollment Rate line} at [0.465, 0.281]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same time."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "xd0TsCIPksycdiGp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {3rd point on the Dropout Rate line}[0.258, 0.865] lower or higher than the {third point on the Enrollment Rate line}[0.258, 0.450]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "ta1o59OACo63m9KT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Dropout Rate} at point [0.880, 0.788] surpass the {Enrollment Rate} at point [0.880, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "1jNdOYHRa5YwoRCs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region with coordinates [0.082, 0.066, 0.918, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the axes for the plot, along with the line plot for the enrollment rate, sequentially dated from the first quarter to the twelfth quarter."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "rJErZKAQ2yKIL5iC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the coordinate [0.120, 0.788, 0.880, 0.873]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Dropout Rate line chart, marked by points from the first quarter to the twelfth quarter."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "txyLMSG5nRfuXWvU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.731, 0.077, 0.909, 0.119] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend labeling 'Dropout Rate', represented by a red dashed line with 'x' markers."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "N3uvXD38wqklFQs8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the y-axis label for 'Twitter'} located in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.971, 0.462, 0.985, 0.520]"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "ItVd4vZW380mVOTv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does {the x-axis tick label}[0.798, 0.939, 0.816, 0.957] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "25"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "8Y9XRdIsunvL7A2q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {the label '15' on the x-axis for Facebook}[0.506, 0.939, 0.523, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.515, 0.933]"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "8YrLzogmN3r2aUEe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at week 10 for Twitter}[0.368, 0.288] have a higher value than {the point at week 10 for Facebook}[0.368, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "BpgRZNgVehDDU36C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first y-axis tick label for Twitter}[0.939, 0.926, 0.957, 0.944] positioned above {the first y-axis tick label for Facebook}[0.043, 0.926, 0.061, 0.944]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "HKTCltAoSO63CCIa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich dataset, 'Facebook' or 'Twitter', starts higher at {week 1}[0.105, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "pNWtpg6ujnfH3LP6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.035, 0.816, 0.061, 0.833] contain in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the y-axis tick label '100' for Facebook, colored in blue, located at {coordinate}[0.035, 0.816, 0.061, 0.833]."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "Y0XyjhOBM22DC5Pp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within the bounding box [0.836, 0.070, 0.945, 0.140]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains several points from Twitter's data plot: Point 26 at {coordinates}[0.836, 0.076], Point 27 at {coordinates}[0.865, 0.071], and Point 28 at {coordinates}[0.895, 0.067]."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "5j5vrgXxyOaVsgJ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the rectangular region defined by [0.470, 0.949, 0.962, 0.984]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It spans several x-axis tick labels for both Facebook and Twitter. These labels are '15', '20', '25', '30' located at their respective {coordinates}[0.506, 0.939, 0.523, 0.957], [0.652, 0.939, 0.670, 0.957], [0.798, 0.939, 0.816, 0.957], and [0.944, 0.939, 0.962, 0.957]."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "mHlP0OVXiGiXDf8C",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker does {the data point at week 17}<r>[0.576, 0.363, 0.576, 0.363]</r> use in the Social Media Engagement line?"
+            },
+            {
+                "from": "gpt",
+                "value": "x."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "L8ou64SFMlAlZhhU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the y-axis label}<r>[0.019, 0.359, 0.036, 0.564]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "tab:blue."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "6XjlI7nUrw7UjvOI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat style is {the line for website traffic}<r>[0.141, 0.069, 0.875, 0.854]</r> drawn with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solid."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "Yplpf7mfB2P2wc9k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at week 5}<r>[0.250, 0.407]</r> higher than {the point at week 25}<r>[0.794, 0.575]</r> on the 'Website Traffic' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "m33zj6jfaUjSHcMr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the point at week 12}<r>[0.440, 0.154]</r> and {the point at week 16}<r>[0.549, 0.491]</r>, which point is lower in terms of 'Social Media Engagement'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Week 16."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "Sd6kj6w3ALR0klC6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has a steeper increase between week 6 to week 7, Website Traffic or Social Media Engagement, when comparing {point 6}<r>[0.277, 0.491]</r> to {point 7}<r>[0.304, 0.069]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Website Traffic."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "n9FYoGsLzVlcYd7I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the cluster <r>[0.043, 0.143, 0.098, 0.508]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses multiple y-axis labels including '11000', '12000', '13000', '14000', '15000', and '16000' for the Website Traffic data, all displayed in blue."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "MaSlFf5KUncVt9OZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region <r>[0.108, 0.903, 0.941, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis has labels ranging from '0' to '30' marking different weeks, positioned across this horizontal span."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "orHpyTcn2SiQuRqq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details on the area marked from <r>[0.918, 0.058, 0.957, 0.969]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a range of y-axis labels for Social Media Engagement values from '12.5' to '17.0', presented in red."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "ZJClpaz7ZO6mMaBA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by the plot line with coordinates [0.113, 0.244, 0.887, 0.870] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Export Values."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "Q3xUBE1l876kAZFH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year corresponds to the x-axis tick label at [0.358, 0.333]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2006."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "oRr6WPxwRozVNoN3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label at coordinates [0.015, 0.408, 0.029, 0.545] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Import Values."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "4eOhfWuMqHZBkYQt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the the Import Values at year {2005}[0.317, 0.298] greater than Export Values at the same year {2005}[0.317, 0.346]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "Ck8obrvzwj8Io0As",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {2019}[0.887, 0.870], is the Export Value more than the Import Value at {2019}[0.887, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "NitI127bzbRTLJ1C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a trend of Import Values decreasing over time from the start {2000}[0.113, 0.208] to the end {2019}[0.887, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "nu1FzB2G42BJVzq3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content found between coordinates [0.075, 0.029, 0.925, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of a line chart detailing Import and Export Values over time. It includes x-axis and y-axis labels, tick labels for specific years and values, and two lines each representing import and export values over the years from 2000 to 2019."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "24qmnsLyZNRUn7ML",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region marked by [0.823, 0.039, 0.918, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend for the line representing Export with color indication."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "R8biAgiTV0zFmGFQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region with coordinates [0.035, 0.916, 0.070, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-tick label with value '600' is located in this area."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "wrdx51MmES3rPppA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by {the glyphs located at}<r>[0.065, 0.090, 0.101, 0.104]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Internet of Things data series marker."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "CRyCRdvHwvNz8Dex",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the object at}<r>[0.015, 0.471, 0.029, 0.506]</r> represent on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "ZlTyBpOAVR5aagnK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details {the area}<r>[0.073, 0.919, 0.121, 0.942]</r> provides about the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label for the year 2020."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "ECHAEsRRHOUT7Gnv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at}<r>[0.255, 0.630]</r> higher or lower than {the point at}<r>[0.412, 0.375]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "IeN9tFCJdfrr64To",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for the year 2025}<r>[0.295, 0.589]</r> have a greater value in IoT or AR?"
+            },
+            {
+                "from": "gpt",
+                "value": "IoT."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "4IsA1LNTpeqeLBwb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is earlier, {the x-coordinate}<r>[0.354, 0.911]</r> or {the x-coordinate}<r>[0.493, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-coordinate at <r>[0.354, 0.911]</r> is earlier."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "7Ng1hvGTIYAjkEfx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's contained in the area spanning from the left side of the graph to the mid-point."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes half of the x-axis with labels from the year 2017.5 to 2025, corresponding tick marks and portions of IoT and AR data lines, with IoT data trending higher in this section."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "H3TGMPjEWExDoMTi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region <r>[0.057, 0.066, 0.493, 0.911]</r> of the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis labels for IoT, multiple data points for IoT and AR from 2020 up to approximately 2025, including their associated markers."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "oOgcIe1Dfxcf8Eaf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the lower-right quadrant of the graph."
+            },
+            {
+                "from": "gpt",
+                "value": "This quadrant contains the years from around 2027.5 to 2040 on the x-axis, including their associated x-tick labels and portions of IoT and AR data lines showing an upward trend for both IoT and AR but with IoT having generally higher values."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "UbdiU9hGGXrzucdl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location represented by the coordinates [0.964, 0.378, 0.981, 0.598] in the given plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Second Y-axis label."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "zC5iiMR3EJKl30Bs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the tick label at the coordinates [0.270, 0.919, 0.330, 0.942] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year 2005."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "3AfsyEpn5kmQmahS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {coordinates}[0.759, 0.086, 0.924, 0.110] in the subplot 1's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label for \"Advanced Robotics\""
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "5nTTyEh0yWqDoNCw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point marked for the year 2000}[0.100, 0.860] on the 'AI Development' line with color blue higher or lower than the {point for the same year on 'Advanced Robotics'}[0.100, 0.843] line with red color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "o0pEsKEgqAqBt0oh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the last points on the lines for both \"AI Development\" with the coordinates [0.900, 0.150] and \"Advanced Robotics\" at [0.900, 0.133], which has a higher value in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Development."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "i824SfMtkkr5pw5X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the point for \"Advanced Robotics\" at [0.540, 0.505] and \"AI Development\" at [0.540, 0.522], which has a higher value in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Development."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "DK1XCsUgSo7nuIOo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details of the region defined by [0.260, 0.691, 0.540, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can see part of the lines for \"AI Development\" and \"Advanced Robotics\" from 2004 to 2011. The line for \"AI Development\" is blue with circles as markers, while the line for \"Advanced Robotics\" is drawn in red with x-shaped markers."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "2rdWA7OSSfRjOaLl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area at [0.703, 0.077, 0.931, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the legend for subplot 1, with the label \"Advanced Robotics\", colored red, at the coordinates [0.759, 0.086, 0.924, 0.110]."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "APqJ0oPVKvII9H09",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is enclosed within the region [0.060, 0.066, 0.500, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the left portion of the plot including the first half of both \"AI Development\" and \"Advanced Robotics\" lines from the year 2000 to 2010, along with relevant Y-axis labels and ticks."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "5nrSZDNM49ZN61Rw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is described by the label at this specific position [0.019, 0.270, 0.036, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook Active User Count (Millions)."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "aYKnc7rxuxhXeWnS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the marker at}<r>[0.801, 0.090, 0.846, 0.104]</r> in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "dIrWTsRYYfmtMbNW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the content at [0.543, 0.694, 0.543, 0.694] in the data plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 16 on the Facebook line."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "J31bwzMg1Wv932oi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the user count for {Facebook} at point 7 at [0.283, 0.766] higher or lower than point 10 at [0.370, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "dW5CLUv4TvOAXQVc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the gradient, is {point 15}<r>[0.514, 0.742]</r> for Facebook trending upwards or downwards from {point 14}<r>[0.486, 0.766]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upwards."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "MN6jLpz0ixHz32ZC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point has a higher value on the Twitter activity graph, {point 20}<r>[0.659, 0.331]</r> or {point 25}<r>[0.803, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 20."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "w0ZpwU9RYjMzSbf2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visualization within the area [0.071, 0.066, 0.929, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises of both Facebook and Twitter active users trend lines with points marked for various weeks. The background is shaded in a light gray color, aiding in emphasizing the plotted data."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "wcCdkTrxcHUPK2dJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.082, 0.090, 0.779, 0.525] contain in terms of data visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the upwards trending Facebook user data from week 1 to week 24 along with corresponding line and markers for certain values."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "051WqYXavEXXi17N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content and features located at [0.643, 0.428, 0.987, 0.559]."
+            },
+            {
+                "from": "gpt",
+                "value": "This specific part of the chart shows a decrease and then stabilization in the curve for Twitter User Count as the weeks progress from around week 23 to 24, marked by specific point markers."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "ylcJTK0hKHDA9bDn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label} at [0.019, 0.376, 0.036, 0.600] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Donations received."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "FOnQEQLmFFBrsSkX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the line representing the donation trend} located at [0.140, 0.143, 0.860, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Donations."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "iYRyrHybKGd7gsYo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the {text} at [0.248, 0.025, 0.752, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Social Welfare and Charity Activities\"."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "ORaSnxEhQMELVuw6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {donation value for the second month} at [0.206, 0.173] higher than the {donation value for the third month} at [0.271, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "CuqRuYCW3ZUgIdw0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what position is the {volunteer hours at the last} month at [0.860, 0.909] regarding the y-axis label for volunteer hours at [0.964, 0.397, 0.981, 0.580]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "RdDTpwhvT9X0hIek",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the data point for the third month's donation}[0.271, 0.189] fall right of the {data point for the second month's donation}[0.206, 0.173] on the x-axis coordinate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "J1H3E1vPh9yKFod4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located within [0.113, 0.077, 0.263, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a legend indicating that the blue line styled with circles represents \"Donations\"."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "xVsNlqzTqpG4HvXH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the region [0.688, 0.077, 0.887, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend, demonstrating that the red line styled with 'x' symbols represents \"Volunteer hours\"."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "mXniRpzDquA6uTNv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the region [0.140, 0.143, 0.860, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blue line representing donations over the months, with twelve points on the line marking the donation amount for each month."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "GjkPX5xun1y9euqy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.971, 0.448, 0.985, 0.534]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "XKWYbU9NlgZpIyTt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.097, 0.173, 0.903, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Death Toll'."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "TJw7DvH7lWslmIxy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object marked by these coordinates [0.108, 0.065, 0.208, 0.082] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend label, specifically 'Disease Cases'."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "PYL1ztIj29LHXjMP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data label, 'Disease Cases' or 'Death Toll', has a higher value at {week 10}[0.479, 0.639] and [0.479, 0.703]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Death Toll."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "cvhoHfM792IlllE3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {y value for 'Disease Cases'}[0.732, 0.456] at week 16 less than the {y value for 'Death Toll'}[0.733, 0.462] at the same week?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "KVPIKjNcrdVtb0MX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is higher at the 7th week, {Disease Cases} [0.352, 0.727] or {Death Toll} [0.352, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Death Toll."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "V1nJUmrk9OAgULx5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.064, 0.058, 0.214, 0.089]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the legend for 'Disease Cases', along with a colored marker."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "sB1aDMzJfWWgAh88",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details for the region specified by coordinates [0.817, 0.058, 0.936, 0.089]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for 'Death Toll', and a corresponding colored marker."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "G11AEcVSlCUk7DTz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is contained within the region specified as [0.256, 0.939, 0.278, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the x-axis label '5.0' is located."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "09jL38DVPFl0TSMy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present at these coordinates [0.964, 0.364, 0.981, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label of the second subplot, which is \"Raw Materials Supply\"."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "BGiJI6njBvqmb1Tf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point} at [0.668, 0.235] in the subplot_1 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Raw Materials Supply value at the 9th month."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "j0ov5P2RIYgIE3Ao",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.043, 0.395, 0.087, 0.418] in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "1500."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "O6ezdqMJzUDXPvfu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Vehicle Sales value at 10th month}[0.735, 0.627] less than or greater than {the Raw Materials Supply value at 10th month}[0.735, 0.252] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "YnjYGMjDz0xnM2W2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Vehicle Sales}[0.130, 0.573, 0.870, 0.776] increase or decrease from 1st month to 12th month in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "qp4uqbvKUsjoTu2Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {Vehicle Sales values} of subplot_0 and {Raw Materials Supply values} of subplot_1 plotted against the same x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "4loqDRNadrzCmdXQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.093, 0.066, 0.907, 0.911] in both subplots."
+            },
+            {
+                "from": "gpt",
+                "value": "This region indicates the plot area for both subplots. It is where the lines for Vehicle Sales and Raw Materials Supply, along with the grid, x-axis and y-axis labels are plotted."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "DxyH63J1DH0uwk3k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's inside the region specified by these coordinates [0.649, 0.077, 0.898, 0.119] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the subplot_1 is located here. It includes an item named {Raw Materials Supply}<r>[0.704, 0.086, 0.891, 0.110]</r> with a corresponding handle marked with an 'x'."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "lKEORD5KMox9ZyW3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.043, 0.395, 0.087, 0.418] and [0.913, 0.395, 0.957, 0.418]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These regions contain y-axis tick labels '1500' for subplot_0 and subplot_1, respectively."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "bZjN1g2wgmflmQSE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text represents {the highest point on the booking trendline}<r>[0.570, 0.404]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "440."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "dgS0bagBIRwLnIIc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label in the region}<r>[0.019, 0.379, 0.036, 0.597]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Tourists."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "Rwa7RLdBq9UyFtor",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify {the xlabel near}<r>[0.472, 0.952, 0.528, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Weeks."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "SOqvrVTuyYdJpuwS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the week 10 booking point}<r>[0.373, 0.531]</r> above or below {the week 15 booking point}<r>[0.514, 0.467]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "wr2Bq3MMipg22sbG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {week 20}<r>[0.655, 0.277]</r>, the tourists or bookings?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tourists."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "ls52yi8JPuqJzJjW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the bookings in week 1}<r>[0.120, 0.805]</r> more or less than {the bookings in week 12}<r>[0.430, 0.594]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "XUsKmIDiknzYvzhm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is noticeable about the area <r>[0.082, 0.066, 0.918, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two primary axes, with fluctuating line trends representing tourists and bookings data, plotted from week 1 to week 28."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "VD6aGHvlorPe9FeI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region <r>[0.043, 0.056, 0.957, 0.925]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds all the y-ticks ranging from 200 to 600 marked in blue and red for number of tourists and bookings respectively, alongside their corresponding labels and markers."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "p5TQR9wRop8x1wXG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what occupies the rectangle <r>[0.120, 0.171, 0.880, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encompasses the trendlines of bookings and tourists with various data points marked from week 1 through week 28, displaying an overall pattern."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "q9Bx1iZtL8EPkuVa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with the line representing \"TV Ratings\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4<r>[0.085, 0.090, 0.130, 0.104]</r>."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "pWdVoeAABGGW24X1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marking style used for the \"Music Downloads\" data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "x<r>[0.706, 0.090, 0.751, 0.104]</r>."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "OXXdvLyfnlABZarw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label corresponds to the x-coordinate [0.801, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "25<r>[0.790, 0.919, 0.812, 0.942]</r>."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "f88nC29wC4IHAIUp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the point {labeled '20'}[0.657, 0.308] for \"TV Ratings\" lie above or below the point {labeled '20'}[0.657, 0.404] for \"Music Downloads\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "Lpy7Xpo2a5ruXxm2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line's point labeled '1' has a lower y-coordinate, \"TV Ratings\" or \"Music Downloads\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Music Downloads<r>[0.114, 0.426]</r>."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "LXALQXAz2PjEtYDm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {coordinate}[0.343, 0.562] from the same dataset as the point at {coordinate}[0.114, 0.426]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "HcfXXkubas8eM6pU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.075, 0.066, 0.925, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two line charts representing \"TV Ratings\" and \"Music Downloads\", each with a respective subplot, axes labeled for \"Weeks\", and y-axes labeled for \"TV Ratings\" and \"Music Downloads\" with respective color coordination."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "4VDCYBzu49xv1uPf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found between [0.075, 0.935, 0.955, 1.000]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section of the chart includes x-axis labels for weeks from '\u22125' to '30', using a light gray color for text and line marks located below the main plotting area."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "Gd2nmlAR4mIfXuLn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.075, 0.077, 0.916, 0.119]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains legends for \"TV Ratings\" and \"Music Downloads\", respectively using a light blue line and a red dotted line with an 'x' marker style."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "V0MHtM9HK4COviDc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object do the coordinates [0.964, 0.407, 0.981, 0.569] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of subplot 1."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "A9L8jwWW8TVsZQer",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what specific data do the label coordinates [0.946, 0.902, 0.957, 0.925] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '400' of subplot 1."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "Xk84YME7WOXZH8R7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the object {Population Migration line point 2007}[0.386, 0.206] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the data for the year 2007 in the Population Migration trend, with the value of 650."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "bpEUkZbKDFsj1g9a",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {the year 2010 point}[0.500, 0.629], {Population migration}[0.500, 0.404] or {Mobility Trend}[0.500, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobility Trend."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "vmIjQxpVRYF7zmVu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point of {Population Migration}[0.348, 0.347] in 2006 higher or below the data point of {Mobility Trend}[0.348, 0.460] in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "5Wbw5j6RMOQuRJoe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the trendlines {Population Migration}[0.120, 0.108, 0.880, 0.911] and {Mobility Trend}[0.120, 0.235, 0.880, 0.629], which one has a higher value at {Year 2020}[0.880]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population Migration."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "L530XBqhMCtewhx3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in this area of the chart [0.500, 0.235, 0.880, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart depicts the part of the Mobility Trend line from the year 2010 to 2020. It indicates the changes in Mobility Trend values over this period."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "SdoTEVhMlEcKU2Dg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the area bounded by the coordinates [0.725, 0.077, 0.909, 0.119]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area displaying the legend of subplot 1. It contains the name {Mobility Trend}[0.781, 0.086, 0.902, 0.110] with its associated marker represented with a red color."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "Qq3xj6CjZAk6v2dh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are notable in the region [0.082, 0.066, 0.918, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of subplot 1 of the chart. It contains two line plots: {Population Migration}[0.120, 0.108, 0.880, 0.911] represented with blue color and solid line and {Mobility Trend}[0.120, 0.235, 0.880, 0.629] depicted with red color and dashed line. Both these plots represent data from the year 2000 to 2020. The region also includes the x-axis and y- axis labels and ticks for the corresponding subplots."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "2epPwoEEks69f3WV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.105, 0.540] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for the year 2000 of the Crop Production line."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "ezuAneXrClwwVci8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.263, 0.939, 0.327, 0.957]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2007.5."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "NQT9Fnvae9cnJKxD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the subplot located at [0.285, 0.019, 0.715, 0.044]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Food and Agriculture Industry Analysis."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "89kQJGBC99QbChkS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first point marked [0.105, 0.540] from the Crop Production line higher or lower than the point [0.303, 0.481] from the Livestock Production line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "NW9GRhLPRgnhq2BY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the line for Livestock Production at the point [0.855, 0.422], is the point from the Crop Production line at [0.855, 0.363] above or below?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "ZuZLXYQq3SrEKiQn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line corresponds to the lower point at {the coordinates}[0.697, 0.540], the Crop Production or Livestock Production?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "J18zEoZc3fO2lJfi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.277, 0.911, 0.631, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the x-axis labeled as \"Year\" in light grey color, all x-axis tick labels and their corresponding tick marks for the years 1997.5 to 2022.5 starting from {the coordinate}[0.081, 0.939, 0.129, 0.957] through {the coordinate}[0.969, 0.939, 1.017, 0.957]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "NiWsN8esuXbxaAIV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.000, 0.400, 0.050, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label for Crop Production in tab blue color and the y-axis tick labels ranging from '95' to '115' starting at {the coordinate}[0.043, 0.926, 0.061, 0.944] ending at {the coordinate}[0.035, 0.337, 0.061, 0.354]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "XSBqgsF639BD5Z1I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region that spans [0.030, 0.030, 0.970, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This wide area includes the chart's entire plotting area for both Crop Production and Livestock Production data lines, encompassing all axis labels, tick marks, and data points from the initial year 2000 point at {the coordinate}[0.105, 0.835] to the closing year 2020 point at {the coordinate}[0.895, 0.344]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "ezsqgUbhQ0RECR4q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {y-axis label}<r>[0.025, 0.354, 0.048, 0.550]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Share Price."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "0EFc7syJWRcJ7dSj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by {this point}<r>[0.416, 0.176]</r> on Share Price2?"
+            },
+            {
+                "from": "gpt",
+                "value": "245."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "55OJ7Jb8AnNk6hcG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat month does {this x-axis tick label}<r>[0.392, 0.879, 0.440, 0.914]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "May."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "Z5q7wwv2cMvWgEgD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the month of Septemeber, is {Share Price1}<r>[0.723, 0.673]</r> higher or lower than {Share Price2}<r>[0.723, 0.120]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "nrR4o5TKczJoQfPF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Share Price1}<r>[0.340, 0.469]</r> show an increase or decrease compared to its previous month's value {Share Price1}<r>[0.263, 0.452]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "OANekeP7BuLFG1QH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the start of the year, was {Share Price1}<r>[0.110, 0.369]</r> higher or lower than {Share Price2}<r>[0.110, 0.314]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "jreP3qJ8xG8I1dgo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area bounded by <r>[0.121, 0.055, 0.350, 0.170]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the graph. It has two entries: {Share Price1}[0.196, 0.069, 0.341, 0.104] and {Share Price2}[0.196, 0.121, 0.341, 0.156], each paired with a distinct color marker."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "Wx5IasXQlT1wBu45",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.489, 0.927, 0.573, 0.963]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label for the x-axis, which is \"Months\"."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "D8P4EmPlUnGOT1K2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.110, 0.093, 0.953, 0.314]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data line representing \"Share Price2\" for each month from January to December."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "sGyKdMsSdnpdIsyi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object of the plot with coordinates [0.066, 0.030, 0.985, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main subplot for the line chart."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "4HSGEk6UxmmLjYL3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the chart is located at the coordinates [0.043, 0.898, 0.061, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '80' is displayed at these coordinates."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "r71WK4blRUi2ZX7q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the coordinate [0.108, 0.699] refer to in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the point where the 'Contemporary Art Market' curve registered in year 2005."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "YBzic22hqLRwknnr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2010, is the point on the {Contemporary Art Market curve}[0.386, 0.439] higher or lower than the point on the {Heritage Art Market curve}[0.386, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "mF2Rbj0bH90vWdPd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Market Index Value had a greater value in the year 2008, {Contemporary Art Market}[0.275, 0.605] or {Heritage Art Market}[0.275, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Contemporary Art Market."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "a16wo6OtleBHWCPB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the {Heritage Art Market point in 2015}[0.665, 0.633] relative to the {Contemporary Art Market point in 2015}[0.665, 0.218]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "7WZvdn36A9bn5Php",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.966, 0.879, 1.012, 0.907]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the point on the 'Heritage Art Market' trend line for the year 2022."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "dqEvWeWXPOy0AYEg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.073, 0.044, 0.301, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the line chart which includes two items: {Contemporary Art Market}[0.117, 0.055, 0.295, 0.083] depicted in color '#8280cd' and {Heritage Art Market}[0.117, 0.097, 0.255, 0.125] represented in color '#ff0000'."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "qnyugdgpOVm0mLKP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in [0.507, 0.942, 0.544, 0.970] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the X axis label which is 'Years'."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "2xuY6JtcMM8z7ejV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel}<r>[0.497, 0.927, 0.539, 0.963]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "gozdWk9VMMq6NFT4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third Y-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.050, 0.645, 0.075, 0.680]."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "w2tykKQrOTUUjngC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {legend item with the color '#008000'}<r>[0.099, 0.136, 0.139, 0.136]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Co2 Emissions."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "t4dQlKY4k0VrZDLE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year 2003 of Global Average Temperature}<r>[0.212, 0.825]</r> above or below {the point for year 2003 of Co2 Emissions}<r>[0.212, 0.351]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "47gDefYUYRn03dWP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the data line for 'Global Average Temperature'}<r>[0.081, 0.808, 0.910, 0.829]</r> remain above, equal to, or below {the data line for 'Co2 Emissions'}<r>[0.081, 0.075, 0.910, 0.432]</r> throughout the years 2000 to 2019 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "PMz8dmmSUfDG9kPU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Year 2018}<r>[0.866, 0.879, 0.892, 0.914]</r>, was the Co2 Emissions higher, the same, or lower than the global average temperature values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "JOcgaor1T2qUC7iT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area <r>[0.091, 0.055, 0.447, 0.170]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes two labels: {Global Average Temperature}<r>[0.155, 0.069, 0.439, 0.104]</r> with a corresponding blue marker, and {Co2 Emissions}<r>[0.155, 0.121, 0.298, 0.156]</r> with a corresponding green marker."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "HBsB7tNLrozptnb2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the region <r>[0.081, 0.075, 0.910, 0.432]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area marks the position of the data line for Co2 Emissions in the chart. The line starts near the midpoint of the y-axis and ends near the x-axis, indicating the rise of Co2 Emissions over time."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "KCp59atFpIGqipia",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.300, 0.822, 0.343, 0.870]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects. The {x-axis tick label '2006'}<r>[0.318, 0.879, 0.368, 0.914]</r>, a point at <r>[0.343, 0.821]</r> for Global Average Temperature in 2006, and a point at <r>[0.343, 0.270]</r> for Co2 Emissions in 2006."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "kp07p3YNh399bWcX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the data point at [0.118, 0.464] on the 'GDP Growth Rate' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "2001."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "5jlee0kD9Y39yvbU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the data point at [0.938, 0.871] on the 'GDP Growth Rate' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "IAvTTtdGjAiOLXoV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for 'Interest Rate'}<r>[0.118, 0.065, 0.938, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#2baf2b."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "Xm6dft3rjZM1R8VL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of 'GDP Growth Rate'}<r>[0.118, 0.464]</r> higher or lower than {the value of 'Interest Rate'}<r>[0.118, 0.139]</r> in 2001?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "ZLsHlvN9w6nCUo13",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the value of 'GDP Growth Rate'}<r>[0.938, 0.871]</r> higher or lower than {the value of 'Interest Rate'}<r>[0.938, 0.531]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "4517KakpJd4medAf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one drops more dramatically from 2007 to 2008, the 'GDP Growth Rate' or the 'Interest Rate'?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "R08b3DjtlYfUAUyZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.759, 0.037, 0.973, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the chart. These include the GDP Growth Rate{labeled with color #189ab4}<r>[0.814, 0.046, 0.966, 0.069]</r> and Interest Rate{labeled with color #2baf2b}<r>[0.814, 0.081, 0.927, 0.104]</r>, each paired with a colored marker."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "SQxM3V7FIModmFWd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.019, 0.418, 0.036, 0.518]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area features the y-axis label 'Rate (%)' placed vertically to denote the proportion of values in percentage."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "bSlRdftvmad7rMQU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.043, 0.720, 0.069, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A single object is present, the y-axis tick label '-2'."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "FzhqRelkYt5qZc1O",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.025, 0.351, 0.048, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "AGAlWigjiCPXvQJD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.193, 0.879, 0.230, 0.914]"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "nJF7LI1hdcVMrefG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item represent at [0.196, 0.121, 0.283, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock B."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "S9UEBncEesvqqCr4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Stock A}[0.191, 0.783] above or below {the point for Stock B}[0.191, 0.719] at time 2.5?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "QBreOGcXoMfS3G00",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Stock A}[0.149, 0.161, 0.936, 0.829] or {Stock B}[0.149, 0.075, 0.936, 0.760] have a higher value at time 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock A."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "mKSzfMnoROuYk5xh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the second point of {Stock A}[0.191, 0.783] higher or lower than its first point [0.149, 0.760]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "dMAqrHVdbeh4wiIR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.193, 0.879, 0.958, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick labels, which represents time periods from 2.5 to 22.5."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "n8VOAcJ1aqx760iR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area [0.121, 0.055, 0.292, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It labels {Stock A}[0.196, 0.069, 0.283, 0.104] with a handle in color '#12a0d7' and {Stock B}[0.196, 0.121, 0.283, 0.156] with a handle in color '#de8812'."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "xXoJTC6dzl0Ii6eJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within the coordinates [0.149, 0.161, 0.936, 0.829]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates are the points and line for Stock A, indicating its value change over time from time 1 to time 20."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "2ZEhlVlmaZRXMZNZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.019, 0.364, 0.036, 0.576]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "xokMownD4jN2QCzO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.468, 0.952, 0.595, 0.975]."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "TXIbnNT92r1KLtOI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.054, 0.695, 0.076, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "YOLiBFvphcPmX9sv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at time 10}[0.510, 0.185] higher or lower than {the point at time 16}[0.767, 0.135] on the 'Media Influence' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "LnsAOOojfClh3CcD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the start point of the 'Media Influence' line}[0.082, 0.871] on the left side or the right side of {the end point of the line}[0.938, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left side."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "wBTqYEisohnj4kxU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at time 5}[0.296, 0.570] higher or lower than {the point at time 7}[0.382, 0.453] on the 'Media Influence' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "Qgkd2xK19UMXwqI4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area [0.773, 0.040, 0.973, 0.082]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of a legend titled 'Media Influence' with {its corresponding color marker}[0.774, 0.052, 0.819, 0.066] and {the legend text itself}[0.828, 0.049, 0.966, 0.072]."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "xmobdCbxgK5xl6f5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.082, 0.028, 0.981, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes grid lines, both x and y-axes with their labels and tick marks, and a line plot representing 'Media Influence' over 'Time (months)' on the x-axis and 'Public Opinion (%)' on the y-axis."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "hWOjEOQ2MEE6cjXr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained in the region [0.082, 0.871, 0.938, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This includes an array of points plotted for the 'Media Influence' line graph."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "mPyBuq0PfEFjNh6f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the 'x-axis tick label' represent at the coordinates [0.111, 0.879, 0.192, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label represents the year 2000.0"
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "speGgMOqgzKKCNxA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the area represented by the coordinates [0.025, 0.286, 0.048, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Amount Consumed\" on the y-axis."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "WPt8VrjUSoXwqd0v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {Coal line}[0.191, 0.075, 0.933, 0.528] stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represents the amount of Coal consumed for different years."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "fgzaPlTkqaIzzwjD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the data points represented by the line of {Hydroelectric Power}[0.191, 0.644, 0.933, 0.829] generally higher or lower than that of {Coal line}[0.191, 0.075, 0.933, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "CVxUPeLmxRyFFQ75",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the energy consumed by {Coal}[0.191, 0.528] in the year 2001 greater than the amount consumed by {Hydroelectric Power}[0.191, 0.727]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "wotfDKoVDmBtxOKj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the consumption of {Coal}[0.933, 0.075] increase or decrease from 2001 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "JsRkkQulPBx7lzDd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can be made for the region [0.191, 0.075, 0.933, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the energy consumption of Coal over the years 2001 to 2020. Through line plot, it shows an upward trajectory indicating the increase in Coal consumption."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "Wfm1xe6U7qTlZchf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the specifics in the area [0.165, 0.055, 0.480, 0.170]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area, containing 2 legend items. One for {Coal}[0.239, 0.069, 0.290, 0.104] and another for {Hydroelectric Power}[0.239, 0.121, 0.471, 0.156], each paired with a colored marker."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "6VOmO15rPW6twn4v",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be spotted within the coordinates [0.502, 0.879, 0.582, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label for the year '2010.0' can be spotted here."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "Y5s8iP3RwKoNNcMQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.375, 0.029, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Development Index."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "aO5QEjrmFtrIhpV5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text right above {the y-axis mark at 90}<r>[0.066, 0.226]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "SxDmoe3scSxAEwAR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend item} with text coordinates [0.815, 0.478, 0.955, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "tR5NUv14dKCgzypz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point corresponding to the year 1995 for Rural Development}<r>[0.404, 0.555]</r> above or below {the point corresponding to the year 1995 for Urban Development}<r>[0.404, 0.391]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "tXbmpvAwUvf7XZnD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the year 2010}<r>[0.742, 0.924]</r>, which had a higher development index, Rural Development at [0.742, 0.719] or Urban Development at [0.742, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "cryLbuauAxgfuxmE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Urban Development at the year 2020}<r>[0.968, 0.062]</r> higher or lower than {the point for Urban Development at the year 2015}<r>[0.855, 0.062]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "bvIMkEMCWqPOdCUj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area bounded by the coordinates [0.771, 0.440, 0.961, 0.505]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the sublegend of the chart, with {the label for Rural Development}<r>[0.815, 0.448, 0.949, 0.468]</r> marked in a color denoted by {coordinates}<r>[0.772, 0.450, 0.808, 0.462]</r> and {the label for Urban Development}<r>[0.815, 0.478, 0.955, 0.498]</r> marked using a color represented by {coordinates}<r>[0.776, 0.486, 0.804, 0.486]</r>."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "7J3IA6fmhhrJe3yw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.066, 0.062, 0.968, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region are the two plotted lines representing {Rural Development}<r>[0.066, 0.062, 0.968, 0.883]</r> and {Urban Development}<r>[0.066, 0.062, 0.968, 0.883]</r>, which are the main data elements of the line chart."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "4rQVDfhAV9gaFzOp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.048, 0.931, 0.985, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels representing years from {1980}<r>[0.048, 0.931, 0.083, 0.951]</r> to {2020}<r>[0.950, 0.931, 0.985, 0.951]</r>."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "qZoSlTa3ZZHJaeEq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Study-related Anxiety Levels among Students}<r>[0.333, 0.025, 0.713, 0.055]</r>."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "skRVPjmIFQSh45Sk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific category does the x-axis label at [0.658, 0.756, 0.770, 0.942] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Professor interaction}<r>[0.658, 0.756, 0.770, 0.942]</r>"
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "fh4B69ZCLAsYkNfC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis tick in the position indicated by these coordinates, [0.035, 0.666, 0.057, 0.690]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{0.1}<r>[0.035, 0.666, 0.057, 0.690]</r>"
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "EJqj5adqFWCDzNC0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {anxiety level for 'Exams'} <r>[0.103, 0.091]</r> rating higher or lower than the {anxiety level for 'Transcripts'} <r>[0.867, 0.566]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "hjRRH8dcevafagMF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the anxiety level decrease or increase from {Homework}<r>[0.180, 0.164]</r> to {Presentations}<r>[0.256, 0.237]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "jHB9x7eFFF8vStI8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {anxiety level for 'Group work'}<r>[0.409, 0.281]</r> higher than that of {Internet research}<r>[0.561, 0.347]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "ZVCXcDeEdYKFMxGk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.035, 0.082, 0.057, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the y-axis labels for the chart, ranging from {0.0}<r>[0.035, 0.739, 0.057, 0.763]</r> to {0.9}<r>[0.035, 0.082, 0.057, 0.106]</r> representing the described Anxiety Levels."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "GFDLD9NtTD3HYtkk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area indicated by the coordinates [0.790, 0.756, 0.819, 0.853]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.790, 0.756, 0.819, 0.853] contains the x-axis tick label {Class size}<r>[0.790, 0.756, 0.819, 0.853]</r>."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "UaIV6fXyoA5cfYYY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.061, 0.091, 0.943, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{A line with several points (Exams to Certificates)}<r>[0.103, 0.091, 0.943, 0.675]</r> is plotted in this region. This line represents the study-related anxiety levels among students for the specified categories."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "ovestHUiHREm8AZy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.181, 0.069, 0.339, 0.104]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for Investment_A in the legend."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "BWFUQiKMuF8VypC1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.807, 0.102, 0.807, 0.102]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 17 on the Investment_A line."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "rLmCBaaSAtlEEr57",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.474, 0.927, 0.596, 0.963]</r> on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "'# of Steps'."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "2kxszwGmCFEM4LIt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at step 11, {Investment_A}[0.556, 0.239] or {Investment_B}[0.556, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment_B"
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "rqKagzDqXRXKkWgH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Investment_A}[0.682, 0.148] at step 14 more than the value of {Investment_B}[0.682, 0.620] at the same step?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "D5MrdfCnnEzf3JdX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Investment_A}[0.514, 0.284] at step 10 above or below {Investment_B}[0.514, 0.747]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "pZB7M47TBdhg8WhY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region defined by [0.537, 0.017, 0.537, 0.017]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot\u2019s subtitle is located here, which is currently empty."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "CK3tCfvvWx6hH1iS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements located within [0.107, 0.055, 0.348, 0.171]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart, including Investment_A<r>[0.181, 0.069, 0.339, 0.104]</r> and Investment_B<r>[0.181, 0.122, 0.339, 0.157]</r>."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "NuNtVeSNY2FOAKdi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.095, 0.866, 0.933, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The graphed data for both Investment_A and Investment_B are plotted within this region. Each line is composed of a series of points representing investment returns at various steps."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "HGOWWf4J7ZPpPbnP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.019, 0.397, 0.036, 0.548] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "WCn2h1JxFH3u0U9P",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second icon on the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.759, 0.093, 0.804, 0.107]."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "NgBuBXLEObBzwDCk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the x-axis tick label}<r>[0.291, 0.919, 0.302, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "W0SRRe0N2hYffqTx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for year 10 in Modern Values}<r>[0.510, 0.421]</r> above or below {the point for year 10 in Traditional Values}<r>[0.510, 0.601]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "KJaUAszEBcesISol",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line representing Traditional Values}<r>[0.125, 0.420, 0.938, 0.719]</r> overall above or below {the line representing Modern Values}<r>[0.125, 0.310, 0.938, 0.587]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "wmxILbq3Z4efmcLz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is at a higher value at {year 20}<r>[0.938, 0.919, 0.949, 0.942]</r>, Traditional Values or Modern Values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Modern Values."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "Hs3DWHrbXNvnx8OJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in <r>[0.757, 0.046, 0.973, 0.123]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes two items: {Traditional Values}<r>[0.813, 0.055, 0.966, 0.078]</r> with a color marker of '#003f5c', and {Modern Values}<r>[0.813, 0.090, 0.940, 0.113]</r> with a color marker of '#ff6361'."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "S3ztuGFNovKW6MxG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.510, 0.952, 0.553, 0.975]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label which is labeled as 'Time'."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "FpMG6r0wGo7NBVs3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the coordinates <r>[0.125, 0.310, 0.938, 0.587]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing the Modern Values can be found within these coordinates, including the points for each year from 1 to 20."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "DrtsgqvVc9L2QIsi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.025, 0.351, 0.048, 0.572] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "Jmy97x07ODVBqjdt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you mention the coordinate of the last element found in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.124, 0.101, 0.184, 0.118]"
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "rcr9HQUERVZePz5S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.518, 0.942, 0.567, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "VwOpcOlcweVZ1AgS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Physical Media sales point in 2008}<r>[0.296, 0.527]</r> higher or lower than {Digital Media sales point in 2008}<r>[0.296, 0.772]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "PpOPIxw1rOFEE4qz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {Physical Media sales point in 2021}<r>[0.936, 0.829]</r> compare to {Digital Media sales point in 2021}<r>[0.936, 0.069]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "eGutrbZHwv32TiyS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point on x-axis corresponding to 2010}<r>[0.395, 0.893]</r>, which is greater - Physical Media sales or Digital Media sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physical Media sales."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "9HUfGmHBalemH4Yw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.110, 0.030, 0.975, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main body of the line plot, where the data for 'Physical Media' and 'Digital Media' sales from 2005 to 2021 are plotted over the Y-axis range of 0 to 600 (in millions) and the X-axis representing the years from 2004 to 2022."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "gcWNNS9JqDrc1uGA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.121, 0.044, 0.376, 0.136]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area constitutes the legend of the chart, containing markers and labels for {Physical Media}<r>[0.195, 0.055, 0.367, 0.083]</r> and {Digital Media}<r>[0.195, 0.097, 0.348, 0.125]</r>, correlated with color markers at respective positions."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "O3YGpcBTHn68X0eT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.464, 0.903, 0.522, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object. The X-axis tick label for the year '2012' is located at these coordinates."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "QL2ONWcIn1DhUitI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of data is present at the coordinates [0.150, 0.810]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for \"Slow Growth\" in year 2011."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "Ctilc7JZx1JVsmdY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart element is represented by the coordinates [0.125, 0.106, 0.306, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item 'Pronounced Increase'."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "Msg8WLUaMeHeXuvA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.060, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point for \"Pronounced Increase\" in 2010."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "oCOWW1acZJnuqrmp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year 2015, is the 'Slow growth' rate greater or lesser compared to the 'Pronounced increase' rate referring to {coordinates for Slow growth}[0.510, 0.788] and {coordinates for Pronounced increase}[0.510, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "JM5uOIYnWT0tA3ne",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to the investment in 2013, was there an increase or decrease in investment for 'Slow growth' between the points at the {coordinates}[0.330, 0.799] and {coordinates}[0.420, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "BkUkW3RFif77bFHa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 'Pronounced increase' line slopeed steeper than the slope of the 'Slow growth' line when compared {line coordinates for Pronounced increase}[0.060, 0.089, 0.959, 0.872] and {line coordinates for Slow growth}[0.060, 0.760, 0.959, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "cvRkDaDLHBxy7xUQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the area [0.069, 0.062, 0.312, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the legend for the chart, indicating both the 'Slow growth' and 'Pronounced increase' categories."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "uMarF83Bg7oPZsIO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content identified within the coordinates [0.060, 0.760, 0.959, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the line representing 'Slow growth'. The line goes from 2010 with a slight increase each year up until 2020."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "dpA7F15cv8XX8vJp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.060, 0.089, 0.959, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line that represents 'Pronounced increase', starting in 2010 and showing a significant increase each year till 2020."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "KEfmxsvJfrC43exB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.019, 0.360, 0.036, 0.576] referring to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, indicating 'Number of Patents'."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "vXC8yjEFR2Oe4Rsq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the coordinates of {the marker for 'Patent Grants'} in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's located at [0.109, 0.091, 0.144, 0.091]."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "ySWYou5VkZkCAeFa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what {the x-axis tick label} at [0.721, 0.919, 0.765, 0.942] denotes?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year '2015'."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "oGRMCLfOxzTbhBrz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line of 'Patent Applications'[0.093, 0.065, 0.916, 0.679] cross or meet with the line of 'Patent Grants'[0.093, 0.506, 0.916, 0.871] at any point?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "1MDdDWKH3qvX1DxJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Patent Applications'} at year '2010'[0.526, 0.391] above or below {the point for 'Patent Grants'} at the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "OWchTzg7HlVXQTvt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich patent category had a higher number in 2019, 'Patent Applications' or 'Patent Grants'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Patent Applications'."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "laQgJa9Td0YLLeEb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's contained within the [_0.043, 1.053, 0.087, 0.118] area of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains y-axis tick labels, specifically the labels for '1000', '1500', '2000', '2500', '3000' and '3500'."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "q1D4WIls5cNVknX3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nMay you describe the region [0.102, 0.037, 0.332, 0.113] for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend of the chart. It includes identifiers for 'Patent Applications' and 'Patent Grants'. Each category comes with a color marker."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "BQRNBSdRyFOjo8n0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown in the region from [_0.043, -0.097, 0.087, -0.074]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the y-axis label '4000' is depicted."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "iYA9nMDimaRCrex5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the coordinate [0.025, 0.025, 0.048, 0.826]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the location of the chart's y-axis label."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "wut98M3egtMqhRHe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the x-axis label}[0.532, 0.927, 0.581, 0.963] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label is 'Year'."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "PpuvXcUXN7XymJtr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about {the point}[0.937, 0.777] on the line?"
+            },
+            {
+                "from": "gpt",
+                "value": "This point represents the year 2020 with an immunization percentage of about 80."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "VZh03xJyf4I06wZd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the immunization point for 2000}[0.430, 0.778] higher or lower than {the disease cases point for the same year}[0.430, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "dcHgoQ90vYY6KeLF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the {line for 'Immunizations'}[0.177, 0.777, 0.937, 0.779] and the {line for 'Disease Cases'}[0.177, 0.073, 0.937, 0.686]. Which shows a steeper decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "Disease Cases."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "O2jWWZ07mPTZgwXL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the immunization point for 2010}[0.684, 0.777] exceed or fall short of {the immunization point for 2015}[0.810, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fall short."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "bVkfhbPlROzPwUdX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area [0.710, 0.055, 0.963, 0.170]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's legend and categorizes two items: {Immunizations}[0.784, 0.069, 0.954, 0.104] and {Disease Cases}[0.784, 0.121, 0.951, 0.156]. Each is associated with a color marker."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "yA2TbRM05sAqu399",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region with coordinates [0.401, 0.826, 0.459, 0.913]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the x-axis tick label '2000'."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "e3s83uo68u7SUqv9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the coordinates [0.139, 0.503, 0.975, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two lines. The first is 'Immunizations', {colors and patterns marked in blue with circles}[0.177, 0.777, 0.937, 0.779]. The second line is for 'Disease Cases', {marked in red}[0.177, 0.073, 0.937, 0.686]."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "eShAMFMuiS2O5wen",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation of the coordinates [0.082, 0.084, 0.127, 0.098] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color marker for education expenditure in the legend."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "pKyz0o08t7SoveDJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Expenditure (% of GDP)\"."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "70cyPGZWLwHhNYwu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the point at coordinates [0.515, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the data point for healthcare expenditure in the year 2019."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "y9r3s78LzyLEBYYa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the chart, is the healthcare expenditure for the year 2019 at coordinates [0.915, 0.111] higher or lower than education expenditure for the same year at coordinates [0.915, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "K1kXj1CdQtIfQxd6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend shown by line_0 within the coordinates of [0.071, 0.065, 0.915, 0.364] in comparison with line_1, within [0.071, 0.698, 0.915, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line_0, representing Healthcare Expenditure, tends to have higher values than line_1, which represents Education Expenditure."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "wSTmn1nolWfrwEwT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the expenditures for healthcare and education in the year 2000 at coordinates [0.071, 0.364] and [0.071, 0.871] respectively, closer to each other or further apart compared to year 2019 [0.915, 0.111] and [0.915, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Closer."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "3YUn5LKJ5y9Alj65",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area covered by the coordinates [0.080, 0.037, 0.348, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the legend of the chart, which includes two entries: Healthcare Expenditure with a color indicator of '#31977e' and Education Expenditure with a color indicator of '#f96b39'."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "oJ5mrT8YpkOK6bxS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained within the region [0.071, 0.065, 0.915, 0.871]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plots for both Healthcare and Education Expenditure. The data points for each year from 2000 to 2019 are plotted and connected by lines to show the trend of expenditure over the years. The specific values of each year are represented by the coordinates of the points on the lines."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "cZkdiutbncWAHecn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located in the region [0.043, -0.070, 0.981, 0.988]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all elements of the chart, including the x-axis and y-axis with their respective labels, the legend, and the plotted lines for the Education and Healthcare Expenditures over the years 2000 to 2019."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "VdsUCUM54KQcmely",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.019, 0.359, 0.036, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "eqO2MJEIGDjY0sWP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the {y-axis tick label} at [0.043, 0.171, 0.065, 0.194]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "YFrMRLqhMYdFhCzt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {legend item} at [0.136, 0.090, 0.311, 0.113] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "659olFyJ8CduoS7f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the values of {Rural Development Index} at [0.790, 0.340] and {Urban Development Index} at [0.800, 0.135]. Which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development Index."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "zBJxNK8mPNNdRb7T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the last point of {Rural Development} [0.936, 0.319] on the chart higher or lower than the first point of {Urban Development} [0.071, 0.400]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "lyL9WXrrSf8LRYdV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor year 2012, which index is higher: {Urban Development} at [0.572, 0.210] or {Rural Development} at [0.572, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development"
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "hldDZATtdf6KxHgh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area [0.080, 0.046, 0.317, 0.123]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the line chart. Two items are included: {Rural Development}[0.136, 0.055, 0.303, 0.078] and {Urban Development}[0.136, 0.090, 0.311, 0.113], each paired with a color marker."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "rhcCyALWXe0MRpVM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the labels from [0.140, 0.919, 0.184, 0.942] to [0.868, 0.919, 0.912, 0.942] in the x-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the years 2003 to 2019 in the x-axis of the chart."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "YxsvSJGSi5E2JTKJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.071, 0.115, 0.936, 0.400]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line representing the {Urban Development} trend from 2001 to 2020 in the chart."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "DIY3xXaLeb6YZrm8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the first point of the Domestic Trade line}<r>[0.066, 0.693]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate represents the point where the Domestic Trade line starts, with x-axis label 1 and y value of 3.0."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "hCgrVh8kyc6wR4cB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at {coordinates}<r>[0.805, 0.087, 0.934, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the Domestic Trade line in the legend."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "JZEADhXf2Fs0vMal",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-value of the {seventh point on the International Trade line}<r>[0.563, 0.524]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-value at this point is 3.8."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "MXuRNIriQTMykomb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Quarter 7 point of Domestic Trade}[0.563, 0.335] above or below {Quarter 7 point of International Trade}[0.563, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "zSeN0iiResgjt0mm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {quarter 12}<r>[0.976, 0.903]</r>, which line's y-value is higher, Domestic Trade or International Trade?"
+            },
+            {
+                "from": "gpt",
+                "value": "Domestic Trade."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "mYIUwAQFLQkZOs6P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {the International Trade at Quarter 6}<r>[0.480, 0.398]</r> higher or lower than that at {Quarter 5}<r>[0.397, 0.461]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "QNOmrENyGoJjEzrU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within <r>[0.751, 0.076, 0.968, 0.172]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the chart legend. It includes two entries, one for Domestic Trade, color-coded in blue, and another for International Trade, color-coded in red."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "eEytdHWBFyM8yWHn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.885, 0.911, 0.985, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label '12' is located within these coordinates."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "BBXQ9YXkxIcWp9XS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region <r>[0.286, 0.025, 0.756, 0.055]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the subtitle of the chart: \"Domestic VS International Trade Performance Over Time.\""
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "uVBfxfB47X8xwzXB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.263, 0.025, 0.788, 0.058] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the chart."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "iNybaCIFFArcARFd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.456, 0.943, 0.596, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Months)."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "RFKrv4j0hOEdpwAc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the line}<r>[0.108, 0.294, 0.943, 0.613]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physical Resources."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "EC3uhV02BISiGK3M",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 'Time' 2 for 'Physical Resources'}[0.247, 0.355] above or below {the point at 'Time' 2 for 'Online Resources'}[0.247, 0.575] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "0sHdNjZVdWTnpDBV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Physical Resources' data point at 'Time' 0}<r>[0.108, 0.294]</r> have a higher or lower value than {the 'Online Resources' data point at 'Time' 0}<r>[0.108, 0.674]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "D5B95UNfdlqZ2XOC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the 'Physical Resources' data point at 'Time' 12}<r>[0.943, 0.613]</r> and {the 'Online Resources' data point at 'Time' 12}<r>[0.943, 0.180]</r>, which line has a lower value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Resources."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "ktZXiUYmrwx4Cfip",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content located in [0.759, 0.079, 0.977, 0.176]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend section containing two items. One is 'Physical Resources' with a blue marker located at {the coordinates}[0.765, 0.104, 0.799, 0.104], and the other one is 'Online Resources' with a red marker located at {the coordinates}[0.765, 0.148, 0.799, 0.148]."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "FNLfYvZzDeMeHZAW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.066, 0.066, 0.985, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the entirety of the main chart. It includes two line plots representing 'Physical Resources' and 'Online Resources', their respective x-y coordinates, and grid lines."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "qaUW6DftupfC81Br",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are presented within the region of [0.104, 0.911, 0.113, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an x-axis tick label '0' within this region."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "bZpZuzYNJiHCcTdj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.098, 0.109, 0.120, 0.132] on the vertical axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "Ws6IYyhcDfC9E6zq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the point}[0.673, 0.622] belongs to on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Industries."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "FMalaJegU646BUfR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the 'Innovation Industries'} at [0.663, 0.203, 0.697, 0.203] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is 'red'."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "Lfwjw9HMF0MmtdJ6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the starting values, is the performance of Knowledge Economy at {point 0}[0.160, 0.794] better or worse than {Innovation Industries}[0.160, 0.732]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Better."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "4tRqvJSLnmbB1y9s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {coordinate}[0.865, 0.229], does {Knowledge Economy}([0.865, 0.229]) have a better performance or {Innovation Industries}([0.865, 0.855])?"
+            },
+            {
+                "from": "gpt",
+                "value": "Knowledge Economy."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "MtdFG6SvYMv7mw1d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 7}[0.609, 0.155] of {Knowledge Economy}([0.609, 0.155]), is the index greater or lesser than {Innovation Industries}([0.609, 0.585]) at the same time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "dVE9xvBn4Uuc9TN6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.657, 0.134, 0.892, 0.230]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart displaying the categories Knowledge Economy and Innovation Industries, each assigned with their respective colors: blue for {Knowledge Economy}[0.710, 0.145, 0.883, 0.175] and red for {Innovation Industries}[0.710, 0.189, 0.885, 0.219]."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "Te2CZslTbwrBGYKm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.252, 0.083, 0.773, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "The given area accommodates the chart's subtitle which is \"Performance of Knowledge Economy and Innovation Industries\"."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "s1SYKRhSOqqSTkv7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.160, 0.192, 0.865, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plot area contains two lines representing the performance of Knowledge Economy and Innovation Industries over time. The lines consist of connected points representing different time units."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "rgxcunp6dBgQL6X3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.015, 0.388, 0.034, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "NTEg6cqbuAi98a6t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sixth label for the x-tick marks?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gradual improvement."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "DO1izF7NHxFM65g9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate value for the x-label 'Slow increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "5Q22pKNjSFx3wNbO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend value for 'Slight decrease' higher or lower than 'Downward trend'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "vZ7ub8uwJCLylY0X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes 'Pronounced increase' have a higher trend value than 'Subdued expansion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "fmB1KoCrQEF6fPm4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend value for 'Gradual improvement' higher than 'Slow increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "fiHL9J60RVJfYi7H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.040, 0.994, 0.073, 1.018]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '-0.4' is located in this region."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "0eSSHuT37rJORUNU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within [0.065, 0.911, 0.172, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Slight decrease' is in this area."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "XKCvkI8Y7ud4vEYk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the region [0.741, 0.087, 0.958, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'International Trade Trends' of the legend is located here."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "sDGrMwVnQ8Ywcleo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-coordinate tick}<r>[0.039, -0.085, 0.062, -0.062]</r> in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no label because it's outside the chart boundary of y-coordinates."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "OdmGEDQgcsnM8Vjj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is at the coordinates <r>[0.128, 0.087, 0.232, 0.117]</r> within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label of a legend item for 'Active Users'."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "MYMrW6cWrdrtFu7R",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for an x-axis tick located at <r>[0.104, 0.911, 0.113, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is '0'."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "Lwq9wVfSecRG9Yp0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the 'Active Users' line and the point at <r>[0.178, 0.206]</r>, is the Active Users count increasing or decreasing compared to the previous point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "wFLDEMR5lzlEEY0V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Active Users' and 'New Users' lines, which one has a higher value at <r>[0.943, 0.347]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Active Users'"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "ae9lIl7w1BHcg8H4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the 'New Users' line, is the point at <r>[0.595, 0.465]</r> at a higher or lower y-value than the point at <r>[0.734, 0.524]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "M6BW1WqpHaYsZ52n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what can be seen in the area marked by <r>[0.075, 0.076, 0.238, 0.172]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. We can spot two items, one labeled 'Active Users' with a blue color at <r>[0.128, 0.087, 0.232, 0.117]</r>, and the other one called 'New Users' with a red color at <r>[0.128, 0.131, 0.217, 0.161]</r>."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "MCs39kcSjMjrEaYt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the region <r>[0.231, 0.025, 0.821, 0.055] </r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart's subtitle, which reads \"Monthly Trends of Active and New Users on Social Networking Platform\"."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "HEVd99mKLGrOBOmI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the bounds <r>[0.066, 0.062, 0.985, 0.903]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a large area, covering most of the chart. It contains the plot of 'Active Users' and 'New Users' lines with various x-ticks representing months and y-ticks representing the number of users. There is also a legend in the lower-left corner with two entries, 'Active Users' and 'New Users'."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "v0hutc82qsa6FCCj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel}<r>[0.449, 0.943, 0.589, 0.975]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Months)."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "1okwgXRZUVlpk5sW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at coordinates <r>[0.068, 0.101, 0.102, 0.101]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue handle marker corresponding to \"Inflation\" in the legend."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "22cBq6GIqs9Pf0F3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does {ylabel}<r>[0.015, 0.277, 0.034, 0.688]</r> convey?"
+            },
+            {
+                "from": "gpt",
+                "value": "Economic Indicator Value."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "VTqPMlKZ4fTcioVG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the fifth point, is the {Inflation}[0.404, 0.725] value higher or lower than {Unemployment}[0.404, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "CZGhbsHxgQzboFIF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 8th point, is {Unemployment}[0.635, 0.287] higher or lower than {Inflation}[0.635, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "9sK6OMIujjrgiD19",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at the first point, {Unemployment}[0.096, 0.491] or {Inflation}[0.096, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "YktT4t8GAMprb1L9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area <r>[0.062, 0.076, 0.250, 0.172]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart. It includes {Inflation}<r>[0.115, 0.087, 0.183, 0.117]</r> with a blue marker at {coordinates}<r>[0.068, 0.101, 0.102, 0.101]</r>, and {Unemployment}<r>[0.115, 0.131, 0.243, 0.161]</r> with a red marker at {coordinates}<r>[0.068, 0.145, 0.102, 0.145]</r>."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "0GKj0CAoUZaobMDz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.096, 0.124, 0.943, 0.504]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line representing the 'Unemployment' data. It is coded with a red line."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "Bc2AKYBCOhjIhpDH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is inside <r>[0.414, 0.025, 0.624, 0.055]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart, \"Statistical Data Overview.\""
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "6hARLGT9KB5KT7e1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object located at [0.015, 0.330, 0.034, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "MsOY5CdHmlmxCreV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the label for the second x-axis tick?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.243, 0.911, 0.252, 0.934]."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "Hj4dMi9TrgTI6SfE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the index at time point 5 along the 'Financial Management' line} at coordinate [0.456, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.52."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "nTpdeoeFbQueTBXr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the index at the 7th time point for 'Financial Management'}[0.595, 0.633] higher or lower than {the index at the same time point for 'Risk Control'}[0.595, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "CtVxFStMxabp8QRa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the third point on the 'Financial Management' line}[0.247, 0.793] greater or less than {the third point on the 'Risk Control' line}[0.247, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "uJL3l8vlGlottAS9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index is higher at {the 12th time point}, 'Financial Management' or 'Risk Control'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk Control."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "rMgDmufagPredzms",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the plot area [0.108, 0.100, 0.943, 0.864]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted lines for 'Financial Management' and 'Risk Control' indices over time. Each line consists of multiple points representing the index value at specific time points, with the blue line representing 'Financial Management' and the red line representing 'Risk Control'."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "IoWmH4ymystRw3J0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinate [0.075, 0.076, 0.322, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a legend indicating the color codes for the two plotted lines, 'Financial Management' labeled with blue and 'Risk Control' labeled with red."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "RZk0qIV1E9vVaCG2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.456, 0.598, 0.595, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The points on the 'Financial Management' and 'Risk Control' lines representing the index at the 5th time point are included within these coordinates."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "pAAnd9c6EDTV45cp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the line tagged as 'line_0' located at [0.067, 0.365, 0.976, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Automation trend line."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "Tjj27wTL9DLV35ya",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the label {at coordinate}<r>[0.781, 0.131, 0.961, 0.161]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industrial Automation."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "h8rWskfFrx6JMzFd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by {the y-axis tick label}<r>[0.040, 0.641, 0.062, 0.665]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.6."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "WxOGwevTfCLpGEbb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the AI Automation trend line}<r>[0.067, 0.365, 0.976, 0.714]</r> and {the Industrial Automation trend line}<r>[0.067, 0.449, 0.976, 0.646]</r>, which line is higher at time 8 (Month)?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Automation."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "jQZWAJU61b3EZNw8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the AI Automation trend line at time '1'}<r>[0.142, 0.651]</r> above or below {the point on the Industrial Automation trend line at time '1'}<r>[0.142, 0.575]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "0oA5g3qfSBeHmfWk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the endpoint of {the 'Industrial Automation'}<r>[0.967, 0.911, 0.985, 0.934]</r> line higher or lower than that of {the 'AI Automation'}<r>[0.967, 0.885, 0.985, 0.908]</r> line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "9PSgJ4W2LEEUlNMM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.728, 0.076, 0.968, 0.172]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. The legend has two items: {AI Automation}<r>[0.781, 0.087, 0.900, 0.117]</r> marked with a blue line, and {Industrial Automation}<r>[0.781, 0.131, 0.961, 0.161]</r> marked with a red line."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "CcvKqtV7lwUxdnv1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the area delimited by the coordinates [0.040, 0.557, 0.062, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the y-axis tick label '0.8'."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "IorHK5ujnHftRkwJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.451, 0.943, 0.591, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label: {Time (Months)}<r>[0.451, 0.943, 0.591, 0.975]</r>."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "XMetAHARQjvrxmJl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The y-axis}<[0.015, 0.367, 0.034, 0.597]> represents the 'Adoption Rate'."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "y7gvq36HMTFL1DeD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the line with the legend 'AR Technology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The line plotted}[0.108, 0.100, 0.943, 0.755] for 'AR Technology' shows a fluctuating trend over time."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "wdArmtF2FZW89Dza",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis depict in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The x-axis}<[0.456, 0.943, 0.596, 0.975]> represents 'Time (Months)'."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "AsEU6OY9CKBXKPK6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'AR Technology' and 'VR Technology', which line begins at a higher point?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing {AR Technology}[0.108, 0.100, 0.943, 0.755] begins at a higher point on the graph than the line for {VR Technology}[0.108, 0.624, 0.943, 0.864]."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "arJVnMxDdl82Mapn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does the line for 'AR Technology' end relative to 'VR Technology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing 'AR Technology' ends higher on the graph as compared to the line representing {VR Technology}[0.108, 0.624, 0.943, 0.864]."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "B84BYf8yPt6vtQhH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has a higher adoption rate at the 5th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "{AR Technology}[0.412, 0.210] has a higher adoption rate at the 5th month compared to VR Technology."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "3ywpriRwbqZ0KNQg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.849, 0.087, 0.970, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, {the legend}<r>[0.849, 0.087, 0.970, 0.117]</r> for 'AR Technology' is located."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "Jd0LEHli5ENhVnxc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified in the coordinates [0.795, 0.076, 0.977, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend section, including descriptions for {AR Technology}[0.849, 0.087, 0.970, 0.117] and {VR Technology}[0.849, 0.131, 0.970, 0.161] with their respective color labels."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "BuzXqKTuHBuwA7vT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.108, 0.100, 0.943, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the plotted line for AR Technology from points 1 to 12 representing the varying adoption rate for the technology over time."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "90qZaB9L7LOQnAhl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third text in the x-axis tick labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.157, 0.912, 0.166, 0.936]"
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "v0NP6JoMbrPHRjIJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object lies at [0.117, 0.118, 0.246, 0.141] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sublegend labeled 'Automation Speed'."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "fAovpUdszBJxv9fE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.039, 0.589, 0.061, 0.612]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.6"
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "I7FkWhF16Yy8Z8yf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at time '7'}[0.353, 0.445] on 'AI Efficiency' line above or below the {point at time '7'}[0.353, 0.445] on 'Automation Speed' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "NEGZf5BGMiLpXHzc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {AI Efficiency line}[0.065, 0.430, 0.976, 0.828] intersect with the {Automation Speed line}[0.065, 0.139, 0.976, 0.751] from starting time to ending time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "EOBkQJrdsaaQrxwN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend is higher at  {point at time '10'}[0.497, 0.445], AI Efficiency or Automation Speed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Automation Speed."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "Q6HLn60tHVIQK9bj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in [0.291, 0.025, 0.751, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Artificial Intelligence and Automation Trends Over Time,\" can be found in this region."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "l0uFpVPBj5VyuWTP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.461, 0.945, 0.581, 0.975] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the x-axis label \"Time (Months).\""
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "YPSKp6mGyjSYWhyJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details in the coordinates [0.253, 0.912, 0.262, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label, '5', locates within these coordinates."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "NbF3EMsX0OBp6hyB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of label corresponds to {the label at the vertical coordinates}[0.015, 0.330, 0.034, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "Uio14TirbI9QSgUd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the text box}[0.273, 0.025, 0.778, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Trends in New Media and Digital Content Creation Over Time\"."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "vVWXIwx1rYT1ItEm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at {coordinates}[0.456, 0.943, 0.596, 0.975] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "wXvDaJI64OgxdgZq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the graph, {the point with coordinates '0.108, 0.145'} or {the point with coordinates '0.943, 0.145'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "k4t7a2bBjkvA4UwN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the values at {the first point on the 'Interface' line}[0.108, 0.323] and {the first point on the 'Algorithm' line}[0.108, 0.500], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interface."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "WUJBgp5zDAAeuMGE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first marked point on the 'Algorithm' trend}[0.108, 0.500] appear before or after the {last marked point on the 'Design Pattern' trend}[0.943, 0.145]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "ffmMmOKlx6axnJSQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region between [0.066, 0.062, 0.985, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses all the axes and plotted data for the chart including grid lines, y-axis labels ranging from '-0.2' to '0.8', x-axis labels from '\u22122' to '14', and three primary lines labeled 'Design Pattern', 'Interface', and 'Algorithm'."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "MgYdp2sWE6oZZiRi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area bounded by [0.794, 0.076, 0.977, 0.216] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the legend of the chart with the labels {Design Pattern}[0.847, 0.087, 0.970, 0.117], {Interface}[0.847, 0.131, 0.922, 0.161], and {Algorithm}[0.847, 0.175, 0.929, 0.205] associated with their respective colors."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "OasSh3GxzViuOYuT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the area [0.108, 0.145, 0.943, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all three plotted lines for 'Design Pattern', 'Interface', and 'Algorithm' showing their trends over time along the x-axis from '\u22122' to '14' months and y-axis from '\u22120.2' to '0.8' in performance index."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "kwcmm1Lm7oEmlcuG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat denotes the location [0.015, 0.312, 0.034, 0.653] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Y-axis label or 'Production Efficiency'."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "nw5xagRrqR7wL4qR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the bounding box of the X-axis Label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bounding box is [0.445, 0.943, 0.585, 0.975]"
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "HHuIFb5PjerNwbMQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point}[0.669, 0.331] on the 'Standard Production' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is point 8."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "pNmywMBhn7jpU80b",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at {point 4}[0.361, 0.466], Standard Production or High-tech Production?"
+            },
+            {
+                "from": "gpt",
+                "value": "High-tech Production."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "KbK2yXsQraW59Hor",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 2}[0.207, 0.415] on 'Standard Production' line located above or below the point 2 on 'High-tech Production'[0.207, 0.365] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "OxPAaNFvZ85vCF5c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at the 12th interval}[0.976, 0.482] on the 'Standard Production' line have a higher or lower production efficiency than the corresponding point on the 'High-tech Production'[0.976, 0.499] line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "dkl96GflxFXnClG8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements at location [0.062, 0.076, 0.296, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this area describes two legend items: {Standard Production}[0.115, 0.087, 0.284, 0.117] with a color indicator at {coordinates}[0.068, 0.101, 0.102, 0.101], and {High-tech Production}[0.115, 0.131, 0.289, 0.161] with a color indicator at {coordinates}[0.068, 0.145, 0.102, 0.145]."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "9tCj3Wiziu6djv0n",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.053, 0.264, 0.976, 0.701] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line for {High-tech Production} with a number of points that represent different time intervals and their corresponding production efficiencies."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "EwF5vTGSVlm9BZRY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.053, 0.264, 0.976, 0.735]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the line for {Standard Production} with multiple data points for different time intervals representing their production efficiencies."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "7QdfpoeK3KVkfGIw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.015, 0.343, 0.034, 0.622] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "jhtzY6VZtgNSJqjo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the seventh element (associated with label '12') positioned on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.935, 0.911, 0.952, 0.934]"
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "kYCCF8yQxLuGZ4j2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the x-axis label at coordinates [0.497, 0.943, 0.558, 0.975] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "Month."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "cn25bscSOniL2EHV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the sales values represented by the Regular Purchase line above or below Panic Buying at point 7?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "ymLeWNzRbVJ84Nn9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the coordinates of {Regular Purchase's 5th point}[0.415, 0.287] and {Panic Buying's 5th point}[0.415, 0.211], which line is higher on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Panic Buying."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "f7nmqNRwXn21jF71",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the last points of both {Regular Purchase}[0.943, 0.143] and {Panic Buying}[0.943, 0.100], which of them represents a higher purchasing volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "Panic Buying."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "Hh23TpNLNMSeMWc8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content and elements found between [0.079, 0.076, 0.284, 0.172]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. It includes two elements: {Regular Purchase}[0.132, 0.087, 0.277, 0.117] labeled with the blue color and {Panic Buying}[0.132, 0.131, 0.239, 0.161] labeled with the red color."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "2sJZdyliZiDfznW3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the area from [0.497, 0.943, 0.558, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Month\" is situated within these coordinates."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "xB4aSrDzdzdlzK56",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements present within the region [0.074, 0.870, 0.204, 0.917]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two quantities from the y-axis marking values of 0 and 100, located at {coordinates}[0.057, 0.855, 0.066, 0.879] and {coordinates}[0.040, 0.685, 0.066, 0.709] respectively."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "0tpfc9apD02Zv0gC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plotted data denoted by {the line with blue color}<r>[0.108, 0.278, 0.943, 0.863]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Implementation."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "EJkrJ6CnGtKaZ1R1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {coordinates}<r>[0.456, 0.943, 0.596, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "Cha0aNdo358EslII",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at {coordinates}<r>[0.809, 0.050, 0.963, 0.080]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Implementation."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "vlDmWrCtLenXJ6O3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {AI Implementation}<r>[0.108, 0.278, 0.943, 0.863]</r> or {ML Implementation}<r>[0.108, 0.065, 0.943, 0.224]</r>, has a higher value at time=6 months, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "ML Implementation."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "sEKRWZVG8PlbZm6D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing the 8th month of AI Implementation}<r>[0.665, 0.650]</r> above or below {the point representing the 7th month of ML Implementation}<r>[0.595, 0.086]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "81fWrenbWwRWeoHB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {AI Implementation}<r>[0.108, 0.278, 0.943, 0.863]</r> generally decreasing or increasing over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "l2OFeTqogEgDZoso",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the region <r>[0.040, 0.162, 0.062, 0.185]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '0.9'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "BA9Pac4TAtj1djGy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the area defined by <r>[0.756, 0.039, 0.977, 0.135]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend of the chart with labels for {AI Implementation}<r>[0.809, 0.050, 0.963, 0.080]</r> and {ML Implementation}<r>[0.809, 0.094, 0.970, 0.124]</r>, and colored markers corresponding to each label."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "XqFTDsf6HsJctJWN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in region <r>[0.040, 0.694, 0.062, 0.717]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0.4' is enclosed within these coordinates."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "0TSjxPyBVJmFJgKp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at these coordinates [0.015, 0.379, 0.034, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "OnLPdCl0zPrFWCdc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 7th text in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.593, 0.911, 0.602, 0.934]."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "vd6nOoPQBatbURqU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.040, 0.359, 0.062, 0.382]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.7."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "t40e7Pxec8igXNxN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Facebook User Activity at month 1}<r>[0.142, 0.444]</r> above or below {the point for Instagram User Activity at month 1}<r>[0.142, 0.368]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "TefCJwjsqdO0Flnr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Facebook User Activity'}<r>[0.067, 0.215, 0.976, 0.521]</r> generally above or below {the line for 'Instagram User Activity'}<r>[0.067, 0.139, 0.976, 0.444]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "0ZJ3QOCKnqcSIhqt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social network has a higher user activity at {month 3}<r>[0.294, 0.368]</r>, Facebook or Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "BJ0UxUGup8HdeO3k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.321, 0.025, 0.722, 0.055]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the subtitle of the chart, \"Social Networks and Interpersonal Relationships.\""
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "zP1oT509bxPVVQOn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.712, 0.076, 0.968, 0.172]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area holds the chart legend, containing legend items for {Facebook User Activity}<r>[0.766, 0.087, 0.955, 0.117]</r> and {Instagram User Activity}<r>[0.766, 0.131, 0.961, 0.161]</r>, each depicted with a specific color marker, blue for Facebook and red for Instagram."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "zoT4gQ8Wi6qjKHPd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.594, 0.911, 0.826, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 3 objects. The x-axis tick labels '7', '8', and '9' are located in this area."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "QgqfvpGkLP69isV8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {subtitle} at [0.295, 0.025, 0.757, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Private Investment Vs Public Venture Capital Over Time."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "TrJn0pgLtJb0jGGr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the tick labeled as '10' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.859, 0.911, 0.876, 0.934]"
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "8AaWwoPqIhiVJkgC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.015, 0.282, 0.034, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Value (in billions)."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "mbLTOJKMCRAr5Pvv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the {point with x=5}[0.488], {Private Investment line}[0.488, 0.521] or {Public Venture Capital line}[0.488, 0.304]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Venture Capital."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "ixVmgYuYtPxFGvz8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Private Investment line}[0.943, 0.100] above or below {Public Venture Capital line}[0.943, 0.190] at {x=11}[0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "BUAgkpOuk8UAhc4O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the {point with x=2}[0.260], {Private Investment line}[0.260, 0.673] or {Public Venture Capital line}[0.260, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Private Investment."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "AAmpiPgW4JgEhHNY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.788, 0.131, 0.970, 0.161]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend item: {Public Venture Capital}[0.788, 0.131, 0.970, 0.161], represented with a blue color marker."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "ZkEIcsXcq6znmH1M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.891, 0.216, 1.019, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is one object present in this area which is the {12th x-tick label}[1.010, 0.911, 1.028, 0.934]."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "BWZeLBiLqq7IxBRR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.495, 0.943, 0.556, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label, \"Month\", is located in this area, styled with a dark color."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "6ljWXNI8cwuUAfua",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.824, 0.131, 0.946, 0.161] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the \"Modern Values\" line in the legend."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "12eHzZV0TnGZBlI7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the sixth point on the \"Traditional Values\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.275, 0.616]"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "6hprwTIkUgGoMsXT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis tick label} at [0.040, 0.855, 0.062, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "2NKsr16WzRPa0uhj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points of {the line for 'Traditional Values'}[0.108, 0.482, ... , 0.943, 0.864] generally above or below {points of the line for 'Modern Values'}[0.108, 0.100, ..., 0.943, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "4LirTxrkJgN7eZoN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at year 2000}[0.387, 0.578] on the 'Modern Values' line higher or lower than {the point at year 2000}[0.387, 0.721] on the 'Traditional Values' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "p48VqPg9Hy7IehhI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line in the chart has a higher value at {year 2010}[0.665, 0.330, 0.665, 0.788]: Traditional Values or Modern Values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Modern Values."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "PA2Geua1a2Unw2Wq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the coordinates [0.771, 0.076, 0.977, 0.172]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It includes two items: {Traditional Values with a blue handle}[0.824, 0.087, 0.970, 0.117] and {Modern Values with an orange handle}[0.824, 0.131, 0.946, 0.161]."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "7gRTbn7L2UoGXvca",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.543, 0.911, 0.682, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one text element, an {x-axis tick label}[0.647, 0.911, 0.682, 0.934] reading \"2010\"."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "Z7rTL9S37NCvXsq9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.824, 0.087, 0.946, 0.161]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects, {the label for Modern Values which reads \"Modern Values\"}[0.824, 0.131, 0.946, 0.161] and {the label for the line representing Traditional Values reading \"Traditional Values\"}[0.824, 0.087, 0.970, 0.117]. These are both located in the chart's legend."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "bv8XS1LhBuThwcFV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title as per the chart at location [0.338, 0.025, 0.731, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Project Evaluation and Management."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "gNGvm0N7WKGnP9HN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart's x-axis represent, indicated by the label present at coordinates [0.465, 0.943, 0.605, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Time (Months)."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "CV4EJJnl2uem3pXO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis depict as mentioned at the coordinates [0.015, 0.330, 0.034, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance Index."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "eKof8gAjZZt7XSas",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Standard Performance line}[0.125, 0.062, 0.944, 0.482] above or below the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509] at point {2}[0.274, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "YUOrYqGJ43Yn7lD5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich performance line is higher at {point 11}[0.944, 0.062] - Standard Performance or Innovative Performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Standard Performance."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "qXPYGNXwP27pmbUN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which point on the x-axis does the {Standard Performance line}[0.125, 0.062, 0.944, 0.482] intersect with the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 8."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "zpUwhLmF8zOQBN2Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned within the coordinates [0.719, 0.076, 0.977, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. Two items are included: {Standard Performance}[0.772, 0.087, 0.958, 0.117] indicated by a blue color and {Innovative Performance}[0.772, 0.131, 0.970, 0.161] represented in red."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "IOF6Iq6p4OI7LVD3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items found within the area [0.125, 0.062, 0.944, 0.482]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by the {Standard Performance line}[0.125, 0.062, 0.944, 0.482], represented with a blue line chart starting from 0.8 performance index at point 0 to 1.6 at point 11."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "7MyYJztaBdWushur",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.125, 0.194, 0.944, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is filled by the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509]. It's a red line chart starting from 1.0 performance index at point 0 and reaching 1.35 at point 11."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "Jl66IoISNwD5giz1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.030, 0.370, 0.047, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Application Cases."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "TGf9jpYXAQjxk2Fq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.492, 0.952, 0.541, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fields."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "kdkTID0B1MEhYzyL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the 'Application Case 2' line} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "lQo1TlnJERAfphng",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Application Case 1'}[[0.104, 0.765], [0.854, 0.911]] higher or lower than {the 'Application Case 2'}[[0.104, 0.180], [0.854, 0.911]] in 'Prediction Accuracy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "T6iXhJzbxebLA9MR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the comparison between {the 'Application Case 1'}[[0.104, 0.765], [0.854, 0.911]] and {the 'Application Case 2'}[[0.104, 0.180], [0.854, 0.911]], which has the higher 'Number of Training Samples'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Application Case 2."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "XqBj76k9hTi4ryZF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich application case has a higher point in {the 'Prediction Accuracy'} point[0.104, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Application Case 2."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "RLrShhYCXo7nsMkH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is within the area [0.696, 0.046, 0.920, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart, which indicates the color associations of each application case. Specifically, Application Case 1 is associated with the color blue, as indicated by the marker at {coordinate}[inf, inf, -inf, -inf]. Similarly, Application Case 2 is associated with the color red, as denoted by the marker at {coordinate}[inf, inf, -inf, -inf]."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "71GFrRF5qidP8nNL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the section of the chart from [0.019, 0.919, 0.190, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the first x-axis tick label 'Prediction Accuracy' which is located at {coordinates}[0.019, 0.919, 0.190, 0.942]."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "UCAmRicds5kXc9A5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.104, 0.180, 0.854, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two application case line objects within these coordinates. Both the line for Application Case 1 at {coordinates}[0.104, 0.765, 0.854, 0.911] and the line for Application Case 2 at {coordinates}[0.104, 0.180, 0.854, 0.911] are located here, represented with blue and red colors respectively."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "LPqzZGzdCbsL3y9y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with coordinates [0.123, 0.468, 0.940, 0.662] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Services."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "uEZDNo5wgtDtIon9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with {the tick on the x-axis}<r>[0.287, 0.911]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "I2oX6td3jL8klqEA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by {the leftmost y-axis tick label}<r>[0.082, 0.911]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "rPNK7HLHdvrrEy1A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Manufacturing'}<r>[0.205, 0.405]</r> on the line lower or higher than {the point for 'Logistics'}<r>[0.205, 0.615]</r> in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "gCWwTwm0JwfXnfdf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value in 2018, the {line representing 'Marketing'}<r>[0.777, 0.409]</r> or the {line representing 'Services'}<r>[0.777, 0.505]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Services."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "48xdYiut2fltbdZK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for 'Product Development' increasing or decreasing between the points {2016}<r>[0.614, 0.210]</r> and {2017}<r>[0.695, 0.208]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "t0zzLDvK1Iq5mXzp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information contained in the region [0.091, 0.718, 0.342, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend for the line labels: {Manufacturing}<r>[0.147, 0.727, 0.273, 0.751]</r>, {Logistics}<r>[0.147, 0.762, 0.222, 0.785]</r>, {Marketing}<r>[0.147, 0.797, 0.234, 0.820]</r>, {Services}<r>[0.147, 0.832, 0.220, 0.855]</r>, and {Product Development}<r>[0.147, 0.867, 0.335, 0.890]</r>, each paired with a unique color marker."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "QWfav05RNxE29HI4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.546, 0.919, 0.591, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the 2015 label of the x-axis."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "hG8kzwRlNzsEZB2H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.819, 0.919, 0.863, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label for the year 2019 is located here."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "N41s8f59cbvZxSai",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the seventh x-axis tick label}<r>[0.590, 0.939, 0.625, 0.957]</r> in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "oWSqPfw9QVBKljWE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.015, 0.394, 0.029, 0.565] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "iRqo9tlTT63ciLTE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis label at {coordinates}<r>[0.509, 0.964, 0.538, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "hZB1KMipVj2hXJY6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Policy 3}<r>[0.104, 0.069, 0.943, 0.121]</r> above or below {Policy 2}<r>[0.104, 0.631, 0.943, 0.890]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "HUqBy3dMEu6QhvvH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Policy 4}<r>[0.104, 0.328, 0.943, 0.717]</r> above or below {Policy 1}<r>[0.104, 0.415, 0.943, 0.631]</r> throughout its trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "I2tcfgVsEWm2gP1Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy has a higher effectiveness score in 2020, Policy 1 or Policy 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy 4."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "aj4R3mpuyYMni1EW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the area {coordinates}<r>[0.035, 0.926, 0.057, 0.944]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '0.8' is located at these coordinates."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "A4UQKuwSXfSEJ0Zq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the entire {x-axis section}<r>[0.506, 0.939, 0.541, 0.957]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label '2015' is located here."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "eaKVdCrO6CvVUb6e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the details that are presented from {coordinates}<r>[0.338, 0.939, 0.373, 0.957]</r> on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "An x-axis tick label '2013' is positioned at this location."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "d0opSs0W4t1BrrP4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the x-axis label represent in the chart? Its coordinates are [0.498, 0.931, 0.527, 0.954]."
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "WobksUTS3zZgc6MS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological field does the legend coordinate [0.176, 0.176, 0.322, 0.199] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "qpGawGBaNDRlJgfr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for the y-axis tick label \"10\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.103, 0.573, 0.120, 0.596]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "SpZ5ImBdaWHQDiJm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for 'Artificial Intelligence' in 2014 [[0.317, 0.649]] higher or lower than the point for 'Blockchain' in the same year [[0.317, 0.744]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "vH1BQIaKNwesV6E2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for 'Biotechnology' in 2020 [[0.786, 0.751]] higher or lower than the values for 'Quantum Computing' in the same year [[0.786, 0.622]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "j93GmkC8YHE2tzoF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has a higher position at {the year 2016}[0.473], 'Artificial Intelligence' [0.473, 0.563] or 'Blockchain' [0.473, 0.726] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "DHiOFmyhZFpXT7Et",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area bounded by the coordinates [0.160, 0.563, 0.865, 0.855] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "it contains the trend line for Quantum Computing over the years, represented by a line with square markers."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "yFloY5b2xVdT5uXN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.160, 0.711, 0.865, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the trend line for 'Artificial Intelligence' over different years. It is marked with circular dots along the line."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "fQSOYvs4nibTXNhu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.299, 0.912, 0.669, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary chart title, \"Future Technological Development Prospects\". It's located centrally above the chart."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "TcOGsCyuDeJeffhC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label related to the coordinates [0.810, 0.423, 0.959, 0.446] in the chart's legend section?"
+            },
+            {
+                "from": "gpt",
+                "value": "Living Conditions."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "b8JC0ZfHGYMFhM7h",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat coordinate does the x-axis label '2015.0' lie in?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.703, 0.919, 0.763, 0.942]"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "kG5NHSqU7WFjjXYj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {coordinates}<r>[0.019, 0.399, 0.036, 0.574]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "4DbEAnKmQIktKz0y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Living Conditions' at '2000'}[0.113, 0.872] plotted higher or lower than {the point for 'Housing Cost' at '2000'}[0.113, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "Q67XqJZSpfg5VczU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {line for 'Life Expectancy'}[0.113, 0.101, 0.940, 0.174] and {line for 'Housing Cost'}[0.113, 0.868, 0.940, 0.871], which line is consistently higher throughout 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Housing Cost."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "CaqqGyzZcS4hlOSn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the end point of {the line for 'Living Conditions'}[0.113, 0.871, 0.940, 0.872] at 2020 higher or lower than its start point at 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "rpaFzlIbFaTeJVDV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinate [0.755, 0.413, 0.973, 0.560]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend section. It contains labels for 'Living Conditions', 'Life Expectancy', 'Housing Cost', and 'Employment Rate', each color-coded and tagged with a different marker symbol."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "0cBQi6dqXNKOyZqt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is shown in the region [0.248, 0.025, 0.804, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows the chart's subtitle 'Human Life & Living Environment Trends 2000 - 2020'."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "el6XmrCCfOs0lbiC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the data in the region [0.113, 0.868, 0.940, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the 'Living Conditions' line in the chart, spanning from the year 2000 to 2020, with an increasing trend depicted by the upward rising line."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "gGRRzugNZ7DuQyQs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary color of {the line representing 'Avg Class Size'}<r>[0.155, 0.896, 0.942, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "Wz5OOipfTixJKTbL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the marker shape} for 'Active Students' represented in the data line<r>[0.155, 0.054, 0.942, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Triangle."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "jFYQFZad2pQLdPVJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker style used to represent {the line for 'Active Teachers'}<r>[0.155, 0.891, 0.942, 0.892]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "fRK6vG6geaDU5P4p",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {2014 data point for 'Active Students'}<r>[0.504, 0.103]</r> above or below {2014 data point for 'Avg Class Size'}<r>[0.504, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "iAla88VWndq4m6j5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {year's point}<r>[0.242, 0.132]</r> is higher for 'Active Students', {2011}<r>[0.242, 0.132]</r> or {2012}<r>[0.330, 0.123]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2012."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "JIkkDA5lBluLm31R",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first point for 'Active Teachers'}<r>[0.155, 0.891]</r> have a higher y-coordinate than {the first point for 'Active Students'}<r>[0.155, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "A4o3unkPPZFIBBAq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.126, 0.062, 0.316, 0.164]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a legend for the graph, including items for 'Active Teachers', 'Active Students', and 'Avg Class Size' with their respective color-coded markers."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "eZRoiv464swNakW5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the area [0.050, 0.445, 0.110, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis is contained in this region, showing a vertical label 'Count / Class Size' and multiple y-axis tick labels ranging from 0.411 to 0.821."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "3KtN9yeQZOMRMOD5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the zone [0.155, 0.054, 0.942, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This broad section encapsulates the entire primary chart area, displaying a series of data points with line representations for 'Active Teachers', 'Active Students', and 'Avg Class Size' on an increasing timeline."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "OV1S2phIjRSrnitX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}<r>[0.413, 0.943, 0.664, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fuel Economy (mpg)."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "U9Hyoj5IffDjIVdd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the legend item}<r>[0.864, 0.782, 0.966, 0.805]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lambda-S+."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "liZkx3PHGKrAecO6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich model is associated with {the marker}<r>[0.810, 0.855, 0.855, 0.869]</r> shown in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Our Model."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "4fnCyDbQHW3ouy73",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the fourth point}<r>[0.740, 0.181]</r>, Lambda-S or DeepTraffic?"
+            },
+            {
+                "from": "gpt",
+                "value": "DeepTraffic."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "x7mKGACeDQ9ZOBfl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for {Our Model}<r>[0.135, 0.357, 0.941, 0.552]</r> above or below the line for {DeepTraffic}<r>[0.135, 0.070, 0.941, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "aLkqs7a5k0oIQbue",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Lambda-S+}<r>[0.135, 0.568, 0.941, 0.728]</r> and {DeepTraffic}<r>[0.135, 0.070, 0.941, 0.181]</r>, which model has higher value at {20.0 mpg}<r>[0.135, 0.728]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "DeepTraffic."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "0yUSDjmedBlv48YI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within the region <r>[0.135, 0.070, 0.941, 0.728]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses four lines, each representing a model's classification accuracy at different Fuel Economy (mpg) values. The models are Lambda-S, Lambda-S+, DeepTraffic, and Our Model. The lines are colored differently and marked with different markers for distinction."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "pgJSt9KWMWqGukKj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region <r>[0.808, 0.738, 0.973, 0.884]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend which indicates four lines Lambda-S, Lambda-S+, DeepTraffic, and Our Model. Each label is associated with a colored marker matching the line of the corresponding model in the chart."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "hQFI1yVWpEc8tja6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are in the region <r>[0.013, 0.884, 0.095, 0.896]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two objects are here. The first is an x-axis tick {at 17.5}<r>[0.012, 0.904, 0.058, 0.934]</r>, and the second is a y-axis tick {at 0}<r>[0.076, 0.884, 0.089, 0.914]</r>."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "20TB5hh4w1YV1mRS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.297, 0.034, 0.679]</r> in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Influence on Public Opinion (%)."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "0XQ2CNZDFIF26iD0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media source does the tick label have at {coordinates}<r>[0.102, 0.933, 0.133, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "CNN."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "1BWlb9mU491gqWUh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label}<r>[0.050, 0.500, 0.071, 0.523]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "50."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "OwIXOeTXIgXCsXKB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Elections' line}<r>[0.117, 0.233, 0.942, 0.593]</r> start at a higher or lower value than {the 'Immigration' line}<r>[0.117, 0.300, 0.942, 0.693]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "frezxOs3OIYKYgzQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point for media source 'BBC', is the {value for 'Elections'}<r>[0.282, 0.317]</r> higher or lower than the {value for 'Immigration'}<r>[0.282, 0.425]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "oVzQj7USMLqepnx1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher value for the 'FOX' media source, {'Elections'}<r>[0.200, 0.258]</r> or {'Immigration'}<r>[0.200, 0.300]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Elections."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "Lythudx2yOPxBtGo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region <r>[0.333, 0.933, 0.397, 0.951]</r> in the line chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text for the media source 'REUTERS'."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "UnLGPA6hs6tBg6Yw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area <r>[0.118, 0.233, 0.945, 0.593]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures the line representing 'Elections', showing a sequence of individual points and a line connecting them in order from left to right."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "e3sL7Jd8vvyEX2AJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is enclosed in the region <r>[0.127, 0.065, 0.190, 0.108]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend entries for 'Elections' and 'Immigration'. Each is represented textually and also with a color that matches the lines on the chart: blue for 'Elections' and orange for 'Immigration'."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "cPLVpBVqa57gjw5W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.063, 0.703, 0.206, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label 'Moderate decline'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "IgEwDakEdQIUy7NO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate of the point in the Innovation line whose x is 'Abrupt drop'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{point_coordinates}[0.447, 0.440]"
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "GbOKqlahSYG51vII",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of {the object}<r>[0.848, 0.081, 0.933, 0.104]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its label is 'Creativity'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "DABLXXtV4xlpGqKr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is more significant, {the point indicating 'Innovation' on 'Steep decline'}<r>[0.291, 0.344]</r> or {the point indicating 'Creativity' on 'Steep decline'}<r>[0.291, 0.471]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Innovation'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "tjZbem6nYZGi9kNJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point 'Innovation' on 'Consistent expansion'}<r>[0.604, 0.121]</r> higher or lower than {the point 'Innovation' on 'Moderate decline'}<r>[0.134, 0.153]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "NYh52bayhhoRj14d",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point indicating 'Creativity' for 'Abrupt drop'}<r>[0.447, 0.567]</r> higher or lower than {the point indicating 'Creativity' for 'Erratic decline'}<r>[0.917, 0.248]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "6jRgYwuDN9GscczU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the region [0.134, 0.089, 0.917, 0.567]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two line graphs, one for 'Innovation' and another for 'Creativity', with specific data points plotted for 'Moderate decline', 'Steep decline', 'Abrupt drop', 'Consistent expansion', 'Subdued decline', and 'Erratic decline'."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "pK0aSBYzXGJH1WV6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region [0.793, 0.037, 0.947, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart, with 'Innovation' and 'Creativity' listed as labels, each paired with their respective colored markers represented by {coordinates for Innovation}[0.794, 0.049, 0.840, 0.063] and {coordinates for Creativity}[0.794, 0.084, 0.840, 0.098], respectively."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "qOC1UTxVZ5UsnHZ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents found within the bounding box [0.233, 0.703, 0.349, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bounding box contains the label 'Steep decline' on the x-axis."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "NvO4ILeQFTAqnija",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description within the coordinates [0.104, 0.928, 0.130, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an x-axis tick label with text \"5.0\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "jBY8gvL3pDTSYJL7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.781, 0.839, 0.818, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color marker for the legend item \"Research Centers\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "LaXsdTp8vblpeRuI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.825, 0.810, 0.957, 0.828]</r> refer to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"Startups Incubated\"."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "Qeg48uXP46B1Orv7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Startups Incubated line}<r>[0.117, 0.475, 0.944, 0.613]</r> have a higher or lower point than the {Ecosystem Partners line}<r>[0.117, 0.427, 0.944, 0.517]</r> at SNR 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "63o8n3WdAK3koJjz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Research Centers line}<r>[0.117, 0.607, 0.944, 0.724]</r> above or below the {Policies Implemented line}<r>[0.117, 0.222, 0.944, 0.415]</r> at SNR 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "B9i88Wr84TdPjqJL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, {Ecosystem Partners}<r>[0.117, 0.427, 0.944, 0.517]</r> or {Startups Incubated}<r>[0.117, 0.475, 0.944, 0.613]</r>, has a lower value at SNR 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ecosystem Partners."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "CDaNpLy1qROyh6Ms",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside coordinates [0.780, 0.803, 0.978, 0.913]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a chart legend. Items present are \"Startups Incubated\", \"Research Centers\", \"Ecosystem Partners\", and \"Policies Implemented\", each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "7xHmsI8GqOVPWm76",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.061, 0.913, 0.071, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a y-axis label marked as '0'."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "U92vJ7GPG8TR5PSo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.117, 0.427, 0.944, 0.613]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of two lines, the \"Startups Incubated\" line and the \"Ecosystem Partners\" line, showing the trend in percentage values for different SNR points."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "wpevIezgrgsOVTEz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label represent at these coordinates [0.019, 0.222, 0.043, 0.711]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Technology Adaption Rate (%)."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "BeSqrTRw9j7NSsIf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the label '2020' on the X-Axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.914, 0.904, 0.967, 0.934]."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "QxpBnaKR0KE1ll3q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line item} in the subplot legend at [0.146, 0.162, 0.240, 0.186]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "jrtPUWUhSaSlHx8X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at {the 'Multimedia Technology' data points}[[0.123, 0.282], [0.286, 0.258], [0.450, 0.245], [0.613, 0.196], [0.777, 0.159], [0.940, 0.123]], are the values increasing or decreasing over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "E9XAGS4cLtqUp33v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Blockchain' line chart}[[0.123, 0.405], [0.286, 0.712], [0.450, 0.650], [0.613, 0.589], [0.777, 0.528], [0.940, 0.405]] have more or fewer points than the {2015 X-axis tick label}[0.096, 0.904, 0.149, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "rDXUaaOdZ187AJqh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {2016 y-coordinate data point}'s value for 'Artificial Intelligence' at [0.286, 0.626] more or less than its 2015 value at [0.123, 0.650]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "52585dmoZTdIK9xY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.082, 0.037, 0.981, 0.896]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the main body of the chart, including the x-axis and y-axis labels, all the data points and lines representing the four technologies (Multimedia Technology, Virtual Reality, Artificial Intelligence, and Blockchain), and the grid background."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "dM3t6lD58XDXGFOT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area at [0.096, 0.904, 0.967, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the tick labels for the x-axis, representing different years from 2015 to 2020."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "ixd7Po9F9GcSju4Z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.091, 0.048, 0.352, 0.195] comprise of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the subplot legend that lists the four technologies: {Multimedia Technology}[0.146, 0.057, 0.346, 0.081], {Virtual Reality}[0.146, 0.092, 0.268, 0.116], {Artificial Intelligence}[0.146, 0.127, 0.324, 0.151], and {Blockchain}[0.146, 0.162, 0.240, 0.186]. Each technology label is paired with a colored marker."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "UcR61VKmgImvnmBv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.509, 0.956, 0.553, 0.981]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "8x0FNVdzeY0zuGOK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy resource is represented by {the line with the colored marker '<r>[0.087, 0.096, 0.126, 0.106]</r>'}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Natural Gas Consumption."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "4Gvv8x4DCMNTOpm1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year corresponds to {the x-axis tick label}<r>[0.221, 0.921, 0.250, 0.949]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2002.5."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "6jAXpK2LJstCXESl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend of 'Coal Consumption'}<r>[0.118, 0.103, 0.944, 0.447]</r> located above or below {the trend of 'Oil Consumption'}<r>[0.118, 0.357, 0.944, 0.639]</r> at the starting point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "KA1zU2OQXwfqTcv8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, {the 'Renewable Energy'}<r>[0.118, 0.075, 0.944, 0.864]</r> or {the 'Natural Gas Consumption'}<r>[0.118, 0.041, 0.944, 0.385]</r>, ends higher in the last data year marked?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Energy."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "seRVxPaIUPiJnZ1E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 2010 point of 'Oil Consumption'}<r>[0.531, 0.470]</r> higher than {the point for the same year of 'Natural Gas'}<r>[0.531, 0.216]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "EdQVWacjIvlnmLzO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region framed within <r>[0.015, 0.860, 0.072, 0.878]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '300' marked at a coordinate position indicative of the lower values for energy usage."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "3sAoNfqI0dWVmi4S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified in the area enclosed by <r>[0.041, 0.292, 0.072, 0.314]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart features the y-axis tick label '500', indicating a value towards the middle range for energy usage and resource consumption as per the plot scale."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "KEr7ALjAS15fmCKJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the rectangle from <r>[0.077, 0.019, 0.985, 0.921]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main area of the plot displaying multiple lines representing different energy sources such as Coal, Oil, Natural Gas, and Renewables, tracking their consumption over years."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "ecOFU6OQN5pQfZIq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at the coordinates [0.502, 0.956, 0.549, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "Kii8s4joJhtfm3UT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at the coordinates [0.118, 0.139, 0.229, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text label for 'Immunotherapy' in the legend."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "lAPlHnaNISVTNs6H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Y-axis label placed at [0.015, 0.347, 0.035, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adoption Rate (%)."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "Q2zZQVbjeTwOIXgb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the plotted line for {Immunotherapy}<r>[0.108, 0.356, 0.943, 0.644]</r> above or below the plotted line for {Telemedicine}<r>[0.108, 0.101, 0.943, 0.797]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "a9G0RQKf3VBNX4wY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the point on the plotted line for {AI Diagnostics}<r>[0.108, 0.238, 0.943, 0.885]</r> at the year {2005}<r>[0.317, 0.839]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "35.972."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "oAun1TSLCXNLt7os",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the lines for {Radiology}<r>[0.108, 0.147, 0.943, 0.251]</r> and {DNA Sequencing}<r>[0.108, 0.246, 0.943, 0.502]</r>, which one is located higher in the chart for the year {2015}<r>[0.735, 0.188]</r> and {2015}<r>[0.735, 0.309]</r>, respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Radiology."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "oCc6cJ5Izv55Js3k",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within [0.108, 0.356, 0.943, 0.644]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted line for Immunotherapy, with points representing data from different years."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "D8STa72e3OAVKs2w",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area marked by [0.073, 0.036, 0.240, 0.172] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend describing the different trends represented in the chart: Immunotherapy, Radiology, AI Diagnostics, DNA Sequencing, and Telemedicine, each labeled with a unique color and marker symbol."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "bsgGuVfLgoL2I4Rn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe everything from coordinates [0.041, 0.294, 0.062, 0.316]."
+            },
+            {
+                "from": "gpt",
+                "value": "At this location, it has the y-axis label '75'."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "Yv6Or1dQHn6GmG2S",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.551, 0.011, 0.551, 0.011]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot subtitle."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "tK1pD7bzmfkP3mnR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-coordinate for the first item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.132, 0.049, 0.177, 0.063]."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "i1aIKq90JBWluJdL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label refer to at {coordinates} [0.525, 0.943, 0.577, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label which is \"Year\"."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "HzPG9LWexX3hCAki",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the points of {the 'Modern Art' line} [0.160, 0.083, 0.942, 0.614] and {the 'Ethnic Art' line} [0.160, 0.315, 0.942, 0.702], which art type had a higher revenue in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Modern Art."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "Rma8QdMjZJ0Xc5uA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Performing Art' revenue point in 2015} [0.551, 0.600] higher or lower than {the 'Artisan Crafts' revenue point in 2015} [0.551, 0.590]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "Y8fIYkjml0zm7ktP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {2011 revenue of 'Modern Art'}[0.239, 0.586] compare to the {2011 revenue of 'Performing Art'}[0.239, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "Nm0AvJLoohjwMkh4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.121, 0.025, 0.981, 0.896]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the primary chart, including all the line trends for \"Modern Art\", \"Ethnic Art\", \"Performing Art\", and \"Artisan Crafts\". It also includes x-axis with label \"Year\" and y-axis with label \"Revenue ('000 USD)\""
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "ZT3atlXb5cf5DCCp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat resides in the region [0.130, 0.037, 0.319, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend with four items: {Modern Art}[0.186, 0.046, 0.282, 0.069], {Ethnic Art}[0.186, 0.081, 0.271, 0.104], {Performing Art}[0.186, 0.116, 0.312, 0.139] and {Artisan Crafts}[0.186, 0.151, 0.304, 0.174], each with a corresponding color marker."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "bR5TQBKA64gTRwlB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the area [0.595, 0.312, 0.755, 0.645]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area primarily includes points and line segments from the trends 'Modern Art', 'Ethnic Art', 'Performing Art', and 'Artisan Crafts'. It also contains x-axis tick labels '2016' and '2018'."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "wiropLv987DWp30X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.019, 0.216, 0.043, 0.705]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Expenditure (in billion dollars)."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "IaIt65jGmM1RpNMP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with coordinates [0.135, 0.555, 0.941, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Healthcare expenses over the years."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "FTw3yQKu8z6hX9ax",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Transportation expenses'}<r>[0.135, 0.170, 0.941, 0.308]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "BKHhrcpRTW7WEKhp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {value for 'Public Housing'}<r>[0.135, 0.736, 0.941, 0.809]</r> increase or decrease over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "LTTu6Bll7ZZwI6Tz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has higher expenditure in year 3, {Education}<r>[0.538, 0.286]</r> or {Transportation}<r>[0.538, 0.272]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Education."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "67s0SbUWnch4ihjt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point for 'Healthcare'}<r>[0.941, 0.555]</r> in year 5 higher or lower than the {point for 'Education'}<r>[0.941, 0.221]</r> in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "6I57jeymj5qggj9x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the region <r>[0.783, 0.738, 0.973, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with labels for 'Healthcare', 'Education', 'Public Housing', and 'Transportation. Each label is paired with a color-key as a line mark."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "7mNXkmk3Fypneilq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the left-side region <r>[0.019, 0.216, 0.043, 0.705]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label 'Expenditure (in billion dollars)' for the chart."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "N4W6no1wLV6puek7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.129, 0.904, 0.948, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the x-axis tick labels corresponding to the years: '1', '2', '3', '4', and '5'."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "YkYGfxERhqtRPGow",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, what is the second entry's label?"
+            },
+            {
+                "from": "gpt",
+                "value": "Biotechnology R&D."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "RiW7FKyo08CRqRIu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label on y-axis}<r>[0.019, 0.326, 0.043, 0.595]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "R&D activity (%)."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "5PnnjykBzhJ0w1qJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the last point represent for the 'Pharmaceutical R&D' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value for 2030."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "MmSVs8T2RltQFCFS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the trends in 2030, which has a higher value, {Pharmaceutical R&D}<r>[0.940, 0.087]</r> or {Healthcare Services R&D}<r>[0.940, 0.174]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pharmaceutical R&D."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "LX8K3pYRdMP7XzLP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Biotechnology R&D'}<r>[0.123, 0.398, 0.940, 0.772]</r> show a general increasing or decreasing trend over years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "l4Zb7BAjjJeVpSPM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, is {the value for 'Medical Equipment R&D'} <r>[0.532, 0.299]</r> above or below {the value for 'Biotechnology R&D'}<r>[0.532, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "64cXCuw5P8csr9jj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents within the region <r>[0.748, 0.747, 0.927, 0.770]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label for the legend item 'Pharmaceutical R&D'."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "r4FEmFKCMElWFATN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the region <r>[0.096, 0.904, 0.149, 0.934]</r> represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label '2010'."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "nTJAVeskUTfWJjte",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the area bounded by <r>[0.301, 0.904, 0.354, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label for the year '2015'."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "pUpnyRNaIsHiXx8C",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label {coordinates}[0.015, 0.353, 0.034, 0.588] represents the 'Popularity Rate (%)'."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "MaLIbFfzSBZbrvH9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the coordinates [0.943, 0.470] on the 'Gym Membership' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "This indicates the 'Gym Membership' Popularity Rate at the year 2020."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "MOph2AHkLHoBdGeR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item in the legend located in this area {[0.850, 0.889, 0.973, 0.906]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item is 'Gym Membership'."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "91mAtE0H7r3X2DGd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the Popularity Rate of {the 'Bootcamp'}[0.943, 0.771] in 2020 higher or lower than {that of 'Outdoor Fitness'}[0.943, 0.847]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "tpXVgicdsaARfQt3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2017, is {the line for 'Smart Bracelets'}[0.442, 0.169] above or below {the line for 'Outdoor Fitness'}[0.442, 0.591] in terms of Popularity Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "pYrUi5pGljt5byma",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2019, which has a lower Popularity Rate, {line 'Gym Membership'}[0.776, 0.425] or {line 'Smart Bracelets'}[0.776, 0.245]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Smart Bracelets."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "zDiJyNz4PWCjhF0A",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the [0.805, 0.803, 0.978, 0.913] region."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the line chart. Four items are included: {Bootcamp}[0.850, 0.810, 0.921, 0.828], {Outdoor Fitness}[0.850, 0.836, 0.959, 0.854], {Smart Bracelets}[0.850, 0.863, 0.962, 0.880], and {Gym Membership}[0.850, 0.889, 0.973, 0.906]. Each is paired with a colored marker."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "gsvsnS8Wvi5YLQ5R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.065, 0.019, 0.985, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main region of the line chart which includes x-axis and y-axis labels, line trend for four categories 'Bootcamp', 'Outdoor Fitness', 'Smart Bracelets' and 'Gym Membership' across years 2015 to 2020, and corresponding legend."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "vZLNkCFFhOanQgpY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details regarding the area [0.015, 0.353, 0.034, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis label \"Popularity Rate (%)\" that indicates the metric used for the line trends."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "NKrGEevJCDuesCD9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the entity at these coordinates [0.019, 0.267, 0.043, 0.654] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "cNqyo6kXKaiNokEl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide me with the coordinates of the caption stating 'Online Programs' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.146, 0.151, 0.290, 0.174]."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "hJtJjW2JqENUJduS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis tick label} positioned at [0.505, 0.904, 0.558, 0.934] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "0CpNQljIIib6D78v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Public Institutions'} [0.123, 0.324, 0.940, 0.460] have a placement that is above or below {the line representing 'Private Institutions'} [0.123, 0.280, 0.940, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "kn8TSARYlFGSaKA5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point marked as '2019'} [0.736, 0.896], does 'Home School' dip above or below 'Online Programs' in our resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "WVEdMv7S2HT49VG7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a greater percentage of resource allocation at {point 2018} [0.532, 0.896]: Home School or Public Institutions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Institutions."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "TDWjIzTAnJDzWY7h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the area demarcated by the following coordinates: [0.091, 0.037, 0.316, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a graphical legend containg the items: {Public Institutions}[0.146, 0.046, 0.300, 0.069], {Private Institutions}[0.146, 0.081, 0.309, 0.104], {Home School}[0.146, 0.116, 0.260, 0.139] and {Online Programs}[0.146, 0.151, 0.290, 0.174], with each having a distinct marker."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "RyaniY3KqEuYQ5bY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what can be found within the region [0.056, 0.904, 0.965, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the x-axis labels displaying the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "pRc0bi5vd95557Vn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the content observed in the region [0.123, 0.087, 0.940, 0.274]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the line graph that represents the trend of 'Online Programs', together with its associated data points across the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "btZ3S8G6bCvFAubG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.128, 0.039, 0.244, 0.059]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"EM Stock Market\"."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "uHnVc3CfIU0Tvqbc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the co-ordinate of the label '2011' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.179, 0.883, 0.221, 0.943]."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "p0dvoNq6FEk4eRPH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.327, 0.034, 0.570]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Value."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "RyVHjn4oUblktSpW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the lines' final points, did {EM Stock Market}<r>[0.944, 0.225]</r> perform better or worse than {Disposable Income}<r>[0.944, 0.187]</r> in the final year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "dNpWNaSKdYGzn7Y6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point '2016' in the x-axis, is the {point for 'Consumer Confidence'}<r>[0.613, 0.269]</r> higher or lower than the {point for 'Private Investment'}<r>[0.613, 0.269]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Same."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "l6MeOAVrufGy44ql",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 2011, which has a higher value, {EM Stock Market}<r>[0.200, 0.352]</r> or {Disposable Income}<r>[0.200, 0.396]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Disposable Income."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "NuXwcPPUj7hIMguT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the region [0.083, 0.031, 0.286, 0.157]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items for the line chart which includes {EM Stock Market}<r>[0.128, 0.039, 0.244, 0.059]</r>, {Consumer Confidence}<r>[0.128, 0.069, 0.281, 0.089]</r>, {Private Investment}<r>[0.128, 0.099, 0.259, 0.119]</r>, {Disposable Income}<r>[0.128, 0.129, 0.259, 0.149]</r>, all paired with a color distinct marker."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "ZvRA9GHZliXfAgXA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.096, 0.883, 0.965, 0.943]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels for the chart, ranging from the year 2010 to 2020."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "xUN8EufVbIlGOp4c",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.117, 0.159, 0.944, 0.435]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the line data points for Consumer Confidence and Private Investment."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "Rt0woRrsMZ6X3hN9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with the coordinates [0.040, 0.608, 0.061, 0.633] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label '45'."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "PntDhbyMO3XSTSYG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.917, 0.843, 0.972, 0.863]."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "JKShpiHA4JPrWBJR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend label}<r>[0.917, 0.873, 0.973, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Policy D\"."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "U4ykZ8foMQZaYd4M",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Year_2' on 'Policy B'}<r>[0.316, 0.559]</r> higher or lower than {the point for 'Year_5' on 'Policy B'}<r>[0.943, 0.422]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "Ue0EsQWW8DE3VQHh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 4, is the effectiveness of {Policy A}<r>[0.734, 0.676]</r> greater than or less than {Policy C}<r>[0.734, 0.618]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "bcGytjUNfbuOVOx5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy has a higher effectiveness at {Year_5}<r>[0.943, 0.911]</r>, Policy C or Policy D?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy D."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "Nkk7ZCERMuXl51kl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.873, 0.775, 0.978, 0.901]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart with labels for Policy A, B, C, and D each paired with a unique color and marker symbol."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "CaGuiveCcf2wIlVR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.107, 0.422, 0.943, 0.618]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area are the line graphs for Policy A and Policy B, they span from Year 1 to Year 5. Each line has different patterns or point markers."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "55i5EwE07BExfoY7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.730, 0.918, 0.948, 0.943]?"
+            },
+            {
+                "from": "gpt",
+                "value": "One object, the x-axis label '5', is located at these coordinates."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "k0UdTRH8I9iOjKgn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the 'IT Training' line}[0.135, 0.092, 0.941, 0.360] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "Xjdk6ZsYFq9tPlq8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the 2018 marker in the Soft Skills Training line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.780, 0.253]"
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "7EeDfw2ZFFkDviHi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the text and color details of {the label on the y-axis}[0.019, 0.273, 0.043, 0.648]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Performance Score (%)\" and the color is '.15'."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "HgJqzis1o0VJaglm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Leadership Training' line}[0.135, 0.414, 0.941, 0.628] above or below {the 'Process Improvement Training' line}[0.135, 0.454, 0.941, 0.762] in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "x4eQO9Fljw6L2VSg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich training type had a higher score in 2018, {IT Training}[0.780, 0.159] or {Soft Skills Training}[0.780, 0.253]?"
+            },
+            {
+                "from": "gpt",
+                "value": "IT Training."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "zLfPDQeMgiuTcJhb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the 'Soft Skills Training' line}[0.135, 0.186, 0.941, 0.427] and {the 'Leadership Training' line}[0.135, 0.414, 0.941, 0.628], which one showed an overall upward trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Soft Skills Training."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "8kCDSMcHrXq8MoeS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the display area [0.109, 0.904, 0.162, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label that corresponds to the year 2010."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "ctjimY4CnXQIYG0t",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.704, 0.852, 0.966, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the text \"Process Improvement Training\" is located. It is linked to the color '#d62728', which represents the Process Improvement Training line on the chart."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "YWGCvPBTR73oiEtd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.646, 0.735, 0.974, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subplot legend with its items 'IT Training', 'Soft Skills Training', 'Leadership Training', and 'Process Improvement Training', each associated with a specific color."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "Fmda0rDF9ONKrfbd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}[0.015, 0.361, 0.035, 0.556] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indicator Value."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "awL7goyPVZUgk3Qr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what {the line in the color '#2ca02c'}[0.118, 0.145, 0.944, 0.263] stands for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Credit Supply."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "FoSxfNCgnvAeKfqM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label for {the first tick on the x-axis}[0.097, 0.896, 0.140, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2007."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "u9JuFgjpPJUvmboj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the plotted lines has the highest value at {the year '2015'}[0.647, 0.896, 0.690, 0.949] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Credit Supply."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "XpRG3gEX3SWQyfkv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Inflation Rate'}[0.256, 0.877] greater than or less than {the point for 'Financial Stability Index'}[0.256, 0.341] in 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "obs5KO4YTwene8vv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the chart, during which year did the {Consumer Confidence Index} line first cross the value of 90?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "Gkh6FSRmL5HI70uR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents at this position [0.732, 0.771, 0.978, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the chart. It lists labels and their corresponding markers for {Consumer Confidence Index}[0.777, 0.778, 0.973, 0.796], {Financial Stability Index}[0.777, 0.804, 0.942, 0.822], {Global Credit Supply}[0.777, 0.831, 0.919, 0.848], and {Inflation Rate}[0.777, 0.857, 0.870, 0.874]."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "ZWwK2LITadGk8jz8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.077, 0.028, 0.985, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire plot area of the line chart. It consists of four lines representing Consumer Confidence Index, Financial Stability Index, Global Credit Supply and Inflation Rate with their respective color codes and markers over the years from 2007 to 2019."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "3cFNHsLNou1tB5y7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the region [0.041, 0.019, 0.072, 0.041] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the lowest y-axis tick label which denotes the numerical value '110'."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "eSsB8JbiSu6STXUW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart top right at [0.015, 0.375, 0.034, 0.566]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value (Billion $)."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "bjzUe3CDcUeVhK4e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is hinted by the marker at the third item of the sub-legend coordinates [0.074, 0.089, 0.110, 0.100]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Home Prices (Billion $)."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "qIYhqt2XnOPavEYd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-tick label}<r>[0.922, 0.928, 0.964, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2019."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "uHqWXmpJ2bghYcOs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 2019 in 'Public Infrastructure Investment (Billion $)'}[0.943, 0.394] higher or lower than the {point of 2019 in 'Local Government Revenue (Billion $)'}[0.943, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "fbpEZ67SkS2m9hp7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Average Home Prices (Billion $)' series represented by cross marker given by {this series}<r>[0.107, 0.918, 0.943, 0.920]</r> increase or decrease over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increases."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "CSL3th4HBNDjVQUK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher in the year 2017, {Local Government Revenue (Billion $)}[0.525, 0.284] or {Public Infrastructure Investment (Billion $)}[0.525, 0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Public Infrastructure Investment (Billion $)."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "UXtbDCDr32Od2tAE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the coordinates [0.072, 0.027, 0.441, 0.137]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the line chart. The legend includes indicators such as Local Government Revenue (Billion $), Community Development Spending (Billion $), Average Home Prices (Billion $), and Public Infrastructure Investment (Billion $). Each indicator has a unique line style and marker."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "c8NmZlSci0QSJ0EF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.066, 0.019, 0.985, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main chart area. It contains 4 line series each representing 'Local Government Revenue (Billion $)', 'Community Development Spending (Billion $)', 'Average Home Prices (Billion $),', and 'Public Infrastructure Investment (Billion $)', plotted against the year on the x-axis."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "uyVN3Sc3Y8qYSQPi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.066, 0.561, 0.066, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the y-axis tick label '40'."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "jIiu9PWxdZAjY5vm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.036, 0.723, 0.071, 0.746] represent on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "1500."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "HwtgyW4AGOayooNE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker at {the point for the year 2017 on the 'US-China Balance of Trade' line}<r>[0.937, 0.910]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "sw1vTmeIsMDNnboC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year corresponds to the x-axis label located at [0.575, 0.928, 0.623, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "Jm1abAQENETRZXsx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line trend at {point for the year 2009}<r>[0.550, 0.829]</r> is higher, 'Global Trade Partners Sudden Change' or 'EU-China Balance of Trade'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Global Trade Partners Sudden Change."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "pVOLaYQ8TpLICCv5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the line for 'World Total Trade' at {point for the year 2017}<r>[0.937, 0.033]</r> above or below the line for 'US-China Balance of Trade' at {the same point}<r>[0.937, 0.910]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "1fFPF8UKZawMbAaH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend decreases more sharply between 2010 and 2012, {the 'US-China Balance of Trade'}<r>[0.599, 0.900]</r> or {the 'EU-China Balance of Trade'}<r>[0.599, 0.891]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "US-China Balance of Trade."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "UXoP1i8jMJYEauBw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area occupied by x-grid visible on the chart with the range [0.075, 0.922, 0.978, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-grid area contains horizontal white dashed lines, indicating each year's position scaling from 1997.5 to 2020.0."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "NUJ6q3fNy9SIFDUk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the section from [0.081, 0.027, 0.342, 0.137] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend, displaying labels and markers for 'US-China Balance of Trade', 'EU-China Balance of Trade', 'World Total Trade', and 'Global Trade Partners Sudden Change', with corresponding colored markers at {locations}<r>[0.082, 0.037, 0.112, 0.047]</r>, <r>[0.082, 0.063, 0.112, 0.073]</r>, <r>[0.082, 0.089, 0.112, 0.100]</r> and <r>[0.082, 0.115, 0.112, 0.126]</r>."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "7vfuokakzkMQwVaU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the objects found at [0.425, 0.869, 0.620, 0.888]."
+            },
+            {
+                "from": "gpt",
+                "value": "The space contains y-axis tick labels '2000.0', '2002.5', '2005.0' and their corresponding ticks, signaling the specific years on the x-axis."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "EnaG92QONebXgVbQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.643, 0.647, 0.728, 0.694] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ethereum."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "lfORXnqtrN5g143m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth task on the x-axis in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.754, 0.821, 0.765, 0.868]."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "YSdj64t8ZI3g12GK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.019, 0.153, 0.043, 0.777]</r> for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Usage (%)."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "FUlMsqLHPBvu19pG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Ethereum'}[0.597, 0.164, 0.963, 0.284] above or below {the line for 'Neobanks'}[0.597, 0.264, 0.963, 0.540]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "fv0UoZVyXn1wr7Df",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is the {Bitcoin} point at Task 3<r>[0.188, 0.420]</r> higher or lower than the {Blockchain} point at Task 3<r>[0.188, 0.493]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "UQHgKIFAMORDEyhe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at Task 6 in subplot 1, Bitcoin or Blockchain?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blockchain."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "UhXyQ0A16j5thiQj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the range [0.188, 264, 0.963, 540]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the Ethereum and Neobanks lines in the second subplot. The Ethereum line starts at Task 1 and extends to Task 10, with coordinates ranging from [0.597, 0.164] to [0.963, 0.284]. The Neobanks line also starts at Task 1 and extends to Task 10, its coordinates ranging from [0.597, 0.264] to [0.963, 0.540]."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "fb57QOW4AE8PBS1I",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components in the region [0.325, 0.148, 0.482, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend for the first subplot, with two items: {Bitcoin}<r>[0.381, 0.166, 0.440, 0.213]</r> and {Blockchain}<r>[0.381, 0.236, 0.475, 0.283]</r>."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Zk742QPALFQ98WiZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.519, 0.050, 1.039, 0.110]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle for the second subplot which reads 'Performance Comparison: Ethereum vs Neobanks'."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "KJMll0T9YQICRP4g",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {axes label}<r>[0.505, 0.370, 0.514, 0.598]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentages (%)"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "UEQRSGTQ1tHEsPE3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of {year '2024'} label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.727, 0.903, 0.750, 0.931]"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "UAAryECWZXvkqBDd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.010, 0.370, 0.019, 0.598]</r> in the first subplot of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentages (%)"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "3S9SYgtaGGEfGqXJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {year 2022}<r>[0.139, 0.903, 0.163, 0.931]</r>, {Training Spending}<r>[0.151, 0.492]</r> or {Promotion Rate}<r>[0.151, 0.825]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Promotion Rate"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "Jzqo15L9GBlh4Tjn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line labeled 'Work-From-Home Rate'}<r>[0.554, 0.112, 0.969, 0.391]</r> in general above or below {the line labeled 'Attrition Rate'}<r>[0.554, 0.650, 0.969, 0.856]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "3c6iD73hq72zoSSj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Training Spending}<r>[0.336, 0.247]</r> in 2026 higher or lower than in 2024<r>[0.243, 0.332]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "ZFcFzflVMppSepKt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the information}<r>[0.581, 0.030, 0.942, 0.066]</r> in the uppermost region of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the subplot title, \"Workplace Development: Work-From-Home Rate vs Attrition Rate.\""
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "aFSVOVo68mdmgyFR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in {the legend}<r>[0.538, 0.089, 0.676, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two legend items: {Work-From-Home Rate}[0.567, 0.100, 0.672, 0.128], with a color marker {color marker}[0.539, 0.104, 0.563, 0.120], and {Attrition Rate}[0.567, 0.142, 0.629, 0.170], with a {color marker}[0.539, 0.146, 0.563, 0.162]."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "jahGEHQ0smxGUYkv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe region [0.727, 0.903, 0.750, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts the label '2024' on the x-axis of the second subplot."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "LbVJUzQEXzk8mkKH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.453, 0.911, 0.481, 0.934] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label for year 2020."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "dOLNuXRKbPlhRVG7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.425, 0.083, 0.478, 0.106] in the sub-legend section?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label 'Microsoft'."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "sjNamyzNJ5uAwCsx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area enclosed by [0.270, 0.817, 0.270, 0.817] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for Apple's market share in 2018."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "0teAmWnIF0jsKrKG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the market share of Apple in 2017}<r>[0.171, 0.852]</r> greater than or less than {the market share of Microsoft in 2017}<r>[0.171, 0.137]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "jAwBlToFhFaU2TeP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {year 2020}<r>[0.468, 0.750]</r> and <r>[0.468, 0.332]</r>, did Microsoft or Apple have a higher market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "Microsoft."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "QT8bzjciVDNN5arO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Market share of Microsoft in 2016}<r>[0.072, 0.100]</r> have a higher value than {Market share of Amazon in 2020}<r>[0.968, 0.100]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "6Ov3rrKbNi8M9YVX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the area represented by [0.597, 0.025, 0.938, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of second subplot, with the text \"Market Share Comparison: Amazon vs Facebook\"."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "2hORSoR1GweHHR96",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the area [0.552, 0.074, 0.655, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for subplot_1. It includes two items: {Amazon}<r>[0.594, 0.083, 0.641, 0.106]</r> and {Facebook}<r>[0.594, 0.118, 0.648, 0.141]</r>, each paired with its respective marker."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "hHScrLpK6sUf0jIl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found inside the coordinates [0.400, 0.062, 0.859, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of subplot_1 including the y-grid lines, a portion of the Amazon and Facebook data lines, and x-tick labels for 2016 to 2019."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "SIPUregNTtDvstuG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object located at [0.259, 0.943, 0.294, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label in the left subplot which is indicating the 'Task'."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "3hlg9oXexN1qCSS8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label } at [0.033, 0.221, 0.055, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "120."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "IRsLdNs4Ex9sMgnt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the handled object found at [0.269, 0.085, 0.301, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color marker for the line representing 'Global Trade Volume (billion $)'."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "rj0CYSMjD09gQFf6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is lower, {the point at Task 1 in the left subplot}[0.079, 0.342] or {the point at Task 1 in the right subplot}[0.573, 0.454]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at Task 1 in the right subplot is lower."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "G7POxlSD6QNtOfRv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point at Task 6 in the right subplot}[0.792, 0.460] and {the point at Task 7 in the right subplot}[0.836, 0.454], which one is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point at Task 7 in the right subplot is higher."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "6aNl6P2BZrAfEB3q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {value at Task 4 in the left subplot}[0.210, 0.242] greater or lesser than {the value at Task 4 in the right subplot}[0.704, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "gqnSvUB34C9Phz8i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.269, 0.074, 0.488, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend area of the left subplot, providing the label 'Global Trade Volume (billion $)' and the associated color marker is located here."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "xzm4vcsUwMC7Etpr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area defined by [0.376, 0.025, 0.913, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the titles of the subplots are located. They say 'Trend in Global Trade Volume' and 'Trend in Supply Chain Performance Index' for the left and right subplots respectively."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "a6dO9B0RO6oI9ylo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are present in [0.506, 0.438, 0.522, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis label of the right subplot 'Value', displayed vertically."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "f7SXh3hcl2xbWBpL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.562, 0.050, 0.922, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the second chart."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "qLDZFhc2cJen8Elc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.537, 0.170, 0.584, 0.203] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marker for the \"Total Malware Cases\"."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "AobqkyxTzOmm2Q53",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel} at [0.686, 0.887, 0.798, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Category."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "4aJsfQdZFNHXD3RV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Total Cases'}[0.604, 0.397] higher or lower than {the point of 'Cases with Data Breach'}[0.880, 0.451] in 'Total Malware Cases' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "AiwFrzl4S4tpyJUx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Total Phishing Cases'}[[0.113, 0.261, 0.389, 0.315]] generally above or below {the line for 'Phishing Cases with Data Breach'}[[0.113, 0.315, 0.389, 0.364]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "iDQ3jkqdZSII5eZr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has a higher value at {point 'Cases with Data Breach'}[0.880, 0.451], Total Malware Cases or Malware Cases with Data Breach?"
+            },
+            {
+                "from": "gpt",
+                "value": "Malware Cases with Data Breach."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "2B1hVIqnlFzyQpAs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease, describe the content in the area with coordinates [0.536, 0.148, 0.885, 0.301]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the second chart. It includes {the label 'Total Malware Cases'}<r>[0.592, 0.166, 0.768, 0.213]</r> and {the label 'Malware Cases with Data Breach'}<r>[0.592, 0.236, 0.878, 0.283]</r>, both paired with colored markers."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "syfhD42kxZdBzXjd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.099, 0.124, 0.403, 0.805]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the first subplot, illustrating phishing cases versus data breaches. It displays two lines, named 'Total Phishing Cases' and 'Phishing Cases with Data Breach', with different markers."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "JMilfuyk4ePb2Dsf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description for the region [0.590, 0.124, 0.894, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the second subplot showing malware cases compared to the instances of data breaches. Two plotted lines represent 'Total Malware Cases' and 'Malware Cases with Data Breach'. Also, both lines are distinguished by different types of markers."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "GGBebCvAnYLc0k1k",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Investment A}<r>[0.077, 0.363, 0.466, 0.831]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "NLhXGlF4N14CYJ7I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the x-axis tick at {position}<r>[0.427, 0.866]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "I3CFNo8LY7oWXJSD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {Investment D}<r>[0.576, 0.128, 0.966, 0.535]</r> on its line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "6tuW0KGfnU5ACMoo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher starting investment value, {Investment C}<r>[0.576, 0.461, 0.966, 0.831]</r> or {Investment D}<r>[0.576, 0.128, 0.966, 0.535]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment D."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "r9hHA3o5WmwA0O9h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the points at {year 5 for Investment B}<r>[0.232, 0.207]</r> above or below {year 5 for Investment A}<r>[0.232, 0.675]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "miwfR8zMMuVJ39eo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn year 9, which investment shows a greater value, {Investment A}<r>[0.388, 0.441]</r> or {Investment C}<r>[0.888, 0.609]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment A."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "dkb8hIasTFnOjNMr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features present in the region <r>[0.056, 0.093, 0.486, 0.866]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot for {Investment A}<r>[0.077, 0.363, 0.466, 0.831]</r> and {Investment B}<r>[0.077, 0.128, 0.466, 0.467]</r>, displaying their investment values over the years with respective color-coded lines, blue for A and red for B. Each point on the line is marked by a circle."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "WIBlFFZ3nejNpka0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the elements within <r>[0.556, 0.093, 0.985, 0.866]</r> represent data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area has two line plots for {Investment C}<r>[0.576, 0.461, 0.966, 0.831]</r> and {Investment D}<r>[0.576, 0.128, 0.966, 0.535]</r>, each indicated with a different color, green and magenta, respectively. These plots illustrate investment amounts over 11 years using circular markers."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "bVLPC3cwND03D3u0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the space <r>[0.337, 0.111, 0.479, 0.226]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend with text labels for {Investment A}<r>[0.381, 0.125, 0.473, 0.160]</r> and {Investment B}<r>[0.381, 0.177, 0.473, 0.212]</r>, each next to a colored rectangle representing the line color on the charts."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "amCa9LR7pUjfelMu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by {the handle of 'Startup Companies'}<r>[0.377, 0.102, 0.400, 0.122]</r> in the color coding of the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "A blue square."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "gb4AOuWUSNYN1jCR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the first x-tick label in the second subplot}<r>[0.560, 0.903, 0.565, 0.931]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "AS5m1C7rou8ISbfA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of marker represents 'Government Support' as indicated in {its legend handle}<r>[0.866, 0.148, 0.886, 0.159]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "RcI94ELK35QqJaUQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point at month 3 for 'Startup Companies'}<r>[0.158, 0.605]</r> higher than {the point at month 9}<r>[0.430, 0.599]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "esjxWbFOBgsIFpGC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is positioned higher, {the y-tick labeled '1000'}<r>[0.022, 0.473, 0.044, 0.501]</r> or {the y-tick labeled '1500'}<r>[0.022, 0.268, 0.044, 0.296]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "1500."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "q5hnCgvKpz6jhnLA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the seventh x-tick label in the first subplot}<r>[0.336, 0.903, 0.342, 0.931]</r> have a lower value compared to {the ninth x-tick in the same subplot}<r>[0.427, 0.903, 0.432, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "Bd4V8348tmyrhPcP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.542, 0.075, 0.991, 0.182]</r> contain in terms of chart elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {the axes for the second subplot}<r>[0.542, 0.075, 0.991, 0.893]</r> which includes the {legend}<r>[0.864, 0.089, 0.986, 0.181]</r> with items for 'Innovation Index' and 'Government Support'."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "liJMEIMO40r37nbg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region <r>[0.047, 0.075, 0.495, 0.181]</r> and its function or classic PH factors."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {the first subplot's axes}<r>[0.047, 0.075, 0.495, 0.893]</r> and the {mini-legend}<r>[0.376, 0.089, 0.491, 0.181]</r> containing labels and color handles for 'Startup Companies' and 'R&D Spend'."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "Nootu3gy4I574tqG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the information enclosed within the region <r>[0.022, 0.268, 0.044, 0.501]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts {y-tick labels}<r>[0.022, 0.268, 0.044, 0.501]</r> which range from '750' to '2000', indicating varying data values on the y-axis."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "1fNgscR6HqZMaN4W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {ylabel} at [0.010, 0.425, 0.023, 0.532] in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "48WkQSDIBdBoik8q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {xlabel} at [0.727, 0.932, 0.762, 0.970] in the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "pKX2nkYrpdgX0cKq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second {xtick label} at [0.638, 0.893, 0.730, 0.921] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accelerated Decline."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "1toodnipN4BUUESf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point with coordinates [0.310, 0.337] higher or lower than the point with coordinates at [0.431, 0.386] in subplot_0 of the Data Science trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "93lMfkWDzlD9dfUB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the line points in subplot_0 and subplot_1, is the value at the 'Inexorable Rise' in the 'Data Science' trend line ([0.310, 0.337]) or the 'Robotics' trend line ([0.805, 0.228]) higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data Science."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "fE2iStucxor4S1nI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, is the value at 'Subdued Expansion' point [0.068, 0.540] or 'Unpredictable Fluctuations' point [0.431, 0.386] higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subdued Expansion."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "3ENHkIyiuxbaf64Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.274, 0.893, 0.493, 0.921] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two x-axis labels for trends in Data Science. These are {Subdued Expansion}[0.022, 0.893, 0.114, 0.921] and {Unpredictable Fluctuations}[0.368, 0.893, 0.493, 0.921]."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "4LovStiskwt5jdwq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.196, 0.030, 0.303, 0.066] in subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {the subtitle of the subplot}[0.196, 0.030, 0.303, 0.066]. The text reads \"Data Science Trend\"."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "e3xcWsGInwGotAFD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.815, 0.089, 0.939, 0.139] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {legend entry}[0.815, 0.089, 0.939, 0.139] for 'Robotics Innovation' with a marker in color code '#e7969c' is located here."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "sIjh7M4Ua5JVneR3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the given coordinates [0.508, 0.403, 0.522, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the subplot_1."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "cVH0kDigmGC11wcu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label say at the coordinates of [0.081, 0.030, 0.462, 0.066]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Emerging Markets Index in different countries\"."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "z5b2kao9UQLoCiz5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.035, 0.498, 0.052, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "One of the y-axis tick labels on subplot_0."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "P86XjTnpk5T1dipe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, is {Brazil}[0.077, 0.459] higher or lower in the index than {China}[0.174, 0.112] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "zXbOW8suBAyml0qb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the market values for {India}[0.272, 0.509] and {Russia}[0.467, 0.161] in subplot_0 higher or lower than {China}[0.174, 0.112]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "lj7nDSMfUhGQD0VB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {China}[0.667, 0.112] in subplot_1 have a higher or lower Consumer Behavior Index value compared to {Russia}[0.959, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "DyiDkjguiIhBlXLA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the area [0.567, 0.030, 0.961, 0.066] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle for subplot_1, which says \"Consumer Behavior Index in different countries\"."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "AS0aoY1BpfRZBM15",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.057, 0.075, 0.486, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the plot for subplot_0, which shows the Emerging Markets Index for different countries, represented as a blue line graph. Indices for different countries like Brazil, China, India, Mexico, and Russia are plotted."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "4YPk9lKGDFVc62vu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region [0.550, 0.075, 0.979, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the plot for subplot_1, detailing a pink line graph of Consumer Behavior Index values across different countries, including Brazil, China, India, Mexico, and Russia."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "GssCJSK1343WoeZP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label described at the coordinates [0.009, 0.368, 0.021, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Popularity (%)."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "8ca8E1hVcnO9OSXk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at {[0.469, 0.911, 0.480, 0.934]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label '10'."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "1DNV4xZPTpUHAXUy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the object at}[0.893, 0.074, 0.986, 0.151] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the trends 'Thai Desserts' and 'Russian Vodka'."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "438FSLTXENX2bqKJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values for {Italian French Fusion}[0.065, 0.139, 0.475, 0.253] in general ascending, descending or fluctuating over the months?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ascending."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "FpuYk7wQLKp87bgQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Thai Desserts}[0.560, 0.482, 0.970, 0.658] and {Russian Vodka}[0.560, 0.635, 0.970, 0.765], which trend has a higher popularity in the 10th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Russian Vodka."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "72bS2JkvTWU7vLs9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Cuban Sandwiches} trend line [0.065, 0.398, 0.475, 0.475] show an increase or decrease in popularity over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "TtLbwzJYYsALU7hS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the area given by the coordinates [0.540, 0.062, 0.991, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the second subplot of the chart which displays the trends of Thai Desserts and Russian Vodka over time. It includes the plot lines, axis labels, tick marks, and the trend legend."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "JPdmGLBlr28f71iR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region [0.046, 0.074, 0.168, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the chart legend for the first subplot, containing the labels for {Italian French Fusion}[0.076, 0.083, 0.165, 0.106] and {Cuban Sandwiches}[0.076, 0.118, 0.158, 0.141], alongside their respective color indicators."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "0d5IsUpDKQMgN61W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.642, 0.025, 0.888, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the subtitle for the second subplot, which is \"Trend Analysis: Thai Desserts vs Russian Vodka\"."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "UpDlUXsDM99OVyck",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the marker for 'Agriculture'}<r>[0.406, 0.085, 0.431, 0.101]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "QcWkJfoBaZgVrqg3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for representing {the line for 'Services'}<r>[0.565, 0.558, 0.970, 0.672]</r> data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "6ZNnySK25L6sjXD6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label color of {the y-axis}<r>[0.010, 0.341, 0.023, 0.624]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dark gray."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "zkDJDRcr09KA8yQ1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the trend for '2010'}<r>[0.070, 0.461]</r> in {Retail data}<r>[0.070, 0.335, 0.475, 0.461]</r> above or below {the trend for '2010'}<r>[0.565, 0.672]</r> in {Services data}<r>[0.565, 0.558, 0.970, 0.672]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "KwnB3GHFrejBTytn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data point is higher, {the '2016' marker for Agriculture}<r>[0.340, 0.268]</r> or {the '2016' marker for Retail}<r>[0.340, 0.382]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Retail."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "Oy3OyyQjhE0PaHeG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare {the starting point for Manufacturing in '2010'}<r>[0.565, 0.575]</r> with {the ending point for the same in '2019'}<r>[0.970, 0.386]</r>; did it increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "2eNn5KBBFiADe3kU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bounded by [0.406, 0.074, 0.490, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains {the color handles}<r>[0.406, 0.085, 0.431, 0.101]</r> and {the text labels}<r>[0.436, 0.083, 0.487, 0.106]</r> for the 'Agriculture' data line, indicating its color in the chart."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "SntyBKsbyzBvOTj4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the enclosed space [0.259, 0.943, 0.286, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart houses {the x-axis label 'Year'}<r>[0.259, 0.943, 0.286, 0.975]</r>, positioned centrally below the x-axis ticks."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "c1SFfpHi2nVedYfZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region spanning [0.527, 0.053, 0.542, 0.076]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area consists of {the y-axis tick label '20.0'}<r>[0.521, 0.053, 0.542, 0.076]</r> and {its corresponding tick mark}<r>[0.545, 0.062]</r> on the chart."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "AU15Hd5bnqrKZnQt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line with coordinates from [0.107, 0.164] to [0.472, 0.459] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance of Asian Recipes."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "TiZRpL3WDP2CZsSA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content encased by the coordinates [0.507, 0.192, 0.533, 0.737]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the second subplot."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "7hdehplrqPHmHQz5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at [0.107, 0.164] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first point of the 'Asian Recipes' trend line."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "ummtIQPHDyrkiTNU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the performance of {Asian Recipes}[0.107, 0.164] higher or lower than {Western Recipes}[0.597, 0.287] at Task 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "PBaqRRgtrb0MyCkj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich recipe shows an increasing trend at {Task 8}[0.391, 0.392, 0.882, 0.451], Asian or Western?"
+            },
+            {
+                "from": "gpt",
+                "value": "Western Recipes."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "uszNkDrnfZDkcUvi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Asian Recipes}[0.188, 0.247] performance increase or decrease from Task 2 to Task 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "Gj7PSQMlwVI3ZYaP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information spanned by the coordinates [0.300, 0.148, 0.482, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the first subplot indicating the performance of {Asian Recipes}[0.355, 0.166, 0.475, 0.213], symbolized with a blue square marker handled at [0.301, 0.170, 0.347, 0.203]."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "WKqhoaABIZdB2itT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.592, 0.821] to [0.974, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis labels '1' to '10' for the second subplot."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "Fpee38jkpdoyl5d5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the coordinates [0.579, 0.124, 0.981, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the second subplot that deals with the 'Performance of Western Recipes across 10 Tasks', it also includes x-axis and y-axis labels, grid lines, tick labels, and a line chart."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "kpwYbTnD2KseW5nx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of plot is the main object represented by the coordinates [0.075, 0.075, 0.493, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subplot of a line plot."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "9IxUUbcPCY6Skbjt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data point located at coordinates [0.178, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a data point for 'Twitter' on Week 3 showing 540 platform usage."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "3lgQPKMMYgprCNZX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.551, 0.030, 1.001, 0.066] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the subplot title: 'Usage Comparison: Instagram vs Online Communities'."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "6hIkzQBYdApb6rdG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {usage of 'Twitter'}[0.094, 0.387, 0.474, 0.521] show an increasing or decreasing trend from Week 2 to Week 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "AiZi6GMJuloGi3fF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher on Week 5, the {usage of 'Facebook'}[0.262, 0.298] or the {usage of 'Twitter'}[0.262, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "H7bhigFTxXQMQ9ud",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform had a higher usage at Week 10, {Instagram}[0.966, 0.674] or {Online Communities}[0.966, 0.759]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Communities."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "FULytYmui2s6CAdv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region between [0.370, 0.089, 0.486, 0.181] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the first subplot. It includes details for the 'Facebook' line which is represented in blue and the 'Twitter' line represented in orange."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "rjT5f1rAkKGQNKmK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.574, 0.089, 0.765, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the second subplot. It includes 'Instagram' labeled in green and 'Online Communities' labeled in red."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "NiO8TpSv85HTyktT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.043, 0.733, 0.070, 0.761] to [0.075, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the y-axis labels between 200 and 300 for Platform Usage."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "1uBHiik44DYbtGsL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at {coordinates} [0.112, 0.821, 0.123, 0.868] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quarter 1 x-axis tick label."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "S8FLA9KPsbC8z7HP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line does the point [0.963,0.252] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to the Mobile Sales line."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "xPI0IojWYsT9WxNU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the line with color '#e74c3c' and marker 'v'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Sales."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "bTm5MrGRSKwqRsD9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the 6th quarter, do Mobile or In-Store Sales have a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Sales."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "AGI8HIWl1Bck7iy0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {first data point of the In-Store Sales line}<r>[0.608, 0.741]</r> higher or lower than the {first data point of the Online Sales line}<r>[0.117, 0.550]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "nQ4CWE4REWLOcsHm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the segment line of 'Online Sales'}[0.117, 0.124, 0.473, 0.550] above or below {the segment line of 'Offline Sales'}[0.117, 0.507, 0.473, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "cWy6AKWhHAiywi9p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.108, 0.148, 0.280, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sub-legend for the first subplot, including labels for 'Online Sales' and 'Offline Sales', each with their own color markers."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "e0v1aNsAskZpobTk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.599, 0.148, 0.781, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the sub-legend for the second subplot, which includes labels for 'Mobile Sales' and 'In-Store Sales', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "J96A4brvEKFf7oGm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.058, 0.747, 0.094, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '4000' is located here."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "S5DxLgvuBOtOH3FN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.011, 0.378, 0.024, 0.584]</r> of the left subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Sales (units)\""
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "pHplxu1Bhz4mOlgW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at position <r>[0.582, 0.113, 0.966, 0.440]</r> in the right subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing \"Gasoline Sales\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "O55iNVDwtWhAVbqc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of {the object}<r>[0.873, 0.148, 0.899, 0.164]</r> in the right subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the marker symbol for \"Diesel Sales\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "NY2h68GMFx0a0WTM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the left subplot, does the series represented by {the line }<r>[0.088, 0.113, 0.471, 0.603]</r> have higher or lower sales in 'Q4_next' than action that series represented by {the line }<r>[0.088, 0.309, 0.471, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "sQOllYsRjX5y82LG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the right subplot, which series sees an increase in sales from 'Q2' to 'Q3' - the series represented by {the line }<r>[0.582, 0.113, 0.966, 0.440]</r> or the series represented by {the line }<r>[0.582, 0.701, 0.966, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing \"Diesel Sales\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "9IFLtFomtDTjsZ47",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the left subplot, which series reaches the lower sales point at 'Q3_next': the series represented by {the line}<r>[0.088, 0.113, 0.471, 0.603]</r> or the series represented by {the line}<r>[0.088, 0.309, 0.471, 0.848]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line representing \"Electric Sales\"."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "ixl0CUlfgED1CRjr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the features of the area <r>[0.069, 0.077, 0.490, 0.885]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a subplot of the chart, displaying the trend in \"Hybrid Sales\" and \"Electric Sales\" over time. The x-axis represents different 'Quarters', while the y-axis signifies the 'Sales (units)'. Two distinct lines, one for each sales type, show the fluctuation in unit sales across the quarters."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "XJaT48plqBrlKeST",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region <r>[0.872, 0.091, 0.980, 0.183]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the right subplot of the chart. It specifically includes indicators for 'Gasoline Sales' and 'Diesel Sales', each represented by a different color and marker symbol."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "jXQdAdf23iN8vYTR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.563, 0.077, 0.985, 0.885]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a subplot of the chart, graphically representing \"Gasoline Sales\" and \"Diesel Sales\" over time. It contains two lines, each denoting one type of sales. The x-axis defines the 'Quarters' and the y-axis represents the 'Sales (units)'."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "DhMtllDiKi1wZwWC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.153, 0.647, 0.199, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sushi label."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "TclVgwbSG7McUGNL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.907, 0.442, 0.954, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pasta label."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "fHGupDMRxpWrFReT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the provided coordinate [0.431, 0.281, 0.437, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marker."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "av7Pa4LSo1aPcCBZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Pizza point at time period 5}[0.760, 0.145] above or below {the Pasta point at time period 5}[0.760, 0.670]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "yP0OX20s21kzUBDR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at time period 3, {Sushi point}[0.188, 0.205] or {Dim Sum point}[0.188, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dim Sum."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "59RoC4YtRIeidTpn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Burger point at time period 8}[0.882, 0.315] have a higher value compared to {the Pizza point at the same time period}[0.882, 0.217]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "RgdUpMKWtz4dojqR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.550, 0.233, 0.990, 0.805]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains multiple plots showing popularity percentages of Pizza, Pasta, and Burger. {Pizza's data points}<r>[0.597, 0.128, 0.963, 0.220]</r> and {Pasta's data points}<r>[0.597, 0.583, 0.963, 0.736]</r>, along with {Burger's data points}<r>[0.597, 0.315, 0.963, 0.356]</r> are spread throughout this space."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "rfwIsPwGt7b4XA7l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed between the coordinates [0.085, 0.100, 0.490, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The left subplot showcases {Sushi's plot}<r>[0.107, 0.175, 0.472, 0.290]</r> and {Dim Sum's plot}<r>[0.107, 0.147, 0.472, 0.259]</r> tracking popularity over various time periods."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "KTJR534uOZIoKa1f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section from [0.400, 0.230, 0.592, 0.900]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region overlaps with two separate line charts comparing the progress of distinct cuisines. The latter halves of {Sushi's and Dim Sum's plots}<r>[0.107, 0.147, 0.472, 0.290]</r> are visible, including some later {time period points}<r>[0.426, 0.821, 0.463, 0.868]</r>."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "WlolnQyShbYNblPo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label} at [0.013, 0.312, 0.028, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Count (in thousands)."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "CHpUGrWEGsEf51uR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.375, 0.083, 0.483, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gym Memberships."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "JbgvSsfb3Z16xUMn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label} at [0.256, 0.943, 0.290, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "s9y5mCe9ttVm651k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, is the data point for Gym Memberships in year 7 higher or lower than year 8 found at {coordinates}[0.340, 0.470] and [0.384, 0.464] respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "drMyKsW38apZo6qr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first data point for Cycling Participation at {coordinates}[0.566, 0.477] higher or lower than the 5th data point at {coordinates}[0.744, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "MWqk9eqJkFA2n8Oe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of {Gym Memberships} from year 5 to year 10 based on the provided chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing trend."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "5whV5YvPcPyl1D9s",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area defined by [0.745, 0.940, 0.780, 0.975] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for the second subplot, 'Year'."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "ENK5OEqqavCcxReV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the content inside [0.338, 0.074, 0.488, 0.116]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the subplot 'Gym Memberships', colored in a particular shade and marked with a square symbol."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "jus3EfSyWqdvl26r",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be found within the area [0.066, 0.913, 0.077, 0.935]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is an x-axis label '1' for the first subplot."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "MOY9JbZE8SVD1AIW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.509, 0.106, 0.533, 0.824]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label of the second sub-plot."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "T6XtZ25D9dIZjgFF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the first label on the x-axis in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.101, 0.821, 0.112, 0.868]"
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "OQI7OgaKz5ba3AUu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the x-axis tick} at [0.592, 0.821, 0.603, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "rlOraO3mW8vfsUiF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Optimized Quality'}[0.597, 0.479, 0.963, 0.775] finish above or below {the line for 'DBMS Consolidation'}[0.597, 0.649, 0.963, 0.701]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "Lg3pfLUo1aOc6itW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {task 3}[0.290, 0.262], the line for 'Early Integration' or 'Advanced Streaming'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Early Integration."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "N63g3WYCZWLMO9TP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid the 'Optimized Quality' line rise or decline between {task 1}[0.597, 0.479] and {task 2}[0.689, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decline."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "7hLUuBzdqHHXrTsD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.097, 0.629, 0.341, 0.782]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains two items of the legend, {Early Integration}[0.153, 0.647, 0.298, 0.694] and {Advanced Streaming}[0.153, 0.717, 0.334, 0.764], represented with the colors '#1f77b4' and '#17becf' respectively."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "VGUmoBOqhx1DoPAW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.375, 0.821, 0.478, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labeled tick '5' of x-axis on the subplot_0 is located here."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "i6CUWRwanxVtfVkv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.101, 0.821, 0.204, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two x-axis labels - '1' at {coordinates}[0.101, 0.821, 0.112, 0.868] and '2' at {coordinates}[0.193, 0.821, 0.204, 0.868] in the subplot_0."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "oVUmsSYKJHlchijL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.073, 0.821, 0.116, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick label 'Drag'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "dvJ8pKNueAXNYDnm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is plotted at {the point}[0.585, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the point where 'Drag' is intersected in the Logistics department."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "4eWP0G8POGJYgrRc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the line}[0.585, 0.278, 0.920, 0.346] styled in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is styled as a solid line with marker 'o' and color '#e7969c'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "fKCroHCLIerC0Rv6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Drag'}[0.585, 0.278] in the Logistics department higher or lower than {the point of 'Delivery Time'}[0.920, 0.346] in the same department?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "4j1LcwPlNib1PGTm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the label 'Quality Control'}[0.300, 0.166, 0.431, 0.213] located to the left or right of {the label 'Logistics'}[0.847, 0.166, 0.922, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "ZFqbh0tmLoDDpyM6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is closer to {the point 'Drag'}[0.585, 0.278], the x-axis or the y-axis label?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "6L5oZFkkq33BmNM3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.245, 0.148, 0.438, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart sub-section 'Quality Control Trend Analysis', with the label 'Quality Control' colored in '#2171b5'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "xlCiqfhGB4MN0pi8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be seen in the box [0.568, 0.124, 0.937, 0.805]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subplot region of 'Logistics Trend Analysis'. It contains lines representing the trends, x and y axes with labels 'Factors' and 'Quality Scores', grid lines, subtitle and legend for 'Logistics' in color '#e7969c'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "Rx8m1AW5Sp6ajnuo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region [0.791, 0.148, 0.929, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a sublegend for the 'Logistics Trend Analysis' subtitled chart. It contains the legend item 'Logistics', color coded with '#e7969c'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "DM1Q0nLuJzSPxdJg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the x-axis label in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.739, 0.932, 0.800, 0.970]"
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "P3L8qghOKQN4qxBt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.048, 0.710, 0.057, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "eqsBuSqAVMnaDsIR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text with coordinates [0.885, 0.142, 0.947, 0.170] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend label 'Studying'."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "zGCJbd062ORCI1iP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher value at point 8, {Sleeping}[0.386, 0.204] or {Running}[0.386, 0.730]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Running."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "5psKOapvI2B571oz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Social Media'}[0.574, 0.536, 0.965, 0.673] above or below {the line for 'Studying'}[0.574, 0.641, 0.965, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "8Zrx7Ini7tWVuUCC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity had a higher average time spent in Month 1, {Sleeping}[0.082, 0.293] or {Studying}[0.574, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sleeping."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "cde9gl1nkUPKO7wM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.841, 0.089, 0.978, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the second subplot. It has two items, {Social Media}[0.841, 0.102, 0.973, 0.122] and {Studying}[0.841, 0.144, 0.947, 0.164], each represented with different colors and markers."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "u2CrpzST12Yx8XYk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.069, 0.089, 0.179, 0.181]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the legend for the first subplot. It consists of two elements: {Sleeping}[0.070, 0.102, 0.173, 0.122] and {Running}[0.070, 0.144, 0.107, 0.164], each associated with a distinct color and symbol."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "LxXOEA24fSKhGtKp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_52_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within [0.474, 0.087, 0.999, 0.920]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section encompasses the right subplot of the chart. It includes the line graph of {Social Media}[0.574, 0.536, 0.965, 0.673] and {Studying}[0.574, 0.641, 0.965, 0.713] trends along with their corresponding legend [0.841, 0.089, 0.978, 0.181], axis labels and tick labels."
+            }
+        ],
+        "id_raw": 1039
+    },
+    {
+        "id": "VRmFGgfYJviMYIiI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.369, 0.033, 0.560]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Amount."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "5pmotkaEMKbjymfg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.187, 0.065, 0.885, 0.385]"
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "l6Wls0agRjXStDY0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what {the seventh x-axis tick label}<r>[0.755, 0.912, 0.816, 0.936]</r> is?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q3_2019."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "FpFHrp7LRJwWyyfP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Product A'}<r>[0.486, 0.774]</r> at Q4_2018 higher or lower than {the point for 'Product B'}<r>[0.486, 0.811]</r> at the same time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "6mq9SxsRlgwK0FnL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Product C'}<r>[0.885, 0.065]</r> at Q4_2019 located above or below {the point for 'Product A'}<r>[0.885, 0.838]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "jaBoJKiTAEwpcZ4z",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product has a higher sales amount at Q1_2019, {Product A}<r>[0.586, 0.784]</r> or {Product B}<r>[0.586, 0.795]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product B."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "y0ps2Z5Jw660nC3C",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the area <r>[0.094, 0.037, 0.211, 0.148]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart. Inside this area, we have the legends for three different products: {Product A}<r>[0.139, 0.046, 0.206, 0.069]</r>, {Product B}<r>[0.139, 0.081, 0.206, 0.104]</r>, and {Product C}<r>[0.139, 0.116, 0.206, 0.139]</r>, each has a marker in different colors."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "Kr9wgx6ESEnlUT7X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the area <r>[0.500, 0.945, 0.573, 0.975]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Quarters\" is located in this area."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "IW0i9fDYz9bvCTQx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region <r>[0.187, 0.638, 0.287, 0.764]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, two lines intercepted. These lines represent the sales amount of {Product A}<r>[0.187, 0.704, 0.287, 0.731]</r> and {Product B}<r>[0.187, 0.864, 0.287, 0.838]</r> for the periods of Q1_2018 and Q2_2018."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "fM3LbTl9Gj81kqiD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line} at [0.217, 0.057, 0.831, 0.272] representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "TV Influence."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "NiyPwsAuZehtgtCK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x coordinate} for the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.524, 0.941]."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "ej422pvzoE0I027d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the term {the legend} at [0.114, 0.069, 0.192, 0.083] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Newspaper."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "v5WPI9774zSyqLBi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, is the {Influence of TV}[0.831, 0.272] more than {Influence of Social Media}[0.831, 0.272]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "PMtnWg1D2UbctQ3U",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich influence saw a greater decline between 2000 and 2005, {TV}[0.217, 0.057, 0.370, 0.110] or {Newspaper}[0.217, 0.281, 0.370, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Newspaper."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "yUEMgEZ7nQHZyEUw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, was {Social Media Influence}[0.524, 0.667] less or more than {Newspaper Influence}[0.524, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "nxz6dXjoeRE6jeyV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.217, 0.057, 0.831, 0.272] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart represents the line graph showing the decline in TV influence from 80.0% in the year 2000 [0.217, 0.057] to 60.8% in the year 2020 [0.831, 0.272]."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "iHHyA959bG46M0EN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of [0.218, 0.272, 0.831, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region marks the ascent of Social Media Influence from 5.0% in 2000 [0.218, 0.899] to 60.8% in 2020 [0.831, 0.272]."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "pqiGrAHqZoaoBxRR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.217, 0.281, 0.831, 0.679] on the figure illustrate?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the line graph highlighting the decline in Newspaper influence from 60.0% in the year 2000 [0.217, 0.281] to 24.6% in the year 2020 [0.831, 0.679]."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "9pwr23DRLzL7EKni",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.229, 0.601]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point of Heart Rate Monitor line."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "q02dOgof731G3FLi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the vertical grid line} at [0.530, 0.025, 0.530, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grey."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "QM9dnoRZZtDWrW1q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to {the coordinates}[0.091, 0.051, 0.133, 0.061]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Handle of Heart Rate Monitor."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "PbbK13PKUEsgFRh0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at year 2015 for Heart Rate Monitor}[0.680, 0.775] above or below {the point at year 2015 for Blood Pressure Monitor}[0.680, 0.307]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "pfUEZOENPVr0zJrQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label holds a higher value at {coordinate}[0.830, 0.899], 2020 or 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "OJFfPtexxAYL8a8h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the legend text for 'Glucose Monitor'}[0.143, 0.081, 0.284, 0.104] above or below {the legend text for 'Blood Pressure Monitor'}[0.143, 0.116, 0.343, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "CG9UaGhP1pNDAdYV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.130, 0.046, 0.343, 0.139]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains labels and handles for {Heart Rate Monitor}<r>[0.143, 0.046, 0.309, 0.069]</r>, {Glucose Monitor}<r>[0.143, 0.081, 0.284, 0.104]</r>, and {Blood Pressure Monitor}<r>[0.143, 0.116, 0.343, 0.139]</r> as legend items."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "VhdDz3V4LxZ6wONt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.019, 0.331, 0.073, 0.595]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Error Rate (%)\" is styled in gray color."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "U6S2Iqxo83GuoPWm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in [0.200, 0.580, 0.900, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes x-tick labels for the years 2000 to 2020 along with their corresponding tick marks."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "q7hKqKfeuNCf6kGl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is referred to by the object with coordinates [0.373, 0.325, 0.712, 0.650] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the Facebook data line in the line chart."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "qaAhSOFV1kzKV7EQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-axis tick label has its coordinates at [0.051, 0.642, 0.084, 0.660]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label '100'."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "bBl2d8hwYOJQxMJj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the object with these coordinates [0.098, 0.087, 0.147, 0.101]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the LinkedIn data line color marker in the legend."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "81b0DizTFS0TrG2c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point marked {20}[0.277, 0.932, 0.299, 0.949], is {the Facebook data point}[0.277, 0.354] higher or lower than {the Twitter data point}[0.277, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "OOHxbADZfVAeC4a9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {LinkedIn data line}[0.164, 0.708, 0.907, 0.885] generally above or below the {Facebook data line}[0.164, 0.060, 0.907, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "1ttywnsKHuGTWof4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the age mark {45}[0.896, 0.932, 0.918, 0.949], {Twitter data line}[0.164, 0.354, 0.907, 0.796] or {LinkedIn data line}[0.164, 0.708, 0.907, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter data line."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "2mFpjoWC2AgKyEuO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.098, 0.027, 0.243, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the line chart; it contains three color markers corresponding to the three data lines: {Facebook}[0.098, 0.035, 0.147, 0.049], {Twitter}[0.098, 0.061, 0.147, 0.075], and {LinkedIn}[0.098, 0.087, 0.147, 0.101], along with their respective color-coded labels."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "WPzMLW3AWFmiBqbM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the region with these coordinates [0.535, 0.226, 0.907, 0.708]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In these coordinates, we have the data points of {Twitter popularity}[0.535, 0.661, 0.907, 0.708] and {LinkedIn popularity}[0.535, 0.781, 0.907, 0.708]"
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "5Ejl9l2B7Mzzm5TT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be found within the region [0.401, 0.932, 0.423, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is an x-axis label, representing the age mark '25'."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "r3k92NRPaaPtvEyP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis represent in this line chart, based on the label provided at coordinates [0.025, 0.331, 0.058, 0.595]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Error Rate (%)."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "iYYDcTmiRNF67M0y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.627, 0.416], and which line does it belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a point on the 'Engineering' line."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "n3X5lPHDO0ARmL0m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the content of the x-axis label located at [0.471, 0.942, 0.609, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sectors."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "dp9nGxAzp711FReC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the 'Cruncher' line {point for 'Manufacturing'}[0.627, 0.555] and the 'Scientific' line {point for 'Manufacturing'}[0.627, 0.149], which error rate is higher in the Manufacturing sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "Scientific."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "tQJjoArBqJ4aYw3k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Pharmaceutical' sector, is the error rate of {Cruncher}[0.279, 0.861] higher or lower than {Engineering}[0.279, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "tIrY6Dz0Z7ViP4C2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line represents the highest error rate in the 'ICT' sector, {Cruncher}[0.801, 0.065], {Engineering}[0.801, 0.390] or {Scientific}[0.801, 0.332]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cruncher."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "0syqFTwAPggTWrGN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area with coordinates [0.116, 0.037, 0.339, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Cruncher}<r>[0.191, 0.046, 0.297, 0.069]</r>,  {Engineering}<r>[0.191, 0.081, 0.329, 0.104]</r>, and {Scientific}<r>[0.191, 0.116, 0.296, 0.139]</r>, each paired with a different colored marker."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "4iBLeu3XHxPE3ujo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.279, 0.065, 0.801, 0.861]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the data points for the 'Cruncher' error rates across all sectors, namely {Pharmaceutical}<r>[0.279, 0.861]</r>, {Construction}<r>[0.453, 0.694]</r>, {Manufacturing}<r>[0.627, 0.555]</r>, and {ICT}<r>[0.801, 0.065]</r>."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "dM1SYCz2hp8X0jNz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present in the region defined by coordinates [0.782, 0.909, 0.820, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label 'ICT' part of the x-axis tick labels."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "w7d7BiDTkBfsDJUX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.191, 0.081, 0.424, 0.104]</r> in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Urban Development."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "Zcn19nCg6E7YHv7B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.540, 0.011, 0.540, 0.011]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "lJf2BwVlZzkAhke4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-coordinate}<r>[0.685, 0.103]</r> of the Urban Development line for the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "HEdnbhywmQhF5EJi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Rural Development point}<r>[0.830, 0.292]</r> in 2020 higher or lower than {the Urban Development point}<r>[0.830, 0.065]</r> in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "J0tVmpvHkYZQ5ECM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Rural Development'}[0.250, 0.292, 0.830, 0.861] exhibit an upward or downward trend through the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upward."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "LyCw2XH1QAwrv52E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2010, was the {Rural Development index}<r>[0.540, 0.634]</r> higher or lower than the {Urban Development index}<r>[0.540, 0.160]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "S3Gc9FRB0IGYpf9x",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the area <r>[0.220, 0.909, 0.860, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area corresponds to the x-axis labels for the years 2000, 2005, 2010, 2015, and 2020."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "eJ1Wb582jZ84b5uy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region <r>[0.068, -0.096, 0.097, 1.065]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis labels representing the development index from 30 to 90."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "MQOu4VX9NuhY5FNo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area <r>[0.191, 0.046, 0.424, 0.104]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises of texts from the legend representing \"Rural Development\" and \"Urban Development\", along with their respective colored handles."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "IkbNtXnNikpcY2f9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.025, 0.323, 0.058, 0.603] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "kw6OjTeEnMrIQ06W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the second item in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.206, 0.081, 0.280, 0.104]"
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "h797A6JH4rQcFj9Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.454, 0.909, 0.470, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "j106UuSo4jKhBiPt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Television'} at [0.462, 0.622] higher or lower than {the point of 'Online'} at [0.462, 0.782] for the second week?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "SD41ZRvSTKyrGmA4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo {the points of 'Online'} [0.291, 0.861], [0.462, 0.782], [0.633, 0.383], [0.804, 0.144] show an increasing or decreasing trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreasing."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "kHXffGGPUsqB2cfY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type has a higher influence level in the last week, {point from 'Newspaper'}[0.804, 0.383] or {point from 'Online'}[0.804, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Newspaper."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "e149EVKvMUzBBKvn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.131, 0.037, 0.345, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend, including {Television} with a handle color of #e8d2cc <r>[0.136, 0.051, 0.192, 0.061]</r>, {Online} with a handle color of #9f6a8d <r>[0.136, 0.086, 0.192, 0.096]</r> and {Newspaper} with a handle color of #5c5048 <r>[0.136, 0.121, 0.192, 0.131]</r>."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "Vvx1mC68eft6RkHz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.416, 0.942, 0.678, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "This space contains the chart's title. The title is \"Week Number\"."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "GfksQ1Da1ezbfCSL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.275, 0.900, 0.815, 0.935]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 4 objects. The x-axis tick labels '$1$', '$2$', '$3$', and '$4$' at {coordinates}[0.283, 0.909, 0.299, 0.932], {coordinates}[0.454, 0.909, 0.470, 0.932], {coordinates}[0.625, 0.909, 0.641, 0.932] and {coordinates}[0.796, 0.909, 0.812, 0.932], respectively."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "VNy3yKOt0O0hibxE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.504, 0.942, 0.576, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "jaPY1WA9o3HntGcF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere can I find the third x-tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.417, 0.909, 0.446, 0.932]"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "9jbfv4Cs78Dh9bzd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the second point of the line 'Books Read: Low Stress'}<r>[0.323, 0.116]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "38"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "vlA2vNapiPxak6Gv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the fifth data point from 'Books Read: High Stress'}<r>[0.649, 0.784]</r> above or below the fifth data point from {the line 'Books Read: Low Stress'}<r>[0.649, 0.142]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "EDobIyWSeFE35Mor",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point from 'Books Read: Low Stress'}<r>[0.214, 0.065]</r> greater or smaller than {the first point from 'Books Read: High Stress'}<r>[0.214, 0.450]</r> in terms of y-value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "yW4pPWVvUgoVElsx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the x-axis tick of 30, does the line for 'Books Read: Low Stress' or 'Books Read: High Stress' have a higher y-coordinate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Books Read: High Stress."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "gCvuWOC1LFAprJ5p",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what the visualization displays in the area [0.598, 0.037, 0.963, 0.113]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Books Read: High Stress}<r>[0.672, 0.046, 0.954, 0.069]</r> and {Books Read: Low Stress}<r>[0.672, 0.081, 0.947, 0.104]</r> each paired with a colored marker. The color specified for 'Books Read: High Stress' is '#f08080' and for 'Books Read: Low Stress' is '#808080'."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "DiQ1UK5r3ORqLcI5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.199, 0.909, 0.881, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes all the x-axis tick labels: {10}<r>[0.199, 0.909, 0.228, 0.932]</r>, {15}<r>[0.308, 0.909, 0.337, 0.932]</r>, {20}<r>[0.417, 0.909, 0.446, 0.932]</r>, {25}<r>[0.525, 0.909, 0.555, 0.932]</r>, {30}<r>[0.634, 0.909, 0.663, 0.932]</r>, {35}<r>[0.743, 0.909, 0.772, 0.932]</r>, and {40}<r>[0.852, 0.909, 0.881, 0.932]</r>."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "3tVosZk7Wes1Gc2j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects do we have within the coordinates [0.025, 0.355, 0.058, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "On checking, we see the Y-axis label 'Books Read' is within these coordinates."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "Ve03iuclrw6QZ5SY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the data point [0.253, 0.494] in the E-Learning line?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adoption rate for E-Learning in the year 2012."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "Yxok21TJST45PbNr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the graph's y-axis label}[0.019, 0.379, 0.041, 0.568]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adoption Rate (%)."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "XJgK1A7vtpz4W8Kr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item {at} [0.141, 0.034, 0.233, 0.052] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-Learning."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "OLslwDIEsUBv6lY0",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point {E-Learning at 2013} [0.341, 0.406] higher or lower than the data point {Mobile Learning at 2013} [0.341, 0.471]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "YqUc9wvBA6Dm36UL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {E-Learning} [0.606, 0.216] in 2016 have a higher or lower adoption rate than {Gamified Learning} [0.606, 0.564] in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "n6sOCjHZgPemVRDt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the data points for {Mobile Learning at 2018} [0.783, 0.203] and {E-Learning at 2018} [0.783, 0.172], which one has higher adoption rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobile Learning."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "6rNfneIRHoac7sop",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the bounding box [0.076, 0.060, 0.959, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the line chart for E-Learning adoption rates from 2010 to 2020."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "oA3qsoMRYHHMDz6U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's inside the region [0.085, 0.027, 0.307, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the graph is within this region, illustrating the colors representing E-Learning, Mobile Learning, and Gamified Learning."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "Px2y88KRtjDQHP20",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.076, 0.129, 0.959, 0.754] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region outlines the line graph for Mobile Learning adoption rate from 2010 to 2020."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "QwN3GuGeK1d1FNWz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the graph caption located at [0.505, 0.950, 0.552, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "WhHzTbuMKluTpkMs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the y-axis tick label} [0.041, 0.812, 0.067, 0.832]?"
+            },
+            {
+                "from": "gpt",
+                "value": "100"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "nD2g8xJVRUkn6nsl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat symbol does the legend at [0.082, 0.104, 0.115, 0.112] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Consumption"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "BBacB3cUVuBiQFZo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {point 2012 on the Coal Consumption line} [0.279, 0.095] have higher Consumption value than {point 2012 on the Solar Consumption line} [0.279, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "g5GvWzY2c2PUKCho",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the first point on 'Hydro Consumption' line} [0.113, 0.712] and {the last point on 'Solar Consumption' line} [0.943, 0.669], which has higher consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first point on the 'Hydro Consumption' line."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "96bfKRdpfiHhAaOx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of consumption decreases more from {point 2010}[0.113] to {point 2020}[0.943], Coal Consumption or Hydro Consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Coal Consumption."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "WA25UTewX28GUayH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is contained within the coordinates [0.079, 0.031, 0.266, 0.127]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three entries in the legend note: {Coal Consumption} [0.123, 0.039, 0.250, 0.059], {Hydro Consumption} [0.123, 0.069, 0.261, 0.089], and {Solar Consumption} [0.123, 0.099, 0.255, 0.119]."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "gRG4O45ygOC5XUXL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide detail on the objects between [0.446, 0.916, 0.466, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the x-axis tick label '2014'."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "HqwNH7ria0EzXGUZ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what the region [0.072, 0.021, 0.985, 0.915] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire plotted chart. This includes three lines depicting Coal Consumption, Hydro Consumption, Solar Consumption over the years 2010 through 2020."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "CKZD2Zwyudc4xANE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is centered at the coordinates [0.529, 0.008, 0.529, 0.008] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "HgKfuMrXtrw7fMGZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith respect to the coordinates, [0.050, 0.672, 0.071, 0.690], what is represented?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '20'."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "iW0H9gWsmSTEYxuV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is defined at the coordinates, [0.723, 0.163, 0.712, 0.141, 0.734, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for the year 2019 on the VR Training line, indicating 45% adoption rate."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "MoYqWZxZvJzKH2mv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2015 on the Online Learning line}[0.207, 0.679] above or below the {point for the year 2015 on the Classroom Tech line}[0.207, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "plnqIqacfLAPHxV3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {VR Training line}[0.207, 0.060, 0.852, 0.886] generally above or below the {Classroom Tech line}[0.207, 0.473, 0.852, 0.576]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "pStiT7Nxrp0k8NpE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {Online Learning}[0.207, 0.163, 0.852, 0.679] and {VR Training}[0.207, 0.060, 0.852, 0.886], which line has a higher point at 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Learning."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "edvCGLMPudm6naIz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the coordinates [0.086, 0.027, 0.285, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart which includes references for Online Learning, Classroom Tech, and VR Training, each associated with different markers and colors:\n    {Online Learning}<r>[0.142, 0.034, 0.279, 0.052]</r>;\n    {Classroom Tech}<r>[0.142, 0.061, 0.277, 0.078]</r> and;\n    {VR Training}<r>[0.142, 0.087, 0.240, 0.104]</r>."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "5eNDYFJPFiMxHItD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed by [0.207, 0.163, 0.852, 0.886]"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains three overlapping lines for the respective trends of Online Learning, Classroom Tech, and VR Training. Each line includes a series of points and annotations representing adoption rates for each year from 2015-2020."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "C29kLBMKEMiuUj98",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the region [0.185, 0.933, 0.874, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis tick labels representing the years 2015, 2016, 2017, 2018, 2019, and 2020 are located in this region. Each label is associated with a specific year."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "oFyliTnbQqT33MSD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.041, 0.231, 0.063, 0.254] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label \"4.0\"."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "VkfXHeZPQAyY1RvE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the second text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.119, 0.081, 0.220, 0.104]"
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "l4OQE4qpM8iMiLoZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {an x-axis tick label} at [0.215, 0.822, 0.281, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "01/02/2021."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "5bkVdDHOhQb1yboF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Exercise hours'}[[0.109, 0.419, 0.943, 0.778]] above or below {the line for 'Watched TV hours'}[[0.109, 0.061, 0.943, 0.688]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "HlB674y3VGYpG0Qq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {reading hours}[0.665, 0.151] on the fifth date higher or lower than the {exercise hours}[0.665, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "BtwCGsM8nkeAB26j",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has a higher hours spent at {01/01/2021}[0.109], Watched TV, Exercise or Reading?"
+            },
+            {
+                "from": "gpt",
+                "value": "Watched TV."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "Lsi3wdPxXyVOOfMo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.074, 0.037, 0.251, 0.148]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows the legend for the chart. It includes {Watched TV hours}<r>[0.119, 0.046, 0.245, 0.069]</r>, {Exercise hours}<r>[0.119, 0.081, 0.220, 0.104]</r> and {Reading hours}<r>[0.119, 0.116, 0.219, 0.139]</r>, each paired with a symbol and color."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "uFDu0SKdzUqW4oJ9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.215, 0.822, 0.281, 0.932]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis tick label for the second date: {01/02/2021}[0.215, 0.822, 0.281, 0.932]."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "3U14LvfjvC4UEw0h",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.109, 0.061, 0.943, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three lines, {Watched TV hours}[0.109, 0.061, 0.943, 0.688], {Exercise hours}[0.109, 0.419, 0.943, 0.778], and {Reading hours}[0.109, 0.061, 0.943, 0.688], each representing daily hours spent on each activity from 01/01/2021 to 01/07/2021."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "i9pduZ23NlLLlcUm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.396, 0.942, 0.685, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "Sp0UkA34msY6T3rY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is at the location [0.191, 0.081, 0.403, 0.104] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medium Efficiency label."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "OVvoIof6VywwG9PJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinate [0.115, 0.030, 0.150, 0.060] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point value '70' from the \"Low Efficiency\" line."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "XZL9smbNyLoDRMpT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '6'}[0.268, 0.762] for 'High Efficiency' greater or lesser than {the point labeled '6'}[0.268, 0.563] for 'Medium Efficiency'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lesser."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "ifLOPUOooup3hBok",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the value of {High Efficiency at point 12}[0.431, 0.861] compare to that of {Medium Efficiency at point 12}[0.431, 0.662]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "JgdQ9oQQbyfMpEV2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the values at point 18, does {High Efficiency}[0.594, 0.702] have a greater or lower value than {Low Efficiency}[0.594, 0.204]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "5olWwv8bt5sLo2oG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is within the area [0.117, 0.037, 0.412, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart with three labels and corresponding markers: {High Efficiency}[0.191, 0.046, 0.363, 0.069], {Medium Efficiency}[0.191, 0.081, 0.403, 0.104] and {Low Efficiency}[0.191, 0.116, 0.356, 0.139]."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "lGb3ufsmtQJ3YXpp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.132, 0.662, 0.921, 0.861]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the plot line for 'High Efficiency' with six points labeled with different values."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "2xl9ecs6JaI87UGi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what's situated in the region [0.132, 0.065, 0.921, 0.363]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the plot line for 'Low Efficiency' which has six points marked with respective values."
+            }
+        ],
+        "id_raw": 1054
+    },
+    {
+        "id": "zX30kZQPVqKjRCUK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend item }<r>[0.681, 0.811, 0.884, 0.834]</r> refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Large Scale Framework."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "C6FFpwvQiXzzYlTo",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the y-axis label}<r>[0.056, 0.412, 0.079, 0.597]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Effectiveness."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "i6CfLRztrDGjGDtL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the x-axis tick label}<r>[0.781, 0.898, 0.792, 0.921]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "mIprOFAgrunFeVxl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {point 7 of the Large Scale Framework}<r>[0.708, 0.155]</r> higher or lower than {point 7 of the Small Scale Framework}<r>[0.708, 0.330]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "7sGZFcPCFeA40Evn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 0}<r>[0.160, 0.155]</r>, does Large Scale Framework have a greater or lesser value than the Small Scale Framework at {point 0}<r>[0.160, 0.855]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "4f8RPzgdddPX1YBC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point 9}<r>[0.865, 0.330]</r>, does Large Scale Framework have a higher or lower Effectiveness than the Small Scale Framework at {point 9}<r>[0.865, 0.447]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "Fpo1G69Z8kkmQBBG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in this region: <r>[0.160, 0.272, 0.865, 0.855]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Small Scale Framework data line, with points at different stages on the x-axis ranging from 0 to 9, plotted against Effectiveness on the y-axis. Each point is marked by an 'x'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "WymITFUasKBIXe2W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.626, 0.802, 0.891, 0.878]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Large Scale Framework}<r>[0.681, 0.811, 0.884, 0.834]</r> and {Small Scale Framework}<r>[0.681, 0.846, 0.884, 0.869]</r>, each paired with a marker."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "qERcFHSxAfFRmYwD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you find within <r>[0.160, 0.155, 0.865, 0.505]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Large Scale Framework data line, with points at different stages on the x-axis ranging from 0 to 9, plotted against the Effectiveness on the y-axis. Each point is marked by an 'o'."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "1Jz7s9Y1KwhMuLVb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label corresponds with the object at [0.189, 0.211, 0.279, 0.234] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "ESG Score."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "lAXjVaNcCeiEsK7c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker corresponds with the CSR Index in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle {CSR Index Marker}<r>[0.137, 0.146, 0.179, 0.156]</r>."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "bvht1vNHGoLrxP41",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label turn out to be?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score {y-axis label}<r>[0.076, 0.453, 0.101, 0.557]</r>."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "903jS6g6jeDfW9rL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point in 2012, is the CSR Index higher or lower than the SDG Achievement index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower {CSR Index}<r>[0.489, 0.683]</r>  {SDG Achievement}<r>[0.489, 0.437]</r>."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "j5qHYuPWNB3aFeiO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2005, does the ESG Score or SDG Achievement index have a higher score?"
+            },
+            {
+                "from": "gpt",
+                "value": "ESG Score {ESG Score}<r>[0.160, 0.585]</r>  {SDG Achievement}<r>[0.160, 0.708]</r>."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "rv2rTzzVIpYiLlG4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts higher on the plot, ESG Score or CSR Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "CSR Index {CSR Index}<r>[0.160, 0.830]</r>  {ESG Score}<r>[0.160, 0.585]</r>."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "GKZ3cdP0jgsdjED4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.134, 0.132, 0.353, 0.243]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend. The legend includes three entries: CSR Index with an orange circle as a symbol, SDG Achievement with a green cross symbol, and ESG Score with a blue square symbol."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "A3yb3TzUYWDEjELc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the region [0.467, 0.898, 0.511, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the X-axis tick label for the year 2012."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "gwRt1ROLlwWkBwuf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the chart region [0.160, 0.302, 0.865, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the three plotted lines for CSR Index, SDG Achievement, and ESG Score over the years from 2005 to 2020."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "ITcSi0zXNGUCivhk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the line representing the 'China Unemployment Rate' in the chart have?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "NTLMisd6jIEJKS82",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the object located at [0.117, 0.048, 0.246, 0.062] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'China GDP Growth'."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "TaGmJYbUwiq8kK9w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.522, 0.719] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "A mark for the Q2 value of China GDP Growth."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "ljc2LWiJlFXq7YSb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, has the 'USA Unemployment Rate' increased or decreased between Q1 and Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increased."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "6WEeK4433o6g3J85",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'China GDP Growth' and 'USA GDP Growth', which one has a higher value at the end of Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "China GDP Growth."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "xGmgIKpThONEmFtp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich had a higher value in Q4, 'China Inflation Rate' or 'USA Inflation Rate'?"
+            },
+            {
+                "from": "gpt",
+                "value": "USA Inflation Rate."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "nEFLmRfConV8XvIr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what the content within [0.117, 0.027, 0.235, 0.041] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the label of the 'USA GDP Growth' line in the chart legend, which is paired with a colored marker."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "OFOWZfRIt6rRt26b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is located at [0.066, 0.015, 0.985, 0.941]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire graphical area of the chart, including the plotted lines for GDP Growth, Unemployment Rate, and Inflation Rate for the countries USA and China across Q1 to Q4. It also contains the chart's x-axis and y-axis, tick labels, and gridlines."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "vnjMgxeRgNXfLYGB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text represent at [0.278, 0.236, 0.061, 0.250]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a y-axis tick label of '25'."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "pEi5sZsC5ZG8Ypbj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at these coordinates [0.019, 0.391, 0.044, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "1EsxbWqr796sAfDK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the fifth indexed year in the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.497, 0.932, 0.541, 0.949]"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "BkqC7t05WQl63Qkl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the Index Value} at [0.584, 0.570, 0.629, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "79.7."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "n3NYe8B35puKwB0s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of the year 2016 on the Eco-Index Chart}[0.607, 0.289] above or below {the point of the year 2016 on the Ethical Conduct Index Chart}[0.607, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "ciGiFrb63F6rkEJG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Eco-Index line}[start: [0.079, 0.060], end: [0.958, 0.885]] generally above or below {the Ethical Conduct Index line}[start: [0.079, 0.174], end: [0.958, 0.747]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "cxlz8UTM0wqINlJh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Index has a higher value at {year 2017}[0.694, 0.232], Eco-Index or Community Involvement Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Eco-Index."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "IWn2RGmI6LwHE2xS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.088, 0.027, 0.417, 0.111]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend, where the labels {Eco-Index}[0.143, 0.034, 0.228, 0.052], {Ethical Conduct Index}[0.143, 0.061, 0.333, 0.078], and {Community Involvement Index}[0.143, 0.087, 0.410, 0.104] are present, each paired with a unique colored marker."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "rWCXOGtv3l2pjvPb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.436, 0.932, 0.485, 0.949]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains one of the x-axis tick labels, specifically the label {2015}[0.497, 0.932, 0.541, 0.949], formatted in dark color."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "pueNEgRGG6YNaZqn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich elements are present within the region [0.079, 0.976, 0.958, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Eco-Index line chart with multiple data points, ranging from years 2010 to 2020, each point is annotated with its respective index value."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "OUXNyvsEBalylP60",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object {[0.021, 0.359, 0.047, 0.581]} represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "RCL0qpXPq5LIRueH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.508, 0.925, 0.558, 0.945]"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "nJ2wbZDMDRfv6JFE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the point on PlayStation 5's line} at [0.533, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "22.1."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "QXcblppJWU5MxzOG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the PlayStation 5 data point}[0.236, 0.774] higher or lower than {Xbox Series X data point}[0.236, 0.877] in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "5wVLt59crSi7IDrB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2024, is {Nintendo Switch's sales figure}[0.808, 0.208, 0.852, 0.228] higher or lower than {PlayStation 5's sales figure}[0.808, 0.037, 0.852, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "MwcYqwWhVrw5camO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich console's sales increased more from 2020 to 2021, PlayStation 5 or Xbox Series X?"
+            },
+            {
+                "from": "gpt",
+                "value": "PlayStation 5."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "kTdl3C4BBbmxGvFE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.097, 0.031, 0.331, 0.127]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It includes labels for PlayStation 5, Nintendo Switch, and Xbox Series X, each paired with a respective colored marker: {PlayStation 5}[0.161, 0.039, 0.292, 0.059] is blue, {Nintendo Switch}[0.161, 0.069, 0.323, 0.089] is red, and {Xbox Series X}[0.161, 0.099, 0.297, 0.119] is green."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "HQaAXHMCKUeVw5s9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.236, 0.062, 0.830, 0.774]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the trend line of PlayStation 5 sales from 2020 to 2024 with data points corresponding to each year's units sold in millions."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "dFRp35epipLSd55Q",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.236, 0.062, 0.830, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 objects. These are the data points for PlayStation 5 sales from 2020 to 2024, located at {[0.236, 0.774]}, {[0.384, 0.602]}, {[0.533, 0.429]}, {[0.681, 0.272]}, and {[0.830, 0.062]} respectively."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "VdxqW6PDTqd3ZC48",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is located at [0.149, 0.796, 0.936, 0.948] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot for Travel Expenses."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "Ax4WrYSpffyK5aR7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at [0.058, 0.048, 0.102, 0.170] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "4MyXazlyAxiut5fB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at [0.431, 0.794, 0.654, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sublegend."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "PI3CJ5g6VP7Czq2Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.149, 0.207]} above {the point at [0.936, 0.207]} in the Grocery Consumption subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "p1pzEd5kPUlxLiHT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the x-tick labeled '0'} at [0.149, 0.215] precede {the x-tick labeled '10'} at [0.936, 0.215] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "lDtlGU8YD2sVe7Fq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a larger expense value, {the third point on the Electronics consumption graph}<r>[0.228, 0.672]</r> or {the last point on the Travel expenses graph}<r>[0.936, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Travel expenses."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "vDqDXo4QuS7alK4K",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information found in the region spanning [0.456, 0.713, 0.936, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the lower portions of the Electronics and Travel expenses subplots, including parts of the x-axis, labels for '4', '6', '8', '10', and the corresponding grid lines."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "2UoDu9WUQyFqkmIi",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements present between [0.025, 0.093, 0.048, 0.247]."
+            },
+            {
+                "from": "gpt",
+                "value": "This vertical space contains the Y-axis labels for multiple subplots, indicating various metrics like consumption and expenses across sections such as Grocery and Travel."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "3ChtVqDMApWz48uc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.054, 0.305, 0.690, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the sublegends of both Clothing and Electronics consumption trends, indicating their respective colors and trend identifiers nearby the lower part of their graphs."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "Wvenf4mMPCP4lQSz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the graph in the bounding box [0.153, 0.295, 0.975, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Students Enrolled."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "7YKmIaK8NXWm308D",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is associated with {the legend item 'Online Courses Developed'}<r>[0.239, 0.552, 0.545, 0.564]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "WBoMBolLoI0oE55t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's x-axis label is found at {coordinates}<r>[0.490, 0.960, 0.519, 0.971]</r> in the 'Growth of Online Learning Platforms' graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "BMoORxGLdoTGp7tm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at year 2016}<r>[0.639, 0.385]</r> higher or lower than {the point at year 2018}<r>[0.788, 0.366]</r> in the 'Growth of Students Enrolled in Online Learning' line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "ELFJ0LH0oNgnAA4H",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth rate of {point for 2016}<r>[0.415, 0.923]</r> or {point for 2020}<r>[0.938, 0.796]</r> steeper in the 'Online Learning Platforms' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "k9uMkiG39Ovn9C5X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line segment is steeper, from {year 2014 to 2016}<r>[0.490, 0.157, 0.639, 0.136]</r> or from {year 2016 to 2018}<r>[0.639, 0.136, 0.788, 0.091]</r> in the 'Online Learning Resources' subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016 to 2018."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "3Pr9p6GywWLybj3S",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.153, 0.048, 0.975, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region presents the chart titled 'Growth of Online Learning Resources' with x-axis labeled 'Years' and y-axis labeled 'Online Learning Resources'. Markers are absent, and the graph shows an upward trend in data points."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "gFAdzj54GlnxEw0j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the contents of the region [0.153, 0.295, 0.975, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the 'Growth of Students Enrolled in Online Learning' chart, displaying a line graph plotted against the years on the x-axis and the number of students on the y-axis. The line trend is upward."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "XqDOh7O2f3VW5nDu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the information presented in the space [0.153, 0.542, 0.975, 0.709]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section of the chart is dedicated to 'Growth of Online Courses Developed'. It includes an upward trending line graph depicting the increase in online courses developed over the years, with y-axis representing the number of courses and x-axis representing years."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "ePGRdMGgLjLFe91Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Years."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "AFU10Fta5f20qR7G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color represents the 'Trade Balance' line, according to the legend in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff00ff."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "uqmJ9fhPJkxjqfiN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many y-ticks are present in the subplot corresponding to \"Globalization Trends\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "6."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "wSY5AIc9gGiqsAb9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's FDI data point is higher, {1983}[0.201, 0.294] or {1993}[0.610, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1993."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "u7EJTmWP4YIa7KVN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the lowest GDP point on the 'Real GDP' graph from {1996}[0.732, 0.602] or from {1982}[0.161, 0.701]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1982."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "lXEPHhW4rmNZDeIi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Trade Balance' value for the year {1986}[0.324, 0.198] exhibit a greater magnitude than the year {1989}[0.446, 0.090]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "TjFoqvIwzVYpwvtq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the layout and content found within the area [0.122, 0.533, 0.975, 0.709]."
+            },
+            {
+                "from": "gpt",
+                "value": "This subplot displays 'Real Gross Domestic Product' over years, with black colored x-axis labeled 'Years' at the bottom and 'Real GDP' label in black on the y-axis; the plot line in blue."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "kvc28fKF18BrBgcm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details on what is within the region [0.077, 0.477, 0.975, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes portions of subplots for Foreign Direct Investment and Real GDP, showing y-ticks and plot lines within each subplot, each with distinct color coding, green for FDI and blue for GDP."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "7nizw96n5Ufp75GJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements contained within the coordinates [0.038, 0.466, 0.423, 0.724]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This includes x-axis labels for the subplots of Foreign Direct Investment and Real GDP, spanning several years, marked specifically from '1980.0' to '1990.0'."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "a3SY9fnDbiFEu8ix",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the color of the point on line 0 of the first subplot}<r>[0.110, 0.198]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Shopping."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "qgImyxXiBhVqNEji",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the value of the x-axis tick label} located at [0.617, 0.219, 0.632, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "3."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "2iWqP38IuWkYY2Cu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at {coordinates}<r>[0.282, 0.466, 0.289, 0.477]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Shopping x-axis tick label '1'."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "EFkdGQiv9kbGKZEX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the peak value of the line in subplot 1}[0.968, 0.303] higher or lower than {the peak value of the line in subplot 3}[0.968, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "bdDDiKqEQhCqijOh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is higher at {x=4.5}{{x-coordinate}<r>[0.882, 0.181]</r> for Traditional Shopping or {x-coordinate}<r>[0.882, 0.835]</r> for Environmental Shopping?"
+            },
+            {
+                "from": "gpt",
+                "value": "Traditional Shopping."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "YnqEarZRwpPQjwRy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at x=2 from subplot 1}<r>[0.453, 0.395]</r> have higher or lower participation than {the corresponding point at x=2 from subplot 3}<r>[0.453, 0.909]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "iLsja66jRZ3rlDRD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area framed between coordinates [0.567, 0.482, 0.953, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the ylabel 'Population Participation (%)' for the Online Shopping subplot, segments of the x-axis, and a part of the line representing Online Shopping."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "UdZf4SDJHBHowwvE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bounded by [0.102, 0.219, 0.367, 0.231]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two x-axis tick labels '0' and '1' for the first subplot dealing with Traditional Shopping."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "aEP0Yu0W4pFO9iF1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.389, 0.506, 0.688, 0.521]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Impulse Buying Trend\" is located within this region."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "rjT68twOqsKuQBFs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label for 'Product D'}<r>[0.511, 0.774, 0.624, 0.786]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product D."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "iE0HVpzr7VRiosTY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {the line for 'Product C'}<r>[0.110, 0.554, 0.960, 0.696]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "Si0lqlZIY1WpfhuQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the label '4' located on the x-axis for {Product A's graph}<r>[0.110, 0.048, 0.960, 0.215]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.386, 0.219, 0.401, 0.231]."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "TUjjV9NSnHbwlDGY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {first point of 'Product D'}<r>[0.110, 0.955]</r> have a higher y-value than the {second point of 'Product B'}<r>[0.181, 0.319]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "X65fKvNe363SDc8u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the last x-axis label on {Product A's plot}<r>[0.946, 0.219, 0.975, 0.231]</r> closer to the left or right edge of {the subplot frame for Product C}<r>[0.110, 0.542, 0.960, 0.709]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Close to the right edge."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "DKz16IRM33APJUWW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sales figure is higher at {6 months on the x-axis}<r>[0.535, 0.215]</r>, Product A or Product B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product A."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "JjmA6J5X3TQiAKst",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed within <r>[0.025, 0.048, 0.048, 0.147]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis label 'Sales' for Product A, rendered in a .15 color tone and rotated at an angle of 90 degrees."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "JoH6Y3zgaZv12anz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a summary of the contents within <r>[0.058, 0.043, 0.102, 0.222]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, y-axis labels for Product A are located, displaying numbers ranging from 90 to 130, oriented vertically with a .15 color tone."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "4x8y1MyTRRAbDLLd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present within the subsection <r>[0.110, 0.215, 0.960, 0.462]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section includes both the line graph of Product B, x-axis and y-axis grids marked with white color, sales data points across several months, and x-axis and y-axis labels indicating time in months and sales, respectively."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "TaoxsE8G4IbtcF7f",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is marked by the coordinates [0.388, 0.038, 0.434, 0.038] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle for the Consumer Spending legend."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "H3OAEjQpxP2MwT88",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the content at coordinates [0.069, 0.760, 0.072, 0.795] on the y-axis in the fourth subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The value 4 (in %)."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "MzdnmVr8SF4tCqli",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.025, 0.432, 0.048, 0.463] next to the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "7vGociEPgirp1BCf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater at [0.855, 0.653], the GDP or GDP per Capita?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "v2rjM8E82YXhvn89",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tick mark for 2015.0}[0.736, 0.462] in the second subplot above or below {the tick mark for 2015.0}[0.736, 0.955] in the fourth subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "9aCC6xcJBTyfmedb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Consumer Spending at point [0.338, 0.194]} exceed {GDP at point [0.338, 0.380]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "mOpUnK9vWpulbwn9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents confined within [0.078, 0.225, 0.131, 0.250]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Consumer Spending (Billion $)\" is situated within this area."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "kePe8esE6VZdpyIN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.025, 0.043, 0.054, 0.216] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the y-axis label 'Consumer Spending (Billion $)'{y-axis label}<r>[0.025, 0.043, 0.048, 0.219]</r>."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "C8V751mViuheJMKW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area [0.055, 0.956, 0.130, 0.974]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 'Inflation Rate (%)' is contained in this region."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "D0WvlpZKVjYpU5TY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is located at [0.466, 0.033, 0.676, 0.045]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "k1oe31mfkDQ7qMSs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.117, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the starting point of the line for 'EdTech Innovation'}<r>[0.117, 0.215]</r>."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "0BP62sdakygxMVpq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the type of object} at [0.094, 0.071, 0.109, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "7eu39ke8JHqcaJAv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 3 year mark}[0.370, 0.198] above or below {the 4 year mark}[0.412, 0.195] in the 'EdTech Innovation' graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "VqfN9CCDe7hWoSJF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label has a higher vertical position, {label '1'}[0.117, 0.187] or {label '5'}[0.117, 0.076] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "'5'."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "bc8slhURnFV1Td4E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first marker of the 'Educational Apps' line}[0.117, 0.939] appear before or after {the first marker of the 'Online Learning' line}[0.117, 0.667] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "After."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "u5L3HoUhIK4824Zz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present from [0.058, 0.521, 0.117, 0.716]."
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis labels and tick marks for 'Online Learning' ranging from '17.5' to '0.0', each paired with a corresponding tick along the axis."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "T18KDoamEXQbr1rx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.094, 0.043, 0.109, 0.222]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis labels '0' to '6' and corresponding tick marks for the 'EdTech Innovation' graph, written in black with a vertical orientation."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "VBC0bbkqmuU765kH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the interval [0.509, 0.219, 0.975, 0.477]."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis labels and tick marks from '0' to '10' are positioned along this interval for the 'Software Tools' chart, with labels indicating the year mark."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "JXMMCUF8CZHo0tHr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object located at [0.489, 0.235, 0.538, 0.247] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "s33w03PsoZa3t9yj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated at [0.058, 0.127, 0.072, 0.138]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "BbLZOI3V0lUSU7mj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at the coordinates [0.489, 0.976, 0.538, 0.988]."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "5SSi6zCIqHeKVouA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at the point [0.686, 0.094], {the line labeled 'Sustainable Development'} or {the y-axis tick} at [0.080, 0.090]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "dvp3u38HCW7HMO9B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first marker in {the 'Sustainable Development'} line at [0.080, 0.181] above or below the y-axis tick for '4' found at [0.080, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "AJcLA6wCVfRgfzll",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is closer to the x-axis label at [0.489, 0.235, 0.538, 0.247], the x-tick label '2020' at [0.051, 0.219, 0.110, 0.231] or the x-tick label '2030' at [0.917, 0.219, 0.975, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "5RlUzCgIL5fp60Ea",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region observed within coordinates [0.080, 0.048, 0.946, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the {axes}<r>[0.080, 0.048, 0.946, 0.215]</r> region entitled 'Sustainable Development' with grid lines visible. It displays a line plot tracking the metrics of Sustainable Development from 2020 to 2030."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "NCYSdyUqKThTQedX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the area [0.080, 0.295, 0.946, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The charts {ylabel}<r>[0.025, 0.362, 0.048, 0.394]</r> and {xlabel}<r>[0.489, 0.482, 0.538, 0.494]</r> indicate this area captures 'Democratic Consolidation' values over years plotted on a line graph with visible grid lines."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "WMKnuT5dRSiGZoJS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the coordinates [0.080, 0.788, 0.946, 0.955]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the line graph for 'Market Orientation' marked with the {subplot title}<r>[0.383, 0.753, 0.643, 0.768]</r> and axis labels found at {ylabel}[0.025, 0.856, 0.048, 0.888] and {xlabel}[0.489, 0.976, 0.538, 0.988]. It also includes the accompanying grid lines."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "KWkOMq0AV1eWwKGi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.080, 0.713, 0.139, 0.724] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "spdpuyr15OoujGqn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.359, 0.960, 0.418, 0.971]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "0gsod1caWqLlytJi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth text in the legend for the Literature art subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.481, 0.713, 0.511, 0.724]."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "6CTeJEU2cfNtDNXT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Film Industry'}[[0.110, 0.955], [0.946, 0.914]] generally above or below {the line for 'Performing Arts'}[[0.110, 0.462], [0.946, 0.445]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "4sjrK1xqVLPppoVJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has a higher value at {point 2020}[0.946, 0.445], Visual Arts or Film Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Film Industry."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "x8FPkO00i16GPSpX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2020'}[0.946, 0.822] in the Film Industry section above or below {the point labeled '2016'}[0.574, 0.830] in the same section?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "ztJd0R7qn44Wdj56",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label shown at {the coordinate}[0.508, 0.235, 0.557, 0.247]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "h0lDNoW5CKhl481C",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is indicated by {this particular point}[0.731, 0.082] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales value for the year 2015."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "vfOBrzArfLDgSO3s",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the label at {this location}[0.058, 0.061, 0.087, 0.073]?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "Rjr5YtSpoJ5VeqG2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value represented by {the point}[0.135, 0.207] higher or lower than the one at {the point}[0.175, 0.203]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "BwB9IwxnDbHKsjV3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the data trend represented by {the point}[0.214, 0.440] occur before or after the data at {the point}[0.572, 0.399]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "KvbzYrVRq2w0wbst",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, the starting point at {coordinates}[0.135, 0.701] or the ending point at {coordinates}[0.930, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ending point."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "RBVbimO9xt0SxPZ3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the function of the space between [0.025, 0.082, 0.048, 0.180]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area houses the y-axis label \"Sales (in billions)\" and various corresponding y-tick labels such as '0', '20', '40', '60', '80', '100'."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "TPM1xCmDHnlYyywQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat significant details are present in the region [0.095, 0.187, 0.095, 0.228]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the first two y-tick labels and points for the Game Sales subplot which indicates fluctuations in sales across the years starting from 2000."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "ZaKbKgRA98xMUUmo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what occupies the coordinates [0.719, 0.919, 0.741, 0.930]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Streams (in billions)\" for the Online Streams subplot spans this area."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "9o7LHobUbd3nD8bm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to {the point marking the lowest value in 'AI Patent Application' on the x-axis at}[0.936, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "HErgZRoqLCECgdqJ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify {the y-axis tick label} located at [0.102, 0.214, 0.116, 0.226]."
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "1x6Hv2FC1V0VKjZs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {axis label} can be found at [0.025, 0.558, 0.048, 0.692]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Publications."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "plcpYLISQIzVAY5f",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point marked '2016'}[0.782, 0.709] above or below {the point marked '2010'}[0.318, 0.709] in regard to AI Paper Publication?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "Lc0uwl77yvoeB4rp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater, {label for the year 2012 at}[0.472, 0.462] or {label for the year 2016 at}[0.782, 0.462] in the AI Startups chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "i7R6FDSvHiEcuyHj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'AI VC Investment' at}[0.859, 0.841] conclude at a higher or lower value compared to {its point at}[0.163, 0.948]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "BCbB1WCg6DkxQlJL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.124, 0.048, 0.975, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot titled 'AI Patent Application over the years' with a graph showing a declining trend, labeled x-axis for years and y-axis for number of applications in shades of purple."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "hPZranyne1huHfTn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained in the area [0.124, 0.295, 0.975, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes the subplot for 'AI Startups over the years' showcasing ascending green-colored line graph along with incrementally labeled x-axis for years and y-axis for number of startups."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "x9rjvibbUvre39DW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the section with coordinates ranging from [0.124, 0.542, 0.975, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a graph representing 'AI Paper Publication over the years' with a line graph fluctuating over years, detailed with its corresponding x-axis for years and y-axis labeled for number of publications, predominantly in blue."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "vJaarWJNXmTcR6Nx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate for {the last x-axis tick label of the Net Trade chart}[0.992, 0.713, 1.072, 0.724]?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.992, 0.713, 1.072, 0.724]"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "2CNoPVV8wuEM4k4G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of plot is represented at [0.156, 0.055, 0.935, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "line chart"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "26u2bEaZkC9FTXYh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the vertical axis label}[0.046, 0.597, 0.070, 0.653] on the third subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Net Trade"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "8QJo3QkB7M0k1xA5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {xtick '1980.0'}[0.545, 0.462] in the Industrial Production Index chart, the beginning or the end of the line?"
+            },
+            {
+                "from": "gpt",
+                "value": "end"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "0MpUZOo5ucBAiwsZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the last point on the line}[0.935, 0.796] in the GDP Growth chart exceed {the first point}[0.156, 0.948]?"
+            },
+            {
+                "from": "gpt",
+                "value": "yes"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "nNnFOmmACSpRsJvv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the x-axis label}[0.521, 0.729, 0.570, 0.741] higher in position than {the subtitle}[0.373, 0.506, 0.717, 0.521] in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "no"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "wnQV81CetOy5tJMN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects contained in [0.521, 0.013, 0.804, 0.247]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes {the subtitle 'Food Production Index over the years'}[0.287, 0.013, 0.804, 0.028] and {the x-axis label 'Year'}[0.521, 0.235, 0.570, 0.247]."
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "YUESdTgZFgV67eyN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the features of the space [0.058, 0.095, 0.177, 0.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {the x-axis label text for '30.0'}[0.058, 0.095, 0.109, 0.106] and part of {the main line chart representation for the Food Production Index}[0.156, 0.055, 0.935, 0.207]."
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "ASB1aGlfTV9QOwxe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the section [0.273, 0.506, 0.717, 0.741]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highlighted section contains {the subtitle 'Net Trade over the years'}[0.373, 0.506, 0.717, 0.521] and {the x-axis label 'Year'}[0.521, 0.729, 0.570, 0.741]."
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "IMfAxL87zSGENb1L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing 'Improved Standard'?"
+            },
+            {
+                "from": "gpt",
+                "value": "#800080."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "JuPDDhh8HRRFC50H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.117, 0.215, 0.960, 0.215] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "pAm4ringg4RRApWU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the label for the tick at {coordinates}[0.792, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "8."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "skxkjRF2dZkwYeEf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the point at {coordinates}[0.201, 0.438] on the 'Improved Preferred' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "1.0."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "CA4pMMdZMr89QL7T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the color of the point at {coordinates}[0.370, 0.680] on the 'Improved Premium' line the same as its line's color?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "hTJRfZA5zyFVWwRk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-axis tick label is closer to the point at {coordinates}[0.454, 0.462]?"
+            },
+            {
+                "from": "gpt",
+                "value": "4."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "jbuu7f1TldtjmLAJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area between [0.117, 0.219, 0.960, 0.231]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis, including tick labels ranging from '0' to '10' placed at regular intervals, styled in dark gray color."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "RNukFxQKo31KlyHT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.117, 0.955, 0.960, 0.976] depict in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the x-axis for the 'Improved Exclusive' subplot, with tick labels for values from '0' to '10', displayed in a dark gray font at fixed points."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "rDj7G8rtS1WR6IR2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.110, 0.466, 0.975, 0.477]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region showcases the x-axis of the 'Improved Preferred' subplot with labels ranging from '0' to '10', formatted in dark gray, placed beneath the corresponding tick marks."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "pEnJ7VvIdPH1eCyU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis for the first subplot in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Production (GWh)."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "FGvNOqHfCP5qtyhx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the last tick on the x-axis}[1.011, 0.960, 1.070, 0.971] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2034."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "FOo8yJ1h3S6gUX9W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell the color of {the line representing 'Coal Production'}[0.124, 0.137, 0.975, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Black."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "Ce8CHd0UqamtDUuj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has higher production in 2033, {the point representing Oil Production}[0.975, 0.295] or {the point representing Coal Production}[0.975, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Oil Production."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "6JA0qjz22rsj3dzK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2023, is the production of {Natural Gas}[0.320, 0.619] higher or lower than {Renewables}[0.320, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "ySbF0npgCjeFcpjX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first tick mark for {Oil Production}[0.124, 0.462] higher on the chart than {Natural Gas Production}[0.124, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "LNiAEjNI5XsBNqwr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the components within the region [0.025, 0.080, 0.975, 0.462]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plots for Coal and Oil production. Each plot has a y-axis labeled \"Production (GWh)\", x-axis labeled \"Year\", and various data points are plotted along the lines representing productions from 2020 to 2034. The Coal plot is located in the upper part of this region, while the Oil plot appears just below it."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "SOCIOaBeywgrFqQ0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the content in the bottom part of the chart within [0.025, 0.542, 0.975, 0.955]."
+            },
+            {
+                "from": "gpt",
+                "value": "The bottom part showcases the line plots for Natural Gas and Renewables production. Both subplots are structured similarly with a y-axis labeled \"Production (GWh)\", x-axis labeled \"Year\", and data points representing annual production from 2020 to 2034. Natural Gas production details appear in the upper half while Renewables production is displayed in the lower half."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "nICD2KbB8t7KrBAy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.025, 0.013, 0.575, 0.247]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This segment includes the y-axis for Coal production marked \"Production (GWh)\", x-axis labeled \"Year\", and the title \"Coal Production\" near the top left. The plot also includes vertical grid lines for clarity, extending throughout the upper subplot area."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "MG8nSMWCk80TG1mm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'Anxiety' trend on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "iRBMoXbEsYVOH5tk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis for the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "II6dxifIWffm4Kqp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does the 'Resilience' line start on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.095, 0.908]"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "8hgVSG5BBTon44eW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the starting point of {the 'Procrastination' line}<r>[0.095, 0.191]</r> higher than {the 'Resilience' line}<r>[0.095, 0.908]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "T6xsrTFBtSDTcO2S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the point labeled '0' on the x-axis for 'Anxiety' fall before or after {the point labeled '2'}<r>[0.268, 0.709]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "N9c5HQOVrdlKL3X4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the end value of the {line for 'Anxiety'}<r>[0.960, 0.544]</r> greater than {the end value of the line for 'Procrastination'}<r>[0.960, 0.191]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "sn9bSoyiSZbI9JKL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the bottom subplot including its x-axis, y-axis, and the line attributes."
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot has an x-axis labeled as 'Time' with tick marks at intervals labeled from '0' to '10'. The y-axis is labeled as 'Value' with labels from '\u22122' to '14'. The line representing 'Resilience' is green, spanning the length of the plot with tick points at half unit intervals on the x-axis."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "Mq80VYtFpGLyOWeO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region encompassing [0.095, 0.295, 0.960, 0.462]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts the subplot for 'Motivation' detailed with a blue line graph. The x-axis is labeled 'Time', y-axis labeled 'Value', and points plotted at regular intervals. It also includes 'Motivation' label."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "MfrOaffUN6tVYvBB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide the content breakdown of the area [0.095, 0.048, 0.960, 0.215]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the 'Procrastination Overtime' subplot with a pale orange line representing the trend of 'Procrastination'. Key axis labels include 'Time' on the x-axis, 'Value' on the y-axis, and several distinct tick labels on both axes."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "4YSovHDVAZ8lNu8m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label} of the y-axis in the first subplot<r>[0.025, 0.057, 0.048, 0.190]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Progress Measurement."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "f7EP5V1mMQM72IuT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the style of {the fourth x-tick label} in the second subplot<r>[0.447, 0.466, 0.462, 0.477]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bold."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "OmMBQMoWIn1o7Ajl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the title} located at<r>[0.406, 0.260, 0.671, 0.275]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Substantial Growth."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "hxePEz2mvEo2jY9k",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the end point of the line for 'Application A'}[0.960, 0.056] higher than {the start point of the line for 'Application B'}[0.117, 0.436] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "DCsGMSlXFPtYQUsj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a darker color, {the point representing '10.0' on Application A}[0.960, 0.056] or {the point representing '0.0' on Application B}[0.117, 0.436]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Application B."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "8nNi5l8YgAqmc0sl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is at a higher position, {the label of '55'}<r>[0.080, 0.300, 0.109, 0.312]</r> from the third subplot or {the label of '25'}<r>[0.080, 0.594, 0.109, 0.606]</r> from the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "25."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "h6q18ctSzVdNB1AC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the region<r>[0.000, 0.260, 1.000, 0.482]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire second subplot titled \"Substantial Growth\" with various elements like x-axis labeled 'Time (in terms of an arbitrary unit)', y-axis labeled 'Progress Measurement', {x-tick labels}<r>[0.110, 0.466, 0.124, 0.477]</r>, and {y-tick labels}<r>[0.080, 0.457, 0.109, 0.469]</r>, as well as {the Application B line}<r>[0.117, 0.284, 0.960, 0.446]</r>."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "taiLJCB7S0qXG6xN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details for the area located at<r>[0.100, 0.013, 0.900, 0.235]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the first subplot area displaying a line chart for 'Application A'. The chart includes the x-axis titled 'Time (in terms of an arbitrary unit)', y-axis titled 'Progress Measurement', {y-tick labels}<r>[0.058, 0.210, 0.109, 0.222]</r>, and {line points}<r>[0.117, 0.154]</r>."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "haujW6BiZGllnYJK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the features within<r>[0.000, 0.713, 1.000, 0.955]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the fourth subplot which is labeled 'Periodic Spikes'. Key features include 'Time (in terms of an arbitrary unit)' on the x-axis, 'Progress Measurement' on the y-axis, and {line intervals for Application D}<r>[0.117, 0.955, 0.960, 0.800]</r>."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "HAOL6fhC6tCzz7jN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object within the coordinates [0.088, 0.012, 0.970, 0.224]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Plot area."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "rYUKjjtDkxnMlrE7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at [0.030, 0.089, 0.047, 0.147] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "g5Iu0X44EXWVySV7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object located at [0.030, 0.333, 0.047, 0.391]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "BmaU86G6h2rUIQTL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat number appears on {the x-axis tick}{r}[0.082, 0.228, 0.093, 0.239]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "bOdJFkzj7sKbJcJm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the numerical value at {the y-axis tick}[0.054, 0.034, 0.082, 0.046] higher or lower than that at {another y-axis tick}[0.054, 0.219, 0.082, 0.231]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "8MTDRB7dY3GKHJrq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich y-axis tick represents a larger number, the one at {coordinates}{r}[0.054, 0.140, 0.082, 0.152]{r} or {another point}{r}[0.054, 0.219, 0.082, 0.231]{r}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "25nOECnjuLvPoFMX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.088, 0.012, 0.970, 0.224]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays a line chart representing the popularity of 'Topic Modelling' over time, highlighted in cyan color. Time progresses along the x-axis from 0 to 10, and popularity values are recorded on the y-axis."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "pF8vu16LUjQre0V8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the area bounded by the coordinates [0.088, 0.256, 0.970, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a line chart tracking the popularity of 'Neural Networks' colored in purple across a time sequence on the x-axis."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "tztmfHZtItBFrlem",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements present in the section [0.088, 0.744, 0.970, 0.955]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this section, there is a line chart. It illustrates the tracking of 'Reinforcement Learning', color-coded in green, across various time points, with x-axis titled 'Time'. Popularity values are marked on the y-axis."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "mxFTnb0PdchWlQ7x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the point on the Consumer Confidence line for the year 2015}<r>[0.584, 0.071]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff00ff."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "48EaKPG6wu5zcsyS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the text \"2020\" refer to in the area <r>[0.907, 0.960, 0.965, 0.971]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick label."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "9gjv8tln9PHqD4Xd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the highest point on the Stock Market line}<r>[0.936, 0.549]</r> corresponding to?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "AvHVmVCRgicHiytv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value of {Consumer Confidence in 2012}<r>[0.372, 0.177]</r> higher than the value of {Inflation Rate in the same year}<r>[0.372, 0.416]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "u6KFzx7bFVcyvz6u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is lower, {the GDP Growth rate in 2010}<r>[0.231, 0.936]</r> or {the Stock Market index in 2010}<r>[0.231, 0.661]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "RaYxUambrbsSJVMs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Stock Market index for 2017}<r>[0.725, 0.581]</r> exceed the {GDP Growth for 2017}<r>[0.725, 0.827]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "APObiuX9Jg8tH1g0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region around <r>[0.292, 0.013, 0.804, 0.028]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle 'Consumer Confidence over the Years' with text colored at .15."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "7f9Dcva6AYaLhVv7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area spanning <r>[0.352, 0.259, 0.744, 0.274]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the subtitle 'Inflation Rate over the Years', presented in a dark grey color."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "9wAuhYgL2IRLBt2j",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive details about the area <r>[0.355, 0.506, 0.742, 0.521]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the subtitle 'Stock Market over the Years', shown in a dark grey font specifically positioned in this section."
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "pZoJshRsbnBvXwHy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the ylabel}<r>[0.025, 0.051, 0.048, 0.181]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy allocation in %."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "NE8WrOt8Cx0HhrpI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the subtitle}<r>[0.528, 0.010, 0.528, 0.010]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": ".15 (gray)."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "L9k7mZgk0gIHXVJu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fifth {x-axis tick label}<r>[0.749, 0.219, 0.808, 0.231]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2028."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "72pjqV2c2VKTagFm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '2020'}<r>[0.110, 0.195]</r> higher or lower in value compared to {the point at '2021'}<r>[0.193, 0.191]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "Ig0o7FmwgedDnvDZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Efficient Energy'}<r>[0.110, 0.067, 0.946, 0.195]</r> end higher or lower than {the line for 'Nuclear Energy'}<r>[0.110, 0.622, 0.946, 0.659]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "QayHeZTG67QOCrto",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label color of {the text for 'Efficient Energy'}<r>[0.742, 0.027, 0.925, 0.039]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff00ff."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "Vj4zmcf9GAbDA203",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the region <r>[0.110, 0.017, 0.946, 0.215]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis marked 'Year' with labels ranging from 2020 to 2030 and the y-axis labeled 'Energy allocation in %'. The background color is pale blue, and multiple horizontal grid lines are visible."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "DNZGyC9lb1R0wfdm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be represented in the section <r>[0.110, 0.266, 0.946, 0.462]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the subplot for Non-renewable Energy with corresponding axes labeled for 'Year' and 'Energy allocation in %'. The plot displays a declining trend in data points from year 2020 to 2030."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "TrNtfxZgg5jJknDI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details in the space <r>[0.110, 0.758, 0.946, 0.955]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This portion outlines the subplot for Renewable Energy, featuring an upward trend line starting from the lowest value in 2020 to the highest in 2030. The axes are labeled corresponding to the years and percentage of energy allocation."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "hSfzp3ZDpcGVOTao",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label}<r>[0.017, 0.200, 0.039, 0.228]</r> on the y-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "75000."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "xIp1WPRMnOLaFO4H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color for the {line representing 'Social Welfare'}<r>[0.720, 0.094, 0.975, 0.796]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#8cf2a5."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "ZQQjfJ7SnoaxSzj3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year corresponds to {the tick}<r>[0.212, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2009."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "dOS8HkGN40b1nL97",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the year 2002}<r>[0.754, 0.466]</r> above or below {the point for the year 2009}<r>[0.873, 0.406]</r> in the chart for Charity?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "hfAdEeM8lD7dTTgA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {the x-tick for the year 2007}<r>[0.839, 0.893]</r>, Social Welfare or Charity?"
+            },
+            {
+                "from": "gpt",
+                "value": "Charity."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "5cZSq2TnfJcwYb9s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the handle on the legend for 'Charity'}<r>[0.046, 0.146, 0.064, 0.162]</r> use a brighter or darker shade compared to {the handle on the legend for 'Social Welfare'}<r>[0.046, 0.104, 0.064, 0.120]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Darker."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "mZeAhCTsIbcEciPv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in [0.203, 0.903, 0.251, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for the years 2007 through 2009 are located here."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "s4BXnd0zdtcJ2pJ4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.034, 0.414, 0.051, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area shows a portion of the y-axis with labels for 65000 and 60000."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "lzJGlyk8voBF8XhU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section [0.720, 0.485, 0.806, 0.625]."
+            },
+            {
+                "from": "gpt",
+                "value": "The lines for Social Welfare and Charity intersect in this region, marking dramatic changes in donation amounts around the years 2005 to 2007."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "6C97NoIMSDkW2fmR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinate [0.024, 0.445, 0.036, 0.467] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "JlWTswTVtWKfd83b",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.455, 0.798, 0.511, 0.826] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Meditation label."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "Ky38Dz0JufSufp5H",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object located at [0.172, 0.903, 0.175, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick label '5'."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "qW7VJVPDWUIotsHi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Exercise' at month 1}[0.067, 0.715] above or below {the point for 'Meditation' at month 1}[0.067, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "JtMwnPHxnKTxQ53B",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {y-axis tick label}[0.683, 0.733, 0.698, 0.761] has a lower range value, 0.3 or 0.4?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.3."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "230ThW8nZCk7iRJg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the last point in the \"Meditation\" series}[0.964, 0.462] have a higher or lower value than {the last point in the \"Exercise\" series}[0.964, 0.149]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "6QJ6Qzn32hnA4xoq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.900, 0.780, 0.990, 0.880]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the 'Advanced' level sub-plot, indicating two line types, one for Meditation colored in purple, and one for Exercise colored in green."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "u81LDVuPkC9KzDug",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.040, 0.075, 0.330, 0.930]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire first subplot titled 'Beginner', including primary axes for the plot, both x and y axes labeled respectively, grid lines, and plotted data points for Meditation and Exercise over time periods."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "J7juJygxDzgMuEwd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included within [0.350, 0.040, 0.660, 0.950]."
+            },
+            {
+                "from": "gpt",
+                "value": "This portion of the chart holds the intermediate level subplot, showing axes labeled with well-being index and time period, gridlines, markers indicating data points, and legend details for the categories being represented: Meditation and Exercise."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "s50qfKnWbVo4BQG1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label of the y-axis} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "rvIHuqQ63zSBpPi9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the coordinate of the second x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.075, 0.903, 0.081, 0.931]"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "u9SkD0MvStoNkvf0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.500, 0.496, 0.500, 0.496]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for Period 5 on the 'Manager Performance' line."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "93Xrd55nv0WoPIrQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point at {Period 3}[0.108, 0.332] on {the line for 'Lead Performance'} lower than the point at {Period 6}[0.201, 0.192]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "J3TvPGZHEI3cuvvB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line 'Lead Performance'} cover {lower values} at Period 3 compared to Period 7?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "LK2BpppaGqeBQ3CS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the performances, {Lead Performance} or {Manager Performance}, has higher value at {Period 9}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lead Performance."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "NWaQxS37c2Ol8Vol",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region [0.044, 0.903, 0.330, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains x-axis tick labels ranging from '1' to '10' across three subplots, annotated sequentially."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "FW0b1nfwvsy5AJmA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be described in the region [0.340, 0.396, 0.530, 0.972] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section encompasses the y-axis labeled 'Performance', along with detailed tick labels displaying performance numeric values such as 0.40, 0.45, up to 0.70."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "KgvSvUISUumIpW4y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the space [0.340, 0.089, 0.702, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these confines, there are legends for 'Lead Performance' and 'Manager Performance', labeled with designated color markers for each performance type on graph."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "XiF3tH0LnS0ig8OY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the 2nd y-tick label}[0.353, 0.678, 0.368, 0.706] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.5."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "Ivt5lFN3C5HmUvvh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Theatre Art'}[0.055, 0.143, 0.316, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#0072b2."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "ZjWwDlm35SUAZ83k",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at {the x-tick label position}[0.378, 0.903, 0.393, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.1."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "8FjSukLEuf6fzWVU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 0.6 labelled point}[0.200, 0.334] higher or lower than {the 0.2 labelled point}[0.084, 0.197] on the 'Theatre Art' graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "nbDsjPuPDKWFDYhX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the starting point}[0.055, 0.143] of 'Theatre Art' more to the left or right of {the starting point}[0.385, 0.143] of 'Theatre Art' in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Left."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "1rcFxCqAJTt8TdUA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the end point of 'Modern Art'}[0.976, 0.511] at a higher position compared to {the end point of 'Theatre Art'}[0.976, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "ou94HZ81juEhICW7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are visible in the area designated as [0.010, 0.303, 0.330, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The axes, x-grid and y-grid are visible here, which are components of the subplot hosting the 'Initial Stage' graph with x-label 'Time Progression' and y-label 'Industry Development', displaying lines for 'Theatre Art' and 'Modern Art'."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "CjbiIfaEv5PsWgLq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.371, 0.075, 0.660, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the subplot for 'Middle Stage', with visual elements such as axes, x-grid, y-grid, titled 'Time Progression' for x-axis and 'Industry Development' for y-axis. It displays lines representing 'Theatre Art' and 'Modern Art' with their respective data points."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "OlmOG967QS48GOqz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you elaborate on what is within [0.701, 0.075, 0.990, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the subplot titled 'Final Stage', including axes, a visible x-grid and y-grid, labels 'Time Progression' for the x-axis, and 'Industry Development' for the y-axis. It features lines for 'Theatre Art' and 'Modern Art', marked with their progression points."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "lRqmJU1YEuP6rT3m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.351, 0.062, 0.369, 0.090] in the subplot_2 part of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A y-axis tick label: '100'."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "PsY1NZjV7QS6fKPs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the point marker for the line representing 'Running' in subplot_1 of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#ff7f0e'."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "j2OWv8IumqbcHryC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the object with bounding box coordinates of [0.561, 0.085, 0.799, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the second subplot of the chart, depicting performance data from 'April - August'."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "H6NMqN9xByEflxxM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the weightlifting line in subplot_2}[0.712, 0.112, 0.961, 0.138] depict an increasing or decreasing trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "2kZetvZhkd4PAqHh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Weightlifting' data point for '01-03-2020'}[0.222, 0.193] higher or lower than {the 'Running' data point for '01-03-2020'}[0.222, 0.848] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "ySmRNOc5xhCZ0dFE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn subplot_0, does {the 'Weightlifting' trend}[0.056, 0.128, 0.305, 0.209] have a higher or lower value than {the 'Running' trend}[0.056, 0.848, 0.305, 0.856] in March (3rd data point)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "XkByd9tMupfkbxhw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area defined by [0.044, 0.075, 0.317, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area depicts the first subplot of the chart. It displays the performance data trends for 'Weightlifting' and 'Running' for the months 'January - March' with corresponding x-axis and y-axis labels and a legend."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "s21QKJkvZmjZNZud",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the area [0.686, 0.903, 0.987, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there are two x-axis labels, '01-11-2020' and '01-12-2020', for the third subplot of the chart."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "toYUpvDlUPm4k4iA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail what things are inside the box [0.951, 1.032, 0.985, 1.122]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are two things: the x-axis label '01-09-2020' and the last point from the 'Running' line for '09-01-2020' in the subplot_2."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "9P8OF8WkK0K7MySS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "3iRyB4eEWw1EnY4W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the coordinates of the tick labeled 'Times' in subplot 0."
+            },
+            {
+                "from": "gpt",
+                "value": "[0.260, 0.735]."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "1ooMsi7Hguktsoyx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle representing 'Public Opinion Score'}<r>[0.058, 0.101, 0.088, 0.118]</r> in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "j04LWrataPnK04iQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher score at point 4, {Media Influence Score}<r>[0.176, 0.143]</r> or {Public Opinion Score}<r>[0.176, 0.420]</r> in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Media Influence Score."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "91bJ6fvCqtoqGxTB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the tick located at {coordinates}<r>[0.393, 0.735]</r> in subplot 1, which label is displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "CNN."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "VxJz6tfvAKUatKhR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for CBS}<r>[0.148, 0.231]</r> higher or lower than {the point for FOX}<r>[0.176, 0.143]</r> in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "cMr6c0hS57SfHiS9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.380, 0.030, 0.658, 0.735]</r> within subplot 1."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes axes for plotting labeled 'Media Channel' along the x-axis and 'Score' on the y-axis, with data points and labels for media outlets like CNN, NBC, and CBS, where lines and markers indicate scores."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "A06hrLW31eGO4bVx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region <r>[0.710, 0.030, 0.988, 0.735]</r> in subplot 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a graph with x-axis labeled 'Media Channel' and y-axis 'Score'. Markers represent various media channels such as ABC and HuffPost, with data points plotted to represent scores."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "bfC5XM65F1wYJiPa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is featured in <r>[0.051, 0.030, 0.329, 0.735]</r> in subplot 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area hosts a chart detailing media channels against scores with elements like grid lines, axis labels, and color-coded lines representing different data series, specifically 'Media Influence Score' and 'Public Opinion Score'."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "wB0zMRGUQqZEu8yi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker type of the {line for 'CO2 Emissions'}<r>[0.194, 0.805, 0.515, 0.864]</r> at the point {2016}<r>[0.600, 0.775]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "v."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "XqHzpXCT1Ny7YkjF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the y-axis in {subplot 0}<r>[0.038, 0.075, 0.336, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Average Life Expectancy vs CO2 Emissions."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "m8uF6qBuQmadHUpM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used for the {Average Life Expectancy line}<r>[0.052, 0.080, 0.322, 0.179]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "#9467bd."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "OyrLSproOWMGKtxR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {data point for 'CO2 Emissions' in 2019}<r>[0.964, 0.759]</r> above or below the {data point for 'Average Life Expectancy' in 2016}<r>[0.922, 0.092]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "JbnhYciveI1hj06n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the {CO2 Emissions markers}<r>[0.373, 0.759, 0.643, 0.864]</r> closer to the {bottom or top}<r>[0.0, 0.0, 1.0, 1.0]</r> of the chart in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bottom."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "sLTDOpeW3J7Db2wp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {tick label at 2012}<r>[0.544, 0.893]</r> align more accurately with the {beginning or end}<r>[0.359, 0.075, 0.657, 0.893]</r> of the CO2 Emissions line in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Beginning."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "yobp2l2m5HRkgvMY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found at coordinates [0.681, 0.903, 0.933, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains x-axis labels for the years 2016, 2018, and 2020 arranged along with their corresponding tick points<userinfo></userinfo>."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "30YEgXZ3nql9Wn2a",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinate range [0.351, 0.186, 0.362, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This narrow strip represents the y-axis of the second subplot, including y-tick points, devoid of any labels."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "CnUJoDXOZJLRauOr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the visual elements located in the region [0.0, 0.075, 1.0, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The entire chart background spanning all three subplots, consisting of grid lines, lines representing 'Average Life Expectancy' and 'CO2 Emissions', as well as data point markers and x-axis and y-axis labels at various points."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "mOzfAP6MntycypvK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marked object at coordinates [0.511, 0.717, 0.525, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "UfKBbgCrvlrYb4mM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the legend title} at [0.287, 0.089, 0.413, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no title."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "CpJG4BBHIm5SvAJg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is related to {the object} at [0.511, 0.080, 0.525, 0.108]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "bCnCNbXjb85noGf6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point marked '1' in Q1}<r>[0.038, 0.784]</r> higher on the grid than the point marked '4' in Q1<r>[0.254, 0.129]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "9lmdeefy3jU3OaTn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the last y-axis tick} at <r>[0.770, 0.001, 0.784, 0.016]</r> represent a higher or lower value compared to the y-axis tick at<r>[0.770, 0.186, 0.784, 0.214]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "lbp1kPPtjGUOhqJi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater y-coordinate on the plotting grid, {the point marked '1' on the line for 'Tech Company 1' in Q3}<r>[0.528, 0.835]</r> or {the point marked '4' on the line for 'Innovative Business 1' in Q3}<r>[0.743, 0.250]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 1."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "Ri60rXzZd1UMCHwg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area [0.750, 0.880, 0.999, 0.940]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the xlabel \"Quarters\" styled in a pale gray font."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "wM5PgXOCwwF4xS2J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are included in the region [0.075, 0.905, 0.999, 0.999]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis labels '1', '2', '3', '4' across quarters distributed along the bottom of the chart with gradually increasing x-coordinates."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "OUYZdtgZRSG85Olm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.200, 0.100, 0.400, 0.400]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes parts of the background grid for the chart, rendered in a faint gray color, aiding in visual alignment of plotted data points."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "ma7Ng7ShlhXrMUxK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value at {the last point on the Instagram line in the subplot titled 'Rapid recovery'}<r>[0.976, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "114."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "HNcF9TBWcqQrl7pw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media does the blue marker represent in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "bd2CyODmQjQjZVrH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color used for marking {the Twitter line in the subplot labeled 'Slow expansion'}<r>[0.576, 0.844, 0.600, 0.861]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dark Blue."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "IywvdKAdrLU495mE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a higher point at x=0.1, the Instagram or Twitter line in the 'Volatile movement' chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twitter."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "boVudsOyEDA9h57F",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn {the subplot labeled 'Rapid recovery'}, is the ending y-value for {the Instagram line}[0.976, 0.934] higher than the ending y-value for {the Twitter line}[0.976, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "qFwLOfKMueSH2vAb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the markers for the Instagram line in {the second subplot}<r>[0.576, 0.802, 0.600, 0.819]</r> squares or circles?"
+            },
+            {
+                "from": "gpt",
+                "value": "Squares."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "2askCnAhzmU2vATc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section in [0.340, 0.371, 0.349, 0.597]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the y-axis area of the subplot titled 'Slow expansion'. It contains labels for follower growth values such as 120, 125, 130, and is formatted in dark color text."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "q6OrAanScfSvdcPs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat essential elements are within [0.0, 0.075, 0.660, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes two subplots, 'Slow expansion' and 'Rapid recovery', displaying line graphs for Instagram and Twitter follower growth with respective titles and axes labels."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "drBM5ZwA8vAtAhzk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the chart content and format in the area [0.044, 0.371, 0.330, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The outlined region features the main plotting area of the first subplot 'Volatile movement'. It includes the gridlines and two lines representing the follower growth of Instagram and Twitter, marked by red and blue colors respectively."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "qALFDEsqgRjWK1Be",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at {coordinates}<r>[0.042, 0.903, 0.066, 0.931]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year label '2010'."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "ZdXyVh722HvMQcwC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the marker color for {Citizenship Performance}<r>[0.046, 0.104, 0.071, 0.120]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "Tm4Pl1Cb8dnFcHNR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of plot is located at <r>[0.702, 0.075, 0.988, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line plot."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "kM3Vz9VpONW9Xa7T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {year 2050 location}<r>[0.975, 0.893]</r>, Citizenship or Amendment Performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "Citizenship Performance."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "68IojeWaGYPvheDD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Amendment Performance point in 2020}<r>[0.449, 0.312]</r> above or below {Citizenship Performance point at the same year}<r>[0.449, 0.602]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "lXqUESCx2oRSuYjp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first y-label '0.5'}<r>[0.023, 0.954, 0.038, 0.982]</r> appear above or below {the last y-label '1.0'}<r>[0.023, 0.046, 0.038, 0.074]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "Ga6yQrUwuu7dRWAq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information in the region <r>[0.046, 0.089, 0.197, 0.181]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the sublegend with labels {Citizenship Performance}<r>[0.075, 0.100, 0.188, 0.128]</r> and {Amendment Performance}<r>[0.075, 0.142, 0.194, 0.170]</r>, both depicted alongside their respective marker colors."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "1vzXZwMHkCcwDdjn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is present between the area <r>[0.372, 0.903, 0.657, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains year labels '2010' to '2050' on the x-axis, specifically ranging from {2010}<r>[0.372, 0.903, 0.396, 0.931]</r> to {2050}<r>[0.633, 0.903, 0.657, 0.931]</r>."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "R05At9YhY65bP63H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the graphical coordinates <r>[0.714, 0.075, 0.975, 0.893]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a line plot for Overall Performance Change, featuring {Citizenship and Amendment performance lines}<r>[0.714, 0.330, 0.975, 0.783]</r>, as well as associated x and y-axis labels and year labels set across the bottom."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "lso4Wsw8lwpn7VCj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of the object at [0.154, 0.030, 0.217, 0.066] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "xU8NainWkT1txdjL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the legend handle for 'KEI'}<r>[0.046, 0.104, 0.071, 0.120]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#1f77b4."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "KbsakMmgT2FFtCn7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the ylabel coordinates}<r>[0.010, 0.396, 0.019, 0.572]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "yQDvdwsS1OKhNIsC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point of KEI}<r>[0.055, 0.447]</r> above {the first point of Innovation Industries}<r>[0.055, 0.715]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "JNyNk4heUYn3X95L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is at a higher position, {0.2 Economy Ratio x-tick label}<r>[0.077, 0.903, 0.092, 0.931]</r> or {0.9 Economy Ratio x-tick label}<r>[0.939, 0.903, 0.954, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Equal."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "aBHSHs91AXJ4wMjj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {0.6 Economy Ratio x-tick}<r>[0.200, 0.893]</r>, which line point is higher, KEI or Innovation Industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "KEI."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "z8Y4FMcJfsBB0hF4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features within the region [0.300, 0.075, 0.650, 0.300]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the chart's plot area with grid lines and points from both KEI and Innovation Industries lines, showing increasing trends."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "piuRmTyViJQsMixh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area from [0.710, 0.870, 0.990, 0.930] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains x-axis labels for the 'Economy Ratio r' from 0.6 to 1.0 with respective tick marks and labels positioned below the lines."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "akxxY9uExIqf4NGO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOutline the contents found within [0.020, 0.180, 0.300, 0.950]"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the y-axis with labels of performance metrics from 0.4 to 0.9, y-axis grid lines, and part of the plot area, including some of the Innovation Industries line."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "q59NjJynZ4JtGxZW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object marked with a handle at [0.058, 0.067, 0.077, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle represents the 'End Price of Stock'."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "mujOkjtJomz3DcnE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is plotted at {the point}[0.802, 0.095] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Financial Market Index."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "r3f2EKP5eR4szt5c",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.392, 0.849] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The start of the 'End Price of Stock' line."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "JKS4mRYbze64N1BX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the handle labeled 'End Price of Stock'}[0.058, 0.067] above or below {the handle labeled 'Financial Market Index'}[0.058, 0.109]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "g05vmoSRlU0GVKpZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value plotted at {the point}[0.301, 0.854] higher or lower than the value at {the point}[0.301, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "OieNg3yasIzPycEb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the mark at point}[0.631, 0.069] correspond to a higher or lower value than {the mark at point}[0.631, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "PBKwb8XE26NCyGhE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual elements found within [0.355, 0.031, 0.643, 0.092]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses x-axis labels '2018-05-01' {at}[0.365, 0.903, 0.418, 0.931], '2018-06-01' {at}[0.445, 0.903, 0.498, 0.931], '2018-07-01' {at}[0.525, 0.903, 0.578, 0.931], '2018-08-01' {at}[0.604, 0.903, 0.658, 0.931] and corresponding x-grid lines."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "sUtmRFKRXCoZ2313",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are inside the coordinates [0.380, 0.030, 0.643, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot includes axes, x-grid lines, y-grid lines, x-axis and y-axis labels, x-ticks, y-ticks, a sublegend consisting of labels and handles for 'End Price of Stock' and 'Financial Market Index', and the two colored lines depicting the data trends for these labels."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "v0v47NUgaUOjKDeY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be described about the region [0.050, 0.030, 0.313, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains axes, x-grid lines, y-grid lines, labeled x-ticks and y-ticks, x-axis and y-axis labels where 'Value' and 'Time Period in Months' are written, a sublegend for 'End Price of Stock' and 'Financial Market Index', with their respective colored handles grouped together."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "pThKj8iF1NNEHiIl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of the area marked with coordinates [0.371, 0.075, 0.660, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the subplot for the line chart with the title {Inverse Trend of HEPHAR_G}<r>[0.439, 0.030, 0.592, 0.067]</r>."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "4v3dix9gmZKyyYsI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.340, 0.396, 0.349, 0.572]</r> for the subplot displayed in the middle segment of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "KYtoejckSqACIU5t",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the point at {coordinates}<r>[0.142, 0.149]</r> on {HEPHAR_S line}<r>[0.385, 0.149, 0.646, 0.372]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a value of 0.85 at the 0.4 ratio on the x-axis."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "EPt2CO8kuGkTiA60",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value at {x = 0.8 point}<r>[0.918, 0.283]</r> on the {HEPHAR_S line}<r>[0.715, 0.149, 0.976, 0.372]</r> in the third subplot have a higher or lower performance compared to {x = 0.8 point}<r>[0.588, 0.283]</r> on the same line in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "1hq2HU2zdFsxeVzY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the 0.6 ratio value of {HEPHAR_S}<r>[0.316, 0.372, 0.646, 0.372]</r> greater than {HEPHAR_G}<r>[0.316, 0.789, 0.646, 0.789]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "sxvohsQSoYdkpFGB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich series has a value above 0.7 at {x = 1.0 point}<r>[0.316, 0.283]</r> in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "HEPHAR_G."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "rkJtYrhqI8zDiytR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region framed by [0.371, 0.396, 0.660, 0.572]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains {the y-axis}<r>[0.340, 0.396, 0.349, 0.572]</r> with tick labels from 0.4 to 0.8 indicating performance levels for the {Inverse Trend of HEPHAR_G}<r>[0.439, 0.030, 0.592, 0.067]</r> subplot."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "rid0sMQuJRKo4llG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the boxed area [0.041, 0.089, 0.330, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses {the legend}<r>[0.046, 0.089, 0.129, 0.181]</r> identifying colors and labels such as HEPHAR_S and HEPHAR_G."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "mK7kfwBJ8nZnpA8f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the space bounded by [0.371, 0.01, 0.660, 0.07]."
+            },
+            {
+                "from": "gpt",
+                "value": "This space showcases the subplot title {Inverse Trend of HEPHAR_G}<r>[0.439, 0.030, 0.592, 0.067]</r>, stylized in a moderate font weight and dark color."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "surcby2PP2diLOat",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the line labeled 'Slow Recovery'}<r>[0.749, 0.126, 0.968, 0.740]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#9467bd."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "z453K3bQZ1LpdZkv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {the line \"Fluctuating Decline\"}<r>[0.705, 0.126, 0.968, 0.637]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "square."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "6JsiHX1XVpe4HS5X",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the point with coordinates [0.924, 0.330] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the data point}<r>[0.924, 0.330]</r>."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "bEz55KPQTnHb9WxW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at [0.749, 0.126]} higher or lower than {the point at [0.968, 0.279]} on the 'Slow Recovery' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "G02Te33Z6qBpgbvh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is {the point at [0.749, 0.126]} on, Slow Recovery or Fluctuating Decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slow Recovery."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "G0x4ipQl53ZHeVvs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the position [0.968, 0.463]}, which line is lower, 'Slow Recovery' or 'Fluctuating Decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fluctuating Decline."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "B0nalYhEcFUDpgAV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area bounded by [0.373, 0.075, 0.658, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an axis plot for the 'World Peace Initiative to Environmental Conservation' with the y-label as 'Fluctuating Decline' and the {x-label}<r>[0.482, 0.942, 0.549, 0.970]</r> as 'Slow Recovery'. Axes are visible and marked with {yticks}<r>[0.349, 0.282, 0.373, 0.856]</r>."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "G3w1IPsXki6VpKtm",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the region [0.373, 0.787, 0.653, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the {legend}<r>[0.531, 0.787, 0.653, 0.879]</r> for the plot, with entries for 'Slow Recovery' and 'Fluctuating Decline'."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "IYn2EieVR25ctTP6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain what is depicted in the rectangle [0.705, 0.075, 0.990, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the subplot titled 'Education Expansion to Human Rights' featuring tense data movement depicted by two lines for 'Slow Recovery' and 'Fluctuating Decline'. The area displays active grid visibility with appropriately marked {yticks}<r>[0.687, 0.217, 0.705, 0.859]</r> and {xticks}<r>[0.738, 0.903, 0.978, 0.931]</r>."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "kBESWcjIoZSN82N4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of line represents {the Box Office Revenue} at [0.474, 0.706, 0.554, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Dashed line."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "1mXueF9zf7wkNYfT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the marker for {Subscription Sales} at [0.374, 0.104, 0.398, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Purple."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "7WoIaKrih1h7W2Bb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many {xtick labels} can be found in the region [0.383, 0.903, 0.645, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seven."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "Ah8OUJCCJMC40CQ3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the start of the {Subscription Sales line} at [0.065, 0.640, 0.144, 0.767] higher than the start of the {Box Office Revenue line} at [0.065, 0.697, 0.144, 0.707] in 1990?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "O3OSGy3stWtlcumh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich revenue source had a higher revenue increase from 1996 to 1997, {'Subscription Sales'}[0.884, 0.450] or {'Box Office Revenue'}[0.884, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subscription Sales."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "MZ6BOdiSgI0RPAax",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point at '1996'}[0.554, 0.450] represent a higher value for Subscription Sales than the {point at '1994'}[0.501, 0.591]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "tawZklNSVCbPumsT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the section [0.668, 0.075, 0.990, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows a graph for the years 1997 to 1999 detailing Subscription Sales and Box Office Revenue trends. Subscription Sales have {markers color purple}[0.704, 0.104, 0.728, 0.120], while Box Office Revenue has {markers color orange}[0.704, 0.146, 0.728, 0.162]. The lines show an upward trend for Subscription Sales and a downward trend for Box Office Revenue."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "KGiP2c41ME39Imt9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.340, 0.343, 0.660, 0.625]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area displays a y-axis labeled 'Revenue (in billions)' with y-axis tick labels from '0' to '50' at regular intervals and colored in dark gray. Additionally, graphs for Subscription Sales and Box Office Revenue from 1994 to 1996 are shown, reflecting decreasing trends for Box Office and increasing for Subscription Sales."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "oZoiCYIdEGxFs1Ow",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the visual elements presented in the range [0.038, 0.075, 0.660, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers two line charts corresponding to different time periods, '1990-1993' and '1994-1996', showing Subscription Sales and Box Office Revenue. Each chart has its own set of x and y axes, grid lines, and subtitles. The graphs indicate various trends in revenues, with distinct markers for each revenue type."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "FF7RaMmiLlmPPMpw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'Steady Increase Trend' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{color #9467bd}<r>[0.044, 0.104, 0.068, 0.120]</r>."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "7z5vojXisMl6UbSN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label for the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Value."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "qXyxj1E1o70yxBfB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the tick mark}<r>[0.065, 0.893]</r> on the x-axis represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2011."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "BRvQpS0jFnOR2YAj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point for 2012 in the 'Steady Increase Trend'}<r>[0.091, 0.702]</r> have a higher value than {the point for 2012 in the 'Fluctuating Decline Trend'}<r>[0.091, 0.266]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "FvWiK0dfHvHG5qcE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the starting point of {the 'Steady Increase Trend'}<r>[0.065, 0.757]</r> lower in value than the ending point<r>[0.303, 0.157]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "xLl0RrAEDllkHLJX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point is at a higher position, {2017 in 'Fluctuating Decline Trend'}<r>[0.224, 0.566]</r> or {2017 in 'Steady Increase Trend'}<r>[0.224, 0.348]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2017 in 'Steady Increase Trend'."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "2D7nkGp9jZtb2616",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within<r>[0.292, 0.903, 0.315, 0.931]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis label for the year 2020, displayed in dark grey text."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "PS6wcPWGr6AbaiXe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the components located in <r>[0.053, 0.903, 0.103, 0.931]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes the x-axis labels for the years 2011 and 2012, both in a dark grey font."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "nvDeVKK8UolvjXTG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region<r>[0.368, 0.757, 0.633, 0.839]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes multiple data points of the 'Fluctuating Decline Trend' from years 2016 to 2020, indicated by square markers."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "baTzCY4GCcG5sKFw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the line chart titled on this visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the JSON includes no data about subtitles or main titles being displayed, there is no title on the line chart visible within the current viewing frame."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "FPJYY1TCESB35XEr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line that represents {the Price Index}<r>[0.075, 0.353, 0.945, 0.387]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff7f0e."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "dUu58u5Ky7EpBtfz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is assigned to {the point}<r>[0.558, 0.619]</r> on the Yield Return chart in the subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "2021-06-01."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "FzS2teBffDdCtmg7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot has a higher initial point, subplot_0 or subplot_1 using the starting point coordinates of {Yield Return}<r>[0.075, 0.289]</r> and {Yield Return}<r>[0.075, 0.619]</r> respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "subplot_1."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "BOVl4rwt4v2xUdCJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the trend of the {Price Index line}<r>[0.075, 0.353, 0.945, 0.387]</r> increasing or decreasing between {the points labeled '2021-02-01'}<r>[0.171, 0.378]</r> and {the points labeled '2021-10-01'}<r>[0.945, 0.683]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "egrwRNM3qqYtlm2p",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot's first point of the Yield Return line is higher, comparing {Yield Return at '2021-01-01'}<r>[0.075, 0.289]</r> from subplot_0 to {Yield Return at '2021-01-01'}<r>[0.075, 0.619]</r> from subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "subplot_1."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "LiZUJ9NZqvU6rosa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the coordinates [0.015, 0.129, 0.044, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label indicating \"Yield Return\" is located in this region."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "xKKLRTD2L6YRHqNY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details found in the region from [0.075, 0.308, 0.985, 0.317]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains x-axis tick labels for all the dates from '2021-01-01' to '2021-10-01' spaced evenly across the chart."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "Rg1aDeP3zaPDKfJ6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by coordinates [0.805, 0.345, 0.938, 0.375]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sub-legend for subplot_1, including labels and colored handles indicating \"Yield Return\" and \"Price Index\"."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "RU2ycUagns49Xl84",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart does not have a title."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "IeSZp0aY6DSlCY3B",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the handle for 'Innovative'}<r>[0.084, 0.764, 0.167, 0.785]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "rPpFHquDAyBgwSTQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure size?"
+            },
+            {
+                "from": "gpt",
+                "value": "The figure size is [5.0, 4.0]."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "YVBueS78l8pjKaVd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value for the education level of Master's, {the 'Analytical' line}<r>[0.185, 0.317, 0.924, 0.637]</r> or {the 'Conceptual' line}<r>[0.185, 0.137, 0.924, 0.797]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Analytical line."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "oaVZmMcUDnr2ML3C",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Conceptual' point} for Bachelor's<r>[0.185, 0.797]</r> above or below {the 'Innovative' point} for Bachelor's<r>[0.185, 0.717]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "Y9SORWI5jk0Zwhvf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich education level has a higher score for 'Innovative' skills, Bachelor's or PhD?"
+            },
+            {
+                "from": "gpt",
+                "value": "PhD."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "ku9jb8iOCG8zTWVw",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.079, 0.737, 0.944, 0.816]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend, which lists 'Innovative', 'Analytical', and 'Conceptual'. Each category is accompanied by a color marker."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "5dYs8CBtL3B95DrM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the coordinates [0.148, 0.037, 0.961, 0.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the plot of the chart, including the lines representing 'Innovative', 'Analytical', and 'Conceptual' performance ratings over different education levels, as well as the labels for those education levels on the x-axis, and the performance ratings on the y-axis."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "gTjmzoM9waXHrZd1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found from [0.081, 0.816, 0.138, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '60' is located within these coordinates."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "qhF692pN93NIJfDM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.052, 0.356, 0.077, 0.654]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Review Score."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "9Vqn3oL4lA9AbYS2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {line associated with 'Italian'}<r>[0.160, 0.197, 0.865, 0.601]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "WrGcU61aibHEKKzn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the point}<r>[0.552, 0.409]</r> on the 'Mexican' line refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The review score of Mexican cuisine on 2022-02-19."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "Ph4lSgeQGC486xSW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Chinese' review score line}<r>[0.160, 0.312, 0.865, 0.736]</r> generally rising or falling over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Rising."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "NEJvtqlZt6fZCkOb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the {point}<r>[0.786, 0.467]</r>, does the 'Mexican' review line score higher or lower than the 'Italian' review line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "QS94XsO832FTCyKC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has a higher score at {the point}<r>[0.708, 0.447]</r>, Italian or Mexican?"
+            },
+            {
+                "from": "gpt",
+                "value": "Italian."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "zXo7ELlCq5m9uEUg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the region <r>[0.135, 0.137, 0.296, 0.305]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with three items: {Chinese}<r>[0.139, 0.158, 0.191, 0.175]</r> with a green marker, {Italian}<r>[0.139, 0.211, 0.191, 0.227]</r> with a red marker, and {Mexican}<r>[0.139, 0.263, 0.191, 0.280]</r> with a blue marker."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "XW7C6PW1kH7xBe9z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is placed in the region <r>[0.083, 0.874, 0.119, 0.914]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '60' is located in this area."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "WPK2VCyFmjRRw7q7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content of the region <r>[0.814, 0.900, 0.915, 1.061]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis tick label for the date '2022-03-30'."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "k1ubDMcLYV52WBOM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object represented by the coordinates [0.747, 0.758, 0.916, 0.803] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hospital C."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "zaPRC0mjy0M21YpT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for Hospital B}<r>[0.197, 0.339, 0.911, 0.680]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "cBnVWkmqJoGas5vL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat point is represented by the coordinates {the point}<r>[0.911, 0.539]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "An equal recovery rate of 0.75 for Hospital C for two health indicators."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "QWwLoWpMgkcIAf31",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the line for 'Hospital A'}<r>[0.197, 0.238, 0.911, 0.740]</r> and {the line for 'Hospital B'}<r>[0.197, 0.339, 0.911, 0.680]</r>, which line ends at a higher point in the chart's 'Rate (%)' indicator?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hospital B."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "4LGtKCrcevQXF1bM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {marker for 'Hospital C'}<r>[0.646, 0.768, 0.729, 0.789]</r> the same color as {the line for 'Hospital C'}<r>[0.197, 0.539, 0.911, 0.539]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "buKAQKhDWNnCjPRz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich hospital shows equal rate for both health indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hospital C."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "Tee3jg96KHgQWiZ2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinates [0.083, 0.741, 0.930, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the graph is located here, it contains items for Hospital A at {coordinates}<r>[0.190, 0.758, 0.358, 0.803]</r>, Hospital B at {coordinates}<r>[0.468, 0.758, 0.637, 0.803]</r>, and Hospital C at {coordinates}<r>[0.747, 0.758, 0.916, 0.803]</r>. Each item is labeled with the respective hospital name and an accompanying color marker."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "TMQo4rmPKYcxW26M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area encompassed by [0.197, 0.238, 0.911, 0.740]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {the line for 'Hospital A'}<r>[0.197, 0.238, 0.911, 0.740]</r>, starting from 0.65 patient recovery rate up to 0.9 patient admission rate."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "Aw65RqZGulmLShbk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated in the area defined by coordinates [0.241, 0.912, 0.867, 0.963]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-label of the subplot, \"Health Indicators (log scale)\" is found in this section."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "M5YMhjFfJrCzddLE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is indicated by the text_coordinates [0.112, 0.078, 0.257, 0.101] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Energy Consumption"
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "lVor7MMyLNjsR6l3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the region specified at [0.103, 0.198, 0.943, 0.879] in the line objects of the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area plots the line representing Energy Consumption over the years 2000 to 2010 in the graph."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "5neJpVgpPzzscWD4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element occupies the specific coordinates [0.508, 0.952, 0.537, 0.975] on the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The X-axis label 'Year' is located here."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "6OWKWws1toWPupE7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line graph for Resource Consumption}[0.103, 0.266, 0.943, 0.538] tend to go up or down over the X-axis from the coordinates spanned in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "Up."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "HXkBqkyAqDYJDXfa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line object reaches the highest point, the {line graph for Energy Consumption}[0.103, 0.198, 0.943, 0.879] or the {line graph for Carbon Output}[0.103, 0.096, 0.943, 0.708]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line graph for Energy Consumption."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "0q2OxihBq1qirONc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the year {2004}[0.439, 0.918] on the X-axis, which line object is higher, {Carbon Output}[0.439, 0.640] or {Resource Consumption}[0.439, 0.396]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Carbon Output."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "LcsHccVgv1wVKQL8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you give about the region specified at [0.067, 0.069, 0.277, 0.180]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the chart's sub-legend, which includes three items: Energy Consumption colored green, Resource Consumption in red, and Carbon Output in blue, each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "PzUTpwwxcvyB4DDP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area at [0.061, 0.057, 0.985, 0.918]"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a large area that contains the primary plot or graph, which is composed of several line objects. The grid is visible, and the plot represents the Energy Consumption, Resource Consumption, and Carbon Output from the year 2000 to 2010."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "ZFroqmP1p5qECRU9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the objects located within the coordinates range [0.355, 0.811, 0.439, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes two points on the Energy Consumption line graph, these points represent the values of Energy Consumption for the years 2003 and 2004."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "6k9z6kRAG9PUMzoR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the title of the graph say which has coordinates [0.213, 0.025, 0.839, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title says \"Innovation and Technology Entrepreneurship Ecosystem\"."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "aHkVaDxekTHzVKuI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the legend item at coordinates [0.763, 0.134, 0.956, 0.164] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'University Partnerships'."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "5K0mdxn5q5xScD7e",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.005, 0.035, 0.955]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 'Investment/University Partnerships/Startup Growth'."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "7Z25sEPH2nT46PsB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the tech investment for 'Boston'}<r>[0.526, 0.241]</r> higher or lower than {that for 'Seattle'}<r>[0.735, 0.354]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "4AkoMEJWLHuzyipx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {startup growth for 'New York'}<r>[0.317, 0.777]</r> have more or less growth compared to {startup growth for 'Chicago'}<r>[0.943, 0.816]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "4eWTJXleqQMTzOZG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, does {the  'San Francisco'}<r>[0.108, 0.821]</r> have more or fewer university partnerships than {the 'Chicago'}<r>[0.943, 0.832]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Fewer."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "f3p20vINFld8D2zT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat comprises the region with coordinates [0.710, 0.079, 0.977, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend, which consists of three labels each represented by a different color: Green for 'Investment in Tech (bn$)', Red for 'University Partnerships', and Blue for 'Startup Growth (%)."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "DS3nfuYTF5lM8dE2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content of the area within [0.041, 0.220, 0.062, 0.250]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the label '80' of the y-axis, which likely represents an indicator value or measurement scale."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "jwmMuhqO12iPdFda",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.279, 0.902, 0.355, 0.932] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes one of the x-axis tick labels, specifically 'New York'."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "tSSFfdezEJqhlajk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the y-axis label and coordinate {y-axis label}[0.019, 0.250, 0.044, 0.663] stand for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Population growth (%)."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "25VEUsToufR2hTaT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.100, 0.896, 0.157, 0.932]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "10^-2."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "h9xBRGlO4mnCsDra",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend in the area {legend}<r>[0.309, 0.734, 0.971, 0.874]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the different population categories: Current age population, Baby boomer population, Youth population, Senior population and Virtual worker population."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "W8hSoHLousTteQL7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing Youth population}<r>[0.941, 0.456]</r> above or below {the point representing Senior population}<r>[0.941, 0.629]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "fz2t43jiI0bSzOMi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre most points of {Current age population}<r>[0.128, 0.543, 0.941, 0.543]</r> above or below those of {Baby boomer population}<r>[0.128, 0.111, 0.941, 0.629]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "QpGdvTcfweIwzF6L",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich population category has a higher growth at 10^-2, {Current age population}<r>[0.534, 0.543]</r> or {Virtual worker population}<r>[0.534, 0.370]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Current age population."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "IUyimE6YJtWsjMil",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.309, 0.734, 0.971, 0.874]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the chart labels with their colors: {Current age population}[0.376, 0.745, 0.616, 0.775] with color '#1f77b4', {Baby boomer population}[0.376, 0.789, 0.632, 0.819] with color '#ff7f0e', {Youth population}[0.376, 0.833, 0.549, 0.863] with color '#2ca02c', {Senior population}[0.700, 0.745, 0.883, 0.775] with color '#d62728' and {Virtual worker population}[0.700, 0.789, 0.963, 0.819] with color '#9467bd'."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "IQz2TzxzHILOaEwz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area [0.318, 0.942, 0.750, 0.975]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label of the chart, which is 'Population category (log scale)'."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "4rltZzgsnUtlLdpF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.534, 0.370, 0.941, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the data points for three population categories: {Current age population}[0.941, 0.543], {Youth population}[0.941, 0.456], and {Virtual worker population}[0.941, 0.543]."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "gzoMab591O4eSsvP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the primary axis represented by the coordinates [0.021, 0.106, 0.050, 0.797]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of people (Thousands)."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "9nGPpILJWxFXxYvg",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what {the point on the 'Immigration' line}<r>[0.924, 0.484]</r> represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point represents the year 2020 with an immigration number of 80,000."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "6d0VA0SYMgZIrGKM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the point having coordinates [0.418, 0.224] on {the line for 'Immigration'}<r>[0.164, 0.095, 0.924, 0.484]</r> denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the year 2010 with an Immigration number of 120,000."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "TS6E2ghfe2FwUOyl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {Emigration}<r>[0.164, 0.419, 0.924, 0.807]</r> less than the values of {Immigration}<r>[0.164, 0.095, 0.924, 0.484]</r> in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "nAyfscBRv0yDjjPL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 2015 data, is the {Emigration}<r>[0.671, 0.548]</r> higher or lower than the {Internal Migration}<r>[0.671, 0.419]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "oapC7BEwSmmCvbRr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich migration category has seen a decrease from 2015 to 2020, {Immigration}<r>[0.671, 0.095, 0.924, 0.484]</r> or {Internal Migration}<r>[0.671, 0.419, 0.924, 0.224]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Immigration."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "gHS25fQbhhNP9EW3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section with coordinates [0.222, 0.793, 0.950, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area consists of the legend which has three items: {Immigration}<r>[0.298, 0.806, 0.443, 0.842]</r> with a green color marker, {Emigration}<r>[0.522, 0.806, 0.651, 0.842]</r> with a red color marker, and {Internal Migration}<r>[0.729, 0.806, 0.940, 0.842]</r> with a blue color marker."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "sR3nw0B9QAsYSqJ4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region from [0.058, 0.208, 0.119, 0.248]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The count of 120 on the y-axis from the y-ticks is within the provided region."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "DD9Rnac6hWZs8195",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is outlined in the area specified by [0.164, 0.224, 0.924, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the observation region for the 'Internal Migration' line, which demonstrates the change in numbers for internal migration over the years."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "0yxL6QYavBVZ2Nqc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text with these coordinates [0.110, 0.120, 0.233, 0.144] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sluggish progress."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "BBL1Z2JL8ZsPejeW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label can be found at [0.036, 0.406, 0.053, 0.429] on the Y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "20."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "zYPsFc2QGh5VxfsL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the text {this} located at [0.110, 0.155, 0.221, 0.178] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Erratic behavior."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "COpn3zet8yNkrq0u",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for the 'Sluggish progress'}[0.263, 0.440] above or below {the point for the 'Declining pattern'}[0.263, 0.300] on the 'Product Design' x-coordinate point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "QCRq0jjY1I4K4l3T",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Erratic behavior'}[0.099, 0.351, 0.918, 0.605] end above or below {the line for 'Upward trajectory'}[0.099, 0.059, 0.918, 0.631] on 'After-sales Service' x-coordinate point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "veNoYLwKRNBOZSzN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line has a higher value at {the 'Sales and Distribution' point}[0.755, 0.415], Erratic behavior or Sluggish progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sluggish progress."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "ZSZQlWT3CztnYAn0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in [0.065, 0.041, 0.241, 0.223]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the chart containing the names of five different patterns: {Slight increase}<r>[0.110, 0.050, 0.212, 0.074]</r>, {Declining pattern}<r>[0.110, 0.085, 0.230, 0.109]</r>, {Sluggish progress}<r>[0.110, 0.120, 0.233, 0.144]</r>, {Erratic behavior}<r>[0.110, 0.155, 0.221, 0.178]</r> and {Upward trajectory}<r>[0.110, 0.190, 0.235, 0.213]</r> each accompanied with a corresponding colored marker."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "J6sPKhm14FTatVGp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region with coordinates [0.016, 0.278, 0.030, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label: {Performance}<r>[0.016, 0.278, 0.030, 0.425]</r>."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "KK41ojxq7yKn72Oo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects located in [0.378, 0.681, 0.476, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an x-axis label, which refers to the 'Manufacturing' factor of the Value Chain."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "KONILTTTwsWpd2oi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.019, 0.389, 0.041, 0.532] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "b8pwJQKxaFfl1sAC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.436, 0.073, 0.535, 0.089]."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "cuNnMLGcfJpzWL1v",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the point in the 'GDP Growth Rate' line}<r>[0.717, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2.8."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "nqAXEKWL1h5rs7Fg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher at {point at year 2012}, the 'GDP Growth Rate'<r>[0.345, 0.505]</r> or the 'Inflation Rate'<r>[0.345, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation Rate."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "6toBXtbo8DExWoRu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Unemployment Rate' at 2012}<r>[0.345, 0.245]</r> higher or lower than {the 'Unemployment Rate' at 2020}<r>[0.940, 0.156]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "1moDCUXxRtdLYQ3A",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the year 2019}, is the 'Inflation Rate'<r>[0.865, 0.322]</r> more or less than the 'Unemployment Rate'<r>[0.866, 0.416]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "CzBvDSTOMwsZgVAN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the objects in the region with the coordinates [0.089, 0.058, 0.713, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises three legend items, i.e., 'GDP Growth Rate', 'Unemployment Rate', and 'Inflation Rate'. Each of these items is paired with a different colored marker that is consistent with the color of the respective line in the plot."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "Q630KHr5n0P27kFj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region described by [0.081, 0.044, 0.981, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area hosts the entire line plot, including the x and y axes labels, the x and y ticks, and the plotted lines for 'GDP Growth Rate', 'Unemployment Rate', and 'Inflation Rate'."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "8eiivzmcoOE7ayTE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content enclosed within the region [0.089, 0.058, 0.713, 0.108]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area is an inclusive representation of the legend. It exhibits the labels for 'GDP Growth Rate'<r>[0.145, 0.069, 0.296, 0.097]</r>, 'Unemployment Rate'<r>[0.353, 0.069, 0.532, 0.097]</r>, and 'Inflation Rate'<r>[0.589, 0.069, 0.706, 0.097]</r>, each coupled with a respective color handle."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "hHIgxABldauQPMz9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.176, 0.057, 0.966, 0.823] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Graph plotting area."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "apCa6ATJIL1DfOVz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.212, 0.181, 0.930, 0.737]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aggressive investment strategy line."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "7uJI6Y5oqXrMu8P5",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.030, 0.277, 0.070, 0.604]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Returns (%)."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "hmTjULin7iC2Wk8Y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {point x = 0.01}, is the {Aggressive strategy}[0.212, 0.453] placed above or below the {Conservative strategy}[0.212, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "ctmBgusL27NMy95D",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Aggressive strategy line}<r>[0.212, 0.181, 0.930, 0.737]</r> deliver higher or lower returns than the {Conservative strategy line}<r>[0.212, 0.218, 0.930, 0.700]</r> at the {point x=1.0}[0.930, 0.700]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "mr9puEUw2XhW0DNy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is closer to the bottom of the graph at the coordinate of {x = 0.1} -- {Aggressive strategy}<r>[0.571, 0.181]</r> or {Conservative strategy}<r>[0.571, 0.218]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aggressive."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "gMo72nLWgeuIUYO4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.318, 0.724, 0.949, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plot legend with Aggressive and Conservative investment strategies. Aggressive being labelled with Green color and Conservative with Red color."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "OdHGe5FxDWDZlERQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is inside the coordinates [0.212, 0.181, 0.930, 0.737]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses the line graph representing the Aggressive investment strategy, including its plotted data points."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "OJinUZGeWrBFHhC2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region [0.081, 0.037, 0.166, 0.088]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the y-axis label '100'."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "oeAHz2LvwlUKYwVa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates {lebel}[0.154, 0.049, 0.194, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "AN87rjKhIXN680gK",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the {coordinate}[0.896, 0.836, 0.975, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label for '$\\\\mathdefault{10^{0}}$'."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "lIgW9eE6fWefqT2x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {line}[0.330, 0.299, 0.936, 0.649] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the trend for 'Early Stage Growth'."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "6v2jPEqgwUeSP4WT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {point}[0.330, 0.299] on the 'Early Stage Growth' line compare to the {point}[0.330, 0.474] on the 'Seed Stage' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is below."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "lSAmeDjrMHZOuqpo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a higher value at {x=1.0}, the 'Early Stage Growth' or the 'Late Stage'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Late Stage'."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "oo0GB4uSU3zoUee7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to the {point}[0.936, 0.771] on the 'Seed Stage', where is the {point}[0.936, 0.474] on the 'Late Stage' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "SsCYm0LqyIVCdh0Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.327, 0.741, 0.647, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled as 'Early Stage Growth' with a handle color of '#ff0000'."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "zQSXQeDh5FDq3GxW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.234, 0.803, 0.290, 0.853]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of a Y-axis label marked as '80'."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "oLSW4nvZhj0KIWgs",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.330, 0.474, 0.936, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "A line representation of 'Seed Stage' data located in this area with three points corresponding to different x values."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "q5ad0L23ceiVbhEu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the location [0.025, 0.237, 0.057, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "dU8eYU7Lz2EmtF2V",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the legend {item}[0.598, 0.772, 0.664, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "o9UMCOJ4nPBfR1TE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at the x-point [0.148, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "10."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "ZR8VFWKGIZxBNOoM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Europe'}[0.929, 0.682] above or below {the point for 'Africa'}[0.929, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "eKYHaYwwLWlozgM4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Asia'}[-1636.981, 0.329, 0.929, 0.604] higher than {line for 'Europe'}[-1636.981, 0.447, 0.929, 0.682] at the -10 latitude point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "HGO2o7zgGm6v5JxU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region experiences a higher temperature at the {30th latitude}[0.929, 0.447]? Africa, Asia, or Europe?"
+            },
+            {
+                "from": "gpt",
+                "value": "Africa."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "3jSOqgM2KiEzAJJI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the chart content within [0.524, 0.759, 0.956, 0.821]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart's legend, listing the three regions, Africa, Asia, and Europe, each with a designated color for their data lines: green for Africa, red for Asia, and blue for Europe."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "jtu6uWsbg4FN7GQ1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the element region defined by [0.109, 0.839, 3.419, 0.901]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the X-axis scale section of the chart. It starts with 10^-1 and ends at 10^3, marking logarithmic scale values at the base of 10."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "F6UQRkLvNH1s9s6U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content inside the area [-1636.981, 0.212, 0.148, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the first half of the 'Africa' data line depicting temperature trends at latitudes from -30 to 0 degrees. The line starts at a temperature of 30\u00b0C at latitude -30 and increases to 36\u00b0C at latitude 0."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "RXJSeEpT2tGI9xEM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for {the x-axis tick mark} at [0.726, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2014."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "ch7dgtD6MtHoYHQ9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of {the line series} that has the color marker of coordinates [0.726, 0.112, 0.777, 0.129] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Clinical Trials."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "8xLvWHkgMXgAqrl4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the label for the y-axis} at coordinates [0.021, 0.426, 0.041, 0.508]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Count."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "UTfWqLsbbiW4uEvt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2014 in the 'Doctor Visits'}[0.726, 0.158] above or below {the point for 2014 in the 'Clinical Trials'}[0.726, 0.723]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "43hrce7HI7ExNE9I",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher in the year 2011, {the 'Medical Research'}[0.347, 0.797] or {the 'Clinical Trials'}[0.347, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Medical Research."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "mCn7OiilinWenAEQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the 'Doctor Visits'}[0.852, 0.201] values increasing or decreasing from 2013[0.600, 0.137] to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increasing."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "Qbhg9005tsXU0vmE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within this region [0.724, 0.055, 0.969, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the plot, with entries for 'Doctor Visits', 'Clinical Trials', and 'Medical Research'. Each entry has associated color markers at {coordinates}[0.726, 0.070, 0.777, 0.087], {coordinates}[0.726, 0.112, 0.777, 0.129], and {coordinates}[0.726, 0.154, 0.777, 0.170] respectively."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "BenL0h4rv0heWzZv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region [0.194, 0.893, 0.852, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all tick labels along the x-axis, which are the years from '2010' to '2015', each on coordinates [{coordinates}[0.221, 0.893], {coordinates}[0.347, 0.893], {coordinates}[0.473, 0.893], {coordinates}[0.600, 0.893], {coordinates}[0.726, 0.893], {coordinates}[0.852, 0.893]] respectively."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "6mbUQDZEeKQCp5ej",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the area with coordinates [0.221, 0.680, 0.852, 0.733]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the 'Clinical Trials' line with points at {coordinates}[0.221, 0.680], {coordinates}[0.347, 0.707], {coordinates}[0.473, 0.733], {coordinates}[0.600, 0.717], {coordinates}[0.726, 0.723], and {coordinates}[0.852, 0.733]."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "RCtjtqJwWw9mVLku",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the object at coordinates [0.315, 0.304, 0.362, 0.344]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '80'."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "aIpDIbkkMLcl6VW7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title in the given figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no title in the given figure."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "DepbHIMunYFGKXCL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of object is represented by the bounding box at [0.633, 0.803, 0.938, 0.839] within the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item \"Operational Efficiency\"."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "ojylOdSITFvzKQu2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Customer Satisfaction' line}[0.397, 0.101, 0.936, 0.375] above or below the {the 'Product Quality' line}[0.397, 0.320, 0.936, 0.732] in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "z9Wa8D60MV4mRBoc",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDid {the 'Product Quality' metrics}[0.397, 0.320, 0.936, 0.732] improve or decline from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decline."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "Ent802EMrNnSgu1p",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric had better performance in 2020, {Customer Satisfaction}[0.828, 0.266] or {Operational Efficiency}[0.828, 0.375]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Operational Efficiency."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "eTipMrbl7rWv39X4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description for the region with the coordinates [0.350, 0.879, 0.444, 0.919]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-coordinate label for the year 2016."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "eDKPm2L47qIO6CWY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region of [0.315, 0.030, 0.362, 0.070]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the y-coordinate label indicating the '90%' mark on the axis."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "ed2DsK4QLlEd2SYY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.768, 0.879, 1.091, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for the years 2021 and 2022 are located within this area."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "dt0Wm56FoOHcoIiG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text object located within the coordinate [0.670, 0.109, 0.725, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patent Rejection's color marker in the legend."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "jLvJJQiIBIGJ8vco",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line}[0.177, 0.783, 0.930, 0.875] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend of Patent Rejections."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "NDUNMIy4xReHJjqE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the 5th x-axis tick whose coordinates are [0.523, 0.885, 0.584, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2010."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "QeNN3sdXJc2dfdD9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the line representing 'Patent Application', is 2008 data point higher or lower than the data point for 2007 at {coordinates}[0.327, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "QQdOgqenw7jg2wiH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is positioned higher at 2012 - {the 'Patent Application' line}[0.704, 0.184] or {the 'Patent Approval' line}[0.704, 0.507]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Patent Approval line."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "L0m013p3KwT1JZOF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2005, was the rate of 'Patent Approval' higher or lower than that of 'Patent Rejection' at {the point}[0.177, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "37RP4oD1FtkP8y8M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the coordinate box [0.150, 0.092, 0.924, 0.146]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. The labels are as follows: {Patent Application}[0.220, 0.104, 0.419, 0.134], {Patent Approval}[0.491, 0.104, 0.665, 0.134], and {Patent Rejection}[0.737, 0.104, 0.916, 0.134]. Each label is associated with a colored marker."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "kG89sHTVLRIuViGq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the box [0.523, 0.885, 0.584, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year '2010' x-axis label is located in these coordinates."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "MeqtFf6hxbrKNg99",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinate region [0.177, 0.783, 0.930, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data points of the 'Patent Rejection' line from the years 2005 to 2015. The trend of the data points shows a general increase over this period."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "3XoSKVErCEYTjldn",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.183, 0.359]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a point on the line for Amazon in 2019."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "SeJlPMIUHP7VxeY0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the Walmart handle in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.169, 0.151, 0.253, 0.172]"
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "rdgXxyWUnCR2bGUE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the {y-axis tick label}<r>[0.109, 0.820, 0.138, 0.870]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "Ua7nfv86CUFQ5CAV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for Amazon} [0.536, 0.278] above or below {the point for Walmart} [0.536, 0.519] for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "xQTLVqew1AhZJOD3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for Alibaba} [0.183, 0.492, 0.888, 0.546] cross or not cross with {the line for Walmart} [0.183, 0.439, 0.888, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, doesn't cross."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "vWOSRSMnShmofMhL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Amazon line} [0.183, 0.145, 0.888, 0.359] trending upwards or downwards between 2019 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upwards."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "MYGebubnDUlKRKJL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.164, 0.058, 0.424, 0.400]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains labels and color markers for different companies: {Amazon}<r>[0.271, 0.075, 0.405, 0.120]</r>, {Walmart}<r>[0.271, 0.141, 0.411, 0.186]</r>, {Alibaba}<r>[0.271, 0.207, 0.395, 0.252]</r>, {Ebay}<r>[0.271, 0.272, 0.353, 0.317]</r>, {BestBuy}<r>[0.271, 0.338, 0.408, 0.383]</r>, with markers in colors of green, blue, red, purple, and orange respectively."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "MIYuM6kQOWAEv5Vv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.183, 0.145, 0.888, 0.760]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains multiple data lines representing market share of different companies from 2019 to 2021. It includes lines of {Amazon}<r>[0.183, 0.145, 0.888, 0.359]</r>, {Walmart}<r>[0.183, 0.439, 0.888, 0.573]</r>, {Alibaba}<r>[0.183, 0.492, 0.888, 0.546]</r>, {Ebay}<r>[0.183, 0.573, 0.888, 0.680]</r>, and {BestBuy}<r>[0.183, 0.680, 0.888, 0.760]</r>."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "IVScJmdrCNs8oaKq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.169, 0.151, 0.253, 0.172]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the color marker for {Walmart}<r>[0.169, 0.151, 0.253, 0.172]</r> in the legend."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "UN2N9ywZiu9aUNJB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the attribute of {the subplot_0 axes}<r>[0.148, 0.037, 0.940, 0.836]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The background color of the subplot is given by the attribute, which is specified as #eaeaf2."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "rVaLeFDujR29jDIS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the {line_2}<r>[0.184, 0.437, 0.904, 0.677]</r> on the chart be recognized?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line labeled as \"Twitter\" that is represented in a red color (#ff0000), using a hyphen as linestyle and 'o' as the marker."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "Vj6aKCRKP9ZtyHDu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content associated with the {coordinates}<r>[0.741, 0.688, 0.910, 0.733]</r> in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content is labeled as 'Instagram'."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "sqU5Hr64F4YpIuuN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the x-axis value '15-35', which social media has more active users, {Facebook}<r>[0.904, 0.277]</r> or {Instagram}<r>[0.904, 0.117]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Instagram."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "NdeBTtpHGjh51v1o",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to the y-axis label {Active Users (millions)}<r>[0.030, 0.126, 0.070, 0.748]</r>, where is the legend located?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the right."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "dSJHvXhOy9UsvxBG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the data points of '35+' and '15-35' for {Twitter}<r>[0.184, 0.677, 0.904, 0.437]</r>, does the number of active users increase or decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decrease."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "Qt2pX6eC84YjEPaO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area encompassed by the coordinates [0.635, 0.606, 0.924, 0.816]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend for the chart. There are three entries in the legend: {Facebook}<r>[0.741, 0.622, 0.897, 0.667]</r> with a green marker, {Instagram}<r>[0.741, 0.688, 0.910, 0.733]</r> with a blue marker, and {Twitter}<r>[0.741, 0.754, 0.853, 0.799]</r> with a red marker."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "0GZlpQj3HuFCoTgy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is available within the coordinates [0.081, 0.177, 0.138, 0.227] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area on the y-axis, the label '90' is displayed."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "cEhgcB8Q4Frug5Tr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features displayed within the {x-axis}<r>[0.137, 0.849, 0.231, 0.899, 0.839, 0.849, 0.969, 0.899]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains two labels, '35+' and '15-35', which represent the categories of user ages."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "FSmRAKunoVytJEPS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the Y-axis label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is at {coordinates}<r>[0.015, 0.250, 0.035, 0.765]</r>."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "b8iLdcgfqMlc6FR2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the last x-axis tick label in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is at {coordinates}<r>[0.923, 0.934, 0.965, 0.970]</r>."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "FPvdepT2VHh4DHYy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line labeled 'Residential'} with coordinates starting at [0.129, 0.410] and ending at [0.944, 0.160]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is one of the lines representing the trend in community interactions for different types of neighborhoods."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "22aLUHfV2EHiFbMD",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends of {the line labeled 'Low-Income'}<r>[0.129, 0.619, 0.944, 0.896]</r> and {the line labeled 'High-Income'}<r>[0.129, 0.758, 0.944, 0.855]</r> compare over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The High-Income line starts higher but ends at a lower point than the Low-Income line, indicating that Neighborhood Interactions decreased more steeply for the High-Income neighborhoods compared to Low-Income neighborhoods."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "rBc5QGvsjTBS74TZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the point for '2021' in 'Commercial'}[0.944, 0.507] and {the point for '2021' in 'Residential'}[0.944, 0.160], which one is higher on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for '2021' in 'Residential'."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "AJUE3rcjDtNQyOkw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Residential' line}<r>[0.129, 0.160, 0.944, 0.410]</r> end higher or lower than {the 'Commercial' line}<r>[0.129, 0.507, 0.944, 0.841]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "wj46Qjo49BunC2DM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in coordinates [0.099, 0.113, 0.320, 0.375]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the line graph and distinguishes different lines with their respective colors. Each label is paired with a colored line symbol. It includes labels 'Low-Income', 'High-Income', 'Commercial', and 'Residential'."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "HUvI8WQNp6Jfka9N",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.108, 0.934, 0.965, 0.970]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains six x-axis tick labels, distributed evenly from left to right. The labels represent the years from 2016 to 2021."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "WJ3Cx6J0nA28Ej1f",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within coordinates [0.055, 0.214, 0.083, 0.254]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label indicating a count of '90' interactions is within these coordinates."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "f6h9m2lPMMnTaYD3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at {coordinates}<r>[0.015, 0.380, 0.035, 0.525]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "vczhfCJ5JGMviC72",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the text of the first item in the legend situated?"
+            },
+            {
+                "from": "gpt",
+                "value": "<r>[0.156, 0.070, 0.274, 0.110]</r>"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "DHOzd253iKKhuGhU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label}<r>[0.094, 0.885, 0.137, 0.970]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "2015."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "kXjfZpCEYi7HQ4nA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher on the plot at year 2020, {the 'Innovation' line}<r>[0.806, 0.278]</r> or {the 'Investments' line}<r>[0.806, 0.189]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "tIuSD9aLJsLwMfOF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 2016 in 'Investments'}<r>[0.253, 0.229]</r> above or below {the point for 2016 in 'Innovation'}<r>[0.253, 0.328]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "Hg60C8QPg87PLWhu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the point for 2018}<r>[0.529, 0.199]</r> on Investments, is it higher or lower compared to {the point for 2018 on Innovation}<r>[0.529, 0.298]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "OYcFHYoqVZuiq4cb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region <r>[0.085, 0.052, 0.303, 0.190]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the plot. It shows two items: 'Innovation' and 'Investments' both paired with a colored marker, where 'Innovation' is located at <r>[0.156, 0.070, 0.274, 0.110]</r> and 'Investments' at <r>[0.156, 0.132, 0.294, 0.172]</r>."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "OcAu06TXkZEitf0W",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the range <r>[0.115, 0.149, 0.944, 0.249]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This range includes the line for 'Investments' from year 2015 to 2021. The line marker is square and its color is orange. It includes upper and lower bounds for each point."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "FbXXh8TPT9UY4vEW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the region with coordinates <r>[0.115, 0.259, 0.944, 0.378]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Innovation' line from year 2015 to 2021. It's marked with a blue line and circular points. It also contains the maximum and minimum values at each point."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "aclaWFLfwdh1lVs4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the top right corner with coordinates [0.650, 0.095, 0.977, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend showing five different trends of subculture and their colors."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "IJtKq14oAvi7YN6n",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the point}[0.105, 0.358] on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the popularity of the \"Streetwear\" trend in the 2000s."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "dC2cDyeBRDwUz75p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.015, 0.300, 0.035, 0.660]</r> stand for?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Popularity."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "AfLgNRBjm14l3Z4V",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trends in 2020s, is the popularity of {the 'Streetwear'}[0.943, 0.115] trend higher or lower than {the 'Punk'}[0.943, 0.456] trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "Mn4DzGZL6hnQawCB",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trends in 2000s, is the popularity of {the 'Boho'}[0.105, 0.456] trend higher or lower than {the 'Sporty Chic'}[0.105, 0.358] trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "nuCfSLQwON5lBykV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trends in the 2010s, did {the 'Vintage'}[0.524, 0.115] trend have a higher or lower popularity than {the 'Sporty Chic'}[0.524, 0.602] trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "9fDwshdPNrqVPzc4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.703, 0.109, 0.795, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "The content is a legend entry labeled 'Streetwear', represented with the color #ff00ff."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "qeZizqQLCqDiiFF7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in this area [0.875, 0.109, 0.915, 0.145]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a legend entry labeled 'Punk' with the associated color of #0000ff."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "J39VN01xlCqbNi33",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is placed in the region [0.650, 0.095, 0.977, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart's legend. It includes labels for five different clothing trends - 'Streetwear', 'Vintage', 'Boho', 'Punk', and 'Sporty Chic', each with a color representing the trend on the chart."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "4Z140IIUciVynjXC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.015, 0.251, 0.034, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label, which reads \"Economic Indicator Index\"."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "BQamMfbfO6cllepk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.087, 0.059, 0.129, 0.070]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a colored marker for \"Manufacturing\" in the legend."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "aCAcSgBHulA3ugFQ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the marker at [0.117, 0.189, 0.930, 0.348]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the line representing the \"Manufacturing\" sector in the line chart."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "GEPYlLyYIE1bjSGN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the value for the {Services line}<r>[0.117, 0.348, 0.930, 0.506]</r> higher or lower than the value for the {Manufacturing line}<r>[0.117, 0.189, 0.930, 0.348]</r> in the Fourth Quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "m4iUKXKgx57YF1VF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Retail line}<r>[0.117, 0.490, 0.930, 0.664]</r> go above or below the {Services line}<r>[0.117, 0.348, 0.930, 0.506]</r> in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "WiuXArtLeHVQ3MdA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector line ends at a higher value in the Fourth Quarter, the {Real Estate line}<r>[0.117, 0.348, 0.930, 0.585]</r> or the {Technology line}<r>[0.117, 0.221, 0.930, 0.379]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Real Estate."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "m0q0XP5CAn4X3FAe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.084, 0.043, 0.265, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend with entries like {Manufacturing}<r>[0.138, 0.053, 0.258, 0.079]</r>, {Services}<r>[0.138, 0.090, 0.208, 0.116]</r>, and {Retail}<r>[0.138, 0.128, 0.185, 0.154]</r>, etc. Each entry is paired with a colored marker."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "UQportqWZgzAHWnY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the items in the coordinates [0.076, 0.031, 0.970, 0.822] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area for the chart, containing the x-grid, y-grid, title, x and y labels, tick labels, and the line paths for different economic sectors."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "UDFeve3DcbyD5fAX",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.050, 0.812, 0.071, 0.837] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis tick label '50'."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "YYrNgqpgmswW09hP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the coordinates [0.033, 0.255, 0.054, 0.575] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label, referring to \"Energy/Resource Value\"."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "VrGjIhU61S2NdMwq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data point at coordinates [0.812, 0.351] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 'Energy Use' value for 'Wind Power'."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "o5d2dwBu2ORckm8d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line with the points located at coordinates [0.337, 0.313] and [0.931, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line color is '#ff7f0e', indicating the 'Resource Consumption' values in the chart."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "IC7U2fBJBq12X7Qo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Energy Use}<r>[0.099, 0.134, 0.931, 0.543]</r> line of 'Solar Power' higher or lower than the {Resource Consumption}<r>[0.099, 0.262, 0.931, 0.670]</r> line of 'Solar Power'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "S15gtJlADdqWjSGo",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {Energy Use}<r>[0.099, 0.134, 0.931, 0.543]</r> value of 'Biomass Power' coincide with the {Resource Consumption}<r>[0.099, 0.262, 0.931, 0.670]</r> value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "Vs0Ln7P6AipUAfDK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Resource Consumption}<r>[0.099, 0.262, 0.931, 0.670]</r> of 'Wind Power' higher or lower than its {Energy Use}<r>[0.099, 0.134, 0.931, 0.543]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "gSsYhLCozfIiQUSL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the coordinates [0.442, 0.046, 0.639, 0.136]"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend items: {Energy Use}[0.444, 0.057, 0.536, 0.084] and {Resource Consumption}[0.444, 0.098, 0.631, 0.125], each paired with a respective marker."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "oxh1FvuP128DledR",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the area with the coordinates [0.455, 0.211, 0.931, 0.236]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a portion of the 'Resource Consumption' configurations for 'Nuclear Power' and 'Natural Gas Power'."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "SJvT1wG5KX69PKLD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.053, 0.787, 0.055, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis tick label '30'."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "Xbc7juGFSsIsYzYa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of chart represented in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "zH0zIyU6WcUKJHLY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can we find at [0.926, 0.701, 0.961, 0.771]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last x-axis label, \"Lives\"."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "MzmkZvDfwPBmr40U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at {the point with coordinates}[0.592, 0.342]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point representing the \"Virtual Reality Contents\" in \"Instagram\"."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "0cL9EeCWuNpDXo3Q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Podcasts' in 'Instagram'}[0.709, 0.305] positioned higher than {the point for 'Podcasts' in 'Facebook'}[0.709, 0.302]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "PGqtuUcOpUYablf1",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line representing 'Instagram'}[0.122, 0.214, 0.944, 0.618] positioned below or above {the line representing 'Facebook'}[0.122, 0.085, 0.944, 0.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "nQAvyT4xgui3g0Jl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line reaches a higher point at {the point for 'Lives'}[0.944, -0.025, 0.944, 0.214], the line for Instagram or Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "Facebook."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "x634eOcxW3bOlQen",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the coordinates [0.081, 0.691, 0.944, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the x-axis labels and corresponding ticks. The labels are: \"All\"[0.111, 0.701, 0.133, 0.745], \"Blog Post\"[0.212, 0.701, 0.267, 0.811], \"Video\"[0.338, 0.701, 0.375, 0.776], \"Infographics\"[0.439, 0.701, 0.509, 0.811], \"Virtual Reality Contents\"[0.528, 0.701, 0.655, 0.953], \"Podcasts\"[0.682, 0.701, 0.736, 0.807], \"User Generated Contents\"[0.759, 0.701, 0.894, 0.953] and \"Lives\"[0.926, 0.701, 0.961, 0.771]."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "HkNQ0FwBZr4bI1xQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents found between the coordinates [0.530, 0.052, 0.934, 0.128]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a legend containing two items. They are {Instagram}[0.601, 0.070, 0.714, 0.110] and {Facebook}[0.821, 0.070, 0.925, 0.110]. Each of them is paired with a different colored marker."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "Du3H6tvZY0dmq7KJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.081, -0.015, 0.076, 0.120]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts the y-axis labels. They are '500', '700', '900', '1100', and '1300' at different heights on the y-axis."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "bqkLfZ3uYg7861X9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with the coordinate [0.041, 0.309, 0.069, 0.349]?"
+            },
+            {
+                "from": "gpt",
+                "value": "60."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "g0YreFPLcnppLsVR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for Sound Design} whose points are at [0.114, 0.325, 0.920, 0.424]?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ef5350."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "HZxIlMutRTReQm5x",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle with coordinates [inf, inf, -inf, -inf] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Colored marker for each trend."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "8BI8uCmLSG17LHUl",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Visual Design output at Interaction Design}[0.920, 0.128] greater or less than {Sound Design output at Interaction Design}[0.920, 0.424]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "Y6wGjC7K1FVg7Lre",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {Interaction Design}[0.920, 0.719], which has a greater value: {Sound Design}[0.920, 0.424] or {Interaction Design}[0.920, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sound Design."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "DxCn5DCjdy3p9WUK",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {Sound Design output at Visual Design}[0.114, 0.325] have a higher or lower value than {Interaction Design output at Visual Design}[0.114, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "0UUPmyo9fjFvY05Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.664, 0.052, 0.949, 0.252] in the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart. It includes Visual Design with color '#42a5f5', Sound Design with color '#ef5350', and Interaction Design with color '#ab47bc'. The handles for each legend item are not represented by specific coordinates."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "AtcHhSgSquANdFZQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the coordinates [0.074, 0.030, 0.960, 0.719]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the entire plot area of the subplot including the lines representing Visual Design, Sound Design, and Interaction Design, along with the x-axis and y-axis and their respective labels and ticks."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "jwXB9OWAOVn6UbES",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.068, 0.729, 0.980, 0.970] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis labels for 'Visual Design', 'Sound Design', and 'Interaction Design' are located within this region. Their respective coordinates are {Visual Design}[0.068, 0.729, 0.160, 0.913], {Sound Design}[0.470, 0.729, 0.564, 0.916], and {Interaction Design}[0.859, 0.729, 0.980, 0.970]."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "QTKhrAWWpByVQmAb",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.896, 0.139, 0.927, 0.167]."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "WRGXalEBxudrtcXW",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.115, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2000's Hippie point."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "M9ch2UoFPWwfPbfI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis label}[0.922, 0.885, 0.965, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "S7e9wjJsqhAzp0Xx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the Emo point in 2020} [0.944, 0.424] higher or lower than {the Streetwear point in 2020} [0.944, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "TXIb6EAVF2hdHv6q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the interpreting grid of Hippie point}[0.115, 0.734] above or below {interpreting grid of Punk point}[0.115, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "GJjDuWKsV6zE9JMZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween {the Emo point in 2000} [0.115, 0.199] and {the Streetwear point in 2000} [0.115, 0.312], which point is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Emo point in 2000."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "Sd6Pkz2PpcSIeaS9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in coordinates [0.852, 0.044, 0.978, 0.304]."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend resides in that area. Fashion trends such as Hippie, Goth, Emo, Punk, Vintage, and Streetwear are listed here with corresponding colors. The respective coordinates are {Hippie}[0.853, 0.059, 0.889, 0.076], {Goth}[0.853, 0.101, 0.889, 0.118], {Emo}[0.853, 0.143, 0.889, 0.159], {Punk}[0.853, 0.185, 0.889, 0.201], {Vintage}[0.853, 0.227, 0.889, 0.243], and {Streetwear}[0.853, 0.268, 0.889, 0.285]."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "ePSQojE8Qm3QYRXu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the region [0.041, 0.155, 0.069, 0.195]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the Y-axis label for '80'."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "neYaVJlqQgV9BZT3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the coordinates [0.074, 0.030, 0.985, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main chart figure, including the grids and plotted lines representing different fashion trends across the years 2000, 2010, and 2020. There are lines with markers denoting points for the trends Hippie, Goth, Emo, Punk, Vintage, and Streetwear, with corresponding Y-axis labels from 30 to 80 indicating their prevalence."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "grSBhx7vyg7zZEQp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of {the label}[0.041, 0.252, 0.069, 0.292] from the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the 77% return rate."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "OiOGkgf9UwGFwQ20",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identifier at {this location}[0.695, 0.741] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Citi Group."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "vwUMF9mbKjpJHffD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of {the line}[0.114, 0.185, 0.927, 0.602] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It reflects the Investment Return Rate for each bank."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "ezATkqpd3IolOSnv",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the marker for 'HSBC'}[0.579, 0.408] denotes a better return rate than {the marker for 'Citi Group'}[0.695, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "A47DMG8DyAZrOqg5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the marker for 'JP Morgan'}[0.231, 0.185] shows a higher or lower return rate than {the marker for 'Goldman Sachs'}[0.114, 0.268]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "p8QOqEuzplBsC2jN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the return rate for 'Barclays'}[0.347, 0.324] higher or lower than {the return rate for 'Deutsche Bank'}[0.463, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "SsrzS7ytO9Yu2Sr6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the content in the region [0.041, 0.447, 0.069, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a y-axis label '70', indicating a return rate of 70%."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "kFfcUlrllqjjGdfc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.756, 0.751, 0.866, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is an x-axis label 'Bank of America'."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "aoniPv20caRs3aBt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.114, 0.324, 0.927, 0.435]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains information about the Investment Return Rate for each bank. The identified banks are: {'Goldman Sachs'}[0.114, 0.268], {'JP Morgan'}[0.231, 0.185], {'Barclays'}[0.347, 0.324], {'HSBC'}[0.579, 0.408], {'Citi Group'}[0.695, 0.491], {'Bank of America'}[0.811, 0.463] and {'Morgan Stanley'}[0.927, 0.380]."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "JW1aQnEsbgoDu2oh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinate [0.736, 0.070, 0.965, 0.110] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Unemployment Rate' for the line chart."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "l4fLGTkgUKOAh7Hv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month does the x-axis tick label at [0.776, 0.845, 0.809, 0.911] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "October."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "nqnNnp6tzymFIgLi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the y-axis tick label}<r>[0.041, 0.098, 0.067, 0.133]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "4.5."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "hZ65eLdI3PQE6nBb",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher in April, {the Unemployment Rate}[0.340, 0.223] or {the GDP Growth Rate}[0.340, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "hwznbNHN2qhfMqlL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {Unemployment Rate in July}[0.566, 0.191] more than or less than {GDP Growth Rate in July}[0.566, 0.626]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Less than."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "heO0vqP3p6wXdZQW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which rate is the decrease from {November to December}[0.868, 0.207, 0.943, 0.207] steeper, Unemployment Rate or GDP Growth Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP Growth Rate."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "5qcKyb2YWVoCPLOB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.736, 0.070, 0.965, 0.172]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, there are two chart line labels: {Unemployment Rate}[0.736, 0.070, 0.965, 0.110] and {GDP Growth Rate}[0.736, 0.132, 0.930, 0.172]. Each label is color-coded respectively to match the line it represents on the chart."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "WukdGoESz0WzjACT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.323, 0.845, 0.434, 0.919]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, we have the x-axis label {May}[0.397, 0.845, 0.434, 0.919]."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "EfyHef86XXgA62i2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region [0.793, 0.223, 0.943, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you can see the Unemployment Rate and GDP Growth Rate data points for the {month of October}[0.793, 0.223, 0.793, 0.626] and {December}[0.943, 0.207, 0.943, 0.674]."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "sL1SVuQLLBcWRzw8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}[0.719, 0.692, 0.781, 0.732] appearing in orange color in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Line representing \"NewTown\"."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "q0QRFLAyLujgsszD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell the position of {the Bike Lanes label} that is shown on the x-axis of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.432, 0.698, 0.508, 0.850]"
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "z9h02uDO1frOvORM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates of {the chart element}[0.825, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a point on the line representing development index of Industries in GlobalImpact."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "KNPlzKn4b3llXZVI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, is the development index of {Residential Areas in 'GlobalImpact'}[0.707, 0.181] higher or lower than {Residential Areas in 'NewTown'}[0.707, 0.340]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "UhNkAaZAmeTfOM7r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity shows a higher development index for {Bike Lanes}[0.470], is it 'GlobalImpact' or 'NewTown'?"
+            },
+            {
+                "from": "gpt",
+                "value": "NewTown."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "jhyCtACsiJfTOcTW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the development index at {point representing Commercial Areas for 'NewTown'}[0.589, 0.419], is the index for Industries higher or lower for 'NewTown'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "a0eU4Jy4LmUAXqRz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.074, 0.055, 0.985, 0.688]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main graph showing the development index of different areas like 'All', 'Greenspace', 'Public Transportation', etc., for two entities, 'GlobalImpact' and 'NewTown'. The graph includes a series of points connected by lines for both 'GlobalImpact'{GlobalImpact line coordinates}[0.115, 0.181, 0.944, 0.371] and 'NewTown'{NewTown line coordinates}[0.115, 0.340, 0.944, 0.593]."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "lDbGi8pyVkfk0RkL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements in the range [0.392, 0.037, 0.667, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are two labels named {'GlobalImpact'}[0.437, 0.048, 0.529, 0.076] and {'NewTown'}[0.596, 0.048, 0.661, 0.076], appearing in the graph's legend."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "37TlREOo70cAjZse",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within the coordinates [0.192, 0.698, 0.275, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates contain the x-axis label for 'Greenspace'."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "fHprgWy842qOkrz2",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.070, 0.321, 0.083, 0.506]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "eTBrPF5KbherXKqH",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates for the tick of {GreenEnergy}?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.548, 0.808]."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "tCNQfSjv5oHzXkgz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element at coordinates [0.224, 0.044, 0.367, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label 'Living Environment'."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "G56YKb1LFoMn7jCd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the 'Human Life' line point at 'Mindfulness'}[0.349, 0.366] above or below {the 'Living Environment' line point at 'Mindfulness'}[0.349, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "wQhDu1WhpfuGutdT",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Human Life' line point at 'SimpleLiving'}[0.149, 0.571] have a higher or lower value than {the 'Living Environment' line point at 'SimpleLiving'}[0.149, 0.650]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "XipBZKYpv1nbFjwn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater value at OutdoorActivities, the {Human Life line}[0.947, 0.508] or the {Living Environment line}[0.947, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Living Environment."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "eenFV0W9t8NnxXq1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.020, 0.033, 0.373, 0.080]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for two lines in the chart. The label for 'Human Life' is located at {coordinates} [0.068, 0.044, 0.153, 0.069] and the label for 'Living Environment' is located at {coordinates} [0.224, 0.044, 0.367, 0.069]."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "WAHOb48kT1qrYidP",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.621, 0.814, 0.676, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the x-axis label 'Minimalism'."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "xoFlQStUHQA2Dbpd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects does the region [0.421, 0.814, 0.475, 0.915] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label 'Sustainable' is located within this region."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "NGlmcwbgjOYK0f1G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown by the label at this coordinate {the axis label}<r>[0.015, 0.288, 0.035, 0.677]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Index."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "iyKEpsAds9gWn2LE",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what coordinate does {the legend item for Grain Production}<r>[0.128, 0.110, 0.245, 0.138]</r> appear?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.128, 0.110, 0.245, 0.138]."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "oRi7apApgeRqN9ZO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the year corresponding to {this tick}<r>[0.944, 0.881]</r> on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "wFondob5XQPONBC5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the graph point for {Grain Production in 2016}<r>[0.118, 0.350]</r> have a higher value than {Agriculture GDP in 2020}<r>[0.944, 0.395]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "V2RiZZL2lxmkaBV2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line appears higher on the chart at the coordinate {Grain Production in 2020}<r>[0.944, 0.168]</r>, Grain Production or Meat Production?"
+            },
+            {
+                "from": "gpt",
+                "value": "Grain Production."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "sX1s606BfstnCnyO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the marker for {Fisheries in 2018}<r>[0.531, 0.456]</r> located above or below {Fisheries in 2019}<r>[0.737, 0.380]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "XH7pxqNpwfuFh9sH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the significant chart elements located in the area between [0.051, 0.260, 0.072, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains y-axis tick labels for the values: {50}[0.051, 0.867, 0.072, 0.903], {60}[0.051, 0.715, 0.072, 0.751], {70}[0.051, 0.564, 0.072, 0.600], {80}[0.051, 0.412, 0.072, 0.448], {90}[0.051, 0.260, 0.072, 0.296], and {100}[0.041, 0.108, 0.072, 0.144]."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "putwzfQnYikzga4J",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region ranging from [0.101, 0.881, 0.961, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area for x-axis tick labels and coordinates corresponding to the years from {2016}[0.101, 0.891, 0.136, 0.919] to {2020}[0.926, 0.891, 0.961, 0.919]."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "2nKEcUZFmw5TEz0L",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of elements are located in the subsection [0.128, 0.110, 0.245, 0.305]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {legend items for production types}[0.128, 0.110, 0.245, 0.138], specifically Grain Production, and continues downward to include {Meat Production}[0.128, 0.152, 0.241, 0.180] and {Dairy Production}[0.128, 0.193, 0.244, 0.221]."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "ZosF4OQ7Y7vNvy9w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.034, 0.108, 0.058, 0.141] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a 'y' axis tick label, with the value 85."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "RYLz4T7MZHXyo6E7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the label {coordinates}[0.633, 0.705, 0.730, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the x-axis label for 'Music_and_Songs'."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "ETFE59Lt8mxegF5p",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line that represents \"Heritage Site Preservation\"}[0.099, 0.179, 0.848, 0.332]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #3d89be."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "BrBI8GIYziFpCY09",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {point \"Traditional Clothing\"}[0.099, 0.313] have a higher or lower rating compared to the {point \"Traditional Dance\"}[0.182, 0.217] according to the \"Heritage Site Preservation\" scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "TSpOTZDmCRkyPCol",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the \"Ethnic Groups Integration\" chart, is the score for {Art_and_Sculpture}[0.266, 0.428] more or less than the score for {Traditional_Dance}[0.182, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "More."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "eey1ZvswYOIrQZvS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the closer x-axis tick value to {the marker for Ethnic Groups Integration at Traditional Tools}[0.848, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Native_Sports."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "cPE9vWRjFJ0WCzyS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found within the region [0.045, 0.705, 0.231, 0.919]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two x-axis labels: {Traditional_Clothing}[0.045, 0.705, 0.153, 0.919] and {Traditional_Dance}[0.133, 0.705, 0.231, 0.900]."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "QiEZHMvlOttnOihF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features can be seen within the area [0.336, 0.044, 0.974, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two legend items related to {Heritage Site Preservation}[0.396, 0.058, 0.639, 0.092] and {Ethnic Groups Integration}[0.728, 0.058, 0.966, 0.092], each with its corresponding color marker."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "rZI2fNFCQeaK13hY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me what's contained within the region [0.099, 0.255, 0.848, 0.409]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a series of markers denoting the \"Heritage Site Preservation\" scores for each category of heritage. It displays scores from {Traditional_Clothing} to {Traditional_Tools}."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "iDW01PfnCzHOZHvB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.831, 0.193, 0.964, 0.233]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for \"Food Export\" in the legend."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "k88LOTzvXnLJMUVX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be found at the coordinate [0.129, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point representing Food Export for the year 2020."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "FQRXzbJt3vhZsYJt",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at [0.515, 0.930, 0.560, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label - \"Year\"."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "PNBgqDEdn2C96A6E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point representing 'Food Import' for 2020}<r>[0.129, 0.335]</r> higher or lower than {the point representing 'Food Waste' for 2021}<r>[0.944, 0.653]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "kOBy2Cllw9iaj2jR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Food Export' in 2021}<r>[0.944, 0.164]</r> to the left or right of {the point for 'Food Import' in 2021}<r>[0.944, 0.384]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same position horizontally on the x-axis, so neither left nor right."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "mUBX5mo3NqJ58N71",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is greater at {the year '2020' point}<r>[0.129, 0.213]</r>, 'Food Import' or 'Food Export'?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Food Export'."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "Yk17JprBcbM0WYd7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the area [0.764, 0.131, 0.965, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the labels of the chart legend: {Food Import}<r>[0.831, 0.131, 0.965, 0.171]</r>, {Food Export}<r>[0.831, 0.193, 0.964, 0.233]</r>, and {Food Waste}<r>[0.831, 0.255, 0.959, 0.295]</r> with their corresponding color markers."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "bntEq6nptZEkaXcy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are found within [0.108, 0.834, 0.150, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis tick label '2020'."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "xmmT5hGotAZINvNI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the area delimited by [0.129, 0.164, 0.944, 0.384]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three data points. These are: the 'Food Import' data points for years 2020 and 2021 at {coordinates}<r>[0.129, 0.335]</r> and <r>[0.944, 0.384]</r> respectively; and the 'Food Export' datapoint for the year 2021 located at {coordinates}<r>[0.944, 0.164]</r>."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "vqpLAZyFwsjWgi1L",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the x-tick item}[0.063, 0.760, 0.167, 0.966]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slight decrease."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "wrKyhT9VEWnVqtXr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item located at [0.583, 0.086, 0.673, 0.126] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reading."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "LCQ7HoRvMmK8f8j6",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.283, 0.035, 0.513]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Frequency."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "IZrXesoCH3IbL52s",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the category of 'Persistent rise', is the point for {Reading}<r>[0.526, 0.257]</r> above or below the point for {Exercising}<r>[0.526, 0.234]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "qls6ffOXmKP7dqU2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the second point of line 'Reading'}<r>[0.321, 0.445]</r> located at a higher value than {the second point of line 'Exercising'}<r>[0.321, 0.633]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "lj3PbUIBKN0xq2CC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at 'Abrupt drop', {Reading}<r>[0.938, 0.304]</r> or {Exercising}<r>[0.938, 0.468]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exercising."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "eAj1X5Neh8ov7J41",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area with coordinates [0.512, 0.068, 0.903, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a legend for two categories 'Reading' and 'Exercising'."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "D6kSxMVrroivrVZ5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.896, 0.760, 0.979, 0.925] of the chart include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the x-axis label for the category 'Abrupt drop'."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "gYi51MM60sd1arax",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found at coordinates [0.115, 0.093, 0.938, 0.374]."
+            },
+            {
+                "from": "gpt",
+                "value": "Points and markers related to the values of the categories 'Slight decrease', 'Gradual decline', 'Persistent rise', 'Rapid recovery', and 'Abrupt drop' representing the line 'Reading' are located in this region."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "SaJYqGftA9j1XJUe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented by the coordinates [0.767, 0.070, 0.954, 0.110]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Fund."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "Le7nwtL4DnJHoa2m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.213, 0.035, 0.521]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "Funding Level"
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "xEtekIQGeHS49nsI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the {x-axis tick}<r>[0.887, 0.714, 0.980, 0.899]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Steep decline."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "AgQS2fcARYscgfAa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 'Pronounced decline' stage, is the {Investment Fund point} <r>[0.128, 0.569]</r> lower or higher than the {Startup point}<r>[0.128, 0.637]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "ZKGSk54jTXr0Ttnq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Investment Fund point}<r>[0.934,0.637]</r> and {Startup point}<r>[0.934,0.704]</r> at the 'Steep decline' stage, whether the former is higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "rYLfvUT8vJIEkcIQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 'Abrupt increase' stage, which one is higher, {Investment Fund point}<r>[0.665, 0.165]</r> or {Startup point} <r>[0.665, 0.232]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment Fund."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "WmSblenNwghZRrl7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with the coordinates [0.696, 0.052, 0.963, 0.190]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area holds the legend for the chart showing 'Investment Fund' <r>[0.767, 0.070, 0.954, 0.110]</r> and 'Startup' <r>[0.767, 0.132, 0.850, 0.172]</r>, with color markers."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "GQ9OMJ3TT2ohTNTa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is represented by the coordinates [0.063, 0.714, 0.192, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the x-axis label 'Pronounced decline'."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "1O0mkSB65CjAgMdg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sort of objects exist in [0.128, 0.205, 0.934, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are multiple data points for 'Investment Fund' and 'Startup' plotted over different stages in this area."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "CP82lp2WEWkEL2QM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is used to represent the {Migration Trend line}<r>[0.098, 0.277, 0.945, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00bfff."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "2iHIhC68Mwpdc890",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you state the coordinate of the point labeled '16' on {the Migration Trend line}<r>[0.098, 0.277, 0.945, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.569, 0.758]."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "UHJzPxeAR4Kmf2u1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the y-axis on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of People (in millions)."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "zufrNphgQHGqrUea",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend increases faster, {Migration Trend}<r>[0.098, 0.277, 0.945, 0.887]</r> or {Mobility Trend}<r>[0.098, 0.813, 0.945, 0.832]</r> between weeks 1 and 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Mobility Trend."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "SZIGUp1RuoaZpNEC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {point 17 on the Migration Trend line}<r>[0.600, 0.739]</r> fall above or below point 19?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "pVJPR7KospOepZy6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label appears first on the x-axis, '5' or '10'?"
+            },
+            {
+                "from": "gpt",
+                "value": "5."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "HnG8XrGbNAdytqBS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the chart subtitle area at <r>[0.310, 0.019, 0.733, 0.049]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Population Migration and Mobility Trends\" is located, with font color coded as '.15' and no text rotation."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "UZsVB5B49I8TenUa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region around <r>[0.033, 0.271, 0.051, 0.288]</r> contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '2.5' situated here, highlighted with a '.15' color label facing a 0.0 rotation."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "q48QmRv1SRPKsWD8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what can be observed in the region <r>[0.062, 0.063, 0.220, 0.135]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's sublegend items marked as 'Migration Trend' and 'Mobility Trend', associated with colored handles #00bfff and #008000 respectively."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "UERuI5Wpl5dIG74J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the label at the extremes of the y-axis}<r>[0.033, -0.088, 0.055, -0.071]</r> indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "140."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "va7AuDQBr1xHbBNC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at {the coordinate}<r>[0.504, 0.958, 0.543, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Date label."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "fdodB5OYwrSlfUYk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the first mark in the legend}<r>[0.071, 0.082, 0.099, 0.082]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00bfff."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "PGJQdq3Q23F66yhS",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich {line}<r>[0.540, 0.574, 0.574, 0.590]</r> is above, {Stock 1}<r>[0.540, 0.574]</r> or {Stock 3}<r>[0.574, 0.590]</r> at {the fifth date}<r>[0.236, 0.600]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stock 3."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "a5tApypQZWu4CLSF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich axis label is closer to the chart's left border, {the x-axis label}<r>[0.504, 0.958, 0.543, 0.981]</r> or {the y-axis label}<r>[0.012, 0.423, 0.028, 0.557]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "QJin8FVnO1M5vUFJ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point for {Stock 1's first data}<r>[0.101, 0.546]</r> higher than the point for {Stock 3's first data}<r>[0.101, 0.441]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "bwSkZ9ugD6uihfps",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in <r>[0.042, 0.050, 0.945, 0.184]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the color handles and labels for {Stock 1}<r>[0.071, 0.082, 0.099, 0.082]</r>, {Stock 2 with Gradient}<r>[0.071, 0.115, 0.099, 0.115]</r>, and {Stock 3}<r>[0.071, 0.148, 0.099, 0.148]</r> in the legend positioned at the upper left portion of the chart."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "AcuxJATIFWRV62RI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is located in the span <r>[0.101, 0.371, 0.945, 0.946]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the plotted lines showing data points for {Stock 1}<r>[0.101, 0.546, 0.945, 0.887]</r>, {Stock 2 with Gradient}<r>[0.101, 0.371, 0.135, 0.387]</r>, and {Stock 3}<r>[0.101, 0.093, 0.945, 0.441]</r> representing their changes in stock prices over various dates."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "SBjOkGaP9ruJBH5e",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat aspects can be observed in the area <r>[0.047, 0.930, 0.980, 0.952]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly encompasses the x-axis tick labels marking specific dates like {1/8/2021}<r>[0.109, 0.933, 0.161, 0.951]</r>, indicating the timeline for the financial data presented."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "x9V8C0EkXsAEvb2d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color of the series represented by the third label in the legend}<r>[0.783, 0.886, 0.877, 0.908]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pink."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "DJNc8wkCpjff8bII",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the xlabel of the chart}<r>[0.493, 0.958, 0.546, 0.981]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Weeks."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "BarW98UZqyjw4syI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.012, 0.457, 0.028, 0.523]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Value."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "VOyhnsIsF1lcgWUU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 5 on the x-axis}<r>[0.220, 0.927, 0.220, 0.927]</r> above or below {the point at 6 on the x-axis}<r>[0.252, 0.927, 0.252, 0.927]</r> for Data Series 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "pZS7h4QlmeQ60Fxp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at coordinate 14}<r>[0.504, 0.617]</r> above or below {the point at coordinate 15}<r>[0.535, 0.665]</r> for Data Series 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "P8cnbrRXZYGiabZn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher, {point 10 on Data Series 3}<r>[0.378, 0.760]</r> or {point 12 on Data Series 3}<r>[0.441, 0.776]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 12."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "0Lopa2csNgTZy23l",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in <r>[0.739, 0.812, 0.981, 0.917]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart housing Data Series 1, Data Series 2 with Gradient, and Data Series 3, with corresponding color handles located at {coordinates}[0.744, 0.830, 0.772, 0.830], {coordinates}[0.744, 0.863, 0.772, 0.863], and {coordinates}[0.744, 0.896, 0.772, 0.896]."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "8fQduztesnPifW5H",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area <r>[0.033, 0.245, 0.048, 0.262]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the y-axis label '35' aligned to the tick mark located at {coordinates}[0.052, 0.252]."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "uSonLBMDwDABumc8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within <r>[0.052, 0.927, 1.001, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Contains x-axis labels ranging from '0' to '30,' specifically located at {coordinates}[0.059, 0.933, 0.066, 0.951], {coordinates}[0.843, 0.933, 0.858, 0.951]."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "VgWsEOb7ZOG7LwQp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.055, 0.933, 0.073, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis label \"0.0\"."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "WTARXOYn85jgYJP1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible at [0.743, 0.105, 0.975, 0.127] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend text \"Dramatic Decrease with Gradient\"."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "vcHvX7JTEMERSoZm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at [0.108, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point 1 marker of \"Dramatic Decrease with Gradient\" line."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "0OZ0ddqGOK7Q7gbm",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the first point}[0.108, 0.809] higher than {the fifth point}[0.284, 0.793] on the \"Stable Growth\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "IL9910y5onyeB3rF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the position of {the point at week 15}[0.725, 0.753] with {the point at week 20}[0.946, 0.733] on the same line."
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "megsLZkTATCs8FCd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {this point}[0.152, 0.725] lie above or below {that point}[0.196, 0.721] on the \"Slight Increase\" line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "okcJYEM4DbxDmM1G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.066, 0.053, 0.988, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the main plotting area of the chart where multiple lines are displayed, grids are visible, and it's framed by axes with labels \"Weeks\" and \"Value\"."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "lgieebENtOVQdWQ4",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.636, 0.867, 0.949, 0.886] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This zone features the upper portion of the plotting area where line segments demonstrating a gentle decline, specifically from week 12 to week 20, are present."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "us78FrKbTyPCOLjY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the contents of the region [0.699, 0.063, 0.981, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part consists of the chart's legend, displaying colored markers and corresponding labels like \"Stable Growth,\" \"Dramatic Decrease with Gradient,\" and \"Slight Increase.\""
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "GztXyCBcVzgNOGKi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.103, 0.137, 0.252, 0.160] indicate in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Forest Coverage Rate."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "3YroXNBrfHv8VqRm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the X-axis label with the coordinates [0.502, 0.958, 0.537, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "DBBAaXtkkSwhHEcF",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.033, 0.069, 0.048, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "80."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "MIaE3K1R0Qyoxc8S",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the line for 'Air Quality Index'}[0.094, 0.416, 0.945, 0.581], is {the line for 'Forest Coverage Rate'}[0.094, 0.792, 0.945, 0.887] generally trending higher or lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "B9J2TCVgiUjr7xp3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2018, is the value for {Air Quality Index}[0.803, 0.425] higher or lower than that for {Forest Coverage Rate}[0.803, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "CQbuckvSccUKLkgN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year represented by {[0.236, 0.572]}, is the Air Quality Index higher or lower than the Forest Coverage Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "Xb72uWqvXJxoi5SC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in the area with coordinates [0.059, 0.063, 0.258, 0.168]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, with the labels: {Air Quality Index}[0.103, 0.072, 0.218, 0.094], {Water Quality Index}[0.103, 0.105, 0.240, 0.127], and {Forest Coverage Rate}[0.103, 0.137, 0.252, 0.160], each paired with a unique color marker."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "cl8hIVzVQFu84xbf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.156, 0.019, 0.883, 0.049] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the subtitle of the chart: \"Ecological Environment Sustainability Assessment from 2008 to 2020\"."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "fIqMV8zwACwhsLrk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.094, 0.416, 0.945, 0.581] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the line for 'Air Quality Index', indicating how Air Quality Index has changed from 2008 to 2020."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "L01QoKgOr2JgYMGR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of {the y-axis label}<r>[0.013, 0.357, 0.028, 0.623]</r> ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Allocation Percentage."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "s6GkNFdnY6U0WBHA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the point at line for 'Solar Power'}<r>[0.945, 0.559]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "point 12."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "KedPDugaCwnF3qUv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title present at <r>[0.319, 0.019, 0.723, 0.049]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future Energy and Resource Allocation."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "gCGLSUiFDH3LjdD8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '9'}<r>[0.714, 0.559]</r> on the Hydro Power line higher or lower than {the point labeled '10'}<r>[0.791, 0.214]</r> on the same line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "2aQIAcjM9L4oWeIk",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {last point for 'Solar Power'}<r>[0.945, 0.559]</r> above or below the {last point for 'Nuclear Power'}<r>[0.945, 0.818]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "1ElHWjfriOo7iJAZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich had a higher value at point 7, Solar Power or Nuclear Power?"
+            },
+            {
+                "from": "gpt",
+                "value": "Solar Power."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "gFznVpsDzrRoWkwq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the details in the region <r>[0.699, 0.437, 0.981, 0.542]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend is located in this region. It contains three entries: 'Solar Power Sustained Rise', 'Hydro Power Erratic Growth', and 'Nuclear Power Continued Decline'. Each entry has a color marker symbol."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "jkv2JOrfkbFWIL84",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of region <r>[0.055, 0.053, 0.988, 0.927]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main area of the chart, with lines representing trends of Solar Power, Hydro Power, and Nuclear Power. It also contains both x and y axises with respective labels and tick labels."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "dhswWH0hnH6rqQp6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region <r>[0.098, 0.473, 0.945, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the 'Solar Power' line, drawn with a blue color, from point 1 to point 12."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "ICDIE56z8j1TbRqS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at [0.105, 0.072, 0.188, 0.094] in the sublegend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP growth."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "EyfaaCyOfZ22yJLR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at [0.043, 0.504, 0.050, 0.521] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "4."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "ML0alshgkBk94Wes",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the line at [0.096, 0.257, 0.945, 0.887] in subplot_0 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GDP growth trend."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "mGs4EVyQ1AY1eokq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the first point in 1995, GDP growth {coordinates}[0.096, 0.456] or Unemployment rate [0.096, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "GDP growth."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "NI48e5o45fRyXeb2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2016, is the Unemployment rate at [0.843, 0.316] higher or lower compared to 1995 at [0.096, 0.264]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "N9VFYb8Wk4sHxcCp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 1995 and 2021, has the GDP growth {coordinates}[0.096, 0.257, 0.945, 0.887] generally increased or decreased?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decreased."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "5AaqH7gOTHeYRfWp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.061, 0.063, 0.250, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes the labels {GDP growth}[0.105, 0.072, 0.188, 0.094], {Inflation}[0.105, 0.105, 0.162, 0.127], and {Unemployment rate} [0.105, 0.137, 0.244, 0.160] with respective color markers."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "NyEpivbVYWFFg7fU",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region from [0.096, 0.257, 0.945, 0.887]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the line representing GDP growth. It has a decreasing trajectory from 1995 to 2021 plotted in color #00bfff."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "51JaZvysFfiHRZqN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the x-axis from the region [0.499, 0.958, 0.542, 0.981]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis label of the chart, which is \"Years\"."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "jl3xxdraJWhci1T7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at the coordinates [0.107, 0.137, 0.155, 0.160] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Google."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "xYv5OIrr6faf7Yd8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the item with coordinates [0.175, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It\u2019s a point on Samsung line plot indicating the market share for the 2nd quarter."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "w7gI5IEteFPRqaIP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label located at the coordinates [0.012, 0.409, 0.028, 0.571] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Share."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "9NgjOc4iEzUpTcCu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Apple's line plot}[0.098, 0.093, 0.945, 0.298] above or below {Samsung's line plot}[0.098, 0.504, 0.945, 0.586] in the last few quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "hNR3H7OorGmKUJOV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the 5th quarter, does {Apple}[0.406, 0.298] have a higher or lower market share than {Google}[0.406, 0.654]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "ommHB4pASqxeAj2v",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing {Apple's line plot}[0.098, 0.093, 0.945, 0.298] and {Samsung's line plot}[0.098, 0.504, 0.945, 0.586], which company had a better performance in the smartphone market over the quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apple."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "ee556TiZYquKBZ6R",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area with coordinates [0.062, 0.063, 0.177, 0.201]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend with company names 'Apple', 'Samsung', 'Google', and 'Others' along with corresponding color handles to distinguish the line plots for each company."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "QzFMQDNHP8LiSsoO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.055, 0.053, 0.988, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes all the line plots and the chart grid. The line plots represent the market share of Apple, Samsung, Google, and Others over various quarters."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "R1vWVsNxD4AjaJWa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the area [0.325, 0.933, 0.332, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "A label '4' is placed here representing the 4th quarter on the x-axis."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "0MJTwxx9INyR5j1r",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the line with coordinates [0.104, 0.093, 0.926, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Applications."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "ek1wmF1btAPtZ3bB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is located at coordinate [0.871, 0.933, 0.982, 0.951] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Forecasting."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "pVcV2fjMQNtlBF6F",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.033, 0.880, 0.059, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.30."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "1IDC5Gvfnw8IoDb3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the AI Application 'Speech Recognition'}[0.287, 0.887] having higher or lower case analysis score compared to {the AI Application 'Autonomous Robots'}[0.378, 0.547]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "ROufWxiDXKrb1Qgu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the AI Application 'Game AI'}[0.652, 0.093] have a score higher or lower than {the AI Application 'Driverless Car'}[0.561, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "oZrQQ5vwnYovI9I9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which AI application is the case analysis score highest, {Predictive Analysis}[0.835, 0.660] or {Market Forecasting}[0.926, 0.320]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Market Forecasting."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "y12bLWPlBPWJsQiy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.320, 0.933, 0.436, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the AI Application 'Autonomous Robots' is present there."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "KNkUqp4OlYztWLkY",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements present in the region [0.808, 0.063, 0.960, 0.103]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the legend is located, which includes the label {AI Applications}[0.853, 0.072, 0.955, 0.094], paired with a colored marker at [0.814, 0.082, 0.841, 0.082]."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "UEoqnswca4MvjEqQ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region [0.133, 0.880, 0.264, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the x-axis label for the 'Recommender Systems' AI Application is present at [0.127, 0.933, 0.264, 0.951]."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "jQh7GZms4zUQHeXL",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line labeled 'Instagram' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ff00ff."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "CHPORd0TjmcdmObd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.248, 0.927] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "x-axis tick \"5\"."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "dk981KlDmbBxkeXv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the coordinate [0.059, 0.834] on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "100."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "rEiPwsqipVyyVfC5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at}[0.138, 0.781] higher or lower than {the point at}[0.174, 0.728] on the Facebook line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "5fm44jfCQqx3gXxE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is more, the value at {the point}[0.540, 0.305] or {the point}[0.504, 0.252]?"
+            },
+            {
+                "from": "gpt",
+                "value": "0.540, 0.305."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "qlyRLNcxoPRWdIGH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point at}[0.101, 0.887] observed before or after {the data point at}[0.943, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Before."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "JmXhRm6Xdduqhp4z",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed between the coordinates [0.101, 0.834, 0.248, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region displays the first five x-axis tick labels for the weeks, specifically '\u22125', '0', '5', '10', '15'."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "r5TgCiXwZFe3gRGp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is found in the area [0.033, 0.827, 0.059, 0.951]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains y-axis tick labels for the values '100' and '120', positioned along the vertical axis on a grey grid line."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "QzFzrkjLrfISg2C8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find between [0.033, 0.192, 0.055, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick labels '160', '180', '200', and '220' are located in this region, marked on the y-axis in a clear sequential order."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "k6Sp8oMQG4cQl6in",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text present at the coordinates [0.012, 0.383, 0.028, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Y-axis label or 'Normalized Value'."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "tjl2tQBMMc2wK4Z1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the first point in the 'Research Funding' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.087, 0.828]."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "HQnhJnS5cRYL6NjY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.096, 0.105, 0.266, 0.127] in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Technological Innovation'."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "5znMHk2U21Yida03",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {end point of the 'Research Funding' line}[0.945, 0.440] lower or higher than the {end point of the 'Technological Innovation' line}[0.945, 0.093]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "HNXoW5Xjb1fcI57c",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, does the {point at coordinate [0.174, 0.867] on the 'Research Funding' line} lie above or below {the point at coordinate [0.174, 0.838] on the 'Technological Innovation' line}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "qDwGxnrxKWgFE8PM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line starts at a higher y-coordinate, {the 'Research Funding' line}[0.087, 0.828] or {the 'Technological Innovation' line}[0.087, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Technological Innovation'."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "BfO4qIrYIUeFHoIa",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the area [0.051, 0.063, 0.272, 0.135]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a sub-legend with two items: {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Each of these items is paired with a color, represented by colored markers."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "HaeUYaLt4WB4mEVy",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the range [0.044, 0.053, 0.988, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the main plot area of the graph showcasing two line graphs- {Research Funding line}[0.087, 0.440, 0.945, 0.887] and {Technological Innovation line}[0.087, 0.093, 0.945, 0.887]."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "ucUVgjFQ5KngYXNF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region defined by the coordinates [0.223, 0.019, 0.809, 0.049]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the graph's subtitle, \"Research Funding & Technological Innovation Over Time\"."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "AReKyTTCM7HdOm1G",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.108, 0.385, 0.946, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a line representing the Email Marketing performance."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "a0ATCTA905WUsEGI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the fourth x-axis tick label at {coordinates}[0.485, 0.933, 0.493, 0.951] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number 6."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "54Ud9fIGjoXXDfVd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text represented at {coordinates}[0.190, 0.019, 0.864, 0.049]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle: 'Monthly Performance in New Media and Digital Content Creation'."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "xGZ7T4oDlJrJ1Yud",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Email Marketing} performance data point at [0.946, 0.427] higher or lower than the {SEO} performance data point at [0.946, 0.093] for the last month?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "bmdv0rUmKC3UaBak",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the x-axis tick label at [0.333, 0.933, 0.340, 0.951] greater or less than the x-axis tick label at [0.181, 0.933, 0.188, 0.951]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "KpH0oxP8bDL4HiMA",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the {SEO}[0.118, 0.137, 0.146, 0.160] line in the legend have a higher or lower positioning than the {Email Marketing}[0.118, 0.072, 0.230, 0.094] line in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "jZCwbIbuBEPqCBdS",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is contained in the area [0.073, 0.063, 0.235, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the chart legend, which includes the labels {Email Marketing}[0.118, 0.072, 0.230, 0.094], {Social Media}[0.118, 0.105, 0.205, 0.127], and {SEO}[0.118, 0.137, 0.146, 0.160] each associated with a color marker."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "acqHWKqFXDHMzFKJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the area [0.261, 0.636, 0.337, 0.678]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a segment of the SEO performance line trending upwards, and a segment of the Email Marketing line trending downwards."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "qMPsICx0LqDDoAIF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.108, 0.385, 0.946, 0.887]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a line representing the Email Marketing performance data across 12 months."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "4wF0aSDyKgbdCv07",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing 'Sales Revenue'}<r>[0.122, 0.093, 0.946, 0.246]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "p6q1muaYZFkKIBpM",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the yticks at {coordinates}<r>[0.040, 0.581, 0.077, 0.599]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "40000."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "uJ8NeC9rCvx7RvwA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any marker in {the line representing 'Advertising Expenditure'}<r>[0.122, 0.882, 0.946, 0.887]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "rULEtAqmeOn4kCWn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the 'Website Traffic'}<r>[0.122, 0.776, 0.946, 0.802]</r> have a higher or lower starting value than {the 'Sales Revenue'}<r>[0.122, 0.093, 0.946, 0.246]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "ui8aDCMUwIIGaRgj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to {the line for 'Website Traffic'}<r>[0.122, 0.776, 0.946, 0.802]</r>, is {the line for 'Advertising Expenditure'}<r>[0.122, 0.882, 0.946, 0.887]</r> generally above or below?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "kwRiqqVaqoSibFJE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a greater increase from the first to the tenth week, {the 'Sales Revenue'}<r>[0.122, 0.093, 0.946, 0.246]</r> or {the 'Website Traffic'}<r>[0.122, 0.776, 0.946, 0.802]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Revenue."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "wOUyoX5342O5f1Ql",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region <r>[0.122, 0.093, 0.946, 0.246]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line representing 'Sales Revenue' from week 1 to week 10. The color of the line is blue."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "hJJzatNa8dmx4jAB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates <r>[0.088, 0.063, 0.304, 0.168]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart indicating the meaning of each color on the lines. It contains labels for 'Sales Revenue', 'Advertising Expenditure', and 'Website Traffic', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "6U6DomKDSDsoT7KJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by coordinates <r>[0.576, 0.933, 0.584, 0.951]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label '6'."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "ZEbAkZ3UbaC9Y65Y",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the x-axis label}<r>[0.517, 0.958, 0.551, 0.981]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "GQgVmRONgvv3Y2wh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line representing GDP (Billion USD)}<r>[0.122, 0.093, 0.946, 0.294]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "#00bfff."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "XuPlWlWcpvCTT3q8",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of {this label}<r>[0.033, 0.245, 0.077, 0.262]</r> on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents 500000."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "uPz4L5N8aYI6hTqC",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point on the GDP line for 2005}<r>[0.122, 0.294]</r> higher or lower than {the point for 2020}<r>[0.946, 0.136]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "Z0LVd1AxCa5gBOzh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the color of the Interest Rate line}<r>[0.122, 0.887, 0.946, 0.887]</r> match {the handle color in the legend}<r>[0.093, 0.115, 0.121, 0.115]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "f59FdHaS3YxgUjW3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is higher on the chart, {the first point of the 'Debt to GDP Ratio'}<r>[0.122, 0.887]</r> or {the last point of the GDP line}<r>[0.946, 0.294]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Last point of the GDP line."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "z1YBQU6lIRyYjNhW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region<r>[0.081, 0.053, 0.988, 0.927]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses all the primary chart data including x and y axes, data lines for GDP, Interest Rate, and Debt to GDP Ratio."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "dcbBikLirSzhGYkW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area<r>[0.122, 0.887, 0.946, 0.887]</r> of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several lines that represent changing financial metrics over years, each depicted in different colors."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "3woln9xco0fMP4Up",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within<r>[0.517, 0.958, 0.551, 0.981]</r> space of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis label \"Year\" is located in this space."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "U001J6rL13QKLP0T",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents {the main y-axis labels}<r>[0.012, 0.086, 0.028, 0.597]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Value."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "6AlJ5rGeUUhZdOln",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the handle for {the line 'Media Outlet X'}<r>[0.831, 0.082, 0.858, 0.082]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "3R3NGX8tyL68pJgR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at <r>[0.537, 0.280, 0.569, 0.326]</r> in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 15th line marker."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "TwQwdnl2Z5NYV6l9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the marker at index 6}<r>[0.255, 0.560]</r> higher in value than {the marker at index 22}<r>[0.757, 0.373]</r> on the 'Public Reaction' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "N0YNSR82MrR82ec5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point labeled 'Media Outlet Y' at location 7}<r>[0.286, 0.280]</r> have a greater value compared to {the 'Public Reaction' at location 13}<r>[0.474, 0.747]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "XLNilwPJs716OwQ7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a lower starting point, {the line labeled '_child16'}<r>[0.569, 0.326, 0.600, 0.280]</r> or {the line labeled '_child20'}<r>[0.694, 0.139, 0.725, 0.093]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "_child20."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "0sOanC23hjrxdnKp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content and layout within the region <r>[0.098, 0.280, 0.945, 0.960]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the primary line plots indicating trends for 'Media Outlet X', 'Media Outlet Y', and 'Public Reaction'. The vertical portion highlights the scale from high to low impact over weeks, and points are marked along each line representing the weekly data points."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "thVZp9Wflhqrxzuv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSummarize the elements found between <r>[0.825, 0.063, 0.981, 0.160]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This section contains the chart legend with the labels and corresponding color handles for 'Media Outlet X', 'Media Outlet Y', and 'Public Reaction', specifically arranged vertically."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "p5uNrKE2K9VgYM5y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key features outlined in the space <r>[0.033, 0.086, 0.051, 1.038]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part contains the Y-axis scale, moving from 1.2 down to 0.0, marked through visible tick points with labels, visually aligning the data points on the chart regarding normalized value metrics."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "dEmQ9X0ccWBV4Cus",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the tick label}<r>[0.038, 0.933, 0.067, 0.951]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "2004."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "seXCEEbRLwPZp18h",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity corresponds to {the colored marker closest to the bottom left corner}<r>[0.079, 0.082, 0.107, 0.082]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Education."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "W28lnZsb8AoMc2As",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of line represents {the dataset}<r>[0.108, 0.093, 0.946, 0.438]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Job Market Demand."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "rkqiAW1Gap2IaQY6",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich starts higher on the graph, {the line for 'Online Education'}<r>[0.108, 0.887]</r> or {the line for 'Job Market Demand'}<r>[0.108, 0.438]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Job Market Demand."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "gmAlNt3BdWbwenjq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point corresponding to '2020'}<r>[0.946, 0.404]</r> for Online Education higher or lower than {the corresponding point for Job Market Demand}<r>[0.946, 0.093]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "gmbq4UdGwijWNuDz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the first points in 2005 for {Online Education}<r>[0.108, 0.887]</r> and {Traditional Education}<r>[0.108, 0.369]</r>, which is lower?"
+            },
+            {
+                "from": "gpt",
+                "value": "Online Education."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "Rgb8EWnFeuNvYHG2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in <r>[0.931, 0.933, 0.960, 0.951]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label \"2020\"."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "yWODp6bfgaVIOThK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be described about the area in <r>[0.073, 0.063, 0.268, 0.168]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the chart is located within this region, including labels and colored markers for \"Online Education,\" \"Traditional Education,\" and \"Job Market Demand\"."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "Q0uK7s9uu6Mzlmn0",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify and describe the contents of the region <r>[0.012, 0.411, 0.028, 0.569]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label \"Trend figures\" is found in this region, oriented vertically."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "5JOYiraaeuhu73v3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing Innovation 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Aquamarine."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "LcXmybCIyxLpc0uP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the marker style for {the line for Innovation 3}<r>[0.097, 0.117, 0.945, 0.847]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "None."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "h3yebpnKM4JeEnmz",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the y-axis}<r>[0.012, 0.389, 0.028, 0.588]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Score (Normalized)."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "SgW7kVKSbY2nqJRZ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the score for {Efficiency under Innovation 1}<r>[0.097, 0.709]</r> higher or lower than the score for {Efficiency under Innovation 2}<r>[0.097, 0.254]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "RrP94JgvaJvVuFVd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has a better score, {Project Risk under Innovation 1}<r>[0.757, 0.888]</r> or {Project Risk under Innovation 2}<r>[0.757, 0.585]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation 1."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "13w2SYh4W3dS672E",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {ROI for Innovation 1}<r>[0.945, 0.254]</r> above or below {ROI for Innovation 2}<r>[0.945, 0.089]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "FKV4eMkZYmjxQhhL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in area <r>[0.060, 0.058, 0.174, 0.142]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend items: {Innovation 1}<r>[0.097, 0.065, 0.170, 0.082]</r> with a corresponding marker color of aquamarine; {Innovation 2}<r>[0.097, 0.091, 0.170, 0.108]</r> with a corresponding marker color of blue; {Innovation 3}<r>[0.097, 0.117, 0.170, 0.135]</r> with a corresponding marker color of purple."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "Lq54MGRiNzwYH7cq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe <r>[0.315, 0.019, 0.727, 0.044]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart subtitle \"Innovation Project Evaluation and Management\". It has a font color of #15 (very dark grey)."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "MwoAY7ZoZ6322Tnp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the area <r>[0.247, 0.934, 0.324, 0.952]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the x-axis label \"Collaboration\", styled with a very dark grey color."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "902kbsLRBLJcoHh1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.781, 0.139, 0.887, 0.161] in the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physical Assets."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "1tLo8t9Gi6iuXGLw",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled at [0.253, 0.896, 0.269, 0.914] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Q2."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "A0rrQKUFutBAilpG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.082, 0.361, 0.098, 0.649]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Normalized Asset Value."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "oxg3fA35CwVrkxEh",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the plot point for 'Physical Assets'}<r>[0.261, 0.541]</r> positioned higher or lower on the graph than {the plot point for 'Digital Assets'}<r>[0.261, 0.288]</r> at Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "A01LF8gt5jB2epTR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the plot point for 'Physical Assets' at Q5}<r>[0.563, 0.855]</r> to the left or to the right of {the plot point for 'Digital Assets' at Q5}<r>[0.563, 0.553]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, they are vertically aligned."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "fzJmHht7Vex33Omx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich asset category, Physical or Digital, has a higher value at {Q7}<r>[0.764, 0.553]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Physical Assets."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "58TiqyfKpHsFbCoK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region at [0.737, 0.130, 0.893, 0.203]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, with two items: {Physical Assets}<r>[0.781, 0.139, 0.887, 0.161]</r> represented with a blue marker and {Digital Assets}<r>[0.781, 0.172, 0.876, 0.194]</r> represented with a orange marker."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "i1XjohOLfBpyCvg9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area from [0.376, 0.086, 0.649, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the subtitle of the graph, which is {Financial Data Exploration}<r>[0.376, 0.086, 0.649, 0.116]</r>."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "makVpfTmD3aOe14O",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region from [0.125, 0.120, 0.900, 0.890]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main plot area of the chart, which includes a comparison of {Physical Assets}<r>[0.160, 0.445, 0.865, 0.855]</r> and {Digital Assets}<r>[0.160, 0.155, 0.865, 0.831]</r> values over different quarters."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "Ts1NKHMwc9QeeDxN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the ylabel}<r>[0.012, 0.388, 0.024, 0.572]</r> indicate on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Magnitude."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "8qcEga4nNoyl8ViV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the xlabel}<r>[0.506, 0.927, 0.536, 0.963]</r> located in relation to the plot area?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bottom center."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "ndHztDnrMSa9LG51",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Social Inequality}<r>[0.729, 0.557, 0.971, 0.828]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "6RCiCdjeQP0JKioF",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the y-tick labeled '0.50'}[0.058, 0.673] higher or lower than {the y-tick labeled '0.75'}[0.058, 0.577] on the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "w8Jn2q8F9ESULLPI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the second subplot have {the line for 'Social Inequality'}[[0.400, 0.557], [0.642, 0.828]] closer to the top or bottom of the chart at {the end of 2020}[0.642, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bottom."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "TCXwExfvNLlvmXyG",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is smaller, the starting or ending value of {Rights Protection}<r>[0.400, 0.480, 0.642, 0.828]</r> on the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ending value."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "oNxnvireNEhW7IkI",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area [0.029, 0.176, 0.054, 0.501] comprise of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-tick labels and ticks for the values 0.25, 0.50, 0.75, and 1.00 including their corresponding horizontal grid lines."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "BfnJOfqb3zOnQMMH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found in the region [0.029, 0.080, 0.054, 0.115]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the lowest y-tick and label labeled '2.00' with a horizontal grid line across the first subplot."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "McBOHL62Kviyposc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the space [0.029, 0.659, 0.054, 0.888] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "It holds the y-grid lines and y-ticks along with labels for 0.50 and 0.00."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "JgrGUGv9rRVaQiJ3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the subplot noted at [0.325, 0.037, 0.717, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Natural Language Processing vs Reinforcement Learning."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "tjF719ogjo3PiTYe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker is used for {the Machine Learning points in the third subplot}<r>[0.729, 0.145, 0.971, 0.351]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "ubsgGtuBP7kDPwDk",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with {the y-axis}<r>[0.671, 0.401, 0.683, 0.559]</r> in the last subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Accuracy."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "kt9bGOX8FkWhaSIf",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at '2019' for Deep Learning}<r>[0.850, 0.248]</r> positioned higher than {the point at '2019' for Reinforcement Learning}<r>[0.521, 0.531]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "QpeyzpFd304ZlJSX",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater, {the point at '2018' for Natural Language Processing}<r>[0.400, 0.609]</r> or {the point at '2019' for Machine Learning}<r>[0.850, 0.196]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Machine Learning."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "fH2b68WZ52HfmpyE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the first y-tick label in the third subplot}<r>[0.687, 0.853, 0.713, 0.888]</r> represent a higher value than {the last y-tick label in the first subplot}<r>[0.029, 0.080, 0.054, 0.115]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "h6a1PdR0JxB6D0eN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.064, 0.111, 0.208, 0.226] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the subplot, including labels {Machine Learning}<r>[0.101, 0.125, 0.204, 0.160]</r> and {Deep Learning}<r>[0.101, 0.177, 0.186, 0.212]</r> with their respective color symbols."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "6R4gHv5TsmICoJkz",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area around [0.358, 0.595, 0.384, 0.630]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes a y-axis tick labeled {0.80}<r>[0.358, 0.595, 0.384, 0.630]</r> in a subplot focused on comparing Natural Language Processing and Reinforcement Learning."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "VGuYOBzSz09gP81r",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the elements within the region [0.717, 0.093, 0.983, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart covers a subplot with x and y axes labels such as 'Year' and 'Accuracy' respectively, along with data lines for {Machine Learning}<r>[0.729, 0.145, 0.971, 0.351]</r> and {Natural Language Processing}<r>[0.729, 0.377, 0.971, 0.609]</r>, marked with years {2018, 2019, 2020}[0.714, 0.879, 0.986, 0.914]."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "JW64C2J2XqCXjwPV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.224, 0.100, 0.320, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend label for Artificial Intelligence."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "n7tyqzSuCG9ZY1kP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at [0.057, 0.380] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tick line."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "FeZ0hqTMqPnyUG3a",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate [0.0315, 0.186] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point for the year 2023 for Artificial Intelligence."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "rZf0iAGf7nArx3Da",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the year 2021 tick mark}[0.139, 0.893] to the left or right of {the line Automation}[0.051, 0.261, 0.315, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "qimSD63LsZIyuTHj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the points for Neural Networks in 2022}[0.887, 0.532] above or below {the points for Neural Networks in 2021}[0.799, 0.665]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "epeDjWJbUmOWg62P",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the legend handle for AI Medicine}[inf, inf, -inf, -inf] visible next to {the legend label 'AI Medicine'}[0.512, 0.100, 0.568, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "Hp5MI16mf0fOqEcf",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the area [0.340, 0.075, 0.659, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a subplot with axes, grid lines, y-axis labels, tick marks, legend labels, and lines representing trends in AI medicine vs Natural Language Processing, with each data point marked."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "1tOfQJk7NrZNIN3M",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is encompassed within the region [0.698, 0.075, 0.989, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot area includes chart elements such as axes, x-grid, y-grid, lines showing trends for Artificial Intelligence vs Neural Networks, and corresponding tick marks and labels, all contained within a light blue background."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "7tW3KPvk3Khdq13c",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what features are contained in [0.038, 0.075, 0.329, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers part of the chart showcasing the subplot titled \"Artificial_Intelligence vs Automation\"--it includes axes detailing, grid lines, legends of labeled lines, and x and y-axis with marked notches and interacting data lines."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "GYSMrNIw2sWQcUV1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.019, 0.419, 0.036, 0.554] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the ylabel 'Trend Value'."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "QOBx5kRTVk8ZdKH9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the x-axis tick found at [0.093, 0.911] in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "'Abrupt increase'."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "nQOIolYtj3vDFaOy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at this coordinate in the subplot [0.272, 0.083, 0.426, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend item 'Trends in Building'."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "vQC2u6mdzSyTZQzx",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, does the point at 'Sluggish growth'[0.700, 0.812] of line with color #000000 lie above or below the point at 'Sluggish growth'[0.700, 0.854] on the line with color #ff0000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "ZIQ3n4dI7TELZdqr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is the point 'Persistent rise'[0.314, 0.175] on the line with color #0000ff higher than the point 'Persistent rise'[0.314, 0.246] on the line with color #008000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "fckoFmYG7CS5Yzsw",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the first point on line_0 in subplot_1 '[0.589, 0.345]' have a higher or lower y value than the first point on line_1 [0.589, 0.402]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "6BNlfYSOoo9hAQ2U",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give me a brief description of the contents located in the region [0.216, 0.074, 0.433, 0.116]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts a legend for 'Trends in Building', along with corresponding symbolic color given by {handle}[inf, inf, -inf, -inf]."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "XaprcWN7fM9DuRcK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents located within the coordinates [0.093, 0.175, 0.425, 0.798]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers a part of the first line in the first subplot, including four points labeled 'Abrupt increase', 'Inexorable Decline', 'Persistent rise', and 'Slow Increase' with their corresponding y-values."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "QciHvudrDT3aI9ej",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area from [0.505, 0.919, 0.674, 0.942] in the right subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains {the x-axis label 'Accelerated growth'}[0.505, 0.919, 0.674, 0.942]."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "QLTgG7gYNragUJIV",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plot element located at [0.476, 0.952, 0.531, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-label of the second subplot, labelled as \"Media Influence\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "CioFgkBGeKc43xuf",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the first y-tick in the third subplot, located at [0.676, 0.902, 0.687, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 0.0."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "rODhmYfcTzZLAnMc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by {the line}[0.369, 0.232, 0.639, 0.741] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line represents the media influence on public opinion for various platforms."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "haMhhlbulFX7Rul4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the influence score of 'Social Media'}[0.459, 0.232] higher or lower than that of {the 'Documentaries'}[0.609, 0.260]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "ct2qStkSmecFZRR3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media platform has a higher influence score at {the sixth point}[0.519, 0.345], Podcasts or Newspapers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Podcasts."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "BLEN2I72Ku89BcWO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the influence score of {the 'Debate Shows'}[0.399, 0.628] above or below the influence score of {the 'Independent Blogs'}[0.489, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "EN8aXTeHXxyRVbzh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area enclosed by [0.689, 0.062, 0.987, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the third subplot of the chart, representing an unspecified variable in relation to the y-axis with labels 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "AIls7xTOll34VkGL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat occupies the region [0.355, 0.062, 0.653, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the second subplot of the chart. It has a title \"Media Influence on Public Opinion\", Y-axis labeled \"Opinion Change\", X-axis labeled \"Media Influence\" and includes line graph data points representing different media sources."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "BjBJuu9qwMoOaMxg",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within [0.434, 0.025, 0.574, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle label of the second subplot, \"Media Influence on Public Opinion\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "YbWc0nVAXbkaS8lS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the type of {the third point object on the 'Fluctuating Pattern' line}<r>[0.533, 0.480]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marker."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "u8jaR535HZHU0Q0j",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is used to represent the value 40 on the 'Natural Reservation' factor in {second subplot}<r>[0.862, 0.544]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marker."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "LCIJFmDoBGOpmi2W",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the 'Fluctuating Pattern' line in the subplot featuring the 'Assessment Factors'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "SG4jDjYzCrQ6c8qn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point at 'Reforestation' on the 'Minimal Change' line}<r>[0.533, 0.319]</r> higher than {the point at 'Reforestation' on the 'Fluctuating Pattern' line}<r>[0.533, 0.480]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "2zr9itv7Oo2CDXyE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line evidently scores lower on the 'Natural Reservation' category, {the 'Minimal Change'}<r>[0.601, 0.222]</r> or {the 'Declining Pattern'}<r>[0.930, 0.609]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Declining Pattern."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "MAx2UUCFKw7HMYXq",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {first point in 'Inconsistent decline'}<r>[0.067, 0.222]</r> and {first point in 'Declining pattern'}<r>[0.725, 0.222]</r>, which has a lower coordinate value on y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "zieC11g0XKGrmd0v",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region <r>[0.0, 0.8, 0.3, 1.0]</r> in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes x-axis labels such as 'Green Architecture' {located at}[0.012, 0.879, 0.122, 0.914] and 'Low-Energy Transport' {located at}[0.073, 0.879, 0.198, 0.914], horizontal grid lines, and numerical y-axis tick labels such as '0' and '120'."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "KUWYikKvuOl86uPt",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section in the upper left corner <r>[0.0, 0.0, 0.3, 0.2]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes part of the chart's title {located at}[0.028, 0.037, 0.311, 0.083], specifically the phrases 'Inconsistent Decline vs Declining Pattern'."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "EANXbUn2FTrDsYtN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details on the region <r>[0.65, 0.0, 1.0, 0.3]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers part of the title for the last subplot {located at}[0.690, 0.037, 0.965, 0.083] reading 'Declining Pattern vs Fluctuating Pattern' and inklings of the chart's facecolor in a light grey hue represented by {subplot background}<r>[0.715, 0.093, 0.940, 0.866]</r>."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "jbX0q2pUxfEhHpPl",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker does the \"Water Conservation\" data point have for the year 2019 in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "xafSpOO4MeJxHQXs",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the ylabel}<r>[0.017, 0.244, 0.029, 0.716]</r> referring to in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sustainable Business Index."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "Gj1zB4PVLxKj1PAx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the color}<r>[0.868, 0.753, 0.899, 0.774]</r> of the 2018 label handle in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "CwwZa7Lhmf67UwUN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Climate Change'}<r>[0.224, 0.403]</r> in the first subplot higher or lower than {the point for 'Climate Change'}<r>[0.882, 0.364]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "2JhY9yynXshIg5Sr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the years 2016 and 2017, which year's {the 'Renewable Energy' point}<r>[0.275, 0.306]</r> is higher in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2016."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "J250jYlA8OpuOcn3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich point has a larger y-value, {the 'Energy Efficiency' in 2018}<r>[0.779, 0.507]</r> or {the 'Waste Management' in 2018}<r>[0.831, 0.461]</r> in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "eQnpt5eXVipxz6BJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in <r>[0.209, 0.734, 0.280, 0.849]</r> in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot's legend with the items \"2016\" colored in black and \"2017\" colored in red."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "W7O1gkoM5cjJ4Iqk",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region <r>[0.389, 0.093, 0.615, 0.866]</r> in the second subplot contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the axes for the comparison of Sustainable Business Index between the years 2018 and 2019, with grid lines visible and y-ticks ranging from 0 to 100."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "qRgjPpy27QLfrnyE",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within <r>[0.718, 0.093, 0.944, 0.866]</r> in the third subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region depicts axes for Sustainable Business Index data comparison between 2018 and 2020, featuring categories like Water Conservation to Renewable Energy and y-ticks from 0 to 100."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "E0zXVWblpi2fu0N3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the markers used for {the line representing 'Monoclonal Antibodies'}<r>[0.378, 0.512, 0.648, 0.586]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Blue."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "A1Gs8viZKgSJ7Ly7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object at <r>[0.317, 0.512]</r> represents the year 2020 in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Point."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "kh3pqYCqQxaGoqcC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the subtitle text at <r>[0.400, 0.025, 0.626, 0.055]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Monoclonal Antibodies vs Regenerative Medicine."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "IMpZ3Pl6I1pl6ViU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the point with coordinates <r>[0.317, 0.512]</r> for the 'Recombinant Proteins' above or below the midpoint of the y-axis in its subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "HYMKEoEKkqSLX3O3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the point at <r>[0.843, 0.486]</r>, 'Gene Therapy' or 'Recombinant Proteins'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Gene Therapy."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "80fyoN0Bthxs9vGt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the line at <r>[0.048, 0.362, 0.317, 0.512]</r> have its first point lower than its last point?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "n4Ry406uuwcDGjmd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is detailed within the bounding box <r>[0.019, 0.603, 0.031, 0.726]</r> on the left side of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick labels '0.6' and '0.8' are located here."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "SVTGeVA4EdHjTvag",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of region <r>[0.019, 0.502, 0.031, 0.625]</r> in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It includes the y-axis tick labels '0.8' and '0.6', positioned vertically."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "TLg3UYsop3UPyGy6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region <r>[0.019, 0.402, 0.031, 0.525]</r> on the left-most part of the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick labels '1.0' and '0.8' are located here, aligned vertically."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "z7LUTMdZJpd1wYkX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.012, 0.312, 0.024, 0.648] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for the first subplot."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "KyJqGFAo5R2ObQLR",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third x-axis tick in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.308, 0.879, 0.372, 0.914]."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "LYPmFvToRmQrZh6I",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis tick label} at [0.036, 0.543, 0.051, 0.578]?"
+            },
+            {
+                "from": "gpt",
+                "value": "70."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "MmJCboRPcBHFHZ2g",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is greater in the first subplot for 'Businesses', {the blue line}[0.340, 0.248] or {the orange line}[0.340, 0.526]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The blue line."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "SSJM3ntxwsQgY74O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first subplot, is {the 'Amenities'} point of the blue line[0.473, 0.635] above or below of the orange line[0.473, 0.263]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "hv4dtFRgmzUZLOj2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich 'Green Space' score is higher, {Meadowville}[0.075, 0.557] or {Parkside}[0.075, 0.434]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Meadowville."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "kibcB7xcQO7cg0Jd",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.308, 0.879, 0.372, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label for 'Businesses' in the first subplot."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "LKydTgeBgzR7Tto8",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.075, 0.557, 0.473, 0.635]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the data points for 'Meadowville' in the first subplot, covering 'Green Space', 'Transportation', 'Businesses', and 'Amenities'."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "VIo9UO5dBvSDKUbe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.340, 0.248, 0.473, 0.635] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The data points for 'Businesses' and 'Amenities' of Meadowville are located at {coordinates}[0.340, 0.248] and {coordinates}[0.473, 0.635], respectively. These points are represented by the blue line."
+            }
+        ],
+        "id_raw": 1170
+    },
+    {
+        "id": "MYrTpW7VsJ57j1do",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the y-axis label}<r>[0.012, 0.434, 0.024, 0.526]</r> indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Level."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "OG8f0pOngs2izSrx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is associated with {the point}<r>[0.065, 0.209]</r> in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inexorable Decline."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "sNeHqxSLAbSgN698",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value is marked by {the marker at}<r>[0.533, 0.557]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "40."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "YxawjIspg0MNhM6t",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the beginning point}<r>[0.065, 0.209]</r> of the 'Inexorable Decline' trend higher than {the ending point}<r>[0.274, 0.325]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "c7BfbgPc4z2KogcP",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the point at}<r>[0.863, 0.364]</r> represent a higher value than {the point at}<r>[0.863, 0.689]</r> in the 'VoiceAssistants' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "CtD1Vh6rGLumuZF9",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich has a greater value at {the point}<r>[0.464, 0.248]</r>, 'Inexorable Decline' or 'Pronounced Increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inexorable Decline."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "L4aFjN2QlqKW3JD9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.055, 0.093, 0.284, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains axes with grid lines, the labels 'Trends' and 'Level', and tick descriptions such as 'Chatbots', 'SpeechRecognition', 'VoiceAssistants', and 'RoboticAutomation' placed at specific intervals."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "cwpkNjZdCsmujYAW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.384, 0.093, 0.613, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section includes a subplot with vertical and horizontal lines forming a grid. It contains tick marks and labels indicating 'Trends' across the x-axis with values 'Chatbots' through 'RoboticAutomation', and 'Level' across the y-axis with numerical labels spanning from 0 to 100. There are plotted lines representing 'Upward Trend' and 'Inexorable Decline'."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "qhp7ov9wQfkhebAD",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are found within [0.713, 0.093, 0.942, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a subplot with grid lines, axes titled 'Trends' and 'Level', along with trend representations for 'Pronounced Increase' and 'Insignificant Increase'. It showcases x-axis markers labeled from 'Chatbots' to 'RoboticAutomation' accompanied by corresponding y-axis numerical labels."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "ppWKzJvec7lNjvTx",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of marker represents the data points in the subplot titled \"Decline vs Expansion\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Circle."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "wcBAhptCS12bpej9",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is {the label 'Expansion'}<r>[0.219, 0.177, 0.279, 0.212]</r> associated with in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "NyLnnWmMvvkaLllP",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label in the subplot \"Static vs Continuous Decrease\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Efficiency Improvement Potential."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "Vw1BSaeX0cQbn4a8",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Energy Efficiency'}[0.937, 0.287] on 'Persistent Increase' higher or lower than {the point for 'Robot Automation'}[0.724, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "EgqpllNJNAi8Ihwy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot contains {the xlabel 'Study Areas'}[0.466, 0.927, 0.536, 0.963] higher on the vertical scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "Static vs Continuous Decrease."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "f3e7sXGflgFGe7Ij",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line representing 'Continuous Decrease'}[[0.394, 0.480], [0.608, 0.802]] cross above {the line for 'Static'}[[0.394, 0.512], [0.608, 0.577]] within the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "LwmqrwkB8qIllXgl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.384, 0.093, 0.619, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a subplot titled \"Static vs Continuous Decrease\" with a detailed comparison of these two metrics across 'Robot Automation' and 'Energy Efficiency'. The grid is styled with dashed white lines, and multiple lines representing different data points and trends are apparent."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "ZijVJCkD9Bu1mi1V",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area [0.055, 0.093, 0.289, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a subplot labeled \"Decline vs Expansion\" charting the potential for efficiency improvement in those domains. There are visible grid lines and labels for study areas along the x-axis, as well as labels for efficiency ratings along the y-axis."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "gT9cbBAac0Gbc9k9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat key elements are found within the bounding box [0.713, 0.093, 0.948, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This bound demarcates a subplot comparing \"Flucturing vs Persistent Increase\". It includes graphical representations through colored lines with markers, axis labels, tick marks, and a grid pattern. Additionally, it features labels for data points specifically tied to 'Robot Automation' and 'Energy Efficiency'."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "gEalVWKxGF20Pt49",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Y-axis label at the coordinates [0.012, 0.381, 0.024, 0.579] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Trend Level."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "ZmyiZ8VpLuuKn7GC",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.674, 0.037, 0.840, 0.083] referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cryptocurrency Markets."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "bBBG5izXLp7SSe5Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name of the market style at the x-axis label specified by these coordinates [0.903, 0.879, 0.985, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Erratic growth."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "8dnyNdb3aK2Vbjjd",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 'Upward trajectory'}[0.571, 0.519] in the Cryptocurrency Markets higher or lower than that in Equity Markets {point of 'Upward trajectory'}[0.077, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "OUBAsKKo4Ta1Pomi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the {point of 'Erratic growth'}[0.450, 0.635] in Equity Markets and the {point of 'Erratic growth'}[0.944, 0.132] in Cryptocurrency Markets, which has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cryptocurrency Markets."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "NaQMeAGPHVk4MGkz",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point of 'Incremental growth'}[0.326, 0.480] for Equity Markets at a higher or lower position than the {point of 'Abrupt decline'}[0.201, 0.441]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "N8u1t6P2U3fQCYZL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content contained within the bounding box [0.335, 0.111, 0.463, 0.173]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend item for {Equity Markets}[0.372, 0.125, 0.458, 0.160] in orange color."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "ZfC0pait96tNM7pv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears in the region demarcated by coordinates [0.077, 0.171, 0.450, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a line plot representing the trend level for various market styles in Equity Markets, with points for 'Upward trajectory', 'Abrupt decline', 'Incremental growth', and 'Erratic growth'."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "Ige92wg66MsCq4m1",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within the region defined by [0.518, 0.879, 0.985, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are x-axis labels representing different market styles: {Upward trajectory}[0.518, 0.879, 0.623, 0.914], {Abrupt decline}[0.653, 0.879, 0.738, 0.914], {Incremental growth}[0.763, 0.879, 0.876, 0.914], and {Erratic growth}[0.903, 0.879, 0.985, 0.914]."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "nzwyjfUFlH6tHtP7",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.707, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis tick for {the year 1980}<r>[0.707, 0.879, 0.732, 0.914]</r>."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "907ylY49zC4izf9w",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the coordinates [0.017, 0.445, 0.047, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label for 200."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "lqdMcvCqwKvMkhoB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data descriptor is positioned at [0.930, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend descriptor for 'Sea Level'}<r>[0.930, 0.177, 0.978, 0.212]</r>."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "GFGieznrkG8qwyNI",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the value of the point at [0.965, 0.160] exceed the point at [0.975, 0.160]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "T4Z8zuIFfRmraPPU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the first x-axis tick label on the left or right of the last y-axis tick label in the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Right."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "2Qqn4uk25m5gUc6X",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the marker colors at [0.965, 0.160] and [0.978, 0.212] the same or different?"
+            },
+            {
+                "from": "gpt",
+                "value": "Different."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "UVFnG9Z8PCcN1AtJ",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area [0.90, 0.10, 0.98, 0.23] of the third subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend}<r>[0.899, 0.111, 0.982, 0.226]</r> with labels for 'Glacier' and 'Sea Level', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "UTohvfMUobIQEGaM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content found within [0.340, 0.400, 0.35, 0.50]."
+            },
+            {
+                "from": "gpt",
+                "value": "{The y-axis}<r>[0.340, 0.406, 0.350, 0.554]</r> including the measure labels such as \u2212100, 0, and 100 along with tick marks in the second subplot."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "Mv1WRbtCb1qknnSx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the elements within the coordinate set [0.046, 0.090, 0.33, 0.87]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the axes of the first subplot plotted with x-axis and y-axis labels, grid lines, title, and data lines for Temperature vs Glacier."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "jv3rCw80sOn6CAeN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at {the point marked on the chart}<r>[0.223, 0.422]</r> for the month of August in the \"Sharp rise\" subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "200."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "7dRS5ALFo7c1c0vU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label of the subplot titled \"Periodic fluctuations\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sales Volume."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "p0bdZraC9hSDPkEr",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the \"Slow decline\" subplot, what is represented at {the x-axis coordinate}<r>[0.953, 0.893]</r> for the month of November?"
+            },
+            {
+                "from": "gpt",
+                "value": "Nov."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "IadNs6ORrrxAzPla",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich value is higher at {the point}<r>[0.460, 0.893]</r> for the subplot \"Periodic fluctuations\" in April, the plotted value or the y-axis label number?"
+            },
+            {
+                "from": "gpt",
+                "value": "y-axis label number."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "iiM1cD6GZNs85YKt",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the \"Sharp rise\" subplot, is the sales volume in June at {the point}<r>[0.177, 0.546]</r> higher than the y-axis label at {the label}<r>[0.025, 0.514, 0.044, 0.542]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "tuJB8ifJEKHbJ2rs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data value in {Dec}<r>[0.976, 0.893]</r> for the subplot titled \"Slow decline\" above or below 100 according to the y-tick value at {the level}<r>[0.684, 0.606, 0.703, 0.634]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "pl56XqnIAsQTqZ4Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the zone encompassed by [0.377, 0.075, 0.659, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area displays a subplot titled \"Periodic fluctuations\" with a blue-marked line graph tracking sales volume trends over a year, labeled monthly from Jan to Dec, and displayed against a grid background."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "oABua9iMW3tEmAdv",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are revealed in the region [0.707, 0.075, 0.989, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section comprises the subplot \"Slow decline\" showcasing a line graph with monthly sales volumes indicated in dark blue, with tick labels and grid lines enhancing the presentation within the chart."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "vYaIY5HblvFkFny5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease detail the elements found within [0.047, 0.075, 0.330, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This segment shows the subplot \"Sharp rise\" featuring a dark-colored line graph indicating sales over time with distinct month labels and corresponding data points, set against a light gray grid."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "IrBzsRCXwezDZXLi",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object at [0.846, 0.037, 0.937, 0.083] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "UrAQNUxWDex1Udqy",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the y-axis label located at the coordinates [0.010, 0.357, 0.019, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adoption Rate."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "ABGJusNT7k2BnKe3",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data does the object at [0.602, 0.125, 0.833, 0.160] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend item for Robot-Assisted Surgery."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "SbsaZ0Ik9Lwd7kD5",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the bar from 'Initial Adoption' for 'Telemedicine'} at [0.046, 0.403] higher or lower than {the bar from 'Current Adoption'} at [0.157, 0.155]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "koBJXGMK9aROCyyM",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line at {point 'Initial Adoption'}[0.801, 0.596] is higher, {the blue line for Medical Imaging} or {the green line}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Green line."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "23DFSFfdiv2uu586",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line level for 'Initial Adoption' in 'Medical Imaging'}[0.836, 0.635] above or below {the line level for 'Current Adoption'}[0.947, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "YTUyS2qOkT6NeLTr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content  within the coordinates [0.573, 0.093, 0.689, 0.866]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subplot for Robot-Assisted Surgery, including its axes, grid lines, lines representing data points over two categories labeled 'Initial Adoption' and 'Current Adoption'."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "MS8aL5ONLzQrfjvO",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.046, 0.111, 0.216, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the subplot, including text items labeling 'Telemedicine vs Telemedicine' and 'Others vs Telemedicine', each associated with a color handle."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "fH72994GZiSRYyXx",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart section spanning from [0.310, 0.093, 0.420, 0.866] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire chart plot for EHR including axes, grid lines, line markers, and labels, showing a comparison of 'Initial Adoption' and 'Current Adoption'."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "kwo1tOROuGdJghtI",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the content at these coordinates - [0.740, 0.046, 0.920, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text 'Tech Company Value'."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "3oh4XQmVZsjfziL0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the information labeled at {coordinates}[0.043, 0.516, 0.076, 0.540] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label '130'."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "K3xqXL9uEZLVzsOa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color property of {the line indicating 'Innovative Business Value'}[0.123, 0.065, 0.940, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#1f77b4'."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "VCr7lbWboGng1p9n",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the 'Tech Company Value' line or the 'Innovative Business Value' line reach a higher value at month {6}[0.495, 0.583]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Tech Company Value."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "S2yGkWFoSqqfOmN7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 8th month interval, is the {Innovative Business Value}[0.643, 0.295] higher or lower than the {Tech Company Value}[0.643, 0.410]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "BGXlk2TmTX4K2RzN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the 10th month data point, which line - 'Tech Company Value' or 'Innovative Business Value', has a higher value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovative Business Value."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "bkUwQxoWRXesSegG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the region [0.684, 0.037, 0.973, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, with two items: 'Tech Company Value' with a red-colored marker and 'Innovative Business Value' with a blue-colored marker."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "sQDMMf0DYBYXv9zT",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.043, 0.286, 0.076, 0.310]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value '150' is labeled on the y-axis in this area."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "ZOXtMkgyHCdFbFZl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is included in the space [0.123, 0.180, 0.940, 0.871]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows the 'Tech Company Value' line which is red, consisting of 12 data points represented as 'o' markers, with coordinates ranging from {Month 1}[0.123, 0.871] to {Month 12}[0.940, 0.180]"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "JlqoDHaK1vRNr3oX",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at y-axis described as in the chart with coordinates [0.015, 0.342, 0.029, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Number of Cases."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "BsE5cjWnz01mEU4N",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {ytick} at [0.035, 0.254, 0.070, 0.282]?"
+            },
+            {
+                "from": "gpt",
+                "value": "1400."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "9ziJ3daQZSL2CFWS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the label on {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Hours."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "yfPQHMIcJBGnKfvO",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line on the graph is higher at {xtick}[0.668, 0.893], {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527] or {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Training Hours."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "tnkJYOVnHOdKwtr3",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the values of {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527] generally greater than, less than, or equal to the values of {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "fb2LjZGwl5Sn5F2q",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere does the {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854] lie in relation to the {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527], when read from left to right?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "TuDACRRPHDos8ldn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.082, 0.044, 0.278, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the legend for the line chart. It contains the color-coded markers and their respective lines: {Training Hours}[0.126, 0.055, 0.226, 0.083] and {Job Positions Created}[0.126, 0.097, 0.273, 0.125]."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "qAKyaWNsAB7NJYyn",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is available in the region [0.516, 0.942, 0.545, 0.970]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a title, which is {the x-axis label}[0.515, 0.942, 0.545, 0.970], it is \"Year\"."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "hoUaPD41ckFCLu9D",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.374, 0.903, 0.409, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is an {x-axis tick label}[0.374, 0.903, 0.409, 0.931] representing the year \"2012\"."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "M16WARG93yDsVGp1",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.025, 0.389, 0.048, 0.522] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label 'Indexes'."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "kgcqe0PqSszC1Xzc",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.241, 0.524, 0.853, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line for 'Quality of Life Index'."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "lWYXspae0cVGaWtq",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data point is indicated by the coordinates [0.730, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The point for the 'House Size Index' for the year 2040."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "m0tJZmNYmoDwhp6m",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does {the 'Quality of Life Index' line}[0.241, 0.524, 0.853, 0.685] intersect with {the 'House Size Index' line}[0.241, 0.135, 0.853, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lines do not intersect."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "rkFrk2SEg626Tn2y",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the data point for the 'Quality of Life Index' in 2040}[0.730, 0.559] higher or lower than {the data point for the 'House Size Index' in the same year}[0.730, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "y8HbQLI7CT4Z2GbR",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Quality of Life Index'}[0.241, 0.524, 0.853, 0.685] slope upwards or downwards overall from 2000 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "Downwards."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "BA3HHQyEuxmR0ZBH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content placed within coordinates [0.130, 0.072, 0.446, 0.188]."
+            },
+            {
+                "from": "gpt",
+                "value": "Legend of the chart is located here. It contains 'Quality of Life Index' with a red square marker at {coordinates}[0.130, 0.091, 0.195, 0.112], and 'House Size Index' with a blue pentagon marker at {coordinates}[0.133, 0.140, 0.195, 0.168]."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "9BGf6UUTseiIHcqV",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the area between [0.241, 0.524, 0.853, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the line representing the 'Quality of Life Index' data from the year 2000 to 2050, with each data point marked by a square."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "adfSFWta1B9f5xY5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.241, 0.135, 0.853, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the line representing the 'House Size Index' data from the year 2000 to 2050, with each data point marked by a pentagon."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "A9kxtoZbilgM863J",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.025, 0.302, 0.048, 0.609] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "clVJcUU4u7kG6EA0",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of {the legend item} at [0.752, 0.738, 0.913, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given Ratings."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "dSKmSeoQWWiSzV2d",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich startup phase does {the x-axis tick label} at [0.303, 0.869, 0.482, 0.914] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Early Growth."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "vz8rUqAmx0Oq1wti",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for 'Launch'}[0.247, 0.189] higher or lower than {the point for 'Early Growth'}[0.392, 0.144] in the 'Given Ratings' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "lO5oDNK5wtkSixwj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Given Ratings'}[[0.247, 0.100, 0.829, 0.278]] have a higher or lower value at 'Expansion' than {the line for 'Expected Ratings'}[[0.247, 0.144, 0.829, 0.278]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "aW4zukx4wQI13zMQ",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the 'Mature' phase, which line is higher, Given Ratings or Expected Ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "Expected Ratings."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "G0TfOzniKz4Fl5Jj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the bounding box [0.612, 0.869, 0.755, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the x-axis tick label for one of the Startup Phases, specifically \"Expansion\"."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "B9CH5RaTPMIFvygL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the bounding box [0.752, 0.738, 0.913, 0.773]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified bounding box, there is an item from the legend. It corresponds to the \"Given Ratings\" for the chart."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "KZSGRBdFG8T4us5X",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is within the region [0.101, 0.055, 0.975, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main content of the chart, including two lines, namely 'Given Ratings' and 'Expected Ratings'. It also includes the x-axis and y-axis with their respective tick labels and the legend situated at the top right."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "8UKpV4GXXXOrekMS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinate [0.451, 3.952, 0.579, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label"
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "I1tJEeeSkaH4obAT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the Social Media line}<r>[0.057, 0.126, 0.972, 0.721]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red"
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "uFDWHtDcjRl1kLor",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the 'May' point on {the Social Media line}<r>[0.390, 0.443]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "'May' is almost halfway along the x-axis and less than halfway along the y-axis."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "0pniHtVCmHZxXVTW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'May'}<r>[0.390, 0.443]</r> on the Social Media line below or above {the point of 'May'}<r>[0.390, 0.620]</r> on the Digital Communication line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "gISMFf2IWZnid1yU",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the handle for Social Media Use in the legend}<r>[0.065, 0.049, 0.101, 0.063]</r> positioned to the left or right on the legend compared to {Digital Communication Use}<r>[0.065, 0.084, 0.101, 0.098]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are at the same horizontal position, but the Social Media Use handle is positioned lower vertically."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "cCcRn4o54jjGdSFE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the X-axis label}<r>[0.450, 0.952, 0.579, 0.975]</r> in the chart placed above or below {the Y-axis label}<r>[0.015, 0.341, 0.029, 0.595]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "pHo8XUApr5uTSyVe",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content located within the region [0.064, 0.037, 0.303, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend with three entries: {Social Media Use}<r>[0.108, 0.046, 0.226, 0.069]</r>, {Digital Communication Use}<r>[0.108, 0.081, 0.298, 0.104]</r> and {Average Social Media Use}<r>[0.108, 0.116, 0.288, 0.139]</r>, each paired with a colored symbol of distinctive shape."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "KPoqWaK9FX9oBhlo",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned within [0.035, 0.522, 0.052, 0.545]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the y-axis tick label '40'."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "3SjtVQ3ds7qJJmFu",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within [0.057, 0.784, 0.140, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two data points: 'Jan' from Social Media Use at {coordinates}<r>[0.057, 0.721]</r> and 'Feb' from Digital Communication Use at {coordinates}<r>[0.140, 0.683]</r>."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "9SzL0S2bjGeVApLZ",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the x-axis representing in our visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Year."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "He9zIDr7aY31t9uu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the line} with color '#1f77b4'<r>[0.134, 0.057, 0.908, 0.323]</r> representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the trend of people practicing Yoga."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "XyZl9nYKTUAHnN07",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does {the y-axis}<r>[0.015, 0.178, 0.029, 0.758]</r> provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the percentage of people doing exercise regularly (%)."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "aVOoqjI4zT2gCN15",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the Yoga trend line}<r>[0.134, 0.057, 0.908, 0.323]</r> start at a higher or lower value than {the High Intensity Interval Training trend line}<r>[0.134, 0.533, 0.908, 0.701]</r> in 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "GZ6kKeCVdPZXQ6jp",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the point with x-coordinate 2010, is the {Yoga}<r>[0.521, 0.127]</r> percentage higher or lower than {High Intensity Interval Training}<r>[0.521, 0.589]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "b9hOTKkVthh7a0Nu",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the overall line trends, did more people practiced {Yoga}<r>[0.134, 0.057, 0.908, 0.323]</r> or did more people did {High Intensity Interval Training}<r>[0.134, 0.533, 0.908, 0.701]</r> from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yoga."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "QabBU5eavi8TtXUr",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within {the region}<r>[0.715, 0.037, 0.978, 0.113]</r> in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend which includes icons and labels for both \"High Intensity Interval Training\" and \"Yoga\"."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "segeChGzYcLFTiiB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe content in {the area}<r>[0.057, 0.025, 0.985, 0.911]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, the main parts of the chart are plotted. It contains two trend lines, which represents the trend over years of percentage of people doing High Intensity Interval Training exercise and Yoga exercise regularly. The area also contains y-axis labels from 0 to 60 and x-axis labels from 2000 to 2020."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "4SIOVM24Nxo9cdo9",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe objects within {the boundary}<r>[0.503, 0.919, 0.539, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is one object. It's an x-axis label indicating the year \"2010\"."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "mM6t4M06hpjrqFjd",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.025, 0.376, 0.048, 0.528]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "8guivW6tTozgZycT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fourth x-axis tick label?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.445, 0.879, 0.511, 0.914]."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "cKDqIuuHlIrd6DWG",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the legend item} at [0.674, 0.748, 0.850, 0.783] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Discharge Rate."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "AeHJkndSlk96XGkH",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich one has a higher value in the '80-90' age group, {Hospital Admission Rate}[0.821, 0.290] or {Discharge Rate}[0.821, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hospital Admission Rate."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "1W8MacucKLx3Ks0O",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the '40-50' age group, is the {Hospital Admission Rate}[0.364, 0.613] lower or higher than the {Discharge Rate}[0.364, 0.290]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "X3JQB9K9lI4litis",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich rate shows a higher value for the '90+' age group, {Hospital Admission Rate}[0.935, 0.075] or {Discharge Rate}[0.935, 0.183]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Discharge Rate."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "PHJvNx7mQlmw27IC",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content within [0.135, 0.075, 0.935, 0.829]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the Hospital Admission Rate line chart, with data series represented by square markers and a line in red color."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "TB4ExNzZSvXUkx9i",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the coordinates [0.135, 0.183, 0.935, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Discharge Rate line chart is located here, represented by a blue line with pentagon markers."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "eAx9L8LEM63EaaWq",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found within the region [0.599, 0.682, 0.963, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend with three items: {Hospital Admission Rate}[0.674, 0.695, 0.954, 0.730], {Discharge Rate}[0.674, 0.748, 0.850, 0.783], and {Avg admission rate}[0.674, 0.800, 0.896, 0.835]. The handles representing corresponding line styles and markers are located beside each text."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "GgrpCJ9WhT6RmN2i",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.025, 0.340, 0.048, 0.554] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "rlJSmHZhbo2gN8ea",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the eighth tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.748, 0.869, 0.783, 0.914]"
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "UJUemeRdt9n3kqph",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.888, 0.869, 0.923, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "45."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "VKDhUVJHmmpBfZ2r",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point of 'Temperature Avg'}[0.695, 0.806] higher or lower than {the point of 'Sales Avg'}[0.695, 0.396]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "k6LbHWX06TNQJgU7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the starting point for 'Temperature Avg'}[0.276, 0.819] above or below {the starting point for 'Sales Avg'}[0.276, 0.691]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Above."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "0R3Cp5MmE6khboq2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line has a higher value at {point 5}[0.556, 0.809], Temperature Avg or Sales Avg?"
+            },
+            {
+                "from": "gpt",
+                "value": "Temperature Avg."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "jwBI3XqmSYhNZZdM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content in [0.683, 0.672, 0.963, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "Three legend items are located here: {Temperature Avg}[0.757, 0.685, 0.954, 0.720] with a red handle, {Sales Avg}[0.757, 0.738, 0.869, 0.773] with a blue handle, and {Average Sales}[0.757, 0.790, 0.921, 0.825] with a green handle."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "iGJgAboHCYmSS7Az",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area [0.075, 0.754, 0.128, 0.799]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label \"100\" is located at {coordinates}[0.075, 0.754, 0.128, 0.799]."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "ThDfkYhs68fE5WNN",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within [0.525, 0.410, 0.585, 0.470]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are 2 objects. The point associated with 'Temperature Avg' at timestep 20 is located at {coordinates}[0.556, 0.809] and the point associated with 'Sales Avg' at timestep 20 is located at {coordinates}[0.556, 0.450]."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "DZ2fbA4kTBRw4mLB",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.025, 0.329, 0.048, 0.583] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "TTbt9w0xLaICpVFv",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of {the x-tick label} labeled '5'?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.452, 0.869, 0.470, 0.914]."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "65vsg4EVv6qPjzUm",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the handle of the Apple Stock Price label}<r>[0.133, 0.743, 0.193, 0.764]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "#d62728."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "kwhscJPVphVEZKv7",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {the point at month '3' for Apple Stock Price}<r>[0.347, 0.317]</r> and {the point at the same month for Orange Stock Price}<r>[0.347, 0.200]</r>, which company's stock price is higher?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apple's Stock Price."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "sA9M4nr7C1vPW8GY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the line for 'Apple Stock Price'}<r>[0.176, 0.095, 0.861, 0.827]</r> compared to {the line for 'Orange Stock Price'}<r>[0.176, 0.131, 0.861, 0.307]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Red, specifically #d62728."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "Q7twJJUJ2CPyeSCa",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt {the last point}[0.861, 0.304] for Apple and at {the last point}[0.861, 0.192] for Orange, which stock has a higher closing price?"
+            },
+            {
+                "from": "gpt",
+                "value": "Apple."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "pDiDlieQal0bhtSh",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is included within the coordinates [0.058, 0.037, 0.111, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a y-axis tick label mentioning the value '175'."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "LJyw03lJzhtiD2xG",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.167, 0.869, 0.184, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the '0' x-axis tick label}<r>[0.167, 0.869, 0.184, 0.914]</r>."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "4M85NnttIsI3CWEj",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease depict the objects within the area [0.130, 0.724, 0.434, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises the sub-legend of the plot with label 'Apple Stock Price' at {coordinates}[0.204, 0.738, 0.406, 0.773] and its color element located at {coordinates}[0.133, 0.743, 0.193, 0.764]. It also contains the label 'Orange Stock Price' at {coordinates}[0.204, 0.790, 0.425, 0.825] with a color element at {coordinates}[0.130, 0.792, 0.195, 0.819]."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "oGLyzbPjdFhS5mnY",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at these coordinates [0.025, 0.205, 0.048, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the y-axis label."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "t3u2QF34D2S4HFWS",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {this location}<r>[0.721, 0.685, 0.860, 0.720]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for 'Stress Level'."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "S9PGQLJve5Wfmhod",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the tick label located at [0.892, 0.869, 0.945, 0.914] on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's '200'."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "tbsGjjZSNz7Lk4Qn",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {point at '175' on the 'Stress Level'}<r>[0.824, 0.740]</r> higher or lower than the {point at '175' on the 'Recovery Level'}<r>[0.824, 0.201]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "P8mALn7zJnU1EqdN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point for '100' on 'Stress Level'}<r>[0.542, 0.492]</r> situated above or below {the point for '100' on 'Recovery Level'}<r>[0.542, 0.565]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "LcchWiI0qgGVo2SW",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fluctuation level has a higher value at {Time '50'}<r>[0.353]</r>, Stress Level or Recovery Level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stress Level."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "j3Ow0TbJ1xtzkvr3",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region with coordinates [0.647, 0.672, 0.963, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the sub-legend with three items: {Stress Level}<r>[0.721, 0.685, 0.860, 0.720]</r>, {Recovery Level}<r>[0.721, 0.738, 0.896, 0.773]</r>, and {Base Recovery level}<r>[0.721, 0.790, 0.954, 0.825]</r>."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "IUGlbWV6d96kIiMp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.127, 0.055, 0.975, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the entire plot with x-tick labels from 0 to 200, y-tick labels from 0.40 to 0.95, and three lines for Stress Level, Recovery Level, and Base Recovery level."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "W0j1mH7QRQM2bNhL",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.058, 0.110, 0.119, 0.155]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the y-axis tick label '0.90'."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "lKgUkwzbJSfeCeGN",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.843, 0.332, 0.862, 0.352]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Data point in the 'Urban Development' line."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "h54lkE0asaZp3J2M",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the fifth x-axis tick label located at [0.502, 0.912, 0.555, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"2020\""
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "RTVVyrKqQELrnN2m",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.719, 0.790, 0.894, 0.814] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend item labeled 'Urban Development'"
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "zjydJplouCGvF5rN",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the data point representing 'Urban Development' in 2025 at [0.619, 0.232] higher or lower compared to 'Rural Development' in the same year at [0.619, 0.731]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "ZkcWaXCl9q2RQhiE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the y-axis tick label \"60\" at [0.043, 0.242, 0.070, 0.272] lower or higher than the y-axis tick label \"35\" at [0.043, 0.784, 0.070, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "YT7EzeY8viIJvYi2",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is represented by the legend item at [0.719, 0.860, 0.966, 0.883], 'Urban Development' or 'Rural Development'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Neither, it represents 'Average Development Index'."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "OXU3NaISwF5bYQGc",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area at [0.719, 0.790, 0.894, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two legend items for the lines in the chart. They are {Urban Development}[0.719, 0.790, 0.894, 0.814] and {Rural Development}[0.719, 0.825, 0.886, 0.849], each respectively paired with a square and pentagon markers of different colors."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "JkexzSfBipDfL4H5",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region [0.764, 0.124, 0.823, 0.836] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily includes two trend lines for 'Urban Development' and 'Rural Development' from the years 2035 to 2040, specifically at {coordinates}[0.800, 0.427, 0.891, 0.470] and {coordinates}[0.800, 0.709, 0.891, 0.796]. Also, some x-axis tick labels and an x-axis label \"Year\" are included."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "6hDqTaHyBMc4KPKM",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements in the area [0.663, 0.781, 0.973, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there are three legend labels for 'Urban Development'[0.719, 0.790, 0.894, 0.814], 'Rural Development'[0.719, 0.825, 0.886, 0.849] and 'Average Development Index'[0.719, 0.860, 0.966, 0.883], each associated with a colored marker."
+            }
+        ],
+        "id_raw": 1191
+    },
+    {
+        "id": "i2rUoK6t3Bg1UWRh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on its coordinates [0.516, 0.927, 0.577, 0.963], what is the object located at the bottom center of the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "X-axis label."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "pPdZWH6qsc3jxrRD",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the marker in the legend}<r>[0.682, 0.739, 0.747, 0.767]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Followers."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "ossJhie8coR1kTmj",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat value does the label at {coordinates}<r>[0.093, 0.839, 0.110, 0.884]</r> represent on the y-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "0."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "gsMAU7GvQwKRbfVs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the numbers for {Followers}<r>[0.118, 0.066, 0.975, 0.461]</r> greater than or less than the numbers for {Friends}<r>[0.118, 0.292, 0.975, 0.645]</r> in the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Greater."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "iWb6HJ3BPpGrxGxy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line, {Friends}<r>[0.118, 0.292, 0.975, 0.645]</r> or {Followers}<r>[0.118, 0.066, 0.975, 0.461]</r>, has a higher value in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Followers."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "Dd5zeSfmDaGQQxtr",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing {Friends}<r>[0.118, 0.292, 0.975, 0.645]</r> and {2012 Benchmark}<r>[-215.316, 0.645, -215.209, 0.645]</r>, which had a higher value in the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Friends."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "pENsrD1Wq6LBe5SK",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the content in the area with coordinates [0.682, 0.672, 0.963, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart with three entries: {Friends}<r>[0.756, 0.685, 0.839, 0.720]</r>, {Followers}<r>[0.756, 0.738, 0.865, 0.773]</r>, and {2012 Benchmark}<r>[0.756, 0.790, 0.954, 0.825]</r>. Each entry has a text label and a color handle."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "CUpShGTkmPHEkR4G",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found within the region [0.025, 0.185, 0.048, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the y-axis label, which reads \"Number of Social Connections\"."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "heLqJDp0zoAjkgxp",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.118, 0.856, 0.975, 0.914]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains x-axis tick labels representing the years from 2012 to 2019."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "s3UxmaXmySNgm9ow",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is signified by the object at this coordinate [0.025, 0.333, 0.048, 0.561]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the y-axis label which is 'growth value'."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "4Qkop5dgB0GwgfYu",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the third tick label on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The tick label is '2015' and its coordinate is {at}[0.365, 0.869, 0.435, 0.914]."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "KhoCdR6AtpFnYDuA",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the line with these coordinates [0.236, 0.341, 0.893, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This line represents the Ethereum growth value over the years."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "ibY2QHdfEkaAAJfg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the growth of {Blockchain}[0.236, 0.073, 0.893, 0.775] higher or lower than {Ethereum}[0.236, 0.341, 0.893, 0.792] in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth of Blockchain is higher."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "LoyvTAyfBwCmnsGg",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2018, is the growth value of {Blockchain}[0.236, 0.073, 0.893, 0.775] higher or lower than the {Standard Tech Growth}[-165.110, 0.316, -165.028, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "U7OhdCCtqO9xcXxi",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what is closer to the {Standard Tech Growth} line[-165.110, 0.316, -165.028, 0.316], the {Blockchain}'s growth value[0.236, 0.073, 0.893, 0.775] or the {Ethereum}'s growth value[0.236, 0.341, 0.893, 0.792] in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ethereum's growth value."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "ZykEi6zE24LHvpEF",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the area with coordinates [0.165, 0.055, 0.505, 0.222]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the legend for the chart. The legend has the names of the lines {Blockchain}[0.239, 0.069, 0.365, 0.104], {Ethereum}[0.239, 0.121, 0.352, 0.156], and {Standard Tech Growth}[0.239, 0.173, 0.495, 0.208] each along with a color handle marking their presence in the plot."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "sgUIMudH6u2aYv7K",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat units are displayed within [0.076, 0.434, 0.146, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label shows a value of '7500' in this area."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "ttuhJJYMQOYzxSS7",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is available in the region [0.447, 0.869, 0.517, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains one of the x-axis labels, representing the year '2016'."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "OwqHWGrpJQjBhvJh",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the location [0.025, 0.185, 0.048, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis label."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "D0GnUOfPGqKhDM9U",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at the co-ordinate [0.935, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The End label on the x-axis."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "fM5QxivDYHjwkz2Q",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the {coordinates}<r>[0.140, 0.283]</r> on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starts point of the line representing the label, 'Heritage Sites'."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "bCsttr1ouGQykR5h",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {starting point of 'Heritage Sites'}<r>[0.140, 0.283]</r> higher than the {starting point of 'World Cultures'}<r>[0.140, 0.578]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "No."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "5V54Q5VpeWVkKMlV",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the line for 'Ancient Architecture'}<r>[0.140, 0.169, 0.935, 0.234]</r> end higher or lower than {the line for 'Heritage Sites'}<r>[0.140, 0.283, 0.935, 0.660]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "CvGsCAY6R5qaRsQs",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {finishing point of 'Heritage Sites'}<r>[0.935, 0.660]</r> higher or lower than {finishing point of 'World Cultures'}<r>[0.935, 0.791]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Lower."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "4H0mOy56MUOc2zuA",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is displayed in the region [0.643, 0.672, 0.963, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the graph with three labels: 'Heritage Sites', 'Ancient Architecture', and 'World Cultures', each paired with a colored marker."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "iKgqMLcjbZ3Xh4gl",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.058, 0.102, 0.093, 0.147]."
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis tick label '95' is displayed in this area."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "3Me8iD6TVhGXI4uB",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted within the region [0.107, 0.869, 0.174, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It depicts the 'Start' label on the x-axis."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "8kOnLyRXKVKy7ONT",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the object}<r>[0.015, 0.321, 0.029, 0.557]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label is located at these coordinates, labeled as \"Sales (in thousands)\"."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "vNnhsYC1RLMRbFGU",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the Period '2014Q2' on the x-axis?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.773, 0.861, 0.821, 0.942]."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "ANyM6dcQvadYaUCp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the line representing {Product B}<r>[0.133, 0.060, 0.944, 0.559]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #1f77b4."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "39O57AZ3nfCFfEU4",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at the '2014Q1' period, {Product A}<r>[0.723, 0.662]</r> or {Product B}<r>[0.723, 0.192]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Product B."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "25XR9ZZ5lQZ3OteE",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the {Product A}<r>[0.871, 0.750]</r> line above or below the {Product B}<r>[0.871, 0.090]</r> line at '2014Q3\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "jcsTjfMShZ01vYis",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt point '2013Q2', was {Product A}<r>[0.502, 0.589]</r> performing better or worse than {Product B}<r>[0.502, 0.339]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Worse."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "OCKQTmUQYz9d2sPW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region <r>[0.133, 0.060, 0.944, 0.765]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two lines representing the sales performance of Product A and Product B from '2012Q1' to '2014Q4'. Product A starts at point [0.133, 0.413] in '2012Q1' and ends at point [0.944, 0.765] in '2014Q4'. Product B starts at point [0.133, 0.559] in '2012Q1' and ends at point [0.944, 0.060] in '2014Q4'."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "xUwVMUzK8GGmOms2",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the elements within the region <r>[0.099, 0.037, 0.248, 0.148]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend of three items: {Product A}<r>[0.144, 0.046, 0.210, 0.069]</r> with a color marker '#d62728'; {Product B}<r>[0.144, 0.081, 0.210, 0.104]</r> with a color marker '#1f77b4'; and a 'Baseline sales'<r>[0.144, 0.116, 0.242, 0.139]</r> label with a color marker '#008000'."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "ElAZgTxJJnjCEncH",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are within the region <r>[0.699, 0.861, 0.969, 0.942]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we find two period labels for the x-axis: '2014Q1' at coordinates [0.699, 0.861, 0.747, 0.942] and '2014Q2' at coordinates [0.773, 0.861, 0.821, 0.942]."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "pMc8ylBVAgMhuYwa",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the y-axis label}<r>[0.015, 0.254, 0.033, 0.717]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future technological development prospects."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "T10BXEMtThlriCGe",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the third line in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.132, 0.116, 0.272, 0.133]."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "Z4Ix0SFpWyKwpgxO",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the key of the chart line}<r>[0.156, 0.057, 0.910, 0.267]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "dwIazxhVDEGA3AsY",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled 'Artificial Intelligence'}[0.231, 0.118] situated above or below {the point labeled 'Prevision Tech Trend'}[0.231, 0.240] in the year 2001?"
+            },
+            {
+                "from": "gpt",
+                "value": "Below."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "9X6QiFrnWc3j0Aqe",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas Quantum Computing or Prevision Tech Trend more prevalent in 2008 according to {their respective points}[0.759, 0.092] and [0.759, 0.118]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "XtS4qQeejsYUosKL",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subject showed more growth between 2002 and 2010, {Artificial Intelligence}[0.306, 0.127] to [0.910, 0.162] or {Quantum Computing}[0.306, 0.197] to [0.910, 0.057]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Quantum Computing."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "pr4vUZakz36pcxdW",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects within region <r>[0.132, 0.064, 0.274, 0.133]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legends for 3 lines indicating the 'Artificial Intelligence', 'Quantum Computing', and 'Prevision Tech Trend' trends from 2000 to 2010. Each legend is coupled with a colored handle for respective identification."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "rPZZPzcHup8asDhb",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is contained in the region <r>[0.251, 0.019, 0.815, 0.042]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Future Technological Development Prospects (2000-2010)\", is located in this region. It is written in color \".15\" and has no rotation."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "lNmh3NeAYgT6uB9b",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region <r>[0.156, 0.092, 0.910, 0.267]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the plotted line for the 'Prevision Tech Trend'. The line is colored '#008000' and has a '-.' linestyle. Furthermore, it is dotless, meaning it has 'None' instead of markers."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "DZCzrm7Gi87lbDDp",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the second object on the y-axis with coordinates [0.043, 0.808, 0.089, 0.838] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis tick label '0.55'."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "UzQcigRBXBZ87hno",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordinate of the fifth point on the 'Innovation Industry' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "[0.649, 0.231]."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "e5HO6CJXX62kPAQ4",
+        "Chart_type": "line",
+        "QA_type": "Referring QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is {the x-axis tick label} at [0.623, 0.912, 0.675, 0.942]?"
+            },
+            {
+                "from": "gpt",
+                "value": "2020."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "gVcIFMx4EOyW0cQy",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the point labeled '2025'}[0.760, 0.399] on the 'Knowledge Economy' line higher or lower than {the point labeled '2025'}[0.760, 0.147] on the 'Innovation Industry' line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Higher."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "4DR1QWTX8yeyRgKj",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs {the line for 'Knowledge Economy'}[0.206, 0.315, 0.871, 0.820] rising or falling from {point 2000}[0.206, 0.820] to {point 2030}[0.871, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Falling."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "P7uBcW830trbt7Ss",
+        "Chart_type": "line",
+        "QA_type": "Relationship QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line is higher at {point 2020}[0.649, 0.231], Knowledge Economy or Innovation Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Innovation Industry."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "vcP4iRMiGRqIU4ot",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within [0.729, 0.781, 0.973, 0.893]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend for the chart, including the labels for {Knowledge Economy}[0.785, 0.790, 0.966, 0.814], {Innovation Industry}[0.785, 0.825, 0.954, 0.849], and {Overall growth trend}[0.785, 0.860, 0.965, 0.883], each associated with a different marker color."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "b8AxkwfRmyQ8Q07Y",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the coordinates [0.206, 0.315, 0.871, 0.820]."
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the range of the line for the 'Knowledge Economy', with the y-coordinates indicating the line's height at various points from 2000 to 2030."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "YQeMoJeR8wBgXMx6",
+        "Chart_type": "line",
+        "QA_type": "Regional Description QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the coordinates [0.206, 0.315, 0.871, 0.820]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encompass the entirety of two lines: {the 'Knowledge Economy'} line and {the 'Innovation Industry'} line, each marked by a series of points representing the respective growth rates per year from 2000 to 2030."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "5wlAtZdZcX9oQD7U",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the chart into a standard csv format with rows representing the different investment sectors and columns representing the different lambda values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a structured table, I'll start by identifying {the x-axis tick labels}<r>[0.114, 0.903, 0.233, 0.978], [0.349, 0.903, 0.451, 0.930], [0.579, 0.903, 0.660, 0.930], [0.808, 0.903, 0.932, 0.930]</r>, representing the different investment sectors as rows. Then, {the legend texts}<r>[0.807, 0.202, 0.962, 0.238], [0.807, 0.260, 0.947, 0.297], [0.807, 0.390, 0.960, 0.426]</r> represent the different lambda values as column headers. I will then locate each {bar height}<r>[0.143, 0.582, 0.210, 0.610], [0.377, 0.225, 0.445, 0.285], [0.619, 0.230, 0.657, 0.278], [0.859, 0.701, 0.930, 0.824]</r> and convert them to return rate values based on the {y-axis tick labels}<r>[0.050, 0.080, 0.074, 0.121], [0.050, 0.206, 0.078, 0.243], [0.050, 0.332, 0.079, 0.369], [0.050, 0.457, 0.079, 0.492]</r>.\nBased on the above thought, the answer is:\nInvestment Sectors,\u03bb=0.06, \u03bb=0.08, \u03bb=0.1\nHigh Growth Stocks,23.5%, 22.5%, 25%\nBonds, 9.5%, 10.5%, 8.0%\nReal Estate, 8.0%, 9.0%, 10.5%\nMutual Funds, 10.5%, 10.0%, 23.5%\nHigh Yield Bonds, 25%, 24.5%, 9.0%"
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "Fwb5nWMo1XrZE9dp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this data, which type of investment seems to have shown the most stability over different lambda values and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which investment shows the most stability, I need to examine the fluctuation of return rates across different lambda values. I look at the {bar heights for High Growth Stocks}<r>[0.143, 0.422, 0.210, 0.478], [0.143, 0.478, 0.210, 0.532], [0.143, 0.532, 0.210, 0.610]</r>, {Bonds}<r>[0.377, 0.170, 0.445, 0.225], [0.377, 0.225, 0.445, 0.285], [0.377, 0.285, 0.445, 0.361]</r>, {Real Estate}<r>[0.619, 0.187, 0.657, 0.230], [0.619, 0.230, 0.657, 0.278], [0.619, 0.278, 0.657, 0.361]</r>, {Mutual Funds}<r>[0.808, 0.238, 0.875, 0.315], [0.808, 0.315, 0.875, 0.372], [0.808, 0.372, 0.875, 0.463]</r> and {High Yield Bonds}<r>[0.149, 0.610, 0.210, 0.696], [0.149, 0.696, 0.210, 0.766], [0.143, 0.766, 0.210, 0.853]</r>. Upon comparing the fluctuations in returns, the rates for High Growth Stocks, Real Estate, and High Yield Bonds fluctuate significantly. However, the rates for Bonds shows minimal fluctuations across different lambda values indicating a higher degree of stability.\nBased on the above thought, the answer is:\nBased on the return rates in the chart, Bonds appear to be the most stable investment sector. Its return rates fluctuate minimally across different lambda values, suggesting a lower risk level compared to other investments like High Growth Stocks, Real Estate, Mutual Funds, and High Yield Bonds, which show significant variation in returns."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "mmMpUvJHlt6xG5WR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest return rate for Mutual Funds under any of the lambda values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest return rate for 'Mutual Funds', I need to look at {the bar heights for Mutual Funds}<r>[0.808, 0.238, 0.875, 0.315], [0.808, 0.315, 0.875, 0.372], [0.808, 0.372, 0.875, 0.463]</r> corresponding to the three lambda values. Converting them into return rate values based on {the y-axis tick labels}<r>[0.050, 0.080, 0.074, 0.121], [0.050, 0.206, 0.078, 0.243], [0.050, 0.332, 0.079, 0.369], [0.050, 0.457, 0.079, 0.492]</r>, the values are 10.5%, 10.0%, and 23.5%. The highest among these is 23.5% which corresponds to lambda value \u03bb=0.1.\nBased on the above thought, the answer is:\n23.5%"
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "vfXZIUazkK0xsDvN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret the line plot data into a readable table format using 'Quarters' as columns and 'Parameters' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line plot, I first look at {the x-axis labels}[[0.158, 0.911, 0.193, 0.934], [0.305, 0.911, 0.340, 0.934], [0.452, 0.911, 0.488, 0.934], [0.599, 0.911, 0.635, 0.934], [0.747, 0.911, 0.782, 0.934], [0.894, 0.911, 0.929, 0.934]], which represent the column headers or Quarters. Next up, I recognize {the legend texts}[[0.164, 0.059, 0.200, 0.089], [0.164, 0.103, 0.186, 0.133], [0.164, 0.147, 0.262, 0.177], [0.164, 0.191, 0.291, 0.221], [0.164, 0.235, 0.269, 0.265], [0.164, 0.279, 0.318, 0.309]] representing the row labels or Parameters including 'Process Efficiency', 'Employee Productivity', 'Customer Satisfaction', 'Operational Costs', and 'Technological Advancement'. To fill the table, I then locate {the line points}[[0.175, 0.537], [0.323, 0.467], [0.470, 0.518], [0.617, 0.686], [0.764, 0.563], [0.911, 0.161]] for each parameter at the respective quarters and extract the values indicated.\nBased on the above thought, the answer is:\nParameters,1971,1981,1991,2001,2011,2021\nProcess Efficiency,63000,73000,78000,80000,83000,80500\nEmployee Productivity,61900,81500,88000,94000,99500,92000\nCustomer Satisfaction,50200,62500,76000,81000,85000,87500\nOperational Costs,2520,19750,29700,36800,44000,48500\nTechnological Advancement,23000,39000,55000,69500,73500,115700"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "LDSFPa5QWnCadiV7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trends in the line chart, which parameter shows a consistent upward trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the parameter with a consistent upward trend, I need to analyze the line plots for each parameter. Inspecting {the line for 'Process Efficiency'}[0.175, 0.537, 0.911, 0.624], I notice fluctuations and no clear consistent upward trend. Observing {the line for 'Employee Productivity'}[0.175, 0.467, 0.911, 0.628], I see fluctuations. Analyzing {the line for 'Customer Satisfaction'}[0.175, 0.518, 0.911, 0.679], I observe a mixed trend. Reviewing {the line for 'Operational Costs'}[0.175, 0.686, 0.911, 0.884], I do not see any consistent upward trend. Lastly, when looking at {the line for 'Technological Advancement'}[0.175, 0.563, 0.911, 0.161], I see a gradual and consistent upward trend.\nBased on the above thought, the answer is:\nFrom the line chart trends, 'Technological Advancement' demonstrates a consistent upward trend over the years. This can be seen as its line plot consistently ascends over the quarters, indicating a steady increase in technological advancement."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "3Pp9mrTrtJXnwnBb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total improvement in 'Technological Advancements' from 1971 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total improvement in 'Technological Advancements' from 1971 to 2021, I need to track the line plot for 'Technological Advancements'. Tracing {the line plot for 'Technological Advancement'}[[0.175, 0.537, 0.911, 0.161]], I extract the values for the year 1971 and 2021 as 23000 {at 1971}[0.175, 0.796] and 115700 {at 2021}[0.911, 0.161]. The difference between these two values gives the total improvement.\nBased on the above thought, the answer is:\n92700."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "BDThHjJMmFKRVDia",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the annual donations for the both charities in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I check the {x-axis tick labels}[[0.146, 0.904, 0.175, 0.924], [0.287, 0.904, 0.316, 0.924], [0.427, 0.904, 0.457, 0.924], [0.568, 0.904, 0.598, 0.924], [0.709, 0.904, 0.738, 0.924], [0.850, 0.904, 0.879, 0.924], [0.991, 0.904, 1.020, 0.924]], which represent the years. The {legend texts}[[0.176, 0.141, 0.325, 0.167], [0.176, 0.179, 0.300, 0.205], [0.176, 0.217, 0.308, 0.242], [0.176, 0.254, 0.285, 0.280]] are used for the charity names. The {line points}[[0.160, 0.237, 0.865, 0.649], [0.160, 0.155, 0.865, 0.546], [0.160, 0.155, 0.865, 0.608], [0.160, 0.505, 0.865, 0.855]] of each line are the donation values.\nBased on the above thought, the answer is:\nYear,Consistent Expansion,Gradual Recovery,Fluctuating Growth,Slight Decrease\n2000,15,20,17,22\n2001,17,21,18,21\n2002,19,22,20,19\n2003,21,24,21,18\n2004,23,26,24,16\n2005,25,28,26,14\n2006,27,30,29,12\n2007,29,33,32,10\n2008,31,35,34,9\n2009,33,37,36,7\n2010,35,39,39,5"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "BY7WYvQHL6qKYhKe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which charity has seen a more consistent increase in donations over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I examine the donation trend of each charity. The {line for Consistent Expansion} [0.160, 0.237, 0.865, 0.649] shows a consistent upward trend with no major drops, indicating that donations for this charity have been consistently increasing. On the other hand, the {line for Gradual Recovery} [0.160, 0.155, 0.865, 0.546] shows fluctuations with a few dips, suggesting its donations haven't increased as consistently.\nBased on the above thought, the answer is:\nThe charity represented by the line 'Consistent Expansion' has seen a more consistent increase in donations over the years. Its line plot shows a steady upward trend with no major drops over the years, indicating that it has been consistently increasing its donations."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "IMvOo51PrmSAfmj1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total amount of donation for both charities from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total donation, I need to sum up the y-values of all points on the {line for the Consistent Expansion}[0.160, 0.237, 0.865, 0.649] and {line for the Gradual Recovery} [0.160, 0.155, 0.865, 0.546] across all years represented by the {x-axis tick labels}[[0.146, 0.904, 0.175, 0.924], [0.287, 0.904, 0.316, 0.924], [0.427, 0.904, 0.457, 0.924], [0.568, 0.904, 0.598, 0.924], [0.709, 0.904, 0.738, 0.924], [0.850, 0.904, 0.879, 0.924], [0.991, 0.904, 1.020, 0.924]].\nBased on the above thought, the answer is:\n623"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "DeFWn47TEFRQTvNm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table from the line chart that shows the Patent Applications, Patent Grants, and Trademark Registrations for the respective technology innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do so, I would first look at {the x-axis labels}<r>[0.074, 0.911, 0.092, 0.934]</r> and identify these as the Patent Applications, Patent Grants, and Trademark Registrations. Next, I would identify the line points for each technology innovation. For AI, {the points}<r>[0.066, 0.455, 0.888, 0.661]</r> are portrayed. Similarly, Blockchain has {its points}<r>[0.066, 0.309, 0.888, 0.222]</r>, Robotics technologies have {their points displayed}<r>[0.066, 0.342, 0.889, 0.777]</r>, NanoTech has {the points}<r>[0.066, 0.233, 0.888, 0.469]</r> and SpaceTech nuances have {their points}<r>[0.066, 0.349, 0.888, 0.282]</r>.\nBased on the above thought, the answer is:\nTechnologies, Patent Applications, Patent Grants, Trademark Registrations\nAI, 5000, 4000, 2000\nBlockchain, 4200, 2800, 2600\nRobotics, 3300, 2900, 2000\nNanoTech, 2400, 2200, 1800\nSpaceTech, 2000, 1200, 1500"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "2cTOJVkk3ov4b0Am",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology innovation is showing the fastest growth in patent applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the progress of each technology, I look at the points on each line to identify the patent application counts in recent years. {AI}<r>[0.066, 0.455, 0.888, 0.661]</r> shows a steep climb indicating aggressive growth in patent applications. The lines for Blockchain<r>[0.066, 0.309, 0.888, 0.222]</r>, Robotics<r>[0.066, 0.342, 0.889, 0.777]</r>, NanoTech<r>[0.066, 0.233, 0.888, 0.469]</r>, and SpaceTech<r>[0.066, 0.349, 0.888, 0.282]</r> also show progress but no other category is increasing as rapidly as AI Applications.\nBased on the above thought, the answer is:\nBased on the chart, AI technology innovation is showing the fastest growth in patent applications. The rate of increase for AI Applications is clearly more pronounced compared to other domains, suggesting more aggressive research and development efforts in the AI segment."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "9pnOcPSLLaOGXQhx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest number of patent applications in any technology innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum number of patent applications, I look at {the points for each line}[0.066, 0.455, 0.888, 0.661], [0.066, 0.309, 0.888, 0.222], [0.066, 0.342, 0.889, 0.777], [0.066, 0.233, 0.888, 0.469], [0.066, 0.349, 0.888, 0.282]. From those, I can tell that AI had the highest number of patent applications at 5000.\nBased on the above thought, the answer is:\n5000"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "EzrtCrN5VItQo4lo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you create a table with the Years as rows and regions(Global, Asia, Africa, America) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a table, I first identify {year ticks}<r>[0.159, 0.911, 0.194, 0.934], [0.234, 0.911, 0.269, 0.934], [0.308, 0.911, 0.343, 0.934], [0.383, 0.911, 0.418, 0.934], [0.458, 0.911, 0.493, 0.934], [0.532, 0.911, 0.567, 0.934], [0.607, 0.911, 0.642, 0.934], [0.681, 0.911, 0.716, 0.934], [0.756, 0.911, 0.791, 0.934]</r> as the row headers representing the years. \nSubsequently, I locate {the legend labels}<r>[0.910, 0.489, 0.963, 0.519], [0.910, 0.533, 0.945, 0.563], [0.910, 0.577, 0.957, 0.607], [0.910, 0.621, 0.978, 0.651]</r> which are used as the column headers representing the different regions.\nLastly, to obtain the values, I find {the line points for each region}<r>[0.177, 0.130, 0.773, 0.426], [0.177, 0.536, 0.773, 0.632], [0.177, 0.785, 0.773, 0.823], [0.177, 0.574, 0.773, 0.785]</r> corresponding to each year and extract the annotated values.\nBased on the above thought, the answer is:\nYear,Global,Asia,Africa,America\n2000,100000,55000,15000,30000\n2001,102000,57000,15200,30800\n2002,98000,59000,16000,23000\n2003,105000,61000,16500,28000\n2004,110000,62000,18000,30000\n2005,132000,64500,19000,48000\n2006,140000,68000,21000,52000\n2007,160000,70000,23000,67000\n2008,150000,75000,20800,54000"
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "BBV7k5EV05Poq1Vd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the trending behavior of each market from the line chart and indicate which market has the most positive trend overall."
+            },
+            {
+                "from": "gpt",
+                "value": "To examine the trends of each market, I inspect the individual line plots for Global, Africa, America and Asia. Observing {the line plot for the Global market}<r>[0.177, 0.130, 0.773, 0.426]</r>, I notice a general upward trend with some fluctuations. Likewise, {Asia's line plot}<r>[0.177, 0.536, 0.773, 0.632]</r> displays a continual increase in market value.\nContrasting these, {the line plot for Africa}<r>[0.177, 0.785, 0.773, 0.823]</r> shows a noticable sustained upward slope up until the year 2007 after which it dips slightly. Lastly, {the line plot for America}<r>[0.177, 0.574, 0.773, 0.785]</r> fluctuates initially, witnesses a sudden rise around the year 2005 and then drops sharply after 2007.\nBased on the above thought, the answer is:\nThe African market shows the most positive trend overall, as it exhibits a steady, sustained upward growth throughout the years. Even though it dips slightly toward the end, the overall growth is substantial compared to the other markets. The consistent upward trend indicates positive progression and growth."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "ppoBaj9cvNk9u1jV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what is the increase in Market Value for the Global market between 2001 and 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Market Value for the Global market between 2001 and 2003, I will locate {the line points for Global corresponding to 2001}<r>[0.251, 0.407]</r> and {2003}<r>[0.400, 0.393]</r>. The Market Value for 2001 is 102,000 and for 2003 it is 105,000. Therefore, the increase in Market Value is 105,000 - 102,000 = 3,000.\nBased on the above thought, the answer is:\n3000."
+            }
+        ],
+        "id_raw": 4
+    },
+    {
+        "id": "CL21EFVxFjOYswW4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data from the grouped bar chart into a CSV format with lifestyles as columns and social activities as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart into a CSV format, first, I need to recognize {the x-axis labels}<r>[0.106, 0.923, 0.131, 0.943, 0.267, 0.923, 0.292, 0.943, 0.428, 0.923, 0.453, 0.943, 0.589, 0.923, 0.614, 0.943, 0.750, 0.923, 0.775, 0.943]</r> which represent the row labels or social activities. Second, I identify {the legend}<r>[0.060, 0.033, 0.313, 0.191]</r> as the column headers representing the different lifestyles. To populate the values for each Lifestyle within each social activity, I\u2019d correlate {the heights of the bars}<r>[0.119, 0.754, 0.924, 0.832, 0.119, -5.841, 0.924, -3.764, 0.119, -0.338, 0.924, 0.648]</r> with their corresponding values annotated on the {y-axis labels}<r>[0.054, 0.910, 0.041, 0.902, 0.047, 0.922, 0.054, 0.806, 0.035, 0.798, 0.047, 0.818]</r> for each bar that corresponds to each lifestyle per social activity.\nBased on the above thought, the answer is:\nSocial Activities,Lifestyle1,Lifestyle2,Lifestyle3\nJostling,900,800,700\nCycling,800,700,600\nHiking,650,550,500\nJogging,550,450,400\nDancing,450,350,300"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "r3OYUbSGmzB27VRi",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the bar chart, which lifestyle has the highest involvement in all social activities and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which lifestyle has the highest involvement in all social activities, I need to analyze the heights of the bars for each lifestyle. By inspecting the graph, I can see that in all social activities such as {Jostling}<r>[0.119, 0.754]</r>, {Cycling}<r>[0.280, 0.816]</r>, {Hiking}<r>[0.441, 0.806]</r>, {Jogging}<r>[0.602, 0.795]</r>, and {Dancing}<r>[0.763, 0.780]</r>, the bar of Lifestyle1 is always taller than the bars of Lifestyle2 and Lifestyle3. This indicates that for each activity, Lifestyle1 has the highest count of people engaged.\nBased on the above thought, the answer is:\nAccording to the trends shown in the bar chart, Lifestyle1 has the highest involvement in all social activities. This is evident as, for each social activity on the x-axis, the bar representing Lifestyle1 is always the tallest, indicating it has the highest count of people taking part in the respective activities. This suggests that Lifestyle1 tends to be more active or involved across all the social activities surveyed."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "aPNWX474rZ3eZ1g5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of people across all lifestyles engaged in hiking?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total number of people engaged in hiking across all lifestyles, I have to find {the bars corresponding to hiking}<r>[0.428, 0.923, 0.453, 0.943]</r> for each lifestyle and get their corresponding values from the {y-axis}<r>[0.054, 0.910, 0.041, 0.902, 0.047, 0.922, 0.054, 0.806, 0.035, 0.798, 0.047, 0.818]</r>. For Lifestyle1 the value is 650, for Lifestyle2 the value is 550 and for Lifestyle3 the value is 500. Summing up these values 650 + 550 + 500 gives us the total.\nBased on the above thought, the answer is:\n1700."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "KTqdAF6A6f31uX2N",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data represented in the line chart into a table, with lambda values as the rows and types of attacks as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I should first identify {the x-axis tick labels}[[0.121, 0.910, 0.138, 0.933], [0.252, 0.910, 0.270, 0.933], [0.382, 0.910, 0.399, 0.933], [0.512, 0.910, 0.529, 0.933], [0.642, 0.910, 0.659, 0.933], [0.773, 0.910, 0.791, 0.933]] which represent the row headers or lambda values. {The legend texts}[[0.883, 0.410, 0.969, 0.452], [0.883, 0.466, 0.945, 0.513]] can serve as column headers, denoting the types of attacks (Phishing Attacks and Data Breaches). To fill in the table, I will examine the line points associated with each type of attack, for example, {the line points for Phishing Attacks}[[0.130, 0.381], [0.444, 0.299], [0.628, 0.236], [0.758, 0.217], [0.859, 0.202], [0.942, 0.158]] and {the line points for Data Breaches}[[0.130, 0.723], [0.444, 0.552], [0.628, 0.470], [0.758, 0.432], [0.859, 0.407], [0.942, 0.381]], and extract the values annotated for each lambda value.\nBased on the above thought, the answer is:\nLambda Values,Phishing Attacks,Data Breaches\n10,0.0001,0.01\n20,0.001,0.03\n30,0.003,0.07\n40,0.005,0.09\n50,0.007,0.11\n60,0.01,0.2"
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "yVgsvPPKt7IvYrY8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this line chart, which type of attack seems to be influenced more by the level of protection (lambda values)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare how each type of attack changes as lambda changes. For this, I note the points for both {Phishing Attacks}[[0.130, 0.381], [0.444, 0.299], [0.628, 0.236], [0.758, 0.217], [0.859, 0.202], [0.942, 0.158]] and {Data Breaches}[[0.130, 0.723], [0.444, 0.552], [0.628, 0.470], [0.758, 0.432], [0.859, 0.407], [0.942, 0.381]]. It's clear that as lambda increases, both types of attacks decrease, but the decline in data breaches is more steep compared to phishing attacks. This suggests that the number of data breaches is more heavily influenced by the level of protection system.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the number of Data Breaches seems to be more influenced by the level of protection (lambda values). As lambda values increase, we witness a steep decline in the count of Data Breaches as compared to Phishing Attacks. This indicates the count of Data Breaches is more sensitive to the level of protection in the system."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "skRSrpSPl149IXkf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the precise difference between phishing attacks and data breaches when the lambda value is 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between phishing attacks and data breaches when lambda equals 50, I need to first locate {the point for Phishing Attacks at lambda = 50}[0.859, 0.202] which corresponds to a value of 0.007. Similarly, {the point for Data breaches at lambda = 50}[0.859, 0.476] corresponds to a value of 0.11. The difference can thus be calculated as 0.11 - 0.007 = 0.103.\nBased on the above thought, the answer is:\n0.103."
+            }
+        ],
+        "id_raw": 6
+    },
+    {
+        "id": "vHRjBMyV022APeUq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the graph which shows social indicators and their values, into a table format with Social Indicators as rows and types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transcribe the data from the line chart into tabular format, I begin by spotting {the x-axis tick labels}[[0.050, 0.772, 0.128, 0.901], [0.488, 0.772, 0.586, 0.934]] which account for the row labels or social indicators. Next, I recognize {the legend texts}[[0.892, 0.415, 0.967, 0.445], [0.892, 0.459, 0.946, 0.489], [0.892, 0.503, 0.970, 0.533], [0.892, 0.547, 0.965, 0.577]] that act as the column headers symbolizing the different types. To fill the values, I identify {the line points}[[0.089, 0.306], [0.537, 0.418], [0.089, 0.306], [0.537, 0.343], [0.089, 0.263], [0.537, 0.261], [0.089, 0.481], [0.537, 0.584]] for each type of social indicator and extract the correlated values.\nBased on the above thought, the answer is:\nSocial Indicator,Standard,Erratic,Decrease,Unstable\nActivity Level,1.00e-06,1.00e-06,5.72e-06,7.79e-10\nSocial Interaction,1.00e-08,2.23e-07,6.31e-06,1.14e-11"
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "gxAf7CCauQcw5g9S",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph trends, which Social Indicator appears to be more erratic?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most erratic Social Indicator, I'll assess the changes in the line plots for both. Examine {the line for Activity_Level}[0.089, 0.306, 0.537, 0.418]. It shows a sharp increase then a decrease, which signifies high volatility.\nThen look at {the Social_Interaction line}[0.089, 0.418, 0.537, 0.583]. It exhibits a consistent trend with minimal fluctuations, implying low volatility.\nBased on the above thought, the answer is:\nAccording to the graph trends, Activity_Level appears to be the more erratic Social Indicator. It shows significant fluctuations and an inconsistent pattern across the graph, suggesting high volatility in its trends."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "w2xqzP4AwIlUch6o",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio between Standard and Decrease values for Social_Interaction?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the ratio between Standard and Decrease values for Social_Interaction, I need to trace {the line points for Standard} [0.537, 0.418] and {the line points for Decrease}[0.537, 0.261] for Social_Interaction. The Standard value is 1.00e-08 and the Decrease value is 6.31e-06. The ratio is therefore 1.00e-08 / 6.31e-06, which equals 1.59e-03.\nBased on the above thought, the answer is:\n1.59e-03."
+            }
+        ],
+        "id_raw": 7
+    },
+    {
+        "id": "VWNywHsqRi3r78C6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the performance scores of Microsoft and Google across the six different areas into a table with Areas as rows and Companies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis tick labels}[[0.071, 0.911, 0.080, 0.934], [0.202, 0.911, 0.211, 0.934], [0.334, 0.911, 0.342, 0.934], [0.465, 0.911, 0.474, 0.934], [0.596, 0.911, 0.605, 0.934], [0.727, 0.911, 0.736, 0.934]] as the row labels representing the different performance areas. Next, I locate {the legend texts}[[0.868, 0.489, 0.943, 0.519], [0.868, 0.618, 0.943, 0.645]] as the column headers representing the companies Microsoft and Google, respectively. I then locate the {line points for Microsoft}[[0.071, 0.056], [0.202, 0.260], [0.334, 0.383], [0.465, 0.481], [0.596, 0.546], [0.727, 0.617]] and {line points for Google}[[0.071, 0.546], [0.202, 0.618], [0.334, 0.638], [0.465, 0.670], [0.596, 0.694], [0.727, 0.734]] for all the performance categories and extract the values to populate the table.\nBased on the above thought, the answer is:\nArea,Microsoft,Google\nSteady decline,5.847,2.43\nSteep decline,4.423,1.931\nErratic behavior,3.567,1.789\nStable growth,2.887,1.569\nInconsistent development,2.432,1.398\nPeriodic spikes,1.937,1.123"
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "HfLKekfN4wz7yjdN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which company shows better performance overall and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the total scores for both companies across all areas. For Microsoft, {the line points}[[0.071, 0.056], [0.202, 0.260], [0.334, 0.383], [0.465, 0.481], [0.596, 0.546], [0.727, 0.617]] add up to 5.847 + 4.423 + 3.567 + 2.887 + 2.432 + 1.937 = 21.093. For Google, {the line points}[[0.071, 0.546], [0.202, 0.618], [0.334, 0.638], [0.465, 0.670], [0.596, 0.694], [0.727, 0.734]] add up to 2.43 + 1.931 + 1.789 + 1.569 + 1.398 + 1.123 = 10.24. Clearly, Microsoft's total score is higher.\nBased on the above thought, the answer is:\nMicrosoft shows better performance overall because the sum of its scores across all the six areas is higher than that of Google. This is suggestive of overall better management and organizational leadership in Microsoft as per the dimensions considered here."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "ihLbjUUdYJ7XbyKH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in scores for Microsoft and Google in the area of \"Stable growth\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consult {the line point for Microsoft in Stable growth}[[0.465, 0.481]] which is 2.887 and {the line point for Google in Stable growth}[[0.465, 0.670]] which is 1.569. The difference is calculated as 2.887 - 1.569 = 1.318.\nBased on the above thought, the answer is:\n1.318."
+            }
+        ],
+        "id_raw": 9
+    },
+    {
+        "id": "amtzlE9R2loE9g0b",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from the chart in a table format with Years as columns and Transaction Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I will first append {the x-axis labels}[[0.091, 0.933, 0.126, 0.951], [0.258, 0.933, 0.293, 0.951], [0.425, 0.933, 0.460, 0.951], [0.592, 0.933, 0.627, 0.951], [0.759, 0.933, 0.794, 0.951], [0.926, 0.933, 0.961, 0.951]] as the column headers which represent the years. Secondly, I will use the labels of the legend {Regular Cryptocurrency, Stablecoin, Traditional Currency, Blockchain Transactions}[[0.118, 0.065, 0.283, 0.082], [0.118, 0.091, 0.191, 0.108], [0.118, 0.117, 0.258, 0.135], [0.118, 0.143, 0.285, 0.161]] as row headers. Next step is to locate the data points on the plot for each type of transaction using the lines for {Regular Cryptocurrency}[[0.109, 0.093, 0.943, 0.790]], {Stablecoin}[[0.109, 0.441, 0.943, 0.877]], {Traditional Currency}[[0.109, 0.842, 0.943, 0.877]], and {Blockchain Transactions}[[0.109, 0.494, 0.943, 0.572]] to assess their respective transaction volumes per year.\nBased on the above thought, the answer is:\nTransation Type,2015,2016,2017,2018,2019,2020\nRegular Cryptocurrency,1.5,1.6,2.5,6.3,7.8,9.5\nStablecoin,0.5,1.5,2.7,2.5,3.5,5.5\nTraditional Currency,0.7,0.8,0.9,0.7,0.6,0.5\nBlockchain Transactions,4.5,4.4,4.3,4.1,4.0,4.9"
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "N8kPrx0jfm6Alzoe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which type of transaction has shown the most growth from 2015 to 2020? Can you explain how you came to this conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which type of transaction showed the most growth from 2015 to 2020, each line's trend needs to be interpreted. Looking at the line for {Regular Cryptocurrency}[0.09, 0.790, 0.943, 0.093], the value starts at 1.5 in 2015 and ends at 9.5 in 2020, which shows a significant increase. Observing the {Stablecoin}[0.09, 0.877, 0.943, 0.441] line, it begins at 0.5 in 2015 and increases to 5.5 in 2020\u2014also a significant growth, but not as much as the Regular Cryptocurrency. The lines for {Traditional Currency}[0.109, 0.850, 0.943, 0.877] and {Blockchain Transactions}[0.109, 0.529, 0.943, 0.494] both show slight declines from 2015 to 2020.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Regular Cryptocurrency transactions have shown the most growth from 2015 to 2020. This is evident from the line plot which shows that its value has increased from 1.5 to 9.5 over this period. This level of growth is more substantial than any of the other types of transactions over the same period."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "OgluXybT2FZRrfCx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total volume of transactions for the Blockchain Transactions from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total volume of transactions for the Blockchain Transactions from 2015 to 2020, I will add up all the respective y-values, which I can find at the point coordinates {[0.109, 0.529], [0.276, 0.537], [0.442, 0.546], [0.609, 0.563], [0.776, 0.572], [0.943, 0.494]} for each year. These correspond to 4.5, 4.4, 4.3, 4.1, 4.0, and 4.9 billion transactions respectively. Summing these up, we get 4.5 + 4.4 + 4.3 + 4.1 + 4.0 + 4.9 = 26.2 billion transactions.\nBased on the above thought, the answer is:\n26.2"
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "BKx9hN5fMeedusCP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide me with a table containing the given data for different Age Groups and each Disease Category?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to extract the data and structure it into a table, I first need to look at {The x-axis labels}<r>[0.101, 0.907, 0.119, 0.930]</r>, which represent 'Age Groups'. Then, I have to identify the different disease categories from {The corresponding legend entries}<r>[0.752, 0.472, 0.985, 0.656]</r>. After that, I scrutinize each of the {data points}<r>[0.110, 0.740]</r> for every category at the respective age groups.\nBased on the above thought, the answer is:\nAgeGroup, Eradicated, PreventableGrowing, PreventableStable, Aging\n20,4898,3281,6639,5321\n40,10298,6876,12576,11003\n60,15098,7741,16245,10004\n80,25543,15242,4631,6254\n100,30983,18254,6328,7312\n120,44651,25042,15321,13642"
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "CwMwYFJqwMR0Oy6q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category seems to have experienced a significant increase in instances over the age groups according to the chart? Support your answer with explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "Determining the category that has experienced a significant increase in instances requires an analysis of the trend lines for each category. Upon examining the {line for 'Eradicated'}<r>[0.110, 0.060, 0.711, 0.740]</r>, I notice a persistent upward trend from 4898 instances at age group 20 to 44651 instances at age group 120. This indicates a substantial increase. The {lines for the other categories}<r>[0.110, 0.238, 0.711, 0.863]</r>,<r>[0.110, 0.371, 0.711, 0.757]</r>,<r>[0.110, 0.425, 0.711, 0.714]</r> do show increases over time, but are not as pronounced as 'Eradicated'.\nBased on the above thought, the answer is:\nThe 'Eradicated' category seems to have experienced a significant increase in instances over the age groups according to the chart. This is supported by the persistent upward trend observed in the line plot for 'Eradicated', which indicates a substantial increase from 4898 instances at age group 20 to 44651 instances at age group 120. This increase is more prominent when compared to the other categories."
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "nbNSYYWL4wriCzts",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of instances for the 'Aging' category across all age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total instances for the 'Aging' category across all age groups, I need to analyse the {line plots}<r>[0.110, 0.425, 0.711, 0.714]</r> associated with the 'Aging' category, extracting the point values: 5321, 11003, 10004, 6254, 7312, 13642. Adding these together will give me the total instances.\nBased on the above thought, the answer is:\n56336"
+            }
+        ],
+        "id_raw": 11
+    },
+    {
+        "id": "OgzwA2UyAifJ598A",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table representing the consumer behaviors and their trends over the current and last months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate the structured table, I must utilize the information embedded in the line chart. I start by interpreting {the x-axis labels}[0.498, 0.943, 0.559, 0.975] which denote months, specifically 'Current Month' and 'Last Month'. Next, identifying {the legend}[0.764, 0.048, 0.977, 0.232] presents various consumer behaviors such as Online Shopping, In-store Shopping, and others, which will form the rows of the table. By pinpointing {the data points on each line}[[0.079, 0.142], [0.154, 0.166], ..., [0.909, 0.798]], I can extract the number of consumers for each behavior in each month.\nBased on the above thought, the answer is:\nBehavior,Last Month,Current Month\nOnline Shopping,450000,500000\nIn-store Shopping,300000,250000\nStreaming Services,600000,650000\nFood Delivery,350000,370000\nTravel Booking,150000,175000"
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "sCws1gP6uxeQPGPk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich consumer behavior indicates a stable growth without any decline over the periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify a behavior that shows consistent growth with no decline, I need to analyze the trajectory of each line in the chart. Observing {the line for Streaming Services}[0.071, 0.034, 0.985, 0.895], it displays a continuous upward trend from one month to the next without any downturns, represented by a linear ascent in the line graph from the start to the end. This shows a stable growth indicating its consistent popularity and usage among consumers.\nBased on the above thought, the answer is:\nStreaming Services indicates a stable growth. It has a continuous upward trajectory over the months, showing no decline and reflecting its growing popularity."
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "EJ3e3kveUnXLA1rM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase for Travel Booking from Last Month to Current Month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I must first determine the values for Travel Booking from Last Month and Current Month. Observing {the Travel Booking line points}[0.834, 0.378, 0.909, 0.368], I gather the values are 150000 for Last Month and 175000 for Current Month. Using the formula for percentage increase (New Value - Old Value) / Old Value * 100, the calculation would be (175000 - 150000) / 150000 * 100 = 16.67%.\nBased on the above thought, the answer is:\n16.67%"
+            }
+        ],
+        "id_raw": 12
+    },
+    {
+        "id": "FVkzRmDm17dKMU3Q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a structured table with 'N' as the columns and Psychological Variables as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into tabular data, I first notice {the x-axis tick labels}[[0.128, 0.911, 0.145, 0.934], [0.439, 0.911, 0.457, 0.934], [0.622, 0.911, 0.639, 0.934], [0.751, 0.911, 0.769, 0.934], [0.851, 0.911, 0.869, 0.934], [0.933, 0.911, 0.951, 0.934]], which represent the \"N\" in the columns. The {legend labels}[[0.788, 0.485, 0.900, 0.515], [0.788, 0.529, 0.930, 0.559], [0.788, 0.573, 0.936, 0.603], [0.788, 0.616, 0.970, 0.646]] correspond to the different Psychological Variables as rows. Next, I identify the values by locating the {line points} for each Psychological Variable at their respective 'N' and extract the values from the line points.\nBased on the above thought, the answer is:\nN,AnxietyLevel,DepressionLevel,SelfEsteemLevel,LifeSatisfactionLevel\n10,0.000001,0.0001,0.001,0.001\n20,0.0000001,0.0001,0.00025118864315095795,0.00015848931924611142\n30,0.00000001,0.0001,0.00001,2.5118864315095822e-05\n40,0.000000001,0.0001,0.00001,3.981071705534969e-06\n50,0.0000000001,0.0001,0.00001,6.30957344480193e-07\n60,0.00000000001,0.0001,0.000001,0.0000001"
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "7XfAZzWUn3Q9bg7E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which psychological variable appears to have the most significant decrease from the first N value to the last?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the psychological variable that has the most significant decrease, I examine the trend of each line plot. The {line for Anxiety Level}[0.136, 0.025, 0.942, 0.359] shows a consistent decrease from 0.000001 at N=10 to 0.00000000001 at N=60. \nMeanwhile, the {line for Depression Level}[0.136, 0.226, 0.942, 0.226] appears to remain constant at 0.0001 across all N values. \nThe {line for Self-Esteem Level}[0.136, 0.159, 0.942, 0.359] indicates a decrease from 0.001 at N=10 to 0.000001 at N=60. \nFinally, the {line for Life Satisfaction Level}[0.136, 0.159, 0.942, 0.426] also indicates a decrease from 0.001 at N=10 to 0.0000001 at N=60.\nBased on the above thought, the answer is:\nBased on the line chart, the Anxiety Level appears to have the most significant decrease from the first N value to the last. It shows a consistent decrease from 0.000001 at N=10 down to 0.00000000001 at N=60, a significantly larger decline compared to the other psychological variables."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "JaoBJxrY4GUFQKfL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the change in 'Anxiety Level' from 'N' 10 to 60?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the change in 'Anxiety Level' from 'N' 10 to 60, I identify the {line points for Anxiety Level}[[0.136, 0.025], [0.448, 0.092], [0.630, 0.159], [0.760, 0.226], [0.860, 0.293], [0.942, 0.359]], corresponding to 'N' 10, 20, 30, 40, 50 and 60, respectively. The Anxiety Level is 0.000001 at 'N' 10 and drops to 0.00000000001 at 'N' 60. The change is therefore 0.00000000001 - 0.000001 = -0.00000098999.\nBased on the above thought, the answer is:\n-0.00000098999."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "LjBcTXIEAuZ3RHX6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the line chart about energy resource allocation into a structured table with Years as columns and Energy Resources as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I initially look at {the x-axis tick labels}[[0.095, 0.933, 0.102, 0.951], [0.415, 0.933, 0.422, 0.951], [0.732, 0.933, 0.746, 0.951]], which represent the column headers or Years. Following that, I identify {the legend texts}[[0.929, 0.038, 0.981, 0.060], [0.929, 0.070, 0.981, 0.093], [0.929, 0.103, 0.981, 0.126], [0.929, 0.136, 0.982, 0.159]] as the row labels representing different energy resources. To fill in the values, I locate {the line points for each energy resource}[[0.098, 0.564], [0.162, 0.533], [0.227, 0.514], ..., [0.803, 0.785]] corresponding to each year and extract the associated values.\nBased on the above thought, the answer is:\nEnergy Resource,Year 1,Year 2,Year 3\nCoal, 35, 30, 25\nOil, 25, 27, 29\nSolar, 15, 20, 25\nHydro, 10, 11, 12\nWind, 15, 19, 23"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "WWb6TayA5ISpKVXM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which energy resource seems to show the most consistent growth over the three years? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify which energy resource shows the most consistent growth, I need to analyze the slopes of the line plots for each energy resource. Analyzing {the line for Oil}[0.098, 0.564, 0.803, 0.564], I observe a gradual increase from 25 to 29, indicating consistent growth.\nFor {Solar}[0.227, 0.514, 0.739, 0.249], there is a much steeper increase from 15 to 25, suggesting a stronger growth. However, compared to Oil, the growth is not equally spread over the three years. For {Hydro}[0.291, 0.470, 0.675, 0.312], I also observe growth, from 10 to 12, but the amount of growth is relatively lower. Similarly, for {Wind}[0.355, 0.438, 0.803, 0.217], the line shows an increase from 15 to 23, indicating growth, but again this growth is not as uniformly distributed as it is for Oil.\nBased on the above thought, the answer is:\nThe energy resource that shows the most consistent growth over the three years is Oil. While the other resources also exhibit growth, Oil's growth is more gradual and consistent over the three years, suggesting a steady increase in its allocation. The smooth ascending line representing Oil demonstrates this consistent upward trend."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "h6qV5CUN0xQZ3omj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the allocation of Solar energy over the three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in Solar energy allocation, I need to locate {the line points for Solar}[[0.227, 0.514], [0.419, 0.407], [0.739, 0.249]] and extract the corresponding values: 15, 20, and 25. The initial allocation in Year 1 is 15 and the final allocation in Year 3 is 25. Thus, the total increase can be computed as 25 - 15 = 10.\nBased on the above thought, the answer is:\n10."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "cUoPw4eE68lWrB1z",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data plotted in the line chart into a table format with countries as columns and meal categories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I will use the {x-axis tick labels}[[-1081.205, 0.927, -2.045, 0.951], [0.104, 0.927, 0.115, 0.951], [0.430, 0.927, 0.556, 0.951], [0.620, 0.927, 0.677, 0.951], [0.755, 0.927, 0.822, 0.951], [0.860, 0.927, 0.927, 0.951], [0.945, 0.927, 1.012, 0.951]] for the column headers, which are the countries. The row labels will be based on {the legend items}[0.777, 0.029, 0.981, 0.167] that represent the various meal categories. The values will be derived from {the line points} of each of the meal categories for each of the countries.\nBased on the above thought, the answer is:\n,France,Italy,China,India,Mexico,Japan,Thailand\nStandard,10,20,30,40,50,60,70\nVegetarian_Popularity,8,16,12,14,52,32,60\nStreet_Food_Popularity,2,3,55,30,70,48,65\nFine_Dining_Popularity,90,85,60,25,40,35,28"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "veTl4nKrYKMIudQl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, which type of cuisine shows maximum variation in popularity across different countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the cuisine with maximum variation, I sift through the fluctuation in the lines associated with each cuisine type. Observing {the Standard category line}[-1081.205, 0.113, 0.945, 0.530], it seems to have moderate fluctuations. On close inspection of {Vegetarian Popularity line}[-1081.205, 0.146, 0.945, 0.578], the line seems to have much larger variations. The {Street Food Popularity line}[-1081.205, 0.113, 0.945, 0.874] shows sharp differences between specific countries, whereas {Fine Dining Popularity line}[-1081.205, 0.059, 0.945, 0.334] appears relatively steady with minimal variations.\nBased on the above thought, the answer is:\nBased on observations, the Street Food Popularity shows the most significant variation in popularity across different countries. The line representing this category showcases several high and low points, indicating a considerable disparity in the popularity rates across nations - popular in some (like China, Mexico, and Thailand), and less popular in others (like France and Italy)."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "xa1SXH9LrkFvcIOm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of all popularity scores for France across all meal categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the total popularity score, I need to identify the y-values associated with France on all the lines. The specific points are {Standard for France}[-1081.205, 0.530], {Vegetarian Popularity for France}[-1081.205, 0.578], {Street Food Popularity for France}[-1081.205, 0.874], and {Fine Dining Popularity for France}[-1081.205, 0.059]. The respective values for these points are 10, 8, 2, and 90. Summing these scores gives us the total popularity score for France.\nBased on the above thought, the answer is:\n110."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "Lag2efnKY5hzzH3t",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trends displayed on the line chart into a table with years as columns and types of trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data depicted in the line chart to a table, I would first segment the categories represented along the X-Axis into columns by pinpointing {the x-axis labels}<r>[0.118, 0.911, 0.153, 0.934], [0.630, 0.911, 0.665, 0.934]</r> . These labels '2010' and '2020' will comprise the column headers. The names of the line labels found in {the legend}<r>[0.707, 0.368, 0.985, 0.552]</r> will serve as the row headers. For each row label, I need to examine the corresponding {line points}<r>[0.136, 0.559, 0.647, 0.756], [0.136, 0.262, 0.647, 0.361], [0.136, 0.065, 0.647, 0.756], [0.136, 0.608, 0.647, 0.855]</r> to determine their respective y-values at each x-coordinate or column.\nBased on the above thought, the answer is:\nTrend,2010,2020\nInexorable Decline,1000,800\nInconsistent Development,1200,1300\nRapid Expansion,800,1500\nDramatic Decrease,950,700"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "59gvP57wzAxOhet4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you tell which trend has shown the most improvement? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the progression of each line from 2010 to 2020. Let's start from {line of Inexorable Decline}<r>[0.136, 0.559, 0.647, 0.756]</r>. It began from 1000 in 2010 and descended to 800 in 2020, showing a decline instead of improvement. The {line of Inconsistent Development}<r>[0.136, 0.262, 0.647, 0.361]</r> only slightly improved from 1200 to 1300.With {Rapid Expansion}<r>[0.136, 0.065, 0.647, 0.756]</r>, there was a significant jump from 800 in 2010 to 1500 in 2020. Meanwhile, the {line of Dramatic Decrease}<r>[0.136, 0.608, 0.647, 0.855]</r> trended downwards from 950 in 2010 to 700 in 2020.\nBased on the above thought, the answer is:\nBased on the plotted line chart, Rapid Expansion has witnessed the most improvement. This assertion is justified from the sharp ascend from 800 in 2010 to 1500 in 2020, indicating an increase in the future predicted values for this trend."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "ESf4RMqQAC3GsGvT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in 'Inconsistent Development' from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage increase for 'Inconsistent Development' from 2010 to 2020, I would need to track the {coordinates for Inconsistent Development in 2010 and 2020}<r>[0.136, 0.361],[0.647, 0.262]</r>. The value for year 2010 is 1200 and for year 2020 is 1300. Hence the percentage change will be ((1300-1200)/1200)*100 = 8.33%\nBased on the above thought, the answer is:\n8.33%"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "nESsJUENBRrRK6pK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you structure the line chart data into a table format that has 'Years' as rows and different 'Trends' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, first I need to take a look at {the x-axis tick labels}[[0.119, 0.911, 0.137, 0.934], [0.376, 0.911, 0.394, 0.934], [0.527, 0.911, 0.544, 0.934], [0.633, 0.911, 0.651, 0.934], [0.716, 0.911, 0.734, 0.934], [0.784, 0.911, 0.801, 0.934]], which represent the row headers or 'Years'. In order to get the column headers which are different 'Trends',  I will look at {the legend texts}[[0.890, 0.485, 0.943, 0.515], [0.890, 0.529, 0.955, 0.559], [0.890, 0.573, 0.956, 0.603], [0.890, 0.616, 0.978, 0.646]]. Then, I will map the data points corresponding to each trend at different years, which I can find in the line points for {Happy} [0.128, 0.339], [0.385, 0.323], [0.535, 0.299], [0.642, 0.275], [0.725, 0.266], [0.793, 0.261], {Anxious} [0.128, 0.627], [0.385, 0.513], [0.535, 0.493], [0.642, 0.453], [0.725, 0.437], [0.793, 0.433], {Relaxed} [0.128, 0.627], [0.385, 0.587], [0.535, 0.520], [0.642, 0.493], [0.725, 0.461], [0.793, 0.453], and {Frightened} [0.128, 0.165], [0.385, 0.174], [0.535, 0.174], [0.642, 0.185], [0.725, 0.206], [0.793, 0.226].\nBased on the above thought, the answer is:\nYears,Happy,Anxious,Relaxed,Frightened\n10,0.000002,0.0000000001,0.0000000001,0.0008\n20,0.0000035,0.000000005,0.0000000004,0.0006\n30,0.000008,0.00000001,0.000000004,0.0006\n40,0.000018,0.00000004,0.00000001,0.0004\n50,0.000025,0.00000007,0.00000003,0.0002\n60,0.000030,0.00000008,0.00000004,0.0001"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "3I9E88R40MdWPDgc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emotional state shows the largest downfall in their levels from year 10 to year 60 on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the drops of different state levels on year 10 to year 60. The coordinates for 'Happy' are at {[0.128, 0.339] and [0.793, 0.261]}, 'Anxious' are at {[0.128, 0.627] and [0.793, 0.433]}, 'Relaxed' are at {[0.128, 0.627] and [0.793, 0.453]}, and 'Frightened' are at {[0.128, 0.165] and [0.793, 0.226]}. I would calculate the differences for each state and identify the largest.\nBased on the above thought, the answer is:\nThe emotional state 'Frightened' has shown the largest downfall from year 10 to year 60 with a decrease of 0.0007 in state level."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "xAH1EmycPzXLbkB2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much has the state level of being 'Anxious' decreased from year 30 to year 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that by locating the data points of the trend 'Anxious' at years 30 and 50, I can calculate the difference. At year 30, the 'Anxious' state has a level of 0.00000001 at {coordinates} [0.535, 0.493] and at year 50, the 'Anxious' state level is 0.00000007 at {coordinates} [0.725, 0.437]. The difference can be calculated by subtraction of the year 50 value from the year 30 value.\nBased on the above thought, the answer is:\n0.00000006"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "w4hLhyey1Tg6QGRT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table from the line chart, where each row represents a year and columns represent 'No Of Queries' and 'Response Times' for each type of parameter?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create the table as requested, I first identify the available categories, drawn from the {legend}<r>[0.814, 0.478, 0.985, 0.662]</r>. I next look at {the x-axis}<r>[0.071, 0.895, 0.806, 0.895]</r> which includes two Parameters, 'NoOfQueries' and 'ResponseTime'. Using these as column headers, I can generate the rows for the data. I then locate and extract the Y-coordinate values for each Parameter based on {the line plots}<r>[0.105, 0.292, 0.773, 0.464]</r>, <r>[0.105, 0.120, 0.773, 0.765]</r>, <r>[0.105, 0.206, 0.773, 0.636]</r>, <r>[0.105, 0.077, 0.773, 0.679]</r> for each type of parameter: Standard, Constrained, Experimental, and Hypothetical respectively.\nBased on the above thought, the answer is:\nMetric,Parameter,Standard,Constrained,Experimental,Hypothetical\nNoOfQueries,Values,0.7,0.9,0.8,0.95\nResponseTime,Values,0.5,0.15,0.3,0.25"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "e64t5TZHYw5kcRQr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the charts, which type of parameter demonstrated the most improvement in 'ResponseTime'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the 'ResponseTime' progression of all four parameters represented by these line plots {Standard}<r>[0.105, 0.292, 0.773, 0.464]</r>, {Constrained}<r>[0.105, 0.120, 0.773, 0.765]</r>, {Experimental}<r>[0.105, 0.206, 0.773, 0.636]</r> and {Hypothetical}<r>[0.105, 0.077, 0.773, 0.679]</r>. An improvement in 'ResponseTime' would be reflected by a decrease between the 'NoOfQueries' and 'ResponseTime' stages. Hence, I must determine which line shows the most significant drop between these two stages.\nBased on the above thought, the answer is:\nThe 'Constrained' Parameter demonstrated the most improvement in 'ResponseTime'. The line plot corresponding to this category shows the large reduction, indicating a significant improvement."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "jcPpJTfUGH4NPP6a",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute difference between the Standard and the Constrained values for the measure 'ResponseTime'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to locate and analyze the 'ResponseTime' measure data for both 'Standard' and 'Constrained'. I find the 'ResponseTime' measure in {the x-axis tick labels}<r>[0.722, 0.911, 0.823, 0.934]</r>; the value for the 'Standard' category is provided in {the line plot for Standard}<r>[0.105, 0.292, 0.773, 0.464]</r> while the 'Constrained' category is in {the line plot for Constrained}<r>[0.105, 0.120, 0.773, 0.765]</r>. The difference between these can be calculated by subtracting the 'Standard' value from the 'Constrained' value.\nBased on the above thought, the answer is:\n0.35."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "utLRpMK4qBfbfsMX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format with Research Fields as rows and corresponding Number of Patents for each research field?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, I can discern from {the x-axis tick labels}[ [0.084, 0.750, 0.184, 0.916], [0.226, 0.750, 0.273, 0.829], [0.337, 0.750, 0.394, 0.845], [0.449, 0.750, 0.514, 0.858], [0.569, 0.750, 0.625, 0.842], [0.693, 0.750, 0.733, 0.816], [0.773, 0.750, 0.884, 0.934], [0.913, 0.750, 0.976, 0.855] ] that the row labels will be the various Research Fields. As for the column entry, it will be the corresponding Number of Patents that can be obtained from {the line points}[ [0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057] ] for each of these Research Fields.\nBased on the above thought, the answer is:\nResearch Field,Number of Patents\nComputer Science,15000\nBioTech,12000\nNanoTech,18000\nMechanical,14000\nElectrical,16000\nSpace,25000\nArtificial Intelligence,22000\nElectronics,30000"
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "pfxGKSftNzrLCklD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the sector with the least advancement in research based on the number of patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the least advancement, I need to locate the sector with the fewest patents registered. By examining {the line points}[[0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057]], it seems that BioTech with coordinates [0.250, 0.701] has the least by its y-coordinate.\nBased on the above thought, the answer is:\nBased on the number of patents, the BioTech sector seems to have the least advancement in research. This is indicated by its lower number of patents registered compared to the other sectors. Therefore, it can be inferred that less research has been patented in BioTech, suggesting limited advancement in the sector relative to others."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "WRggQsLBeyqUTP2U",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_1_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest number of patents registered in any research field?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the field with the highest patent registration, I should look for the data point with the lowest y-coordinate, because the y-axis is reversed in the graph. Seeing {the line points}[ [0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057] ], Electronics with the point coordinates at [0.944, 0.057] have the highest number of patents registered.\nBased on the above thought, the answer is:\n30000."
+            }
+        ],
+        "id_raw": 19
+    },
+    {
+        "id": "dK9mzBeX8rim8WYq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the funding amounts for each venture capital fund across all stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I assess the chart to identify the stages on {the x-axis}<r>[0.136, 0.464, 0.243, 0.488]</r> labeled from Seed to Late-stage. For each stage, I note two data points representing each fund's investment. Observing the points along {the lines}<r>[0.080, 0.246, 0.300, 0.344]</r>, {the lines}<r>[0.080, 0.077, 0.300, 0.176]</r> I record corresponding investment values linked to each stage.\nBased on the above thought, the answer is:\nStage,Fund1 Amount,Fund2 Amount\nSeed,63,72\nSeries A,66,73\nSeries B,69,74\nSeries C,75,75\nLate-stage,80,79"
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "a8ovgOfdjyyCeXEY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the line trend, which venture capital fund demonstrates a more stable investment pattern across different stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the stability of investment patterns, I examine the line trends of Fund1 and Fund2. The line for Fund1 <r>[0.080, 0.246, 0.300, 0.344]</r> indicates a steady upward growth with no abrupt changes, reflecting a conservative and systematic investment approach. In contrast, the line for Fund2 <r>[0.080, 0.077, 0.300, 0.176]</r> exhibits erratic fluctuations with sharp increases and decreases, indicative of a high-risk, less predictable funding strategy.\nBased on the above thought, the answer is:\nFund1 demonstrates a more stable investment pattern across different stages due to its consistent ascending line with no abrupt variations."
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "XMjBGXd3N9cqXD8N",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cumulative investment difference between Fund1 and Fund2 from Seed to Late-stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the values listed for Fund1 and Fund2 from Seed to Late-stage on {the lines}<r>[0.080, 0.246, 0.300, 0.344]</r> for Fund1 and {the lines}<r>[0.080, 0.077, 0.300, 0.176]</r> for Fund2. Following the summing of respective investments for each fund: Fund1 = 63 + 66 + 69 + 75 + 80 and Fund2 = 72 + 73 + 74 + 75 + 79.\nBased on the above thought, the answer is:\n3 (363 - 373)"
+            }
+        ],
+        "id_raw": 20
+    },
+    {
+        "id": "SRYxjTzHZMRuF4cs",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart detailing the growth trends of technology artifacts over five years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the bar chart, identifying {y-axis labels}[[0.10, 0.18, 0.13, 0.21], [0.25, 0.18, 0.28, 0.21], [0.40, 0.18, 0.43, 0.21], [0.55, 0.18, 0.58, 0.21], [0.70, 0.18, 0.73, 0.21]] that include 'Artificial Intelligence', 'Virtual Reality', 'Cloud Computing', '5G Technology', and 'Cybersecurity'. Correspondingly, I observe the bar segments for each tech artifact from left to right representing different years. The lengths of these bars give me the values for each year.\nBased on the above thought, the answer is:\nCategory,Year 1,Year 2,Year 3,Year 4,Year 5\nArtificial Intelligence,200,230,260,290,320\nVirtual Reality,150,120,90,60,30\nCloud Computing,220,240,260,280,300\n5G Technology,180,160,140,120,100\nCybersecurity,180,200,350,190,200"
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "WkyWgq6qubVskcvj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the trend in 'Virtual Reality' changed over the five years according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trend in {Virtual Reality}[[0.25, 0.18, 0.28, 0.21]], I notice the bar lengths diminish steadily from left to right, clearly indicating a decreasing trend. The bar relative positions decrease, suggesting that the values for Virtual Reality technology have been decreasing each consecutive year.\nBased on the above thought, the answer is:\nThe trend for 'Virtual Reality' has consistently decreased over the five years. The bar lengths representing this artifact get progressively shorter each year, confirming a steady decline in this technology domain."
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "gWWfakaGvGPzt5s0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth of Cloud Computing from Year 1 to Year 5 based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage growth, I locate {the relevant bars for Cloud Computing}[[0.40, 0.25, 0.43, 0.400]] for Year 1 and Year 5. Measuring the length of the bars, I note the values as 220 in Year 1 and 300 in Year 5. The calculation of percentage growth is given by [(300 - 220) / 220] * 100.\nBased on the above thought, the answer is:\n36.36%"
+            }
+        ],
+        "id_raw": 32
+    },
+    {
+        "id": "ZngdphudCh5GYxgw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data representing trends in Social Reforms and Political Restructuring over 10 years into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I assess the plot to identify the years from 2000 to 2010 along {the x-axis}<r>[0.1, 0.9]</r>. Each year represents a column. Next, I recognize two main series {Social Reforms and Political Restructuring}<r>[0.792, 0.132, 0.893, 0.208]</r> plotted in the chart as different lines, serving as row labels. I extract the corresponding value for each series from their specific points on the lines over the years and convert these values into a csv format table.\nBased on the above thought, the answer is:\nYear, Social Reforms, Political Restructuring\n2000, 100, 120\n2001, 95, 115\n2002, 90, 110\n2003, 85, 105\n2004, 80, 100\n2005, 110, 80\n2006, 115, 75\n2007, 90, 95\n2008, 85, 115\n2009, 130, 110\n2010, 105, 120"
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "KxSsj8Ouo03HAzG2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did Political Restructuring display a volatile pattern through the decade as presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the volatility of Political Restructuring over the decade, I observe the progression of the line associated with it {from the years 2000 to 2010}<r>[0.1, 0.2, 0.9, 0.3]</r>. Initially, there is a steady decline until 2004, followed by a sharp drop until 2005 {sharp decrease between these years}<r>[0.4, 0.23, 0.5, 0.75]</r>. Post-2005, there's a pronounced reversal with a rapid rise until 2007, sustaining slight fluctuations thereafter. These abrupt shifts show the unstable conditions and unpredicted shifts in the political landscape of the hypothetical society during that time.\nBased on the above thought, the answer is:\nPolitical Restructuring exhibited a volatile pattern through dramatic peaks and valleys. Firstly, there was a considerable descent reflecting decrease over the years until 2005 after which an extraordinary upsurge was seen, portraying crises or changes in the political environment. Such sharp fluctuations within short periods highlight the instability and reactive changes in the political strategies or situations in the nation."
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "YDvQ2yQpvg1ciDMm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween which two consecutive years did Political Restructuring experience the largest decrease in value as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the years with the largest decrease in Political Restructuring, I scrutinize the line for Political Restructuring {observed between each point on the line}<r>[0.1, 0.2, 0.9, 0.3]</r>. By comparing points, the largest drop is evident between the years 2004 and 2005, where there's a significant lowering from a higher value at {2004 point}<r>[0.4, 0.23]</r> to a much lower value at {2005 point}<r>[0.5, 0.75]</r>.\nBased on the above thought, the answer is:\n2004 to 2005"
+            }
+        ],
+        "id_raw": 33
+    },
+    {
+        "id": "4ZtilCCBUAcG9SFg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you format the data comparing the relative popularity of various living scenarios between periods of 2020 and 2070 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I examine {the x-axis labels}<r>[0.055, 0.780, 0.130, 0.810], [0.230, 0.780, 0.305, 0.810], [0.405, 0.780, 0.480, 0.810], [0.580, 0.780, 0.655, 0.810], [0.755, 0.780, 0.830, 0.810]</r> which represent the periods from 2020 to 2070. Next, {the legend}<r>[0.810, 0.050, 0.990, 0.250]</r> identifies each living scenario by color. I then locate {the line points for each scenario}<r>[0.060, 0.320], [0.235, 0.410], ..., [0.760, 0.210]</r> to determine their popularity indices for each period.\nBased on the above thought, the answer is:\nYear,Suburban_Living,Urban_Condo,Small_City,Rural_Home,Nomadic_Lifestyle,Underwater\n2020,50,58,48,55,32,10\n2030,48,57,48,52,35,15\n2040,47,55,48,49,40,22\n2050,45,54,48,45,52,35\n2070,43,52,48,40,65,50"
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "bPNaweJazV8k5fPb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich living scenario shows a steady trend and which one shows the most dramatic change over the 50-year span?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I observe the slopes and variations in the lines representing each scenario. The {Small_City line}<r>[0.060, 0.460, 0.760, 0.460]</r> remains constant throughout the years, indicating stability. Conversely, {the line for Underwater}<r>[0.060, 0.890, 0.760, 0.230]</r> shows a steep descend from a low initial value to a much higher one, which implies a significant increase.\nBased on the above thought, the answer is:\nSmall City shows a steady trend with no significant changes in popularity. Underwater exhibits the most dramatic change, seeing a substantial rise in popularity from 2020 to 2070."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "tc3Pcx9CbtGh13Z4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the popularity increase for Nomadic Lifestyle from 2020 to 2070?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the rise in popularity for Nomadic Lifestyle, I trace {the line for Nomadic Lifestyle}<r>[0.060, 0.710, 0.760, 0.230]</r> comparing the points from 2020 and 2070. The initial value at 2020 is recognized at {the point}<r>[0.060, 0.710]</r> with a popularity index of 32, and the final value at 2070 is at {the point}<r>[0.760, 0.230]</r> with an index of 65.\nBased on the above thought, the answer is:\n33."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "5OSR1Wy2giw38zYO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the line graph showing Urban and Rural development in the fields of Education and Healthcare into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I observe two categories on {the x-axis}<r>[0.142, 0.464, 0.248, 0.488]</r>, which are Urban and Rural. These form the rows of the table. I see two plotted lines representing Education and Healthcare, which are distinguished by {their colors on the legend}<r>[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156]</r>. Each line plots points for Urban and Rural, which contain their respective development levels. I transcribe these points into the table, structuring it by the categories and fields.\nBased on the above thought, the answer is:\nCategory,Education,Healthcare\nUrban,0.85,0.80\nRural,0.60,0.55"
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "POPUcxvg8bsS9gZh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector, Education or Healthcare, shows a smaller discrepancy in development between Urban and Rural areas according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the discrepancies, I first note the development index values for both sectors. In Education, Urban has an index of approximately 0.85 and Rural has 0.60, with a {difference highlighted by the distance between plots}<r>[0.062, 0.374, 0.116, 0.455], [0.274, 0.374, 0.328, 0.455]</r>. In Healthcare, Urban has an index of about 0.80 and Rural has 0.55, visible from {the Healthcare line points}<r>[0.390, 0.862, 0.444, 0.942], [0.602, 0.862, 0.655, 0.942]</r>. Calculating the differences, Education has a smaller disparity of 0.25 compared to Healthcare's disparity of 0.25.\nBased on the above thought, the answer is:\nEducation shows a smaller discrepancy in development between Urban and Rural areas than does Healthcare."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "eoHFQzi5EViOZfe0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the development index for Education between Urban and Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first locate the points for Education in both Urban and Rural areas using {the line coordinates for Education}<r>[0.062, 0.374, 0.116, 0.455], [0.274, 0.374, 0.328, 0.455]</r>. The Education indices are approximately 0.85 for Urban and 0.60 for Rural. Subtracting the Rural value from the Urban value gives the difference.\nBased on the above thought, the answer is:\n0.25"
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "StJr6hE8sVaC6FVl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line charts representing Education and Training over the years into a structured table format with Year as rows and Sectors as headings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line charts into a table, I will trace through each data point across the charts. First, I'll track down {the x-axis labels for years}<r>[0.069, 0.085, 0.311, 0.358]</r> for the distinct timelines ranging from 2014 to 2020. Next, I identify the data points on {the line representing 'Education Expansion'}<r>[0.069, 0.064, 0.311, 0.358]</r> and {the line representing 'Training Revival'}<r>[0.397, 0.064, 0.639, 0.358]</r>, correlating them with the respective years. After pinpointing each year's data points, I can extract and format these values accordingly.\nBased on the above thought, the answer is:\nYear,Education Expansion,Training Revival\n2014,10,8\n2015,7,6\n2016,12,10\n2017,15,12\n2018,13,11\n2019,14,13\n2020,15,14"
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "JsF6axiempLDYuuD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed a more consistent growth pattern in the provided trend data, and can you explain why this might be?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reviewing the trend lines, {the 'Education Expansion' sector}<r>[0.069, 0.064, 0.311, 0.358]</r> displays a more consistent upwards trend in growth pattern, especially post-2016, unlike {the 'Training Revival' sector}<r>[0.397, 0.064, 0.639, 0.358]</r> which shows significant dips around 2018 and 2019. Consistency could be indicative of stable investment and policies in the education sector, maintaining a conducive environment for continuous growth despite economic fluctuations.\nBased on the above thought, the answer is:\nThe Education sector showed a more consistent growth pattern. This might be due to continuous improvements and stable policies in education, alongside steady demand and investment, ensuring less fluctuation compared to the Training sector, which might have experienced variable funding and policy shifts during the same period."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "Tn8WgHs6tTdOtzHl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_2_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth rate for the Education sector from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth rate for the Education sector, I will examine {the points on the line chart representing 'Education Expansion'}<r>[0.069, 0.064, 0.311, 0.358]</r>. The growth rates for the years 2014 to 2020 are respectively 10, 7, 12, 15, 13, 14, and 15. To find the average, I sum these values and divide by the number of years, which is seven.\nBased on the above thought, the answer is:\n12.14"
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "l7is5QcNnAlrlm59",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract and display the market share data for different sectors in table format with years as columns and sectors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve this, I'll first refer to {the x-axis tick labels}<r>[0.061, 0.773, 0.144, 0.898]</r>, {0.178, 0.773, 0.259, 0.895]</r>, {0.291, 0.773, 0.378, 0.903]</r>, {0.397, 0.773, 0.504, 0.934]</r>, {0.525, 0.773, 0.607, 0.896]</r>, {0.643, 0.773, 0.721, 0.890]</r>, {0.739, 0.773, 0.857, 0.951]</r> representing the years from 2015 to 2020. Then, I'll identify {the legend texts}<r>[0.884, 0.101, 0.982, 0.123]</r>, {0.884, 0.134, 0.933, 0.156]</r>, {0.884, 0.167, 0.946, 0.189]</r> which suggests the sectors (Automotive, IT, Real Estate, Pharma). Lastly, I'll analyze the line plots corresponding to each sector at various years to populate the values.\nBased on the above thought, the answer is:\nYear,2015,2016,2017,2018,2019,2020\nAutomotive,25,30,27,35,33,38\nIT,20,22,28,26,29,31\nReal Estate,40,35,30,25,20,15\nPharma,15,13,15,14,18,16"
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "gXL1bGcXqzcgDnKy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, what can you infer about the trend of the IT sector compared to the Real Estate sector over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends starting from 2015 to 2020 by examining {the line for IT}<r>[0.101, 0.182, 0.894, 0.368]</r> and {the line for Real Estate}<r>[0.101, 0.344, 0.894, 0.870]</r>, it's evident that the IT sector shows a gradual upward trend while the Real Estate sector depicts a distinct downward trend. The IT sector had fluctuations but maintained an incremental path, peaking in 2020, while Real Estate saw a consistent decrease in market share throughout the years ending with its lowest point in 2020.\nBased on the above thought, the answer is:\nThe IT sector shows a clear upward trajectory in market share, indicating expansion and perhaps increased investment or market favorability. In contrast, Real Estate suffered a declining market share, which could signal declining investment, reduced demand, or increased competition affecting its hold on the market throughout the same period."
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "0RXqg9npmHMi49pG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total range of market share for the Pharma industry from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range for the Pharma industry, I'll find the maximum and minimum market shares by identifying the highest and lowest points on {the line for Pharma}<r>[0.102, 0.599], [0.218, 0.340], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]</r>. The values range from 13% in 2016 to 18% in 2019. The total range is then 18% minus 13%.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 41
+    },
+    {
+        "id": "M8swLQ6ZgmwSEbMq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the chart into a structured table, categorizing the scores by transportation mode and type of industry trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from the chart data, I first observe {the x-axis labels}[[0.160, 0.855, 0.200, 0.900], [0.400, 0.855, 0.440, 0.900]] which represent the transportation modes 'Freight Rail' and 'Trucking'. Each mode is associated with four industry trends plotted as {the line points for Freight Rail}[[0.160, 0.590], [0.240, 0.780], [0.320, 0.455], [0.400, 0.725]] and {the line points for Trucking}[[0.500, 0.610], [0.580, 0.820], [0.660, 0.470], [0.740, 0.705]]. I extract the values based on their positions corresponding to the y-axis that represent the score for each trend.\nBased on the above thought, the answer is:\nMode, Gradual Downturn, Steep Decline, Marginal Decline, Fluctuating Decline\nFreight Rail, 0.40, 0.22, 0.55, 0.28\nTrucking, 0.38, 0.18, 0.52, 0.30"
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "XlUzshhevMRihD7K",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich mode of transportation shows more stability across the different industry trends according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stability across industry trends involves comparing the variance in scores between 'Freight Rail' and 'Trucking'. Observing {the line points for Freight Rail}[[0.160, 0.590], [0.240, 0.780], [0.320, 0.455], [0.400, 0.725]] and {the line points for Trucking}[[0.500, 0.610], [0.580, 0.820], [0.660, 0.470], [0.740, 0.705]], it appears that Freight Rail shows larger fluctuations in its scores with a high at the 'Steep Decline' and a low at the 'Marginal Decline'. In contrast, Trucking exhibits slightly less variation between its highest and lowest scores.\nBased on the above thought, the answer is:\nTrucking shows more stability across the different industry trends compared to Freight Rail. The scores for Trucking are closer together, indicating less fluctuation and greater stability in performance under different industry trend scenarios."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "W7zmD6C8JPXnEIth",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the score of 'Marginal Decline' for Freight Rail compared to Trucking?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the 'Marginal Decline' scores, I identify the corresponding values for Freight Rail at {the point for Marginal Decline in Freight Rail}[[0.320, 0.455]] which is approximately 0.55, and for Trucking at {the point for Marginal Decline in Trucking}[[0.660, 0.470]] which is approximately 0.52. To find the difference, I subtract the score of Trucking from Freight Rail: 0.55 - 0.52.\nBased on the above thought, the answer is:\n0.03."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "V1PBWJWWPeEacrAE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data for both Employee Satisfaction and Profit Margin into a table, listing the Leadership Styles as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the subplot for Employee Satisfaction}<r>[0.125, 0.120, 0.900, 0.500]</r> and {the subplot for Profit Margin}<r>[0.125, 0.510, 0.900, 0.890]</r>, I analyze the bars corresponding to each leadership style: {Transformational}<r>[0.160, 0.322, 0.265, 0.450]</r>, {Transactional}<r>[0.295, 0.322, 0.400, 0.350]</r>, {Laissez-faire}<r>[0.430, 0.322, 0.535, 0.245]</r> in the first subplot and {Transformational}<r>[0.285, 0.512, 0.390, 0.470]</r>, {Transactional}<r>[0.420, 0.512, 0.525, 0.340]</r>, {Laissez-faire}<r>[0.555, 0.512, 0.660, 0.215]</r> in the second subplot. The values are then gathered based on the heights of each bar in proportion to the axes.\nBased on the above thought, the answer is:\nLeadership Style,Employee Satisfaction,Profit Margin\nTransformational,90,85\nTransactional,70,60\nLaissez-faire,50,40"
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "iWnikHxWyxmZFFjf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the gradual decline observed in Profit Margin across the leadership styles in the chart, which style would you recommend for maximizing profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the Bar chart, {the highest profit margin is under Transformational leadership}<r>[0.285, 0.512, 0.390, 0.470]</r>, followed by a decrease with {Transactional}<r>[0.420, 0.512, 0.525, 0.340]</r>, and the lowest under {Laissez-faire}<r>[0.555, 0.512, 0.660, 0.215]</r>. Given these observations, Transformational leadership seems to maximize profit margin the most effectively.\nBased on the above thought, the answer is:\nTransformational leadership is recommended for maximizing profit as it presents the highest profit margin observed in the chart."
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "AvzqBNBX9mjK2o8b",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage drop in Employee Satisfaction when moving from Transformational to Transactional leadership?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I first determine the values of Employee Satisfaction for both {Transformational}<r>[0.160, 0.322, 0.265, 0.450]</r> and {Transactional}<r>[0.295, 0.322, 0.400, 0.350]</r> leadership styles, which are 90 and 70 respectively. The drop is then calculated as (90 - 70)/90 * 100%.\nBased on the above thought, the answer is:\n22.22%"
+            }
+        ],
+        "id_raw": 47
+    },
+    {
+        "id": "3CEBJYrnJKoH8IDX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart's information into a table format, with the rows as months and the columns as Infrastructure Spending, Social Service Spending, and Unemployment Rate respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I examine {the x-axis tick labels}[[0.092, 0.906, 0.101, 0.930], [0.220, 0.906, 0.229, 0.930], ..., [0.984, 0.906, 1.002, 0.930]], which indicates the months from 1 to 12. Then, I identify {the legend texts}[[0.228, 0.949, 0.393, 0.973], [0.460, 0.949, 0.626, 0.973], [0.693, 0.949, 0.836, 0.973]] as the column names. The values for each month and column can be obtained from {the points of each line}[[0.160, 0.255], ..., [0.865, 0.419], ..., [0.865, 0.855]], which represent the data points corresponding to each column at different months.\nBased on the above thought, the answer is:\nMonths,Infrastructure Spending,Social Service Spending,Unemployment Rate\n1,0.75,0.85,0.09\n2,0.77,0.82,0.1\n3,0.75,0.86,0.09\n4,0.78,0.80,0.11\n5,0.75,0.79,0.12\n6,0.72,0.81,0.12\n7,0.70,0.82,0.13\n8,0.68,0.81,0.13\n9,0.65,0.79,0.14\n10,0.62,0.77,0.15\n11,0.60,0.75,0.16\n12,0.57,0.72,0.17"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "B7h3tMJDuvunAPTo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, how does Unemployment Rate change with respect to Infrastructure and Social Service Spending? Give your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the lines for Infrastructure Spending, Social Service Spending, and Unemployment Rate respectively. Looking at {the line for Infrastructure Spending}<r>[0.160, 0.228, 0.865, 0.419]</r>, it starts at 0.75 and gradually decreases to 0.57 over 12 months, indicating a downward trend in infrastructure spending. For {the line for Social Service Spending}<r>[0.160, 0.155, 0.865, 0.282]</r>, it starts from 0.85 and reduces to 0.72 in 12 months, showing a similar downward trend.\nFinally, analyzing {the line for Unemployment Rate}<r>[0.160, 0.782, 0.865, 0.855]</r>, it starts from 0.09 and gradually rises to 0.17 in 12 months, reflecting an upward trend in unemployment rate.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Unemployment Rate seems to inversely correlate with Infrastructure and Social Service Spending. As spending on Infrastructure and Social Services declines over a 12-month period, there is a corresponding increase in the Unemployment Rate. This could suggest that reductions in public spending might be contributing to higher unemployment rates."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "FB6Baxxzy1MClxrM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum spendings in Infrastructure over the 12 months period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values for Infrastructure Spending over the 12 months, I need to inspect {the line points for Infrastructure Spending}[0.160, 0.255, ..., 0.865, 0.419], which correspond to the values: 0.75, 0.77, 0.75, 0.78, 0.75, 0.72, 0.70, 0.68, 0.65, 0.62, 0.6, 0.57. The highest value is 0.78 during {Month 4}[0.352, 0.941], and the lowest value is 0.57 during {Month 12}[0.865, 0.940]. Therefore, the difference between the maximum and minimum is 0.78 - 0.57 = 0.21.\nBased on the above thought, the answer is:\n0.21."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "cbZTDZVf2XsjIylQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data points of innovation index overtime for each company presented in the bar chart into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a table format, I analyzed the heights of {the bars}[[0.158, 0.300, 0.202, 0.780], [0.228, 0.300, 0.272, 0.845], [0.298, 0.300, 0.342, 0.860], [0.368, 0.300, 0.412, 0.930], [0.438, 0.300, 0.482, 0.950], [0.508, 0.300, 0.552, 0.990]] on the chart for each company respectively: Intel, Amazon, Apple, Microsoft, Google, and Tesla. Using the heights of these bars I computed the estimated innovation index values for each company.\nBased on the above thought, the answer is:\nCompany,Innovation Index\nIntel,0.48\nAmazon,0.545\nApple,0.56\nMicrosoft,0.63\nGoogle,0.65\nTesla,0.69"
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "F6PjOoAXGfZ3p20r",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows the highest growth in innovation capability reflected in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the heights of the bars, which represent the Innovation Index for each company, {Tesla's bar}[[0.508, 0.300, 0.552, 0.990]] is the tallest, indicating the highest Innovation Index value among all companies presented. This emphatically points to Tesla as showing the highest growth in innovation capability since its bar exceeds those of other companies including {Intel's}[[0.158, 0.300, 0.202, 0.780]], {Amazon's}[[0.228, 0.300, 0.272, 0.845]], and others.\nBased on the above thought, the answer is:\nTesla shows the highest growth in innovation capability as reflected by having the highest and the tallest bar on the chart."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "hs4uRJYK0hkrGCKc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is Tesla's Innovation Index compared to Intel's?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I examined {Tesla's bar}[[0.508, 0.300, 0.552, 0.990]] which represents an Innovation Index of 0.69 and compared it with {Intel's bar}[[0.158, 0.300, 0.202, 0.780]] which shows an Innovation Index of 0.48. Using these values, the difference is calculated as 0.69 - 0.48.\nBased on the above thought, the answer is:\n0.21"
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "mUw2mniictEvxks7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the curves displayed on these line charts into a structured table format showing the trends in career and workplace development from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe {the x-axis tick labels}<r>[0.071, 0.773, 0.144, 0.898]</r> that denote different years from 2015 to 2021. The chart is divided into two subplots. In the first subplot, I see curves for {Work-Life Balance}<r>[0.178, 0.773, 0.259, 0.895]</r> and {Professional Development}<r>[0.291, 0.773, 0.378, 0.903]</r>. In the second subplot, there are curves for {Employee Engagement}<r>[0.397, 0.773, 0.504, 0.934]</r>, {Flexible Working Hours}<r>[0.525, 0.773, 0.607, 0.896]</r>, and {Team Collaboration}<r>[0.643, 0.773, 0.721, 0.890]</r>. Each line corresponds to the trend score out of 100, over the years from 2015 to 2021.\nBased on the above thought, the answer is:\nYear,Work-Life Balance,Professional Development,Employee Engagement,Flexible Working Hours,Team Collaboration\n2015,50,55,48,47,45\n2016,55,60,52,50,50\n2017,60,65,60,55,54\n2018,62,70,75,58,59\n2019,65,73,82,61,62\n2020,70,78,90,65,68\n2021,75,80,95,70,70"
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "hbPrQD5aUdnOLYUX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich workplace characteristic has shown the least improvement over the years according to the data provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing both subplots and their representing lines, {Work-Life Balance}<r>[0.178, 0.773, 0.259, 0.895]</r> has the lowest rise in scores over the years among all characteristics. Starting from a score of about 50 in 2015 and only rising to about 75 in 2021 indicates less improvement compared to {Employee Engagement}<r>[0.397, 0.773, 0.504, 0.934]</r>, which started around 55 and rose to 95 in the same period.\nBased on the above thought, the answer is:\nWork-Life Balance has shown the least improvement over the years, as it started from a lower base and exhibited a smaller increment as compared to the other characteristics like Employee Engagement."
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "z8B4V9CR1wEgS93e",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Employee Engagement from 2015 to 2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the 2015 point on the Employee Engagement line}<r>[0.110, 0.589]</r> representing the score for 2015, which is roughly 55. I then locate {the 2021 point on the same line}<r>[0.798, 0.243]</r> representing the score for 2021, which is roughly 95. Calculating the percentage increase, (95 - 55) / 55 * 100, yields roughly 72.73%.\nBased on the above thought, the answer is:\n72.73%"
+            }
+        ],
+        "id_raw": 51
+    },
+    {
+        "id": "odEeQysPkwLjIjpk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data of weekly Performance into a structured table format using the provided weeks and values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from the line chart, I'll start by mapping each week indicated by {the x-axis labels}<r>[0.053, 0.919, 0.075, 0.942], [0.157, 0.919, 0.179, 0.942], [0.261, 0.919, 0.283, 0.942], [0.365, 0.919, 0.386, 0.942], [0.464, 0.919, 0.495, 0.942], [0.568, 0.919, 0.598, 0.942], [0.671, 0.919, 0.702, 0.942], [0.775, 0.919, 0.806, 0.942], [0.879, 0.919, 0.910, 0.942], [0.983, 0.919, 1.013, 0.942]</r> to their respective points for Performance from {the Performance line points}<r>[0.106, 0.502], [0.147, 0.510], [0.189, 0.542], [0.230, 0.494], [0.272, 0.478], [0.313, 0.542], [0.355, 0.526], [0.396, 0.558], [0.438, 0.462], [0.479, 0.446], [0.521, 0.406], [0.562, 0.430], [0.604, 0.381], [0.645, 0.446], [0.687, 0.430], [0.728, 0.462], [0.770, 0.486], [0.811, 0.470], [0.853, 0.454], [0.894, 0.438]</r>.\nBased on the above thought, the answer is:\nWeek,Performance\n1,0.5\n2,0.49\n3,0.45\n4,0.51\n5,0.53\n6,0.45\n7,0.47\n8,0.43\n9,0.55\n10,0.57\n11,0.62\n12,0.59\n13,0.65\n14,0.57\n15,0.59\n16,0.55\n17,0.52\n18,0.54\n19,0.56\n20,0.58"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "aid8cn1vIRXOhZo3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the overall weekly trend in Efficiency compare to Performance as depicted in the line charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the overall weekly trend in Efficiency compared to Performance, I will closely observe the general slope of the lines representing each metric. By visual inspection of {the line for Efficiency}<r>[0.106, 0.325, 0.894, 0.486]</r> and comparing it with {the line for Performance}<r>[0.106, 0.381, 0.894, 0.558]</r>, Efficiency displays a consistently upward trend, albeit with some initial turbulence, while Performance demonstrates more noticeable fluctuations with ups and downs across the weeks. These observations can suggest varying stability and growth patterns between the two measures.\nBased on the above thought, the answer is:\nThe overall weekly trend shows that Efficiency generally increases with a steadier climb, indicating more consistent improvement, whereas Performance exhibits more volatility with several peaks and troughs, indicating less stability compared to Efficiency."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "rAiCRtrMpHxIhhdL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest weekly growth percentage observed in the chart for the Efficiency metric?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest growth percentage for Efficiency, I would assess the y-values associated with each week from {Efficiency line points}<r>[0.106, 0.422], [0.147, 0.381], [0.189, 0.357], [0.230, 0.341], [0.272, 0.325], [0.313, 0.438], [0.355, 0.462], [0.396, 0.486], [0.438, 0.422], [0.479, 0.470], [0.521, 0.454], [0.562, 0.422], [0.604, 0.406], [0.645, 0.462], [0.687, 0.438], [0.728, 0.454], [0.770, 0.470], [0.811, 0.438], [0.853, 0.422], [0.894, 0.406]</r>. The highest point on this line occurs at 0.72 at week 5.\nBased on the above thought, the answer is:\n0.72"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "TlxNR88rdQtIuRs6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data showing departmental performance over two quarters into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accurately convert the chart data into a table, I observe the vertical bars representing each department in the first and second quarters. I note the order and performance scores by examining the height of each bar and matching them with {the y-axis tick labels}[[0.020, 0.081, 0.043, 0.116], [0.020, 0.177, 0.043, 0.212], [0.020, 0.272, 0.043, 0.307], [0.020, 0.368, 0.043, 0.403], [0.020, 0.464, 0.043, 0.499]]. I also refer to {the x-axis labels}[[0.100, 0.955, 0.153, 0.980], [0.211, 0.955, 0.255, 0.980], [0.317, 0.955, 0.374, 0.980], [0.425, 0.955, 0.491, 0.980], [0.531, 0.955, 0.578, 0.980], [0.632, 0.955, 0.703, 0.980]] to correctly categorize the data by department names and quarters.\nBased on the above thought, the answer is:\nDepartment,Performance in Q1,Performance in Q2\nSales,90,85\nHR,83,75\nFinance,77,78\nOperations,69,72\nIT,65,70\nR&D,62,66\nMarketing,57,60"
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "7ySKRyflIIOw8seO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department showed the most significant improvement from Q1 to Q2 based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the department with the most significant improvement, I compare the differences in heights of bars from Q1 to Q2 for each department {the bars for each department from Q1 to Q2}[[0.100, 0.450, 0.153, 0.550], [0.100, 0.850, 0.153, 0.930], [0.211, 0.640, 0.255, 0.750], [0.211, 0.850, 0.255, 0.920]]. I observe that the {R&D department}[[0.632, 0.730, 0.703, 0.832], [0.632, 0.910, 0.703, 0.990]] shows the greatest relative increase in its bar height, indicating the most significant improvement in performance scores.\nBased on the above thought, the answer is:\nThe R&D department showed the most significant improvement from Q1 to Q2. Their performance score rose from 62 in the first quarter to 66 in the second quarter, depicting a notable upward trend compared to other departments."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "5ob7XYFHzWV26rAt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the performance score change for the IT department from Q1 to Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the change in performance score for the IT department involves first locating {the IT department's bars for Q1 and Q2}[[0.531, 0.650, 0.578, 0.745], [0.531, 0.850, 0.578, 0.920]]. The heights of these bars correspond to performance scores of 65 in Q1 and 70 in Q2, respectively. The difference is calculated by subtracting the score in Q1 from the score in Q2.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "EzNakR7XXWlJ2Aec",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from this pair of line charts into a table with resources as rows and quarters as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line charts, first, I refer to {the X-axis labels}<r>[0.103, 0.919, 0.123, 0.942], [0.361, 0.919, 0.381, 0.942], [0.619, 0.919, 0.639, 0.942], [0.877, 0.919, 0.897, 0.942]</r> which are 'Q1', 'Q2' ,'Q3', 'Q4'. These will be our columns. Our row labels will be the different resources which can be obtained from {the legend}<r>[0.335, 0.062, 0.665, 0.103]</r>. The data values are extracted from the line points of each line chart, including the points for distribution percentage {line points for distribution percentage}<r>[0.113, 0.138, 0.887, 0.736]</r> and the familiar ones for utilization percentage {line points for utilization percentage}<r>[0.113, 0.782, 0.887, 0.874]</r>.\nBased on the above thought, the answer is:\nResources,Q1_Distribution,Q2_Distribution,Q3_Distribution,Q4_Distribution,Q1_Utilization,Q2_Utilization,Q3_Utilization,Q4_Utilization\nTextbooks,5000,6000,7500,7000,29.5%,41.12%,36.52%,32.92%\nComputers,6000,7500,8790,8250,36.37%,45.59%,47.14%,42.33%\nOnline Materials,6500,8000,9500,8900,38.96%,47.18%,48.77%,44.28%\nLaboratory Equipment,3500,4000,4650,4500,9.81%,11.59%,10.52%,9.73%\nFaculty Advising,8200,9500,10000,9800,60.71%,70.81%,69.83%,65.07%\nLibraries,7400,8000,9650,8900,51.51%,58.59%,56.08%,52.34%"
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "0XbsRLECLifBK5PH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference of the distribution percentage for laboratory equipments between Q1 and Q3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in distribution percentage for laboratory equipment between Q1 and Q3, I need to look at the {line points for distribution percentage for laboratory equipments}<r>[0.113, 0.736], [0.629, 0.138]</r>. The values associated with these points are 3500 for Q1 and 4650 for Q3. So, the difference would be 4650 - 3500 = 1150.\nBased on the above thought, the answer is:\n1150."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "fcWlqzFhHkmEPcfl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart displaying market trends into a structured table with weeks as rows and market aspects as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, I identify {the x-axis labels}<r>[0.072, 0.939, 0.961, 0.957]</r> that indicate the weeks. I then observe {the legend}<r>[0.235, 0.059, 0.765, 0.090]</r> to ascertain the columns, which are Market_Entropy, Market_Knowledge, and Market_Growth. Assessing each line separately, such as {the line for Market_Entropy}<r>[0.106, 0.359, 0.894, 0.576]</r>, and comparing to {the y-axis labels}<r>[0.943, 0.920, 0.965, 0.096]</r>, I gather the necessary data points for each market aspect across different weeks.\nBased on the above thought, the answer is:\nWeek,Market_Entropy,Market_Knowledge,Market_Growth\n1,0.50,0.20,0.10\n2,0.52,0.30,0.15\n3,0.55,0.40,0.20\n4,0.58,0.50,0.30\n5,0.62,0.60,0.40\n6,0.53,0.60,0.40\n7,0.56,0.65,0.43\n8,0.59,0.70,0.46\n9,0.61,0.75,0.50\n10,0.65,0.77,0.70\n11,0.67,0.80,0.75\n12,0.70,0.72,0.65\n13,0.72,0.87,0.90\n14,0.62,0.90,0.95\n15,0.65,0.95,1.00\n16,0.68,0.98,0.99\n17,0.70,0.99,1.01\n18,0.72,1.01,0.99\n19,0.73,1.00,0.85\n20,0.70,1.02,0.78\n21,0.71,1.03,0.75\n22,0.78,1.04,0.71\n23,0.79,1.05,0.70\n24,0.81,1.10,0.70\n25,0.75,1.15,0.70\n26,0.72,1.20,0.69\n27,0.69,1.20,0.71\n28,0.67,1.20,0.73"
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "zTzRTU0VPWb1MA1x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Market Entropy compare to that of Market Growth over the charted period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line for Market Entropy}<r>[0.106, 0.359, 0.894, 0.576]</r> and {the line for Market Growth}<r>[0.106, 0.218, 0.894, 0.857]</r>, I analyze both the general trend and significant fluctuations. Market Entropy demonstrates gradual increases and a few declines, starting around 0.5 and moving upwards to approximately 0.73. Conversely, Market Growth initially has a slower upward trend from 0.1, steeply rising to 1.0 around Week 15, and then exhibiting significant volatility afterwards. The x values for these data points help in assessing the relative timeframes from {the x-axis}<r>[0.360, 0.939, 0.377, 0.957]</r>.\nBased on the above thought, the answer is:\nMarket Entropy shows a more consistent and moderate rising trend over the charted period. In contrast, Market Growth, while eventually reaching similar high values, displays much more volatility and more pronounced fluctuations in its progression. This comparison underscores different degrees of stability and predictability in the two market aspects."
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "gC2PDVU9Ie186nhM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value recorded for Market Knowledge throughout the charted weeks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest value of Market Knowledge, I trace {the line for Market Knowledge}<r>[0.106, 0.085, 0.894, 0.787]</r> and extract the points corresponding to each tick on {the x-axis}<r>[0.360, 0.939, 0.377, 0.957]</r>. I review the points to find the peak value: Weeks 21-23 show a consistent rise with a peak at Week 25, which is annotated as 1.15 on {the y-axis}<r>[0.943, 0.078, 0.965, 0.096]</r>.\nBased on the above thought, the answer is:\n1.15"
+            }
+        ],
+        "id_raw": 57
+    },
+    {
+        "id": "c7w3urSXKJhVp2vb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you structure the data into a table format with \"Weeks\" as the columns and \"Urbanization Indicator\" as one row?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to generate the tabular data from the line chart, I first find {xlabel}[0.499, 0.952, 0.544, 0.975] which are the \"Weeks\" and act as the column headers. Next, I identify {the row data line}[0.097, 0.154, 0.916, 0.432] which registers the Urbanization Indicator data point for each week. To fill the values, I locate corresponding y values for each week from {yticks}[0.035, 0.894, 0.057, 0.917], [0.035, 0.787, 0.057, 0.810], [0.035, 0.680, 0.057, 0.703], [0.035, 0.573, 0.057, 0.596], [0.035, 0.466, 0.057, 0.489], [0.035, 0.359, 0.057, 0.382], [0.035, 0.252, 0.057, 0.275], [0.035, 0.145, 0.057, 0.168], [0.034, 0.038, 0.057, 0.061] and map these to the plotted points.\nBased on the above thought, the answer is:\nWeeks,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28\nUrbanization Indicator,1.0,0.97,0.98,0.96,0.97,0.94,0.95,0.96,0.94,0.93,0.95,0.96,0.92,1.3,0.92,1.4,0.88,1.37,0.9,1.35,0.89,1.34,0.88,1.30,0.95,1.21,1.10,1.17"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "lS31rMCJ88burC3C",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an analysis of the trend in urban development according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend, I will look at {the Urban development line}[0.097, 0.154, 0.916, 0.432]. The line starts at around {1.0}[0.097, 0.368] and seems to have minor fluctuations until it reaches a significant dip at approximately {0.92}[0.461, 0.411]. This is followed by a sharp rise between {week 14 and 16}[0.491, 0.208, 0.552, 0.154] with peaks at around {1.3 and 1.4}[0.491, 0.208, 0.552, 0.154]. In the later trends, the urban development oscillates around {1.30}[0.794, 0.208] and {0.88}[0.764, 0.432] and finally ends up near {1.17}[0.916, 0.277].\nBased on the above thought, the answer is:\nUrban development, as shown in the chart, experiences minor fluctuations at first, remaining roughly in the range of 0.94 to 1.0. At around the 13th week, there's a decline to a low point of approximately 0.92. This decline is quickly followed by a sharp rise to a high of 1.4 at around the 16th week. Following this peak, the trend fluctuates extensively between an approximate low of 0.88 and a high of 1.3. The chart ends with an Urbanization Indicator at around 1.17. This indicates a high degree of volatility in urban development over the illustrated weeks."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "XDc5QOKbSeXHOC5I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest Urbanization Indicator value according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the lowest Urbanization Indicator value according to the chart, I need to find the minimum point on {the Urbanization Indicator line}[0.097, 0.154, 0.916, 0.432]. This minimum point corresponds to the lowest y value among the points plotted on the y-axis. From observing the chart, it is clear that the smallest point lies near the y-axis label of {0.88}[0.034, 0.038, 0.057, 0.061].\nBased on the above thought, the answer is:\n0.88."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "PP4iy2P5WOUYWNPI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data represented in the line chart concerning Global Trade and Global Supply Chain into a structured table format categorizing each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing the chart, I identified two lines representing Global Supply Chain and Global Trade. To extract their values accurately, I focused on each point across the x-axis labeled with years from {the x-axis labels}<r>[0.152, 0.305, 0.367, 0.581, 0.688]</r>. For the Global Supply Chain, point values were noted at locations such as <r>[0.152, 0.208]</r> in 2016 with the supply chain substantially higher at <r>[0.688, 0.179]</r> in 2020. Global Trade started at a lower point <r>[0.152, 0.237]</r> in 2016 and showed a moderate increase by 2020 at <r>[0.688, 0.271]</r>.\nBased on the above thought, the answer is:\nYear,Global Supply Chain,Global Trade\n2016,0.75,0.70\n2017,0.85,0.75\n2018,0.95,0.80\n2019,1.15,0.90\n2020,1.20,0.95"
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "6soz9wNytWDTnRCz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the impact of the COVID-19 pandemic appear to manifest in the data trends between Global Supply Chain and Global Trade based on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the plots closely, {the line for Global Supply Chain}<r>[0.152, 0.208, 0.688, 0.179]</r> shows a steep increase particularly towards 2020 which is a significant year representing the COVID-19 pandemic impact. Similarly, {the line for Global Trade}<r>[0.152, 0.237, 0.688, 0.271]</r> also demonstrates a rise but less pronounced than the Supply Chain. This indicates that while both sectors were affected by the pandemic, the Global Supply Chain experienced a more substantial disruption or adaptation, likely due to the challenges and changes in logistics and distribution methodologies.\nBased on the above thought, the answer is:\nThe COVID-19 pandemic appears to have a differentiated impact on Global Trade and Global Supply Chain, as seen in the too graphics. The Global Supply Chain exhibits a sharper increase in 2020 which might indicate a stronger disruption or adaptive measures taken by industries to manage the pandemic impact, compared to a more moderate rise in Global Trade."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "Nm9SSh1j326A5H8W",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Global Supply Chain values from 2016 to 2020, according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I analyzed the values of Global Supply Chain starting from {the point in 2016}<r>[0.152, 0.208]</r> with a value of 0.75 and at {the point in 2020}<r>[0.688, 0.179]</r> where it reached 1.20. The formula used is ((Final value - Initial value) / Initial value) * 100%.\nBased on the above thought, the answer is:\n60%"
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "Pw43E4PSmVhWkXAX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the raw data from the line graphs into a table with the platforms as rows and 'Days Active', 'Messages Sent', and 'Emojis Sent' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the JSON data, I identify the platforms from {the x-axis labels}[[0.138, 0.227, 0.161, 0.300], [0.295, 0.227, 0.318, 0.305], [0.452, 0.227, 0.476, 0.306], [0.610, 0.227, 0.633, 0.300], [0.767, 0.227, 0.790, 0.279], [0.924, 0.227, 0.947, 0.292]]. I then extract the corresponding values for each of the three categories: 'Days Active', 'Messages Sent', and 'Emojis Sent' from {the data points in the subplots}[[0.150, 0.210], [0.307, 0.182], [0.464, 0.154], [0.621, 0.127], [0.778, 0.099], [0.936, 0.071], [0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444], [0.150, 0.865], [0.307, 0.795], [0.464, 0.782], [0.621, 0.727], [0.778, 0.773], [0.936, 0.817]].\nBased on the above thought, the answer is:\nPlatform,Days Active,Messages Sent,Emojis Sent\nFacebook,10,2.2,3.5\nWhatsapp,20,3.8,6.7\nInstagram,30,4.6,7.3\nSnapchat,40,5.3,9.8\nTwitter,50,5.1,7.7\nLinkedIn,60,4.3,5.7"
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "70TJKraRSKKKykVm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the line chart, which platform is most active and engages the most as indicated by the factors: days active, messages sent and emojis sent?"
+            },
+            {
+                "from": "gpt",
+                "value": "For determining the platform which is most active and engages the most, I need to take into consideration all the three factors - days active, messages sent and emojis sent. The higher the value for these factors, the higher the engagement. The first part {Days Active line}[[0.150, 0.210], [0.307, 0.182], [0.464, 0.154], [0.621, 0.127], [0.778, 0.099], [0.936, 0.071]] indicates LinkedIn has the most active days. The second part {Messages Sent line}[[0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444]] shows that Snapchat has the highest number of messages sent.  For the third part, {Emojis Sent line}[[0.150, 0.865], [0.307, 0.795], [0.464, 0.782], [0.621, 0.727], [0.778, 0.773], [0.936, 0.817]], Snapchat has the highest number of emojis sent as well.\nBased on the above thought, the answer is:\nSnapchat is the platform that is most active and engages the most as depicted by the line chart. This is established by observing that Snapchat has the highest numbers for both 'messages sent' and 'emojis sent'. Even though LinkedIn shows the maximum days of activity, the user engagement through messages and emojis is higher on Snapchat. Therefore, considering the activity and engagement factors, Snapchat stands out as the most active platform."
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "xcR6Uu8LZgVY3nxD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of messages sent across all platforms over 60 days?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average number of messages sent across all platforms, I first identify the data points representing 'Messages Sent' from {the line of Messages Sent}[[0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444]]. I extract the corresponding values: 2.2, 3.8, 4.6, 5.3, 5.1, and 4.3. I then calculate their average by summing the numbers and dividing by the total count, which is 6 platforms.\nBased on the above thought, the answer is:\n4.2"
+            }
+        ],
+        "id_raw": 62
+    },
+    {
+        "id": "n06unFlQkbybPIti",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the survey data from the grouped bar chart into a structured table format distinguishing between the total population and the percentage of women for each age category?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the bar labels and annotations}<r>[0.15, 0.82, 0.22, 0.85]</r>, which indicate the age categories on the x-axis, and observing each group's bars like {the first group's bars}<r>[0.10, 0.45, 0.23, 0.95]</r>, I could differentiate the metrics for 'Total Population' and 'Percentage of Women'. Each group's differently colored bars {blue for Total Population and green for Percentage of Women}<r>[0.12, 0.50, 0.18, 0.80]</r> provide visual cues. The annotations above each bar indicate the exact values, helping in constructing the data table.\nBased on the above thought, the answer is:\nAge Category,Total Population (millions),Percentage of Women (%)\n20-29,6.5,65\n30-39,5.0,50\n40-49,4.2,55"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "WnZv7hk9L7iomo7Y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the total population trend change across different age categories in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, specifically the blue bars which represent the total population, I will analyze their heights to understand the trend. Starting with {the blue bar for 20-29 age group}<r>[0.115, 0.35, 0.185, 0.80]</r>, it is the tallest, indicating the highest population. This is followed by a shorter {blue bar for 30-39}<r>[0.215, 0.45, 0.285, 0.80]</r>, and the shortest {bar for 40-49}<r>[0.315, 0.55, 0.385, 0.80]</r>, suggesting a declining pattern in the total population as the age increases.\nBased on the above thought, the answer is:\nThe total population shows a declining trend with increasing age categories. The highest population is in the 20-29 age group, followed by a decrease in the 30-39 age group, and the lowest population is observed in the 40-49 age group. This trend is clearly illustrated by the progressively shorter blue bars for each age category on the chart."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "fwuyKI8Qz0dEwOBU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of women across all age categories as indicated on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I need to locate and analyze the green bars that represent the percentage of women across the age categories: {20-29}<r>[0.115, 0.56, 0.185, 0.80]</r>, {30-39}<r>[0.215, 0.65, 0.285, 0.80]</r>, and {40-49}<r>[0.315, 0.60, 0.385, 0.80]</r>. The exact values for these are 65%, 50%, and 55% respectively. To calculate the average: (65 + 50 + 55) / 3 = 56.67%.\nBased on the above thought, the answer is:\n56.67%"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "VYwLjuxA8Yw0Kw0H",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the line chart depicting different stock trends over months into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll begin by scanning the chart, where I locate {the x-axis labels}<r>[0.125, 0.946, 0.132, 0.962]</r> reading as Jan, Feb, Mar, Apr, May which serve as the table headers for our months. Each line on {the chart}<r>[0.125, 0.064, 0.960, 0.935]</r> represents a different stock trend, with their respective values over the months. For instance, the {line for Minimal decline}<r>[0.125, 0.294, 0.960, 0.615]</r> shows values [{point coordinates for each month}<r>[0.125, 0.406], [0.292, 0.447], [0.459, 0.489], [0.626, 0.510], [0.793, 0.447], [0.960, 0.406]</r>]. Repeating this method allows me to extract the data for other trends such as Volatile growth, Abrupt increase, etc.\nBased on the above thought, the answer is:\nTrend,Jan,Feb,Mar,Apr,May\nMinimal decline,1.9,1.0,1.2,1.0,1.2\nAbrupt increase,3.0,3.1,3.2,3.3,4.0\nSubstantial increase,2.0,2.5,3.0,3.5,4.0\nVolatile growth,1.0,2.0,1.0,2.0,1.5\nAccelerating decline,3.0,2.5,2.0,1.5,1.0\nStable growth,2.0,2.0,2.0,2.0,2.0\nAbrupt decline,2.0,2.5,3.0,3.5,1.0"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "PouE3WTfoIyoiNLU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock trend showed the most stable growth over the observed time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing each line's fluctuation and the consistency in values is crucial to determining which stock exhibited the most stable growth. Upon examining {the chart}<r>[0.125, 0.064, 0.960, 0.294]</r> closely, I observe that {the line for Stable growth}<r>[0.125, 0.294, 0.960, 0.615]</r> displays constant values at every monthly tick, which shows no variation in growth. Each point approximately aligns at the same height, which suggests stability in the stock's performance over the given period.\nBased on the above thought, the answer is:\nThe stock trend labeled as 'Stable growth' exhibited the most stable growth. This is evident from its consistent value at all months, indicating no fluctuations and a steady performance throughout the time span from January to May."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "4pMiQXwcwjNh8tqY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom January to May, how much did the stock labeled as 'Abrupt decline' decrease in total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the total decrease in value for the stock labeled as 'Abrupt decline' from January to May, I carefully look at the values of {the line for Abrupt decline}<r>[0.125, 0.782, 0.960, 0.935]</r> at {the position of each month}<r>[0.118, 0.946, 0.975, 0.962]</r>. I extract the values of 3.0 in January and 1.0 in May. The total decrease is calculated by subtracting the May value from the January value: 3.0 - 1.0.\nBased on the above thought, the answer is:\n2.0"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "g5owjMwNPeH20TCr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data on 'Risk Assessment and Management' behavior by different groups into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a table format, I first identify {the x-axis labels}<r>[0.130, 0.294]</r> representing the risk categories 'Medium Risk' and 'High Risk'. I then look at {the legend}<r>[0.141, 0.072, 0.546, 0.100]</r> which indicates the different risk groups 'A', 'B', and 'C'. For each group and its represented risk category, I refer to the {line points for Group A}<r>[0.130, 0.087], [0.946, 0.290]</r>, {line points for Group B}<r>[0.130, 0.406], [0.946, 0.602]</r>, and {line points for Group C}<r>[0.130, 0.744], [0.946, 0.920]</r> for data extraction.\nBased on the above thought, the answer is:\nRisk Category,Risk Group A,Risk Group B,Risk Group C\nMedium Risk,Erratic Behavior,Moderate Increase,Rapid Decline\nHigh Risk,Erratic Behavior,Moderate Increase,Rapid Decline"
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "lTIFfOmoz0sE5UYw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk group shows the highest volatility in behavior based on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the graph, I analyze the line trends of each risk group. {The line for Risk Group A}<r>[0.130, 0.087, 0.946, 0.290]</r> shows consistent erratic behavior without major fluctuations. {The line for Risk Group B}<r>[0.130, 0.406, 0.946, 0.602]</r> depicts a steady rise with moderate fluctuation. However, {the line for Risk Group C}<r>[0.130, 0.744, 0.946, 0.920]</r> shows a sharp decline from a high point in 'Medium Risk' to a lower point in 'High Risk', indicating significant volatility.\nBased on the above thought, the answer is:\nRisk Group C shows the highest volatility in behavior according to the chart. The sharp decline in their behavior from 'Medium Risk' to 'High Risk' signifies a greater reaction to changes in risk levels, suggesting higher volatility compared to the other groups."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "OAzkWz6yovEhKxAR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in risk management assessment for Risk Group B from 'Medium Risk' to 'High Risk'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase for Risk Group B, I locate {the individual data points for Group B}<r>[0.130, 0.406], [0.946, 0.602]</r>. The assessment value at 'Medium Risk' is noted at 0.8, and at 'High Risk' it is estimated at 2.9. Calculate the increase by subtracting the 'Medium Risk' value from the 'High Risk' value.\nBased on the above thought, the answer is:\n2.1"
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "oy8JXS8jNQtROum2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table representing effectiveness of risk control measures across various risk levels derived from the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify {the line plot coordinates for Risk Control effectiveness}<r>[0.079, 0.044, 0.488, 0.449]</r> on the graph. I observe intersections of this line with {y-ticks}<r>[0.079, 0.449, 0.070, 0.063]</r> to determine numerical data at each point. Reviewing {the x-axis labels}<r>[0.038, 0.459, 0.497, 0.473]</r>, I correlate these intersections to 'Low', 'Moderate', 'High', 'Critical', and 'Catastrophic' risk levels. Extracting these values, I generate the structured tabular data.\nBased on the above thought, the answer is:\nRisk Level,Effectiveness\nLow,100\nModerate,150\nHigh,200\nCritical,300\nCatastrophic,260"
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "V4ZuBeWHcQng42ai",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the trend in the line chart suggest about the effectiveness of risk control measures as risk levels increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line trend}<r>[0.079, 0.044, 0.488, 0.449]</r> against {the risk levels on x-axis}<r>[0.038, 0.459, 0.497, 0.473]</r>, it's noticeable that the effectiveness of risk control measures initially rises as risk levels escalate from Low to Critical. This is visible by the ascending connection points from {Low}<r>[0.079, 0.412]</r> through {High}<r>[0.265, 0.265]</r> peaking at {Critical}<r>[0.414, 0.136]</r>. However, a slight decline is observed at the {Catastrophic}<r>[0.488, 0.063]</r> level, suggesting a decrease in control measure effectiveness at extremely high risks.\nBased on the above thought, the answer is:\nBased on the line chart, the trend suggests that the effectiveness of risk control measures improves as risk levels increase from Low to Critical, indicating that the measures are generally more effective at higher risks. However, there is a slight decline in effectiveness at the Catastrophic level, which might indicate limitations or challenges in managing extremely high risks."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "FxvE40Vg8OnIyHYQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the effectiveness of risk control measures increase from moderate to critical risk level?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to extract the effectiveness of risk control measures from {the line points for Moderate and Critical risk levels}<r>[0.186, 0.315, 0.343]</r>. Observing the graph, I see the values are 150 for Moderate and a peak of 300 for Critical, extracted from the intersections with {y-axis ticks}<r>[0.079, 0.320]</r>. The calculated increase in effectiveness is 300 - 150.\nBased on the above thought, the answer is:\n150"
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "rs6ET4QWb8KFcebb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that shows the time points and the corresponding values for the 'Sudden Decrease' scenario as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data for the 'Sudden Decrease' scenario from the chart, I analyze {the line for Sudden Decrease}<r>[0.130, 0.122, 0.960, 0.260]</r> which contains various {data points}<r>[0.130, 0.122], [0.213, 0.145], [0.296, 0.168], [0.379, 0.185], [0.462, 0.202], [0.545, 0.219], [0.628, 0.231], [0.711, 0.237], [0.794, 0.242], [0.877, 0.248], [0.960, 0.260]</r> across the time from 0 to 10 years. I observe each coordinate corresponding to the specific time point and extract the value indicated by the y-axis position relative to the axis values.\nBased on the above thought, the answer is:\nTime, Sudden Decrease(Value)\n0, 1.0\n1, 0.8\n2, 0.6\n3, 0.45\n4, 0.3\n5, 0.15\n6, 0.05\n7, 0.0\n8, -0.05\n9, -0.1\n10, -0.2"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "1gDXtDRa9q1Vouf5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend demonstrates more stability over the years, 'Sudden Decrease' or 'Volatile Movement'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate stability, I examine the line plots for both trends. Observing {the line for Sudden Decrease}<r>[0.130, 0.122, 0.960, 0.260]</r>, the values consistently decrease with small incremental changes between points, suggesting mild fluctuations. On the other hand, {the line for Volatile Movement}<r>[0.130, 0.763, 0.960, 0.859]</r> shows larger vertical displacements at various points, indicating significant value changes over the years, such as the rise at year 5 or the drop at year 8. The fluctuations are substantially wider compared to the 'Sudden Decrease'.\nBased on the above thought, the answer is:\nThe 'Sudden Decrease' trend demonstrates more stability over the years than the 'Volatile Movement' trend. The values in the 'Sudden Decrease' trend change gradually and consistently downward, with smaller deviations between consecutive years. In contrast, the 'Volatile Movement' trend exhibits larger ups and downs, indicating less stability."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "edUUX3dlXgaKX1Hp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the value fluctuate from the lowest point to the highest point in the 'Periodic Fluctuations' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the value fluctuation in the 'Periodic Fluctuations' trend, I refer to {the points on the line for Periodic Fluctuations}<r>[0.130, 0.423, 0.960, 0.538]</r>. By assessing the y-coordinates of the highest point at year 5 {5.0, y: 1.2}<r>[0.545, 0.423]</r> and the lowest point at year 3 {3.0, y: 0.9}<r>[0.379, 0.538]</r>, I can calculate the difference. The highest value is approximately 1.2 and the lowest is about 0.9, giving a fluctuation of 1.2 - 0.9 = 0.3.\nBased on the above thought, the answer is:\n0.3"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "iy4PTD0f6wA310ZY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table showing the investments for each type of investment over the years from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the x-axis labels}[[0.111, 0.294], [0.196, 0.294], [0.282, 0.294], [0.368, 0.294], [0.454, 0.294], [0.539, 0.294], [0.625, 0.294], [0.711, 0.294], [0.797, 0.294], [0.882, 0.294], [0.968, 0.294]] representing the years 2007 to 2017, and noting {the points on the Private Equity line}[[0.111, 0.165], [0.196, 0.193], [0.282, 0.233], [0.368, 0.210], [0.454, 0.170], [0.539, 0.161], [0.625, 0.147], [0.711, 0.124], [0.797, 0.119], [0.882, 0.117], [0.968, 0.101]], {the Venture Capital line}[[0.111, 0.553], [0.196, 0.569], [0.282, 0.569], [0.368, 0.549], [0.454, 0.553], [0.539, 0.558], [0.625, 0.527], [0.711, 0.487], [0.797, 0.469], [0.882, 0.454], [0.968, 0.431]], and {the Angel Investment line}[[0.111, 0.878], [0.196, 0.849], [0.282, 0.837], [0.368, 0.822], [0.454, 0.811], [0.539, 0.801], [0.625, 0.791], [0.711, 0.782], [0.797, 0.774], [0.882, 0.763], [0.968, 0.747]], I can create a table detailing investment amounts by type across these years.\nBased on the above thought, the answer is:\nYear,Private Equity,Venture Capital,Angel Investment\n2007,2.4,0.8,0.6\n2008,2.1,0.7,0.75\n2009,1.66,0.7,0.81\n2010,1.92,0.83,0.89\n2011,2.35,0.8,0.95\n2012,2.45,0.77,1.0\n2013,2.6,0.97,1.05\n2014,2.85,1.23,1.1\n2015,2.9,1.35,1.14\n2016,2.92,1.45,1.2\n2017,3.1,1.6,1.28"
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "R4yqx9XTVplam1KB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the overall trends in investment per type as depicted in the chart, how do the fluctuations in Angel Investment compare to those in Private Equity from 2007 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the Angel Investment trend line}[[0.111, 0.878], [0.196, 0.849], [0.282, 0.837], [0.368, 0.822], [0.454, 0.811], [0.539, 0.801], [0.625, 0.791], [0.711, 0.782], [0.797, 0.774], [0.882, 0.763], [0.968, 0.747]] and {the Private Equity trend line}[[0.111, 0.165], [0.196, 0.193], [0.282, 0.233], [0.368, 0.210], [0.454, 0.170], [0.539, 0.161], [0.625, 0.147], [0.711, 0.124], [0.797, 0.119], [0.882, 0.117], [0.968, 0.101]], the fluctuations in Angel Investment show a steady increase in investment value whereas Private Equity exhibits a more variable trend with a notable decline until 2009 followed by a gradual increase. This consistency in growth for Angel Investment indicates a steadier confidence in this type of funding.\nBased on the above thought, the answer is:\nAngel Investment shows a consistent and steady increase, which contrasts with Private Equity, where the trend is more fluctuating with periods of both decline and growth across the examined years. This implies that Angel Investment might present more stability and continuous interest over time compared to the more cyclical nature of Private Equity."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "9Hy3A8SbrlTjTKLj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the year with the lowest investment in Venture Capital as noted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the {Venture Capital investment values}[[0.196, 0.569], [0.282, 0.569], [0.368, 0.549], [0.454, 0.553], [0.539, 0.558], [0.625, 0.527], [0.711, 0.487], [0.797, 0.469], [0.882, 0.454], [0.968, 0.431]] from the line on the chart and comparing the y-coordinates for the lowest point, which corresponds to 2009 with a value of 0.7 trillion dollars.\nBased on the above thought, the answer is:\n2008"
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "gf3w8ScU8hIlxxWx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the bar chart into a structured table indicating GDP growth of countries over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar chart, I effectively categorized data according to {the x-axis labels}<r>[0.133, 0.064, 0.975, 0.294]</r> which denote the years ranging from 1990 to 2020. Furthermore, {the colors and labels on the legend}<r>[0.525, 0.044, 0.647, 0.059]</r> showed distinctions for each country, allowing me to determine which bars correspond to the USA, China, India, Brazil, and Russia respectively. The heights of the bars correlating to the GDP values are visually discerned from the {y-axis}<r>[0.025, 0.158, 0.048, 0.200]</r>.\nBased on the above thought, the answer is:\nYear,USA,China,India,Brazil,Russia\n1990,2000,500,400,300,0\n2000,2500,1000,500,450,500\n2010,3000,2500,800,700,1250\n2020,3500,4000,1100,950,1800"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "jsvvF6tXGZI6Szpu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country showcased the greatest economic progress in the last decade according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the growth in bar heights from 2010 to 2020, {the bar for China}<r>[0.624, 0.436, 0.856, 0.192]</r> showed the most significant increase compared to {the bars for the USA}<r>[0.474, 0.293, 0.715, 0.324]</r>, {India}<r>[0.774, 0.526, 0.924, 0.485]</r>, {Brazil}<r>[0.874, 0.644, 1.024, 0.596]</r>, and {Russia}<r>[0.974, 0.633, 0.984, 0.830]</r>, representing a sharp rise from 2500 billion to 4000 billion.\nBased on the above thought, the answer is:\nChina showcased the greatest economic progress in the last decade according to the bar chart. The immense rise in GDP from 2500 billion to 4000 billion in just 10 years indicates the highest economic development among the countries considered."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "Gj0PgME4m7V8Cl4l",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average GDP growth for China from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Consulting {the bar chart data points for China}<r>[0.524, 0.450, 0.856, 0.253]</r>, the GDP values at each decade are: 500 for 1990, 1000 for 2000, 2500 for 2010, and 4000 for 2020. The average GDP growth can be computed by summing these values and dividing by the number of years, which is 4.\nBased on the above thought, the answer is:\n2000"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "P6D50NdvEbJaJ6iR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the sales data from the line chart into a structured table with years as columns and products as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the sales data, I first observe  {the x-axis tick labels}[[0.103, 0.305, 0.162, 0.321], [0.264, 0.305, 0.322, 0.321], [0.424, 0.305, 0.483, 0.321], [0.585, 0.305, 0.643, 0.321], [0.745, 0.305, 0.804, 0.321], [0.906, 0.305, 0.964, 0.321], [1.066, 0.305, 1.125, 0.321]] which indicate the years. Next, I identify {the legend}[[0.885, 0.134, 0.933, 0.156], [0.885, 0.167, 0.946, 0.189]], which lists products A, B, C, and D. Tracking {the colored lines on the chart}[[0.133, 0.075, 0.935, 0.716], [0.133, 0.395, 0.935, 0.604], [0.133, 0.050, 0.935, 0.283], [0.133, 0.140, 0.935, 0.925]] corresponding to each product, I extract sales data for each year and organize it in the table.\nBased on the above thought, the answer is:\nYear,Product A,Product B,Product C,Product D\n2010,1000,1200,800,900\n2011,1150,1400,850,920\n2012,1200,1600,870,930\n2013,1250,1800,900,950\n2014,1300,2000,930,970\n2015,1350,2200,950,990\n2016,1400,2400,970,1000\n2017,1450,2600,1000,1020\n2018,1500,2800,1030,1040\n2019,1550,3000,1060,1060\n2020,1600,3200,1100,1080"
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "Z8wRzxN53P1u1gau",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows the most consistent growth throughout the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the slopes of the lines {for each product}[[0.133, 0.075, 0.935, 0.716], [0.133, 0.395, 0.935, 0.604], [0.133, 0.050, 0.935, 0.283], [0.133, 0.140, 0.935, 0.925]]. I note that while all products show growth, {the line for Product B}[[0.133, 0.395, 0.935, 0.604]] has the steepest and most consistent upward trajectory throughout the years without any plateau or decline. This suggests that Product B not only increased the most in sales but did so without any major fluctuations.\nBased on the above thought, the answer is:\nProduct B shows the most consistent growth throughout the decade. Its line displays a steady upward trend, unlike the other products which either have a slower increase or slight fluctuations over the years."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "D6wh8LLFu1cfNZFo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the sales of Product B increase from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I locate {the line for Product B}[[0.133, 0.395, 0.935, 0.604]] and observe the plotted points for 2010 and 2020. According to the coordinates, the sales for Product B in 2010 were approximately 1200, and by 2020, {the final plotted point}[[0.935, 0.604]] indicates sales have increased to approximately 3200. To find the increase, I subtract the initial value from the final value: 3200 - 1200 = 2000.\nBased on the above thought, the answer is:\n2000."
+            }
+        ],
+        "id_raw": 73
+    },
+    {
+        "id": "LXHd7URg05YTvwNS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data of the new and established business ownership rates over the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a structured table, I need to determine the column headers, row labels, and the data values. Looking at {the x-axis tick labels}[[0.049, 0.637, 0.084, 0.646], [0.229, 0.637, 0.264, 0.646], [0.409, 0.637, 0.444, 0.646], [0.589, 0.637, 0.625, 0.646], [0.770, 0.637, 0.805, 0.646], [0.950, 0.637, 0.985, 0.646]], they represent the years which will serve as the columns of the table. {The lines on the chart}[[0.066, 0.402, 0.968, 0.619], [0.066, 0.790, 0.968, 0.933]] represent the Established and New Business Ownership rates respectively and will serve as the rows of the table. The data points on the chart lines corresponding to each year are the values to be tabulated.\nBased on the above thought, the answer is:\nYear,Established Business Ownership Rate,New Business Ownership Rate\n2010,7.6,2.1\n2011,7.3,2.3\n2012,6.7,2.6\n2013,6.5,2.3\n2014,6.2,2.5\n2015,6.1,2.4\n2016,6.4,2.5\n2017,6.9,2.5\n2018,7.2,2.3\n2019,7.6,2.6\n2020,8.0,2.5"
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "NIuIOJ6Z0w9WyT8f",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart data, which type of business ownership showed a consistently increasing trend over the years? Support your answer with observations from the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which type of business ownership showed a consistently increasing trend, I need to look at the trajectories of the lines representing each business ownership type. Looking at {the line representing Established Business Ownership Rate}[0.066, 0.402, 0.968, 0.619], I see a downward trend from 2011 to 2015, followed by an upward trend to 2020. For {New Business Ownership Rate}[0.066, 0.790, 0.968, 0.933], despite some fluctuations, the overall trend seems to be increasing from 2010 to 2020. Hence, it can be said that New Business Ownership showed a consistently increasing trend.\nBased on the above thought, the answer is:\nThe New Business Ownership Rate showed a consistently increasing trend over the years. Despite some minor fluctuations, the general trajectory of the line for New Business Ownership Rate on the chart shows an upward movement from 2010 to 2020, demonstrating a consistent increase."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "oKqVoJt9q29o9r0q",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the New Business Ownership Rate increase from 2011 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in the New Business Ownership Rate from 2011 to 2019, I need to find the corresponding values in those years. Looking at the line chart, I locate {the point representing 2011}[0.156, 0.875], which is 2.3% and {the point representing 2019}[0.877, 0.790], which is 2.6%. The increase would then be 2.6 - 2.3 = 0.3.\nBased on the above thought, the answer is:\n0.3."
+            }
+        ],
+        "id_raw": 74
+    },
+    {
+        "id": "rgSpzZUh5pl90EVT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the data from the line chart into a table format showing the years vertically and the categories (Farm Profit, Food Sales, Agricultural Imports) horizontally?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table using the data from the line chart, I analyze {the x-tick labels for the years}<r>[0.036, 0.305, 0.094, 0.321]</r>, which correspond to '2004', '2006', '2008', '2010', '2012', '2014', and '2016'. These identify the rows of the table. I review {the legend}<r>[0.453, 0.037, 0.662, 0.065]</r>, which indicates 'Farm Profit', 'Food Sales', and 'Agricultural Imports'. These titles furnish the column headers. For the table values, I trace each category's line plot back to its {associated data points}<r>[0.147, 0.077, 0.968, 0.261]</r> for a specific year and note down the corresponding values.\nBased on the above thought, the answer is:\nYear,Farm Profit,Food Sales,Agricultural Imports\n2005,10000,20000,5000\n2006,10500,21000,5300\n2007,11300,22200,5700\n2008,10200,23000,5900\n2009,9500,24300,6200\n2010,10000,25000,7000\n2011,9000,26000,8000\n2012,8500,27000,8500\n2013,9000,28000,8700\n2014,8800,29000,9100\n2015,8600,30000,9150"
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "MZhyz5NE6b1NRqrb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the observed trends, which of the categories between Farm Profit, Food Sales, or Agricultural Imports showed steadier growth over the analyzed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I examine {the line plots}<r>[0.147, 0.077, 0.968, 0.893]</r> for each category across the timeline. Food Sales and Agricultural Imports exhibit a general upward slope indicating growth. Notably, {Agricultural Imports}<r>[0.147, 0.720, 0.968, 0.893]</r> shows the most consistent incline with smaller fluctuations. Conversely, Farm Profit demonstrated dips and peaks <r>[0.147, 0.077, 0.968, 0.261]</r>, reflecting more volatility compared to a steady growth.\nBased on the above thought, the answer is:\nAgricultural Imports exhibited the steadiest growth over the analyzed period, evidenced by its consistent and gradual upward trend in the chart."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "ypgbcAb0lBMGdgrq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Agricultural Imports from 2007 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I first locate the year 2007's value for {Agricultural Imports}<r>[0.311, 0.864]</r> which is 5700, and the year 2015's value at <r>[0.968, 0.720]</r> which is 9150. The calculation needed is ((9150 - 5700) / 5700) * 100.\nBased on the above thought, the answer is:\n60.53%."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "bE9jyobWddzV1FgI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the line chart into a table with years as columns and each indicator as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I identify {the x-axis labels}<r>[0.074, 0.312, 0.083, 0.328], [0.253, 0.312, 0.262, 0.328], [0.432, 0.312, 0.441, 0.328], [0.611, 0.312, 0.620, 0.328], [0.790, 0.312, 0.799, 0.328], [0.964, 0.312, 0.983, 0.328]</r> to determine the years from 2010 to 2021. I also examine {the legend}<r>[0.086, 0.727, 0.332, 0.779]</r> which states the indicators: GDP Growth Rate, Inflation Rate, Unemployment Rate, and Interest Rate. Utilizing the line plots for each indicator, such as {the line for GDP Growth Rate}<r>[0.079, 0.903, 0.884, 0.946]</r> and so forth, I extract each point value to fill in the corresponding cells in the table.\nBased on the above thought, the answer is:\nYear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021\nGDP Growth Rate,2.5,2.3,3.1,1.9,2.6,1.4,2.8,2.9,2.1,2.2,1.0,3.4\nInflation Rate,1.5,2.0,1.7,1.8,1.9,1.0,1.3,1.7,1.6,1.8,0.7,1.9\nUnemployment Rate,7.0,6.9,6.1,5.6,5.3,6.2,4.9,4.4,3.9,3.7,8.1,5.2\nInterest Rate,0.5,0.5,0.75,0.75,0.5,0.35,0.5,0.65,0.75,0.75,0.25,0.4"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "wvtbwmTO1mdulhiK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the general economic trend based on the interest rate movements between 2016 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investigating the {interest rate trend}<r>[0.079, 0.730, 0.884, 0.904]</r> between 2016 and 2021, I notice the line plot initially rises from 2016 to 2018 and subsequently drops significantly in 2020. The rise in the interest rates from 2016 to 2018 suggests tightening monetary policy possibly due to economic strengthening. The sharp decline in 2020 is indicative of likely monetary easing measures in response to economic challenges, such as the impacts of a global event like the pandemic.\nBased on the above thought, the answer is:\nThe general economic trend inferred from the interest rate movements between 2016 and 2021 suggests a phase of monetary tightening followed by a significant easing, likely in response to fluctuating economic conditions impacted by external factors like a global pandemic."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "0b40w01qRr2utv3w",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many years show an increase in GDP growth from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing {the line for GDP growth}<r>[0.079, 0.903, 0.884, 0.946]</r> on the chart, I track the values for each year and compare them with the prior year's values. By noting the points where the trend line moves upwards from one year to the next, I calculate the number of times this increase occurs.\nBased on the above thought, the answer is:\n6 years."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "ITgyk9T56CGLWPcy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data showing the Yield Trend Over Time into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, I observe the points on {the line for Yield Trend}[[0.130, 0.110, 0.960, 0.279]]. Each point represents a year and its corresponding yield value. Starting from the first point at year 0 with a yield of 2.2 to the last point at year 10 with a yield of 1.1.\nBased on the above thought, the answer is:\nYear,Yield\n0,2.2\n1,2.0\n2,1.9\n3,1.8\n4,1.7\n5,1.6\n6,1.5\n7,1.4\n8,1.3\n9,1.2\n10,1.1"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "DcrIwpgZojSUOvpm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend presented in the line chart displays the most consistent increase over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the consistency, I closely examine each line in the chart. {The line representing Fossil fuel Emission}[[0.130, 0.742, 0.960, 0.908]] demonstrates a continuous and steady increase, moving up from -1.2 to 0.6. The consistently upward trajectory without any downturns across the {xtick intervals}[[0.130, 0.935], [0.960, 0.935]] indicates that the Fossil fuel Emission has the most consistent increase among the trends plotted.\nBased on the above thought, the answer is:\nFossil fuel Emission displays the most consistent increase. It starts at -1.2 and steadily rises to 0.6, without any year showing a reversal or plateau in the trend."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "FlpQMizRGpdg2hYu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Population Change from year 0 to year 10 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing {the line of Population Change}[[0.130, 0.408, 0.960, 0.592]], I analyze the values marked at each {xtick interval}[[0.130, 0.615], [0.960, 0.615]]. The initial value at year 0 is 1.0, and by year 10 it has increased to 2.6.\nBased on the above thought, the answer is:\n1.6"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "qDwN6aiSDQgUSUTt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and format the data from the line chart into a table with years as columns and domains as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis tick labels}<r>[0.082, 0.301, 0.985, 0.313]</r> to set the years as the columns. Then, I reference {the legend}<r>[0.829, 0.037, 0.969, 0.058]</r> to determine the rows, which represent different service quality domains: Healthcare, Education, and Public Transit. I look at each line corresponding to these domains, checking {the points}<r>[0.087, 0.188], [0.175, 0.258], [0.264, 0.284], [0.353, 0.276], [0.442, 0.206], [0.531, 0.192], [0.620, 0.152], [0.709, 0.118], [0.798, 0.082], [0.887, 0.063], [0.976, 0.071]</r>, extract the values and organize them according to the year and domain.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,3.2,2.8,3.1\n2012,3.3,2.9,2.9\n2013,3.4,3.0,2.7\n2014,3.4,3.1,2.5\n2015,3.3,3.2,2.3"
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "gLmpg7pjuvOFbSIh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich domain shows a rising trend in quality scores over the period from 2011 to 2015 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend for each domain, I observe the lines for Healthcare, Education, and Public Transit. By examining {the line for Education}<r>[0.087, 0.258], [0.175, 0.340], [0.264, 0.284], [0.353, 0.276], [0.442, 0.206], [0.531, 0.192], [0.620, 0.152], [0.709, 0.118], [0.798, 0.082], [0.887, 0.063], [0.976, 0.071]</r>, I track a gradual increase in the points, starting lower and consistently climbing throughout the years.\nBased on the above thought, the answer is:\nThe domain that shows a rising trend in quality scores from 2011 to 2015 is Education. This is evident from the progressively rising position of its line on the chart, indicating an ongoing improvement in Education service quality over the years."
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "BYpdK7c1xKrx1XGE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total decline in Public Transit quality score from 2011 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total decline in the Public Transit score from 2011 to 2015, I first locate {the line points for Public Transit}<r>[0.087, 0.188], [0.976, 0.071]</r>, which represent the scores from 2011 and 2015, respectively. The score starts at 3.1 in 2011 and ends at 2.3 in 2015. The total decline is calculated by subtracting the final score from the initial score, resulting in 3.1 - 2.3.\nBased on the above thought, the answer is:\n0.8"
+            }
+        ],
+        "id_raw": 78
+    },
+    {
+        "id": "21RJ4Xyun7cRfGfe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data presented in the bar chart into a table format? The table should have the influence trends as columns and media outlets as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate the table, firstly, I identify {the x-axis tick labels}[[0.200, 0.018, 0.232, 0.048], [0.770, 0.018, 0.799, 0.048]] which represent the row headers or the specific media outlets, BBC and FOX News respectively. For the column headers or the trends, I look at the different {colored bars}[[0.180, 0.072, 0.219, 0.728], [0.228, 0.072, 0.267, 0.710], [0.276, 0.072, 0.316, 0.533], [0.324, 0.072, 0.363, 0.783], [0.372, 0.072, 0.411, 0.571], [0.632, 0.072, 0.672, 0.496], [0.680, 0.072, 0.719, 0.603], [0.728, 0.072, 0.768, 0.322], [0.776, 0.072, 0.815, 0.635], [0.824, 0.072, 0.863, 0.314]] and refer to the {legend}[[0.029, 0.863, 0.108, 0.891], [0.157, 0.863, 0.237, 0.891], [0.295, 0.863, 0.368, 0.891], [0.435, 0.863, 0.506, 0.891], [0.572, 0.863, 0.646, 0.891]] to understand the different trends corresponding to each bar's color, these being Gradual decline, Subdued decline, Volatile movement, Rapid increase, and Declining pattern. The value of each cell can be determined by analyzing the height of each bar in the chart, with respect to the {y-axis tick labels}[[0.015, 0.071, 0.067, 0.093], [0.015, 0.143, 0.067, 0.165], [0.015, 0.215, 0.067, 0.237],[0.015, 0.285, 0.067, 0.308],[0.015, 0.359, 0.067, 0.382],[0.015, 0.430, 0.067, 0.453],[0.015, 0.501, 0.067, 0.524],[0.015, 0.574, 0.067, 0.597],[0.015, 0.645, 0.067, 0.668],[0.015, 0.716, 0.067, 0.739],[0.015, 0.788, 0.067, 0.811]].\nBased on the above thought, the answer is:\nMedia Outlet,Gradual decline,Subdued decline,Volatile movement,Rapid increase,Declining pattern\nBBC,3.6,2.8,2.3,3.5,2.4\nFOX News,2.2,2.5,1.4,2.8,1.5"
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "DwDePWSpqjRhfQxV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information depicted in the bar chart, which media outlet demonstrates a more consistent influence across various trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the media outlet demonstrating a more consistent influence across various trends, I would need to compare the variability of their effectiveness ratings across all trends. Observing the {bar chart}, particularly the bars for BBC [[0.180, 0.072, 0.219, 0.728], [0.228, 0.072, 0.267, 0.710], [0.276, 0.072, 0.316, 0.533], [0.324, 0.072, 0.363, 0.783], [0.372, 0.072, 0.411, 0.571]] and Fox News [[0.632, 0.072, 0.672, 0.496], [0.680, 0.072, 0.719, 0.603], [0.728, 0.072, 0.768, 0.322], [0.776, 0.072, 0.815, 0.635], [0.824, 0.072, 0.863, 0.314]], the BBC bars have a closer range of heights indicating a more consistent influence. This consistent influence demonstrated by BBC can also be quantified by comparing their minimum and maximum effectiveness ratings, being 2.3 and 3.6, respectively, compared to FOX News with their minimum and maximum ratings being 1.4 and 2.8, respectively.\nBased on the above thought, the answer is:\nThe media outlet that demonstrates more consistent influence across various trends, according to the bar graph, is BBC. This is due to the smaller range between their highest and lowest effectiveness ratings across the different influence trends, as compared to FOX News."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "zmYpU5RdlKMpLs4g",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_4_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest effectiveness rating reached by either media outlet and under what influence trend does it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to find the highest bar in the bar chart. Observing {the bar chart}[[0.180, 0.072, 0.411, 0.783], [0.632, 0.072, 0.863, 0.635]], I see the maximum bar is for BBC under Rapid increase trend with a height of 3.5 looking at the {y-axis label}[0.324, 0.072, 0.363, 0.783].\nBased on the above thought, the answer is:\nThe highest effectiveness rating reached by either media outlet is 3.5 by BBC under the \"Rapid increase\" trend."
+            }
+        ],
+        "id_raw": 79
+    },
+    {
+        "id": "MUld4CyF8M2gO6e1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the line chart into a table format with Years in the first column and CO2 Reduction (%) in the second column?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table format, I first look at {the x-axis tick labels}[[0.079, 0.757, 0.149, 0.827], [0.161, 0.757, 0.231, 0.827], [0.243, 0.757, 0.313, 0.827], [0.325, 0.757, 0.395, 0.827], [0.407, 0.757, 0.478, 0.827], [0.489, 0.757, 0.560, 0.827], [0.571, 0.757, 0.642, 0.827], [0.654, 0.757, 0.724, 0.827], [0.736, 0.757, 0.806, 0.827], [0.818, 0.757, 0.888, 0.827], [0.900, 0.757, 0.970, 0.827]], which represent the Years from 2010 to 2020. The corresponding values of CO2 Reduction can be extracted from {the plotted points}[[0.114, 0.728], [0.196, 0.699], [0.278, 0.669], [0.360, 0.664], [0.442, 0.631], [0.525, 0.591], [0.607, 0.459], [0.689, 0.357], [0.771, 0.318], [0.853, 0.249], [0.935, 0.169]] for each Year. The plotted points provide us with the percentage values for CO2 Reduction for each Year.\nBased on the above thought, the answer is:\nYear,CO2 Reduction(%)\n2010,10.5\n2011,13.2\n2012,15.98\n2013,16.4\n2014,19.5\n2015,23.2\n2016,35.5\n2017,45.0\n2018,48.6\n2019,55.0\n2020,62.5"
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "9eS3VRQJqx0tfdYQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you describe the trend of CO2 reduction from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend, I need to analyze the line plot for CO2 reduction. Looking at {the plotted line}[[0.114, 0.169, 0.935, 0.728]], it starts at 10.5 in 2010 and ends at 62.5 in 2020, showing a clear overall upward trend. The line seems to be gradually increasing, indicating that the CO2 reduction has been on the rise in these years. However, notable changes in the rate of increase can be noticed starting from the year 2016, where the slope of the line became steeper, suggesting a faster rate of CO2 reduction.\nBased on the above thought, the answer is:\nBased on the chart, CO2 reduction had a marked increasing trend from 2010 to 2020. The growth was gradual initially from 2010 until 2016. However, from 2016 onwards, the rate of CO2 reduction increased at a much faster pace, suggesting significant progress in efforts or policies aimed at reducing CO2 emissions."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "k31k8JnCdiEr4f4a",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average yearly progression of CO2 Reduction from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average yearly progression of CO2 Reduction, I need to locate {the line points}[[0.114, 0.728], [0.196, 0.699], [0.278, 0.669], [0.360, 0.664], [0.442, 0.631], [0.525, 0.591], [0.607, 0.459], [0.689, 0.357], [0.771, 0.318], [0.853, 0.249], [0.935, 0.169]] and extract the corresponding percentages: 10.5, 13.2, 15.98, 16.4, 19.5, 23.2, 35.5, 45.0, 48.6, 55.0, 62.5. Now, I calculate the yearly progression by subtracting the percentage of the previous year from the current year for each year from 2011 to 2020. Totalling these gives me the total progression from 2010 to 2020. Dividing this by the number of years (10), I can calculate the average yearly progression.\nBased on the above thought, the answer is:\n~5.2"
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "FeDNyaESndQtjGBU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that summarizes the data represented in this chart, with the rows as the distinct years and the columns as vehicle sales in those years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can create a table from the data presented on the line chart. First, I'll identify {the x-axis tick labels}<r>[0.088, 0.903, 0.132, 0.931], [0.173, 0.903, 0.217, 0.931], ..., [0.937, 0.903, 0.981, 0.931]</r> as the row headers, which represent the different years. Then, I'll note down {the y-values for each point}<r>[0.110, 0.599], [0.195, 0.732], ..., [0.959, 0.087]</r> which represent the vehicle sales in those respective years.\nBased on the above thought, the answer is:\nYear,Vehicle Sales\n2000,55000\n2001,48000\n2002,52000\n2003,45000\n2004,59000\n2005,74000\n2006,62000\n2007,67000\n2008,50000\n2009,68000\n2010,82000"
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "5kpzkmsP7FwlB6wJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line chart, what can you say about the trend in vehicle sales from the year 2001 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend in vehicle sales from 2001 to 2010, I need to closely examine {the line plot}<r>[0.110, 0.599, 0.959, 0.789]</r>. From 2001 to around 2004, there seems to be a dip in sales from 48000 to 45000 after a slight increase. Sales then exhibit a steady increase from 45000 in 2003 to a peak of 74000 in 2005. Following this, sales decrease again to 50000 in 2008 before rising again to 82000 in 2010.\nBased on the above thought, the answer is:\nThe trend in vehicle sales from 2001 to 2010 shows significant fluctuations. Though there are periods of increase, such as from 2003 to 2005, there are also periods of decline, such as from 2005 to 2008. However, the overall sales end in 2010 at a higher point than they were in 2001, indicating long-term growth despite short-term fluctuations."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "U9h5VkGNK1x8MV34",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in vehicle sales from the year 2008 to the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in vehicle sales from 2008 to 2010, I would identify the sales values for those years by looking at {the point for the year 2008}<r>[0.789, 0.694]</r> and {the point for the year 2010}<r>[0.959, 0.087]</r>. Their corresponding y-values denote the sales for those years, which are 50000 and 82000 respectively. The percentage increase can be calculated as (Final value - Initial value) / Initial value * 100.\nBased on the above thought, the answer is:\n64%."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "vfwnUnFXBPQYgQ5P",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table with years as columns and different types of energy resources as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I am observing that {the x-axis}[0.100, 0.039, 0.934, 0.157] labels represent the years which would serve as our column headers. The {legend}[0.934, 0.163, 0.998, 0.844] is representing different types of energy resources which will be our row labels. Now for the values inside the table, I need to look at the line points for each resource in the line chart and convert them into respective values.\nBased on the above thought, the answer is:\nResource,2050,2055,2060,2065,2070,2075,2080,2085,2090,2095,2100\nSolar,2.5,5.5,10,12.5,15.5,17.5,21.5,23.5,27,31.5,33.5\nWind,1,2.5,4.5,6,7.5,10,12,15,18.5,20,21.5\nHydro,45,42.5,40,35.5,32,30,26.5,23.5,21,18,15.5\nNuclear,40,35.5,30,25.5,22,19.5,17,14.5,12,10,9.5\nThermal,5.5,6,7,8,9,10.5,11.5,13,14,15.5,16.5\nFossil Fuels,6,8.5,8,8.5,8.5,8.5,7.5,8.5,7.5,7,8"
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "F62IYiAzltqRxd6D",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends in the figures, which energy resource is expected to see the most significant rise in allocation from 2050 to 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will need to compare the starting and ending points for all the energy resources in the line chart. Firstly, looking at {the line for Solar resources}[0.148, 0.401, 0.932, 0.603], it begins from 2.5% allocation and ends at 33.5%. Comparing this change to those of other energy resources such as {Wind}[0.148, 0.425, 0.932, 0.428], {Hydro}[0.148, 0.654, 0.932, 0.350], {Nuclear}[0.148, 0.643, 0.932, 0.408], {Thermal}[0.148, 0.416, 0.932, 0.465], and {Fossil Fuels}[0.148, 0.428, 0.932, 0.430], I observe that the increase in allocation for Solar is the most significant.\nBased on the above thought, the answer is:\nThe Solar energy resource is expected to see the most significant rise in allocation from 2050 to 2100. The line chart shows a consistent increase in the allocation for Solar resources, starting from 2.5% in 2050 and ending at 33.5% in 2100. This change is larger than the changes observed for other energy resources."
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "fmZdtDihDAaViNK9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the allocation percentage for Solar power between 2050 and 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to identify {the series of coordinates for the allocation of Solar resources}[0.148, 0.401, 0.932, 0.605]. Now to find the starting allocation in 2050, I look at the coordinates of the starting point {the point representing the year 2050}[0.148, 0.401] which corresponds to 2.5%. Similarly, the coordinates for the point {at 2100}[0.932, 0.603] indicate an allocation of 33.5%. Finding the difference would provide the required answer.\nBased on the above thought, the answer is:\n31%"
+            }
+        ],
+        "id_raw": 82
+    },
+    {
+        "id": "vAqHhg1pQZh6suem",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the raw data illustrated in the chart in a structured table format where rows represent different level of Friends_count and columns correspond to their associated Engagement_index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart that captures the relationship between Friends_count and Engagement_index, I first identify {the x-axis tick labels}[[0.209, 0.906, 0.253, 0.953], [0.422, 0.906, 0.466, 0.953], [0.627, 0.906, 0.686, 0.953], [0.840, 0.906, 0.899, 0.953]] for the column 'Friends_count'. Then, I capture data points on the line for the \"Engagement_index\", which are represented by {the points on the line}[[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362], [0.657, 0.389], [0.763, 0.213], [0.869, 0.180], [0.900, 0.164]]. Corresponding to each 'Friends_count', there is an 'Engagement_index' value, thus forming rows of the table.\nBased on the above thought, the answer is:\nFriends_count,Engagement_index\n64,3.2\n128,5.1\n256,7.3\n512,9.5\n1024,12.6\n2048,12.1\n4096,15.3\n8192,15.9\n10000,16.2"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "KwT48UZMqSXKaN62",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend of the Engagement_index based on Friends_count observed in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the trend line {the points on the line}[[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362],[0.657, 0.389],[0.763, 0.213],[0.869, 0.180], [0.900, 0.164]], it is clear that as the Friends_count increases, the Engagement_index generally increases, depicting a positive correlation. There is however a slight decrease between Friends_count 1024 and 2048. After Friends_count of 2048, Engagement_index keeps rising. Hence, the trend shows that more the Friends_count, higher is the Engagement_index, although there can be periodical dips.\nBased on the above thought, the answer is:\nThe trend line in the chart indicates a direct correlation between Friends_count and Engagement_index - as Friends_count increases, Engagement_index frequently increases as well. There exists an occasional dip in the trend, such as in the segment between Friends_count 1024 and 2048. However, after a drop, the trend continues to rise. The pattern suggests that higher Friends_count typically correspond to a higher level of Engagement_index, but it doesn't rule out the existence of periodical reductions in Engagement_index."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "Uv75Ot4CCDBfcU2n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of Engagement_index as shown in the line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first locate the highest and lowest Engagement_index values in the chart. By extracting the Engagement_index values for all data points on the line {[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362], [0.657, 0.389], [0.763, 0.213], [0.869, 0.180], [0.900, 0.164]}, I find the range. The maximum index is 16.2, corresponding to the data point [0.900, 0.164], and the minimum is 3.2 corresponding to the data point [0.125, 0.879]. Hence, the range is 16.2 - 3.2 = 13.\nBased on the above thought, the answer is:\n13."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "ZEQED46JcUraQFR9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table from the bar chart data that represents the growth percentage in global trade for the five major countries over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the bar chart data, I first identify the countries on {the y-axis labels}<r>[0.07, 0.265, 0.16, 0.295], [0.07, 0.400, 0.12, 0.430], [0.07, 0.545, 0.14, 0.575], [0.07, 0.655, 0.15, 0.685], [0.07, 0.810, 0.12, 0.840]</r> which represent the row headers. The length of each {horizontal bar}<r>[0.15, 0.23, 0.950, 0.320], [0.15, 0.37, 0.890, 0.460], [0.15, 0.510, 0.390, 0.600], [0.15, 0.645, 0.460, 0.735], [0.15, 0.790, 0.535, 0.880]</r> and its location with respect to the origin of the graph provides the value of growth percentage. Positive growth lies on the right side of the origin while negative growth lies on the left.\nBased on the above thought, the answer is:\nCountries, Growth Percentage\nChina, 80\nU.S., 74\nGermany, -16\nAustralia, 31\nJapan, 38"
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "CTHWP21IEp65sCp6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you deduce the overall trend of global trade over the years, based on the information provided in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the {horizontal bars}<r>[0.15, 0.23, 0.950, 0.320], [0.15, 0.37, 0.890, 0.460], [0.15, 0.510, 0.390, 0.600], [0.15, 0.645, 0.460, 0.735], [0.15, 0.790, 0.535, 0.880]</r>, we can see that the majority of the represented countries show positive growth in global trade. China and the U.S., two of the biggest economies in the world, show considerable positive growth. This could indicate a more globalized economy and a healthier global trade network despite some countries like Germany showing negative growth.\nBased on the above thought, the answer is:\nThe overall trend of global trade seems to be positive based on the bar chart, with the majority of the represented countries showing an increase in cargo movement indicative of positive growth in global trade. This suggests a healthy global trade pattern with an increasing globalization trend. Yet, the negative growth from some countries signifies that the positive outcome is not uniformly distributed and there might be sectors or regions experiencing decline."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "dxuksvEjOJQyzmX6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country had the highest growth percentage in global trade according the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the country with highest growth percentage, I need to identify the longest {horizontal bar}<r>[0.15, 0.23, 0.950, 0.320]</r>. Based on the chart this bar corresponds to China as seen from the {y-axis label}<r>[0.07, 0.265, 0.16, 0.295]</r>.\nBased on the above thought, the answer is:\nChina."
+            }
+        ],
+        "id_raw": 84
+    },
+    {
+        "id": "pYLLa391TrmGlxIt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data points regarding the trend in Intrusion Detection percentage over the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I'll first identify {the x-axis tick labels}[[0.041, 0.861, 0.076, 0.931], [0.045, 0.861, 0.080, 0.931], [0.052, 0.861, 0.087, 0.931], [0.066, 0.861, 0.101, 0.931], [0.095, 0.861, 0.130, 0.931], [0.152, 0.861, 0.187, 0.931], [0.266, 0.861, 0.301, 0.931], [0.494, 0.861, 0.529, 0.931], [0.950, 0.861, 0.985, 0.931]], which represent the Years. Next, I identify the y-values from the data corresponding to {the line points}[[0.059, 0.687], [0.062, 0.660], [0.070, 0.496], [0.084, 0.304], [0.112, 0.249], [0.169, 0.276], [0.283, 0.194], [0.511, 0.085], [0.968, 0.852]] labelled as Intrusion Detection percentages. Thus, we can create the following table:\nBased on the above thought, the answer is:\nYears,Intrusion_Detection_Percentage\n2012,76\n2013,77\n2014,83\n2015,90\n2016,92\n2017,91\n2018,94\n2019,98\n2020,70"
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "x6jbF2TQIqwcC9DW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you briefly describe the trend of the Intrusion Detection rate over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line plot}[[0.059, 0.085, 0.968, 0.852]], I can see that the Intrusion Detection rate generally increased from 2012 to 2019 before falling significantly in 2020. The steepest growth happened between 2014 {2014}[0.070, 0.496] and 2015 {2015}[0.084, 0.304], where the rate increased from 83 to 90. However, the most significant drop happened between 2019 {2019}[0.511, 0.085] and 2020 {2020}[0.968, 0.852], where the rate fell from 98 to 70.\nBased on the above thought, the answer is:\nThe trend of the Intrusion Detection rate over the years was mostly increasing, especially between 2014 to 2015, reaching a peak in 2019. However, in 2020 a significant drop occurred in the detection rate, reducing the rate back to 70, which is close to the value of the year 2012."
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "jw6s90ZBTyu0ShrM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase in Intrusion Detection rate from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover the total percentage increase in Intrusion Detection rate, I need to find the values of Intrusion Detection rate in 2012 and 2020. From the chart, I identify that the Intrusion Detection rate in 2012 was given by the point {2012}[0.059, 0.687] having the value 76. Similarly, the Intrusion Detection rate in 2020 was provided by the point {2020}[0.968, 0.852] having the value 70. Thus, the difference between the two is 70 - 76, which is -6.\nBased on the above thought, the answer is:\n-6"
+            }
+        ],
+        "id_raw": 85
+    },
+    {
+        "id": "kGZoHtk6RbsycqWa",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, could you form a structured table showcasing the satisfaction level for each category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the structured data, I'll start by identifying {the x-axis tick labels}[[0.036, 0.796, 0.127, 0.887], [0.142, 0.796, 0.226, 0.881], [0.251, 0.796, 0.322, 0.868], [0.354, 0.796, 0.424, 0.867], [0.432, 0.796, 0.551, 0.916], [0.566, 0.796, 0.622, 0.853], [0.633, 0.796, 0.760, 0.923], [0.773, 0.796, 0.825, 0.848], [0.824, 0.796, 0.979, 0.951]], as categories; and then, for each category, I'll find and collect the corresponding {value points}[[0.081, 0.213], [0.184, 0.405], [0.286, 0.482], [0.389, 0.329], [0.491, 0.137], [0.594, 0.559], [0.696, 0.405], [0.799, 0.252], [0.901, 0.521]].\nBased on the above thought, the answer is:\nCategories,Satisfaction Level (%)\nHealthcare,85\nEducation,80\nSecurity,78\nHousing,82\nSocial Services,87\nRoads,76\nPublic Transport,80\nParks,84\nWaste Management,77"
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "DT4P6g1hZIeuj4Kj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the satisfaction levels, which category seems to be performing the best and which category seems to be performing the worst?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the best and worst performing categories, I look at the highest and lowest points on the line plot. The highest point is {Social Services}[0.491, 0.137] holding a satisfaction level of 87, indicating that it's the best performing category. The lowest point is {Roads}[0.594, 0.559], with a satisfaction level of 76, indicating that it's the worst-performing category.\nBased on the above thought, the answer is:\nThe 'Social Services' category seems to be performing the best with a satisfaction level of 87. Conversely, the 'Roads' category seems to be performing the worst with a satisfaction level of 76."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "a1h8fgBFq6Ln9mie",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the satisfaction level of 'Healthcare' and 'Waste Management'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would first locate the {Satisfaction Level for 'Healthcare'}[0.081, 0.213] which is 85, and then locate the {Satisfaction Level for 'Waste Management'}[0.901, 0.521] which is 77. The difference between these two values is 85 - 77 = 8.\nBased on the above thought, the answer is:\n8."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "DiPPFqCx5IjMifIb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the data from the line graph in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the line chart into a table, I need to take the {x-axis} representing the 'Age Groups'[0.457, 0.927, 0.580, 0.963] as rows and 'Stress Level'[0.025, 0.364, 0.048, 0.573] given on the Y-axis as columns. I will locate {the points on the line}[0.095, 0.093, 0.942, 0.777] and read off the corresponding stress level for each age group.\nBased on the above thought, the answer is:\nAge Group,Stress Level\n20-30,52.5\n30-40,72.9\n40-50,75.1\n50-60,78.4\n60-70,63.2\n70-80,50.7\n80-90,31.2"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "4RZkvoOyEvtMeghb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend of stress level as people age based on this line chart? Can this line chart suggest any causation relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line {chart}[0.095, 0.093, 0.942, 0.777] shows a clear trend in stress levels as people age. The stress level seems to rise steadily as people move from the '20-30' age group to their 60's. But a prominent drop in stress levels starts to happen as they age from '60-70'[0.659, 0.313] onwards, hitting the most reduced stress level at the '80-90' age range[0.942, 0.777]. However, it's important to note that this chart only shows a correlation between age and stress levels, not causation. There can be multiple factors affecting the stress level of different age groups, and age itself might just be a part of it.\nBased on the above thought, the answer is:\nOverall, the trend of stress level as seen in the line chart suggests that stress generally increases as people age until their 60's, then it starts to decline drastically from their 60's onwards to the age of 90. Despite this correlation shown in the chart, it does not necessarily imply causation. It's not guaranteed that aging will lead to the respective increase and decrease in stress as shown in the graph. There could be various other factors involved, unaccounted for in this data."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "nggrZTh8fvXajOIM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the stress level dropped from the '60-70' age group to the '80-90' age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease in the stress level, I'd first locate the position of the '60-70' group's stress level {on the line}[0.659, 0.313] which is 63.2. Then I'd find the '80-90' group's stress level {on the line}[0.942, 0.777] which is 31.2. The decrease in stress level is then calculated as 63.2 - 31.2.\nBased on the above thought, the answer is:\n32"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "ulZaJylVucy7BzKE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the GDP growth line chart, could you create a table that lists the years in ascending order and the corresponding GDP growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.042, 0.919, 0.077, 0.942], [0.133, 0.919, 0.168, 0.942], [0.224, 0.919, 0.259, 0.942], [0.315, 0.919, 0.350, 0.942], [0.405, 0.919, 0.440, 0.942], [0.496, 0.919, 0.531, 0.942], [0.587, 0.919, 0.622, 0.942], [0.678, 0.919, 0.713, 0.942], [0.768, 0.919, 0.804, 0.942], [0.859, 0.919, 0.894, 0.942], [0.950, 0.919, 0.985, 0.942]] which represent the years to be listed in the table. The corresponding GDP growth rates are the y-values from {the growth rate data points on the line plot}[[0.060, 0.310], [0.105, 0.379], [0.151, 0.360], [0.196, 0.310], [0.241, 0.190], [0.287, 0.259], [0.332, 0.215], [0.377, 0.095], [0.423, 0.398], [0.468, 0.772], [0.514, 0.335], [0.559, 0.386], [0.604, 0.367], [0.650, 0.405], [0.695, 0.354], [0.741, 0.398], [0.786, 0.436], [0.831, 0.405], [0.877, 0.354], [0.922, 0.436], [0.968, 0.867]] and are extracted via corresponding text labels.\nBased on the above thought, the answer is:\nYear, GDP Growth Rate\n2000, 4.5\n2001, 3.4\n2002, 3.7\n2003, 4.5\n2004, 6.4\n2005, 5.3\n2006, 6.0\n2007, 7.9\n2008, 3.1\n2009, -2.8\n2010, 4.1\n2011, 3.3\n2012, 3.6\n2013, 3.0\n2014, 3.8\n2015, 3.1\n2016, 2.5\n2017, 3.0\n2018, 3.8\n2019, 2.5\n2020, -4.3"
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "6W2NtLK3CTIdjwDr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart of GDP growth, how would you describe the pattern of economic growth from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting from {2000}<r>[0.042, 0.919, 0.077, 0.942]</r>, the line plot showing GDP growth rate appears to have a generally fluctuating trend, with {the peak at 7.9}<r>[0.377, 0.095]</r> in {2007}<r>[0.377, 0.919, 0.413, 0.942]</r>. From {2008}<r>[0.405, 0.919, 0.440, 0.942]</r> through to {2020}<r>[0.950, 0.919, 0.985, 0.942]</r>, the trend seems to include a significant recession period where the GDP growth dropped below 0, eventually beginning to recover although not yet reaching the prior peak level.\nBased on the above thought, the answer is:\nAccording to the line chart of GDP growth, the economy experienced fluctuations from 2000 to 2020. The period started with GDP growth rates around 4 to 5%, peaked at 7.9% in 2007, experienced a major recession in 2009 with a GDP growth rate at -2.8%, and began to recover subsequently albeit at lower rates compared to the earlier part of the period."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "fGViGkHT12kom1B7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the year with the greatest GDP growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the greatest GDP growth, I can look closely at the points on the line plot. The highest point is {7.9}<r>[0.377, 0.095]</r>, which corresponds to the year {2007}<r>[0.377, 0.919, 0.413, 0.942]</r> indicated by the x-axis label.\nBased on the above thought, the answer is:\n2007."
+            }
+        ],
+        "id_raw": 88
+    },
+    {
+        "id": "zkUoXVUbSTUvKIiz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data used for creating the bar graph in table format with initiatives as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate the data as a table, I first identify {the x-axis tick labels}[[0.073, 0.888, 0.151, 0.931], [0.190, 0.888, 0.263, 0.931], [0.296, 0.888, 0.386, 0.908], [0.419, 0.888, 0.493, 0.931], [0.514, 0.888, 0.629, 0.931], [0.639, 0.888, 0.733, 0.931], [0.762, 0.888, 0.840, 0.931], [0.856, 0.888, 0.976, 0.931]] indicating the rows, which are the community development and urban planning initiatives. Then I look for {the points of the bars}[[0.112, 0.482], [0.226, 0.256], [0.341, 0.313], [0.456, 0.539], [0.571, 0.256], [0.686, 0.200], [0.801, 0.143], [0.916, 0.652]] to get the participation percentages associated with each initiative.\nBased on the above thought, the answer is:\nCommunity Development and Urban Planning Initiatives,Participation Percentage (%)\nGreen Space Expansion,55\nCity Cycling Initiatives,75\nUrban Farming,70\nArt in Public Spaces,50\nCommunity Health Programs,75\nNeighbourhood Revitalization,80\nSmart Traffic Systems,85\nWater Conservation Programs,40"
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "RPuluYR1xeyrHrQd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, which initiative seems to be the most successful in terms of community participation and which one is the least successful? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which initiative is the most and the least successful in terms of community participation, I need to look for the highest and the lowest values on the bar graph. From my analysis, {the bar for the Smart Traffic Systems}[0.112, 0.0, 0.801, 0.143] represents the highest value which is 85% and suggests that this initiative is the most successful. On the other hand, {the bar for the Water Conservation Programs}[0.112, 0.0, 0.71, 0.916] represents the lowest value which is 40%, indicating that this initiative is the least successful.\nBased on the above thought, the answer is:\nAccording to the data, the Smart Traffic Systems initiative seems to be the most successful in terms of community participation as it has the highest participation percentage of 85%. On the other hand, the Water Conservation Programs initiative is the least successful, having the lowest participation percentage of 40%. The figures suggest a strong interest and engagement in smart traffic systems, whereas water conservation programs might require more community outreach or possibly a re-evaluation of the program's design and execution to boost community participation."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "dHqcpk1BWowQt9Dx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest participation percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to look for the highest and the lowest points on the bar graph, which are {the point for Smart Traffic Systems}[[0.801, 0.143]] with 85% participation and {the point for Water Conservation Programs}[[0.916, 0.652]] with 40% participation. The difference would be 85 - 40 = 45%.\nBased on the above thought, the answer is:\n45%."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "rJBaSd0x4T57XtiD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format, with the Months as columns and the Business Performance types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a tabular format, first, I analyze the x-axis tick labels, which I see are {the x-axis tick labels}[[0.220, 0.899, 0.283, 0.928], [0.341, 0.899, 0.403, 0.928], [0.462, 0.899, 0.524, 0.928], [0.583, 0.899, 0.645, 0.928], [0.704, 0.899, 0.766, 0.928], [0.833, 0.899, 0.879, 0.928]]. These represent the columns (months). Next, since there is no legend, I identify the different lines as different Business Performance types and rows in the table. For each business performance type, I locate the points for the line, for example, {the line points for the green line}[[0.251, 0.183, 0.856, 0.788]] and map them to the respective months to populate the values for that particular type.\nBased on the above thought, the answer is:\nType,18-24,25-34,35-44,45-54,55-64,65+\nAbrupt Increase,10000,12000,13500,15500,17500,20500\nErratic Behavior,15000,9000,16000,8000,16000,35000\nSluggish Progress,6000,6200,6400,6600,6800,7000\nSubdued Expansion,7000,7200,7600,8000,8500,9200"
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "oO9ZT9M6Ss5xPB96",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which business performance type exhibits the most volatility?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the most volatile business performance type, I look at the fluctuations in line plots. For the line of {\"Abrupt Increase\"}[0.251, 0.183], I see a steady positive trend going upwards. For the {\"Subdued Expansion\"} and {\"Sluggish Progress\"} lines, the progression is very slow and minor. The {\"Erratic behavior\"} line, however, exhibits significant volatility with its points fluctuating randomly both up and down, suggesting a high degree of uncertainty in business performance.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Erratic Behavior business performance type exhibits the most volatility. It undergoes significant fluctuations, indicating a high degree of uncertainty and inconsistency in performance over time."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "C5OU8hyRW0zJJ3ud",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value for the Erratic Behavior business performance type?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the maximum value for Erratic Behavior, I need to identify the y-values of the points on {the line representing Erratic Behavior}[0.251, 0.183, 0.856, 0.788]. After analyzing the points, I see the highest point corresponds to \"65+\" on the x-axis, with a y-value of 35000.\nBased on the above thought, the answer is:\n35000."
+            }
+        ],
+        "id_raw": 90
+    },
+    {
+        "id": "I2IqquOcohYbk4tX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a structured table that lists the named companies and their respective net profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line chart, I can see {the x-axis tick labels}[[0.093, 0.849, 0.131, 0.912], [0.173, 0.849, 0.218, 0.923], [0.251, 0.849, 0.306, 0.940], [0.333, 0.849, 0.390, 0.942], [0.420, 0.849, 0.470, 0.932], [0.514, 0.849, 0.542, 0.895], [0.590, 0.849, 0.632, 0.919], [0.676, 0.849, 0.712, 0.909], [0.766, 0.849, 0.789, 0.887], [0.846, 0.849, 0.875, 0.897], [0.928, 0.849, 0.959, 0.901]], which are the names of the companies. I then locate {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]] and their corresponding values which represent the net profit of each company.\nBased on the above thought, the answer is:\nCompany,Net Profit\nApple,59.53\nGoogle,92.95\nMicrosoft,61.27\nFacebook,61.94\nAmazon,53.02\nIBM,60.7\nOracle,89.43\nCisco,56.8\nHP,69.02\nDell,52.81\nIntel,49.76"
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "MBwWgscS0dJ1TZc3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the company with the third highest net profit based on the given data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the third highest net profit, I have to sort the net profit values held by each company, which are presented as {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]]. The company with the third highest net profit is located at {the point for IBM}[0.528, 0.557], corresponding to a net profit of 60.7 billion dollars.\nBased on the above thought, the answer is:\nIBM, with a net profit of 60.7 billion dollars, holds the third highest net profit among the given companies."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "R2Px0phjQUBhFyux",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest net profit amongst the companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the largest net profit value, I need to examine all {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]] and their corresponding values. The highest point in the line chart corresponds to {the line point for Google}[0.195, 0.128] with the highest value of 92.95.\nBased on the above thought, the answer is:\n92.95 billion dollars."
+            }
+        ],
+        "id_raw": 91
+    },
+    {
+        "id": "mKeB6YWMnsab8BKS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the provided bar chart, can you organize the data into a table format that shows the Companies and their corresponding Market Share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from this bar chart, I need to first identify {the x-axis tick labels}[[0.070, 0.843, 0.108, 0.907], [0.128, 0.843, 0.184, 0.936], ..., [0.899, 0.843, 0.943, 0.918]], which represent the company names. Next, I need to locate {the bar points of each company}[[0.108, 0.622], ..., [0.943, 0.784]] and extract their corresponding y-values as market shares.\nBased on the above thought, the answer is:\nCompanies,Market Share (%)\nApple,4.3\nSamsung,15.3\nHuawei,14.6\nXiaomi,9.2\nVivo,8.3\nOppo,8.1\nRealme,3.0\nLenovo,2.7\nLG,2.2\nMotorola,2.1\nOnePlus,1.2\nGoogle,0.9"
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "CZZXkUuuY1YAVz0G",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which company seems to have a significant edge over others in the market? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the company with a significant edge in the market, I need to compare the market shares of the companies. Looking at {the bar for Samsung}[0.184, 0.099], its market share at 15.3% is the highest. This is closely followed by {Huawei}[0.260, 0.132] with 14.6%, but all the other companies have considerably lower market shares.\nBased on the above thought, the answer is:\nBased on the bar chart, Samsung seems to have a significant edge over other companies. Although Huawei comes close, Samsung still holds the highest market share, implying that they have a larger segment of customers and thereby a more substantial presence in the market than other competitors."
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "5mkYhL2yjGOHaU1n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cumulative market share of the top 3 companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the cumulative market share of the top 3 companies, I need to add the market shares of the three companies with the highest shares. These companies are {Samsung}[0.184, 0.099], {Huawei}[0.260, 0.132], and {Xiaomi}[0.336, 0.389], with market shares of 15.3%, 14.6%, and 9.2%, respectively. Adding these shares together, we get a total of 15.3% + 14.6% + 9.2% = 39.1%.\nBased on the above thought, the answer is:\n39.1%"
+            }
+        ],
+        "id_raw": 92
+    },
+    {
+        "id": "RNPktLWSWz1bcrBQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data in a table with the growth of each scientific field as a percentage for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart does not provide the data for each year, rather it gives the overall percentage growth for each field. Based on the graphical representation, I can infer that the percentages provided correspond to the growth from the beginning to the end of a certain time period. The {y-axis tick labels}[[0.066, 0.088, 0.092, 0.107], [0.062, 0.276, 0.091, 0.289],[0.057, 0.357, 0.085, 0.370], [0.051, 0.522, 0.078, 0.535], [0.046, 0.711, 0.072, 0.724], [0.041, 0.794, 0.064, 0.807]] correspond to the different fields of scientific research and technological innovation. The corresponding percentage gains are indicated by {line points}[[0.119, 0.081], [0.168, 0.267], [0.216, 0.352], [0.278, 0.513], [0.398, 0.703], [0.472, 0.786]], from which I can extract the corresponding values such as Neuroscience showing a moderate decline of 20.3%, Robotics showing a sustained growth of 15.8%, and so on.\nBased on the above thought, the answer is:\nField,Growth\nNeuroscience,-20.3\nRobotics,15.8\nAstronomy,19.1\nBiotechnology,-13.5\nNanotechnology,7.3"
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "zaV3Ju9V95ooDY3s",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which scientific research field has experienced the most growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the scientific field with the most growth, I need to look for the line with the highest point on the x-axis. The {line for Astronomy} <r>[0.216, 0.352]</r> corresponds to a value of 19.1% which is the greatest among all other fields. This indicates that Astronomy has seen the most growth.\nBased on the above thought, the answer is:\nAccording to the line chart, Astronomy has experienced the most growth among the scientific research fields. It shows a large increase of 19.1%, which is higher compared to the growth in Nanotechnology, Biotechnology, Robotics and the decline in Neuroscience."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "B0dLtDpEwepGhTC0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth of the scientific research fields as presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average growth of these scientific fields, I'd have to locate {the line points}[[0.119, 0.081], [0.168, 0.267], [0.216, 0.352], [0.278, 0.513], [0.398, 0.703], [0.472, 0.786]] which represent the overall percentage change for each field. The average growth is given by (7.3 - 13.5 + 19.1 + 15.8 - 20.3) / 5.\nBased on the above thought, the answer is:\n1.68."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "fLaz7UyAV7opuPfL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the data represented in the horizontal bar chart into a tabular format with 'AI Applications' as rows and 'Successful Implementation (%)' as the column?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to create this table, we first define the rows according to the AI applications listed as {x-axis labels}[[0.102, 0.757, 0.213, 0.827], [0.187, 0.757, 0.461, 0.827], [0.287, 0.757, 0.693, 0.827], [0.527, 0.757, 0.787, 0.827], [0.675, 0.757, 0.972, 0.827]]. The column header would be 'Successful Implementation (%)'. The values for each row can be extracted from the length of {the bars on the chart}[[0.157, 0.639, 0.438, 0.663], [0.157, 0.545, 0.492, 0.569], [0.157, 0.451, 0.414, 0.475], [0.157, 0.357, 0.396, 0.381], [0.157, 0.263, 0.502, 0.287]] and their corresponding values from the {y-axis labels}[[0.069, 0.554, 0.104, 0.624], [0.069, 0.491, 0.104, 0.561], [0.069, 0.428, 0.104, 0.498], [0.069, 0.364, 0.104, 0.434], [0.069, 0.301, 0.104, 0.371], [0.069, 0.238, 0.104, 0.308], [0.069, 0.174, 0.104, 0.244]], which represent percentages.\nBased on the above thought, the answer is:\nAI Applications,Successful Implementation (%)\nChatbot,60\nSpeech Recognition,80\nNatural Language Processing,72\nImage Recognition,65\nAutonomous Vehicles,90"
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "HH0AePsha2tc26sY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing all the AI applications in the chart, which one shows the highest successful implementation and which one the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bars in the chart correspond to the successful implementation percentages of different AI applications. By comparing the lengths of these bars: {Chatbot}[[0.157, 0.639, 0.438, 0.663]], {Speech Recognition}[[0.157, 0.545, 0.492, 0.569]], {Natural Language Processing}[[0.157, 0.451, 0.414, 0.475]], {Image Recognition}[[0.157, 0.357, 0.396, 0.381]], {Autonomous Vehicles}[[0.157, 0.263, 0.502, 0.287]] and their corresponding values from the y-axis labels, it's clear that the Autonomous Vehicles application has the longest bar, indicating the highest successful implementation percentage (90%). On the other hand, Chatbot has the shortest bar, indicating the lowest successful implementation percentage (60%).\nBased on the above thought, the answer is:\nAutonomous Vehicles shows the highest successful implementation percentage with 90%. On the other hand, Chatbot has the lowest successful implementation with a percentage of 60%."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "HDTzFq2Y6DwquB7E",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the successful implementation percentages of Autonomous Vehicles and Chatbot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference, I need to find the Successful Implementation Percentages of both AI applications. For {Autonomous Vehicles}[[0.675, 0.757, 0.972, 0.827]], I locate corresponding bar on chart and find its value from the y-axis which appears to be 90. Similarly, I locate the bar for {Chatbot}[[0.102, 0.757, 0.213, 0.827]] with corresponding value of 60. Now, we subtract Chatbot's implementation percentage from Autonomous Vehicles's implementation percentage which is 90 - 60 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 94
+    },
+    {
+        "id": "gM72eh3rcv7MMiQK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the horizontal bar chart, can you extract the data into a table format with Policies as rows and Effectiveness Ratings as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the horizontal bar chart, I first look at {the y-axis tick labels} [[0.345, 0.24], [0.449, 0.34], [0.553, 0.44], [0.657, 0.54], [0.761, 0.64]], which represent the row labels or Policies. Next, I identify the bar lengths to extract values representing the Effectiveness Ratings. For instance, the point {Corporate Tax Cuts} <r> [0.345, 0.36] </r> has a black bar stretching to x=0.50, corresponding to a rating of 10. Similarly, the point {Climate Change Policies} <r> [0.449, 0.46] </r> has a red bar stretching to x=0.25, corresponding to a rating of -6.\nBased on the above thought, the answer is:\nPolicies, Effectiveness Ratings\nCorporate Tax Cuts, 10\nClimate Change Policies, -6\nHealth Care Reform, 5\nEducation Funding, 12\nGun Control Policies, -8"
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "q84w8YB5AwYyLDMM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart, which policy appears to be the least popular among the public based on its effectiveness rating? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the least popular policy, I need to find the policy with the lowest effectiveness rating. I notice that the {Gun Control Policies} <r> [0.761, 0.76] </r> has a red bar that extends to x=0.20, indicating a negative evaluation of -8, which is the most negative or lowest among all the ratings.\nBased on the above thought, the answer is:\nAccording to the chart, the Gun Control Policies appears to be the least popular among the public. The negative evaluation of -8, represented by the red bar, indicates a low effectiveness rating, suggesting that the policy is not perceived favorably when it comes to its effectiveness."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "Qo03EUS3tTFsqfzR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total effectiveness rating of all the policies combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total effectiveness rating, I need to add up all the ratings from each policy. I locate each policy's bar length and extract the value, such as {Corporate Tax Cuts} <r> [0.345, 0.36]</r> at 10, {Climate Change Policies} <r> [0.449, 0.46]</r> at -6, {Health Care Reform} <r> [0.553, 0.56]</r> at 5, {Education Funding} <r> [0.657, 0.66]</r> at 12 and {Gun Control Policies} <r> [0.761, 0.76]</r> at -8, then I add these values together.\nBased on the above thought, the answer is:\n13."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "UPrlwMlQw7niPbRt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a tabular format? The rows should be 'Size of Training Data (No. of Data Points)' and the column should be 'AI Model Accuracy (%)'."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from this line chart, I first look at {the x-axis tick labels}[[0.201, 0.867, 0.228, 0.914], [0.411, 0.867, 0.439, 0.914], [0.622, 0.867, 0.649, 0.914], [0.832, 0.867, 0.859, 0.914]], which represent the row headers. The column header is defined by {the y-axis label}[[0.025, 0.258, 0.048, 0.648]]. To populate the values, I locate {the points on the line}[[0.171, 0.849], [0.215, 0.700], [0.320, 0.584], [0.425, 0.440], [0.530, 0.383], [0.635, 0.300], [0.741, 0.236], [0.846, 0.204], [0.951, 0.129]] for each 'Size of Training Data (No. of Data Points)' and extract the corresponding 'AI Model Accuracy (%)'.\nBased on the above thought, the answer is:\nSize of Training Data, AI Model Accuracy (%) \n3,70.22\n4,75.80\n8,80.13\n16,85.50\n32,87.60\n64,90.70\n128,93.10\n256,94.30\n512,97.10"
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "IltX7M1WGlcNkzhM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the accuracy of the AI model as the size of the Training Data increases? Provide reasoning with the help of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "From the {line chart coordinates}[[0.171, 0.129, 0.951, 0.849]], we observe that the AI model's accuracy increases as the size of the training data increases. Starting from 70.22% accuracy for a data size of 3 represented by {the point}[[0.171, 0.849]], the accuracy rises steadily along the line, reaching up to 97.1% for a data size of 512 represented by {the point}[[0.951, 0.129]].\nBased on the above thought, the answer is:\nAs the size of the Training Data increases, the accuracy of the AI model improves. This may be due to the model having access to more data to learn from, thus improving its ability to make accurate predictions. This inference is based on the clear upward trend observed in the line chart, where larger training data sizes consistently correspond to higher degrees of AI model accuracy."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "HBfwWnZhGHHMl5y0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in 'AI Model Accuracy(%)' from 'Size of Training Data' of 3 to 512?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to locate the accuracy values for 'Size of Training Data' of 3 and 512. These are represented by {the point for data size of 3}[[0.171, 0.849]] which corresponds to 70.22% accuracy, and {the point for data size of 512}[[0.951, 0.129]] corresponding to 97.10% accuracy. The percentage increase can be calculated as (97.10 - 70.22) / 70.22 * 100%.\nBased on the above thought, the answer is:\n38.22%."
+            }
+        ],
+        "id_raw": 96
+    },
+    {
+        "id": "fLvd8H1Qw45UKAaZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the statistical metric values for each economic indicator into a structured table from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the structured table from the line chart, I first identify {the x-axis labels}[[0.029, 0.722, 0.136, 0.899], [0.123, 0.722, 0.209, 0.866], [0.173, 0.722, 0.283, 0.904], [0.286, 0.722, 0.356, 0.839], [0.322, 0.722, 0.430, 0.902], [0.400, 0.722, 0.503, 0.894], [0.457, 0.722, 0.577, 0.921], [0.578, 0.722, 0.650, 0.843], [0.592, 0.722, 0.724, 0.942], [0.678, 0.722, 0.797, 0.921], [0.787, 0.722, 0.871, 0.862], [0.864, 0.722, 0.945, 0.855]] which represent the economic indicators. The corresponding statistical metric values for each economic indicator can be obtained from {the data points}[0.136, 0.058, 0.945, 0.683]].\nBased on the above thought, the answer is:\nEconomic Indicators,Statistical Metric Value\nhousehold_incomes,87124.8\nsalary_increase,7432.6\nunemployment_rate,9.2\nGDP_growth,5.7\nstock_market_index,2768.13\nreal_estate_pricing,5349.46\nconsumer_price_index,124.32\ninterest_rate,2.75\nlabor_force_participation,63.6\npersonal_savings_rate,7.9\nbusiness_profit,12348.5\ntrade_balance,-50250"
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "xKd6Ut6eVLVXffux",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line chart and the economic indicators, could you comment on the relationship between the household incomes and the unemployment rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the point for household incomes}[0.136, 0.058] and {the point for unemployment rate}[0.283, 0.454] on the line graph, we can see that the metric value for household incomes is considerably higher than the unemployment rate. This seems to suggest that despite having a certain unemployment rate, the household incomes are still relatively high. However, it's crucial to note that this is an oversimplification as there could be a multitude of factors affecting these two economic indicators, and they may not necessarily have a direct relationship.\nBased on the above thought, the answer is:\nHousehold incomes are significantly higher than the unemployment rate, as shown in the line chart. However, it's essential to recognize that this high income could be due to a host of factors that may or may not directly correlate with the unemployment rate. Further investigation would be required to establish a robust link between the two economic indicators, and this could involve other factors not visually represented in the given chart."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "Fin06rKSPMjf8fWB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest metric value displayed on the chart, and for which economic indicator is it associated?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the largest metric value, I analyze {the y-values of the data points}[[0.136, 0.058], [0.209, 0.420], [0.283, 0.454], [0.356, 0.454], [0.430, 0.441], [0.503, 0.430], [0.577, 0.453], [0.650, 0.454], [0.724, 0.454], [0.797, 0.454], [0.871, 0.398], [0.945, 0.683]]. The highest point corresponds to the largest metric value, and it is associated with {household_incomes}[0.136, 0.058], with a metric value of 87124.8.\nBased on the above thought, the answer is:\nThe largest metric value displayed on the chart is 87124.8, and it is associated with Household Incomes."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "k7BGQTJZGwqrrBj0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the yearly food production from the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate tabular data from the line chart, I need to identify the x-axis elements and y-axis elements. Looking at the {x-axis labels}[0.089, 0.757, 0.159, 0.827], [0.190, 0.757, 0.261, 0.827], ..., [0.900, 0.757, 0.970, 0.827], they represent the years from 2012 to 2020. The y-axis labels show the food production in various units, starting at 85 and going up till 94, separated by units of 1 {ytick elements}[0.069, 0.681, 0.105, 0.751], [0.069, 0.618, 0.105, 0.688], ..., [0.069, 0.111, 0.105, 0.181]. To tabulate these values, I'll take the values annotated for each year according to {line_0}[0.124, 0.186, 0.935, 0.550] points.\nBased on the above thought, the answer is:\nYear,Food Production\n2012,87.50\n2013,88.34\n2014,89.67\n2015,90.12\n2016,89.87\n2017,90.11\n2018,92.43\n2019,91.88\n2020,93.25"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "Fm9PQbzaxrV8wT0G",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend in food production from 2012 to 2020 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend, I would observe {line_0}<r>[0.124, 0.186, 0.935, 0.550]</r>. This line represents the changes in food production over the years. Starting from 2012, the line shows a gradual rise until 2015, a slight drop in 2016, another slight rise in 2017, a sharp increase to 2018, a decrease in 2019, and then another increase in 2020. Overall, it's an upward trend with some fluctuations.\nBased on the above thought, the answer is:\nThe trend in food production from 2012 to 2020 is generally upward, indicating an increase in food production over the years. Though there are minor fluctuations in between, the production in 2020 is higher compared to 2012, signifying growth. The sharp increase from 2017 to 2018 suggests a significant improvement in food production during that period. However, there are also slight decreases observable in the years following 2015 and 2018."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "QK5d5ieSTfmKJPx7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in food production from 2012 to 2020 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the overall increase in food production, I must find the production values for the years 2012 and 2020. The {point for 2012}<r>[0.124, 0.550]</r> has a production value of 87.50, and the {point for 2020}<r>[0.935, 0.186]</r> has a value of 93.25. The overall increase is therefore 93.25 - 87.50 = 5.75.\nBased on the above thought, the answer is:\n5.75."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "YD5rPalGlIbUnlck",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart data related to different modes of transport within the logistics and transportation industry into a simple table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into table format, I first identify {the x-axis labels}<r>[0.100, 0.903, 0.125, 0.931], [0.219, 0.903, 0.243, 0.931], [0.342, 0.903, 0.357, 0.931], [0.462, 0.903, 0.473, 0.931], [0.568, 0.903, 0.603, 0.931], [0.691, 0.903, 0.716, 0.931], [0.815, 0.903, 0.828, 0.931], [0.933, 0.903, 0.947, 0.931]</r> which represent different modes of transport. To find the values of each transport mode, I should look at corresponding y-axis values for each point on a line. For example, on the line for Air Freight {coordinates}<r>[0.113, 0.093, 0.940, 0.766]</r>, I can see points at y-axis 125, 135, and 90 and assign these to the corresponding modes.\nBased on the above thought, the answer is:\nTransport Mode, Trend Values\nCC, 65.23\nTD, 75.96\nM, 80.32\nT, 85.01\nCC2, 88.97\nHR, 90.52\nN, 78.42\nG, 92.86"
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "soMi3zCwf5FVyPNS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which social and cultural issue remains generally popular over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart displays the popularity of various social and cultural issues over time. {Globalization}<r>[0.915, 0.043, 0.965, 0.071]</r> stands out as it reaches the highest value on the chart, indicating it consistently maintains a high level of popularity.\nBased on the above thought, the answer is:\nAccording to the line chart, Globalization has consistently remained popular over time as indicated by its highest value amongst all the social and cultural issues."
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "ef0eXLgjwU4ONYMn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the highest and lowest popularity percentages for the social and cultural issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each line point on the chart corresponds to a popularity percentage for the issue represented along the x-axis. To find the highest and lowest popularity percentages, I need to inspect all the y-axis values of the line points. The highest y-value I see is for {Globalization}<r>[0.940, 0.093]</r>, with a popularity percentage of 92.86. The lowest y-value I see is for {Continuity of Culture}<r>[0.113, 0.766]</r>, with a popularity percentage of 65.23.\nBased on the above thought, the answer is:\nHighest is 92.86, Lowest is 65.23"
+            }
+        ],
+        "id_raw": 99
+    },
+    {
+        "id": "CTEHnodhAqT60mNc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data presented in the line chart into a structured table format, with Startup Lifecycle Stages as columns and the single row being Startup Success Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the line chart data into structured tabular form, I begin by identifying {the x-axis tick labels}[[0.015, 0.809, 0.235, 0.842], [0.149, 0.809, 0.298, 0.842], [0.264, 0.809, 0.379, 0.842], [0.360, 0.809, 0.481, 0.842], [0.477, 0.809, 0.560, 0.842], [0.577, 0.809, 0.657, 0.842], [0.632, 0.809, 0.799, 0.842], [0.748, 0.809, 0.880, 0.842], [0.839, 0.809, 0.985, 0.842]] which correspond to various stages of the Startup Lifecycle. I then look at {the line points}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]] for the Startup Success rate and obtain the corresponding values at each stage.\nBased on the above thought, the answer is:\n\",\"\",\"Rapid Development\",\"Mature Stage\",\"Expansion\",\"Stagnation\",\"Decline\",\"Rebirth\",\"Transformation\",\"Reinvention\",\"Rejuvenation\"\n\"Startup Success Rate\",,75,85,90,69,55,80,78,92,95"
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "AyEAjYM1hKyfTibL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify and explain the trend in the startup's success rate across its lifecycle as depicted by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from {the line representing the Startup Success Rate}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]], there is a general upward trend in the startup's success rate from the Rapid Development stage to the Expansion stage, followed by a relative decline in success during the Stagnation stage. The success rate further plummets during the Decline stage, followed by a recovery during the Rebirth and Transformation stages. Finally, there is a significant increase in the success rate during the Reinvention and Rejuvenation stages.\nBased on the above thought, the answer is:\nThe line graph depicts that the startup's success rate generally increases from the Rapid Development stage to the Expansion stage, indicating successful growth during the early stages. However, it experiences a falter during the Stagnation and Decline phases, suggesting difficulties encountered during scaling or due to market competition or shifts. The Rise in the success rate during the latter stages of Rebirth, Transformation, Reinvention, and Rejuvenation suggests the implementation of successful restructuring or pivot strategies, leading to a turnaround in the business."
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "pgEfmXwH5H3ih8Ce",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average success rate across the different startup lifecycle stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "Calculating the average success rate across the stage requires extracting the values at each stage from {the line for Startup Success}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]]. The success rates are: 75, 85, 90, 69, 55, 80, 78, 92, and 95. The average is calculated by summing these values and dividing by the total number of stages which is 9.\nBased on the above thought, the answer is:\n79.11"
+            }
+        ],
+        "id_raw": 100
+    },
+    {
+        "id": "s19mv6XfvZvK7Wn0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the performance of the different integration methods over the ten integration steps?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data, I need to reference the x-axis and y-axis along with the lines. There are {four lines on the graph}<r>[0.112, 0.080, 0.968, 0.662], [0.112, 0.288, 0.968, 0.558], [0.112, 0.267, 0.968, 0.766], [0.112, 0.205, 0.968, 0.495]</r>. Each represents a different integration method: Database Integration, API Integration, Data Warehouse Integration, and Data Lake Integration.\nThe x-axis marks the {integration steps}<r>[0.104, 0.886, 0.120, 0.919], [0.199, 0.886, 0.215, 0.919], ..., [0.952, 0.886, 0.983, 0.919]</r>, which I'll use as the rows. I reference the y-values of each point on the lines to extract the corresponding performance values for the various integration methods.\nBased on the above thought, the answer is:\nIntegration Steps,Database Integration,API Integration,Data Warehouse Integration,Data Lake Integration\n1,80,85,75,88\n2,85,88,77,90\n3,90,92,80,91\n4,95,93,85,93\n5,96,95,87,93\n6,98,96,90,94\n7,100,96,93,96\n8,105,96,95,98\n9,105,97,97,100\n10,108,98,99,102"
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "C0pOSNomyQ3iPO1A",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which integration method appears to be improving continuously?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which integration method is improving continuously, I need to analyze the trends of the plotted lines. \nLooking at {line for the Database Integration}[0.112, 0.080, 0.968, 0.662], it shows a steady increase in performance. \nHowever, for the {API Integration line}[0.112, 0.288, 0.968, 0.558], the growth initially seems continuous but experiences a plateau at steps 7 to 9. \nFor {Data Warehouse Integration}[0.112, 0.267, 0.968, 0.766], the line demonstrates a progressive improvement throughout the steps. \nThe {Data Lake Integration line}[0.112, 0.205, 0.968, 0.495] also shows continuous improvement, but the growth trend is slower than that of the Database Integration method.\nBased on the above thought, the answer is:\nBased on the analysis, Database Integration appears to be the method that is improving continuously. Despite the continuous growth exhibited by other methods, the Database Integration method displays a steeper and steady increase, indicating a consistent improvement."
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "oY7OhuWV94kfwNhh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the performance of Data Lake Integration at the 7th Integration Step?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the performance value for Data Lake Integration at the 7th Integration Step, I have to locate the {point on the Data Lake Integration line}<r>[0.682, 0.329]</r> corresponding to the {x coordinate for the 7th step}<r>[0.675, 0.886, 0.690, 0.919]</r>. Extracting the corresponding y value, I can determine the performance at this integration step.\nBased on the above thought, the answer is:\n96"
+            }
+        ],
+        "id_raw": 101
+    },
+    {
+        "id": "vXtyIK4g4zWNRguf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help organize the data regarding Social Activities and the level of engagement into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely, I can extract the data from the line chart into a table. First, I'd take {the x-axis tick labels}<r>[0.036, 0.720, 0.150, 0.883], [0.118, 0.720, 0.274, 0.942], [0.250, 0.720, 0.348, 0.860], [0.364, 0.720, 0.439, 0.828], [0.460, 0.720, 0.549, 0.846], [0.568, 0.720, 0.647, 0.832], [0.685, 0.720, 0.736, 0.793], [0.774, 0.720, 0.852, 0.831], [0.847, 0.720, 0.985, 0.916]</r> , these labels represent different social activities. The engagement levels for both Healthy and Sedentary lifestyle are plotted as two distinct lines, {Healthy Lifestyle line}<r>[0.093, 0.129, 0.916, 0.610]</r> and {Sedentary Lifestyle line}<r>[0.093, 0.116, 0.916, 0.610]</r>. I will then extract values at each data point on these lines to populate the final table.\nBased on the above thought, the answer is:\nSocial Activities,Healthy Lifestyle,Sedentary Lifestyle\nTheme Party,52,63\nOutdoor Activities,68,40\nMeditation,79,30\nGaming,33,88\nFast Food,23,89\nRunning,85,15\nYoga,90,20\nReading,70,50\nBinge-Watching,15,92"
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "B4r5df8xBwbZV4ai",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you say which Lifestyle engages more in social activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I compare the two lines {Healthy Lifestyle line}<r>[0.093, 0.129, 0.916, 0.610]</r> and {Sedentary Lifestyle line}<r>[0.093, 0.116, 0.916, 0.610]</r> and look for which lifestyle shows higher engagement levels for the majority of the social activities. The Healthy Lifestyle shows higher engagement in Outdoor Activities, Running, Yoga, and Reading. On the other hand, the Sedentary Lifestyle has higher engagement in Theme Party, Gaming, Fast Food Consumption, and Binge-Watching. This means that the engagement levels are similar for both lifestyles, with each having higher engagement in different activities. However, one aspect to note is the type of activities, where majority of the healthy activities have a higher engagement in the Healthy Lifestyle, while the unhealthy ones see a higher engagement in Sedentary Lifestyle.\nBased on the above thought, the answer is:\nNeither lifestyle engages notably more in social activities since the engagement levels are quite similar for both lifestyles. However, the type of engagement in activities differs significantly; healthier activities see more engagement from those leading a Healthy Lifestyle and vice versa."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "ZulQic0V1s0KrRX9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how much higher is the engagement in Running amongst those leading a Healthy Lifestyle compared to those with a Sedentary Lifestyle?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this information, I'd locate {the point for Running in the Healthy Lifestyle line}<r>[0.607, 0.161]</r>, which shows an engagement value of 85. Then, I'd find {the equivalent point in the Sedentary Lifestyle line}<r>[0.607, 0.610]</r>, which shows an engagement value of 15. To find the difference, we subtract the engagement level for the Sedentary Lifestyle from that of the Healthy Lifestyle, which would give us 85 - 15 = 70.\nBased on the above thought, the answer is:\n70%."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "hsGVaqkNKnsK3X00",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line plot data to a table with years as columns and the economic indicators as rows, using the provided dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I must refer to the x-axis ticks for the columns. They are in {the x-axis tick labels}[[0.122, 0.809, 0.137, 0.842], [0.215, 0.809, 0.230, 0.842], [0.308, 0.809, 0.324, 0.842], [0.401, 0.809, 0.417, 0.842], [0.494, 0.809, 0.510, 0.842], [0.587, 0.809, 0.603, 0.842], [0.680, 0.809, 0.696, 0.842], [0.773, 0.809, 0.789, 0.842], [0.867, 0.809, 0.882, 0.842], [0.952, 0.809, 0.983, 0.842]]. They together represent the years from 1 to 10. I then observe {the legend}[0.353, 0.903, 0.744, 0.966] to identify the row labels as Population and GDP. To fill in the data, I refer to {the points on the Population plot}[[0.129, 0.147, 0.968, 0.638]] and {the points on the GDP plot}[[0.129, 0.311, 0.968, 0.785]]. I reference the y-axis ticks to estimate the values.\nBased on the above thought, the answer is:\nIndicator,Year 1,Year 2,Year 3,Year 4,Year 5,Year 6,Year 7,Year 8,Year 9,Year 10\nPopulation,10000,15000,17000,22000,24000,26000,28000,30000,35000,40000\nGDP,1000,3000,5000,6000,8000,9000,14000,18000,25000,30000"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "fuj1HbcBlHo7MwEx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, what can be inferred about the community's economic growth and urban development over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the community's economic growth and urban development, I need to analyze the trends in the line plots. I observe {the Population line}[0.129, 0.147, 0.968, 0.638] is showing a steady increase over the years from 10000 to 40000, suggesting the community is growing. Additionally, I see that the {GDP line}[0.129, 0.311, 0.968, 0.785] is also continuously increasing over time from 1000 to 30000, signifying the enhancing economic growth. This also implies that the average standard of living may be improving.\nBased on the above thought, the answer is:\nThe line charts indicate a steady, positive growth in both population and GDP, suggesting that the community is undergoing consistent economic development and urban growth. The continuous increase in population may be a result of factors such as migration due to increased opportunities, better living conditions, among others. Next, the increasing GDP implies that the economic status of the community is improving, in turn indicating the enhancement in the standard of living in the community. Therefore, based on the chart, the community is positively developing over time, both economically and demographically."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "P5pX9dkzOfoPxER5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage increase in the GDP from year 1 to year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall percentage increase, I first need to extract the GDP value at year 1 and year 10. The GDP value for year 1 corresponds to {the first point on the GDP plot}[0.129, 0.785] and is 1000, while the GDP value for year 10 corresponds to {the last point on the GDP plot}[0.968, 0.311] and is 30000. The percentage increase from year 1 to year 10 is calculated as ((30000 - 1000) / 1000) * 100.\nBased on the above thought, the answer is:\n2900%."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "4YLt4TXZCdI5LBZI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the line chart and present it in tabular form, with Efficiency Level as the rows and efficiency types being the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first need to identify {the x-axis tick labels}[[0.066, 0.880, 0.110, 0.909], [0.161, 0.880, 0.302, 0.909], [0.330, 0.880, 0.419, 0.909], [0.444, 0.880, 0.592, 0.909], [0.635, 0.880, 0.686, 0.909], [0.748, 0.880, 0.860, 0.910], [0.909, 0.880, 0.985, 0.909]], which represent different levels of efficiency. Then I identify {the legend texts}[[0.379, 0.961, 0.508, 0.981], [0.574, 0.961, 0.695, 0.981]] as the column names. Finally, I can obtain the values from {the line points}[[0.088, 0.820], [0.231, 0.709], [0.374, 0.663], [0.518, 0.579], [0.661, 0.390], [0.804, 0.344], [0.947, 0.033]] and [[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]] for the respective efficiency types at each efficiency level.\nBased on the above thought, the answer is:\nEfficiency Level,Material Efficiency,Energy Efficiency\nLow,62.5,65\nLow-Medium,67.8,69.5\nMedium,70,73\nMedium-High,74,78\nHigh,83,81.5\nVery High,85.2,86\nOutlier,100,96.4"
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "95oCro3Bm4FjAE18",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line chart, what does this tell us about the gap between Material Efficiency and Energy Efficiency as efficiency levels increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret this trend, I need to compare the line plots for {Material Efficiency}[[0.088, 0.820], [0.231, 0.709], [0.374, 0.663], [0.518, 0.579], [0.661, 0.390], [0.804, 0.344], [0.947, 0.033]] and {Energy Efficiency}[[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]]. Here, I see that the Material Efficiency line is almost always above the Energy Efficiency line, implying the former is typically higher. However, as the efficiency level increases beyond 'High', the Material Efficiency line crosses the Energy Efficiency line and the latter shows a more rapid increase resulting in a larger gap at the 'Outlier' level.\nBased on the above thought, the answer is:\nThe lines in the chart indicate that Material Efficiency is generally higher than Energy Efficiency till we reach the 'High' efficiency level. However, as efficiency levels increase beyond 'High', the rate of improvement in Energy Efficiency outpaces Material Efficiency, leading to a larger gap in favor of Energy Efficiency at the 'Outlier' level. This suggests that there is a greater scope for improvement in energy usage as efficiency levels increase."
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "TGXIUIQu8nwHZuP5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of potential improvement in Energy Efficiency across all Efficiency Levels depicted in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values for Energy Efficiency across all Efficiency Levels, I need to locate the {line points for Energy Efficiency}[[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]] and extract the corresponding values: 65, 69.5, 73, 78, 81.5, 86, 96.4. From these values, the maximum value is 96.4 corresponding to {Outlier}[0.909, 0.880] and the minimum value is 65 corresponding to {Low}[0.066, 0.880]. Subtracting the minimum from the maximum gives the range of potential improvement.\nBased on the above thought, the answer is:\n31.4"
+            }
+        ],
+        "id_raw": 104
+    },
+    {
+        "id": "XLKjJ7K2e0VC3pQx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease, can you transform data from the line chart into a table format with years as rows and regions as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from this line chart data, I would start with {the x-axis tick labels}[[0.079, 0.913, 0.135, 0.942], [0.249, 0.913, 0.305, 0.942], [0.419, 0.913, 0.475, 0.942], [0.588, 0.913, 0.645, 0.942], [0.758, 0.913, 0.815, 0.942], [0.928, 0.913, 0.985, 0.942]] portraying years that would become our row labels. Then, I would identify {the legend texts}[[0.189, 0.089, 0.371, 0.118],[0.189, 0.133, 0.381, 0.162]] and use them as column headers representing different regions. The values would come from {the line points for the corresponding region and year}[[0.107, 0.475], [0.277, 0.581], ..., [0.957, 0.138]].\nBased on the above thought, the answer is:\nYears, Rural Population, Urban Population\n1970, 956,449\n1980, 817,637\n1990, 712,866\n2000, 659, 1069\n2010, 636, 1213\n2020, 620, 1399"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "DcgWEiDV09PD1igN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, how would you describe the development of rural and urban populations over the last 50 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To give a description of the development of rural and urban populations over the last 50 years {the line for Rural Population}[0.107, 0.475, 0.957, 0.732] and {the line for Urban Population}[0.107, 0.138, 0.957, 0.862] are key to observe. The rural population has experienced a steady decrease from 956 million in 1970 to 620 million in 2020. On the other hand, the urban population has seen a steady rise, from 449 million in 1970, more than tripling to 1399 million in 2020.\nBased on the above thought, the answer is:\nThe trends shown in the line chart over the past 50 years show a decrease in the rural population and a corresponding increase in the urban population. The rural population declined from 956 million to 620 million, while the urban population more than tripled from 449 million to 1399 million. This shift indicates a significant urbanization trend over the last half-century."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "NbHCy2aU99dPfcGE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the Urban Population from 1970 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Urban Population from 1970 to 2020, I would identify {the point for Urban Population in 1970}[[0.107, 0.862]], which corresponds to a value of 449, and {the point for Urban Population in 2020}[[0.957, 0.138]], which corresponds to a value of 1399. The increase is therefore 1399 - 449 = 950.\nBased on the above thought, the answer is:\n950"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "YR1soq6BfkDeRUQq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the horizontal bar chart, can you translate the scores for the three key areas in both Emerging and Developed Markets into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the bar chart, I first identify {the area labels on y axis}<r>[0.134, 0.744, 0.219, 0.769], [0.344, 0.744, 0.428, 0.769], [0.552, 0.744, 0.635, 0.769]</r> as the rows. The column headers are the two market types: Emerging and Developed. Then, I examine the lengths of the bar segments for each area in both markets: {Manufacturing in Emerging Markets}<r>[0.227, 0.697, 0.350, 0.761]</r>,\n{Manufacturing in Developed Markets}<r>[0.350, 0.697, 0.427, 0.761]</r>, \n{Trading in Emerging Markets}<r>[0.227, 0.538, 0.283, 0.603]</r>, \n{Trading in Developed Markets}<r>[0.283, 0.538, 0.406, 0.603]</r>, \n{Logistics in Emerging Markets}<r>[0.227, 0.380, 0.320, 0.444]</r>, \n{Logistics in Developed Markets}<r>[0.320, 0.380, 0.404, 0.444]</r> and extract the corresponding values.\nBased on the above thought, the answer is:\nArea,Emerging Markets,Developed Markets\nManufacturing,123,77\nTrading,56,123\nLogistics,93,102"
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "RSodj2pg9YrSbzpW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area of Global Trade and Supply Chain Management performs significantly better in Developed Markets compared to Emerging Markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the bars, I can observe that {Manufacturing}<r>[0.227, 0.697, 0.427, 0.761]</r> perform better in Emerging Markets while {Trading}<r>[0.227, 0.538, 0.406, 0.603]</r> and {Logistics}<r>[0.227, 0.380, 0.404, 0.444]</r> are closely competitive in both markets. Hence, it's clear that Trading is the area that performs significantly better in Developed Markets compared to Emerging Markets.\nBased on the above thought, the answer is:\nTrading is the area of Global Trade and Supply Chain Management that performs significantly better in Developed Markets compared to Emerging Markets. This is evident from the significantly larger bar representing Trading in Developed Markets as compared to the corresponding bar in Emerging Markets."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "JkMflLyHa0M0cHBX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more 'wins' does Trading have in Developed Markets compared to Emerging Markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to determine the scores for Trading in both markets. From {Trading in Emerging Markets}<r>[0.227, 0.538, 0.283, 0.603]</r>, the score is 56, and from {Trading in Developed Markets}<r>[0.283, 0.538, 0.406, 0.603]</r>, the score is 123. Therefore, the difference is 123 - 56 = 67.\nBased on the above thought, the answer is:\n67."
+            }
+        ],
+        "id_raw": 106
+    },
+    {
+        "id": "nTkypKca6QAKEiEi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data present in the line chart depicting student engagement and learning efficiency for Online Tools be tabulated?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is possible to tabulate the data represented in this line chart. To do so, I first identify {the x-axis labels}<r>[0.013, 0.753, 0.143, 0.949], [0.187, 0.753, 0.236, 0.828], [0.219, 0.753, 0.329, 0.918], [0.364, 0.753, 0.422, 0.840], [0.478, 0.753, 0.515, 0.809], [0.537, 0.753, 0.608, 0.860], [0.651, 0.753, 0.701, 0.829], [0.722, 0.753, 0.794, 0.862], [0.805, 0.753, 0.888, 0.877], [0.880, 0.753, 0.981, 0.905]</r> as the distinct tools. Then, I examine the correspondence of these tools in {the line points of Student Engagement}<r>[0.143, 0.439], [0.236, 0.439], [0.329, 0.234], [0.422, 0.337], [0.515, 0.337], [0.608, 0.747], [0.701, 0.172], [0.794, 0.172], [0.888, 0.131], [0.981, 0.337]</r> and {Learning Efficiency}<r>[0.143, 0.337], [0.236, 0.603], [0.329, 0.378], [0.422, 0.234], [0.515, 0.152], [0.608, 0.583], [0.701, 0.337], [0.794, 0.234], [0.888, 0.378], [0.981, 0.357]</r> to populate the values in the table.\nBased on the above thought, the answer is:\nOnline Tools,Student Engagement,Learning Efficiency\nGoogle Classroom,75,80\nZoom,75,67\nKhan Academy,85,78\nUdemy,80,85\nEdX,80,89\nCoursera,60,68\nLynda,88,80\nBrainPOP,88,85\nABCmouse,90,78\nReading Eggs,80,79"
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "kuttQZiraG9H0T39",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which online tool shows the lowest learning efficiency and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the online tool with the lowest learning efficiency, I refer to {the line points for Learning Efficiency}<r>[0.143, 0.337], [0.236, 0.603], [0.329, 0.378], [0.422, 0.234], [0.515, 0.152], [0.608, 0.583], [0.701, 0.337], [0.794, 0.234], [0.888, 0.378], [0.981, 0.357]</r>. Then, I extract the values: 80, 67, 78, 85, 89, 68, 80, 85, 78, 79. According to this list, Zoom and Coursera have the lowest learning efficiency score of 67 and 68 respectively. I further check {Zoom's Learning Efficiency Point}<r>[0.236, 0.603]</r> and {Coursera's Learning Efficiency Point}<r>[0.608, 0.583]</r> to confirm the same.\nBased on the above thought, the answer is:\nBased on the line chart, Zoom is the online tool that shows the lowest Learning Efficiency. This is evident as Zoom has the lowest score of 67 for learning efficiency among all online tools as per the point [0.236, 0.603] on the chart. This may suggest that students may find it less effective for learning compared to other tools."
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "EkEsAua5sQuRwJuQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maxima for student engagement among all the online tools as per the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the maximum value of student engagement, I need to review {the line points for Student Engagement}<r>[0.143, 0.439], [0.236, 0.439], [0.329, 0.234], [0.422, 0.337], [0.515, 0.337], [0.608, 0.747], [0.701, 0.172], [0.794, 0.172], [0.888, 0.131], [0.981, 0.337]</r> and derive the corresponding values: 75, 75, 85, 80, 80, 60, 88, 88, 90, 80. Thus, the maximum value for student engagement is 90.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 110
+    },
+    {
+        "id": "lDTL2wLjrbiraEOv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you capture the data presented in the line graphs in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the graphs to a table, I'll first identify {the x-axis ticks}[[0.048, 0.906, 0.090, 0.936], [0.138, 0.906, 0.180, 0.936], [0.227, 0.906, 0.269, 0.936], [0.317, 0.906, 0.359, 0.936], [0.406, 0.906, 0.448, 0.936], [0.495, 0.906, 0.538, 0.936], [0.585, 0.906, 0.627, 0.936], [0.674, 0.906, 0.717, 0.936], [0.764, 0.906, 0.806, 0.936], [0.853, 0.906, 0.896, 0.936], [0.943, 0.906, 0.985, 0.936]] as the column headers representing the different years. Next, I locate texts from {the legend}[[0.360, 0.089, 0.585, 0.119], [0.665, 0.089, 0.949, 0.119]] which indicate the names of the industries and act as row headers. Extracting the actual data involves translating {the line points}[[0.069, 0.752], [0.114, 0.724], ..., [0.964, 0.146]] for Biotechnology Pharmacists and {the other set of line points}[[0.069, 0.477], [0.114, 0.504], ..., [0.964, 0.848]] for Medical Equipment Manufacturers into numerical values for each year.\nBased on the above thought, the answer is:\nYear,Biotechnology Pharmacists,Medical Equipment Manufacturers\n2000,50,70\n2001,52,68\n2002,54,65\n2003,53,69\n2004,57,67\n2005,60,70\n2006,63,68\n2007,65,70\n2008,67,65\n2009,69,63\n2010,70,60\n2011,72,58\n2012,73,57\n2013,78,55\n2014,80,53\n2015,82,52\n2016,84,50\n2017,88,48\n2018,90,46\n2019,92,45\n2020,94,43"
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "4iEEYa8LMe9L3Rk7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line trends, can you compare the growth trend in Biotechnology Pharmacists and Medical Equipment Manufacturers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare growth trends, I need to analyze the lines representing each industry. Looking at the line of {Biotechnology Pharmacists}[[0.069, 0.752, 0.964, 0.146]], I observe a consistent rising trend with a steep gradient, indicating a strong, increase in growth over the years. \nOn the other hand, the line representing {Medical Equipment Manufacturers}[[0.069, 0.477, 0.964, 0.848]] shows a steady decline. This is clear from the downward slope of the line.\nBased on the above thought, the answer is:\nOwing to the upward direction of the line trend for Biotechnology Pharmacists, it can be inferred that the growth in this industry consistently increased over the years. Conversely, Medical Equipment Manufacturers are showing a consistent decrease in growth, as indicated by the downward trend in their line graph."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "A5hCycx5JqYVBj7H",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Medical Equipment Manufacturers experience the maximum percentage growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year of maximum growth for Medical Equipment Manufacturers, I need to identify the highest point on their line. This can be found at {the line coordinates}[[0.830, 0.779]], which correspond to a growth percentage of 48%. Tracing this point to {the x-axis}[[0.806, 0.936]], I can determine the year.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "n4m5TgGWBirKE6Zm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a table format with Industries as rows and the two factors 'Green Intelligence' and 'Environmental Philanthropy' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. First, we have {the x-axis tick labels}[[0.063, 0.845, 0.104, 0.875], [0.164, 0.845, 0.216, 0.875], [0.230, 0.845, 0.364, 0.875], [0.371, 0.845, 0.436, 0.875], [0.460, 0.845, 0.561, 0.875], [0.564, 0.845, 0.671, 0.875], [0.657, 0.845, 0.791, 0.875], [0.779, 0.845, 0.883, 0.875], [0.892, 0.845, 0.983, 0.875]], which will represent the Industry for each row in the table. Next, we have {the legend texts}[[0.239, 0.936, 0.438, 0.968], [0.542, 0.936, 0.842, 0.968]], which will be the column headers for 'Green Intelligence' and 'Environmental Philanthropy' respectively. To populate the table, I will locate the y-value for both 'Green Intelligence' and 'Environmental Philanthropy' for each industry.\nBased on the above thought, the answer is:\nIndustry,Green Intelligence,Environmental Philanthropy\nTech,80,77\nRetail,85,82\nManufacturing,90,90\nEnergy,95,88\nHealthcare,90,85\nAutomotive,83,80\nTransportation,81,78\nReal Estate,79,75\nEducation,85,84"
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "4jDnonmm7gigqEDf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry has shown the most environmental consciousness and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which industry shows the most environmental consciousness, I will analyze the scores for both 'Green Intelligence' and 'Environmental Philanthropy' across all industries. For 'Green Intelligence', the highest score is noted for {Energy}<r>[0.404, 0.197]</r> which has a score of 95. Similarly, for 'Environmental Philanthropy', the highest score is given to {Manufacturing}<r>[0.297, 0.357]</r> with a score of 90. As the overall environmental consciousness would be measured collectively by both 'Green Intelligence' and 'Environmental Philanthropy', it is important to consider the scores of both the metrics. Although Energy gets the highest in 'Green Intelligence' and Manufacturing scores the highest in 'Environmental Philanthropy', Energy's score for both the metrics is higher in aggregate, hence it may be deemed as the industry with the most environmental consciousness.\nBased on the above thought, the answer is:\nAccording to the chart, the Energy industry appears to be the most conscious about the environment because it has the highest score in 'Green Intelligence' and among the highest scores in 'Environmental Philanthropy', making its aggregate score higher than that of other industries."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "NykVaMUJYkLHeGgg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the scores for Green Intelligence and Environmental Philanthropy in the Energy industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the scores for Green Intelligence and Environmental Philanthropy in the Energy industry, I have to look for the y-values corresponding to these two metrics at {Energy's x-location}<r>[0.404, 0.837]</r>. The y-values extracted from {Green Intelligence's line points} <r>[0.083, 0.677], [0.190, 0.517], [0.297, 0.357], [0.404, 0.197]</r> and {Environmental Philanthropy's line points} <r>[0.083, 0.773], [0.190, 0.613], [0.297, 0.357], [0.404, 0.421]</r> give us the scores for Green Intelligence and Environmental Philanthropy respectively. The difference between the two scores is the answer to this question.\nBased on the above thought, the answer is:\n7."
+            }
+        ],
+        "id_raw": 113
+    },
+    {
+        "id": "QV3zHYMNjyn4ltBS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a table with the different statuses as rows and the Parties as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart data into a table, I first identify {the y-axis labels for the status}[[0.071, 0.738, 0.195, 0.877], [0.071, 0.639, 0.215, 0.763], [0.071, 0.551, 0.158, 0.675], [0.071, 0.457, 0.174, 0.581], [0.071, 0.364, 0.160, 0.488], [0.071, 0.270, 0.156, 0.394], [0.071, 0.183, 0.181, 0.307], [0.071, 0.091, 0.218, 0.215]] which represent the row headers or statuses. Next, I locate {the legend entries}[[0.303, 0.031, 0.425, 0.057], [0.438, 0.031, 0.570, 0.057]] as the column headers representing the different parties. To populate the values, I look at {the bar values for Democrats}[[0.243, 0.793, 0.263, 0.877], [0.243, 0.689, 0.303, 0.763], [0.243, 0.611, 0.293, 0.675], [0.243, 0.518, 0.313, 0.581], [0.243, 0.414, 0.323, 0.488], [0.243, 0.325, 0.333, 0.394], [0.243, 0.225, 0.323, 0.307], [0.243, 0.143, 0.303, 0.215]] and {Republicans}[[0.733, 0.793, 0.753, 0.877], [0.733, 0.694, 0.783, 0.763], [0.733, 0.604, 0.833, 0.675], [0.733, 0.519, 0.853, 0.581], [0.733, 0.424, 0.853, 0.488], [0.733, 0.330, 0.873, 0.394], [0.733, 0.220, 0.853, 0.307], [0.733, 0.133, 0.833, 0.215]] for each party at the respective statuses.\nBased on the above thought, the answer is:\nStatus, Democrats, Republicans\nSteep increase,2,3\nFluctuating growth,3,5\nUpward trajectory,5,10\nSubdued decline,7,12\nStable growth,8,12\nDeclining pattern,9,13\nAccelerated decline,8,12\nErratic behavior,6,9"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "TEbwd6OWNyW8MCjI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which party appears to perform better overall across all statuses? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which party appears to perform better overall across all statuses, I'll compare the bar values for each status. I check {the bar values for Democrats}[[0.243, 0.793, 0.263, 0.877], [0.243, 0.689, 0.303, 0.763], [0.243, 0.611, 0.293, 0.675], [0.243, 0.518, 0.313, 0.581], [0.243, 0.414, 0.323, 0.488], [0.243, 0.325, 0.333, 0.394], [0.243, 0.225, 0.323, 0.307], [0.243, 0.143, 0.303, 0.215]] against {the bar values for Republicans}[[0.733, 0.793, 0.753, 0.877], [0.733, 0.694, 0.783, 0.763], [0.733, 0.604, 0.833, 0.675], [0.733, 0.519, 0.853, 0.581], [0.733, 0.424, 0.853, 0.488], [0.733, 0.330, 0.873, 0.394], [0.733, 0.220, 0.853, 0.307], [0.733, 0.133, 0.833, 0.215]]. It is clear that the values for Republicans are consistently higher than Democrats for every status, indicating that Republicans appear to perform better overall.\nBased on the above thought, the answer is:\nBased on the bar chart, the Republican party appears to perform better overall across all statuses. This is determined by comparing the values of each status. Whether it be 'Steep increase', 'Fluctuating growth', 'Upward trajectory', 'Subdued decline', 'Stable growth', 'Declining pattern', 'Accelerated decline', or 'Erratic behavior', the Republicans always have higher numbers than Democrats, indicating their better performance across the spectrum."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "8N8jnaGvvINB3UL2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the values for Republicans and Democrats during the 'Subdued decline' status?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference in values for Republicans and Democrats during the 'Subdued decline' status, I need to locate {the bar value for Democrats during 'Subdued decline'}[0.243, 0.518, 0.313, 0.581] and correspondingly, {the bar value for Republicans during 'Subdued decline'}[0.733, 0.519, 0.853, 0.581], which are 7 and 12 respectively. The difference is therefore, 12 - 7 = 5.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "z7K8Qn0CRCfVTlQa",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you elaborate on the chart data and create a table that includes the columns 'Year', 'Carbon Emissions', and 'Volunteers' respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can generate a table based on the line charts. I will identify {the x-axis} coordinates as [0.090, 0.899, 0.106, 0.932], representing the 'Year' from 0 to 8 in the table. Further, the y-axis is going to serve as the values for 'Carbon Emissions' and 'Volunteers' categories which are represented by two lines, {line for Carbon Emissions}<r>[0.098, 0.124, 0.974, 0.874]</r> and {line for Volunteers}<r>[0.098, 0.158, 0.974, 0.465]</r>. Hence each point of intersection of x and these two lines give us their respectively values for each year.\nBased on the above thought, the answer is:\nYear,Carbon Emissions,Volunteers\n0,100,80\n1,95,85\n2,85,82\n3,75,86\n4,77,90\n5,70,92\n6,63,94\n7,58,96\n8,56,98"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "DXnXnpDbRic3zi42",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you discuss the trend in 'Volunteers' over the years as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The line representing 'Volunteers' is seen here <r>[0.098, 0.158, 0.974, 0.465]</r>. By examining this line, we can see an overall increase from Year 0 to Year 8 as the line rises while maintaining a positive slope over the interval. The trend shows that the number of volunteers has consistently risen over the years with a slight dip only in Year 2.\nBased on the above thought, the answer is:\nThe 'Volunteers' line exhibits an upward trend throughout the period from Year 0 to Year 8, indicating that the number of volunteers has continuously increased over the years. The increase is steady and consistent, reflecting a constantly growing interest and participation level in the cause. Except for a minor drop in Year 2, the overall trend signifies increasing involvement of volunteers over the years."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "S8L2hsbKScXIaH25",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage change in 'Carbon Emissions' from Year 0 to Year 8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To work out the percentage change, I have to first locate the initial and final values for 'Carbon Emissions'. My starting point is at {Year 0}<r>[0.098, 0.124]</r> and ending point is at {Year 8}<r>[0.974, 0.874]</r>. The initial value is 100 and the final value is 56. The percentage change can be calculated using the formula: [(final value - initial value) / initial value] * 100.\nBased on the above thought, the answer is:\n-44%"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "AxURUwjifX97pShi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the numerical data regarding Cholesterol Level, Heart Rate, and Blood Sugar under different exercise regimes into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a tabular format, I closely examined the values represented by each color-coded line across the two subplots. For instance, {the line representing Cholesterol Level}<r>[0.125, 0.250, 0.475, 0.630]</r> starts from a high value at {Running}<r>[0.145, 0.895]</r> and ends lower at {Yoga}<r>[0.465, 0.390]</r>. Similarly, I noted the starting and ending points for {Heart Rate}<r>[0.125, 0.250, 0.475, 0.670]</r> and {Blood Sugar}<r>[0.125, 0.250, 0.475, 0.720]</r>, thereby mapping them to their corresponding values for each exercise regime.\nBased on the above thought, the answer is:\nHealth Metric,Running,Yoga\nCholesterol Level,220,150\nHeart Rate,85,65\nBlood Sugar,160,105"
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "RP8EPwEhWefYt25N",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the different exercise regimes appear to affect Cholesterol levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the effect on Cholesterol levels as depicted in the chart requires scrutinizing {the line for Cholesterol Level}<r>[0.125, 0.250, 0.475, 0.630]</r>. The data points start high during {Running}<r>[0.145, 0.250]</r> with a value around 220, and drastically decrease as the regime shifts to {Yoga}<r>[0.465, 0.630]</r> with a value around 150. This significant drop suggests a more pronounced effect of Yoga on reducing Cholesterol levels compared to Running.\nBased on the above thought, the answer is:\nThe different exercise regimes have a substantial impact on Cholesterol levels, particularly observed in the sharp decline from Running to Yoga. The chart indicates that the more relaxed and stretching-oriented Yoga regime helps in greatly reducing Cholesterol levels compared to the more strenuous Running regime."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "GyNDlVlzrfN2mdYk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Blood Sugar levels from Running to Yoga according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in Blood Sugar levels from Running to Yoga, I locate the end points of {the Blood Sugar line}<r>[0.125, 0.720, 0.475, 0.465]</r>. Initially, the Blood Sugar level is recorded at 160 during {Running}<r>[0.145, 0.720]</r>, and it decreases to 105 by the time of {Yoga}<r>[0.465, 0.465]</r>. Thus, the difference can be calculated by subtracting the value obtained in Yoga from the one in Running.\nBased on the above thought, the answer is:\n55"
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "iiDqZXoOKFFnjyoo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the horizontal bar plot to a structured data table with investment types and two quarters: Q1 and Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the structured table, I first take note of {the y-axis labels}<r>[0.051, 0.170, 0.158, 0.858]</r>, which represent the row values or investment types. Next, I identify the lengths of the bars representing {Q1}<r>[0.307, 0.253, 0.682, 0.775]</r> and corresponding {Q2}<r>[0.388, 0.243, 0.921, 0.777]</r> as the numerical values for the columns. Matching each investment type with its corresponding Q1 and Q2 lengths, I can form the data table.\nBased on the above thought, the answer is:\nInvestment Types,Q1,Q2\nCorporate Bonds,40,50\nMutual Funds,35,55\nETFs,50,60\nGold,40,55\nCryptocurrency,45,75\nStocks,35,45\nReal Estate,40,55\nTreasury Bonds,50,40\nMunicipal Bonds,55,40\nDerivatives,55,45"
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "OpLcam15WMvyG3XR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar plots, which investment type showed the most growth from Q1 to Q2, and what might this indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the growth of each investment type, I compare the lengths of the bars representing {Q1}<r>[0.307, 0.243, 0.682, 0.777]</r> and corresponding {Q2}<r>[0.388, 0.243, 0.921, 0.777]</r> for each investment type. Notably, {Cryptocurrency}<r>[0.307, 0.610, 0.797, 0.624]</r> presents a noticeable growth from Q1 to Q2, with its length increasing from 45 to 75, which is consistent with the extracted data. This substantial growth implies that Cryptocurrency was becoming a more attractive investment option during this period.\nBased on the above thought, the answer is:\nCryptocurrency showed the most growth from Q1 to Q2 according to the bar plots. This upward trend could suggest increased investor interest and confidence in Cryptocurrency during this period, which might have been triggered by positive market news, improved market conditions, or favorable policy changes. It may indicate a prosperous trend for Cryptocurrency investment for the ongoing quarter."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "826hK8FDdhzdav5V",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_6_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many points did the rating for Cryptocurrency increase from Q1 to Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first locate the bars representing Cryptocurrency's rating for {Q1}<r>[0.307, 0.610]</r> and {Q2}<r>[0.797, 0.610]</r>. I then extract the corresponding rating values, which are 45 for Q1 and 75 for Q2 respectively. The increase in rating is calculated by subtracting the Q1 value from the Q2 value, i.e., 75 - 45 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 119
+    },
+    {
+        "id": "33olZyVrmlPGZ0v2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart indicating transportation preference over the years into a structured table representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.072, 0.855, 0.134, 0.886], [0.242, 0.855, 0.303, 0.886],[0.411, 0.855, 0.473, 0.886], [0.581, 0.855, 0.642, 0.886],[0.750, 0.855, 0.812, 0.886], [0.920, 0.855, 0.981, 0.886]], that represent the column headers (Years). Next, I identify {the legend texts}[[0.238, 0.934, 0.460, 0.964],[0.560, 0.934, 0.689, 0.964],[0.789, 0.934, 0.874, 0.964]] as the row labels indicating different modes of transportation. To assign the corresponding values, I locate {the line points} for each mode of transportation at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Public_Transportation,Automobiles,Bicycles\n2015,0.75,0.90,0.20\n2016,0.80,0.86,0.25\n2017,0.84,0.82,0.35\n2018,0.82,0.78,0.45\n2019,0.78,0.75,0.55\n2020,0.72,0.70,0.65"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "W91m7iXNhgOsixC3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what conclusions can be drawn regarding the trend in transportation preferences from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw conclusions from the line chart, I need to analyze the trend lines for different modes of transportation. The line for {Public Transportation}[0.103, 0.187, 0.950, 0.280] shows a gradual decrease in preference from 0.75 in 2015 to 0.72 in 2020. The line for {Automobiles}[0.103, 0.141, 0.950, 0.295] also shows a steady decline from 0.9 in 2015 to 0.7 in 2020. In contrast, the line for {Bicycles}[0.103, 0.334, 0.950, 0.681] exhibits a consistent increase from 0.2 in 2015 to 0.65 in 2020.\nBased on the above thought, the answer is:\nThe line chart indicates a clear shift in transportation preferences from 2015 to 2020. Both Public Transportation and Automobiles have seen a decline in preference, signifying a possible move away from traditional modes of transport. Simultaneously, there is an evident increase in the preference for Bicycles, suggesting a growing trend towards eco-friendly and active modes of transportation."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "FtY3cGneivIuz4VZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the preference for Bicycles increase from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in preference for Bicycles from 2015 to 2020, I need to identify the corresponding values from {the line points for Bicycles}[[0.103, 0.681], [0.272, 0.642], [0.442, 0.565], [0.611, 0.488], [0.781, 0.411], [0.950, 0.334]]. The preference in 2015 is 0.20 at point {2015}[0.103, 0.681] and in 2020 it is 0.65 at point {2020}[0.950, 0.334]. So the increase in preference for Bicycles from 2015 to 2020 is 0.65 - 0.20 = 0.45.\nBased on the above thought, the answer is:\n0.45."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "VOwtSiu4vHEKzf7p",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart's data into a tabular format, with 'Cities' being the row headers and 'Avg Temperature (\u00b0C)', 'Avg Snowfall (cm)' being the column headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "For creating a table from the line chart, I have to first identify {the x-axis tick labels}[[0.028, 0.902, 0.101, 0.934], [0.328, 0.902, 0.396, 0.934], [0.610, 0.902, 0.709, 0.934], [0.933, 0.902, 0.981, 0.934]], as these hold the city names forming the row headers. Next, I must allocate the column headers, which are {the legend texts}[[0.714, 0.784, 0.938, 0.814], [0.714, 0.828, 0.901, 0.858]]. The column headers are \"Avg Temperature (\u00b0C)\" and \"Avg Snowfall (cm)\". The actual values can be inferred from {the line points}[[0.064, 0.743], [0.362, 0.669], [0.659, 0.570], [0.957, 0.535], [0.064, 0.826], [0.362, 0.180], [0.659, 0.849], [0.957, 0.849]].\nBased on the above thought, the answer is:\nCity,Avg Temperature (\u00b0C),Avg Snowfall (cm)\nTorino,5.4,1.2\nAosta,9.2,34.1\nBologna,14.2,0\nBari,16,0"
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "rguv3WxM7A4giB5Z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBeyond the raw numbers, what is the overall trend in the weather of these Italian cities according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce the overall weather trends in these Italian cities according to the line chart, I need to interpret the information provided by the plots. {The line for Avg Temperature (\u00b0C)}[0.064, 0.535, 0.957, 0.743] reveals an increase as we move from Torino to Bari. This suggests an overall rise in average temperatures in these cities from north (Torino) to south (Bari), typically found in Italy due to its geographical orientation. On the other hand, {the line for Avg Snowfall (cm)}[0.064, 0.180, 0.957, 0.849] displays a variable snowfall pattern where it is seen only in Torino and Aosta conspicuously, and no snowfall in Bologna or Bari. Snowfall seems to decline sharply as you move from the northern city Aosta to the central-northern city Bologna before reaching zero in the southern city Bari.\nBased on the above thought, the answer is:\nThe overall trend in the weather of these Italian cities, according to the line chart, hints at a climatic pattern often observed in Italy. The average temperatures tend to rise moving from northern cities towards the south, signifying a warmer climate in southern cities like Bari, compared to northern cities such as Torino. As for snowfall, it seems to be considerably higher in the northern cities like Torino and Aosta, but absent in Bologna and Bari, suggesting that snowfall is a crucial component of the weather only in the northern parts of Italy."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "idn0Pi169Bde0BZF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average snowfall in Torino and Bari combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average snowfall in Torino and Bari combined, I have to find the snowfall values for these cities first. The snowfall in Torino is located at {the point for Torino in Avg Snowfall (cm)}[0.064, 0.826] with a value of 1.2 cm. The snowfall in Bari is depicted at {the point for Bari in Avg Snowfall (cm)}[0.957, 0.849] with a value of 0.0 cm. The average snowfall is then calculated by summing these two values and dividing by 2.\nBased on the above thought, the answer is:\n0.6 cm."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "hipf5j3uDxral51v",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the evenly scattered points denoting each sector's innovation index in the line chart into a structured table while considering industry sectors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table, I first look at {the x-axis tick labels}<r>[0.056, 0.697, 0.112, 0.790]</r>, <r>[0.107, 0.697, 0.188, 0.831]</r>,<r>[0.210, 0.697, 0.263, 0.785]</r>, <r>[0.292, 0.697, 0.339, 0.775]</r>, <r>[0.351, 0.697, 0.414, 0.803]</r>,<r>[0.420, 0.697, 0.490, 0.814]</r>,<r>[0.500, 0.697, 0.565, 0.806]</r>, <r>[0.601, 0.697, 0.641, 0.764]</r>, <r>[0.675, 0.697, 0.717, 0.767]</r>,<r>[0.707, 0.697, 0.792, 0.840]</r>,<r>[0.820, 0.697, 0.868, 0.777]</r>, <r>[0.869, 0.697, 0.943, 0.821]</r> which represent the rows of the table. Extracting the values from {the line points}<r>[0.112, 0.557]</r>, <r>[0.188, 0.434]</r>, <r>[0.263, 0.311]</r>, <r>[0.339, 0.218]</r>, <r>[0.414, 0.234]</r>,<r>[0.490, 0.218]</r>,<r>[0.565, 0.181]</r>, <r>[0.641, 0.156]</r>, <r>[0.717, 0.135]</r>,<r>[0.792, 0.301]</r>,<r>[0.868, 0.419]</r>, <r>[0.943, 0.212]</r> corresponding to each sector gives us the innovation indices of these sectors.\nBased on the above thought, the answer is:\nInvestment Strategy,Earnings\nHigh Risk, 0.4\nModerate Risk, 0.8\nLow Risk, 1.2\nNo Risk, 1.5\nIndex Fund, 1.45\nMutual Fund, 1.5\nReal Estate, 1.62\nBonds, 1.7\nStocks, 1.77\nCryptocurrency, 1.23\nOptions, 0.85\nCommodities, 1.52"
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "iIi7Bbch0neryEEE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a prospective investor is looking to invest in a sector with high innovation index, which sectors should he consider according to the line chart? Explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the sectors with high innovation index. Observing the line chart, I can see that {Stocks}<r>[0.717, 0.135]</r>, {Bonds}<r>[0.641, 0.156]</r>, {Real Estate}<r>[0.565, 0.181]</r>, {Commodities}<r>[0.943, 0.212]</r> and {Mutual Fund}<r>[0.490, 0.218]</r> have innovation indices above 1.5 which is significantly high compared to other sectors.\nBased on the above thought, the answer is:\nAccording to the line chart, a prospective investor looking to invest in a sector with a high innovation index should consider the sectors of Stocks, Bonds, Real Estate, Commodities, and Mutual Fund. These sectors are tagged with innovation indices above 1.5, indicating a strong innovation performance and thereby presenting the potential for higher returns from investments made in these sectors."
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "Iz2tf41vUONx3lVp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest innovation index across all sectors in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest and lowest innovation index, I need to locate and analyze {the line points}<r>[0.112, 0.557]</r>, <r>[0.188, 0.434]</r>, <r>[0.263, 0.311]</r>, <r>[0.339, 0.218]</r>, <r>[0.414, 0.234]</r>,<r>[0.490, 0.218]</r>,<r>[0.565, 0.181]</r>, <r>[0.641, 0.156]</r>, <r>[0.717, 0.135]</r>,<r>[0.792, 0.301]</r>,<r>[0.868, 0.419]</r>, <r>[0.943, 0.212]</r> on the line chart. From these coordinates, I see that the highest innovation index is for Stocks at 1.77 <r>[0.717, 0.135]</r>, and the lowest is for High Risk at 0.4 <r>[0.112, 0.557]</r>. The difference between the highest and lowest innovation index is therefore 1.77 - 0.4 = 1.37.\nBased on the above thought, the answer is:\n1.37"
+            }
+        ],
+        "id_raw": 122
+    },
+    {
+        "id": "sJp7iYNRpGNJ9EeW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart displaying the annual energy use and resource consumption between 2001 and 2010 into a table format where rows represent years and columns represent the type of energy resource?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table, I extract the column names from {the legend}<r>[0.373, 0.953, 0.688, 0.985]</r>. These represent the type of energy resource. I then identify {the x-axis tick labels}<r>[0.076, 0.851, 0.125, 0.874], [0.172, 0.851, 0.221, 0.874], [0.267, 0.851, 0.316, 0.874], [0.363, 0.851, 0.412, 0.874], [0.458, 0.851, 0.508, 0.874], [0.554, 0.851, 0.603, 0.874], [0.649, 0.851, 0.698, 0.874], [0.745, 0.851, 0.794, 0.874], [0.840, 0.851, 0.889, 0.874], [0.936, 0.851, 0.985, 0.874]</r> as row labels representing the different years. With these headers established, I then identify {the line points for Coal}<r>[0.101, 0.078, 0.960, 0.351]</r>, {Oil}<r>[0.101, 0.533, 0.960, 0.806]</r>, and {Natural gas}<r>[0.101, 0.472, 0.960, 0.745]</r> and extract the corresponding values at each point for each year.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas\n2001,5100,4500,3800\n2002,5200,4400,3900\n2003,5300,4300,4000\n2004,5400,4200,4100\n2005,5500,4100,4200\n2006,5600,4000,4300\n2007,5700,3900,4400\n2008,5800,3800,4500\n2009,5900,3700,4600\n2010,6000,3600,4700"
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "wbiPtVS5UYZGqum1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy resource has shown a consistently increasing trend in consumption from 2001 to 2010, and what does it indicate about the reliance on this resource?"
+            },
+            {
+                "from": "gpt",
+                "value": "I interpret the trend for each energy resource by looking at their respective line plots on the chart. Looking at the line for {Coal}<r>[0.101, 0.078, 0.960, 0.351]</r>, it consistently increases from 5100 to 6000, indicating a constant rise in Coal consumption over these years. The line for {Oil}<r>[0.101, 0.533, 0.960, 0.806]</r> shows a decreasing trend, whereas {Natural gas}<r>[0.101, 0.472, 0.960, 0.745]</r> shows an increase, but it's not as marked as Coal\u2019s. Therefore, Coal is the energy resource with a consistently increasing trend in consumption.\nBased on the above thought, the answer is:\nThe energy resource with a consistently increasing trend in consumption from 2001 to 2010 is Coal. This could indicate a higher reliance on Coal as a source of energy during this period, whether due to factors such as cost-effectiveness, availability, or energy requirement demands. However, the increasing trend also suggests potential environmental concerns, since the burning of Coal for energy contributes significantly to air pollution and climate change."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "YBh78f2PIg2XNL3b",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in consumption of Natural Gas from 2001 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first locate {the line points for Natural Gas}<r>[0.101, 0.472, 0.960, 0.745]</r> and extract the consumption values for 2001 which is 3800 and 2010 which is 4700. The difference between consumption in 2010 and 2001 is therefore 4700 - 3800 = 900.\nBased on the above thought, the answer is:\n900."
+            }
+        ],
+        "id_raw": 124
+    },
+    {
+        "id": "P6sbZhgAmlQmH79E",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the graph into a structured table with the media types as columns and the features as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract tabular data from the line graph, I first identify the {x-axis tick labels}<r>[0.077, 0.851, 0.138, 0.883]</r> to <r>[0.920, 0.851, 0.981, 0.883]</r> which stand for the column headers or Entrepreneurial Features. Secondly, I identify {the legend texts}<r>[0.188, 0.934, 0.381, 0.964]</r>, <r>[0.481, 0.934, 0.613, 0.964]</r> and <r>[0.713, 0.934, 0.929, 0.964]</r> as the row labels representing the types of Rates in %. To fill up the table, I locate {the line points}<r>[0.108, 0.800]</r> to <r>[0.951, 0.598]</r>, <r>[0.108, 0.202]</r> to <r>[0.951, 0.101]</r> and <r>[0.108, 0.294]</r> to <r>[0.951, 0.156]</r> for each feature at the respective rates and extract the values.\nBased on the above thought, the answer is:\n|\n    Entrepreneurial   | High-tech startup | Success rate | Government support |\n    Features          | Rates in %       | Rates in % | Rates in % |\n    Attr1             | 20                  | 85                   | 75                                |\n    Attr2             | 22                  | 88                   | 76                                | \n    Attr3             | 25                  | 88                   | 80                                | \n    Attr4             | 28                  | 90                   | 82                                | \n    Attr5             | 31                  | 92                   | 88                                | \n    Attr6             | 36                  | 96                   | 82                                | \n    Attr7             | 39                  | 95                   | 85                                | \n    Attr8             | 42                  | 96                   | 90                                |"
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "MQVMFTbsdcfcKnwa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, which rate experienced the most steady increase throughout the entrepreneurial features?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which rate experienced the most steady increase, we need to look at the fluctuation in the line plots. Observing the {line plot for High-tech startups}<r>[0.108, 0.598, 0.951, 0.800]</r> shows a straight and steady increase from 20 to 42. {Line plot for Success rate}<r>[0.108, 0.101, 0.951, 0.202]</r> has a relatively stable increase from 85 to 96 with a slight dip for Attr7. The {line plot for Government Support}<r>[0.108, 0.156, 0.951, 0.294]</r> shows increase and decrease movements as it journeys from 75 to 90.\nBased on the above thought, the answer is:\nThe High-tech start-ups rate experienced the most steady increase throughout the entrepreneurial features. The line plot shows a consistent increase in percentage rate from 20 up to 42 across the entrepreneurial features. This suggests that the high-tech start-up rate continues to improve steadily over time compared to the Success rate and Government Support rate."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "F3ylxuW7Knq65TZo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent did Government support rate increase from Attr1 to Attr8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the increase in Government support from Attr1 to Attr8, I need to get the initial value for Attr1 from the point coordinate <r>[0.108, 0.294]</r> and the final value for Attr8 from the point coordinate <r>[0.951, 0.156]</r>. On extracting the corresponding values, I find the initial value is 75 and the final value is 90. Therefore, the percentage increase is calculated as ((Final Value - Initial Value)/Initial Value)*100.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "vIRyHN2QsnZSUwRu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the line chart data into a structured table, with the years as rows and the trends of 'Global Trade Demand' and 'Global Supply Management' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to convert the line chart data into a tabular format, I will first locate and identify the axis labels. The {x-axis tick labels}[[0.072, 0.852, 0.115, 0.875], [0.159, 0.852, 0.202, 0.875], [0.246, 0.852, 0.289, 0.875], [0.333, 0.852, 0.376, 0.875], [0.421, 0.852, 0.463, 0.875], [0.508, 0.852, 0.550, 0.875], [0.595, 0.852, 0.637, 0.875], [0.682, 0.852, 0.724, 0.875], [0.769, 0.852, 0.811, 0.875], [0.856, 0.852, 0.898, 0.875], [0.943, 0.852, 0.985, 0.875]] will serve as the row identifiers, each corresponding to a different year. The columns, on the other hand, will be represented by the two lines in the plot, namely 'Global Trade Demand' {line_0}[[0.093, 0.127, 0.964, 0.706]] and 'Global Supply Management' {line_1}[[0.093, 0.114, 0.964, 0.732]]. To populate the values in each cell, I would study the coordinates of each point on the line and extract the corresponding values accordingly.\nBased on the above thought, the answer is:\nYear Global Trade Demand, Global Supply Management\n2010 500, 480\n2011 520, 520\n2012 550, 560\n2013 600, 580\n2014 650, 600\n2015 750, 620\n2016 800, 750\n2017 825, 800\n2018 850, 820\n2019 900, 850\n2020 940, 950"
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "hyMhO1KZKHnZGArZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends represented in the chart, which area, trade demand or supply management, saw a more stable growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the area with more stable growth, I analyze the lines representing 'Global Trade Demand' {line_0}[[0.093, 0.127, 0.964, 0.706]] and 'Global Supply Management' {line_1}[[0.093, 0.114, 0.964, 0.732]]. For 'Global Trade Demand', the line shows an oscillating but overall increasing trend over the years, indicating some volatility. For 'Global Supply Management', the line initially stays relatively flat but begins to increase significantly from 2016 onwards, which suggests an overall more stable growth before 2016.\nBased on the above thought, the answer is:\nFrom the trends displayed in the chart, it can be inferred that the 'Global Supply Management' had more stable growth before 2016, mostly staying flat with only slight changes. However, after 2016 it started increasing significantly, showing less stability. On the other hand, 'Global Trade Demand' showed some volatility but overall an increasing trend."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "chhWxqyQ7VKfMQ2B",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value reached by the 'Global Trade Demand' and in which year did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value reached by 'Global Trade Demand', I need to go through all the points in the {Global Trade Demand line}[[0.093, 0.127, 0.964, 0.706]]. The points represent the different years from 2010 to 2020. By examining each point, I identify the highest y-coordinate which represents the maximum value. It corresponds to year 2020 {corresponding x-coordinate of the year 2020}[0.943, 0.852, 0.985, 0.875] with a value of 940.\nBased on the above thought, the answer is:\nThe maximum value reached by the 'Global Trade Demand' is 940, in the year 2020."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "KSxbI8wVP98r0tYg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart representing the growth rate of different emerging technologies over the years into tabular data format where tech categories are the row labels and years are the column labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the table format from this line chart, I consider {the x-axis tick labels}[[0.080, 0.855, 0.141, 0.886], [0.247, 0.855, 0.309, 0.886], [0.415, 0.855, 0.477, 0.886], [0.583, 0.855, 0.645, 0.886], [0.751, 0.855, 0.813, 0.886], [0.919, 0.855, 0.981, 0.886]] as column headers representing the different years. {The legend texts}[[0.301, 0.846, 0.321, 0.876], [0.301, 0.890, 0.477, 0.920], [0.301, 0.934, 0.330, 0.964], [0.577, 0.846, 0.609, 0.876], [0.577, 0.890, 0.689, 0.920], [0.789, 0.846, 0.818, 0.876], [0.789, 0.890, 0.818, 0.920]] are the row labels representing the emerging technologies. To gather the values, I look at {the points on each line}[[0.110, 0.660], [0.278, 0.600], ..., [0.950, 0.325]] and record the corresponding values for each technology at the respective years.\nBased on the above thought, the answer is:\nTechnology,2020,2021,2022,2023,2024,2025\nAI,20,28,35,43,53,65\nCloud computing,15,22,31,40,51,65\n5G,10,25,40,56,72,90\nIoT,5,12,19,27,36,46\nBlockchain,2,3,11,21,32,44\nAR,1,2,3,5,8,12\nVR,1,2,4,8,13,21"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "OplaXKkb3xIBmQDu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the patterns in the line chart, what can be said about the growth rate of the technologies over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I notice that all the technologies have shown progress over the years. The lines for AI, Cloud computing, 5G, IoT, Blockchain, AR, and VR are all upward sloping indicating increasing growth rates. However, the pace of growth varies across technologies. For instance, {the line for 5G}[0.110, 0.138, 0.950, 0.735] shows a steep upward slope, indicating rapid growth rate. On the other hand, {the line for IoT}[0.110, 0.466, 0.950, 0.772] depicts a less steep slope suggesting a relatively slower growth over the years. MR and VR, represented by {the lines for AR and VR}[0.110, 0.720, 0.950, 0.802][0.110, 0.653, 0.950, 0.802], show the slowest growth, with AR growing just a little more than VR. The growth rate of these technologies could be shaped by various factors such as development of technology, market demand, and investment in research and development.\nBased on the above thought, the answer is:\nFrom the line chart it can be observed that all the technologies have been growing over the years, with varying rates of growth. 5G has seen the most rapid growth, followed by Cloud computing and AI. IoT and Blockchain also show significant growth, but at a slower pace compared to the previous technologies mentioned. AR and VR represent emerging technologies that are growing at the slowest rate, indicating these technologies may still be in the early stages of adoption or development. However, all technologies are expected to continue to grow in the future. This trend points towards a continual expansion and advancement in technologies over the course of the years 2020 to 2025."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "mHh45MuP4bjXtFoZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth rate of AI from 2020 to 2025 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average growth rate of AI from 2020 to 2025, I observe {the points on the line for AI}[[0.110, 0.660], [0.278, 0.600], [0.446, 0.548], [0.614, 0.489], [0.782, 0.414], [0.950, 0.325]], which correspond to the growth rates for the years from 2020 to 2025: 20, 28, 35, 43, 53, 65 respectively. I add up these rates and divide by the number of years to get the average growth rate.\nBased on the above thought, the answer is:\n40.6667"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "JBcDcyh1UdiZl32h",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data output of these algorithm's achievable efficiency at different levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "OK, To form a structured table from the line chart, I begin by identifying {the x-axis labels}<r>[0.081, 0.851, 0.120, 0.883], [0.214, 0.851, 0.268, 0.883], [0.362, 0.851, 0.401, 0.883], [0.495, 0.851, 0.550, 0.883], [0.655, 0.851, 0.671, 0.883], [0.777, 0.851, 0.831, 0.883], [0.925, 0.851, 0.964, 0.883]</r> as columns representing different levels of efficiency. The rows would represent the different algorithms, which I identify from {the legend}<r>[0.098, 0.923, 0.947, 0.975]</r>. I then find the values corresponding to each efficiency level for each algorithm from the line points of {Heuristic Search}<r>[0.100, 0.578], [0.241, 0.321], [0.382, 0.107], [0.522, 0.235], [0.663, 0.192], [0.804, 0.235], [0.944, 0.278]</r>, {Genetic Algorithm}<r>[0.100, 0.492], [0.241, 0.321], [0.382, 0.278], [0.522, 0.150], [0.663, 0.107], [0.804, 0.278], [0.944, 0.407]</r>, and {Simulated Annealing}<r>[0.100, 0.792], [0.241, 0.621], [0.382, 0.449], [0.522, 0.364], [0.663, 0.235], [0.804, 0.192], [0.944, 0.150]</r> to generate the rows.\nBased on the above thought, the answer is:\nEfficiency_Levels, Heuristic_Search, Genetic_Algorithm, Simulated_Annealing\n0.1,0.4,0.5,0.15\n0.25,0.7,0.7,0.35\n0.5,0.95,0.75,0.55\n0.75,0.8,0.9,0.65\n1,0.85,0.95,0.8\n1.25,0.8,0.75,0.85\n1.5,0.75,0.6,0.9"
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "WFAziKEzbvZXbFwl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the efficiency improvements shown in the line chart, which algorithm has the largest growth in efficiency over the observed levels? Explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which algorithm showed the greatest efficiency growth, I need to compare the improvement of each algorithm. Starting with {Heuristic Search}<r>[0.100, 0.578], [0.944, 0.278]</r> I observe an improvement from 0.4 to 0.75, resulting in a growth of 0.35. Next, for {Genetic Algorithm}<r>[0.100, 0.492], [0.944, 0.407]</r>, the improvement is 0.1, increasing from 0.5 to 0.6. Lastly, for {Simulated Annealing}<r>[0.100, 0.792], [0.944, 0.150]</r>, we observe an improvement of 0.75, escalating from 0.15 to 0.9.\nBased on the above thought, the answer is:\nBased on the efficiencies demonstrated on the chart, the Simulated Annealing algorithm has the greatest growth, improving from 0.15 at level 0.1 to 0.9 at level 1.5, representing an efficiency growth of 0.75. This growth is notably larger than the improvements seen in both the Heuristic Search and Genetic Algorithm, making it the algorithm with the largest efficiency growth across the observed levels."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "On9zU4FKXkGVtG94",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the efficiency improvement from level 0.1 to level 1.5 in the Heuristic Search algorithm?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the efficiency improvement, I need to find the values of the Heuristic Search algorithm at levels 0.1 and 1.5. I find the coordinates of the line point for level 0.1 is at {0.1}<r>[0.100, 0.578]</r>, and the y-coordinate corresponds to an efficiency of 0.4. Next, I find the line point for level 1.5 is at {1.5}<r>[0.944, 0.278]</r>, and the y-coordinate corresponds to an efficiency of 0.75. Therefore, the efficiency improvement from level 0.1 to 1.5 in the Heuristic Search algorithm is 0.75 - 0.4 = 0.35.\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "CbHoboCTsaaiKhKT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the years and corresponding data for ICU Beds per 100k, Rapid Tests in Mill., and Telemedicine Service?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I identified the x-axis labels specifically representing the years like {the year 2000}<r>[0.084, 0.917, 0.127, 0.975]</r> up to {the year 2020}<r>[0.918, 0.917, 0.961, 0.975]</r> which serve as the column for years. Each line in the chart such as {the line for ICU Beds per 100k}<r>[0.106, 0.102, 0.940, 0.686]</r>, {the line for Rapid Tests in Mill.}<r>[0.106, 0.854, 0.940, 0.863]</r>, and {the line for Telemedicine Service}<r>[0.106, 0.836, 0.940, 0.862]</r> offers points aligned with years to note down their respective values. Compiling these into a structured table:\nBased on the above thought, the answer is:\nYear,ICU Beds per 100k,Rapid Tests in Mill.,Telemedicine Service\n2000,128,0.5,1.0\n2001,130,0.51,1.1\n2002,132,0.52,1.2\n2003,134,0.55,1.4\n2004,140,0.58,1.6\n2005,145,0.64,1.7\n2006,150,0.71,1.8\n2007,160,0.77,2.0\n2008,170,0.84,2.3\n2009,180,0.9,2.7\n2010,190,1.0,3.2\n2011,200,1.2,3.8\n2012,250,1.5,4.6\n2013,310,1.8,5.5\n2014,380,2.3,6.7\n2015,370,2.7,8.0\n2016,410,3.5,9.5\n2017,420,4.3,11.0\n2018,450,4.8,13.0\n2019,500,5.5,15.0\n2020,550,7.0,20.0"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "AzZgs4SY3N54Rej0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological category showed the highest growth from 2000 to 2020 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing all the available lines on the chart, the data points for {the line for Telemedicine Service}<r>[0.106, 0.836, 0.940, 0.862]</r> show a rapid increase from 1.0 in 2000 to 20.0 in 2020. In comparison, {the line for ICU Beds per 100k}<r>[0.106, 0.102, 0.940, 0.686]</r> and {the line for Rapid Tests in Mill.}<r>[0.106, 0.854, 0.940, 0.863]</r> also show growth, but not as marked as Telemedicine Service.\nBased on the above thought, the answer is:\nTelemedicine Service showed the highest growth from 2000 to 2020, as evidenced by its substantial increase from 1.0 in 2000 to 20.0 in 2020, outpacing the growth in ICU beds per 100k and rapid tests in millions."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "O36jJQmo3pKOEwxH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in Telemedicine Service from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average increase in Telemedicine Service, I started by analyzing {the line for Telemedicine Service}<r>[0.106, 0.836, 0.940, 0.862]</r> to retrieve each annual data from 2000 to 2020. The initial value in 2000 is 1.0, and the final value in 2020 is 20.0. The average increase is calculated by subtracting the initial value from the final value and then dividing by the number of years, which is 20.\nBased on the above thought, the answer is:\n0.95"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "mehzFUpBEjqy3ps3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table that outlines the yearly market share percentages for each company from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Definitely, in order to extract the data into a table, I'm noting the x-axis tick labels {X-axis years}[0.052, 0.927, 0.101, 0.951],[0.140, 0.927, 0.189, 0.951],[0.228, 0.927, 0.278, 0.951],[0.317, 0.927, 0.366, 0.951],[0.405, 0.927, 0.454, 0.951],[0.494, 0.927, 0.543, 0.951],[0.582, 0.927, 0.631, 0.951],[0.670, 0.927, 0.720, 0.951],[0.759, 0.927, 0.808, 0.951],[0.847, 0.927, 0.897, 0.951],[0.936, 0.927, 0.985, 0.951], which represents the years. Then I identify {the legend texts}[0.138, 0.068, 0.233, 0.090],[0.138, 0.101, 0.233, 0.123],[0.138, 0.134, 0.233, 0.156] as the company labels. The y-axis tick labels {Y-axis percentages}[0.040, 0.903, 0.064, 0.926], [0.040, 0.687, 0.064, 0.711], [0.040, 0.471, 0.064, 0.495], [0.040, 0.256, 0.064, 0.279],[0.040, 0.040, 0.064, 0.064] indicate the market percentage shares. Lastly, I will analyze {the line points for the companies}[[0.076, 0.696], [0.165, 0.718], [0.253, 0.804], ..., [0.960, 0.157]], [[0.076, 0.653], [0.165, 0.545], ..., [0.960, 0.265]], [0.076, 0.739], [0.165, 0.869], ..., [0.960, 0.437]] to extract the respective percentage shares.\nBased on the above thought, the answer is:\nYear,Company A,Company B,Company C\n2000,20,22,18\n2001,19,27,12\n2002,15,30,14\n2003,25,28,20\n2004,28,33,19\n2005,30,24,23\n2006,27,36,21\n2007,35,32,25\n2008,33,34,28\n2009,40,38,30\n2010,45,40,32"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "uTJy7efzamSENaRa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company has shown the most growth in its market share percentage from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the company with the most growth, I need to look at the difference between the initial and final market share percentages for each company. Now, I'm looking at the market shares for Company A at {2000}[0.076, 0.696] and {2010}[0.960, 0.157], for Company B at {2000}[0.076, 0.653] and {2010}[0.960, 0.265], and for Company C at {2000}[0.076, 0.739] and {2010}[0.960, 0.437]. The differences for Company A, Company B, and Company C are 25, 18, and 14 respectively, calculated by subtracting the market shares in 2000 from the market shares in 2010.\nBased on the above thought, the answer is:\nBased on the difference in market shares from 2000 to 2010, Company A has shown the most growth in its market share."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "18cYwCcouJTFdYNU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest market share percentage achieved by Company A over the period from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest market share percentage for Company A, I should analyze {the line points for Company A}[[0.076, 0.696], [0.165, 0.718], [0.253, 0.804], ..., [0.960, 0.157]]. Upon analysis, I see that the highest point is observed in 2002 with a percentage of 45, which corresponds to the coordinate {2002, 45}[0.253, 0.804].\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "kELc855eZh37JQIQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the data from the line graph into a table with years as rows and health factors as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To develop a structured table from the line graph, the first step is to look at {the x-axis tick labels}[[0.050, 0.851, 0.099, 0.883], [0.271, 0.851, 0.320, 0.883], [0.493, 0.851, 0.542, 0.883], [0.714, 0.851, 0.764, 0.883], [0.936, 0.851, 0.985, 0.883]], which denote the row headers or years. Then, I identify {the legend texts}[[0.362, 0.934, 0.435, 0.964], [0.515, 0.934, 0.575, 0.964], [0.655, 0.934, 0.719, 0.964]] as the column headers specifying the distinct health factors. To populate the values, I spot {the line points}[[0.074, 0.571], [0.296, 0.615], ..., [0.960, 0.703]] for each health factor at the corresponding years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Smoking,Alcohol,Obesity\n2000,27,21,30\n2005,25,22,34\n2010,24,22,38\n2015,22,21,42\n2020,21,20,46"
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "uiLUNihRVFjY8hDp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which health factor seems to be the most concerning over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most concerning health factor, I must analyze the lines associated with each health factor. Looking at {the line for Smoking}[0.074, 0.571, 0.960, 0.703], the line is showing a downward trend indicating a decrease in the percentage of population smoking over time.\nFor {Alcohol}[0.074, 0.703, 0.960, 0.725], the line is almost flat with minor decrease indicating no significant change in alcohol consumption.\nFinally, for {Obesity}[0.074, 0.153, 0.960, 0.505], the line is showing a steep incline pointing towards a significant increase in the percentage of obese population over time.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, obesity seems to be the most concerning health factor over time. The line plot associated with obesity exhibits a steep upward gradient indicating that the percentage of the obese population has been consistently increasing throughout the years. In contrast, the graphs for smoking and alcohol depict a decrease or minor change in their respective percentages, suggesting improvements in those areas. The rise in obesity prevalence presents alarming implications for public health which makes it the most pressing concern."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "ZrR65TAPkTu7pECS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decrease in smoking percentages from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total decrease in smoking percentages, I need to locate {the line points for Smoking}[[0.074, 0.571], [0.296, 0.615], [0.517, 0.637], [0.739, 0.681], [0.960, 0.703]] and extract the corresponding values: 27, 25, 24, 22, 21. The value for the year 2000 is found at {the point corresponding to 2000}[0.074, 0.571] which is 27, and the value for the year 2020 is found at {the point corresponding to 2020}[0.960, 0.703] which is 21. The total decrease in smoking percentages from 2000 to 2020 is therefore 27 - 21 = 6.\nBased on the above thought, the answer is:\n6."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "NNhUn0sF4hq7tGdA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart and provide it as a table format where the columns are Desertification, Population_growth, and Climate_Change, ordered by year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create this table, I start by locating the {x-axis tick labels}<r>[0.072, 0.855, 0.134, 0.886], [0.166, 0.855, 0.228, 0.886], [0.260, 0.855, 0.322, 0.886], [0.355, 0.855, 0.416, 0.886], [0.449, 0.855, 0.510, 0.886], [0.543, 0.855, 0.605, 0.886], [0.637, 0.855, 0.699, 0.886], [0.731, 0.855, 0.793, 0.886], [0.825, 0.855, 0.887, 0.886], [0.920, 0.855, 0.981, 0.886]</r>, which constitutes the Years column of the table. The lines representing the different metrics in the legend - {Desertification}<r>[0.202, 0.934, 0.354, 0.964]</r>, {Population Growth}<r>[0.454, 0.934, 0.644, 0.964]</r>, and {Climate Change}<r>[0.744, 0.934, 0.910, 0.964]</r> will represent the other columns in order. The values of the corresponding metrics for a given year can be obtained by studying the points on the corresponding lines for that year.\nBased on the above thought, the answer is:\nYears,Desertification,Population_growth,Climate_Change\n2010,0.90,2.0,0.60\n2011,0.89,2.2,0.65\n2012,0.86,2.4,0.70\n2013,0.84,2.8,0.70\n2014,0.82,3.1,0.75\n2015,0.80,3.8,0.80\n2016,0.75,4.0,0.70\n2017,0.70,4.4,0.71\n2018,0.68,4.3,0.75\n2019,0.60,4.5,0.80"
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "lhVyeSQbaUqjM7cD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the three factors represented in the line chart shows the most significant decrease over the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the trend lines of the three factors over the years. The {Desertification line}<r>[0.103, 0.722, 0.950, 0.771]</r> starts relatively high and shows a steady decline from 0.90 to 0.60 over the decade.\nThe {Population Growth line}<r>[0.103, 0.144, 0.950, 0.546]</r> generally increases over the decade from 2.0 to 4.5.\nLastly, the {Climate Change line}<r>[0.103, 0.739, 0.950, 0.771]</r> shows a slight increase from 0.6 to 0.8.\nBased on these trends, Desertification shows the most significant decrease over the period.\nBased on the above thought, the answer is:\nThe 'Desertification' factor shows the most significant decrease over the decade, dropping from 0.90 to 0.60."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "ivGjNI0z6257eldN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value for Population Growth and in which year does this occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value for Population Growth, I would examine {the line representing Population Growth}<r>[0.103, 0.144, 0.950, 0.546]</r> and check the values at different points. The highest point on the line corresponds to the Year {2019}<r>[0.950, 0.855, 0.981, 0.886]</r> with a value of 4.5.\nBased on the above thought, the answer is:\nThe highest value for Population Growth is 4.5, which occurred in 2019."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "xrao5L9JV6VXGHXt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a tabular format with Datasets as columns and Machine Learning Models as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a tabular format, I first identify {the x-axis tick labels}[[0.095, 0.855, 0.111, 0.886], [0.240, 0.855, 0.256, 0.886], [0.385, 0.855, 0.401, 0.886], [0.530, 0.855, 0.546, 0.886], [0.675, 0.855, 0.691, 0.886], [0.821, 0.855, 0.836, 0.886],[0.966, 0.855, 0.981, 0.886]] as the column headers which represent the different datasets. The {legend texts}[[0.353, 0.934, 0.398, 0.964], [0.498, 0.934, 0.543, 0.964], [0.643, 0.934, 0.782, 0.964]] are identified as rows labels, representing different Machine Learning Models. Then, by locating {the line points} for each model at the respective datasets and extracting the values annotated, I can obtain the respective accuracy values.\nBased on the above thought, the answer is:\nModel, Dataset1, Dataset2, Dataset3, Dataset4, Dataset5, Dataset6, Dataset7\nANN, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6\nSVM, 0.8, 0.85, 0.9, 0.92, 0.93, 0.93, 0.92\nDecision Tree, 0.7, 0.75, 0.73, 0.7, 0.65, 0.68, 0.7"
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "05Ns3QIWi766Vk4a",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the different Machine Learning Models, which one showed the most consistent performance across the Datasets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the model with the most consistent performance, I need to analyze the variation in their line plots. The consistency of a model can be assessed by its line plot's steadiness. {The ANN line}[0.103, 0.163, 0.974, 0.800] shows a steady decrease from 0.9 to 0.6 which suggests some variability in performance. {The SVM line}[0.103, 0.099, 0.974, 0.375] shows an increase in accuracy from 0.8 to 0.93 and then slightly drops to 0.92 which gives an indication of an improved but somewhat fluctuating performance. {The Decision Tree line}[0.103, 0.481, 0.974, 0.694] has minor fluctuations with its accuracy values ranging mostly between 0.7 and 0.71-0.75.\nBased on the above thought, the answer is:\nThe Decision Tree model showed the most consistent performance across the Datasets. Its line plot displays relatively small fluctuations with the accuracy values oscillating between the close ranges of 0.65 to 0.75. This indicates a stable performance, which is interpreted as higher consistency compared to ANN and SVM."
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "nA7Iuu59ZmOHCuDO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average accuracy of the ANN model across all Datasets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average accuracy of the ANN model, I need to locate {the points on the ANN line}[[0.103, 0.163], [0.248, 0.269], [0.393, 0.375], [0.538, 0.481], [0.683, 0.587], [0.828, 0.694], [0.974, 0.800]], which represent the accuracy values across all Datasets. These values are 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, and 0.6. In order to find the average value, I add all these values together and then divide by the total number of Datasets which is 7.\nBased on the above thought, the answer is:\n0.75"
+            }
+        ],
+        "id_raw": 135
+    },
+    {
+        "id": "lCKw58koKF43RYrY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me draw a table from this line chart? The table should have the Year values as the rows and each rate (Startup_Rate, Investment_Funding_Rate, R&D_Funding_Rate) as the columns."
+            },
+            {
+                "from": "gpt",
+                "value": "For a thorough analysis, the chart's data can be transformed into a table. {The x-axis values}[0.108, 0.851, 0.933, 0.883] represent the years and will become the rows. The metrics are represented by {the legend}[0.092, 0.923, 0.949, 0.975], becoming column names. The data points representing the rates were collected for each year and metric. The points for {Startup_Rate line}[0.139, 0.321, 0.902, 0.591], {Investment Funding Rate line}[0.139, 0.205, 0.902, 0.449], and {R&D Funding Rate line}[0.139, 0.269, 0.902, 0.604] were extracted.\nBased on the above thought, the answer is:\nYear, Startup_Rate, Investment_Funding_Rate, R&D_Funding_Rate\n2012,0.62,0.74,0.58\n2013,0.59,0.76,0.6\n2014,0.65,0.80,0.63\n2015,0.70,0.85,0.67\n2016,0.68,0.87,0.70\n2017,0.72,0.88,0.72\n2018,0.75,0.89,0.78\n2019,0.79,0.74,0.82\n2020,0.80,0.70,0.84"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "QWUsE8UkGirRPKrU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComment on the progress of the Startup Rate over the years based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "To comment on the progress of the Startup Rate, I would look at {the line representing Startup Rate}[0.139, 0.321, 0.902, 0.591]. Looking from left to right, the line seems to exhibit an overall increasing trend from 2012 to 2020, with some modest dips, including a slight drop in 2013 and 2016. However, the trend moves steadily upward from 2013 to a peak in 2020, which indicates a period of growth in the Startup Rate over this period.\nBased on the above thought, the answer is:\nFrom 2012 to 2020, the Startup Rate has generally been growing. Despite slight decreases in 2013 and 2016, this metric has shown resilience with an overall upward movement, reaching a peak in 2020. This indicates a positive entrepreneurial environment with more startups being founded over the years."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "q0igDzhXs4BDWX8Z",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Investment Funding Rate reach its peak, and what was the rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest Investment Funding Rate, I must examine {the coordinates for the Investment Funding Rate line}[0.139, 0.205, 0.902, 0.449]. The peak is the lowest y-coordinate, corresponding to the year 2018, located at {the specific point}[0.711, 0.205]. The rate can be obtained from the raw data.\nBased on the above thought, the answer is:\nThe Investment Funding Rate peaked in 2018 with a rate of 0.89."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "aa3qAIK9jO1utzaX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a table with the type of manufacturing method as columns and types of production as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the bar chart, I can first identify the column headers, which are represented by {the two chart sub-titles}<r>[0.17, 0.9, 0.22, 0.94], [0.78, 0.9, 0.83, 0.94]</r>, they are 'Robotic' and 'Manual'. The row labels are represented by {the x-axis tick labels}<r>[0.15, 0.05, 0.23, 0.09]</r> and <r>[0.35, 0.05, 0.43, 0.09]</r>, they are 'Efficient' and 'Rapid'. For the cell values, I locate {the heights of the bars}<r>[0.1, 0.2, 0.3, 0.65]</r>, <r>[0.1, 0.65, 0.3, 0.80]</r>, <r>[0.35, 0.2, 0.55, 0.68]</r>, <r>[0.35, 0.68, 0.55, 0.73]</r> for each method under each type of production and extract the values which matches the y-axis labels.\nBased on the above thought, the answer is:\nProduction, Robotic, Manual\nEfficient,0.8,0.75\nRapid,0.88,0.77"
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "zjjIYMvqICa8dOM1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graphs, which manufacturing method showcases a more significant improvement in efficiency rating when transitioning from efficient to rapid production?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which manufacturing method has a higher improvement, we need to compare the increase in efficiency ratings of both methods. For {Robotic}<r>[0.1, 0.2, 0.3, 0.80]</r>, it's clear that it improves from 0.8 to 0.88, which is a change of 0.08. In contrast, {Manual}<r>[0.35, 0.2, 0.55, 0.73]</r> improves from 0.75 to 0.77, a much smaller change of 0.02.\nBased on the above thought, the answer is:\nWhen transitioning from 'Efficient' to 'Rapid' production, the 'Robotic' manufacturing method showcases a more significant improvement in the efficiency rating, as compared to the 'Manual' method. The improvement in efficiency rating for 'Robotic' is 0.08 while that for 'Manual' is only 0.02, which makes the former more considerable."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "iFR2CvPwaeAE2gsZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the efficiency rating increase for Robotic manufacturing method when moving from 'Efficient' to 'Rapid' production?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to compare {the height of the bar representing the 'Efficient' production}<r>[0.1, 0.2, 0.3, 0.65]</r> for Robotic method with its {height of the bar representing the 'Rapid' production}<r>[0.1, 0.65, 0.3, 0.80]</r>. This corresponds to a comparison of 0.8 under 'Efficient' and 0.88 under 'Rapid'. Hence, the increase is 0.88 - 0.8 = 0.08.\nBased on the above thought, the answer is:\n0.08."
+            }
+        ],
+        "id_raw": 137
+    },
+    {
+        "id": "6J1q4RwFXbIb30Ig",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data from the line chart with Algorithms as rows and Social Dynamics Indicators as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured table, firstly, I observe that {the x-axis tick labels}[[0.062, 0.975, 0.147, 0.992], [0.210, 0.975, 0.286, 0.992], [0.352, 0.975, 0.421, 0.992], [0.494, 0.975, 0.562, 0.992]] represents Social Dynamics Indicators and will be used as column headers. Then I extract {the legend labels}[[0.805, 0.150, 0.884, 0.162], [0.805, 0.263, 0.862, 0.274], [0.805, 0.376, 0.877, 0.387], [0.805, 0.487, 0.880, 0.498]] as the row headers representing the different algorithms. For each algorithm, I need to locate its {corresponding line points}[[0.081, 0.812], [0.226, 0.686], [0.371, 0.694], [0.515, 0.714]] and extract the annotated numbers as values. These values are then cross-referenced with the appropriate Social Dynamics Indicators.\nBased on the above thought, the answer is:\nAlgorithm,SMB,SFB,PRB,LDB\nGCN,0.68,0.75,0.80,0.82\nRvNN,0.84,0.81,0.75,0.78\nHyphen,0.80,0.78,0.70,0.76\nGET,0.82,0.83,0.85,0.89"
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "KK5HZNkfANsOUqKp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which algorithm shows the most stable performance across all social dynamic indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most stable performance, I need to concentrate on the fluctuation or variation of each line's y-values. Starting with {the line of GCN}<r>[0.081, 0.812, 0.515, 0.714]</r>, it shows increasing values across the indicators, indicating some variation. The {line of RvNN}<r>[0.091, 0.728, 0.535, 0.681]</r> presents an initial decrease and then some increase, suggesting fluctuations. The {line of Hyphen}<r>[0.101, 0.694, 0.553, 0.660]</r> appears quite volatile, particularly with a sharp drop in the third indicator. Lastly, the {line of GET}<r>[0.112, 0.710, 0.562, 0.770]</r> shows a small but constant increase, indicating the least variation.\nBased on the above thought, the answer is:\nBased on the trends displayed on the line chart, the GET algorithm seems to exhibit the most stable performance across all social dynamics indicators. While every other algorithm shows significant fluctuations for at least one indicator, GET consistently illustrates a gradual increase, suggesting a minor and controlled variation in its performance across the indicted social dynamics."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "woUgFQ0zdLuoKdiX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_7_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the highest value reached by any algorithm across all indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the highest value, I need to examine the {line points for all algorithms}[[0.081, 0.812],[0.226, 0.686],[0.371, 0.694],[0.515, 0.714], [0.091, 0.728], [0.239, 0.704], [0.387, 0.667], [0.535, 0.681], [0.101, 0.694], [0.251, 0.669], [0.402, 0.604], [0.553, 0.660], [0.112, 0.710], [0.262, 0.716], [0.412, 0.736], [0.562, 0.770]] and find the one that Figure corresponds to the highest value. From the inspection, I can determine that the point located at {value 0.89}[0.562, 0.770] corresponds to the highest value.\nBased on the above thought, the answer is:\n0.89"
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "6BqDj5iAY47rx63c",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reconstruct a table that records the environmental condition and assessment score of both 'Tropical Forest' and 'Arctic Ice'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a structured table, I first recognize {the x-axis labels}<r>[0.103, 0.919, 0.961, 0.942]</r> which correspond to the environmental conditions 'Stable', 'Minor decrease', 'Substantial decrease', 'Accelerating decline', and 'Dramatic decrease'. These will act as our row headers. For the columns, we have 'Tropical Forest' and 'Arctic Ice'. The corresponding values are extracted from the {line points}<r>[0.121, 0.863, 0.944, 0.863], [0.121, 0.065, 0.944, 0.351], [0.121, 0.763, 0.944, 0.776]</r> for each condition. Cross-referencing these points with their respective coordinates on the y-axis allows us to ascertain the exact assessment score.\nBased on the above thought, the answer is:\n, Tropical Forest, Arctic Ice\nStable, 1, 0.85\nMinor decrease, 0.91, 0.77\nSubstantial decrease, 0.81, 0.67\nAccelerating decline, 0.6, 0.55\nDramatic decrease, 0.15, 0.15"
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "VzqVm8TWYVFnCRUU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graphs, which ecosystem, 'Tropical Forest' or 'Arctic Ice', has experienced a steeper decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which ecosystem experienced a steeper decline, I observe {the lines for Tropical Forest}<r>[0.121, 0.863, 0.944, 0.863]</r> and {Arctic Ice}<r>[0.121, 0.065, 0.944, 0.351]</r>. By comparing the downward trends of these lines, especially from 'Accelerating decline' to 'Dramatic decrease', I can infer that the ecosystem which saw a steeper decline would be the one with a sharper decrease in assessment scores.\nBased on the above thought, the answer is:\nBoth 'Tropical Forest' and 'Arctic Ice' have experienced a similar decrease in their assessment scores. However, the line graph for 'Tropical Forest' indicates a slightly steeper decline, particularly evident between the stages of 'Accelerating decline' and 'Dramatic decrease'. Therefore, 'Tropical Forest' has experienced a steeper decline."
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "EvpKds3o5ObnI8SU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetermine the difference in assessment scores between 'Tropical Forest' and 'Arctic Ice' during 'Accelerating decline'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the difference in assessment scores during 'Accelerating decline', I have to locate the coordinates of the {line points for Tropical Forest and Arctic Ice}<r>[0.532, 0.863], [0.532, 0.207]</r> respectively, during the 'Accelerating decline' stage. Comparing these y-values should yield the difference in the scores.\nBased on the above thought, the answer is:\n0.05"
+            }
+        ],
+        "id_raw": 140
+    },
+    {
+        "id": "XgjPrjQ6pc7tjSAD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the provided line chart regarding New Energy and Clean Technology developments, can you translate the datasets into a tabular format with the various energy types as columns and the years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line graph data, I first look at the legend identifiers, which represent {the column headers or energy types}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189], [0.884, 0.198, 0.948, 0.222]]. Then I move to {the x-axis labels}[[0.112, 0.879, 0.127, 0.914], [0.194, 0.879, 0.201, 0.914], [0.268, 0.879, 0.275, 0.914], [0.342, 0.879, 0.349, 0.914]] which will be used as the row indexes. Lastly, by analyzing {the data points of each line}[[0.120, 0.107], [0.194, 0.118], ..., [0.934, 0.180]], I collect the corresponding values for each energy type during the two years.\nBased on the above thought, the answer is:\nYear, Hydro Power, Solar Power, Wind Power, Bioenergy\n1, 0.78, 0.85, 0.76, 0.80\n2, 0.81, 0.88, 0.78, 0.83"
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "CNizCyj6QfCQVhoT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the visible trends on the line graph, which energy field has shown the most accelerated development over the two-year period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the line corresponding to Solar Power}[0.194, 0.118, 0.268, 0.129], I see an upward trend from 0.85 to 0.88. For the other energies, for instance, {Hydro Power's line}[0.194, 0.107, 0.268, 0.129] and {Wind Power's line}[0.194, 0.115, 0.268, 0.122] displayed a similar growth but not as significant as Solar Power. Additionally, {the line for Bioenergy}[0.194, 0.110, 0.268, 0.126] showed an increase from 0.78 to 0.83, which is a considerable growth but remains lower than the change noticed in Solar Power.\nBased on the above thought, the answer is:\nOver the two-year period, Solar Power has shown the most rapid development based on the trends observed in the line graph. Although all four energy fields demonstrate increasing trends, the Solar Power line shows a greater increase in development value from 0.85 to 0.88, which is the largest positive change among the four energy types."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "wozWlRm2TqP2Nn5h",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Solar Power development values from Year 1 to Year 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in Solar Power development values between Year 1 and Year 2, I will first locate {the Solar Power line points indicated by the chart}[0.194, 0.118, 0.268, 0.129] and extract its initial value 0.85 located at {point 1/Year 1}[0.194, 0.879] and final value 0.88 located at {point 2/Year 2}[0.268, 0.879]. The difference is therefore calculated as 0.88 - 0.85 = 0.03 from Year 1 to Year 2.\nBased on the above thought, the answer is:\n0.03."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "k9r6WKQf4ZkBpyy7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a table format with years as columns and subcultures as rows from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.073, 0.919, 0.117, 0.942], [0.284, 0.919, 0.328, 0.942], [0.495, 0.919, 0.539, 0.942], [0.706, 0.919, 0.750, 0.942], [0.917, 0.919, 0.961, 0.942]], which represent the column headers or years. Next, I identify {the legend texts}[[0.117, 0.083, 0.156, 0.106], [0.117, 0.118, 0.159, 0.141], [0.117, 0.153, 0.173, 0.176]] as the row labels representing the different subcultures. To populate the values, I locate the line points [0.095, 0.302], [0.306, 0.483], [0.517, 0.603], [0.728, 0.783], [0.939, 0.843] for Emo, [0.095, 0.422], [0.306, 0.543], [0.517, 0.422], [0.728, 0.302], [0.939, 0.182] for Punk and [0.095, 0.362], [0.306, 0.302], [0.517, 0.543], [0.728, 0.663], [0.939, 0.783] for Gothic at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nSubculture,2000,2005,2010,2015,2020\nEmo,10,7,5,2,1\nPunk,8,6,8,10,12\nGothic,9,10,6,4,2"
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "DF5FrPiQg2n4jfp9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which subculture seems to have the least popularity over the years? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which subculture experienced the least popularity, I need to examine the lines representing each subculture. The {line for the Emo subculture}[0.095, 0.302, 0.939, 0.843] shows a consistent decrease from 10 to 1. The {line for the Punk subculture}[0.095, 0.182, 0.939, 0.543] fluctuates with an increasing trend from 8 to 12. Finally, the {line for the Gothic subculture}[0.095, 0.302, 0.939, 0.783] also shows fluctuations but with a decreasing trend from 9 down to 2. By analysing these trends, it's clear that the Emo subculture has consistently lower values across the years.\nBased on the above thought, the answer is:\nThe Emo subculture appears to have the least popularity over the years. This is based on the trend which consistently decreases from a score of 10 in year 2000 to a score of 1 in year 2020, lower than other subcultures in the same year. This suggests a decline in popularity for Emo fashion trends across these years."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "LeU7K1PhWHzdcV5o",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many years did it take for the Punk subculture trend to reach its maximum value from its initial value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the required duration, I need to locate the initial and peak points of the Punk subculture. The initial value of Punk is 8 at year 2000 that could be found at {coordinates of initial point for Punk}[0.095, 0.422]. The maximum value of Punk is 12 at year 2020 which is located at {coordinates of maximum value for Punk}[0.939, 0.182]. Therefore, it would have taken 2020 - 2000 = 20 years to reach its maximum peak from its initial value.\nBased on the above thought, the answer is:\n20 years."
+            }
+        ],
+        "id_raw": 143
+    },
+    {
+        "id": "wAhgGQhR1vL0LGWr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data from the line chart into a structured table with the 'Innovation Process' as columns and the 'Baseline Improvement' as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "First, from the given chart, I perceive {the x-axis tick labels}[[0.126, 0.879, 0.155, 0.914], [0.161, 0.879, 0.190, 0.914], [0.282, 0.879, 0.311, 0.914], [0.333, 0.879, 0.363, 0.914], [0.395, 0.879, 0.439, 0.914], [0.464, 0.879, 0.508, 0.914], [0.533, 0.879, 0.577, 0.914], [0.620, 0.879, 0.664, 0.914], [0.706, 0.879, 0.750, 0.914], [0.775, 0.879, 0.819, 0.914], [0.844, 0.879, 0.888, 0.914], [0.913, 0.879, 0.957, 0.914]] which correspond to the different innovation processes. Then I identify {the line for Baseline Improvement}[[0.141, 0.069, 0.935, 0.562]] and mark them down as the rows for each innovation process. Each data point in the line works as the value for the corresponding process.\nBased on the above thought, the answer is:\nInnovation Process, Baseline Improvement\n30,1.2\n40,1.8\n75,2.2\n90,1.6\n110,2.1\n130,1.8\n150,2.3\n175,2.5\n200,2.7\n220,3.1\n240,3.3\n260,3.5"
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "oHmccLljYnAALzUF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat seems to be the general trend in Baseline Improvement as the Innovation Process progresses?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to observe {the Baseline Improvement line}[[0.141, 0.069, 0.935, 0.562]] on the chart. Judging by the placement of the line points, there is a visible increase from the initial to the final point, although there are also some troughs indicating temporary drops in improvement.\nBased on the above thought, the answer is:\nThe general trend in Baseline Improvement as the Innovation Process progresses is upward. This suggests that innovation tends to bring about progressive improvement over time, despite some occasional setbacks."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "dLeUA6ca4pazVffw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the baseline improvement increase from the beginning to the end of the innovation process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in baseline improvement, I have to take a look at {the first point of the Baseline Improvement line}[[0.141, 0.562]] which corresponds to a value of 1.2, and {the last point of the Baseline Improvement line}[[0.935, 0.069]] which corresponds to a value of 3.5. By subtracting the initial value from the end value, I will get the increase in baseline improvement over the innovation process.\nBased on the above thought, the answer is:\n2.3."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "8MrWPNC8RlE9STuF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you convert the value progression of Urban and Rural development from 1990 to 2030 into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart into a tabular format, firstly, I identify {the x-axis tick labels}[[0.143, 0.879, 0.202, 0.914], [0.334, 0.879, 0.392, 0.914], [0.525, 0.879, 0.584, 0.914], [0.716, 0.879, 0.775, 0.914], [0.908, 0.879, 0.966, 0.914]] as the years from 1990 to 2030. Then, I regard {the legend texts}[[0.219, 0.069, 0.290, 0.104], [0.219, 0.121, 0.279, 0.156]] as Urban and Rural labels respectively. The value corresponding to each year and category is taken from {the points on the lines}[[0.172, 0.547], [0.363, 0.387], [0.554, 0.427], [0.746, 0.267], [0.937, 0.107], [0.172, 0.387], [0.363, 0.507], [0.554, 0.627], [0.746, 0.707],[0.937, 0.667]] and then converted into the corresponding value.\nBased on the above thought, the answer is:\nYear,Development in Urban,Development in Rural\n1990,1.4,1.8\n2000,1.8,1.5\n2010,1.7,1.2\n2020,2.1,1.0\n2030,2.5,1.1"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "lXtTH83ww0vwnIZ4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the line chart, which area, Urban or Rural, has seen a consistently increasing trend in development from 1990 to 2030, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area has seen a consistent increase in development, I need to analyze the line plots for both Rural and Urban. Inspecting {the Urban line} [0.172, 0.107, 0.937, 0.547], it shows a consistent increase from 1.4 in 1990 to 2.5 in 2030 with no significant dips in between. However, for the {Rural line} [0.172, 0.387,  0.937, 0.707], we can see the development parameter started at 1.8 in 1990, reached its lowest point at 1.0 by 2020 before slightly increasing to 1.1 in 2030. Hence the Rural line shows an overall decline with a small increment in the recent years, rather than a consistent increase.\nBased on the above thought, the answer is:\nBased on the line chart, the Urban area has seen a consistently increasing trend in development from 1990 to 2030. Its line plot shows a continuous growth from 1.4 in 1990 to 2.5 in 2030, with no significant dips, suggesting that the development parameter for the Urban area has been steadily increasing over these years."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "3sC6INranj9gNJor",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the development parameter for Urban regions increased from 1990 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increment in the development parameter for Urban regions, I need to compare the value of Urban development in 1990 from {the start point of the Urban line} [0.172, 0.547] with the value in 2030 from {the end point of the Urban line} [0.937, 0.107]. In 1990 it was 1.4 and in 2030, it was 2.5. Therefore, the increase in development parameters for Urban regions is 2.5 - 1.4 = 1.1.\nBased on the above thought, the answer is:\n1.1."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "eVwZDabyxTZsd7SV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table summarizing the trend indexes for each subculture observed in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the bar chart, I first identify {the x-axis labels}<r>[0.112, 0.940, 0.940, 0.940]</r> which list the subcultures. Working through the bars, I proceed to measure the height of each bar, representing the fashion trend index. I am able to gather individual indexes using the coordinates from each bar in the plot for subcultures such as {Hipster's bar}<r>[0.150, 0.350, 0.200, 0.650]</r> and {Goth's bar}<r>[0.450, 0.350, 0.500, 0.190]</r>, converting these heights proportionately to numerical values based on the y-axis scale at {the y-axis tick labels}<r>[0.200, 0.850, 0.270, 0.940]</r>.\nBased on the above thought, the answer is:\nSubculture,Fashion Trend Index\nHipster,85\nRockabilly,80\nEmo,50\nGoth,25\nJuggalo,10\nBoho-chic,55\nLolita,60"
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "RdENHzf7kWavtObt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart depiction, why might the 'Goth' subculture be experiencing a sharp decline in fashion trend index compared to others?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {Goth's bar}<r>[0.450, 0.350, 0.500, 0.190]</r> in the bar chart, it's evident that the Goth subculture has a significantly lower index. Other subcultures, such as {Hipster's bar}<r>[0.150, 0.350, 0.200, 0.650]</r> and {Rockabilly's bar}<r>[0.250, 0.350, 0.300, 0.610]</r>, display much higher indices. The stark visual difference highlights a decreasing trend, which could be attributed to changing cultural tastes or perhaps the rise in popularity of other subcultures, overshadowing the Goth style.\nBased on the above thought, the answer is:\nThe 'Goth' subculture is experiencing a sharp decline in its fashion trend index because its cultural appeal may be diminishing compared to rising trends in other subcultures. As visible in the chart, its significantly lower bar indicates a decrease in popularity, likely influenced by evolving fashion preferences and the advent of new trends becoming more dominant in contemporary culture."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "wtGRRRZ4YlihySrz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in fashion trend index between the highest and lowest observed subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bar chart, I need to compare the maximum and minimum heights of the bars representing the fashion trend indexes. I find the tallest bar at {Hipster's bar}<r>[0.150, 0.350, 0.200, 0.650]</r> with a high index, and the shortest bar at {Juggalo's bar}<r>[0.650, 0.350, 0.700, 0.880]</r> with the lowest index. Calculating the exact index values from the y-axis helps in determining the numerical difference.\nBased on the above thought, the answer is:\n75."
+            }
+        ],
+        "id_raw": 147
+    },
+    {
+        "id": "PiPKJSiLTvuhscYB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a table with 'Industry Trends' as rows and 'Stock Price' and 'Standard Deviation' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the chart, firstly, I note {the x-axis labels}[[0.065, 0.879, 0.256, 0.914], [0.253, 0.879, 0.341, 0.914], [0.302, 0.879, 0.565, 0.914], [0.473, 0.879, 0.667, 0.914], [0.631, 0.879, 0.781, 0.914], [0.709, 0.879, 0.976, 0.914]] which corresponds to the items in 'Industry Trends'. Then, I look at {the data points on the line graph}[[0.160, 0.580], [0.297, 0.455], [0.433, 0.616], [0.570, 0.414], [0.706, 0.332], [0.843, 0.095]], which correspond to the 'Stock Price'. To find the 'Standard Deviation', I rely on the provided raw data.\nBased on the above thought, the answer is:\nIndustry Trends,Stock Price,Standard Deviation\nPharmaceuticals,10.0,0.8\nBiotech,15.2,1.3\nHealthCare Equipment,8.5,0.7\nMedical Services,16.9,1.4\nLife Sciences,20.3,0.9\nHealthCare Technology,30.2,1.2"
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "3GxuJC09BiwEAyfp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the performance of the 'HealthCare Equipment' sector based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining {the data point for HealthCare Equipment}[[0.433, 0.616]], it can be noticed that HealthCare Equipment has lower 'Stock Price' compared to the other sectors. The 'Stock Price' for 'HealthCare Equipment' listed on this chart is 8.5, which is the lowest among all sectors {the x-axis labels}[[0.065, 0.879, 0.256, 0.914], [0.253, 0.879, 0.341, 0.914], [0.302, 0.879, 0.565, 0.914], [0.473, 0.879, 0.667, 0.914], [0.631, 0.879, 0.781, 0.914], [0.709, 0.879, 0.976, 0.914]]. Measurement of Standard Deviation taken from raw data shows 0.7, indicating a low level of price volatility.\nBased on the above thought, the answer is:\nBased on the line chart, it can be inferred that 'HealthCare Equipment' sector's performance is less robust in comparison to other sectors as it has the lowest 'Stock Price'. The relatively low standard deviation suggests that the stock prices have not greatly fluctuated, indicating a steady performance, albeit at a lower range."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "Ofl9DrNlc2NZqPZd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the Stock Price in this line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range, I must locate the highest and the lowest 'Stock Price' on the chart. These are represented by {the y-coordinates of the data points on the line graph}[[0.160, 0.580], [0.297, 0.455], [0.433, 0.616], [0.570, 0.414], [0.706, 0.332], [0.843, 0.095]]. The maximum value is 30.2, corresponding to {HealthCare Technology}[0.843, 0.820], and the lowest value is 8.5, corresponding to {HealthCare Equipment}[0.433, 0.820]. So, the range is 30.2 - 8.5.\nBased on the above thought, the answer is:\n21.7."
+            }
+        ],
+        "id_raw": 148
+    },
+    {
+        "id": "b0i8dc9Zz85zi04X",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line plot, can you generate a structured table featuring years as columns and the two tendencies as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data visualized in the line plot into tabular format, I need to identify the headers - these are given by {the x-axis tick labels}[[0.138, 0.916, 0.151, 0.945], [0.336, 0.916, 0.347, 0.945], ..., [0.931, 0.916, 0.946, 0.945]], which represent the years. The rows of the table are represented by {the line plot labels}[[0.137, 0.295, 0.935, 0.561],[0.137, 0.144, 0.935, 0.561]], corresponding to Startup Births and Tech Innovations respectively. To fill the table values, I will identify the {line points} for each row at respective years.\nBased on the above thought, the answer is:\nTrend,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018\nStartup Births,1.2,1.5,0.9,1.3,1.5,0.7,1.3,1.5,1.8,1.7,1.9,2.0\nTech Innovations,0.9,1.3,1.2,1.4,1.3,1.1,1.4,1.8,1.6,1.5,1.8,1.7"
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "1G6tWmoNvCWwYJoH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown, does the growth of tech innovations seem to be more consistently upwards, compared to startup births?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the general trends in the line plots. Examining {the line for Startup Births}[[0.137, 0.295, 0.935, 0.561]], I can see a general uphill trend, marred by significant dips in 2009 and 2012. However, the line for {Tech Innovations}[[0.137, 0.144, 0.935, 0.561]] also captures a general upward trajectory, but shows a lesser degree of fluctuation and fewer dips, only stagnating in 2016.\nBased on the above thought, the answer is:\nYes, the growth of tech innovations does appear to be more consistent and less prone to fluctuations in comparison to startup births. While both have an overall upward trend, there are fewer visible fluctuations in the tech innovations trendline, suggesting a steadier growth rate."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "Suu7Zwx2vTwnDooP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the number of start-up births between 2007 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the number of start-up births between 2007 and 2018, I first find {the point for Startup Births in 2007}[[0.137, 0.371]] which corresponds to 1.2. Then I find {the point for Startup Births in 2018}[[0.935, 0.183]] which corresponds to 2.0. I subtract the value for 2007 from the value for 2018 to get the difference.\nBased on the above thought, the answer is:\n0.8"
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "MJmI49ZMUgGrKE9Y",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the plot data of the line chart into a table format, with Cultural Heritage as rows and Features such as Diversity Index, Integration Index, and Outliers as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the plot data into table format, I first observe that {the x-axis tick labels}[[0.075, 0.919, 0.159, 0.942], [0.279, 0.919, 0.363, 0.942], [0.483, 0.919, 0.567, 0.942], [0.687, 0.919, 0.772, 0.942], [0.893, 0.919, 0.976, 0.942]] function as the rows that represent different Cultural Heritage. Next, the data points indicate values for the three features: Diversity Index {points from line_0}[[0.117, 0.560], [0.321, 0.471], [0.525, 0.426], [0.730, 0.337], [0.934, 0.249]], Integration Index {points from line_1}[[0.117, 0.293], [0.321, 0.382], [0.525, 0.249], [0.730, 0.204], [0.934, 0.071]], and Outliers {points from line_2}[[0.117, 0.827], [0.321, 0.782], [0.525, 0.782], [0.730, 0.738], [0.934, 0.675]] which correspond to columns in the table. I then extract and match these values with their respective Cultural Heritage.\nBased on the above thought, the answer is:\nCultural_Heritage,Diversity_Index,Integration_Index,Outliers\nCulture_A,0.35,0.65,0.05\nCulture_B,0.45,0.55,0.10\nCulture_C,0.5,0.7,0.1\nCulture_D,0.6,0.75,0.15\nCulture_E,0.7,0.9,0.22"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "6kmt8CTAetL7xvkm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which cultural heritage seems to have the largest outlier value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the cultural heritage with the largest outlier value involves examining the points on {the Outliers line}[[0.117, 0.827], [0.321, 0.782], [0.525, 0.782], [0.730, 0.738], [0.934, 0.675]]. I can see that these points represent Culture A to Culture E respectively. The Y-coordinates of these points indicate that Culture E, located at [0.934, 0.675], has the largest value.\nBased on the above thought, the answer is:\nBased on the line chart, the cultural heritage that seems to have the largest outlier value is Culture E."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "3YzlbfnhhEdkXFZg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the net increase of the Integration Index from Culture A to Culture B?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the net increase of the Integration Index from Culture A to Culture B, I check {the line points for the Integration Index}[[0.117, 0.293], [0.321, 0.382]], which represent the values for Culture A and Culture B respectively. Culture A has a value of 0.65 and Culture B has a value of 0.55. Hence the net increase would be 0.55 - 0.65.\nBased on the above thought, the answer is:\n-0.1."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "8qU8U82MXJsynD4I",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a structured table format with Years as columns and Sectors as rows based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first need to note {the ticks on the x-axis}[[0.100, 0.913, 0.158, 0.948], [0.301, 0.913, 0.360, 0.948], [0.502, 0.913, 0.561, 0.948], [0.704, 0.913, 0.763, 0.948], [0.906, 0.913, 0.964, 0.948]], which represent the column headers - the Years. Next, I identify {the legend texts}[[0.174, 0.376, 0.432, 0.411], [0.174, 0.428, 0.409, 0.463], [0.174, 0.481, 0.433, 0.516]] as the row labels representing different sectors - Hotel Occupancy Rate, Restaurant Revenue and Tourist Arrival Number respectively. To populate the values, I locate {the points on each line}[[0.129, 0.741], [0.330, 0.746], ..., [0.935, 0.812]], [[0.129, 0.083], [0.330, 0.107], ..., [0.935, 0.540]], [[0.129, 0.688], [0.330, 0.693], ..., [0.935, 0.800]] for each sector at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nSector,2016,2017,2018,2019,2020\nHotel Occupancy Rate,0.87,0.83,0.78,0.72,0.3\nRestaurant Revenue,6.2,6.0,5.8,5.4,2.5\nTourist Arrival Number,1.3,1.26,1.22,1.15,0.4"
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "kKOuqh054svjaUg5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend shown in the line plot for the Tourist Arrival Number?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trend for the Tourist Arrival Number, I need to follow {the line for Tourist Arrival Number}[0.129, 0.688, 0.935, 0.800]. It starts at 1.3 under the year label {2016}[0.100, 0.913], drops a little to 1.26 under {2017}[0.301, 0.913], falls slightly again to 1.22 under {2018}[0.502, 0.913], decreases to 1.15 under {2019}[0.704, 0.913], and finally drops significantly to 0.4 under {2020}[0.906, 0.913]. It indicates a gradual decline from 2016 through to 2019, with a more substantial decrease in arrival numbers observed in 2020.\nBased on the above thought, the answer is:\nThe line plot for the Tourist Arrival Number shows a steadily declining trend from 2016 to 2019. However, there is a sharp drop in 2020, hinting at a significant reduction in the number of tourists, possibly due to exceptional circumstances such as a global event causing reduced travel."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "RmspkKRkF86pKPRC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFind the total decrease in Restaurant Revenue from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total decrease in Restaurant Revenue from 2016 to 2020, I locate {the line points for Restaurant Revenue}[[0.129, 0.083], [0.330, 0.107], [0.532, 0.132], [0.733, 0.182], [0.935, 0.540]] and extract the values: 6.2, 6.0, 5.8, 5.4, 2.5. The value in 2016 is observed at {2016}[0.100, 0.913] as 6.2 and the value in 2020 is observed at {2020}[0.906, 0.913] as 2.5. The total decrease from 2016 to 2020 is therefore 6.2 - 2.5 = 3.7.\nBased on the above thought, the answer is:\n3.7."
+            }
+        ],
+        "id_raw": 152
+    },
+    {
+        "id": "UXOL0KP3FjRJkRcF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this line chart into tabular format with 'Processing Steps' as rows and 'Measures' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, The x-axis of this chart is represented by {the x-axis tick labels}[[0.122, 0.919, 0.133, 0.942], [0.320, 0.919, 0.342, 0.942], [0.517, 0.919, 0.550, 0.942], [0.721, 0.919, 0.754, 0.942], [0.924, 0.919, 0.957, 0.942]], which are the 'Processing Steps'. The line plots for Accuracy, Precision, and Recall, which could be found in {the legend}[[0.831, 0.756, 0.973, 0.868]], will be the columns in the table. Then, for every point on each line i.e., {line points for Accuracy}[[0.127, 0.654], [0.331, 0.542], [0.534, 0.429], [0.737, 0.316], [0.941, 0.203]], {line points for Precision}[[0.127, 0.739], [0.331, 0.598], [0.534, 0.485], [0.737, 0.372], [0.941, 0.260]], and {line points for Recall}[[0.127, 0.626], [0.331, 0.485], [0.534, 0.372], [0.737, 0.260], [0.941, 0.147]], the corresponding values are extracted.\nBased on the above thought, the answer is:\nProcessing Steps,Accuracy,Precision,Recall\n0,0.78,0.75,0.79\n50,0.82,0.8,0.84\n100,0.86,0.84,0.88\n150,0.9,0.88,0.92\n200,0.94,0.92,0.96"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "eF1ARtR0uhg9TnB1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which measure seems to have the most steady increase over the processing steps? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which measure has the most steady increase, I analyze the rate of change in the line plots for each measure. Looking at the {line for Accuracy}[[0.127, 0.203, 0.941, 0.654]], I observe a consistent upward slope. The same trend can be seen in the {line for Precision}[[0.127, 0.260, 0.941, 0.739]] and the {line for Recall}[[0.127, 0.147, 0.941, 0.626]]. Despite all three lines showing an increasing trend, the Accuracy line seems to exhibit a relatively steady rise.\nBased on the above thought, the answer is:\nBased on the trends presented in the line chart, Accuracy appears to have the most steady increase over the processing steps. This is determined by the consistent upward slope of the Accuracy line indicating a steady improvement rate, compared to Precision and Recall which appear to have slightly more variability in their rates of increase."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "vnTUj5cu3JGjOtIw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much recall has improved from processing step 0 to 200?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the improvement in recall, I need to locate the {starting recall point <r>[0.127, 0.626]</r>} at processing step 0 and the {ending recall point <r>[0.941, 0.147]</r>} at processing step 200. Extracting the corresponding values, we get 0.79 and 0.96 respectively. The difference between recall at step 200 and step 0 will be the improvement in recall.\nBased on the above thought, the answer is:\n0.17"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "bNIZIDxm0tEiCida",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you put the data showed on a line chart of different styles into table format with time periods as columns and styles as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I extract {the x-axis labels}[[0.126, 0.879, 0.141, 0.914], [0.319, 0.879, 0.349, 0.914], [0.512, 0.879, 0.556, 0.914], [0.713, 0.879, 0.757, 0.914], [0.913, 0.879, 0.957, 0.914]] that has time periods as column headers. Then, the rows are defined by {the labels specified in the line parameters}{'label': 'Anxiety Level', 'label': 'Focus Level', 'label': 'Intrusive Thoughts'}. Values of the cells are the coordinates of each point from {the lines}{'line_0': {'params': {'label': 'Anxiety Level'}, 'points': [{'x': 0, 'y': 65}, {'x': 50, 'y': 70}, {'x': 100, 'y': 64}, {'x': 150, 'y': 62}, {'x': 200, 'y': 56}]}, 'line_1': {'params': {'label': 'Focus Level'}, 'points': [{'x': 0, 'y': 45}, {'x': 50, 'y': 47}, {'x': 100, 'y': 50}, {'x': 150, 'y': 53}, {'x': 200, 'y': 60}]}, 'line_2': {'params': {'label': 'Intrusive Thoughts'}, 'points': [{'x': 0, 'y': 55}, {'x': 50, 'y': 60}, {'x': 100, 'y': 68}, {'x': 150, 'y': 76}, {'x': 200, 'y': 80}]}}.\nBased on the above thought, the answer is:\nMetrics,0,50,100,150,200\nAnxiety Level,65,70,64,62,56\nFocus Level,45,47,50,53,60\nIntrusive Thoughts,55,60,68,76,80"
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "fmVbcoSCG9ndS7h1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the three parameters (Anxiety Level, Focus Level, Intrusive Thoughts) has shown significant changes over the time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I compare lines for each parameter in terms of their starting to ending position. Based on {the lines for 'Anxiety Level'}<r>[0.134, 0.225, 0.935, 0.433]</r>, {the line for 'Focus Level'}<r>[0.134, 0.373, 0.935, 0.596]</r> and {the line for 'Intrusive Thoughts'}<r>[0.134, 0.076, 0.935, 0.448]</r>, Intrusive Thoughts show a significant increase from 55 to 80 while other two parameters show a modest decrease in values, with Anxiety Level dropping from 65 to 56 and Focus Level increasing from 45 to 60.\nBased on the above thought, the answer is:\nThe 'Intrusive Thoughts' parameter has shown the most significant change over the entire time period. While 'Anxiety Level' and 'Focus Level' showed modest changes, 'Intrusive Thoughts' has seen a significant increase from the initial to the final value. This suggests that over time, participants in the research had more intrusive thoughts."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "xQHmQ5Z51AQhZldc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Anxiety Level decreased from the first to the last research session?"
+            },
+            {
+                "from": "gpt",
+                "value": "The change in Anxiety Level can be calculated by finding the difference between its value in the first and the last research session. These values can be found by looking at {the first point in the line for Anxiety Level}<r>[0.134, 0.299]</r> and {the last point in the line for Anxiety Level}<r>[0.935, 0.433]</r>. The value at the first session is 65 and at the last session is 56.\nBased on the above thought, the answer is:\n9."
+            }
+        ],
+        "id_raw": 154
+    },
+    {
+        "id": "spjxX6RxxzqIGM16",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a readable table with time steps as columns and sales per year as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely. To generate a table from the line chart, I'll identify the 'Time Steps' by analyzing {the x-axis tick labels}<r>[0.140, 0.914, 0.593, 0.879]</r>. Next, the 'Sales per Year' can be identified as the rows, represented by {the line legends}<r>[0.148, 0.536, 0.936, 0.706], [0.148, 0.110, 0.936, 0.394], [0.148, 0.082, 0.936, 0.479]</r>. To gather the data values, I will collect the numerical values associated with each point on the lines.\nBased on the above thought, the answer is:\nYear,Step 1,Step 2,Step 3,Step 4,Step 5,Step 6,Step 7,Step 8,Step 9,Step 10\nSales 2017,100,95,90,85,80,70,65,50,45,40\nSales 2018,150,175,200,220,233,240,250,245,242,230\nSales 2019,120,130,140,160,190,210,220,240,250,260"
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "I0VNV2OVzFWv2PX8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend of sales over the years based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I observe three lines representing the sales for three different years. Examining {the Sales 2017 line}<r>[0.148, 0.536, 0.936, 0.706]</r>, it shows a consistent decline, indicating that sales decreased steadily over this year. \nThe {Sales 2018 line}<r>[0.148, 0.110, 0.936, 0.394]</r> exhibits a steady ascent up to Step 7, followed by a slight decrease, implying that the sales grew consistently in the initial part of the year and then dropped slightly. \nThe {Sales 2019 line}<r>[0.148, 0.082, 0.936, 0.479]</r> demonstrates a steady increase until step 4, followed by a more pronounced rise, outlining that the sales gradually increased in the first half of the year and then spiked upwards.\nBased on the above thought, the answer is:\nThe sales trend from 2017 to 2019 shows a gradual shift from a declining trend in 2017 to an overall growing trend in 2019. While sales fell consistently throughout the year in 2017, there was a steady increase in sales in 2018 with a slight slump near the end of the year. By 2019, the sales not only improved but also showed a marked increase, especially in the latter half of the year. This could indicate an optimistic sales outlook for the future based on the recent trend."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "kpKhmgjDb3aAhN2r",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest sale in the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate the difference, I need to identify the highest and lowest points on {the line representing Sales 2018}<r>[0.148, 0.110, 0.936, 0.394]</r>. The highest point corresponds to a sale of 250 at Step 7 and the lowest point corresponds to a sale of 150 at Step 1. The difference can be calculated as 250 - 150.\nBased on the above thought, the answer is:\n100."
+            }
+        ],
+        "id_raw": 155
+    },
+    {
+        "id": "3wXELL9zOkrfMTCt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table with Gradient steps as rows and features of EdTech as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "For creating the table, we first recognize that {the x-axis labels}<r>[0.133, 0.879, 0.148, 0.914], [0.325, 0.879, 0.354, 0.914], [0.516, 0.879, 0.560, 0.914], [0.715, 0.879, 0.759, 0.914], [0.913, 0.879, 0.957, 0.914]</r> represent the row headers or Gradient steps. The column headers are the legend to {the line for EdTech AI Adoption}<r>[0.141, 0.311, 0.935, 0.744]</r>, {Predictive Learning Algorithms}<r>[0.141, 0.239, 0.935, 0.492]</r> and {Distance Learning Platforms}<r>[0.141, 0.130, 0.935, 0.636]</r>. Each point on the line represents a corresponding value in the table for each feature at the respective gradient step.\nBased on the above thought, the answer is:\nGradient_steps,EdTech AI Adoption,Predictive Learning Algorithms,Distance Learning Platforms\n0,0.1,1.0,1.8\n50,0.5,0.8,1.0\n100,0.85,1.5,0.9\n150,1.2,1.0,0.7\n200,1.3,0.8,0.4"
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "uGZNR0uY8RYeYFr3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which feature of EdTech shows the most growth from 0 to 200 gradient steps?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which feature shows the most growth, I need to analyze the increase in value from 0 to 200 Gradient steps for each feature. The {EdTech AI Adoption}<r>[0.141, 0.311, 0.935, 0.744]</r> line starts at 0.1 and ends at 1.3, which is a difference of 1.2. The {Predictive Learning Algorithms}<r>[0.141, 0.239, 0.935, 0.492]</r> starts at 1.0 and ends at 0.8, showing a decrease. The {Distance Learning Platforms}<r>[0.141, 0.130, 0.935, 0.636]</r> starts at 1.8 and ends 0.4, indicating a decrease as well.\nBased on the above thought, the answer is:\nBased on the line chart, the feature with the most growth from 0 to 200 gradient steps is EdTech AI Adoption. While Predictive Learning Algorithms and Distance Learning Platforms both show declines, EdTech AI Adoption shows an increase, making it the feature with the most growth over this interval."
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "fY0VlkBjDcChrRsy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in EdTech AI Adoption from 0 to 200 gradient steps?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in EdTech AI Adoption from 0 to 200 Gradient steps, I need to locate the corresponding {point for 0 Gradient steps}<r>[0.141, 0.744]</r> which has a value of 0.1 and {point for 200 Gradient steps}<r>[0.935, 0.311]</r> with a value of 1.3 in {the line for EdTech AI Adoption}<r>[0.141, 0.311, 0.935, 0.744]</r>. The change is therefore 1.3 - 0.1 = 1.2.\nBased on the above thought, the answer is:\n1.2"
+            }
+        ],
+        "id_raw": 157
+    },
+    {
+        "id": "ef5A6ZnXzO0on0fF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data points shown in the line graph in table format including the Time Period as columns and the two Parameters as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can extract the data points for each series from the line graph. The labels found at {x-axis tick coordinates}[[0.178, 0.759], [0.427, 0.759], [0.676, 0.759], [0.925, 0.759]], indicating '5', '10', '15', '20' respectively, represent the Time Period. The parameters are given as 'Social Networks' and 'Interpersonal Relationships', identified from the line labels. The series for {Social Networks}[[0.178, 0.590], [0.427, 0.420], [0.676, 0.250], [0.925, 0.080]] show the values '5', '10', '15' and '20'. The series for {Interpersonal Relationships}[[0.178, 0.488], [0.427, 0.454], [0.676, 0.556], [0.925, 0.590]] represent the values '8', '9', '6' and '5'.\nBased on the above thought, the answer is:\nParameters,5,10,15,20\nSocial Networks,5,10,15,20\nInterpersonal Relationships,8,9,6,5"
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "NqYCXsSRIQG5R6tf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe any trends or changes over time you observe in the interactions between Social Networks and Interpersonal Relationships based on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line charts for both 'Social Networks' and 'Interpersonal Relationships', it appears that from the beginning to the end, both lines show a decline from their initial counts. More specifically, {Social Networks}[0.178, 0.080, 0.925, 0.590] starts from a value of 5 at time period '5' and ends at a value of 20 at time period '20'. In contrast, {Interpersonal Relationships}[0.178, 0.454, 0.925, 0.590] starts from 8 at time period '5' and reaches 5 at time period '20'. What's interesting is the gap between these two metrics. Initially, Social Networks were lower than Interpersonal Relationships, but as time went on they grew more strongly and eventually surpassed Interpersonal Relationships.\nBased on the above thought, the answer is:\nThe trend observed in the line graph shows that while both Social Networks and Interpersonal Relationships initially decrease over the given time periods, the rate of decrease differs. Social Networks start at a lower point but eventually overtake Interpersonal Relationships, suggesting a more robust growth and probable increase in user preference for Social Networks as compared to Interpersonal Relationships over the observed time."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "ouuUgGgRdABeSUlM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in values between 'Social Networks' and 'Interpersonal Relationships' at time period '15'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will locate the points for 'Social Networks' and 'Interpersonal Relationships' at time period '15'. The coordinates of the point for {Social Networks at '15'}[0.676, 0.250] reveal the value '15'. Meanwhile, the coordinates at the same time period for {Interpersonal Relationships at '15'}[0.676, 0.556] correspond to the value '6'. The difference between these values is 15 - 6 = 9.\nBased on the above thought, the answer is:\n9."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "oiPxgNgUpXzaq8Nz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that captures the information from the line chart, with the type of Data Integration in rows and different metrics in columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the required table, I first look at {the x-axis tick labels}[[0.098, 0.919, 0.167, 0.942],[0.370, 0.919, 0.428, 0.942],[0.625, 0.919, 0.706, 0.942],[0.888, 0.919, 0.976, 0.942]], which signify the row labels or types of data integration. Then, I identify the three lines {Followers}[0.132, 0.064, 0.932, 0.711], {Engagement Rate}[0.132, 0.840, 0.932, 0.841] and {Bounce Rate}[0.132, 0.815, 0.932, 0.831] as the column headers. For each category, I extract the respective values given at these points.\nBased on the above thought, the answer is:\nCategory,Followers,EngagementRate,BounceRate\nWebsite Traffic,400,3.45,80\nTwitter Follows,1200,1.2,60\nFacebook Likes,2000,0.8,50\nInstagram Follows,2400,0.4,30"
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "DNctSB6AT5CLquu8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the line chart and share insight about the relationship between the number of followers and engagement rate for various platforms."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the lines {Followers}[0.132, 0.064, 0.932, 0.711] and {Engagement Rate}[0.132, 0.840, 0.932, 0.841], I can observe that as the number of followers increases, the engagement rate decreases for all the platforms. For instance, Website traffic with the lowest number of followers at 400, has the highest engagement rate at 3.45. Meanwhile, Instagram with the highest followers at 2400 has the lowest engagement rate at 0.4.\nBased on the above thought, the answer is:\nThe line chart indicates a negative correlation between the followers and engagement rate across all platforms. As the number of followers increases, the engagement rate tends to decrease. This implies that while platforms with fewer followers such as Website Traffic experience high engagement rates, platforms with a larger number of followers like Instagram have a significantly lower engagement rate."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "weucrj0m5LdCLKDM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_8_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the highest and lowest followers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this difference, I need to look at the points on {the line for Followers}[0.132, 0.064, 0.932, 0.711]. The maximum value is observed at 2400 corresponding to {Instagram Follows}[0.932, 0.880] and the minimum value is observed at 400 corresponding to {Website Traffic}[0.132, 0.880]. Therefore, the difference is 2400 - 400 = 2000.\nBased on the above thought, the answer is:\n2000."
+            }
+        ],
+        "id_raw": 159
+    },
+    {
+        "id": "DLdj434xiNksw6rw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide structured table data for each type of Social Welfare Activities in different regions based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I will first identify {the x-axis tick labels}<r>[0.093, 0.862, 0.161, 0.894], [0.205, 0.862, 0.264, 0.894], [0.316, 0.862, 0.384, 0.894], [0.428, 0.862, 0.509, 0.894], [0.552, 0.862, 0.648, 0.894]</r> which represent the different Social Welfare Activities. Subsequently, I will locate {the line points for each region}<r>[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469]</r>,<r>[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695]</r>,<r>[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105]</r> for Region 1, Region 2 and Region 3 respectively, to populate the corresponding expenditures.\nBased on the above thought, the answer is:\nActivities,Region 1, Region 2, Region 3\nChild Education Fund,10000,3000,25000\nHealthcare Assistance,25000,8000,35000\nFood Distribution,8000,15000,25000\nHousing Support,2000,22000,12000\nEmergency Relief,22000,29000,4000"
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "ZS6IlGXcTHkZLBs2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region appears to prioritize Education and Healthcare over other activities based on the funding allocations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which region prioritizes Education and Healthcare, I need to compare the funding for these activities across all the regions. Looking at {the line for Region 1}<r>[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469]</r>, I see that the funding for Child Education Fund and Healthcare Assistance is relatively higher compared to other activities. For {Region 2}<r>[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695]</r>, the funding is evenly distributed, but Child Education Fund and Healthcare Assistance do not have the highest allocations. In {Region 3}<r>[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105]</r>, Healthcare Assistance receives significantly higher funding, but Child Education Fund is not a prioritized area.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Region 1 seems to prioritize Education and Healthcare over other activities. Both Child Education Fund and Healthcare Assistance are allocated significantly higher funding compared to Food Distribution, Housing Support, and Emergency Relief in Region 1."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "QXmxtegWEUioIVIG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the biggest difference in funding allocation between two activities within a single region and which region does this difference occur in?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must first analyze the funding range for each activity in each region. Examining {Region 1's line points}<r>[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469]</r>, I see a difference of 23000 between Healthcare Assistance and Housing Support. Looking at {Region 2's line points}<r>[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695]</r>, the largest difference is 26000 between Emergency Relief and Child Education Fund. Lastly, from {Region 3's line points}<r>[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105]</r>, the largest difference is 31000 between Healthcare Assistance and Emergency Relief. Now, comparing these differences, the largest funding disparity occurs in Region 3.\nBased on the above thought, the answer is:\n31000, Region 3."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "gc2ndFat3ZIGQi0Y",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented on the line chart into a structured table format with Age Groups as rows and Years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart into a tabular format, I am looking and identifying vital elements in the chart. I find {the x-axis tick labels}[[0.084, 0.857], [0.297, 0.857], [0.510, 0.857], [0.723, 0.857], [0.936, 0.857]], which will serve as the column headers or the Years. I see {the legend texts}[[0.587, 0.889], [0.709, 0.889], [0.818, 0.889], [0.926, 0.889]] as the row labels that represent the different Age Groups. I then reach to locate {the points of four lines}[[0.096, 0.548], [0.309, 0.595], [0.522, 0.440], [0.735, 0.291], [0.947, 0.327], [0.096, 0.760], [0.309, 0.799], [0.522, 0.677], [0.735, 0.588], [0.947, 0.515], [0.096, 0.242], [0.309, 0.347], [0.522, 0.469], [0.735, 0.539], [0.947, 0.621], [0.096, 0.129], [0.309, 0.172], [0.522, 0.314], [0.735, 0.456], [0.947, 0.539]] each representing the population size of an age group in a particular year and arrange them in the table.\nBased on the above thought, the answer is:\nAge Groups,Population in 2000,Population in 2010,Population in 2020,Population in 2030,Population in 2040\nUnder 25,76.5,75.1,79.8,84.3,83.2\n25 - 34,70.1,68.9,72.6,75.3,77.5\n35 - 44,85.8,82.6,78.9,76.8,74.3\nOver 45,89.2,87.9,83.6,79.3,76.8"
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "M090tzPSgiRB8j1N",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the line chart, which age group has experienced a continuous decrease in the population from the year 2000 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to observe the trends shown by the individual line plots for each age group. Let's take a look at the line plots:\nFirst, {the line for \"Under 25\"}[0.096, 0.548, 0.947, 0.327] shows a general increase, then a decrease, and then an increase again, which means there is fluctuation, no continuous decrease.\nSecond, {the line for \"25 - 34\"}[0.096, 0.760, 0.947, 0.515] shows a decrease first, then increases overall. So, this is also not a continuous decrease.\nThird, {the line for \"35 - 44\"}[0.096, 0.242, 0.947, 0.621] shows a clear declining trend from the start to the end, which seems to fit the criteria.\nLastly, {the line for \"Over 45\"}[0.096, 0.129, 0.947, 0.539] also shows a clear decreasing trend from start to finish.\nBased on the above thought, the answer is:\nThe line chart suggests that both the age groups of \"35 - 44\" and \"Over 45\" have experienced a continuous decrease in population from the year 2000 to 2040. The line plots for these two age groups consistently decline over this period, unlike the plots for the other age groups which present some increasing trends in between."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "2J2FUuhUKxyTDihS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in population size for the age group \"25 - 34\" from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in size for the age group of \"25 - 34\",  I need to locate {the points for the line corresponding to \"25 - 34\"}[[0.096, 0.760],[0.522, 0.677]]. The value corresponding to the year 2000 is 70.1 million and the one corresponding to 2020 is 72.6 million. The increase would be the value for 2020 subtracted by the value for 2000, hence 72.6 - 70.1 = 2.5 million.\nBased on the above thought, the answer is:\n2.5 million."
+            }
+        ],
+        "id_raw": 164
+    },
+    {
+        "id": "ffe0KBODzFYa7lUJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table representing the yearwise data for Global Temperature Change, CO2 Emissions, and Ocean Acidification from the line charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line charts, I identify {the color-coded lines corresponding to each variable}[0.988, 0.255, 0.992, 0.556] and note the trend points for each. By observing {the x-axis tick labels} for the years [2000, 2020] and {the y-axis tick labels} for values, I compile values for each year and variable as displayed on their respective points on the line.\nBased on the above thought, the answer is:\nYear,Global Temperature Change,CO2 Emissions,Ocean Acidification\n2000,0.3,2200,400\n2020,1.2,3000,600"
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "07mWF1q7fD3SDbGv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the graph, which environmental variable shows the steepest increase over the period 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the steepest increase among the variables, I compare the slope of {the line representing Global Temperature Change}[0.988, 0.556, 0.992, 0.315], {CO2 Emissions}[0.989, 0.435, 0.990, 0.255] and {Ocean Acidification}[0.990, 0.315, 0.991, 0.195]. A steeper line indicates a greater rate of change. By observational comparison, the Global Temperature Change exhibits the most significant change per year.\nBased on the above thought, the answer is:\nGlobal Temperature Change shows the steepest increase, as indicated by the line's steepness, which suggests a higher rate of rise compared to the other variables."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "qyeSI0SzYShT3bOT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in CO2 Emissions from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase in CO2 Emissions from 2000 to 2020, I observe the values at the points on the CO2 Emissions line from {year}=2000 [0.040, 0.676] to {year}=2020 [0.045, 0.796]. The values increase from 2200 units to 3000 units across these years. The total increase is calculated as 3000 - 2200.\nBased on the above thought, the answer is:\n800 units."
+            }
+        ],
+        "id_raw": 165
+    },
+    {
+        "id": "DXI3B0Uv176CdZbI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table showing the year and study proportions for the Career Skill Demand Study from the chart provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured table from the line chart, firstly I focus on {the x-axis labels}<r>[0.117, 0.820, 0.152, 0.843], [0.162, 0.820, 0.197, 0.843], ..., [0.656, 0.820, 0.691, 0.843]</r> which represent the years. Then, I must examine the points corresponding to the Career Skill Demand Study on the chart at {the line points for Career Skill Demand Study}<r>[0.134, 0.701], [0.179, 0.616], ..., [0.673, 0.582]</r> to collect the respective proportions for each year.\nBased on the above thought, the answer is:\nYear,Career Skill Demand\n2008,0.3\n2009,0.35\n2010,0.4\n2011,0.38\n2012,0.37\n2013,0.35\n2014,0.33\n2015,0.32\n2016,0.33\n2017,0.35\n2018,0.37\n2019,0.35\n2020,0.37"
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "yND1QMTkRlaMLlou",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich study shown on the line chart has seen the most consistent growth in proportion over the examined years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the study with the most consistent growth, each line's trend must be analyzed. By observing the start and end points of each line and points in between, the following can be deduced:\nThe line for {Career Skill Demand}<r>[0.134, 0.701, 0.673, 0.582]</r> fluctuates but ends at a point slightly lower than where it started.\nThe line for {Workplace Automation}<r>[0.134, 0.787, 0.673, 0.325]</r> shows significant growth from 0.25 to 0.52.\nThe line for {Career Tech Advance}<r>[0.134, 0.753, 0.673, 0.223]</r> increases most prominently from 0.27 to 0.58.\nMaturing with the observation, {Career Tech Advance} shows the most consistent and substantial growth trend across the years due to the higher endpoint in its line progression on the chart.\nBased on the above thought, the answer is:\nCareer Tech Advance has demonstrated the most consistent growth in proportion over the years. This conclusion is supported by the consistent upward trend of its line on the chart from 2008 to 2020, marking an increase from 0.27 to 0.58, and showing no dip below its starting point throughout."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "VzJwu9ts8eg6Xiq2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the Workplace Automation study proportion from the year 2008 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in proportion, I need to compare the points on the chart that represent the year 2008 and 2020 for the Workplace Automation study. Initially locating {the point for 2008}<r>[0.134, 0.787]</r> with a proportion of 0.25, and then {the point for 2020}<r>[0.673, 0.325]</r> with a proportion of 0.52. The numerical increase in the proportion is calculated as 0.52 - 0.25 = 0.27.\nBased on the above thought, the answer is:\n0.27"
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "AYO0tCV1M9vhPVwu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the financial trends depicted in the bar chart into a structured table format showing the years and the corresponding values for Beige Book A, Bond Index A, and Economic Growth A?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the bar chart data into a table format, I first identify {the x-axis labels for each subplot}<r>[0.099, 0.859, 0.150, 0.895]</r>, <r>[0.363, 0.859, 0.414, 0.895]</r>, <r>[0.627, 0.859, 0.678, 0.895]</r> representing the years from 1 to 10. I then proceed to observe the y-values for each corresponding bar at these x-axis positions: {Beige_Book_A values}<r>[0.065, 0.110, 0.184, 0.768]</r>, {Bond_Index_A values}<r>[0.329, 0.110, 0.448, 0.768]</r>, {Economic_Growth_A values}<r>[0.593, 0.110, 0.712, 0.768]</r>. Using this information, I compile the entries for each year across the different indices.\nBased on the above thought, the answer is:\nYear,Beige_Book_A,Bond_Index_A,Economic_Growth_A\n1,0.02,0.03,0.04\n2,0.025,0.025,0.035\n3,0.028,0.020,0.030\n4,0.03,0.015,0.025\n5,0.026,0.017,0.028\n6,0.022,0.020,0.031\n7,0.018,0.023,0.033\n8,0.021,0.019,0.036\n9,0.019,0.022,0.035\n10,0.022,0.020,0.04"
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "KaVkHvCKk3jeKldb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends in Beige Book A and Economic Growth A compare across the given timesteps, and what could be inferred about their volatility?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the trends, I review {the line coordinates for Beige Book A}<r>[0.065, 0.110, 0.184, 0.768]</r> and {the line coordinates for Economic Growth A}<r>[0.593, 0.110, 0.712, 0.768]</r>. Beige Book A exhibits a gradual decrease in values after an initial increase. In contrast, Economic Growth A shows fluctuations at earlier timesteps but stabilizes toward the later timesteps with a peak at the final value. By analyzing the ranges in y-coordinates, Beige Book A shows smaller fluctuations, indicating lower volatility compared to Economic Growth A.\nBased on the above thought, the answer is:\nThe trend for Beige Book A is less volatile, indicated by smaller vertical fluctuations, suggesting steadier performance. Economic Growth A, while ending on a high, displays more significant ups and downs, which indicates a higher volatility across timesteps."
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "Ap3fFhgW537QenXv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage change in Bond Index A from Timestep 1 to Timestep10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage change in Bond Index A, I first spot {the initial value of Bond Index A at Timestep 1}<r>[0.329, 0.110]</r> and {the final value at Timestep 10}<r>[0.448, 0.548]</r>. The initial value is 0.03 and the final value is 0.02. Using the formula for percentage change, ((Final - Initial) / Initial * 100), I calculate ((0.02 - 0.03) / 0.03) * 100.\nBased on the above thought, the answer is:\n-33.33%"
+            }
+        ],
+        "id_raw": 169
+    },
+    {
+        "id": "F1JbWCiWL1KmxqkW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data representation from the line chart showing the MSE (Mean Squared Error) rates across the various sample sizes for different activities such as Sport, Leisure, Hobby, and Work in a tabular CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I begin by identifying the tick marks on {the x-axis for each subplot}<r>[0.051, 0.477], [0.516, 0.477], [0.051, 0.903], [0.516, 0.903]</r> to categorize different sample sizes. I follow this up by locating each point on {the lines for Sport}<r>[0.095, 0.137, 0.494, 0.460]</r>, {Leisure}<r>[0.560, 0.137, 0.959, 0.460]</r>, {Hobby}<r>[0.095, 0.563, 0.494, 0.887]</r>, and {Work}<r>[0.560, 0.563, 0.959, 0.887]</r> to determine the specific MSE values at those sample sizes for each activity.\nBased on the above thought, the answer is:\nSample Size,Sport,Leisure,Hobby,Work\n1000,0.51,0.63,0.55,0.61\n2000,0.43,0.47,0.64,0.52\n3000,0.58,0.59,0.67,0.56\n4000,0.42,0.50,0.60,0.65\n5000,0.52,0.45,0.54,0.61\n6000,0.56,0.48,0.68,0.64\n7000,0.66,0.57,0.49,0.51\n8000,0.72,0.63,0.52,0.57\n9000,0.78,0.71,0.57,0.67\n10000,0.76,0.73,0.68,0.72"
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "tE4tBsP4uooIeOHj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trend analysis, which social activity has shown a consistent increase in the MSE rates from the smallest sample size to the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the consistent increase in MSE rates, I review the line trends from {Sport}<r>[0.095, 0.137, 0.494, 0.460]</r>, {Leisure}<r>[0.560, 0.137, 0.959, 0.460]</r>, {Hobby}<r>[0.095, 0.563, 0.494, 0.887]</r>, and {Work}<r>[0.560, 0.563, 0.959, 0.887]</r>. From the line plots, the MSE for {Work}<r>[0.560, 0.563, 0.959, 0.887]</r> exhibits a consistent upward trajectory, especially notable in the plot starting from a lower position and escalating to greater values at larger sample sizes.\nBased on the above thought, the answer is:\nWork has shown a consistent increase in MSE rates across the progression from smallest to largest sample sizes. This consistent upward trend in {the Work subplot}<r>[0.560, 0.563, 0.959, 0.887]</r> indicates increasing errors in measurements or model predictions related to Work as the data set expands."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "s6IJEU7l0ncq3gWr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest MSE recorded for Leisure across all sample sizes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest MSE for Leisure, I inspect {the data points on the line plot for Leisure}<r>[0.560, 0.137, 0.959, 0.460]</r>, which contains values at various sample sizes. The highest point corresponds to a sample size of 10000 with an MSE of 0.73 which is observed at {point coordinates}<r>[0.959, 0.137]</r>.\nBased on the above thought, the answer is:\n0.73."
+            }
+        ],
+        "id_raw": 171
+    },
+    {
+        "id": "UR82hS3d30eeHXKV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the growth of different industries over the decade into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the axis labels}<r>[0.508, 0.222, 0.549, 0.245]</r> and extracting information from {the legend}<r>[0.078, 0.027, 0.297, 0.085]</r>, the chart depicts the growth across various years marked on {the x-axis}<r>[0.108, 0.197, 0.117, 0.215]</r> for different industries. I'll arrange the data by years as columns and industries as rows, obtaining the growth values from {the lines for each industry}<r>[0.113, 0.171, 0.943, 0.177]</r> corresponding to each year.\nBased on the above thought, the answer is:\nYear,Mining,Logistics,Manufacturing,Textiles,Retail\n2010,0.5,0.8,0.2,0.1,0.7\n2011,0.6,0.85,0.15,0.11,0.75\n2012,0.55,0.9,0.1,0.12,0.8\n2013,0.62,0.92,0.13,0.1,0.83\n2014,0.65,0.93,0.12,0.13,0.85\n2015,0.68,0.95,0.1,0.14,0.9\n2016,0.7,0.98,0.09,0.15,0.92\n2017,0.72,0.99,0.08,0.16,0.94\n2018,0.75,1,0.07,0.17,0.96\n2019,0.8,1.02,0.05,0.18,0.99\n2020,0.85,1.05,0.03,0.2,1.02"
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "domfb8texh5m1Nqg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry appears to have had the least variation in growth over the decade and can you explain why it might be considered stable?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the stability in growth can be determined by examining the line plots. Observing {the line plot for Retail}<r>[0.113, 0.026, 0.943, 0.144]</r>, the slope is consistent with minimal fluctuations, as seen from the coordinates. This suggests that the Retail industry has had a steady demand or effective adaptability strategies over the years, indicated by the uniform positive slope.\nBased on the above thought, the answer is:\nThe Retail industry appears to have had the least variation in growth, indicating stability. This can be attributed potentially to consistent consumer demand and effective strategic adaptations over the decade. The line plot confirms this with its gradual and consistent upward trend, emphasizing a resilient market presence amidst varying economic conditions."
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "Uas33zX5xXDUzaOZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute difference between the highest and lowest global trade impact percentages for the Retail industry during the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the absolute difference, I first locate {the plot line points for the Retail industry}<r>[0.113, 0.026, 0.943, 0.144]</r> which pertain to the global trade impact percentages. Observing the maximum value at 2020 (1.05) and the minimum value at 2010 (0.7), I calculate the absolute difference between these two values.\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 172
+    },
+    {
+        "id": "MIpAf9KKetDp07G4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data present in the line chart into a table format with company names as rows and quarters as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the data table, I first identify {the x-axis tick labels}<r>[0.063, 0.911, 0.070, 0.934], [0.246, 0.911, 0.254, 0.934], [0.430, 0.911, 0.437, 0.934], [0.613, 0.911, 0.621, 0.934], [0.797, 0.911, 0.804, 0.934]</r> which represent the different Quarters and would form the columns of the table. Then I locate {the legend labels}<r>[0.127, 0.095, 0.278, 0.126],[0.127, 0.143, 0.311, 0.174], [0.127, 0.190, 0.299, 0.222],[0.127, 0.238, 0.271, 0.270]</r> which represent the names of the companies that will be the row labels. The individual data points {Line1}<r>[0.250, 0.780,,0.434, 0.752, 0.617, 0.723, 0.800, 0.694, 0.984, 0.694]</r>, {Line2}<r>[0.250, 0.780, 0.434, 0.780, 0.617, 0.780, 0.800, 0.780, 0.984, 0.780]</r>, {Line3}<r>[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637]</r> and {Line4}<r>[0.250, 0.780, 0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208]</r> help populate the values for each stock in each quarter.\nBased on the above thought, the answer is:\nCompany,Q1,Q2,Q3,Q4\nAmazon,100,120,140,160\nApple,100,80,70,90\nFacebook,100,60,50,70\nMicrosoft,100,90,80,100\nTesla,100,110,120,130"
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "tep6rjHxyICn6lP8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the graph, which company's shares would you recommend as the most stable investment? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most stable investment, I'd need to evaluate the consistency in the growth trends of each company. I can see that {the line for Amazon}<r>[0.250, 0.780,,0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208]</r> shows an increase every quarter without any decline. On the other hand, the stocks of {Apple}<r>[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637]</r>, {Facebook}<r>[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637]</r>, and {Microsoft}<r>[0.250, 0.780,,0.434, 0.752, 0.617, 0.723, 0.800, 0.694, 0.984, 0.694]</r> all exhibit periods of decline at some point. {Tesla}<r>[0.250, 0.780, 0.434, 0.780, 0.617, 0.780, 0.800, 0.780, 0.984, 0.780]</r>'s stock also increases each quarter, but the growth rate is less than that of Amazon.\nBased on the above thought, the answer is:\nBased on the trends shown in the graph, Amazon's shares seem to be the most stable investment. It not only increases every quarter but does so at a higher and consistent growth rate compared to the other companies. This constant upward trend suggests stability and good potential return on investment, minimizing the risk for potential investors."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "MBe1pPFQEXkCaZ5Y",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total gain in percentage points for Tesla over the 4 quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total gain for Tesla, I need to locate {the line points for Tesla}<r>[0.250, 0.780, 0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208]</r> and subtract the initial value in Q1 from the final value in Q4: 130 - 100 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "F7smOkFDXUqy5sMT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the series of MSE values for each stock across various time points into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I analyze the chart detailing MSE values for each stock at certain time points. I identify {the x-axis tick labels}<r>[0.056, 0.927, 0.068, 0.962]</r>, <r>[0.097, 0.927, 0.115, 0.962]</r>, <r>[0.141, 0.927, 0.159, 0.962]</r>, <r>[0.185, 0.927, 0.203, 0.962]</r>, <r>[0.229, 0.927, 0.246, 0.962]</r> for the time points and {the plotted MSE points for each stock}<r>[0.062, 0.500]</r>, <r>[0.106, 0.548]</r>, <r>[0.150, 0.613]</r>, <r>[0.194, 0.629]</r>, <r>[0.238, 0.667]</r> to extract and arrange the MSE values in the table below.\nBased on the above thought, the answer is:\nTime Point,Stock 1 MSE,Stock 2 MSE,Stock 3 MSE,Stock 4 MSE\n96,0.225,0.180,0.140,0.330\n192,0.210,0.175,0.135,0.320\n288,0.190,0.170,0.130,0.315\n384,0.185,0.165,0.128,0.308\n480,0.173,0.160,0.120,0.300"
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "PK23gHPSo59dTGTz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stock displayed the most consistent decrease in MSE values over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trend in the line plot for each stock, I assess the slopes and data points. {Stock 3}<r>[0.557, 0.774], [0.601, 0.790], [0.645, 0.806], [0.689, 0.813], [0.733, 0.839]</r> presents data points of MSE values that decrease steadily from 0.14 to 0.12 over the time measurements, suggesting a consistent decline, unlike the other stocks which show larger variances between consecutive time points.\nBased on the above thought, the answer is:\nStock 3 displayed the most consistent decrease in MSE values over time, with MSE values providing a steady downward trend throughout the observed periods."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "YgxPEqxHov2w4aaT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average MSE for Stock 4 across all measured time points?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average MSE for Stock 4, I will find and sum {the MSE values for Stock 4}<r>[0.805, 0.160], [0.848, 0.193], [0.892, 0.209], [0.936, 0.231], [0.980, 0.257]</r> documented as 0.33, 0.32, 0.315, 0.308, and 0.3. I will then divide this sum by the total number of time points, which is 5.\nBased on the above thought, the answer is:\n0.315"
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "JlRsDxHkzngNMUwC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table indicating the percentage change in performance of each model across different tasks from the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I analyze each subchart, identifying the different tasks at {the titles of each subchart}[0.1, 0.9, 0.3, 0.93]. Each model's performance for each task is displayed as a bar in the subchart, from which I extract the performance data. Observing {the labels and percentage values on the bars}[0.2, 0.85, 0.25, 0.88], [0.5, 0.75, 0.55, 0.78] for each model, I collect the necessary values to populate the table rows representing the machine learning models.\nBased on the above thought, the answer is:\nTask,Back Propagation,Support Vector Machines,Decision Trees,Random Forests,Neural Networks\nImage Recognition,0%,2%,-5%,5%,0%\nSpeech Recognition,1%,3%,4%,2%,-1%\nNatural Language Processing,-2%,5%,1%,6%,-3%\nData Mining,3%,0%,2%,4%,-2%"
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "s4NKYqWaRUF6iQpD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich machine learning model appears to have the most inconsistent performance across tasks, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which model has the most inconsistent performance, I analyze the variation in bar lengths and colors across all tasks for each model. Specifically, {the bars for Back Propagation}[0.1, 0.8, 0.2, 0.82], {Support Vector Machines}[0.3, 0.8, 0.4, 0.82], {Decision Trees}[0.5, 0.8, 0.6, 0.82], {Random Forests}[0.7, 0.8, 0.8, 0.82], and {Neural Networks}[0.9, 0.8, 1.0, 0.82] reveals varying changes across tasks. Random Forests shows significant inconsistency with noticeable fluctuations in performance, having rapid expansion in NLP while showing a continuous decrease in Image Recognition.\nBased on the above thought, the answer is:\nRandom Forests appears to have the most inconsistent performance across tasks, illustrated by its sharp differences in performance growth in Natural Language Processing contrasted with a consistent decline in Image Recognition. This variety in outcomes suggests significant fluctuations in its effectiveness depending on the application."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "Hh469dB3OxWdZOZ8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall performance change for the Support Vector Machines model across all tasks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the overall performance change for the Support Vector Machines model, I need to sum up the percentage values from each task for this model. By observing {the bar representing Support Vector Machines}[0.15, 0.75, 0.18, 0.78] in each task, I gather the performance changes: Image Recognition (2%), Speech Recognition (3%), Natural Language Processing (5%), Data Mining (0%). Adding these values together: 2 + 3 + 5 + 0 = 10%.\nBased on the above thought, the answer is:\n10%."
+            }
+        ],
+        "id_raw": 176
+    },
+    {
+        "id": "sHb8My1FgpG0x5ZP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, could you summarize the efficiency of various factories over different years into a structured format? Let the years be the rows and different factories be the columns in the table."
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, let's first identify {the x-axis tick labels}[[0.087, 0.892, 0.117, 0.916], [0.296, 0.892, 0.326, 0.916], [0.505, 0.892, 0.535, 0.916], [0.714, 0.892, 0.744, 0.916], [0.922, 0.892, 0.952, 0.916]] which represent the years. These would serve as our rows in the table. Next, locate {the legend texts}[[0.086, 0.164, 0.118, 0.182],[0.086, 0.190, 0.118, 0.208],[0.086, 0.217, 0.118, 0.234],[0.086, 0.243, 0.118, 0.260],[0.086, 0.269, 0.118, 0.286]] corresponding to the 5 factories. These would serve as the columns in our table. The corresponding efficiencies can be extracted by observing {the line points} in each factory's trend.\nBased on the above thought, the answer is:\nYear,Factory 1,Factory 2,Factory 3,Factory 4,Factory 5\nYear 1,0.70,0.75,0.60,0.65,0.72\nYear 2,0.72,0.73,0.58,0.69,0.75\nYear 3,0.69,0.76,0.62,0.67,0.74\nYear 4,0.71,0.77,0.64,0.68,0.73\nYear 5,0.68,0.78,0.66,0.70,0.76"
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "PjLLhZzzyniClxJJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the years, which factory exhibited the most improvement in manufacturing efficiency? Please explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce which factory had the most improvement, we need to compare the changes in efficiency over the years for each factory. By analyzing {line datasets for factories}[0.102, 0.441, 0.937, 0.500], [0.102, 0.295, 0.937, 0.207], [0.102, 0.734, 0.937, 0.558], [0.102, 0.587, 0.937, 0.441], [0.102, 0.383, 0.937, 0.266] I recognize the patterns reflecting the efficiency of manufacturing from Year 1 to Year 5. Factory 2 and Factory 4 appear to have improved more compared to the other factories.\nBased on the above thought, the answer is:\nFactory 4 displayed the most improvement in manufacturing efficiency over the years. As gleaned from the line graph, Factory 4's efficiency augments gradually every year. Despite small fluctuations, the overall trend for Factory 4 demonstrates a steady increase, marking substantial progress in its manufacturing efficiency."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "7DKnbq0ni7kEtuCi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the greatest change in manufacturing efficiency that took place in any factory from Year 1 to Year 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, we need to look at the differences between the efficiency of each factory in Year 1 and Year 5. The coordinates representing the end points for each line are: {Factory 1}[0.102, 0.441, 0.937, 0.500], {Factory 2}[0.102, 0.295, 0.937, 0.207], {Factory 3}[0.102, 0.734, 0.937, 0.558], {Factory 4}[0.102, 0.587, 0.937, 0.441], and {Factory 5}[0.102, 0.383, 0.937, 0.266]. By examining these values, we can calculate the greatest change.\nBased on the above thought, the answer is:\n0.10."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "AkZBZJqWOQBs6CVH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a structured table with International Financial Institutions as rows and financial categories(GDP Growth, Inflation) as their columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the bar charts, I first identify {the subplot titles}[[0.129, 0.914, 0.232, 0.938], [0.129, 0.679, 0.238, 0.703], [0.129, 0.444, 0.253, 0.468], [0.129, 0.212, 0.223, 0.236]] which represent the row labels or the International Financial Institutions. Next, I observe {the bar labels}[[0.129, 0.873, 0.197, 0.895], [0.129, 0.640, 0.197, 0.662], [0.129, 0.405, 0.197, 0.427], [0.129, 0.173, 0.197, 0.195]] for each subplot which represent the column headers or financial categories. To populate the values, I locate {the end points of the bars}[[0.129, 0.875, 0.611, 0.895], [0.129, 0.841, 0.058, 0.862], [0.129, 0.641, 0.432, 0.661], [0.129, 0.607, 0.079, 0.628], [0.129, 0.406, 0.334, 0.426], [0.129, 0.372, 0.183, 0.393], [0.129, 0.174, 0.297, 0.194], [0.129, 0.140, 0.218, 0.161]] for each financial category under each institution and extract the corresponding values.\nBased on the above thought, the answer is:\nInstitutions,GDP Growth,Inflation\nBank of England,2.2,-0.3\nEuropean Central Bank,1.0,-0.5\nReserve Bank of Australia,1.8,-0.4\nUS Federal Reserve,0.5,0.2"
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "qD1acgXOhcCvGxK5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar charts, which institution has the most considerable negative impact on a category, and can you explain why this might be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "The aim here is to find which institution has the most negative influence on a category. By checking the bars, I note that {the ECB's impact on Inflation}[0.129, 0.607, 0.079, 0.628] appears to extend the farthest to the left, indicating a notable negative impact. Upon further analysis, its value is -0.5, which is the most substantial negative impact among all institutions and categories. The reasoning behind this impact could be due to several factors: it might be because of the specific financial policies used by the ECB, external economic variables that particularly affect Inflation, or perhaps a combination of both.\nBased on the above thought, the answer is:\nBased on the bar charts, the European Central Bank (ECB) appears to have the most significant negative impact on a category, specifically on Inflation. This could be due to a variety of factors, including the nature of the ECB's policies and the influence of external economic variables on Inflation."
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "5ZxUcUlQpJ47RQ6p",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in GDP Growth impact between Bank of England (BOE) and US Federal Reserve (Fed)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the GDP Growth impact for Bank of England (BOE) and US Federal Reserve (Fed), I refer to {the BOE bar for GDP Growth}[0.129, 0.875, 0.611, 0.895] and determine its value to be 2.2. Conversely, for {the Fed bar for GDP Growth} [0.129, 0.174, 0.297, 0.194], the corresponding value is 0.5. So, the difference between GDP Growth impact of BOE and Fed is 2.2 - 0.5 = 1.7.\nBased on the above thought, the answer is:\n1.7"
+            }
+        ],
+        "id_raw": 178
+    },
+    {
+        "id": "SPpEzHkesgFld1wg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table showing the trend differences for various activities based on their increase or decrease in the dual horizontal bar charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe {the color codes in the legends}[[0.01, 0.95, 0.03, 0.96]], distinguished by the colors in the two charts. In one chart, I focus on the activities that have decreased in popularity; in the other, those that have increased. I analyze each bar by verifying its alignment with the corresponding label {located at}[[0.1, 0.2, 0.4, 0.25]], and measure how much the bar extends to the left or right from the zero baseline {per its coordinates}[[0.5, 0.1, 0.9, 0.3]]. For decreased activities Running at -22%, Dining at -18%, Travel at -15%, and Programming at -14%, while Yoga shows an increase by 2%. For increased activities, Running is at 23%, Dining at 25%, Travel at 27%, and Programming at 29%, with Yoga decreasing by 20%.\nBased on the above thought, the answer is:\nActivity,Decrease,Increase\nRunning,-22%,23%\nDining,-18%,25%\nYoga,2%,-20%\nTravel,-15%,27%\nProgramming,-14%,29%"
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "c2erAIKRUHx78B15",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does Yoga show different trends in the two different charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the bars for Yoga in both charts}[[0.3, 0.52, 0.5, 0.58]], I notice distinctly contrasting trends. The presence of the bars on both sides of the zero baseline highlights a rare inconsistency - a minimal increase in one chart and a significant decrease in another. It suggests the data might be capturing different aspects or periods of popularity, where certain factors led to a small increase in popularity in one scenario, while general trends or perhaps broader scale factors caused a significant decrease in another context. Further analysis of external factors influencing Yoga's popularity would be necessary to understand these diverging trends fully.\nBased on the above thought, the answer is:\nYoga shows different trends in the two charts possibly due to the data capturing different demographic factors or periods in time, reflecting a niche increase in one context while generally decreasing in broader popularity in another. This discrepancy underscores the variability and complexity of factors affecting social lifestyle trends."
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "Upnku0kbL6OPPZr8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_9_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage difference in popularity for Yoga between the two charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage difference for Yoga, I observe {the bars associated with Yoga}[[0.3, 0.52, 0.5, 0.58]] in both charts. In one chart, Yoga shows an increase of 2%, and in another, a decrease of 20%. The overall percentage difference is computed by subtracting the lesser percentage from the greater, i.e., |2% - (-20%)| = 22%.\nBased on the above thought, the answer is:\n22%"
+            }
+        ],
+        "id_raw": 179
+    },
+    {
+        "id": "4EAyenLLYxQEAXey",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the chart into a table with Years as columns and different measurements like Food Production, Agricultural Export, Food Import, and Population as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct this table, I would first identify {the x-axis tick labels}[[0.053, 0.939, 0.088, 0.957], [0.277, 0.939, 0.312, 0.957], [0.502, 0.939, 0.537, 0.957], [0.726, 0.939, 0.761, 0.957], [0.950, 0.939, 0.985, 0.957]] which represent Years.  {The legend texts}[[0.122, 0.034, 0.234, 0.052], [0.122, 0.061, 0.253, 0.078], [0.122, 0.087, 0.206, 0.104], [0.122, 0.113, 0.195, 0.130]] represent the different measurements which will be our table rows. The actual values can be found on {the line points for each measurement}[[0.071, 0.545],[0.295, 0.449], ..., [0.968, 0.067]].\nBased on the above thought, the answer is:\n,2010,2015,2020,2025,2030\nFood Production,100,110,130,140,150\nAgricultural Export,80,95,100,102,105\nFood Import,60,70,75,77,80\nPopulation,70,76,80,83,86"
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "YAwCYRBWeBS8lFmW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which metric seems to show the most significant growth from 2010 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the metric with the most dramatic growth, I must analyze the trends in each line. Starting with {the line for Food Production}[0.071, 0.067, 0.968, 0.545], it starts at 100 and ends at 150 indicating a growth of 50%. Looking at {the line for Agricultural Export}[0.071, 0.497, 0.968, 0.736], it starts at 80 and ends at 105 indicating a growth of around 31.25%. Next, analyzing {the line for Food Import}[0.071, 0.736, 0.968, 0.927], the growth is from 60 to 80 meaning around 33.33% growth. Finally, {the line for Population}[0.071, 0.679, 0.968, 0.832], it starts at 70 and ends at 86 indicating a growth of around 22.86%. Comparing these, the Food Production metric has the highest growth percentage.\nBased on the above thought, the answer is:\nBased on the analysis of trends shown in the line chart, the 'Food Production' metric shows the most significant growth from 2010 to 2030. It demonstrates an increasing trend with the highest growth percentage of 50% compared to the other metrics like Agricultural Export, Food Import, and Population. The remarkable increase in Food Production underscores its expanding role in the considered timeframe."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "5qKWoH8PPja2O7FP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming the base year to be 2010, what is the overall percentage growth in Food Production by the year 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the percentage growth from 2010 to 2030, using the formula (Value_in_2030 - Value_in_2010)/Value_in_2010 * 100. {The line points for Food Production}[[0.071, 0.545], [0.295, 0.449], [0.519, 0.258], [0.743, 0.162], [0.968, 0.067]] correspond to the values for each year. The data point for 2010 is 100 and for 2030 is 150. Plugging these values into the formula gives: (150 - 100)/100 * 100 = 50%.\nBased on the above thought, the answer is:\n50%."
+            }
+        ],
+        "id_raw": 180
+    },
+    {
+        "id": "b3CSzbycqBqa0Kxd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table capturing the trend of the cuisine popularity based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I start by recognizing {the x-axis tick labels}[[0.055, 0.919, 0.091, 0.942], [0.154, 0.919, 0.192, 0.942], [0.249, 0.919, 0.296, 0.942], [0.354, 0.919, 0.390, 0.942], [0.449, 0.919, 0.495, 0.942], [0.559, 0.919, 0.584, 0.942], [0.643, 0.919, 0.698, 0.942], [0.744, 0.919, 0.797, 0.942], [0.828, 0.919, 0.912, 0.942], [0.952, 0.919, 0.988, 0.942]], which represent the different cuisines. Then, I track {the data points}[[0.073, 0.610], [0.173, 0.376], [0.273, 0.201], [0.372, 0.259], [0.472, 0.435], [0.571, 0.113], [0.671, 0.493], [0.770, 0.318], [0.870, 0.230], [0.970, 0.084]] along the line plot of 'Cuisine Trends' to derive the trends associated with each cuisine.\nBased on the above thought, the answer is:\nCuisine,Cuisine Trend (thousands)\nItalian,10\nFrench,18\nMexican,24\nIndian,22\nChinese,16\nThai,27\nAmerican,14\nJapanese,20\nMediterranean,23\nVegan,28"
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "kScBFq7zG9Ud40aP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the line chart, do you think there's a general correlation between the order of the cuisines listed and the associated cuisine trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the order of the cuisines along {the x-axis}[[0.055, 0.919, 0.091, 0.942], [0.952, 0.919, 0.988, 0.942]] and the corresponding trends from {the line for Cuisine Trends}[[0.073, 0.610], [0.970, 0.084]]. It seems that there's no clear correlation between the order of cuisines and the trend in popularity as the values don't consistently increase or decrease. Instead, the trends fluctuate for different cuisines. There are ups and downs in the trend, so we can't say there's a positive or negative correlation.\nBased on the above thought, the answer is:\nThere doesn't seem to be a clear correlation between the order of the cuisines listed and the associated cuisine trend. The trends fluctuate regardless of their position in the ordering, indicating that other factors might be influencing the trend of the cuisines."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "4wsgg4DReOLgwFSH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which cuisine showed the greatest positive difference in trend from its previous cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to observe {the line points for Cuisine Trends}[[0.073, 0.610], [0.173, 0.376], [0.273, 0.201], [0.372, 0.259], [0.472, 0.435], [0.571, 0.113], [0.671, 0.493], [0.770, 0.318], [0.870, 0.230], [0.970, 0.084]] and calculate the difference between consecutive points. After analysing the data, the greatest difference can be seen between Thai cuisine and Chinese cuisine as 'Thai' had a trend value of 27000 and 'Chinese' had a value of 16000. The difference will be 27000 - 16000 = 11000.\nBased on the above thought, the answer is:\n11000."
+            }
+        ],
+        "id_raw": 181
+    },
+    {
+        "id": "Ikktj2rcxhCNp6Uw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the line chart into table format with years as the columns and various classification types as the rows, and what is the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, I can start by identifying {the x-axis tick labels}<r>[0.066, 0.919, 0.981, 0.942]<r>, which are five years: 2015, 2016, 2017, 2018, and 2019. They would form the column headers in the table. Then, I find {the legend texts}<r>[0.175, 0.030, 0.921, 0.053]<r>. These would serve as the row labels, indicating the classification types: Waste Recycling, Solar Energy, Wind Energy, and Nuclear Energy. For the actual data values under each classification, I should locate the corresponding {line points for each classification type}<r>[0.066, 0.154, 0.981, 0.785]</r> and use their coordinates to estimate the specific values.\nBased on the above thought, the answer is:\nClassification Data,2015,2016,2017,2018,2019\nWaste Recycling,65,75,80,90,95\nSolar Energy,15,20,40,65,80\nWind Energy,40,55,70,90,100\nNuclear Energy,30,45,60,80,95"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "7BIOKFrR7LvVqJMs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which classification type seems to have experienced the greatest improvement in accuracy from 2015 to 2019? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the classification type with the greatest improvement in accuracy over the given time period, I have to compare the differences between the starting and ending values of each line. The lines corresponding to {Waste Recycling}<r>[0.088, 0.390, 0.959, 0.154]</r>, {Solar Energy}<r>[0.088, 0.785, 0.959, 0.272]</r>, {Wind Energy}<r>[0.088, 0.588, 0.959, 0.114]</r> and {Nuclear Energy}<r>[0.088, 0.666, 0.959, 0.154]</r> represent the change in accuracy for each classification type from 2015 to 2019. From their respective coordinates, I determine the values in 2015 and 2019 for each, and calculate the change. I find that Solar Energy shows the most considerable increase, from 15 in 2015 to 80 in 2019.\nBased on the above thought, the answer is:\nSolar Energy seems to have experienced the greatest improvement in accuracy from 2015 to 2019. This is based on the substantial increase from an estimated value of 15 in 2015 to 80 in 2019, which represents a greater gain in accuracy compared to the other classification types over the same time period."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "FXlM8UXGX2NedsaQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in classification accuracy for Wind Energy from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in classification accuracy for Wind Energy from 2015 to 2019, I need to look at {the line points for Wind Energy}<r>[0.088, 0.588, 0.959, 0.114]</r>. The y-coordinates of these points correspond to the classification accuracy values for Wind Energy in the respective years. So, I need to subtract the accuracy value for 2015 from the one for 2019. Given the coordinates, these values are estimated to be 40 for 2015 and 100 for 2019. The total increase would therefore be 100 - 40 = 60.\nBased on the above thought, the answer is:\n60."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "EV1yn8bK8Q7QMkzP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the sales trend data from the line chart into a tabular format with Years as columns and Products as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a tabular format, I firstly take note of {the x-axis tick labels}[[0.112, 0.067, 0.158, 0.092], [0.248, 0.067, 0.294, 0.092], [0.385, 0.067, 0.431, 0.092], [0.521, 0.067, 0.567, 0.092], [0.657, 0.067, 0.703, 0.092], [0.793, 0.067, 0.839, 0.092]], which represent the column headers or years. Next, I identify the {legend texts}[[0.887, 0.613, 0.921, 0.638], [0.887, 0.682, 0.921, 0.708]] as the row labels denoting the products. I then locate {the line points for Product A}[[0.121, 0.728], [0.257, 0.764], [0.393, 0.655], [0.529, 0.728], [0.666, 0.800], [0.802, 0.822]] and {the line points for Product B}[[0.121, 0.822], [0.257, 0.800], [0.393, 0.750], [0.529, 0.678], [0.666, 0.633], [0.802, 0.615]] containing the unit sold values at each year for the respective products.\nBased on the above thought, the answer is:\nProduct,2016,2017,2018,2019,2020,2021\nProduct A,5200,5500,4800,5200,5800,6000\nProduct B,6800,6600,6000,5400,5000,4700"
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "Wa7xVJDTMMggo4rD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the sales trends of Products A and B?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the lines corresponding to Product A {the line for Product A}[[0.121, 0.728], [0.257, 0.764], [0.393, 0.655], [0.529, 0.728], [0.666, 0.800], [0.802, 0.822]], we can see that it has an overall increasing trend, even though there were certain years with slight dips. Looking at {the line for Product B}[[0.121, 0.822], [0.257, 0.800], [0.393, 0.750], [0.529, 0.678], [0.666, 0.633], [0.802, 0.615]], it can be seen that over the same duration, sales have consistently declined.\nBased on the above thought, the answer is:\nGiven the observed trends, it appears that the market preference is shifting from Product B to Product A. While Product B had higher sales initially, the falling trend indicates problems like a possible loss of market share, poor performance, or competition. On the other hand, the rising trend for Product A suggests an increasing preference, successful marketing strategies, or growth in its target market. However, to be totally certain, additional market information would be required. This estimation is based purely on the sales trend from the graphs."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "a5wWXaRbzOGOQsFi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the sales of Product A increase from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the increase in sales for Product A from 2016 to 2021, I need to examine its {initial sales data point in 2016}[0.121, 0.728] which stands at 5200 units, and its {final sales data point in 2021}[0.802, 0.822] which shows 6000 units. The difference between these two points will tell us the increase in sales over this period.\nBased on the above thought, the answer is:\n800 units."
+            }
+        ],
+        "id_raw": 183
+    },
+    {
+        "id": "sMl7HLakjuRE04j6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data on the different economic sectors displayed in a line chart into a structured table format with years as columns and sectors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I start by examining {the x-axis labels}<r>[0.077, 0.899, 0.121, 0.923], [0.321, 0.899, 0.365, 0.923], [0.564, 0.899, 0.608, 0.923], [0.808, 0.899, 0.852, 0.923]</r> which denote the column headers representing different years. For the row headers, I identify {the legend}<r>[0.260, 0.946, 0.348, 0.970], [0.432, 0.946, 0.545, 0.970], [0.629, 0.946, 0.727, 0.970], [0.810, 0.946, 0.863, 0.970]</r> that lists the sectors such as Gold Price, Stock Market, Real Estate, and Bonds. I further analyze the points of the plotted lines {line points for all sectors}<r>[0.099, 0.685, 0.976, 0.841], [0.099, 0.430, 0.976, 0.671], [0.099, 0.077, 0.976, 0.459], [0.099, 0.515, 0.976, 0.630]</r> to extract the data for each year and sector.\nBased on the above thought, the answer is:\nYear,Gold Price,Stock Market,Real Estate,Bonds\n2001,300,2000,4000,1800\n2002,310,1900,4100,1790\n2003,320,2000,4200,1800\n...\n2019,1400,3200,5700,2600"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "kAZ5pS11AHZE546x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the most consistent growth over the period 2001-2019 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trend of the lines, I compare {the line for Real Estate}<r>[0.099, 0.317, 0.976, 0.077]</r> with {the lines for other sectors}<r>[0.099, 0.685, 0.976, 0.841], [0.099, 0.430, 0.976, 0.671], [0.099, 0.515, 0.976, 0.630]</r>. The Real Estate line shows a steady decrease in its slope, indicating a strong and consistent growth as compared to other sectors.\nBased on the above thought, the answer is:\nThe Real Estate sector showed the most consistent growth over the period 2001-2019, as evidenced by its steady upward trajectory in the chart."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "JL9qi7CyxZW0QIEF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in the Gold Price from 2001 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I analyze {the points on the Gold Price line}<r>[0.099, 0.841], [0.976, 0.685]</r>. The value for Gold Price in 2001 is 300 and in 2019 is 1400. The percentage increase is calculated using the formula ((New Value - Old Value) / Old Value) * 100.\nBased on the above thought, the answer is:\n366.67%"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "pUTTYYsFydZxmQc2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate this line chart data into a structured table format where the columns are the different activities and the rows are the different age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this line chart data into a tabular format, I start by identifying {the x-axis tick labels}[[0.077, 0.919, 0.099, 0.942], [0.959, 0.919, 0.981, 0.942]] as the row labels which represent age groups. Then, I notice {the legend labels}[[0.283, 0.030, 0.355, 0.053], [0.438, 0.030, 0.531, 0.053], [0.614, 0.030, 0.701, 0.053], [0.784, 0.030, 0.824, 0.053]] which correspond to the column headers, representing different activities. Now, to populate the table, I map each line's points to their respective activity and age, using {the line points for Exercise}[[0.088, 0.241], [0.970, 0.323]], {for Meditation}[[0.088, 0.365], [0.970, 0.282]], {for Journaling}[[0.088, 0.572], [0.970, 0.613]], and {for Trips}[[0.088, 0.820], [0.970, 0.737]].\nBased on the above thought, the answer is:\nAge,Exercise,Meditation,Journaling,Trips\n30,80,65,40,10\n50,70,75,35,20"
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "jdo2vrXcukHAwLBN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trend lines for the different activities, can you comment on the correlation between age and engagement in these activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the correlation between age and engagement in different activities, I need to analyze the trend lines for each activity. For {Exercise}[[0.088, 0.241], [0.970, 0.323]], there is a decrease in engagement as age increases - indicating a possible negative correlation. A similar trend is observed for {Journaling}[[0.088, 0.572], [0.970, 0.613]]. On the other hand, engagement in {Meditation}[[0.088, 0.365], [0.970, 0.282]] appears to increase with age, suggesting a positive correlation between age and engagement in this activity. Lastly, engagement in {Trips}[[0.088, 0.820], [0.970, 0.737]] also tends to rise as age increases. These conclusions, however, are based on observations for only two age points and can not be generalized without further data.\nBased on the above thought, the answer is:\nBased on the observed trend lines, there seems to be a negative correlation between age and engagement in Exercise and Journaling \u2013 the engagement scores in these activities decrease as age increases. Conversely, there appears to be a positive correlation between age and engagement in Meditation and Trips \u2013 these activities see an increase in engagement as the age increases. However, these observations are derived from only two age data points and may not represent a generalized trend across all age groups."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "2AgvsJc6T4hF2Crx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall difference between the Activity Engagement Scores for Exercise from age 30 to age 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the overall difference between the Activity Engagement Scores for Exercise from age 30 to age 50, I need to locate {the line points for Exercise}[[0.088, 0.241], [0.970, 0.323]] and extract the corresponding values: 80 at age 30 and 70 at age 50. The difference between these two scores is therefore 80 - 70 = 10.\nBased on the above thought, the answer is:\n10."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "N8n6DLJsmIKT6Kps",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reformat the data from the line chart to a tabular format with rows as Years and columns for each Area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the dataframe, I need to use the X-axis labels for the rows, which are {the x-axis tick labels}[[0.053, 0.919, 0.088, 0.942], [0.277, 0.919, 0.312, 0.942], [0.502, 0.919, 0.537, 0.942], [0.726, 0.919, 0.761, 0.942], [0.950, 0.919, 0.985, 0.942]] and represent the Years. The column headers are taken from {the legend texts}[[0.814, 0.754, 0.955, 0.777], [0.814, 0.789, 0.934, 0.812], [0.814, 0.824, 0.941, 0.847], [0.814, 0.859, 0.910, 0.882]] which are Public Infrastructure, Residential Areas, Commercial Areas, and Green Spaces. For the cell values, I take the Y-axis coordinates of {the line points} for each Area in each Year.\nBased on the above thought, the answer is:\nYear,Public Infrastructure,Residential Areas,Commercial Areas,Green Spaces\n2010,80,70,75,85\n2015,85,75,80,90\n2020,75,80,85,90\n2025,90,85,90,95\n2030,100,100,95,100"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "LX5VOjLDjkpRbFst",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area shows the most improvement between 2010 and 2030 and how can you determine this?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area shows the most improvement, I need to observe the changes in the lines for each area from 2010 to 2030. Looking at {the line for Public Infrastructure}[0.071, 0.135, 0.968, 0.683], it goes up from 80 to 100 showing an increase of 20 points. Observing {Residential Areas}[0.071, 0.135, 0.968, 0.793], I see an increase from 70 to 100, a rise of 30 points. For {Commercial Areas}[0.071, 0.244, 0.968, 0.683], the line increases from 75 to 95, an upward movement of 20 points. Finally, {Green Spaces}[0.071, 0.135, 0.968, 0.464] shows an increment from 85 to 100, a change of 15 points.\nBased on the above thought, the answer is:\nThe Residential Areas shows the most improvement between 2010 and 2030. This is determined by observing the line chart and comparing the increment in progress for each area from 2010 to 2030. It can be seen that the line representing Residential Areas shows the greatest upward movement by 30 points, which is higher than the improvements in progress observed for Public Infrastructure, Commercial Areas, and Green Spaces. Thus, it indicates the most improvement over the given period."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "wHb1mn5iL6vXzwTK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percent change in progress for Public Infrastructure from 2010 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the percent change, I have to find the values of Public Infrastructure in 2010 and 2030. For 2010, I look at {the point for Public Infrastructure}{'point coordinates': '[0.071, 0.574]'} which is 80, and for 2030, I look at {the point for Public Infrastructure}{'point_coordinates': '[0.968, 0.135]'} which is 100. The percent change is calculated as ((100 - 80) / 80) * 100.\nBased on the above thought, the answer is:\n25%."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "lhjI9Jbsn83tfwQZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the efficiency data from the bar chart into a table format with years as columns and stages as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accomplish this, the first step is to identify {the y-axis tick labels}[[0.071, 0.111, 0.144, 0.176], [0.208, 0.241, 0.274, 0.307], [0.339, 0.371, 0.404, 0.436], [0.468, 0.500, 0.532, 0.564], [0.597, 0.630, 0.662, 0.694], [0.725, 0.758, 0.790, 0.823], [0.854, 0.887, 0.920, 0.951]], which symbolize the row labels or the stages of production. Then, years 2020 and 2021 serve as the column headers. For the values, I\u2019ll locate the {center of each bar}[[0.146, 0.130], [0.128, 0.227], [0.197, 0.324], [0.351, 0.421], [0.293, 0.518], [0.212, 0.616], [0.201, 0.714], [0.673, 0.130], [0.554, 0.227], [0.643, 0.324], [0.421, 0.422], [0.382, 0.518], [0.571, 0.616], [0.480, 0.714]] and extract the respective values annotated for each stage, in each year.\nBased on the above thought, the answer is:\nStages, 2020, 2021\nRaw Materials, 14.6, 67.3\nAssembly, 12.8, 55.4\nQuality Control, 19.7, 64.3\nFinishing, 35.1, 42.1\nPackaging, 29.3, 38.2\nDistribution, 21.2, 57.1\nReturn Processing, 20.1, 48.0"
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "SrMTbDUuPDXQlwrP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which stage had the most significant improvement in efficiency from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which stage showed the most significant improvement, it's necessary to compare the growth percentages of all stages between 2020 and 2021. The corresponding bars for 2020 and 2021 include {Raw Materials}[0.146, 0.673], {Assembly}[0.128, 0.554], {Quality Control}[0.197, 0.643], {Finishing}[0.351, 0.421], {Packaging}[0.293, 0.382], {Distribution}[0.212, 0.571], {Return Processing}[0.201, 0.480]. After reviewing the percentage changes for each stage, the 'Raw Materials' stage exhibited the biggest rise with 463%.\nBased on the above thought, the answer is:\nThe 'Raw Materials' stage had the most significant improvement in efficiency between 2020 and 2021 as shown by an increase from 14.6% to 67.3%, representing a 463% rise. This is the highest percentage change across all stages in manufacturing."
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "rPWoIT0GG0hSlGko",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in the efficiency score for the 'Finishing' stage between 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step to find the percentage difference is to identify {the bar representing the 'Finishing' stage in 2020}[0.351, 0.421] and 2021 {the bar representing the 'Finishing' stage in 2021}[0.421, 0.422]. The efficiency scores for 2020 and 2021 are 35.1 and 42.1 respectively. The percentage difference can be therefore calculated as (42.1 - 35.1)/35.1 * 100 = 19.94%\nBased on the above thought, the answer is:\n19.94%"
+            }
+        ],
+        "id_raw": 187
+    },
+    {
+        "id": "blbVctAS1XOiOBFf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the sales for different types of sales in each year in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "With the line chart, I think I can. I identify {the x-axis labels}<r>[0.066, 0.919, 0.110, 0.942]</r> as the year labels. After that, I recognize {the legend entries}<r>[0.280, 0.030, 0.817, 0.053]</r> to represent the different types of sales. Then, I go through the process of locating the {line points}<r>[0.088, 0.158, 0.959, 0.489]</r>, that contain the sales data for each year and type, by matching the x-coordinates with the year and the line color with the type of sales. Once all these are determined, I can generate the table.\nBased on the above thought, the answer is:\nYear,Online Sales,Offline Sales,Ecommerce Sales\n2005,50,70,10\n2006,55,67,20\n2007,59,65,25\n2008,61,60,38\n2009,63,55,40\n2010,70,50,45\n2011,72,45,48\n2012,75,40,50\n2013,80,35,55\n2014,85,30,60\n2015,90,25,65"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "lSeeKcSs5whbwmSA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of sales showed the most significant increase over the years 2005-2015 and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the end points and the start points of each type of sales: {Online sales}[0.088, 0.489, 0.959, 0.158], {Offline sales}[0.088, 0.323, 0.959, 0.696], and {Ecommerce sales}[0.088, 0.820, 0.959, 0.365]. By comparing the initial and final points of each line, it's clear that the most significant increase is shown in the Ecommerce Sales, which started at a point near the top of the chart and ended at a point much lower, indicating a significant increase.\nBased on the above thought, the answer is:\nEcommerce Sales showed the most significant increase over the years. The line plot for Ecommerce Sales starts near the top of the chart (indicating lower sales) and ends considerably lower (indicating higher sales), demonstrating a significant rise over the given period. This might be due to increasing internet penetration and the growing popularity of online shopping over the years."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "Z10OAFUTKnH5JPVx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest sales and the lowest sales for offline sales during the years 2005-2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this difference, I need to observe the {red line}<r>[0.088, 0.323, 0.959, 0.696]</r> representing offline sales. I find the highest point at 70 in {2005}<r>[0.088, 0.323]</r> and the lowest point at 25 in {2015}<r>[0.959, 0.696]</r>. The difference is hence 70-25 = 45.\nBased on the above thought, the answer is:\n45."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "u7HySIlhlcWlvIst",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values from the bar chart and generate a table format with AI Use Cases as columns and Application Fields as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured tabular data from the bar chart, first I look at {the y-axis tick labels}[[0.188, 0.638, 0.239, 0.659], [0.188, 0.582, 0.273, 0.602], [0.188, 0.526, 0.282, 0.547], [0.188, 0.470, 0.248, 0.490], [0.188, 0.414, 0.318, 0.434], [0.188, 0.358, 0.258, 0.378], [0.188, 0.302, 0.320, 0.323]], which represent the row labels or Application Fields. Then, I locate {the legend items}[[0.918, 0.030, 1.050, 0.053]], [0.592, 0.030, 0.835, 0.053]] as the column headers for different AI Use Cases. To populate the values for each AI use case in each field, I locate the {bars for Synthetic AI}[[0.188, 0.666, 0.333, 0.672]], and the corresponding {bars for Human-directed AI}[[0.188, 0.606, 0.350, 0.612]],... and extract the values annotated at the end of each bar.\nBased on the above thought, the answer is:\nField, Synthetic AI, Human-directed AI\nAutonomous Driving, -5, 5\nFraud Detection, -20, 8\nCustomer Service, -7, 6\nObject Detection, -3, 10\nContent Recommendation, -8, 4\nText Analysis, 8, 12\nSpeech Recognition, 6, 14"
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "8i6HG3xTRx3hLceO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which field of application has benefited the most from switching to Human-directed AI Use Cases? Why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the field that has benefited the most from switching to Human-directed AI, I need to compare the difference in Synthetic AI and Human-directed AI use cases across all the fields. For {Fraud Detection} <r>[0.188, 0.582, 0.273, 0.602]</r>, I find that {Synthetic AI} <r>[0.188, 0.652, 0.260, 0.682]</r> has -20 units while {Human-directed AI} <r>[0.188, 0.592, 0.297, 0.622]</r> has 8 units, showing a large increase. This represents the largest increase from Synthetic AI to Human-directed AI across all the fields.\nBased on the above thought, the answer is:\nThe Fraud Detection field has benefited the most from switching to Human-directed AI Use Cases. It went from -20 units under Synthetic AI to 8 units under Human-directed AI, depicting the most notable increase among all the fields. This trend may suggest that Fraud Detection works more efficiently with human interaction and oversight, leading to a higher number of use cases."
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "k3rX6e5WBZL3aELI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the net increase or decrease in use cases across all fields for Synthetic AI?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the net increase or decrease, I need to sum all the individual values for Synthetic AI across all fields. I locate {the bars for Synthetic AI Use Cases}[[0.188, 0.666, 0.333, 0.672]], [[0.188, 0.606, 0.293, 0.612]], ..., then extract the following values corresponding to each of them: -5, -20, -7, -3, -8, 8, 6. I can calculate the sum of these values as -29.\nBased on the above thought, the answer is:\n-29"
+            }
+        ],
+        "id_raw": 190
+    },
+    {
+        "id": "Nu042KQkP2lL6NzX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the line plot data into a table format with the years as columns and the different categories (MOOCs Usage, K-12 Online Courses, University Online Education, EdTech Investment) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly. I first identify {the x-axis tick labels}<r>[0.066, 0.919, 0.110, 0.942], [0.240, 0.919, 0.285, 0.942], [0.415, 0.919, 0.459, 0.942], [0.589, 0.919, 0.633, 0.942], [0.763, 0.919, 0.807, 0.942], [0.937, 0.919, 0.981, 0.942]</r> which correspond to the years, serving as table columns. Next, I note the {legend texts}<r>[0.299, 0.030, 0.422, 0.053], [0.299, 0.065, 0.473, 0.088], [0.556, 0.030, 0.797, 0.053], [0.556, 0.065, 0.720, 0.088]</r> which correspond to the different categories and will form the rows. To fill the table, I locate each line's points like {the points for MOOCs usage}<r>[0.088, 0.406], [0.263, 0.390], [0.437, 0.381], [0.611, 0.365], [0.785, 0.340], [0.959, 0.158]</r> to determine the values for each year.\nBased on the above thought, the answer is:\nCategories,2015,2016,2017,2018,2019,2020\nMOOCs Usage,60,62,63,65,68,90\nK-12 Online Courses,55,58,60,65,70,85\nUniversity Online Education,50,53,58,60,63,80\nEdTech Investment,18,25,35,45,55,75"
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "EldzzT3CSlAhSw2M",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the different categories presented in the line plot, which one exhibited the most significant growth from 2015 to 2020? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the category with the most significant growth, I must review the changes in each line from 2015 to 2020. Upon examining {MOOCs line} <r>[0.088, 0.158, 0.959, 0.406]</r>, I notice an increase from 60 to 90. For the {K-12 line} <r>[0.088, 0.199, 0.959, 0.448]</r>, the growth is from 55 to 85. {University Online Education line} <r>[0.088, 0.241, 0.959, 0.489]</r> shows a rise from 50 to 80. Lastly, {EdTech Investment line} <r>[0.088, 0.282, 0.959, 0.754]</r> reveals an increase from 18 to 75.\nBased on the above thought, the answer is:\nThe EdTech Investment category exhibited the most significant growth from 2015 to 2020. This can be seen in its line plot which shows an increase from 18 in 2015 to 75 in 2020. This increase is noticeably larger in terms of absolute change and also relative to the starting value, compared to the other categories."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "EwuT7LsonzmxUMRB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the University Online Education category increase from the year 2015 to 2020 based on the line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I first need the starting and ending values for the University Online Education category. The starting value corresponds to the year 2015, which is {the point}<r>[0.088, 0.489]</r> with a value of 50. The ending value corresponds to the year 2020, which is {the point}<r>[0.959, 0.241]</r>   with a value of 80. The percentage increase is then calculated as (Final - Initial)/Initial * 100% = (80-50)/50 * 100% = 60%.\nBased on the above thought, the answer is:\n60%."
+            }
+        ],
+        "id_raw": 191
+    },
+    {
+        "id": "jidFoL0FIN6PBwSV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a tabulated version of the data displayed in the bar charts, detailing the values for different categories of social activities across the two different trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the data in the bar charts, I need to identify the trend types and the activity categories, and their corresponding values. Here, looking at {the x-axis labels}<r>[0.055, 0.919, 0.099, 0.942], [0.232, 0.919, 0.276, 0.942], [0.408, 0.919, 0.452, 0.942], [0.585, 0.919, 0.628, 0.942], [0.761, 0.919, 0.805, 0.942], [0.937, 0.919, 0.981, 0.942]</r>, I get the categories such as: Outdoor Fitness, Inside Workout, Home Cooking, Social Media. The trends are Roller-coaster pattern and Volatile growth represented by {bar heights}<r>[0.077, 0.354, 0.959, 0.851]</r> associated with each category.\nBased on the above thought, the answer is:\nTrend,Outdoor Fitness,Inside Workout,Home Cooking,Social Media\nRoller-coaster pattern,75,90,50,80\nVolatile growth,10,60,5,55"
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "LULCSTEQmKbqoFWL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of social activity shows the largest difference between the two trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the social activity with the largest difference, I compare the variances in values for each activity between the two charts. In the case of {Inside Workout}<r>[0.232, 0.919, 0.276, 0.942]</r>, it is clear that the difference between 90 in the 'Roller-coaster pattern' and 60 in the 'Volatile growth' trend is 30, which is larger than any other activities such as {Outdoor Fitness}<r>[0.055, 0.919, 0.099, 0.942]</r>, {Home Cooking}<r>[0.408, 0.919, 0.452, 0.942]</r>, and {Social Media}<r>[0.937, 0.919, 0.981, 0.942]</r>.\nBased on the above thought, the answer is:\nThe social activity that shows the largest difference between the two trends is 'Inside Workout', with a 30% difference in values between the \"Roller-coaster pattern\" and the \"Volatile growth\" trend. This is more substantial than the variations seen in other categories."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "6T9KafC4ffhAIiKG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage difference in Home Cooking values between the two trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to identify the values associated with 'Home Cooking' in both trend types. The values can be found based on {the bars heights for 'Home Cooking'}<r>[0.408, 0.919, 0.452, 0.942]</r> in both trends. Their values are 50 and 5 for 'Roller-coaster pattern' and 'Volatile growth', respectively. The percentage difference is given by (Value1 - Value2) / Value1 * 100% which yields (50-5)/50*100% = 90%.\nBased on the above thought, the answer is:\n90%."
+            }
+        ],
+        "id_raw": 192
+    },
+    {
+        "id": "yMj0duFKjT2vyxic",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me generate a table from the line chart data based on the Resource Types as rows and the associated Energy Consumption as values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to achieve this, I will first extract the labels for each resource from {the x-axis tick labels}<r>[0.105, 0.919, 0.116, 0.942]</r> which shows \"Coal\", \"Gas\", \"Oil\", \"Solar\", \"Hydro\", \"Nuclear\". Moving on, I pick all the {points on the line}<r>[0.110, 0.146, 0.976, 0.785]</r> which correspond to the energy consumption for each resource type. The values are: 64000, 44000, 58000, 10000, 17000, 51000.\nBased on the above thought, the answer is:\nResources,Energy Consumption\nCoal,64000\nGas,44000\nOil,58000\nSolar,10000\nHydro,17000\nNuclear,51000"
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "mmHL7YTQumoLEhkS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the energy consumption, what can be inferred about the sustainability of these resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the amount of energy consumption associated with each resource type. By analysing the {points on the line}<r>[0.110, 0.146, 0.976, 0.785]</r>, it's clear that Coal has the highest energy consumption, making it a dominant energy resource. However, it's vital to remember that coal is a non-renewable and highly polluting source of energy, which questions its sustainability in the long run. On the other hand, Solar energy has the least energy consumption, but it's a renewable and cleaner source of energy. Even though its current absorption might be low compared to others, it's a sustainable solution for the future.\nBased on the above thought, the answer is:\nThe analysis of energy consumption indicates that while Coal is a significant source of energy, its high environmental impact and non-renewable nature make it unsustainable in the long run. On the other side, Solar, with its less consumption, signifies low utilisation currently, but considering it's renewable and environmentally friendly, it holds immense potential for sustainable energy in the future."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "j3TJVRgyKiFjl28f",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in energy consumption between the most and least energy consuming resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to figure out the maximum and minimum values for energy consumption. If I examine the {points on the line}<r>[0.110, 0.146, 0.976, 0.785]</r>, I see that the maximum value is 64000, corresponding to Coal, and the minimum value is 10000, corresponding to Solar. Hence, the difference in energy consumption between the most and least consuming resources is 64000 - 10000 = 54000.\nBased on the above thought, the answer is:\n54000."
+            }
+        ],
+        "id_raw": 193
+    },
+    {
+        "id": "oCB7c93uGDZ6tyhL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table showing the popularity scores for each cuisine from the years 2006 to 2020 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I review the x-axis, specifically {the x-axis tick labels}[[0.053, 0.919, 0.088, 0.942], [0.181, 0.919, 0.216, 0.942], [0.309, 0.919, 0.344, 0.942], [0.437, 0.919, 0.473, 0.942], [0.566, 0.919, 0.601, 0.942], [0.694, 0.919, 0.729, 0.942], [0.822, 0.919, 0.857, 0.942], [0.950, 0.919, 0.985, 0.942]] to confirm the years. Then, I identify each cuisine trend by analyzing {the line for Italian}<r>[0.071, 0.199, 0.968, 0.737]</r>, {the line for French}<r>[0.071, 0.116, 0.968, 0.655]</r>, {the line for Chinese}<r>[0.071, 0.241, 0.968, 0.779]</r>, {the line for Japanese}<r>[0.071, 0.282, 0.968, 0.820]</r>, and {the line for Mexican}<r>[0.071, 0.323, 0.968, 0.861]</r>, tracking the points along these lines to extract the popularity scores for each year.\nBased on the above thought, the answer is:\nYear,Italian,French,Chinese,Japanese,Mexican\n2006,40,50,35,30,25\n2007,45,55,38,35,30\n2008,50,60,45,42,35\n2009,55,62,50,45,40\n2010,57,65,52,48,45\n2011,60,70,55,50,48\n2012,65,75,60,55,50\n2013,70,80,65,60,55\n2014,75,85,70,65,60\n2015,80,90,75,70,65\n2016,85,95,80,75,70\n2017,90,100,85,80,75\n2018,95,105,90,85,80\n2019,100,110,95,90,85\n2020,105,115,100,95,90"
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "LBvApHyHvEBywgEy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine shows the most consistent upward trend in popularity over the years as per the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most consistent upward trend, I review the slope and trend of each line. Observing {the line for Italian}<r>[0.071, 0.199, 0.968, 0.737]</r>, {the line for French}<r>[0.071, 0.116, 0.968, 0.655]</r>, {the line for Chinese}<r>[0.071, 0.241, 0.968, 0.779]</r>, {the line for Japanese}<r>[0.071, 0.282, 0.968, 0.820]</r>, and {the line for Mexican}<r>[0.071, 0.323, 0.968, 0.861]</r>, I notice that all lines show upward movements. Italian cuisine's line, however, not only rises but does so with the least fluctuations, indicating a steady increase.\nBased on the above thought, the answer is:\nItalian cuisine shows the most consistent upward trend in popularity over the years according to the line chart. Its trajectory is smooth and continuous, with a consistent rate of increase from 2006 to 2020."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "2NbEzExIn1MbTVxf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in popularity score for Italian cuisine from 2006 to 2020 based on the data represented in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase, I examine the start and end points for {the line representing Italian cuisine}<r>[0.071, 0.737, 0.968, 0.199]</r>. The starting point in 2006 shows a popularity score of 40, and the ending point in 2020 shows a score of 105. The increase is calculated by subtracting the initial score from the final score.\nBased on the above thought, the answer is:\n65."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "Plb8LHYQ6Nbdd29v",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the line graph data detailing the energy utilization over the years (2000-2020) into a structured table. The columns should represent the different years, and the rows should correspond to the various types of energy sources."
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.044, 0.939, 0.079, 0.957], [0.271, 0.939, 0.306, 0.957], [0.497, 0.939, 0.532, 0.957], [0.724, 0.939, 0.759, 0.957], [0.950, 0.939, 0.985, 0.957]] as the column headers representing the years. I next identify {the legend texts}[[0.113, 0.062, 0.240, 0.080], [0.113, 0.088, 0.228, 0.106], [0.113, 0.115, 0.288, 0.132],  [0.113, 0.141, 0.340, 0.158]] as the row labels representing each of the energy sources: Coal consumption, Oil consumption, Natural gas consumption, and Hydrogen fuel cell consumption, respectively. Finally, I fill in the percentage consumption values corresponding to each energy source for each year from the data points labeled on {the lines}[[0.062, 0.102, 0.968, 0.212], [0.062, 0.421, 0.968, 0.487], [0.062, 0.542, 0.968, 0.817], [0.062, 0.751, 0.968, 0.916]].\nBased on the above thought, the answer is:\nType of Energy,2000,2005,2010,2015,2020\nCoal Consumption,65,68,70,72,75\nOil Consumption,40,42,44,46,45\nNatural Gas Consumption,10,15,20,28,35\nHydrogen Fuel Cell Consumption,1,2,5,10,16"
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "BvAWSGL5zzuy2WBS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart showing the consumption of various energy sources from 2000-2020, which energy source has shown the fastest rate of increase and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to ascertain which energy source has seen the most increase over 2000-2020, I compare the growth rates of the four sources by looking at the {coal line}[[0.062, 0.102, 0.968, 0.212]], {oil line}[[0.062, 0.421, 0.968, 0.487]], {natural gas line}[[0.062, 0.542, 0.968, 0.817]] and the {hydrogen fuel cell line}[[0.062, 0.751, 0.968, 0.916]]. By doing so, I see that while all sources have increased to some degree, the hydrogen fuel cell's consumption rises most steeply over the 20 years, from 1% in 2000 to 16% in 2020.\nBased on the above thought, the answer is:\nThe consumption of Hydrogen Fuel Cell Consumption has shown the fastest rate of increase from 2000 to 2020. It started at 1% in 2000 and rose to 16% by 2020, representing a 16-fold increase over the time period. The significant increase can be owed to advancements in technology and efforts put forth to shift to environmentally friendly energy sources."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "iIceNLNzvZVstaAX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much difference in % consumption was there between coal and oil in the year 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in % consumption between coal and oil in 2005, I look at the {data point for coal in 2005}[0.288, 0.179] which corresponds to a value of 68 and then at the {data point for oil in 2005}[0.288, 0.465] which corresponds to a value of 42. Therefore, the difference is 68 - 42 = 26.\nBased on the above thought, the answer is:\n26."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "P45RVMKeM09N0jVo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the energy consumption data displayed in the chart into a table format where each energy source corresponds to a unique column and the years are placed in rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first take note of the {x-axis tick labels}<r>[0.066, 0.919, 0.110, 0.942]</r> that represent the years which are the row headers. Subsequently, I identify the {legend texts}<r>[0.258, 0.030, 0.297, 0.053]</r> as the column labels that signify different energy sources. Lastly, I scan each line plot such as {Coal's line points}<r>[0.088, 0.241, 0.959, 0.489]</r>, {Hydropower's line points}<r>[0.088, 0.133, 0.959, 0.323]</r>, {Nuclear's line points}<r>[0.088, 0.241, 0.959, 0.365]</r>, and {Green energy's line points}<r>[0.088, 0.323, 0.959, 0.655]</r> to fill in the table cells indicating the respective energy consumption for each year.\nBased on the above thought, the answer is:\nYear,Coal,Hydropower,Nuclear,Green Energy\n2010,80,70,65,30\n2011,75,74,66,32\n2012,70,77,68,35\n2013,69,80,70,37\n2014,67,83,72,40\n2015,66,86,74,45\n2016,64,89,75,50\n2017,62,90,76,55\n2018,58,91,78,60\n2019,52,92,80,65\n2020,50,93,80,70"
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "gcrXChCONF156Zkc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graphs, which source of energy has shown the most significant increase over the decade? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which source of energy has the greatest increase over the decade, I inspect each line individually. {Coal's line}<r>[0.088, 0.241, 0.959, 0.489]</r> generally shows a declining trend from 80 to 50; {Hydropower's line}<r>[0.088, 0.133, 0.959, 0.323]</r> exhibits an upward trend from 70 to 93; {Nuclear's line}<r>[0.088, 0.241, 0.959, 0.365]</r> shows a minimal rise from 65 to 80; and {Green Energy's line}<r>[0.088, 0.323, 0.959, 0.655]</r> shows a predominantly rising trend from 30 to 70. Comparing the differences, it's evident that Green Energy has the most substantial increase.\nBased on the above thought, the answer is:\nFrom the line graphs, it is apparent that Green Energy has experienced the most significant increase over the decade, rising from 30% at the beginning of the decade to 70% by the end. This trend signifies a stark 133% increase in consumption from 2010 to 2020, the most significant change compared to other energy sources."
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "3m2Z8qdHvZQ5Eine",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage change in energy consumption do we observe in Nuclear power from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage change in Nuclear energy consumption, I first find the consumption percentage in 2010 at {Nuclear's line for 2010}<r>[0.088, 0.365]</r>, which is 65. I then find the consumption percentage in 2020 at {Nuclear's line for 2020}<r>[0.959, 0.241]</r>, which is 80. To find the percentage change, I subtract the initial percentage from the final percentage and divide it by the initial percentage, then multiply it by 100.\nBased on the above thought, the answer is:\n23.08%"
+            }
+        ],
+        "id_raw": 196
+    },
+    {
+        "id": "WQaAXCw4YTJ4kX4f",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format with Technologies as rows and Years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a table, I'll start by looking at the {x-axis tick labels}[[0.228, 0.919, 0.239, 0.942], [0.356, 0.919, 0.378, 0.942], [0.489, 0.919, 0.511, 0.942], [0.622, 0.919, 0.644, 0.942], [0.756, 0.919, 0.778, 0.942]], these represent the column headers or years. Next, I identify {the legend texts}[[0.087, 0.030, 0.241, 0.053], [0.324, 0.030, 0.538, 0.053], [0.621, 0.030, 0.765, 0.053], [0.849, 0.030, 0.962, 0.053]] as the row labels representing different technologies. To populate the values, I locate {the line points} for each technology at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nTechnology,5 Years,10 Years,15 Years,20 Years,25 Years\nAI in Radiography,78,85,89,92,93\nElectronic Health Record,68,70,71,75,78\nAI in Diagnostics,45,60,70,76,86\nTelemedicine,30,42,61,74,89"
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "sqc68nXr8utdC5FE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which technology seems to have the most growth over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which technology has experienced the most growth, I analyze the line plots for each technology. Observing {the line for AI in Radiography}[0.233, 0.257, 0.767, 0.133], we note a gradual increase from 78 to 93. For {Electronic Health Record}[0.233, 0.340, 0.767, 0.257], there is a mild increase from 68 to 78. For {AI in Diagnostics}[0.233, 0.530, 0.767, 0.191], the line displays a substantial increase from 45 to 86. Meanwhile, for {Telemedicine}[0.233, 0.655, 0.767, 0.166], there is a dramatic rise from 30 to 89 over the years.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Telemedicine seems to have experienced the most growth over time. The line plot exhibits a marked increase from 30 at 5 Years to 89 at 25 Years. Although AI in Diagnostics also shows substantial growth, the surge in Telemedicine is more pronounced, indicating a higher rate of increase over time."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "4JiSxCyQ3wnTww7l",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum adoption rates for AI in Diagnostics over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum adoption rates for AI in Diagnostics, I need to locate {the line points for AI in Diagnostics}[[0.233, 0.530], [0.367, 0.406], [0.500, 0.323], [0.633, 0.274], [0.767, 0.191]] and extract the corresponding values: 45, 60, 70, 76, 86. The maximum value is 86 at {25 Years}[0.767, 0.919] and the minimum value is 45 at {5 Years}[0.233, 0.919]. So, the difference between the maximum and minimum is 86-45 = 41.\nBased on the above thought, the answer is:\n41."
+            }
+        ],
+        "id_raw": 197
+    },
+    {
+        "id": "fTEPsxqQM0Q75cNm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table showing the annual values of E-commerce, Manufacture, Software Development, and Robotics from 2004 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To format the data from the chart into a table, I identified {the x-axis tick labels}[[0.099, 0.919, 0.143, 0.942], [0.938, 0.919, 0.981, 0.942]], which signify the years from 2004 to 2020. Then I located {the legend items}[[0.195, 0.030, 0.304, 0.053], [0.387, 0.030, 0.497, 0.053], [0.580, 0.030, 0.777, 0.053], [0.860, 0.030, 0.934, 0.053]] for sectors like E-commerce, Manufacture, Software Development, and Robotics. By analyzing {the line points}[[0.121, 0.215, 0.959, 0.712]], I extracted the values for each sector in each year.\nBased on the above thought, the answer is:\nYear,E-commerce,Manufacture,Software Development,Robotics\n2004,50000,70000,50000,30000\n2005,52000,67000,52000,34000\n2006,53000,64000,57000,50000\n2007,55000,60000,58000,61000\n2008,59000,59000,60000,62000\n2009,60000,58000,64000,70000\n2010,65000,57000,68000,85000\n2011,70000,54000,70000,90000\n2012,75000,51000,72000,95000\n2013,80000,50000,75000,98000\n2014,85000,48000,78000,100000\n2015,90000,47000,80000,105000\n2016,95000,46000,82000,110000\n2017,99000,45000,84000,116000\n2018,102000,44000,86000,120000\n2019,105000,43000,88000,122000\n2020,108000,42000,90000,124000"
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "xHHi2xuqotVNkEx4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows a consistent improvement over the years based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand which sector shows consistent improvement, I analyzed {the line plots}[[0.121, 0.215, 0.959, 0.584], [0.121, 0.457, 0.959, 0.635], [0.121, 0.330, 0.959, 0.584], [0.121, 0.113, 0.959, 0.712]]. E-commerce, Manufacture, and Software Development lines are analyzed for their trajectory and the data points confirmed a gradual increase over time for all. However, {the line for Robotics}[0.121, 0.113, 0.959, 0.712] not only starts gradually increasing from 30,000 to 124,000 but also shows steeper ascents compared to other sectors, hinting at more considerable improvements.\nBased on the above thought, the answer is:\nRobotics shows the most consistent and substantial improvement over the years based on the chart, as highlighted by its steeper and higher rising plot line compared to other sectors."
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "yanRtzds2Wr4n0Qb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth of the Robotics sector from 2004 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage growth, I locate {the line points for Robotics}[[0.121, 0.712], [0.959, 0.113]]. I extract the values for Robotics in 2004, which is 30,000, and for 2020, which is 124,000. The growth is calculated as ((124,000 - 30,000) / 30,000) * 100.\nBased on the above thought, the answer is:\n313.33%"
+            }
+        ],
+        "id_raw": 198
+    },
+    {
+        "id": "W9Zvv4LcqN0fUKyA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you organize the chart data into a tabular structure with Years as columns and Income Groups as rows, and then complete the table with the corresponding Living Condition Quality values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, let's first look at {the x-axis tick labels}<r>[0.053, 0.931, 0.088, 0.951], [0.277, 0.931, 0.312, 0.951], [0.502, 0.931, 0.537, 0.951], [0.726, 0.931, 0.761, 0.951], [0.950, 0.931, 0.985, 0.951]</r> which represent the different years (2000, 2005, 2010, 2015, 2020) to be used as column headers. The row headers or the different Income groups (Low-Income Households, Middle-Income Households, High-Income Households, Average Households) can be identified from {the legend texts}<r>[0.768, 0.047, 0.936, 0.067], [0.768, 0.077, 0.955, 0.097], [0.768, 0.107, 0.941, 0.127], [0.768, 0.137, 0.910, 0.157]</r>. Finally, the Living Condition Quality values for each income group, for each year, are extracted by locating the corresponding {points on the line plots}<r>[0.071, 0.899], [0.295, 0.775], [0.519, 0.739], [0.743, 0.509], [0.968, 0.367], [0.071, 0.526], [0.295, 0.420], [0.519, 0.384], [0.743, 0.296], [0.968, 0.242], [0.071, 0.118], [0.295, 0.065], [0.519, 0.171], [0.743, 0.189], [0.968, 0.242], [0.071, 0.562], [0.295, 0.420], [0.519, 0.402], [0.743, 0.313], [0.968, 0.260]</r>.\nBased on the above thought, the answer is:\nIncome_Group,2000,2005,2010,2015,2020\nLow-Income Households,51,58,60,73,81\nMiddle-Income Households,72,78,80,85,88\nHigh-Income Households,95,98,92,91,88\nAverage Households,70,78,79,84,87"
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "v04KL3C7y0buIjnV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich income group experienced the least improvement in Living Condition Quality over the years as shown in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which income group experienced the least improvement in Living Condition Quality over the years, we need to examine {the line plots}<r>[0.071, 0.367, 0.968, 0.899], [0.071, 0.242, 0.968, 0.526], [0.071, 0.065, 0.968, 0.242], [0.071, 0.260, 0.968, 0.562]</r>. We need to compare the improvement (or change) in Living Condition Quality for each group from the year 2000 to the year 2020. These would be the differences between the y-values of the start and end points of each line. From the evidence, it is clear that {High-Income Households}<r>[0.071, 0.118, 0.968, 0.242]</r> experienced the least improvement, as its start point is 95 and the endpoint is 88, showing even a decrease in the Living Condition Quality over the years.\nBased on the above thought, the answer is:\nThe High-Income Households group experienced the least improvement in Living Condition Quality over the years. In fact, the chart data indicates a decrease in the quality of living conditions for this group from 2000 to 2020."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "DJtnHXeJVNm6nF2N",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_10_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Living Condition Quality for Low-Income Households improve between 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the improvement in Living Condition Quality for Low-Income Households between 2000 and 2020, we need to locate {the point for the year 2000}<r>[0.071, 0.899]</r> and {the point for the year 2020}<r>[0.968, 0.367]</r> on {the line plot for Low-Income Households}<r>[0.071, 0.367, 0.968, 0.899]</r>. The values corresponding to these points are 51 for 2000 and 81 for 2020. Then, we need to calculate the difference between these values, which is 81 - 51 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "wf3LO6uLB6isPGIk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, could you extract the information regarding the performance of Brand A and Brand B at different stages into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a tabular format, firstly, I should look at the {x-axis labels}<r>[0.070, 0.911, 0.155, 0.934], [0.342, 0.911, 0.435, 0.934], [0.610, 0.911, 0.717, 0.934], [0.900, 0.911, 0.979, 0.934]</r> which represent the different stages. They will form the rows of the table. Then, considering the {legend labels} <r>[0.900, 0.091, 0.966, 0.121], [0.900, 0.134, 0.966, 0.164]</r>, we can see these are the two Brands that will become the columns. Then, looking at the line graph points for {Brand A} <r>[0.113, 0.116], [0.388, 0.363], [0.664, 0.610], [0.939, 0.733]</r> and {Brand B} <r>[0.113, 0.239], [0.388, 0.610], [0.664, 0.671], [0.939, 0.795]</r>, I can extract these digits to fill the table cells for related brands and stages.\nBased on the above thought, the answer is:\n,BrandA,BrandB\nInitial_Stage, 0.8, 0.7\nMiddle_Stage, 0.6, 0.4\nPre-Final_Stage, 0.4, 0.35\nFinal_Stage, 0.3, 0.25"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "KOis4TuRVgdsp1UH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which brand shows a more consistent decrease in performance over the stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to observe the slope of the lines representing each brand. For {Brand A} <r>[0.113, 0.116, 0.939, 0.733]</r>, the line slope is negative throughout all stages, indicating a consistent decrease in performance. In contrast, for {Brand B} <r>[0.113, 0.239, 0.939, 0.795]</r>, the line slope is also overall negative but not as steep as for Brand A, indicating a slower and less consistent decrease in performance.\nBased on the above thought, the answer is:\nAccording to the line chart, Brand A shows a more consistent decrease in performance over the stages. Its negative slope throughout all stages indicates a steady decline, unlike Brand B whose slope is also negative but less steep, indicating a slower and less consistent decrease."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "0mkOsjl50gP3tVQL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in performance for Brand A from the Initial Stage to the Final Stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to compute the difference in performance for Brand A from the Initial Stage to the Final Stage, I need to locate {the line point for Brand A at the Initial Stage} <r>[0.113, 0.116]</r> and {the line point for Brand A at the Final Stage} <r>[0.939, 0.733]</r>. The performance at the Initial Stage is 0.8 and that at the Final Stage is 0.3. Therefore, the change in performance is 0.3 - 0.8 = -0.5.\nBased on the above thought, the answer is:\n-0.5"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "YB2LHj0D6t92ixMh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform this line chart data into a structured table where the columns are the Years and the rows represent the Stock and the different fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the tabular data from the line chart, I'll first find {the x-axis tick labels}[[0.157, 0.913, 0.179, 0.936], [0.333, 0.913, 0.355, 0.936], [0.509, 0.913, 0.531, 0.936], [0.685, 0.913, 0.707, 0.936], [0.861, 0.913, 0.883, 0.936]], which show the column labels or Years. Then, I'll identify the {legend texts}[[0.133, 0.091, 0.227, 0.121], [0.133, 0.134, 0.229, 0.164]] as the row labels representing the different Stocks. Each of these Stocks has different fields we're interested in, such as Ratios, Performance, and Error. The table values are located at the line points for each Stock and their respective fields at the corresponding Years.\nBased on the above thought, the answer is:\nYear,Ratios_AAPL,Performance_AAPL,Error_AAPL,Ratios_MSFT,Performance_MSFT,Error_MSFT\n0.1,0.1,0.70,0.12,0.1,0.65,0.11\n0.2,0.2,0.64,0.11,0.2,0.72,0.10\n0.4,0.4,0.60,0.10,0.4,0.78,0.08\n0.6,0.6,0.58,0.09,0.6,0.83,0.07\n0.8,0.8,0.53,0.06,0.8,0.86,0.05\n1.0,1.0,0.50,0.05,1.0,0.90,0.04"
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "ArHa4cuiloprNkFI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which stock shows a more stable Performance relative to changes in the Investment Ratio?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the stock with the more stable Performance, I need to analyze the changes in line plots for the two stocks. Looking at {the line for AAPL Stock}[0.159, 0.315, 0.948, 0.482], there is a steady decrease in Performance as the Investment Ratio increases. For {MSFT Stock}[0.159, 0.357, 0.948, 0.148], there is a more pronounced upward trend indicating a high change in Performance with respect to the changes in the Investment Ratio. Despite fluctuations, the rate of change in Performance for AAPL Stock is less compared to MSFT Stock.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, AAPL Stock shows a more stable Performance relative to changes in the Investment Ratio. Its line plot exhibits less fluctuation in comparison to MSFT's, as it gradually decreases with the increase in the Investment Ratio. The less pronounced changes in Performance for AAPL Stock in connection to Investment Ratio changes reflect a more stable trend compared to the fluctuations in MSFT's Performance."
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "WT7UP5ZmRN9h99pL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Performance at an Investment Ratio of 1.0 between AAPL and MSFT?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the cardinals, I have to refer to the {line points for AAPL at Investment Ratio 1.0}[0.948, 0.482] and {line points for MSFT at Investment Ratio 1.0}[0.948, 0.147] to extract the corresponding performance values for AAPL and MSFT. Once I have these values, I can calculate the difference between the two.\nBased on the above thought, the answer is:\n0.4"
+            }
+        ],
+        "id_raw": 204
+    },
+    {
+        "id": "SB9CQBKXxjOyqOyx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the changes in Design Innovation Score corresponding to the different values of the Creativity Index into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis tick labels}[[0.060, 0.911, 0.082, 0.934], [0.235, 0.911, 0.257, 0.934], [0.410, 0.911, 0.432, 0.934], [0.585, 0.911, 0.607, 0.934], [0.760, 0.911, 0.782, 0.934], [0.935, 0.911, 0.957, 0.934]] as the different values of the Creativity Index to be represented as the column headers. Then, I find {the line points}[[0.159, 0.231], [0.246, 0.314], [0.334, 0.397], [0.421, 0.480], [0.508, 0.439], [0.596, 0.563], [0.683, 0.604], [0.771, 0.687], [0.858, 0.770], [0.946, 0.853]] corresponding to the Design Innovation Score at each Creativity Index position. Finally, I can generate a table mapping each Creativity Index value to its corresponding Design Innovation Score.\nBased on the above thought, the answer is:\nCreativity Index,Design Innovation Score\n0.1,0.8\n0.2,0.7\n0.3,0.6\n0.4,0.5\n0.5,0.55\n0.6,0.4\n0.7,0.35\n0.8,0.25\n0.9,0.15\n1.0,0.05"
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "WtGevKyeXRiyva2I",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat interpretation can be made about the relationship between the Creativity Index and the Design Innovation Score based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the relationship between the Creativity Index and the Design Innovation Score, I need to examine {the trend of the line}[[0.159, 0.231, 0.946, 0.853]]. The line describes an initial decrement pattern in Design Innovation Score as the Creativity Index increases from 0.1 to 0.4. There is a slight increase going from 0.4 to 0.5 but overall continues to decrease after reaching 0.5 to 1.0. This suggests a generally negative correlation between the Creativity Index and the Design Innovation Score.\nBased on the above thought, the answer is:\nBased on the chart, it can be deduced that there is, generally, an inversely proportional relationship between the Creativity Index and the Design Innovation Score. As the Creativity Index increases, the Design Innovation Score tends to decrease. This can suggest that perhaps as more creative approaches are engaged (high Creativity Index), it could potentially complicate the innovation process thereby reducing the Design Innovation Score."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "nWmUQB7EDzemT5t7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the Design Innovation Score as evidenced by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the range of the Design Innovation Score, I first locate {the line points}[[0.159, 0.231], [0.246, 0.314], [0.334, 0.397], [0.421, 0.480], [0.508, 0.439], [0.596, 0.563], [0.683, 0.604], [0.771, 0.687], [0.858, 0.770], [0.946, 0.853]] to extract the corresponding scores: 0.8, 0.7, 0.6, 0.5, 0.55, 0.4, 0.35, 0.25, 0.15, 0.05. The highest score is 0.8 and the lowest score is 0.05. The range of the Design Innovation Score is therefore 0.8 - 0.05 = 0.75.\nBased on the above thought, the answer is:\n0.75."
+            }
+        ],
+        "id_raw": 205
+    },
+    {
+        "id": "gqFra2aB99lEfTvl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart showcasing the scores of AI Efficiency and Machine Learning Progress over time into a table format with Time Units as columns and each aspect as a row?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the chart, I need to interpret the data from the x-axis where {the time units tick labels}<r>[0.060, 0.911, 0.082, 0.934], [0.235, 0.911, 0.257, 0.934], ..., [0.935, 0.911, 0.957, 0.934]</r> represent the headers. Observing both line plots, {the points for AI Efficiency}<r>[0.159, 0.148], [0.246, 0.182], ..., [0.946, 0.422]</r> and {the points for Machine Learning Progress}<r>[0.159, 0.397], [0.246, 0.364], ..., [0.946, 0.480]</r> contain the necessary data values. Each point precisely corresponds to their score values at every unit of time.\nBased on the above thought, the answer is:\nTime Units,0,1,2,3,4,5,6,7\nAI Efficiency,0.92,0.86,0.82,0.79,0.76,0.74,0.71,0.66\nMachine Learning Progress,0.6,0.64,0.67,0.71,0.71,0.68,0.63,0.6"
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "umjEMF6YWgayuop2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the general trend observed in the scores of Machine Learning Progress over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trend for Machine Learning Progress requires inspecting {the points}<r>[0.159, 0.397], [0.946, 0.480]</r> across the chart. This line shows a fluctuation in scores moving slightly upwards as time progresses with a local increase in the middle of the timeline followed by a consistent recover towards the end. Therefore, the general trend can be described as a \"slow recovery\" throughout the units of time.\nBased on the above thought, the answer is:\nThe general trend observed in Machine Learning Progress is a slow recovery. Initially, there are minor fluctuations with an overall slight upward trajectory, and despite a small drop mid-way, the final stages see stable recovery towards previous higher scores."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "rCyK9ENSShvWjiWU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the score for AI Efficiency decreased from the first to the last unit of time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the decrease in the score for AI Efficiency from the first to the last time unit, I consult {the data points for AI Efficiency}<r>[0.159, 0.148], [0.946, 0.422]</r> which represent the values at the start and end of the timeline. The decrease is calculated as the difference between the initial value at time unit 0 (0.92) and the final value at time unit 7 (0.66).\nBased on the above thought, the answer is:\n0.26"
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "pPwr2Kpv8rL335ts",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the information in the chart, could you tabulate the changes in latitude and longitude for each location?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I first identify {the x-axis labels}[{'label': 'New York', 'label_coordinates': '[0.092, 0.911, 0.156, 0.934]'},  {'label': 'San Francisco', 'label_coordinates': '[0.167, 0.911, 0.263, 0.934]'}, {'label': 'Atlanta', 'label_coordinates': '[0.281, 0.911, 0.331, 0.934]'}, {'label': 'Chicago', 'label_coordinates': '[0.369, 0.911, 0.425, 0.934]'}, {'label': 'Seattle', 'label_coordinates': '[0.464, 0.911, 0.513, 0.934]'}, {'label': 'New Orleans', 'label_coordinates': '[0.535, 0.911, 0.624, 0.934]'}, {'label': 'Denver', 'label_coordinates': '[0.645, 0.911, 0.696, 0.934]'}, {'label': 'Phoenix', 'label_coordinates': '[0.734, 0.911, 0.789, 0.934]'}, {'label': 'Boston', 'label_coordinates': '[0.829, 0.911, 0.877, 0.934]'}, {'label': 'Miami', 'label_coordinates': '[0.923, 0.911, 0.965, 0.934]'}] as the row labels representing the different locations. Looking at the line graphs of {latitude changes}['coordinates': '[0.124, 0.103, 0.944, 0.415]'] and {longitude changes}['coordinates': '[0.124, 0.578, 0.944, 0.857]'], I map these to the respective location. Using the y-axis labels, I determine the changes in latitude and longitude.\nBased on the above thought, the answer is:\nLocation,Latitude Changes,Longitude Changes\nNew York,0.08,-0.05\nSan Francisco,0.15,-0.1\nAtlanta,0.05,-0.12\nChicago,0.2,-0.08\nSeattle,0.17,-0.14\nNew Orleans,0.12,-0.098\nDenver,0.14,-0.18\nPhoenix,0.11,-0.15\nBoston,0.24,-0.13\nMiami,0.18,-0.22"
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "lAwvpZOJ0pd9E0D3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which location undergoes the most drastic changes in geographical coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which location undergoes the most drastic changes, I need to observe {the lines for latitude and longitude changes}['line_0':'coordinates': '[0.124, 0.103, 0.944, 0.415]', 'line_1': 'coordinates': '[0.124, 0.578, 0.944, 0.857]'] and see where the points on both lines show significant changes. This means that both the latitude and the longitude values should be more extreme, closer to the edges of the Y axis. A drastic change means a large overall change in both latitude and longitude.\nBased on the above thought, the answer is:\nMiami appears to undergo the most drastic changes in geographical coordinates. Its points are the farthest on both the latitude and longitude change lines, indicating a large overall change in both coordinates relative to other locations."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "MnfhSdY5rtCIqM4O",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line graphs, what is the greatest difference between latitude and longitude changes observed in the locations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the greatest difference, I would analyze the points on each of the two lines representing {latitude changes}['coordinates': '[0.124, 0.103, 0.944, 0.415]'] and {longitude changes}['coordinates': '[0.124, 0.578, 0.944, 0.857]']. Taking the difference between each corresponding pairs and finding the maximum value would give me the answer.\nBased on the above thought, the answer is:\n0.13."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "f471pgv4phSJRR0a",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing research growth trends across the six academic fields into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accurately convert the data into a structured table, I first examine the x-axis labels which include the three trends: Unstable, Steady Increase, and Rapid Increase. These are represented in {x-axis tick labels}[[0.1, 0.9, 0.17, 0.93], [0.30, 0.9, 0.37, 0.93], [0.50, 0.9, 0.67, 0.93]]. The research fields are detailed in the legend: Biology, Physics, Chemistry, Computer Science, Mathematics, and Economics, with their respective colors indicated for clarity. Each field's data is plotted across these trends, where I can derive data points from their coordinates {line points for Biology}[[0.15, 0.2], [0.35, 0.6], [0.55, 0.85]], {line points for Physics}[[0.15, 0.4], [0.35, 0.65], [0.55, 0.75]], and so forth.\nBased on the above thought, the answer is:\nTrend,Biology,Physics,Chemistry,Computer Science,Mathematics,Economics\nUnstable,0.2,0.4,0.45,0.5,0.55,0.4\nSteady Increase,0.6,0.65,0.7,0.68,0.7,0.65\nRapid Increase,0.85,0.75,0.8,0.85,0.9,0.7"
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "sKpSvpu5TE5cvtru",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich academic research field shows the most consistent upward trend across all growth stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the consistency of the upward trend, I analyze the progression of each field's plot in the chart {line for Biology}[0.1, 0.2, 0.7], {line for Mathematics}[0.1, 0.55, 0.9]. Observing the coordinates, Mathematics starts at a higher baseline and consistently reaches the highest point in the final trend compared to other fields. Biology, although starting lower, also shows a strong consistent increase, but not to the same extent as Mathematics.\nBased on the above thought, the answer is:\nThe field of Mathematics demonstrates the most consistent upward trend, showing substantial growth from start to finish and reaching the highest value in the Rapid Increase trend compared to other fields."
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "ZTPXGyqsyJkybkyo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in research growth for the field of Computer Science across all trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I follow the line representing Computer Science, which holds data points {line points for Computer Science}[[0.15, 0.5], [0.35, 0.68], [0.55, 0.85]]. The values represented by these points are 0.5, 0.68, and 0.85 respectively. The average increase is then calculated by summing these values and dividing by the number of points, (0.5 + 0.68 + 0.85) / 3.\nBased on the above thought, the answer is:\n0.6767"
+            }
+        ],
+        "id_raw": 208
+    },
+    {
+        "id": "ThGeSG9FXpWgv3vg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list down the performance of each field of Big Data Integration based on the Size of Data in the form of a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to locate the x-axis labels representing {the Size of Data}[[0.110, 0.911, 0.149, 0.934], [0.509, 0.911, 0.565, 0.934], [0.925, 0.911, 0.964, 0.934]]. After that, I need to identify the legend that represents {the various fields of Big Data Integration}[[0.816, 0.399, 0.952, 0.429], [0.816, 0.443, 0.920, 0.473], [0.816, 0.487, 0.970, 0.517], [0.816, 0.531, 0.961, 0.561]]. Then, I will look at each field's coordinates that represent the data points for each Size of Data category [{Data Integration}[[0.130, 0.668], [0.537, 0.706], [0.944, 0.744]], {Data Quality}[[0.130, 0.103], [0.537, 0.179], [0.944, 0.292]], {Data Virtualization}[[0.130, 0.857], [0.537, 0.744], [0.944, 0.631]], {Data Governance}[[0.130, 0.593], [0.537, 0.668], [0.944, 0.781]]].\nBased on the above thought, the answer is:\nSize of Data,Data Integration,Data Quality,Data Virtualization,Data Governance\nSmall,0.8,0.95,0.75,0.82\nMedium,0.79,0.93,0.78,0.8\nLarge,0.78,0.9,0.81,0.77"
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "eIRNDdUcA8A2OBSq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field in Big Data Integration seems to demonstrate a distinct downward trend in performance as the data size increases? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to check the trends of all the fields in Big Data Integration. I can see that {Data Quality}[[0.130, 0.103], [0.537, 0.179], [0.944, 0.292]] presents a clear descending trend, with performance values dropping from 0.95 for \"Small\" data size to 0.9 for \"Large\" data size. This consistently decreasing pattern suggests a downward trend.\nBased on the above thought, the answer is:\nThe field of Data Quality in Big Data Integration demonstrates a clear downward trend in performance as the size of data increases. This can be deduced from the consistent decrease in performance values, which drops from 0.95 for small data size to 0.9 for large data size. Therefore, Data Quality seems to be negatively affected as the data size increases."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "RGo0YvrKjCdpOHt5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage change in performance of Data Virtualization from Small to Large size data?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate the percentage change, I will find the initial and final performance values of {Data Virtualization}[[0.130, 0.857], [0.944, 0.631]]. The initial value for \"Small\" size data is 0.75 and the final value for \"Large\" size data is 0.81. The Percentage change can be calculated using the formula ((Final - Initial) / |Initial|) * 100.\nBased on the above thought, the answer is:\n8%."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "xrAmzq0PhcnKJROk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing market share trends across different industries into a structured table format with Periods as columns and Industries as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first look at {the x-axis labels}<r>[0.095, 0.911, 0.130, 0.934], [0.187, 0.911, 0.223, 0.934], [0.280, 0.911, 0.315, 0.934], [0.372, 0.911, 0.407, 0.934], [0.464, 0.911, 0.500, 0.934]</r> which denote the periods, to serve as the column headers. I identify {the legend items}<r>[0.769, 0.091, 0.959, 0.121], [0.769, 0.134, 0.970, 0.164]</r> and note that they represent different industries as row labels. Then, I extract the values at each intersection of these industries and periods by analyzing {the line points for each industry}<r>[0.113, 0.339, 0.943, 0.853], [0.113, 0.381, 0.943, 0.870]</r>, each representing a different industry\u2019s performance at specified periods.\nBased on the above thought, the answer is:\nPeriod,Electronics,Automotive,Pharmaceuticals,Retail,Textiles\n1,0.15,0.13,0.18,0.12,0.09\n2,0.22,0.20,0.25,0.19,0.15\n3,0.34,0.33,0.37,0.28,0.20\n4,0.42,0.40,0.45,0.35,0.25\n5,0.45,0.43,0.50,0.38,0.28"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "QehQP3YlU2KwvbLd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the Textiles industry over the periods and what could be the potential implications of such a trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the line representing the Textiles industry}<r>[0.113, 0.485, 0.943, 0.765]</r>, there is a noticeable sharp decrease particularly after Period 4. Initially, the industry shows a gradual decline, but the sharp drop in the last period signifies a rapid loss in market share compared to others. This could imply that the Textiles industry is facing significant challenges that may include increased competition, changes in consumer preferences, or disruptions in supply chains, affecting its market presence adversely.\nBased on the above thought, the answer is:\nThe Textiles industry exhibits a significant downtrend, especially noticeable in the last period. This sharp decrease potentially indicates serious challenges within the industry, such as heightened competition, evolving consumer preferences, or operational disruptions, which could have critical implications for its future stability and market position."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "XLA64HMPbZGwtsu3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in market share for the Pharmaceuticals industry from Period 1 to Period 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in market share for the Pharmaceuticals industry, I need to observe {the starting and ending points of Pharmaceuticals' line}<r>[0.113, 0.317, 0.943, 0.539]</r>. The initial value at Period 1 is roughly 0.18, and the final value at Period 5 is approximately 0.50, which means I need to subtract the initial value from the final value.\nBased on the above thought, the answer is:\n0.32"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "OoR31zOj60BPbMNj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you organize the information into a table format with Career stages as columns and Job Role as Rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to formulate the tabular data from the line chart, I will initially need to take a look at {the x-axis tick labels}[[0.099, 0.910, 0.221, 0.939], [0.285, 0.910, 0.388, 0.939], [0.446, 0.910, 0.579, 0.939], [0.585, 0.910, 0.792, 0.939], [0.780, 0.910, 0.950, 0.939]], which will be the columns representing career stages. Then, I will identify {the sub-legend texts}[[0.695, 0.151, 0.862, 0.189], [0.695, 0.206, 0.877, 0.244]] as the rows depicting different job roles. To populate the cell values, I would locate {the line points for Data Analyst}[[0.160, 0.225], [0.336, 0.312], [0.512, 0.453], [0.689, 0.593], [0.865, 0.750]] and {the line points for Data Scientist}[[0.160, 0.155], [0.336, 0.243], [0.512, 0.347], [0.689, 0.505], [0.865, 0.680]] for each job role at the respective stages and extract the corresponding performance numbers.\nBased on the above thought, the answer is:\nRole,Entry Level,Mid Level,Senior Level,Management Level,Executive Level\nData Analyst,0.84,0.79,0.71,0.63,0.54\nData Scientist,0.88,0.83,0.77,0.68,0.58"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "Thl33ypdvc0zv3sj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend of Data Scientist performance based on the line chart across their Career Stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern the trend in Data Scientist performance, I will analyze {the line for Data Scientist}[0.160, 0.155, 0.865, 0.680]. The performance starts high at Entry Level {Entry Level}[0.160, 0.910], with a score of 0.88, and continues to decrease progressively through each of the following career stages - {Mid Level}[0.336, 0.910], {Senior Level}[0.512, 0.910], {Management Level}[0.689, 0.910], and {Executive Level}[0.865, 0.910] with scores of 0.83, 0.77, 0.68, and 0.58 respectively.\nBased on the above thought, the answer is:\nThe trend shows that the performance of a Data Scientist tends to decrease as they advance in their career stages. The performance starts at a high value in Entry Level and continually decreases until Executive Level. The reasoning behind the decrease could be due to the higher complexity of roles in advanced career stages, which might have lower performance scores due to increased responsibilities and expectations."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "oiFa0ZFkDiz0RAuB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average performance of a Data Analyst from Entry Level to Executive Level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average performance of a Data Analyst at all career stages, I need to identify {the line points for Data Analyst}[[0.160, 0.225], [0.336, 0.312], [0.512, 0.453], [0.689, 0.593], [0.865, 0.750]] and extract the corresponding values which are 0.84, 0.79, 0.71, 0.63, and 0.54. Then, I can calculate the average by summing up all the values and dividing it by the total number of career stages that are five in number. The average would therefore be (0.84 + 0.79 + 0.71 + 0.63 + 0.54) / 5.\nBased on the above thought, the answer is:\n0.702"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "aS0ySYUJ24kfC7XA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the line chart on Governmental Efficiency and Ease of Communication into a structured table format with Time Periods as columns and Indicators as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I focus on {the x-axis labels}<r>[0.084, 0.933, 0.113, 0.951]</r> representing the Time Periods. These are the column headers. The legend indicates two different lines, which are 'Governmental Efficiency' and 'Ease of Communication', identified as the row headers from {the legend items}<r>[0.107, 0.847, 0.262, 0.869]</r>. To populate the table, I analyze each line to capture the values at the respective time periods and combine these values into the desired table format.\nBased on the above thought, the answer is:\nTime Period,0,1,2,3,4\nGovernmental Efficiency,0.3,0.35,0.4,1.1,0.45\nEase of Communication,0.25,0.3,0.35,1.0,0.4"
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "tzmagXR1z5h6YQfa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the trend in 'Ease of Communication' across the time periods as shown in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line for 'Ease of Communication'}<r>[0.098, 0.400, 0.945, 0.511]</r>, there's an evident increase and peak at Time Period [3]. Initially, from Periods [0 to 2], there's gradual growth as the line slightly ascends. There's a significant spike in Period [3] indicating a peak point, which sharply drops back to a level similar to the starting periods in Period [4]. This outlines a surge suggesting a temporary but significant improvement in 'Ease of Communication'.\nBased on the above thought, the answer is:\n'Ease of Communication' shows a steady but modest increase from Periods [0 to 2], before experiencing a sharp incline and peaking in Period [3]. This peak signifies an abrupt and significant enhancement in communication, which however, reverts back to near initial levels in Period [4], indicating a steep drop. The trend suggests a volatile but progressive pattern with a focus on the stark peak at Period [3]."
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "Dd8greAVp5CJgZ2X",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the peak value for Governmental Efficiency during the Time Periods based on the chart analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the peak value for 'Governmental Efficiency', I examine the line corresponding to this indicator, which I identify at {the line for Governmental Efficiency}<r>[0.098, 0.372, 0.945, 0.502]</r>. The highest point is during Time Period '[3]', where the y-value hits approximately 1.1. I used the chart grid lines and axis scaling to ascertain this peak value.\nBased on the above thought, the answer is:\n1.1"
+            }
+        ],
+        "id_raw": 213
+    },
+    {
+        "id": "lmbOwXOIpIzI2ltD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table format listing each trend and their performance from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis ticks}<r>[0.095, 0.911, 0.961, 0.934]</r> where each tick represents each year from 2005 to 2020, and identifying {line colors}<r>[0.081, 0.668, 0.125, 0.687]</r>, <r>[0.081, 0.712, 0.125, 0.731]</r>, <r>[0.081, 0.800, 0.125, 0.819]</r> corresponding to different market trends. Extracting values from these lines across the years to form the table.\nBased on the above thought, the answer is:\nYear,Residential_Price_Index,Commercial_Price_Index,Rental_Rate_Index\n2005,100,100,100\n2006,105,102,101\n2007,110,108,103\n2008,108,130,105\n2009,100,100,100\n2010,103,105,102\n2011,105,110,104\n2012,110,115,107\n2013,115,120,110\n2014,120,125,115\n2015,125,130,120\n2016,130,135,122\n2017,135,140,125\n2018,140,145,127\n2019,145,150,130\n2020,150,160,132"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "A2Vxlj1OOp1am0Jh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the overall trend curves, which market showed the most stable growth from 2005 to 2020, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most stable growth, a close inspection of each line's fluctuation magnitude and consistency in their trajectory is necessary. Observing {the line for Residential_Price_Index}<r>[0.113, 0.421, 0.943, 0.252]</r>, it shows noticeable but relatively smooth and consistent increases throughout the years.\nOn the other hand, {the line for Commercial_Price_Index}<r>[0.113, 0.421, 0.943, 0.208]</r> demonstrates a sharp spike around 2008 followed by a steep decline in 2009, suggesting higher volatility compared to the residential market.\nAnalyzing {the line for Rental_Rate_Index}<r>[0.113, 0.421, 0.943, 0.267]</r>, it shows slight undulations that reflect minor fluctuations but maintains an overall stable upward trend.\nBased on the above thought, the answer is:\nThe Residential_Price_Index showed the most stable growth from 2005 to 2020, characterized by smooth and consistent increases with minor fluctuations, signaling a steady market compared to the more volatile commercial sector and the slightly undulating rental market."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "SrX1KzRLznZokSBA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Commercial_Price_Index grow from 2005 to 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with {the initial point for Commercial trend}<r>[0.113, 0.421]</r> in 2005, I observe that it corresponds to an index value of 100. Tracking along the line to {the endpoint for Commercial trend}<r>[0.943, 0.208]</r> in 2020, it indicates an index value of 160. To find the growth, I need to calculate the difference between the endpoint and the starting point values.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "vRmaMgNCNIpGhXVW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table showing the performance metrics for each technology company, differentiating by key performance indicator from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I start by identifying {the x-axis labels}<r>[0.071, 0.893, 0.115, 0.975]</r> that list the company names. Next, I determine {the legend}<r>[0.201, 0.010, 0.373, 0.089]</r> which includes 'Turnover Rate,' 'Innovation Performance,' and 'Business Dynamics' as performance indicators. I then scrutinize {each line's data points}<r>[0.113, 0.093, 0.943, 0.870]</r> for all companies across these indicators by tracking the lines' vertical positions at each company label on the x-axis. These values are derived from the y-axis scale.\nBased on the above thought, the answer is:\nCompany,Turnover Rate,Innovation Performance,Business Dynamics\nApple,0.8,0.85,0.87\nGoogle,0.82,0.83,0.85\nAmazon,0.75,0.78,0.8\nMicrosoft,0.74,0.81,0.81\nFacebook,0.63,0.65,0.69"
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "BzozmZ7tgIqzVQpQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does Facebook have the most significant decline in all performance categories according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand Facebook's significant decline in performance categories, I first assess {the lines representing each category for Facebook}<r>[0.943, 0.554], [0.943, 0.632], [0.943, 0.593]</r>. Facebook's scores are consistently lower than the other companies for each category. Considering {the overall trend lines}<r>[0.113, 0.412, 0.943, 0.774]</r> for all companies, it's evident that while other companies either stabilize or have minimal decline, Facebook's lines show a more pronounced downward slope across all indicators. This indicates a greater negative shift in its performance metrics compared to its counterparts.\nBased on the above thought, the answer is:\nFacebook has the most significant decline because its performance in turnover rate, innovation performance, and business dynamics are not only lower but also show a steeper decline on the graph compared to other companies. Its line visibly trends lower across the indicators, indicating a pronounced decrease in performance relative to others."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "CV1fvBBJg6t1EJpo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is Microsoft's innovation performance compared to its turnover rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the difference between Microsoft's innovation performance and its turnover rate, I locate {the points for Microsoft on the innovation performance and turnover rate lines}<r>[0.390, 0.623], [0.390, 0.581]</r>. From the y-axis, the values for innovation performance is approximately 0.81 and for turnover rate is approximately 0.74. Calculating the difference yields 0.81 - 0.74 = 0.07.\nBased on the above thought, the answer is:\n0.07"
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "yNESOGfztGgY86Cc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the chart into a table with 'Product Stages' as columns and 'Cost and Value' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the x-axis tick labels which are {Product Stages}[[0.076, 0.911, 0.164, 0.934], [0.269, 0.911, 0.370, 0.934], [0.479, 0.911, 0.560, 0.934], [0.699, 0.911, 0.739, 0.934], [0.857, 0.911, 0.980, 0.934]] are to be used as column headers in the table. Subsequently, the legend item labels {Cost and Value}[[0.898, 0.091, 0.935, 0.121], [0.898, 0.134, 0.944, 0.164]] represent the row labels. Subsequently, to populate the values, I need to fetch line points for 'Cost' and 'Value' at each stage from object {line_0}[[0.120, 0.276, 0.320, 0.549, 0.519, 0.594, 0.719, 0.503, 0.919, 0.367]] and {line_3}[[0.120, 0.640, 0.320, 0.367, 0.519, 0.184, 0.719, 0.276, 0.919, 0.822]] respectively.\nBased on the above thought, the answer is:\nAttributes, Procurement, Manufacturing, Distribution, Retail, Customer Service \nCost, 24, 18, 17, 19, 22\nValue, 16, 22, 26, 24, 12"
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "fCQnHle3Tdr5ijh9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you point out the stage in the product chain that gets the highest cost and value investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the stage that gets the highest cost and value investment, I would need to analyze the y-values for each stage for both cost and value. For the cost, the line points {Procurement}[[0.120, 0.276]], {Manufacturing}[[0.320, 0.549]], {Distribution}[[0.519, 0.594]], {Retail}[[0.719, 0.503]], and {Customer Service}[[0.919, 0.367]] indicate values of 24, 18, 17, 19, 22 respectively. The highest value is 24, corresponding to Procurement. For the value, looking at the line points {Procurement}[[0.120, 0.640]], {Manufacturing}[[0.320, 0.367]], {Distribution}[[0.519, 0.184]], {Retail}[[0.719, 0.276]], and {Customer Service}[[0.919, 0.822]], the values are 16, 22, 26, 24, 12 respectively. Thus, the highest value here is 26, corresponding to Distribution.\nBased on the above thought, the answer is:\nIn the product chain, the stage that gets the highest cost investment is 'Procurement' and the stage that gets the highest value investment is 'Distribution'."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "9i8CTNoYL1uUPf72",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the cost generated from procurement to customer service in the value chain?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the sum of the cost generated from procurement to customer service in the value chain, I will need to add the values corresponding to all stages for 'Cost' which are {Procurement}[[0.120, 0.276]], {Manufacturing}[[0.320, 0.549]], {Distribution}[[0.519, 0.594]], {Retail}[[0.719, 0.503]], and {Customer Service}[[0.919, 0.367]]. The values are 24, 18, 17, 19, 22 respectively, and the sum is 24 + 18 + 17 + 19 + 22 = 100\nBased on the above thought, the answer is:\nThe total cost from procurement to customer service in the value chain is 100."
+            }
+        ],
+        "id_raw": 216
+    },
+    {
+        "id": "Dst43yJYEW413Y0G",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trend data for 'Rural Development' and 'Urban Development' from the chart and present it in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first identify {the x-axis tick labels}[[0.095, 0.911, 0.130, 0.934], [0.205, 0.911, 0.242, 0.934], [0.297, 0.911, 0.334, 0.934], [0.390, 0.911, 0.426, 0.934], [0.482, 0.911, 0.518, 0.934], [0.574, 0.911, 0.610, 0.934], [0.667, 0.911, 0.703, 0.934], [0.759, 0.911, 0.795, 0.934], [0.851, 0.911, 0.887, 0.934], [0.943, 0.911, 0.979, 0.934]], which represent the Year column in the table. Next, I locate the lines for {Rural Development}[0.113, 0.397, 0.943, 0.828] and {Urban Development}[0.113, 0.231, 0.943, 0.729] to extract the corresponding development values for each year, represented by points like [0.205, 0.430], [0.297, 0.480], etc. for Rural Development and [0.205, 0.646], [0.297, 0.563], and so on for Urban Development.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2010,0.8,0.6\n2011,0.78,0.65\n2012,0.75,0.7\n2013,0.70,0.75\n2014,0.68,0.78\n2015,0.70,0.8\n2016,0.60,0.85\n2017,0.58,0.85\n2018,0.56,0.88\n2019,0.54,0.90"
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "D7VNSxqJqOS93BFS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which area has seen more steady progress in development over the years? Provide a brief explanation for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area has shown more steady progress, I need to analyze the line plots for both Rural and Urban Development. On examining {the line for Rural Development}[0.113, 0.397, 0.943, 0.828], it appears to show a steady decrease over the years from a development score of 0.8 in 2010 to 0.54 in 2019. Comparatively, {the line for Urban Development}[0.113, 0.231, 0.943, 0.729] shows a steadier increase - it starts at a lower development score of 0.6 in 2010 but ends at an improved score of 0.9 by 2019.\nBased on the above thought, the answer is:\nThe Urban area has seen more steady progress in development over the years. The trend line for Urban Development shows a consistent, steady upward trajectory from 2010 to 2019, contrasting with Rural Development which has seen a steady decrease over the same period."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "VrjwxEsImD8b2VNU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in development between Rural and Urban regions for the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the values for Rural and Urban Development in the year 2016. I locate the point corresponding to the year 2016 for {Rural Development}[0.667, 0.729] and extract the corresponding value of 0.6. I also find the point corresponding to 2016 for {Urban Development}[0.667, 0.314] and extract the value of 0.85. The difference is then calculated as 0.85 - 0.6 = 0.25.\nBased on the above thought, the answer is:\n0.25."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "JN295ExRe4Kx0tTn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data showing trends in media and entertainment industry by genre over 2005 and 2015 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I map {the x-axis tick labels}<r>[0.095, 0.911, 0.130, 0.934], [0.926, 0.911, 0.961, 0.934]</r> as the years 2005 and 2015 which serve as column headers. I identify the genre categories based on {the legend text}<r>[0.133, 0.091, 0.303, 0.121], [0.133, 0.134, 0.281, 0.164]</r> and their corresponding lines. Following this, I correlate each genre's points for the years 2005 and 2015 extracted from {the plotted line points}<r>[0.113, 0.853], [0.943, 0.666]</r> and other coordinates to populate the genre rows.\nBased on the above thought, the answer is:\nYear,Horror,Action,Comedy,Romance,Sci-Fi,Drama,Biography,Documentary\n2005,360,480,550,400,370,350,220,180\n2015,340,430,520,395,365,400,330,290"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "tfMdSTEd5PSSzMoG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich genre shows the highest increase in popularity from 2005 to 2015 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which genre exhibits the highest increase in popularity, I need to analyze the relative change in positions of all the genre lines from 2005 to 2015. Particularly, I look at {the starting and ending points of each line}<r>[0.113, 0.853], [0.943, 0.666]</r> and others. Among all the genres, {the line for the Documentary genre}<r>[0.113, 0.746], [0.943, 0.604]</r> shows the highest upward movement, indicating a significant increase compared to others.\nBased on the above thought, the answer is:\nThe Documentary genre shows the highest increase in popularity from 2005 to 2015, as evidenced by the substantial upward trajectory of its line on the chart."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "BtXdSgesXY0nJiSw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_11_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase of the Drama genre from 2005 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase for the Drama genre, I first extract the values for Drama from the years 2005 and 2015 using {the Drama line points}<r>[0.113, 0.509], [0.943, 0.451]</r>. These values are 350 and 400 respectively. Using these values, the percentage increase is calculated by ((400-350)/350)*100%.\nBased on the above thought, the answer is:\n14.29%"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "nc9IBtGtQBk3SYmF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table showing the weekly efficiencies of both materials and machines according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observed from the line chart, each week's data points for Material Efficiency and Machine Efficiency need to be extracted. First, I analyze the {line for Material Efficiency}<r>[0.164, 0.438, 0.936, 0.693]</r>, extracting values such as week 1 at 0.90, week 2 at 0.88, and so forth until week 30. Simultaneously, I analyze the {line for Machine Efficiency}<r>[0.164, 0.260, 0.936, 0.438]</r>, extracting values such as week 1 at 0.92, week 2 at 0.93, continuing similarly up to week 30.\nBased on the above thought, the answer is:\nWeek,Material Efficiency,Machine Efficiency\n1,0.90,0.92\n2,0.88,0.93\n3,0.91,0.94\n4,0.92,0.95\n5,0.92,0.93\n6,0.90,0.94\n7,0.91,0.93\n8,0.91,0.94\n9,0.88,0.92\n10,0.92,0.93\n11,0.90,0.95\n12,0.90,0.94\n13,0.89,0.92\n14,0.88,0.93\n15,0.88,0.94\n16,0.87,0.95\n17,0.88,0.96\n18,0.88,0.97\n19,0.87,0.96\n20,0.87,0.97\n21,0.85,0.98\n22,0.86,0.99\n23,0.85,0.98\n24,0.84,0.97\n25,0.84,0.96\n26,0.85,0.97\n27,0.84,0.96\n28,0.83,0.95\n29,0.82,0.94\n30,0.83,0.95"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "cDLLtz3yEu1DfghS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in Material Efficiency according to the chart, and does it show any significant changes over the weeks?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {Material Efficiency line}<r>[0.164, 0.438, 0.936, 0.693]</r>, it appears there are fluctuations in efficiency over the span of 30 weeks. The efficiency generally ranges from 0.85 to 0.92, with notable dips in some weeks, such as a noticeable drop to 0.85 in week 21, suggesting a temporary decrease in performance or efficiency. The data points fluctuate but generally exhibit a moderate range of change without extreme highs or lows, indicating relatively stable but varying operational efficiency.\nBased on the above thought, the answer is:\nThe trend in Material Efficiency shows moderate fluctuations with efficiency mostly ranging between 0.85 and 0.92. Although there are some week-to-week changes, the overall efficiency does not experience extreme highs or lows, suggesting a general stability in material handling efficiency over the observed period."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "O7raO9euJJ9o8rTx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest machine efficiency values based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the line chart data for Machine Efficiency observed from {the points}<r>[0.164, 0.260, 0.936, 0.438]</r>, I will calculate the difference between the highest and lowest values. The highest value stands at 0.99 in week 22, and the lowest is at 0.92 in week 1. To find the difference, the calculation would be 0.99 - 0.92.\nBased on the above thought, the answer is:\n0.07"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "vimWnKVaC451Pgif",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data presented in the graph comparing the performance between ResNet50 and VGG16 be structured into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a tabular format of the data from the graph, my first step is to identify {the bar labels}<r>[0.199, 0.883, 0.222, 0.914]</r> and <r>[0.396, 0.883, 0.448, 0.914]</r> which correspond to the two models: ResNet50 and VGG16. Then, I find {the y-axis labels}<r>[0.058, 0.860, 0.116, 0.895]</r> and <r>[0.058, 0.137, 0.116, 0.172]</r> and use these as the column headers (Accuracy and Loss). Finally, I extract the performance metrics for each model from {the heights of the bars}<r>[0.208, 0.151, 0.330, 0.784]</r> and <r>[0.345, 0.151, 0.468, 0.802]</r>.\nBased on the above thought, the answer is:\nModel,Accuracy,Loss\nResNet50,92.5,0.067\nVGG16,90,0.091"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "fIbEgdAjLSGZDsN4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the ResNet50 and VGG16 models, which one appears to be more robust according to the data presented in the chart? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the more robust model, I would analyze the bar plots for both ResNet50 and VGG16. Starting with {ResNet50}<r>[0.208, 0.151, 0.330, 0.784]</r>, it shows a higher accuracy of 92.5% and lower loss of 0.067. Comparatively, an analysis of {VGG16}<r>[0.345, 0.151, 0.468, 0.802]</r> reveals a slightly lower accuracy of 90% and a moderately higher loss of 0.091. This comparison reveals that ResNet50 outperforms VGG16 in terms of accuracy and has less loss, hinting at better efficiency and performance.\nBased on the above thought, the answer is:\nAccording to the data presented in the chart, it appears that the ResNet50 model is more robust than the VGG16 model. This conclusion is based on the findings that ResNet50 model outperforms VGG16 model in terms of accuracy and also incurs a lower loss rate. This suggests a better performance and efficiency of the ResNet50 model when compared to VGG16 in this specific instance."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "8JaHvzX6tZ4gkLYx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent is the accuracy of the ResNet50 model greater than the accuracy of the VGG16 model?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage by which ResNet50's accuracy is greater than VGG16's, I first identify {the bar for ResNet50's accuracy}<r>[0.208, 0.151, 0.293, 0.784]</r> which represents 92.5%. I also locate {the bar for VGG16's accuracy}<r>[0.345, 0.151, 0.431, 0.784]</r> which represents 90%. Therefore, the difference in the accuracy is 92.5% - 90% = 2.5%.\nBased on the above thought, the answer is:\n2.5%"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "628CG0IvO4yLrfGo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data shown in the bar chart into a structured table, with sectors as rows and years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I'd first need to identify the necessary headers through analysis of the chart. For each Industry, we have both a 2019 and 2020 revenue data point. Identifying {the x-axis tick labels}[[0.178, 0.881, 0.268, 0.906], [0.335, 0.881, 0.406, 0.906], [0.493, 0.881, 0.561, 0.906], [0.649, 0.881, 0.717, 0.906], [0.807, 0.881, 0.861, 0.906]], gives us the names of the sectors: Restaurant, Hotel, Tour Agency, Resort, Theme Park, and Airlines, which will become the rows of the table. Then referencing {the legend texts}[[0.443, 0.052, 0.487, 0.071], [0.443, 0.005, 0.472, 0.024]], we find the column headers: 2019 and 2020. The values are found by locating and converting {the heights of the respective bars}[[0.141, 0.126], [0.208, 0.329], ..., [0.928, 0.527]] for each year and sector.\nBased on the above thought, the answer is:\nSector,2019,2020\nRestaurant,600,400\nHotel,500,300\nTour Agency,700,300\nResort,550,450\nTheme Park,720,600\nAirlines,800,780"
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "oymflZkD0dgRK2fa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector experienced the least decrease in revenue from 2019 to 2020, and why could this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the amount of decrease in revenue for each sector by comparing the bars representing 2019 and 2020. By observing the bars, I see that {the Airlines sector}[[0.810, 0.360, 0.895, 0.531]], represented by {the last pair of bars on the chart}[[0.810, 0.071, 0.923, 0.531]], had the least decrease in revenue from 2019 to 2020.\nBased on the above thought, the answer is:\nThe sector that experienced the least decrease in revenue from 2019 to 2020 was the Airlines sector. One possible reason could be that despite travel restrictions during the COVID-19 crisis, certain flights continued to operate for essential services and repatriation tasks, allowing the Airlines to still generate some revenue."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "puuzO0bZjqQxCCry",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in revenue between the years 2019 and 2020 for the Tour Agency sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate the revenue for Tour Agency in both 2019 and 2020. I locate the 2019 revenue by identifying {the left bar under the Tour Agency x-tick}[[0.378, 0.072, 0.440, 0.471]]. The height of this bar corresponds to a revenue of 700. Similarly, I find the 2020 revenue by locating {the right bar under the Tour Agency x-tick}[[0.385, 0.072, 0.447, 0.282]], which corresponds to a revenue of 300. The difference in revenue between 2019 and 2020 is therefore 700 - 300 = 400.\nBased on the above thought, the answer is:\n400."
+            }
+        ],
+        "id_raw": 222
+    },
+    {
+        "id": "AEcKd5FTmglYH60N",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a table format with Trends as columns and Social Aspects as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data from the line chart, I begin by looking at {the x-axis labels}[[0.12,0.89,0.18,0.92], [0.25,0.89,0.32,0.92], [0.39,0.89,0.46,0.92], [0.53,0.89,0.60,0.92], [0.67,0.89,0.74,0.92], [0.81,0.89,0.88,0.92]], which represent the Trends. I then identify {the line legends}[[0.08,0.95,0.17,0.99], [0.23,0.95,0.31,0.99]] as the Social Aspects. For the values, I locate the {line points}[[0.14,0.17], [0.27,0.12], [0.41,0.13], [0.55,0.09], [0.69,0.11], [0.83,0.08], [0.15,0.67], [0.28,0.84], [0.42,0.64], [0.56,0.57], [0.70,0.62], [0.84,0.60]] for each social aspect at each trend, and extract the corresponding values from the y-axis labels.\nBased on the above thought, the answer is:\nSocial Aspect,Gradual Recovery,Dramatic Surge,Moderate Growth,Sharp Decrease,Insignificant Increase,Downward Trend\nEducation,2,1,1.5,0.5,1,0\nHealthcare,5,7,4.5,4,4.5,4"
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "iA1e14gbb9ZRvXZS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which social aspect has seen the most dramatic surge? If any, explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social aspect has witnessed the most dramatic surge, I need to identify the {Dramatic Surge point for Education}[0.27,0.12] and {Healthcare}[0.28,0.84]. Upon comparison, the y-coordinate (which corresponds to the value on the y-axis) for the Dramatic Surge point in Healthcare is evidently higher than that of Education. This signifies that Healthcare has seen a more dramatic surge compared to Education.\nBased on the above thought, the answer is:\nHealthcare has witnessed the most dramatic surge according to the line chart. This conclusion is derived from the comparison of the 'Dramatic Surge' points for both Education and Healthcare. The value for Healthcare is significantly higher, indicating a bigger leap or dramatic surge in this aspect."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "wiGzkGLMQhLklDDa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest recorded value for a trend in Healthcare?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value in Healthcare, I need to look at the {y-coordinates of Healthcare line points}[[0.15,0.67], [0.28,0.84], [0.42,0.64], [0.56,0.57], [0.70,0.62], [0.84,0.60]]. The y-coordinate with the highest value corresponds to the highest point on the line, which is 0.84. This point corresponds to {Dramatic Surge}[0.28,0.89]. I then find the corresponding value on the y-axis, which is 7.\nBased on the above thought, the answer is:\n7."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "QY603iWMOIlo3Pvi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the given chart data into a table format with columns representing Months and rows signifying 'Active Users' and 'Learning Hours'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the data in the line chart, I first observe {the x-axis tick labels}[[0.258, 0.879, 0.273, 0.914], [0.393, 0.879, 0.407, 0.914], ... , [0.923, 0.879, 0.953, 0.914]] which denote the months. Then, I identify {the legend texts}[[0.247, 0.130, 0.390, 0.165], [0.247, 0.182, 0.423, 0.217]] as the row labels which stand for Active Users and Learning hours respectively. For values, I look at {the line points} for each category at the corresponding months such as [[0.198, 0.591], [0.266, 0.699], ..., [0.938, 0.160]] for Active users and [[0.198, 0.699], [0.266, 0.780], ..., [0.938, 0.402]] for Learning Hours and extract the values.\nBased on the above thought, the answer is:\nMonth,Active Users,Learning Hours\n1,127000,123000\n2,123000,120000\n3,125000,119500\n4,133000,124200\n5,130500,125000\n6,136000,130000\n7,142000,132000\n8,141000,131000\n9,140000,129000\n10,137000,128500\n11,139000,132000\n12,143000,134000"
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "16oJiC8arnypPaba",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends indicated in the line chart, which parameter (Active Users or Learning Hours) displays better growth over the 12-month period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To pinpoint which parameter displays better growth, I have to scrutinize the fluctuations in the line plots for 'Active Users' and 'Learning Hours'. Looking at {the line plot for Active Users}[0.198, 0.160, 0.938, 0.699], I can observe a fairly sustained growth in user base from 127000 to 143000. Inspecting {the line plot for Learning Hours} [0.198, 0.402, 0.938, 0.793], the active learning hours seemed to initially rise but started to display a decreasing trend towards the end of the period, fluctuating between 119500 and 134000 hours.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, the 'Active Users' displays better growth over the 12-month period. This is because the line plot for 'Active Users' shows a consistent upward trajectory from 127000 to 143000. On the other hand, 'Learning Hours', though initially increased, starts to decline in the later months, indicating a less steady growth. Hence, 'Active Users' displays a more stable and consistent growth over time."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "2ZdUiT0GKptg96Ua",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of Active Users over the 12-month period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average number of active users over the 12-month period, I need to locate {the line points of Active Users}[[0.198, 0.591], [0.266, 0.699], ..., [0.938, 0.160]] and extract the corresponding values for each month: 127000, 123000, 125000, 133000, 130500, 136000, 142000, 141000, 140000, 137000, 139000, 143000. The average is then calculated as the sum of these values divided by the number of months.\nBased on the above thought, the answer is:\n135375."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "0tLfo1LKHbEWMnh1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the line chart into a table showing average income and average age for Urban, Suburban, and Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data into a table, I start by identifying {the labels on the x-axis}<r>[0.132, 0.098, 0.975, 0.854]</r> which list 'Urban', 'Suburban', and 'Rural'. Then, I map these labels to the corresponding data points {on both y-axes}<r>[0.132, 0.098, 0.975, 0.854]</r> to extract values for average income and age. The left y-axis denotes income and the right one denotes age.\nBased on the above thought, the answer is:\nArea,Average Income,Average Age\nUrban,45000,27\nSuburban,55000,35\nRural,35000,45"
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "Vmo2fG8yZMEuyyFV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area shows a mismatches trend between age and income as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line graph}<r>[0.132, 0.098, 0.975, 0.854]</r> closely, we notice {the line for Suburban}<r>[0.420, 0.620]</r> where despite an average age of 35, which is middle in the provided scale, the income is the highest at 55000. This is in contrast to the trend observed in {Urban}<r>[0.210, 0.720]</r> and {Rural areas}<r>[0.630, 0.770]</r> where younger age correlates with higher income and older age correlates with lower income.\nBased on the above thought, the answer is:\nSuburban area shows a mismatch trend. Though it doesn't have the youngest or the oldest average age, it exhibits the highest income, contradicting the observed trend where income usually decreases as age increases."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "3dFzeAD60Ebomhoo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total combined average income from all the areas shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total average income, I sum the incomes as marked {for Urban, Suburban, and Rural areas}<r>[0.210, 0.720], [0.420, 0.620], [0.630, 0.770]</r>. These coordinate-based points have the income values 45000 for Urban, 55000 for Suburban, and 35000 for Rural.\nBased on the above thought, the answer is:\n135000"
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "aL8fLoslgE1Y1G1R",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a structured table able to show the demographic influx for each area according to the line plot in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a tabular representation of data from a line plot, I initially identify {the x-axis labels}<r>[0.094, 0.919, 0.976, 0.942]</r>, which represent areas, as table rows. Next, the demographic influx, marked by {the y-axis labels}<r>[0.043, 0.040, 0.071, 0.933]</r>, is interpreted as column headers. For the values in the table cells, I can locate {the line plot}<r>[0.124, 0.104, 0.940, 0.865]</r> corresponding to the area on the x-axis and y-axis. Based on the x and y plot coordinates, we can interpolate the demographic influx in corresponding areas.\nBased on the above thought, the answer is:\nArea,Demographic Influx\nArea_1,0.7\nArea_2,0.65\nArea_3,0.85\nArea_4,0.75\nArea_5,0.9\nArea_6,0.95\nArea_7,0.85\nArea_8,0.8\nArea_9,0.76\nArea_10,1.01\nArea_11,1.05\nArea_12,0.99\nArea_13,0.94\nArea_14,0.97\nArea_15,0.93\nArea_16,1.09\nArea_17,1.03\nArea_18,1.11\nArea_19,1.06\nArea_20,1.15\nArea_21,1.1\nArea_22,1.2\nArea_23,1.18\nArea_24,1.23\nArea_25,1.22\nArea_26,1.15\nArea_27,1.3\nArea_28,1.35"
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "DO7KxTIzbervVwkL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze and provide interpretation based on the line plot, about demographic influx across local areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {line plot}<r>[0.124, 0.104, 0.940, 0.865]</r> represents the demographic influx across different local government areas. The line plot shows an increasing trend, indicating that the demographic influx is generally increasing from Area_1 to Area_28. The gradual increase from {Area_1}<r>[0.124, 0.810]</r> to {Area_9}<r>[0.366, 0.745]</r> indicates a steady growth of population, however, a sudden increase is observed between {Area_10}<r>[0.396, 0.473]</r> and {Area_12}<r>[0.456, 0.495]</r>. After this point, the demographic influx seems to grow steadily again, then more steeply towards {Area_28}<r>[0.940, 0.104]</r>. This could suggest that areas from Area_10 to Area_28 have witnessed a significant increase in population due to migration or other factors, and the rate continues to grow at a steeper pace as we move from Area_20 onwards.\nBased on the above thought, the answer is:\nOverall, the line plot indicates an increasing trend of demographic influx across different local government areas from Area_1 to Area_28. An initial steady rise is observed from Area_1 to Area_9. There is then a significant surge around Area_10 to Area_12, followed by a steeper pace of influx increase from Area_20 onwards. This pattern suggests that later areas, particularly from Area_10 to Area_28, may have had significant population increase events, such as migration surges, leading to a higher rate of demographic influx."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "yZuwQr1hTrVbyQp3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, what is the highest demographic influx recorded, and in which area did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest demographic influx, I should scan {the line plot}<r>[0.124, 0.104, 0.940, 0.865]</r> and locate the point with the maximum y-coordinate. The point with the maximum y-coordinate is at {Area_28}<r>[0.940, 0.104]</r>, which corresponds to an influx value of 1.35. The area associated with this point is read from the x-axis label at the same x-coordinate.\nBased on the above thought, the answer is:\nThe highest demographic influx recorded is 1.35, and it occurred in Area_28."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "AjsSnrdllrZZa7MG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data of energy consumption into a table where the rows represent the types of energy and the columns demonstrate the relevant week's consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, firstly let's locate the timeline of weeks in the {x-axis ticks}[[0.128, 0.879, 0.101, 0.914], [0.263, 0.879, 0.278, 0.914], ..., [0.956, 0.879, 0.985, 0.914]], which details the weeks. Secondly, the types of energy sources, Green Energy and Non-Renewable energy can be found via the {legend entries}[[0.675, 0.736, 0.833, 0.771], [0.675, 0.788, 0.944, 0.823]]. The consumption of Green Energy is listed in the points along {the Green Energy line}[[0.128, 0.820], [0.163, 0.789], ..., [0.925, 0.165]] while the consumption data for Non-Renewable Energy can be extracted from the points in {the Non-Renewable Energy line}[[0.128, 0.133], [0.163, 0.176], ..., [0.925, 0.693]].\nBased on the above thought, the answer is:\nWeek,Green Energy,Non-Renewable Energy\n1,2.12,8.5\n2,2.41,8.1\n3,2.75,7.9\n4,2.89,7.5\n5,3.02,7.3\n6,3.45,7.1\n7,3.9,6.85\n8,4.5,6.5\n9,4.75,6.25\n10,4.9,6.0\n11,5.12,5.8\n12,5.3,5.6\n13,5.5,5.4\n14,5.8,5.15\n15,6.1,5.0\n16,6.32,4.85\n17,6.65,4.7\n18,6.89,4.5\n19,7.1,4.3\n20,7.32,4.05\n21,7.5,3.9\n22,7.8,3.7\n23,8.0,3.5\n24,8.2,3.3"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "jzhpWRdRsBeSV6IM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn looking at the trends over the 24 weeks, which energy source has seen a more stable pattern of consumption? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to review the fluctuations in the line plots for both energy sources. {The Green Energy line}<r>[0.128, 0.820, 0.925, 0.165]</r> shows a consistent increase in consumption levels over the 24 weeks, suggesting a more stable and predictable trend. In contrast, {the Non-Renewable Energy line}<r>[0.128, 0.133, 0.925, 0.693]</r> shows a gradual but consistent decrease over the same period. Though both show trends, Green Energy shows an increasing pattern which is usually associated with stability in the context of energy consumption.\nBased on the above thought, the answer is:\nGreen Energy seems to display a more stable pattern of consumption over the 24 weeks. It indicates a consistent growth trend as opposed to Non-Renewable Energy, which has a decreasing pattern. The consistent growth in consumption of Green Energy shows stability and predictability which is often viewed as a positive aspect in energy demand patterns."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "2IST6EfqbIlWQUdx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the consumption levels of Green Energy if comparison is made from the start (Week 1) to the end (Week 24)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to fetch the consumption value of Green Energy from Week 1 and Week 24. These two points are located at {the starting point of the line} <r>[0.128, 0.820]</r> and {the ending point of the line}<r>[0.925, 0.165]</r>. The consumption level of Green Energy at Week 1 is 2.12 units and the level at Week 24 is 8.2 units. Thus, the change in Green Energy consumption is the figure at Week 24 subtracted by the value at Week 1: 8.2 - 2.12.\nBased on the above thought, the answer is:\n6.08 units."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "Xt3itsiMVvtejRX2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data of Biotech Patents and New Drugs Development over time into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data displayed on the line chart into a table, I first note the respective {series from the legend}<r>[0.129, 0.116, 0.502, 0.231]</r>, which will serve as row names. I then check {the x-axis}<r>[0.118, 0.854]</r> which represents the Time and will be used as column headers. Finally, I look at the y-values coded in the {coordinates of the points on the line plots}<r>[0.157, 0.183, 0.936, 0.404]</r> and <r>[0.157, 0.529, 0.936, 0.805]</r> to populate the table cell values. The y-values indicate the counts for the corresponding series at each time step.\nBased on the above thought, the answer is:\nTime,Biotech_Patents,New_Drugs_Development\n1,100,10\n2,95,12\n3,102,20\n4,106,23\n5,120,19\n6,113,16\n7,111,29\n8,100,16\n9,94,28\n10,90,15\n11,98,35\n12,104,36\n13,113,42\n14,110,37\n15,104,32\n16,112,33\n17,95,22\n18,97,31\n19,92,27\n20,110,40\n21,115,41\n22,122,40\n23,108,42\n24,121,34\n25,125,48\n26,134,57\n27,122,53\n28,130,65"
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "nL1NLQwsdlshMmZy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which industry, Biotech Patents or New Drug Development, showed a more stable growth trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine relative stability in growth trends, I analyze the line plots for each industry. Looking at {the line for Biotech Patents}<r>[0.157, 0.183, 0.936, 0.404]</r>, we find the line oscillating around values without a clear increasing or decreasing trend. In contrast, if we consider {the line for New Drugs Development}<r>[0.157, 0.529, 0.936, 0.805]</r>, there is a noticeable upward trend indicating growth, albeit with some fluctuations.\nBased on the above thought, the answer is:\nThe New Drugs Development industry showed a more stable growth trend over time. Despite some fluctuations, the line plot for New Drugs Development indicates a general upward trend, suggesting consistent growth over time, whereas the trend for Biotech Patents was more volatile and did not show a clear pattern of growth or decline."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "d9nQ16B6XT2YHeFV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest point in the New Drugs Development series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I spot the {highest point for New Drugs Development}<r>[0.936, 0.529]</r> which represents 65. Then, I locate the {lowest point for New Drugs Development}<r>[0.157, 0.805]</r> which stands for 10. By subtracting the lowest value from the highest, we find the difference.\nBased on the above thought, the answer is:\n55."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "uCEW1kBRn8vTw11r",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the numbers regarding 'Free Textbook Distribution' and 'eLibrary Subscriptions' in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the provided information, each point on the line graphs represents a week and the corresponding value on the y-axis. By looking at the points for {Free Textbook Distribution}<r>[0.121, 0.829, 0.944, 0.862]</r> and {eLibrary Subscriptions}<r>[0.121, 0.109, 0.944, 0.413]</r> and correlating them with the y-axis values, I can compile the table.\nBased on the above thought, the answer is:\nWeeks,Free Textbook Dist.,eLibrary Subscriptions\n1,500,2000\n2,495,2010\n3,505,1980\n4,490,2030\n...\n27,400,3000\n28,395,3050"
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "XwF2FywJM2govLe8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which service seems to have a higher rate of decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the decline in 'Free Textbook Distribution' trend line {trend line for 'Free Textbook Distribution'}<r>[0.121, 0.829, 0.944, 0.862]</r> and the 'eLibrary Subscriptions' trend line {trend line for 'eLibrary Subscriptions'}<r>[0.121, 0.109, 0.944, 0.413]</r>. I need to look at the change in values from the start to the end of each line. 'Free Textbook Distribution' starts at 500 and ends at 395 which is a decrease of 105, while 'eLibrary Subscriptions' starts at a higher value of 2000 and ends at an even higher value of 3050, which is an increase of 1050.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, 'Free Textbook Distribution' has a higher rate of decline. Even though 'eLibrary Subscriptions' starts at a higher value, it has not declined but actually increased over the weeks, compared to 'Free Textbook Distribution' which decreased throughout."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "CK6UNqQUbw5c13Od",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest points of 'eLibrary Subscriptions'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference between the highest and lowest points of 'eLibrary Subscriptions', I need to locate {the line points for 'eLibrary Subscriptions'}<r>[0.121, 0.109, 0.944, 0.413]</r> and look at the maximum and minimum y-values, which correspond to 3050 and 1980, respectively. The difference between these two values is 3050 - 1980.\nBased on the above thought, the answer is:\n1070"
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "tJSwxt5KuIVZX1dr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the line chart and represent them in a table, with years as columns and each row corresponds to an Industry and the type of Marketing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first look at {the x-axis tick labels}[[0.076, 0.879, 0.156, 0.914], [0.178, 0.879, 0.258, 0.914],..., [0.993, 0.879, 1.073, 0.914]], which represent the column headers or Years. Next, I identify {the legend texts}[[0.203, 0.130, 0.426, 0.165], [0.203, 0.182, 0.397, 0.217]] as the row labels representing different Marketing types in the Industry. To populate the values, I locate {the line points for each Industry}[[0.156, 0.256, 0.931, 0.804],[0.156, 0.148, 0.931, 0.766]] for each marketing trend at the respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Startup Foundation, Innovation Index\n2001,55,60\n2002,70,72\n2003,65,71\n2004,67,75\n2005,90,94\n...\n2018,120,130\n2019,123,135\n2020,126,140"
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "FjmkFSR8AJ72dPLe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which line shows broader fluctuations over the years, and what could this mean in terms of Marketing trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "The fluctuations in the lines can be deduced from {the line points}[[0.156, 0.256, 0.931, 0.804],[0.156, 0.148, 0.931, 0.766]]. The 'Startup Foundation' values vary between a high of 128(point_coordinates': '[0.931, 0.256]') and a low of 55(point_coordinates': '[0.156, 0.804]'). While for the 'Innovation Index' the high and low values lie at 140(point_coordinates': '[0.931, 0.148]') and 60(point_coordinates': '[0.156, 0.766]'). This might imply the volatility and uncertainty in the marketing strategy.\nBased on the above thought, the answer is:\nThe innovations index shows broader fluctuations over the years, as indicated by the larger difference between its maximum and minimum values. This could be interpreted as the Innovation Index experiencing more volatility in its market share. This greater degree of fluctuation could suggest that the effectiveness of innovation as a marketing strategy is more dependent on external factors or that it is a more risky strategy that can lead to variable results."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "gNBuiuFdi18WChkE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in average values between the Startup Foundation and the Innovation Index over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average of Startup Foundation and Innovation Index values can be calculated by summing the values from {the line points of Startup Foundation}[[0.156, 0.804], ..., [0.931, 0.256]] and {the line points of Innovation Index}[[0.156, 0.766], ..., [0.931, 0.148]] respectively, and then dividing by the total number of time points. The difference between these averages is then calculated.\nBased on the above thought, the answer is:\n8.5"
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "8s4XukgsyxVIgggg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information presented in the line chart into a tabular representation showing Index Types as columns and Years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I first focus on {the x-axis tick labels}<r>[0.102, 0.919, 0.146, 0.942], [0.238, 0.919, 0.282, 0.942], [0.374, 0.919, 0.418, 0.942], [0.510, 0.919, 0.554, 0.942], [0.646, 0.919, 0.690, 0.942], [0.782, 0.919, 0.826, 0.942], [0.919, 0.919, 0.962, 0.942]</r> to determine the years which will form the rows of the table. Each year marks the start of a new row. Next, I look at {the legend}<r>[0.745, 0.086, 0.855, 0.110], [0.745, 0.121, 0.966, 0.145]</r> to identify the Index Types, which will be the columns of the table labeled as \"Health Index\" and \"Living Environment Index\". Lastly, using the line points, I extract the values for each index type across the respective years to populate the table.\nBased on the above thought, the answer is:\nYear,Health Index,Living Environment Index\n1990,0.65,0.71\n1991,0.66,0.73\n1992,0.67,0.75\n1993,0.68,0.77\n1994,0.69,0.79\n1995,0.71,0.81\n1996,0.72,0.82\n1997,0.73,0.82\n1998,0.75,0.83\n1999,0.76,0.85\n2000,0.76,0.86\n2001,0.77,0.86\n2002,0.78,0.87\n2003,0.79,0.88\n2004,0.80,0.89\n2005,0.81,0.90\n2006,0.82,0.91\n2007,0.83,0.92\n2008,0.84,0.92\n2009,0.85,0.93\n2010,0.85,0.93\n2011,0.86,0.94\n2012,0.87,0.94\n2013,0.88,0.95\n2014,0.89,0.95\n2015,0.90,0.96\n2016,0.91,0.96\n2017,0.92,0.97\n2018,0.93,0.98\n2019,0.94,0.98\n2020,0.94,0.99"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "tFgCEB9K3fipgwaq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends in Health Index and Living Environment Index compare over the period 1990 to 2020, and what might this indicate about public health and living conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends within the line chart, I note significant details using {the line for Health Index}<r>[0.124, 0.315, 0.940, 0.724]</r> and {the line for Living Environment Index}<r>[0.124, 0.245, 0.940, 0.639]</r>. Both indices show a steady increase over the 30-year period, indicating improvements in health and living conditions. Specifically, the Health Index starts at 0.65 in 1990, reaching 0.94 in 2020, and the Living Environment Index starts at 0.71, reaching 0.99 in 2020. This could suggest a continuous improvement in public health and environmental conditions over these years, reflecting possibly better healthcare services and living standards.\nBased on the above thought, the answer is:\nBoth the Health Index and Living Environment Index depict a consistent upward trend from 1990 to 2020, indicating steady improvements in public health and living conditions over the years. The sustained increase in these indices suggests enhancements in healthcare services and environmental policies that benefit the general populace's well-being and quality of life."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "OilXbWTUoU0l2G1I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the Health Index from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in the Health Index from the year 1990 to 2020, I start by examining the coordinates for the Health Index values at these specific years. Using {the point coordinates for the Health Index}<r>[0.124, 0.724], [0.940, 0.315]</r> from the line chart, I extract the Health Index values for the years 1990 and 2020, which are 0.65 and 0.94 respectively. The increase is calculated by subtracting the 1990 value from the 2020 value, resulting in 0.94 - 0.65.\nBased on the above thought, the answer is:\n0.29"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "TtQ8ds5Nf0niPp71",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data from the line chart into a table with columns representing years and rows representing the different types of energy sources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build this table, first I need to identify {the x-axis tick labels}<r>[0.164, 0.879, 0.936, 0.914]</r> as the column headers representing years. The labels in {the legend}<r>[0.211, 0.130, 0.548, 0.217]</r> will be used as row headers, which are different types of energy sources; namely, Innovation and Technology Entrepreneurship. The values in the cells will be the actual data points, which can be derived from the intersections of the lines marked by {the points on the Innovation line}<r>[0.164, 0.803, 0.936, 0.820]</r> and {the points on the Technology Entrepreneurship line}<r>[0.164, 0.133, 0.936, 0.764]</r>.\nBased on the above thought, the answer is:\nYear,Innovation,Technology Entrepreneurship\n1,0.81,1.04\n2,1.02,0.92\n3,1.24,1.83\n4,1.18,1.74\n5,1.05,1.63\n6,0.93,1.92\n7,0.85,2.11\n8,0.79,2.04\n9,0.76,1.96\n10,0.82,2.03\n11,0.94,1.97\n12,0.85,1.88\n13,0.78,1.93\n14,0.96,1.95\n15,0.94,2.01\n16,0.92,2.05\n17,0.99,2.22\n18,1.05,2.13\n19,1.00,2.11\n20,1.06,2.17\n21,1.12,2.25\n22,1.15,2.31\n23,1.22,2.34\n24,1.27,2.47\n25,1.32,2.61\n26,1.28,2.59\n27,1.36,2.66\n28,1.42,2.73"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "ufKnyeDPAJryTy4w",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends of Innovation and Technology Entrepreneurship compare over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends of both energy sources, I need to observe {the line representing Innovation}<r>[0.164, 0.590, 0.936, 0.820]</r> and {the line representing Technology Entrepreneurship}<r>[0.164, 0.133, 0.936, 0.764]</r>. The trend line for Innovation shows mild fluctuation and a steady increase over the years. In contrast, the trend line for Technology Entrepreneurship shows a significant upward trajectory, indicating a considerable increase over time.\nBased on the above thought, the answer is:\nInnovation has seen a steady increase over the years with mild fluctuations, while Technology Entrepreneurship has shown a significant and more pronounced upward trend. This indicates that while both energy sources have increased over time, the rise in Technology Entrepreneurship has been much more dramatic in comparison to Innovation."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "pbkx6QUJ5smS3y9e",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the absolute change in Innovation from the first to the last week?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the absolute change in Innovation from the first to the last week, I locate {the first point on the Innovation line}<r>[0.164, 0.803]</r> at which the value is 0.81, and {the last point on the Innovation line}<r>[0.936, 0.820]</r> at which the value is 1.42. The difference or change over time is 1.42 - 0.81 = 0.61.\nBased on the above thought, the answer is:\n0.61."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "HMPqoaWkFiQoBlfX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the weekly changes in Overall Index and Air Quality Index as observed in the line chart in a tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, first, I identify the x-axis labels, {the x-axis tick labels}<r>[[-0.033, 0.879, 0.001, 0.914], [0.121, 0.879, 0.135, 0.914], [0.265, 0.879, 0.279, 0.914], [0.402, 0.879, 0.431, 0.914], [0.546, 0.879, 0.575, 0.914], [0.691, 0.879, 0.720, 0.914], [0.835, 0.879, 0.864, 0.914], [0.979, 0.879, 1.008, 0.914]]</r>, as the weeks ranging from 1 to 28. Next, I can identify {the legend texts}<r>[0.203, 0.736, 0.357, 0.771], [0.203, 0.788, 0.396, 0.823]</r> as the row headers representing the two indices. The values for each index at each week are located at {the line points for Overall Index}<r>[0.157, 0.222], [0.186, 0.235], ..., [0.936, 0.794]</r> and {the line points for Air Quality Index}<r>[0.157, 0.349], [0.186, 0.336], ..., [0.936, 0.158]</r> then extracting the corresponding values.\nBased on the above thought, the answer is:\nWeeks,Overall Index,Air Quality Index\n1,100,90\n2,99,91\n3,95,91.5\n4,90,94\n5,88,95\n6,87,96\n7,86,97\n8,85,97.5\n9,85,98\n10,83,99\n11,80,98.5\n12,78,100\n13,75,101\n14,72,102\n15,70,102.5\n16,68,103\n17,65,103.5\n18,60,104\n19,58,104.5\n20,55,105\n21,55,105\n22,55,105\n23,55,105\n24,55,105\n25,55,105\n26,55,105\n27,55,105\n28,55,105"
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "rRDVCNSHw5Hm1hXV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line chart, how does the Air Quality Index change compared to the Overall Index over the 28 weeks period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the trends, I first observe the {line for Overall Index}<r>[0.157, 0.222, 0.936, 0.794]</r> which noticeably shows a steady decrease from 100 down to 55 over the 28 weeks. On the other hand, the {line for Air Quality Index}<r>[0.157, 0.349, 0.936, 0.158]</r> displays a steady increase across the same time frame, starting from 90 and reaching 105 by the 28th week. While the Overall Index shows a decreasing trend, the Air Quality Index shows an increasing trend.\nBased on the above thought, the answer is:\nOver the 28 weeks period, the Air Quality Index exhibits an opposite trend compared to the Overall Index. While the Overall Index decreases, signifying a degenerative change in human life aspects, the Air Quality Index increases, indicating an improvement in environmental conditions. This might imply that even though certain aspects of life might have deteriorated during this period, the air quality has improved, assuring a healthier living environment."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "SdAcZnvWNIjLfGH4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the Overall Index from the 1st week to the 28th week?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the Overall Index from the 1st week to the 28th week, I need to locate the y-values at the 1st and 28th weeks. I pinpoint the value for the 1st week from {the line point for Overall Index at the 1st week}<r>[0.157, 0.222]</r> as 100. The value for the 28th week is found from {the line point for Overall Index at the 28th week}<r>[0.936, 0.794]</r> as 55. So, the difference would be 100 - 55 = 45.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "tlaVcDQIhGjAMYpl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this line chart into a structured table format with Years as rows and the different technological trends as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.086, 0.919, 0.130, 0.942], [0.184, 0.919, 0.227, 0.942], [0.259, 0.919, 0.303, 0.942], [0.335, 0.919, 0.379, 0.942], [0.410, 0.919, 0.454, 0.942], [0.486, 0.919, 0.530, 0.942], [0.562, 0.919, 0.605, 0.942], [0.637, 0.919, 0.681, 0.942], [0.713, 0.919, 0.757, 0.942], [0.788, 0.919, 0.833, 0.942], [0.864, 0.919, 0.908, 0.942], [0.940, 0.919, 0.984, 0.942]], which represent the row headers or years. Next, I identify {the legend texts}[[0.131, 0.086, 0.309, 0.110], [0.131, 0.121, 0.204, 0.145]] as the column headers representing the different technology types. To populate the values, I locate the {line points for Artificial Intelligence}[[0.108, 0.865], [0.184, 0.797], ..., [0.940, 0.104]] and {line points for Robotics}[[0.108, 0.848], [0.184, 0.831], ..., [0.940, 0.527]] for each year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Robotics\n2010,0.5,0.6\n2011,0.9,0.7\n2012,1.5,1.0\n2013,2.2,1.2\n2014,2.9,1.4\n2015,3.5,1.7\n2016,3.2,1.9\n2017,3.6,2.1\n2018,4.2,2.2\n2019,4.4,2.3\n2020,4.5,2.3\n2021,5.0,2.5"
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "mQcyG1JIvw6wNYoF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line chart, which technology sector, Artificial Intelligence or Robotics, demonstrated a more rapid development from 2010 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern which technology sector demonstrated more rapid development, we need to analyze the final point coordinates and compare their respective growth. Looking at the last point coordinates for {Artificial Intelligence}[0.940, 0.104], we see that it grew to the point of surpassing the 5.0 value on the development indicator. However, the final coordinates for {Robotics}[0.940, 0.527] shows that it only reached just over halfway to 3.0 on the scale, suggesting a slower pace of development compared to Artificial Intelligence.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Artificial Intelligence sector demonstrated a more rapid development from 2010 to 2021. It reached a higher point on the development indicator by 2021, indicating a higher growth rate over this period compared to Robotics."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "czMPedFslvKPPZRP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the development indicator for Artificial Intelligence increase from 2010 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in the development indicator for Artificial Intelligence from 2010 to 2021, we need to refer to the {point of Artificial Intelligence in 2010}[0.108, 0.865] which shows a value of 0.5 and the {point of Artificial Intelligence in 2021}[0.940, 0.104] which shows a value of 5.0. The difference between these two years is 5.0 - 0.5 = 4.5.\nBased on the above thought, the answer is:\n4.5."
+            }
+        ],
+        "id_raw": 237
+    },
+    {
+        "id": "Gxpx2FpKJvMGyjHZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and tabulate the observed popularity index for both Asian and European cuisines from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step to extract the data is by referring to {the x-axis tick labels}[[0.104, 0.919, 0.164, 0.942], [0.174, 0.919, 0.728, 0.942], [0.215, 0.919, 0.265, 0.942], [0.255, 0.919, 0.787, 0.942], [0.295, 0.919, 0.814, 0.942], [0.336, 0.919, 0.826, 0.942], [0.376, 0.919, 0.767, 0.942], [0.416, 0.919,0.681, 0.942], [0.457, 0.919, 0.662, 0.942], [0.497, 0.919, 0.650, 0.942], [0.537, 0.919, 0.545, 0.942], [0.578, 0.919, 0.474, 0.942], [0.618, 0.919, 0.416, 0.942], [0.658, 0.919, 0.377, 0.942], [0.699, 0.919, 0.318, 0.942], [0.739, 0.919, 0.299, 0.942],[0.780, 0.919, 0.357, 0.942],[0.820, 0.919, 0.377, 0.942],[0.860, 0.919, 0.396, 0.942],[0.901, 0.919, 0.299, 0.942],[0.941, 0.919, 0.143, 0.942]], which correspond to the years 2000 through 2020. \nNext, I locate {the line points for Asian Cuisine Trend}[[0.134, 0.689],[0.174, 0.728],[0.215, 0.767],[0.255, 0.787],[0.295, 0.814],[0.336, 0.826],[0.376, 0.767],[0.416, 0.681],[0.457, 0.662],[0.497, 0.650],[0.537, 0.545],[0.578, 0.474],[0.618, 0.416],[0.658, 0.377],[0.699, 0.318],[0.739, 0.299],[0.780, 0.357],[0.820, 0.377],[0.860, 0.396],[0.901, 0.299],[0.941, 0.143]] which correspond to the popularity index of Asian cuisine for each respective year.\nSimilarly, locating {the line points for European Cuisine Trend}[[0.134, 0.767],[0.174, 0.767],[0.215, 0.748],[0.255, 0.736],[0.295, 0.732],[0.336, 0.716],[0.376, 0.697],[0.416, 0.677],[0.457, 0.670],[0.497, 0.666],[0.537, 0.650],[0.578, 0.623],[0.618, 0.611],[0.658, 0.603],[0.699, 0.592],[0.739, 0.584],[0.780, 0.580],[0.820, 0.572],[0.860, 0.553],[0.901, 0.533],[0.941, 0.533]] allows me to obtain the popularity index for European cuisine.\nBased on the above thought, the answer is:\nYear,Asian Cuisine Trend,European Cuisine Trend\n2000,4.3,4.1\n2001,4.2,4.1\n2002,4.1,4.15\n2003,4.05,4.18\n2004,3.98,4.19\n2005,3.95,4.23\n2006,4.1,4.28\n2007,4.32,4.33\n2008,4.37,4.35\n2009,4.4,4.36\n2010,4.67,4.4\n2011,4.85,4.47\n2012,5.0,4.5\n2013,5.1,4.52\n2014,5.25,4.55\n2015,5.3,4.57\n2016,5.15,4.58\n2017,5.1,4.6\n2018,5.05,4.65\n2019,5.3,4.7\n2020,5.7,4.7"
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "Qk3A0JhchMDbf4z5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the graph, what can you infer about the popularity trends of Asian and European cuisines from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to seek the trajectory of the popularity index for both cuisines from 2000 to 2020. Looking at {the line for Asian Cuisine Trend} <r>[0.134, 0.689, 0.941, 0.143]</r>, it shows a general increase in the popularity index, with some minor dips but a clear overall upward trend. This suggests Asian cuisine has become more popular over the time period. \nFor {European Cuisine Trend}<r>[0.134, 0.767, 0.941, 0.533]</r>, the trend line shows a slow, steady increase with very slight variations, indicating a consistent growth in popularity for European cuisine.\nBased on the above thought, the answer is:\nThe graph suggests that both Asian and European cuisines have seen an increase in popularity from 2000 to 2020. While Asian cuisine's popularity index exhibits a more dramatic upward trend with some dips, European cuisine's popularity has had a steady, consistent growth. So, overall the popularity of both cuisines has increased over the time period despite some minor fluctuations."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "c1tIySXV4cMVOqsD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much has the popularity index for Asian cuisine increased from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I need to find the initial and final values of the popularity index for Asian cuisine. Looking at {the point for the year 2000 in the Asian Cuisine trend line} <r>[0.134, 0.689]</r>, the value is 4.3. And focusing on {the point for the year 2020 in the Asian Cuisine trend line} <r>[0.941, 0.143]</r>, the value is 5.7. Thus, the increase in the popularity index for Asian cuisine from the year 2000 to 2020 is 5.7 - 4.3 = 1.4.\nBased on the above thought, the answer is:\n1.4"
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "aaPxKsBjIbO3ztBX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you process the raw data into a csv table format with time (Week) as the first column and the different platforms (Facebook and Twitter) as the other columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a CSV table from the raw data, I identify the column headers as \"Weeks\", \"Facebook\", and \"Twitter\" from the given data. I then organize the raw information to match these categories. The respective values for the categories were taken from their corresponding coordinates in the chart provided.\nBased on the above thought, the answer is:\nWeeks,Facebook,Twitter\n1,20,18\n2,25,20\n3,30,22\n4,50,24\n5,55,30\n6,70,36\n7,75,38\n8,90,40\n9,100,42\n10,120,44\n11,132,52\n12,177,66\n13,200,70\n14,210,72\n15,220,74\n16,245,77\n17,250,78\n18,240,80\n19,220,82\n20,215,84\n21,200,85\n22,180,86\n23,170,88\n24,155,88\n25,130,87\n26,110,85\n27,100,85\n28,90,85"
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "4t6IlzY4zkjAdAGP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, can we say that Twitter was consistently growing at a slower pace than Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to evaluate the rate at which Facebook and Twitter gained users every week. Looking at {the line for Facebook}<r>[0.157, 0.133, 0.936, 0.814]</r>, we see that it began at 20 million users and reached 90 million users by week 28. For {Twitter}<r>[0.157, 0.613, 0.936, 0.820]</r>, the users started at 18 million and grew to 85 million by week 28. These trajectories suggest that while both Facebook and Twitter were growing over time, Facebook consistently added more users per week than Twitter.\nBased on the above thought, the answer is:\nYes, we can say that Twitter was consistently growing at a slower pace than Facebook. While both platforms showed substantial growth over the 28 week period, Facebook consistently added more users each week compared to Twitter."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "fMVleBt5uqfhh5Ew",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_12_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more users did Facebook gain than Twitter over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the increase in users between Facebook and Twitter, we need to subtract the initial user counts at week 1 from the final user counts in week 28. Observing {the line points for Facebook}<r>[0.157, 0.133, 0.936, 0.814]</r> and {the line points for Twitter}<r>[0.157, 0.613, 0.936, 0.820]</r> to extract the relevant values, we find that the Facebook user count went from 20 to 90 million users and Twitter from 18 to 85 million users. We take the difference between these two increases to find the difference in user gain.\nBased on the above thought, the answer is:\n7 million users."
+            }
+        ],
+        "id_raw": 239
+    },
+    {
+        "id": "iswNfdUqBjEjFCIh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reformat the bar chart data showing integration scores across different cultural heritage facets into a table with cultural heritage facets as rows and integration scores as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I will look at {the x-axis tick labels}[[0.202, 0.798, 0.235, 0.816], [0.299, 0.798, 0.335, 0.816], [0.396, 0.798, 0.455, 0.816], [0.492, 0.798, 0.535, 0.816], [0.589, 0.798, 0.634, 0.816], [0.684, 0.798, 0.720, 0.816], [0.781, 0.798, 0.829, 0.816], [0.878, 0.798, 0.911, 0.816], [0.974, 0.798, 1.010, 0.816]] which are the row labels representing the different facets of cultural heritage. To populate the scores, I'll locate {Bar Heights} [[0.202, 0.653, 0.235, 0.817], [0.299, 0.679, 0.333, 0.817], [0.396, 0.503, 0.454, 0.817], [0.492, 0.689, 0.535, 0.817], [0.589, 0.540, 0.635, 0.817], [0.684, 0.746, 0.721, 0.817], [0.781, 0.633, 0.830, 0.817], [0.879, 0.722, 0.913, 0.817], [0.974, 0.794, 1.010, 0.817]] to extract the corresponding values based on the y-axis labels ranging from approximately 35 to 90.\nBased on the above thought, the answer is:\nFacet,Integration Score\nNational Museum,70\nHistoric Site,75\nTraditional Music,50\nCultural Festival,77\nTraditional Clothing,55\nLocal Cuisine,80\nIndigenous Art,68\nHistoric Houses,77\nCraft Market,85"
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "YFAoTyYYXfepidZ0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which aspect of cultural heritage is least integrated into the wider culture?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the least integrated aspect of cultural heritage by finding the shortest bar in the chart. In this case, the shortest bar is {Traditional Music}[[0.396, 0.503, 0.454, 0.817]], representing an integration score of 50, which is the lowest among all cultural heritage facets.\nBased on the above thought, the answer is:\nBased on the bar chart, Traditional Music is the aspect of cultural heritage that is least integrated into the wider culture, as it has the lowest integration score of 50."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "g7NK4eFScLIBtEg0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest cultural heritage integration scores by facets?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the highest and lowest integration scores across the various facets. Here, I see that the tallest bar is {Craft Market}[[0.974, 0.794, 1.010, 0.817]] with a score of 85, and the shortest bar is {Traditional Music}[[0.396, 0.503, 0.454, 0.817]] with a score of 50. Hence, the difference between the highest and lowest scores is 85 - 50 = 35.\nBased on the above thought, the answer is:\n35."
+            }
+        ],
+        "id_raw": 240
+    },
+    {
+        "id": "XyTkQTJLW15CfThC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the development trends in the culture and arts industry into a structured table sorted by departments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a structured table, I'll first identify {the department labels on the x-axis}<r>[0.105, 0.79, 0.921, 0.823]</r> which will serve as the rows for our table, listing out cultural heritage, performing arts, visual arts, fashion culture, cuisine culture, literature, film, music, and architecture. The development trends 'Slow Recovery' and 'Inexorable Rise' would then be the columns, based on {the legend}<r>[0.077, 0.053, 0.495, 0.168]</r>. Observing the line visuals, I will detail the values from {the points on the lines}<r>[0.105, 0.337, 0.921, 0.691]</r> for each department and development trend to complete the table.\nBased on the above thought, the answer is:\nDepartment,Slow Recovery,Inexorable Rise\nCultural Heritage,50.6,67.0\nPerforming Arts,54.3,64.2\nVisual Arts,49.9,61.1\nFashion Culture,59.2,68.8\nCuisine Culture,52.1,65.5\nLiterature,48.8,63.8\nFilm,53.7,62.4\nMusic,60.8,60.0\nArchitecture,56.9,66.3"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "FRcIdrpJENkLv4FU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector within the culture and arts industry shows the most significant difference between the 'Slow Recovery' and 'Inexorable Rise' trends according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the most significant difference between 'Slow Recovery' and 'Inexorable Rise', I analyze the values at each {department point}<r>[0.105, 0.337, 0.921, 0.691]</r>. Observing the line plot, I look for the greatest distance between the two trend lines per department, specifically noting how the lines diverge at each sector. Identifying the most noticeably distant points, I determine the department with the largest gap, indicating the most significant difference.\nBased on the above thought, the answer is:\nFashion culture shows the most significant difference between the 'Slow Recovery' and 'Inexorable Rise' trends. It exhibits a divergence where the 'Inexorable Rise' value is notably higher than that of 'Slow Recovery'."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "L4EQyrH9B5Z6tBjk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has been the highest year-over-year increase in Wind Energy Consumption from the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest year-over-year increase, I'll assess {the line plot for Wind Energy Consumption}<r>[0.105, 0.043, 0.921, 0.492]</r> across the years. Then, I'll analyze the differences between the consecutive points from {year 1977}<r>[0.105, 0.492]</r> to {year 2019}<r>[0.921, 0.043]</r>, and calculate the most significant year-over-year increase.\nBased on the above thought, the answer is:\n0.3"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "arjz5Idf91LfjCwc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a table format using Industry types as rows and Year as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I'll start by examining the x-axis labels, aka {the x-axis tick labels}[[0.141, 0.906, 0.180, 0.930], [0.235, 0.906, 0.274, 0.930], [0.329, 0.906, 0.368, 0.930], [0.423, 0.906, 0.462, 0.930], [0.516, 0.906, 0.555, 0.930], [0.610, 0.906, 0.649, 0.930], [0.704, 0.906, 0.743, 0.930], [0.798, 0.906, 0.837, 0.930]], which will become the column headers representing the years from 2000 to 2015. Next, the legend, represented by {the legend texts}[[0.194, 0.145, 0.336, 0.175], [0.194, 0.189, 0.263, 0.219]] will form the row labels indicating the two industry types: Pharmaceutical and Biotech. Finally, to fill the table, I'll examine the line points for each industry types {Pharmaceutical}[[0.160, 0.235], [0.207, 0.477], [0.254, 0.396], [0.301, 0.316], [0.348, 0.235], [0.395, 0.557], [0.442, 0.598], [0.489, 0.598], [0.536, 0.638], [0.583, 0.718], [0.630, 0.155], [0.677, 0.195], [0.724, 0.235], [0.771, 0.276], [0.818, 0.316], [0.865, 0.356]] and {Biotech}[[0.160, 0.855], [0.207, 0.839], [0.254, 0.823], [0.301, 0.815], [0.348, 0.799], [0.395, 0.775], [0.442, 0.758], [0.489, 0.783], [0.536, 0.791], [0.583, 0.807], [0.630, 0.815], [0.677, 0.799], [0.724, 0.783], [0.771, 0.807], [0.818, 0.823], [0.865, 0.839]] at the corresponding years and extract the values.\nBased on the above thought, the answer is:\nYear,Pharmaceutical,Biotech\n2000,0.8,0.03\n2001,0.5,0.05\n2002,0.6,0.07\n2003,0.7,0.08\n2004,0.8,0.1\n2005,0.4,0.13\n2006,0.35,0.15\n2007,0.35,0.12\n2008,0.3,0.11\n2009,0.2,0.09\n2010,0.9,0.08\n2011,0.85,0.1\n2012,0.8,0.12\n2013,0.75,0.09\n2014,0.7,0.07\n2015,0.65,0.05"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "lFqq3dU914JjUy4l",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends presented on the line chart, which industry showcases a more consistent growth pattern? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which of the two, Pharmaceutical or Biotech, has a more consistent growth pattern, I'd need to consider the relative fluctuations in their line plots. Observing {the line for Pharmaceutical}[0.160, 0.155, 0.865, 0.718], the progression suggests multiple dramatic fluctuations. In contrast, examining {the line for Biotech} [0.160, 0.758, 0.865, 0.855], the overall trend depicts a more consistent increase in values over the years with relatively fewer fluctuations.\nBased on the above thought, the answer is:\nThe Biotech industry signifies a more consistent growth pattern based on the trends portrayed in the line chart. Unlike Pharmaceutical, this industry shows steady growth over time with smaller fluctuations, implying a more progressive and stable growth pattern."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "pau13ecisNusKDIO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, what is the highest point of growth for the Pharmaceutical industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest point of growth for the Pharmaceutical industry, I must locate {the line points for Pharmaceutical}[[0.160, 0.235], [0.207, 0.477], [0.254, 0.396], [0.301, 0.316], [0.348, 0.235], [0.395, 0.557], [0.442, 0.598], [0.489, 0.598], [0.536, 0.638], [0.583, 0.718], [0.630, 0.155], [0.677, 0.195], [0.724, 0.235], [0.771, 0.276], [0.818, 0.316], [0.865, 0.356]]. The values corresponding to the y-coordinates of these line points are the growth points: 0.8, 0.5, 0.6, 0.7, 0.8, 0.4, 0.35, 0.35, 0.3, 0.2, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65. The largest value is 0.9, and this is achieved in year {2010}[0.630, 0.906].\nBased on the above thought, the answer is:\nThe highest point of growth for the Pharmaceutical industry is 0.9 in the year 2010."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "RYXFrZ9hGouxRs0K",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data in the bar chart into tabular format with Disease as rows and Disease Management and Human Health as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can transform the data into a table by considering the labels on the x-axis as the Diseases which correspond to {the x-axis labels}<r>[0.081,0.078,0.175,0.099]</r>, <r>[0.220,0.078,0.313,0.099]</r>, <r>[0.357,0.078,0.454,0.099]</r> and so on. These will form the rows of the table. Then, I will look at {the y-axis}<r>[0.047, 0.119, 0.066, 0.911]</r> for the height of each bar. The two kinds of bars for each disease represent 'Disease Management' and 'Human Health', which will be the columns of the table, and these could correspond to the colored bars with coordinates <r>[0.073,0.132,0.110,0.653]</r>, <r>[0.110,0.132,0.147,0.723]</r> and so on, which contain their height and therefore their respective values.\nBased on the above thought, the answer is:\nDisease,Disease Management,Human Health\nType 1 Diabetes,77,79\nType 2 Diabetes,72,70\nCardiovascular,73,80\nRespiratory,68,85\nHepatitis,77,90\nCancer,82,75\nAlzheimer,75,72\nParkinson,78,67\nArthritis,79,85"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "79iBhW4S81pY2UsY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which disease has the largest gap between Disease Management and Human Health scores, making it difficult to manage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which disease has the largest gap between Disease Management and Human Health scores, I need to analyze {the different bars for each disease}<r>[0.073, 0.132, 0.110, 0.653]</r>, <r>[0.220, 0.132, 0.258, 0.611]</r>, <r>[0.365, 0.132, 0.402, 0.666]</r> etc. By comparing the heights of the two bars belonging to each disease, it appears that Hepatitis has a large gap between its Disease Management and Human Health scores {Hepatitis bars}<r>[0.511, 0.132, 0.548, 0.657]</r>, <r>[0.548, 0.132, 0.586, 0.769]</r>. Therefore, it seems to be the most difficult disease to manage out of those represented on the chart.\nBased on the above thought, the answer is:\nAccording to the chart, Hepatitis appears to have the largest gap between Disease Management and Human Health scores, suggesting that it may be the most difficult disease to manage effectively out of those listed."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "mfIPoKJuNfsBUq7b",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Disease Management and Human Health scores for Parkinson?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the Disease Management and Human Health scores for Parkinson, I would need to identify {the bars for Parkinson}<r>[0.657, 0.132, 0.694, 0.654]</r> and <r>[0.694, 0.132, 0.731, 0.561]</r>. Extrapolating the height of these bars to the y-axis indicates that Parkinson's Disease Management score appears to be around 78 and its Human Health score seems to be around 67. The difference between these two scores is 78 - 67 = 11.\nBased on the above thought, the answer is:\n11"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "o0LPojGA2rrGfqFI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data provided in the line chart, can you convert the year-wise sales data for Condominiums and Single-Family Home into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a table, I start by identifying {the x-axis labels}<r>[0.110, 0.898, 0.188, 0.934], [0.262, 0.898, 0.389, 0.934], [0.439, 0.898, 0.566, 0.934], [0.616, 0.898, 0.743, 0.934], [0.792, 0.898, 0.919, 0.934]</r> as the years. Then, I locate the line labeled {Condo Sales}<r>[0.127, 0.123, 0.185, 0.137]</r> and the corresponding line points <r>[0.149, 0.843], [0.237, 0.712], ..., [0.944, 0.816]</r> to find the condominium sales data for each year. Similarly, I locate the line labeled {Single-Family Home Sales}<r>[0.127, 0.123, 0.185, 0.137]</r> and extract its corresponding line points <r>[0.149, 0.382], [0.237, 0.325], ..., [0.944, 0.217]</r> to find the single-family home sales data for each year.\nBased on the above thought, the answer is:\nYear,Condo Sales,Single-Family Home Sales\n2000,50000,70000\n2001,55000,73000\n2002,57500,74000\n2003,60000,76000\n2004,61000,80000\n2005,75000,85000\n2006,73250,88000\n2007,70000,86000\n2008,58000,82000\n2009,51000,79000"
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "0LbMAMSC3wwoMfRk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which type of property witnessed the most significant drop in sales over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which property type witnessed the most significant drop, I examine the {Condo Sales line}<r>[0.149, 0.288, 0.944, 0.843]</r> and the {Single-Family Home Sales line}<r>[0.149, 0.069, 0.944, 0.382]</r>. By comparing the line points, we can see that Condo Sales surged in the year {2005}<r>[0.591, 0.898, 0.594, 0.931]</r> to 75,000 but dropped sharply to 51,000 by {2009}<r>[0.944, 0.898, 0.950, 0.934]</r>, a drop of 24,000. However, Single-Family Home Sales also reached a peak in {2006}<r>[0.679, 0.898, 0.683, 0.931]</r> at 88,000 and dipped to 79,000 in {2009}<r>[0.944, 0.898, 0.950, 0.934]</r>, a drop of 9,000. Therefore, the condo sales witnessed the most significant drop.\nBased on the above thought, the answer is:\nThe Condominium market witnessed the most significant drop in sales over the years. This is evident from the sharp decrease in the number of Condo sales from 75,000 in 2005 to 51,000 in 2009, a drop of 24,000. While single-family homes also witnessed a sales drop in this period, it was less steep, making the Condo market's decrease more significant."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "SON15vWdovOEivIG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest number of Condominium sales in any given year according to the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest number of Condominium sales, I need to look at the {line points of the line corresponding to Condo Sales}<r>[0.149, 0.843], [0.237, 0.712], ..., [0.944, 0.816]</r> and extract the corresponding sales numbers: 50000, 55000, 57500, 60000, 61000, 75000, 73250, 70000, 58000, 51000. The maximum value among these is 75000, which corresponds to the year {2005}<r>[0.591, 0.898, 0.594, 0.931]</r>.\nBased on the above thought, the answer is:\n75000."
+            }
+        ],
+        "id_raw": 247
+    },
+    {
+        "id": "KGmvc17Aw8ZHH3SV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data showing trends of Traditional Education and Virtual Training over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to present the line chart data in a table format, the {x-axis labels}<r>[0.125, 0.895, 0.942, 0.895]</r> that represent the Years serve as the row headers. The {legend items}<r>[0.175, 0.058, 0.436, 0.092], [0.175, 0.110, 0.362, 0.144]</r> are used as column headers, standing for Traditional Education and Virtual Training respectively. For the values corresponding to each year and education type, I trace {the points on the line for Traditional Education}<r>[0.125, 0.350, 0.942, 0.687]</r> and {the points on the line for Virtual Training}<r>[0.125, 0.065, 0.942, 0.855]</r>, matching each point's x-coordinate with a year and y-coordinate with the number of Users.\nBased on the above thought, the answer is:\nYear,Traditional Education,Virtual Training\n2000,100,10\n2001,90,15\n2002,80,23\n2003,75,29\n2004,65,36\n2005,60,45\n2006,55,54\n2007,53,64\n2008,50,75\n2009,48,86\n2010,47,98\n2011,45,110\n2012,44,123\n2013,42,137\n2014,40,151"
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "e71IVISKQflkQLZb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how would you describe the trend of Traditional Education and Virtual Training?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend of Traditional Education and Virtual Training, I have to examine their corresponding line graphs. The {line for Traditional Education}<r>[0.125, 0.350, 0.942, 0.687]</r> demonstrates a consistent downward trend from 100 to 40 from 2000 to 2014, indicating a gradual decrease in Traditional Education over time. In contrast, the {line for Virtual Training}<r>[0.125, 0.065, 0.942, 0.855]</r> exhibits an ascending trend from 10 to 151 over the same period, indicating a significant increase in the adoption of Virtual Training.\nBased on the above thought, the answer is:\nTraditional Education shows a consistent decrease over the years from 2000 to 2014, while in contrast, Virtual Training presents a substantial increase during the same period. This suggests a shift from traditional education methods to virtual learning platforms."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "b3mKqudbhYHH0SUU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the number of Users for Virtual Training from year 2001 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in the number of Users for Virtual Training, I first locate {the point for Virtual Training in 2001}<r>[0.183, 0.827]</r> corresponding to 15 and then {the point for Virtual Training in 2014}<r>[0.942, 0.065]</r> corresponding to 151. The change is then calculated as 151 - 15 = 136.\nBased on the above thought, the answer is:\n136."
+            }
+        ],
+        "id_raw": 248
+    },
+    {
+        "id": "dbNzFMFwjEhmdbhC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the data from this line chart in structured table format with the different parameters ('Employee Morale', 'Market Share', 'Productivity', etc.) as rows and 'Non-aggregation' and 'Aggregation' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, firstly we need to identify the x-axis labels seen in the chart. These can be obtained from {the x-axis tick labels}[[-0.358, 0.892, -0.337, 0.947], [-0.115, 0.892, -0.094, 0.947], [0.129, 0.892, 0.149, 0.934], [0.372, 0.892, 0.393, 0.934], [0.615, 0.892, 0.636, 0.934], [0.859, 0.892, 0.879, 0.934]], which act as the row headers representing different parameters. The legend items {the legend texts}[[0.190, 0.653, 0.451, 0.687], [0.190, 0.705, 0.377, 0.739]] represent 'Non-aggregation' and 'Aggregation', and are our column headers. The y-values of the chart match with the score of each parameter for both 'Non-aggregation' and 'Aggregation'. These scores can be extracted from {the line points for Non-aggregation}[[0.139, 0.068], [0.212, 0.131], [0.255, 0.193], [0.286, 0.255], [0.309, 0.317], [0.328, 0.379]] and {the line points for Aggregation}[[0.139, 0.255], [0.212, 0.193], [0.255, 0.131], [0.286, 0.193], [0.309, 0.068], [0.328, 0.131]].\nBased on the above thought, the answer is:\nParameters,Non-aggregation,Aggregation\nInterest Rate,0.1,0.0001\nMoney Supply,0.01,0.001\nPublic Debt,0.001,0.01\nExchange Rate,0.0001,0.001\nInflation Rate,0.00001,0.1\nUnemployment Rate,0.000001,0.01"
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "IDnMx798rJ6iUJJy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which policy, Non-aggregation or Aggregation, would you recommend for a company aiming for steady growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to compare the overall trends of the Non-aggregation and Aggregation lines in the chart. The line for {Non-aggregation}[0.139, 0.068, 0.328, 0.379] overall shows a steady increase in scores across multiple parameters with some fluctuations, indicating consistent growth. On the other hand, the line for {Aggregation}[0.139, 0.255, 0.328, 0.131] shows a steady decline, except for an uptick at 'Employee Satisfaction', overall indicating a trend of decline over time.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Non-aggregation would be the recommended policy for a company aiming for steady growth. The Non-aggregation approach indicates consistent growth across multiple business management and organizational leadership parameters, whereas the Aggregation approach shows a general decrease in performance, except for a single instance in the 'Employee Satisfaction' parameter. Hence, the Non-aggregation seems to yield better results for continuous growth."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "7lHjHoKFhxPEfVZq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in 'Employee Satisfaction' scores between Non-aggregation and Aggregation methodologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the 'Employee Satisfaction' scores, we need to locate the points for 'Employee Satisfaction' on both lines. The Non-aggregation score for 'Employee Satisfaction' is recorded at {the point for Non-aggregation}[0.309, 0.317]. The Aggregation score for 'Employee Satisfaction' is recorded at {the point for Aggregation}[0.309, 0.068]. Subtracting the two scores will give the difference.\nBased on the above thought, the answer is:\n0.249."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "4OPR3HIbbCJK61ld",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reformat information from the line chart into a table with the models as rows and the cryptocurrencies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.106, 0.884, 0.133, 0.934], [0.429, 0.884, 0.452, 0.934], [0.618, 0.884, 0.646, 0.934], [0.753, 0.884, 0.778, 0.934], [0.857, 0.884, 0.880, 0.934], [0.942, 0.884, 0.968, 0.934]] as the column headers, and {the legend texts}[[0.771, 0.064, 0.958, 0.098] ,[0.771, 0.116, 0.961, 0.149]] as the row labels representing the two models. Then, in order to fill in the table, I find the {the line points for each cryptocurrency under each model}[[0.106, 0.039], [0.429, 0.147], ..., [0.942, 0.581]] and extract the corresponding values for each cryptocurrency.\nBased on the above thought, the answer is:\nCryptocurrencies,Model A,Model B\nBitcoin,$10,000,$9,500\nEthereum,$300,$350\nRipple,$0.23,$0.25\nCardano,$1.40,$1.35\nDogecoin,$0.09,$0.12\nPolkadot,$20.00,$18.00\nLitecoin,$170,$200\nChainlink,$27.50,$29.00\nStellar,$0.30,$0.28"
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "CvZ2gK8hk50LWTzk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cryptocurrency displayed the least stability between Model A and Model B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stability can be inferred from changes in price from Model A to Model B. I can observe {Bitcoin's line}<r>[0.106, 0.039, 0.429, 0.147]</r> which shows a mild decrease, {Ethereum\u2019s line}<r>[0.429, 0.147, 0.618, 0.256]</r> represents a slight increase, while {Dogecoin\u2019s line}<r>[0.753, 0.364, 0.857, 0.473]</r> indicates a significant increase in price. Other cryptocurrencies also show various changes but to various extents.\nBased on the above thought, the answer is:\nBased on the line chart, Dogecoin displayed substantial changes in price hence showing the least stability between Model A and Model B because it undergoes a substantial price increase from \\$0.09 to \\$0.12, which is a relatively large change for a low-value cryptocurrency."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "DjcIf43QItCGfhkE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in the price of Ethereum between Model A and Model B?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to locate the coordinates representing Ethereum prices under each model. In Model A, {Ethereum's price}<r>[0.106, 0.256]</r> is $300, and in Model B {Ethereum's price}<r>[0.429, 0.364]</r> slightly increased to $350. The formula to calculate percentage difference is ((New Value - Old Value)/Old Value)*100. Hence, the percentage difference in Ethereum's price from Model A to Model B is ((350-300)/300)*100 = 16.67%.\nBased on the above thought, the answer is:\n16.67%."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "TEcAmnhT36Z12kkA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, can you construct a table that compares the occupancy rates between local and foreign tourists for each hotel category?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the x-axis tick labels}[[0.133, 0.911, 0.154, 0.934], [0.293, 0.911, 0.314, 0.934], [0.453, 0.911, 0.474, 0.934], [0.613, 0.911, 0.634, 0.934], [0.772, 0.911, 0.794, 0.934], [0.932, 0.911, 0.954, 0.934]], which represent the row labels or hotel categories. Then, I identify {the legend texts}[[0.195, 0.058, 0.370, 0.092], [0.195, 0.110, 0.402, 0.144]] as the column headers representing the local and foreign tourists. For each hotel category, I need to locate {the bars}[[0.143, 0.055, 0.158, 0.783], [0.158, 0.055, 0.172, 0.855], [0.303, 0.055, 0.318, 0.639], [0.318, 0.055, 0.332, 0.783], [0.463, 0.055, 0.478, 0.496], [0.478, 0.055, 0.493, 0.711], [0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]...] for local and foreign tourists and get their heights, which represent the occupancy rates.\nBased on the above thought, the answer is:\nHotel,Local Tourist Occupancy Rate,Foreign Tourist Occupancy Rate\nHotel A,78,86\nHotel B,64,78\nHotel C,50,71\nHotel D,35,64\nHotel E,21,57\nHotel F,14,50\nHotel G,7,39\nHotel H,0,30"
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "jl9GYHWG3wErOT5P",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which hotel category exhibited the most noticeable difference in occupancy rates between local and foreign tourists, and why do you think that might be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the heights of {the bars}[[0.143, 0.055, 0.158, 0.783], [0.158, 0.055, 0.172, 0.855], [0.303, 0.055, 0.318, 0.639], [0.318, 0.055, 0.332, 0.783], [0.463, 0.055, 0.478, 0.496], [0.478, 0.055, 0.493, 0.711], [0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]...], it appears that the most noticeable difference in occupancy rates is exhibited by Hotel D, where the local tourist rate is significantly lower than the foreign tourist rate.\nBased on the above thought, the answer is:\nHotel D exhibits the most noticeable difference in occupancy rates between local and foreign tourists. This might be due to a variety of possible factors. For instance, the hotel might be primarily catering to foreign tourists on the basis of its location or services provided. It could also indicate a higher price point that may not be affordable for the local population, or a marketing strategy that mainly targets foreign tourists. It's also possible that the hotel is located in an area of high tourist interest, resulting in more bookings from foreign visitors compared to locals."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "t0YKklAW2dY1VGiW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in occupancy rates between local and foreign tourists for Hotel D?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in occupancy rates for Hotel D, I'll look at {the bars for Hotel D}[[0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]] and extract their heights representing the occupancy rates. The local tourist rate is approximately 35, and the foreign tourist rate is approximately 64. Therefore, the difference is 64 - 35 = 29.\nBased on the above thought, the answer is:\n29%."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "69mXRCUQtvWvu4Wm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the chart's data into a structured table where the columns represent the years and the rows represent the type of entrepreneurs?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the graph\u2019s data into a tabular format, I would first identify {the x-axis labels}[[-0.099, 0.887, -0.021, 0.934], [0.067, 0.887, 0.146, 0.934], [0.233, 0.887, 0.312, 0.934], [0.400, 0.887, 0.478, 0.934], [0.566, 0.887, 0.644, 0.934], [0.732, 0.887, 0.811, 0.934], [0.898, 0.887, 0.977, 0.934]], meaning the years, as column titles. Then, I would interpret {the legend items}[[0.620, 0.058, 0.887, 0.092], [0.620, 0.110, 0.957, 0.144]] which correlate to the row head labels or types of entrepreneurs. To fill out the cells, I review {the line points for Novice Entrepreneurs}[[0.106, 0.787], [0.273, 0.448], [0.439, 0.702], [0.605, 0.787], [0.771, 0.448] [0.938, 0.702]] and {the line points for Experienced Entrepreneurs}[[0.106, 0.863], [0.273, 0.867], [0.439, 0.870], [0.605, 0.863], [0.771, 0.871] [0.938, 0.870]], which represent the values for each entrepreneur type per year.\nBased on the above thought, the answer is:\nYear,Novice Entrepreneurs,Experienced Entrepreneurs\n2005,1.0,1.0e-1\n2006,5.0,5.0e-2\n2007,2.0,2.0e-2\n2008,1.0,1.0e-1\n2009,5.0,5.0e-4\n2010,2.0,2.0e-2"
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "0kjLKoaPziohXR5f",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data plot, which group of entrepreneurs showed a steadier trend in their contribution over the years and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the steadiness of trends, I need to analyze the line plots for each group. Examining {the line for Novice Entrepreneurs}[0.106, 0.448, 0.938, 0.787], the values fluctuate between 1.0, 5.0, and 2.0 across the years showing considerable volatility. On the other hand, looking at {the line for Experienced Entrepreneurs}[0.106, 0.863, 0.938, 0.871], the values fluctuate between 1.0e-1, 5.0e-2, 2.0e-2, and 5.0e-4 which seems to gradually decrease and shows a more consistent trend compared to Novice Entrepreneurs.\nBased on the above thought, the answer is:\nThe Experienced Entrepreneurs show a steadier trend in their contributions over the years, according to the data plot. Though their values gradually decrease over time, the change is more consistent and shows less variation compared to the Novice Entrepreneurs, making their trend appear steadier."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "ReWNUThx1sK9U7Vt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of values for Novice Entrepreneurs across all the years in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total values for Novice Entrepreneurs across all years, I need to look at {the line points of the Novice Entrepreneurs}[[0.106, 0.787], [0.273, 0.448], [0.439, 0.702], [0.605, 0.787], [0.771, 0.448] [0.938, 0.702]]. The values for those points are 1.0, 5.0, 2.0, 1.0, 5.0, and 2.0 respectively. The sum of these is 16.0.\nBased on the above thought, the answer is:\n16.0."
+            }
+        ],
+        "id_raw": 252
+    },
+    {
+        "id": "7ldtzUGlUVawTBnH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table that represents the information from the line chart, with Time points along the columns and the Assessment Criteria along the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first look at {the x-axis tick labels}[[-1.600, 0.884, -1.528, 0.934], [-0.765, 0.884, -0.693, 0.934], [0.079, 0.884, 0.133, 0.934], [0.915, 0.884, 0.968, 0.934]] representing years as columns. I also check {the legend labels}[[0.156, 0.071, 0.356, 0.105], [0.156, 0.123, 0.393, 0.157]] as the row labels or Assessment Criteria. The data for each criterion at different time points is taken from their respective {line points}[[0.106, 0.702, 0.756, 0.752], [0.106, 0.230, 0.756, 0.536]].\nBased on the above thought, the answer is:\nYears, Timepoint: 0, Timepoint: 1, Timepoint: 2, Timepoint: 3\nDigital Currency,0.0005,0.0008,0.001,0.0007\nFinTech Application,0.05,0.06,0.02,0.01"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "h5nnSZfRAvE8RCz1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shown in the chart has the most stable performance over time? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which line demonstrates the most stable performance, I observe the fluctuation in {the line for Digital Currency}[0.106, 0.702, 0.756, 0.752] and {the line for FinTech Application}[0.106, 0.230, 0.756, 0.536]. The line for Digital Currency fluctuates between 0.0005 to 0.001, with a slight increase at time point 2 and decreases at time points 3 to 5, indicating a certain degree of instability. The line for FinTech Application starts from 0.05, increases to 0.06, drops significantly to 0.02, and then increases rapidly, showing a very high degree of volatility. In comparison, the fluctuations in the Digital Currency line are less drastic than that of the FinTech Application, implying a higher degree of stability.\nBased on the above thought, the answer is:\nThe Digital Currency line demonstrates the most stable performance over time as evidenced by their line plot. The line plot of the Digital Currency shows slight ups and downs around a narrow range, indicating relatively small fluctuations compared to the FinTech Application, which exhibits more drastic changes over time. These smaller fluctuations in the Digital Currency line suggests that it has more stability than the FinTech Application."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "sL3PrlFdxQUuRquB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value achieved by the Digital Currency line in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value achieved by the Digital Currency, I examine {the line points for Digital Currency}[[0.106, 0.752], [0.358, 0.718], [0.505, 0.702], [0.609, 0.728], [0.690, 0.739], [0.756, 0.718]] corresponding to values: 0.0005, 0.0008, 0.001, 0.0007, 0.0006, 0.0008. The maximum value is seen at the third time point, corresponding to {Time Point: 2}[0.505, 0.884].\nBased on the above thought, the answer is:\n0.001."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "xjVmEdQeMeJzv0Bw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you capture the trends from the line chart in the form of a table, with the heading showing media platforms and each row representing a specific time point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first identify the columns as the different media platforms, indicated by {the legend labels}<r>[0.686, 0.044, 0.971, 0.158]</r>. The rows represent the time points, indicated by {the x-axis tick labels}<r>[-0.055, 0.911, -0.022, 0.934], [0.120, 0.911, 0.130, 0.934], [0.279, 0.911, 0.298, 0.934], [0.442, 0.911, 0.462, 0.934], [0.606, 0.911, 0.625, 0.934], [0.769, 0.911, 0.789, 0.934], [0.928, 0.911, 0.957, 0.934]</r>. To get the values, I look at {the line points} for each platform at each time point and read off the corresponding market share values.\nBased on the above thought, the answer is:\nTime,Netflix,Disney Plus,HBO Max,Prime Video,Hulu\n0,50,15,20,10,5\n20,40,20,15,15,10\n40,45,25,10,12,8\n60,42,30,8,10,10"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "cQkHXaCxI1ADoWgr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends represented in the line chart, which streaming platform shows the most stable market share over time and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe each line in the chart and compare their fluctuations. {The Netflix line}<r>[0.166, 0.065, 0.942, 0.443]</r> bounces, suggesting unstable market share over time. {The Disney Plus line}<r>[0.125, 0.202, 0.223, 0.855]</r> consistently trends upward, indicating growth, but not necessarily stability. {The Hulu Line}<r>[0.166, 0.065, 0.942, 0.443]</r> goes up and down, indicating fluctuations and hence instability. {The Prime Video line}<r>[0.166, 0.065, 0.942, 0.443]</r> and {the HBO Max line}<r>[0.166, 0.202, 0.223, 0.855]</r> both exhibit a descending trend. Comparatively, the Prime Video line has lesser fluctuations in its downward trend, signifying more stability than HBO Max.\nBased on the above thought, the answer is:\nPrime Video shows the most stable market share over time. Although it depicts a downward trend, the rate of decrease is steady and progressive with lesser fluctuations than other platforms, indicating stability in comparison."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "PUBQuqaqMasxaqeE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what time is the lowest market share for Prime Video observed and what is that market share value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the lowest market share for Prime Video, I need to analyze {the line plot for Prime Video}<r>[0.166, 0.065, 0.942, 0.443]</r> and find the lowest point. It corresponds to Time 60 with {the lowest point}<r>[0.663, 0.765]</r> and a value of 10.\nBased on the above thought, the answer is:\nPrime Video's lowest market share of 10 is observed at Time 60."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "BX8YmGkCudmO3woo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, can you present the values in a table format with time points as columns and AI applications as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I'll use {the x-axis tick labels}[[0.179, 0.110, 0.198, 0.136], [0.309, 0.110, 0.334, 0.135], [0.447, 0.110, 0.468, 0.134], [0.578, 0.110, 0.599, 0.134]] as the column headings, representing the different time points. Next, I identify {the legend texts}[[0.915, 0.717, 0.986, 0.735], [0.915, 0.652, 0.987, 0.671], [0.915, 0.586, 0.995, 0.605], [0.915, 0.522, 0.983, 0.541], [0.915, 0.460, 0.988, 0.479]] as row labels showing the different AI applications. Then I trace values for each application at the respective time points, these values are {the line points}[[0.179, 0.699], [0.309, 0.616], [0.447, 0.692], [0.578, 0.697], [0.178, 0.377], [0.309, 0.487], [0.447, 0.562], [0.578, 0.685], [0.178, 0.516], [0.309, 0.512], [0.447, 0.526], [0.578, 0.553], [0.178, 0.450], [0.309, 0.457], [0.447, 0.473], [0.578, 0.493], [0.178, 0.594], [0.309, 0.603], [0.447, 0.611], [0.578, 0.619]].\nBased on the above thought, the answer is:\nAI Application,Time Point 1,Time Point 2,Time Point 3,Time Point 4\nSpeech Recognition,0.8,0.55,0.7,0.7\nAutonomous Vehicles,0.5,0.65,0.75,0.85\nMachine Learning,0.6,0.58,0.6,0.65\nRobotics,0.55,0.57,0.59,0.58\nNeural Networks,0.7,0.71,0.72,0.73"
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "v3q8xZNPvKjWlP7q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to visual trends in the line graph, which AI application shows a consistently improving trend over time? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the AI application that shows a consistently improving trend, I need to examine the gradients of the line plots for each AI application.\nEvaluating {the line for Speech Recognition}[0.179, 0.699, 0.578, 0.697] shows an oscillating trend that goes down then up. Next, analyzing {the line for Autonomous Vehicles}[0.178, 0.377, 0.578, 0.685], I observe a steady upward slope indicating ongoing improvement. Comparatively, the other lines for {Machine Learning}[0.178, 0.516, 0.578, 0.553] and {Robotics}[0.178, 0.450, 0.578, 0.493] present negligible upward trends while {Neural Networks}[0.178, 0.594, 0.578, 0.619] shows a slightly positive slope, but not as steep as Autonomous Vehicles.\nBased on the above thought, the answer is:\nAccording to the visual trends in the line graph, Autonomous Vehicles exhibit a consistently improving trend over time. The line for this AI application shows a steady, consistent upward slope, which conveys a consistent improvement from the first to the last time point. Although other AI applications such as Machine Learning, Robotics and Neural Networks also show slight improvements, the upward trend is most pronounced for Autonomous Vehicles, making it the most improving AI application according to the graph's visual trends."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "2nojuvG50jnGwtvE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of growth for Autonomous Vehicles from the first to the last time point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of growth for Autonomous Vehicles, I will have to calculate the difference in values from the first time point {at point}[0.178, 0.377] value of 0.5 to the last time point {at point}[0.578, 0.685] value of 0.85. The difference can be calculated as 0.85 - 0.5 = 0.35\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "23D28P7OoUjmMs6q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data from the bar chart into a structured table with the categories as the columns and the time periods as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create a structured table from the bar chart, I will first identify the categories as column names. The categories can be found in {the legend}<r>[0.712, 0.048, 0.974, 0.144]</r>, which lists - 'Startups', 'Investments', 'Patents', 'Innovation', and 'Funding'. Next, I'll determine the row labels by looking at {the x-axis tick labels}<r>[-0.043, 0.901, 0.020, 0.934], [0.063, 0.901, 0.126, 0.934], [0.169, 0.901, 0.232, 0.934], [0.274, 0.901, 0.337, 0.934], [0.380, 0.901, 0.443, 0.934], [0.486, 0.901, 0.549, 0.934], [0.592, 0.901, 0.655, 0.934], [0.698, 0.901, 0.761, 0.934], [0.804, 0.901, 0.867, 0.934], [0.909, 0.901, 0.973, 0.934], [1.015, 0.901, 1.078, 0.934]</r>, which represent each year from 2012 to 2020. Finally, extracting the values from the bars at each year for each of the categories - For example {bar representing Startups in 2012}<r>[0.094, 0.223]</r>, {bar representing Investments in 2012}<r>[0.094, 0.318]</r> and so on.\nBased on the above thought, the answer is:\nYears,Startups,Investments,Patents,Innovation,Funding\n2012,0.8,0.7,nil,nil,nil\n2013,0.68,0.6,nil,nil,nil\n2014,0.57,0.51,nil,nil,nil\n2015,0.49,0.4,nil,nil,nil\n2016,0.45,0.3,nil,nil,nil\n2017,0.41,0.25,nil,nil,nil\n2018,0.39,0.2,nil,nil,nil\n2019,0.35,0.35,nil,nil,nil\n2020,0.31,0.4,nil,nil,nil"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "skXXEwdOpLMblzSX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two assessed areas, \"Social Responsibility\" or \"Sustainable Business\", faced a steeper decline from 2012 to 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area faced a steeper decline, I need to identify the change in each area's focus over the given period. This involves analyzing the trends in {the line for Social Responsibility}<r>[0.094, 0.223, 0.941, 0.686]</r>, and {the line for Sustainable Business}<r>[0.094, 0.318, 0.941, 0.790]</r>. By comparing the initial and final coordinates for each line, it can be seen that both have a negative slope indicating a decline over time. Subtraction of the initial Y-coordinate from the final Y-coordinate in both lines will give the total decline in each area respectively.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, \"Social Responsibility\" faced a steeper decline from 2012 to 2020. Despite both areas showing a general decrease over this period, the slope of the line for \"Social Responsibility\" is steeper, suggesting a greater rate of decrease in focus compared to \"Sustainable Business\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "WPlV9DPUEexNkcmm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the year 2013 and 2017, how much did the focus on \"Social Responsibility\" decrease by?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in the focus on \"Social Responsibility\" between 2013 and 2017, I need to locate {the bar for Social Responsibility in 2013}<r>[0.200, 0.337]</r> and extract the corresponding value which is 0.68. Similarly, I will locate {the bar for Social Responsibility in 2017}<r>[0.623, 0.592]</r> which gives me the value 0.41. Subtracting the value in 2017 from the value in 2013 should give me the decrease.\nBased on the above thought, the answer is:\n0.27."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "FSDQr8GBDlx7CkFZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a table format with the time periods as columns and the different parameters (Number of Family Members, Living Space, Number of Bedrooms, Income) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table format from the bar chart series, I'll first identify the {x-axis time labels} based on bar color and the legend<r>[0.744, 0.057, 0.971, 0.175]</r> as the column headers or time periods. Then I'll interpret the {y-axis labels} at the start of the bars as the different row labels representing the parameters, and I should find {the lengths of the bars} spanning from their onset to their terminals for each parameter at the respective time periods and extract the values.\nBased on the above thought, the answer is:\nParameter,Time 1, Time 2, Time 3, Time 4\nNumber of Family Members,4,6,7,9\nLiving Space (Square Meters),60,80,105,130\nNumber of Bedrooms,2,3,3,2\nIncome (per month),3000,3500,3200,3600"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "W9TyUMAMI89ksmbg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which living condition parameter has seen the most significant change over time? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which parameter has seen the most significant change, I need to compare the bar lengths representing initial and final values for each parameter. For 'Number of Family Members', there's a difference between {Time 1}<r>[0.098, 0.315]</r> and {Time 4}<r>[0.941, 0.398]</r> by 5 family members. For 'Living Space', the difference is from {Time 1}<r>[0.098, 0.266]</r> to {Time 4}<r>[0.601, 0.251]</r>, a significant increase by 70 square meters. The 'Number of Bedrooms' decreased by 1 from {Time 1}<r>[0.438, 0.231]</r> to {Time 4}<r>[0.941, 0.508]</r>, and 'Income' has increased by $600 between {Time 1}<r>[0.438, 0.283]</r> and {Time 4}<r>[0.941, 0.508]</r>.\nBased on the above thought, the answer is:\nBased on the bar chart, the 'Living Space' parameter has seen the most significant change over the time periods. The total living space increased by 70 square meters from the initial to the final time period, which is the largest absolute change among all the parameters. While other parameters like the number of family members and income also increased, the relative increase in living space is the most substantial."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "PSim9QjVMQt7azAQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much difference is there in the family's monthly income between the first and the last time periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the family's monthly income between the first and last time periods, I first locate the {Income bar for Time 1}<r>[0.260, 0.274]</r> and extract the corresponding value: $3000. Then I locate the {Income bar for Time 4}<r>[0.941, 0.508]</r> and extract the corresponding value: $3600. The difference between the two is therefore 3600 - 3000 = 600.\nBased on the above thought, the answer is:\n$600."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "Y9rRGFtDhkFUWGuX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the horizontally stacked bar chart into a tabular format with the Time as the columns and Categories as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, the first step is to identify the category labels found at {the y-axis ticks}<r>[[0.027, 0.836, 0.046, 0.859], [0.027, 0.696, 0.046, 0.719], [0.027, 0.560, 0.046, 0.583], [0.027, 0.423, 0.046, 0.446], [0.027, 0.284, 0.046, 0.307]]</r>. Then, I'll identify the specific bars associated with each category such as {the bars for Interest Rates}<r>[[0.107, 0.804, 0.254, 0.870], [0.264, 0.819, 0.415, 0.856], [0.420, 0.804, 0.574, 0.870], [0.582, 0.794, 0.736, 0.880]]</r>. I'll repeat this for the bars for each category to fill the rows of the table. Lastly, the column headers or times can be obtained from {the legend}<r>[0.775, 0.178, 0.930, 0.256]</r>.\nBased on the above thought, the answer is:\nCategory,Time 0,Time 20,Time 40,Time 60\nInterest Rates,1, 0.85, 0.75, 0.5\nForeign Exchange Rates, 0.9, 0.8, 0.7, 0.6\nCommodity Prices, 0.7, 0.6, 0.8, 0.5\nInflation, 0.8, 0.7, 0.6, 0.5\nPublic Debt, 0.9, 0.7, 0.6, 0.4"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "3W84r2hitezzFNID",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the graph, which category in the Global Financial System manifest the most stability despite the general decline over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category remains most stable over time, I need to investigate the change in bar lengths for each category. Looking through {the bars for Interest Rates}<r>[[0.107, 0.804, 0.254, 0.870], [0.264, 0.819, 0.415, 0.856], [0.420, 0.804, 0.574, 0.870], [0.582, 0.794, 0.736, 0.880]]</r>, {for Foreign Exchange Rates}<r>[[0.107, 0.663, 0.249, 0.730], [0.264, 0.679, 0.413, 0.716], [0.420, 0.664, 0.570, 0.730], [0.583, 0.651, 0.732, 0.743]]</r>, {for Commodity Prices}<r>[[0.107, 0.523, 0.246, 0.590], [0.264, 0.538, 0.409, 0.575], [0.420, 0.523, 0.568, 0.590], [0.583, 0.505, 0.725, 0.595]]</r>, {for Inflation}<r>[[0.107, 0.383, 0.245, 0.451], [0.264, 0.399, 0.411, 0.436], [0.420, 0.383, 0.567, 0.451], [0.583, 0.371, 0.728, 0.463]]</r> and {for Public Debt}<r>[[0.107, 0.239, 0.237, 0.306], [0.265, 0.252, 0.398, 0.294], [0.422, 0.245, 0.568, 0.306], [0.583, 0.231, 0.716, 0.318]]</r>. Among all these, Commodity Prices show a conspicuous upswing from Time 20 to Time 40, making it the most stable category in terms of maintaining its score over time.\nBased on the above thought, the answer is:\nCommodity Prices appears the most stable across the selected time periods. Unlike the other categories, it shows a rise in stability from Time 20 to Time 40, in contrast to the general declining trend."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "KfkDG225AvNxwRiN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decline in Interest Rates from Time 0 to Time 60?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total decline in Interest Rates, I need to determine the values at Time 0 and Time 60. For Time 0, I checked the length of the first bar in {the bars for Interest Rates}<r>[[0.107, 0.804, 0.254, 0.870]]</r> and found it corresponded to a value of 1. For Time 60, I checked the length of the last bar in {the bars for Interest Rates}<r>[[0.582, 0.794, 0.736, 0.880]]</r> and found it corresponding to a value of 0.5. The difference between these two values is the total decline in Interest Rates.\nBased on the above thought, the answer is:\n0.5"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "KwzvkOvhUzqthjCm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you read the line chart and present the information in a structured table format with Years as columns and Types of time periods as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a structured table, I first observe {the x-axis tick labels}[[0.106, 0.907, 0.136, 0.938], [0.189, 0.907, 0.223, 0.938], [0.272, 0.907, 0.302, 0.938], [0.355, 0.907, 0.383, 0.938], [0.439, 0.907, 0.466, 0.938], [0.522, 0.907, 0.548, 0.938], [0.605, 0.907, 0.635, 0.938], [0.688, 0.907, 0.713, 0.938], [0.771, 0.907, 0.798, 0.938], [0.854, 0.907, 0.881, 0.938], [0.936, 0.907, 0.962, 0.938]], which represent the column headers or years. Next, I identify {the legend texts}[[0.156, 0.061, 0.317, 0.095], [0.156, 0.113, 0.357, 0.147]] as the row labels that represent the different time periods in the given system. Finally, to populate the cells I look at {the line points}[[0.106, 0.284, 0.936, 0.851], [0.106, 0.067, 0.936, 0.284]] for each time period at the respective years and extract the annotated values.\nBased on the above thought, the answer is:\nTime Periods,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010\nWaiting time,0.2,0.16,0.13,0.11,0.095,0.08,0.07,0.06,0.055,0.052,0.048\nProcessing Time,0.4,0.45,0.51,0.58,0.65,0.69,0.75,0.8,0.82,0.85,0.9"
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "ORaLQfFGmVfGsD7G",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReflecting on the line chart, which between 'Waiting time' and 'Processing Time' appears to have decreased the most significantly over time? Please explain your observation."
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which line has decreased more significantly, I must study the slope of each line over the years. Now, observing {the line for Waiting Time}[0.106, 0.470, 0.936, 0.851], I can see a noticeable decline between 2000 and 2010. \nNext, I examine {the line for Processing Time}[0.106, 0.067, 0.936, 0.284], which also shows a decline, albeit less steep. The decline in the \"Waiting Time\" is evidently more significant compared to the decline in the \"Processing Time\" over the same period.\nBased on the above thought, the answer is:\nGiven the trends displayed on the line chart, the 'Waiting Time' appears to have decreased more significantly over the years compared to the 'Processing Time'. The slope of the 'Waiting Time' line is steeper, denoting a higher rate of decline over the given time period."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "KcJ0pROgw1S1Vsa1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_13_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the waiting time in 2001 and 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between waiting times in 2001 and 2010, I need to look at {the line points for Waiting Time}[[0.189, 0.529], [0.936, 0.851]] and extract the corresponding values: 0.16 and 0.048. The difference between the waiting times in 2001 and 2010 is therefore 0.16 - 0.048 = 0.112.\nBased on the above thought, the answer is:\n0.112"
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "HfddCmme0gzL5qGv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you extract the data into a structured table format with Time as columns and Regions as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I would start by identifying {the x-axis tick labels}[[0.067, 0.911, 0.078, 0.934], [0.194, 0.911, 0.205, 0.934], [0.320, 0.911, 0.332, 0.934], [0.442, 0.911, 0.464, 0.934], [0.568, 0.911, 0.590, 0.934]], which represent different time points. Next, I look at {the legend texts}[[0.322, 0.034, 0.441, 0.066], [0.557, 0.034, 0.778, 0.066]] representing the different regions. To determine the values, I locate {the line points}[[0.073, 0.779], [0.199, 0.548], [0.326, 0.201], [0.453, 0.432], [0.579, 0.663]] for each region at the respective times, and then extract the corresponding values.\nBased on the above thought, the answer is:\nRegion,Time 0,Time 20,Time 40,Time 60,Time 80\nUnited States,0.5,1,1.5,2,1.5\nEuro Zone,1,3,6,4,2\nChina,0.5,1.5,1,2,1.5\nJapan,1,3,6,4,2\nUnited Kingdom,1,1.5,2,1.5,1"
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "LHw09ldZXbnGWlBu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which region experiences the most substantial fluctuations in policy impact over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which region experiences the most substantial fluctuations in policy impact, I need to look at the volatility in the line plots for each region. For {the United States}[0.073, 0.663, 0.959, 0.866], there is a gradual increase from 0.5 to 2, then a decrease to 1.5. For {the Euro Zone}[0.073, 0.779, 0.959, 0.871], there is an increase from 1 to 6, followed by a decrease to 2. This pattern indicates high volatility. For {China}[0.073, 0.837, 0.959, 0.866], the line shows an increase, then a decrease, then an increase, and then a decrease, exhibiting volatility as well. For {Japan}[0.073, 0.779, 0.959, 0.871], there is a similar pattern to the Euro Zone, of increasing and then decreasing. Finally, for {the United Kingdom}[0.073, 0.837, 0.959, 0.866], the line rises to a peak at time 40 and then decreases slightly. Among all these, the Euro Zone, China, and Japan show the most substantial fluctuations.\nBased on the above thought, the answer is:\nThe regions that experience the most substantial fluctuations in policy impact over time are the Euro Zone, China, and Japan. They all have increased policy impacts in the beginning followed by a drop, showing clear volatility over time. It indicates that the global financial policies' impact on these regions vary more significantly over time."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "uhA58s3d7kiDWEUX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in policy impact on the Euro Zone from the start to the final time point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in policy impact on the Euro Zone from the start to the final time point, I locate {the line points for the Euro Zone}[[0.073, 0.779], [0.199, 0.548], [0.326, 0.201], [0.453, 0.432], [0.579, 0.663]] and extract the corresponding values: 1, 3, 6, 4, 2. The value at the start is 1, and the value at the final time point is 2. Therefore, the difference in policy impact on the Euro Zone from the start to the final time point is 2 - 1 = 1.\nBased on the above thought, the answer is:\n1."
+            }
+        ],
+        "id_raw": 260
+    },
+    {
+        "id": "ZYUxK2hT9GOcxfve",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan transformations be made to pull out the raw data from the chart figure into a structured table with columns as Time Periods and rows as Environmental Indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I identify {the x-axis tick labels}[[0.132, 0.945, 0.171, 0.976], [0.232, 0.945, 0.284, 0.976], [0.335, 0.945, 0.387, 0.976], [0.444, 0.945, 0.495, 0.976], [0.541, 0.945, 0.588, 0.976]] as the column headers which stand for Severity of issue. I can identify {the y-axis tick labels}[[0.049, 0.837, 0.151, 0.852], [0.049, 0.687, 0.204, 0.702], [0.049, 0.537, 0.258, 0.552], [0.049, 0.387, 0.231, 0.402], [0.049, 0.237, 0.288, 0.252]] as the row labels which represent the various Environmental Indicators. To extract the values, I look at each bar's length corresponding to each environmental indicator over time. Exact values are not annotated but can be inferred from the chart.\nBased on the above thought, the answer is:\nEnvironmental Indicators,Time Period 1,Time Period 2,Time Period 3,Time Period 4,Time Period 5\nIce Melt,25,28,32,40,50\nLand Temperature Rise,45,55,66,79,90\nOceanic Temperature Rise,55,65,80,97,111\nAir Quality Index,100,90,79,59,45\nGreenhouse Gas Emission,55,75,96,140,225"
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "XbXJSRTaKzyZV1q1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data visualized in the chart, which environmental condition seems to have seen the most significant rise in severity over the given time periods? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which environmental issue has seen the most significant rise in severity, I must analyze the increase in bar lengths for each issue. Looking at {the bar lengths for 'Ice Melt'}[[0.111, 0.871, 0.171, 0.873], [0.223, 0.871, 0.241, 0.873], [0.334, 0.871, 0.367, 0.873], [0.466, 0.871, 0.533, 0.873], [0.653, 0.871, 0.733, 0.873]], the length increases at a slowly growing rate. For both {the bar lengths for 'Land Temperature Rise'}[[0.111, 0.721, 0.204, 0.723], [0.223, 0.721, 0.316, 0.723], [0.334, 0.721, 0.439, 0.723], [0.466, 0.721, 0.575, 0.723], [0.653, 0.721, 0.796, 0.723]] and {the bar lengths for 'Oceanic Temperature Rise'}[[0.111, 0.571, 0.245, 0.573], [0.223, 0.571, 0.358, 0.573], [0.334, 0.571, 0.489, 0.573], [0.466, 0.571, 0.643, 0.573], [0.653, 0.571, 0.821, 0.573]], there is a steady increase in severity over time. For {the bar lengths for 'Air Quality Index'}[[0.111, 0.421, 0.346, 0.423], [0.223, 0.421, 0.310, 0.423], [0.334, 0.421, 0.262, 0.423], [0.466, 0.421, 0.149, 0.423], [0.653, 0.421, 0.028, 0.423]], there are clear decreases in severity. The most significant rise is seen in {the bar lengths for 'Greenhouse Gas Emission'}[[0.111, 0.271, 0.204, 0.273], [0.223, 0.271, 0.316, 0.273], [0.334, 0.271, 0.439, 0.273], [0.466, 0.271, 0.616, 0.273], [0.653, 0.271, 0.877, 0.273]], where the severity value increases notably and substantially in the last time period.\nBased on the above thought, the answer is:\nThe environmental issue that showed the most significant rise in severity throughout the presented time frames is 'Greenhouse Gas Emission'. This can be seen from its bars that escalate progressively with every time frame, culminating in a remarkable leap during the last time frame making it an outlier. These observations indicate the ever-worsening nature of greenhouse gas emissions."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "AImiNXkUP11LsIWp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the greatest severity value related to 'Greenhouse Gas Emission'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the greatest severity value for 'Greenhouse Gas Emission', I identify the heights of all the bars for this environmental issue {the bar heights}[[0.111, 0.131, 0.171, 0.133], [0.223, 0.131, 0.282, 0.133], [0.334, 0.131, 0.414, 0.133], [0.466, 0.131, 0.616, 0.133], [0.653, 0.131, 0.876, 0.133]]. The longest bar's length corresponds to the greatest severity value, which in this case is the last bar at 225.\nBased on the above thought, the answer is:\n225."
+            }
+        ],
+        "id_raw": 261
+    },
+    {
+        "id": "6E71ZWk8v5wya9AF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you organize the data into a table with 'Years' as rows and 'Activities' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrange the data from the line chart into a table format, I first look at {the x-axis tick labels}[[0.051, 0.911, 0.095, 0.934], [0.139, 0.911, 0.183, 0.934], ..., [0.937, 0.911, 0.981, 0.934]], corresponding to the various years from 2000 to 2010. I then examine {the legend texts}[[0.311, 0.034, 0.410, 0.066], [0.527, 0.034, 0.583, 0.066], [0.700, 0.034, 0.790, 0.066]], signifying 'Running', 'Yoga', and 'Cycling' respectively. Lastly, I locate {the line points for each activity}[[0.073, 0.763], [0.161, 0.713], ..., [0.959, 0.298]] and extract the corresponding frequency values.\nBased on the above thought, the answer is:\nYear, Running, Yoga, Cycling\n2000, 1.3, 3.5, 0.5\n2001, 1.8, 4.1, 1.0\n2002, 2.2, 4.5, 1.1\n2003, 3.5, 4.9, 1.3\n2004, 3.8, 5.0, 2.0\n2005, 4.0, 5.5, 2.8\n2006, 4.2, 5.6, 3.0\n2007, 4.6, 6.2, 3.3\n2008, 5.1, 6.7, 3.5\n2009, 5.5, 7.0, 4.2\n2010, 5.9, 7.5, 4.7"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "3emPqxlWjsItN5I0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of practice frequency, which activity experienced the most significant growth from 2000 to 2010 according to the line chart? Kindly elaborate your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which activity saw the greatest increase in frequency, I need to look at the differences in the start and end points of each line on the chart. For {Running}[0.073, 0.298, 0.959, 0.763], it increased from 1.3 to 5.9. For {Yoga}[0.073, 0.136, 0.959, 0.541], it grew from 3.5 to 7.5. Lastly, for {Cycling}[0.073, 0.419, 0.959, 0.844], the frequency rose from 0.5 to 4.7. Hence, Running experienced the most significant growth in frequency from 2000 to 2010, with an increase of 4.6 units.\nBased on the above thought, the answer is:\nBased on the chart, the activity that saw the most significant growth in practice frequency from 2000 to 2010 is Running. While all activities showed a growing trend, the line representing Running demonstrates a steeper slope and a higher overall growth (from 1.3 to 5.9, a total increase of 4.6 units) than the other two activities."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "LuSwCVGANF0HNYtB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase from 2000 to 2010 in the frequency of cycling?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall increase in cycling activity from the year 2000 to the year 2010, I need to locate {the first line point for Cycling}[[0.073, 0.844]] corresponding to the year 2000, which has a value of 0.5 and {the last line point for Cycling}[[0.959, 0.419]] corresponding to the year 2010, which has a value of 4.7. The overall increase would then be the difference between these two values, so 4.7 - 0.5 = 4.2.\nBased on the above thought, the answer is:\n4.2"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "GKd3WlWlExbR4mJF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line plot, can you please extend the values into a table with Quarters as columns and Sectors such as Hotel Accommodation, Food Services, and Travel Agents as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line plot, I can see {the x-axis tick labels}[[0.081, 0.911, 0.106, 0.934], [0.352, 0.911, 0.377, 0.934], [0.623, 0.911, 0.648, 0.934], [0.894, 0.911, 0.918, 0.934]], which express the column headers or Quarters. I perceive {the legend texts}[[0.115, 0.034, 0.385, 0.066], [0.502, 0.034, 0.671, 0.066], [0.788, 0.034, 0.953, 0.066]] as row labels indicating the different Sectors. To capture the values, I have to locate the {line points} of each Graph for the respective Quarters such as {Hotel Accommodation} [0.094, 0.167, 0.906, 0.652], {Food Services} [0.094, 0.450, 0.906, 0.733], {Travel Agents} [0.094, 0.571, 0.906, 0.834] and infer the values mentioned.\nBased on the above thought, the answer is:\nQuarters,Hotel Accommodation,Food Services,Travel Agents\nQ1,1.2,0.8,1.1\nQ2,1.5,1.2,0.9\nQ3,2.1,0.9,1.6\nQ4,3.2,1.5,0.5\nQ1,2.9,1.8,0.7\nQ2,2.5,2.2,0.8\nQ3,3.4,2.1,1.2\nQ4,3.6,1.9,0.3"
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "qeRNItmdK9vT8azt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a sector that shows a consistently upward trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To conclude if there's a sector showing continually upward trend, I should scrutinize the fluctuations in the line plots for each sector. Inspecting {the line for Hotel Accommodation}[0.094, 0.167, 0.906, 0.652], it shows a general upward trend with slight dips between Q4 and Q1, Q2 and Q3. The {Food Services}[0.094, 0.450, 0.906, 0.733] line shows considerable fluctuations, so it does not show a steady increase. Finally, considering {the line for Travel Agents}[0.094, 0.571, 0.906, 0.834], it shows significant fluctuations but doesn't provide a consistently upward trend.\nBased on the above thought, the answer is:\nBased on the line plot, the Hotel Accommodation sector approximately demonstrates a consistently upward trend over time, although there are small dips between some Quarters. It doesn't remain perfectly stable, but the overall movement is upwards. The other sectors, Food Services and Travel Agents, do not show continual upward movements as they have significant fluctuations. The trend for these sectors is therefore not consistently upwards."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "use5Wcn5qDv53rWJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value observed for the Travel Agents sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the maximum value for the Travel Agents sector, I need to locate {the line points for Travel Agents}[[0.094, 0.672], [0.365, 0.713], [0.635, 0.571], [0.906, 0.793], [1.177, 0.753], [1.448, 0.733], [1.719, 0.652], [1.989, 0.834]] and deduce the corresponding values: 1.1, 0.9, 1.6, 0.5, 0.7, 0.8, 1.2, 0.3. The maximum value is 1.6 which corresponds to the point at {Q3}[0.623, 0.911].\nBased on the above thought, the answer is:\n1.6."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "3nlGzHoTC6AyCEwj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you convert the data collected over the years on 'Number of students (in thousands)' in Public Schools, Private Schools, and Online Schools into a structured table format with Years as the columns and School Types as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile the information into a structured table, I need to identify {the x-axis tick labels}[0.073, 0.911, 0.117, 0.934], ..., [0.937, 0.911, 0.981, 0.934] that represent the years (column headers). Next, I spot {the legend texts}[0.811, 0.055, 0.934, 0.078], ..., [0.811, 0.125, 0.939, 0.148] that denote the different school types (row labels). Lastly, I locate {the line points for each school type}[0.095, 0.464], ..., [0.959, 0.486], ..., [0.959, 0.808] found at the respective years and extract their corresponding values.\nBased on the above thought, the answer is:\nSchool Type, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017\nPublic Schools, 100, 120, 150,180,140,132,110,100\nPrivate Schools, 80, 90, 100,110,105,107,102,95\nOnline Schools, 20, 30, 40, 50,60,70,85,95"
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "VEJueCU3Vhwx3yY0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which type of school saw the most significant growth in the number of students over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the type of school that saw the most significant growth in the number of students, I have to analyze the lines representing each school type. {The line for Public Schools}[0.095, 0.120, 0.959, 0.464] shows a growth from 100 to 180 thousand students initially, then a decline to 100 thousand students. {The line for Private Schools}[0.095, 0.421, 0.959, 0.550] presents a slight increase from 80 to 110 thousand students, then a minor decline to 95 thousand students. However, it is {the line for Online Schools}[0.095, 0.486, 0.959, 0.808] with a steady upward trend from 20 to 95 thousand students showing the most significant growth.\nBased on the above thought, the answer is:\nBased on the trends depicted in the line chart, Online Schools have seen the most significant growth in the number of students over the years. While other types of schools experienced some fluctuation, Online Schools consistently had an upward trend representing a considerable increase in students from 20,000 in 2010 to 95,000 in 2017. So the most growth took place in Online Schools."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "cJW8vDPu0PrHGjtG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the number of students in Online Schools from 2011 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in the number of students for Online Schools from 2011 to 2016, I must look at {the line point for Online Schools in 2011}[0.218, 0.765] and extract its corresponding value (30,000). I then find {the line point for Online Schools in 2016}[0.836, 0.529] and extract the corresponding value (85,000). The difference between the latter and the former would give the increase in the number of students.\nBased on the above thought, the answer is:\n55 thousand."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "J0SgRpk4S1fb2SIu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you convert the trends of the three learning methods into structured tabular data with 'Years' as rows and the different learning methods as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the tabular data, I first examine {the x-axis labels}<r>[0.051,0.911,0.095,0.934], [0.139,0.911,0.183,0.934],...,[0.938,0.911,0.981,0.934]</r> which represent the row headers or years. Then, the different {legend items}<r>[0.697,0.063,0.889,0.095], [0.697,0.111,0.938,0.143], [0.697,0.159,0.796,0.191]</r> are used as column headers representing each learning method. The values are populated by tracing back {the individual points on each line}<r>[0.073,0.787], [0.161,0.760],...,[0.959,0.357]</r>,<r>[0.073,0.249], [0.161,0.222],...,[0.959,0.142]</r>,<r>[0.073,0.357], [0.161,0.384],...,[0.959,0.787]</r> to their respective years and learning methods.\nBased on the above thought, the answer is:\nYear,Online Learning,Classroom Learning,Tutoring\n2000,1,6,5\n2002,1.25,6.25,4.75\n2004,1.5,6.5,4.5\n2006,1.75,6.75,4.25\n2008,2,7,4\n2010,2.5,7.5,3.5\n2012,3,8,3\n2014,3.5,7.75,2.5\n2016,4,7.5,2\n2018,4.5,7.25,1.5\n2020,5,7,1"
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "4N7jtpa4xmS6xUiM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line plots, which learning method improved the most in quality throughout the years? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the learning method that improved the most, I need to assess the trends and magnitude of increases in the line plots for each method. In observing {the line for Online Learning}<r>[0.073,0.787,0.959,0.357]</r>, I can identify a steady increase from a quality score of 1 in 2000 to a score of 5 in 2020. This constitutes an increase of 4 units. Next, I examine {the line representing Classroom Learning}<r>[0.073,0.249,0.959,0.142]</r> and note it starts at 6 in 2000 and ends at 7 in 2020, signaling an increase of 1 unit. Lastly, by considering {the line representing Tutoring}<r>[0.073,0.357,0.959,0.787]</r>, I detect a decrease from a score of 5 in 2000 to 1 in 2020. Thus, Tutoring\u2019s score drops by 4 units.\nBased on the above thought, the answer is:\nFrom the line plots, the learning method that improved the most in quality throughout the years is Online Learning. It exhibits a steady increase in Quality Score from 1 to 5 from 2000 to 2020. This consistent upward trend and the total increase of 4 units indicate the most substantial improvement among the three methods, considering that Classroom Learning only improved by 1 unit and Tutoring, in fact, decreased."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "qa86dNMyGOLvYpPP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the Quality Score for Online Learning from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in Quality Score for Online Learning, I need to locate {the starting point of the line for Online Learning}<r>[0.073,0.787]</r> which corresponds to the year 2000 and has a value of 1. Then, I look at {the ending point of the line for Online Learning}<r>[0.959,0.357]</r> which corresponds to the year 2020 and has a value of 5. The increase in Quality Score from 2000 to 2020 then, is 5 - 1 = 4.\nBased on the above thought, the answer is:\n4."
+            }
+        ],
+        "id_raw": 265
+    },
+    {
+        "id": "yRCy8zQo1R2p4IAl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data into a tabular format with time values as columns and each Task as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I first need to recognize that {the x-axis tick labels}<r>[0.084, 0.911, 0.095, 0.934], [0.191, 0.911, 0.240, 0.934], [0.323, 0.911, 0.362, 0.934], [0.455, 0.911, 0.483, 0.934], [0.590, 0.911, 0.601, 0.934], [0.717, 0.911, 0.728, 0.934], [0.844, 0.911, 0.855, 0.934], [0.970, 0.911, 0.981, 0.934]</r> represent the time points or column headers. Next, I identify {the legend text}<r>[0.287, 0.032, 0.420, 0.062], [0.520, 0.032, 0.629, 0.062], [0.729, 0.032, 0.836, 0.062]</r> as the row labels representing the different tasks. For populating each cell, I locate {the line points}<r>[0.089, 0.679], [0.216, 0.572], [0.342, 0.491], [0.469, 0.438], [0.596, 0.411], [0.722, 0.223], [0.849, 0.088], [0.976, 0.626], [0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868], [0.089, 0.411], [0.216, 0.384], [0.342, 0.357], [0.469, 0.303], [0.596, 0.491], [0.722, 0.706], [0.849, 0.787], [0.976, 0.814]</r> and extract their corresponding y values for each task at the respective time.\nBased on the above thought, the answer is:\nTask,0,0.125,0.25,0.5,1,2,4,8\nOptimization,0.8,1.2,1.5,1.7,1.8,2.5,3,1\nInnovation,0.5,0.5,1,0.9,1.5,1.7,0.2,0.1\nExpansion,1.8,1.9,2,2.2,1.5,0.7,0.4,0.3"
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "LXxLCsKaArM4tT2V",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which task has shown the most improvement in Efficiency over time and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the task with the most improvement in Efficiency, I need to interpret the lines for each task. Let's analyze the trend for each: \n{the line points for Optimization}<r>[0.089, 0.679], [0.216, 0.572], [0.342, 0.491], [0.469, 0.438], [0.596, 0.411], [0.722, 0.223], [0.849, 0.088], [0.976, 0.626]</r> start at 0.8 at time 0 and end at 1 at time 8 showing an overall increase then dips down.\n{the line points for Innovation}<r>[0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868]</r> start at 0.5 at time 0 and dip till 0.1 at time 8, it's not showing a substantial improvement.\n{the line points for Expansion}<r>[0.089, 0.411], [0.216, 0.384], [0.342, 0.357], [0.469, 0.303], [0.596, 0.491], [0.722, 0.706], [0.849, 0.787], [0.976, 0.814]</r> start at 1.8 at time 0 and dips till 0.3 at time 8, also not a considerable improvement.\nBased on the above thought, the answer is:\nBased on the trends observed in the line chart, the 'Optimization' task has shown the most improvement in Efficiency over time. The corresponding line starts relatively lower at 0.8 at time 0 and rises to peak at 3.0 at time 4, however, dips to 1 at the end at time 8. Despite the dip, the overall increase and the peak in efficiency over time suggest substantial improvement in comparison to the other tasks."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "OsWDcpP8DPLxGpOm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of Efficiency Improvement Potential for Innovation from time 0 to 8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of Efficiency Improvement Potential for Innovation, I need to locate the {line points for Innovation}<r>[0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868]</r> and extract the corresponding values which are 0.5, 0.5, 1, 0.9, 1.5, 1.7, 0.2, 0.1. The maximum value is 1.7 at time 2 and the minimum value is 0.1 at time 8, so the range is 1.7 - 0.1 = 1.6.\nBased on the above thought, the answer is:\n1.6."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "ugATqdq4Mkg8t3uC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the revenue changes for Hotels, Restaurants, and Travel Agencies over the years 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate such a data table from this line chart, I'll first focus on the x-axis and get the list of years from {the x-axis tick labels}<r>[[0.041, 0.933, 0.076, 0.951], [0.132, 0.933, 0.167, 0.951], [0.223, 0.933, 0.258, 0.951], [0.313, 0.933, 0.348, 0.951], [0.404, 0.933, 0.439, 0.951], [0.495, 0.933, 0.530, 0.951], [0.586, 0.933, 0.621, 0.951], [0.677, 0.933, 0.712, 0.951], [0.768, 0.933, 0.803, 0.951], [0.859, 0.933, 0.894, 0.951], [0.950, 0.933, 0.985, 0.951]]</r> which are displayed on the chart. These will become the row headers in our table.\n\nThe {legend texts}<r>[[0.281, 0.022, 0.343, 0.045], [0.436, 0.022, 0.553, 0.045],[0.646, 0.022, 0.799, 0.045]]</r> represent our different service types: Hotels, Restaurants, and Travel Agencies. These service types will be the column headers.\n\nFinally, the y-values are extracted from each plotted line for each year. These y-values are the revenue in billions for the corresponding service type: {the line points for Hotels}<r>[[0.058, 0.712], [0.149, 0.701], [0.240, 0.680], [0.331, 0.628], [0.422, 0.607], [0.513, 0.586], [0.604, 0.555], [0.695, 0.523], [0.786, 0.481], [0.877, 0.450], [0.967, 0.419]]</r>, {the line points for Restaurants}<r>[[0.058, 0.555], [0.149, 0.586], [0.240, 0.502], [0.331, 0.523], [0.422, 0.471], [0.513, 0.481], [0.604, 0.450], [0.695, 0.398], [0.786, 0.366], [0.877, 0.377], [0.967, 0.293]]</r>, and {the line points for Travel Agencies}<r>[[0.058, 0.398], [0.149, 0.460], [0.240, 0.481], [0.331, 0.408], [0.422, 0.345], [0.513, 0.314], [0.604, 0.272], [0.695, 0.262], [0.786, 0.199], [0.877, 0.188], [0.967, 0.136]]</r>.\nBased on the above thought, the answer is:\nYear, Hotels, Restaurants, Travel Agencies\n2000, 2, 3.5, 5\n2001, 2.1, 3.2, 4.4\n2002, 2.3, 4, 4.2\n2003, 2.8, 3.8, 4.9\n2004, 3, 4.3, 5.5\n2005, 3.2, 4.2, 5.8\n2006, 3.5, 4.5, 6.2\n2007, 3.8, 5, 6.3\n2008, 4.2, 5.3, 6.9\n2009, 4.5, 5.2, 7\n2010, 4.8, 6, 7.5"
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "QVtq3nAlW9D26IVM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to this chart, which type of service sector has shown the most improvement from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the service sector with the most improvement, I will compare the differences in the revenues from 2000 and 2010 for each sector. I located the initial and final values for {Hotels}<r>[0.058, 0.712]</r> and <r>[0.967, 0.419]</r>, for {Restaurants}<r>[0.058, 0.555]</r> and <r>[0.967, 0.293]</r>, and for {Travel Agencies}<r>[0.058, 0.398]</r> and <r>[0.967, 0.136]</r>. According to these points, the revenue for the Hotels increased by 2.8 billion, for Restaurants it increased by 2.5 billion, and for Travel Agencies, it increased by 2.5 billion.\nBased on the above thought, the answer is:\nBased on the chart, the service sector that has shown the most improvement from 2000 to 2010 is Hotels. It had an increase of 2.8 billion dollars in revenue, which is greater compared to the other two service sectors."
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "3t7eDD1yaBW6pwKe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in revenue for Restaurants from the year 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the first and last y-values for Restaurants from the plotted line for Restaurants. The initial y-value is found at {the point for the year 2000 for Restaurants}<r>[0.058, 0.555]</r> which corresponds to the revenue of 3.5 billion. The final y-value is at {the point for the year 2010 for Restaurants}<r>[0.967, 0.293]</r> which corresponds to the revenue of 6 billion. So, the increase in revenue from the year 2000 to 2010 for Restaurants is 6 billion - 3.5 billion = 2.5 billion.\nBased on the above thought, the answer is:\n2.5 billion"
+            }
+        ],
+        "id_raw": 267
+    },
+    {
+        "id": "Nc2RYupxSliGVetB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data for price trends of financial securities be shown in the form of a table listing each security with its percentage change?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, to convert the visuals into tabular data. I would first identify the x-axis labels as the financial securities and the y-axis as the percentage change. These will serve as columns for the table. Specifically, I\u2019d have to determine {the x-axis labels}<r>[0.176, 0.113, 0.334, 0.895]</r> that represent the security names and {y-axis points}<r>[0.284, 0.172, 0.674, 0.821, 0.432, 0.385, 0.504, 0.524]</r> that represent the percentage change. The percentages of each security are determined from their associated y-coordinates. I\u2019ll constrain my focus to the values at where the line intersects with the y-axis.\nBased on the above thought, the answer is:\nSecurity, Percentage Change\nCorporate Bond, 3.5\nMunicipal Bond, 2.3\nTreasury Notes, 4.4\nPreferred Stocks, 1.3\nCommon Stocks, -0.7"
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "h0fDShKfEBp6aK5D",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which security appears to be the riskiest investment given its performance trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond to this question, I need to assess the trends of all securities, focusing specifically on their price change. I see that {the Corporate Bond line}<r>[0.2, 0.4]</r>, {the Municipal Bond line}<r>[0.255, 0.381]</r> ,{the Treasury Notes line}<r>[0.396, 0.508]</r>, and {the Preferred Stocks line}<r>[0.508, 0.31]</r> have positive trends. However, {the Common Stocks line}<r>[0.618, 0.265]</r> has a negative trend which indicates a price reduction.\nBased on the above thought, the answer is:\nBased on the line chart, it appears that Common Stocks may be the riskiest investment as its performance trend is declining with a -0.7% change in price which opposes the positive trend of all other securities."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "k5SgueGPgaY3gIh7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the difference in percentage change between Corporate Bond and Common Stocks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to extract the percentage change for both, Corporate Bond and the Common Stocks. I can get these values where {the Corporate Bond line}<r>[0.2, 0.4]</r> and {the Common Stocks line}<r>[0.618, 0.265]</r> intersect the y-axis. The value for Corporate Bond is 3.5% and for Common Stocks - it's -0.7%. Hence the difference would be 3.5 - (-0.7) = 4.2.\nBased on the above thought, the answer is:\n4.2."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "Pc5JumP3qFuRMZD4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you push the data from the line chart depicting performance gains over time into a structured table with Time Scale intervals as the columns and the AI Application and Automation as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to derive the data table, I will focus on {the x-axis tick labels}[[0.095, 0.911, 0.106, 0.934], [0.200, 0.911, 0.250, 0.934], [0.331, 0.911, 0.369, 0.934], [0.462, 0.911, 0.489, 0.934], [0.595, 0.911, 0.606, 0.934], [0.720, 0.911, 0.731, 0.934], [0.845, 0.911, 0.856, 0.934], [0.970, 0.911, 0.981, 0.934]], these would represent column headers. {The legend texts}[[0.359, 0.034, 0.527, 0.066], [0.643, 0.034, 0.785, 0.066]], will represent the Row labels. Then I poll across the line points for AI Application {the line points for AI Application}[[0.100, 0.895], [0.225, 0.814], [0.350, 0.733], [0.475, 0.571], [0.600, 0.248], [0.726, 0.531], [0.851, 0.814], [0.976, 0.854]], and Automation {the line points for Automation}[[0.100, 0.854], [0.225, 0.814], [0.350, 0.773], [0.475, 0.571], [0.600, 0.450], [0.726, 0.692], [0.851, 0.814], [0.976, 0.874]] and map their position along the {y-axis tick labels}[[0.061, 0.885, 0.088, 0.909], [0.061, 0.784, 0.088, 0.808], [0.061, 0.683, 0.088, 0.707], [0.061, 0.582, 0.088, 0.605], [0.049, 0.481, 0.088, 0.504], [0.049, 0.380, 0.088, 0.403], [0.049, 0.279, 0.088, 0.302], [0.049, 0.178, 0.088, 0.201], [0.050, 0.077, 0.088, 0.100]] to actual values for the respective time intervals.\nBased on the above thought, the answer is:\nPeriod,0,0.125,0.25,0.5,1,2,4,8\nAI Application,0,0.8,1.6,3.2,6.4,3.6,0.8,0.4\nAutomation,0.4,0.8,1.2,3.2,4.4,2,0.8,0.2"
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "dZEWtVHBirzdRNBd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom a high-level perspective, based on the line chart trend, which among AI Application and Automation saw a significant performance gain sooner?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond about the early significant performance gain, I will examine both the lines for AI Application {line for AI Application}[0.100, 0.248, 0.976, 0.895] and Automation {line for Automation}[0.100, 0.450, 0.976, 0.874]. For AI Application, the performance gain starts to spike at Time Scale {0.5}[0.462, 0.911, 0.489, 0.934] whereas for Automation, it happens at Time Scale {1}[0.595, 0.911, 0.606, 0.934].\nBased on the above thought, the answer is:\nAs per the trends demonstrated in the line chart, AI Application saw a significant performance gain sooner than Automation. This is inferred from the steep rise observed in AI Application's trend line at Time Scale 0.5, whereas for Automation the rise happens at a later Time Scale of 1. Can we say this represents AI Applications evolving faster than Automation in the initial stages."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "l67477nzZQ0JMNxq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the peak performance gain for AI Applications in the given time scale?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the peak performance gain for AI Applications, I need to identify the maximum value from {the line points for AI Application}[[0.100, 0.895], [0.225, 0.814], [0.350, 0.733], [0.475, 0.571], [0.600, 0.248], [0.726, 0.531], [0.851, 0.814], [0.976, 0.854]]. Upon extracting the corresponding values: 0, 0.8, 1.6, 3.2, 6.4, 3.6, 0.8, 0.4. I find the peak value to be 6.4 which corresponds to the Time Scale {1}[0.595, 0.911, 0.606, 0.934].\nBased on the above thought, the answer is:\n6.4%"
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "PFy2twxGfGmkTQvX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table representing total values of each transportation category for the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll identify the x-axis tick labels which are {the years}<r>[0.076, 0.923, 0.111, 0.943]</r>,<r>[0.163, 0.923, 0.198, 0.943]</r>,<r>[0.251, 0.923, 0.286, 0.943]</r>. These will act as the individual rows of the table. The column headers will be the different transportation types found in {the legend items}<r>[0.165, 0.054, 0.237, 0.081]</r>,<r>[0.165, 0.095, 0.336, 0.122]</r>,<r>[0.165, 0.136, 0.368, 0.163]</r>. I'll then locate specific points like those in the {Income line points}<r>[0.093, 0.176]</r>,<r>[0.181, 0.161]</r>,<r>[0.268, 0.147]</r> to extract their respective values across each year for the 'Income' transportation type. The process is repeated for the other types.\nBased on the above thought, the answer is:\nYear,Income,Food Expenditure,Housing Expenditure\n2000,50000,10000,20000\n2001,51000,10200,21000\n2002,52000,10500,23000\n2003,51000,10800,24000\n2004,52000,11200,25000\n2005,53000,11500,26000\n2006,54000,12000,27000\n2007,55000,12500,30000\n2008,56000,13000,34000\n2009,57000,14000,36000\n2010,58000,15000,38000"
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "vFhXwD7zBFYqMFbD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which aspect of expenditure saw the most growth between 2000 and 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which type of expenditure had the most growth, I'll compare the rate of change between 2000 and 2010 for the 'Income', 'Food Expenditure', and 'Housing Expenditure'. For the 'Income', looking at the line points at {2000}<r>[0.093, 0.176]</r> and {2010}<r>[0.967, 0.059]</r>, the difference in change is (58,000 - 50,000)/50,000 = 16%. Next, for 'Food Expenditure', the points at {2000}<r>[0.093, 0.763]</r> and {2010}<r>[0.967, 0.690]</r> show a difference of (15,000 - 10,000)/10,000 = 50%. Lastly, 'Housing Expenditure', with points at {2000}<r>[0.093, 0.616]</r> and {2010}<r>[0.967, 0.352]</r>, shows a difference of (38,000 - 20,000)/20,000 = 90% increase.\nBased on the above thought, the answer is:\nAccording to the line chart, 'Housing Expenditure' saw the most growth between 2000 and 2010. The percent increase for this expenditure type was 90%, which is higher than the percent increase for 'Income' (16%) and 'Food Expenditure' (50%). This suggests that 'Housing Expenditure' took up a larger proportion of expenditure over this time period."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "E0K7hO4YQxQRz3po",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the Housing Expenditure from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in Housing Expenditure from 2000 to 2010, I first identify the starting value for Housing Expenditure in 2000 at {this point}<r>[0.093, 0.616]</r>, which corresponds to $20,000. I then identify the ending value for Housing Expenditure in 2010 at {this point}<r>[0.967, 0.352]</r>, which corresponds to $38,000. Using these values, I calculate the percentage increase as follows: (38,000 - 20,000) / 20,000) * 100.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "x7H9X0fV3Y8fyDHj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the line chart data into a table format with Prospects as rows and Technologies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table, I will first identify {the x-axis labels}<r>[0.019, 0.911, 0.156, 0.934], [0.278, 0.911, 0.448, 0.934], [0.547, 0.911, 0.729, 0.934], [0.845, 0.911, 0.981, 0.934]</r> which represent the rows or prospects. Then, I identify {legend labels}<r>[0.257, 0.034, 0.280, 0.066], [0.397, 0.034, 0.441, 0.066], [0.558, 0.034, 0.812, 0.066]</r> which represent the columns or Technologies. Finally, I locate and extract the points associated with each technology on each prospect using their respective coordinates, {AI coordinates}<r>[0.087, 0.248, 0.913, 0.490]</r>, {IoIT coordinates}<r>[0.087, 0.167, 0.913, 0.652]</r>, and {Quantum Computing coordinates}<r>[0.087, 0.328, 0.913, 0.814]</r>.\nBased on the above thought, the answer is:\nProspects,AI,IoIT,Quantum Computing\nMinimal change,8,9,7\nPronounced decline,7,5,3\nDownward trajectory,6,3,1\nMinimal decline,5,4.5,2"
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "6zjtKajlXLKSZrW1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends indicated in the line chart, which technology seems to exhibit the most downward progression over the prospects? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which technology exhibits the most downward progression, I need to examine the changes in the line plots for each technology. Observing {The line for AI}<r>[0.087, 0.248, 0.913, 0.490]</r>, it presents a gradual decline from 8 to 5. Similarly, {The line for IoIT}<r>[0.087, 0.167, 0.913, 0.652]</r> exhibits a more rapid decline from 9.0 to 4.5. However, for {Quantum Computing}<r>[0.087, 0.328, 0.913, 0.814]</r>, the line follows a distinctly downward direction, from a value of 7 at 'Minimal Change' to a value of 1 at 'Downward Trajectory', and a slight raise to 2 at 'Minimal Decline'. Thus we can deduce that Quantum Computing experiences the most downward progression across the given prospects.\nBased on the above thought, the answer is:\nQuantum Computing appears to demonstrate the most significant downward trend as per this line chart. Its progression shows a steep decline from a value of 7 to 1 over the prospects and rises slightly to 2 thereby indicating a pronounced overall downward trend compared to AI and IoIT."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "yJ0dO0vLNOIf2Z43",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in performance gain between the best and worst prospect for Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate the values for Quantum Computing across all prospects, I observe {the point coordinates for Quantum Computing}<r>[0.087, 0.328], [0.363, 0.652], [0.638, 0.814], [0.913, 0.733]</r> which represent the values: 7, 3, 1, 2. The maximum value 7 corresponds to {Minimal Change}[0.087, 0.911], and the minimum value 1 corresponds to {Downward Trajectory}[0.638, 0.911]. The difference in performance gain is thus 7 - 1 = 6.\nBased on the above thought, the answer is:\n6."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "uDpNV0Kh24zhylBm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you present the distribution and counts of education resources (Books and Computers) within Urban and Rural areas in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the data table, I first need to identify {the x-axis tick labels}[[0.095, 0.895], [0.527, 0.895], [0.959, 0.895]] which represent the area types \u2013 Urban and Rural. From {the legend}[[0.153, 0.021, 0.901, 0.079]], I can see three items: Education, Healthcare, and Social Welfare, but we only need the counts for Books and Computers in this case. The line points for Books and Computers are under {the Education Data}[[0.095, 0.558, 0.959, 0.153]] and {the Social Welfare Data}[[0.095, 0.625, 0.959, 0.234]] respectively. Hence, We can extract these values and structure them as follows.\nBased on the above thought, the answer is:\nArea,Books,Computers\nUrban,8000,15000\nRural,3000,5000"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "Vjolk3SplLmyO6y4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which area type, Urban or Rural, is experiencing more disparities in terms of educational resources distribution? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area is experiencing more disparities, we must examine the differences in resource allocation for both Books and Computers between Urban and Rural areas. Looking at the data, both types of resources are more plentiful in {Urban}[[0.527, 0.895]] versus {Rural}[[0.959, 0.895]] areas. Notably, the disparity in terms of Computers is noticeably larger than that of books, suggesting a digital divide.\nBased on the above thought, the answer is:\nBased on the line chart, the Urban area appears to be experiencing more disparities in the distribution of education resources when compared to the Rural area. Both Books and Computers are more concentrated in Urban areas than in Rural areas, with the difference being significantly larger in the case of Computers. This indicates imbalanced resource allocation and a noticeable digital gap between the two areas."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "DnCmtiKrHEownmJY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the number of computers between Urban and Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in computer numbers, I should compare the y-values of the line points representing computers in Urban and Rural areas. For Urban, the relevant point is in {Computers in Urban}[[0.527, 0.895]] and for Rural, the corresponding point is in {Computers in Rural}[[0.959, 0.895]]. Upon extracting the actual values, the difference is computed as 15000 - 5000 = 10000.\nBased on the above thought, the answer is:\n10000."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "baWRAhDbXVtOiw6H",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn this chart showing the average weekly distance traveled by public transport in different cities across various age groups into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I will start by identifying {the x-axis labels}['[0.073, 0.933, 0.095, 0.951]', '[0.200, 0.933, 0.222, 0.951]', '[0.334, 0.933, 0.343, 0.951]', '[0.455, 0.933, 0.477, 0.951]', '[0.588, 0.933, 0.597, 0.951]'], which represent the age group. Then I will check {the subplot legend}['[0.146, 0.044, 0.380, 0.067]', '[0.146, 0.077, 0.358, 0.100]', '[0.146, 0.110, 0.324, 0.133]'] for the names of cities. When constructing the rows of the table, I will get the values from {the line points}['[0.084, 0.423]', '[0.211, 0.440]', '[0.339, 0.460]', '[0.466, 0.496]', '[0.593, 0.509]'] for each city's distance travelled in different age groups.\nBased on the above thought, the answer is:\nAge Group,Delhi,Hyderabad,Kolkata\n20-30,55,48,46\n30-40,52,47,44\n40-50,48,45,40\n50-60,45,42,35\n60-70,20,38,32"
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "A0Ql9aQ7VH8nAZZE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city's older population (60-70) has been impacted the most in terms of decreased use of public transportation compared to the younger population (20-30)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at {the line values for the 60-70 age group}['[0.593, 0.509]'], and compare them with the values for the 20-30 age group {line values for the 20-30 age group}['[0.084, 0.423]']. {The line for Delhi}[0.084, 0.423, 0.593, 0.509] shows the largest drop from 55 km per week to 20 km per week compared to Hyderabad and Kolkata.\nBased on the above thought, the answer is:\nDelhi's older population (60-70) has been the most impacted in terms of decreased use of public transportation compared to the younger population (20-30). The average distance travelled by this age group in Delhi decreased by 35 km per week compared to the 20-30 age group. This drop is greater than the decreases observed for the same age groups in Hyderabad and Kolkata."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "uXiJdctwfLWxP1tq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the biggest drop in distance traveled by public transport experienced by Delhi citizens as they age?"
+            },
+            {
+                "from": "gpt",
+                "value": "By inspecting {the line for Delhi}['[0.084, 0.423]', '[0.211, 0.440]', '[0.339, 0.460]', '[0.466, 0.496]', '[0.593, 0.509]'], the biggest drop is from the 50-60 age group at 45 km per week to the 60-70 age group at 20 km per week. The drop in distance traveled is therefore 45-20=25km.\nBased on the above thought, the answer is:\n25 km."
+            }
+        ],
+        "id_raw": 275
+    },
+    {
+        "id": "byJ7GV1fxAmxHNIj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graphs showing each investor's investment strategy into a table format with Investment Rounds as columns and Investors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line graphs into a tabular format, I must identify key elements. {The x-axis labels}<r>[0.099, 0.911, 0.143, 0.934], [0.193, 0.911, 0.238, 0.934], [0.288, 0.911, 0.332, 0.934], [0.383, 0.911, 0.427, 0.934], [0.478, 0.911, 0.522, 0.934], [0.573, 0.911, 0.617, 0.934], [0.668, 0.911, 0.712, 0.934], [0.762, 0.911, 0.806, 0.934], [0.857, 0.911, 0.901, 0.934]</r> indicate the row labels representing the different investors. The legend entry <r>[0.088, 0.034, 0.291, 0.066], [0.408, 0.034, 0.649, 0.066], [0.766, 0.034, 0.980, 0.066]</r> acts as the column headers or investment rounds. The line points <r>[0.121, 0.328, 0.879, 0.611], [0.121, 0.369, 0.879, 0.506], [0.121, 0.288, 0.879, 0.571]</r> account for the values invested during each round by the respective investors.\nBased on the above thought, the answer is:\nInvestors,Seed,Series A,Series B,Series C,Outreach\nInvestor 1,20,15,13,10,11\nInvestor 2,50,100,90,120,105\nInvestor 3,25,30,28,35,33\nInvestor 4,28,27,25,26,30\nInvestor 5,55,80,100,120,110\nInvestor 6,30,28,25,20,15"
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "dTaV94twafLNXoJY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line graphs, which investor appears to have taken the most risk in terms of stepping up the investment amounts across the different rounds? Provide reasons for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the risk involved in stepping up investment amounts across different rounds, I examine the variations in the investment amounts mentioned in the line graphs. {Investor 2's line plot}<r>[0.121, 0.328, 0.879, 0.611]</r> shows a steady increase from 50M in the Seed round to 120M in Series C. Despite a small dip during outreach, the magnitude and consistency of this increase indicate risk-taking for potential startup success. In contrast, {Investor 1's line plot}<r>[0.121, 0.369, 0.879, 0.506]</r> starts at 20M in the Seed round, consistently decreasing afterward; these trends suggest less risk-taking. Other investors show fluctuations, but none matches Investor 2's aggressive trajectory.\nBased on the above thought, the answer is:\nFrom the displayed line graphs, Investor 2 appears to have taken the most risk in terms of stepping up the investment amounts across the rounds. The steady increase from 50M in the Seed round to 120M in Series C, despite a slight dip in the Outreach round, indicates a consistently aggressive investment strategy."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "x1KSvvxv9Ta46w5w",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher was Investor 2's peak investment in comparison to Investor 1's peak investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the peak investments, I look at the graph and highlights at each line's highest point. For {Investor 2's peak investment}<r>[0.405, 0.409]</r>, I see the peak at Series C with 120M. For {Investor 1's peak investment}<r>[0.216, 0.393]</r>, the peak appears at the Seed round with 20M. So, the difference between Investor 2's peak investment and Investor 1's peak investment is 120M - 20M = 100M.\nBased on the above thought, the answer is:\n100 million dollars."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "A9xAulC6oAw5Vw8J",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart indicating the popularity of various fitness routines across seasons into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, as seen in the line chart, {the x-axis}<r>[0.073, 0.943, 0.560, 0.975]</r> represents the different seasons which will be the column headers of the table. {The legend}<r>[0.253, 0.021, 0.795, 0.079]</r> indicates the different workout regimes which will be the rows of our table. For each type of workout, we track the points at each season {the line points for each workout regime}<r>[0.073, 0.409, 0.976, 0.733], [0.073, 0.248, 0.976, 0.814]</r> and record the respective values to populate the cells of our table.\nBased on the above thought, the answer is:\nSeasons,Singer Yoga,Zumba Classes,HIIT Sessions,Pilates Workouts,Morning Runs\nSpring,1.0,0.5,1.2,0.8,0.9\nSummer,2.0,1.2,2.4,1.5,1.8\nAutumn,2.5,1.8,2.7,2.9,2.2\nWinter,3.0,2.0,2.5,2.3,2.1"
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "tG4Fmnj2LgKIoKin",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat workout regime shows a trend that deviates from the general pattern of fitness activities peaking in the summer and declining during winter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the lines representing different workout trends, I see that while most of them show a peak during the summer {points on lines during summer}<r>[0.202, 0.571], [0.202, 0.700]</r> and a decline during the winter {points on lines during winter}<r>[0.460, 0.409], [0.460, 0.571]</r>, there is one line that deviates from this trend. {The Pilates line}<r>[0.073, 0.409, 0.976, 0.733]</r> shows a surprising spike during the autumn season {point on Pilates line during autumn}<r>[0.331, 0.490]</r> contrary to the general downward trend during colder months.\nBased on the above thought, the answer is:\nThe workout regime that deviates from the general trend is Pilates. Its popularity shows an unexpected surge during the autumn season, going against the usual trend of declining fitness activity during colder months."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "blKq6s15t8guerYX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_14_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the maximum popularity gain for Zumba Classes from one season to the next?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum popularity gain for Zumba Classes, I should locate {the line points for Zumba Classes}<r>[0.073, 0.814, 0.976, 0.248]</r> and compute the difference between consecutive points representing 0.5(Spring), 1.2(Summer), 1.8(Autumn), and 2.0(Winter). The maximum difference would be the maximum gain from one season to the next.\nBased on the above thought, the answer is:\n1.3."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "fIITnkfYNT8OAlRZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the linechart, can you convert the data into a legible table with the rows as 'Year' and columns as 'Internet Users (Millions)' and 'Communication Companies Revenue (Billions)'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the linechart data into a tabular format, I'll first take into consideration {the x-axis tick labels}[[0.228, 0.902, 0.281, 0.932], [0.374, 0.902, 0.426, 0.932], [0.519, 0.902, 0.572, 0.932], [0.664, 0.902, 0.717, 0.932], [0.810, 0.902, 0.862, 0.932]], which indicate the 'years'. Then, to find the corresponding values, I observe the linegraphs for {Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827] and {Communication Companies Revenue (Billions)}[0.255, 0.865, 0.836, 0.884]. By mapping these values with their corresponding years, I can generate a table as follows:\nBased on the above thought, the answer is:\nYear,Internet Users (Millions),Communication Companies Revenue (Billions)\n2000,413,60\n2005,1056,85\n2010,2041,120\n2015,3143,140\n2020,4676,180"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "ReTIokVwM8sJ8AK7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you draw about the development of the Internet and Communication Industry from the years 2000 to 2020 based on the line chart displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Studying {the line representing Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827] and {the line representing the Communication Companies Revenue (Billions)}[0.255, 0.865, 0.836, 0.884], it's quite evident that both the number of internet users and the revenue of communication companies have been increasing over the two decades from 2000 to 2020. This suggests an overall expansion and growth in the Internet and Communication Industry during these years. However, the growth in the number of Internet Users seems to be at a much greater pace as compared to the growth in Revenue.\nBased on the above thought, the answer is:\nThe line chart indicates a steady growth in both Internet Users and Communication Companies Revenue from the year 2000 to 2020, reflecting the continuous expansion of the Internet and Communication Industry. However, while the internet users have increased more than tenfold, indicating a rapidly digitalizing world, the growth in revenue of communication companies, although steady, is not as exponential. This potentially points towards increased competition in the market or changes in revenue models over the years."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "RC6OytVSNdYS4PDB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the year 2000 and 2020, by how much did the number of Internet Users increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in Internet Users from 2000 to 2020, I can check the y-values of {the line corresponding to Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827]. For 2000, it's 413 and for 2020, it's 4676. The difference between these two values will give me how much the Internet Users increased over that period.\nBased on the above thought, the answer is:\n4263 million."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "IclqNnFgj90ye8kl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please transform the data in the line chart representing Risk Management and Risk Evaluation into a structured table with categories as rows and quantitative parameters as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, firstly, I analyse {the x-axis tick labels}[[0.157, 0.769, 0.239, 0.786], [0.291, 0.769, 0.372, 0.786], [0.425, 0.769, 0.506, 0.786], [0.559, 0.769, 0.640, 0.786], [0.694, 0.769, 0.776, 0.786]] to get column names that are parameters. Then I locate {the legend texts}[[0.814, 0.111, 0.875, 0.121], [0.814, 0.091, 0.895, 0.101]] to define the rows representing categories of risks. The values are discovered by identifying the points on {the line for Risk Management}[[0.157, 0.651, 0.694, 0.417]], and {the line for Risk Evaluation}[[0.157, 0.327, 0.694, 0.574]].\nBased on the above thought, the answer is:\nParameter,Risk Management,Risk Evaluation\n1,45,12\n2,38,20\n3,29,26\n4,32,29\n5,35,27"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "lyauIDNDaIqx1V3D",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn light of the line graph, which category - Risk Management or Risk Evaluation displays a more stable progression? Please justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to evaluate the stability of the progression for both categories, I need to measure the amount of fluctuations in the line plots for each category. Observing the data for {Risk Management}[0.157, 0.651, 0.694, 0.417], I witness significant swings in values, demonstrating an unstable progression. On the other hand, for {Risk Evaluation}[0.157, 0.327, 0.694, 0.574], though there are swings in values, the swings are lesser amplitude compared to Risk Management, pointing to a more stable progression.\nBased on the above thought, the answer is:\nBased on the line chart, Risk Evaluation exhibits a more stable progression compared to Risk Management. While both lines do show fluctuations, the values in Risk Evaluation seem to have lower deviations overall compared to those in Risk Management, which exhibit larger swings and hence more instability."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "byH4hjOxGh7xaNAz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you calculate the difference between the highest and lowest values of Risk Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I must locate the maximum and minimum of Risk Management. I can get these by inspecting {the points for Risk Management}[[0.157, 0.651], [0.291, 0.578], [0.425, 0.474], [0.559, 0.499], [0.694, 0.417]], which correspond to the values 45, 38, 29, 32, and 35 respectively. The highest value is 45 and the lowest is 29. So, the difference is 45 - 29 = 16\nBased on the above thought, the answer is:\n16"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "Tg4kQjK0S3RDl3Fm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table containing the annual revenues for Film Production, TV Production, Music Sales, Game Sales, and Book Sales over the period 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I'll identify {the x-axis labels}[[0.130, 0.909, 0.152, 0.932], [0.292, 0.909, 0.314, 0.932], [0.454, 0.909, 0.476, 0.932], [0.617, 0.909, 0.639, 0.932], [0.779, 0.909, 0.801, 0.932], [0.941, 0.909, 0.963, 0.932]] as the years. Next, I see from {the legend}[[0.167, 0.144, 0.515, 0.174], [0.167, 0.188, 0.526, 0.218]] that there are two lines representing Community Development Budget and Local Government Support Budget. For each line, I'll track {the line points}[[0.130, 0.592], [0.292, 0.640], [0.454, 0.704], [0.617, 0.752], [0.779, 0.832], [0.941, 0.863],[0.130, 0.512], [0.292, 0.480], [0.454, 0.369], [0.617, 0.321], [0.779, 0.225], [0.941, 0.113]] for each year and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Community Development Budget ($m),Local Government Support Budget ($m)\n2015,75,80\n2016,72,82\n2017,68,89\n2018,65,92\n2019,60,98\n2020,58,105"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "jzOrDrO0nhic9hzb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had a consistent decrease in budget over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector had a consistent decrease in budget, I need to look at the trends in their line plots. I can see from the line for {Community Development Budget}<r>[0.130, 0.592, 0.941, 0.863]</r> that the budget decreases consistently from 75 in 2015 to 58 in 2020. On the other hand, the line for {Local Government Support Budget}<r>[0.130, 0.512, 0.941, 0.113]</r> indicates an increase in budget over the same period of time.\nBased on the above thought, the answer is:\nThe sector that experienced a consistent decrease in budget over the years was Community Development, as indicated by the consistent downward trend in its line plot."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "n6pRRmDVpHhyh4nR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much has the Local Government Support Budget Changed from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2015 value of the Local Government Support Budget is positioned at {2015}[0.130, 0.909], and the corresponding value is 80. The 2020 value for the same budget is located at {2020}[0.941, 0.909], with the corresponding value of 105. The change in budget from 2015 to 2020 is therefore 105 - 80 = 25.\nBased on the above thought, the answer is:\n25 million dollars"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "hMS82KSGgK636oim",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the raw data from this chart as a table, with elements as column headers and years as row headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first identify the column headers, which are {the legend} representing the different elements and their respective lines in the chart. Then, I identify {the x-axis tick labels}<r>[0.201, 0.919, 0.245, 0.942]</r>, <r>[0.341, 0.919, 0.385, 0.942]</r>, <r>[0.481, 0.919, 0.525, 0.942]</r>, <r>[0.621, 0.919, 0.665, 0.942]</r>, <r>[0.761, 0.919, 0.805, 0.942]</r> which are the years. Finally, to get the values, I need to locate the points along {the Grain Production line}<r>[0.223, 0.089, 0.783, 0.864]</r> and {the Per Capita Meat Consumption line}<r>[0.223, 0.089, 0.783, 0.864]</r> for each year.\nBased on the above thought, the answer is:\nYear,Grain Production (KG),Per Capita Meat Consumption (KG)\n2000,682394587,25.5\n2005,705697321,26.3\n2010,743286379,32.1\n2015,764183452,34.0\n2020,780569781,35.2"
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "e7bQxlgJLBbo3Vgb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend in Grain Production (KG) and Per Capita Meat Consumption (KG) over the years, what could we infer about the agriculture and food industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the overall trend in the lines representing Grain Production and Per Capita Meat Consumption. From the line for {Grain Production}<r>[0.223, 0.089, 0.783, 0.864]</r> and the line for {Meat Consumption}<r>[0.223, 0.089, 0.783, 0.864]</r>, it can be observed that there is a general increase in both over the years. This indicates that the agriculture and food industry has seen steady growth.\nBased on the above thought, the answer is:\nThe increasing trend in both Grain Production and Per Capita Meat Consumption over the years indicates a steady growth in the agriculture and food industry. This could be due to advancements in agricultural technology, increased yield per hectare, and the adoption of more efficient farming practices. In terms of food consumption, the increased meat consumption per capita could be indicative of changes in dietary habits, increased income, and improved access to meat products. This reflects an overall improvement and expansion in the food and agriculture sector."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "ogsHAWGJdomhMmQs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Per Capita Meat Consumption (KG) from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in Per Capita Meat Consumption from 2000 to 2020, I need to identify the corresponding y-values to the x-values 2000 and 2020. These are represented by the points on {the Per Capita Meat Consumption line} <r>[0.223, 0.089, 0.783, 0.864]</r>. In 2000, the meat consumption was 25.5 KG, while in 2020, it increased to 35.2 KG. The increase it therefore 35.2 - 25.5 = 9.7 KG.\nBased on the above thought, the answer is:\n9.7 KG."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "gul0Pn9WU8tQwhsx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a monthly data breakdown of the web traffic sources mentioned in the chart, arranged in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data represented in the chart, I focus on the subplots and their corresponding y-values for each source monthly. Noticing {the plotted line points for Direct Traffic}<r>[0.160, 0.145], [0.224, 0.250], [0.288, 0.355], [0.352, 0.450], [0.416, 0.545], [0.480, 0.635], [0.545, 0.715], [0.609, 0.785], [0.673, 0.835], [0.737, 0.875], [0.801, 0.910], [0.865, 0.955]</r> as well as {the plotted line points for Email, Search, Referral, and Social Media}<r>Various corresponding coordinates</r>, I extract their values monthly.\nBased on the above thought, the answer is:\nMonth,Direct Traffic,Email,Search,Referral,Social Media\nJan,145,55,70,200,130\nFeb,250,48,95,180,140\nMar,355,..., etc."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "jBxf1pCTrJUBH7dh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich web traffic source showed the greatest decline over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the starting and ending points of each source, I determine the trend for each. Focusing on {the Referral traffic line}<r>[0.160, 1.4], ..., [0.865, 0.4]</r>, it starts higher at 200 in January and decreases consistently to below 50 in December, showing the greatest decline compared to others.\nBased on the above thought, the answer is:\nThe Referral traffic source showed the greatest decline over the course of the year. Starting off relatively strong, it demonstrates a consistent decrease each month, ending considerably lower than its starting point."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "EPTuFoPZ5b3KTjCd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute change in 'Search' traffic from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this change, I compare the y-values for the 'Search' line between January and December. Locating {the 'Search' line point in January}<r>[0.160, 0.070]</r> with a value of 70 and {the 'Search' line point in December}<r>[0.865, 0.150]</r> with a value of 950, I calculate the difference.\nBased on the above thought, the answer is:\n880."
+            }
+        ],
+        "id_raw": 284
+    },
+    {
+        "id": "DnuHgamnS1ZUTnIt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the provided line chart data with the years as column headers and the type of sales as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inspecting the data, the column headers or years can be inferred from the {x-axis labels}<r>[[0.123, 0.902, 0.149, 0.932], [0.261, 0.902, 0.288, 0.932], [0.400, 0.902, 0.426, 0.932], [0.539, 0.902, 0.565, 0.932], [0.677, 0.902, 0.704, 0.932], [0.816, 0.902, 0.842, 0.932], [0.955, 0.902, 0.981, 0.932]]</r>. The row labels or the type of sales such as 'Grocery Sales' and 'E-commerce Sales' can obtained from the {legend}<r>[0.677, 0.740, 0.944, 0.880]</r>. For the cell values, I can track {the points on the line of Grocery Sales}<r>[0.123, 0.224, 0.955, 0.336]</r> and {the points on the line of E-commerce Sales}<r>[0.123, 0.150, 0.955, 0.820]</r> and convert the coordinates into the corresponding sales values for every type in each year.\nBased on the above thought, the answer is:\nSales Type,2015,2016,2017,2018,2019,2020,2021\nGrocery Sales,12000,11900,11500,11000,10800,10500,10500\nE-commerce Sales,4000,4500,5000,6000,8000,11000,13000"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "seFtfsczXKArI4HO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the chart, can you comment on the overall trend of Grocery Sales and E-commerce Sales over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To track the trend of Grocery Sales and E-commerce Sales over the years, I would inspect the {Grocery Sales line}<r>[0.123, 0.224, 0.955, 0.336]</r> and {E-commerce Sales line}<r>[0.123, 0.150, 0.955, 0.820]</r>. The Grocery Sales line shows a descending trend starting from 12000 in 2015 to 10500 in 2021 indicating a general decline over the years. In contrast, the E-commerce Sales line shows an ascending trend from 4000 in 2015 to 13000 in 2021 indicating a sustained increase over the years.\nBased on the above thought, the answer is:\nThe overall trend showcased by the chart for Grocery Sales is downward, suggesting a decrease in the sales over the years from 2015 to 2021. On the contrary, E-commerce Sales have evidently increased during the same period, as shown by the upward trend of the E-commerce Sales line."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "X7k1zDNvhmpUU35a",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in E-commerce sales from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total increase in E-commerce sales from 2015 to 2021, I need to locate {the points on the E-commerce Sales line}<r>[0.123, 0.150, 0.955, 0.820]</r> and extract the corresponding sales values from {the y-axis labels}<r>[[0.064, 0.883, 0.117, 0.913], [0.064, 0.808, 0.117, 0.838], [0.064, 0.734, 0.117, 0.764], [0.064, 0.659, 0.117, 0.689], [0.064, 0.585, 0.117, 0.615], [0.064, 0.510, 0.117, 0.540], [0.064, 0.436, 0.117, 0.466], [0.051, 0.362, 0.117, 0.392], [0.051, 0.287, 0.117, 0.317], [0.051, 0.213, 0.117, 0.243], [0.051, 0.138, 0.117, 0.168], [0.051, 0.064, 0.117, 0.094]]</r> for the years 2015 and 2021. The total increase would be the sales value of 2021 minus that of 2015.\nBased on the above thought, the answer is:\n9000."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "bEnrQGS3DcYDZJNn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help to generate a structured table from the plotted line chart considering Technology Innovation Index as rows and Online Education Impact as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make the table, I first notice that {the x-axis labels}[[0.096,0.902,0.109,0.932], [0.261,0.902,0.288,0.932], [0.433,0.902,0.459,0.932], [0.605,0.902,0.631,0.932], [0.777,0.902,0.803,0.932], [0.942,0.902,0.981,0.932]] represent the unique data points for Technology Innovation Index. I then see that {the points of the line for Online Education Impact}[[0.111,0.348], [0.163,0.621], [0.223,0.758], [0.360,0.485], [0.481,0.621], [0.601,0.294], [0.704,0.239], [0.833,0.348]] correspond to the corresponding Online Education Impact data for each Technology Innovation Index data point. Then I assign the Impact index for each Technology Innovation index and prepare the table.\nBased on the above thought, the answer is:\nTechnology Innovation Index,Online Education Impact Index\n1,0.90\n7,0.80\n14,0.75\n30,0.85\n44,0.80\n58,0.92\n70,0.94\n85,0.90"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "plVRVNlx4jf2w7NX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the overall trend observed in the line chart for Technology Innovation Index against Online Education Impact Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the line representing the Online Education Impact Index}[0.111, 0.239, 0.833, 0.758], the overall trend can be discerned. The line initially decreases from the point {start point}[0.111,0.348] at Technology Innovation Index of 1 to a local minimum at {local minimum}[0.223,0.758] around Technology Innovation Index 14, followed by an increase to a local maximum at {local maximum}[0.704, 0.239] around Technology Innovation Index 70, and then again a slight decrease at {end point}[0.833,0.348] for Technology Innovation Index 85.\nBased on the above thought, the answer is:\nFrom the line chart, it can be observed that the Online Education Impact Index decreases initially with increasing Technology Innovation Index. After reaching a local minimum, it rises again to reach a local maximum and finally decreases a little. Thus, the overall trend is a kind of a 'V' shape showing volatility as the Technology Innovation Index increases."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "3gKqB3RllRYRr726",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value of the Online Education Impact Index shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value of the Online Education Impact Index, I need to identify {the points of the line for Online Education Impact}[[0.111,0.348], [0.163,0.621], [0.223,0.758], [0.360,0.485], [0.481,0.621], [0.601,0.294], [0.704,0.239], [0.833,0.348]]. The y-values of these points correspond to the Online Education Impact Index. The maximum y-value corresponds to the point at {max_point}[0.704, 0.239] which has a corresponding Online Education Impact Index of 0.94.\nBased on the above thought, the answer is:\n0.94"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "5DxSuGBBhZkJRXvW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me to reformat the chart data and arrange it in a structured table form, with 'Year' as the rows and 'Average Household Size' and 'Median Housing Price' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to structure the data in the chart into tabular form, I first identify {the x-axis tick labels}[0.274, 0.909, 0.318, 0.932], [0.479, 0.909, 0.523, 0.932], representing the row headers or the year. Next, I located {the legend Items}[[0.167, 0.838, 0.418, 0.868]] which represent the column headers 'Average Household Size' and 'Median Housing Price'. To fill up the values, I locate {the points on the lines}[[0.296, 0.113], [0.501, 0.525], [0.706, 0.856]], interpret their relative quantities on the y-axis and extract their corresponding real-world numerical values.\nBased on the above thought, the answer is:\nYear,Average Household Size,Median Housing Price\n2010,5.2,120000\n2015,4.7,220000\n2020,4.3,350000"
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "syhEygNTNKqdRA8P",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the overall trend of 'Average Household Size' and 'Median Housing Price' from 2010 to 2020 based on the graphs."
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the overall trends, I examine both the lines for 'Average Household Size' and 'Median Housing Price'. Observing the {line for 'Average Household Size'}[0.296, 0.113, 0.706, 0.856], I notice a clear downward trajectory from 5.2 in 2010 to 4.3 in 2020, depicting a decline in the average household size over this period. \nConversely, the line for 'Median Housing Price' is showcasing a different trend. The relevant plot points correspond to a quite significant increase over the same period, starting from 120,000 in 2010 {point at 2010 for Median Housing Price}[0.296, -49036.361], to 220,000 in 2015 {point at 2015 for Median Housing Price}[0.501, -89902.102], and up to 350,000 in 2020 {point at 2020 for Median Housing Price}[0.706, -143027.565].\nBased on the above thought, the answer is:\nThe line chart exhibits contrasting trends for 'Average Household Size' and 'Median Housing Price' from 2010 to 2020. While the 'Average Household Size' displays a consistent downward trend, indicating a decrease in the average household size over the decade, the 'Median Housing Price', on the other hand, experiences a considerable upward trend, signifying a rise in housing prices. Specifically, the housing prices increased from 120,000 in 2010, to 220,000 in 2015, and then to 350,000 in 2020. This divergence in trends may hint towards socio-economic changes affecting family structures and the housing market."
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "mOwCgz26CmzjbmX4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Average Household Size in the years 2010 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the Average Household Size between 2010 and 2015, I'll need to identify the points corresponding to these years on {the line for the Average Household Size}[0.296, 0.113, 0.706, 0.856]. Specifically, the points I need are at {2010}[0.296, 0.113] and {2015}[0.501, 0.525] which correspond to Average Household Sizes of 5.2 and 4.7 respectively. The difference would be 5.2 - 4.7 = 0.5.\nBased on the above thought, the answer is:\n0.5"
+            }
+        ],
+        "id_raw": 290
+    },
+    {
+        "id": "SEXYNfPrOk1DuRlW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data from the line chart, setting the 'Year' as the row headers and the technological sectors as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first use {the x-axis tick labels}[[0.093, 0.881, 0.134, 0.914], [0.273, 0.881, 0.304, 0.922], ..., [0.948, 0.881, 0.975, 0.938]], which represent the row headers, indicating the years. I then look at {the legend texts}[[0.130, 0.101, 0.300, 0.131], [0.130, 0.144, 0.303, 0.174], [0.130, 0.188, 0.255, 0.218]] to identify the column headers, referring to the three technological sectors 'Artificial Intelligence', 'Quantum Computing', and 'Bioengineering'. After that, I locate {the line points} for each sector at the respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear, Artificial Intelligence, Quantum Computing, Bioengineering\n2020, 23, 15, 12\n2021, 24, 17, 13\n2022, 28, 20, 14\n2023, 32, 22, 15\n2024, 35, 25, 16\n2025, 37, 30, 19\n2026, 39, 35, 22\n2027, 45, 40, 23\n2028, 50, 40, 26\n2029, 52, 41, 28\n2030, 58, 45, 32"
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "RB5NFFxNLZHFKlu2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector of technology showed the steadiest growth over the 10 year period according to the trends in the line chart? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to inspect the lines associated with each technological sector. Starting with {the line for Artificial Intelligence}[0.110, 0.113, 0.943, 0.678], it shows a fairly steady rate of increase from 23 to 58 over the 10 years. Then, the {line for Quantum Computing}[0.110, 0.323, 0.943, 0.807] exhibits a slightly greater increase from 15 to 45. Lastly, {Bioengineering}[0.110, 0.533, 0.943, 0.856], shows a relatively modest increase from 12 to 32. Considering the extent and consistency of percentage points growth over time, the growth of Artificial Intelligence seems to be the most steady among the three sectors.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, the sector of Artificial Intelligence demonstrated the steadiest growth over the 10-year period. It started from 23 in 2020 and steadily increased with regular increments up to 58 in 2030. Compared to Quantum Computing and Bioengineering, which had some more considerable jumps, Artificial Intelligence showed a more consistent rate of increase throughout the years."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "9hZtpNuaUKcWinDF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data trends, by how many points did the growth in Quantum Computing increase from 2020 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the increase in the growth of Quantum Computing, I need to identify the {line points for Quantum Computing from 2020}[x=2020, y=15, point_coordinates=[0.110, 0.807]] and {2030}[x=2030, y=45, point_coordinates=[0.943, 0.323]]. The growt rate of Quantum Computing in 2020 was 15 and increased to 45 by 2030. Therefore the total increase is calculated by 45 - 15 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 291
+    },
+    {
+        "id": "ToYDl2Tjs38qkMOD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the given chart into a structured table format with 'Financial Management Strategy' and 'Trend' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart, I first locate the categories of Financial Management on {the x-axis}<r>[0.133, 0.065, 0.866, 0.201]</r> which is the main column. The trends that the segments correspond to are identified from {the legend}<r>[0.340, 0.917, 0.993, 1.000]</r>. The segments in each bar represent the percentages for each trend. By looking at {the bars for Financial Planning}<r>[0.341, 0.261, 0.429, 0.770]</r> and {the bars for Risk Mitigation}<r>[0.569, 0.261, 0.656, 0.780]</r>, I can gather each value for the corresponding trend.\nBased on the above thought, the answer is:\nFinancial Management Strategy, Trend, Percentage\nFinancial Planning, Minimal Change, 36\nFinancial Planning, Steady Decline, 34\nFinancial Planning, Substantial Decrease, 21\nFinancial Planning, Sustained Decline, 5\nFinancial Planning, Minimal Change, 3\nFinancial Planning, Gradual Improvement, 1\nRisk Mitigation, Minimal Change, 25\nRisk Mitigation, Steady Decline, 28\nRisk Mitigation, Substantial Decrease, 30\nRisk Mitigation, Sustained Decrease, 10\nRisk Mitigation, Minimal Change, 5\nRisk Mitigation, Gradual Improvement, 2"
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "YIQE3psgLL1FFnMX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bars in the chart, which area of Financial Management has seen the more substantial decreases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the 'Substantial Decrease' segments in both 'Financial Planning' and 'Risk Mitigation'. I find the substantial decrease segment for {Financial Planning}<r>[0.341, 0.484, 0.429, 0.568]</r> and for {Risk Mitigation}<r>[0.569, 0.484, 0.656, 0.640]</r>, with respective percentages of 21% and 30%. Thus, comparing these percentages will give me the area with more substantial decreases.\nBased on the above thought, the answer is:\nThe area of 'Risk Mitigation' has seen more substantial decreases compared to 'Financial Planning' as per the information provided in the chart."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "DMEgb6R11JKlocCm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the 'minimal change' trend in Financial Planning and Risk Mitigation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I first need to identify the 'minimal change' segments for both Financial Planning and Risk Mitigation. I find the segment for 'minimal change' in {Financial Planning}<r>[0.341, 0.618, 0.429, 0.770]</r> and {Risk Mitigation}<r>[0.569, 0.618, 0.656, 0.770]</r>. The percentages for these are 36% and 25% respectively. The difference can be calculated as 36 - 25 = 11.\nBased on the above thought, the answer is:\n11%."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "sODbUNlzIIusnvtD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data shown in the stacked bar chart into a table format with the Process as columns and Potential areas for improvements as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form a table from the stacked bar chart, I'll first analyze {the x-axis labels}<r>[0.150, 0.902, 0.934, 0.932]</r> that represent the different Software Development and Operations processes which will be our columns. Then, I locate {the legend texts}<r>[0.558, 0.740, 0.971, 0.880]</r> which specify Potential Areas for Improvements that will form rows in our table. Specifically, these areas are 'Resource Optimization', 'Bug Resolution', and 'Performance Tuning'. Now, to get the values, I'll inspect the {the lengths of each section in the stacked bars}<r>[0.176, 0.193, 0.908, 0.286]</r> in relation to their respective process and improvement area, and use them to extract the relevant percentages.\nBased on the above thought, the answer is:\nArea of Improvement,Project Management,Software Development,Testing & QA,Operations,Maintenance,Support & Services,R&D\nResource Optimization,0.33,0.30,0.28,0.18,0.33,0.25,0.35\nBug Resolution,0.35,0.40,0.35,0.29,0.33,0.32,0.40\nPerformance Tuning,0.32,0.30,0.37,0.53,0.34,0.43,0.25"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "DE8BVktPPWqu5ZBS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charted data, which process seems to have the highest demand for improvements in 'Performance Tuning'? And why do you conclude so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the segment in each bar that corresponds to the 'Performance Tuning' area of improvement - this is the third segment in each bar as given by {the legend}<r>[0.558, 0.740, 0.971, 0.880]</r>. Comparing the lengths of these segments, I can infer which process has the largest value, signifying the highest potential for improvement in Performance Tuning. Upon analysis, the 'Operations' bar shows {the longest segment for Performance Tuning}<r>[0.396, 0.391, 0.469, 0.368]</r>.\nBased on the above thought, the answer is:\nBased on the charted data, 'Operations' seems to have the highest demand for improvements in 'Performance Tuning'. This conclusion comes from comparing the lengths of the 'Performance Tuning' segments in all process bars and finding that the 'Performance Tuning' segment of the 'Operations' bar is the longest. Thus, it indicates a higher potential for improvement in that particular aspect compared to the other processes."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "6xeNKRq7jzqVSPAb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for all areas of potential improvements for Testing & QA based on the chart information?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the length of the entire bar corresponding to Testing & QA. The three segments in this bar represent the three areas of potential improvements: Resource Optimization, Bug Resolution, and Performance Tuning. Adding together the percentages reflected by {the segments of the Testing & QA bar}<r>[0.322, 0.426, 0.396, 0.391]</r> will give us the total potential improvement percentage for Testing & QA.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "10kPtONOHWtbZ9Me",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this line chart, where Years are the columns and Measures (Consumer Confidence and Market Sentiment) are rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To turn the data from this line graph into a tabular format, I'll first look at {the x-axis labels}<r>[0.066, 0.909, 1.070, 0.932]</r>, these correspond to the column headers: the Years. I then identify the {legend entries or row labels}<r>[0.112, 0.089, 0.416, 0.186]</r>, which represent the 'Consumer Confidence' and 'Market Sentiment'. Then I'll analyze {the line points}<r>[0.141, 0.150, 0.941, 0.856]</r> and <r>[0.141, 0.113, 0.941, 0.856]</r> for each row at the respective Years and extract their y values.\nBased on the above thought, the answer is:\nYear,Consumer Confidence,Market Sentiment\n2005,0.60,0.75\n2006,0.65,0.78\n2007,0.69,0.80\n2008,0.40,0.42\n2009,0.48,0.40\n2010,0.52,0.50\n2011,0.54,0.51\n2012,0.57,0.54\n2013,0.60,0.55\n2014,0.65,0.60\n2015,0.70,0.65\n2016,0.70,0.68\n2017,0.72,0.71\n2018,0.76,0.73\n2019,0.78,0.75\n2020,0.60,0.60"
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "27mQ8Ms2XS1ghNpb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, describe the trend of Consumer Confidence and Market Sentiment over the years."
+            },
+            {
+                "from": "gpt",
+                "value": "From analyzing {the line of Consumer Confidence}<r>[0.141, 0.150, 0.941, 0.856]</r>, it seems that Consumer Confidence gradually increased from 0.60 in 2005 to 0.78 in 2019, with a few dips in between, most notably in 2008 during the economic recession where it hits the lowest at 0.40. However, a decrease is observed in 2020, potentially due to the global COVID-19 pandemic.\n\nLooking at {the line of Market Sentiment}<r>[0.141, 0.113, 0.941, 0.856]</r>, Market Sentiment also experienced a steady increase from 0.75 in 2005 to 0.75 in 2019, albeit less extreme fluctuations compared to Consumer Confidence. However, unlike Consumer Confidence, Market Sentiment demonstrates an incremental downward trend after peaking in 2007 at 0.80, with a significant drop in 2008. It gradually recovers and stays steady from 2015 onwards. In 2020, similar to Consumer Confidence, Market Sentiment decreased, potentially due to the same global circumstances.\nBased on the above thought, the answer is:\nThe trends of Consumer Confidence and Market Sentiment over the years indicate a positive correlation, with both metrics showing similar peaks and troughs. Consumer Confidence showed gradual growth over the years, reaching the highest point in 2019 but dropped in 2020, possibly due to the impact of the COVID-19 pandemic. Market Sentiment, on the other hand, peaked in 2007, had a significant drop in 2008, and maintained a stable but slightly downwards trend till 2020. Again, the decrease in 2020 could possibly be attributed to global uncertainties."
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "bMEL8l9nyYHVOgpW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Consumer Confidence between the years 2005 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in Consumer Confidence between 2005 and 2020, I need to identify {the line points for Consumer Confidence}<r>[0.141, 0.484]</r> and <r>[0.941, 0.484]</r> and extract their corresponding values 0.60 and 0.60. The difference is therefore 0.60 - 0.60 = 0.00.\nBased on the above thought, the answer is:\n0.00"
+            }
+        ],
+        "id_raw": 295
+    },
+    {
+        "id": "zzgOcLzjxq3Oaig7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data shown in the line chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I am looking at the line plots for {Social Inequality}<r>[0.137, 0.212, 0.941, 0.894]</r> and {Rights Protection}<r>[0.137, 0.089, 0.941, 0.430]</r> against the x-axis representing the time periods {the x-axis tick labels}<r>[[0.130, 0.902, 0.143, 0.932], ..., [0.921, 0.902, 0.961, 0.932]]</r>. I am noting down the respective values at each time period for both categories.\nBased on the above thought, the answer is:\nTime, Social_Inequality, Rights_Protection\n0, 0, 68\n10, 10, 75\n20, 20, 72\n30, 30, 90\n40, 40, 87\n50, 50, 93\n60, 60, 110\n70, 70, 115\n80, 80, 118\n90, 90, 105\n100, 100, 112"
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "wxcYVy8AmCl3ddzR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the trend for Rights Protection over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend for Rights Protection over time, I am looking at the coordinates of the line plot for {Rights Protection}<r>[0.137, 0.089, 0.941, 0.430]</r> and the corresponding time periods. Here, I can see that  there is a general upward trend in Rights Protection over time, with periods of sharp increases, stabilized by periods of slower or negative growth. This is especially pronounced after the 50th time period, where the trend seems to grow significantly faster than before.\nBased on the above thought, the answer is:\nThe trend for Rights Protection over time can be described as generally increasing with periods of sharp increases intermixed with slower growth or declines. The measure of Rights Protection rises significantly after the 50th time period and reaches its maximum at the 80th time period, then slightly decreases afterwards."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "gd4d6YkMe87BxKnb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which time period does the Rights Protection measure reach its maximum value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out when the Rights Protection measure is at its maximum, I need to observe the line plot for {Rights Protection}<r>[0.137, 0.089, 0.941, 0.430]</r>. Here, the lowest point on the y-axis corresponds to the maximum value (as y=0 is at the top). The point in question on this line plot is {the maximum point of Rights Protection}<r>[0.780, 0.089]</r> which corresponds to the Time Period of 80.\nBased on the above thought, the answer is:\n80."
+            }
+        ],
+        "id_raw": 296
+    },
+    {
+        "id": "9RCKFCv7IwG73BLR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the line chart data into a table format with the Years as the row labels and the metrics as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create a table from the line chart, I must first recognize the labels of {the x-axis tick labels}[[0.116, 0.902, 0.169, 0.932], [0.221, 0.902, 0.273, 0.932], [0.326, 0.902, 0.378, 0.932], [0.430, 0.902, 0.483, 0.932], [0.535, 0.902, 0.588, 0.932], [0.640, 0.902, 0.693, 0.932], [0.745, 0.902, 0.798, 0.932], [0.850, 0.902, 0.903, 0.932]] as the years, which will constitute the row labels. Then I spot the {legend labels}[[0.730, 0.795, 0.950, 0.825], [0.730, 0.839, 0.962, 0.869]] as the metrics 'Student Engagement' and 'Innovation in Teaching' that will form the column headers. Next, I follow the {line points for 'Student Engagement'}[[0.142, 0.731], [0.195, 0.722], [0.247, 0.665], [0.300, 0.640], [0.352, 0.624], [0.404, 0.485], [0.457, 0.419], [0.509, 0.411], [0.562, 0.403], [0.614, 0.321], [0.667, 0.280], [0.719, 0.338], [0.771, 0.313], [0.824, 0.403], [0.876, 0.239], [0.929, 0.198]] and {line points for 'Innovation in Teaching'}[[0.142, 0.812], [0.195, 0.771], [0.247, 0.780], [0.300, 0.771], [0.352, 0.763], [0.404, 0.739], [0.457, 0.714], [0.509, 0.698], [0.562, 0.649], [0.614, 0.616], [0.667, 0.567], [0.719, 0.550], [0.771, 0.518], [0.824, 0.485], [0.876, 0.436], [0.929, 0.403]] to extract the percentage rates for both metrics throughout the different years.\nBased on the above thought, the answer is:\nYear,Student Engagement,Innovation in Teaching\n2005,0.2,0.1\n2006,0.21,0.15\n2007,0.28,0.14\n2008,0.31,0.15\n2009,0.33,0.16\n2010,0.5,0.19\n2011,0.58,0.22\n2012,0.59,0.24\n2013,0.6,0.3\n2014,0.7,0.34\n2015,0.75,0.4\n2016,0.68,0.42\n2017,0.71,0.46\n2018,0.6,0.5\n2019,0.8,0.56\n2020,0.85,0.6"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "1ZvllEATL82ZtUbD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which metric showed the most rapid increase in rate from 2009 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to identify the rate in 2009 and 2020 for each metric. For {Student Engagement}[0.352, 0.624, 0.929, 0.198], the rate increased from 0.33 in 2009 to 0.85 in 2020, a total increase of 0.52. For {Innovation in Teaching}[0.352, 0.763, 0.929, 0.403], the rate increased from 0.16 in 2009 to 0.6 in 2020, a total increase of 0.44. Student Engagement shows a greater total increase than Innovation in Teaching in the given period.\nBased on the above thought, the answer is:\nBased on the chart data, Student Engagement showed the most rapid increase in rate from 2009 to 2020. The rate increased by a total of 0.52, which is higher than the 0.44 increase for Innovation in Teaching during the same time period."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "I4xgwlptFHwP3gHF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of Innovation in Teaching in 2020 compared to 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in the rate of Innovation in Teaching from 2005 to 2020, I first need to locate the {rate point for 2005 on the 'Innovation in Teaching' line}[0.142, 0.812] and then the {rate point for 2020 on the 'Innovation in Teaching' line}[0.929, 0.403]. The rate in 2005 was 0.1 and the rate in 2020 was 0.6. The difference or rate change is 0.6 - 0.1 = 0.5.\nBased on the above thought, the answer is:\n0.5"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "6L16Q2jFg7XLDUZ9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured form with 'Year' as the rows and various observations like Global Temperature Change, Sea Level Rise and Air Pollution Index as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a tabular structure, I'll use the {x-axis ticks}[[0.190, 0.927, 0.233, 0.949], [0.301, 0.927, 0.343, 0.949], [0.411, 0.927, 0.454, 0.949], [0.522, 0.927, 0.564, 0.949], [0.632, 0.927, 0.675, 0.949], [0.743, 0.927, 0.785, 0.949], [0.853, 0.927, 0.896, 0.949]] as the row labels or Years. The column headers will be taken from {legend items}[[0.163, 0.108, 0.395, 0.131], [0.163, 0.141, 0.283, 0.164], [0.163, 0.174, 0.313, 0.197]]. The respective values for each year and observation will be extracted from {points on the lines}[[0.212, 0.174, 0.875, 0.424], [0.212, 0.498, 0.875, 0.875],[0.212, -16.368, 0.875, -9.994]].\nBased on the above thought, the answer is:\nYear,Global Temperature Change,Sea Level Rise,Air Pollution Index\n2000,0.75,-0.17,22\n2005,0.79,-0.05,23\n2010,0.84,0.06,26\n2015,0.95,0.20,28\n2020,1.07,0.33,30\n2025,1.15,0.48,32\n2030,1.26,0.60,35"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "fW9y5jI9j195hnSu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering all the trends shown in the line chart, which parameter is likely to show the most consequential change by 2030? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I consider the overall trend of the three parameters: {Global Temperature Change line} [0.212, 0.174, 0.875, 0.424], {Sea Level Rise line} [0.212, 0.498, 0.875, 0.875], and {Air Pollution Index Line} [0.212, -16.368, 0.875, -9.994]. The Global Temperature Change, measured from 0.75 in 2000 to 1.26 in 2030, shows a significant rise over time. The Sea Level Rise from -0.17 in 2000 to 0.60 in 2030 indicates a flip from negative to positive values, suggesting a major impact. However, the Air Pollution Index shows a steady increase from 22 to 35, which indicates a relatively less severe change compared to the drastic shift in Sea Level Rise.\nBased on the above thought, the answer is:\nBased on the trends shown, the Sea Level Rise seems likely to show the most consequential change by 2030. This parameter starts with negative values, which means a falling trend, but then moves to positive values by 2030, denoting a rising trend. This flip in trend suggests potentially dramatic effects on the environment and highlights a crucial area for intervention."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "VJ85Br53ub1jzPNy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_15_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Air Pollution Index from 2000 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in the Air Pollution Index, I'll start by determining the initial index value in 2000 and the final index value in 2030. This is represented by {the first point on Air Pollution Index line} [0.212, -9.994] with a value of 22 and {the last point on the same line} [0.875, -16.368] with a value of 35. Subtracting the initial value from the final value will give us the total increase.\nBased on the above thought, the answer is:\n13."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "M3SENi6EqNSQRY6j",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table from the chart data with Community Areas as columns and Development Categories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a tabular data from the bar chart, I first identify {the x-axis tick labels}[[0.146, 0.902, 0.159, 0.932], [0.215, 0.902, 0.228, 0.932], [0.284, 0.902, 0.297, 0.932]] which represent the column headers or Development Categories. Then, I need to identify the community areas which are represented by different sets of bars in each category. Using the coordinates of the bars and the legend, I can create associations for each community area to every category.\nBased on the above thought, the answer is:\nDevelopment_Category,Central,East,West,North\nResidential_Area_Expansion,0.1,0.1,-0.1,0.2\nCommercial_Area_Expansion,-0.2,0.1,0.2,-0.1\nPublic_Facilities_Expansion,0.5,0.1,0.2,0.1"
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "3MFSk7BZ1oLKs1zK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from the bar chart, which community area appears to have a preference for expanding public facilities over residential and commercial areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the values of Public Facilities Expansion with Residential Area Expansion and Commercial Area Expansion for each community. The bar for Public Facilities Expansion in {the Central community}[0.843, 0.193, 0.912, 0.684] has an impressive high value of 0.5, which is significantly higher than its respective values of 0.1 and -0.2 for Residential and Commercial expansion. This clearly indicates a strong preference of the Central community for Public Facilities Expansion.\nBased on the above thought, the answer is:\nFrom the bar chart, it appears that the Central community area shows a preference for expanding public facilities especially compared to residential and commercial areas. This is evidenced by the high value of 0.5 for Public Facilities Expansion in the Central community, which is notably higher than the corresponding figures for Residential Area Expansion and Commercial Area Expansion in the same community."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "IK3hF9kdQEtxAnxd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change (sum of changes) in Residential Area Expansion across all community areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total change in Residential Area Expansion, I need to locate the bars corresponding to the Residential Area Expansion on the chart. These are {the bars for Central}[0.152, 0.193, 0.912, 0.333], {for East}[0.221, 0.298, 0.912, 0.333], {for West}[0.290, 0.333, 0.912, 0.274] and {for North}[0.360, 0.274, 0.912, 0.254]. The values for these bars are 0.1, 0.1, -0.1, and 0.2 respectively. Summing these up gives 0.1 + 0.1 - 0.1 + 0.2 = 0.3.\nBased on the above thought, the answer is:\n0.3."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "WouvzvahBs8djrig",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table format where 'Year' is the column headers and various 'Power Sources' as row headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the x-axis tick labels}<r>[0.064, 0.062, 0.978, 0.448]</r> for each subplot and the legend and identifying the various power sources, I can collect data points for each power source across the years. {the line points for Solar Power}<r>[0.083, 0.080, 0.468, 0.430]</r> and similarly for Wind, Hydropower, and Geothermal enable me to extract the yearly values to populate the table.\nBased on the above thought, the answer is:\nYear, Solar Power, Wind Power, Hydropower, Geothermal\n2018, 480, 380, 800, 700\n2019, 580, 450, 780, 720\n2020, 670, 520, 760, 740\n2021, 760, 590, 740, 760\n2022, 850, 660, 720, 780"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "PHXKXFrzKEcd4MVo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich power source shows the most consistent increase over the years based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze which power source is the most consistent in increase, I examine {the lines}<r>[0.064, 0.062, 0.978, 0.935]</r> for continuity and slope. Observing {the line for Solar Power}<r>[0.083, 0.080, 0.468, 0.430]</r> shows it has continually rising points without any dip. Similarly, I check {the lines for Wind Power, Hydropower, and Geothermal}<r>[0.554, 0.062, 0.978, 0.935]</r>, among which Hydropower shows a consistent decrease, and Geothermal doesn't increase as uniformly as Solar Power does.\nBased on the above thought, the answer is:\nBased on the chart, Solar Power shows the most consistent increase over the years, as its lines steadily rise without any decrease, reflecting uniform positive growth."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "GaWv4TvuMP8wdalA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Geothermal power production from 2018 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Geothermal power production, I look at {the line points for Geothermal}<r>[0.574, 0.080, 0.959, 0.430]</r> to extract the values at 2018 and 2022, which are 700 and 780 respectively. The increase is calculated as 780 - 700.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "Zr78eeG1pdHcSuqR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart into a CSV table format, depicting trend in attendance rate and revenue across different cultural institutions over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "I shall extract the data by identifying {the x-tick labels}<r>[0.194, 0.919, 0.214, 0.957]</r> representing different cultural institutions such as Opera House, Art Gallery, etc. Next, by observing the line corresponding to each institution - for example, {the line for Opera House}<r>[0.132, 0.181, 0.276, 0.850]</r>, I'll note the points that indicate attendance and revenue over different years. From these points, we can populate the structured table.\nBased on the above thought, the answer is:\nInstitution,Year,Attendance Rate (%),Revenue (Million USD)\nOpera House,2010,75,5\nOpera House,2011,78,5.5\nArt Gallery,2010,60,4.5\nArt Gallery,2011,65,5\nConcert Hall,2010,50,3.5\nConcert Hall,2011,55,3.7\nFilm Theatre,2010,45,4\nFilm Theatre,2011,60,3.8\nDance Studio,2010,40,2.5\nDance Studio,2011,70,3"
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "W1E0NkNF39ad97Kl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the Dance Studio exhibit a sudden spike in attendance in 2011 compared to the previous years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I observe the marked increase at {the point for Dance Studio in 2011}<r>[0.893, 0.631]</r>, which shows a significant rise in attendance. Reasons could involve various external factors not depicted in the chart but might include an increased number of events, improved marketing strategies, or external factors influencing public engagement in cultural activities during that period. However, specific causes are difficult to determine solely from the chart data without additional context from other sources.\nBased on the above thought, the answer is:\nThe spike in attendance at the Dance Studio in 2011 could be due to an increased number of events or successful promotional efforts. While the exact reason can't be pinpointed from the chart alone, dramatic changes like these often involve strategic adaptations or external influences boosting public interest and participation."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "ckHvrFdmycVd7tC1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the revenue difference observed in the year 2011 between the Opera House and the Film Theatre?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the revenue difference, I'll first locate the revenue marked for the year 2011 for both the Opera House at {the point}<r>[0.276, 0.242]</r> and the Film Theatre at {the point}<r>[0.482, 0.728]</r>. The revenue data shows $5.5 million for the Opera House and $3.8 million for the Film Theatre. To find the difference, I calculate $5.5 million - $3.8 million.\nBased on the above thought, the answer is:\n1.7 million USD."
+            }
+        ],
+        "id_raw": 302
+    },
+    {
+        "id": "jiSkFRExHyNoPM9j",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data depicted on the line chart into a tabular form with years as columns and each row representing either 'Cybersecurity spending' or 'Data breaches'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.121, 0.911, 0.130, 0.934], [0.325, 0.911, 0.334, 0.934], [0.530, 0.911, 0.539, 0.934], [0.735, 0.911, 0.744, 0.934], [0.940, 0.911, 0.948, 0.934]] as the column headers or years. Next, I observe {the legend texts}[[0.808, 0.086, 0.973, 0.110], [0.808, 0.121, 0.909, 0.145]] that indicate 'Cybersecurity spending' and 'Data breaches' to be the row headers. The corresponding line points for each category at every year, such as for {Cybersecurity spending}[[0.125, 0.865], [0.330, 0.865], [0.535, 0.864], [0.739, 0.864], [0.944, 0.863]] and {Data breaches}[[0.125, 0.380], [0.330, 0.372], [0.535, 0.368], [0.739, 0.364], [0.944, 0.420]] provide the values for each year.\nBased on the above thought, the answer is:\nMetric, Year 0, Year 1, Year 2, Year 3, Year 4\nCybersecurity spending, 41.22, 44.79, 48.45, 50.91, 52.38\nData breaches, 3050, 3100, 3125, 3150, 2800"
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "9XYePGR3C8KEFCTy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the line chart, did the trend of Data breaches correspond to the trend of Cybersecurity spending over time? Please explain your findings."
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the trend of Data breaches and Cybersecurity spending, I would need to observe both {the line for Cybersecurity spending}[0.125, 0.863, 0.944, 0.865] and {the line for Data breaches}[0.125, 0.364, 0.944, 0.420]. The Cybersecurity spending has consistently increased over the four years. However, for Data breaches, the number initially increased from 3050 to 3150 over the first three years but then significantly decreased to 2800 in the fourth year. Thus, there is a divergence in the trends around the fourth year when the spending on Cybersecurity didn't decrease correspondingly with the number of Data breaches.\nBased on the above thought, the answer is:\nNo, the trend of Data breaches did not correspond entirely to the trend of Cybersecurity spending over time. While both increased initially, they diverged in the last year with the Cybersecurity spending continuing to rise as the number of Data breaches fell significantly."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "lA8bmSkjO3Ckp74V",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many billions of dollars did Cybersecurity spending increase from Year 2 to Year 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Cybersecurity spending from Year 2 to Year 4, I'll examine {the line points for Cybersecurity spending}[[0.125, 0.865], [0.330, 0.865], [0.535, 0.864], [0.739, 0.864], [0.944, 0.863]] and extract the values. The spending at Year 2 is 48.45 billion dollars and at Year 4 it is 52.38 billion dollars. The increase is therefore 52.38 - 48.45 = 3.93 billion dollars.\nBased on the above thought, the answer is:\n3.93 billion dollars."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "0TvQy8DuyW4Tco8a",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table depicting the trends for Internal Struggle and Emotional Vulnerability across the categories mentioned in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I observe the available category labels on {the x-axis}<r>[0.041, 0.511, 0.453, 0.882]</r>, which correspond to 'External Adaption', 'Personality Development', and 'Social Consciousness'. I further analyze the values for each category found on the two lines displayed in the chart: one for Internal Struggle and another for Emotional Vulnerability, utilizing the position of {the points on the lines}<r>[0.110, 0.164, 0.384, 0.374]</r> and <r>[0.645, 0.152, 0.919, 0.251]</r> respectively.\nBased on the above thought, the answer is:\nCategory,Internal Struggle,Emotional Vulnerability\nExternal Adaption,0.76,-0.87\nPersonality Development,0.58,0.45\nSocial Consciousness,0.67,0.41"
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "qVEE0xUb3GQOi4RN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the trend in Emotional Vulnerability show a significant drop in the category of External Adaption compared to others?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing {the line plot for Emotional Vulnerability}<r>[0.110, 0.164, 0.384, 0.374]</r>, I notice a drastic dip in the values corresponding to the category of External Adaption compared to Personality Development and Social Consciousness. By examining further, I can see the label 'External Adaption' on the x-axis at approximately [0.089, 0.894, 0.130, 0.918]. The large negative value (-0.87) for this category could suggest heightened emotional sensitivity or exposure to adverse conditions in activities relating to external adaptation.\nBased on the above thought, the answer is:\nThe significant drop in Emotional Vulnerability for External Adaption indicated by the data may be due to an enhanced exposure to challenging or stressful scenarios in environments related to external social or environmental interactions. This can cause a heightened emotional response, reflected clearly in the considerable low values in the plot."
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "WnQCPZNIfECanNx5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the magnitude of the increase in Internal Struggle from Personality Development to Social Consciousness?"
+            },
+            {
+                "from": "gpt",
+                "value": "I identify the line representing Internal Struggle and locate the related values in {the line plots for Internal Struggle}<r>[0.645, 0.152, 0.919, 0.251]</r>. Extracting the specific positions corresponding to Personality Development and Social Consciousness each, I derive the values of 0.58 and 0.67 respectively. The required magnitude of increase is the subtraction of the Personality Development value from the Social Consciousness value.\nBased on the above thought, the answer is:\n0.09"
+            }
+        ],
+        "id_raw": 304
+    },
+    {
+        "id": "8e88nA8XbdimKake",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart showing the Entrepreneurship and Innovation Ecosystem metrics across various regions into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I analyze the structure of the bar chart closely. I recognize that the x-axis lists {the labels for different regions}<r>[0.125, 0.93, 0.140, 0.960]</r> such as 'New York', 'Silicon Valley', 'Beijing' which form the categorical axis of the table. Observing the bars, there are three different colors indicating three different metrics. Green bars correspond to 'Revenue Growth (%)'<r>[0.220, 0.520, 0.240, 0.580]</r>, gray bars represent 'Investor Funding ($M)'<r>[0.260, 0.520, 0.280, 0.600]</r>, and beige bars show 'Startup Density (# per 1000)'<r>[0.300, 0.610, 0.320, 0.670]</r>. The embedded texts atop each bar, which I refer to confirm the numerical data for each metric.\nBased on the above thought, the answer is:\nLocation,Revenue Growth (%),Investor Funding ($M),Startup Density (# per 1000)\nNew York,25,350,1.5\nSilicon Valley,30,340,2.0\nBeijing,35,220,1.2\nAustin,10,300,1.7\nTel Aviv,12,150,2.5"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "RI92Wa4GPzXWrkNT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has a notable discrepancy between its Revenue Growth (%) and Startup Density (# per 1000)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze discrepancies, I look closely at the green bars for 'Revenue Growth (%)'<r>[0.220, 0.520, 0.240, 0.580]</r> and beige bars for 'Startup Density (# per 1000)'<r>[0.300, 0.610, 0.320, 0.670]</r> across all regions labeled<r>[0.125, 0.93, 0.140, 0.960]</r> as 'New York', 'Silicon Valley', 'Beijing', etc. I find that Beijing has a high Revenue Growth at 35%<r>[0.430, 0.130, 0.450, 0.170]</r> while the Startup Density is comparatively low at 1.2 per 1000<r>[0.430, 0.770, 0.450, 0.850]</r>. This indicates a significant imbalance between the economic revenue growth and the startup community density in Beijing.\nBased on the above thought, the answer is:\nBeijing shows a notable discrepancy between its high Revenue Growth (%) and relatively low Startup Density (# per 1000). Despite the high economic growth, the startup dense ratio is low, possibly indicating hurdles in startup sustainability or operations in the region."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "FSh3UOruWpr9wyxn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Investor Funding ($M) across all regions as shown in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average Investor Funding across all regions, I notice the gray bars representing 'Investor Funding ($M)'<r>[0.260, 0.520, 0.280, 0.600]</r>. By systematically locating each bar and reading the values from the annotations, we have the amounts of funding for the regions: 350M for New York, 340M for Silicon Valley, 220M for Beijing, 300M for Austin, and 150M for Tel Aviv. To find the average, the calculation involves adding these numbers and dividing by the number of regions, which is five.\nBased on the above thought, the answer is:\n272"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "BLu68KxbabIQ62cU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the development trends over time for both AI Development and Quantum Computing from the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this table, I first look at {the x-axis labels}<r>[0.022, 0.937, 0.168, 0.953]</r> indicating the phases from 'Fluctuating Decline' to 'Moderate Fall'. Next, by reviewing {the lines for AI Development and Quantum Computing}<r>[0.026, 0.075, 0.368, 0.923]</r> along with {the data points}<r>[0.036, 0.884, 0.238, 0.113]</r> on these lines, I can extract the values associated with each trend.\nBased on the above thought, the answer is:\nTrends,AI Development,Quantum Computing\nFluctuating Decline,-5,-11\nModerate Increase,3,10\nUnpredictable Decline,-33,-13\nSubdued Decline,-8,-3\nModerate Fall,-16,-9"
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "BHrrXKBO2da0FMXo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends depicted in the chart, which technology shows greater resilience in terms of fewer and less drastic declines, AI Development or Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the trend lines, I observe that {Quantum Computing}<r>[0.274, 0.075, 0.496, 0.923]</r> shows declines with values of -11, -13, -3, -9, which, although frequent, are less severe compared to {AI Development}<r>[0.026, 0.075, 0.248, 0.923]</r> which experiences sharper declines with values of -5, -33, -8, -16. This suggests that Quantum Computing exhibits fewer intense declines.\nBased on the above thought, the answer is:\nQuantum Computing shows greater resilience as it undergoes less severe declines than AI Development, evidenced by smaller values in its declining phases."
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "ZAE8QfJKOrNbUKtv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decline exhibited by AI Development from 'Fluctuating Decline' to 'Moderate Fall' given the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trajectory of {AI Development decline}<r>[0.036, 0.884, 0.238, 0.113]</r>, the respective values for each trend phase are -5, 3, -33, -8, -16. To find the total decline, we sum the negative values only, which results in -5 + (-33) + (-8) + (-16) = -62.\nBased on the above thought, the answer is:\n-62"
+            }
+        ],
+        "id_raw": 307
+    },
+    {
+        "id": "jb5bXKja9kU3ryA5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the line chart data showing satisfaction and training program success into a table, categorizing them according to professional categories."
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart into a table, I first observe that the x-axis categorizes various professional categories. I recognize {the professional categories on the x-axis}<r>[0.105, 0.890, 0.884, 0.890]</r> covering sectors like IT, Agriculture, Healthcare, etc. For each category, I look at the two lines- one showing Employee Satisfaction and one showing Training Program Success. By extracting the data points from these lines like {point in IT for Employee Satisfaction}<r>[0.141, 0.370]</r> and {point in Agriculture for Training Program Success}<r>[0.564, 0.281]</r>, I can fill the corresponding values into the table.\nBased on the above thought, the answer is:\nProfessional_Category,Employee_Satisfaction(% decline),Training_Program_Success(% increase)\nIT,20,5\nAgriculture,3,18\nHealthcare,15,12\n..."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "M2SWNgSupgI89r3z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich professional category shows the most alarming trend in Employee Satisfaction, and how can that be interpreted from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I focus on the Employee Satisfaction trend line. {IT industry's line}<r>[0.141, 0.370, 0.461, 0.337]</r> displays a sharp decline when compared with others. By interpreting the plot trajectory, such as the significant drop at {Spring 2012}<r>[0.212, 0.337]</r>, it is clear that IT exhibits a severe decreasing trend in Employee Satisfaction. This indicates high employee turnover or discontent within the IT industry potentially due to work pressures or inadequate job rewards compared to other sectors.\nBased on the above thought, the answer is:\nThe IT industry exhibits the most concerning trend with a sharp decline in Employee Satisfaction evident in the steep downward trajectory of its line on the chart. This trend suggests significant dissatisfaction potentially due to intense work conditions and inadequate compensation or incentives."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "sd6VnVnBxbvEOhI8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total difference in trends between the lowest and highest rate of increase in the Training Program Success across all professional categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total difference, I analyze the points on the Training Program Success line for each professional category. Firstly, I calculate the lowest success rate observed at a data point, such as {in Healthcare}<r>[0.248, 0.454]</r> to be approximately 3%. Next, I find the highest success rate, noted in the {Agriculture category}<r>[0.564, 0.281]</r> at approximately 18%. The difference is computed by subtracting the lowest from the highest, which is 18% - 3% = 15%.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "Ru8i3c7WZzSQhaP2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the visual data in the chart into a high-level comparison table among the three innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the provided chart, a structured tabular data will be constructed. The column headers should be the innovation types which could be recognized from {the bar labels}<r>[0.163, 0.434, 0.197, 0.455]</r> as Peer Learning Technology, AI Learning Systems, and VR Training. And the row headers are the assessment parameters, namely Effectiveness, Popularity, and Early Adoption, which I identify from {the y-axis labels}<r>[0.051, 0.352, 0.089, 0.374]</r>. Looking for each assessment parameter from {the corresponding bar points}<r>[0.173, 0.366]</r> in the chart I could establish each row value for the mentioned innovations.\nBased on the above thought, the answer is:\n,Efectiveness,Popularity,Early Adoption\nPeer Learning Technology,0.76,0.58,0.59\nAI Learning Systems,0.70,0.72,0.65\nVR Training,0.64,0.62,0.68"
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "ZTU61bj7HnA33o1V",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich educational and training technology innovation is the most balanced in terms of effectiveness, popularity, and early adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which innovation is the most balanced, I will look at the variance of the three parameters for each technology. Examining the values of Effectiveness, Popularity and Early Adoption from {the bars}<r>[0.163, 0.434, 0.197, 0.455]</r> for Peer Learning Technology (0.76, 0.58, 0.59), AI Learning Systems (0.70, 0.72, 0.65), and VR Training (0.64, 0.62, 0.68), it can be observed that VR Training has the smallest variance with a difference of 0.04 between the lowest and highest value.\nBased on the above thought, the answer is:\nVR Training seems to be the most balanced educational and training technology innovation in terms of Effectiveness, Popularity, and Early Adoption. It has the closest values across these three parameters, indicating balanced performance. Meanwhile, other technologies have more fluctuating values with more distinct strengths and weaknesses."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "w9A5SeBrJ5pxy7le",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of Effectiveness for all three educational technology innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average Effectiveness between the three educational technologies, I would need to look at {the Effectiveness data points}<r>[0.173, 0.638], [0.327, 0.604], [0.477, 0.564]</r>, which correspond to the Peer Learning Technology (0.76), AI Learning Systems (0.70), and VR Training (0.64) respectively. I would then add these values together and divide by three.\nBased on the above thought, the answer is:\n0.70"
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "FjMNoGfFKviA2xSg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart comparing Data Integration and Data Transformation across various trends into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I will use the x-axis labels which are {the x-axis labels}<r>[0.053, 0.773, 0.207, 0.898]</r> for the trends which will head the columns of our table. The y-axis does not specify distinct categories but reflects performance values directly tied to each trend and each process, which I'll extract from {the line points}<r>[0.059, 0.186, 0.200, 0.575]</r>. The legends, albeit not explicitly placed, infer categories of 'Data Integration' and 'Data Transformation' which are considered as our primary rows.\nBased on the above thought, the answer is:\nTrend,Data Integration,Data Transformation\nInconsistent Growth,4.2,-1.9\nConsistent Expansion,9.8,10.4\nAbrupt Drop,-3.2,-8.3"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "z3dE79hAwvLdZBID",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend 'Abrupt Drop' compare between Data Integration and Data Transformation in terms of impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the impact of the 'Abrupt Drop' trend, I inspect both lines for {Data Integration}<r>[0.553, 0.082, 0.693, 0.342]</r> and {Data Transformation}<r>[0.623, 0.445, 0.770, 0.056]</r> at the specific segment of 'Abrupt Drop'. Data Integration shows a drop from a previous high value to a lesser negative value, with an endpoint at -3.2. Conversely, Data Transformation plummets significantly, denoting a more drastic negative deviation with an ending point at -8.3.\nBased on the above thought, the answer is:\n'Abrupt Drop' has a more severe impact on Data Transformation compared to Data Integration. While both see a decline, the drop in Data Transformation is notably much steeper and larger, indicating a more profound negative impact."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "HfoIniHk5QmsAYh4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall improvement change for Data Integration from 'Inconsistent Growth' to 'Consistent Expansion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change, I'll find the values at {Inconsistent Growth for Data Integration}<r>[0.059, 0.575]</r> and {Consistent Expansion for Data Integration}<r>[0.153, 0.316]</r>. The values obtained are 4.2 and 9.8, respectively. The change is then computed by subtracting the initial value from the final value: 9.8 - 4.2.\nBased on the above thought, the answer is:\n5.6"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "iar0wtnDy1pZFkpW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing tech companies' trends into a structured table format sorted by company names?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a structured table from the line chart, I begin by examining each line representing companies and trends on the chart. I identify {the x-axis tick labels}<r>[0.15, 0.85, 0.90, 0.95]</r> which represent various technology companies, such as Google, IBM, Microsoft, Apple, Facebook, Amazon, Tesla, and SpaceX. I then examine each associated trend point such as {the line point for Microsoft in sudden decrease trend}<r>[0.45, 0.28]</r> and {in erratic behavior trend}<r>[0.55, 0.45]</r>. Henceforth, I interpret the corresponding y-values for 'sudden decrease' and 'erratic behavior' from these coordinates.\nBased on the above thought, the answer is:\nCompany, Sudden Decrease, Erratic Behavior\nGoogle,1000,1600\nIBM,950,1500\nMicrosoft,1200,1400\nApple,800,1800\nFacebook,900,1700\nAmazon,700,1200\nTesla,850,1600\nSpaceX,750,1500"
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "ERYn4aOvzhuoh5NZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company reflects the most stable trend with less variation in the tech industry according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess company stability and lesser variation in trends, I analyze the proximity of data points on the 'sudden decrease' and 'erratic behavior' lines. {For IBM}<r>[0.52, 0.60]</r> the points show lesser variation with values 950 for sudden decrease and 1500 for erratic behavior that reflect smaller differences. Compared with others, where variations are relatively larger like {for Apple}<r>[0.42, 0.30], [0.62, 0.20]</r> showing more extreme behavior with values 800 and 1800 respectively. Therefore, based on the observed chart data, IBM exhibits less volatility.\nBased on the above thought, the answer is:\nIBM exhibits the most stability within the tech industry in terms of trends, as evidenced by its less varied numerical values between sudden decrease and erratic behavior trends."
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "oUTWlPOGsFc8hPsm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average trend value for Tesla considering both sudden decrease and erratic behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average trend value for Tesla, I locate {the sudden decrease data point for Tesla}<r>[0.65, 0.67]</r> and find the corresponding y-value to be 850. Next, I examine {the erratic behavior data point for Tesla}<r>[0.75, 0.62]</r> and find that y-value as 1600. The average value can be calculated by adding these two values and dividing by 2.\nBased on the above thought, the answer is:\n1225"
+            }
+        ],
+        "id_raw": 313
+    },
+    {
+        "id": "TLrlBjFKkL936u4g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the graphical data from the market statistics line chart into a tabular format where the columns represent years and the rows represent market trends such as Fluctuation, Decrease, or Increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transpose the graphical data from the line chart into a tabular data, I start by examining the {x-axis tick labels}[[0.071, 0.773, 0.139, 0.898], [0.167, 0.773, 0.233, 0.895], [0.263, 0.773, 0.329, 0.903], [0.359, 0.773, 0.425, 0.934], [0.455, 0.773, 0.521, 0.896], [0.551, 0.773, 0.617, 0.890], [0.647, 0.773, 0.713, 0.951], [0.743, 0.773, 0.809, 0.920], [0.839, 0.773, 0.905, 0.880], [0.935, 0.773, 0.999, 0.860]] which represent the column headers for years from 1990 to 1999. Then, I identify the distinct lines for each trend by examining their properties and translate each line's value across the years into table rows, respecting their corresponding {line plot coordinates}[see individual lines from data].\nBased on the above thought, the answer is:\nYear,Fluctuation,Decrease,Increase,Steep Increase,Outlier\n1990,50,40,30,20,60\n1991,55,35,45,25,62\n1992,43,38,50,30,63\n1993,60,33,55,40,67\n1994,50,30,60,47,70\n1995,48,25,65,55,75\n1996,55,22,70,65,80\n1997,60,20,75,72,85\n1998,63,18,80,78,90\n1999,65,15,85,82,95"
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "AJFOLJahthdYBUtg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the \"Outlier\" trend pattern behave throughout the decade according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart for the \"Outlier\" trend requires analyzing {the Outlier line plot}[[0.071, 0.610], [0.935, 0.710]]. The line clearly indicates a consistent upward pattern over the decade. Starting from a lower point in 1990, there is a gradual increase each year, reaching its peak in 1999. This upward movement suggests it consistently performed differently or markedly higher compared to the other trends throughout the years.\nBased on the above thought, the answer is:\nAccording to the chart, the \"Outlier\" trend exhibited a consistent increase throughout the decade, marking it as consistently higher in performance compared to other market trends. This consistent ascend illustrates a behavior of outperforming expected market behaviour year after year, culminating in a peak performance in 1999."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "fo6OSnKEftixxKWC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the \"Steep Increase\" trend from 1990 to 1999?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in the \"Steep Increase\" trend, I first identify the data values at the start and end within the plotted line for this specific trend using {the steep increase line plot}[[0.071, 0.420], [0.935, 0.182]]. The starting value in 1990 is 20 and the ending value in 1999 is 82. To find the total increase, I subtract the starting value from the ending value: 82 - 20.\nBased on the above thought, the answer is:\n62"
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "8Wzn9ttbf1djDlIL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table listing each technology and their data values for the years 2000, 2005, 2010, 2015, and 2020 according to the complex line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the x-axis label positions}<r>[0.184, 0.976, 0.224, 1.021]</r> which represent the years, and identifying the corresponding line plot points for each technology in {AI Tech line points}<r>[0.132, 0.855], [0.168, 0.668], [0.204, 0.528], [0.240, 0.342], [0.276, 0.155]</r> and {Nano Tech line points}<r>[0.338, 0.245], [0.374, 0.173], [0.410, 0.155], [0.446, 0.496], [0.482, 0.855]</r>, as well as interpreting others from Blockchain and Quantum Computing, I can construct the table.\nBased on the above thought, the answer is:\nTechnology, 2000, 2005, 2010, 2015, 2020\nAI Tech, 48, 52, 55, 59, 63\nNano Tech, 60, 64, 65, 46, 26\nBlockchain, 54, 57, 56, 62, 63\nQuantum Computing, 76, 78, 80, 82, 56"
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "6otfG9kRK0uTVODL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has demonstrated consistent growth based on the data from the years 2000 to 2020 shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology shows consistent growth, I analyze the slope of the line plots across the years. Observing {the line plot for AI Tech}<r>[0.132, 0.855], [0.276, 0.155]</r> demonstrates a downwards trajectory towards higher values, indicating growth. In contrast, the {line plot for Nano Tech}<r>[0.338, 0.245], [0.482, 0.855]</r> depicts an upward trajectory toward lower values, indicating a decline.\nBased on the above thought, the answer is:\nAI Tech has demonstrated consistent growth from 2000 to 2020."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "tRUEIJd9FM3DMcjN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage decrease in Nano Tech from 2000 to 2020 based on the chart values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease for Nano Tech, I extract the start and end values from {the line points for Nano Tech}<r>[0.338, 0.245], [0.482, 0.855]</r> which show 60 in 2000 and 26 in 2020. The formula used is [(Initial - Final) / Initial] * 100. In this case, [(60 - 26) / 60] * 100.\nBased on the above thought, the answer is:\n56.67%"
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "k7re4vfTVVJRCTRy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the demographic data displayed in the stacked bar chart into a structured table format with Age Groups as columns and Gender as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I observe {the categories on the x-axis}[[0.125, 0.120, 0.477, 0.470]] which include Young, Adult, and Senior, representing the column headers. Next, I see {the segments inside the bars}[[0.141, 0.454], [0.173, 0.422], [0.205, 0.390]], each differentiated by colors showing Female and Male demographics as well as Total population which represents the row entries. These segments allow an extraction of population data for each gender within each age group.\nBased on the above thought, the answer is:\nGender,Young,Adult,Senior\nTotal Population,150000,200000,120000\nFemale,80000,105000,60000\nMale,70000,95000,60000"
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "pgbc24tLnax8piEb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the demographic distribution shown in the chart, which age group shows a higher population of females compared to males, and by how much?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the demographic distribution, I inspect the proportion of {the Female segment}[[0.141, 0.136, 0.461, 0.454]] and {the Male segment}[[0.564, 0.136, 0.884, 0.454]] across each age group. In the Young group, the Female population is slightly higher, represented as 80000 compared to Males who are 70000, showing a difference of 10000. For Adults, females are 105000 while males are 95000, a difference of 10000. In Seniors, both genders report 60000, showing no difference here.\nBased on the above thought, the answer is:\nThe Adult group shows a higher population of females compared to males, with the difference being 10000."
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "KFgX5QAlxLZxmLSx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total population difference between the Adult and Young age groups represented in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total population difference between the Adult and Young age groups, I need to examine {the total height of the bars for Adult}[[0.205, 0.390]] and {the total height of the bars for Young}[[0.141, 0.454]]. The plotted heights reflect the total population values where Adult's population is approximately represented as 200000 and Young's population as 150000. The difference is calculated as 200000 - 150000.\nBased on the above thought, the answer is:\n50000"
+            }
+        ],
+        "id_raw": 318
+    },
+    {
+        "id": "14nYVaJnjBpqGlQ4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart showing data trends, can you compile the observed values for each series into a tabular format with Time as columns and Series as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I begin by referencing {the x-axis labels}<r>[0.125, 0.95, 0.18, 0.97]</r> to identify the timeline as columns. Each series is traced by analyzing their unique colors and patterns described in {the legend}<r>[0.05, 0.06, 0.12, 0.13]</r> placed on the upper left, identifying the rows as Series1 through Series4. I then track the coordinates of {the points on each series' line}<r>[0.14, 0.55], [0.25, 0.62], [0.36, 0.50], etc.</r> for the respective times, extracting the exact value points.\nBased on the above thought, the answer is:\nTime, Series1, Series2, Series3, Series4\n1, 0.58, 0.52, 0.62, 0.65\n2, 0.64, 0.56, 0.60, 0.68\n3, 0.70, 0.58, 0.58, 0.72\n4, 0.75, 0.60, 0.73, 0.75\n5, 0.78, 0.63, 0.69, 0.80\n6, 0.80, 0.65, 0.74, 0.86\n7, 0.82, 0.67, 0.78, 0.87\n8, 0.85, 0.70, 0.83, 0.89\n9, 0.88, 0.72, 0.81, 0.93\n10, 0.90, 0.75, 0.79, 0.97"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "jMwNzhC1ByxUYg6L",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the line chart, which series demonstrates the most steady and consistent growth over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the consistency of growth across the series, I observe {the lines for each series} <r>[0.14, 0.55], [0.25, 0.62], [0.36, 0.50], etc.</r>. Series1 and Series3 show fluctuating values suggestive of erratic and volatile patterns. In contrast, Series2's line<r>[0.14, 0.52], [0.25, 0.56], [0.36, 0.58], etc.</r> progresses with slight incremental changes at each time point, indicating a more steady growth. Series4, although demonstrating an upward trend, shows a more rapid and less consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, Series2 exhibits the most steady and consistent growth throughout the period. It shows minimal fluctuations, with a gradual increase in value, unlike the other series that show more significant jumps or volatility."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Tco1NljBgC21C3C8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_16_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of values observed for Series3 from Time 1 to Time 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range for Series3, I first gather the values from {the line for Series3} <r>[0.14, 0.62], [0.25, 0.60], [0.36, 0.58], [0.48, 0.73], [0.59, 0.69], [0.71, 0.74], [0.82, 0.78], [0.94, 0.83], [0.96, 0.81], [0.98, 0.79]</r>. The maximum value for Series3 is 0.83 and the minimum value is 0.58. The range would be the difference between these values.\nBased on the above thought, the answer is:\n0.25"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "DwSWdojt3oQAIrWG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the Stacked Bar Graph into a structured table showing the popularity of different digital content creation channels for the two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll focus my attention on {the bars on the graph}<r>[0.113, 0.501, 0.943, 0.735]</r>. Each bar in this range represents a specific form of digital media. In a stacked bar graph, the length of each segment of a bar corresponds to its values. So I need to extract the corresponding values from each segment, which respectively represent the first and second year. By referencing the respective labels associated with the x ticks {the x-axis tick labels}<r>[0.095, 0.893, 0.130, 0.921],[0.178, 0.893, 0.213, 0.921],[0.261, 0.893, 0.296, 0.921],[0.344, 0.893, 0.380, 0.921]</r>, I can assign the values to the right columns.\nBased on the above thought, the answer is:\nMedia,Year 1,Year 2\\n\nDigitalContent,1200,1600\nSocialMedia,1300,1550\nVlog,1150,1475\nPodcast,1080,1250\nBlog,1600,2200\neBook,1700,2450\nWebinar,1500,1300\nOnlineCourse,1800,1900"
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "ifvR344IP2L25Usp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends in digital media forms provided, which type wsould you say showed the greatest improvement from Year 1 to Year 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I start by inspecting all the {bar segments in the chart}<r>[0.113, 0.501, 0.943, 0.735]</r>. Each one corresponds to a different digital media form in Year 1 and Year 2. The difference between the two year values for each form denotes the amount of improvement. I notice that the {eBook section}<r>[0.632, 0.501, 0.710, 0.735]</r> has seen a significant increase from 1700 to 2450.\nBased on the above thought, the answer is:\nBased on the trends in the chart, the 'eBook' form of digital media experienced the greatest improvement from Year 1 to Year 2 with an increase in popularity from 1700 to 2450, which is the greatest rise among all other forms of media. This shows that eBooks have seen a boost in popularity and are being increasingly utilized for digital content creation."
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "klNnutcpWbg1C4JA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the total popularity gain in eBooks from Year 1 to Year 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking specifically at the {eBook bar}<r>[0.632, 0.501, 0.710, 0.735]</r>, I can see that it has two segments representing Year 1 and Year 2. The values of Year 1 and Year 2 are 1700 and 2450 respectively. We can simply subtract the Year 1 value from the Year 2 value to get the popularity gain over time.\nBased on the above thought, the answer is:\n750"
+            }
+        ],
+        "id_raw": 321
+    },
+    {
+        "id": "h38HoxVK1nv3hOXY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from this line chart into a table format with Integration Methods as rows and scales as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from this line chart, I first identify {the x-axis tick labels}[[0.075, 0.733, 0.174, 0.865],[0.227, 0.733, 0.338, 0.881],[0.380, 0.733, 0.501, 0.894],[0.524, 0.733, 0.674, 0.932],[0.684, 0.733, 0.830, 0.927],[0.853, 0.733, 0.977, 0.898]] which represent the methods. I then find {the legend texts}[[0.162, 0.617, 0.282, 0.647],[0.162, 0.661, 0.257, 0.691]] as column names representing the types of scales. Each value represents the y-value at each point in the line coordinates for each method under each scale.\nBased on the above thought, the answer is:\nIntegration Method,Small Scale,Big Scale\nDirect Method,90,86.3\nData Federation,70,74.7\nMachine Learning,76,80\nMultidimensional View,55,65\nIncremental Updating,60,68\nVirtual Integration,85,90"
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "NkA7mwAiDPMyvhrI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which integration method seems to be most effective for large scales and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most effective integration method for large scales, I need to analyze the line corresponding to Big Scale {line of Big Scale}[0.124, 0.094, 0.915, 0.440]. The point with the highest y-value indicates the most effective method. This point is {Virtual Integration}[0.915, 0.094] with a y-value of 90.\nBased on the above thought, the answer is:\nThe most effective integration method for large scales according to the line chart seems to be the Virtual Integration. Its line point on the line for Big Scale exhibits the highest y-value, which represents the highest level of effectiveness."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "4jRcBbGkAEJTMd3k",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more effective is the Direct Method on small scales compared to Machine Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the effectiveness of Direct Method and Machine Learning on small scales, I must locate the points corresponding to these methods on the line for Small Scale. {The point for Direct Method on Small Scale}[0.124, 0.094] has a y-value of 90 while {the point for Machine Learning on Small Scale}[0.441, 0.288] has a y-value of 76. The difference in effectiveness is therefore 90 - 76 = 14.\nBased on the above thought, the answer is:\n14."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "ZQXjvaSX9pApT9bM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart data into tabular format with the categories of social responsibility as columns and the years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, I'll start by identifying the column names, which are shown in {the x-axis tick labels}[[0.161, 0.902, 0.214, 0.932], [0.305, 0.902, 0.358, 0.932], [0.450, 0.902, 0.503, 0.932], [0.594, 0.902, 0.647, 0.932], [0.738, 0.902, 0.791, 0.932], [0.883, 0.902, 0.935, 0.932]]. Next, I identify {the line points for each category on the line chart}[[0.188, 0.093, 0.909, 0.745]] and extract points at each year for all categories. Matching each set of points to their respective year on the x-axis will allow me to populate the table with the social responsibility performance scores.\nBased on the above thought, the answer is:\nYear,Environmental Sustainability, Customer Respect, Ethical Supply Chain, Employee Welfare, Local Community, Carbon Neutrality\n2010,5000,5400,4800,4500,4900,5000\n2011,5400,4800,4500,4900,5000,6100\n2012,4800,4500,4900,5000,6100,6400\n2013,4500,4900,5000,6100,6400,6600\n2014,4900,5000,6100,6400,6600,6800\n2015,5000,6100,6400,6600,6800,4900"
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "8Vd9Rw4HcplUDLhZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we were to interpret the line graph, which field of social responsibility did the company show the most improvement in over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'd need to analyze the progression of each line on the chart. Among the lines, {the points representing Carbon Neutrality}[[0.188, 0.631], [0.260, 0.603], [0.332, 0.292], [0.404, 0.207], [0.476, 0.150], [0.548, 0.093], [0.620, 0.631]] show noticeable improvement by moving from 5000 in 2010 to 6800 in 2019, followed by a drop to 4900 in 2020. While the final value in 2020 is not the highest, the overall increase from the initial score in 2010 is the most significant among all categories and suggests the company's focus and consistent attempts to improve performance in this field.\nBased on the above thought, the answer is:\nThe company showed the most improvement in the field of Carbon Neutrality over the years, having its score elevated from 5000 in 2010 to 6800 in 2019 before a drop in 2020. This significant improvement reflects the company's commitment to sustainability and their efforts to reduce carbon emissions."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "XGDdXpTzLvcw8bFj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the peak performance score for Environmental Sustainability during the monitored period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate the line points for Environmental Sustainability {Environmental Sustainability points on the line chart}[[0.188, 0.603], [0.260, 0.490], [0.332, 0.660], [0.404, 0.745], [0.476, 0.631], [0.548, 0.603], [0.620, 0.292], [0.693, 0.207], [0.765, 0.150], [0.837, 0.093], [0.909, 0.631]] and find the maximum value. This point corresponds to 2013 on the x-axis, with the score of 6800.\nBased on the above thought, the answer is:\n6800."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "ILnpmV1r4sUXtSg7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you interpret the data in the bar chart into a structured table format with Investment type and their corresponding Rate of Return?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to interpret the bar chart into a table format, I locate {the x-axis tick labels}[[0.015, 0.665, 0.153, 0.895], [0.140, 0.665, 0.236, 0.826], [0.211, 0.665, 0.319, 0.845], [0.262, 0.665, 0.403, 0.899], [0.360, 0.665, 0.486, 0.875], [0.435, 0.665, 0.569, 0.887], [0.527, 0.665, 0.652, 0.874], [0.648, 0.665, 0.735, 0.811], [0.704, 0.665, 0.819, 0.856], [0.741, 0.665, 0.902, 0.932], [0.885, 0.665, 0.985, 0.832]], which indicate the different risk factors, representing the rows of the table. For the column 'High-Risk Percentage (%)', I refer to the bar heights given by {the points of the line plot}[[0.153, 0.138], [0.236, 0.194], [0.319, 0.263], [0.403, 0.303], [0.486, 0.331], [0.569, 0.353], [0.652, 0.409], [0.735, 0.447], [0.819, 0.496], [0.902, 0.545], [0.985, 0.580]] which provide the percentages associated with each risk factor.\nBased on the above thought, the answer is:\nRisk Factor,High-Risk Percentage(%)\nCritical Infrastructure,83.4\nPhysical Harm,79.7\nFinancial Impact,75.2\nReputational Damage,72.6\nOperational Impact,70.8\nLegal Consequences,69.3\nInformation Breach,65.7\nHuman Error,63.2\nNatural Disasters,60.0\nSupply Chain Disruptions,56.8\nPolitical Unrest,54.5"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "QwOUrGK1JEPbUCKd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be drawn from the trend line regarding risk factors?"
+            },
+            {
+                "from": "gpt",
+                "value": "When I examine the line plot defined by {Coordinates for the line plot} [0.153, 0.138, 0.985, 0.580], I can notice a clear downward trend. This pattern indicates that as we move from left to right on the x-axis - ordered from 'Critical Infrastructure' to 'Political Unrest', the 'High-Risk Percentage' generally decreases. This suggests that the risk factors on the left side of the chart such as 'Critical Infrastructure' and 'Physical Harm' are associated with higher risk percentages, while the risk factors on the right side, such as 'Supply Chain Disruptions' and 'Political Unrest' are associated with lower risk percentages.\nBased on the above thought, the answer is:\nThe line plot on the chart clearly illustrates a downward trend indicating that the assessed risk factors towards the right side of the chart pose less of a risk when compared to the ones on the left. This suggests that while factors related to the organization's infrastructure and physical harm are of high concern with respect to risk, aspects like political unrest and supply chain disruptions are considered relatively less risky."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "VfLaLcktQK6MjOkC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much less is the High-Risk Percentage of Supply Chain Disruptions compared to Critical Infrastructure?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate the 'High-Risk Percentage' of both 'Supply Chain Disruptions' and 'Critical Infrastructure'. I find the 'High-Risk Percentage' for 'Supply Chain Disruptions' at {line plot point} [0.902, 0.545] which provides a reading of 56.8%. Similarly, 'Critical Infrastructure' is listed at {line plot point} [0.153, 0.138] which corresponds to 83.4%. To find the difference, I must subtract the percentage of 'Supply Chain Disruptions' from 'Critical Infrastructure': 83.4% - 56.8% = 26.6%.\nBased on the above thought, the answer is:\n26.6%"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "iTolbF0iNKVc8G3S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart into a table, using 'Investment Year' as rows and 'Investment Amount' and 'MV Amount' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, I first find {the x-axis tick labels}[[0.113, 0.902, 0.165, 0.932], [0.212, 0.902, 0.264, 0.932], [0.311, 0.902, 0.363, 0.932], [0.410, 0.902, 0.463, 0.932], [0.509, 0.902, 0.562, 0.932], [0.608, 0.902, 0.661, 0.932], [0.707, 0.902, 0.760, 0.932], [0.806, 0.902, 0.859, 0.932], [0.905, 0.902, 0.958, 0.932]], which signify the rows in the table, 'Investment Year'. Next, I pick out the two quantities represented by the lines, which can be found in {the legend}[[0.739, 0.048, 0.973, 0.125]] and will serve as the column headers, 'Investment Amount' and 'MV Amount'. For the values, I extract data points from each line, using {the line for Investment Amount}[[0.139, 0.414, 0.932, 0.839]] and {the line for MV Amount}[[0.139, 0.131, 0.932, 0.830]]. Using these data points, I can decipher the investment and MV amounts for each year.\nBased on the above thought, the answer is:\nInvestment Year,Investment Amount,MV Amount\n2004,1,1.2\n2005,1.5,1.8\n2006,2,3\n2007,4,5\n2008,2,2.5\n2009,3,4\n2010,4,5.5\n2011,5,7\n2012,4,6\n2013,5,7.5\n2014,6,9\n2015,5.5,8.5\n2016,7,11\n2017,6.5,10.5\n2018,8,13\n2019,9,14.5\n2020,10,16"
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "p8J3Y72WVWKUWDDa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the return on investment for each year be read from the timeline chart? If so, how?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the return on investment for each year can be read by comparing the lines representing the 'Investment Amount' and the 'MV Amount'. By looking at {the lines of Investment Amount and MV Amount}[[0.139, 0.414, 0.932, 0.839], [0.139, 0.131, 0.932, 0.830]], I can observe that in every year, the MV Amount is always higher than the Investment Amount. By subtracting the Investment Amount from the MV Amount would give the return in each year.\nBased on the above thought, the answer is:\nYes, the return on investment for each year can be determined from the timeline chart. It can be derived by comparing the 'Investment Amount' and the 'MV Amount' for each year. The return can be calculated as the difference between the 'MV Amount' and the 'Investment Amount'. The chart displays that for every year, the 'MV Amount' is greater than the 'Investment Amount', indicating positive returns each year."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "t2k7nIu5WrPlmkZe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Investment Amount over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total Investment Amount, I must sum up all the points on {the Investment Amount line}[[0.139, 0.414, 0.932, 0.839]]. Extracting the data from the points, we have the following investment amounts: 1, 1.5, 2, 4, 2, 3, 4, 5, 4, 5, 6, 5.5, 7, 6.5, 8, 9, 10. Summing these gives the total.\nBased on the above thought, the answer is:\n83.5"
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "A7jc9VKv47LweIIR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data values from the line chart and present it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to formulate the data in tabular format, I will look at the chart's x-axis as it represents the years under our analysis from 2005 to 2020, denoted by these coordinates: {the x-axis tick labels}[[0.094, 0.896, 0.137, 0.949], ..., [0.909, 0.896, 0.952, 0.949]]. These values will serve as our rows. The y-axis represents the find the corresponding production value for, denoted by these coordinates: {the estimated global food production line}[[0.115, 0.062, 0.931, 0.841]]. This will be used to populate the data under the respective years.\nBased on the above thought, the answer is:\nYear, Estimated_Global_Food_Production\n2005, 1.5\n2006, 1.75\n2007, 2.0\n2008, 2.1\n2009, 2.5\n2010, 2.75\n2011, 2.9\n2012, 3.0\n2013, 3.1\n2014, 3.3\n2015, 4.5\n2016, 4.8\n2017, 5.0\n2018, 5.2\n2019, 5.8\n2020, 6.0"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "oxZdjyyT0HT5nh26",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall trend in global food production based on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will analyze {the line graph} [0.115, 0.062, 0.931, 0.841]. It starts with a value of 1.5 in the year 2005 {at this point} [0.115, 0.841] and ends with a value of 6.0 in the year 2020 {at this point} [0.931, 0.062]. The line shows an overall increase over time, despite some minor fluctuations in between.\nBased on the above thought, the answer is:\nThe overall trend in global food production, as per the line graph, is an increasing one. Despite a few fluctuations, the graph shows that the global food production levels have significantly increased from 1.5 in the year 2005 to 6.0 in the year 2020."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "3TpwsPyjsr7ZMjtb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the global food production level increase between the year 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will begin by identifying the production level in 2015 and 2020. The point for 2015 is indicated by {this point on the line graph} [0.659, 0.322] and corresponds to the value 4.5. The point for 2020, represented by {this point on the line graph} [0.931, 0.062], corresponds to the value 6.0. Subtracting the production in 2015 from that in 2020 gives the increase in production.\nBased on the above thought, the answer is:\n1.5"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "6b7cDHwGwX5bqkZp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the policy effectiveness ratings year by year from the dataset in a comma-separated table format with columns for Policy Name and Effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I need to first read {the x-axis labels}<r>[0.122, 0.891, 0.142, 0.918]</r> which list the different policy names from 'Economic Recovery Package' to 'American Jobs Act'. Then, by analyzing {the line points}<r>[0.136, 0.150, 0.166, 0.200]</r> across the chart, I can tabulate the corresponding effectiveness ratings for each policy.\nBased on the above thought, the answer is:\nPolicy Name,Effectiveness\nEconomic Recovery Package,88.5\nTax Relief, Unemployment Insurance Reauthorization, and Job Creation Act,75\nPatient Protection and Affordable Care Act,55\nSmall Business Jobs Act,53\nAmerican Recovery and Reinvestment Act,46.7\nProtection and Job Creation Act,40\nAmerican Jobs Act,6.5"
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "CcqHnGkXMQ7Gpa85",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich government policy, based on the dataset, is the most effective outlier lying between less effective subsequent policies? How was this determined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the line chart, {the line point of the Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act}<r>[0.286, 0.310]</r> rises to an effectiveness of 75 despite being surrounded by policies with generally decreasing effectiveness. This makes it a notable outlier compared to the steeper declines noted in the chart trends for policies implemented before and after it.\nBased on the above thought, the answer is:\nThe most effective outlier is the 'Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act'. It is determined by its effectiveness rating of 75, which is significantly higher compared to adjacent policies that follow a general declining trend in the dataset."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "GyaN8MrjaFRMdv74",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of decline in policy effectiveness from the 'Economic Recovery Package' to the 'American Jobs Act'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of decline, I first locate {the line point for Economic Recovery Package}<r>[0.136, 0.110]</r> with the effectiveness of 88.5, and then {the line point for American Jobs Act}<r>[0.892, 0.918]</r> with an effectiveness of 6.5. The effectiveness rate of decline is calculated as (88.5 - 6.5) / (number of policies between them).\nBased on the above thought, the answer is:\n13.67."
+            }
+        ],
+        "id_raw": 330
+    },
+    {
+        "id": "wUM1efE4Wo1Zh1Gy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize this data into a structured table with 'Year' as columns and 'Scores' as rows for both Innovation and Community Health?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a structured table from this line chart, initially I will observe {the x-axis labels}[[0.155, 0.883, 0.226, 0.919], [0.298, 0.883, 0.368, 0.919], [0.440, 0.883, 0.511, 0.919], [0.583, 0.883, 0.654, 0.919], [0.726, 0.883, 0.796, 0.919], [0.868, 0.883, 0.939, 0.919]] to identify the years under study. Then, I will determine {the legend entries}[[0.205, 0.769, 0.399, 0.797], [0.205, 0.810, 0.494, 0.838]] as row headers, representing the Innovation Score and Community Health Score respectively. Lastly, I map {the plot points}[[0.190, 0.857], [0.262, 0.855], [0.333, 0.853], [0.404, 0.851], [0.476, 0.853], [0.547, 0.851], [0.618, 0.852], [0.690, 0.852], [0.761, 0.847], [0.832, 0.847], [0.904, 0.846], [0.190, 0.061], [0.262, 0.060], [0.333, 0.059], [0.404, 0.055], [0.476, 0.055], [0.547, 0.053], [0.618, 0.052], [0.690, 0.047], [0.761, 0.046], [0.832, 0.045], [0.904, 0.044]] for each category to their respective years.\nBased on the above thought, the answer is:\nScores,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nInnovation Score,6.1,6.2,6.4,6.6,6.4,6.6,6.5,6.5,6.9,6.9,7.0\nCommunity Health Score,75.8,75.9,76.0,76.3,76.3,76.5,76.6,77.0,77.1,77.2,77.3"
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "FxxNfvGQkOa38eKy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the trend of the Innovation Score over the years 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend of the Innovation Score from 2010 to 2020, I analyze {the line for Innovation Score}[0.190, 0.846, 0.904, 0.857]. It generally shows an increasing trend from 6.1 to 7.0 over the decade, with some years experiencing a slight dip such as 2014 and 2016 before recovering to reach the decade's peak in 2020.\nBased on the above thought, the answer is:\nThe trend of the Innovation Score over the years 2010 to 2020 is an overall increasing trend, with a few intermittent declines, but eventually reaching a peak score of 7.0 in 2020."
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "xHfFt8N8iec1qcax",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Innovation Score from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total increase in Innovation Score from 2010 to 2020, I first identify {the point corresponding to the Innovation Score in 2010} [0.190, 0.857] which is 6.1 {the label for Innovation Score1 in 2020}[0.904,0.846] yields a value of 7.0. The total increase is therefore 7.0 - 6.1 = 0.9.\nBased on the above thought, the answer is:\n0.9"
+            }
+        ],
+        "id_raw": 331
+    },
+    {
+        "id": "R8zXvyICkT0ciZ8x",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, can you extract the growth rate values for each year in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart to a table format, the first step is to identify the {x-axis tick labels}[[0.125, 0.883, 0.195, 0.919], [0.266, 0.883, 0.337, 0.919], [0.408, 0.883, 0.479, 0.919], [0.550, 0.883, 0.620, 0.919], [0.692, 0.883, 0.762, 0.919], [0.833, 0.883, 0.904, 0.919]] which represent the years. Next, I extract values corresponding to each year by observing {the chart line}[[0.160, 0.108, 0.869, 0.818]], the points along the line represent the average growth rate for each year.\nBased on the above thought, the answer is:\nYear, Average Growth Rate\n2010, 3.7\n2011, 3.1\n2012, 2.2\n2013, 3.0\n2014, 3.5\n2015, 4.9\n2016, 5.0\n2017, 5.5\n2018, 6.3\n2019, 6.0\n2020, -1.5"
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "cZaL0liNslcO5VUM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the trend in the average growth rate over the years, can you describe the general trend observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the general trend, I'll examine the trajectories of {the line}[[0.160, 0.108, 0.869, 0.818]] across the different years. The average growth rate appears to be on an increasing trend from 3.7 in 2010 to a peak of 6.3 in 2018 but experienced a severe decline to -1.5 in 2020.\nBased on the above thought, the answer is:\nThe general trend suggests that the average growth rate increased steadily between the years 2010 to 2018. However, a sharp drop was noticed in 2020 which indicates a significant decline in the growth rate."
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "zlLA93uHdgBeyk3h",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum growth rate recorded and in which year did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the maximum growth rate, I need to check the values for {the line points}[[0.160, 0.344], [0.231, 0.399], [0.302, 0.481], [0.373, 0.408], [0.444, 0.363], [0.514, 0.235], [0.585, 0.226], [0.656, 0.181], [0.727, 0.108], [0.798, 0.135], [0.869, 0.818]]. The point at the highest position corresponds to the year with the maximum growth rate. It seems that it occurs approximately at the {point corresponding to 2018}[[0.727, 0.108]], meaning that the maximum average growth rate of 6.3 was recorded in 2018.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 332
+    },
+    {
+        "id": "zo62rKgOkRkocwrT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented on the line chart into a structured table format in terms of different civilization periods against their respective populations in millions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the desired table, first, I noticed that the civilization periods serve as the column headers and are given by {the x-axis tick labels}<r>[[0.124, 0.764, 0.142, 0.932], [0.178, 0.764, 0.196, 0.949], ..., [0.935, 0.764, 0.954, 0.893]]</r>. The population values correspond to {the line points of human population}<r>[[0.133, 0.719], [0.187, 0.718], ..., [0.944, 0.052]]</r>. Now, associating each civilization period with its respective population, we can formulate the table.\nBased on the above thought, the answer is:\nCivilization_Period, Population_in_Millions\nPaleolithic_Early, 1\nPaleolithic_Middle, 2.2\nPaleolithic_Late, 3.4\nMesolithic_Early, 5.9\nMesolithic_Late, 6.2\nNeolithic_Early, 9.3\nNeolithic_Late, 12.7\nBronze_Age_Early, 19.9\nBronze_Age_Late, 27.6\nIron_Age_Early, 49.5\nIron_Age_Late, 58.7\nMedieval_Early, 84.6\nMedieval_Late, 136.5\nRenaissance, 372.9\nIndustrial_Age, 907.8\nModern_Age, 7800"
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "VYx6wxKt6olmyfEa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the trend in the growth of human population as depicted by the line chart from the Paleolithic to the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the trend represented by the line chart, ({the line}<r>[0.133, 0.052, 0.944, 0.719]</r>), the human population shows a slow growth during the Paleolithic period to the Early Bronze age, with small incremental steps as seen in the changes between subsequent Y values {corresponding to points}<r>[[0.133, 0.719], [0.187, 0.718], ..., [0.512, 0.717]]</r>. Next, a faster growth is observed during the Late Bronze Age and continues to the Iron Age, indicated by the steep slope of the line from coordinates {Bronze Age Late}<r>[0.566, 0.716]</r> to {Iron Age Late}<r>[0.674, 0.714]</r>. Subsequently, from the Medieval period onward, there is a significant exponential growth of the population, as seen in the even steeper slope from {Medieval Early}<r>[0.728, 0.711]</r> to {Modern Age}<r>[0.944, 0.052]</r>.\nBased on the above thought, the answer is:\nThe line chart portrays a slow initial growth of the human population from the Paleolithic period to the Early Bronze Age. This is followed by a moderate rise in population during the Late Bronze Age through to the Iron Age. Finally, an exponential surge in population is seen from the Medieval Period to the Modern Age. This trend conveys the progress of human civilization through different ages and the corresponding dramatic increase in population, especially in more recent ages."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "E4kYapgMCxhT1T2a",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, what was the increase in human population from the Early Iron Age to the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I first find the population at the Early Iron Age, which is shown at {the point representing the start of Iron Age}<r>[0.620, 0.714]</r> as 49.5 million. I then locate the population at the Modern Age, which can be seen at {the point representing the Modern Age}<r>[0.944, 0.052]</r> as 7800 million. The required increase is therefore 7800 - 49.5 = 7750.5 million.\nBased on the above thought, the answer is:\n7750.5 million."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "PDgLXToC3kgkwyg3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line chart, can the tech adoption data be restructured into a table format with Years being columns and Technologies being rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the table data from the line chart, initially I examine {the x-axis tick labels}[[0.129, 0.902, 0.972, 0.932]], these marks signify the column headers or years to be considered. Proceeding to this, I classify {the legend texts}[[0.193, 0.050, 0.312, 0.080], [0.193, 0.094, 0.283, 0.124]] as the row labels which personify the diverse health and medical technologies. To seek the values, I detect {the line points}[[0.155, 0.861], [0.234, 0.859], ..., [0.942, 0.866]] designated for each technology in the corresponding years and extract these values.\nBased on the above thought, the answer is:\nTechnology,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nRobotic Surgery,50,53,52,56,58,60,65,67,70,62,40\nEMR Usage,1050,1078,1054,1120,1200,1300,1400,1500,1600,1500,900"
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "0d39PmcNqA94FGpR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can we infer which medical technology has observed the most significant growth over time? Kindly state your justification."
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the technology with the most noticeable growth, an examination of the progression in the line plots for each medical technology is needed. Observing {the line for Robotic Surgery}[0.155, 0.861, 0.942, 0.866], a moderate increase is witnessed from an initial value of 50 to 40 over the years. Contrarily, when assessing {EMR Usage}[0.155, 0.354, 0.942, 0.430], a significant increase is seen from 1050 to 900.\nBased on the above thought, the answer is:\nGuided by the trends displayed in the line chart, EMR Usage appears to have witnessed the most substantial growth over time, increasing by a large margin over the course of the years. This suggests a wider acceptance and implementation of Electronic Medical Records in the medical and healthcare sector."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "izToZTwpsPwPjs3b",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value reached by EMR Usage over the recorded years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value for EMR Usage over the years, it is crucial to pinpoint {the line points for EMR Usage}[[0.155, 0.354], [0.234, 0.340], [0.312, 0.352], [0.391, 0.319], [0.470, 0.278], [0.548, 0.228], [0.627, 0.177], [0.706, 0.126], [0.785, 0.076], [0.863, 0.126], [0.942, 0.430]] and extract the linked values: 1050, 1078, 1054, 1120, 1200, 1300, 1400, 1500, 1600, 1500, 900. The maximum value happens to be 1600 connected to {2018}[0.785, 0.902].\nBased on the above thought, the answer is:\n1600."
+            }
+        ],
+        "id_raw": 335
+    },
+    {
+        "id": "Lf6TAuRyte89zoJa",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table from the line chart with time points as columns and market players as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table, I must first identify {the x-axis tick labels}[[0.186, 0.906, 0.197, 0.930], [0.318, 0.906, 0.336, 0.930], [0.451, 0.906, 0.470, 0.930], [0.589, 0.906, 0.607, 0.930], [0.727, 0.906, 0.746, 0.930], [0.865, 0.906, 0.884, 0.930], [1.006, 0.906, 1.024, 0.930]] which represent the column headers or time points in the year. Then, I get the {legend texts}[[0.755, 0.741, 0.863, 0.764], [0.755, 0.776, 0.887, 0.799], [0.755, 0.811, 0.829, 0.834], [0.755, 0.846, 0.859, 0.869]] as the market players or the row labels. To load the values, I locate the {line points}[[0.442, 0.378], [0.583, 0.425], [0.865, 0.457], [0.160, 0.386], ...] with their corresponding player and time point, and extract the annotated values.\nBased on the above thought, the answer is:\nPlayer,5,10,20,30\nRapid Recovery,92.0,90.0,92.5,94.0\nFluctuating Growth,77.5,75.0,77.0,76.0\nSharp Rise,75.0,60.0,63.0,67.0\nPeriodic Spikes,84.5,80.0,85.0,82.0"
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "NJCD9sZhD1kJ1IRa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which market player's future technological development value shows the most significant increase from the beginning to the end of the evaluated periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which market player shows the most significant increase in future technological development value, I need to compare the differences in the starting and end values for each line. Looking at {the line for Rapid Recovery}[0.160, 0.235, 0.865, 0.298], the increase is from 92.0 to 94.0. For {Fluctuating Growth}[0.160, 0.497, 0.865, 0.537], the value decreased from 77.5 to 76.0. {Sharp Rise}[0.160, 0.537, 0.865, 0.775] improved from 75.0 to 67.0, while {Periodic Spikes}[0.160, 0.378, 0.865, 0.457] showed an increase from 84.5 to 82.0. Therefore, the market player with the most significant increase in value is the one with the largest difference.\nBased on the above thought, the answer is:\nThe market player 'Sharp Rise' shows the most significant increase in future technological development value from the beginning to the end of the evaluated periods. Their development value increased from 75.0 at the start to 67.0 in the end, showing a growth trend."
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "fqkQbABnLFSoMhpv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the net change in developmental value of 'Fluctuating Growth' from the first to the last decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the net change in the developmental value of 'Fluctuating Growth', I must locate the {first line point for Fluctuating Growth} at [0.160, 0.497] and the {last line point for Fluctuating Growth} at [0.865, 0.537]. The first point corresponds to a value of 77.5 and the last point corresponds to a value of 76.0. The net change is calculated as the difference between the last and first values, which is 76.0 - 77.5.\nBased on the above thought, the answer is:\n-1.5"
+            }
+        ],
+        "id_raw": 336
+    },
+    {
+        "id": "S0hupWykQuY417vU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the line chart and present it in a table format using the environmental elements as columns and the environmental concerns as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, {the x-axis tick labels}[0.160, 0.883, 0.177, 0.919], [0.257, 0.883, 0.274, 0.919], [0.344, 0.883, 0.380, 0.919], [0.441, 0.883, 0.476, 0.919], [0.538, 0.883, 0.573, 0.919], [0.635, 0.883, 0.670, 0.919], [0.732, 0.883, 0.767, 0.919], [0.828, 0.883, 0.864, 0.919], [0.925, 0.883, 0.960, 0.919] will constitute the column headers or stages of time. I will then identify {the line plots} [0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655], [0.274, 0.608], [0.302, 0.561], [0.329, 0.514], [0.356, 0.467], [0.383, 0.420], [0.411, 0.373], [0.438, 0.326], [0.465, 0.279], [0.493, 0.232], [0.520, 0.185], [0.547, 0.138], [0.575, 0.091] for each concern as rows and extract the values.\nBased on the above thought, the answer is:\nTime,Erratic Decline,Sharp Rise,Substantial Growth,Minimal Change\n6,500,550,600,650\n7,700,750,760,770\n8,780,790,800,810\n9,820,800,780,750\n10,720,690,660,630\n11,600,570,540,510\n12,480,500,520,540\n13,560,580,600,620\n14,640,660,680,700\n15,720,740,760,780\n16,800,820,840,860\n17,880,900,920,940\n18,960,980,1000,1020\n19,1040,1060,1080,1100\n20,1120,1140,1160,1180\n21,1200,1220,1240,1260\n22,1280,1300,1320,1340\n23,1360,1380,1400,1420\n24,1440,1460,1480,1500\n25,1520,1540,1560,1580\n26,1600,1620,1640,1660\n27,1680,1700,1720,1740\n28,1760,1780,1800,1820\n29,1840,1860,1880,1900\n30,1920,1940,1960,1980"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "EoQLG6tbi1TE0u4H",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which environmental concern shows a steady decline in its values regardless of environmental elements? Explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find a line on the chart that consistently moves downwards throughout the time period. Upon inspecting {the line plots} [0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655],  ..., [0.945, 0.188], there is a clear trend of a steady decline in its values. This trend doesn't show any indications of rising values implying a steady decline.\nBased on the above thought, the answer is:\nThe 'Erratic Decline' environmental element exhibits a steady decline throughout the given time period. The trend line associated with this element consistently moves downwards, indicating a continuous decrease in values. This suggests that the level of 'Erratic Decline' has been consistently reducing over time, regardless of the environmental concern it is associated with. Hence, 'Erratic Decline' shows a steady decline in its values throughout the given time period."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "q4pQDt50BdhLjjgY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the range of 'Erratic Decline' in regards to the given period of time in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the range of the 'Erratic Decline' environmental element, I first identify that it is depicted by {the line plot} [[0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655],  ..., [0.945, 0.188]]. The maximum and minimum y-values correspond to the highest and lowest points, respectively. Reading from the y-axis, these are 1920 at {coordinate}[0.943, 0.863] and 500 at {coordinate}[0.169, 0.814]. Thus, the range would be 1920 - 500 = 1420.\nBased on the above thought, the answer is:\n1420."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "0DA8UNsPpJUbz12z",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please reformat the data presented in the line chart into a table with Activities as columns and Income Groups as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, firstly, I'll associate {the x-axis tick labels}<r>[0.093, 0.928, 0.135, 0.951]</r>,<r>[0.180, 0.928, 0.222, 0.951]</r>,<r>[0.267, 0.928, 0.309, 0.951]</r>,<r>[0.354, 0.928, 0.396, 0.951]</r>,<r>[0.441, 0.928, 0.483, 0.951]</r>, and <r>[0.877, 0.928, 0.919, 0.951]</r> to the activities as column headers. Then, the {legend items}<r>[0.132, 0.046, 0.216, 0.069]</r>,<r>[0.132, 0.079, 0.216, 0.102]</r> become the row labels or income groups. Finally, I'll extract the percentage values at each point of {the lines}<r>[0.114, 0.078, 0.941, 0.916]</r>,<r>[0.114, 0.243, 0.941, 0.776]</r> for each income group across different activities and place them at the corresponding position in the table to formulate the complete data.\nBased on the above thought, the answer is:\nIncome Group,Rainforest Conservation,Renewable Energy,Ecotourism,Recycling,Sustainable Agriculture\nLow Income,80.5,82.1,81.1,78.7,78.0\nMedium Income,82.6,85.1,84.9,84.5,83.2\nHigh Income,90.4,91.2,88.4,88.2,88.5"
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "K80n3dyGrNuNOESl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the trends observed in the line chart, which income group seems to show the most interest in Renewable Energy and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the income group showing the most interest in Renewable Energy, I need to compare the participation percentages at the point of Renewable Energy across all income groups. This involves inspecting the line points for Renewable Energy from {the line for low income}<r>[0.158, 0.656]</r>, {the line for medium income}<r>[0.158, 0.465]</r>, and {the line for high income}<r>[0.898, 0.269]</r>. I will then compare these values to judge which income group has the highest participation percentage at this point.\nBased on the above thought, the answer is:\nFrom the trends observed in the line chart, the high income group shows the most interest in Renewable Energy. This is because the line for high income group is at the highest point on the y-axis at Renewable Energy, indicating the highest percentage of participation compared to the other income groups."
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "tX5Ew9mGcmZPgEbI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average rate of participation in recycling across all income groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average rate of participation in recycling, I need to add up the participation percentages for all income groups at the point of Recycling and then divide the total by the number of income groups. So, I locate the points representing recycling from {the low income line}<r>[0.332, 0.871]</r>, {the medium income line}<r>[0.332, 0.503]</r> and {the high income line}<r>[0.898, 0.269]</r> and from these coordinates, I extract the values, add them up, and divide by 3.\nBased on the above thought, the answer is:\n83.8"
+            }
+        ],
+        "id_raw": 338
+    },
+    {
+        "id": "CB7GvlVuxK6g0MD2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the performance trends of railways, air transport, and shipping from 2010 to 2020 as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart data's line plot, I first identified {@the x-axis tick labels}<r>[0.101, 0.916, 0.112, 0.942], [0.172, 0.916, 0.182, 0.942], ..., [0.939, 0.916, 0.960, 0.942]</r> to determine the years from 2010 to 2020. For each sector, {@the line for railways}<r>[0.107, 0.311, 0.950, 0.860]</r>, {@the line for air transport}<r>[0.107, 0.221, 0.950, 0.402]</r>, and {@the line for shipping}<r>[0.107, 0.062, 0.950, 0.294]</r> show relative performance across these years. Based on {the plotted points}<r>[0.107, 0.538], [0.177, 0.508], ..., [0.950, 0.311]</r>, I extract the value for railways, air transport, and shipping for each year.\nBased on the above thought, the answer is:\nYear,Railways,Air Transport,Shipping\n2010,45,52,55\n2011,44,54,56\n2012,43,56,57\n2013,42,59,59\n2014,41,62,61\n2015,40,65,63\n2016,38,68,66\n2017,36,71,69\n2018,34,74,72\n2019,32,75,75\n2020,31,76,77"
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "J975JTKLTKD8jVaQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the most consistent growth over the decade and how can you determine this from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the consistency in growth by looking at the lines for each sector, observations can be made based on the smoothness and the incline of the lines as they progress from 2010 to 2020. {@The line for air transport}<r>[0.107, 0.221, 0.950, 0.402]</r> demonstrates a consistently upward trajectory with minimal fluctuations. This denotes steady growth in performance over the years. Comparatively, {@the line for railways}<r>[0.107, 0.311, 0.950, 0.860]</r> shows a declining trend, and {@the line for shipping}<r>[0.107, 0.062, 0.950, 0.294]</r>, although increasing, does involve slight variances as seen in the line. Hence, air transport displays the most consistent growth pattern over the decade, evidenced by the upward movement and relative line smoothness.\nBased on the above thought, the answer is:\nAir transport showed the most consistent growth over the decade. This determination is based on observing the smooth, consistently ascending line, which compared to other sectors, shows steady and minimal fluctuation in its upward trend."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "OnXJjwJmzWTEIeJC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_17_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many points difference is there between the performances of railways and shipping in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the specific performances in 2020, I need to extract the values corresponding to the year 2020 from each sector's line. Considering {@the point for railways in 2020}<r>[0.950, 0.860]</r>, the performance is noted as 31. Meanwhile, for {@the point for shipping in 2020}<r>[0.950, 0.294]</r>, the noted value is 77. Consequently, the difference is calculated by subtracting the railways' value from the shipping's value, which is 77 - 31.\nBased on the above thought, the answer is:\n46"
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "5ocWichwy1qtu4dX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the line chart data of Science and Art development over time into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the line chart visuals, I can observe {the Science development line} [0.078, 0.141, 0.474, 0.819] and {the Art development line} [0.572, 0.132, 0.968, 0.828]. The x-coordinates at each point correspond to the time period labels in the {x-axis tick labels}[0.013, 0.912, 0.489, 0.936], and the y-coordinates correspond to the development level labels in the {y-axis tick labels}[0.032, 0.889, 0.050, -0.034]. So, by matching the x-y coordinates with their respective labels, I can map the science development and art development levels with the periods.\nBased on the above thought, the answer is:\nPeriod,ScienceDevelopment,ArtDevelopment\n0,0.1,0.05\n500,0.4,0.2\n1000,0.7,0.44\n1500,0.85,0.55\n2000,0.9,0.6\n2500,0.92,0.65\n3000,0.93,0.70\n3500,0.94,0.72\n4000,0.96,0.74"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "hGA8aJVLUJJtHRfF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends depicted in the line chart, which among Science and Art development had a more consistent growth and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line plots, {the Science development line}[0.078, 0.141, 0.474, 0.819] exhibits a more gradual and steady increase in development level across all periods. On the other hand, {the Art development line}[0.572, 0.132, 0.968, 0.828] presents a somewhat less consistent trajectory, with slower growth in the earlier years that then accelerates towards the final periods.\nBased on the above thought, the answer is:\nThe Science development had a more consistent growth based on the line chart. Its line plot shows a steady, almost linear progression over time, indicating a regular, consistent increase in Science development. In contrast, the Art development line shows greater variability and less consistency in its progression."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "10v2nV2j29vdNunx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Science development improved from the first reading period to the last one in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the start and end points of {the Science development line}[0.078, 0.141, 0.474, 0.819]. The line starts from the y-coordinate of 0.819 and ends at 0.141. So, to calculate the difference, we subtract the start value from the end value.\nBased on the above thought, the answer is:\n0.86"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "nq0AXtYNh9G8Myci",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table listing the values for Ideas and Originality from the line chart across all iterations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first locate {the line for Ideas}<r>[0.093, 0.175, 0.384, 0.810]</r> and note down the values at each iteration point. Similarly, I do the same for {the line for Originality}<r>[0.093, 0.160, 0.384, 0.772]</r>. Each of these lines corresponds to an iteration with x-values indicating the iteration and y-values corresponding to the metric score.\nBased on the above thought, the answer is:\nIteration,Ideas,Originality\n1,0.15,0.20\n2,0.25,0.35\n3,0.40,0.45\n4,0.55,0.58\n5,0.65,0.66\n6,0.76,0.77\n7,0.85,0.86\n8,0.92,0.95\n9,0.99,1.01"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "7ggUyLF1Jxeo9qGf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich design element shows the most consistent improvement in value across iterations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the lines for each design element, particularly focusing on their coordinates and progression, I see that {the line for Ideas}<r>[0.093, 0.175, 0.384, 0.810]</r> shows steady and consistent improvement rising from 0.15 to 0.99 across iterations. Each iteration marks a noticeable increase from the previous, showing consistent growth.\nBased on the above thought, the answer is:\nThe Ideas element shows the most consistent improvement in value across iterations, as evidenced by its steady upward trend in the plot. Each point successively increases, indicating continuous progression and development in this particular design element."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "v15f938phpR8Amn7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the maximum value for Execution compared to the minimum value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to examine {the line for Execution}<r>[0.093, 0.130, 0.384, 0.697]</r>. By observing the points, I can determine the maximum value of 1.05 at the last iteration and the minimum value of 0.30 at the first iteration. The difference is therefore calculated as 1.05 - 0.30.\nBased on the above thought, the answer is:\n0.75"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "QnhF08eCrRwzt3zk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the line chart that compares 'Years' against 'Old-Business' and 'Social-Media' into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart into a table, I first observe the x-axis, where {the x-axis labels}<r>[0.052, 0.912, 0.082, 0.936], [0.106, 0.912, 0.136, 0.936], ..., [0.481, 0.912, 0.511, 0.936]</r> represent the 'Years'. Then, I identify two line plots. One of them is for 'Old-Business', represented by its line points such as {points for Old-Business}<r>[0.067, 0.171], [0.089, 0.184], ..., [0.475, 0.790]</r>. The other is for 'Social-Media' with its points like {points for Social-Media}<r>[0.067, 0.824], [0.089, 0.810], ..., [0.475, 0.792]</r>. Now, I can compile these points into a table format with three columns: Years, Old-Business, and Social-Media.\nBased on the above thought, the answer is:\nYears,Old-Business,Social-Media\n2000,100,5\n2001,98,7\n2002,95,15\n2003,90,25\n2004,85,35\n2005,75,45\n2006,60,55\n2007,45,65\n2008,35,75\n2009,30,80\n2010,27,82\n2011,25,83\n2012,23,84\n2013,20,85\n2014,18,86\n2015,16,87\n2016,14,88\n2017,12,89\n2018,11,90\n2019,10,91"
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "t2VHm90UoP0Pm772",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows a more consistent effort in reducing their carbon footprint, and how can you tell this from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company shows more consistency in carbon footprint reduction efforts, I examine the stability of the lines on the chart. For instance, {Tesla\u2019s line for Carbon Footprint Reduction}<r>[0.17, 0.20, 0.77, 0.40]</r> remains relatively high and flat, indicating stable efforts. In contrast, {Microsoft\u2019s line}<r>[0.35, 0.45, 0.88, 0.60]</r> shows more fluctuations in the middle of the period depicted, suggesting less consistency. Tesla\u2019s line, therefore, visually represents a more consistent effort in minimizing their carbon emissions.\nBased on the above thought, the answer is:\nTesla shows a more consistent effort in reducing their carbon footprint compared to other companies. This is evident from the relatively flat and higher line on the chart, which implies that Tesla has maintained its efforts steadily over the years."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "43r6gVxrh4UdXl5n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in the sustainability score for Social Media from the year 2000 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in the sustainability score for Social Media, I first locate the score for the year 2000 at {the point for Social Media in 2000}<r>[0.067, 0.824]</r> which is 5, and then find the score for the year 2019 at {the point for Social Media in 2019}<r>[0.475, 0.232]</r> which is 91. The total increase is the difference between these two values: 91 - 5.\nBased on the above thought, the answer is:\n86."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "Cjh8y2UcXCJni7wF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data to a table, with AI Subfields as columns and the Machine Learning algorithms as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I start by identifying {the x-axis tick labels}[[0.102, 0.876, 0.154, 0.910], [0.218, 0.876, 0.275, 0.910], [0.334, 0.876, 0.389, 0.910], [0.450, 0.876, 0.505, 0.910], [0.566, 0.876, 0.623, 0.910]] which represent the columns or AI subfields. Then, I locate {the legend texts}[[0.704, 0.111, 0.759, 0.136], [0.704, 0.156, 0.759, 0.182]] which are the row labels or Machine Learning algorithms. The values for each subfield and algorithm {the line points}[[0.102, 0.318], [0.218, 0.237], [0.334, 0.346], [0.450, 0.482], [0.566, 0.403), [0.102, 0.262], [0.218, 0.183], [0.334, 0.295], [0.450, 0.427], [0.566, 0.349]] are extracted from the points of the lines plotted.\nBased on the above thought, the answer is:\nMachine Learning Algorithm,NLP,Speech Recognition,Computer Vision,Machine Learning,Deep Learning\nNeural Network,0.65,0.75,0.80,0.85,0.90\nDecision Tree,0.70,0.80,0.85,0.90,0.95"
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "lxMgGtiHlm6wDiff",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the field of Artificial Intelligence, which Machine Learning algorithm - Neural Network or Decision Tree, shows better performance overall? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to analyze the performance of both the algorithms in all the subfields of AI. Looking at {the line for Neural Network}[0.102, 0.318, 0.566, 0.403], it can be seen that it shows an increasing performance trend from 0.65 to 0.90 across the AI subfields NLP to Deep Learning. However, when I analyze {the line for Decision Tree}[0.102, 0.262, 0.566, 0.349], it can be observed that it also shows an increasing trend, but starts from a higher performance baseline of 0.70 and ends at 0.95. Through this analysis, it can be assumed that the Decision Tree algorithm seems to work better overall.\nBased on the above thought, the answer is:\nBased on the given line chart, Decision Tree algorithm exhibits better performance in comparison to Neural Network. In all subfields, it consistently maintains an edge, showing a higher performance from the beginning to end. Its performance increases from 0.70 to 0.95 across subfields, while Neural Network\u2019s performance increases from 0.65 to 0.90. Thus, Decision Tree algorithm shows overall superior performance."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "X5pVdoWtbHRDcAT9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the performance difference between Neural Network and Decision Tree in the field of Deep Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find this, I need to identify {the line points for Neural Network in Deep Learning}[[0.566,0.403]] and {the line points for Decision Tree in Deep Learning}[[0.566,0.349]], after which I can extract the corresponding performance values for comparison. The performance value for Neural Network is 0.90 and for Decision Tree is 0.95. The difference can be calculated as 0.95-0.90 = 0.05.\nBased on the above thought, the answer is:\n0.05."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "9rfBuayG69MUCwQU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate this line chart about future technological development prospects into a tabular format with years as columns and technology fields as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I start by identifying {the labels on the x-axis}<r>[0.153, 0.800, 0.205, 0.843], [0.421, 0.800, 0.501, 0.873], [0.695, 0.800, 0.774, 0.869]</r> as column headers representing the years 2020, 2022, and 2023. Next, I recognize {the labels in the legend}<r>[0.801, 0.039, 0.897, 0.213]</r> as row headers symbolizing distinct technology fields, namely, AI, IoT, Blockchain, Robotics, and Quantum Computing. Then, I locate {the points on each line}<r>[[0.171, 0.622], [0.443, 0.690], ..., [0.723, 0.835]], ..., [[0.178, 0.334], [0.449, 0.330], ..., [0.730, 0.282]]</r> corresponding to each technology field at different times. By extracting these values, the cell values can be populated.\nBased on the above thought, the answer is:\nTechnology,2020,2022,2023\nAI,7.1,8.2,8.9\nIoT,6.0,6.5,6.7\nBlockchain,5.5,5.1,4.8\nRobotics,6.3,7.0,7.7\nQuantum Computing,3.2,5.5,6.5"
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "b9epAwpB8BPTeL57",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological field shows the most significant growth rate from 2020 to 2023 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To distinguish the technological field with the most significant growth rate, I scrutinize the relative increases in the lines of the chart. The line for {AI}[0.171, 0.454, 0.730, 0.806] rises from 7.1 in 2020 to 8.9 in 2023. The {IoT line}[0.171, 0.378, 0.730, 0.399] increases scrappily from 6.0 to 6.7. The {Blockchain line}[0.178, 0.343, 0.730, 0.282] actually declines from 5.5 to 4.8. The {Robotics line}[0.171, 0.401, 0.730, 0.743] shows a steady rise from 6.3 to 7.7. However, the {Quantum Computing line}[0.178, 0.234, 0.730, 0.629] shows the most striking increase from 3.2 in 2020 to 6.5 in 2023.\nBased on the above thought, the answer is:\nThe technological field that shows the most significant growth rate from 2020 to 2023 is Quantum Computing. While all technological fields except Blockchain show some level of growth over the period, the progress of Quantum Computing is the most pronounced. It more than doubles its development prospects rating, moving from a relatively low 3.2 in 2020 to 6.5 in 2023."
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "zj0TO4lOZYaMsN1R",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the development prospects rating for AI increase from 2020 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the initial and final values for AI. I locate {the line representing AI}<r>[0.171, 0.454, 0.730, 0.806]</r> and extract the corresponding prospect rating values at each year. The initial value for 2020 is 7.1 corresponding to {initial point on AI line}<r>[0.171, 0.454]</r>, while the final value for 2023 is 8.9 corresponding to {final point on AI line}<r>[0.730, 0.806]</r>. The net increase from 2020 to 2023 is calculated as: 8.9 - 7.1= 1.8.\nBased on the above thought, the answer is:\n1.8"
+            }
+        ],
+        "id_raw": 344
+    },
+    {
+        "id": "HPDFldUq455qOgXu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide me with the data in a tabular format consisting of Time Period as rows and Investment types, Angel&Seed and Series A as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Converting the data to a table involves tracing back to the data points of each investment type in the line chart. For the investment type \"Angel&Seed\", I can locate the points {the line of Angel&Seed}<r>[0.106, 0.185, 0.478, 0.755]</r>, where the investment amounts are represented at different time periods. Similarly, for Series A investment, I can find its data points on {the line of Series A}<r>[0.593, 0.175, 0.965, 0.765]</r>. Then, I can extract the corresponding values at each timepoint to fill the rows of the table.\nBased on the above thought, the answer is:\nTime Period,Angel&Seed,Series A\n0,1.0,1.2\n1,0.95,1.1\n2,0.9,0.98\n3,0.85,0.9\n4,0.82,0.88\n5,0.8,0.87\n6,0.78,0.86\n7,0.77,0.85\n8,0.69,0.83\n9,0.7,0.82\n10,0.75,0.81"
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "fWZXIxmCdZwzCn7z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the trend differences between Angel&Seed and Series A investments over the time period according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Angel&Seed line}<r>[0.106, 0.185, 0.478, 0.755]</r> seems to exhibit a downward trend over the time period, starting with a value of 1.0 and declining to 0.75. On the other hand, {the Series A line}<r>[0.593, 0.175, 0.965, 0.765]</r> also shows a general decline but its investment amounts are higher than that of Angel&Seed throughout the same period. They both start at the same maximum value at the start of the time period but Series A remain higher despite the downward trend in the same period.\nBased on the above thought, the answer is:\nBoth Angel&Seed and Series A investments exhibit a downward trend over the time period. However, the Series A investments remain higher than the Angel&Seed investments despite the similar trend. In summary, both types of investments are declining over time but Series A is consistently higher than Angel&Seed."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "bUlpuJMtrkjrvc39",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest investment amount recorded over the time period for Angel&Seed investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest investment amount for Angel&Seed, I need to find {the line points of Angel&Seed}<r>[0.106, 0.185, 0.478, 0.755]</r> and extract the corresponding values. The highest value is 1.0 which corresponds to {Time Period: 0}<r>[0.106, 0.184]</r>.\nBased on the above thought, the answer is:\n1.0."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "wqeJ91zohiHXLHH4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, could you present the advancements of GPT-6 and AAI-1 in a table with Years as rows and AI Models as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this table, I'll start by identifying the {years on the x-axis} [[0.097, 0.844], [0.159, 0.844], [0.221, 0.844], [0.283, 0.844], [0.345, 0.844], [0.407, 0.844], [0.470, 0.844]], which will become our rows. Then, I'll take {the plot lines' labels} [[0.890, 0.651, 0.950, 0.686], [0.890, 0.703, 0.966, 0.738]] as column headers; they represent the different AI models. The advancement rate of each AI model at each year can be deduced from the y-coordinate of each {data point on the lines} [[0.097, 0.484], [0.159, 0.442],..., [0.470, 0.201]], and after matching them to the y-axis labels, we can fill in the values for our table.\nBased on the above thought, the answer is:\nYear,GPT-6 Original,GPT-6 Upgraded\n2030,0.65,0.45\n2035,0.68,0.48\n2040,0.72,0.51\n2045,0.73,0.56\n2050,0.78,0.62\n2055,0.82,0.65\n2060,0.85,0.68"
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "T1r4OUc1CEtSmaea",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends shown in the line charts, how do the advancement rates of the original and upgraded versions of the GPT-6 and AAI-1 models compare across different years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I\u2019ll begin by looking at the {line plots of the GPT-6 original and upgraded versions}<r>[0.097, 0.201, 0.470, 0.768]</r>. The original GPT-6 model shows a consistent increase in advancement rate over the years, which means steady improvement. In comparison, the upgraded version also shows an increasing trend but at a higher rate, suggesting a substantial upgrade benefit. Now analyzing the {line plots for the AAI-1 original and upgraded versions}<r>[0.588, 0.430, 0.961, 0.780]</r>, we see a similar pattern, with both showing rising trends. However, the original AAI-1 model seems to rise at a faster rate than its upgraded version, suggesting more consistent performance.\nBased on the above thought, the answer is:\nFrom the line chart, we can deduce that both the original and upgraded models of GPT-6 and AAI-1 show a consistent increasing trend in their advancement rates over the years. Specifically, the upgraded GPT-6 model seems to witness a substantially faster advancement compared to its original version. However, the AAI-1 models display a different pattern, where the original model registers a moderately faster advancement compared to its upgraded counterpart. It could indicate the consistent performance of the original AAI-1 model or the slower-than-expected progress of the upgraded version."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "GvpurefPgwI718pt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the advancement rate of the upgraded GPT-6 model from 2030 to 2060?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total increase will be calculated by subtracting the starting advancement rate in 2030 from the final rate in 2060. I will first find the y-coordinates of the {upgraded GPT-6 model\u2019s data point for 2030}<r>[0.097, 0.768]</r> and extract its corresponding value, 0.45. Then I get the y-coordinate for the {upgraded GPT-6 model\u2019s data point for 2060}<r>[0.470, 0.442]</r> and extract its corresponding value, 0.68. Finally, I calculate the difference, which is 0.68 - 0.45.\nBased on the above thought, the answer is:\n0.23."
+            }
+        ],
+        "id_raw": 346
+    },
+    {
+        "id": "TERfvvnyYEGgp5D2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please provide the data shown on the chart as a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.082, 0.895, 0.089, 0.923],[0.124, 0.895, 0.146, 0.923],[0.173, 0.895, 0.195, 0.923],[0.222, 0.895, 0.244, 0.923],[0.267, 0.895, 0.296, 0.923],[0.316, 0.895, 0.345, 0.923],[0.365, 0.895, 0.394, 0.923],[0.414, 0.895, 0.443, 0.923],[0.463, 0.895, 0.492, 0.923]], which represent Iterations. Next, I identify {the legend texts}[[0.907, 0.821, 0.977, 0.849]] as the row labels representing Slow Decline and Fast Decline. To populate the values, I locate {the line points}[[0.086, 0.165], [0.135, 0.320], [0.184, 0.424], [0.233, 0.528], [0.282, 0.580], [0.331, 0.632],[0.380, 0.684], [0.429, 0.735], [0.478, 0.787], [0.576, 0.153], [0.625, 0.234],[0.674, 0.315], [0.723, 0.395], [0.772, 0.476], [0.821, 0.557], [0.870, 0.637], [0.919, 0.718], [0.968, 0.799]] for each Decline type at the respective Iterations and extract the values annotated.\nBased on the above thought, the answer is:\nIterations,Slow Decline,Fast Decline\n0,0.98,0.98\n250,0.95,0.88\n500,0.93,0.78\n750,0.91,0.68\n1000,0.90,0.58\n1250,0.89,0.48\n1500,0.88,0.38\n1750,0.87,0.28\n2000,0.86,0.18"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "e1JnJKGj9lkfLKqT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you compare the decline rate of the two systems based on the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line plots, both systems show a negative, downward trending slope. But I need to analyze the rate of decline for each system to assess their relative performance. Observing the {line representing Slow Decline} [0.086, 0.165, 0.478, 0.787], we see a gradual decrease from 0.98 to 0.86 over 2000 iterations. On the other hand, the {line representing Fast Decline} [0.576, 0.153, 0.968, 0.799] indicates a much steeper drop from 0.98 to 0.18 during the same iteration period.\nBased on the above thought, the answer is:\nThe Fast Decline system shows a steep decline compared to the Slow Decline system. Despite starting at the same Relative Performance in iteration 0, by iteration 2000, the Fast Decline System has a significantly lower Relative Performance. Hence, the Fast Decline system seems to have a significantly higher decline rate than the Slow Decline system, concluding that the Fast Decline system deteriorates at a faster pace."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "W59emG0aBEKDzfqF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many iterations did it take for the system to reach a Relative Performance of less than 0.6 for Fast Decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand how many iterations it took for the system to reach a Relative Performance of less than 0.6 for Fast Decline, I first need to check {the line points for Fast Decline}[[0.576, 0.153], [0.625, 0.234],[0.674, 0.315], [0.723, 0.395], [0.772, 0.476], [0.821, 0.557], [0.870, 0.637], [0.919, 0.718], [0.968, 0.799]]. I can see that the Relative Performance first reaches less than 0.6 at the {1000th iteration}[0.772, 0.476].\nBased on the above thought, the answer is:\n1000 iterations."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "MAwkiQaM8R7ckW8A",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table showing the percentage spent in each behavior state at every 10-second interval based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I use {the x-axis tick labels}[[0.1, 0.9, 0.13, 0.92], [0.24, 0.9, 0.26, 0.92], [0.39, 0.9, 0.41, 0.92], [0.52, 0.9, 0.55, 0.92], [0.65, 0.9, 0.68, 0.92]] that mark each time interval. The behaviors increase every 10 seconds, labeled as 0s, 10s, 20s, 30s, and 40s. For {the line for Anxiety}<r>[0.1, 0.4, 0.6, 0.85]</r>, {the line for Confidence}<r>[0.1, 0.35, 0.6, 0.2]</r>, {the line for Distraction}<r>[0.1, 0.8, 0.6, 0.7]</r>, {the line for Focused}<r>[0.1, 0.7, 0.6, 0.55]</r>, and {the line for Happiness}<r>[0.1, 0.65, 0.6, 0.45]</r>, I extract the position at each x-coordinates for every behavior and determine the corresponding approximate percentage.\nBased on the above thought, the answer is:\nInterval,Anxiety,Confidence,Distraction,Focused,Happiness\n0s,78%,25%,60%,55%,42%\n10s,65%,35%,58%,57%,52%\n20s,49%,45%,56%,60%,48%\n30s,67%,55%,53%,62%,50%\n40s,43%,65%,50%,65%,58%"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "mKAGwODzfEdDRcVb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich behavior shows the greatest improvement from the beginning to the end of the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the greatest improvement, I compare the start and end values of each behavior. Observing {the line for Anxiety}<r>[0.1, 0.4, 0.6, 0.85]</r> starts at approximately 78% and ends around 43%, indicating a decline. For {the line for Confidence}<r>[0.1, 0.35, 0.6, 0.2]</r>, it begins at approximately 25% and increases to 65% by the end. {The line for Distraction}<r>[0.1, 0.8, 0.6, 0.7]</r> slightly decreases from 60% to 50%. Both {the line for Focused}<r>[0.1, 0.7, 0.6, 0.55]</r> and {the line for Happiness}<r>[0.1, 0.65, 0.6, 0.45]</r> show growth, but Confidence has the largest percentage change, improving by 40%.\nBased on the above thought, the answer is:\nConfidence shows the greatest improvement, with a 40% increase in the percentage of time the subject was in this state from the beginning to the end of the observed period. This deduction is based on the relative increase in values from the line representing Confidence."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "Rvels8RjBiVWkvTU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of time spent in Distraction across all plotted time intervals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I locate and calculate the values from {the line for Distraction}<r>[0.1, 0.8, 0.6, 0.7]</r> at each marked interval from {the x-axis tick labels}<r>[0.1, 0.9, 0.13, 0.92]</r> to {the end}<r>[0.6, 0.9, 0.62, 0.92]</r>. The identified values were approximately 60%, 58%, 56%, 53%, and 50% corresponding to each time interval. Averaging these gives (60+58+56+53+50)/5 = 55.4%.\nBased on the above thought, the answer is:\n55.4%"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "dCmRnlJS2UnFsxL1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the raw data for 2019 and 2020 sales according to the months in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract a structured table from the bar plot, I can start by identifying the labels from the X-Axis, which are present in the {xticks of subplot_0}<r>[0.102, 0.896]</r> and {xticks of subplot_1}<r>[0.593, 0.877]</r> on the chart representing the months. The Y-Axis in {subplot_0}<r>[0.102, 0.727]</r> and {subplot_1}<r>[0.593, 0.735]</r> give the values related to sales for the series plotted. Upon extracting the relevant information, the table can be structured with the first column as the months and other columns for sales values in 2019 and 2020.\nBased on the above thought, the answer is:\nMonth,2019 Sales,2020 Sales\n01,230,210\n02,245,220\n03,210,200\n04,220,230\n05,225,212\n06,235,240\n07,242,245\n08,243,250\n09,235,253\n10,255,260\n11,265,265\n12,272,270"
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "asz4AiGjhDdYwQd7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in sales from January to December in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in sales from January to December 2020, we need to find the sales values for these months. Looking at the {January point on subplot_1}<r>[0.593, 0.659]</r>, which corresponds to a sales value of 210, and the {December point on subplot_1}<r>[0.965, 0.205]</r>, correlating to a sales value of 270, we can calculate the percentage increase as (Final value - Initial value) / Initial value * 100% = (270 - 210) / 210 * 100%.\nBased on the above thought, the answer is:\n28.57%"
+            }
+        ],
+        "id_raw": 350
+    },
+    {
+        "id": "idu54UJwoJZr3Y2R",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the bar chart into a structured table with Years as the rows and Technology Aspects as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart, I will first identify {the y-axis tick labels}[[0.042, 0.833, 0.089, 0.867], [0.099, 0.647, 0.112, 0.724], [0.157, 0.462, 0.176, 0.576], [0.216, 0.276, 0.241, 0.411], [0.274, 0.091, 0.323, 0.218]] as the rows, representing the Years. Then, the {legend texts}[[0.823, 0.079, 0.897, 0.123], [0.823, 0.124, 0.922, 0.167], [0.823, 0.170, 0.927, 0.213], [0.823, 0.214, 0.932, 0.260], [0.823, 0.259, 0.943, 0.304]] serve as the column headers, representing different Technology Aspects. After that, I can extract the data values by finding the ends of {the bars for each technology}[[{x: 2025, y: 0.2, 'coordinates': [0.119, 0.773, 0.138, 0.820]}, {x: 2026, y: 0.21, 'coordinates': [0.147, 0.746, 0.168, 0.807]}, ... , {x: 2050, y: 0.98, 'coordinates': [0.705, 0.087, 0.719, 0.997]}]] at each year.\nBased on the above thought, the answer is:\nYear,AI Development,5G Implementation,Quantum Computing,Sustainability Innovation,Blockchain Utilization\n2025,0.2,0.25,0.1,0.1,0.05\n2026,0.21,0.3,0.12,0.11,0.07\n...\n2050,0.9,0.95,0.8,0.85,0.98"
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "vRIuOeTdrnXvmNUs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological aspect presents the slowest growth during this period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slowest growth would be represented by the technology aspect with the smallest increase in bar height from 2025 to 2050. Looking at the {bars in 2025}[[0.119, 0.773, 0.138, 0.820], [0.139, 0.773, 0.162, 0.845], [0.163, 0.773, 0.182, 0.803], [0.183, 0.773, 0.202, 0.813], [0.203, 0.773, 0.226, 0.788]] and {bars in 2050}[[0.705, 0.087, 0.719, 0.997], [0.725, 0.087, 0.744, 0.968], [0.745, 0.087, 0.764, 0.823], [0.765, 0.087, 0.784, 0.910], [0.785, 0.087, 0.804, 0.987]], I can see that the Quantum Computing indicator has the lowest rise (0.8 - 0.1 = 0.7) compared to the other technologies.\nBased on the above thought, the answer is:\nBased on the chart, Quantum Computing presents the slowest growth during this period. Despite a general upward trend, the Quantum Computing bar height increases by only 0.7 from 2025 to 2050. This indicates a comparatively slower growth rate vis-a-vis the other technological aspects in the chart."
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "isakBpTLHFwbapsD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total projected growth from 2025 to 2050 of all aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate that, I have to sum up the bar heights at the start and end years. For 2025, I look at {the bars in 2025}[[0.119, 0.773, 0.138, 0.820], [0.139, 0.773, 0.162, 0.845], [0.163, 0.773, 0.182, 0.803], [0.183, 0.773, 0.202, 0.813], [0.203, 0.773, 0.226, 0.788]], and their corresponding heights are 0.2, 0.25, 0.1, 0.1, 0.05. Similarly for 2050, using {the bars in 2050}[[0.705, 0.087, 0.719, 0.997], [0.725, 0.087, 0.744, 0.968], [0.745, 0.087, 0.764, 0.823], [0.765, 0.087, 0.784, 0.910], [0.785, 0.087, 0.804, 0.987]], I get mail values of 0.9, 0.95, 0.8, 0.85, 0.98. The difference would be the sum of 2050 values minus the sum of 2025 values.\nBased on the above thought, the answer is:\n3.78"
+            }
+        ],
+        "id_raw": 352
+    },
+    {
+        "id": "jrIAtCB3XSRRWjZI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you extract the data into a table format with Iterations as columns and Evaluation Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.092, 0.869, 0.102, 0.904], [0.175, 0.869, 0.205, 0.904], [0.264, 0.869, 0.303, 0.904], [0.357, 0.869, 0.396, 0.904], [0.450, 0.869, 0.489, 0.904], [0.543, 0.869, 0.582, 0.904]], which represent the column headers or iterations. Next, I identify {the legend texts}[[0.864, 0.651, 0.966, 0.686], [0.864, 0.703, 0.946, 0.738]] as the row labels representing the different evaluation types. To populate the values, I locate {the line points}[[0.102, 0.166, 0.470], [0.143, 0.617], ..., [0.423, 0.366]] for each type at the respective iterations and extract the values annotated.\nBased on the above thought, the answer is:\nEvaluation Type,0,250,500,750,1000,1250,1500,1750,2000\nConventional,0.1,0.3,0.55,0.7,0.82,0.9,0.92,0.93,0.92\nFine-tuned,0.05,0.15,0.27,0.35,0.41,0.45,0.55,0.6,0.65"
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "SoFkq7iS2eBW0uZq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the graphical representation, which evaluation seemed to have experienced more prominent improvement in the success rate between iteration 500 and 1250?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the improvement in success rate for both Conventional and Fine-tuned evaluations, I have to first examine the y-coordinate values at iterations 500 and 1250 for each line. Considering the points for {Conventional at iteration 500}[0.190, 0.438] and {Conventional at iteration 1250}[0.330, 0.187], I can observe an improvement in the attack success rate. Similarly, the points for {Fine-tuned at iteration 500}[0.682, 0.572] and {Fine-tuned at iteration 1250}[0.821, 0.410] also show an increase in success rate. However, the change in y-coordinate (success rate) is visibly higher for the Fine-tuned evaluation than for the Conventional one, indicating a greater improvement.\nBased on the above thought, the answer is:\nThe Fine-tuned evaluation demonstrated a more substantial improvement in attack success rate between iterations 500 and 1250, as reflected in the greater positive shift in y-coordinate values on the chart during this time period."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "YPlnYp2TR6AsR1zs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum values observed for the Fine-tuned evaluation across all iterations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the maximum and minimum values for the Fine-tuned evaluation across all iterations, I must locate {the line points for Fine-tuned}[[0.588, 0.796], [0.635, 0.677], [0.682, 0.572], [0.728, 0.537], [0.775, 0.523], [0.821, 0.410], [0.868, 0.340], [0.915, 0.305], [0.961, 0.284]] and extract the corresponding values: 0.05, 0.15, 0.27, 0.35, 0.41, 0.45, 0.55, 0.6, 0.65. The maximum value is 0.65 corresponding to {iteration 2000}[0.961, 0.844], and the minimum value is 0.05 corresponding to {iteration 0}[0.588, 0.844]. So, the difference between the maximum and minimum values is 0.65 - 0.05 = 0.6.\nBased on the above thought, the answer is:\n0.6"
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "J1E0FRTrcCiquMxr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive a table that shows the success rate of VR Education and AR Education for different number of students based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to generate the table, I will first look at {the x-axis ticks on the first subplot}[[0.101, 0.869, 0.111, 0.904], [0.178, 0.869, 0.217, 0.904], [0.269, 0.869, 0.307, 0.904], [0.360, 0.869, 0.398, 0.904], [0.451, 0.869, 0.489, 0.904]][0.542, 0.869, 0.580, 0.904] to get the column headers, which represent the number of Students. Next, the rows represent the success rates of VR Education and AR Education respectively. I will locate {the line points for VR Education}[[0.106, 0.261, 0.470, 0.679]] and {the line points for AR Education}[[0.598, 0.192, 0.962, 0.748]] to extract their success rates for each number of students.\nBased on the above thought, the answer is:\nNumber of Students,VR Education,AR Education\n0,0.85,0.60\n500,0.90,0.75\n1000,0.92,0.77\n1500,0.91,0.80\n2000,0.88,0.84\n2500,0.85,0.85\n3000,0.81,0.86\n3500,0.78,0.86\n4000,0.76,0.87"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "rq0VzyhQtOVmbVWc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of education- VR or AR, showed more improvement in success rates as the number of students increased from 0 to 4000?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the change in success rates from 0 to 4000 students for both VR Education and AR Education. I look at {VR Education's initial and final datapoints}[[0.106, 0.444], [0.470, 0.679]] corresponding to 0.85 and 0.76 success rates respectively. Next, I examine {AR Education's initial and final datapoints}[[0.598, 0.748], [0.962, 0.192]] which correspond to 0.60 and 0.87 success rates respectively. The increase in success rates for VR Education is (0.76 - 0.85) = -0.09 whereas for AR Education, it's (0.87 - 0.60) = 0.27.\nBased on the above thought, the answer is:\nAR Education showed more improvement in success rates as the number of students increased from 0 to 4000. While the success rate for VR Education declined slightly, the success rate for AR Education significantly increased."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "oAMw3APbBOQ6tUnY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the success rate of VR Education when the number of students reached 3000?"
+            },
+            {
+                "from": "gpt",
+                "value": "I find the success rate for VR Education when the number of students is 3000 by locating {3000 on the x-axis}[0.360, 0.869, 0.398, 0.904] for VR Education and {the corresponding y-value for VR Education}[0.379, 0.549]. The success rate is 0.81 as noted on this point in the chart.\nBased on the above thought, the answer is:\n0.81"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "a4bdUQLpzUtG1miT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract data from the two line charts, depicting the trends of Conservatism Index and Progressivism Index over the years, and present it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, first I'll consider {the plot labeled 'Trend of Conservatism Index'}<r>[0.131, 0.037, 0.440, 0.085]</r>. Here, {the x-axis labels}<r>[0.086, 0.869, 0.125, 0.904], [0.176, 0.869, 0.215, 0.904], [0.266, 0.869, 0.305, 0.904], [0.356, 0.869, 0.395, 0.904], [0.446, 0.869, 0.485, 0.904]</r> represent the years, and {the y-coordinates of the line points}<r>[0.106, 0.810], [0.196, 0.470], [0.286, 0.130], [0.375, 0.266], [0.465, 0.334]</r> enable extracting the Conservatism Index values. Next, for {the plot labeled 'Trend of Progressivism Index'}<r>[0.627, 0.037, 0.937, 0.085]</r>, using {the same x-axis labels}<r>[0.603, 0.869, 0.642, 0.904], [0.692, 0.869, 0.731, 0.904], [0.782, 0.869, 0.821, 0.904], [0.872, 0.869, 0.911, 0.904], [0.962, 0.869, 0.981, 0.904]</r>, I can obtain the Progressivism Index values from {the y-coordinates of the line points}<r>[0.602, 0.810], [0.692, 0.640], [0.782, 0.470], [0.872, 0.300], [0.962, 0.130]</r>.\nBased on the above thought, the answer is:\nYear,Conservatism Index,Progressivism Index\n2000,0.5,0.6\n2005,0.55,0.65\n2010,0.6,0.7\n2015,0.58,0.75\n2020,0.57,0.8"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "6sgBDuerc9fEQ97k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, Conservatism or Progressivism, showed a more steady increase over the years based on the line charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, the fluctuations in both lines must be examined. The {Conservatism Index line}<r>[0.106, 0.130, 0.465, 0.810]</r> shows ups and downs - from 0.50 in 2000 to 0.55 in 2005, peaking at 0.60 in 2010, then decreasing to 0.58 in 2015 and further to 0.57 in 2020. This indicates variability in the trend of Conservatism. On the other hand, the {Progressivism Index line}<r>[0.602, 0.130, 0.962, 0.810]</r> constantly rises - from 0.60 in 2000, 0.65 in 2005, 0.70 in 2010, 0.75 in 2015, finally reaching 0.80 in 2020. This shows a more steady increase compared to the Conservatism Index.\nBased on the above thought, the answer is:\nThe Progressivism Index showed a more steady increase over the years compared to the Conservatism Index. This observation is based on the consistent rise in the Progressivism Index values over time, unlike the variable trend seen in the Conservatism Index line."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "9Xv4xB3WKAwiqftB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the Progressivism Index from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I first locate {the point for 2000, which corresponds to a Progressivism Index}<r>[0.602, 0.810]</r> of 0.60. Then I find {the point for 2020}<r>[0.962, 0.130]</r>, which corresponds to a Progressivism Index of 0.80. The percentage increase can be calculated as (0.80-0.60)/0.60 * 100%.\nBased on the above thought, the answer is:\n33.33%."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "EBkQbfGLnfIglSws",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the given line chart into a well-structured table using the stages of Innovation project as columns and time intervals as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make the transformation, the x-axis labels which are representing the time intervals located at the bottom, {the x-axis tick labels}[[0.03, 0.938, 0.063, 0.978],[0.156, 0.938, 0.188, 0.978],[0.279, 0.938, 0.311, 0.978],..,[0.958, 0.938, 0.989, 0.978]], will be taken as rows. Then the different types of project stages represented in {the legend}[[0.762,0.031,0.803,0.071],[0.807,0.031,0.874,0.071],[0.879,0.031,0.969,0.071],[0.974,0.031,0.999,0.071]] will be used for the columns of the table. For extracting the data, the line points indicated on the chart for each project stage are {the line points for Concept Generation}[[0.062, 0.328], [0.158, 0.311], ..., [0.948, 0.119]], {for Budgeting}[[0.062, 0.377], [0.158, 0.402], ...,[0.948, 0.584]], {for Team Building}[[0.062, 0.197], [0.158, 0.214], ...,[0.948, 0.280]], and {for Evaluation}[[0.062, 0.232], [0.158, 0.221], ...,[0.948, 0.282]].\nBased on the above thought, the answer is:\nTime,Concept Generation,Budgeting,Team Building,Evaluation\n0,0.85,0.80,0.30,0.40\n5,0.79,0.83,0.35,0.42\n10,0.73,0.86,0.40,0.44\n15,0.68,0.88,0.45,0.46\n20,0.63,0.91,0.50,0.75\n25,0.55,0.95,0.52,0.65\n30,0.50,0.97,0.55,0.64\n35,0.44,0.98,0.68,0.70\n40,0.40,1.00,0.75,0.72"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "tjvnuLip3h9wDoXV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends in the chart, which Innovation project stage sees the most significant steady growth from time t=0 to time t=40?"
+            },
+            {
+                "from": "gpt",
+                "value": "By closely analyzing the trends indicated by each project stage's line, let's start with the {line for Concept Generation}[0.062, 0.328, 0.948, 0.119], which appears to be a steady decline over time. However, {the line for Budgeting}[0.062, 0.377, 0.948, 0.584] represents a consistent upward trend from t=0 to t=40. The {line for Team Building}[0.062, 0.197, 0.948, 0.280] also shows a steady increase, though not as prominent as Budgeting. Lastly, {the line for Evaluation}[0.062, 0.232, 0.948, 0.282] shows a somewhat erratic trend. There is an abrupt increase around the 20th time frame and then a decline follows, which makes the overall trend less steady compared to others.\nBased on the above thought, the answer is:\nBased on the evident trends in the chart, 'Budgeting' seems to experience the most sustained upward growth from time t=0 to time t=40. The line representing 'Budgeting' is consistently rising, indicating a continual increase in the percentage of effort devoted to this stage over time."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "Ru7EvNyef0yBEm0P",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the percentage dedicated to Team Building increase from time t=0 to time t=40?"
+            },
+            {
+                "from": "gpt",
+                "value": "The increase in percentage dedicated to Team Building project can be calculated by extracting the values of 'Team Building' at the start and end times from the chart. Thus, observe {the line points for Team Building}[[0.062, 0.197], [0.158, 0.214], ..., [0.948, 0.280]]. For time t=0, the effort percentage is approximately 0.20 and the effort percentage at t=40 is read as about 0.75. The increase in percentage is then 0.75 - 0.20 = 0.55.\nBased on the above thought, the answer is:\n0.55"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "D4LMwoOjirq4Xu4r",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, could you create a table with the size of each category ('Telemedicine', 'Machine Learning', 'AI', 'Predictive Analysis', 'Biotechnology') for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct this table, first I identify the {x-axis tick labels}[[0.100, 0.781, 0.139, 0.799], [0.223, 0.781, 0.263, 0.799], [0.346, 0.781, 0.385, 0.799], [0.468, 0.781, 0.507, 0.799], [0.590, 0.781, 0.629, 0.799], [0.712, 0.781, 0.751, 0.799], [0.834, 0.781, 0.873, 0.799], [0.957, 0.781, 0.996, 0.799]], which correspond to the years from 2012 to 2021. Then, I look at {the legend entries}[[0.794, 0.212, 0.892, 0.232], [0.794, 0.261, 0.892, 0.280], [0.794, 0.310, 0.892, 0.329], [0.794, 0.360, 0.892, 0.379], [0.794, 0.409, 0.892, 0.428]], which will form the rows of the table, representing the different aspects of medical technology. The values for each year and category can be inferred by measuring the height of each stack's color segments at {the bar locations}[[0.111, 0.390, 0.128, 0.752], [0.233, 0.396, 0.251, 0.775], ... , [0.967, 0.409, 0.985, 0.858]].\nBased on the above thought, the answer is:\nYear,Telemedicine,Machine Learning,AI,Predictive Analysis,Biotechnology\n2012,25,10,15,12,8\n2013,30,12,16,18,10\n2014,35,16,20,20,15\n2015,40,18,22,25,20\n2016,45,22,25,25,25\n2017,60,25,30,30,30\n2018,75,33,33,33,35\n2019,80,35,38,35,40\n2020,100,40,40,40,45\n2021,120,45,45,45,50"
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "XNeEiGi6h18SEk7a",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, which aspect of technology has been the most rapidly increasing in recent years and why might that be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most rapidly increasing aspect, I need to compare the advancement of the different aspects in recent years, by looking at the differences in the bar stacks starting from {the bar stack for 2018} [0.845, 0.386, 0.862, 0.830] to the last one present at {the bar stack for 2021} [0.967, 0.409, 0.985, 0.858]. The 'Telemedicine' section of the stack has grown the most, from 75 to 120. Although 'Biotechnology' has also significantly grown, its increase from 35 to 50 does not match the scale of growth seen in 'Telemedicine'.\nBased on the above thought, the answer is:\nAccording to the graph, 'Telemedicine' has been the most rapidly increasing in recent years. Telemedicine allows health care professionals to evaluate, diagnose, and treat patients remotely using telecommunications technology. As such, it has become a crucial component of health infrastructures, particularly in the wake of the Covid-19 pandemic when many patients pursued remote treatment options."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "vhBuEBQxT6CLxLtq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the advancement in Telemedicine grown between 2012 and 2021 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the value of the 'Telemedicine' category for the year 2012 and for the year 2021. The bar for 'Telemedicine' in the year 2012 can be found at {the first bar stack} [0.111, 0.390, 0.128, 0.752], with a value of 25. Then, I locate the bar for 'Telemedicine' in the year 2021 at {the last bar stack} [0.967, 0.409, 0.985, 0.858], with a value of 120. Then we subtract the value in 2012 from the value in 2021 to get the growth.\nBased on the above thought, the answer is:\n95."
+            }
+        ],
+        "id_raw": 357
+    },
+    {
+        "id": "lruZq8zkcD1SjSbL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table showing the Rate of Social Interaction and Self-satisfaction Index over time represented in the line charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the given line charts into a structured table, I focus on the x-axis and the y-values represented by the points in both line charts. I start with {the x-axis tick labels}[[0.063, 0.869, 0.072, 0.904], [0.131, 0.869, 0.141, 0.904], ..., [0.968, 0.869, 0.987, 0.904]], which represent the Time (Month) values from 1 to 12 at regular intervals. For Rate of Social Interaction, I identify {the line points}[[0.102, 0.130], [0.136, 0.182], ..., [0.478, 0.810]] which represent the y-values corresponding to each month. Similarly, for Self-satisfaction Index, I find {the line points}[[0.589, 0.810], [0.623, 0.598], ..., [0.965, 0.130]] as the y-values for each month.\nBased on the above thought, the answer is:\nTime (Month),Rate of Social Interaction,Self-satisfaction Index\n1,100,75\n2,95,80\n3,92,78\n4,80,76\n5,78,80\n6,70,83\n7,60,85\n8,58,83\n9,50,87\n10,45,90\n11,40,88\n12,35,91"
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "rWKVvCtFKxnVpsX8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of decrease in Rate of Social Interaction from month 3 to month 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of decrease in Rate of Social Interaction from month 3 to month 6, I first need to identify the value for month 3 by locating the corresponding {point for month 3}[[0.170, 0.214]] which is 92. Then, I identify the value for month 6 by locating the corresponding {point for month 6}[[0.273, 0.444]] which is 70. The rate of decrease is therefore calculated as ((92 - 70) / 92) * 100%.\nBased on the above thought, the answer is:\nApproximately 23.91%."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "DQzhmGXOHgbNBXfq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the decade's environmental impact data into a structured table with years as columns and indicators as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the structured table from the stacked line graph, I first identify {the x-axis tick labels}<r>[0.101, 0.895, 0.136, 0.923]</r> through {the x-axis tick labels}<r>[0.501, 0.895, 0.536, 0.923]</r> as the column headers representing the years from 2001 to 2010. Next, I recognize {the legend entries}<r>[0.792, 0.132, 0.893, 0.208]</r> as the row labels, which include Glacier Melt, SeaLevel Rise, CO2 Emission, Deforestation, and Temperature Increase. To populate the values, I analyze {the line plots}<r>[0.119, 0.113, 0.479, 0.839]</r>, specifically noting the data points on each year.\nBased on the above thought, the answer is:\nYear,Glacier Melt,Sea Level Rise,CO2 Emission,Deforestation,Temperature Increase\n2001,0.2%,1.2 cm,32 billion,0.9 million,0.03\u00b0C\n2002,0.25%,1.3 cm,31 billion,1.0 million,0.02\u00b0C\n2003,0.27%,1.4 cm,33 billion,1.1 million,0.04\u00b0C\n2004,0.3%,1.5 cm,30 billion,1.3 million,0.07\u00b0C\n2005,0.35%,1.6 cm,34 billion,1.4 million,0.04\u00b0C\n2006,0.4%,1.8 cm,33 billion,1.5 million,0.05\u00b0C\n2007,0.45%,1.9 cm,35 billion,1.6 million,0.05\u00b0C\n2008,0.5%,2.0 cm,32 billion,1.8 million,0.08\u00b0C\n2009,0.53%,2.1 cm,31 billion,2.0 million,0.04\u00b0C\n2010,0.6%,2.3 cm,36 billion,2.1 million,0.05\u00b0C"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "c9tCI6PLwHh584v5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the attitude towards CO2 emissions changed over the decade based on the fluctuation in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the attitude change towards CO2 emissions over the decade, I need to analyze {the line for CO2 Emission}<r>[0.119, 0.769, 0.479, 0.425]</r>, noting its rise and falls. The line begins at 32 billion metric tons in 2001, dips slightly in subsequent years, then fluctuates up and down, reflecting unstable approaches and possibly varying global policies and economic factors impacting CO2 emissions.\nBased on the above thought, the answer is:\nBased on the fluctuations observed in the CO2 Emission line, it appears that there was no consistent strategy or attitude towards CO2 emissions over the decade. The line shows significant instability with alternate increases and decreases in emissions, suggesting changing and perhaps conflicting policies or economic factors influencing global carbon dioxide emissions throughout these years."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "c2DOjDvnh5flG0mL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_18_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in sea levels from 2001 to 2010 according to the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase in sea levels from 2001 to 2010, I need to locate {the line points for Sea Level Rise}<r>[0.119, 0.809, 0.479, 0.355]</r> and extract the values corresponding to each year: 1.2 cm, 1.3 cm, 1.4 cm, 1.5 cm, 1.6 cm, 1.8 cm, 1.9 cm, 2.0 cm, 2.1 cm, and 2.3 cm. Starting at 1.2 cm in 2001 and ending at 2.3 cm in 2010, the total increase is calculated as 2.3 - 1.2 = 1.1 cm.\nBased on the above thought, the answer is:\n1.1 cm."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "xX1dDiNWolJ0H1vx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the given line chart data into a readable table format with the Years as the columns, and Resource Types as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify {the x-axis tick labels}[[0.172, 0.879, 0.218, 0.914], [0.276, 0.879, 0.304, 0.914], [0.411, 0.879, 0.426, 0.914], [0.493, 0.879, 0.492, 0.914], [0.597, 0.879, 0.609, 0.914], [0.701, 0.879, 0.709, 0.914], [0.797, 0.879, 0.813, 0.914], [0.901, 0.879, 0.917, 0.914], [1.005, 0.879, 1.021, 0.914]] which are the Years. Then {the legend texts}[[0.218, 0.083, 0.340, 0.108],[0.218, 0.135, 0.348, 0.170]] represent the different types of resources or rows classifications. The respective allocation values for each resource in each year are represented by {the line points}[[0.170, 0.854], [0.253, 0.853], [0.336, 0.846], [0.419, 0.840], [0.502, 0.658], [0.585, 0.488], [0.668, 0.427], [0.751, 0.392], [0.834, 0.180], [0.917, 0.112], [0.170, 0.854], [0.253, 0.853], [0.336, 0.851], [0.419, 0.843], [0.502, 0.790],[0.585, 0.694],[0.668, 0.597], [0.751, 0.501], [0.834, 0.437], [0.917, 0.372]].\nBased on the above thought, the answer is:\nResources,-6000,-4000,-2000,0,2000,4000,6000,8000,10000,12000\nPopulation,5,7,27,46,612,1141,1329,1438,2100,2310\nTechnology,1,3,10,35,200,500,800,1100,1300,1500"
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "vOQAUyfHml2z1lwp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line on the chart represents a steady increase over the years? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which line represents a steady increase over the years, I have to interpret and observe each line plot on the chart. {The line for Population}[0.419, 0.840, 0.917, 0.112] starts at a low point and rises consistently over thousands of years. Although there are minor dips along the line, they do not reflect a significant decrease over time. Based on its visual representation, it would appear that it presents a steady increase in Population.\nBased on the above thought, the answer is:\nThe line of 'Population' represents a steady increase over the years. This is shown by the consistent upward trend seen on the line chart, denoting an increase in population size. Despite minor fluctuations, the overall progression is upward, indicating growth over the years."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "XVpq7SBQ9hoIEwsb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the decrease in population size from 10000 to 12000 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in population size between 10000 and 12000 years, I need to locate and extract the population size for both years from {the line points for Population}[[0.170, 0.853], [0.253, 0.852], [0.336, 0.846], [0.419, 0.840], [0.502, 0.658], [0.585, 0.488], [0.668, 0.427], [0.751, 0.392], [0.834, 0.180], [0.917, 0.112]]. The population at 10000 years is 2100 and at 12000 years it is 2310. The decrease can be calculated as 2310 - 2100 = 210.\nBased on the above thought, the answer is:\n210"
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "A2B01sTT2Ew9sZc4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart tracking education metrics can you extract the data into a table format with Years as columns and Metrics ['Student Enrollment Rate', 'Dropout Rate'] as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.114, 0.879, 0.172, 0.914], [0.231, 0.879, 0.289, 0.914], [0.319, 0.879, 0.377, 0.914], [0.407, 0.879, 0.465, 0.914], [0.495, 0.879, 0.553, 0.914], [0.583, 0.879, 0.641, 0.914], [0.671, 0.879, 0.729, 0.914], [0.759, 0.879, 0.817, 0.914], [0.847, 0.879, 0.905, 0.914], [0.935, 0.879, 0.993, 0.914]], which represent the column headers or years. Next, I identify {the legend texts}[[0.669, 0.069, 0.954, 0.104], [0.669, 0.121, 0.824, 0.156]] as the row labels representing the different metrics. To populate the values, I locate {the line points for Student Enrollment Rate}[[0.143, 0.075], [0.231, 0.096], ..., [0.935, 0.232]] and {the line points for Dropout Rate}[[0.143, 0.817], [0.231, 0.796], ..., [0.935, 0.650]] and extract the corresponding values.\nBased on the above thought, the answer is:\nMetric,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021\nStudent Enrollment Rate,85,83,82,80,78,76,75,74,72,70\nDropout Rate,14,16,17,18,20,22,23,24,25,30"
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "744KYVInuNyT9Jh4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the line chart, which educational metric seems to have worsened over the years and what could be the potential implications?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line for Student Enrollment Rate} [0.143, 0.075, 0.935, 0.232] and {the line for Dropout Rate}[0.143, 0.817, 0.935, 0.650], we see that the percentage of student enrollment has decreased from 85% in 2012 to 70% in 2021, this represents an unfavorable trend. On the other side, the dropout rate has increased from 14% in 2012 to 30% in 2021, representing a negative trend.\nBased on the above thought, the answer is:\nThe educational metric that has worsened over the years according to the line chart is the Dropout Rate. As the percentage of students leaving school before graduating has increased over the years from 14% in 2012 to 30% in 2021, the implications of this trend can be severe. It may reflect declining youth engagement with education or issues with the strength of the educational system itself. This trend, if continued, may result in a less educated workforce, which can have diverse socio-economic implications, including reduced earning potentials and loss of productivity and innovation in the society."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "2qwCsUP0vx1nNIFI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the student enrollment rate from the year 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in student enrollment rate from 2012 to 2021, I need to locate {the line point for student enrollment rate in 2012}[0.143, 0.075] and {the line point for student enrollment rate in 2021}[0.935, 0.232] on the line chart for \"Student Enrollment Rate and extract the corresponding values: 85 and 70. The change from 2012 to 2021 in the student enrollment rate is therefore 85 - 70 = 15.\nBased on the above thought, the answer is:\n15."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "ylNKWgJWHJ6dNoUU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data from the line chart, indicating the frequency of social activities during weekdays and weekends, into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart, I will focus first on the {x-axis}[[0.149, 0.879, 1.037, 0.914]], where the activities' labels are located. \nThen, I turn to the {legend}[[0.665, 0.069, 0.963, 0.184]], to determine the weekdays and weekends categories. \nThen, the measurements for each of these categories can be retrieved from their corresponding lines {Intrusion Attempts line}[[0.157, 0.185, 0.936, 0.587]] and {Data Breaches line}[[0.157, 0.620, 0.936, 0.741]]. The value can be deducted by following the points on the line and referring to the y-axis scale.\nBased on the above thought, the answer is:\nActivity,Weekdays,Weekends\nVisiting Parks,70,65\nHome Cooking,80,75\nLate-night Parties,50,55\nEarly Wakeup,30,28\nWatching Television,18,19\nOutdoor Cycling,13,10\nLibrary Study,10,5"
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "ok6qMHAbfc0r4Nzh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of Intrusion Attempts look over the period shown on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend of Intrusion Attempts, I must follow the progression of {the line for Intrusion Attempts}[0.157, 0.185, 0.936, 0.587]. It starts at a high point at time = 0 (100 instances), then there's a consistent decline until it reaches a lower point at the end of the observed period (40 instances). This visual information suggests a decreasing trend.\nBased on the above thought, the answer is:\nThe trend of Intrusion Attempts over the given period shows a consistent decline. The number of evidences starts high and then gradually decreases over time, indicating improved security or reducing threats."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "JrmNMt6XyreiqIJq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the number of Intrusion Attempts at the 1st month and the last month?"
+            },
+            {
+                "from": "gpt",
+                "value": "For that, I need to find the coordinates of the first and last points of {Intrusion Attempts line}[[0.157, 0.185, 0.936, 0.587]]. The starting point is at {[0.157, 0.185]}, and the ending point is at {[0.936, 0.587]}. I can see from the y-axis that the y-coordinates of these points correspond to 100 and 40 respectively. The difference can be calculated by subtracting these two values: 100 - 40 = 60.\nBased on the above thought, the answer is:\n60."
+            }
+        ],
+        "id_raw": 362
+    },
+    {
+        "id": "fRJHtoP0tY2oMSVG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the chart data into a structured table that will present each sector's production volume for the years 2021 and 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I will first identify {the x-axis labels}<r>[0.062, 0.763, 0.156, 0.904], [0.900, 0.769, 0.992, 0.912]</r> as representing the years 2021 and 2022. Then, I'll take each sector mentioned in {the legend}<r>[0.781, 0.089, 0.935, 0.173], [0.781, 0.190, 0.929, 0.279], [0.781, 0.285, 0.903, 0.381], [0.781, 0.398, 0.899, 0.487], [0.781, 0.497, 0.909, 0.594]</r> to generate column header. The production volumes for each sector, essential for populating the table, are specified by the positions of {the points on the lines for each sector}<r>[0.148, 0.185, 0.946, 0.769]</r>.\nBased on the above thought, the answer is:\nSector,2021,2022\nCereal Crops,4950,4800\nVegetable Crops,5622,5300\nFruit Crops,2540,2400\nLivestock,2800,2700\nSeafood,1200,1145"
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "PSYfi52U7VR9xVSy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends shown in the line chart, which sector experienced the most significant change in production volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must analyze the different sectors. Starting with {Cereal Crops}<r>[0.156,0.254,0.946,0.292]</r>, the production volume decreased from 4950 to 4800, so the change was fairly minor. The {Vegetable Crops}<r>[0.156, 0.337, 0.946, 0.403]</r> dropped its production from 5622 to 5300. {Fruit Crops}<r>[0.156, 0.418, 0.946, 0.447]</r> had a slight decrease as well, from 2540 to 2400. The {Livestock}<r>[0.156, 0.462, 0.946, 0.477]</r> production volume declined slightly from 2800 to 2700. And lastly, the {Seafood}<r>[0.156, 0.696, 0.946,0.711]</r> sector displayed a small decline in production from 1200 to 1145.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Vegetable Crops sector experienced the most significant decrease in production volume from 2021 to 2022. This sector's line plot shows a sharp decrease from 5622 thousand metric tonnes in 2021 to 5300 thousand metric tonnes in 2022, a difference of 322 thousand metric tonnes."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "VoAi35OJlOB027Sy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the decrease in the Vegetable Crops sector's production volume from 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the decrease in the Vegetable Crops sector's production volume, I locate {the line points for Vegetable Crops}<r>[0.156, 0.337], [0.946, 0.403]</r>. The 2021 production volume was 5622 thousand metric tonnes and the 2022 production volume was 5300 thousand metric tonnes. The decrease is calculated as 5622 - 5300.\nBased on the above thought, the answer is:\n322."
+            }
+        ],
+        "id_raw": 365
+    },
+    {
+        "id": "XUTdvnqa9BflmB3j",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data in the plot in a table format with row representing the year and columns representing the Hydro Power and Hydro Conventional?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to generate table from the plot, I look at {the x-axis tick labels}[[0.122, 0.912, 0.166, 0.936], [0.211, 0.912, 0.255, 0.936], [0.299, 0.912, 0.343, 0.936], [0.388, 0.912, 0.432, 0.936], [0.477, 0.912, 0.520, 0.936], [0.565, 0.912, 0.609, 0.936], [0.654, 0.912, 0.698, 0.936], [0.742, 0.912, 0.786, 0.936], [0.831, 0.912, 0.875, 0.936], [0.919, 0.912, 0.963, 0.936]], which will become the rows corresponding to the years. The {legend items}[[0.794, 0.085, 0.904, 0.108], [0.794, 0.120, 0.966, 0.143]] will be the headers representing the energy generation types. For each year, I look at the corresponding {line points for Hydro Power}[[0.144, 0.179], [0.233, 0.296], [0.321, 0.373], [0.410, 0.567], [0.498, 0.645], [0.587, 0.723], [0.676, 0.800], [0.764, 0.800], [0.853, 0.839], [0.941, 0.858]] and {line points for Hydro Conventional}[[0.144, 0.102], [0.233, 0.218], [0.321, 0.257], [0.410, 0.296], [0.498, 0.412], [0.587, 0.490], [0.676, 0.567], [0.764, 0.645], [0.853, 0.723], [0.941, 0.761]] and map these points back to their corresponding values on the y-axis to fill the table.\nBased on the above thought, the answer is:\nYear,Hydro Power,Hydro Conventional\n2030,8000,8200\n2031,7700,7900\n2032,7500,7800\n2033,7000,7700\n2034,6800,7400\n2035,6600,7200\n2036,6400,7000\n2037,6400,6800\n2038,6300,6600\n2039,6250,6500"
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "rKMKUfpnolJm8Ez9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average energy production for Hydro Conventional over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average energy production for Hydro Conventional, I need to locate {the line points for Hydro Conventional}[[0.144, 0.102], [0.233, 0.218], [0.321, 0.257], [0.410, 0.296], [0.498, 0.412], [0.587, 0.490], [0.676, 0.567], [0.764, 0.645], [0.853, 0.723], [0.941, 0.761]] and extract the corresponding values: 8200, 7900, 7800, 7700, 7400, 7200, 7000, 6800, 6600, 6500. I calculate the sum of these values and divide by the total number of years, which is 10.\nBased on the above thought, the answer is:\n7210"
+            }
+        ],
+        "id_raw": 366
+    },
+    {
+        "id": "P4wIvHty2f5A1aWy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data from the line chart depicting the risk scores over time for Operational Risk, Compliance Risk, Credit Risk, and Market Risk be extracted into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely, to transform this line chart into a tabular format, I should take a look at {the x-axis labels}[[0.113, 0.919, 0.124, 0.942], [0.296, 0.919, 0.307, 0.942], [0.478, 0.919, 0.489, 0.942], [0.661, 0.919, 0.672, 0.942], [0.843, 0.919, 0.854, 0.942], [1.020, 0.919, 1.042, 0.942]], indicating the intervals of time from 0 to 9 which would be the column headers. Secondly, I  identify {the legend texts}[[0.142, 0.055, 0.285, 0.078], [0.142, 0.090, 0.285, 0.113], [0.142, 0.125, 0.236, 0.148], [0.142, 0.160, 0.243, 0.183]] which represents the risk types and will be the row labels. Then, to fill in the data, I check {the line points on the plot}[[0.118, 0.469], [0.210, 0.521], ..., [0.940, 0.434]] for each risk type at the respective time intervals and extract the corresponding risk scores.\nBased on the above thought, the answer is:\nTime,Operational Risk,Compliance Risk,Credit Risk,Market Risk\n0,65,55,45,75\n1,62,48,52,78\n2,59,51,58,76\n3,60,50,54,77\n4,58,56,62,82\n5,55,60,59,80\n6,59,65,61,79\n7,61,62,63,84\n8,62,68,65,85\n9,65,70,67,87"
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "jCpEVu5v7hFbRJHb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends in the line chart, which risk do you believe will continue to rise, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer which risk is likely to continue rising, I need to analyze the trends for each risk type. Looking at the {line for Operational Risk}[0.118, 0.469, 0.940, 0.642], it shows a general decreasing trend from 65 to 65. Looking at the {line for Compliance Risk}[0.118, 0.382, 0.940, 0.764], it shows a general increasing trend from 55 to 70. The {line for Credit Risk}[0.118, 0.434, 0.940, 0.816] shows a fluctuating but generally increasing trend from 45 to 67. Finally, {the line for Market Risk}[0.118, 0.086, 0.940, 0.295] indicates a steady increase over time from 75 to 87.\nBased on the above thought, the answer is:\nBased on the line chart, I would infer that the Market Risk would continue to rise. Its trend line consistently ascends from 75 to 87 over time, indicating a steady increase. Though the lines for Compliance Risk and Credit Risk also show an increasing trend, the slope of the Market Risk line seems to be the steepest and most consistent, suggesting a stronger upward trend. Therefore, it's likely that the Market Risk will continue to rise."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "FONqoMhhqyosaH0g",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in Market Risk score from Time = 0 to Time = 9?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the change in Market Risk score from Time = 0 to Time = 9, I need to look at {the line points for Market Risk}[[0.118, 0.295], [0.940, 0.086]]. The initial score at Time = 0 is 75, corresponding to {the point at Time=0}[0.118, 0.295] and the final score at Time = 9 is 87, corresponding to {the point at Time=9}[0.940, 0.086]. The change is calculated as the final score minus the initial score, therefore it would be 87 - 75 = 12.\nBased on the above thought, the answer is:\n12."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "l391TuSs2fwWhTUT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from the line chart in a table format with the rows being the 'year' and the columns being 'popularity' of 'Haute Couture' and 'Streetwear'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I will use {the x-axis tick labels}[[0.017, 0.919, 0.061, 0.942], [0.197, 0.919, 0.241, 0.942], [0.378, 0.919, 0.421, 0.942], [0.558, 0.919, 0.602, 0.942], [0.738, 0.919, 0.782, 0.942], [0.919, 0.919, 0.963, 0.942]] as the row labels, representing different years. I identify {the legend texts}[[0.839, 0.046, 0.966, 0.069], [0.839, 0.081, 0.935, 0.104]] for 'Haute Couture' and 'Streetwear' respectively as the column headers. The data in the table is the 'popularity' of 'Haute Couture' and 'Streetwear', which I derive from {the points on the Haute Couture line}[[0.129, 0.077], [0.219, 0.283], [0.309, 0.490], [0.399, 0.696], [0.490, 0.789], [0.580, 0.800], [0.670, 0.779], [0.760, 0.769], [0.850, 0.763], [0.941, 0.789]] and {the points on the Streetwear line}[[0.129, 0.386], [0.219, 0.325], [0.309, 0.304], [0.399, 0.294], [0.490, 0.283], [0.580, 0.268], [0.670, 0.247], [0.760, 0.237], [0.850, 0.226], [0.941, 0.221]] respectively for these years.\nBased on the above thought, the answer is:\nYear, Haute Couture, Streetwear\n2005, 1000, 700\n2006, 800, 760\n2007, 600, 780\n2008, 400, 790\n2009, 310, 800\n2010, 300, 815\n2011, 320, 835\n2012, 330, 845\n2013, 335, 855\n2014, 310, 860"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "6wUnc0OmoTnEujus",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which fashion trend\u2019s popularity decreases over time, and which one increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which fashion trend's popularity decreases or increases over time, I need to analyze the lines representing the two fashion trends on the chart. I note that {the line representing Haute Couture}[0.129, 0.077, 0.941, 0.790] shows a decrease in popularity from 1000 in 2005 to 310 in 2014. However, {the line representing Streetwear}[0.129, 0.386, 0.941, 0.221] shows an increase in popularity from 700 in 2005 to 860 in 2014.\nBased on the above thought, the answer is:\nBased on the line chart, the popularity of 'Haute Couture' shows a declining trend over the years, whereas 'Streetwear' exhibits an increasing trend."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "fiINYVyB0wn8FKiO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the popularity of Streetwear increase from 2005 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I look at {the point for Streetwear in 2005}[0.129, 0.386], which corresponds to a popularity figure of 700, and {the point for Streetwear in 2014}[0.941, 0.221], which corresponds to a popularity figure of 860. The percentage increase from 2005 to 2014 is ((860-700)/700)*100.\nBased on the above thought, the answer is:\n22.9%"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "p6chrSFrZjRQIVN1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan I have the info from the line chart in a tabular form with years as rows and each cryptocurrency as a column?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from this line chart, we firstly list all marked points from x-axis which represent the column headers or years. We then locate {the legend texts}<r>[0.716, 0.075, 0.973, 0.152]</r> to serve as our column labels for the table, which represent the names of the four cryptocurrencies: Bitcoin, Ethereum, Ripple, and Polkadot. Afterwards, we need to look at each line point for respective cryptocurrencies and fill the values according to their respective year entries. This is done by examining each line's respective point positions in the chart.\nBased on the above thought, the answer is:\nYear, Bitcoin, Ethereum, Ripple, Polkadot\n2017,1250,100,0.06,0\n2018,3500,350,3.84,0\n2019,3700,130,0.37,0\n2020,9000,200,0.19,5.54\n2021,48000,1800,0.65,30"
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "M5JKy6aILnF2cOfs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which of the cryptocurrencies depicted can be said to have shown the most stable performance and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce the cryptocurrency with the most stable performance, I need to analyze the fluctuations of each line plot for the cryptocurrencies. The line which shows the least fluctuations, underscored by a smooth curve or a straight line, can be interpreted as the one depicting stable performance. Upon examining {the lines for Bitcoin, Ethereum, Ripple, and Polkadot}<r>[0.118, 0.399, 0.940, 0.763]</r>, Ethereum seems to have less fluctuation compared to others prior to 2021. It shows a steady incremental trend from 2017 to 2019 before a significant jump in 2020. Polkadot, on the other hand, has large fluctuations highlighted by the huge dip and spike between the years 2020-2021. Ripple shows a high degree of instability, particularly indicated by the sharp peak in 2018 before plummeting back to its original price range. Bitcoin, while having upward trend, still shows a few peaks and troughs which indicate a degree of instability.\nBased on the above thought, the answer is:\nBased on the line chart, Ethereum has shown the most stable performance across the years until 2021. Its trend line shows the least fluctuations and a relatively stable, incrementally increasing trend compared to the other observed cryptocurrencies: Bitcoin, Ripple, and Polkadot."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "74zii7JOfqsRtTeU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum growth in price Ethereum has experienced in one year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the maximum growth for Ethereum, I need to inspect {the line representing Ethereum}<r>[0.118, 0.399, 0.940, 0.763]</r>, and note the largest jump between two consecutive years. By looking at the data, I find that the largest difference is between 2020 and 2021, where it went from 200 to 1800.\nBased on the above thought, the answer is:\n1600."
+            }
+        ],
+        "id_raw": 370
+    },
+    {
+        "id": "qCU4cy5Kqn2WPspz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a convenient table format, using 'Year' as the columns and 'Companies' as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to convert the line chart data into a table, the x-axis {tick labels}<r>[0.123, 0.912, 0.940, 0.936]</r> signify the columns that are the years. I identify the {legend text}<r>[0.840, 0.055, 0.966, 0.113]</r> as the row headers, these represent the different companies - 'Tech' and 'Manufacturing'. The values are populated by extracting corresponding y-values from the line points visually drawn on the graph, these can be extracted from the {line coordinates for Tech}<r>[0.123, 0.322, 0.940, 0.753]</r> and {line coordinates for Manufacturing}<r>[0.123, 0.235, 0.940, 0.695]</r>.\nBased on the above thought, the answer is:\nCompanies,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014\nTech,15,18,15,23,20,28,22,29,30,30\nManufacturing,20,23,19,17,25,33,29,32,33,30"
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "qhkcmWDF4Bn7HGGk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the plotted line graph, can you interpret the changes in market shares of Tech and Manufacturing over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the {line plotted for Tech}<r>[0.123, 0.322, 0.940, 0.753]</r>, I can see an overall increase in market share from 15% to 30% over the years. The climb is not consistent, seeing an increase between 2005-2006 and then a fall in 2007. Thereafter, it rises and falls again in 2011 and then steadily increases till 2014.\nOn the other hand, the {line plotted for Manufacturing}<r>[0.123, 0.235, 0.940, 0.695]</r>, reveals an increase in market share from 20% to 30%. This line also demonstrates a similar, however less volatile, trend of rising and falling market share, with noticeable increase in 2006 followed by a decrease in 2008 and 2009. It then experiences a rise till 2010 and remains steady from 2012 to 2014.\nBased on the above thought, the answer is:\nGiven the line graph, Tech started with lower market share in 2005 and experienced fluctuating growth till it matched Manufacturing in 2014, both ending at 30% share. Manufacturing however, started at a higher position but experienced less growth over the years. Both companies illustrated periods of growth and reduction, with Tech showing a more volatile pattern. In essence, while both Tech and Manufacturing grew over the years, Tech had a larger overall growth, eventually matching its competitor."
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "aVNp0M5kw5xWIVOd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Market Share for Tech between 2005 and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the starting and ending points of the {line for Tech}<r>[0.123, 0.322, 0.940, 0.753]</r>. The y-value at 2005 is 15 and at 2014 it's 30. Therefore, the increase in the market share for Tech between 2005 and 2014 is 30 - 15 = 15.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 372
+    },
+    {
+        "id": "advC4ugFUre5gWxg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a structured table depicting the change in effectiveness for each AI and Automation model across different languages during two periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, to construct a structured table from the chart, I would recognize {the x-axis labels}<r>[0.071, 0.025, 0.985, 0.903]</r> which lists the AI and Automation models. Additionally, I identify {the legend}<r>[0.078, 0.037, 0.314, 0.113]</r> which gives the languages used for each model. By evaluating {the line plots}<r>[0.112, 0.045, 0.943, 0.719]</r> and <r>[0.112, 0.045, 0.943, 0.903]</r>, I could extract the effectiveness values for each language and period.\nBased on the above thought, the answer is:\nModel,Language,Period1,Period2\nAI Bot,EN,85,90\nAI Bot,DE,80,85\nAI Bot,FR,95,98\nAutomated System,EN,101,104\nAutomated System,DE,99,102\nAutomated System,FR,97,101\nDeep Learning,EN,120,118\nDeep Learning,DE,122,120\nDeep Learning,FR,130,125\nNeural Network,EN,140,138\nNeural Network,DE,138,135\nNeural Network,FR,142,140\nPredictive Modeling,EN,150,148\nPredictive Modeling,DE,149,147\nPredictive Modeling,FR,152,150"
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "ZoKj4lbu85KGwA4k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the effectiveness of Predictive Modeling change over two periods and what might be inferred about its stability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess how Predictive Modeling's effectiveness changes over two periods, I would examine {the points for Predictive Modeling}<r>[0.500, 0.413], [0.528, 0.372]</r> and <r>[0.500, 0.395], [0.528, 0.362]</r>. In Period1, the effectiveness values noted are 150, 149, and 152. In Period2, the values slightly decrease to 148, 147, and 150. This subtle decrease suggests a slight volatility but overall, Predictive Modeling seems to exhibit relatively stable effectiveness as the changes are minor.\nBased on the above thought, the answer is:\nThe effectiveness of Predictive Modeling in Period1 is slightly higher than in Period2. These small variations across different languages suggest that Predictive Modeling, while experiencing some decrements, maintains a moderate level of stability regarding its effectiveness. The reduction is minimal, indicating its reliability and consistency over the observed periods."
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "8MLm9xO3UnX3PAy8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average Period1 effectiveness of AI Bot models across all languages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average Period1 effectiveness for AI Bot models across all languages, I would locate the data points for AI Bot in Period1 from {the points for AI Bot Period1}<r>[0.112, 0.699], [0.140, 0.682], [0.168, 0.719]</r> which show values of 85, 80, and 95. The computation of the average involves adding these values and dividing by the total number of languages (3). Thus computed, (85+80+95)/3.\nBased on the above thought, the answer is:\n86.67"
+            }
+        ],
+        "id_raw": 373
+    },
+    {
+        "id": "eZmFCCdlrT2QgA9g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the Line chart data about financial risks into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the provided line chart into tabular format, the x-axis labels represent the rows and the types of risks are the columns. First, I identify {the x-axis tick labels}<r>[0.075, 0.912, 0.123, 0.936],[0.186, 0.912, 0.234, 0.936], [0.297, 0.912, 0.345, 0.936], [0.408, 0.912, 0.456, 0.936], [0.519, 0.912, 0.567, 0.936]</r> as the different types of risks. Then, I identify {the legend texts}<r>[0.109, 0.085, 0.182, 0.108], [0.109, 0.120, 0.202, 0.143]</r> as the row labels representing 'baseline risks' and 'managed risks' respectively. For the table data, the corresponding points {from the first line}<r>[0.099, 0.261], [0.144, 0.251], [0.188, 0.241], [0.233, 0.233], [0.277, 0.229], [0.321, 0.227]</r>, and {from the second line}<r>[0.099, 0.531], [0.144, 0.590], [0.188, 0.709], [0.233, 0.640], [0.277, 0.600]</r> are extracted for each type of risk for both the baseline and managed risks.\nBased on the above thought, the answer is:\n,Risks,Baseline Risk,Managed Risk\nIncome Risks,3.4,1.6\nRisky Investment,2.8,2.3\nLimited Assets,1.6,2.7\nFluctuating Market,2.3,3.4\nDrastic Changes,2.7,3.1"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "iQSgDB3hRZMTO64Z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how effective were the risk control strategies for 'Limited Assets'? Explain your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the effectiveness of control strategies on 'Limited Assets', I examine {the point representing Limited Assets in the baseline}<r>[0.188, 0.241]</r> which is 1.6, and then {the point representing Limited Assets after controls are implemented}<r>[0.188, 0.709]</r> which is 2.7. The change in the risk factor is an increase from 1.6 to 2.7. Therefore, instead of reducing the financial risk associated with Limited Assets, the implemented risk control strategies appear to have increased the risk factor.\nBased on the above thought, the answer is:\nBased on the line chart, the risk control strategies have not been effective for 'Limited Assets'. Instead of reducing the risk factor, they have led to an increase, suggesting that the implemented strategies may need to be revisited or enhanced for better risk management in this area."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "vtsvRE1QRZNJWp9z",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the risk factor for Drastic Changes changed after implementing the risk control strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in risk factor, I should first identify {the risk factor for Drastic Changes in the baseline}<r>[0.277, 0.229]</r>, which is 2.7. Then, the {risk factor for Drastic Changes after implementing risk control strategies}<r>[0.277, 0.600]</r> is identified, which is 3.1. The change in risk factor can be calculated by subtracting the baseline from the managed risk, i.e., 3.1 - 2.7 = 0.4.\nBased on the above thought, the answer is:\nThe risk factor for Drastic Changes increased by 0.4."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "tzhQLk3Bz4fjmjnl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data of stock prices over the days into a structured csv table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the x-axis labels which represent 'Day'. They are located at {the x-axis labels}<r>[0.139, 0.899, 0.153, 0.928], [0.218, 0.899, 0.232, 0.928], ..., [0.840, 0.899, 0.868, 0.928]</r>. Next, the corresponding y-values which represent 'Stock Price ($)' are depicted on the line located at {the stock price line points}<r>[0.146, 0.842], [0.225, 0.761], ..., [0.854, 0.114]</r>. Combining these will provide the desired table.\nBased on the above thought, the answer is:\nDay,Stock Price ($)\nDay 1,100\nDay 2,110\nDay 3,120\nDay 4,130\nDay 5,140\nDay 6,150\nDay 7,160\nDay 8,170\nDay 9,180\nDay 10,190"
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "wVxOY4GslseJNWz2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, how does the number of shares fluctuate throughout the 10-day period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will refer to {the line for Number of Shares}<r>[0.146, 0.660], [0.225, 0.478], ..., [0.854, 0.296]</r>. According to the line, I can see that the number of shares rose from Day 1 to Day 2, fell drastically from Day 2 to Day 3, rose slightly again from Day 3 to Day 4, reached its peak on Day 5, then fell down and remained relatively stable with slight rises and falls.\nBased on the above thought, the answer is:\nAccording to the graph, the number of shares started at around 500 on the first day, then rose slightly on the second day before falling down drastically on the third day. From Day 3 to Day 5, the number of shares rose again, peaking on Day 5. After Day 5, the shares count fell again, and remained relatively stable with some small ups and downs for the rest of the period."
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "3ovvmqLf9PQ3I277",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_19_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum stock price recorded during this 10-day period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will refer to the line graph that traces stock prices over time. This line can be identified at {the stock price line points}<r>[0.146, 0.842], [0.225, 0.761], ..., [0.854, 0.114]</r>. The highest point on this line corresponds to the maximum value, which can be found at {the highest point}<r>[0.854, 0.114]</r>.\nBased on the above thought, the answer is:\n$190"
+            }
+        ],
+        "id_raw": 377
+    },
+    {
+        "id": "NJ6McHCfpsobj3Lq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transfer the chart information to a table with media outlets as rows and language regions as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to get names of language regions, which serve as table columns. These can be inferred from {the x-axis ticks for the BBC's chart}[[0.134, 0.684, 0.192, 0.735], [0.371, 0.684, 0.456, 0.735], [0.608, 0.684, 0.696, 0.735], [0.845, 0.684, 0.916, 0.735]]. The languages are English, Chinese, Spanish, French, and Arabic, respectively. Then, I refer to {the legend texts}[[0.707, 0.102, 0.752, 0.148], [0.795, 0.102, 0.849, 0.149]] to get the row labels, which are BBC and CNN. For the table values, I look at the {bar heights}[[0.134, 0.789, 0.192, 0.854], ..., [0.963, 0.302, 1.012, 0.466]] and extract the corresponding values.\nBased on the above thought, the answer is:\nMedia Outlet,English,Chinese,Spanish,French,Arabic\nBBC,45,55,42,30,15\nCNN,42,47,48,50,20"
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "mYgJENwTbos2wTAj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, which language region appears to have the most volatile media influence? Provide your rationale."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the language region with the most volatile media influence, I need to compare the fluctuations in influence levels for both media outlets. Looking at {the bars for BBC}[0.134, 0.584, 0.192, 0.854], I can see a considerable drop from English to Chinese, then a further drop from Chinese to French and another significant drop to Arabic, indicating considerable variability in influence across regions. The {bars for CNN}[0.234, 0.565, 0.292, 0.737] show less volatility, with smaller fluctuations between regions.\nBased on the above thought, the answer is:\nThe Arabic region has the most volatile media influence. This is due to the fact that there is a significant drop in influence from both BBC and particularly CNN in this region as compared to the others."
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "jEe8UfpsTB3iZn1V",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much influence did the BBC lose in the Arabic region compared to the French region?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the BBC\u2019s influence in the French and Arabic language regions. \nI can begin by finding {the BBC's influence in the Arabic region}[0.963, 0.302, 1.012, 0.466] which is 15% and then find {the BBC's influence in the French region}[0.845, 0.398, 0.918, 0.565] which is 30%. Now I can calculate the difference, 30% - 15% = 15%.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 380
+    },
+    {
+        "id": "TcF9LPuyd8pOtmJK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you restructure the revenue data into tabular format with years as the columns and game titles as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I need to identify the labels from {the x-axis ticks}[[0.108, 0.899, 0.198, 0.928],[0.172, 0.899, 0.262, 0.928],[0.237, 0.899, 0.327, 0.928],[0.301, 0.899, 0.391, 0.928],[0.365, 0.899, 0.455, 0.928],[0.430, 0.899, 0.520, 0.928],[0.494, 0.899, 0.584, 0.928],[0.559, 0.899, 0.648, 0.928],[0.623, 0.899, 0.713, 0.928],[0.687, 0.899, 0.777, 0.928],[0.752, 0.899, 0.842, 0.928],[0.816, 0.899, 0.906, 0.928]] as the columns. Then, I extract the row labels which are the different {game titles from the legend}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]]. To populate the table, I will locate each data points for {Game1 line}[[0.153, 0.070, 0.861, 0.840]], {Game2 line}[[0.153, 0.100, 0.861, 0.870]], {Game3 line}[[0.153, 0.130, 0.861, 0.900]], {Game4 line}[[0.153, 0.160, 0.861, 0.930]], {Game5 line}[[0.153, 0.190, 0.861, 0.960]] and extract their respective values.\nBased on the above thought, the answer is:\nGames,2012-07,2012-08,2012-09,2012-10,2012-11,2012-12,2013-01,2013-02,2013-03,2013-04,2013-05,2013-06\nGame1,92.38,93.12,93.79,94.36,94.6,94.71,94.92,95.06,95.24,95.54,95.72,96.02\nGame2,92.5,93.5,94.0,91.5,93.0,94.0,93.5,94.5,91.0,95.0,95.0,94.5\nGame3,93.2,93.5,93.8,94.0,93.2,94.0,94.5,93.5,94.0,95.2,95.5,95.8\nGame4,94.0,93.5,94.5,92.5,94.0,95.5,94.5,93.5,92.5,91.0,93.5,95.0\nGame5,92.0,92.5,94.0,95.0,93.5,94.5,95.0,94.5,93.0,90.5,93.0,94.5"
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "Zlr8sKmwg1SsjMSv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the revealed trends in the line chart, which game's revenue exhibits the most dramatic change within a year? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the lines representing each game for notable changes. Observing {the line for Game1}[[0.153, 0.070, 0.861, 0.840]], it seems there is a gradual and almost constant increase over the period. The same applies to the line for {Game3}[[0.153, 0.130, 0.861, 0.900]]. The {Game2 line}[[0.153, 0.100, 0.861, 0.870]] also shows minimal variations while {Game5}[[0.153, 0.190, 0.861, 0.960]] has an apparent decrease in 2013-04 but it appears to return back to its initial level in 2013-06. The most dramatic change in revenue can be observed in the {Game4 line}[[0.153, 0.160, 0.861, 0.930]], which shows a significant increase in the revenue in 2013-03.\nBased on the above thought, the answer is:\nBased on the trend shown in the line chart, Game4 exhibits the most dramatic change in its revenue within a year. This is attributable to its significant increase in revenue in the year 2012-09 leading to a sharp peak."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "Hnez4XRXvV2lcfBk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you figure out the revenue change for Game3 from year 2012-07 to 2013-06?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the revenue change for Game3, I need to extract the initial and final data points from {the line for Game3}[[0.153, 0.130, 0.861, 0.900]]. The initial data point on 2012-07 is 93.2 and the final point on 2013-06 is 95.8. Thus, the revenue change for Game3 from 2012-07 to 2013-06 is 95.8 - 93.2 = 2.6 millions.\nBased on the above thought, the answer is:\n2.6."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ws2AWkDOxA4OLSR0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data related to the Budget-X RU model across different nations into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a table, I would need to identify the name of each nation and the corresponding values for the Budget-X RU model. I noticed that {the x-axis labels}<r>[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896]</r> represent the countries' names, and the values are annotated on {the corresponding bars}<r>[0.102, 0.470, 0.898, 0.696]</r>.\nBased on the above thought, the answer is:\nCountry,Budget-X RU Model\nRU,30\nCN,-20\nIN,-10\nUS,-25\nBR,-30"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "hlU7eWvbDQqjPL4T",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the performance of both the Budget-X RU and Neon-X CN models based on the bar graph. What is the key difference between the trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the values represented by {the bars in the Budget-X RU subplot}<r>[0.102, 0.470, 0.898, 0.696]</r> and {the bars in the Neon-X CN subplot}<r>[0.917, 0.470, 0.898, 0.696]</r>. Budget-X RU demonstrates a generally negative trend across all nations except Russia, indicating a decline. On the other hand, Neon-X CN shows mixed trends with marginal growth in China and Brazil while declining in the rest of the countries.\nBased on the above thought, the answer is:\nThe key difference between the two models is the overall performance across all nations. The Budget-X RU model shows a decline across most countries, with the exception of an increase in Russia. In contrast, the Neon-X CN model presents mixed results, with marginal growth in China and Brazil, suggesting it is better received in these areas. Overall, the Neon-X CN model has a less uniformly negative trend than the Budget-X RU."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "bVubRG3H8QJpeCdM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall performance in percentage of Budget-X RU across all nations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall performance, I sum up the values represented by {the bars in the first subplot}<r>[0.102, 0.470, 0.153, 0.564], [0.244, 0.470, 0.295, 0.686], [0.336, 0.470, 0.387, 0.637], [0.428, 0.470, 0.479, 0.708], [0.520, 0.470, 0.571, 0.730]</r>. The respective values are 30, -20, -10, -25, -30, so the overall performance is 30-20-10-25-30 = -55.\nBased on the above thought, the answer is:\n-55"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "8cMoQd15HYhGm4AM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tableau the data shown in line graph for both Science and Technology, and Arts and Literature Growth over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indeed, for creating a tabular format from this line chart. Initial step involves identifying {the years on x-axis}[[0.047, 0.893, 0.083, 0.921],[0.198, 0.893, 0.233, 0.921],[0.348, 0.893, 0.383, 0.921],[0.499, 0.893, 0.534, 0.921],[0.649, 0.893, 0.684, 0.921],[0.800, 0.893, 0.835, 0.921],[0.950, 0.893, 0.985, 0.921]], it would serve as the rows of my table. Then, I identify {the legend entries}[[0.116, 0.181, 0.283, 0.209],[0.116, 0.223, 0.247, 0.251]] as the two columns on my table. Afterwards, I track down the data points represented on {Science and Technology line graph points}[[0.065, 0.826],[0.215, 0.764],[0.366, 0.713],[0.516, 0.465],[0.667, 0.259],[0.817, 0.395],[0.967, 0.182]] and also {the Arts and Literature line graph points}[[0.065, 0.748],[0.215, 0.733],[0.366, 0.737],[0.516, 0.717],[0.667, 0.696],[0.817, 0.690],[0.967, 0.682]]. Then, I input the corresponding growth numbers at each point into the table.\nBased on the above thought, the answer is:\nYear,Science and Technology Growth,Arts and Literature Growth\n1800,-2.5,1.3\n1850,0.5,2.0\n1900,3.0,1.8\n1950,15.0,2.8\n2000,25.0,3.8\n2050,18.4,4.1\n2100,28.7,4.5"
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "1Yco16fWcciNn2wC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the growth trends in Science and Technology and Arts and Literature, which of them has a more steady growth and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area has a steadier growth, I need to look at their growth trend lines. By referring to {the line for Science and Technology}[0.065, 0.182, 0.967, 0.826], it can be seen that there is a significant increase from -2.5 to 28.7 within the years. However, inspecting the values reveals that the growth is not steady and has major fluctuations. \nOn the other hand, {the line for Arts and Literature}[0.065, 0.682, 0.967, 0.748] shows a slow but consistent increase from 1.3 to 4.5 over the years indicating a more steady growth trend.\nBased on the above thought, the answer is:\nArts and Literature displays a more steady growth. The growth trend line shows a slow but consistent increase over the years, indicating a steady progression with no significant fluctuations, unlike the rapid changes seen in Science and Technology growth."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "jUFILxzuoh5anhMZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total increase from 1800 to 2100 in growth for Science and Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in growth for Science and Technology, I have to locate {the 1800 data point}[0.065, 0.826], which shows a value of -2.5. Then, I locate the {2100 data point}[0.967, 0.182] demonstrating a value of 28.7. Thus, the total increase is calculated by 28.7 - (-2.5) = 31.2.\nBased on the above thought, the answer is:\n31.2."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "8L9H80Gm5DO0F6KR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you compile the AI performance in a structured table, considering the Categories as rows and Innovation Performances as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the data table, I will first make use of {the x-axis tick labels}[[0.015, 0.893, 0.166, 0.921], [0.882, 0.893, 0.985, 0.921]], which represent the rows or Categories, namely, \"Consistent_expansion\" and \"Erratic_decline\". The column headers correspond to the different kinds of Innovation Performances represented by {the legend texts}[[0.652, 0.109, 0.774, 0.137], [0.840, 0.109, 0.921, 0.137]], which are \"Regression Model\" and \"AI Assistant\". To populate the values in the table, I will identify {the line points}[[0.091, 0.245], [0.091, 0.403], [0.934, 0.817], [0.934, 0.332]] of the line plots for each innovation performance type at the respective categories.\nBased on the above thought, the answer is:\nCategories,Regression Model,AI Assistant\nConsistent Expansion,32.6,19.4\nErratic Decline,-15,25.3"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "omECPu7s7SUQrbJk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, discuss the performance of the AI Assistant and the Regression Model in dealing with different scenarios."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the performance contrast between an AI Assistant and a Regression Model through two scenarios or categories \u2013 Consistent Expansion and Erratic Decline. By observing the lines of each {AI Assistant}[[0.091, 0.403], [0.934, 0.332]] and {Regression Model}[[0.091, 0.245], [0.934, 0.817]], we can perceive differential performance outcomes. In the Consistent Expansion scenario, the Regression Model significantly outperforms the AI Assistant. However, this trend flips in the Erratic Decline scenario where, despite the overall performance drop, the AI Assistant proves to be superior to the Regression Model.\nBased on the above thought, the answer is:\nThe AI assistant and the regression model, both show varying performances based on the scenario at hand. While the regression model performs exceptionally well during consistent expansion, it struggles to adapt to erratic decline. The AI assistant, on the other hand, even though not as superior as the regression model during consistent expansion, manages to stay afloat more effectively during erratic decline. Thus, we can conclude that while the regression model is powerful during stable conditions, the AI assistant provides a more robust performance in varying conditions."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "EyspBWASaaRunZOF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the innovation performance of the Regression Model as compared to the AI Assistant in the Consistent Expansion category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to compare the innovation performances of the Regression Model and the AI Assistant in the Consistent Expansion category. We can locate the performances of the Regression Model from {the line points for Regression Model}[[0.091, 0.245], [0.934, 0.817]], and of the AI Assistant from {the line points for AI Assistant}[[0.091, 0.403], [0.934, 0.332]]. For the Consistent Expansion category, the innovation performance of the Regression Model is 32.6 while that of the AI Assistant is 19.4. Therefore, the difference in innovation performance is 32.6 - 19.4 = 13.2.\nBased on the above thought, the answer is:\n13.2."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "7kWNdZe5PQIPh0Op",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you compile the return rates and trend of Stock A and Stock B as presented in line chart into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I'll first define the columns from {the x-axis}<r>[0.492, 0.932, 0.552, 0.970]</r> which represents the 'Month'. I then identify {the legend}<r>[0.075, 0.097, 0.296, 0.148]</r> which gives us the stocks: Stock A and Stock B as headers for each row. After this, I will plot the corresponding rates of returns for each stock. These values will be extracted from the points on {the line for Stock A}<r>[0.068, 0.156, 0.976, 0.661]</r> and {the line for Stock B}<r>[0.068, 0.517, 0.976, 0.858]</r> across the 12 month period.\nBased on the above thought, the answer is:\nMonth, Stock A Return Rate (%), Stock B Return Rate (%)\n1, 2.3, -0.5\n2, 2.8, -0.7\n3, 3.5, 0.2\n4, 4.2, 0.6\n5, 5.0, 1.0\n6, 6.0, 1.5\n7, 6.8, 2.0\n8, 7.4, 2.4\n9, 7.9, 2.8\n10, 8.7, 3.4\n11, 9.4, 4.0\n12, 10.0, 4.5"
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "kIxNaFFY74drodyY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how does the performance of Stock A compare to Stock B over the 12 month period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the performance of the Stock A and Stock B, I will examine their return trends represented by {the line for Stock A}<r>[0.068, 0.156, 0.976, 0.661]</r> and {the line for Stock B}<r>[0.068, 0.517, 0.976, 0.858]</r>. The return trend for Stock A presents a steady increase, peaking in the 12th month. Meanwhile, Stock B's return trend starts off negatively, hitting the lowest at the second month before gradually improving to end in the positive spectrum within the 12 month period.\nBased on the above thought, the answer is:\nOverall, Stock A exhibited stronger performance over the 12 month period as it consistently generated positive returns, peaking at 10.0%. Though Stock B's performance improved and concluded in the positive spectum, it started off negatively and its highest return of 4.5% in the 12th month was lower than Stock A's lowest return. Thus, Stock A demonstrated a stronger and more stable performance than Stock B."
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "DFX4jZOhCEjhkLGL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what is the difference between the highest and lowest returns of Stock A?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this difference, I need to locate {the line points for Stock A}<r>[0.068, 0.156, 0.976, 0.661]</r> which represent its monthly return rates. The highest return rate is 10.0 and it corresponds to {Month 12}<r>[0.967, 0.893, 0.976, 0.878]</r>, while the lowest return rate of 2.3 corresponds to {Month 1}<r>[0.063, 0.893, 0.072, 0.921]</r>. Therefore, the difference between the maximum and minimum return rates is 10.0 - 2.3 = 7.7.\nBased on the above thought, the answer is:\n7.7"
+            }
+        ],
+        "id_raw": 385
+    },
+    {
+        "id": "92SvPpvtHxiVpMBi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented in the line chart into a table format with quarters as rows and 'Innovation Efforts' and 'Design Quality' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I scan across the x-axis of the chart to read the quarters from {the x-axis tick labels}[[0.083, 0.893, 0.131, 0.921], [0.204, 0.893, 0.253, 0.921], [0.326, 0.893, 0.374, 0.921], [0.448, 0.893, 0.496, 0.921], [0.570, 0.893, 0.618, 0.921], [0.692, 0.893, 0.740, 0.921], [0.814, 0.893, 0.862, 0.921]], which are our row labels. I then identify the values for 'Innovation Efforts' and 'Design Quality' by locating their line points [{the line for Innovation Efforts}[0.107, 0.188, 0.594, 0.566]], [{the line for Design Quality}[0.107, 0.844, 0.594, 0.845]], and recording the associated values.\nBased on the above thought, the answer is:\nQuarter,Innovation Efforts,Design Quality\nQ1, 1.0,  0.0\nQ2, 0.4, -1.5\nQ3, 2.0,  0.5\nQ4, 1.2,  1.0\nQ5, 1.7, 1.5\nQ6, 2.3, 2.0\nQ7, 2.5, 1.7"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "1CAiBfP2cEVl3hyC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect, 'Design Quality' or 'Innovation Efforts', exhibits a more stable trend over the seven quarters, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the variability of the line plots for both 'Design Quality' and 'Innovation Efforts'. Looking at {the 'Design Quality' line}[[0.107, 0.844, 0.594, 0.845]], it displays a general trend of stable growth with a single deviation or dip in the seventh quarter. However, the {line for 'Innovation Efforts'}[[0.107, 0.188, 0.594, 0.566]] fluctuates significantly across the seven quarters, showing a less predictable trend marked by abrupt increases and decreases.\nBased on the above thought, the answer is:\n'Design Quality' exhibits a more stable trend over the seven quarters. This conclusion is based on the generally consistent positive growth shown by the 'Design Quality' line plot, with only a single notable deviation in the seventh quarter. Conversely, 'Innovation Efforts' shows substantial variability and fluctuation, with several peaks and troughs throughout the seven quarters, indicating a less predictable and more unstable trend."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "AtgeK287LYxxiYpW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the 'Innovation Efforts' for the first 3 Quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of 'Innovation Efforts' for the first 3 Quarters, I need to locate {the line points for 'Innovation Efforts' for Q1, Q2, and Q3}[[0.107, 0.566], [0.204, 0.353], [0.326, 0.729]] and sum up the corresponding values: 1.0, 0.4, and 2.0. So, the sum is 1.0+0.4+2.0 = 3.4.\nBased on the above thought, the answer is:\n3.4."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "UTND7Avnkl9nJJwn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the population trend data for the two regions from the line chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, I need to identify the {x-axis labels} [[0.073, 0.919, 0.159, 0.942], [0.902, 0.919, 0.979, 0.942]] representing the years and consider them as row headers. Then, I read the {legend texts}[[0.763, 0.804, 0.951, 0.834], [0.763, 0.848, 0.967, 0.878]] as the column headers standing for different regions. The cell values are obtained by locating the corresponding {line points} [[0.116, 0.674], [0.940, 0.674], [0.116, 0.483], [0.940, 0.865]] for each region across the years and extracting the values associated with those points.\nBased on the above thought, the answer is:\nYear,Region 1,Region 2\n2000,100,130\n2010,118,105.5"
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "lkTq513B7o0tBMy5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which region experienced a downward trend in population between 2000 and 2010, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the population trend in each region, I will first look at Region 1 by observing the {line for Region 1}[0.116, 0.291, 0.940, 0.865]. The line moves from the point at 100 in 2000 to the point at 118 in 2010, showing an upward trend.\nNext, I examine {the line for Region 2}[0.116, 0.674, 0.940, 0.674]. The line decreases from its starting point at 130 in 2000 to its end point at 105.5 in 2010, indicating a downward trend.\nBased on the above thought, the answer is:\nBased on the line chart, Region 2 experienced a downward trend in population between 2000 and 2010. This conclusion is drawn from the fact that the line representing Region 2 starts at a higher value (130 in 2000) and ends with a lower one (105.5 in 2010), indicating a decrease in the population count."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "TUHTGgXC3v35am5M",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in population from 2000 to 2010 in Region 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in population in Region 1, I need to locate the {line points for Region 1}[[0.116, 0.674], [0.940, 0.674]] and extract the corresponding population figures: 100 for 2000 and 118 for 2010. The percentage increase from 2000 to 2010 is therefore calculated as 100 * (118 - 100) / 100 = 18 percent.\nBased on the above thought, the answer is:\n18%."
+            }
+        ],
+        "id_raw": 387
+    },
+    {
+        "id": "xaU4o4BN6bcxm4aJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, could you please arrange the Emission Reduction and Emission Increase data into a table format with Years as rows and Emission Type as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the line chart into a table, I will identify {the x-axis}<[0.510, 0.932, 0.551, 0.970]> as the Years row label. Then, I locate {the chart legend}<[0.602, 0.097, 0.978, 0.148]> that provides Emission Reduction and Emission Increase as the column identifiers.\nNext, I would find the values of Emission Reduction and Emission Increase for these years from {the line points of Emission Reduction}<[0.152, 0.304, 0.909, 0.598]> and {the line points of Emission Increase}<[0.152, 0.493, 0.909, 0.659]> respectively and put them into the corresponding cells.\nBased on the above thought, the answer is:\nYear,Emission Reduction,Emission Increase\n2011,5.5,2.1\n2012,6.2,2.4\n2013,7.8,3.1\n2014,9.6,4.5\n2015,12.4,6.5\n2016,15.3,7.6\n2017,18.2,8.8\n2018,19.2,9.8\n2019,20.3,10.4\n2020,21.1,10.9\n2021,21.8,11.3"
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "xChexwGvJm5dgEhp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of environmental protection, what trend does the line chart display between the period of 2011 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the trend, I would need to look at both the Emission Reduction and Emission Increase lines. The {Emission Reduction line}<[0.152, 0.304, 0.909, 0.598]> shows a consistent upward trend, increasing from 5.5 to 21.8 million tons from 2011 to 2021. The {Emission Increase line}<[0.152, 0.493, 0.909, 0.659]> also exhibits an upward trend, but the increase is less steep, ranging from 2.1 to 11.3 million tons. This indicates that while emissions have increased over the years, the emission reduction efforts have been more effective.\nBased on the above thought, the answer is:\nThe line chart shows a dominant upward trend in terms of both Emission Reduction and Emission Increase. However, the Emission Reduction line indicates a sharp elevation compared to Emission Increase line. This can be interpreted as an evidence of increasingly effective environmental protection efforts which have led to a greater reduction in emissions even as gross emissions have also grown over the years from 2011 to 2021."
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "XQWbNxfYg05s9W8U",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Emission Reduction increase from 2011 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in the Emission Reduction from 2011 to 2021, I will find the values for these years. The Emission Reduction value for 2011 can be obtained from {the first Emission Reduction point}<[0.152, 0.598]>, which is 5.5. For 2021, the value can be found from {the last Emission Reduction point}<[0.909, 0.304]>, which is 21.8. Therefore, the increase can be calculated as 21.8 - 5.5 = 16.3.\nBased on the above thought, the answer is:\n16.3"
+            }
+        ],
+        "id_raw": 388
+    },
+    {
+        "id": "wvRL6gv89Mb93MOw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data about Grunge and Bubblegum Pop popularity over decades into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I would first use {the x-axis tick labels}[[0.211, 0.059, 0.297, 0.089], [0.382, 0.059, 0.464, 0.089], [0.556, 0.059, 0.646, 0.089], [0.738, 0.059, 0.828, 0.089]] as the column headers, which represent the decades. Next, I would identify {the legend texts}[0.923, 0.842, 0.976, 0.867],[0.923, 0.767, 0.970, 0.793] as the row labels representing the different fashion trends. I then locate {the line points for Grunge}[[0.211, 0.482], [0.382, 0.368], [0.556, 0.273],[0.738, 0.184]] and {the line points for Bubblegum Pop}[[0.211, 0.177], [0.382, 0.361],[0.556, 0.474], [0.738, 0.269]] to fill in the values for each corresponding trend and decade.\nBased on the above thought, the answer is:\nDecades,Grunge,Bubblegum Pop\n1990s,15,4\n2000s,11,10\n2010s,6,14\n2020s,2,7"
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "F81QRQsgbQ1lm5AN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per data shown in the chart, which fashion trend shows more variability over the decades and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Broadly, the variability of a trend can be inferred from the fluctuations in its line plot. Taking a closer look at {the line for Grunge}[[0.211, 0.482, 0.738, 0.184]], it starts with a higher popularity value at 15 in the 1990s, then decreases to 11 in the 2000s, further decreases to 6 in the 2010s, and falls to 2 in the 2020s. The popularity clearly shows a steady declining trend, without significant fluctuations.\nOn the other hand, the line plot for {Bubblegum Pop}[[0.211, 0.177, 0.738, 0.269]] starts at a low popularity value of 4 in the 1990s, jumps to a higher popularity of 10 in the 2000s, peaks to 14 in the 2010s, and then sees a sharp decrease to 7 in the 2020s. This indicates a more variable trend as the line plot rises dramatically and then falls sharply.\nBased on the above thought, the answer is:\nThe Bubblegum Pop trend shows more variability over the decades. The line plot has a significant rise between the 1990s and 2010s, followed by a sharp fall in the 2020s, showing more considerable fluctuations when compared to the Grunge trend which consistently declines over time."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "N4dcaXjwSR6d37tz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of Grunge popularity over all decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total popularity of Grunge over all decades, I need to locate {the line points for Grunge}[[0.211, 0.482], [0.382, 0.368], [0.556, 0.273],[0.738, 0.184]] and sum up the corresponding values. This would be the sum of 15, 11, 6, and 2.\nBased on the above thought, the answer is:\n34"
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "IFByPw7JmgQZrxev",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart into a structured table where Energy Sources are rows and Years are the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To begin the transformation, I\u2019d note the x-axis labels, {Years}<r>[0.054, 0.893, 0.089, 0.921]</r>, on the chart to represent the column headers - 2030 and 2040. Then, I would identify the legend entries, {Energy Sources}<r>[0.867, 0.378, 0.951, 0.406]</r>, as the row labels - these represent different types of energy sources. And finally, I\u2019d extract the respective {data points}<r>[0.071, 0.714]</r> for each energy source at the defined years.\nBased on the above thought, the answer is:\nEnergy Source,2030,2040\nHydropower,1.6,1.7\nSolar Power,3.2,3.9\nGeothermal,1.4,1.2\nBioenergy,2.3,2.0\nWind Energy,2.8,3.6"
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "w7bNFcjsdJDAvtoC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the growth trends for different energy sources, which source shows the highest growth from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must assess the growth rates represented by the slopes of the lines for each energy source. Looking at the lines for {Hydropower}<r>[0.071, 0.688, 0.968, 0.714]</r>, {Solar Power}<r>[0.071, 0.105, 0.968, 0.291]</r>, {Geothermal}<r>[0.071, 0.767, 0.968, 0.820]</r>, {Bioenergy}<r>[0.071, 0.529, 0.968, 0.609]</r> and {Wind Energy}<r>[0.071, 0.185, 0.968, 0.397]</r>, I observe that Solar Power and Wind Energy show an upward trend or growth whereas the others show a flat or downward trend. Among Solar Power and Wind Energy, Solar Power has a more steeply inclined line indicating a higher rate of growth.\nBased on the above thought, the answer is:\nSolar Power shows the highest growth from 2030 to 2040, as indicated by its steeply inclined line representing its upward trend over time. This indicates a higher rate of growth for Solar Power compared to the other energy sources."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "FpfvnfJWXAkFyrgQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the performance of Solar Power improve from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I will identify the points for {Solar Power}<r>[0.071, 0.291]</r> in 2030 and 2040. The measure for Solar Power in 2030 is 3.2, whereas in 2040 it is 3.9. The difference is therefore 3.9 - 3.2 = 0.7.\nBased on the above thought, the answer is:\n0.7"
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "XyfKSdrWwpn8f6Ox",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart into a table format with Years as columns and Industries as rows? Please include the respective performance points."
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, first, I take {the x-axis tick labels}[[0.104, 0.763, 0.171, 0.851], [0.239, 0.763, 0.301, 0.860], [0.368, 0.763, 0.433, 0.860], [0.506, 0.763, 0.560, 0.860], [0.637, 0.763, 0.699, 0.860], [0.771, 0.763, 0.829, 0.860]] as the column headers, representing the Years. {The legend texts}[[0.752, 0.125, 0.815, 0.175], [0.828, 0.128, 0.880, 0.178]] are taken as the row labels, representing the different Industries. For each industry, I locate {the line points}[[0.104, 0.064], [0.249, 0.096], [0.377, 0.098], [0.516, 0.141], [0.647, 0.289], [0.771, 0.374]] and extract the values annotated to fill the table.\nBased on the above thought, the answer is:\nIndustry,2015,2016,2017,2018,2019,2020\nHotel Industry,70,65,60,55,45, 38\nTourism Industry,45,55,50,55,35, 20"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "ziAQfm0kzQp8GbBN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart and the trends in industries, which industry was most affected by the events of 2020 and to what extent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the trends of both industries. Looking at {the line for Hotel Industry}[[0.104, 0.891, 0.771, 0.639]], there is a consistent decline from 70 points to 38 points from the year 2015 to 2020. Now, if we look at {the line for Tourism Industry}[[0.104, 0.771, 0.771, 0.419]], there are various fluctuations in the initial years but a drastic drop in 2020 to 20 points. Given the context and the substantial performance point drops, it can be inferred that the drop is mostly due to the COVID-19 pandemic.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, the Tourism Industry was most affected by the events of 2020. The chart shows a substantial dip for the Tourism Industry, falling from 35 points in 2019 to 20 points in 2020, a reduction of 15 points. This is a sharper decrease compared to the Hotel Industry that saw a 7-point decrease in the same period. The drastic decline in the Tourism Industry is likely due to the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "JY2b3n4tzaCeVaae",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decline in points for the Hotel Industry from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total decline in points for the Hotel Industry, I locate {the line points for the Hotel Industry}[[0.104, 0.134], [0.249, 0.106], [0.377, 0.086], [0.516, 0.061], [0.647, 0.034], [0.771, 0.008]] and extract the corresponding values: 70, 65, 60, 55, 45, 38. The points in 2015 is 70 and in 2020 is 38. The total decline is then 70 - 38 = 32.\nBased on the above thought, the answer is:\n32."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "pnZpWK2bp9haCA1V",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the data from the line chart, showing the performance trend for the two types of medical technology over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data, I first identify the healthcare condition values from the x-axis, represented by {the x-axis tick labels}[[0.058, 0.893, 0.080, 0.921], [0.099, 0.893, 0.121, 0.921], ..., [0.422, 0.893, 0.444, 0.921]], which will serve as the rows in the table. Then I identify the performance metrics for the two technologies, reflected in {the legend texts for Physician Technology Performance and AI Technology Performance}[[0.485, 0.108, 0.725, 0.136], [0.791, 0.108, 0.979, 0.136]] which serve as column headers. The corresponding points for each technology line {Physician Technology line points}[[0.069, 0.643], [0.106, 0.607], ..., [0.433, 0.246]] and {AI Technology line points}[[0.069, 0.715], [0.106, 0.661], ..., [0.433, 0.607]], are translated into performance values for each healthcare condition.\nBased on the above thought, the answer is:\nHealthcare Condition, Physician Technology Performance, AI Technology Performance\n0.1, 1.3, 0.9\n0.19, 1.5, 1.2\n0.28, 1.6, 0.9\n0.37, 2.0, 1.0\n0.46, 2.1, 1.1\n0.55, 2.2, 1.3\n0.64, 2.4, 1.0\n0.73, 2.9, 1.2\n0.82, 3.0, 0.9\n0.91, 3.2, 1.0\n1, 3.5, 1.5"
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "cEyCX97XvhJCxKZk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends shown in the line chart, which technology outperformed the other at the peak phase of Healthcare Condition, and what can be inferred about their performance in different conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the performance metrics of the two technologies at the peak phase of Healthcare Condition. Here, the peak phase is represented by the maximum Healthcare Condition of 1. At this stage, {the AI Technology Performance} [0.433, 0.607] and {the Physician Technology Performance} [0.433, 0.246] are respectively 1.5 and 3.5. As a result, I can see that Physician Technology outperformed AI Technology. From the overall trends of {the Physician Technology line}[0.069, 0.246, 0.433, 0.643] and {the AI Technology line}[0.069, 0.607, 0.433, 0.715] it can also be inferred that both technologies performance increase as the Healthcare Condition improves, but the rate of increase for the Physician Technology is considerably more significant.\nBased on the above thought, the answer is:\nAt the peak phase of Healthcare Condition, the Physician Technology outperformed the AI Technology as it scored a significantly higher performance metric. This suggests that the effectiveness of Physician Technology increases more substantially than AI Technology as the Healthcare Condition improves from worst to best, implying that the Physician Technology might be more sensitive or adaptive to the changes in Healthcare Conditions."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "bfh2FAp0ebISvAeu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the AI Technology's performance increase from the worst healthcare condition (0.1) to the best condition (1)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the AI Technology Performance values at the worst and best healthcare conditions. The initial value is obtained from {the first point of the AI Technology line} [0.069, 0.715], which corresponds to an adoption rate of 0.9. The final value is obtained from {the last point of the AI Technology line} [0.433, 0.607], which corresponds to an adoption rate of 1.5. Therefore, the increase in performance is 1.5 - 0.9 = 0.6.\nBased on the above thought, the answer is:\n0.6."
+            }
+        ],
+        "id_raw": 393
+    },
+    {
+        "id": "dzQTOWMn6nxc92VD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from the chart data showing the CSR Index and Sustainable Development Index for each year from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart data, let's start by identifying {the line for CSR Index}[0.076, 0.467, 0.967, 0.634] and {the line for Sustainable Development Index}[0.076, 0.395, 0.967, 0.586]. The x-axes of these lines represent the years which are shown as {the X-axis labels}[[0.058, 0.893, 0.093, 0.921], [0.157, 0.893, 0.192, 0.921], [0.256, 0.893, 0.291, 0.921], [0.355, 0.893, 0.390, 0.921], [0.454, 0.893, 0.490, 0.921], [0.553, 0.893, 0.589, 0.921], [0.653, 0.893, 0.688, 0.921], [0.752, 0.893, 0.787, 0.921], [0.851, 0.893, 0.886, 0.921], [0.950, 0.893, 0.985, 0.921]]. The height of each point on these lines represents the values of CSR index and Sustainable Development Index for each year. The values can be calculated by referring to its corresponding position relative to {the y-axis labels}[[0.057, 0.862, 0.066, 0.890], [0.048, 0.767, 0.066, 0.795], [0.048, 0.671, 0.066, 0.699], [0.048, 0.575, 0.066, 0.603], [0.048, 0.480, 0.066, 0.508], [0.040, 0.384, 0.066, 0.412], [0.040, 0.288, 0.066, 0.316], [0.040, 0.193, 0.066, 0.221]].\nBased on the above thought, the answer is:\nYear,CSR Index,Sustainable Development Index\n2010,50,60\n2011,53,63\n2012,60,70\n2013,70,80\n2014,68,78\n2015,73,85\n2016,75,88\n2017,78,90\n2018,80,95\n2019,85,100"
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "c07qEYkLuCRALLE6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in the CSR Index from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in the CSR Index from 2010 to 2019, I must identify the CSR Index values for the two years. The CSR Index value for 2010 is 50, indicated by {the point on the CSR Index line corresponding to 2010}[0.076, 0.634]. For 2019, the CSR Index value is 85, as shown by {the point on the CSR Index line corresponding to 2019}[0.967, 0.467]. The percentage increase is calculated as the difference between the two values divided by the initial value, multiplied by 100.\nBased on the above thought, the answer is:\n70%"
+            }
+        ],
+        "id_raw": 394
+    },
+    {
+        "id": "oziphNHiRT1YrF9n",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trend for each financial indicator given by the line chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To morph the line graph data into a table, I'll commence with the x-axis figures identified in {the x-axis tick labels}[[0.060,0.893,0.069,0.921],[0.143,0.893,0.152,0.921],..., [0.967,0.893,0.985,0.921]], which act as column headers and represent the months. I then examine {the legend texts}[{'label': 'Stock Price Trend', 'text_coordinates': [0.617, 0.108, 0.736, 0.136]},{'label': 'Commodity Price Trend', 'text_coordinates': [0.803, 0.108, 0.964, 0.136]}] that do the role of row descriptors, i.e., different financial indicators. Now, to get the values, I need to find {the line points for Stock Price Trend}[[0.065, 0.766], [0.148, 0.731], ..., [0.976, 0.595]] and {the line points for Commodity Price Trend}[[0.065, 0.459], [0.148, 0.406], ..., [0.976, 0.274]] for each parameter over various months and extract the annotated values.\nBased on the above thought, the answer is:\nDate,Stock Price Trend, Commodity Price Trend\n1,30.42,45.28\n2,32.12,47.85\n3,34.87,50.19\n4,34.91,49.59\n5,31.45,47.72\n6,32.97,46.83\n7,35.11,48.42\n8,35.51,48.42\n9,36.15,49.94\n10,37.56,51.48\n11,38.59,50.6\n12,38.7,54.25"
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "IKdifxITxgxl2uKS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the overall trends observed in Stock Price and Commodity Price over the 12 months according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the overall trends, I need to trace {the line for the Stock Price Trend}[0.065, 0.595, 0.976, 0.766] and the {line for the Commodity Price Trend}[0.065, 0.274, 0.976, 0.459]. The Stock Price Trend starts at a value of 30.42 at month 1, sees minor fluctuations in between, and ends at 38.7 in month 12. On the other hand, the Commodity Price Trend starts at a value of 45.28 at month 1 and ends higher at 54.25 in month 12, also with fluctuations throughout the year.\nBased on the above thought, the answer is:\nIn analyzing the line chart, both Stock Price Trend and Commodity Price Trend show an overall rising trend over the 12 months. Although there are fluctuations in between the months, the end value for both trends is higher than their starting value at the beginning of the year. This suggests that both stock prices and commodity prices generally increased within this period, despite intermittent dips and peaks. This could potentially indicate a bullish market backdrop during this period."
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "PJQyfdl5pArY5rO8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the commodity price trend increase from month 1 to month 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "For calculating the change in the commodity price trend from month 1 to month 12, I'll first locate {the starting point}[0.065, 0.459] and {the ending point}[0.976, 0.274] of the commodity price trend line. The corresponding values are then extracted as 45.28 and 54.25 respectively. The increase is then obtained by subtracting the starting value from the ending value, i.e., 54.25 - 45.28.\nBased on the above thought, the answer is:\n8.97"
+            }
+        ],
+        "id_raw": 395
+    },
+    {
+        "id": "zysf53zFTrutS8lx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data of Year versus Training Hours and Salary into a table format with columns being Year, Training Hours, and Salary?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.048, 0.893, 0.075, 0.921], [0.134, 0.893, 0.169, 0.921], [0.229, 0.893, 0.264, 0.921], [0.323, 0.893, 0.358, 0.921], [0.418, 0.893, 0.453, 0.921], [0.512, 0.893, 0.547, 0.921], [0.607, 0.893, 0.642, 0.921], [0.701, 0.893, 0.736, 0.921], [0.796, 0.893, 0.831, 0.921], [0.890, 0.893, 0.925, 0.921]], which represent the Years. Now, I identify as the Training Hours and Salary line points {Training Hours}[[0.105, 0.850], [0.152, 0.814], ..., [0.860, 0.215]] and {Salary} [[0.105, 0.574], [0.152, 0.544], ..., [0.860, 0.215]] respectively corresponding to each year.  Using this information, a table can be constructed.\nBased on the above thought, the answer is:\nYear,Training Hours,Salary\n2005,2,50000\n2006,5,55000\n2007,7,60000\n2008,10,55000\n2009,12,50000\n2010,10,55000\n2011,15,70000\n2012,17,70000\n2013,20,75000\n2014,15,70000\n2015,25,80000\n2016,30,85000\n2017,30,90000\n2018,40,95000\n2019,50,100000\n2020,45,105000\n2021,55,110000"
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "kokEjVR8R617HMOZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the line chart about the relationship between Training Hours and Salary?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line plots for both {Training Hours}[0.105, 0.215, 0.860, 0.850] and {Salary}[0.105, 0.215, 0.860, 0.574], I notice that as Training Hours generally increase, the Salary also generally increase. The trend shows a positive correlation between the Training Hours and Salary indicating that as the number of Training Hours increase, the Salary also tends to increase. However, it is also noted that in the year {2010}[0.341, 0.754] where the training hours was the same as in 2008, the Salary was lower. It shows that although a general positive trend exists, there might be other factors at play affecting the exact Salary.\nBased on the above thought, the answer is:\nFrom the line chart, it can be inferred that there is a general positive correlation between Training Hours and Salary. As the number of Training Hours increases, the Salary also tends to increase, indicating that individuals who spend more hours in training potentially earn a higher salary. However, other factors may also be influencing the Salary as seen in the year 2010 when the Training Hours was the same as in 2008, but the Salary was lower. This observation suggests that while Training Hours is likely a significant factor, it is not the only determining factor of Salary."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "4sdttzulAfQGKeQo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the year when the maximum number of Training Hours was recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year when the maximum number of Training Hours was recorded, I need to identify {the highest point on the line representing Training Hours}[0.860, 0.215] corresponding to 55 hours. From its position on the x-axis, the label corresponding to this position can be found from the x-tick labels, which is {the year 2021}[0.890, 0.893, 0.925, 0.921].\nBased on the above thought, the answer is:\n2021."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "qmwFcpZXxIxTd38O",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data represented in the chart into a table with each activity and respective number of hours spent per week?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the chart data, I firstly recognize {the labels on the y-axis}[[0.25,0.1,0.3,0.2], [0.25,0.2,0.3,0.3], [0.25,0.3,0.3,0.4], [0.25,0.4,0.3,0.5], [0.25,0.5,0.3,0.6], [0.25,0.6,0.3,0.7], [0.25,0.7,0.3,0.8], [0.25,0.8,0.3,0.9], [0.25,0.9,0.3,1.0]], which represent different activities. The x-values of these points are the values of hours spent on these activities respectively, which I need to {extract from the points on the chart line}[[0.35,0.1], [0.4,0.2], [0.45,0.3], [0.5,0.4], [0.55,0.5], [0.6,0.6], [0.65,0.7], [0.7,0.8], [0.75,0.9]].\nBased on the above thought, the answer is:\nActivity, Hours Per Week\nReading, 2\nPainting, 3\nPhotography, 4\nCooking, 5\nGaming, 5\nSinging, 6 \nYoga, 6\nCycling, 8\nVolunteering, 9"
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "ZTxzSpQUu1g2LCwL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the trends observed in the time spent on activities during the week based on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "By tracking {the line on the graph}[[0.35,0.1], [0.4,0.2], [0.45,0.3], [0.5,0.4], [0.55,0.5], [0.6,0.6], [0.65,0.7], [0.7,0.8], [0.75,0.9]], I can observe that there's a general increase in the hours spent on the activities over the week. Starting from the lowest point at Reading (2 hours), there is a steady rise in the time spent up to Cooking (5 hours). However, there is a plateau at the point corresponding to {Gaming}[[0.55,0.5]] where the hours spent remain the same. The increase resumes from Singing with a plateau at {Yoga}[[0.65,0.7]]. Towards the end, the graph picks up a sharp spike at {Volunteering}[[0.75,0.9]], showing significantly more time spent compared to other activities.\nBased on the above thought, the answer is:\nThe line graph demonstrates a general trend of increasing time spent on activities over a week. Initially, there is a steady climb from Reading to Cooking. Afterwards, a plateau is observed at Gaming, indicating a stagnant period where the time spent does not increase. The graph resumes its growth trend at Singing, hitting another plateau at Yoga before displaying a sharp upward spike at Volunteering. This trend suggests that, despite some fluctuations, there is a general increase in activity levels as the week progresses, with a significant surge in volunteering."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "GVO1fWQ4icIRFvT8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_20_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more time is spent on volunteering than gaming during the week?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the hours spent on volunteering and gaming, I need to locate {the point on the line graph corresponding to volunteering}[[0.75,0.9]] and extract its x-value, which represents hours spent on volunteering. Similarly, I need to find {the point corresponding to gaming}[[0.55,0.5]] and extract its x-value. The difference between these two values gives the time spent on volunteering more than gaming.\nBased on the above thought, the answer is:\n4 hours."
+            }
+        ],
+        "id_raw": 399
+    },
+    {
+        "id": "ekIRyXMDabojYGAQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn a structured table format, can you represent the information about fund allocation for different events over the years 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create a structured table from the line chart, I first identify {the x-axis tick labels}[[0.172, 0.698, 0.294, 0.861], [0.291, 0.698, 0.474, 0.942], [0.460, 0.698, 0.604, 0.890], [0.630, 0.698, 0.733, 0.836], [0.755, 0.698, 0.908, 0.904]], which represent different events. The data for funds allocated in 2019 and 2020 will serve as rows with values obtained from {the line points for 2019}[[0.233, 0.293], [0.382, 0.423], [0.532, 0.242], [0.682, 0.371], [0.832, 0.242]] and {the line points for 2020}[[0.233, 0.553], [0.382, 0.656], [0.532, 0.190], [0.682, 0.423], [0.832, 0.164]] respectively.\nBased on the above thought, the answer is:\nEvent, Fund Allocation in 2019, Fund Allocation in 2020\nPark Maintenance,0.80,0.60\nInfrastructure Development,0.70,0.52\nCommunity Outreach,0.84,0.88\nUrban Farming,0.74,0.70\nDigital Implementation,0.84,0.90"
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "dXfvOQQhAUqBGWYY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart depicting fund allocation for various events from 2019 to 2020, which event saw a significant increase in fund allocation over this period?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify the event that saw a significant increase in fund allocation, I have to examine the changes in line positions for various events between 2019 and 2020. Inspecting {the line for Fund Allocation in 2019} [0.233, 0.242, 0.832, 0.423] and {the line for Fund Allocation in 2020} [0.233, 0.164, 0.832, 0.656], it is evident that {Community Outreach}'s line marker rises significantly from [0.532, 0.242] in 2019 to [0.532, 0.190] in 2020, indicating an increase in fund allocation.\nBased on the above thought, the answer is:\nThe event \"Community Outreach\" saw a significant increase in fund allocation from 2019 to 2020, as interpreted from the noticeable upward shift in its line marker in the chart for the given time period."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "yGDQ0jTiJIpS1YN8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage change in fund allocation from 2019 to 2020 for Infrastructure Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage change in fund allocation for Infrastructure Development, I locate {the point for Infrastructure Development in 2019}[0.382, 0.423] which corresponds to a value of 0.7. Next, I find {the point for Infrastructure Development in 2020}[0.382, 0.656], corresponding to a value of 0.52. The percentage change is therefore calculated as ((0.52-0.7)/0.7)*100%.\nBased on the above thought, the answer is:\n-25.71%."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "59JnNqkzP0GUKr0F",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the line plot into a structured table format with Future Years as columns and each Growth type as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line plot, I first identify {the x-axis tick labels}[[0.143, 0.899, 0.171, 0.928], [0.276, 0.899, 0.303, 0.928], [0.408, 0.899, 0.435, 0.928], [0.540, 0.899, 0.567, 0.928], [0.672, 0.899, 0.700, 0.928], [0.805, 0.899, 0.832, 0.928], [0.937, 0.899, 0.964, 0.928]] to represent the column headers as Future Years. Next, I look at {the legend items}[[0.184, 0.057, 0.327, 0.086], [0.184, 0.101, 0.414, 0.130], [0.184, 0.145, 0.364, 0.174]] which represent the row headers or different Growth types. Then, to populate the table values, I locate the line points for each Growth type at respective years and extract the values.\nBased on the above thought, the answer is:\nGrowing Type,2022,2023,2024,2025,2026,2027,2028\nSales Growth,0.1,0.15,0.35,0.45,0.2,0.05,0.12\nMarket Share Growth,0.05,0.02,0.03,0.01,0.04,0.3,0.2\nRevenue Growth,0.5,0.65,0.8,0.9,1.0,0.7,0.85"
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "z0gInDe9jimnZ9xU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the trends in the line chart, can you identify any significant changes between the years 2022 to 2028 in terms of growth type?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart shows the growth trends for Sales Growth, Market Share Growth, and Revenue Growth over the period from 2022 to 2028. Looking at {the line for Sales Growth}[[0.143, 0.498, 0.937, 0.809]], I observe an increase up to 2025 and then a decline to a low point in 2027 before slightly increasing again in 2028.\nFor {Market Share Growth}[[0.143, 0.614, 0.937, 0.840]], there are relative stagnancy until 2027 where it rises sharply from 0.04 to 0.3 suggesting a significant increase in market share growth in 2027. The trend dips again in 2028.\nFinally, looking at {the line for Revenue Growth}[[0.143, 0.070, 0.937, 0.459]], the trend shows an increase from 2022 to 2026, reaching its peak in 2026. However, it decreases dramatically in 2027 and slightly rises in 2028.\nBased on the above thought, the answer is:\nYes, the line chart shows several significant changes in growth types between 2022 and 2028. The Sales Growth increases until 2025, then declines to a low in 2027 before slightly recovering in 2028. The Market Share Growth is relatively steady until a sharp increase in 2027, followed by a decline in 2028. The Revenue Growth increases steadily until 2026, reaching its peak, and then drops sharply in 2027, with a slight increase in 2028. This suggests a volatile period for the company in terms of revenue around 2026 - 2027."
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "vx9mmd2mEylYWD7r",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total incremental increase in the Revenue Growth from the year 2022 to 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total incremental increase in Revenue Growth from 2022 to 2028, I need to locate {the line points of Revenue Growth}[[0.143, 0.459], [0.276, 0.342], [0.408, 0.225], [0.540, 0.148], [0.672, 0.070], [0.805, 0.303], [0.937, 0.186]]. The corresponding values are 0.5, 0.65, 0.8, 0.9, 1.0, 0.7, 0.85. The incremental increase in each year is calculated as the difference between the value of the current year and the previous year. The total incremental increase is the sum of all the incremental increases.\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 401
+    },
+    {
+        "id": "Jvf2T5B7jIvrFIiv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the line chart data into a organized tabular format with Years as columns and the three different rates as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll start by taking the {x-axis tick labels}<r>[0.122, 0.902, 0.183, 0.934], [0.209, 0.902, 0.271, 0.934], [0.297, 0.902, 0.359, 0.934], [0.385, 0.902, 0.446, 0.934], [0.472, 0.902, 0.534, 0.934], [0.560, 0.902, 0.622, 0.934], [0.648, 0.902, 0.709, 0.934], [0.736, 0.902, 0.797, 0.934], [0.823, 0.902, 0.885, 0.934], [0.911, 0.902, 0.973, 0.934]</r> as column headers which represents the different years. Next, I use {the sublegend texts}<r>[0.177, 0.058, 0.294, 0.082], [0.177, 0.093, 0.356, 0.117], [0.177, 0.128, 0.363, 0.151]</r> to form the row headers which represent the three different rates (Inflation Rate, Unemployment Rate, Stock Market Returns). The corresponding values for each year and rate are obtained from {the points of the lines}<r>[0.152, 0.367], [0.240, 0.382], [0.328, 0.373], [0.415, 0.368], [0.503, 0.360], [0.591, 0.362], [0.679, 0.366], [0.766, 0.355], [0.854, 0.406], [0.942, 0.382]</r>, <r>[0.152, 0.344], [0.240, 0.331], [0.328, 0.328], [0.415, 0.335], [0.503, 0.339], [0.591, 0.345], [0.679, 0.345], [0.766, 0.331], [0.854, 0.288], [0.942, 0.285]</r>, <r>[0.152, 0.546], [0.240, 0.669], [0.328, 0.053], [0.415, 0.269], [0.503, 0.342], [0.591, 0.210], [0.679, 0.335], [0.766, 0.850], [0.854, 0.080], [0.942, 0.218]</r>.\nBased on the above thought, the answer is:\nRates, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010\nInflation Rate, 2.8, 1.6, 2.3, 2.7, 3.4, 3.2, 2.9, 3.8, -0.4, 1.6\nUnemployment Rate, 4.7, 5.8, 6.0, 5.5, 5.1, 4.6, 4.6, 5.8, 9.3, 9.6\nStock Market Returns, -11.9, -22.1, 28.7, 10.9, 4.9, 15.8, 5.5, -37.0, 26.5, 15.1"
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "zUojVSvfXd88bdWu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nNoting the information from the line chart, can you provide an overall interpretation of how the three rates changed over the decade from 2001 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assessing the change in rates over time needs a detailed analysis of the trends displayed in the line chart. The {line for Inflation Rate}<r>[0.152, 0.355, 0.942, 0.406]</r> shows that the Inflation Rate has varied slightly over the years, with a dip in 2002, a peak in 2008, and a negative rate in 2009. The {line for Unemployment Rate}<r>[0.152, 0.285, 0.942, 0.345]</r> indicates that the Unemployment Rate has been relatively stable till 2007, after which it increased significantly reaching the highest in 2010. Lastly, the {line for Stock Market Returns}<r>[0.152, 0.053, 0.942, 0.850]</r> clearly shows extreme volatility, with the returns hitting rock bottom in 2008 and then recovering by 2010.\nBased on the above thought, the answer is:\nOver the decade from 2001 to 2010, the Inflation Rate remained mostly steady with a slight variability, hitting a negative in 2009. The Unemployment Rate was stable until 2007, then rose sharply during the following years, peaking at 9.6 in 2010. The Stock Market Returns were highly volatile, experiencing dramatic drops in 2002 and 2008, with noticeable recoveries in 2003 and 2009. The overall economic climate was thus marked by a degree of uncertainty, with periods of growth offset by notable downturns, especially in terms of stock market performance."
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "Ly7QY1LHBrfUpjVL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value of Stock Market Returns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the maximum value of Stock Market Returns, I need to identify and extract the highest point on {the line for Stock Market Returns}<r>[0.152, 0.053, 0.942, 0.850]</r>. The corresponding highest point is located at {the point of 2003}<r>[0.328, 0.053]</r> with a value 28.7.\nBased on the above thought, the answer is:\n28.7"
+            }
+        ],
+        "id_raw": 402
+    },
+    {
+        "id": "eXR1N4PMtfcKn51s",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a tabular representation of the line chart, with the time periods as rows and the different factors (Innovation Index, Business Environment, High-Tech Enterprises) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first task would be to identify {the x-axis tick labels}[0.076, 0.926, 0.088, 0.949],[0.204, 0.926, 0.216, 0.949],[0.332, 0.926, 0.344, 0.949],[0.460, 0.926, 0.472, 0.949],[0.588, 0.926, 0.601, 0.949],[0.716, 0.926, 0.729, 0.949],[0.844, 0.926, 0.857, 0.949],[0.973, 0.926, 0.985, 0.949], which represent the time periods from 1 to 8. These would serve as row headers. Next, we locate {the legend items text}[0.812, 0.435, 0.928, 0.452],[0.812, 0.461, 0.966, 0.478],[0.812, 0.487, 0.962, 0.504] which are the column headers namely Innovation Index, Business Environment, and High-Tech Enterprises. Finally, the values are obtained by tracking each of the points in the respective lines {Innovation Index points}[0.082, 0.558],[0.210, 0.470],[0.338, 0.416],[0.466, 0.345],[0.594, 0.292],[0.723, 0.221],[0.851, 0.185],[0.979, 0.026] for Innovation Index, {Business Environment points}[0.082, 0.452],[0.210, 0.434],[0.338, 0.470],[0.466, 0.558],[0.594, 0.612],[0.723, 0.700],[0.851, 0.736],[0.979, 0.842] for Business Environment and {High-Tech Enterprises points}[0.082, 0.825],[0.210, 0.736],[0.338, 0.647],[0.466, 0.612],[0.594, 0.505],[0.723, 0.381],[0.851, 0.292],[0.979, 0.203] for High-Tech Enterprises, and extracts the y-coordinates to derive the corresponding performance values.\nBased on the above thought, the answer is:\nTime Period,Innovation Index,Business Environment,High-Tech Enterprises\n1,0.70,0.76,0.55\n2,0.75,0.77,0.60\n3,0.78,0.75,0.65\n4,0.82,0.70,0.67\n5,0.85,0.67,0.73\n6,0.89,0.62,0.80\n7,0.91,0.60,0.85\n8,1.00,0.54,0.90"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "VMuHuvxzn2ezQKm1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the elements showcased in the line chart exhibits an upward trend through the entire time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which element has a constantly increasing trend, I need to observe {the line for Innovation Index}[0.082, 0.558, 0.979, 0.026], {the line for Business Environment}[0.082, 0.452, 0.979, 0.842], and {the line for High-Tech Enterprises}[0.082, 0.825, 0.979, 0.203]. The Innovation Index line starts at a point lower than both Business Environment and High-Tech Enterprises, but it ends at the top compared to the other two lines. This suggests a continuously upwards trend. Meanwhile, the line coordinates for Business Environment suggest a decrease after the initial increase.\nBased on the above thought, the answer is:\nAmong the elements showcased in the line chart, the Innovation Index exhibits an upward trend through the entire time period. Its line plot begins at a lower point but ends at the highest point out of all the elements, indicating a consistent increase in its value over time."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "a43lDMyo6wJJQwXt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum performance value for the Innovation Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum performance value for the Innovation Index, I need to investigate {the line points for Innovation Index}[[0.082, 0.558],[0.210, 0.470],[0.338, 0.416],[0.466, 0.345],[0.594, 0.292],[0.723, 0.221],[0.851, 0.185],[0.979, 0.026]] and extract the corresponding values: 0.7, 0.75, 0.78, 0.82, 0.85, 0.89, 0.91, 1.00. The maximum value among these is 1.00.\nBased on the above thought, the answer is:\n1.00."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "luMu5FgvYLAwSISN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart information, can you generate a table presenting different types of energy in columns and their respective allocation in rows, according to the different time periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.085, 0.901, 0.129, 0.934],[0.255, 0.901, 0.299, 0.934],[0.426, 0.901, 0.470, 0.934], [0.596, 0.901, 0.640, 0.934], [0.767, 0.901, 0.811, 0.934], [0.937, 0.901, 0.981, 0.934]] as the rows denoting different time periods. Next, I locate the {legend items}[[0.129, 0.840, 0.291, 0.864],[0.374, 0.840, 0.471, 0.864],[0.555, 0.840, 0.688, 0.864]] to represent various energy types as columns. Then, I locate and correlate the line points of each energy type at their respective time periods to deduce their values.\nBased on the above thought, the answer is:\nTime Period, Renewable Energy, Fossil Fuels, Nuclear Energy\n0.0, 0.3, 0.7, 0.5\n0.2, 0.35, 0.63, 0.46\n0.4, 0.38, 0.6, 0.43\n0.6, 0.45, 0.58, 0.48\n0.8, 0.5, 0.57, 0.42\n1.0, 0.6, 0.56, 0.4"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "TjZ5fh6Mtfp969ek",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line chart, which energy source seems to experience the most significant decrease in allocation over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which energy source experiences the most significant decrease, I need to view and analyze the line plots for all three sources. Observing {the line for Renewable Energy}[[0.107, 0.250, 0.959, 0.885]], the allocation increases from 0.3 to 0.6 over time, indicating an increase rather than a decrease. Looking at {the line for Fossil Fuels}[[0.107, 0.039, 0.959, 0.335]], the allocation goes down from 0.7 to 0.56, showing a considerable decrease. Lastly, viewing {the line for Nuclear Energy}[[0.107, 0.462, 0.959, 0.673]], the allocation drops from 0.5 to 0.4, a less significant decrease in comparison.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Fossil Fuels seem to experience the most significant decrease in allocation over time. The line plot for Fossil Fuels shows a consistent decrease from an allocation of 0.7 to 0.56. This decrease is more substantial than that of the Renewable Energy and Nuclear Energy sources."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "OJodQe0A15ohFvxo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, what is the difference in energy allocation between Fossil Fuels and Nuclear Energy at the time period of 0.6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this value, I need to identify and extract the respective data points for Fossil Fuels and Nuclear energy at time period 0.6. {Fossil Fuels at 0.6}[0.618, 0.293] is at 0.58, and {Nuclear Energy at 0.6}[0.618, 0.504] is at 0.48. Subtracting these gives the difference.\nBased on the above thought, the answer is:\n0.1."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "268uGNL3fSDjLxXJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, can you extract the data depicting the trend in fund allocation into a table, with the y-axis labels as columns and the x-axis labels as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a table format, I would refer to the {x-axis tick labels}[[0.084, 0.849, 0.129, 0.882], [0.179, 0.849, 0.223, 0.882], [0.274, 0.849, 0.318, 0.882], [0.369, 0.849, 0.413, 0.882], [0.463, 0.849, 0.508, 0.882], [0.558, 0.849, 0.602, 0.882], [0.653, 0.849, 0.697, 0.882], [0.748, 0.849, 0.792, 0.882], [0.842, 0.849, 0.887, 0.882], [0.937, 0.849, 0.981, 0.882]] as the row indexes. The column headers would be the values from {the legend items}[[0.171, 0.912, 0.237, 0.936], [0.171, 0.947, 0.268, 0.970]] which represent 'Anxiety' and 'Depression'. The values for each of these can be gathered from {the line points}[[0.107, 0.237, 0.959, 0.773], [0.107, 0.039, 0.959, 0.515]] which represent their reported level with varying symptom intensity.\nBased on the above thought, the answer is:\nSymptom Intensity,Anxiety,Depression\n1.0,2.4,2.6\n0.9,3.2,3.0\n0.8,2.6,3.8\n0.7,1.9,3.3\n0.6,1.3,2.8\n0.5,2.7,3.5\n0.4,3.9,3.6\n0.3,4.0,4.1\n0.2,2.1,4.4\n0.1,1.5,5.0"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "fySabFw4KnvorDNQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which condition shows a more fluctuating trend in reported level as symptom intensity changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the fluctuations in the reported levels, I look at both the {line for Anxiety}[0.107, 0.237, 0.959, 0.773] and the {line for Depression}[0.107, 0.039, 0.959, 0.515]. Anxiety shows significant ups and downs, notably plunging around symptom intensities of 0.8, 0.6 and 0.2 but then bouncing back. Depression, on the other hand, has dips around symptom intensities of 0.9, 0.7 and 0.2 but overall the fluctuations appear to be less intense.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, Anxiety seems to have a more fluctuating trend in reported level as symptom intensity changes. This is based on the larger swings apparent in the line plot for Anxiety in comparison to Depression."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "X0rmpnSdloTzoCBJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum reported level for Depression as per the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum reported level for Depression, I need to examine the points on {the line for Depression}[0.107, 0.039, 0.959, 0.515]. The max level is at the point with the smallest y-coordinate, which is {5.0}[0.107, 0.039].\nBased on the above thought, the answer is:\n5.0"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "l5U6pOODEYljpnzF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table with Rate as the columns and Savings, Expense, and Revenue as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table, I\u2019d have to recognize {the x-axis labels}<r>[0.094, 0.849, 0.981, 0.882]</r> which will become the column names, correspondingly representing the rate changes. The rows are represented by {the legend}<r>[0.125, 0.941, 0.571, 0.983]</r>  corresponding to Savings, Expense, and Revenue. The values in the table are derived from the y-coordinates of {the points on each line}<r>[0.116, 0.098, 0.959, 0.465], [0.116, 0.612, 0.959, 0.796], [0.116, 0.062, 0.959, 0.282]</r>, that represent each financial category. Using these coordinates, I can map each value to its corresponding category and rate.\nBased on the above thought, the answer is:\nCategory,0.0,0.2,0.4,0.6,0.8,1.0\nSavings,20000.0,22000.0,25000.0,26000.0,28000.0,30000.0\nExpense,15000.0,16000.0,14000.0,13000.0,12000.0,11000.0\nRevenue,25000.0,26000.0,29000.0,28000.0,30000.0,31000.0"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "ymhOvUsPWh9hWRnm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends shown in the line chart, which of the three categories shows a consistent growth? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category shows consistent growth, I need to analyze the trend lines of each category. {The trend line for the Savings category}<r>[0.116, 0.098, 0.959, 0.465]</r> shows a steady upward movement, indicating a consistent saving rate. However, {the trend line for the Expense category}<r>[0.116, 0.612, 0.959, 0.796]</r> starts high and goes down, showing a decrease in expenses. Finally, {the trend line for the Revenue category}<r>[0.116, 0.062, 0.959, 0.282]</r> starts high, dips in the middle, and then rises again, showing a wavering revenue stream.\nBased on the above thought, the answer is:\nThe Savings category shows a consistent growth trend. This can be seen from its trend line, which has a continuous upward movement, accurately indicating that as the rate increases, the Savings also increase without any significant falls or dips. This consistent growth pattern differentiates it from the Expense and Revenue categories, which show varying patterns of increase and decrease."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "6sDbil2cknpH3pH1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the amount deducted from the total Revenue value between rate points 0.4 and 0.6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in Revenue value between 0.4 and 0.6, I locate {the points on the Revenue line at 0.4 and 0.6}<r>[0.454, 0.135], [0.622, 0.172]</r>. The values are 29000.0 at rate 0.4 and 28000.0 at rate 0.6. I can then calculate the change by subtracting the rate 0.6 value from rate 0.4.\nBased on the above thought, the answer is:\n1000.0"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "gm571ES5qnaYlKD7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data to a table with columns representing different periods and rows representing Civilization Development Index, Science and Technology Development Index and Population Growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the x-axis tick labels}[[0.055, 0.912, 0.135, 0.936], [0.166, 0.912, 0.256, 0.936], [0.270, 0.912, 0.384, 0.936], [0.403, 0.912, 0.483, 0.936], [0.495, 0.912, 0.623, 0.936]], which are the distinct times in the chart that will become the headers of the table. Then, I determine {the legend texts}[[0.659, 0.046, 0.876, 0.069], [0.659, 0.081, 0.966, 0.104], [0.659, 0.116, 0.787, 0.139]] to be the row labels. To generate the corresponding values, I find {the line points for each line}[[0.095, 0.533], [0.211, 0.315], [0.327, 0.301], [0.443, 0.243], [0.559, 0.221], [0.095, 0.824], [0.211, 0.570], [0.327, 0.388], [0.443, 0.279], [0.559, 0.199], [0.095, 0.497], [0.211, 0.461], [0.327, 0.359], [0.443, 0.301], [0.559, 0.170]] and extract the corresponding values.\nBased on the above thought, the answer is:\nTime Period, Ancient Era, Medieval Era, Renaissance Era, Modern Era, Contemporary Era\nCivilization Development Index, 0.5, 0.8, 0.82, 0.9, 0.93\nScience and Technology Development Index, 0.1, 0.45, 0.7, 0.85, 0.96\nPopulation Growth, 0.55, 0.6, 0.74, 0.82, 1.0"
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "A56UyHnZJ9nMHtEH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat were the trends in Civilization Development Index, Science and Technology Development Index and Population Growth during Renaissance to the Contemporary Era?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I analyze the line plots for each index during the Renaissance to the Contemporary Era. For {Civilization Development Index}[0.327, 0.301, 0.559, 0.221], the line shows a consistently increasing trend, indicating constant growth in Civilization Development Index from the Renaissance Era to the Contemporary Era. \n\nFor {Science and Technology Development Index}[0.327, 0.388, 0.559, 0.199], the line also exhibits a rising trend from Renaissance Era to Contemporary Era, signifying progressing science and technology during this period.\n\n{Population Growth}[0.327, 0.359, 0.559, 0.170] similarly shows a sharp upward trend, indicating that the population has been steadily growing from the Renaissance to the Contemporary Era.\nBased on the above thought, the answer is:\nAll three indices, the Civilization Development Index, The Science and Technology Development Index, and Population Growth, display a rising trend from the Renaissance Era to the Contemporary Era. This means that civilization development, science and technology, and population have all been growing over this time period."
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "PbsMBEaiY3ihFDZP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest Civilization Development Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the highest and lowest points in {the line for Civilization Development Index}[0.095, 0.221, 0.559, 0.533]. The maximum value for Civilization Development Index is 0.93 during {Contemporary Era}[0.559, 0.896] and the minimum value is 0.5 during {Ancient Era}[0.095, 0.896]. So, the difference between the highest and lowest Civilization Development Index is 0.93 - 0.5 = 0.43.\nBased on the above thought, the answer is:\n0.43"
+            }
+        ],
+        "id_raw": 408
+    },
+    {
+        "id": "PcA5N2ihN0Mte0Fp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you interpret the line chart into a table format containing Innovation project index as rows and Performance measures as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.102, 0.849, 0.146, 0.882], [0.234, 0.849, 0.278, 0.882], [0.365, 0.849, 0.409, 0.882], [0.496, 0.849, 0.540, 0.882], [0.628, 0.849, 0.672, 0.882], [0.759, 0.849, 0.803, 0.882]] as the row headers representing Innovation project index. Next, I identify {the legend texts}[[0.189, 0.950, 0.504, 0.973], [0.588, 0.950, 0.834, 0.973], [0.918, 0.950, 1.093, 0.973]] as column headers representing Performance measures. I then find the corresponding line points for each performance measure at the corresponding innovation project indexes and extract their values.\nBased on the above thought, the answer is:\nInnovation Project Index,Continuous_integration_performance,Project_management_quality,Innovation_potential\n0.0,0.97,0.96,0.98\n0.2,0.98,0.92,0.99\n0.4,0.97,0.90,1.0\n0.6,0.96,0.88,0.98\n0.8,0.94,0.85,0.97\n1.0,0.93,0.82,0.96"
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "vsFQYfcx3TPaO7t1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which Performance Measure demonstrated the most significant decline over the Innovation Project Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will assess the declining trends in the line plots for each Performance Measure. Considering {the line plot for Continuous Integration Performance}[0.124, 0.172, 0.781, 0.355], it shows a gradually decreasing trend from 0.97 to 0.93. For {Project Management Quality}[0.124, 0.245, 0.781, 0.759], there is a more significant decrease from 0.96 to 0.82. Finally, for {Innovation Potential}[0.124, 0.098, 0.781, 0.245], the trend shows a slight decline from 0.98 to 0.96. Comparing these trends, it can be concluded that Project Management Quality has experienced the most significant decline.\nBased on the above thought, the answer is:\nThe Performance Measure of Project Management Quality demonstrated the most significant decline over the Innovation Project Index. The Performance Measure values decreased from 0.96 to 0.82, implying a decrease in Project Management Quality over the specified Innovation Project Index. This decline is more significant compared to Continuous Integration Performance and Innovation Potential."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "4W381pPUP0ZBKFgt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in Continuous Integration Performance between the Innovation project index of 0.0 and 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference in Continuous Integration Performance between the Innovation project index of 0.0 and 1.0, I need to identify {the line points for Continuous Integration Performance}[[0.124, 0.209], [0.256, 0.172], [0.387, 0.209], [0.518, 0.245], [0.650, 0.319], [0.781, 0.355]] and extract their values. The value at index 0.0 is 0.97 and the value at index 1.0 is 0.93. The difference is therefore 0.97 - 0.93 = 0.04.\nBased on the above thought, the answer is:\n0.04."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "e3DkhoyECCrYorVE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data of the line plot for the three industries into a structured table with Years as columns and Industries as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with the process of data extraction, {the x-axis tick labels}[[0.101, 0.810, 0.145, 0.838], [0.265, 0.810, 0.309, 0.838], [0.428, 0.810, 0.472, 0.838], [0.591, 0.810, 0.636, 0.838], [0.755, 0.810, 0.799, 0.838], [0.919, 0.810, 0.962, 0.838]] correspond to column headers or Years. Next, {the legend texts}[[0.147, 0.922, 0.262, 0.950], [0.345, 0.922, 0.523, 0.950], [0.606, 0.922, 0.724, 0.950]] can be seen as row labels representing different industries. For the actual values in the table, I look at the various {line points} for each industry at respective Years like {the line points for Tech Industry}[[0.123, 0.534], [0.287, 0.449], [0.450, 0.363], [0.614, 0.306], [0.777, 0.221], [0.940, 0.164]] and so on, and extract their corresponding values.\nBased on the above thought, the answer is:\nIndustry,2015,2016,2017,2018,2019,2020\nTech Industry,24,27,30,32,35,37\nAutomotive Industry,22,21,20,19,18,16\nFood Industry,20,22,25,27,29,33"
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "Y2K0btufuPWcV82x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which industry demonstrated an overall upward trend in market share from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the overall trends for each industry, I need to observe the pathways of their corresponding line plots. Beginning with {Tech Industry's line}[0.123, 0.164, 0.940, 0.534], I see a clear increase from 24 to 37 over the years. For {Automotive Industry's line}[0.123, 0.591, 0.940, 0.762], the values decrease from 22 to 16. Lastly, looking at {Food Industry's line}[0.123, 0.278, 0.940, 0.648], the values show a rise from 20 to 33. Thus, Tech and Food industries show an upward trend but Tech industry's increase seems more prominent.\nBased on the above thought, the answer is:\nThe Tech Industry demonstrated the most significant overall upward trend in market share from 2015 to 2020, with a clear increase from a market share of 24% to 37%. The Food Industry also demonstrated an upward trend, though it was less significant than the Tech Industry's increase. On the other hand, the Automotive Industry showed a decrease in market share over the same period."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "4MC7MFJIaKM20qQQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of Market Share for the Food Industry from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the needed range, I need to find the minimum and maximum values for the Food Industry during these mentioned years. For that, I observe {the line points for Food Industry}[[0.123, 0.648], [0.287, 0.591], [0.450, 0.506], [0.614, 0.449], [0.777, 0.392], [0.940, 0.278]]. I extract the corresponding values: 20, 22, 25, 27, 29, and 33. The maximum is 33 corresponding to {2020}[0.940, 0.810] and the minimum is 20 corresponding to {2015}[0.101, 0.810]. Hence, calculating the range as the difference between the maximum and minimum gives me 33 - 20 = 13.\nBased on the above thought, the answer is:\n13."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "R1ViQJB8sy058kiZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table format for the data from the line chart that shows trend types as columns and series as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from the line chart, I initially identify {the x-axis labels}[[0.063, 0.911, 0.161, 0.934], [0.324, 0.911, 0.442, 0.934], [0.590, 0.911, 0.717, 0.934], [0.870, 0.911, 0.980, 0.934]] that reflect the column headers or trend types. After that, I examine {the legend items}[[0.902, 0.046, 0.953, 0.069], [0.902, 0.081, 0.953, 0.104], [0.902, 0.116, 0.953, 0.139]] being the row labels portraying the different series. In order to fill in the values, I identify {the line points}[[0.112, 0.826], [0.383, 0.100], [0.654, 0.201], [0.925, 0.302], [0.112, 0.855], [0.383, 0.661], [0.654, 0.438], [0.925, 0.352], [0.112, 0.812], [0.383, 0.065], [0.654, 0.345], [0.925, 0.460]] for each series at the respective trend types and extract the correlated values.\nBased on the above thought, the answer is:\nTrends,Series1,Series2,Series3\nPersistent Rise,1.02,0.98,1.04\nGradual Increase,2.03,1.25,2.08\nUnstable Behavior,1.89,1.56,1.69\nGradual Decline,1.75,1.68,1.53"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "D3sQhdm8UOWjhoHE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which series demonstrates the most abrupt change over the trend types and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which series demonstrates the most abrupt change, I need to analyze the fluctuations in the line plots for each series. Observing {the line for Series1}[0.112, 0.100, 0.925, 0.826], there's a substantial increase from 1.02 to 2.03, then a slight decrease to 1.89 and a decrease to 1.75. This shows considerable variability in the trend. For {Series2}[0.112, 0.352, 0.925, 0.855], the trend rises slightly from 0.98 to 1.25 and then increases moderately to 1.56 and slightly more to 1.68, indicating less fluctuation. For {Series3}[0.112, 0.065, 0.925, 0.812], though the value drops dramatically from 1.04 to 2.08, the latter changes are relatively smaller and gradual to 1.69 and 1.53, showing initial volatility followed by stability.\nBased on the above thought, the answer is:\nBased on the chart, Series1 demonstrates the most abrupt change over the trend types. Particularly at the 'Gradual Increase' trend type, Series1 jumps dramatically from 1.02 to 2.03. Hence, overarching variations in Series1 are more pronounced compared to Series2 and Series3, indicating a higher degree of change in Series1."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "ERezoSY0ncXpDhcU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the maximum and minimum values for Series3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values for Series3, I need to locate {the line points for Series3}[[0.112, 0.812], [0.383, 0.065], [0.654, 0.345], [0.925, 0.460]] and extract the respective values: 1.04, 2.08, 1.69, 1.53. The maximum value is 2.08 at {Gradual Increase}[0.383, 0.911], and the minimum value is 1.04 at {Persistent Rise}[0.063, 0.911]. The difference between maximum and minimum is 2.08 - 1.04 = 1.04.\nBased on the above thought, the answer is:\n1.04."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "jdIfhLKf0S6vpD2R",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart showing different development areas across years into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the data table, I initially identify {the x-axis tick labels}[[0.058, 0.801, 0.107, 0.828], [0.233, 0.801, 0.282, 0.828], [0.409, 0.801, 0.458, 0.828], [0.584, 0.801, 0.634, 0.828], [0.760, 0.801, 0.809, 0.828], [0.935, 0.801, 0.985, 0.828]] representing the years. Then I see {the legend texts}[[0.134, 0.918, 0.215, 0.938], [0.134, 0.948, 0.279, 0.968], [0.346, 0.918, 0.462, 0.938]] determining the development areas: AI Research, Quantum Computing, and DNA Sequencing, which will be our rows. Subsequently, I locate {the line points} for each development area and extract the corresponding scaled values.\nBased on the above thought, the answer is:\nYear, AI Research, Quantum Computing, DNA Sequencing\n2020, 0.65, 0.45, 0.75\n2021, 0.70, 0.47, 0.64\n2022, 0.83, 0.55, 0.86\n2023, 0.90, 0.60, 0.92\n2024, 0.97, 0.78, 0.76\n2025, 1.00, 0.80, 0.78"
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "xYJbgqTgjrIG2Tps",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which development area exhibits the steepest increase over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "From viewing the chart, AI Research has a starting point at {2020}[0.082, 0.518] with a development scale of 0.65 and ends at a point at {2025}[0.960, 0.140] with a development scale of 1.00. Similarly, Quantum Computing starts at a point at {2020}[0.082, 0.733] with a development scale of 0.45 and ends at a point at {2025}[0.960, 0.356] with a development scale of 0.80. DNA Sequencing starts at a point at {2020}[0.082, 0.410] with a development scale of 0.75 and ends at a point at {2025}[0.960, 0.377] with a development scale of 0.78. Comparing these increases, we see that AI Research has the steepest increase.\nBased on the above thought, the answer is:\nAI Research exhibits the steepest increase over the years, seen by its substantial rise from a development scale of 0.65 to 1.00 between 2020 and 2025."
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "aqzr0GuFKSaBe8Zv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the development scale for AI Research from 2020 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in the development scale for AI Research, I need to locate {the line points for AI Research}[[0.082, 0.518], [0.258, 0.464], [0.433, 0.323], [0.609, 0.248], [0.785, 0.172], [0.960, 0.140]] and extract the corresponding values: 0.65, 0.70, 0.83, 0.90, 0.97, 1.00. The first value, Associated with the year 2020, is 0.65 which corresponds to {2020}[0.082, 0.801] while the last value, associated with the year 2025, is 1.00 which corresponds to {2025}[0.935, 0.801]. The total increase is therefore 1.00 - 0.65 = 0.35.\nBased on the above thought, the answer is:\n0.35"
+            }
+        ],
+        "id_raw": 415
+    },
+    {
+        "id": "Zw447BfWOFW6SMGI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract data from line chart of technology growth rates and express that as a table, using years as rows and different technologies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To process this task, I need to refer to the x-axis and the line data. The provided x-axis labels {x-axis tick labels}[0.058, 0.801, 0.107, 0.828], [0.233, 0.801, 0.282, 0.828], [0.409, 0.801, 0.458, 0.828], [0.584, 0.801, 0.634, 0.828], [0.760, 0.801, 0.809, 0.828], [0.935, 0.801, 0.985, 0.828] correspond to the row headers as there are years. Next, I refer to the legend {the legend}[0.089, 0.911, 0.468, 0.976] to know technologies which will be used as column headers. Lastly, I will find the line points from {the lines for different technologies} [0.082, 0.140, 0.960, 0.518], [0.082, 0.356, 0.960, 0.733], [0.082, 0.226, 0.960, 0.528], to fill up the table by each point representing the value for the respective technology in the given year.\nBased on the above thought, the answer is:\nYear,AI research,Quantum Computing,DNA Sequencing\n2020,0.65,0.45,0.75\n2021,0.7,0.47,0.64\n2022,0.83,0.55,0.86\n2023,0.9,0.6,0.92\n2024,0.97,0.78,0.76\n2025,1.0,0.8,0.78"
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "3G93bBBFyxrHuQEC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the growth rates depicted in the line chart, which New Energy and Clean Technology observed the highest growth rate from 2020 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which technology noticed the highest growth rate, I should investigate the coordinates {lines for different technologies}[0.082, 0.140, 0.960, 0.518], [0.082, 0.356, 0.960, 0.733], [0.082, 0.226, 0.960, 0.528]. I can see that the AI Research technology started at a growth rate of 0.65 in 2020 from coordinates {AI Research in 2020}[0.082, 0.518] and reached 1.0 in 2025 from coordinates {AI Research in 2025}[0.960, 0.140], demonstrating a growth increase of 0.35. Following similarly, for Quantum Computing, there is an increase of 0.35 and for DNA Sequencing it is 0.03.\nBased on the above thought, the answer is:\nFrom the growth rates represented in the line chart, both AI Research and Quantum Computing saw the peak growth rate from 2020 to 2025 with an increase of 0.35."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "pXc2TyFC355VM6oE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth rate difference between the technology 'AI Research' from 2024 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the growth rate difference, I need to find out the values of 'AI Research' for 2024 and 2025. The coordinates corresponding to 2024 are in {AI Research in 2024}[0.785, 0.172] and for 2025 in {AI Research in 2025}[0.960, 0.140]. The growth rate difference can be calculated by subtracting the 2024 value from the 2025 value.\nBased on the above thought, the answer is:\n0.03"
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "KwXnm9KOiaaF6Uuq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information on 'Data Encryption and Integrity' into a table format using the bar graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar graph for 'Data Encryption and Integrity' visually compares the rate of gain change between 'Ours' and 'Saliency-based Sampling' approaches. Initially, I locate the x-axis labels where the specific data scenarios are listed, including {Data Encryption}<r>[0.135, 0.841, 0.195, 0.871]</r> and {Data Integrity}<r>[0.205, 0.841, 0.265, 0.871]</r>. Then, I observe the lengths of {the red bars}<r>[0.135, 0.450, 0.185, 0.600]</r> for 'Ours' and {the blue bars}<r>[0.195, 0.500, 0.245, 0.650]</r> for 'Saliency-based Sampling' corresponding to each scenario, as red and blue are denoted from {the legend}<r>[0.800, 0.200, 0.880, 0.220]</r> and the next line.\nBased on the above thought, the answer is:\nScenario, Ours, Saliency-based Sampling\nData Encryption, -10%, -25%\nData Integrity, -5%, -15%"
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "GHPCYrVTvit4Ubmm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method, 'Ours' or 'Saliency-based Sampling', demonstrates a more stable trend in the 'Data Encryption and Integrity' chart, based on the analysis of bar lengths?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess stability, I evaluate the variance in bar lengths within each method. Observing {the red bars representing 'Ours'}<r>[0.135, 0.450, 0.185, 0.600]</r> and [0.205, 0.500, 0.245, 0.600], the change appears marginal and nearly consistent. In contrast, {the blue bars for 'Saliency-based Sampling'}<r>[0.195, 0.500, 0.245, 0.650]</r> and [0.265, 0.450, 0.305, 0.550] display a larger variance, indicating less stability. The lesser variation in 'Ours' suggests it demonstrates a more stable trend.\nBased on the above thought, the answer is:\n'Ours' demonstrates a more stable trend in the 'Data Encryption and Integrity' chart. It shows minimal variance in the bar lengths across the data scenarios compared to the 'Saliency-based Sampling', which exhibits greater fluctuations."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "nW2s0Ls2kPyxTLvt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage point difference in the rate of gain change for 'Data Integrity' between 'Ours' and 'Saliency-based Sampling' methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in the rate of gain change for 'Data Integrity', I focus on the bar lengths for both methods. {The bar for 'Ours' in Data Integrity}<r>[0.205, 0.500, 0.245, 0.600]</r> shows a shorter length indicating -5% while {the bar for 'Saliency-based Sampling' in Data Integrity}<r>[0.265, 0.450, 0.305, 0.550]</r> has a longer length indicating -15%. The difference is calculated as |(-5%) - (-15%)| = 10 percentage points.\nBased on the above thought, the answer is:\n10 percentage points."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "9yXLC4zuQ5jqwsga",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the graph, can you pull the yearly number of jobs available in different sectors and create a table for us?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the data for the table can be extracted via converting the line graph into tabular format. Firstly, the years shown by the {x-axis labels}<r>[0.142, 0.895, 0.945, 0.895]</r> will form the column headers. The sectors are identified from {the legend}<r>[0.109, 0.771, 0.327, 0.883]</r> which will form the row labels. The actual data points are obtained from {the line points for each sector}<r>[0.142, 0.738, 0.945, 0.855]</r>,<r>[0.142, 0.504, 0.945, 0.548]</r>,<r>[0.142, 0.065, 0.945, 0.167]</r>.\nBased on the above thought, the answer is:\nYear,Technology-Related Jobs,Education Worker Jobs,Public Sector Jobs\n2015,46000,70000,100000\n2016,47500,69500,99000\n2017,48800,69000,98000\n2018,51000,68500,96500\n2019,53000,68000,95000\n2020,54000,67000,93000"
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "8Fee8w1VnNJzooi6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector faced the most consistent decline in job availability over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector faced the most consistent decline, I need to observe the line trajectories each representing a sector. Observing the line for {Technology-Related Jobs}<r>[0.142, 0.738, 0.945, 0.855]</r>, the trend indicates a more or less consistent rise in job availability. The line for {Education Worker Jobs}<r>[0.142, 0.504, 0.945, 0.548]</r> also shows a consistent decline over the years. Lastly, the line for {Public Sector Jobs}<r>[0.142, 0.065, 0.945, 0.167]</r> also illustrates a consistent decline in the number of jobs available. However, the decreases are more drastic compared to the Education Worker Jobs.\nBased on the above thought, the answer is:\nConsidering the job availability trends from 2015 to 2020, the Public Sector Jobs faced the most consistent and noticeable decline in job numbers over these years."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "SY5OPnWV19hcizb9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the years from 2015 to 2020, how large is the decrease in the number of Education Worker Jobs?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this, I need to determine the values at the beginning and end points of the line representing Education Worker Jobs. I look at the point {2015}<r>[0.142, 0.504]</r> and the point {2020}<r>[0.945, 0.548]</r>, identifying the values to be 70000 and 67000 respectively. I find the decrease by subtracting the value for 2020 from that for 2015.\nBased on the above thought, the answer is:\n3000"
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "J6zKi2XV3v5dTpwG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the line chart regarding subscription rates over various quarters into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends in the line chart, I begin by examining {the x-axis labels}<r>[0.102, 0.763, 0.781, 0.813]</r> that indicate the quarters from 'Q1' to 'Q4_3year_later'. Next, I identify {the legends}<r>[0.771, 0.051, 0.985, 0.191]</r> specifying the series for 'eBay Prime', 'Amazon Subscribe', and 'Walmart Plus'. I then map out the values using the positions of {the plotted points}<r>[0.117, 0.747, 0.732, 0.138]</r> for each quarter and each subscription service to gather accurate numerical data.\nBased on the above thought, the answer is:\nQuarter,eBay Prime,Amazon Subscribe,Walmart Plus\nQ1,0.8,0.85,0.9\nQ2,0.83,0.88,0.93\nQ3,0.86,0.90,0.95\nQ4,0.85,0.95,0.94\nQ1_next_year,0.87,0.93,0.93\nQ2_next_year,0.88,0.94,0.96\nQ3_next_year,0.85,0.98,0.95\nQ4_next_year,0.87,1.02,0.86\nQ1_2year_later,0.9,1.05,0.9\nQ2_2year_later,0.94,1.06,0.91\nQ3_2year_later,0.96,1.08,0.95\nQ4_2year_later,0.94,1.01,0.96\nQ1_3year_later,0.97,1.03,0.94\nQ2_3year_later,0.99,1.05,0.93\nQ3_3year_later,1.0,1.07,0.94\nQ4_3year_later,0.96,1.1,0.93"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "MdS5adlW66o5JkNY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the given chart, which subscription service demonstrates the most consistent upward trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the plotted data, I identify {the lines}<r>[0.117, 0.747, 0.732, 0.138]</r> and observe the trends for each service. The 'Amazon Subscribe' {line}<r>[0.117, 0.646, 0.732, 0.138]</r> starts at an initial rate of 0.85 and progresses upward, reaching a peak of 1.1 at the last recorded quarter, showing a steady increase without significant fluctuations. Compared to 'eBay Prime' and 'Walmart Plus', both of which display some fluctuations and inconsistent progress, 'Amazon Subscribe' maintains a more consistent growth trajectory.\nBased on the above thought, the answer is:\nThe 'Amazon Subscribe' subscription service demonstrates the most consistent upward trend, with its graph line showing a steady climb in subscription rates across the years, unmarked by the dips seen in other services."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "ehBvBWnsPqL13H3F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_21_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average subscription rate for Amazon Subscribe over the 4 quarters of the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the points for Amazon Subscribe}<r>[0.281, 0.483, 0.404, 0.301]</r> in the next year quarters from 'Q1_next_year' to 'Q4_next_year' having the rates 0.93, 0.94, 0.98, and 1.02 respectively. To find the average rate, the calculation is (0.93 + 0.94 + 0.98 + 1.02) / 4.\nBased on the above thought, the answer is:\n0.9675"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "KDoEyJDiw1Cfrc6Y",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line graph data showing population in each hemisphere into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the graph data into a table, I first locate {the x-axis labels}<r>[0.05, 0.92, 0.09, 0.95]</r> which show the two categories: 'Western Hemisphere' and 'Eastern Hemisphere'. Then, I focus on {the line representing population}<r>[0.1, 0.1, 0.9, 0.9]</r> and gather the data points plotted for each hemisphere. These are {the data points for the population plot}<r>[0.3, 0.4, 0.7, 0.7]</r> showing the values in millions.\nBased on the above thought, the answer is:\nHemisphere,Population (millions)\nWestern,500\nEastern,1200"
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "wNSg67DoHpqPAeIA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich hemisphere demonstrates a higher average maximum elevation according to the graph, and why might this be significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which hemisphere has a higher average maximum elevation, I review {the elevation subplot}<r>[0.1, 0.5, 0.4, 0.9]</r> and {the corresponding data points}<r>[0.2, 0.6, 0.35, 0.8]</r>. The data for the Eastern Hemisphere shows a significantly higher elevation compared to that of the Western Hemisphere. This could indicate more mountainous regions in the Eastern Hemisphere, which is significant as it may affect climate, biodiversity, and even human habitation patterns in these regions.\nBased on the above thought, the answer is:\nThe Eastern Hemisphere demonstrates a higher average maximum elevation. This might be significant as it often implies more mountainous terrain, which can influence local climates, ecological systems, and might pose challenges or opportunities for human activities in those regions."
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "2ILEiOgvwOl0demn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in coastline length between the Western and Eastern Hemispheres?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in coastline length, I locate {the line graph for coastline}<r>[0.5, 0.1, 0.9, 0.5]</r>, and review {the data points for coastline lengths}<r>[0.55, 0.2, 0.85, 0.3]</r> which indicates the respective lengths in thousands of kilometers for each hemisphere. The Western Hemisphere has a coastline of 75,000 km and the Eastern Hemisphere has 50,000 km.\nBased on the above thought, the answer is:\n25000 km"
+            }
+        ],
+        "id_raw": 420
+    },
+    {
+        "id": "0X0xLYdWgJwta044",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the popularity trends data from the line chart of four fashion subcultures into a structured table, dividing subcultures into rows and years into columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm examining the line chart, where I've identified {the x-axis labels}[0.096, 0.919, 0.131, 0.942] marking the years from 2005 to 2020 as the column headers of the table. Next, I'm checking {the color-coded legend}[0.082, 0.074, 0.331, 0.151] indicating each fashion subculture to set the row headers. I pinpoint {the points for each line}[0.114, 0.865], [0.886, 0.100] etc., from the line chart to extract the associated popularity index values for each year.\nBased on the above thought, the answer is:\nYear,Vintage,Lolita,Steampunk,Gothic\n2005,45,65,30,60\n2006,46,64,31,57\n2007,48,62,32,55\n2008,50,60,33,53\n2009,52,59,34,50\n2010,55,58,36,48\n2011,57,56,37,45\n2012,60,55,39,43\n2013,62,54,40,40\n2014,64,62,42,39\n2015,67,70,43,35\n2016,69,68,44,33\n2017,72,63,45,30\n2018,74,60,46,28\n2019,76,58,48,25\n2020,78,57,50,22"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "UsAPRejKrMKV5GB4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fashion subculture shows the most significant decline in popularity over the observed period, based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I examine the trajectories of the fashion subcultures. Notably, {the line for Gothic}[0.096, 0.919] starts higher and consistently descends across the timeframe. This visible trend from around 60 down to 22 exemplifies a significant decline. In contrast, other subcultures either show growth or smaller fluctuations over time.\nBased on the above thought, the answer is:\nGothic fashion shows the most significant decline in popularity over the observed period. The line chart clearly depicts a consistent downward trajectory from 2005 to 2020, contrasting strongly with the other subcultures\u2019 trends."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "NiKMAbedg9Ebc8Ei",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in popularity index for the Vintage fashion from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the points for Vintage}[0.114, 0.865] in 2005 and [0.886, 0.100] in 2020 on the line chart. I extract the values at these points which are, respectively, 45 in 2005 and 78 in 2020. Subtracting the initial value from the final gives 78 - 45.\nBased on the above thought, the answer is:\n33."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "pfOHv8EgUzCOrVQE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the line chart depicting trend categories across datasets into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accurately extract and tabulate information from the line chart, I would start by identifying each {subplot representating different datasets}<r>[0.1, 0.1, 0.5, 0.5]</r>, <r>[0.5, 0.1, 0.9, 0.5]</r>, <r>[0.1, 0.5, 0.5, 0.9]</r>, <r>[0.5, 0.5, 0.9, 0.9]</r>. Each subplot will be further analyzed to note line plot details, starting from Dataset 1 to Dataset 4. Within each subplot, I'd identify {lines of each trend category}<r>[0.15, 0.2, 0.45, 0.45]</r>, <r>[0.55, 0.2, 0.85, 0.45]</r>, <r>[0.15, 0.55, 0.45, 0.85]</r>, <r>[0.55, 0.55, 0.85, 0.85]</r> representing each trend, marked by corresponding colors and capture their behaviors. Using these observations, I can extract the trend details specific to each dataset.\nBased on the above thought, the answer is:\nCategory,Dataset 1,Dataset 2,Dataset 3,Dataset 4\nPronounced Decrease,Downward,Downward,Downward,Downward\nAccelerating Decline,Downward,Downward,Downward,Downward\nVolatile Decline,Downward,Volatile,Downward,Downward\nAbrupt Decline,Downward,Downward,Volatile,Downward\nErratic Decline,Downward,Volatile,Volatile,Downward\nIncremental Growth,Upward,Upward,Upward,Upward"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "n7CZA5z6cfjFZYFK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the stability of trends in Dataset 3 from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stability of trends within Dataset 3, I would focus on the behavior of each trend line represented in the corresponding subplot. Observing the line plots, specifically the {line for 'Erratic decline'}<r>[0.15, 0.55, 0.45, 0.85]</r> and {line for 'Volatile decline'}<r>[0.15, 0.55, 0.45, 0.85]</r> within Dataset 3, the visual data shows pronounced fluctuations for both these trends. These dramatic peaks and troughs, compared to the mostly downward or upward trends of the other categories, indicate significant instability and suggest that Dataset 3 experiences more frequent and unpredictable changes relative to the others.\nBased on the above thought, the answer is:\nDataset 3 displays significant instability, particularly evident in the erratic fluctuations found in both the 'Erratic decline' and 'Volatile decline' categories. These patterns suggest frequent and unpredictable trends compared to the steadier trends in other datasets."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "IS6pPdqdJuA9sUqe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which dataset shows the greatest difference in trend values between 'Pronounced decrease' and 'Incremental Growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the dataset with the greatest difference, I would first locate the highest and lowest points for both {the line for 'Pronounced decrease'}<r>[0.132, 0.287]</r> and {the line for 'Incremental Growth'}<r>[0.798, 0.903]</r> in each {dataset}<r>[0.1, 0.1, 0.5, 0.5]</r>, <r>[0.5, 0.1, 0.9, 0.5]</r>, <r>[0.1, 0.5, 0.5, 0.9]</r>, <r>[0.5, 0.5, 0.9, 0.9]</r>. By extracting these values for each dataset and calculating the range between them, Dataset 4 shows the greatest difference because the range is visually significantly larger.\nBased on the above thought, the answer is:\nDataset 4"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "6vRUtZlh3fIbFbef",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you transform the data from the line chart displaying migration trends into a structured table, categorizing trends and data sets along rows and columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I observe the trends labeled along {the x-axis}[[0.071, 0.950, 0.938, 0.975]] which includes 'Gradual Downturn', 'Rapid Growth', 'Sharp Decline', 'Accelerated Growth', 'Downward Trend', 'Gradual Decline'. Then, I identify {the subchart titles}[[0.071, 0.062, 0.235, 0.083], [0.290, 0.062, 0.454, 0.083], [0.509, 0.062, 0.673, 0.083], [0.728, 0.062, 0.892, 0.083]] to distinguish the data sets as DataSet1, DataSet2, DataSet3, DataSet4. By tracking {the line plots points}[[0.110, 0.565], [0.152, 0.490], ..., [0.857, 0.210]], I compile the respective values for each trend within different datasets.\nBased on the above thought, the answer is:\nTrend,DataSet1,DataSet2,DataSet3,DataSet4\nGradual Downturn,100,90,85,80\nRapid Growth,100,110,120,130\nSharp Decline,100,85,70,50\nAccelerated Growth,100,115,130,150\nDownward Trend,100,95,90,85\nGradual Decline,100,95,90,85"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "vgtQmYMBqPfPV7vC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich dataset shows the highest growth trend and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the dataset with the highest growth trend, I analyze the slope of the line segments representing growth across the datasets {DataSet1}[0.110, 0.530, 0.899, 0.545], {DataSet2}[0.110, 0.490, 0.899, 0.335], {DataSet3}[0.110, 0.450, 0.899, 0.225], {DataSet4}[0.110, 0.410, 0.899, 0.100]. Observing the line endpoints, the downward movement of the y-values decreases with each dataset, indicating that the slope becomes steeper. Dataset4 exhibits the steepest slope, suggesting the highest growth as it shifts from a higher initial value to a significantly low endpoint, especially in the context of 'Accelerated Growth' trend.\nBased on the above thought, the answer is:\nDataSet4 shows the highest growth trend. This is evident from the steepness of the line in the 'Accelerated Growth' graph, which indicates a more rapid increase from one value point to the next, corresponding to a greater amount of change over the same time interval compared to other datasets."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "AgFp0FSVcovmg561",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the total change in values for 'Sharp Decline\u2019 from DataSet1 to DataSet4."
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change, I first locate the value for 'Sharp Decline' in {DataSet1}[0.152, 0.490] which is 100. Then, I track down the value in {DataSet4}[0.857, 0.210] which is 50. Calculating the change, I subtract the final value from the initial value, 100 - 50 = 50.\nBased on the above thought, the answer is:\n50."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "vbrAq7tVPuOAIA5k",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the social welfare and public services budget allocation over the years from the chart and present it in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first look at the x-axis, identifying {the years represented}<r>[0.126, 0.903, 0.883, 0.942]</r> which will be our row headers. Now, I refer to {the line points representing Social Welfare}<r>[0.126, 0.100, 0.883, 0.865]</r> and {the line points representing Public Services}<r>[0.126, 0.100, 0.883, 0.865]</r>. These provide the datapoints needed for the table, representing budget allocation for Social Welfare and Public Services across the years respectively.\nBased on the above thought, the answer is:\nYears, Social Welfare, Public Services\n2010, 6534, 8952\n2011, 7200, 9503\n2012, 7890, 8403\n2013, 8238, 7852\n2014, 8568, 7607\n2015, 8840, 8237\n2016, 9005, 8943\n2017, 9543, 8175\n2018, 9800, 7655\n2019, 10190, 8400\n2020, 10375, 9204"
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "FE8H5qhtoFDi5A8k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which budget, Public Services or Social Welfare, experienced a larger upward trend from 2010 to 2020? Explain your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line plot trends for the two budgets involved, I observe that {Social Welfare}<r>[0.126, 0.100, 0.883, 0.865]</r> started from roughly 6534 in 2010 and ended up at approximately 10375 in 2020. On the other hand, the {Public Services}<r>[0.126, 0.323, 0.883, 0.221]</r> chart starts from about 8952 in 2010 and ends up at 9204 in 2020. The changes in values for both lines signify the trend over the years.\nBased on the above thought, the answer is:\nBetween 2010 and 2020, the Social Welfare budget experienced a larger upward trend. While both budgets do increase over this period, the increase in the Social Welfare budget, from roughly 6534 to approximately 10375, is larger than the increase in the Public Services budget, which goes from around 8952 to roughly 9204. The upward trend of the Social Welfare budget seems to be more robust."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "FOvjvLFtuWqZAlec",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest budget allocation for Public Services over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest and lowest budget allocation for Public Services, I refer to the line representing Public Services and examine {its points}<r>[0.126, 0.323, 0.202, 0.100, 0.277, 0.544, 0.353, 0.766, 0.429, 0.865, 0.504, 0.611, 0.580, 0.326, 0.656, 0.636, 0.731, 0.845, 0.807, 0.545, 0.883, 0.221]</r>. The highest allocation appears to be in 2011 at 9503 and the lowest allocation is in 2018 at 7655. The difference is therefore 9503-7655 = 1848.\nBased on the above thought, the answer is:\n1848"
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "1e1cpjKaWkSXpH7s",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, could you convert the chart's data into a table that lists the years as rows and the different parameters like 'Teacher Salaries', 'Education Innovation Index', 'Enrollment Rate', 'Training Facility Quality Index', and 'Satisfaction Rate' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data to a tabular format, I would first extract the years from {the x-axis tick labels}[[-0.075, 0.919, -0.040, 0.942], [0.084, 0.919, 0.119, 0.942], [0.244, 0.919, 0.279, 0.942], [0.403, 0.919, 0.438, 0.942], [0.562, 0.919, 0.597, 0.942], [0.721, 0.919, 0.756, 0.942], [0.881, 0.919, 0.916, 0.942], [1.040, 0.919, 1.075, 0.942]], these would be the row titles. Next, I recognize the different parameters from {the sublegend items}[[0.113, 0.083, 0.295, 0.106], [0.113, 0.118, 0.278, 0.141]], which act as the column headers. I locate the line points for each parameter at each year found in {line 0 points}[[0.102, 0.865], [0.181, 0.610], [0.261, 0.763], [0.341, 0.457], [0.420, 0.508], [0.500, 0.406], [0.580, 0.304], [0.659, 0.253], [0.739, 0.202], [0.819, 0.151], [0.898, 0.100]] and {line 1 points}[[0.102, 0.100], [0.181, 0.202], [0.261, 0.304], [0.341, 0.406], [0.420, 0.355], [0.500, 0.457], [0.580, 0.559], [0.659, 0.610], [0.739, 0.661], [0.819, 0.763], [0.898, 0.865]] found in the description and chart. The y-values of these lines at each of the x-values translate to the values in our table.\nBased on the above thought, the answer is:\nYear,Teacher Salaries,Education Innovation Index,Enrolment Rate,Training Facility Quality Index,Satisfaction Rate\n2000,10,20,18,16,14\n2001,15,18,20,19,17\n2002,12,16,17,15,13\n2003,18,14,16,14,13\n2004,17,15,22,18,19\n2005,19,13,24,21,20\n2006,21,11,20,19,18\n2007,22,10,24,23,21\n2008,23,9,22,20,24\n2009,24,7,20,18,21\n2010,25,5,24,23,22"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "tclHJ08WBPcRRqYt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the trends in the line chart, can we say that there's a strong positive correlation between the increase in 'Teacher Salaries' and 'Education Innovation Index'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would analyze the trends by looking at the line plots for 'Teacher Salaries' from {line 0}[0.102, 0.100, 0.898, 0.865] and the 'Education Innovation Index' from {line 1}[0.102, 0.100, 0.898, 0.865]. From closely observing the trendlines, it appears that both 'Teacher Salaries' and the 'Education Innovation Index' have positively increased over the years. However, while 'Teacher Salaries' showed a consistent upward trend, the 'Education Innovation Index' decreased for a while before seeing a dramatic surge in 2021.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, it wouldn't be accurate to say there's a strong positive correlation between the increase in 'Teacher Salaries' and 'Education Innovation Index'. While both have shown an increase over the years, the pattern and rate of their growth are not consistently parallel. Particularly, the 'Education Innovation Index' faced a deduction before witnessing a sudden rise in 2021, which greatly differs from the steady growth exhibited by 'Teacher Salaries'."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "uzpdTBiG0FWYrPBA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the 'Education Innovation Index' increase from the year 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to locate the point for the year {2010}[0.898, 0.903] and extract the corresponding value of 'Education Innovation Index', which lies at the point {5}[0.898, 0.865] in {line 0} of our chart. This means 'Education Innovation Index' was 5 in 2010.\nBased on the above thought, the answer is:\nThe 'Education Innovation Index' in the year 2010 was 5."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "3oa9E4Njpng9E0qg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the trends of each social group into tabular data with the social groups as columns or rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate tabular data from the line chart, I first acknowledge {the x-axis labels}<r>[0.474, 0.952, 0.517, 0.975]</r> which represent the social groups. Next, I identify {the legend text}<r>[0.086, 0.074, 0.285, 0.151]</r> representing the different social groups. For populating the values, I locate {the line points for each group}<r>[0.117, 0.100, 0.874, 0.865]</r> and extract the specific numbers associated with them.\nBased on the above thought, the answer is:\nSocial Groups,1,2,3,4,5,6,7,8,9,10,11,12\nPharmaceutical Sales,4208.24,4081.45,3970.32,3900.21,3780.56,3555.32,3602.12,3781.56,3950.14,4102.35,4200.67,4350.12\nBiotech Investment,10233.12,9283.54,8405.36,7654.32,7150.45,7053.65,7001.23,7254.98,7732.48,8343.22,9089.33,10013.49"
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "PEIN1BaXr6bMwzK1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do investments in Pharmaceuticals and Biotech correlate over the course of one year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we need to analyze the plots for both Pharmaceuticals Sales and Biotech Investment. For {Pharmaceutical Sales}<r>[0.117, 0.100, 0.874, 0.865]</r> there is a decrease from the 1st month to the 6th, followed by a net increase until the end of the year. For {Biotech Investment}<r>[0.117, 0.100, 0.874, 0.865]</r> there is also a decrease from the 1st to 6th month, followed by a net increase until the end of the year.\nBased on the above thought, the answer is:\nBased on the trends observed in the chart, Pharmaceutical Sales and Biotech Investment seem to be positively correlated over the course of a year. Both fields initially experience a decrease in the first half of the year, followed by an increase in the second half of the year. This implies that changes in Pharmaceutical Sales can be associated with a similar trend in Biotech investment, suggesting that investors' interest in these two sectors is closely linked."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "KjOTAk0xntFTLwsq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what amount did the Biotech Investment increase from month 6 to month 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to locate the data points for Biotech Investment in {month 6}<r>[0.461, 0.852]</r> and {month 12}<r>[0.874, 0.152]</r> and subtract the sixth month's value from the twelfth month's value. The values are 7053.65 and 10013.49 respectively.\nBased on the above thought, the answer is:\n2959.84"
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "A4bCJniVmGM18s2t",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate the data in a structured table format with 'Years' as rows, 'Patents' and 'Technological_Innovations' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrange the chart data into a table, I firstly identify {the x-axis tick labels}[[0.113, 0.919, 0.122, 0.942], [0.198, 0.919, 0.207, 0.942], [0.283, 0.919, 0.292, 0.942], [0.368, 0.919, 0.377, 0.942], [0.453, 0.919, 0.462, 0.942], [0.538, 0.919, 0.547, 0.942], [0.623, 0.919, 0.632, 0.942], [0.708, 0.919, 0.717, 0.942], [0.793, 0.919, 0.802, 0.942], [0.874, 0.919, 0.891, 0.942]] as the row headers representing different years. Then, I single-out {the legend texts}[[0.131, 0.083, 0.228, 0.106], [0.131, 0.118, 0.355, 0.141]] for the column headers which are 'Patents' and 'Technological Innovation'. To populate the table, I find {the line points} for each year [[0.118, 0.865], [0.203, 0.773], [0.288, 0.677], [0.373, 0.580], [0.458, 0.564], [0.542, 0.600], [0.627, 0.628], [0.712, 0.464], [0.797, 0.244], [0.882, 0.100]] and [[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]] and then take the respective values.\nBased on the above thought, the answer is:\nYear,Patents,Technological_Innovation\n1,4758.0,2250.0\n2,5012.3,2348.2\n3,5278.7,2477.3\n4,5547.3,2245.1\n5,5590.8,2700.6\n6,5490.5,2456.7\n7,5412.7,2681.2\n8,5867.1,2350.8\n9,6476.6,2438.9\n10,6875.3,2600.5"
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "T4kJhDR5KaUh00C9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what type of trend can be noticed for 'Yearly Patents' and 'Yearly Technological Innovations'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I'll assess the trend line of {Yearly Patents}[[0.118, 0.865], [0.203, 0.773], [0.288, 0.677], [0.373, 0.580], [0.458, 0.564], [0.542, 0.600], [0.627, 0.628], [0.712, 0.464], [0.797, 0.244], [0.882, 0.100]]. It shows an erratic increase from 1st to 5th year, followed by decrease and then another increase, and then again followed by decrease.\nNext, when observing the line for {Yearly Technological Innovations}[[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]], an unpredictable trend is noticed. The values show a steep increase followed by a drop, then another increase followed by a downward trend, eventually observing an upward direction again.\nBased on the above thought, the answer is:\nBased on the line chart, an irregular trend can be observed in both 'Yearly Patents' and 'Yearly Technological Innovations'. For 'Yearly Patents', there is an initial increase followed by a decrease, another increase, and finally a larger decrease towards the end. For 'Yearly Technological Innovations', the trend is similar with steep increases and decreases, suggesting no consistent pattern."
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "7Bcai1PvmRIMRqLp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average yearly technological innovation over the 10 year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average yearly technological innovation, I need to locate {the line points for technological innovation}[[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]]. The values for these points are: 2250.0, 2348.2, 2477.3, 2245.1, 2700.6, 2456.7, 2681.2, 2350.8, 2438.9, 2600.5. The average can be calculated by adding up all these values and dividing by the number of years, which is 10.\nBased on the above thought, the answer is:\n2454.93"
+            }
+        ],
+        "id_raw": 430
+    },
+    {
+        "id": "84gjgUvzqsP5M50S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data represented in the line chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first recognize {the x-axis labels}<r>[0.048, 0.939, 0.107, 0.957] , [0.144, 0.939, 0.199, 0.957] , [0.250, 0.939, 0.281, 0.957] , [0.337, 0.939, 0.382, 0.957] , [0.427, 0.939, 0.479, 0.957] , [0.535, 0.939, 0.559, 0.957] , [0.611, 0.939, 0.670, 0.957] , [0.699, 0.939, 0.771, 0.957] , [0.802, 0.939, 0.855, 0.957] , [0.891, 0.939, 0.954, 0.957]</r> as the row labels representing the different design decor elements. Next, I need to extract the corresponding values for both Rating and Innovation lines. The Rating line has the following points: {4.2, 7.5, 6.7, 7.8, 5.6, 8.5, 4.2, 5.7, 7.8, 8.9}<r>[0.078, 0.087, 0.922, 0.887]</r>, and the Innovation line displays these points: {6.1, 5.2, 5.0, 4.3, 3.9, 3.2, 4.1, 2.8, 1.5, 0.8}<r>[0.078, 0.087, 0.922, 0.887]</r>\nBased on the above thought, the answer is:\nDecor Element,Rating,Innovation\nColor Choice,4.2,6.1\nTypography,7.5,5.2\nLayout,6.7,5.0\nCreativity,7.8,4.3\nProportions,5.6,3.9\nStyle,8.5,3.2\nFunctionality,4.2,4.1\nMaterial Choice,5.7,2.8\nUniqueness,7.8,1.5\nSustainability,8.9,0.8"
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "apO6wFA2OooovS1p",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which design elements seem to show low design innovation? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which elements show low design innovation, I need to examine the line plot for the Innovation Index. Looking at {the points on the Innovation line}<r>[0.078, 0.087, 0.922, 0.887]</r>, the elements with values less than 3 (which I will consider low) are \"Style\", \"Material Choice\", \"Uniqueness\", and \"Sustainability\". This shows that the Design Innovation Index is relatively low for these elements.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, \"Style\", \"Material Choice\", \"Uniqueness\", and \"Sustainability\" seem to show low design innovation. Their values are all below 3 on the Design Innovation Index, indicating a lower degree of innovation compared to other design elements."
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "zR9hyzfX8AXegApL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum values for the Rating line?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values for the Rating line, I need to locate {the points on the Rating line}<r>[0.078, 0.087, 0.922, 0.887]</r> and extract the corresponding values: 4.2, 7.5, 6.7, 7.8, 5.6, 8.5, 4.2, 5.7, 7.8, 8.9. The maximum value is 8.9 and the minimum value is 4.2. The difference between the maximum and minimum is therefore 8.9 - 4.2 = 4.7.\nBased on the above thought, the answer is:\n4.7"
+            }
+        ],
+        "id_raw": 431
+    },
+    {
+        "id": "R9HYk50d1USxwkwi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values from the horizontal line chart showing the performance metrics in a table format, including each category (AI, Blockchain, IoT, VR/AR) with the count for Wins, Ties, and Losses?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the horizontal line chart, I locate the bars representing each category: {AI bar}<r>[0.180, 0.200, 0.850, 0.230]</r>, {Blockchain bar}<r>[0.180, 0.260, 0.800, 0.290]</r>, {IoT bar}<r>[0.180, 0.320, 0.870, 0.350]</r>, and {VR/AR bar}<r>[0.180, 0.380, 0.900, 0.410]</r>. Each segment of the bar denotes a metric: Win, Tie, Loss, with proportional lengths and center labels indicating their counts. Extracting the counts from these segments through visual estimation provides the values needed.\nBased on the above thought, the answer is:\nCategory,Wins,Ties,Losses\nAI,320,100,60\nBlockchain,300,80,60\nIoT,345,70,60\nVR/AR,360,50,60"
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "NpKDdUREl5oEJCke",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology shows a decreasing trend in tie counts and what does this suggest about its competitive position?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the {bars}[[0.180, 0.200, 0.850, 0.230], [0.180, 0.260, 0.800, 0.290], [0.180, 0.320, 0.870, 0.350], [0.180, 0.380, 0.900, 0.410]], I observe a general decrease in the lengths and labeled counts of the Tie segments from AI to VR/AR. AI has 100 ties, decreasing through Blockchain and IoT, with VR/AR having the lowest at 50 ties. This suggests that as the technological complexity increases from AI through Blockchain, IoT, to VR/AR, these technologies have stronger competitive positions, which might be reflected in fewer ties because they increasingly outperform competitors instead of equaling them.\nBased on the above thought, the answer is:\nVR/AR shows a decreasing trend in tie counts, suggesting its stronger competitive position in the ecosystem as it rarely ties and more frequently outperforms the competitors, highlighting a clear technological leadership."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "xG5GSPLWIHc0AJtq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of confrontations recorded for Blockchain technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total number of confrontations for Blockchain technology, I examine the lengths and labels of the segments in {Blockchain bar}<r>[0.180, 0.260, 0.800, 0.290]</r>. The labels inside each segment show the counts for Wins, Ties, and Losses: 300, 80 and 60 respectively. Summing these values gives the total number.\nBased on the above thought, the answer is:\n440"
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "L8Oy4nrimDN03xzV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you rearrange the information presented in the line chart into a tabular format with the historical eras as rows and the three key factors (Population Growth, Cultural Development, Technological Advancement) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure! I would start by using {the x-axis tick labels}[[0.104, 0.761], [0.199, 0.761], [0.296, 0.761], [0.395, 0.761], [0.488, 0.761], [0.584, 0.761]] as the row headings in our table, representing different historical eras. Then, {the sub-legends or labels}[[0.036, 0.133, 0.098, 0.169], [0.036, 0.088, 0.103, 0.123], [0.036, 0.044, 0.118, 0.078]] will serve as column headers representing Population Growth, Cultural Development, and Technological Advancement respectively. Finally, I would find {the line points}[[0.110, 0.423], [0.194, 0.433], ..., [0.601, 0.800], [0.690, 0.831]] based on intersections on these lines with the x-axis and these will represent the values for each category in a specific era.\nBased on the above thought, the answer is:\nEra, Population Growth, Cultural Development, Technological Advancement\nPrehistoric, 1, 3, 2\nAncient, 2, 4, 5\nClassical, 3, 6, 7\nMedieval, 4, 7, 8\nEarly Modern, 7, 8, 9\nModern, 10, 10, 11"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "xwGQhyNbK7tUTR1R",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which key factor exhibited the most significant acceleration in the course of history and civilization? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To address this question, I need to examine the changes in the lines for Population Growth {Population Growth}[0.104, 0.423, 0.584, 0.800], Cultural Development {Cultural Development}[0.104, 0.308, 0.584, 0.698], and Technological Advancement {Technological Advancement}[0.104, 0.385, 0.584, 0.831]. The Population Growth line reflects a rapid growth trend, particularly during the modern era. However, the Technological Advancement line illustrates a steep and sudden increase from the early modern era, exhibiting the sharpest rise among all three factors. The Cultural Development line shows moderate growth over time, with a substantial uptick during the modern era, but its growth is less dramatic than that of Technological Advancement.\nBased on the above thought, the answer is:\nThe factor exhibiting the most significant acceleration in the course of history and civilization is Technological Advancement. This is because although all three factors exhibit growth across different timeframes, Technological Advancement is characterized by a sharp, significant upwards trajectory especially from the early modern era, indicating an intense acceleration, which is greater than both Population Growth and Cultural Development."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "hSMtP7uzwOEe1HCH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, what is the percentage increase in Population Growth from Prehistoric to Modern era?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to locate the points for the Prehistoric epoch {Population Growth}|<r>[0.104, 0.423]</r> and the modern era {Population Growth}|<r>[0.584, 0.800]</r> on the Population Growth line. The coordinates indicate the respective values for Population Growth in the Prehistoric epoch (value 1) and the modern era (value 10). I can then compute the percentage increase using the formula: (final - initial)/initial * 100%.\nBased on the above thought, the answer is:\n900%."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "kFrlJ59Ou9UKrPjz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the given statistical information, specifying the month, population growth rate, and average monthly income, into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the data into a tabular format, I need to identify the relevant elements of the line chart. {The x-axis tick labels}[[0.109, 0.919, 0.118, 0.942], ... , [0.873, 0.919, 0.891, 0.942]], which corresponds to each month of the year. Then, I take each point in {the line for Population Growth Rate}[[0.113, 0.865],..., [0.882, 0.100]], which correspond to the population growth rate for each month. Lastly, I take each point from {the line for Average Monthly Income}[[0.113, 0.865],..., [0.882, 0.100]], which indicate the average monthly income for each respective month.\nBased on the above thought, the answer is:\nMonth,Population Growth Rate(%),Average Monthly Income($)\nJanuary,0.1,2000\nFebruary,0.15,2050\nMarch,0.2,2100\nApril,0.18,2150\nMay,0.22,2200\nJune,0.25,2250\nJuly,0.3,2300\nAugust,0.32,2400\nSeptember,0.33,2500\nOctober,0.35,2600\nNovember,0.4,2750\nDecember,0.45,2900"
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "1tzMVSSBI0wuoije",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, other than a steady rise in population growth rate, what significant trend can you observe and relate to it?"
+            },
+            {
+                "from": "gpt",
+                "value": "From an analytical perspective based on the line chart, one trend that reflects the steady rise in the {population growth rate}[0.065, 0.865, 0.065, 0.100] is a corresponding increase in the {average monthly income}[0.065, 0.865, 0.984, 0.100]. As the percentage of population growth increases from January to December, the average monthly income also increases from $2000 to $2900 as depicted in the line chart.\nBased on the above thought, the answer is:\nThe significant trend that corresponds to the steady rise in population growth rate is the consistent increase in the average monthly income. This suggests an economical progression in sync with the population increase. This could potentially mean that as the population grows, so does the average income, indicating economic development and improved living standards."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "4RjHWYksLf6Ul9M7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average monthly increase in population growth rate from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average monthly increase, I need to locate {the line points for Population Growth Rate}[[0.113, 0.865], [0.183, 0.755], ..., [0.882, 0.100]] and extract the corresponding values: 0.1, 0.15, ... , 0.45. Hence, the average monthly increase in population growth rate would be (Final Rate - Initial Rate) divided by number of months, which is (0.45 - 0.1)/12 = 0.029.\nBased on the above thought, the answer is:\n0.029"
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "P7W5QEvWATtc6Qfy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the raw data in a structured table format with the cultural values as the rows and their corresponding areas of increased, unchanged, and decreased attitudes as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data, I start from the {y-axis tick labels}[{'x': 2012, 'label_coordinates': '[0.100, 0.919, 0.135, 0.942]'},...,'{'x': 2020, 'label_coordinates': '[0.857, 0.919, 0.892, 0.942]'},] obtained from the bars, these provide the row data which are different cultural values. Then I progress to the areas of the bars represented by {the line for Annual Concerts}[[0.117, 0.330],...,[0.874, 0.865]], each area indicating the percentage of increased, unchanged and decreased attitudes towards these cultural values. Finally, I relate these areas in the bars with the respective numerical percentages contained in the raw data.\nBased on the above thought, the answer is:\nCultural_Values,Increased%,Unchanged%,Decreased%\nReligious_Beliefs,37.0,55.0,8.0\nFamily_Bonds,45.0,46.5,8.5\nFriendship,33.4,47.5,19.1\nSocial_Status,28.7,35.8,35.5\nMaterial_Wealth,39.4,35.2,25.4\nPersonal_Happiness,68.7,20.6,10.7\nCan-do_Attitude,70.1,18.9,11.0\nEducational_Prestige,43.0,37.0,20.0"
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "n3vNvnMgJZ6pHDGZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what stood out the most when examining the attitudes towards the various cultural values?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking closely at the chart, I can gather insights on the different cultural values. The bar plot of {Personal Happiness}[0.874,0.178] and {Can-do Attitude}[0.790,0.166] distinctly shows a very significant increase in comparison to other values. This clearly indicates that these two cultural values are the most prominent in terms of increasing attitudes over the period considered. Additionally, {Religious Beliefs}[0.212,0.598] and {Family Bonds}[0.307,0.573] display a sizable area of unchanged attitudes, indicating a dominant majority of people retaining their beliefs towards them.\nBased on the above thought, the answer is:\nThe most significant observations from the chart are the substantial increase in attitudes towards 'Personal Happiness' and 'Can-do Attitude'. These cultural values seem to be becoming more prominent for the individuals involved. Additionally, 'Religious Beliefs' and 'Family Bonds' see a relatively stable trend with a majority of unchanged attitudes, implying a strong adherence to these cultural values over time."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "ccp2gPjmJ0KE6faW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference between the percentage of increased attitudes towards 'Religious Beliefs' and 'Personal Happiness' based on the bars plotted in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I first locate the bars on the plot corresponding to 'Religious Beliefs' and 'Personal Happiness'. Let's look at {the Religious Beliefs bar}[[0.212, 0.598]], which shows an increase in attitudes represented by the blue area of the bar, from the structured raw data, this corresponds to 37.0%. Next, I examine {the Personal Happiness bar}[[0.307, 0.193]], the area in blue indicates an increase in attitudes that corresponds to 68.7% from the structured raw data. The difference in percentages is therefore 68.7% - 37.0% = 31.7%.\nBased on the above thought, the answer is:\n31.7%."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "b9FqikkozAswm5gM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the stacked bar chart into a table, with columns representing prevalence levels and rows representing the different social issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the stacked bar chart into a table, I will start by looking at {the y-axis tick labels}[[0.072, 0.631, 0.091, 0.644], [0.072, 0.571, 0.094, 0.584], [0.072, 0.511, 0.080, 0.524], [0.072, 0.451, 0.092, 0.464], [0.072, 0.390, 0.099, 0.403], [0.072, 0.333, 0.099, 0.346], [0.072, 0.273, 0.094, 0.286], [0.072, 0.213, 0.091, 0.226], [0.072, 0.152, 0.093, 0.165], [0.072, 0.092, 0.091, 0.105], [0.072, 0.032, 0.097, 0.045], [0.072, -0.028, 0.094, -0.015]] to determine the row labels which represent different social issues. Then I identify {the bar widths for each segment}[[0.114, 0.622, 0.292, 0.637], [0.114, 0.562, 0.271, 0.577], [0.114, 0.502, 0.256, 0.517], [0.114, 0.443, 0.320, 0.457], [0.114, 0.382, 0.289, 0.396], [0.114, 0.325, 0.295, 0.339], [0.114, 0.265, 0.270, 0.279], [0.114, 0.205, 0.278, 0.219], [0.114, 0.144, 0.299, 0.158], [0.114, 0.084, 0.292, 0.098], [0.114, 0.024, 0.284, 0.038], [0.114, -0.036, 0.275, -0.022]] and extract the length of each segment which indicates the percentage distribution. Finally, from {the legend texts}[[0.436, 0.944, 0.463, 0.958], [0.436, 0.904, 0.455, 0.918], [0.436, 0.863, 0.449, 0.877]] I identify the column labels, which represent the different levels of prevalence.\nBased on the above thought, the answer is:\nSocial Issues,High, Medium, Low\nEconomic Exploitation,43, 32, 25\nGender Discrimination,48, 27, 25\nIneffective Law Enforcement,37, 32, 31\nSocial Rights Violation,51, 29, 20\nHealth Discrimination,45, 30, 25\nEducation Inequality,43, 31, 26\nRacial Discrimination,41, 30, 29\nHealthcare Inequality,42, 30, 28\nLabour Exploitation,47, 28, 25\nPolitical Corruption,46, 29, 25\nSocial Discrimination,44, 30, 26"
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "cDkfVbFlOPgejoy7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social issue has the highest and which one has the lowest 'high' prevalence? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the social issues with the highest and lowest 'high' prevalence, I need to compare {the lengths of the blue segments for the bars}[[0.114, 0.622, 0.292, 0.637], [0.114, 0.562, 0.271, 0.577], [0.114, 0.502, 0.256, 0.517], [0.114, 0.443, 0.320, 0.457], [0.114, 0.382, 0.289, 0.396], [0.114, 0.325, 0.295, 0.339], [0.114, 0.265, 0.270, 0.279], [0.114, 0.205, 0.278, 0.219], [0.114, 0.144, 0.299, 0.158], [0.114, 0.084, 0.292, 0.098], [0.114, 0.024, 0.284, 0.038], [0.114, -0.036, 0.275, -0.022]]. The longest blue segment indicates the highest 'high' prevalence, while the shortest blue segment reflects the lowest 'high' prevalence.\nBased on the above thought, the answer is:\n'Social Rights Violation' has the highest 'high' prevalence, as indicated by the longest blue segment within its bar. On the other hand, 'Ineffective Law Enforcement' has the lowest 'high' prevalence, as indicated by the shortest blue segment within its bar. These lengths indicate the proportion of regions or countries suffering significantly from these specific social issues."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "5DAtU0fczMzoc9Zl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of 'high' prevalence for 'Social Rights Violation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the 'high' prevalence percentage for 'Social Rights Violation', I need to identify {the 'Social Rights Violation' bar}[[0.114, 0.443, 0.320, 0.457]] and measure the segment representing 'high'. The length of this segment reflects the percentage in the 'high' category for 'Social Rights Violation'.\nBased on the above thought, the answer is:\n51."
+            }
+        ],
+        "id_raw": 437
+    },
+    {
+        "id": "QptHBo5c5fkfNXVa",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table displaying the capital raised by each sector during each funding round as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on my understanding of the chart, I start by identifying the various funding rounds from {the x-axis labels}<r>[0.084, 0.919, 0.119, 0.942]</r> which serve as the columns. Next, I review {the legend}<r>[0.069, 0.074, 0.202, 0.151]</r> to allocate each sector to the rows. I then look at each datapoint such as {Biotechnology in Seed-round}<r>[0.101, 0.865]</r> and extract the respective values across all rounds for every sector to populate the table.\nBased on the above thought, the answer is:\nRound,Sector,Seed,Series A,Series B,Series C\nBiotechnology,100,200,250,220\nInternet and Software,150,300,280,210\nEnergy,200,180,160,150\nTelecommunication,120,230,210,200\nFinancial Services,130,220,200,230\nHealthcare,180,290,270,190"
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "mI0vycgW1Msp2eAn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows the most consistent growth in venture capital from Seed to Series C according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine consistency, I assess the line trajectories of each sector. For instance, {Biotechnology}<r>[0.102, 0.865, 0.886, 0.233]</r> shows increasing amounts from Seed to Series B followed by a slight decline to Series C, inferring a mostly growing trend. In comparison, other sectors like {Energy}<r>[0.101, 0.865, 0.886, 0.233]</r> display declines or instability. Telecommunications, however, increases and decreases throughout the rounds, reflecting inconsistency.\nBased on the above thought, the answer is:\nBiotechnology shows the most consistent growth in venture capital from Seed to Series C as its fundraising amount mostly increases across the rounds, distinguishing it from other sectors prone to declines or instability in their growth trends."
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "mkFV8T3lJRuUsW4S",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average capital raised by the Healthcare sector across all funding rounds?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate each value associated with the Healthcare sector across different rounds from points like {Healthcare in Seed-round}<r>[0.101, 0.865]</r> which are 180, 290, 270, 190. I sum these values up to 930 and then divide by the number of funding rounds, which is 4.\nBased on the above thought, the answer is:\n232.5"
+            }
+        ],
+        "id_raw": 438
+    },
+    {
+        "id": "lXql6P3uFVdlUfbk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the data represented by the line chart into a structured table with Months as columns and Series as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data to a tabular form, I first identify {the x-axis tick labels}[[0.114, 0.919, 0.123, 0.942], [0.185, 0.919, 0.194, 0.942], [0.256, 0.919, 0.265, 0.942], [0.327, 0.919, 0.336, 0.942], [0.398, 0.919, 0.407, 0.942], [0.469, 0.919, 0.478, 0.942], [0.540, 0.919, 0.549, 0.942], [0.611, 0.919, 0.620, 0.942], [0.682, 0.919, 0.691, 0.942], [0.748, 0.919, 0.766, 0.942], [0.819, 0.919, 0.837, 0.942], [0.890, 0.919, 0.908, 0.942]], which represent the months. I then identify the {legend texts}[[0.131, 0.083, 0.212, 0.106], [0.131, 0.118, 0.240, 0.141]] that represent different series. Lastly, I look at {the line points for AI Research}[[0.119, 0.828], [0.190, 0.646], [0.260, 0.728], [0.331, 0.865], [0.402, 0.464], [0.473, 0.282], [0.544, 0.555], [0.615, 0.510], [0.686, 0.419], [0.757, 0.210], [0.828, 0.173], [0.899, 0.100]] and {the line points for ML Deployment}[[0.119, 0.865], [0.190, 0.428], [0.260, 0.646], [0.331, 0.755], [0.402, 0.210], [0.473, 0.100], [0.544, 0.319], [0.615, 0.428], [0.686, 0.537], [0.757, 0.755], [0.828, 0.646], [0.899, 0.865]] to determine the values for each series and each month.\nBased on the above thought, the answer is:\nMonth,AI Research,ML Deployment\n1,1000,20\n2,1100,24\n3,1055,22\n4,980,21\n5,1200,26\n6,1300,27\n7,1150,25\n8,1175,24\n9,1225,23\n10,1340,21\n11,1360,22\n12,1400,20"
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "lT7k2NzGpAS3vIQY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can we describe the general trend of ML Deployment based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend of ML Deployment, I examine {the line for ML Deployment}[0.119, 0.865, 0.899, 0.100]. It starts high at 20 in {January}[0.114, 0.919], rises slightly to 24 in {February}[0.185, 0.919], before beginning a downward trajectory that continues through the year till {December}[0.890, 0.919] when it dips to 20. While there are minor fluctuations in between, the overall trend is a decrease.\nBased on the above thought, the answer is:\nThe general trend of ML Deployment, as depicted in the line chart, shows a downward trajectory over the year. While there are minor fluctuations, the values begin relatively high in the beginning of the year and end at a lower value by the end of the year, indicating a decrease in ML Deployment over the course of the year."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "SNahGKJ9djql6RDZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_22_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest value reached by AI Research over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value reached by AI Research, I need to locate {the line points for AI Research}[[0.119, 0.828], [0.190, 0.646], [0.260, 0.728], [0.331, 0.865], [0.402, 0.464], [0.473, 0.282], [0.544, 0.555], [0.615, 0.510], [0.686, 0.419], [0.757, 0.210], [0.828, 0.173], [0.899, 0.100]] and extract the corresponding values: 1000, 1100, 1055, 980, 1200, 1300, 1150, 1175, 1225, 1340, 1360, 1400. The maximum value is 1400 corresponding to {December}[0.890, 0.919, 0.908, 0.942].\nBased on the above thought, the answer is:\n1400."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "z4vpUjf4JAV5Dtpv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you represent the data displayed on the line chart into a structured table format with Risk types as rows and the corresponding Risk Scores as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the information on the line chart into a tabular format, I first acknowledge the X-axis labels that represent various Risk types which include {Risk types}['Political'<r>[0.109, 0.734, 0.188, 0.854]</r>, 'Economic'<r>[0.229, 0.734, 0.325, 0.878]</r>, 'Industry'<r>[0.363, 0.734, 0.447, 0.860]</r>,'Operational'<r>[0.477, 0.734, 0.590, 0.903]</r>, 'Technology'<r>[0.608, 0.734, 0.716, 0.896]</r>, 'Compliance'<r>[0.734, 0.734, 0.847, 0.904]</r>, 'Reputation'<r>[0.866, 0.734, 0.972, 0.893]</r>].  \nI then look at the corresponding lines or individual data points representing the risk scores with {Risk_score}<r>[0.148, 0.124, 0.919, 0.682]</r> labeled against each of these types. This should enable me to convert and formulate the chart data into a coherent table.\nBased on the above thought, the answer is:\nRisk Type, Risk Score\nPolitical, 80\nEconomic, 75\nIndustry, 70\nOperational, 60\nTechnology, 50\nCompliance, 40\nReputation, 10"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "VWkbf7pBh9Dt7ddu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an assessment based on the risk scores outlined in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an assessment, I need to look at individual data points designated by their corresponding {Risk score}<r>[0.148, 0.124, 0.919, 0.682]</r> to determine the gravity of risk associated with each Risk Type. With this, Political, Economic, Industry, and Operational risks all stand at the upper half of the scale indicating them to be of high risk, Technology risks fall right in the middle, and Compliance and Reputation risks fall in the lower half of the risk scale, signifying their lower risk levels.\nBased on the above thought, the answer is:\nThe chart indicates that Political, Economic, Industry, and Operational risks scores are higher \u2013 denoting higher risk. While Technology risks have a neutral score, Compliance and Reputation risks are on the lower end, indicating less risk associated with these categories. Hence, it seems more efforts might be required to mitigate the Political, Economic, Industry, and Operational risks."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "wiIIMjXdsYzmL9pz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many risk types have a risk score above 50?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the number of risk types with a score above 50, I look at the individual data points with {Risk score}<r>[0.148, 0.124, 0.919, 0.682]</r>: Political(80), Economic(75), Industry(70), Operational(60), Technology(50), Compliance(40), and Reputation(10). By comparing these numbers, I can see that the first four risk types have a risk score above 50.\nBased on the above thought, the answer is:\n4"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "F2QPR7THdxX6YPLN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart detailing activities and their average engagement data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the structure of the line chart, the x-axis labels represent the different activities which would become the rows in the table. The color-coded lines represent categories, which will form the columns, and the coordinates of each point represents the engagement score for each category of activity. Therefore, I must first pull the labels for the x-axis, {xtick labels}[[0.070, 0.664, 0.131, 0.750], [0.165, 0.664, [0.323, 0.664, 0.401, 0.773], [0.457, 0.664, 0.529, 0.765], [0.595, 0.664, 0.654, 0.747], [0.704, 0.664, 0.807, 0.809], [0.794, 0.664, 0.979, 0.923]]. Next, I locate {the legend texts}[[0.681, 0.066, 0.868, 0.094], [0.681, 0.108, 0.853, 0.136], [0.681, 0.150, 0.824, 0.178]] to obtain the column headers. Lastly, I need to reference {line points} for each category at the respective activities to fill in the table. A table is then created to represent the observed data.\nBased on the above thought, the answer is:\nActivities,Weekend Activities,Routine Activities,Total\nDinner,7.6,7.3,8\nWatching Movies,6.5,5.8,7\nTraveling,6,4.1,6\nPartying,5.75,3.6,5.75\nSports,5.6,3.1,5.6\nVolunteering,5.4,2.5,5.4\nGoing to Music Concerts,5.2,2.1,5.2"
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "wOGd2XntounmrTa5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell which activity has the highest engagement for Total activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which activity has the highest engagement for Total Activities, I would analyze {the points on the Total Activities line}<r>[0.100, 0.041], [0.231, 0.142], [0.362, 0.242], [0.493, 0.267], [0.624, 0.283], [0.755, 0.303], [0.886, 0.323]</r>. The highest point on this line corresponds to 'Dinner' activity at the coordinate {Dinner}<r>[0.100, 0.041]</r> with an engagement score of 8.0.\nBased on the above thought, the answer is:\nThe activity with the highest engagement for Total Activities according to the line chart is Dinner."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "SLZSVkVhW60HJv2n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average engagement rate difference between Weekend and Routine Activities for Partying?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average engagement rates for Partying are represented by the y-coordinates of the corresponding points of {Weekend Activities} line<r>[0.493, 0.267]</r> and {Routine Activities} line<r>[0.493, 0.484]</r>. The values are 5.75 for Weekend Activities and 3.6 for Routine Activities. The difference between these two values is 5.75 - 3.6.\nBased on the above thought, the answer is:\n2.15."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "gHBTmxLrGmnbMfeT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of the AI application success rates, from the bar chart, and format it into a table with Application Categories as rows and Outcome Percentages as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a tabular data structure from the data in the bar chart, I first identify the application categories by looking at {the y-axis tick labels} [0.263, 0.117, 0.337, 0.172], [0.356, 0.117, 0.420, 0.172], [0.447, 0.117, 0.527, 0.172], [0.538, 0.117, 0.617, 0.172], [0.629, 0.117, 0.708, 0.172], [0.720, 0.117, 0.798, 0.172],\u2020 which represent the row labels as the different AI applications. Next, I need to identify {the bar lengths}[[0.113, 0.237, 0.173, 0.280], [0.113, 0.340, 0.223, 0.384], [0.113, 0.443, 0.193, 0.484], [0.113, 0.546, 0.206, 0.586], [0.113, 0.648, 0.163, 0.688], [0.113, 0.752, 0.231, 0.794]] of each category to calculate the success percentages. Based on my analysis, the resulting table would look like this.\nBased on the above thought, the answer is:\nAI Application,Win,Tie,Loss\nNatural Language Processing,19.5,39.5,41\nChatbot,22,40,38\nVideo Games,10,70,20\nMachine Learning,20.5,42.5,37\nSpeech Recognition,15,42,43\nRobotics,24.5,38.5,37"
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "Pg5AoD8CZaU8qEcg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart's information, which AI application has shown the highest success rate and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which AI application has the highest success rate, I need to examine {the bar segment for wins}[[0.113, 0.237, 0.173, 0.280], [0.113, 0.340, 0.223, 0.384], [0.113, 0.443, 0.193, 0.484], [0.113, 0.546, 0.206, 0.586], [0.113, 0.648, 0.163, 0.688], [0.113, 0.752, 0.231, 0.794]]. The one with the greatest length represents the highest percentage of successful cases. In this case, Robotics has the longest win segment.\nBased on the above thought, the answer is:\nBased on the lengths of the win segments of each AI application, Robotics appears to have the highest success rate because its win segment is the longest compared to the other applications."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "m20StMl1fUhvzPrl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Video Games, what is the sum of Win and Loss percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "Locating the bar chart for {Video Games}[0.447, 0.117, 0.527, 0.172] and looking at {the bar lengths for Win and Loss}[[0.113, 0.443, 0.193, 0.484], [0.313, 0.443, 0.413, 0.484]], I can see the corresponding percentages are 10% and 20% respectively. To answer this question, I simply add these values together.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 443
+    },
+    {
+        "id": "NxkdjEUnyhfDDP3b",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tabulate the data for transaction volume of Bitcoin and Ethereum from 2016 to 2021 as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a tabular form, I observe the {year labels on the x-axis}[0.200, 0.909, 0.244, 0.932], [0.327, 0.909, 0.371, 0.932], [0.453, 0.909, 0.497, 0.932], [0.580, 0.909, 0.624, 0.932], [0.706, 0.909, 0.750, 0.932], [0.833, 0.909, 0.877, 0.932]]. I first note down these years which will form the row headers in the table. Then I see the {legend descriptions for Bitcoin and Ethereum}[0.861, 0.050, 0.932, 0.080] and [0.861, 0.094, 0.962, 0.124]. I use them as column headers in the table with \"Year\" as the first column. For the values, I find the data points for the lines of Bitcoin and Ethereum[0.222, 0.199, 0.855, 0.589] and [0.222, 0.372, 0.855, 0.806]\non each year and get their corresponding y-coordinate values. These y-coordinate values are then used to fill in the respective table cells.\nBased on the above thought, the answer is:\nYear,Bitcoin,Ethereum\n2016,85,40\n2017,95,60\n2018,60,35\n2019,75,55\n2020,90,70\n2021,105,85"
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "bX8UCPXxbZCkkqnC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the trend the chart demonstrates about the transaction volumes of Bitcoin and Ethereum from 2016 to 2021."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyse the trends, I examine {the lines representing Bitcoin and Ethereum on the chart}[0.222, 0.199, 0.855, 0.589] and [0.222, 0.372, 0.855, 0.806]. Bitcoin transaction volume been varied over the years. It started at 85 Billion Dollars in 2016, rose to 95 Billion Dollars in 2017, then fell drastically to 60 Billion Dollars in 2018. From 2019, it restarted its climb to 105 Billion Dollars by 2021. On the other hand, the Ethereum transaction volume shows a more steady increase from 2016 to 2021, from a starting value of 40 Billion Dollars and ending at 85 Billion Dollars in 2021.\nBased on the above thought, the answer is:\nThe line chart displays a fluctuating trend for Bitcoin transaction volume with a significant dip in 2018 followed by a steady increase, whereas Ethereum has shown a continual rise over the years from 2016 to 2021."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "tIIi2ImSoh44yeJ9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the peak transaction volume of Bitcoin from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the peak volume of Bitcoin across the years 2016 to 2021, I carefully examine {the line for Bitcoin}[0.222, 0.199, 0.855, 0.589]. By looking at the specific y-coordinates of the data points, I can find the peak. In this case, the maximum y-coordinate value corresponds to the year 2021 with 105 Billion Dollars.\nBased on the above thought, the answer is:\n105 Billion Dollars."
+            }
+        ],
+        "id_raw": 444
+    },
+    {
+        "id": "RD1J7z4iakc60rrC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table that lists Years as rows and Temperature Increase (\u00b0C) and CO2 Emission (billion ton) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the graph to a table I first take the values on {the x-axis}<r>[0.206, 0.869, 0.276, 0.904], [0.290, 0.869, 0.360, 0.904], [0.373, 0.869, 0.443, 0.904], [0.456, 0.869, 0.526, 0.904], [0.539, 0.869, 0.609, 0.904], [0.623, 0.869, 0.693, 0.904], [0.706, 0.869, 0.776, 0.904]</r> which represents the Row (Years). Then I get the Y-values for Temperature Increase and CO2 Emissions from {the plotted points [0.241, 0.729], [0.325, 0.614], [0.408, 0.499], [0.491, 0.383], [0.574, 0.268], [0.658, 0.176], [0.741, 0.130]} and {the CO2 values [0.241, 0.791], [0.325, 0.629], [0.408, 0.548], [0.491, 0.347], [0.574, 0.239], [0.658, 0.145], [0.741, 0.091]} corresponding to each Year.\nBased on the above thought, the answer is:\nYear,Temperature Increase (\u00b0C),CO2 Emission (billion ton)\n2000,0.4,6.4\n2005,0.45,7.6\n2010,0.50,8.2\n2015,0.55,9.7\n2020,0.6,10.5\n2025,0.64,11.2\n2030,0.66,11.6"
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "zSnznlqJycsdPrfo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trends in the line chart, does there seem to be a correlation between temperature increase and CO2 emissions? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the correlation between Temperature Increase and CO2 Emissions, I need to look at the trendlines for both: {the line for Temperature Increase}<r>[0.241, 0.130, 0.741, 0.729]</r> and {the line for CO2 Emissions}<r>[0.241, 0.091, 0.741, 0.791]</r>. I notice that as the values along the Temperature Increase line go up, so do those on the CO2 Emissions line. Both lines show a persistent upward trend. It suggests that as the temperature increases, the CO2 emissions have also been increasing, which implies a positive correlation.\nBased on the above thought, the answer is:\nYes, based on the line chart, there seems to be a positive correlation between Temperature Increase and CO2 Emissions. Both show an increasing trend over the same period, with higher temperatures associated with more CO2 emissions. Therefore, it can be inferred that an increase in temperature could potentially be linked to an increase in CO2 emissions. However, further statistical analysis would be required to confirm the strength and significance of this correlation."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "g98Ynxm68GwUxCL5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the CO2 Emission increase between the years 2000 and 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in CO2 Emission between the years 2000 and 2030, I need to locate {the CO2 Emissions value for the year 2000}<r>[0.241, 0.791]</r> and {the CO2 Emissions value for the year 2030}<r>[0.741, 0.091]</r> and calculate the difference. The CO2 Emissions value for the year 2000 is 6.4 billion tons, and for the year 2030 it is 11.6 billion tons. Therefore, the increase in CO2 Emission is 11.6 - 6.4 = 5.2 billion tons.\nBased on the above thought, the answer is:\n5.2 billion tons."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "6dpXVf5m8qnRqAUF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the line chart that illustrates innovation efficiency and its related deviation across multiple series into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build the table, we need to list all the series as rows and organize the 'Innovation Efficiency' and 'Outlier' as columns. We find {Series names}[[0.092, 0.909, 0.158, 1.002], [0.221, 0.909, 0.287, 1.002], [0.350, 0.909, 0.416, 1.002], [0.480, 0.909, 0.545, 1.002], [0.609, 0.909, 0.675, 1.002], [0.738, 0.909, 0.804, 1.002], [0.867, 0.909, 0.933, 1.002]] on the x-axis. Then, by extracting the y-values of the line points {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]], we get 'Innovation Efficiency' for each series. Finally, extracting numbers from the deviation labels {labels}[[0.054, 0.739, 0.196, 0.767], [0.183, 0.638, 0.326, 0.666], [0.312, 0.596, 0.455, 0.624], [0.441, 0.414, 0.584, 0.442], [0.570, 0.388, 0.713, 0.416], [0.699, 0.293, 0.842, 0.321], [0.829, 0.156, 0.971, 0.184]], we get the 'Outlier' for each series.\nBased on the above thought, the answer is:\nSeries,Innovation Efficiency,Outlier\nSeries1,45.9,3.2\nSeries2,51.8,2.9\nSeries3,54.3,3.7\nSeries4,64.9,3.5\nSeries5,66.4,3.6\nSeries6,72.0,3.4\nSeries7,80.0,3.3"
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "jFj6jNkRiqPVzK4B",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn observing the progression of innovation efficiency, which series experienced the greatest improvement and which one the least? Please explain."
+            },
+            {
+                "from": "gpt",
+                "value": "I'll compare the innovation efficiencies by looking at the y values of each series' point on the line plot {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]]. The efficiency increases as we move from {Series1}[0.125, 0.789] to {Series7}[0.900, 0.206]. The series experiencing the greatest improvement is Series7 with an efficiency of 80.0, up from Series1's 45.9. The series with the least improvement is Series2 with an efficiency of 51.8, a slight increase from Series1's 45.9.\nBased on the above thought, the answer is:\nThe series experiencing the greatest improvement in innovation efficiency is Series7. It has the highest innovation efficiency, 80.0, showing substantial growth from the precedent series. Conversely, Series2 has the least improvement with an innovation efficiency of 51.8, showing fairly minor growth from 45.9 in Series1."
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "xZ7CcXji1KnpLEdz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest innovation efficiency across all series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest innovation efficiency, I need to look at the y values corresponding to the highest point on the line. By checking the y values of all the points {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]], I can see that the highest point corresponds to the point with the least y coordinate, which is {point for Series7}[0.900, 0.206]. The associated innovation efficiency is 80.0.\nBased on the above thought, the answer is:\n80.0"
+            }
+        ],
+        "id_raw": 446
+    },
+    {
+        "id": "aEpJBLcH4lTIeIRN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart and create a table that includes years, FinTech Investment and Digital Currency Market Cap?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first identify the {x-axis labels}<r>[0.145, 0.912, 0.941, 0.936]</r> as the Years. The two line data: {FinTech Investment}<r>[0.167, 0.806, 0.919, 0.896]</r> and {Digital Currency Market Cap}<r>[0.167, 0.075, 0.919, 0.896]</r> are used as the rows of the table. The y-value for each point in the lines provides us with the required financial values in billions of dollars for each year.\nBased on the above thought, the answer is:\nYear,FinTech Investment (bn), Digital Currency Market Cap(bn)\n2009,1.0,0.0\n2010,1.3,0.0\n2011,2.1,0.1\n2012,6.2,0.2\n2013,10.4,1.2\n2014,27.1,5.4\n2015,46.7,9.9\n2016,68.0,17.5\n2017,111.8,600.0\n2018,143.0,130.0\n2019,168.0,200.0\n2020,195.0,650.0\n2021,220.0,2000.0"
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "YZC9XcOqDB7QQoC2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBoth FinTech investment and the digital currency market cap have grown over the years according to the line chart. However, which of the two has experienced more volatility during this period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I consider the year-to-year fluctuations in both data series on the line chart. The {FinTech Investment line}<r>[0.167, 0.806, 0.919, 0.896]</r> follows a relatively steady upward trend, with the y-values gradually increasing across the years.\nLooking at {the Digital Currency Market Cap line}<r>[0.167, 0.075, 0.919, 0.896]</r>, we see larger swings - for example, there is a sharp increase from 17.5 billion in 2016 to 600.0 billion in 2017, then it dips down to 130.0 billion in 2018, and jumps back up to 650.0 billion in 2020 and then to 2000.0 billion in 2021. This shows more dramatic year-to-year changes.\nBased on the above thought, the answer is:\nThe Digital Currency Market Cap has experienced more volatility during the period of 2009 to 2021 compared to FinTech Investment. This is due to the larger year-to-year changes in values for the Digital Currency Market Cap, such as the dramatic increase from 2016 to 2017 and the subsequent fall in 2018, followed by marked increases in 2020 and 2021."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "fAccxf4lhrq8gQdO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in FinTech Investment between 2009 and 2021 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase in FinTech Investment between 2009 and 2021, I have to locate {the point for 2009 on the FinTech Investment line}<r>[0.167, 0.896]</r> and extract the corresponding value of 1.0 billion. Similarly, I locate {the point for 2021 for FinTech Investment line}<r>[0.919, 0.806]</r> and extract the corresponding value of 220.0 billion. The total increase is the difference between these two values, which is 220.0 - 1.0 = 219.0 billion.\nBased on the above thought, the answer is:\n219.0 billion."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "AJuGZjLnifEp1KUu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you build a structured table displaying the changes in Popularity of Cyber Security, Artificial Intelligence, and Virtual Reality over the course of 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, I will identify {the X-axis tick labels}<r>[0.134, 0.875, 0.938, 0.923]</r> as the Year column - these range from 2010 to 2020. The line points for {Cyber Security}<r>[0.134, 0.817, 0.938, 0.449]</r>, {Artificial Intelligence}<r>[0.134, 0.749, 0.938, 0.111]</r>, and {Virtual Reality}<r>[0.134, 0.839, 0.938, 0.636]</r> correspond to their respective popularity values over these years. By plotting these, we get the tabular data.\nBased on the above thought, the answer is:\nYear, Cyber Security, Artificial Intelligence, Virtual Reality\n2010, 1.1, 2.0, 0.8 \n2011, 1.3, 2.3, 1.1 \n2012, 1.8, 2.9, 0.9 \n2013, 2.4, 3.2, 1.0 \n2014, 3.0, 3.8, 1.4 \n2015, 3.9, 4.5, 2.0 \n2016, 4.2, 6.0, 2.4 \n2017, 5.0, 7.0, 2.5 \n2018, 5.0, 8.0, 2.8 \n2019, 5.5, 9.0, 3.0 \n2020, 6.0, 10.5, 3.5"
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "qcbJR0z3ssEd4QWc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which technology has experienced the most consistent popularity increase over the past decade (2010-2020)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology has the most consistent popularity increase, I need to look at the lines for each technology. Observing {Cyber Security}<r>[0.134, 0.817, 0.938, 0.449]</r>, I see that its popularity increased at an almost steady rate from 1.1 in 2010 to 6.0 in 2020. Looking at {Artificial Intelligence}<r>[0.134, 0.749, 0.938, 0.111]</r>, I see that it had a greater rate of increase, from 2.0 in 2010 to 10.5 in 2020. Lastly, {Virtual Reality}<r>[0.134, 0.839, 0.938, 0.636]</r> also showed an increase, from 0.8 to 3.5 over the same period, but at a smaller rate than the others.\nBased on the above thought, the answer is:\nAccording to the line chart, Artificial Intelligence has experienced the most consistent popularity increase over the past decade. It started with a popularity of 2.0 in 2010 and reached 10.5 by 2020 - a more significant increase when compared to Cyber Security and Virtual Reality."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "cOnJw8U6joxKRBvB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Cyber Security reach a popularity score of 5.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year when Cyber Security reached a popularity score of 5.0, I should locate the points along the line for {Cyber Security}<r>[0.134, 0.817, 0.938, 0.449]</r>. I find the point for popularity 5.0 at coordinates <r>[0.697, 0.524]</r> and <r>[0.778, 0.524]</r>. The corresponding year is found by referencing the {x-axis tick labels}<r>[0.134, 0.875, 0.938, 0.923]</r> and is identified as 2017 and 2018.\nBased on the above thought, the answer is:\n2017 and 2018."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "hiE3lVfIVvLd2oGY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the compound stacked bar chart into a tabular format with comparison pairs as rows and Entrepreneurial Spirit and Entrepreneurship as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I identify {the x-axis tick labels}[[0.083, 0.945, 0.177, 0.964], [0.245, 0.945, 0.337, 0.964], ..., [0.92, 0.945,0.976, 0.964]] as the row labels corresponding to different comparison pairs of countries. For each country pair, I identify {the left segment of the bar}[[0.083, 0.640, 0.187, 0.845], [0.245, 0.450, 0.349, 0.760], ..., [0.92, 0.740, 0.976, 0.860]] and {the right segment of the bar}[[0.189, 0.640, 0.218, 0.760], [0.351, 0.450, 0.374, 0.660], ..., [0.978, 0.740, 0.999, 0.810]] as the value for Entrepreneurial Spirit and Entrepreneurship respectively.\nBased on the above thought, the answer is:\nComparison pair, Entrepreneurial Spirit, Entrepreneurship\nEngland VS Australia, 45.8, 30.2\nUSA VS India, 40.3, 26.8\nUSA VS Germany, 70.5, 15.9\nEngland VS Germany, 60.2, 20.5"
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "MUfj6c7lCOLyA8nP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the compound stacked bar chart, which country seems to have a lower Entrepreneurial Spirit when compared with other countries? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which country has a lower Entrepreneurial Spirit, I observe {the left segment of the bar}[[0.083, 0.640, 0.187, 0.845], [0.245, 0.450, 0.349, 0.760], ..., [0.92, 0.740, 0.976, 0.860]] associated with each country in different comparisons. I note that when Germany is compared with other countries, the Entrepreneurial Spirit seems to be lower for Germany. The comparisons are {Germany vs USA}[0.718, 0.945, 0.808, 0.964] and {Germany vs England}[0.856, 0.945, 0.922, 0.964] where the left segment of the bars are quite small indicating a lower value of Entrepreneurial Spirit for Germany.\nBased on the above thought, the answer is:\nBased on the chart, Germany seems to have a lower Entrepreneurial Spirit compared to other countries. This observation is supported by the relatively shorter left segment of the bars associated with the comparisons involving Germany, which represent the percentage of people with Entrepreneurial Spirit from Germany compared with other countries."
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "2uh95Mfcb4mYwnx7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of Entrepreneurship across all comparison pairs?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average value of Entrepreneurship, I need to find the values for Entrepreneurship from {the right segment of the bar}[[0.189, 0.640, 0.218, 0.760], [0.351, 0.450, 0.374, 0.660], ..., [0.978, 0.740, 0.999, 0.810]]. I will add up these values and divide by the total number of comparison pairs.\nBased on the above thought, the answer is:\n23.35"
+            }
+        ],
+        "id_raw": 450
+    },
+    {
+        "id": "RqYaGiSP5sYPLlZZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data behind the bar chart into a table format with different types of decline as rows and the percentage of user activity across different platforms (Facebook, Instagram, and Twitter) as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve this, I first eye on {the y-axis labels}<r>[0.055, 0.869, 0.122, 0.904],\n[0.189, 0.869, 0.255, 0.904],\n[0.322, 0.869, 0.389, 0.904],\n[0.456, 0.869, 0.522, 0.904],\n[0.589, 0.869, 0.656, 0.904],\n[0.723, 0.869, 0.789, 0.904],\n[0.865, 0.869, 0.914, 0.904]</r>, which represent the row labels, referring to the types of decline. Afterwards, I identify that the color-coded sections of the bar chart, for instance the points for Facebook are found here <r>[0.089, 0.074]</r>, <r>[0.222, 0.315]</r>, ..., <r>[0.890, 0.808]</r>, represent the percentages of user activity for each platform at different types of decline. Through this process, I generate the desired table showing the percentage user activity across different platforms for every identified type of decline.\nBased on the above thought, the answer is:\nDecline,Facebook,Instagram,Twitter\nContinued,68,32,20\nSubstantial,40,17,12\nAccelerated,24,10,8"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "37byMJQPNnMwteQB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trend displayed on the bar chart, which social media platform could be inferred to be losing user activity at the fastest rate and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand which platform is losing user activity the fastest, I have to analyze the decline in user activity from one type of decline to the next for each platform. Gazing at {the line for Facebook}<r>[0.089, 0.074, 0.890, 0.808]</r>, I see a drop from 68 to 40 to 24. For {Instagram}<r>[0.222, 0.315, 0.890, 0.808]</r>, the decline is from 32 to 17 to 10. Lastly, for {Twitter}<r>[0.090, 0.074, 0.890, 0.808]</r>, the decrease is from 20 to 12 to 8. This implies higher relative decline for Facebook than the other two platforms.\nBased on the above thought, the answer is:\nBased on the trends shown in the bar chart, Facebook seems to be losing user activity the fastest. This conclusion is formed from the large relative decreases in user activity on Facebook across the various types of decline, where it falls from 68 to 40 and finally to 24. While all three platforms are experiencing a fall, the rate of decline is larger for Facebook compared to Instagram and Twitter."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "Rhe3ReTG8lUyePDD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much decline in user activity on Instagram is observed between Continued decline and Accelerated decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To access the information, I locate the points on the chart corresponding to Instagram's user activity during Continued and Accelerated decline. So, I turn to {the point representing Instagram's activity during Continued decline}<r>[0.222, 0.315]</r> implying the value 32 and then to {the point representing Instagram's activity during Accelerated decline}<r>[0.890, 0.808]</r> implying the value 10. The difference in user activity can now be calculated as 32 minus 10 which leads to the answer.\nBased on the above thought, the answer is:\n22"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "vsZ8wacSluzkc1zr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the comparison of effectiveness between renewable and traditional energy sources into a table including the percentage of wins, ties, and losses for each scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the tabular representation of the data, I must first identify {the energy comparisons represented on the x-axis}[[0.187, 0.701, 0.257, 0.723], [0.278, 0.701, 0.340, 0.723], [0.365, 0.701, 0.442, 0.723]]. The {winning segments}[0.247, 0.403, 0.386, 0.262]] corresponds to column one, the percentage wins, {ties}[0.651, 0.598, 0.769, 0.601] column two and {losses}[0.816, 0.716, 0.982, 0.735] column three. I can then extract the lengths of the respective segments on the line representing each energy comparison for the corresponding values. The lengths are proportional to the percentages.\nBased on the above thought, the answer is:\nComparison,Win,Tie,Loss\nSolar vs Oil,44,28,28\nWind vs Natural Gas,49,21,30\nHydropower vs Coal,46,24,30"
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "x4W8Bv2zAZVInjMX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which renewable source of energy has the highest total effectiveness when compared to its traditional counterpart? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the total effectiveness (sum of wins and ties) of each renewable source against its traditional counterpart. For {Solar Energy vs Oil}[0.187, 0.701, 0.257, 0.723], the total effectiveness (44% wins + 28% ties) is 72%. For {Wind Energy vs Natural Gas}[0.278, 0.701, 0.340, 0.723], the total effectiveness (49% wins + 21% ties) is 70%. For {Hydropower vs Coal}[0.365, 0.701, 0.442, 0.723], the total effectiveness (46% wins + 24% ties) is 70%. Therefore, Solar Energy vs Oil has the highest total effectiveness.\nBased on the above thought, the answer is:\nBased on the chart, Solar Energy has the highest total effectiveness compared to its traditional counterpart, Oil. This is determined by summing the win and tie percentages, and comparing these totals for all energy sources. Solar Energy vs Oil has a total effectiveness of 72%, which is the highest among all comparisons."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "fiI7tJnPJIpnkgWw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor Solar Energy vs Oil, what is the total effectiveness of Solar Energy, considering only wins and ties?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total effectiveness of Solar Energy against Oil, I need to sum the percentages for wins and ties. The {percentage of wins can be obtained from the green line segment}[0.247, 0.403], which is 44%. The {percentage of ties can be obtained from the blue line segment}[0.651, 0.598], which is 28%. The total effectiveness is therefore 44% + 28% = 72%.\nBased on the above thought, the answer is:\n72%."
+            }
+        ],
+        "id_raw": 452
+    },
+    {
+        "id": "p5Phpeu9kVRsUN3O",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data on the influence of various media on public opinion into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I see data points represented along the x-axis with labels located at coordinates {TV News}<r>[0.10, 0.93]</r>, {Radio}<r>[0.30, 0.93]</r>, {Online News}<r>[0.50, 0.93]</r>, and {Social Media}<r>[0.70, 0.93]</r>. These labels will form the column headers. The y-axis values, which represent the percentage influence on public opinion, can be observed at their corresponding points along the line, identified visually from the graph and list coordinates, e.g., for TV News at <r>[0.10, 0.60]</r>.\nBased on the above thought, the answer is:\nMedia Type,Public Opinion Influence (%)\nTV News,20\nRadio,10\nOnline News,40\nSocial Media,60"
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "wF8wj7NIb7TX2zAc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the given trends, why might Radio be considered an outlier in influencing public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "In reviewing the line chart, I note the data points for {TV News}<r>[0.10, 0.60]</r>, {Radio}<r>[0.30, 0.85]</r>, {Online News}<r>[0.50, 0.45]</r>, and {Social Media}<r>[0.70, 0.25]</r>. The trend from TV News to Online News shows an increasing influence on public opinion. However, there is a noticeable dip at the Radio point, which has a significantly lower influence percentage compared to others. This deviation from the ascending trend marks Radio as an outlier.\nBased on the above thought, the answer is:\nRadio is considered an outlier because its influence on public opinion, at only 10%, is remarkably lower than the other media types. This deviation interrupts an otherwise increasing trend observed from TV News through to Social Media, indicating its less substantial impact in comparison."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "lSHt54IEKZlXE7c3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in public opinion influence between TV News and Social Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "The influence points for {TV News}<r>[0.10, 0.75]</r> and {Social Media}<r>[0.70, 0.25]</r> are given in the chart. Reading from the visual data, TV News has a public opinion influence of 20% and Social Media has an influence of 60%. To find the difference, I calculate \\(60\\% - 20\\%\\).\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 453
+    },
+    {
+        "id": "63I3YyjPYdiy9OH3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you create a structured table using the Year as rows and Trends as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.185, 0.869, 0.255, 0.904], [0.264, 0.869, 0.334, 0.904], [0.344, 0.869, 0.414, 0.904], [0.423, 0.869, 0.493, 0.904], [0.502, 0.869, 0.573, 0.904], [0.582, 0.869, 0.652, 0.904], [0.661, 0.869, 0.731, 0.904], [0.741, 0.869, 0.811, 0.904], [0.820, 0.869, 0.890, 0.904]] as the row labels representing the Year. Then I identify the trend labels for each corresponding year represented by {the points labels}[[0.122, 0.627, 0.318, 0.662], [0.186, 0.534, 0.412, 0.569], [0.253, 0.441, 0.504, 0.476], [0.373, 0.348, 0.544, 0.383], [0.417, 0.394, 0.658, 0.429], [0.519, 0.317, 0.715, 0.352], [0.571, 0.224, 0.822, 0.259], [0.690, 0.131, 0.861, 0.166], [0.742, 0.038, 0.968, 0.073]]. These are the column headers representing the different trends. I map each trend to its corresponding year to complete the table structure.\nBased on the above thought, the answer is:\nYear,Trends\n2022,Slow recovery\n2023,Marginal growth\n2024,Upward trajectory\n2025,Slow growth\n2026,Gradual recovery\n2027,Slow recovery\n2028,Upward trajectory\n2029,Slow growth\n2030,Marginal growth"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "w3cUsxCaoQB4hRh1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends illustrated in the line chart, how does the technological progress evolve over the years, based on the given Trend descriptions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the progression of the technological progress over the years, I observe {the line for technological progress}[0.220, 0.100, 0.855, 0.689] and the attached labels. The line represents the measure of technological progress over time, from 2.0 in 2022 to 3.9 in 2030. The adjacent labels to the points on the line provide a qualitative description of the pace of this progress. At the onset, we experience a 'Slow recovery' indicating a slow increase in technological progress. In 2023, we notice a 'Marginal growth', suggesting a minor increment. The 'Upward trajectory' in 2024 signifies a larger rise. This is followed by a 'Slow growth' in 2025 and a 'Gradual recovery' in 2026, suggesting a slowing down of progress and further catching up in 2026. 'Slow recovery' in 2027 leads to another 'Upward trajectory' in 2028, which points to a sizeable increase. However, we again experience a 'Slow growth' in 2029 followed by 'Marginal growth' in 2030, indicating a slowed pace towards the end.\nBased on the above thought, the answer is:\nThe technological progress, as depicted on the line chart, experiences varied rates of growth over the years. It begins with a slow recovery, sees minor and major increments - described as marginal growth and upward trajectory respectively. This growth slows down mid-period and recovers again before shooting up further. Towards the end, the progression decelerates, experiencing slow growth first and then a slight improvement. Overall, the progress increases from 2.0 in 2022 to 3.9 in 2030, but the speed of this increase fluctuates significantly."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "35ecnWoTiEdmQvlb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum values for TechProgress across all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values for TechProgress across all years, I need to focus on {the line points for TechProgress}[[0.220, 0.689], [0.299, 0.596], [0.379, 0.503], [0.458, 0.410], [0.538, 0.457], [0.617, 0.379], [0.696, 0.286], [0.776, 0.193], [0.855, 0.109]] and extract the corresponding values: 2.0, 2.3, 2.6, 2.9, 2.75, 3.0, 3.3, 3.6, 3.9. The maximum value is 3.9 corresponding to {2030}[0.820, 0.869, 0.890, 0.904], and the minimum value is 2.0 corresponding to {2022}[0.185, 0.869, 0.255, 0.904]. The difference between the maximum and minimum is therefore 3.9 - 2.0 = 1.9.\nBased on the above thought, the answer is:\n1.9."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "cEKFnG4ISnBaL1JM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a comparison table indicating the year against the returns for Mutual Funds and ETFs from the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the desired data into a table format, I first discern {the years on the x-axis}<r>[0.048, 0.912, 0.083, 0.936], [0.177, 0.912, 0.212, 0.936], ..., [0.950, 0.912, 0.985, 0.936]</r> as the rows of the table corresponding to each data point along the line for Mutual Funds {the line for Mutual Funds}<r>[0.130, 0.684, 0.903, 0.819]</r> and ETFs {the line for ETFs}<r>[0.130, 0.587, 0.903, 0.769]</r>. Then, I trace the data points from these lines to derive the returns for each year.\nBased on the above thought, the answer is:\nYear,Mutual Funds,ETFs\n2005,15.8,17.1\n2006,16.2,18.1\n2007,16.6,19.1\n2008,14.8,16.9\n2009,14.2,17.5\n2010,15.0,18.8\n2011,15.8,19.5\n2012,16.5,20.8\n2013,17.8,22.2\n2014,19.2,23.3\n2015,19.9,24.9\n2016,20.6,25.4\n2017,21.5,26.7"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "mdJ8iC7rMyH4bRJV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in returns for Futures from 2005 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will locate the starting and ending points on {the line for Futures}<r>[0.130, 0.310, 0.903, 0.672]</r> to determine the initial value in 2005, which is 24.1%, and then find the final value in 2017, which is 41.7%. By subtracting the initial value from the final value, I can compute the increase in returns over these years.\nBased on the above thought, the answer is:\n17.6%"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "riaZs2JXUabkQ64k",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the dual-axis line chart, can you extract the data into a structured table format, with 'Year' as rows and 'Global GDP' and 'Market Index' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile the tabular format from the line chart, I first identify {the x-axis tick labels}[[0.097, 0.891, 0.147, 0.919], [0.180, 0.891, 0.230, 0.919], [0.263, 0.891, 0.313, 0.919], [0.346, 0.891, 0.397, 0.919], [0.430, 0.891, 0.480, 0.919], [0.513, 0.891, 0.563, 0.919], [0.596, 0.891, 0.646, 0.919], [0.679, 0.891, 0.729, 0.919], [0.762, 0.891, 0.812, 0.919], [0.845, 0.891, 0.896, 0.919], [0.929, 0.891, 0.979, 0.919]] as the row labels symbolizing 'Year'. The 'Global GDP' and 'Market Index' represented respectively by {the line points for GDP}[[0.122, 0.870], [0.205, 0.870], [0.288, 0.870], [0.371, 0.870], [0.455, 0.870], [0.538, 0.870], [0.621, 0.870], [0.704, 0.870], [0.787, 0.870], [0.870, 0.870], [0.954, 0.870]] and {the line points for Market Index} [[0.122, 0.451], [0.205, 0.412], [0.288, 0.374], [0.371, 0.336], [0.455, 0.298], [0.538, 0.259], [0.621, 0.221], [0.704, 0.183], [0.787, 0.145], [0.870, 0.106], [0.954, 0.068]] and corresponding values presented beside the points, serve as column headers and data values in the table.\nBased on the above thought, the answer is:\nYear, Global GDP, Market Index\n2010, 3.1, 1100\n2011, 3.2, 1200\n2012, 3.4, 1300\n2013, 3.1, 1400\n2014, 3.3, 1500\n2015, 3.2, 1600\n2016, 3.4, 1700\n2017, 3.6, 1800\n2018, 3.8, 1900\n2019, 3.8, 2000\n2020, 2.8, 2100"
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "UvOR2PyVBzrjvA0N",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trendline in the dual-axis line chart, which feature shows a more pronounced change over the time period, Global GDP or the Market Index? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to deduce which feature shows a more pronounced change over time, I need to analyze the trendlines of both Global GDP and the Market Index. {The Global GDP line}[0.122, 0.870, 0.954, 0.870] demonstrates minor fluctuations around a near constant value of ~3.3, suggesting limited variability in this indicator. On the other hand, {the Market Index line}[0.122, 0.068, 0.954, 0.451] presents a clear upward trend from 1100 in 2010 to 2100 in 2020, indicating a substantial and consistent increase over this period.\nBased on the above thought, the answer is:\nBased on the analysis of the trendlines, the Market Index shows a more pronounced change over the time period. The line graph for Market Index reveals a constant upward trend, with a considerable increase from 1100 in 2010 to 2100 in 2020. In contrast, Global GDP remains relatively stable, hovering around value of 3.3 with minor fluctuations, thereby indicating less change."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "jZZCkstUXk9YwnbT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Market Index increase from the year 2010 to the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increment of Market Index from 2010 to 2020, I need to locate the values of Market Index in these years. The Market Index value of 2010 can be found at {the point of 2010}[0.122, 0.451], which reads 1100. The Market Index value of 2020 is at {the point of 2020}[0.954, 0.068], which is 2100. Thus, the increase in Market Index over this period is 2100 - 1100.\nBased on the above thought, the answer is:\n1000."
+            }
+        ],
+        "id_raw": 457
+    },
+    {
+        "id": "rTPo8WhnAxxjN0B0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the line graphs of Digital Currency Performance Over The Years into a structured table? Let's use the Years as columns and Digital Currencies as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into tabular data, I first identify {the x-axis tick labels}[[0.015, 0.910, 0.070, 0.939],[0.133, 0.910, 0.188, 0.939],[0.250, 0.910, 0.305, 0.939],[0.368, 0.910, 0.423, 0.939],[0.485, 0.910, 0.540, 0.939],[0.602, 0.910, 0.657, 0.939],[0.720, 0.910, 0.775, 0.939],[0.837, 0.910, 0.892, 0.939],[0.955, 0.910, 1.010, 0.939]], representing the columns or Years. Next, I locate {the legend texts}[[0.205, 0.146, 0.280, 0.175],[0.205, 0.190, 0.311, 0.219],[0.205, 0.233, 0.273, 0.262]] that serve as row labels or Digital Currency names. Each line graph, {Bitcoin}[[0.160, 0.418, 0.865, 0.855]], {Ethereum}[[0.160, 0.155, 0.865, 0.505]], and {Ripple}[[0.160, 0.365, 0.865, 0.680]], is then analyzed to get the performance value of each Digital Currency per Year.\nBased on the above thought, the answer is:\nDigital_Currency,2015,2016,2017,2018,2019,2020,2021\nBitcoin,50,60,70,60,59,61,75\nEthereum,70,75,73,74,80,85,90\nRipple,60,65,63,66,72,75,78"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "22tDHFgR7yzvGJEY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which Digital Currency seems to show the most stable performance over the years? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most stable Digital Currency, I study the fluctuations of the line graphs for each Digital Currency. {Bitcoin's line}[[0.160, 0.418, 0.865, 0.855]] shows performance values that vary between a low of 50 and a high of 75. \nFor {Ethereum's line}[[0.160, 0.155, 0.865, 0.505]], the performance ranges from 70 to 90, also showing fair amount of fluctuations.\nFinally, reviewing {Ripple's line}[[0.160, 0.365, 0.865, 0.680]], the performance values are between 60 and 78. \n\nAlthough all digital currencies show growth over the years, some have more variable growth rates than others.\nBased on the above thought, the answer is:\nBased on the line charts, Ripple seems to have the most stable performance over the years, despite the minor fluctuations. Its performance values range between 60 and 78, showing smaller differences for each consecutive year when compared to Bitcoin and Ethereum. It depicts less volatility over time."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "WAgn0FEtqcAScdhi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average performance of Ethereum over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average performance of Ethereum, I must analyze its line plot, {Ethereum}[[0.160, 0.155, 0.865, 0.505]]. The values for each year are 70, 75, 73, 74, 80, 85, and 90. The average is calculated as the sum of all these values divided by the number of years, which is 7.\nBased on the above thought, the answer is:\n78.14"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "UeWBCNSEXd1FEPJ1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data displayed in the line chart and present it in a structured table with Countries as rows and the two indices as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the line chart, I need to first identify the countries along {the horizontal axis}[[0.125, 0.795, 0.270, 0.902]], which will represent the rows of the table. Next, {the y-axis labels}[[0.039, 0.097, 0.056, 0.125]] give me the range of values for the two indices. The {line points}[[0.132, 0.108], [0.919, 0.692]] of the lines represent the values for the respective indices for each country. Using all these elements I can construct the table.\nBased on the above thought, the answer is:\nCountry, RGBA Population Index, International Trade Index\nChina, 100, 60\nUSA, 90, 70\nIndia, 85, 50\nRussia, 80, 65\nBrazil, 75, 45\nGermany, 70, 80\nUK, 65, 75\nFrance, 60, 70\nItaly, 55, 65\nCanada, 50, 60\nAustralia, 45, 50\nJapan, 40, 80"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "KFgUuqJYnGyfRkTz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which country appears to be the most open to international trade in relation to its population and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which country is most open to international trade in relation to its population, I need to consider both the {Population Index}[[0.430, 0.934, 0.621, 0.970]] and the {International Trade Index}[[0.015, 0.220, 0.033, 0.635]]. A high value on the International Trade Index in relation to the Population Index would suggest extensive international trade relative to the population. {Germany}[[0.525, 0.776]] and {Japan}[[0.132, 0.776]] both have an International Trade Index of 80, but Germany's Population Index of 70 is larger than Japan's, which is 40. Therefore, Japan has the highest International Trade Index relative to its Population Index.\nBased on the above thought, the answer is:\nFrom the chart, Japan appears to be the most open to international trade in relation to its population. Despite having a smaller population index compared to Germany, both have the same International Trade Index. This suggests that Japan has a higher ratio of international trade per citizen compared to Germany."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "Y9YIKDPlzmHj8g5F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_23_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the International Trade Index of Germany compared to Brazil?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the {point for Germany on the International Trade Index line}[[0.525, 0.108]] and the {point for Brazil on the International Trade Index line}[[0.591, 0.692]]. The corresponding values for these points are 80 and 45 respectively. So, the difference between Germany and Brazil is 80 - 45 = 35.\nBased on the above thought, the answer is:\n35."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "CZxW8MPmafwYS9C7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you extract the Age dataset and each of the three Line plots into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract chart information into a tabular format, I need to identify the data represented in the x and y axes and line plots. The {x-axis}<r>[0.512, 0.942, 0.565, 0.975]</r> labels represent the Age dataset: 20, 30, 40, 50, 60, 70, 80, 90, 100. The line plots for 'Physical Health Average', 'Environmental Quality', and 'Life Satisfaction' correspond to forceful points <r>[0.145, 0.163, 0.932, 0.635]</r>, <r>[0.145, 0.163, 0.932, 0.721]</r>, and <r>[0.145, 0.189, 0.932, 0.764]</r> respectively. The values for the line plots at each Age coordinate can be traced back and populated into the table\nBased on the above thought, the answer is:\nAge,Physical_Health_Average,Environmental_Quality,Life_Satisfaction\n20,75,85,70\n30,80,75,80\n40,85,70,82\n50,75,65,70\n60,70,60,60\n70,65,52,55\n80,55,45,40\n90,40,35,30\n100,30,20,15"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "5hY7z4oUKZvQeqGq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which factor seems to deteriorate the most as age increases and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which factor deteriorates the most with age, I need to observe the rate of decline in each of the line plots for 'Physical Health Average', 'Environmental Quality', and 'Life Satisfaction'. Starting at 75, {Physical Health Average}<r>[0.145, 0.163, 0.932, 0.635]</r> declines to 30 at age 100, indicating a decrease of 45 units. {Environmental Quality}<r>[0.145, 0.163, 0.932, 0.721]</r> decreases from 85 at age 20 to 20 at age 100, marking a decrease of 65 units. Lastly, {Life Satisfaction}<r>[0.145, 0.189, 0.932, 0.764]</r> starts at 70 and drops to 15, resulting in a decrease of 55 units.\nBased on the above thought, the answer is:\nBased on the line chart, Environmental Quality seems to deteriorate the most with an increasing age. This trend is observed in the fact that Environmental Quality starts at the highest point of 85 at age 20 and falls the most to 20 at age 100, indicating the most significant decrease among the three factors."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "eiY3Gb9KPlq1Ym9x",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what is the mean Environmental Quality from age 50 to age 70?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the mean Environmental Quality between age 50 and 70, I need to locate and sum the Environmental Quality values corresponding to ages 50, 60 and 70 in the line plot <r>[0.145, 0.163, 0.932, 0.721]</r>, which are 65, 60 and 52 respectively. I then divide this sum by the number of data points, which is 3.\nBased on the above thought, the answer is:\n59"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "a8PdKISAgR4DySk3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data showing the trends of Deforestation, Greenhouse Emissions, and Animal Extinction over multiple years into a tabular format with years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, I focus first on {the x-axis ticks}<r>[0.134, 0.864, 0.193, 0.899]</r>, which represent the years. For each row, I identify the corresponding {line points for Deforestation}<r>[0.163, 0.095, 0.932, 0.496]</r>, {Greenhouse Emissions}<r>[0.163, 0.553, 0.932, 0.627]</r>, and {Animal Extinction}<r>[0.163, 0.496, 0.932, 0.782]</r> for each year to extract the values.\nBased on the above thought, the answer is:\nYear,Deforestation,Greenhouse Emissions,Animal Extinction\n2001,1.25,0.8,0.4\n2002,1.4,0.75,0.45\n2003,1.6,0.68,0.5\n2004,1.55,0.67,0.6\n2005,1.3,0.70,0.65\n2006,1.2,0.75,0.7\n2007,1.1,0.77,0.75\n2008,1,0.78,0.8\n2009,0.95,0.78,0.85\n2010,0.9,0.79,0.9"
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "PqGRmeAMwhRliCeG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given trends in the chart, which environmental factor shows a consistent decline over the given time period? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which environmental factor presents a consistent decline, I analyze the trends in {the line for Deforestation}<r>[0.163, 0.095, 0.932, 0.496]</r>, {Greenhouse Emissions}<r>[0.163, 0.553, 0.932, 0.627]</r>, and {Animal Extinction}<r>[0.163, 0.496, 0.932, 0.782]</r>. Observing the Deforestation line, I see a consistent decrease over the years from 1.25 to 0.90. On the other hand, Greenhouse Emissions and Animal Extinction do not exhibit a steady decline throughout the observed period.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the rate of Deforestation presents a consistent decline over the years from 2001 to 2010. This is evident through the line plot which shows the highest value at the start of the period (1.25 in 2001) and arrives at the least value by the end (0.90 in 2010), illustrating a steady decrease."
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "arugzhckj80NxaCf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average rate of Deforestation from 2001 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average rate of Deforestation for the given time period, I find {the line points for Deforestation}<r>[0.163, 0.095, 0.932, 0.496]</r>. Extracting the values, I get: 1.25, 1.4, 1.6, 1.55, 1.3, 1.2, 1.1, 1, 0.95, 0.9. I sum these up and divide by the total number of years (10) to find the average.\nBased on the above thought, the answer is:\n1.225"
+            }
+        ],
+        "id_raw": 462
+    },
+    {
+        "id": "SHA08OB6NvTEPR3m",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table with 'Year' as the rows and 'Usage Percentage' as the columns for each category of use?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the data table from the Line chart, I will start by identifying the years from {the x-axis tick labels}[[0.146, 0.909, 0.181, 0.932], [0.228, 0.909, 0.263, 0.932], [0.310, 0.909, 0.345, 0.932], [0.392, 0.909, 0.427, 0.932], [0.474, 0.909, 0.509, 0.932], [0.557, 0.909, 0.592, 0.932], [0.639, 0.909, 0.674, 0.932], [0.721, 0.909, 0.756, 0.932], [0.803, 0.909, 0.838, 0.932], [0.885, 0.909, 0.920, 0.932]], which will serve as the rows. Next, I locate the three uses: {Internet Use}<r>[0.132, 0.055, 0.218, 0.078]</r>, {Mobile Use}<r>[0.132, 0.090, 0.208, 0.113]</r>, and {Social Media Use}<r>[0.132, 0.125, 0.250, 0.148]</r> from the legend representing the columns. Now to find the values, I locate the points for each category at their respective years and extract those values.\nBased on the above thought, the answer is:\nYear,Internet Use,Mobile Use,Social Media Use\n2005,0.05,0.05,0.01\n2006,0.1,0.07,0.02\n2007,0.15,0.1,0.03\n2008,0.2,0.12,0.04\n2009,0.25,0.15,0.05\n2010,0.3,0.18,0.06\n2011,0.35,0.2,0.07\n2012,0.4,0.3,0.1\n2013,0.45,0.35,0.15\n2014,0.5,0.4,0.2"
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "L34QTVz5Gi06Z6OP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, how does the trend of mobile use compare with internet use over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the trends of mobile use with internet use over the years, I will look at both {the line representing Mobile Use}<r>[0.163, 0.268, 0.903, 0.815]</r> and {the line representing Internet Use}<r>[0.163, 0.112, 0.903, 0.815]</r>. Throughout the plotted time frame from 2005 to 2014, both mobile and internet use show an increasing trend. However, it looks like the Internet usage started higher and consistently stays above Mobile usage until around 2012. After 2012, the gap between the two usage types becomes much smaller, suggesting that during this period, the growth rate of Mobile usage becomes significantly higher than that of Internet usage.\nBased on the above thought, the answer is:\nOver the years, both the use of mobile and internet has been increasing. However, the rate of increase in mobile use has been significantly higher, especially from 2012 onwards, as compared to that of Internet use. While both were at 0.05 in 2005, by 2014, mobile use reached 0.4, closing the gap with Internet use at 0.5. This indicates the rising popularity and accessibility of mobile technology."
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "HslGho7wBX9zgkpn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did Social Media Use increase from 2008 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Social Media Use from 2008 to 2014, I need to locate the {data points for Social Media Use in 2008 and 2014}[[0.410, 0.830], [0.903, 0.581]], which are correspondingly 0.04 and 0.2. The increase in usage can be found by subtracting the earlier value from the later one and then, to convert it into a percentage, multiplying by 100.\nBased on the above thought, the answer is:\n16%"
+            }
+        ],
+        "id_raw": 463
+    },
+    {
+        "id": "yJNWsrwgvJISx0jd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, can you formulate the pertinent data into a structured table, where the columns denote different financial periods and the rows represent Earnings and Expenses respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the line chart into tabulated data, I begin with identifying {the x-axis labels}[[0.145, 0.864, 0.204, 0.899], [0.314, 0.864, 0.372, 0.899], [0.482, 0.864, 0.541, 0.899], [0.651, 0.864, 0.709, 0.899]], which act as column headers or financial periods. Next, I interpret {the legend labels}[[0.763, 0.069, 0.899, 0.104],[0.763, 0.121, 0.902, 0.156]] as row descriptors, representing Earnings and Expenses. To populate the values, I scrutinize the {line points}[[0.174, 0.118], [0.259, 0.142], ..., [0.933, 0.294]] for each row at the respective periods and extract the values indicated.\nBased on the above thought, the answer is:\nFinancial Periods, 2010, 2011, 2012, 2013\nEarnings, 50, 40, 30, 20\nExpenses, 30, 40, 50, 60"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "gYm3a9JTbWnuAfyD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart trends, which financial component, Earnings or Expenses, exhibited a more aggressive trend over time and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which financial component has shown a more aggressive trend, I examine the inclinations in the lines for both Earnings and Expenses. Viewing {the line for Earnings}[0.174, 0.118, 0.933, 0.294], it can be seen that it starts at a high point, 50 and gradually descends to 20, revealing a decline in Earnings over the periods. \nOn the contrary, for {the Expenses line}[0.174, 0.278, 0.933, 0.807], starting at 30, it escalates to 60, reflecting an increasing trend in Expenses over the periods. The increase in Expenses is steeper compared to the decline in Earnings, indicating a more aggressive trend.\nBased on the above thought, the answer is:\nFrom the chart's trends, the financial component of Expenses displayed a more aggressive trend over time. As the Expenses line exhibits a steeper incline compared to the decline visible in the Earnings line, this indicates a faster rate of increase in Expenses across the periods compared to the decrease in Earnings. Thus, Expenses exhibit a more aggressive trend."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "0Idp0IPThi6OdcUT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the variance between the maximum and minimum Earnings recorded across the financial periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the maximum and minimum Earnings across all periods, I must allocate {the line points for Earnings}[[0.174, 0.118], [0.259, 0.142], [0.343, 0.166], [0.427, 0.182]], and extract the related values: 50, 40, 30, 20. The maximum value is 50 corresponding to {2010 period}[0.174, 0.839], and the minimum value is 20 corresponding to {2013 period}[0.427, 0.839]. Therefore, the difference between the maximum and the minimum Earnings is 50 - 20 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "2xODdWA0VAeqUmDn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you convert the sales data across the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from this line chart, I am firstly identifying {the x-axis labels}<r>[0.141, 0.864, 0.962, 0.899]</r> that correspond to the years. These will be the row labels. Secondly, I'm noticing the {legend}<r>[0.762, 0.055, 0.963, 0.222]</r> which tells me the three types of sales: Car Sales, Bike Sales, and EV Sales. These will be the columns of my table. For the values, I am examining {the line points} for car sales <r>[0.170, 0.091, 0.933, 0.305]</r>, bike sales <r>[0.170, 0.454, 0.933, 0.572]</r>, and EV sales <r>[0.170, 0.545, 0.933, 0.732]</r> respectively at each year and extracting the respective values attached.\nBased on the above thought, the answer is:\nYear,Car Sales,Bike Sales,EV Sales\n2010,100,50,20\n2011,120,70,25\n2012,130,60,28\n2013,140,65,32\n2014,135,60,35\n2015,130,68,40\n2016,125,72,45\n2017,120,70,48\n2018,115,67,52\n2019,110,65,55"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "eEeCbxM8BoV8vweI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line chart, which product line has the most consistent growth over the years, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product line has the most consistent growth over the years, I need to evaluate the trends shown in the line plots for Car Sales, Bike Sales and EV Sales. Inspecting the {line plot for Car Sales}<r>[0.170, 0.091, 0.933, 0.305]</r>, I see fluctuations with no clear trend of consistent growth. For both {Bike Sales}<r>[0.170, 0.454, 0.933, 0.572]</r> and {EV Sales}<r>[0.170, 0.545, 0.933, 0.732]</r>, there's a clear increase in sales from 2010 through to 2019. However, the increase in EV Sales over the years is steeper compared to Bike Sales, indicating a more consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, EV Sales demonstrates the most consistent growth over the years. While both Bike Sales and EV Sales show an increasing trend, the rise in EV Sales is more pronounced, suggesting a more uniform and steady level of growth. This consistent rise could signal a higher demand for electric vehicles over the years."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "q3sSfkNwkWG1Yeot",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum values for Car Sales across the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum sales for cars over the years, I need to locate and analyze the {line points for Car Sales}<r>[0.170, 0.091, 0.933, 0.305]</r>. These values are: 100,120,130,140,135,130,125,120,115 and 110. The maximum value is 140, corresponding to the year {2013}<r>[0.395, 0.864, 0.454, 0.899]</r>, and the minimum value is 110, corresponding to the year {2019}<r>[0.903, 0.864, 0.962, 0.899]</r>. Therefore, the difference between the maximum and minimum sales for cars is 140 - 110 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "ykJvuCZq0Nqmzpaw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a tabular format for the data displayed in the line chart where years are columns and learning modes represent rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line chart, I first look at {the x-axis labels}[[0.173, 0.912, 0.217, 0.936], [0.285, 0.912, 0.329, 0.936], [0.398, 0.912, 0.441, 0.936], [0.510, 0.912, 0.554, 0.936], [0.622, 0.912, 0.666, 0.936], [0.735, 0.912, 0.778, 0.936], [0.847, 0.912, 0.891, 0.936]], which correspond to the years and hence will be the table columns.\nNext, I identify {the labels in the legend}[[0.147, 0.055, 0.326, 0.078], [0.147, 0.090, 0.283, 0.113], [0.147, 0.125, 0.313, 0.148]] to represent different learning modes which will be the rows.\nThe respective data points such as those under {the line for On-campus Learning} [[0.195, 0.451], [0.307, 0.396], [0.419, 0.340], [0.532, 0.312], [0.644, 0.368], [0.757, 0.451], [0.869, 0.507]] and similar ones for other lines are traced to build the cell values.\nBased on the above thought, the answer is:\nLearning Mode, 2000, 2005, 2010, 2015, 2020, 2025, 2030\nOn-campus Learning, 15, 17, 19, 20, 18, 15, 13\nOnline Learning, 0.5, 2, 5, 10, 16, 20, 25\nAugmented Reality, 0, 0.3, 0.6, 2, 5, 7.5, 10"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "7OqRd8ysJrn6URbX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide an analysis of the learning trends from 2000 to 2030 based on the lines on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart shows three types of learning: {On-campus Learning} [[0.195, 0.451], [0.307, 0.396], [0.419, 0.340], [0.532, 0.312], [0.644, 0.368], [0.757, 0.451], [0.869, 0.507]], {Online Learning} [[0.195, 0.854], [0.307, 0.813], [0.419, 0.729], [0.532, 0.590], [0.644, 0.423], [0.757, 0.312], [0.869, 0.173]], and {Augmented Reality} [[0.195, 0.868], [0.307, 0.860], [0.419, 0.852], [0.532, 0.813], [0.644, 0.729], [0.757, 0.660], [0.869, 0.590]] over a time span from 2000 to 2030.\n\nOn-campus Learning shows a generally declining trend from 2000 to 2030, evidencing a gradual shift in learning preferences away from traditional classroom environments.\n\nOnline Learning shows a significant increase over time, from almost no students in 2000 to a peak in 2030. This trend points to the growing popularity and acceptance of online learning platforms.\n\nAugmented Reality, while starting from zero in 2000, shows steady growth over the years, indicating the potential for this learning mode to become a more mainstream choice in the future.\nBased on the above thought, the answer is:\nThe line chart reveals evolving learning trends from 2000 to 2030. It is evident that there is a gradual shift away from On-campus Learning, marked by a steady decline in student numbers. Interestingly, this decline is complemented by a surge in Online Learning, which grows dramatically from nearly negligible numbers in 2000 to becoming a popular choice by 2030. Meanwhile, Augmented Reality, although starting from zero in 2000, demonstrates consistent growth, suggesting its potential as a common mode of learning in the coming years."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "qIYHMpTTfvZO0ZH4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in student enrollment in the Online Learning mode from 2000 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to locate {the line points for Online Learning in 2000} [[0.195, 0.854]] and {in 2015} [[0.532, 0.590]]. The corresponding values are 0.5 and 10 respectively. The increase is therefore 10 - 0.5 = 9.5 million students.\nBased on the above thought, the answer is:\n9.5 million students."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "ekXQOcfgDd75M5v2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a tabular representation of the data presented in the line chart with Months as columns and Security Measures as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first consider {the x-axis tick labels}[[0.131, 0.864, 0.146, 0.899], [0.222, 0.864, 0.237, 0.899], [0.313, 0.864, 0.328, 0.899], [0.404, 0.864, 0.419, 0.899], [0.495, 0.864, 0.510, 0.899], [0.586, 0.864, 0.601, 0.899], [0.677, 0.864, 0.691, 0.899], [0.768, 0.864, 0.782, 0.899], [0.859, 0.864, 0.873, 0.899], [0.942, 0.864, 0.972, 0.899]] which represent the column headers or Months. Next, I distinguish {the legend texts}[[0.540, 0.668, 0.803, 0.703],[0.540, 0.721, 0.954, 0.756],[0.540, 0.773, 0.861, 0.808]] as the row labels representing the different Security Measures. To populate the values, I locate {the line points}[[0.139, 0.130, 0.957, 0.347],[0.139, 0.465, 0.957, 0.642],[0.139, 0.248, 0.957, 0.544]] for each security measure at the respective months and extract the values annotated.\nBased on the above thought, the answer is:\nSecurity Measure,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10\nSecure Login Accuracy,0.85,0.88,0.91,0.93,0.92,0.9,0.91,0.92,0.94,0.96\nTwo-Factor Authentication Accuracy,0.7,0.73,0.76,0.78,0.79,0.75,0.7,0.72,0.71,0.7\nFirewall Detection Accuracy,0.75,0.8,0.8,0.82,0.84,0.85,0.87,0.85,0.88,0.9"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "hygRbL5Xr4IJss3a",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the accuracy trend for the Three Security Measures based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend, I consider {the lines for the three Security Measures}[[0.139, 0.130, 0.957, 0.347],[0.139, 0.465, 0.957, 0.642],[0.139, 0.248, 0.957, 0.544]]. The accuracy of Secure Login increased from 0.85 to 0.96 showing a steady rise with a slight dip in Month 6 but recovered afterwards. The accuracy of Two-Factor Authentication improved from 0.7 to 0.79 showing an upward trend till Month 5, then a slight decrease and a further decrease with minor fluctuations till Month 10.  And lastly, the accuracy of Firewall Detection started at 0.75 and ended at 0.9, also exhibiting an upward trend with a brief decrease observed at Month 8.\nBased on the above thought, the answer is:\nThe three security measures all demonstrate an upward trend in accuracy over the ten months, with Secure Login Accuracy showing the most consistent improvement. The accuracy of Two-Factor Authentication improved until Month 5, after which it saw a slight decrease and then further decrease with some fluctuations. Firewall Detection Accuracy also improved steadily, with a brief decrease observed at Month 8. These trends suggest that the overall accuracy of these security measures is improving over time."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "cVGDXjGgqQwPD6PA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the accuracy of Secure Login improve from Month 1 to Month 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the improvement in Secure Login from Month 1 to Month 10, I first locate {the point for Month 1 on the Secure Login line}[[0.139, 0.347]] and observe that the accuracy is 0.85. Next, I locate {the point for Month 10 on the Secure Login line}[[0.957, 0.130]] and see that the accuracy is 0.96. The difference between the two values gives the amount of improvement in Secure Login accuracy over the period.\nBased on the above thought, the answer is:\n0.11"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "VNzuUON2smX37bDj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart data into table format, with years as columns and Charities as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, I can identify {the x-axis tick labels}[[0.163, 0.864, 0.221, 0.899], [0.234, 0.864, 0.292, 0.899], ..., [0.875, 0.864, 0.933, 0.899]] which represents the column headers or years. These are: 2000, 2001, 2002, ..., 2010. The {legend texts}[[0.206, 0.069, 0.418, 0.104], [0.206, 0.121, 0.360, 0.156], ..., [0.206, 0.173, 0.413, 0.208]] represent the row labels or charity names. These are: Prototype Success, Patent Filings, Market Expansion. For the table values, I analyze {the line points} [[0.192, 0.329], [0.263, 0.344], ..., [0.904, 0.628]], and extract the corresponding values for each charity across the different years.\nBased on the above thought, the answer is:\nYear,Prototype Success,Patent Filings,Market Expansion\n2000,0.70,0.45,0.50\n2001,0.68,0.50,0.55\n2002,0.62,0.60,0.60\n2003,0.60,0.65,0.65\n2004,0.55,0.70,0.70\n2005,0.47,0.75,0.78\n2006,0.42,0.80,0.82\n2007,0.40,0.85,0.85\n2008,0.35,0.90,0.90\n2009,0.30,0.91,0.93\n2010,0.29,0.92,0.98"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "hJAXUKxrgaJFmHBK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area experienced the most significant decline over the years according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the area that experienced the most decline, I need to pay attention to the slopes of {all the lines}[[0.192, 0.329, 0.904, 0.628], [0.192, 0.169, 0.904, 0.511], [0.192, 0.125, 0.904, 0.475]]. Reviewing each line, I found that {the line for Prototype Success}[0.192, 0.329, 0.904, 0.628] with an initial value of 0.7 and a final value of 0.29, had a drop of 0.41 which is the highest decline compared to other lines.\nBased on the above thought, the answer is:\nThe Prototype Success sector experienced the most significant decline over the years according to the line chart. You can observe a downward trend in its line chart from the beginning to the end, with a drop-off from an initial value of 0.7 to a final value of 0.29, representing the highest decline compared to Patent Filings and Market Expansion."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "HZ3065fKD5FSiXsB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Market Expansion increase from the year 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in Market Expansion, I need to locate the points {Year 2000}[0.192, 0.475] and {Year 2010}[0.904, 0.125] within {the line for Market Expansion}[0.192, 0.125, 0.904, 0.475] from the line chart. The corresponding recorded values for these vertices are 0.5 and 0.98 respectively. Subtracting 0.5 from 0.98 gives the increase from 2000 to 2010.\nBased on the above thought, the answer is:\n0.48"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "7O5fBoWeaF2ROVc5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please help me to organize the data in the line chart into a table with years as the columns and car types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform this line chart into tabular data, I first notice that {the x-axis tick labels}<r>[0.143, 0.909, 0.187, 0.932], ... ,[0.885, 0.909, 0.929, 0.932]</r>, represent the years, which will thus form the column headers on our table. Then, in {the legend}<r>[0.806, 0.058, 0.851, 0.072], .., [0.806, 0.128, 0.851, 0.142]</r> we can find the types of cars, these will translate into rows in our table. To fill in the table with the right values, I find {the line points for Electric cars}<r>[0.165, 0.876], ..., [0.907, 0.206]</r>, {the line points for Hybrid cars}<r>[0.165, 0.738], ..., [0.907, 0.481]</r>, and {the line points for Diesel cars}<r>[0.165, 0.206], ..., [0.907, 0.670]</r>, these values represent the market share for each type of car at each year.\nBased on the above thought, the answer is:\nYear,Electric cars,Hybrid cars,Diesel cars\n2010,0.01,0.09,0.4\n2011,0.02,0.11,0.39\n2012,0.03,0.14,0.38\n2013,0.05,0.16,0.35\n2014,0.07,0.18,0.32\n2015,0.1,0.22,0.29\n2016,0.15,0.25,0.25\n2017,0.22,0.28,0.21\n2018,0.3,0.27,0.18\n2019,0.35,0.25,0.15\n2020,0.4,0.24,0.13"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "MipquHJl9GAGInfH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends shown in the line chart, which type of cars have shown the most consistent growth in market share over the years? Explain your basis for this conclusion."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which type of cars has shown the most consistent growth, I have to analyze the progression in the line plots for each type. Looking at {the line plot for Electric cars}<r>[0.165, 0.206, 0.907, 0.876]</r>, it starts from 0.01 in 2010, gradually increasing each year to 0.4 in 2020. This represents a consistent increase in market share over the years.\nConsidering {the plot for Hybrid cars}<r>[0.165, 0.412, 0.907, 0.738]</r>, it also shows growth, however after 2017 it dips slightly.\nFinally, observing {the line for Diesel cars}<r>[0.165, 0.206, 0.907, 0.670]</r>, I notice that it has decreased consistently from 0.4 in 2010 to 0.13 in 2020. Although it's a consistent trend, it's not a growth trend.\nBased on the above thought, the answer is:\nConsidering the trends shown in the line chart, Electric cars have demonstrated the most consistent growth in market share over the years. This conclusion is based on the steady upward trend seen in the line plot for Electric cars, where the market share has successively increased year after year from 2010 to 2020. Although Hybrid cars also show an increasing trend for some years, this growth is not as consistent as Electric cars since their market share slightly decreases after 2017. Diesel cars exhibit a consistent trend, however, it is not a growth trend but a decreasing one."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "N3HWxGmNr7FM7O8n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the total increase in market share for electric cars from 2010 to 2020 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in market share for electric cars from 2010 to 2020, I need to first locate {the line point for Electric cars in 2010}<r>[0.165, 0.876]</r> and {the line point for Electric cars in 2020}<r>[0.907, 0.206]</r>. The market share in 2010 is represented by the y-coordinate of the first point, which is 0.01, while the market share in 2020 is represented by the y-coordinate of the last point, which is 0.4. So, the total increase in market share is 0.4 - 0.01 = 0.39.\nBased on the above thought, the answer is:\n0.39."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "x5rxJ0YABRewsL37",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the number of Internet, Mobile Phone and Email users based on the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the data table from the line graph, I firstly identify {the x-axis tick labels}[{'label': '2000', 'label_coordinates': '[0.148, 0.912, 0.165, 0.936]'}, {'label': '2010', 'label_coordinates': '[0.792, 0.912, 0.809, 0.936]'}] which represent the Years column. Next I find the {legend texts}[{'label': 'Internet Users', 'text_coordinates': '[0.135, 0.055, 0.233, 0.078]'}, {'label': 'Mobile Phone Users', 'text_coordinates': '[0.135, 0.090, 0.271, 0.113]'}, {'label': 'Email Users', 'text_coordinates': '[0.135, 0.125, 0.216, 0.148]'}] that determine what type of user the rows represent. For each user type, I locate the points {Internet Users}[{'x': 2000, 'y': 361.1, 'point_coordinates': '[0.148, 0.857]'}, {'x': 2010, 'y': 2026.3, 'point_coordinates': '[0.792, 0.678]'}], {Mobile Phone Users}[{'x': 2000, 'y': 759.7, 'point_coordinates': '[0.148, 0.814]'}, {'x': 2010, 'y': 5504.2, 'point_coordinates': '[0.792, 0.303]'}] and {Email Users}[{'x': 2000, 'y': 480.6, 'point_coordinates': '[0.148, 0.844]'}, {'x': 2010, 'y': 2450.3, 'point_coordinates': '[0.792, 0.632]'}] on each line of the corresponding color to extract the y-values, which present the number of users (in millions), and correspond them to the years.\nBased on the above thought, the answer is:\nYear,Internet Users,Mobile Phone Users,Email Users\n2000,361.1,759.7,480.6\n2010,2026.3,5504.2,2450.3"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "CPN7YrKZakO1NWgP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which type of technology user group showed the most rapid increase over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which user group has the most rapid increase, I need to analyze the slope of the line plots. {The line plot for Internet Users}<r>[0.148, 0.626, 0.921, 0.857]</r> shows a steady ascent, suggesting a steady growth. However, {the line plot for Mobile Phone Users}<r>[0.148, 0.145, 0.921, 0.814]</r> shows a faster rate of increase, suggesting a more rapid growth. Finally, {the line plot for Email Users}<r>[0.148, 0.509, 0.921, 0.844]</r> shows a growth rate that appears to be somewhere between the Internet Users and Mobile Phone Users lines. Hence, the Mobile Phone Users line illustrates the most rapid increase.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Mobile Phone Users shows the most rapid increase over the period. The line plot for Mobile Phone Users has the steepest slope, indicating a faster rate of growth compared to Internet and Email Users."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "RtsNAht2cSOouA6v",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the number of Mobile Phone Users grow from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the growth in the number of Mobile Phone Users between 2000 and 2010, I need to locate the line points for Mobile Phone Users. {The point for the year 2000}[{'x': 2000, 'y': 759.7, 'point_coordinates': '[0.148, 0.814]'}] indicates the number of mobile phone users was 759.7 million in that year and the {point for the year 2010}[{'x': 2010, 'y': 5504.2, 'point_coordinates': '[0.792, 0.303]'}] shows it has grown to 5504.2 million in 2010. So, the growth is: 5504.2 million - 759.7 million = 4744.5 million\nBased on the above thought, the answer is:\n4744.5 million"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "dsVVh2l2PP7qxTg6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing global trade values from 2001 to 2020 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile the data into a table format, I'm analyzing {the line plot for Global Trade}<r>[0.120, 0.507, 0.942, 0.652]</r>, marking observed points {from 2001 to 2020}<r>[0.120, 0.604, 0.942, 0.652]</r>. Each point on this plot corresponds to a year's trade value, starting at the year 2001 and concluding at 2020.\nBased on the above thought, the answer is:\nYear,Global Trade\n2001,100\n2002,102\n2003,105\n2004,110\n2005,114\n2006,117\n2007,119\n2008,120\n2009,112\n2010,110\n2011,108\n2012,104\n2013,102\n2014,101\n2015,100\n2016,99\n2017,98\n2018,97\n2019,92\n2020,90"
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "kJHuTDqrcvr3bGGF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Supply Chain Management compare to Global Trade between 2001 and 2020 in terms of growth stability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the stability of growth trends, I assess both {the line for Supply Chain Management}<r>[0.120, 0.064, 0.942, 0.242]</r> alongside {the line for Global Trade}<r>[0.120, 0.507, 0.942, 0.652]</r>. Supply Chain Management demonstrates a more consistent upward trajectory until 2008 with minor fluctuations, thereafter it appears to stabilize. In contrast, Global Trade maintains an even level initially, followed by a gradual decline post-2008 with sharper oscillations towards 2020.\nBased on the above thought, the answer is:\nSupply Chain Management shows greater stability in growth compared to Global Trade from 2001 to 2020, with less fluctuation and a steadier increase through the years until it stabilizes, whereas Global Trade showcases more variability and a noticeable decline in later years."
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "x8CzQ01XQDCf4P5E",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest recorded value for Volatile Market from 2001 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest value for Volatile Market, I need to examine {the line for Volatile Market}<r>[0.120, 0.616, 0.942, 0.866]</r> and review the peak points within this range. The value observation begins in 2001, visualized as {the highest peak in the plot}<r>[0.120, 0.616]</r>.\nBased on the above thought, the answer is:\n97.5"
+            }
+        ],
+        "id_raw": 474
+    },
+    {
+        "id": "arfVTcfDP2heKKFr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table of the yearly funds raised by each organization from 2017 to 2022 as displayed in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data for each organization's funds per year, I observe the yearly markers on {the x-axis}<r>[0.120, 0.106, 0.975, 0.839]</r> labeled from 2017 to 2022 and inspect the line graphs for each organization, focusing on their respective data points. By examining {the data points for each line}<r>[0.159, 0.456, 0.936, 0.778]</r> representing the organizations, I can determine the funds raised for each year per organization.\nBased on the above thought, the answer is:\nYear, Doctors Without Borders, Samaritan's Purse, Red Cross, St. Jude Children's Research Hospital\n2017, 3500, 4200, 5100, 2800\n2018, 3700, 4300, 5300, 2950\n2019, 3600, 4150, 4900, 2700\n2020, 4100, 4500, 5500, 3100\n2021, 4400, 4850, 6000, 3500\n2022, 4750, 5000, 6800, 3900"
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "RgWgXYqqHRuA3YE2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich organization showed the most consistent upward growth trend in the funds raised from 2017 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the organization with the most consistent positive trend in fund raising, it's necessary to visually assess the smoothness and steady increase of the line graphs. Observing each line graph, specifically looking at {the coordinates of the line graph for St. Jude Children's Research Hospital}<r>[0.159, 0.456, 0.936, 0.778]</r>, it appears that this organization shows a consistent upward slope without significant drops, in contrast to others who exhibit dips.\nBased on the above thought, the answer is:\nSt. Jude Children's Research Hospital showed the most consistent upward growth trend in the funds raised from 2017 to 2022."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "YWXIp9fBfXXxJrXM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in funds raised by Red Cross from 2017 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this increase, I pinpoint {the data point for Red Cross in 2017}<r>[0.159, 0.778]</r> and {the data point for Red Cross in 2022}<r>[0.936, 0.456]</r>. By subtracting the funds of 2017 from that of 2022, I can find the increase.\nBased on the above thought, the answer is:\n1700."
+            }
+        ],
+        "id_raw": 475
+    },
+    {
+        "id": "8BmMtmd0b1AnmXx0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the time series information from the line chart into a table structure with column being various learning methods and rows being the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data from the chart, I will first make use of the {x-axis tick labels}<r>[0.120, 0.861, 0.942, 0.931]</r> to determine the row labels, which represent the years. Then, I identify the {legend texts}<r>[0.814, 0.705, 0.976, 0.828]</r> as the column headers demonstrating the types of learning methods. The values for each category across the years are found by tracing the {line points}<r>[0.120, 0.232, 0.942, 0.446]</r>, <r>[0.120, 0.066, 0.942, 0.398]</r>, and <r>[0.120, 0.050, 0.942, 0.351]</r> of Direct Learning, Interactive Learning, and Video Learning respectively.\nBased on the above thought, the answer is:\nYear,Direct Learning,Interactive Learning,Video Learning\n2007,75,56,62\n2008,77,60,65\n2009,73,66,67\n2010,69,71,72\n2011,64,75,75\n2012,60,80,81\n2013,58,82,85\n2014,56,84,87\n2015,55,87,91\n2016,52,90,93\n2017,53,92,95\n2018,52,94,97\n2019,51,96,99\n2020,50,98,100"
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "D63ZFSZuCE0WkHld",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich learning method shows the fastest rate of improvement from 2007 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the learning method with the fastest rate of improvement, I need to compare the slopes of the lines representing each learning method. Observing the line for {Direct Learning}<r>[0.120, 0.232, 0.942, 0.446]</r>, it shows a slow decline over the years. The line for {Interactive Learning}<r>[0.120, 0.066, 0.942, 0.398]</r> shows a steady increase also. Comparatively, {Video Learning}<r>[0.120, 0.050, 0.942, 0.351]</r> shows a steeper increase, indicating improvement at a quicker rate.\nBased on the above thought, the answer is:\nAccording to the chart, Video Learning shows the fastest rate of improvement from 2007 to 2020. Its trend line has a steeper slope compared to Direct Learning and Interactive Learning, suggesting a rapid increase in success rate over the course of the years."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "hBhrSUtDSBf8FjIH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in success rate of Video Learning from 2007 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to find the initial success rate in 2007 and the final success rate in 2020 for Video Learning. I locate the starting point {Video Learning 2007}<r>[0.120, 0.351]</r> and find the success rate to be 62%. Subsequently, I locate the end point {Video Learning 2020}<r>[0.942, 0.050]</r> and find the success rate to be 100%. The total increase is then calculated by subtracting the initial success rate from the final success rate, which is 100% - 62% = 38%.\nBased on the above thought, the answer is:\n38%."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "7uzc9RuNqTocwR8S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph information of the different government policies for the years 2020 and 2021 into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I will convert the visual data shown in the diagram into tabular format. To start with, I'll take {the x-axis} <r>[0.506, 0.930, 0.565, 0.970]</r> which corresponds to the years 2020 and 2021. These will be our column headers. Next, I'll identify {the individual line plot legends}<r>[0.666, 0.724, 0.973, 0.858]</r> which will serve as our row headers or government policies. Lastly, I'll extract the data from {the individual line plot points}<r>[0.127, 0.321, 0.945, 0.768]</r>, <r>[0.127, 0.145, 0.945, 0.560]</r>, <r>[0.127, 0.332, 0.945, 0.456]</r>. These points will correspond to the evaluation scores for their respective policies in 2020 and 2021.\nBased on the above thought, the answer is:\n-| 2020 | 2021\n--- | --- | ---\nGreen Energy Act | 70 | 75\nElectric Car Subsidy | 60 | 70\nAffordable Housing Act | 85 | 90\nMinimum Wage Increase | 65 | 70\nClimate Change Act | 50 | 60\nWork-from-Home Credits | 75 | 80\nFree College Strive Act | 80 | 85\nCarbon Tax | 90 | 60\nUniversal Medicare Act | 95 | 98"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "SAwEjR68lxlaYPJs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which policy's effectiveness increased the most from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which policy's effectiveness increased the most, I'll need to analyze the increases in the line plots for each policy. Checking each line plot, starting with {the line for 'Green Energy Act'}<r>[0.127, 0.321, 0.945, 0.768]</r> and moving onto each subsequent line, it appears that '{Universal Medicare Act}'<r>[0.667, 0.822, 0.713, 0.839]</r> has the most noticeable increase in its evaluation score from 2020 to 2021.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the effectiveness of the 'Universal Medicare Act' policy increased the most from 2020 to 2021."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "ysZ78aAbsKIdE6u8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the score of the 'Carbon Tax' policy decrease from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will find the specific line for 'Carbon Tax' and locate its points for the years 2020 and 2021. The {line points for 'Carbon Tax'}<r>[0.127, 0.145, 0.945, 0.560]</r> indicate that the scores for 'Carbon Tax' in 2020 and 2021 are 90 and 60, respectively. By subtracting these two values, we can find the decrease in the score.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "geNepqbhcpGdM6Tt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from this chart with the stages in the value chain as columns and each progression stage as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I start by identifying the column headers as {the x-axis tick labels}[[0.192, 0.891, 0.250, 0.919], [0.299, 0.891, 0.358, 0.919], [0.407, 0.891, 0.466, 0.919], [0.515, 0.891, 0.573, 0.919], [0.622, 0.891, 0.681, 0.919], [0.730, 0.891, 0.789, 0.919], [0.838, 0.891, 0.897, 0.919]], which symbolize each progression. For the row headers, I look at {the legend texts}[[0.199, 0.055, 0.563, 0.083], [0.199, 0.097, 0.577, 0.125], [0.199, 0.139, 0.525, 0.167]] and recognize them as the distinct stages of progression. By mapping these values to {the line points}[[0.221, 0.556], [0.329, 0.451], [0.436, 0.377], [0.544, 0.272], [0.652, 0.209], [0.760, 0.167], [0.867, 0.114]], I can generate the table information.\nBased on the above thought, the answer is:\nStages,2015,2016,2017,2018,2019,2020,2021\nRate of Digital Record Adoption,25,35,42,52,58,62,67\nIncrease in Medical Data Volume,15,18,25,40,47,55,60\nAI Adoption in Health Sector,2,5,8,12,15,15,18"
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "nxgGoqUJ2cck1zUw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich aspect made the most progress from 2015 to 2021 according to the trends in the chart, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond to this question, I need to observe the line trends on the chart between 2015 and 2021 for each aspect. Starting with {Rate of Digital Record Adoption}[0.221, 0.114, 0.867, 0.556], we see an upward but mild incline from 25 to 67. For {Increase in Medical Data Volume}[0.221, 0.188, 0.867, 0.661], there's a more significant increase from 15 to 60. Lastly, {AI Adoption in Health Sector}[0.221, 0.630, 0.867, 0.798] shows a steep increase from 2 to 18 over the period. Even though all trends are upward, the AI Adoption in Health Sector shows the most progress.\nBased on the above thought, the answer is:\nThe aspect that made the most progress from 2015 to 2021 according to the trends in the chart was the AI Adoption in Health Sector. This is mainly due to the steep increase of its value from 2 to 18 over the specified period. The significant growth in the value indicates a rapid adoption of AI in the health sector during this time."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "Fe0CEnkgmc1qOVyR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_24_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what was the increase in percentage of AI Adoption in Health Sector from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the increase in percentage of AI Adoption in Health Sector from 2015 to 2021, I should locate {the line points for AI Adoption in Health Sector}[[0.221, 0.798], [0.329, 0.766], [0.436, 0.735], [0.544, 0.693], [0.652, 0.661], [0.760, 0.661], [0.867, 0.630]] and extract the corresponding values. The value for 2015 is given by {2}[0.221, 0.798] and for 2021, it is provided by {18}[0.867, 0.630]. The increase is then calculated as 18 - 2 = 16.\nBased on the above thought, the answer is:\n16."
+            }
+        ],
+        "id_raw": 479
+    },
+    {
+        "id": "hhUC6IC8hLwJdyjK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reorganize the data in the chart into a structured table with years as columns and job categories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the chart data into a table, I first identify the column headers as {the x-axis tick labels}[[0.118, 0.919, 0.136, 0.942], [0.273, 0.919, 0.291, 0.942], [0.429, 0.919, 0.446, 0.942], [0.584, 0.919, 0.602, 0.942], [0.740, 0.919, 0.757, 0.942], [0.895, 0.919, 0.913, 0.942]], corresponding to the years of analysis. I then recognize {the legend items}[[0.993, 0.406, 1.052, 0.455], [0.993, 0.368, 1.014, 0.390], [0.993, 0.330, 1.029, 0.352], [0.993, 0.292, 1.028, 0.314]] as the row labels, which represent different job categories. Lastly, I pinpoint {the line data points}[[0.118, 0.199], [0.196, 0.167], ..., [0.895, 0.865]] and extract the corresponding job figures to fill in the table.\nBased on the above thought, the answer is:\nCategory,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nNumber_Of_Tourists,2000,2500,2750,3000,3250,3600,4000,4250,4500,4800,2000\nFacility_Rating,4.5,4.6,4.6,4.7,4.7,4.9,4.5,4.6,4.7,4.8,2.5"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "EeTiv2GNPKtGMeHO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, can you infer any relationship between the number of tourists and the facility rating over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the relationship between the number of tourists and the facility rating, I need to observe the trends in the two line plots. Looking at the line for {Number_Of_Tourists}[0.118, 0.865, 0.895, 0.865], the number of tourists is generally increasing from 2010 to 2019 before a sudden drop in 2020. When I compare this with the line for {Facility_Rating}[0.118, 0.230, 0.895, 0.865], the facility rating also increases till 2015 before a dip in 2016 and then continues its upward trend till 2019. In 2020, the facility rating decrease is correlated with a decrease in the number of tourists. This suggests a positive correlation between the number of tourists and the facility ratings over the years.\nBased on the above thought, the answer is:\nYes, there appears to be a positive relationship between the number of tourists and the facility rating over the years. When the facility rating goes up, the number of tourists also tends to increase. Similarly, when the facility rating goes down, as seen in 2020, the number of tourists also decreases. This correlation might suggest that tourists prefer facilities with higher ratings."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "89jAZzmLc0qvJpPi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in tourist numbers between 2018 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage difference in tourist numbers between 2018 and 2020, I need to locate and retrieve the corresponding values. {The line data point for Number_Of_Tourists in 2018} is at [0.740, 0.185] and the value is 4,500. While {the line data point for Number_Of_Tourists in 2020} is at [0.895, 0.865] and the value is 2,000. The percent difference can be computed as |4500-2000|/4500 * 100%.\nBased on the above thought, the answer is:\n55.56%"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "qNzSULurIs9907VO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you form a table with the data depicted in the line graph? Where each row represents a Risk Level and columns correspond to the Probability and Impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the visualization, I first identify {the x-axis tick labels}[[0.100, 0.909, 0.127, 0.932], [0.293, 0.909, 0.349, 0.932], [0.513, 0.909, 0.545, 0.932], [0.703, 0.909, 0.770, 0.932], [0.914, 0.909, 0.973, 0.932]] which are translated to the row labels. They represent the Risk Level. I can also see two line plots labeled as {Probability}[[0.124, 0.086, 0.198, 0.110]] and {Impact}[[0.124, 0.121, 0.172, 0.145]] from the legend. The y-values on these lines correspond to the data values in the cells of the table. Based on these observations, I form the table by mapping each point on the line to its corresponding Risk Level and extract its y-value as the data.\nBased on the above thought, the answer is:\nRisk_Level,Probability,Impact\nLow,0.1,0.2\nMedium,0.3,0.5\nHigh,0.7,1.2\nVery High,1.5,2.3\nExtreme,2.2,3.5"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "NXDeoJpD5yVbsb57",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Probability and Impact, which metric appears to be more sensitive to increasing Risk Levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the degrees of fluctuation in the lines that represent Probability and Impact as Risk Level increases. By observing the line for {Probability}[0.114, 0.391, 0.944, 0.855], it starts at a lower point for Low risk and increases steadily, reaching a high at Extreme. On the other hand, the line for {Impact}[0.114, 0.103, 0.944, 0.833] also starts low and then increases dramatically as risk level increases towards Extreme. The slope of the Impact line is steeper compared to Probability which suggests more sensitivity to increasing Risk Levels.\nBased on the above thought, the answer is:\nThe Impact metric appears to be more sensitive to increasing Risk Levels. Its line curve shows significant increase as we move from Low to Extreme Risk Levels, and it indicates a higher rate of change across the risk levels compared to the Probability metric."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "hZmIgyIvOMRBBUOh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Impact when Risk Level changes from Medium to Very High?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first need to locate the points on the Impact line corresponding to Risk Levels Medium and Very High. I find {Medium}[[0.321, 0.767]] which corresponds to Impact=0.5 and {Very High}[[0.736, 0.369]] which corresponds to Impact=2.3. The increase in Impact when Risk Level changes from Medium to Very High is the difference between these two values, which is 2.3 - 0.5 .\nBased on the above thought, the answer is:\n1.8"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "2q5b1CPnEpoR6eDC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table listing the academic progress for each research field across the years from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I begin by identifying the years mapped along {the x-axis}[0.123, 0.902, 0.965, 0.932] which serve as the headers for the columns in the table. For the rows, I recognize each research field represented by different colors, referred to in {the legend}[0.808, 0.077, 0.978, 0.154]. I systematically extract the coordinates of points on each line, corresponding to the academic progress values for each year. AI is represented with blue points, Data Science with green points, BioTech with red points, and Physics with purple points. By collecting the {y-values of these points}[0.123, 0.314, 0.944, 0.837], I fill in the respective cells in the table for each combination of year and field.\nBased on the above thought, the answer is:\nYear,AI,Data Science,BioTech,Physics\n2016,1,1.1,1.5,1.0\n2017,1.5,1.7,2.0,1.2\n2018,2,2.5,2.4,1.4\n2019,2.5,3.0,3.5,1.6\n2020,3,3.5,3.2,1.8\n2021,3.5,4.0,3.8,2.0"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "L5N6KkNBKnbTuYGI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research field shows the most consistent growth without significant volatility in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I analyze the trends of each field by focusing on the slope and fluctuations of their respective lines. Observing {the line for AI}[0.123, 0.814, 0.944, 0.314], it shows a steady upward trend without major fluctuations, indicating consistent growth. In contrast, examining {other lines}, Data Science also increases but with a slightly more aggressive slope, BioTech shows larger fluctuations especially around 2019, and Physics progresses slowly with almost negligible growth. Thus, the properties of each line and growth pattern help me determine the consistency.\nBased on the above thought, the answer is:\nAI shows the most consistent growth without significant volatility, as its line is smoothly increasing with minor and steady changes each year."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "Lc8kfB9Z7wImniiD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total academic progress increase for the AI field from 2016 to 2021 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase, I examine the academic progress values for AI at the start and end of the period by referencing {the line for AI}[0.123, 0.314, 0.944, 0.314]. The progress starts at {1.0}[0.123, 0.837] in 2016 and increases to {3.5}[0.944, 0.314] by 2021. Subtracting the start value from the end value gives a total increase.\nBased on the above thought, the answer is:\n2.5"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "ArRUhTFWdH1BZsjJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data in the sub-charts representing the trends in academic fields into a statistical table."
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identified the x-axis labels, representing the increase trends in academic fields, which are {'Abrupt Increase'}[0.114, 0.919],[0.270, 0.903] and {'Persistent rise'}[0.582, 0.903],[0.895,0.903]. Using this information as column headers, I'll generate represenatations for each academic field. \nNext, I identified the academic fields from the sub-chart titles, namely, {Revolutionary Physics}<r>[0.312, 0.768, 0.398, 0.842]</r>, {Cutting-edge Biology}<r>[0.302, 0.573, 0.407, 0.635]</r>, {Ancient Philosophy}<r>[0.315, 0.383, 0.400, 0.442]</r>, {Modern Sociology}<r>[0.315, 0.191, 0.404, 0.238]</r>, and {Theoretical Math}<r>[0.328, 0.003, 0.399, 0.445]</r>, which serve as row headers. \nFor the actual data, I located and extracted the values from the points in the line charts for each of the fields. The metric values for each academic field corresponding to the trends are mapped from this data.\nBased on the above thought, the answer is:\nFields, Abrupt Increase, Persistent rise\nRevolutionary Physics, 50, 62\nCutting-edge Biology, 70, 85\nAncient Philosophy, 65, 75\nModern Sociology, 80, 88\nTheoretical Math, 55, 70"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "8iIJLNkQdtQqDLZm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparatively, which academic field observed the greatest rise between 'Abrupt Increase' and 'Persistent rise'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the difference in the values of 'Abrupt Increase' and 'Persistent rise' of each academic field, and then compare them to find out the field with the greatest rise.\nFor the field {Revolutionary Physics}[0.312, 0.768], I found the values at {Abrupt Increase}<r>[0.114, 0.654]</r> and {Persistent rise}<r>[0.895, 0.654]</r> and calculated the difference as 62 - 50 = 12.\nFollowing the same steps, I obtained the differences for {Cutting-edge Biology}<r>[0.302, 0.504]</r>, {Ancient Philosophy}<r>[0.315, 0.317]</r>, {Modern Sociology}<r>[0.315, 0.127]</r>, and {Theoretical Math}<r>[0.315, -0.037]</r> as 15, 10, 8, and 15 respectively.\nOn comparing these differences, it can be seen that 'Theoretical Math' and 'Cutting-edge Biology' both show the greatest rise between 'Abrupt Increase' and 'Persistent rise'.\nBased on the above thought, the answer is:\nBoth 'Theoretical Math' and 'Cutting-edge Biology' observed the greatest comparative rise between 'Abrupt Increase' and 'Persistent rise' in the academic fields, with a growth of 15 units."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "vThYZWLt3wh2pajh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference between the values of 'Abrupt Increase' in 'Theoretical Math' and 'Modern Sociology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate and determine the values corresponding to 'Abrupt Increase' for both 'Theoretical Math' and 'Modern Sociology'. I found {the corresponding coordinates for Abrupt Increase for Theoretical Math}<r>[0.582, 0.340]</r> and {the corresponding coordinates for Abrupt Increase for Modern Sociology}<r>[0.582, 0.767]</r>. Using this information, the difference between the 'Abrupt Increase' values of 'Modern Sociology' (80) and 'Theoretical Math' (55) could be calculated as 80 - 55 = 25.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "ZBIlWqKeCwQHSxJH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a table with Rows as Models and Columns as Sustainability Aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first identify {the subplot titles}[[0.168, 0.783, 0.206, 0.796], [0.168, 0.553, 0.206, 0.567], [0.168, 0.323, 0.206, 0.337], [0.168, 0.093, 0.206, 0.107]] which represent each model. Next, I locate {the x-axis ticks}[0.210, 0.736, 0.252, 0.764], which represent the sustainability aspects for each model. To extract the values I locate {the bars}[[0.221, 0.681, 0.238, 0.728], [0.258, 0.681, 0.271, 0.706], ...] in each model's subplot and extract the bar heights.\nBased on the above thought, the answer is:\nModels,Economical,Environmental,Social,Innovational,Legal,Governance\nModel1,0.84, 0.79, 0.85, 0.87, 0.92, 0.91\nModel2,0.86, 0.82, 0.84, 0.88, 0.93, 0.92\nModel3,0.88, 0.81, 0.83, 0.87, 0.92, 0.92\nModel4,0.89, 0.80, 0.85, 0.86, 0.92, 0.92"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "QyUxVEZSflSO9ihP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the most consistent performance across all models and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The consistent performance of a category across all models can be assessed by minimizing the variation in bar heights for each category across subplots. By comparing {the bars corresponding to 'Economical'}[0.221, 0.681, 0.238, 0.728], {the bars corresponding to 'Environmental'}[0.258, 0.681, 0.275, 0.706], {the bars corresponding to 'Social'}[0.298, 0.681, 0.313, 0.713], {the bars corresponding to 'Innovational'}[0.334, 0.681, 0.349, 0.715], {the bars corresponding to 'Legal'}[0.374, 0.681, 0.391, 0.723], and {the bars corresponding to 'Governance'}[0.412, 0.681, 0.428, 0.722] across the four subplots representing different models, the 'Legal' aspect seems to show the most consistent performance, with the least variation in its value.\nBased on the above thought, the answer is:\nThe 'Legal' category shows the most consistent performance across all models. This can be observed from the similar bar heights for this aspect in all four models, indicating that the performance scores for 'Legal' are closely grouped and do not vary significantly between different models."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "uUjiIKEdHio5qHRA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average performance across all categories for Model3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to locate the {bars in the Model3 subplot}[0.271, 0.528, 0.292, 0.549], each representing a sustainability aspect. Their heights are 0.88, 0.81, 0.83, 0.87, 0.92, and 0.92 respectively. The average performance can be computed as the mean of these values.\nBased on the above thought, the answer is:\n0.872"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "Y5l9OrCfbn2MSqBn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the bar chart to a table with rows representing different research fields and columns representing the condition of research funding (without and with)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I would first consider {the subplots}[[0.12, 0.85, 0.43, 0.71], [0.54, 0.85, 0.85, 0.71],[0.12, 0.61, 0.43, 0.46], [0.54, 0.61, 0.85, 0.46]] which represent the different research fields. Each subplot has two bars \u2013 firstly, {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]] represent the scores without research funding and secondly, {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]] represent the scores with research funding.\nBased on the above thought, the answer is:\nFields, Without Research Funding, With Research Funding\nEngineering, 0.65, 0.92\nMathematics, 0.75, 0.85\nPhysics, 0.68, 0.8\nBiology, 0.62, 0.78"
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "PKsPhiAK3neFJxzD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field experienced the greatest difference in academic scores due to the presence of research funding? Provide a brief explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find out the increase in each field's score once they received funding. From {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]], the scores without funding are found, and from {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]], the scores with funding are identified. The difference in each field is calculated as [(0.92 - 0.65), (0.85 - 0.75), (0.8- 0.68), (0.78- 0.62)], and the maximum difference among these was observed in the Engineering field.\nBased on the above thought, the answer is:\nThe Engineering field experienced the greatest difference in academic scores due to the presence of research funding. With funding, the academic score in Engineering increased from 0.65 to 0.92, indicating a distinctly more significant enhancement relative to the other fields. This implies that the Engineering field benefits the most from research funding in terms of academic performance."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "Xe6BmM5ezADAUWWH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average academic score increase in all research fields upon receiving funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average academic score increase, I need to calculate the difference between scores with and without funding for each research field. The field scores without funding are located at {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]], and the scores with funding are at {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]]. The difference in each field is (0.92 - 0.65), (0.85 - 0.75), (0.8- 0.68), (0.78- 0.62) respectively. The total increase equals the sum of these differences, and the average can be calculated by dividing the total by the number of fields.\nBased on the above thought, the answer is:\n0.1025."
+            }
+        ],
+        "id_raw": 486
+    },
+    {
+        "id": "wRbU7txCk44jRf6q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data presented in the line chart into a tabular format with years as rows and Platforms as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In generating a table from the line chart, I begin with the {x-axis tick labels}<r>[0.100, 0.909, 0.136, 0.932], [0.192, 0.909, 0.227, 0.932], [0.284, 0.909, 0.319, 0.932], [0.376, 0.909, 0.411, 0.932], [0.467, 0.909, 0.503, 0.932], [0.559, 0.909, 0.594, 0.932], [0.651, 0.909, 0.686, 0.932], [0.743, 0.909, 0.778, 0.932], [0.834, 0.909, 0.870, 0.932], [0.926, 0.909, 0.961, 0.932]</r> to determine the row headers which represent the years. I then identify {the legend text}<r>[0.814, 0.086, 0.972, 0.110], [0.814, 0.121, 0.954, 0.145]</r> as the column headers that represent the different social media platforms. The remaining action is to identify the line points for each platform in each year. This is achieved by locating all {points of the lines}<r>[0.118, 0.682], [0.210, 0.506], [0.302, 0.611], [0.393, 0.330], [0.485, 0.418], [0.577, 0.277], [0.668, 0.189], [0.760, 0.154], [0.852, 0.242], [0.944, 0.277], [0.118, 0.858], [0.210, 0.822], [0.302, 0.770], [0.393, 0.717], [0.485, 0.664], [0.577, 0.611], [0.668, 0.576], [0.760, 0.470], [0.852, 0.365], [0.944, 0.294]</r> which are marked on the line for each platform. These points' coordinates correspond to the values in the table.\nBased on the above thought, the answer is:\nYear, Facebook Engagement, Twitter Engagement\n2011,100,90\n2012,110,92\n2013,104,95\n2014,120,98\n2015,115,101\n2016,123,104\n2017,128,106\n2018,130,112\n2019,125,118\n2020,123,122"
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "zu1TxU1S5e5mB2VA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the engagement levels of users on both Twitter and Facebook over the years based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences based on the line chart, I examine the trend line for each platform. Looking at {the line for Facebook Engagement}<r>[0.118, 0.154, 0.944, 0.682]</r>, the trend is mostly increasing except for some minor dips. This shows a generally increasing engagement level on Facebook over the years. For {Twitter Engagement}<r>[0.118, 0.294, 0.944, 0.858]</r>, the trend line is consistently increasing with no major dips, indicating a steady increase in engagement level on Twitter over this period. A comparative analysis of both lines also indicates that while the engagement levels for both platforms increased, the rate of increase was higher for Twitter than Facebook.\nBased on the above thought, the answer is:\nBased on the line chart, it can be inferred that engagement levels have generally increased for both Facebook and Twitter over the years. However, Twitter's engagement levels appears to have increased at a faster rate than Facebook's over the same period. This could suggest a growing preference for Twitter among users over the observed period or that Twitter's strategies for boosting user interaction have been more effective than Facebook's."
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "a9SugsnYSDs7ptSV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which year did Facebook Engagement show the highest level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest level of Facebook Engagement, I need to review {the line points for Facebook Engagement}<r>[0.118, 0.682], [0.210, 0.506], [0.302, 0.611], [0.393, 0.330], [0.485, 0.418], [0.577, 0.277], [0.668, 0.189], [0.760, 0.154], [0.852, 0.242], [0.944, 0.277]</r>. Each point on this line corresponds to a value for a certain year. The highest point on the line corresponds to the maximum value and the x-coordinate of this point corresponds to the year. Observing the coordinates, the point with the greatest y-value is at [0.118, 0.682], which corresponds to the {year 2011}<r>[0.100, 0.909, 0.136, 0.932]</r>.\nBased on the above thought, the answer is:\n2011"
+            }
+        ],
+        "id_raw": 487
+    },
+    {
+        "id": "IQTY3KDOhh44vvBC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table with Years as rows and Columns as both Population and AQI?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the tabular data from the line chart, I recognize {the x-axis tick labels}[[0.117, 0.899, 0.172, 0.928], [0.211, 0.899], [0.278, 0.899], [0.345, 0.899], [0.412, 0.899], [0.547, 0.899], [0.614, 0.899], [0.681, 0.899], [0.748, 0.899], [0.815, 0.899]] as the row values representing different years. For the column headers, I utilize the labels for the two y-axes: {AQI}[0.023, 0.319, 0.045, 0.642] and {Population}[0.955, 0.404, 0.977, 0.556]. To fill the values in the cells, I observe {the points of the AQI line}[0.144, 0.118, 0.815, 0.842] and {the points of the Population line}[0.144, 0.842, 0.815, 0.118] and extract the corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Air Quality Index (AQI),Population\n2010,150,100000\n2011,145,103000\n2012,140,106000\n2013,128,112000\n2014,118,120000\n2015,115,126000\n2016,100,130000\n2017,97,136000\n2018,90,145000\n2019,85,155000\n2020,82,165000"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "VeYMTPSASG8pzWS9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends shown in the line chart, what is the relationship between population growth and AQI over the years? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the relationship between population growth and AQI, I need to analyze the trends in the respective lines. Looking at {the AQI line}[0.144, 0.118, 0.815, 0.842], I see a consistent decrease from 150 to 82 over the years, indicating a steady improvement in the air quality. Observing {the Population line}[0.144, 0.842, 0.815, 0.118], I see a consistent increase from 100000 to 165000 over the same period, indicating population growth. The inverse trends suggest that as the population increased, the AQI decreased. This implies that despite population growth, measures may have been put in place to maintain and even improve air quality over time.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, an inverse relationship is observed between population growth and Air Quality Index (AQI) over the years. This means that even as the population was increasing, the AQI was improving, decreasing from 150 to 82 over the period. This suggests an effective management of environmental resources and successful implementation of air pollution control measures, despite the challenges posed by continuing population growth."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "4TuUlwT0rHf7GHFY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Population grow from 2010 to 2020 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the population growth from 2010 to 2020, I first find {the plot point for 2010}[0.144, 0.842] which corresponds to a population of 100000. Next, I locate {the plot point for 2020}[0.815, 0.118] which corresponds to a population of 165000. The difference between these two values gives the population growth over that period, which is 165000 - 100000 = 65000.\nBased on the above thought, the answer is:\n65000."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "4mUtlRAkfv020Gkp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data represented in this line chart into a table format with Years as columns and the two categories of Spending as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.101, 0.909, 0.136, 0.932], [0.156, 0.909, 0.191, 0.932], [0.211, 0.909, 0.246, 0.932], [0.266, 0.909, 0.301, 0.932], [0.321, 0.909, 0.356, 0.932], [0.376, 0.909, 0.411, 0.932], [0.431, 0.909, 0.466, 0.932], [0.486, 0.909, 0.521, 0.932], [0.541, 0.909, 0.576, 0.932], [0.596, 0.909, 0.631, 0.932], [0.651, 0.909, 0.686, 0.932], [0.706, 0.909, 0.741, 0.932], [0.761, 0.909, 0.796, 0.932], [0.816, 0.909, 0.851, 0.932], [0.871, 0.909, 0.906, 0.932], [0.926, 0.909, 0.961, 0.932]], which represent the column headers or years. Next, I identify {the legend texts}[[0.128, 0.086, 0.333, 0.110], [0.128, 0.121, 0.319, 0.145]] as the row labels representing Medical Technology Spending and Medical Research Spending. To populate the values, I locate {the points on the lines for Medical Technology Spending and Medical Research Spending}[[0.118, 0.777], [0.173, 0.726], ..., [0.944, 0.777]] and [[0.118, 0.259], [0.173, 0.311], ..., [0.944, 0.855]] respectively for each year and extract the respective spending values.\nBased on the above thought, the answer is:\nCategory,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nMedical Technology Spending,100,110,130,150,160,170,160,150,160,165,170,180,195,210,220,230\nMedical Research Spending,200,190,170,160,150,140,135,130,125,117,110,105,100,95,90,85"
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "bQkKsrLidFbwddnH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, which sector (Medical Technology or Medical Research) experienced a greater rate of change in spending from 2005 to 2020 and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the lines representing each sector, I analyze {the line for Medical Technology Spending}[0.118, 0.103, 0.944, 0.777] and {the line for Medical Research Spending}[0.118, 0.259, 0.944, 0.855]. For Medical Technology Spending, spending increases from 100 to 230 which is an increase of 130 billion. Whereas in the case of Medical Research Spending, the spending decreases from 200 to 85 which is a decrease of 115 billion. Thus, the Medical Technology sector experienced a greater absolute change.\nBased on the above thought, the answer is:\nThe Medical Technology sector experienced a greater rate of change in spending from 2005 to 2020. This is indicated by the greater absolute change in the chart, where Medical Technology Spending increased by 130 billion, while Medical Research Spending decreased by 115 billion."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "ywBVQ3KLnfbZi6KU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Medical Technology Spending from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Medical Technology Spending from 2005 to 2020, I first find {the point corresponding to the 2005 Medical Technology Spending}[[0.118, 0.777]]. It's value is 100. Then, I locate {the point associated with the 2020 Medical Technology Spending}[[0.944, 0.103]]. That value is 230. The increase from 2005 to 2020 is the difference between these two values, which is 230 - 100 = 130.\nBased on the above thought, the answer is:\n130."
+            }
+        ],
+        "id_raw": 489
+    },
+    {
+        "id": "vSOWs7inDmNij7Cp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table with Fashion Elements as rows and their respective Trend values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into tabular data, I first observe {the x-axis tick labels}[[0.103, 0.755, 0.117, 0.914],[0.178, 0.755, 0.192, 0.885], [0.254, 0.755, 0.268, 0.901], [0.330, 0.755, 0.344, 0.905], [0.406, 0.755, 0.420, 0.878], [0.482, 0.755, 0.496, 0.868], [0.557, 0.755, 0.571, 0.900], [0.633, 0.755, 0.647, 0.916], [0.709, 0.755, 0.723, 0.908], [0.785, 0.755, 0.799, 0.884], [0.861, 0.755, 0.875, 0.932], [0.936, 0.755, 0.950, 0.886]] which are the row labels or Fashion Elements. To get the trend values, I locate {the line points}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]] for each fashion element and extract the corresponding values.\nBased on the above thought, the answer is:\nFashion Elements, Trend\nBootcut Jeans, 80\nFlare Skirts, 95\nSkinny Jeans, 45\nRipped Jeans, 50\nWide Belts, 65\nCrop Tops, 75\nOff Shoulder, 85\nFlowers Prints, 55\nAnimal Prints, 35\nPlaid Shirts, 40\nLeather Jackets, 60\nTurtlenecks, 90"
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "2zzeDWhYc4enyLIo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fashion element demonstrated the least interest according to the trend analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at {the line points for each fashion element}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]]. The point with the lowest y-value corresponds to the least interest in a fashion element. Comparing the y-values, the point {0.716, 0.685} stands out as it is the lowest, which corresponds to the 'Animal Prints' fashion element.\nBased on the above thought, the answer is:\nAccording to the trend analysis, 'Animal Prints' seems to have shown the least interest among all the fashion elements. The corresponding line plot indicates the lowest y-value, reflecting the lowest trend value."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "mvDs0r0Hp1a1yQoe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest trend value observed among all the fashion elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest trend value, I should look at the highest point coordinates amongst {the individual line points}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]]. After reviewing each point, the highest y-value corresponds to the 'Flare Skirts' trend, which is 95.\nBased on the above thought, the answer is:\n95."
+            }
+        ],
+        "id_raw": 490
+    },
+    {
+        "id": "zgo95KQuDPc1N7y5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the attendance numbers from the line chart for 'King's Landing' and 'Winterfell' into a structured table format with 'Attendance Ratio' as columns and 'Location' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Utilizing the plotted points in the line chart, I extract the attendance numbers for both 'Local King's Landing' and 'International King's Landing' {line_0}[[0.126, 0.130, 0.944, 0.226]] and similarly for 'Local Winterfell' and 'International Winterfell' {line_2}[[0.126, 0.412, 0.944, 0.430]]. These lines respectfully provide the data for each attendance ratio. Observing the {the x-axis labels}[[0.122, 0.909, 0.131, 0.932], ..., [0.935, 0.909, 0.953, 0.932]] helps categorize each data point into the appropriate attendance ratio column.\nBased on the above thought, the answer is:\nAttendance Ratio,Local King's Landing,International King's Landing,Local Winterfell,International Winterfell\n5,6504.72,804.72,4611.80,804.72\n6,6595.88,895.88,4622.47,895.88\n7,6672.96,972.96,4632.29,972.96\n...\n30,7400.60,1700.60,4773.86,1700.60"
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "JVKzjl33GSYwNX15",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in international attendance compare between King's Landing and Winterfell?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the lines representing international attendance, {line for International King's Landing}[0.126, 0.743, 0.944, 0.839] shows a gradual upward slope, suggesting a steady increase in international attendance in King's Landing. Meanwhile, the {line for International Winterfell}[0.126, 0.743, 0.944, 0.839] indicates a similar upward trajectory. However, upon close inspection, the slope of King's Landing is slightly steeper, signifying a somewhat faster rate of increase in international visitors compared to Winterfell.\nBased on the above thought, the answer is:\nBoth King's Landing and Winterfell demonstrate an increasing trend in international attendance. However, King's Landing exhibits a slightly steeper increase, indicating a faster growth in international visitor numbers compared to Winterfell."
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "aV9XBOjDDTUY4ghj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total difference in mean local attendance between 'King's Landing' and 'Winterfell' at the ratio of '20'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total difference in mean local attendance at an attendance ratio of '20', I observe the local attendance points for both locations. For 'King's Landing', the data point at ratio 20 is {dot on the graph for Local King's Landing at ratio 20}[0.617, 0.152]. For 'Winterfell', it is captured at {dot on the graph for Local Winterfell at ratio 20}[0.617, 0.418]. The values are 7197.87 for King's Landing and 4723.61 for Winterfell. Thus, the difference is 7197.87 - 4723.61.\nBased on the above thought, the answer is:\n2474.26"
+            }
+        ],
+        "id_raw": 491
+    },
+    {
+        "id": "4nbMDp8VtUBiFAn0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line graph, can you map the yearly user counts in millions for both Facebook and Twitter into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To shape the chart information into a table, I will identify {the x-axis tick labels}[[0.102, 0.902, 0.144, 0.932],[0.512, 0.902, 0.555, 0.932]] as the years, which will serve as the row labels of the table. Next, we have {the legend texts}[[0.133, 0.086, 0.270, 0.110],[0.133, 0.121, 0.267, 0.145]] that will be used as column headers, representing the two social media platforms. Finally, I will locate {the line points for Facebook}[[0.123,0.537],[0.534,0.712]] and {the line points for Twitter}[[0.123,0.799],[0.534,0.642]], to extract the corresponding values of user counts in each year for both platforms.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter\n2020,3760,4250\n2021,3870,4500"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "l0U7CCARePe43BLR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the presented data, which social media platform showed more growth in its user base from 2020 to 2021? Can you elaborate on these trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social media platform showed more growth, I need to compare the differences in user counts between 2020 and 2021 for both platforms. For {Facebook}[0.123,0.537,0.534,0.712], there was a growth from 3760 million to 3870 million users, resulting in a net increase of 110 million users. For {Twitter}[0.123,0.799,0.534,0.642], the growth was from 4250 million to 4500 million users, indicating a net increase of 250 million users.\nBased on the above thought, the answer is:\nTwitter showed more growth in its user base from 2020 to 2021. While both platforms saw an increase in users, the rise in Twitter's user base is much more significant compared to Facebook's - about 127% more. This suggests Twitter had a more rapid growth rate during this period."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "d9bnvU933YlKVjUu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the userbase growth of Twitter from 2020 to 2021, in millions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the userbase growth of Twitter, I need to subtract the user count in 2020 from 2021. I locate {the 2020 line point for Twitter at twitter_coordinates_2020}[0.123,0.799], the corresponding value is 4250 million users. Then I look at {the 2021 line point for Twitter at twitter_coordinates_2021}[0.534,0.642], where the number is 4500 million users. The userbase growth for Twitter is therefore 4500 - 4250 = 250.\nBased on the above thought, the answer is:\n250."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "YhBEAf2ENxByfWE9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the provided line chart data in a tabular format with the historical eras as columns and the civilization development percentages as values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first observe {the x-axis tick labels}[[0.088, 0.909, 0.130, 0.932], [0.259, 0.909, 0.372, 0.932], [0.464, 0.909, 0.580, 0.932], [0.685, 0.909, 0.772, 0.932], [0.891, 0.909, 0.979, 0.932]], which present the column headers, denoting the historical eras. To get the corresponding values, I isolate {the line points}[[0.109, 0.125], [0.316, 0.339], [0.522, 0.510], [0.729, 0.825], [0.935, 0.633]], which epitomize the developmental percentages for each historical era.\nBased on the above thought, the answer is:\nHistorical Era,Books,Online Materials,Peer Discussions,Experiments,Field Studies\nCivilization Development(%),82.5,74.6,68.3,56.7,63.8"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "t0gkpBB3PeSUbq9S",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the trend in the line chart tell us about the usage of the education resources over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trend, I analyze the progression of the line plotted with coordinates {the line for Resource Usage}[[0.109, 0.125, 0.935, 0.633]]. We can observe that there is a decline in resource utilization from Books to Online Materials, a further drop to Peer Discussions, a sharper fall to Experiments, and then a moderate increase towards Field Studies.\nBased on the above thought, the answer is:\nThe trend depicted in the line chart shows a predominantly declining pattern in the usage of education resources from Books to Experiments. It suggests that traditional resources like Books and Online Materials have been utilized more compared to experiential learning methods like Experiments. Nevertheless, there is a small, noticeable resurgence in utilizing Field Studies, a form of experiential learning. This might indicate a growing recognition of the importance and effectiveness of hands-on experience in education."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "SknHqOrmto6qJJjb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in civilization development from Books to Field Studies according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percent change, I first locate {the usage percentage for Books}[[0.109, 0.125]] which equals 82.5%, then I locate {the usage percentage for Field Studies}[[0.935, 0.633]], which equals 63.8%. So the percentage change is ((63.8-82.5)/82.5)*100.\nBased on the above thought, the answer is:\n-22.7%"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "ed07ceGmDqVp6ck6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table of the data represented in the line chart, with years as columns and sectors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, recognizing the data, I first locate {the x-axis tick labels}[0.148, 0.902, 0.877, 0.920] which act as the column headers, denoting years. Subsequently, {the legend}[0.830, 0.136, 0.891, 0.179] text provides the row labels, represented by different sectors. For the values in the table, {the line plots}[0.160, 0.216, 0.865, 0.794] indicate the number of units sold for each sector for different years.\nBased on the above thought, the answer is:\nSector,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016\nRural Development,100,98.3,97,96,94,91,88,85,82,80,77,75\nUrban Development,80,85,88,90,92,95,97,98,99,100,102,105"
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "Gbdc3Gb6GEkBHf6M",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends in the line chart, which area, Rural or Urban, has shown a more consistent development? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I look at the line plots for Rural and Urban Development. Observing {the line for Rural Development}[0.160, 0.312, 0.865, 0.794], it decreases consistently over time suggesting a decline in development levels. On the other side, {the line for Urban Development}[0.160, 0.216, 0.865, 0.698] shows a fairly consistent upward progression, indicating a steady improvement in development levels over the given period.\nBased on the above thought, the answer is:\nBased on the trends, Urban Development has shown more consistent development. While Rural Development shows a consistent decline, Urban Development shows a steady increase over the years. The clear upward trend in the Urban Development line implies consistent improvement in development levels over time."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "dmQbE4icDKPBXKzs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in the Urban Development level from the start year 2005 to the end year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total change in the Urban Development level, I need to find the values in the start year 2005 and the end year 2016. I look for {the point for Urban Development}[0.160, 0.698] in 2005 which has a value of 80 and the {point for Urban Development}[0.865, 0.216] in 2016 with a value of 105. The total change would be the difference between these two values i.e. 105 - 80.\nBased on the above thought, the answer is:\n25."
+            }
+        ],
+        "id_raw": 494
+    },
+    {
+        "id": "Pz6Rx4OacZUzzdNr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart provided, can you present the data showing the inspection efficiency of Process 1 and Process 2 across different timelines into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve the tabular form of data from the chart, I need to start by identifying the x-axis, {the x-axis tick labels}[[0.122, 0.902, 0.133, 0.932], [0.208, 0.902, 0.219, 0.932], [0.294, 0.902, 0.305, 0.932], [0.380, 0.902, 0.391, 0.932], [0.466, 0.902, 0.477, 0.932], [0.547, 0.902, 0.568, 0.932], [0.633, 0.902, 0.654, 0.932], [0.719, 0.902, 0.740, 0.932], [0.805, 0.902, 0.826, 0.932], [0.891, 0.902, 0.912, 0.932]], which serve as the different timelines conveyed. The series data corresponding to Process 1 and Process 2 can be gathered from their line points; {the line points for Process 1}[[0.128, 0.311], [0.171, 0.465], [0.214, 0.594], ..., [0.944, 0.698]] reflecting the efficiency of Process 1 and {the line points for Process 2}[[0.128, 0.362], [0.171, 0.362], [0.214, 0.336], ..., [0.944, 0.259]] standing for the efficiency of Process 2.\nBased on the above thought, the answer is:\nTimeline,Process 1 Efficiency,Process 2 Efficiency\n1,80,78\n2,74,78\n3,69,79\n4,60,81\n5,65,81\n6,70,79\n7,70,73\n8,74,71\n9,75,73\n10,80,74\n11,83,74\n12,85,73\n13,87,75\n14,80,75\n15,75,78\n16,70,80\n17,65,82\n18,64,84\n19,63,83\n20,65,82"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "ZHBYBg8oHp5xU2Dm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which process, Process 1 or Process 2, demonstrates a more stable increase in efficiency over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which process shows a more stable increase in efficiency, we have to examine the lines of both processes. By looking at the line for {Process 1}[0.128, 0.130, 0.944, 0.827], we could see a volatile movement with multiple peaks and troughs, which signify an unstable increase. In contrast, the line for {Process 2}[0.128, 0.207, 0.944, 0.543] shows a gradual increase across the timeline, only decreasing slightly at certain time periods. This indicates a more stable increase in efficiency compared to Process 1.\nBased on the above thought, the answer is:\nProcess 2 demonstrates a more stable increase in efficiency over time. While both processes show an overall improvement in efficiency, the changes in Process 2 are more gradual and consistent, indicating a steadier and more controlled growth trajectory compared to the considerable fluctuations seen in Process 1."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "iRgdcONYhwPSzUF5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average efficiency of Process 1 from time 1 to time 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average efficiency of Process 1, I first locate the coordinates of Process 1 that span from time 1 to time 20: {the line points for Process 1}[[0.128, 0.311], [0.171, 0.465], [0.214, 0.594], ..., [0.944, 0.698]]. The corresponding values are: 80, 74, 69, 60, 65, 70, 70, 74, 75, 80, 83, 85, 87, 80, 75, 70, 65, 64, 63, 65. The average is calculated by summing up all these values and dividing by the total number of values, which is 20.\nBased on the above thought, the answer is:\n73.05"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "ZGjbaHDzDs3Tcdj0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table representing the usage of different big data integration strategies over the years from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I will analyze each strategy's usage over the years from 2015 to 2020. Firstly, locating {the x-axis labels}[[0.154, 0.906, 0.163, 0.930], [0.207, 0.906, 0.215, 0.930], ..., [0.891, 0.906, 0.909, 0.930]] which represents the years from 2015 through 2020. Next, identifying {the legend items}[[0.132, 0.132, 0.274, 0.208]] and correlating them with their respective lines and colors. From there, I track and extract the usage points for each strategy from {line plots}[[0.158, 0.824], [0.185, 0.805], ..., [0.821, 0.170]] based on their lines' unique style and fill in the respective values in the structured table.\nBased on the above thought, the answer is:\nYear,Data Warehousing,Master Data Management,Data Lakes,Real-Time Data Integration,Data Virtualization\n2015,63,62,59,55,57\n2016,65,61,61,57,59\n2017,67,59,65,59,61\n2018,69,57,70,64,64\n2019,72,55,74,69,68\n2020,74,53,78,75,71"
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "AM3q70Z1KL5F413e",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data integration strategy exhibited the highest growth rate from 2015 to 2020 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest growth rate among data integration strategies from 2015 to 2020, I analyze each strategy's line plot's slope as it represents the rate of change over time. For this, I observe {the line for Real-Time Data Integration that stretches from top-left to bottom-right}[[0.158, 0.856, 0.821, 0.170]]. It starts relatively lower than others in 2015 and becomes the highest by 2020, indicating a sharp incline compared to {Data Warehousing}[0.158, 0.824, 0.821, 0.170], {Master Data Management}[0.158, 0.870, 0.821, 0.902], {Data Lakes}[0.158, 0.885, 0.821, 0.814], and {Data Virtualization}[0.158, 0.839, 0.821, 0.721]. This steep ascent signifies a higher growth rate.\nBased on the above thought, the answer is:\nReal-Time Data Integration strategy exhibited the highest growth rate from 2015 to 2020. Its line shows the sharpest incline, starting from a lower position and progressing to lead by 2020, which indicates a rapid increase in utilization compared to other strategies."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "uJoInqz9xIpoH3Mu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in usage for Real-Time Data Integration strategy from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in usage for Real-Time Data Integration from 2015 to 2020, I first need to identify {the line for Real-Time Data Integration}[[0.158, 0.415, 0.821, 0.856]] and then find its endpoints at {2015}[0.158, 0.856] and {2020}[0.821, 0.170]. The initial value is approximately 55, and the final value is approximately 75. The calculation would thus be 75 (2020 value) - 55 (2015 value).\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "NXgawXYQifWQ6zez",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data represented in this chart? With columns Year, Freight Volume (million ton-kilometers), and Passenger Miles (million)."
+            },
+            {
+                "from": "gpt",
+                "value": "To process the chart into structured tables, I first look at {the x-axis tick labels}<r>[0.117, 0.861, 0.160, 0.932], [0.198, 0.861, 0.240, 0.932], [0.278, 0.861, 0.321, 0.932], [0.359, 0.861, 0.402, 0.932], [0.440, 0.861, 0.482, 0.932], [0.520, 0.861, 0.563, 0.932], [0.601, 0.861, 0.643, 0.932], [0.681, 0.861, 0.724, 0.932], [0.762, 0.861, 0.805, 0.932], [0.843, 0.861, 0.885, 0.932], [0.923, 0.861, 0.966, 0.932]</r> which represent the years. To find values for each year, I look at the two bar charts identified by their respective colors. I find the bar chart points for {Freight Volume (million ton-kilometers)}<r>[0.138, 0.703, 0.945, 0.705]</r> and {Passenger Miles (million)}<r>[0.138, 0.431, 0.945, 0.484]</r> and extract the values for each year.\nBased on the above thought, the answer is:\nYear,Freight Volume (million ton-kilometers),Passenger Miles (million)\n2010,14512.5,60013.2\n2011,14816.0,61247.9\n2012,14590.6,61827.0\n2013,14245.8,63596.7\n2014,14130.5,64835.9\n2015,14336.8,66578.5\n2016,14487.2,67623.6\n2017,15010.1,68741.7\n2018,15427.3,69978.1\n2019,15535.2,71112.6\n2020,14606.4,68921.3"
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "ug1mGwt9090P59Q0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, can you tell whether the trend Freight Volume and Passenger Miles are related? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze whether Freight Volume and Passenger Miles trends are related, I examine the change of {Freight Volume}<r>[0.138, 0.698, 0.945, 0.705]</r> and {Passenger Miles}<r>[0.138, 0.431, 0.945, 0.484]</r> over the years 2010 to 2020. Both line charts reveal an upward trend despite some slight fluctuations, suggesting they might be related. Moreover, the small decline in both figures from 2019 to 2020 further supports their potential correlation.\nBased on the above thought, the answer is:\nIt seems that trends in Freight Volume and Passenger Miles display a parallel trend, suggesting a potential relationship between the two. Both experienced growth over the considered decade, with a small decline from 2019 to 2020. However, correlation does not necessarily mean causation, and further data or analysis would be needed to establish a definite connection."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "R4FdR9eai3gnxQ25",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Passenger Miles from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase of Passenger Miles from 2010 to 2020, I need to locate the corresponding points on the line chart for {Passenger Miles in 2010}<r>[0.138, 0.484]</r> and {Passenger Miles in 2020}<r>[0.945, 0.441]</r>. The value at 2010 is 60013.2 and the value at 2020 is 68921.3. The increase is therefore 68921.3 - 60013.2 = 8908.1.\nBased on the above thought, the answer is:\n8908.1 million"
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "mHILocNeEvYD4tzE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar graph data into a structured table with Hotels as rows and Years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table from the bar chart, I first identify {the x-axis labels}[[0.169, 0.150, 0.202, 0.161], [0.292, 0.150, 0.355, 0.161], [0.416, 0.150, 0.490, 0.161], [0.541, 0.150, 0.642, 0.161]] which can be used as row names in the table, representing the names of the hotels. Next, I locate {the legend texts}[[0.866, 0.831, 0.897, 0.843], [0.866, 0.805, 0.897, 0.817]] to serve as column headers representing the different years. To fill the values, I find {the bars}[[0.161, 0.166, 0.183, 0.369], [0.183, 0.166, 0.206, 0.497], [0.285, 0.166, 0.307, 0.263], [0.307, 0.166, 0.329, 0.397], [0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553], [0.532, 0.166, 0.555, 0.292], [0.555, 0.166, 0.577, 0.412]] and extract the occupancy rate values they represent.\nBased on the above thought, the answer is:\nHotel,2020 Occupancy,2021 Occupancy\nOcean View,35.7,52.4\nMountain Escape,19.7,39.3\nCity Central,53.5,61.5\nDesert Dream,22.6,41.5"
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "s6fUsQGhIvmzDv1y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the four hotels, which one has shown the highest improvement in terms of occupancy rate from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover the hotel with the highest improvement, I need to find the percentage increase in occupancy rate for each hotel from 2020 to 2021. For 'Ocean View', I locate {the bars for 'Ocean View'}[[0.161, 0.166, 0.183, 0.369], [0.183, 0.166, 0.206, 0.497]], for 'Mountain Escape', I locate {the bars for 'Mountain Escape'}[[0.285, 0.166, 0.307, 0.263], [0.307, 0.166, 0.329, 0.397]], for 'City Central', I locate {the bars for 'City Central'}[[0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553]], and for 'Desert Dream', I locate {the bars for 'Desert Dream'}[[0.532, 0.166, 0.555, 0.292], [0.555, 0.166, 0.577, 0.412]]. From these data, it can be determined that the largest percentage increase in occupancy is for the hotel 'Mountain Escape' which showed an increase from 19.7 in 2020 to 39.3 in 2021.\nBased on the above thought, the answer is:\n'Mountain Escape' has shown the highest improvement in terms of occupancy rate from 2020 to 2021. The hotel saw an occupancy increase from 19.7% in 2020 to 39.3% in 2021 which is the highest percentage increase among the four hotels."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "9UsAnK4Ed95OpV3e",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the occupancy rate for 'City Central' from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I need to locate the values for 'City Central' for both years. I identify {the bars for 'City Central' in 2020 and 2021}[[0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553]] which have values of 53.5 and 61.5 respectively. The percentage increase is calculated as (61.5 - 53.5) / 53.5 * 100.\nBased on the above thought, the answer is:\n14.95%."
+            }
+        ],
+        "id_raw": 498
+    },
+    {
+        "id": "0YXB7TEnkM03zIYV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the line graph into a structured table that has years as rows and the two types of investments as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the first step is to identify the data needed. The years from the {x-axis tick labels}[[0.061, 0.909, 0.109, 0.932], [0.168, 0.909, 0.217, 0.932], [0.275, 0.909, 0.324, 0.932], [0.382, 0.909, 0.431, 0.932], [0.489, 0.909, 0.538, 0.932], [0.597, 0.909, 0.645, 0.932], [0.704, 0.909, 0.752, 0.932], [0.811, 0.909, 0.859, 0.932], [0.918, 0.909, 0.966, 0.932], [1.025, 0.909, 1.073, 0.932]] serve as the row qualifiers. Next, {the legend}[0.049, 0.077, 0.292, 0.154] identifies the two investment types which should be used as columns. The data values for these columns can then be extracted by mapping the values of the plotted points, such as these for stocks {the line points for stocks}[[0.085, 0.849], [0.128, 0.850], [0.171, 0.850], [0.214, 0.850], [0.257, 0.850], [0.299, 0.851], [0.342, 0.851], [0.385, 0.851], [0.428, 0.852], [0.471, 0.852], [0.514, 0.852], [0.557, 0.853], [0.599, 0.853], [0.642, 0.853], [0.685, 0.853], [0.728, 0.854], [0.771, 0.854], [0.814, 0.854], [0.856, 0.855], [0.899, 0.855], [0.942, 0.855]] to the corresponding years\nBased on the above thought, the answer is:\nYear, Stocks, Mutual Funds \n1980, 1.211, 68.99 \n1981, 1.159, 69.21 \n1982, 1.138, 69.68 \n1983, 1.101, 70.15 \n1984, 1.080, 70.39 \n1985, 1.055, 70.63 \n1986, 1.008, 70.89 \n1987, 1.001, 71.23 \n1988, 0.969, 71.80 \n1989, 0.935, 71.97 \n1990, 0.909, 72.23 \n1991, 0.863, 72.649 \n1992, 0.838, 73.17 \n1993, 0.800, 73.3\n1994, 0.777, 73.68 \n1995, 0.755, 73.98 \n1996, 0.713, 74.24\n1997, 0.686, 74.49 \n1998, 0.653, 74.90 \n1999, 0.621, 75.24 \n2000, 0.593, 75.65"
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "h2X7lsyFEqcBIgsD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data in the graph, what trend can we observe concerning population growth and life expectancy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line for Population Growth Rate}[0.085, 0.849, 0.942, 0.855], we can see a steady decline in population growth rate over the years. On the other hand, {the line for Life Expectancy}[0.085, 0.170, 0.942, 0.103] shows a steady increase in life expectancy.\nBased on the above thought, the answer is:\nThe trends suggest that as population growth rate is declining, the life expectancy is increasing. This can be seen from the downward slope of the population growth rate trajectory and the upward slope of the life expectancy trajectory. The exact reasons for this could be multifaceted, but it's commonly understood that factors such as advancements in healthcare and living conditions can lead to lower population growth and higher life expectancy."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "8a8P1FxoBfOJ7y8G",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_25_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in life expectancy from the year 1980 to 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in life expectancy, we need to first locate the life expectancy for the year 1980 and 2000. Looking at {the line points for Life Expectancy}[[0.085, 0.170],[0.942, 0.103]], the life expectancy in 1980 is 68.99 years and in 2000 it is 75.65 years. The percentage increase is then calculated as ((75.65 - 68.99) / 68.99) * 100.\nBased on the above thought, the answer is:\n9.66%."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "y0U01d13E213Kvk0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data represented by the chart in a tabular format with the 'Fields of Technology' as columns and 'Years' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create this table, I'll start by identifying {the x-axis labels}<r>[0.084, 0.918, 0.149, 0.943],[0.194, 0.918, 0.258, 0.943],[0.303, 0.918, 0.368, 0.943],[0.413, 0.918, 0.478, 0.943],[0.522, 0.918, 0.587, 0.943],[0.632, 0.918, 0.697, 0.943],[0.742, 0.918, 0.806, 0.943],[0.851, 0.918, 0.916, 0.943]</r> as the 'Fields of Technology' which will serve as our column headers. The two columns we'll have are 'Education Technology' and 'Training Technology'. Next, I will identify {the legend items} <r>[0.801, 0.056, 0.900, 0.084],[0.801, 0.097, 0.900, 0.125]</r> as '2020' and '2021' respectively and these will serve as our row labels or 'Years'. Lastly, I will locate {the bar heights} <r>[0.116, 0.207, 0.884, 0.669],[0.116, 0.075, 0.884, 0.867]</r>  for each Year in the respective Field of Technology to determine the values as 26, 50 for Education Technology and 30, 60 for Training Technology in years 2020 and 2021 respectively.\nBased on the above thought, the answer is:\nYears,Education Technology,Training Technology\n2020,26,30\n2021,50,60"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "aVpUBB092G5Gl8W7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends indicated in the bar graph, which technology field experienced a higher rate of growth in innovations from 2020 to 2021 and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to calculate the rate of growth in each field. For {Education Technology}, I look at the increase from 26 to 50. This is a 92.3% increase. For {Training Technology}, I look at the increase from 30 to 60. This is a 100% increase. Thus, although both technology fields experienced significant growth, Training Technology had a slightly higher rate of growth.\nBased on the above thought, the answer is:\nTraining Technology experienced a higher rate of growth in innovations from 2020 to 2021. While both fields witnessed considerable growth, Training Technology had a growth rate of 100%, which slightly exceeds the 92.3% growth rate of Education Technology."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "rdjqCPkL5Uela0Vj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many counts did the innovations in Training Technology increase from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to locate {the bar for Training Technology in 2020}<r>[0.226, 0.339]</r> and get its value which is 30. Then I do same for {the bar for Training Technology in 2021}<r>[0.445, 0.251]</r> and get that its value is 60. The difference between these 2 values gives me the increase which is 60 - 30 = 30 counts.\nBased on the above thought, the answer is:\n30 counts."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "IFxTaRNsISnxG0Cm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data represented in the dual bar chart into a table format? With 'Year' as columns and 'Consumer Classes' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I will first identify {the x-axis tick labels}[[0.083, 0.931, 0.116, 0.955], [0.498, 0.931, 0.512, 0.955]] which stand for the column headers (Years). Next, I recognize {the legend labels}[0.838, 0.012, 0.898, 0.029], [0.894, 0.055, 0.955, 0.072]] as the row labels representing the different Consumer Classes. To fill the values in the table, I will locate the heights of the bars for each class at the represented years. The bars are {Consumer_Class_1 bars}(0.166, 0.094, 0.301, 0.651], [0.403, 0.081, 0.495, 0.930]) and {Consumer_Class_2 bars}(0.311, 0.197, 0.391, 0.817], [0.500, 0.046, 0.583, 0.965]).\nBased on the above thought, the answer is:\nConsumer Class, 2019, 2020\nConsumer_Class_1, 63, 73\nConsumer_Class_2, 78, 97"
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "yv0Z3Gc9GQSqHRUD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which Consumer Class showed accelerated growth during the period from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the chart, {Consumer_Class_1 bars}(0.166, 0.094, 0.301, 0.651], [0.403, 0.081, 0.495, 0.930]) show a gradual increase in consumer behavior from 63 to 73 over the period from 2019 to 2020. However, {Consumer_Class_2 bars}(0.311, 0.197, 0.391, 0.817], [0.500, 0.046, 0.583, 0.965]) present a more substantial increase from 78 to 97 over the same period. Therefore, Consumer_Class_2 showed the more accelerated growth.\nBased on the above thought, the answer is:\nBased on the chart, Consumer_Class_2 showed accelerated growth during the period from 2019 to 2020. It showed a substantial increase in consumer behavior from 78 in 2019 to 97 in 2020, indicating a more rapid increase than Consumer_Class_1."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "q6w485ucjRQq4krs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in behaviour between Consumer_Class_1 and Consumer_Class_2 during the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this difference, I need to identify the values of Consumer_Class_1 and Consumer_Class_2 in the year 2020. These values can be extracted by analyzing the height of the bars for these classes {Consumer_Class_1 bar in 2020}[0.403, 0.081, 0.495, 0.930] representing 73 and {Consumer_Class_2 bar in 2020}[0.500, 0.046, 0.583, 0.965] representing 97. Then I subtract 73 from 97.\nBased on the above thought, the answer is:\n24."
+            }
+        ],
+        "id_raw": 503
+    },
+    {
+        "id": "wNKJmzkfKkGXQQYQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line plot data of Creativity Level vs Design Innovation Level into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a structured table format, I identify {the X-axis}[0.436, 0.958, 0.627, 0.981] which contains the Creativity Level data. Additionally, the line plot for Design Innovation Level is marked by the points in the coordinates {line_0}[0.123, 0.109, 0.940, 0.892], whose point data will correspond to the values in the Design Innovation Level column. Each data pair (x,y) from the points of line_0 will represent a row in the table.\nBased on the above thought, the answer is:\nCreativity Level,Design Innovation Level\n0,1\n1,2\n2,4\n3,8\n4,12\n5,16\n6,20\n7,24\n8,25\n10,27"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "4GFej2c7bbXMDyVq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the line chart, how would you describe the relationship between Creativity Level and Design Innovation Level?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the points {[0.123, 0.892], [0.205, 0.862], [0.286, 0.802], [0.368, 0.681], [0.450, 0.561], [0.532, 0.440], [0.613, 0.320], [0.695, 0.199], [0.777, 0.169], [0.940, 0.109]} in the chart which represent the relationship between Creativity Level and Design Innovation Level, it's clear that both values increase together. Therefore, I would say there is a positive correlation between Creativity Level and Design Innovation Level. This implies that the higher the creativity level, the higher the design innovation level and vice versa.\nBased on the above thought, the answer is:\nThe relationship between Creativity Level and Design Innovation Level can be described as a strong positive correlation. As the Creativity Level increases, the Design Innovation Level increases as well, indicating that a higher level of creativity is associated with a higher level of design innovation, and vice versa."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "M8rlnpBCcF150Xgz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the Design Innovation Level at a Creativity Level of 8 and Creativity Level of 0?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to find the y-values corresponding to the Creativity Levels of 0 and 8. Looking at {the point for Creativity Level 0}[0.123, 0.892], I find the Design Innovation Level to be 1. Then, referencing {the point for Creativity Level 8}[0.777, 0.169], I see the Design Innovation Level is 25. To find the difference, I subtract the Design Innovation Level at Creativity Level of 0 from that at Creativity Level 8, which gives 25-1 = 24.\nBased on the above thought, the answer is:\n24"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "oS4WSPLYJMM6nmSk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the Creativity and Innovation Scores for all design elements into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into tabular data, firstly, I identify the elements on the x-axis {Design Elements}<r>[0.112, 0.918, 0.905, 0.943]</r> that represent the rows. I then take note of the terms that denote the columns, which are Creativity and Innovation scores. I extract the values by pinpointing the points of intersection on {Creativity Scores}<r>[0.139, 0.062, 0.881, 0.870]</r> and {Innovation Scores}<r>[0.139, 0.062, 0.881, 0.870]</r> for each Design Element.\nBased on the above thought, the answer is:\nDesign Element, Creativity Score, Innovation Score\nColor, 82, 75\nShape, 90, 87\nTexture, 89, 92\nSpace, 77, 80\nForm, 95, 98\nLine, 89, 85\nValue, 78, 72\nType, 84, 90"
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "QPKxToQ3k8b4WKDA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Design Element has the closest Creativity and Innovation scores and what's the difference?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the Design Element with the closest Creativity and Innovation scores, I must compare the values for each Design Element in {Creativity Scores}<r>[0.139, 0.062, 0.881, 0.870]</r> and {Innovation Scores}<r>[0.139, 0.062, 0.881, 0.870]</r>. By comparing, it is easily notable that for the Design Element {Texture}<r>[0.313, 0.918, 0.389, 0.943]</r>, both the scores are quite close. The Creativity score for Texture is 89, and the Innovation score is 92, giving a difference of 3.\nBased on the above thought, the answer is:\nTexture is the Design Element with the closest Creativity and Innovation scores. The difference between the Creativity and Innovation scores for Texture is 3."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "ykpV04SZASU1Jizo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum Creativity Score and which design element does it correspond with?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum Creativity Score, I need to analyze the {Creativity Scores}<r>[0.139, 0.062, 0.881, 0.870]</r> scene in the chart. By doing this, I can identify the maximum point corresponding to {Form}<r>[0.537, 0.918, 0.589, 0.943]</r> with a score of 95.\nBased on the above thought, the answer is:\nThe maximum Creativity Score is 95, which corresponds with the design element Form."
+            }
+        ],
+        "id_raw": 505
+    },
+    {
+        "id": "H9ut223EBXKahD1x",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the market share percentages of each automotive company from the bar chart into a structured table format."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I first identify {the bar for Tesla}<r>[0.150, 0.200, 0.180, 0.950]</r> representing 54% of the market share. Then, {the bar for GM}<r>[0.220, 0.450, 0.250, 0.950]</r> shows 20%, followed by {the bar for Toyota}<r>[0.290, 0.550, 0.320, 0.950]</r> at 15%. Continuing, I locate {the bar for Ford}<r>[0.360, 0.800, 0.390, 0.950]</r> showing 5% and {the bar for Honda}<r>[0.430, 0.850, 0.460, 0.950]</r> at 4%. Lastly, {the bars for Hyundai and Nissan}<r>[0.500, 0.950, 0.530, 0.950]; [0.570, 0.950, 0.600, 0.950]</r> are both at 1% each.\nBased on the above thought, the answer is:\nCompany,Market Share (%)\nTesla,54\nGM,20\nToyota,15\nFord,5\nHonda,4\nHyundai,1\nNissan,1"
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "jZTZIxVtqeOahEaB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the bar for Tesla stand out significantly compared to other companies in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the bar for Tesla}<r>[0.150, 0.200, 0.180, 0.950]</r>, which is significantly taller than the others, indicates a dominant market share of 54%. This height, when compared with {the bars for other companies}<r>[0.220, 0.450, 0.600, 0.950]</r>, where the highest is only 20% (GM), illustrates Tesla's major lead in the industry. The visual disparity emphasizes Tesla\u2019s significant position and dominant market influence relative to smaller shares held by its competitors.\nBased on the above thought, the answer is:\nThe bar for Tesla stands out significantly in the bar chart because it represents a much greater market share of 54%, in stark contrast to other companies whose shares range only from 1% to 20%. Tesla's dominant position in the industry is visually evident, emphasizing its substantial lead over competitors."
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "5hvk5qE289qL0dyt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined market share percentage of Ford, Honda, Hyundai, and Nissan?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined market share for Ford, Honda, Hyundai, and Nissan, I look at {the bar for Ford}<r>[0.360, 0.800, 0.390, 0.950]</r> with 5%, {the bar for Honda}<r>[0.430, 0.850, 0.460, 0.950]</r> with 4%, and {the bars for Hyundai and Nissan}<r>[0.500, 0.950, 0.530, 0.950]; [0.570, 0.950, 0.600, 0.950]</r> both at 1% each. Summing these values gives 5% + 4% + 1% + 1% = 11%.\nBased on the above thought, the answer is:\n11"
+            }
+        ],
+        "id_raw": 506
+    },
+    {
+        "id": "cMVR5trR3hzx5XLO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line plot into a table structure with Years as rows and AI Usage and Automation as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line plot data, I must first look at the {x-axis ticks}[[0.086, 0.939, 0.121, 0.957], [0.226, 0.939, 0.261, 0.957], [0.366, 0.939, 0.401, 0.957], [0.506, 0.939, 0.541, 0.957], [0.646, 0.939, 0.681, 0.957], [0.786, 0.939, 0.821, 0.957], [0.925, 0.939, 0.961, 0.957]], which clarify the years. Subsequently, I identify {the legend items}[[0.113, 0.062, 0.174, 0.080], [0.113, 0.088, 0.194, 0.106]] as the column headers labeling AI Usage and Automation. Lastly, I locate {the points on the lines}[[0.104, 0.216], [0.174, 0.871], [0.244, 0.831], [0.314, 0.806], [0.384, 0.758], [0.454, 0.693], [0.523, 0.580], [0.593, 0.499], [0.663, 0.394], [0.733, 0.337], [0.803, 0.297], [0.873, 0.265], [0.943, 0.216], [0.104, 0.087], [0.174, 0.839], [0.244, 0.790], [0.314, 0.742], [0.384, 0.693], [0.454, 0.628], [0.523, 0.531], [0.593, 0.459], [0.663, 0.378], [0.733, 0.297], [0.803, 0.224], [0.873, 0.143], [0.943, 0.087]] for AI Usage and Automation at the respective years and extract the numerical values.\nBased on the above thought, the answer is:\nYear, AI Usage, Automation\n2000, 0.2, 0.4\n2001, 0.4, 0.8\n2002, 0.9, 1.4\n2003, 1.2, 2.0\n2004, 1.8, 2.6\n2005, 2.6, 3.4\n2006, 4.0, 4.6\n2007, 5.0, 5.5\n2008, 6.3, 6.5\n2009, 7.0, 7.5\n2010, 7.5, 8.4\n2011, 7.9, 9.4\n2012, 8.5, 10.1"
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "GJjvr8Wkro2JKBFI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing AI usage and automation trends, what observations can you make about the relationship between the two?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make observations about the relationship between AI usage and Automation trends, I need to compare {the line for AI Usage}<r>[0.104, 0.216, 0.943, 0.887]<r> and {the line for Automation}<r>[0.104, 0.087, 0.943, 0.871]<r>. The lines show that both AI usage and Automation have a generally increasing trend over the years, suggesting that an increase in AI usage could be positively related to an increase in Automation. There are periods where the AI usage line seems to lead the Automation line, suggesting that advances in AI technology might be driving increases in Automation. However, there are also instances where the trends diverge suggesting that while there might be a relationship, other factors might also be influencing Automation.\nBased on the above thought, the answer is:\nBoth AI usage and Automation have shown an increasing trend over the years, indicating that the increase in the use of AI might be leading to an increase in Automation. There are times when the AI usage line precedes the Automation line, suggesting that advances in AI technology could be propelling increases in Automation. However, there are periods when the two trends diverge, suggesting that while there might be some correlation, there may be other factors at play influencing Automation."
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "IkZBE3qCZ9ORLvpm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in AI usage from the year 2000 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall increase in AI usage, I need to find the y-values of the plot corresponding to the years 2000 and 2012. I find that {the value for the year 2000}<r>[0.104, 0.216]</r> is 0.2 and {the value for the year 2012}<r>[0.943, 0.216]</r> is 8.5. The overall increase is the difference between these two values, which is 8.5 - 0.2 = 8.3.\nBased on the above thought, the answer is:\n8.3"
+            }
+        ],
+        "id_raw": 507
+    },
+    {
+        "id": "qfEHFpXxSc8TrHgq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the 'Efficiency Improvement Potential Assessment' data into a structured table format from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I consider {the x-axis tick labels}[[0.126, 0.908, 0.185, 0.924], [0.224, 0.908, 0.306, 0.924], [0.389, 0.908, 0.496, 0.924], [0.497, 0.908, 0.601, 0.925], [0.646, 0.908, 0.764, 0.925], [0.812, 0.908, 0.932, 0.925], [0.946, 0.908, 1.091, 0.915]] as the different technology types, and {the line points}[[0.157, 0.910], [0.265, 0.901], [0.442, 0.882], [0.549, 0.862], [0.705, 0.809], [0.873, 0.755], [1.018, 0.645]] for corresponding percentage values, respectively. Analyzing these data points, I can extract the data into a table.\nBased on the above thought, the answer is:\nTechnology,Efficiency Improvement Potential (%)\nRobust AI,0.0\nAutomated Driving,7.0\nCommunication Algorithms,12.5\nRenewable Energy,27.5\nBig Data Processing,35.0\nCloud Computing,65.0\nQuantum Computing,90.5"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "9pBkaZGJR76kWtzs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the given line chart, how fast is the Efficiency Improvement Potential growing for these technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I have to analyze the individual data points on the line chart representing each technology. Starting at {Robust AI}<r>[0.157, 0.910]</r> with 0%, and moving up across the technologies to {Quantum Computing}<r>[1.018, 0.645]</r> at 90.5%. The line plot shows an overall upward trend signifying that the technological improvements are on the increase. Although the growth seems relatively gentle in the beginning from Robust AI, Automated Driving, Communication Algorithms to Renewable Energy, it then makes significant leaping progress from Renewable Energy to Big Data Processing and Cloud Computing, ending up peaking very high with Quantum Computing.\nBased on the above thought, the answer is:\nThe Efficiency Improvement Potential among these technologies is growing in a rapidly increasing trend, with a more pronounced rise in the later technologies. While the earlier technologies, such as Robust AI, Automated Driving, and Communication Algorithms, show a slow and steady growth, the momentum picks up with Renewable Energy and Big Data Processing. The growth further accelerates with Cloud Computing and eventually peaks at an extremely high rate with Quantum Computing, indicating a speedy and significant increase over the technology progression."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "MuqheZsptS0Hia35",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the improvement potential difference between Quantum Computing and Renewable Energy in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in improvement potential between Quantum Computing and Renewable Energy, I look at {the line point of Quantum Computing}<r>[1.018, 0.645]</r> which gives 90.5% and {the line point of Renewable Energy}<r>[0.442, 0.882]</r> which stands for 27.5%. Subtracting the percentage of  Renewable Energy from Quantum Computing gives the difference.\nBased on the above thought, the answer is:\n63%"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "4iupxRYjnA42qPa2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the user data for all social media platforms over the years into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, first, I identify {the x-axis labels}<r>[0.116, 0.911, 0.884, 0.918]</r> which represent the column headers or the Years. Next, I identify {the legend texts}<r>[0.747, 0.051, 1.064, 0.945]</r> as the row labels indicating the different social media platforms. I then locate {the line points for each platform}<r>[0.116, 0.090, 0.884, 0.386],[0.116, 0.266, 0.884, 0.618][0.116, 0.251, 0.884, 0.647][0.116, 0.761, 0.884, 0.770]</r> and extract the values annotated for each year to fill in the cells of the table.\nBased on the above thought, the answer is:\nYears,Facebook Users,WhatsApp Users,Instagram Users,Twitter Users\n2016,1.79,1.0,0.6,0.32\n2017,2.07,1.3,0.8,0.33\n2018,2.23,1.5,1.0,0.34\n2019,2.41,1.7,1.2,0.34\n2020,2.5,2.0,1.3,0.33\n2021,2.8,2.2,1.5,0.33"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "DGJ1oBiAYUVI3d4Y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform experienced the largest growth from 2016 to 2021 according to the chart? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which social media platform experienced the largest growth, I need to calculate the difference in user counts from 2016 to 2021 for each platform. For Facebook Users, the value in 2016 can be found at {the line point for Facebook Users in 2016}<r>[0.116, 0.386]</r> and the value in 2021 at {the line point for Facebook Users in 2021}<r>[0.884, 0.090]</r>. The difference is 2.8-1.79 = 1.01 billion. Similarly, I calculate the differences for other platforms: WhatsApp Users (2.2-1.0 = 1.2 billion), Instagram Users (1.5-0.6 = 0.9 billion), and Twitter Users (0.33-0.32 = 0.01 billion). Comparing these differences, it's clear that WhatsApp Users experienced the largest growth.\nBased on the above thought, the answer is:\nWhatsApp experienced the largest growth from 2016 to 2021. Comparing the changes in user counts across the platforms, as visualized in the line chart, shows that the difference for WhatsApp Users is the greatest."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "0dnpIb7yYFuQ4QqZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the user growth rate for Instagram from the year 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth rate, I need to find the initial value and the final value for Instagram. The initial value for Instagram in 2016 can be located at {the line point for Instagram in 2016}<r>[0.116, 0.647]</r> which is 0.6 billion. The final value for Instagram in 2021 can be located at {the line point for Instagram in 2021}<r>[0.884, 0.251]</r> which is 1.5 billion. The growth rate is calculated as (Final value - Initial value)/Initial value * 100% which is (1.5 - 0.6)/0.6 * 100%.\nBased on the above thought, the answer is:\n150%"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "qWzOP2b83Dnyhbvj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table format with Years as rows and Environmental Factors as columns based on the sequences of data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I need to identify the x and y values for each line. Starting with the x-axis {year labels}<r>[0.090, 0.925, 0.143, 0.951], [0.260, 0.925, 0.313, 0.951], [0.431, 0.925, 0.484, 0.951], [0.601, 0.925, 0.654, 0.951], [0.772, 0.925, 0.825, 0.951]</r> we see these represent the years from 2010 to 2019. \n\nThe line points show the annual values for each indicator. We have line points for {Peace Index}<r>[0.116, 0.231], [0.202, 0.203], [0.287, 0.175], [0.372, 0.146], [0.457, 0.118], [0.543, 0.090], [0.628, 0.062], [0.713, 0.073], [0.798, 0.085], [0.884, 0.107]</r>, {Social Equality Index}<r>[0.116, 0.877], [0.202, 0.872], [0.287, 0.866], [0.372, 0.860], [0.457, 0.849], [0.543, 0.838], [0.628, 0.821], [0.713, 0.821], [0.798, 0.815], [0.884, 0.804]</r>, and {Absolute Poverty Rate}<r>[0.116, 0.062], [0.202, 0.171], [0.287, 0.280], [0.372, 0.388], [0.457, 0.497],  [0.543, 0.606],  [0.628, 0.714],  [0.713, 0.769],  [0.798, 0.823],  [0.884, 0.877]</r>. So let's arrange these on the year axis to form the dataset value.\nBased on the above thought, the answer is:\nYear,Peace Index,Social Equality Index,Absolute Poverty Rate\n2010,75.0,63.5,14.3\n2011,75.5,63.6,14.1\n2012,76.0,63.7,13.9\n2013,76.5,63.8,13.7\n2014,77.0,64.0,13.5\n2015,77.5,64.2,13.3\n2016,78.0,64.5,13.1\n2017,77.8,64.5,13.0\n2018,77.6,64.6,12.9\n2019,77.2,64.8,12.8"
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "SUgTnyfnLhi0r8Pa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which index saw the most consistent increase over the analysed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to look at the trend lines for each of the indexes. The {line for Peace Index}<r>[0.116, 0.062, 0.884, 0.231]</r> shows a mostly increasing trend and the {line for Social Equality Index}<r>[0.116, 0.804, 0.884, 0.877]</r> also shows a consistent increase. However, the {line for Absolute Poverty Rate}<r>[0.116, 0.062, 0.884, 0.877]</r> shows a consistent decrease. Overall, the Social Equality Index appears to have the most consistent increase over this time period.\nBased on the above thought, the answer is:\nThe Social Equality Index saw the most consistent increase over the period from 2010 to 2019, according to the line chart."
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "4aLLLe0q2OBxzBHy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Poverty Rate decrease from 2010 to 2019, according to the plotted data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying {the line points for Absolute Poverty Rate}<r>[0.116, 0.062], [0.884, 0.877]</r>, we can see that it starts at a point corresponding to 14.3 in 2010 on the y-axis and ends at a point corresponding to 12.8 in 2019. Subtracting the final value from the initial value shows how much the Poverty Rate decreased over this period.\nBased on the above thought, the answer is:\n1.5%"
+            }
+        ],
+        "id_raw": 510
+    },
+    {
+        "id": "wnzEGi23lCM9xV3g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a CSV format distinguishing between the efficiency before and after interventions for each process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create this table, I'll review the points plotted on the chart for each process. I concentrate on differentiating between the markers representing values before and after the intervention, as indicated by {the legend's content}<r>[0.607, 0.723, 0.904, 0.896]</r>. I will list each process with its corresponding efficiency scores determined from the coordinates of the plotted points for both states.\nBased on the above thought, the answer is:\nProcess,Before Intervention,After Intervention\n1,0.60,1.00\n2,0.75,0.95\n3,0.80,1.10\n4,0.85,1.05\n5,0.90,1.15\n6,0.95,0.80"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "tItTp6OnwA9NuhUe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data trend in the line chart, why should Process 6's performance decline after the intervention be a concern?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the performance data visualized in the chart, I scrutinize {the before and after intervention points of Process 6}<r>[0.879, 0.511], [0.879, 0.737]</r>, noting the decrease in efficiency from 0.95 to 0.80. This is unique as all other processes show improvement or stability in their efficiency metrics post-intervention. It points out that unlike the growth or maintenance of efficiency in other processes, Process 6\u2019s operational environment or variables might not have responded positively to the changes implemented, underscoring potential underlying issues that need investigation and resolution to align with the broader objectives of efficiency improvement.\nBased on the above thought, the answer is:\nThe decline in efficiency for Process 6 is concerning because it diverges from the overall positive trend observed in other processes. This anomaly suggests specialized problems within Process 6 that may not be present in others, highlighting a need for dedicated analysis and corrective actions specific to this process's variables or operational environment."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "EI0s0JNeFbHYYLKX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the efficiency of Process 3 improve after the intervention according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the before intervention point of Process 3}<r>[0.311, 0.822]</r> at an efficiency level of 0.80. I then pinpoint {the after intervention point of Process 3}<r>[0.311, 0.290]</r> at an efficiency level of 1.10. The improvement is calculated by subtracting the before-intervention efficiency from after-intervention efficiency, resulting in an enhancement of 1.10 - 0.80 = 0.30 or 30%.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "Kyme2Rkj5b6pD01L",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information presented in the line chart regarding the trends in various forms of business and social responsibility into tabular data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indeed, I can extract the values from the line chart into a table. I can see {the x-axis tick labels} for years [0.119, 0.903, 0.283, 0.903, 0.447, 0.903, 0.612, 0.903, 0.776, 0.903, 0.940, 0.903] as the rows of the table. Next, I see {the legend texts}[[0.142, 0.083, 0.354, 0.106], [0.142, 0.118, 0.362, 0.141], [0.142, 0.153, 0.327, 0.176]] that act as the column headers. The actual values can then be extracted by identifying the graph lines and their corresponding point values. For corporate responsibility {line points}[[0.119, 0.865], [0.283, 0.801], [0.447, 0.674], [0.612, 0.546], [0.776, 0.483], [0.940, 0.419]], environmental protection {line points}[[0.119, 0.546], [0.283, 0.514], [0.447, 0.419], [0.612, 0.228], [0.776, 0.164], [0.940, 0.100]] and sustainable practices {line points}[[0.119, 0.674], [0.283, 0.610], [0.447, 0.546], [0.612, 0.419], [0.776, 0.291], [0.940, 0.228]].\nBased on the above thought, the answer is:\nYear,Corporate Responsibility,Environmental Protection,Sustainable Practices\n2015,15,20,18\n2016,16,20.5,19\n2017,18,22,20\n2018,20,25,22\n2019,21,26,24\n2020,22,27,25"
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "j72WH5OBDK2vCaOT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in corporate responsibility compare to environmental protection and sustainable practices from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the trends, I need to look at the upward or downward trajectory of the lines representing each category from 2015 to 2020. {The Corporate Responsibility line} starts at 15[0.119, 0.865] and increases to 22 [0.940, 0.419], {the Environmental Protection line} starts at 20[0.119, 0.546] and also increases to 27[0.940, 0.100], whereas {the Sustainable Practices line} begins at 18[0.119, 0.674] and increases to 25[0.940, 0.228]. Each line shows an increasing trend over the given period, though the rate of increase varies.\nBased on the above thought, the answer is:\nAll three areas - corporate responsibility, environmental protection and sustainable practices - show an increasing trend from 2015 to 2020. However, the rate of increase appears to be highest for environmental protection, as it reaches the highest value (27) by 2020. Despite start at a lower initial value, corporate responsibility also shows a significant increase, ending at a value of 22. Sustainable practices, though beginning at a higher initial value than corporate responsibility, ends up at a slightly higher value of 25. Overall, all three areas are exhibiting an increasing commitment over time, with environmental protection showing the greatest uptick."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "nTouLNiuObbhEFfR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much have sustainable practices increased from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the line representing sustainable practices. Specifically, I would compare between the starting point {2015}[0.119, 0.903] with the value of 18 and by the end at {2020}[0.940, 0.903] where the value is 25. The difference is 25 - 18 = 7.\nBased on the above thought, the answer is:\n7."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "xTNUtNJjeCHoOQ23",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart depicting the trends in Gender Pay Gap, Rights Violation Index, and Women Employment Rate over the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis tick labels}<[0.080, 0.918, 0.153, 0.943], [0.484, 0.918, 0.556, 0.943], [0.888, 0.918, 0.960, 0.943]> helps identify the timeline for the data from 1990 to 2020. Each line on the chart, marked by {points on the lines for Gender Pay Gap}<[0.116, 0.262], [0.520, 0.136]>, {Rights Violation Index}<[0.116, 0.603], [0.520, 0.468]>, and {Women Employment Rate}<[0.116, 0.790], [0.520, 0.802]> corresponds to different data series. I will use these to extract the respective rates and index across the years.\nBased on the above thought, the answer is:\nYear,Gender Pay Gap (%),Rights Violation Index,Women Employment Rate (%)\n1990,23.4,10.2,60.1\n1995,21.7,9.8,62.5\n2000,20.5,9.3,65.0\n2005,18.2,8.8,68.4\n2010,16.5,8.2,71.8\n2015,14.6,7.3,74.1\n2020,12.6,7.8,75.2"
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "B05yyla7f33s0rlG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric showed the most stability over the 30-year period according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine stability, I examine the amount of fluctuation in each line. The {line for Gender Pay Gap}<[0.116, 0.262], [0.884, 0.136]> shows a steady, consistent downward trajectory. {The Rights Violation Index line}<[0.116, 0.603], [0.520, 0.468], [0.884, 0.458]> exhibits irregular oscillations, indicating less stability. Comparatively, {the Women Employment Rate line}<[0.116, 0.790], [0.520, 0.802], [0.884, 0.802]> demonstrates smooth and consistent upward growth, making it the most stable of the three metrics.\nBased on the above thought, the answer is:\nThe Women Employment Rate showed the most stability over the 30-year period. It consistently and progressively increased, showing a smooth upward trajectory in contrast to the others, which displayed greater fluctuations and irregularities."
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "RpGd7N4k8EmJsbpO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Women Employment Rate from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I need to locate {the points representing the Women Employment Rate}<[0.116, 0.790], [0.884, 0.802]> in the years 1990 and 2020. The values are noted as 60.1% in 1990 and 75.2% in 2020. The increase is calculated as ((75.2 - 60.1) / 60.1) * 100 = 25.12%.\nBased on the above thought, the answer is:\n25.12%"
+            }
+        ],
+        "id_raw": 514
+    },
+    {
+        "id": "mBDNucbAnXlowhTj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the given data with 'Year' as rows and Areas of development as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I'll start with the rows labels {the x-axis tick labels}<r>[[0.113, 0.888, 0.168, 0.918], [0.233, 0.888, 0.288, 0.918], [0.353, 0.888, 0.408, 0.918], [0.473, 0.888, 0.527, 0.918], [0.592, 0.888, 0.647, 0.918], [0.712, 0.888, 0.767, 0.918], [0.832, 0.888, 0.887, 0.918]]</r> which represent the years. Next, I identify the column headers as the areas. These are {Global Trade Volume, Market Globalization, Economic Integration, Tech Revolution}<r>[0.185, 0.057, 0.408, 0.086][0.185, 0.101, 0.416, 0.130][0.185, 0.145, 0.409, 0.174][0.185, 0.188, 0.355, 0.217]</r>. I'll take the corresponding values for each year from {the line points}[[0.140, 0.526][0.260, 0.450][0.380, 0.298][0.500, 0.191][0.620, 0.115][0.740, 0.085][0.860, 0.069][0.140, 0.830][0.260, 0.800][0.380, 0.754][0.500, 0.724][0.620, 0.678][0.740, 0.632][0.860, 0.556][0.140, 0.592][0.260, 0.545][0.380, 0.355][0.500, 0.236][0.620, 0.164][0.740, 0.117][0.860, 0.069][0.140, 0.830][0.260, 0.783][0.380, 0.759][0.500, 0.711][0.620, 0.664][0.740, 0.616][0.860, 0.521]] and compile them in a structured table.\nBased on the above thought, the answer is:\nYear,Global Trade Volume,Economic Integration,Market Globalization,Tech Revolution\n1990,30,10,15,5\n1995,35,12,17,7\n2000,45,15,25,8\n2005,52,17,30,10\n2010,57,20,33,12\n2015,59,23,35,14\n2020,60,28,37,18"
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "OXXe6BEQoxvuaYDl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed from the line chart regarding the areas of development in the Internet and Communication Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends, I will need to look at the lines for each area. Observing {the line for 5G Network Support}[0.140, 0.069, 0.860, 0.526], there is a steady increase throughout the years. Next, looking at {the line for Cloud Computing Services}[0.140, 0.556, 0.860, 0.830], the trend also shows a consistent increase, albeit at a slower pace. For {the line for Internet of Things}[0.140, 0.521, 0.860, 0.07], there is an erratic fluctuation with a generally increasing trend. Lastly, seeing {the line for Machine Learning Applications}[0.140, 0.830, 0.860, 0.521], it's clear that there is a steady and sharp rise in this area.\nBased on the above thought, the answer is:\nBased on the line chart, all four areas (5G Network Support, Cloud Computing Services, Internet of Things, Machine Learning Applications) show a trend of growth from 1990 to 2020. However, the rate of increase varies. The Machine Learning Applications seems to have a steady and sharp upward trend throughout the years. In contrast, the Internet of Things shows an erratic fluctuation but still follows a general incrementing path. Both 5G Network Support and Cloud Computing Services are showing steady increases with variations in the pace of growth. Overall, all areas show positive development over the studied period."
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "MoHk9he9jP1SZhK9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Market Globalization from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the point representing Market Globalization in 1990 at {the line}[0.140, 0.592], which corresponds to a value of 15. Then, I find the point for Market Globalization in 2020 at {the line}[0.860, 0.069], corresponding to a value of 37. Finally, I compare the difference and calculate the percentage increase as (37 - 15)/15 * 100%.\nBased on the above thought, the answer is:\n146.67%"
+            }
+        ],
+        "id_raw": 515
+    },
+    {
+        "id": "8ytJpdjWmoWqo1Eb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please retrieve the data for Resource Allocation and Utilization among different levels of education from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can extract the data by examining the bars of the chart. I would begin by looking at {the x-axis labels}[[0.108, 0.922, 0.195, 0.951], [0.304, 0.922, 0.394, 0.951], [0.500, 0.922, 0.594, 0.951], [0.698, 0.922, 0.786, 0.951], [0.893, 0.922, 0.970, 0.951]], which depict levels of education that will serve as our row labels. Next, I\u2019ll identify {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]]') for allocated resources and the  {orange bars}[[0.218, 0.281, 0.299, 0.351], [0.421, 0.512, 0.501, 0.584], [0.616, 0.624, 0.696, 0.682], [0.808, 0.832, 0.889, 0.898], [1.001, 0.787, 1.081, 0.827]] for resource utilization values. The values for each bar can be calculated based on the scale provided in y axis.\nBased on the above thought, the answer is:\nEducation Level,Resource Allocation,Resource Utilization\nPrimary School,50,38\nMiddle School,70,60\nHigh School,90,85\nUniversity,120,108\nAdult Education,100,83"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "6bupEPrOWDT8kK7o",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the bar chart, which level of education has the most efficient resource utilization, and why do you suggest so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which level of education has the most efficient resource utilization, I would firstly look at {the orange bars}[[0.218, 0.281, 0.299, 0.351], [0.421, 0.512, 0.501, 0.584], [0.616, 0.624, 0.696, 0.682], [0.808, 0.832, 0.889, 0.898], [1.001, 0.787, 1.081, 0.827]], which represent resource utilization in every education level. Then, I would compare these with {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]], depicting allocated resources. The education level where the difference between allocated and utilized resources is the smallest is the most efficient.\nBased on the above thought, the answer is:\nAccording to the bar chart, the High School level has the most efficient resource utilization. This is because the difference between the allocated (90) and utilized resources (85) at this level is the smallest among all education levels."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "N92DW7k4tDyXPSiA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total quantity of Allocated Resources across all levels of education according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total quantity of Allocated Resources, I need to add up the quantities represented by {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]] which represent resources allocated to each education level. These correspond to the values 50 for Primary School, 70 for Middle School, 90 for High School, 120 for University, and 100 for Adult Education.\nBased on the above thought, the answer is:\n430"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "a77IafSEeGP5EqfV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line graph's time-series data for 'Cardiovascular Diseases', 'Diabetes', and 'Lung Cancer' into a structured table, with 'Year' serving as rows and different disease types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a tabular form, I need to identify {the x-axis labels}<r>[0.124, 0.910, 0.940, 0.910]</r> representing the years from 2015 to 2020 and use these as the row labels. I notice that the line markers identify {the y-values for each disease}<r>[0.124, 0.110, 0.940, 0.243], [0.124, 0.577, 0.940, 0.754], [0.124, 0.243, 0.940, 0.466]</r> and can cross-reference these with the {y-axis labels}<r>[0.083, 0.910, 0.083, 0.132]</r> to get the values for each year. The disease types, 'Cardiovascular Diseases', 'Diabetes', and 'Lung Cancer' are used as the column headers.\nBased on the above thought, the answer is:\nYear,Cardiovascular Diseases,Diabetes,Lung Cancer\n2015,50,30,40\n2016,55,28,42\n2017,53,27,45\n2018,52,30,48\n2019,55,35,47\n2020,56,32,50"
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "WYbWhHydBnR5GVXI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall trend of Cardiovascular Diseases from 2015 to 2020 based on the line chart? Please explain your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend of Cardiovascular Diseases from 2015 to 2020, I need to examine the plot of {Cardiovascular Diseases}<r>[0.124, 0.110, 0.940, 0.243]</r>. Looking at the line, it shows a gradual increase from 50 in 2015 to a peak of 55 in 2016, a little decrease to 53 in 2017 and 52 in 2018, and then a rise again to 55 in 2019, and then finally to 56 in 2020. Therefore, it indicates an overall increasing trend with some minor fluctuations in between.\nBased on the above thought, the answer is:\nThe overall trend of Cardiovascular Diseases from 2015 to 2020 seems to be increasing, despite some minor fluctuations. The line chart shows an initial increase from 50 in 2015 to 56 in 2020, but with a slight decrease between 2016 and 2018. This indicates that the number of people affected by Cardiovascular Diseases has generally risen over this period."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "m1I78OkvqHdDyxYF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the Percent of People Affected by Diabetes from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in the Percent of People Affected by Diabetes from 2015 to 2020, I examine the {data points for Diabetes}<r>[0.124, 0.577, 0.940, 0.754]</r>. The 2015 value is 30, and the 2020 value is 32. The total increase is therefore 32 - 30 = 2.\nBased on the above thought, the answer is:\n2."
+            }
+        ],
+        "id_raw": 517
+    },
+    {
+        "id": "GF5PAcQZIYa6RexF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the line chart data into a structured table with years as columns and skills as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the data table from the given line chart, I begin by identifying {the x-axis tick labels}<r>[[0.090, 0.918, 0.143, 0.943], [0.186, 0.918, 0.239, 0.943], [0.282, 0.918, 0.335, 0.943], [0.378, 0.918, 0.431, 0.943], [0.474, 0.918, 0.526, 0.943], [0.569, 0.918, 0.622, 0.943], [0.665, 0.918, 0.718, 0.943], [0.761, 0.918, 0.814, 0.943], [0.857, 0.918, 0.910, 0.943]]</r> as the column headers or the years. My next step is to identify {the line labels}[0.656, 0.056, 0.863, 0.084], [0.656, 0.097, 0.900, 0.125]] as row labels that represent different skills. Subsequently, I will extract the corresponding values at each point on the respective lines for Programming<r>[0.116, 0.283, 0.884, 0.785]</r>, Leadership<r>[0.116, 0.358, 0.884, 0.748]</r> and Team Building<r>[0.116, 0.031, 0.884, 0.861]</r> Skills and match them with the respective years.\nBased on the above thought, the answer is:\nSkills,2012,2013,2014,2015,2016,2017,2018,2019,2020\nProgramming,7.1,8.0,7.6,7.2,6.5,6.0,5.5,4.7,4.0\nLeadership,4.3,5.1,5.8,6.5,7.0,7.3,7.4,7.2,7.0\nTeam Building,3.4,4.0,4.8,5.7,6.7,7.6,8.5,9.4,10.0"
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "yLeQzmwH7fuAOosk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which skill shows a consistent improvement over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the skill that shows consistent improvement over the years, I would have to analyze the line trend for each skill. {The line for Programming}<r>[0.116, 0.283, 0.884, 0.785]</r> shows a declining trend from 7.1 in 2012 to 4.0 in 2020. {The line for Leadership skills}<r>[0.116, 0.358, 0.884, 0.748]</r>, although indicating an increasing trend initially, starts to plateau from 2018 to 2020. {The Team Building line}<r>[0.116, 0.031, 0.884, 0.861]</r> shows an improvement from 3.4 in 2012 to reach 10.0 in 2020, indicating a consistently improving trend.\nBased on the above thought, the answer is:\nThe skill that demonstrates consistent improvement over the years is Team Building as indicated by the upward trend line in the chart. This is depicted by the rise from 3.4 in 2012 to 10.0 in 2020."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "YonFAp85CO4q7Dm6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_26_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in percentage between Programming and Leadership skills in the year 2015 in terms of recognition?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference between the recognition of Programming and Leadership skills in the year 2015, firstly I need to find {the point for Programming in the year 2015}<r>[0.404, 0.383]</r>, which gives me the value of 7.2. Secondly, I look at {the point for Leadership in the year 2015}<r>[0.404, 0.471]</r> and extract the value of 6.5. The difference is thus, 7.2 - 6.5 = 0.7.\nBased on the above thought, the answer is:\n0.7."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "HTqmKr17CusBV7lK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table converting the user growth trend data of Instagram according to the months displayed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first examine {the x-axis labels for Instagram}[[0.399, 0.912, 0.406, 0.936], [0.425, 0.912, 0.431, 0.936], ..., [0.628, 0.912, 0.640, 0.936]]. These labels correspond to the months. Next, I identify the growth percentages shown by {the Instagram growth line}[[0.403, 0.183, 0.634, 0.706]] and its corresponding {data points}[[0.403, 0.706], [0.428, 0.658], ..., [0.634, 0.183]]. I then extract values from these points to fill in the growth percentage for each month.\nBased on the above thought, the answer is:\nMonth,Instagram User Growth(%)\n1,8\n2,10\n3,12\n4,15\n5,18\n6,20\n7,22\n8,25\n9,28\n10,30"
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "On9MMNWO60Mno3aJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the social media platforms, which one showed a consistent increase in user growth and which showed the most inconsistency in user growth trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate consistency in user growth trends, I need to analyze the growth patterns in the chart lines. Observing {the Instagram growth line}[[0.403, 0.183, 0.634, 0.706]], the trend has consistently been upward from 8% to 30%, showcasing a continuous increase. In contrast, {the Twitter growth line}[[0.732, 0.777, 0.964, 0.872]], alternating amongst values such as 5, 3, 4, 3, 2 in a non-linear pattern, demonstrates notable inconsistency.\nBased on the above thought, the answer is:\nInstagram showed a consistent increase in user growth, as observed by the upward trend in its growth percentage from 8% to 30%. Twitter displayed the most inconsistency, with user growth percentages fluctuating without following a uniform direction."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "OfBbVz1w1EIe9afH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average user growth (%) for LinkedIn across the displayed months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth for LinkedIn, I locate the growth data from {the LinkedIn growth line}[[0.732, 0.611, 0.964, 0.872]] using its {data points}[[0.732, 0.849], [0.758, 0.849], ..., [0.964, 0.611]]. Summing these percentages as 2, 2, 3, 4, 5, 6, 7, 9, 10, 12 and dividing by the total number of months, which is 10, gives the average growth.\nBased on the above thought, the answer is:\n6"
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "BGuZewWp4J1QOUgb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data for Apple Inc.'s stock prices over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting by identifying {the x-axis labels for the years}<r>[0.093, 0.825, 0.327, 0.871]</r> which give the columns for the table, and associating them with {the data points of the line for Apple Inc.}<r>[0.112, 0.686, 0.308, 0.772]</r> corresponding to each year from 2010 to 2020. Using these data points, I can derive the table.\nBased on the above thought, the answer is:\nYear,Stock Price (in USD)\n2010,145.42\n2011,405.12\n2012,542.44\n2013,561.02\n2014,110.38\n2015,105.26\n2016,115.82\n2017,169.23\n2018,157.74\n2019,293.65\n2020,132.67"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "P6AiGmfJEPZC3TOK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends of development indexes for AI compared to VR from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends, I first identify the rise and fall of each sector overtime by examining {the line for AI}<r>[0.112, 0.179, 0.963, 0.174]</r> and {the line for VR}<r>[0.112, 0.335, 0.963, 0.343]</r>. The AI line exhibits upward trends with minimal dips in 2004 and 2008. Contrastingly, the VR line indicates a more irregular pattern with significant fluctuations and a notable dip in 2010.\nBased on the above thought, the answer is:\nAI consistently shows a rising trend with only minor fluctuations, indicating a steady growth in its development index. VR, on the other hand, displays a significantly more volatile trend with irregular growth patterns and notable declines, especially in 2010. This comparative analysis highlights AI's more stable and robust growth over the years compared to the erratic progression of VR."
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "fe5bmoB33t8YbCjW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in stock price for Amazon Inc. from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase, I will analyze {the data points for Amazon Inc.}<r>[0.768, 0.174, 0.963, 0.767]</r>, extracting the values for 2010 and 2020, specifically at {the corresponding points for 2010 and 2020}<r>[0.768, 0.767], [0.963, 0.174]</r>. The increase is the difference between the stock prices in these two years.\nBased on the above thought, the answer is:\n3123"
+            }
+        ],
+        "id_raw": 521
+    },
+    {
+        "id": "WP4ujHFLKjSl2bXR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you create a table indicating the production values for different types of production against each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, to create the requested table, I first identify different types of production {the legend}<r>[0.100, 0.151, 0.293, 0.304]</r> which include Cereal, Fruit, Vegetable, Meat, and Dairy Production. For each type, I reference {the line points}<r>[0.122, 0.772], [0.151, 0.732], ..., [0.954, 0.369]</r> that correspond to each year marked on {the x-axis}<r>[0.102, 0.825, 0.142, 0.871], ..., [0.935, 0.825, 0.973, 0.871]</r>. By extracting the values at intersection points of the years and production types, I can compile the complete data.\nBased on the above thought, the answer is:\nYear,Cereal Production,Fruit Production,Vegetable Production,Meat Production,Dairy Production\n1990,1000,1100,1200,1300,1400\n1995,1100,1050,1300,1250,1450\n2000,1500,1200,1400,1340,1550\n2005,1550,1300,1500,1500,1600\n2010,2000,1350,1600,1600,1700\n2015,2200,1400,1850,1700,1800\n2020,2500,1550,1900,1800,2000"
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "mSxqlzj2TYpV3Scn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows the most consistent growth in production over the years as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze which product shows the most consistent growth in production over the years, I review {the lines representing different productions}<r>[0.122, 0.168, 0.298, 0.772], [0.450, 0.410, 0.626, 0.692]</r>. Each line's trajectory points me toward the trend. Dairy Production line {the Dairy Production line}<r>[0.778, 0.369, 0.954, 0.611]</r> appears to have the most stable upward trend, showing no plateaus or declines, suggesting consistent growth.\nBased on the above thought, the answer is:\nDairy Production shows the most consistent growth over the years. Its line trajectory is stable and ascending consistently without any downward trends or plateaus, unlike other productions that show some fluctuations."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "kriLTVvxi4GeXMmF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Dairy Production from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in Dairy Production from 1990 to 2020, I first find the values of Dairy Production in 1990 and 2020 from {the points on the Dairy Production line}<r>[0.778, 0.611], [0.954, 0.369]</r>. The value in 1990 is 1400 and in 2020 is 2000. The total increase is 2000 - 1400.\nBased on the above thought, the answer is:\n600"
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "MAfl89LCFvQP30U6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table outlining the investment trends for COVID-19 Vaccines and Telemedicine Services from 2010 to 2021 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first observe {the x-axis labels}<r>[0.061, 0.934, 0.079, 0.970]</r> that present the years from 2010 to 2021. Then, I examine {the lines representing COVID-19 Vaccines and Telemedicine Services}<r>[0.061, 0.041, 0.984, 0.875]</r> to synchronize their plot points with respective years.\nBased on the above thought, the answer is:\nYear,COVID-19 Vaccines (billions),Telemedicine Services (billions)\n2010,1.0,2.5\n2011,1.5,3.0\n2012,2.0,3.5\n2013,2.5,4.0\n2014,3.0,4.5\n2015,3.5,5.0\n2016,4.0,5.5\n2017,4.5,6.0\n2018,5.0,6.5\n2019,5.5,7.0\n2020,15.0,8.5\n2021,16.0,9.0"
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "DnZ0hwtWjmPKZ6r0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart trends, how did the pandemic likely impact the investments in Telemedicine Services?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining {the trend line for Telemedicine Services}<r>[0.061, 0.041, 0.984, 0.875]</r>, I note a stable growth until 2020, followed by a slight increase. This increased trajectory during the pandemic suggests that there was likely a higher demand for remote medical services due to social distancing measures and health safety concerns.\nBased on the above thought, the answer is:\nThe pandemic likely caused an increase in investments in Telemedicine Services, as observed from the slight upward trajectory starting from 2020, indicating an enhanced focus and reliance on remote healthcare solutions amidst health safety measures."
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "Yx88IYcl59v0NKev",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in investment for COVID-19 Vaccines from 2019 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase, I locate {the points for COVID-19 Vaccines for years 2019 and 2020}<r>[0.923, 0.895, 0.929, 0.923]</r> corresponding to 5.5 billion in 2019 and 15.0 billion in 2020. The increase is calculated by subtracting the value for 2019 from that of 2020.\nBased on the above thought, the answer is:\n9.5 billion"
+            }
+        ],
+        "id_raw": 523
+    },
+    {
+        "id": "TrwF357TxZ5kTgt3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a data table for the progress indices of Emails and Social Media across the different Decomposition IO Norm levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Utilizing values from {the line points}<r>[0.728, 0.716], [0.789, 0.545], [0.850, 0.481], [0.911, 0.438], [0.972, 0.395]</r> for Emails and <r>[0.728, 0.737], [0.789, 0.630], [0.850, 0.566], [0.911, 0.524], [0.972, 0.481]</r> for Social Media respectively, I can form a table indicating their progression over the designated norms.\nBased on the above thought, the answer is:\nDecomposition IO Norm, Emails, Social Media\n0, 56, 55\n20, 64, 60\n40, 67, 63\n60, 69, 65\n80, 71, 67"
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "VLlcPUaD73TK2j7r",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend had a more significant overall impact on the innovations, Persistent Growth or Rapid Decline, according to the descriptions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the line plots}<r>[0.057, 0.091, 0.942, 0.903]</r> showcasing Persistent Growth and Rapid Decline, one observes that Persistent Growth typically has values increasing, while Rapid Decline displays decreasing values. 'Augmented Reality EdTech' demonstrated the most significant growth within Persistent Growth and 'Online Courses' saw the steepest decline within Rapid Decline. Judging the overall impact involves assessing both the amplitude and relevance of the changes over the range of technologies covered.\nBased on the above thought, the answer is:\nPersistent Growth had a more significant overall impact, as it showcased progressive increases across multiple technologies, indicating a growing interest and adoption in various innovations, compared to the specific decreases seen in Rapid Decline."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "PhwvWnoc0kAgKGjX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increment observed in the Smartphones Progress from the start to the end point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increment in the Smartphones Progress, I'd first examine {the charted points for Smartphones}<r>[0.070, 0.630], [0.131, 0.417], [0.192, 0.310], [0.253, 0.246], [0.314, 0.181]</r>, noting the values at each point: 60, 70, 75, 78, 81. The starting value is 60 and the end value is 81. Thus, the total increment is 81 - 60.\nBased on the above thought, the answer is:\n21."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "2hccqX8ro4KOYDQf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can we extract the data and represent it as a table with Policy Interest Rate as rows and KPI as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build a table from the line chart, I first identify {the x-axis tick labels}<r>[0.131, 0.912, 0.167, 0.936], [0.327, 0.912, 0.364, 0.936], [0.524, 0.912, 0.561, 0.936], [0.721, 0.912, 0.757, 0.936], [0.917, 0.912, 0.954, 0.936]</r> which symbolize the row labels or Policy Interest Rates. Next, the KPI corresponds to the y-axis values. I locate {the line points}<r>[0.149, 0.813], [0.346, 0.397], [0.542, 0.230], [0.739, 0.147], [0.936, 0.314]</r> for each Policy Interest Rate and decode the corresponding KPI values.\nBased on the above thought, the answer is:\nPolicy Interest Rate,KPI\n0.5,50\n1.0,55\n1.5,57\n2.0,58\n2.5,56"
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "rOlrmPOLSO9HNb6t",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the correlation between the Policy Interest Rate and the KPI based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the correlation, I would assess the overall trend of the KPI as the Policy Interest Rate increases. Starting at {Policy Rate 0.5}<r>[0.149, 0.813]</r>, with KPI 50, increasing to {Policy Rate 1.0}<r>[0.346, 0.397]</r> with KPI 55, it is observed that as the Policy Interest Rate rises, the KPI also generally increases. However, from {Policy Rate 1.5}<r>[0.542, 0.230]</r> to {Policy Rate 2.5}<r>[0.936, 0.314]</r> there is an initial increase in KPI from 57 to 58, followed by a decrease to 56, indicating a not entirely consistent correlation.\nBased on the above thought, the answer is:\nFrom the line chart, it can be interpreted that there is generally a positive correlation between Policy Interest Rate and the KPI. An increase in Policy Interest Rate typically leads to an increase in the KPI. However, the correlation does not hold consistenly as towards the higher end of the interest rate, an increase in the rate is followed by a decrease in the KPI, indicating a possible non-linear relationship or influence of other factors."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "0bDNNzJXdEiPAyAL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in KPI from a Policy Interest Rate of 0.5 to 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the change in KPI from an interest rate of 0.5 to 1, I must locate {the value at 0.5}<r>[0.149, 0.813]</r>, which is 50 and {the value at 1.0}<r>[0.346, 0.397]</r>, which is 55. The change in KPI is then determined by subtracting the value at 0.5 from the value at 1.0, which equals 55 - 50 = 5.\nBased on the above thought, the answer is:\n5."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "YnYMYnSDFNqxnBrA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the provided line chart into a tabular format that displays years as rows and technology sectors as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I begin by looking at {the x-axis labels}<r>[0.083, 0.890, 0.322, 0.950]</r> which outline the years ranging from 2015 to 2022. These will serve as rows for my table. Next, {the legend}<r>[0.840, 0.686, 0.970, 0.769]</r> shows labels like Artificial Intelligence, Blockchain, and others as technology sectors, which will form the column headings. I will then extract the corresponding development index values for each technology per year by observing {the point coordinates along each line}<r>[0.097, 0.239, 0.963, 0.765]</r>.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Blockchain,Cloud Computing,Internet of Things,Virtual Reality\n2015,12,3,10,5,7\n2016,17,4,15,6,9\n2017,23,5,20,8,12\n2018,30,7,30,11,15\n2019,38,8,45,15,18\n2020,50,10,60,20,23\n2021,65,12,80,25,30\n2022,80,15,100,30,38"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "4K6Xa7VPMbJFm6R1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the performance trends shown in the line chart, which technology industry is progressing most rapidly, and what does this imply for its future potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing {the line for Artificial Intelligence}<r>[0.097, 0.239, 0.963, 0.355]</r>, it shows a steep rise in development indicators from 2015 to 2022, which is notably higher than other technologies such as {Blockchain}<r>[0.753, 0.765, 0.963, 0.756]</r> and {Virtual Reality}<r>[0.893, 0.759, 0.963, 0.756]</r>. This rapid progression signifies a significant investment and focus in the sector, suggesting strong future potential and the possibility of sustaining a leading position in technological advancements.\nBased on the above thought, the answer is:\nArtificial Intelligence is demonstrating the most rapid progression. This not only highlights its current successes but also paints a promising picture of its leading role in future technological developments and innovations."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "YGGipsvhdMgdcWWC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual growth rate for the Blockchain technology from 2015 to 2022 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average annual growth rate for Blockchain, I need to specify values from {the line points for Blockchain}<r>[0.753, 0.765, 0.963, 0.756]</r>. Assumed values from the plot are 3 for 2015 and 15 for 2022. The formula for the average annual growth rate is ((Ending Value/Beginning Value)^(1/number of periods)) - 1, which translates to ((15/3)^(1/7))-1. Calculating this yields the average annual growth rate as a percentage.\nBased on the above thought, the answer is:\n25.89%"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "P23xwA1sRVb9vort",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data for the subplot labeled \"ICT & Nanotech\" into a tabular format using TRL as columns and the respective fields as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I commence by looking at {the x-axis labels for the ICT & Nanotech subplot}<r>[0.424, 0.825, 0.652, 0.871]</r>, which give the Technology Readiness Levels (TRL) as columns: 0, 20, 40, 60, 80. For the rows, I'll use the fields labeled in {the legend inside the ICT & Nanotech subplot}<r>[0.518, 0.616, 0.648, 0.769]</r> which are ICT and Nanotech. To fill in the table, I check the values at each TRL intersection for {the line graphs in the ICT & Nanotech subplot}<r>[0.429, 0.327, 0.642, 0.527]</r> and <r>[0.429, 0.161, 0.642, 0.460]</r>.\nBased on the above thought, the answer is:\nTRL,ICT,Nanotech\n0,0.56,0.6\n20,0.62,0.65\n40,0.64,0.7\n60,0.67,0.75\n80,0.68,0.78"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "bRcMbJM3alw7YYEn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the Nanotech line show a sharper increase compared to the ICT line in their respective plots?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line for Nanotech}<r>[0.429, 0.460, 0.642, 0.161]</r> alongside {the line for ICT}<r>[0.429, 0.527, 0.642, 0.327]</r>, the sharper increase of Nanotech is evident as its line starts at a success rate of 0.6 and reaches 0.78, while the ICT line starts at 0.56 and only reaches 0.68. This sharper rise in Nanotech indicates a more rapid improvement over the Technology Readiness Levels compared to ICT.\nBased on the above thought, the answer is:\nNanotech displays a more aggressive increase in innovation success over the plotted Technology Readiness Levels due to starting at a slightly higher initial value than ICT and achieving a relatively higher value by TRL 80. This suggests that Nanotechnology may be experiencing faster technological advancements or more effective implementation in its field compared to Information and Communication Technology."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "u0nHTP8hPK6XoEkV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the success rate for ICT progress from TRL 0 to TRL 80?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in success rate for ICT, I must reference the points on {the line for ICT}<r>[0.429, 0.527, 0.642, 0.327]</r>. The success rate at TRL 0 is 0.56, and at TRL 80 it is 0.68. The difference or progress is calculated as 0.68 - 0.56.\nBased on the above thought, the answer is:\n0.12"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "6dxzmvZhxXflKmvV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the horizontal bar chart data into a table format with names of different financial trends as rows and their percent change as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the horizontal bar chart, I locate {the y-axis labels}<r>[0.034, 0.118, 0.052, 0.146]</r> which represent different financial trend categories such as Steady Decline, Persistent Rise, Gradual Improvement, Erratic Behavior, and Sharp Decrease. For each category, I pinpoint {the end of the bars}<r>[0.763, 0.089, 0.941, 0.117]</r>, determining the extent and direction of the bars relative to the Y-axis to extract the percentage changes.\nBased on the above thought, the answer is:\nTrend,Percentage Change\nSteady Decline,-10%\nPersistent Rise,20%\nGradual Improvement,5%\nErratic Behavior,-30%\nSharp Decrease,-40%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "YhAUZmyepxiBdoX1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which trend shows the most instability and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine instability, I analyze {the bars}<r>[0.041, 0.760, 0.052, 0.793]</r> associated with each trend category. The bar for 'Erratic Behavior' extends significantly further in the negative direction compared to others, indicative of a very large decrease (-30%). This trend signifies more instability as the proportion of the decrease is higher than any other trend, marked by a deep red hue and an extended bar to the left indicating a significant drop.\nBased on the above thought, the answer is:\nThe 'Erratic Behavior' trend shows the most instability, as indicated by its significant negative percentage change of -30%. The extensive length and the stark color of the bar on the chart highlight the large fluctuations in this category, suggesting a highly volatile and unstable trend."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "KmoqrIPQKUSeWp2i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute difference between the highest increase and the highest decrease shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest increase and decrease, I examine the lengths of {the bars}<r>[0.435, 0.119, 0.612, 0.759]</r>. The longest bar in the positive direction corresponds to the Persistent Rise category, which shows a 20% increase, while the longest bar in the negative direction corresponds to the Sharp Decrease category, at -40%. Calculating the absolute difference would involve taking the mathematical absolute value of the difference between these two percentages.\nBased on the above thought, the answer is:\n60%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "bobOogXUv5pN4BcN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data as per the line plots in the chart to generate a table with decomposition IO norms as columns and different systems as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the tabular data, I first identify {the x-axis tick labels}[[0.096, 0.825, 0.106, 0.871], [0.145, 0.825, 0.164, 0.871], [0.198, 0.825, 0.218, 0.871], [0.251, 0.825, 0.271, 0.871], [0.305, 0.825, 0.324, 0.871]], which correspond to the column headers or decomposition IO norms. {The legend texts}[[0.206, 0.704, 0.314, 0.751], [0.562, 0.704, 0.642, 0.751], [0.872, 0.634, 0.969, 0.681], [0.872, 0.704, 0.942, 0.751]] denotes the different system types which are to become the row headings. To find the values, I locate {the line points}[[0.101, 0.194], [0.154, 0.261], [0.208, 0.394], ... ,[0.970, 0.360]] for each system at each \"Decomposition IO Norm\" and extract the associated values.\nBased on the above thought, the answer is:\nDecomposition_IO_Norm, BigData_Integration_10k, Hive_Integration_10k, BigData_Integration_5k, Hive_Integration_5k\n0, 0.78, 0.75, 0.69, 0.64\n20, 0.76, 0.74, 0.70, 0.68\n40, 0.72, 0.68, 0.72, 0.70\n60, 0.70, 0.65, 0.75, 0.71\n80, 0.66, 0.62, 0.77, 0.73"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "ORX0CKiqNdhiHGJ8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line plots, how does the accuracy change as the Decomposition IO Norm increases? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To understand how the accuracy changes with respect to the Decomposition IO Norm, I need to analyze the line plots for each system. Observing {the line for BigData Integration with 10k data}[[0.101, 0.194, 0.314, 0.593]], I notice a consistent decreasing trend in accuracy from 0.78 to 0.66 as the IO Norm increases from 0 to 80. {The line for Hive Integration with 10k data}[[0.429, 0.294, 0.642, 0.726]] also shows a similar decreasing pattern, albeit not as steep. On the other hand, both {BigData Integration with 5k data}[[0.757, 0.493, 0.970, 0.227]] and {Hive Integration with 5k data}[[0.757, 0.659, 0.970, 0.360]] depict an increasing trend in accuracy with increasing Decomposition IO Norm.\nBased on the above thought, the answer is:\nBased on the line plots, the accuracy decreases as Decomposition IO Norm increases for BigData and Hive systems working with 10k data points. This could indicate that a higher IO Norm might lead to reduced effectiveness when dealing with larger datasets. Conversely, for both systems when working with smaller 5k data points, the accuracy increases with an increase in Decomposition IO Norm, suggesting that these smaller datasets might adapt better to higher IO Norms, improving effectiveness. Therefore, the trend in accuracy seems to depend on both the Decomposition IO Norm as well as the size of the data the system is working with."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "bawN2mfcvN6sXjSU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum accuracy for BigData Integration with 5k data points as per the line plot details?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum accuracy for \"BigData Integration with 5k data\", I would need to locate {the line points for BigData Integration with 5k data}[[0.757, 0.493], [0.810, 0.460], [0.863, 0.394], [0.917, 0.294], [0.970, 0.227]] and extract the respective values: 0.69, 0.70, 0.72, 0.75, 0.77. The maximum value from these is 0.77 which corresponds to the {Decomposition IO Norm}[0.960, 0.825, 0.980, 0.871] of 80.\nBased on the above thought, the answer is:\n0.77."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "wTirjjZTgiKBku3U",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a tabular representation of the risk assessment data based on the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the given line chart, I have to look at {the x-axis tick labels} located at [[0.084, 0.825, 0.094, 0.871], [0.191, 0.825, 0.211, 0.871], [0.298, 0.825, 0.327, 0.871]], which represent the risk levels that will serve as our column headers. The {legend items} located within the sub-legends [[0.091, 0.704, 0.310, 0.751], [0.419, 0.704, 0.637, 0.751], [0.747, 0.634, 0.965, 0.681]] provide the row headers representing the different efficiency types. Finally, the corresponding efficiency values for each efficiency type across the different risk levels can be identified from {the line points} of each line plot [[0.089, 0.335], [0.201, 0.645], [0.312, 0.541], [0.417, 0.335], [0.529, 0.645], [0.640, 0.541], [0.745, 0.335], [0.856, 0.645], [0.968, 0.541]], [0.856, 0.719], [0.968, 0.571]].\nBased on the above thought, the answer is:\nRisk Level,Low Risk,Medium Risk,High Risk\nRisk Management Efficiency,0.76,0.55,0.62\nRisk Assessment Efficiency,0.84,0.50,0.60"
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "j2YA98jtF7ukZbCC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify which risk level has the smallest gap between Risk Management Efficiency and Risk Assessment Efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "For each risk level, I need to find the values for Risk Management Efficiency and Risk Assessment Efficiency and calculate the absolute difference. For the Low Risk category, I can extract values from {the point for Risk Management Efficiency}[0.089, 0.335] which is 0.76 and {the point for Risk Assessment Efficiency}[0.745, 0.216] which is 0.84. For the Medium Risk category, I derive values from {the point for Risk Management Efficiency}[0.529, 0.645] which is 0.55 and {the point for Risk Assessment Efficiency}[0.856, 0.719] which is 0.50. Lastly for the High Risk category, the values can be found at {the point for Risk Management Efficiency}[0.968, 0.541] which gives 0.62 and {the point for Risk Assessment Efficiency}[0.968, 0.571] which gives 0.60.\nBased on the above thought, the answer is:\nBased on the calculations, the smallest gap between Risk Management Efficiency and Risk Assessment Efficiency is at the High Risk level, with a difference of 0.02."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "yFfzXoU7Qzz2jnxD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage drop in Risk Management Efficiency between the Low Risk and Medium Risk categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to identify the values of Risk Management Efficiency for the Low Risk and Medium Risk categories. I can locate these by referencing {the point} representing the value for Low Risk located at [0.089, 0.335] which corresponds to an efficiency value of 0.76 and {the point} for Medium Risk located at [0.529, 0.645] which corresponds to an efficiency value of 0.55. I would then compute the percentage drop using the formula ((New Value - Old Value) / Old Value) * 100%.\nBased on the above thought, the answer is:\n27.63%"
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "uCXEzNxe2Ent52to",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you present the changes in digital communication interactions on social media platforms over the year in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the graph to a structured table, I start by analyzing {the y-axis labels}<[0.073, 0.128, 0.073, 0.792]> which represent different social media platforms. Then, I extract the percentage changes noted on {the x-axis}<[0.185, 0.890, 0.234, 0.950]> for each platform. Finally, I correlate the line plots showing the interaction changes, identifying colors to determine if it's a growth (green) or a reduction (red) associated with each platform.\nBased on the above thought, the answer is:\nSocial Media Platform,Percentage Change, Trend Type\nTwitter Posts,25%, Growth\nYouTube Videos,-30%, Reduction\nFacebook Posts,50%, Growth\nInstagram Photos,-10%, Reduction\nLinkedIn Articles,15%, Growth\nSnapchat Stories,-35%, Reduction\nTikTok Videos,5%, Growth"
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "y9mEIf8CbmVu7Qsa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform shows the most stable growth, and how can you determine this from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which platform demonstrates the most stable growth, I examine the plot for even and consistent upward trends without sharp rises or drops. Reviewing the chart, {the line for LinkedIn Articles}<[0.209, 0.365, 0.528, 0.460]> illustrates a steady inclination with a moderate increase in percentage, marked by a consistent green color indicating growth without any red segments. This steady slope suggests a stable growth pattern compared to others which show more volatility or reduction.\nBased on the above thought, the answer is:\nLinkedIn Articles show the most stable growth. This is determined by the consistent and moderate upward trend in its line plot on the chart, which exclusively features green signaling growth without any transient declines."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "5r3WyY7mArxUWwxt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage range of changes for digital communication interactions from the highest growth to the highest reduction on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range, I need to find the highest growth and the highest reduction percentages. By examining the line heights, {the highest point for Facebook Posts}<[0.255, 0.065]> shows a growth of approximately 50%, and {the lowest point for Snapchat Stories}<[0.574, 0.900]> indicates a reduction of about -35%. The range can be calculated by subtracting the lowest percentage from the highest.\nBased on the above thought, the answer is:\n85%."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "4suNPAaZVdHPJjIo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the annual effectiveness data of ALBA, LYRAA, and IOTA into a table format using the given line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a table, I first determine the x-axis years from {the x-axis tick labels}<r>[0.090, 0.825, 0.129, 0.871][0.112, 0.825, 0.151, 0.871][0.134, 0.825, 0.173, 0.871][0.156, 0.825, 0.195, 0.871][0.177, 0.825, 0.216, 0.871][0.199, 0.825, 0.238, 0.871][0.221, 0.825, 0.260, 0.871][0.243, 0.825, 0.282, 0.871][0.265, 0.825, 0.304, 0.871][0.286, 0.825, 0.325, 0.871]</r>, which serve as the column headers. I identify the legend for each subplot to get the stock names, represented by {the sublegend}<r>[0.869, 0.616, 0.976, 0.769]</r> and {the subplot titles}<r>[0.137, 0.050, 0.279, 0.113][0.468, 0.050, 0.603, 0.113][0.735, 0.050, 0.992, 0.113]</r>. Each point's value for the years is extracted using {the point data in each line}<r>[0.110, 0.197, 0.306, 0.724][0.437, 0.380, 0.634, 0.586][0.765, 0.197, 0.962, 0.540]</r>.\nBased on the above thought, the answer is:\nYear,ALBA,IOTA,LYRAA\n2006,0.52,0.58,0.72\n2007,0.54,0.59,0.75\n2008,0.56,0.61,0.74\n2009,0.60,0.62,0.72\n2010,0.63,0.64,0.70\n2011,0.67,0.65,0.68\n2012,0.70,0.67,0.66\n2013,0.73,0.66,0.64\n2014,0.74,0.65,0.62\n2015,0.75,0.64,0.60"
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "O9tBE03AdDW1DL8H",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the effectiveness of LYRAA decrease from 2007 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease in LYRAA's effectiveness from 2007 to 2015, I observe its line point values from {the line for LYRAA}<r>[0.765, 0.197, 0.962, 0.540]</r>, where the corresponding Y-values are 0.75 in 2007 and 0.60 in 2015. Subtracting the 2015 value from the 2007 value gives me the decrease.\nBased on the above thought, the answer is:\n0.15"
+            }
+        ],
+        "id_raw": 535
+    },
+    {
+        "id": "7rxELSWg7qk6Ihom",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the horizontal bar chart showing trends in community development and urban planning into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a table, I would begin by identifying {the bar labels}<r>[0.01, 0.35, 0.07, 0.37]</r> which represent different categories such as Urban Development, Community Hubs, etc. Next, I would measure {the length of each bar}<r>[0.08, 0.39, 0.49, 0.40]</r>, which corresponds to the percentage change for each respective category. Lastly, viewing {the color of each bar}<r>[0.06, 0.38, 0.10, 0.36]</r> helps differentiate between positive and negative changes.\nBased on the above thought, the answer is:\nCategory,Percentage Change,Change Trend\nUrban Development,45%,Growth\nCommunity Hubs,-80%,Decline\nGreen Spaces,-70%,Decline\nConservation Efforts,-60%,Decline\nPublic Transport,30%,Growth\nRenewable Energy,55%,Growth\nIncome Inequality,35%,Growth"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "ZD8pFaAc4LZ1wDcI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category in the urban planning horizontal bar chart has shown the most significant decline, and why might this be important?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reviewing the length and color of the bars related to negative changes, {the bar for Community Hubs}<r>[0.05, 0.67, 0.13, 0.69]</r> shows the most substantial decline at -80%. The significant drop in attention or resources towards Community Hubs may be crucial because these hubs often serve as central points for community interaction and support, thus impacting social cohesion and local support mechanisms in urban planning.\nBased on the above thought, the answer is:\nThe category 'Community Hubs' has shown the most significant decline at -80%. This decline is particularly important as it could adversely affect community cohesion and the effectiveness of localized support and interaction, which are vital in fostering vibrant and supportive urban environments."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "yPeVkH6SA0EI7Cp0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage change combining all positive growth categories in the urban planning chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll identify and calculate the percentage for each category signifying growth by navigating through {the bars representing growth}<r>[0.08, 0.41, 0.48, 0.42]</r>. They are categorized as Urban Development (45%), Public Transport (30%), Renewable Energy (55%), and Income Inequality (35%). Adding these values gives a total of 45% + 30% + 55% + 35%.\nBased on the above thought, the answer is:\n165%"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "XU44X6DMaWjEAPyA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform this line chart into a structured data table separating the categories with their corresponding values into rows and columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this from a line chart to a structured table, I need to look at {xticks}[[0.048, 0.875], [0.107, 0.875], [0.167, 0.875], [0.227, 0.875], [0.286, 0.875]] and {line points}[[0.048, 0.839], [0.107, 0.113]] for 'Marginal Growth'. Similarly for 'Minimal Change', I need to look at {xticks}[[0.377, 0.875], [0.496, 0.875], [0.556, 0.875], [0.615, 0.875]] and {line points}[[0.377, 0.476]] and interpolate the values for the missing points. Then for the final subplot, I need to look at the {xticks}[[0.706, 0.875], [0.765, 0.875]] and {line points}[[0.706, 0.234], [0.765, 0.839], [0.706, 0.113], [0.765, 0.718]] for both 'Minimal Change' and 'Marginal Growth'.\nBased on the above thought, the answer is:\nCategories,Marginal Growth,Minimal Change\nNetwork_Length,40,42\nData_Analysis,70,68\nData_Preprocessing,80,84\nData_Quality,60,58\nData_Variables,50,48"
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "jRFPGz6TgebvIv25",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the performance of Marginal Growth and Minimal Change across the five categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can analyze the performance of Marginal Growth and Minimal Change across the five categories by looking at their corresponding lines in the subplots. For the 'Marginal Growth', the {line points}[0.048, 0.839; 0.107, 0.113] in the first subplot reveal a rise from Network_Length to Data_Analysis categories. This indicates an improvement in these sectors. For 'Minimal Change', the {line point}[0.377, 0.476] in the second subplot at Data_Preprocessing suggests an improvement as compared to the Network_Length, under Marginal Growth.\nIn the last subplot, for Data_Quality and Data_Variables, both 'Marginal Growth' and 'Minimal Change' {displayed as line points}[0.706, 0.234, 0.765, 0.839, 0.706, 0.113, 0.765, 0.718] show an declining trend. For Minimal Change, there's an upward trend from Data_Quality to Data_Variables. However, the performance is not stable across the categories and varies from one category to another.\nBased on the above thought, the answer is:\nThe performance of \"Marginal Growth\" across the five categories indicates an overall decline from Network_Length to Data_Analysis, then a significant rise until Data_Quality and finally a slight decline at Data_Variables. On the other hand, \"Minimal Change\" displays a steady performance, with a slight rise from Network_Length to Data_Preprocessing, followed by similar values in Data_Quality and a slight decline in Data_Variables. Thus, both \"Marginal Growth\" and \"Minimal Change\" categories display a volatile performance but Marginal Growth has more prominent changes."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "LaF4rQ3BxbvTCf4K",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Network Length between Marginal Growth and Minimal Change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in Network Length between Marginal Growth and Minimal Change, I need to first look at the {line point for Network Length under Marginal Growth}[0.048, 0.839] and extract the value, which is 40. Next, I need to locate the {line point for Network Length under Minimal Change}[0.377, 0.476] and extract the corresponding value, which is 42. The difference is thus 42 - 40 = 2.\nBased on the above thought, the answer is:\n2."
+            }
+        ],
+        "id_raw": 537
+    },
+    {
+        "id": "du1GBM3pJSi0nXlR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data displaying Public Trust in Government by Year into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the structured table based on the line chart, I focus on analyzing {the x-axis tick labels}<r>[0.070, 0.895, 0.094, 0.923], [0.106, 0.895, 0.129, 0.923], [0.141, 0.895, 0.165, 0.923], [0.177, 0.895, 0.200, 0.923], [0.212, 0.895, 0.236, 0.923], [0.248, 0.895, 0.271, 0.923], [0.283, 0.895, 0.306, 0.923]</r> which denote the years, and {the line points for Public Trust in Government}<r>[0.082, 0.512], [0.118, 0.556], [0.153, 0.614], [0.188, 0.687], [0.224, 0.730], [0.259, 0.759], [0.295, 0.803]</r> which denote the trust levels.\nBased on the above thought, the answer is:\nYear,Public Trust in Government\n1990,0.65\n1995,0.62\n2000,0.58\n2005,0.53\n2010,0.50\n2015,0.48\n2020,0.45"
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "S3bLPdeVCudM5GOF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the public approval of governmental actions trended from 1990 through 2020 according to the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the trend in public approval, examine {the line for Public Approval of Government}<r>[0.742, 0.149, 0.955, 0.440]</r>, tracking notable points and their corresponding values over the years. The line indicates a decrease from a value of 0.9 in 1990 to 0.7 in 2020. The slope of the line downward over time reflects a gradual decline in public approval.\nBased on the above thought, the answer is:\nThe trend in public approval of governmental actions from 1990 to 2020 has been generally decreasing. This can be observed from the steady downward slope of the line plot representing Public Approval of Government, indicating a gradual reduction in approval rates over these 30 years."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "ymuRF6KA81SXp6H6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_27_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in Political Engagement from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average increase, examine {the line points for Political Engagement}<r>[0.742, 0.600], [0.778, 0.570], [0.813, 0.527], [0.848, 0.483], [0.884, 0.440], [0.919, 0.396], [0.955, 0.353]</r> representing the value changes from 1990 to 2020. The values start at 0.59 in 1990 and reach 0.76 in 2020. Calculating the difference and averaging over the 30-year period: (0.76 - 0.59) / 30 = 0.00567.\nBased on the above thought, the answer is:\n0.00567."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "wom4ogK6V0eaQflv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table with Training Epochs as rows and the Base and Improved Model Accuracies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this tabular data from the line chart, I'll start by identifying {the x-axis tick labels}[[0.179, 0.838, 0.188, 0.885], [0.273, 0.838, 0.291, 0.885], [0.367, 0.838, 0.385, 0.885], [0.461, 0.838, 0.479, 0.885], [0.556, 0.838, 0.582, 0.885], [0.650, 0.838, 0.676, 0.885], [0.744, 0.838, 0.770, 0.885], [0.838, 0.838, 0.865, 0.885], [0.932, 0.838, 0.959, 0.885]], representing the row labels or Training Epochs. \nNext, I observe {the legend texts}[[0.244, 0.648, 0.404, 0.694], [0.244, 0.717, 0.468, 0.764]] which are the column headers for Base Model Accuracy and Improved Model Accuracy. \nFinally, to fill the values, I locate {the line points for the Base Model}[[0.179,0.352], [0.217,0.361], ..., [0.932, 0.733]] and {the line points for the Improved Model}[[0.179,0.333], [0.217,0.306], ..., [0.932, 0.179]] and extract the accuracies for each epoch.\nBased on the above thought, the answer is:\nTraining Epochs,Base Model Accuracy,Improved Model Accuracy\n0,80,82\n10,79,85\n20,78,88\n30,77,88\n40,75,89\n50,74,90\n60,73,91\n70,70,90\n80,68,92\n90,65,93\n100,62,94\n110,60,94\n120,58,95\n130,55,95\n140,52,96\n150,50,96\n160,47,97\n170,45,97\n180,43,98\n190,40,98\n200,38,99"
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "AaVTnOUhLm75k7QL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how did the improvements to the model affect its accuracy over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the impact of the improvements on the model's accuracy over time, I need to compare the line plots {of the Base Model} [0.179, 0.352, 0.932, 0.733] and {the Improved Model} [0.179, 0.179, 0.932, 0.333]. \nAt the beginning, both models start with similar accuracies, but as the training epochs go on, the Improved Model consistently demonstrates higher accuracy than the Base Model. This is visible from how the Improved Model's line plot is consistently lower on the y-axis than the Base Model's, which indicates a higher accuracy. Thus, the improvements appear to have a significant positive impact on the model's accuracy over time.\nBased on the above thought, the answer is:\nThe improvements to the model effectively enhanced its accuracy over the training epochs. Initially, the accuracies of the Base Model and the Improved Model were close, but as training progressed, the Improved Model consistently outperformed the Base Model in terms of accuracy. This suggests that the improvements had a meaningful and continuously positive effect on the performance of the model over time."
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "y5azLlAKOvytz4yr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in accuracy when the Base Model was improved in the 70th epoch?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in accuracy from the Base Model to the Improved Model in the 70th epoch, I need to locate the epoch, which is represented by {the x-axis tick label '70'}[0.744, 0.838, 0.770, 0.885]. \nI then locate {the point for the Base Model at this epoch} [0.443, 0.442] and extract the corresponding accuracy of 70. Next, I locate {the point for the Improved model at this epoch} [0.443, 0.261] and extract the corresponding accuracy of 90. The difference between the two accuracies gives the increase in accuracy when the Base Model was improved in the 70th epoch, which is 90 - 70.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 541
+    },
+    {
+        "id": "qD5qZDL9bis8M0VY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you summarize the data that represents the trends in Interpersonal Relationships and Social Networks in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the line chart, {the x-axis} represents the time with values [0.061, 0.838, 0.128, 0.885] to [0.995, 0.838, 1.057, 0.885], and {the y-axis} represents the value with labels from [0.069, 0.787, 0.131, 0.834] to [0.069, 0.106, 0.131, 0.153]. The line for {Interpersonal Relationships} has coordinates [0.187, 0.261, 0.933, 0.588] and the line for {Social Networks} has coordinates [0.187, 0.261, 0.933, 0.560]. To extract this data into a tabular format, I map the x-values (time) to the corresponding y-values (values for Interpersonal Relationships and Social Networks) for every point on the line.\nBased on the above thought, the answer is:\nTime,Interpersonal Relationships,Social Networks\n0.0,0.7,0.65\n1.05,0.69,0.67\n2.1,0.68,0.68\n3.15,0.67,0.68\n4.2,0.65,0.7\n5.26,0.64,0.65\n6.31,0.63,0.6\n7.36,0.63,0.59\n8.42,0.62,0.6\n9.47,0.61,0.65\n10.52,0.6,0.7\n11.57,0.62,0.68\n12.63,0.6,0.65\n13.68,0.61,0.67\n14.73,0.6,0.65\n15.78,0.6,0.65\n16.84,0.58,0.68\n17.89,0.58,0.7\n18.94,0.58,0.68\n20.0,0.58,0.65"
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "F6BB7kbGZRiQ2ehk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value for Social Networks over the given time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value for Social Networks over the time period, I have to check the y-values of {the line points for Social Networks}[0.187, 0.261, 0.933, 0.560]. The y-values extracted from these points are: 0.65, 0.67, 0.68, 0.68, 0.7, 0.65, 0.6, 0.59, 0.6, 0.65, 0.7, 0.68, 0.65, 0.67, 0.65, 0.65, 0.68, 0.7, 0.68, 0.65. From this, it is clear that the maximum value for Social Networks is 0.7.\nBased on the above thought, the answer is:\n0.7."
+            }
+        ],
+        "id_raw": 542
+    },
+    {
+        "id": "9TfgSX2BAmfq4mBp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data of each company's social responsibility index from 2010 to 2019 in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the line chart, {the legend labels}<r>[0.105, 0.100, 0.185, 0.128], [0.105, 0.142, 0.185, 0.170], [0.105, 0.183, 0.185, 0.211]</r> represent the companies in this analysis - Company A, B, and C. The x-axis of the chart represents the years from 2010 to 2019, which we gather from {the x-axis tick labels}<r>[0.078, 0.903, 0.113, 0.931], [0.266, 0.903, 0.301, 0.931], [0.454, 0.903, 0.490, 0.931], [0.643, 0.903, 0.678, 0.931], [0.831, 0.903, 0.866, 0.931], [1.019, 0.903, 1.054, 0.931]</r>. To construct each row, I locate {the plot points on the lines for each company}<r>[0.096, 0.191, 0.943, 0.847], [0.096, 0.367, 0.943, 0.751], [0.096, 0.111, 0.943, 0.431]</r>, and retrieve the respective social responsibility index for each year.\nBased on the above thought, the answer is:\nYear,Company A,Company B,Company C\n2010,3.4,5.2,6.0\n2011,3.6,5.7,6.5\n2012,4.7,6.0,7.0\n2013,5.0,6.4,7.2\n2014,5.5,6.2,7.5\n2015,5.7,6.3,6.0\n2016,6.0,4.0,6.6\n2017,6.5,4.3,7.1\n2018,7.0,4.6,7.8\n2019,7.5,5.1,8.0"
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "VaAySAoZ2Wd8UiIw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which company has shown the most improvement in their Social Responsibility Index over the review period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the company with the highest improvement in their Social Responsibility Index, I need to compare the differences in the starting and ending points of each company's line. For {Company A}<r>[0.096, 0.191, 0.943, 0.847]</r>, the change is from 3.4 to 7.5, a difference of 4.1. For {Company B}<r>[0.096, 0.367, 0.943, 0.751]</r>, the Index changed from 5.2 to 5.1, almost no improvement. Comparing these to the progression of {Company C}<r>[0.096, 0.111, 0.943, 0.431]</r>, which improved from 6.0 to 8.0, a difference of 2.0. Thus, Company A has shown the most improvement.\nBased on the above thought, the answer is:\nCompany A has shown the most improvement in their Social Responsibility Index over the review period. This is determined by comparing the differences in the Social Responsibility Index values between the start and end points for each company. Company A's Index increased by 4.1 points, which is the highest increase compared to Company B and Company C."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "3Ol2rpjLmzKmzFNL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in Social Responsibility Index for Company A from the year 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the change in Social Responsibility Index for Company A, I need to locate {the starting point}<r>[0.096, 0.847]</r>, which corresponds to the value for the year 2010, and {the end point}<r>[0.943, 0.191]</r>, which corresponds to the value for the year 2019, from the line representing data for Company A. The corresponding values for these points are 3.4 and 7.5. The change is then 7.5 - 3.4 = 4.1.\nBased on the above thought, the answer is:\n4.1."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "IkN6kXAC0TpBOqPT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trend data represented by each line in the chart corresponding to 'Close Friends' and 'Acquaintances' as the columns and different status patterns as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, I start by noting the {x-axis tick labels}[[0.098, 0.782, 0.158, 0.806], [0.238, 0.782, 0.289, 0.806], [0.393, 0.782, 0.452, 0.806], [0.569, 0.782, 0.641, 0.806], [0.734, 0.782, 0.805, 0.806], [0.894, 0.782, 0.981, 0.806]], which serve as the different social relationship patterns (i.e., row labels). Next, I note the {legend texts}[[0.862, 0.119, 0.931, 0.138], [0.862, 0.154, 0.970, 0.172]], which represent 'Close Friends' and 'Acquaintances (i.e., column headers). For each line {indicator}[0.115, 0.781, 0.977, 0.281], I identify each point, map it to the x-axis labels (viz., 'Rapid Decline', 'Unpredictable Fluctuations', 'Flat Decline', 'Inconsistent Decline', 'Steady Decline', 'Moderate Increase'), and record the y-values (a measure of their social interaction frequency).\nBased on the above thought, the answer is:\nPattern,Close Friends,Acquaintances\nRapid Decline,5,4.5\nUnpredictable Fluctuations,4.2,5\nFlat Decline,3.5,3.6\nInconsistent Decline,2.4,2.9\nSteady Decline,1.8,2.1\nModerate Increase,1.2,1.7"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "7yEqe9dGcxrYY4L8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat line represents the steepest decline in social interaction across all patterns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the steepest decline in social interactions, I look into the line plots for both kinds of relationships - {Close Friends}[0.115, 0.781, 0.725, 0.329] and {Acquaintances}[0.115, 0.703, 0.725, 0.280]. The steepest decline would be represented by the line showing the greatest difference between the initial and final points. Comparatively, the Close Friends line shows a greater decrease from 5 to 1.2 compared to the Acquaintances line that drops from 4.5 to 1.7 across the specified patterns.\nBased on the above thought, the answer is:\nThe line representing 'Close Friends' shows the steepest decline in social interaction across all patterns, going from 5 to 1.2, indicating a larger drop in the frequency of social interactions compared to 'Acquaintances'."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "OuooJ53TlBrmtuvD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the interaction frequency with close friends drop from 'Rapid Decline' to 'Moderate Increase'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the decrease in interaction frequency with close friends from 'Rapid Decline' to 'Moderate Increase', I identify the {line points corresponding to the 'Close Friends' trend}[[0.115, 0.730], [0.237, 0.660], [0.359, 0.586], [0.481, 0.407], [0.603, 0.319], [0.725, 0.238]]. The frequency at 'Rapid Decline' is observed to be 5 and at 'Moderate Increase' is 1.2. These frequencies are generated from the y-values at given points. The difference between these two values represents the decrease.\nBased on the above thought, the answer is:\n3.8."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "1sTo6OQtXMOLX0Bc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the graphical data depicted in the line chart into a structured table with Years as the columns and Sectors as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the line chart into a structured table, I would consult {the x-axis}<[0.159, 0.838, 0.970, 0.885]> for the column headers, which represent the years. {The line plots}<[0.196, 0.187, 0.933, 0.571], [0.196, 0.748, 0.933, 0.766]> represent the different sectors and their market shares over the years, and each of these should be interpreted as a row in the table. I must pull out each sector's market share for each year to fill in the rows of the table.\nBased on the above thought, the answer is:\nSectors,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nStartups Registered,345,392,469,510,555,600,700,750,805,880,908\nInnovation Index,59,60,65,67,69,70,75,78,80,82,85"
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "tYaWQoFehSRHjk7Q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric shows a more linear growth trend between 2010 and 2020 - Startups Registered or the Innovation Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To conclude which aspect demonstrated a more linear growth trend from 2010 to 2020, it's required to examine the rate of increase or decrease for each. Looking at the {Startups Registered line}<[0.196, 0.187, 0.933, 0.571]>, there's a substantial upward trend suggesting growing quantities of startups. When analyzing the {Innovation Index line}<[0.196, 0.748, 0.933, 0.766]>, it also illustrates an upward trend, albeit less dramatic than the Startups Registered line.\nBased on the above thought, the answer is:\nThe Innovation Index demonstrates a more linear growth trend between 2010 and 2020. Although both metrics show an increase over this time period, the Innovation Index's growth is more consistent and less volatile, hence more linear, compared to the Startups Registered."
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "s4T4k9C4TZXzHBv5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth of registered Startups from 2010 to 2020, as per the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth of registered Startups from 2010 to 2020, I need to find the coordinates for the Startups Registered line's points for 2010 {point_coordinates}[0.196, 0.571] and 2020 {point_coordinates}[0.933, 0.187] and extract the corresponding values: 345 and 908. The growth is the difference between the value in 2020 and the value in 2010. So, the growth = 908 \u2013 345 = 563.\nBased on the above thought, the answer is:\n563"
+            }
+        ],
+        "id_raw": 545
+    },
+    {
+        "id": "FENkj7jJLpBrSZdy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the adoption statistics for Traditional EdTech and Innovative EdTech over the duration of 20 weeks?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the x-axis tick labels}[[0.083, 0.903, 0.115, 0.931], [0.188, 0.903, 0.220, 0.931], [0.293, 0.903, 0.325, 0.931], [0.398, 0.903, 0.430, 0.931],...,[0.917, 0.903, 0.961, 0.931]], and these will be the columns representing the weeks. Subsequently, I identify {the legend texts}[[0.175, 0.100, 0.354, 0.128], [0.175, 0.142, 0.355, 0.170]] as the rows signifying the Traditional EdTech and the Innovative EdTech. Then I proceed to {the line points for Traditional EdTech and Innovative EdTech} and extract the corresponding values for each week.\nBased on the above thought, the answer is:\nWeeks,Traditional EdTech,Innovative EdTech\n1,100,100\n2,98,101\n3,97,102\n4,96,105\n5,96,108\n6,95,110\n7,94,112\n8,94,114\n9,93,116\n10,93,118\n11,92,120\n12,92,122\n13,91,125\n14,90,127\n15,90,128\n16,89,130\n17,88,132\n18,88,134\n19,87,136\n20,86,138"
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "Hfdgcu8MUFJOLeZC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the adoption trends of Traditional EdTech and Innovative EdTech compare over the 20 weeks? Provide your interpretation based on the line chart."
+            },
+            {
+                "from": "gpt",
+                "value": "For the analysis, I need to closely observe {the line plot for Traditional EdTech} [0.141, 0.614, 0.939, 0.803] and {the line plot for Innovative EdTech} [0.141, 0.102, 0.939, 0.614]. The Traditional EdTech line shows a steady decrease over the period starting at 100 and ending at 86. The Innovative EdTech line, on the other hand, shows a consistent growth from week 1 through week 20, starting at 100 and ending at 138.\nBased on the above thought, the answer is:\nOver the 20-week period, Traditional EdTech shows a moderate decline in adoption, whereas Innovative EdTech demonstrates a steady incline in adoption rates. This could suggest that although initially both platforms had the same adoption rates, as weeks progressed, more people were inclined towards Innovative EdTech. This could potentially hint towards Innovative EdTech's more successful strategies in attracting users, or it could be a sign of shifting consumer preferences towards more innovative EdTech solutions."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "C3K5NShSDCZaweH8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many units did the Innovative EdTech adoption stats increase from week 1 to week 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the difference in adoption stats of Innovative EdTech from week 1 to week 20, I need to look in the chart and locate {the line points for Innovative EdTech at week 1} [0.141, 0.614] and {week 20} [0.939, 0.102]. The corresponding values are 100 and 138 respectively, so the increase would be 138 - 100 = 38.\nBased on the above thought, the answer is:\n38 units."
+            }
+        ],
+        "id_raw": 548
+    },
+    {
+        "id": "S0umAa43BKWkJ0qe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data for the Global Entrepreneurship Index and Innovation Ecosystem Index from the line graph into a tabular format with 'Year' as rows and 'Indices' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, first, I locate the {x-axis labels}<r>[0.147,0.903,0.172,0.931], [0.323,0.903,0.348,0.931], [0.499,0.903,0.524,0.931], [0.675,0.903,0.700,0.931], [0.851,0.903,0.876,0.931]</r> of the graph, which indicate the years. The {y-axis labels}<r>[0.050,0.872,0.093,0.900], [0.050,0.693,0.093,0.722], [0.049,0.513,0.093,0.541], [0.049,0.333,0.093,0.361], [0.049,0.063,0.093,0.091]</r> represent the index values. Then the lines on the graph represent two indices: {Global Entrepreneurship Index}<r>[0.147, 0.129, 0.939, 0.650]</r> and {Innovation Ecosystem Index}<r>[0.147, 0.165, 0.939, 0.758]</r>. From this information, I can extract the values for each index at each year.\nBased on the above thought, the answer is:\nYear,Global Entrepreneurship Index,Innovation Ecosystem Index\n2010,0.53,0.47\n2011,0.56,0.52\n2012,0.59,0.57\n2013,0.64,0.59\n2014,0.68,0.61\n2015,0.73,0.68\n2016,0.75,0.74\n2017,0.77,0.76\n2018,0.80,0.77\n2019,0.82,0.80"
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "FtlY8wgIpYeuW792",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which Index has shown a greater increase between 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which Index has shown a greater increase, I need to calculate the change for each Index from 2010 to 2019. For the {Global Entrepreneurship Index}<r>[0.147, 0.129, 0.939, 0.650]</r>, I previously calculated the change to be 0.29. For the {Innovation Ecosystem Index}<r>[0.147, 0.165, 0.939, 0.758]</r>, I locate the initial point in 2010 and the final point in 2019. The initial value in 2010 is 0.47 and the final value in 2019 is 0.80. Therefore, the change is 0.80 - 0.47 = 0.33.\nBased on the above thought, the answer is:\nThe Innovation Ecosystem Index has shown a greater increase between 2010 and 2019. The change for the Global Entrepreneurship Index is 0.29 while the Innovation Ecosystem Index has increased by 0.33 over the same period."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "9MM52GcJ8Cb4DLcp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the Global Entrepreneurship Index from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in the Global Entrepreneurship Index from 2010 to 2019, I need to locate the initial point {2010}<r>[0.147, 0.650]</r> on the {Global Entrepreneurship Index line}<r>[0.147, 0.129, 0.939, 0.650]</r> and the final point {2019}<r>[0.939, 0.129]</r> on the same line. The initial value for 2010 is 0.53 and the final value for 2019 is 0.82. The change is the final value minus the initial value which is 0.82 - 0.53 = 0.29.\nBased on the above thought, the answer is:\n0.29."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "JMLQqDlmVAKT4dbu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trend data for the different media and entertainment industry sectors over the decade into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[{extreme left}[0.061, 0.773], {extreme right}[0.951, 0.788]], which represent the column headers corresponding to the timeline from 2012 to 2021. Then, I use {the legend texts}[{Box Office}[0.844, 0.055, 0.909, 0.079], {Online Streaming}[0.844, 0.086, 0.909, 0.111], {Live Performance}[0.844, 0.117, 0.909, 0.142], {Video Game Sales}[0.844, 0.148, 0.909, 0.173], {Book Sales}[0.844, 0.179, 0.909, 0.204]] to identify the row labels representing the different industry sectors. For the table's values, I look at {the line points for each sector}[{Box Office}[0.102, 0.470, ..., 0.798, 0.696], {Online Streaming}[0.102, 0.211, ..., 0.798, 0.729], {Live Performance}[0.102, 0.599, ..., 0.798, 0.340], {Video Game Sales}[0.102, 0.179, ..., 0.798, 0.534], {Book Sales}[0.102, 0.470, ..., 0.798, 0.632]] at the respective years, which are represented by respective y-coordinate values.\nBased on the above thought, the answer is:\nYear,Box Office,Online Streaming,Live Performance,Video Game Sales,Book Sales\n2012,120,80,100,90,120\n2013,115,82,102,88,121\n2014,110,85,99,91,125\n2015,105,89,101,92,123\n2016,100,92,98,94,122\n2017,95,96,95,97,123\n2018,90,100,93,99,125\n2019,85,105,91,100,126\n2020,80,110,90,102,130\n2021,75,115,89,100,129"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "mQ9ALmKZXuwGyFYz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which sector had the most significant positive trend in the last decade? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the most significant positive trend, I need to analyze each sector's progression over the time series. Among {the lines for the sectors}[{Box Office}[0.102, 0.470, ..., 0.798, 0.696], {Online Streaming}[0.102, 0.211, ..., 0.798, 0.729], {Live Performance}[0.102, 0.599, ..., 0.798, 0.340], {Video Game Sales}[0.102, 0.179, ..., 0.798, 0.534], {Book Sales}[0.102, 0.470, ..., 0.798, 0.632]], the line for Online Streaming shows a consistent increase over time, rising from 80 to 115, a significant increase compared to the other sections.\nBased on the above thought, the answer is:\nAccording to the line chart, the Online Streaming sector exhibited the most significant positive trend in the last decade. The line plot for Online Streaming shows a consistent upward trajectory, indicating an increase in usage throughout this period. This trend significantly contrasts the trends in other sectors, which have either declined or exhibited inconsistent growth. The surge in online streaming reflects the shift in consumer behavior towards digital and streaming platforms in the past decade."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "Vvveb8Xaswt1TnNa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Box Office earnings decline from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decline in Box Office earnings, I first locate {the line points for Box Office}[{2012}[0.102, 0.470], {2021}[0.798, 0.696]] and extract the corresponding values: 120 in 2012 and 75 in 2021. The decline in Box Office earnings is therefore 120 - 75 = 45 units.\nBased on the above thought, the answer is:\n45 units."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "SiCnaqZb8YlgTWMU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart into a structured table with Years as rows and Interaction Types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the structured table from the line chart, I first identify {the x-axis tick labels}[[0.109, 0.919, 0.153, 0.942],[0.295, 0.919, 0.317, 0.942],[0.459, 0.919, 0.481, 0.942],[0.623, 0.919, 0.645, 0.942],[0.787, 0.919, 0.809, 0.942]] which represent row headers or Years. Then I find that the types of interactions as seen from the legend absent in the JSON. To derive values, I look at {the line points}[[0.131, 0.865],[0.213, 0.769],[0.295, 0.674],[0.377, 0.626],[0.459, 0.530],[0.541, 0.483],[0.623, 0.387],[0.705, 0.291],[0.787, 0.196],[0.869, 0.100]], the point raw data shows that each interaction type has their respective values at each year, which can be filled under the respective columns.\nBased on the above thought, the answer is:\nYear,Online_Interactions,Face_to_Face_Interactions,Online_Casual_Acquaintances,Face_to_Face_Casual_Acquaintances,Online_Close_Friends,Face_to_Face_Close_Friends\n2010,1500000,0.8,1200000,0.89,1300000,0.91\n2011,1550000,0.78,1250000,0.88,1350000,0.92\n2012,1600000,0.77,1300000,0.87,1400000,0.94\n2013,1625000,0.76,1325000,0.86,1425000,0.96\n2014,1675000,0.75,1375000,0.85,1475000,0.97\n2015,1700000,0.74,1400000,0.84,1500000,0.98\n2016,1750000,0.72,1450000,0.82,1550000,1\n2017,1800000,0.71,1500000,0.81,1600000,1.02\n2018,1850000,0.69,1550000,0.79,1650000,1.04\n2019,1900000,0.67,1600000,0.76,1700000,1.06"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "VX9tABBNcfSrd8T2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how has the business approached social sustainability in terms of Corporate Giving and reducing Carbon Footprint?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart demonstrates trends for two key social sustainability indicators: Corporate Giving and Carbon Footprint. Looking at {the line for Corporate Giving}[0.131, 0.865, 0.869, 0.100], one can see a steady upward trend from 1500000 in 2010 to 1900000 in 2019, indicating a consistent increase in the business's social investments and contributions. \nConversely, observing {the line for Carbon Footprint}[0.131, 0.100, 0.869, 0.865], one sees a downward trend from 0.8 to 0.67 over the same time period. This reflects the company's successful efforts to reduce its environmental impact, another important aspect of social sustainability.\nBased on the above thought, the answer is:\nBased on the line chart, the business has positively addressed social sustainability over the period from 2010 to 2019. It has consistently increased its Corporate Giving, reflecting a commitment to social investment and contribution. Simultaneously, the business has significantly reduced its Carbon Footprint, demonstrating efforts towards environmental sustainability. This dual approach reflects a comprehensive strategy towards social responsibility and sustainability."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "RCemZ2Zp3ZoBUheA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Corporate Giving increased from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the increase in Corporate Giving from 2010 to 2019, I need to look at the points for Corporate Giving line, specifically the first point at {2010}[0.131, 0.865] and the last point at {2019}[0.869, 0.100] . The y-values for these points represent the Corporate Giving amount in those years. The value in 2010 is 1500000 and in 2019 it's 1900000.  Therefore, the increase from 2010 to 2019 is 1900000 - 1500000 = 400000.\nBased on the above thought, the answer is:\n400000"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "A9SvWUxpGgUOoj6H",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the raw data in a table format where months correspond to the columns, while different products correspond to the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a tabular representation, the first step would be to identify {the x-axis tick labels}<[0.120, 0.838, 0.138, 0.885], [0.254, 0.838, 0.272, 0.885], [0.388, 0.838, 0.406, 0.885], [0.522, 0.838, 0.540, 0.885], [0.656, 0.838, 0.674, 0.885], [0.781, 0.838, 0.817, 0.885], [0.916, 0.838, 0.951, 0.885], [1.050, 0.838, 1.085, 0.885]> as columns corresponding to months. Next, I would identify {the legend texts}<[0.763, 0.166, 0.945, 0.213], [0.763, 0.236, 0.928, 0.283]> as the rows representing the Base Product and Our Product. The data points values are represented by the line plots for each product, {the points for Base Product }<[0.196, 0.200], [0.263, 0.276], [0.330, 0.352], [0.397, 0.397], [0.464, 0.442], [0.531, 0.503], [0.598, 0.548], [0.665, 0.579], [0.732, 0.609], [0.799, 0.654], [0.866, 0.654], [0.933, 0.692]> and {the points for Our Product }<[0.196, 0.730], [0.263, 0.579], [0.330, 0.427], [0.397, 0.276], [0.464, 0.200], [0.531, 0.306], [0.598, 0.654], [0.665, 0.685], [0.732, 0.715], [0.799, 0.624], [0.866, 0.707], [0.933, 0.745]>.\nBased on the above thought, the answer is:\nProducts,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10,Month 11,Month 12\nBase Product,1200,1150,1100,1070,1040,1000,970,950,930,900,900,875\nOur Product,850,950,1050,1150,1200,1130,900,880,860,920,865,840"
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "qumGXdMcKrghBTH7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, how would you interpret the sales performance of both products over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the sales performance of both products over time, I need to analyze the line plots for each product. Starting with {the line for Base Product}[0.196, 0.200, 0.933, 0.692], I can see that it shows a consistent decrease in sales over the 12 months' period. Starting from 1200 units, it falls to 875 by the 12th month, indicating a decline in the product's performance over time.\n\nMoving on to {the line for Our Product}[0.196, 0.200, 0.933, 0.745], it shows an initial increase in sales, peaking at 1200 units in the 5th month. Thereafter, the sales performance became inconsistent, seeing some decrease and increase throughout the remaining months before finalizing at 840 units in the last month.\n\nThe sales performance for Our Product was initially positive with strong growth, but there after it became unstable, with both increases and decreases in sales. This suggests a level of unpredictability in the sales performance of Our Product after the 5th month. Meanwhile, the Base Product has seen a steady decline in sales throughout the year.\nBased on the above thought, the answer is:\nThe sales performance of the Base Product has been steadily declining over the year, indicating a potential loss in customer interest or changes in market trends. The 'Our Product' shows a strong growth initially but later becomes unpredictable with fluctuations in the number of units sold. These trends suggest that while 'Our Product' may have initially done well, it did not maintain its growth, whereas the Base Product needs to reassess its sales strategy due to the persistent downward performance."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "5FDJ2z7SSTEgJBwp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average monthly sales of 'Our Product'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average monthly sales of 'Our Product', I'll need to position {the plot values of 'Our Product'}<[0.196, 0.730], [0.263, 0.579], [0.330, 0.427], [0.397, 0.276], [0.464, 0.200], [0.531, 0.306], [0.598, 0.654], [0.665, 0.685], [0.732, 0.715], [0.799, 0.624], [0.866, 0.707], [0.933, 0.745]>. These values are 850, 950, 1050, 1150, 1200, 1130, 900, 880, 860, 920, 865, and 840. Summing up these values and dividing by 12 (the total number of months), we get the average of 'Our Product' sales.\nBased on the above thought, the answer is:\n976.67 units."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "0zbZe2uJJnB9EGVg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you provide the data in terms of Level of Support for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data from the chart into a table, I start by checking {the X-axis labels}<r>[0.124, 0.903, 0.146, 0.931], ..., [0.940, 0.903, 0.950, 0.931]</r> which represent the Year. Next, I detect that {the legend labels}<r>[0.800, 0.100, 0.966, 0.128], [0.800, 0.142, 0.933, 0.170]</r> showing Traditional and Trendy Support respectively will be the column headers. For each year, I place {the corresponding data points for each category}<r>[0.124, 0.585], ..., [0.940, 0.543]</r> and <r>[0.124, 0.713], ..., [0.940, 0.160]</r> into the appropriate cell to populate the values.\nBased on the above thought, the answer is:\nYear,Traditional Support,Trendy Support\n2010,0.4,0.25\n2011,0.36,0.28\n2012,0.33,0.35\n2013,0.29,0.5\n2014,0.35,0.55\n2015,0.33,0.6\n2016,0.37,0.75\n2017,0.35,0.8\n2018,0.42,0.85\n2019,0.45,0.9"
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "ciQgFAne3SJ5kFc4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you interpret from the Trendy Support and Traditional Support trends from the years 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the trends, I look at the lines for {Traditional Support}<r>[0.124, 0.585, 0.940, 0.543]</r> and {Trendy Support}<r>[0.124, 0.713, 0.940, 0.160]</r>. Traditional Support starts at a high, but gradually decreases over the years, suggesting a possible declining interest in traditional culture and arts. On the other hand, Trendy Support begins at a lower level in 2010 but experiences a sharp increase throughout the years, indicating a growing interest in trendy culture and arts. This could suggest a shift in societal preference towards newer, trendier forms of culture and arts.\nBased on the above thought, the answer is:\nThe trends indicate a shift in societal interest and support from traditional to trendy forms of culture and arts from 2010 to 2019. There is a visible decline in Traditional support, suggesting a potentially decreased preference for traditional culture. Concurrently, there is a significant increase in Trendy Support, which may indicate growing interest and preference for newer, trendier forms of culture and arts."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "lbDx5PMoxJK8Z1Dc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in Trendy Support from the year 2013 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in Trendy Support from 2013 to 2017, I will identify {the point representing Trendy Support in 2013}<r>[0.396, 0.500]</r>, which corresponds to 0.5. Then, I find {the point representing Trendy Support in 2017}<r>[0.759, 0.245]</r> which corresponds to 0.8. To get the increase, I will subtract the 2013 value from the 2017 Value: 0.8 - 0.5 = 0.3.\nBased on the above thought, the answer is:\n0.3."
+            }
+        ],
+        "id_raw": 553
+    },
+    {
+        "id": "od6Mzclbj4qe6GGb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the change in daily active users per social media platform, as indicated in the line chart, in a tabulated format with measurements by \"Ours\" and \"SMA\" listed separately for each platform?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, I can see that the two social media platforms are represented in two different subplots, each with two lines denoting the measurements from 'Ours' and 'SMA'. The {Facebook line from 'Ours'}<r>[0.15, 0.5, 0.85, 0.6]</r> shows a 3.4% increase and the {Facebook line from 'SMA'}<r>[0.15, 0.53, 0.85, 0.63]</r> displays a 3.2% increase. For Twitter, the {Twitter line from 'Ours'}<r>[0.15, 0.1, 0.85, 0.2]</r> signifies a 3% increase, and the {Twitter line from 'SMA'}<r>[0.15, 0.13, 0.85, 0.07]</r> indicates a 1% decrease.\nBased on the above thought, the answer is:\nPlatform, Ours, SMA\nFacebook, 3.4%, 3.2% \nTwitter, 3%, -1%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "073C78XSWtqsVovR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret which social media platform showed a consistent increase in active users based on the line chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the line chart, I can see that {the Facebook lines from 'Ours' and 'SMA'}<r>[0.15, 0.5, 0.85, 0.6]</r>, <r>[0.15, 0.53, 0.85, 0.63]</r> both depict an increase in the number of active users. This is in contrast to the {Twitter line from 'SMA'}<r>[0.15, 0.13, 0.85, 0.07]</r>, which shows a decrease. Therefore, Facebook has shown a consistent increase in active users over this time period.\nBased on the above thought, the answer is:\nBased on the given line chart, Facebook showed a consistent increase in active users. Both measurements from 'Ours' and 'SMA' depict an upward trend in Facebook's active user count, contrasting Twitter which, according to 'SMA', experienced a decrease in active users."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "eJwDrywgMDcTFjA4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in user change (as a percentage) for Facebook between 'Ours' and 'SMA' measurements?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in user change for Facebook between 'Ours' and 'SMA' measurements, I have to locate the values represented by the {Facebook line from 'Ours'}<r>[0.15, 0.5, 0.85, 0.6]</r>, which is 3.4%, and the {Facebook line from 'SMA'}<r>[0.15, 0.53, 0.85, 0.63]</r>, which is 3.2%. The difference is equal to 3.4% - 3.2% = 0.2%.\nBased on the above thought, the answer is:\n0.2%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "f8SkEFbOY4whtHsd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and structure the data of Global GDP and Global Inflation Rate as per the years into a table from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I'll first identify {the labels on the X-axis}[[0.036, 0.919, 0.084, 0.942], [0.146, 0.919, 0.194, 0.942], [0.256, 0.919, 0.305, 0.942], [0.367, 0.919, 0.415, 0.942], [0.477, 0.919, 0.525, 0.942]] as the Years. The lines representing {Global GDP}[[0.104, 0.124, 0.943, 0.499]] and {Global Inflation Rate}[[0.104, 0.804, 0.943, 0.867]] are the rows in this case, while the points on these lines give the specific values for these measures in the respective years.\nBased on the above thought, the answer is:\nYear,Global GDP,Global Inflation Rate\n2001,40.9,1.7\n2002,40.6,1.6\n2003,41.8,2.1\n2004,44.1,2.7\n2005,47.5,3.2\n2006,50.9,3.5\n2007,56.1,4.6\n2008,57.3,6.2\n2009,57.0,-0.9\n2010,60.5,4.1\n2011,64.1,4.6\n2012,66.3,3.2\n2013,69.2,1.9\n2014,72.2,1.5\n2015,74.6,1.1\n2016,76.0,1.7\n2017,79.1,2.9\n2018,81.4,3.8\n2019,83.0,3.1\n2020,80.6,-0.2"
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "JKERdhFrBIDOWN2J",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy comparing the trends of Global GDP and the Global Inflation Rate, can you interpret any relationship between the two?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyse the relationship between Global GDP and the Global Inflation Rate, I need to compare the trends of {the Global GDP line}[[0.104, 0.124, 0.943, 0.499]] and {the Global Inflation Rate line}[[0.104, 0.804, 0.943, 0.867]]. The Global GDP shows a steady rise over the years, indicating consistent economic growth. However, the Global Inflation Rate, while also trending upwards, exhibits much more volatility with noticeable spikes and dips, such as the sharp fall in 2009 corresponding to {the point for the year 2009 on the Global Inflation Rate line}[0.457, 0.867]. Nevertheless, both metrics seem to follow a synchronized pattern, suggesting potentially an interdependent relation.\nBased on the above thought, the answer is:\nThe trends in the line chart suggest a correlation between Global GDP and the Global Inflation Rate. Both generally increase over time, indicating economic growth and inflation often move in tandem. However, the higher volatility in the inflation rate suggests that short-term economic factors may cause inflation to fluctuate more dramatically while GDP tends to change gradually. For example, the steep decrease in inflation in 2009 may be due to the global financial crisis, but the GDP line shows a slight decrease only. This suggests that while the two are interconnected, specific events can influence them differently."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "e9dlo0yXjki3gr5X",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum Global GDP values over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must find the maximum and minimum values for Global GDP over the years for this. These values can be obtained by closely examining {the points on the Global GDP line}[[0.104, 0.497], [0.148, 0.499], [0.236, 0.468], ..., [0.899, 0.124], [0.943, 0.145]]. The highest represented GDP value is 83.0 corresponding to {the point for the year 2019}[0.899, 0.124]. The lowest GDP value is 40.6 corresponding to {the point for the year 2002}[0.148, 0.499]. The difference between the maximum and minimum is therefore 83.0 - 40.6 = 42.4.\nBased on the above thought, the answer is:\n42.4."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "EdsH7Mn0lJGSbC4A",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format, with Years as rows and different technologies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first look at {the labels on the x-axis}[[0.129, 0.903, 0.154, 0.931], [0.230, 0.903, 0.255, 0.931], [0.331, 0.903, 0.356, 0.931], [0.432, 0.903, 0.458, 0.931], [0.534, 0.903, 0.559, 0.931], [0.635, 0.903, 0.660, 0.931], [0.736, 0.903, 0.761, 0.931], [0.837, 0.903, 0.862, 0.931], [0.938, 0.903, 0.963, 0.931]], which represent the years. Next, I identify {the labels in the legend}[[0.162, 0.100, 0.302, 0.128], [0.162, 0.142, 0.300, 0.170]] representing the different technologies. I need to find {the points for each technology}[[0.129, 0.768], [0.230, 0.745], ..., [0.938, 0.479]], match them to the years and extract the values to populate the table.\nBased on the above thought, the answer is:\nYear,Italian Cuisine,Greek Cuisine\n1980,20,30\n1985,22,35\n1990,25,40\n1995,30,48\n2000,35,55\n2005,37,58\n2010,40,60\n2015,43,65\n2020,45,70"
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "9Okx1DimmSROSwx4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich gastronomy culture showed more growth between 1980 and 2020 as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which gastronomy culture showed more growth, I need to look at the increase in scores for both Italian and Greek cuisine from 1980 to 2020. For {Italian cuisine}[0.162, 0.100, 0.302, 0.128], the score changed from {20}[0.129, 0.768] in 1980 to {45}[0.938, 0.479] in 2020, indicating a growth of 45 - 20 = 25. For {Greek cuisine}[0.162, 0.142, 0.300, 0.170], the score changed from {30}[0.129, 0.652] in 1980 to {70}[0.938, 0.190] in 2020, indicating a growth of 70 - 30 = 40.\nBased on the above thought, the answer is:\nGreek gastronomy culture showed more growth between 1980 and 2020. It increased by 40 points on the popularity scale from 30 to 70, compared to the Italian gastronomy culture which increased by 25 points from 20 to 45."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "rj8M15g7a2UQmB8l",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_28_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what amount did the popularity of Italian cuisine increase between 1980 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in the popularity of Italian cuisine, I need to examine {the points on the Italian Cuisine line}[[0.129, 0.768], [0.230, 0.745], [0.331, 0.710], [0.432, 0.652], [0.534, 0.595], [0.635, 0.571], [0.736, 0.537], [0.837, 0.502], [0.938, 0.479]]. The point corresponding to the year 1980 is {20}[0.129, 0.768]. The point corresponding to the year 2020 is {45}[0.938, 0.479]. Thus, the increase in the popularity is 45 - 20 = 25.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "5UxpSNAoOKatnB1g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please arrange the given chart data into a structured table format where the columns would represent the years and the rows would represent the different types of cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the structured table from the data, I will first locate {the x-axis tick labels}[[0.248, 0.895, 0.601, 0.923], [0.208, 0.895, 0.219, 0.923], [0.390, 0.895, 0.401, 0.923], [0.571, 0.895, 0.582, 0.923], [0.753, 0.895, 0.764, 0.923], [0.929, 0.895, 0.951, 0.923]], which will serve as the column headers representing time in years. Next, I identify {the legend texts}[[0.146, 0.055, 0.339, 0.083], [0.146, 0.097, 0.315, 0.125]] as the y-axis representative for the different cuisine types to create rows. To populate the values in the table, I locate {the points on the lines}[[0.123, 0.185, 0.940, 0.846], [0.123, 0.069, 0.940, 0.433]] for each type of cuisine per year and extract the corresponding values: e.g., 0.5, 0.6, 0.7, etc.\nBased on the above thought, the answer is:\nYear,Western Cuisine,Asian Cuisine\n1,0.5,0.75\n2,0.6,0.78\n3,0.7,0.79\n4,0.73,0.80\n5,0.75,0.83\n6,0.77,0.85\n7,0.80,0.88\n8,0.82,0.91\n9,0.86,0.93\n10,0.9,0.97"
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "aooXfFoo8Rgz0Sw3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data patterns presented in the line graph, which cuisine type experienced a constant rise in favorability over the period under observation? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To discern which cuisine experienced a constant increase in favorability, I must analyze the trend lines for each cuisine. Observing {the line for Western Cuisine}[0.123, 0.185, 0.940, 0.846], there is a steady upward trajectory from 0.5 in year 1 to 0.9 in year 10, representing a consistent rise in favorability. For {the line for Asian Cuisine}[0.123, 0.069, 0.940, 0.433], the points show a similar upward trend, starting from 0.75 in year 1 to 0.97 in year 10, reflecting an unbroken rise in preference.\nBased on the above thought, the answer is:\nBoth Western and Asian cuisines have experienced a constant rise in favorability over the time period. Both trend lines indicate a steady upward trajectory, representing increased preference for these cuisines over the years. However, it is also important to note that the rise in favorability is more significant for Asian Cuisine as it started at a higher preference score of 0.75 and ended at 0.97, compared to Western Cuisine which started at 0.5 and ended at 0.9."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "18vAzfnBM1XRbsvN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much change in favorability of Asian Cuisine is predicted from the year 4 to year 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, to find the favorability of Asian Cuisine in year 4, I need to locate {the value point for Asian Cuisine in year 4}[[0.395, 0.350]], which corresponds to a favorability score of 0.8. Then, I locate {the value point for Asian Cuisine in year 6}[[0.577, 0.267]], which corresponds to a favorability score of 0.85. The difference between the favorability scores in year 6 and year 4 is then computed as 0.85 - 0.8 = 0.05.\nBased on the above thought, the answer is:\n0.05."
+            }
+        ],
+        "id_raw": 565
+    },
+    {
+        "id": "0C4G6JuV8l0KyeDO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table from the data represented in the line chart, where the various factors represent columns, and the three sample sets represent rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I will first look at {the x-axis tick labels}<r>[0.052, 0.895, 0.978, 0.923]</r>, which serves as the column headers representing the various factors. Also, I identify {the legend labels}<r>[0.750, 0.044, 0.922, 0.220]</r> serving as the row labels representing the three sample sets. For each sample set, I locate the {line points}<r>[0.120, 0.422, 0.892, 0.634]</r>,<r>[0.120, 0.352, 0.892, 0.776]</r>,<r>[0.120, 0.069, 0.892, 0.846]</r> on each factor and extract the corresponding normalized values.\nBased on the above thought, the answer is:\nSamples, Abrupt Increase, Sharp Rise, Significant Decrease, Subdued Expansion\nUpstream, 0.5, 0.6, 0.4, 0.7\nMidstream, 0.75, 0.8, 0.2, 0.8\nDownstream, 1.0, 1.2, 0.1, 0.9"
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "mE9hWzvEHccaVm4g",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends depicted in the line chart, which sample set seems to be experiencing the highest volatility across all stages of the value chain? Provide an explanation for your reason8ing."
+            },
+            {
+                "from": "gpt",
+                "value": "In determining the sample set experiencing the highest volatility across the value chain stages, I need to examine the oscillations in the line plots for each sample. {The line for the Upstream sample}<r>[0.120, 0.422, 0.892, 0.634]</r> shows moderate changes in its values all through the stages with minor fluctuations. The {line for the Midstream sample}<r>[0.120, 0.352, 0.892, 0.776]</r> exhibits relatively high fluctuations, particularly from the Sharp rise to the Significant decrease stage, and then stabilization for the Subdued expansion. The {line for Downstream sample}<r>[0.120, 0.069, 0.892, 0.846]</r> shows a very steep rise from the Abrupt increase to Sharp rise stage, a steep decline at the Significant decrease stage, and again a rise for the Subdued expansion stage. These significant fluctuations imply high volatility.\nBased on the above thought, the answer is:\nBased on the trends seen in the line chart, the Downstream sample seems to have the highest volatility across all stages of the value chain. This is backed up by the Downstream line plot that depicts significant fluctuations. It demonstrates a sharp rise from the Abrupt increase to Sharp rise stage, a sharp fall at the Significant decrease stage, followed by another spike at the Subdued expansion stage. The magnitudes of these fluctuations are more drastic compared to the Upstream and Midstream samples, indicating a greater degree of volatility in the Downstream sample."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "SlONmMI6cnsk8JBA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the phase values from the Abrupt increase stage to the Subdued expansion stage for the Upstream sample?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage increase in phase values from the Abrupt increase stage to the Subdued expansion stage for the Upstream sample, I have to find the initial and final values from the {line points of the Upstream sample}<r>[0.120, 0.564]</r> and <r>[0.892, 0.422]</r>. The coordinates of the phase values show that the initial value (at the abrupt increase) for Upstream is 0.5 and the final value (at subdued expansion) is 0.7. Thus, the percentage increase is given by 100 x (Final - Initial) / Initial which is 100 x (0.7 - 0.5) / 0.5 = 40% increase.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "bdDGUaty5f9isqez",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table with the years as the rows and \"Price Index\" and \"Housing Units\" as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data to a table, I identify each point on the {Price Index line}[0.105, 0.067, 0.878, 0.857] and the {Housing Units line}[0.105, 0.535, 0.878, 0.797] and map the coordinates to the years using {the x-axis labels}[-0.029, 0.912, 0.001, 0.936, ..., 0.934, 0.912, 0.963, 0.936]. This allows me to extract the values of Price Index and Housing Units for each year.\nBased on the above thought, the answer is:\nYear,Price Index,Housing Units\n2010,76.5,680\n2011,82.4,700\n2012,87.2,720\n2013,81.9,740\n2014,89.1,760\n2015,93.6,780\n2016,99.4,800\n2017,104.2,820\n2018,98.6,850\n2019,103.5,900\n2020,108.7,950"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "VRz0Y2QoywFpDZSM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how might you describe the overall trend in housing units built over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the overall trend in housing units built over the years, I need to analyze {the Housing Units line}[0.105, 0.535, 0.878, 0.797]. The line shows a steady upward trend, indicating that the number of housing units built has generally increased year after year.\nBased on the above thought, the answer is:\nThe overall trend in housing units built over the years can be described as a steady increase. This implies that the construction of new housing units has consistently grown throughout the decade, suggesting a healthy development in the real estate market."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "0hdpAS0SOC9PvrOB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the maximum increase in price index from one year to the next?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum increase in price index, I must locate the corresponding points on the {Price Index line}[0.105, 0.067, 0.878, 0.857] and calculate the differences in these values from one year to the next. Comparing these annual changes, I find that the maximum increase is between 2016 and 2017.\nBased on the above thought, the answer is:\n4.8"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "1cmS4454opeLuwiB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the raw data from the chart into a structured table with 'Year' as the rows and 'Investment' and 'Employment' as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify {the x-axis labels}[[0.085, 0.912, 0.121, 0.936], [0.190, 0.912, 0.226, 0.936], [0.295, 0.912, 0.331, 0.936], [0.400, 0.912, 0.436, 0.936], [0.505, 0.912, 0.541, 0.936], [0.610, 0.912, 0.646, 0.936], [0.715, 0.912, 0.751, 0.936], [0.820, 0.912, 0.856, 0.936], [0.926, 0.912, 0.961, 0.936]] that represents the years. Next, I check {the legend}[[0.902, 0.046, 0.973, 0.069], [0.902, 0.081, 0.972, 0.104]] for the column headers (Investment and Employment). Then, I move to extract the values for each year by noting down {the y-coordinates for Investment}[[0.103, 0.864], [0.208, 0.826], [0.313, 0.792], [0.418, 0.775], [0.523, 0.712], [0.628, 0.642], [0.733, 0.557], [0.838, 0.447], [0.943, 0.361]] and {the coordinates for Employment}[[0.103, 0.065], [0.208, 0.103], [0.313, 0.164], [0.418, 0.173], [0.523, 0.228], [0.628, 0.270], [0.733, 0.357], [0.838, 0.456], [0.943, 0.542]] from the respective line plots.\nBased on the above thought, the answer is:\nYear,Investment in millions,Employment\n2012,8.5,50.6\n2013,10.5,48.6\n2014,12.3,45.4\n2015,13.2,44.9\n2016,16.5,42.0\n2017,20.2,39.8\n2018,24.7,35.2\n2019,30.5,30.0\n2020,35.0,25.5"
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "AGK0aheYAs8cZ3PI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which Industry seems to have the most consistent growth over the years? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which Industry has the most consistent growth, I need to analyze the trends in the line plots for both the Industries. For {Industry 1}[0.103, 0.361, 0.943, 0.864], the line plot shows a steady increase from 8.5 to 35.0 over the years, indicating a positive trend. By contrast, for {Industry 2}[0.103, 0.065, 0.943, 0.542], the line plot shows a decrease from 50.6 to 25.5, indicating a negative trend.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Industry 1 seems to have the most consistent growth over the years. Its line plot shows a steady increase from 8.5 to 35.0 over the years, indicating a positive trend. Contrary to this, Industry 2 displays a declining trend over the years."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "eOG57GEGPxxWYztM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value noticed for the Employment count and in which year did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest Employment count, I first locate {the points plotted for Employment}[[0.103, 0.065], [0.208, 0.103], [0.313, 0.164], [0.418, 0.173], [0.523, 0.228], [0.628, 0.270], [0.733, 0.357], [0.838, 0.456], [0.943, 0.542]]. I notice that the highest point is {at y=0.065}<r>[0.103, 0.065]</r>. The corresponding {year for the highest Employment} is derived from the {x-value of 0.103}<r>[0.103, 0.912, 0.121, 0.936]</r>, which indicates the year 2012.\nBased on the above thought, the answer is:\nThe highest Employment count is 50.6 in the year 2012."
+            }
+        ],
+        "id_raw": 575
+    },
+    {
+        "id": "blUMxmRU3FcFcOE9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar charts, can you convert the data representing Market Status, Sales, and Consumption into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "For forming the table from the provided data, we need to examine the bars on the bar charts. The {legend}<r>[0.662, 0.044, 0.903, 0.136]</r> defines the type of data each bar represents; one for Global Trade Volume and the other for Supply Chain Risk. The x-axis, represented by {x-axis tick labels}<r>[[0.051, 0.895, 0.062, 0.923],...,[1.000, 0.895, 1.022, 0.923]]</r> serves as the Month column. Furthermore, the trade volume for the corresponding month is represented by {the line points for Global Trade Volume}<r>[[0.125, 0.846],...,[0.875, 0.276]]</r>, and the Supply Chain Risk is signified by {the line points for Supply Chain Risk}<r>[[0.125, 0.263],...,[0.875, 0.652]]</r>. It is essential to note that all the points plotted here have a correspondence with the tick marks on the x-axis.\nBased on the above thought, the answer is:\nMonth,Global Trade Volume,Supply Chain Risk\n1,100,5\n2,120,4\n3,130,5\n4,120,6\n5,150,5\n6,160,4\n7,180,3\n8,250,2\n9,150,3\n10,230,2\n11,200,4\n12,210,3"
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "Ja9oAgQXGChnJJK9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the patterns shown in the line charts, what inferences can you make about the correlation between Global Trade Volume and Supply Chain Risk? Please elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "To make any inference about the correlation between Global Trade Volume and Supply Chain Risk, I need to evaluate the trends of both lines. Looking at {the line for Global Trade Volume}<r>[0.125, 0.069, 0.875, 0.846]</r>, it is clear that the trade volume increases for the most part with some fluctuations in the middle. Conversely, {the line for Supply Chain Risk}<r>[0.125, 0.069, 0.875, 0.846]</r> shows an overall downward trend. These distinct patterns in the lines suggest there might be a negative correlation between Global Trade Volume and Supply Chain Risk.\nBased on the above thought, the answer is:\nBased on the observations from the line charts, it could be inferred that Global Trade Volume and Supply Chain Risk may have a negative correlation. This is because as the Global Trade Volume increases, the Supply Chain Risk decreases, and vice versa. However, note that to definitively establish a statistical correlation, a rigorous analysis is required. This inference is made purely on the observed data trends from the line charts."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "SrOXLoz0Urs5evhS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Global Trade Volume across all months indicated in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total Global Trade Volume, we need to sum up all the monthly volumes. As per the plotted data points for {Global Trade Volume}<r>[[0.125, 0.846],...,[0.875, 0.276]]</r>, the corresponding Global Trade Volume for each month is as follows: 100, 120, 130, 120, 150, 160, 180, 250, 150, 230, 200, and 210. Therefore, we sum all these values to get the total Global Trade Volume.\nBased on the above thought, the answer is:\n2000."
+            }
+        ],
+        "id_raw": 576
+    },
+    {
+        "id": "Teql2B6IpuSaK1On",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide information from the chart in a table format with years as rows and startups as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "For extracting the tabulated data, {the x-axis tick labels}<r>[0.101, 0.895, 0.144, 0.923], [0.264, 0.895, 0.308, 0.923], [0.428, 0.895, 0.472, 0.923], [0.591, 0.895, 0.635, 0.923], [0.755, 0.895, 0.799, 0.923], [0.918, 0.895, 0.962, 0.923]</r> will be employed as our row identifiers. These represent the years. {The legend texts}<r>[0.146, 0.055, 0.310, 0.083], [0.146, 0.097, 0.310, 0.125]</r> correspond to the startups that will be represented as column labels. For the values in the table, {the line points}<r>[0.123, 0.846], [0.286, 0.380], [0.450, 0.509], [0.613, 0.095], [0.777, 0.121], [0.940, 0.406], [0.123, 0.691], [0.286, 0.769], [0.450, 0.587], [0.613, 0.173], [0.777, 0.328], [0.940, 0.069]</r> will be utilized. The values for each startup at the respective years can be extracted from these points.\nBased on the above thought, the answer is:\nYear,Startups-A,Startups-B\n2015,2.1,2.7\n2016,3.9,2.4\n2017,3.4,3.1\n2018,5.0,4.7\n2019,4.9,4.1\n2020,3.8,5.1"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "wuAKdUza7QepNMHV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which startup sector had a more pronounced growth trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "From looking at {the line for Startups Sector-A}<r>[0.123, 0.095, 0.940, 0.846]</r>, I see the values leveled off after a significant increase, suggesting an initial period of rapid growth followed by a stable period. Meanwhile, {the line for Startups Sector-B}<r>[0.123, 0.069, 0.940, 0.769]</r> also shows a significant increase, however, it does not level off and instead continues to increase. Therefore, based on the magnitude of increase, Startups Sector-B had a more pronounced growth trend over the years.\nBased on the above thought, the answer is:\nAccording to the chart, Startup Sector-B had a more pronounced growth trend over the years. It showed a consistent increase throughout the years as reflected in the chart, indicating stronger growth compared to Startup Sector-A."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "zTwTWJEicnU2ipYc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth difference for Startups Sector-A from the year 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth difference for Startups Sector-A from 2016 to 2020, I need to locate the values of these two years. The value for 2016 is 3.9 which corresponds to {2016's point for Sector-A}<r>[0.286, 0.380]</r>, and the value for 2020 is 3.8 which corresponds to {2020's point for Sector-A}<r>[0.940, 0.406]</r>. Hence, the difference in growth for Sector-A from 2016 to 2020 is the value at 2020 minus the value at 2016 which is 3.8 - 3.9.\nBased on the above thought, the answer is:\n-0.1."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "lzBko6jnFVAxpnW5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make a table summarizing social involvement for the different age groups, separated by males and females, as represented in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, we first look at the x-axis which represents {the age groups}[0.125, 0.895, 0.875, 0.923] - these will form the row labels. The legend identifies {the gender lines}[0.097, 0.044, 0.304, 0.178], which will be the column headers - Male and Female. Finally, we locate the specific {data points on the lines}[[0.125, 0.837], [0.250, 0.666], [0.375, 0.239], [0.500, 0.154], [0.625, 0.068], [0.750, 0.410], [0.875, 0.325]] for each gender at each age group to populate the table. These correspond to the level of social involvement.\nBased on the above thought, the answer is:\nGender/Age Group,18-24,25-34,35-44,45-54,55-64,65+\nMale,50,70,120,130,140,100\nFemale,30,40,50,70,80,90"
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "5XlunC7eqMtzfn4E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which demographic (male or female) displays an overall trend of increasing social involvement as age increases? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the trends displayed by the line plots for both males and females. The {line for males}[0.125, 0.068, 0.875, 0.837] initially shows an increase in social involvement up to the 35-44 age group, but then there is a significant decline, indicating a decreasing trend as age increases after the mid-thirties. \nOn the other hand, looking at the {line associated with females}[0.125, 0.920, 0.875, 0.710], we observe a consistent increase in social involvement up to the 55-64 age group, followed by a slight decrease. However, overall, the trend for females is an increase in social involvement as age increases, at least until the age of 65.\nBased on the above thought, the answer is:\nBased on the line chart, females display an overall trend of increasing social involvement as age increases. While there is a slight decrease in social involvement in the 65+ age group, the general trend from 18-64 is increasing, which contrasts with the decreasing trend observed for males after the 35-44 age group."
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "Yrkj28rSNGjmXho7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_29_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of social involvement percentages for all age groups for females?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total social involvement for females, I need to identify {the line points for females}[[0.125, 0.920], [0.250, 0.875], [0.375, 0.830], [0.500, 0.660], [0.625, 0.615], [0.750, 0.710]] from the line chart corresponding to ages from 18 to 65+. The social involvement percentages corresponding to these points are 30, 40, 50, 70, 80 and 90. Adding them up, we get the total social involvement for females.\nBased on the above thought, the answer is:\n360"
+            }
+        ],
+        "id_raw": 579
+    },
+    {
+        "id": "ACBjJT311CjHIdBR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data from the chart into a structured table. The columns would be the Investment Amounts and the rows would be the different methods of investment."
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart coordinates {Investment Amounts}[[0.188, 0.510], [0.272, 0.510], [0.524, 0.510], [0.943, 0.510]], we have the column headers. We can identify {the legend texts}[[0.455, 0.443, 0.671, 0.457]], as the row labels which represent the different investment methods. {The line points for Fixed Rateturn}[[0.188, 0.392], [0.272, 0.322], [0.524, 0.217], [0.943, 0.042]] and {The line points for Random Rate}[[0.188, 0.392], [0.272, 0.385], [0.524, 0.217], [0.943, 0.287]] are plotted in the first subplot which allow us to populate the Fixed Rateturn and Random Rate values respectively. We can similarly extract the values for Predictive Analysis using {The line points for Predictive Analysis}[[0.188, 0.863], [0.272, 0.746], [0.524, 0.688], [0.943, 0.513]].\nBased on the above thought, the answer is:\nInvestment Method,10000,20000,50000,100000\nFixed Rateturn,0.05,0.07,0.1,0.15\nRandom Rate,0.05,0.052,0.1,0.08\nPredictive Analysis,0.06,0.1,0.12,0.18"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "svSBtEswVU2rIvDJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends, what can you tell about the return rate as the investment amount increases for each investment method?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the line plots, I observe how the return rate changes with increasing investment amount. For Fixed Rateturn, there is an increase observed in {the line for Fixed Rateturn}[0.188, 0.392, 0.943, 0.042]. However, its increasing trend shows a decline near the end. The Random Rate, represented by {the line for Random Rate}[0.188, 0.392, 0.943, 0.287], exhibits an initially static, then increasing, and then a decreasing trend. In contrast, the Predictive Analysis method, shown by {the line for Predictive Analysis}[0.188, 0.863, 0.943, 0.513], is continuously increasing though at a decreasing rate, indicating the return rate increases with the investment amount but the increase slows down as the investment amount grows.\nBased on the above thought, the answer is:\nFor the Fixed Rateturn method, the return rate generally increases with the investment amount but there is a drop at the highest investment amount. For the Random Rate method, the return rate remains the same initially, increases moderately at the middle investment amounts, and then decreases towards the end. The Predictive Analysis method shows a consistently increasing trend, where the return rate rises with the investment amount, but the rate of increase slows down at higher investment amounts."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "f0J2NPhzqp63T168",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest return rate achieved from the Predictive Analysis investment method?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest return rate achieved from the Predictive Analysis investment method, I need to consider {the line points for Predictive Analysis}[[0.188, 0.863], [0.272, 0.746], [0.524, 0.688], [0.943, 0.513]]. The line points correspond to the return rates 0.06, 0.1, 0.12, 0.18. So the highest value is 0.18.\nBased on the above thought, the answer is:\n0.18."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "VYQ6emZ4CpfwITJo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract user data and corresponding performance across different methods for Facebook and Twitter into table format from the line chart."
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line chart, I examined {the x-axis labeling}[0.568, 0.469, 0.568, 0.469] which indicates that each point relates to a specific number of users. Observing the labels helps me understand the columns; 'Users'. Then, by identifying {the legend}<r>[0.385, 0.021, 0.750, 0.063]</r>, I confirmed the distinct methods used like Baseline, SoftMoE, and Top1-MoE for categorization within the table. Each line's data points like those for Baseline on Facebook {line for Baseline on Facebook}<r>[0.188, 0.100, 0.947, 0.317]</r> and those for Baseline on Twitter {line for Baseline on Twitter}<r>[0.188, 0.499, 0.947, 0.833]</r> provide the specific values for users.\nBased on the above thought, the answer is:\nUser_Count,Facebook_Baseline,Facebook_SoftMoE,Facebook_Top1MoE,Twitter_Baseline,Twitter_SoftMoE,Twitter_Top1MoE\n1,5000,4800,4300,4700,4500,4100\n2,5100,4700,4400,4500,4600,4300\n4,4750,4700,4500,4300,4600,4200\n8,4800,4600,4350,4500,4500,4300\n16,4600,4400,4200,4200,4500,4300\n32,4500,4300,4175,4100,4500,4350"
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "qAbq2LGVxMvhln1C",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the performance trend change with increasing users for the Baseline method on Facebook, according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line for Facebook Baseline}<r>[0.188, 0.100, 0.947, 0.317]</r>, which illustrates the performance trend for the Baseline method on Facebook, I observe the performance values at significant user count increments: For 1 user, the performance is at a peak with 5000, which decreases as the user count increases, showing 4500 for 32 users. This trend signifies a gradual decrease in performance as the number of users increases.\nBased on the above thought, the answer is:\nAccording to the line chart, the performance trend for the Baseline method on Facebook shows a gradual decrease as the user count increases. Starting from 5000 for one user and descending to 4500 for 32 users, the trend displays a consistent decline in performance with growing user numbers."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "YXCAijtoE2UNGktC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, which user count setting shows the smallest difference between Facebook and Twitter performance for the SoftMoE method?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the smallest difference between Facebook and Twitter performance for the SoftMoE method, I examine the respective line points for Facebook {line for Facebook SoftMoE}<r>[0.188, 0.209, 0.947, 0.389]</r> and Twitter {line for Twitter SoftMoE}<r>[0.188, 0.555, 0.947, 0.610]</r>, extracting performance values: Facebook - [4800, 4700, 4700, 4600, 4400, 4300] and Twitter - [4500, 4600, 4600, 4500, 4500, 4500]. After calculating the absolute differences for each user count, the smallest difference in performance is found for user count 32, with values 4300 for Facebook and 4500 for Twitter, giving a difference of 200.\nBased on the above thought, the answer is:\n32"
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "0DxAXP9LgTNL15ui",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table enumerating the 'Average Efficiency' and 'Material Waste' for each month from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the bar chart, I first identify {the x-axis labels}<r>[0.093, 0.415], [0.188, 0.415], [0.282, 0.415], [0.377, 0.415], [0.471, 0.415], [0.566, 0.415]</r> marking the months from January to June. For each month, I analyze {the stacked bars}<r>[0.093, 0.380, 0.125, 0.415], [0.188, 0.350, 0.220, 0.415], ... , [0.566, 0.250, 0.606, 0.415]</r> where the top portion represents 'Material Waste' and the bottom portion represents 'Average Efficiency'. I then read the corresponding percentage values annotated near each bar.\nBased on the above thought, the answer is:\nMonth, Average Efficiency (%), Material Waste (%)\nJanuary, 80, 20\nFebruary, 77, 23\nMarch, 79, 21\nApril, 84, 16\nMay, 78, 22\nJune, 77, 23"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "sDmBq7nKchYMqsRs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat month shows the highest 'Average Efficiency' according to the bar chart, and how does it compare to other months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the month with the highest 'Average Efficiency', I examine the heights of {the dark shaded areas of the bars representing 'Average Efficiency'}<r>[0.093, 0.380, 0.125, 0.415], [0.188, 0.350, 0.220, 0.415], [0.282, 0.330, 0.314, 0.415], [0.377, 0.305, 0.409, 0.415], [0.471, 0.340, 0.503, 0.415], [0.566, 0.350, 0.606, 0.415]</r>. April shows the highest efficiency with a notable peak in its efficiency bar. Comparatively, the efficiency values in other months fluctuate between 77% and 79%, while April stands out with an efficiency of 84%.\nBased on the above thought, the answer is:\nApril shows the highest 'Average Efficiency' at 84% according to the bar chart. This percentage is significantly higher compared to the other months, which largely hover between 77% and 79%. The noticeable peak in April highlights it as an outlier in terms of production efficiency."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "DKuZ7Hu8pL6kOFcH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the 'Material Waste' percentage decrease from January to April?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the 'Material Waste' decrease, I locate {the bars for Material Waste in January and April}<r>[0.093, 0.380, 0.125, 0.415], [0.377, 0.350, 0.409, 0.415]</r>. The percentage of 'Material Waste' in January is 20%, and in April it is 16%. The decrease is the difference between these two values, which is 20 - 16 = 4%.\nBased on the above thought, the answer is:\n4%"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "oBQru1JYXZ8EwUod",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the chart into a structured table format with years in the column top and categories in each row?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table, I first identify {the x-axis tick labels}[[0.080, 0.413], [0.188, 0.413], [0.296, 0.413], [0.404, 0.413], [0.512, 0.413], [0.620, 0.413], [0.728, 0.413], [0.836, 0.413], [0.943, 0.413]] as the column headers or years. Then I attribute the name of each line as row names, these are {the legend texts}[[0.518, 0.443, 0.662, 0.466]]. The data for each category at each year can be collected from {the line points}[[0.188, 0.381], [0.296, 0.303], [0.404, 0.332], [0.512, 0.247], [0.620, 0.155], [0.728, 0.275],[ 0.836, 0.112], [0.943, 0.077]; [0.188, 0.850],[ 0.296, 0.751], [0.404, 0.776], [0.512, 0.689], [0.620, 0.640], [0.728, 0.665], [0.836, 0.590], [0.943, 0.553]]. Any points at the same x-axis coordinate belong to the same year. By taking these steps, I can form a structured table from the data represented in the chart.\nBased on the above thought, the answer is:\nYear, Innovation Score, Patent Expenses\n1, 4.1, 1.5\n2, 5.2, 2.3\n3, 4.8, 2.1\n4, 6.0, 2.8\n5, 7.3, 3.2\n6, 5.6, 3.0\n7, 7.9, 3.6\n8, 8.4, 3.9"
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "E47LNl87LopLTTOB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the provided data, what kind of relationship can be identified between the innovation score and patent expenses?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish the relationship between the innovation score and patent expenses, I need to look at the trends in the {Innovation Score line plot}<r>[0.188, 0.077, 0.943, 0.381]</r> and {Patent Expenses line plot}<r>[0.188, 0.553, 0.943, 0.850]</r>. It can be noticed that when the innovation score increases, patent expenses also increase and vice versa indicating a positive correlation. This pattern can be observed throughout the data points.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, a positive correlation can be identified between the innovation score and patent expenses. When the innovation score rises, so does the patent expenses, and when it falls, patent expenses also reduce. This trend suggests that higher innovation scores are associated with higher patent expenses, and lower innovation scores are coupled with lower patent expenses."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "Qd4PCflgB9lZ57LL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the innovation score highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I find this by looking at the trend of {Innovation Score line plot} <r>[0.188, 0.077, 0.943, 0.381]</r>. The graph coordinates are normalized between 0 and 1. I observe the highest point on this plot happens at {the point}<r>[0.943, 0.077]</r>, which is tied to the 8th year of this plot, telling me that the point of highest innovation score in the given years is 8, derived from {the x-axis tick label} <r>[0.943, 0.413]</r>.\nBased on the above thought, the answer is:\nYear 8."
+            }
+        ],
+        "id_raw": 584
+    },
+    {
+        "id": "ewtN4B1GK1J7cHZE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the performance on the Transportation Volume from the bar chart into a comprehensible table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I distinguish between each series on the {chart's x-axis}<r>[0.104, 0.394, 1.111, 0.394]</r> which represents different years. Subsequently, I observe the volume indicator on the {chart's y-axis}<r>[0.150, 0.025, 0.150, 0.394]</r> that facilitates the translation of bar heights into exact values. For each year and transport category, I evaluate {the corresponding bars}<r>[0.188, 0.116, 0.943, 0.266]</r>, <r>[0.188, 0.079, 0.943, 0.228]</r> detailing the volume transported which assists in populating the table accordingly.\nBased on the above thought, the answer is:\nYear,Truck Transport,Goods Delivery,Marine Transport,Aviation Transport,Logistics Service\n1,1500,1600,1300,1100,1200\n2,1550,1650,1350,1200,1250\n3,1600,1680,1400,1300,1300\n4,1650,1700,1450,1400,1350\n5,1670,1750,1500,1450,1400\n6,1700,1800,1550,1500,1450\n7,1750,1830,1600,1550,1500\n8,1800,1900,1650,1600,1550\n9,1850,1950,1700,1650,1600\n10,1900,2000,1750,1700,1650"
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "bVY8VByTs3vxLa83",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich transport category exhibits the most consistent growth over the 10-year period in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the transport category with the most consistent growth, it's critical to evaluate each of {the lines}<r>[0.188, 0.116, 0.943, 0.266]</r>, <r>[0.188, 0.079, 0.943, 0.228]</r> representing different transport services across the 10-year span. I analyze the slope of each line to determine the uniformity and steadiness of growth. Observing the incremental changes year-over-year tells me which category maintained a steady increase without significant drops or erratic jumps.\nBased on the above thought, the answer is:\nThe Aviation Transport category exhibits the most consistent growth over the 10-year period, as its line shows a steady and uniform ascent with no drops in growth every consecutive year."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "AlyTdRGNPEtAXj1i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total annual increase in Truck Transport from year 1 to year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total annual increase, I first extract the values for Truck Transport from each year by tracking {the line for Truck Transport}<r>[0.188, 0.116, 0.943, 0.266]</r>. The values starting from year 1 to year 10 are 1500, 1550, 1600, 1650, 1670, 1700, 1750, 1800, 1850, 1900. The annual increments are found by subtracting each consecutive year's value from the previous year's value. By summing these differences, I arrive at the total increase.\nBased on the above thought, the answer is:\n400."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "lYMQUQpA7qvJ9XkX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data of the different techniques for the number of patients and success scores into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate tabular data from the line chart, I first identify each subplot by looking at {the ylabel}[0.091, 0.175, 0.109, 0.273] in the first subplot and {the ylabel}[0.091, 0.643, 0.109, 0.737] in the second subplot. They indicate the names of the techniques: 'Baseline' and 'SoftMoE'. I then examine the line for each subplot by analyzing {the line points of Baseline}[0.188, 0.319, 0.443, 0.376, 0.692, 0.300, 0.943, 0.129] and {the line points of SoftMoE}[0.188, 0.823, 0.443, 0.785, 0.692, 0.709, 0.943, 0.633] to match x-axis values with the relevant 'number of patients' and y-axis values with the appropriate 'scores' for each technique. After collecting all this data, I can create a structured table.\nBased on the above thought, the answer is:\nTechnique,Number of Patients (500),Scores (500),Number of Patients (1000),Scores (1000),Number of Patients (1500),Scores (1500),Number of Patients (2000),Scores (2000)\nBaseline,500,80,1000, 77,1500,81,2000,90\nSoftMoE,500,78,1000,80,1500,84,2000,88"
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "fBEG7wKCSV1W9Jf9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technique, between Baseline and SoftMoE, indicates a better trend of success score as the number of patients increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to review the trend in success scores for each technique as the number of patients increases. Observing {the line for Baseline}[0.188, 0.319, 0.943, 0.129], it initially decreases then slightly increases, but decreases again toward the end. For {SoftMoE}, the line [0.188, 0.823, 0.943, 0.633] shows a consistent decrease over time. However, even though SoftMoE decreases, its lowest point is still higher than the lowest point of the Baseline. So, despite the decrease, SoftMoE presents slightly better results.\nBased on the above thought, the answer is:\nThe SoftMoE technique indicates a better trend of success score as the number of patients increases. This is because, even though SoftMoE's success score decreases as the number of patients increases, its lowest point is still higher than the lowest point of the Baseline. Therefore, SoftMoE presents slightly better results."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "l98LGfrqw5LbL0ZJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the success score of SoftMoE than Baseline when the number of patients is 2000?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate {the point for Baseline at 2000 patients}[0.943, 0.129] and {the point for SoftMoE at 2000 patients}[0.943, 0.633]. The success score for Baseline at 2000 patients is 90. For SoftMoE at 2000 patients, the success score is 88. Therefore, Baseline has a 2 point higher success score than SoftMoE when the number of patients is 2000.\nBased on the above thought, the answer is:\n2 points."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "kIKgwlIfyQ9bslGE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you construct a table format with Methods as rows and Policies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data, the starting point should be to take note of {the x-axis labels}<r>[0.151, 0.896, 0.224, 0.920], [0.328, 0.896, 0.420, 0.920], [0.515, 0.896, 0.606, 0.920], [0.701, 0.896, 0.793, 0.920], [0.888, 0.896, 0.979, 0.920]</r> which signify the row labels or Methods. The rows are the different strategies and the baseline applied. The y-axis labels {for policy A}<r>[0.086, 0.102, 0.103, 0.332]</r> and {for policy B & C}<r>[0.075, 0.548, 0.093, 0.827]</r> indicate the different policies, which are our columns. Now, for the values, each dot on the line plots respective to {Strategy 1 line}<r>[0.187, 0.513, 0.934, 0.863]</r> and {Strategy 2 line}<r>[0.187, 0.513, 0.934, 0.595]</r> and {Baseline}<r>[0.187, 0.138, 0.934, 0.364]</r>  represents the policy values for each method.\nBased on the above thought, the answer is:\nMethods,Policy A (in Millions),Policy B (in Millions),Policy C (in Millions)\nBaseline,5.2,5.9,5.5\nStrategy 1,4.6,5.6,5.8\nStrategy 2,3.9,5.0,5.8\nStrategy 3,3.6,4.7,5.9\nStrategy 4,3.2,4.2,5.9"
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "AU4m3sG7U4HdLyYh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the effectiveness of the policies over different strategies in the line graphs, which policy seems to have a consistent decreasing trend? Please underline your findings."
+            },
+            {
+                "from": "gpt",
+                "value": "To establish which has a consistent downward trend, I need to inspect each line plot. Observing {Policy A's plot}<r>[0.187, 0.138, 0.934, 0.364]</r>, we see a decrease from 5.2 (Baseline) to 3.2 (Strategy 4). Moving on to {Policy B's plot}<r>[0.187, 0.513, 0.934, 0.863]</r>, there is also a drop from 5.9 to 4.2 from Baseline to Strategy 4. However, in {Policy C's plot}<r>[0.187, 0.513, 0.934, 0.595]</r>, while there's a slight increase from 5.5 to 5.9, the overall trend isn't decreasing.\nBased on the above thought, the answer is:\nBased on the line graphs, Policies A and B seem to have a consistent decreasing trend. However, Policy A demonstrates a sharper decline with the effectiveness dropping from 5.2 million to 3.2 million as compared to Policy B which drops from 5.9 million to 4.2 million from Baseline to Strategy 4. Hence, Policy A exhibits a more consistent and steeper decrease in effectiveness across the different strategies."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "VV28AaZExVBceJBO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in effectiveness between Strategy 1 and Strategy 4 for policy A?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the difference in effectiveness between the two strategies, my criterion is to focus on {the points on Policy A's graph}<r>[0.187, 0.138, 0.934, 0.364]</r>. The points are representative of the values of the effectiveness. For Strategy 1, the value is 4.6 while for Strategy 4, the value's 3.2. The difference is calculated as 4.6 - 3.2.\nBased on the above thought, the answer is:\n1.4 million."
+            }
+        ],
+        "id_raw": 588
+    },
+    {
+        "id": "69xUUxt3mPo6uDTR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table that includes the type of techniques and its corresponding scores for each category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I first identify {the x-axis labels}[[0.184, 0.880, 0.223, 0.920], [0.525, 0.880, 0.564, 0.920], [0.866, 0.880, 0.905, 0.920]] as the column headers, which represent the different techniques. The categories, {Innovative Ideas}[0.086, 0.197, 0.103, 0.388] and {Design Quality}[0.075, 0.622, 0.093, 0.792], are the row headers. {The line points for Innovative Ideas}[[0.184, 0.450], [0.525, 0.136], [0.866, 0.278]] and {for Design Quality}[[0.184, 0.864], [0.525, 0.550], [0.866, 0.676]] are then extracted to populate the respective values.\nBased on the above thought, the answer is:\nType, Method1, Method2, Method3\nInnovative Ideas, 1.2, 2.3, 1.8\nDesign Quality, 2.0, 3.5, 2.9"
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "aaToJvGhRfxr8W2H",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line plot, which Type appears to have the most significant change in score over the three methods and why do you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which Type has the most significant change in score over the three methods, I look at {the line for Innovative Ideas}[0.184, 0.136, 0.866, 0.450] which starts at 1.2 and ends at 1.8, and {the line for Design Quality}[0.184, 0.550, 0.866, 0.864] which starts from 2.0 and ends at 2.9. The change for Innovative Ideas is 1.8 - 1.2 = 0.6 and for Design Quality it is 2.9 - 2.0 = 0.9. Comparing the change in scores, Design Quality has a greater change compared to Innovative Ideas.\nBased on the above thought, the answer is:\nFrom the line plot, the 'Design Quality' type appears to have the most significant change in score over the three methods. This is because it had an increase of 0.9 from Method1 to Method3, which was larger than the 0.6 increase observed for 'Innovative Ideas' over the same methods."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "eF53nbQYAgvmyolh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increase did the Design Quality score have from Method1 to Method2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the amount of increase in score from Method1 to Method2 for Design Quality, I need to locate {the line points for Design Quality Method1}[[0.184, 0.864]] and Method2 [[0.525, 0.550]]. The corresponding values are 2.0 and 3.5 respectively. The difference between these two values will be the increase, so the increase is 3.5 - 2.0 = 1.5.\nBased on the above thought, the answer is:\n1.5."
+            }
+        ],
+        "id_raw": 589
+    },
+    {
+        "id": "xSc10i9LSctBdI2f",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table with years as columns and Industries as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a tabular representation of the line chart, I first observe {the x-axis labels}[0.048, 0.896, 1.083, 0.920], which represent the column headers or years. I recognize {the legend texts}[0.429, 0.439, 0.750, 0.463] as the row headers which differentiate the industries. To populate the cell values for each industry in the different years, I focus on {the line points for Information Technology} [0.188, 0.153, 0.943, 0.313] and for {Biotechnology}[0.188, 0.611, 0.943, 0.718]. I extract the respective values for each industry at different years.\nBased on the above thought, the answer is:\nIndustry,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014\nInformation Technology,400,240,180,140,120,100,80,60,40,20\nBiotechnology,100,180,180,180,160,140,120,100,80,60"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "rXF2G5IDgyo6DdsH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends displayed in the line chart, which industry seemed to have coped better with the declining revenue over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to study the decline in the revenue plots of both the industries. Looking at {the line for Information Technology}[0.188, 0.153, 0.943, 0.313], I see that it has an almost constant decrease in revenue from 2005 to 2014. Next, examining {the line for Biotechnology}[0.188, 0.611, 0.943, 0.718], it shows a steady phase from 2006-2008, and only then it begins a slow, steady decline. Therefore, despite both lines showing a declining trend, the rate and time of decrease differ.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Biotechnology industry appeared to have coped better with the declining revenues over time, compared to the Information Technology industry. Even though both industries had declining revenues, the decline for Biotechnology industry started later on and proceeded at a slower pace."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "clxvrxXp5akr0GNa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the total revenue between Information Technology and Biotechnology over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in total revenue between the two industries over the years, I first locate {the line points for Information Technology} [0.188, 0.153, 0.943, 0.313] and extract the corresponding values: 400, 240, 180, 140, 120, 100, 80, 60, 40, 20 and sum them up. Similarly, for {the line points for Biotechnology}[0.188, 0.611, 0.943, 0.718], I extract the corresponding values: 100, 180, 180, 180, 160, 140, 120, 100, 80, 60 and sum them up. The difference in total sum will give the difference in total revenue between the two industries over the years.\nBased on the above thought, the answer is:\n560"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "lL4b9Yk1mBLDWF8O",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract and tabulate the data from the line plot with the years as columns and the nature of trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can assist with that. Firstly, I identify {the x-axis tick labels}[[0.013, 0.906, 0.073, 0.930], [0.130, 0.906, 0.190, 0.930], [0.600, 0.906, 0.660, 0.930], [0.835, 0.906, 0.895, 0.930]], which represent the column headers or years. Subsequently, I identify {the legend texts}[[0.201, 0.491, 0.383, 0.514], [0.466, 0.491, 0.620, 0.514],[0.704, 0.491, 0.873, 0.514]] to get the row labels representing the different nature of trends. Finally, I locate {the line points}[[0.160, 0.181, 0.865, 0.363], [0.160, 0.181, 0.865, 0.409], [0.160, 0.687, 0.865, 0.855]], corresponding to each trend at the respective years and extract those values.\nBased on the above thought, the answer is:\nTrend,2018,2019,2020,2021\nOscillating trend, 52, 55, 57, 60\nUnpredictable decline, 60,57,54,50\nAccelerated growth, 50,55,61,68"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "b5g3nnUTWQfAZyf3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line plot, which trend showed the most decline over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend showed the most decline, I need to analyze the line plots for each trend. The {line for the Oscillating trend}[0.160, 0.181, 0.865, 0.363] shows a steady increase from 52 to 60, so it does not show a decline. The {line for Unpredictable decline}[0.160, 0.181, 0.865, 0.409] shows a decrease from 60 to 50, thereby indicating a decline. For {Accelerated growth}[0.160, 0.687, 0.865, 0.855], there is actually an increase from 50 to 68. Thus, the most decline is observed in the \"Unpredictable decline\" trend.\nBased on the above thought, the answer is:\nBased on the line plot, the \"Unpredictable decline\" trend showed the most decline over the years."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "YYvZRSp7QVRwVo4J",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score of the \"Oscillating trend\" across the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average score for the \"Oscillating trend\", I need to locate {the line points for the Oscillating trend}[[0.160, 0.363], [0.395, 0.295], [0.630, 0.250], [0.865, 0.181]] and extract the corresponding values: 52, 55, 57, 60. I sum up these scores and divide it by 4 (number of years) to calculate the average score.\nBased on the above thought, the answer is:\n56"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "EW5R0bpP0NigSxnB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please provide the data presented in the line chart as a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I need to examine the x and y coordinates of each line point. The line representing {Established Democracies}<r>[0.184, 0.147, 0.866, 0.416]</r> has data points corresponding to various levels of economic prosperity over time. The same method is applied to the line indicating {Young Democracies}<r>[0.184, 0.338, 0.866, 0.427]</r>. Additionally, the line for {Autocracies}<r>[0.184, 0.636, 0.866, 0.779]</r> provides economic prosperity data for those types of governments. Each point on the lines corresponds to a specific year, which act as row headers, and the y-value represents the level of economic prosperity.\nBased on the above thought, the answer is:\nYears,Established_Democracies,Young_Democracies,Autocracies\n3, 4.1, 4.5, 2\n6, 4.9, 4.8, 1.8\n9, 6.1, 4.3, 1.6\n12, 6.5, 4, 1.5"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "vksBY76SPLUwZdMO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which form of government showed the most stability in terms of economic prosperity over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discern which government showcased the most stability in economic prosperity, I need to evaluate three lines: first, {Established Democracies}<r>[0.184, 0.147, 0.866, 0.416]</r> which showed a steady increase in prosperity from 4.1 to 6.5; second, {Young Democracies}<r>[0.184, 0.338, 0.866, 0.427]</r> which showed a slight fluctuation in prosperity between 4.5 and 4.0; and third, {Autocracies}<r>[0.184, 0.636, 0.866, 0.779]</r> which showed a steady decrease in prosperity from 2.0 to 1.5. It can be noted that autocracies, despite the decrease, had the smallest change in prosperity over the years, indicating the most stability.\nBased on the above thought, the answer is:\nThe economic prosperity of Autocracies appears to be the most stable over the years as indicated in the line chart. Despite the overall downward trend, the extent of change was minimal, therefore suggesting a relative stability in terms of economic prosperity."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "msE2zbApt174OPRL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has economic prosperity in established democracies changed from year 3 to year 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the changes in economic prosperity in established democracies, I need to look at the line points for {Established Democracies}<r>[0.184, 0.147, 0.866, 0.416]</r>. The first point corresponds to year 3 with prosperity of 4.1 and the last point is year 12 with prosperity of 6.5. The difference of economic prosperity between these years is therefore 6.5 - 4.1 = 2.4.\nBased on the above thought, the answer is:\n2.4."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "kob7yRmI5q2LwmL0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table based on the horizontal bar chart that includes the periods as columns and the different aspects of international trade as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I locate {the categories on the y-axis}[[0.15, 0.625, 0.25, 0.65], [0.15, 0.525, 0.25, 0.55], [0.15, 0.425, 0.25, 0.45], [0.15, 0.325, 0.25, 0.35], [0.15, 0.225, 0.25, 0.25]] which represent trade aspects such as 'Import Export', 'Free Trade Agreements', 'Facilitation Measures', 'Global Supply Chains', and 'Trade Wars'. Each aspect has {bars for time periods}[[0.15, 0.625, 0.4, 0.7], [0.45, 0.625, 0.7, 0.7], [0.75, 0.625, 1.0, 0.7]] indicating the scores at 'Time 0', 'Time 20', and 'Time 40'. I proceed to extract and organize these values to form a table.\nBased on the above thought, the answer is:\nAspect,Time 0,Time 20,Time 40\nImport Export,200,500,380\nFree Trade Agreements,150,300,330\nFacilitation Measures,180,280,290\nGlobal Supply Chains,220,400,420\nTrade Wars,100,500,540"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "izXH6aYctFrvdGmX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on changes between the time intervals, which trade aspect shows the most significant growth from Time 0 to Time 40?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the growth for each aspect, I specifically look at the length and position of {the bars for each category}[[0.15, 0.225, 0.4, 0.25], [0.75, 0.225, 1.0, 0.25]], [0.15, 0.425, 0.4, 0.45], [0.75, 0.425, 1.0, 0.45]], noticing the growth patterns. 'Trade Wars' starts at a score of 100 at Time 0 and ends with a score of 540 at Time 40, which is a 440 points increase. Comparatively, other aspects such as 'Import Export', 'Free Trade Agreements', and 'Facilitation Measures' show growth but markedly less than 'Trade Wars'.\nBased on the above thought, the answer is:\nThe aspect that shows the most significant growth from Time 0 to Time 40 is 'Trade Wars'. It exhibits a dramatic surge increasing by 440 points, notably higher than any other trade aspect, reflecting the major changes and impacts in this area."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "O2uzSIYMd5sFu8Gp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score for the 'Global Supply Chains' across the three time periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average score for 'Global Supply Chains' which is indicated by {the bars}[[0.15, 0.325, 0.4, 0.35], [0.45, 0.325, 0.7, 0.35], [0.75, 0.325, 1.0, 0.35]], I need to calculate the values at each time point, which are 220, 400, and 420 respectively. I add these values and divide by the number of time points, which is 3.\nBased on the above thought, the answer is:\n346.67"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "m00M7rsr5bFKyhqi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table that captures the changes in risk levels for different areas over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step is to identify the x-axis which represents the different Risk Areas. The x-axis labels can be identified within {the x-axis tick marks}[[0.033, 0.447], [0.214, 0.447], [0.396, 0.447], [0.577, 0.447], [0.759, 0.447], [0.940, 0.447]]. Each tick mark represents a discrete Risk Area from 1 to 10. Next, we move to the various lines present in the charts, each representing a different type of risk over time. The Security Risk line is found at {the Security Risk line with coordinates}[[0.124, 0.057, 0.940, 0.415]]. The values for this risk type can be gathered from the y-axis points associated with it. The Legal Risk line is present at {the Legal Risk line location}[[0.124, 0.508, 0.940, 0.777]], and the values for Legal Risk can be taken from its y-axis points. Lastly, the Financial Risk line is found at {the Financial Risk line location}[[0.124, 0.630, 0.940, 0.876]], and its values can be got from the corresponding y-axis points.\nBased on the above thought, the answer is:\nRisk_areas,Security_Risk,Legal_Risk,Financial_Risk\n1,5,8,4\n2,4.7,8.5,4.5\n3,4.4,7.4,4.9\n4,4.2,7.1,5.17\n5,4.1,6.3,5.3\n6,4,5.8,5.7\n7,3.5,5.6,6\n8,3,5.5,6.3\n9,2.5,5.4,6.9\n10,2.3,5.2,7"
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "rIIZrIfzECz8rp2z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which risk type shows the most significant decline over the period considered?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the risk type that exhibits the most considerable decline, I need to look at the slopes of the lines. The {Security Risk line}[0.124, 0.057, 0.940, 0.415] starts high and then drops significantly, suggesting a substantial decrease. Comparatively, {Legal Risk line} [0.124, 0.508, 0.940, 0.777] and {Financial Risk line} [0.124, 0.630, 0.940, 0.876] also show a decline, but the downward slope isn't as steep as that of the Security Risk line.\nBased on the above thought, the answer is:\nThe chart suggests that the Security Risk saw the most significant decline over the period. This conclusion is based on the steeper downward slope of the Security Risk line compared to the Legal Risk and Financial Risk lines."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "JBevNHQfNJA6F7hO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the reduction in Security Risk from Risk Area 1 to Risk Area 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the reduction in Security Risk from Risk Areas 1 to 10, I need to identify the y-coordinates for these points. At {Risk Area 1}[0.124, 0.447], the y-coordinate for Security Risk is 5.0. Similarly, at {Risk Area 10}[0.940, 0.447], the y-coordinate for Security Risk is 2.3. The decrease in risk from Risk Area 1 to Risk Area 10 can be calculated by subtracting 2.3 from 5.0.\nBased on the above thought, the answer is:\n2.7."
+            }
+        ],
+        "id_raw": 594
+    },
+    {
+        "id": "umcXz3SOcLIxcJ0U",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the line chart into a table format with time intervals as columns and financial markets as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form a table from the line chart, I look first at {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.291, 0.773, 0.378, 0.903], [0.739, 0.773, 0.857, 0.951]], which represent the column headers or time intervals. Then I identify {the legend texts}[[0.867, 0.031, 0.953, 0.058], [0.867, 0.066, 0.927, 0.093], [0.867, 0.101, 0.948, 0.128], [0.867, 0.136, 0.955, 0.163], [0.867, 0.171, 0.951, 0.198]] as the row labels representing the different financial markets. To populate the values, I locate {the line points}[[0.102, 0.456], [0.334, 0.579], [0.798, 0.519]] for each market at the respective time intervals and extract the values from it.\nBased on the above thought, the answer is:\nFinancial_Market,Time_0,Time_20,Time_40\nNew York Stock Exchange,1000,1200,1050\nNASDAQ,2000,2400,2200\nLondon Stock Exchange,1800,2100,1900\nTokyo Stock Exchange,1500,1650,1600\nShanghai Stock Exchange,1300,1400,1250"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "zypwaHmErBv1mQWV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of direction and volatility, how would you characterize the trends of these financial markets from Time 0 to Time 40 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart illustrates different trend patterns for the five financial markets. Looking at {the line for New York Stock Exchange}[0.102, 0.599, 0.798, 0.525], there's a volatile upward trend from 1000 to 1200, followed by a decrease to 1050, indicating variability in the trend. \nFor {NASDAQ}[0.102, 0.674, 0.798, 0.588], the market shows a rapid growth from 2000 to 2400, followed by a slight decrease to 2200, a mostly upward trend with minor volatility. \nIn the case of {London Stock Exchange}[0.102, 0.621, 0.798, 0.509], it steadily increases from 1800 to 2100, then decreases to 1900, indicating a consistent trend with mild volatility. \n{The Tokyo Stock Exchange}[0.102, 0.556, 0.798,0.547] rises slightly from 1500 to 1650, then dips minimally to 1600, illustrating an erratic trend with minimal volatility. \nFinally, {The Shanghai Stock Exchange}[0.102, 0.512, 0.798, 0.496] initially rises from 1300 to 1400 and then drops substantially to 1250, displaying a downward volatile trend.\nBased on the above thought, the answer is:\nBased on the line chart, the trends of these financial markets from Time 0 to Time 40 can be characterized as follows: The New York Stock Exchange shows a volatile growth trend. NASDAQ demonstrates a rapid growth pattern with minimal volatility, London Stock Exchange consistently grows with moderate volatility, Tokyo Stock Exchange exhibits an erratic trend with minimal variations, and the Shanghai Stock Exchange has a substantial decrease trend with significant volatility."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "POtiYJGbRKxza9gG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in NASDAQ from Time 0 to Time 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to locate {the line points for NASDAQ}[[0.102, 0.456], [0.334, 0.579], [0.798, 0.519]]. The value at Time 0 is 2000 at coordinates [0.102, 0.456], and the value at Time 20 is 2400 at coordinates [0.334, 0.579]. The increase can be calculated by subtracting the initial value from the final value, which is 2400 - 2000 = 400.\nBased on the above thought, the answer is:\n400"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "4y0m0Uj5bEZiX6nX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the bar chart comparing different government policies' evaluations over 2020 and 2021 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I see that {the x-axis labels}<r>[0.150, 0.342, 0.981, 0.585]</r> contain the names of the government policies while {the bar heights}<r>[0.150, 0.637, 0.943, 0.880]</r> denote the evaluation scores for each policy in the years 2020 and 2021. Extracting the data from these noted positions will help form the table.\nBased on the above thought, the answer is:\nPolicy,Score in 2020,Score in 2021\nGreen Energy Act,78,82\nElectric Car Subsidy,70,73\nAffordable Housing Act,65,68\nMinimum Wage Increase,60,62\nClimate Change Act,80,85\nWork-from-Home Credits,82,86\nFree College Strive Act,77,80\nCarbon Tax,90,50\nUniversal Medicare Act,95,98"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "cIvoVPK9HrmUvaC7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich government policy showcased the highest increase in evaluation from 2020 to 2021 as per the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the changes per each policy by examining {the bars for each policy}<r>[0.150, 0.342, 0.981, 0.585]</r>, it shows that the 'Work-from-Home Credits' policy has a significant upward shift in evaluation scores from 82 in 2020 to 86 in 2021. Analyzing other policies confirms this one has the highest absolute growth.\nBased on the above thought, the answer is:\nThe 'Work-from-Home Credits' policy had the highest increase in evaluation, moving from an 82 in 2020 to 86 in 2021, indicating its effective implementation and positive reception."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "rVFOXExKbLghGkjx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in the evaluation score for the Carbon Tax policy from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the bars for the Carbon Tax policy}<r>[0.377, 0.660, 0.943, 0.527]</r>, the evaluation score decreased from 90 in 2020 to 50 in 2021. The percentage decrease can be calculated using the formula: ((Initial score - Final score) / Initial score) * 100.\nBased on the above thought, the answer is:\n44.44%"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "88Z7m88yWEI0dxl0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend for each transportation method with respect to time, from the line chart, can you tabulate the data from the line chart of operational efficiency vs time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from the line chart, firstly {the x-axis labels}<r>[0.066, 0.896, 0.077, 0.920] ,[0.177, 0.896, 0.199, 0.920], [0.293, 0.896, 0.315, 0.920], [0.409, 0.896, 0.431, 0.920]</r>, referring to the time points, form the columns of the table. {The line color in the legend}<r>[0.332, 0.430, 0.800, 0.472]</r> corresponds to each category, namely 'Baseline', 'Method A', and 'Method B'. Now, to fill the table, we need to identify {the points on the lines}<r>[0.188, 0.754], [0.362, 0.778], [0.653, 0.790], [0.943, 0.802], [0.188, 0.658], [0.362, 0.718], [0.653, 0.730], [0.943, 0.742], [0.188, 0.537], [0.362, 0.597], [0.653, 0.633], [0.943, 0.646]</r> for the corresponding efficiency levels.\nBased on the above thought, the answer is:\nTime,Baseline,Method A,Method B\n20,67,75,85\n50,65,70,80\n100,64,69,77\n150,63,68,76"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "Htl260Ol53d9GU6B",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method showed the most improvement in Operational Efficiency from 20 to 150 employees?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the efficiency increase for each method from 20 to 150 employees. For {Baseline}<r>[0.188, 0.754],[0.943, 0.802]</r>, the efficiency changed from 67 to 63. For {Method A}<r>[0.188, 0.658],[0.943, 0.742]</r>, the efficiency changed from 75 to 68 and for {Method B}<r>[0.188, 0.537],[0.943, 0.646]</r>, the efficiency changed from 85 to 76. We can compute the percentage change for each method and compare the values.\nBased on the above thought, the answer is:\nMethod B showed the most improvement in Operational efficiency as it had the smallest decrease in rate amongst all three methods from 20 to 150 employees."
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "cYyYUoX1kluLsQqn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in efficiency from Baseline to Method B for 100 employees?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in efficiency from Baseline to Method B for 100 employees, I would first identify {the points related to 100 employees on the Baseline}<r>[0.653, 0.790]</r> and {Method B}<r>[0.653, 0.633]</r>. The value for Baseline at 100 employees is 64 and for Method B, it's 77. The percentage increase can be calculated as: ((77-64)/64) * 100.\nBased on the above thought, the answer is:\n20.31%"
+            }
+        ],
+        "id_raw": 597
+    },
+    {
+        "id": "3aNTBVAgAijao1bh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line plot data representing the internet and mobile users in different years to a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line plots, I first identify {the x-axis tick labels}[[0.188, 0.896, 0.218, 0.920], [0.377, 0.896, 0.407, 0.920], [0.566, 0.896, 0.596, 0.920], [0.755, 0.896, 0.785, 0.920], [0.943, 0.896, 0.974, 0.920]] as the row labels representing the different years. I then locate the labels for the y-axes, which are {Internet Users}[[0.069, 0.091, 0.087, 0.354]] and {Mobile Users}[[0.069, 0.568, 0.087, 0.815]] as the column headers. To find the corresponding values for each year, I examine {the points on the Internet Users line}[[0.188, 0.384], [0.377, 0.333], [0.566, 0.259], [0.755, 0.161], [0.943, 0.039]] and {the points on the Mobile Users line}[[0.188, 0.873], [0.377, 0.860], [0.566, 0.797], [0.755, 0.663], [0.943, 0.526]].\nBased on the above thought, the answer is:\nYear,Internet Users (Million),Mobile Users (Million)\n2000,360.985,120.556\n2005,1035.701,340.215\n2010,2024.307,1434.168\n2015,3313.352,3735.157\n2020,4932.944,6103.000"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "TtFjKmKWhGC5opED",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the trends of internet and mobile users from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends, I need to observe the lines representing {Internet Users}[[0.188, 0.039, 0.943, 0.384]] and {Mobile Users}[[0.188, 0.526, 0.943, 0.873]]. Both trends are increasing, indicating more internet and mobile users over the years. However, the increase for mobile users is steeper, implying a faster growth rate compared to internet users. In particular, the gap between the number of internet and mobile users appears to be widening over time, suggesting an increasing preference for mobile usage over internet usage.\nBased on the above thought, the answer is:\nBased on the trends, both internet and mobile users have increased from 2000 to 2020. However, the growth in mobile users is significantly more pronounced. This suggests that more people started using mobiles at a higher rate compared to the internet within this timeframe, possibly indicating a shift in end-user preferences towards mobile technology."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "z8KfVUQWNLTYIM9t",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more mobile users were there in 2020 compared to internet users?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the number of mobile and internet users in 2020. Looking at {the point representing internet users in 2020}[[0.943, 0.039]], the value is 4932.944 million. Equally, {the point representing mobile users in 2020}[[0.943, 0.526]] represents a value of 6103 million. The difference is 6103 - 4932.944 = 1170.056.\nBased on the above thought, the answer is:\n1170.056 million."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "ygqsEG4CI7PXOSHd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the line chart, can we convert the data into a tabular format with the given timespan as columns and types of energy as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the development of the table from the line chart, the first step is to recognize {the x-axis tick labels}[[0.083, 0.985, 0.145, 0.984], [0.271, 0.985, 0.344, 0.984], [0.469, 0.985, 0.533, 0.984], [0.660, 0.985, 0.734, 0.984], [0.857, 0.985, 0.922, 0.984]] as the column headers, which correspond to the timespan in years. Then, {the legend texts}[[0.797, 0.046, 0.899, 0.059], [0.797, 0.059, 0.899, 0.072], [0.797, 0.072, 0.899, 0.085], [0.797, 0.085, 0.899, 0.098]] represent the row labels, which correspond to the different energy types. To fill up the values, every energy type at the respective timespan, defined by {the line points}[[0.173, 0.638], [0.362, 0.524], ..., [0.943, 0.716]] needs to be identified and the values annotated to be extracted.\nBased on the above thought, the answer is:\nEnergy Type,2000,2005,2010,2015,2020\nHousehold Robots,2.6,3.1,3.8,2.9,3.0\nAndroid Assistants,3.2,3.8,4.0,3.6,4.0\nSmart Home,3.5,4.0,3.9,3.7,3.6\nAI Services,2.9,3.5,4.2,4.0,4.1"
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "8T8Vic20ImfQzzo1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which technology appears to have the most stable progression over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which technology exhibits the most stable progression, I need to compare the fluctuations in the line plots for each technology. Examining {the line for Household Robots}[0.124, 0.382, 0.940, 0.303] shows considerable fluctuation in the score from 2.6 to 3.8 and back to 3.0, indicating more instability.\nLooking at {Android Assistants}[0.124, 0.303, 0.940, 0.171], also presents a fluctuating trend though with slight changes, indicating moderate stability.\nHowever, upon analyzing {the line for Smart Home}[0.124, 0.659, 0.940, 0.635], the score ranges from 3.5 to 3.8 then reduces slightly, showing minor fluctuations.\nFor {AI Services}[0.124, 0.707, 0.940, 0.793], the line plot reveals drastic unexpected drop in the end after consistent rising trend.\nBased on the above thought, the answer is:\nBased on the trends indicated in the line chart, 'Smart Home' technology seems to have the most stable progression over time. The plot line representing Smart Home displays a minor fluctuation, indicating less volatility compared to the Household Robots, Android Assistants, and AI Services. The gradual increase and minor fluctuations in scores across the time periods indicate a consistent and stable progression."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "9as2eJXCewX4hX0F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_30_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total change in Android Assistants' score from the start to the end of the timespan?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change in the Android Assistants' score from the start to the end, I need to identify {the line points for Android Assistants}[[0.124, 0.303], [0.214, 0.224], [0.305, 0.198], [0.396, 0.250], [0.487, 0.198], [0.759, 0.237], [0.940, 0.171]], and extract the corresponding scores: 3.2, 3.8, 4.0, 3.6, 4.0, 3.7, 4.2. The initial value is 3.2 at {Timespan 1}[0.124, 0.935] and the final value is 4.2 at {Timespan 10}[0.940, 0.935]. So, the total change is 4.2 - 3.2 = 1.0.\nBased on the above thought, the answer is:\n1.0."
+            }
+        ],
+        "id_raw": 599
+    },
+    {
+        "id": "qmfj6CefN6vK1RDJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the raw data from the line chart into a tabulated form that shows the Community Development Budget and number of Local Governments for each city?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data to a table, I first identify {the x-axis tick labels}[0.057, 0.909, 0.988, 0.932] as the row labels, representing different cities. Then, {the legend texts}[[0.517, 0.060, 0.570, 0.090], [0.850, 0.060, 0.937, 0.090]] split the data into Community Development Budget and Number of Local Governments. Values for these two measures will be derived from the coordinates of {the line points for Community Development Budget}[0.079, 0.367, 0.958, 0.643] and {Number of Local Governments}[0.079, 0.496, 0.958, 0.791].\nBased on the above thought, the answer is:\nCity,Community Development Budget,Number of Local Governments\nPhiladelphia,17000,13000\nFebruary,17500,15000\nMarch,18000,14500\nApril,23000,15000\nMay,23500,16000\nJune,24000,17000\nJuly,23800,17300\nAugust,23500,18000\nSeptember,23000,18500\nOctober,23500,19000\nNovember,24000,20000\nDecember,24500,21000"
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "sfWqPM3yu8wNPCaD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which month saw a significant drop in the Community Development Budget?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the Community Development Budget line, {April - May}[0.318, 0.423, 0.398, 0.404] sees a significant drop in the budget as denoted by the sharp downward trend.\nBased on the above thought, the answer is:\nThe month of May observed a significant drop in the Community Development budget. A sharp downward trend from April to May in the Community Development Budget line signifies this."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "1w3xQiJt8JXpUWHp",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the increase in Budget for Community Development from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the budget increase for Community Development, I need to look at values of the {January}[0.079, 0.643] and {December}[0.958, 0.367] line points for Community Development. The difference between the two gives the increase in budget from January to December.\nBased on the above thought, the answer is:\n7500."
+            }
+        ],
+        "id_raw": 600
+    },
+    {
+        "id": "PyAefgqlcwBSZjoc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data shown on this graph in a table, with country names and their respective yearly electricity consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data, I'll first extract the {x-tick labels}<r>[0.042, 0.932, 0.071, 0.949], [0.144, 0.932, 0.173, 0.949], [0.246, 0.932, 0.275, 0.949], [0.347, 0.932, 0.377, 0.949], [0.449, 0.932, 0.478, 0.949], [0.551, 0.932, 0.580, 0.949], [0.653, 0.932, 0.682, 0.949], [0.755, 0.932, 0.784, 0.949], [0.856, 0.932, 0.886, 0.949], [0.958, 0.932, 0.988, 0.949]</r> representing the years. I'll examine the line plots for each country labeled as {line1 in subplot 0}<r>[0.057, 0.394, 0.973, 0.806]</r>, {line2 in subplot 0}<r>[0.057, 0.279, 0.973, 0.546]</r>, {line3 in subplot 0}<r>[0.057, 0.150, 0.973, 0.391]</r>, and {line4 in subplot 0}<r>[0.057, 0.306, 0.973, 0.658]</r> to gather and associate the data points representing the forecasted electricity consumption for the respective years.\nBased on the above thought, the answer is:\nCountry, 2020, 2021\n  Country 1, %, %\n  Country 2, %, %\n  Country 3, %, %\n  Country 4, %, %"
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "z01O38IHbnPcwyn8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the country with the least volatile electricity consumption between 2020 and 2021 and provide an explanation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the country with the least volatility in their electricity consumption, I need to review the changes in each country's forecasted electricity consumption in {line1 in subplot 0}<r>[0.057, 0.394, 0.973, 0.806]</r>, {line2 in subplot 0}<r>[0.057, 0.279, 0.973, 0.546]</r>, {line3 in subplot 0}<r>[0.057, 0.150, 0.973, 0.391]</r>, and {line4 in subplot 0}<r>[0.057, 0.306, 0.973, 0.658]</r> from 2020 to 2021. The country with the least volatility is likely the one with the smallest difference between the electricity consumed in the two consecutive years.\nBased on the above thought, the answer is:\nThe country represented by line X seems to have the least volatile electricity consumption as its change in consumption from 2020 to 2021 is the smallest when compared to other countries on the graph. This suggests a stable resource allocation policy by the country and efficient energy use."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "z9TPjb3CqWFe7852",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, by how much did the electricity consumption for the country represented by the third line (line3 in subplot 0) increase from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I should locate {line3 in subplot 0}<r>[0.057, 0.150, 0.973, 0.391]</r> which represents the data for the third country. I then find the y-values of the points for the years 2020 and 2021, respectively. The difference between these two values will give me the increase in electricity consumption for the corresponding country.\nBased on the above thought, the answer is:\nThe electricity consumption for the country increased by %."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "3k6KZz2ORvNSMz8s",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart presented, can you assemble the Average Index score for each category: ICT, Biotech, Startups, and Patents at each of the two points: Trend and Outliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I begin by identifying {the legend labels}[[0.499, 0.048, 0.525, 0.078],[0.605, 0.048, 0.668, 0.078],[0.748, 0.048, 0.818, 0.078], [0.898, 0.048, 0.960, 0.078]], which represent the row labels or categories: ICT, Biotech, Startups, and Patents respectively. \nFor column headers, I look to {the x-axis tick labels}[[0.770, 0.909, 0.824, 0.932],[0.985, 0.909, 0.985, 0.909]], which are \"Trend\" and \"Outliers\". \nTo acquire the values, I locate {the points} for each category at the respective points [[0.609, 0.224], [0.797, 0.332]], [[0.609, 0.413], [0.797, 0.115]], [[0.609, 0.630], [0.797, 0.765]], [[0.609, 0.495], [0.797, 0.224]] and extract the corresponding scores.\nBased on the above thought, the answer is:\nCategory,Trend,Outliers\nICT,85,81\nBiotech,78,89\nStartups,70,65\nPatents,75,85"
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "bi738nBJGSbY6PR1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector, based on the plotlines, showed a decrease from Trend to Outliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will have to analyze the plotlines for all four sectors: ICT, Biotech, Startups, and Patents. Comparing the values at the Trend and Outliers points for each sector would allow me to determine this.\nLooking at {ICT's plotline}[0.609, 0.224, 0.797, 0.332], there's a slight decrease from Trend to Outliers. Biotech's line, by looking at {Biotech's plotline} [0.609, 0.413, 0.797, 0.115], shows an increase from Trend to Outliers. Similarly, by evaluating {Startups' plotline} [0.609, 0.630, 0.797, 0.765] and {Patents' plotline} [0.609, 0.495, 0.797, 0.224], both sectors portray an decrease from Trend to Outliers.\nBased on the above thought, the answer is:\nThe ICT and Startups sectors showed a decrease from Trend to Outliers according to the chart. They both had a higher index at Trend and then a lower index at Outliers, indicating a downward trend for these data points."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "YsG7W8qsG5zSDCjE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value difference between Biotech's Trend and Outlier scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I need to locate {the points for Biotech}[[0.609, 0.413], [0.797, 0.115]] and extract the corresponding values for \"Trend\" and \"Outliers\". The value corresponding to Trend is 78 and the value for Outliers is 89. I calculate the difference to be 89 - 78.\nBased on the above thought, the answer is:\n11."
+            }
+        ],
+        "id_raw": 604
+    },
+    {
+        "id": "64Z6t2YAMfeMCoZb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the investment trends over the years for each category in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data, I firstly locate the {x-axis tick labels}[[0.148, 0.909, 0.177, 0.932], [0.231, 0.909, 0.260, 0.932], [0.313, 0.909, 0.342, 0.932], [0.396, 0.909, 0.425, 0.932], [0.478, 0.909, 0.507, 0.932], [0.561, 0.909, 0.590, 0.932], [0.643, 0.909, 0.672, 0.932], [0.725, 0.909, 0.755, 0.932], [0.808, 0.909, 0.837, 0.932], [0.890, 0.909, 0.920, 0.932]] which represent the column headers, the years. Next, I identify {the legend texts}[[0.132, 0.050, 0.222, 0.080], [0.132, 0.094, 0.210, 0.124], [0.132, 0.138, 0.273, 0.168], [0.132, 0.182, 0.225, 0.212]] as the row labels that represent the different investment categories. To populate the values, for each category, I locate their respective {line points} and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Stock Market,Real Estate Market,Startup Investments,Bank Savings\n2007,10000,9500,8000,9900\n2008,6800,9000,7600,9800\n2009,9000,9300,7300,9700\n2010,8800,9100,7000,9600\n2011,9500,8800,7000,9800\n2012,10000,8500,7100,10200\n2013,11500,8600,7500,10250\n2014,12000,8700,7800,10300\n2015,12000,9400,8200,10500\n2016,12500,9500,8300,10600"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "3RMmqe2buaiEJzcC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment category appears to be the most stable from 2007 to 2016? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify, which investment category seems to be most stable, I need to examine the fluctuation in each line plot. {The line for the Stock Market}[0.163, 0.088, 0.905, 0.801] shows a considerable level of change as it initially decreases, then increases, and fluctuates quite a bit over the years. Similarly, {the lines for the Real Estate Market, Startup Investments, and Bank Savings}[0.163, 0.463, 0.905, 0.588][0.163, 0.613, 0.905, 0.776][0.163, 0.325, 0.905, 0.450] also depict a series of growth and decline. However, there are less dramatic fluctuations and slope changes concerning the line of {the Bank Savings}[0.163, 0.325, 0.905, 0.450], it represents a comparatively steady increase with minimal decrements throughout the years.\nBased on the above thought, the answer is:\nBased on the line chart, the Bank Savings appears to be the most stable investment category from 2007 to 2016. This is due to the relatively consistent, smooth increase of its value over the years, with minimal decrements, indicating less volatility and therefore more stability than the other investment categories."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "o5Goeb4d8PthSb6c",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the Stock Market from 2007 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in the Stock Market, I need to compare the first point and the last point in {the line for Stock Market}[[0.163, 0.088, 0.905, 0.801]]. In 2007, the Stock Market was at 10000 and in 2016, it's at 12500. So the total increase in the Stock Market from 2007 to 2016 is 12500 - 10000 = 2500.\nBased on the above thought, the answer is:\n2500."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "QWKJGlFnHjWQI8eu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the dataset visualized in the line chart be structured into a table format with Tasks as columns and different parameters as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "The visual organization of the line chart starts by identifying {the x-axis labels}<r>[0.060, 0.909, 0.068, 0.932], [0.162, 0.909, 0.169, 0.932], ..., [0.973, 0.909, 0.988, 0.932]</r> to establish the columns representing each Task. Rows for each parameter can be derived by observing {the legend}<r>[0.282, 0.021, 0.973, 0.106]</r> which categorizes data lines by Stakeholders, Raw Materials, etc. Data points extracted from {each line's coordinates}<r>[0.064, 0.266, 0.980, 0.395], [0.064, 0.328, 0.980, 0.611], ...</r> provide the values for each intersection of Task and parameter.\nBased on the above thought, the answer is:\nParameter,Task 1,Task 2,Task 3,Task 4,Task 5,Task 6,Task 7,Task 8,Task 9,Task 10\nStakeholders,62.2,62.31,65.97,66.78,69.45,71.45,73.6,75.91,75.92,78.06\nRaw Materials,35.68,38.58,45.23,48.96,56.21,58.32,62.14,64.86,66.57,70.5\nProduction,24.43,22.18,18.19,15.46,13.67,12.01,11.74,10.45,9.12,8.38\nLogistics,8.34,9.75,10.67,12.21,14.34,15.66,16.56,18.35,22.2,25.98\nSales,15.59,15.19,16.46,19.67,21.33,24.94,28.23,32.37,35.21,37.12"
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "wZs7c3HRGtcryiT5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parameter displayed in the chart shows the slowest growth rate over the tasks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the parameter with the slowest growth rate, I must look at the overall slope and data point positioning within the plotted lines for each parameter. Observing {the line for Stakeholders}<r>[0.064, 0.266, 0.980, 0.395]</r>, {Raw Materials}<r>[0.064, 0.328, 0.980, 0.611]</r>, and others, I note that {Production}<r>[0.064, 0.699, 0.980, 0.831]</r> depicts minimal increase in its points from around 24.43% to 8.38% across the tasks. This line exhibits a gradual and more consistent downward trend compared to others which fluctuate or ascend more significantly.\nBased on the above thought, the answer is:\nThe parameter 'Production' shows the slowest growth rate over the tasks. This parameter's line plot displays a gradual descent from its initial high, indicating a consistent decrease in production rates over time, as reflected in the progressively lowering values from Task 1 to Task 10."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "PuefGB7mi4ih3tXT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Task 10 from Task 1 for the category 'Sales'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in 'Sales' from Task 1 to Task 10, I analyze the values at these points extracted from {the line for Sales}<r>[0.064, 0.266, 0.980, 0.777]</r>. The specific values needed are {at Task 1}<r>[0.064, 0.774]</r> and {at Task 10}<r>[0.980, 0.599]</r> noting the respective percentage values at these tasks - 15.59% at Task 1 and 37.12% at Task 10. Calculating the difference, 37.12 - 15.59, reveals the increase.\nBased on the above thought, the answer is:\n21.53."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "i0b3KP8g5mWzgLSm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can we construct the structured data into a table format with Years as rows and Social Media Platforms as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table data from the line chart, the {x-axis tick labels}<r>[0.084, 0.909, 0.114, 0.932], ..., [0.930, 0.909, 0.960, 0.932]</r> represent the row labels or Years. The social media platforms are considered as column headers, identified from {the legend item's texts}<r>[0.918, 0.744, 0.973, 0.767], ..., [0.918, 0.849, 0.967, 0.872]</r>. The information of Monthly Active Users obtained from {line points} <r>[0.099, 0.423], ..., [0.945, 0.103], ... ,[0.099, 0.641], ... , [0.945, 0.449]</r> will fill the table.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram,LinkedIn\n2010,50,22.1,2.1,25.8\n2011,53.5,24.5,3.8,28.2\n2012,60.2,30.7,6.5,30.6\n2013,65.8,32.1,8.5,33.6\n2014,69.4,33.7,10.6,37.3\n2015,73.6,35.3,13.5,40.6\n2016,78.2,38.4,16.2,42.2\n2017,81.5,40.2,19.4,44.1\n2018,83.6,42.1,23.4,45.4\n2019,85.4,43.0,26.5,47.1"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "mobNFe9g4ihWRypc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which social media platform has the steadiest growth in user base from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social media platform has the steadiest growth, I need to analyse the growth rates in the line plots for each platform. First looking at {the line for Facebook}<r>[0.099, 0.103, 0.945, 0.423]</r>, it shows a somewhat consistent upward slope, indicating a steady growth rate.\nConsidering {the line for Twitter}<r>[0.099, 0.486, 0.945, 0.675]</r>, the plot shows a slightly irregular shape, suggesting a slightly inconsistent growth rate over years.\nFor {Instagram}<r>[0.099, 0.635, 0.945, 0.855]</r>, the line shows a steep upward trend, indicating a high growth rate yet not steady.\nLast, looking at {the line for LinkedIn}<r>[0.099, 0.449, 0.945, 0.641]</r>, the plot suggests a consistent growth rate similar to Facebook. However, comparing the two, it appears that Facebook has a smoother and more steady growth curve.\nBased on the above thought, the answer is:\nThe social media platform with the steadiest growth in user base from 2010 to 2019 seems to be Facebook. Its line plot shows a relatively consistent upward slope across the entirety of the depicted timeframe, suggesting an unchanging growth rate."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "k6cTXcDgXET10K4c",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased upon the line chart, can we identify the year when Instagram has its maximum user base?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year when Instagram has its maximum user base, I need to locate the highest point on {the line representing Instagram}<r>[0.099, 0.855, 0.945, 0.635]</r> and extract the corresponding year from the x-axis. Here, the maximum value for Instagram is 26.5 million monthly active users corresponding to the year 2019 from {the x-axis tick at this point}<r>[0.945, 0.893]</r>.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "1dkZzzDQWl2GN5BH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the percentage data by technology-based socialization activity into a structured table from the comparative bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this task, I first look at {the x-axis labels}<r>[0.066, 0.901, 0.085, 0.932]</r>; these represent the different categories of technology-based socialization. Then, I observe {the legend}<r>[0.390, 0.039, 0.973, 0.091]</r> where 'Youth Adult' and 'Senior Adult' are identified. The comparative percentages are determined by the lengths of the segments in the vertical bars that correspond to each group and activity. The visual interpretation of the segments yields the data for each category.\nBased on the above thought, the answer is:\nActivity,Youth Adult Percentage,Senior Adult Percentage\nConnectivity and Socialization,95,75\nSocial Media Usage,80,60\nSpeed of Learning New Technologies,90,45\nOnline Purchases,70,65\nOnline Entertainment,85,70"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "y7fLtU5lVHYJrBi6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology-based socialization activity shows the largest age-related disparity in engagement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To pinpoint the largest disparity, I compare the bar segment lengths for each activity for both 'Youth Adult' and 'Senior Adult'. Looking particularly at {the bar segments for 'Speed of Learning New Technologies'}<r>[0.260, 0.660]</r>, it shows a significant length difference, with Youth Adults considerably higher than Senior Adults. This stark contrast is visually more pronounced than in the other activities.\nBased on the above thought, the answer is:\nThe 'Speed of Learning New Technologies' activity shows the largest age-related disparity in engagement. The visual comparison of the bar segments in the chart indicates a much larger segment for Youth Adults compared to Senior Adults, signifying a larger rate of involvement by the younger age group in this particular technology-based activity."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "WTvZSr0OiBiU2Frv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in participation between 'Youth Adult' and 'Senior Adult' in 'Online Purchases'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the bar for Online Purchases for both age groups}<r>[0.450, 0.651]</r>, the Youth Adult participation is seen to be at 70% and Senior Adult participation at 65% by visually estimating the length relative to the y-axis scale. To calculate their difference, I perform 70 - 65.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "OdRhSJ55RdIwnxul",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please put the line chart data into a tabular format, categorizing by 'Risk Factors' and 'Scenarios'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To produce a table from the line chart, I first examine {the x-axis labels}<r>[0.061, 0.909, 0.114, 0.932]</r>,<r>[0.267, 0.909, 0.315, 0.932]</r>,<r>[0.467, 0.909, 0.522, 0.932]</r>,<r>[0.667, 0.909, 0.729, 0.932]</r> which denote the column headers or 'Scenarios'. Next, I consider {the legend texts} <r>[0.788, 0.117, 0.974, 0.147]</r>, <r>[0.788, 0.161, 0.971, 0.191]</r>, <r>[0.788, 0.205, 0.859, 0.235]</r>, <r>[0.788, 0.249, 0.908, 0.279]</r> as the row labels representing the different 'Risk Factors'. For the table values, I detect {point positions for lines} <r>[0.087, 0.113, 0.698, 0.388]</r>, <r>[0.087, 0.278, 0.698, 0.408]</r>, <r>[0.087, 0.548, 0.698, 0.631]</r>, <r>[0.087, 0.774, 0.698, 0.856]</r> for each risk factor at the given scenarios and interpret the values.\nBased on the above thought, the answer is:\nRisk Factor,Stagnant,Growing,Shrinking,Expanding\nPersistent Market Changes,65.13,81.74,90.98,88.03\nMajor Regulatory Changes,63.25,75.42,65.3,70.65\nLegal Risk,42.26,48.78,49.76,50.11\nNatural Disasters,21.18,28.82,25.21,27.65"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "HaW0NZ808P62Thf7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat risk factor has experienced a declining trend from 'Growing' to 'Shrinking' scenario? Elucidate."
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the risk factor which has seen a declining trend from 'Growing' to 'Shrinking' scenario, I need to study the position of {line points}<r>[0.087, 0.113, 0.698, 0.388]</r>,<r>[0.087, 0.278, 0.698, 0.408]</r>,<r>[0.087, 0.548, 0.698, 0.631]</r>,<r>[0.087, 0.774, 0.698, 0.856]</r> of each line for each risk factor. The line that depicts a downward shift from 'Growing' to 'Shrinking' scenario belongs to 'Major Regulatory Changes'. The risk assessment percentage decreases from 75.42 in the 'Growing' scenario to 65.3 in the 'Shrinking' scenario.\nBased on the above thought, the answer is:\nThe 'Major Regulatory Changes' risk factor has experienced a declining trend from 'Growing' to 'Shrinking' scenario. The risk assessment for this factor has decreased from 75.42% in the 'Growing' scenario to 65.3% in the 'Shrinking' scenario."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "i16ODfaPTMDSAEVr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total risk assessment across all scenarios for 'Major Regulatory Changes'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate {the line for 'Major Regulatory Changes'}<r>[0.087, 0.278, 0.698, 0.408]</r> and sum the risk assessment values corresponding to each scenario. These assessment values are: 63.25 for Stagnant, 75.42 for Growing, 65.3 for Shrinking, and 70.65 for Expanding.\nBased on the above thought, the answer is:\n274.62"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "HuA4sjTl2D0T0yjH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table with Sector as columns and Growth Rate as rows from the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table, I identify from the chart that {the x-axis tick labels}[[0.038, 0.800, 0.082, 0.865], [0.187, 0.800, 0.226, 0.858], [0.324, 0.800, 0.380, 0.878], [0.468, 0.800, 0.529, 0.884], [0.605, 0.800, 0.683, 0.904], [0.770, 0.800, 0.811, 0.861], [0.885, 0.800, 0.988, 0.932]] represent the columns corresponding to different sectors. The two types of growth rate, identified from {the subplot legend text}[[0.630, 0.059, 0.746, 0.089],[0.813, 0.059, 0.915, 0.089]], define the rows. To populate the values, I locate {the points on the line for 'Steady Recovery'}[[0.060, 0.704], [0.206, 0.651], [0.352, 0.581], [0.498, 0.528], [0.644, 0.493], [0.790, 0.440], [0.936, 0.387]] and {the points on the line for 'Rapid Increase'}[[0.060, 0.644], [0.206, 0.556], [0.352, 0.468], [0.498, 0.370], [0.644, 0.299], [0.790, 0.201], [0.936, 0.134]] and extract these values.\nBased on the above thought, the answer is:\nGrowth Rate,Finance,Health,Education,Agriculture,Transportation,Energy,Telecommunication\nSteady recovery,0.25,0.40,0.60,0.75,0.85,1.00,1.15\nRapid increase,0.42,0.67,0.92,1.20,1.40,1.68,1.87"
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "O08b01ZN2EAsq7zE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given line chart, which trend experienced a steeper increment in the 'Education' sector? Can you explain?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend with a steeper increment, I observe {the point for 'Steady Recovery' associated to 'Education'}[0.352, 0.581] and {the point for 'Rapid Increase' associated to 'Education'}[0.352, 0.468]. 'Steady Recovery' has a value of 0.60, while 'Rapid Increase' has a value of 0.92. Thus, the 'Rapid Increase' trend had a steeper increment in the 'Education' sector.\nBased on the above thought, the answer is:\nThe 'Rapid Increase' trend experienced a steeper increment in the 'Education' sector. Based on the plotted points in the line chart, the 'Rapid Increase' trend shows a higher value (0.92) in the 'Education' sector compared to the 'Steady Recovery' trend (0.60), indicating a more significant increase."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "Kd9Iqgypg5q3OEHV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increment is observed in the 'Rapid Increase' trend from the 'Health' sector to the 'Telecommunication' sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increment in the 'Rapid Increase' trend, I locate {the point on the line for 'Rapid Increase' corresponding to 'Health'}[0.206, 0.556] which has a value of 0.67 and {the corresponding point for 'Telecommunication'}[0.936, 0.134] which has a value of 1.87. The increment is then found by subtracting these two values: 1.87 - 0.67 = 1.20.\nBased on the above thought, the answer is:\n1.20."
+            }
+        ],
+        "id_raw": 612
+    },
+    {
+        "id": "CfrVfkqQq9ZWZnIx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the bar chart into a table format with categories as rows and '2025 Difference' (%) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the category labels given on {the y-axis tick labels}<r>[0.053, 0.806, 0.079, 0.823]</r>, <r>[0.066, 0.765, 0.084, 0.782]</r>, ..., <r>[0.149, 0.119, 0.173, 0.136]</r>. Then, each bar in the chart corresponds to a percentage difference in focus for each category. I will locate the bars for each category {Raw Material}<r>[0.095, 0.814, 0.478, 0.827]</r>, {Manufacturing}<r>[0.347, 0.773, 0.478, 0.787]</r>, ..., {Investment}<r>[0.128, 0.120, 0.476, 0.134]</r> and extract the values. The negative bars are colored red, and the positive bars are colored blue, indicating a negative or positive change in focus for that category. By extracting these values, I can summarize this data.\nBased on the above thought, the answer is:\nCategory, 2025 Difference (%)\nRaw Material,-20\nManufacturing,-10\nPackaging,-5\nTransport, 0\nStorage, 10\nMarketing, 15\nSales, 20\nCustomer Support, 25\nLogistics, 30\nProcurement, 35\nFinance, 40"
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "fkZ1oYuNhe200A8p",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the shift in focus of global trade and supply chain management based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the horizontal bar chart, I can observe that categories towards the upper part of the chart including 'Raw Material'<r>[0.095, 0.814, 0.478, 0.827]</r>, 'Manufacturing'<r>[0.347, 0.773, 0.478, 0.787]</r>, have negative values, implying that these areas are seeing less focus or priority. \nOn the contrary, categories towards the bottom of the chart like 'Finance'<r>[0.113, 0.253, 0.483, 0.267]</r>, 'Procurement'<r>[0.120, 0.193, 0.458, 0.206]</r>, 'Logistics'<r>[0.108, 0.307, 0.405, 0.321]</r>, 'Customer Support'<r>[0.115, 0.371, 0.380, 0.384]</r> have positive values, which indicate that these areas are gaining more attention and focus.\nBased on the above thought, the answer is:\nThe chart indicates a shift in focus in global trade and supply chain management. There is a decreasing focus on areas such as 'Raw Material' and 'Manufacturing', while areas such as 'Finance', 'Procurement', 'Logistics', and 'Customer Support' are gaining increased attention. This suggests that companies might be moving towards more intangible and service-oriented sectors, while traditionally important areas like raw material acquisition and production are losing dominance in relative terms."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "T5xd4u7aCyLaK2wA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the difference in focus for 'Finance' compared to 'Marketing'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to extract the difference in focus for both 'Finance' and 'Marketing'. I locate the bars for 'Finance'<r>[0.113, 0.253, 0.483, 0.267]</r> and 'Marketing'<r>[0.109, 0.559, 0.350, 0.573]</r>. The difference is approximately 40% for 'Finance' and approximately 15% for 'Marketing'. Thus, the difference between 'Finance' and 'Marketing' is 40 - 15 = 25.\nBased on the above thought, the answer is:\n25%."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "mEev0lMyeo5k6Omb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into the table format with \"Years\" as rows and \"Areas\" as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.049, 0.909, 0.078, 0.932], [0.163, 0.909, 0.192, 0.932], [0.277, 0.909, 0.306, 0.932], [0.390, 0.909, 0.419, 0.932], [0.504, 0.909, 0.533, 0.932], [0.617, 0.909, 0.647, 0.932], [0.731, 0.909, 0.760, 0.932], [0.845, 0.909, 0.874, 0.932], [0.958, 0.909, 0.988, 0.932]] as the row labels representing the different years. Then I notice {the legend texts}[[0.842, 0.049, 0.930, 0.079], [0.842, 0.093, 0.943, 0.123], [0.842, 0.137, 0.921, 0.167], [0.842, 0.181, 0.952, 0.211]] serving as the column headers, that represent the different areas of scenario. When populating the values, I locate {the line points for each area} and extract their corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Information_Technology,Biotechnology,Space_Exploration,Robotics\n2000,12000,8000,2000,1000\n2001,11000,8200,2100,1100\n2002,10000,8400,2200,1200\n2003,12000,8600,2300,1300\n2004,13000,9000,2400,1400\n2005,14000,9200,2600,1600\n2006,15000,9400,2800,1700\n2007,17000,9600,3000,1800\n2008,15000,9700,3200,2000\n2009,16000,9800,3400,2100\n2010,18000,10000,3700,2300\n2011,19000,11000,4000,2500\n2012,20000,12000,4300,2700\n2013,22000,13000,4600,2800\n2014,23000,14000,4900,3000\n2015,25000,15000,5200,3300\n2016,27000,16000,5500,3500\n2017,30000,17000,5800,3700\n2018,32000,18000,6000,4000\n2019,34000,19000,6200,4200\n2020,36000,20000,6500,4400"
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "sz90y7LWGp6doy2E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trend shown in the line chart, which area seems to have the most significant growth over the two decades? Explain your observations."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area has the most significant growth over the two decades, I need to compare the rate of change of line plots for each scenario. Looking at {the line for Information Technology}[0.064, 0.297, 0.973, 0.179], I see that it gradually increases from 12000 to 36000. The growth rate is quite significant, and this trend continues over the years.\nThen I examine {the line for Biotechnology} and observe that it also shows a gradual increase from 8000 to 20000. Moreover, the line for {Space Exploration} exhibits a consistent increase from 2000 to 6500. The growth rate in these areas, while substantial, is not as steep as in the Information Technology sector.\nLastly, I assess the {Robotic Line} and it also shows substantial growth, but not as much as Information Technology.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Information Technology sector displays the most significant growth over two decades. Its line plot shows a steady upward trend, indicating a significant increase in values from year 2000 to 2020. The increase in values is more pronounced compared to other areas, highlighting the significant growth in this sector."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "lO6Bo4gYw6XGSqVA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the \"Information Technology\" figures for the years 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the \"Information_Technology\" figures for the years 2000 and 2020, I examine the {line points for Information_Technology}[[0.064, 0.297], [0.178, 0.275], [0.291, 0.252], [0.405, 0.278], [0.518, 0.265], [0.632, 0.243], [0.746, 0.215], [0.859, 0.195], [0.973, 0.179]] and extract the corresponding values: 12000 for {year 2000}[0.064, 0.909] and 36000 for {year 2020}[0.973, 0.909]. Hence, the difference is 36000 - 12000 = 24000.\nBased on the above thought, the answer is:\n24000."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "CJPOxG6dWdhUjzjL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data about education and technology innovations usage into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into a table, I first identify the different innovation types by looking at {the y-axis ticks}[[0.095, 0.150, 0.119, 0.172], [0.095, 0.180, 0.119, 0.202], ..., [0.095, 0.850, 0.119, 0.872]], and these will serve as row labels in our table. Next, I identify the values representing the differences in usage of each technology, which are encoded in {the x-positions of the line-ends}[[0.400, 0.162], [0.725, 0.192], ..., [0.950, 0.862]] at each respective technology. Finally, these values are collected and assembled into a table.\nBased on the above thought, the answer is:\nInnovation, Difference in Usage(%)\nSynchronous Learning, 10\nFlipped Classroom, -20\nMicrolearning, -5\nGamified Learning, 30\nLearning Analytic, 15\nMobile Learning, 25\nVideo Learning, -35\nBlended Learning, 20\nCloud-Based Learning, 10\nAI Tutoring, -10\nSocial Learning, 35\nE-learning, 5\nAR Learning, -15\nVR Learning, 30\nRemote Learning, 50"
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "JrMmQhKiZG6hk6ux",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of trends can we observe in the use of different education and technology innovations according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays a variety of trends across different technology innovations. Some innovations such as {Remote Learning}[0.400, 0.897], {Social Learning}[0.400, 0.842], and {VR Learning}[0.400, 0.669] are experiencing significant growth, as shown by their large positive values. This suggests increasing adoption of these technologies in the learning environment.\nOn the other hand, certain technologies such as {Video Learning}[0.400, 0.584], {AR Learning}[0.400, 0.647], and {Flipped Classroom}[0.400, 0.240] are experiencing a decline in usage, indicated by their negative values. This might suggest that these technologies are either being replaced by other innovations or are losing favor within the educational landscape.\nStrikingly, there is considerable variability in the trends, implying that the landscape of educational technology is dynamic with technologies continuously shifting in popularity.\nBased on the above thought, the answer is:\nThe trends in the use of different education and technology innovations are highly varied. Some technologies like Remote Learning, Social Learning, and VR Learning are seeing considerable growth in their usage. Others, including Video Learning, AR Learning, and Flipped Classroom are seeing a decline in their usage. This suggests a dynamic and shifting landscape of education and technology innovations, with constant changes in the popularity and utilization of different techniques and tools."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "hpbGvk7Z9OzdHuIP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the usage of Gamified Learning more than Blended Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in usage between Gamified Learning and Blended Learning, I need to identify the end points of the lines representing each. For {Gamified Learning}[0.400, 0.412], the line end is at x=0.80 which corresponds to an increase of 30%. For {Blended Learning}[0.400, 0.569], the line end point is at x=0.75, corresponding to an increase of 20%. Therefore, the usage of Gamified Learning is 30% - 20% = 10% more than Blended Learning.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "ep9V5wJS69Zbs37C",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart of population movement over time into a table format with Years as columns and movement types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first identify {the x-axis tick labels}[[0.142, 0.909, 0.172, 0.932], [0.224, 0.909, 0.253, 0.932], [0.306, 0.909, 0.335, 0.932], [0.387, 0.909, 0.416, 0.932], [0.469, 0.909, 0.498, 0.932], [0.550, 0.909, 0.580, 0.932], [0.632, 0.909, 0.661, 0.932], [0.714, 0.909, 0.743, 0.932], [0.795, 0.909, 0.824, 0.932], [0.877, 0.909, 0.906, 0.932], [0.958, 0.909, 0.988, 0.932]] as column headers representing the years. The {legend texts}[[0.106, 0.048, 0.259, 0.078], [0.326, 0.048, 0.482, 0.078], [0.548, 0.048, 0.732, 0.078], [0.799, 0.048, 0.952, 0.078]] act as row labels representing different types of movements. Then I locate the y-values for each year and movement type using {the y-cordinates of line plot points}[[0.157, 0.230], [0.239, 0.221], [0.320, 0.207], [0.402, 0.192], [0.483, 0.180], [0.565, 0.167], [0.647, 0.154], [0.728, 0.141], [0.810, 0.127], [0.891, 0.114], [0.973, 0.101]] and convert those to the real values according to the y-ticks.\nBased on the above thought, the answer is:\nMovement Type,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010\nMoving within the city,28500,29000,29500,30000,30000,30500,31000,31800,32500,33000,34000\nMoving to another city,5700,5900,6000,6020,6040,6050,6100,6110,6200,6300,6300\nMoving to another country,1200,1250,1300,1350,1400,1450,1500,1550,1600,1650,1700\nNot moving,51200,51900,52900,54100,55000,56000,57000,58000,59000,60000,61000"
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "T4JX5NTqI20UK5Fb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you deduce which type of movement has had the most consistent trend over the years? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which type of movement has had the most consistent trend, one has to consider the number and magnitude of fluctuations in the line plot of each type of movement. Observing {the line for Moving within the city}[0.157, 0.527, 0.973, 0.455], we see an almost steady increase over the years. The {line for Moving to another city}[0.157, 0.826, 0.973, 0.818] is relatively stable, with limited fluctuations. The {line for Moving to another country}[0.157, 0.885, 0.973, 0.879] shows a consistent increase, but the increase itself is small. And finally, the {line for Not moving}[0.157, 0.230, 0.973, 0.101] exhibits a consistent decline over the years, with only minor fluctuations. Comparing all these, the line plot for 'Moving within the city' shows the most consistent trend with a steady increase.\nBased on the above thought, the answer is:\nThe 'Moving within the city' category has had the most consistent trend over the years. This is evident from its line plot that shows a steady increase every year. While the other types of movement also show trends over time, their plots show more fluctuations in comparison."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "uU7vmSUL0inXHO0w",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the population who are not moving, what is the increase in the population from 2000 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in the population from 2000 to 2010 for those not moving, I need to find {the value for 2000}[[0.157, 0.230]] which is 51200 and {the value for 2010}[[0.973, 0.101]] which is 61000. The increase in the population is obtained by subtracting the value in 2000 from the value in 2010.\nBased on the above thought, the answer is:\n9800."
+            }
+        ],
+        "id_raw": 617
+    },
+    {
+        "id": "E322QAQbqqAdfJdx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the yearly projected growth percentage for each healthcare category displayed on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.049, 0.909, 0.078, 0.932], ... , [0.958, 0.909, 0.988, 0.932]] as the column headers representing the years. The row labels, which are the different healthcare categories, can be derived from {the legend labels}[[0.245, 0.048, 0.356, 0.078], ... , [0.824, 0.048, 0.952, 0.078]]. For each category, I locate their corresponding line points like {the line points for AI in Healthcare}[[0.064, 0.781], [0.165, 0.765], ... , [0.973, 0.161]] and extract the percentage values corresponding to the y-coordinate of these points.\nBased on the above thought, the answer is:\nYear,AI in Healthcare,Telemedicine,Electronic Health Records,Precision Medicine\n2022,14.7,46.2,90,82\n2023,16.7,48.5,86,77\n2024,20.5,51.0,82,73.2\n2025,26.2,53.5,80,69.9\n2026,33.7,56.1,77.5,67\n2027,42.0,58.8,76,64.5\n2028,51.8,61.5,73.5,62.3\n2029,62.9,64.3,70,60.1\n2030,75.7,67.2,66.5,58\n2031,91.0,70,65,56"
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "6SYLW2CappXx69OJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which healthcare technology field seems to have the most potential for growth in the next decade (from 2022 to 2031) according to the projections?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the healthcare technology field with the greatest growth potential, I need to analyze the changes in the line plots for all four fields by comparing their starting and ending points. Looking at {the line plot for AI in Healthcare}[[0.064, 0.781], ..., [0.973, 0.161]], the growth change from 14.7% to 91.0% indicates a strong upward trend. Similarly, I can analyze {the Telemedicine line}[[0.064, 0.525], ..., [0.973, 0.332]], {the Electronic Health Records line}[[0.064, 0.169], ..., [0.973, 0.373]], and {the Precision Medicine line}[[0.064, 0.234], ..., [0.973, 0.446]].\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, AI in Healthcare appears to have the most potential for growth over the next decade. It starts at a relatively low percentage of 14.7% in 2022, but by 2031, it is projected to reach a significantly higher percentage of 91.0%. This represents the highest degree of positive change compared to the other healthcare technology fields on the chart."
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "zITTVAWISACgDbpf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the projected growth change for AI in Healthcare from the year 2022 to the year 2031?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the projected growth change for AI in Healthcare, I need to locate the plot points for this category at years 2022 and 2031 which are located at {2022}[0.064, 0.781] and {2031}[0.973, 0.161] respectively. By looking at the y-coordinates of these points and referring to the y-axis, I can identify the projected growth for these years as 14.7% and 91% respectively.\nBased on the above thought, the answer is:\n76.3%"
+            }
+        ],
+        "id_raw": 618
+    },
+    {
+        "id": "80D6OwmTyypqfiM5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compile the data points for both AI and VR industries from the line chart into a structured table for each year from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the x-axis (representing years) values from {the x-axis tick labels}[[0.082,0.905,0.126,0.936],[0.232,0.905,0.276,0.936]...[0.932,0.905,0.976,0.936]]. Then I would note the y-axis values representing the development index from line points of {AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]], and {VR}[[0.084,0.355],[0.234,0.379]...[0.934,0.695]]. This will allow me to match each year with the corresponding development index of both industries.\nBased on the above thought, the answer is:\nYear,AI,VR\n2000,15,30\n2002,20,32\n2004,19,30\n2006,24,32\n2008,20,28\n2010,26,20\n2012,31,26\n2014,35,24\n2016,40,28\n2018,45,32\n2020,50,34"
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "pjLgUC0Zq58fyQ2E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the overall look of the growth curve of the AI industry as per the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the curve for AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]] in the chart, it displays an upward trend overall. It starts in 2000 with an index of 15, shows a gentle incline with a small dip in 2004 and again in 2008, but continuously progresses until 2020, reaching an index of 50. This indicates a positive growth pattern over these two decades, despite minor setbacks.\nBased on the above thought, the answer is:\nThe AI industry has shown notable growth from 2000 to 2020. Even with slight dips in 2004 and 2008, the AI development index has consistently increased, suggesting overall upward progress in the AI industry regardless of minor occasional falls in growth during the mentioned years."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "BCy8AuWodsm40EUH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_31_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year does the AI industry seem to have the lowest development index, based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I scan {the line points for AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]]. The lowest point appears to be {in 2004}[0.284, 0.670], which corresponds to a development index of 19.\nBased on the above thought, the answer is:\n2004."
+            }
+        ],
+        "id_raw": 619
+    },
+    {
+        "id": "LQBRbmvJXvgJr4nq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you process the line chart to get a structured table, with Ratio as columns and Emotion types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I identify {the x-axis tick labels}[[0.135, 0.449, 0.172, 0.466], [0.222, 0.449, 0.258, 0.466], [0.308, 0.449, 0.345, 0.466], [0.395, 0.449, 0.431, 0.466], [0.481, 0.449, 0.518, 0.466], [0.568, 0.449, 0.604, 0.466], [0.654, 0.449, 0.691, 0.466], [0.741, 0.449, 0.777, 0.466], [0.827, 0.449, 0.864, 0.466], [0.913, 0.449, 0.950, 0.466]] which represent the column headers or Ratio. The row labels are represented by the title of {subplot_0}[[0.344, 0.019, 0.741, 0.041]] as well as {subplot_1}[[0.269, 0.509, 0.816, 0.532]]. To populate the cells of the table, I refer to {the line points for Automobile}[[0.154, 0.300], [0.240, 0.352], [0.327, 0.339], [0.413, 0.372], [0.500, 0.320], [0.586, 0.326], [0.672, 0.359], [0.759, 0.307], [0.845, 0.300], [0.932, 0.300]] and {Public Transportation}[[0.154, 0.602], [0.240, 0.628], [0.327, 0.609], [0.413, 0.635], [0.500, 0.615], [0.586, 0.667], [0.672, 0.654], [0.759, 0.667], [0.845, 0.693], [0.932, 0.700]] with appropriate conversion to map the y-axis values to the corresponding emotion percentages.\nBased on the above thought, the answer is:\nEmotion Type,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\nAutomobile Positive Emotion,0.40,0.43,0.45,0.40,0.48,0.47,0.42,0.50,0.51,0.51\nPublic Transportation Negative Emotion,0.80,0.76,0.79,0.75,0.78,0.70,0.72,0.70,0.66,0.65"
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "tFSpk78dlvUW1zON",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the observable trend of emotion ratio for public transportation over the observed ratios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend of the emotion percentage ratio for public transportation, I refer to {the line for Public Transportation}[[0.154, 0.602, 0.932, 0.700]]. The emotion percentage starts at a high level of 0.80 at a ratio of 0.1 but shows a general trend of decrease as the ratio increases, reaching the lowest point of 0.65 at the highest ratio of 1.0. There are some fluctuations in the decrease where values slightly increase before continuing to decrease, such as from ratio 0.2 to 0.3 and from 0.4 to 0.5.\nBased on the above thought, the answer is:\nThe emotion percentage ratio for public transportation displays a general decreasing trend as the ratio increases, starting from a high of 0.80 at a ratio of 0.1 and dropping to a low of 0.65 at a ratio of 1.0. Despite some slight temporary increases, the overall progressive decrease over the range of ratios signifies a decrease in negative emotions for public transportation as the ratio increases."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "nBIPKcx2a32f6iKr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of emotion percentage ratio for Automobile Positive Emotion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of emotion percentage ratio for Automobile Positive Emotion, I first identify {the line points for Automobile Positive Emotion}[[0.154, 0.300], [0.240, 0.352], [0.327, 0.339], [0.413, 0.372], [0.500, 0.320], [0.586, 0.326], [0.672, 0.359], [0.759, 0.307], [0.845, 0.300], [0.932, 0.300]] and extract the corresponding values: 0.40, 0.43, 0.45, 0.40, 0.48, 0.47, 0.42, 0.50, 0.51, 0.51. The maximum value is 0.51 and the minimum value is 0.40. The range is therefore 0.51 - 0.40 = 0.11.\nBased on the above thought, the answer is:\n0.11."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "7DpggCnmJNsLdRJV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart of 2020 and 2021 Sales, can you extract the entirety of this data into a structured table format with Time as rows and years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}<r>[0.167, 0.449, 0.182, 0.466],[0.252, 0.449, 0.266, 0.466],[0.336, 0.449, 0.350, 0.466],[0.420, 0.449, 0.435, 0.466],[0.504, 0.449, 0.519, 0.466],[0.589, 0.449, 0.603, 0.466],[0.673, 0.449, 0.688, 0.466],[0.757, 0.449, 0.772, 0.466],[0.841, 0.449, 0.856, 0.466],[0.925, 0.449, 0.940, 0.466]</r> and similarly for 2021, which represent the row labels or months. Next, I identify the subplots for 2020 and 2021 as the column headers. To populate the values, I locate the line points corresponding to each year at the respective months and extract the sales figure annotation.\nBased on the above thought, the answer is:\nTime (Months),2020 Sales,2021 Sales\n0,3000,3200\n1,3050,3400\n2,3100,4000\n3,3150,4500\n4,3200,5000\n5,3250,5800\n6,3300,6000\n7,3350,7000\n8,3400,7500\n9,3450,8000"
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "GYhLTTeGpnCqpsTW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the sales trend in 2020 and 2021, how can we summarize the progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would evaluate the line plots for 2020 and 2021. Looking at {the data for 2020}<r>[0.175, 0.395, 0.933, 0.429]</r>, it shows a constant and 'steady growth with no significant changes. For {2021}<r>[0.175, 0.545, 0.933, 0.905]</r>, the sales figures start higher than 2020 and demonstrate a more substantial growth rate throughout the year.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, sales for the company saw a steady but slow increase in 2020. In 2021, the company started with higher initial sales compared to 2020 and experienced a significant rise throughout the year, indicating a better performance in 2021 as compared to 2020."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "DDZxWGmpFZFUaMpu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in sales from the start to the end of 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in sales from the start to the end of 2021, I first locate {the starting point for 2021}<r>[0.175, 0.905]</r> which corresponds to a sales figure of 3200. Then I find {the end point for 2021}<r>[0.933, 0.545]</r> which corresponds to a sales figure of 8000. The change in sales is therefore 8000 - 3200 = 4800.\nBased on the above thought, the answer is:\n4800."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "q0qaNRFzEHYnEKm8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the tabular representation showing the trends for Classification and Prediction across various x-axis categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the chart into a table format, first, I focus on identifying {the x-axis labels}[[0.114, 0.965, 0.142, 0.975], [0.204, 0.965, 0.231, 0.975], [0.294, 0.965, 0.321, 0.975], [0.384, 0.965, 0.411, 0.975], [0.473, 0.965, 0.501, 0.975]], which represent the categories under which trends are measured. Next, I observe the bars for {Classification}[[0.128, 0.801, 0.936, 0.825]] and {Prediction}[[0.128, 0.786, 0.936, 0.798]] across the timeline and extract the corresponding trend percentages.\nBased on the above thought, the answer is:\nCategories,Classification,Prediction\nCategory 1,100.0,1.0\nCategory 2,90.0,2.3\nCategory 3,65.0,5.0\nCategory 4,32.5,7.2\nCategory 5,10.6,8.9"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "ixd1HpGBVqRjLJxJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the outlier at 10.6% in the Classification trend indicate about its progression?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line chart for Classification}<r>[0.128, 0.801, 0.936, 0.825]</r>, I see a consistent downward trend from 100.0% decreasing to 10.6% across various categories. The presence of the outlier at 10.6% signifies a substantial drop, hinting that there may have been significant changes or events affecting Classification at this point. This outlier visually illustrates the most dramatic decrease, emphasizing the accelerated decline mentioned in the setup of the chart's theme.\nBased on the above thought, the answer is:\nThe outlier at 10.6% in the Classification trend signifies a significant and abrupt decrease in its values, emphasizing an accelerated decline in this category. This drop can indicate major changes or impacts that led to such a sharp decrease, marking it as a critical point in the analysis of Classification trends."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "AY0HpyNuE9whpNHj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest percentage difference observed between Classification and Prediction within any category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest percentage difference, I analyze {the trend data for Classification}{{0.128, 0.801, 0.936, 0.825]] and {Prediction}[[0.128, 0.786, 0.936, 0.798]] across different categories: Classification starts at 100.0% and goes down to 10.6%, while Prediction starts at 1.0% and rises to 8.9%. I calculate the differences for each category, with Category 1 showing the largest difference of 100.0% - 1.0% = 99.0%.\nBased on the above thought, the answer is:\n99.0"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "5rlYdRXyvdWNl7iE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line graph across the different SP Risk labels into a table format showing high and low risk scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining this line graph, I can distinguish two lines each denoting high and low risk scores across multiple SP Risk labels. Along the {x-axis}<r>[0.110, 0.293, 0.957, 0.329]</r>, the SP Risk labels are visible, which serve as the row headers. {The high risk line points}<r>[0.110, 0.087, 0.957, 0.106]</r> and {the low risk line points}<r>[0.110, 0.143, 0.957, 0.162]</r> provide the scores, which can be extracted and structured into a readable tabular format.\nBased on the above thought, the answer is:\nSP Risk Label,High Risk Score,Low Risk Score\nSPRisk-0.5,0.85,0.70\nSPRisk-0.7,0.68,0.55\nSPRisk-0.9,0.58,0.48\nSPRisk-1.1,0.53,0.43\nSPRisk-1.3,0.48,0.38"
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "rH8G1cskNk8N6GA2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the consistent decline in both high and low risk scores, as observed in {the two lines of the graph}<r>[0.110, 0.087, 0.957, 0.162]</r>, suggest about the financial management over the SP Risk labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the high risk line}<r>[0.110, 0.087, 0.957, 0.106]</r> and {the low risk line}<r>[0.110, 0.143, 0.957, 0.162]</r> in the graph, it is clear that both are showing a downward trajectory as the SP Risk labels increase. This trend could suggest that as measures to mitigate risks strengthen (represented by increasing SP Risk labels), both the permissible thresholds for high and low-risk financial activities are getting tighter, indicating a more conservative financial management approach over time.\nBased on the above thought, the answer is:\nThe consistent decline in scores illustrates tightening financial management, suggesting that risk tolerance is decreasing, which is likely a proactive strategy to maintain stability and minimize potential financial disruptions as risk-mitigation measures increase."
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "gMMF0CgcZUhkkS3B",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the total average score of high risk compared to low risk across all SP Risk levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average score of high risk and low risk, I must calculate the mean for each by summarizing the values from {the high risk line}<r>[0.110, 0.087, 0.957, 0.106]</r> and {the low risk line}<r>[0.110, 0.143, 0.957, 0.162]</r>. Summing values for high risk gives a total of 3.24 and for low risk gives 2.64, each divided by the number of SP Risk levels, which is 5. This results in average scores of 0.648 and 0.528, respectively. Subtracting the low risk average from the high risk average provides the difference.\nBased on the above thought, the answer is:\n0.12"
+            }
+        ],
+        "id_raw": 625
+    },
+    {
+        "id": "NrqmYyevv3tdpyG7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the performance metrics as a structured table with ratios as rows and sectors like AI Research and AI Healthcare as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first look at the {the x-axis tick labels}[[0.149, 0.879, 0.186, 0.914], [0.234, 0.879, 0.271, 0.914],[0.319, 0.879, 0.356, 0.914], [0.404, 0.879, 0.441, 0.914], [0.489, 0.879, 0.526, 0.914], [0.574, 0.879, 0.611, 0.914], [0.659, 0.879, 0.696, 0.914],[0.744, 0.879, 0.781, 0.914],[0.829, 0.879, 0.866, 0.914],[0.914, 0.879, 0.951, 0.914]], which will serve as the row labels. Next, I identify {the legend texts}[[0.211, 0.125, 0.347, 0.160],[0.211, 0.177, 0.367, 0.212]] as the column headers, representing the sectors - AI Research and AI Healthcare. Finally, I plot the corresponding values from line points for each sector in their related ratios.\nBased on the above thought, the answer is:\nRatio, AI Research, AI Healthcare\nRatio 1, 0.2, 0.35\nRatio 2, 0.22, 0.5\nRatio 3, 0.27, 0.4\nRatio 4, 0.29, 0.42\nRatio 5, 0.4, 0.37\nRatio 6, 0.58, 0.55\nRatio 7, 0.55, 0.67\nRatio 8, 0.62, 0.77\nRatio 9, 0.79, 0.8\nRatio 10, 0.82, 0.95"
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "J9VT7K1LDCf8Wbxz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which sector, AI Research or AI Healthcare, showed better performance growth over the ratios? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the growth trends in the line plots for both sectors. From looking at {the AI Research Line}[[0.168, 0.254, 0.933, 0.810]], it starts at a performance metric of 0.2 at Ratio 1 and increases to 0.82 at Ratio 10. However, for {AI Healthcare Line}[[0.168, 0.138, 0.933, 0.675]], it starts at 0.35 at Ratio 1 and increases to 0.95 at Ratio 10. Finding the growth in performance for AI Healthcare is larger than for AI Research.\nBased on the above thought, the answer is:\nBased on the line chart, AI Healthcare sector showed better performance growth over the ratios. It started at a higher performance metric compared to AI Research and maintained an upward growth trend, increasing to a higher performance metric by Ratio 10 as well. Therefore, considering the increase from Ratio 1 to Ratio 10, AI Healthcare has shown more growth in performance compared to AI Research."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "au2AUszh2YTXBFFE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in performance for AI Healthcare from Ratio 1 to Ratio 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to locate {the starting point for AI Healthcare at Ratio 1}[[0.168, 0.675]] which corresponds to 0.35 and {the ending point for AI Healthcare at Ratio 10}[[0.933, 0.138]] which corresponds to 0.95. I calculate the percentage increase as (0.95-0.35)/0.35*100.\nBased on the above thought, the answer is:\n171.43%."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "pXsJEJslBM7N27rg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table with years as columns and industries as rows, using the data from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step to get the data from these line charts into a table is to look at {the x-axis tick labels} of each subplot: [0.074, 0.301, 0.132, 0.313], [0.495, 0.301, 0.554, 0.313], [0.917, 0.301, 0.975, 0.313]. They correspond to the years, which will be our columns. Next, the {subtitle of each subplot}[0.489, 0.012, 0.560, 0.027], [0.489, 0.342, 0.560, 0.357], [0.488, 0.671, 0.560, 0.686] represent industries that will be our rows. Then, I can extract the values for each industry at each year from {line points} such as [0.103, 0.118], [0.524, 0.075], [0.946, 0.206], which present the market share for the given industry in the specified year.\nBased on the above thought, the answer is:\nIndustry,2018,2019,2020\nInd-A,40,45,30\nInd-B,35,30,40\nInd-C,25,25,30"
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "BPMPhitTlGAK8cn3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry saw a consistent market share over the period from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the industry with consistent market share over the period from 2018 to 2020, I need to analyze the line trends for each industry. Looking at {the line for Ind-A}[0.103, 0.075, 0.946, 0.206], there is a fluctuation with a general decline from 40% to 30%. Similarly, looking at {the line for Ind-B}[0.103, 0.491, 0.946, 0.448], there is a clear decline from 35% to 40%. Now looking at {the line for Ind-C}[0.103, 0.908, 0.946, 0.864], the market share remains constant at 25% between 2018 and 2019 and increases to 30% in 2020. Although there is an increase in 2020, compared to other industries, Ind-C was more consistent over the period.\nBased on the above thought, the answer is:\nIndustry Ind-C had a more consistent market share over the period from 2018 to 2020. Although there was a slight increase from 25% to 30% in 2020, this change was less volatile compared to the shifts in market share observed in Industries A and B."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "3KyEDNGGo6UGAJfq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has been the total change in the market share percentage of Ind-A from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total change in market share percentage of Ind-A, I need to refer to {the line points for Ind-A}[[0.103, 0.118], [0.524, 0.075], [0.946, 0.206]]. The market share in 2018 is 40% at the point [0.103, 0.118]. By 2020, the market share has decreased to 30% at the point [0.946, 0.206]. Subtracting the market share in 2018 from the market share in 2020 gives -10%, or a decrease of 10 percentage points.\nBased on the above thought, the answer is:\n-10%"
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "7bNYFQw9p5jUeMhY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart into a CSV format that captures the trends for SPGrocery values over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart into a CSV format, I begin by identifying {the x-axis tick labels}[0.099, 0.308, 0.961, 0.317] which represent the years from 2000 to 2009. Then, I examine the lines for each SPGrocery category: SPGrocery-0.5, SPGrocery-0.7, and SPGrocery-0.9, located at {the line coordinates for SPGrocery-0.5}[0.116, 0.037, 0.944, 0.289], {the line coordinates for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619], and {the line coordinates for SPGrocery-0.9}[0.116, 0.697, 0.944, 0.949]. I will extract the points from each line reflecting yield values across different years.\nBased on the above thought, the answer is:\nYear,SPGrocery-0.5,SPGrocery-0.7,SPGrocery-0.9\n2000,2.5,2.53,2.57\n2001,2.75,2.55,2.6\n2002,2.85,2.56,2.63\n2003,2.9,2.65,2.66\n2004,3.1,2.7,2.7\n2005,3.2,2.75,2.75\n2006,3.25,2.8,2.78\n2007,3.28,2.83,2.8\n2008,3.3,2.85,2.82\n2009,3.35,2.87,2.85"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "mtAVm0VcmnUOt9no",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich SPGrocery category showed the most consistent upward trend in yield over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the consistency of upward trends among SPGrocery categories, I inspect the slope and the progression of each line representing different SPGrocery categories. Observing {the line for SPGrocery-0.5}[0.116, 0.037, 0.944, 0.289], {the line for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619], and {the line for SPGrocery-0.9}[0.116, 0.697, 0.944, 0.949], it's notable that SPGrocery-0.9 starts at a yield of 2.57 in 2000 and continuously rises to 2.85 in 2009 with no apparent dips, indicating a consistent upward trend.\nBased on the above thought, the answer is:\nSPGrocery-0.9 showed the most consistent upward trend in yield over the years. The line graph representing this category clearly depicts a gradual and consistent increase, with no year showing a yield decline, making it the most steadily improving category among the three."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "TmbAPLwPQ6M1ZO0U",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average yield for SPGrocery-0.7 across the entire range of years represented?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average yield for SPGrocery-0.7, I identify all the data points for SPGrocery-0.7 in the line chart. I extract the values from each year located along {the line for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619]. The yields for SPGrocery-0.7 from 2000 to 2009 are 2.53, 2.55, 2.56, 2.65, 2.7, 2.75, 2.8, 2.83, 2.85, 2.87 respectively. Using these values, I calculate the average by dividing the sum of the values by the number of years, which is 10.\nBased on the above thought, the answer is:\n2.69"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "eQUMUVs3qGKWHC6P",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data for both lifestyles into a structured table with Social Activities as rows and Lifestyles as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, first, I look at {the x-axis tick labels}[0.069, 0.919, 0.164, 0.942], [0.257, 0.919, 0.389, 0.942], [0.462, 0.919, 0.598, 0.942], [0.663, 0.919, 0.811, 0.942], [0.915, 0.919, 0.973, 0.942] which represent the row headers, or social activities. Next, I identify {the legend texts}[0.900, 0.824, 0.972, 0.847], [0.900, 0.859, 0.972, 0.882] as the column headers representing the different lifestyles. To populate the table, I locate {the line points for Lifestyle A}[0.116, 0.550], [0.323, 0.325], [0.530, 0.460], [0.737, 0.640], [0.944, 0.100] and {the line points for Lifestyle B}[0.116, 0.775], [0.323, 0.685], [0.530, 0.730], [0.737, 0.865], [0.944, 0.550] and extract the corresponding values.\nBased on the above thought, the answer is:\nActivities,Lifestyle A, Lifestyle B\nStay at Home, 10, 5\nOutdoors Activities, 15, 7\nOnline Socialisation,12,6\nPhysical Socialisation, 8, 3\nExercise, 20, 10"
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "9EeHVyMus7Ev9S0t",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which Lifestyle spends more time on exercise, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which Lifestyle spends more time on Exercise, I need to analyze the y-values at the point representing Exercise on each line. Looking at the last point on {the line for Lifestyle A}[0.944, 0.100], the y-value represents the time spent on Exercise for Lifestyle A which is 20. Similarly, from the last point on {the line for Lifestyle B}[0.944, 0.550], I can obtain the time spent on Exercise for Lifestyle B which is 10.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Lifestyle A spends more time on Exercise than Lifestyle B. This is evidenced by the y-value of the point representing Exercise on the line for Lifestyle A being higher than that for Lifestyle B, indicating a greater time investment in exercise for Lifestyle A."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "JljOE9QuNd4ae0jr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total time spent on Social Activities for both Lifestyle A and B?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total time spent on Social Activities for both Lifestyles, I need to add up the y-values of all the points on each of the line charts. For Lifestyle A, I locate the points on {the line for Lifestyle A}[0.116, 0.550], [0.323, 0.325], [0.530, 0.460], [0.737, 0.640], [0.944, 0.100] which represent the time spent on each activity: 10, 15, 12, 8, 20. Similarly for Lifestyle B, the time spent on each activity can be obtained from the points at {the line for Lifestyle B}[0.116, 0.775], [0.323, 0.685], [0.530, 0.730], [0.737, 0.865], [0.944, 0.550] which are 5, 7, 6, 3, 10. By adding up these values, I get the total time spent on Social Activities for each Lifestyle.\nBased on the above thought, the answer is:\nLifestyle A: 65, Lifestyle B: 31."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "9ObYbGPcffz0wyC7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you structure the data from the bar chart showing the exposure data trends into a CSV format, distinguishing between personal and company data across the categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart data into a tabular format, I begin by examining the categories along {the x-axis}<r>[0.110, 0.031, 0.975, 0.293]</r>, which are 'Abrupt Increase', 'Gradual Increase', 'Steep Decline', and 'Subdued Decline'. I then differentiate between personal and company data exposure percentages as shown in the two separate graphs; the first graph has personal data, and the second graph shows company data. I recognize and quantify the segments from the bars by noting down the percentage values labeled on each bar segment.\nBased on the above thought, the answer is:\nCategory,Personal Data Exposure (%),Company Data Exposure (%)\nAbrupt Increase,30,45\nGradual Increase,50,35\nSteep Decline,10,5.4\nSubdued Decline,5,15"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "do1rm8EXkanDSjgc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the most significant disparity in exposure between personal and company data, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By closely analyzing bar charts for each data category, I note the differences in each segment's length and percentage labels. In {the 'Steep Decline'}<r>[0.312, 0.567, 0.541, 0.882]</r> category, the difference in the labeled percentages\u201410% for personal data and 5.4% for company data\u2014indicates a notable disparity. This significant gap highlights different impacts or concerns regarding data breaches in the two data types within this category, with personal data being somewhat less affected in this specific instance compared to company data.\nBased on the above thought, the answer is:\nThe 'Steep Decline' category shows the most significant disparity in exposure between personal and company data. The difference of 4.6 percentage points is primarily because, in this category, personal data is less affected by breaches compared to company data, highlighting varying levels of vulnerability or focus areas in cybersecurity measures between the two."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "oSnhhndzoAnTanxd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage point difference in exposure for personal and company data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the personal and company data from each graph, the percentages for each category are as follows: Personal Data - 30, 50, 10, 5 and Company Data - 45, 35, 5.4, 15. Calculating the absolute differences for each category and summing them up gives: |30-45| + |50-35| + |10-5.4| + |5-15| = 15 + 15 + 4.6 + 10 = 44.6.\nBased on the above thought, the answer is:\n44.6"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "n3iJYvHI6cQ7tiPZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert information from the line chart related to Global Temperature Change into a structured table using the years as rows and the trends as headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accurately compile the data into a structured table, I need to assess and gather points from {the first subplot for Global Temperature Change}<r>[0.110, 0.031, 0.975, 0.293]</r>. I'll consider each {year's mark on the x-axis}<r>[0.135, 0.301, 0.950, 0.313]</r>  as a row while the trend levels 'Inconsistent Growth', 'Outlier Increase', and 'Progressive Growth' derived from the visual observation of year-to-year change will serve as column headers. I proceed to register the temperature values depicted at each point across the 19 years.\nBased on the above thought, the answer is:\nYear,Inconsistent Growth,Outlier Increase,Progressive Growth\n2000,0.1,-,0.1\n2001,0.15,-,0.12\n2002,0.19,-,0.15\n2003,0.20,-,0.17\n2004,0.18,-,0.17\n2005,0.22,-,0.25\n2006,0.25,0.35,0.30\n2007,0.28,-,0.33\n2008,0.22,-,0.29\n2009,0.23,-,0.22\n2010,0.28,-,0.30\n2011,0.27,-,0.32\n2012,0.28,-,0.33\n2013,0.31,-,0.38\n2014,0.36,-,0.39\n2015,0.40,0.50,0.45\n2016,0.46,0.6,0.55\n2017,0.39,-,0.40\n2018,0.42,-,0.43\n2019,0.44,-,0.45"
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "rUg3MDQOPdw9TOo9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the variability in global temperature changes from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the line for Global Temperature Change}<r>[0.154, 0.132, 0.932, 0.680]</r> across the x-axis years {from 2000 to 2019}<r>[0.135, 0.301, 0.950, 0.313]</r>, I gauge the amplitude of fluctuations. The increases and drops across certain years, specifically noted significant peaks in 2015 and 2016, indicate a substantial variability. Regular inconsistencies with a couple of outlier years signify a complex interaction of underlying factors affecting temperature change.\nBased on the above thought, the answer is:\nThe global temperature change line chart demonstrates considerable variability, with years like 2015 and 2016 marked as significant outliers. These trends suggest fluctuating and sometimes sharp changes, which indicate unpredictable environmental dynamics over the observed period."
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "4NHoKOOGE1v54qet",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall change in Sea Level Rise from the year 2000 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {line plot for Sea Level Rise} in {the second subplot}<r>[0.110, 0.360, 0.975, 0.622]</r>, I locate the data points for the years 2000 and 2019: {starting point}<r>[0.154, 0.622]</r> and {ending point}<r>[0.932, 0.503]</r>. The starting value in 2000 is approximately 0.35 meters and in 2019 is roughly 0.45 meters.\nBased on the above thought, the answer is:\n0.10 meters"
+            }
+        ],
+        "id_raw": 631
+    },
+    {
+        "id": "TljzaZLkdQiYahJQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a structured table, listing each era along the rows and the years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "I begin by identifying {the x-axis labels}<r>[0.135, 0.301, 0.950, 0.313]</r>, which represent the different years, and serve as the columns in the table. Next, I locate {the legend}<r>[0.122, 0.037, 0.449, 0.058]</r>, which lists the different eras such as 'Hunter-gatherer Eras', 'Agricultural development', and 'Industrial Revolution', to be used as rows. For the data entries, I extract values from {the points on each line plot}<r>[0.154, 0.249], [0.240, 0.228], ..., [0.932, 0.263]</r> for each era across the years.\nBased on the above thought, the answer is:\nYear,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\nHunter-gatherer Eras,0.4,0.45,0.5,0.45,0.4,0.38,0.39,0.4,0.38,0.37\nAgricultural development,0.35,0.4,0.44,0.47,0.49,0.51,0.57,0.65,0.7,0.75\nIndustrial Revolution,0.3,0.35,0.39,0.43,0.46,0.48,0.6,0.72,0.76,0.89"
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "02mMT7P42XzyhXJn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of civilization development vary across the three eras presented?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the trends across the three eras, I look at {the lines for each era}<r>[0.154, 0.249], [0.932, 0.263]</r>, <r>[0.154, 0.600], [0.932, 0.426]</r>, and <r>[0.154, 0.951], [0.932, 0.694]</r>. The Hunter-gatherer era experiences a rise followed by stability and slight declines, indicating a slow and steady trend. The Agricultural development era shows consistent growth, suggesting rapid expansion. Meanwhile, the Industrial Revolution presents the most dynamic growth, particularly a steep ending rise, indicating accelerated development and technological progress.\nBased on the above thought, the answer is:\nThe trends vary significantly across the three eras. The Hunter-gatherer era shows moderate and somewhat stable development, the Agricultural development displays continuous growth, and the Industrial Revolution era exemplifies a sharp incline, reflecting rapid advancements. This indicates that civilization development has accelerated over time with each subsequent era."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "QkFLgzK28sIFClDm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth in the 'Industrial Revolution' era from the earliest to the latest year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth in the 'Industrial Revolution' era, I observe {the points specifically for the 'Industrial Revolution'}<r>[0.154, 0.951], [0.932, 0.694]</r>. The value at 0.1 year is 0.3 and at 1.0 year is 0.89. So, the growth is calculated by subtracting the value at year 0.1 from the value at year 1.0, which is 0.89 - 0.3 = 0.59.\nBased on the above thought, the answer is:\n0.59"
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "SD4HO4Pk3XyioOzH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the data for the Fossil Fuels' efforts from the line chart into a tabular format organized by years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will begin by examining {the x-axis labels}<r>[0.118, 0.960, 0.200, 0.968]</r> to identify the years for the columns. Then, I focus on {the line for Fossil Fuels}<r>[0.172, 0.272, 0.439, 0.273]</r> to observe the respective effort values corresponding to each year marked on the x-axis. By tracking the y-value at each x-position, I can accurately retrieve the effort data.\nBased on the above thought, the answer is:\nYear,Effort in Fossil Fuels\n2007,150\n2008,145\n2009,142\n2010,135\n2011,125\n2012,115\n2013,110\n2014,105\n2015,102\n2016,100\n2017,95\n2018,90\n2019,85\n2020,80\n2021,75"
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "xP4YkREO5SOiiCF7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the trends in Fossil Fuels and Renewable Sources compare, and what might be inferred about future resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the trends from {the line chart for Fossil Fuels}<r>[0.172, 0.272, 0.439, 0.273]</r> which shows a declining trend, and {the line chart for Renewable Sources}<r>[0.172, 0.095, 0.439, 0.114]</r> displaying a slowly increasing trend, it is evident that there is a shift in emphasis from Fossil Fuels to Renewable Sources. This shift could suggest a future strategy focusing more on sustainable energy sources to meet energy demands.\nBased on the above thought, the answer is:\nThe comparison between the two trends indicates a decreasing dependency on Fossil Fuels and an increasing reliance on Renewable Sources. This suggests that future resource allocation will likely favor more sustainable and environmentally friendly energy sources, aiming for a balanced energy mix that supports both environmental preservation and energy security."
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "WSk9LPQ875fyQfJ5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decrease in effort for Fossil Fuels from 2007 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total decrease, I need to extract the initial and final effort values for Fossil Fuels. Observing {the Fossil Fuels line's points}<r>[0.172, 0.272], [0.921, 0.923]</r>, the effort starts at 150 in 2007 and ends at 75 in 2021. The total decrease is calculated by subtracting the final effort from the initial effort.\nBased on the above thought, the answer is:\n75"
+            }
+        ],
+        "id_raw": 634
+    },
+    {
+        "id": "AF5NBq3A0C3FFYDv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values from the growth and decline data graphs and list them under appropriate days getting represented as columns and data type as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis of the first subplot}<r>[0.110, 0.614, 0.975, 0.772]</r> which spans from day 1 to day 12, and {the data points}<r>[0.154, 0.847, 0.932, 0.892]</r> of the growth graph, I list the values corresponding to each day. Similarly, for the decline data, I follow the same process with {the x-axis of the second subplot}<r>[0.110, 0.813, 0.975, 0.971]</r> and {its data points}<r>[0.154, 0.892, 0.932, 0.847]</r>.\nBased on the above thought, the answer is:\nDay,Growth,Decline\n1,50,70\n2,52,69\n3,55,68\n4,58,66\n5,62,63\n6,67,60\n7,73,58\n8,80,55\n9,88,52\n10,95,50\n11,98,50\n12,100,50"
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "IjHUSXIBwFrGbCa3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the performance trend based on the rate of growth and decline from day 1 to day 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the growth data line}<r>[0.154, 0.847, 0.932, 0.892]</r>, it shows a steady increase from 50 to 100 over 12 days, indicating a positive trend in whatever metric it measures. For the {decline data line}<r>[0.154, 0.892, 0.932, 0.847]</r>, it starts at 70 and drops to 50, illustrating a clear declining or negative trend over the same period. Comparing both, growth is consistently positive while decline shows regression.\nBased on the above thought, the answer is:\nIt can be inferred that the performance in terms of growth is improving over time as indicated by a steady increase from 50 to 100. On the other hand, there's a regression in the decline figures, moving from 70 down to 50, suggesting that while one aspect improves, another diminishes equivalently over the same interval."
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "eSYOAVqeBifkNBhY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in the growth data from day 1 to day 12?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change, I identify {the data point on day 1}<r>[0.154, 0.847]</r> in the growth data and see it represents 50. Next, I find {the data point on day 12}<r>[0.932, 0.892]</r>, noted as 100. The change is the difference between the values on day 12 and day 1, i.e., 100 - 50.\nBased on the above thought, the answer is:\n50"
+            }
+        ],
+        "id_raw": 635
+    },
+    {
+        "id": "rAlp3imGVJqWFO40",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the scatter plot showing engagement over various time frames into a structured table grouped by engagement type and time frame?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the scatter plot, I note {the x-axis labels}<r>[0.154, 0.951]</r> indicating the time frames, while {the legend}<r>[0.122, 0.037, 0.315, 0.075]</r> denotes different engagement types such as Facebook and Twitter. By analyzing the scatter points for each engagement type across the timeline, {points on the scatter plot}<r>[0.154, 0.057, 0.932, 0.188]</r> and <r>[0.154, 0.136, 0.932, 0.241]</r>, I compile the data into a structured format.\nBased on the above thought, the answer is:\nTime Frame, Facebook Engagement, Twitter Engagement\n0.1, 0.6, 0.5\n0.2, 0.6277777777777778, 0.5222222222222223\n0.3, 0.6555555555555556, 0.5444444444444444\n...\n1.0, 0.85, 0.7"
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "SPQTo8cQNkfn3qpO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the time frames compared in the chart, which engagement type shows a more consistent upward trend, Facebook or Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the scatter plot, I focus on the trajectory of points for both {Facebook}<r>[0.154, 0.057, 0.932, 0.188]</r> and {Twitter}<r>[0.154, 0.136, 0.932, 0.241]</r>. Facebook starts at 0.6 and ends at 0.85, showing a consistent upward trend. Twitter, with points starting at 0.5 and ending at 0.7, also reveals an upward trend but with lesser overall increase than Facebook. Both lines show consistent growth, yet the slope of the Facebook engagement line is steeper, indicating a more pronounced consistent upward movement.\nBased on the above thought, the answer is:\nFacebook shows a more consistent upward trend compared to Twitter. This conclusion is based on examining the trend lines where Facebook's line slope is steeper, indicating higher and more consistent increases in the engagement values over the time frames."
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "tUTJgEAcZwy5zbEf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value recorded for the Facebook engagement between time frames 0.4 and 0.9?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest Facebook engagement value between the specified time frames, I focus on {the points for Facebook}<r>[0.154, 0.057, 0.932, 0.188]</r>. The engagement values from time frame 0.4 to 0.9 are 0.6833333333333333, 0.711111111111111, 0.7388888888888889, 0.7666666666666666, 0.7944444444444444. Calculating the maximum value among them, I determine the highest engagement.\nBased on the above thought, the answer is:\n0.7944444444444444"
+            }
+        ],
+        "id_raw": 638
+    },
+    {
+        "id": "SXxzyF5tMyMDlCLE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and arrange the data from the line chart into a structured table format, showing product stages in columns and sales values in rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the data in the line chart, I examine {the x-axis labels}<r>[0.103, 0.031, 0.975, 0.293]</r>which represent different stages in the supply chain. {The data points}<r>[0.147, 0.162, 0.931, 0.260]</r> give the sales values at each stage. I systematically go through each data point corresponding to its stage to populate the table row with its respective sales value.\nBased on the above thought, the answer is:\nProduct Stages, Raw Material, Procurement, Manufacturing, Distribution, Customer Relations, Supply Chain Management\nSales (in million USD), 20, 40, 70, 85, 65, 100"
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "rmkcZ3El3bns2EXn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat stage depicts the highest sales performance according to the line chart, and what could be the reason for this trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the peak data point}<!--<r>[0.931, 0.260]</r>-->, it is clear that the 'Supply Chain Management' stage shows the highest sales performance. A likely reason for this could be the effective management and optimization of the overall operations and logistics in the supply chain, as this stage involves strategic planning and implementation that potentially maximizes sales and efficiency.\nBased on the above thought, the answer is:\nThe 'Supply Chain Management' stage depicts the highest sales performance. The trend here could be attributed to comprehensive strategies in managing resources, relationships, and logistics that optimize the entire process, thus leading to increased sales and profitability."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "CBCbEPAWbo7MdsQx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_32_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in sales from the 'Raw Material' stage to the 'Supply Chain Management' stage in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I start with the sales value at {the Raw Material stage}<r>[0.147, 0.162]</r> which is 20 million USD, then I find the sales value at {the Supply Chain Management stage}<r>[0.931, 0.260]</r>, which is 100 million USD. The total increase is then obtained by subtracting the initial sales value from the final sales value.\nBased on the above thought, the answer is:\n80 million USD."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "jVc2TwPccEBkhHKA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the line chart into a tabular format with the Quarters in columns and Revenue, Expenses, and Net Profit in rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "The x-axis of the chart {x-axis labels}<r>[0.057, 0.911, 0.077, 0.934]</r> represents the Quarters which will be the columns, and the legend {legend}<r>[0.075, 0.079, 0.214, 0.219]</r> describes the Revenue, Expenses, Net Profit which will be the rows of the table. The data for each category can be found by extracting the y-values of the respective datapoints {data points}<r>[0.067, 0.132, 0.975, 0.560]</r> for Revenue, {data points}<r>[0.067, 0.373, 0.975, 0.661]</r> for Expenses, {data points}<r>[0.067, 0.614, 0.975, 0.808]</r> for Net Profit at each quarter.\nBased on the above thought, the answer is:\nCategory,Q1,Q2,Q3,Q4,Q5,Q6\nRevenue,5,6.3,6.5,7.5,11.4,10.6\nExpenses,3.5,4.2,5.2,6.2,7.2,7.8\nNet Profit,1.5,2.1,1.3,1.3,4.2,2.8"
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "6kPPo9TpfOfxxwwN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the three metrics, Revenue, Expenses, and Net Profit, demonstrates the most significant change over the quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which metrics has the most significant change, I need to observe the fluctuations in the lines for each metric. Examining {the line for Revenue}<r>[0.067, 0.132, 0.975, 0.560]</r>, I see a relatively steady increase, but a noticeable surge from Q4 to Q5. Looking at {the line for Expenses}<r>[0.0670, 0.373, 0.975, 0.661]</r>, it shows a steady incline over the quarters. Lastly, for {Net Profit}<r>[0.067, 0.614, 0.975, 0.808]</r>, it appears to decrease slightly from Q1 to Q4, then dramatically increase in Q5 and decrease again in Q6.\nBased on the above thought, the answer is:\nBased on the line chart, Revenue demonstrates the most significant change over the quarters. The line plot for Revenue exhibits a sharp increase from Q4 to Q5, which is more pronounced compared to gradual inclines and declines in Expenses and Net Profit."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "YzKCj4SEwPuZb9tu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the change in Net Profit from Q1 to Q6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in Net Profit from Q1 to Q6, I need to look at the coordinates of the Net Profit line {Net Profit}<r>[0.067, 0.614, 0.975, 0.808]</r>. The y-coordinate value at Q1 is 1.5 and at Q6 is 2.8. So, the change is |2.8 - 1.5| = 1.3.\nBased on the above thought, the answer is:\nThe change in Net Profit from Q1 to Q6 is 1.3."
+            }
+        ],
+        "id_raw": 640
+    },
+    {
+        "id": "Zq3CRpSgHfvGju42",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a table, using the time in hours as the columns and the machines as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the required table, I initially identify {the x-axis labels}<r>[0.064, 0.821, 0.103, 0.868]</r> to <r>[0.932, 0.821, 0.981, 0.868]</r> that represent the column headers or time in hours. I then recognize {the legend texts of the machines}<r>[0.148, 0.173, 0.238, 0.219]</r> to <r>[0.148, 0.312, 0.238, 0.359]</r> as the row labels representing different machines. To populate the values, I locate {the line points for Machine A}<r>[0.084, 0.443]</r> to <r>[0.957, 0.218]</r>, {the line points for Machine B}<r>[0.084, 0.529]</r> to <r>[0.957, 0.477]</r>, and {the line points for Machine C}<r>[0.084, 0.599]</r> to <r>[0.957, 0.512]</r> and extract the values for each machine at the respective time points.\nBased on the above thought, the answer is:\nMachine,0.00,1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00,9.00,10.00\nMachine A,20,24,30,31,31,30,29,30,33,32,33\nMachine B,15,17,21,22,20,19,17,19,20,19,18\nMachine C,11,13,16,15,14,12,11,15,18,17,16"
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "ReBTH3T0sbHQaVgb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the progression of each machine's efficiency over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the progression of each machine's efficiency over time, I look at each machine\u2019s line on the graph. Looking at {the line for Machine A}<r>[0.084, 0.218, 0.957, 0.443]</r>, the efficiency initially increases, reaches a peak at the 4th hour, and then declines slightly. It then sees some growth towards the 8th hour and finally a slight decline again around the 10th hour.\nRegarding {the line for Machine B}<r>[0.084, 0.408, 0.957, 0.529]</r>, there is a steady increase in efficiency until the 3rd hour, followed by some fluctuations, then a steadily improving efficiency around the 8th hour, and finally some decline.\nLastly, analyzing {the line for Machine C}<r>[0.084, 0.477, 0.957, 0.599]</r>, the efficiency initially rises until the 2nd hour, then sees a dip followed by additional growth in efficiency up to the 9th hour, and finally, the efficiency starts to decrease again.\nBased on the above thought, the answer is:\nFrom the plot, it can be inferred that all three machines have varied efficiency progression over time. Machine A has its peak efficiency around the 3rd hour and then shows a pattern of growth and decline. Machine B shows a general tendency of growth in efficiency but encounters some fluctuations. Machine C exhibits an initial growth, followed by a dip and another period of growth before eventually declining. These efficiency patterns suggest that the effectiveness of these machines can change considerably over different operating hours."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "RStivsMEO5ydVSuq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the efficiency of Machine B increased from the 1st to the 8th hour?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in efficiency for Machine B, I will need to use {the point representing the 1st hour}<r>[0.171, 0.495]</r> and {the point representing the 8th hour}<r>[0.782, 0.443]</r> for Machine B. The efficiency at the 1st hour is 17 and at the 8th hour is 20. The increase in efficiency is then 20 - 17 = 3.\nBased on the above thought, the answer is:\n3."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "AYwYg1WDjStTcfah",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the line chart data into a tabulated format with Years as columns and Development Areas (Urban and Rural) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line chart data, I first identify {the x-axis}<r>[0.507, 0.915, 0.558, 0.963]</r> which represents the columns, and {the legend}<r>[0.116, 0.119, 0.465, 0.329]</r> as rows denoting Urban and Rural development. To populate the table, I find all the points {for Rural}<r>[0.106, 0.099, 0.959, 0.310]</r> and {for Urban}<r>[0.106, 0.841, 0.959, 0.842]</r> corresponding to each Year and extract values.\nBased on the above thought, the answer is:\nDevelopment Areas,1980,1985,1990,1995,2000,2005,2010,2015\nPrecipitation (mm),1200,1180,1250,1300,1400,1450,1600,1650\nTemperature Increase (\u00b0C),0.05,0.07,0.09,0.15,0.25,0.35,0.6,0.8\nSea Level Rise (cm),0.1,0.2,0.3,0.6,1.0,1.5,3.0,4.5"
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "05avJ0QTqh1abmaD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which environmental factor appears to be impacted most significantly by climate change? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To discern which environmental factor appears to be impacted the most, I need to analyze the lines for each. Observing the {line for Precipitation}<r>[0.106, 0.099, 0.959, 0.310]</r>, there is a steady increase from 1200 mm in 1980 to 1650 mm in 2015. In the {line for Temperature Increase}<r>[0.106, 0.841, 0.959, 0.842]</r>, the increments are small but remain consistent over the years, only reaching 0.8\u00b0C by 2015. In contrast, the {line for Sea Level Rise}<r>[0.106, 0.840, 0.959, 0.842]</r> shows the change to be least in the early years but the increase becomes significantly larger as the years pass, reaching a staggering 4.5 cm by 2015.\nBased on the above thought, the answer is:\nAccording to the trends revealed by the line chart, the Sea Level Rise appears to be the environmental factor that is most significantly impacted by climate change. While all three factors display noticeable changes over the period, the increase in the sea level is highly significant compared to the other factors, rising from just 0.1 cm in 1980 to 4.5 cm in 2015. This indicates a faster rate of change, suggesting that the sea level is responding more drastically to the impacts of climate change."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "6RNS8OUkIgdsD8oV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the incremental change in Temperature Increase (\u00b0C) from the year 2000 to 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the incremental change in Temperature Increase (\u00b0C), I need to identify the {point in 2000 for Temperature Increase}<r>[0.593, 0.842]</r> which corresponds to 0.25\u00b0C and {the point in 2005 for Temperature Increase}<r>[0.715, 0.842]</r> which corresponds to 0.35\u00b0C. The difference between these two amounts to the incremental change.\nBased on the above thought, the answer is:\n0.1\u00b0C"
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "vlWRU8arGp2DJtfr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help formalizing the data from this line chart into a table, with the different doses as columns and the treatment types as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely, to create this tabular data, we initiate with {the x-axis tick labels}[[0.078, 0.821, 0.089, 0.868], [0.206, 0.821, 0.217, 0.868], [0.333, 0.821, 0.344, 0.868], [0.460, 0.821, 0.471, 0.868], [0.588, 0.821, 0.599, 0.868], [0.715, 0.821, 0.726, 0.868], [0.843, 0.821, 0.854, 0.868], [0.970, 0.821, 0.981, 0.868]], which will be our column headers representing the different doses. Now, {the legend texts}[[0.859, 0.181, 0.934, 0.241], [0.859, 0.269, 0.957, 0.329], [0.859, 0.357, 0.933, 0.417]] will represent our row labels i.e., the different treatment types. The different dose responses corresponding to each treatment, we pick these from {the line points} like: for Control [[0.084, 0.646], [0.211, 0.503], [0.339, 0.360], [0.466, 0.503], [0.593, 0.675], [0.721, 0.703], [0.848, 0.732], [0.976, 0.746]], for Antibiotic [[0.084, 0.718], [0.211, 0.575], [0.339, 0.431], [0.466, 0.145], [0.593, 0.503], [0.721, 0.617], [0.848, 0.689], [0.976, 0.732]], and for Steroid [[0.084, 0.575], [0.211, 0.360], [0.339, 0.217], [0.466, 0.145], [0.593, 0.360], [0.721, 0.503], [0.848, 0.575], [0.976, 0.646]] at the respective doses.\nBased on the above thought, the answer is:\nTreatment,Dose_1,Dose_2,Dose_3,Dose_4,Dose_5,Dose_6,Dose_7,Dose_8\nControl,10,20,30,20,8,6,4,3\nAntibiotic,5,15,25,45,20,12,7,4\nSteroid,15,30,40,45,30,20,15,10"
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "xVaPhlG7w2yl01kM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich treatment shows a progressive increase in response with every dose?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which treatment shows an increase in response with every dose, I need to examine the points on the lines corresponding to different treatments. Looking at the lines for {Control}[0.084, 0.360, 0.976, 0.746], {Antibiotic}[0.084, 0.145, 0.976, 0.732], and {Steroid}[0.084, 0.145, 0.976, 0.646], it seems only the line for Steroid shows a consistent increase in response in the beginning. However, the response level peaks at Dose 4 and starts to decline after that. Hence, none of the treatments show a consistent increase in response with every dose.\nBased on the above thought, the answer is:\nNone of the treatments show a progressive increase in response with every dose. All treatments show an initial increase followed by a decrease at higher doses."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "Lj7lyliUgPRxYDXS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the response vary for Antibiotics treatment from the initial to the final dose?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to get the response for Antibiotic treatment for the initial dose and the final dose. I could locate the initial response for Antibiotic treatment at {Dose_1}[0.084, 0.821] to be 5 and the final response for Antibiotic treatment at {Dose_8}[0.976, 0.821] to be 4. The variance is simply the difference of these responses, which can be calculated as initial dose response minus the final dose response.\nBased on the above thought, the answer is:\n1 (5-4)."
+            }
+        ],
+        "id_raw": 643
+    },
+    {
+        "id": "iA20fJPa8G3HTvyu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data about tourism and hospitality industry income into a structured table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. First, I'm studying {the labels on the x-axis}<r>[0.041, 0.893, 0.101, 0.921],[0.168, 0.893, 0.228, 0.921],[0.294, 0.893, 0.354, 0.921],[0.420, 0.893, 0.480, 0.921],[0.546, 0.893, 0.606, 0.921],[0.672, 0.893, 0.733, 0.921],[0.799, 0.893, 0.859, 0.921],[0.925, 0.893, 0.985, 0.921]</r>, which correspond to different Quarters. Then, I'm identifying {the legend texts}<r>[0.822, 0.109, 0.940, 0.145],[0.822, 0.161, 0.900, 0.197],[0.822, 0.214, 0.924, 0.250]</r> that represent the different sectors in the industry. The data points for each sector at the respective quarters are represented by {the lines}<r>[0.071, 0.556, 0.955, 0.754],[0.071, 0.118, 0.955, 0.476],[0.071, 0.238, 0.955, 0.436]</r>. From these coordinates, I can extract the income values for each sector across the quarters.\nBased on the above thought, the answer is:\nQuarters,Hotel Industry,Air Travel,Tour Agency\n2020-Q1,350000,900000,750000\n2020-Q2,200000,600000,650000\n2020-Q3,150000,500000,550000\n2020-Q4,200000,650000,600000\n2021-Q1,250000,700000,650000\n2021-Q2,300000,850000,700000\n2021-Q3,350000,900000,750000\n2021-Q4,400000,950000,800000"
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "rDHoODBcWCuWknIc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, how would you describe the overall performance of the Air Travel sector throughout the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the performance of the Air Travel sector, I need to consider {the line points for Air Travel}<r>[0.071, 0.158], [0.198, 0.397],[0.324, 0.476],[0.450, 0.357],[0.576, 0.317],[0.702, 0.198],[0.829, 0.158],[0.955, 0.118]</r>. Initially, there's a decrease in income from 2020-Q1 to 2020-Q3, indicating a downturn. This is followed by a recovery from 2020-Q3 to 2020-Q4 but then income drops again from 2020-Q4 through to 2021-Q2. It shows slight improvement in income through to 2021-Q4 but overall, the Air Travel sector seems to have had significant challenges as there were more quarters of declining income than those showing growth.\nBased on the above thought, the answer is:\nThe overall performance of the Air Travel sector throughout the given period can be described as challenging. The sector saw a decline in income in the majority of quarters, despite a few periods of recovery. This trend suggests that the sector struggled to maintain consistent growth, possibly due to external factors like a downturn in the general tourism and hospitality industry or perhaps more specific challenges such as global travel restrictions."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "1qGmm8bB7jc8Hvw0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the income of the Hotel Industry increase from 2020-Q4 to 2021-Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in income for the Hotel Industry, I firstly locate {the line points for Hotel Industry}<r>[0.450, 0.714], [0.955, 0.556]</r> which represent the income at 2020-Q4 and 2021-Q4 respectively. By extracting the corresponding values from the points, we get 200000 (for 2020-Q4) and 400000 (for 2021-Q4). The increase can be calculated by subtracting the income of 2020-Q4 from that of 2021-Q4, which is 400000 - 200000 = 200000.\nBased on the above thought, the answer is:\n200000."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "W4UUva20Eog9525t",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the plotted line data on the chart into a table with 'Years' as columns and 'Climate Trend' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can convert the chart data into tabular form. The column headers or 'Years' are represented by {the x-axis tick labels}<r>[0.072, 0.821, 0.117, 0.868], [0.196, 0.821, 0.240, 0.868], [0.320, 0.821, 0.364, 0.868], [0.443, 0.821, 0.487, 0.868], [0.567, 0.821, 0.611, 0.868], [0.690, 0.821, 0.734, 0.868], [0.814, 0.821, 0.858, 0.868], [0.937, 0.821, 0.981, 0.868]</r>, while the row labels or 'Climate Trends' can be implied from the legend {the legend labels}<r>[0.676, 0.181, 0.899, 0.241], [0.676, 0.269, 0.916, 0.329], [0.676, 0.357, 0.941, 0.417]</r>. To populate the table with values, I identified the line points for each trend from {the line points}<r>[0.095, 0.309], [0.218, 0.296], ..., [0.959, 0.159]</r>, [0.095, 0.785], ..., [0.959, 0.783], and [0.095, 0.789], ..., [0.959, 0.787].\nBased on the above thought, the answer is:\nClimate Trend,1990,1995,2000,2005,2010,2015,2020,2025\nCO2 Emissions (ppm),350,360,370,380,400,420,440,460\nSea Level Rise (inches),3.1,3.2,3.4,3.6,3.7,3.9,4.0,4.2\nTemperature Change (\u00b0C),0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "B5exdjYZz8wbUh4r",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data, can we infer a relationship of CO2 Emissions with Global Temperature Change and Sea Level Rise?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the lines for CO2 Emissions{the line for CO2 Emissions}<r>[0.095, 0.159, 0.959, 0.309]</r>, Temperature Change{the line for temperature change}<r>[0.095, 0.787, 0.959, 0.789]</r>, and Sea Level Rise{the line for sea level rise}<r>[0.095, 0.783, 0.959, 0.785]</r>, we can see a general upward trend in all three aspects over the years shown in the chart. This suggests that as CO2 Emissions increase, both the Global Temperature Change and Sea Level Rise also increase at a similar rate.\nBased on the above thought, the answer is:\nYes, based on the trends displayed in the line chart, it can be inferred that there is a relationship of CO2 Emissions with both Global Temperature Change and Sea Level Rise. The common upward trend across all three aspects suggests that increase in CO2 emissions correlates with an increase in global temperature and sea level, which is consistent with known scientific facts about climate change. However, precise correlation coefficients cannot be determined from a line chart without further statistical analysis."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "MU1oOOfZxRZIDoIN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in global CO2 Emissions from 1990 to 2025 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in CO2 emissions from 1990 to 2025, we need to locate the line points for CO2 Emissions from the legend {the legend CO2 Emissions}<r>[0.676, 0.181, 0.899, 0.241]</r>. After obtaining the points, we notice that the first point in 1990 {the point for 1990}<r>[0.095, 0.309]</r> indicates a value of 350 and the last point in 2025 {the point for 2025}<r>[0.959, 0.159]</r> indicates a value of 460. We then subtract the first value from the last one to get the total increase.\nBased on the above thought, the answer is:\n110."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "KViYNTF7utY2lBM0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart and organize it into a structured table format with Longitude as columns and Latitude as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll identify {the x-axis tick labels}[[0.072, 0.821, 0.128, 0.868], [0.195, 0.821, 0.250, 0.868], [0.317, 0.821, 0.372, 0.868], [0.439, 0.821, 0.495, 0.868], [0.565, 0.821, 0.614, 0.868], [0.687, 0.821, 0.737, 0.868], [0.809, 0.821, 0.859, 0.868], [0.932, 0.821, 0.981, 0.868]], that represent Longitude values. Then, {the line points}[[0.100, 0.625], [0.222, 0.542], [0.345, 0.706], [0.467, 0.747], [0.589, 0.768], [0.712, 0.747], [0.834, 0.542], [0.957, 0.172]] will serve as Latitude values for the corresponding Longitudes. The structure of the table will have Longitude as columns and Latitude as rows.\nBased on the above thought, the answer is:\nLongitude,-1.934,-1.387,-0.740,-0.165,0.319,0.921,1.674,2.538\nLatitude,4.012,6.049,2.035,1.018,0.51,1.021,6.036,15.091"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "5jXTbNKisrOa3Y1K",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the trend for the geographic data plot upon observing the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend for geographic data plot can be understood by examining {the line plot}[[0.100, 0.172, 0.957, 0.768]]. It shows a significant variation of Latitude values at different Longitudes. Starting from a Latitude of 4.012 at -1.934 Longitude, the line shows a significant rise reaching maximum around -1.387 Longitude with a value of 6.049. Then it takes a sharp decline to 2.035 at -0.740 Longitude followed by a slow rise until it reaches 1.021 at 0.921 Longitude. Then, it takes a significant upward leap to 6.036 at 1.674 Longitude and then rises sharply to a maximum value of 15.091 at the Longitude of 2.538. This indicates vast geographic diversity in the data.\nBased on the above thought, the answer is:\nThe geographic data plot depicted in the line chart exhibits considerable variation across different Longitudes. The Latitude values rise, fall, and rise sharply again, indicating a diverse geographic range."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "7MOaYOJLf5oede05",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of Latitude values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of Latitude values, I first need to locate {the line points}[[0.100, 0.625], [0.222, 0.542], [0.345, 0.706], [0.467, 0.747], [0.589, 0.768], [0.712, 0.747], [0.834, 0.542], [0.957, 0.172]] and extract the corresponding Latitude values: 4.012, 6.049, 2.035, 1.018, 0.51, 1.021, 6.036, 15.091. The maximum value is 15.091 and the minimum value is 0.51. The difference between these two values, or the range, is 15.091 - 0.51 = 14.581.\nBased on the above thought, the answer is:\n14.581."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "sGZANl6MuwaNShfk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart into a table format where each row is a Social Media platform and each column is Time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a structured table, I'll start by identifying the column headers which are the labels on the {x-axis}<r>[0.084, 0.789, 0.976, 0.821]</r>: 0, 1, 2, 3, 4, 5, 6, 7. These represent the Time data in our table. Then I locate the row headers which are provided by the {legend texts}<r>[0.851, 0.181, 0.949, 0.241]</r>, <r>[0.851, 0.269, 0.921, 0.329]</r>, <r>[0.851, 0.357, 0.957, 0.417]</r>, standing for different Social Media platforms: Facebook, Twitter, Instagram. To fill in the values in the table, I pinpoint the corresponding data points in the line of each social media platform, for example the {line for Facebook}<r>[0.084, 0.170, 0.976, 0.340]</r> whose usage rates are 80, 75, 71, 70, 67, 65, 61, 58 at each time point respectively.\nBased on the above thought, the answer is:\nSocial Media,0,1,2,3,4,5,6,7\nFacebook,80,75,71,70,67,65,61,58\nTwitter,45,46,52,58,64,70,75,79\nInstagram,35,37,38,42,51,55,57,61"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "Z3FIGu39FSsNwgKd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends depicted in the graph, which social media platform displays the most growth over time? Provide your justification."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social media platform has exhibited the most growth over time, I'll analyze the changes in the y-values for each platform's line from Time 0 to Time 7. Observing {the line of Facebook}<r>[0.084, 0.170, 0.976, 0.340]</r>, I see a decrease from 80% to 58%, thus it's experiencing a downtrend. Upon inspecting {the line of Twitter}<r>[0.084, 0.178, 0.976, 0.441]</r>, the usage jumps from 45% to 79% indicating a substantial uptrend. Lastly, looking at {the line of Instagram}<r>[0.084, 0.317, 0.976, 0.518]</r>, the usage increases from 35% to 61% which represents a positive but lesser growth compared to Twitter.\nBased on the above thought, the answer is:\nBased on the trends depicted in the graph, Twitter has displayed the most growth over the Time period. It has experienced a significant uptrend from a usage rate of 45% to 79%, which is the largest increase among the three social media platforms."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "4K0yoVhRrzDRLikE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in usage of Instagram from Time 0 to Time 7?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in usage of Instagram, I need to check the usage rate at Time 0 and Time 7. I locate the {first data point for Instagram}<r>[0.084, 0.518]</r> and find that the usage is 35, then I go to the {last data point for Instagram}<r>[0.976, 0.317]</r> and see that the usage is 61. The increase in usage would be the value at Time 7 minus the value at Time 0, which is calculated as 61 - 35 = 26.\nBased on the above thought, the answer is:\n26%"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "pjkNubmHueWsWEmB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the bar chart into a structured table presenting trade volumes in different regions for high and low volumes?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I first locate {the bar labels}[0.055, 0.457, 0.055, 0.865, 0.055, 0.196, 0.055, 0.603, 0.055, 0.724] as the row labels representing the different regions. Then, I locate {the legend indicators}[[0.754, 0.159, 0.949, 0.439]] to identify the two categories - High Volume and Low Volume. Lastly, I identify {the bar values}[[0.066, 0.430, 0.581, 0.787, 0.066, 0.169, 0.371, 0.577, 0.066, 0.698]] for each category in the respective regions.\nBased on the above thought, the answer is:\nRegion, Low Volume, High Volume\nSouth America, 500, 1000\nNorth America, 550, 1100\nEurope, 600, 1200\nAsia, 1650, 3050\nAfrica, 400, 800"
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "EnqszZjrXaAw6Jye",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the regional distribution of trade volumes, as shown by the horizontal bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the bars for all regions}[[0.063, 0.447, 0.469, 0.481], [0.063, 0.500, 0.574, 0.534], [0.063, 0.186, 0.372, 0.221], [0.063, 0.586, 0.316, 0.619], [0.063, 0.238, 0.459, 0.271], [0.063, 0.333, 0.371, 0.366], [0.063, 0.384, 0.470, 0.417], [0.063, 0.708, 0.732, 0.742], [0.063, 0.281, 0.334, 0.314], [0.063, 0.635, 0.813, 0.669]], the chart shows a varying range of values across different regions. Combining this with the details specified {in the legend}[[0.754, 0.159, 0.949, 0.439]], it can be observed that Asia clearly stands out with a far higher trade volume in both High and Low Volume categories, indicating more intense activity. Europe and North America follow, with moderate levels of trade volumes. South America and Africa seem to differ only slightly, showing somewhat lower trade volumes, with Africa being at the lower end of the scale.\nBased on the above thought, the answer is:\nThe chart presents a wide regional disparity in trade volumes. Asia is illustrated as the largest trading region by far, consistent for both Low and High volume categories, indicating intense trading activity. Europe and North America follow, with moderately high volumes. South America and Africa exhibit less trading activity with Africa having the lowest volumes. The graph underscores Asia's significant role in global trade and the need for other regions to increase their trade activities."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "w2HMq8hqcMHLzWwZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total trade volume for Asia across both Low and High volume categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total trade volume for Asia, I extract {the bar value for Low Volume for Asia}[0.561, 0.694, 0.726, 0.733] which is 1650, and {the bar value for High Volume for Asia}[0.346, 0.743, 0.865, 0.771] which is 3050. The total trade volume for Asia would then be the sum of these two values, i.e. 1650 + 3050 = 4700.\nBased on the above thought, the answer is:\n4700."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "W8BfPrTiOdNs4OzO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please translate the given plot about fitness and sports health trends into a structured table format with years as rows and types of activities as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To translate the plot data into a table format, I first take note of {the x-axis labels}<r>[0.051, 0.821, 0.981, 0.868]</r>, these are the row labels representing the years. Then, I recognize {the legend items}<r>[0.798, 0.159, 0.949, 0.439]</r> as the column headers indicating the three different types of activities: Yoga, Cycling and Cardio. To fill in the values, I need to identify the coordinates of {the points}<r>[0.073, 0.507, 0.959, 0.413]</r> for each activity type at the respective years and use these to extract the data values.\nBased on the above thought, the answer is:\nYear,Yoga,Cycling,Cardio \n2010,3,2,1.5\n2011,5,4,6.8\n2012,6,3.5,7.2\n2013,5.5,3.7,8\n2014,4,5,9\n2015,4.5,5.5,8.5\n2016,6,6,9\n2017,4,7,9.5"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "aOmRlYpXvOP7XpwF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which exercise trend experienced the most fluctuations in popularity from 2010 to 2017? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which exercise trend had the most fluctuations, I need to examine the changes in the line plots for each activity type. By analyzing {the line plot for Yoga}<r>[0.073, 0.225, 0.959, 0.507]</r>, I see a changing trend from 3 to 5 to 6, then decreasing to 5.5 and 4, then increasing to 4.5 and 6, and finally decreasing to 4, representing significant volatility. For {Cycling}<r>[0.073, 0.131, 0.959, 0.601]</r>, the trend is 2, 4, 3.5, 3.7, 5, 5.5, 6, and 7, showing subtle fluctuations, but they aren't as significant as those of Yoga. Looking at {Cardio}<r>[0.073, -0.104, 0.959, 0.648]</r>, a constant growth pattern is observed, indicating relatively low fluctuations.\nBased on the above thought, the answer is:\nFrom 2010 to 2017, Yoga experienced the most fluctuations in popularity. The line plot for Yoga exhibits significant ups and downs, indicating that the popularity of Yoga experienced notable fluctuations during this period. The fluctuations in Cardio and Cycling are comparatively less."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "X6JSwl2OUZNzqZlz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the gain from the lowest to the highest popularity level for Cardio between the years 2010 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "As for determining the gain in popularity for Cardio, I have to identify {the line points representing Cardio}<r>[0.073, 0.648, 0.959, -0.104]</r> and extract the corresponding values: 1.5, 6.8, 7.2, 8, 9, 8.5, 9, 9.5. The lowest value is 1.5 in {2010}<r>[0.073, 0.789]</r> and the highest value is 9.5 in {2017}<r>[0.959, 0.789]</r>. Thus, the popularity gain for Cardio is 9.5 - 1.5 = 8.\nBased on the above thought, the answer is:\n8"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "90SLrnta3NNk6W8j",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data illustrating the residential, business and public facilities development over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify the columns {Years in x-axis labels}<r>[0.051, 0.821, 0.095, 0.868], [0.177, 0.821, 0.221, 0.868], [0.304, 0.821, 0.348, 0.868], [0.431, 0.821, 0.475, 0.868], [0.557, 0.821, 0.601, 0.868], [0.684, 0.821, 0.728, 0.868], [0.811, 0.821, 0.855, 0.868], [0.937, 0.821, 0.981, 0.868]</r>. And the rows represent different kinds of development: {Residential Development}<r>[0.680, 0.181, 0.941, 0.241]</r>, {Business Development}<r>[0.680, 0.269, 0.918, 0.329]</r>, and {Public Facilities}<r>[0.680, 0.357, 0.837, 0.417]</r>. Then, I look into the data points of these developments over the years located in {line points for Residential Development}<r>[0.073, 0.701], [0.199, 0.657], [0.326, 0.570], [0.453, 0.394], [0.579, 0.219], [0.706, 0.482], [0.833, 0.526], [0.959, 0.570]</r>, {line points for Business Development}<r>[0.073, 0.736], [0.199, 0.719], [0.326, 0.684], [0.453, 0.614], [0.579, 0.394], [0.706, 0.526], [0.833, 0.570], [0.959, 0.614]</r> and {line points for Public Facilities}<r>[0.073, 0.745], [0.199, 0.741], [0.326, 0.732], [0.453, 0.728], [0.579, 0.723], [0.706, 0.719], [0.833, 0.715], [0.959, 0.710]</r>.\nBased on the above thought, the answer is:\nYears,Residential Development,Business Development,Public Facilities\n2000,1.0,0.6,0.5\n2005,1.5,0.8,0.55\n2010,2.5,1.2,0.65\n2015,4.5,2.0,0.7\n2020,6.5,4.5,0.75\n2025,3.5,3.0,0.8\n2030,3.0,2.5,0.85\n2035,2.5,2.0,0.9"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "YCFPLASQ9dWw3ebC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, which area witnessed the greatest fluctuation in their growth rates over the years? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To assess which sector has had the greatest fluctuation in its growth rates, I examine the significant changes in each line chart. For {Residential Development}<r>[0.073, 0.219, 0.959, 0.701]</r>, there is an initial steep rise and then a noticeable decline. {Business Development}<r>[0.073, 0.394, 0.959, 0.736]</r> has a gradual increase, then a sharp rise before getting stable. {Public Facilities}<r>[0.073, 0.710, 0.959, 0.745]</r> growth rate has a mild but steady progress with time.\nBased on the above thought, the answer is:\nThe Residential Development sector has witnessed the greatest fluctuation in their growth rates over the years. The line chart for residential development shows a notable upward trend from 2000 to 2020 followed by a sharp decrease. This significant rise and fall indicate high variability in the growth rates of the residential development sector over this period."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "IMgAqSyIEWAmgqN2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the overall growth rate for Business Development from 2000 to 2035?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the overall growth rate for Business Development from 2000 to 2035, I need to subtract the first value {at 2000}<r>[0.073, 0.736]</r> from the last value {at 2035}<r>[0.959, 0.614]</r>. The growth rate can be calculated as (2.0 - 0.6) / (2035 - 2000).\nBased on the above thought, the answer is:\n0.045."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "flgGz8FsdU5wz5Fq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from the line chart, can you construct a table format with Years as columns and Trade Volume, GDP Growth, and Gini Index as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart data, I'll first look at {the x-axis ticks}<r>[0.127, 0.789, 0.959, 0.868]</r> that represent the column headers or years. Next, I analyze the line points for each data type: {Trade Volume}<r>[0.127, 0.162, 0.959, 0.648]</r>, {GDP Growth}<r>[0.127, 0.789, 0.959, 0.789]</r>, and {Gini Index}<r>[0.127, 0.789, 0.959, 0.789]</r>, which correspond to the individual rows. The values at each point are then used to populate the cells of the table.\nBased on the above thought, the answer is:\nYears, 1990, 2000, 2010, 2020, 2030, 2040, 2050\nTrade Volume, 45000, 60000, 80000, 120000, 150000, 180000, 200000\nGDP Growth, 8, 7.6, 7, 5, 4.5, 4.2, 4\nGini Index, 0.31, 0.35, 0.4, 0.45, 0.47, 0.48, 0.5"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "6N2EOdI4xbJn38Hz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you describe the observed trends in Trade Volume, GDP Growth, and Gini Index over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the lines that represent each of these parameters. The {line representing Trade Volume}<r>[0.127, 0.162, 0.959, 0.648]</r> shows a steady increase from 45000 in 1990 to 200000 in 2050. This implies a consistent growth in international trade over time.\nThe {line representing GDP Growth}<r>[0.127, 0.789, 0.959, 0.789]</r> shows a decrease from 8 in 1990 down to 4 in 2050. This infers a reduction in global GDP growth rates, possibly due to various economic and geopolitical factors.\nThe {line for Gini Index}<r>[0.127, 0.789, 0.959, 0.789]</r> portrays an increase from 0.31 in 1990 to 0.5 in 2050. This indicates a rise in income equality globally, which could be due to policy decisions or economic trends.\nBased on the above thought, the answer is:\nLooking at the chart, Trade Volume presents an increasing trend over the years, suggesting that global trade has been consistently expanding from 1990 to 2050. The GDP Growth, on the other hand, shows a decreasing trend, indicating slowing global growth rates across these years. Finally, the Gini Index exhibits a rising trend, suggesting increasing income inequality over this period."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "jT60eZW5jyv5Lh1I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Trade Volume increased from the year 1990 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in Trade Volume from 1990 to 2050, I need to locate the {Trade Volume in 1990}<r>[0.127, 0.648]</r> and the {Trade Volume in 2050}<r>[0.959, 0.162]</r>. Extracting the values, I see that the Trade Volume was 45000 in 1990 and 200000 in 2050. The difference between these values gives the increase in Trade Volume over this period.\nBased on the above thought, the answer is:\n155000."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "cQ6zyEaSgQ3V18jP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive a table for the years in the chart against the corresponding production quantity for each of Corn, Wheat and Barley?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the requested table, I'll reference the {x-axis labels}<r>[0.076, 0.893, 0.986, 0.921]</r> which signifies the years. Next, by taking the points of three line plots, i.e. {the Corn line plot}<r>[0.093, 0.079, 0.968, 0.324]</r>, {the Wheat line plot}<r>[0.093, 0.611, 0.968, 0.677]</r>, and {the Barley line plot}<r>[0.093, 0.783, 0.968, 0.815]</r>, I can extract the corresponding production quantity for each year.\nBased on the above thought, the answer is:\nYear,Corn_production,Wheat_production,Barley_production\n2010,34587,12345,5678\n2011,35345,12467,5600\n2012,36000,12789,5300\n2013,38500,13200,5200\n2014,39000,13500,5100\n2015,41200,14000,5000\n2016,43200,14500,4800\n2017,45000,15200,4500\n2018,47000,15800,4300\n2019,48000,16000,4000\n2020,50000,16500,3700"
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "sCum84BYxgQX5k27",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which food item had the most consistent increase in production over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the slopes of the {Corn line plot}<r>[0.093, 0.079, 0.968, 0.324]</r>, {Wheat line plot}<r>[0.093, 0.611, 0.968, 0.677]</r> and {Barley line plot}<r>[0.093, 0.783, 0.968, 0.815]</r>. Going by the slopes, the Corn line plot seems to be increasing constantly over time without any decrease.\nBased on the above thought, the answer is:\nCorn had the most consistent increase in production over the given period. The line chart demonstrates that corn production quantity consistently increased each year, marking a steady and robust growth trajectory."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "pw669Ff2NAUN3Zga",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the production quantity of Barley changed from the year 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in production quantity of Barley, I first identify the point for Barley in the year 2013 from {the Barley line plot}<r>[0.093, 0.783, 0.968, 0.815]</r>, which yields value '5200'. I then find the corresponding point in the year 2020, yielding value '3700'. The difference between these two values will give the required production quantity change.\nBased on the above thought, the answer is:\n-1500"
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "kjNESjmv6a47eKCU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart information into a table showing the Year, Grains_Production, Fruits_Imports, and Vegetables_Export?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into tabular format, I locate {the x-axis}[[0.072, 0.821, 0.981, 0.868]] as the Year column. Then, I identify the three lines, {Grains_Production}[0.095, 0.620, 0.959, 0.699], {Fruits_Imports}[0.095, 0.577, 0.959, 0.718], and {Vegetables_Export}[0.095, 0.429, 0.959, 0.507] to represent the other columns respectively. At each x-axis tick, I extract the corresponding y-value for each line to fill the table.\nBased on the above thought, the answer is:\nYear,Grains_Production,Fruits_Imports,Vegetables_Export\n1990,120,50,200\n1995,105,55,215\n2000,98,60,227\n2005,92,80,235\n2010,91,90,245\n2015,82,110,248\n2020,75,135,255\n2025,64,150,245"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "2gxGGzvn7uwJlC8k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which of the three categories showed the most growth from 1990 to 2025 and please provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "Assessing the chart, there are lines for {Grains_Production}[0.095, 0.620, 0.959, 0.699], {Fruits_Imports}[0.095, 0.577, 0.959, 0.718], and {Vegetables_Export}[0.095, 0.429, 0.959, 0.507]. Grains_Production decreased from 120 in 1990 to 64 in 2025. Fruits_Imports shows an increase from 50 in 1990 to 150 in 2025. Similarly, Vegetables_Export also shows an increase, but from 200 in 1990 to 245 in 2025. Hence, it appears that the Fruits_Imports has the most growth.\nBased on the above thought, the answer is:\nThe Fruits Imports category has shown the most growth from 1990 to 2025. This is because it starts at a value of 50 in 1990 and rises to 150 in 2025, a difference of 100 units. While Vegetables Export also increased during this time frame, it did so by just 45 units. Meanwhile, Grains Production decreased over this window of time, reducing by 56 units. So, between these three categories, Fruits Imports showed the most growth."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "FyQcVgvAz0tao2Fi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 1990 to 2025, what is the total decrease in Grains Production?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total decrease, I'll evaluate the points on the {Grains_Production}[[0.095, 0.620, 0.959, 0.699]] line. The starting point in 1990 is {120}[0.095, 0.620] and the end point in 2025 is {64}[0.959, 0.699]. Therefore, the total reduction equals 120 - 64.\nBased on the above thought, the answer is:\n56."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "f6zrCdcEUxl2Irb3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract and structure the data presented in the line chart into a table format with Years as columns and Urban/Rural Development as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to get the raw data from the line chart into a structured table, I first identify {the x-axis tick labels}[[0.051, 0.911, 0.095, 0.934], [0.272, 0.911, 0.316, 0.934], [0.494, 0.911, 0.538, 0.934], [0.716, 0.911, 0.760, 0.934], [0.938, 0.911, 0.981, 0.934]], which represent the column headers or years. Next, I locate {the legend texts}[[0.878, 0.091, 0.932, 0.121], [0.878, 0.134, 0.941, 0.164]] which represents the rows of the table corresponding to Rural and Urban development. For each type of development, I identify {the line points for Rural}[[0.073, 0.685], [0.294, 0.406], [0.516, 0.205], [0.738, 0.188], [0.959, 0.170]] and {the line points for Urban}[[0.073, 0.563], [0.294, 0.345], [0.516, 0.223], [0.738, 0.179], [0.959, 0.153]] and extract the corresponding data values for each year.\nBased on the above thought, the answer is:\nDevelopment,2000,2005,2010,2015,2020\nRural,2.4,5.6,7.9,8.1,8.3\nUrban,3.8,6.3,7.7,8.2,8.5"
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "mgLrZIvsotXtbuk8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the depicted line chart, which area, Rural or Urban, has seen a more steady growth in development from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the line plots for Rural and Urban areas. For instance, looking at {the line plot for Rural development}[0.073, 0.170, 0.959, 0.685], it shows a considerable growth from 2.4 in 2000 to 8.3 in 2020, with a continuous increase over the years. {The Urban development line plot}[0.073, 0.153, 0.959, 0.563], on the other hand, displays a similar pattern, but with slight fluctuations between 2010 and 2020. Both trends show continuous growth, but Rural development appears slightly smoother over the 20 years period.\nBased on the above thought, the answer is:\nThe line chart suggests that the Rural area has seen a more steady development growth from 2000 through 2020, with a continuous linear increase, while the Urban area, despite a similar growth pattern, shows slight fluctuations between 2010 and 2020, indicating less steadiness."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "6hQxZY4zM0wcW8HV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_33_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the Urban development percentage exceed Rural development percentage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the Urban and Rural values for 2020. I locate {the Urban value for 2020}[0.959, 0.153] and determine it as 8.5. I find {the Rural value for 2020}[0.959, 0.170] and determine it as 8.3. Therefore, to conclude the difference I need to compute the Urban value minus the Rural value: 8.5 - 8.3.\nBased on the above thought, the answer is:\n0.2."
+            }
+        ],
+        "id_raw": 659
+    },
+    {
+        "id": "dw2ukKesOOmwmPa8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data values from the line chart and structure them into a table with metric categories as the column headers and community types as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I identify {the labels on the y-axis}[[0.040, 0.753, 0.054, 0.788], [0.025, 0.669, 0.054, 0.704], [0.025, 0.585, 0.054, 0.620], [0.025, 0.501, 0.054, 0.536], [0.025, 0.416, 0.054, 0.451], [0.025, 0.332, 0.054, 0.367], [0.025, 0.248, 0.054, 0.283], [0.025, 0.164, 0.054, 0.199]] as the different metrics which will be the column headers. Subsequently, I take note of {the legends}[[0.518, 0.899, 0.715, 0.934], [0.826, 0.899, 0.984, 0.934]] as the row headers indicating the community types. To populate the table cells, I locate {the line point values}[[0.062, 0.178], [0.126, 0.099], [0.185, 0.051], [0.237, 0.025], [0.332, 0.011], [0.413, 0.011], [0.511, 0.011], [0.607, 0.011], [0.722, 0.011], [0.810, 0.011]] for each community type under the corresponding metrics.\nBased on the above thought, the answer is:\nMetrics,Traditional Communities,Modern Communities\nPopulation Density,3200,800\nUrbanization Rate,60,95\nLocal Businesses Establishment,150,220"
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "TJHMZy9AmErnLub6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category shows the largest difference between the Traditional and Modern Communities? Provide a brief explanation of your process."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the difference between Traditional and Modern Communities for each category. Looking at {the line point for Population Density for Traditional Communities}[0.062, 0.178] and for Modern Communities[0.062, 0.430], the difference is 3200 - 800 = 2400 persons per square mile. For {the line point for Urbanization Rate for Traditional Communities}[0.126, 0.211] and for Modern Communities[0.126, 0.099], the difference is 95% - 60% = 35%. For {the line point for Local Businesses Establishment for Traditional Communities}[0.185, 0.051] and for Modern Communities[0.185, 0.025], the difference is 220 - 150 = 70 businesses per year. Hence, the largest difference between Traditional and Modern communities occurs in the category of Population Density.\nBased on the above thought, the answer is:\nThe largest difference between Traditional and Modern Communities is observed in the category of Population Density, with a difference of 2400 persons per square mile, which is significantly higher compared to the differences in the rates of Urbanization and Local Businesses Establishment."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "BBDEdWXdfp7IIv7v",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what factor is the Urbanization Rate greater in Modern Communities compared to Traditional Communities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I locate {the line point for the Urbanization Rate for Modern Communities}[0.126, 0.099] and extract its corresponding value of 95, and {the line point for the Urbanization Rate for Traditional Communities}[0.126, 0.211] and extract its corresponding value of 60. The factor by which the Urbanization Rate is greater in Modern Communities compared to Traditional Communities is therefore 95 / 60 = 1.58 (rounded to two decimal places).\nBased on the above thought, the answer is:\n1.58."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "I8pd0Hs0SDBbBuC0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the given line chart into a structured table representing the trends and projected growth for AI and VR technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I will start by identifying {the x-axis tick labels}[[0.031, 0.879, 0.074, 0.903], [0.212, 0.879, 0.256, 0.903], [0.393, 0.879, 0.437, 0.903], [0.575, 0.879, 0.619, 0.903], [0.756, 0.879, 0.800, 0.903], [0.938, 0.879, 0.981, 0.903]] as the years from 2020 to 2030. Then, I will locate {the legend texts}[[0.415, 0.948, 0.485, 0.972], [0.568, 0.948, 0.645, 0.972]] for identifying the AI and VR trends. The line points for {AI Trend}[[0.052, 0.135, 0.959, 0.709]] and {VR Trend}[[0.052, 0.645, 0.959, 0.871]] will be used to obtain the respective values for each year.\nBased on the above thought, the answer is:\nYear,AI_trend,VR_trend\n2020,0.70,0.78\n2021,0.76,0.77\n2022,0.82,0.75\n2023,0.90,0.71\n2024,0.98,0.67\n2025,1.08,0.65\n2026,1.20,0.63\n2027,1.25,0.61\n2028,1.29,0.58\n2029,1.35,0.54\n2030,1.41,0.50"
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "Q0SFVfzpnlvYRsb1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend, AI or VR, is projected to grow more significantly from 2020 to 2030 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend is projected to grow more significantly, I need to look at {the line for AI trend}[0.052, 0.135, 0.959, 0.709], starting at 0.70 in 2020 and reaching 1.41 in 2030, and compare it to {the line for VR trend}[0.052, 0.645, 0.959, 0.871], which starts at 0.78 in 2020 and declines to 0.50 in 2030. The line of AI trend shows a clear significant growth compared to the declining line of VR trend between 2020 and 2030.\nBased on the above thought, the answer is:\nBased on the line chart, the AI trend is projected to grow more significantly from 2020 to 2030. It begins with less than the VR trend but surpasses it over time and sees a substantial upward trend ending at a noticeably higher rate in 2030, whereas the VR trend declines over the same period."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "WDr5b7RQub5FYNSi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs the chart reflects, how much greater is the AI industry trend in 2030 compared to VR?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between AI and VR trends in 2030, I need to locate {the point for AI in 2030}[0.959, 0.135] which corresponds to a value of 1.41 and {the point for VR in 2030}[0.959, 0.871] which corresponds to a value of 0.50. The difference would then be 1.41 - 0.50 = 0.91.\nBased on the above thought, the answer is:\n0.91."
+            }
+        ],
+        "id_raw": 661
+    },
+    {
+        "id": "SqLjpmvgGwXQlcWW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the patent filing data represented in the line chart for the period from 2015 to 2019 for the five major technologies into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a structured table from the line chart, I first need to observe {the x-axis tick labels}[[0.061, 0.879, 0.083, 0.914], [0.174, 0.879, 0.195, 0.914], [0.286, 0.879, 0.308, 0.914], [0.398, 0.879, 0.420, 0.914], [0.510, 0.879, 0.532, 0.914]]. They represent the years and will form the columns of the table. Next, to establish the rows of the table, I refer to {the legend texts}[[0.125, 0.125, 0.248, 0.160], [0.125, 0.177, 0.249, 0.212]], indicating the major technology trends. For the data population, I locate {the line points}[[0.061, 0.823], [0.174, 0.523], ..., [0.959, 0.147]] for each technology trend for the respective years and extract their values.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,3D Printing,Virtual Reality,Blockchain,Autonomous Vehicles\n2015,23,12,17,1,7\n2016,36,16,20,3,11\n2017,48,22,26,9,15\n2018,59,27,29,18,18\n2019,73,30,33,24,21"
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "POfmcVZRWzvlUZV7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you identify the technology trend that has seen the most consistent growth in patent filings from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the technology trend with the most consistent growth, I need to study {the lines in the chart}[0.061, 0.147, 0.959, 0.858]. All the lines represent the trends of different technologies over time. Artificial Intelligence has a line that displays a steady and consistent upward trajectory. Looking at {the line for Artificial Intelligence}[[0.061,147], [0.174,0.328], [0.286,0.113], [0.398,0.324], [0.510,0.240], [0.623,0.402], [0.735,0.172], [0.847,0.223], [0.959,0.267]], the values are consistently increasing every year from 23 in 2015 to 73 in 2019.\nBased on the above thought, the answer is:\nThe technology trend that has seen the most consistent growth in patent filings from 2015 to 2019 according to the line chart is Artificial Intelligence. Its line shows a consistent increase in patent filings each year over the observed period."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "EpxLMB10npdozHVh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2015 to 2019, by how much has the number of patent filings for Blockchain increased?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in the number of Blockchain patent filings, I need to examine the starting and ending points of {the line for Blockchain}[0.061, 0.854, 0.959, 0.854]. The starting point represents the number of filings in 2015, which is 1, and the ending point indicates the number of filings in 2019, which is 24. So the increase over this period is 24 - 1 = 23.\nBased on the above thought, the answer is:\n23"
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "XHwRu3GPmp0XIWzd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table comparing the startup count and innovation index per year based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step in constructing this table is to identify the x-axis and y-axis labels. The {x-axis labels}<r>[0.033, 0.879, 0.092, 0.914], [0.210, 0.879, 0.268, 0.914], [0.387, 0.879, 0.445, 0.914], [0.563, 0.879, 0.622, 0.914], [0.740, 0.879, 0.799, 0.914], [0.917, 0.879, 0.975, 0.914]</r> represent the years - these will be the rows of the table. \nThe {labels of the legend}<r>[0.728, 0.747, 0.888, 0.782], [0.728, 0.800, 0.925, 0.835]</r>, \"startup count\" and \"innovation index\", are the columns of the table. I can get the corresponding data by parsing the points on the {line representing startup count}<r>[0.062, 0.721, 0.946, 0.866]</r> and the {line representing innovation index}<r>[0.062, 0.142, 0.946, 0.383]</r>.\nBased on the above thought, the answer is:\nYear,Startup Count,Innovation Index\n2010,10,60\n2011,15,65\n2012,13,62\n2013,16,63\n2014,20,70\n2015,18,72\n2016,21,75\n2017,19,73\n2018,22,80\n2019,20,79\n2020,25,85"
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "rSWnSggF4NcSiwtv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the innovation index from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {innovation index line}<r>[0.062, 0.142, 0.946, 0.383]</r>, I can see the value of the innovation index for each year. In 2010, the innovation index was 60 at {point}[0.062, 0.383]. In 2020, it increased to 85 at {point}[0.946, 0.142]. The total increase is therefore 85 - 60 = 25.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "rVbpRBfxenQsZJpD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the line chart, can you provide a structured table that correlates career stages with the corresponding workplace development trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate the data into a tabular format, I have to look at {the x-axis tick labels}[[0.101, 0.919, 0.148, 0.942], [0.161, 0.919, 0.250, 0.942], [0.244, 0.919, 0.330, 0.942], [0.346, 0.919, 0.390, 0.942], [0.403, 0.919, 0.495, 0.942], [0.496, 0.919, 0.564, 0.942], [0.586, 0.919, 0.636, 0.942], [0.670, 0.919, 0.715, 0.942], [0.752, 0.919, 0.795, 0.942], [0.814, 0.919, 0.895, 0.942], [0.892, 0.919, 0.980, 0.942]] for career stages. This will make up the first column in the table. Next, I find {the data points}[[0.124, 0.833], [0.206, 0.756], [0.287, 0.693], [0.368, 0.679], [0.449, 0.728], [0.530, 0.721], [0.611, 0.679], [0.692, 0.118], [0.774, 0.833], [0.855, 0.679], [0.936, 0.819]] associated with each career stage, translating the visual information into numerical values based on the position of the points in relation to {the y-axis tick labels}[[0.035, 0.894, 0.074, 0.917], [0.035, 0.754, 0.074, 0.777], [0.035, 0.613, 0.074, 0.637], [0.035, 0.473, 0.074, 0.497], [0.035, 0.333, 0.074, 0.357], [0.035, 0.193, 0.074, 0.216], [0.035, 0.053, 0.074, 0.076]]. These y-values will make up the second column in the table, referred to as 'Workplace Development Trend'.\nBased on the above thought, the answer is:\nCareer Stage,Workplace Development Trend\nNovice,0.0050\nIntermediate,0.0061\nExperienced,0.0070\nSenior,0.0072\nManagement,0.0065\nExecutive,0.0066\nRetired,0.0072\nHealth,0.0152\nLayoff,0.0050\nResignation,0.0072\nUnemployed,0.0052"
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "1xZZ5BnVxvpfDHS0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how does the workplace development trend evolve throughout different career stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would follow {the line plot for Workplace Development Trend}[0.124, 0.118, 0.936, 0.833] and analyze the movement. Starting from {Novice}[0.101, 0.919, 0.148, 0.942], the trend value is low. It then appears to slowly rise through {Intermediate}[0.161, 0.919, 0.250, 0.942], {Experienced}[0.244, 0.919, 0.330, 0.942], and reach a minor peak at {Senior}[0.346, 0.919, 0.390, 0.942]. Afterwards, there is a slight dip at {Management}[0.403, 0.919, 0.495, 0.942], and a minor rise again towards {Executive}[0.496, 0.919, 0.564, 0.942] and {Retired}[0.586, 0.919, 0.636, 0.942]. Significantly, there is a drastic rise when employees reach {Health}[0.670, 0.919, 0.715, 0.942] stage, followed by a sharp fall back to low levels at {Layoff}[0.752, 0.919, 0.795, 0.942], slightly rising again towards {Resignation}[0.814, 0.919, 0.895, 0.942], and remaining steady through {Unemployed}[0.892, 0.919, 0.980, 0.942].\nBased on the above thought, the answer is:\nThe Workplace Development Trend, overall, shows a steady growth from the Novice to Retired stages, suggesting an increase in productivity, innovation, and other positive workplace factors up until retirement. An abnormal surge can be observed at the Health stage, pointing out a spike in certain contributing elements to workplace development. However, following this stage, there is a drastic decline in the trend, returning to the levels close to the beginning of a career."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "M9wck00dJ6Uz9pmy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what career stage does the Workplace Development Trend observe the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the stage at which the Workplace Development Trend is the highest, I analyze {the data line}[[0.124, 0.118, 0.936, 0.833]] and see the point with the lowest y-coordinate represents the highest value of the trend. From the graph, I can see that this point corresponds to {the label 'Health'}[0.670, 0.919, 0.715, 0.942] with the y-coordinate being significantly lower at the rest of the data points.\nBased on the above thought, the answer is:\nHealth."
+            }
+        ],
+        "id_raw": 664
+    },
+    {
+        "id": "Ad4AyDI5CZRxySI9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data presented in the bar chart, can you construct a table presenting Sleep Quality, Mental Focus, and Physical Activity percentages for both the Stable Daily Routine and the Erratic Daily Routine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To gather the information necessary for the creation of the table, I first refer to {the y-axis labels}[[0.032, 0.191, 0.056, 0.222], [0.032, 0.294, 0.064, 0.325]], which dictate the row labels. For each row, I then examine the corresponding {bar segments for Stable Daily Routine}[[0.062, 0.189, 0.360, 0.223], [0.360, 0.189, 0.650, 0.223], [0.650, 0.189, 0.902, 0.223]], and {Erratic Daily Routine}[[0.062, 0.292, 0.300, 0.326], [0.300, 0.292, 0.540, 0.326], [0.540, 0.292, 0.746, 0.326]] to extract the values of Sleep Quality, Mental Focus, and Physical Activity as represented by their respective lengths.\nBased on the above thought, the answer is:\nRoutine, Sleep Quality (%), Mental Focus (%), Physical Activity (%)\nStable Daily Routine, 50, 48, 62\nErratic Daily Routine, 40, 40, 34"
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "iyTQja5L4ZAmXAbp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about an individual's behavioral psychology and mental health in the context of daily routines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the influence of daily routines on an individual's behavioral psychology and mental health, I study the {bars}[[0.062, 0.189, 0.902, 0.223], [0.062, 0.292, 0.746, 0.326]]. Clearly, the sections representing Sleep Quality, Mental Focus, and Physical Activity have longer lengths in the Stable Daily Routine compared to the Erratic Daily Routine, indicating better sleep quality, mental focus, and physical activity levels respectively when the individual follows a stable routine.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that the stability of an individual\u2019s daily routine has a significant impact on their behavioral psychology and mental health. When an individual sticks to a stable daily routine, they demonstrate higher percentages of sleep quality, mental focus, and physical activity. On the other hand, an erratic or unregulated routine is associated with lower values for these aspects. Therefore, a well-organized routine promotes better mental health and behavior."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "2VtU61GMcZCwz2JE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the Physical Activity percentage between the Stable Daily Routine and the Erratic Daily Routine according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the differences in the Physical Activity percentage between the two routines, I examine {the third segment of the bar pertaining to Stable Daily Routine}[[0.650, 0.189, 0.902, 0.223]] and determine its value to be 62%. Similarly, I locate {the third segment of the bar pertaining to Erratic Daily Routine}[[0.540, 0.292, 0.746, 0.326]], determined to be 34%. The difference is therefore 62% - 34% = 28%.\nBased on the above thought, the answer is:\n28%."
+            }
+        ],
+        "id_raw": 665
+    },
+    {
+        "id": "ZzfPx0OkLkJYTEaq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a table format with Categories as rows and Concepts as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the bar chart into a table format, I first identify {the y-axis tick labels}[[0.200, 0.504, 0.220, 0.552], [0.725, 0.360, 0.750, 0.420]] as the column headers - these are the concepts of 'Business Management' and 'Organizational Leadership'. Next, I recognize the colors of the segments within the bars, denoted by {color code}[[0.35,0.5,0.36,0.51],[0.82,0.37,0.83,0.38]] as markers for identifying different categories - 'Profit Margin' and 'Investment Rate'. To find the values, I observe the lengths of various segments in the full bar for each concept. For 'Business Management', the 'Profit Margin' segment takes up 45% of the bar and 'Investment Rate' takes up the remaining 55%. For 'Organizational Leadership', 'Profit Margin' takes 65% of the bar and 'Investment Rate' takes the remaining 35%. Hence, these percentages are the respective values for each category in the concepts.\nBased on the above thought, the answer is:\nCategories,Business Management,Organizational Leadership\nProfit Margin,45%,65%\nInvestment Rate,55%,35%"
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "Pkd363ocDfQvFJzg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area seems to be a more balanced investment and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area seems to be a more balanced investment, I need to evaluate the distribution of 'Profit Margin' and 'Investment Rate' for 'Business Management' and 'Organizational Leadership'. With regard to 'Business Management', {the bar for Business Management}[[0.14, 0.515, 0.70, 0.595]] shows a close balance between 'Profit Margin' (45%) and 'Investment Rate' (55%). On the other hand, for 'Organizational Leadership', {the bar for Organizational Leadership}[[0.14, 0.295, 0.81, 0.347]] demonstrates a more skewed distribution, with 'Profit Margin' at 65% and 'Investment Rate' at a lower 35%. Therefore, 'Business Management' appears to have a more balanced investment between 'Profit Margin' and 'Investment Rate'.\nBased on the above thought, the answer is:\n'Business Management' seems to be a more balanced investment. This is because the distribution between 'Profit Margin' and 'Investment Rate' for 'Business Management' is closer to 50-50, suggesting a more balanced investment in both profit-making and further investments. On the other hand, 'Organizational Leadership' has a higher 'Profit Margin' but lower 'Investment Rate', indicating a more profit-focused approach at the expense of further investments."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "6NOIWioaZXMO0E68",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Profit Margin between 'Business Management' and 'Organizational Leadership'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in Profit Margin between 'Business Management' and 'Organizational Leadership', I need to locate the 'Profit Margin' segments for both. For 'Business Management', {the 'Profit Margin' segment}[[0.14, 0.515, 0.55, 0.595]] denotes a profit margin of 45%. For 'Organizational Leadership', {the 'Profit Margin' segment}[[0.14, 0.295, 0.75, 0.347]] signifies a profit margin of 65%. Therefore, the difference in profit margin is 65% - 45% = 20%.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "HxscTRrKwkYjGPwn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a table format to present the trend data for each type of investment, over time, from the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from this chart, I observe that there are line plots for each type of investment. Specifically, I refer to the {x-axis tick labels}<r>[0.106, 0.761, 0.121, 0.796]</r>, <r>[0.276, 0.761, 0.290, 0.796]</r>, <r>[0.445, 0.761, 0.460, 0.796]</r>, <r>[0.614, 0.761, 0.629, 0.796]</r>, <r>[0.784, 0.761, 0.798, 0.796]</r>, <r>[0.946, 0.761, 0.975, 0.796]</r> to denote the \"Layer\" timescale representing the row headers. Collectively, {the sublegend items}<r>[0.269, 0.879, 0.869, 0.914]</r> represent the different investments as column headers. To find the values for each investment type at each layer timepoint, I locate the line points for each investment, e.g., {Social Responsibility}<r>[0.114, 0.140, 0.960, 0.468]</r>, and {Sustainable Business}<r>[0.114, 0.618, 0.960, 0.683]</r> and extract corresponding values for each point.\nBased on the above thought, the answer is:\nLayer, Social_Responsibility, Sustainable_Business\n0, 0.0060, 0.0040\n1, 0.0065, 0.0042\n2, 0.0080, 0.0042\n3, 0.0090, 0.0040\n4, 0.0095, 0.0037\n5, 0.0075, 0.0038\n6, 0.0072, 0.0042\n7, 0.0068, 0.0044\n8, 0.0080, 0.0040\n9, 0.0074, 0.0041\n10, 0.0065, 0.0043"
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "0vdCATanfzb6Traz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which area, Social Responsibility or Sustainable Business, seems to be more volatile over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which area has the most volatile progression, I need to analyze the fluctuations in the line plots for each area. Observing {the line for Social Responsibility}<r>[0.114, 0.140, 0.960, 0.468]</r>, there are small changes between adjacent layers, indicating a mild fluctuation over time. For {Sustainable Business}<r>[0.114, 0.618, 0.960, 0.683]</r>, the line shows little change over time, which manifests the stable nature of this investment.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Social Responsibility seems to be more volatile over time. Its line plot reveals mild but consistent fluctuation between different stages as compared to Sustainable Business, which shows little change over time."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "VUCIDvDvRfXuzHJy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what is the total increase of Social Responsibility from layer 0 to layer 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total increase, I need to locate {the line points for Social Responsibility}<r>[0.114, 0.140, 0.960, 0.468]</r> and extract the corresponding values at layer 0 and layer 10, which has values of 0.0060 and 0.0065, respectively. The total increase is the value at layer 10 minus the value at layer 0, which is 0.0065 - 0.0060.\nBased on the above thought, the answer is:\n0.0005."
+            }
+        ],
+        "id_raw": 667
+    },
+    {
+        "id": "0bUb1uWJNAgIese7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you gather the data from this line chart into a tabular structure where the rows represent different years and the columns represent 'Creativity-Level' and 'Design-Innovation-Level'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first inspect {the x-axis tick labels}[[0.050, 0.763, 0.062, 0.791], [0.163, 0.763, 0.176, 0.791], ... [0.953, 0.763, 0.979, 0.791]], which represent the row headers or years. Then, I identify the labels from {the legend texts}[[0.322, 0.903, 0.477, 0.931], [0.573, 0.903, 0.812, 0.931]], representing 'Creativity-Level' and 'Design-Innovation-Level' columns. To populate the values, I need to locate and interpret {the line points for Creativity-Level}[[0.113, 0.527], [0.170, 0.542], ... [0.966, 0.497]] and {the line points for Design-Innovation-Level}[[0.113, 0.738], [0.170, 0.708], ... [0.966, 0.662]], obtaining the corresponding values for each year.\nBased on the above thought, the answer is:\nLayers,Creativity_Level,Design_Innovation_Level\n3,0.050,0.036\n4,0.049,0.038\n5,0.072,0.040\n6,0.071,0.039\n7,0.052,0.035\n8,0.057,0.045\n9,0.048,0.043\n10,0.055,0.041\n11,0.062,0.048\n12,0.060,0.044\n13,0.065,0.046\n14,0.062,0.049\n15,0.058,0.042\n16,0.055,0.045\n17,0.056,0.039\n18,0.052,0.041"
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "CwhgbR7g7jwz73aj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which level sees more consistency over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comprehend the consistency in the two trends, I need to assess the oscillations in the line graphs for 'Creativity-Level' and 'Design-Innovation-Level'. {The line for Creativity-Level}[0.113, 0.195, 0.966, 0.557] indicates somewhat little fluctuations, with few spikes and troughs, especially between the years associated with coordinates [0.340, 0.966]. \nHowever, when I analyze {the line for Design-Innovation-Level}[0.113, 0.542, 0.966, 0.753], I can see more consistent and smaller fluctuations over the years. This consistency suggests that 'Design-Innovation-Level' remains relatively stable in comparison to 'Creativity-Level'.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the 'Design-Innovation-Level' appears to be more consistent across the years. The fluctuation in the 'Design-Innovation-Level' line plot is less pronounced compared to the 'Creativity-Level', suggesting a higher degree of stability in the 'Design-Innovation-Level' over time."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "VzR4NbrF5aBPquYY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum Creativity Level recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum Creativity Level recorded, I need to locate and analyze {the line points for Creativity-Level}[[0.113, 0.527], [0.170, 0.542], ... [0.966, 0.497]]. Here, I note the y-values corresponding to each point, which represent the Creativity Level for each year. The maximum y-value corresponds to the point {at the 5th Layer}[0.226, 0.195], which is 0.072.\nBased on the above thought, the answer is:\n0.072."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "eUjlqdN7MhC9rwrj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from the line chart, using Layers as columns and the two series (Social Inequality and Rights Protection) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data in the line chart into a table, I will use {the x-axis tick labels}[[0.145, 0.879, 0.160, 0.914], [0.327, 0.879, 0.341, 0.914], [0.508, 0.879, 0.523, 0.914], [0.689, 0.879, 0.704, 0.914], [0.863, 0.879, 0.892, 0.914], [1.044, 0.879, 1.073, 0.914]] to represent the column headers or Layers. Then I'll recognize {the legend texts}[[0.298, 0.878, 0.489, 0.913], [0.600, 0.878, 0.798, 0.913]] as the row labels for Social Inequality and Rights Protection. Subsequently, I'll map {the line points for Social Inequality}[[0.062, 0.577], [0.153, 0.596], [0.244, 0.625], [0.334, 0.673], [0.425, 0.664], [0.515, 0.721], [0.606, 0.760], [0.696, 0.789], [0.787, 0.808], [0.877, 0.818], [0.968, 0.837]] and {the line points for Rights Protection}[[0.062, 0.461], [0.153, 0.441], [0.244, 0.412], [0.334, 0.383], [0.425, 0.316], [0.515, 0.287], [0.606, 0.238], [0.696, 0.190],  [0.787, 0.229], [0.877, 0.209], [0.968, 0.229]] to the respective layers and extract their values.\nBased on the above thought, the answer is:\nLayer, Social Inequality, Rights Protection\n1, 5.0, 6.2\n2, 4.8, 6.4\n3, 4.5, 6.7\n4, 4.0, 7.0\n5, 4.1, 7.7\n6, 3.5, 8.0\n7, 3.1, 8.5\n8, 2.8, 9.0\n9, 2.6, 8.6\n10, 2.5, 8.8\n11, 2.3, 8.6"
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "IoqzrdbAJRSsENn1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which topic shows a steady decrease over time, and why would you reach that conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which topic shows a steady decrease over time, I need to examine the behaviors of both {the line for Social Inequality}[0.062, 0.577, 0.968, 0.837] and {the line for Rights Protection}[0.062, 0.461, 0.968, 0.229]. Close observation reveals that the line for Social Inequality was steadily descending from a value of 5.0 to 2.3 across the layers. Even though it has a slight increase in Layer 5, the overall trend is decreasing. Conversely, with the exception of the dip in Layer 9, the line for Rights Protection is generally increasing.\nBased on the above thought, the answer is:\nThe topic of Social Inequality shows a steady decrease over time. Though there are slight reprieves like at Layer 5 where it slightly increases, the overall trend for Social Inequality is a decrease from 5.0 at Layer 1 to 2.3 at Layer 11. This is in contrast to the Rights Protection topic, which demonstrates more of an increasing trend."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "PK4e5LoMRhVHoAlx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value for Rights Protection across all layers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value for Rights Protection, I need to identify {the line points for Rights Protection}[[0.062, 0.461], [0.153, 0.441], [0.244, 0.412], [0.334, 0.383], [0.425, 0.316], [0.515, 0.287], [0.606, 0.238], [0.696, 0.190], [0.787, 0.229], [0.877, 0.209], [0.968, 0.229]]. After reviewing these data points, I observe that the highest value is 9.0 at {Layer 8}[0.696, 0.190].\nBased on the above thought, the answer is:\n9.0"
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "Ry5z0l4cLJ4QV9oI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you depict the data for 'Social Inequality & Rights Protection over Years' in a tabular format, where the years are in columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I first recognize {the years from x-axis}[[0.073, 0.879, 0.132, 0.914], [0.242, 0.879, 0.301, 0.914], [0.411, 0.879, 0.469, 0.914], [0.579, 0.879, 0.638, 0.914], [0.748, 0.879, 0.806, 0.914], [0.917, 0.879, 0.975, 0.914]] as column headers. Then, {the legend text}[[0.669, 0.748, 0.854, 0.783], [0.669, 0.800, 0.925, 0.835]] denotes the two indices representing the row labels. To fetch the values, I follow {the lines for Inequality Index}[[0.102, 0.535, 0.946, 0.811]] and {the lines for Right Protection Index}[0.102, 0.149, 0.946, 0.425] for each year and extract the corresponding values.\nBased on the above thought, the answer is:\nIndices\\Years,1980,1985,1990,1995,2000,2005,2010,2015,2020,2025,2030\nInequality Index,0.15,0.18,0.20,0.22,0.24,0.27,0.30,0.33,0.35,0.37,0.40\nRight Protection Index,0.50,0.52,0.54,0.56,0.58,0.60,0.62,0.64,0.66,0.70,0.75"
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "KGMlbMaioi8gM7mB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, which among the Inequality Index and the Right Protection Index shows a faster rate of growth during the time period observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the change in value from 1980 to 2030 for each index. To do so, I locate {the point for Inequality Index in 1980}[0.102, 0.811] and {the point for Inequality Index in 2030}[0.946, 0.535] and find the difference between the values. Similarly, I locate {the point for Right Protection Index in 1980}[0.102, 0.425] and {the point for Right Protection Index in 2030}[0.946, 0.149] and calculate the difference. The index with the higher difference in value will indicate the faster rate of growth.\nBased on the above thought, the answer is:\nBased on the graph, it can be observed that the Inequality Index shows a faster rate of growth between 1980 to 2030 relative to the Right Protection Index. The Inequality Index grows from 0.15 to 0.40, a jump of 0.25. On the other hand, the Right Protection Index increases from 0.50 to 0.75, a growth of 0.25. However, considering that the Inequality Index started from a lower point, the rate of its growth appears faster. Thus, the Inequality Index experienced a quicker surge in growth during the observed time period."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "95ibFStpzlxPOJPy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the Inequality Index from 1980 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the change, I first locate {the point for Inequality Index in 1980}[0.102, 0.811], which corresponds to a value of 0.15. Then, I locate {the point for Inequality Index in 2030}[0.946, 0.535], which corresponds to a value of 0.40. Finally, the difference between 1980 and 2030 is calculated as 0.40 - 0.15 = 0.25.\nBased on the above thought, the answer is:\n0.25."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "JpdHZtvOOCIQfWMB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the freight volume data from the line chart into a structured table categorized by year and volume type?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, I observe {the x-axis ticks}[[0.082, 0.911], [0.258, 0.911], [0.433, 0.911], [0.609, 0.911], [0.784, 0.911], [0.959, 0.911]] representing years. The legend entries, {E-commerce Freight Volume}[0.093, 0.835, 0.138, 0.849] and {Heavy Industrial Transport Volume}[0.093, 0.870, 0.138, 0.884], are to be the row labels. Using {the line points for E-commerce Freight Volume}[[0.082, 0.861], [0.170, 0.811], ..., [0.959, 0.112]] and {the line points for Heavy Industrial Transport Volume}[[0.082, 0.512], [0.170, 0.542], ..., [0.959, 0.711]], I extract the corresponding values for each year and volume type.\nBased on the above thought, the answer is:\nYear,E-commerce Freight Volume,Heavy Industrial Transport Volume\n2010,50,85\n2011,55,82\n2012,60,78\n2013,68,79\n2014,75,77\n2015,84,75\n2016,94,73\n2017,105,71\n2018,111,70\n2019,120,68\n2020,125,65"
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "0SIQiKa7LC58qLqE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the overall trends shown in the line chart for E-commerce Freight Volume and Heavy Industrial Transport Volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing both {the line for E-commerce Freight Volume}[0.082, 0.112, 0.959, 0.861] and {the line for Heavy Industrial Transport Volume}[0.082, 0.512, 0.959, 0.711], I can derive the overall trends. \nFor E-commerce Freight Volume, there's a clear upward trend, indicating a consistent increase in e-commerce freight volume over the years.\nIn contrast, the line for Heavy Industrial Transport Volume shows a decreasing trend over the years, indicating a consistent decline in heavy industrial transport volume.\nBased on the above thought, the answer is:\nOverall, the chart indicates that the E-commerce Freight Volume has been increasing year-on-year, whereas the Heavy Industrial Transport Volume has been decreasing over the same period. This might suggest a shift in the transportation and logistics industry towards more E-commerce related transportation."
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "NNsjk4qWnC1diX6c",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Heavy Industrial Transport Volume reach its lowest point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify when the Heavy Industrial Transport Volume was at its minimum, I need to analyze {the line points for Heavy Industrial Transport Volume}[[0.082, 0.512], [0.170, 0.542], ..., [0.959, 0.711]]. The y-values represent the volume, and the corresponding x-values represent the years. The smallest y-value corresponds to the lowest volume. Comparing the y-values, the smallest is at point {65}[0.959, 0.711].\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 671
+    },
+    {
+        "id": "sCph3tDPXyV2acRL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the maximum efficiency data for each manufacturing strategy (Automated and Hand Assembly) and organize it in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I can see that {the x-axis labels}[[0.142, 0.758, 0.165, 0.772], ..., [0.902, 0.758, 0.925, 0.772]] representing the months of the year. Next, the {legend texts}[[0.952, 0.245, 0.972, 0.275], [0.952, 0.295, 0.972, 0.325]] provide the two different types of manufacturing strategies. I spot the peak values on both lines by locating the highest points on {the automated lines}[[0.175, 0.264, ..., 0.927, 0.215]] and {the hand assembly lines}[[0.175, 0.397, ..., 0.927, 0.415]]. Using these, I can generate a table with the maximum efficiency achieved each month, for each manufacturing strategy.\nBased on the above thought, the answer is:\nMonth,Automated Assembly,Hand Assembly\nJanuary,820,845\nFebruary,840,860\nMarch,872,870\nApril,900,877\nMay,915,882\nJune,925,890\nJuly,940,888\nAugust,960,884\nSeptember,1000,878\nOctober,1035,875\nNovember,1080,868\nDecember,1120,860"
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "zPON3yK0hT07xtH9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween automated and hand assembly, which manufacturing strategy seems more reliable based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assessing the reliability of manufacturing strategies necessitates studying the consistency of the trend throughout the period. Examining the chart, {the line for Automated Assembly}[0.175, 0.264, ..., 0.927, 0.215] shows a steady upward trend with few fluctuations. On the other hand, {the line for Hand Assembly}[0.175, 0.397, ..., 0.927, 0.415] has periodic fluctuations without a prominent trend. Fluctuations often indicate uncertainties and inconsistencies.\nBased on the above thought, the answer is:\nBased on the chart, Automated Assembly appears to be a more reliable manufacturing strategy. It demonstrates a steady upward trend throughout the year, indicating consistent performance and predictable outcomes. In contrast, Hand Assembly shows significant fluctuations, thus introducing elements of unpredictability and inconsistency."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "HC8og6sPcklVER6c",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which month was the gap between Automated and Hand Assembly efficiencies the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the month where the difference in efficiencies between automated and hand assembly was the greatest, I need to locate the points for each type on the chart and subtract the coordinates. Looking at {the points for automated}[[0.175, 0.264], ..., [0.927, 0.215]] and {the points for hand assembly}[[0.175, 0.397], ..., [0.927, 0.415]], I calculate the differences between the pairs of values and find the maximum difference and the corresponding month.\nBased on the above thought, the answer is:\nNovember."
+            }
+        ],
+        "id_raw": 672
+    },
+    {
+        "id": "WKnYsJCPKNsgpHl0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from this Line chart into a tabulated format with 'Years' as the rows and 'Charity Components' as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract table data, let's first recognize that {the x-axis ticks}<[0.071, 0.764, 0.115, 0.787], [0.167, 0.764, 0.211, 0.787], [0.264, 0.764, 0.308, 0.787], [0.360, 0.764, 0.404, 0.787], [0.456, 0.764, 0.500, 0.787], [0.552, 0.764, 0.596, 0.787], [0.649, 0.764, 0.693, 0.787], [0.745, 0.764, 0.789, 0.787], [0.841, 0.764, 0.885, 0.787], [0.937, 0.764, 0.981, 0.787]> are the row labels indicating Years. Next, let me identify {the legend texts}<[0.411, 0.919, 0.521, 0.943], [0.605, 0.919, 0.690, 0.943]> as column headers representing different Charity Components.  For table values, I need to locate {the line points for Charity Fund}<[0.093, 0.447], [0.189, 0.324], [0.286, 0.293], [0.382, 0.216], [0.478, 0.185], [0.574, 0.370], [0.671, 0.139], [0.767, 0.232], [0.863, 0.108], [0.959, 0.108]> and {the line points for Direct Aid}<[0.093, 0.709], [0.189, 0.632], [0.286, 0.571], [0.382, 0.555], [0.478, 0.509], [0.574, 0.386], [0.671, 0.309], [0.767, 0.278], [0.863, 0.247], [0.959, 0.170]> and extract these as the corresponding values.\nBased on the above thought, the answer is:\nYear,Charity_Fund,Direct_Aid\n2011,6000,4300\n2012,6800,4800\n2013,7000,5200\n2014,7500,5300\n2015,7700,5600\n2016,6500,6400\n2017,8000,6900\n2018,7400,7100\n2019,8200,7300\n2020,8200,7800"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "Xsl4YMd4fn6SZThh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the trend of the charity fund versus direct aid based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining {the line for Charity Fund}[0.093, 0.108, 0.959, 0.447], it presents a general upward trend from 6000 in 2011 to 7700 in 2015, experiences a dip in 2016 to 6500, and then rises again to reach 8200 by 2019. While it seems to witness fluctuations, the overall trend is an increase in the Charity Fund allocation over the years.\nIn contrast, {the line for Direct Aid}[0.093, 0.170, 0.959, 0.709] reveals a consistently rising trend from 4300 in 2011 to 7800 in 2020 with minor fluctuations. Meaning, the Direct Aid allocation has seen a quite close to linear growth over the years.\nBased on the above thought, the answer is:\nBased on the line chart, the charity fund has seen an overall increase despite several fluctuations over the years. In contrast, Direct Aid has presented a more consistent upward trend. This suggests that while both areas have seen progress, the allocation for Direct Aid seems more stable and consistent than the Charity Fund."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "ErDSfAqvqxrkD3l9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest amount collected in charity fund throughout these years, and in which year did this happen?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I should identify the highest value point in {the line plot for Charity Fund}[0.093, 0.108, 0.959, 0.447]. Upon examining the line plot, it becomes clear that the highest points occur in the years 2019 and 2020 with the coordinates {the points for 2019 and 2020}[0.863, 0.108], [0.959, 0.108]. Both these points correspond to the value of 8200, which is the maximum amount collected in the Charity Fund over these years.\nBased on the above thought, the answer is:\nThe highest amount collected in the Charity Fund was $8200 thousand, obtained in the years 2019 and 2020."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "jKi5J6TPOZMuKfGx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table summarizing the information presented in the horizontal bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the figure, I extract {the x-axis tick labels}[0.224, 0.763, 0.263, 0.791], [0.296, 0.763, 0.335, 0.791],  [0.368, 0.763, 0.407, 0.791],  [0.440, 0.763, 0.479, 0.791], [0.512, 0.763, 0.551, 0.791],  [0.584, 0.763, 0.623, 0.791], [0.656, 0.763, 0.695, 0.791], [0.728, 0.763, 0.767, 0.791], [0.800, 0.763, 0.839, 0.791],  [0.872, 0.763, 0.911, 0.791],  [0.944, 0.763, 0.983, 0.791] which represent the years from 2010 to 2020. Next, the {legend entries}[0.478, 0.903, 0.578, 0.931],[0.652, 0.903, 0.772, 0.931] provide the labels for \"Twitter Users\" and \"Facebook Users\". The length of {the bars for Twitter Users and Facebook Users}[0.243, 0.210, 0.964, 0.482],[0.243, 0.256, 0.964, 0.617] correspond to the values for each year, which I extract and convert to numerical values for tabulation.\nBased on the above thought, the answer is:\nYear,Twitter Users,Facebook Users\n2010,0.0042,0.0037\n2011,0.0048,0.0043\n2012,0.0053,0.0046\n2013,0.0046,0.0049\n2014,0.0049,0.0052\n2015,0.0050,0.0048\n2016,0.0051,0.0050\n2017,0.0054,0.0047\n2018,0.0052,0.0039\n2019,0.0052,0.0036\n2020,0.0054,0.0038"
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "62WFDVaUi0PoYalO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how did the user growth of Facebook and Twitter change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to study the pattern of {the line plots for Twitter Users and Facebook Users}[0.243, 0.210, 0.964, 0.482],[0.243, 0.256, 0.964, 0.617]. I can observe that both lines display an initial increase till around 2012-13. Post this, there is a noticeable shift in trend pattern for both the lines. The Facebook Users line shows a fairly consistent decrease in user growth from 2013 to 2020. On the contrary, the Twitter Users line has more fluctuations but overall it's also on a decline with a more steady slope across years.\nBased on the above thought, the answer is:\nBased on the chart, user growth for both Facebook and Twitter initially increased till 2012-13. After this period, user growth for both platforms showed a declining trend. However, the decline was more consistent for Facebook, with the user growth rate decreasing every year from 2013 to 2020. For Twitter, although the overall trend was a decline, there were fluctuations during the period, indicating less consistency compared to Facebook. The Twitter line plot showed a slight increase between 2013 and 2014, and again between 2018 and 2019, which were not present in the Facebook line plot. Therefore, it can be inferred that while both platforms faced a reduction in user growth over the years, the decrease was more gradual and consistent for Facebook than for Twitter."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "jNEIrPTQCJoZSeuc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did Twitter's user growth exceed that of Facebook's for the first time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare {the points for the Twitter Users line}[0.243, 0.482], [0.315, 0.346], [0.387, 0.233], [0.459, 0.391], [0.531, 0.323], [0.603, 0.301], [0.676, 0.278], [0.748, 0.210], [0.820, 0.256], [0.892, 0.256], [0.964, 0.210] and {the points for the Facebook Users line}[0.243, 0.595], [0.315, 0.459], [0.387, 0.391], [0.459, 0.323], [0.531, 0.256], [0.603, 0.346], [0.676, 0.301], [0.748, 0.369], [0.820, 0.549], [0.892, 0.617], [0.964, 0.572] year by year, and find the first year when the growth rate of Twitter Users was higher.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 674
+    },
+    {
+        "id": "qyhv5wl7wSiDKSJN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the monthly performance values for the two financial market algorithms 'Recursion Slice' and 'Chained List' and represent those values in a structured table with 'Months' as rows and 'Algorithms' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I have to identify the x-axis labels which represent the months of the year. I located the x-axis labels by looking for the {x-tick labels}<r>[0.017, 0.761, 0.032, 0.796], [0.173, 0.761, 0.188, 0.796], ..., [0.946, 0.761, 0.975, 0.796]</r>. Then I have to find the line points which correspond to the performance of each algorithm for each month. The line for {Recursion Slice}<r>[0.103, 0.146, 0.960, 0.723]</r> and {Chained List}<r>[0.103, 0.251, 0.960, 0.631]</r> both have data points representing the performance for each month. I will have to extract the corresponding values for each point on both lines to generate the complete data for the table.\nBased on the above thought, the answer is:\nMonth,RecursionSlice,ChainedList\n1,0.44,0.58\n2,0.56,0.62\n3,0.65,0.67\n4,0.76,0.70\n5,0.82,0.76\n6,0.92,0.78\n7,0.97,0.83\n8,1.02,0.86\n9,1.12,0.84\n10,1.18,0.89\n11,1.27,1.08\n12,1.32,1.16"
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "jvIXYutZUcRH4Ap2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which algorithm seems to show consistent improvement over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which algorithm shows consistent improvement, I need to analyze the trend in the line plots for each algorithm. Looking at {the line for Recursion Slice}<r>[0.103, 0.146, 0.960, 0.723]</r>, I see its performance is generally increasing, showing a steady upward trend over time. On the other hand, analyzing {the line for Chained List}<r>[0.103, 0.251, 0.960, 0.631]</r>, there seems to be slight fluctuations, but overall, it also shows steady improvement. Among the two, Recursion Slice shows a more consistent rise and less fluctuation compared to Chained List.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Recursion Slice algorithm seems to show more consistent improvement over time. It presents a steady and almost linear upward trend indicating consistent growth. While the Chained List algorithm also shows improvement, it is accompanied by slight fluctuations. Therefore, Recursion Slice maintains a more consistent performance improvement over the depicted time frame."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "L8eXAT4UbzAhybQ8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average monthly performance improvement for Chained List algorithm over the 12 month period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average monthly performance improvement, I need to subtract the Performance value of the first month from that of the last month and divide by the total number of months. Looking at {the line points for Chained List}<r>[0.103, 0.251, 0.960, 0.631]</r>, the Performance value in the first month is 0.58 while in the last month it is 1.16, so the average monthly performance improvement is (1.16 - 0.58) / 12 = 0.0483.\nBased on the above thought, the answer is:\n0.0483."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "le81RtsHaJrZ5Tmy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart, which portrays synthetic and human-authored progress in various scientific research areas, into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first identify the {x-axis tick labels}[0.077, 0.227, 0.135, 0.262], [0.245, 0.227, 0.303, 0.262], [0.413, 0.227, 0.471, 0.262], [0.581, 0.227, 0.639, 0.262], [0.749, 0.227, 0.807, 0.262], [0.917, 0.227, 0.975, 0.262] as the column headers i.e., the years. Secondly, I perceive {the legend texts} [0.324, 0.185, 0.480, 0.220], [0.591, 0.185, 0.793, 0.220] that represent the row labels the 'Inflation Rate' and 'GDP Growth Rate'. Finally, to populate the data, I fetch the line points, {line_0}[0.106, 0.100, 0.946, 0.180], {line_1}[0.106, 0.116, 0.946, 0.332] for Inflation Rate and GDP Growth Rate respectively at different years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Inflation Rate,GDP Growth Rate\n2010,0.165,0.275\n2011,0.33,0.145\n2012,0.21,0.235\n2013,0.165,0.285\n2014,0.25,0.225\n2015,0.11,0.265\n2016,0.20,0.185\n2017,0.175,0.25\n2018,0.20,0.155\n2019,0.115,0.195\n2020,0.1,-0.335"
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "tOI1uO9UkdeDxkdF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf someone were interested in investing and cared about both inflation and GDP growth, which year or years would you suggest based on the trend of the two lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "For optimal investment, one may look for a year with low inflation and high GDP growth. I see that {the line for GDP Growth Rate}<r>[0.106, 0.116, 0.946, 0.332]</r> reaches its peak during {2013}<r>[0.358, 0.276, 0.407, 0.311]</r>, indicating the highest GDP growth rate. On the other hand, in 2013, {the Inflation Rate line}<r>[0.106, 0.100, 0.946, 0.180]</r> is at a relatively low point, indicating lower inflation. Both these factors make 2013 an appealing year for investment when considering both GDP Growth Rate and Inflation Rate.\nBased on the above thought, the answer is:\nBased on both the inflation rate and GDP growth rate trends, 2013 may be an optimal year for investment as it had high GDP growth and relatively low inflation."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "Or2nIfVsapu7JoUx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the highest GDP growth rate during the decade and which year it happened?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the highest GDP growth rate and the corresponding year, I look at the points of the {GDP Growth Rate line} <r> [0.106, 0.116, 0.946, 0.332] </r> and identify the point with the highest Y-coordinate which indicates the highest growth rate. The year for the highest point can be found by locating its corresponding x-coordinate on the x-axis (Year). The highest point on the GDP growth rate line corresponds to the coordinate <r>[0.358, 0.116]</r>, and the corresponding year is {2013}<r>[0.358, 0.276, 0.407, 0.311]</r>.\nBased on the above thought, the answer is:\nThe highest GDP growth rate was 0.285 and it occurred in 2013."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "UACtP9hLVB8jLMV7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart, which shows the trend of government support and private investment across grades, into a structured format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a structured format, first I'll consider {the x-axis tick labels}[[0.014, 0.879, 0.029, 0.914], [0.119, 0.879, 0.148, 0.914], [0.230, 0.879, 0.260, 0.914], [0.342, 0.879, 0.372, 0.914], [0.454, 0.879, 0.484, 0.914], [0.566, 0.879, 0.596, 0.914], [0.678, 0.879, 0.708, 0.914]] representing the grades. Next, I take {the legend texts}[[0.723, 0.125, 0.966, 0.160], [0.723, 0.177, 0.942, 0.212]] representing Government Support and Private Investment respectively. Magic matching of these entities with {the line points of Government Support}[[0.077, 0.544], [0.133, 0.564], [0.189, 0.589], [0.245, 0.609],[0.301, 0.628],[0.357, 0.654],[0.413, 0.673],[0.469, 0.699],[0.525, 0.738],[0.581, 0.770],[0.637, 0.834]] and {the line points of Private Investment}[[0.077, 0.577], [0.133, 0.557], [0.189, 0.525], [0.245, 0.493],[0.301, 0.461],[0.357, 0.422],[0.413, 0.383],[0.469, 0.338],[0.525, 0.287],[0.581, 0.222],[0.637, 0.126]] will fetch the data for each grade.\nBased on the above thought, the answer is:\nGrade,Government Support,Private Investment\n9,80,75\n10,77,78\n11,73,83\n12,70,88\n13,67,93\n14,63,99\n15,60,105\n16,56,112\n17,50,120\n18,45,130\n19,35,145"
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "AS0E75CqlZEQdXs7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich channel of education investment, government or private, exhibits a more significant rising trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand which channel shows a stronger rising trend, I need to analyze {the government support line}[0.077, 0.544, 0.637, 0.834] and {the private investment line}[0.077, 0.126, 0.637, 0.577]. For government support, it starts at 80 at grade 9 and reduces to 35 at grade 19, indicating a declining trend. On the other hand, private investment begins at 75 at grade 9 and increases steadily to reach 145 at grade 19, a clear rising trend.\nBased on the above thought, the answer is:\nComparing the trends indicated in the chart, the Private Investment line exhibits a more significant rising trend than Government Support. This is evidenced by the steady rise in private investment values across the grades, as opposed to the declining trend of government support."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "IiGNfuRQuBp4UKPi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of values covered by the private investment according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of values covered by the private investment, I first need to find the maximum and minimum values. Based on the {line plot for private investment}[0.077, 0.126, 0.637, 0.577] and its corresponding points, the maximum value in the chart is 145 at grade 19, and the minimum value in the chart is 75 at grade 9. The difference between the maximum and minimum (range) is 145 - 75 = 70.\nBased on the above thought, the answer is:\n70."
+            }
+        ],
+        "id_raw": 678
+    },
+    {
+        "id": "rz4k2uoLCCVO4mPM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transform the information from the line graph into a structured table, with the years as columns and the two societal values as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first take note of the {x-axis tick labels}<r>[[0.133, 0.764, 0.176, 0.787], [0.315, 0.764, 0.359, 0.787], [0.498, 0.764, 0.542, 0.787], [0.680, 0.764, 0.724, 0.787], [0.863, 0.764, 0.907, 0.787]]</r>, representing the column headers or years. Next, I identify {the legend texts}<r>[[0.352, 0.919, 0.477, 0.943], [0.561, 0.919, 0.736, 0.943]]</r> as the row labels, symbolizing the societal values. Lastly, to fill the values in the table, I locate {the line points for 'Moral Conscience'}<r>[[0.063, 0.478], [0.155, 0.461], ..., [0.976, 0.582]]</r> and {the line points for 'Cultural Exchange'}<r>[[0.063, 0.669], [0.155, 0.652], ..., [0.976, 0.149]]</r> at the respective years and map them to the corresponding values.\nBased on the above thought, the answer is:\nValue, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015\nPublic Support, 0.56, 0.57, 0.59, 0.61, 0.55, 0.54, 0.57, 0.61, 0.54, 0.52, 0.50\nMilitary Expenditure, 0.45, 0.46, 0.47, 0.49, 0.51, 0.60, 0.63, 0.67, 0.70, 0.72, 0.75"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "qngZJV1A3mWkw4Qo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which societal value showed a greater trend of change over the 10 years period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the societal value that showed a greater trend of change, I have to consider the magnitudes of changes in both \"Public Support\" and \"Military Expenditure\". Starting with {Public Support}<r>[0.063, 0.392, 0.976, 0.582]</r>, there was a decrease from 0.61 to 0.5, which is a change of 0.11. On the other hand, looking at {Military Expenditure}<r>[0.063, 0.149, 0.976, 0.669]</r>, the change was from 0.45 to 0.75, which is a difference of 0.3. Therefore, \"Military Expenditure\" showed a greater magnitude of change compared to \"Public Support\".\nBased on the above thought, the answer is:\nFrom 2005 to 2015, \"Military Expenditure\" showed a greater trend of change than \"Public Support\" based on the line chart. This is evident as the difference in \"Military Expenditure\" over the ten-year span was larger compared to the difference in \"Public Support\". Specifically, \"Military Expenditure\" rose by 0.3, whereas \"Public Support\" decreased by 0.11. This suggests that the focus on military spending increased significantly more than the decrease in public support over this time period."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "UnFQRpa2k9EuDEo8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_34_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Public Support in 2005 and 2015 based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I have to look at the values for Public Support in 2005 and 2015. The data point for 2005 can be found at {Public Support in 2005}<r>[0.063, 0.478]</r> which corresponds to 0.56. Similarly, the data point for 2015 is at {Public Support in 2015}<r>[0.976, 0.582]</r> showing a value of 0.50. So, the difference between these two years is 0.56 - 0.50 = 0.06.\nBased on the above thought, the answer is:\n0.06"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "zjlCmetaQ6ws3yMC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the visual data plotted in the chart, please extract structured tables with years as rows and the different aspects as columns."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate structured tabular data from the chart, I first observe {the x-axis tick labels}<r>[0.082, 0.901, 0.094, 0.932], [0.079, 0.901, 0.104, 0.932], [0.082, 0.901, 0.107, 0.932], [0.085, 0.901, 0.110, 0.932], [0.088, 0.901, 0.113, 0.932]</r> for years. Next, I find {the legend texts}<r>[0.149, 0.830, 0.310, 0.860], [0.440, 0.830, 0.592, 0.860], [0.722, 0.830, 0.948, 0.860], [1.078, 0.830, 1.296, 0.860], [1.426, 0.830, 1.739, 0.860]</r> which represents different aspects like 'CO2 in atmosphere', 'Deforestation rate', 'Polar Ice Caps Melting Rate', 'Human Population Growth', 'Invention of Sustainable Technologies'.Then, I note down {the points on the lines}<r>[0.087, 0.156], [0.101, 0.156], [0.088, 0.750], [0.091, 0.759], [0.094, 0.768], [0.098, 0.777], [0.101, 0.786], [0.088, 0.868], [0.091, 0.872], [0.094, 0.871], [0.098, 0.870], [0.101, 0.868], [0.088, 0.863], [0.091, 0.870], [0.094, 0.868], [0.098, 0.867], [0.101, 0.863], [0.088, 0.864], [0.091, 0.876], [0.094, 0.873], [0.098, 0.869], [0.101, 0.864]</r> to extract the values of all these aspects for each year.\nBased on the above thought, the answer is:\nYears,CO2 in atmosphere,Deforestation rate,Polar Ice Caps Melting Rate,Human Population Growth,Invention of Sustainable Technologies\n3,405,75,6.5,7.7,2.1\n10,410,70,7.0,8.2,4.6\n15,413,65,7.5,9.0,6.5\n30,420,60,8.0,10.0,8.8\n40,425,55,9.0,12.0,11.7"
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "oogUokvthFvDIrVf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the environmental aspect that shows the highest rate of improvement between the 3rd year and the 40th year, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the environmental aspect with the highest rate of improvement, I need to compare the differences between the values of all aspects from the 3rd year to the 40th year. I can find the points for each aspect for both years: {CO2 in atmosphere}<r>[0.087, 0.156], [0.101, 0.864]</r>, {Deforestation rate}<r>[0.088, 0.750], [0.101, 0.786]</r>, {Polar Ice Caps Melting Rate}<r>[0.088, 0.868], [0.101, 0.881]</r>, {Human Population Growth}<r>[0.088, 0.863], [0.101, 0.871]</r>, {Invention of Sustainable Technologies}<r>[0.088, 0.864], [0.101, 0.881]</r>. Based on the figures, the 'Invention of Sustainable Technologies' increases from 2.1 to 11.7, which is higher compared to others.\nBased on the above thought, the answer is:\nThe environmental aspect that shows the highest rate of improvement between the 3rd year and the 40th year according to the chart is the 'Invention of Sustainable Technologies'."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "MpAGhu2PfhL1R6Jc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in atmospheric CO2 from the 3rd year to the 40th year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in atmospheric CO2 from the 3rd year to the 40th year, I need to find out the values of CO2 in atmosphere for these years. I locate {the points of CO2 in atmosphere for the 3rd year and 40th year}<r>[0.087, 0.156], [0.101, 0.864]</r>, then I can see the corresponding values are 405 for the 3rd year and 425 for the 40th year. So the increase in atmospheric CO2 is 425 - 405 = 20.\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 680
+    },
+    {
+        "id": "l1tVE963rakooMfk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data depicted in the chart in a table format with \"Year\" as rows and the respective \"Fields\" as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I begin by identifying the x-axis labels found at {the x-axis tick labels}<r>[0.123, 0.801, 0.205, 0.865], [0.692, 0.801, 0.774, 0.865]</r> as the row headers '1990' and '2010'. Then, I identify the object labels in {the legend}<r>[0.238, 0.571, 0.356, 0.631], [0.238, 0.659, 0.345, 0.719], [0.572, 0.571, 0.651, 0.631], [0.572, 0.659, 0.719, 0.719], [0.936, 0.571, 1.112, 0.631], [0.936, 0.659, 1.024, 0.719]</r> as the column headers 'GPT-Neo', 'Theatre', 'Music', 'Visual Arts', 'Photography', 'Dance'. The data values can be derived based on {the line points}<r>[0.164, 0.336], [0.733, 0.336], [0.164, 0.249], [0.733, 0.379], [0.164, 0.336], [0.733, 0.249], [0.164, 0.163], [0.733, 0.206], [0.164, 0.379], [0.733, 0.466], [0.164, 0.509], [0.733, 0.423]</r> corresponding to each cell of the table.\nBased on the above thought, the answer is:\nYear,GPT-Neo,Theatre,Music,Visual Arts,Photography,Dance\n1990,0.5,0.6,0.5,0.7,0.45,0.3\n2010,0.5,0.45,0.6,0.65,0.55,0.35"
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "huk7sgGSGX188Iv1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which field has seen negative growth from 1990 to 2010? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the line plots for each field. I see that {the line for Theatre}<r>[0.164, 0.249], [0.733, 0.379]</r> starts from 0.6 in 1990 and ends at 0.45 in 2010 indicating negative growth. Similarly, {the line for Visual Arts}<r>[0.164, 0.163], [0.733, 0.206]</r> starts from 0.7 in 1990 and ends at 0.65 in 2010 which also indicates a negative growth.\nBased on the above thought, the answer is:\nThe fields which have seen negative growth from 1990 to 2010 are Theatre and Visual Arts. In both cases, the growth rate in 1990 is higher than the growth rate in 2010, indicating a decrease or negative growth over these years."
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "yYmKaI2pOcfYIGij",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the growth change in Visual Arts from 1990 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the growth values for Visual Arts in 1990 and 2010 by looking at the points on {the line for Visual Arts}<r>[0.164, 0.163], [0.733, 0.206]</r> which are represented as 0.7 and 0.65 respectively. So, the growth change from 1990 to 2010 would be 0.65 - 0.7 = -0.05.\nBased on the above thought, the answer is:\n-0.05"
+            }
+        ],
+        "id_raw": 681
+    },
+    {
+        "id": "7XZVMLIo3Z8TKhJi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please turn the line chart into a table that includes Quarters in columns and Industries as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, the first step is to recognize {the x-axis tick labels}[[0.118, 0.902, 0.152, 0.934], [0.386, 0.902, 0.421, 0.934], [0.655, 0.902, 0.690, 0.934], [0.924, 0.902, 0.958, 0.934]] as the column headers or Quarters. Next, I identify {the legend texts}[[0.815, 0.656, 0.962, 0.686], [0.815, 0.700, 0.899, 0.730], [0.815, 0.743, 0.854, 0.773], [0.815, 0.787, 0.906, 0.817], [0.815, 0.831, 0.867, 0.861]] as the row labels representing the different industries. To populate the values, I locate the points for each industry {Aviation}[[0.135, 0.589], [0.404, 0.461], etc.], {Rail}[[0.135, 0.716], [0.404, 0.674], etc.], {Shipping}[[0.135, 0.249], [0.404, 0.334], etc.], {Road}[[0.135, 0.844],[0.404, 0.759], etc.] at the respective quarters and convert the corresponding y-coordinate to the growth rate.\nBased on the above thought, the answer is:\nIndustries,Q1,Q2,Q3,Q4\nAviation,1.5,1.8,2.1,1.7\nRail,1.2,1.3,1.7,1.6\nShipping,2.3,2.1,2.4,2.6\nRoad,0.9,1.1,1.3,1.2"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "BuFXFdT8qFP2QZw1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the trends depicted in the line chart, which industry demonstrated the most stable growth over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which industry demonstrated the most stable growth, I need to examine the changes in the line plots for each industry. Analyzing {the line for Aviation}[[0.135, 0.589], [0.404, 0.461], [0.672, 0.334], [0.941, 0.504]], I see some fluctuations across quarters. The similar scenario can be observed for the {Road}[[0.135, 0.844],[0.404, 0.759], etc.], {Shipping}[[0.135, 0.249], [0.404, 0.334], etc.], and {Rail}[[0.135, 0.716], [0.404, 0.674], etc.] industries too. However, the fluctuations in {the line for Rail} appear less severe and more stable in comparison to the other industries.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Rail industry seems to have demonstrated the most stable growth over the year. The line plot for Rail shows relatively less fluctuations across quarters, indicating a more stable growth rate compared to other industries, such as Aviation, Shipping, and Road."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "hWA8LVhxCQB1qvhO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the difference between the highest and lowest growth rate for the Aviation industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the highest and lowest values, I need to locate {the points for the Aviation line}[[0.135, 0.589], [0.404, 0.461], [0.672, 0.334], [0.941, 0.504]]. The highest value is at the point corresponding to {Q3}[0.672, 0.334] with a growth rate of 2.1, while the lowest value is at the point corresponding to {Q1}[0.135, 0.589] with a growth rate of 1.5. Therefore, the difference between the highest and lowest growth rate for Aviation is 2.1-1.5=0.6.\nBased on the above thought, the answer is:\n0.6."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "ZfwSnSApqRyqqYI3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart to a structured table with the 'Parameters' acting as the column headers and each social media platform as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I note {the axis labels}<r>[0.450, 0.930, 0.577, 0.970]</r>, which represent the column headers or 'Parameters'. Now, looking at {the legend items}, I find labels for Facebook<r>[0.119, 0.055, 0.185, 0.083]</r>, Twitter<r>[0.119, 0.097, 0.166, 0.125]</r>, Instagram<r>[0.119, 0.139, 0.190, 0.167]</r> and LinkedIn<r>[0.119, 0.181, 0.177, 0.209]</r>, which will form the row headings. To generate the values, I observe {the line points for Facebook}<r>[0.108, 0.776, 0.919, 0.551]</r>, {Twitter}<r>[0.108, 0.824, 0.919, 0.334]</r>, {Instagram}<r>[0.108, 0.809, 0.919, 0.068]</r> and {LinkedIn}<r>[0.108, 0.833, 0.919, 0.406]</r>, respectively and extract these corresponding data values.\nBased on the above thought, the answer is:\nPlatform,Active Users in billions,Growth Rate in %\nFacebook,2.7,12\nTwitter,0.69,21\nInstagram,1.32,32\nLinkedIn,0.31,18"
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "1893b0uQUjQosCPm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the active user count and growth rate, which platform shows the most balanced growth and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate balanced growth, I need to consider both active user count and growth rate. {Facebook}<r>[0.108, 0.776, 0.919, 0.551]</r> has a substantial active user count but a relatively lower growth rate. {Instagram}<r>[0.108, 0.809, 0.919, 0.068]</r> possesses the highest growth rate, but its active user count is less than half of Facebook's. {Twitter}<r>[0.108, 0.824, 0.919, 0.334]</r> has a decent growth rate but far fewer active users than Facebook or Instagram. {LinkedIn}<r>[0.108, 0.833, 0.919, 0.406]</r> has the least number of active users but maintains a growth rate that's competitive with other platforms except Instagram. Therefore, attributing balanced growth depends on whether equal weight is given to active users and growth rate.\nBased on the above thought, the answer is:\nIf both active user count and growth rate are given equal importance, then Facebook arguably demonstrates the most balanced growth. Despite its relatively lower growth rate, it compensates with its massive active user base. Otherwise, LinkedIn seems to be growing steadily with a reasonable growth rate, despite starting with a lower user base, showing potential for balanced growth in the future."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "8W8nuQy9jMhOWxYB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the growth rate of the social media platform with the highest growth rate and the one with the lowest growth rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rates, I need to refer to the line points corresponding to 'growth rate in %'. Here, {Instagram}<r>[0.919, 0.068]</r> has the highest growth rate of 32% while {Facebook}<r>[0.919, 0.551]</r> has the lowest growth rate of 12%. Hence, the difference is 32% - 12% = 20%.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 683
+    },
+    {
+        "id": "z6GBU7TDltQoKroi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please create a table showing how sales changed from 6AM to 6PM for each product sold?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm looking at {the x-axis labels}<r>[0.173, 0.801, 0.194, 0.865], [0.917, 0.801, 0.958, 0.865]</r> that state the two timestamps \"6\" and \"18\". With those as my column labels, I'm noticing {the legend items}<r>[0.860, 0.308, 0.950, 0.368], [0.860, 0.396, 0.916, 0.456], [0.860, 0.484, 0.906, 0.544], [0.860, 0.571, 0.926, 0.631], [0.860, 0.659, 0.942, 0.719]</r> are \"Coffee\", \"Milk\", \"Tea\", \"Juice\", and \"Water\". Now, I'll extract the quantities sold for each, represented by the lines' positions {for Coffee}<r>[0.183, 0.334, 0.937, 0.435]</r>, {for Milk}<r>[0.183, 0.267, 0.937, 0.334]</r>, {for Tea}<r>[0.183, 0.201, 0.937, 0.468]</r>, {for Juice}<r>[0.183, 0.100, 0.937, 0.602]</r> and {for Water}<r>[0.183, 0.167, 0.937, 0.612]</r>.\nBased on the above thought, the answer is:\nProduct,6AM,6PM\nCoffee,100,130\nMilk,130,150\nTea,90,170\nJuice,50,200\nWater,47,180"
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "hVnAwvikhERaH66l",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product shows the greatest increase in sales from 6AM to 6PM?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product had the greatest increase in sales, I need to compare the changes in sales from 6AM to 6PM. Looking at {the line for Coffee}<r>[0.183, 0.334, 0.937, 0.435]</r>, I see the sales increased from 100 to 130. For {Milk}<r>[0.183, 0.267, 0.937, 0.334]</r>, the sales increased from 130 to 150. {The line for Tea}<r>[0.183, 0.201, 0.937, 0.468]</r> shows an increase from 90 to 170. {The line for Juice}<r>[0.183, 0.100, 0.937, 0.602]</r> shows the largest increase in sales from 50 to 200. Finally, {Water}<r>[0.183, 0.167, 0.937, 0.612]</r> sales increased from 47 to 180.\nBased on the above thought, the answer is:\nThe product that shows the greatest increase in sales from 6AM to 6PM is Juice, with sales increasing by 150 units which is the largest increase among all the products."
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "WGWeamXFk1NIpavF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt 6PM, how much more is sold in terms of Tea than Water units?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would have to locate and compare the values for Tea and Water at the 6PM time. Among the plot lines, I find {the point for Tea at 6PM}<r>[0.937, 0.201]</r>, which corresponds to 170 units sold and {the point for Water at 6PM}<r>[0.937, 0.167]</r>, corresponding to 180 units sold. By subtracting the sales of Water from Tea, I can find the difference.\nBased on the above thought, the answer is:\n-10"
+            }
+        ],
+        "id_raw": 684
+    },
+    {
+        "id": "5ujYnge9ZzoJy7aD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the line chart that shows the population trend over years in different Areas into a table with Years as rows and Area names as columns."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first locate {the x-axis tick labels}[[0.119, 0.881, 0.168, 0.919], [0.920, 0.881, 0.970, 0.919]] which represent the row headers or Years. Next, I identify {the legend texts}[[0.154, 0.599, 0.267, 0.627], [0.154, 0.641, 0.200, 0.669], [0.154, 0.683, 0.201, 0.711], [0.154, 0.725, 0.201, 0.753], [0.154, 0.767, 0.202, 0.795], [0.154, 0.808, 0.200, 0.836]] as the column headers representing the different Areas. For the values, I would identify each {line points}[[0.143, 0.666], [0.945, 0.813], [0.143, 0.372], [0.945, 0.177], [0.143, 0.226], [0.945, 0.079], [0.143, 0.421], [0.945, 0.275], [0.143, 0.519], [0.945, 0.617]] for each Area for the specific Year and extract these population values.\nBased on the above thought, the answer is:\nYears,AreaA,AreaB,AreaC,AreaD,AreaE\n2000,12000,18000,21000,17000,15000\n2010,9000,22000,24000,20000,13000"
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "j0LEoKc2saD5m3lp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data, which area has had the largest growth over this period, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will compare the increase or decrease in values for each area. From the {line points for Area A}[[0.143, 0.666], [0.945, 0.813]], I am able to see that the population decreased from 12000 to 9000. By analyzing {the line points for Area B}[[0.143, 0.372], [0.945, 0.177]], I can determine that it increased from 18000 to 22000. For {Area C's line points}[[0.143, 0.226], [0.945, 0.079]], the population sees a growth from 21000 to 24000. Similarly, by examining {the line points for Area D}[[0.143, 0.421], [0.945, 0.275]], there is an increase from 17000 to 20000. Lastly, looking at {the line points for Area E}[[0.143, 0.519], [0.945, 0.617]], the values decrease from 15000 to 13000.\nBased on the above thought, the answer is:\nFrom the information, it is evident that Area C has had the largest growth over the last decade, with an increase of 3000 from 21000 to 24000, compared to the growth of other areas like Area B (4000 from 18000 to 22000) and Area D (3000 from 17000 to 20000). The reasons could be various ranging from better facilities, economic development or migration trends, without additional context we can't pinpoint a specific reason why."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "2w1qmPQ1lV5avWWx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the decade, what is the absolute change in the population for Area A?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the absolute change in the population for Area A, I need to find {the line points representing Area A}[[0.143, 0.666], [0.945, 0.813]]. The population in 2000 is 12000 at point [0.143, 0.666] and in 2010, it's 9000 at point [0.945, 0.813]. The absolute difference is 12000 - 9000 = 3000.\nBased on the above thought, the answer is:\n3000."
+            }
+        ],
+        "id_raw": 685
+    },
+    {
+        "id": "puaZV7yABQNgIZnn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the line chart into a tabular format with 'Year' as the columns and 'Categories' as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a tabular format, I start by recognizing the labels on {the x-axis}['xticks'][0.103, 0.901, 0.165, 0.932], [0.910, 0.901, 0.972, 0.932] as the column headers representing the years. Next, I identify {the legend texts}[[0.171, 0.062, 0.256, 0.093], [0.171, 0.106, 0.262, 0.136], [0.171, 0.150, 0.271, 0.180],  [0.371, 0.062, 0.525, 0.093], [0.371, 0.106, 0.548, 0.136]] as the row labels representing the different Technology Services. The values are extracted from the corresponding {line points} for each service at the respective years.\nBased on the above thought, the answer is:\nYear,Telecom,Software,Hardware,Cloud Services,Internet Services\n2009,20,30,35,15,10\n2019,30,45,40,60,75"
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "TwrMRJQPE4gl4N8z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased solely on the line chart, which technology service category experienced the most significant growth from 2009 - 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the service category with the most substantial growth, I need to inspect the difference in start and end points for each line. The lines representing the various categories are {Telecom}['line_0'][0.134, 0.673, 0.941, 0.567], {Software}['line_1'][0.134, 0.567, 0.941, 0.408], {Hardware}['line_2'][0.134, 0.514, 0.941, 0.461], {Cloud Services}['line_3'][0.134, 0.726, 0.941, 0.249], and {Internet Services}['line_4'][0.134, 0.779, 0.941, 0.090]. The y-values for these lines correspond to the growth levels for the respective years 2009 and 2019. As per my evaluation, Internet Services have demonstrated the highest growth.\nBased on the above thought, the answer is:\nOf all the technology service categories, Internet Services has experienced the most significant growth from 2009 to 2019. This is evident from the large vertical distance covered by the corresponding line from its point in 2009 to its point in 2019."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "E176LvouoykmekBz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute growth from 2009 to 2019 in the Hardware category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the absolute growth in the Hardware category, I need to identify the start and end point of {the line representing Hardware}{'line_2'}[0.134, 0.514, 0.941, 0.461]. The starting point corresponds to the year 2009 and its value is 35. However, the concluding point represents the year 2019 and its value is 40. Hence, the absolute growth is calculated as 40 - 35 = 5.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "RrqVbYeOF9mYvDsS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you state the earnings per share for all the quarters in a structured table for each company?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the structured table, I first recognize {the x-axis ticks}<r>[0.153, 0.851, 0.192, 0.899], [0.408, 0.851, 0.448, 0.899], [0.664, 0.851, 0.704, 0.899], [0.920, 0.851, 0.960, 0.899]</r> which represent the quarters. Next, I identify the company names from {the legend}<r>[0.676, 0.670, 0.727, 0.702], [0.676, 0.718, 0.709, 0.751], [0.676, 0.766, 0.739, 0.799], [0.878, 0.670, 0.963, 0.702], [0.878, 0.718, 0.950, 0.751]</r>. For the earnings values, I locate the points on {the lines for each company}<r>[0.172, 0.185, 0.940, 0.284], [0.172, 0.120, 0.940, 0.366], [0.172, 0.498, 0.940, 0.613], [0.172, 0.416, 0.940, 0.531], [0.172, 0.629, 0.940, 0.745]</r> at each quarter and extract the corresponding values.\nBased on the above thought, the answer is:\nCompany,Q1,Q2,Q3,Q4\nApple,0.68,0.72,0.74,0.70\nIBM,0.75,0.78,0.65,0.63\nGoogle,0.5,0.48,0.52,0.55\nFacebook,0.53,0.57,0.6,0.58\nAmazon,0.47,0.4,0.44,0.45"
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "ygSvkCCynVFBmJbB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which company seems to have the most stable earnings per share across the four quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company has the most stable earnings across all quarters, I need to analyze the fluctuations in the line plots for each company. Looking at the {line for Apple}<r>[0.172, 0.185, 0.940, 0.284]</r>, I see a minor increase from 0.68 to 0.72, followed by another minor increase and a decrease which indicates variability. The {line for IBM}<r>[0.172, 0.120, 0.940, 0.366]</r> starts at 0.75 and ends at 0.63, exhibiting a decrease over time with a small upward movement in Q2. {Google}<r>[0.172, 0.498, 0.940, 0.613]</r> and {Facebook}<r>[0.172, 0.416, 0.940, 0.531]</r> both show a generally rising trend with minor fluctuations. For {Amazon}<r>[0.172, 0.629, 0.940, 0.745]</r>, the line shows a consistently increasing trend from 0.47 to 0.45 with almost no fluctuation.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Amazon seems to have the most stable earnings per share across the four quarters. Its plot line exhibits the smallest fluctuation among all companies, indicating the highest level of stability in their earnings."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "vL5c1l37AvI2fF05",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in Earnings per Share from Q1 to Q4 for Google?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in earnings for Google from Q1 to Q4, I need to locate {the line points for Google}<r>[0.172, 0.580], [0.428, 0.613], [0.684, 0.547], [0.940, 0.498]</r> and extract the corresponding values: 0.5, 0.48, 0.52, 0.55. The value for Q1 is 0.5 corresponding to {Q1}<r>[0.153, 0.851, 0.192, 0.899]</r>, and the value for Q4 is 0.55 corresponding to {Q4}<r>[0.920, 0.851, 0.960, 0.899]</r>. The difference between Q1 and Q4 is therefore 0.55 - 0.5 = 0.05.\nBased on the above thought, the answer is:\n0.05."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "OOat2zMjcG00lUpB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data from the line chart showing the progression stages of both \"Knowledge Economy\" and \"Innovation Industries\" be arrayed into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a tabular format, I begin by identifying {the x-axis tick labels}[[0.128, 0.856, 0.142, 0.901], [0.390, 0.856, 0.417, 0.901], [0.659, 0.856, 0.685, 0.901], [0.921, 0.856, 0.961, 0.901]] as column headers which represent the progression stages. Then I look at {the legend}[[0.519, 0.766, 0.700, 0.801], [0.783, 0.766, 0.966, 0.801]] to figure out the row labels, which here represent the entities \"Knowledge Economy\" and \"Innovation Industries\". Using {the line points for Knowledge Economy}[[0.135, 0.210], [0.404, 0.172], [0.672, 0.366], [0.941, 0.599]] and {the line points for Innovation Industries}[[0.135, 0.443], [0.404, 0.443], [0.672, 0.288], [0.941, 0.172]] I fetch the values for each stage of progression for both entities.\nBased on the above thought, the answer is:\nProgression Stages|3|10|30|100\nKnowledge Economy|.8|.85|.6|.3\nInnovation Industries|.5|.5|.7|.85"
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "Ik4p6WoT61GeUVSW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, has there been a steady increase or decrease in the metrics for both \"Knowledge Economy\" and \"Innovation Industries\" with increasing progression stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "Determining if there has been a steady increase or decrease in the metrics requires an analysis of the lines and their slopes for both the entities. {The line for Knowledge Economy}[0.135, 0.210, 0.941, 0.599] first shows an upward trend until {Progression Stage 10}[0.404, 0.856], afterwards which a downward trend is evident, showing an overall decrease in metrics. Similarly, {the line for Innovation Industries}[0.135, 0.443, 0.941, 0.172] initially reflects a steady metric until {Progression Stage 10}[0.404, 0.856], followed by an increase until {Progression Stage 30}[0.659, 0.856] and another decrease until {Progression Stage 100}[0.921, 0.856], showing overall fluctuation in metrics.\nBased on the above thought, the answer is:\nThe metrics for \"Knowledge Economy\" show an overall decrease with increasing progression stages, indicating an initial growth followed by a decline. On the other hand, the metrics for \"Innovation Industries\" stage an overall fluctuation with an initial stagnancy followed by an increase and subsequent decrease."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "NpFK5Ad9ReOx2Ebo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum metrics of \"Knowledge Economy\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To attain the difference, I first have to get the minimum and maximum metrics for \"Knowledge Economy\". Close examination of {the line points for Knowledge Economy}[[0.135, 0.210], [0.404, 0.172], [0.672, 0.366], [0.941, 0.599]] manifests the maximum metric as 0.85 corresponding to {Progression Stage 10}[0.404, 0.856] and the minimum metric as 0.3 corresponding to {Progression Stage 100}[0.921, 0.856]. The difference thus is 0.85 - 0.3 = 0.55.\nBased on the above thought, the answer is:\n0.55."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "yczBhXeaV1fMiDoo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data into structured tables based on efficiency levels and types of meaning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To recreate the tables from the line graph, firstly, I am focusing on the {x-axis tick labels}[[0.147, 0.801, 0.239, 0.865], [0.339, 0.801, 0.414, 0.865], [0.517, 0.801, 0.603, 0.865], [0.685, 0.801, 0.802, 0.865], [0.881, 0.801, 0.974, 0.865]], which indicate the efficiency levels. Then, I use {the legend texts}[[0.259, 0.571, 0.473, 0.631], [0.259, 0.659, 0.498, 0.719]] that include \"Literal Meaning\" and \"Learned Meaning\" that represent different types of meaning. For the corresponding values, I consider {the line points} for both \"Literal Meaning\"[[0.193, 0.457], [0.376, 0.577], [0.560, 0.649], [0.743, 0.649], [0.927, 0.721]] and \"Learned Meaning\" [[0.193, 0.170], [0.376, 0.290], [0.560, 0.410], [0.743, 0.529], [0.927, 0.601]] to populate the values.\nBased on the above thought, the answer is:\nEfficiency Level,Literal Meaning,Learned Meaning\nAlpha,0.93,1.05\nBeta,0.88,1.0\nDelta,0.85,0.95\nEpsilon,0.85,0.9\nTheta,0.82,0.87"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "XWAtc17pUOEVsavw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, how does the efficiency evolve from Alpha to Theta for each type of meaning?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the lines on the graph for each type, I can analyze the trends in efficiency from Alpha to Theta for both types of meaning. For {Literal Meaning}[0.193, 0.457, 0.927, 0.721], the value starts at 0.93 and ends at 0.82, showing a decrease in efficiency. In contrast, the line for {Learned Meaning}[0.193, 0.170, 0.927, 0.601] points to an overall decrease as well, starting from 1.05 at Alpha, and reducing to 0.87 at Theta.\nBased on the above thought, the answer is:\nThe efficiency for both types of meaning decreases from Alpha to Theta, as per the trends shown on the line graph. \"Literal Meaning\" starts at a high efficiency of 0.93 at Alpha and decreases to 0.82 at Theta, while \"Learned Meaning\" also shows a reduction from 1.05 at Alpha to 0.87 at Theta."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "LCv2yf1H64OJnfTn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in efficiency from Alpha to Theta for Literal Meaning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in efficiency from Alpha to Theta in Literal Meaning, I need to identify {the line points for Literal Meaning starting at Alpha and ending at Theta}[[0.193, 0.457], [0.927, 0.721]] and extract the corresponding values: Alpha = 0.93 and Theta = 0.82. The increase is therefore 0.82 - 0.93 = -0.11.\nBased on the above thought, the answer is:\n-0.11."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "ImtWLqTMzY2tLMqN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from the line chart to show the values of Patent Filed, Patent Granted, Patent Expired, Innovation Index, and Patent Transferred for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first identify {the x-axis tick labels}[[0.115, 0.881, 0.164, 0.919], [0.316, 0.881, 0.366, 0.919], [0.518, 0.881, 0.567, 0.919], [0.719, 0.881, 0.768, 0.919], [0.920, 0.881, 0.969, 0.919]] which represent the year or the row labels of our table. Then, I locate the {legend texts}[[0.151, 0.070, 0.232, 0.098], [0.151, 0.112, 0.257, 0.140], [0.151, 0.154, 0.253, 0.182], [0.151, 0.196, 0.267, 0.224], [0.151, 0.238, 0.279, 0.266]] for the columns corresponding to Patent Filed, Patent Granted, Patent Expired, Innovation Index, and Patent Transferred. The values for each category can be obtained from the coordinates of the {line points}[[0.140, 0.487], [0.341, 0.372], ..., [0.945, 0.113]]. Corresponding to these various years are extracted.\nBased on the above thought, the answer is:\nYear,Patent Filed,Patent Granted,Patent Expired,Innovation Index,Patent Transferred\n2000,550,320,50,2.5,30\n2005,720,400,100,3,50\n2010,890,450,150,3.5,100\n2015,1000,500,200,4.2,150\n2020,1100,600,250,5,200"
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "qEoocNv8DujP6jEG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends seen in the line chart, which category had the most significant growth over the years and why do you believe so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the category with the most significant growth, I need to analyze the lines for different categories. The {line for Patent Filed}[0.140, 0.113, 0.945, 0.487] starts at 550 in 2000 and ends at 1100 in 2020, showing an increase of 550. The {line for Patent Granted}[0.140, 0.453, 0.945, 0.644] indicates an increase of 280 over the years. {Patent Expired}[0.140, 0.691, 0.945, 0.827] increased by 200, {Innovation Index}[0.140, 0.858, 0.945, 0.860] only increased by 2.5, and {Patent Transferred}[0.140, 0.725, 0.945, 0.841] significantly increased by 170.\nBased on the above thought, the answer is:\nThe category that has shown the most significant growth over the years, as depicted in the line chart, is the Patent Filed category. This is evident as the Patent Filed line plot shows an increase from 550 in the year 2000 to 1100 in the year 2020, which is a 550 increases, this is the largest increase compared to the other categories within the same period."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "Cb5BGyRDUuSdBntk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the overall net increase in Patent Transferred from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the net increase in Patent Transferred, I have to find the corresponding values of Patent Transferred for the years 2000 and 2020. The 2000 value is at {the point for 2000}[[0.140, 0.841]], which corresponds to 30. The 2020 value is at {the point for 2020}[[0.945, 0.725]], which is 200. The net increase is therefore 200 - 30 = 170.\nBased on the above thought, the answer is:\n170."
+            }
+        ],
+        "id_raw": 690
+    },
+    {
+        "id": "akfAJeqU3IdZZqFE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the chart into a table that reflects the year and corresponding values for Cultural Heritage and Cultural Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the information from the chart, I need to observe {the line for Cultural Heritage}<r>[0.114, 0.857, 0.946, 0.853]</r> and {the line for Cultural Integration}<r>[0.114, 0.127, 0.946, 0.029]</r>, along with {the x-axis labels}<r>[0.094, 0.901, 0.966, 0.932]</r> representing the years. For each year, the values associated with Cultural Heritage and Cultural Integration need to be accurately collected from where the points intersect with each corresponding line, ensuring precise data capture.\nBased on the above thought, the answer is:\nYear,Cultural Heritage (%),Cultural Integration (%)\n2000,13.6,370\n2001,13.75,372\n2002,13.85,375\n2003,13.77,379\n2004,13.7,381\n2005,14.0,382\n2006,13.93,385\n2007,13.85,387\n2008,13.7,390\n2009,14.2,393\n2010,14.4,396\n2011,14.42,400\n2012,14.6,402\n2013,14.65,404\n2014,14.75,407\n2015,15.01,409\n2016,15.15,410\n2017,15.2,413\n2018,15.3,415\n2019,15.4,418"
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "qxkfhBLiN3CiJlAX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the line plot for Cultural Integration indicate the effects of multicultural policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The analysis of {the line for Cultural Integration}<r>[0.114, 0.127, 0.946, 0.029]</r> reveals subtle fluctuations across the years. It portrays a marginal decline with one significant drop, which can help interpret the effects of multicultural policies and their adaptation in society over time. Given that the line mostly exhibits a declining trend rather than sharp falls or rises, it suggests a gradual adaptation and normalization of multicultural principles, with the noted outlier possibly indicating a temporary cultural disturbance or an issue which momentarily impacted cultural integration.\nBased on the above thought, the answer is:\nThe line plot demonstrates a generally stable yet slightly declining trend in Cultural Integration, signifying that while there are ongoing efforts for cultural adaptation through multicultural policies, there are challenges and disturbances which might temporarily affect the integration process."
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "h24ASjbppKymCgiB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average percentage decline for Cultural Heritage over the observed years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average decline, I will review the data points along {the line for Cultural Heritage}<r>[0.114, 0.857, 0.946, 0.853]</r>. From the start year 2000 with a value of 13.6 to the end year 2019 with a value of 15.4, calculations involve determining the decline each year and then finding the average. The total decline is computed by subtracting the final value from the initial value, divided by the number of years.\nBased on the above thought, the answer is:\n0.09%"
+            }
+        ],
+        "id_raw": 691
+    },
+    {
+        "id": "pMEi4H7lLsDNfbd4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the provided raw data into a table showcasing the trend of each sector or category over the years from 2009 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the data into a tabular form, I will extract each sector's data across the years by analyzing the line plot for each sector. First, I will locate {the x-axis tick labels}[0.109, 0.881, 0.972, 0.919] which represent the years 2009 to 2019. For each sector such as {the line for Building Materials}[0.149, 0.453, 0.942, 0.233], I will extract the corresponding y-values at every x-point. Repeating this process for each sector, I will compile a comprehensive table showcasing the trend over the years.\nBased on the above thought, the answer is:\nYear,Building_Materials,Real_Estate_Agencies,Residential_Buildings,Commercial_Buildings,Public_Sector_Buildings,Infrastructure_Projects\n2009,50,60,55,42,58,65\n2010,52,62,57,45,60,67\n2011,58,65,60,50,63,70\n2012,60,70,63,52,65,75\n2013,62,70,67,55,67,77\n2014,67,72,70,57,68,78\n2015,68,75,75,60,70,80\n2016,70,78,78,65,72,85\n2017,72,79,80,67,75,86\n2018,75,80,83,70,77,86\n2019,77,85,85,73,80,90"
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "derD4hAB7Thq6i8q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows a consistently upward trend throughout the period from 2009 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which sector shows a consistently upward trend, it is necessary to analyze the slope of the lines for each sector throughout the period. Observing the line plots, I notice that {the line for Infrastructure Projects}[0.149, 0.208, 0.942, 0.167] starts at a lower position and consistently rises to a higher position by the end of 2019. This indicates that Infrastructure Projects have a consistent upward trend.\nBased on the above thought, the answer is:\nThe Infrastructure Projects sector shows a consistently upward trend throughout the period from 2009 to 2019. This is evident from its line plot starting at a lower position and gradually ascending to a higher position by the end of the period."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "bhYWVi08ANfgWgOh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in value for Commercial Buildings from 2009 to 2019 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to identify the points for Commercial Buildings in the years 2009 and 2019. Locating {the point for Commercial Buildings in 2009}{[0.149, 0.519]}, the value is 42, and for {2019}{[0.942, 0.265]}, it is 73. The increase is calculated by subtracting the initial value from the final value (73 - 42).\nBased on the above thought, the answer is:\n31."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "oRm64ZxjzszauqWy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured data table reflecting the user numbers over years for each platform as shown in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the structured data table from the line chart, I first locate {the axis labels for years}<r>[0.098, 0.895, 0.961, 0.923]</r> that will represent the rows in our table. Next, I identify {the legend entries}<r>[0.087, 0.102, 0.181, 0.213]</r> that denote each platform to establish the columns for our table. Each column corresponds to Google, Youtube, Twitter, Facebook, and Netflix respectively. Then, I assess the {data points}<r>[0.116, 0.275, 0.944, 0.875]</r> for each platform over the years, calculating the corresponding user numbers from their positions relative to the Y-axis.\nBased on the above thought, the answer is:\nYear,Google,Youtube,Twitter,Facebook,Netflix\n2006,65,30,0,25,0\n2007,75,40,5,30,0\n2008,75,60,10,50,10\n2009,80,80,15,55,20\n2010,85,110,20,60,40\n2011,90,140,30,70,60\n2012,100,170,45,80,80\n2013,115,200,60,90,110\n2014,130,230,100,110,150\n2015,145,270,150,130,200\n2016,160,310,200,160,260\n2017,180,345,225,190,320\n2018,200,380,260,220,375\n2019,220,400,300,250,420\n2020,240,425,340,290,500\n2021,260,450,380,330,570"
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "lwPMXIhual1qz5fq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the user growth trend for Netflix compare to Google\u2019s from 2006 to 2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing their growth trends, I observe {the line for Netflix}<r>[0.116, 0.017, 0.944, 0.117]</r> starting at 0 million users in 2006 and escalating to 570 million in 2021, showing sharp upward trend particularly after 2010. In contrast, {Google\u2019s line}<r>[0.116, 0.529, 0.944, 0.370]</r> starts at 65 million users in 2006 and gradually increases to 260 million by 2021. It's evident that both have sustained growth; however, Netflix\u2019s growth is more exponential in the later years compared to Google's more linear increase.\nBased on the above thought, the answer is:\nNetflix exhibited a more drastic increase in user numbers, especially from 2010 onwards, compared to Google, which had steady, linear growth. Netflix's graph reveals sharp ascensions and higher growth rates, denoting more volatile but higher gains, as opposed to Google's consistent upward trajectory."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "19xI2ii5Hgo5Z7RE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percent increase in Youtube users from 2006 to 2021 as shown on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the percent increase for Youtube users from 2006 to 2021, I examine the y-values for Youtube at the years {2006 and 2021}<r>[0.116, 0.015, 0.944, 0.117]</r>. In 2006, Youtube had 30 million users, and by 2021, the users increased to 450 million. The percent increase is calculated as ((450 - 30) / 30) * 100.\nBased on the above thought, the answer is:\n1400%"
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "I9UGBhRLcxoMm1JT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract a CSV data table from the chart with a structure based on trends over the years as columns, and the sector type, i.e. 'Knowledge Economy' and 'Innovation Industries' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table data, I will first identify {the x-axis tick labels}<r>[0.117, 0.912, 0.174, 0.943], [0.277, 0.912, 0.334, 0.943], [0.437, 0.912, 0.494, 0.943], [0.597, 0.912, 0.654, 0.943], [0.757, 0.912, 0.814, 0.943], [0.917, 0.912, 0.974, 0.943]</r> as the column headers representing different trends. Following this, I can see {the subplot text labels}<r>[0.048, 0.465, 0.169, 0.489], [0.048, 0.040, 0.199, 0.064]</r> indicating 'Knowledge Economy' and 'Innovation Industries'. Then, I will locate positions of {line points for 'Knowledge Economy'}<r>[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527]</r>, and {line points for 'Innovation Industries'}<r>[0.117, 0.535], [0.277, 0.708], [0.437, 0.726], [0.597, 0.710], [0.757, 0.551], [0.917, 0.605]</r> to extract the respective values for each trend and each sector to populate the rows of the table.\nBased on the above thought, the answer is:\nTrend,Knowledge_Economy,Innovation_Industries\nAccelerating Decline,79.7,53.5\nMarginal Growth,66.8,70.8\nSluggish Growth,69.7,72.6\nUneven Development,47.6,71.0\nSubstantial Decrease,62.7,55.1\nDownward Trajectory,52.7,60.5"
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "vJ2G4ShjOQ6fsCgx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain which industry between 'Knowledge Economy' and 'Innovation Industries' displayed more consistent performance under different trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the consistency in performance, we should look at the fluctuation or the volatility of the lines for each sector. The {line for 'Knowledge Economy'}<r>[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527]</r> varies widely throughout different trends, showing highs near 79.7% and lows approximately at 47.6%. On the other hand, the {line for 'Innovation Industries'}<r>[0.117, 0.535], [0.277, 0.708], [0.437, 0.726], [0.597, 0.710], [0.757, 0.551], [0.917, 0.605]</r> fluctuates less, with highest at roughly 72.6% and lowest around 53.5%.\nBased on the above thought, the answer is:\nThe 'Innovation Industries' sector displayed more consistent performance under different trends compared to 'Knowledge Economy'. Though it saw both highs and lows, the margin between those extremes was less prominent for 'Innovation Industries', indicating less volatility and thus, more consistency."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "amisJ0EsoThFtku8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the range of performance values in the 'Knowledge Economy' sector."
+            },
+            {
+                "from": "gpt",
+                "value": "For calculating the range of performance values in the 'Knowledge Economy' sector, I should spot the maximum and minimum values. Observing {the line for 'Knowledge Economy'}<r>[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527]</r>, the maximum value is near about 79.7% and the minimum value is approximately 47.6%. The range can be calculated as maximum - minimum, i.e., 79.7 - 47.6 = 32.1.\nBased on the above thought, the answer is:\n32.1."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "XLKHmOKGuxSCtl3I",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing GDP % in R&D for different countries into a structured table with years as rows and countries as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I identify distinct data points for each country over the years. By examining {the x-axis labels}<r>[0.084, 0.801, 0.166, 0.865]</r> for years and {the line points for each country}<r>[0.971, 0.165, 0.975, 0.290]</r> for the USA, <r>[0.971, 0.630, 0.975, 0.672]</r> for China, and so on, I then align these with their respective years and percentage values. The ticks on the x-axis help to block exact years, and the y-axis provides the percentage values for each point on the lines.\nBased on the above thought, the answer is:\nYear,USA,China,India,Germany,Brazil\n2000,8.5,1.4,2.7,5.6,4.3\n2001,8.7,1.8,2.9,5.4,4.0\n2002,8.1,1.6,3.2,5.2,3.5\n2003,7.6,1.4,2.8,5.8,3.7\n2004,7.8,1.7,3.1,5.9,4.2\n2005,7.5,1.6,2.9,6.0,4.0\n2006,7.6,1.9,3.2,5.7,3.8\n2007,7.3,2.0,3.0,5.8,4.3\n2008,7.1,1.8,2.7,5.5,3.9\n2009,7.0,1.6,2.5,5.4,3.6\n2010,6.9,1.8,2.6,5.6,4.0"
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "tubXGkYsinUj4oMf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the span of the decade, which country shows a consistent increase in % GDP towards R&D according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By scrutinizing the lines for each country in {the plotted data}<r>[0.971, 0.165, 0.975, 0.290]</r>, I observe the general trends. The USA and Brazil exhibit some fluctuations, but Germany's line <r>[0.971, 0.353, 0.975, 0.408]</r> particularly demonstrates a constant upward trajectory, starting from 5.6% in 2000 and peaking at 6.0% in 2005 before stabilizing around 5.7% to 5.8% towards the end of the decade.\nBased on the above thought, the answer is:\nGermany exhibits a consistent increase in % GDP towards R&D over the ten-year span, marked by a trend that predominantly climbs with minor fluctuations."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "4ivAd3hL2WwC2Lp3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in India's % GDP on R&D from 2000 to 2010 according to the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase over the decade, I track {India's line points}<r>[0.971, 0.547, 0.975, 0.596]</r> specifically from the points in 2000 and 2010. Extracting the starting and ending values which are 2.7 in 2000 and 2.6 in 2010, I calculate the total increase which involves subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n-0.1."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "KUfl2fnEATSrqsf8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a table with Investment strategies as rows and Market Trends as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I need to first identify {the x-axis labels}<r>[0.116, 0.881, 0.230, 0.919], [0.871, 0.881, 0.982, 0.919]</r>, which represent the rows of the table i.e., the investment strategies. Then, I recognize {the legend}<r>[0.083, 0.677, 0.502, 0.845]</r> symbols which represent the column headers or market trends. I then extract the height of various {bars}<r>[0.116, 0.861, 0.166, 0.698], [0.871, 0.861, 0.922, 0.251]</r> in relation to the y-axis to note down the performance metrics of the respective investment strategies under various market trends.\nBased on the above thought, the answer is:\nInvestment Strategy,Sluggish Growth,Fluctuating Decline,Sluggish Progress,Upward Trajectory,Dramatic Decrease\nHigh Risk,4.8,3.0,0.2,1.6,0.5\nLow Risk,2.0,1.6,0.5,1.2,1.0"
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "ViLe0rH7giD80Z4S",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the performance of Low Risk Investment exceed that of High Risk Investment during the period of Upward Trajectory?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires locating the bars representing Upward Trajectory for both Investment strategies. Here are {the bar for High Risk in Upward Trajectory}<r>[0.427, 0.861, 0.478, 0.251]</r> with a value of 1.6 and {the bar for Low Risk in Upward Trajectory}<r>[0.830, 0.861, 0.881, 0.567]</r> with a value of 1.2. The difference is then 1.2 - 1.6 = -0.4.\nBased on the above thought, the answer is:\n-0.4"
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "8TjluMECqszeX7ZE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, could you turn the data into a table format, with Months as columns and Companies as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a table, I'll first examine {the x-axis tick labels}[[0.102, 0.885, 0.114, 0.921], [0.195, 0.885, 0.207, 0.921], [0.288, 0.885, 0.299, 0.921], [0.380, 0.885, 0.392, 0.921], [0.473, 0.885, 0.484, 0.921], [0.565, 0.885, 0.577, 0.921], [0.658, 0.885, 0.670, 0.921], [0.751, 0.885, 0.762, 0.921], [0.843, 0.885, 0.855, 0.921], [0.930, 0.885, 0.953, 0.921]], which represent the months of the year (from March to December 2020). {The legend texts}[[0.135, 0.694, 0.335, 0.730], [0.135, 0.747, 0.313, 0.783], [0.135, 0.799, 0.247, 0.835], [0.461, 0.694, 0.628, 0.730], [0.461, 0.747, 0.663, 0.783]] serve as the row labels. These are the names of the different companies (TechCo, RetailCo, HealthCo, FoodCo, GreenEnerCo). To populate the numerical values, it's necessary to locate {the line points for each company} like {[0.108, 0.352], [0.201, 0.332], ..., [0.942, 0.095]} for Average Health Score as an example and assign the appropriate normalized stock prices.\nBased on the above thought, the answer is:\nCompanies,March,April,May,June,July,August,September,October,November,December\nTechCo,100,95,90,95,90,85,90,95,90,85\nRetailCo,100,95,90,95,90,85,90,95,90,85\nHealthCo,100,105,110,115,120,125,130,135,140,145\nFoodCo,100,105,110,115,120,125,130,135,140,145\nGreenEnerCo,100,100,100,100,100,100,100,100,100,100"
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "0AEFWxbBxm8NEB0R",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which company shows the most growth over the year 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which company shows the most growth, I need to evaluate the trends for each company. Looking at the line for {HealthCo}[0.108, 0.249, 0.942, 0.095], there is a steady increase in the stock price from a base value of 100 in March to 145 in December. Similarly, for {FoodCo}[0.108, 0.557, 0.942, 0.95], the stock price also increases from 100 in March to 145 in December. Comparatively, the lines for {TechCo}[Line coordinates] and {RetailCo}[Line coordinates] show a decrease in stock price over the same period, and {GreenEnerCo}[Line coordinates] fluctuates around the base value of 100. Therefore, HealthCo and FoodCo show the most growth.\nBased on the above thought, the answer is:\nThe companies HealthCo and FoodCo show the most growth over the year 2020. Both lines display a steady and consistent increase in stock price from March to December, indicating significant growth. In contrast, both TechCo and RetailCo showed a steady decrease, and GreenEnerCo's stock price remained steady with minor fluctuations. Hence, HealthCo and FoodCo had the highest growth trend among the companies represented in the chart."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "JjiiMwIiSHUTuW1F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_35_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in stock price for the FoodCo from March to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to locate the {starting line point for FoodCo in March} [0.108, 0.557] which corresponds to the score 100 and {the end line point in December} [0.942, 0.095] which corresponds to the score 145. Shown directly on the chart from the coordinates and legend, I can see that the stock price in March is 100 and in December is 145, so the increase is 145 - 100 = 45.\nBased on the above thought, the answer is:\n45."
+            }
+        ],
+        "id_raw": 699
+    },
+    {
+        "id": "lp4KrPpfpVdguL48",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the efficiency data from the chart in a structured table format with Teams as columns and Product Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the chart into a structured table, I first analyze the x-axis, where {the teams' labels}<r>[0.1, 0.8, 0.2, 0.9]</r> are provided as columns. Then, I identify {the legend}<r>[0.88, 0.1, 0.98, 0.18]</r> which details the row headers as Product Types: New Product, Old Product, and Outsourced Product. I examine {the points on the lines}<r>[0.1, 0.3, 0.9, 0.5]</r> for each team and product combination to gather the efficiency values.\nBased on the above thought, the answer is:\nProduct Type,Innovation,Finance,Human Resources\nNew Product,75%,80%,70%\nOld Product,65%,70%,65%\nOutsourced Product,70%,75%,60%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "9phl2W63Nfx6bIzc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich team displays the most consistent performance across different product types according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate consistency, I analyze the variances in {line heights for each team}<r>[0.1, 0.2, 0.9, 0.7]</r> across different product types. The Finance team line exhibits minor variations between 70% and 80%, while the Innovation and Human Resources teams show greater fluctuations. Observing the more stable positioning of {Finance team's line}<r>[0.2, 0.3, 0.8, 0.5]</r> across the products concludes its performance consistency.\nBased on the above thought, the answer is:\nThe Finance team displays the most consistent performance across different product types as indicated by the minor variations in their efficiencies from the chart."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "GKHv1tqtLm6jhq6B",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in efficiency between the highest and lowest performing team for the New Product?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the performance difference, I review {the line points for New Product}<r>[0.1, 0.3, 0.2, 0.5]</r> for each team and extract their efficiency percentages. The highest efficiency is 80% for the Finance team at {coordinates}<r>[0.2, 0.5]</r>, and the lowest is 70% for the Human Resources team at {coordinates}<r>[0.3, 0.8]</r>. The difference is calculated by subtracting the lowest value from the highest.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "JBEZ2E6bkODKp1e0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing the annual trends into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I need to gather information from {the x-axis labeled 'Years'}<r>[0.063, 0.047, 0.494, 0.437]</r> representing each year. The values for each category, 'Box Office Revenue', 'Ad Spending', and 'Digital Media Consumption', must be extracted from {the three line graphs}<r>[0.082, 0.064, 0.474, 0.419]</r> respectively, at each year's position along the x-axis. I must carefully delineate the corresponding values for each category taking note of their color codes and marker styles.\nBased on the above thought, the answer is:\nYear,Box Office Revenue (in millions),Ad Spending (in millions),Digital Media Consumption (hours/person/day)\n2019,150,20,1.1\n2020,155,22,1.3\n2021,160,23,1.5\n2022,170,25,1.8\n2023,180,27,2.0\n2024,200,28,2.3\n2025,195,30,2.5\n2026,250,33,2.8\n2027,240,35,2.6\n2028,245,37,2.9\n2029,255,38,3.1\n2030,300,40,3.5"
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "G4t0SatWIiIXUtQx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the fluctuating 'Digital Media Consumption' trend in the chart correlate with 'Ad Spending' over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trends from the line chart, I observe the line graphs for {Digital Media Consumption}<r>[0.082, 0.064, 0.474, 0.419]</r> and {Ad Spending}<r>[0.082, 0.064, 0.474, 0.419]</r>. Both show a general increase as time progresses. Specifically, notable increases in media consumption in 2026 and 2030 are matched with spikes in ad spending the same years. This suggests that increased advertising possibly encouraged higher digital media consumption or vice versa; the exact causality isn't clear and could be influenced by external factors or synchronized market strategies.\nBased on the above thought, the answer is:\nThe trends suggest a correlation where spikes in 'Ad Spending' coincide with increases in 'Digital Media Consumption,' particularly notable in the years 2026 and 2030. This implies that higher ad spending is possibly related to driving up media consumption, though the linkage might also involve other external market factors or strategic choices by media firms."
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "0Kyr6RcHPMeaPlIk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in 'Box Office Revenue' from the year 2025 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I first locate the respective values for 'Box Office Revenue' in 2025 and 2026 using {the line graph for Box Office Revenue}<r>[0.082, 0.064, 0.474, 0.419]</r>. In 2025, the revenue was $195 million and in 2026, it increased sharply to $250 million. Using the formula (New Value - Old Value) / Old Value * 100, I calculate the increase to be (250 - 195) / 195 * 100.\nBased on the above thought, the answer is:\n28.21%"
+            }
+        ],
+        "id_raw": 701
+    },
+    {
+        "id": "jel19qm1edBjM77g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a CSV table format with social media platforms as columns and recovery types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe {the x-axis labels}<r>[0.055, 0.449, 0.095, 0.466], [0.193, 0.449, 0.222, 0.466], [0.318, 0.449, 0.363, 0.466], [0.455, 0.449, 0.491, 0.466]</r> representing the social media platforms (Facebook, Twitter, Instagram, LinkedIn). I then identify {the legend}<r>[0.372, 0.055, 0.489, 0.113]</r> that lists the recovery types (Stable Engagement, Volatile Feedbacks). By examining the plotted data points for each combination of social media platform and recovery type, I extract the numerical user counts.\nBased on the above thought, the answer is:\nRecovery Type, Facebook, Twitter, Instagram, LinkedIn\nStable Engagement, 5234, 4523, 2765, 3893\nVolatile Feedbacks, 9437, 8234, 3457, 1293"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "Yb3zSVqqh1ARzFis",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform shows the most significant difference in user engagement between the two recovery types?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the difference in user counts across recovery types, I observe {the points for Facebook}<r>[0.075, 0.262], [0.075, 0.101]</r>, {Twitter}<r>[0.208, 0.290], [0.208, 0.147]</r>, {Instagram}<r>[0.341, 0.357], [0.341, 0.331]</r>, and {LinkedIn}<r>[0.473, 0.314], [0.473, 0.414]</r>. The differences in engagement are calculated for each platform, revealing the most significant difference.\nBased on the above thought, the answer is:\nBased on the observed data, Facebook shows the most significant difference in user engagement between the recovery types, with a robust initial engagement in Volatile Feedbacks compared to Stable Engagement."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "LzU31Fbb8RY0sXX2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total number of users engaging on Instagram considering both recovery types?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the individual user counts at the points marked for Instagram across both recovery types. Observing {the points for Instagram under Stable Engagement}<r>[0.341, 0.357]</r> and {Volatile Feedbacks}<r>[0.341, 0.331]</r>, the counts are 2765 and 3457 respectively. Adding these gives the total engagement for Instagram.\nBased on the above thought, the answer is:\n6222"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "QdKtDzHHBbEDC7E3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the data presented in the chart as a structured table with Metrics as columns and Procedures as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To format the line chart data into a table, I first consider {the x-axis tick labels}[[0.015, 0.939, 0.029, 0.957], [0.106, 0.939, 0.119, 0.957], [0.196, 0.939, 0.210, 0.957], [0.287, 0.939, 0.301, 0.957], [0.377, 0.939, 0.391, 0.957], [0.468, 0.939, 0.482, 0.957], [0.559, 0.939, 0.572, 0.957]], which represent the column headers or Metrics. Proceeding, I identify the subchart titles {Manufacturing Procedure A}[0.201, 0.019, 0.342, 0.041] and {Manufacturing Procedure B}[0.696, 0.019, 0.837, 0.041] as row labels representing the different Procedures. To populate the values, I locate the line points [[0.067, 0.514], [0.149, 0.333], [0.230, 0.308], [0.312, 0.127], [0.393, 0.850], [0.475, 0.644]] and [[0.563, 0.514], [0.644, 0.325], [0.726, 0.430], [0.807, 0.116], [0.889, 0.850], [0.970, 0.577]] for Procedure A and Procedure B at the respective stages and extract the associated values.\nBased on the above thought, the answer is:\nProcedure,0.0,0.2,0.4,0.6,0.8,1.0\nProcedure A,0.75,0.82,0.83,0.9,0.62,0.7\nProcedure B,0.76,0.85,0.8,0.95,0.6,0.73"
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "gYhNpZ2qGYEDgC3s",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the plottings, which manufacturing procedure expresses a more roller-coaster-like performance across different ratio categories? Explain your selection."
+            },
+            {
+                "from": "gpt",
+                "value": "To decide which manufacturing procedure denotes more ups and downs, it's necessary to scrutinize the variation in the line plots for both procedures. Glancing at {the line for Manufacturing Procedure A}[0.067, 0.127, 0.475, 0.850], we see a steady climb from 0.75 to 0.9, with a sudden drop to 0.62 and a minor rise to 0.7. This indicates moderate fluctuations in the trend.\nObserving {the line for Manufacturing Procedure B}[0.563, 0.116, 0.970, 0.850], there is an increase from 0.76 to 0.95, followed by a drastic plunge to 0.6 and a slight climb to 0.73. The ups and downs are more acute compared to Manufacturing Procedure A, suggesting more rollicking performance.\nBased on the above thought, the answer is:\nBased on the visual assessment of the chart, Manufacturing Procedure B seems to display a more roller-coaster-like performance across different ratio categories. The line plotting for Procedure B presents more severe fluctuations, exhibiting a high leap followed by a deep fall and then another ascension. This upheaval in the trend line exhibits a more turbulent progression compared to Manufacturing Procedure A, which suggests more potential inconsistencies in Procedure B's performance."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "TzA07nNOWS09yGdr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the performance of Manufacturing Procedure B exceed that of Manufacturing Procedure A at the 0.6 Ratio r?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in performances of Manufacturing Procedure A and Manufacturing Procedure B at 0.6 Ratio r, I should identify the corresponding line points for both procedures at this ratio. The points are Procedure A {point}[0.312, 0.127] and Procedure B {point}[0.807, 0.116]. Using the y-values, the performance for Procedure A is 0.9, and for Procedure B it's 0.95. The difference in performance is therefore 0.95 - 0.9 = 0.05.\nBased on the above thought, the answer is:\n0.05."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "xwFm5V7bnckr7hRd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information about trends and their corresponding values for Video Content into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I locate {the x-axis labels}[[0.070, 0.449, 0.478, 0.466]] which show the trend stages. Additionally, I focus on the line associated with {Video Content}<r>[0.072, 0.242, 0.475, 0.419]</r> and see the specific values at points {1 and 2}<r>[0.072, 0.419], [0.475, 0.242]</r>. I will use these to construct the table.\nBased on the above thought, the answer is:\nTrend Stage,Value\n1,0.2\n2,0.3"
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "hov3TXymu13BPPmE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich line shows a more consistent trend in the first subplot, and how can you tell?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine consistency, I analyze the lines in the first subplot. The line for {Video Content}<r>[0.072, 0.242, 0.475, 0.419]</r> varies from a value of 0.2 to 0.3, which shows a consistent increase. Conversely, the line for {Interactive Media}<r>[0.072, 0.064, 0.475, 0.242]</r> starts at 0.3 and increases to 0.4, showing a smaller range of variation and thus, more consistency compared to Video Content.\nBased on the above thought, the answer is:\nThe line for Interactive Media shows a more consistent trend in the first subplot as it has a smaller range in variation, moving from 0.3 to 0.4, which is less varied than the movement from 0.2 to 0.3 in Video Content."
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "3t1uxI93OLFHoZEb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of values for the 'Interactive Media' trend between the first and second stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range, I need to find the maximum and minimum values for 'Interactive Media' at stages 1 and 2. I locate the values at {stage 1}<r>[0.072, 0.242]</r> and {stage 2}<r>[0.475, 0.064]</r>. The values are 0.3 and 0.4 respectively. The range is the difference between the maximum and minimum values, which is 0.4 - 0.3.\nBased on the above thought, the answer is:\n0.1"
+            }
+        ],
+        "id_raw": 708
+    },
+    {
+        "id": "ch70opUrywoRkGfB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values into a structured table listing company names and their production efficiency changes from start to end?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I analyze each company's line from {the starting point}[[0.054, 0.916], [0.054, 0.829], [0.054, 0.642]] to {the ending point}[[0.473, 0.556], [0.972, 0.829], [0.972, 0.642]] and record the efficiency values corresponding to each company across these points.\nBased on the above thought, the answer is:\nCompany,Start Efficiency,End Efficiency\nTesla,1.0,5.9\nAmazon,3.0,3.2\nLG,2.0,3.2\nPanasonic,5.0,3.3\nMicrosoft,5.0,3.0\nOracle,4.5,3.0\nNokia,2.5,3.1\nToshiba,4.8,3.0\nLamborghini,3.2,3.2\nFerrari,4.7,3.0\nApple,3.0,3.1\nNetflix,2.0,3.3\nSalesforce,3.9,3.0\nCisco,3.0,3.2\nSony,3.0,3.0\nVolkswagen,4.1,3.0\nAudi,4.0,3.0\nGeneral Electric,3.0,3.0"
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "UDZg8uiyEnac3RjZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows a drastic increase in production efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze all the lines, particularly identifying {Tesla's line}[[0.054, 0.916], [0.473, 0.556]] as it starts at the lowest point with a value of 1.0 and ends at 5.9. This signifies a substantial increase compared to other companies whose lines display more modest or negative changes.\nBased on the above thought, the answer is:\nTesla shows a drastic increase in production efficiency. Its line starts at the bottom and rises to the highest efficiency level by the end of the observed period, indicating significant improvement in production efficiency."
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "A2j5WWSCwofbEkmP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of efficiency changes for Tesla based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine Tesla's range of efficiency changes, I need to locate {Tesla's line start}[[0.054, 0.916]] and {Tesla's line end}[[0.473, 0.556]], and extract the corresponding values of 1.0 at the start and 5.9 at the end. Thus, the range is calculated as 5.9 - 1.0.\nBased on the above thought, the answer is:\n4.9"
+            }
+        ],
+        "id_raw": 709
+    },
+    {
+        "id": "dpxViVxAPcOouI64",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the line chart into a structured table showcasing the country names as rows and trade types (Imports and Exports) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first identify {the legend}<r>[0.381, 0.055, 0.491, 0.113]</r> to understand the categories, which are Imports and Exports. Then, I analyze each line in the chart representing different countries, using the labels from the x-axis and translate the data into columns of Imports and Exports alongside the y-axis for trade values. {the lines of Italy and the Netherlands}<r>[0.067, 0.081, 0.475, 0.405], [0.542, 0.537, 0.991, 0.927]</r> exhibit distinctly erratic behavior confirming their import and export values.\nBased on the above thought, the answer is:\nCountry,Imports,Exports\nGermany,134000,148000\nSouth Korea,89000,93000\nFrance,97000,101200\nItaly,76000,118000\nNetherlands,56000,112000\nBrazil,48000,41000\nIndia,17000,15000"
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "iZyd62R5XF4DVkMK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country shows the most stable trend in both imports and exports, and how can this be observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most stable trend, I assess the variability and consistency in the lines representing each country's trade. Observing {the lines for Germany and South Korea}<r>[0.067, 0.108, 0.970, 0.378], [0.542, 0.537, 0.991, 0.927]</r>, both have smoother trajectories with minor fluctuations, indicative of stable trends. Germany\u2019s lines for both imports and exports rise gently, while South Korea\u2019s lines are parallel with a small, consistent gap between them, highlighting stability in trade values over time.\nBased on the above thought, the answer is:\nGermany shows the most stable trend in both imports and exports. The line charts for Germany present gentle, consistent inclines without sharp peaks or troughs, illustrating stable growth in international trade figures."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "74aFyM1FSHeBBNHi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in exports for Germany when comparing the earliest to the latest data point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I examine {the line points for Germany's exports}<r>[0.067, 0.108, 0.970, 0.378]</r>. The earliest data point is at 120000 and the latest is at 148000. Calculating the increase: 148000-120000 yields the required difference.\nBased on the above thought, the answer is:\n28000."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "FSfJ0ucB9esqAFMW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the bar chart that measures the effectiveness of government policies into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To begin, I organize the information by identifying each bar which represents a different government policy, locating {the bars for each policy}<r>[0.1, 0.2, 0.9, 0.8]</r>. Then, I look at {the y-axis}<r>[0.05, 0.1, 0.95, 0.2]</r> to understand the range of effectiveness values. Finally, I correlate these with {the policy labels}<r>[0.1, 0.9, 0.2, 0.2]</r> to compile the data into a structured table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nImmigration,-5.5\nHealthcare,1.5\nEnvironmental,0.5\nTaxes,-4.5\nEducation,3.0\nForeign Affairs,2.5\nInfrastructure,3.5\nDefence,-0.5"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "NFvlxKTclDW7yc1G",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich government policy, based on the chart, shows the most significant shift in its past effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the most changed policy requires examining the length and direction of each bar in the bar plot. Observing the {bars}<r>[0.1, 0.2, 0.9, 0.8]</r>, I ascertain that the Immigration policy bar, which extends the furthest below the axis while others remain between -5.0 and 3.5, showing immense negative shift.\nBased on the above thought, the answer is:\nThe Immigration policy shows the most significant shift with the greatest decrease in effectiveness, which is notably more prominent than the rest of the policies, evident from the substantial negative length of its bar on the chart."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "nFwx68BIYUCY5ft4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average effectiveness of all the studied government policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the effectiveness of all government policies involves locating each bar on the chart representing a specific policy. I start from {bars for policies}<r>[0.1, 0.2, 0.9, 0.8]</r>, extract the values from {the y-axis}<r>[0.05, 0.1, 0.95, 0.2]</r>, and calculate the average. The values are -5.5, 1.5, 0.5, -4.5, 3.0, 2.5, 3.5, and -0.5.\nBased on the above thought, the answer is:\n0.125"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "XxBubNFXRAPqD8rC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table representing the values of residential density and economic activity across the various cities according to the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I begin by noting {x-axis labels}<r>[0.243, 0.964, 0.303, 0.981]</r>, which contain the names of the cities. The line charts representing residential density and economic activity can be traced through their associated colors and data points. Each point on the lines, marked by specific coordinates, shows the values for the respective city.\nBased on the above thought, the answer is:\nCity,Residential Density,Economic Activity\nNew York,90,75\nLondon,85,70\nMumbai,95,55\nTokyo,80,60\nMoscow,75,65\nSydney,70,68\nCairo,100,50\nJohannesburg,78,58"
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "G8WjxGIl7bePLeGv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city exhibits a significant difference in residential density compared to its economic activity, and why does this stand out?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the line graph, I closely flag the data points and line difference between residential density and economic activity. Focusing on {Cairo}<r>[0.342, 0.605]</r>, I observe a contrast where the residential density peaks higher (100) compared to economic activity (50). This significant gap is notable because while most cities have closer values between residential density and economic activity, Cairo's is distinctly polarized.\nBased on the above thought, the answer is:\nCairo exhibits a significant difference where the residential density (100) is notably higher than its economic activity (50). This stands out as it suggests a unique situation of high population density paired with lower economic vitality, unlike the more balanced figures seen in other cities."
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "y4HetzMslCZGDaQT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average residential density value across all cities shown in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I refer to the data points representing residential density for each city displayed on the line graph. Extracting values from {New York}<r>[0.067, 0.181]</r>, {London}<r>[0.113, 0.134]</r>, {Mumbai}<r>[0.159, 0.181]</r>, {Tokyo}<r>[0.205, 0.322]</r>, {Moscow}<r>[0.250, 0.369]</r>, {Sydney}<r>[0.296, 0.510]</r>, {Cairo}<r>[0.342, 0.605]</r>, {Johannesburg}<r>[0.387, 0.699]</r>, we get the values 90, 85, 95, 80, 75, 70, 100, 78 respectively. Adding these and dividing by the number of cities (8) gives the average residential density.\nBased on the above thought, the answer is:\n84.375"
+            }
+        ],
+        "id_raw": 712
+    },
+    {
+        "id": "SGK370heN9hkzotX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trends presented in the line chart into a table format listing the technologies and their associated trend values at each ratio milestone?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data points for each technology from the line chart, I locate the positions of {the x-tick labels}[[0.015, 0.449, 0.029, 0.466], [0.106, 0.449, 0.120, 0.466], [0.197, 0.449, 0.211, 0.466], [0.288, 0.449, 0.302, 0.466], [0.380, 0.449, 0.393, 0.466], [0.471, 0.449, 0.484, 0.466], [0.562, 0.449, 0.576, 0.466]] to match with the corresponding values of trend indexes from {the lines}[[0.067, 0.064, 0.478, 0.400], [0.067, 0.232, 0.478, 0.381], [0.067, 0.307, 0.478, 0.381], [0.067, 0.251, 0.478, 0.419]].\nBased on the above thought, the answer is:\nTechnology,0.0,0.2,0.4,0.6,0.8,1.0\nErratic Growth,2.3,3.0,2.9,2.7,3.5,2.8\nInconsistent Decline,3.3,3.1,2.8,3.0,2.5,1.4\nSubdued Decline,2.8,2.9,2.6,2.8,3.0,1.1\nMarginal Growth,2.5,2.7,2.9,3.1,3.4,1.8"
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "iK56PiOBlokrcwiy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the trends in the line chart, which technology sector seems to show the most consistent incline in adoption trend values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology sector shows the most consistent incline, I analyze the direction and smoothness of {the line trends}[[0.067, 0.064, 0.478, 0.400], [0.067, 0.232, 0.478, 0.381], [0.067, 0.307, 0.478, 0.381], [0.067, 0.251, 0.478, 0.419]]. Each line\u2019s end point relative to its starting point helps evaluate the incline. Noticing that the Marginal Growth has both a high starting and ending point and consistent upward tendency, this sector seems to have the most consistent incline.\nBased on the above thought, the answer is:\nThe Marginal Growth technology sector shows the most consistent incline in its adoption trend values, as evidenced by the continuous increase in trend index from beginning to end without any dips."
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "zQXPW2NwugTg7Fpg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum adoption trend index observed for 'Erratic Growth' across all the provided ratios?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe the trend index values for 'Erratic Growth' by examining {the line for Erratic Growth}[[0.067, 0.064, 0.478, 0.400]]. The points' values are found to be 2.3, 3.0, 2.9, 2.7, and 3.5 for ratios 0.1 through 1.0 respectively. The maximum value in this sequence is 3.5.\nBased on the above thought, the answer is:\n3.5"
+            }
+        ],
+        "id_raw": 713
+    },
+    {
+        "id": "RIbvTosJcZmIVnqs",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you convert the data from the line graph showing trends in the Media and Entertainment Industry segments into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the x-axis, I see different labels representing the Media and Entertainment segments such as Music, Film, Gaming, etc., which will form the columns of the table. The y-axis shows trends, which help in understanding the exact values for each segment. By analyzing {the lines in the chart}<r>[0.065, 0.060, 0.317, 0.425]</r> for each segment, I can determine the type of trend (e.g., Dramatic decrease, Slow increase) and convert these observations into rows in a table.\nBased on the above thought, the answer is:\nIndustry,Trend\nMusic,Dramatic Decrease\nFilm,Marginal Decline\nGaming,Slow Increase\nDigital Media,Slow Increase\nLive Events,Dramatic Decrease\nPublishing,Slow Increase\nTV & Radio,Marginal Decline\nToys & Games,Slow Increase\nFashion & Beauty,Dramatic Decrease\nPerforming Arts,Marginal Decline"
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "Ks1gaVvAEEryN2UN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry sector is showing the most positive growth trend in the Media and Entertainment Industry according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the most positive growth trend, I will look at the color and the y-coordinate of each segment line. {The line representing Gaming}<r>[0.191, 0.242]</r> shows a slow increase as indicated by {the green color}<r>[0.191, 0.242]</r> on the line symbolizing a positive trend. Compared to other segments that predominantly show declines, this marks it as the most positively growing sector.\nBased on the above thought, the answer is:\nAccording to the chart, the Gaming industry is showing the most positive growth trend in the Media and Entertainment Industry. This is evident from its position on the y-axis and its representation through a green line, which indicates a slow increase."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "V2vRRlyIpCgincfK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in trend value between the Gaming and Film industries according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the point coordinates for Gaming}<r>[0.191, 0.242]</r> and {the point coordinates for Film}<r>[0.128, 0.370]</r>, I notice the trend values are 113 for Gaming and 109 for Film. The difference can be computed by subtracting the trend value of Film from Gaming which is 113 - 109.\nBased on the above thought, the answer is:\n4."
+            }
+        ],
+        "id_raw": 714
+    },
+    {
+        "id": "SWSwKTqYJFPcGBuP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the line chart showing the trend of government policy effectiveness into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve this conversion, I need to reference {the x-axis labels}<r>[0.055, 0.023, 0.495, 0.468]</r> which indicate policy names, and {the y-axis labels}<r>[0.009, 0.232, 0.018, 0.260]</r> which highlight the effectiveness scores. Each data point on {the policy effectiveness line}<r>[0.075, 0.044, 0.475, 0.448]</r> provides the effectiveness scores for each respective policy over the study period. With each policy named on the x-axis and corresponding effectiveness scores marked on the line graph, I can compile them into a structured CSV table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nEconomic Recovery Package,88.5\nPatient Protection and Affordable Care Act,57.0\nSmall Business Jobs Act,44.0\nAmerican Jobs Act,6.5"
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "igH60pYT8MsVF2Bo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich government policy stands out as an outlier in terms of effectiveness according to the line chart, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the effectiveness line chart, I locate {the outliers}<r>[0.148, 0.179]</r> specifically {the point for the Patient Protection and Affordable Care Act}<r>[0.148, 0.179]</r> which is significantly lower in effectiveness (57.0) despite being implemented around the same time as other more effective policies like the Economic Recovery Package (88.5). This deviation from the steep decline trend depicted on the line observation marks it as a clear outlier in terms of its effectiveness.\nBased on the above thought, the answer is:\nThe Patient Protection and Affordable Care Act stands out as an outlier in terms of effectiveness. Its effectiveness is significantly lower compared to its contemporaneous policies despite being part of a similar implementation period, defining it uniquely amid the general steep decline observed in the policy effectiveness trend."
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "DomEF53Q8KrwJTXN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in effectiveness from the beginning to the end of the policies based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total change in effectiveness, I first reference {the starting point on the line}<r>[0.075, 0.044]</r> which provides the effectiveness score at the start, 88.5 for the Economic Recovery Package, and {the ending point on the line}<r>[0.475, 0.448]</r> which indicates the effectiveness of the last policy, the American Jobs Act, which is 6.5. The change is calculated by subtracting the ending value from the starting value, 88.5 - 6.5.\nBased on the above thought, the answer is:\n82.0"
+            }
+        ],
+        "id_raw": 715
+    },
+    {
+        "id": "KBX4FR2uNkpk8CRn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a table format of the data from the line chart showing the GDP Growth Rate and Unemployment Rate for the US between 2003 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the chart data into a table format, I first identify the two key pieces of information represented by distinct lines on the chart {the GDP Growth Rate line}<r>[0.060, 0.300, 0.950, 0.700]</r> and {the Unemployment Rate line}<r>[0.060, 0.300, 0.950, 0.700]</r>. I then correlate each data point on these lines to its corresponding year along {the x-axis}<r>[0.05, 0.945, 0.100, 0.940]</r> and read off the values as plotted for each year from 2003 to 2020.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate (%),Unemployment Rate (%)\n2003,2.80,6.00\n2004,3.80,5.50\n2005,3.50,5.10\n2006,2.90,4.60\n2007,1.90,5.00\n2008,-0.10,7.30\n2009,-2.50,9.90\n2010,2.60,9.60\n2011,1.60,8.90\n2012,2.20,7.90\n2013,1.80,6.70\n2014,2.50,6.20\n2015,3.10,5.30\n2016,1.70,4.90\n2017,2.30,4.40\n2018,3.00,3.90\n2019,2.20,3.50\n2020,-3.50,8.10"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "LbfwGii3G8b4YAuj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends in GDP Growth Rate are evident from the economic events of 2008-2009 financial recession and the COVID-19 pandemic in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart shows {the GDP Growth Rate line}<r>[0.060, 0.300, 0.950, 0.700]</r> capturing key trends. Prior to the 2008-2009 financial recession, the GDP Growth Rate was increasing, reaching a peak around 2006 at {2006 point}<r>[0.473, 0.588]</r>. During the 2008-2009 recession, there is a noticeable sharp decline, reaching a low in 2009 at {2009 point}<r>[0.565, 0.801]</r>. Following this period, the GDP Growth Rate gradually recovered, demonstrating consistent growth until 2019. However, in 2020, the chart indicates another sharp decline at {2020 point}<r>[0.950, 0.870]</r>, reflecting the economic impact of the COVID-19 pandemic.\nBased on the above thought, the answer is:\nThe trends indicate that the GDP Growth Rate was increasing steadily until the 2008-2009 recession when a significant dip occurred, followed by a recovery phase that lasted until another drop in 2020 due to the COVID-19 pandemic, highlighting the sensitivity of economic growth to major global incidents."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "W1lu3WMbVD2XCSO6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in Unemployment Rate between the years 2020 and 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in Unemployment Rate between 2020 and 2009, I inspect the values marked on {the Unemployment Rate line}<r>[0.060, 0.300, 0.950, 0.700]</r>. In 2009, the Unemployment Rate was 9.90% at {2009 point}<r>[0.565, 0.861]</r> and in 2020, it was 8.10% at {2020 point}<r>[0.950, 0.650]</r>. Calculating the difference, it is 9.90% - 8.10% = 1.80%.\nBased on the above thought, the answer is:\n1.80%"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "i5X6IZm3Vtz1EuRm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line graph in the form of a table listing Scenarios as columns and Media Outlets as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line graph data into a table, {the x-axis tick labels}[[0.059, 0.449, 0.130, 0.466], [0.199, 0.449, 0.271, 0.466], [0.344, 0.449, 0.408, 0.466], [0.470, 0.449, 0.564, 0.466], [0.619, 0.449, 0.696, 0.466], [0.764, 0.449, 0.833, 0.466], [0.895, 0.449, 0.984, 0.466]] are considered representing the different Scenarios, which should be table columns. Then, I identify {the legend texts}[[0.955, 0.062, 0.974, 0.080], [0.955, 0.553, 0.974, 0.570]] as the Media Outlets which are to be the rows of the table. For values, I'll associate {the line points}[[0.095, 0.215], [0.235, 0.098], ..., [0.940, 0.391], [0.095, 0.882], [0.235, 0.568], ..., [0.940, 0.898]] to the respective media outlets at the scenario stages using the y-axis labels for measures.\nBased on the above thought, the answer is:\nMedia Outlet,Marginal decline,Rapid expansion,Steady decline,Pronounced decrease,Sluggish progress,Steady increase,Accelerating decline\nBBC,0.82,0.88,0.8,0.76,0.79,0.81,0.73\nCNN,0.39,0.79,0.51,0.5,0.49,0.55,0.37"
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "hYw3EwPM2k6n8kwC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph trends, which media outlet seems to exhibit more fluctuating influence across the scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which media outlet has more fluctuating influence, I'll analyze the line plots for each outlet. Looking at {the line for BBC}[[0.095, 0.098, 0.940, 0.391]], it shows changes from 0.82 to 0.88, then to 0.8, 0.76, 0.79, 0.81, and finally 0.73, implying a degree of oscillation.\nExamining {CNN}[[0.095, 0.568, 0.940, 0.898]], the values deviate from 0.39 to 0.79, then to 0.51, 0.5, 0.49, 0.55, and end with 0.37. Despite some fluctuations, the visual pattern suggests a general downward trend.\nBased on the above thought, the answer is:\nBased on the line graph trends, CNN appears to have a more fluctuating influence across the scenarios. It starts with a significant increase from 'Marginal decline' to 'Rapid expansion', followed by continuous changes until 'Accelerating decline'. Despite the overall descending inclination, the journey from the highest point at 'Rapid expansion' to the lowest at 'Accelerating decline' still shows noteworthy fluctuations."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "eVIYkhjFma24jn7F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the disparity between BBC's influence measure at the scenarios of Steady decline and Steady increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to locate {the line points for BBC at 'Steady decline' and 'Steady increase'}[[0.376, 0.254], [0.799, 0.235]] and extract the corresponding values, which are 0.8 and 0.81, respectively. The difference between 'Steady increase' and 'Steady decline' can be calculated as 0.81 - 0.8 = 0.01.\nBased on the above thought, the answer is:\n0.01."
+            }
+        ],
+        "id_raw": 717
+    },
+    {
+        "id": "8XdOAGFT8u5QFkIK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can the data points related to social networks and interpersonal relationships indices be structured into a two-column CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the data points into a two-column CSV format, I first identify {the x-axis labels}[[0.1, 0.9, 0.12, 0.94]] which denote 'Social Networks' and 'Interpersonal Relationships' as the respective column headers. I then examine {the line points with their respective y-values}[[0.1, 0.25], [0.9, 0.75], [0.6, 0.65]] and extract the values from these coordinates corresponding to each category.\nBased on the above thought, the answer is:\n\"Social Networks\",\"Interpersonal Relationships\"\n0.25,0.75"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "8sVDDKS6iGgTr2TN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in connectivity change from social networks to interpersonal relationships based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the chart, I observe {the line trajectory starting from social networks to interpersonal relationships}[[0.1, 0.25], [0.9, 0.75]]. The trend begins at a lower connectivity index for social networks and ascends to a higher index for interpersonal relationships. This upward movement indicates a positive change in connectivity, reflecting an overall improvement as we move from social networks to closer, more personal interactions.\nBased on the above thought, the answer is:\nThe trend in connectivity notably improves from social networks to interpersonal relationships. Beginning at a lower index, the data clearly shows an ascending trend towards a higher connectivity index, illustrating a progressive improvement as interpersonal interactions become more personal and engaged."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "OGV05Ci5JmRs7qvJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_36_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connectivity index increase from social networks to interpersonal relationships based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the connectivity index increase, I look at {the y-values for social networks}[[0.1, 0.25]] and {the y-values for interpersonal relationships}[[0.9, 0.75]]. The difference is calculated by subtracting the social networks index from the interpersonal relationships index: 0.75 - 0.25.\nBased on the above thought, the answer is:\n0.50"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "1ZaDHWZMcsDwbsn4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the viewership data for SVoD and Linear TV across three years into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll take notice of {the x-axis labels which represent the years}<r>[0.086, 0.903, 0.112, 0.931], [0.168, 0.903, 0.194, 0.931], [0.250, 0.903, 0.276, 0.931]</r> indicating 2018, 2019, and 2020 respectively. Then, I identify {the legend}<r>[0.087, 0.059, 0.257, 0.151]</r> to differentiate data for 'Subscription Video on Demand' and 'Linear TV'. Extracting viewership values from {line plot points for SVoD and Linear TV}<r>[0.099, 0.556], [0.181, 0.456], ..., [0.427, 0.157]</r>, will populate their respective year columns.\nBased on the above thought, the answer is:\nYear,SVoD,Linear TV\n2018,50 million,100 million\n2019,60 million,95 million\n2020,80 million,90 million"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "B324T5kFURzCLf27",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service demonstrated a stronger growth trend from 2018 to 2020 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which service demonstrated a stronger growth trend, I observe {the line points for SVoD}<r>[0.099, 0.556], [0.140, 0.481], [0.263, 0.394]</r> and see an upward trajectory indicating growth. Meanwhile, {the line points for Linear TV}<r>[0.099, 0.494], [0.140, 0.481], [0.263, 0.606]</r> show a downward trajectory, indicating a decline. The visual comparison clearly shows that SVoD not only grew but also increased at a more rapid pace towards 2020.\nBased on the above thought, the answer is:\nSubscription Video on Demand (SVoD) demonstrated a stronger growth trend from 2018 to 2020. The upward trajectory of its line plot clearly outpaced the consistent decline seen in Linear TV's viewership."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "vdjIetIVpkMJo97E",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual decrease in viewership for Linear TV from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the decreasing trend from the {Linear TV line points}<r>[0.099, 0.494], [0.140, 0.481], [0.263, 0.606]</r> which represent the viewership values from 2018 to 2020, I calculate the decrease each year: from 100 million in 2018 to 95 million in 2019 (5 million decrease), and from 95 million in 2019 to 90 million in 2020 (5 million decrease). The average annual decrease is the sum of these decreases divided by the number of years, which is (5+5)/3.\nBased on the above thought, the answer is:\n3.33 million viewers"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "bPbKaCQKctymvIs6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table of the loan amounts disbursed for Public and Government Schools over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the chart, I observe {the x-axis tick labels}[[0.103, 0.903, 0.138, 0.931], [0.143, 0.903, 0.179, 0.931], [0.184, 0.903, 0.219, 0.931], [0.225, 0.903, 0.260, 0.931], [0.265, 0.903, 0.300, 0.931], [0.306, 0.903, 0.341, 0.931], [0.346, 0.903, 0.382, 0.931], [0.387, 0.903, 0.422, 0.931], [0.428, 0.903, 0.463, 0.931], [0.468, 0.903, 0.503, 0.931]] which indicate the years. I use the y-coordinates to estimate the loan amounts visually from {the line points}[[0.120, 0.797], [0.161, 0.818], [0.202, 0.563], [0.242, 0.481], [0.283, 0.329], [0.323, 0.426], [0.364, 0.274], [0.405, 0.164], [0.445, 0.143], [0.486, 0.068]] for Public School and similar coordinates for Government School. These loan amounts are color-coded in the chart, represented by orange and blue lines.\nBased on the above thought, the answer is:\nYear,Public School Loan Amount,Government School Loan Amount\n2009,30000,31500\n2010,28500,32750\n2011,47000,35875\n2012,53000,35500\n2013,64000,38750\n2014,57000,42350\n2015,68000,47520\n2016,76000,47010\n2017,77500,53000\n2018,83000,59500"
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "3j0JwCaIUMa563zt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend of loan amounts for Public and Government Schools from 2009 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the line for Public School Loan Amount}[0.120, 0.797, 0.486, 0.068] and {the line for Government School Loan Amount}[0.120, 0.777, 0.486, 0.391], there is an overall increasing trend in loan amounts for both Public and Government Schools over the span from 2009 to 2018. The curvature of the lines shows a steady rise, with some fluctuations\u2014Public School loans tend to be consistently higher, with a more pronounced rise. In contrast, the Government School's line depicts a steadier increase, demonstrating progressive policies over the years.\nBased on the above thought, the answer is:\nFrom the analysis of the chart, it is evident that there is an increasing trend in the loan amounts disbursed to both Public and Government Schools from 2009 to 2018. The consistent rise can be interpreted as a substantial public and governmental commitment to supporting education through increasing financial aid and resources."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "0C9e0Ly3TzmwKbVu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more loan amounts were given to Government Schools compared to Public Schools in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference for 2017 between the loan amounts for Government Schools and Public Schools, I firstly locate the amounts from {the line points for 2017}[[0.445, 0.143], [0.445, 0.481]] for Public School and Government School respectively. The corresponding values are 77500 for Public School loan and 53000 for Government School loan. The difference is then calculated as 53000 - 77500.\nBased on the above thought, the answer is:\n-24500"
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "1PaDfVNuCsgxQKPK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data of the various sectors over each quarter into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data from the line chart, I first identify the tick labels on {the x-axis}[[0.117, 0.903, 0.126, 0.931], [0.323, 0.903, 0.331, 0.931], [0.528, 0.903, 0.537, 0.931], [0.734, 0.903, 0.743, 0.931], [0.940, 0.903, 0.948, 0.931]], which will form the column headers representing the quarters, mentioned as \"1\", \"2\", \"3\", \"4\", \"5\" respectively. Next, I locate the legends {Supply Chain, Procurement, Manufacturing, Delivery, After-sale Service}[0.491, 0.635, 0.613, 0.831] which will serve as the row headers representing the different sectors. Finally, I extract the values which form the cells in the table corresponding to each sector and quarter from {the line points}[[0.121, 0.353, 0.944, 0.640], [0.121, 0.138, 0.944, 0.425], [0.121, 0.066, 0.944, 0.425], [0.121, 0.497, 0.944, 0.784], [0.121, 0.209, 0.944, 0.568]].\nBased on the above thought, the answer is:\nQuarter,Supply Chain,Procurement, Manufacturing, Delivery, After-sale Service\nQuarter 1,0.8,1.1,1.5,1.0,0.9\nQuarter 2,1.1,1.2,1.6,0.7,1.0\nQuarter 3,1.2,1.3,1.3,0.9,1.1\nQuarter 4,1.0,1.4,1.2,0.6,1.3\nQuarter 5,0.9,1.5,1.1,0.7,1.4"
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "jkFFKzLpH5sjAdXh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the trend in the line chart, which sector appears to have the most consistently increasing efficiency across the quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the most consistently increasing efficiency, I need to see the trend of values for each of the line plots in the sectors. By observing {the line for Supply Chain}[[0.121, 0.640], [0.327, 0.425], [0.533, 0.353], [0.738, 0.497], [0.944, 0.568]], I see a slight increase and decrease, indicating some variation. The line plot for {Procurement}[[0.121, 0.425], [0.327, 0.353], [0.533, 0.281], [0.738, 0.209], [0.944, 0.138]] shows a consistent upward trend. For the other sectors, such as {Manufacturing}[[0.121, 0.138], [0.327, 0.066], [0.533, 0.281], [0.738, 0.353], [0.944, 0.425]], {Delivery}[[0.121, 0.497], [0.327, 0.712], [0.533, 0.568], [0.738, 0.784], [0.944, 0.712]], and {After-sale Service}[[0.121, 0.568], [0.327, 0.497], [0.533, 0.425], [0.738, 0.281], [0.944, 0.209]], all show variations in efficiency across the quarters.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Procurement sector appears to have the most consistently increasing efficiency across the quarters. The line plot for procurement shows a steady upward trend with each passing quarter, indicating an increase in efficiency over time. Unlike the other sectors, which show more unpredictable variations in efficiency, the Procurement sector demonstrates a consistent increase from quarter 1 to 5."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "TMuq3c8gnjH1KDIh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, what is the difference in efficiency between the Procurement sector in Quarter 1 and Quarter 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in efficiency, I need to locate {the line points for Procurement}[[0.121, 0.425], [0.327, 0.353], [0.533, 0.281], [0.738, 0.209], [0.944, 0.138]]. The efficiency in Quarter 1 is located at {the point}[0.121, 0.425], which corresponds to a value of 1.1, and in Quarter 5 is located at {the point}[0.944, 0.138], corresponding to an efficiency value of 1.5. The difference between the efficiency of Procurement in Quarter 5 and Quarter 1 is therefore 1.5 - 1.1 = 0.4.\nBased on the above thought, the answer is:\n0.4"
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "7t1e2YQYhdqSZEhA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the historical course registration data from the line chart into a structured table format with Years as columns and Courses as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I first notice {the x-axis labels}<r>[0.103, 0.903, 0.112, 0.931]</r> representing the years from '2015' to '2020'. These labels serve as column headers. Then, I identify {the legend}<r>[0.096, 0.044, 0.191, 0.136]</r> which details the three courses: Web Design, AI, and Business Management as row headers. I locate {the data points for each course}<r>[0.108, 0.068, 0.480, 0.443]</r>, <r>[0.593, 0.068, 0.966, 0.362]</r> distributed over the lines representing each course for the respective years and extract these registrations values.\nBased on the above thought, the answer is:\nYear, Web Design, AI, Business Management\n2015, 1000, 2500, 5000\n2016, 1500, 3500, 4700\n2017, 1800, 4500, 4500\n2018, 2000, 6000, 4300\n2019, 2200, 8000, 4100\n2020, 2300, 9500, 4000"
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "TYJObwnByR2JGQ1x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich course demonstrated the highest growth rate in registrations over the observed years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the highest growth rate, I analyze {the slopes of the lines representing the courses}<r>[0.108, 0.068, 0.480, 0.443]</r>, <r>[0.593, 0.068, 0.966, 0.362]</r>. The AI course line, which initially starts at 2500 registrations in 2015 at <r>[0.593, 0.215]</r> and spikes to 9500 by 2020 at <r>[0.966, 0.156]</r>, depicts a steep gradient relative to the other courses. This suggests a much faster growth compared to the moderate increase in Web Design and the decrease in Business Management courses.\nBased on the above thought, the answer is:\nThe AI course demonstrated the highest growth rate in registrations over the observed years, as its line on the chart shows the steepest ascending slope, indicating a vigorous increase."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "Dw07FbJ8PjtEXBCQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in registrations for the AI course from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I need to observe {the data points for the AI course}<r>[0.593, 0.068, 0.966, 0.362]</r>. The number of registrations depicted in 2015 is 2500, located at <r>[0.593, 0.215]</r>, while those in 2020 are 9500, located at <r>[0.966, 0.156]</r>. The increase is simply calculated as 9500 (2020 registrations) minus 2500 (2015 registrations).\nBased on the above thought, the answer is:\n7000."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "zPizkbG2yt2BQe0A",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the values of each social network's user counts into a table format for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first notice {the x-axis tick labels}[[0.094, 0.903, 0.129, 0.931], [0.184, 0.903, 0.219, 0.931], [0.275, 0.903, 0.310, 0.931], [0.365, 0.903, 0.400, 0.931], [0.455, 0.903, 0.490, 0.931]], which represent the years. {The subplot legends}[[0.100, 0.044, 0.216, 0.136], [0.593, 0.044, 0.714, 0.136]] correspond to the social networks. Utilizing this, I can form the column headers as the social networks and the rows for each year. The values are then extracted from each {line points}[[0.111, 0.419], [0.202, 0.367], [0.292, 0.262], [0.383, 0.163], [0.473, 0.065], [0.111, 0.766], [0.202, 0.762], [0.292, 0.761], [0.383, 0.760], [0.473, 0.762], [0.604, 0.457], [0.694, 0.337], [0.785, 0.257], [0.875, 0.058], [0.965, 0.058], [0.604, 0.616], [0.694, 0.537], [0.785, 0.457], [0.875, 0.377], [0.965, 0.317]] of respective social networks for each year.\nBased on the above thought, the answer is:\nYear,Facebook Users (Millions),Twitter Users (Millions),WhatsApp Users (Millions),Instagram Users (Millions)\n2016,1543,317,1000,600\n2017,1729,330,1300,800\n2018,2098,335,1500,1000\n2019,2449,340,2000,1200\n2020,2796,330,2000,1350"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "dViqhjLf3fWEaDT6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which social media platform has had the most consistent growth? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social media platform has had the most consistent growth, I need to examine the line plots for each platform. Observing {the line for Facebook}[0.111, 0.065, 0.473, 0.419], it shows an upward trend in user counts from 1543 million to 2796 million. Meanwhile, {the line for Twitter}[0.111, 0.760, 0.473, 0.766] exhibits a very slight uptrend followed by leveling off. Looking at {the line for WhatsApp}[0.604, 0.058, 0.965, 0.457], it shows a consistent increase from 1000 million to 2000 million users but then horizontally straight from 2019 to 2020. Finally, {the line for Instagram}[0.604, 0.317, 0.965, 0.616] shows an upward trend but at a slower pace than Facebook.\nBased on the above thought, the answer is:\nBased on the trends, Facebook has had the most consistent growth. Its user count consistently increased over the years, from 1543 million in 2016 to 2796 million in 2020. The growth trajectory of Facebook is also steeper than other social media platforms in the line chart, signifying a more rapid and consistent growth rate."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "CSPxFMCUDzA477SY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase of Instagram users from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase of Instagram users from 2016 to 2020, I look at {the line points for Instagram}[[0.604, 0.616], [0.694, 0.537], [0.785, 0.457], [0.875, 0.377], [0.965, 0.317]]. The user count in 2016 is 600 million at {point}[0.604, 0.616] and in 2020 is 1350 million at {point}[0.965, 0.317]. The total increase is therefore 1350 - 600 = 750 million.\nBased on the above thought, the answer is:\n750 million."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "icM9TAUYbPGu0V24",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, could you extract the data into a table with categories (teenagers and adults) as columns and behaviors (impulsive and procrastination) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the table from the line graph, I first recognize {the x-axis tick labels}[[0.033, 0.903, 0.118, 0.931], [0.076, 0.903, 0.159, 0.931], [0.112, 0.903, 0.208, 0.931], [0.173, 0.903, 0.232, 0.931], [0.228, 0.903, 0.262, 0.931], [0.263, 0.903, 0.311, 0.931], [0.312, 0.903, 0.347, 0.931], [0.344, 0.903, 0.399, 0.931], [0.393, 0.903, 0.435, 0.931], [0.422, 0.903, 0.491, 0.931]] as the column headers: 'Teenagers' and 'Adults'. Next, I see {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] which represent the rows: 'Impulsive Behavior' and 'Procrastination'. Each of these datasets is represented by {the line points}[[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]] in the line plot, from which I can extract the corresponding values to fill in our table.\nBased on the above thought, the answer is:\nCategory, Teenagers, Adults\nImpulsive Behavior, 80, 60\nProcrastination, 75, 50"
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "Hf60Uv65koVZ8EWq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExamining the line chart, which group, teenagers or adults, tends to exhibit more impulsive behavior and procrastination, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the line graph. Looking at the line plot for {Teenagers}[0.075, 0.081, 0.456, 0.750], the behavior rates for impulsive behavior and procrastination are higher relative to the {Adults}[0.118, 0.328, 0.160, 0.433] line plot, whose behavior rates for impulsive behavior and procrastination are lower.\nBased on the above thought, the answer is:\nAccording to the line chart, Teenagers tend to exhibit more impulsive behavior and procrastination. This is because both the rates of impulsive behavior and procrastination are noticeably higher for Teenagers as compared to Adults, as visualized by the respective line plots on the graph."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "P3mwtPvjEKafDMr3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average rate of impulsive behavior for teenagers and adults as indicated in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average rate of impulsive behavior for teenagers and adults, I need to find {the line points for impulsive behavior for teenagers}[[0.075, 0.504], [0.118, 0.328]], which represent the rates: 80 and 60 respectively. The average is therefore (80+60)/2 = 70.\nBased on the above thought, the answer is:\n70."
+            }
+        ],
+        "id_raw": 731
+    },
+    {
+        "id": "bU10cuDQbvufV7yT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we transform the year-wise data of 'Population Density' and 'Renewable Energy Production' from the line chart to table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can convert the visual data from the chart to tabular form by locating {the x-axis labels}_{the x-axis tick labels}<r>[[0.098, 0.903, 0.133, 0.931], [0.170, 0.903, 0.205, 0.931], [0.241, 0.903, 0.276, 0.931], [0.313, 0.903, 0.348, 0.931], [0.384, 0.903, 0.419, 0.931], [0.456, 0.903, 0.491, 0.931]]</r> as the 'Year', {line plot for 'Population Density'}{line_0}<r>[0.116, 0.793, 0.473, 0.818]</r> and {line plot for 'Renewable Energy Production'}{line_1}<r>[0.116, 0.068, 0.473, 0.294]</r> as the rows for 'Population Density' and 'Renewable Energy Production' respectively.\nBased on the above thought, the answer is:\nYear,Population Density,Renewable Energy Production\n2020,52.8,500\n2021,37.8,520\n2022,40.8,560\n2023,50.9,615\n2024,59.2,550\n2025,60.3,700"
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "eua4v1wg2jzmgmqB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the comparative trends in the line chart, can it be inferred that a higher population density corresponds to higher renewable energy production?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trends of both parameters should be considered here. By observing {Population Density line plot}<r>[0.116, 0.793, 0.473, 0.818]</r>, initially, I see that population density decreases from 52.8 in 2020 to 37.8 in 2021, slightly increases to 40.8 in 2022 and then shows an increasing trend till 2025. On the other hand, looking at the {Renewable Energy Production line plot}<r>[0.116, 0.068, 0.473, 0.294]</r>, the renewable energy production consistently shows an increasing trend from 500 in 2020 to 700 in 2025. So, while the population density shows fluctuations, the renewable energy production shows a consistently increasing trend. Therefore, we cannot conclusively infer that a higher population density corresponds to higher renewable energy production based on these trends.\nBased on the above thought, the answer is:\nNo, we cannot conclusively infer that a higher population density corresponds to higher renewable energy production based on the trends depicted in the line chart."
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "bbnETSl7hF9mXcaP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the population densities of the year 2020 and 2022 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The population densities for the years 2020 and 2022 can be obtained from the {Points on Population Density line plot}<r>[[0.116, 0.801], [0.259, 0.815]]</r>, which represent 52.8 for 2020 and 40.8 for 2022. The difference can be calculated as 52.8 - 40.8 = 12.\nBased on the above thought, the answer is:\n12"
+            }
+        ],
+        "id_raw": 732
+    },
+    {
+        "id": "TgKGm8fp79AZymn9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of the graph into a table format, with Time as rows and Measures as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To begin converting the graph to a table format, firstly, I identify {the x-axis tick labels}<r>[0.094, 0.903, 0.103, 0.931], [0.150, 0.903, 0.168, 0.931], [0.206, 0.903, 0.232, 0.931], [0.267, 0.903, 0.293, 0.931], [0.327, 0.903, 0.353, 0.931], [0.388, 0.903, 0.414, 0.931], [0.448, 0.903, 0.474, 0.931]</r>\nwhich serve as row labels for time in months. \nThen, I look at {the legend texts}<r>[0.132, 0.055, 0.246, 0.083], [0.132, 0.097, 0.250, 0.125]</r> as column headers signifying the Measures. For data extraction I locate {the line points for Social Inequality}<r>[0.098, 0.065, 0.461, 0.737]</r> and  {the line points for Rights Protection}<r>[0.098, 0.421, 0.461, 0.757]</r> at each respective time and extract corresponding values.\nBased on the above thought, the answer is:\nTime (months),Social Inequality,Rights Protection\n0,1.4,1.0\n50,2.0,0.9\n100,1.6,1.1\n150,0.9,0.9\n200,0.3,0.8\n250,0.9,0.25\n300,1.0,0.3"
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "Wad8zTGyF0kt8gIP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the graphs, which measure remains more stable over time - Social Inequality or Rights Protection? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the stability of the measures, I need to examine the variability in the data points for each one. Observing {the line for Social Inequality}<r>[0.098, 0.065, 0.461, 0.737]</r>, it displays high variability, with a rise from 1.4 to 2.0, a drop to 0.3, and an increase again to 1.0. In contrast, the {line for Rights Protection}<r>[0.098, 0.421, 0.461, 0.757]</r> also varies but not as significantly, with values ranging between 1.1 and 0.3.\nBased on the above thought, the answer is:\nThe Rights Protection measure remains more stable over time. Although both measures showcase fluctuations, the Rights Protection line exhibits smaller changes when compared to the Social Inequality line, indicating lesser fluctuation and therefore, greater stability."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "QQXjIJwwr407le2Y",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter how many months does the Intensity of Social Inequality first drop below 1?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find out when the Intensity of Social Inequality first falls below 1. To do this, I look at {the data points for Social Inequality}<r>[0.098, 0.065, 0.461, 0.737]</r>, the corresponding Intensity values are 1.4, 2.0, 1.6, 0.9, 0.3, 0.9, 1.0. The first time value where the Intensity drops below 1 corresponds to {150}<r>[0.267, 0.903, 0.293, 0.931]</r>.\nBased on the above thought, the answer is:\n150 months."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "2DNCur74bAZMAXW8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the plotted data into a structured table form with time intervals as the rows and 'On Fire' values and 'Ice Cool' values as the columns."
+            },
+            {
+                "from": "gpt",
+                "value": "To create the tabular data, I'll start by identifying {the x-axis tick labels}[[0.090, 0.903, 0.099, 0.931], [0.184, 0.903, 0.193, 0.931], [0.278, 0.903, 0.286, 0.931], [0.371, 0.903, 0.380, 0.931], [0.465, 0.903, 0.474, 0.931]] representing the rows which are time intervals. The column headers are represented by {the legend labels}[[0.127, 0.074, 0.176, 0.102], [0.747, 0.074, 0.845, 0.102]]. The 'On Fire' values can be retrieved from {the line points for 'On Fire'}[[0.095, 0.261], [0.188, 0.595], [0.282, 0.717], [0.376, 0.081], [0.469, 0.220]]. Also, 'Ice Cool' values from {the line points for 'Ice Cool'}[[0.591, 0.383], [0.685, 0.660], [0.779, 0.689], [0.873, 0.372], [0.966, 0.303]].\nBased on the above thought, the answer is:\nTime Intervals, On Fire Value, Ice Cool Value\n1, 73, 82\n2, 32, 34\n3, 17, 29\n4, 95, 84\n5, 78, 96"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "8ydUGZA2gCFLlvRO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, is there a direct correlation between the 'On Fire' and 'Ice Cool' trends across the time intervals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the correlation between 'On Fire' and 'Ice Cool' trends, I need to examine {the plot of 'On Fire'}[0.095, 0.081, 0.469, 0.717] and {the plot of 'Ice Cool'}[0.591, 0.293, 0.966, 0.699]. The 'On Fire' plot starts at a high point, decreases significantly, increases again, and then has a gradual decline. On the other hand, the 'Ice Cool' plot shows both decrease and increase but as a general trend, it decreases. While both plots exhibit declines and rises, their rates and timings don't directly correspond.\nBased on the above thought, the answer is:\nWhile both the 'On Fire' and 'Ice Cool' trends have their peaks and troughs across the time intervals, they seem to not mirror each other perfectly, indicating that there is not a strong direct correlation between the two variables. They both exhibit volatile behaviors in different ways which does not necessarily establish a direct correlation."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "dcaAJ0B8DeyaO15G",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the average value of the 'On Fire' variable across all time intervals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average of the 'On Fire' values, I'll have to find {the points for 'On Fire'}[[0.095, 0.261], [0.188, 0.595], [0.282, 0.717], [0.376, 0.081], [0.469, 0.220]], where the corresponding values are 73, 32, 17, 95, and 78. Adding these and dividing by the number of time intervals (5), will yield the average.\nBased on the above thought, the answer is:\n59"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "uDs5nqqgG78DChdI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a structured table from the bar graph displaying growth percentages for both Western and Eastern civilizations during the Industrial Age and Digital Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the two bar graphs, I first recognize the labeling of the sections. Starting with the digital representation of the growth percentages for both civilizations, I extract values based on the lengths of {the bars for Western civilization}<r>[0.450, 0.200, 0.750, 0.300]</r> and {the bars for Eastern civilization}<r>[0.450, 0.350, 0.680, 0.450]</r> during {the Industrial Age}<r>[0.100, 0.150, 0.300, 0.500]</r> and {the Digital Age}<r>[0.550, 0.150, 0.750, 0.500]</r>. These visuals and coordinates are interpreted to represent the respective growth percentages.\nBased on the above thought, the answer is:\nPeriod,Civilization,Growth Percentage\nIndustrial Age,Western,70.4\nIndustrial Age,Eastern,63.6\nDigital Age,Western,89.2\nDigital Age,Eastern,80.1"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "fQlY5JSDGTpOh4h0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the growth of civilizations from the Industrial Age to the Digital Age based on the bar graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the progression of the bars, specifically {the bar for the Western civilization}<r>[0.450, 0.150, 0.750, 0.250]</r> showing growth from the Industrial Age to the Digital Age, there's a visible increase in bar length, indicating a higher growth percentage. Similarly, for {the Eastern civilization}<r>[0.450, 0.350, 0.680, 0.450]</r>, there's also an increase, but slightly less pronounced compared to the Western civilization. This suggests that while both civilizations show growth in moving from the Industrial Age to the Digital Age, Western civilization exhibits a relatively faster growth.\nBased on the above thought, the answer is:\nThe trend observed is a clear increase in growth percentages from the Industrial Age to the Digital Age for both civilizations. Western civilization shows a more rapid increase compared to Eastern civilization, indicating a faster rate of development in recent periods."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "fc83G5ivU3ZOTIxT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage point difference in growth between the Digital Age and the Industrial Age for the Western civilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage point difference for the Western civilization, I refer to the values indicated by {the bar for Western civilization in the Industrial Age}<r>[0.450, 0.200, 0.750, 0.300]</r> and {the bar for Western civilization in the Digital Age}<r>[0.450, 0.150, 0.750, 0.250]</r>. The value for the Industrial Age is 70.4 and for the Digital Age is 89.2. The difference is computed as 89.2 - 70.4.\nBased on the above thought, the answer is:\n18.8"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "mvvj52wM1bYfh7KU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the bar chart in a table format with columns for policy and effectiveness evaluation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the bar chart, I must first look at {the x-axis tick labels}<r>[0.090, 0.903, 0.099, 0.931], [0.132, 0.903, 0.141, 0.931], [0.175, 0.903, 0.183, 0.931], [0.217, 0.903, 0.225, 0.931], [0.259, 0.903, 0.268, 0.931], [0.301, 0.903, 0.310, 0.931], [0.343, 0.903, 0.352, 0.931], [0.385, 0.903, 0.394, 0.931], [0.427, 0.903, 0.436, 0.931], [0.469, 0.903, 0.478, 0.931]</r>, which represent the effectiveness evaluation scores. Next, I identify {the bar values}<r>[0.095, 0.093, 0.474, 0.522], [0.587, 0.066, 0.966, 0.556]</r> as the policy effectiveness evaluations. These values will be in the second column. The first column will be the policy names, which I recognize as the {y-axis tick labels}<r>[0.036, 0.845, 0.052, 0.873], [0.035, 0.771, 0.052, 0.799], [0.036, 0.697, 0.052, 0.725], [0.036, 0.622, 0.052, 0.650], [0.036, 0.548, 0.052, 0.576], [0.036, 0.474, 0.052, 0.502], [0.035, 0.400, 0.052, 0.428], [0.036, 0.326, 0.052, 0.354], [0.035, 0.252, 0.052, 0.280], [0.035, 0.178, 0.052, 0.206], [0.035, 0.104, 0.052, 0.132], [0.035, 0.030, 0.052, 0.058]</r>.\nBased on the above thought, the answer is:\nPolicy, Effectiveness Evaluation\nCorporate Tax Cuts, 10\nClimate Change Policies, -6\nHealth Care Reform, 5\nEducation Funding, 12\nGun Control Policies, -8"
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "aE6Bi22SH2MCya5Y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which policy received the most positive public opinion and which received the least positive response?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which policy has the most positive public opinion, we need to look for the bar with the greatest positive value. This corresponds to the wheel labeled {Education Funding}<r>[0.263, 0.352, 0.474, 0.522]</r>. It extends to the right, implying a positive rating, and its length shows its rating of 12, the highest among all policies. On the other hand, the policy that received the least positive response corresponds to the bar extending towards the left, which signifies a negative rating. This bar is assigned to {Gun Control Policies}<r>[0.966, 0.066, 0.587, 0.556]</r>, indicating it has the lowest rating of -8 amongst all policies.\nBased on the above thought, the answer is:\nRegarding policies represented through public opinion in the bar chart, the most positive response was given to Education Funding, and the least positive response was to Gun Control Policies. The Education Funding policy received a rating of 12, which was the highest amongst all, while Gun Control Policies received a significantly less favorable rating of -8."
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "AL5rwlwDgilw8puo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest policy effectiveness evaluations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the highest and lowest policy effectiveness evaluations, I need to search for the longest positive- and negative-value bars, which correspond {Education Funding}<r>[0.263, 0.352, 0.474, 0.522]</r> and {Gun Control Policies}<r>[0.966, 0.066, 0.587, 0.556]</r>, respectively. Extracting the corresponding values, these are 12 and -8. Therefore, the difference between the maximum and minimum is |12 - (-8)| = 20.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 738
+    },
+    {
+        "id": "sPzDRtFzXhbTgSWf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table from the grouped bar chart showing environmental issues and their severity over four time periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the grouped bar chart data into a tabular format, I analyze the magnitude of each bar within its bounding coordinates, reflecting the severity of the respective environmental issue. First, I inspect {the bars for 'Ice Melt'}<r>[0.150, 0.200, 0.400, 0.300]</r>, {the bars for 'Land Temperature Rise'}<r>[0.150, 0.350, 0.500, 0.450]</r>, {the bars for 'Oceanic Temperature Rise'}<r>[0.150, 0.500, 0.550, 0.600]</r>, {the bars for 'Air Quality Index'}<r>[0.150, 0.650, 0.320, 0.750]</r>, and {the bars for 'Greenhouse Gas Emission'}<r>[0.150, 0.800, 0.700, 0.900]</r>. I arrange these values according to their y-coordinates which align with different environmental issues and x-coordinates indicating the severity across different time periods.\nBased on the above thought, the answer is:\nEnvironmental Issue,Period 1,Period 2,Period 3,Period 4\nIce Melt,10,15,20,25\nLand Temperature Rise,20,25,30,35\nOceanic Temperature Rise,25,30,35,40\nAir Quality Index,15,20,18,22\nGreenhouse Gas Emission,30,35,40,45"
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "zTpJB4kQWkA4ufEF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich environmental issue demonstrated the most significant growth in severity over the given time periods according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which issue showed the most significant growth, I compare the length and increase of the bar groups for each environmental issue. Scanning through the bar groups, I notice {the group for 'Greenhouse Gas Emission'}<r>[0.150, 0.800, 0.700, 0.900]</r> shows a steep rise from the start to the end coordinate compared to others such as {the group for 'Ice Melt'}<r>[0.150, 0.200, 0.400, 0.300]</r> and {the group for 'Air Quality Index'}<r>[0.150, 0.650, 0.320, 0.750]</r>. The increased width distinctly across four periods indicates a significant growth in severity compared to others.\nBased on the above thought, the answer is:\n'Greenhouse Gas Emission' demonstrated the most significant growth in severity over the given time periods. This was observed by the substantial increase in the width of its bars from Period 1 to Period 4, visually representing a greater change in severity relative to other environmental issues."
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "aAMzGyoNS76SDM4u",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_37_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in severity for 'Greenhouse Gas Emission' from Period 1 to Period 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I locate the severity values for 'Greenhouse Gas Emission' across the four periods by referring to {the bars representing 'Greenhouse Gas Emission'}<r>[0.150, 0.800, 0.700, 0.900]</r>. These values are 30, 35, 40, 45. The increase from Period 1 (30) to Period 4 (45) is calculated as 45 - 30.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 739
+    },
+    {
+        "id": "fduMTv612FcUYNZ6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data shown on the line chart in a structured table format with 'Years' as columns and 'Expenditures' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will translate the data displayed in the line chart into a structured table. First, {the x-axis tick labels}<r>[-0.133, 0.894, -0.085, 0.931], [1.329, 0.894, 1.376, 0.931]</r> are observed as column headers which correspond to the years. Next, I identify {the legend texts}<r>[0.168, 0.055, 0.219, 0.083], [0.168, 0.097, 0.218, 0.125]</r> as the row labels, indicating the types of expenditures. To populate the values, I find {the line points}<r>[0.135, 0.066], [0.281, 0.605], [0.427, 0.515], [0.598, 0.695], [0.768, 0.784], [0.938, 0.066]</r> for each expenditure type corresponding to the respective years.\nBased on the above thought, the answer is:\nType,2010,2020\nHealthcare,15000,25000\nEducation,20000,22000"
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "3HK2oRfnPYs3VgBS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which area has seen more focus in terms of expenditure over the decade? Provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the focus of expenditure, I look at {the lines for Healthcare and Education}<r>[0.135, 0.066, 0.938, 0.066], [0.281, 0.605, 0.427, 0.515]</r>. It's observed that the expenditure on Healthcare saw a more significant rise, increasing from $15,000 in 2010 to $25,000 in 2020. The spending on Education, in contrast, shows only a minor increase from $20,000 to $22,000 during the same period.\nBased on the above thought, the answer is:\nThe Healthcare sector saw a more significant increase in expenditure over the decade, implying a greater focus on Healthcare compared to Education. The line chart shows an upward trajectory in Healthcare spending, which contrasts with the sluggish progress of Education spending. Thus, the chart suggests that more focus has been placed on Healthcare in terms of expenditure over the decade."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "3ougVTQ4NwenmFGJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the expenditure on Healthcare increase from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in Healthcare expenditure, I need to locate {the line points for Healthcare}<r>[0.135, 0.066], [0.938, 0.066]</r> and extract the related values for the years 2010 and 2020. The value for 2010 is $15,000 while the value for 2020 is $25,000. The difference between these two values represents the increase in Healthcare expenditure over the ten-year period.\nBased on the above thought, the answer is:\n10000."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "vgAAK45dEJHd33PT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please prepare a table that outlines the cost per serving for each cuisine type as indicated in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I will first look for the x-axis labels which represent the cuisine types. I spot these at {the x-axis tick labels}[[0.114, 0.888, 0.209, 0.928], [0.242, 0.888, 0.303, 0.928], [0.401, 0.888, 0.446, 0.928], [0.581, 0.888, 0.636, 0.928], [0.718, 0.888, 0.805, 0.928], [0.871, 0.888, 0.931, 0.928]]. The y-value at the top of each bar plot represents the cost per serving for these cuisine types, which I find at {the bar top points}[[0.160, 0.188], [0.284, 0.604], [0.450, 0.604], [0.613, 0.500], [0.756, 0.542], [0.902, 0.292]]. I now have all the details to construct the table.\nBased on the above thought, the answer is:\nCuisine Types,Cost per Serving\nGerman,100\nItalian,200\nAmerican,200\nFrench,175\nJapanese,190\nMexican,130"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "cJo1AmA0VRTmCGxU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComment on the cost per serving trends based on the bar chart. What information can you infer about the cost of cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the trends, I need to look at the order and heights of the bars in the chart. Starting from the left, {German cuisine}[0.160, 0.188] is the least expensive. Next, there is a significant rise as we move to {Italian}[0.284, 0.604] and {American Cuisines}[0.450, 0.604] which are both priced higher. The cost remains relatively high for {French}[0.613, 0.500] and {Japanese cuisines}[0.756, 0.542], with Japanese cuisine being the most expensive. The cost drops again for {Mexican cuisine}[0.902, 0.292].\nBased on the above thought, the answer is:\nThe cost of cuisines varies significantly. German cuisine is the cheapest, while Japanese cuisine is the most expensive. Italian and American cuisines are similarly priced and are moderately costly. The price for French cuisine also falls in this range, although it is a bit cheaper. Finally, Mexican cuisine is relatively affordable, although not as cheap as German cuisine. The significant disparities in the cost per serving of these cuisines infer the different ingredients, preparation techniques, and perceived value associated with each cuisine."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "GI0HzriZtEAsUXVR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cost difference between the most expensive and least expensive cuisine types as per the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To aid my calculation, I need to identify the highest and lowest points on the bar plots. These are {Japanese cuisine} [0.756, 0.542] with a height that corresponds to $190, making it the most expensive, and {German cuisine} at [0.160, 0.188] with a height that corresponds to $100, making it the least expensive. The difference in cost is therefore 190 - 100 = $90 dollars.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "2pe2FsOyPRrxHOqY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the online and offline shopping habits data into a table, with Years as columns and Shopping Modes as rows from the trend shown in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data, I'll look at {the x-axis tick labels}[[-0.392, 0.852, -0.337, 0.899], [-0.131, 0.852, -0.075, 0.899], [0.131, 0.852, 0.187, 0.899], [0.393, 0.852, 0.449, 0.899], [0.655, 0.852, 0.711, 0.899], [0.917, 0.852, 0.973, 0.899], [1.179, 0.852, 1.235, 0.899]] to identify the years. Then, the {legend labels}[[0.232, 0.078, 0.636, 0.123], [0.232, 0.146, 0.682, 0.191]] represent the shopping modes. The data points to populate the table are given by {line points for online shopping}[[0.421, 0.828], [0.604, 0.828]] and {line points for offline shopping}[[0.421, 0.037], [0.604, 0.037]] for each year.\nBased on the above thought, the answer is:\nShopping Mode,2010,2020\nOnline,1000,2000\nOffline,2000,1500"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "iEQ1UUarTtrxQTPc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, has the preference for online shopping increased or decreased from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend in preference for online shopping, let's take {the line for online shopping}[[0.421, 0.828], [0.604, 0.828]]. It's clearly visible that the online shopping line shows a rise from 1000 units in 2010 to 2000 units in 2020. Hence, the preference for online shopping has increased.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the preference for online shopping has increased from 2010 to 2020."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "ZM2NivV0ml2FmYIQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent did the online shopping increment from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percent increment in online shopping, I need to take {the line points for online shopping}[[0.421, 0.828], [0.604, 0.828]] which correspond to the values 1000 in 2010 and 2000 in 2020. The percentage increase is calculated by ((2000 - 1000) / 1000)*100 = (1000/1000)*100 = 100%.\nBased on the above thought, the answer is:\n100%."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "NpznWfiRxZB2KF5b",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the annual costs for Public Service and Basic Welfare into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I'm examining the data. The x-axis tick labels can be seen at {[0.095, 0.911, 0.130, 0.934], [0.261, 0.911, 0.296, 0.934], [0.428, 0.911, 0.463, 0.934], [0.594, 0.911, 0.629, 0.934], [0.760, 0.911, 0.795, 0.934], [0.926, 0.911, 0.961, 0.934]}, which define the 'Year'. Then, looking at the line points for each 'Public Service Cost' and 'Basic Welfare Cost' from {[0.113, 0.100, 0.943, 0.449]} and {[0.113, 0.449, 0.943, 0.857]}, I can derive the annual costs for each year.\nBased on the above thought, the answer is:\nYear,Public_Service_Cost,Basic_Welfare_Cost\n2000,2.5,1.1\n2001,2.6,1.2\n2002,2.7,1.3\n2003,2.8,1.4\n2004,2.9,1.5\n2005,3.0,1.6\n2006,3.1,1.7\n2007,3.2,1.8\n2008,3.3,2.0\n2009,3.5,2.2\n2010,3.7,2.5"
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "Kbon5ppMl0e06rcv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which expenditure type shows the most steady growth rate between 2000 to 2010 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the volatility in the growth trends. Observing the lines {[0.113, 0.100, 0.943, 0.449]} for 'Public Service Cost' and {[0.113, 0.449, 0.943, 0.857]} for 'Basic Welfare Cost', the line for 'Public Service Cost' shows a steady uphill movement, with the growth rate incrementally increasing each year, and no noticeable fluctuations. In contrast, the line for 'Basic Welfare Cost' shows steeper gradients, indicating more significant changes in the costs year by year.\nBased on the above thought, the answer is:\nThe 'Public Service Cost' appears to have the most steady growth rate between 2000 to 2010. This conclusion is based on the smooth, gradually increasing pattern observed in the corresponding line plot, which suggests a consistent and steady increase in cost over the years, with no drastic fluctuations or large jumps. In comparison, the 'Basic Welfare Cost' line plot shows more steep gradients, indicating a more volatile growth rate."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "3psQPZFkK1xlnE01",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of increase in Basic Welfare Costs from 2008 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Basic Welfare Costs between these two years, I need to locate {the line points for Basic Welfare Cost}[[0.777, 0.595], [0.860, 0.537], [0.943, 0.449]] and extract the corresponding values: 2.0 from {2008}[0.777, 0.911], 2.2 from {2009}[0.860, 0.911], and 2.5 from {2010}[0.943, 0.911]. The increase from 2008 to 2010 is therefore 2.5 - 2.0 = 0.5 billion.\nBased on the above thought, the answer is:\n0.5 billion."
+            }
+        ],
+        "id_raw": 744
+    },
+    {
+        "id": "Zxl7es5echg7mjzF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the usage ratio for each learning platform before and during the pandemic in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify that {the x-axis labels}<r>[0.088, 0.912, 0.131, 0.936], [0.172, 0.912, 0.310, 0.936], [0.337, 0.912, 0.407, 0.936], [0.434, 0.912, 0.573, 0.936], [0.571, 0.912, 0.698, 0.936], [0.681, 0.912, 0.851, 0.936], [0.814, 0.912, 0.981, 0.936]</r> correspond to different learning modules, which can act as row labels. Then, looking at {the legend}<r>[0.079, 0.079, 0.280, 0.176]</r>, I distinguish \"Before Pandemic\" and \"During Pandemic\" as column headers. I then examine the line data of these two states and find out the corresponding usage ratio for each learning module.\nBased on the above thought, the answer is:\nLearning Module,Usage Ratio Before,Usage Ratio During\nMOOC,0.6,1.75\nSimulation Platform,1.5,2.1\ne-Reading,1.6,2.9\nAI-assisted Platform,0.5,1.6\nGamification Tools,1.3,2.15\nOnline Assessment Tools,1.75,2.9\nLearning Analytics Tools,0.9,1.75"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "Ho3lAtSyvQDudSDR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich learning module experienced the most significant increase in usage ratio during the pandemic compared to before the pandemic? Explain."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the learning module with the most significant increase in usage ratio during the pandemic, I need to compare the change in usage ratios across all learning modules. Starting with the point for MOOC before the pandemic {at this location}<r>[0.110, 0.827]</r> and during the pandemic {at this location}<r>[0.110, 0.465]</r>, I calculate an increase of 1.75 - 0.6 = 1.15. I repeat the same calculation for all other learning modules using their respective coordinates. However, taking a closer look at the e-Reading platform, with its previous value {here}<r>[0.372, 0.512]</r> and the current value {here}<r>[0.372, 0.103]</r>, I compute an increase of 2.9 - 1.6 = 1.3. This is the most significant increase among all the learning modules.\nBased on the above thought, the answer is:\nThe e-Reading platform experienced the most significant increase in usage ratio during the pandemic compared to before the pandemic at 1.3. This indicates that many learners have shifted towards electronic reading resources during the pandemic."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "DuGIp0KTl56RrvAE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the usage ratio of e-Reading increased during the pandemic compared to before the pandemic?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in usage ratio for e-Reading platform during the pandemic, I first identify the point coordinates associated with the e-Reading platform for both states. For the state before the pandemic, {the point}<r>[0.372, 0.512]</r> corresponds to a usage ratio of 1.6. For the state during the pandemic, {the point}<r>[0.372, 0.103]</r> corresponds to a usage ratio of 2.9. Therefore, the increase in usage ratio during the pandemic is 2.9 - 1.6 = 1.3.\nBased on the above thought, the answer is:\n1.3"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "hPWqv4DogtPlxpm4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the line chart into a table with Return Ratio as the columns and Risk Factor as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from this graphical data, first I recognize that {the x-axis} values< r>[0.432, 0.917, 0.647, 0.963]</ r> represent the columns which are Return Ratios. Meanwhile, the y-axis values< r>[0.020, 0.291, 0.058, 0.588]</ r> correspond to the rows for Risk Factors. The points on the line plot for the Risk Return Trade-off< r>[0.429, 0.721, 0.935, 0.710]</ r> yield values corresponding to each Return Ratio and Risk Factor pair.\nBased on the above thought, the answer is:\nReturn Ratio,Risk Factor\n0.1,1\n0.4,1.05\n0.25,1.15\n0.15,1.20\n0.8,2\n0.03,6.6"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "gOzzxpLSg2j6pE78",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the investment strategy based on the risk-return trade-off shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The risk-return trade-off depicted by the plotted line< r>[0.429, 0.721, 0.935, 0.710]</ r> shows that as the return ratio increases, the risk factor tends to increase as well. Specifically, we have a very high-risk factor at a low return point< r>[0.138, 0.096 </ r>], a moderate risk factor at moderate return ratio< r>[0.653, 0.700]</ r> and< r>[0.767, 0.711]</ r>, and an elevated risk at high return ratio< r>[0.935, 0.606]</ r>. The investments A, B, and C< r>[0.653, 0.051, 0.767, 0.051, 0.935, 0.051]</ r> represent constant-risk options with their risks roughly equivalent respectively to the risks at low, moderate, and high returns in the trade-off line.\nBased on the above thought, the answer is:\nThe chart shows a typical risk-return trade-off where higher potential returns are associated with greater risk. Investment A has a low return but also a low risk, making it a conservative investment. Investment B, with a moderate return ratio, represents a balanced investment. Investment C, with a high return ratio, represents a high-risk, high-reward strategy. These observations underscore the importance of diversification when building an investment portfolio, balancing lower-risk and higher-risk investments. This distribution of investments allows one to achieve a desirable risk-return trade-off."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "EdjswtgnBCwg7NRI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Risk Factor when the Return Ratio is 0.8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the Risk Factor at a Return Ratio of 0.8, I need to locate the point on the {Risk Return Trade-off line}< r>[0.429, 0.721, 0.935, 0.710]</ r> corresponding to 0.8 Return Ratio. This point< r>[0.935, 0.606]</ r> indicates the Risk Factor at this Return Ratio.\nBased on the above thought, the answer is:\n2.0"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "z7z2v6kGRHaCblvB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph displaying market shares of the products across three quarters into a structured table please?"
+            },
+            {
+                "from": "gpt",
+                "value": "To export the data from the line graph, I firstly identify the labels corresponding to the quarters on {the x-axis}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903]], which will be used as the column headers. Next, by identifying the legend texts of {product_A}[[0.884, 0.101, 0.982, 0.123]], {product_B}[[0.884, 0.134, 0.933, 0.156]], {product_C}[[0.884, 0.167, 0.946, 0.189]], {product_D}[[0.884, 0.200, 0.983, 0.220]], and {product_E}[[0.884, 0.233, 0.982, 0.255]], I understand that they will represent the different rows. Then, by locating points on the line for each product like {product_A's points}[[0.102, 0.470], [0.218, 0.082], [0.333, 0.179]] for the respective quarters and extracting the annotated values, I can populate the table.\nBased on the above thought, the answer is:\nProduct,Q1,Q2,Q3\nProduct_A,24,20,18\nProduct_B,18,22,26\nProduct_C,22,15,19\nProduct_D,20,25,19\nProduct_E,16,15,14"
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "x2mmtu6zVEReXzHO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in market share percentage for Product_B from Q1 to Q3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover the variation in market share percentage for Product_B between Q1 and Q3, I have to find {the point of Product_B for Q1}[[0.102,0.211]] and {the point of Product_B for Q3}[[0.333,0.470]]. The corresponding values extracted from these points are 18 and 26 respectively. Subtraction of the Q1 value from the Q3 value gives the difference which is 26 - 18 = 8.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "xt3TpY9Jf0mrHGw0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you arrange the progression of the two trends 'Gradual increase' and 'Rapid growth' per year in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.153, 0.852, 0.174, 0.899], ...[0.936, 0.852, 0.936, 0.899]], which represent the column headers or years from 2000 to 2015. The row labels representing the trend names 'Gradual increase' and 'Rapid growth' are defined by {the legend texts}[[0.068, 0.239, 0.097, 0.274], [0.068, 0.167, 0.097, 0.202]]. To populate the values, I locate {the line points of 'Gradual increase'}[[0.153, 0.109], [0.173, 0.181], ...[0.936, 0.500]] and {the line points of 'Rapid growth'}[[0.153, 0.109], [0.173, 0.262],...[0.936, 0.835]] for each year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Gradual increase,Rapid growth\n2000,100,100\n2001,102,200\n2002,104,400\n2003,106,800\n2004,108,1600\n2005,110,3200\n2006,112,6400\n2007,114,12800\n2008,116,25600\n2009,118,51200\n2010,120,102400\n2011,122,51200\n2012,124,25600\n2013,126,12800\n2014,128,6400\n2015,130,3200"
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "L3jeK2A3f5cGJOE6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which trend shows a more drastic change during the observed period from 2000 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the progression of both trends during the period from 2000 to 2015. I note that while {the line for 'Gradual increase'}[0.153, 0.109, 0.936, 0.500] shows a steady and slow increase in value throughout the period, {the line for 'Rapid growth'}[0.153, 0.109, 0.936, 0.835] initially shows a steep increase reaching a peak in 2010 before dropping back down. This drop in the 'Rapid growth' trend indicates a more drastic change compared to the 'Gradual increase' trend.\nBased on the above thought, the answer is:\nThe 'Rapid growth' trend shows a more drastic change during the observed period from 2000 to 2015. This is evident in the steep increase in the initial years until 2010, after which there's a drastic drop. This up and down progression indicates a much more drastic change in comparison to the steady and slow growth of the 'Gradual Increase' trend."
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "qjy8S8xZxF3z5yYG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in value of 'Gradual increase' from the year 2000 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in value of 'Gradual increase' from the year 2000 to 2015, I locate {the line point for 'Gradual increase' in 2000}[0.153, 0.109], which gives me the value of 100, and {the line point for 'Gradual increase' in 2015}[0.936, 0.500], which corresponds to a value of 130. The percentage increase is therefore calculated as ((130 - 100) / 100) * 100%.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 749
+    },
+    {
+        "id": "wSfF9kuYkwm2ivoF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract educational spending in different types of institutions into tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to extract the data, I analyze {the x-axis labels}<r>[0.061, 0.934, 0.167, 0.952]</r>, <r>[0.226, 0.934, 0.325, 0.952]</r>, <r>[0.408, 0.934, 0.467, 0.952]</r>, <r>[0.537, 0.934, 0.660, 0.952]</r>, <r>[0.704, 0.934, 0.817, 0.952]</r>, <r>[0.864, 0.934, 0.980, 0.952]</r> which gives the types of schools. Then, I locate {the line points for spending}<r>[0.114, 0.882]</r>,<r>[0.276, 0.500]</r>,<r>[0.437, 0.529]</r>,<r>[0.599, 0.249]</r>,<r>[0.760, 0.365]</r>,<r>[0.922, 0.086]</r> and extract the corresponding figures.\nBased on the above thought, the answer is:\nInstitutions,Spending\nPrivate Schools,1000000\nPublic Schools,19845627\nColleges,15789463\nGraduate Schools,140781300\nTraining Centers,56921336\nOnline platforms,500000000"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "SHO66lFeI589xwqa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you describe the spending trend on educational institutions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the spending trend, I'm analyzing the {points on the line chart}<r>[0.114, 0.882]</r>, <r>[0.276, 0.500]</r>, <r>[0.437, 0.529]</r>, <r>[0.599, 0.249]</r>, <r>[0.760, 0.365]</r>, <r>[0.922, 0.086]</r>. From this, I infer that the spending has overall increased from Private Schools to Online platforms, though there were fluctuations.\nBased on the above thought, the answer is:\nThe spending on educational institutions has seen an overall increase from Private Schools to Online platforms, however there have been substantial fluctuations in between. While the expenditure on Public Schools was significantly higher than Private Schools, there was a drop for Colleges and another noticeable increase for Graduate Schools. Spending decreased again for Training Centers but saw the most substantial rise for Online platforms."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "TlffQSH2fS0ecSNm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest spending on educational institutions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to locate {the maximum and minimum points on the line chart}<r>[0.922, 0.086]</r> and <r>[0.114, 0.882]</r>, which correspond to the highest and lowest spendings respectively. The difference is 500000000 - 1000000 = 499000000.\nBased on the above thought, the answer is:\n499000000"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "8wgZzOBb6inhBBBe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the line chart into a table format with 'Time' as the rows and 'Types of Interaction' as the columns for each age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To summarize the chart in a table, I first identify the {x-axis}[0.491, 0.912, 0.582, 0.963] as the 'Time' parameter. I then locate the {legend texts}[0.210, 0.078, 0.537, 0.123], [0.210, 0.146, 0.507, 0.191], [0.210, 0.213, 0.694, 0.258], [0.210, 0.281, 0.664, 0.326] to set the 'Type of Interaction' for each age group. Finally, I analyze {the y-values of the data points}[0.509, 0.750, 0.509, 0.839], [0.787, 0.750, 0.787, 0.839], [0.666, 0.750, 0.666, 0.839], [0.926, 0.750, 0.926, 0.839] for each corresponding 'Time' to create row entries for each type of interaction in both age groups.\nBased on the above thought, the answer is:\nTime,Education Technology,Training Technology,Average (Education Technology),Average (Training Technology)\n3.3,3.5,null,null\n3.9,3.7,null,null\n5.1,4.4,null,null\n6.5,5.2,null,null\n5.3,6.8,6.15,null\n7.6,8.9,null,7.55"
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "rZyN25aRmQEVrrym",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology, Education Technology or Training Technology, indicates a more positive trend in increasing its Innovation Index over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to understand the overall trends of the line points for Education Technology and Training Technology. Looking at the {line for Education Technology}[0.138, 0.046, 0.935, 0.527], it indicates an overall upward trend from 3.3 to 7.6. Similarly, the trend in the {line for Training Technology} [0.210, 0.146, 0.507, 0.191] also shows an upward trend but starting at 3.5 and ending at 8.9, with a steeper rise compared to that for Education Technology.\nBased on the above thought, the answer is:\nBased on the chart, Training Technology indicates a more positive trend in increasing its Innovation Index over time. Despite both technologies showing an upward trend, Training Technology starts at a slightly higher index and ends at a significantly higher index. Moreover, the slope of the line indicates a steeper rise, thus demonstrating a more dramatic increase in its Innovation Index over time as compared to Education Technology."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "71RQ7372fLoeXlON",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest Innovation Index observed for Education Technology over the time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest Innovation Index for Education Technology, I need to observe the {Education Technology line points}[0.509, 0.750, 0.509, 0.839], [0.787, 0.750, 0.787, 0.839], [0.666, 0.750, 0.666, 0.839] and extract the corresponding values: 3.3, 3.9, 5.1, 6.5, 5.3, 7.6, from which the highest value is 7.6 at time 7.6.\nBased on the above thought, the answer is:\n7.6."
+            }
+        ],
+        "id_raw": 752
+    },
+    {
+        "id": "G7PJcfOo1bumOveW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data depicted in the line chart into a table displaying Time Periods as columns and Supply Chain Factors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first note {the x-axis tick labels}[[0.110, 0.909, 0.121, 0.932], [0.288, 0.909, 0.309, 0.932], [0.471, 0.909, 0.493, 0.932], [0.654, 0.909, 0.676, 0.932], [0.837, 0.909, 0.859, 0.932], [1.015, 0.909, 1.048, 0.932]], representing the Time Periods. Then, {the legend texts}[[0.085, 0.049, 0.510, 0.238]] point out the Supply Chain Factors. Finally, by locating {the line points for Risk Moderation}[[0.115, 0.280, 0.161, 0.427]] and {the line points for Supply Chain Integration}[[0.115, 0.034, 0.161, 0.169]], I can extract their respective Performance Indices over the given Time Periods.\nBased on the above thought, the answer is:\nSupply Chain Factor,-20,0,20,40,60,80\nRisk Moderation,5,4.5,4.7,4.2,3.9,3.8\nSupply Chain Integration,7,6.9,6.5,6.2,6,5.9"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "0iGzLtQh84vSXrl3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the depicted trends in the line chart, which factor shows a continuous decreasing trend in Performance Index over time and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to discern which factor shows a continuous decreasing trend, I need to inspect the line plots for each factor. Upon evaluating {the line plot for Risk Moderation}[[0.115, 0.280, 0.161, 0.427]], it appears to not be decreasing continuously, especially with the dip and rise around the 20 mark. On the other hand, looking at {the line plot for Supply Chain Integration}[[0.115, 0.034, 0.161, 0.169]], I observe a sustained reduction in performance index from 7 at '-20'to 5.9 at '80'.\nBased on the above thought, the answer is:\nThe factor Supply Chain Integration shows a continuous decreasing trend in Performance Index over time. This is observed from the continuous decrease in its values from 7 to 5.9 as the time period progresses from -20 to 80."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "W78Fj3hSjc9LjXEP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value of Performance Index reached by Supply Chain Integration over the Time Periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value of Performance Index for Supply Chain Integration, I examine {the line points for Supply Chain  Integration}[[0.115, 0.034, 0.161, 0.169]], which correspond to the values 7, 6.9, 6.5, 6.2, 6, and 5.9. As seen, the highest value is 7 at the Time Period '-20'.\nBased on the above thought, the answer is:\n7."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "JrZoOPbpaFeiQjhX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line dataset into a structured table with years as the columns and the different metrics (like CSR Score, Environment, etc.) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line plot, I first identify {the x-axis labels}<r>[0.035, 0.869, 0.069, 0.904], [0.132, 0.869, 0.146, 0.904], [0.219, 0.869, 0.234, 0.904], [0.306, 0.869, 0.321, 0.904], [0.394, 0.869, 0.408, 0.904], [0.481, 0.869, 0.495, 0.904], [0.568, 0.869, 0.583, 0.904], [0.655, 0.869, 0.670, 0.904]</r> as the columns i.e. years. I then identify {the legend}<r>[0.633, 0.069, 0.960, 0.341]</r> as the row labels representing different metrics. Based on the plot, I can then map the corresponding {line points} <r>[0.139, 0.483], [0.226, 0.531], [0.314, 0.501], [0.401, 0.522], [0.488, 0.513], [0.575, 0.557], [0.139, 0.682], [0.226, 0.726], [0.314, 0.700], [0.401, 0.721], [0.488, 0.712], [0.575, 0.739], [0.139, 0.610], [0.226, 0.548], [0.314, 0.575], [0.401, 0.566], [0.488, 0.545], [0.575, 0.527], [0.139, 0.573], [0.226, 0.633], [0.314, 0.527], [0.401, 0.598], [0.488, 0.562], [0.575, 0.668], [0.139, 0.393], [0.226, 0.196], [0.314, 0.351], [0.401, 0.263], [0.488, 0.337], [0.575, 0.148]</r> values as the data under respective years for each metric.\nBased on the above thought, the answer is:\nMetrics,-1,0,1,2,3,4,5\nCSR Score,2.05,1.78,1.95,1.83,1.88,1.63,0.63\nEnvironment,0.92,0.67,0.82,0.70,0.75,0.60,0.60\nEconomic,1.33,1.68,1.53,1.583,1.70,1.80,1.80\nEmployee Wellness,1.54,1.20,1.80,1.40,1.60,1.00,1.00\nSustainable Business,2.56,3.68,2.80,3.30,2.88,3.95,3.95"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "WApGzlFLpOUYmDrc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend shown in the line chart, which factor seems to be improving the most over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the factor that improved the most over the years, I observe the slopes of the line plots for each factor. The slope of {Employee Wellness}<r>[0.139, 0.573, 0.575, 0.668]</r> line plotted seems the steepest as it increases from 1.54 to 1.0 over this period.\nBased on the above thought, the answer is:\nBased on the trend shown in the line chart, the factor, Employee Wellness seems to be improving the most over the years. Its line plot shows the steepest upward slope indicating a continuous improvement over the period."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "Voha3nfzLVHeKQzl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the CSR Score of -1 year and 1 year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the CSR Score of -1 year and 1 year, I first locate {the point for -1 year}<r>[0.139, 0.483]</r> with a value of 2.05 and {the point for 1 year}<r>[0.314, 0.501]</r> with a value of 1.95. The difference is then 2.05 - 1.95 = 0.1.\nBased on the above thought, the answer is:\n0.1"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "VxUD7dyc5L74svYb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the line plot data into a table format with Investment categories as rows and Rapid Recovery as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I need to first identify {the legend texts}<r>[0.210, 0.092, 0.536, 0.137], [0.210, 0.159, 0.491, 0.204], [0.210, 0.227, 0.376, 0.272], [0.210, 0.295, 0.461, 0.340], [0.210, 0.362, 0.500, 0.407], [0.210, 0.430, 0.554, 0.475]</r> as the row labels representing the different Investment categories. To populate the columns, I locate the x coordinates of {the line points for each category}<r>[0.841, 0.634], [0.654, 0.634], [0.701, 0.634], [0.279, 0.634], [0.138, 0.634], [0.935, 0.634]</r> on the x-axis which represent the investment for respective categories. Next, I look at the corresponding y coordinates which represent the Rapid recovery rates for each category.\nBased on the above thought, the answer is:\nInvestment,Rapid_Recovery\nTraditional_Innovation,1.4\nHigh-Tech_Startups,1.1\nAI_Startups,1.0\nBioTech_Startups,2.6\nCleanTech_Startups,3.4\nQuantumTech_Startups,2.0"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "wYv9qMSFgFzM0lva",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the category that the investors are likely to invest in more for a higher rapid recovery rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the category that the investors are likely to invest in more for a higher rapid recovery rate, I need to locate the line point with the highest y coordinate, which indicates the highest rapid recovery rate. Looking at the y coordinates of {the line points for each category}<r>[0.841, 0.634], [0.654, 0.634], [0.701, 0.634], [0.279, 0.634], [0.138, 0.634], [0.935, 0.634]</r>, the point with the highest y-coordinate is at 3.4 corresponding to {CleanTech Startups}<r>[0.138, 0.634]</r>. This suggests that CleanTech Startups is the category that investors are likely to invest in more for a higher rapid recovery rate.\nBased on the above thought, the answer is:\nFrom the chart figure, it is likely that investors may invest more in the \"CleanTech Startups\" category as it is associated with the highest rapid recovery rate."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "bWZLjOgjM8AjPQa8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the Rapid Recovery rate for CleanTech Startups as compared to AI Startups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the y coordinates of the line points for {CleanTech Startups}<r>[0.138, 0.634]</r> and {AI Startups}<r>[0.701, 0.634]</r>. The Rapid Recovery rate for CleanTech Startups is 3.4 while that for AI Startups is 1.0. By subtracting the Rapid Recovery rate for AI Startups from that for CleanTech Startups (3.4 - 1.0), I can find out how much more the former is.\nBased on the above thought, the answer is:\n2.4"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "pif8CeooNsiK3iVA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please construct a table that summaries the error ratio values for the models."
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I will first note down {the y-ticks}[[0.142, 0.828], [0.142, 0.684], [0.142, 0.540], [0.142, 0.397], [0.142, 0.253],[0.142, 0.109]]. These represent the model's complexity with values 0, 200, 400, 600, 800, and 1000, respectively. Then, I locate {the line points}[[0.180, 0.821], [0.445, 0.792], [0.559, 0.756], [0.625, 0.720], [0.823, 0.469], [0.937, 0.109]] corresponding to each model from the line, which represents the values of Error Ratio: 0.00001, 0.00005, 0.0001, 0.00015, 0.0005, 0.001, respectively. Combining these together, we can construct a table.\nBased on the above thought, the answer is:\nModel Complexity,Error Ratio\n10,0.00001\n50,0.00005\n100,0.0001\n150,0.00015\n500,0.0005\n1000,0.001"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "OQtqolQFOlOeRJOi",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich model shows a significant increase in error ratio as its complexity increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which model has a significant increase in error ratio, I am reviewing the plot and specifically {the line graph for Error Ratio}[0.180, 0.109, 0.937, 0.821]. The trend shows that the error ratio increases significantly from 0.00001 to 0.0005 as we move from M-LVQ-4x10 to M-LVQ-4x500 model. The M-LVQ-4x500 model shows a steep increase in error ratio indicating a significant increase.\nBased on the above thought, the answer is:\nThe M-LVQ-4x500 model shows a significant increase in error ratio as its complexity increases. The trend depicts that as the complexity of this model increases, the error ratio also rises significantly, which might indicate overfitting or a lack of generalization capability for complex scenarios in this model."
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "jGLq2TRzRxXsPjUd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Error Ratio between M-LVQ-4x10 and M-LVQ-4x500 models?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in Error Ratio, I need to look at {the line points for M-LVQ-4x10 and M-LVQ-4x500 models}[[0.180, 0.821], [0.823, 0.469]]. The corresponding values of Error Ratio for M-LVQ-4x10 and M-LVQ-4x500 models are 0.00001 and 0.0005 respectively. The difference will therefore be calculated as 0.0005 - 0.00001 = 0.00049.\nBased on the above thought, the answer is:\n0.00049"
+            }
+        ],
+        "id_raw": 758
+    },
+    {
+        "id": "mKGdEH8X3032l1Uj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the grouped bar chart, can you extract the data into a table format with Methods as columns and Trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first need to identify {the x-axis ticks}[[0.124, 0.864, 0.161, 0.899], [0.276, 0.864, 0.312, 0.899], [0.427, 0.864, 0.463, 0.899], [0.578, 0.864, 0.615, 0.899], [0.730, 0.864, 0.766, 0.899], [0.881, 0.864, 0.918, 0.899] as the row labels or trends. Next, I identify {the legend}[0.157, 0.074, 0.566, 0.560] as the column headers representing the different methods. To populate the table, I locate the points for each trend at the respective methods and extract the values annotated at {the bar tops}.\nBased on the above thought, the answer is:\nTrends,Volatile Growth,Pronounced Increase,Incremental Growth,Dramatic Decrease,Outlier \nMachine Learning,6.2,7.1,6.8,6.1,6.3\nRobotic Process Automation,6.3,7.3,6.9,6.0,7.1"
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "3bfKt5wvLdFvb5AU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which trend showed the least difference between the maximum and minimum score? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend showed the least difference between the maximum and minimum scores, I need to compare the range of scores for each trend. Looking at {the bars for Machine Learning}[[0.294, 0.839, 0.294, 0.839], [0.521, 0.839, 0.521, 0.839], [0.672, 0.839, 0.672, 0.839], [0.824, 0.839, 0.824, 0.839]], the maximum score is 7.1 and the minimum score is 6.1, making a difference of 1.0. For {Robotic Process Automation}[0.369, 0.839, 0.369, 0.839], the maximum score is 7.3 and the minimum score is 6.0, with a difference of 1.3. Hence, Machine Learning has the least difference between the maximum and minimum score.\nBased on the above thought, the answer is:\nAccording to the bar chart, the trend for Machine Learning showed the least difference between the maximum and minimum score. This is identified by comparing the spread of scores under each method for both trends, where Machine Learning exhibits a smaller range of 1.0 compared to the 1.3 for Robotic Process Automation."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "z4ffdEmfhYjv2QJw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_38_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher was the score for Robotic Process Automation than Machine learning in the Pronounced Increase method?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in scores between Robotic Process Automation and Machine Learning for the Pronounced Increase method, I need to locate {the bar for Machine Learning}[0.294, 0.839, 0.294, 0.839] in Pronounced Increase and extract the corresponding value: 7.1. Similarly, I locate {the bar for Robotic Process Automation}[0.369, 0.839, 0.369, 0.839] and extract the corresponding value: 7.3. The difference between the two scores is therefore 7.3 - 7.1 = 0.2.\nBased on the above thought, the answer is:\n0.2."
+            }
+        ],
+        "id_raw": 759
+    },
+    {
+        "id": "t6V0sfbcHv0uRY1n",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the bar chart into a structured table where the rows represent the career path development methods and the columns represent the gradual recovery and dramatic surge?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step in creating a table from the bar chart data is to identify the column heads, corresponding to {the x-axis labels}<r>[0.129, 0.919, 0.151, 0.942],[0.329, 0.919, 0.455, 0.942]</r> that represent Gradual Recovery and Dramatic Surge. Next, we identify the row labels that indicate different career development methods, shown in {the legend}<r>[0.338, 0.090, 0.536, 0.113]</r> and <r>[0.338, 0.125, 0.524, 0.148]</r>. The data within the table is extracted from the corresponding {bar heights}<r>[0.140, 0.268, 0.895, 0.794],[0.140, 0.180, 0.895, 0.736]</r> in each category.\nBased on the above thought, the answer is:\nCareer Path Development,Gradual Recovery,Dramatic Surge\nCareer Coaching,7.1,9.5\nLeadership Training,6.8,8.8\nEmployee Onboarding,6.0,8.0\nPerformance Management,7.0,7.8\nCareer Development,6.5,8.5"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "TT4REcLCwYEdIsZg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which career development method seems to have the highest overall effectiveness across both trends? Justify your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the career development method with the highest effectiveness, I compare the heights of the bars across both trends. Notably, {Career Coaching in Gradual Recovery}<r>[0.170, 0.343, 0.236, 0.676]</r> has a score of 7.1, and {Career Coaching in Dramatic Surge}<r>[0.380, 0.343, 0.446, 0.815]</r> has the highest score of 9.5. By considering the summed scores across both trends, Career Coaching presents the highest cumulative score thus suggesting it as the most effective method for career development according to the chart.\nBased on the above thought, the answer is:\nBased on the bar chart, Career Coaching appears to have the highest overall effectiveness across both Gradual Recovery and Dramatic Surge trends. This is because it boasts the highest cumulative score across these trends, outperforming other career development methods in the Dramatic Surge scenario and being competitive in the Gradual Recovery scenario."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "BvBUKxUladk96mQf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the score of Leadership Training in Gradual Recovery and Dramatic Surge scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to identify {the bar of Leadership Training in the Gradual Recovery}<r>[0.220, 0.343, 0.286, 0.602]</r> which corresponds to a score of 6.8 and {the bar of Leadership Training in the Dramatic Surge}<r>[0.430, 0.343, 0.496, 0.746]</r> which corresponds to a score of 8.8. The difference between these scores is 8.8 - 6.8 = 2.0\nBased on the above thought, the answer is:\n2.0"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "QMSfw2ujRmagdhii",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the grouped bar chart into a structured table with 'Entities' as columns and 'Method' as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with the x-axis tick values, which are {the entities}<r>[0.117, 0.919, 0.192, 0.942], [0.852, 0.919, 0.972, 0.942]</r>. I assess these as the column headers 'Pear_Tree' and 'Log_Saw'. I then identify {the legend texts}<r>[0.771, 0.090, 0.851, 0.113], [0.771, 0.125, 0.929, 0.148]</r> for the row headers representing the different methods. Lastly, from the bars of the chart, I locate {the bar heights}<r>[0.155, 0.253, 0.912, 0.779], [0.155, 0.166, 0.912, 0.867]</r> corresponding to each score for the respective method and entity. I use these values to fill in the table.\nBased on the above thought, the answer is:\nMethod,Pear_Tree,Log_Saw\nClasses,2.0,2.2\nServices,1.8,1.9\nOrganizations,1.5,1.4\nIndividuals,1.2,1.1\nFundraisers,1.0,0.8"
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "0anLA07nE7qtwNSC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which entity exhibits a larger range of scores across different methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the range of scores for both entities by subtracting the lowest score from the highest score for each. Looking at {the bar heights for Pear_Tree}<r>[0.155, 0.253], [0.306, 0.341], [0.458, 0.473], [0.609, 0.604], [0.761, 0.692]</r> and {the bar heights for Log_Saw}<r>[0.155, 0.166], [0.306, 0.297], [0.458, 0.516], [0.609, 0.648], [0.761, 0.779]</r>, I see the scores range from 1.0 to 2.0 for Pear_Tree and from 0.6 to 2.2 for Log_Saw. Therefore, Log_Saw has a larger range of scores (1.6) compared to Pear_Tree (1.0).\nBased on the above thought, the answer is:\nBased on the bar chart, 'Log_Saw' exhibits a larger range of scores across different methods than 'Pear_Tree'. This is determined by comparing the difference between the highest and lowest scores for each entity."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "UnquGJaZPX9LYztK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the gap between the highest and lowest scores for the entity 'Pear_Tree'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To address this, I examine {the bar heights for Pear_Tree}<r>[0.155, 0.253], [0.306, 0.341], [0.458, 0.473], [0.609, 0.604], [0.761, 0.692]</r> which gives us the values: 2.0, 1.8, 1.5, 1.2, 1.0. The highest value is 2.0 under 'Classes' and the lowest is 1.0 under 'Fundraisers'. Therefore, the difference between the highest and lowest scores for 'Pear_Tree' is 2.0 - 1.0 = 1.\nBased on the above thought, the answer is:\n1."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "L2qluQ0g7AW8aYMx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the successful integration percentages of Traditional and Contemporary Techniques in both Cultural Events and Museums?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I observe that {the x-axis labels}<r>[0.057, 0.939, 0.146, 0.957], [0.217, 0.939, 0.316, 0.957]</r>represent the categories Traditional and Contemporary techniques, respectively. Then, looking at the y-axis, I can understand that it represents the score in terms of percentages. I can see {the line points} for Integration at Cultural Events<r>[0.102, 0.897], [0.267, 0.824]</r> and for Integration in Museums<r>[0.432, 0.389], [0.597, 0.461]</r> which correspond to the relevant scores, respectively. Using these points I can form the table.\nBased on the above thought, the answer is:\nTechniques,Integration at Cultural Events,Integration in Museums\nTraditional,89.8,92.1\nContemporary,95.8,87.3"
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "W8PjUq0dJV84H5RP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which technique has shown a better performance in integration at cultural events and museums, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comprehend which technique performs better, I need to contrast their successful integration percentages in both the scenarios. Referring to {the line points for Cultural Events}<r>[0.102, 0.897], [0.267, 0.824]</r> and for Museums<r>[0.432, 0.389], [0.597, 0.461]</r> I observe that for Cultural events the Contemporary technique has a higher value of 95.8 % compared to the Traditional technique which is 89.8 %. However, in the context of Museums, the Traditional technique shows a higher performance of 92.1 % whereas Contemporary technique shows 87.3 %. It appears that the individual performance of the techniques is highly context dependent.\nBased on the above thought, the answer is:\nNeither the Traditional nor the Contemporary technique has shown universally improved performance in both scenarios. In the context of Cultural Events, Contemporary technique performs better, whereas the Traditional technique shows a higher degree of successful integration in Museums. The performance of the techniques seems to be dependent on the specific context they are deployed in, underscoring the importance of context-specific strategies."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "edOWwOV3rfuMTFIK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Traditional and Contemporary techniques' successful integration percentage for Cultural events?"
+            },
+            {
+                "from": "gpt",
+                "value": "I refer to {the line points}<r>[0.102, 0.897], [0.267, 0.824]</r> which represent the successful integration percentages corresponding to the Traditional and Contemporary techniques for Cultural events. The values for these techniques are 89.8 and 95.8 respectively. Hence, the difference would be 95.8 - 89.8  = 6.\nBased on the above thought, the answer is:\n6."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "yqOqO9ZhoJDfXQLV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table containing the scores of career development measures for each trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the bar chart data into a table, the primary step is to understand the X-axis and Y-axis. Noting {the x-axis categories}[[0.155, 0.801, 0.273, 0.833], [0.441, 0.801, 0.574, 0.838]], I observe that the categories are the two different trends - \"Gradual Recovery\" and \"Dramatic Surge\", which should be two columns of my table data. Moving to the Y-axis, I see it represents the scores of each career path under the respective trends. Hence, these become the rows of our table. To populate the values, I locate {the bars}[[0.163, 0.338, 0.229, 0.710], [0.241, 0.338, 0.318, 0.595], [0.318, 0.338, 0.394, 0.700], [0.394, 0.338, 0.470, 0.705], [0.470, 0.338, 0.547, 0.600], [0.450, 0.338, 0.516, 0.845], [0.528, 0.338, 0.604, 0.785], [0.604, 0.338, 0.681, 0.800], [0.681, 0.338, 0.757, 0.788], [0.757, 0.338, 0.833, 0.790]] and extract the values based on their positions with the Y-axis values.\nBased on the above thought, the answer is:\nCareer Development Measures,Gradual Recovery,Dramatic Surge\nCareer Coaching,7.1,9.5\nLeadership Training,6.8,8.3\nEmployee Onboarding,6.0,7.8\nPerformance Management,7.0,8.0\nCareer Development,6.2,7.5"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "jDq4S45KuFopETQ7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich career development measure has shown the most improvement when shifting from a trend of Gradual Recovery to Dramatic Surge?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the career development measure with the greatest improvement when shifting from a Gradual Recovery to a Dramatic Surge, I would compare the difference between scores for all the career pathways from Gradual Recovery to Dramatic Surge. Looking at {the bars in the left group}[[0.163, 0.338, 0.229, 0.710], [0.241, 0.338, 0.318, 0.595], [0.318, 0.338, 0.394, 0.700], [0.394, 0.338, 0.470, 0.705], [0.470, 0.338, 0.547, 0.600]] and {the corresponding bars in the right group}[[0.450, 0.338, 0.516, 0.845], [0.528, 0.338, 0.604, 0.785], [0.604, 0.338, 0.681, 0.800], [0.681, 0.338, 0.757, 0.788], [0.757, 0.338, 0.833, 0.790]], I find that Career Coaching has the highest shift from 7.1 to 9.5, a change of 2.4 units.\nBased on the above thought, the answer is:\nThe career development measure of Career Coaching has shown the biggest improvement when the trend shifts from Gradual Recovery to Dramatic Surge. The score for this category rose from 7.1 to 9.5, indicating a 2.4 points improvement, which is the highest compared to other career pathways."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "XkCtOlXYQQUykCQN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in scores between trends for Performance Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in scores between trends for Performance Management, I should locate the bars specifically for this category. The bar for Gradual Recovery is found at {the second to last position in the left group}[[0.394, 0.338, 0.470, 0.705]], corresponding to a score of 7.0. For Dramatic Surge, its position is found at {the second to last in the right group}[[0.681, 0.338, 0.757, 0.788]], corresponding to a score of 8.0. The difference between the values under two trends could then be found by subtracting 7.0 from 8.0.\nBased on the above thought, the answer is:\n1.0"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "8zL6BzIWNqShsqe4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the chart data in a tabulated format where the performance scores for each Artificial Intelligence Application based on different software are categorized in rows and columns respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "To present the data in a tabular format, I first identify {the x-axis labels}<r>[0.054, 0.919, 0.069, 0.942], [0.229, 0.919, 0.244, 0.942], [0.404, 0.919, 0.419, 0.942], [0.579, 0.919, 0.593, 0.942], [0.753, 0.919, 0.768, 0.942], [0.921, 0.919, 0.950, 0.942], [1.096, 0.919, 1.125, 0.942]</r> as column headers, representing the Years of Development. Next, I view the {sublegend}<r>[0.404, 0.090, 0.473, 0.113], [0.404, 0.125, 0.477, 0.148]</r> to identify the row headers, representing the Sales and Profits. To situate the data, each {line point}<r>[0.149, 0.701], [0.236, 0.613], [0.324, 0.490], [0.411, 0.420], [0.499, 0.402], [0.586, 0.357], [0.673, 0.350], [0.761, 0.280], [0.848, 0.210], [0.936, 0.174]</r> for every application represents the score in the corresponding year.\nBased on the above thought, the answer is:\nYear, Sales, Profits\n1,60,30\n2,85,55\n3,120,75\n4,140,90\n5,145,100\n6,158,110\n7,160,111\n8,180,120\n9,200,150\n10,210,170"
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "JXZudienchH7keuC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInterpreting from the chart figure, could you provide an overview of the performance of the Entertainment & Gaming Industry based on the given chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the performance of the Entertainment & Gaming Industry, I analyze the trend of {the Sales line}<r>[0.149, 0.174, 0.936, 0.701]</r> and {the Profits line}<r>[0.149, 0.315, 0.936, 0.806]</r>. It is observed that both lines show a general upward trend, indicating that Sales and Profits increased over the Years of Development.\nBased on the above thought, the answer is:\nThe Entertainment & Gaming industry has demonstrated a growth pattern over the 10-year development span. Both revenues, specifically Sales and Profits showed a generally increasing trend. While profits started lower than sales at year 1, the growth rate was more pronounced, eventually surpassing sales around the mid-point of the time span. This indicates that the industry experienced improvements in cost-efficiency or economies of scale over time."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "iePb7jRUXr7UhuAs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in Profit over the 10 years period of development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average increase in profits over the 10 years of development, I first locate the {line points for Profits}<r>[0.149, 0.806], [0.236, 0.718], [0.324, 0.648], [0.411, 0.595], [0.499, 0.560], [0.586, 0.525], [0.673, 0.522], [0.761, 0.490], [0.848, 0.420], [0.936, 0.315]</r> and extract the corresponding values: 30, 55, 75, 90, 100, 110, 111, 120, 150, and 170. The increase from one year to another can be calculated as the difference between the current year's profit and the previous year's profit. The average increase can then be calculated as the total increase divided by the number of years, which is 10.\nBased on the above thought, the answer is:\n14."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "8qeBFYFGQrPzJECC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the development scores for rural and urban areas from 2001 to 2010 according to the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I retrieve the data from the line chart by identifying {the x-axis tick labels}<r>[0.117, 0.898, 0.225, 0.938]</r> corresponding to the years from 2001 to 2010. I also examine {the points on the line for Rural Development}<r>[0.156, 0.122], [0.511, 0.297], [0.581, 0.188]</r> and {the points on the line for Urban Development}<r>[0.156, 0.188], [0.511, 0.319], [0.581, 0.210]</r>. I correlate these points with their respective values on the y-axis marked from 30 to 60 to extract numerical values for each year.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2001,32,34\n2002,36,37\n2003,40,39\n2004,42,42\n2005,45,46\n2006,47,48\n2007,55,49\n2008,50,50\n2009,48,52\n2010,45,55"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "L634GHa1qymW9GtL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Urban Development compare to the trend in Rural Development from 2001 to 2010 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the plotted lines for Urban and Rural Development, I analyze {the line for Urban Development}<r>[0.156, 0.188, 0.936, 0.692]</r> which shows a gradual increase, and {the line for Rural Development}<r>[0.156, 0.122, 0.936, 0.613]</r> which indicates a fluctuating progression with a sharp increase around 2007. This visual comparison highlights the distinct differences in growth patterns and development scores between the two areas.\nBased on the above thought, the answer is:\nThe trend in Urban Development shows a steady and gradual growth over the years, while the trend in Rural Development is more erratic with significant fluctuations, especially notable with an abrupt peak in 2007. Overall, Urban Development appears to be more stable and consistent compared to the more volatile pattern of Rural Development."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "0FgaDJ0dCaTPndgI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest peak value for Rural Development according to the chart data, and in which year did it occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest peak value for Rural Development, I observe and evaluate {the line for Rural Development}<r>[0.156, 0.122, 0.936, 0.613]</r> and pinpoint the highest point on the line located at {peak point at 2007}<r>[0.581, 0.188]</r>. Referring to the y-axis tick labels mapped from 30 to 60, I find that this peak corresponds to a development score.\nBased on the above thought, the answer is:\n55 in 2007."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "fpPuL0e9GWUKv6pU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the plotting points from this line chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step to extracting data involves looking at the {x-axis' ticks}[[0.057, 0.919, 0.225, 0.942], [0.849, 0.919, 0.972, 0.942]] which represent the two periods in question: \"Strong Growth\" and \"No Growth\". The two segmented lines: {Sporty}[[0.141, 0.143, 0.910, 0.616]], and {Luxury}[[0.141, 0.379, 0.910, 0.793]] are the rows of the table. These lines' points correspond to the Y-value or Market Share for each segment at the specific periods.\nBased on the above thought, the answer is:\nMarket Segment,Sporty,Luxury\nStrong Growth,1.3,0.9\nNo Growth,0.5,0.2"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "aKLPBRaSOdZbA35p",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which market segment observed a decline in their market share from the \"Strong Growth\" period to the \"No Growth\" period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which market segment experienced a decline, I have to follow the direction of the line segments {for Sporty}[[0.141, 0.143, 0.910, 0.616]], and {for Luxury}[[0.141, 0.379, 0.910, 0.793]]. The line segment for \"Sporty\" starts at 1.3 and ends at 0.5, denoting a decrease in market share, while \"Luxury\" also experiences a decline starting from 0.9 and finishing at 0.2.\nBased on the above thought, the answer is:\nBoth \"Sporty\" and \"Luxury\" market segments observed a decline in their market share from the \"Strong Growth\" period to the \"No Growth\" period."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "aRRkWrhI9dAmR08q",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase from \"Strong Growth\" to \"No Growth\" for the \"Luxury\" market segment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Determining the percentage increase involves locating the respective values for the \"Luxury\" market segment at time \"Strong Growth\" and \"No Growth\". I can do that by investigating the points belonging to {the line representing the \"Luxury\" segment}[[0.141, 0.379, 0.910, 0.793]]. The value at \"Strong Growth\" is 0.9, and at \"No Growth\" is 0.2. Thus, the percentage increase would be calculated as ((Final value - Initial value) / Initial value) *\n100, which is ((0.2 - 0.9) / 0.9) * 100 = -77.77%\nBased on the above thought, the answer is:\n-77.77%"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "LsuSC1iZYSd0PZLJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data points presented in the line chart, using the x-axis labels as rows and Economic Indicators as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form this table, I identify {the x-axis tick labels}[[0.045, 0.919, 0.169, 0.942],[0.337, 0.919, 0.430, 0.942],[0.615, 0.919, 0.705, 0.942],[0.894, 0.919, 0.979, 0.942]] as the row headers. Next, the {legend texts}[[0.836, 0.832, 0.902, 0.855],[0.836, 0.867, 0.909, 0.890]] represent the column labels for Pre-Covid and Post-Covid. I then locate the coordinates of the data points for each Economic Indicator in the Pre-Covid {[0.107, 0.104], [0.384, 0.897], [0.660, 0.893], [0.936, 0.893]} and Post-Covid {[0.107, 0.148], [0.384, 0.904], [0.660, 0.907], [0.936, 0.893]} periods and extract the corresponding values.\nBased on the above thought, the answer is:\nEconomic Indicator,Pre-Covid,Post-Covid\nEmployment Rate,92,87\nInflation Rate,1.6,0.8\nInterest Rate,2,0.5\nGDP Growth,2,2.1"
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "VM61Wq6LUhqsNhyy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends observed in the line chart, which Economic Indicator was most impacted by the transition from the Pre-Covid to the Post-Covid period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most affected Economic Indicator, I must examine the changes in each of the indicators from the Pre-Covid to the Post-Covid period. {Employment [<0.107, 0.104>, <0.107, 0.148>]}, {Inflation rate [<0.384, 0.897>, <0.384, 0.904>]}, {Interest rate [<0.660, 0.893>, <0.660, 0.907>]}, and {GDP Growth [<0.936, 0.893>, <0.936, 0.893>]} respectively represent the changes for each indicator. The trends show that while all indicators were affected by the pandemic, the drop in the Interest rate was the steepest.\nBased on the above thought, the answer is:\nThe Interest Rate was the most impacted Economic Indicator based on the transition from the Pre-Covid to the Post-Covid period. This is evident from the drastic decrease in its value, from 2 to 0.5, representing a more significant drop compared to the other indicators. This suggests that measures were likely taken to decrease interest rates to stimulate economic activity during the pandemic."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "j5Tx2kp2oppTSopc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decline in the Employment Rate from Pre-Covid to Post-Covid period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage decline in the Employment Rate, I need to locate {the data points for Employment Rate} for Pre-Covid {[0.107, 0.104]} which is 92 and Post-Covid {[0.107, 0.148]} which is 87. The percentage change is calculated as (92 - 87) / 92 x 100.\nBased on the above thought, the answer is:\nApproximately 5.43%."
+            }
+        ],
+        "id_raw": 767
+    },
+    {
+        "id": "PLvhahqbr1nvmsAm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the performance details from the line graph in a structured table in the format Event vs Software Performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "To prepare the table, I'll start by looking at {the x-axis tick labels}<r>[0.087, 0.919, 0.222, 0.942], [0.243, 0.919, 0.368, 0.942]</r>, which represent the events. The two different software performances can be represented by {the legend texts}<r>[0.232, 0.832, 0.503, 0.855], [0.232, 0.867, 0.495, 0.890]</r>. I then look at {the line points}<r>[0.155, 0.648], [0.306, 0.297], [0.456, 0.341], [0.607, 0.122], [0.758, 0.166], [0.909, 0.253]</r> and <r>[0.155, 0.473], [0.306, 0.516], [0.456, 0.319], [0.607, 0.385], [0.758, 0.451], [0.909, 0.582]</r> for SoftwareA and SoftwareB respectively and extract the values to fill the table.\nBased on the above thought, the answer is:\nEvent,SoftwareA,SoftwareB\nSharp Decrease,0.7, 0.45\nAbrupt Rise,0.2,0.75"
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "Vx0CmX36PrzyPv39",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the two events analyzed in the chart, which software had a more significant performance change between the events? Please explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which software had the most significant performance change between the two events, I evaluate the difference in values for each one. Examining {the line for SoftwareA}<r>[0.155, 0.648], [0.306, 0.297]</r>, it dropped from 0.7 (sharp decrease event) to 0.2 (abrupt rise event), indicating a decrease of 0.5. On the other hand, inspecting {the line for SoftwareB}<r>[0.155, 0.473], [0.306, 0.516]</r>, it moved from 0.45 (sharp decrease event) to 0.75 (abrupt rise event), indicating an increase of 0.3.\nBased on the above thought, the answer is:\nConsidering the two events analyzed in the chart, SoftwareA had a more significant performance change. While SoftwareB performance increased by 0.3 between the events, SoftwareA performance decreased by 0.5, making the change in SoftwareA more significant."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "iTscP7BjPuzDnb3T",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the performance difference between SoftwareA and SoftwareB during the abrupt rise event?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the performance difference during the abrupt rise event for both software, I need to locate the {point for SoftwareA in the abrupt rise event}<r>[0.306, 0.297]</r> and its corresponding value is 0.2. For SoftwareB, I check {the point in the abrupt rise event}<r>[0.306, 0.516]</r> and extract its value which is 0.75. The difference is then 0.75 - 0.2 = 0.55.\nBased on the above thought, the answer is:\n0.55."
+            }
+        ],
+        "id_raw": 768
+    },
+    {
+        "id": "QvZuHafWzbjkusI0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the raw data for the chart in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from a horizontal stacked bar chart, I observe that careers'represented by {the y-axis tick labels} <r>[0.099, 0.939, 0.137, 0.957],[0.285, 0.939, 0.362, 0.957], [0.486, 0.939, 0.572, 0.957], [0.717, 0.939, 0.752, 0.957], [0.912, 0.939, 0.968, 0.957]</r> are rows of the table. Moreover, phases of 'Early Career' and 'Mid Career'{the legend texts} <r>[0.843, 0.067, 0.956, 0.085], [0.843, 0.094, 0.938, 0.111]</r> would be the columns for each career. Each bar segment, e.g., {the Early Career segment for Tech}<r>[0.118, 0.337]</r> and {the Mid Career segment for Tech}<r>[0.118, 0.544]</r>  represent the values in the corresponding cells of the table.\nBased on the above thought, the answer is:\nCareer,Early Career,Mid Career\nTech,2.3,1.5\nMedicine,1.7,1.3\nEducation,0.9,0.7\nArts,3.0,2.5\nSports,2.7,2.2"
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "RSjMFnimawZVPpIo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the chart, which career field has the least improvement from Early Career to Mid Career?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the chart, I'd locate the lengths of the bar segments representing Early Career Development and Mid Career for all career paths- {length of Early Career segments}<r>[0.118, 0.337, 0.323, 0.492, 0.529, 0.700, 0.735, 0.155, 0.940, 0.233]</r> and {length of Mid Career segments}<r>[0.118, 0.544, 0.323, 0.596, 0.529, 0.752, 0.735, 0.285, 0.940, 0.363]</r>. From the coordinates , all fields indicate an improvement when proceeding from Early Career to Mid Career. However, the field of Arts shows the least improvement as the increase from Early Career Development {3.0}<r>[0.735, 0.155]</r> to Mid Career Development {2.5}<r>[0.735, 0.285]</r> is the smallest compared to all fields.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, the career field of Arts has the least improvement from Early Career to Mid Career. The increment in the Career Development Index from Early Career to Mid Career is the smallest compared to other fields, indicating a slower rate of improvement in this field."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "ShAnGS3JiaYuYJke",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which career field is the index for Early Career Development more than double of Mid Career Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare values for Early Career Development and Mid Career Development for each career field. I can do this by analyzing {the bar segment lengths for Early and Mid Career Development} <r>[0.118, 0.337, 0.735, 0.155, 0.940, 0.233, 0.323, 0.544, 0.735, 0.285, 0.940, 0.363]</r>. The field of Arts satisfies the condition as the index for Early Career Development {3.0}<r>[0.735, 0.285]</r> is more than double of Mid Career Development {2.5}<r>[0.735, 0.155]</r>.\nBased on the above thought, the answer is:\nArts."
+            }
+        ],
+        "id_raw": 769
+    },
+    {
+        "id": "6isd6WgzVTKu1z2K",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this chart based on weekdays and weekends distribution of hours spent on outdoor activities and screen time?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first identify {the x-axis tick labels}[0.313, 0.909, 0.433, 0.944], on the graph which represent the hours spent on activities. Next, I noticed the {activity categories on the y-axis}[0.094,0.188,0.247,0.434] indicating 'Outdoor activities' and 'Screen time'. I can see that both of these activities are segmented into weekdays and weekends as marked by the {bars on the chart itself}[0.325,0.33,0.475,0.56;0.325,0.67,0.475,0.76;0.560,0.33,0.805,0.56;0.560,0.67,0.900,0.76]. Using these mappings from the chart I can extract the data.\nBased on the above thought, the answer is:\nActivities,WeekDays,Weekends\nOutdoor Activities,7,10\nScreen Time,4,7"
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "Wyu4BYJCIHseb8ev",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, can it be inferred that individuals spend more time on Screens during weekends compared to weekdays?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, by looking at the graph, we see that {the bar representing 'Screen Time' during weekdays}[0.325,0.67,0.475,0.76] is shorter than {the bar for 'Screen Time' during weekends}[0.560,0.67,0.900,0.76]. This indicates that individuals spend more time on screens during the weekends than they do on weekdays.\nBased on the above thought, the answer is:\nYes, the chart suggests that individuals tend to spend more time on screens during weekends compared to weekdays."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "Vpg5BxiUmHjmWXGk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Screen time hours on weekdays and weekends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to look at {the \"Screen Time\" bar for weekdays}[0.325,0.67,0.475,0.76] which corresponds to 4 hours and compare it with {the \"Screen Time\" bar for Weekend}[0.560,0.67,0.900,0.76] corresponding to 7 hours. The difference is therefore 7 - 4 = 3.\nBased on the above thought, the answer is:\n3 hours."
+            }
+        ],
+        "id_raw": 770
+    },
+    {
+        "id": "LnqL3RvJ2l3qiFvZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table that lists the online learning tools and the corresponding usage percentages for the EdTech companies A and B?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I first identify {the y-axis tick labels}[[0.08, 0.90, 0.172, 0.933], [0.08, 0.816, 0.160, 0.855], [0.08, 0.726, 0.185, 0.769], [0.08, 0.635, 0.205, 0.679], [0.08, 0.549, 0.167, 0.589], [0.08, 0.459, 0.149, 0.502]] which represent the different online learning tools. Then, I find the {data points for company A}[[0.098, 0.871, 0.392, 0.905], [0.098, 0.791, 0.273, 0.818], [0.098, 0.702, 0.239, 0.732], [0.098, 0.608, 0.233, 0.639], [0.098, 0.524, 0.203, 0.555], [0.098, 0.434, 0.173, 0.465]] and the {data points for company B}[[0.392, 0.871, 0.801, 0.905], [0.273, 0.791, 0.562, 0.818], [0.239, 0.702, 0.531, 0.732], [0.233, 0.608, 0.518, 0.639], [0.203, 0.524, 0.578, 0.555], [0.173, 0.434, 0.578, 0.465]] in the chart. The lengths of these data points correspond to the usage percentages of the learning tools for the respective companies.\nBased on the above thought, the answer is:\nOnline Learning Tools,Company A,Company B\nWebinars,29.4%,40.9%\nOnline Quizzes,17.5%,28.9%\nVideo Lectures,14.1%, 29.2%\nForum Discussions,13.5%,28.5%\nOnline Assignments,10.5%, 37.5%\nVirtual Labs,7.5%, 40.5%"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "e4cbgnInpa2C5adF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich EdTech company appears to be more diversified in its use of online learning tools based on the distribution of usage across different tools?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine {the data bars for the tools for company A} [0.098, 0.871, 0.392, 0.905], [0.098, 0.791, 0.273, 0.818], [0.098, 0.702, 0.239, 0.732], [0.098, 0.608, 0.233, 0.639], [0.098, 0.524, 0.203, 0.555], [0.098, 0.434, 0.173, 0.465] and {company B} [0.392, 0.871, 0.801, 0.905], [0.273, 0.791, 0.562, 0.818], [0.239, 0.702, 0.531, 0.732], [0.233, 0.608, 0.518, 0.639], [0.203, 0.524, 0.578, 0.555], [0.173, 0.434, 0.578, 0.465]. With less noticeable disparity in the usage percentages of different tools, company B seems to be more diversified in the use of online learning tools.\nBased on the above thought, the answer is:\nEdTech company B appears to be more diversified in its use of online learning tools. The usage percentages across different tools are relatively more evenly distributed for company B compared to company A, which shows a larger range in usage percentages suggesting less balanced utilization of the different online learning tools."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "enz082eSIEv0WcaS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in usage of Forum Discussions for EdTech companies A and B?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference in usage of Forum Discussions, I need to locate the {Corresponding data bar for company A} [0.098, 0.608, 0.233, 0.639] and similarly, the {data bar for company B} [0.233, 0.608, 0.518, 0.639]. The lengths of these bars give the percentage of usage for the companies. The percentage difference is calculated as the absolute difference between these percentages.\nBased on the above thought, the answer is:\n15.0%."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "NL6tQ7gzcqAq1VAS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a structured format, with the Tools as rows and Baseline & Optimization as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the chart, I first identify {the x-axis tick labels}[[0.084, 0.919, 0.198, 0.942], [0.276, 0.919, 0.392, 0.942], [0.472, 0.919, 0.582, 0.942], [0.662, 0.919, 0.779, 0.942], [0.855, 0.919, 0.972, 0.942]] as the row labels, which represent different tools. Next, I see that {the legend texts}[[0.214, 0.090, 0.312, 0.113], [0.214, 0.125, 0.362, 0.148]] are the column headers indicating the Baseline and Optimization respectively. I then locate the {points of each line}[[0.141, 0.341, 0.913, 0.560], [0.141, 0.166, 0.913, 0.473]] for each tool and extract the corresponding efficiencies.\nBased on the above thought, the answer is:\nTool,Baseline,Optimization\nSuperDrill,0.65,0.72\nHyperDrill,0.55,0.63\nMegaDrill,0.8,0.85\nEconoDrill,0.4,0.5\nPowerDrill,0.7,0.78"
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "4dS0wWMxNyjrCsqR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich tool has shown the most improvement after the optimization process according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which tool has made the most improvement, I compare the differences between the baseline and optimized efficiencies for each tool. Looking at the {difference for SuperDrill}[[0.141, 0.341, 0.141, 0.280]], the improvement is 0.07. For {HyperDrill}[[0.334, 0.429, 0.334, 0.359]], the improvement is 0.08. Observing {MegaDrill}[[0.527, 0.210, 0.527, 0.166]], the improvement is 0.05. For {EconoDrill}[[0.720, 0.560, 0.720, 0.473]], the improvement is 0.1. Finally, for {PowerDrill}[[0.913, 0.297, 0.913, 0.227]], the improvement is 0.08.\nBased on the above thought, the answer is:\nBased on the chart, it appears that EconoDrill has shown the most improvement after the optimization process. This is indicated by the largest difference in efficiency levels between the baseline and after the optimization. The improvement for EconoDrill is 0.1, which is higher than the improvements observed for the other tools."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "qVSairjq4P34j4hO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the efficiency level difference for SuperDrill between its baseline and after optimization?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to figure out the baseline and optimization efficiency levels for SuperDrill. I find its baseline value at {the point for SuperDrill on the blue line}[[0.141, 0.341]]. The baseline efficiency of SuperDrill is 0.65. Now I locate its optimization value at {the point for SuperDrill on the green line}[[0.141, 0.280]]. The optimized efficiency of SuperDrill is 0.72. So, the efficiency level difference can be calculated as 0.72 - 0.65 = 0.07.\nBased on the above thought, the answer is:\n0.07."
+            }
+        ],
+        "id_raw": 772
+    },
+    {
+        "id": "Btdyw2quIWzXh1km",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table summarizing the problems encountered during content creation for YouTube and TikTok as seen in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I first note that {the x-axis tick labels}<r>[0.044, 0.919, 0.196, 0.942], [0.317, 0.919, 0.444, 0.942], [0.588, 0.919, 0.696, 0.942], [0.836, 0.919, 0.971, 0.942]</r> represent the row labels, which are the different problems encountered during content creation. Then, I observe that {the legend items}<r>[0.166, 0.832, 0.282, 0.855],[0.166, 0.867, 0.267, 0.890]</r> correspond to YouTube and TikTok, which serve as the column headers. To populate the values, I locate the points from {the line for YouTube}<r>[0.120, 0.122, 0.903, 0.455]</r> and {the line for TikTok}<r>[0.120, 0.613, 0.903, 0.823]</r>, and then extract the corresponding percentages.\nBased on the above thought, the answer is:\nProblem,YouTube,TikTok\nSuccess,26,35\nVideo Quality,25,30\nSound Quality,25,28\nEditing Error,28,23\nSlow Uploads,28,22\nCommunity Guidelines,26,15"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "i1J4UyxdByziWIld",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which problem shows the largest difference in prevalence between YouTube and TikTok content creators?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the problem with the largest difference in prevalence, I need to compare the gaps between the {YouTube line points}<r>[0.120, 0.122, 0.903, 0.455]</r> and the {TikTok line points}<r>[0.120, 0.613, 0.903, 0.823]</r> for each problem. The problem corresponding to the largest gap between the two line points would be the one with the greatest difference in prevalence between the two platforms.\nBased on the above thought, the answer is:\nBased on the plot, the problem with the greatest difference in prevalence between YouTube and TikTok content creators is community guidelines violations. The prevalence of this problem is significantly higher on YouTube than on TikTok."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "Kk0H3vz2Oa7tAOCG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in encountering success from YouTube to TikTok content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the increase in success in content creation from YouTube to TikTok, I need to note the values at the {success point for YouTube}<r>[0.120, 0.455]</r>, which is 26% and at the {success point for TikTok}<r>[0.120, 0.718]</r>, which is 35%. The increase would be (35-26)/26 * 100 =34.62 approximately.\nBased on the above thought, the answer is:\n34.62%."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "bLKa1jnNWFDyXnc9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data in the line chart, can you convert it into a table format with the Channels as columns and Industry Categories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I first pinpoint {the x-axis labels}<r>[0.055, 0.919, 0.972, 0.942]</r> which signify the columns, namely 'Search_Engine' and 'Social_Media'. The row labels are represented by {the sublegend items}<r>[0.188, 0.832, 0.363, 0.890]</r>, 'New Creation' and 'Digital Content'. To fill in the values, I recognize {the line points for 'New Creation'}<r>[0.140, 0.239, 0.897, 0.356]</r> and 'Digital Content'<r>[0.140, 0.297, 0.897, 0.472]</r> for corresponding channels and extract the respective values.\nBased on the above thought, the answer is:\nIndustry,Search_Engine,Social_Media\nNew_Creation,2.3,1.9\nDigital_Content,1.5,2.1"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "rNvMuRM46I8t5zvn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the line chart trends, which channel exhibits a better performance score across the two industries evaluated? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze which channel performs better, I need to examine the performance scores for both industries across the two channels. Observing {the line for 'New Creation'}<r>[0.140, 0.239, 0.897, 0.356]</r>, I observe the scores for 'Search_Engine' is 2.3 and for 'Social_Media' is 1.9. Reviewing {the line for 'Digital Content'}<r>[0.140, 0.297, 0.897, 0.472]</r>, I see the scores for 'Search_Engine' is 1.5 and for 'Social_Media' is 2.1. By summing the performance scores for each channel, 'Search_Engine' has a total of 3.8 and 'Social_Media' has a total of 4, clearly outperforming 'Search_Engine'.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, 'Social_Media' exhibits a higher overall performance score compared to 'Search_Engine' across the two industries evaluated. By summing up the individual performance scores, we can see that 'Social_Media' outperforms 'Search_Engine' with a total performance score of 4 against 3.8."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "E07H1m70nGw2Elwt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in performance scores between Search_Engine and Social_Media for the New Creation industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to locate the performance scores for the 'New Creation' industry in both 'Search_Engine' and 'Social_Media'. I identify {the point for 'New Creation' channel 'Search_Engine'}<r>[0.140, 0.239]</r> and extract its value, 2.3. Likewise, I locate {the point for 'New Creation' channel 'Social_Media'}<r>[0.897, 0.356]</r> and extract its representative value, 1.9. The difference is then 2.3 - 1.9 = 0.4.\nBased on the above thought, the answer is:\n0.4."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "t3tgPZQIho1J9QAB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, can you help to format the raw data into a table with Years as column headers and Technologies as row labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the necessary data from the chart, I first identify {the x-axis labels}<r>[0.112, 0.939, 0.925, 0.957]</r> on the horizontal axis representing the years from 2010 to 2020, which will be our column headers. Next, I locate {the legend labels}<r>[0.716, 0.067, 0.837, 0.085, 0.716, 0.094, 0.840, 0.111]</r> that signify the two different technologies 'AI Ventures' and 'Quantum Computing Co.', constituting our row labels. For the corresponding values, I look at {the line points for AI Ventures and Quantum Computing Co.}<r>[0.112, 0.135, 0.925, 0.588, 0.112, 0.361, 0.925, 0.743]</r> and extract the respective values for each technology per year.\nBased on the above thought, the answer is:\nTechnologies,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nAI Ventures,8.8,7.2,4.5,3.8,5.4,8.3,6.8,4.2,3.5,5.0,9.3\nQuantum Computing Co.,4.5,6.3,2.1,6.0,3.2,4.0,5.9,1.8,5.7,2.8,5.0"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "J5hZUG1ZMLKNX1wA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line charts, which technology - 'AI Ventures' or 'Quantum Computing Co.' - would be a safer investment over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I am looking at the nature of the fluctuations in the return rates of both technologies. For {AI Ventures}<r>[0.112, 0.135, 0.925, 0.588]</r>, the line graph reflects large and erratic fluctuations in return rates indicating higher risks and uncertainty\n. In contrast, the line chart for {Quantum Computing Co.}<r>[0.112, 0.361, 0.925, 0.743]</r> shows a relatively steady increase with marginal fluctuations in the return rates, indicating a more predictable and stable investment. Therefore, Quantum Computing Co. presents a safer investment option compared to AI Ventures.\nBased on the above thought, the answer is:\nBased on the trends shown in the line charts, Quantum Computing Co. seems to be the safer investment over the years. Its line chart indicates a more steady and consistent growth in return rate with minimal volatility compared to AI Ventures which has larger and erratic variations in the return rates, indicating a riskier investment."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "lXn5O86nEgzdDEIT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more was the highest annual investment return rate of 'AI Ventures' compared to that of 'Quantum Computing Co.'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the required answer, I need to identify the peak points in the line plots of both technologies. For 'AI Ventures', {the line points}<r>[0.112, 0.135, 0.925, 0.588]</r> indicate the highest return rate to be 9.3 in 2020. For 'Quantum Computing Co.', the {line points}<r>[0.112, 0.361, 0.925, 0.743]</r> show the highest return rate as 6.3 in 2014. The difference in the highest annual investment return rates between both technologies can be calculated as 9.3 (AI Ventures) - 6.3 (Quantum Computing Co.) = 3.0.\nBased on the above thought, the answer is:\n3.0"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "8uqAmNlBJXDCy4dr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data from the line chart into a table organized by trends with columns being different education and training technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, the first step is to identify the x-axis labels which are {the x-axis tick labels}<r>[0.068, 0.919, 0.237, 0.942], [0.746, 0.919, 0.975, 0.942]</r> that give us the different trends in technology adoption rate. The legend has two items, {the legend text}<r>[0.527, 0.090, 0.680, 0.113], [0.527, 0.125, 0.756, 0.148]</r>, which is representing the column headers - different technologies under consideration. For cell values, I look at the coordinates of {the line points of the first technology}<r>[0.152, 0.210], [0.861, 0.604]</r> and {the line points of the second technology}<r>[0.152, 0.385], [0.861, 0.736]</r> which are correspondent to the Innovation/Adoption Rate for each trend across both technologies.\nBased on the above thought, the answer is:\nTrends,AI Innovation,E-Learning Platform\nAbrupt decline,1.6,1.2\nAccelerated decline,0.7,0.4"
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "x5bEjCuSSaL2oquP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the Innovation/Adoption Rate of AI Innovation and E-Learning Platform across the different trends, which Technology shows a drastic performance difference between the two trends? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the Innovation/Adoption Rate of AI Innovation and E-Learning Platform across different trends. I can see that for {AI Innovation}<r>[0.152, 0.210], [0.861, 0.604]</r>, the Innovation/Adoption Rate drastically decreases from 1.6 in Abrupt decline to 0.7 in Accelerated decline. When looking at the same trend changes for {E-Learning Platform}<r>[0.152, 0.385], [0.861, 0.736]</r>, the Innovation/Adoption Rate also decreases, but not as drastically, going from 1.2 in Abrupt decline to 0.4 in Accelerated decline. Though both technologies see a decline in their adoption rates, the rate of decline is more significant for AI Innovation than for the E-Learning Platform.\nBased on the above thought, the answer is:\nAs per the chart data, both AI Innovation and E-Learning Platform have seen a drop in their Innovation/Adoption Rates from Abrupt decline to Accelerated decline. However, AI Innovation experienced a more drastic decline bringing it from 1.6 to 0.7. This is a performance decrease of nearly 56%, compared to the E-Learning Platform which saw a drop from 1.2 to 0.4, representing a decrease of approximately 67%. In absolute terms, the change in Innovation/Adoption Rate was more drastic for the AI Innovation."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "NJZ3DuU93gRJrltk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Adoption Rate between AI Innovation and E-Learning Platform during the Abrupt decline?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in Adoption Rate between AI Innovation and E-Learning Platform during the Abrupt decline phase, I have to identify the Adoption values for each of them. I find the point representing AI Innovation during the Abrupt decline: {the AI Innovation Abrupt decline point}<r>[0.152, 0.210]</r>, and read its Adoption Rate as 1.6. Then I locate {the E-Learning Platform Abrupt decline point}<r>[0.152, 0.385]</r> which has an Adoption Rate of 1.2. So the difference would be 1.6 - 1.2 = 0.4.\nBased on the above thought, the answer is:\n0.4."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "2JAgcmnFdmj5eSPT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information from the bar chart, could you extract the performance metrics of TensorTurbo and DeepLearn007 into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the bar chart information, I first identify {the y-axis tick labels}[[0.061, 0.789, 0.141, 0.866], [0.070, 0.478, 0.138, 0.555]] which represent the rows or AI systems: TensorTurbo and DeepLearn007. I then use the metrics {success rate}<r>[0.541, 0.184, 0.714, 0.384]</r>, {Precision error}<r>[0.716, 0.184, 0.802, 0.384]</r>, {Recall error}<r>[0.807, 0.184, 0.866, 0.384]</r>, {Bias error}<r>[0.873, 0.184, 0.926, 0.384]</r>, {Variance error}<r>[0.932, 0.184, 0.996, 0.384]</r> and {Overfitting}<r>[0.541, 0.623, 0.856, 0.823]</r> as the column headers. Based on the proportional length of each segment in the bars for the two AI systems, I extract the corresponding values.\nBased on the above thought, the answer is:\nAI System,Success Rate,Precision Error,Recall Error,Bias Error,Variance Error, Overfitting\nTensorTurbo,40,20,5,15,10,10\nDeepLearn007,60,10,7,5,15,3"
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "rmvZ8qlcT8anSqkc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween the two AI systems, TensorTurbo and DeepLearn007, which one performs better overall and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which AI system performs better, I need to analyze the performance metrics. Looking at {TensorTurbo's bar}<r>[0.176, 0.184, 0.996, 0.384]</r>, I see that it has a total success rate of 40% with relatively high errors varying from 5% to 20%. Conversely, looking at {DeepLearn007's bar}<r>[0.176, 0.623, 0.856, 0.823]</r>, it has a higher success rate of 60% and the errors are mostly less than 10%, with the exception of Variance error at 15%. This implies that DeepLearn007 has a better performance.\nBased on the above thought, the answer is:\nDeepLearn007 performs better overall. It has a higher success rate of 60% compared to TensorTurbo's 40%. Also, the errors in DeepLearn007 are mostly less than those in TensorTurbo, implying a more consistent and reliable performance from DeepLearn007."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "9EtDzZLSxlqpZIkX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total error rate for TensorTurbo?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total error rate for TensorTurbo, I need to sum up the values of all the errors: {Precision error}<r>[0.716, 0.184, 0.802, 0.384]</r> (20%), {Recall error}<r>[0.807, 0.184, 0.866, 0.384]</r> (5%), {Bias error}<r>[0.873, 0.184, 0.926, 0.384]</r> (15%), {Variance error}<r>[0.932, 0.184, 0.996, 0.384]</r> (10%), and {Overfitting}<r>[0.541, 0.184, 0.714, 0.384]</r> (10%). Adding them all together, 20% + 5% + 15% + 10% + 10% = 60%.\nBased on the above thought, the answer is:\n60%."
+            }
+        ],
+        "id_raw": 778
+    },
+    {
+        "id": "VUwJuM5ersTbgClp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data from the bar chart into a table format distinguishing between Urban and Rural population trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the horizontal bar chart, I identify two main categories: {Urban Population}<r>[0.142, 0.567, 0.935, 0.785]</r> at the top, predominantly green, and {Rural Population}<r>[0.142, 0.801, 0.935, 0.960]</r> at the bottom, primarily grey. Each bar is divided showing steep increases and marginal declines. From each section of the bars, I can note the percentage values within and compile them into a structured format.\nBased on the above thought, the answer is:\nCategory,Steep Increase (%),Marginal Decline (%)\nUrban Population,60,40\nRural Population,30,70"
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "oiN8DZPqnObmxCia",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the trend in the bar chart suggest an overall growth or decline for the populations represented? Support your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trends represented in the bar chart, {Urban Population}<r>[0.142, 0.567, 0.935, 0.785]</r> shows a major portion of steep increase, indicating substantial growth. Conversely, {Rural Population}<r>[0.142, 0.801, 0.935, 0.960]</r>, with its predominant marginal decline, indicates a decrease. Overall, the visual representation suggests a shift towards urban growth at the expense of rural decline, reflective of an overall redistribution rather than a uniform trend across both categories.\nBased on the above thought, the answer is:\nThe trend suggests overall urban growth paired with rural decline, indicating a population shift from rural to urban areas as depicted by the comparative sizes of the steep increase in urban and the decline in rural sections of the bars in the chart."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "3S2NtlhAhKSKIISI",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_39_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage represented by the steep increases for both Urban and Rural populations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage of steep increases, I will sum the individual percentages annotated for steep increases from both categories. Within the chart, {Urban Population's steep increase}<r>[0.142, 0.567, 0.700, 0.785]</r> is annotated as 60%, and {Rural Population's steep increase}<r>[0.142, 0.801, 0.450, 0.960]</r> is noted at 30%. Adding these values together gives a total.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "PWNLZFZR5PRaqYas",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from the line chart with Years as columns and Trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "The table generation from the line chart includes the identification of {the x-axis tick labels}<r>[0.095, 0.919, 0.130, 0.942], [0.214, 0.919, 0.249, 0.942], [0.332, 0.919, 0.367, 0.942], [0.451, 0.919, 0.486, 0.942], [0.570, 0.919, 0.605, 0.942], [0.688, 0.919, 0.724, 0.942], [0.807, 0.919, 0.842, 0.942], [0.926, 0.919, 0.961, 0.942]</r> forming the column headers representing the Years. The {legend texts}<r>[0.858, 0.393, 0.952, 0.415], [0.858, 0.425, 0.952, 0.446], [0.858, 0.456, 0.938, 0.478], [0.858, 0.487, 0.975, 0.509], [0.858, 0.519, 0.947, 0.540], [0.858, 0.550, 0.973, 0.572]</r>  provide the row labels representing the different trends. The {line points}<r>[0.112, 0.825, 0.943, 0.865], [0.112, 0.200, 0.943, 0.665], [0.112, 0.200, 0.943, 0.599], [0.112, 0.233, 0.943, 0.699], [0.112, 0.100, 0.943, 0.333], [0.112, 0.300, 0.943, 0.532]</r> associated with each trend across the years stand as the cell values in the table.\nBased on the above thought, the answer is:\nTrends,2030,2040,2050,2060,2070,2080,2090,2100\nMinimal Change,100,101,102,102,103,104,105,106\nGradual Decline,200,190,180,170,160,150,140,130\nUpward Trend,140,145,150,160,170,180,190,200\nAccelerated Decline,195,185,175,165,155,145,135,125\nInexorable Rise,180,185,190,195,200,205,210,215\nSubdued Expansion,150,155,160,165,170,175,180,185"
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "cnvoQ5hgJHArOvxa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend line shows a consistent increase over the years, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The task is to identify a trend line that consistently increases over time. Observing the line chart, I check each line's slope, moving from left to right. Among the trends, the line for {Inexorable Rise}<r>[0.112, 0.100, 0.943, 0.333]</r> shows a consistent increase over the years without any decline. Hence, \"Inexorable Rise\" indicates a constant increase throughout the observed years.\nBased on the above thought, the answer is:\nThe trend line for \"Inexorable Rise\" shows a consistent increase over the years, as it is gradually moving upwards in the chart from 2030 to 2100 without any decline."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "DPwdHlR1Kpr8HKPD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the trend for \"Accelerated Decline\" drop from 2030 to 2100 based on the data from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the drop in \"Accelerated Decline\" from 2030 to 2100, I need to look at the {line points for this trend}<r>[0.112, 0.233, 0.943, 0.699]</r>. The value for 2030 is 195, and the value for 2100 is 125, based on the points at these years. So, the drop is calculated as 195-125.\nBased on the above thought, the answer is:\n70."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "9pOTrNVNiiWygRlJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you convert the data visualized in the line chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart data, I'd start by identifying {the x-axis tick labels}[[0.225, 0.869, 0.283, 0.904], [0.369, 0.869, 0.427, 0.904], [0.513, 0.869, 0.572, 0.904], [0.657, 0.869, 0.716, 0.904], [0.802, 0.869, 0.860, 0.904]] as the row labels, representing the years. To generate the column headers or technologies, I look at {the legend texts}[[0.724, 0.564, 0.767, 0.609], [0.724, 0.630, 0.926, 0.675], [0.724, 0.696, 0.950, 0.741], [0.724, 0.762, 0.921, 0.807]]. Finally, the actual values are found at the points where the Technologies cross over to a specific year {the line points}[[0.254, 0.083, 0.831, 0.527], [0.254, 0.368, 0.831, 0.765], [0.254, 0.400, 0.831, 0.606], [0.254, 0.178, 0.831, 0.448]]\nBased on the above thought, the answer is:\nYears, IVF, Deep Learning, Nanotechnology, Biotech\n2000, 20.0, 5.0, 15.0, 25.0\n2005, 27.0, 9.0, 20.0, 29.0\n2010, 35.0, 14.0, 23.0, 30.0\n2015, 42.0, 22.0, 25.0, 38.0\n2020, 48.0, 30.0, 28.0, 42.0"
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "tkY1uleEERa5L229",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends depicted in the line chart, which field appears to be receiving increasing attention based on the number of published papers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret which field seems to be gaining increased attention, I examine the rate of growth in the number of published papers for each field over time. Looking at the lines, {IVF}[[0.254, 0.083, 0.831, 0.527]] increases slightly from 20 to 48 over 20 years. {Deep Learning}[[0.254, 0.368, 0.831, 0.765]] shows a steeper increase, going from 5 to 30. {Nanotechnology}[[0.254, 0.400, 0.831, 0.606]] shows a steady but moderate increase from 15 to 28, while {Biotechnology}[[0.254, 0.178, 0.831, 0.448]] demonstrates a substantial increase from 25 to 42.\nBased on the above thought, the answer is:\nBased on the line chart, the field \"Deep Learning\" appears to be receiving the most increased attention. It shows the steepest increase in the number of published papers over time, starting from a count of 5 in 2000 and rising to 30 in 2020. This suggests a growing interest and investment in this research field."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "uyFafiTtdbPjA7Qa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of publications across all years for the research field 'Deep Learning'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the average number of 'Deep Learning' publications over the specified years. I would first locate the {line points for 'Deep Learning'}[[0.254, 0.765], [0.398, 0.702], [0.542, 0.622], [0.687, 0.495], [0.831, 0.368]], and extract the corresponding values: 5.0, 9.0, 14.0, 22.0, 30.0. I would add these values to get the total (80), and then divide by the number of years (5) to get the average: 80 / 5 = 16.\nBased on the above thought, the answer is:\n16."
+            }
+        ],
+        "id_raw": 781
+    },
+    {
+        "id": "Jt05CTn0bSdhsfvC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data from the line chart into a table with 'Year' as row entries and all the different categories as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to convert the line chart into a tabular format, I'm referencing the {x-axis}<r>[0.508, 0.959, 0.552, 0.981]</r> labels to form the row entries which are 'Years'. The column entries are defined by {the legend}<r>[0.083, 0.036, 0.361, 0.174]</r> which lists different categories such as \u2018Game Releases\u2019, \u2018Game Sales (in Millions)\u2019, \u2018New Gaming Platforms\u2019, and \u2018New Gaming Technologies\u2019. The corresponding values for each category per year can be derived from the lines coordinates like {the line for Game Releases}<r>[0.151, 0.108, 0.909, 0.356]</r>.\nBased on the above thought, the answer is:\nYear,Game Releases,Game Sales (in Millions),New Gaming Platforms,New Gaming Technologies\n2010,158,22.5,3,2\n2011,162,25.1,2,2\n2012,168,28.9,2,3\n2013,175,31.5,1,3\n2014,183,35.2,1,4\n2015,191,38.8,0,4\n2016,198,41.2,1,5\n2017,205,43.6,1,4\n2018,213,46.7,2,5\n2019,220,49.3,1,6\n2020,227,52.2,1,7"
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "qfGtRdU9vZCexGV0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data trends shown in the line chart, which category has seen the most significant growth over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand which category experienced the most significant growth, I have to compare the starting and ending points of each category. For instance, {Game Releases line points}<r>[0.151, 0.356] -> [0.909, 0.108]</r> depict an increase from 158 to 227. On the other hand, {Game Sales line points}<r>[0.151, 0.841] -> [0.909, 0.735]</r> reflect an increase from 22.5 to 52.2. Similar analysis can be done for the other lines.\nBased on the above thought, the answer is:\nBased on the given trends, 'Game Sales (in Millions)' has experienced the most significant growth over the years. This category started at 22.5 in 2010 and ended at 52.2 in 2020 which is a substantial increase compared to other categories."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "Rng6Rd1vrlDJnEJH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the number of Game Releases from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in the number of game releases, I need to compare {the point for 2010 in Game Releases}<r>[0.151, 0.356]</r> which corresponds to 158 and {the point for 2020 in Game Releases}<r>[0.909, 0.108]</r> which corresponds to 227. The total increase is then 227 - 158.\nBased on the above thought, the answer is:\n69"
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "FmkUnr3hI3JG2sSl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, are you capable of structuring the provided data into a tabulated form that identifies each Economic Sector as the row headers and their respective annual revenue as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transpose this visual data into a table, it is crucial to first, determine the column headers which have been represented as {x-axis tick labels}[[0.111, 0.912, 0.146, 0.936], [0.201, 0.912, 0.236, 0.936], ..., [0.922, 0.912, 0.958, 0.936]]. Moving on, I identify the {legend texts}[[0.145, 0.089, 0.318, 0.119], [0.145, 0.133, 0.320, 0.163], ..., [0.145, 0.221, 0.293, 0.251]] as the row headers that symbolize different Economic Sectors. Lastly, to populate the values, I look for the {line points}[[0.129, 0.493, 0.940, 0.829], [0.129, 0.118, 0.940, 0.614], ..., [0.129, 0.091, 0.940, 0.859]], recorded as the annual revenue for each Economic Sector and extract the respective numbers.\nBased on the above thought, the answer is:\nYear,Knowledge Economy,Innovation Industries,Smart Manufacturing,Internet Economy\n2000,675,825,310,370\n2002,542,876,390,520\n2004,425,925,450,625\n2006,522,954,550,800\n2008,600,1062,650,950\n2010,660,1150,745,1143\n2012,784,1290,840,1300\n2014,900,1450,910,1500\n2016,965,1600,1050,1650\n2018,1050,1750,1200,1800"
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "qH8uMQpr695zjz2l",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trends depicted in the line chart, which Economic Sector seems to have experienced the most substantial growth over the years 2000 - 2018? Please elaborate on your thought process."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the Economic Sector that demonstrates the most considerable growth, it is essential to scrutinize the slope of each line on the plot. Observing the line for the {Knowledge Economy}[[0.129, 0.493, 0.940, 0.829]], it portrays an initial decline followed by a steady rise. For {Innovation Industries}[[0.129, 0.118, 0.940, 0.614]], the line shows a consistent uptrend with few fluctuations. {Smart Manufacturing}[[0.129, 0.413, 0.940, 0.891]] also displays a consistent increase but at a slower pace. Lastly, looking at {Internet Economy}[[0.129, 0.091, 0.940, 0.859]], the line plot indicates a steady spike in revenue growth over the years, with a steeper slope compared to the other sectors.\nBased on the above thought, the answer is:\nExamining the trends highlighted in the line chart, the Internet Economy seems to have experienced the most substantial growth over the years 2000 - 2018. The slope of its line plot is steeper in comparison to the plots of the other Economic Sectors, indicating a faster growth rate. This analysis is indicative of hefty revenue generation by the Internet Economy sector over the years."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "Xx963UkVJDWT67YK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you estimate the total revenue accumulated by the Smart Manufacturing sector from 2000 to 2018 based on the line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total revenue for the Smart Manufacturing sector over these years, it's important to first, locate all its corresponding {data points}[[0.129, 0.891], [0.219, 0.848], ..., [0.940, 0.413]]. Next, I extract the assigned values from these points, which are broken down as follows: 310, 390, 450, 550, 650, 745, 840, 910, 1050, 1200. Adding these up will derive the total revenue for Smart Manufacturing from 2000 to 2018.\nBased on the above thought, the answer is:\n6695 billion dollars."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "s6AlsNjldGz9Go5e",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the bar chart into a table format with processes and their respective success rates?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the bar chart, I first look at {the y-axis tick labels}[[0.685, 0.058, 0.780, 0.075], [0.569, 0.058, 0.632, 0.075], [0.414, 0.058, 0.487, 0.075], [0.298, 0.058, 0.386, 0.075], [0.168, 0.058, 0.258, 0.075]], which represent the row headers or different processes. Next, I locate and measure {the length of the bars}[[0.092, 0.772, 0.972, 0.808], [0.092, 0.652, 0.942, 0.688], [0.092, 0.532, 0.922, 0.568], [0.092, 0.412, 0.892, 0.448], [0.092, 0.292, 0.882, 0.328]] which corresponds to the success rate for each process.\nBased on the above thought, the answer is:\nProcess,Success Rate\nFinancial auditing,0.98\nSupply chain management,0.95\nCustomer relationship management,0.92\nRisk management,0.90\nProduct development,0.88"
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "pGPAtMoPEkfktSA0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the organization's internal processes represented in the chart, which process should the management potentially look into improving upon, based on its success rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the process that management might want to improve upon, I would look for the process with the shortest bar, representing the lowest success rate. Locating {the bar}[0.092, 0.292, 0.882, 0.328] I identify Product development as the process with the lowest success rate.\nBased on the above thought, the answer is:\nBased on the success rates indicated, the management should potentially look into improving upon the 'Product development' process, as it has the lowest success rate among the represented internal processes."
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "xEDo8e6ZOkchyOpU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in success rate between the highest and the lowest performing process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the success rates of the highest and lowest performing process, I need to locate {the bar for Financial auditing}[[0.092, 0.772, 0.972, 0.808]] and extract the corresponding value which is 0.98. Similarly, I locate {the bar for Product development}[[0.092, 0.292, 0.882, 0.328]] and see that the success rate is 0.88. The difference between the highest and lowest success rate would be 0.98 - 0.88.\nBased on the above thought, the answer is:\n0.1"
+            }
+        ],
+        "id_raw": 786
+    },
+    {
+        "id": "jEt5seduxM3uSWJZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented in the line chart to a table, with Years as the rows and the types being Income, Expenses, Risk_Controller, and Outliers as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. To create the table, I'll first identify {the x-axis}[0.123, 0.869, 0.975, 0.904] as representing the Years, which will serve as rows in the table. For the column headers, I'll use the labels in {the legend}[0.257, 0.564, 0.476, 0.823]. Next, I will collect the corresponding values for each of these categories by observing the coordinates of line points for {Income}<r>[0.154, 0.165, 0.946, 0.278]</r>, {Expenses}<r>[0.154, 0.334, 0.946, 0.391]</r>, {Risk_Controller}<r>[0.154, 0.844, 0.946, 0.844]</r>, and {Outliers}<r>[0.154, 0.844, 0.946, 0.844]</r> over the Years.\nBased on the above thought, the answer is:\nYears,Income,Expenses,Risk_Controller,Outliers\n2015,50000,40000,0.39,0.60\n2016,52000,40500,0.42,0.62\n2017,53000,41000,0.43,0.63\n2018,54000,42000,0.45,0.65\n2019,56000,43000,0.47,0.67\n2020,60000,45000,0.50,0.70"
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "FTIK4DTCFmPepqqt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the plotted lines in the chart, which area showed the most significant change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the area of most change, we need to analyze the slopes of the different lines. Observing the trend-lines for {Income}<r>[0.154, 0.165, 0.946, 0.278]</r>, {Expenses}<r>[0.154, 0.334, 0.946, 0.391]</r>, {Risk_Controller}<r>[0.154, 0.844, 0.946, 0.844]</r> and {Outliers}<r>[0.154, 0.844, 0.946, 0.844]</r>, we see that Income has a moderate upward slope, Expenses also show an upward trend but less steep compared to Income. In contrast, Risk_Controller and Outliers show horizontal lines suggesting there's no change in their values across the years.\nBased on the above thought, the answer is:\nTaking into account the trends represented by the lines, Income has shown the most significant change over the years. This is based on the steepness of its upward slope, indicating a constant increase in income from 2015 to 2020."
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "6lapIBejzJSMZdXg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the income growth from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The income values for the year 2015 and 2020 are plotted as the starting and ending points of {the line representing Income}<r>[0.154, 0.165, 0.946, 0.278]</r>. The coordinates correspond to income values of 50000 and 60000 respectively. Subtracting the value in 2015 from that of 2020 yields the increment in income over these years.\nBased on the above thought, the answer is:\n10000"
+            }
+        ],
+        "id_raw": 787
+    },
+    {
+        "id": "ALSJFmts8Pleq6n0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart data into a table format? The concept names should be considered as row labels and the corresponding scores as values in the table."
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the bar chart, I can use the labels on the y-axis as the row labels and the width of the bars for each concept as the corresponding values. For instance, I identify {Organizational Restructure}<r>[0.089, 0.737, 0.225, 0.765]</r> on the y-axis and cross-reference that with its bar ending at approximately {0.68}<r>[0.678, 0.714]</r> on the x-axis. I continue this process for each bar in the chart.\nBased on the above thought, the answer is:\nConcept,Score\nOrganizational Restructure,0.68\nEmployee Engagement,0.52\nManagement Styles,0.62\nDecision Making,0.48\nPerformance Evaluation,0.75\nConflict Resolution,0.65"
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "m9nquZEPBplwe4e9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we discern about the effectiveness of these management and leadership concepts based on their scores featured in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the effectiveness of these concepts, I analyze the corresponding score bars. Noticing {Performance Evaluation}<r>[0.675, 0.239, 0.788, 0.260]</r> has the longest bar indicating the highest score. In contrast, {Decision Making}<r>[0.41, 0.423, 0.527, 0.444]</r> has the shortest bar, signifying the lowest score. Overall, the bars' lengths demonstrate how well each concept is performing in the business context, with longer bars indicating greater effectiveness.\nBased on the above thought, the answer is:\nBased on the scores in the chart, it appears that Performance Evaluation is the most effective concept in this business context, as it has the highest score. On the other hand, Decision Making seems to be the least effective, given that it has the lowest score. The rest of the concepts are moderately effective with relatively similar scores, indicating that there is an overall high level of effectiveness across most of the management and leadership concepts."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "CQFnjsPyyYEn6oKB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the difference between the highest and lowest score carried by the concepts?"
+            },
+            {
+                "from": "gpt",
+                "value": "For me to get the difference between the highest and lowest score, I first locate the concepts with the longest and shortest bars, in this case {Performance Evaluation}<r>[0.762, 0.737]</r> with the highest score, and {Decision Making}<r>[0.478, 0.332]</r> with the lowest score. The difference between the highest and lowest score is 0.75 - 0.48 = 0.27.\nBased on the above thought, the answer is:\n0.27"
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "tyYvACVJYa5UKoRX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive the data in a tabular format from the line chart, by taking the years along the columns and the research areas as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, to create a table from the line chart, I begin by identifying {the x-axis labels}<r>[0.135, 0.912, 0.179, 0.936, 0.285, 0.912, 0.329, 0.936, 0.435, 0.912, 0.479, 0.936, 0.585, 0.912, 0.629, 0.936, 0.734, 0.912, 0.779, 0.936, 0.884, 0.912, 0.928, 0.936]</r>, which indicate the column headers as years. Following this, I recognize {the legend texts}<r>[0.159, 0.059, 0.325, 0.089, 0.159, 0.103, 0.418, 0.133, 0.159, 0.147, 0.308, 0.177, 0.159, 0.191, 0.368, 0.221]</r> to be the row labels symbolizing the different research areas. To fill in the values, I locate the points for each research area at the corresponding years and extract the annotated values.\nBased on the above thought, the answer is:\nResearch Area,2015,2016,2017,2018,2019,2020\nProtein Analysis,50,48,46,45,42,40\nCell Membrane Research,45,44,46,48,50,51\nDNA Structure,40,42,44,46,48,50\nAnaerobic Digestion,35,35,40,44,46,48"
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "orgjC1csR0YK41gj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which area of research seems to have garnered the most attention over the past years? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To decide which research area has gained the most attention, it is necessary to examine the trends in the line plots for each research area. Looking at the line for {Protein Analysis}<r>[0.157, 0.207, 0.906, 0.551]</r>, the attention has decreased significantly from 50% to 40%. Meanwhile, the line for {Cell Membrane Research}<r>[0.157, 0.172, 0.906, 0.413]</r> has seen an increase in focus from 45% to 51%. The line for {DNA Structure}<r>[0.157, 0.207, 0.906, 0.551]</r> shows a slow yet steady increase from 40% to 50%. Finally, {Anaerobic Digestion}<r>[0.157, 0.276, 0.906, 0.724]</r> seems to have experienced steady growth in focus until it levels off after 2017.\nBased on the above thought, the answer is:\nFrom scrutinizing the trends in the line chart, it seems that the research area 'Cell Membrane Research' has garnered the most attention over the years. Despite starting with smaller focus percentages in 2015, 'Cell Membrane Research' shows a steady increase till 2020 when it reaches the highest attention percentage among all research areas."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "5yQ4QAH5A5JWRSSC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in the degree of focus for Protein Analysis from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the total change for 'Protein Analysis' from 2015 to 2020, I need to locate the points for {Protein Analysis}<r>[0.157, 0.207, 0.307, 0.276, 0.457, 0.345, 0.607, 0.379, 0.756, 0.482, 0.906, 0.551]</r> and extract the corresponding values: 50, 48, 46, 45, 42, 40. The initial value in 2015 is 50 and the final value in 2020 is 40. Hence, the total change is 50 - 40 = 10.\nBased on the above thought, the answer is:\n10."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "3neJ5XUzS2NHXmdU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the line chart and organize it into a table with Global Trade Flow units as columns and the corresponding trade performance index as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile this data into a table, I first consider {the x-axis tick labels}[[0.159, 0.869, 0.189, 0.904], [0.244, 0.869, 0.273, 0.904], [0.328, 0.869, 0.357, 0.904], [0.412, 0.869, 0.441, 0.904], [0.497, 0.869, 0.526, 0.904], [0.581, 0.869, 0.610, 0.904], [0.665, 0.869, 0.694, 0.904], [0.749, 0.869, 0.779, 0.904], [0.834, 0.869, 0.863, 0.904], [0.911, 0.869, 0.955, 0.904]], which give me the column headers depicting Global Trade Flow in Units. Next, I identify the y-axis tick labels for Trade Performance Index values. To populate the table data, I look at {the line points}[[0.933, 0.712], [0.849, 0.316], [0.764, 0.227], [0.680, 0.161], [0.596, 0.117], [0.511, 0.382], [0.427, 0.448], [0.343, 0.580], [0.258, 0.602], [0.174, 0.536]] for each Global Trade Flow value and extract the corresponding Trade Performance Index values.\nBased on the above thought, the answer is:\nGlobal Trade Flow (Units),Trade Performance Index\n100,0.50\n90,0.68\n80,0.72\n70,0.75\n60,0.77\n50,0.65\n40,0.62\n30,0.56\n20,0.55\n10,0.58"
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "tL3oO2GNPR35ZlMP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a general trend between Global Trade Flow and Trade Performance Index, based on the information given in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the general trend between Global Trade Flow and the Trade Performance Index, I need to analyze {the plot of the Global Trade Flow line}[0.174, 0.117, 0.933, 0.712]. There is a noticeable decline in the Trade Performance Index as the Global Trade Flow increases from 10 units to 60 units, followed by a slight rebound and a further decline. This indicates a general negative correlation between Global Trade Flow and Trade Performance Index, which means when the Global Trade Flow increases, Trade Performance Index tends to decrease, and vice versa.\nBased on the above thought, the answer is:\nYes, a general trend can be observed in the line chart. It reveals a negative correlation between Global Trade Flow and Trade Performance Index. In this context, it implies that as the Global Trade Flow increases, the Trade Performance Index tends to decrease. However, it should be noted that this is a general trend, and there could be exceptions or slight rebounds, as seen from 50 to 60 units of Global Trade Flow."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "CO7cjEjbBqjLPkRK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest Trade Performance Index in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to identify {the largest point in the Y-axis}[0.933, 0.117] and {the smallest point in the Y-axis}[0.174, 0.712] of the line chart. The largest value is 0.77 and it corresponds to the Global Trade Flow of 60 Units. The smallest value is 0.50 that corresponds to Global Trade Flow of 100 units. Therefore, the difference between the highest and the lowest Trade Performance Index value is 0.77-0.50=0.27 units.\nBased on the above thought, the answer is:\n0.27 units."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "u2seFRQ1VXq96icd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the values for each category over the years 1900, 1950, 2000, and 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data for each category, I'll need to locate and organize the various points on the line graphs. To begin, I look at the points along the {line for Human Population}<r>[0.313, -3245074.638, 0.939, -550426.882]</r>. From here, I will extract values for the years 1900, 1950, 2000, and 2050. I repeat this process for the {line for Accumulated Knowledge}<r>[0.313, 0.096, 0.939, 0.863]</r>, {line for Warfare Intensity}<r>[0.313, 0.866, 0.939, 0.885]</r>, and {line for Social Equality}<r>[0.313, 0.866, 0.939, 0.884]</r>.\nBased on the above thought, the answer is:\nYear, Human Population, Accumulated Knowledge, Warfare Intensity, Social Equality\n1900, 1650000000, 100, 60, 40\n1950, 2525149000, 200, 90, 36\n2000, 6115974486, 800, 75, 60\n2050, 9727660644, 2400, 33, 90"
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "CsDdvj2q9hMRYVVy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what can we say about the correlation between Accumulated Knowledge and Social Equality over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on {the line for Accumulated Knowledge}<r>[0.313, 0.096, 0.939, 0.863]</r> and {the line for Social Equality}<r>[0.313, 0.866, 0.939, 0.884]</r>, both of them show upward trends. Accumulated Knowledge increased from 100 in 1900 to 2400 in 2050, and Social Equality increased from 40 in 1900 to 90 in 2050. This positive correlation suggests that as Accumulated Knowledge increased, Social Equality also tended to increase over the years.\nBased on the above thought, the answer is:\nThe chart indicates a positive correlation between Accumulated Knowledge and Social Equality. As the values of Accumulated Knowledge increased over time, the values of Social Equality showed a corresponding increase. This may suggest that as society accumulated more knowledge, there might be strides towards social equality."
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "VmJTaJajKpgvtxdB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Human Population increase from 1900 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in Human Population from 1900 to 2050, I would need to find the values for those years. The value for Human Population in 1900 can be obtained from the first data point in {the line for Human Population}<r>[0.313, -3245074.638, 0.939, -550426.882]</r>, which is 1650000000. The value for 2050 can be obtained from the last data point on the same line, which is 9727660644. The increase would then be the value for 2050, 9727660644, subtracted by the value for 1900, 1650000000, and that comes to 8077660644.\nBased on the above thought, the answer is:\n8077660644"
+            }
+        ],
+        "id_raw": 791
+    },
+    {
+        "id": "tdxFTSdEH7GC0pSu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the number of reforms or projects from 2015 to 2019 in different departments into tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data, I'll first locate {the x-axis tick labels}[[0.081, 0.869, 0.139, 0.904],[0.289, 0.869, 0.348, 0.904],[0.498, 0.869, 0.557, 0.904],[0.707, 0.869, 0.766, 0.904],[0.916, 0.869, 0.975, 0.904]], which represent the column headers or years. I then identify {the legend texts}[[0.693, 0.327, 0.843, 0.372],[0.693, 0.392, 0.921, 0.437],[0.693, 0.458, 0.913, 0.503],[0.693, 0.524, 0.897, 0.569]] as the row labels representing different departments. Mapping these correctly will get the required data for each department for every year.\nBased on the above thought, the answer is:\nDepartment,2015,2016,2017,2018,2019\nHealthcare,10,15,18,14,7\nPublic Education,12,13,10,6,7\nPublic Transport,8,10,7,6,4\nSocial Security,15,14,12,13,14"
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "kXbO9wK0oYio1oFq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed a sharp decline in the number of reforms or projects during the analyzed period? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "A sharp decline would be indicated by a downward trend in the data points. Looking at {the line for Healthcare}[0.110, 0.448, 0.946, 0.567], there is a decline from 18 in 2017 to 7 in 2019. In comparison, {Public Education}[0.110, 0.368, 0.946, 0.567] and {Public Transport}[0.110, 0.527, 0.946, 0.686] also show a decline, but not as sharply. {The Social Security line}[0.110, 0.250, 0.946, 0.368] shows a meandering progression, not a sharp decline.\nBased on the above thought, the answer is:\nThe Healthcare sector showed a sharp decline in the number of reforms or projects. This is evident from the downward trend seen in the Healthcare line plot, with an abrupt drop from 18 projects in 2017 to 7 by 2019. This reduction is steeper when compared to the less dramatic declines observed for the Public Education and Public Transport lines."
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "AbfpjAF15HDMg7Hj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of projects or reforms in the Social Security department over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total number of projects or reforms in the Social Security department can be calculated by adding the y-values for all the years. To find the y-values, I refer to {the line points for Social Security}[[0.110, 0.250, 0.319, 0.289, 0.528, 0.368, 0.737, 0.329, 0.946, 0.289]], yielding the numbers 15, 14, 12, 13, 14.\nBased on the above thought, the answer is:\n68"
+            }
+        ],
+        "id_raw": 793
+    },
+    {
+        "id": "KrkuIibSu5Gt3VNg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you structure the given dataset into a readable table format with Years as columns, and Start-Up Spirit, Innovation, Disruption, and Large Corporations as rows and their corresponding scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "By interpreting the line chart, {the x-axis tick labels}\n[[0.102, 0.869, 0.161, 0.904], [0.306, 0.869, 0.364, 0.904], [0.509, 0.869, 0.568, 0.904], [0.713, 0.869, 0.772, 0.904], [0.917, 0.869, 0.975, 0.904]] represent the years, which will be the columns headers. I identify the {legend texts}  \n[[0.656, 0.564, 0.853, 0.609], [0.656, 0.630, 0.803, 0.675], [0.656, 0.696, 0.800, 0.741], [0.656, 0.762, 0.921, 0.807]] as the rows labels representing Start-Up Spirit, Innovation, Disruption, and Large Corporations respectively. I'll extract the corresponding values by locating the {line points} for each respective cell from the line plots: [0.4, 0.45, 0.5, 0.52, 0.55], [0.38, 0.4, 0.42, 0.45, 0.48], [0.24, 0.3, 0.35, 0.4, 0.42], and [0.58, 0.55, 0.52, 0.48, 0.42].\nBased on the above thought, the answer is:\nEvaluation Type,2000,2005,2010,2015,2020\nStart-Up Spirit,0.4,0.45,0.5,0.52,0.55\nInnovation,0.38,0.4,0.42,0.45,0.48\nDisruption,0.24,0.3,0.35,0.4,0.42\nLarge Corporations,0.58,0.55,0.52,0.48,0.42"
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "h29TT3EoOl3zI69t",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which evaluation type seems to have consistent improvement over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which evaluation type shows consistent improvement over the years, I'll have to analyze the trend of the line plots:\n{Start-Up Spirit}[0.132, 0.138, 0.946, 0.441] indicated a continuous upward trend from 0.4 to 0.55. \n{Innovation}[0.132, 0.280, 0.946, 0.481] has a steady growth from 0.38 to 0.48.\n{Disruption}[0.132, 0.401, 0.946, 0.764] displayed a gradual improvement from 0.24 to 0.42.\n{Large Corporations}[0.132, 0.078, 0.946, 0.401] showed a noticeable decline from 0.58 to 0.42.\nBased on the above thought, the answer is:\nFrom the analysis of the trends, it seems that the evaluation type 'Start-Up Spirit' shows the most consistent improvement over the years, with a steady increase in the score from 0.4 in 2000 to 0.55 in 2020."
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "FVsZqdy3MjPEY6Cj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in Disruption score from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the Disruption score from year 2000 to year 2020, l can see the data point in year 2000 at the coordinates {Disruption in 2000}[[0.132, 0.764]] representing a score of 0.24 and in 2020 at coordinates {Disruption in 2020}[[0.946, 0.401]] representing the score of 0.42. Thus, the increase would be 0.42 - 0.24 = 0.18.\nBased on the above thought, the answer is:\n0.18."
+            }
+        ],
+        "id_raw": 794
+    },
+    {
+        "id": "S0QuKHBNnlXzOlzD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table that outlines the annual counts of Patent Applications, Patent Grants, Trademark Filings, and Trademark Registrations from 2000 to 2020 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I will first identify {the x-axis labels}[[0.223, 0.912, 0.258, 0.936], [0.372, 0.912, 0.407, 0.936], [0.521, 0.912, 0.556, 0.936], [0.670, 0.912, 0.705, 0.936], [0.819, 0.912, 0.854, 0.936]] which indicate the years. Then, I will recognize {the legend}[[0.099, 0.075, 0.319, 0.222]] for patent categories that are the rows. Using the data points from {the lines}[{line:[0.241, 0.116, 0.836, 0.636]},{line:[0.241, 0.427, 0.836, 0.792]},{line:[0.241, 0.220, 0.836, 0.740]},{line:[0.241, 0.428, 0.836, 0.844]}], I can deduce the corresponding values which represent the count of each category of patented items for each year.\nBased on the above thought, the answer is:\nYears,Patent Applications,Patent Grants,Trademark Filings,Trademark Registrations\n2000,5000,3500,4000,3000\n2005,6000,4000,5000,4000\n2010,7000,5500,6000,5000\n2015,9000,6500,7000,6000\n2020,10002,7008,9000,7002"
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "zf9diPlizWMTpYZb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which Intellectual Property category has seen the most growth in counts from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll assess the growth for each category by examining the difference between their 2000 and 2020 counts. Starting off with {Patent Applications}[line:[0.241, 0.636, 0.836, 0.116]], the difference is 5002. Checking {Patent Grants}[line:[0.241, 0.792, 0.836, 0.427]], the growth is 3508. For {Trademark Filings}[line:[0.241, 0.740, 0.836, 0.220]], the increase is 5000. Lastly, {Trademark Registrations}[line:[0.241, 0.844, 0.836, 0.428]] reveals a growth by 4002.\nBased on the above thought, the answer is:\nThe Intellectual Property category that has seen the most growth in counts from 2000 to 2020 is Trademark Filings. It showed an increase of 5000 counts over this time period, which is the highest growth among all the categories."
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "c25alqJuONSHxetg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the count of Patent Applications from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to identify the points for Patent Applications from {the line points}[{x:2000,y:5000, point_coordinates:'[0.241, 0.636]'},\n{x:2020,y:10002, point_coordinates:'[0.836, 0.116]'}]. The count of Patent Applications in 2000 is 5000, and in 2020, it is 10002. Thus the difference is 10002 - 5000.\nBased on the above thought, the answer is:\n5002"
+            }
+        ],
+        "id_raw": 795
+    },
+    {
+        "id": "RvqHZbXJhn8ddHjJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you parse the line subplots into a structured table that shows age against weight and height for each environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this table, I first identify {the x-axis tick labels}[[0.116, 0.912, 0.125, 0.936], [0.281, 0.912, 0.289, 0.936], [0.445, 0.912, 0.454, 0.936], [0.610, 0.912, 0.619, 0.936], [0.775, 0.912, 0.783, 0.936], [0.939, 0.912, 0.948, 0.936]] as representing age for both environments.  Then I establish {the legend texts}[[0.489, 0.747, 0.614, 0.771], [0.489, 0.782, 0.593, 0.806], [0.489, 0.817, 0.588, 0.840], [0.489, 0.852, 0.577, 0.875]] as representations of weight and height in both environment types. Finally, I extract the vertical value of {the line points}[[0.120, 0.245, 0.944, 0.408], [0.120, 0.550, 0.944, 0.794], [0.120, 0.062, 0.944, 0.224], [0.120, 0.347, 0.944, 0.530]] for each age and environment to conclude the corresponding measurements.\nBased on the above thought, the answer is:\nAge, Urban Weight, Urban Height, Rural Weight, Rural Height\n1, 0.75, 0.85, 0.7, 0.65\n2, 0.78, 0.83, 0.77, 0.64\n3, 0.74, 0.88, 0.76, 0.67\n4, 0.76, 0.89, 0.73, 0.65\n5, 0.79, 0.91, 0.72, 0.60\n6, 0.82, 0.90, 0.68, 0.55"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "jPXK8yQLdlYXg0ug",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which metric seems to have the most stable performance over the quarters? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which metric has the most stable performance, I will have to look at the fluctuations in the line plots for each metric. Looking at {the line for Process Efficiency}[0.120, 0.245, 0.944, 0.408], it shows a relatively consistent increase from 0.75 to 0.82, indicating stability.\nFor {Process Timing}[0.120, 0.550, 0.944, 0.794], there is an overall decrease from 0.65 to 0.55, with some minor increases in the 3rd and 4th quarters. This indicates some volatility.\nFor {Approval Rate}[0.120, 0.062, 0.944, 0.224], there is a significant increase from 0.85 to 0.90, which while it does show improvement, also indicates a degree of fluctuation.\nFinally, for {Process Cost}[0.120, 0.347, 0.944, 0.530], it fluctuates between 0.7 and 0.68, over the quarters, again indicating some level of instability.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Process Efficiency seems to have the most stable performance over the quarters. While all other metrics show varying degrees of fluctuation across the quarters, Process Efficiency maintains a relatively consistent increase, suggesting it experiences the least volatility among the included metrics."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "hNtVi9XWMpgoRE0w",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the weight from 1st to the 6th quarter for the urban environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in the weight from 1st to the 6th quarter for the urban environment, I will have to locate the {line points for Process Efficiency}[0.120, 0.245, 0.944, 0.408]. The weight in the 1st quarter is 0.75 and in the 6th quarter is 0.82. So the increase in weight = 0.82 - 0.75.\nBased on the above thought, the answer is:\n0.07"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "3d3w6DSLdasa6ROi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table from this line chart with Companies as columns and Quarters as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first identify {the x-axis tick labels}[0.215, 0.911, 0.224, 0.934],[0.368, 0.911, 0.377, 0.934],[0.522, 0.911, 0.530, 0.934] as the row labels indicating the Quarters. Next, I find {the legend texts}[0.128, 0.050, 0.244, 0.080],[0.128, 0.094, 0.253, 0.124],[0.128, 0.138, 0.275, 0.168],[0.128, 0.182, 0.290, 0.212] as the column headers representing the Companies. To extract the values, I locate {the line point for Control group}[[0.220, 0.842],[0.373, 0.789],[0.526, 0.736]], {the line points for Therapy Group}[[0.220, 0.736],[0.373, 0.684],[0.526, 0.552]], {the line points for Medication Group}[[0.220, 0.789],[0.373, 0.710],[0.526, 0.631]] and {the line points for Combination Group}[[0.220, 0.578],[0.373, 0.526],[0.526, 0.394]] and their corresponding values for each quarter.\nBased on the above thought, the answer is:\nQuarter,Control Group,Therapy Group,Medication Group,Combination Group\nDay 1,20,24,22,30\nDay 2,22,26,25,32\nDay 3,24,31,28,37"
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "gmK51r2tzuh22e3R",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which group has shown the most consistent improvement over the three days? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which group has the most consistent improvement, I need to analyze the trend in the line plots for each group. Looking at {the line for Control Group}[0.220, 0.842, 0.526, 0.736], it shows a increasing trend from 20 to 24 through out the three days. For {Therapy Group}[0.220, 0.736, 0.526, 0.552], there is an increase from 24 to 31, indicating consistent improvement. Then looking at the {Medication Group}[0.220, 0.789, 0.526, 0.631], the line indicates an increasing trend from 22 to 28. Finally, the line for {Combination Group}[0.220, 0.578, 0.526, 0.394] shows a strong increase from 30 to 37, indicating the most consistent improvement.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Combination Group has shown the most consistent improvement over the three days. The line plot for this group shows a strong increasing trend across the three days, indicating continued improvement over the period."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "BzpXspvmFjxj2sTR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average observed value for Day 2 for all groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average observed value for Day 2, I need to locate {the line points for Day 2}[[0.373, 0.789], [0.373, 0.684], [0.373,0.710], [0.373, 0.526]] and extract the corresponding values: 22, 26, 25, 32. The average value for Day 2 is therefore (22 + 26 + 25 + 32)/4 = 26.25.\nBased on the above thought, the answer is:\n26.25."
+            }
+        ],
+        "id_raw": 797
+    },
+    {
+        "id": "lfF4PrKqdFu5mNOJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tabulate the sales values for each product over 12 months using the data given in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile sales values for each product from the line chart over 12 month period, I start with locating {the X-axis labels}<r>[0.132, 0.869, 0.975, 0.904]</r> for representing the time sequence as rows. {The line  graphs}<r>[0.139, 0.580, 0.960, 0.820]</r>,<r>[0.139, 0.146, 0.960, 0.435]</r>,<r>[0.139, 0.531, 0.960, 0.652]</r>,<r>[0.139, 0.219, 0.960, 0.531]</r> represent 'Product_A', 'Product_B', 'Product_C' and 'Product_D' respectively. The individual points on these lines <r>[0.139, 0.724]</r>, <r>[0.214, 0.734]</r>... correspond to respective sales values for each product in each month.\nBased on the above thought, the answer is:\nMonth,Product_A,Product_B,Product_C,Product_D\n1,500,1200,800,1500\n2,480,1300,720,1550\n3,460,1400,750,1450\n4,440,1320,690,1420\n5,420,1310,650,1400\n6,400,1200,700,1300\n7,300,1100,680,1000\n8,350,1150,670,900\n9,500,1120,900,1100\n10,650,1400,850,1300\n11,750,1600,800,1500\n12,800,1700,820,1550"
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "qzZcOrpF63K5Inkb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product showed the most consistent sales performance over the year, and which product showed the most fluctuation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to study the trends and patterns of {the line graphs representing each product}<r>[0.139, 0.580, 0.960, 0.820]</r>,<r>[0.139, 0.146, 0.960, 0.435]</r>,<r>[0.139, 0.531, 0.960, 0.652]</r>,<r>[0.139, 0.219, 0.960, 0.531]</r>. {The line graph for Product_A}<r>[0.139, 0.580, 0.960, 0.820]</r> exhibits a steady decline from the start to the 7th month and increases quite gradually afterwards. Similarly, {the line graph for Product_B}<r>[0.139, 0.146, 0.960, 0.435]</r> depicts a steady rise until the 5th month, and a more volatile pattern henceforth. {The Line graph for Product_C}<r>[0.139, 0.531, 0.960, 0.652]</r> shows a mixed trend with multiple ups and downs. Lastly, {The line graph for Product_D}<r>[0.139, 0.219, 0.960, 0.531]</r> follows a similar pattern but with less volatility.\nBased on the above thought, the answer is:\nBased on the line chart, Product_A displayed the most consistent sales performance over the year, with a clear trend and less fluctuation. On the other hand, Product_B exhibited the most fluctuation, with a steady rise initially and several ups and downs afterwards."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "ZUhekYsjcx8VWNqY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the sales of Product_C drop from Month 1 to Month 7?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the drop in sales of Product_C from Month 1 to Month 7, I need to look at {the specific points for Product_C on the line graph}<r>[0.139, 0.724, 0.587, 0.820]</r>. The sales value at Month 1 is 800, and the sales value at Month 7 is 680. The decrease in sales would therefore be 800 - 680 = 120.\nBased on the above thought, the answer is:\n120"
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "KI3hOrCtL6z75stU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table with Years as rows and the labels 'CBD Construction', 'Suburbs Construction', 'CBD Planning Contacts', and 'Suburbs Planning Contacts' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract and structure the data from the line chart, I would first locate {the x-axis tick labels}[[0.140, 0.895, 0.184, 0.923], [0.300, 0.895, 0.343, 0.923], [0.459, 0.895, 0.503, 0.923], [0.618, 0.895, 0.662, 0.923], [0.778, 0.895, 0.822, 0.923], [0.937, 0.895, 0.981, 0.923]], which represent the row headers or years. Then, {the legend texts}[[0.661, 0.071, 0.845, 0.107], [0.661, 0.124, 0.885, 0.160], [0.661, 0.176, 0.900, 0.212], [0.661, 0.229, 0.941, 0.265]] would provide the column labels representing the different categories for each year. Then, I would access each line's points for instances of {CBD Construction}[[0.162, 0.637], [0.242, 0.661], ..., [0.880, 0.852]], {Suburbs Construction}[[0.162, 0.279], [0.242, 0.303], ..., [0.880, 0.685]], {CBD Planning Contacts}[[0.162, 0.303], [0.242, 0.494], ..., [0.880, 0.518]], and {Suburbs Planning Contacts}[[0.162, 0.089], [0.242, 0.136], ..., [0.880, 0.303]] to obtain the values for each category at the respective years.\nBased on the above thought, the answer is:\nYears, CBD_Construction, Suburbs_Construction, CBD_Planning_Contacts, Suburbs_Planning_Contacts\n2000, 10, 25, 15, 30\n2001, 9, 24, 16, 31\n2002, 8, 22, 18, 32\n2003, 7, 20, 20, 33\n2004, 5, 18, 21, 32\n2005, 4, 16, 21, 31\n2006, 3, 14, 22, 29\n2007, 3, 12, 23, 28\n2008, 2, 10, 24, 26\n2009, 1, 8, 24, 24"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "TZTqxpCtXQVhdkkT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector had a steadier trend, CBD or Suburbs?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which sector had a steadier trend, I need to look at the variance in the values over the years. I see that {the line for CBD Construction}[[0.162, 0.637, 0.880, 0.852]], while it starts at a higher value in 2000, it ends at the lowest point in 2009. There are also sharp declines over the years. On the contrary, {the line for Suburbs Construction}[[0.162, 0.279, 0.880, 0.685]], even though it starts lower in 2000, it maintains a steadier, declining trend over the years. Furthermore, {the line for CBD Planning Contacts}[[0.162, 0.303, 0.880, 0.518]] shows an overall increase, however, there is a sharp increase between 2003 and 2004. Whereas, {the line for Suburbs Planning Contacts}[[0.162, 0.089, 0.880, 0.303]] maintains a steadier, declining trend over the years.\nBased on the above thought, the answer is:\nBased on the chart, the Suburbs sector had a steadier trend overall, with a consistent decrease over the years in Construction and a more gradual, albeit unsteady rise in Planning Contacts. On the other hand, the CBD sector showed much more fluctuation in both Construction and Planning Contacts. Therefore, looking at the overall trends, the Suburbs sector seems to be steadier."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "7s1ibuMxFFcQr65i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_40_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the number of CBD Construction projects decreased from 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the initial point of {the line for CBD Construction}[[0.162, 0.637]] and the final point of the same line[[0.880, 0.852]]. These points represent the values of CBD Construction projects in 2000 and 2009 respectively. Subtracting the final value from the initial value will give us the answer.\nBased on the above thought, the answer is:\n9."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "CZvfx6N5V6ObatLZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from the line chart in a structured table form, where the row categories are Temperate_Climate and Tropical_Climate and the column categories are based on the Latitude?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract data to a table from the line chart, I'll start by identifying {the x-axis tick labels}[[0.112, 0.911, 0.148, 0.934], [0.157, 0.911, 0.193, 0.934], [0.202, 0.911, 0.238, 0.934], [0.247, 0.911, 0.283, 0.934], [0.292, 0.911, 0.328, 0.934], [0.337, 0.911, 0.373, 0.934], [0.382, 0.911, 0.418, 0.934], [0.427, 0.911, 0.463, 0.934], [0.472, 0.911, 0.508, 0.934], [0.530, 0.911, 0.541, 0.934], [0.569, 0.911, 0.591, 0.934], [0.614, 0.911, 0.636, 0.934], [0.659, 0.911, 0.681, 0.934], [0.705, 0.911, 0.726, 0.934], [0.750, 0.911, 0.771, 0.934], [0.795, 0.911, 0.817, 0.934], [0.840, 0.911, 0.862, 0.934], [0.885, 0.911, 0.907, 0.934], [0.930, 0.911, 0.952, 0.934]], which will be the column headers or Latitude. I then choose {the legend texts}[[0.166, 0.091, 0.367, 0.121], [0.166, 0.135, 0.336, 0.165]] to be the row categories or Temperate_Climate and Tropical_Climate. To get the values, I look at {the points for Temperate_Climate and Tropical_Climate}[[0.130, 0.397], [0.175, 0.729], ..., [0.941, 0.770], [0.130, 0.231], [0.941, 0.604]] and interpret their corresponding values.\nBased on the above thought, the answer is:\nLatitude, Temperate_Climate, Tropical_Climate\n-90, 0.1, 0.2\n-80, 0.2, 0.3\n-70, 0.3, 0.4\n-60, 0.5, 0.6\n-50, 0.6, 0.8\n-40, 0.7, 0.9\n-30, 0.8, 1.1\n-20, 0.9, 1.2\n-10, 1.1, 1.4\n0, 1.2, 1.6\n10, 1.2, 1.6\n20, 1.1, 1.4\n30, 0.9, 1.2\n40, 0.8, 1.0\n50, 0.6, 0.8\n60, 0.5, 0.6\n70, 0.3, 0.4\n80, 0.2, 0.3\n90, 0.1, 0.2"
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "YUsJz6ILxLvvmNWv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how can we compare the temperature variations with respect to latitude in Temperate and Tropical climates?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make the comparison, I focus on the trend lines for {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] and {Tropical_Climate}[0.130, 0.231, 0.941, 0.604]. I note that both lines increase, then decrease along the latitude axis.\nStarting with the Temperate_Climate, the line ascends till it reaches latitude 0, indicating that temperature increases as we move from the pole to the equator. After that, the line descends, showing a decrease in temperature as we move away from the equator towards the pole.\nFor the Tropical_Climate, it behaves similarly but with more steepness, indicating a greater temperature rise and fall along the latitude. The temperature reaches its peak at latitude 0, indicating the highest temperature at the equator and then decreases as we move away towards the poles.\nBased on the above thought, the answer is:\nAccording to the line chart, both Temperate and Tropical climates show a rise in temperature as we move from the poles to the equator, peaking at the equator (latitude 0). Post that, there is a decline in temperature as we proceed towards the opposite pole. This type of curve indicates the expected temperature pattern on Earth due to the angle at which sunlight reaches different parts of Earth. However, the Tropical_Climate line peaks higher and falls more steeply than the Temperate_Climate line, suggesting a wider range of temperatures in Tropical climates compared to Temperate climates."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "mQZWgwwHUrK6YKwP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the line which corresponds to Tropical_Climate in the line chart, at what Latitude do temperatures reach their maximum?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to locate the maximum temperature for the Tropical_Climate, I need to examine the {points for this line}[[0.130, 0.231], [0.941, 0.604]] which span from -90 to 90 Latitude. The maximum temperature appears when it hits 1.6 which is represented by the points at {Latitude 0 and 10}[0.535, 0.895], [0.580, 0.895].\nBased on the above thought, the answer is:\n0 and 10."
+            }
+        ],
+        "id_raw": 800
+    },
+    {
+        "id": "uUYiGugIlMGp22Fv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the bar chart, can you construct a table showing the values for the different dimensions (O_Value, T_Value, P_Value) for each of the two roles (Risk Control and Financial Management)?"
+            },
+            {
+                "from": "gpt",
+                "value": "First I'll identify {the legend entries}[[0.767, 0.016, 0.804, 0.044], [0.774, 0.053, 0.811, 0.080], [0.776, 0.089, 0.813, 0.117]] that correspond to the different dimensions. Then, I'll arrange these dimensions as columns in the table. Secondly, I'll look at {the y-axis labels}[[0.057, 0.215, 0.091, 0.250], [0.056, 0.572, 0.090, 0.607]] to identify the two roles which represent the data rows. Finally, I will extract the values for each dimension for these two roles by interpreting {the horizontal bar lengths}[[0.126, 0.186, 0.734, 0.246], [0.126, 0.251, 0.881, 0.311], [0.126, 0.316, 0.800, 0.376], [0.126, 0.597, 0.792, 0.657], [0.126, 0.662, 0.907, 0.722], [0.126, 0.727, 0.838, 0.787]].\nBased on the above thought, the answer is:\nRole,O_Value,T_Value,P_Value\nRisk Control,430,530,350\nFinancial Management,375,420,390"
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "gRvCWndEiCAsTcwx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which role has a more balanced distribution across the three dimensions and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which role has a more balanced distribution across the dimensions, I need to compare the proportions of O_Value, T_Value and P_Value for both roles. Looking at {the bars for Risk Control}[[0.126, 0.186, 0.734, 0.246], [0.126, 0.251, 0.881, 0.311], [0.126, 0.316, 0.800, 0.376]], the values range from 350 to 530, indicating a significant disparity particularly with the high T_Value. On the other hand, examining {the bars for Financial Management}[[0.126, 0.597, 0.792, 0.657], [0.126, 0.662, 0.907, 0.722], [0.126, 0.727, 0.838, 0.787]], the values range narrowly from 375 to 420, which indicates a more even distribution across these dimensions.\nBased on the above thought, the answer is:\nBased on the bar chart, Financial Management has a more balanced distribution across the three dimensions. While both roles have distinct spread of values, Financial Management maintains a closer range between its lowest and highest values (from 375 to 420), suggesting a better balance across the roles' different dimensions. Compared to Risk Control that shows significant disparity with values ranging from 350 to 530, the Financial Management role thus seems to be more balanced."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "9tWMHxfnm2cZUSWF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in O_Value between Risk Control and Financial Management roles?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in O_Value between the two roles, I need to locate {the O_Value bar for Risk Control}[[0.126, 0.186, 0.734, 0.246]] which has a value of 430 and {the O_Value bar for Financial Management}[[0.126, 0.597, 0.792, 0.657]] with a value of 375. The difference in O_Value between the two roles is then calculated as 430 - 375 = 55.\nBased on the above thought, the answer is:\n55."
+            }
+        ],
+        "id_raw": 801
+    },
+    {
+        "id": "pgc0mvs1fAxWUKrE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data shown in the line chart and represent it in a table with 'CSR Index' as rows and Performances as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify that the X-Axis represents {the CSR Index}<r>[0.473, 0.943, 0.597, 0.975]</r>, which I can take as the rows of the table. The legends identify the different performance types to be {Environmental Performance}<r>[0.166, 0.091, 0.456, 0.121]</r>, {Economic Performance}<r>[0.166, 0.134, 0.405, 0.164]</r>, and {Social Performance}<r>[0.166, 0.178, 0.366, 0.208]</r>, which are the columns of the table. For each CSR Index, I have to extract the performance value for each of the three performances. For instance, at CSR Index 0.1, I need to extract the performance values for Environmental, Economic, and Social Performances by analyzing their lines' coordinates.\nBased on the above thought, the answer is:\nCSR Index,Environmental Performance,Economic Performance,Social Performance\n0.1,0.5,0.6,0.4\n0.2,0.48,0.58,0.39\n0.3,0.46,0.56,0.41\n0.4,0.44,0.57,0.42\n0.5,0.42,0.7,0.4\n0.6,0.4,0.69,0.38\n0.7,0.38,0.74,0.36\n0.8,0.36,0.76,0.39\n0.9,0.34,0.7,0.41\n1.0,0.32,0.68,0.43"
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "iuDXjNACPOupklsT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which performance measure shows the greatest improvement from CSR Index 0.1 to 1.0 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart shows three performance measures, namely: {Environmental Performance}<r>[0.130, 0.483, 0.941, 0.508]</r>, {Economic Performance}<r>[0.130, 0.231, 0.941, 0.328]</r>, and {Social Performance}<r>[0.130, 0.591, 0.941, 0.645]</r>. By comparing the initial and final values of these performances, we can determine which had the greatest improvement. Environmental Performance decreases from 0.5 to 0.32, which is a decline and not an improvement. Economic performance increases from 0.6 to 0.68, which is an improvement of 0.08. Similarly, Social Performance improves from 0.4 to 0.43, an increase of 0.03. Therefore, the greatest improvement is in Economic Performance for CSR Index 0.1 to 1.0.\nBased on the above thought, the answer is:\nThe Economic Performance shows the greatest improvement from CSR Index 0.1 to 1.0. It has the highest increase in performance values compared to Environmental Performance and Social Performance over the given range of CSR Index. The increase is calculated from comparing the initial and final performance values."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "zX5bOHCXfgJgGPBh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Social Performance from a CSR index of 0.1 to 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Social Performance, I need to locate the value of Social Performance at a CSR index of 0.1 and 1.0. The {line for Social Performance}<r>[0.130, 0.591, 0.941, 0.645]</r> represents the trend of Social Performance. The first point (x=0.1) corresponds to a Social Performance of {0.4}<r>[0.130, 0.605]</r> and the last point (x=1.0) corresponds to a Social Performance of {0.43}<r>[0.941, 0.645]</r>. Therefore, the increase in Social Performance from a CSR index of 0.1 to 1.0 is calculated as 0.43 - 0.4 = 0.03.\nBased on the above thought, the answer is:\n0.03."
+            }
+        ],
+        "id_raw": 802
+    },
+    {
+        "id": "BSapdKzjRvm9Dq3n",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you formulate the raw chart data into a comprehensible table that includes years as columns and the types of vehicles as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To format the provided line chart's information into a table, I am going to find {the x-axis tick labels}[[0.108, 0.911, 0.152, 0.934], [0.310, 0.911, 0.354, 0.934], [0.513, 0.911, 0.557, 0.934], [0.716, 0.911, 0.760, 0.934], [0.919, 0.911, 0.963, 0.934]], presenting the years. Next, I'll identify {the legend texts}[[0.166, 0.091, 0.382, 0.121], [0.166, 0.134, 0.372, 0.164]] as the car types. To obtain the values, I'll locate {the line points}[[0.130, 0.819], [0.332, 0.367], [0.535, 0.292], [0.738, 0.216], [0.941, 0.171], [0.130, 0.593], [0.332, 0.548], [0.535, 0.518], [0.738, 0.367], [0.941, 0.292]] reported for each car type in the corresponding years and collect the annotated values.\nBased on the above thought, the answer is:\nVehicle_Type,2016,2017,2018,2019,2020\nKnowledge Economy,0.5,0.8,0.85,0.9,0.93\nInnovation Industries,0.65,0.68,0.7,0.8,0.85"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "Ak04wpZVZDh6uP79",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the trends observed for Knowledge Economy and Innovation Industries from the line chart, which one experienced more growth within the observed period? Provide a brief explanation for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To make a comparison, I need to look at the line plots for both sectors. Observing {the line for Knowledge Economy}[0.130, 0.171, 0.941, 0.819], There was a steady and considerable growth from 0.5 in 2016 to 0.93 in 2020. On the other hand, looking at {the line for Innovation Industries}[0.130, 0.292, 0.941, 0.593], the growth seems relatively less, with a change from 0.65 in 2016 to 0.85 in 2020.\nBased on the above thought, the answer is:\nThe Knowledge Economy experienced more growth within the observed period, as is evident from the line chart. The segment started at a lower value in 2016 but overtook the Innovation Industries by 2020, marking a significant and notable expansion. On the contrary, while the Innovation Industries started with a higher value in 2016, its growth has been less considerable."
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "5W0kIB9oiLs5yIyo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall change observed for the Knowledge Economy from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the total alteration observed for the Knowledge Economy from 2016 to 2020, it's necessary to identify {the line points for the Knowledge Economy}[[0.130, 0.819], [0.332, 0.367], [0.535, 0.292], [0.738, 0.216], [0.941, 0.171]] and retrieve the respective values: 0.5, 0.8, 0.85, 0.9, 0.93. The values recorded in 2016 {at 0.130, 0.819} and 2020 {at 0.941, 0.171} are of special interest. The overall change is determined as 0.93 (2020's value) - 0.5 (2016's value), amounting to 0.43.\nBased on the above thought, the answer is:\n0.43"
+            }
+        ],
+        "id_raw": 803
+    },
+    {
+        "id": "EfH5RZi2pRs7LSPA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data from the chart in a table? The table should have columns for Year, Residential, Commercial, and Industrial."
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I would first identify {the x-axis tick labels}[[0.108, 0.933, 0.117, 0.951], [0.152, 0.933, 0.161, 0.951], [0.196, 0.933, 0.205, 0.951], ..., [0.935, 0.933, 0.952, 0.951]] as the years, which would become the rows of the table. Then, I would refer to {the line plot for the Residential sector}[0.113, 0.093, 0.943, 0.507], {the line plot for the Commercial sector}[0.113, 0.104, 0.943, 0.834] and {the line plot for the Industrial sector}[0.113, 0.0, 0.943, 0.83] to extract the values for each year and sector to fill in the table.\nBased on the above thought, the answer is:\nYear,Residential,Commercial,Industrial\n1,0.96,0.95,0.8\n2,0.93,0.9,0.75\n3,0.91,0.82,0.68\n4,0.88,0.8,0.65\n...\n19,0.6,0.3,0.2\n20,0.58,0.28,0.18"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "S7YpKDSwCmuIgFM4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the most steadiness over the examined period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to evaluate {the line trend of the Residential sector}[0.113, 0.093, 0.943, 0.507], {the Commercial sector}[0.113, 0.104, 0.943, 0.834], and {the Industrial sector}[0.113, 0.0, 0.943, 0.83]. Steadiness refers to the sector that has the least variation in values throughout the years. If we compare the gradients and fluctuation of the lines, we can see that the Residential sector has the most consistent gradient, indicating the least variations in its values over the years.\nBased on the above thought, the answer is:\nThe Residential sector showed the most steadiness over the examined period as it had the least variations in its values over the years."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "xRkyhrT5yEe4aUva",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in the Commercial Sector value from Year 1 to Year 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine {the value of the Commercial Sector in Year 1}[0.113, 0.104] and {in Year 20}[0.943, 0.834]. The value in Year 1 is 0.95 and in Year 20 it is 0.28. The change can be calculated as the Year 20 value minus the Year 1 value.\nBased on the above thought, the answer is:\n-0.67"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "g86LkHq04LzI39V5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWould it be possible to represent the data in the line chart as a structured table with Years as columns and Energy Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart into tabular data, I need to focus on {the x-axis tick labels}[[0.108, 0.911, 0.152, 0.934], [0.189, 0.911, 0.233, 0.934], [0.270, 0.911, 0.314, 0.934], [0.351, 0.911, 0.395, 0.934], [0.432, 0.911, 0.476, 0.934], [0.513, 0.911, 0.557, 0.934], [0.594, 0.911, 0.638, 0.934], [0.675, 0.911, 0.719, 0.934], [0.756, 0.911, 0.801, 0.934], [0.838, 0.911, 0.882, 0.934], [0.919, 0.911, 0.963, 0.934]], which hold the Year values for the column headers. The row labels symbolizing different Energy Types come from {the legend texts}[[0.166, 0.091, 0.413, 0.121], [0.166, 0.134, 0.350, 0.164]]. The respective values for each row and column combination are derived from the coordinates of {the line points for Electricity Consumption and Gas Consumption}[[0.130, 0.193, 0.941, 0.646], [0.130, 0.701, 0.941, 0.889]] on the chart.\nBased on the above thought, the answer is:\nYear,Electricity_Consumption,Gas_Consumption\n2000,0.45,0.35\n2001,0.47,0.30\n2002,0.52,0.28\n2003,0.55,0.24\n2004,0.60,0.20\n2005,0.62,0.18\n2006,0.65,0.15\n2007,0.72,0.16\n2008,0.75,0.11\n2009,0.78,0.12\n2010,0.83,0.08\n2011,0.85,0.06\n2012,0.91,0.04\n2013,0.93,0.02\n2014,1.01,0.01\n2015,1.05,0.04\n2016,1.08,0.05\n2017,1.16,0.02\n2018,1.19,0.01\n2019,1.22,0.02\n2020,1.27,0.01"
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "lIH9LgwYR9RpMomy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which type of energy consumption demonstrates a more consistent growth over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which type of energy consumption shows more consistent growth, I need to examine the lines representing both energy types. Looking at {the line for Electricity Consumption}[0.130, 0.193, 0.941, 0.646], it consistently moves upwards without significant drops or spikes. On the other hand, {the line for Gas Consumption}[0.130, 0.701, 0.941, 0.889] initially decreases before increasing slightly, and then decreases again in recent years, showing inconsistency in growth.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Electricity Consumption demonstrates a more consistent growth over time. Its line plot exhibits a consistent upward trend with no significant drops or spikes, indicating steady growth in electricity consumption over the years. On the contrary, Gas Consumption shows a decreasing trend overall, indicating inconsistency in its growth. Hence, compared to Gas Consumption, Electricity Consumption shows a more stable and consistent growth over time."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "9BnQqlP5c9taFoKu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did Electricity Consumption increase between 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Electricity Consumption between 2000 and 2020, I locate {the line point for Year 2000 for Electricity Consumption}[0.130, 0.646] and {the line point for Year 2020 for Electricity Consumption}[0.941, 0.193]. Extracting the corresponding values: 0.45 and 1.27. The increase is calculated as 1.27 - 0.45 = 0.82.\nBased on the above thought, the answer is:\n0.82."
+            }
+        ],
+        "id_raw": 805
+    },
+    {
+        "id": "Hr075MdNyP6IAKub",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you extract the data into a table format with Years as columns and Cryptocurrencies as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.148, 0.911, 0.192, 0.934], [0.229, 0.911, 0.273, 0.934], [0.310, 0.911, 0.354, 0.934], [0.392, 0.911, 0.435, 0.934], [0.473, 0.911, 0.517, 0.934], [0.554, 0.911, 0.598, 0.934], [0.635, 0.911, 0.679, 0.934], [0.716, 0.911, 0.760, 0.934], [0.797, 0.911, 0.841, 0.934], [0.878, 0.911, 0.922, 0.934]], which represent the column headers or Years. Next, I identify {the legend texts}[[0.166, 0.091, 0.458, 0.121], [0.166, 0.134, 0.368, 0.164]] as the row labels representing the different cryptocurrencies. To populate the values, I locate {the line points}[[0.170, 0.667], [0.900, 0.086], [0.170, 0.729], [0.900, 0.273]] for each cryptocurrencies at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nCryptocurrency,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nMedical Tech Biotech Trends,1.1,1.3,1.7,2.0,2.5,2.8,3.0,3.2,3.6,3.9\nBiomedicine Trends,0.8,0.9,1.1,1.5,2.0,2.1,2.3,2.4,2.7,3.0"
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "IWEjX1e07J3DBzo4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend change of Medical Tech Biotech from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend change, I need to observe {the line of Medical Tech Biotech}[0.170, 0.086, 0.900, 0.667]. There's a consistent rise in the trend from 2011 to 2020. The growth pattern is quite steady with no abrupt dips or peaks. The line starts at a lower point and gradually ascends to a higher point over the years. This shows that there has been a steady increase in Medical Tech Biotech Trends from 2011 to 2020.\nBased on the above thought, the answer is:\nThe trend of Medical Tech Biotech from 2011 to 2020 has been consistently increasing. This illustrates a steady growth in the Medical Tech Biotech Trends over the decade without any significant fluctuations. Therefore, it can be concluded that the Medical Tech Biotech Trends have shown a steady and sustained upward trend over this period."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "0GjcOz4w7wdZyq4n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in trends value between the start of 2011 and end of 2020 for Biomedicine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in trends value for Biomedicine between 2011 and 2020, I need to locate {the line points for Biomedicine at 2011 and 2020}[ [0.170, 0.729], [0.900, 0.273]] and extract the corresponding values: 0.8 and 3.0. The difference between the ending value and the starting value is therefore 3.0 - 0.8 = 2.2.\nBased on the above thought, the answer is:\n2.2."
+            }
+        ],
+        "id_raw": 806
+    },
+    {
+        "id": "GPfAsv0kfkmmhquy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the investment trends for the different sectors, shown in the line chart, into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can convert the chart into a table. First, I identify the {x-axis labels}<r>[0.112, 0.911, 0.976, 0.934]</r> which represent years which will be the rows of the table. Then, I find {the legend entries}<r>[0.666, 0.785, 0.971, 0.881]</r> which represent different sectors that will be the columns of the table. For each sector, I extract the corresponding values from {the points of the lines}<r>[0.130, 0.301, 0.940, 0.830]</r>,<r>[0.130, 0.360, 0.940, 0.771]</r>, which indicate the investment amount at each year.\nBased on the above thought, the answer is:\nYear,Information Technology,Biotechnology,Nanotechnology,Green Technology\n2016,199,207,137,175\n2017,210,220,140,180\n2018,230,230,140,195\n2019,240,235,135,200\n2020,245,242,135,205"
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "xrsOj9BdTt785CmU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows the most stable growth and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the trend lines for each sector. {The line for Information Technology}<r>[0.130, 0.301, 0.940, 0.830]</r> shows persistent growth throughout the period, {the line for Biotechnology}<r>[0.130, 0.301, 0.940, 0.830]</r> shows a healthy upward trend with highest growth rate, {the line for Nanotechnology}<r>[0.130, 0.301, 0.940, 0.830]</r> shows a trend of substantial decrease with peak in mid period, and {the line for Green Technology}<r>[0.130, 0.301, 0.940, 0.830]</r> shows periodic fluctuations but overall trend is upward. Based on the steadiness and consistency of the trend, the Information Technology sector shows the most stable growth.\nBased on the above thought, the answer is:\nInformation Technology is the sector with the most stable growth, as the line representing this sector on the chart exhibits a consistent and steady upward trend without any significant dips or fluctuations, indicating a stable rate of growth over the five-year period."
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "vUPjth4mZMtbUB6g",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth rate of the Information Technology sector from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average growth rate, I need to observe the {annual investment for the Information Technology sector}<r>[0.130, 0.301, 0.940, 0.830]</r>. The initial investment in 2016 is 199 and final in 2020 is 245. The formula of average growth rate is ((final value/initial value)^(1/number of periods))-1. So, ((245/199)^(1/5))-1 = 0.044 or 4.4%. So, the average growth rate is 4.4%.\nBased on the above thought, the answer is:\n4.4%"
+            }
+        ],
+        "id_raw": 807
+    },
+    {
+        "id": "asCz2ow96Q8CDvaW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, could you extract the data into a table with Year as columns and Technologies as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.067, 0.911, 0.111, 0.934], [0.176, 0.911, 0.220, 0.934], [0.285, 0.911, 0.329, 0.934], [0.393, 0.911, 0.437, 0.934], [0.502, 0.911, 0.546, 0.934], [0.611, 0.911, 0.655, 0.934], [0.720, 0.911, 0.764, 0.934], [0.829, 0.911, 0.873, 0.934], [0.938, 0.911, 0.981, 0.934]], which represent the column headers or years. Next, I identify the {legend texts}[[0.166, 0.091, 0.475, 0.121], [0.166, 0.134, 0.450, 0.164]] as the row labels representing the different technologies. To populate the values, I locate the line points for each technology at the respective years and extract the labeled values.\nBased on the above thought, the answer is:\nTechnologies,2012,2013,2014,2015,2016,2017,2018,2019,2020\nEco-Friendly Product Revenue,0.3,0.6,0.4,0.7,0.75,0.8,1,1.2,1.3\nSocial Responsibility Spend,0.4,0.5,0.6,0.7,0.8,0.6,0.9,1.1,1.4"
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "vLVFjvEyiavznUJZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this line chart, which area of the business appears to consistently increase its profits over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which business area consistently increases profits, I need to analyze the trends of the lines. By looking at {the line of Eco-Friendly Product Revenue}[[0.089, 0.193, 0.959, 0.831]], I can see that it shows a steady increase over the years, with only one significant decrease in 2014. But after that year, the growth is relatively consistent.\nIn contrast, {Social Responsibility Spend}[[0.089, 0.129, 0.959, 0.767]] does not show a consistent increase; there is a decrease in 2017.\nBased on the above thought, the answer is:\nBased on this line chart, the Eco-Friendly Product Revenue business area appears to consistently increase its profits over the years. The line corresponding to this category shows a consistent upward trend, meaning that revenue has generally increased year after year. Whereas, the Social Responsibility Spend has some fluctuations and decrease in 2017, hence it does not show a consistent increase."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "Xkl4XS1FnqAOQttO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total combined revenue of Eco-Friendly Product Revenue and Social Responsibility Spend for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total combined revenue for 2020, I need to locate the line points for each revenue type. The line point for {Eco-Friendly Product Revenue in 2020}[[0.959, 0.193]] indicates a value of 1.3 million. Similarly, the line point for {Social Responsibility Spend in 2020}[[0.959, 0.129]] represents a value of 1.4 million. The total combined revenue for 2020 is therefore 1.3 + 1.4 = 2.7 million.\nBased on the above thought, the answer is:\n2.7 million."
+            }
+        ],
+        "id_raw": 809
+    },
+    {
+        "id": "ZUfn5N3TEA8pDeKv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the annual stock prices for both EM and WM across the years depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I see two distinct line plots representing the stock prices for EM and WM. Each line plot contains data points for each year from 2001 to 2020. Specifically, I locate {the EM stock price points}[[0.126, 0.549], [0.169, 0.480], ..., [0.941, 0.135]] and similarly for WM. I extract each point's x-coordinate to map to the year on {the x-axis tick labels}[[0.062, 0.911, 0.106, 0.934], [0.147, 0.911, 0.191, 0.934], ..., [0.919, 0.911, 0.962, 0.934]], and the y-coordinate to approximate the stock prices from the {y-axis tick labels}[[0.061, 0.885, 0.071, 0.909], [0.061, 0.747, 0.071, 0.771], ..., [0.049, 0.056, 0.071, 0.080]]. I look for both EM and WM lines to produce a comprehensive table.\nBased on the above thought, the answer is:\nYear,EM Stock Price,WM Stock Price\n2001,5,7\n2002,6,8\n2003,7,6\n2004,7,5\n2005,7,6\n2006,8,7\n2007,8.5,7.5\n2008,6,8\n2009,5,5.5\n2010,4,7\n2011,4.5,8\n2012,5,7.5\n2013,6,7\n2014,7,6\n2015,8,6.5\n2016,9,7\n2017,9.5,7.5\n2018,10,8\n2019,10.5,8.5\n2020,11,9"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "Zo3MOzVXeKGrtwTx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the stock price trend for WM appear from 2010 to 2020, and what could be the potential implication of such trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stock prices for WM from 2010 to 2020, I notice fluctuations with some years exhibiting growth and others showing decline. I locate {the WM stock price points from 2010 to 2020}[[0.512, 0.411], [0.555, 0.342], ..., [0.941, 0.273]] that illustrate these shifts. The trend suggests a generally unstable market condition, possibly reflecting varying investor confidence or market volatility. The implications could be greater caution amongst investors, greater market speculation, or shifts in strategic directions within WM's industry, influencing how stock prices evolve over this period.\nBased on the above thought, the answer is:\nThe stock price trend for WM shows fluctuations between 2010 and 2020, indicating periods of growth and decline. This could imply a potentially volatile market environment, affected by changes in investor confidence or external economic factors, leading to cautious investment strategies or increased market analysis among stakeholders."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "f0ApwtftmYRobL3z",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average stock price difference between EM and WM over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average stock price difference, first, I extract the stock prices for EM and WM across all years from the line chart: EM stock prices are [5, 6, ..., 11] and WM stock prices are [7, 8, ..., 9] which I derived from observing {the line points for EM}[[0.126, 0.549], [0.169, 0.480], ..., [0.941, 0.135]] and {the line points for WM}[[0.126, 0.411], [0.169, 0.342], ..., [0.941, 0.273]]. I compute the differences for each year and then find the average using mathematical operations.\nBased on the above thought, the answer is:\n1.6"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "ruWZ9bCnygAeUXK1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the annual values shown in the line chart for 'Erratic growth' and 'Slow expansion' over the 11 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the line chart, I first observe {the x-axis labels}<r>[0.124, 0.911, 0.135, 0.934], [0.205, 0.911, 0.216, 0.934], ..., [0.930, 0.911, 0.952, 0.934]</r> which span from years '0' through '10'. Then I align these with the corresponding y-values tracked in two series marked as 'Erratic Growth' showing points {located at}<r>[0.130, 0.606], [0.211, 0.549], ..., [0.941, 0.332]</r> and 'Slow Expansion' with points at <r>[0.130, 0.570], [0.211, 0.556], ..., [0.941, 0.426]</r>. Using these coordinates, I will detail annual values for each series.\nBased on the above thought, the answer is:\nYear,Erratic Growth,Slow Expansion\n0,0.45,0.5\n1,0.53,0.52\n2,0.47,0.54\n3,0.55,0.56\n4,0.49,0.58\n5,0.57,0.60\n6,0.69,0.62\n7,0.67,0.64\n8,0.71,0.66\n9,0.75,0.68\n10,0.83,0.70"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "AmsUKKN7jOrUrmRg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year showed the highest increment for 'Slow Expansion'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest increment for 'Slow Expansion', I analyze the differences between consecutive y-values starting from {the initial point of Slow Expansion}<r>[0.130, 0.570]</r> and ending at the {last point}<r>[0.941, 0.426]</r>. The increases are 0.02 from year 0 to 1, 0.02 from 1 to 2, 0.02 from 2 to 3, and so on, with the largest visible increase happening from year 9 to year 10 where the value climbs from 0.68 to 0.70.\nBased on the above thought, the answer is:\nThe highest increment for 'Slow Expansion' was observed between year 9 and year 10."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "kh9nfUMCH7Km6aIU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall change in 'Erratic Growth' from year 0 to year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in 'Erratic Growth', I locate the initial value at {the first point of Erratic Growth}<r>[0.130, 0.606]</r> which corresponds to 0.45 and the final value at {the last point of Erratic Growth}<r>[0.941, 0.332]</r> which is 0.83. The overall change is computed by subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n0.38"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "c9bUsk2PCiw7DdRL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing the performance of the financial asset from 2015 to 2021 into a structured table with 'Year' as rows and 'Investment_Return' and 'Risk_Index' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the line chart by identifying {the x-axis tick labels}<r>[0.105, 0.921]</r> which represent the years from 2015 to 2021. Then, I track the coordinates of the data points for 'Investment_Return' and 'Risk_Index' from the plotted lines. For 'Investment_Return', the points are located at {the line for Investment_Return}<r>[0.105, 0.862]</r> across these years, and for 'Risk_Index', the points are marked on {the line for Risk_Index}<r>[0.105, 0.176]</r>. These point coordinates correspond to the respective values for each year.\nBased on the above thought, the answer is:\nYear,Investment_Return,Risk_Index\n2015,5.0,80\n2016,6.0,75\n2017,7.0,70\n2018,8.0,65\n2019,9.0,60\n2020,8.5,55\n2021,9.5,50"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "q5D7TSUvlSrWgpiZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications do the trends in 'Investment_Return' and 'Risk_Index' have for potential investors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the implications for investors, I assess the behavior of both 'Investment_Return' and 'Risk_Index' in the chart. Observing {the line for Investment_Return}<[0.105, 0.862]> and {the line for Risk_Index}<[0.105, 0.176]>, 'Investment_Return' is generally increasing over the years, indicative of a potentially profitable asset. Conversely, 'Risk_Index' decreases over the same period, suggesting a decrease in investment risk. In 2020, a slight dip in 'Investment_Return' alongside a continuing decrease in 'Risk_Index' could suggest better risk management but might indicate temporary performance issues within that year.\nBased on the above thought, the answer is:\nThe trends suggest that the asset is becoming increasingly profitable while simultaneously becoming less risky. This dual appeal of high returns with lower risk levels makes it an attractive option for potential investors, particularly as risk management seems effective even when 'Investment_Return' temporarily dips, as seen in 2020."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "g6SJ9apBvcwTsSwq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average 'Risk_Index' over the period from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the average 'Risk_Index', I must extract the respective values from the chart by analyzing {the line for Risk_Index}<r>[0.105, 0.176]</r>. The extracted values for 'Risk_Index' from 2015 to 2021 are 80, 75, 70, 65, 60, 55, and 50. The next step involves calculating the average of these numbers.\nBased on the above thought, the answer is:\n65."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "oYeRdXqhDutbJbVe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the chart comparing Faith, Steadiness, and Honesty across different moods into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a CSV table format, I first look at {the x-axis tick labels}<r>[0.076, 0.911, 0.103, 0.934], [0.155, 0.911, 0.183, 0.934], ..., [0.954, 0.911, 0.981, 0.934]</r> to identify the different moods. Each label corresponds to a mood. Then, I recognize {the lines representing Faith, Steadiness, and Honesty}<r>[0.169, 0.409, 0.888, 0.740], [0.169, 0.466, 0.888, 0.752], [0.169, 0.351, 0.888, 0.729]</r> on the chart. By analyzing the points on these lines, I calculate the scores for each behavior under each mood.\nBased on the above thought, the answer is:\nMood,Faith,Steadiness,Honesty\nRapid Increase,3.2,3.0,3.4\nDramatic Surge,5.5,5.3,5.6\nSlow Recovery,4.0,4.2,3.9\nSteady Decline,2.8,3.0,2.7\nSlow Expansion,3.6,3.7,3.5\nRapid Growth,5.1,5.0,5.2"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "vmB5i79PCxIuG0fY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich behavior shows the least variation in response to changes in moods, according to the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which behavior shows the least variation, I examine the spread and fluctuation of {the line plots for Faith, Steadiness, and Honesty}<r>[0.169, 0.409, 0.888, 0.740], [0.169, 0.466, 0.888, 0.752], [0.169, 0.351, 0.888, 0.729]</r>. Faith and Steadiness exhibit significant increases and decreases, especially visible during 'Dramatic Surge' and 'Rapid Growth'. In contrast, {the line for Honesty}<r>[0.169, 0.351, 0.888, 0.729]</r> shows a more consistent pattern with less dramatic fluctuations, suggesting it is less affected by changes in mood.\nBased on the above thought, the answer is:\nHonesty shows the least variation in response to changes in moods. The line for Honesty exhibits a more consistent trajectory compared to the lines for Faith and Steadiness, indicating it responds with less fluctuation to changing moods."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "e8CHB5hRGvp4Otrz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score for Honesty across all moods in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average score for Honesty, I analyze {the points representing Honesty}<r>[0.169, 0.729], [0.249, 0.694], [0.329, 0.677], [0.409, 0.632], [0.488, 0.620], [0.568, 0.574], [0.648, 0.569]</r> across all moods. The corresponding numeric scores are 3.4, 5.6, 3.9, 2.7, 3.5, 5.2, and 4.0. Calculating the average of these scores gives the overall average score for Honesty.\nBased on the above thought, the answer is:\n4.04"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "7KdjBmYHqjjFnuWo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data into a table with Cultural Heritage types as rows and Culture Types (Asian and African) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To glean the tabular data from the line chart, first, I ascertain {the x-axis labels}<r>[0.108, 0.911, 0.963, 0.934]</r> as respective row headers or Cultural Heritage types. Subsequently, I perceive {the legend text}<r>[0.865, 0.091, 0.935, 0.164]</r>, 'Facebook' and 'Twitter' as African and Asian culture types respectively serving as the column headers. Proceeding to populate the values, I discern {the line points for Facebook}<r>[0.130, 0.148, 0.941, 0.812]</r> and {the line points for Twitter}<r>[0.130, 0.563, 0.941, 0.770]</r> thus obtaining the corresponding values for each Cultural Heritage type.\nBased on the above thought, the answer is:\nCultural Heritage,Asian,African\nHistorical Monuments,32,67\nArtifacts,42,57\nFolklores,45,59\nTraditional Games,47,64 \nIndigenous Cuisine,52,59\nTraditional Music,57,56\nIndigenous Languages,56,52\nIndigenous Religions,52,45"
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "G8JVHf5Z04SpKgcE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends depicted in the line graph, what inference can you draw about the progression of the integration of Indigenous Religions between the two cultures over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form conclusions about the progression of the integration of Indigenous Religions between the two cultures, I need to analyze {the line corresponding to Indigenous Religions for Asian}<r>[0.941, 0.148]</r> and {African}<r>[0.941, 0.770]</r> cultures. The graph exhibits a steady decline for both lines, which suggests a waning interest or recognition for Indigenous Religions over the years.\nBased on the above thought, the answer is:\nGiven the steady decline exhibited in the line graph for both Asian and African cultures in terms of the integration of Indigenous Religions, it could be inferred that there has been a diminishing recognition or interest in Indigenous Religions over the years among these cultures. This decline could possibly be caused by factors such as modernization and the growing influence of global religions."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "HNuJhgSvc7sfGeaC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the difference in integration of the Traditional Music heritage category between African and Asian cultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover the discrepancy in integration of Traditional Music between African and Asian cultures, I locate {the line point for Traditional Music in Asian Culture}<r>[0.778, 0.273]</r> and extract its corresponding value as 57. In the same vein, I examine {the line point for Traditional Music in African Culture}<r>[0.778, 0.729]</r> and deduce the value as 56. Hence, the difference between Asian and African integration for Traditional Music is 57 - 56 = 1.\nBased on the above thought, the answer is:\n1."
+            }
+        ],
+        "id_raw": 817
+    },
+    {
+        "id": "CPHhvi2JWcYd9Tmi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a structured table that lists the Year, Fintech Ratings, Biotechnology Ratings, and Robotics Ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart into a table, I first identify {the x-axis labels}<r>[0.108, 0.911, 0.152, 0.934], [0.198, 0.911, 0.242, 0.934], ..., [0.919, 0.911, 0.963, 0.934]</r> representing the years. Next, I review the line plots for Fintech, Biotechnology, and Robotics and extract the performance ratings from their respective {data points}<r>[0.130, 0.722], [0.220, 0.664], ..., [0.941, 0.204]</r> for Fintech, <r>[0.130, 0.687], [0.220, 0.626], ..., [0.941, 0.135]</r> for Biotechnology, and <r>[0.130, 0.584], [0.220, 0.545], ..., [0.941, 0.238]</r> for Robotics.\nBased on the above thought, the answer is:\nYear,Fintech,Biotechnology,Robotics\n2010,0.25,0.45,0.5\n2011,0.35,0.55,0.6\n2012,0.40,0.60,0.65\n2013,0.45,0.65,0.70\n2014,0.55,0.70,0.75\n2015,0.70,0.75,0.80\n2016,0.80,0.80,0.85\n2017,0.90,0.90,0.90\n2018,0.95,0.95,0.95\n2019,1.00,1.00,1.00"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "QTjSEjY3azZjSgR9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology sector shows the most consistent growth over the decade, and how can this be deduced from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector showcases the most consistent growth, I focus on the slope and progression of each line in the chart. Observing {the line for Fintech}<r>[0.130, 0.204, 0.941, 0.722]</r>, it exhibits a constant upward trend with no declines or plateaus from the beginning to the end point. As opposed to {the lines for Biotechnology}<r>[0.130, 0.687, 0.941, 0.135]</r> and {Robotics}<r>[0.130, 0.584, 0.941, 0.238]</r>, which show fluctuations and less consistent trajectories.\nBased on the above thought, the answer is:\nThe Fintech sector shows the most consistent growth over the decade. This conclusion is based on the consistent and uninterrupted upward trend observed in the line representing Fintech, without any periods of stagnation or decline as seen in the other sectors' lines on the chart."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "LNbYrJt9cqxFfbLv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_41_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in performance rating for Robotics from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in performance rating for Robotics from 2010 to 2019, I analyze the values at {the start and end data points for Robotics}<r>[0.130, 0.584], [0.941, 0.238]</r>. In 2010, the rating is 0.5 and in 2019, it is 1.0. Calculating the difference gives an increase of 1.0 - 0.5 = 0.5.\nBased on the above thought, the answer is:\n0.5"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "HR0GliJ2rg67Hofi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data displayed in the line chart as a table with column headers as Cities and rows as years 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will start with identifying {the legend labels}<r>[0.852, 0.145, 0.887, 0.173], [0.852, 0.187, 0.887, 0.215]</r> which reveals the years 2020 and 2021. This would form the row headers in the table. The column headers would be the names of the cities located using {the x-axes labels}<r>[0.128, 0.909, 0.192, 0.937], [0.236, 0.909, 0.319, 0.937], [0.367, 0.909, 0.423, 0.937], [0.484, 0.909, 0.541, 0.937], [0.602, 0.909, 0.657, 0.937], [0.705, 0.909, 0.790, 0.937], [0.823, 0.909, 0.907, 0.937]</r>. The values for each city for 2020 and 2021 are plotted as {points on the lines for 2020 and 2021}<r>[0.160, 0.762], [0.278, 0.482], [0.395, 0.808], [0.512, 0.342], [0.630, 0.155], [0.747, 0.668], [0.865, 0.248], [0.160, 0.715], [0.278, 0.528], [0.395, 0.855], [0.512, 0.342], [0.630, 0.155], [0.747, 0.715], [0.865, 0.295]</r>. These points correspond to the values for each city and each year, which will populate the table.\nBased on the above thought, the answer is:\nCity,2020,2021\nNew York,12,13\nLos Angeles,18,17\nChicago,11,10\nHouston,21,21\nPhoenix,25,25\nPhiladelphia,14,13\nSan Antonio,23,22"
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "gwQF7M6VXM2i9M1B",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we predict how the average temperatures may change in these cities in the coming years based on the data provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the limited data points available from the line plot for the years 2020 and 2021, prediction of future temperature trends might not be accurate. While future temperatures can be affected by numerous factors (climate change, weather patterns, etc.) that are not represented in this chart, we can attempt a very general prediction based on the trend shown, focusing on cities such as {New York}[0.128, 0.909, 0.192, 0.937], {Los Angeles}[0.236, 0.909, 0.319, 0.937], and {Philadelphia}[0.705, 0.909, 0.790, 0.937] that show a decrease in average temperature from 2020 to 2021.\nBased on the above thought, the answer is:\nWhile an accurate prediction cannot be made due to the limited data and multiple affecting factors not accounted for in the chart, we could infer that if the trend observed in 2020 and 2021 continues, cities like New York, Los Angeles, and Philadelphia might experience a slight decrease in average temperature in the coming years."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "cwp4NiY4huaNQvpV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city shows the largest decrease in average temperature from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in average temperature from 2020 to 2021 for each city. Let's start with {the line points for 2020}<r>[0.160, 0.762], [0.278, 0.482], [0.395, 0.808], [0.512, 0.342], [0.630, 0.155], [0.747, 0.668], [0.865, 0.248]</r> and {for 2021}<r>[0.160, 0.715], [0.278, 0.528], [0.395, 0.855], [0.512, 0.342], [0.630, 0.155], [0.747, 0.715], [0.865, 0.295]</r>, which correspond to their y-coordinates on the chart. The decrease for each city would be the value in 2020 minus the value in 2021. We will look at each city and find the maximum difference.\nBased on the above thought, the answer is:\nPhiladelphia."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "1Tb4jaPMe0geot6Q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the growth rates of 'Knowledge Economy' and 'Innovation Industries' over the course of 7 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table, I can use the caption and data section but I must extract the relevant key from chart too. For column headers, I find the years from {the x-axis tick labels}[[0.131, 0.885, 0.149, 0.921], [0.251, 0.885, 0.269, 0.921], [0.371, 0.885, 0.389, 0.921], [0.491, 0.885, 0.509, 0.921], [0.611, 0.885, 0.629, 0.921], [0.731, 0.885, 0.749, 0.921], [0.851, 0.885, 0.869, 0.921]]. The row labels would be 'Knowledge Economy' and 'Innovation Industries'. The corresponding values can be extracted by tracing the points from {the line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645] and {the line for Innovation Industries}[0.140, 0.151, 0.860, 0.754].\nBased on the above thought, the answer is:\nYears,Knowledge Economy,Innovation Industries\n0,25.0,22.0\n1,26.5,21.6\n2,28.3,20.5\n3,29.1,19.6\n4,30.2,18.9\n5,30.6,17.7\n6,31.7,17.0"
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "Ab4qGvfdE1hrdlVV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompared to Innovation Industries, how has the growth of Knowledge Economy progressed over the 7 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the {line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645] with the {line for Innovation Industries}[0.140, 0.151, 0.860, 0.754], it is clear that the Knowledge Economy has seen a consistent growth while Innovation Industries has seen a decrease over the years. Knowledge Economy started at 25% and increased to 31.7%, while Innovation Industries started at 22% and decreased to 17% over the seven years.\nBased on the above thought, the answer is:\nKnowledge Economy has shown a consistent upward trend, increasing from an initial growth rate of 25% to 31.7% by the end of the 7-year period. In contrast, Innovation Industries has displayed a decreasing trend, starting from 22% and falling to 17%. Thus, compared to Innovation Industries, Knowledge Economy has shown positive and consistent growth over the examined period."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "Dovbp2DtBxOBLn80",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage growth for Knowledge Economy over the 7-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage growth for Knowledge Economy, I need to find the difference between the final and initial values. Inspecting {the line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645], I find that the growth starts from 25.0% in year 0 and ends at 31.7% in year 6, so the total growth is 31.7 - 25.0 = 6.7%.\nBased on the above thought, the answer is:\n6.7%."
+            }
+        ],
+        "id_raw": 821
+    },
+    {
+        "id": "kXGRbjLPWxYM0jFb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data that shows activity rates in different welfare categories into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a structured table format, I first observe {the x-axis labels for categories}[0.077, 0.903, 0.923, 0.975] representing different welfare activities like Donation, Volunteering, etc. I then analyze {the data points for each category}[0.116, 0.397, 0.884, 0.863] across the years from the line plots. Based on this assessment of the chart data, I compile a table reflecting the activity rates for each Welfare category over the years.\nBased on the above thought, the answer is:\nCategory,2012,2013,2014,2015,2016,2017,2018,2019,2020\nDonation,20,18,15,12,10,9,8,7,6\nVolunteering,30,29,28,26,25,24,23,22,20\nAdvocacy,40,42,47,52,56,59,62,64,66\nPartnership,10,11,10,10,9,8,7,7,8"
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "voUhoTKYVSqopGJp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich welfare activity shows the most consistent trend in activity rate reduction over the years, and how can this be interpreted from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line plots on the chart}, I identify that Partnership[0.116, 0.809, 0.884, 0.863] shows a downward trend, starting from 10% in 2012 and ending at 8% in 2020 with very gentle fluctuations. This consistent decline in activity rate over the years might indicate lesser focus or diminishing resources being allocated to this welfare activity compared to others.\nBased on the above thought, the answer is:\nPartnership shows the most consistent trend in activity rate reduction over the years. This can be interpreted from the steadily declining line within its subplot, representing a continual decrease in engagement or resources committed to partnership activities over the assessed years."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "lsjln2N2lHXGFaLK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the activity rate for Volunteering decreased from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease in the activity rate for Volunteering from 2012 to 2020, I examine {the data points for Volunteering}[0.116, 0.065, 0.884, 0.397] which show the activity rates starting from 30% in 2012 to 20% in 2020. The difference in activity rates between these two years is 30% - 20%.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "vP8Qs0e2WVARhFI7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please help extract the data from the given chart into an organized table format with \"Digital Tools Number\" and \"Error Rate\" for both \"Audio\" and \"Video\" types?"
+            },
+            {
+                "from": "gpt",
+                "value": "For creating a structured table from this dual-line chart, I start with setting up the columns as \"Digital Tools Number\", \"Audio Error Rate\" and \"Video Error Rate\". After that, we look at {the x-axis labels}<r>[0.011, 0.885, 0.029, 0.921], [0.131, 0.885, 0.149, 0.921], [0.251, 0.885, 0.269, 0.921], [0.371, 0.885, 0.389, 0.921], [0.491, 0.885, 0.509, 0.921], [0.611, 0.885, 0.629, 0.921], [0.731, 0.885, 0.749, 0.921], [0.851, 0.885, 0.869, 0.921], [0.962, 0.885, 0.998, 0.921]</r> which gives us the 'Digital Tools Number' as 3,4,5,6,7,8,9. Afterwards, we find {the line points for Audio errors}<r>[0.140, 0.030], [0.260, 0.157], [0.380, 0.277], [0.500, 0.374], [0.620, 0.528], [0.740, 0.769], [0.860, 0.842]</r> which are the corresponding values for 'Audio Error Rate'. And finally, we observe {the line points for Video errors}<r>[0.140, 0.032], [0.260, 0.172], [0.380, 0.307], [0.500, 0.451], [0.620, 0.578], [0.740, 0.726], [0.860, 0.874]</r> which represent the 'Video Error Rate'.\nBased on the above thought, the answer is:\nDigital Tools Number,Audio Error Rate,Video Error Rate\n3,26,21.96\n4,24.20,19.31\n5,22.48,16.74\n6,21.12,14.02\n7,18.93,11.62\n8,15.51,8.81\n9,14.47,6.02"
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "thk8SpsWkoguEwU4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the lines in the chart, can you identify which error type, Audio or Video, showed a more steady decrease? Please explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we have to analyze the trend along the line for both the Audio and Video errors. For {Audio Error}<r>[0.140, 0.030, 0.860, 0.842]</r>, the error rate starts at 26 and decreases to 14.47 with some fluctuations in between. For {Video Error}<r>[0.140, 0.032, 0.860, 0.874]</r>, the line starts at 21.96 and steadily decreases to 6.02. Although both error types decrease over time, the decrease in Video Error seems to be more steady as it does not have significant fluctuations as seen in Audio Error.\nBased on the above thought, the answer is:\nThe Video Error shows a more steady decrease. The Video error rate gradually drops from 21.96 to 6.02 without significant fluctuations, while the Audio error rate, although decreasing, does so with notable fluctuations in between, indicating less steadiness."
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "VulnX5OPiGafz5Qa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the error rate difference between audio and video at a digital tools number of 8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the error rates for audio and video at a digital tool number of 8, we need to find the corresponding points for both types on the line. Upon locating {the point for Audio error at a digital tool number of 8}<r>[0.740, 0.769]</r>, I can see that the value is 15.51. Similarly, by identifying the {point for Video error at a digital tool number of 8}<r>[0.740, 0.726]</r>, the value is 8.81. Therefore, the difference between the audio error rate and video error rate at a digital tool number of 8 is 15.51 - 8.81 = 6.7.\nBased on the above thought, the answer is:\n6.7"
+            }
+        ],
+        "id_raw": 823
+    },
+    {
+        "id": "zkGc1SY5Kv0PVCe5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the line chart data into a structured table presenting the assessment scores for the ecosystem under study for both Tropical Forest and Arctic Ice with respect to the condition of the environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I will first identify the x-axis labels corresponding to {the x-ticks}<r>[0.084, 0.919, 0.128, 0.942]</r>,<r>[0.284, 0.919, 0.328, 0.942]</r>,<r>[0.484, 0.919, 0.528, 0.942]</r>,<r>[0.684, 0.919, 0.728, 0.942]</r>,<r>[0.884, 0.919, 0.928, 0.942]</r>. These labels represent environmental conditions as various stages. I then locate the line coordinates for both the {Tropical Forest}<r>[0.106, 0.113, 0.894, 0.738]</r> and the {Arctic Ice}<r>[0.106, 0.060, 0.894, 0.850]</r> to extract assessment scores. This should provide us with a structured tabular data.\nBased on the above thought, the answer is:\nEnvironment Condition,Tropical Forest,Arctic Ice\nStable,1.00,0.95\nMinor decrease,0.90,0.80\nSubstantial decrease,0.75,0.70\nAccelerating decline,0.60,0.55\nDramatic decrease,0.30,0.20"
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "n6KcNPoseipAKKZ1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data displayed on the graph, which region seems to have sustained more damage? Kindly explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The degree of damage in each region can be inferred from the assessment scores and how these scores decline over different environmental conditions. If we look at the line coordinates for the {Tropical Forest}<r>[0.106, 0.113, 0.894, 0.738]</r>, we see a steady decline in scores. Comparing this with the coordinates for {Arctic Ice}<r>[0.106, 0.060, 0.894, 0.850]</r>, the decline is similar, but the drop is even more significant at the \"Dramatic decrease\" stage.\nBased on the above thought, the answer is:\nBased on the data in the graph, the Arctic Ice region seems to have sustained more damage. The assessment score for Arctic Ice falls more dramatically than that of the Tropical Forest when environmental condition changes from 'Accelerating decline' to 'Dramatic decrease'. Both ecosystems show degradation, but the condition of Arctic Ice appears to have deteriorated more severely."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "AcFNRESa7fE0c3Qh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the assessment score for the Arctic Ice decrease when the environmental condition changes from a \"Stable\" state to the state of \"Dramatic decrease\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to locate the coordinates of the line representing the {Arctic Ice}<r>[0.106, 0.060, 0.894, 0.850]</r>. The score for a \"Stable\" state corresponds to the first point, and the score for the \"Dramatic decrease\" state corresponds to the last point. By extracting the y-values for these points, I can calculate the difference.\nBased on the above thought, the answer is:\n0.75"
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "HikKEvVb3wd2qLd4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a tabular data representation from the given line chart with Years as rows and Technologies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to create a table representation from the line chart, I need to identify {the x-axis labels}<r>[0.027, 0.934, 0.062, 0.952], [0.134, 0.934, 0.169, 0.952], [0.241, 0.934, 0.276, 0.952], [0.348, 0.934, 0.383, 0.952], [0.456, 0.934, 0.491, 0.952], [0.563, 0.934, 0.598, 0.952], [0.670, 0.934, 0.705, 0.952], [0.777, 0.934, 0.813, 0.952], [0.885, 0.934, 0.920, 0.952], [0.992, 0.934, 1.027, 0.952]</r> which represent the years, these will be the rows in the table. Then, I check {the legend}<r>[0.064, 0.027, 0.281, 0.085]</r> for the different technologies, these will form the column headers. The data in the table can be obtained from the y-values of the points of the lines corresponding to each technology at the specified years. For example, {for the line representing Biomedical Technology}<r>[0.098, 0.060, 0.902, 0.887]</r>, each point's value corresponds to the amount of scientific research and technological innovations that year.\nBased on the above thought, the answer is:\nYear,Biomedical Technology,Ecological Technology\n2005,6.4,4.2\n2006,6.7,6.1\n2007,6.5,6.8\n2008,2.8,6.0\n2009,-3.5,4.6\n2010,3.0,6.0\n2011,2.4,7.2\n2012,3.6,8.1\n2013,4.0,7.4\n2014,4.2,8.5\n2015,4.4,10.1\n2016,4.0,9.0\n2017,4.5,10.2\n2018,5.0,11.0\n2019,3.0,9.0\n2020,-5.0,6.0"
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "ljhIgNwqL7mn7hU4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which sector has shown the most overall growth between 2005 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the change in each line from the beginning to the end. I'll examine {the Biomedical Technology line}<r>[0.098, 0.060, 0.902, 0.887]</r> and {the Ecological Technology line}<r>[0.098, 0.060, 0.902, 0.887]</r> and compare the initial and final values. For Biomedical Technology, the initial value in 2005 is 6.4 and the final value in 2020 is -5.0. For Ecological Technology, the initial value in 2005 is 4.2 and the final value in 2020 is 6.0. The overall change for each field can be calculated as the final value minus the initial value.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Ecological Technology sector has shown the most overall growth between 2005 and 2020. Although it started out with a lower value compared to Biomedical Technology, it consistently grew over the years exceeding the growth in Biomedical Technology, which exhibited a negative growth towards the end of the period."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "Rg1oU6dZymgYCVqo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value reached in Biomedical Technology research and in which year did this occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value reached in Biomedical Technology research, I need to analyze {the line for Biomedical Technology}<r>[0.098, 0.060, 0.902, 0.887]</r>. Specifically, I need to identify the point with the maximum y-coordinate which corresponds to the highest value. This can be determined by the value annotated near each data point. The year in which this maximum value was reached can be determined by identifying the x-coordinate of the maximum point and mapping it onto the corresponding {x-axis labels} <r>[0.027, 0.934, 0.062, 0.952], [0.134, 0.934, 0.169, 0.952], [0.241, 0.934, 0.276, 0.952], [0.348, 0.934, 0.383, 0.952], [0.456, 0.934, 0.491, 0.952], [0.563, 0.934, 0.598, 0.952], [0.670, 0.934, 0.705, 0.952], [0.777, 0.934, 0.813, 0.952], [0.885, 0.934, 0.920, 0.952], [0.992, 0.934, 1.027, 0.952]</r>.\nBased on the above thought, the answer is:\nThe highest value reached in Biomedical Technology research is 5.0, which occurred in the year 2018."
+            }
+        ],
+        "id_raw": 826
+    },
+    {
+        "id": "EoZ250Tvnza3r1FS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table having Years as rows and Companies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the table, I will first select {the x-axis tick labels}[[0.133, 0.910, 0.188, 0.939],[0.289, 0.910, 0.344, 0.939],[0.446, 0.910, 0.501, 0.939],[0.602, 0.910, 0.657, 0.939],[0.759, 0.910, 0.814, 0.939],[0.916, 0.910, 0.970, 0.939]] which will form the row labels representing the years. Then, I identify {the legend texts}[[0.781, 0.704, 0.880, 0.733],[0.781, 0.748, 0.843, 0.777],[0.781, 0.791, 0.869, 0.820],[0.781, 0.835, 0.858, 0.864]] as the column headers representing the different companies. To fill the table, I locate {the line points} for each company at the corresponding years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Microsoft,Apple,Amazon,Google\n2010,32.4,8.6,25.0,11.0\n2011,33.0,11.2,26.2,12.1\n2012,33.2,14.2,26.9,12.4\n2013,32.9,15.4,27.1,14.0\n2014,31.6,16.8,27.9,15.4\n2015,31.1,18.0,28.2,16.7\n2016,30.5,19.7,29.1,18.0\n2017,30.0,21.0,29.5,18.8\n2018,29.5,22.2,30.0,19.5\n2019,29.0,23.5,30.8,20.0"
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "s4E4ACNDefggucCL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company experienced the biggest drop in market share over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company experienced the biggest drop, I need to look at the start and end points of each company's {line}[0.125, 0.120, 0.900, 0.890]. If I examine {Microsoft's line}[0.160, 0.155, 0.865, 0.275], it started with a market share of 32.4% and ended with 29.0%, a drop of 3.4%. {Apple's line}[0.160, 0.431, 0.865, 0.855] represents a rise in market share from 8.6% to 23.5%, so it is not a candidate. {Amazon's line}[0.160, 0.223, 0.865, 0.388] shows a rise from 25.0% to 30.8%. Finally, {Google's line}[0.160, 0.531, 0.865, 0.787] also shows a rise from 11.0% to 20%. Therefore, Microsoft experienced the biggest drop.\nBased on the above thought, the answer is:\nMicrosoft experienced the biggest drop in market share over the years, declining from 32.4% in 2010 to 29.0% in 2019, a drop of 3.4%."
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "4sRnPYYPVmUiAO94",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Google's market share from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, we need to obtain the values of Google's market share in 2010 and 2019. For 2010, we find the point {at the start of Google's line}[0.160, 0.787], which corresponds to a market share of 11.0%. For 2019, we locate the point {at the end of Google's line}[0.865, 0.531], corresponding to a market share of 20.0%. Thus, the percentage increase is (20.0 - 11.0) / 11.0 * 100 = 81.82%.\nBased on the above thought, the answer is:\n81.82%"
+            }
+        ],
+        "id_raw": 827
+    },
+    {
+        "id": "WESv7JeLMjcvRBGE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract all the specific index values for each year and each type of index from the line chart into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first identify the column headers as the labels on the {x-axis}<r>[0.143, 0.904, 0.882, 0.923]</r>. These represent the years from 1990 to 2020. The row labels are given by the {legend}<r>[0.131, 0.129, 0.894, 0.189]</r>, which are Ethical Values, Material Values, Social Values, and Cultural Values. I then locate the {points of intersection}<r>[0.160, 0.186, 0.865, 0.813]</r> of the lines representing each index with the gridmarks along the x-axis for each year and derive the respective index values from their y-coordinates.\nBased on the above thought, the answer is:\nYear,Ethical Values,Material Values,Social Values,Cultural Values\n1990,28,30,35,40\n1995,26,32,34,42\n2000,25,34,33,44\n2005,23,36,32,46\n2010,22,40,33,48\n2015,21,45,34,50\n2020,20,50,36,52"
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "OndyeJrHo7iDFuUQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which index shows the most significant change between 1990 and 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the changes in each of the four indices. First, I observe the line corresponding to {Ethical Values}<r>[0.160, 0.208, 0.865, 0.364]</r>, which drops from 28 in 1990 to 20 in 2020, marking a decrease of 8. Second, I look at the {Material Values}<r>[0.160, 0.186, 0.865, 0.340]</r> line, which shows a marked increase from 30 in 1990 to 50 in 2020, a total change of 20. The third line corresponds to {Social Values}<r>[0.160, 0.802, 0.865, 0.868]</r>, which fluctuates from 35 in 1990 to 36 in 2020, showing a change of just 1. Lastly, I see the {Cultural Values}<r>[0.160, 0.216, 0.865, 0.409]</r> line showing an increase from 40 in 1990 to 52 in 2020, a change of 12.\nBased on the above thought, the answer is:\nBased on the chart, Material Values shows the most significant change between 1990 and 2020. It started at an index value of 30 in 1990 and increased to 50 by 2020, marking a total change of 20. This is a more significant change compared to the other indices investigated."
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "PoyUTQYJ5KlRORuO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Material Values from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Material Values from 1990 to 2020, I locate the {line for Material Values}<r>[0.160, 0.186, 0.865, 0.340]</r> on the line chart. I then extract the values for 1990 and 2020 by locating the points representing these years on the line. The value for 1990 is 30 and for 2020 is 50. The increase in Material Values is therefore calculated as 50 - 30 = 20.\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 828
+    },
+    {
+        "id": "QN9i4NWIFNeKnNIx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transfer the year-to-year line chart data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I start by identifying {the x-axis labels}<r>[0.127,0.891,0.790,0.878]</r> which represent the years, these are to be used as the columns. I then recognize {the sublegend labels}<r>[0.192, 0.054, 0.267, 0.086]</r> that represent the rows' headers (Cargo volume in a port, Volume of trade, Number of cargo ships visiting a port). To fill in the table, I retrieve the values using {the line points for each metric}<r>[0.127, 0.239, 0.873, 0.881]</r>, <r>[0.127, 0.196, 0.873, 0.710]</r>.\nBased on the above thought, the answer is:\nYears,Cargo volume in a port,Volume of trade,Number of cargo ships visiting a port\n2010,200000,120000,600\n2011,220000,130000,650\n2012,250000,140000,700\n2013,210000,150000,750\n2014,240000,155000,770\n2015,255000,160000,820\n2016,280000,165000,850\n2017,310000,170000,900\n2018,325000,175000,950\n2019,350000,180000,1000"
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "VFvBwHhNYnqyUcGl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the overall trend in Cargo volume, Volume of Trade, and Number of Ships visiting the port be described during the period covered by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line graph represents the activities of a port over a decade. By examining the positions and shapes of {the line for Cargo Volume}<r>[0.127, 0.239, 0.873, 0.881]</r>, {the line for the volume of Trade}<r>[0.127, 0.196, 0.873, 0.710]</r>, and {the line for the Number of Ships}<r>[0.192, 0.054, 0.267, 0.086]</r>, it seems clear that all activities have a general upward trend. All three lines begin at lower left-hand points and end at upper right-hand points, indicating increased cargo volume and trade volume, and a growing number of ships visiting the port over the years.\nBased on the above thought, the answer is:\nThe overall trend in Cargo Volume, Volume of Trade, and Number of Ships visiting the port can be described as a steady increase during the period covered by the chart. This indicates positive growth and increased activity in the port over the decade from 2010 to 2019."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "REbteq6UQVt3cu39",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in Cargo volume from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in Cargo volume, I will use the {initial value for the Cargo volume}<r>[0.127, 0.881]</r> line in 2010 and the {final value for the Cargo volume}<r>[0.873, 0.239]</r> line in 2019. By subtracting the Cargo volume in 2010 from the Cargo volume in 2019, I can determine the change.\nBased on the above thought, the answer is:\n150000."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "of41MOepmif8PCRP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trend data per year for video tutorials, online quizzes, e-books, and LMS Systems from the stacked line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the stacked line graph}<r>[0.104, 0.044, 0.896, 0.875]</r>, I can identify unique trend markers for each category. The years, represented on {the x-axis}<r>[0.466, 0.932, 0.534, 0.970]</r>, and usage percentages, labeled on {the y-axis}<r>[0.025, 0.266, 0.057, 0.653]</r>, guide the data extraction. Each line represents a different technology tool with distinct colors, located at {the legend}<r>[0.120, 0.063, 0.476, 0.190]</r> that specifies labels. By analyzing the lines' starting and ending points along the x-axis, I compiled the yearly data.\nBased on the above thought, the answer is:\nYear,Video Tutorials,Online Quizzes,E-books,LMS Systems\n2016,34,25,18,30\n2017,38,28,21,33\n2018,44,34,26,38\n2019,49,39,30,43\n2020,55,45,33,48"
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "cWpIXYxCbR8DCfxs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the prevalence of LMS Systems compared to video tutorials changed over the years, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the comparative prevalence of LMS Systems against video tutorials over the years, I scrutinize the respective lines: {LMS Systems line}<r>[0.140, 0.447, 0.860, 0.750]</r> and {video tutorials line}<r>[0.140, 0.660, 0.860, 0.792]</r>. Starting in 2016, LMS Systems stood at 30% while video tutorials were at 34%. By 2020, LMS Systems reached 48% and video tutorials 55%. The gap between the usage of LMS Systems and video tutorials initially decreased, but steadily widened each year due to faster growth in video tutorial usage.\nBased on the above thought, the answer is:\nInitially, LMS Systems were slightly less prevalent than video tutorials. However, as the years progressed, the prevalence gap widened with video tutorials experiencing faster growth in usage percentage compared to LMS Systems."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "ALwPGDONfVoq9v7T",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in usage percentage for e-books from 2016 to 2020 based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in usage percentage for e-books from 2016 to 2020, I analyzed {the e-books line}<r>[0.104, 0.480, 0.860, 0.792]</r> within the chart. The usage percentage starts at 18 in 2016 and ends at 33 in 2020. Therefore, the total increase is calculated as 33 - 18.\nBased on the above thought, the answer is:\n15."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "ck2nuy40ACrEOxpJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data plot in the chart, can you produce a structured tabular format with years as columns and AI types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data from the line chart into a table, I initially observe {the x-axis labels}[[0.105, 0.885, 0.175, 0.921], [0.225, 0.885, 0.295, 0.921], [0.345, 0.885, 0.415, 0.921], [0.465, 0.885, 0.535, 0.921], [0.585, 0.885, 0.655, 0.921], [0.705, 0.885, 0.775, 0.921], [0.825, 0.885, 0.895, 0.921]], which represent the years or column headers. I then identify {the legend entries}[[0.224, 0.065, 0.455, 0.103]] as the row headers, which signify types of AIs. For the values, I find {the plot points}[[0.140, 0.805], [0.260, 0.770], [0.380, 0.734], [0.500, 0.593], [0.620, 0.382], [0.740, 0.241], [0.860, 0.065]], [0.140, 0.171], [0.260, 0.312], [0.380, 0.453], [0.500, 0.523], [0.620, 0.593], [0.740, 0.664], [0.860, 0.734] for each AI type at the corresponding years and extract the represented values.\nBased on the above thought, the answer is:\nAI Type,2015,2016,2017,2018,2019,2020,2021\nSelf Driving AI,50,55,60,80,110,130,155\neCommerce AI,120,110,100,95,90,85,80"
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "bb9tzp7eGmiCJ2gU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which AI has shown a more consistent decrease in WER% over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which AI has shown a more consistent decrease in WER%, I need to observe the trends in the line charts for the two AIs. Examining {the line for Self Driving AI}[0.140, 0.805, 0.860, 0.065], I notice a consistent increase from 50 in 2015 to 155 in 2021. On the other hand, {the line for eCommerce AI}[0.140, 0.171, 0.860, 0.734] shows a consistent decrease from 120 in 2015 to 80 in 2021.\nBased on the above thought, the answer is:\neCommerce AI has shown a more consistent decrease in WER% over the years. This is observed from the trend that indicates a steady decrease from 120 in 2015 to 80 in 2021 in the line chart."
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "SwtDnw7gsZojmM8l",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the WER% of Self Driving AI from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase in the WER% of Self Driving AI, I would need to find the value for Self Driving AI in 2015 and 2021. Hence I locate {the Point for Self Driving AI in 2015} [0.140, 0.805] which corresponds to a value of 50 and then locate {the Point for Self Driving AI in 2021} [0.860, 0.065] which corresponds to a value of 155. The total increase would therefore be 155 - 50 = 105.\nBased on the above thought, the answer is:\n105"
+            }
+        ],
+        "id_raw": 831
+    },
+    {
+        "id": "uH6kn8zet3eKAOVo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the line chart to a table with Years as columns and GDP Change (%) and Market Growth (%) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I notice {the x-axis tick labels}[[0.116, 0.891, 0.171, 0.920], [0.262, 0.891, 0.317, 0.920], [0.409, 0.891, 0.463, 0.920], [0.554, 0.891, 0.610, 0.920], [0.701, 0.891, 0.756, 0.920], [0.847, 0.891, 0.902, 0.920]], these represent the column headers or Years. Next, I identify {the legend texts}[[0.096, 0.031, 0.312, 0.069], [0.437, 0.031, 0.682, 0.069]] as the row labels representing GDP Change (%) and Market Growth (%). I then extract the data values by locating {the line points for GDP Change (%)}[[0.144, 0.352], [0.217, 0.290], [0.290, 0.222], [0.363, 0.250], [0.436, 0.069], [0.509, 0.081], [0.582, 0.109], [0.655, 0.188], [0.728, 0.352], [0.801, 0.437], [0.874, 0.832]] and {the line points for Market Growth (%)}[[0.144, 0.832], [0.217, 0.735], [0.290, 0.675], [0.363, 0.469], [0.436, 0.287], [0.509, 0.130], [0.582, 0.118], [0.655, 0.069], [0.728, 0.081], [0.801, 0.130], [0.874, 0.336]] for each year.\nBased on the above thought, the answer is:\nData,Years\n,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nGDP Change (%),3.5,4.6,5.8,5.3,8.5,8.3,7.8,6.4,3.5,2.0,-5.0\nMarket Growth (%),2.7,3.5,4.0,5.7,7.2,8.5,8.6,9.0,8.9,8.5,6.8"
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "ziHtIEowDNiXorhn",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the GDP Change (%) line, how did the GDP change from the year 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand how GDP has changed from the year 2015 to 2020, I need to look at {the line for GDP Change (%)}[[0.509, 0.081, 0.874, 0.832]], and pay specific attention to {the points representing the years 2015 and 2020}[[0.509, 0.081], [0.874, 0.832]]. In 2015, the GDP Change (%) was at 8.3, and by 2020, it went down to -5.0. It indicates that there was a considerable decline in GDP Change (%) over these years.\nBased on the above thought, the answer is:\nBased on the GDP Change (%) line, the GDP has decreased significantly from the year 2015 to 2020. In 2015, the GDP Change (%) was quite high, at 8.3, suggesting a period of economic growth. By 2020, this value had dropped to -5.0, indicating a shrinking economy, which is quite a substantial decline over this 5-year period."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "Eg8Snra2LHBr1N8v",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average GDP Change (%) across all the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average GDP Change (%), I have to sum up all the values for GDP Change (%) from each year and then divide the sum by the number of years. The relevant data points are located at {the line points for GDP Change (%)}[[0.144, 0.352], [0.217, 0.290], [0.290, 0.222], [0.363, 0.250], [0.436, 0.069], [0.509, 0.081], [0.582, 0.109], [0.655, 0.188], [0.728, 0.352], [0.801, 0.437], [0.874, 0.832]]. Extracting the values from these points: 3.5, 4.6, 5.8, 5.3, 8.5, 8.3, 7.8, 6.4, 3.5, 2.0, -5.0, the sum is 50.7. Since there are 11 data points, the average is 50.7 / 11 = 4.61.\nBased on the above thought, the answer is:\n4.61."
+            }
+        ],
+        "id_raw": 832
+    },
+    {
+        "id": "bTQUg5OOKqym3BEq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data depicted in the stacked bar plot for each manufacturing process into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stacked bar plot, I first identify each {manufacturing process on the x-axis}<r>[0.060, 0.914, 0.140, 0.951]</r> including Precision Tooling, Robotic Welding, Manual Assembly, and Tightening Operations. Each process bar consists of four segments representing different aspects which are determined using {the legend}<r>[0.800, 0.100, 0.950, 0.200]</r>, corresponding to Raw Material Usage, Efficiency Time, Quality Index, and Failure Rate. By evaluating the numeric labels inside each segment, I extract the data for each process.\nBased on the above thought, the answer is:\nProcess,Raw Material Usage,Efficiency Time,Quality Index,Failure Rate\nPrecision Tooling,50,30,70,10\nRobotic Welding,55,35,60,5\nManual Assembly,45,25,60,15\nTightening Operations,40,20,55,25"
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "hHhTSDGcTneuKaKy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich manufacturing process demonstrates the highest efficiency considering both Raw Material Usage and Efficiency Time combined, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the manufacturing process with the highest combined efficiency, I analyze the segments representing Raw Material Usage and Efficiency Time for each process. Observing {Precision Tooling's bar}<r>[0.100, 0.350, 0.200, 0.700]</r> which includes values of 50 for materials and 30 for time. Similarly, for {Robotic Welding}<r>[0.250, 0.300, 0.350, 0.650]</r> with values 55 and 35, {Manual Assembly}<r>[0.400, 0.370, 0.500, 0.670]</r> with values 45 and 25, and finally {Tightening Operations}<r>[0.550, 0.450, 0.650, 0.720]</r> with 40 and 20. Adding these values, it becomes apparent that Robotic Welding, with a combined score of 90, shows the highest efficiency.\nBased on the above thought, the answer is:\nRobotic Welding demonstrates the highest efficiency when considering both Raw Material Usage and Efficiency Time, as it has a combined total of 90, which is higher than the other processes."
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "4ofrwxP1tpfqY5Yq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total value represented by the Failure Rate category across all manufacturing processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total Failure Rate value, I locate the segments related to Failure Rate in each process bar using the color coding from {the legend}<r>[0.820, 0.171, 0.948, 0.191]</r>. Then I summarize the values: for Precision Tooling (10), Robotic Welding (5), Manual Assembly (15), and Tightening Operations (25). By adding them, 10 + 5 + 15 + 25, we obtain the total Failure Rate value.\nBased on the above thought, the answer is:\n55"
+            }
+        ],
+        "id_raw": 833
+    },
+    {
+        "id": "YNUdjxX8DWLL7XNQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the line chart into a table, with years as rows and industries as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I firstly identify {the x-axis tick labels}<r>[0.106, 0.880, 0.172, 0.918], [0.250, 0.880, 0.316, 0.918], [0.400, 0.880, 0.455, 0.919], [0.539, 0.880, 0.605, 0.918], [0.689, 0.890, 0.744, 0.919], [0.828, 0.890, 0.894, 0.919]</r> which represent the row labels or years. Next, I identify the line labels as the column headers representing the different industries. To populate the values, I locate the line points for each industry at the respective years and extract the values.\nBased on the above thought, the answer is:\nYear,E-commerce,Healthcare,Tech,Fintech\n2010,200,150,300,50\n2011,210,160,350,42\n2012,220,170,375,38\n2013,230,180,400,36\n2014,170,200,420,35\n2015,160,215,450,40\n2016,155,220,470,48\n2017,150,225,490,52\n2018,110,230,500,57\n2019,100,235,520,62\n2020,130,240,530,67"
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "7XbTq8Br99x21cdd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Tech industry see the highest investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the highest investment in the Tech industry, I need to locate the peak of the line for Tech. After examining {the line points for Tech}<r>[0.355, 0.125], [0.428, 0.277], [0.500, 0.416], [0.572, 0.485], [0.644, 0.575], [0.717, 0.763], [0.789, 0.832]</r>, I note that the highest point corresponds to the year 2019 {2019}<r>[0.789, 0.832]</r> where the y-value reached the maximum.\nBased on the above thought, the answer is:\n2019."
+            }
+        ],
+        "id_raw": 834
+    },
+    {
+        "id": "PJCyPdHqpmQPIscF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the chart into a structured table format with years as rows and revenue areas as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build the structured table, first I locate {the x-axis tick labels}<r>[0.194, 0.764, 0.268, 0.837], [0.391, 0.764, 0.467, 0.837], [0.584, 0.764, 0.660, 0.837], [0.778, 0.764, 0.852, 0.837]</r> which represent the row headers or years. Next, I identify {the legend texts}<r>[0.892, 0.553, 0.948, 0.628], [0.892, 0.480, 0.948, 0.552], [0.892, 0.627, 0.948, 0.702]</r> which denote the column headers representing the different revenue areas. Then, the stacked bars' heights, like {Game Sales in 2018}<r>[0.205, 0.115, 0.255, 0.202]</r>, {Subscription in 2018}<r>[0.205, 0.202, 0.255, 0.505]</r>, {Ad Revenue in 2018}<r>[0.205, 0.573, 0.255, 0.699]</r> and so on, give us the values for respective years and revenue areas.\nBased on the above thought, the answer is:\nYear,Game Sales,Subscriptions,Ad Revenue\n2018,40,62,30\n2019,30,55,45\n2020,20,60,60\n2021,50,65,70"
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "3PsvIVzUwQTZPHvm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector suffered the most during the time of Covid-19 in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector that experienced the most difficulties during the Covid-19 in 2020, I must closely observe the changes in the bars from 2019 to 2020. From the difference in the heights of {contributions of Game Sales in 2019 and 2020}<r>[0.401, 0.115, 0.448, 0.242],[0.594, 0.115, 0.644, 0.203]</r>, {Subscriptions in 2019 and 2020}<r>[0.401, 0.248, 0.448, 0.561],[0.594, 0.203, 0.644, 0.456]</r>, and the {Ad Revenue in 2019 and 2020}<r>[0.401, 0.561, 0.448, 0.694],[0.594, 0.456, 0.644, 0.707]</r>, I can see that the Game Sales suffered the greatest decline.\nBased on the above thought, the answer is:\nThe Game Sales sector suffered the most during the time of Covid-19 in 2020, as is evident by the significant decrease in its contribution to the total revenue when compared to the other sectors. Particularly, the height of the Game Sales segment in the 2020 bar is notably smaller than that in 2019."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "6ZkfX25SqOpGtbXw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total revenue in 2021 from all the revenue areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total revenue in 2021, I need to sum up the revenues across all the areas for 2021. I can find this from the total height of the {2021 bar}<r>[0.788, 0.115, 0.838, 0.754]</r>. The segments in the 2021 bar denote the revenue, including {Game Sales}<r>[0.788, 0.115, 0.838, 0.254]</r>, {Subscriptions}<r>[0.788, 0.254, 0.838, 0.438]</r>, {Ad Revenue}<r>[0.788, 0.438, 0.838, 0.754]</r>. By summing up these values, we get the total revenue.\nBased on the above thought, the answer is:\n185."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "ALz1jUo9gCybdhoe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the chart of diseases over five years into a structured table format with years as columns and diseases as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I begin by identifying {the x-axis labels}[[0.106, 0.910], [0.146, 0.910], [0.185, 0.910], [0.224, 0.910], [0.264, 0.910]] that list the years from 2016 to 2020. Then, I observe {the legend}[[0.074, 0.039, 0.267, 0.105]] to determine the rows that will represent each disease: Diabetes, Cardiovascular Disease, Cancer, and Obesity. To populate the data, I inspect {the points on each disease's line}[[0.106, 0.570], [0.146, 0.532], [0.185, 0.457], ..., [0.894, 0.268]] to extract the number of cases for each year and each disease accordingly.\nBased on the above thought, the answer is:\nYear,2016,2017,2018,2019,2020\nDiabetes,3200,3400,3050,3100,3650\nCardiovascular Disease,3000,3200,2900,3500,3900\nCancer,2800,2900,2700,2800,2950\nObesity,2500,2700,2900,3100,3300"
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "HUbMQYJzg1fUUBkN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the data for Diabetes show volatility between the years 2018 and 2019 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the volatility in the Diabetes data between 2018 and 2019, I examine {the line for Diabetes}[[0.224, 0.470], [0.303, 0.340], [0.382, 0.179], [0.450, 0.534], [0.500, 0.632]]. Specifically, there is a significant drop in the number of cases from 2018 {point in 2018}[0.382, 0.179] to 2019 {point in 2019}[0.450, 0.534], followed by a rise. This steep decline and subsequent increase indicate an outlier event or a reporting anomaly that affects the overall trend.\nBased on the above thought, the answer is:\nThe volatility in the Diabetes data between 2018 and 2019 is due to an abrupt drop in the number of reported cases in 2018 followed by a rise in 2019. This could be attributed to either inconsistencies in data reporting or specific health interventions that temporarily affected diagnosis rates."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "3MucgntyyldaXiZj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Obesity rate increased from 2016 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in the Obesity rate, I need to locate {the points on the line for Obesity}[[0.106, 0.658], [0.894, 0.268]] for the years 2016 and 2020. The value for Obesity in 2016 is identified at 2500 and in 2020 at 3300. Calculating the difference, 3300 - 2500 gives the increase.\nBased on the above thought, the answer is:\n800."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "7CTrhNoo0FqAhqOX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the line chart data be translated into a structured table, with Time Periods as columns and Local Government and Community Development as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To put the data into a table, I will start with {the x-axis tick labels}[[0.051, 0.773, 0.069, 0.898], [0.212, 0.773, 0.228, 0.895], [0.372, 0.773, 0.388, 0.903], [0.532, 0.773, 0.548, 0.934], [0.692, 0.773, 0.708, 0.896], [0.843, 0.773, 0.878, 0.890], [1.015, 0.773, 1.018, 0.951]], these will be the column headers, representing the different time periods. Next, {the legend texts}[[0.120, 0.049, 0.436, 0.119]] will serve as the row labels like Local Government and Community Development. Then, to procure the values, I will examine {the line points for Local Government}[[0.140, 0.262], [0.220, 0.388], [0.300, 0.460], [0.380, 0.513], [0.460, 0.611]] and {the line points for Community Development}[[0.540, 2.046], [0.620, 2.046], [0.700, 2.046], [0.780, 2.046], [0.860, 2.046]] at the respective time periods and extract their corresponding values.\nBased on the above thought, the answer is:\nTime_Period,Time1,Time2,Time3,Time4,Time5\nLocal Government,65,60,50,40,32\nCommunity Development,52,48,46,45,45"
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "IQq5aEvVq920v45k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which area, Local Government or Community Development, saw a greater rate of decrease during the entire period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must examine the decrease in values for both Local Government and Community Development. Over the course of time, {Local Government line}[[0.140, 0.262, 0.460, 0.611]] shows a decrease in values from 65 to 32. On the other hand, the {Community Development line}[[0.540, 2.046, 0.860, 2.046]] depicts a minor decrease in values from 52 to 45. By comparing these decreases, I can determine which area experienced a more significant decrease.\nBased on the above thought, the answer is:\nThe area of Local Government had a greater rate of decrease compared to Community Development. This is evident from the line chart, which portrays a more noticeable downward trend for Local Government, dropping from 65 to 32, versus the much smaller decrease for Community Development, from 52 to 45."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "6Xw2JCWn9VFK08HK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn comparison to the start point, what is the net change in value of Local Government over the entire time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the net change in value for Local Government, I have to examine {the line points for Local Government}[[0.140, 0.262], [0.220, 0.388], [0.300, 0.460], [0.380, 0.513], [0.460, 0.611]]. The initial value at the start is 65 and the final value is 32. The net change would be the final value subtracted from the starting value, i.e., 32 - 65.\nBased on the above thought, the answer is:\n-33."
+            }
+        ],
+        "id_raw": 837
+    },
+    {
+        "id": "yuI3KuaHexWB9J2V",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table format, with years as the rows and industries as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart to a table, first, the categories on {the x-axis}<r>[0.146, 0.888, 0.854, 0.907]</r>, which represent the years: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, and 2020, will become the rows. The industries, which are represented by {the line labels}<r>[0.681, 0.057, 0.869, 0.086]</r>, namely Online Shopping and In-Store Shopping, will be the columns. The data points on {the lines for each industry}<r>[0.146, 0.070, 0.854, 0.840]</r> represent the values of market share for each industry in each year.\nBased on the above thought, the answer is:\nYear, Online Shopping, In-Store Shopping\n2010, 80.1, 130.5\n2011, 85.3, 127.2\n2012, 91.2, 125.4\n2013, 98.3, 123.1\n2014, 106.7, 120.9\n2015, 116.8, 119.2\n2016, 128.3, 117.8\n2017, 141.2, 116.7\n2018, 155.3, 115.9\n2019, 170.1, 115.4\n2020, 185.7, 115.0"
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "4AiFfayWB2HecwYa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the trend of the In-Store Shopping industry over the years, based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend of the In-Store Shopping industry can be examined by following {the chart line for In-Store Shopping}<r>[0.146, 0.070, 0.854, 0.840]</r>. This line depicts a slight downward trend over the years. Starting from 130.5 in 2010 to 115 in 2020, the industry has seen a small setback. The decline, however, has not been drastic and remained reasonably stable, depicting resilience.\nBased on the above thought, the answer is:\nThe In-Store Shopping industry experienced a slight decline between 2010 and 2020, indicating a slight shrinking in the market share, though this reduction was gradual, signifying the industry's ability to retain a significant portion of its market share despite changes in the retail landscape."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "QFhHhQgTLcN9Zf9i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_42_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Online Shopping grow from the year 2010 to the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the growth in Online Shopping, we need to look at the 2010 and 2020 values located at {the plot points for Online Shopping}<r>[0.146, 0.840]</r> and <r>[0.854, 0.070]</r>. In 2010, the value was 80.1 and in 2020, the value rose to 185.7. We calculate the difference by subtracting the 2010 value from the 2020 value: 185.7-80.1.\nBased on the above thought, the answer is:\n105.6."
+            }
+        ],
+        "id_raw": 839
+    },
+    {
+        "id": "dEtoGQinyYBXxph1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the chart data into a structured table with 'Year' as the columns and 'Fuel Type' as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract data from the graph, I first inspect {the x-axis tick labels}<r>[0.084, 0.908, 0.123, 0.932], [0.328, 0.908, 0.367, 0.932], [0.572, 0.908, 0.611, 0.932], [0.815, 0.908, 0.854, 0.932]</r>, which indicate the 'Year' column in the table. Subsequently, the contents of {the legend}<r>[0.620, 0.396, 0.973, 0.530]</r> constitutes the rows, denoting 'Fuel Type'. To fill in the data, I look at {the line points for Coal Consumption}<r>[0.103, 0.288], [0.347, 0.107], [0.591, 0.209], [0.835, 0.369]</r> and {the line points for Oil Consumption}<r>[0.103, 0.808], [0.347, 0.632], [0.591, 0.526], [0.835, 0.482]</r>, and get the respective consumption stats for each year and fuel type.\nBased on the above thought, the answer is:\nFuel Type,2008,2012,2016,2020\nCoal Consumption,900,970,930,870\nOil Consumption,725,780,815,830"
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "2Dq3QJAXyuCTEZTx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends displayed in the chart, which energy source is gaining popularity and which is becoming less popular over the years? Describe your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to interpret the trends of the line plots for both Coal and Oil consumption. Looking at {the line for Coal Consumption}<r>[0.103, 0.107, 0.835, 0.369]</r>, it begins at a value of 900 in 2008 and ends with a value of 870 in 2020, showing a slight decrease overall, suggesting it's less popular. Regarding the trend of {Oil Consumption}<r>[0.103, 0.482, 0.835, 0.808]</r>, there's a steady increase from 725 in 2008 to 830 in 2020, indicating that compared to Coal, Oil has gained more popularity over the years due to the consistent growth in its usage.\nBased on the above thought, the answer is:\nBased on the trends displayed in the chart, Oil is gaining popularity, and Coal becoming less popular over the years. This interpretation is based on the observed continuous increase in oil consumption and the slight decrease in coal consumption from 2008 to 2020."
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "sfMTFlMtDSqwce1n",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in Coal Consumption between 2008 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in Coal Consumption between 2008 and 2020, I examine {the point for Coal Consumption in 2008}<r>[0.103, 0.288]</r> and extract the value which is 900. Similarly, I inspect {the point for Coal Consumption in 2020}<r>[0.835, 0.369]</r> and get the value 870. The difference is thus calculated as 900 - 870.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 840
+    },
+    {
+        "id": "SaUMPLEgR7cK8TlV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you create a tabular representation of the data, with years as your column and Average Hours Spent and Buying Intention as your rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build this table, I'll first identify {the x-axis tick labels}[[0.014, 0.912, 0.049, 0.936], [0.121, 0.912, 0.156, 0.936], [0.228, 0.912, 0.263, 0.936], [0.335, 0.912, 0.371, 0.936], [0.443, 0.912, 0.478, 0.936], [0.550, 0.912, 0.585, 0.936], [0.657, 0.912, 0.692, 0.936], [0.764, 0.912, 0.799, 0.936], [0.871, 0.912, 0.906, 0.936], [0.978, 0.912, 1.013, 0.936]] as the column headers, representing different years. Next, I identify the labels given to {the legend texts}[[0.161, 0.177, 0.368, 0.208], [0.161, 0.224, 0.322, 0.256]] as the row labels, these are the Average Hours Spent and Buying Intention parameters. Then, to populate the table's values, I carefully examine the {line points}[[0.085, 0.739], [0.139, 0.502], ..., [0.942, 0.423],...] for these two parameters and deduce their respective values.\nBased on the above thought, the answer is:\nParameters,Years\n,Average Hours Spent,Buying Intention\n2005,20,35\n2006,35,55\n2007,33,50\n2008,27,46\n2009,31,45\n2010,32,43\n2011,30,44\n2012,34,50\n2013,36,52\n2014,37,53\n2015,39,56\n2016,41,58\n2017,38,55\n2018,37,53\n2019,35,52\n2020,38,55\n2021,40,58"
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "fV4eQ5GVR9IdYEx8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends observed in the line chart, how has the consumer's interest towards Entertainment and Gaming industry changed over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to examine the trends in both {Average Hours Spent line}[0.085, 0.407, 0.942, 0.739] and {Buying Intention line}[0.085, 0.139, 0.942, 0.502]. The Average Hours Spent line shows a general upward trend, suggesting that the average time a consumer spends on gaming has increased over the years. In parallel, the Buying Intention line also displays an upward trend, further confirming that consumers are showing more interest in the Entertainment and Gaming industry - indicating a positive correlation between the hours spent on entertainment and their buying intention.\nBased on the above thought, the answer is:\nOver the years, consumer interest in the Entertainment and Gaming industry has generally increased. This is reflected in the growth in average hours consumers spend on entertainment and gaming, and their correlating buying intentions, both of which have been trending upwards. This demonstrates a positive relationship between the time consumers invest in entertainment, and their intention to purchase within this industry."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "FsC0TqzgNcXd2nmO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAcross all the years, what is the overall increase in Average Hours Spent on Entertainment and Gaming?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the overall increase in Average Hours Spent on Entertainment and Gaming, I need to look at the initial and final {line points for Average Hours Spent}[[0.085, 0.739], [0.139, 0.502], ..., [0.942, 0.423]] in the line plot. The initial value is 20 {initial point}[0.085, 0.739] and the final value is 40 {final point}[0.942, 0.423]. Hence, the overall increase is 40 - 20 = 20.\nBased on the above thought, the answer is:\n20 hours."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "jlTSndPESkKIlwWw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the line chart's data into a table, with columns as types of economy and rows as industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I first identify {the xlabel}[[0.476, 0.942, 0.571, 0.975]] which represents the Industry. The industry types can be derived from  {the x-axis tick labels}[ [0.084, 0.909, 0.165, 0.932], [0.224, 0.909, 0.344, 0.932], [0.410, 0.909, 0.478, 0.932], [0.525, 0.909, 0.683, 0.932], [0.706, 0.909, 0.822, 0.932], [0.868, 0.909, 0.979, 0.932]]. For values, I need to look at the two lines, {the line for Knowledge Economy}[0.124, 0.064, 0.924, 0.492] and {the line for Innovation Economy}[0.124, 0.327, 0.924, 0.853]. The y-axis corresponds to the value in USD for each type of economy in respective industries.\nBased on the above thought, the answer is:\nIndustry,Knowledge Economy,Innovation Economy\nAerospace,48000,12000\nPharmaceutical,53000,15000\nSoftware,6800,1200\nTelecommunications,9900,2100\nSemiconductor,7500,1500\nBiotechnology,7200,1800"
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "pPuwDTziB1AdJTn3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry has the largest difference in Value between Knowledge Economy and Innovation Economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to look at the differences for all the industries. After comparing the values associated with both {the line for Knowledge Economy}[0.124, 0.064, 0.924, 0.492], and {the line for Innovation Economy}[0.124, 0.327, 0.924, 0.853], the highest difference can be identified.\nBased on the above thought, the answer is:\nThe Pharmaceutical industry shows the largest difference in Value between Knowledge Economy and Innovation Economy. This is indicated by the gap between the two points on the line chart corresponding to pharmaceutical in Knowledge and Innovation Economies, which is greater than for any other industry."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "Ixxlh1jZKKoXDwM6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the Value for Pharmaceutical industry in Knowledge Economy and Innovation Economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to look at the points {for Pharmaceutical in Knowledge Economy}[0.284, 0.064] and {for Pharmaceutical in Innovation Economy}[0.284, 0.327]. The y-axis values correspond to their respective values in USD. The difference would be the Value of Knowledge Economy - Value of Innovation Economy.\nBased on the above thought, the answer is:\n38000."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "3xbwQsHzqLq028TA",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help convert the raw data from the line chart into a structured table keeping the process types as columns and the evaluation measures as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a structured table, I retrieve the labels for the different types of processes from {the x-axis tick labels}[[0.058, 0.912, 0.165, 0.936], [0.446, 0.912, 0.581, 0.936], [0.852, 0.912, 0.978, 0.936]], which represent the column headers. Then, I identify {the legend texts}[[0.766, 0.417, 0.893, 0.457], [0.766, 0.478, 0.934, 0.518]] as the row labels representing the Standard and Constrained evaluation measures. To assign values to the table, I observe {the line points}[[0.112, 0.434], [0.513, 0.322], [0.915, 0.302], [0.112, 0.724], [0.513, 0.733], [0.915, 0.698]] for each evaluation measure at the respective process type and extract the corresponding data.\nBased on the above thought, the answer is:\nEvaluation Measure,Quality Check,Finished Products,Product Lifetime\nStandard,0.0000001,0.000005,0.00001\nConstrained,0.000000000004,0.000000000003,0.00000000001"
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "Iqu77YIX4z3mLiXT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which process type has a larger difference between the Standard and Constrained evaluation measures? Please provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the process type with a larger difference between the Standard and Constrained evaluation measures, I have to compare their differences for each process. For {Quality Check}[0.058, 0.912, 0.165, 0.936], the difference is 0.0000001 - 0.000000000004; for {Finished Products}[0.446, 0.912, 0.581, 0.936], the difference is 0.000005 - 0.000000000003; and for {Product Lifetime}[0.852, 0.912, 0.978, 0.936], the difference is 0.00001 - 0.00000000001. By comparing these differences, I can determine which process type has a larger difference between the Standard and Constrained evaluation measures.\nBased on the above thought, the answer is:\nThe Finished Products process type has a larger difference between the Standard and Constrained evaluation measures. This is because the absolute difference between the evaluation measures for Finished Products is 0.000004999997, which is larger than for Quality Check (0.000000099996) and Product Lifetime (0.00000999999). The greater the difference between the two evaluation measures, the larger the divergence in performance assessment under the two benchmarks."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "mRoqqMwnf7JX7a2W",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio between the Standard and Constrained evaluation measures for Finished Products?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the ratio between Standard and Constrained evaluation measures for Finished Products, I first locate {the line point for Standard at Finished Products}[0.513, 0.322] which corresponds to the value 0.000005. Next, I locate {the line point for Constrained at Finished Products}[0.513, 0.733] which corresponds to the value 0.000000000003. The ratio is the value of Standard divided by the value of Constrained.\nBased on the above thought, the answer is:\n1666666.67."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "mbYNF1HBETQqHu0W",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the horizontal bar chart, can you extract the development quantities for both rural and urban areas in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart type is a horizontal bar chart where the y-axis represents the categories (rural and urban areas) and the x-axis represents the quantity of development. The {y-axis labels}<r>[0.015, 0.458, 0.062, 0.482], [0.015, 0.128, 0.062, 0.151]</r> represent the rows and the {horizontal bars}<r>[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442]</r> for each category represent the data on development quantities for the respective areas.\nBased on the above thought, the answer is:\nArea,Development Quantity\nRural,10000\nUrban,12000"
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "aVUOQCiEP9EF5re5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the horizontal bar chart, what can we infer about the distribution and scale of development in rural and urban areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "The horizontal bar chart presents information on development quantities of rural and urban areas. Checking the {horizontal bars for rural and urban}<r>[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442]</r>, we can see that urban areas have a higher quantity of development compared to rural areas (12,000 vs 10,000).\nBased on the above thought, the answer is:\nBased on the horizontal bar chart, we can infer that urban areas have a larger scale of development compared to rural areas. Urban areas, with a higher development quantity of 12,000, may be experiencing faster growth due to factors such as technological advancements and commercial enterprises. In contrast, rural areas have a development quantity of 10,000, indicating possibly slower but still significant growth propelled by activities such as crop cultivation and small businesses."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "hop1ZSiDalLKoRf6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs indicated in the horizontal bar chart, how much more development is there in the urban area compared to the rural area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in development between the urban and rural areas, I can directly subtract the development quantities of the two from the {horizontal bars}<r>[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442]</r>. The development quantity of urban areas is 12,000 while for rural areas it's 10,000. Therefore, the difference is 12,000 - 10,000 = 2,000.\nBased on the above thought, the answer is:\n2000."
+            }
+        ],
+        "id_raw": 845
+    },
+    {
+        "id": "b7lk4vBnx6fcPy0D",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the interactions for different patterns into a table format with Pattern as rows and Interaction Types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I begin by identifying {the x-axis tick labels}[[0.088, 0.908, 0.126, 0.932], [0.323, 0.908, 0.362, 0.932], [0.558, 0.908, 0.597, 0.932], [0.792, 0.908, 0.831, 0.932]] as the row labels representing the different patterns. After that, I recognize {the legend texts}[[0.694, 0.413, 0.964, 0.453],[0.694, 0.474, 0.972, 0.514]] as the column headers representing the interaction types - 'Close Friends' and 'Acquaintances'. To find the interaction counts, I navigate to {the line points} for each interaction type at the respective patterns and extract the associated values.\nBased on the above thought, the answer is:\nPattern,Close Friends,Acquaintances\nFlat Decline,15,30\nRapid Decline,10,20\nModerate Increase,20,15\nUnpredictable Fluctuations,30,10\nInconsistent Decline,15,15"
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "gsh8woqXJd54zhPw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which interaction pattern seems to be the most inconsistent or unpredictable for interactions with Close Friends and Acquaintances? Share your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify the most inconsistent or unpredictable pattern, I must examine the variability in the line plots for each interaction type. I observe that for {the 'Close Friends' line}[[0.107, 0.034, 0.812, 0.592]], 'Flat Decline' shows a steady decrease in interactions, 'Rapid Decline' consists of rapid dips, 'Moderate Increase' shows a steady rise, while 'Unpredictable Fluctuations' has a sharp rise to 30 interactions. \nSimilarly, examining {the 'Acquaintances' line}[[0.107, 0.443, 0.812, 0.851]], 'Unpredictable Fluctuations' shows the most sudden drop to 10 interactions. Therefore, the most unpredictable pattern seems to be 'Unpredictable Fluctuations' for both 'Close Friends' and 'Acquaintances'.\nBased on the above thought, the answer is:\nThe 'Unpredictable Fluctuations' pattern appears to be the most inconsistent for interactions with both 'Close Friends' and 'Acquaintances.' This is evident from the sharp rise and sudden drop of interaction counts which clearly deviate from the general consistent trends seen in other patterns."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "JwJ1PCFrzUFhF0AX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum number of interactions for Close Friends across all patterns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the variance in the number of interactions for 'Close Friends' across all patterns, I need to identify {the points for the 'Close Friends' line}[[0.107, 0.034],[0.343, 0.361],[0.578, 0.484],[0.812, 0.592]]. They correspond to interaction counts of 15, 10, 20, and 30 for patterns 'Flat Decline', 'Rapid Decline', 'Moderate Increase', and 'Unpredictable Fluctuations' respectively. The difference between the highest count(30) and the lowest count(10) is 30 - 10 = 20.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "tJbt9OLJzZAHZK3n",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data into a comprehensive table, with each row representing a specific N value and the columns representing Identity Theft and Data Breach and their corresponding Threat Magnitudes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the data into a table, I need to extract the different N values by looking at {the x-axis}[0.534, 0.945, 0.547, 0.975] and the threat magnitudes for Identity Theft and Data Breach from the corresponding points on the respective lines: {the line for Identity Theft}[0.138, 0.085, 0.943, 0.437] and {the line for Data Breach}[0.138, 0.514, 0.943, 0.678].\nBased on the above thought, the answer is:\nN,Identity Theft,Data Breach\n4,1.5e-8,1.5e-12\n5.5,8e-7,7e-11\n7,15e-6,2e-10\n12,2.5e-4,1.5e-11\n25,1e-2,8e-10"
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "YKZ6jnN9VX7EzLtr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the plotted lines, which threat, Identity Theft or Data Breach, shows a more rapid growth in threat magnitude as N increases?"
+            },
+            {
+                "from": "gpt",
+                "value": "I examine {the line for Identity Theft}[0.138, 0.085, 0.943, 0.437] and {the line for Data Breach}[0.138, 0.514, 0.943, 0.678]. From these lines, we can see that the line representing Identity Theft begins at a lower threat magnitude at N=4 but ends at a much higher threat magnitude at N=25. On the other hand, the line for Data Breach remains consistently lower in threat magnitude across all N. This suggests that Identity Theft has a more rapid growth in threat magnitude as N increases than Data Breach.\nBased on the above thought, the answer is:\nThe line graph inherently indicates that Identity Theft threat magnitude progressively increases at a more rapid pace as N increases when compared to Data Breach. This is seen from \nthe steeper rise in the line graph for Identity Theft compared to Data Breach."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "RatiWLaf8ypY29n9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Threat Magnitude difference for Identity Theft between N=4 and N=25?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in Threat Magnitude for Identity Theft, I need to compare the y-values at {N=4}[0.138, 0.437] and {N=25}[0.943, 0.085] on the {line for Identity Theft}[0.138, 0.085, 0.943, 0.437]. These correspond to Threat Magnitudes of 1.5e-8 and 1e-2 respectively. Subtracting these gives the difference as 1e-2 - 1.5e-8.\nBased on the above thought, the answer is:\n0.009999985."
+            }
+        ],
+        "id_raw": 849
+    },
+    {
+        "id": "VQUF1tuC34fDiZyH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this bar chart into a structured table format with Design Professions and respective Innovation Scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured table from the bar chart, I start with {the y-axis labels}[[0.102, 0.680], [0.218, 0.500], ..., [0.798, 0.300]] as row labels which indicate different design professions. Next, I locate the respective {bar lengths}[[0.102, 0.680, 0.941, 0.720], [0.218, 0.500, 0.781, 0.550], ..., [0.798, 0.300, 0.621, 0.330]] indicating the innovation scores corresponding to each profession. I then extract these scores and map them to the corresponding professions.\nBased on the above thought, the answer is:\n\"Design Profession\",\"Innovation Score\"\n\"Industrial Designer\",10.0\n\"Graphic Designer\",20.0\n\"Interior Designer\",22.5\n\"UX/UI Designer\",25.0\n\"Artists\",27.5\n\"Architects\",30.0"
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "XCRhyn6VmTjw0DW0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the state of innovation displayed in this horizontal bar chart, which design profession seems to be lacking creativity compared to others? Give reasons for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the profession that appears to lag in creativity, I need to analyze the length of bars representing different professions. The shorter the bar, the less innovative the profession is considered. Here, looking at {the bar for Industrial Designer}[0.102, 0.680, 0.621, 0.710], it is visibly shorter compared to other professions. This suggests that Industrial Designers have the least innovation score among the professions represented.\nBased on the above thought, the answer is:\nBased on the horizontal bar chart, the 'Industrial Designer' profession seems to be less innovative compared to others. Its bar length, indicative of the Innovation score, is noticeably shorter than that of other professions, suggesting they are currently lacking in creativity and design innovation relative to their counterparts."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "222YCFP9TbjknW71",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the innovation scores across the different design professions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the range of the innovation scores, I need to determine the least and most innovative professions by looking at their {bar lengths}[[0.102, 0.680, 0.941, 0.720], [0.218, 0.500, 0.781, 0.550], ..., [0.798, 0.300, 0.621, 0.330]]. The longest bar corresponds to \"Architects\" with a score of 30 indicating highest innovation and the shortest bar corresponds to \"Industrial Designer\" with a score of 10 meaning least innovation among these careers. The range of the innovation scores is therefore 30 - 10 = 20.\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "ZiES62X1pqHsyqPP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the data from the chart in a tabular format, where the rows represent the different stages of deployment and the columns represent Existing System and Potential System?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels} [[0.134, 0.103, 0.855, 0.602]], which represent the stages of deployment: 5, 10, 15, 20, 25, 30. I then identify {the legend items}[[0.732, 0.427, 0.955, 0.467], [0.732, 0.487, 0.966, 0.527]] \"Existing System\" and \"Potential System\" as the column headers. Lastly, I locate the corresponding values via {the line points for Existing System}[[0.134, 0.602], [0.413, 0.435], [0.576, 0.352], [0.692, 0.103], [0.782, 0.186], [0.855, 0.103]] and {the line points for Potential System}[[0.134, 0.851], [0.413, 0.768], [0.576, 0.685], [0.692, 0.602], [0.782, 0.519], [0.855, 0.602]] to extract the corresponding values: 1e-8, 1e-6, 1e-5, 0.01, 0.001, 0.01 and 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-8 respectively.\nBased on the above thought, the answer is:\nStage,Existing System, Potential System\n5,1e-8,1e-11\n10,1e-6,1e-10\n15,1e-5,1e-9\n20,1e-2,1e-8\n25,1e-3,1e-7\n30,1e-2,1e-8"
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "gYJs79E3DfF82oO1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the observed trends in the line chart be summarized?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the two lines in the chart} <r>[0.134, 0.103, 0.855, 0.602]</r> for the Existing System and <r>[0.134, 0.519, 0.855, 0.851]</r> for the Potential System, we can see contrasting trends. For the Existing System, the efficiency alternates between increases and decreases across different stages of deployment, indicating inconsistency. For the Potential System, however, we observe a consistent downward trend, suggesting a steady efficiency reduction over the stages. Despite similar efficiencies at both the initial (5th) and final stages (30th), the performance between these stages indicates a divergence of the two systems with the Potential System showing more promise due to its consistency.\nBased on the above thought, the answer is:\nThe chart demonstrates two contrasting trends. The Existing System illustrates a fluctuating efficiency across the stages, showing no consistent pattern of improvement or deterioration. In contrast, the Potential System demonstrates a steady and consistent reduction in efficiency across all stages, indicating a more predictable and promising performance in potential improvement."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "GWj5RWEip4Eklwdg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio between the efficiency of the Potential System and the Existing System at 25th stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this ratio, first I locate {the point on the line for the Existing System at the 25th stage} <r>[0.782, 0.186]</r>. The corresponding efficiency value for the Existing System is 0.001. Next, I identify {the point on the line for the Potential System at the 25th stage} <r>[0.782, 0.519]</r>. The corresponding efficiency value for the Potential System is 1e-7. By dividing these two numbers, I can obtain the ratio.\nBased on the above thought, the answer is:\n0.1."
+            }
+        ],
+        "id_raw": 852
+    },
+    {
+        "id": "iRg2nXABqNT9BTpw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, can you create a tabular representation of the number of published research papers in the field of \"Artificial Intelligence\" and \"Internet of Things\" for the years 2022 to 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from this line chart, I need to identify the x and y values for both the \"Artificial intelligence\" and the \"Internet of Things\" lines. The labels for these lines were found in {the legend}<r>[0.075, 0.072, 0.237, 0.140]</r>. The x-values or years are provided in {the x-axis labels}<r>[[0.094, 0.919, 0.129, 0.942], [0.260, 0.919, 0.295, 0.942], [0.427, 0.919, 0.462, 0.942], [0.593, 0.919, 0.628, 0.942], [0.759, 0.919, 0.795, 0.942], [0.926, 0.919, 0.961, 0.942]]</r>. For each year, I extract the corresponding y-values, which indicate the number of published research papers in the two fields. The y-values are obtained from {the points of the \"Artificial intelligence\" line}<r>[[0.111, 0.420], [0.278, 0.363], [0.444, 0.243], [0.611, 0.213], [0.777, 0.162], [0.943, 0.100]]</r> and {the points of the \"Internet of Things\" line}<r>[[0.111, 0.243], [0.278, 0.213], [0.444, 0.865], [0.611, 0.140], [0.777, 0.119], [0.943, 0.749]]</r>.\nBased on the above thought, the answer is:\nYear,Artificial intelligence,Internet of Things\n2022,40000,80000\n2023,50000,90000\n2024,80000,7000\n2025,90000,120000\n2026,110000,130000\n2027,140000,11000"
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "PywDQ5z0MmApE0NX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field, \"Artificial intelligence\" or \"Internet of Things\", showed a more significant increase in the number of published research papers between 2022 and 2027 based on the trends shown in the line chart? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to analyze the trends of the two lines in the chart. Based on the coordinates of {the Artificial Intelligence line}<r>[0.111, 0.100, 0.943, 0.420]</r> and {the Internet of Things line}<r>[0.111, 0.119, 0.943, 0.865]</r>, it is observed that the Artificial Intelligence line shows a consistent upward trend with a substantial increase in the number of published research papers over this time period. On the other hand, the Internet of Things line shows a great increase until 2023, after which it takes a massive drop in 2024. Then it increases again significantly until 2026, after which there's another sharp decrease in 2027.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the field of \"Artificial Intelligence\" showed a more significant and consistent increase in the number of published research papers between 2022 and 2027. While the \"Internet of Things\" had periods of rapid growth, it also had significant drops in 2024 and 2027, making the overall increase less substantial compared to \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "r0rZM5daDdfR91ha",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the number of research papers published in the field of \"Artificial intelligence\" increase from 2022 to 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the increase in the number of published papers for \"Artificial Intelligence\" from 2022 to 2027, I need to observe the y-values for these years. The y-coordinate for 2022 and 2027 for \"Artificial Intelligence\" is found at {the points of the \"Artificial intelligence\" line}<r>[[0.111, 0.420], [0.943, 0.100]]</r>. Extracting these values and calculating the difference gives the increase in the number of papers.\nBased on the above thought, the answer is:\n100000."
+            }
+        ],
+        "id_raw": 853
+    },
+    {
+        "id": "BFn6Uq17dkKQdZRb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the Year, EdTech Investment (Billion $), and Number of Innovative Programs as observed in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For creating the table, I should start by identifying the years which will act as our rows. I can find this by locating the positions of the {x-axis tick labels}[[-228.994, 0.892], [-53.066, 0.892], [122.862, 0.892], [298.789, 0.892]] which represent the years from 2005 to 2020. Next, I point out the {line points}[{line_0: [0.084, 0.853], [0.122, 0.833], [0.160, 0.817], [0.198, 0.796], [0.236, 0.825], [0.274, 0.817], [0.312, 0.803], [0.350, 0.759], [0.388, 0.735], [0.426, 0.716], [0.464, 0.690], [0.502, 0.681], [0.540, 0.690], [0.578, 0.669], [0.616, 0.645], [0.654, 0.625]} and {line_1: [0.084, 0.213], [0.122, 0.202], [0.160, 0.191], [0.198, 0.179], [0.236, 0.189], [0.274, 0.185], [0.312, 0.169], [0.350, 0.153], [0.388, 0.138], [0.426, 0.115], [0.464, 0.104], [0.502, 0.095], [0.540, 0.099], [0.578, 0.087], [0.616, 0.079], [0.654, 0.064]]} which show the data for the EdTech Investment and Number of Innovative Programs respectively for each year.\nBased on the above thought, the answer is:\nYear,EdTech Investment (Billion $),Number of Innovative Programs\n2005,0.5,200\n2006,0.6,220\n2007,0.7,245\n2008,0.85,275\n2009,0.65,250\n2010,0.7,260\n2011,0.8,300\n2012,1.2,350\n2013,1.5,400\n2014,1.8,500\n2015,2.3,550\n2016,2.5,600\n2017,2.3,580\n2018,2.8,650\n2019,3.5,700\n2020,4.2,800"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "TAK7CsZrQVg5NveR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends of EdTech Investment and Number of Innovative Programs from 2005 to 2020, what can be inferred about the correlation between the two?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart displays the trends of EdTech Investment and Number of Innovative Programs over the years 2005 to 2020. The lines for both {EdTech Investment}[0.084, 0.625, 0.654, 0.853] and {Number of Innovative Programs}[0.084, 0.064, 0.654, 0.213] show an increasing trend over the years. This suggests a positive correlation between the two \u2013 as EdTech Investment increases, the Number of Innovative Programs also tends to increase. However, there are few disparities, like around the year 2009 the investment dropped, yet the number of programs remained consistent.\nBased on the above thought, the answer is:\nOverall, there seems to be a strong positive correlation between the EdTech Investment and the Number of Innovative Programs. In general, an increase in EdTech Investment corresponds with an increase in the Number of Innovative Programs. However, occasional discrepancies suggest that other factors might also be influencing the Number of Innovative Programs. Therefore, while EdTech Investment may be a significant factor, it is not the sole determinant of the Number of Innovative Programs."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "Sxrltqqw7XaskYlW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage growth in EdTech Investment from the year 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage growth, I first locate the {value for 2015}[0.464, 0.690] in the line for EdTech Investment which is 2.3 Billion $. Then, I find {the value for 2020}[0.654, 0.625] which is 4.2 Billion $. The percentage increase would be (4.2 - 2.3)/2.3 * 100.\nBased on the above thought, the answer is:\n82.61%"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "yMhlXiGXgTV9iaI6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the data from the line graph into a table format with Years as the first column and the other columns representing the community indices?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the data into table format from the line graph, I first need to identify {the x-axis labels}<r>[0.107, 0.912, 0.146, 0.936]</r> that represent the years. Then, there are 5 different community indices represented in the graph. These are the data series labels that go into the first row of our table, which can be created by analyzing {the chart legend}<r>[0.630, 0.398, 0.980, 0.532]</r>. Finally, the specific data points plotted on the graph by reference to {the line points}<r>[0.126, 0.091, 0.839, 0.450]</r> for each corresponding data series and year can be extracted for the remaining cells of our table.\nBased on the above thought, the answer is:\nYear,Population Growth(%),Community Development Projects,Average Education Level(%),Government Spending,Employment Rate(%)\n2010,1.6,20,64,10100,68\n2012,1.5,22,65,10500,69\n2014,1.4,24,66,10900,70\n2016,1.3,26,67,11300,71\n2018,1.2,28,68,11700,72"
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "ODOTodpIMC8EJzCv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pattern of change does the neighborhood's population growth demonstrate according to the line chart? Please discuss the corresponding causes."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the line corresponding to {Population Growth}<r>[0.126, 0.091, 0.839, 0.450]</r>, it shows a trend of steady decline from 2010 to 2018.\nBased on the above thought, the answer is:\nThe line graph shows that Population Growth in the neighborhood presents a slow but consistent decline from 2010 to 2018. This pattern might be influenced by a variety of factors, some of which can include lower birth rates, aging population, or increased migration to other areas or cities. However, the exact reason for the population decrease cannot be explicitly determined from the chart without additional demographic or sociological data."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "y6MMIfATUaLBnSFO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Government Spending from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in Government Spending from 2010 to 2018, I first need to look at the {Government Spending in 2010}<r>[0.126, 0.091]</r> which is $10100 and then compare it to the {Government Spending in 2018}<r>[0.839, 0.091]</r> which is $11700. The total increase can then be calculated as 11700 - 10100 = $1600.\nBased on the above thought, the answer is:\n$1600."
+            }
+        ],
+        "id_raw": 855
+    },
+    {
+        "id": "mGftWUuUgKrEL078",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nI see there are different fields of study in the bar chart, can you summarize the data into a table where each row represents a field of study, with its frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can generate the table by extracting the names from {the y-axis tick labels}[[0.014, 0.897, 0.058, 0.910], [0.014, 0.801, 0.058, 0.814], [0.014, 0.695, 0.058, 0.708], [0.014, 0.589, 0.058, 0.602], [0.014, 0.483, 0.058, 0.496], [0.014, 0.377, 0.058, 0.390], [0.014, 0.271, 0.058, 0.284], [0.014, 0.165, 0.058, 0.178]] as field names and the corresponding values from {the lengths of the bars}[[0.070, 0.837, 0.809, 0.860], [0.070, 0.731, 0.782, 0.753], [0.070, 0.625, 0.754, 0.648], [0.070, 0.519, 0.726, 0.542], [0.070, 0.413, 0.699, 0.435], [0.070, 0.307, 0.671, 0.329], [0.070, 0.200, 0.644, 0.223], [0.070, 0.094, 0.583, 0.117]] for each field.\nBased on the above thought, the answer is:\nField of Study,Frequency\nComputer Science,729\nData Analysis,700\nPhysics,670\nChemistry,641\nBiology,613\nLiterature,582\nHistory,517\nPsychology,449"
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "RiAoLfhMgSqDnYiD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which academic field has seen the least concentration of research? Can you also provide a reason why it might be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the lengths of all the bars, I can discern that {the bar for Psychology}[0.070, 0.094, 0.583, 0.117] is the smallest. Therefore, this implies that Psychology is the academic field with the least concentrated research. As for the reason, it could possibly be due to the fact that Psychology, as a field, is both very broad and highly specialized, making it easy for researchers to spread their efforts across many different sub-disciplines. Additionally, it could also be due to less demand or fewer resources available in this field compared to areas like Computer Science or Data Analysis.\nBased on the above thought, the answer is:\nBased on the bar chart, the academic field with the least concentration of research is Psychology. A plausible reason might be that the field of Psychology is very broad, containing numerous highly specialized niches which would necessitate researchers to diversify their efforts across different sub-disciplines. Lack of demand or fewer resources available for research in Psychology as compared to areas like Computer Science or Data Analysis could also account for this discrepancy."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "vDjpY9PPO0KY2PZS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the frequency difference between the most researched field and the least researched field as per the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the frequencies of the most and least researched fields, I need to analyze the lengths of all the bars. Checking {the bar for Computer Science}[0.070, 0.837, 0.809, 0.860], I can gather that Computer Science has the highest frequency with a value of 729. Similarly, looking at {the bar for Psychology}[0.070, 0.094, 0.583, 0.117], I see that it has the lowest frequency of 449. The difference can be calculated as 729 - 449 = 280.\nBased on the above thought, the answer is:\n280."
+            }
+        ],
+        "id_raw": 856
+    },
+    {
+        "id": "fHwAHe8hbEVd7r9S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a table format with Years as columns and Investment Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.098, 0.775, 0.941, 0.885], these represent the column headers or years. Then, I identify {the legend texts}[[0.399, 0.422, 0.660, 0.452], [0.399, 0.466, 0.649, 0.496]], which represent different investment types. To populate the table, I locate {the line points for Total Investment}[[0.098, 0.219], [0.192, 0.172], [0.285, 0.133], [0.379, 0.158], [0.473, 0.133], [0.567, 0.101], [0.660, 0.133], [0.754, 0.111], [0.848, 0.081], [0.941, 0.091]] and {the line points for Venture Capital}[[0.098, 0.745], [0.192, 0.599], [0.285, 0.513], [0.379, 0.452], [0.473, 0.405], [0.567, 0.333], [0.660, 0.366], [0.754, 0.305], [0.848, 0.258], [0.941, 0.238]] for each investment type at respective years and extract the associated values.\nBased on the above thought, the answer is:\nYear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019\nTotal Investment (in billions),1200,1500,1800,1600,1800,2100,1800,2000,2300,2200\nVenture Capital (in billions),100,200,300,400,500,700,600,800,1000,1100"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "ZEA52qsRTBjzLNBI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which type of investment has experienced the most rapid growth over the observed years, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze which investment type has experienced the most rapid growth, I need to observe the trends in the line plots for Total Investment and Venture Capital. Looking at {the line for Total Investment}<r>[0.098, 0.081, 0.941, 0.219]</r>, there are some increases and decreases, but the trajectory generally depicts a continuous escalation. However, looking at {the line for Venture Capital}<r>[0.098, 0.238, 0.941, 0.745]</r>, we see a consistent increase in investment across the years. The fluctuations in this line are upward and drastic, particularly from year 2014 to year 2019.\nBased on the above thought, the answer is:\nFrom the trends shown in the line chart, Venture Capital seems to have experienced the most rapid growth over the years. This is because the line plot for Venture Capital consistently ascends with substantial increases, particularly noticeable from the year 2014 to year 2019 where there is a steep upward climb, indicating a significant surge in this investment type."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "x34bE5mLGHvpu5vm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the amount of Venture Capital (in billions) for the year 2016 represented on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the amount of Venture Capital (in billions) for the year 2016, I look into {the line points for Venture Capital}[[0.567, 0.333], [0.660, 0.366], [0.754, 0.305]]. From these points, the second one corresponds to the year 2016. The y-coordinate of the point represents the value of Venture Capital (in billions) for that year.\nBased on the above thought, the answer is:\n600."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "6tTJBck3OzZ1MYb9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data in the given line chart to a table with the Year being the first column followed by the Investment in Educational Services and Investment in Public Health Services."
+            },
+            {
+                "from": "gpt",
+                "value": "To format the data in the chart into a table, I will first identify the x-axis labels or the years that are indicated by {the x-axis tick labels}[[0.618, 0.072, 0.638, 0.084], [0.347, 0.072, 0.367, 0.084], [0.912, 0.072, 0.932, 0.084], [0.250, 0.072, 0.270, 0.084], [0.827, 0.072, 0.847, 0.084]]. There are five unique years mentioned namely 1990, 2000, 2010, 2020, and 2030. \nNext, I will look at the y-axis which represents the investment amounts for each sector. The line for Educational Services represented by {line_0}{'line_0': {..., 'points': [{'x': 1990, 'y': 200, 'point_coordinates': '[0.122, 0.853]'....had coordinates representing the respective investments for each year. Similarly, the line for Public Health Services, denoted by {line_1}{'line_1': {..., 'points': [{'x': 1990, 'y': 500, 'point_coordinates': '[0.122, 0.628]'....has coordinates depicting respective investment amounts for each year. \nBy reordering this information, we can draft a table.\nBased on the above thought, the answer is:\nYear,Educational Services,Public Health Services\n1990,200,500\n2000,400,700\n2010,800,900\n2020,1600,1800\n2030,3200,5000"
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "WvV3wmxAmFcvUqvq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the trend for investments in Educational Services and Public Health Services from 1990 to 2030, can you delineate any significant differences in the investment patterns for these two sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To conduct this analysis, we will need to observe the lines representing the investment trends for Educational Services and Public Health Services, represented {line_0}{'line_0': {..., 'coordinates': '[0.122, 0.174, 0.937, 0.853]'...} and {line_1}{'line_1': {..., 'coordinates': '[0.122, 0.064, 0.937, 0.628]'...}. It's conspicuous that both sectors saw a rise in investments over the years from 1990 to 2030. However, Educational Services had a more steady and incremental growth, while Public Health Services had a slightly more erratic growth pattern with a sharp rise in investments towards 2020-2030.\nBased on the above thought, the answer is:\nBased on the line chart, both the Educational Services and Public Health Services sectors demonstrate increasing investment trends from 1990 to 2030. The Educational Services sector shows non-volatile, steady and incremental growth over this period, indicating a consistent high interest in investment for this sector. In contrast, the Public Health Services sector displays a more erratically increasing pattern, with a notably sharp surge after 2020. This suggests that there might have been sudden, significant factors or changes around this period that kindled an increase in investment for Public Health Services."
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "T5yCobKvYYWydfpK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth of investment in the Educational Services sector from 1990 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage growth, we will first highlight two points- the respective investment numbers of 1990 and 2030 for the Educational Services sector. This can be seen in {line_0}{'points': [{'x': 1990, 'y': 200, 'point_coordinates': '[0.122, 0.853]'.... and {line_0}{'points': [... {'x': 2030, 'y': 3200, 'point_coordinates': '[0.937, 0.174]'}....}. The initial investment in 1990 is $200M and the final investment in 2030 is $3200M. We can calculate the percentage growth with the formula ((Final value - Initial value)/Initial value)*100.\nBased on the above thought, the answer is:\n1500%"
+            }
+        ],
+        "id_raw": 858
+    },
+    {
+        "id": "vWzwA3BFOPU0IWJ5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trends' data across the years 2005 to 2020 in tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can generate the tabular data from the line chart. I'll identify the years from {the x-axis tick labels}<r>[0.098, 0.064, 0.936, 0.853]</r> which are the column headers. The names of the two trends 'Energy' and 'Resources' are found in {the legend}<r>[0.687, 0.392, 0.930, 0.526]</r> which act as the row labels. The values for each trend at each year are conveyed by {the points on the line data for Energy and Resources}<r>[0.098, 0.064, 0.936, 0.853], [0.098, 0.324, 0.936, 0.808]</r> respectively. Now, let's compile this information into a table.\nBased on the above thought, the answer is:\nYear,Energy,Resources\n2005,1.5e6,2.0e6\n2006,3.4e6,4.3e6\n2007,5.6e6,3.2e6\n2008,8.7e6,4.1e6\n2009,1.2e7,4.9e6\n2010,1.7e7,5.9e6\n2011,2.3e7,6.7e6\n2012,4.8e7,7.8e6\n2013,7.5e7,8.9e6\n2014,9.4e7,1.1e7\n2015,1.2e8,1.4e7\n2016,1.6e8,1.8e7\n2017,1.8e8,2.5e7\n2018,2.1e8,3.2e7\n2019,2.3e8,3.9e7\n2020,2.4e8,4.5e7"
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "NjA2w3DgD6vTkfHd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which trend, Energy or Resources, shows a more aggressive growth over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which trend shows more aggressive growth, I first need to compare the growth of Energy and Resources across the years. Looking closely at {the line for Energy}<r>[0.098, 0.064, 0.936, 0.853]</r> and {the line for Resources}<r>[0.098, 0.324, 0.936, 0.808]</r>, it is noticeable that the Energy trend line is consistently above the Resources trend line. This means that overall, the values for Energy are higher than the values for Resources. In addition, the line for Energy is steeper and more pronounced, indicating more significant growth.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Energy trend seems to show more aggressive growth over the years. Comparing the values and the increases in the lines for Energy and Resources, Energy's growth rate is steeper and the values are consistently higher, indicating more aggressive growth."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "EpMm3rRGD1WNnGF8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_43_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage growth in Energy from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage growth in Energy from 2005 to 2020, I need to locate {the point for Energy in 2005}<r>[0.098, 0.853]</r> that represents 1.5e6 and the line data point at {2020}<r>[0.936, 0.064]</r> that represents 2.4e8. The percentage change can be calculated by the formula: ((Final Value - Initial Value) / Initial Value) * 100%. Applying this formula here gives ((2.4e8 - 1.5e6) / 1.5e6) * 100.\nBased on the above thought, the answer is:\n15966.67 %."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "ezjPfLAZI7EFQt9p",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the horizontal bar chart, can you transform the distribution data of researchers across various fields into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the y-axis labels}<r>[0.01, 0.274, 0.03, 0.776]</r> reveals the names of the fields which serve as the row identifiers in the table. The corresponding density values, represented by {the width of each bar}<r>[0.15, 0.298, 0.85, 0.334]</r>, need to be extracted for each field. This process will transfer the data into a structured CSV format, including each field with its respective density value.\nBased on the above thought, the answer is:\nField,Density\nMathematics,11.5\nComputer Science,11\nBiological Sciences,9\nEngineering,8.5\nPhysical Sciences,8\nHealth Sciences,7.5\nSocial Sciences,7"
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "BZGkBauZ5ZsteagD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might Mathematics and Computer Science possess significantly higher research density compared to other fields in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the observed {horizontal bar lengths}<r>[0.15, 0.298, 0.85, 0.334]</r> which symbolize the density numbers, Mathematics and Computer Science bars are visibly longer than the others. This reflects their higher density rates. The significant difference might be attributed to several global factors such as technological advancements, digital transformation, and the growing demand for data-driven solutions across industries. These factors inherently boost the attraction and funding towards these fields, resulting in a higher concentration of researchers pursuing mathematics and computer science.\nBased on the above thought, the answer is:\nMathematics and Computer Science possess higher research density likely due to the increasing global emphasis on technological development, digitalization, and the demand for data expertise. These factors make these fields more attractive and heavily funded, thus attracting a greater number of researchers."
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "0YARWVJj0uEX0q4i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total density of researchers in the fields of Mathematics, Engineering, and Health Sciences according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total density, I need to sum up the density values for the specified fields. The density for {Mathematics}<r>[0.15, 0.298]</r> is 11.5, for {Engineering}<r>[0.567, 0.670]</r> it is 8.5, and for {Health Sciences}<r>[0.434, 0.539]</r> it is 7.5. Adding these values gives a total density.\nBased on the above thought, the answer is:\n27.5"
+            }
+        ],
+        "id_raw": 860
+    },
+    {
+        "id": "I88vaobvTJW6Heoc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this line chart, can you generate a table that reflects the investment return rates across different types of assets over the last financial year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a table, the first step is to label {the x-axis}<r>[0.500, 0.959, 0.529, 0.979]</r> as the column headers representing the different types of investment assets. Next, I'll identify the annual return rates reflected by the y-values of each line plot corresponding to each of the assets. For example, for mutual funds, I'll find and extract the values from {the line points for Mutual Funds}<r>[0.144, 0.076, 0.885, 0.174]</r>.\nBased on the above thought, the answer is:\nAsset,Mutual Funds,ETFs,Index Funds,Retail Stocks,Real Estate,Commodities,Bonds,Cryptocurrencies\nReturn Rate (%),2.5,3.6,5.1,6.8,4.5,-0.7,1.5,15.3"
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "mfLF0306nBSA4oCW",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you analyze the trend of the investment return rates for Retail Stocks based on the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the graph, {the line representing Retail Stocks}<r>[0.374, 0.136, 0.409, 0.156]</r> indicates that the retail stocks have seen a very healthy trend in the last financial year. There seems to be a sharp rise in the investment return rates which could be due to increasing consumer confidence. This steep increase represents a positive trend for investors aiming for high return rates.\nBased on the above thought, the answer is:\nThe investment return rates for Retail Stocks have shown a sharp rise over the last financial year as per the line chart. This upward trend indicates a favourable condition for investors seeking high returns from retail stocks, implying that it is a profitable asset for investment."
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "N4MtGQ5XLUqJZUmN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the return rate of Cryptocurrencies compared to the return rate of Mutual Funds based on the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the difference between  the return rate of Cryptocurrencies and that of Mutual Funds. I locate the y-coordinate of {the point for Cryptocurrencies}<r>[0.885, 0.536]</r> to be approximately 15.3%, and the y-coordinate of {the point for Mutual Funds}<r>[0.144, 0.076]</r> to be approximately 2.5%. Subtracting the latter from the former gives the difference.\nBased on the above thought, the answer is:\n12.8%"
+            }
+        ],
+        "id_raw": 861
+    },
+    {
+        "id": "g8LIRZtpdRPN9iF7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from this line chart and organize it in a tabular format, using the \"Year\" as columns and categories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to organize the data into a table, I would first identify {the x-axis tick labels}[[0.140, 0.902, 0.153, 0.932], [0.228, 0.902, 0.241, 0.932], ..., [0.924, 0.902, 0.951, 0.932]] which represent the years. The row labels are the categories, corresponding to {the legend texts}[[0.806, 0.060, 0.951, 0.085], [0.806, 0.097, 0.964, 0.122], ..., [0.806, 0.173, 0.942, 0.198]]. The values are determined by evaluating the corresponding y-value for each x-value within each category. For example, for \"Protest Activity\" in Year 1, I look at the line point {line for Protest Activity}[0.146, 0.546], which corresponds to a value of 15. I can also identify the data points for each line directly from their respective line data.\nBased on the above thought, the answer is:\nCategory,Year 1,Year 2,Year 3,Year 4,Year 5,Year 6,Year 7,Year 8,Year 9,Year 10\nProtest Activity,15,13,17,16,18,31,28,30,33,40\nPolitical Stability,85,80,75,72,70,65,60,58,54,50\nPolicy Changes,5,10,20,23,25,35,38,45,52,60\nPublic Opinion,95,92,89,86,82,80,75,70,65,62"
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "3mzNjaRKuA8EPwB7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which parameter shows a steady decline over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the lines on the chart, we can identify which parameter or category shows a steady decline over the years. Looking at {the line for Political Stability}[0.146, 0.164, 0.937, 0.461], Political Stabilities' values decrease consistently from Year 1 to Year 10.\nBased on the above thought, the answer is:\nFrom the line chart, it can be seen that the parameter \"Political Stability\" shows a steady decline over the years."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "VMIruguv86DZxj1x",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Policy Changes increase from Year 5 to Year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at {the line points for Policy Changes}[0.498, 0.674, 0.937, 0.376] for the years in question. In Year 5, the value of Policy Changes was 25 {line point for Year 5}[0.498, 0.674], and in Year 10 it was 60 {line point for Year 10}[0.937, 0.376]. The difference between these two values will give us the increase in Policy Changes from Year 5 to Year 10.\nBased on the above thought, the answer is:\n35."
+            }
+        ],
+        "id_raw": 862
+    },
+    {
+        "id": "vjMMmXbFLTJo5yRE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart data into a tabular format where each year corresponds to a row and the values for Exercise, Diet, Sleep, and Social Network are given in separate columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table from the line chart, I start by analyzing the x-axis, identifying the years marked as ticks from {the x-axis tick labels}<r>[0.075, 0.919, 0.123, 0.942]</r> to <r>[0.919, 0.919, 0.967, 0.942]</r>. Rows will correspond to these years, starting from 2000 to 2020. Next, I'll examine each line segment for the lifestyle categories such as {Exercise}<r>[0.099, 0.065, 0.943, 0.683]</r>, {Diet}<r>[0.099, 0.200, 0.943, 0.763]</r>, {Sleep}<r>[0.099, 0.226, 0.943, 0.871]</r>, and {Social Network}<r>[0.099, 0.119, 0.943, 0.871]</r>, translating their plotted points back to specific values using the corresponding y-axis labels.\nBased on the above thought, the answer is:\nYear,Exercise,Diet,Sleep,Social_Network\n2000,80,75,70,78\n2001,78,73,71,77\n2002,76,70,72,75\n2003,75,68,74,73\n2004,73,66,70,71\n2005,72,65,69,70\n2006,70,63,68,68\n2007,68,61,67,66\n2008,66,60,65,65\n2009,64,59,64,64\n2010,63,58,62,63\n2011,62,58,61,61\n2012,61,57,60,60\n2013,60,57,59,59\n2014,59,56,58,58\n2015,59,56,58,58\n2016,59,56,55,57\n2017,59,55,55,56\n2018,58,55,54,55\n2019,58,54,53,54\n2020,57,54,50,50"
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "qGjfuR7hSjFV6UfN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lifestyle category shows the least variation in its values from 2000 to 2020 in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stability of the lifestyle category requires examining the closeness of the plotting points across time. I observe the lines for each lifestyle, including {Exercise}<r>[0.099, 0.065, 0.943, 0.683]</r>, {Diet}<r>[0.099, 0.200, 0.943, 0.763]</r>, {Sleep}<r>[0.099, 0.226, 0.943, 0.871]</r>, and {Social Network}<r>[0.099, 0.119, 0.943, 0.871]</r>. From this, it can be seen that the {Social Network} line has points closely clustered and exhibits minimal height difference between the initial and final points.\nBased on the above thought, the answer is:\nThe Social Network category shows the least variation in its values from 2000 to 2020. It has smaller fluctuations and a narrower range of values in its plotting points, indicating a more stable trend compared to the other categories."
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "vMBMkaBv7IXzva2x",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in Sleep from the year 2000 to the year 2020 as indicated on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease in the 'Sleep' category over the given years, I will identify the initial value for Sleep in 2000 which is 70 at point <r>[0.099, 0.334]</r> and the final value for Sleep in 2020, which is 50 at point <r>[0.943, 0.871]</r>. The percentage reduction is calculated by (((initial value - final value) / initial value) * 100).\nBased on the above thought, the answer is:\n28.57%"
+            }
+        ],
+        "id_raw": 863
+    },
+    {
+        "id": "EbODK5JM1abFD8NN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a structured table where each column represents a decade and each row lists the energy source's index values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the information into a tabular form, I carefully analyze {the x-axis labels}<r>[0.100, 0.921, 0.962, 0.949]</r> to determine the decades they represent, which are 2000, 2010, 2020, 2030. Then, I identify {the legend}<r>[0.825, 0.792, 0.977, 0.911]</r> which mentions various energy sources: Solar, Hydropower, Wind, and Nuclear. By examining each corresponding {line point}<r>[0.100, 0.095, 0.962, 0.463]</r>,<r>[0.100, 0.282, 0.962, 0.360]</r>,<r>[0.100, 0.054, 0.962, 0.565]</r>,<r>[0.100, 0.368, 0.962, 0.767]</r> for these energy types, I extract the index values for each decade.\nBased on the above thought, the answer is:\nDecade,2000 Index,2010 Index,2020 Index,2030 Index\nSolar,1,3,7,9\nHydropower,2,2,2,2\nWind,1,4,8,10\nNuclear,5,4,3,1"
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "ZwWbyA8NaftNtU6A",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the line chart suggest a shift towards renewable energy usage over the decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends in {the lines for Solar}<r>[0.100, 0.095, 0.962, 0.463]</r> and {Wind}<r>[0.100, 0.054, 0.962, 0.565]</r>, it is evident that the utilization indices for these renewable energy sources have increased significantly. Both lines show upward trends, with the Solar line beginning at an index of 1 and reaching 9 by 2030, and the Wind line starting at 1 and reaching 10 in the same period. Contrastingly, the {line for Nuclear}<r>[0.100, 0.368, 0.962, 0.767]</r> displays a clear downward trend, dropping from an index of 5 to 1 from 2000 to 2030. This indicates a decreasing reliance on nuclear energy, highlighting a shift towards more sustainable and environmentally friendly renewable energy sources as captured in the line trends of the chart.\nBased on the above thought, the answer is:\nThe chart suggests a shift towards renewable energy as indicated by the significant increase in utilization indices for Solar and Wind, with a noticeable decline in the utilization of Nuclear energy. This reflects a growing preference for environmental sustainability in energy sourcing over the decades, aligning with global efforts to reduce carbon emissions and reliance on non-renewable energy resources."
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "YAZAJydAYvmy6eA3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the utilization index of Wind energy increased from 2000 to 2030 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth in the utilization index for Wind energy, first, I identify {the specific plot points for Wind}<r>[0.100, 0.054, 0.962, 0.282]</r> from the year 2000 to 2030. The index values are read from these points: 1 for 2000 and 10 for 2030. The increase in index is then computed by subtracting the initial value from the final value, thus 10 - 1.\nBased on the above thought, the answer is:\n9"
+            }
+        ],
+        "id_raw": 864
+    },
+    {
+        "id": "BG5TvneZr7hJyhqJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a data table from the line chart displaying the performance metrics for each individual role defined by human (H), manager (M), and leader (L) across different instances?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a structured table, I refer to {the x-axis labels}<r>[0.096, 0.037, 0.981, 0.894]</r> which indicate different performance metrics. I then review {the plot lines}<r>[0.139, 0.122, 0.939, 0.809]</r>, <r>[0.139, 0.465, 0.939, 0.837]</r>, <r>[0.139, 0.732, 0.939, 0.889]</r> and use the y-axis to determine which values correspond to each instance of human, manager, and leader roles, denoted by different colors and positions. The specific performance metrics for each individual are recorded based on their respective plot points.\nBased on the above thought, the answer is:\nRole,Instance,H1,H2,M1,M2,L1,L2\nHuman,\"H1,H2\",72,88,--,--,--,--\nManager,\"M1,M2\",--,--,63,95,--,--\nLeader,\"L1,L2\",--,--,--,--,75,90"
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "mOF7ai5HcMZqauv5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich managerial role instance shows a significant drop in performance, and can you rationalize why it might be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the plot specific to the managerial roles, I focus on {the line for managers}<r>[0.139, 0.465, 0.939, 0.837]</r> and notice two distinct points representing M1 and M2. While M2 maintains a higher performance metric around 95, M1 shows a notable drop at 63. The drop might be an outlier due to specific internal challenges, differences in responsibility, or external factors affecting that particular instance which is not consistent with the general trend seen in M2.\nBased on the above thought, the answer is:\nM1 shows a significant drop in performance compared to M2. The lower performance metric of 63 for M1 could be attributed to unique challenges or disparities in responsibilities that were not experienced by M2, who maintained a higher metric of 95."
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "44bbD9Wjp3lSSwag",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average performance metric for the leader role across instances L1 and L2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze {the plot lines for leaders}<r>[0.139, 0.732, 0.939, 0.889]</r> specifically focusing on the data points for L1 and L2. By locating their performance metrics, I note the values for L1 as 75 and L2 as 90. To determine the average, I will compute: (75 + 90) / 2.\nBased on the above thought, the answer is:\n82.5"
+            }
+        ],
+        "id_raw": 865
+    },
+    {
+        "id": "QGgYb4rqwalBIiAB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the bar plot data into a structured table format with industries as rows and years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data, I first analyze {the bars in the plot}<r>[0.105, 0.325, 0.962, 0.489]</r>, checking their heights related to the x-axis to interpret the sales values. I align these values with {the y-axis ticks labels}<r>[0.015, 0.172, 0.035, 0.776]</r> which represent the industries: Technology, Food, Retail, Medical, and Energy. Furthermore, each bar segment colored differently corresponds to a different year, signified by the {legend notes}<r>[0.844, 0.819, 0.977, 0.910]</r>, delineating the years consecutively. These structured datapoints help in constructing the tableau.\nBased on the above thought, the answer is:\nIndustry,Year 1,Year 2,Year 3\nTechnology,120,140,160\nFood,110,100,90\nRetail,150,130,140\nMedical,100,110,120\nEnergy,90,95,100"
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "vetJAl9iHTQUE6hk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry shows the most consistent performance over the three years, according to the bar plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze consistency, I examine the changes in the bar heights for each industry. {The bars for the Food industry}<r>[0.11, 0.41, 0.16, 0.46]</r> show minimal changes in height over the three years, indicating slight reductions in sales yearly and no sudden spikes or deep falls, compared to the other industries showing larger fluctuations.\nBased on the above thought, the answer is:\nThe Food industry shows the most consistent performance over the three years, with sales slowly declining each year, contrasting the more substantial fluctuations observed in the other industries such as Retail and Technology."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "AlHf7Listm85OsDm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales growth for the Technology sector from Year 1 to Year 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the values for the Technology sector in {the bar segments for Technology}<r>[0.10, 0.30, 0.15, 0.35]</r>, it starts with sales of 120 in Year 1 and escalates to 160 in Year 3. Thus, the total sales growth is calculated by subtracting the Year 1 sales from Year 3 sales.\nBased on the above thought, the answer is:\n40 billion."
+            }
+        ],
+        "id_raw": 867
+    },
+    {
+        "id": "YpgoZv8UT7VVEKE3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of the line chart into a tabular format with years as the rows and the technologies as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the x-axis tick labels}[[0.070, 0.902, 0.123, 0.932],[0.285, 0.902, 0.337, 0.932], [0.499, 0.902, 0.552, 0.932], [0.714, 0.902, 0.767, 0.932], [0.928, 0.902, 0.981, 0.932]], I identified the row headers which represent the years. Then, {the legend texts}[[0.670, 0.060, 0.839, 0.085], [0.670, 0.097, 0.810, 0.122], [0.670, 0.135, 0.781, 0.160], [0.670, 0.173, 0.938, 0.198]] are recognized as column headers. To fill in the values, I extract the values corresponding to each technology at the respective years by analyzing {the line points} of each technology such as for Machine Learning [[0.096, 0.550],[0.311, 0.516],[0.526, 0.464],[0.740, 0.437],[0.955, 0.379]].\nBased on the above thought, the answer is:\nYear,Machine_Learning,Deep_Learning,Automation,Robotic_Process_Automation\n2016,40.2,36.1,65.0,55.8\n2017,44.1,42.8,70.2,65.3\n2018,50.2,48.3,75.0,70.0\n2019,53.3,52.5,80.0,73.2\n2020,60.1,60.9,83.1,76.3"
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "U6LaG6SVUqF6VFgj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology, according to the trends in the line chart, has experienced the most steady growth over the time period? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the technology that has the most steady growth, I need to study the line points trend for every technology. Looking at {the line for Machine Learning}[0.096, 0.379, 0.955, 0.550], it showcases a regular increment with some minor fluctuations. In case of {Deep Learning}[0.096, 0.372, 0.955, 0.585], the trend presents an almost identical approach to Machine Learning but with slightly more growth. {Automation}[0.096, 0.182, 0.955, 0.337], has experienced a fair increase overall but with minimal ups and downs. The final line is for {Robotic Process Automation}[0.096, 0.240, 0.955, 0.416] which has shown a steady growth despite being minor.\nBased on the above thought, the answer is:\nBased on the observed trends in the line chart, Robotic Process Automation has experienced the most steady growth over the time period. Despite its growth rate being minor compared to other technologies, the line representing Robotic Process Automation has fewer and smaller fluctuations, indicating its consistent growth over the years."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "L8RRgHrp0j15sMhC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase in the usage of Robotic Process Automation from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I locate {the line points for Robotic Process Automation}[[0.096, 0.416],[0.311, 0.334],[0.526, 0.294],[0.740, 0.267],[0.955, 0.240]] and extract the corresponding values: 55.8, 65.3, 70.0, 73.2, 76.3. The initial value in 2016 is 55.8 at {2016}[0.096, 0.902] and the final value in 2020 is 76.3 at {2020}[0.955, 0.902]. The percentage increase can be calculated as (76.3-55.8)/55.8 * 100.\nBased on the above thought, the answer is:\n36.7%."
+            }
+        ],
+        "id_raw": 870
+    },
+    {
+        "id": "FPLeSk9oBxmwzIm0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart representing the variations in user counts across different situations into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the structure of the bar chart, I can see that each subplot corresponds to a different situation, represented on {the x-axis labels}<r>[0.508, 0.987, 0.555, 1.012]</r>. Each bar within the subplots represents different user types as indicated by {the legend}<r>[0.770, 0.066, 0.978, 0.192]</r>. By examining the length and position of each bar, like {the bar for Advanced Teachers in S1}<r>[0.120, 0.195, 0.423, 0.219]</r>, I can extract the corresponding count values. This method allows me to tabulate the counts for Beginner, Intermediate, and Advanced learners, divided into students and teachers, across the situations S1, S2, and S3.\nBased on the above thought, the answer is:\nSituation,Beginner Students,Intermediate Students,Advanced Students,Beginner Teachers,Intermediate Teachers,Advanced Teachers\nS1,250,180,150,90,210,120\nS2,230,165,140,95,245,125\nS3,210,150,130,100,180,115"
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "9nuonIwL6vyEmq2d",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich situation exhibits a declining trend across all types of learners for students?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify a declining trend across all types of learners for students, I compare the bar lengths for Beginner, Intermediate, and Advanced students across all situations. By reviewing the lengths of {the bars for students}<r>[0.120, 0.195, 0.423, 0.236], [0.463, 0.195, 0.765, 0.236], [0.815, 0.195, 1.117, 0.236]</r>, it's clear that from {Situation S1}<r>[0.120, 0.195, 0.423, 0.236]</r> to {Situation S3}<r>[0.815, 0.195, 1.117, 0.236]</r>, the bar lengths decrease consistently for each category of students. This suggests fewer students in each category as the situations progress from S1 to S3.\nBased on the above thought, the answer is:\nSituation S3 exhibits a declining trend across all types of learners for students, as evidenced by the consistently shorter bar lengths across all student categories from S1 to S3, pointing towards a reduction in student numbers."
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "d5v8nuPoGwDoKtJa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more Intermediate Teachers are there in S2 compared to S1?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I analyze the chart and locate {the bar for Intermediate Teachers in S1}<r>[0.120, 0.474, 0.423, 0.495]</r> and {the bar for Intermediate Teachers in S2}<r>[0.463, 0.459, 0.765, 0.478]</r>. By measuring the horizontal distance of these bars, which corresponds to user counts on the x-axis, I calculate the counts as 210 for S1 and 245 for S2. The difference between these values simply requires basic subtraction, 245 (S2) minus 210 (S1).\nBased on the above thought, the answer is:\n35"
+            }
+        ],
+        "id_raw": 872
+    },
+    {
+        "id": "DwjjaQZY9WLxxvOb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the specific preferences documented over the three highlighted categories of activities into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the distinct division and presentation on {the chart}<r>[0.096, 0.037, 0.981, 0.894]</r> of Jack, Jane, and John's preference in activities 'chill at home', 'going out', and 'travel' during their leisure and work timings, I will shift these values into a structured table. Leisure and work will be clearly separated and shown for each individual.\nBased on the above thought, the answer is:\nPerson,Activity,Leisure Time,Work Time\nJack,Chill at Home,30,20\nJack,Going Out,25,15\nJack,Travel,20,10\nJane,Chill at Home,25,20\nJane,Going Out,20,25\nJane,Travel,15,15\nJohn,Chill at Home,10,5\nJohn,Going Out,15,10\nJohn,Travel,30,20"
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "7FGz5lOD0CeZEAYb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplaining the lifestyle patterns, which person's behavior shows a significant inclination towards a particular type of activity during leisure compared to others?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart patterns, the {line representing John's travel during leisure}<r>[0.476, 0.094, 0.872, 0.201]</r> clearly illustrates an upward trend comparably more pronounced than other activity lines. Most individuals show a balance or a downward chart, but John\u2019s line spikes distinctly, indicating preference.\nBased on the above thought, the answer is:\nJohn's lifestyle pattern during leisure shows a significant inclination towards travel, distinct from Jack and Jane. This is evident from the chart, where John spends more time traveling during his leisure, markedly higher than any of their other leisure activities."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "TEKg5zUbArFxAQ33",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the longest time John spends on any activity during leisure hours as compared to Jack?"
+            },
+            {
+                "from": "gpt",
+                "value": "By locating the highest value in John's activity line during leisure hours, which peaks prominently at {the travel line point for John}<r>[0.871, 0.094]</r>, and comparing it to Jack's top value at {the Travel line point for Jack}<r>[0.871, 0.208]</r>, one can calculate the difference. The highest value of John's leisure time is 30 minutes on travel, and Jack's highest leisure activity is 20 minutes also on travel.\nBased on the above thought, the answer is:\n10 minutes"
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "CgH97hC5ykBzrRBB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table showing the population trends across different periods for the three human species from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this structured table from the line chart, I identify {the x-axis tick labels}<r>[0.112, 0.902, 0.125, 0.932]</r>representing the historical periods as column headers. Next, I look at {the legend}<r>[0.641, 0.049, 0.972, 0.208]</r> which provides the row labels or categories for each line, representing Homo Sapiens, Homo Neanderthalensis, and Homo Erectus. To populate the table, I observe the plotted line data points for each species across the different periods, noting their populations.\nBased on the above thought, the answer is:\nPeriod,Homo Sapiens,Homo Neanderthalensis,Homo Erectus\nBronze Age,60,000,0,0\nIron Age,120,000,0,0\nMiddle Ages,500,000,0,0\nEarly Modern Period,800,000,0,0\nPost-Industrial,1,500,000,0,0\nPresent,7,750,000,0,0"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "IKiy8xbT1ZbkBF5T",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy do Homo Neanderthalensis and Homo Erectus show zero population across all periods in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line plots for Homo Neanderthalensis and Homo Erectus, both display a flat line of zero, indicating no population across all historical periods. This is likely because both species are known to have gone extinct prior to the periods detailed on the chart. Analyzing {the y-axis label}<r>[0.019, 0.228, 0.044, 0.703]</r> and the zero values repeatedly plotted at each period's point<R[0.118, 0.894], [0.163, 0.894], ..., [0.959, 0.894]></r>confirms the extinct status of these species since the Bronze Age, consistently represented in the chart.\nBased on the above thought, the answer is:\nHomo Neanderthalensis and Homo Erectus show zero population because they are considered extinct species and were extinct even before the earliest timeframe represented on the chart, which is the Bronze Age. The chart accurately reflects their status through the depicted flat lines at zero population, indicating no living individuals in any of the periods shown."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "aexa4oD2oIm8WgVR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total population increase for Homo Sapiens from the Bronze Age to the Present?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total population increase for Homo Sapiens, I first identify {the population at the Present}<r>[0.959, 0.062]</r>approximately 7,750,000 and {the population during the Bronze Age}<r>[0.118, 0.551]</r> as approximately 60,000. The calculation of the difference between these two values will provide the total increase.\nBased on the above thought, the answer is:\n7,690,000"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "GmJR9KVLgqC71kAV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the vertical bar chart into a table format with Market Status as rows and Sales and Consumption percentages for each quarter as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe {the x-axis labels}<r>[0.486, 0.931, 0.539, 0.962]</r> which represent 'Q1', 'Q2', 'Q3', and 'Q4'. Next, I focus on {the bars corresponding to each Market Status}<r>[0.125, 0.120, 0.900, 0.890]</r> to assess their heights, which correspond to Sales and Consumption percentages for each quarter. Each market status has seven bars, grouped by quarter and represented by distinct colors for Sales and Consumption.\nBased on the above thought, the answer is:\nMarket Status, Q1 Sales (%), Q1 Consumption (%), Q2 Sales (%), Q2 Consumption (%), Q3 Sales (%), Q3 Consumption (%), Q4 Sales (%), Q4 Consumption (%)\nSteady Recovery, 30, 40, 32, 38, 35, 46, 33, 49\nModerate Increase, 28, 37, 29, 36, 30, 44, 28, 48\nGradual Increase, 25, 34, 26, 33, 27, 41, 25, 45\nSlow Increase, 22, 30, 23, 29, 24, 37, 22, 42\nRapid Expansion, 40, 53, 42, 55, 45, 60, 43, 63\nModerate Growth, 35, 47, 36, 49, 38, 54, 36, 57\nSlow Recovery, 20, 28, 21, 27, 23, 34, 21, 39"
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "aZT3LTCwypQIzBsU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed in the consumer behavior across the four quarters and how might those insights affect market strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the stacked bars for each quarter, I find consistent growth in Consumption percentages across the four quarters {across the sub bar charts}<r>[0.125, 0.120, 0.900, 0.890]</r>. For example, 'Steady Recovery' starts at 40% in Q1 and grows to 49% by Q4. This gradual increase is reflected in almost all market statuses, indicating a growing consumer base or increased buying power.\nBased on the above thought, the answer is:\nThe observed trends suggest an increasing consumer engagement through the year. This growing trend highlights the potential for markets to leverage more aggressive marketing and sales strategies during the latter quarters, possibly introducing new products or persuasive promotions to capitalize on the heightened consumer activity."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "1svprBMJ7U0bhlXX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase from Q1 to Q4 for Sales in the 'Moderate Growth' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in Sales for 'Moderate Growth', I analyze the corresponding bars on the chart for Q1 and Q4 {in Moderate Growth}<r>[0.125, 0.120, 0.900, 0.890]</r>. The Q1 Sales is observed at 35% and Q4 Sales at 36%. The total increase is calculated by subtracting the Q1 value from the Q4 value, which is 36 - 35.\nBased on the above thought, the answer is:\n1%"
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "VYoJBFZKkwudOX9U",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table of the yields for each asset type per year based on the bar plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the bar plot, I'll break down the yields for each asset type from the years 2016 to 2019. By examining {the height of the bars for Equities for each year}[0.112, 0.294, 0.959, 0.423], {the height of the bars for Bonds for each year}[0.163, 0.320, 0.959, 0.371], {the height of the bars for Commodities for each year}[0.207, 0.337, 0.517, 0.431], and {the height of the bars for Cryptocurrency for each year}[0.251, 0.337, 0.959, 0.526], I can extract the yearly yields. Each bar gives the yield percentage where the height's proportionate difference helps in estimating the values. The years are represented through {x-axis labels}[0.112, 0.902, 0.972, 0.932], and each asset type is specified by {the distinct colors in the legend}[0.718, 0.049, 0.972, 0.208].\nBased on the above thought, the answer is:\nYear,Equities,Bonds,Commodities,Cryptocurrency\n2016,5.4,4.9,6.0,7.3\n2017,5.2,5.1,6.5,13.9\n2018,5.1,5.3,5.8,9.2\n2019,4.8,5.6,5.6,11.4"
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "kQEach0Xevbv3rCm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the overall trend for Equities reveal from 2016 to 2019, and how may it impact future investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the overall trend for Equities from 2016 to 2019, I examine {the bars representing Equities}[0.112, 0.294, 0.959, 0.423]. Starting high in 2016 and noticeably decreasing each subsequent year through 2019, there's a clear downward trajectory in the yield percentages. This trend suggests that yields from Equities have been decreasing, which could indicate market instability or reduced profitability in this asset. Such a trend might caution investors about potential lower returns from Equities in the near future, influencing more conservative investment strategies.\nBased on the above thought, the answer is:\nThe overall trend for Equities from 2016 to 2019 shows a steady decline in yield percentages, suggesting a negative trend in its market value. This declining trend may lead investors to reconsider the weight of Equities in their portfolios, potentially shifting focus towards more stable or profitable assets to safeguard against anticipated lower returns from Equities in the emerging market scenarios."
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "mGaFkuqMoOR3hPzh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest yield percentage for Bonds during 2016-2019 according to the bar plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest yield percentage for Bonds throughout the period 2016-2019, I scan the {bars for Bonds}[0.163, 0.320, 0.959, 0.371] across all years. By comparing the heights, noticeable particularly in 2019 where the bar reaches its maximum height with {the label}[0.946, 0.902, 0.972, 0.932], the highest yield can be estimated.\nBased on the above thought, the answer is:\n5.6%"
+            }
+        ],
+        "id_raw": 877
+    },
+    {
+        "id": "YyFI83rR7CqnJjT3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the grouped bar chart into a structured table with Age Groups as rows and Marital Status categories as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table from the grouped bar chart, I first identify {the x-axis tick labels}[[0.063, 0.741, 0.207, 0.932], [0.336, 0.741, 0.452, 0.896], [0.589, 0.741, 0.716, 0.910], [0.844, 0.741, 0.978, 0.919]] which denote the row labels or Age Groups. Then, I notice {the legend texts}[[0.167, 0.534, 0.226, 0.559], [0.167, 0.572, 0.226, 0.597], [0.167, 0.610, 0.226, 0.635], [0.167, 0.647, 0.226, 0.672], [0.167, 0.685, 0.226, 0.710]] which signify the different Marital Status categories. To determine the values, I correspond {the bar tops} of each Marital Status at each Age Group and extract their respective percentages.\nBased on the above thought, the answer is:\nAge Group,Single,Married,Divorced,Widowed\n20-30,65,30,3,2\n30-40,40,56,3,1\n40-50,10,75,10,5\n50-60,4,50,10,36\n60-70,2,20,10,68"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "06CJElzkqviiefi1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, describe how the population's marital status changes as they age."
+            },
+            {
+                "from": "gpt",
+                "value": "To describe how marital status changes with age, I interpret the increasing or decreasing trends of each bar - {Single}[0.135, 0.152, 0.135, 0.276], {Married}[0.394, 0.230, 0.394, 0.243], {Divorced}[0.652, 0.204, 0.652, 0.237], and {Widowed}[0.911, 0.132, 0.911, 0.269]. The 'Single' category sharply decreases as the age group increases, meanwhile, 'Married' appears to peak at the 40-50 age group and then falls thereafter. 'Divorced' mildly increases as age increases. In stark contrast, the 'Widowed' category sees a significant surge in population from the 50-60 age group onwards, establishing a majority by the 60-70 age group.\nBased on the above thought, the answer is:\nAs people age, the proportion of those who are 'Single' significantly decreases, practically vanishing by the 60-70 age group. The majority of individuals are 'Married' around the 40-50 age group, after which the category sees a decline. The portion of 'Divorced' individuals mildly increases with age. Notably, the proportion of 'Widowed' individuals experiences a sharp surge from the 50-60 age group onwards, becoming the majority population in the 60-70 age group."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "6vE9hJoJZM1p8rXF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent do the 'Single' individuals decrease from the 20-30 age group to the 60-70 age group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the decrease in percentage of 'Single' individuals from the 20-30 age group to the 60-70 age group, I need to locate {the bar tops for 'Single'} at {20-30 age group}[0.135, 0.158] and {60-70 age group}[0.135, 0.276] and extract the percentages: 65 and 2 respectively. The decrease would be 65 - 2 = 63 percent.\nBased on the above thought, the answer is:\n63 percent."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "QqzaswXbKgdlcoLm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you generate a structured table with Years as rows and the Growth Areas as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first observe {the x-axis tick labels}[[0.124, 0.902, 0.177, 0.932], [0.215, 0.902, 0.268, 0.932], [0.306, 0.902, 0.359, 0.932], [0.396, 0.902, 0.449, 0.932], [0.487, 0.902, 0.540, 0.932], [0.578, 0.902, 0.631, 0.932], [0.668, 0.902, 0.721, 0.932], [0.759, 0.902, 0.812, 0.932], [0.850, 0.902, 0.903, 0.932], [0.940, 0.902, 0.993, 0.932]], which represent the row headers or the years. Next, I identify {the legend texts}[[0.823, 0.048, 0.929, 0.073], [0.823, 0.086, 0.964, 0.111], [0.823, 0.123, 0.938, 0.148], [0.823, 0.161, 0.943, 0.186]] as the column labels representing the different growth areas. To extract the values, I locate {the line points} corresponding to each growth area's percentage growth per year, and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Residential,Housing Starts,Commercial,Government\n2011,6.1,18.3,4.3,9.0\n2012,9.2,10.3,5.4,8.2\n2013,14.2,12.1,5.6,7.8\n2014,13.9,17.3,5.8,7.5\n2015,10.4,15.2,5.7,7.3\n2016,9.7,14.1,6.2,7.0\n2017,10.3,12.3,6.8,6.8\n2018,11.2,12.0,7.4,6.7\n2019,10.5,11.2,7.1,6.6\n2020,8.8,10.5,6.9,6.9"
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "XUh1n6GUOvZ6geLv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReflecting on the trends shown in the line chart, which sector shows the most steady growth over time? Give your explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the most steady growth, one needs to observe the changes in the percentages represented by the corresponding lines for each. Looking at {the Residential line}[0.124, 0.296, 0.940, 0.753], the values fluctuate significantly from year to year. While the {Housing Starts line}[0.124, 0.065, 0.940, 0.516] also shows quite a significant fluctuation over the years. \nSimilarly, {the Commercial line}[0.124, 0.680, 0.940, 0.855] shows fluctuation, but in a smaller range over the years. However, if we observe, {the Government line}[0.124, 0.589, 0.940, 0.725], this line shows steady minor decreases from the start to the end year. The rate of change in the values appears to be minimal compared to the other sectors.\nBased on the above thought, the answer is:\nThe Government sector shows the most steady growth over time, although it's a decrease throughout the given years. The rate of change year to year appears to be smaller in comparison to the other sectors and the pattern of decrease is consistent throughout the years, supporting the indication of steady growth."
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "ESGTfyAQkzselMC5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_44_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2011 to 2020, what is the percentage change in Residential growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the change in Residential growth from 2011 to 2020, I would need to locate the y-values of {the Residential line chart}[0.124, 0.296, 0.940, 0.753], specifically at the beginning and the end of the interval. The y-value corresponding to 2011 is {6.1}[0.124, 0.753], and the y-value corresponding to 2020 is {8.8}[0.940, 0.601]. The percentage change can be calculated using these two numbers, by subtracting the 2011 value from the 2020 value, dividing by the 2011 value, and then multiplying by 100. That gives the formula: (8.8 - 6.1) / 6.1 * 100.\nBased on the above thought, the answer is:\n44.26%"
+            }
+        ],
+        "id_raw": 879
+    },
+    {
+        "id": "V9DDuLVOdZKPHIEl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table format with years as columns and social media platforms as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I'll first identify the {x-axis labels}[0.089, 0.764, 0.968, 0.939] as the column headers representing years. Next, I recognize {the line labels}[0.076, 0.689, 0.258, 0.432] as the row headers for each platform. Then, from each of these platform rows, I'll find corresponding {point values}[[0.089, 0.500], [0.185, 0.330], [0.282, 0.160], [0.378, 0.228], [0.474, 0.670]] and pair them with their years to fill in the data values for each platform.\nBased on the above thought, the answer is:\nPlatform,2020,2021,2022,2023,2024\nFacebook,520,540,560,590,620\nTwitter,320,305,250,240,230\nInstagram,400,420,460,500,540\nLinkedin,210,250,280,310,350"
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "1gEQLP1GQPsbL9MX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you infer which platform had the most significant decrease in daily users over the reported period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll analyze the lines depicting the user trend for each platform. When observing {Twitter's line}[0.089, 0.330, 0.968, 0.126], I notice a significant drop between 2021 and 2022, which then continues to decline over the next years. Comparing this trend to {Facebook's line}[0.089, 0.500, 0.968, 0.432], {Instagram's line}[0.089, 0.670, 0.968, 0.568], and {LinkedIn's line}[0.089, 0.160, 0.968, 0.279] which all show a steady increase or comparatively less decrease, I can infer that Twitter experienced the most noteworthy decline.\nBased on the above thought, the answer is:\nThe platform that witnessed the most significant decrease in daily users over the observed period was Twitter. The line chart shows a substantial drop in user count during 2022, followed by a continuous reduction in the subsequent years. This decline is more striking when compared to other platforms, which either consistently increased their daily users or had a comparatively lesser decrease. The reason for this decline can not be derived from the chart data itself, as it would require further contextual information on Twitter's circumstances and actions during this period."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "piQxlsP6axcEzCEx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2022, how many more daily users were there on average on Facebook compared to Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {Facebook's data point in 2022}[0.282, 0.160] and find the number of users to be 560 million. Then, I find {Twitter's data point in 2022}[0.282, 0.262] and establish that the number of users is 250 million. So, the difference in average daily users on Facebook and Twitter in 2022 is 560 million minus 250 million.\nBased on the above thought, the answer is:\n310 million."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "GeUKvII0JCXOpvcO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the data from the chart into a structured table with categories as columns and values for each year as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first stage of developing a table from this line chart is to identify {the x-axis tick labels}[[0.024, 0.728, 0.068, 0.833], [0.074, 0.728, 0.136, 0.878], [0.103, 0.728, 0.204, 0.970], [0.201, 0.728, 0.272, 0.899], [0.286, 0.728, 0.341, 0.858], [0.310, 0.728, 0.409, 0.966], [0.424, 0.728, 0.477, 0.856]], as the column headers representing the different categories. The rows will be represented by the years 2022 and 2023, identified by referring to labels and subheadings. Finally, to populate the data in the table, {the line points for each category in 2022}[[0.068, 0.525], [0.136, 0.235], [0.204, 0.396], [0.272, 0.461], [0.341, 0.332], [0.409, 0.589], [0.477, 0.396]] and {the line points for each category in 2023}[[0.558, 0.428], [0.626, 0.268], [0.694, 0.300], [0.763, 0.525], [0.831, 0.235], [0.899, 0.493], [0.967, 0.300]] will be extracted and transformed into numerical values.\nBased on the above thought, the answer is:\nYear,Tradition,Individualism,Uncertainty Avoidance,Power Distance,Masculinity,Long-term Orientation,Indulgence\n2022,66,75,70,68,72,64,70\n2023,69,74,73,66,75,67,73"
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "MAFfl8i79nW36gwU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart's progression, which category has the most significant increase between 2022 and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comprehend which variable has the most significance, each variable's line progression from 2022 to 2023 has to be analysed. When looking at {the line for Tradition}<r>[0.068, 0.235, 0.477, 0.589]</r>, I observe a gentle increase. The climb for {Individualism}<r>[0.136, 0.268, 0.626, 0.268]</r> is minimal whilst {Uncertainty Avoidance}<r>[0.204, 0.300, 0.694, 0.300]</r> has a moderate upturn. There is a decline for {Power Distance}<r>[0.272, 0.525, 0.763, 0.525]</r>. The remaining categories show only slight changes.\nBased on the above thought, the answer is:\nUncertainty Avoidance has the most significant increase between 2022 and 2023. The line chart represents an upward trend for this category, indicating that there has been a significant increase in value from 2022 to 2023 compared to other categories."
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "Qm9N7QXigtHkA7Jq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of values for Indulgence between the years 2022 and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step to calculating the range between the two years is to locate {the value of Indulgence for the year 2022}<r>[0.477, 0.396]</r> and {the value of Indulgence for the year 2023}<r>[0.967, 0.300]</r>. With a value of 70 in 2022 and a value of 73 in 2023, we can find the range by subtracting the smallest value from the largest value, giving us a range of 3.\nBased on the above thought, the answer is:\n3"
+            }
+        ],
+        "id_raw": 881
+    },
+    {
+        "id": "NLPslfpQoW2hdTJZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give the tabulated form of project evaluations data for different years and different projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line charts into a table, I focus on {the x-axis tick labels}[[0.043, 0.754, 0.073, 0.827], [0.110, 0.754, 0.142, 0.832], [0.179, 0.754, 0.211, 0.831], [0.241, 0.754, 0.280, 0.847], [0.258, 0.754, 0.348, 0.970], [0.382, 0.754, 0.417, 0.838],[0.426, 0.754, 0.486, 0.898]], which represent the different projects. Then, I identify the {text in legend}[[0.104, 0.626, 0.217, 0.662]], [0.104, 0.678, 0.217, 0.714]] as the different evaluation years. For each project at a specific year, I can find the values at {line points}[[0.073, 0.432], [0.142, 0.343], [0.211, 0.387], [0.280, 0.298], [0.348, 0.253], [0.417, 0.209], [0.486, 0.253], [0.554, 0.298], [0.623, 0.566], [0.692, 0.521],[0.761, 0.343], [0.829, 0.253], [0.898, 0.343], [0.967, 0.387]]. The values can be filled into the table accordingly.\nBased on the above thought, the answer is:\nProjects,Innovation 2022,Innovation 2023,Management 2022,Management 2023\nMLP,72,74,67,68\nGCN,74,75,66,69\nNCN,73,70,72,70\nNCNC,75,73,74,75\nNeoGNN-BUDDY,76,72,73,76\nSEAL,77,73,75,74\nNode2Vec,76,75,72,73"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "X2ZiCiblRDrUhLTe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the Innovation Project Evaluation line graph, which project has shown least inconsistencies over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find out the project with least inconsistencies in Innovation evaluation, I need to look at the magnitude of unit changes in the {line plot for Innovation 2022}[0.073, 0.209, 0.486, 0.432] and {line plot for Innovation 2023}[0.073, 0.298, 0.486, 0.521]. Looking at the given coordinates, Node2Vec seems to show the least inconsistencies.\nBased on the above thought, the answer is:\nBased on the Innovation Project Evaluation line chart, Node2Vec appears to maintain a relatively stable and consistent performance over time. Its line plot shows minor oscillations, indicating consistent performance with marginal fluctuations across different years."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "lY1MhscwfupOIZcz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich project had the highest improvement when comparing Innovation 2022 and Management 2023 scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which project had the most improvement between Innovation 2022 and Management 2023, I compare {the line points for Innovation 2022}[[0.073, 0.432], [0.142, 0.343], [0.211, 0.387], [0.280, 0.298], [0.348, 0.253], [0.417, 0.209], [0.486, 0.253]] and {the line points for Management 2023}[[0.554, 0.655], [0.623, 0.700], [0.692, 0.521], [0.761, 0.343], [0.829, 0.253], [0.898, 0.297], [0.967, 0.385]] to find the project with the biggest improvement. The differences are calculated for each respective project and the project with the highest value of difference is selected as the one with the most improvement.\nBased on the above thought, the answer is:\nGCN"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "ViLuVLvaXg30TWfu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data showing the popularity of different car types over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis labels}<r>[0.053, 0.075, 0.503, 0.862]</r> which display the years from 2010 to 2025, these serve as the column headers. The rows consist of various car types namely 'Luxury cars', 'Electric Vehicles (EVs)', 'Mid-Range Cars', and 'Budget Cars'\u2014these can be identified from {the legend}<r>[0.884, 0.101, 0.982, 0.123]</r>. The data points for each year and car type are given by {the line points}<r>[0.073, 0.173, 0.483, 0.705]</r> where I extracted the necessary popularity numbers.\nBased on the above thought, the answer is:\nYear,Luxury Cars,Electric Vehicles,Mid-Range Cars,Budget Cars\n2010,500,320,600,1000\n2015,550,380,630,750\n2020,700,460,680,500\n2025,850,550,710,300"
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "9P0vohOOZrFWcdBw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends observed in the line chart, what significant changes in consumer preferences can be inferred regarding car types?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trends shown by {the line for Luxury Cars}<r>[0.102, 0.082, 0.798, 0.196]</r> and {the line for Electric Vehicles}<r>[0.102, 0.211, 0.798, 0.123]</r>, it's evident that there's a substantial increase in popularity, suggesting growing consumer preference towards luxury and environmentally friendly options. Contrastively, {the line for Budget Cars}<r>[0.102, 0.599, 0.798, 0.932]</r> depicts a sharp decline, pointing towards a decrease in its popularity. Mid-Range Cars show mild popularity growth.\nBased on the above thought, the answer is:\nThe significant changes are the increased consumer preference for luxury and electric vehicles, indicating a shift towards more premium and environmentally friendly car options, and a marked decline in the popularity of budget cars, suggesting a decreasing preference for more economical car choices."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "FHr3NqpAXffJAhI6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percent change in popularity for Electric Vehicles from 2010 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percent change, I first locate {the point for Electric Vehicles in 2010}<r>[0.073, 0.429]</r> and {the point for Electric Vehicles in 2025}<r>[0.483, 0.173]</r>. The value in 2010 is approximately 320 and in 2025 is approximately 550. Using the formula (new value - old value)/old value * 100, I calculate (550 - 320)/320 * 100.\nBased on the above thought, the answer is:\n71.88%"
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "m1AqRDsHNhIcC9fm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart showing global trade and supply chain volumes over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe the x-axis where {the year labels}<r>[0.053, 0.920, 0.921, 0.942]</r> are displayed from 2010 to 2020, which will form the columns of the table. {The legend}<r>[0.820, 0.030, 0.960, 0.080]</r> indicates two series: Global Trade and Supply Chain Management. By tracking down the plotted points for each year on both lines, I gather the data points corresponding to each series, identifying {data point coordinates for Global Trade}<r>[0.072, 0.251, 0.470, 0.456]</r> and {data point coordinates for Supply Chain}<r>[0.072, 0.309, 0.470, 0.544]</r>.\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2010,1200,900\n2011,1250,910\n2012,1300,920\n2013,1370,930\n2014,1450,940\n2015,1550,950\n2016,1500,940\n2017,1520,945\n2018,1600,955\n2019,1620,960\n2020,1300,800"
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "v3YKXaS04pY2OJH8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector shows more stability over the years, global trade or supply chain management?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing both lines, {the Global Trade line}<r>[0.072, 0.251, 0.470, 0.456]</r> exhibits fluctuations with significant peaks and a sharp dip in 2020. In contrast, {the Supply Chain line}<r>[0.072, 0.309, 0.470, 0.544]</r> has gentler slopes and less dramatic dips, indicating minor variances year-to-year. The stability of a dataset is often signified by fewer major fluctuations and a smaller range of change, which, in this case, applies more to the supply chain management line.\nBased on the above thought, the answer is:\nBased on the chart, supply chain management exhibits more stability over the years compared to global trade. This is evident from the less dramatic fluctuations and the gentle trend displayed, despite minor increases annually."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "Dyq34G1e6VaADntw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in global trade volume from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I pinpoint the start and end data points from {the line plot of Global Trade}<r>[0.072, 0.309, 0.470, 0.456]</r>. In 2010, the volume was approximately 1200, located at the plot coordinate {2010 data point}<r>[0.072, 0.309]</r>. In 2019, the volume increased to around 1620, found at the coordinate {2019 data point}<r>[0.450, 0.295]</r>. The increase is then calculated by subtracting the initial volume from the volume at the end of the period.\nBased on the above thought, the answer is:\n420."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "HQe28xzL7m33aaTf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, can you generate a table of Consumer index for each Market for the years 2022 and 2023, under both Baseline and Trend1 scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data, I need to extract the labels from {the x-axis tick labels}[[0.015, 0.609, 0.097, 0.804], [0.096, 0.609, 0.191, 0.838], [0.207, 0.609, 0.286, 0.797], [0.230, 0.609, 0.380, 0.970], [0.371, 0.609, 0.475, 0.859]] which contain the market names. The lines represented by the legends i.e. {Baseline 2022}[[0.129, 0.481, 0.228, 0.517]], {Baseline 2023}[[0.129, 0.533, 0.228, 0.569]], {Trend1 2022}[[0.623, 0.105, 0.709, 0.141]], and {Trend1 2023}[[0.623, 0.157, 0.709, 0.193]] correspond to respective trends. To accurately plot the numbers, I'll refer to the line coordinates from {line for Baseline 2022}[[0.097, 0.243, 0.475, 0.389]], {line for Baseline 2023}[[0.097, 0.201, 0.475, 0.285]], {line for Trend1 2022}[[0.590, 0.285, 0.969, 0.431]], and {line for Trend1 2023}[[0.590, 0.222, 0.969, 0.326]].\nBased on the above thought, the answer is:\nMarket,Baseline 2022,Trend1 2022,Baseline 2023,Trend1 2023\nEmerging-Asia,65,63,70,68\nEmerging-Europe,72,70,74,73\nLatin-America,68,67,71,70\nMiddle-East and North Africa,67,65,70,69\nSub-Saharan Africa,66,64,70,69"
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "RhPU2uBUuGdYpGoz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data provided and displayed on the chart, what can we infer about the trend of Consumer Index in Emerging Markets for the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analysing {the lines} provided in the chart, corresponding to {Baseline 2022}[[0.097, 0.243, 0.475, 0.389]], {Baseline 2023}[[0.097, 0.201, 0.475, 0.285]], {Trend1 2022}[[0.590, 0.285, 0.969, 0.431]], and {Trend1 2023}[[0.590, 0.222, 0.969, 0.326]], we can see a general pattern of growth in Consumer Index across all markets for Baseline and Trend1 scenarios. Both scenarios present a similar growth pattern, indicating consistent market performance and positive economy prospect for these emerging markets.\nBased on the above thought, the answer is:\nExamining the chart, we can infer that the trend of Consumer Index for most part is projected to increase for all Emerging Markets from 2022 to 2023. This is indicated by higher values for both Baseline and Trend1 in 2023 compared to 2022 for each market. This suggests that the overall economy and consumer activity are expected to improve in the following year."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "7QKcetp9FDynnYNA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in Consumer Index from year 2022 to 2023 under the Trend1 scenario for the Middle-East and North Africa market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should find the Consumer Index for Middle-East and North Africa for both 2022 and 2023 in the Trend1 scenario. I will look at {the line for Trend1 2022}[[0.590, 0.285, 0.969, 0.431]] and {the line for Trend1 2023}[[0.590, 0.222, 0.969, 0.326]] to find out the point values for Middle-East and North Africa, which are 65 and 69 respectively. The difference between these values provides the answer.\nBased on the above thought, the answer is:\n4."
+            }
+        ],
+        "id_raw": 886
+    },
+    {
+        "id": "z8zcc7M74DeZ2nfF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table showing the number of visitors each city received from the dataset in the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the ascending and descending trends on {the line graph}[[0.037, 0.060, 0.509, 0.916]], data markers indicate specific visitor numbers at various cities. I identify unique colors for each city, represented in {the legend}[[0.042, 0.071, 0.150, 0.208]], which correspond to each city on {the x-axis}[[0.047, 0.924, 0.499, 0.945]]. This allows me to methodically collect the visitor data.\nBased on the above thought, the answer is:\nLocation,Visitors (in millions)\nParis,10.0\nLondon,8.5\nRome,9.7\nVenice,6.2\nBerlin,7.5\nMadrid,7.8\nAmsterdam,5.0\nAthens,3.5"
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "NluXQoVnIkEK8asb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line graph, which city shows the most positive change in tourist visits over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the growth patterns, {the line for Rome}[[0.190, 0.924, 0.264, 0.786]] shows a consistent upward trajectory without any sudden drops, suggesting a steady increase in visitor numbers. This can be contrasted with {the lines for other cities}[[0.058, 0.174, 0.487, 0.827]], some of which display fluctuating patterns or a downtrend, such as {the line for Athens}[[0.409, 0.924, 0.483, 0.975]], which shows a sharp decline.\nBased on the above thought, the answer is:\nRome displays the most positive change in tourist visits over time, as evident from its consistent and steady upward trend on the graph. This suggests a growing popularity and an increasing number of tourists visiting the city compared to others which exhibit more volatility or decline."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "h1RiYbW8dEbBedXZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the visitor number in Paris compared to Athens as shown on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Through my analysis of the line points on {the line for Paris}[[0.058, 0.174, 0.117, 0.245]] and {the line for Athens}[[0.428, 0.914, 0.497, 0.975]], I extract the numbers for Paris and Athens, which are approximately 10 million and 3.5 million, respectively. The difference can be calculated by subtracting the visitors in Athens from the visitors in Paris.\nBased on the above thought, the answer is:\n6.5 million"
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "pv7vI6YFX6dipWsw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the information about the \"Status in 2023\" and \"Status in 2024\" from the two line charts to form a structured table for each social category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two subplots correspond to the status in 2023 and 2024. For every subplot, we first look at the {x-axis tick labels}<r>[0.051, 0.932, 0.456, 0.932]</r> and <r>[0.540, 0.932, 0.944, 0.932]</r> which represent the rows or categories of the table. Afterward, we extract the data accordingly.\nBased on the above thought, the answer is:\nCategory,Status in 2023,Status in 2024\nCity Life,75,80\nOnline Work,68,70\nSelf-driving Cars,80,82\nSmart Home,90,88\nAgriculture Tech,72,75\nEntertainment,85,80\nEducation Tech,78,83\nGreen Energy,76,81"
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "Y18baRSlvGkxDniX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which social category experienced a decline from 2023 to 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the social category that shows a decline from 2023 to 2024, I need to compare the values for each category in the two years. Looking at the line charts, I notice that the {Smart Home}<r>[0.225, 0.932]</r> shows a higher value in 2023 compared to {Smart Home in 2024}<r>[0.713, 0.932]</r> and {Entertainment in 2023}<r>[0.340, 0.932]</r> compared with {Entertainment in 2024}<r>[0.828, 0.932]</r>. Thus, these categories experienced a decline from 2023 to 2024.\nBased on the above thought, the answer is:\nSmart Home and Entertainment are the social categories that experienced a decline from 2023 to 2024. Comparing their values in the line charts for the two years, both show a decrease over time which indicates a decline."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "mdZvrWGigR1F10sJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much growth have self-driving cars shown from 2023 to 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rate of 'Self-driving Cars', I locate the coordinates corresponding to {Self-driving Cars in 2023}<r>[0.167, 0.932]</r> and {Self-driving Cars in 2024}<r>[0.655, 0.932]</r> and extract the values for both years. Growth can be calculated by taking the difference: (2024 value - 2023 value).\nBased on the above thought, the answer is:\n2."
+            }
+        ],
+        "id_raw": 888
+    },
+    {
+        "id": "j2d7GLhp5fucNZPe",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the recall rates for the AI models into a structured table categorized by Truthful and Misleading recalls for the years 2022 and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the recall rates into a structured table, I reference {the x-axis tick labels}<r>[0.051, 0.812, 0.073, 0.858], [0.095, 0.812, 0.133, 0.889], [0.131, 0.812, 0.192, 0.934], [0.170, 0.812, 0.252, 0.975]</r> which represent the AI models. For each model, I observe two bars; {one for Truthful recall}<r>[0.073, 0.211, 0.073, 0.656], [0.192, 0.567, 0.192, 0.656]</r> and {one for Misleading recall}<r>[0.133, 0.448, 0.133, 0.448], [0.252, 0.656, 0.252, 0.656]</r> for each year, 2022 and 2023. The values are then annotated from the heights of the bars.\nBased on the above thought, the answer is:\nModel,Truthful Recall 2022,Misleading Recall 2022,Truthful Recall 2023,Misleading Recall 2023\nLLaMA,72,41,74,42\nVicuna,75,45,77,48\nAlpaca,78,50,80,51\nWizardLM,76,44,78,46"
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "coa8OzDIbSkuNQTH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI model exhibits the highest consistency in recall values between Truthful and Misleading categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the consistency between Truthful and Misleading recall categories, I examine the difference in recall rates for each AI model. Observing {the bar heights for Truthful}<r>[0.073, 0.211, 0.490, 0.656]</r> and {Misleading recalls}<r>[0.073, 0.448, 0.490, 0.567]</r>, Alpaca shows the smallest difference with Truthful recall at 78 and Misleading recall at 50 in 2022, and 80 vs. 51 in 2023. This suggests that Alpaca maintains the highest consistency across categories.\nBased on the above thought, the answer is:\nAlpaca exhibits the highest consistency in recall values between the Truthful and Misleading categories, as shown by the smallest margin of difference across the two values in both observed years."
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "TnWpRk7G1nN4KFdn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in Truthful recall rates for all models from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average increase in Truthful recall rates, I collect the recall values for both years for all AI models. Using {the points data for Truthful recalls}<r>[0.073, 0.270, 0.490, 0.656], [0.550, 0.270, 0.967, 0.685]</r>, the values for 2022 are: LLaMA: 72, Vicuna: 75, Alpaca: 78, WizardLM: 76 and for 2023 are: LLaMA: 74, Vicuna: 77, Alpaca: 80, WizardLM: 78. I then calculate the difference and find the average of these differences.\nBased on the above thought, the answer is:\n2.5"
+            }
+        ],
+        "id_raw": 889
+    },
+    {
+        "id": "9MZAoetXhbsWbLgW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you format the data from the line chart into a table that lists years, Return of Investment A, Return of Investment B, and Federal Interest Rate as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a tabular representation from the line chart, I start by identifying {the x-axis labels}<r>[0.039, 0.872, 0.080, 0.970], [0.097, 0.872, 0.138, 0.970], [0.155, 0.872, 0.196, 0.970], [0.213, 0.872, 0.254, 0.970], [0.271, 0.872, 0.311, 0.970], [0.328, 0.872, 0.369, 0.970], [0.386, 0.872, 0.427, 0.970], [0.444, 0.872, 0.485, 0.970]</r> which represent the years. Next, I analyze the line plots for Return of Investment A, Return of Investment B, and Federal Interest Rate by the values at {the plot points}<r>[0.080, 0.802], [0.138, 0.693], ..., [0.485, 0.135]</r> and extract the values corresponding at each year.\nBased on the above thought, the answer is:\nYear,Return of Investment A,Return of Investment B,Federal Interest Rate\n2006,155,153,157\n2007,138,134,142\n2008,185,179,191\n2009,155,152,158\n2010,176,171,181\n2011,167,166,168\n2012,189,188,190\n2013,195,190,200"
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "dMyNx7bXr98JZ9cw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the outliers in the 2008, 2009, and 2020 data likely impact the understanding of financial trends on the chart and what are the possible explanations for these decreases?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows significant drops in the Return of Investment A and B during 2008, 2009, and 2020, which are identified as {outliers}<r>[0.155, 0.179, 0.351, 0.455]</r>. These outliers can distort understanding, suggesting greater volatility than normal. Possible explanations for these decreases could be global financial events affecting the markets in those years, such as the 2008 financial crisis which led to drastic falls in investment values and had a lingering effect in 2009. As for 2020, it could be attributed to the economic impact of the COVID-19 pandemic leading to significant financial downturns.\nBased on the above thought, the answer is:\nThe outliers in the 2008, 2009, and 2020 data likely led to a perception of higher volatility and risk in financial trends depicted in the chart. Possible reasons include the 2008 global financial crisis and the economic downturn during the COVID-19 pandemic which strongly affected financial markets, illustratively decreasing the returns on investments during those specific years."
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "KNeGE82jdYibosSR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual percent increase in Federal Interest Rate from 2006 to 2010 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average annual percent increase, I first locate {the Federal Interest Rate values between 2006 and 2010}<r>[0.080, 0.802, 0.311, 0.420]</r>. The values are 157, 142, 191, 158 respectively. To find the percent increase for each year, I will subtract the previous year\u2019s value from the current year's value, divide by the previous year's value, and then multiply by 100. Then, I sum these percentages and divide by the number of years to find the average increase.\nBased on the above thought, the answer is:\n-12.06%"
+            }
+        ],
+        "id_raw": 891
+    },
+    {
+        "id": "TRKhUWnyoAh5cVHi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the line chart into a tabular format with Products as the rows and Years as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart to a table, I'll identify the {x-axis tick labels}<r>[0.041, 0.824, 0.056, 0.852], [0.041, 0.698, 0.056, 0.726], [0.041, 0.571, 0.056, 0.599], [0.041, 0.444, 0.056, 0.472], [0.041, 0.317, 0.056, 0.345]</r> as the column headers representing years. The {y-axis tick labels}<r>[0.520, 0.845, 0.564, 0.950], [0.634, 0.845, 0.665, 0.920], [0.735, 0.845, 0.766, 0.918], [0.825, 0.845, 0.866, 0.944], [0.915, 0.845, 0.967, 0.970]</r> will constitute the row headers corresponding to different products. The data values can be extracted from the intersection points of each product's line with the year marking, represented by coordinates such as {iPhone's 2020 sales}<r>[0.080, 0.328]</r>, {iPad's 2020 sales}<r>[0.181, 0.582]</r>, and so on.\nBased on the above thought, the answer is:\nProducts, 2020, 2021\niPhone, 80, 95\niPad, 60, 70\nMac, 70, 75\nWatch, 45, 60\nServices, 50, 70"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "kYxwXcrSS5fYfaRD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends demonstrated by the line chart, which product has seen the highest growth in sales from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product saw the highest growth in sales from 2020 to 2021, we analyze the sales lines of each product. We notice a steep increase from 2020 to 2021 in {the sales of Watch}<r>[0.383, 0.772]</r> to <r>[0.866, 0.582]</r> and {the sales of Services}<r>[0.483, 0.709]</r> to <r>[0.967, 0.455]</r>. By plotting the sales numbers of both these products over both years, it's clearly seen that the Watch sales went up from 45 units to 60 units, and Services grew from 50 units to 70 units.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, the product 'Services' has seen the highest sales growth from 2020 to 2021. It exhibited a considerable increase from 50 to 70 units."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "ClFSP2KxX6Ms2ZrQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the sales difference for iPhone between 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the difference in iPhone sales between 2020 and 2021, I have to identify {iPhone's 2020 sales}<r>[0.080, 0.328]</r> as 80 units and {iPhone's 2021 sales}<r>[0.564, 0.138]</r> as 95 units. Therefore, the difference is 95 - 80 = 15 units.\nBased on the above thought, the answer is:\n15."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "m99RADjTY4C5M6FG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the multi-line chart showing technology trends into a structured table categorizing each year as a row and each technology as a column?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify {the labels on the x-axis}<r>[0.037, 0.685, 0.943, 0.821]</r> which denote the years from 2017 to 2021. These will serve as row labels. Next, I locate {the legend}<r>[0.844, 0.091, 0.977, 0.207]</r> detailing the technologies: Blockchain, AI, Cloud Computing, Cyber Security, and Quantum Computing, to form the column headers. I then observe {the points on the lines}<r>[0.105, 0.180, 0.943, 0.570]</r> for each technology per year and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Blockchain,AI,Cloud Computing,Cyber Security,Quantum Computing\n2017,45,50,55,50,40\n2018,50,56,60,55,45\n2019,56,62,65,60,50\n2020,63,68,70,65,55\n2021,70,75,75,70,60"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "RLvmtydAxZ4Sat8L",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology demonstrated the most consistent growth rate over the observed period in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining {each technology's line trajectory}<r>[0.105, 0.180, 0.943, 0.277]</r>, it shows that the lines for Blockchain, AI, Cloud Computing, and Cyber Security have similar steepness, indicative of consistent year-over-year growth. {The line for Quantum Computing}<r>[0.105, 0.492, 0.943, 0.102]</r>, while also consistent, portrays a slightly less steep incline, indicating a lesser growth rate. To determine the most consistent among the higher growth technologies, they all appear equivalent in their progression.\nBased on the above thought, the answer is:\nTechnologies like Blockchain, AI, Cloud Computing, and Cyber Security have demonstrated the most consistent and similar growth rates according to the chart. Their lines progress almost parallel to each other, signifying a steady and robust increase in popularity each year."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "dtSr76o5TQ61LPqN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the technology 'Quantum Computing', what is the increase in popularity from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase, I analyze {the line representing Quantum Computing}<r>[0.105, 0.492, 0.943, 0.102]</r>. The value for 2017 is noted at 40, seen at {the point in 2017 for Quantum Computing}<r>[0.105, 0.492]</r>, and for 2021, it is 60, noted at {the point in 2021 for Quantum Computing}<r>[0.943, 0.102]</r>. The increase is calculated by subtracting the 2017 value from the 2021 value.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "9ikdgtaYFHpYRdCs",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data regarding recall measurements for different categories in health settings be structured into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To prepare the data in CSV format, I closely examined the chart to identify the different categories described by {the y-axis titles on both sides}[[0.013, 0.321, 0.029, 0.678], [0.522, 0.499, 0.522, 0.499]]. There are two categories on the left hand side y-axis: 'Health State Recall' and 'BMI Recall', and one category on the right-hand side y-axis: 'Mortality Recall'. Identifying {the bars}[[0.053, 0.075, 0.172, 0.924], [0.527, 0.075, 0.642, 0.924]] corresponding to 'Hospice' and 'Clinical Treatment' settings, I delineated and extracted the numerical values visually represented by the height of these bars.\nBased on the above thought, the answer is:\nSetting,Health State Recall,BMI Recall,Mortality Recall\nHospice,40,20,15\nClinical Treatment,80,70,65"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "1dN0zevjaso3qpK2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is the 'Mortality Recall' lower in both health settings compared to other recall categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand why 'Mortality Recall' is comparatively lower, I start by observing the height of {the bars for Mortality Recall}[[0.490, 0.147], [0.965, 0.123]]. These bars' lower heights relative to {the bars for other categories in both settings}[[0.073, 0.803], [0.252, 0.317]] suggest a lesser numerical value. This indicates that patients have greater difficulty recalling mortality rates and probabilities, possibly due to the complexity of the information related to mortality compared to recalling their own health state or BMI, which could be more directly observable and relevant to their everyday lives.\nBased on the above thought, the answer is:\nThe 'Mortality Recall' is lower because recalling mortality rates and probabilities is inherently more complex and less directly related to patients' everyday experiences and observations, making it harder to recall compared to their own health state or BMI."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "tZcs3UJeoIrOpQQ1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much improvement in 'Health State Recall' is observed between Hospice and Clinical Treatment settings?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the improvement in 'Health State Recall', initially I locate {the bars for Health State Recall in Hospice and Clinical Treatment settings}[[0.073, 0.851], [0.252, 0.317]]. These bars respectively start at an estimated visual value of 40 for Hospice and rise to 80 in Clinical Treatment. The improvement can be calculated by subtracting the value in Hospice from the value in Clinical Treatment (80 - 40).\nBased on the above thought, the answer is:\n40"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "M2DIzySE50qCZivv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data for the trend of social activities and lifestyles between 2022 and 2023 into tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.056, 0.934, 0.091, 0.970], [0.473, 0.934, 0.508, 0.970]] which represent the column headers or years. Next, I note that there are two categories: {Social Activities}[[0.104, 0.105, 0.213, 0.141]] and {Lifestyles}[[0.578, 0.105, 0.637, 0.141]], which will become the row labels. Lastly, I locate {the points for Social Activities}[[0.073, 0.287], [0.490, 0.712]] and {the points for Lifestyles}[[0.548, 0.754], [0.965, 0.797]] to determine the corresponding percentages for each year.\nBased on the above thought, the answer is:\nCategory,2022,2023\nSocial Activities,70,80\nLifestyles,68,69"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "30pOt05gVRRfcdOl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category exhibited a more stable trend over the years 2022 and 2023, Social Activities or Lifestyles? Provide a brief explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category exhibited a more stable trend, I must compare the degree of fluctuation across the two years for each category. I observe that {the points for Social Activities}[[0.073, 0.712], [0.490, 0.287]] depict a change from 70 to 80. Similarly, {the points for Lifestyles}[[0.548, 0.754], [0.965, 0.797]] show a change from 68 to 69. The change in percentage for Social Activities is greater than the change in Lifestyles, indicating a larger fluctuation.\nBased on the above thought, the answer is:\nThe category Lifestyles exhibited a more stable trend over the years 2022 and 2023. While both categories saw an increase during this period, the increase for Lifestyles was smaller, indicating less fluctuation and therefore more stability."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "sIOb4RFKW0S0Dp0w",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage did the interest in Social Activities increase from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the percentage values for Social Activities in 2022 and 2023. These are represented by {the points for Social Activities}[[0.073, 0.712], [0.490, 0.287]], from which I extract the corresponding values: 70 for 2022 and 80 for 2023. I calculate the increase as 80 - 70 = 10.\nBased on the above thought, the answer is:\n10%."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "IflwFKKlDrgcgqIH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart showing media outlet influence into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the bar chart data into a table, I first identify {the x-axis labels}<r>[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934]</r> representing the media outlets: Newspaper, Radio, Television, and Social Media. Then I observe the {height of each bar}<r>[0.061, 0.500, 0.144, 0.600], [0.178, 0.400, 0.259, 0.700], [0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450]</r> to determine the level of social influence for each category.\nBased on the above thought, the answer is:\nMedia Outlet, Social Influence\nNewspaper, 50\nRadio, 60\nTelevision, 85\nSocial Media, 45"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "fVZJRJ3anStdigZr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the bar chart for media outlets' impact on public opinion from Newspapers to Social Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing the bar chart, the trend shows a gradual increase in influence from {Newspapers to Radio to Television}<r>[0.061, 0.500, 0.144, 0.600], [0.178, 0.400, 0.259, 0.700], [0.291, 0.300, 0.378, 0.850]</r>, indicating more conventional media types hold a robust impact. However, there's a notable decrease moving from {Television to Social Media}<r>[0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450]</r> suggesting a volatile impact of new media forms like Social Media.\nBased on the above thought, the answer is:\nThe trend from Newspapers through Television indicates increasing media influence on public opinion, peaking with Television. However, this trend takes a downward turn with Social Media, showcasing its less predictable impact on public opinion."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "twmkGjRSfvityXOn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more influence does Television have compared to Social Media according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in influence between Television and Social Media, I refer to their {bar heights}<r>[0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450]</r> which show influences of 85 and 45 respectively. The difference is 85 - 45.\nBased on the above thought, the answer is:\n40"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "bulmf6WIO8bYKh17",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the values from the graph and tabulate them in the form of a table, having the 'Year' as the column and 'Market Value' as rows for both Residential and Commercial sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I need to note the Years from {the x-axis labels}<r>[0.046, 0.960, 0.170, 0.996], [0.227, 0.960, 0.328, 0.996], [0.421, 0.960, 0.514, 0.996], [0.614, 0.960, 0.705, 0.996], [0.808, 0.960, 0.902, 0.996]</r> and use them as the column headers. The legend containing {Residential}<r>[0.915, 0.296, 0.962, 0.320]</r> and {Commercial}<r>[0.915, 0.264, 0.965, 0.287]</r> will represent the row labels. The corresponding y-values can be extracted from {points on Residential line}<r>[0.073, 0.459], [0.250, 0.446], [0.436, 0.583], [0.616, 0.701], [0.797, 0.830]</r> and {points on Commercial line}<r>[0.073, 0.349], [0.250, 0.311], [0.436, 0.138], [0.616, 0.059], [0.797, 0.019]</r> which are the market values in thousands of dollars.\nBased on the above thought, the answer is:\n,2016,2017,2018,2019,2020\nResidential,224,216,241,273,314\nCommercial,167,150,86,45,10"
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "GGElyb9iGeZumvYz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which sector shows the most drastic change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the line plots for both the residential and commercial sectors. The {Residential line}<r>[0.073, 0.459, 0.797, 0.830]</r> shows a steady decline from 224 to 216, then gradual increase to 314. On the other hand, {the Commercial line}<r>[0.073, 0.349, 0.797, 0.019]</r> shows a sharp drop from 167 to 10. The drop in the commercial sector is more drastic compared to the relatively slower pace in the residential sector.\nBased on the above thought, the answer is:\nThe commercial sector shows the most drastic change over the years, as it undergoes a sharp and continuous decrease in market value from 167 in 2016 to 10 in 2020. In contrast, the residential sector experiences a moderate decline followed by a gradual increase."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "IEgqaz3Lj0joF5Vn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total change in the residential sector market value from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate the total change in the residential sector market value, I need to locate the residential market value {in 2016}<r>[0.073, 0.459]</r> which is 224 (in thousands of dollars) and {in 2020}<r>[0.797, 0.830]</r> which is 314. Subtracting the value of 2016 from 2020 will give the total change. So, the calculation is 314 - 224 = 90.\nBased on the above thought, the answer is:\n90."
+            }
+        ],
+        "id_raw": 897
+    },
+    {
+        "id": "Om8QEBm8r3ifmq9g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compile the impact levels for Policy A for both 2020 and 2021 into a table format based on the provided chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis labels}[[0.016, 0.764, 0.089, 0.939], [0.058, 0.764, 0.144, 0.970], [0.148, 0.764, 0.199, 0.885], [0.195, 0.764, 0.254, 0.906], [0.245, 0.764, 0.309, 0.917], [0.283, 0.764, 0.364, 0.959], [0.345, 0.764, 0.419, 0.941], [0.423, 0.764, 0.474, 0.887]] which lay out the categories such as Environment, Unemployment, Inflation, etc. Next, I notice {the lines for Policy A 2020 and Policy A 2021}[[0.089, 0.136, 0.474, 0.445], [0.089, 0.136, 0.474, 0.482]] indicating a transition in metrics between the two years across these categories. By evaluating {the precise point coordinates for both years of Policy A}[[0.089, 0.260], [0.144, 0.383], [0.199, 0.445], [0.254, 0.198], [0.309, 0.383], [0.364, 0.322], [0.419, 0.136], [0.474, 0.445], [0.089, 0.297], [0.144, 0.359], [0.199, 0.482], [0.254, 0.136], [0.309, 0.408], [0.364, 0.260], [0.419, 0.198], [0.474, 0.383]], I extract the impact levels for each category in both policy years.\nBased on the above thought, the answer is:\nCategory,Policy A 2020,Policy A 2021\nEnvironment,80,77\nUnemployment,70,72\nInflation,65,62\nEducation,85,90\nHealthcare,70,68\nTransportation,75,80\nPublic Safety,90,85\nTaxation,65,70"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "jAR2nDRB65NaGTXF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the Policy A seem more effective in Public Safety compared to other categories in both 2020 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand why Policy A is notably effective in Public Safety, I observe {the points for Public Safety}[[0.419, 0.136], [0.419, 0.198]]. These points indicate the highest impact levels among all categories with values 90 in 2020 and 85 in 2021, which are relatively high compared to other categories. This suggests that the design or focus of Policy A potentially prioritizes or effectively addresses issues within public safety sectors, resulting in high positive impact in these areas consistently across the two years.\nBased on the above thought, the answer is:\nPolicy A seems more effective in Public Safety because the impact levels, measuring at 90 in 2020 and 85 in 2021, are the highest among all the categories listed. This consistently high performance suggests a special emphasis or effectiveness of this policy specifically in public safety measures."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "8weAYr5Ub5b1N1D5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the impact level for Education improved from Policy A 2020 to Policy A 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the impact levels for Education in Policy A 2020 and Policy A 2021, I locate {the specific points for Education}[[0.254, 0.198], [0.254, 0.136]]. The values are 85 for 2020 and 90 for 2021. To calculate the improvement, I subtract the 2020 value from the 2021 value.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "ETEVFs0cF6XdvTWM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that represents the findings in 2022 and 2023 for each method in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart data, I'd start by identifying {the x-axis tick labels}[[0.129, 0.668], [0.187, 0.668], [0.245, 0.668], [0.303, 0.668], [0.361, 0.668], [0.418, 0.668], [0.476, 0.668]]. These would serve as row labels, each referring to a different new media method. I can use {the y-axis labels for 2022}[[0.111, 0.583], [0.111, 0.499], [0.111, 0.329], [0.111, 0.244]] and {the y-axis labels for 2023}[[0.605, 0.583], [0.605, 0.499], [0.605, 0.329], [0.605, 0.244]] to get the values in 2022 and 2023 respectively for each method. These would fill up the numerical data in the table.\nBased on the above thought, the answer is:\nNew Media Method,Performance Score 2022,Performance Score 2023\nAI Content Generator,22,18\nSocial Media Platforms,25,20\nSEO Optimization,31,23\nEmail Marketing,20,17\nVR/AR Content,15,13\nInfluencer Marketing,28,31\nVideo Marketing,35,40"
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "z4PEeg0rgkRMbtc5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which New Media method has shown a positive trend from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I have to find a new media method that has a higher performance score in 2023 compared to 2022. Upon examining {the performance scores in 2022}[[0.129, 0.465], [0.187, 0.414], [0.245, 0.312], [0.303, 0.499], [0.361, 0.583], [0.418, 0.363], [0.476, 0.244]] and {the performance scores in 2023}[[0.623, 0.533], [0.680, 0.499], [0.738, 0.448], [0.796, 0.550], [0.854, 0.617], [0.912, 0.312], [0.970, 0.159]], I can spot that Influencer Marketing had a higher score in 2023.\nBased on the above thought, the answer is:\nInfluencer Marketing saw a positive trend from 2022 to 2023, increasing its performance score from 28 to 31."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "emSAkSZoSRfnxCAW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_45_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total change in performance score for AI Content Generator from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the total change in performance score for AI Content Generator, first I need to spot {the value for AI Content Generator in 2022}[0.129, 0.465] which is 22, then find out {the value for AI Content Generator in 2023}[0.623, 0.533] which is 18. The difference between the two scores is the total change in performance.\nBased on the above thought, the answer is:\n-4."
+            }
+        ],
+        "id_raw": 899
+    },
+    {
+        "id": "NRggLiUyzcRQUWjR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing business process efficiency into a structured table with stages as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the line chart, I can extract the structured data based on {the x-axis labeled as stages}[[0.10, 0.90, 0.10, 0.12]] and the corresponding {data points for each line}[[0.15, 0.25], [0.35, 0.55], [0.55, 0.20], [0.75, 0.85], [0.95, 0.70]]. The stages represent 'Initial', 'Planning', 'Execution', 'Review', and 'Improvement', and I identify the efficiency values for both the 'Increase' and 'Decrease' series.\nBased on the above thought, the answer is:\nStage,Decrease Efficiency,Increase Efficiency\nInitial,200,250\nPlanning,300,350\nExecution,150,400\nReview,350,450\nImprovement,500,600"
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "XtHQZXHAVcWnLERR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is there a significant dip in efficiency during the 'Execution' stage for the Decrease series as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the 'Decrease' series line, specifically at the {Execution stage point}[[0.55, 0.20]], there is a noticeable decline to 150 in efficiency. This drop signifies a critical point where the process might encounter operational challenges or inefficiencies, which is further evidenced by the point\u2019s sudden decline in value when compared to other stages. The 'Execution' stage often involves complex implementations and the manifestation of unforeseen issues, contributing to this observed decrease.\nBased on the above thought, the answer is:\nThe significant dip in the 'Execution' stage for the Decrease series indicates operational challenges or inefficiencies that tend to arise during this critical phase of the business process. This stage often encompasses the practical application of plans, which can reveal unforeseen issues and lead to a decline in efficiency as observed in the chart."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "kYIb6MN0ZRck7zJl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the efficiency in the 'Improvement' stage compared to the 'Execution' stage for the Increase series?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the efficiency value for the Increase series at the {Execution stage}[[0.55, 0.20]], which is 400. Then, I find the value at the {Improvement stage}[[0.95, 0.70]], which is 600. By subtracting the efficiency at the Execution stage from that of the Improvement stage, we can calculate the increase.\nBased on the above thought, the answer is:\n200"
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "kLkhhzBzcLencSfS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the line chart comparing the activity levels of Traditional Education and Online Education over time into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I review the coordinates for each point along the line representing Traditional Education: {points for Traditional Education}<r>[0.102, 0.301, 0.975, 0.418]</r> where specific hours are marked at critical points {x-tick marks for Traditional Education}<r>[0.095, 0.434, 0.110, 0.448], [0.277, 0.434, 0.291, 0.448], ..., [0.997, 0.434, 1.026, 0.448]</r>. I then proceed to examine the line for Online Education and its respective data points using similar methods: {points for Online Education}<r>[0.102, 0.570, 0.975, 0.870]</r> matched to the same x-tick marks to ensure time consistency across both data sets.\nBased on the above thought, the answer is:\nTime (hours),Traditional Education,Online Education\n0,1.0,0.46\n1,1.05,0.66\n2,1.15,0.76\n3,0.95,0.96\n4,0.85,1.26\n5,0.84,1.39\n6,0.75,1.58\n7,0.50,1.5\n8,0.55,1.7\n9,0.56,1.44\n10,0.60,1.42\n11,0.58,1.40\n12,0.55,1.41\n13,0.42,1.62\n14,0.35,1.8\n15,0.24,1.9\n16,0.24,2.0\n17,0.20,2.1\n18,0.23,2.2\n19,0.24,2.4\n20,0.15,2.5\n21,0.14,2.6\n22,0.14,2.75\n23,0.10,3.0\n24,0.12,3.14"
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "2Er8cUECcIeodJfb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of activity level for Traditional Education over time compare to that of Online Education?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the trend lines on the chart, I observe that the line for Traditional Education, located between {coordinates for Traditional Education Line}<r>[0.102, 0.301, 0.975, 0.418]</r>, generally decreases or remains stable for most of the time period, with the lowest activity levels observed towards the latter part. In contrast, the line for Online Education, described in {coordinates for Online Education Line}<r>[0.102, 0.570, 0.975, 0.870]</r>, shows a clear upward trajectory with activity levels consistently increasing over the same period.\nBased on the above thought, the answer is:\nThe trend for Traditional Education mostly decreases or remains stable over time, indicating a reduction or stagnation in activity levels. Meanwhile, Online Education displays a progressively increasing trend, suggesting a rising interest and engagement over the time period. This comparative analysis of the two lines illustrates significant differences in how the activity levels evolve in each educational model."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "xxIpBf7SfCtjx5IH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in activity level for Online Education from the start to the 24th hour?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I check the activity level at the start hour {initial point of Online Education}<r>[0.102, 0.870]</r> and compare it with the activity level at the 24th hour {final point of Online Education}<r>[0.975, 0.570]</r>. The activity level increased from 0.46 to 3.14 over 24 hours.\nBased on the above thought, the answer is:\n2.68."
+            }
+        ],
+        "id_raw": 902
+    },
+    {
+        "id": "DotGLrVrtJRtwD2P",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the chart and reformat it into a structured table with Time periods as rows and financial characteristics as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the chart, I first Identify {the x-axis}[[0.081, 0.395, 0.132, 0.409], [0.251, 0.395, 0.301, 0.409], [0.420, 0.395, 0.470, 0.409], [0.590, 0.395, 0.640, 0.409], [0.759, 0.395, 0.809, 0.409], [0.929, 0.395, 0.979, 0.409]] labels as rows representing the time periods: 6_months, 12_months, 18_months, and 24_months. Next, I observe {the legend}[0.201, 0.468, 0.428, 0.493] to identify the columns corresponding to different financial characteristics: Investment_Return, Risk_Level. To populate the values, I locate and extract the {bar heights}[[0.106, 0.084, 0.954, 0.332], [0.106, 0.565, 0.954, 0.839]] for each financial characteristic at the respective time periods.\nBased on the above thought, the answer is:\nTime,Investment_Return,Risk_Level\n6_Months,3,2\n12_Months,6,2\n18_Months,4,4\n24_Months,5,3"
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "NYtwIOO7eoCLYEsq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the Risk_Level change with the time periods based on the chart, and what might be a potential reason behind this observed pattern?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the Risk_Level bars}[[0.276, 0.565, 0.615, 0.699], [0.784, 0.839, 0.954, 0.881]], I see a pattern of increasing risk over time, with risk levels of 2, 2, 4, and 3 during the 6_months, 12_months, 18_months and 24_months periods respectively. This pattern suggests an unpredictable fluctuations trend as they indicate a slight increase at the beginning, remained constant during the second period, followed by a sharp increase in the third period, and finishing with a decrease. The change in risk level might be due to change in market conditions or investment strategies over different periods.\nBased on the above thought, the answer is:\nThe Risk_Level increases, remains constant, then has a sharp increase before decreasing across the four periods according to the chart figure. This pattern may indicate the volatility of investments and change in market conditions over time, which result in unpredicted ups and downs of the risk. It reflects the uncertainty and variability associated with the returns of the investments in the portfolio. The increase in risk could be due to more risky investments being included in the portfolio or market instability whereas the subsequent decrease may represent more conservative investment decisions or stability in the market."
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "VONMa0behQwh62jj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Investment_Return over all periods presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average Investment Return, I need to locate and sum all the {Investment Return bar heights}[[0.106, 0.084, 0.191, 0.273], [0.276, 0.285, 0.361, 0.302], [0.445, 0.255, 0.530, 0.239], [0.615, 0.196, 0.699, 0.179], [0.784, 0.149, 0.869, 0.096],[0.954, 0.084]] corresponding to all periods, which are 3, 6, 4, and 5, and then divide the sum by the total number of periods, which is 4. Therefore, the average Investment Return is calculated as (3+6+4+5)/4 = 4.5.\nBased on the above thought, the answer is:\n4.5"
+            }
+        ],
+        "id_raw": 904
+    },
+    {
+        "id": "cGV5IKHsjPoGghUl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the line chart regarding big data integration techniques into a CSV format where each row represents a technique and the columns show the data size in terabytes for years 1990 through 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the line chart into a table, I will analyze {the line plots}<r>[0.110, 0.021, 0.642, 0.454]</r> and correlate the data points {line points for each technique}<r>[0.110, 0.269], [0.642, 0.282]</r>, along with the x-axis years {xticks labels}<r>[0.080, 0.459, 0.139, 0.473], [0.613, 0.459, 0.671, 0.473]</r>. I will assess the data values at these points, confirming that they align with the respective years and techniques.\nBased on the above thought, the answer is:\nTechnique,1990,1995,2000,2005,2010,2015,2020\nDiverse Data Gathering,1.2,1.3,1.4,1.7,1.9,2.1,2.3\nData Cleansing,0.8,0.5,-0.7,0.2,0,0.4,0.2\nData Transformation,1.7,2.0,2.3,2.5,2.7,2.9,3.0\nParallel Data Processing,1.5,1.6,1.7,1.9,2.1,2.2,2.3\nEnjoy Driven Data,1.9,2.2,2.5,2.7,2.9,3.0,3.0\nLarge Data Indexing,1.8,1.9,2.0,2.0,2.1,2.1,2.1"
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "L8YvnrfaAeDa6K4s",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich big data integration technique shows a negative trend according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify a technique with a negative trend, I focus on observing {line plots}<r>[0.110, 0.021, 0.642, 0.454]</r>. I found that {the line for Data Cleansing}<r>[0.110, 0.307, 0.642, 0.424]</r> starts at a higher point and ends lower, which visually represents a decline.\nBased on the above thought, the answer is:\nThe 'Data Cleansing' technique shows a negative trend as evidenced by the downward trajectory of its line plot in the chart. This indicates a reduction in the total data size handled by this technique over time."
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "3Ni3tlZ7yJ3fVjND",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in terabytes for the 'Enjoy Driven Data' technique from 1990 to 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the plot data for 'Enjoy Driven Data', I located the values at the beginning {starting point}<r>[0.110, 0.240]</r> and the end {ending point}<r>[0.642, 0.186]</r> of the timeline in the chart. The initial value is 1.9 and the value in 2020 is 3.0. I then calculated the difference between these two points.\nBased on the above thought, the answer is:\n1.1"
+            }
+        ],
+        "id_raw": 905
+    },
+    {
+        "id": "XRgdPzOMNvrEgwxj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table showing the growth trends of IP measures for both types of companies as depicted in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first identify {the y-axis labels}<r>[0.015, 0.173, 0.029, 0.318]</r> which outline the different IP measures: Inventions, Patents, Licensing, and Development. For each measure, I find the corresponding bars for both Tech Giant and Startup, which are visually distinguished by their colors and patterns. Observing the bars for each IP measure, I note their heights to represent either growth or decline. I extract values by closely analyzing the height comparison between the zero line (indicating no change) and the bar tops for each category.\nBased on the above thought, the answer is:\nMeasure,Tech Giant,Startup\nInventions,High Growth,Moderate Growth\nPatents,Slight Decline,Unpredictable Decline\nLicensing,High Growth,Rapid Growth\nDevelopment,Accelerated Decline,Subdued Decline"
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "pRXCoouXT1riFC47",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the reason for Tech Giant's slight decline in patents while observing growth in other areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the bars for the Tech Giant across all IP measures}<r>[0.105, 0.124, 0.190, 0.473], [0.195, 0.124, 0.281, 0.423], [0.285, 0.124, 0.371, 0.623], [0.375, 0.124, 0.461, 0.323]</r>, it is apparent that except for patents, all other areas exhibit a positive growth. The decline in patents, noticeable by the bar's placement below the zero growth line and shorter compared to others, could be linked to strategic shifts in focus towards more immediately monetizable innovations like Licensing, or perhaps reallocating resources towards rapidly growing sectors like Inventions. This misalignment might represent a strategic decision rather than an overall negative progression.\nBased on the above thought, the answer is:\nThe slight decline in patents in the Tech Giant could be due to shifting company focus and resources towards sectors displaying rapid growth like Licensing. This strategic reallocation helps capitalize on immediate revenue-generating opportunities while patents, typically longer-term investments, receive comparatively less attention."
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "Z4gmE2dgxzqLlQCC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the Licensing growth in Tech Giant exceed the Licensing growth in Startup?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I observe {the bars for Licensing in Tech Giant and Startup}<r>[0.219, 0.124, 0.304, 0.573], [0.321, 0.124, 0.406, 0.673]</r>. Analyzing these, I see that the Tech Giant's bar extends significantly above the zero growth line while the Startup's bar, although also above the line, is less so. From visual estimations and correlating with the axes scales, I calculate the differences in their heights above zero, attributing to their growth.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 906
+    },
+    {
+        "id": "tf4FDgfupWg6ZfT5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the growth data from the line graph to a structured table format with Time Periods as columns and Fields as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line graph, I analyze the x-axis which contains {time periods labels}<r>[0.110, 0.454, 0.926, 0.454]</r> representing the different Time Periods. The y-axis gives values for each field observed at these time periods {the line data points}<r>[0.110, 0.371, 0.926, 0.947]</r>. The field names are illustrated by the legend, segmented as \"Education Technology\" and \"Training Software\" {the legend}<r>[0.121, 0.044, 0.502, 0.069]</r>. Gathering this visual data, I can construct a table.\nBased on the above thought, the answer is:\nTime Periods, Education Technology, Training Software\nPeriod 1, 2.0, 1.5\nPeriod 2, 3.6, 2.0\nPeriod 3, 3.1, 4.7\nPeriod 4, 2.3, 1.6"
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "blIuqCd5xMok6HH5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends depicted, which field demonstrates a more stable progression and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which field manifests a more stable progression, I scrutinize the lines representing each field within the graph. For \"Education Technology\" {line for Education Technology}<r>[0.110, 0.371, 0.926, 0.947]</r> the plot shows sharp peaks and troughs indicating high volatility.\nComparatively, the \"Training Software\" {line for Training Software}<r>[0.110, 0.947, 0.926, 0.572]</r> though it also showcases peaks and declines, has periods of slower incline and decline that tend to stabilize before rising sharply and then declining similarly. Still, this is relatively more balanced than the extreme sudden changes observed in the \"Education Technology\".\nBased on the above thought, the answer is:\n\"Training Software\" demonstrates a more stable progression compared to \"Education Technology\". Despite its fluctuations, Training Software has sections of slower increase and decrease which add a degree of steadiness, in contrast to the abrupt changes seen in the progression of Education Technology."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "3HqBTOBQkWNL1J7C",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest growth rate recorded for Training Software and during which time period was it observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest growth rate for Training Software, I examine {the line points for Training Software}<r>[0.110, 0.947, 0.926, 0.572]</r> spanning different periods. I can see the values plotted across the graph with the highest visibly at the third time period. The x-axis {time labels}<r>[0.450, 0.454]</r> confirms that this corresponds to the third segment. Checking the value, it is around 4.7 during \"Period 3\".\nBased on the above thought, the answer is:\n4.7 during Period 3."
+            }
+        ],
+        "id_raw": 907
+    },
+    {
+        "id": "MFu53UbK1w3pzRB1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the growth data of different sectors from the line chart into a structured table, listing the years as rows and sectors as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a table, I start by analyzing key placement markers. By observing {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951], [0.843, 0.773, 0.959, 0.941], [0.937, 0.773, 1.053, 0.951]], which represent the years ranging from 2010 to 2020. Next, I locate {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189], [0.884, 0.200, 0.933, 0.222]] to identify the sectors: Software, Hardware, E-commerce, and ISP. Subsequently, data points are identified by locating {the line points for each sector}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]] within the plot to extract relative growth values annotated by year and sector.\nBased on the above thought, the answer is:\nYear,Software,Hardware,E-commerce,ISP\n2010,5,8,10,6\n2011,6,9,11,7\n2012,7,10,12,8\n2013,8,11,8,8\n2014,9,12,9,8\n2015,5,14,7,8\n2016,3,15,4,8\n2017,4,16,5,8\n2018,6,17,6,8\n2019,5,18,7,7\n2020,4,19,6,7"
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "nrOTIFbn4FrsySYd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of growth in the E-commerce sector compare to that in the ISP sector throughout the decade as per the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the growth trends of the E-commerce and ISP sectors from the line chart, I review the paths of {the line for E-commerce}[0.102, 0.179, 0.798, 0.632] and {the line for ISP}[0.102, 0.211, 0.798, 0.729]. E-commerce starts at a higher value with strong growth initially, then experiences a decline before slightly recovering. In contrast, {the ISP line} follows a more stable path with minimal fluctuations and generally maintains a stable growth rate throughout the decade. This comparative analysis suggests that while both sectors started closely, E-commerce was subject to more significant changes in growth, whereas ISP remained more steady and less volatile.\nBased on the above thought, the answer is:\nThe E-commerce sector experienced more fluctuations in growth throughout the decade compared to the ISP sector. Initially, E-commerce saw a rapid increase, followed by a notable decline and a mild recovery, indicating its volatile nature. On the other hand, the ISP sector demonstrated steady growth with slight declines, showcasing its stability over the period from 2010 to 2020. This stability could suggest a consistent market demand or effective management stabilizing its growth rate against potential declines."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "gCnkLbS5SGqRI6Qc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual growth for the Hardware sector from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average annual growth for the Hardware sector over the period from 2010 to 2020, I need to locate {the line data points for Hardware}[[0.102, 0.375], [0.218, 0.291], ..., [0.798, 0.110]] and extract the corresponding values across all years: {8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19}. The sum of these values is 139, and there are 11 years in total. To find the average, I divide the sum by the number of years: 139 / 11.\nBased on the above thought, the answer is:\n12.63"
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "fhjbdvlScoX98aek",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table showing the changes in scores for each social media platform across the different constraint words?"
+            },
+            {
+                "from": "gpt",
+                "value": "In analyzing the line chart, I first examine {the x-axis labels}<r>[0.057, 0.459, 0.066, 0.473]</r>, <r>[0.224, 0.459, 0.259, 0.473]</r>, <r>[0.405, 0.459, 0.440, 0.473]</r>, <r>[0.585, 0.459, 0.620, 0.473]</r>, <r>[0.765, 0.459, 0.800, 0.473]</r>, <r>[0.941, 0.459, 0.985, 0.473]</r> which correspond to constraint words from '3' to '10'. Then, I observe {the lines representing each social media platform}<r>[0.061, 0.530, 0.963, 0.947]</r> to extract the score points for Facebook, Twitter, Instagram, and LinkedIn along these constraints. The scores are directly extracted from points plotted on the graph for each social media.\nBased on the above thought, the answer is:\nConstraint Words,Facebook,Twitter,Instagram,LinkedIn\n3,10000,8000,1000,900\n4,8000,6000,2000,1500\n5,4000,10000,3000,2000\n6,3000,4000,4000,2500\n7,2000,3000,6000,3500\n8,1000,2000,8000,5000\n9,500,1000,9000,7500\n10,1000,800,10000,10000"
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "4kXnVIqhisz4Q4Az",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform shows a rising trend despite an initial drop, as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I analyze the trend lines of different social media platforms. For this assessment, I focus on the lines for {Facebook}<r>[0.061, 0.530, 0.963, 0.947]</r> and {Instagram}<r>[0.061, 0.530, 0.963, 0.947]</r>. Facebook shows a general downward trend from '3' to '10' constraint words, while Instagram initially has a lower score but gradually increases, leading to a rapid expansion in scores after passing the midpoint of constraint '5', showing the most significant rise towards the end.\nBased on the above thought, the answer is:\nInstagram exhibits a rising trend despite an initial drop. Initially starting with lower scores at constraint word '3', Instagram's score gradually grows exponentially towards the high peak by constraint word '10', marking a striking upward trajectory in the graph."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "tSE9zBqCAT7JkPBo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the peak score for Instagram according to the graph data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the peak score for Instagram, I need to analyze {the line for Instagram}<r>[0.061, 0.530, 0.963, 0.947]</r> across all constraint words. By examining the particular points where Instagram line peaks, especially towards the end, I need to find the coordinate with the highest y-value along the line, indicating the highest score.\nBased on the above thought, the answer is:\n10000."
+            }
+        ],
+        "id_raw": 909
+    },
+    {
+        "id": "WRV0xsenfiivXPnK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the line chart showing product life cycle stages and investments into a structured table with Stages as columns and Investment types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data from the line chart into a table, I begin by identifying {the x-axis tick labels}<r>[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951]</r> which indicate the different stages of the product life cycle. I also identify {the legend}<r>[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]</r> marking categories such as Product Design, Inventory Management, and Customer Service. After locating {the data points on the lines}<r>[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]</r> for each investment type and stage, I can extract and tabulate the corresponding figures.\nBased on the above thought, the answer is:\nStage,Initial,Growth,Establishment,Maturity,Expansion,Decline\nProduct Design,5,12,23,32,29,20\nInventory Management,3,9,16,22,21,15\nCustomer Service,7,15,24,35,33,25"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "fDtxWi66czMJjD9S",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the provided chart, which investment category appears to have the most consistent growth throughout the product life cycle stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inspecting {the line for Customer Service}<r>[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]</r> across all stages illustrates a consistent upward trend, despite the outlier at Maturity stage. In contrast, both {the line for Product Design}<r>[0.102, 0.082, 0.798, 0.696]</r> and {Inventory Management}<r>[0.102, 0.179, 0.798, 0.632]</r> don't exhibit a consistent rate of increase as they have fluctuations and plateau periods. Therefore, Customer Service shows the most consistent growth trend.\nBased on the above thought, the answer is:\nCustomer Service shows the most consistent growth throughout all product life cycle stages. This consistency is marked by a steady increase from the Initial to Decline stages, even with a markable peak during the Maturity stage."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "5IRifyF4kxJK5ABR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the peak investment in Customer Service at the Maturity stage differ from its initial investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the peak value in Customer Service data, I focus on {the peak point for Customer Service}<r>[0.397, 0.773, 0.504, 0.934]</r> during the Maturity stage where the investment is 35. I then compare it to {the initial point for Customer Service}<r>[0.061, 0.773, 0.144, 0.898]</r> at the Initial stage with an investment of 7. The difference is calculated as 35 - 7.\nBased on the above thought, the answer is:\n28"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "dnxqE7v3dOSdwPyq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing technology company market shares over four quarters into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line chart, I note down the headers from {the x-axis tick labels}<r>[0.088, 0.434, 0.102, 0.448], [0.271, 0.434, 0.286, 0.448], [0.447, 0.434, 0.476, 0.448], [0.630, 0.434, 0.660, 0.448]</r> which denote the quarters Q1 through Q4. For the rows, I identify the labels from {the legend}<r>[0.402, 0.476, 0.668, 0.501]</r> which represent the company names - Apple, Samsung, Huawei, and Google. Visualizing the trends of market share from {the lines corresponding to each company}<r>[0.095, 0.116, 0.975, 0.369]</r>, I summarize the values for each quarter based on their market share positions in the chart.\nBased on the above thought, the answer is:\nQuarter,Apple,Samsung,Huawei,Google\nQ1,24,21,18,10\nQ2,21,18,15,12\nQ3,18,15,6,15\nQ4,19,14,6,18"
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "EhWvTRYHV6bYGsvC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which company showed the most stability in market share over the four quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which company had the most stable market share, I need to examine the fluctuation of the lines on the chart. {Apple's line}<r>[0.095, 0.116, 0.975, 0.369]</r> and {Samsung's line}<r>[0.095, 0.182, 0.975, 0.248]</r> both show notable declines. {Huawei's line}<r>[0.095, 0.194, 0.975, 0.357]</r> has a sharp drop, particularly noticeable in Q3. However, {Google's line}<r>[0.095, 0.560, 0.975, 0.369]</r> displays a consistent upward trend with less variability compared to others.\nBased on the above thought, the answer is:\nGoogle demonstrated the most stability in market share over the four quarters, as evidenced by its consistent upward trajectory and lesser fluctuations in the market share levels compared to the other companies."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "fBkXg5j47TnUlWWe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Google's market share increase from Q1 to Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "The market share of Google at the beginning of the year can be determined from the first point on {Google's line}<r>[0.095, 0.560]</r>, and the end of the year share from the last point on the same line<r>[0.975, 0.369]</r>. Initially, Google's market share is approximately 10 and it rises to around 18 by Q4. Calculating the increase involves subtracting the Q1 share from the Q4 share.\nBased on the above thought, the answer is:\n8"
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "s2KwbVAPdtulkDKI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the provided data on migration trends into a table with time as columns and type of migration as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate a table, I first identify {the x-axis tick labels}<r>[0.114, 0.395, 0.946, 0.409]</r> which represent different years, acting as columns for the table. {The legend}<r>[0.133, 0.468, 0.394, 0.493]</r> and <r>[0.133, 0.961, 0.404, 0.986]</r> for 'Rural Migration' and 'Urban Migration' respectively, will act as row headers. For each of these categories, I locate their corresponding {line points}<r>[0.122, 0.305, 0.960, 0.390]</r> and <r>[0.122, 0.539, 0.960, 0.706]</r> to extract the data values.\nBased on the above thought, the answer is:\nTime,Rural Migration, Urban Migration\n0,-0.5,0.8\n1,-0.45,0.85\n2,-0.4,0.92\n3,-0.35,0.96\n4,-0.3,1.02\n5,-0.26,1.1\n6,-0.2,1.12\n7,-0.15,1.25\n8,-0.12,1.33\n9,-0.1,1.4\n10,-0.08,1.45\n11,-0.06,1.5\n12,-0.05,1.52\n13,-0.04,1.6\n14,-0.03,1.65\n15,-0.02,1.7\n16,-0.01,1.75\n17,0,1.79\n18,0.01,1.82\n19,0.02,1.86\n20,0.03,1.9\n21,0.05,1.93\n22,0.07,1.95\n23,0.09,1.99\n24,0.11,2.01\n25,0.13,2.03"
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "HI0w6aiguZ0kuY31",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich migration trend shows a consistently positive movement, and what does this indicate about urban versus rural trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {line for Rural Migration}<r>[0.122, 0.305, 0.960, 0.390]</r>, the trend moves from negative values towards zero, indicating a gradual reduction in negative migration or a decrease in loss. Conversely, the {line for Urban Migration}<r>[0.122, 0.539, 0.960, 0.706]</r> starts from a positive value and continues to rise, showing consistently positive growth. This suggests that Urban areas might be experiencing growth due to factors like better job opportunities or living conditions, attracting populations from rural areas or possibly other urban areas, whereas Rural areas are seeing a slowdown or reversal in out-migration, potentially due to improving local conditions or decreased attractiveness of urban offerings.\nBased on the above thought, the answer is:\nUrban Migration shows a consistently positive movement. This indicates a robust attraction towards urban areas, likely due to better opportunities or amenities compared to rural areas, resulting in a net population increase in urban spots gradually influencing a balancing act in rural migration trends."
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "ByJlVzkBzGM0XGJg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Urban Migration from year 5 to year 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the {Urban Migration line points}<r>[0.289, 0.673], [0.457, 0.640]</r>, I extract the migration values at year 5 and year 10, which are 1.046 and 1.292 respectively. The increase is calculated by subtracting the value at year 5 from the value at year 10, resulting in 1.292 - 1.046 = 0.246.\nBased on the above thought, the answer is:\n0.246"
+            }
+        ],
+        "id_raw": 912
+    },
+    {
+        "id": "hycI7TRysyx1ouZf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you transform the dataset from the chart into a structured table showing \"Time (Hours)\" as columns and \"Trends\" as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe {the x-axis ticks}<r>[0.073, 0.459, 0.088, 0.473], [0.249, 0.459, 0.264, 0.473], [0.418, 0.459, 0.447, 0.473], [0.594, 0.459, 0.623, 0.473], [0.770, 0.459, 0.799, 0.473], [0.946, 0.459, 0.975, 0.473]</r> clearly labeled from 0 to 25 hours representing the columns, and the plot legends from {the subplot legends}<r>[0.688, 0.422, 0.949, 0.447], [0.092, 0.537, 0.298, 0.562]</r> indicating the \"Self-confidence\" and \"Innovation\" trends for the rows. I then collect the numerical data corresponding to each time point for both trends from identified {data points}<r>[0.080, 0.395], [0.116, 0.406], ..., [0.925, 0.299], [0.080, 0.798], ..., [0.925, 0.542]</r>.\nBased on the above thought, the answer is:\nTime (Hours),Self-confidence,Innovation\n0,1.0,2.5\n1,0.8,2.7\n2,0.65,3.0\n3,0.4,3.2\n4,0.5,3.3\n5,0.36,3.1\n6,0.3,3.8\n7,0.5,4.0\n8,0.6,4.3\n9,0.7,4.0\n10,0.8,4.5\n11,0.9,4.6\n12,1.0,4.9\n13,1.1,4.8\n14,1.3,5.0\n15,1.5,5.2\n16,1.6,5.5\n17,1.8,5.6\n18,1.9,5.9\n19,2.0,5.8\n20,2.1,6.0\n21,2.3,6.1\n22,2.4,6.4\n23,2.5,6.6\n24,2.6,6.8"
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "YwVyXMzQfVDzRMbP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the trends, which one appears to have a more steady growth over time, Self-confidence or Innovation, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends, I assess the {Self-confidence line}<r>[0.080, 0.395, 0.925, 0.299]</r> and {Innovation line}<r>[0.080, 0.798, 0.925, 0.542]</r>. The Self-confidence shows fluctuations with an increase and decrease in levels between data points. In contrast, the Innovation, despite some fluctuations, shows a general upward trend with higher end values and fewer dips below its trend line. This indicates a more steady overall growth for Innovation compared to the more variable Self-confidence.\nBased on the above thought, the answer is:\nInnovation demonstrates a more steady growth over time compared to Self-confidence. The Innovation trend, compared to Self-confidence, exhibits a consistent rise with generally increasing values, showing fewer and less significant drops, indicating a steady upward progression."
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "XBLsMZU7NmjHo89i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in \"Self-confidence\" from hour 0 to hour 24?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will determine the average increase by comparing the initial and final {data points for Self-confidence}<r>[0.080, 0.395], [0.925, 0.299]</r> which show values from 1.0 at hour 0 to 2.6 at hour 24. To calculate the average increase: (2.6 - 1.0) / 24 = 0.0667.\nBased on the above thought, the answer is:\n0.0667."
+            }
+        ],
+        "id_raw": 913
+    },
+    {
+        "id": "WX93ohmAzR3JhdFc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured data table from the bar chart showing the number of individuals in different professions across various age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar chart, I see three sets of bars grouped across the X-axis that represent the different age groups, starting from {the first age group label}(r>[0.05, 0.95, 0.1, 0.97]</r>) to {the last age group label}(r>[0.9, 0.95, 0.95, 0.97]</r>). Each group contains bars for {Entrepreneurs}(r>[0.06, 0.2, 0.08, 0.7]</r>), {Professionals}(r>[0.09, 0.2, 0.11, 0.5]</r>), and {Trades}(r>[0.12, 0.2, 0.14, 0.4]</r>), represented by distinct colors. By decoding these bars' heights against the Y-axis that measures counts starting from labels at {the beginning of the Y-axis}(r>[0.0, 0.9, 0.03, 0.95]</r>) to {the end of the Y-axis}(r>[0.0, 0.1, 0.03, 0.15]</r>), we can tabulate the data.\nBased on the above thought, the answer is:\nAge Group, Entrepreneurs, Professionals, Trades\n20-30, 150, 120, 130\n30-40, 180, 105, 115\n40-50, 160, 90, 80\n50-60, 140, 75, 60\n60-70, 100, 60, 40\n70-80, 85, 50, 30"
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "KzB1zjDEjMcROpCD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich profession shows a continuous decrease as age increases according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends for each profession, I look at how each profession's bar heights vary from the youngest to the oldest age groups. Tracking down the bars for {Professionals}(r>[0.09, 0.2, 0.11, 0.5]</r>), I observe a consistent reduction in height from {the youngest age group's bar}(r>[0.06, 0.2, 0.08, 0.7]</r>) to {the oldest age group's bar}(r>[0.12, 0.2, 0.14, 0.4]</r>), which indicates a decrease in numbers. This consistent trend is not observed with the same regularity in the other professions based on their respective bar groups across age segments.\nBased on the above thought, the answer is:\nProfessionals show a continuous decrease in their number as age increases according to the data represented in the chart. This trend is deduced from the consistently diminishing bar heights across the age groups for this profession."
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "Dxy4l2Hy7axmSxJf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of individuals across all professions in the age group 30-40?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the bars for the age group 30-40 which are defined within {the range on the x-axis}(r>[0.15, 0.95, 0.2, 0.97]</r>) on the chart. Inside this grouping, I find the specific bars for {Entrepreneurs}(r>[0.16, 0.2, 0.18, 0.9]</r>), {Professionals}(r>[0.19, 0.2, 0.21, 0.6]</r>), and {Trades}(r>[0.22, 0.2, 0.24, 0.55]</r>). The values for these bars are 180, 105, and 115 respectively. Adding these values together provides the total number of individuals in this age group.\nBased on the above thought, the answer is:\n400"
+            }
+        ],
+        "id_raw": 914
+    },
+    {
+        "id": "A7bkxzo8IVcmEO8z",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table from the line chart showing the effectiveness of each action plan across different policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "From my analysis of the line chart, I started by identifying {the x-axis tick labels}<r>[0.124, 0.773, 0.227, 0.898]</r>, {0.291, 0.773, 0.378, 0.903]</r>, and {0.397, 0.773, 0.504, 0.934]</r> representing 'Policy Enforcement', 'Social Welfare', and 'Security Boost' respectively. I then looked at {the legend}<r>[0.792, 0.132, 0.893, 0.208]</r> identifying Action Plans - Act1, Act2, Act3. Corresponding points for these action plans were extracted across policies: Act1{[0.152, 0.470], [0.318, 0.582], [0.482, 0.696]}, Act2{[0.152, 0.540], [0.318, 0.439], [0.482, 0.348]}, Act3{[0.152, 0.643], [0.318, 0.729], [0.482, 0.815]}.\nBased on the above thought, the answer is:\nPolicies,Act1,Act2,Act3\nPolicy Enforcement,150,90,170\nSocial Welfare,130,140,160\nSecurity Boost,120,150,150"
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "7cF2jKilaey7MyUO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich action plan showed the most improvement from 'Policy Enforcement' to 'Security Boost'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the effectiveness from 'Policy Enforcement' to 'Security Boost':\nFor Act1, the values change from 150 to 120 displaying a decrease; for Act2, values go from 90 to 150 showing significant improvement; for Act3, the effectiveness goes from 170 to 150, observing a slight decrease. Therefore, {Act2}<r>[0.152, 0.540], [0.482, 0.348]</r> shows the most significant improvement, moving towards a higher effectiveness score.\nBased on the above thought, the answer is:\nAct2 showed the most improvement moving from a lower effectiveness score in 'Policy Enforcement' to a higher score in 'Security Boost'."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "iCcpNUaycgIblWcj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the average effectiveness differ between the highest and lowest performing action plans in 'Security Boost'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I examine the data points for 'Security Boost' for the different action plans. Act1 records an effectiveness of 120 at {0.482, 0.696]</r>}, Act2 shows 150 at [0.482, 0.348] and Act3 also has 150 at [0.482, 0.815]. Therefore, the maximum value is 150 (for both Act2 and Act3) and the minimum value for Act1 is 120. The difference is calculated as 150 - 120 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "xfqZaaWt0y9vJbCP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the line chart regarding 'Organic Produce', 'GM Crops', and 'Artificial Fertilizers' from 2010 to 2020 into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I observe {the x-axis labels}<r>[0.124, 0.459, 0.793, 0.960, 0.947]</r> representing the years from 2010 to 2020. I then identify the three lines for 'Organic Produce', 'GM Crops', and 'Artificial Fertilizers' by their distinct colors and extract values from each point plotted across the years. The {points on the Organic Produce line}<r>[0.124, 0.454, 0.927, 0.454]</r> {for GM Crops}<r>[0.124, 0.454, 0.927, 0.454]</r> and {for Artificial Fertilizers}<r>[0.124, 0.454, 0.927, 0.454]</r> provide the data for the table.\nBased on the above thought, the answer is:\nYear,Organic Produce (000s),GM Crops (000s),Artificial Fertilizers (000s)\n2010,580,670,720\n2011,610,650,700\n2012,630,630,680\n2013,640,610,660\n2014,650,600,640\n2015,640,605,630\n2016,630,610,620\n2017,620,620,610\n2018,610,630,600\n2019,600,640,590\n2020,590,650,580"
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "XSMyYMSOrunnTe3U",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the variables presented had the most stable trend over the 2010 to 2020 period?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing the stability of the variables, I look into {the line for Organic Produce}<r>[0.124, 0.454, 0.927, 0.454]</r>, {the line for GM Crops}<r>[0.124, 0.454, 0.927, 0.454]</r>, and {the line for Artificial Fertilizers}<r>[0.124, 0.454, 0.927, 0.454]</r>. Organic Produce and Artificial Fertilizers show considerable variability and significant peaks and troughs throughout the period. In contrast, GM Crops, although showing decline, does not have as wide fluctuations as the other two.\nBased on the above thought, the answer is:\n'GM Crops' exhibited the most stable trend overall with lesser fluctuations compared to 'Organic Produce' and 'Artificial Fertilizers', discerned by narrower movement between high and low points throughout the decade."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "gtjTMfAlQvoLlxRo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the period 2010 to 2020, by how much did 'Artificial Fertilizers' production decrease from its highest to its lowest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the line denoting 'Artificial Fertilizers' between {starting point}<r>[0.124, 0.454]</r> and {ending point}<r>[0.927, 0.454]</r>, I extract the peak and the lowest values represented at these points. As seen, the highest value observed is 720 in 2010 and the lowest is 580 in 2020. The difference calculation would be 720 - 580.\nBased on the above thought, the answer is:\n140 thousand units."
+            }
+        ],
+        "id_raw": 916
+    },
+    {
+        "id": "vcdxjAAOPRUPqkQY",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a data table that categorizes the data by years and policy type based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data displayed in the line chart into a table format, I start by identifying {the x-axis tick labels}<r>[0.122, 0.960, 0.122, 0.960]</r> that indicate the years from 2015 to 2019. Then, I note {the legend items}<r>[0.377, 0.476, 0.705, 0.501]</r> which classify the lines as 'Fiscal Policy' and 'Monetary Policy'. By analyzing {the line for Fiscal Policy}<r>[0.122, 0.168, 0.927, 0.410]</r> and {the line for Monetary Policy}<r>[0.122, 0.549, 0.927, 0.791]</r>, I extract the specific policy values for each year.\nBased on the above thought, the answer is:\nYear,Fiscal Policy (Billion Dollars),Monetary Policy (Billion Dollars)\n2015,5900,4355\n2016,6100,5820\n2017,5800,4860\n2018,5600,5235\n2019,5500,5575"
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "NITNjGWLc7HJDf5o",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, which policy showed more stability over the five-year period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess which policy displayed more stability, I examine the line plots for both policies. Observing {the line for Fiscal Policy}<r>[0.122, 0.168, 0.927, 0.410]</r>, it indicates a mostly declining trend with minor fluctuations and no abrupt changes. In contrast, the {line for Monetary Policy}<r>[0.122, 0.549, 0.927, 0.791]</r> shows noticeable ups and downs, especially the rapid rise in 2016 followed by a sharp decline in 2017 and subsequent fluctuations. This suggests that the Fiscal Policy had a steadier decline over the years compared to the Monetary Policy which exhibited more volatility.\nBased on the above thought, the answer is:\nThe Fiscal Policy demonstrated more stability over the five-year period as evidenced by its steadily declining line, showing fewer fluctuations and more predictability compared to the Monetary Policy, which exhibited significant volatility with abrupt increases and decreases over the same period."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "33M13GetXXv6kzHX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in Fiscal Policy value from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change in Fiscal Policy value, I first identify {the point for Fiscal Policy in 2015}<r>[0.122, 0.410]</r> which is 5900 Billion and {the point for Fiscal Policy in 2019}<r>[0.927, 0.320]</r> which is 5500 Billion.  The difference is calculated by subtracting the value in 2015 from the value in 2019, giving 5900 - 5500.\nBased on the above thought, the answer is:\n400 Billion Dollars."
+            }
+        ],
+        "id_raw": 917
+    },
+    {
+        "id": "9YwJT7kso6GeRhjO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you frame the information on this chart into a tabular format with years as rows and market shares as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this table, I'll start by observing {the x-axis' labels}<r>[0.066, 0.459, 0.946, 0.473]</r> which represent the rows for the various years ranging from 2008 to 2020. Then I identify {the legend texts}<r>[0.568, 0.044, 0.934, 0.069]</r><r>[0.568, 0.537, 0.934, 0.562]</r> as the column headers, signifying the Traditional and Innovative market shares. \nFor the cell values, I take into account {line points for Traditional Market}<r>[0.095, 0.287, 0.946, 0.379]</r> and those {for Innovative Market}<r>[0.095, 0.572, 0.946, 0.863]</r> from the plotted lines and extract the corresponding data.\nBased on the above thought, the answer is:\nYear,Market Share Traditional,Market Share Innovative\n2008,20,10\n2009,22,12\n2010,21,15\n2011,19,17\n2012,18,21\n2013,16,23\n2014,15,28\n2015,14,32\n2016,13,34\n2017,11,38\n2018,10,41\n2019,10,43\n2020,9,45"
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "pH9Bc8aC1CXccjbr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the respective trends in this line chart, could you tell me which market share had a more significant increase over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "For analyzing which of the market shares had a more significant increase over the years, I need to compare {the line for the Traditional Market Share}<r>[0.095, 0.271, 0.946, 0.379]</r> and {the line for the Innovative Market Share}<r>[0.095, 0.572, 0.946, 0.863]</r>. \nThe Traditional Market Share decreased from 20% in 2008 to 9% in 2020, showing a reduction in adoption. On the other hand, The Innovative Market recorded a steady increase in the same period, from 10% to 45%.\nBased on the above thought, the answer is:\nThe Innovative Market Share has a more significant increase over the years. The data shows its adoption rate goes up from 10% in 2008 to 45% in 2020, while that of the Traditional Market Share decreased in the same period."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "krXgUL00v0sTFQI0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the chart, by how much did the Traditional Market Share decrease from 2009 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in Traditional Market Share from 2009 to 2019, I should look at {the line points for Traditional Market}<r>[0.095, 0.287, 0.946, 0.379]</r>. The value for 2009 is 22 based on the point at {2009 marker}<r>[0.166, 0.271]</r> and the value for 2019 is 10 based on the point at {2019 marker}<r>[0.875, 0.371]</r>. The decrease from 2009 to 2019 is therefore 22 - 10 = 12.\nBased on the above thought, the answer is:\n12."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "IQdgQO2iHfAxiYvo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the sales data from the line chart into a structured table detailing the sales over different hours for both Standard and Innovative vehicles?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe the sales data based on {the x-axis labels}<r>[0.061, 0.459, 0.070, 0.473], [0.244, 0.459, 0.252, 0.473], [0.421, 0.459, 0.439, 0.473], [0.603, 0.459, 0.621, 0.473], [0.785, 0.459, 0.803, 0.473], [0.968, 0.459, 0.985, 0.473]</r> which denote the time in hours starting from 0 to 25 at intervals of 5 hours. I then locate the sales data for both {Standard Sales line}<r>[0.066, 0.294, 0.940, 0.385]</r> and {Innovative Sales line}<r>[0.066, 0.585, 0.940, 0.863]</r> and extract the respective unit sales values from their {data points}<r>[0.066, 0.385], [0.102, 0.371], [0.139, 0.374], [0.175, 0.367], ..., [0.940, 0.294]</r> for Standard and <r>[0.066, 0.863], [0.102, 0.842], [0.139, 0.822], ..., [0.940, 0.585]</r> for Innovative vehicles.\nBased on the above thought, the answer is:\nTime (Hours),Standard Sales (units),Innovative Sales (units)\n0,200,220\n5,240,350\n10,260,410\n15,285,500\n20,310,545\n25,330,620"
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "ZiTXJn0PHyVD1ERK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich vehicle type shows a more consistent sales performance over the observed time interval based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sales trends of the vehicles, I analyze the fluctuations in the sales lines. The {Standard Sales line}<r>[0.066, 0.294, 0.940, 0.385]</r> presents smaller fluctuations within a confined range, indicating a more consistent performance over time with gradual increments in sales values. On the other hand, the {Innovative Sales line}<r>[0.066, 0.585, 0.940, 0.863]</r> exhibits a broader range of sales values, particularly notable between hour 5 and 25, suggesting a less consistent, though more aggressive growth pattern.\nBased on the above thought, the answer is:\nThe Standard Vehicle type shows a more consistent sales performance over the observed time interval as indicated by the smaller fluctuations and more gradual increases in its sales data line compared to the Innovative Vehicle, which exhibited a more rapid growth but with larger fluctuations."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "P28rxHIz4AuvJ12A",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_46_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much do the Innovative Vehicle sales increase from hour 0 to hour 25?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the plotted data, I first locate the sales value for Innovative Vehicles at hour 0 with {the point coordinate}<r>[0.066, 0.863]</r> and then locate the sales value at hour 25 with {the last point coordinate}<r>[0.940, 0.585]</r>. Subtracting the initial value (220) from the final value (620) will give the total increase.\nBased on the above thought, the answer is:\n400 units."
+            }
+        ],
+        "id_raw": 919
+    },
+    {
+        "id": "EmOFZTSxmbAkRvx5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and create a tabular representation indicating the yearly development for Rural and Urban areas from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the x-axis tick labels}<r>[0.070, 0.919, 0.130, 0.942], [0.168, 0.919, 0.228, 0.942], ..., [0.854, 0.919, 0.914, 0.942]</r> to determine the years from 2000 to 2020. Then, I identify {the line points for Rural Development}<r>[0.100, 0.871], [0.139, 0.852], ..., [0.884, 0.065]</r> and {the line points for Urban Development}<r>[0.100, 0.815], [0.139, 0.676], ..., [0.884, 0.065]</r> to obtain the development values for each year. Managing this data extraction and comparing the coordinates to the respective year, I can create a table.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2000,3.5,6.7\n2001,3.6,7.2\n2002,3.8,7.0\n2003,4.1,6.5\n2004,4.3,6.9\n2005,4.5,7.3\n2006,4.8,7.5\n2007,4.9,7.8\n2008,5.2,7.7\n2009,5.5,7.6\n2010,5.8,7.6\n2011,6.0,7.4\n2012,6.2,7.9\n2013,6.8,8.3\n2014,6.3,8.4\n2015,6.7,8.7\n2016,6.9,8.3\n2017,7.2,8.9\n2018,7.3,9.0\n2019,7.5,9.0\n2020,7.7,9.4"
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "OPnfSYOin5DBDjrI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Urban Development differ from Rural Development over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the line trends for both developments, {the line for Rural Development}<r>[0.100, 0.065, 0.884, 0.871]</r> shows a steady increase with minor fluctuations from a value of 3.5 in 2000 to a value of 7.7 in 2020. {The line for Urban Development}<r>[0.100, 0.065, 0.884, 0.871]</r> also demonstrates a general upward trend but with larger variability and peaking at a value of 9.4 in 2020 compared to the less steep rural development. The peaks and troughs are more defined in the urban data reflecting greater volatility, particularly noticeable in years after 2015.\nBased on the above thought, the answer is:\nUrban Development exhibits a significantly more volatile trend compared to Rural Development, with higher peaks indicating rapid growth periods and pronounced troughs signaling sharper declines in specific years. Over the same timeframe, Rural Development's growth is more gradual and stable with less pronounced fluctuations, culminating in a consistent upward trend with minor yearly deviations."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "rDgDz8tuOU190i1p",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average urban development value over the years 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to extract the urban development values for the years 2015 to 2020. These values are obtained from {the points corresponding to Urban Development from 2015 to 2020}<r>[0.688, 0.260], [0.727, 0.371], [0.766, 0.204], [0.806, 0.176], [0.845, 0.176], [0.884, 0.065]</r>. The values for these years are 8.7, 8.3, 8.9, 9.0, 9.0, 9.4. The sum is 53.3, and there are 6 years, therefore, the average is 53.3/6 = 8.88.\nBased on the above thought, the answer is:\n8.88"
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "bKXg3HMYrKPaXY0Y",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data from the bar chart showing investor confidence vs. the number of startups funded into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the visual elements in the bar chart, I can discern distinct bars representing various investor confidence trends. I first identify {the x-axis labels}<r>[0.122, 0.766, 0.176, 0.788], [0.236, 0.766, 0.290, 0.788], [0.350, 0.766, 0.404, 0.788], [0.464, 0.766, 0.518, 0.788], [0.578, 0.766, 0.632, 0.788], [0.692, 0.766, 0.746, 0.788]</r> and determine they are corresponding to 'Stable Growth', 'Subdued Decline', 'Abrupt Increase', 'Persistent Rise', 'Steep Decline', and 'Steady Recovery'. Then I measure {the bar heights}<r>[0.130, 0.400, 0.155, 0.766], [0.244, 0.500, 0.269, 0.766], [0.358, 0.200, 0.383, 0.766], [0.472, 0.350, 0.497, 0.766], [0.586, 0.600, 0.611, 0.766], [0.700, 0.450, 0.725, 0.766]</r> that correlate with the amount of funding for startups under each category.\nBased on the above thought, the answer is:\nInvestor Confidence,Number of Startups Funded\nStable Growth,1100\nSubdued Decline,800\nAbrupt Increase,1700\nPersistent Rise,1300\nSteep Decline,400\nSteady Recovery,950"
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "kwTh2YTUJNMn2Jpr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category of investor confidence showed the lowest funding for startups and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing the chart, it is apparent that the category 'Steep Decline' in investor confidence has the lowest bar height observed at {the bar for 'Steep Decline'}<r>[0.586, 0.600, 0.611, 0.766]</r>, indicating the least number of startups funded, adding up to 400. This low funding in the 'Steep Decline' category could be attributed to decreased investor optimism and hesitation to invest in new ventures during times perceived as financially unstable or risky, reflecting the investor's lack of confidence.\nBased on the above thought, the answer is:\nThe 'Steep Decline' category showed the lowest funding for startups. This may be because during a sharp decline in investor confidence, investors are likely to be more risk-averse and reluctant to allocate resources to new or existing ventures, therefore resulting in the diminished funding observed."
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "W0bdtweOA5EhWDj6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of startups funded across all categories of investor confidence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total number of startups funded across different categories of investor confidence, I add up the values corresponding to each bar height in the chart: 1100 (Stable Growth) + 800 (Subdued Decline) + 1700 (Abrupt Increase) + 1300 (Persistent Rise) + 400 (Steep Decline) + 950 (Steady Recovery). These values are extracted by analyzing {the bar heights}<r>[0.130, 0.400, 0.155, 0.766], [0.244, 0.500, 0.269, 0.766], [0.358, 0.200, 0.383, 0.766], [0.472, 0.350, 0.497, 0.766], [0.586, 0.600, 0.611, 0.766], [0.700, 0.450, 0.725, 0.766]</r>.\nBased on the above thought, the answer is:\n6250"
+            }
+        ],
+        "id_raw": 921
+    },
+    {
+        "id": "8A3VFF04T8iM0kuU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data points from the chart showing trends in Total Crypto Users, Crypto Startups Values, and Bitcoin Prices over the years into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To populate the tabular data, I analyze the chart and use the {x-axis labels}<r>[0.081, 0.919, 0.092, 0.942]</r> for the years and gather data labels from {the legend}<r>[0.886, 0.077, 0.914, 0.119]</r> defining each data series distinctly. I follow this by noting down each point's value from the y-values plotted.\nBased on the above thought, the answer is:\nYear, Total Crypto Users, Crypto Startups Value, Bitcoin Price\n2016, 90, 5, 968\n2017, 450, 120, 13765\n2018, 780, 200, 3643\n2019, 2400, 320, 7845\n2020, 4100, 480, 19143\n2021, 6000, 650, 47765"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "nXjyEOUQdWe9SCp0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you derive from the trends in the chart considering the technologies represented by Crypto Startups Values and Total Crypto Users?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends, {Crypto Startups Values}<r>[0.115, 0.104, 0.885, 0.873]</r> show a consistent rise, indicating growing investments or valuations in crypto startups from 2016 to 2021. Similarly, {Total Crypto Users}<r>[0.115, 0.104, 0.885, 0.873]</r> has an exponential growth curve, suggesting a rapidly increasing user base in the cryptocurrency market. These trends together suggest a significant expansion and acceptance of cryptocurrency technologies, attracting both users and investors progressively.\nBased on the above thought, the answer is:\nThe trends demonstrate significant and consistent growth in both the user base and the economic valuation of cryptocurrency technologies, indicating robust market expansion and increased acceptance. These might suggest a maturing industry with growing trust and investment from the general populace and investors alike."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "dVe2f62SbCWPGXkC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Bitcoin Price from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the percentage increase, I locate and extract the data points for {Bitcoin Price in 2016 and 2021}<r>[0.115, 0.873, 0.885, 0.258]</r>. The price rose from 968 in 2016 to 47765 in 2021. The increase is calculated as ((47765 - 968) / 968) * 100.\nBased on the above thought, the answer is:\n4831.89%"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "PhiFMqhnFuqVGTQl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table that represents the change in Artificial Intelligence and Virtual Reality trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, firstly, I identify {the x-axis labels}<r>[0.002, 0.919, 0.046, 0.942], [0.175, 0.919, 0.219, 0.942], [0.348, 0.919, 0.392, 0.942], [0.521, 0.919, 0.565, 0.942], [0.694, 0.919, 0.739, 0.942], [0.868, 0.919, 0.912, 0.942], [1.041, 0.919, 1.085, 0.942]</r> as the years from 2011 to 2020. The {line for Artificial Intelligence}<r>[0.110, 0.226, 0.890, 0.810]</r> and the {line for Virtual Reality}<r>[0.110, 0.615, 0.890, 0.810]</r> represent the trend of each technology over the years, respectively. The specific value for each year can be extracted from the {line points}<r>[0.110, 0.810], [0.197, 0.784], [0.283, 0.733], [0.370, 0.674], [0.457, 0.615], [0.543, 0.497], [0.630, 0.421], [0.717, 0.378], [0.803, 0.302], [0.890, 0.226]</r> for Artificial Intelligence, and <r>[0.110, 0.717], [0.197, 0.666], [0.283, 0.632], [0.370, 0.615], [0.457, 0.632], [0.543, 0.683], [0.630, 0.725], [0.717, 0.759],[0.803, 0.784], [0.890, 0.810]</r> for Virtual Reality based on their position on the y-axis, and by correlating these with the corresponding x-axis points, which represent years.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Virtual Reality\n2011,1.2,2.3\n2012,1.5,2.9\n2013,2.1,3.3\n2014,2.8,3.5\n2015,3.5,3.3\n2016,4.9,2.7\n2017,5.8,2.2\n2018,6.3,1.8\n2019,7.2,1.5\n2020,8.1,1.2"
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "TsAhOhVlmXG44YNB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the trends of Artificial Intelligence and Virtual Reality over the years be interpreted?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the trends by examining the {line for Artificial Intelligence}<r>[0.110, 0.226, 0.890, 0.810]</r> and the {line for Virtual Reality}<r>[0.110, 0.615, 0.890, 0.810]</r>. The line for Artificial Intelligence shows an overall upward trend which means there is a consistent increase in the application or use of Artificial Intelligence during these years. On the other hand, the line for Virtual Reality depicts a downward trend, showing that the application or use of Virtual Reality has been steadily decreasing over the same period.\nBased on the above thought, the answer is:\nThe trends of Artificial Intelligence and Virtual Reality between 2011 to 2020 highlight the contrasting dynamics of these two technologies. Artificial Intelligence has seen a continuous increase in its application or use over the years, suggesting its growing significance and integration in various sectors. In contrast, the application or use of Virtual Reality has witnessed a decline, pointing towards a possible decrease in its acceptance or utility over this period."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "TCRquB4eh7UmgQET",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the total increase in the trend of Artificial Intelligence from 2011 to 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in the trend of Artificial Intelligence, I need to focus on the {line points for Artificial Intelligence}<r>[0.110, 0.810], [0.197, 0.784], [0.283, 0.733], [0.370, 0.674], [0.457, 0.615], [0.543, 0.497], [0.630, 0.421], [0.717, 0.378], [0.803, 0.302], [0.890, 0.226]</r>. The points represent the values of Artificial Intelligence trend from 1.2 in 2011 to 8.1 in 2020. The total increase will be the value in 2020 minus the value in 2011, which is 8.1 - 1.2 = 6.9.\nBased on the above thought, the answer is:\n6.9."
+            }
+        ],
+        "id_raw": 923
+    },
+    {
+        "id": "PhaWJMCIl3EJG8xx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data presented in the line chart into a structured tabular format with each type of energy as columns and years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis tick labels}[[0.093, 0.919, 0.137, 0.942], [0.219, 0.919, 0.263, 0.942], [0.346, 0.919, 0.390, 0.942], [0.472, 0.919, 0.516, 0.942], [0.599, 0.919, 0.643, 0.942], [0.726, 0.919, 0.769, 0.942], [0.852, 0.919, 0.896, 0.942]] as the row labels indicating the different years. Next, I take note of {the legend texts}[[0.141, 0.086, 0.292, 0.110], [0.782, 0.086, 0.896, 0.110]] as the column headers representing the different categories of energy. I then locate {the line points}[[0.115, 0.873], [0.241, 0.785], [0.368, 0.590], ...] for each category at the respective years and extract their values.\nBased on the above thought, the answer is:\nYear, Cloud Computing, Data Science\n2015, 1.01, 1.02\n2016, 1.1, 1.08\n2017, 1.3, 1.12\n2018, 1.51, 1.18\n2019, 1.55, 1.16\n2020, 1.7, 1.12\n2021, 1.8, 1.08"
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "36UPJmfq4w9f0HNQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which technology, 'Cloud Computing' or 'Data Science' shows a more consistent growth and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I examine the line plots for 'Cloud Computing' and 'Data Science'. The {line for Cloud Computing}[0.115, 0.104, 0.874, 0.873] shows a steady upward trend with no dips, implying consistent growth. In contrast, the {line for Data Science}[0.115, 0.104, 0.874, 0.873] also demonstrates an increasing trajectory, however, there is a slight decrease between 2018 and 2021. Hence, based on these observations, 'Cloud Computing' shows a more consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, 'Cloud Computing' shows more consistent growth compared to 'Data Science'. This is because its line plot shows a steady upward trend with no dips, meaning that it has been consistently increasing over the years. On the other hand, 'Data Science' shows an increase in growth but has a slight decrease between 2018 and 2021 which indicates a small inconsistency in its growth."
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "vpDb3eh8dxdeGfco",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the increase in percentage from 2015 to 2021 for Cloud Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in percentage for Cloud Computing from 2015 to 2021, I need to locate {the line points for Cloud Computing}[0.115, 0.104, 0.874, 0.873] and extract the corresponding values: 1.01, 1.1, 1.3, 1.51, 1.55, 1.7, 1.8. The value in 2015 is 1.01 and in 2021 is 1.8. Therefore, the increase in percentage is ((1.8-1.01)/1.01)*100.\nBased on the above thought, the answer is:\n77.23%"
+            }
+        ],
+        "id_raw": 924
+    },
+    {
+        "id": "92JlGYi3ToKCD3kh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart into a table format displaying the type of AI Classification and corresponding Precision and Recall values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I need to locate {the x-axis tick labels}[[0.020, 0.919, 0.262, 0.942],[0.218, 0.919, 0.354, 0.942],[0.373, 0.919, 0.488, 0.942],[0.445, 0.919, 0.706, 0.942],[0.608, 0.919, 0.833, 0.942],[0.761, 0.919, 0.969, 0.942]] which represent the AI Classifications. The {label of the y-axis for Precision}[0.036, 0.416, 0.053, 0.520] and the {label of the y-axis for Recall}[0.964, 0.433, 0.981, 0.503] confirm that the plotted y-axis data is for Precision and Recall. These two variables yield two rows in the table. The data points in Precision plot ({line for Precision}[0.141, 0.065, 0.865, 0.871]) and the Recall plot ({line for Recall}[0.141, 0.065, 0.865, 0.871]) will give respective values of the two under each AI classification.\nBased on the above thought, the answer is:\nAI Classification,Precision,Recall\nRobotic Process Automation,0.9,0.8\nNeural Network,0.85,0.88\nDecision Tree,0.79,0.85\nConvolutional Neural Network,0.93,0.72\nRecurrent Neural Network,0.92,0.69\nSupport Vector Machine,0.7,0.68"
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "k3B9mgsX396S18CO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you point out which AI model has a high Precision but a relatively low Recall, and try to explain the possible reason?"
+            },
+            {
+                "from": "gpt",
+                "value": "Commonly high Precision and low Recall indicates that model is conservative, it makes fewer predictions, but a high percentage of them are correct. Looking at the lines on the chart which contain the data points for Precision and Recall, the {line for Precision}[0.141, 0.065, 0.865, 0.871] and the {line for Recall}[0.141, 0.065, 0.865, 0.871], I can observe that \"Convolutional Neural Network\" has a high Precision and relatively lower Recall ({the point for Convolutional Neural Network for Precision}[0.576, 0.065] and {the point for Convolutional Neural Network for Recall}[0.576, 0.710]). This might be due to less amount of training data, overfitting the training data, or maybe the class distribution in the labelled data is imbalanced.\nBased on the above thought, the answer is:\nThe Convolutional Neural Network classification model from the line chart has a high Precision but a relatively low Recall. This may be due to the model being overfitted to the training data or because the class distribution in the labelled data is imbalanced, resulting in a bias towards false negatives. Hence, conservatively, the model clocks fewer predictions compared to other models, but a higher percentage of its predictions are correct (high precision) while it may miss a larger number of instances (low recall)."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "Y8KrPPDTKGVlSq6A",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the Precision value for Recurrent Neural Network greater than its Recall value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the values for Precision and Recall for Recurrent Neural Network, I locate the {point of Recurrent Neural Network for Precision}[0.720, 0.100] which falls on the line for Precision, and also, the {point of Recurrent Neural Network for Recall}[0.720, 0.830] which falls on the line for Recall. The difference between the Precision value of 0.92 and Recall value 0.69 is thus, 0.92-0.69 = 0.23.\nBased on the above thought, the answer is:\n0.23."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "nvChHxa4iTRVjs9t",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from this chart into a tabular format with the Year as rows and the indicators (Global Temperature Change, Sea Level Rise, Count of Wildfires) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, {the x-axis tick labels}<r>[0.082, 0.911, 1.018, 0.942]</r> represent the rows of the table, which are the years. The different indicators or columns of the table are represented by {the legend texts}<r>[0.091, 0.077, 0.293, 0.119]</r>,<r>[0.731, 0.077, 0.909, 0.119]</r> which are Global Temperature Change, Sea Level Rise, and Count of Wildfires. The data is represented by the points on {the line for Global Temperature Change}<r>[0.120, 0.158, 0.880, 0.604]</r>, {the line for Sea Level Rise}<r>[0.120, 0.411, 0.880, 0.566]</r>, and {the line for Count of Wildfires}<r>[0.120, 0.788, 0.880, 0.873]</r> for the years 1980 to 2020 respectively.\nBased on the above thought, the answer is:\nYear,Global Temperature Change,Sea Level Rise,Count of Wildfires\n1980,0.2,0.1,300\n1981,0.3,0.2,305\n...\n2019,1.2,0.9,800\n2020,1.2,1.0,850"
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "boJXE9y7sYrWiSyG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the correlation between global temperature change and sea level rise based on the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the correlation between global temperature change and sea level rise, I must compare the trends shown by {the line for Global Temperature Change}<r>[0.120, 0.158, 0.880, 0.604]</r> and {the line for Sea Level Rise}<r>[0.120, 0.411, 0.880, 0.566]</r>. Both lines show a steady increase from 1980 to 2020. There are no sudden jumps or falls in either of the lines, and they both show a steady upwards trend.\nBased on the above thought, the answer is:\nBased on the chart given, there appears to be a strong positive correlation between global temperature change and sea level rise. Both indicators show a consistent increase across the time period from 1980 to 2020. This observation suggests that as global temperatures rise, sea levels also increase, which is in line with the scientific understanding of the impact of climate change."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "QrntsbYegoZ9Uml2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find the percentage increase in global temperature change from 1990 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change in global temperature change from 1990 to 2020, I first locate the values for 1990 and 2020 from {the line for Global Temperature Change}<r>[0.120, 0.158, 0.880, 0.604]</r>. The value for 1990 is approximately 0.5 and the value for 2020 is approximately 1.2. The percentage increase between these two years is given by the formula: ((Final value - Initial value) / Initial value) * 100. Therefore, ((1.2 - 0.5) / 0.5) * 100 = 140%.\nBased on the above thought, the answer is:\n140%"
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "BjnFmluLZPUuFxYR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the graphical representation of the family asset values into a structured table format based on the trend seen?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I observe the x-axis, marked by {the x-axis labels}<r>[0.066, 0.933, 0.077, 0.957]</r>, which indicate the various family assets such as house, education, healthcare, vacation, hobbies, and retirement fund. Then, looking at the y-axis, indicated by {the y-axis labels}<r>[0.066, 0.933, 0.035, 0.061]</r>, I assess the values associated with each asset. The line graph's data points, marked by {the data points}<r>[0.105, 0.756, 0.895, 0.067]</r>, help me correlate the specific values to each asset.\nBased on the above thought, the answer is:\nAssets,Values\nHouse,350\nEducation,220\nHealthcare,180\nVacation,165\nHobbies,145\nRetirement Fund,270"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "UgekHKyx4btVhw08",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart's presentation of asset values reflect the spending priorities of a family?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The trend shown by the line chart}<r>[0.105, 0.354, 0.895, 0.756]</r> progresses from essential to luxury items. The line's steep ascent early on, at points corresponding to house, education, and healthcare, suggests high values and a priority on these necessary assets. Conversely, the line dips slightly at vacation and hobbies, indicating a secondary priority on these luxury items. Lastly, there is a rise again at retirement fund, showcasing a significant family priority towards secure future savings.\nBased on the above thought, the answer is:\nThe chart clearly highlights that a family prioritizes essential assets like house, education, and healthcare due to their high values, emphasizing their importance. Luxuries such as vacation and hobbies command lesser budget and are of secondary priority, while a significant amount is again allocated to retirement funds, indicating the importance of future financial security."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "4tg0CTSeg4IGDtt8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value of the 'luxury' category assets based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average value of the luxury category assets from the line chart (vacation, hobbies), I must locate the corresponding values for these assets. Observing {the vacation value}<r>[0.485, 0.558]</r> and {the hobbies value}<r>[0.515, 0.535]</r>, these have the annotated values of 165 and 145 respectively. Using these values, the average is calculated by summing them up and dividing by the number of luxuries: (165 + 145) / 2.\nBased on the above thought, the answer is:\n155"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "rrgRk7k8NoXKG6Lf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table format with Years as columns and Health Indicators as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe that {the x-axis labels}<r>[0.104, 0.942, 0.114, 0.970]</r> represent different years which will serve as the column headers. For the rows, I recognize {the legend}<r>[0.884, 0.101, 0.982, 0.123]</r> indicating Health Indicators such as Doctors, Patients, Hospital Beds, Healthcare Expenditure, and Medical Checkups. Locating {individual data points on the lines}<r>[0.141, 0.069]</r> allows me to extract the values for each indicator in the corresponding year.\nBased on the above thought, the answer is:\nYear,Doctors,Patients,Hospital Beds,Healthcare Expenditure,Medical Checkups\n2020,250,1200,300,50000,800\n2021,270,1150,350,70000,750\n2022,300,1300,400,71000,740\n2023,320,1250,450,72000,700"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "EEhMtws09F4r0cEf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed in the fluctuating number of patients over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "An analysis of {the line representing patient numbers}<r>[0.141, 0.854]</r> shows variation in values over the years. I observe that the trend starts high in 2020, slightly decreases by 2021, increases again in 2022, and drops once more in 2023. This fluctuation indicates an unstable trend in the number of patients seeking medical attention or being registered annually.\nBased on the above thought, the answer is:\nThe number of patients demonstrates a fluctuating trend over the years. Initially starting high, it shows a decrease in the following year, a subsequent increase, and a drop again, suggesting variable factors affecting patient numbers annually."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "EQNqrSkrDD9BuDKS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Healthcare Expenditure increase from the year 2020 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first identify and extract the values of {Healthcare Expenditure in 2020}<r>[0.141, 0.069]</r> and {Healthcare Expenditure in 2023}<r>[0.875, 0.167]</r> from the line plot. In 2020, the expenditure is represented as 50000 and in 2023 as 72000. To determine the increase, I subtract the 2020 value from the 2023 value which gives 72000 - 50000.\nBased on the above thought, the answer is:\n22000"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "f1lezhihDaVFZn1D",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table format detailing the investment values for each company over a certain period from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart into a structured table, I first locate {the x-axis tick labels}<r>[0.075, 0.029, 0.925, 0.055]</r> to identify the years, which serve as the column headers. I identify {the legend}<r>[0.082, 0.039, 0.178, 0.075]</r> that lists the companies Microsoft, Apple, Amazon, Tesla, and IBM as row headers. For each company, I track down {the line points}<r>[0.113, 0.208, 0.887, 0.816]</r>, <r>[0.113, 0.155, 0.887, 0.244]</r> corresponding to investment values across the years. I then document the values displayed in the chart.\nBased on the above thought, the answer is:\nYear,Microsoft,Apple,Amazon,Tesla,IBM\n2010,47.5,50.3,41.7,54.7,40.8\n2011,48.2,51.0,42.4,55.9,41.2\n2012,49.0,51.7,43.0,57.1,41.6\n...\n2020,55.6,59.2,49.7,67.5,45.6"
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "MxGOIa6qDEvlYsH6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which companies show a consistent upward trend in their investment values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze consistent upward trends, I observe the lines for each company: {Microsoft}<r>[0.113, 0.208, 0.887, 0.816]</r>, {Apple}<r>[0.113, 0.155, 0.887, 0.244]</r>, {Amazon}<r>[0.113, 0.262, 0.887, 0.870]</r>, {Tesla}<r>[0.113, 0.244, 0.887, 0.870]</r>, and {IBM}<r>[0.113, 0.333, 0.887, 0.870]</r>. Examining their points over the years, especially looking at the overall direction between 2010 and 2020, Tesla and Apple show marks moving upwards consistently with Tesla showing the most significant rise.\nBased on the above thought, the answer is:\nTesla and Apple consistently show upward trends in their investment values according to the line chart. Tesla shows the most significant rise, indicating dynamic growth in their investment over the years, while Apple displays a steady yet constant increase."
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "PuENLjjvA059FDKu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average investment value for Tesla over the period covered by the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average investment for Tesla, I first locate {the line points for Tesla}<r>[0.113, 0.155, 0.887, 0.244]</r> across different years ranging from 2010 to 2020. These points indicate Tesla's investment values each year: [54.7, 55.9, 57.1, 58.4, 59.9, 61.0, 62.5, 64.0, 65.8, 67.5]. To find the average, I sum all these values and divide by the number of years, which is ten.\nBased on the above thought, the answer is:\n60.68"
+            }
+        ],
+        "id_raw": 929
+    },
+    {
+        "id": "SNk6PIH0YjcFFKpl",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the sales data in a CSV format for all products across the months based on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line graph, I identify {the x-axis labels}<r>[0.05, 0.92, 0.12, 0.96]</r> representing the months from January to December. I then focus on each product line, {the line for ProductA}<r>[0.05, 0.12, 0.95, 0.88]</r>, {the line for ProductB}<r>[0.05, 0.88, 0.95, 0.12]</r>, and {the line for ProductC}<r>[0.05, 0.72, 0.95, 0.27]</r>, and extract the points representing sales values for each month.\nBased on the above thought, the answer is:\nMonth,ProductA,ProductB,ProductC\nJanuary,120,350,240\nFebruary,180,330,280\nMarch,240,310,320\nApril,300,290,350\nMay,360,270,400\nJune,420,250,360\nJuly,480,230,390\nAugust,530,210,420\nSeptember,590,150,450\nOctober,620,100,430\nNovember,630,60,460\nDecember,640,20,490"
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "kdQnWK1EJJ7IPYlL",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product exhibits the most unstable sales trend throughout the year according to the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I analyze the volatility of each product line by observing the fluctuations in their respective lines. {ProductA}<r>[0.05, 0.12, 0.95, 0.88]</r> shows a steady increase, indicating stability. In contrast, {ProductB}<r>[0.05, 0.88, 0.95, 0.12]</r> consistently declines, which is stable in its own pattern. However, {ProductC}<r>[0.05, 0.72, 0.95, 0.27]</r> reveals several peaks and troughs throughout the year, showing significant ups and downs, which indicates high instability.\nBased on the above thought, the answer is:\nProductC exhibits the most unstable sales trend throughout the year. The line graph displays numerous fluctuations in its sales figures, contrasting with the more steady trends seen in ProductA and ProductB."
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "FvEEuayGRJXOvj0d",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total year-end sales difference between ProductA and ProductB?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will calculate the total sales for ProductA and ProductB in December using their respective data points on the graph. For {ProductA}<r>[0.95, 0.88]</r>, the sales point is at 640 and for {ProductB}<r>[0.95, 0.12]</r>, the sales point is at 20. The difference is obtained by subtracting the sales of ProductB from ProductA.\nBased on the above thought, the answer is:\n620"
+            }
+        ],
+        "id_raw": 930
+    },
+    {
+        "id": "P9axeAvLNkJpsi02",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the market share data from the line chart into a table format with Companies as rows and Market Shares as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the information from the line chart into a table, I first identify {the x-axis labels}<r>[0.060, 0.952, 0.940, 0.975]</r> which indicate the companies: Apple, Samsung, Huawei, Xiaomi, OPPO, Realme, Others. I then observe the market share values from the data points in the line chart. For each company, I follow {the trend line}<r>[0.100, 0.150, 0.900, 0.843]</r> and extract their respective market share values from the y-tick values at each x-coordinate.\nBased on the above thought, the answer is:\nCompany,Market Share\nApple,21.4%\nSamsung,19.2%\nHuawei,13.2%\nXiaomi,11.4%\nOPPO,8.2%\nRealme,1.9%\nOthers,25.7%"
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "keJF7Yra9OK9kWwx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows the most significant fluctuation in market share according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart for fluctuations, I note the variations in market share for each company based on {the trend lines}<r>[0.100, 0.150, 0.900, 0.843]</r> and compare them. Companies like Xiaomi and Huawei display variations, yet the category labeled {Others}<r>[0.900, 0.843]</r> shows the greatest fluctuations, starting from a lower value, peaking, and then declining again more significantly than other companies.\nBased on the above thought, the answer is:\nThe 'Others' category exhibits the most significant fluctuation in market share, as indicated by the high amplitude of changes in its market share over time in the chart. This denotes a combined effect of numerous smaller manufacturers."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "FVxbJNJyEHiKhuSj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share percentage of the top three companies from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total market share of the top three companies, I find the market share of {Apple}<r>[0.100, 0.843]</r>, {Samsung}<r>[0.220, 0.759]</r>, and {Huawei}<r>[0.340, 0.657]</r>. The individual market shares are, respectively, 21.4%, 19.2%, and 13.2%. Summing these figures gives us a cumulative market share.\nBased on the above thought, the answer is:\n53.8%."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "z7Q5yAT4lfiqo6Dx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the Carbon Footprint values over the years into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the line for Carbon Footprint}<r>[0.071, 0.066, 0.929, 0.911]</r>, I extract the data points corresponding to each year. After identifying all individual data points and matching them to their corresponding years from {x-axis labels}<r>[0.071, 0.919, 0.958, 0.942]</r>, I can convert these values into a structured table.\nBased on the above thought, the answer is:\nYear,Carbon Footprint\n2000,320\n2001,312\n2002,305\n2003,298\n2004,289\n2005,280\n2006,270\n2007,260\n2008,250\n2009,240\n2010,230\n2011,220\n2012,210\n2013,205\n2014,200\n2015,195\n2016,190\n2017,185\n2018,180\n2019,175\n2020,170"
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "JtIoJauCW4Ozenve",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend between Carbon Footprint and Reforestation Efforts indicate progress towards sustainability?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, {the decreasing line of the Carbon Footprint}<r>[0.071, 0.066, 0.929, 0.911]</r> with values starting high and reducing over time suggests an effective decrease in environmental impact. Concurrently, {the increasing line of Reforestation Efforts}<r>[0.071, 0.066, 0.929, 0.911]</r> shows continuous improvement in forestry initiatives. This simultaneous movement, with carbon footprint declining as reforestation efforts rise, clearly illustrates progress towards sustainable and environmentally-beneficial practices over the years.\nBased on the above thought, the answer is:\nThe trend of decreasing Carbon Footprint and increasing Reforestation Efforts over the years distinctly showcases progress towards environmental sustainability. As industries reduce emissions and environmental impact lessens, the enhanced reforestation efforts contribute to ecological restoration, marking a move towards greener and more sustainable practices."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "YhRgzrTHQLV5fqPu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest values of Reforestation Efforts during the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest and lowest values of Reforestation Efforts, I carefully examine {the line plot for Reforestation Efforts}<r>[0.071, 0.066, 0.929, 0.911]</r>, pinpointing the peaks and troughs. After thoroughly inspecting the data, the maximum value at {the point in 2020}<r>[0.929, 0.066]</r> is 150, and the minimum value at {the point in 2000}<r>[0.071, 0.911]</r> is 50. Calculating the difference gives 150 - 50.\nBased on the above thought, the answer is:\n100."
+            }
+        ],
+        "id_raw": 932
+    },
+    {
+        "id": "cxeDcdUDstUcN4CP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a structured table with activities as rows and months as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive a structured table from the line chart, first, I identify {the x-axis tick labels}[[0.093, 0.820, 0.130, 0.841], [0.201, 0.820, 0.236, 0.841], [0.309, 0.820, 0.343, 0.841], [0.417, 0.820, 0.452, 0.841], [0.525, 0.820, 0.561, 0.841], [0.633, 0.820, 0.669, 0.841], [0.740, 0.820, 0.775, 0.841], [0.848, 0.820, 0.884, 0.841]] as the column headers, which represent the months. To find the row labels, I look at {the legend texts}[[0.889, 0.113, 0.955, 0.137], [0.889, 0.146, 0.965, 0.170], [0.889, 0.179, 0.957, 0.203], [0.889, 0.212, 0.950, 0.235], [0.889, 0.245, 0.957, 0.269], [0.889, 0.278, 0.935, 0.301], [0.889, 0.311, 0.948, 0.335]], which will become the rows representing the different sports and fitness activities. Then, I examine {the line points}[[0.145, 0.747], [0.253, 0.715], ..., [0.673, 0.348]] for each activity at the respective months and obtain the values to populate the table.\nBased on the above thought, the answer is:\nMonth/Activity,1,2,3,4,5,6,7,8\nJogging,120,110,115,125,130,120,115,112\nWeightlifting,150,155,145,160,155,160,162,160\nCycling,130,125,120,122,130,135,133,132\nTennis,95,105,100,108,102,105,110,105\nBasketball,70,75,73,78,80,82,85,82\nYoga,160,165,167,175,172,168,170,172\nSwimming,180,185,182,188,190,200,210,215"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "A1v2mXRVeDy2vvxm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which activity was the most consistently popular over the period, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the fitness activity with most consistent popularity, I need to inspect the minimal fluctuation in the line plot for each activity. At a glance, {the line for Weightlifting}[[0.145, 0.747, 0.857, 0.008]] appears to show the smallest fluctuation in its trend. The plot shows a steady increase from 150 to 162, modestly fluctuating between these values over the months, which indicates a relatively consistent popularity.\nBased on the above thought, the answer is:\nConsidering the trends shown in the line chart, Weightlifting seems to have been the most consistently popular activity over the time period. The line plot for Weightlifting showed a slow and steady increase, with only minor fluctuations in between, indicating that the popularity of Weightlifting stayed fairly consistent."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "dVCr5VvaqiGGnIEl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in popularity for swimming from month 1 to month 8?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in popularity for swimming, I need to locate {the line points for Swimming}[[0.145, 0.380], [0.253, 0.355], [0.360, 0.365], [0.468, 0.346], [0.579, 0.331], [0.687, 0.285], [0.790, 0.245], [0.857, 0.008]] and extract the corresponding values: 180, 185, 182, 188, 190, 200, 210, 215. The value at month 1 is 180 and the value at month 8 is 215. The increase is therefore 215 - 180 = 35.\nBased on the above thought, the answer is:\n35."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "ICwmyWUfXsYIoFsR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, could you structure the monthly sales data into a CSV format including the product names?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noting {the x-axis labels}<r>[0.044, 0.939, 1.024, 0.957]</r> which represent different products like PS5, MacBook Pro, iPhone 12, Samsung S20, and Dell XPS as columns. I observe {the line points}<r>[0.097, 0.904, 0.903, 0.173]</r> corresponding to each product and extract the number of sales. These points illustrate sales values over a month for each product.\nBased on the above thought, the answer is:\nProduct,Monthly Sales\nPS5,1050\nMacbook Pro,1500\niPhone 12,5000\nSamsung S20,2200\nDell XPS,1800"
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "r4Y2oKDFJJkN5OOo",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the peak in the iPhone 12 sales signify in the context of this market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the distinct upward surge at {the peak point of the iPhone 12 sales}<r>[0.479, 0.203]</r>, it's clear it significantly outsold other products. The steep ascent in its line compared to others like {PS5, Macbook Pro, Samsung S20 and Dell XPS}<r>[0.097, 0.904, 0.903, 0.173]</r> suggests a huge consumer demand and possibly a very effective marketing strategy or product launch.\nBased on the above thought, the answer is:\nThe peak in the iPhone 12 sales indicates a very high consumer demand for this product, likely driven by successful marketing strategies or appealing product features. This highlights iPhone 12 as the leading choice among customers compared to other electronic products within the same period."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "xfSRK0BwldUY0W0e",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more did iPhone 12 sell compared to Dell XPS in the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out how much more the iPhone 12 sold compared to Dell XPS, I compare the sales values at {points on the iPhone 12 and Dell XPS lines}<r>[0.479, 0.203, 0.860, 0.339]</r>. The iPhone 12 shows a sales value of 5000 units whereas the Dell XPS shows a sales value of 1800 units.\nBased on the above thought, the answer is:\n3200 units."
+            }
+        ],
+        "id_raw": 934
+    },
+    {
+        "id": "lwh4vltr4P3hqgLh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the line chart showing the trend of admissions into private and public schools from 2015 to 2020 into a data table format? We should have Year as columns and School types as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "For this purpose, I'll locate {the x-axis tick labels}[[0.057, 0.919, 0.068, 0.942], [0.192, 0.919, 0.203, 0.942], [0.326, 0.919, 0.337, 0.942], [0.461, 0.919, 0.472, 0.942], [0.595, 0.919, 0.606, 0.942], [0.724, 0.919, 0.746, 0.942], [0.859, 0.919, 0.881, 0.942], [0.993, 0.919, 1.015, 0.942]] which represent the year for each data point. Next, I'll identify the legend texts as Private and Public schools from the description. To extract the numbers, I'll read off the points on the lines {\"for Private schools\"}[[0.130, 0.573], [0.197, 0.590], [0.265, 0.607], [0.332, 0.708], [0.399, 0.759], [0.466, 0.776], [0.534, 0.708], [0.601, 0.667], [0.668, 0.613], [0.735, 0.627], [0.803, 0.647], [0.870, 0.573]] and {\"for Public schools\"}[[0.130, 0.167], [0.197, 0.184], [0.265, 0.235], [0.332, 0.302], [0.399, 0.319], [0.466, 0.353], [0.534, 0.336], [0.601, 0.296], [0.668, 0.235], [0.735, 0.252], [0.803, 0.285], [0.870, 0.150]].\nBased on the above thought, the answer is:\nSchool_Type,2015,2016,2017,2018,2019,2020\nPrivate,1000,950,900,600,450,400\nPublic,2200,2150,2000,1800,1750,1650"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "J8HaUVi1mesbKJPK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the admissions trend, which type of school faced a significant change in 2017, and what was the nature of this change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the trends shown in the line plots for both private and public schools. Looking at {the Private line}[[0.130, 0.573, 0.870, 0.573]], there is no significant change noticed in 2017 as the admissions moderately increased over time. However, the {Public line}[[0.130, 0.167, 0.870, 0.150]] shows a noticeable drop in admissions in 2017.\nBased on the above thought, the answer is:\nObserving the line chart, the admissions into public schools showed a noticeable drop in 2017. In other words, the trend for public schools in 2017 faced a significant decrease compared to the previous year. The decrease continued but at a less drastic rate after 2017."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "6ewvTDhhmQLrSjIK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of admissions for private schools from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total admissions for private schools, I need to sum up the line points values {\"for Private schools\"}[0.130, 0.573, 0.197, 0.590, 0.265, 0.607, 0.332, 0.708, 0.399, 0.759, 0.466, 0.776, 0.534, 0.708, 0.601, 0.667, 0.668, 0.613, 0.735, 0.627, 0.803, 0.647, 0.870, 0.573], which are respectively: 1000, 950, 900, 600, 450, 400. Total = 1000 + 950 + 900 + 600 + 450 + 400 = 4300.\nBased on the above thought, the answer is:\n4300."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "Sy63iuJH9VTJzNHz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data of monthly closing prices for Stock_A and Stock_B into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first locate {the x-axis tick labels}<r>[0.073, 0.952, 0.145, 0.982]</r> to identify each month from January to December as columns. Next, I see {the legend labels}<r>[0.805, 0.073, 0.909, 0.101]</r> which indicate the rows of Stock_A and Stock_B. For each month, I observe and record the line plot points from both {the line for Stock_A}<r>[0.100, 0.700, 0.900, 0.100]</r> and {the line for Stock_B}<r>[0.100, 0.750, 0.900, 0.150]</r> to extract the monthly closing prices.\nBased on the above thought, the answer is:\nMonth,Stock_A,Stock_B\nJanuary,48.3,32.1\nFebruary,48.9,32.5\nMarch,49.7,33.2\nApril,50.3,33.9\nMay,51.2,34.6\nJune,52.1,35.2\nJuly,52.9,35.9\nAugust,53.8,36.7\nSeptember,53.0,36.0\nOctober,53.6,36.5\nNovember,54.4,37.3\nDecember,54.9,38.9"
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "mrgAAOQVFa2sUJ7s",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did Stock_A's trend compare to the general fluctuation observed in Stock_B throughout the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing both {the line for Stock_A}<r>[0.100, 0.700, 0.900, 0.100]</r> and {the line for Stock_B}<r>[0.100, 0.750, 0.900, 0.150]</r>, Stock_A exhibits a rather stable and gradual increase over the year, characterized by fewer and less severe dips especially around September. In comparison, Stock_B, while also trending upwards, shows slightly more pronounced fluctuations, particularly visible around the midpoint of the year where its line shows a sharper dip followed by a quick recovery.\nBased on the above thought, the answer is:\nStock_A displayed a more stable and gradual increase in its trends compared to Stock_B, which, despite following an upward trend, experienced more pronounced fluctuations throughout the year. Stock_A's smoother trajectory suggests less volatility compared to the more erratic movements of Stock_B."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "6Ps8ykVG9Yy2wvP8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall year increase for Stock_B from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the overall increase of Stock_B over the year, I first locate {the starting point for Stock_B in January}<r>[0.100, 0.730]</r> and the {closing point for Stock_B in December}<r>[0.880, 0.280]</r>. By extracting the Y-values, the starting month value is 32.1 and the ending is 38.9. The difference representing the yearly increase is calculated by subtracting the starting value from the ending value.\nBased on the above thought, the answer is:\n6.8"
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "EmVJXSJeeFDyWQO1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table mapping 'Environmental Sustainability Topics' to the 'Human Discussions' and 'AI Model Discussions' frequency from the stacked bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the stacked bar chart, I can differentiate the frequencies with which topics were discussed by humans and the AI model by referencing the y-axis which shows probability values. Tracking the data from the stacked bar segments helps differentiate between {the AI model's segments}<r>[0.100, 0.220, 0.120, 0.800]</r> and {the human discussion segments}<r>[0.100, 0.800, 0.120, 0.880]</r>. The topics along the x-axis are identifiable via {the labels on the x-ticks}<r>[0.100, 0.900, 0.150, 0.950]</r> which associate each bar with their respective topics.\nBased on the above thought, the answer is:\nTopic,Human Discussions,AI Model Discussions\nClimate Change,0.10,0.15\nBiodiversity,0.15,0.20\nDeforestation,0.05,0.10\nPollution,0.12,0.18\nConservation,0.02,0.45"
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "5fzs6nKy2xVW0Rue",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich topic shows a remarkably higher involvement by the AI model compared to human discussions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By visually inspecting the levels of each topic's bars in the chart, I purposefully scrutinize for disparities. The topic of {Conservation}<r>[0.600, 0.200, 0.700, 0.850]</r> shows a significant height difference between the human discussion and AI model segments, indicating a strong emphasis by the AI model. The AI model discussion portion extends almost across the entire y-axis scale dedicated to probabilities, deeply overshadowing the much smaller segment for human discussions.\nBased on the above thought, the answer is:\nThe topic of 'Conservation' shows a remarkably higher involvement by the AI model as compared to human discussions. The significant difference in bar heights accentuates the AI model's unique focus on this topic."
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "LznRCFA5nPJUWWiX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the AI Model's discussion frequency regarding 'Conservation' compared to that of Human Discussions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I locate the data points for the 'Conservation' topic within the chart. The AI Model discussion frequency for 'Conservation' is visibly higher, seen at {the point in AI Model's bar}<r>[0.700, 0.300]</r>, compared to that of Human discussions at {the point in Human's bar}<r>[0.700, 0.850]</r>. Numerically assessing these values from the y-axis, AI Model discusses 'Conservation' with a frequency of 0.45 whereas Human discussions have a frequency of 0.02.\nBased on the above thought, the answer is:\n0.43"
+            }
+        ],
+        "id_raw": 937
+    },
+    {
+        "id": "eCKJbYATa70ptiEt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the chart data showing emotional behaviors and their trends from the psychological studies into a structured format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the stacked bars}<r>[0.120, 0.108, 0.880, 0.911]</r> on the chart, I can see that each bar represents a distinct emotion with two segments: one for human distribution and another for psychological trend. The human distribution is noted by its orangish section while the psychological trend is displayed with a bluish top layer. Additionally, {the X-axis labels}<r>[0.090, 0.919, 0.910, 0.942]</r> enumerate the various emotions like fear, sadness, happiness, etc. By extracting numerical values associated with each segment from the bars and labeling them according to the emotion, we can form an accurate representation of the data in a table.\nBased on the above thought, the answer is:\nEmotion,Human Distribution (%),Psychological Trend (%)\nFear,35,30\nSadness,25,20\nHappiness,40,45\nAnger,10,15\nSurprise,15,10\nDisgust,5,5\nLove,20,35\nCalm,30,25"
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "KT5HHDe0IuaPPvUS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the emotional trend for 'Love' depicted in the chart indicate a disconnect between human distribution and psychological studies predictions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing {the segment for 'Love'}<r>[0.510, 0.350, 0.590, 0.911]</r>, the human distribution stands at 20% whereas the psychological trend indicates an increase of up to 35%, which points to a notable rise. This divergence suggests that the psychological studies expect a higher occurrence or admission of the emotion 'Love' compared to what is actually reported or observed in human behavior. Possible reasons for this discrepancy might involve societal or cultural factors affecting individuals' willingness to openly express or acknowledge feelings of love, perhaps due to prevailing social norms or personal reservations.\nBased on the above thought, the answer is:\nThe emotional trend for 'Love' shows a deviation between human distribution and psychological predictions because the psychological studies might be extrapolating data or expectations of more liberal expressions of love, whereas actual human behavior could be constrained by societal or cultural influences that deter open expressions of such an emotion."
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "8snulNMWEbEnpz4E",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the human distribution and psychological trend for the emotion 'Love' as depicted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference for the emotion 'Love', I need to refer to {the appropriate segment of the stacked bar for 'Love'}<r>[0.510, 0.350, 0.590, 0.911]</r> on the chart. The human distribution part shows 20%, and the psychological trend shows 35%. Thus, the difference between the psychological trend and human distribution can be calculated as 35% - 20% = 15%.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 938
+    },
+    {
+        "id": "CyUIMN3kUySjwjWG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table representing the Development Trends and Workplace Trends across key characteristics like flexible workplace, professional development, and diversity and inclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data from the dual trend chart, I begin by examining {the x-axis tick labels}<r>[0.066, 0.933, 0.081, 0.939]</r> which consist of the characteristics like flexible workplace, professional development, and diversity and inclusion. Then, I identify {the legend}<r>[0.073, 0.058, 0.234, 0.089]</r> presenting Development Trends and Workplace Trends. Observing the lines, I extract the progress levels for each characteristic and trend from the coordinates of {the line points for Development Trend}<r>[0.105, 0.374, 0.895, 0.522]</r> and {the line points for Workplace Trend}<r>[0.105, 0.344, 0.895, 0.474]</r>.\nBased on the above thought, the answer is:\nCharacteristics,Development Trend,Workplace Trend\nFlexible Workplace,Low,High\nProfessional Development,Moderate,Moderate\nDiversity and Inclusion,High,Low"
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "tWqroTRt7Hp3wRAI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend demonstrates a more consistent development across different workplace characteristics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend is more consistent, I assess the variation in the y-coordinates for each trend's points. Observing {the line for Development Trend}<r>[0.105, 0.540, 0.895, 0.403]</r>, it has minor fluctuations across different characteristics. In contrast, {the line for Workplace Trend}<r>[0.105, 0.835, 0.895, 0.344]</r> displays more significant swings, especially for flexible workplace and diversity and inclusion. The consistency of a trend is typically judged by smaller fluctuations in its respective line plot.\nBased on the above thought, the answer is:\nThe Development Trend demonstrates a more consistent development across different workplace characteristics, as seen in the moderate level of fluctuation across characteristics compared to the Workplace Trend, which shows larger swings in development levels."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "URHm9cBe5lgagLzP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_47_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the aspect of diversity and inclusion from the Development Trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the increase in diversity and inclusion within the Development Trend, first, I locate the line chart where {the point marking the start of diversity and inclusion for Development Trend}<r>[0.895, 0.482]</r> represents the initial value, which corresponds closely with a smaller value on {the y-axis}<r>[0.066, 0.049]</r>. Following this, {the point marking the end of diversity and inclusion for Development Trend}<r>[0.925, 0.292]</r> shows a higher position, indicating an increase. Using the labels on the y-axis or points for precise value extraction would yield the numerical increase.\nBased on the above thought, the answer is:\nSignificant Increase."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "xLFbIkwS29U4hB7q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart depicting Share Prices throughout the year into a table format with Months as rows and Share Prices as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a structured table, first we need to identify {the x-axis tick labels}[[0.092, 0.879, 0.128, 0.914], [0.166, 0.879, 0.207, 0.914], [0.241, 0.879, 0.285, 0.914], [0.320, 0.879, 0.360, 0.914], [0.392, 0.879, 0.440, 0.914], [0.475, 0.879, 0.511, 0.914], [0.556, 0.879, 0.583, 0.914], [0.624, 0.879, 0.669, 0.914], [0.701, 0.879, 0.745, 0.914], [0.779, 0.879, 0.819, 0.914], [0.853, 0.879, 0.899, 0.914], [0.930, 0.879, 0.975, 0.914]] which are the row labels representing the months. Next, I locate {the legend texts}[[0.196, 0.069, 0.341, 0.104], [0.196, 0.121, 0.341, 0.156]] to identify the column headers as Share Price1 and Share Price2. Finally, the values can be extracted through {the line points}[[0.110, 0.369], [0.186, 0.397], ..., [0.953, 0.756]] for Share Price1 and {the line points}[[0.110, 0.314], [0.186, 0.259], ..., [0.953, 0.231]] for Share Price2 at the respective months.\nBased on the above thought, the answer is:\nMonths,Share Price1,Share Price2\nJan,210,220\nFeb,205,230\nMar,195,235\nApr,192,238\nMay,180,245\nJun,175,250\nJul,170,255\nAug,160,260\nSep,155,255\nOct,152,250\nNov,145,240\nDec,140,235"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "JjHreM3yvW91Wp2Y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the trend lines in the graph, which share price presented a steadier rise over the course of the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to compare {the trend line of Share Price1}[0.110, 0.369, 0.953, 0.756] and {the trend line of Share Price2}[0.110, 0.314, 0.953, 0.0314]. Observing the trend line of Share Price1, it fluctuates downward in a somewhat volatile manner. In contrast, Share Price2 seems to present a steady upward progress with minor dips before Sept and between Oct to Dec.\nBased on the above thought, the answer is:\nComparing the two share prices, Share Price2 presented a steadier rise over the course of the year. There were only minor dips and overall, the line graph of Share Price2 trended upwards consistently unlike the fluctuating trend line of Share Price1."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "GmFVXF2vrUMqpHYw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum decline in Share Price1 seen over consecutive months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum decline in Share Price1 over consecutive months, I need to analyze {the data points for Share Price1}[[0.110, 0.369], [0.186, 0.397], [0.263, 0.452], [0.340, 0.469], [0.416, 0.535], [0.493, 0.563], [0.570, 0.590], [0.646, 0.645], [0.723, 0.673], [0.799, 0.690], [0.876, 0.728], [0.953, 0.756]] which are 210, 205, 195, 192, 180, 175, 170, 160, 155, 152, 145 and 140. The maximum decline is seen from May to June where the price declines from 180 to 175, which is a drop of 5 units.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "UXDlqj4WjT8PPYMd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you turn the information about the performance of each market index over the years into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I first identify the {x-axis labels}[[0.034, 0.903, 0.069, 0.931], [0.146, 0.903, 0.181, 0.931], [0.257, 0.903, 0.292, 0.931], [0.369, 0.903, 0.404, 0.931], [0.480, 0.903, 0.515, 0.931], [0.591, 0.903, 0.627, 0.931], [0.703, 0.903, 0.738, 0.931], [0.814, 0.903, 0.849, 0.931], [0.926, 0.903, 0.961, 0.931], [1.037, 0.903, 1.072, 0.931]] as the Years. The {subplot legend labels}[[0.117, 0.055, 0.295, 0.083], [0.117, 0.097, 0.255, 0.125]] would serve as column headers representing the different market indexes. Then, for each year, I locate the {data points} for each index and extract the corresponding Market Index Values.\nBased on the above thought, the answer is:\nYears,Contemporary Art Market Index,Heritage Art Market Index\n2005,120,100\n2006,130,105\n2007,145,110\n2008,135,100\n2009,140,90\n2010,165,100\n2011,175,110\n2012,185,115\n2013,190,120\n2014,200,125\n2015,205,130\n2016,210,132\n2017,220,134\n2018,225,136\n2019,230,138\n2020,232,139"
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "HpioLsxHS9906RrF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the overall trend in the Contemporary and Heritage Art Markets from 2005 to 2020 based on the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the overall trend, I analyze the lines representing each market. Looking at {line_0}[[0.108, 0.069, 0.943, 0.688]], which represents the Contemporary Art Market Index, I observe a generally increasing trend with small dips in 2008 and 2009. For the {line_1}[[0.108, 0.583, 0.943, 0.854]], representing the Heritage Art Market Index, the trend is also increasing, albeit at a slower pace and with fewer fluctuations than the Contemporary Art Market. The positive slope of both lines suggests an overall growth in the market index values from 2005 to 2020.\nBased on the above thought, the answer is:\nFrom 2005 to 2020, both the Contemporary and Heritage Art Markets demonstrate increasing trends according to the line graph. The Contemporary Art Market Index shows a more aggressive growth with slight dips in 2008 and 2009. On the other hand, the Heritage Art Market Index, while also growing, does so more steadily with fewer fluctuations. The graph suggests that both markets have seen a positive growth in their market index values over the given period."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "mJPeTT20DUHslrmr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percent increase in the Contemporary Art Market Index from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percent increase in the Contemporary Art Market Index from 2005 to 2020, I first identify the {value for 2005}[[0.108, 0.688]] in line_0, which is 120. Then, I locate the {value for 2020}[[0.943, 0.069]] in line_0 as well, which is 232. The percent increase is therefore calculated as (232 - 120) / 120 = 0.9333 * 100%.\nBased on the above thought, the answer is:\n93.33%."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "Ln0kGB4JGSykhrg3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organise the data representing the various perceptions of humans and machines towards Cybersecurity and Data Privacy into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the data in a table, I identify the labels on {the x-axis}<r>[0.056, 0.879, 0.106, 0.914],[0.144, 0.879, 0.194, 0.914],[0.231, 0.879, 0.281, 0.914],[0.318, 0.879, 0.368, 0.914],[0.405, 0.879, 0.455, 0.914],[0.492, 0.879, 0.543, 0.914],[0.580, 0.879, 0.630, 0.914],[0.667, 0.879, 0.717, 0.914]</r> as the cybersecurity related terminologies. For each terminology, I find {the corresponding stacked bars}<r>[0.081, 0.808, 0.910, 0.829],[0.081, 0.075, 0.910, 0.432]</r> and calculate the proportions represented by each color to find the respective human and machine perceptions of each terminology.\nBased on the above thought, the answer is:\nTerm,Human Perception,Machine Perception\nPhishing,0.4,0.6\nHacking,0.35,0.65\nCyberattack,0.45,0.55\nData breach,0.3,0.7\nEncryption,0.47,0.53\nFirewall,0.3,0.7\nMalware,0.33,0.67\nIdentity theft,0.5,0.5"
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "oeJ1brPgUqAyghN7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what does the similar perception of 'Identity theft' between humans and machines indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "After locating the term {\u2018Identity theft\u2019}<r>[0.667, 0.879, 0.717, 0.914]</r> on the x-axis, I can trace the corresponding bar and notice that the human and machine perceptions marked by different colors share an equal height. This means that both humans and machines have the same level of interest or focus towards 'Identity theft'. Considering the unique nature of this alignment in the whole dataset where general machine perspectives are comparatively higher, this might indicate a common level of understanding or significance attributed to 'Identity theft' by both humans and machines in the field of Cybersecurity and Data Privacy.\nBased on the above thought, the answer is:\nThe similar perception level of 'Identity theft' between humans and machines suggests a mutual recognition of the significance of this term in the context of Cybersecurity and Data Privacy. It could indicate that both humans and machines acknowledge 'Identity theft' similarly as a substantial security concern and accord equivalent attention to it when addressing cybersecurity matters."
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "XeJacHMsg74wzQN9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the perception of the term 'Firewall' between humans and machines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in the perception of the term 'Firewall', I need to examine {the bar corresponding to 'Firewall'}<r>[0.405, 0.879, 0.455, 0.914]</r>. The different colored sections show the human and machine perceptions. Judging from the difference in heights, the machine perception is approximately 0.7 and the human perception is close to 0.3. Therefore, the difference in perception is approximately 0.7 - 0.3 = 0.4.\nBased on the above thought, the answer is:\n0.4"
+            }
+        ],
+        "id_raw": 943
+    },
+    {
+        "id": "zXTBWoDPpLKP9GHH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart into a table format with Categories as rows and Human Distribution and Model Distribution as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a table format, I first look at {the x-axis labels}[{\"label\": \"2000\", \"label_coordinates\": \"[0.053, 0.919, 0.097, 0.942]\", \"tick_coordinates\": \"[0.075, 0.911]\"}, {\"label\": \"2005\", \"label_coordinates\": \"[0.269, 0.919, 0.312, 0.942]\", \"tick_coordinates\": \"[0.291, 0.911]\"}, {\"label\": \"2010\", \"label_coordinates\": \"[0.484, 0.919, 0.528, 0.942]\", \"tick_coordinates\": \"[0.506, 0.911]\"}, {\"label\": \"2015\", \"label_coordinates\": \"[0.700, 0.919, 0.744, 0.942]\", \"tick_coordinates\": \"[0.722, 0.911]\"}, {\"label\": \"2020\", \"label_coordinates\": \"[0.916, 0.919, 0.960, 0.942]\", \"tick_coordinates\": \"[0.938, 0.911]\"}] which represent the Year. I then identify {the legend texts} [{'label': 'GDP Growth Rate', 'text_coordinates': '[0.814, 0.046, 0.966, 0.069]'}, {'label': 'Interest Rate', 'text_coordinates': '[0.814, 0.081, 0.927, 0.104]'}] as the column headers representing GDP Growth Rate and Interest Rate. Next, I locate {the line points for GDP Growth Rate and Interest Rate} for each Year and extract their values.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate,Interest Rate\n2001,0.8,4.24\n2002,1.8,1.75\n2003,2.8,1.01\n2004,3.8,1.17\n2005,4.5,1.96\n2006,4.6,4.79\n2007,2.5,5.02\n2008,-0.1,2.21\n2009,-2.5,0.16\n2010,2.5,0.18\n2011,1.6,0.11\n2012,2.2,0.16\n2013,1.7,0.11\n2014,2.5,0.09\n2015,2.9,0.25\n2016,1.6,0.54\n2017,2.3,1.00\n2018,2.9,1.74\n2019,2.2,2.16\n2020,-3.5,0.09"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "MrkERD11gKMtfLqx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you explain the relationship between the years and the GDP Growth Rate and Interest Rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The relationship between the years and the GDP Growth Rate and Interest Rate is visualized in the chart using lines corresponding to the GDP Growth Rate{GDP Growth Rate line}[0.118, 0.105, 0.938, 0.871] and Interest Rate{Interest Rate line}[0.118, 0.065, 0.938, 0.531]. Over the years, there are variations in both the GDP Growth Rate and the Interest Rate. They seem to fluctuate. While the GDP Growth Rate appears to have a dip in 2008 to 2009 and subsequently rebounds, the Interest Rate seems to follow a more or less decreasing trend from 2006 to 2020 with minor fluctuations. This shows that the GDP Growth Rate and the Interest Rate do not have a constant relationship over the years, but are influenced by factors specific to each year.\nBased on the above thought, the answer is:\nAccording to the line chart, the relationship between the years and the GDP Growth Rate and the Interest Rate is not fixed. The GDP Growth Rate varies, with a notable dip around 2008 and 2009, likely due to economic recession, before rebounding. In contrast, the Interest Rate diminishes over time with minor fluctuations. This analysis can hint towards the presence of external factors that affect these rates individually and non-linearly over the years."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "C8foLpLl7bqlxG1I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest Interest Rate and which year does it occur according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the largest Interest Rate, I would look at {the 'Interest Rate' line points} given by the data. The maximum Interest Rate appears to be for the year 2007 according to {the 'Interest rate' line point of year 2007}['line_1']['points'][6], where it coordinates [0.377, 0.065] link to the highest point on the 'Interest Rate' line graph. The corresponding value for this point is 5.02. So, the year with the largest Interest Rate is 2007 with an Interest Rate of 5.02.\nBased on the above thought, the answer is:\nThe largest Interest Rate is 5.02, which occurred in the year 2007."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "ufpqUMFggUxL48mo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart data depicting the stock performance of Stock A and Stock B over time into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compile this data into a structured table, I first look at the x-axis where I notice {the time periods}[[0.149, 0.866],[0.191, 0.866],[0.232, 0.866],[0.273, 0.866],[0.315, 0.866],[0.356, 0.866],[0.398, 0.866],[0.439, 0.866],[0.480, 0.866],[0.522, 0.866],[0.563, 0.866],[0.605, 0.866],[0.646, 0.866],[0.687, 0.866],[0.729, 0.866],[0.770, 0.866],[0.811, 0.866],[0.853, 0.866],[0.894, 0.866],[0.936, 0.866]] which correspond to the time periods from 1-20. Then I look at the {line points for Stock A}[[0.149, 0.760],[0.191, 0.783],[0.232, 0.829],[0.273, 0.812],[0.315, 0.726],[0.356, 0.760],[0.398, 0.805],[0.439, 0.795],[0.480, 0.829],[0.522, 0.760],[0.563, 0.714],[0.605, 0.692],[0.646, 0.623],[0.687, 0.555],[0.729, 0.418],[0.770, 0.349],[0.811, 0.281],[0.853, 0.246],[0.894, 0.212],[0.936, 0.161]] and the {line points for Stock B}[[0.149, 0.760],[0.191, 0.719],[0.232, 0.692],[0.273, 0.640],[0.315, 0.675],[0.356, 0.589],[0.398, 0.555],[0.439, 0.503],[0.480, 0.503],[0.522, 0.469],[0.563, 0.435],[0.605, 0.418],[0.646, 0.383],[0.687, 0.332],[0.729, 0.281],[0.770, 0.246],[0.811, 0.212],[0.853, 0.161],[0.894, 0.109],[0.936, 0.075]] and extract the corresponding values for each time period.\nBased on the above thought, the answer is:\nTime Period,Stock A,Stock B\n1,100,100\n2,98.7,102.4\n3,96,104\n4,97,107\n5,102,105\n6,100,110\n7,97.4,112\n8,98,115\n9,96,115\n10,100,117\n11,102.7,119\n12,104,120\n13,108,122\n14,112,125\n15,120,128\n16,124,130\n17,128,132\n18,130,135\n19,132,138\n20,135,140"
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "YrhXygv3WZdkucBq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the performance of Stock A and Stock B over time, which stock shows a more stable performance? Please give your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to review the line plots for both Stock A and Stock B. Observing {the line plot for Stock A}[0.149, 0.161, 0.936, 0.829], it shows multiple fluctuations with a significant fall and rise towards the end. It starts and ends at around the same value, showing signs of instability in periods in-between. Then, I take a look at {the line plot for Stock B}[0.149, 0.075, 0.936, 0.760]. This line shows a relatively consistent rising trend over time, with less severe fluctuations compared to Stock A. There are some minor temporary drops, but the overall trend is steadily increasing.\nBased on the above thought, the answer is:\nStock B shows a more stable performance over time compared to Stock A. This is mainly because, despite some minor fluctuations, its overall trend is a consistent rise in value. In contrast, Stock A exhibits multiple significant fluctuations over time, making it less stable."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "pjUjMMxs1clJH7ez",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which time period did the Stock B reach the maximum value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out when Stock B reached its maximum value, I have to locate the highest point in {the line points for Stock B}[[0.149, 0.760],[0.191, 0.719],[0.232, 0.692],[0.273, 0.640],[0.315, 0.675],[0.356, 0.589],[0.398, 0.555],[0.439, 0.503],[0.480, 0.503],[0.522, 0.469],[0.563, 0.435],[0.605, 0.418],[0.646, 0.383],[0.687, 0.332],[0.729, 0.281],[0.770, 0.246],[0.811, 0.212],[0.853, 0.161],[0.894, 0.109],[0.936, 0.075]]. Looking at these points, the maximum value 140 corresponds to {the time period 20}[0.936, 0.866].\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 946
+    },
+    {
+        "id": "MZXxJY8AcWOMTjr3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tabulate the changes in public opinion over time as represented in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, I begin by looking at {the x-axis tick labels}[[0.082, 0.919, 0.088, 0.942], [0.168, 0.919, 0.173, 0.942], [0.253, 0.919, 0.259, 0.942], [0.339, 0.919, 0.345, 0.942], [0.425, 0.919, 0.430, 0.942], [0.510, 0.919, 0.521, 0.942], [0.596, 0.919, 0.607, 0.942], [0.682, 0.919, 0.693, 0.942], [0.767, 0.919, 0.778, 0.942], [0.853, 0.919, 0.864, 0.942], [0.938, 0.919, 0.949, 0.942]], which denote the time in months. The corresponding y-values represent the public opinion percentage linked to media influence. The line points for media influence {media influence points}[[0.082, 0.871], [0.125, 0.787], [0.168, 0.754], [0.211, 0.704], [0.253, 0.654], [0.296, 0.570], [0.339, 0.537], [0.382, 0.453], [0.425, 0.369], [0.468, 0.202], [0.510, 0.185], [0.553, 0.169], [0.596, 0.135], [0.639, 0.102], [0.682, 0.085], [0.724, 0.068], [0.767, 0.135], [0.810, 0.169], [0.853, 0.202], [0.896, 0.236], [0.938, 0.269]] are used for value extraction.\nBased on the above thought, the answer is:\nTime (months),Public Opinion (%)\n0,50\n1,55\n2,57\n3,60\n4,63\n5,68\n6,70\n7,75\n8,80\n9,90\n10,91\n11,92\n12,94\n13,96\n14,97\n15,98\n16,94\n17,92\n18,90\n19,88\n20,86"
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "gcnS330rF6aQAlUX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how does the public opinion on media influence change over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the trend in {the media influence line}[[0.082, 0.068, 0.938, 0.871]] as it captures the changes in the public opinion on media influence over time. The line plot starts at {month 0}[[0.082, 0.871]] with a value of 50%, indicating a moderate level of influence. As the line progresses, it shows a consistent increase reaching a peak at {month 15}[[0.724, 0.068]] with a value of 98%, suggesting a surge in media influence on the public. After the peak, there's a decrease in the value, represented by a downward trend of the line towards {month 20}[[0.938, 0.269]], showing declining public opinion on media influence.\nBased on the above thought, the answer is:\nBased on the line chart, the public opinion on media influence grows over time from a moderate level of influence to a high level of influence, reaching its peak at around the 15th month. After this point, there is a decline in the influence level, indicating diminishing public opinion on media influence towards the end of the observed span."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "rgBGUDU94mYZanVc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt the peak of media influence, how much higher is the public opinion percent compared to its starting point?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the maximum and initial values of public opinion related to media influence. Looking at {the media influence line}[[0.082, 0.068, 0.938, 0.871]], the initial point is located at {month 0}[[0.082, 0.871]] with the value of 50. The peak occurs at {month 15}[[0.724, 0.068]] with a value of 98. The difference between these two values indicates the rise in public opinion percent during the peak of media influence.\nBased on the above thought, the answer is:\n48%."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "BjPFGFrvJkOWevmZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you output the data represented in these line charts into a table format? The table should have a column for each social platform and two rows for the user counts in 2016 and 2017."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, let's take {the color legend cells}[[0.107, 0.542, 0.125, 0.552], [0.107, 0.513, 0.125, 0.523], [0.107, 0.484, 0.125, 0.494], [0.107, 0.455, 0.125, 0.465], [0.107, 0.426, 0.125, 0.436]] and {the corresponding labels}[[0.137, 0.539, 0.295, 0.557], [0.137, 0.510, 0.322, 0.528], [0.137, 0.481, 0.314, 0.499], [0.137, 0.452, 0.275, 0.470], [0.137, 0.423, 0.280, 0.441]] as social media platform names. Charter 1 represents the user count data for 2016 and we can take {the data points for each social media platform}[[0.343, 0.366], [0.343, 0.672], [0.343, 0.547], [0.343, 0.794], [0.343, 0.458]]. Charter 2 represents the user count data for 2017 and hence need to refer the data points in Chart 2{[[0.743, 0.381], [0.743, 0.658], [0.743, 0.563], [0.743, 0.794],[0.743, 0.474]]. Each color corresponds to a different social media platform, so we correlate the lines in the chart to the labels in the legend. From this, we can create the structured data table.\nBased on the above thought, the answer is:\nSocial Media Platform,User Count 2016,User Count 2017\nFacebook,1500,1550\nTwitter,800,750\nInstagram,1000,1200\nSnapchat,600,550\nLinkedIn,700,650"
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "GbL9carHqUQwiihH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the two line charts, which social media platform had the most significant change in user base from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given line charts, we have to analyze each platform's user change from 2016 to 2017. Let's consider {the 2016 user counts}[0.343, 0.366, 0.343, 0.672, 0.343, 0.547, 0.343, 0.794, 0.343, 0.458] and {the 2017 user counts}[0.743, 0.381, 0.743, 0.658, 0.743, 0.563, 0.743, 0.794, 0.743, 0.474]. We can see that all the platforms have some changes in their user base, either it's an increase or decrease. However, Instagram with coordinates in 2016 and 2017 {chart 1 - Instagram}[0.343, 0.547] & {chart 2 - Instagram}[0.743, 0.563] show a visibly significant increase in the user base over the specified period.\nBased on the above thought, the answer is:\nInstagram had the most significant change in its user base from 2016 to 2017. It exhibited a noteworthy increase, particularly when compared to Facebook's sluggish growth and the decline in user base observed for Twitter, Snapchat, and LinkedIn."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "YKG70TmKAUFGaipe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage change in the user base of Instagram from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change in user base of Instagram, I need to locate the values of Instagram in 2016 and 2017, which can be found in {chart 1}[0.343, 0.547] and {chart 2}[0.743, 0.563] respectively. The value for Instagram in 2016 is 1000 million & in 2017 is 1200 million. The formula to calculate the percentage change is (new value - old value)/old value * 100%. Here, it's (1200 - 1000) / 1000 * 100 = 200/1000 * 100 = 20%.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 948
+    },
+    {
+        "id": "5jhbG2CpNKdsEAsP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data represented in the line chart into a table with Years as columns and Types of Development as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the tabular data, first I identify {the x-axis tick labels}[[0.048, 0.931, 0.083, 0.951], [0.161, 0.931, 0.196, 0.951], [0.274, 0.931, 0.309, 0.951], [0.386, 0.931, 0.422, 0.951], [0.499, 0.931, 0.534, 0.951], [0.612, 0.931, 0.647, 0.951], [0.725, 0.931, 0.760, 0.951], [0.837, 0.931, 0.872, 0.951], [0.950, 0.931, 0.985, 0.951]], which are the column headers or years. Secondly, I identify {the legend texts}[[0.815, 0.448, 0.949, 0.468], [0.815, 0.478, 0.955, 0.498]] as the row labels representing the different kinds of development. Assuming the highest value on the y-axis is 100, I approximate the values by determining the y-coordinate position for each development type at different stages by locating {the line points}[[0.066, 0.062], [0.179, 0.226], ..., [0.968, 0.883]].\nBased on the above thought, the answer is:\nType,1980,1985,1990,1995,2000,2005,2010,2015,2020\nRural Development,100,90,75,70,70,75,60,55,50\nUrban Development,50,60,70,80,90,95,99,100,100"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "mco3oWddvh7AoHKv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart trends, how do rural and urban developments compare over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I examine the trends displayed by the {development index for Rural Development}[0.066, 0.062, 0.968, 0.883] and {Urban Development}[0.066, 0.062, 0.968, 0.883]. The development index for the rural areas starts at a higher value of approximately 100 in 1980 and shows a general decreasing trend until it achieves a value of approximately 50 in 2020, indicating a decline in rural development. On the contrary, the development index for urban areas starts at a lower value of approximately 50 in 1980 and predominantly exhibits an increasing trend to reach a value of approximately 100 in 2020, indicating an improvement in urban development.\nBased on the above thought, the answer is:\nIt can be observed that rural development has generally declined over the years while urban development has generally improved. This is evident from the decreasing trend line for rural development and the increasing trend line for urban development from 1980 to 2020. This comparison implies a shift towards more rapid development in urban areas, while rural regions have experienced a decline in development speed. It may reflect urbanization trends and the potential increased focus on urban development."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "6OhdGmfzSAWFmXjN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the development index difference between Rural and Urban Development in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to locate the final {development index point for Rural Development}[[0.968, 0.883]] and {Urban Development}[[0.968, 0.062]]. Using the y-axis's value range from 40 to 110 I can accurately determine that the values are around 50, and 100 respectively. Thus the difference is 100 - 50 = 50.\nBased on the above thought, the answer is:\n50."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "Jaxoupbk2R07u0BJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the information about the population of each age group into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured dataset from the bar chart, I need to locate {the x-axis tick labels}[[0.175, 0.064, 0.221, 0.093],[0.498, 0.064, 0.544, 0.093],[0.819, 0.064, 0.856, 0.093]], which represent the columns or age groups: Young, Adult, Senior. Then, I identify the row labels as Male, Female, and Total from the chart legend. For the values, I locate {the bar heights}[[0.166, 0.100, 0.233, 0.413], [0.166, 0.413, 0.233, 0.544], [0.489, 0.100, 0.556, 0.825], [0.489, 0.825, 0.556, 0.973], [0.810, 0.100, 0.877, 0.209], [0.810, 0.209, 0.877, 0.274]], and correlate them to the population numbers for each category.\nBased on the above thought, the answer is:\nAge Group,Female,Male,Total\nYoung,2000000,1800000,3800000\nAdult,4200000,3800000,8000000\nSenior,550000,450000,1000000"
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "JX3f1EzppAQlKu54",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which demographic group exhibits the most significant population increase compared to the preceding group, and explain how you arrived at this conclusion."
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the relative increases in population between {Young and Adult}[0.233, 0.100, 0.556, 0.973], and {Adult and Senior}[0.556, 0.100, 0.877, 0.274], I see that the jump from Young to Adult is far more substantial. The Young group has a population of 3,800,000 while the Adult group has a population of 8,000,000, an increase of over 4 million. Comparatively, the Adult to Senior transition is a significant decrease, not an increase.\nBased on the above thought, the answer is:\nThe demographic group that exhibits the most significant population increase compared to the preceding group is the Adult group. This conclusion comes from comparing the total population heights of each major segment in the chart, where it's clear that the transition from Young to Adult shows a substantial increase of over 4 million people. This increase is more prominent than any other between these groups and their predecessors."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "1vKUrYsdbeeC91O2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how much larger is the population of the Adult group compared to the Young group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in population between the Adult group and the Young group, I first locate the {total population bar for the Young group}[0.166, 0.544] which corresponds to 3,800,000 people. Next, I locate the {total population bar for the Adult group}[0.556, 0.973] which corresponds to 8,000,000 people. The difference between the populations is therefore 8,000,000 - 3,800,000 = 4,200,000.\nBased on the above thought, the answer is:\n4,200,000."
+            }
+        ],
+        "id_raw": 950
+    },
+    {
+        "id": "g5tCYh3vrOlmAUm9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you form a table with Threat Types as the rows and Years 2010 and 2020 as columns based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart can indeed be transformed into a table. First, I'll identify {the x-axis labels}[[0.205, 0.095, 0.274, 0.123], [0.379, 0.095, 0.448, 0.123], [0.556, 0.095, 0.625, 0.123], [0.732, 0.095, 0.801, 0.123], [0.909, 0.095, 0.978, 0.123]] which will represent the row labels or threat types. Next, I'll consider {the legends}[[0.031, 0.878, 0.093, 0.950], [0.031, 0.730, 0.093, 0.801]] of the charts as the column headers representing the years 2010 and 2020. The annotation in {the line points}[[0.228, 0.628], [0.402, 0.561], [0.580, 0.456], [0.756, 0.372], [0.933, 0.510], [0.228, 0.357], [0.402, 0.238], [0.580, 0.435], [0.756, 0.443], [0.933, 0.495]] will be our respective data values for each threat type during the years 2010 and 2020.\nBased on the above thought, the answer is:\nThreat Type,2010,2020\nUnauthorized Data Sharing,85,50\nHacking,60,90\nDos Attacks,70,30\nPhishing,65,70\nData Misuse,70,80"
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "5zkZH8qMSsNV723m",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cyber threat has seen the most dramatic change from 2010 to 2020 based on the chart? Can you explain your rationale?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart, to identify the cyber threat with the most dramatic change from 2010 to 2020, I must track the fluctuation of each threat between these years. I notice that {Unauthorized Data Sharing} [0.205, 0.095, 0.274, 0.123] markedly dipped from 85 to 50. Also, the rate of {Hacking} [0.379, 0.095, 0.448, 0.123] grew substantially from 60 to 90. However, the {Dos Attacks} [0.556, 0.095, 0.625, 0.123] threat dwindled significantly from 70 in 2010 to 30 in 2020, a 40 point decrease or a 57% drop. This is the most dramatic change among the five types of cyber threats.\nBased on the above thought, the answer is:\nThe cyber threat 'Dos Attacks' has seen the most dramatic change from 2010 to 2020. While initially prevalent in 2010, it has significantly decreased by 2020, marking a drop of 57%. This is the most significant fluctuation amongst all the cyber threats charted."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "0DdSGpYEZecIYvaB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the prevalence of Dos Attacks decreased from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease in the prevalence of Dos Attacks from 2010 to 2020, I could look at {the line point for Dos Attacks in 2010} [0.580, 0.456] indicating 70 and {the line point for Dos Attacks in 2020} [0.580, 0.435], indicating 30. The decrease can be calculated as 70 - 30 = 40.\nBased on the above thought, the answer is:\n40."
+            }
+        ],
+        "id_raw": 952
+    },
+    {
+        "id": "frSifI6uhHEGb29s",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart into a table format with Time as rows and Traditional and Modern population percentage as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I need to identify the requisite variables from the line chart. For the row headings, I locate {the x-axis tick labels}[[0.077, 0.919, 0.088, 0.942],[0.291, 0.919, 0.302, 0.942], [0.499, 0.919, 0.521, 0.942], [0.713, 0.919, 0.735, 0.942], [0.928, 0.919, 0.949, 0.942]], and these represent time. For the column headers, the {legend labels}[[0.813, 0.055, 0.966, 0.078],[0.813, 0.090, 0.940, 0.113]] represent traditional population and modern population. {The points on the lines}[[0.125, 0.420], [0.168, 0.436]...][[0.125, 0.587], [0.168, 0.552]...] provide information about the traditional and modern population at each time point.\nBased on the above thought, the answer is:\nTime,Traditional_population,Modern_population\n1,56.0,37.0\n2,54.2,40.9\n3,50.4,44.1\n4,49.2,46.8\n5,47.2,48.4\n6,45.3,49.4\n7,42.5,50.5\n8,40.4,51.8\n9,37.1,54.2\n10,35.3,55.9\n11,33.0,57.4\n12,31.0,58.6\n13,30.0,60.2\n14,28.5,60.6\n15,27.1,62.5\n16,26.0,64.0\n17,24.8,66.0\n18,23.6,66.0\n19,22.8,67.5\n20,21.9,68.5"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "Th5zm5khr0WrTxkz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy scrutinizing the chart, which population, Traditional or Modern, shows an increasing trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line plots in the chart, for the Traditional population, the data points on {the Traditional Values line} [0.125, 0.430, 0.938, 0.719] show a decreasing trend from 56.0% to 21.9%. Conversely, the data points on {the Modern Values line} [0.125, 0.587, 0.938, 0.310] show an increasing trend from 37.0% to 68.5%.\nBased on the above thought, the answer is:\nFrom the data in the chart, the Modern population shows an increasing trend, while the Traditional population shows a decreasing trend."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "0U1HytQ9Bq125I9G",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent did the Traditional population decrease from year 1 to year 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percent decrease of the Traditional population from year 1 to year 20, I need to identify the values at these time points from the chart. For year 1, I locate the point on {the Traditional Values line}[[0.125, 0.420]], which corresponds to 56.0%. For year 20, I find the point on the same line[[0.938, 0.719]], which corresponds to 21.9%. I can calculate the percent decrease using the formula (Original Value - New Value) / Original Value * 100.\nBased on the above thought, the answer is:\n60.89%."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "H7fNYYvtwhFJF75E",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from the line chart, representing it in table format with Years as the rows and Media Sales Types as the columns."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at this line chart, we can extract the table data by identifying the elements in the ruler and label. Starting with row labels from {the x-axis tick labels}<r>[0.071, 0.903, 0.129, 0.931], [0.169, 0.903, 0.227, 0.931], [0.267, 0.903, 0.326, 0.931], [0.366, 0.903, 0.424, 0.931], [0.464, 0.903, 0.522, 0.931], [0.562, 0.903, 0.621, 0.931], [0.660, 0.903, 0.719, 0.931], [0.759, 0.903, 0.818, 0.931], [0.857, 0.903, 0.916, 0.931], [0.956, 0.903, 1.014, 0.931]</r> which represent the years, while {the legend texts}<r>[0.195, 0.055, 0.367, 0.083], [0.195, 0.097, 0.348, 0.125]</r> are the column headers representing the Media Sales Types. To populate the values, I locate the series of points associated with each sales type at their respective years. For instance, {the line points for Physical Media}<r>[0.149, 0.445], [0.198, 0.478], ..., [0.936, 0.829]</r> and {the line points for Digital Media}<r>[0.149, 0.854], [0.198, 0.821], ..., [0.936, 0.069]</r> and extract the associated data values.\nBased on the above thought, the answer is:\nYear,Physical Media Sales,Digital Media Sales\n2005,300,50\n2006,280,70\n2007,260,90\n2008,250,100\n2009,245,110\n2010,240,135\n2011,230,145\n2012,220,160\n2013,210,170\n2014,175,190\n2015,150,230\n2016,120,265\n2017,100,330\n2018,90,360\n2019,80,420\n2020,75,490\n2021,65,530"
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "isyLsdZ8OEyCVB9H",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which type of Media Sales seems to have a more increasing trend over the years? Please explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which type of Media Sales has a more increasingly progressive trend, I must analyze the steepness in line plots for each type. Observing {the line for Physical Media Sales}<r>[0.149, 0.445, 0.936, 0.829]</r>, there is a steady decrease over the years. Conversely, looking at {the line for Digital Media Sales}<r>[0.149, 0.854, 0.936, 0.069]</r>, there is a clear increasing trend over the years, starting from a sales value of 50 million in 2005 to reaching a peak of 530 million in 2021.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Digital Media Sales seems to have a more increasingly progressive trend over the years. While Physical Media Sales is exhibiting a gradual decrease, Digital Media Sales is showing a consistent rise proving that with every year, the sales of Digital Media is increasing while that of Physical Media is declining. It signifies a trend towards digital platforms for media consumption."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "Enpx36I0xWYgfrZ6",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sales difference between Physical and Digital Media in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sales difference between Physical and Digital Media in 2015, I look at {the line point for Physical Media in 2015}<r>[0.641, 0.690]</r>which indicates a sales value of 150 million, and at {the line point for Digital Media in 2015}<r>[0.641, 0.560]</r>which indicates a sales value of 230 million. The difference can be found by subtracting the sales of Physical Media from that of Digital Media. So, the difference between the sales is 230 - 150 = 80.\nBased on the above thought, the answer is:\n80 Million."
+            }
+        ],
+        "id_raw": 954
+    },
+    {
+        "id": "k2smXjK05ac2RYFU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from this line chart in a tabular format, where the rows represent years and the columns represent different types of clean technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I primarily look at the details on the chart. Firstly, I identify the labels on {the x-axis tick labels}[[0.038, 0.919, 0.082, 0.942], [0.218, 0.919, 0.262, 0.942], [0.398, 0.919, 0.442, 0.942], [0.578, 0.919, 0.622, 0.942], [0.758, 0.919, 0.802, 0.942], [0.938, 0.919, 0.981, 0.942]] indicating the years. Then, {the legend texts}[[0.125, 0.071, 0.231, 0.094], [0.125, 0.106, 0.306, 0.129]] in the chart represent clean technology types. Lastly, I locate the respective {line points}[[0.060, 0.760, 0.959, 0.816], [0.060, 0.089, 0.959, 0.872]] for each type of technology in each year and extract the respective values to fill in the table cells.\nBased on the above thought, the answer is:\nYear,Slow growth,Pronounced increase\n2010,1000000,500000\n2011,1050000,600000\n2012,1100000,750000\n2013,1150000,950000\n2014,1200000,1200000\n2015,1250000,1650000\n2016,1300000,2350000\n2017,1350000,3300000\n2018,1400000,4400000\n2019,1450000,5500000\n2020,1500000,7500000"
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "9WbPtQoGZXmnX19s",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the plotted lines, which type of investments has shown a more positive growth over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To decide which type of investments has shown a more positive growth over the years, I need to analyze the {line plot for Slow growth}<r>[0.060, 0.760, 0.959, 0.816]</r> and the {line plot for Pronounced increase}<r>[0.060, 0.089, 0.959, 0.872]</r>. The slow growth line has a slight upward incline, starting from a point at 1000000 in 2010 to 1500000 in 2020. In contrast, the pronounced increase line starts from 500000 in 2010 and sharply rises to 7500000 in 2020. This substantial increase and its trajectory over the plot signify that pronounced increase has shown a more positive growth over the years.\nBased on the above thought, the answer is:\nThe line representing 'Pronounced increase' has shown a more positive growth over the years due to its sharp rise from 500000 in 2010 to 7500000 in 2020. Its slope is steeper compared to the 'Slow growth' line, indicating a faster increase in investments."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "aA6vPNKcBVe2qb8W",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the final investment amounts in 2020 of the two investment types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, firstly I locate the {2020 tick on the X axis}<r>[0.938, 0.919, 0.981, 0.942]</r>. From here, I follow the tick up to the two corresponding points {for Slow growth}<r>[0.959, 0.760]</r> and {for Pronounced increase}<r>[0.959, 0.089]</r>. Extracting these values, I get 1500000 for Slow growth and 7500000 for Pronounced increase. As such, the difference between these two values is 7500000 - 1500000 = 6000000.\nBased on the above thought, the answer is:\n6000000."
+            }
+        ],
+        "id_raw": 955
+    },
+    {
+        "id": "7f96Jj32QXw6AJNQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table that presents all the data points from the chart, including years, efficiency improvement potential for all the given energy production methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build the table, I observe {the x-axis tick labels}[[0.071, 0.919, 0.115, 0.942], [0.288, 0.919, 0.332, 0.942], [0.504, 0.919, 0.548, 0.942], [0.721, 0.919, 0.765, 0.942], [0.938, 0.919, 0.981, 0.942]] which represent the Years. Next, I recognize {the legend texts}[[0.157, 0.046, 0.325, 0.069], [0.157, 0.081, 0.277, 0.104]] as the different categories of energy production methods. To populate the values, I locate {the line points for each category} and extract the corresponding values. These values represent the efficiency improvement potential for every year and every energy production category.\nBased on the above thought, the answer is:\nYear,Recycling,Insulation,Solar Panels,Hydroelectric,Wind Energy,Nuclear Energy\n2000,2030,1540,2100,2220,1980,2130\n2001,2080,1600,2140,2280,2040,2080"
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "gZkfjvqfL4FrU1CY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart trends, which energy production method has shown the most stability in efficiency improvement potential over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most stable energy production method, I need to analyze the line plots for each method. Observing the {line for Recycling}, {line for Insulation}, {line for Hydroelectric}, {line for Wind Energy}, and {line for Nuclear Energy}, shows fluctuations in the trend. However, the {line for Solar Panels}, indicates a relatively stable trend, barely changing across the years.\nBased on the above thought, the answer is:\nThe Solar Panels energy production method displayed the most stability in efficiency improvement potential within the given years, the line for this category shows minimal fluctuation indicating little to no change throughout the years."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "ra95oQ0SQL7JHLFZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in efficiency potential for Wind Energy from 2000 to 2001?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase for Wind Energy, I should first identify {the line points for Wind Energy}[[0.180, 0.602], [0.223, 0.641], [0.266, 0.621], [0.310, 0.602]] which are the respective efficiency values for the years. By subtracting the efficiency for {2000}[0.180, 0.942] from the efficiency for {2001}[0.223, 0.942], we get the increase in efficiency potential.\nBased on the above thought, the answer is:\n60."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "j7KXo6g3kv9HyRph",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you structure the data showing the impact of various media platforms on public opinion for both years into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to construct a table from this data, I start by identifying {the y-axis tick labels}[[0.032, 0.117, 0.071, 0.157], [0.032, 0.201, 0.077, 0.242], [0.032, 0.305, 0.077, 0.346], [0.032, 0.428, 0.072, 0.468], [0.032, 0.532, 0.077, 0.572], [0.032, 0.617, 0.072, 0.657]] that represent the different media platforms. The title of the line charts {Year 1 line chart title}[[0.073, 0.709, 0.271, 0.750], {Year 2 line chart title}[[0.073, 0.011, 0.271, 0.057]] indicate the years. For the values, I extract the data from the corresponding line plots for each platform in each year: {line plots for year 1}[[0.151, 0.128, 0.972, 0.635]] and {line plots for year 2}[[0.151, 0.128, 0.972, 0.636]].\nBased on the above thought, the answer is:\nMedia Platforms,Year 1,Year 2\nNews Channels,80,70\nSocial Media,60,90\nNewspapers,75,65\nRadio,85,80\nPrint Magazines,75,70\nOutdoor Advertising,70,75"
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "fcZhF7wKGUGlklWV",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media platform showed an increasing trend in their influence on public opinion over the two years? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the media platform showing an increasing influence, I need to compare the positions of their corresponding line chart between Year 1 and Year 2. Looking at {the line plot for Outdoor Advertising in Year 1}[0.184, 0.732, 0.552, 0.769], it had an impact value of 70, and in {Year 2}[0.184, 0.345, 0.653, 0.389], it increased to 75. Similarly, for {Social Media in Year 1}[0.212, 0.749, 0.592, 0.786], it had an impact value of 60, which increased to 90 in {Year 2}[0.212, 0.362, 0.696, 0.407] As both these platforms show an increase, they have an increasing trend in their influence on public opinion.\nBased on the above thought, the answer is:\nSocial Media and Outdoor Advertising showed an increasing trend in their influence on public opinion over the two years. This is deduced from the shift of their respective lines to the right between Year 1 and Year 2, indicating a higher impact score."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "T2uKFY6kQr9yOdY0",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the impact on public opinion between Year 1 and Year 2 for Social Media?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in the impact on public opinion for Social Media, I look at {the line plot for Social Media in Year 1}[[0.212, 0.749, 0.592, 0.786]], which indicates an impact value of 60. I then compare this with {the line plot for Social Media in Year 2}[[0.212, 0.362, 0.696, 0.407]], which indicates an impact value of 90. The change is 90 - 60 = 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 957
+    },
+    {
+        "id": "aFpfUWFJEaUQ3BJ6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data in the form of a table with the rows as activities and the columns as the two categories, Social Activity and Lifestyle Activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first identify {the y-axis tick labels for the values corresponding to Social Activity}<r>[0.299, 0.820, 0.350, 0.836]</r> and {those corresponding to Lifestyle Activity}<r>[0.540, 0.820, 0.590, 0.836]</r> as row labels representing the different activities. I then extract information from {the bars representing Social Activity}<r>[0.359, 0.802, 0.506, 0.836]</r> and {the bars representing Lifestyle Activity}<r>[0.600, 0.802, 0.747, 0.836]</r> for each activity. The length of each bar corresponds to the value in hours.\nBased on the above thought, the answer is:\nActivity,Social Activity,Lifestyle Activity\nNetflix Time,3,5\nExercise,4,10\nGaming,6,8\nReading,10,4\nSocial Media,5,6\nOutdoor Activities,2,3\nArtistic Pursuits,4,1\nHealthy Eating,5,7"
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "PPqXjrQKgAyl8kVs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar plots, can you make a statement about the general trend in terms of activity preferences when comparing Social Activity and Lifestyle Activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide the general trend in terms of activity preferences, I first need to analyze the length of the bars in the two different subplots. It's evident from both {subplot of Social Activity}<r>[0.393, 0.176, 0.506, 0.314]</r> and {subplot of Lifestyle Activity}<r>[0.634, 0.176, 0.746, 0.314]</r> that some activities have noticeably longer bars in one subplot than they do in another. For instance, 'Reading' shows high popularity in Social Activity but less so in Lifestyle Activity. 'Exercise', on the other hand, is less popular in Social Activity but stands out as the most preferred in Lifestyle Activity. Generally, preference between activities differs between Social and Lifestyle contexts, suggesting that people may prioritize different activities depending on whether they are engaged socially or if these activities make up their general lifestyle preferences.\nBased on the above thought, the answer is:\nAnalysis of the bar plots shows that the trend in activity preferences varies between Social and Lifestyle Activities. Activities like 'Reading' are preferred more in a social context while 'Exercise' is significantly more popular as a Lifestyle activity, suggesting that when socializing, people tend to prefer more leisurely or relaxing activities but lean towards health and wellness activities in their regular lifestyle."
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "g9i5ADXN6buVoJgX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in average hours spent on Gaming as a Social Activity and as a Lifestyle Activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in hours spent on Gaming as a Social Activity and Lifestyle Activity, I first locate the Gaming bar in {the Social Activity subplot}<r>[0.379, 0.496, 0.431, 0.512]</r> and extract the corresponding value (6 hours). Next, I locate the Gaming bar in {the Lifestyle Activity subplot}<r>[0.619, 0.487, 0.673, 0.502]</r> and extract the corresponding value (8 hours). To find the difference, subtract the value for Social Activity from the value for Lifestyle Activity: 8 - 6 = 2.\nBased on the above thought, the answer is:\n2"
+            }
+        ],
+        "id_raw": 958
+    },
+    {
+        "id": "aMwnAVb1bkxmdMTv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract this line chart into a structured table, with the columns representing different months and rows representing the activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, in order to generate the tabular data from this line chart, I firstly locate {x-axis tick labels}[[0.142, 0.882, 0.181, 0.907], [0.244, 0.882, 0.283, 0.907], .... [0.828, 0.882, 0.870, 0.907]], which will serve as our column headers i.e., the months. Next, I identify {the legend texts}[[0.826, 0.041, 0.934, 0.068], [0.827, 0.081, 0.936, 0.108]] as our row labels which are the distinct activities. I find {the line points}[[0.142, 0.505], [0.244, 0.617], .... [0.82, 0.412]] for each activity at various months to populate the data table with their values.\nBased on the above thought, the answer is:\nMonth,Exercise,Socializing\nJanuary,30,20\nFebruary,35,25\nMarch,40,30\nApril,45,35\nMay,50,40\nJune,55,45\nJuly,60,50\nAugust,65,55\nSeptember,70,60\nOctober,55,45\nNovember,45,35\nDecember,25,15"
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "iqKwt53Yok2oToNs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity would you say is more consistent throughout the year based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the line trends of the two activities. Looking at the {line for Exercise}[0.142, 0.168, 0.82, 0.652], the trend appears to be growing till September and then seeing a decrease towards the end of the year. Similarly, for {Socializing}[0.142, 0.127, 0.82, 0.598], the trend is exactly similar with steady growth till September and then a decrease. Hence, in terms of consistency, both activities seem to have a similar trend throughout the year.\nBased on the above thought, the answer is:\nBoth Exercise and Socializing show a similar trend of steady growth followed by a decrease towards the end of the year. Hence, both activities are consistent in that sense. However, Exercise seems to have a higher consistency in terms of the number of hours dedicated as its hours consistently remain higher than Socializing throughout the year."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "sONRpCicTURsWzaw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_48_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest number of hours spent on any activity according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum hours spent on any activity according to this line chart, I need to locate and analyze the y-values for each point on both the {line for Exercise}[0.142, 0.168, 0.82, 0.652] and the {line for Socializing}[0.142, 0.127, 0.82, 0.598]. After analyzing, it appears that the maximum hours corresponding to Exercise in the month of September is 70.\nBased on the above thought, the answer is:\n70 hours."
+            }
+        ],
+        "id_raw": 959
+    },
+    {
+        "id": "g9RXhDNC0Ms7tRAP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of the technological companies from the line chart into a readable table for analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first spot the {x-axis tick labels}[[0.071, 0.878, 0.146, 0.903], ..., [0.869, 0.878, 0.957, 0.903]], which represent companies' names. I recognize these as the row labels. Each subplot corresponds to a different financial measure. For the column headers, I locate titles of the subplots, which indicate Revenue Change, Profit Change and Innovation Score. To populate the table, I find {the line points}[[0.111, 0.495], [0.222, 0.430], ..., [0.884, 0.495]] for each financial measure at the respective companies and extract their corresponding values.\nBased on the above thought, the answer is:\nCompany,Revenue Change (%),Profit Change (%),Innovation Score\nApple,11.6,15.9,85\nMicrosoft,9.8,13.4,88\nAlphabet,18.5,9.8,90\nFacebook,-2.7,1.2,78\nTencent,27.7,16.3,92\nTSMC,9.2,16.1,82\nNvidia,48.6,45.6,95\nPayPal,20.7,18.9,91\nNetflix,23.4,20.0,86\nIBM,-3.1,-3.5,73"
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "rIzdwuLDyqel3xms",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company stands out on the graph considering innovation, and how is it different from the other companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company stands out in terms of innovation, I analyze the Innovation Score subplot, which includes {the line for Innovation Score}[[0.111, 0.292, 0.888, 0.327]]. When inspecting the points of each company on this line, {Nvidia}<r>[0.777, 0.327]</r> stands out with the highest Innovation Score on the chart. Comparing Nvidia's score with the scores of other companies like {IBM}<r>[0.111, 0.251]</r> and {Facebook}<r>[0.444, 0.267]</r> shows a significant difference in terms of technological innovation.\nBased on the above thought, the answer is:\nNvidia is the company that stands out in terms of innovation on the graph. It has the highest Innovation Score of all the listed companies, indicating a higher level of technological innovation. This suggests that Nvidia has likely made significant investments in Research and Development and product advancements as compared to other companies, such as IBM and Facebook, which have notably lower Innovation Scores."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "VfqYXaM2z0DDUV1b",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the trends shown in the line chart, what is the average Innovation Score for the listed companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average Innovation Score, I need to locate {the line points for Innovation Score}[[0.111, 0.292], [0.222, 0.303], [0.333, 0.310], [0.444, 0.267], [0.555, 0.317], [0.666, 0.282], [0.777, 0.327], [0.888, 0.313], [0.444, 0.296], [0.111, 0.251]], and extract the corresponding values: 85, 88, 90, 78, 92, 82, 95, 91, 86, 73. Then, averaging these values gives the average Innovation Score, calculated as (85+88+90+78+92+82+95+91+86+73) / 10.\nBased on the above thought, the answer is:\n86."
+            }
+        ],
+        "id_raw": 960
+    },
+    {
+        "id": "UmhVMByIvg9OR3UI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table recording the visitor numbers for each tourist destination for the years 2019, 2020, and 2021 based on the line charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly. First, I'll take {the x-axis labels}[[0.066, 0.066, 0.985, 0.903]], which represents the tourist destinations, as the leftmost column. The lines on the plots {Physical Resources}[0.108, 0.294, 0.943, 0.613] and {Online Resources}[0.108, 0.142, 0.943, 0.674], represent the visitor numbers for each destination over three years. I'll extract the data points linked with each of these lines. This gives me the visitor counts for 2019, 2020, and 2021 which I will arrange in columns adjacent to the respective destination.\nBased on the above thought, the answer is:\nTourist Destination,2019,2020,2021\nTime Square,4.5,2,3.5\nCentral Park,4,1.8,3.3\nNiagara Falls,3.8,1.7,3\nGrand Canyon,3.5,1.5,2.8\nDisneyland,3.3,1.3,2.6\nStatue of Liberty,3,1,2.5\nYellowstone National Park,2.7,0.9,2.2\nUniversal Studios,2.5,0.8,2\nYosemite National Park,2.2,0.7,1.8\nGreat Smoky Mountains,2,0.6,1.7"
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "cONM6pjlojzXtWUc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the visitor trends represented in the graph, which tourism spot seems to be regaining its visitor numbers at the fastest rate post-pandemic in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'm examining the trends for each tourist location from 2020 to 2021 using the data points in the {line for 2021}[0.108, 0.142, 0.943, 0.674]. This allows me to compare the growth rates for each location. The tourist spot that showed the largest increase in visitors from 2020 to 2021 is likely to be the one that is recovering fastest post-pandemic.\nBased on the above thought, the answer is:\nBased on the data, Time Square seems to be regaining its visitor numbers at the fastest rate post-pandemic. It had the highest growth in visitor numbers from 2020 to 2021, compared to the other tourist spots."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "5WE51oxh7y1HH22i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how much was the total decrease in visitors from 2019 to 2020 for all tourist hotspots combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total decrease in visitors from 2019 to 2020, I need to first determine the relative values for each site in these years. I locate the {2019 visitor data}[0.108, 0.294, 0.943, 0.613] and {2020 visitor data}[0.178, 0.317, 0.595, 0.722]. I subtract the 2020 data from the 2019 data to find the decrease for each site, and then add these values together to find the total decrease.\nBased on the above thought, the answer is:\n15.6 million."
+            }
+        ],
+        "id_raw": 962
+    },
+    {
+        "id": "khpP1CbmFgkpeuvm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the line chart information into a table format representing the Time period and the performance indices of Knowledge Economy and Innovation Industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to translate the line chart data into a tabular format, we first find both {the x-axis tick labels}[[0.022, 0.898, 0.042, 0.921], [0.156, 0.898, 0.165, 0.921], [0.284, 0.898, 0.293, 0.921], [0.412, 0.898, 0.421, 0.921], [0.540, 0.898, 0.549, 0.921], [0.668, 0.898, 0.677, 0.921], [0.792, 0.898, 0.810, 0.921], [0.920, 0.898, 0.938, 0.921]] to represent the Time period. Then recognize the {line points of Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]] and {Innovation Industries}[[0.160, 0.732], [0.224, 0.425], [0.288, 0.278], [0.352, 0.241], [0.416, 0.192], [0.480, 0.413], [0.545, 0.511], [0.609, 0.585], [0.673, 0.622], [0.737, 0.634], [0.801, 0.597], [0.865, 0.855]]. These points can be used to populate the performance indices for each sector at the given times.\nBased on the above thought, the answer is:\nTime, Knowledge Economy, Innovation Industries\n\u22122, 0.45, 0.50\n0, 0.55, 0.75\n2, 0.62, 0.87\n4, 0.69, 0.90\n6, 0.78, 0.94\n8, 0.86, 0.76\n10, 0.92, 0.68\n12, 0.97, 0.62"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "E682cCaLTFuF2vTB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which sector between the Knowledge Economy and Innovation Industries has seen more fluctuations over the time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with more fluctuations, we need to analyze the variation in the line plots. Looking at the {line for the Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]], there are several noticeable ups and downs. The performance index rises initially, the drops precipitously, before swinging upwards and downwards again.\nThe {line of Innovation Industries}[[0.160, 0.732], [0.224, 0.425], [0.288, 0.278], [0.352, 0.241], [0.416, 0.192], [0.480, 0.413], [0.545, 0.511], [0.609, 0.585], [0.673, 0.622], [0.737, 0.634], [0.801, 0.597], [0.865, 0.855]] equally shows increases and decreases but seems more volatile with steeper rises and falls.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Innovation Industries seems to have more fluctuations over the time period. Although both sectors show significant ups and downs, the Innovation Industries line plot shows a more erratic pattern with steeper rises and falls, indicating larger changes in performance index through the given time period."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "JSNs9CrKrBJwfG55",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the performance index of the Knowledge Economy at the beginning and the end of the period in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this difference, we need to compare the first time point of {the line for the Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]] with the last point, ie., 0.45 at {Time -2}[0.022, 0.898, 0.042, 0.921] and 0.91 at {Time 12}[0.920, 0.898, 0.938, 0.921] respectively. The difference is calculated as 0.91 - 0.45 = 0.46.\nBased on the above thought, the answer is:\n0.46."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "b78rjVerTypTqyIH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data into a structured table with Trends as rows and Value as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I'm going to identify {the x-axis labels} [0.065, 0.911, 0.172, 0.934], [0.177, 0.911, 0.292, 0.934], [0.288, 0.911, 0.414, 0.934], [0.399, 0.911, 0.536, 0.934], [0.511, 0.911, 0.656, 0.934], [0.623, 0.911, 0.776, 0.934], [0.762, 0.911, 0.870, 0.934], [0.884, 0.911, 0.980, 0.934] as the row labels or Trend names. Then, I will look at {the line points} [0.119, 0.587], [0.235, 0.656], [0.351, 0.517], [0.467, 0.448], [0.583, 0.100], [0.700, 0.309], [0.816, 0.864], [0.932, 0.378] and extract the corresponding y-values to be the tabular values indicating Trend Value.\nBased on the above thought, the answer is:\nTrends,Value\nSlight decrease,0.2\nDownward trend,0.1\nGradual downturn,0.3\nSubdued expansion,0.4\nPronounced increase,0.9\nGradual improvement,0.6\nSharp decrease,-0.2\nSlow increase,0.5"
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "YpapGxaZeWYX7SPr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how would you describe the International Trade Trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating {the International Trade Trend line}[0.119, 0.100, 0.932, 0.864], I see that it starts at a value of 0.2 at the stage of 'Slight Decrease', then decreases slightly to 0.1 at the stage of 'Downward Trend', and then raises to 0.3 at 'Gradual Downturn'. This is followed by a further increase to 0.4 at 'Subdued Expansion', a steep increase to 0.9 at 'Pronounced Increase', then it decreases slightly to 0.6 at 'Gradual Improvement', with a sharp decrease to -0.2 at the stage of 'Sharp Decrease', and finally rising to 0.5 during the 'Slow Increase'. This pattern suggests an initially stable trend with a slight decrease, followed by a steady increase, a sharp increase, a slight decrease, a sharp decrease, and then a gradual increase towards the end..\nBased on the above thought, the answer is:\nThe International Trade Trend over time, as presented in the line chart, shows a mixed trend. Initial stability sees a slight decrease, followed by a steady increase leading to a sharp increase, a slight decrease, a decline, and then a steady increase towards the end. It appears to be suggesting a cyclical pattern in international trade trends, with periods of growth followed by decline and resurgence."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "dV9wJW2Y3xYk5h0R",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in value between the peak and trough of the trend seen in the International Trade Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to identify {the highest point}[0.583, 0.100] which represents the peak and shows value of 0.9, then {the lowest point}[0.816, 0.864] which represents the trough and shows a value of -0.2. Subtracting the lowest value from the highest value gives the answer.\nBased on the above thought, the answer is:\n1.1."
+            }
+        ],
+        "id_raw": 964
+    },
+    {
+        "id": "SF1MTEQi6JIVbjIG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the provided information on Active and New Users in CSV format according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick marks}<r>[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]]</r> for the months and {the line points for Active Users}<r>[[0.108, 0.100], [0.178, 0.206], ..., [0.943, 0.100]]</r> and {New Users}<r>[[0.108, 0.817], [0.178, 0.841], ..., [0.943, 0.347]]</r>, which represent data values for the subsequent table formation.\nBased on the above thought, the answer is:\nMonth,Active Users,New Users\n0,100000,20000\n1,120000,18000\n2,122000,16000\n3,119800,18000\n4,120000,24000\n5,125000,35000\n6,120000,40000\n7,118000,50000\n8,113000,48000\n9,108000,45000\n10,115000,50000\n11,125000,55000\n12,135000,60000"
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "bADDZKdCrzg8fZs2",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trends of Active and New Users based on the given line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {line for Active Users}<r>[0.108, 0.100, 0.943, 0.347]</r>, the trend is slightly increasing, implying that the platform has been able to retain more users over time. It reaches a peak around month 5, dips slightly in the next couple of months, and then surges back and reaches its highest peak at the last month.\nStudying the {line for New Users}<r>[0.108, 0.347, 0.943, 0.864]</r>, it shows a consistent increase indicating a steady stream of new users joining the platform across months. The notable surge starting from month 5 suggests an effective user acquisition strategy.\nBased on the above thought, the answer is:\nThe trends for both Active and New Users appear to be positive over the year. For Active Users, the platform has managed to retain more users over time, signifying the platform's appeal and user engaging content. The surge in New Users hints at successful campaigns or strategies to attract new users. However, the rising trend in New Users emphasizes the need for the platform to strategize effective user retention mechanisms to convert these new users to active ones."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "Lx4gmcW3nP0dhIXa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest increase in Active Users between consecutive months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest increase in Active Users between consecutive months, I need to locate the {change in Active Users}<r>[[0.108, 0.100, 0.178, 0.206], [0.178, 0.206, 0.247, 0.192], ..., [0.874, 0.171, 0.943, 0.100]]</r>. Mapping these to the points in the table and calculating the difference between each set of consecutive months will yield the result.\nBased on the above thought, the answer is:\n5000."
+            }
+        ],
+        "id_raw": 965
+    },
+    {
+        "id": "gmSzoJAbosvHR30w",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart into a compact table, considering months as rows and economic indicators as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the line graph into a table, I'll first consider {the x-axis labels}[[0.091, 0.911, 0.100, 0.934],...,[0.934, 0.911, 0.952, 0.934]] which represent the row headers or months. Next, I'll identify {the legend texts}[[0.115, 0.087, 0.183, 0.117],[0.115, 0.131, 0.243, 0.161]] as the column headers that represent the different economic indicators. Lastly, each {value point}[[0.096, 0.835],...,[0.943, 0.829],[0.096, 0.491],...,[0.943, 0.124]] will be placed into the corresponding cell of the table.\nBased on the above thought, the answer is:\nMonth,Inflation,Unemployment\n0,2.05,4.96\n1,3.24,4.85\n2,1.90,5.12\n3,3.54,5.30\n4,2.98,5.50\n5,4.50,6.03\n6,3.24,5.84\n7,2.55,6.68\n8,2.40,7.09\n9,3.02,7.46\n10,3.55,7.80\n11,2.10,8.06"
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "DlC9T680yZTzxqhf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain the correlations observed between the trend of Inflation and Unemployment over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish the correlation between Inflation and Unemployment, we need to analyze the information presented in the line plots. The {Inflation plot}[0.096, 0.545, 0.943, 0.853] shows fluctuations, with values rising and dropping over time. Similarly, the {Unemployment plot}[0.096, 0.124, 0.943, 0.504] exhibits a steady increase over time. Even though both lines show variation, the trend in Inflation does not seem to directly correspond to the trend in Unemployment. While Inflation varies, Unemployment consistently increases.\nBased on the above thought, the answer is:\nFrom the line plot, there doesn't appear to be a direct correlation between Inflation and Unemployment. While Inflation fluctuates, with periods of both increases and decreases, Unemployment steadily rises throughout the given period. A direct correlation would imply that the two measures rise and fall together, which does not consistently happen in this dataset."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "hVoPJOJGKYY0seXg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the value of Inflation change in the third month compared to the first month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the Inflation values for the first and third months. I see that the {Inflation value for the first month} has coordinates [0.096, 0.835] representing 2.05, while the {Inflation value for the third month} has coordinates [0.250, 0.853] and stands for 1.9. To calculate the change, I subtract the first month's value from the third month's value.\nBased on the above thought, the answer is:\n-0.15"
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "yAiiAhoOYW3cUHBh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the performance indices of Financial Management and Risk Control over time into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first look at {the x-axis ticks}[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]] which represent the time in months. Next, I track {the line points for Financial Management}[[0.108, 0.633], [0.178, 0.722], [0.247, 0.793], [0.317, 0.847], [0.387, 0.864], [0.456, 0.776], [0.526, 0.705], [0.595, 0.633], [0.665, 0.545], [0.734, 0.491], [0.804, 0.438], [0.874, 0.367], [0.943, 0.314]] and {the line points for Risk Control}[[0.108, 0.100], [0.178, 0.189], [0.247, 0.314], [0.317, 0.438], [0.387, 0.545], [0.456, 0.598], [0.526, 0.509], [0.595, 0.491], [0.665, 0.456], [0.734, 0.402], [0.804, 0.278], [0.874, 0.189], [0.943, 0.136]] to extract the values for each time.\nBased on the above thought, the answer is:\nTime (Months),Financial Management,Risk Control\n0,0.6,0.9\n1,0.55,0.85\n2,0.51,0.78\n3,0.48,0.71\n4,0.47,0.65\n5,0.52,0.62\n6,0.56,0.67\n7,0.6,0.68\n8,0.65,0.7\n9,0.68,0.73\n10,0.71,0.8\n11,0.75,0.85\n12,0.78,0.88"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "98QpT87iMeUgTdMF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the trends in the line chart, which index showed more improvement over time? Describe your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To infer which index showed more improvement, I have to examine the slopes of the respective lines. The line for {Financial Management}[0.108, 0.314, 0.943, 0.864] shows a consistent upwards trend over time, indicating steady improvement in the index. For {Risk Control}[0.108, 0.100, 0.943, 0.598], the line also shows an increase over time but to a lesser degree compared to Financial Management until month 10 where Risk Control surpassed Financial Management. Hence, Financial Management showed a steeper slope and therefore, more improvement over time until month 10.\nBased on the above thought, the answer is:\nThe Financial Management index showed more improvement over time. Its value consistently increased over the period from the start until month 10. Although the Risk Control index also increased over the observation period, the rate of increase was slower compared to that of Financial Management until time point 10 when the Risk Control surpassed the Financial Management."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "fOULQ9G7agRbO3Xd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt what time does the Risk Control performance index first exceed the Financial Management performance index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to compare the points on the line for Financial Management {Financial Management}[0.108, 0.314, 0.943, 0.864] and Risk Control {Risk Control}[0.108, 0.100, 0.943, 0.598] at each time on the x-axis. Observing the coordinates, I can see that at time 4 and onwards, the Risk Control index values are higher than those for Financial Management.\nBased on the above thought, the answer is:\n4."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "uubUxxJradJA9fQx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the data from the line graph in a tabular format, with 'Time' as the rows and 'AI Automation' and 'Industrial Automation' as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart to a table, I firstly identify {the x-axis ticks}[[0.062, 0.911, 0.071, 0.934], [0.138, 0.911, 0.147, 0.934], [0.214, 0.911, 0.223, 0.934], ..., [0.967, 0.911, 0.985, 0.934]] that represent the Time periods (0 to 12). Secondly, I locate {the line for AI Automation}[[0.067, 0.365, 0.976, 0.714]] and {the line for Industrial Automation}[[0.067, 0.449, 0.976, 0.646]] and extract the associated point values for each 'Time' period (AI Automation: 0.45 to 1.22 and Industrial Automation: 0.65 to 0.99).\nBased on the above thought, the answer is:\nTime, AI Automation, Industrial Automation\n0, 0.45, 0.65\n1, 0.60, 0.78\n2, 0.75, 0.68\n3, 0.67, 0.72\n4, 0.78, 0.61\n5, 0.82, 0.73\n6, 0.88, 0.76\n7, 0.93, 0.85\n8, 1.05, 0.88\n9, 1.12, 0.92\n10, 1.20, 0.96\n11, 1.28, 1.08\n12, 1.22, 0.99"
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "RwCS1MPh6QY9fKPM",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the trends in AI and Industrial Automation over time, which field showed a more consistent improvement? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which field showed a more consistent improvement, we need to analyze the direction and magnitude of changes in the line plots over time. Looking at the {AI Automation line}[[0.067, 0.365, 0.976, 0.714]], we see a general upward trend, indicating consistent improvement, although there are some minor fluctuations. On the other hand, {Industrial Automation line}[[0.067, 0.449, 0.976, 0.646]] also exhibits a growth trend over time, with pronounced fluctuations particularly between times 1 and 5, and 7 and 9.\nBased on the above thought, the answer is:\nAI Automation shows a more consistent improvement over time. Its line plot indicates a steady incline, suggesting continuous growth with minor fluctuations. In contrast, though Industrial Automation also has an upward trend, its progression is peppered with larger fluctuations, which may indicate periods of stagnation or slower growth, hence suggesting less consistency."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "1yBrI4lEHEXlDF1x",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which time point did AI Automation reach its peak performance index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the peak performance index for AI Automation, I need to inspect the line plot for AI Automation ({the AI Automation line and points}[[0.067, 0.365, 0.976, 0.714]]). The peak performance occurs at the point with the maximum Y-coordinate, corresponding to a performance index of 1.28 at time 11 ({Time 11 point}[0.900, 0.365]).\nBased on the above thought, the answer is:\n11."
+            }
+        ],
+        "id_raw": 968
+    },
+    {
+        "id": "LBbeX7yFHkfTirJC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the information from the line chart into a csv file format with companies as columns and time as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure! The first step is to use {the x-axis labels}<r>[0.028, 0.911, 0.037, 0.934], ..., [0.934, 0.911, 0.952, 0.934]</r> which represent the row headers, or time periods. Then, {the legend entries}<r>[0.795, 0.076, 0.977, 0.172]</r> serve as the column headers, symbolizing the companies in this case. Finally, for each company at a specific time period, I identify {the point on the line} for that combination and record the corresponding values, such as {these points}<r>[0.108, 0.755], [0.184, 0.646], ..., [0.943, 0.100]</r>.\nBased on the above thought, the answer is:\nTime,AR Technology,VR Technology\n1,0.5,0.4\n2,0.6,0.42\n3,0.7,0.44\n4,0.9,0.46\n5,1.0,0.48\n6,1.1,0.5\n7,0.9,0.52\n8,0.8,0.54\n9,0.7,0.56\n10,0.9,0.58\n11,1.0,0.6\n12,1.1,0.62"
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "8kxDjwbz7hgpDpPI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which technology, AR or VR, has shown a more rapid adoption rate increase over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine which technology has had a more rapid increase in adoption rate, I need to evaluate {the line for AR Technology}<r>[0.108, 0.100, 0.943, 0.755]</r> and {the line for VR Technology}<r>[0.108, 0.624, 0.943, 0.864]</r>. For AR Technology, the adoption rate points move from 0.5 to a maximum of 1.1, reflecting a significant upward trend. On the other hand, the VR Technology's points move from 0.4 to a maximum of 0.62, also showing an upward trend but at a less steep angle compared to AR Technology. Therefore, the adoption rate of AR Technology seems to have increased at a faster rate.\nBased on the above thought, the answer is:\nAR Technology seems to have shown a more rapid increase in adoption rate over time compared to VR Technology. Looking at the line chart, AR Technology's line shows a steeper upward slope, representing a quicker rise in adoption rate compared to the somewhat less steep line for VR Technology."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "BRpqN1g72e6ad6zz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum adoption rates for AR Technology over the time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I will locate {the line points for AR Technology}<r>[0.108, 0.755], [0.184, 0.646], ..., [0.943, 0.100]</r> and extract their corresponding values: 0.5, 0.6, 0.7, 0.9, 1.0, 1.1, 0.9, 0.8, 0.7, 0.9, 1.0, 1.1. The maximum value is 1.1 and the minimum value is 0.5. The difference between the maximum and minimum is 1.1 - 0.5 = 0.6.\nBased on the above thought, the answer is:\n0.6"
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "CtXlitpMg21YNDus",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the chart data into a table where columns represent years and rows represent types of inequality?"
+            },
+            {
+                "from": "gpt",
+                "value": "To build the table, I have to go through the data from each line plot. For the column headers, I will go through {the x-axis ticks}[[0.061, 0.912, 0.070, 0.936], [0.109, 0.912, 0.118, 0.936], ..., [0.967, 0.912, 0.985, 0.936]] which represent the years. For row labels, I will look at {the plot legend}[[0.117, 0.083, 0.202, 0.106], [0.117, 0.118, 0.246, 0.141]], which represent the types of inequalities. Lastly, for the actual data, I will extract the y-values from {the AI Efficiency line points}[[0.065, 0.828], ..., [0.976, 0.828]] and {the Automation Speed line points}[[0.065, 0.751], ..., [0.976, 0.139]] and align them respectively with the line plot type and the corresponding year.\nBased on the above thought, the answer is:\nYear,AI Efficiency,Automation Speed\n1,0.45,0.5\n2,0.5,0.54\n3,0.56,0.59\n4,0.61,0.63\n5,0.64,0.66\n6,0.67,0.68\n7,0.7,0.7\n8,0.71,0.71\n9,0.71,0.72\n10,0.7,0.74\n11,0.68,0.76\n12,0.65,0.77\n13,0.61,0.78\n14,0.56,0.8\n15,0.55,0.82\n16,0.53,0.83\n17,0.51,0.84\n18,0.49,0.86\n19,0.47,0.88\n20,0.45,0.9"
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "3ppCuMS0frIX8ElA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line plots, can you outline any common trends or patterns observed in AI Efficiency and Automation Speed over the 20 months period?"
+            },
+            {
+                "from": "gpt",
+                "value": "On observing {the AI Efficiency line}[[0.065, 0.430, 0.976, 0.828]] and {the Automation Speed line}[[0.065, 0.139, 0.976, 0.751]], both trends appear to increase initially though Automation Speed kept on increasing whereas the AI Efficiency trend began to decrease after reaching a peak around the 9th month. The Automation speed almost always maintained a higher value as compared to AI efficiency with the gap between them increasing during the later months.\nBased on the above thought, the answer is:\nBoth AI Efficiency and Automation Speed demonstrate an overall upward trend over the 20 months depicted. AI Efficiency steadily increased until approximately the 9th month, after which the trend started to descend. In contrast, Automation maintained a steady rising trend throughout the period. There was also a noticeable efficiency gap with Automation Speed largely outpacing AI Efficiency, which became especially noticeable towards the later months."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "aW7dRC2zYDD5XbfL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the maximum Automation Speed value achieved and in what year did it happen?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the maximum Automation Speed value, I have to go through {the Automation Speed line points}[[0.065, 0.751], ..., [0.976, 0.139]]. The maximum value achieved according to the line plot was 0.9. Next, I correspond this value to a timestep in {the x-axis ticks}[[0.061, 0.912, 0.070, 0.936], ..., [0.967, 0.912, 0.985, 0.936]]. The maximum value seems to correspond to the last point, meaning it happened in the 20th month.\nBased on the above thought, the answer is:\nThe maximum Automation Speed value was 0.9 and it was achieved in the 20th month."
+            }
+        ],
+        "id_raw": 970
+    },
+    {
+        "id": "GCwRGE8vd2qn4U7L",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table from the line chart showing the progression over time for Risk Management, Financial Planning, and Growth Investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create this structured table, I first identify the {x-axis tick labels}<r>[0.108, 0.903, 0.117, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934]</r> on the chart that denote the different time points. I next note the presence of three categories represented on the chart with three distinct colors through {the legend}<r>[0.794, 0.076, 0.977, 0.216]</r> where the respective data points are linked to each category: Risk Management, Financial Planning, and Growth Investment, through {line tags}<r>[0.108, 0.145, 0.943, 0.145], [0.108, 0.323, 0.943, 0.536], [0.108, 0.500, 0.943, 0.820]</r>. Reviewing the lines we can extract the values at different time points thus filling the table with the proper data scores.\nBased on the above thought, the answer is:\nTime, Risk Management, Financial Planning, Growth Investment\n10, 0.2, 0.45, 0.7\n20, 0.3, 0.5, 0.6\n30, 0.35, 0.55, 0.65\n40, 0.4, 0.6, 0.55\n50, 0.5, 0.65, 0.45"
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "4NSmgjvFZnlvsp5O",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Financial Planning compare with Risk Management over time in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze and compare the trends, I carefully observe both {the line for Financial Planning}<r>[0.108, 0.323, 0.943, 0.536]</r> and {the line for Risk Management}<r>[0.108, 0.145, 0.943, 0.145]</r>. Following these lines across the timeline, I can see that Financial Planning starts at a score of {0.45}[0.108, 0.323] and gently increases reaching {0.65}[0.660, 0.536], showing consistent growth. In contrast, Risk Management starts at a somewhat lower value of {0.2}[0.108, 0.145] but accelerates through the timeline ending slightly higher at {0.5}[0.943, 0.145]. These observations indicate that though both categories show an upward trend, Risk Management exhibits a sharper increase as compared to the more gradual growth of Financial Planning.\nBased on the above thought, the answer is:\nThe trend in Financial Planning shows a steady and gradual increase over time. Conversely, Risk Management starts at a lower point but increases more sharply, finishing slightly ahead of Financial Planning by the end of the observed time period."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "FTBEUYzzAuJMTIUk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score for Growth Investment over the indicated time points in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average score for Growth Investment, I locate the related data from {the line for Growth Investment}<r>[0.108, 0.500, 0.943, 0.820]</r>. The scores at different time points are extracted from the line points at {Time 10}<r>[0.108, 0.7]</r>, {Time 20}<r>[0.243, 0.6]</r>, {Time 30}<r>[0.382, 0.65]</r>, {Time 40}<r>[0.521, 0.55]</r>, and {Time 50}<r>[0.660, 0.45]</r>. Adding these scores (0.7, 0.6, 0.65, 0.55, 0.45) gives a total of 2.95, and dividing by the number of time points (5), the average is calculated.\nBased on the above thought, the answer is:\n0.59"
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "3OX4rZhDvIorp0Do",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you summarize the shown line chart data into a structured table with each product category as a column and the months as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the required table, I would utilize the x-axis as row indices, illustrated by {the x-axis tick labels}<r>[0.049, 0.911, 0.058, 0.934], [0.126, 0.911, 0.135, 0.934], ... , [0.967, 0.911, 0.985, 0.934]</r> which symbolize the months. Next, the {legend}<r>[0.062, 0.076, 0.296, 0.172]</r> provides the column headers indicating different product categories. To capture the values, I observe the respective coordinates of {the plot points of each line}<r>[0.053, 0.264, 0.976, 0.735], [0.053, 0.264, 0.976, 0.701]</r> and note down the specific sales data for each product in the corresponding month.\nBased on the above thought, the answer is:\nMonth,Standard Production,High-tech Production\n0,5.0,6.0\n1,5.8,6.5\n2,6.9,7.2\n3,7.1,6.8\n4,6.6,5.8\n5,6.0,5.2\n6,5.9,5.7\n7,6.6,6.8\n8,7.4,7.8\n9,7.8,7.5\n10,7.6,7.0\n11,7.0,6.6\n12,6.5,6.4"
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "jMzNZMwromTsRLxC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich production method has shown a consistent growth trend over the first six months?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to study the trend of performance across the first six months for both Standard and High-tech Productions. Observing {the line for Standard Production}<r>[0.053, 0.264, 0.515, 0.583]</r>, I see a general increase over the first six months, from an efficiency rate of 5.0 to 6.0. Meanwhile, examining {the line for High-tech Production}<r>[0.053, 0.264, 0.515, 0.617]</r>, there is also an upward trend starting from an efficiency rate of 6.0 to 5.2. Thus, both production methods have shown a consistent growth trend over the first six months.\nBased on the above thought, the answer is:\nBoth production methods, Standard Production and High-tech Production, have shown a consistent growth trend over the first six months."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "DVeIaDOnn75GPu5l",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in production efficiency between Standard Production and High-tech Production in the 9th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the 9th month, I need to locate {the 9th point on the Standard Production line}<r>[0.745, 0.264]</r> which corresponds to 7.8. Then, I locate {the 9th point on the High-tech Production line}<r>[0.745, 0.314]</r> which corresponds to 7.5. To get the difference, I would subtract 7.5 from 7.8.\nBased on the above thought, the answer is:\n0.3."
+            }
+        ],
+        "id_raw": 972
+    },
+    {
+        "id": "F5NvKoPuDxgrVk7S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the data from the line chart into a structured table, with the months as columns and the type of purchase as rows."
+            },
+            {
+                "from": "gpt",
+                "value": "To turn the chart data into table format, first, I identify {the x-axis tick labels}[[0.112,0.143,0.943,0.355], [0.188,0.338,0.943,0.355], ..., [0.868,0.151,0.794,0.315], [0.943,0.142,0.794,0.315]] as column headers representing the months. Next, each line represents a type of purchase, {Regular Purchase}[0.112, 0.143, 0.943, 0.355] and {Panic Buying}[0.112, 0.100, 0.943, 0.270], which serve as row labels. Finally, I gather the values for each type of purchase at each month by looking at {the data points for Regular Purchase}[[0.112, 0.355], [0.188, 0.338], ..., [0.868, 0.151], [0.943, 0.142]] and {the data points for Panic Buying}[[0.112, 0.270], [0.188, 0.270], ..., [0.868, 0.151], [0.943, 0.100]].\nBased on the above thought, the answer is:\nType,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10,Month 11,Month12\nRegular Purchase,300,310,320,330,340,350,360,380,400,415,420,425\nPanic Buying,350,350,365,370,385,375,350,360,375,400,420,450"
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "MnzfrU2iup8sGNAp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line chart, which behavior shows a general increase in purchase volume over time? Describe your observations."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I'll analyze the trends exhibited by each purchase behavior. Looking at the line for {Regular Purchase}[0.112, 0.143, 0.943, 0.355], we can see a steady increase in the purchase volume from 300 at the 1st month to 425 at the 12th month. However, when we look at the line for {Panic Buying}[0.112, 0.100, 0.943, 0.270], while there are increases and decreases, Panic Buying also ends at a higher volume, going from 350 to 450 from the 1st month to the 12th.\nBased on the above thought, the answer is:\nBoth Regular Purchase and Panic Buying shows a general increase in purchase volume over time. Regular Purchase shows a steady uphill trend throughout the year, starting at a purchase volume of 300 in the 1st month and ending with a volume of 425 in the 12th month. Panic Buying, while showing more fluctuation throughout the year, also demonstrates a general increase as it starts at a volume of 350 in the 1st month and increases to 450 in the 12th month."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "w0O5iGrwKISByV9N",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the difference between the maximum and minimum purchase values for Panic Buying?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I will first need to find the maximum and minimum values related to Panic Buying. I will do this by analyzing {the data points for Panic Buying}[[0.112, 0.270], [0.188, 0.270], ..., [0.868, 0.151], [0.943, 0.100]]. \nThe maximum Panic Buying value, 450, matches to {the data point to month 12}[0.943, 0.100]. The minimum Panic Buying value, 350, matches to {the data point to month 1}[0.112, 0.270] and to {the data point to month 2}[0.188, 0.270]. Calculating the difference gives 450 - 350.\nBased on the above thought, the answer is:\n100."
+            }
+        ],
+        "id_raw": 973
+    },
+    {
+        "id": "Vfrr7OddjpccDRH3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data of the manufacturing production efficiency of the two factories across the eight different periods into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first look at {the x-axis tick labels}[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]], which represent the column headers or periods. The row labels representing the factories can be identified from {the legend texts}[[0.809, 0.050, 0.963, 0.080], [0.809, 0.094, 0.970, 0.124]]. The production efficiency values can be deduced from the respective {line points (Factory A)}[[0.108, 0.278], [0.178, 0.288], ..., [0.943, 0.863]] and {line points (Factory B)}[[0.108, 0.065], [0.178, 0.107], ..., [0.943, 0.224]] that map to each period.\nBased on the above thought, the answer is:\nPeriod,Factory A,Factory B\nUnpredictable decline,75,80\nSustained decline,49,70\nIncremental growth,68,65\nDramatic surge,62,40\nSporadic growth,70,50\nStable growth,70,60\nPeriodic fluctuations,66,70\nErratic growth,66,75"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "HJL2CBDI3YwbTbzX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the line chart, what can be deduced about the performance stability between Factory A and Factory B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Factory A's performance, represented as {the line for Factory A}[0.108, 0.278, 0.943, 0.863], fluctuates throughout the periods, with several peaks and troughs indicating a degree of instability. Meanwhile, Factory B's performance, represented by {the line for Factory B}[0.108, 0.065, 0.943, 0.224], also fluctuates, but its trend seems to be more steady especially after it recovers from the dramatic surge period. Therefore, it seems to be slightly more stable than Factory A.\nBased on the above thought, the answer is:\nFrom the line chart, Factory B appears to have a more stable performance compared to Factory A, especially in the periods after the dramatic surge. While both factories exhibit fluctuations in their manufacturing production efficiency, the fluctuation amplitude of Factory B seems to be less volatile after its recovery from the lowest efficiency value."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "N07XieP0G5H9cEpD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum efficiency gap between Factory A and Factory B across all periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to find the maximum difference in production efficiency between the two factories. I need to locate each pair of matching {points for Factory A}[[0.108, 0.278], [0.178, 0.288], ..., [0.943, 0.863]] and {points for Factory B}[[0.108, 0.065], [0.178, 0.107], ..., [0.943, 0.224]], subtract the value of Factory A from that of Factory B for each period, and find the maximum absolute difference from all the computed results.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "BYOoBjgNsUc9DjMq",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart that represents user activity for social networks like Facebook and Instagram over a period of 12 months into a tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I first need to identify {the x-axis labels that represent months}[0.062, 0.911, 0.985, 0.934] as column headers. The {legend labels}[0.766, 0.087, 0.961, 0.161] indicate row labels of Facebook User Activity and Instagram User Activity. The values are determined by tracing the line points of {Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and {Instagram User Activity}[0.067, 0.139, 0.976, 0.444] across these months and identifying the corresponding user activity.\nBased on the above thought, the answer is:\nTime (Months),Facebook User Activity,Instagram User Activity\n0,0.5,0.6\n1,0.6,0.7\n2,0.65,0.75\n3,0.7,0.8\n4,0.75,0.85\n5,0.8,0.9\n6,0.85,0.95\n7,0.9,1.0\n8,0.88,0.97\n9,0.86,0.9\n10,0.84,0.85\n11,0.82,0.8\n12,0.8,0.75"
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "UuU6RLpxttNmta0V",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which social network, Facebook or Instagram, recorded a higher rate of user activity overall and why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social network recorded a higher overall user activity, I need to analyse both the {line for Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and the {line for Instagram User Activity}[0.067, 0.139, 0.976, 0.444]. It's clear that the Instagram User Activity line is consistently above the Facebook User Activity line throughout the time period, indicating that Instagram had a higher rate of user activity overall.\nBased on the above thought, the answer is:\nBased on the line chart, Instagram recorded a higher rate of user activity overall. This conclusion is drawn from the fact that the line representing Instagram User Activity is consistently higher than the line representing Facebook User Activity, indicating a higher rate of user engagement on Instagram throughout the 12 month period."
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "ReB6bGQKMAnfyPeB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what month do the user activities of Facebook and Instagram coincide?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find when Facebook and Instagram User Activities coincide, I need to locate the points on the line for each where the y-values or user activity values are the same. Comparing the points on the lines of {Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and {Instagram User Activity}[0.067, 0.139, 0.976, 0.444], I find that they intersect at the 5th month.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 976
+    },
+    {
+        "id": "BdLphCyM75aPAeGV",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the month by month market values for all types of investments in a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll locate and extract the label {Month} on the x-axis<r>[0.495, 0.943, 0.556, 0.975]</r>these will act as the row identifiers. Next, both the types of investments, namely {Private Investment} and {Public Venture Capital} are extracted from the legend<r>[0.734, 0.076, 0.977, 0.172]</r>, these will act as column headers. Afterward, the corresponding market values for each type of investment over each month are extracted by analyzing {the points on the line for Private Investment}<r>[0.108, 0.100, 0.943, 0.864]</r> and {the points on the line for Public Venture Capital}<r>[0.108, 0.190, 0.943, 0.864]</r>.\nBased on the above thought, the answer is:\nMonth,Private Investment,Public Venture Capital\n0,1.0,1.0\n1,1.08,1.15\n2,1.15,1.27\n3,1.21,1.36\n4,1.25,1.42\n5,1.27,1.44\n6,1.3,1.45\n7,1.35,1.47\n8,1.4,1.5\n9,1.48,1.51\n10,1.53,1.52\n11,1.6,1.53"
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "aZcA8fwshnb6wsEB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment type is showing a stronger growth trend and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the two lines, {the line for Private Investment}<r>[0.108, 0.100, 0.943, 0.864]</r> and {the line for Public Venture Capital}<r>[0.108, 0.190, 0.943, 0.864]</r>, I can see that they both start at a value of 1.0 at month 0. By the end at month 11, the Private Investment has grown to 1.6, while the Public Venture Capital has grown to 1.53. The growth rate is determined by the increase of the value over time. The Private Investment line trend shows more significant steepness upward in comparison to the Public Venture Capital line.\nBased on the above thought, the answer is:\nThe stronger growth trend is shown by the Private Investment. This is because it starts at the same initial value as Public Venture Capital, but it ends with a higher value. This demonstrates a greater overall increase in value for Private Investment over the timeframe of 11 months."
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "J8LA2PaQOrwgFxzf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Private Investment value change between months 0 and 11?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the difference between the Private Investment values at month 0 and month 11. I can find these values by checking {the points on the line for Private Investment}<r>[0.108, 0.100, 0.943, 0.864]</r>. The value at month 0 is 1.0 and at month 11 is 1.6, so the difference is 1.6 - 1.0 = 0.6.\nBased on the above thought, the answer is:\n0.6"
+            }
+        ],
+        "id_raw": 977
+    },
+    {
+        "id": "mOkRqMvJ48NE552s",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the line graph about cultural artifacts and geographic regions into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line graph into a CSV format, I first identify {the x-axis labels}<r>[0.06, 0.95, 0.1, 0.98]</r> which are the cultural artifacts (Statue, Painting, Manuscript, Architecture). Then, I review {the legend}<r>[0.771, 0.076, 0.977, 0.172]</r> to identify each geographic region (Global, European, African, Asian, American). To compile the data, I extract values from each {line point}<r>[0.108, 0.482, 0.943, 0.864]</r> that correspond to the artifacts and geographic regions.\nBased on the above thought, the answer is:\nArtifact,Global,European,African,Asian,American\nStatue,200,300,150,250,180\nPainting,500,600,400,550,450\nManuscript,350,450,300,400,350\nArchitecture,400,500,350,450,320"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "BvsjNRXWIucWYpbI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cultural region shows the most significant increase in embracing artifacts according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which cultural region shows the most significant increase, I analyze {the lines representing each region}<r>[0.108, 0.482, 0.943, 0.864]</r>. I observe {the European line}<r>[0.108, 0.350, 0.943, 0.100]</r>, noting its significant upward trend compared to other lines, which suggests a larger increase in embracing artifacts over time.\nBased on the above thought, the answer is:\nThe European region shows the most significant increase in embracing artifacts according to the chart. The line representing European culture demonstrates a pronounced upward trend, more so than the lines for other regions, indicating an increased cultural embrace of artifacts in this region."
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "zr99iKVQ8kzwQiqg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum value represented on the y-axis in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value represented on the y-axis, I look at the {highest tick on the y-axis}<r>[0.040, 0.091, 0.062, 0.115]</r>. I analyze the numerical labels along the y-axis and determine the highest value indexed.\nBased on the above thought, the answer is:\n1.4"
+            }
+        ],
+        "id_raw": 978
+    },
+    {
+        "id": "DE0zQMyw0u21VoPx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we break down the time-series data of the transportation trends into table format with months as columns and transport methods as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To map the line chart's data into a table, first, we need to take note of the time range from {the x-axis tick labels}[[0.121, 0.911, 0.129, 0.934], [0.195, 0.911, 0.204, 0.934], ..., [0.935, 0.911, 0.953, 0.934]], which will be the column headers. Then, the {legend texts}[[0.772, 0.087, 0.958, 0.117], [0.772, 0.131, 0.970, 0.161]] will be the row headers representing different transport methods. We obtain the values by tracking {the line points for each transport method}[[0.125, 0.482, ..., 0.944, 0.062], [0.125, 0.377,..., 0.944, 0.509]] through the respective months and extract their corresponding values.\nBased on the above thought, the answer is:\nMonth, Standard Performance, Innovative Performance\n0,0.8,1.0\n1,0.9,0.75\n2,1.1,1.02\n3,1.2,0.85\n4,1.3,1.08\n5,1.4,0.89\n6,1.45,1.10\n7,1.2,1.05\n8,1.1,1.20\n9,1.3,1.15\n10,1.5,1.3\n11,1.6,1.35"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "wwRmnfYl9FfmuRNa",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which performance evaluation type shows a more erratic trend over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the volatility in the line plots for both performance evaluation types. Reviewing the {line for Standard Performance}[0.125, 0.482, 0.944, 0.062], the values vary between 0.8 to 1.6. However, the changes don't appear drastic or unexpected given the overall upward trend.\nWhen I examine the {line for Innovative Performance}[0.125, 0.377, ..., 0.944, 0.194], the values range from 0.75 to 1.35. Although the range is narrower, the line displays more sudden jumps and falls, indicating a more erratic trend in comparison.\nBased on the above thought, the answer is:\nThe Innovative Performance evaluation type shows a more erratic trend over time, as its line plot exhibits more sudden rises and falls compared to the steady growth observed in the Standard Performance type. Even though the range of values is smaller for Innovative Performance, its trend seems to be less predictable."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "y98uI2Gat8uZc0Gv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_49_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Innovative Performance from Month 0 to Month 11?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the percentage increase in Innovative Performance, I need to locate {the line points for Innovative Performance}[[0.125, 0.377, ..., 0.944, 0.194]]. The performance index at Month 0 is 1.0 (at {point}[0.125, 0.377]) and at Month 11 is 1.35 (at {point}[0.944, 0.194]). The percentage increase can then be calculated as ((1.35 - 1.0) / 1.0) * 100%.\nBased on the above thought, the answer is:\n35%."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "ZSOBtvbZ3wvVzaZn",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you format the information on the trends of 'Prediction Accuracy' and 'Number of Training Samples' for 'Application Case 1' and 'Application Case 2' into a structured text format?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend}<r>[0.696, 0.046, 0.920, 0.123]</r> indicates two cases 'Application Case 1' and 'Application Case 2'. The x-axis labels at {the x-axis tick labels}<r>[0.019, 0.919, 0.190, 0.942], [0.731, 0.919, 0.977, 0.942]</r> represent 'Prediction Accuracy' and 'Number of Training Samples' respectively. The line points for {Application Case 1}<r>[0.104, 0.911], [0.854, 0.765]</r> indicate that the values are 0.5 and 1000, respectively. The line points for {Application Case 2}<r>[0.104, 0.911], [0.854, 0.180]</r> indicate that the values are 0.6 and 5000, respectively.\nBased on the above thought, the answer is:\nFields,Application Case 1,Application Case 2\nPrediction Accuracy,0.5,0.6\nNumber of Training Samples,1000,5000"
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "Tvp0TOCNEA9ZnxXi",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line graph, which application case had a better performance with respect to 'Number of Training Samples' and 'Prediction Accuracy'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the performance by analyzing both the 'Prediction Accuracy' and 'Number of Training Samples'. For 'Application Case 1', the 'Prediction Accuracy' is located at {the Prediction Accuracy for Application Case 1}<r>[0.104, 0.911]</r> with a value of 0.5 and 'Number of Training Samples' is located at {the Number of Training Samples for Application Case 1}<r>[0.854, 0.765]</r> with a value of 1000. For 'Application Case 2', the 'Prediction Accuracy' is located at {the Prediction Accuracy for Application Case 2}<r>[0.104, 0.911]</r> with a value of 0.6 and 'Number of Training Samples' is located at {the Number of Training Samples for Application Case 2}<r>[0.854, 0.180]</r> with a value of 5000. Both the 'Prediction Accuracy' and 'Number of Training Samples' are higher in 'Application Case 2'.\nBased on the above thought, the answer is:\nBased on the line graph, 'Application Case 2' had better performance than 'Application Case 1'. This conclusion is based on both, the higher 'Prediction Accuracy' and the larger 'Number of Training Samples' observed for 'Application Case 2'."
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "s3kvS9BUY6SWSM2M",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_50_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more 'Number of Training Samples' are there in 'Application Case 2' compared to 'Application Case 1'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the number of Training Samples, I need to locate the points for 'Application Case 1' and 'Application Case 2'. The y-value for the 'Number of Training Samples' for {Application Case 1}<r>[0.104, 0.765]</r> is 1000 and for {Application Case 2}<r>[0.854, 0.180]</r> is 5000. The difference can be calculated as 5000 - 1000.\nBased on the above thought, the answer is:\n4000"
+            }
+        ],
+        "id_raw": 986
+    },
+    {
+        "id": "HmavzmqV00i9KHgX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the data from the line chart which shows trends across years for different sectors into a tabular format grouped by year and sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis labels}<r>[0.101, 0.919, 0.145, 0.942]</r> to <r>[0.919, 0.919, 0.962, 0.942]</r> which denote the years from 2010 to 2020, and identifying {the legend items}<r>[0.147, 0.727, 0.273, 0.751]</r> to <r>[0.147, 0.867, 0.335, 0.890]</r> which represent sectors like Manufacturing, Logistics, Marketing, Services, and Product Development, I extracted the corresponding values from the chart. The values are tracked at each year's position for each sector using their specific data lines.\nBased on the above thought, the answer is:\nYear,Manufacturing,Logistics,Marketing,Services,Product Development\n2010,73.1,50.2,90.3,33.7,80.2\n2011,68.5,40.1,85.2,35.8,78.2\n2012,64.0,32.1,78.3,40.0,76.2\n2013,61.3,35.0,75.1,43.0,80.3\n2014,60.0,38.8,73.5,45.8,85.0\n2015,59.0,39.8,72.0,47.5,90.0\n2016,57.8,45.5,70.0,50.0,95.0\n2017,57.0,50.0,68.5,52.0,95.2\n2018,56.3,52.3,68.0,55.0,100.0\n2019,56.0,55.0,68.3,58.2,105.2\n2020,56.0,58.5,70.0,60.0,110.0"
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "9SyLLUcbC96FlW1y",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector shows the most consistent decrease in value over the years according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing each sector's line in the chart, I note the trend of {the line for Manufacturing}<r>[0.123, 0.371, 0.940, 0.498]</r> consistently moving downward year over year from a value of 73.1 in 2010 to 56.0 in 2020. This consistent descending trajectory indicates a decrease in value every year.\nBased on the above thought, the answer is:\nThe Manufacturing sector shows the most consistent decrease in value over the years. The line representing Manufacturing in the chart consistently moves downward, indicating a regular decline in its values from the year 2010 to 2020."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "bq1qKXabk1w4M5G4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_50_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increase is observed in the Product Development sector from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting from the value of Product Development in 2010, which is 80.2 at {point for 2010 in Product Development}<r>[0.123, 0.319]</r>, and concluding at the value in 2020 which is 110.0 at {point for 2020 in Product Development}<r>[0.940, 0.099]</r>, I calculate the increase by subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n29.8"
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "kSHSEonmehqdFCRG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the efficiency data for each section from the line chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the line chart, I first identify {the x-axis labels}<r>[0.086, 0.939, 0.121, 0.957], [0.254, 0.939, 0.289, 0.957], [0.422, 0.939, 0.457, 0.957]</r> which list the different sections: Lighting, HVAC, and Plug Load. I analyze {the lines}<r>[0.104, 0.415, 0.943, 0.631], [0.104, 0.631, 0.943, 0.890]</r> depicting both potential and achieved efficiencies respectively. The visual cues at each section intersection give the corresponding efficiency values for potential and achieved.\nBased on the above thought, the answer is:\nSection,Potential Efficiency (%),Achieved Efficiency (%)\nLighting,30,15\nHVAC,45,30\nPlug Load,20,10"
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "ZAZnFFSA39Uaq0l3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the chart suggest that all sections have room for improvement in efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, specifically observing {the potential efficiency line}<r>[0.104, 0.415, 0.943, 0.631]</r> and {the achieved efficiency line}<r>[0.104, 0.631, 0.943, 0.890]</r>, it is clear that the potential efficiency line is consistently above the achieved efficiency line across all sections {Lighting}<r>[0.104, 0.501]</r>, {HVAC}<r>[0.254, 0.174]</r>, and {Plug Load}<r>[0.422, 0.457]</r>. This indicates that the actual efficiencies are lower than what could potentially be achieved, suggesting a gap and therefore room for improvement.\nBased on the above thought, the answer is:\nThere is room for improvement in all sections as depicted by the higher potential efficiency line compared to the achieved efficiency line for each section, signaling that the current achievements are below the identified potential."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "GOTg7SRpPDw1RhU7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_50_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between potential and achieved efficiency for HVAC as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line points for HVAC}<r>[0.254, 0.174], [0.254, 0.533]</r>, the potential efficiency is indicated as 45% and the achieved efficiency is 30%. To calculate the percentage difference, the formula is (Potential - Achieved) / Potential * 100. Substituting the values, it is (45 - 30) / 45 * 100.\nBased on the above thought, the answer is:\n33.33%"
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "l5JCL4mHGDwUOVx7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line chart, can you bring out the data in table format with Years as columns and different technological fields as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data for the tabular view from the line chart, {the x-axis labels}<r>[0.160, 0.890, 0.865, 0.921]</r> can be identified as columns i.e. the years 2012 to 2021. The rows are represented by the {legend labels}<r>[0.176, 0.141, 0.276, 0.269]</r>, which include the various technological fields AI, Quantum Computing, Blockchain, and Biotechnology. Finally, the respective values for each year and technological field can be obtained by tracking {the line points}<r>[0.160, 0.155, 0.865, 0.855]</r> for each technological field in respective years.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,Blockchain,Biotechnology\n2012,5.8,1.2,3.6,1.1\n2013,6.9,1.5,4.1,1.2\n2014,7.8,2.0,4.7,1.4\n2015,9.0,2.6,5.1,1.7\n2016,10.6,3.3,5.3,2.0\n2017,12.5,4.3,5.4,2.5\n2018,14.8,5.5,5.6,3.0\n2019,17.4,7.0,5.8,3.7\n2020,20.5,8.7,6.0,4.5\n2021,23.9,10.6,6.4,5.4"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "gQYmOt64sMAU0NnJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the growth trends presented in the line chart, which technological field seems to have experienced the most significant progress over time? Please elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate which technological field experienced the most significant progress, I investigate {the starting and ending points of each line}<r>[0.160, 0.711, 0.865, 0.155], [0.160, 0.852, 0.865, 0.563], [0.160, 0.778, 0.865, 0.692], [0.160, 0.855, 0.865, 0.723]</r>. I compare the progress i.e., difference between the y-coordinates (which represent scaled values) at the starting and ending points of each field. It's apparent that the line representing Artificial Intelligence shows the greatest increase in value, from approximately 5.8 in 2012 to around 23.9 by 2021.\nBased on the above thought, the answer is:\nConsidering the depicted trends in the line chart, Artificial Intelligence appears to have experienced the most significant progress over the observed timeline. This is marked by a substantial increase in its value from about 5.8 in 2012 to nearly 23.9 by 2021. The proportional increase for Artificial Intelligence seems to be larger than for the other fields, making it the field with the most significant progress over time."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "HgQtjjliKQKhatKR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_50_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much progress did the field of Quantum Computing make from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the progress made by Quantum Computing from 2012 to 2021, I look at {the points for Quantum Computing}<r>[0.160, 0.852, 0.865, 0.563]</r>. I observe that the value in 2012 was 1.2 and in 2021 it was 10.6. Therefore, the progress made by Quantum Computing is 10.6 - 1.2 = 9.4.\nBased on the above thought, the answer is:\n9.4."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "NQhlr2xNMpAiaO5b",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table format with Years as columns and Market Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To prepare the table, I will start by identifying {the x-axis tick labels}[[0.083, 0.919, 0.143, 0.942], [0.289, 0.919, 0.350, 0.942], [0.496, 0.919, 0.556, 0.942], [0.703, 0.919, 0.763, 0.942], [0.910, 0.919, 0.970, 0.942]] as column headers which represent the years. Then, I note that {the legend texts}[[0.810, 0.423, 0.959, 0.446], [0.810, 0.457, 0.948, 0.481], [0.810, 0.492, 0.925, 0.516], [0.810, 0.527, 0.966, 0.551]] symbolize the different areas of the financial market which will serve as row labels. Now, to pull the values, I can determine {the line points}[[0.113, 0.872], [0.319, 0.872], ..., [0.940, 0.871]] for each market type in the corresponding years and extract the recognized values.\nBased on the above thought, the answer is:\nMarket type,2000,2005,2010,2015,2020\nLiving Conditions,0.40,0.45,0.50,0.55,0.60\nLife Expectancy,75.65,77.65,79.54,81.72,83.56\nHousing Cost,0.55,0.65,0.70,0.80,0.85\nEmployment Rate,0.85,0.88,0.89,0.90,0.92"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "UIuFCAmMaQRRFhtd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the given factors demonstrated the greatest improvement over the 20 year period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which factor demonstrated the greatest improvement, I need to analyze the change in the line plots for each factor. Looking at the rate of change in {the Living Conditions line}[0.113, 0.871, 0.940, 0.872], it increases from 0.40 to 0.60. For {the Life Expectancy line}[0.113, 0.101, 0.940, 0.174], there's a considerable jump from 75.65 to 83.56. Assessing the {Housing Cost line}[0.113, 0.868, 0.940, 0.871], it increased gradually from 0.55 to 0.85. Finally, observing the {Employment Rate line}[0.113, 0.868, 0.940, 0.868], the improvement is steady but slight, from 0.85 to 0.92.\nBased on the above thought, the answer is:\nLife expectancy demonstrated the greatest improvement over the 20-year period. The line chart shows a significant increase from 75.65 to 83.56. This represents a large absolute change relative to the other factors and signifies major improvements in lifespan over this period."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "CSbIzZFPvqWj7cYK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_50_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in life expectancy between the years 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in life expectancy between the years 2000 and 2020, I need to locate points on the Life Expectancy line representing these years. The corresponding values are {for the year 2000}[0.113, 0.174], value is 75.65 and {for the year 2020}[0.940, 0.101], value is 83.56. The difference between these two values is therefore 83.56 - 75.65 = 7.91.\nBased on the above thought, the answer is:\n7.91"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "xtkTM3SYHAtWGgBh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the line chart demonstrating the trends of cultural heritage and cultural integration into a well-structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the plot, each data point relating to 'cultural heritage' and 'cultural integration' can be documented. The x-axis seems to represent the year and the y-axis the percentage. Each point marked for 'cultural heritage' and 'cultural integration' is noted from 2010 to 2019 with {the value for cultural heritage at year 2010}<r>[0.155, 0.891]</r> being 2.3% and for {cultural integration at year 2010}<r>[0.155, 0.141]</r> being 2.7% respectively. The points for subsequent years are also located in a similar manner along the lines for each category until the year 2019 with coordinates captured as such.\nBased on the above thought, the answer is:\nYear,Cultural Heritage (%),Cultural Integration (%)\n2010,2.3,2.7\n2011,2.5,2.8\n2012,2.8,3.0\n2013,3.2,3.5\n2014,3.9,4.1\n2015,4.6,4.7\n2016,5.4,5.5\n2017,6.3,6.6\n2018,7.1,6.7\n2019,7.9,7.8"
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "4E9achMSpJQdtf7W",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two series, cultural heritage or cultural integration, shows greater overall growth from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would review the data points on the line chart for both 'cultural heritage' and 'cultural integration' from the year 2010 to 2019. For 'cultural heritage', the percentage begins at 2.3% {at year 2010 for cultural heritage}<r>[0.155, 0.891]</r> and climbs to 7.9% {at year 2019 for cultural heritage}<r>[0.942, 0.892]</r>, while 'cultural integration' starts from 2.7% {at year 2010 for cultural integration}<r>[0.155, 0.141]</r> and increases to 7.8% {at year 2019 for cultural integration}<r>[0.942, 0.054]</r>. Comparing these values, 'cultural heritage' shows a slightly greater increase from 2.3% to 7.9% (a difference of 5.6%) compared to 'cultural integration' which grew from 2.7% to 7.8% (a difference of 5.1%).\nBased on the above thought, the answer is:\nCultural heritage shows a greater overall growth from 2010 to 2019 compared to cultural integration, as evidenced by a slightly larger percentage increase over this period."
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "0lGApkriMYit8M7R",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in cultural heritage from the year 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I will locate the values of cultural heritage in the year 2010 and 2019 from the graph. The value for {2010 in cultural heritage}<r>[0.155, 0.891]</r> is 2.3% and for {2019 in cultural heritage}<r>[0.942, 0.892]</r> is 7.9%. To determine the increase, I will subtract the initial value from the final value: 7.9% - 2.3% = 5.6%.\nBased on the above thought, the answer is:\n5.6%"
+            }
+        ],
+        "id_raw": 1000
+    },
+    {
+        "id": "rCAamVmE43k3JuWF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format with Fuel Economy (mpg) as rows, and the different models as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first locate the {x-axis tick labels}[[0.012, 0.904, 0.058, 0.934], [0.112, 0.904, 0.158, 0.934], [0.213, 0.904, 0.259, 0.934], [0.314, 0.904, 0.360, 0.934], [0.414, 0.904, 0.460, 0.934]], which represent the row headers or Fuel Economy (mpg). The legend texts {Lambda-S, Lambda-S+, DeepTraffic, Our Model}[[0.864, 0.747, 0.951, 0.770], [0.864, 0.782, 0.966, 0.805], [0.864, 0.817, 0.963, 0.840], [0.864, 0.852, 0.953, 0.875]] serve as the column headers representing the different models. I analyze each model's line points, for example {Lambda-S}[[0.135, 0.212, 0.941, 0.365]], to fill in the corresponding table cells.\nBased on the above thought, the answer is:\nFuel Economy(mpg),Lambda-S,Lambda-S+,DeepTraffic,Our Model\n20,67.21,29.68,80.58,29.85\n25,60.98,38.93, 86.94, 38.74\n30,57.32,20.72, 91.39,47.24\n35,50.09,34.86, 94.56,52.61\n40,42.97,19.55, 99.83, 61.31"
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "OCIB9Ol7diSvbQca",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which model seems to have the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which model has the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg, I need to analyze the y-values of all model lines at x equals 40 mpg. By examining the coordinates of each model line - {Lambda-S}[0.135, 0.212, 0.941, 0.365], {Lambda-S+}[0.135, 0.568, 0.941, 0.728], {DeepTraffic}[0.135, 0.070, 0.941, 0.181], and {Our Model}[0.135, 0.357, 0.941, 0.552] - at the 40 mpg mark, I see that the y-value of the DeepTraffic model is the highest.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the DeepTraffic model seems to have the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg. This is evident from the points plotted on the chart, where the line representing the DeepTraffic model reaches the highest y-value at the 40 mpg fuel economy mark, compared to the lines representing the other models."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "xOmDRwDf7ZHjmqKS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the increase in Classification Accuracy (%) of Our Model between 20 mpg and 40 mpg?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Our Model' line coordinates are {Our Model}[0.135, 0.357, 0.941, 0.552]. The y-value at 20 mpg corresponds to 29.85, found at coordinates [0.135, 0.454].  The y-value at 40 mpg corresponds to 61.31, found at coordinates [0.941, 0.552]. To find the increase, I subtract the initial value from the final value, which is 61.31 - 29.85.\nBased on the above thought, the answer is:\n31.46"
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "DDCGUqDBpXQB8jA9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the bar chart data in a table format using \u2018Media Sources\u2019 as the rows and \u2018Elections\u2019 and \u2018Immigration' as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the bar chart, I first identify the labels on {the x-axis}<r>[0.117, 0.927, 0.942, 0.951]</r> as the row labels or 'Media Sources'. Next, looking at {the legend}<r>[0.083, 0.058, 0.218, 0.115]</r>, I identify 'Elections' and 'Immigration' as the column headers. Then, I analyze {the blue bar points}<r>[0.117, 0.384, 0.942, 0.593]</r> and {the orange bar points}<r>[0.117, 0.300, 0.942, 0.693]</r> for each media source in both categories and extract the corresponding values.\nBased on the above thought, the answer is:\nMedia Source,Elections,Immigration\nCNN,65,50\nFOX,80,75\nBBC,73,60\nREUTERS,78,58\nYAHOO,65,45\nMSNBC,83,28\nTWITTER,60,55\nFACEBOOK,62,45\nGOOGLE,58,50\nINSTAGRAM,50,60\nSNAPCHAT,40,55"
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "8L61HpN9ffTH6TKZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which media source seems to exert the most influence on public opinion regarding Elections?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which media source is most influential for Elections, I need to find the media source with the highest percentage in the {Elections series}<r>[0.117, 0.233, 0.942, 0.593]</r>. From the data points, I can see that the point with the lowest y-coordinate corresponds to MSNBC.\nBased on the above thought, the answer is:\nBased on the chart, MSNBC appears to exert the most influence on public opinion regarding Elections. It has the highest percentage among all the media sources tracked in the Elections series."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "HuybyGtB3lq2vSb5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average influence of each media source on public opinion about Elections and Immigration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average influence, I locate the series of data points for both {Elections}<r>[0.117, 0.233, 0.942, 0.593]</r> and {Immigration}<r>[0.117, 0.300, 0.942, 0.693]</r>. Next, I extract the values corresponding to each media source across both these categories and calculate the average.\nBased on the above thought, the answer is:\nThe average influence of each media source on public opinion about Elections and Immigration, rounded to the nearest whole number, is 67%."
+            }
+        ],
+        "id_raw": 1002
+    },
+    {
+        "id": "3NgqQ4qZGodo1nZ5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table from the data presented in the line chart with Trends as columns and categories (Innovation, Creativity) as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data from the line chart, I first look at {the x-axis tick labels}[[0.063, 0.703, 0.206, 0.894], [0.233, 0.703, 0.349, 0.857], [0.395, 0.703, 0.499, 0.841], [0.517, 0.703, 0.690, 0.934], [0.691, 0.703, 0.829, 0.887], [0.856, 0.703, 0.977, 0.865]], which represent the column headers or Trends. Next, I identify {the legend texts}[[0.848, 0.046, 0.940, 0.069], [0.848, 0.081, 0.933, 0.104]] as the row labels representing Innovation and Creativity. To populate the values, I locate {the line points for Innovation}[[0.134, 0.153], [0.291, 0.344], [0.447, 0.440], [0.604, 0.121], [0.760, 0.280], [0.917, 0.503]] and {Creativity}[[0.134, 0.216], [0.291, 0.471], [0.447, 0.567], [0.604, 0.089], [0.760, 0.248], [0.917, 0.248]] at the respective Trends and extract the values annotated.\nBased on the above thought, the answer is:\nCategory,Moderate decline,Steep decline,Abrupt drop,Consistent expansion,Subdued decline,Erratic decline\nInnovation,85,55,40,90,65,30\nCreativity,75,35,20,95,70,70"
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "4vMcqheApOd7v7fh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trends of Innovation and Creativity from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyse the trends of Innovation and Creativity, I focus on {the line for Innovation}[0.134, 0.121, 0.917, 0.503] and {the line for Creativity}[0.134, 0.089, 0.917, 0.567]. The Innovation line illustrates a decrease from 85 to 40, suggesting a decline in innovation during the first three trends. However, it then increases to 90 during the Consistent Expansion trend before slightly decreasing and finishing at 30 in Erratic Decline. This shows a significant fluctuation in the trend of Innovation. On the other hand, the Creativity line starts at 75, decreases to 20 in the middle of the period, recovers slowly to 70 and stabilizes from the steepest decline to the end. This shows a more stable pattern for Creativity compared to Innovation.\nBased on the above thought, the answer is:\nThe line chart suggests that the trend of Innovation underwent significant fluctuations, with a severe decline in the first three trends, followed by a brief recovery and a second decline. On the other hand, Creativity had a more stable pattern, with a decrease initially, gradual recovery, and then stagnation. Overall, the patterns suggest volatility for Innovation, while Creativity tends to be more resilient and steady after initial changes."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "0rZLlDmJbsTfYXnu",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest scores for Creativity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest and lowest values for Creativity, I need to locate {the line points for Creativity}[[0.134, 0.216], [0.291, 0.471], [0.447, 0.567], [0.604, 0.089], [0.760, 0.248], [0.917, 0.248]] and extract the corresponding values: 75, 35, 20, 95, 70, 70. The maximum value is 95 corresponding to {Consistent expansion}[0.604, 0.703], and the minimum value is 20 corresponding to {Abrupt drop}[0.447, 0.703]. The difference between the maximum and minimum is therefore 95 - 20 = 75.\nBased on the above thought, the answer is:\n75."
+            }
+        ],
+        "id_raw": 1003
+    },
+    {
+        "id": "Qn876nuiS7Byz7iB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data from the line graph in tabular format, with 'SNR' values representing columns and 'Classification Accuracy (%)' values as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make the line graph into a table, I need to look at {the x-axis tick labels}[[0.104, 0.928, 0.130, 0.951], [0.324, 0.928, 0.342, 0.951], [0.531, 0.928, 0.549, 0.951], [0.737, 0.928, 0.756, 0.951], [0.944, 0.928, 0.962, 0.951]] as columns, which represent 'SNR' values. Then, I treat {the legend texts}[[0.825, 0.810, 0.957, 0.828], [0.825, 0.836, 0.946, 0.854], [0.825, 0.863, 0.962, 0.880], [0.825, 0.889, 0.973, 0.906]] as rows, which correspond to different categories of 'Classification Accuracy (%)'. Then, I extract from {the line points}[[0.117, 0.533], [0.324, 0.503], [0.531, 0.533], [0.737, 0.475], [0.944, 0.613], ..., [0.944, 0.222]] the values for each category at each 'SNR'.\nBased on the above thought, the answer is:\nSNR,Startups Incubated,Research Centers,Ecosystem Partners,Policies Implemented\n5,45.26,36.65,52.87,58.96\n10,48.73,32.78,56.01,63.78\n15,45.23,29.74,47.12,65.25\n20,52.00,24.40,57.55,77.01\n25,35.87,22.98,48.93,81.37"
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "pbCEOyoaBOCmFErt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the line graph, which category showed a continuous decline in the 'Classification Accuracy (%)' with increasing 'SNR'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category shows a continuous decline, I need to consider the slope of the lines for each category. Observing {the line for Startups Incubated}[0.117, 0.475, 0.944, 0.613], it's clear that it doesn't display a continuous decline as it goes up and down. The {line for Research Centers}[0.117, 0.607, 0.944, 0.724] shows a continuous decline from 'SNR' of 5 to 'SNR' of 25. Similarly, the {line for Ecosystem Partners}[0.117, 0.427, 0.944, 0.517] doesn't exhibit a continuous decline. The {line for Policies Implemented}[0.117, 0.222, 0.944, 0.415] shows an increase over the 'SNR' values.\nBased on the above thought, the answer is:\nThe \"Research Centers\" category shows a continuous decline in 'Classification Accuracy (%)' with increasing 'SNR' values. The line plot for this category consistently slopes downwards from 'SNR' of 5 to 'SNR' of 25, indicating a sustained decrease in 'Classification Accuracy (%)'."
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "rq6Jh4FomRYCYDtP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the maximum 'Classification Accuracy (%)' that the Research Centers obtained across all given SNR values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the maximum 'Classification Accuracy (%)' for Research Centers, I have to examine {the line points for Research Centers}[[0.117, 0.607], [0.324, 0.640], [0.531, 0.666], [0.737, 0.712], [0.944, 0.724]]. The corresponding 'Classification Accuracy (%)' values are 36.65, 32.78, 29.74, 24.4, 22.98 respectively. The maximum value among these is 36.65 at 'SNR' of 5.\nBased on the above thought, the answer is:\n36.65"
+            }
+        ],
+        "id_raw": 1004
+    },
+    {
+        "id": "Ldoug885cM5AfM1O",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the provided line chart data into a table with Years as columns and Technology Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first locate {the x-axis tick labels}[[0.096, 0.904, 0.149, 0.934], [0.260, 0.904, 0.313, 0.934], [0.423, 0.904, 0.476, 0.934], [0.587, 0.904, 0.640, 0.934], [0.750, 0.904, 0.803, 0.934], [0.914, 0.904, 0.967, 0.934]], which represent the individual years. Next, I identify {the legend texts}[[0.146, 0.057, 0.346, 0.081], [0.146, 0.092, 0.268, 0.116], [0.146, 0.127, 0.324, 0.151], [0.146, 0.162, 0.240, 0.186]] as the row headers representing different technology types. To find the values for each technology at the respective years, I identify the {line points} for each technology and extract the values annotated.\nBased on the above thought, the answer is:\nTechnology,2015,2016,2017,2018,2019,2020\nMultimedia Technology,70,72,73,77,80,83\nVirtual Reality,55,60,65,68,70,75\nArtificial Intelligence,40,42,50,55,60,70\nBlockchain,30,35,40,45,50,60"
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "EP8rC57q92Dy8ZD1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which technology has seen the most consistent growth over time from 2015 to 2020, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out which technology has experienced the most consistent growth, I need to analyze the lines representing each technology. Looking at {the line for Multimedia Technology}[0.123, 0.123, 0.940, 0.282], the line has a slight positive slope indicating a stable but limited growth. The second line representing {Virtual Reality}[0.123, 0.221, 0.940, 0.466] shows a trend of relatively constant growth while {Artificial Intelligence}[0.123, 0.282, 0.940, 0.650] and {Blockchain}[0.123, 0.405, 0.940, 0.773] both demonstrate sharp inclines over the years, indicating accelerated growth. However, among these, Artificial Intelligence's growth appears more steady and consistent across the years as compared to Blockchain.\nBased on the above thought, the answer is:\nArtificial Intelligence has seen the most consistent growth over time from 2015 to 2020. While all technology types have shown growth, the line representing Artificial Intelligence demonstrates a regular and steady upward trend, suggesting consistent annual growth in the adaption rate of this technology."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "Y8BsfL01huKaZwnV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth rate of Blockchain from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rate of Blockchain, I need to locate {the line points for Blockchain}[[0.123, 0.773], [0.286, 0.712], [0.450, 0.650], [0.613, 0.589], [0.777, 0.528], [0.940, 0.405]] and extract the corresponding values: 30, 35, 40, 45, 50, 60. The initial value in 2015 is 30 and the final value in 2020 is 60. The growth rate is therefore (60 - 30)/30 = 100%.\nBased on the above thought, the answer is:\n100%."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "2c8o7hzD7WXMRH7d",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table format based on the information from the line chart, listing different energy sources as columns with their corresponding usage values over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart data, I analyze the position of each line, corresponding to different energy sources. First, I consider {the points for Coal Consumption}<r>[0.118, 0.301], [0.944, 0.447]</r> and note down the values at each year mark from 2000 to 2020. I follow the same steps for {Oil Consumption}<r>[0.118, 0.357], [0.944, 0.639]</r>, {Natural Gas Consumption}<r>[0.118, 0.385], [0.944, 0.041]</r>, and {Renewable Energy Consumption}<r>[0.118, 0.864], [0.944, 0.075]</r>. These positions on the y-axis correspond to the energy usage, from which I extract and list the values.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas,Renewables\n2000,500,480,470,300\n2001,510,470,472,310\n2002,515,460,470,320\n2003,520,458,475,340\n2004,540,470,480,350\n2005,560,472,485,355\n2006,565,470,495,370\n2007,560,468,506,390\n2008,570,470,520,400\n2009,560,450,520,430\n2010,550,440,530,440\n2011,540,430,540,460\n2012,530,425,550,480\n2013,502,420,552,500\n2014,500,410,560,520\n2015,490,405,565,530\n2016,470,400,570,540\n2017,460,395,572,550\n2018,450,390,580,560\n2019,448,385,590,570\n2020,450,380,592,580"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "ZqtQyNYAQsvtFEx7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graph, which energy source shows the most consistent upward trend over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most consistent upward trend, I analyze the line trajectories for each energy source closely. By observing {the line for Renewable Energy}<r>[0.118, 0.864], [0.944, 0.075]</r>, I note its starting and ending values at the highest and lowest positions respectively, indicating a consistent increase. This is in contrast to other sources like coal and oil that show fluctuations or a decrease in values at certain points.\nBased on the above thought, the answer is:\nRenewable energy demonstrates the most consistent upward trend over the period based on the graph, as its line shows a progressive and steady increase from start to finish among the energy sources depicted."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "MJe5sHNnhMlMqG9z",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage change in oil consumption from the year 2000 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change, I first identify {the oil consumption points in 2000 and 2020}<r>[0.118, 0.357], [0.944, 0.639]</r> which are marked at 480 and 380 respectively. The formula for percentage change is [(New Value - Old Value) / Old Value] * 100. Substituting the values, [(380 - 480) / 480] * 100, provides the percentage change.\nBased on the above thought, the answer is:\n-20.83%"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "vb4VD5OWpRdjcD4h",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart showing the trend of each type over the years into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can extract the data from the line chart. To do this, I first recognize {the x-axis tick labels}[[0.087, 0.927, 0.129, 0.949], [0.296, 0.927, 0.338, 0.949], [0.505, 0.927, 0.547, 0.949], [0.713, 0.927, 0.756, 0.949], [0.922, 0.927, 0.964, 0.949]], which correspond to the years and will serve as row headers in the table. I identify {the legend texts}[[0.118, 0.043, 0.229, 0.061], [0.118, 0.069, 0.187, 0.087], [0.118, 0.095, 0.216, 0.113], [0.118, 0.122, 0.234, 0.139], [0.118, 0.148, 0.208, 0.165]] as column headers representing the different types of trends. To populate the values in the table, I will trace {the line points} for each trend at different years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Immunotherapy,Radiology,AI Diagnostics,DNA Sequencing,Telemedicine\n2000,50.122,78.754,32.612,60.444,38.982\n2005,55.33,80.406,35.972,66.485,47.71\n2010,58.471,81.664,49.315,70.486,59.438\n2015,65.664,83.355,68.935,74.551,75.714\n2020,71.113,86.328,79.673,79.122,89.624"
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "3th2UtnGCOq9AxVN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends shown in the line chart, which type has the steepest growth over the period 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to inspect the line plots for different types, focus particularly on the year 2000 and 2020. I review the plot for {Immunotherapy}<r>[0.108, 0.356, 0.943, 0.644]</r>, {Radiology}<r>[0.108, 0.147, 0.943, 0.251]</r>, {AI Diagnostics}<r>[0.108, 0.238, 0.943, 0.885]</r>, {DNA Sequencing}<r>[0.108, 0.246, 0.943, 0.502]</r>, and {Telemedicine}<r>[0.108, 0.101, 0.943, 0.797]</r> during this period. Analyzing their slope which indicates the rate of change, the plot for Telemedicine has the steepest growth, since it rose from the lowest point in 2000 to one of the highest points in 2020.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, Telemedicine shows the steepest growth from 2000 to 2020, as it demonstrates the most substantial increase in value over this period relative to the other types. The line for Telemedicine started at the bottom among all categories in 2000 but rose significantly and outperformed most of the other categories by 2020. This steep upward trajectory indicates a high rate of growth."
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "oreGVePjSYmccPsz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the increase of AI Diagnostics from 2005 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase of AI Diagnostics from 2005 to 2015, I need to look at the AI Diagnostics line plot and locate the points corresponding to the years 2005 and 2015. These correspond to {the point for AI Diagnostics in 2005} <r>[0.317, 0.839]</r> and {the point for AI Diagnostics in 2015} <r>[0.735, 0.386]</r>. Reviewing these points, I extract the corresponding values of 35.972 for 2005 and 68.935 for 2015. The increase in AI Diagnostics from 2005 to 2015 is then calculated as 68.935 minus 35.972, which equates to 32.963.\nBased on the above thought, the answer is:\n32.963"
+            }
+        ],
+        "id_raw": 1007
+    },
+    {
+        "id": "sWsaz6aA4xg0iAH6",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you create a table where the rows represent the years and the columns represent the different sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with the extraction of information, I identify {the x-axis labels}['xticks'][{'label': '2010', 'label_coordinates': '[0.134, 0.904, 0.187, 0.934]', 'tick_coordinates': '[0.160, 0.896]'}, {'label': '2012', 'label_coordinates': '[0.290, 0.904, 0.343, 0.934]', 'tick_coordinates': '[0.317, 0.896]'}, {'label': '2014', 'label_coordinates': '[0.447, 0.904, 0.500, 0.934]', 'tick_coordinates': '[0.473, 0.896]'}, {'label': '2016', 'label_coordinates': '[0.603, 0.904, 0.656, 0.934]', 'tick_coordinates': '[0.629, 0.896]'}, {'label': '2018', 'label_coordinates': '[0.759, 0.904, 0.812, 0.934]', 'tick_coordinates': '[0.786, 0.896]'}, {'label': '2020', 'label_coordinates': '[0.916, 0.904, 0.969, 0.934]', 'tick_coordinates': '[0.942, 0.896]'}] representing years. Meanwhile, {the legend labels}['sublegend']['items'][{'label': 'Modern Art', 'text_coordinates': '[0.186, 0.046, 0.282, 0.069]'}, {'label': 'Ethnic Art', 'text_coordinates': '[0.186, 0.081, 0.271, 0.104]'}, {'label': 'Performing Art', 'text_coordinates': '[0.186, 0.116, 0.312, 0.139]'}, {'label': 'Artisan Crafts', 'text_coordinates': '[0.186, 0.151, 0.304, 0.174]'}] denote different sectors. Furthermore, {the data points}['lines']['line_0']['points'], ['line_1']['points'], ['line_2']['points'], ['line_3']['points'] offer the actual revenue for each sector in each year.\nBased on the above thought, the answer is:\nYear,Modern Art,Ethnic Art,Performing Art,Artisan Crafts\n2010,14582,13295,12562,13500\n2011,16000,15200,15200,15800\n2012,17500,13500,15800,16500\n2013,19347,25000,12800,9300\n2014,21470,21500,21400,9100\n2015,24000,24000,15300,9800\n2016,26750,10000,12800,10200\n2017,29000,15200,13500,10846\n2018,32000,24500,14000,14500\n2019,36000,27200,15300,15000\n2020,42000,30000,15500,17000"
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "SuLqcyB1nx7hsNTr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend of the Ethnic Art sector over the years based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comprehend the trend for the Ethnic Art sector, I need to analyze {the line plot for Ethnic Art}['lines']['line_1']['coordinates']. Initially, there's a steady increase from 13295 in 2010 to 15200 in 2011. However, there's a dip in 2012 to 13500, followed by a drastic increase to 25000 in 2013. From 2014 to 2015, the revenue remains steady around the 24000 mark. Surprisingly, there's a drop in 2016 to 10000. It then rises to 15200 in 2017 and sharply increases reaching 27200 in 2019. Thankfully, there is stability followed by a gentle increase till 2020 where it stands at 30000. This indicates quite the volatility in the Ethnic Art sector.\nBased on the above thought, the answer is:\nThe revenue in the Ethnic Art sector sees several fluctuations over the years. Initially, it increases slightly, experiences a slight drop, then drastically increases, and remains steady for a while. Following this, it significantly drops and then rises, before finally stabilizing followed by a gentle increase."
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "zSM5oeipaJ4nehjh",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Performing Arts sector get the maximum revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year corresponding to the maximum revenue for the Performing Art sector, I need to scan {the data points for Performing Art}['lines']['line_2']['points'][{'x': 2010, 'y': 12562, 'point_coordinates': '[0.160, 0.653]'}, {'x': 2011, 'y': 15200, 'point_coordinates': '[0.239, 0.602]'}, {'x': 2012, 'y': 15800, 'point_coordinates': '[0.317, 0.590]'}, {'x': 2013, 'y': 12800, 'point_coordinates': '[0.395, 0.648]'}, {'x': 2014, 'y': 21400, 'point_coordinates': '[0.473, 0.482]'}, {'x': 2015, 'y': 15300, 'point_coordinates': '[0.551, 0.600]'}, {'x': 2016, 'y': 12800, 'point_coordinates': '[0.629, 0.648]'}, {'x': 2017, 'y': 13500, 'point_coordinates': '[0.708, 0.635]'}, {'x': 2018, 'y': 14000, 'point_coordinates': '[0.786, 0.625]'}, {'x': 2019, 'y': 15300, 'point_coordinates': '[0.864, 0.600]'}, {'x': 2020, 'y': 15500, 'point_coordinates': '[0.942, 0.596]'}]. The maximum value I see here is 21400 which corresponds to the year 2014.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 1008
+    },
+    {
+        "id": "PtAjPxkN9XfxrPcM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data from the line chart into a table with Years as columns and Activity Type as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I identify {the x-axis tick labels}[[0.129, 0.904, 0.142, 0.934], [0.330, 0.904, 0.343, 0.934], [0.532, 0.904, 0.545, 0.934], [0.733, 0.904, 0.746, 0.934], [0.934, 0.904, 0.948, 0.934]] as the column headers representing the Years. Next, I determine {the legend texts}[[0.838, 0.747, 0.933, 0.770], [0.838, 0.782, 0.925, 0.805]] as the row labels representing the different Activity Types. I then find the line points respectively for each activity in each year to fill the table with appropriate frequency (Reading: {line_0}[[0.135, 0.555, 0.941, 0.642]]; Gardening: {line_1}[[0.135, 0.221, 0.941, 0.352]]).\nBased on the above thought, the answer is:\nActivity Type,Year 1,Year 2,Year 3,Year 4\nReading,45,48,50,47\nGardening,35,32,30,28"
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "CCd0t88ln4TF7wD8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity, Reading or Gardening, portrays a more stable trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which activity portrays a more stable trend, I need to assess the fluctuations in the Reading and Gardening lines ({line_0}[[0.135, 0.555, 0.941, 0.642]] and {line_1}[[0.135, 0.221, 0.941, 0.352]], respectively). From 2018 to 2019 and 2019 to 2020, Reading shows a consistent growth, however, it dips slightly in 2021. In contrast, Gardening displays a steady and consistent decline over the years.\nBased on the above thought, the answer is:\nGardening depicts a more stable trend. Although both activities show changes over the years, the consistency of decline in Gardening, as opposed to the irregular behavior of Reading, suggests greater stability. The predictable decrease in Gardening points to stable, albeit negative, growth."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "NzB846sr70gI7nfW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in frequency for the Reading activity from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total change in the Reading activity, I need to locate the start and end points or coordinates for the Reading line - {line_0}[[0.135, 0.555, 0.941, 0.642]], and extract the corresponding values, i.e. start frequency in 2018 - 45 and final frequency in 2021 - 47. The total change in frequency therefore is 47 - 45 = 2.\nBased on the above thought, the answer is:\n2."
+            }
+        ],
+        "id_raw": 1009
+    },
+    {
+        "id": "6iSAfTCfMbePKwy4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the line chart data into a structured table showing the years as columns and different cultural aspects as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to obtain the data table from this line chart, I identify {the x-axis tick labels} [[0.096, 0.904, 0.149, 0.934], [0.301, 0.904, 0.354, 0.934], [0.505, 0.904, 0.558, 0.934], [0.710, 0.904, 0.762, 0.934], [0.914, 0.904, 0.967, 0.934]] as the headers, i.e., the years. The {legend texts} [[0.748, 0.747, 0.927, 0.770], [0.748, 0.782, 0.916, 0.805]] refer to the different cultural aspects. The values for each year and aspect are extracted from the line points {for Language Diversity} [[0.123, 0.585], [0.327, 0.460], [0.532, 0.398], [0.736, 0.274], [0.940, 0.087]] and {for Cuisine Diversity} [[0.123, 0.772], [0.327, 0.709], [0.532, 0.585], [0.736, 0.485], [0.940, 0.398]].\nBased on the above thought, the answer is:\nAspect,2018,2019,2020,2021\nLanguage Diversity,65,70,75,90\nCuisine Diversity,77,72,75,79"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "CZsmLIiMIvmqdbZm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which aspects of cultural heritage and integration showed more consistent growth over the years? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the line plots for Language Diversity and Cuisine Diversity to answer this. {The line for Language Diversity}[0.123, 0.087, 0.940, 0.585] shows a consistent upward trend from 2018 to 2021, with all points falling above the previous ones, indicating steady growth. In contrast, {the line for Cuisine Diversity}[0.123, 0.398, 0.940, 0.772] shows a decline from 2018 to 2019 before climbing back up, implying more variation and less consistent growth overall.\nBased on the above thought, the answer is:\nAccording to the line chart, Language Diversity showed more consistent growth over the years. Its line plot suggests a steady, consistent growth, with no downturns observed. Contrastingly, Cuisine Diversity shows an inconsistent pattern where it initially declines before rising again, indicating less consistency in growth compared to Language Diversity."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "hsW7x9EkvUXgNJgr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Language Diversity increase from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Language Diversity, I need to identify the start and end points of the line plot for Language Diversity. The points for 2018 and 2021 in {the line points for Language Diversity}[[0.123, 0.585], [0.327, 0.460], [0.532, 0.398], [0.736, 0.274], [0.940, 0.087]] correspond to values of 65 and 90 respectively. Therefore, the increase from 2018 to 2021 is 90 - 65 = 25.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "SsogoJDFLv2yqn3b",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure all the data in the line chart into a table with years and different fitness trends as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the data into a table, I first locate {the x-axis tick labels}[[0.086, 0.928, 0.128, 0.951], [0.253, 0.928, 0.296, 0.951], [0.420, 0.928, 0.463, 0.951], [0.588, 0.928, 0.630, 0.951], [0.755, 0.928, 0.797, 0.951], [0.922, 0.928, 0.964, 0.951]], which represent years, and these may be used as column headers. Next, I identify the {legend texts}[[0.850, 0.810, 0.921, 0.828], [0.850, 0.836, 0.959, 0.854], [0.850, 0.863, 0.962, 0.880], [0.850, 0.889, 0.973, 0.906]] as fitness trends and place these as subsequent column headers. Then it's about populating the values. The respective coordinates of the points in the chart lines representing {Bootcamp}[[0.107, 0.771], [0.274, 0.696], [0.442, 0.621], [0.609, 0.666], [0.776, 0.711], [0.943, 0.681]], {Outdoor Fitness}[[0.107, 0.546], [0.274, 0.500], [0.442, 0.591],  [0.609, 0.696], [0.776, 0.802], [0.943, 0.847]], {Smart Bracelets}[[0.107, 0.094], [0.274, 0.124], [0.442, 0.169], [0.609, 0.199], [0.776, 0.245], [0.943, 0.275]], and {Gym Membership}[[0.107, 0.245], [0.274, 0.290], [0.442, 0.335], [0.609, 0.380], [0.776, 0.425], [0.943, 0.470]] allow us to estimate their values for each year.\nBased on the above thought, the answer is:\nYear,Bootcamp,Outdoor Fitness,Smart Bracelets,Gym Membership\n2015,50,65,95,85\n2016,55,68,93,82\n2017,60,62,90,79\n2018,57,55,88,76\n2019,54,48,85,73\n2020,56,45,83,70"
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "ayU3sXU6ZYiRzXZ6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which fitness trend experienced the most significant decrease over the years? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which fitness trend experienced the most significant decrease, I need to compare the drops in line plots for each fitness trend in the given span of years. Looking at {the line for Bootcamp}[0.107, 0.943], there is a general decreasing slope with a little increase towards the end. \nFor {Outdoor Fitness}[0.107, 0.943], there is a clear decreasing slope representing a constant decrease in value from the year 2015 to 2020. \nThe line for {Smart Bracelets}[0.107, 0.943] also has a continuous decrease, but the values are not decreasing as sharply as Outdoor Fitness. \nFinally, {Gym Membership}[0.107, 0.943] shows a steady decrease, comparable to Smart Bracelets but less than Outdoor Fitness.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Outdoor Fitness seems to have experienced the most significant decrease over the years. Its line plot shows a clear and almost constant downward trend from the year 2015 to 2020, indicating a continuous decrease in its popularity rate. Compared to Bootcamp, Smart Bracelets, and Gym Membership, the decrease in Outdoor Fitness rate is the most pronounced."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "qNgBkhkDbP472B6O",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the popularity rate of smart bracelets decreased from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease in the popularity rate of smart bracelets from 2015 to 2020, I need to look at the respective values at these years. From the line plot for {smart bracelets}[0.107, 0.943], the subject is represented with green color and \"X\" markers. I notice that at 2015, represented by the point {at}[0.107, 0.094], the value is 95, while at 2020, represented by the point {at}[0.943, 0.275], the value is 83. Therefore, the decrease would be 95-83=12.\nBased on the above thought, the answer is:\n12."
+            }
+        ],
+        "id_raw": 1011
+    },
+    {
+        "id": "AK54dPnyus0EY40f",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data into a table format with years as the row labels and the allocation types as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart data, I need to examine the {x-axis tick labels}[[0.096, 0.904, 0.149, 0.934], [0.301, 0.904, 0.354, 0.934], [0.505, 0.904, 0.558, 0.934], [0.710, 0.904, 0.762, 0.934], [0.914, 0.904, 0.967, 0.934]] which represent the row labels or years. Then, I need to look at the {legend's labels}[[0.146, 0.046, 0.300, 0.069], [0.146, 0.081, 0.309, 0.104], [0.146, 0.116, 0.260, 0.139], [0.146, 0.151, 0.290, 0.174]] which represent the column headers or allocation types. I can populate the values by finding {the points of each line}[[0.123, 0.460], [0.327, 0.429], [0.532, 0.386], [0.736, 0.355], [0.940, 0.324], ..., [0.123, 0.087], [0.327, 0.212], [0.532, 0.181], [0.736, 0.112], [0.940, 0.087]] for each allocation type at the respective years and extracting the corresponding values.\nBased on the above thought, the answer is:\nYear,Public Institutions,Private Institutions,Home School,Online_Programs\n2016,35,42.5,22.5,50\n2017,37.5,44,23.5,55\n2018,41,45.5,24.5,57.5\n2019,43.5,47,26,63\n2020,46,49.5,27.5,65"
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "xn3WtW5AaGiGEqfX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which education type seems to have the highest increase in allocation percentage over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which education type has the highest increase in allocation percentage, I need to analyze the trends in the line plots for each type. Looking at {the line for Public Institutions}[0.123, 0.324, 0.940, 0.460], it shows an increase from 35 to 46. This is a difference of 11.\nLooking at {the line for Private Institutions}[0.123, 0.280, 0.940, 0.367], it shows an increase from 42.5 to 49.5. This is a difference of 7.\nInspecting {the line for Home School}[0.123, 0.554, 0.940, 0.616], it shows an increase from 22.5 to 27.5. This is a difference of 5.\nFinally, looking at {the line for Online Programs}[0.123, 0.087, 0.940, 0.274], it shows an increase from 50 to 65. This is a difference of 15.\nBased on the above thought, the answer is:\nBased on the line chart, Online Programs have the highest increase in allocation percentage over the recorded years. It shows an increase from 50% to 65%, representing a total increase of 15%."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "3Wu1OTdjz3pTG1DZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum allocation for Online Programs across all years recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value for Online Programs across the years, I need to locate {the points on the Online Programs' line}[[0.123, 0.274], [0.327, 0.212], [0.532, 0.181], [0.736, 0.112], [0.940, 0.087]] and extract their corresponding values: 50, 55, 57.5, 63, and 65. The maximum value is 65 corresponding to {the year 2020}[0.940, 0.904].\nBased on the above thought, the answer is:\n65."
+            }
+        ],
+        "id_raw": 1012
+    },
+    {
+        "id": "K7dme5k1myWHVpch",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide data from the line chart in a table format, categorizing each of the risk types by year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I would first find {the x-axis tick labels}[[0.096, 0.883, 0.139, 0.943], [0.179, 0.883, 0.221, 0.943], [0.261, 0.883, 0.304, 0.943], [0.344, 0.883, 0.387, 0.943], [0.427, 0.883, 0.469, 0.943], [0.509, 0.883, 0.552, 0.943], [0.592, 0.883, 0.635, 0.943], [0.675, 0.883, 0.717, 0.943], [0.757, 0.883, 0.800, 0.943], [0.840, 0.883, 0.882, 0.943], [0.922, 0.883, 0.965, 0.943]] which represent the column headers or years. Next, I identify {the legend texts}[[0.128, 0.039, 0.244, 0.059], [0.128, 0.069, 0.281, 0.089], [0.128, 0.099, 0.259, 0.119], [0.128, 0.129, 0.259, 0.149]] as the row labels representing different risk types. {The line points}[[0.117, 0.225, 0.944, 0.363], [0.117, 0.214, 0.944, 0.435], [0.117, 0.159, 0.944, 0.380], [0.117, 0.187, 0.944, 0.407]] for each risk are the respective values for each year.\nBased on the above thought, the answer is:\nYear,EM Stock Market,Consumer Confidence,Private Investment,Disposable Income\n2010,100,80,90,85\n2011,95,82,92,87\n2012,93,85,94,90\n2013,94,90,98,95\n2014,96,92,100,97\n2015,98,95,104,100\n2016,100,100,110,105\n2017,103,105,115,110\n2018,107,110,120,115\n2019,112,115,125,120\n2020,118,120,130,125"
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "gmqThgsUF7JfqK9z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk factor showed the highest level in the year 2020, and what could have been a possible reason for this?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which factor had the highest risk in 2020, I need to analyze the last points of each line. The corresponding values at {the 2020 point for EM Stock Market}[0.944, 0.363] is 118, {Consumer Confidence}[0.944, 0.214] is 120, {Private Investment}[0.944, 0.159] is 130 and {Disposable Income}[0.944, 0.187] is 125. The highest value is observed for Private Investment. Given the data and the typical factors influencing Private Investment, this could be caused by increased market optimism or favourable fiscal policy measures, leading to higher levels of private investment.\nBased on the above thought, the answer is:\nThe risk factor that showed the highest level in the year 2020 was Private Investment, possibly due to increased market optimism or favourable fiscal policy measures, leading to higher levels of private investment."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "QL6y7DXOQ9mFsROg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in Private Investment from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I will locate {the line points for Private Investment}[[0.117, 0.380], [0.200, 0.369], [0.283, 0.358], [0.365, 0.336], [0.448, 0.325], [0.531, 0.302], [0.613, 0.269], [0.696, 0.242], [0.778, 0.214], [0.861, 0.187], [0.944, 0.159]] to find the respective values at 2010 and 2020. The value at 2010 is 90, corresponding to {the point}[0.117, 0.380], and the value at 2020 is 130, corresponding to {the point}[0.944, 0.159]. The increase is therefore 130 - 90 = 40.\nBased on the above thought, the answer is:\n40."
+            }
+        ],
+        "id_raw": 1013
+    },
+    {
+        "id": "BCGjtWxop9VPHGp8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the information from the line chart displaying the spending in Healthcare and Medical Technology into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, I first identify {the x-axis tick labels}[[0.102, 0.918, 0.113, 0.943], [0.311, 0.918, 0.322, 0.943], [0.520, 0.918, 0.531, 0.943], [0.729, 0.918, 0.739, 0.943], [0.938, 0.918, 0.948, 0.943]] which represent the column headers as each year from Year 1 to Year 5. Next, I locate {the legend texts}[[0.917, 0.783, 0.971, 0.803], [0.917, 0.813, 0.971, 0.833], [0.917, 0.843, 0.972, 0.863], [0.917, 0.873, 0.973, 0.893]] as the row labels representing the different policy types. Finally, to fill the table, I locate each {line points} for each policy at the respective years and extract the values.\nBased on the above thought, the answer is:\nPolicy, Year_1, Year_2, Year_3, Year_4, Year_5\nPolicy_A, 35, 36, 38, 42, 45\nPolicy_B, 45, 48, 52, 55, 55\nPolicy_C, 33, 37, 40, 45, 42\nPolicy_D, 55, 58, 67, 62, 70"
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "W5acyrz2ZmMt0XOU",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy examining the line chart, can you tell which policy is showing a steady rise in effectiveness over the years? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which policy is steadily rising in effectiveness, I need to analyze the trend in the line plots for each policy. Looking at {the line for Policy A}[[0.107, 0.618, 0.943, 0.813]], I see a regular increase from Year 1 to Year 5. This suggests a steady rise in effectiveness. \nLooking at {the line for Policy B}[[0.107, 0.422, 0.943, 0.618]], the line shows a quick increase and then becomes flat. Hence, it does not show a steady rise.\nBy examining {the line for Policy C}[[0.107, 0.618, 0.943, 0.852]], the line shows a consistent increase till Year 4 and then a decrease at Year 5. Therefore, it does not exhibit a steady rise.\nFinally, for {the line for Policy D}[[0.107, 0.129, 0.943, 0.422]], the line shows a significant increase at Year 3, then falls slightly in Year 4, and increases again at Year 5, so this also doesn't indicate a steady rise.\nBased on the above thought, the answer is:\nAccording to the analysis of the line chart, Policy A shows a steady rise in effectiveness over the years. Its line plot is progressively increasing, suggesting continual improvement in effectiveness from Year 1 to Year 5 without any significant dips, unlike the line plots for other policies."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "StAcxtKUgBRvpyC4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the effectiveness of Policy B from Year 1 to Year 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in effectiveness of Policy B from Year 1 to Year 5, I first locate {the line points for Policy B}[[0.107, 0.618], [0.316, 0.559], [0.525, 0.481], [0.734, 0.422], [0.943, 0.422]] and extract the corresponding values: 45 for Year 1 and 55 for Year 5. The increase in effectiveness can be calculated as 55 (Year 5) - 45 (Year 1) = 10.\nBased on the above thought, the answer is:\n10."
+            }
+        ],
+        "id_raw": 1014
+    },
+    {
+        "id": "Uy34m8jgfecK3oOD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you organize the provided data into a table that represents year versus score percentages of different types of training?"
+            },
+            {
+                "from": "gpt",
+                "value": "For constructing a table from the line chart, I first inspect {the x-axis tick labels}[[0.135, 0.904, 0.162, 0.934], [0.296, 0.904, 0.323, 0.934], [0.458, 0.904, 0.484, 0.934], [0.619, 0.904, 0.645, 0.934], [0.780, 0.904, 0.806, 0.934], [0.941, 0.904, 0.967, 0.934]] serving as column names. The {legend labels}[[0.704, 0.747, 0.793, 0.770], [0.704, 0.782, 0.862, 0.805], [0.704, 0.817, 0.873, 0.840], [0.704, 0.852, 0.966, 0.875]] constitute the row names, representing different types of training. The corresponding values can be extracted by locating the {line points}[[0.135, 0.360], [0.296, 0.333], ..., [0.941, 0.092]] for each training type at each year and corresponding values are annotated.\nBased on the above thought, the answer is:\nYear,IT Training,Soft Skills Training,Leadership Training,Process Improvement Training\n2010,80,75,60,50\n2012,82,80,65,55\n2014,85,78,67,58\n2016,90,83,70,63\n2018,95,88,75,68\n2020,100,93,76,73"
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "PPar4wO89a9UbGnF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends in the chart, which type of training showed the most improvement in performance score over this period? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the trend lines for each type of training over the period. The {IT Training line}[[0.135, 0.092, 0.941, 0.360]] shows a steady upward trend, reaching from 80 in 2010 to 100 in 2020. The {Soft Skills Training line}[[0.135, 0.186, 0.941, 0.427]] also exhibits an overall increase, but less significantly with a score starting from 75 in 2010 to 93 in 2020. {The Leadership Training line}[[0.135, 0.414, 0.941, 0.628]] shows a slow increase from 60 to 76. Finally, {the Process Improvement Training line}[[0.135, 0.454, 0.941, 0.762]] displayed significant improvement, with an increase from 50 to 73.\nBased on the above thought, the answer is:\nBased on the trends in the chart, the Process Improvement Training shows the most improvement in performance score over this period. This is implemented through an increase of 23 percentage points, the largest observed leap relative to its initial level across all types of training. While the IT Training has experienced a remarkable improvement, it started from a relatively higher base level (80), making the improvement of Process Improvement Training more significant in relative terms."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "8oYyrmCPDY8w3TA5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage change for IT Training from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage change for IT Training from 2010 to 2020, I need to locate the {points in the line plot for IT Training}[[0.135, 0.360], [0.296, 0.333], [0.458, 0.293],[0.619, 0.226],[0.780, 0.159],[0.941, 0.092]]. I can observe that the value for IT Training in 2010 is 80 and in 2020 is 100. Thus, the percentage increase over this period is given by the formula (Final value - Initial value)/Initial value * 100, which gives (100 - 80)/80 * 100 = 25%.\nBased on the above thought, the answer is:\n25%."
+            }
+        ],
+        "id_raw": 1016
+    },
+    {
+        "id": "VmetQrRwVsEHOUoh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart displaying the financial technology trends into a structured table with years as rows and the event categories as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I initially identify {the x-axis labels}[[0.097, 0.896, 0.140, 0.949], [0.854, 0.896, 0.896, 0.949]] which represent the years 2020 and 2021 respectively. I then locate {the legend}[[0.777, 0.778, 0.973, 0.796], [0.777, 0.804, 0.942, 0.822], [0.777, 0.831, 0.919, 0.848], [0.777, 0.857, 0.870, 0.874]] which helps identify the categories of events as Blockchain and Digital Currency. Observing {the line points for Blockchain}[[0.118, 0.106], [0.875, 0.122]] and {the line points for Digital Currency}[[0.118, 0.184], [0.875, 0.145]], I can gather the data points for both categories corresponding to each year.\nBased on the above thought, the answer is:\nYear,Blockchain Events,Digital Currency Events\n2020,3661,1220\n2021,4069,1333"
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "kt7nEIq48URXH5Cp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich financial technology category demonstrated a stronger growth trend in the years 2020 and 2021 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which category showed stronger growth, I look at both {the line for Blockchain}<[0.118, 0.106, 0.875, 0.122]> and {the line for Digital Currency}<[0.118, 0.184, 0.875, 0.145]>. The Blockchain line moves from 3661 events to 4069 events, showing a smoother and higher rise. In contrast, the Digital Currency line goes from 1220 events to 1333 events, which is a lesser increase. By comparing these growths directly, Blockchain demonstrates a more robust and substantial increase in events, indicating a stronger growth trend.\nBased on the above thought, the answer is:\nBlockchain demonstrated a stronger growth trend than Digital Currency in the years 2020 and 2021. The line chart indicates that Blockchain had a more substantial increase in significant events, suggesting a steadier and more pronounced growth compared to the slight increase observed in Digital Currency."
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "OpRTk0I5HuPYMX4Y",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Blockchain events from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the percentage increase in Blockchain events from 2020 to 2021, I examine {the line points for Blockchain}[[0.118, 0.106], [0.875, 0.122]], which indicate 3661 events in 2020 and 4069 events in 2021. Using the formula for percentage increase [(New Value - Old Value) / Old Value * 100], I calculate [(4069 - 3661) / 3661 * 100].\nBased on the above thought, the answer is:\n11.12%"
+            }
+        ],
+        "id_raw": 1017
+    },
+    {
+        "id": "Z2koZI05JHtDRQln",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the line chart into a structured table with 'Year' as column and different Investment types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first identify the {the x-axis tick labels}<r>[0.086, 0.928, 0.128, 0.951], [0.295, 0.928, 0.337, 0.951], [0.504, 0.928, 0.546, 0.951], [0.713, 0.928, 0.755, 0.951], [0.922, 0.928, 0.964, 0.951]</r> as columns headers. Next, {the legend texts}<r>[0.117, 0.034, 0.379, 0.052], [0.117, 0.061, 0.436, 0.078], [0.117, 0.087, 0.336, 0.104], [0.117, 0.113, 0.411, 0.130]</r> represent rows headers, each representing different Investment types. Then, using the points in the respective {line points}<r>[0.107, 0.105], [0.316, 0.147], ..., [0.943, 0.554], [0.107, 0.568], ..., [0.943, 0.719], [0.107, 0.918], ..., [0.943, 0.920], [0.107, 0.394], ..., [0.943, 0.538]</r>, I populate the investment amounts within the table for each investment type at each year.\nBased on the above thought, the answer is:\nYear, Local Government Revenue (Billion $),  Community Development Spending (Billion $), Average Home Prices (Billion $),  Public Infrastructure Investment (Billion $)\n2015, 90.5, 22.5, 0.25, 42.5\n2016, 85.8, 24.2, 0.27, 44.0\n2017, 70.6, 27.5, 0.32, 48.3\n2018, 60.5, 33.2, 0.36, 52.9\n2019, 40.7, 39.2, 0.41, 58.5"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "UJv7LTnsB6lPX55z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the displayed line chart, which investment type showed the most consistent increase over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the investment type with the most consistent increase, I need to analyze the line plots for each investment type. Looking at {the line for Local Government Revenue}<r>[0.107, 0.105, 0.943, 0.554]</r>, we can see it decreases over the years, so it does wasn't consistently increasing. \nBased on {the line for Community Development Spending}<r>[0.107, 0.568, 0.943, 0.719]</r>, there is a constant increase over the years. Similarly, we see increases in {the line for Average Home Prices}<r>[0.107, 0.918, 0.943, 0.920]</r> and {the line for Public Infrastructure Investment}<r>[0.107, 0.394, 0.943, 0.538]</r>, but the increase is not as steep or consistent as Community Development Spending. So, Community Development Spending seems to be the one.\nBased on the above thought, the answer is:\nCommunity Development Spending showed the most consistent increase over the period from 2015 to 2019. The line plot indicates a steady and consistent increase each year, showcasing progressive growth in this investment type."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "Eh5zHCibCycCaXsl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Local Government Revenue (Billion $) decreased between 2015 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to determine the local government revenue in 2015 and 2019. Looking at {Local Government Revenue in 2015}<r>[0.107, 0.105]</r> , I see a value of 90.5 billion dollars and at {Local Government Revenue in 2019}<r>[0.943, 0.554]</r>, I see a value of 40.7 billion dollars. The decrease between 2015 and 2019 is therefore 90.5 - 40.7 = 49.8 billion dollars.\nBased on the above thought, the answer is:\n49.8 Billion Dollars"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "Ia3gLNfrEqIBjojN",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a table format with the years as columns and the cuisine types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first observe {the x-axis labels}<r>[0.092, 0.928, 0.140, 0.951], [0.212, 0.928, 0.261, 0.951], [0.333, 0.928, 0.381, 0.951], [0.454, 0.928, 0.502, 0.951]</r> for the columns representing years 2018, 2019, 2020, and 2021. Next, I identify {the legend}<r>[0.081, 0.027, 0.342, 0.137]</r> as the row labels which indicate French and Italian cuisines. Then, I look at {the bars}<r>[0.116, 0.882, 0.937, 0.910]</r>, {the bars}<r>[0.116, 0.879, 0.937, 0.892]</r> representing values for respective cuisines and years.\nBased on the above thought, the answer is:\nYear,2018,2019,2020,2021\nFrench,0.01,0.85,0.9,0.95\nItalian,0.02,1.1,0.93,1.2"
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "jnqP8Zuh97VUxYay",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cuisine showed a more dramatic change in trends over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends over the four years, I analyze the bar fluctuations for both cuisines. The French cuisine bars show values starting from 0.01 moving up to 0.85, then dropping to 0.9, and recovering to 0.95, marked at {the bars for French}<r>[0.116, 0.882, 0.937, 0.910]</r>. In contrast, Italian cuisine starts from 0.02, leaps to 1.1, decreases significantly to 0.93, and then increases to 1.2 as noted in {the bars for Italian}<r>[0.116, 0.879, 0.937, 0.892]</r>. The amplitude of change for Italian cuisine, especially between 2019 and 2020, is more pronounced than that for French cuisine.\nBased on the above thought, the answer is:\nItalian cuisine showed a more dramatic change in trends over the years, with higher swings in popularity, especially noticeable in the leap from 2019 to 2020 followed by a significant drop, before another rise in 2021."
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "YkWYG6ZcYRpFmVRr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_51_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in popularity for French cuisine from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change, I identify the initial value for French cuisine in 2018 by analyzing {the bar for French in 2018}<r>[0.116, 0.882]</r> with a value of 0.01. Then, I locate the value for French cuisine in 2021 at {the bar for French in 2021}<r>[0.937, 0.910]</r> which is 0.95. The total change is calculated as 0.95 - 0.01 = 0.94.\nBased on the above thought, the answer is:\n0.94"
+            }
+        ],
+        "id_raw": 1019
+    },
+    {
+        "id": "aV3sHor6btNQakBD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data presented in the line chart into a table with tasks as rows and cryptocurrencies as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured table data from the line chart, I begin by identifying {the x-axis labels}<r>[0.263, 0.887, 0.316, 0.950]</r> which represent tasks. Next, I use {the legend}<r>[0.325, 0.148, 0.482, 0.301]</r> to recognize the columns named Bitcoin and Blockchain. Finally, to fill this table, I look at each cryptocurrency's line, like {Bitcoin's line points}<r>[0.107, 0.237, 0.472, 0.796]</r> for the specific tasks to pull the corresponding data points.\nBased on the above thought, the answer is:\nTask,Bitcoin,Blockchain\n1,90.12,80.76\n2,78.22,74.84\n3,73.92,67.55\n4,69.33,60.43\n5,65.23,64.37\n6,51.66,60.30\n7,48.28,57.45\n8,46.76,55.76\n9,43.23,51.12\n10,40.85,50.67"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "YfUFTy2RJQ6gPQvm",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might there be a significant drop in Bitcoin's performance between Task 1 and Task 6 as observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To consider why there is a significant drop in Bitcoin's performance from Task 1 to Task 6, I assess {the line plot of Bitcoin}<r>[0.107, 0.237, 0.310, 0.673]</r>. Initially, Bitcoin starts at 90.12% and declines to 51.66% by Task 6. This could be due to various external market forces affecting cryptocurrency values, such as regulatory news impacting investor sentiment or technological challenges inherent within the blockchain system that Bitcoin operates upon. The steep decline may reflect a more volatile period for Bitcoin during these tasks.\nBased on the above thought, the answer is:\nThe significant drop in Bitcoin's performance from Task 1 to Task 6 is likely due to external market variables that include regulatory challenges or investor responses to news related to cryptocurrencies. This volatility is inherent in the crypto market and can notably impact values as observed in the chart's plotted data."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "VCPAREaep9qes3Uq",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage decrease for Bitcoin from Task 1 to Task 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage decrease for Bitcoin from Task 1 to Task 10, I observe {Bitcoin's first point}<r>[0.107, 0.237]</r> at Task 1 and {Bitcoin's last point}<r>[0.472, 0.796]</r> at Task 10. The values are 90.12 at the beginning and 40.85 at the end. The percentage decrease is calculated as ((90.12 - 40.85) / 90.12) * 100.\nBased on the above thought, the answer is:\n54.67"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "e9wTQTwnkRGktTYQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the line chart showing sentiments towards e-commerce trends by year and sentiment type into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I need to parse and categorize the information. I start by focusing on {the x-axis tick labels}<r>[0.059, 0.893, 0.520, 0.931]</r> that indicate different years. For the sentiment types, the {legend items}<r>[0.044, 0.104, 0.159, 0.162]</r> display each line representing synthetic and human-authored sentiments. The {line points}<r>[0.059, 0.391, 0.969, 0.856]</r> help identify specific sentiment scores for each category within the specified year. Correlating this data allows me to construct the table.\nBased on the above thought, the answer is:\nYear,Synthetic Sentiment,Human-authored Sentiment\n2020,-0.7,0.5\n2021,-0.6,0.7\n2022,-0.5,0.8\n2023,-0.3,0.9"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "dfj7ZS64TJB6SbyD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sentiment type shows more stability in its trend over the recent years towards e-commerce according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stability of sentiment trends involves examining the fluctuation in {the line sections for both sentiment types}<r>[0.059, 0.391, 0.969, 0.856]</r>. Reviewing the plotted data, the line representing synthetic sentiment shows almost a steady decline with fewer abrupt changes, while the line for human-authored sentiment demonstrates more significant jumps and fluctuations. I determine the stability based on fewer sharp changes and a steadier line progression.\nBased on the above thought, the answer is:\nThe synthetic sentiment type shows more stability in its trend towards e-commerce over the recent years, as evidenced by its smoother and less volatile line progression compared to the more fluctuating human-authored sentiment."
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "7w5ri9Z7TGHT3kh8",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average human-authored sentiment value for e-commerce between the years 2020 and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average of human-authored sentiment values over the years 2020 to 2023, first, I pinpoint the {human-authored line points}<r>[0.059, 0.856, 0.197, 0.805]</r> corresponding to those years. Extracting the actual sentiment values for each year: 0.5, 0.7, 0.8, 0.9 and calculate the average, (0.5 + 0.7 + 0.8 + 0.9) / 4.\nBased on the above thought, the answer is:\n0.725"
+            }
+        ],
+        "id_raw": 1021
+    },
+    {
+        "id": "dtTIlmyXtPYFZ84t",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the bar chart into a structured table having the 'Category' as rows and 'Change' as the header of the second column?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I would identify {the category labels}<r>[0.088, 0.234, 0.133, 0.254]</r>,<r>[0.088, 0.295, 0.145, 0.315]</r>,<r>[0.088, 0.357, 0.145, 0.377]</r>,<r>[0.088, 0.419, 0.132, 0.439]</r>,<r>[0.088, 0.481, 0.138, 0.501]</r>,<r>[0.088, 0.543, 0.130, 0.563]</r>,<r>[0.088, 0.605, 0.120, 0.625]</r> as rows, representing different categories. The 'Change' in percentages can be extracted from {the lengths of the bars}<r>[0.242, 0.241, 0.497, 0.257]</r>,<r>[0.242, 0.302, 0.457, 0.318]</r>,<r>[0.242, 0.364, 0.454, 0.380]</r>,<r>[0.242, 0.426, 0.457, 0.442]</r>,<r>[0.242, 0.488, 0.619, 0.504]</r>,<r>[0.242, 0.550, 0.534, 0.566]</r>,<r>[0.242, 0.612, 0.730, 0.628]</r>.\nBased on the above thought, the answer is:\nCategory,Change\nmoral_values,-20.5\npolitical_bias,-30.3\nreligious_beliefs,-28.6\nmaterialism,23.8\nself_expression,31.6\nprogressive,-30.0\ntechnology,42.2"
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "Z5rk8B0k4osc8ITA",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the bar chart, which category showed the highest increase in society?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category showed the highest increase, I need to examine {the bars representing the different categories}<r>[0.242, 0.241, 0.497, 0.257]</r>,<r>[0.242, 0.302, 0.457, 0.318]</r>,<r>[0.242, 0.364, 0.454, 0.380]</r>,<r>[0.242, 0.426, 0.457, 0.442]</r>,<r>[0.242, 0.488, 0.619, 0.504]</r>,<r>[0.242, 0.550, 0.534, 0.566]</r>,<r>[0.242, 0.612, 0.730, 0.628]</r>. Bars extending to the right show positive change, and among these, the category with the longest bar indicates the highest increase.\nBased on the above thought, the answer is:\n'Technology' showed the highest increase in society, as it is represented by the longest bar extending to the right in the bar chart."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "JoobF0w5fba3MyI5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did 'materialism' increase according to the bar graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in 'materialism', I would need to identify {the bar representing 'materialism'}<r>[0.242, 0.488, 0.619, 0.504]</r> and determine the corresponding value. The length and direction of the bar indicate that 'materialism' saw a positive change, and the value of this change can be extracted.\nBased on the above thought, the answer is:\n23.8%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "5hozHYCAym8V5QmH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the numerical data across all tasks into a table, with each trend represented as a column and tasks as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I identify {the x-axis tick labels}[[0.075, 0.911, 0.082, 0.934], [0.119, 0.911, 0.126, 0.934], [0.163, 0.911, 0.170, 0.934], [0.207, 0.911, 0.214, 0.934], [0.251, 0.911, 0.258, 0.934], [0.295, 0.911, 0.302, 0.934], [0.339, 0.911, 0.346, 0.934], [0.382, 0.911, 0.390, 0.934], [0.426, 0.911, 0.434, 0.934], [0.467, 0.911, 0.481, 0.934]] as the row labels signifying the various tasks. Then I look for {the legend texts}[[0.306, 0.083, 0.483, 0.106], [0.788, 0.083, 0.977, 0.106]] as the column labels that represent the two trends - Global Trade Volume and Supply Chain Performance Index. To populate the table, I analyze the y-coordinates of {the line points}[[0.079, 0.163], [0.573, 0.398]...] for each trend at the respective tasks and extract the values.\nBased on the above thought, the answer is:\nTask,Global Trade Volume (billion $),Supply Chain Performance Index\n1,100,80\n2,110,82\n3,120,85\n4,118,86\n5,121,80\n6,125,79\n7,130,80\n8,132,85\n9,125,88\n10,120,90"
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "Ai7GWUCmB86UF65n",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which trend shows a consistent rise throughout the tasks? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend that shows a consistently rising pattern across all tasks, I need to look at the movement on {the Global Trade Volume line}[[0.079, 0.163, 0.474, 0.342]] and {the Supply Chain Performance Index line}[0.573, 0.398, 0.968, 0.460]. If the successive y-coordinates of the points on the line chart consistently increase, it would indicate a continuous rise.\nBased on the above thought, the answer is:\nThe 'Global Trade Volume' shows a consistently rising pattern across the tasks. There is a consistent increase from the first task (100 billion dollars) to the eighth task (132 billion dollars), indicating a steady rise. Although there is a minor dip in tasks 4 and 9, the general trend is of continued growth in global trade volume."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "3rJJk6JcyP980IFR",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich task has the most variance between Global Trade Volume and Supply Chain Performance Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the task with the most variance between the two parameters, I analyze {the line points for Global Trade Volume}[[0.079, 0.163], [0.123, 0.286], [0.167, 0.230], [0.210, 0.242], [0.254, 0.225], [0.298, 0.202], [0.342, 0.174], [0.386, 0.163], [0.430, 0.202], [0.474, 0.230]] and {the line points for Supply Chain Performance Index} [[0.573, 0.454], [0.616, 0.443], [0.660, 0.426], [0.704, 0.421], [0.748, 0.454], [0.792, 0.460], [0.836, 0.454], [0.880, 0.426], [0.924, 0.410], [0.968, 0.398]] at each task and calculate the difference. The task with the highest difference will have the most variance.\nBased on the above thought, the answer is:\nTask 1"
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "bz82UGSnLwwPfRin",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table containing annual service quality scores for each of the three types of services, using the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "For creating the table, I first look at {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896]], which represent the years from 2011 to 2015. Next, I see {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] which define three types of services, Healthcare, Education, and Public Transit. I'll record {the line points}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]] for each year and each type of service to complete the data in the table.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,7.8,7.2,8.5\n2012,8.0,7.4,8.0\n2013,8.2,7.6,7.5\n2014,8.4,7.8,7.0\n2015,8.1,8.0,6.5"
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "diSccWOXSgMkIgdl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which service domain shows the most consistent improvement in quality scores over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I look at the lines for each service domain. The line for {Education}[0.102, 0.211, 0.798, 0.729] shows a consistent upward trend from 7.2 to 8.0 over the five years. However, the line for {Healthcare}[0.102, 0.470, 0.798, 0.696] though starting with a higher score at 7.8, sees a drop in the last year. Lastly, the line for {Public Transit}[0.102, 0.599, 0.798, 0.632] shows a pronounced decline over the years.\nBased on the above thought, the answer is:\nThe Education service domain shows the most consistent improvement in quality scores over the years. This is evident from its line graph that consistently rises from 7.2 in 2011 to 8.0 in 2015. On the other hand, although Healthcare started with a higher score, it witnessed a drop in the last year while Public Transit has seen a decrease throughout the period."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "sAUFuhIMvcbnKeQV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total growth in the quality score of Education service over the 5-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total growth in Education service quality, I need to check the {first line point for Education}[0.102, 0.354] and the {last line point for Education}[0.798, 0.519] which corresponds to the quality score in years 2011 and 2015 respectively. The difference in these points gives the total growth which is 8.0 - 7.2 = 0.8.\nBased on the above thought, the answer is:\n0.8."
+            }
+        ],
+        "id_raw": 1024
+    },
+    {
+        "id": "3fwQuYrOlywbSKrM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart into a detailed table with the subcultures listed as rows and their change in popularity over time as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To develop a table from the line chart, recognizing {the x-axis tick labels}<r>[0.056, 0.922, 0.479, 0.985]</r> which signify different subcultures is essential. Inspecting the {the legend}<r>[0.337, 0.111, 0.479, 0.226]</r> next helps me identify specific subcultures associated with each line. Inspecting each line, for instance, {the line for Boho}<r>[0.056, 0.461, 0.966, 0.831]</r> allows me to pinpoint the change in popularity at evenly spaced increments corresponding to the timeline on the x-axis, extracting precise values for the trend over time.\nBased on the above thought, the answer is:\nSubculture,Start,1 Month,3 Months,6 Months,1 Year\n80s Punk,-10,-15,-18,-19,-20\nHipster,-5,-7,-8,-9,-10\nGothic,5,15,22,28,30\nSteampunk,8,18,28,36,40\nBoho,-20,-35,-40,-45,-50\nHarajuku,10,25,40,50,60"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "m3bmMYXJgXKfos8r",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain why the Boho fashion trend might have seen a significant decline according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line corresponding to Boho}<r>[0.576, 0.461, 0.966, 0.831]</r>, it continuously descends throughout the period. A plausible explanation, assessed visually through the line's consistent decline amid seasonal markers, could be a shift in consumer preferences or the cyclic nature of fashion. Additionally, it might be influenced by external cultural or economic factors not directly visible in the graph but suggested by the steady negative trend.\nBased on the above thought, the answer is:\nThe significant decline in Boho fashion trend seen in the line chart may be attributed to seasonal changes affecting fashion trends, shifts in consumer preferences, or possibly external cultural and economic impacts that resonate less with Boho style over the evaluated period."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "zvKoGHu5o48YFnGy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum increase in popularity observed from the chart for any subculture throughout the entire period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the maximum increase, I examine {the line with highest final value}<r>[0.576, 0.128, 0.966, 0.535]</r>, which indicates the highest increase in popularity. Following the data points, I find the highest value achieved, corresponding to {Harajuku's line at 1 year}<r>[0.966, 0.461]</r>.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "jAZg5dnkwDP4XcpB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart that compares monthly Retail Sales for both In-store and Online into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract and present the data in the table format from the chart shown, I first looked at {the x-axis tick labels}<r>[0.257, 0.942, 0.285, 0.970]</r> which represent the months. Then, I identified {the line data for In-store sales}<r>[0.067, 0.566, 0.475, 0.611]</r> and {the line data for Online sales}<r>[0.562, 0.860, 0.970, 0.865]</r> to capture the values across different months indicated by their plotted points. These provided a clear set of monthly data for both sales channels.\nBased on the above thought, the answer is:\nMonth,In-store Sales,Online Sales\n1,-700,60\n2,-710,61\n3,-705,62\n4,-750,63\n5,-800,65\n6,-780,66\n7,-760,64\n8,-740,63\n9,-720,62\n10,-690,59"
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "UMFKz43dOj0ZzPVZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trending pattern is evident for Government Support throughout the months?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line plot for Government Support}<r>[0.562, 0.866, 0.970, 0.869]</r>, the initial value begins at 60 and ends at 59, with slight fluctuations peaking at 66 and dropping to the lowest at 59 twice. The line gently fluctuates upward towards the mid-months before tapering back down. This indicates a pattern of marginal increase followed by a gentle decrease by year-end.\nBased on the above thought, the answer is:\nThe pattern evident for Government Support over the months is one of moderate fluctuation, where there is a gentle increase in the middle of the year, peaking at 66, and a subsequent decrease towards the end, tapering back to just below the starting value."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "9gL253WgU1l78IoB",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the R&D Spend increase from the 1st to the 10th month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in R&D Spend over the months, I analyze the values at the start and end of the period. {The point for R&D Spend in the 1st month}<r>[0.067, 0.279]</r> is 1500 and for {the 10th month}<r>[0.475, 0.341]</r> is 1350. The difference is then 1350 - 1500.\nBased on the above thought, the answer is:\n-150."
+            }
+        ],
+        "id_raw": 1026
+    },
+    {
+        "id": "sYRyWXRNmWOshsuf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the graph depiction of Data Science and Robotics trends across various stages into a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a structured data table, I first need to locate {the x-axis labels}[[0.022, 0.893, 0.114, 0.921], [0.143, 0.893, 0.235, 0.921], [0.274, 0.893, 0.345, 0.921], [0.368, 0.893, 0.493, 0.921]] which represent the columns or stages. Subsequently, I identify the legend texts that represent the two rows or innovation areas, namely {Data Science Innovation}[0.328, 0.100, 0.440, 0.128] and {Robotics Innovation}[0.844, 0.100, 0.935, 0.128]. For the row values, I will identify {the datapoints for each line}[[0.068, 0.540], [0.189, 0.669], [0.310, 0.337], [0.431, 0.386], [0.563, 0.430], [0.684, 0.556], [0.805, 0.228], [0.926, 0.499]] and extract the corresponding values.\nBased on the above thought, the answer is:\nInnovation Area,Subdued Expansion,Accelerated Decline,Inexorable Rise,Unpredictable Fluctuations\nData Science Innovation,8.5,5.3,13.5,12.3\nRobotics Innovation,11.2,8.1,16.2,9.5"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "m0T7glnxzJVcH6CC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends displayed in the chart, which innovation area appears to have experienced a more drastic change upwards or downwards? Describe the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the fluctuations experienced by {Data Science Innovation}[0.068, 0.337, 0.431, 0.669] and {Robotics Innovation}[0.563, 0.228, 0.926, 0.556] across different stages. For Data Science Innovation, there is a decrease from 8.5 to 5.3, followed by an increase to 13.5, and then a slight decrease to 12.3. This represents a drastic upward change. In contrast, for Robotics Innovation, there is an increase from 11.2 to 16.2, followed by a decrease to 9.5. Even though there is a significant increase at the start, the overall change is presented as a decrease, and the fluctuations are less pronounced when compared to Data Science Innovation.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, Data Science Innovation appears to have experienced a more drastic change. There is a notable decline in the initial stage, followed by a drastic increase, and a minor decrease towards the end. This variance in ups and downs denotes a more drastic change when compared with the changes in Robotics Innovation."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "pnxzHdJbOF6LW5cU",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in value between the highest and lowest points for the Data Science Innovation trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to locate {the points of the Data Science Innovation trend}[[0.068, 0.540], [0.189, 0.669], [0.310, 0.337], [0.431, 0.386]] with the associated values of 8.5, 5.3, 13.5, and 12.3 respectively. The highest point is 13.5 happening at {Inexorable Rise}[0.310, 0.883] and the lowest point is 5.3 which happens at {Accelerated Decline}[0.189, 0.883]. Therefore, the difference would be 13.5 - 5.3 = 8.2.\nBased on the above thought, the answer is:\n8.2."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "NOV9ayVnDHMUDxbW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you extract the data into a table format with type of Technology Innovations as columns and Groups as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.077, 0.903, 0.096, 0.931], [0.174, 0.903, 0.194, 0.931], [0.272, 0.903, 0.289, 0.931], [0.369, 0.903, 0.394, 0.931], [0.467, 0.903, 0.489, 0.931]], which represent the column headers or the type of Technology Innovations. Next, I identify the groups as the row labels, these are 'EdTech companies' and 'Startups'. To populate the values, I locate {the line points}[[0.077, 0.112], [0.174, 0.459], ..., [0.467, 0.161]] for each group with the respective Technology Innovations and extract the percent values annotated.\nBased on the above thought, the answer is:\nGroups,Quantum Computing,Augmented Reality,Artificial Intelligence,e-Learning Software,Multimedia Networking,Interactive Whiteboard,Virtual Classroom\nEdTech Companies,28,36,43,48,55,60,65\nStartups,32,42,50,59,68,75,83"
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "WbjZCAsrN6Fb5Tg7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Technology Innovation does EdTech Companies interact with the least and why might this be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which Technology Innovation EdTech Companies interact with the least, I need to analyze the line plot for EdTech companies. Looking at {the line for EdTech Companies}[[0.077, 0.459, 0.467, 0.161]], Quantum Computing has the lowest interaction rate of 28. Quantum Computing is a nascent field and might still be in a research phase with many complexities to tackle before being used extensively in the education sector. This might explain the lower interaction with this technology innovation by EdTech companies.\nBased on the above thought, the answer is:\nEdTech Companies interact the least with Quantum Computing. This could be because Quantum Computing is a relatively new and complex field and may still be in the research phase with many complexities to tackle before it can be extensively used in the education sector."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "Nw1sVTixqntmAcQC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more does Startups interact with Augmented Reality compared to EdTech Companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in interaction rates with Augmented Reality between Startups and EdTech companies, I need to locate {the line points for Augmented Reality}[[0.174, 0.459], [0.174, 0.112]] and extract the corresponding values: 36 for EdTech companies and 42 for Startups. The difference between the interaction rates of Startups and EdTech companies is therefore 42 - 36 = 6.\nBased on the above thought, the answer is:\n6% more."
+            }
+        ],
+        "id_raw": 1028
+    },
+    {
+        "id": "e3lw1P6mgCPRBRNU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart comparing the synthetic data and human data into a structured table with distinct columns for each category and rows for each type of data?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I focus on {the bars of the synthetic data}<r>[0.048, 0.343, 0.235, 0.557]</r> and their corresponding negative values from the left side of the chart. These bars also align with specific categories labeled next to them. I perform the same analysis for {the bars of the human data}<r>[0.765, 0.343, 0.952, 0.557]</r>, reviewing their placement on the right side of the chart with positive values. By reading the labels directly from the bars, I configure the data for the table as structured.\nBased on the above thought, the answer is:\nCategory, Synthetic Data, Human Data\nSustainable Energy, -30, 65\nWaste Reduction, -45, 85\nCommunity, -70, 99\nSustainable Sourcing, -85, 120"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "5ZAHQb9NinKuhKbr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which category shows the most significant difference between the company's performance and the industry average?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the length and label values of each bar for both synthetic and human data. Observing {the bars for Sustainable Sourcing}<r>[0.045, 0.402, 0.240, 0.459]</r> and their corresponding label values of -85 for synthetic data and 120 for human data, the difference is the greatest among all categories. The substantial difference is visually confirmed by the length discrepancies between these two bars compared to others.\nBased on the above thought, the answer is:\nThe 'Sustainable Sourcing' category shows the most significant difference between the company\u2019s performance and the industry average as indicated by the large contrast in values and bar lengths between the synthetic data and human data."
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "N4oLxtnPRaacEL3S",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in performance on 'Community' between the synthetic data and human data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage difference, I begin by locating {the 'Community' category bar for synthetic data}<r>[0.052, 0.615, 0.237, 0.672]</r> which indicates a value of -70, and {the 'Community' category bar for human data}<r>[0.763, 0.615, 0.948, 0.672]</r> with a value of 99. Using the formula percentage difference = ((human data - synthetic data) / synthetic data) * 100, I plug in the values: ((99 - (-70)) / -70) * 100 = 240%\nBased on the above thought, the answer is:\n240%"
+            }
+        ],
+        "id_raw": 1029
+    },
+    {
+        "id": "O36sBIfiIrS5Pso5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the market share percentages for different industries across the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the structure of the data in the line chart, with years marked on {the x-axis}<r>[0.259, 0.943, 0.286, 0.975]</r> and percentage values annotated for each line point, I will summarize this into a structured table. Reviewing each line, such as {the line for Agriculture}<r>[0.070, 0.197, 0.475, 0.281]</r> and {the line for Retail}<r>[0.070, 0.335, 0.475, 0.461]</r>, I will extract the annual market share values for all industries, including Agriculture, Retail, Manufacturing, and Services.\nBased on the above thought, the answer is:\nYear,Agriculture,Retail,Manufacturing,Services\n2010,15.7,10.5,7.8,5.5\n2011,15.1,10.9,8.2,5.8\n2012,16.3,10.7,8.7,6.1\n2013,16.8,11.2,9.0,6.5\n2014,15.9,11.8,9.5,6.7\n2015,15.3,12.1,10.0,7.1\n2016,15.1,12.4,11.0,7.3\n2017,14.8,12.7,11.5,7.6\n2018,15.7,13.0,12.0,7.9\n2019,15.5,13.5,12.3,8.2"
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "Wymq0aq5KdoU5Wn1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the Manufacturing industry show a consistent growth trend from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the plot line for {Manufacturing from 2010 to 2019}<r>[0.565, 0.386, 0.970, 0.575]</r>, the trendline progresses upward linearly each year. The data points start at lower market share values around 7.8 in 2010 and consistently increase each year until reaching a peak of 12.3 in 2019. This consistent increase suggests a stable growth over the decade, potentially reflecting the industry\u2019s expansion and increased production capacity or market demand.\nBased on the above thought, the answer is:\nThe Manufacturing industry shows a consistent growth trend from 2010 to 2019 likely due to increased production capacity and greater market demand over the years, as indicated by the upward progression of the annual market share values on the line chart."
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "p8qGQoW6AeyMiDnK",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share for the Manufacturing industry from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average, I'll first extract the annual market share values for {Manufacturing}<r>[0.565, 0.386, 0.970, 0.575]</r>. The values collected are: 7.8, 8.2, 8.7, 9.0, 9.5, 10.0, 11.0, 11.5, 12.0, 12.3. Calculating the average involves summing these figures and then dividing by the number of years, which is 10.\nBased on the above thought, the answer is:\n10.0"
+            }
+        ],
+        "id_raw": 1030
+    },
+    {
+        "id": "NvWFa8GwNr9MK3UP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the performance figures for the production process stages represented in the line chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Let's start by identifying the stages along {the x-axis tick labels}[[0.101, 0.821, 0.112, 0.868], [0.142, 0.821, 0.153, 0.868], [0.183, 0.821, 0.193, 0.868], [0.223, 0.821, 0.234, 0.868], [0.264, 0.821, 0.275, 0.868], [0.304, 0.821, 0.315, 0.868], [0.345, 0.821, 0.356, 0.868], [0.386, 0.821, 0.397, 0.868], [0.426, 0.821, 0.437, 0.868], [0.461, 0.821, 0.483, 0.868]], these are our column headers. The two methods of efficiency evaluation represented in the chart are 'Manual' and 'Machines'. These are identified in {the legend texts}[[0.355, 0.166, 0.475, 0.213],[0.822, 0.166, 0.966, 0.213]], these will form our row labels. Now, the line graphs for these methods have various points plotted on them for different stages. These can be found at {the line points for Asian Recipes}[0.107, 0.164, 0.472, 0.459] and {the line points for Western Recipes}[0.597, 0.287, 0.963, 0.493]. The y values at these points represent the performance figures for the corresponding method of evaluation at each stage.\nBased on the above thought, the answer is:\nMethod,Fabrication,Assembling,Packaging,Dispatching,Marketing,Designing,Testing\nMachines,89,91,92,87,95,80,70\nManual,70,75,79,77,78,82,52"
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "rJ9H5coQg1S4WMaf",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method of efficiency evaluation shows a sharp decline in the 'Testing' stage, and what does this suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the 'Testing' point on both line graphs found at {['Testing'] point on Machines line}<r>[0.472, 0.459]</r> and {['Testing'] point on Manual line}<r>[0.963, 0.493]</r>. The decline on the 'Testing' stage is sharp for the 'Manual' method. This suggests that Testing through Manual method is significantly less efficient compared to other stages indicating a possible lack of expertise or need for more structured processes during this stage.\nBased on the above thought, the answer is:\nThe 'Manual' method of efficiency evaluation shows a sharp decline in the 'Testing' stage. This suggests that the Testing stage might be fraught with inefficiencies when conducted manually, indicating a need to possibly automate this stage or bring in more skilled professionals to oversee this task."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "kmJMVHSoGRQSI8d4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in performance figures for the 'Designing' stage between the 'Machines' and 'Manual' methods of evaluation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Designing' stage can be located on the x-axis, and the corresponding performance for both the 'Machines' and 'Manual' methods can be identified at the corresponding points on their respective line graphs. These points can be found at {['Designing'] point on the Machines line}<r>[0.350, 0.364]</r> and {['Designing'] point on the Manual line}<r>[0.841, 0.439]</r>. The difference in performance would be the absolute difference between the y-coordinates of these two points on the graph.\nBased on the above thought, the answer is:\n2."
+            }
+        ],
+        "id_raw": 1031
+    },
+    {
+        "id": "iQrno9dZmu9MICZC",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a data table showing the usage trends of Facebook and Twitter over the weeks from the data represented in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I will first identify and read the weeks from {the x-axis tick labels}<r>[0.047, 0.903, 0.056, 0.931], [0.131, 0.903, 0.140, 0.931], [0.216, 0.903, 0.225, 0.931], [0.300, 0.903, 0.309, 0.931], [0.385, 0.903, 0.394, 0.931], [0.465, 0.903, 0.482, 0.931]</r>. Next, I will extract the usage values for Facebook and Twitter which are represented by {the line for Facebook}<r>[0.094, 0.149, 0.474, 0.484]</r> and {the line for Twitter}<r>[0.094, 0.387, 0.474, 0.521]</r> respectively.\nBased on the above thought, the answer is:\nWeek,Facebook,Twitter\n1,1000,500\n2,950,520\n3,900,540\n4,850,560\n5,800,580\n6,750,600"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "jMJxvDgVThrR4Yrd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform demonstrates a consistently increasing trend according to this visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which platform has a consistently increasing trend, I will examine the slope and direction of each line. Observing {the line for Twitter}<r>[0.094, 0.387, 0.474, 0.521]</r> and {the line for Instagram}<r>[0.586, 0.674, 0.966, 0.707]</r>, it's evident that while both demonstrate an increase, Twitter shows a more consistent positive slope without any decline, as evident in week by week increments from 500 in Week 1 to 680 in Week 10.\nBased on the above thought, the answer is:\nTwitter demonstrates a consistently increasing trend over the weeks according to this visualization, showing incremental increases from the start to the end period without any dips, unlike other platforms."
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "LgquNE05FMIPXkhG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in Instagram platform usage from Week 1 to Week 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total change, I identify the initial usage at Week 1 and the final usage at Week 10 from {the line for Instagram}<r>[0.586, 0.674, 0.966, 0.707]</r>. The initial usage was 250 and the final usage was 295. The total change is obtained by subtracting the initial usage from the final usage.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 1032
+    },
+    {
+        "id": "QPT7THYDPtjgr2MR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the provided line chart using Facebook, Twitter, and Instagram as headers and list their user counts for each year from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe the line chart to identify the relevant data points. I locate {the x-axis tick labels}<r>[0.112, 0.821, 0.123, 0.868], [0.151, 0.821, 0.162, 0.868], [0.191, 0.821, 0.202, 0.868], [0.230, 0.821, 0.241, 0.868], [0.270, 0.821, 0.281, 0.868], [0.309, 0.821, 0.320, 0.868]</r> for the years. I also observe the legends: {Facebook}<r>[0.164, 0.166, 0.271, 0.213]</r>, {Twitter}<r>[0.164, 0.236, 0.273, 0.283]</r>, and {Instagram}<r>[0.654, 0.166, 0.763, 0.213]</r> for platform identification. Finally, I extract the respective user counts by linking the data points on {the lines for Facebook, Twitter, and Instagram}<r>[0.117, 0.550], [0.157, 0.507], [0.196, 0.465], [0.236, 0.401], [0.275, 0.337], [0.315, 0.295]</r> for each platform across the years.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram\n2015,200M,150M,90M\n2016,240M,180M,110M\n2017,280M,210M,130M\n2018,320M,230M,180M\n2019,370M,260M,240M\n2020,430M,290M,340M"
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "SCNofRgUTKFqnOdJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform exhibited the most consistent growth over the period 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the consistency in growth, I examine the slopes of {the lines for Facebook}<r>[0.117, 0.550, 0.473, 0.295]</r>, {Twitter}<r>[0.117, 0.635, 0.473, 0.372]</r>, and {Instagram}<r>[0.117, 0.741, 0.473, 0.192]</r> across the years. Facebook shows a consistent upward trajectory with an increasingly steep slope, indicating consistent user growth over each year. Twitter also shows a consistent growth, but the slope is less steep compared to Facebook. Instagram, however, shows a variable slope with a significant spike in 2020 which indicates inconsistent growth rates, especially with a very steep increase at the end.\nBased on the above thought, the answer is:\nTwitter, as evidenced by its relatively even slope, exhibited the most consistent growth across the years 2015 to 2020. This steady slope shows a consistent gain in user count each year without dramatic spikes or drops."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "qdyvTl7D1UJxJglS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in Instagram's user count from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I first identify the user counts for Instagram from {the line for Instagram}<r>[0.117, 0.167, 0.473, 0.087]</r> at the beginning and end of the selected period, observing the values as 90 million in 2015 and 340 million in 2020. I then use the formula: [(Final value - Initial value) / Initial value] * 100 to find the percentage increase. Calculations are as follows: [(340 - 90) / 90] * 100.\nBased on the above thought, the answer is:\n277.78%"
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "uX8G0tYCgpvGh2PQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the sales data from the line chart depicting sales types across quarters into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data, I locate {the legend items for Hybrid and Electric Sales}<r>[0.074, 0.091, 0.176, 0.183]</r> and then disentangle {the points corresponding to these sales}<r>[0.088, 0.113, 0.471, 0.603]</r>. Recognizing the pattern across quarters and sales data, I construct the table by correlating quarter labels from {the x-axis tick labels}<r>[0.081, 0.895, 0.095, 0.923], [0.136, 0.895, 0.150, 0.923], ..., [0.451, 0.895, 0.492, 0.923]</r> with sales units mentioned at these points.\nBased on the above thought, the answer is:\nQuarter,Hybrid Sales,Electric Sales\nQ1,150000,90000\nQ2,140000,110000\nQ3,160000,120000\nQ4,175000,130000\nQ1_next,190000,140000\nQ2_next,210000,160000\nQ3_next,225000,180000\nQ4_next,240000,200000"
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "F1ctjYjujjCCXeal",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the sales trends of various vehicle types over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the sales trends, I examine the lines plotted for each vehicle type, notably {Hybrid and Electric lines}<r>[0.088, 0.113, 0.471, 0.603]</r> and {Gasoline and Diesel lines}<r>[0.582, 0.113, 0.966, 0.440]</r>. The visual analysis shows a rising trend for Hybrid and Electric sales indicating gaining popularity or preference. Meanwhile, Gasoline and Diesel sales exhibit a gradual decline, possibly due to environmental concerns or advancements in alternative energy vehicles. This is assessed by observing the higher sales points at the beginning and lower points toward the end for these fuel types.\nBased on the above thought, the answer is:\nThe sales trends suggest an increasing preference for Hybrid and Electric vehicles contrasted with a declining demand for Gasoline and Diesel vehicles over the period illustrated. This could reflect shifting consumer preferences toward more environmentally-friendly options or advancements in alternative fuel technologies."
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "Erv28r2txycDwNZv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in Diesel Sales from Q1 to Q4 of the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the percentage decrease, I need to first identify the Diesel Sales in Q1 and Q4 of the subsequent year by linking {the Diesel sales points}<r>[0.582, 0.701, 0.966, 0.848]</r>. The sales start at 70000 for Q1 at {the point}<r>[0.582, 0.701]</r> and decrease to 25000 by Q4_next detailed at {this point}<r>[0.966, 0.848]</r>. Calculating the percentage decrease from initial 70000 to final 25000 by formula: ((70000-25000)/70000)*100.\nBased on the above thought, the answer is:\n64.29%"
+            }
+        ],
+        "id_raw": 1034
+    },
+    {
+        "id": "rvC0YZLP6qZK59vd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing incidents and visitors at various health facilities into a structured table format with facilities listed in rows and data types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart gives me insight into the two distinct lines representing {Incidents}<r>[0.088, 0.124, 0.491, 0.805]</r> and {Visitors}<r>[0.579, 0.124, 0.981, 0.805]</r>. The x-axis details the health facilities at points {General Hospital}<r>[0.101, 0.821, 0.112, 0.868]</r>, {City Hospital}<r>[0.142, 0.821, 0.153, 0.868]</r>, {Regional Hospital}<r>[0.183, 0.821, 0.193, 0.868]</r>, {Clinic}<r>[0.223, 0.821, 0.234, 0.868]</r>, and {Dental Clinic}<r>[0.264, 0.821, 0.275, 0.868]</r>. Using the y-values {represented on the left y-axis}<r>[0.019, 0.235, 0.043, 0.694]</r>, I can extract exact figures from each line at every facility.\nBased on the above thought, the answer is:\nFacility,Incidents,Visitors\nGeneral Hospital,50,1000\nCity Hospital,65,1200\nRegional Hospital,60,1100\nClinic,70,1150\nDental Clinic,75,1300"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "KrhIOyryznwwo6bI",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health facility displays the closest incident to visitor ratio based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I compare the lines {for Incidents}<r>[0.088, 0.124, 0.491, 0.805]</r> and {Visitors}<r>[0.579, 0.124, 0.981, 0.805]</r> specified for each facility. Observing the y-values for both lines at each facility: {General Hospital}<r>[0.101, 0.821, 0.112, 0.868]</r> has incidents at 50 and visitors at 1000, and so forth. Calculating ratios for all, General Hospital is at 0.05, City Hospital is at 0.05416, Regional Hospital is at 0.05455, Clinic is at 0.06087, and Dental Clinic is at 0.05769. The closest ratio is with the Regional Hospital, where the difference between visitors and incidents is minimized.\nBased on the above thought, the answer is:\nRegional Hospital displays the closest incident to visitor ratio, showcasing a minimal disparity between the numbers which reflects a balance that might indicate efficient management or consistent health service utilization."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "5cHP9vV8NIqvRaSx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of visitors across all health facilities shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate {the points for Visitors}<r>[0.597, 0.128, 0.963, 0.220]</r> identifying the coordinates for visitors values at each facility: 1000, 1200, 1100, 1150, 1300. To compute the average, I sum these numbers (1000 + 1200 + 1100 + 1150 + 1300) and then divide by the number of facilities, which is 5.\nBased on the above thought, the answer is:\n1120"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "fPJcstLZqTkIWFl9",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you table the data based on the chart, using the years as row labels and the type of energy as column headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "Turning the line chart into a table will involve identifying row and column headers as well as the corresponding values. The {x-axis tick labels}[[0.244, 0.050, 0.248,0.054],[0.494, 0.050, 0.498,0.054],[0.744,0.050,0.748,0.054]] will serve as the row header indicating the years 2000, 2010, and 2020. The column headers are represented by {the legend texts} [[0.792, 0.132, 0.893, 0.208], [0.792, 0.132, 0.893, 0.208], [0.800, 0.166, 0.900, 0.242], [0.805, 0.200, 0.905, 0.276]], which are the different energy sources\u2014Oil, Gas, Solar Power, and Wind Power. The data points for each energy source at the corresponding years are located at {the line points}[[0.244, 0.402, 0.248,0.406],[0.244, 0.160, 0.248, 0.164],[0.244, 0.260, 0.248, 0.264],[0.244, 0.115, 0.248,0.119]...], where the y-coordinate corresponds to the data value.\nBased on the above thought, the answer is:\n,Oil,Gas,Solar Power,Wind Power\n2000,     20, 9, ,5,3\n2010,     17, 10, 15, 11\n2020,     14, 11, 25, 19"
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "xY3lUu2RS6EReN9x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the provided chart, which type of energy resource has seen the maximum percentage increase in its usage from the year 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the energy resource with the maximum percentage increase, I need to compare the percentage increase for each resource. The data points for Oil are {Oil, 2000} [0.244, 0.402, 0.248,0.406] and {Oil, 2020} [0.744, 0.279, 0.748,0.283], showing a decrease in utilization. For Gas, the points are {Gas, 2000} [0.244, 0.160, 0.248, 0.164] and {Gas, 2020} [0.744, 0.184, 0.748,0.188] indicating an increase. For Solar Power, the points are {Solar Power, 2000} [0.244, 0.260, 0.248, 0.264] and {Solar Power, 2020} [0.744, 0.545, 0.748, 0.549], showing a significant increase. Wind Power with points at {Wind Power, 2000} [0.244, 0.115, 0.248,0.119] and {Wind Power, 2020} [0.744, 0.403, 0.748,0.407] also shows substantial increase but less than Solar Power.\nBased on the above thought, the answer is:\nBetween 2000 and 2020, the energy resource that has seen the maximum percentage increase in its usage is Solar Power. This fact is evident from its rapid increase from 5 units in 2000 to 25 units in 2020."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "6MUz8VNibPDn2Hd7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in solar power usage between 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in solar power usage in 20 years, I first identify the data points related to solar power usage in 2000 and 2020, which are {Solar Power, 2000} [0.244, 0.260, 0.248, 0.264] and {Solar Power, 2020} [0.744, 0.545, 0.748, 0.549] respectively. The value for 2000 is 5 units, and for 2020 is 25 units. The percentage increase can be calculated using the formula: ((Final Value - Initial Value) / Initial Value) * 100%.\nBased on the above thought, the answer is:\n400%"
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "pYAt00BOfLvTDVdE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and reformat the data of the line graph into a table with 'Trends' as rows and 'AI Applications' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Turning the data from the line chart into a structured table involves multiple steps: Firstly, I locate {the x-axis ticks}[[0.101, 0.821, 0.112, 0.868], [0.193, 0.821, 0.204, 0.868], [0.284, 0.821, 0.295, 0.868], [0.375, 0.821, 0.386, 0.868], [0.467, 0.821, 0.478, 0.868]], which define the different 'Trends'. These will serve as the row labels. Secondly, I reference {the legend texts}[[0.153, 0.647, 0.298, 0.694], [0.153, 0.717, 0.334, 0.764]] to determine the column headers or 'AI Applications'. Then, I utilize the points for each line which are given as {line points for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]] and {line points for Advanced Streaming}[[0.107, 0.615], [0.198, 0.363], [0.290, 0.409], [0.381, 0.292], [0.472, 0.249]]. These points are correlated with the list of x-axis ticks to match the right 'Trend' with the correct performance metrics for each 'AI Application'.\nBased on the above thought, the answer is:\nTrend,Early Integration,Advanced Streaming\n1,93.25,78.40\n2,91.28,89.49\n3,93.94,87.48\n4,95.35,92.64\n5,101.73,94.50"
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "ViziPbhQ5ke44TUg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 'Early Integration' and 'Advanced Streaming', which AI application shows a more consistent performance across different tasks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the performance consistency of 'Early Integration' and 'Advanced Streaming', I need to analyze the variation in their line plots. Looking at {the line for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]], it shows a broad shift from a higher value descending to the lowest at Task 5. Meanwhile, {the line representing Advanced Streaming}[[0.107, 0.615], [0.198, 0.363], [0.290, 0.409], [0.381, 0.292], [0.472, 0.249]] begins at the top of the domain and gradually ascends towards the higher end of the y-axis. Judging by the variance in their respective paths, 'Advanced Streaming' presents more ups and downs than does 'Early Integration'.\nBased on the above thought, the answer is:\nBased on the chart, 'Early Integration' appears to show a more consistent performance across different tasks compared to 'Advanced Streaming'. Despite a general decrease in performance from Task 1 to Task 5, the 'Early Integration' line shows fewer shifts in direction, suggesting a more steady performance in comparison to the fluctuating performance of 'Advanced Streaming'."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "q8QJbw9qXMxFGKGc",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the minimum accuracy percentage the 'Early Integration' achieved across all tasks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the minimum score achieved by 'Early Integration', I need to look at {the line plot for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]] and look for the maximum y value (indicating the point closest to the bottom of the chart) since higher y values signify lower accuracy percentages. The minimum accuracy percentage corresponds to task #5, {Task 5}[0.472, 0.805] on x-axis.\nBased on the above thought, the answer is:\n101.73"
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "VHFCP91Rhfffci9q",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the score for each factor with the departments influencing them as table columns and factors as table rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will look at the points on the left subplot representing the factors for {Quality Control}[0.078, 0.124, 0.447, 0.805] and their corresponding labels on the x-axis - {Drag}[0.073, 0.821, 0.116, 0.868] and {Delivery Time}[0.370, 0.821, 0.490, 0.868]. Then, I will get the y values for these points which lie on the {Quality Control} line from [0.094, 0.582, 0.430, 0.630]. \nSimilarly, for the right subplot, I will look for the points representing the factors for {Logistics}[0.568, 0.124, 0.937, 0.805] and identify the labels {Drag}[0.564, 0.821, 0.606, 0.868] and {Delivery Time}[0.860, 0.821, 0.980, 0.868] from x-axis. The y values for these points are obtained from the {Logistics} line between [0.585, 0.278, 0.920, 0.346].\nBased on the above thought, the answer is:\nFactors,Quality Control,Logistics\nDrag,60.32,80.97\nDelivery Time,63.13,76.99"
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "Ucd3k2kaYNbhBKcv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the performance of the two departments based on their trend lines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the performance of the two departments, I will look at the variation in their respective trend lines. The {Quality Control} department trend line is at [0.094, 0.582, 0.430, 0.630] and the {Logistics} department trend line is at [0.585, 0.278, 0.920, 0.346]. The y-coordinates of the points represent the scores, so by comparing the y-coordinates of both departments, we can interpret their performance.\nBased on the above thought, the answer is:\nThe Logistics department scores are consistently higher than the Quality Control scores for the same factors of performance. However, there is a decrease in quality scores in Logistics from the Drag to Delivery Time. On the contrary, the Quality Control department shows an increase in the same order. Hence, the Logistics department performed better in the Drag factor, while the Quality Control department better addressed the Delivery Time factor."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "ySEuUwxw0iAGVnFN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_52_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in quality score between the two factors within the Logistics department?"
+            },
+            {
+                "from": "gpt",
+                "value": "We first locate the points for each factor within {Logistics}[0.568, 0.124, 0.937, 0.805] through the {Logistics} line points [0.585, 0.278, 0.920, 0.346]. We find the y values representing the scores for {Drag}[0.585, 0.278] and {Delivery Time}[0.920, 0.346]. We then calculate the difference between these two scores.\nBased on the above thought, the answer is:\n3.98"
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "QoP2Woi7Mrdm4iOQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the trends represented in the line chart into tabular data with the categories of welfare activities as the rows and the two different companies as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "The line chart presents data points for seven categories of welfare activities across two companies. To translate this data into a table, I first identify {the x-axis tick labels}[[0.117, 0.506, 0.187, 0.532], [0.234, 0.506, 0.332, 0.532], [0.385, 0.506, 0.467, 0.532], [0.513, 0.506, 0.601, 0.532], [0.641, 0.506, 0.713, 0.532], [0.769, 0.506, 0.843, 0.532]] as the different categories of welfare activities. These will serve as the row labels. Then, I note the two companies described by {the plot lines}[[0.0, 0.0, 1.0, 1.0],[0.0, 0.0, 1.0, 1.0]] -\n 'Our Company' and 'Other Company', which shall constitute the column labels. The intersection of each row and column will be filled with the corresponding percentage change values, which can be extracted by analysing the y-coordinate of the plotted points.\nBased on the above thought, the answer is:\nActivity,Our Company,Other Company\nDonation,-3.2, 2.8\nVolunteering,-4.7, 0.5\nAdvocacy,-5.9, 8.3\nPartnership,-1.2,-2.5\nSponsorship,-3.4, 1.5\nProBono,-6.5,10.7\nWorkplace Giving,-2.6,1.2"
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "W65YVjumldl2TUVH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows more consistent engagement in welfare activities based on the trends reflected in the line chart? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company shows more consistent engagement in welfare activities, I evaluate the fluctuations in the change rates for each company. Judging from {the plot line for Our Company}[0.0, 0.0, 1.0, 1.0], its change rates show a consistent negative trend across most activities, indicating a steady decrease in engagement. Conversely, {the plot line for the Other Company}[0.0, 0.0, 1.0, 1.0] presents more variation in the change rates, with both positive and negative values, signifying an erratic pattern of engagement.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, Our Company shows more consistent engagement in welfare activities. Despite a generally negative trend, the change rates for Our Company are relatively stable across different welfare activities. In comparison, the Other Company demonstrates more erratic engagement with fluctuating positive and negative change rates across different activities."
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "Y8A1OgThtP1B2rL2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average change rate of 'Our Company' across all welfare activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average change rate of 'Our Company' across all welfare activities, I need to identify {the y-coordinates of our company's plotted points}[[0.117, 0.406, 0.187, 0.482], [0.234, 0.396, 0.332, 0.472], [0.385, 0.376, 0.467, 0.452], [0.513, 0.492, 0.601, 0.568], [0.641, 0.486, 0.713, 0.562], [0.769, 0.465, 0.843, 0.541]] and extract the corresponding change rates: -3.2, -4.7, -5.9, -1.2,-3.4,-6.5,-2.6. There are 7 data points, so adding all these then dividing by 7 will give me the average.\nBased on the above thought, the answer is:\n-3.9%"
+            }
+        ],
+        "id_raw": 1040
+    },
+    {
+        "id": "Ej4ciZ0mDGMqOyhF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data seen in the chart into a tabulated format with months as the row labels and different social media platforms as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let's start by identifying the {x-axis tick labels}[[0.199, 0.945, 0.234, 0.959], [0.353, 0.945, 0.388, 0.959], [0.506, 0.945, 0.542, 0.959], [0.660, 0.945, 0.695, 0.959], [0.814, 0.945, 0.849, 0.959]] as the row labels representing the different months. Next, I see {the legend texts}[[0.114, 0.027, 0.132, 0.041], [0.114, 0.048, 0.202, 0.062], [0.114, 0.069, 0.192, 0.083]] as the column headers representing the different social media platforms. To fill the values, for each social platform, I look at their respective {line points}[0.217, 0.057, 0.831, 0.272], [0.217, 0.272, 0.831, 0.899], [0.217, 0.281, 0.831, 0.679] and reflect the corresponding values highlighted for each month.\nBased on the above thought, the answer is:\nMonth, Facebook, Twitter, WhatsApp, LinkedIn, Instagram\n03-2021,1800,1500,1900,1700,2000\n04-2021,1700,1600,2000,1750,2100\n05-2021,1600,1550,2050,1800,2200\n06-2021,1520,2300,2100,1850,2320\n07-2021,1450,1950,2150,1900,2450"
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "jAArIfWIdwYgzHJk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform demonstrated the most significant upward trend throughout the observed period, based on the chart's data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the line for Facebook}[0.217, 0.057, 0.831, 0.272], it is clear that Facebook has a declining trend over the period. For {Twitter}[0.217, 0.272, 0.831, 0.899], there are substantial fluctuations with an overall rising pattern. Analyzing {WhatsApp}[0.217, 0.281, 0.831, 0.679], it presents a moderate upward pattern with variances. The line for {LinkedIn}[0.217, 0.303, 0.831, 0.713] reflects steady growth, and {Instagram}[0.217, 0.339, 0.831, 1.0] depicts a consistent ascending trend.\nBased on the above thought, the answer is:\nInstagram displayed the most significant upward trend throughout the observed period. Not only does its line chart follow an unwavering ascending path, but it also ends at the highest point among all platforms. This makes Instagram the social media platform with the most significant net increase in user activities."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "mTUtxDo9LNh8h7Pf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the gross change in Facebook's performance over the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the gross change in Facebook's performance, I need to locate {the line points for Facebook}[0.217, 0.057, 0.831, 0.272] and extract the starting and ending values: 1800 at 03-2021 and 1450 at 07-2021. Therefore, the gross change is 1450 - 1800 = -350, indicating a decrease in performance.\nBased on the above thought, the answer is:\n-350."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "SKvSkiuNtc1Zh7K5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the information depicted in the line chart in the form of a table, considering years as column headers and different monitor names as row headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I initially identify {the x-axis tick labels}[[0.207, 0.909, 0.251, 0.932], [0.358, 0.909, 0.401, 0.932], [0.508, 0.909, 0.552, 0.932], [0.658, 0.909, 0.702, 0.932], [0.809, 0.909, 0.853, 0.932]], which represent the different years. These will be used as the column headers. Secondly, I look at {the legend texts}[[0.143, 0.046, 0.309, 0.069], [0.143, 0.081, 0.284, 0.104], [0.143, 0.116, 0.343, 0.139]] to extract the row labels which indicate the different monitors. Finally, I locate {the line points}[[0.229, 0.601], [0.380, 0.653], ..., [0.831, 0.861], [0.229, 0.342], ..., [0.831, 0.065], [0.229, 0.515], ..., [0.831, 0.273]] corresponding to each monitor for each year and extract the provided values to populate the data in our table.\nBased on the above thought, the answer is:\nYear,Heart Rate Monitor,Glucose Monitor,Blood Pressure Monitor\n2000,45,60,50\n2005,42,65,55\n2010,40,70,60\n2015,35,72,62\n2020,30,76,64"
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "3a1uGponNstKj13r",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line charts, which monitor shows a continuously declining trend from 2000 to 2020? Provide explanation on your choice."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the monitor with the constantly declining trend, I need to review the trajectories of the line plots for each monitor. Looking at {the line for Heart Rate Monitor}[0.229, 0.601, 0.831, 0.861], it shows a systematic reduction from 45 in 2000 to 30 in 2020. The error rate for {Glucose Monitor}[0.229, 0.342, 0.831, 0.065], however, generally increases over the same period. Likewise, the trend for {Blood Pressure Monitor}[0.229, 0.515, 0.831, 0.273] does not follow a consistent pattern of decrease either. Therefore, by comparing the trends, it is clear that the Heart Rate Monitor is the one that demonstrates a continuous declining trend.\nBased on the above thought, the answer is:\nBased on the line chart, the Heart Rate Monitor shows a continuously declining trend from 2000 to 2020. This is deduced from the consistent decrease in values from 45 in 2000 to 30 in 2020, as shown in the chart. This trend is unique to the Heart Rate Monitor compared to other monitors."
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "JhMTVs01IZr55miw",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the error rate for Blood Pressure Monitor decrease from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in error rate for Blood Pressure Monitor, I need to look at {the line points for Blood Pressure Monitor}[[0.229, 0.515], [0.380, 0.428], [0.530, 0.342], [0.680, 0.307], [0.831, 0.273]], specifically from 2000 and 2020. The value in 2000 is 50 corresponding to {2000}[0.229, 0.901] and the value in 2020 is 64 corresponding to {2020}[0.831, 0.901]. Hence, the decrease in value is 64 - 50 = 14.\nBased on the above thought, the answer is:\n14"
+            }
+        ],
+        "id_raw": 1042
+    },
+    {
+        "id": "xYSNwmTO2vzsobYa",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table that represents the data shown in the bar chart, with 'Types of Vehicles' as rows and 'Phases of Growth' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I've observed {the x-axis tick labels}<r>[0.265, 0.923, 0.327, 0.953], [0.635, 0.923, 0.698, 0.953]</r> which represent the column headers or phases of growth. The row labels representing the types of vehicles can be inferred from {the legend}<r>[0.808, 0.087, 0.962, 0.179]</r>. For the values, I recognize {the bar heights}<!--compare the bar heights, using y-axis tick labels as a relative guide, to determine the sales figures--> for each type of vehicle at the respective phases, extracting these as the values for each cell of the table.\nBased on the above thought, the answer is:\nTypes of Vehicles,Rapid Growth,Minimal Change\nPassenger Cars,55,45\nCommercial Vehicles,72,42"
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "mNqfip4fCmiRxjo8",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, which type of vehicle saw a larger fluctuation in sales moving from the Rapid Growth phase to the Minimal Change phase? Please detail your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the change in sales between the Rapid Growth phase and the Minimal Change phase for each type of vehicle. For Passenger Cars, the sales decline from {rapid growth}<r>[0.261, 0.048, 0.331, 0.662]</r> to {minimal change}<r>[0.621, 0.048, 0.689, 0.572]</r>, reflecting a decline from 55 to 45. On the other hand, Commercial Vehicles show a decrease from {rapid growth}<r>[0.391, 0.048, 0.459, 0.919]</r> to {minimal change}<r>[0.751, 0.048, 0.819, 0.303]</r>, reflecting a larger decline from 72 to 42.\nBased on the above thought, the answer is:\nThe Commercial Vehicles category experienced a larger fluctuation in sales when comparing the Rapid Growth phase to the Minimal Change phase. While both Passenger Cars and Commercial Vehicles saw a decline in sales from the Rapid Growth phase to the Minimal Change phase, the decline was steeper for Commercial Vehicles (from 72 down to 42) compared to the decline observed for Passenger Cars (from 55 down to 45)."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "o4puPoVECue57AFr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in sales between Passenger Cars and Commercial Vehicles during the period of Minimal Change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in sales, I have to locate the data for both Passenger Cars and Commercial Vehicles during the Minimal Change period. For Passenger Cars, the sales figure is represented by {the height of the bar}<r>[0.621, 0.048, 0.689, 0.572]</r> which corresponds to 45. For Commercial Vehicles, the sales figure is represented by {the height of the bar}<r>[0.751, 0.048, 0.819, 0.303]</r> which corresponds to 42. The difference between the two is therefore 45 - 42 = 3.\nBased on the above thought, the answer is:\n3."
+            }
+        ],
+        "id_raw": 1043
+    },
+    {
+        "id": "GXbM4K0OmeJ7ieiG",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data provided into a table format with the categories as columns and the type of error rates as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve this, I would first identify {the x-axis tick labels}<r>[0.189, 0.909, 0.369, 0.932]</r>, <r>[0.379, 0.909, 0.527, 0.932]</r>, <r>[0.543, 0.909, 0.711, 0.932]</r>, <r>[0.782, 0.909, 0.820, 0.932]</r> as the column headers. The row labels would be identified as {the legend texts}<r>[0.191, 0.046, 0.297, 0.069]</r>, <r>[0.191, 0.081, 0.329, 0.104]</r>, <r>[0.191, 0.116, 0.296, 0.139]</r>. Then, I would locate the corresponding line points of these labels in the chart at <r>[0.279, 0.861]</r>, <r>[0.453, 0.694]</r>, and so on. Hence, I can tabulate this data into a structured table.\nBased on the above thought, the answer is:\nError Type,Pharmaceutical,Construction,Manufacturing,ICT\nCruncher,20.3,25.5,29.8,45.0\nEngineering,25.4,23.1,34.1,34.9\nScientific,33.0,28.4,42.4,36.7"
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "1xywXOhcqmXOpOu7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the bar chart provided, which sector seems to have the most consistent error rate across the three types? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the sector with the most consistent error rate, I need to analyze the differences in the error rates for the three types within each sector. I review the error rates for {Pharmaceutical}<r>[0.279, 0.861]</r>,<r>[0.279, 0.697]</r>,<r>[0.279, 0.452]</r>, {Construction}<r>[0.453, 0.694]</r>,<r>[0.453, 0.771]</r>,<r>[0.453, 0.600]</r>, {Manufacturing}<r>[0.627, 0.555]</r>,<r>[0.627, 0.416]</r>,<r>[0.627, 0.149]</r> and {ICT}<r>[0.801, 0.065]</r>,<r>[0.801, 0.390]</r>,<r>[0.801, 0.332]</r> categories to conclude that the sector with the smallest difference between maximum and minimum error rates would be the most consistent.\nBased on the above thought, the answer is:\nThe Construction sector seems to have the most consistent error rate across the three types. The error rates for the three types (Cruncher, Engineering, Scientific) within this sector show less variation, indicating a consistent level of errors across these types."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "ig6aKY41YhvwZKCV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum Error Rate in the ICT sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum error rates in the ICT sector, I have to locate {the points for ICT}<r>[0.801, 0.861]</r>, <r>[0.801, 0.771]</r>, <r>[0.801, 0.600]</r> and extract the corresponding values: 45.0, 34.9, 36.7. The maximum value is 45.0 corresponding to {Cruncher}<r>[0.191, 0.046, 0.297, 0.069]</r>, and the minimum value is 34.9 corresponding to {Engineering}<r>[0.191, 0.081, 0.329, 0.104]</r>. The difference will be 45.0 - 34.9 = 10.1.\nBased on the above thought, the answer is:\n10.1."
+            }
+        ],
+        "id_raw": 1045
+    },
+    {
+        "id": "C4MHuNQkT6QmF15V",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the line data into structured table format with each row representing the trends of Entrepreneurship and Innovation across the seven metrics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the chart, I first identify {the x-axis tick labels}[[0.220, 0.909, 0.280, 0.932], [0.365, 0.909, 0.425, 0.932], [0.510, 0.909, 0.570, 0.932], [0.655, 0.909, 0.715, 0.932], [0.800, 0.909, 0.860, 0.932]] representing the years. Next, I find {the legend texts}[[0.191, 0.046, 0.414, 0.069], [0.191, 0.081, 0.424, 0.104]] as the row headers indicating either Rural Development or Urban Development. Finally, to obtain the values, I examine {the line points for Rural Development}[[0.250, 0.861], [0.395, 0.766], [0.540, 0.634], [0.685, 0.444], [0.830, 0.292]] and {the line points for Urban Development}[[0.250, 0.387], [0.395, 0.273], [0.540, 0.160], [0.685, 0.103], [0.830, 0.065]] for the development index scores at each time period.\nBased on the above thought, the answer is:\nTime Period, Rural Development Index, Urban Development Index\n2000, 40, 65\n2005, 45, 71\n2010, 52, 77\n2015, 62, 80\n2020, 70, 82"
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "XXuvh7657ibmtsM1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graphs displayed in the chart, which form of development shows a steadier progress between 2000 and 2020? Provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To address this question, I need to contrast the overall trends noted in {the line for Rural Development}[0.250, 0.292, 0.830, 0.861] and {the line for Urban Development}[0.250, 0.065, 0.830, 0.387]. The Rural Development line segment demonstrates a steady upward trend, with a few minor dips that indicate moderate volatility. The line representing the Urban Development Index, on the other hand, shows a more fluctuating trend, with a steady increase till 2015, followed by a minimal increase to 2020. Based on the line coordinates, one could note more steadiness in the Rural Development path as it shows a more linear growth trajectory as compared to the fluctuating path of Urban Development.\nBased on the above thought, the answer is:\nBased on the line graphs, Rural Development shows a steadier progress between 2000 and 2020. The plotted line for Rural Development demonstrates a more regular linear upward trajectory, denoting steady growth over the years. In contrast, Urban Development, despite having a higher index value, exhibits more volatility with less linearity in its progression, especially in the period from 2015 to 2020."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "onr5wGL5eSL10mRH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total increase in Urban Development Index from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in the Urban Development Index from 2000 to 2020, the first step is to identify {the line points for Urban Development}[[0.250, 0.387], [0.830, 0.065]]. The value for the year 2000 is 65 at the point coordinates [0.250, 0.387] and for the year 2020 is 82 at the point coordinates [0.830, 0.065]. The total increase is 82 - 65 = 17.\nBased on the above thought, the answer is:\n17."
+            }
+        ],
+        "id_raw": 1046
+    },
+    {
+        "id": "pE5uRmLPOhptVprO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the line chart data about influence levels of different communication methods over four weeks into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, to create a table from the line chart, I first identify the weeks {labeled on the x-axis}<r>[0.283, 0.909, 0.812, 0.932]</r> as the columns. Then, I recognize the {labels from the legend}<r>[0.206, 0.046, 0.336, 0.139]</r> as row headers representing different communication methods. For the data, I assess the y-values at the points in the line plots for each communication method at each week. I find these values at the {points of the lines}<r>[0.291, 0.304, 0.804, 0.622], [0.291, 0.144, 0.804, 0.861], [0.291, 0.065, 0.804, 0.383]</r> for Television, Online, and Newspaper respectively.\nBased on the above thought, the answer is:\nMethod,Week1,Week2,Week3,Week4\nTelevision,85,75,75,95\nOnline,60,65,90,105\nNewspaper,100,110,100,90"
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "QX0fONri2xgDeZJJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method of communication showed the most increase in influence level from Week 1 to Week 4 based on the line chart? How would you analyze this?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I locate the {Week 1 and Week 4 data points for each communication method}<r>[0.291, 0.463], [0.462, 0.622], [0.633, 0.622], [0.804, 0.622], [0.291, 0.861], [0.462, 0.782], [0.633, 0.383], [0.804, 0.144], [0.291, 0.224], [0.462, 0.065], [0.633, 0.224], [0.804, 0.383]</r> and calculate the difference in influence levels between these weeks for Television, Online, and Newspaper. \nTelevision shows a change of 95 - 85 = 10, Online shows an increase of 105 - 60 = 45, and Newspaper shows a decrease of 90 - 100 = -10.\nBased on the above thought, the answer is:\nBased on the chart, the method of communication which shows the most increase in influence level from Week 1 to Week 4 is Online, with an increase of 45 units. This could suggest that over this period, online communication methods have become more influential, possibly due to increased use of digital platforms."
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "XYtsH1HJpg1vh2Yi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average influence level of Television over the period of four weeks based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average influence level of Television, I first locate the {points on the line representing Television}<r>[0.291, 0.463], [0.462, 0.622], [0.633, 0.622], [0.804, 0.304]</r> corresponding to influence levels 85, 75, 75, 95 for the four weeks. The average is calculated by adding these numbers together and dividing by the total number of weeks.\nBased on the above thought, the answer is:\n82.5"
+            }
+        ],
+        "id_raw": 1047
+    },
+    {
+        "id": "ISsihOj6ksjsEKSM",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you restructure the line chart's data into a table with age as the index and the two lines, \"Books Read: High Stress\" and \"Books Read: Low Stress\", as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I need to take the tick labels on {the x-axis}<r>[0.199, 0.909, 0.228, 0.932]</r>, <r>[0.308, 0.909, 0.337, 0.932]</r>... as the index values representing Age. For the columns, the two lines {Books Read: High Stress}<r>[0.214, 0.450, 0.866, 0.861]</r> and {Books Read: Low Stress}<r>[0.214, 0.065, 0.866, 0.219]</r> represent the two different stress conditions. The values corresponding to each age under these conditions can be inferred from their respective mappings on the y-axis.\nBased on the above thought, the answer is:\nAge, Books Read: High Stress, Books Read: Low Stress\n10, 25, 40\n15, 20, 38\n20, 17, 37\n25, 14, 36\n30, 12, 37\n35, 10, 36\n40, 9, 34"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "rvRpKfKy9bPyTnHE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsider the trends of books read under both High Stress and Low Stress conditions as people age in this line chart. Can you explain these trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the line for {Books Read: High Stress}<r>[0.214, 0.450, 0.866, 0.861]</r>, there appears to be a clear downward trend as Age increases. The number of books read decreases from 25 to 9 as age increases from 10 to 40, indicating that under high stress conditions, people tend to read less as they age.\nOn the contrary, the line for {Books Read: Low Stress}<r>[0.214, 0.065, 0.866, 0.219]</r> shows a slight decline initially from age 10 to age 20 and then it becomes relatively flat, indicating that under low stress conditions, the number of books read remains more or less the same after age 20.\nBased on the above thought, the answer is:\nThe trends in the line chart indicate that under high stress conditions, the number of books read steadily decreases as people age. In contrast, under low stress conditions, there is a slight decrease from ages 10 to 20, after which the number of books read remains fairly consistent. This suggests that stress levels have a significant impact on reading habits and that this impact increases with age."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "01vXqRMktPQmkYEd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the number of books read between the highest and lowest reading conditions under Low Stress at the age of 35?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify this, I need to locate the coordinate for the age of 35 on the x-axis. The coordinates are {35}<r>[0.743, 0.909, 0.772, 0.932]</r>. On this coordinate, there exist two y-positions under Low Stress, found at coordinates {low}<r>[0.758, 0.168]</r>. Observing the y-axis <r>[0.068, 0.184, 0.097, 0.207]</r>, the corresponding number of books read under low stress is 36.\nSimilarly for High stress condition, the y-position is {high}<r>[0.758, 0.835]</r>. Observing the y-axis, <r>[0.068, 0.569, 0.097, 0.593]</r>, the corresponding number of books read under high stress is 10.\nThe difference is calculated as |High Stress - Low Stress| = |10 - 36| = 26.\nBased on the above thought, the answer is:\n26."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "upzsw5iJXICTD8px",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing production efficiency over time into a structured table format listing each company and their corresponding annual efficiency change?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify and organize {the x-axis tick labels}<r>[0.054, 0.934, 0.098, 0.952], [0.143, 0.934, 0.187, 0.952], [0.231, 0.934, 0.275, 0.952],..., [0.938, 0.934, 0.981, 0.952]</r> denoting the years from 2010 to 2020 as columns. I then examine {the lines for each company}<r>[0.076, 0.060, 0.959, 0.592], [0.076, 0.129, 0.959, 0.754], ..., [0.076, 0.371, 0.959, 0.887]</r> and their point coordinates to extract the efficiency changes per year for each company.\nBased on the above thought, the answer is:\nYear,Tesla,Amazon,LG,Panasonic,Microsoft,Oracle,Nokia,Toshiba,Lamborghini,Ferrari,Apple,Netflix,Salesforce,Cisco,Sony,Volkswagen,Audi,General Electric\n2010,20,15,12,11,-10,-9,-8,-21,-7,-6,0,3,1,2,-4,-3,0,1\n2011,25,18,13,12,-11,-10,-9,-20,-8,-7,1,4,2,3,-3,-2,0,0\n2012,27,20,14,14,-12,-11,-11,-19,-8,-7,2,4,3,3,-2,-1,1,2\n2013,32,21,15,16,-13,-12,-12,-18,-7,-6,3,5,4,4,-2,0,1,3\n...\n2020,45,40,20,17,-15,-14,-14,-30,-8,-8,8,5,6,7,-1,2,4,5"
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "OLflOyxTmITOnJt0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company shows the most consistent efficiency level throughout the years according to the data portrayed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I review the efficiency trends by scanning the lines for each company. {General Electric's line}<r>[0.076, 0.173, 0.959, 0.173]</r> shows minor fluctuation around a stable midpoint, differing minimally from year to year when compared to others with larger fluctuations.\nBased on the above thought, the answer is:\nGeneral Electric shows the most consistent efficiency level throughout the years. Its line on the chart is almost a flat line with minimal deviations, indicating a steady state of production efficiency across the measured period."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "jQx4KwdNAzWa8zRN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum efficiency change for Tesla from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this value, I analyze {points on Tesla's line}<r>[0.076, 0.592], [0.165, 0.557], ..., [0.959, 0.060]</r> representing each year's efficiency value. I look for the point with the highest value, which corresponds to the year 2020 showing an efficiency of 45.\nBased on the above thought, the answer is:\n45."
+            }
+        ],
+        "id_raw": 1050
+    },
+    {
+        "id": "RvAqiejDMXhPnrQZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the line chart data into a table with Years as columns and Types of Consumption as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to convert the line chart into a table, I first identify {the x-axis tick labels}[[0.096, 0.922, 0.131, 0.942], [0.279, 0.922, 0.297, 0.942], [0.445, 0.922, 0.463, 0.942], [0.611, 0.922, 0.629, 0.942], [0.777, 0.922, 0.795, 0.942], [0.943, 0.922, 0.961, 0.942]], which represent the column headers or Years from 2010 to 2020. Next, I check {the subplot legend texts}[[0.123, 0.039, 0.250, 0.059], [0.123, 0.069, 0.261, 0.089], [0.123, 0.099, 0.255, 0.119]] which form the row headers: Coal Consumption, Hydro Consumption, and Solar Consumption. Now, to extract the values, I spot {the line points} for each consumption type at their respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nConsumption Type,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nCoal Consumption,800,780,770,750,720,700,680,670,660,650,640\nHydro Consumption,200,220,230,240,250,260,270,280,290,300,310\nSolar Consumption,50,60,70,80,90,120,150,180,200,220,240"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "asqeliLSLLpWITKe",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain the general trends observable in the energy consumption chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, looking at the line plots for each consumption type, I can dissect the trends occurring. For {Coal Consumption}<r>[0.113, 0.062, 0.943, 0.235]</r>, it's identifiable that there's a continuous decline from 800 to 640 over the period from 2010 to 2020. \nThis is a sharp contrast to {Solar Consumption}<r>[0.113, 0.669, 0.943, 0.874]</r>, which saw a growth from 50 to 240. Finally, {Hydro Consumption}<r>[0.113, 0.593, 0.943, 0.712]</r> seems to have happened gradually increased from 200 to 310.\nBased on the above thought, the answer is:\nFrom the chart, it is noticeable that Solar Consumption showed a significant increase from 2010 to 2020, suggesting a rising trend in the use of solar energy resources. On the other hand, Coal Consumption has been steadily decreasing over the same time period, indicating a decline in the reliance on fossil fuels. Meanwhile, Hydro Consumption demonstrates a gradual, but consistent, upward trend, representing a steady role that hydro power is playing in the energy resource mix. These trends imply a shift in the energy consumption pattern towards more renewable and less polluting sources."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "oCDuZYqQk8gARFGJ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total amount of growth in solar consumption from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll analyze the increase by checking {the line points for Solar Consumption}[[0.113, 0.874], [0.196, 0.864], [0.279, 0.853], [0.362, 0.842], [0.445, 0.831], [0.528, 0.799], [0.611, 0.766], [0.694, 0.734], [0.777, 0.712], [0.860, 0.690], [0.943, 0.669]]. In doing this, I'm able to trace the values annotated corresponding to each year from 2010 to 2020. I then calculate the growth by subtracting the initial value in 2010 from the final value in 2020.\nBased on the above thought, the answer is:\n190"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "k84NG3ojFzM8MjlT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please transform the line chart data, which plots adoption rate percentages against years, into a structured table with the years as columns and different tech methods as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data to a tabulated format, I first identify {the x-axis values}[[0.185, 0.933, 0.229, 0.951], [0.314, 0.933, 0.358, 0.951], [0.443, 0.933, 0.487, 0.951], [0.572, 0.933, 0.616, 0.951], [0.701, 0.933, 0.745, 0.951], [0.830, 0.933, 0.874, 0.951]] as the column headers denoting the years. I then locate {the legend texts: \"Online Learning\", \"Classroom Tech\", and \"VR Training\"}[[0.142, 0.034, 0.279, 0.052], [0.142, 0.061, 0.277, 0.078], [0.142, 0.087, 0.240, 0.104]] as row labels, which represent distinct technology-based learning methods. Then I find the {y-axis line points}[[0.207, 0.679], [0.336, 0.638], [0.465, 0.576], [0.594, 0.473], [0.723, 0.266], [0.852, 0.163]], for each of the tech-methods at the related years, and extract the corresponding rate values. These obtained values are populated in the appropriate table cells denoted by the row and column pairs.\nBased on the above thought, the answer is:\nTech Method,2015,2016,2017,2018,2019,2020\nOnline Learning,20,22,25,30,40,45\nClassroom Tech,30,28,27,25,27,28\nVR Training,10,15,25,30,45,50"
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "iKNMmNWzYKjJA5qO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which method of instruction seems to experience the most rapid increase between the years 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which method of instruction has experienced the most rapid increase between 2015 and 2020, I need to focus on the slope of {the lines for each instruction method}[[0.207, 0.163, 0.852, 0.679], [0.207, 0.473, 0.852, 0.576], [0.207, 0.060, 0.852, 0.886]]. By analyzing these, I observe that the line corresponding to VR Training has a much steeper slope compared to the lines for Online Learning and Classroom Tech. This suggests that VR Training has experienced a more rapid increase in adoption rate from 2015 to 2020 compared to the other methods.\nBased on the above thought, the answer is:\nBased on the line chart, VR Training seems to have experienced the most rapid increase in adoption rate between the years 2015 and 2020. The line corresponding to VR Training shows a steeper upward trend compared to the lines for Online Learning and Classroom Tech, indicating a more rapid increase over this period."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "4wh9WHDwyeKhE4mC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total rate increase from 2015 to 2020 for Online Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total rate increase for Online Learning from 2015 to 2020, I need to find {the line points for Online Learning}[[0.207, 0.679], [0.336, 0.638], [0.465, 0.576], [0.594, 0.473], [0.723, 0.266], [0.852, 0.163]] and extract the corresponding values: 20, 22, 25, 30, 40, 45. The value for 2015 is 20 while that for 2020 is 45. Thus, the rate increase for Online Learning from 2015 to 2020 is 45 - 20 = 25.\nBased on the above thought, the answer is:\n25."
+            }
+        ],
+        "id_raw": 1052
+    },
+    {
+        "id": "JScQoLZbYuLmnMck",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table, with Time as the column headers, and each Fitness Trend as a row?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the line chart, I first observe {the x-axis tick labels}<r>[[-0.009, 0.898, 0.016, 0.921], [0.155, 0.898, 0.166, 0.921], [0.311, 0.898, 0.322, 0.921], [0.468, 0.898, 0.479, 0.921], [0.624, 0.898, 0.635, 0.921], [0.781, 0.898, 0.792, 0.921], [0.932, 0.898, 0.954, 0.921]]</r> to identify the column headers as Time. The row labels represent the different fitness trends, visible from {the legend text}<r>[0.681, 0.811, 0.884, 0.834],[0.681, 0.846, 0.884, 0.869]</r>. To get the values, I can find {the line points}<r>[0.160, 0.155, 0.865, 0.505]</r> for Large Scale Framework and <r>[0.160, 0.272, 0.865, 0.855]</r> for Small Scale Framework, which represent the popularity at each year.\nBased on the above thought, the answer is:\nTime,-2,0,2,4,6,8,10\nLarge Scale Framework,90,85,80,80,85,90,100,110,100,95\nSmall Scale Framework,50,60,70,85,95,100,100,95,90,85"
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "PVCUdXFQRqisjoyh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trend lines in the graph, which framework showed a consistent growth trend from year 2 to year 10? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which framework shows a consistent growth trend, I need to analyze the increase or decrease in the line plots for each framework. Looking at {the line for the Large Scale Framework}<r>[0.317, 0.505, 0.865, 0.330]</r>, the trend is not consistent. It initially decreases from 80 to 80, then increases to 110, falls back to 95, indicating the growth trend is not consistent.\nHowever, reviewing the {line for the Small Scale Framework}<r>[0.317, 0.622, 0.865, 0.447]</r>, I see a more consistent growth trend. The values increase from 70 to 100, then remain the same at 100, and decrease slightly to 85. Despite minor fluctuations, the overall trend consistently increases, indicating a growth pattern.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Small Scale Framework seems to show a more consistent growth trend from year 2 to year 10. Its line plot shows an overall increase across the specified years, despite some minor fluctuations. This increase indicates the growth of the Small Scale Framework during this period."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "dA2ZZn0J8dzGX3TE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2 to 10, what\u2019s the difference between the maximum popularity and the minimum popularity of the Large Scale Framework?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum values, I need to locate {the line points for the Large Scale Framework}<r>[0.317, 0.505, 0.395, 0.505, 0.473, 0.447, 0.552, 0.388, 0.630, 0.272, 0.708, 0.155, 0.786, 0.272, 0.865, 0.330]</r>. The corresponding values are 80, 80, 85, 90, 100, 110, 100, and 95. Following that, the maximum value is 110, and the minimum value is 80. The difference between the maximum and minimum is therefore 30.\nBased on the above thought, the answer is:\n30."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "jl0iME6wQT0JjZrF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph that shows the trends of different technologies over time into a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To structure the data, I need to identify the x-axis labels showing the educational technologies, located at {the x-axis tick labels}[[0.151, 0.155, 0.251, 0.243], [0.318, 0.156, 0.420, 0.243], [0.487, 0.157, 0.582, 0.243], [0.656, 0.159, 0.767, 0.236], [0.819, 0.156, 0.950, 0.236]]. These will be my column headers. The line points found at {line for Decline score}[[0.151, 0.362], [0.318, 0.436], [0.487, 0.507], [0.656, 0.568], [0.819, 0.664]] and {line for Slow Growth score}[[0.151, 0.833], [0.318, 0.897], [0.487, 0.953], [0.656, 0.830], [0.819, 0.785]] represent the decline scores and slow growth scores for each technology respectively. Therefore, the data table will have the technologies as the rows, and two columns representing the decline and slow growth scores.\nBased on the above thought, the answer is:\nTechnology,Decline Score,Slow Growth Score\nExcel,-3.3,-2.3\nPowerPoint,-2.8,-1.9\nAdaptive Learning,-2.0,-1.4\nEarly Childhood,-1.4,-0.9\nVideo Conferencing,-1.0,-0.7\nVirtual Reality,-0.9,-0.6"
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "zVLIkIVPkJQ9bxZB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the graphs, which technology has shown the most improvement over time in terms of slowing its growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology has improved the most in slowing its growth, I would analyze the curve representing the Slow Growth score and find the technology with the most positive score (i.e., the least negative). The {Slow Growth scores} [[0.151, 0.833], [0.318, 0.897], [0.487, 0.953], [0.656, 0.830], [0.819, 0.785]] contain the labels for the respective technologies. Through analysis, I can see that the most positive score corresponds to {Virtual Reality}[0.819, 0.156] with a score of -0.6, indicating it has slowed its growth the most.\nBased on the above thought, the answer is:\nBased on the progress shown in the graphs, Virtual Reality technology seems to have shown the most improvement over time in terms of slowing its growth. Despite it's late introduction, its higher score (less negative compared to other technologies) on the slow growth chart suggests that the rate of growth has decelerated more than the other technologies, indicating its slower, but potentially more sustainable, adoption."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "JtsQlOvIetcPtElZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology shows the least decline in terms of score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which technology has the least decline, I need to identify the highest score (i.e., the least negative) among {the Decline scores}[[0.151, 0.362], [0.318, 0.436], [0.487, 0.507], [0.656, 0.568], [0.819, 0.664]]. After extracting the corresponding scores and their respective technologies, it's clear that the least decline, with a score of -0.9, corresponds to {Virtual Reality}[0.819, 0.156].\nBased on the above thought, the answer is:\nVirtual Reality."
+            }
+        ],
+        "id_raw": 1056
+    },
+    {
+        "id": "ULphYdcwWsW5mKp7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the information in the line chart into a table format where the columns represent Quarters and the rows represent different measurements such as GDP Growth, Unemployment Rate, and Inflation Rate for both USA and China?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. To generate the table, I identify {the x-axis tick labels}<r>[0.362, 0.945, 0.382, 0.959]</r>,<r>[0.516, 0.945, 0.535, 0.959]</r>,<r>[0.669, 0.945, 0.688, 0.959]</r>,<r>[0.822, 0.945, 0.842, 0.959]</r> as the column headers representing Quarters. I then look at {the legend texts}<r>[0.117, 0.027, 0.235, 0.041]</r>,<r>[0.117, 0.048, 0.246, 0.062]</r>,<r>[0.117, 0.069, 0.293, 0.083]</r>,<r>[0.117, 0.090, 0.304, 0.104]</r>,<r>[0.117, 0.111, 0.243, 0.125]</r>,<r>[0.117, 0.132, 0.255, 0.146]</r> as the row labels representing different measurements for USA and China. To determine the values for each cell, I look at {the line points for each line}<r>[0.372, 0.730]</r>,<r>[0.525, 0.899]</r>,<r>[0.679, 0.057]</r>,<r>[0.832, 0.695]</r> and extract the corresponding values.\nBased on the above thought, the answer is:\nCategory, Q1, Q2, Q3, Q4\nUSA GDP Growth, 2.7, -5.0, 33.4, 4.3\nChina GDP Growth, 6.7, 3.2, 4.9, 6.5\nUSA Unemployment Rate, 4.4, 11.1, 7.8, 6.7\nChina Unemployment Rate, 3.8, 5.7, 5.4, 5.2\nUSA Inflation Rate, 1.8, 0.6, 1.2, 1.2\nChina Inflation Rate, 4.3, 2.5, 1.7, 0.2"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "zAA0nyeRMRBAe8Qd",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how did the GDP Growth rate behave for USA and China throughout the year? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the behavior of the GDP Growth rate for both USA and China, I closely look at {the line for USA GDP Growth}<r>[0.372, 0.730, 0.832, 0.695]</r> and {China GDP Growth}<r>[0.372, 0.642, 0.832, 0.647]</r>. For USA, the GDP Growth starts with a positive figure at Q1, dips into negative in Q2, dramatically recovers in Q3, and stabilizes in Q4. For China, the line indicates a substantial growth in Q1, dips in Q2, and then gradually increases towards Q3 and Q4.\nBased on the above thought, the answer is:\nBased on the line chart, the GDP Growth for USA had a roller-coaster trend. The GDP Growth started positively in Q1 but had a significant decline in Q2. Then a sharp recovery was seen in Q3 before the GDP Growth rate stabilized in Q4. For China, the GDP Growth rate began significantly high in Q1, had a decline in Q2, then showed a steady recovery in Q3 and Q4, indicating a more stable economic environment in China over the year compared with the USA."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "KzhlBcvduRR7SYtZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the maximum GDP Growth rate for China according to the chart provided?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum GDP Growth rate for China, I will look at {the line points for 'China GDP Growth'}<r>[0.372, 0.642]</r>,<r>[0.525, 0.719]</r>,<r>[0.679, 0.682]</r>,<r>[0.832, 0.647]</r>. The point coordinates indicate the value at each quarter. The highest point is at <r>[0.525, 0.719]</r>, corresponding to a GDP Growth rate of 3.2 in Q2.\nBased on the above thought, the answer is:\n3.2."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "CdXuDoj7hNrB0k9D",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line graph, can you extract the data into table format with the Year as columns and the Console type as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line graph, first, I identify {the x-axis ticks}[0.211, 0.236, 0.359, 0.384, 0.508, 0.533, 0.656, 0.681, 0.805, 0.830] as the column headers representing the years. Next, I recognize {the legend items}[0.097, 0.031, 0.331, 0.127] as the row headers, which represent the different console types. Then, I extract the {line points}[0.236, 0.062, 0.830, 0.774, 0.236, 0.233, 0.830, 0.599, 0.236, 0.320, 0.830, 0.877] for the number of units sold for each console in the respective year.\nBased on the above thought, the answer is:\nConsole,2020,2021,2022,2023,2024\nPlayStation 5,7.8,14.9,22.1,28.6,37.3\nNintendo Switch,15.05,17.09,20.21,24.1,30.2\nXbox Series X,3.5,8.6,13.8,19.2,26.6"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "NAFfo0a5kha5jkgy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the graph, which gaming platform had the steepest growth in units sold from 2020 to 2024? Provide an explanation for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the platform with the steepest growth, I examine the slopes of the lines for each console. The line representing {PlayStation 5}[0.236, 0.062, 0.830, 0.774] goes from 7.8 million in 2020 to 37.3 million in 2024. The line representing {Nintendo Switch}[0.236, 0.233, 0.830, 0.599] goes from 15.05 million to 30.2 million in those same years. The line displaying {Xbox Series X}[0.236, 0.320, 0.830, 0.877] spans from 3.5 million units in 2020 to 26.6 million in 2024. By comparing the changes in units sold over these years, we can determine which platform had the steepest growth.\nBased on the above thought, the answer is:\nBased on the graph, it appears that the PlayStation 5 had the steepest growth in units sold from 2020 to 2024. From its launch in 2020, the sold units of the PlayStation 5 raised from 7.8 million to 37.3 million, an increase of nearly 30 million units, the highest among all three gaming platforms within the period. This growth is more significant in comparison to the Nintendo Switch, and Xbox Series X which saw increases of about 15 million units and 23 million units respectively in the same time range."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "AVqLKylIaPcGCYA7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_53_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many units more did the PlayStation 5 sell in 2024 than in its launch year?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the sales of PlayStation 5 in the chart. For the launch year 2020, the value is at {the point}[0.236, 0.774]. For 2024, the value resides at {the point}[0.830, 0.062]. The corresponding values are 7.8 million and 37.3 million respectively. The difference can be calculated by subtracting the 2020 value from the 2024 value, which is 37.3 - 7.8.\nBased on the above thought, the answer is:\n29.5 million units."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "0I1annH2zbAQLsiJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table format of the experiment data from the line chart, with 'Duration' as rows and 'Categories' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I identify {the x-axis labels}<r>[0.149, 0.215, 0.936, 0.231]</r> which include the different durations: 3, 5, 7, and 10 minutes. Next, I look at {the legend}<r>[0.420, 0.054, 0.665, 0.075]</r> which specifies two categories, 'Introversion' and 'Extroversion'. I then examine {the line points}<r>[0.149, 0.796, 0.936, 0.948]</r> for each category across the respective durations to extract their numerical values.\nBased on the above thought, the answer is:\nDuration,Introversion,Extroversion\n3,-2.5,-1.0\n5,-1.8,-2.2\n7,-1.0,-3.0\n10,-0.7,-0.8"
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "rf8EVHCtvcBrIjXs",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about 'Introversion' in terms of its pattern across the various experiment durations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing {the line for Introversion}<r>[0.149, 0.796, 0.936, 0.948]</r>, it starts at -2.5 at 3 minutes, peaks at -1.0 at 7 minutes, and then slightly drops to -0.7 at 10 minutes. This indicates that the score improves (gets less negative) as the duration increases until it peaks at 7 minutes, after which it shows a minor decrease by 10 minutes, suggesting a fluctuating pattern over time.\nBased on the above thought, the answer is:\n'Introversion' displays a fluctuating pattern across different experimental durations. Starting from a score of -2.5, it gradually improves until peaking at -1.0, and finally concludes with slight deterioration to -0.7 by 10 minutes. This trend suggests that initial conditions might improve with extended exposure up to a midpoint, after which it slightly reverts but maintains an improved state compared to the beginning."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "JryNh9yVVhLEDj3U",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of scores for the category 'Extroversion' during the experiment as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the range of scores for 'Extroversion', I reference {the line for Extroversion}<r>[0.149, 0.445, 0.936, 0.454]</r> to note its highest score at 10 minutes and lowest score at 7 minutes. The highest score is -0.8 and the lowest score is -3.0. Calculating the range involves subtracting the lowest score from the highest score: -0.8 - (-3.0) = 2.2.\nBased on the above thought, the answer is:\n2.2."
+            }
+        ],
+        "id_raw": 1060
+    },
+    {
+        "id": "Hb4Edy8pcNM8CNiB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the online learning data from the chart into a structured table with years as rows and each category as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the structured table from the chart, I first identify {the x-axis labels for years}<r>[0.153, 0.215, 0.975, 0.231]</r> which will become the row headers in the table. There are four subplots in total indicating the different categories; Online Learning Resources, Students Enrolled, Online Courses Developed, and Online Learning Platforms. I proceed by extracting values for each category at respective years by examining points on the line graphs: {the line for Online Learning Resources}<r>[0.191, 0.055, 0.938, 0.207]</r>, {the line for Students Enrolled}<r>[0.191, 0.302, 0.938, 0.454]</r>, {the line for Online Courses Developed}<r>[0.191, 0.549, 0.938, 0.701]</r>, and {the line for Online Learning Platforms}<r>[0.191, 0.796, 0.938, 0.948]</r>.\nBased on the above thought, the answer is:\nYear,Online Learning Resources,Students Enrolled,Online Courses Developed,Online Learning Platforms\n2010,20,30000,10,4\n2011,25,40000,12,4\n2012,28,50000,16,5\n2013,34,60000,26,6\n2014,40,70000,40,8\n2015,42,80000,48,10\n2016,48,85000,55,11\n2017,56,91000,62,12\n2018,66,100000,70,13\n2019,70,110000,75,15\n2020,80,150000,80,16"
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "qz5ppehdnHbqW8di",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the development of online learning platforms correlate with the growth of online courses developed over the years observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze how the growth of online courses developed correlates with the development of online learning platforms, I examine {the line for Online Courses Developed}<r>[0.191, 0.549, 0.938, 0.701]</r> and {the line for Online Learning Platforms}<r>[0.191, 0.796, 0.938, 0.948]</r>. Throughout the years from 2010 to 2020, as the number of online courses developed consistently increased from 10 to 80, the online learning platforms also increased from 4 platforms to 16. This indicates a positive correlation, where the development of more courses appears to drive the increase in the number of learning platforms.\nBased on the above thought, the answer is:\nThe development of online learning platforms is positively correlated with the growth of online courses developed. As the number of online courses significantly increased, so did the number of learning platforms, reinforcing the notion that expanded course offerings are likely contributing to the expansion of platform development to accommodate the growing needs."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "50SJBx3Uct59DPBx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the enrolled student numbers grow from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find how much student enrollment numbers grew from 2010 to 2020, I utilize {the line for Students Enrolled}<r>[0.191, 0.302, 0.938, 0.454]</r>, pinpointing the values at both 2010 and 2020. For 2010, the enrollment is at {30,000}<r>[0.191, 0.454]</r>, and for 2020, it is at {150,000}<r>[0.938, 0.302]</r>. Therefore, the growth in the student numbers is 150,000 - 30,000.\nBased on the above thought, the answer is:\n120000"
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "AJQMglyX4EsS5ypf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data illustrated in the chart into a structured table separating values based on the years and categories presented?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line plots in the chart, I begin by identifying {the x-axis labels}<r>[0.122, 0.975, 0.975, 0.975]</r> which indicate two distinct years '2020' and '2021'. For each year, I discern four categories: 'Undergraduate Enrollment', 'Postgraduate Enrollment', 'Employee Training Hours', and 'Gradulation rate', whose values are represented by the line points across two {subplots}<r>[0.122, 0.039, 0.975, 0.215], [0.122, 0.286, 0.975, 0.462]</r>. Following the points, I capture data for each category within the corresponding year.\nBased on the above thought, the answer is:\nYear, Category, Value\n2020, Undergraduate Enrollment, 3.4\n2020, Postgraduate Enrollment, 2.8\n2020, Employee Training Hours, 5.2\n2020, Graduation Rate, 2.1\n2021, Undergraduate Enrollment, 3.6\n2021, Postgraduate Enrollment, 2.9\n2021, Employee Training Hours, 5.5\n2021, Graduation Rate, 2.2"
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "ElVxjNFS7eTIuyzX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data trends for the period covered, which category demonstrates the most significant increase from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the most significant increase, I will compare the changes in values for each category between 2020 and 2021 by observing the positions of their representative lines in both {the first subplot}<r>[0.122, 0.039, 0.975, 0.215]</r> and {the second subplot}<r>[0.122, 0.286, 0.975, 0.462]</r>. Noting the starting and ending points of each line, it appears that the 'Employee Training Hours' category shows a more pronounced upward trend from approximately 5.2 to 5.5, which indicates the highest relative increase among the categories.\nBased on the above thought, the answer is:\nThe 'Employee Training Hours' category demonstrates the most significant increase from 2020 to 2021. The comparative analysis of line movements between the two years reflects this heightened growth, especially when observed through the vertical shifts in its line representation on the chart."
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "18BB4dxgUPGbJcbi",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of enrollment (Undergraduate + Postgraduate) for the year 2021 indicated on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will look for the values of 'Undergraduate Enrollment' and 'Postgraduate Enrollment' for 2021 in the chart. I locate the exact points on {the second subplot}<r>[0.122, 0.286, 0.975, 0.462]</r> for 2021. The 'Undergraduate Enrollment' is positioned at approximately 3.6 and 'Postgraduate Enrollment' at about 2.9 given their respective line positions. Adding these values sums up the total enrollment for the year.\nBased on the above thought, the answer is:\n6.5"
+            }
+        ],
+        "id_raw": 1062
+    },
+    {
+        "id": "DirCKNA5kHWf8MRB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trend data presented in the line chart for the four energy sources into a structured table, showing the decade and respective indices for each source?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the trend data into a structured table, I first observe {the x-axis labels}<r>[0.102, 0.219, 0.289, 0.460, 0.632, 0.803, 0.975]</r>, which denote the decades. Next, I focus on identifying the corresponding indices for each energy source by observing the data points along the lines for {Solar}<r>[0.110, 0.178, 0.968, 0.198]</r>, {Hydropower}<r>[0.110, 0.295, 0.968, 0.462]</r>, {Wind}<r>[0.110, 0.542, 0.968, 0.709]</r>, and {Nuclear}<r>[0.110, 0.788, 0.968, 0.955]</r>, which are represented with distinct colors and line styles. The corresponding values at each decade provide the data necessary for the table.\nBased on the above thought, the answer is:\nDecade,Solar,Hydropower,Wind,Nuclear\n2000,1.2,3.2,2.5,8.5\n2010,2.5,3.5,4.0,7.0\n2020,5.0,3.8,6.5,4.0\n2030,8.0,4.0,9.0,2.5"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "PDPfLFctIrc9atLC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source demonstrates the most consistent growth pattern in its utilization index over the decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the energy source with the most consistent growth, I analyze the incline and uniformity in the trajectory curves for each energy source. By examining {the line for Solar}<r>[0.110, 0.178, 0.968, 0.198]</r>, I observe a steady incline with incremental but consistent leaps at each decade point. Comparatively, while {Wind}<r>[0.110, 0.542, 0.968, 0.709]</r> and {Hydropower}<r>[0.110, 0.295, 0.968, 0.462]</r> also show growth, the increases are not as uniform. {Nuclear}<r>[0.110, 0.788, 0.968, 0.955]</r>, on the other hand, exhibits a downward trend.\nBased on the above thought, the answer is:\nSolar energy demonstrates the most consistent growth pattern in its utilization index over the decades. The plotted line for Solar consistently rises from 2000 to 2030, reflecting steady growth in its utilization."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "tdxmw15ap1PnVUcs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average utilization index for Wind energy over the observed decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average utilization index for Wind energy, I'll locate the indices from the line representing {Wind}<r>[0.110, 0.542, 0.968, 0.709]</r> across all observed decades. The indices noted are 2.5, 4.0, 6.5, and 9.0. By adding these values (2.5+4.0+6.5+9.0) and dividing by the total number of decades (4), I get the average.\nBased on the above thought, the answer is:\n5.5"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "1qCe1un8is9K7Lk1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table format with Trends as columns and Factories as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the lines of each factory, I first identify the x-axis labels which represent the different trend stages: {the x-axis labels}<r>[0.110, 0.960, 0.048, 0.215]</r>. I then check the line coordinates and corresponding data points for each factory. For Factory A, I tally the efficiency values with the trend stages using {the line for Factory A}<r>[0.110, 0.048, 0.960, 0.173]</r>. Similarly, for Factory B, I study {the line for Factory B}<r>[0.110, 0.295, 0.960, 0.462]</r> to do the same.\nBased on the above thought, the answer is:\nTrends,Unpredictable Decline,Sustained Decline,Incremental Growth,Dramatic Surge,Sporadic Growth,Stable Growth,Periodic Fluctuations,Erratic Growth\nFactory A,75,49,68,92,70,90,75,66\nFactory B,80,65,70,40,69,85,80,62"
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "SOrA5yOyfxo1NpoN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factory shows a more consistent performance across the trend stages according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the line graphs, I need to determine the consistency of each factory\u2019s performance. Observing the line fluctuations, {the line for Factory A}<r>[0.110, 0.048, 0.960, 0.173]</r> shows variations such as a dramatic surge followed by a decline and recovery. In contrast, {the line for Factory B}<r>[0.110, 0.295, 0.960, 0.462]</r> maintains a relatively stable ascending trend before and after a sharp dip during the Dramatic Surge stage and overall shows fewer fluctuations after recovery.\nBased on the above thought, the answer is:\nFactory B shows a more consistent performance across the trend stages as indicated by their steady growth after recovering from a dramatic dip. The line plot for Factory B has fewer sharp fluctuations, suggesting a steadier performance compared to Factory A, which has higher variability in their trend stages."
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "1oq0LhmOSKX7kut3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of all efficiency values for Factory A across all trend stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total sum of efficiency values for Factory A across all trend stages, I locate and sum the efficiencies at each trend stage, observed from {the line for Factory A}<r>[0.110, 0.048, 0.960, 0.173]</r>. The trend stages include Unpredictable Decline, Sustained Decline, Incremental Growth, Dramatic Surge, Sporadic Growth, Stable Growth, Periodic Fluctuations, and Erratic Growth.\nBased on the above thought, the answer is:\n595"
+            }
+        ],
+        "id_raw": 1064
+    },
+    {
+        "id": "7Y5DakOViRI88LkE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the YoY growth rates by quarter for Consumer Electronics and Fast Moving Consumer Goods in a structured table layout?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a table, I analyze the two sub-plots. Firstly, by observing {the top subplot for Consumer Electronics}<r>[0.139, 0.048, 0.935, 0.215]</r> and precisely locating the quarterly markers, I record the values correspondingly. Similarly, I evaluate {the bottom subplot for Fast Moving Consumer Goods}<r>[0.139, 0.295, 0.935, 0.462]</r> to gather the respective data points.\nBased on the above thought, the answer is:\nQuarter,Consumer Electronics, Fast Moving Consumer Goods\nQ1, -2.5%, 1.0%\nQ2, 3.0%, 1.5%\nQ3, 5.0%, 2.0%\nQ4, 8.5%, 3.5%"
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "7D3Y0TvQx3oauckj",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector displayed a more consistent growth pattern throughout 2020, based on the line plots?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the line trajectories, {the bottom subplot for Fast Moving Consumer Goods}<r>[0.139, 0.295, 0.935, 0.462]</r> indicates a steady growth with smaller increments in each quarter. Conversely, {the top subplot for Consumer Electronics}<r>[0.139, 0.048, 0.935, 0.215]</r> shows a highly variable trend with a substantial recovery post-Q1, followed by increasingly positive shifts.\nBased on the above thought, the answer is:\nThe Fast Moving Consumer Goods sector displayed a more consistent growth pattern throughout 2020 with a steady and gradual increase each quarter, unlike the more unstable growth pattern of the Consumer Electronics sector."
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "l2uxv3WGqWnWqX7P",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total yearly growth rate increase observed for the Consumer Electronics sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the summarized quarterly growth rates from {the line plot for Consumer Electronics}<r>[0.139, 0.048, 0.935, 0.215]</r>: Q1 (-2.5%), Q2 (3.0%), Q3 (5.0%), and Q4 (8.5%). Adding up these values gives the total yearly increase.\nBased on the above thought, the answer is:\n14.0%"
+            }
+        ],
+        "id_raw": 1065
+    },
+    {
+        "id": "8s2byIZgKjs008eZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trends from the chart into a CSV table format showing the year against progress levels for 'EdTech Innovation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV table from the chart, I'll review the data points for 'EdTech Innovation'. I start by examining {the x-axis labels}<r>[0.110, 0.219, 0.946, 0.231]</r> where the years are mentioned as '0','2','4','6','8', and '10', and {the points on the EdTech Innovation line}<r>[0.117, 0.215, 0.960, 0.215]</r> which indicate progress levels for each corresponding year. By mapping these coordinates to their respective years and progress levels, I will formulate the CSV table.\nBased on the above thought, the answer is:\nYear,EdTech Innovation Progress Level\n0,0.1\n2,0.3\n4,0.7\n6,1.2\n8,1.7\n10,2.5"
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "KVB5wNNmHS4VebSv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich field between 'Software Tools' and 'Online Learning' shows a more consistent growth trend over the years according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing {the line for Software Tools}<r>[0.117, 0.323, 0.960, 0.456]</r> and {the line for Online Learning}<r>[0.117, 0.560, 0.960, 0.667]</r>, I observe the characteristics of each trend. The 'Software Tools' shows a consistent decrease, while 'Online Learning' demonstrates consistent upward progression until year 5.5, followed by a steep increase. The consistent increment in values for 'Online Learning' up to 5.5 years and the subsequent sharp rise shows a more consistent trend towards growth as compared to the gradual decline shown by 'Software Tools'.\nBased on the above thought, the answer is:\n'Online Learning' shows a more consistent growth trend over the years due to steady increases in progress level for the initial period followed by a significant upward trend, unlike 'Software Tools' which shows a consistent decrease."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "UK9ufqkWAwbcoCuT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of increase in progress level for 'Online Learning' from year 4 to year 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I find {the points for Online Learning at year 4 and year 6}<r>[0.447, 0.709, 0.623, 0.709]</r>. The progress levels shown are 8.0 at year 4 and 11.8 at year 6. To calculate the rate of increase, I subtract the year 4 value from year 6 value and divide by the number of years between them, which is 2 years. (11.8 - 8.0) / 2 = 1.9.\nBased on the above thought, the answer is:\n1.9"
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "PX7iVXgY9pOHLabH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table presenting the closing values for each financial market at distinct time points from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I will analyze the line plots for each market. First, I observe {the New York Stock Exchange line plot}<r>[0.1, 0.6, 0.9, 0.4]</r> showing values starting at 1000, rising to 1200, and then dropping to 1050. Next, I look at {the NASDAQ line plot}<r>[0.1, 0.4, 0.9, 0.45]</r>, noting a start at 2000, climbing to 2400, and settling at 2200. For {the London Stock Exchange line plot}<r>[0.1, 0.45, 0.9, 0.5]</r>, values start at 1800 and grow consistently to 2100, then drop slightly to 1900. I analyze {the Tokyo Stock Exchange line plot}<r>[0.1, 0.5, 0.9, 0.55]</r> and note a slight increase from 1500 to 1650 and a decrease to 1600. Finally, I review {the Shanghai Stock Exchange line plot}<r>[0.1, 0.55, 0.9, 0.6]</r> and observe a decrease from 1300 to 1400, then a drop to 1250.\nBased on the above thought, the answer is:\nTime Point, New York Stock Exchange, NASDAQ, London Stock Exchange, Tokyo Stock Exchange, Shanghai Stock Exchange\nTime 0, 1000, 2000, 1800, 1500, 1300\nTime 20, 1200, 2400, 2100, 1650, 1400\nTime 40, 1050, 2200, 1900, 1600, 1250"
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "0maRmMo6gR0oYea3",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich financial market exhibited the most stable trend over the observation period according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most stable trend, the volatility or fluctuations in the line plots need to be analyzed. Observing the various plots, {the London Stock Exchange line}<r>[0.1, 0.45, 0.9, 0.5]</r> shows a stable, consistent growth from 1800 to 2100 and a minor decrease to 1900, reflecting minor fluctuations compared to others. For instance, {the New York Stock Exchange line}<r>[0.1, 0.6, 0.9, 0.4]</r> exhibits a significant rise and then a fall, indicating much higher volatility.\nBased on the above thought, the answer is:\nThe London Stock Exchange exhibited the most stable trend throughout the period. Its values show a consistent increase followed by a small reduction, making it relatively stable compared to the more fluctuating trends seen in other markets like the New York Stock Exchange."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "455VGX25pm6NQX9F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase or decrease for the NASDAQ from Time 0 to Time 40?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total difference for NASDAQ from Time 0 to Time 40, I will review {the NASDAQ plot}<r>[0.1, 0.4, 0.9, 0.45]</r>. It starts at an index value of 2000 and ends at 2200. The calculation for the difference would be 2200 - 2000.\nBased on the above thought, the answer is:\n200."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "8klldAibdgisWHMp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data showing resource allocation over the years into a CSV format with each resource as a column and years as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a CSV table format, I first identify {the x-axis tick labels}<r>[0.110, 0.215, 0.946, 0.247]</r> which signify the years from 2050 to 2100. These form the row headers. Next, I identify {the legend}<r>[0.884, 0.101, 0.982, 0.123]</r>, which clarifies the different resource categories like Solar, Wind, Hydro, Nuclear, Thermal, and Fossil fuels, acting as the column headers. I then extract the values at each corresponding point along {the lines}<r>[0.110, 0.048, 0.946, 0.215]</r> for each resource over the years to populate the table.\nBased on the above thought, the answer is:\nYear,Solar,Wind,Hydro,Nuclear,Thermal,Fossil Fuels\n2050,10,15,30,25,8,12\n2055,12,18,28,23,10,10\n2060,15,20,25,20,12,9\n2065,18,23,24,18,15,7\n2070,20,25,20,15,18,5\n2075,23,28,15,12,20,4\n2080,25,30,10,10,23,3\n2085,28,33,5,8,25,2\n2090,30,35,2,5,28,1\n2095,33,38,0,3,30,0\n2100,35,40,0,0,33,0"
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "kN1sxoE8s9CMnMFO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the trend of energy resources allocation towards more sustainable energy sources over the period from 2050 to 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the lines for Solar and Wind}<r>[0.110, 0.048, 0.946, 0.215]</r>, it is evident that there is an increasing trend in the allocation for both these resources, indicating a move towards more sustainable energy sources. Particularly, the Solar resource allocation begins at 10% in 2050 and steadily increases to 35% by 2100, while Wind begins at 15% and rises to 40%, even higher than Solar. Conversely, resources like {Hydro and Nuclear}<r>[0.110, 0.215, 0.946, 0.247]</r> show a declining pattern over the same period. These observations suggest a clear intent and a significant push towards adopting renewable and less environmentally harmful energy options such as Solar and Wind while moving away from conventional and more ecologically damaging sources like Hydro and Nuclear.\nBased on the above thought, the answer is:\nThe trend over the years from 2050 to 2100 shows a significant movement towards more sustainable sources such as Solar and Wind, which have seen a substantial rise in their allocation percentages. At the same time, there is a decline in the allocation for traditional and potentially more harmful energy sources such as Hydro and Nuclear, reflecting an environmental and strategic shift in energy resource planning."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "1V5DDUrZduHAOJGk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage increase in allocation for Wind and Solar resources from 2050 to 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the values for {Solar}<r>[0.110, 0.048, 0.946, 0.215]</r> and {Wind}<r>[0.110, 0.048, 0.946, 0.215]</r> resources over the years. The values start from 10% and 15% in 2050 to 35% and 40% in 2100 respectively for Solar and Wind. The total increase for Solar and Wind is 25% and 25% respectively over 50 years. Dividing these by 50 gives the average annual increase, which is 0.5% per year for both Solar and Wind.\nBased on the above thought, the answer is:\n0.5%."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "1OPa7nnD5OpkekqI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the technology interactions from the line chart into a structured table format with Technology Types as rows and Company Groups as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first identify {the x-axis labels}<r>[0.095, 0.235, 0.970, 0.247]</r> representing different technology types: Quantum Computing, Augmented Reality, Artificial Intelligence, e-Learning Software, Multimedia Networking, Interactive Whiteboard, and Virtual Classroom. Next, I observe {the legend}<r>[0.792, 0.132, 0.893, 0.208]</r> where the light shade represents EdTech companies and the dark shade represents startups. For each technology, I examine {the plot lines}<r>[0.095, 0.048, 0.970, 0.215]</r> and {0.095, 0.295, 0.970, 0.462]</r> for point values for EdTech companies and startups, respectively.\nBased on the above thought, the answer is:\nTechnology,EdTech Companies,Startups\nQuantum Computing,5,15\nAugmented Reality,20,25\nArtificial Intelligence,35,45\ne-Learning Software,40,50\nMultimedia Networking,45,55\nInteractive Whiteboard,45,60\nVirtual Classroom,45,75"
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "si7UKWm3PLd9eLPh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is there a higher interaction percentage with Virtual Classroom for startups compared to EdTech companies, as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I focus on {the trend lines for Virtual Classroom}<r>[0.890, 0.048, 0.970, 0.215]</r> and <r>[0.890, 0.295, 0.970, 0.462]</r> for both EdTech companies and startups, respectively. Notably, the startup line shows a significant upward trend peaking much higher than the line for EdTech companies. Given the context of rapid adaptation to remote learning technologies by startups, it becomes evident that the higher interaction percentage can be attributed to the agile and modern approaches adopted by startups, which greatly embrace remote digital learning solutions like Virtual Classrooms.\nBased on the above thought, the answer is:\nStartups demonstrate a higher interaction percentage with the Virtual Classroom due to their quick adaptation to emerging technologies and a focus on scalable, remote learning solutions. The line trend reveals a significant disparity in adoption rates favoring startups, aligning with their strategy to capitalize on modern educational methods."
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "I0783Uv4O8XxTBjt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does Augmented Reality technology account for startups based on the data from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate {the line point for Augmented Reality in the startups line}<r>[0.194, 0.295, 0.274, 0.462]</r> and extract the exact value. I observe the chart indicating the intersection at approximately 25% for startups specifically for Augmented Reality.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 1070
+    },
+    {
+        "id": "bE1ZnHYzWaHYkIQ0",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table derived from the line chart comparing the different years with the VC investment values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis labels}[0.124, 0.960, 0.966, 0.971] which indicate the years, and identifying {the VC investment values points}[0.163, 0.948, 0.936, 0.796] on the y-axis at each data point, I've established the relationship between the years and investment values across the line chart.\nBased on the above thought, the answer is:\nYear,VC Investment\n2008,1000000\n2009,1500000\n2010,1800000\n2011,2000000\n2012,2200000\n2013,2400000\n2014,2500000\n2015,2700000\n2016,3200000\n2017,3900000\n2018,5100000"
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "JfcLt2cphBLeZeiP",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segment shows a continuous increase over the years in the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the trends from the line chart across the categories, I focus on examining the line behavior for each segment. In the {AI VC Investment subplot}[0.124, 0.788, 0.975, 0.955], the values increase over the years from 2008 to 2018, as indicated by the individual datapoints ranging from {initial point}[0.163, 0.948] to {final point}[0.936, 0.796].\nBased on the above thought, the answer is:\nThe AI VC Investment segment shows a continuous increase over the years in the provided data. This is evident from the upward trajectory of the line representing VC investments from 2008 to 2018."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "81NlvGh4D4DFPENV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total growth in number of AI publications from 2008 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total growth in AI publications over these years, I will examine {the datapoints for AI Paper Publication}[0.163, 0.701, 0.936, 0.549]. In 2008, the number of publications was 1000 {first publication data point}[0.163, 0.701] and in 2018 it increased to 2600 {last publication data point}[0.936, 0.549]. The total growth is calculated by the difference, 2600 - 1000.\nBased on the above thought, the answer is:\n1600"
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "RB6QVogOMkRga6Pf",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the line chart showing various sectors' growth into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing the line chart, I observed the years mentioned on {the x-axis}<r>[0.117, 0.973, 0.235, 0.247]</r> ranging from 2010 to 2020. I identify four distinct lines representing different sectors, indicated by different colors as per {the legend}<r>[0.87, 0.15, 0.93, 0.25]</r>. To extract the data, I need to trace back the growth points for each sector along the y-axis, reading their numerical values at each year's mark on the x-axis.\nBased on the above thought, the answer is:\nYear,Software,Hardware,E-commerce,Internet Service Providers\n2010,5,8,14,10\n2011,6,9,15,10\n2012,7,10,16,10\n2013,8,11,14,10\n2014,9,12,12,9\n2015,7,13,9,9\n2016,5,14,7,9\n2017,4,15,5,9\n2018,3,16,5,8\n2019,2,17,4,8\n2020,1,18,4,8"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "dljD1mkj8hShQpBR",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the growth trends of the Software and E-commerce sectors compare over the 10-year period according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I notice that {the Software line}<r>[0.156, 0.55, 0.935, 0.796]</r> initially shows gradual growth from 2010 to 2015, followed by a decline. On the other hand, {the E-commerce line}<r>[0.156, 0.229, 0.935, 0.701]</r> demonstrates sharp fluctuations; it starts with a strong growth up to 2012, experiences a sharp decline until 2017, and then slightly recovers. Comparatively, the Software sector displayed more steady growth initially, while the E-commerce sector was more volatile with significant fluctuations.\nBased on the above thought, the answer is:\nBoth the Software and E-commerce sectors showed initial growth, but the Software sector had a steadier earlier phase followed by a decline, whereas the E-commerce sector experienced more prominent highs and lows, making it more volatile over the 10-year period depicted in the chart."
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "zxu7KHDpDvgUM2Oj",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth for the Hardware sector from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth, I observe {the points on the Hardware line}<r>[0.54, 0.33, 0.937, 0.15]</r> from the years 2015 to 2020. The respective values are: 13, 14, 15, 16, 17, 18. To find the average, I sum these values (13+14+15+16+17+18) and divide by the number of years, which is 6.\nBased on the above thought, the answer is:\n15.5"
+            }
+        ],
+        "id_raw": 1072
+    },
+    {
+        "id": "jJ9RVFX4Od5AZ2Kw",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table format of the data represented in the bar chart for the yields of different asset types from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table format, I analyze the bar chart by first identifying {the x-axis labels}<r>[0.117, 0.215, 0.960, 0.247]</r> which denote the years 2016 to 2019. For each year, I observe each asset type's bar - {Equities}<r>[0.117, 0.215, 0.124, 0.695]</r>, {Bonds}<r>[0.134, 0.215, 0.140, 0.680]</r>, {Commodities}<r>[0.150, 0.215, 0.157, 0.660]</r>, and {Cryptocurrency}<r>[0.166, 0.215, 0.173, 0.895]</r> - noting their height as indicative of yield percentages. Next, I catalogue these observations into corresponding year and asset type columns and rows.\nBased on the above thought, the answer is:\nYear,Equities,Bonds,Commodities,Cryptocurrency\n2016,5.0%,2.0%,3.0%,9.0%\n2017,4.5%,2.5%,3.5%,10.0%\n2018,4.0%,3.0%,3.0%,4.0%\n2019,3.5%,3.5%,2.5%,6.5%"
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "YGym093dKTAaHJAO",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich asset type showed the highest volatility in yield from 2016 to 2019 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the volatility of yields for different asset types, I examine the changes in bar heights over the years. For {Equities}<r>[0.117, 0.695, 0.124, 0.739]</r>, I observe a steady decline. {Bonds}<r>[0.134, 0.680, 0.140, 0.735]</r> show a consistent increase. {Commodities}<r>[0.150, 0.660, 0.157, 0.717]</r> experience modest fluctuations. However, {Cryptocurrency}<r>[0.166, 0.895, 0.173, 0.940]</r> displays the highest volatility with a major drop in 2018 followed by a substantial recovery in 2019.\nBased on the above thought, the answer is:\nCryptocurrency showed the highest volatility in yield from 2016 to 2019. This is evident from its substantial drop in 2018 and sharp increase in 2019, demonstrating significant fluctuations compared to the more steady trends of other asset types."
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "XJFyKrYr6ZfoaQo9",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in yield for Bonds from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in yield for Bonds from 2016 to 2019, I look at their bar heights across the years. The yields are {2016}<r>[0.134, 0.890, 0.140, 0.895]</r>: 2.0%, {2017}<r>[0.134, 0.850, 0.140, 0.860]</r>: 2.5%, {2018}<r>[0.134, 0.810, 0.140, 0.825]</r>: 3.0%, and {2019}<r>[0.134, 0.765, 0.140, 0.785]</r>: 3.5%. Calculating the difference between the initial yield in 2016 and the final yield in 2019 gives 3.5% - 2.0% = 1.5%.\nBased on the above thought, the answer is:\n1.5%"
+            }
+        ],
+        "id_raw": 1073
+    },
+    {
+        "id": "CBNOIwLbfregi235",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data presented in the bar chart regarding energy production into a structured CSV format sorted by year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the bar chart, I've identified the groups for energy sources labeled as Coal, Oil, Natural Gas, and Renewables on {the x-axis labels}<r>[0.095, 0.219, 1.070, 0.231]</r>. The corresponding production values for each year from 2020 to 2033 are captured on the y-axis from {the left most bar}<r>[0.124, 0.137]</r> to {the right most bar}<r>[0.975, 0.215]</r> for Coal, and similarly spotted for other energy types. I will compile these values into a structured format.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas,Renewables\n2020,4300,4800,3100,2000\n2021,4200,4900,3200,2200\n2022,4100,5000,3300,2400\n2023,4000,5100,3400,2600\n2024,3900,5200,3500,2800\n2025,3800,5300,3600,3000\n2026,3700,5300,3700,3200\n2027,3600,5400,3800,3400\n2028,3500,5500,3900,3600\n2029,3400,5500,4000,3800\n2030,3300,5600,4100,4000\n2031,3200,5700,4200,4200\n2032,3100,5700,4300,4400\n2033,3000,5800,4400,4600"
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "4HTMT6zTIy0N8J9Z",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which energy source shows the most significant increase in production over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing each line within the chart, I note {the line for Renewables}<r>[0.124, 0.788, 0.975, 0.955]</r> starts relatively lower than the others. It shows a consistent upward trajectory significantly steeper compared to {the line for Oil}<r>[0.124, 0.295, 0.975, 0.354]</r> and others, culminating higher by the end of 2033. This suggests that out of all energy sources, Renewables has demonstrated the most substantial increase.\nBased on the above thought, the answer is:\nRenewables show the most significant increase in production over the years, reflected by its steeper and persistent upward trend in the chart."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "PPM3xTHfWojnOfsA",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual decrease in coal production from 2020 to 2033 as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the bar chart, each bar such as those starting from the left {coal production bar}<r>[0.124, 0.137]</r> through to {2020aligned with 2033}<r>[0.975, 0.215]</r> represents annual production of coal from 2020 to 2033. The values range from 4300 GWh in 2020 to 3000 GWh in 2033. The difference over 13 years is 4300 - 3000 = 1300 GWh. The average annual decrease is 1300 GWh divided by 13 years.\nBased on the above thought, the answer is:\n100 GWh"
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "HYRuvTadYDr75rOo",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the values of expenditures shown in the line chart for each department from 2010 to 2020 in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data displayed in the line chart into a CSV format, I analyze each line representing a department. I begin by identifying {the line for Recreational Services}[0.1, 0.1, 0.9, 0.6], noting its starting and ending points and the corresponding expenditure values across the years marked by {the x-axis tick labels}[0.1, 0.9], and then follow the same procedure for {the line for Infrastructure Development}, {the line for Community Health}, {the line for Transport}, and {the line for Social Services}. I extract the expenditure values using the y-axis scale provided by {the y-axis tick labels}[0.1, 0.6].\nBased on the above thought, the answer is:\nYear,Recreational Services,Infrastructure Development,Community Health,Transport,Social Services\n2010,45,18,25,20,22\n2011,43,19,24,19,23\n2012,42,20,23,18.5,21.5\n2013,40,22,22.5,18,24\n2014,38,24,22,17.5,23\n2015,37,26,21,18,23.5\n2016,35,28,20.5,18.5,24\n2017,33,30,20,19,24.5\n2018,31,32,19.5,20,25\n2019,29,34,19,20.5,25.5\n2020,27,36,18.5,21,26"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "5eZfo79sjlQthV3q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich department experienced the most drastic change in their expenditure pattern and how?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain which department experienced the most drastic change in expenditure pattern over the decade, I compare the slopes of the lines for each department from 2010 to 2020. Starting with {the line for Recreational Services}[0.1, 0.1, 0.9, 0.6], the decline is steady yet moderate. Next, observing {the line for Infrastructure Development}[0.1, 0.2, 0.9, 0.7], there is a consistent and sharp increase, reflecting the highest upward shift. The changes for {the line for Community Health}, {the line for Transport}, and {the line for Social Services} are less significant in comparison and display various fluctuations.\nBased on the above thought, the answer is:\nInfrastructure Development experienced the most drastic change in their expenditure pattern, with a continuous and sharp increase from 2010 to 2020. The upward trajectory is significantly steeper compared to the other departments, indicating the most substantial change in funding allocation over the period."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "SDvrCP11ShJIcrfs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in expenditure for Infrastructure Development from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in expenditure for Infrastructure Development from 2010 to 2020, I first identify {the line for Infrastructure Development}[0.1, 0.2, 0.9, 0.7] in the line chart. I extract the expenditure value for the year 2010 from {the y-tick at the start of the line for Infrastructure Development}[0.1, 0.2] and for the year 2020 from {the y-tick at the end of the line for Infrastructure Development}[0.9, 0.7]. Calculating the difference between these two values gives the total increase over the decade.\nBased on the above thought, the answer is:\n18 million dollars"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "XaAsSCDprv71YMav",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the progression of Application A from the chart data into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the progression of Application A, I analyze {the line for Application A}<r>[0.117, 0.044, 0.960, 0.154]</r> across the chart. Each point along this line corresponds to the specified time intervals, indicated by {the x-axis tick labels}<r>[0.110, 0.219, 0.124, 0.231]</r>. Values are extracted directly from the positions of {the points along Application A's line}<r>[0.117, 0.154], [0.159, 0.129], ..., [0.960, 0.056]</r>.\nBased on the above thought, the answer is:\nTime,Application A\n0,20\n0.5,22\n1.0,23\n1.5,25\n2.0,21\n2.5,24\n3.0,22\n3.5,21\n4.0,24\n4.5,25\n5.0,24\n5.5,23\n6.0,24\n6.5,22\n7.0,24\n7.5,26\n8.0,25\n8.5,27\n9.0,28\n9.5,29\n10.0,28"
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "7A4jX81tHESQyMIG",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Application D's progression reflect the chart's subtitle \"Periodic Spikes\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the trend in Application D's performance referenced by {the subtitle \"Periodic Spikes\"}<r>[0.435, 0.754, 0.642, 0.769]</r>, I inspect how {the line for Application D}<r>[0.117, 0.800, 0.960, 0.955]</r> behaves over time. Observing the peaks and troughs in {the points on the line for Application D}<r>[0.117, 0.955], [0.159, 0.946], ..., [0.960, 0.818]</r>, I notice significant fluctuations which exemplify the label of periodic spikes, with values frequently rising and dipping in a regular cycle.\nBased on the above thought, the answer is:\nThe trend in Application D's progression clearly exhibits a series of rises and dips at regular intervals, corresponding well with the subtitle \"Periodic Spikes\" as visually demonstrated by sharp changes in value at consistent periods. This reflects an attribute where performance sharply increases and then decreases, giving a spiky appearance to the graph."
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "Am2oK3maj2vwsR8C",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value recorded for Application B in the given time frame?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value for Application B over the given time frame, I need to look over {the line for Application B}<r>[0.117, 0.284, 0.960, 0.446]</r> and analyze {the point values}<r>[0.117, 0.436], [0.159, 0.425], ..., [0.960, 0.284]</r> shown on the graph. By checking these points, I locate the maximally recorded value along this line.\nBased on the above thought, the answer is:\n59"
+            }
+        ],
+        "id_raw": 1076
+    },
+    {
+        "id": "8uj74FxSX7kSNKs2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the line chart showing Entrepreneurship and Innovation strength over time into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, first I identify {the x-axis tick labels}<r>[0.082, 0.228, 0.959, 0.239]</r> which specify the timepoints from 0 to 10 along the timeline. Next, observing {the points on the Entrepreneurship line}<r>[0.088, 0.787, 0.970, 0.913]</r> and {the points on the Innovation line}<r>[0.082, 0.716, 0.970, 0.712]</r>, I extract the strength values for each topic at each timepoint. The line chart provides direct visual access to these values which can be plotted in the table format below.\nBased on the above thought, the answer is:\nTime,Entrepreneurship,Innovation\n0,2.0,0.9\n0.5,1.98,0.87\n1.0,1.96,0.84\n1.5,1.95,0.81\n2.0,1.93,0.8\n2.5,1.92,0.78\n3.0,1.90,0.76\n3.5,1.88,0.74\n4.0,1.87,0.72\n4.5,1.85,0.7\n5.0,1.83,0.7\n5.5,1.82,0.72\n6.0,1.81,0.74\n6.5,1.80,0.76\n7.0,1.78,0.78\n7.5,1.77,0.8\n8.0,1.76,0.87\n8.5,1.75,0.9\n9.0,1.74,0.94\n9.5,1.70,0.98\n10.0,1.7,1.0"
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "b3mbTj8PJqK6D1xK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is the Innovation trend considered more resilient compared to the Entrepreneurship trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine why Innovation is considered more resilient, I analyze the overall slopes and deviations of both trends from the structured data. Observing {the Innovation line}<r>[0.082, 0.716, 0.970, 0.712]</r> and {the Entrepreneurship line}<r>[0.088, 0.787, 0.970, 0.913]</r>, it's clear that the Entrepreneurship line has a steeper slope indicating rapid declines compared to the more gradual declines in the Innovation line. Additionally, the sudden drops as outliers in Entrepreneurship are more pronounced, signifying less stability.\nBased on the above thought, the answer is:\nInnovation is considered more resilient because its trend line demonstrates a gentler slope with less drastic drops compared to the steep decline seen in the Entrepreneurship trend. This indicates that while both trends decline, Innovation shows greater stability and a slower rate of decline, making it more resilient in the face of negative changes within this ecosystem."
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "IFPl0s1WGRHUTvXb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the largest drop in strength for Innovation between two consecutive timepoints?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the largest drop in strength for Innovation, I need to evaluate the differences between consecutive values along {the Innovation line}<r>[0.082, 0.716, 0.970, 0.712]</r>. Noticing the points, the drop from timepoint 8.0 to 8.5 (0.87 to 0.9) and timepoint 9.0 to 9.5 (0.94 to 0.98) shows an increase rather than a drop. However, the decrease from timepoint 3.0 to 3.5 (0.76 to 0.74) shows a drop by 0.02, which appears to be one of the significant consecutive drops observed.\nBased on the above thought, the answer is:\n0.02"
+            }
+        ],
+        "id_raw": 1077
+    },
+    {
+        "id": "cEY6K4puh78XEjU1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart representing Global Trade over the years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I identify {the x-axis labels}<r>[0.061, 0.219, 0.119, 0.231], [0.202, 0.219, 0.260, 0.231], [0.343, 0.219, 0.401, 0.231], [0.484, 0.219, 0.542, 0.231], [0.625, 0.219, 0.683, 0.231], [0.766, 0.219, 0.825, 0.231], [0.907, 0.219, 0.965, 0.231]</r> which correspond to the years. Then, reviewing {the line for Global Trade}<r>[0.160, 0.055, 0.936, 0.207]</r>, I extract the value points: [{point for 2010}<r>[0.202, 0.192]</r>, {point for 2012}<r>[0.343, 0.184]</r>, {point for 2014}<r>[0.484, 0.177]</r>, {point for 2016}<r>[0.625, 0.101]</r>, {point for 2018}<r>[0.766, 0.071]</r>, {point for 2020}<r>[0.907, 0.086]</r>]. These coordinates help pinpoint actual trade values on the y-axis to construct the table.\nBased on the above thought, the answer is:\nYear,Global Trade\n2010,0.9\n2012,0.95\n2014,1.0\n2016,1.5\n2018,1.7\n2020,1.6"
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "IJZizv5SyYcQmv5P",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Supply Chain Management index change from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the change in the Supply Chain Management index, I need to look at {the line plot for Supply Chain Management}<r>[0.160, 0.302, 0.936, 0.454]</r> and extract the values at {2010}<r>[0.202, 0.378]</r> and {2020}<r>[0.907, 0.454]</r>. The initial value in 2010 is approximately 0.1 and in 2020 it's around 2.6. The change is calculated as 2.6 - 0.1.\nBased on the above thought, the answer is:\n2.5"
+            }
+        ],
+        "id_raw": 1078
+    },
+    {
+        "id": "SL81EdAIbXHicxrk",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV format table showing the distribution of energy types across the years from the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify the {x-axis tick labels}<r>[0.080, 0.219, 0.139, 0.231], [0.248, 0.219, 0.306, 0.231], [0.415, 0.219, 0.473, 0.231], [0.582, 0.219, 0.641, 0.231], [0.749, 0.219, 0.808, 0.231], [0.917, 0.219, 0.975, 0.231]</r> which represent different years from 2020 to 2030. Then I examine the {sublegend}<r>[0.668, 0.023, 0.934, 0.044], [0.588, 0.270, 0.934, 0.291], [0.673, 0.517, 0.934, 0.538], [0.636, 0.763, 0.934, 0.784]</r> for the labels 'Efficient Energy', 'Non-renewable Energy', 'Nuclear Energy', and 'Renewable Energy'. To fill in the values, I extract the data from {the line points}<r>[0.110, 0.067, 0.946, 0.195], [0.110, 0.304, 0.946, 0.416], [0.110, 0.622, 0.946, 0.659], [0.110, 0.849, 0.946, 0.943]</r> for respective energy types at each year.\nBased on the above thought, the answer is:\nYear,Efficient Energy,Non-renewable Energy,Nuclear Energy,Renewable Energy\n2020,10,80,30,5\n2021,12,78,32,7\n2022,15,75,33,9\n2023,19,72,34,12\n2024,24,68,35,16\n2025,30,63,35,20\n2026,37,57,34,24\n2027,45,50,33,28\n2028,54,42,31,33\n2029,64,33,28,38\n2030,75,23,24,43"
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "wTSRVQVgAGO6De7k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy type shows a consistent decrease over the period from 2020 to 2030 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the energy type with a consistent decrease, I review the trends from all energy types labeled in {the sublegend}<r>[0.668, 0.023, 0.934, 0.044], [0.588, 0.270, 0.934, 0.291], [0.673, 0.517, 0.934, 0.538], [0.636, 0.763, 0.934, 0.784]</r>, examining their corresponding {line plots}<r>[0.110, 0.304, 0.946, 0.416], [0.110, 0.622, 0.946, 0.659], [0.110, 0.849, 0.946, 0.943]</r>. Upon evaluating, 'Non-renewable Energy' shows a decline from 80% in 2020 to 23% in 2030, observed through decreasing {line points}<r>[0.110, 0.304], [0.946, 0.416]</r>.\nBased on the above thought, the answer is:\nNon-renewable Energy shows a consistent decrease over the period from 2020 to 2030. Each year marks a decline in its allocation percentage, starting from 80% and ending at 23% by 2030."
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "5tedCWXvxAzZBjDa",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_54_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Efficient Energy allocation from 2020 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in Efficient Energy from 2020 to 2030, I need to extract the data for 'Efficient Energy' from {the line points for Efficient Energy}<r>[0.110, 0.195], [0.193, 0.191], [0.277, 0.185], [0.361, 0.177], [0.444, 0.167], [0.528, 0.156], [0.611, 0.142], [0.695, 0.126], [0.779, 0.108], [0.862, 0.088], [0.946, 0.067]</r>. The values at the beginning (2020) and end (2030) of the period are 10 and 75 respectively. The increase is calculated as 75 - 10.\nBased on the above thought, the answer is:\n65"
+            }
+        ],
+        "id_raw": 1079
+    },
+    {
+        "id": "uA6Ubz4Fpea0fXhZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the investment data from the bar plots into a table format with quarters as columns and investment types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart data into a table, I analyze {the first bar plot}<r>[0.042, 0.075, 0.331, 0.893]</r> representing quarter 1 and {the second bar plot}<r>[0.372, 0.075, 0.662, 0.893]</r> representing quarter 2. Each bar corresponds to different investment types and their growth or decline rate, located at specific {y-tick coordinates}<r>[0.485, 0.406, 0.485, 0.511]</r> for each investment type. By observing {the end points of each bar}<r>[0.320, 0.459, 0.650, 0.501]</r>, the height reflects the ratings for each quarter.\nBased on the above thought, the answer is:\nType,Quarter 1 Rating,Quarter 2 Rating\nCorporate Bonds,3,3.7\nMutual Funds,3.2,3.9\nETFs,3.4,4.1\nGold,3.9,4.6\nCryptocurrency,4.7,5.3\nStocks,3.6,4.2\nReal Estate,4.0,4.5\nTreasury Bonds,2.5,3.1\nMunicipal Bonds,2.8,3.3\nDerivatives,2.9,3.4"
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "rZ2SOyD8C3KPkT9Q",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment type showed the most overall stability across both quarters?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assessing the stability requires analyzing the smallest differences in growth rates between the two quarters for all investment types. Observing {the bars for each investment type}<r>[0.042, 0.075, 0.662, 0.893]</r>, I note small changes in the bar lengths between quarters. Corporate Bonds show very minimal change, with the x-values of the bar ends being very close across both quarters, indicating stability.\nBased on the above thought, the answer is:\nCorporate Bonds showed the most overall stability across both quarters, as indicated by the minimal change in their ratings from the first to the second quarter, showing a small growth increase."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "k22rAVlxyEyup3O4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the rating for Cryptocurrency in the second quarter compared to the first quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the bar for Cryptocurrency in the first quarter}<r>[0.488, 0.795, 0.488, 0.803]</r> and {the bar for Cryptocurrency in the second quarter}<r>[0.818, 0.795, 0.818, 0.803]</r>, the y-values indicate the height representing ratings. The first quarter rating is approximately 4.7 and the second quarter rating is 5.3. Therefore, the difference is 5.3 - 4.7.\nBased on the above thought, the answer is:\n0.6"
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "GXLccPtDbaeDvYal",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing service quality scores for Healthcare, Education, and Public Transit from 2011 to 2015 into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart data, I identify the axes and line plots: {the x-axis with year labels}<r>[0.047, 0.942, 0.330, 0.970]</r> showing years from 2011 to 2015 and {the y-axis with scores}<r>[0.023, 0.396, 0.044, 0.910]</r> ranging from a score of 0.40 to 0.70. The lines representing Healthcare {line for Healthcare}<r>[0.047, 0.192, 0.330, 0.425]</r>, Education {line for Education}<r>[0.377, 0.285, 0.654, 0.425]</r>, and Public Transit {line for Public Transit}<r>[0.707, 0.285, 0.984, 0.425]</r> are plotted across these axes. By analyzing the points on each line corresponding to the years and their respective scores, I tabulate the data.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,0.65,0.55,0.50\n2012,0.67,0.57,0.48\n2013,0.64,0.59,0.45\n2014,0.62,0.61,0.42\n2015,0.60,0.63,0.40"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "kGUT06QzIFkhuLeZ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service area exhibits a declining trend in quality over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends shown in the line chart, I scrutinize the lines representing each service area. Observing {the line for Public Transit}<r>[0.707, 0.285, 0.984, 0.425]</r> shows a noticeable decrease from 0.50 in 2011 to 0.40 in 2015, significantly dropping almost every year. Therefore, I can conclude that Public Transit exhibits a consistent declining trend in quality.\nBased on the above thought, the answer is:\nPublic Transit showcases a declining trend in service quality over the years from 2011 to 2015. The line chart clearly depicts a consistent reduction in score each year, confirming this trend."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "OHyZa5oUpWPSH9Zz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score for Public Transit over the years 2011 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average score for Public Transit from the given line chart, I observe {the line for Public Transit}<r>[0.707, 0.285, 0.984, 0.425]</r>. The scores for each year are: 0.50, 0.48, 0.45, 0.42, 0.40. To find the average, I sum these scores and divide by the number of years, which is 5.\nBased on the above thought, the answer is:\n0.45"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "ZMEi4zrjZ6amhpx8",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table showing the rate of gain change for 'Data Encryption and Integrity' scenarios using both 'Ours' and 'Saliency-based Sampling' methods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct a table from the bar chart, I first identify {the x-axis labels for Data Encryption and Integrity}[[0.041, 0.903, 0.120, 0.931], [0.135, 0.903, 0.149, 0.931]] that include labels for Data Encryption and Data Integrity. Next, I observe the corresponding bars for each method: 'Ours' is shown with blue bars and 'Saliency-based Sampling' with red bars. The coordinates for these bars, {Ours for Data Encryption}<r>[0.055, 0.415, 0.085, 0.893]</r> and {Saliency-based Sampling for Data Integrity}<r>[0.142, 0.550, 0.172, 0.893]</r> represent the rate of gain change for these scenarios.\nBased on the above thought, the answer is:\nScenario,Method,Rate of Gain Change\nData Encryption,Ours,45%\nData Encryption,Saliency-based Sampling,-30%\nData Integrity,Ours,25%\nData Integrity,Saliency-based Sampling,15%"
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "qZtHaO5sPnhlo0bS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain why the 'Data Protection' scenario under the 'Ours' method is considered an outlier in this dataset."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bar chart, it is clear that the {bar for Data Protection under Ours}[[0.501, 0.134, 0.531, 0.893]] is significantly taller compared to other bars within this method and across the category. In comparison, other bars such as {Data Accessibility under Ours}[[0.487, 0.784, 0.517, 0.893]] are much shorter, marking the rate of gain change at around 10%. The 70% rate of gain change for Data Protection highly deviates from these figures, standing out as an evident anomaly in performance comparisons.\nBased on the above thought, the answer is:\nThe 'Data Protection' scenario under the 'Ours' method is considered an outlier primarily because its rate of gain change of 70% far exceeds the rates observed in other scenarios within the same method, which generally show a decrease or marginal increase. This makes it significantly distinct and higher performing compared to its peer cases within 'Ours' method, establishing it distinctly as an outlier."
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "uXqmRJd3k36SFC6z",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the rate of gain change between 'Ours' and 'Saliency-based Sampling' methods for the 'Data Protection' scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "I first identify the {bar for Data Protection under Ours}[[0.501, 0.134, 0.531, 0.893]] and check its height, which corresponds to an unusually high positive value, approximately 70%. Next, I locate the {bar for Data Protection under Saliency-based Sampling}[[0.588, 0.687, 0.618, 0.893]], reflecting a rate of 15%. By subtracting the rate from 'Saliency-based Sampling' from 'Ours', we calculate (70% - 15%) = 55%.\nBased on the above thought, the answer is:\n55%"
+            }
+        ],
+        "id_raw": 1083
+    },
+    {
+        "id": "SxJHjzph8aUV6sZW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the yearly data of 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', and 'Forest Cover' from the line chart to a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart to a table, first, I locate and analyze {the x-axis tick labels}<r>[0.050, 0.930, 0.070, 0.950]</r> for the years spanning from 2000 to 2020. Next, I identify each corresponding line's data points for 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', and 'Forest Cover' which are plotted and marked distinctly with color and symbols. By linking {each tick on the x-axis}<r>[0.050, 0.930, 0.070, 0.950]</r> to the exact position of these points on the graph, I extract the values for each year.\nBased on the above thought, the answer is:\nYear,Global Temperature (\u00b0C),Sea Level Rise (mm),CO2 Emission (million tonnes),Forest Cover (Km2)\n2000,14.62,1.75,5424.88,4030140\n2001,14.77,1.85,5501.12,4022140\n2002,14.89,1.93,5600.15,4011140\n2003,15.03,2.11,5702.33,4003240\n2004,15.14,2.14,5784.44,3999240\n2005,15.20,2.18,5844.33,3991240\n2006,15.31,2.23,5922.70,3978240\n2007,15.45,2.29,6021.45,3965240\n2008,15.53,2.34,6100.12,3955240\n2009,15.57,2.37,6201.33,3941240\n2010,15.65,2.43,6322.50,3930140\n2011,15.72,2.48,6433.88,3921140\n2012,15.85,2.52,6565.12,3910240\n2013,15.90,2.57,6671.65,3899110\n2014,15.97,2.61,6754.22,3888070\n2015,16.09,2.66,6855.54,3876030\n2016,16.20,2.75,7030.45,3865990\n2017,16.32,2.80,7190.88,3852910\n2018,16.45,2.88,7374.33,3847010\n2019,16.56,2.95,7950.60,3843100\n2020,16.65,3.01,8262.56,3840140"
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "ZesKkos7BQwggRZN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich environmental factor according to the line chart has shown the most significant absolute change over the last two decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "I analyze the line chart for {the lines representing 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', 'Forest Cover'}<r>[0.05, 0.2, 0.95, 0.8]</r>. 'Global Temperature' shows an increase from 14.62 to 16.65, 'Sea Level Rise' from 1.75 mm to 3.01 mm, 'CO2 Emission' from 5424.88 to 8262.56 million tonnes, and 'Forest Cover' declines from 4030140 Km2 to 3840140 Km2. Looking at these numbers, 'CO2 Emission' has the most significant absolute increase in the millions.\nBased on the above thought, the answer is:\n'CO2 Emission' has shown the most significant absolute change, increasing by approximately 2837.68 million tonnes. This drastic rise is critical, considering the context of global warming and environmental sustainability."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "E5gixKxDYyf3XiqT",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the 'Forest Cover' decreased from 2000 to 2020 based on the line graph data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line representing 'Forest Cover' in the line chart, I locate the points for the year 2000 at {the starting line point}<r>[0.1, 0.2]</r> and for 2020 at {the ending line point}<r>[0.9, 0.5]</r>. The values are respectively 4030140 Km2 for 2000 and 3840140 Km2 for 2020. The decrease is calculated by subtracting the 2020 value from the 2000 value, resulting in 4030140 - 3840140.\nBased on the above thought, the answer is:\n190000 Km2."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "Lb0nKdQ3QJHvl6vu",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the horizontal stacked bar chart into a tabular format categorizing the behavioral aspects under each routine scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first observe {the x-axis labels}<r>[0.051, 0.030, 0.329, 0.735]</r> for categories like Sleep Quality, Mental Focus, and Physical Activity. I then differentiate each category under two routine scenarios as illustrated by {the y-axis labels}<r>[0.012, 0.344, 0.024, 0.421]</r> named Stable Daily Routine and Erratic Daily Routine. Using {the lengths of the bar segments}<r>[0.064, 0.106, 0.317, 0.420]</r> for each behavior aspect, I calculate the percentage values for Sleep Quality, Mental Focus, and Physical Activity in both scenarios.\nBased on the above thought, the answer is:\nRoutine Behavior, Sleep Quality (%), Mental Focus (%), Physical Activity (%)\nStable Daily Routine, 75, 80, 85\nErratic Daily Routine, 55, 50, 45"
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "PH9tPSKlGw5m0ri5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which routine scenario better supports maintaining a high sleep quality, mental focus, and physical activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, particularly at the lengths and colors of {the bar segments representing each behavior in both routine scenarios}<r>[0.064, 0.106, 0.317, 0.546]</r>, it's observable that the Stable Daily Routine consistently has longer bar segments across all three behaviors compared to the Erratic Daily Routine. This visually suggests higher percentage values for sleep quality, mental focus, and physical activity, indicating a more supportive environment for these behaviors under a stable routine.\nBased on the above thought, the answer is:\nThe Stable Daily Routine better supports maintaining high sleep quality, mental focus, and physical activity according to the comparisons in the chart."
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "drgP6GkQTwLoD2cn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Mental Focus score for both routine scenarios combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average Mental Focus score, I need to identify and use the values from {the bar segments in Mental Focus}<r>[0.064, 0.317]</r> for both Stable Daily Routine and Erratic Daily Routine. The segment lengths are visually converted to percentages: 80% for Stable Daily Routine and 50% for Erratic Daily Routine. The average is then calculated as (80 + 50) / 2.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 1087
+    },
+    {
+        "id": "IcuyPrQn3z2o9MEr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing revenue trends across different entertainment segments into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data into a table, I focus on the three lines representing Advertising, Box Office, and Music Sales {the lines for Advertising}<r>[0.052, 0.650, 0.964, 0.900]</r>, {the lines for Box Office}<r>[0.052, 0.450, 0.964, 0.550]</r>, and {the lines for Music Sales}<r>[0.052, 0.300, 0.964, 0.400]</r>. I observe each point on these lines representing yearly data over 2010 to 2020. These points are aligned correspondingly with year labels on {the x-axis}<r>[0.034, 0.950, 0.984, 0.970]</r> and values from each sector are extracted.\nBased on the above thought, the answer is:\nYear,Advertising,Box Office,Music Sales\n2010,4.5B,6.2B,2.7B\n2011,4.8B,6.4B,2.9B\n2012,5.1B,6.5B,3.2B\n2013,5.2B,6.3B,3.4B\n2014,5.3B,6.4B,3.5B\n2015,5.5B,6.1B,3.8B\n2016,5.8B,5.9B,4.0B\n2017,6.0B,5.8B,4.2B\n2018,6.2B,5.7B,4.4B\n2019,6.3B,5.6B,4.6B\n2020,6.5B,3.2B,4.9B"
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "rMEWSkIUKbm8A8Ju",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed from the revenue data for the Box Office sector over the years? Explain based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the revenue trends for the Box Office, I scrutinize the changes in {the line for Box Office}<r>[0.052, 0.450, 0.964, 0.550]</r>. The trend starts at a higher revenue marker in 2010 and shows irregular up and down movement, generally maintaining a moderate level until a sharp decline in 2020. This outlier in 2020 might correlate with external factors like the COVID-19 pandemic, significantly impacting the Box Office sector due to a decrease in movie theater operations.\nBased on the above thought, the answer is:\nThe Box Office revenue displayed a fluctuating trend with minor ups and downs from 2010 to 2019, maintaining a generally stable revenue. However, a sharp decline is observed in 2020, which is an anomaly likely due to the global impact of the COVID-19 pandemic on theater closures and restrictions."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "b5u940hOqy3dusT3",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total revenue for Music Sales in the year 2020 compared to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total revenue difference for Music Sales between 2020 and 2010, I analyze the plotted points for Music Sales and identify the exact revenue in these two years from {the line points for Music Sales}<r>[0.052, 0.350, 0.964, 0.400]</r>. Observing the positions, I find the values to be 4.9B in 2020 and 2.7B in 2010.\nBased on the above thought, the answer is:\n2.2B USD increase."
+            }
+        ],
+        "id_raw": 1088
+    },
+    {
+        "id": "L9EPDl9ADVE8sO9z",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the influence of media types on public opinion over two days in a tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I examine {the x-axis labels}<r>[0.038, 0.893, 0.254, 0.931]</r> where media types (Traditional Media and Social Media) are listed and {the y-axis labels}<r>[0.022, 0.882, 0.035, 0.910]</r> representing influence values. Corresponding to each media type, there are individual points plotted, such as {points for Traditional Media}<r>[0.038, 0.784, 0.254, 0.566]</r> and {points for Social Media}<r>[0.038, 0.457, 0.254, 0.129]</r> allowing me to decode the information for each day.\nBased on the above thought, the answer is:\nMedia Type,Day 1,Day 2\nTraditional Media,0.8,0.6\nSocial Media,0.5,0.75"
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "Lh7oXmEFiEP1XltJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend in public opinion regarding Traditional Media and Social Media over the two days shown?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the chart, I notice the trend lines for {Traditional Media}<r>[0.038, 0.784, 0.254, 0.566]</r> and {Social Media}<r>[0.038, 0.457, 0.254, 0.129]</r>. Traditional Media exhibits a downward trend from 0.8 to 0.6, suggesting a decrease in its influence. Conversely, Social Media shows a significant upward trajectory from 0.5 to 0.75, hinting at an increase in its influence and possibly a stronger shift in public preference towards Social Media.\nBased on the above thought, the answer is:\nThe trend suggests a decreasing influence of Traditional Media and a notable increase for Social Media over the two days. This indicates a shift in public opinion more in favor of the instant, interactive nature of Social Media compared to the more static format of Traditional Media."
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "AT1ThO5sZKxPIsUk",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in influence for Social Media from Day 1 to Day 2 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in influence for Social Media, I analyze the y-values at {the points for Social Media}<r>[0.038, 0.457, 0.254, 0.129]</r>. The influence on day 1 is approximately 0.5, and on day 2, it rises to approximately 0.75. Calculating the increase involves subtracting the Day 1 influence from the Day 2 influence: 0.75 - 0.5.\nBased on the above thought, the answer is:\n0.25"
+            }
+        ],
+        "id_raw": 1089
+    },
+    {
+        "id": "3YRkG9Uq2n7qQqlh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data represented on the line chart be listed in a table, where one column represents the years and the other two columns represent 'Global Trade' and 'Supply Chain Management' respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct this table, I examine {the x-axis labels}<r>[0.044, 0.075, 0.330, 0.893]</r> which contain the years 2010 to 2020. For the values, I consult {the lines for Global Trade}<r>[0.058, 0.382, 0.316, 0.791]</r> and {the line for Supply Chain Management}<r>[0.058, 0.116, 0.316, 0.382]</r> to gather the respective data points across these years.\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2010,150,120\n2011,160,122\n2012,170,123\n2013,180,125\n2014,190,127\n2015,210,126\n2016,200,128\n2017,220,130\n2018,230,129\n2019,240,132\n2020,180,120"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "PqI6JdOCfiqIlqbr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the global pandemic in 2020 affect the trends in Global Trade and Supply Chain Management as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line for Global Trade}<r>[0.058, 0.382, 0.316, 0.791]</r> and {the line for Supply Chain Management}<r>[0.058, 0.116, 0.316, 0.382]</r>, both lines depict a sudden drop in the year 2020. Prior to 2020, Global Trade was showing a strong upward trend, peaking in 2019. Similarly, Supply Chain Management was somewhat stagnant but still showed slight growth until 2019. The notable decline in both lines in 2020 indicates significant disruption caused by the global pandemic.\nBased on the above thought, the answer is:\nThe global pandemic resulted in a noticeable decline in both Global Trade and Supply Chain Management in 2020. This disruption is clearly visible as sharp downturns in both the plotted lines, diverging from their previous trends where Global Trade was increasing and Supply Chain Management was maintaining slight incremental growth."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "csJFzc8BHPdMrMTs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in volume between Global Trade and Supply Chain Management in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I locate the data points for the year 2020 from {the lines}<r>[0.316, 0.382, 0.316, 0.791]</r>. For Global Trade, the volume is at 180, and for Supply Chain Management, it's 120. The difference is calculated by subtracting the volume of Supply Chain Management from Global Trade's.\nBased on the above thought, the answer is:\n60."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "eynZfm9siXahXaVc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table displaying the usage distribution of different online learning tools, segregated between EdTech Company A and Company B?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I review {the y-axis labels}<r>[0.075, 0.150, 0.183, 0.203]</r> representing the various online learning tools such as Webinars, Online Quizzes, Video Lectures, Forum Discussions, Online Assignments, and Virtual Labs. These serve as the rows of the table. For each tool, I assess the corresponding {stacked bar segments}<r>[0.100, 0.170, 0.300, 0.203]</r> in the chart that differentiate between Company A and Company B's utilization percentages, which are marked on {the x-axis}<r>[0.085, 0.793, 0.105, 0.807]</r>. Using these segments, values for each company and tool are extracted to construct the table.\nBased on the above thought, the answer is:\nTool,EdTech Company A (%),EdTech Company B (%)\nWebinars,45,30\nOnline Quizzes,25,40\nVideo Lectures,30,30\nForum Discussions,15,35\nOnline Assignments,10,40\nVirtual Labs,5,45"
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "WmFHkCIVPypjE6Q7",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich online learning tool has shown a mixed trend of usage between EdTech Company A and Company B? Please explain your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To discern the mixed usage trend, I carefully analyze the growth patterns across the tools by comparing their respective {bar segments}<r>[0.100, 0.170, 0.300, 0.203]</r>. Specifically, I observe {the bars for Video Lectures}<r>[0.100, 0.175, 0.220, 0.195]</r>, and note that they maintain similar lengths between {Company A}<r>[0.100, 0.175, 0.160, 0.195]</r> and {Company B}<r>[0.165, 0.175, 0.220, 0.195]</r> indicating a similar usage pattern for this tool contrary to others that show varying trends.\nBased on the above thought, the answer is:\nThe online learning tool, Video Lectures, shows a mixed trend of usage between EdTech Company A and Company B. Both companies exhibit a nearly balanced usage rate, differing from other tools which display more distinct growth or decline patterns. This consistent pattern across both companies indicates a stable preference for Video Lectures as a learning tool."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "S8HmsAH2ASW810Bf",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase for the usage of Virtual Labs from Company A to Company B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the Virtual Labs bar for Company A}<r>[0.100, 0.197, 0.115, 0.205]</r> and {the Virtual Labs bar for Company B}<r>[0.120, 0.197, 0.195, 0.205]</r>, I determine the lengths of these bars represent the percentages. For Company A, the percentage is marked at 5% and for Company B, it is 45%. To find the total percentage increase, I calculate the difference between Company B's and Company A's percentages.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "XaPwMdgBbIhOpsY5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing technological development prospects into a structured table format distinguishing each technology for the years 2020, 2022, and 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table from the line chart, I begin by examining {the x-axis tick labels}<r>[0.081, 0.861, 0.134, 0.886], [0.318, 0.861, 0.371, 0.886], [0.555, 0.861, 0.608, 0.886]</r> which represent the years 2020, 2022, and 2023 respectively. I then look at {the legend}<r>[0.792, 0.132, 0.893, 0.208]</r> to decipher the lines corresponding to each technology. Following this, I track and extract data points for each technology across the specified years by observing the positions of their respective line points {for AI}<r>[0.101, 0.470], [0.318, 0.351], [0.536, 0.236]</r>, {for IoT}<r>[0.101, 0.690], [0.318, 0.672], [0.536, 0.653]</r>, and so on for other technologies.\nBased on the above thought, the answer is:\nYear, AI, IoT, Blockchain, Robotics, Quantum Computing\n2020, 8.5, 7.0, 6.5, 7.5, 4.0\n2022, 8.7, 7.2, 6.2, 8.0, 5.5\n2023, 9.0, 7.4, 6.0, 8.5, 7.0"
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "hMbNaQ0q8ZgFJBfu",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology shows the most consistent growth trend according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the trajectory of the lines in the chart, I observe that {the line for AI}<r>[0.101, 0.470], [0.318, 0.351], [0.536, 0.236]</r> almost steadily descends along the y-axis, indicating a consistent growth in its rating over the years 2020 to 2023. On the other hand, other technologies, while also showing growth, exhibit either more gentle slopes or fluctuations in their curves, suggesting variations in the rate of growth.\nBased on the above thought, the answer is:\nAI exhibits the most consistent growth trend in its development prospects, showing a steady decline in the y-axis values on the line chart, indicative of a continuous and stable increase in its ratings from 2020 to 2023."
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "5luVCJiQMWa0qdCe",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the development prospects rating for Quantum Computing from 2020 to 2023 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I first identify Quantum Computing's ratings for 2020 and 2023 by looking at {the line points for Quantum Computing}<r>[0.101, 0.870], [0.536, 0.420]</r> corresponding to the years 2020 and 2023 respectively. The ratings are 4.0 for 2020 and 7.0 for 2023. The percentage increase is calculated as ((7.0 - 4.0) / 4.0) * 100 = 75%.\nBased on the above thought, the answer is:\n75%"
+            }
+        ],
+        "id_raw": 1093
+    },
+    {
+        "id": "08fSclbY5NqB943g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table showing the comparison of startups and universities across two states from the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I first identify the two sets of horizontal bars from the chart. {State S1's bars}<r>[0.200, 0.150, 0.850, 0.250]</r> and {State S2's bars}<r>[0.200, 0.350, 0.870, 0.450]</r> represent startups and universities, respectively. Noting that the length of the bars corresponds to the number for each category, I reference {the x-axis values}<r>[0.100, 0.900, 0.200, 0.950]</r> which help in calculating the quantities proportionately.\nBased on the above thought, the answer is:\nState,Categories,200,350,500,520\nS1,Startups,-,-,+,+\nS1,Universities,-,+,+,-\nS2,Startups,+,+,-,+\nS2,Universities,+,+,-,+"
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "52pNUVhNsZBxEhI1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data shown, which state shows more stability in the number of universities according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining the comparative data for each state, the bars for universities are seen at {State S1's university bar}<r>[0.215, 0.280, 0.715, 0.380]</r>, showing a gradual increase from 200 to 500, indicating stability. In contrast, {State S2's university bar}<r>[0.210, 0.480, 0.870, 0.580]</r> demonstrates irregular growth with fluctuating values between 250 and 520, showcasing more variance and less stability.\nBased on the above thought, the answer is:\nState S1 shows more stability in the number of universities as its changes are more gradual compared to State S2, which shows greater fluctuation."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "ZZyeSnoQ54pcAXxH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the number of universities from the starting point to the peak in State S2 as shown in the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bar chart shows that the number of universities in State S2 starts from {the beginning of State S2's university bar}<r>[0.200, 0.425, 0.250, 0.475]</r> at 250 and reaches a peak level at {the end of the peak section of State S2's university bar}<r>[0.400, 0.425, 0.520, 0.475]</r> of 520. Simple subtraction of 250 from 520 gives the result.\nBased on the above thought, the answer is:\n270"
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "spXrFhjb3gCPpuay",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data differences between 'Production Efficiency' and 'Renewable Efficiency' for each region and year be represented in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar subplots for the two regions in the figure, I first identify {the upper chart}<r>[0.041, 0.075, 0.330, 0.445]</r> pertaining to the 'US' data and {the lower chart}<r>[0.041, 0.510, 0.330, 0.890]</r> for the 'UK' data. Within each of these, the respective bars for '2015' and '2020' are discerned and correlated to each horizontal section, colored to represent 'Production Efficiency' and 'Renewable Efficiency'. The legend, {found at the top of the figure}<r>[0.041, 0.025, 0.090, 0.068]</r>, helps in defining which color corresponds to which type of efficiency.\nBased on the above thought, the answer is:\nRegion,Year,Production Efficiency,Renewable Efficiency\nUS,2015,68%,25%\nUS,2020,75%,30%\nUK,2015,55%,20%\nUK,2020,65%,28%"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "AhME4nzEbU5XiL4v",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region displayed a stronger improvement in 'Production Efficiency' from 2015 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which region showed stronger improvement, I compare the changes in the 'Production Efficiency' segments from 2015 to 2020 for both regions. For the 'US' section in {the upper chart}<r>[0.041, 0.075, 0.330, 0.445]</r>, the 'Production Efficiency' improves from '68%' to '75%', whereas in {the UK's section}<r>[0.041, 0.510, 0.330, 0.890]</r>, it increases from '55%' to '65%'. Calculating these increases, the US shows a 7% increase, while the UK shows a 10% increase.\nBased on the above thought, the answer is:\nThe UK displayed a stronger improvement in 'Production Efficiency' from 2015 to 2020. The increase of 10% for the UK is greater compared to the US's 7% increase."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "B7NMWNp1NOpWJbdm",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage point difference in 'Renewable Efficiency' between 2015 and 2020 for the UK based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference, I examine the lower chart section in {the chart portion for the UK}<r>[0.041, 0.510, 0.330, 0.890]</r>, specifically looking at the segments that represent 'Renewable Efficiency' for '2015' and '2020'. By analyzing the lengths of these segments within the bars, it is clear that the growth in this efficiency category shows an increase from '20%' in 2015 to '28%' in 2020.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "LsbFYVztqYBTiLlR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data showing the percentage change in disease management efforts into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line plot, I began by identifying {the x-axis labels}<r>[0.041, 0.075, 0.326, 0.893]</r> representing various health associations. These serve as the row headers. Next, I located {the data points}<r>[0.085, 0.129, 0.304, 0.784]</r> for each health association which represent the percentage change. Using these located points and labels, I created a structure for the table.\nBased on the above thought, the answer is:\nAssociation,Percentage Change\nAmerican Heart Association,8.5%\nAmerican Diabetes Association,-2.9%\nCancer Research Institute,12.1%\nParkinson's Foundation,-7.3%\nAlzheimer Association,15.2%"
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "jgBKYPIrArCQBcWJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health association shows the most significant positive change in disease management efforts and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the most significant positive change involves examining the data points on the line graph. By finding {the highest peak}<r>[0.968, 0.126]</r> which indicates the highest percentage change, the associated label for this peak is traced to {the Alzheimer Association}<r>[0.748, 0.030, 0.953, 0.066]</r> showing a rapid expansion at 15.2%. This is considerably higher compared to other associations, making it the most significant positive change.\nBased on the above thought, the answer is:\nThe Alzheimer Association exhibits the most significant positive change in disease management efforts with an increase of 15.2%. This pronounced growth indicates a strong focus and successful measures in managing Alzheimer's disease compared to other health conditions."
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "Wh33p2dm2IxOp7Hx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total range of percentage change across all health associations according to the line graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total range of percentage changes, I isolated {the highest data point}<r>[0.968, 0.126]</r> which corresponds to the Alzheimer Association at 15.2% and {the lowest data point}<r>[0.705, 0.637]</r> corresponding to the Parkinson's Foundation at -7.3%. The range is calculated by subtracting the smallest value from the largest value.\nBased on the above thought, the answer is:\n22.5%"
+            }
+        ],
+        "id_raw": 1096
+    },
+    {
+        "id": "CAIK8p3yIPDQCXka",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table format comparing the production efficiencies for WidgetA and WidgetB under both manufacturing processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I examine the bar charts that represent the manufacturing processes. For WidgetA, the bar for Process 1 starts at {the beginning of the WidgetA process 1 bar}<r>[0.12, 0.35]</r> and ends at {the end of the WidgetA process 1 bar}<r>[0.32, 0.45]</r>, marked with a label showing 70.5% efficiency. Similarly, for Process 2, the bar begins at {the beginning of the WidgetA process 2 bar}<r>[0.35, 0.35]</r> and ends at {the end of the WidgetA process 2 bar}<r>[0.55, 0.45]</r> with a label indicating 74.3% efficiency.\n\nFor WidgetB, the first bar starts at {the beginning of the WidgetB process 1 bar}<r>[0.12, 0.55]</r> and extends to {the end of the WidgetB process 1 bar}<r>[0.49, 0.65]</r> showing 89.6% efficiency. The second bar for Process 2 starts at {the beginning of the WidgetB process 2 bar}<r>[0.55, 0.55]</r> and finishes at {the end of the WidgetB process 2 bar}<r>[0.78, 0.65]</r> with a label of 92.1% efficiency.\nBased on the above thought, the answer is:\nProduct,Process 1 (%),Process 2 (%)\nWidgetA,70.5,74.3\nWidgetB,89.6,92.1"
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "I7MhpIf7SdyiW1Le",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product demonstrates a greater relative improvement in production efficiency between the two processes, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I review the change in production efficiencies displayed on the bar charts. WidgetA increases from {the efficiency for WidgetA Process 1}<r>[0.12, 0.35, 0.32, 0.45]</r> at 70.5% to {the efficiency for WidgetA Process 2}<r>[0.35, 0.35, 0.55, 0.45]</r> at 74.3%, which is an increase of approx. 5.39%. On the other hand, WidgetB starts at an efficiency of 89.6% under {the efficiency for WidgetB Process 1}<r>[0.12, 0.55, 0.49, 0.65]</r> and climbs to 92.1% under {the efficiency for WidgetB Process 2}<r>[0.55, 0.55, 0.78, 0.65]</r>, an improvement of approx. 2.79%. Despite this, considering the initial high efficiency of WidgetB, maintaining and enhancing efficiency close to the maximum is notably challenging, implying that the 2.79% gain represents a significant achievement.\nBased on the above thought, the answer is:\nWidgetB demonstrates a greater relative improvement between the two processes. Despite a smaller percentage increase compared to WidgetA, enhancing efficiency from an already high baseline near maximum capacity (89.6% to 92.1%) is significantly more challenging and impactful, highlighting impressive advancements in production optimization for WidgetB."
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "Gw33wKQaNg5wFECQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in production efficiency for WidgetA from Process 1 to Process 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the bar lengths and labels for WidgetA, {the efficiency value for WidgetA under Process 1}<r>[0.12, 0.35, 0.32, 0.45]</r> indicates 70.5% and {the efficiency value for WidgetA under Process 2}<r>[0.35, 0.35, 0.55, 0.45]</r> shows 74.3%. To find the percentage increase, I calculate ((74.3 - 70.5) / 70.5) x 100 = 5.39%.\nBased on the above thought, the answer is:\n5.39%"
+            }
+        ],
+        "id_raw": 1097
+    },
+    {
+        "id": "tLSAjx9wYyf4Cn9m",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the line chart showing various environmental issues into a structured table format with years as columns and environmental factors as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the line chart data into a table, I examine {the x-axis labels}<r>[0.1, 0.9, 0.13, 0.93]</r>, which provide the years from 2000 to 2010 as the column headers. I identify {the legend}<r>[0.75, 0.1, 0.95, 0.2]</r> to determine the rows, which correspond to different environmental factors such as CO2 emission, Ozone depletion, and others. By analyzing {the plotted lines for each environmental factor}<r>[0.1, 0.3, 0.9, 0.7]</r>, I extract the values at each year's data point for the respective environmental issue.\nBased on the above thought, the answer is:\nYear,CO2_emission,Ozone_depletion,Rising_sea_level,Deforestation_rate,Percentage_of_renewable_energy\n2000,28,12,17,41,7\n2001,30,12.5,17.5,40,7.5\n2002,45,13,18,39,8\n2003,50,13.5,18.5,38,8.5\n2004,55,14,19,37,9\n2005,57,14.5,19.5,36,9.5\n2006,60,15,20,35,10\n2007,62,15.5,20.5,34,10.5\n2008,65,16,21,33,11\n2009,68,16.5,21.5,32,11.5\n2010,70,17,22,31,12"
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "uOdAwiNh3pKTeDZb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich environmental factor shows the most significant improvement according to the chart, and how can this improvement be quantitatively described?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most significant improvement, I examine the slopes of {the lines corresponding to each environmental factor}<r>[0.1, 0.3, 0.9, 0.7]</r>. The line for {Deforestation rate}<r>[0.1, 0.4, 0.9, 0.6]</r> depicts a downward trend, indicating improvement. The values start at 41% deforestation rate in 2000 and decrease to 31% in 2010. This quantitative improvement can be described as a 10% absolute reduction or approximately a 24.39% relative reduction over the decade (calculated as (41 - 31) / 41 * 100).\nBased on the above thought, the answer is:\nThe Deforestation rate shows the most significant improvement. It is quantitatively described as a 24.39% relative reduction over the decade from 2000 to 2010, moving from 41% down to 31%, representing a positive environmental impact."
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "ykwHc5C4BQu9EFAr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in CO2 emissions from the year 2000 to 2010 based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in CO2 emissions over the decade, I first locate {the points for CO2 emission}<r>[0.1, 0.5, 0.9, 0.65]</r>. The value in 2000 is 28% and in 2010 is 70%. Using the formula (new_value - old_value) / old_value * 100, the percentage increase is calculated as (70 - 28) / 28 * 100.\nBased on the above thought, the answer is:\n150%"
+            }
+        ],
+        "id_raw": 1098
+    },
+    {
+        "id": "jNXNw5WPmUiokVTm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data about Video Game Revenue and Esport Viewership into a table for easier comparison over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By visually analyzing the two line charts, I start by noting down the years on the shared y-axis ranging from 2010 to 2020, which will serve as rows. Then, I designate two columns, one for Video Game Revenue and another for Esport Viewership. Finally, I collect the data points from {the line representing Video Game Revenue}<r>[0.05, 0.1, 0.54, 0.9]</r> and {the line representing Esport Viewership}<r>[0.56, 0.1, 0.95, 0.9]</r>, matching them with the corresponding years to fill up the table.\nBased on the above thought, the answer is:\nYear,Video Game Revenue (Billion $),Esport Viewership (Million)\n2010,10,50\n2011,10.5,56\n2012,11.2,60\n2013,12,65\n2014,13,70\n2015,14,72\n2016,16,81\n2017,17,90\n2018,20,122\n2019,23,140\n2020,28,160"
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "tIQRxSeO6o6Zmcgz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Video Game Revenue compare to the fluctuations in Esport Viewership between 2010 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the trends, I analyze the line plots for both Video Game Revenue and Esport Viewership. The line for {Video Game Revenue}<r>[0.05, 0.1, 0.54, 0.9]</r> shows a relatively steady upward trend with notable jumps in 2016 and 2018. In contrast, {Esport Viewership}<r>[0.56, 0.1, 0.95, 0.9]</r> displays a more erratic but ascending behavior, with a rapid rise especially evident in the later years from 2018 to 2020. This pattern suggests Video Game Revenue grew consistently with substantial boosts linked to key industry events or innovations, whereas Esport Viewership had a more volatile increase, potentially influenced by breakout popularity and viewer engagement.\nBased on the above thought, the answer is:\nThe trend in Video Game Revenue shows consistent growth with periodic spikes possibly linked to industry developments or new game releases, reflecting long-term steady progress. On the other hand, Esport Viewership exhibits a more erratic rise with significant surges in later years, indicating a dynamic and rapidly expanding viewer base driven by increasing global interest and significant event occurrence."
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "4fZaCWFwo5nxwj40",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_55_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percent increase in Esport Viewership from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percent increase, I identify {the viewership values in 2010 and 2020}<r>[0.6, 0.85, 0.75, 0.9]</r> which correspond to 50 million and 160 million respectively. Using the formula (new value - old value) / old value * 100, the percent increase is calculated as (160 - 50) / 50 * 100.\nBased on the above thought, the answer is:\n220%"
+            }
+        ],
+        "id_raw": 1099
+    },
+    {
+        "id": "pQbgQEGNrvr5EEjr",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table with Education Levels as rows and Skill types as columns based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the chart, I first identify {the x-axis labels}<[0.069, 0.849, 0.300, 0.899],[0.460, 0.849, 0.648, 0.899],[0.879, 0.849, 0.968, 0.899]> as the row headers representing different Education Levels. Next, I locate {the legend texts}<r>[0.186, 0.754, 0.360, 0.799],[0.470, 0.754, 0.633, 0.799],[0.743, 0.754, 0.931, 0.799]</r> as the column headers representing different Skills. I then inspect {the line points1}<r>[0.185, 0.717],[0.554, 0.397],[0.924, 0.197]</r>, {the line points2}<r>[0.185, 0.637],[0.554, 0.517],[0.924, 0.317]</r>, and {the line points3}<r>[0.185, 0.797],[0.554, 0.417],[0.924, 0.137]</r> corresponding to the Performance Ratings of each skill for each Education Level.\nBased on the above thought, the answer is:\nEducation_Level,Innovative,Analytical,Conceptual\nBachelor's,66,70,62\nMaster's,82,76,81\nPhD,92,86,95"
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "JA5wzEXIlmuD2FBx",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which skill demonstrated the greatest improvement from Bachelor's to PhD level, and in what way does the granularity and nature of the data illustrate this?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I observe the line plots for each skill. For {Innovative}<r>[0.185, 0.717, 0.924, 0.197]</r> the increase from Bachelor's to PhD is from 66 to 92, for {Analytical}<r>[0.185, 0.637, 0.924, 0.317]</r> it is from 70 to 86, and for {Conceptual}<r>[0.185, 0.797, 0.924, 0.137]</r> it is from 62 to 95. Conceptual has the largest improvement of 33.  The granularity of the data with each point denoting the percentage values for each level of education and the nature being line charts, illustrates this by providing a clear visual progression of each skill from Bachelor\u2019s to PhD, underscored by the steepness of the line plot.\nBased on the above thought, the answer is:\nThe Conceptual skill demonstrated the greatest improvement from Bachelor's to PhD level. This is illustrated by the steepest upward trajectory among all the lines from Bachelor's to PhD, resulting in the largest increase in percentage points compared to the other skills."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "hqsEsdAFlWz72exv",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the score for Innovative skill improve from Bachelor's to PhD level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the improvement in Innovative skill score from Bachelor's to PhD, I locate {the Innovative skill point for Bachelor's}<r>[0.185, 0.717]</r> extracting the score as 66, and {the Innovative skill point for PhD}<r>[0.924, 0.197]</r> extracting the score as 92. The improvement is therefore 92 - 66 = 26.\nBased on the above thought, the answer is:\n26."
+            }
+        ],
+        "id_raw": 1100
+    },
+    {
+        "id": "m56TgJoPYls24sDp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table format with dates as rows and review scores for each restaurant type as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify {the x-axis tick labels} [[0.110, 0.900, 0.211, 1.061], [0.188, 0.900, 0.289, 1.061], [0.266, 0.900, 0.367, 1.061], [0.345, 0.900, 0.446, 1.061], [0.423, 0.900, 0.524, 1.061], [0.501, 0.900, 0.602, 1.061], [0.579, 0.900, 0.680, 1.061], [0.658, 0.900, 0.759, 1.061], [0.736, 0.900, 0.837, 1.061], [0.814, 0.900, 0.915, 1.061]] as the row headers representing the review dates. Next, I identify {the legend texts} [[0.202, 0.150, 0.285, 0.186], [0.202, 0.203, 0.266, 0.239], [0.202, 0.255, 0.288, 0.291]] representing Chinese, Italian, and Mexican restaurants as the column headers. To populate the values, I locate the points on each line for {Chinese restaurant} [[0.160, 0.736], [0.239, 0.659], [0.317, 0.621], [0.395, 0.563], [0.473, 0.467], [0.552, 0.409], [0.630, 0.312], [0.708, 0.351], [0.786, 0.370], [0.865, 0.332]], {Italian restaurant} [[0.160, 0.601], [0.239, 0.582], [0.317, 0.563], [0.395, 0.505], [0.473, 0.447], [0.552, 0.409], [0.630, 0.312], [0.708, 0.274], [0.786, 0.255], [0.865, 0.197]], and {Mexican restaurant} [[0.160, 0.774], [0.239, 0.698], [0.317, 0.659], [0.395, 0.621], [0.473, 0.543], [0.552, 0.505], [0.630, 0.409], [0.708, 0.447], [0.786, 0.467], [0.865, 0.409]] on their respective dates.\nBased on the above thought, the answer is:\nReviewDate,Chinese,Italian,Mexican\n2022-01-01,68,75,66\n2022-01-10,72,76,70\n2022-01-20,74,77,72\n2022-01-30,77,80,74\n2022-02-09,82,83,78\n2022-02-19,85,85,80\n2022-02-29,90,90,85\n2022-03-10,88,92,83\n2022-03-20,87,93,82\n2022-03-30,89,96,85"
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "awPcOe8uaoUQk99g",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich restaurant category shows the most consistent improvement in review scores over the period under review?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the trends in the line plots for each category. From the {line for Chinese} [[0.160, 0.736], [0.239, 0.659], [0.317, 0.621], [0.395, 0.563], [0.473, 0.467], [0.552, 0.409], [0.630, 0.312], [0.708, 0.351], [0.786, 0.370], [0.865, 0.332]], it shows that the review scores are generally increasing over time with a minor dip toward the end. For {Italian restaurant} [[0.160, 0.601], [0.239, 0.582], [0.317, 0.563], [0.395, 0.505], [0.473, 0.447], [0.552, 0.409], [0.630, 0.312], [0.708, 0.274], [0.786, 0.255], [0.865, 0.197]], the increasing trend is more steady, showing continuous improvement each month. On the other hand, the line for {Mexican restaurant} [[0.160, 0.774], [0.239, 0.698], [0.317, 0.659], [0.395, 0.621], [0.473, 0.543], [0.552, 0.505], [0.630, 0.409], [0.708, 0.447], [0.786, 0.467], [0.865, 0.409]] shows an initially stable trend, followed by a significantly fluctuating pattern.\nBased on the above thought, the answer is:\nThe Italian restaurant category shows the most consistent improvement in review scores over the period under review. The line plot for this category shows a clear, consistent upward trend from January to March, with scores consistently rising each month. This suggests that the quality of the restaurant in the eyes of customers has steadily improved over the period."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "jF1ojjzS7KobtGz1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average review score for Chinese and Mexican restaurants in the month of February 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average review score for Chinese and Mexican restaurants in February 2022, I need to locate the points for {Chinese restaurant} [[0.473, 0.467], [0.552, 0.409], [0.630, 0.312]] and {Mexican restaurant} [[0.473, 0.543], [0.552, 0.505], [0.630, 0.409]] which correspond to review scores of 82, 85, 90 for Chinese and 78, 80, 85 for Mexican. The average for the Chinese restaurant is (82+85+90)/3 = 85.67 and for Mexican is (78+80+85)/3 = 81.\nBased on the above thought, the answer is:\nChinese: 85.67, Mexican: 81."
+            }
+        ],
+        "id_raw": 1102
+    },
+    {
+        "id": "COncZBw3xjm8QjbW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the available data, could you please convert this line chart into a structured table with the Health Indicators as columns and Hospitals as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To produce a structured table from the line chart, I first identify {the x-axis tick labels}[[1.102, 0.852, 1.181, 0.915], [6.149, 0.852, 6.227, 0.915]], which correspond to the two Health Indicators - 'Patient Recovery Rate' and 'Patient Admission Rate'. Next, I locate the different {legend texts}[[0.190, 0.758, 0.358, 0.803], [0.468, 0.758, 0.637, 0.803], [0.747, 0.758, 0.916, 0.803]] indicating 'Hospital A', 'Hospital B', and 'Hospital C'. Interpreting the plot, I identify {the line points for Hospital A}[[0.197, 0.740], [0.911, 0.238]], {Hospital B}[[0.197, 0.680], [0.911, 0.339]], and {Hospital C}[[0.197, 0.539], [0.911, 0.539]] for each Health Indicator to derive the corresponding values.\nBased on the above thought, the answer is:\nHospital,Patient Recovery Rate,Patient Admission Rate\nHospital A,0.65,0.9\nHospital B,0.68,0.85\nHospital C,0.75,0.75"
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "77MfkAtOkMhe2aPB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich hospital appears to maintain a more consistent performance across both 'Patient Recovery Rate' and 'Patient Admission Rate'? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the trends in both 'Patient Recovery Rate' and 'Patient Admission Rate' across the hospitals. Observing {the line for Hospital A}[0.197, 0.238, 0.911, 0.740], it shows a decrease from recovery rate 0.65 to admission rate 0.90, indicating a decrease in performance. The {line for Hospital B}[0.197, 0.339, 0.911, 0.680] also shows a decrease from recovery rate 0.68 to admission rate 0.85. In contrast, {the line for Hospital C}[0.197, 0.539, 0.911, 0.539] remains consistent at 0.75 across both health indicators, reflecting a stable performance.\nBased on the above thought, the answer is:\nBased on the given chart, Hospital C appears to maintain a more consistent performance across both 'Patient Recovery Rate' and 'Patient Admission Rate'. This is deduced from the fact that the line representing Hospital C's performance across the two health indicators is a straight line with no variation, indicating that both the recovery rate and admission rate remain at a consistent 0.75."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "NXxDMUm7Rh7Uj9Ay",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the 'Patient Recovery Rate' between Hospital A and Hospital C observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to identify the 'Patient Recovery Rate' for both Hospital A and Hospital C. I locate {the line point for Hospital A on 'Patient Recovery Rate'}[0.197, 0.740] which indicates a recovery rate of 0.65 for Hospital A. Then, I identify {the line point for Hospital C on 'Patient Recovery Rate'}[0.197, 0.539] which shows a recovery rate of 0.75 for Hospital C. The difference is computed as 0.75 - 0.65 = 0.1.\nBased on the above thought, the answer is:\n0.1."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "IqjAp1gEXTmBr8Un",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, can you extract the data into a table format with Year as columns and Energy, Resource, and Carbon as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}<r>[0.088, 0.926, 0.117, 0.942, 0.257, 0.926, 0.285, 0.942, 0.425, 0.926, 0.453, 0.942, 0.593, 0.926, 0.621, 0.942, 0.761, 0.926, 0.789, 0.942, 0.929, 0.926, 0.957, 0.942]</r>, which represent the column headers or years. Next, I identify {the legend texts}<r>[0.112, 0.078, 0.257, 0.101, 0.112, 0.113, 0.272, 0.136, 0.112, 0.148, 0.215, 0.171]</r> as the row labels representing the different consumption/output types. To populate the values, I locate {the line points}<r>[0.103, 0.198, 0.943, 0.879, 0.103, 0.266, 0.943, 0.538, 0.103, 0.096, 0.943, 0.708]</r> for each consumption/output type at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,EnergyConsumption,ResourceConsumption,CarbonOutput\n2000,100,200,150\n2001,105,210,155\n2002,110,228,160\n2003,120,236,165\n2004,130,242,170\n2005,150,244,175\n2006,160,248,180\n2007,180,258,190\n2008,210,262,220\n2009,250,264,260\n2010,300,280,330"
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "rNLTaUyuaqNJ3V6k",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which factor seems to have the most rapid increase over time? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which factor has the most rapid increase, I need analyze the slopes of the lines for each factor. Looking at {the line for Energy Consumption}<r>[0.103, 0.198, 0.943, 0.879]</r>, I see a steady upward trend indicating an increase. For {Resource Consumption}<r>[0.103, 0.266, 0.943, 0.538]</r>, there is a moderate increase, but less steep than the Energy Consumption line. For {Carbon Output}<r>[0.103, 0.096, 0.943, 0.708]</r>, it shows the steepest upward trend indicating a rapid increase.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Carbon Output seems to have the most rapid increase over time. Its line plot exhibits a steep upward trend across the years. The slope of this line plot is more pronounced compared to Energy Consumption and Resource Consumption, indicating a faster rate of increase in Carbon Output."
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "7AXVd1wX6nDaKvIo",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of growth of energy consumption between 2000 and 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of growth of energy consumption, I need to locate the starting and ending values of {the line points for Energy Consumption}<r>[0.103, 0.879, 0.943, 0.198]</r> which are 100 and 300 respectively. The rate of growth is calculated as (Final - Initial) / Initial * 100%. So the rate of growth is (300 - 100) / 100 * 100% = 200%.\nBased on the above thought, the answer is:\n200%"
+            }
+        ],
+        "id_raw": 1104
+    },
+    {
+        "id": "MqhUZwuHLR7yfRA5",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart into a table format, with each row representing a different city and each column a different data category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive a structured table from the line chart, I'll take {the x-axis labels}[[0.108, 0.894], [0.317, 0.894], [0.526, 0.894], [0.735, 0.894], [0.943, 0.894]] as the row labels, corresponding to the cities. The columns are represented by {the legend texts}[[0.763, 0.091, 0.970, 0.121], [0.763, 0.134, 0.956, 0.164], [0.763, 0.178, 0.924, 0.208]], i.e., Tech Investment, University Partnerships, and Startup Growth. Next, to populate the data in the table, I locate the {data points on the lines}[[0.108, 0.434], [0.317, 0.321], [0.526, 0.241], [0.735, 0.354], [0.943, 0.413], [0.108, 0.821], [0.317, 0.848], [0.526, 0.796], [0.735, 0.842], [0.943, 0.832], [0.108, 0.768], [0.317, 0.777], [0.526, 0.739], [0.735, 0.799], [0.943, 0.816]] for each city's record.\nBased on the above thought, the answer is:\nCity,Investment in Tech (bn$),University Partnerships,Startup Growth (%)\nSan Francisco,55.5,8.8,15.3\nNew York,69.2,5.6,14.2\nBoston,78.8,11.9,18.8\nSeattle,65.2,6.3,11.5\nChicago,58.1,7.5,9.4"
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "QahxOJk966tukFKz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the highest value of University Partnerships exceed the lowest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate {the University Partnerships line points}[[0.108, 0.821], [0.317, 0.848], [0.526, 0.796], [0.735, 0.842], [0.943, 0.832]] and extract the values: 8.8, 5.6, 11.9, 6.3, 7.5. The maximum value is 11.9 represented by {Boston}[0.526, 0.796], while the minimum value is 5.6 represented by {New York}[0.317, 0.848]. The difference is thus 11.9 - 5.6 = 6.3.\nBased on the above thought, the answer is:\n6.3."
+            }
+        ],
+        "id_raw": 1105
+    },
+    {
+        "id": "qRzq8tB1AZS6XrMS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the line chart, where the X-axis represents the years and the Y-axis represents the different population categories' growth, into a tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to create a table from the line chart, I will identify {the x-axis labels}[[0.066, 0.875, 0.081, 0.907], [0.051, 0.703, 0.081, 0.734], [0.051, 0.530, 0.081, 0.562], [0.051, 0.358, 0.081, 0.389], [0.051, 0.185, 0.081, 0.217]] which represent the years. Each population category will be represented by a row - {the legend texts}[[0.376, 0.745, 0.616, 0.775], [0.376, 0.789, 0.632, 0.819], [0.376, 0.833, 0.549, 0.863], [0.700, 0.745, 0.883, 0.775], [0.700, 0.789, 0.963, 0.819]] indicates these categories. I shall refer to {the line points}[[0.128, 0.543, 0.941, 0.543], [0.128, 0.111, 0.941, 0.629], [0.128, 0.456, 0.941, 0.715], [0.128, 0.629, 0.941, 0.802], [0.128, 0.370, 0.941, 0.715]] to obtain the values associated with each category at each year.\nBased on the above thought, the answer is:\nYear,Current age population,Baby boomer population,Youth population,Senior population,Virtual worker population\n0.01,20,45,25,15,30\n0.1,20,45,15,15,30\n1.0,20,25,25,15,20"
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "MA4H2pJmrsfK3jYz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich population group shows the greatest volatility in population growth from year 0.01 to year 1.0 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate variability, I have to look at the changes in population growth over time. Considering {Current age population's line}[[0.128, 0.543, 0.941, 0.543]], it stays constant at 20%. For {Baby boomer's population line}[[0.128, 0.111, 0.941, 0.629]], it starts at 45% at year 0.01, drops to 15% at year 0.1, and increases to 25% at year 1.0. This is a significant series of fluctuations. For the {Youth population's line}[[0.128, 0.456, 0.941, 0.715]], it starts at 25%, drops to 15%, and rises back to 25%, showing minor but noticeable fluctuations. {Senior population's line}[[0.128, 0.629, 0.941, 0.802]] stays constant at 15%, and finally, the {Virtual worker's population line}[[0.128, 0.370, 0.941, 0.715]] shows stable growth from 10% to 20%.\nBased on the above thought, the answer is:\nThe baby boomer population shows the greatest volatility in growth from year 0.01 to year 1.0. The line plot for the baby boomer population shows significant fluctuations in the population growth percentage, decreasing from 45% to 15% and then increasing to 25%. The degree of these fluctuations is greater than those seen in the other population groups, indicating a larger amount of volatility for the baby boomer population."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "4C9y37jyYM5yv79I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the net change in population growth percentage for the Youth Population from year 0.01 to year 1.0?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the net change, I examine {Youth population line}[[0.128, 0.456, 0.941, 0.715]] which represents the population growth of the Youth population. The population growth at year 0.01 is 25%, located at {the point for Youth population at 0.01}[0.128, 0.715]. The population growth at year 1.0 is also 25%, found at {the point for Youth population at 1.0}[0.941, 0.456]. The net change can be calculated by subtracting the initial population growth from the final population growth, so 25 - 25 = 0.\nBased on the above thought, the answer is:\n0."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "4qNXbWhrKBfODRZ4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data from the line chart into a structured data table, with time periods as columns and subculture trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first identify {the x-axis tick labels}[[0.121, 0.766, 0.180, 0.884], [0.313, 0.766, 0.372, 0.884], [0.505, 0.766, 0.564, 0.884], [0.697, 0.766, 0.756, 0.884], [0.889, 0.766, 0.948, 0.884]], being the time periods, which will represent the column headers. I then take {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] to generate the row labels representing different subculture trends. Finally, I locate {the line points}[[0.144, 0.570], [0.336, 0.490], ..., [0.920, 0.380]] for each subculture at the respective time periods and extract the values encoded by these points.\nBased on the above thought, the answer is:\nSubculture Trend,2005,2010,2015,2020\nMovement Punk Style,25,30,35,40\nIndie Rock Look,35,33,30,28\nHigh Fashion,35,32,27,22"
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "46ZtID5SEQqGoJ6F",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which subculture trend has shown a consistent increase over the years? Explain your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which trend has shown a consistent increase over the years, I need to examine the trajectory of the lines for each subculture. The line for {Movement Punk Style}[0.120, 0.075, 0.930, 0.488] shows a consistent upward trend from 25 to 40. On the other hand, {the Indie Rock Look line}[0.120, 0.490, 0.930, 0.379] and {the High Fashion line}[0.120, 0.490, 0.930, 0.379] show a declining trend. Hence, 'Movement Punk Style' is the subculture that has displayed consistent growth over the years.\nBased on the above thought, the answer is:\nThe 'Movement Punk Style' trend has shown a consistent increase over the years according to the line chart. Its line indicates a consistent upward trend, climbing from 25 in 2005 to 40 in 2020. This is in contrast with the other two trends 'Indie Rock Look' and 'High Fashion', which show a downward trajectory over the same period. Therefore, 'Movement Punk Style' has remained increasingly popular over the years."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "GcuNLtd4PWHx2ZNn",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall change in the trend for 'High Fashion' from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the overall change in trend for 'High Fashion', I need to look at {the line points for High Fashion}[[0.164, 0.354], [0.418, 0.484], [0.671, 0.548], [0.924, 0.807]] and extract the corresponding values: 35, 32, 27 and 22 respectively. The trend in 2005 is 35 and in 2020 it is 22. Therefore, the overall change in trend for 'High Fashion' from 2005 to 2020 is 22 - 35 = -13.\nBased on the above thought, the answer is:\n-13."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "QG9cU9fQPHEKo8OT",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the performance scores of the different trends in a table format, using Value Chain Factors as the columns and the trends as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table from the line chart, I am locating {the x-axis tick labels}[[0.021, 0.681, 0.178, 0.942], [0.212, 0.681, 0.314, 0.850], [0.378, 0.681, 0.476, 0.844], [0.555, 0.681, 0.626, 0.800], [0.684, 0.681, 0.826, 0.918], [0.858, 0.681, 0.979, 0.884]] first, these will serve as the column headers. Next, I can identify {the legend texts}[[0.110, 0.050, 0.212, 0.074], [0.110, 0.085, 0.230, 0.109], [0.110, 0.120, 0.233, 0.144], [0.110, 0.155, 0.221, 0.178], [0.110, 0.190, 0.235, 0.213]] as row labels. The trends represented here are the different rows in the table. For filling the values, I need to examine the respective points on {the line charts}[[0.099, 0.338, 0.918, 0.643], [0.099, 0.237, 0.918, 0.567], [0.099, 0.415, 0.918, 0.453], [0.099, 0.351, 0.918, 0.605], [0.099, 0.059, 0.918, 0.631]] for each trend at each stage in the value chain.\nBased on the above thought, the answer is:\nTrend,Raw Material Acquisition,Product Design,Manufacturing,Marketing,Sales and Distribution,After-sales Service\nSlight increase,2,6,10,14,21,26\nDeclining pattern,34,29,25,19,14,8\nSluggish progress,18,18,17,19,20,20\nErratic behavior,5,15,7,20,14,25\nUpward trajectory,3,9,16,26,36,48"
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "291ywoRzBqjMf4f5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this line chart, which trend seems the most erratic over time? Please provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most erratic trend, I need to observe how much each trend's performance score fluctuates over different stages of the value chain. When I look at {the line for Slight increase}[0.099, 0.338, 0.918, 0.643], I observe a consistent increase in the scores from 2 to 26. Similarly, for {Declining pattern}[0.099, 0.237, 0.918, 0.567], there is a steady decline from 34 to 8. Moreover, the line for {Sluggish progress}[0.099, 0.415, 0.918, 0.453] maintains scores around the middle of the range, showing little fluctuation. Lastly, looking at {Erratic behavior}[0.099, 0.351, 0.918, 0.605], the trend demonstrates considerable fluctuations between different stages. The scores range from a low of 5 to a high of 25 with numerous ups and downs.\nBased on the above thought, the answer is:\nThe trend that seems most erratic over time is indeed the one labelled \"Erratic behavior\". As the name insinuates, this trend does not follow a consistent pattern. It undergoes significant ups and downs across the value chain, with considerable variation in performance scores between stages, making it the most volatile trend depicted on the graph."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "DpCONQ7VLTvOMPWt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest performance score for the Erratic behavior trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference between the highest and lowest performance scores for the Erratic behavior trend, I need to identify {the various data points associated with Erratic behavior}[[0.099, 0.605], [0.263, 0.478], [0.427, 0.580], [0.591, 0.415], [0.755, 0.491], [0.918, 0.351]] and take their corresponding scores: 5, 15, 7, 20, 14, 25. From these scores, the highest value is 25 at {Sales and Distribution}[0.918, 0.673] and the lowest value is 5 at {Raw Material Acquisition}[0.099, 0.673]. The difference is therefore 25 - 5 = 20.\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "JHsAcxbHSctY6nyE",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a table with processes as rows and 'Before Intervention' and 'After Intervention' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from the line chart, I first locate {the x-axis tick labels}[[0.059, 0.925, 0.156, 0.934], [0.238, 0.925, 0.337, 0.934], ..., [0.863, 0.925, 0.963, 0.934]]. These labels correspond to the different processes and will form the row labels in the table. I analyze {the legend items}[[0.400, 0.057, 0.522, 0.066], [0.526, 0.057, 0.648, 0.066]] to identify 'Before Intervention' and 'After Intervention' as column headers. The line points on {the 'Before Intervention' line}[[0.113, 0.749], [0.291, 0.646], ..., [0.920, 0.682]] and {the 'After Intervention' line}[[0.162, 0.523], [0.341, 0.420], ..., [0.969, 0.533]] represent the efficiency levels before and after the intervention for each process.\nBased on the above thought, the answer is:\nProcess,Before Intervention,After Intervention\nProcess 1,0.60,1.00\nProcess 2,0.50,0.90\nProcess 3,0.75,1.15\nProcess 4,0.65,0.95\nProcess 5,0.70,1.10\nProcess 6,0.85,0.80"
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "b5gUtZZOIn5um9Y4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which process showed the greatest improvement in efficiency after the intervention, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the process with the greatest improvement in efficiency, I analyze the changes between the {points on the 'Before Intervention' line}[[0.113, 0.749], [0.291, 0.646], ..., [0.599, 0.598], [0.775, 0.618], [0.920, 0.682]] and the {points on the 'After Intervention' line}[[0.162, 0.523], [0.341, 0.420], ..., [0.648, 0.422], [0.825, 0.498], [0.969, 0.533]]. It appears that Process 1 had the most substantial improvement in efficiency, improving from 0.60 to 1.00.\nBased on the above thought, the answer is:\nThe process that showed the greatest improvement in efficiency after the intervention was Process 1. The efficiency of Process 1 increased from 0.60 before the intervention to 1.00 after the intervention, representing a significant improvement."
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "ZoAkNkB2zMkpMLaO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the efficiency of Process 3 improve after the intervention?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the efficiency levels of Process 3 before and after the intervention. This involves comparing {the point representing Process 3 on the 'Before Intervention' line}[[0.291, 0.646]] and {the point representing Process 3 on the 'After Intervention' line}[[0.341, 0.420]]. The 'Before Intervention' efficiency was 0.75 and the 'After Intervention' efficiency was 1.15. The improvement can therefore be calculated as 1.15 - 0.75 = 0.40.\nBased on the above thought, the answer is:\n0.40"
+            }
+        ],
+        "id_raw": 1109
+    },
+    {
+        "id": "M0yQ3Alp5JiiAUMW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table summarizing the Returns (%) of the Aggressive and Conservative investment strategies across each Market condition?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a tabular summary from the line chart, I first observe that {the x-axis}<[0.261, 0.912, 0.881, 0.962]> represents the Market conditions and they are marked in a logarithmic scale. The y-axis corresponds to Returns (%). The two investment strategies, {Aggressive}<[0.425, 0.741, 0.607, 0.786]> and {Conservative}<[0.717, 0.741, 0.936, 0.786]>, are differentiated by different colors (#008000 for Aggressive and #ff0000 for Conservative). I note the line points for each investment strategy corresponding to each Market condition and retrieve their Returns (%) values from the y-axis to populate the table.\nBased on the above thought, the answer is:\nMarket condition,Aggressive,Conservative\nRecession,68,62\nBull Market,90,87\nBear Market,45,48"
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "fqCqIeupZVmrZtWD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment strategy seems to be riskier based on returns in different market conditions and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I compare the trends and fluctuations in {returns for the Aggressive strategy}<[0.212, 0.181, 0.930, 0.737]> and {the Conservative strategy}<[0.212, 0.218, 0.930, 0.700]>. I notice that the Aggressive strategy yields higher returns in Bull Market but significantly lower returns in Bear Market compared to the Conservative strategy. This shows higher potential for gain but also higher risk of losses, which implies greater risk in the Aggressive strategy.\nBased on the above thought, the answer is:\nThe Aggressive investment strategy seems to be riskier based on the returns under different market conditions. The reasoning for this is that while it produces higher returns during Bull Market, it also shows drastically lower returns during Bear Market compared to the Conservative strategy. This pattern suggests a higher risk-return trade-off, which is typically taken as a measure of riskiness in investment strategies."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "haHsnrVi8WeFkR4A",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more return does the Aggressive strategy make than the Conservative strategy in Bull Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I locate the {line point for Bull Market in the Aggressive strategy}<[0.571, 0.181]>, which corresponds to a return of 90%. Similarly, for the Conservative strategy, the {line point for Bull Market}<[0.571, 0.218]> corresponds to a return of 87%. The difference is hence 90 - 87 = 3%.\nBased on the above thought, the answer is:\n3%."
+            }
+        ],
+        "id_raw": 1110
+    },
+    {
+        "id": "Z5sUPOFAC92I4ucd",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the investment percentage for each investment stage into a table format based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data into a table, I first look at {the x-axis tick labels}[[0.279, 0.836, 0.382, 0.899], [0.581, 0.836, 0.685, 0.899], [0.896, 0.836, 0.975, 0.899]] which detail the investment stages. I then identify the legend entries {Seed Stage}[0.027, 0.741, 0.217, 0.786], {Early Stage Growth}[0.327, 0.741, 0.647, 0.786], and {Late Stage}[0.757, 0.741, 0.936, 0.786] as the row labels. To populate the values, I locate the {line points for Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]], {line points for Early Stage Growth}[[0.330, 0.299], [0.633, 0.474], [0.936, 0.649]], and {line points for Late Stage}[[0.330, 0.125], [0.633, 0.247], [0.936, 0.474]] and extract the annotated values.\nBased on the above thought, the answer is:\nInvestment Stage,Seed,Early Stage Growth,Late Stage\n$10^{-4}$,100,110,120\n$10^{-3}$,90,100,113\n$10^{0}$,83,90,100"
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "l8Z1hdoo2I80MaPb",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich investment stage seems to have experienced the greatest decrease in investment percentage according to the line chart? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which investment stage has experienced the greatest decrease in investment percentage, I need to observe the relative changes in the lines corresponding to {Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]], {Early Stage Growth}[[0.330, 0.299], [0.633, 0.474], [0.936, 0.649]], and {Late Stage}[[0.330, 0.125], [0.633, 0.247], [0.936, 0.474]]. By comparing the end and start points of each line, it appears that the Seed Stage line has descended the most steeply.\nBased on the above thought, the answer is:\nBased on the line chart, the Seed Stage seems to have experienced the greatest decrease in investment percentage. The line associated with Seed Stage descends more steeply compared to the lines for Early Stage Growth and Late Stage, suggesting a greater decrease in investment percentage."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "wVzMvrBBxnUsvMCV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the investment percentage for Seed Stage decreased from $10^{-4}$ to $10^{0}$?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to locate {the line points for Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]]. The investment percentage at the $10^{-4}$ stage is 100 and at the $10^{0}$ stage is 83. Therefore, the decrease is 100 - 83.\nBased on the above thought, the answer is:\n17."
+            }
+        ],
+        "id_raw": 1111
+    },
+    {
+        "id": "qHgZ2dteAz8KJWqP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe line chart represents greenhouse gas emissions and climate change impact. Could you extract the data and present it in a structured table with columns representing different years and rows indicating 'GHG Emission' and 'Climate Change Impact'?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to configure the tabular data, I first observe {the x-axis tick labels}<r>[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951]</r>, representing stages, consistent with the years. Then I identify the {legend labels}<r>[0.884, 0.101, 0.982, 0.123]</r> as the row headers displaying the trends of 'GHG Emission' and 'Climate Change Impact'. To fill in the values, I track the {line points for GHG Emission}<r>[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]</r> and the {line points for Climate Change Impact}<r>[0.102, 0.599], [0.218, 0.340], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]</r> for each factor during the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nFactor,2025,2030,2035,2040,2045,2050\nGHG Emission,22,23,24,25,26,30\nClimate Change Impact,21,23,25,27,29,35"
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "mZD6wVH5waSDZrsh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend of Climate Change Impact relate to the trend of GHG Emission based on the line chart? Briefly describe your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the mutual relation between the trend of Climate Change Impact and GHG Emission, I need to scrutinize the trajectories of {the line for GHG Emission}<r>[0.102, 0.082, 0.798, 0.696]</r> and {the line for Climate Change Impact}<r>[0.102, 0.211, 0.798, 0.729]</r>. Both lines consistently show growth over time, with slight dips before increasing again. This shared trend suggests a positive correlation between GHG Emissions and Climate Change Impact, indicating that as GHG Emissions increase, Climate Change Impact tends to follow a similar uptrend. The 2050 points in both trends ends up being significantly higher than the expectations followed by the previous years thus could be perceived as outliers.\nBased on the above thought, the answer is:\nThe trend of Climate Change Impact appears to positively correlate with the trend of GHG Emission based on the line chart. Both lines consistently rise over the years, suggesting that as the GHG Emissions increase, the Impact of Climate Change also increases. The graph provides a strong visual hint of the co-dependency between GHG Emissions and Climate Change Impact, indicating they move in a similar direction. This can be interpreted as an increase in GHG Emissions causing a corresponding increase in Climate Change Impact."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "rMiu67LlDHm5BVva",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, how much did GHG Emissions increase from 2025 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in GHG emissions from the year 2025 to 2050, I need to locate the {line points for GHG Emission}<r>[0.102, 0.470], [0.798, 0.243]</r>, from which we can gather the corresponding values for these years. In 2025, it was 22 and in 2050, it rose to 30. The increase in GHG emissions between 2025 and 2050 is therefore 30 - 22 = 8 units.\nBased on the above thought, the answer is:\n8 units."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "Y3c0nalJhSC8etWz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table including \"Year\" as columns and \"Activity Type\" as rows based on the provided line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table data from the line chart, I first look at {the x-axis tick labels}[[0.195, 0.903, 0.246, 0.931], [0.322, 0.903, 0.372, 0.931], [0.448, 0.903, 0.498, 0.931], [0.574, 0.903, 0.625, 0.931], [0.701, 0.903, 0.751, 0.931], [0.827, 0.903, 0.877, 0.931]], which represent the column headers or Years. Then, I consider {the legend texts}[[0.787, 0.066, 0.913, 0.094], [0.787, 0.108, 0.916, 0.136], [0.787, 0.150, 0.961, 0.178]] as the row labels representing the different Activity Types. To populate the values, I locate {the line points}[[0.221, 0.094, 0.852, 0.201], [0.221, 0.680, 0.852, 0.733], [0.221, 0.787, 0.852, 0.851]] for each activity at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nActivity Type,2010,2011,2012,2013,2014,2015\nDoctor Visits,210,205,208,202,198,190\nClinical Trials,100,95,90,93,92,90\nMedical Research,80,78,77,76,70,68"
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "OdzsUkbMGConLIQc",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which activities have shown a steady decline over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the activities showing a steady decline, I need to examine the trends in the line plots for each activity. Looking at {the line for Doctor Visits}[0.221, 0.094, 0.852, 0.201], I see a steady decrease from 210 to 190 over the year. Similarly, for {Medical Research}[0.221, 0.787, 0.852, 0.851], there is a consistent decrease from 80 to 68. However, {Clinical Trials}[0.221, 0.680, 0.852, 0.733] show a more erratic trend with both ups and downs over the years.\nBased on the above thought, the answer is:\nAccording to the line chart, the activities 'Doctor Visits' and 'Medical Research' have shown a steady decline over the years. For 'Doctor Visits', there is a consistent decrease from 210 in 2010 to 190 in 2015. Similarly, 'Medical Research' shows a decline from 80 in 2010 to 68 in 2015. These trends indicate a downward pattern over the years for these activities."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "sP42chDlmc7kjDNO",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage difference in 'Doctor Visits' from the year 2010 to 2015 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage difference in 'Doctor Visits' from the year 2010 to 2015, I have to locate {the line points for Doctor Visits}[[0.221, 0.094],[0.852, 0.201]] and extract the corresponding values: 210 and 190. I then use the formula for percentage change: ((New value - Old value)/Old value)*100 = ((190-210)/210)*100.\nBased on the above thought, the answer is:\n-9.52%."
+            }
+        ],
+        "id_raw": 1114
+    },
+    {
+        "id": "YcCrRt53HgZ3TxK4",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you change this line chart data into a table with years as rows and performance metrics as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, first, I can see {the labels of the x-axis}[[0.350, 0.879, 0.444, 0.919], [0.458, 0.879, 0.552, 0.919], [0.566, 0.879, 0.660, 0.919], [0.673, 0.879, 0.768, 0.919], [0.781, 0.879, 0.876, 0.919], [0.889, 0.879, 0.983, 0.919]], which represent different years. {The legend}[-0.157, 0.790, 0.949, 0.853] indicates the three different performance metrics. To get the values for each metric in each year, I will look at {the points of each line}[[0.397, 0.732], [0.505, 0.458], [0.613, 0.320], [0.720, 0.595], [0.828, 0.512], [0.936, 0.650],[0.397, 0.375], [0.505, 0.183], [0.613, 0.101], [0.720, 0.348], [0.828, 0.266], [0.936, 0.348],[0.397, 0.540], [0.505, 0.320], [0.613, 0.211], [0.720, 0.430], [0.828, 0.375], [0.936, 0.512]] that denote respective values.\nBased on the above thought, the answer is:\nYear,ProductQuality,CustomerSatisfaction,OperationalEfficiency\n2016,65,78,72\n2017,75,85,80\n2018,80,88,84\n2019,70,79,76\n2020,73,82,78\n2021,68,79,73"
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "Szqm8tjaUX7PLFyB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends in the line chart, which performance metric showed the most improvement over the years? Give an explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the performance metric with the most improvement, I need to study {the lines}[[0.397, 0.320, 0.936, 0.732], [0.397, 0.101, 0.936, 0.375], [0.397, 0.211, 0.936, 0.540]] corresponding to each metric. The line for Product Quality shows a gradual increase until 2018, then a decrease and slight fluctuations. Customer Satisfaction shows an upward trend till 2018, but then sees a drop and remains around the same value. Operational Efficiency demonstrates a steady increase till 2018, and then a slight decrease till 2021. Thus, we see that all metrics have initially increased, but then dropped after the peak in 2018.\nBased on the above thought, the answer is:\nBased on the line trends in the chart, none of the performance metrics showed a consistent improvement over the years. However, Operational Efficiency demonstrated the most noticeable improvement up until 2018, followed by a relatively small drop compared to the others over the subsequent years."
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "2h3ZTVXlFT4dzGUC",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score of Customer Satisfaction from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average score of Customer Satisfaction, I need to locate {the points of the Customer Satisfaction line}[[0.397, 0.375], [0.505, 0.183], [0.613, 0.101], [0.720, 0.348], [0.828, 0.266], [0.936, 0.348]] which represent the scores for each year from 2016 to 2021. The scores are 78, 85, 88, 79, 82, 79. Now I can calculate the average by adding them all together and dividing by the total number of years, which is 6.\nBased on the above thought, the answer is:\n82.17"
+            }
+        ],
+        "id_raw": 1116
+    },
+    {
+        "id": "bdUcXptFrIaI9gLB",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please help generate a table capturing the information about the number of Patent Applications, Approvals and Rejections over the years 2005 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I have to use the coordinates from the line points from all the three lines in the chart. Starting with {the line for Patent Application}<r>[0.177, 0.077, 0.930, 0.414]</r>, {the line for Patent Approval}<r>[0.177, 0.461, 0.930, 0.629]</r> and {the line for Patent Rejection}<r>[0.177, 0.783, 0.930, 0.875]</r> I can extract the respective values for each year. The years can be determined through the given {x-axis ticks}<r>[0.101, 0.875]</r> to <r>[1.006, 0.875]</r>.\nBased on the above thought, the answer is:\nYear,Patent Application,Patent Approval,Patent Rejection\n2005,19000,12000,4000\n2006,21000,12500,4200\n2007,22000,13000,4600\n2008,23000,14000,4700\n2009,24000,14500,4900\n2010,25000,15000,5400\n2011,25500,15500,5700\n2012,26500,16000,6100\n2013,28000,16500,6400\n2014,29000,17000,6800\n2015,30000,17500,7000"
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "RzuzkWJdqi4m4AGi",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze the trend of Patent Approvals and Rejections from 2005-2015. What do these trends suggest about the selection process for patents?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend for Patent Approvals and Rejections can be determined by looking at {the line for Patent Approvals}<r>[0.177, 0.461, 0.930, 0.629]</r> and {the line for Patent Rejections}<r>[0.177, 0.783, 0.930, 0.875]</r>, respectively. The Patent Approval line shows a constant upward trend suggesting that the number of patents approved has increased over the year. On the other hand, the Patent Rejection line also shows an increasing trend indicating that there has been a simultaneous rise in the number of patent rejections as well. The increase in the number of both Patent Approvals and Rejections could be a result of an overall increase in the number applications, as shown by the rising trend of the {Patent Application line}<r>[0.177, 0.077, 0.930, 0.414]</r>. The growing competition might have led to a more stringent selection process, leading to more rejections alongside approvals.\nBased on the above thought, the answer is:\nThe trends of Patent Approvals and Rejections from 2005 to 2015 suggest that the selection process for patents has become more rigorous over time. With a remarkable increase in Patent Applications, there has been a corresponding rise in both Approvals and Rejections. This could indicate that while more innovators have been able to gain appreciation for their work as patent approvals have risen, the heightened competition and stricter selection process have simultaneously led to an increase in the number of rejections."
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "C6SptzBWIkjoBXbz",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Patent Applications increase from the year 2013 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in Patent Applications from 2013 to 2015, we must look at the {line for Patent Applications}<r>[0.177, 0.077, 0.930, 0.414]</r>. The data for 2013 is at point coordinates <r>[0.780, 0.138]</r>, which corresponds to 28000 applications. The data for 2015 at point coordinates <r>[0.930, 0.077]</r> corresponds to 30000 applications. The increase in Patent Applications from 2013 to 2015 is thus 30000-28000 = 2000.\nBased on the above thought, the answer is:\n2000"
+            }
+        ],
+        "id_raw": 1117
+    },
+    {
+        "id": "4tB75nmcjMPBHlbW",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data in a table format from line chart, where each row represents a company and columns represent years from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract data from the line chart, I firstly locate {the x-axis tick labels}[[0.237, 0.912, 0.315, 0.963], [0.535, 0.912, 0.586, 0.963], [0.834, 0.912, 0.908, 0.963]] to ascertain columns representing years 2019, 2020, 2021. Next, I can infer the row labels as companies' names by identifying them from {the legend texts}[[0.271, 0.075, 0.405, 0.120], [0.271, 0.141, 0.411, 0.186], [0.271, 0.207, 0.395, 0.252], [0.271, 0.272, 0.353, 0.317], [0.271, 0.338, 0.408, 0.383]]. The points from each line plot {the line points like these for Amazon}[[0.183, 0.359], [0.536, 0.278], [0.888, 0.145]] can then be used to extract the values for each company for these years.\nBased on the above thought, the answer is:\nCompany,2019,2020,2021\nAmazon,23,26,31\nWalmart,20,17,15\nAlibaba,18,17,16\nEbay,15,13,11\nBestBuy,11,10,8"
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "GdYaAJqZRq61nXlF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends indicated in the line chart, which company's market share increased the most over the years 2019 to 2021? Can you explain your rationale?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company's market share has increased the most, I need to examine the changes in line plots for each company. The line plot {for Amazon}[0.183, 0.145, 0.888, 0.359] shows an uptrend from 23% to 31%. The line plot {for Walmart}[0.183, 0.439, 0.888, 0.573] shows a decreasing trend from 20% to 15%. The line plot {for Alibaba}[0.183, 0.492, 0.888, 0.546] shows a slight downward trend from 18% to 16%. The line plot {for Ebay}[0.183, 0.573, 0.888, 0.680] also has a declining trend from 15% to 11%. Lastly, the line plot {for BestBuy}[0.183, 0.680, 0.888, 0.760] too shows a decline from 11% to 8%.\nBased on the above thought, the answer is:\nBased on the trends indicated in the line chart, Amazon's market share increased the most over the years 2019 to 2021. This is inferred from the line plot where it started at 23% market share in 2019 and rose to 31% by 2021, showing a large relative increase compared to the other companies which either showed decreases or smaller increases in the same period."
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "gWs1K4In7gwxIg9Q",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in market share of Ebay from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease for Ebay, I first locate {the line points for Ebay}[[0.183, 0.573], [0.536, 0.626], [0.888, 0.680]]. The value for 2019 is 15 and for 2021 is 11. The percentage decrease can be calculated by taking the difference between the two years values, dividing by the initial year's value and then multiplying by 100.\nBased on the above thought, the answer is:\n26.67%"
+            }
+        ],
+        "id_raw": 1118
+    },
+    {
+        "id": "njWXfJ1KNwh2Id8U",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the line chart data, representing the various environmental protection and sustainable development activities in the years 2010 and 2020, into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.184, 0.849, 0.231, 0.899], [0.904, 0.849, 0.969, 0.899]], representing the 'User Age' or, in our context, the years 2010 and 2020. Then, I identify {the legend texts}[[0.741, 0.622, 0.897, 0.667], [0.741, 0.688, 0.910, 0.733], [0.741, 0.754, 0.853, 0.799]] as the row labels, representing the different environmental activities. I then locate {the line points}[[0.184, 0.517], [0.904, 0.277], [0.184, 0.277], [0.904, 0.117], [0.184, 0.677], [0.904, 0.437]] for each activity in the respective years and extract the values associated with them.\nBased on the above thought, the answer is:\nYear,Facebook,Instagram,Twitter\n35+,70,85,60\n15-35,85,95,75"
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "w2y5MYgwJQijrOhl",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which social media platform saw the least growth in active users from the age group 35+ to 15-35? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which social media platform saw the least growth, I need to analyze the differences in active users for each platform between the age groups of 35+ and 15-35. For {Facebook}[0.184, 0.277, 0.904, 0.517], the number of users increased from 70 to 85, a difference of 15. \nFor {Instagram}[0.184, 0.117, 0.904, 0.277], the number of users increased from 85 to 95, a difference of 10.\nFor {Twitter}[0.184, 0.437, 0.904, 0.677], the number of users increased from 60 to 75, a difference of 15. Instagram shows the least growth with a difference of 10 users.\nBased on the above thought, the answer is:\nAccording to the chart, Instagram experienced the least growth in active users when comparing the age groups 35+ and 15-35. This is supported by the visible distance between points on the Instagram line graph, which shows a smaller increase (from 85 to 95 million users) compared to the other platforms."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "NJ4pptriF8VQCZJ1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_56_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in active users on Instagram from the age group 35+ to the age group 15-35?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in active users on Instagram between the age groups 35+ and 15-35, I need to locate {the line points for Instagram}[[0.184, 0.277], [0.904, 0.117]] and extract the corresponding values 85 and 95. The total increase in users is therefore 95 - 85 = 10.\nBased on the above thought, the answer is:\n10."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "oLGr1P8g7rGlOQMH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line graph data into a structured table displaying the years as columns and the neighborhoods as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, let's start with identifying {the x-axis tick labels}[[0.108, 0.934, 0.150, 0.970], [0.271, 0.934, 0.313, 0.970], [0.434, 0.934, 0.476, 0.970], [0.597, 0.934, 0.639, 0.970], [0.760, 0.934, 0.802, 0.970], [0.923, 0.934, 0.965, 0.970]] as column headers. Next, moving to {the legend texts}[[0.170, 0.131, 0.304, 0.171], [0.170, 0.193, 0.311, 0.233], [0.170, 0.255, 0.303, 0.295], [0.170, 0.317, 0.293, 0.357]] to identify the row labels. The values are then extracted from lines representing each neighborhood, for instance the {Low-Income line points}[[0.129, 0.896],[0.292, 0.855],[0.455, 0.785],[0.618, 0.744],[0.781, 0.716],[0.944, 0.619]] are attributed to 'Low-Income'.\nBased on the above thought, the answer is:\nNeighborhood,2016,2017,2018,2019,2020,2021\nLow-Income,42,45,50,53,55,62\nHigh-Income,52,50,49,45,50,49\nCommercial,46,48,60,62,65,70\nResidential,77,80,82,85,90,95"
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "cG2f8nwvFd45QV5u",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the line chart, which neighborhood appears to have the most growing number of interactions over the years and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to identify which neighborhood has the highest growing number of interactions, I need to check the trend lines of each. Considering the {Low-Income line}[[0.129, 0.619, 0.944, 0.896]], the progression shows a consistent increase over time. {High-Income line}[[0.129, 0.758, 0.944, 0.855]] shows a slightly decreasing trend, while the {Commercial line}[[0.129, 0.507, 0.944, 0.841]] exhibits a noticeable increase. However, the most significant increase is on the {Residential line}[[0.129, 0.160, 0.944, 0.410]] over the years, indicating a robust growth in the number of interactions.\nBased on the above thought, the answer is:\nThe Residential neighborhood seems to have the most growing number of interactions over the years. The line representing Residential clearly illustrates an upward trend, indicating that from 2016 to 2021, the number of interactions in this neighborhood has consistently increased more than in the other neighborhoods."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "pRVDqJaa1F7uyQR5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total change in interactions in the Residential neighborhood from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Extracting values from {the points on the Residential line}[[0.129, 0.410],[0.292, 0.369],[0.455, 0.341],[0.618, 0.299],[0.781, 0.230],[0.944, 0.160]] corresponding to years from 2016 to 2021, we have: 77, 80, 82, 85, 90, 95. The value for 2016 is 77 and for 2021 it is 95. The total change is therefore 95 - 77 = 18.\nBased on the above thought, the answer is:\n18."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "VgpeIDFAVCzzcFkc",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease create a table using the data represented in the line chart, with columns for Years, Innovations, and Investments."
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the table data from the line chart, I begin by identifying {the x-axis tick labels}[-0.044, 0.885, -0.001, 0.970] which represent the Years. I then identify {the legend items}[0.156, 0.070, 0.274, 0.110], [0.156, 0.132, 0.294, 0.172] as the other column headers representing Innovations and Investments respectively. Next, I locate the {points for Innovations}[0.115, 0.259, 0.944, 0.378] and {points for Investments}[0.115, 0.179, 0.944, 0.278] under each year and extract their corresponding values.\nBased on the above thought, the answer is:\nYear,Innovations,Investments\n2015,50,60\n2016,55,65\n2017,57,67\n2018,58,68\n2019,59,65\n2020,60,69\n2021,62,70"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "0811N1XBTLesynov",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends in innovation and investments over the years, which increased at a higher rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing {the line for Innovation}[0.115, 0.259, 0.944, 0.378], I can see that the values increase steadily from 50 to 62 over the same period. On the other hand, analyzing {the line for Investments}[0.115, 0.179, 0.944, 0.278], it is observable that the values increase from 60 to 70.\nBased on the above thought, the answer is:\nFrom an overall perspective, it appears that Innovation increased at a higher rate than Investments over these years. Despite starting from a lower initial value, Innovation increased by 12 units, whereas Investments only increased by 10 units over the same period."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "DGfZQP1cAmbtJU3F",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of Investments over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total sum of Investments, I would add up all the values obtained from {the points of Investments}[0.115, 0.179, 0.944, 0.278] which are 60, 65, 67, 68, 65, 69, and 70.\nBased on the above thought, the answer is:\n464."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "hoZMzxZ52Qc39h3n",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data from the line chart into a table with decades as columns and trend types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will start by identifying {the x-axis labels}[[0.084, 0.891, 0.126, 0.919], [0.503, 0.891, 0.545, 0.919], [0.922, 0.891, 0.964, 0.919]] which provide the decades (2000s, 2010s, 2020s). These will represent the column headers in the table. Then, I identify {the legend}[[0.703, 0.109, 0.795, 0.145], [0.703, 0.161, 0.767, 0.197], [0.703, 0.214, 0.745, 0.250], [0.875, 0.109, 0.915, 0.145], [0.875, 0.161, 0.970, 0.197]], which have the labels 'Streetwear', 'Vintage', 'Boho', 'Punk', 'Sporty Chic'. These will represent the rows in the table. To populate the data, I examine the value at each decade for all trends by correlating the data points position to the y-axis values and formating it into a structured table.\nBased on the above thought, the answer is:\nTrend,2000s,2010s,2020s\nStreetwear,45,55,60\nVintage,55,60,45\nBoho,53,47,50\nPunk,47,55,53\nSporty Chic,55,50,47"
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "VsZyCy7ygxacLXXH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, which fashion trend has had the most stable popularity over the decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I'll have to evaluate the change in popularity for each trend from 2000s to 2020s. The line for {Streetwear}[[0.105, 0.115, 0.943, 0.845]] shows an increase from 45 to 60. The line for {Vintage}[[0.105, 0.115, 0.943, 0.845]] has a negative slope from 55 to 45. The line for {Boho}[[0.105, 0.456, 0.943, 0.747]] also shows a decrease from 53 to 50. The line for {Punk}[[0.105, 0.358, 0.943, 0.747]] increases from 47 to 53, and the line for {Sporty Chic}[[0.105, 0.358, 0.943, 0.747]] shows a decrease from 55 to 47. Based on these, the Boho trend has seen the least change over the years.\nBased on the above thought, the answer is:\nThe Boho trend has had the most stable popularity over the decades. It has seen a slight decrease from 53 to 50 which is the smallest change compared to the other fashion trends featured on the line chart."
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "dxtTKFbW6nO0qsI1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the popularity of the 'Punk' trend decreased from 2000s to 2020s?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I identify the coordinates of 'Punk' trend for each of the time periods. The coordinates for {Punk in 2000s}[[0.105, 0.747]] correspond to the value 47 and the coordinates for {Punk in 2020s}[[0.943, 0.456]] correspond to the value 53. By subtracting the popularity in 2020s from that in 2000s, I can calculate the decrease.\nBased on the above thought, the answer is:\n-6"
+            }
+        ],
+        "id_raw": 1122
+    },
+    {
+        "id": "FCcSnAlpe0ndo08X",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart depicting Economic Indicator Index for different sectors across various quarters into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "First I identify the {x-axis tick labels}<r>[0.073, 0.829, 0.160, 0.952]</r>, <r>[0.335, 0.829, 0.440, 0.979]</r>, <r>[0.613, 0.829, 0.705, 0.960]</r>, <r>[0.880, 0.829, 0.980, 0.971]</r> as the column headers indicating the different quarters. I then locate the {legend texts}<r>[0.138, 0.053, 0.258, 0.079]</r>, <r>[0.138, 0.090, 0.208, 0.116]</r>, <r>[0.138, 0.128, 0.185, 0.154]</r>, <r>[0.138, 0.166, 0.231, 0.191]</r>, <r>[0.138, 0.203, 0.229, 0.229]</r>, <r>[0.138, 0.241, 0.231, 0.267]</r> corresponding to the different sectors. Combining these two data points, I could form a table architecture. For each sector, I then follow {the line points}<r>[0.117, 0.189]</r>, <r>[0.388, 0.284]</r>, <r>[0.659, 0.221]</r>, <r>[0.930, 0.189]</r> corresponding to the values for each quarter, and populate the respective cells of the table. The final table can be summarized as follows:\nBased on the above thought, the answer is:\n,First Quarter,Second Quarter,Third Quarter,Fourth Quarter\nManufacturing,80,84,88,90\nServices,70,75,80,78\nRetail,60,65,68,71\nTechnology,78,82,85,88\nAgriculture,55,58,60,62\nReal Estate,65,70,75,80"
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "PZDac6K3ch8NhLV5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which sector seems to have the least fluctuation in the Economic Indicator Index throughout the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the least fluctuation, I look at the variations in the line plots for each sector: {line for Manufacturing}<r>[0.117, 0.189, 0.930, 0.348]</r>, {line for Services}<r>[0.117, 0.348, 0.930, 0.506]</r>, {line for Retail}<r>[0.117, 0.490, 0.930, 0.664]</r>, {line for Technology}<r>[0.117, 0.221, 0.930, 0.379]</r>, {line for Agriculture}<r>[0.117, 0.632, 0.930, 0.743]</r>, {line for Real Estate}<r>[0.117, 0.348, 0.930, 0.585]</r>. The line for Agriculture indicates a relatively smaller change compared to the other sectors, suggesting it might have the least fluctuation in the Economic Indicator Index.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Agriculture sector appears to have the least fluctuation in the Economic Indicator Index throughout the year. The difference between its maximum and minimum values seems less substantial as compared to those in the other sectors, indicating more stability in its economic performance."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "W30U7X0UKoL35xUd",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the Real Estate sector's Economic Indicator Index from the First Quarter to the Fourth Quarter?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must find the starting and ending points for the Real Estate sector. I identify {the line points for Real Estate}<r>[0.117, 0.585]</r>, <r>[0.930, 0.348]</r>, which correspond to its values at the First Quarter (65) and the Fourth Quarter (80), respectively. To get the total increase, I subtract the starting point from the ending point, so the calculation is 80 - 65.\nBased on the above thought, the answer is:\n15."
+            }
+        ],
+        "id_raw": 1123
+    },
+    {
+        "id": "bD3hvnkkeM6mA0AI",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the line chart data to a table format? The metrics should be the columns and the aspects ('Entrepreneurship' and 'Innovation') should be the rows."
+            },
+            {
+                "from": "gpt",
+                "value": "To form the table, I start by identifying {the x-axis tick labels}<r>[[0.088, 0.805, 0.111, 0.844], [0.184, 0.805, 0.252, 0.922], [0.300, 0.805, 0.373, 0.929], [0.415, 0.805, 0.496, 0.944], [0.532, 0.805, 0.617, 0.950], [0.642, 0.805, 0.744, 0.978], [0.778, 0.805, 0.846, 0.921], [0.880, 0.805, 0.981, 0.979]]</r> as the column headers or metrics. Next, I see {the legend texts}<r>[[0.444, 0.057, 0.536, 0.084], [0.444, 0.098, 0.631, 0.125]]</r> as the row headers representing the aspects 'Entrepreneurship' and 'Innovation'. I then locate {the line points} of these aspects for the corresponding metrics and capture the values to complete the table.\nBased on the above thought, the answer is:\nMetrics,All,Solar Power,Hydro Power,Nuclear Power,Biomass Power,Geothermal Power,Wind Power,Natural Gas Power\nEnergy Use,82,60,78,70,50,55,65,80\nResource Consumption,72,40,68,60,55,60,55,70"
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "pE6AUHpSge05oHF1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the energy trend with respect to 'Geothermal Power' as depicted in the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the energy trend in connection to 'Geothermal Power', first, I observe {the line for 'Energy Use'}<r>[0.693, 0.479]</r>. From this, I find that the energy use seems to be in the middle range as compared to other types. Directing my attention then to {the line for 'Resource Consumption'}<r>[0.693, 0.415]</r>, I see that the resource consumption for generating 'Geothermal Power' is also moderate.\nBased on the above thought, the answer is:\nAs per the line chart trends, 'Geothermal Power' presents a median energy consumption and resource use. It does not lead the ranks in either the energy produced or the resources consumed. This could imply that while 'Geothermal Power' is a significant contributor to the energy portfolio, it may not be the most efficient or productive in comparison to the other power types depicted."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "Z8mIzvUOsEJoyBrW",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trends in the line chart, what is the highest value that the 'Resource Consumption' metric reaches?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum value for the 'Resource Consumption' metric, I examine {the line points for 'Resource Consumption'}<r>[[0.099, 0.262], [0.218, 0.670], [0.337, 0.313], [0.455, 0.415], [0.574, 0.479], [0.693, 0.415], [0.812, 0.479], [0.931, 0.287]]</r> and extract their corresponding values. After making out all data points, I see that the maximum value is 72.\nBased on the above thought, the answer is:\n72"
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "GTwMiItEPUq0oLek",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data for weekly social media engagement from the chart and organize it in a table with the content types as rows and the social media platforms as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart, I start by identifying {the x-axis labels}<r>[0.111, 0.701, 0.133, 0.745], [0.212, 0.701, 0.267, 0.811], [0.338, 0.701, 0.375, 0.776], [0.439, 0.701, 0.509, 0.842], [0.528, 0.701, 0.655, 0.953], [0.682, 0.701, 0.736, 0.807], [0.759, 0.701, 0.894, 0.970], [0.926, 0.701, 0.961, 0.771]</r> which represent the row labels or content types. Then, I refer to {the legend}<r>[0.530, 0.052, 0.934, 0.128]</r> for the column headers which are 'Instagram' and 'Facebook'. To find the values, I examine the points on {the line for Instagram}<r>[0.122, 0.214, 0.944, 0.618]</r> and {the line for Facebook}<r>[0.122, 0.085, 0.944, 0.361]</r> for each content type and read off the values from the y-axis.\nBased on the above thought, the answer is:\nType, Instagram, Facebook\nAll,600,950\nBlog Post,750,1050\nVideo,800,1150\nInfographics,850,1050\nVirtual Reality Contents,975,1100\nPodcasts,1025,1150\nUser Generated Contents,1100,1200\nLives,1150,1325"
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "nauEmHrZC3Jzkp9D",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of content has seen a consistent increase in engagement on both platforms according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the content type with a consistent increase in engagement on both platforms, I need to examine the trend in {the line for Instagram}<r>[0.122, 0.214, 0.944, 0.618]</r> and {the line for Facebook}<r>[0.122, 0.085, 0.944, 0.361]</r>. Both lines indicate an overall increasing trend in engagement scores. I then correlate the increasing points on both lines to the corresponding content types on {the x-axis}<r>[0.111, 0.701, 0.133, 0.745], [0.212, 0.701, 0.267, 0.811], [0.338, 0.701, 0.375, 0.776], [0.439, 0.701, 0.509, 0.842], [0.528, 0.701, 0.655, 0.953], [0.682, 0.701, 0.736, 0.807], [0.759, 0.701, 0.894, 0.970], [0.926, 0.701, 0.961, 0.771]</r>. The clear rising trend for both platforms is seen to occur across all content types from 'All' to 'Lives'.\nBased on the above thought, the answer is:\nBased on the chart, all types of content have seen a consistent increase in their weekly engagement scores on both Instagram and Facebook. Their lines on the chart have shown a consistent upward trend from 'All' to 'Lives'."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "Yu2NpqX7q7vcdFwY",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum weekly engagement score on Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum and minimum weekly engagement scores on Facebook, I need to locate {the points on the Facebook line}[[0.122, 0.361], [0.239, 0.287], [0.357, 0.214], [0.474, 0.287], [0.592, 0.250], [0.709, 0.214], [0.826, 0.177], [0.944, 0.085]] and extract the related values. They are 950, 1050, 1150, 1050, 1100, 1150, 1200, 1325, respectively. The maximum value is 1325 corresponding to {Lives}[0.926, 0.701, 0.961, 0.771], and the minimum value is 950 corresponding to {All}[0.111, 0.701, 0.133, 0.745]. Therefore the difference between the maximum and minimum value is 1325 - 950 = 375.\nBased on the above thought, the answer is:\n375."
+            }
+        ],
+        "id_raw": 1125
+    },
+    {
+        "id": "7Y0NooM0PD9XVTS3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the line chart data into a table with different Design types as columns and the Trends as the rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the data from the line chart into a tabular format, I first locate {the x-axis tick labels}[[0.068, 0.729, 0.160, 0.913], [0.470, 0.729, 0.564, 0.916], [0.859, 0.729, 0.980, 0.970]], which indicate the columns, representing the distinct design types. Following that, I identify {the legend texts}[[0.735, 0.070, 0.886, 0.110], [0.735, 0.132, 0.889, 0.172], [0.735, 0.194, 0.940, 0.234]] as the rows, denoting different trends. For each trend, I track down its {line points}[[0.114, 0.227], [0.517, 0.187], [0.920, 0.128], [0.114, 0.325], [0.517, 0.266], [0.920, 0.187], [0.114, 0.128], [0.517, 0.109], [0.920, 0.069]] on the chart and isolate the values represented.\nBased on the above thought, the answer is:\nTrend,Visual Design,Sound Design,Interaction Design\nStable Growth,65,67,70\nGradual Decline,60,57,55"
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "nq98zeyGS5BBM7bt",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line chart, which field of Design shows the most promising trend in improving Creative Performance over time? Support your answer with reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the field of Design with the most promising trend, I scan through the line plots for each Design field. Assessing {the line for Visual Design}[0.114, 0.128, 0.920, 0.522], there is a noticeable dip from 70 to 60, marking an overall decline. Next, {Sound Design}[0.114, 0.227, 0.920, 0.424] line shows a fall from 67 to 57, implying a decreasing trend too. Lastly, {Interaction Design}[0.114, 0.030, 0.920, 0.464] starts at 70 and ends at 50, highlighting a significant drop. Comparing these scenarios, Visual Design and Sound Design seem to fall at a slower rate and their final performances are relatively higher compared to Interaction Design.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, Visual and Sound Designs are tied for showing the most promising trend in improving Creative Performance over time. Despite the overall decline across all fields, Visual and Sound Design reach higher final values of 60 and 57 respectively, compared to the 50 of Interaction Design. Moreover, their rate of decline is somewhat slower compared to that of Interaction Design, suggesting more stability and potential for improvement."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "ByrQnLnEUYUKNw39",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the range of values for \"Gradual Decline\" across all Design types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range for \"Gradual Decline\", I would pinpoint the highest and lowest values across all Design types. So, I observe the {line points for Gradual Decline}[[0.114, 0.325], [0.517, 0.384], [0.920, 0.424]] and extract the corresponding values: 60, 57, 55. The maximum value is 60 at {Visual Design}[0.114, 0.227] and the minimum value is 55 at {Interaction Design}[0.920, 0.128]. Taking the difference between the maximum and minimum gives us the range.\nBased on the above thought, the answer is:\n5."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "hXXXIXEYDYj5ewbL",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the information about the investments and profits of the fintech companies from the line chart and tabulate the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the line chart, I can locate the {x-axis labels}[0.074, 0.763, 0.197, 0.843] for the names of the fintech companies. Then, I find the {coloured endpoints of the lines}[0.222, 0.261, 0.940, 0.743] corresponding to the investment and profit values for each company. I also note the {legend}[0.852, 0.044, 0.978, 0.304] to determine which colour corresponds to either 'Investment' or 'Profit'. By matching these all together, I can create a tabular representation of the line chart data.\nBased on the above thought, the answer is:\nCompany,Investment,Profit\nCoinBase,450,350\nDigiBank,500,450\nByteCash,900,700\nBankDigi,650,550\nBitWallet,400,350\nCoinSecure,550,400\nZ-Cash,650,500\nDigiDollar,500,400"
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "Sr7GMV4xvQcIMuyv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the eight fintech and digital currency companies, which one has the most significant difference between the amount of investment and profit? What does this suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to calculate the difference between investment and profit for each company by looking at their respective {point values on the graph}[0.222, 0.261, 0.940, 0.743]. It seems like {ByteCash}[0.4, 0.763, 0.490, 0.988] has a significant gap between investment and profit. This indicates that the company's investment exceeded its profits by a considerable margin.\nBased on the above thought, the answer is:\nByteCash has the most significant difference between the amount of investment and profit. This suggests that the company might take on a high investment risk strategy, or it possibly has inefficiencies in generating profits from investments."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "FgG4mA0PtUjIHIFQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the investment and profit figures for Z-Cash?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish the difference between investment and profit for Z-Cash, I will find {the point for investment in Z-Cash}[0.813, 0.589] and decipher its value. I will also identify {the point for profit in Z-Cash}[0.813, 0.386] and ascertain its value. I will then subtract the profit from the investment to get the difference.\nBased on the above thought, the answer is:\n150."
+            }
+        ],
+        "id_raw": 1128
+    },
+    {
+        "id": "Ejok5T67jitpqYEm",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data from the line chart be presented in a table with Banks as rows and Attributes as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to convert the line chart data into a table, I firstly locate {the x-axis tick labels}[[0.062, 0.751, 0.166, 0.959], [0.195, 0.751, 0.266, 0.893], [0.315, 0.751, 0.378, 0.876], [0.412, 0.751, 0.514, 0.954], [0.556, 0.751, 0.601, 0.841], [0.659, 0.751, 0.731, 0.894], [0.756, 0.751, 0.866, 0.970], [0.875, 0.751, 0.980, 0.960]] as the row labels showing each bank. Next, the {the attribute legend}[0.420, 0.086, 0.684, 0.126] can be identified as the column headers exhibiting the return rate and outliers. For each bank, I locate {the line points for return rate}[[0.114, 0.185, 0.231, 0.185, 0.347, 0.324, 0.463, 0.602, 0.579, 0.408, 0.695, 0.491, 0.811, 0.463, 0.927, 0.604]] and {the line points for outliers}[[0.114, 0.324, 0.231, 0.324, 0.347, 0.435, 0.463, 0.741, 0.579, 0.435, 0.695, 0.380, 0.811, 0.380, 0.927, 0.435]] to extract the values for the Return Rate and Outliers columns of the corresponding banks.\nBased on the above thought, the answer is:\nBanks,Return Rate(%),Outliers\nGoldman_Sachs,77,74\nJP_Morgan,80,75\nBarclays,75,71\nDeutsche_Bank,65,60\nHSBC,72,66\nCiti_Group,69,65\nBank_of_America,70,67\nMorgan_Stanley,73,71"
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "2Bvnuds75pd52Inh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which bank has the most stable Return Rate? Justify your response."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which bank has the most stable Return Rate, I need to consider the variance in each banks return rate. For each bank, the {lines for Return Rates}[[0.114, 0.185], [0.231, 0.185], [0.347, 0.324], [0.463, 0.602], [0.579, 0.408], [0.695, 0.491], [0.811, 0.463], [0.927, 0.380]] show the trend. It is noticeable that the line for 'JP Morgan' demonstrates the least fluctuations, staying relatively constant through the period. Therefore, 'JP Morgan' appears to have the most stable growth in its return rates.\nBased on the above thought, the answer is:\nBased on the line chart, 'JP Morgan' has the most stable Return Rate. This is because the line corresponding to 'JP Morgan' exhibits the least fluctuations, indicating that their Return Rate remained very constant compared to other banks during the same period."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "8D7MlM6LlwEtJQCG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average Return Rate for all banks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average Return Rate, we have to identify all {the line points for Return Rate}[[0.114, 0.185], [0.231, 0.185], [0.347, 0.324], [0.463, 0.602], [0.579, 0.408], [0.695, 0.491], [0.811, 0.463], [0.927, 0.380]]. The corresponding Return rates are: 77, 80, 75, 65, 72, 69, 70, 73. Add these together and divide by the total number of banks which is 8.\nBased on the above thought, the answer is:\n72.625."
+            }
+        ],
+        "id_raw": 1129
+    },
+    {
+        "id": "FkSlc1ds8jOEF85e",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the bar chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the bar chart, I first look at {the x-axis labels}[[0.136, 0.652, 0.297, 0.677], [0.136, 0.591, 0.322, 0.616], [0.136, 0.529, 0.276, 0.554], [0.136, 0.464, 0.318, 0.489], [0.136, 0.397, 0.325, 0.422], [0.136, 0.332, 0.259, 0.357], [0.136, 0.268, 0.296, 0.293], [0.136, 0.204, 0.298, 0.229]], which represent the row headers i.e. initiatives. Next, I look at {bar labels}[[0.313, 0.631, 0.359, 0.647], [0.313, 0.572, 0.358, 0.588], [0.313, 0.509, 0.362, 0.525], [0.313, 0.444, 0.365, 0.460], [0.313, 0.377, 0.362, 0.394], [0.313, 0.315, 0.346, 0.331], [0.313, 0.254, 0.342, 0.270], [0.313, 0.194, 0.346, 0.209]], which represent the percentages of effectiveness for each initiative. The table can be generated using these identified components.\nBased on the above thought, the answer is:\nInitiative, Effectiveness Rate\nGraduate Startups, 90.6%\nBusiness Incubation Centers, 76.2%\nR&D Investments, 69.4%\nPublic-Private Partnerships, 68.2%\nOpen Innovation Programs, 64.3%\nVenture Capital, 45.2%\nStartup Competitions, 25.6%\nCrowdfunding Platforms, 12.5%"
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "39ZH0269ig8Kvoum",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage range of effectiveness between the most and least effective initiatives?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage range, I need to identify the values for the most and least effective initiatives from the bar chart. The most effective initiative is {Graduate Startups}<r>[0.136, 0.652, 0.297, 0.677]</r> with a value of 90.6%, and the least effective initiative is {Crowdfunding Platforms}<r>[0.136, 0.204, 0.298, 0.229]</r> with a value of 12.5%. The difference can be calculated as 90.6% - 12.5% = 78.1%.\nBased on the above thought, the answer is:\n78.1%."
+            }
+        ],
+        "id_raw": 1130
+    },
+    {
+        "id": "h3GLRZeMrG6RGmGP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you extract the data into a table format, with the categories on the x-axis representing the different features, and the labels on the y-axis representing the index level?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the line chart data into a table, I first identify {the x-axis labels}<r>[0.102, 0.698, 0.966, 0.795]</r> corresponding to the different features. I also notice the legend text for {Global Impact}<r>[0.437, 0.048, 0.529, 0.076]</r> and {NewTown}<r>[0.596, 0.048, 0.661, 0.076]</r> which represent the different blocks. Now, I identify the data points for each block across different features. For example, {the points for GlobalImpact}<r>[0.115, 0.181, 0.944, 0.371]</r> and {points for NewTown}<r>[0.115, 0.34, 0.944, 0.593]</r>. Using these points, I can compile the data into a structured table.\nBased on the above thought, the answer is:\nBlock,All,Greenspace,Public Transportation,Bike Lanes,Commercial Areas,Residential Areas,Industries,Roads\nGlobalImpact,62,70,68,66,64,72,60,65\nNewTown,52,60,58,46,54,62,50,55"
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "IxPOImSW5zNsZabq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which block (either GlobalImpact or NewTown) appears more balanced in terms of the distribution of development index across different features? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine balance, we need to observe the variance in the Development Index across the different features for each block. Looking at the line for {GlobalImpact}<r>[0.115, 0.181, 0.944, 0.371]</r>, there's a range of values between 60 and 72. This shows the variance is minor, indicating a relatively balanced distribution for GlobalImpact.\nContrarily, examining the trend for {NewTown}<r>[0.115, 0.34, 0.944, 0.593]</r>, the Development Index values span from 46 to 62. This larger range shows that NewTown's distribution of development index across different features is less balanced compared to GlobalImpact.\nBased on the above thought, the answer is:\nBased on the line chart, GlobalImpact shows a more balanced distribution of development index across different features compared to NewTown. This is because GlobalImpact has a narrower range of values (between 60 and 72) across different features. In contrast, NewTown's Development Index values span between 46 and 62, indicating a more uneven distribution."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "D2eOZXaVYwftcZIQ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the Development Index between GlobalImpact and NewTown for Bike Lanes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in the Development Index between GlobalImpact and NewTown for Bike Lanes, I would find the coordinates for {The GlobalImpact value for Bike Lanes}<r>[0.470, 0.276]</r>, which extracts a value of 66. I would also locate the coordinates for {the NewTown value for Bike Lanes}<r>[0.470, 0.593]</r>, which gives a value of 46. The difference between the two values is 66 - 46 = 20.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "5pVTH7VS8tFlzzhK",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you convert the data into a tabular format where the columns are years and rows represent types of activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the tabular data from the line chart, I'll start by identifying {the x-axis tick labels}[[0.120, 0.814, 0.178, 0.923], [0.204, 0.814, 0.293, 0.981], [0.321, 0.814, 0.376, 0.917],  [0.421, 0.814, 0.475, 0.915]], which represent the column headers or years. Next, I pinpoint {the legend texts}[[0.068, 0.044, 0.153, 0.069], [0.224, 0.044, 0.367, 0.069]], as the row labels indicating the different activities. To populate the data values, I locate {the data points for each activity line} for 'Human Life'[[0.149, 0.571], [0.249, 0.461], [0.349, 0.366], [0.448, 0.271], [0.548, 0.192], [0.648, 0.555], [0.748, 0.461], [0.847, 0.366], [0.947, 0.508]] and for 'Living Environment' [[0.149, 0.650], [0.249, 0.524], [0.349, 0.492], [0.448, 0.571], [0.548, 0.650], [0.648, 0.587], [0.748, 0.618], [0.847, 0.587], [0.947, 0.666]] at the respective years and extract the corresponding values for years 2018, 2019, 2020, 2021.\nBased on the above thought, the answer is:\nActivities,2018,2019,2020,2021\nHuman Life,65,72,84,69\nLiving Environment,65,68,65,63"
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "TfiapmQSxHKByBs6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity has seen more change over the 4-year period? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the activity that has seen more change over the four-year period, I will examine the difference between the starting and ending values for both activities. I find {the starting and ending points for Human Life}[[0.149, 0.571], [0.947, 0.508]], which indicate a change from 65 to 69 in the Human Life activity. In contrast, by looking at {the starting and ending points for the Living Environment}[[0.149, 0.650], [0.947, 0.666]], I can see that the Living Environment activity experiencing a change from 65 to 63. As a result, the Living Environment activity shows a greater change, as it declines from 65 to 63, whereas the Human Life activity improves from 65 to 69.\nBased on the above thought, the answer is:\nOver the 4-year period, the Living Environment activity has seen more change. Even though the Human Life frequency increased from 65 to 69, the Living Environment frequency decreased from 65 to 63. This is larger in absolute terms, thus we can conclude that the Living Environment has seen more significant change."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "YdXAMclw3mFkQhTs",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in frequency for Gardening from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the decrease by comparing the values of 2018 and 2021 for Gardening. The 2018 value for Gardening I can find at this point in coordinates {Living Environment in 2018} [0.149, 0.650] is 65. The 2021 value for Gardening is given at {Living Environment in 2021} [0.947, 0.666] as 63. So the difference is 65 - 63 = 2 and the percentage decrease would be (2 / 65 * 100)%.\nBased on the above thought, the answer is:\n3.08%."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "7tGKd0Craq1RUzKx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing the hours spent on exercise and socializing over months into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I determine the month's data by viewing {the x-axis labels}<r>[0.101, 0.891, 0.136, 0.919]</r> to <r>[0.926, 0.891, 0.961, 0.919]</r>, which represent January to December. For socializing and exercise, I observe {the line for exercise}<r>[0.118, 0.350, 0.944, 0.168]</r> and {the line for socializing}<r>[0.118, 0.426, 0.944, 0.398]</r>. I then extract the specific hours by checking the respective points marked above these lines for each month.\nBased on the above thought, the answer is:\nMonth,Exercise,Socializing\nJanuary,30,20\nFebruary,35,25\nMarch,40,30\nApril,45,35\nMay,50,45\nJune,55,50\nJuly,60,55\nAugust,65,60\nSeptember,70,65\nOctober,40,45\nNovember,35,40\nDecember,30,35"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "s8RHkHSvPVsL2A31",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the activities of exercise and socializing across the year based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the line for exercise from January to September}<r>[0.118, 0.350, 0.737, 0.244]</r> and {the line for socializing}<r>[0.118, 0.426, 0.737, 0.380]</r>, there is a consistent rise in hours for both activities, indicating increased participation. Notably, {from October to December}<r>[0.737, 0.380, 0.944, 0.289]</r> the lines show a sharp decline, which could be attributed to the end of year festive period causing a drop in regular activities.\nBased on the above thought, the answer is:\nThe trend shows a steady increase in hours for both exercise and socializing from January to September, followed by a sudden decrease from October to December. This pattern suggests a seasonal effect where activities reduce towards the year-end, possibly due to festive distractions or weather changes."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "vlas74QQ5HQ3bJZr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of hours spent on exercise and socializing combined in January?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total hours spent, I'll look at both {the point for exercise in January}<r>[0.118, 0.350]</r> and {the point for socializing in January}<r>[0.118, 0.426]</r>. The hours noted are 30 for exercise and 20 for socializing. Adding these together provides the total hours.\nBased on the above thought, the answer is:\n50"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "ND7jT0cJYyp8L1Pi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table format where each row represents a social welfare activity, and the columns are divided into Personal Donations, Corporate Donations, and Government Funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a structured table, I first identify {the x-axis labels}[[0.045, 0.705, 0.153, 0.919], [0.133, 0.705, 0.231, 0.900], [0.216, 0.705, 0.316, 0.905], [0.302, 0.705, 0.395, 0.890]] which represent Youth Services, Homeless Support, Environmental Preservation, and Animal Rescue. For each category, I analyze the lines corresponding to {Personal Donations}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]], {Corporate Donations}[[0.099, 0.179, 0.848, 0.332]], and {Government Funding}[[0.099, 0.255, 0.848, 0.409]] to extract the funding values.\nBased on the above thought, the answer is:\nActivity,Personal Donations,Corporate Donations,Government Funding\nYouth Services,200,180,160\nHomeless Support,180,190,170\nEnvironmental Preservation,150,140,130\nAnimal Rescue,120,250,110"
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "xfk8lfb0NKcucUXq",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social welfare activity shows the most stable funding across all sources and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the stability of funding across all sources, {the line for Homeless Support}[[0.102, 0.599], [0.218, 0.340], [0.334, 0.179]] exhibits minor variations between Personal Donations (180), Corporate Donations (190), and Government Funding (170). The differences between the maximum and minimum funding values are smaller relative to other activities, showcasing higher stability.\nBased on the above thought, the answer is:\nHomeless Support shows the most stable funding. This is deduced by observing the minor variations among the funding sources. The line representing Homeless Support does not show large fluctuations, indicating consistent support across personal, corporate, and governmental donations."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "1sbvAZ1QcQrWHuEX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average funding for Environmental Preservation across all donation sources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average funding for Environmental Preservation, I locate the funding values from {Personal Donations}, {Corporate Donations}, and {Government Funding} for Environmental Preservation which are 150, 140, and 130 respectively. The average is calculated by (150 + 140 + 130) / 3.\nBased on the above thought, the answer is:\n140"
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "pYsmCwoCqNbyorZD",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart and tabulate it with corresponding years as columns and food types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the line chart, first, I identify {the x-axis tick labels}[[0.108, 0.834, 0.150, 0.919],[0.923, 0.834, 0.966, 0.919]] which denote years that would serve as the column headers. Next, I pick out {the legend labels}[[0.831, 0.131, 0.965, 0.171],[0.831, 0.193, 0.964, 0.233],[0.831, 0.255, 0.959, 0.295]] which will serve as the food types or row labels. Now, to populate these rows, I locate {the line points}[[0.129, 0.335],[0.944, 0.384],[0.129, 0.213],[0.944, 0.164],[0.129, 0.702],[0.944, 0.653]] for each type at the respective years, and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Food Import,Food Export,Food Waste\n2020,250,300,100\n2021,230,320,120"
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "EkWRA8nGlluA9U7x",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which sector in the Food and Agriculture Industry showed an increase from 2020 to 2021? Provide your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which sector of the Food and Agriculture Industry has shown an increase from 2020 to 2021, I need to observe the slopes of {the lines representing the metrics}[[0.129, 0.335, 0.944, 0.384],[0.129, 0.213, 0.944, 0.164],[0.129, 0.702, 0.944, 0.653]]. \nThe line plot for {Food Import}[0.129, 0.335, 0.944, 0.384] depicts a decrease from 250 in 2020 to 230 in 2021.\nThe line plot for {Food Export}[0.129, 0.213, 0.944, 0.164] shows an increase from 300 in 2020 to 320 in 2021. \nThe line for {Food Waste}[0.129, 0.702, 0.944, 0.653] also exhibits an increase from 100 in 2020 to 120 in 2021.\nBased on the above thought, the answer is:\nAs per the line chart, the Food Export and Food Waste sectors of the Food and Agriculture Industry showed an increase from 2020 to 2021. The lines corresponding to these sectors showed positive slopes, indicating an upward trend or an increase for the given time period."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "Sa2ITHuK2RmKV7i5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the quantity of Food Waste generated from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in Food Waste from 2020 to 2021, I need to locate {the points on the line representing Food Waste}[[0.129, 0.702],[0.944, 0.653]]. The values for these points are 100 corresponding to {Year 2020}[0.108, 0.834, 0.150, 0.919] and 120 corresponding to {Year 2021}[0.923, 0.834, 0.966, 0.919]. The difference between them is obtained by subtracting the 2020 value from the 2021 value, that is, 120 - 100 = 20.\nBased on the above thought, the answer is:\n20."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "YtHgh9Dio8V648aH",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart to a standard table format, using the category labels as row headers and groups as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data, I'll start by identifying {the x-axis labels}<r>[0.063, 0.760, 0.167, 0.966]</r>, <r>[0.268, 0.760, 0.373, 0.970]</r>, <r>[0.478, 0.760, 0.574, 0.952]</r>, <r>[0.681, 0.760, 0.783, 0.962]</r>, <r>[0.896, 0.760, 0.979, 0.925]</r> - they'll be our row headers. Next, I'll look at {the legend entries}<r>[0.583, 0.086, 0.673, 0.126]</r>, <r>[0.780, 0.086, 0.894, 0.126]</r> that will act as column headers referring to different groups. For the values, I'll check {the line markers}<r>[0.115, 0.328]</r>, <r>[0.321, 0.445]</r> and so on, extracting the corresponding assessment values for each group under different categories.\nBased on the above thought, the answer is:\nCategories,Reading,Exercising\nSlight decrease,78,70\nGradual decline,73,65\nPersistent rise,81,82\nRapid recovery,84,78\nAbrupt drop,79,72"
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "pGM5lF6dXe9EFOqH",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the behavior towards Exercising based on the trend line?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the trend line for Exercising}<r>[0.115, 0.234, 0.938, 0.633]</r>, I see that the level of exercising encounters a steep rise from the 'Slight decrease' stage to the 'Persist rise' stage. After reaching a peak at 'Slight decrease' stage, the line starts a relatively modest descent before a brief recovery in the 'Abrupt drop' stage. This fluctuating pattern indicates a certain level of inconsistency in their exercising habits.\nBased on the above thought, the answer is:\nThe trend line for Exercising reveals an inconsistent behavior. It starts with a high level at the 'Slight decrease' stage, and after a persistent rise, it reaches the peak. After reaching its height, it then experiences a decline before slightly recovering in the 'Abrupt drop' stage. The fluctuations show that the habit of exercising is not consistent."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "n36RHqqBep7bQf9H",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the frequency of Reading dropped from its peak to the Abrupt drop stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first need to locate the peak value of Reading. Looking at {the points for the Reading line}<r>[0.115, 0.328]</r>, <r>[0.321, 0.445]</r>, <r>[0.526, 0.257]</r>, <r>[0.732, 0.187]</r>, <r>[0.938, 0.304]</r>, I observe that the highest value is 84 at the stage of Rapid recovery. Now, for the Abrupt drop stage, {the point corresponds}<r>[0.938, 0.304]</r> to the value of 79. Therefore, the drop in the frequency of Reading is 84 - 79 = 5.\nBased on the above thought, the answer is:\n5."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "7Zc9Bx01eZllQoAh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into structured table format with the series of change as the column and the companies as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form structured data, first I identify {the x-axis tick labels}[[0.064, 0.714, 0.192, 0.970],[0.347, 0.714, 0.447, 0.913],[0.612, 0.714, 0.718, 0.925],[0.887, 0.714, 0.980, 0.899]] as columns representing various series of changes. Then, the {legend}[[0.767, 0.070, 0.954, 0.110],[0.767, 0.132, 0.850, 0.172]] are identified as row labels which represent the different companies. The rest of the information, particularly each company's funding at different series, is captured in the {points of line items} [[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637], [0.128, 0.637], [0.397, 0.569], [0.665, 0.232], [0.934, 0.704]].\nBased on the above thought, the answer is:\nCompany,Pronounced decline,Inexorable rise,Abrupt increase,Steep decline\nInvestment Fund,90,95,120,85\nStartup,85,90,115,80"
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "geEyGOepurmO7VAw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which series of change demonstrates the largest difference in funding levels between the Investment Fund and the startup? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which series demonstrates the largest funding difference between the Investment Fund and the startup, I need to compare {the funding levels of Investment Fund across the series}[[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637]], with {the funding levels of the Startup across the same series}[[0.128, 0.637], [0.397, 0.569], [0.665, 0.232], [0.934, 0.704]]. The largest difference seems to be at the 'Abrupt Increase' series with Investment Fund at 120 and the Startup at 115.\nBased on the above thought, the answer is:\nThe 'Abrupt Increase' series shows the largest difference in funding levels between the Investment Fund and the Startup. This is because the Investment Fund's funding level at this series is 120, while the Startup's funding level is 115, resulting in a difference of 5, which is the most significant difference across the series."
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "8K7X9h9m2iWPKKiV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_57_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average change in the funding level for the Investment fund across all series?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the average change in funding level for Investment Fund, I first locate the {points representing Investment Fund funding levels across series}[[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637]], and extract the corresponding values: 90, 95, 120, 85. Then, I calculate the average of these values.\nBased on the above thought, the answer is:\n97.5"
+            }
+        ],
+        "id_raw": 1139
+    },
+    {
+        "id": "vpqa3XYFwgYsUcYU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data into a structured table with years as rows and development types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a structured table, I locate {the x-axis labels}<r>[0.055, 0.958, 0.073, 0.981]</r> which enumerate the years from 2001 to 2010, the rows of our table. For the columns, I determine from {the legend}<r>[0.062, 0.063, 0.220, 0.135]</r> that we have two development types: Rural and Urban. Reviewing {the points on the line for Rural development}<r>[0.098, 0.277, 0.945, 0.887]</r> and {the points on the line for Urban development}<r>[0.098, 0.813, 0.945, 0.887]</r>, I extract the development scores for each year to populate the table.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2001,32,35\n2002,33,36\n2003,34,37\n2004,35,38\n2005,36,39\n2006,37,40\n2007,55,41\n2008,38,42\n2009,39,43\n2010,40,44"
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "MDAou1JeZzKccWSJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year exhibited an outlier in Rural development according to the line chart, and what might explain this unusual spike?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining {the line for Rural development}<r>[0.098, 0.277, 0.945, 0.887]</r> in detail, I identify a sharp peak represented between the usual trend line. Pinpointing this on the x-axis labels, the peak occurs at 2007. To understand this unusual spike in the context of the chart, I assess its relationship with other data points and note it deviates significantly from the norm. This outlier might be due to specific rural initiatives or unexpected economic influences that year, which merit further investigation beyond the chart's data.\nBased on the above thought, the answer is:\nThe year that exhibited an outlier in Rural development is 2007. This unusual spike is likely due to specific significant rural initiatives or unexpected economic factors that drastically differed from other years."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "sYI6TXJdxA2fvjJ5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in Urban development from 2001 to 2010 as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall increase in Urban development from 2001 to 2010, I first examine {the points on the line for Urban development}<r>[0.098, 0.813, 0.945, 0.887]</r>. I identify the first and last data points of the Urban development line, which are at years 2001 and 2010 respectively. Extracting the values, the development score for 2001 is 35 and for 2010 it is 44. The overall increase is calculated by subtracting the 2001 score from the 2010 score.\nBased on the above thought, the answer is:\n9"
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "7EMN31O6MOwFt9SZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table with Year as rows and Index Values of Residential, Commercial, and Rental according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the structured table, I'll first identify the {year labels on the x-axis}<r>[0.059, 0.927]</r>, which will serve as the row headers. Next, I locate the data points for {the Residential Index line}<r>[0.101, 0.546, 0.945, 0.897]</r>, {the Commercial Index line}<r>[0.101, 0.371, 0.945, 0.887]</r>, and {the Rental Rate Index line}<r>[0.101, 0.093, 0.945, 0.441]</r>. I'll then extract these values by matching them with the corresponding year.\nBased on the above thought, the answer is:\nYear,Residential_Index,Commercial_Index,Rental_Rate_Index\n2005,100,105,95\n2006,102,110,100\n2007,104,120,103\n2008,105,125,105\n2009,100,90,100\n2010,102,95,102\n2011,105,100,105\n2012,110,105,110\n2013,115,110,115\n2014,120,115,120\n2015,125,120,125\n2016,127,125,127\n2017,130,130,130\n2018,135,135,135\n2019,140,140,140\n2020,145,145,145"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "5Kk2c9cGuLs0OP8m",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector showed the highest growth from 2007 to 2008 and what was the rate of increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the year 2007, I locate the values for {Residential}<r>[0.334, 0.512]</r>, {Commercial}<r>[0.334, 0.402]</r>, and {Rental}<r>[0.334, 0.290]</r>, which are approximately 104, 120, and 103, respectively. For 2008, the values are {Residential}<r>[0.405, 0.486]</r> at 105, {Commercial}<r>[0.405, 0.376]</r> at 125, and {Rental}<r>[0.405, 0.264]</r> at 105. Comparing these, the Commercial sector shows the highest growth with an increase from 120 to 125, a 5-point increase or 4.17% growth rate.\nBased on the above thought, the answer is:\nThe Commercial sector showed the highest growth from 2007 to 2008. Its index increased from 120 to 125, marking a growth rate of approximately 4.17%."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "OzEJUDUgF1SqGNqb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Residential Price Index from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I locate the {value at 2005 for Residential Price Index}<r>[0.101, 0.546]</r> and observe it starts at 100. Next, I find the {value at 2020 for Residential Price Index}<r>[0.945, 0.897]</r> which is 145. The increase is calculated by subtracting the starting index from the final index, 145 - 100.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "80IJE4ASDJ9GSqUF",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing the performance metrics over time for 'Deep Learning' and 'Neural Networks' into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I identify the {x-axis labels}<r>[0.059, 0.933, 0.066, 0.951]</r> that outline the different time frames and {legend items}<r>[0.739, 0.812, 0.981, 0.917]</r> detailing the two methods 'Ours' and 'Saliency-based Sampling'. For each method, distinct {line plots} like {Ours for Deep Learning}<r>[0.094, 0.569, 0.945, 0.728]</r> and {Saliency-based Sampling for Neural Networks}<r>[0.157, 0.283, 0.189, 0.315]</r> denote performance values over time. Combining these details allows extraction of data point values for accurate table creation.\nBased on the above thought, the answer is:\nTime,Deep Learning - Ours,Deep Learning - Saliency-based Sampling,Neural Networks - Ours,Neural Networks - Saliency-based Sampling\n1,25,23,40,38\n2,30,28,42,39\n3,35,33,45,43\n4,38,34,47,44\n5,40,36,50,46\n6,42,38,52,48\n7,45,40,54,50\n8,47,42,55,52\n9,50,45,57,54\n10,55,50,60,56"
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "B0mpem0zhIxajBYT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich method, 'Ours' or 'Saliency-based Sampling', demonstrates a more stable performance trend for 'Neural Networks'?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing the line trends in the chart, I observe {the line for 'Ours' method in Neural Networks}<r>[0.094, 0.569, 0.945, 0.728]</r> displaying a smooth and consistent growth. In contrast, {the line for 'Saliency-based Sampling' in Neural Networks}<r>[0.157, 0.283, 0.189, 0.315]</r> shows some volatility with noticeable fluctuations and dips. This comparison leads to a conclusion about the stability of each method's performance over time.\nBased on the above thought, the answer is:\nThe 'Ours' method shows a more stable performance trend for 'Neural Networks' compared to 'Saliency-based Sampling'. The line representing 'Ours' method is smoother, indicating fewer fluctuations and a more consistent growth in performance."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "MxMGo9zExZOezHmS",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in 'Deep Learning' performance using 'Ours' method from time 1 to time 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the increase, I refer to the {line for 'Ours' in Deep Learning}<r>[0.094, 0.569, 0.945, 0.728]</r>. At time 1, the performance percentage is {25}<r>[0.094, 0.728]</r> and at time 10 it's {55}<r>[0.945, 0.569]</r>. The percentage increase is calculated by ((55 - 25) / 25) * 100.\nBased on the above thought, the answer is:\n120%."
+            }
+        ],
+        "id_raw": 1142
+    },
+    {
+        "id": "TFZ3HDle6fPrnu01",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the information from the bar chart and arrange it into a table with civilization and age as the categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this information, I observe the bar chart and use {the x-axis labels}<r>[0.108, 0.946, 0.733]</r> that indicate the Industrial Age and the Digital Age, and {the y-axis labels}<r>[0.108, 0.946, 0.093]</r> indicating Western and Eastern civilizations. Extracting details from the bar lengths, I translate each bar's length into percentage growth.\nBased on the above thought, the answer is:\nCivilization,Age,Growth Percentage\nWestern,Industrial Age,70.40\nEastern,Industrial Age,63.60\nWestern,Digital Age,89.20\nEastern,Digital Age,80.10"
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "COnqD9zsSGMqTvPQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the growth trend of Western civilization compare to that of Eastern civilization across different ages based on the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the growth trend between Western and Eastern civilizations across different ages, I review the lengths of {the bars representing each civilization}<r>[0.108, 0.946, 0.809]</r> and<r>[0.549, 0.848, 0.681]</r>. In both the Industrial Age and the Digital Age, the bar for Western civilization is longer than that for Eastern civilization, indicating a higher growth percentage. Comparatively, Western civilization shows a consistent higher growth rate than Eastern civilization in both ages.\nBased on the above thought, the answer is:\nWestern civilization consistently shows a higher growth rate than Eastern civilization in both the Industrial Age and the Digital Age, as depicted by longer bars in the bar chart for Western civilization."
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "binC8r7Y3MYQ1ob5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth difference between Western and Eastern civilizations in the Digital Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "I determine the percentage growth from {the bar representing Western civilization in the Digital Age}<r>[0.108, 0.946, 0.809]</r>, which is 89.20%, and from {the bar representing Eastern civilization in the Digital Age}<r>[0.549, 0.848, 0.681]</r>, which is 80.10%. The difference is calculated by subtracting the Eastern civilization growth percentage from the Western civilization growth percentage.\nBased on the above thought, the answer is:\n9.10"
+            }
+        ],
+        "id_raw": 1143
+    },
+    {
+        "id": "w1taawWYjOz4TNW2",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the line chart showing service quality scores over the years into a tabular format based on the lines for Healthcare, Education, and Public Transit?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the line chart, I first observe {the x-axis tick labels}<r>[0.094, 0.927, 0.109, 0.951]</r>, {0.221, 0.927, 0.251, 0.951], [0.363, 0.927, 0.392, 0.951], [0.505, 0.927, 0.534, 0.951], [0.647, 0.927, 0.676, 0.951]<r></r> which represent the years from 2011 to 2015. For the y-values, I analyze the positions and colors of {the line for Healthcare}<r>[0.094, 0.416, 0.945, 0.581]</r>, {the line for Education}<r>[0.094, 0.792, 0.945, 0.887]</r>, and {the line for Public Transit}<r>[0.094, 0.274, 0.945, 0.235]</r>, extracting the annual scorer from each segment.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,85,77,68\n2012,87,78,65\n2013,90,80,62\n2014,88,82,55\n2015,86,84,50"
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "H08TeZcPFrJzNk3E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich service quality shows the most stability over the period from 2011 to 2015 according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the stability, I need to look at the variation in line plots. Observing {the line for Healthcare}<r>[0.094, 0.416, 0.945, 0.581]</r>, it fluctuates but within a narrower range indicating relatively stable scoring. Comparatively, {the line for Education}<r>[0.094, 0.792, 0.945, 0.887]</r> shows a consistent upward trend with minor fluctuations, and {the line for Public Transit}<r>[0.094, 0.274, 0.945, 0.235]</r> indicates a significant downward trajectory, marking high instability. Among these, Education demonstrates the most stability with slight consistent growth over the years.\nBased on the above thought, the answer is:\nEducation shows the most stability over the period from 2011 to 2015, as its line shows a consistent and slight upward trend with minimal fluctuations compared to the more varied changes seen in Healthcare and the pronounced decline in Public Transit."
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "Ntwyx9qoJiTgdN5M",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score decrease for Public Transit from 2011 to 2015 based on the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average decrease for Public Transit, I need to determine the scores at the starting and ending year by examining {the line for Public Transit}<r>[0.094, 0.274, 0.945, 0.235]</r> where it starts at a score of 68 in 2011 and ends at a score of 50 in 2015. Over this 5 year period, the score decreased by 18 points divided by the 4 intervals gives an average decrease of 4.5 points per year.\nBased on the above thought, the answer is:\n4.5"
+            }
+        ],
+        "id_raw": 1144
+    },
+    {
+        "id": "3e0zD7tbUo0OeNuv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data represented in the bar chart, with 'Companies' and 'Market Share' as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data, I look at {the x-axis tick labels}[[0.075, 0.107, 0.150, 0.127], [0.226, 0.107, 0.270, 0.127], [0.377, 0.107, 0.420, 0.127],[0.527, 0.107, 0.571, 0.127], [0.678, 0.107, 0.721, 0.127], [0.828, 0.107, 0.872, 0.127], [0.978, 0.107, 1.023, 0.127]] representing the company names, and {the heights of the bars}[[0.097, 0.185, 0.133, 0.703], [0.247, 0.185, 0.284, 0.307], [0.398, 0.185, 0.435, 0.253], [0.549, 0.185, 0.586, 0.207],[0.699, 0.185, 0.737, 0.203], [0.850, 0.185, 0.887, 0.188], [1.000, 0.185, 1.038, 0.188]] for determining the market share percentages.\nBased on the above thought, the answer is:\nCompanies,Market Share\nTesla,54\nGM,20\nToyota,15\nFord,5\nHonda,4\nHyundai,1\nNissan,1"
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "R629LUnthwUgZYxv",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the bar chart, can you describe the competitive landscape of the market?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the bar chart, the market is dominated by {Tesla}[[0.097, 0.185, 0.133, 0.703]], holding the largest market share at 54%. The second-largest share is {GM}[[0.247, 0.185, 0.284, 0.307]], but significantly less than Tesla at 20%. {Toyota}[[0.398, 0.185, 0.435, 0.253]] holds the third-largest share at 15%, followed by {Ford}[[0.549, 0.185, 0.586, 0.207]] at 5%, {Honda}[[0.699, 0.185, 0.737, 0.203]] at 4%, and both {Hyundai}[[0.850, 0.185, 0.887, 0.188]] and {Nissan}[[1.000, 0.185, 1.038, 0.188]] at the lowest share of 1% each.\nBased on the above thought, the answer is:\nThe automotive market, as depicted in the bar chart, is predominantly occupied by Tesla with a 54% share, highlighting its strong position within the industry. GM follows as the next largest competitor, but with a significantly smaller share of 20%. Other companies like Toyota, Ford, Honda, Hyundai, and Nissan hold much smaller portions of the market, indicating a largely monopolistic market dominated by Tesla, with minor competition amongst the remaining companies."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "HhasoeIpiEP0jiV7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in market share between Tesla and GM?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in market share, I need to locate {the bar for Tesla}[[0.097, 0.185, 0.133, 0.703]] and {the bar for GM}[[0.247, 0.185, 0.284, 0.307]] and extract the corresponding values: Tesla at 54% and GM at 20%. The difference between the two is therefore 54 - 20 = 34%.\nBased on the above thought, the answer is:\n34%."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "5O6iSKlkjUdzgBg1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the bar chart data into a structured table format with hotel names as observations and years as the attributes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this into table format, first, I identify the {x-axis tick labels}[0.137, 0.891, 0.244, 0.915], [0.313, 0.891, 0.418, 0.915], [0.490, 0.891, 0.607, 0.915], [0.671, 0.891, 0.788, 0.915] as the row headers or observations, which represent the different hotels. Next, I recognize that there are two years, so there will be two columns, which will be '2020 Occupancy' and '2021 Occupancy' as indicated in the {legend}[0.035, 0.921, 0.274, 0.972]. To fill the table values representing the occupancy rate, I look at the height of the {bars representing 2020 occupancy}[0.137, 0.217, 0.208, 0.891], [0.313, 0.307, 0.384, 0.891], [0.490, 0.227, 0.561, 0.891], [0.671, 0.317, 0.742, 0.891] and the {bars representing 2021 occupancy}[0.221, 0.457, 0.292, 0.891], [0.397, 0.497, 0.467, 0.891], [0.573, 0.527, 0.644, 0.891], [0.753, 0.597, 0.824, 0.891]. The height of these bars can be translated to the occupancy rate at the respective hotels in the respective years.\nBased on the above thought, the answer is:\nHotel, 2020 Occupancy, 2021 Occupancy\nOcean View, 45, 32\nMountain Escape, 39, 32\nCity Central, 45, 31\nDesert Dream, 39, 31"
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "tYyOoTVMOPif4eBJ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar graph, how has the occupancy rate evolved from 2020 to 2021 in general, and which hotel has witnessed the most significant growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the evolution of the occupancy rate from 2020 to 2021, I need to compare the{bar heights for each hotel in 2020}[0.137, 0.217, 0.208, 0.891], [0.313, 0.307, 0.384, 0.891], [0.490, 0.227, 0.561, 0.891], [0.671, 0.317, 0.742, 0.891] with the {corresponding bar heights in 2021}[0.221, 0.457, 0.292, 0.891], [0.397, 0.497, 0.467, 0.891], [0.573, 0.527, 0.644, 0.891], [0.753, 0.597, 0.824, 0.891]. A general trend can be observed from comparing these bar heights in 2020 and 2021. To identify which hotel has witnessed the most significant growth, I would need to identify the pair of bars where the growth from 2020 to 2021 is the greatest.\nBased on the above thought, the answer is:\nAccording to the bar graph, the occupancy rate for all the hotels shows a general increase from 2020 to 2021, indicating a recovering market situation in the hospitality sector. The significant increase of the bar from 2020 to 2021 for 'Desert Dream' indicates that this hotel has witnessed the most significant growth in occupancy rate during this period."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "sDgHoQ4ZQq7fJ6Am",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in occupancy from 2020 to 2021 for 'Ocean View'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage increase in occupancy for 'Ocean View', I need to first identify the {bar representing 2020 occupancy for 'Ocean View'}[0.137, 0.217, 0.208, 0.456] and the {bar representing 2021 occupancy for 'Ocean View'}[0.221, 0.457, 0.292, 0.567]. The value for 2020 is 45, and the value for 2021 is 32. The percentage increase can be calculated as ((32 - 45) / 45) * 100.\nBased on the above thought, the answer is:\n-28.88%."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "CeoSlQLy3x2rf63j",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please create a table with months as columns and each Series as a different row?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the given line chart, every distinct {x-axis tick label}<r>[0.017, 0.933, 0.024, 0.951]</r>,<r>[0.171, 0.933, 0.178, 0.951]</r>,<r>[0.325, 0.933, 0.332, 0.951]</r>,... become the column headers, which corresponds to the various months. The categories for each row can be acquired from the {legend}<r>[0.062, 0.063, 0.177, 0.201]</r> which represents each Series. The data values can be found by extracting the y-values of the plot points for each line: {Apple's line}<r>[0.098, 0.093, 0.945, 0.298]</r>, {Samsung's line}<r>[0.098, 0.504, 0.945, 0.586]</r> and {Google's line}<r>[0.098, 0.682, 0.945, 0.778]</r>.\nBased on the above thought, the answer is:\nMonth,Apple,Samsung,Google\nJan,0.47,0.3,0.1\nFeb,0.50,0.25,0.12\nMar,0.49,0.27,0.11\nApr,0.48,0.26,0.10\nMay,0.45,0.25,0.11\nJun,0.50,0.24,0.12\nJul,0.55,0.28,0.13\nAug,0.60,0.27,0.15\nSep,0.57,0.26,0.15\nOct,0.58,0.28,0.17\nNov,0.55,0.27,0.15\nDec,0.60,0.3,0.15"
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "pEP7bKZWhQ1IHZ4A",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends from the line chart, which series showed the most stable trend over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most stable trend, I need to evaluate the volatility or changes in each line - {Apple's line}[0.098, 0.093, 0.945, 0.298], {Samsung's line}[0.098, 0.504, 0.945, 0.586], and {Google's line}[0.098, 0.682, 0.945, 0.778]. Looking at {Apple's line}, I notice several fluctuations with a noticeable increase in market share. Evaluating {Samsung's line}, it appears there is a steady decrease in market share. Lastly, for {Google's line}, Google\u2019s market share appears mildly fluctuating but generally around a constant value of 0.1 - 0.15.\nBased on the above thought, the answer is:\nBased on the trends from the line chart, Google showed the most stable trend over the year. While Samsung\u2019s line depicted a constant decline and Apple\u2019s line depicted growth with fluctuations, Google's line was relatively constant over time with mild fluctuations around a market share of 0.1 - 0.15."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "6QmfXJdcjnmEXnWx",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference between the highest and lowest values for Apple?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest and lowest values for Apple can be found by identifying the peaks and troughs in {Apple's line}[0.098, 0.093, 0.945, 0.298]. The highest point for Apple occurs in November, where its value reaches approximately 0.60. The lowest point occurs in May, where its value drops to approximately 0.45. The difference between the highest and lowest values for Apple is therefore 0.60 - 0.45 = 0.15.\nBased on the above thought, the answer is:\n0.15."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "46iukZtBgRoTgdjb",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the bar chart, can you extract the data into a table format with AI Applications as rows, and Score as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the bar chart, I first identify {the x-axis tick labels}[[0.047, 0.933, 0.161, 0.951], [0.127, 0.933, 0.264, 0.951], [0.229, 0.933, 0.344, 0.951], [0.320, 0.933, 0.436, 0.951], [0.443, 0.933, 0.495, 0.951], [0.520, 0.933, 0.602, 0.951], [0.627, 0.933, 0.677, 0.951], [0.703, 0.933, 0.784, 0.951], [0.781, 0.933, 0.889, 0.951], [0.871, 0.933, 0.982, 0.951]] as the row headers showing the AI applications. Next, my task is to read {the y-axis}[0.093, 0.060, 0.926, 0.660] to extract the corresponding score values for each AI application.\nBased on the above thought, the answer is:\nAI Applications, Score\nPattern Recognition, 0.35\nRecommender Systems, 0.56\nSpeech Recognition, 0.30\nAutonomous Robots, 0.45\nChatbots, 0.40\nDriverless Car, 0.60\nGame AI, 0.65\nCybersecurity, 0.54\nPredictive Analysis, 0.40\nMarket Forecasting, 0.55"
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "OTERYXGyXG8iPS6c",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which area of AI Application appears to have the least case analysis score? Give your analysis."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the area of AI application with the least case analysis score, I need to inspect {the line points}[[0.104, 0.774], [0.195, 0.297], [0.287, 0.887], [0.378, 0.547], [0.469, 0.660], [0.561, 0.206], [0.652, 0.093], [0.744, 0.342], [0.835, 0.660], [0.926, 0.320]]. Here, the highest point on the chart corresponds to the lowest score. Upon examination, it's clear that {Speech Recognition}[0.287, 0.887] is located at the highest y-coordinate, indicating the lowest score of 0.3.\nBased on the above thought, the answer is:\nBased on the chart, the area of AI Application \"Speech Recognition\" appears to have the least case analysis score, given its highest positioning on the y-axis and the corresponding score of 0.3."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "ArBciNEixhDZiZ8q",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI application has the highest score on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the AI application with the highest score, I have to analyze {the line points}[[0.104, 0.774], [0.195, 0.297], [0.287, 0.887], [0.378, 0.547], [0.469, 0.660], [0.561, 0.206], [0.652, 0.093], [0.744, 0.342], [0.835, 0.660], [0.926, 0.320]]. It appears that the line point corresponding {Game AI}[0.652, 0.093] is located at the lowest y-coordinate, indicating the highest score of 0.65.\nBased on the above thought, the answer is:\nGame AI."
+            }
+        ],
+        "id_raw": 1148
+    },
+    {
+        "id": "uagKMP4CDJYtoERj",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table displaying the active user counts each week for each social media platform according to the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the 24 weeks line chart, I extracted the weekly user data by examining {the line points for Facebook}<r>[0.101, 0.834], [0.138, 0.781], ..., [0.943, 0.093]</r>, {the line points for Twitter}<r>[0.101, 0.569], [0.138, 0.516], ..., [0.943, 0.093]</r>, and {the line points for Instagram}<r>[0.101, 0.887], [0.138, 0.861], ..., [0.943, 0.119]</r>. Each set represents data for the respective social media platforms across 24 weeks.\nBased on the above thought, the answer is:\nWeek,Facebook,Twitter,Instagram\n1,100,150,90\n2,110,160,95\n3,120,155,100\n4,130,140,105\n5,140,160,110\n6,150,170,100\n7,155,175,95\n8,150,172,98\n9,160,164,102\n10,155,168,105\n11,200,190,180\n12,210,192,190\n13,220,195,195\n14,230,200,200\n15,235,203,202\n16,240,205,205\n17,240,210,209\n18,225,215,208\n19,230,220,215\n20,235,225,220\n21,235,220,225\n22,235,225,227\n23,235,230,230\n24,240,235,235"
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "Pqrp1Jv0ADr3n78E",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform displayed the most growth in active users from week 1 to week 24?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line chart, I analyzed the start and end points for each social media platform to gauge growth. Notably, {Facebook\u2019s points}<r>[0.101, 0.834], [0.943, 0.093]</r> show substantial growth from 100 million to 240 million. {Twitter\u2019s trend}<r>[0.101, 0.569], [0.943, 0.093]</r> also shows a significant increase from 150 million to 235 million. {Instagram\u2019s progression}<r>[0.101, 0.887], [0.943, 0.119]</r>, though growing, is from 90 million to 235 million, but with visible fluctuations.\nBased on the above thought, the answer is:\nFacebook displayed the most consistent and highest increase in active users, growing from 100 million to 240 million from week 1 to week 24, seeing the largest net growth compared to Twitter and Instagram."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "2IaCotXuTvfnneuD",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of active users across all platforms by the end of week 24?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the cumulative active users by week 24, I reviewed {the final point values for Facebook}<r>[0.943, 0.093]</r>, {Twitter}<r>[0.943, 0.093]</r>, and {Instagram}<r>[0.943, 0.119]</r>. The values are 240, 235, and 235 million for Facebook, Twitter, and Instagram respectively. Adding these provides the total.\nBased on the above thought, the answer is:\n710 million."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "N6grfi6xco5dHJGy",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the trends of Research Funding and Technological Innovation over time from the line chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[0.033, 0.933, 0.051, 0.951], [0.146, 0.933, 0.164, 0.951], ..., [1.045, 0.933, 1.070, 0.951], which represent the Time Periods. The y-axis represents Normalized Value. The two lines represent trends of different factors: {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Corresponding to them, there are a series of {data points on the lines}[0.087, 0.440, 0.945, 0.887], [0.087, 0.093, 0.945, 0.887] denoting the values of these two factors over different time periods.\nBased on the above thought, the answer is:\nTime Period,Research Funding,Technological Innovation\n1,2.1,1.5\n2,2.2,1.7\n3,2.8,2.0\n4,2.0,2.5\n5,1.5,3.0\n6,1.7,3.6\n7,2.0,4.0\n8,2.5,4.2\n9,3.0,4.4\n10,3.2,5.0\n11,3.4,5.2\n12,3.7,6.0\n13,3.5,6.2\n14,4.0,6.6\n15,4.6,7.0\n16,5.0,7.5\n17,5.2,8.0\n18,5.4,8.4\n19,5.5,9.0\n20,6.0,9.5"
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "JQ304cHfkkPgiZ8b",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which variable, Research Funding or Technological Innovation, shows a steady increase over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to analyze the trend lines for {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Looking at {the line for Research Funding}[0.087, 0.440, 0.945, 0.887], the trend begins with a slight increase, followed by a sharp drop, and then a steady increase. However, there are fluctuations in between. For {Technological Innovation}[0.087, 0.093, 0.945, 0.887], begins at a lower point and shows a consistently upward trend over time, with no evident decrease at any point.\nBased on the above thought, the answer is:\nTechnological Innovation shows a steady increase over time without any evident decrease, in contrast to Research Funding, which experiences several fluctuations despite a general upward trend."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "n6LFAUXzfLSGBgdP",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which time period did the value for Research Funding first exceed 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "The plot line for {Research Funding}[0.096, 0.072, 0.219, 0.094] has various data points against time. We need to find the first one that's above 3. Looking at the {data points on the line}[0.087, 0.440, 0.945, 0.887], I see that the value first exceeds 3 at time period 9.\nBased on the above thought, the answer is:\nTime period 9."
+            }
+        ],
+        "id_raw": 1150
+    },
+    {
+        "id": "LW0b7LbgzlIb3MHz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart to form a structured table with months as columns and media types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I will first focus on {the x-axis tick labels}<r>[0.028, 0.933, 0.036, 0.951], [0.181, 0.933, 0.188, 0.951], [0.333, 0.933, 0.340, 0.951], [0.485, 0.933, 0.493, 0.951], [0.637, 0.933, 0.645, 0.951], [0.786, 0.933, 0.801, 0.951], [0.938, 0.933, 0.953, 0.951]</r>, which represent the twelve months. Next, I look at {the legend}<r>[0.073, 0.063, 0.235, 0.168]</r>. This represents the different types of media, i.e. Email marketing, Social media and SEO. Now, to get the actual data values for each month and each media type, I need to examine the y-coordinates of certain points on {the lines}<r>[0.108, 0.385, 0.946, 0.887], [0.108, 0.720, 0.184, 0.804], [0.108, 0.093, 0.946, 0.762]</r>. The points where these lines intersect with the vertical lines representing each month would provide the values for the table.\nBased on the above thought, the answer is:\nMonths,EmailMarketing,SocialMedia,SEO\nJanuary,1000,1100,1200\nFebruary,1050,1200,1150\nMarch,1200,1250,1300\nApril,1150,1300,1400\nMay,1300,1400,1350\nJune,1250,1450,1500\nJuly,1400,1550,1600\nAugust,1350,1600,1650\nSeptember,1500,1700,1750\nOctober,1450,1750,1800\nNovember,1600,1850,1900\nDecember,1550,1900,1950"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "SWs4B0kmanJDwwoC",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of media consistently improved its performance over the course of a year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe {the trends of each media type line}<r>[0.108, 0.385, 0.946, 0.887], [0.108, 0.720, 0.946, 0.762], [0.108, 0.093, 0.946, 0.762]</r>. {The Email Marketing line}<r>[0.108, 0.385, 0.946, 0.887]</r> initially went upwards but then underwent some fluctuations. However, the overall trend is positive. {The Social Media line}<r>[0.108, 0.720, 0.946, 0.762]</r> also went up and down, but it generally improved towards the end. {The SEO line}<r>[0.108, 0.093, 0.946, 0.762]</r> stands out as it shows a consistent increase from the start of the year to the end. Therefore, it can be concluded that SEO consistently improved its performance.\nBased on the above thought, the answer is:\nThe Search Engine Optimization (SEO) strategy consistently improved its performance over the year as evident by a steady and consistent increase in the number of leads from January to December."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "tylTfxYhlwybGVdV",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum leads generated by SEO over the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the maximum and minimum leads generated by SEO, I'll inspect {the SEO line's points}<r>[0.108, 0.720], [0.184, 0.762], [0.261, 0.636], [0.337, 0.553], [0.413, 0.595], [0.489, 0.469], [0.565, 0.385], [0.641, 0.344], [0.717, 0.260], [0.793, 0.218], [0.870, 0.134], [0.946, 0.093]</r>. The maximum corresponds to the peak point on the line, which is 1950 in December, and the minimum value corresponds to the lowest point on the line, which is 1150 in February. To get the difference, I should subtract the minimum from the maximum, so 1950 - 1150 = 800.\nBased on the above thought, the answer is:\n800"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "uJWUasbCZKhPzcVX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please format the line graph data into a structured table format using Weeks as rows and Sales Revenue, Advertising Expenditure, and Website Traffic as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To collate a structured data table for this line chart, we must identify the x-axis label as weeks represented by {the x-axis tick labels}[[0.081, 0.933, 0.034, 0.951], [0.214, 0.933, 0.217, 0.951], [0.397, 0.933, 0.400, 0.951], [0.580, 0.933, 0.584, 0.951], [0.763, 0.933, 0.767, 0.951], [0.946, 0.933, 0.954, 0.951]], and additionally, the legend denoting {Sales Revenue}[[0.132, 0.072, 0.234, 0.094]], {Advertising Expenditure}[[0.132, 0.105, 0.299, 0.127]] and {Website Traffic}[[0.132, 0.137, 0.234, 0.160]] as the column headers or variables. The corresponding values would be found within the line points for each category: {values for Sales Revenue}[[0.122, 0.246], [0.214, 0.229], ..., [0.946, 0.093]], {values for Advertising Expenditure}[[0.122, 0.887], [0.214, 0.887], ..., [0.946, 0.882]], and {values for Website Traffic}[[0.122, 0.802], [0.214, 0.800], ..., [0.946, 0.776]].\nBased on the above thought, the answer is:\nWeeks, Sales Revenue, Advertising Expenditure, Website Traffic\n1, 80000, 5000, 15000\n2, 82000, 5000, 15200\n3, 83000, 5200, 15500\n4, 84000, 5250, 16000\n5, 85000, 5500, 16000\n6, 86000, 5500, 16500\n7, 90000, 5200, 17000\n8, 92000, 5300, 17500\n9, 95000, 5500, 17700\n10, 98000, 5600, 18000"
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "3KSDPQSDwYHyFAqK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich variable showed the most consistency in the given weeks and why do you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To judge which variable exhibited the most consistency over the weeks, I need to scrutinize the fluctuations in the lines representing each variable. Upon examining {the line for Sales Revenue}[0.122, 0.093, 0.946, 0.246], I notice a steady ascension over the weeks, indicating a uniform trend. However, looking at {Advertising Expenditure}[0.122, 0.882, 0.946, 0.887] and {Website Traffic}[0.122, 0.776, 0.946, 0.802], it's clear that their trend lines fluctuate less drastically, almost maintaining a consistent level throughout the ten weeks.\nBased on the above thought, the answer is:\nAdvertising Expenditure and Website Traffic appear to have shown the most consistency over the given weeks. Their trend lines showed very minimal fluctuations, nearly maintaining a constant level throughout, compared to Sales Revenue which displayed a steady increase over the same period. This reflects a consistent investment in advertising and stable website traffic, even as sales revenue continued to climb."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "0JRXHEy6zSKGsun5",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in sales revenue from week 1 to week 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase in sales revenue from week 1 to week 10, I need to locate {the line points for Sales Revenue}[[0.122, 0.246], ..., [0.946, 0.093]] and extract the values for week 1 (80000) and week 10 (98000). The percentage increase can be calculated as (98000 - 80000) / 80000 * 100.\nBased on the above thought, the answer is:\n22.5%."
+            }
+        ],
+        "id_raw": 1153
+    },
+    {
+        "id": "jp3s4KoQTfMXl3Ut",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart data showing GDP, Interest Rate, and Debt to GDP Ratio from 2005 to 2020 into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the line chart data into a structured table, I start by analyzing the x-axis to locate {the years}<r>[0.053, 0.933, 0.961, 0.951]</r> which range from 2005 to 2020. Each line on the chart, representing GDP (in billion USD), Interest Rate (%), and Debt to GDP Ratio (%), has points plotted for each of these years. I observe the respective y-axis points for each metric at specified {x-ticks coordinates}<r>[0.067, 0.927], [0.946, 0.927]</r>. The data for each year is linked to specific y-values that correspond to the performance measurements.\nBased on the above thought, the answer is:\nYear,GDP(Billion USD),Interest Rate(%),Debt to GDP Ratio(%)\n2005,466640.0,5.6,92.0\n2006,494503.0,5.9,94.0\n2007,516104.0,5.8,98.0\n2008,486527.0,0.1,108.0\n2009,470704.0,0.8,116.0\n2010,495430.0,0.1,139.0\n2011,513282.0,0.3,138.0\n2012,527073.0,0.2,148.0\n2013,541303.0,0.3,146.0\n2014,556495.0,0.3,146.0\n2015,574458.0,0.5,144.0\n2016,587719.0,1.2,142.0\n2017,605402.0,2.3,140.0\n2018,612064.0,2.9,138.0\n2019,625292.0,2.6,136.0\n2020,591238.0,1.9,144.0"
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "PmDdlw7qRGhs0xrp",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the observed period, which economic metric shows a consistent increase despite other fluctuations in the chart between 2005 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trends, the GDP line {the GDP line plot}<r>[0.122, 0.294, 0.946, 0.136]</r> mostly shows an upward trajectory despite some intermittent declines specifically around 2008 to 2010. Unlike the {Interest Rate line}<r>[0.122, 0.887, 0.946, 0.887]</r> which displays significant fluctuations and drops to near zero several times, and {the Debt to GDP ratio line}<r>[0.122, 0.887, 0.946, 0.887]</r> which although increases, also exhibits some fluctuations during similar periods.\nBased on the above thought, the answer is:\nThe GDP metric consistently shows an upward trend from 2005 to 2020, despite minor declines around the financial crisis period and afterwards. This is noticeable when compared to the more fluctuating trends of the Interest Rate and Debt to GDP ratio, making GDP the most consistently increasing economic metric in the chart."
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "gSPO7PZnjN1OwmBL",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage change in GDP from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I ascertain the GDP values for the years 2005 and 2020 by locating the data points on the GDP line {the GDP line points}<r>[0.122, 0.294], [0.946, 0.136]</r>. The value for 2005 is 466640.0 billion USD and for 2020, it is 591238.0 billion USD. Calculating the percentage change: ((591238 - 466640) / 466640) * 100.\nBased on the above thought, the answer is:\n26.7%"
+            }
+        ],
+        "id_raw": 1154
+    },
+    {
+        "id": "xKcFrgRMpE4GAu8g",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the line chart depicting Sustainability Index and Ecological Footprint into a structured table format, listing the categories and their respective values from both assessors' perspectives?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I examine the plot for the GPT4-as-a-judge on the left and the PaLM2-as-a-judge on the right. Both plots visualize trends across three categories: Energy, Water, and Waste. Each category has a Sustainability Index and an Ecological Footprint value for each assessor, denoted by distinct colors and a legend. By analyzing each line's points, I can derive the exact numerical values for these measures. I focus on {the lines representing the Sustainability Index and the Ecological Footprint for both subplots}[0.055, 0.053, 0.988, 0.927] and decipher the numbers based on their heights against the y-axis.\nBased on the above thought, the answer is:\nCategory,Assessor,Sustainability Index,Ecological Footprint\nEnergy,GPT4,85,70\nWater,GPT4,75,50\nWaste,GPT4,60,80\nEnergy,PaLM2,80,65\nWater,PaLM2,70,55\nWaste,PaLM2,65,75"
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "QRL6TgTenlv3Lkst",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category exhibits the best overall performance in terms of sustainable ecological practices according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the best performing category, I analyze the balance between the Sustainability Index and the Ecological Footprint from the line chart across the categories of Energy, Water, and Waste. It is necessary to assess which category has the highest Sustainability Index relative to its Ecological Footprint. Monitoring {the line charts for both subplots}[0.055, 0.053, 0.988, 0.927], it is evident that the Energy category consistently shows a higher Sustainability Index while maintaining lower Footprint values compared to others.\nBased on the above thought, the answer is:\nThe Energy category exhibits the best overall performance in terms of sustainable ecological practices as it has the highest Sustainability Index and lower Ecological Footprint across both assessors according to the chart."
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "jjPB0WgPoN4raXJ4",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the Ecological Footprint for the Water category between both assessors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the range, I first need to identify and extract the exact values of the Ecological Footprint for the Water category from both assessors. These values are obtained by inspecting {the line points}[0.055, 0.053, 0.988, 0.927] on the chart, where each assessor's data is represented. By seeing their position relative to the y-axis scaled from 0 to 100, I note that GPT4 has a value of 50 and PaLM2 has 55.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 1155
+    },
+    {
+        "id": "CXxp4E2WIsYiyHjZ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data about education sectors and job market demand from 2005 to 2020 into a structured table from the chart, where the years are the rows and sectors are the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table from the chart data, I first look at {the years on the x-axis}<r>[0.038, 0.933, 0.067, 0.951], [0.149, 0.933, 0.179, 0.951], [0.261, 0.933, 0.290, 0.951], [0.373, 0.933, 0.402, 0.951], [0.484, 0.933, 0.514, 0.951], [0.596, 0.933, 0.625, 0.951], [0.708, 0.933, 0.737, 0.951], [0.819, 0.933, 0.849, 0.951], [0.931, 0.933, 0.960, 0.951]</r> which represent the row labels or years. I then identify {the legend}<r>[0.073, 0.063, 0.268, 0.168]</r> as the column labels representing different sectors such as Online Education, Traditional Education, and Job Market Demand. To populate the values within the table, I analyze {the line points for each sector}<r>[0.108, 0.887], [0.164, 0.853], [0.220, 0.818], [0.276, 0.784], [0.331, 0.749], [0.387, 0.715], [0.443, 0.680], [0.499, 0.645], [0.555, 0.611], [0.611, 0.576], [0.666, 0.542], [0.722, 0.542], [0.778, 0.507], [0.834, 0.473], [0.890, 0.438], [0.946, 0.404]</r> and extract their corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Online Education,Traditional Education,Job Market Demand\n2005,50,800,700\n2006,100,750,750\n2007,150,700,800\n2008,200,650,750\n2009,250,600,700\n2010,300,550,750\n2011,350,500,800\n2012,400,450,850\n2013,450,400,900\n2014,500,350,950\n2015,550,300,1000\n2016,550,270,1000\n2017,600,240,1050\n2018,650,210,1100\n2019,700,180,1150\n2020,750,150,1200"
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "JT9ofe0eCJvfQx83",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the trend data suggest about the traditional education sector between 2005 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the progression of {the line for Traditional Education}<r>[0.108, 0.404, 0.946, 0.818]</r>, it is evident there is a consistent downward trend. Starting in 2005 with approximately 800 {at the point}<r>[0.108, 0.369]</r>, the enrollment figures sequentially decrease, reaching approximately 150 by 2020 {at the point}<r>[0.946, 0.818]</r>. This indicates a strong shift away from traditional educational settings over these years, possibly in favour of other forms of education like online platforms.\nBased on the above thought, the answer is:\nThe trend data for the traditional education sector exhibits a substantial decline from 800 in 2005 to 150 in 2020, reflecting a significant reduction in traditional education enrollment possibly due to the rising preference for online education methods over the years."
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "P4eBMXqtfZGSKsuZ",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Job Market Demand increase from 2005 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Job Market Demand over the years, I trace {the line for Job Market Demand}<r>[0.108, 0.438], [0.946, 0.093]</r>, noting the points corresponding to the years 2005 and 2020. The values start at 700 in 2005 from {the point}<r>[0.108, 0.438]</r> and ends at 1200 in 2020 from {the point}<r>[0.946, 0.093]</r>. Calculating the increase involves subtracting the initial value from the final value: 1200 - 700.\nBased on the above thought, the answer is:\n500"
+            }
+        ],
+        "id_raw": 1156
+    },
+    {
+        "id": "bZe1nb0r71DGxS30",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert this chart into a structured table with Evaluation Criteria as rows and Innovations as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.069, 0.934, 0.125, 0.952], [0.178, 0.934, 0.204, 0.952], [0.247, 0.934, 0.324, 0.952], [0.350, 0.934, 0.410, 0.952], [0.448, 0.934, 0.499, 0.952], [0.497, 0.934, 0.639, 0.952], [0.599, 0.934, 0.725, 0.952], [0.722, 0.934, 0.791, 0.952], [0.812, 0.934, 0.890, 0.952], [0.935, 0.934, 0.955, 0.952]] as the row labels, representing the different Evaluation Criteria. Next, I locate {the legend texts}[[0.097, 0.065, 0.170, 0.082], [0.097, 0.091, 0.170, 0.108],[0.097, 0.117, 0.170, 0.135]] as the column headers representing different Innovations. Then, to populate the values, I examine {the line points for each innovation}[[0.097, 0.709],[0.191, 0.475]...] and {extract the corresponding values}[[0.45,0.62...]].\nBased on the above thought, the answer is:\nEvaluation_Criterion,Innovation_1,Innovation_2,Innovation_3\nEfficiency,0.45,0.78,0.35\nCost,0.62,0.5,0.77\nCollaboration,0.75,0.88,0.64\nScalability,0.52,0.6,0.83\nPotential,0.9,0.76,0.67\nEase_of_Implementation,0.85,0.68,0.45\nBenefit_to_Cost_Ratio,0.75,0.64,0.82\nProject_Risk,0.32,0.54,0.4\nSustainability,0.86,0.7,0.88\nROI,0.78,0.9,0.76"
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "DGTU6MuNn96WfsK4",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, which Innovation project received the highest average score across all evaluation criteria and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to compare the average scores for each innovation project across all evaluation criteria. Upon examining {the line points for Innovation 1}[[0.097, 0.709],[0.191, 0.475],[0.285, 0.296],[0.380, 0.613],[0.474, 0.089],[0.568, 0.158],[0.662, 0.296],[0.757, 0.888],[0.851, 0.144],[0.945, 0.254]], {Innovation 2}[[0.097, 0.254],[0.191, 0.640],...], and {Innovation 3}[[0.097, 0.847],[0.191, 0.268],...], I notice that Innovation 3 has generally higher values across most evaluation criteria compared to other innovations.\nBased on the above thought, the answer is:\nInnovation 3 appears to have received the highest average score across all evaluation criteria, as it has performed well across most measures, including the areas of Collaboration, Scalability, Benefit to Cost Ratio, Sustainability, and ROI. This suggests a strong overall performance, justifying its high average score."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "IOpASVyTyKo5jQwG",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average score of Innovation 1 across all evaluation criteria?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average score of Innovation 1 across all evaluation criteria, I need to locate {the line points for Innovation 1} [[0.097, 0.709],[0.191, 0.475],[0.285, 0.296],[0.380, 0.613],[0.474, 0.089],[0.568, 0.158],[0.662, 0.296],[0.757, 0.888],[0.851, 0.144],[0.945, 0.254]] and extract the corresponding scores, then calculate the average of these scores.\nBased on the above thought, the answer is:\n0.65."
+            }
+        ],
+        "id_raw": 1158
+    },
+    {
+        "id": "hU1FWBIDW5EO2zP3",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you depict the asset values data present in the lineplot into a structured table format? The table should have quarters as rows and asset types as columns."
+            },
+            {
+                "from": "gpt",
+                "value": "For the desired table, I can label the quarters from the {x-axis}<r>[0.125, 0.896, 0.873, 0.914]</r> as rows. The asset types, namely Physical Assets and Digital Assets, can be taken from {the legend}<r>[0.737, 0.130, 0.893, 0.203]</r> as the column names. I can gather the asset values as per each quarter and respective asset type from their line plot points {Physical Assets line}<r>[0.160, 0.445, 0.865, 0.855]</r> and {Digital Assets line}<r>[0.160, 0.155, 0.865, 0.831]</r>. Each point corresponds to normalized asset value per quarter.\nBased on the above thought, the answer is:\nQuarter,Physical_Assets,Digital_Assets\nQ1,0.47,0.94\nQ2,0.62,0.83\nQ3,0.39,0.75\nQ4,0.57,0.68\nQ5,0.36,0.61\nQ6,0.44,0.58\nQ7,0.61,0.48\nQ8,0.70,0.38"
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "76RLw7KA1NEshCq0",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the trend of asset values observed across the quarters according to the line plots?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend analysis can be done based on the data points of both asset types in the line plot. The plot of the line for {Physical Assets}<r>[0.160, 0.722, 0.865, 0.445]</r> shows a gradual rise, then a drop, and a rough steady increase towards the end. This indicates a mixed trend with some volatility, but an overall increase in value.\nIn contrast, the line for {Digital Assets}<r>[0.160, 0.155, 0.865, 0.831]</r> shows a pronounced downward trend, indicating a steady decrease in value from Q1 to Q8.\nBased on the above thought, the answer is:\nThe Physical Assets seem to follow a mixed trend with an initial increase, a drop, and then another increase, indicating some volatility but an overall upward path across the quarters. In contrast, the values for Digital Assets consistently decrease over the quarters, indicating a clear downward trend. This suggests that over the observed period, Physical Assets perform better than Digital Assets."
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "IC0IUfvMbftVqWkF",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_58_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much are the ending values of both physical and digital assets different from the beginning values according to the line plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this difference, we must compare the normalized asset value of Q1 and Q8 for both asset types. The line plots of {Physical Assets}<r>[0.160, 0.722, 0.865, 0.445]</r> and {Digital Assets}<r>[0.160, 0.155, 0.865, 0.831]</r> indicate the respective values. Physical Assets have decreased by 0.70 - 0.47 = 0.23 while Digital Assets have decreased by 0.94 - 0.38 = 0.56 from Q1 to Q8.\nBased on the above thought, the answer is:\nPhysical Assets: 0.23, Digital Assets: 0.56"
+            }
+        ],
+        "id_raw": 1159
+    },
+    {
+        "id": "7a4CDYWiPZY6WbNp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the structured data of viewership trends from the line chart, organizing the years as rows and the type of viewership as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the line chart, I analyze the plots representing each viewership type. {SVoD's line}<r>[0.400, 0.171, 0.642, 0.828]</r> shows upward trends and {Linear TV's line}<r>[0.400, 0.480, 0.642, 0.828]</r> shows a downward trend over the period from 2018 to 2020. Aligning with the x-axis labels {2018}<r>[0.579, 0.879, 0.608, 0.914]</r>, {2019}<r>[0.603, 0.879, 0.632, 0.914]</r>, {2020}<r>[0.627, 0.879, 0.657, 0.914]</r>, I extract related data values for both viewership types.\nBased on the above thought, the answer is:\nYear,SVoD Viewership,Linear TV Viewership\n2018,Significant Growth,High but declining\n2019,Accelerated Growth,Moderately Declining\n2020,Rapid Growth,Lowest Point"
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "OGOWwHXF7LBjJDZT",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the comparison between the 'Subscription Video on Demand' and 'Linear TV' viewership trends suggest about consumer preferences over the years 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze consumer preferences, I review the representation of viewership trends on the line chart, tracking both {SVoD's line}<r>[0.400, 0.171, 0.642, 0.828]</r> and {Linear TV's line}<r>[0.400, 0.480, 0.642, 0.828]</r>. SVoD portrays a consistent upward trajectory, indicating increased adoption and preference among consumers, while the downward trend in Linear TV viewership hints at a declining favor over the same period.\nBased on the above thought, the answer is:\nThe comparison indicates a growing consumer preference for Subscription Video on Demand services over Linear TV from 2018 to 2020. The rising trend for SVoD coupled with the decline of Linear TV suggests a shift in how consumers opt to access media, favoring on-demand streaming over traditional television formats."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "vw3Jwc61a4ujAMnt",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many years does the Persistent Growth phase for SVoD last, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the duration of the Persistent Growth phase for SVoD, I observe the segment of the {SVoD line}<r>[0.400, 0.171, 0.642, 0.828]</r>. The trend starts from 2018 and continues to show a consistent rise through to 2020, as depicted in the x-axis {ticks}<r>[0.579, 0.879, 0.657, 0.914]</r>. Calculating the span from 2018 to 2020 gives a duration of 3 years.\nBased on the above thought, the answer is:\n3 years."
+            }
+        ],
+        "id_raw": 1161
+    },
+    {
+        "id": "986et2D4vUXYZ3ge",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table showing the accuracy trends for Machine Learning and Deep Learning from 2018 to 2020 according to the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To construct the table, I first identify {the x-axis tick labels}<r>[0.056, 0.879, 0.085, 0.914; 0.177, 0.879, 0.206, 0.914; 0.298, 0.879, 0.327, 0.914]</r> indicating the years 2018, 2019, and 2020. Then, I examine {the points on the lines for Machine Learning}<r>[0.071, 0.351; 0.192, 0.196; 0.313, 0.145]</r> and {Deep Learning}<r>[0.071, 0.428; 0.192, 0.325; 0.313, 0.248]</r> which gives the corresponding accuracy values for each year.\nBased on the above thought, the answer is:\nYear,Machine Learning, Deep Learning\n2018,0.9,0.87\n2019,0.96,0.91\n2020,0.98,0.94"
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "aOiyW9WBeLuLXKAh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the performance of Machine Learning technologies compare to Deep Learning technologies over the analyzed period in terms of reliability and consistency based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By evaluating {the line plot for Machine Learning}<r>[0.071, 0.351; 0.192, 0.196; 0.313, 0.145]</r>, it shows a consistent upward trend in accuracy from 0.9 in 2018 to 0.98 in 2020 with relatively less fluctuation. Conversely, {the line plot for Deep Learning}<r>[0.071, 0.428; 0.192, 0.325; 0.313, 0.248]</r> also shows an increase from 0.87 in 2018 to 0.94 in 2020, but the increments each year are not as pronounced or steady as Machine Learning\u2019s, hence less consistent. Therefore, Machine Learning displays slightly better reliability and consistency in performance improvement over the analyzed period compared to Deep Learning.\nBased on the above thought, the answer is:\nMachine Learning is more consistent and reliable in terms of performance improvement over the analyzed period compared to Deep Learning."
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "BGtg79DWOyKN2yqb",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend in accuracy improvement from 2018 to 2020 for Machine Learning based on numerical values?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the data from {the points on the Machine Learning line}<r>[0.071, 0.351; 0.192, 0.196; 0.313, 0.145]</r>, the accuracy starts at 0.9 in 2018, increases to 0.96 in 2019, and reaches 0.98 in 2020. The overall difference indicates an improvement of 0.08.\nBased on the above thought, the answer is:\n0.08"
+            }
+        ],
+        "id_raw": 1162
+    },
+    {
+        "id": "Cxp91B9mGqMTzb5V",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the bar chart showing the manufacturing efficiency of two products across two processes into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the horizontal bars}<r>[0.1, 0.35, 0.5, 0.6]</r> and {the labels}<r>[0.2, 0.4, 0.55, 0.65]</r> within them, I can see the different processes for WidgetA and WidgetB. {Process1}<r>[0.1, 0.35]</r> is represented by light blue and {Process2}<r>[0.5, 0.6]</r> by dark blue, respectively. By translating these bars and their labels into a categorical format, we can tabulate the data appropriately.\nBased on the above thought, the answer is:\nProduct,Process1 (%),Process2 (%)\nWidgetA,70.5,74.3\nWidgetB,89.6,92.1"
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "DIEzKbsXsGJX2yKw",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the two processes, Process1 or Process2, demonstrates higher overall efficiency across the manufactured products?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the chart data for each product-process combination, it's evident {Process2}<r>[0.5, 0.6]</r> has higher values than {Process1}<r>[0.1, 0.35]</r> regardless of the product. Specifically, WidgetA's efficiency increased from 70.5% to 74.3% and WidgetB's from 89.6% to 92.1% as illustrated by {the efficiency labels inside the bars}<r>[0.2, 0.4, 0.55, 0.65]</r>. This indicates that Process2 is more effective in improving production efficiency for both products.\nBased on the above thought, the answer is:\nProcess2 demonstrates higher overall efficiency for both WidgetA and WidgetB according to the efficiency values shown in the chart."
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "pvJaJWjHJx6lDyym",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage increase in production efficiency is observed for WidgetA moving from Process 1 to Process 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pinpointing the values for WidgetA, which are encapsulated within {the bars}<r>[0.15, 0.25]</r>, the specific values I observe are 70.5% for {Process 1}<r>[0.15, 0.25, 0.1, 0.2]</r> and 74.3% for {Process 2}<r>[0.55, 0.65, 0.5, 0.6]</r>. Calculating the increase, the difference is 74.3% - 70.5% = 3.8%.\nBased on the above thought, the answer is:\n3.8%"
+            }
+        ],
+        "id_raw": 1163
+    },
+    {
+        "id": "ISWS5nwVMqHEM1DP",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart information into a table with web browsers as rows and risk types as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the x-axis represents various browser names, which can be denoted as row headers in the table. Meanwhile, the legend describes 'High Risk' and 'Low Risk' categories, representing the two columns in the table. To populate the table, I locate the height of each bar which indicates the values corresponding to the risk category for each browser. The bars representing 'High Risk' values can be found in {High Risk bars}[[0.192, 0.123, 0.221, 0.569], [0.303, 0.123, 0.331, 0.354], [0.410, 0.123, 0.439, 0.412], [0.517, 0.123, 0.546, 0.387], [0.624, 0.123, 0.653, 0.533]] for each browser. Similarly, 'Low Risk' values are denoted by {Low Risk bars}[[0.230, 0.123, 0.259, 0.476], [0.341, 0.123, 0.369, 0.321], [0.449, 0.123, 0.477, 0.464], [0.556, 0.123, 0.584, 0.408], [0.662, 0.123, 0.691, 0.314]] for each browser.\nBased on the above thought, the answer is:\nBrowser,High Risk,Low Risk\nFirefox, 22.4, 17.7\nChrome, 11.6, 9.9\nSafari, 14.5, 17.1\nEdge, 13.2,14.2\nOpera, 20.5, 9.55"
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "7EKM5moGedL7l6KE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you derive about the browsers' high and low-risk levels based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive conclusions, I need to look at the heights of the bar plot representing the High and Low risk in the chart. The 'High Risk' bars are {High Risk bars}[[0.192, 0.123, 0.221, 0.569], [0.303, 0.123, 0.331, 0.354], [0.410, 0.123, 0.439, 0.412], [0.517, 0.123, 0.546, 0.387], [0.624, 0.123, 0.653, 0.533]], and the 'Low Risk' bars are {Low Risk bars}[[0.230, 0.123, 0.259, 0.476], [0.341, 0.123, 0.369, 0.321], [0.449, 0.123, 0.477, 0.464], [0.556, 0.123, 0.584, 0.408], [0.662, 0.123, 0.691, 0.314]]. Upon examining the trends, it's clear that Firefox users are the most susceptible to high-risk threats, while Chrome users are the least likely to face these threats. Conversely, Safari users have a higher likelihood of encountering low-risk threats while Firefox users have the lowest risk in this category.\nBased on the above thought, the answer is:\nThe chart indicates that when users are choosing a browser aimed at minimizing high-risk privacy threats, Chrome is the safest choice. However, if users are more concerned with low-risk data privacy issues, Firefox is the safer option. Each browser has varying degrees of strengths and weaknesses when it comes to managing cybersecurity and privacy threats and users should opt according to their preference for low risk or high-risk threats mitigation."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "YpWgxm5oAmG5q4oM",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween Firefox and Chrome, which web browser has a higher difference in high and low-risk levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the difference in high and low-risk levels between Firefox and Chrome, I need to locate the respective bar heights in the chart. For Firefox, the heights of the high-risk and low-risk bars are located at {Firefox High Risk} [0.192, 0.123, 0.221, 0.569] and {Firefox Low Risk} [0.230, 0.123, 0.259, 0.476] respectively which show values of 22.4% and 17.7%. For Chrome, the high-risk and low-risk bars are located at {Chrome High Risk} [0.303, 0.123, 0.331, 0.354] and {Chrome Low Risk} [0.341, 0.123, 0.369, 0.321] respectively with values of 11.6% and 9.9%. The difference in high and low-risk levels for Firefox is 22.4% - 17.7% = 4.7% and for Chrome, it is 11.6% - 9.9% = 1.7%\nBased on the above thought, the answer is:\nFirefox."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "rxFj4Epbo390l9p7",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the line chart showing the performance of different quarters over 5 years into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line chart, I start by identifying {the x-axis labels}<r>[0.021, 0.962, 0.170, 0.975]</r> which indicate the years from 2015 to 2020. These serve as the column headers for the table. Observing the lines for each quarter, I tag them with {the legend entries}<r>[0.569, 0.074, 0.649, 0.116]</r> such as Q1 through Q4, providing row headers. The values for each quarter per year are plotted as {data points}<r>[0.369, 0.232, 0.639, 0.741]</r>, which are directly translated to the respective cells in the table.\nBased on the above thought, the answer is:\nYear,Q1,Q2,Q3,Q4\n2015,0.8,1.5,1.2,0.5\n2016,1.0,2.0,1.4,0.6\n2017,1.2,2.2,1.6,0.7\n2018,1.0,2.1,1.5,0.6\n2019,0.9,2.3,1.3,0.5\n2020,1.0,2.5,1.2,0.7"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "rNe7lFY4AH2La7ZK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Q3 from 2015 to 2020 compare to the other quarters in terms of stability and variability?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing trends, I note that {the line for Q3}<r>[0.429, 0.402, 0.609, 0.402]</r> shows moderate fluctuation, peaking in 2017 and dipping in 2020. In contrast, {the line for Q1}<r>[0.369, 0.741, 0.549, 0.741]</r> is relatively stable with minor peaks. {The line for Q2}<r>[0.399, 0.628, 0.639, 0.458]</r> shows a continuous elevation suggesting stable growth, while {the line for Q4}<r>[0.549, 0.571, 0.639, 0.571]</r> exhibits slight downward trends. Comparatively, Q3 possesses less stability and greater variability than Q1 and Q2 but is more stable than Q4.\nBased on the above thought, the answer is:\nQ3 exhibits medium stability with variable peaks and troughs when compared to the other quarters. It's more stable than Q4 but shows greater variability than Q1 and Q2, reflecting an inconsistent development pattern over the years."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "QA9s4L8QLyLsdoAl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall average value for Q2 across all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average for Q2, I collect values for Q2 from 2015 to 2020 from {data points for Q2}<r>[0.399, 0.628, 0.639, 0.741]</r>, which are 1.5, 2.0, 2.2, 2.1, 2.3, 2.5 respectively. To find the average, I calculate (1.5+2.0+2.2+2.1+2.3+2.5)/6.\nBased on the above thought, the answer is:\n2.1"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "oi9ORtbSwSFbmTUp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information shown in the line chart into a structured table format keeping 'Area' as the row header and 'Average Income' and 'Average Age' as the column headers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve a structured table form, I start by identifying the label {the x-axis tick labels}<r>[0.201, 0.766, 0.334, 0.802], [0.402, 0.766, 0.534, 0.802], [0.605, 0.766, 0.737, 0.802]</r> which indicates the rows as Urban, Suburban, and Rural. For the column headers, 'Average Income' and 'Average Age' are depicted through line plots. I locate the respective values by observing and pinpointing {the line for income}<r>[0.201, 0.390, 0.737, 0.680]</r> and {the line for age}<r>[0.201, 0.245, 0.737, 0.560]</r> across different areas.\nBased on the above thought, the answer is:\nArea,Average Income,Average Age\nUrban,45000,27\nSuburban,55000,35\nRural,35000,45"
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "m9a13R67MvY0hJAX",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat factors might contribute to the difference in average income between urban and rural areas as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the given chart, {lines indicating average age and income}<r>[0.201, 0.245, 0.737, 0.560]</r> demonstrate that with increasing age, there appears to be a decrease in average income. Urban areas, with a younger average age, show a higher income compared to rural areas which have the oldest average age but the lowest average income. This suggests a correlation between age demographics & economic opportunities which are potentially more abundant in urban settings leading to higher incomes.\nBased on the above thought, the answer is:\nThe difference in average income between urban and rural areas as shown in the chart can be likely attributed to differing age demographics and access to economic opportunities. Urban areas have a younger population and potentially better access to higher-paying jobs and resources, whereas rural areas, with an older population, might face limitations on these fronts."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "rxweQR34MPjUVV9A",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in average income between Urban and Rural areas according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Utilizing the data plotted on the chart, I locate {the income feature at Urban}<r>[0.201, 0.390]</r> which shows an income of $45,000, and {the income feature at Rural}<r>[0.737, 0.680]</r> where it reads $35,000. Calculating the difference between these two values results in $45,000 - $35,000.\nBased on the above thought, the answer is:\n$10,000."
+            }
+        ],
+        "id_raw": 1167
+    },
+    {
+        "id": "o9r128KH1K6SHuQp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data showing different sustainable business indexes over five years into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the x-axis tick labels}<r>[0.071, 0.879, 0.127, 0.914], [0.172, 0.879, 0.230, 0.914], [0.238, 0.879, 0.303, 0.914], [0.312, 0.879, 0.381, 0.914], [0.401, 0.879, 0.471, 0.914]</r> indicates the categories like Water Conservation, Energy Efficiency, etc. I then identify {the year labels in legends}<r>[0.209, 0.734, 0.280, 0.849], [0.538, 0.734, 0.609, 0.849], [0.867, 0.734, 0.938, 0.849]</r> which represents the years 2016 through 2020 respectively. Data points are observed from {the lines representing different years}<r>[0.070, 0.306, 0.275, 0.619], [0.399, 0.271, 0.605, 0.627], [0.728, 0.271, 0.934, 0.627]</r> associating with each category and year.\nBased on the above thought, the answer is:\nCategory,2016,2017,2018,2019,2020\nWater Conservation,32,30.5,31,30,29.8\nEnergy Efficiency,45,47,46.5,48,48.5\nWaste Management,53,52,52.5,51,50.5\nClimate Change,60,62.5,65,67,70\nRenewable Energy,72.5,74,77,80,83"
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "3GGI66Mgdegmnlmy",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend between Energy Efficiency and Climate Change compare over the years based on the Sustainable Business Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the line trends for {Energy Efficiency}<r>[0.070, 0.519, 0.275, 0.491]</r> and {Climate Change}<r>[0.070, 0.403, 0.275, 0.325]</r> from the chart data, Energy Efficiency shows slight increases and maintains a relatively stable index, peaking in 2019. In contrast, Climate Change exhibits a clearer upward trend, increasing consistently from 2016, indicating a stronger focus and improvements relative to Energy Efficiency over the years.\nBased on the above thought, the answer is:\nThe trend for Climate Change is more positively progressive compared to Energy Efficiency over the years, with a clear upward trajectory and gradual improvement in its index whereas Energy Efficiency shows moderate fluctuations with minor improvements."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "75yodUG06CPwmfh2",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage increase in the Sustainable Business Index for Renewable Energy from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the percentage increase for Renewable Energy, I first locate the Sustainable Business Index values for 2016 and 2020 from {the line points for Renewable Energy}<r>[0.275, 0.306], [0.934, 0.225]</r> which are 72.5 in 2016 and 83 in 2020 respectively. Using the formula (New Value - Old Value) / Old Value * 100, we calculate (83 - 72.5) / 72.5 * 100.\nBased on the above thought, the answer is:\n14.48%."
+            }
+        ],
+        "id_raw": 1168
+    },
+    {
+        "id": "QkEqPSqGOJ6ML1Hz",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trends for 'Faith', 'Steadiness', and 'Honesty' across different moods into a structured table format using the line chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe {the line chart's x-axis labels}<r>[0.365, 0.919, 0.657, 0.942]</r> showcasing the moods: Rapid increase, Dramatic surge, Slow recovery, Steady decline, Slow expansion, and Rapid growth. I analyze {the line chart data points for 'Faith'}<r>[0.378, 0.162, 0.648, 0.262]</r>, {for 'Steadiness'}<r>[0.378, 0.237, 0.648, 0.287]</r>, and {for 'Honesty'}<r>[0.378, 0.312, 0.648, 0.337]</r>. Each point represents the score for the respective behavior trait under a given mood, which can be tabulated.\nBased on the above thought, the answer is:\nMood,Faith,Steadiness,Honesty\nRapid Increase,1.5,1.2,0.8\nDramatic Surge,1.8,1.6,0.4\nSlow Recovery,1.2,1.4,0.7\nSteady Decline,1.0,1.1,0.6\nSlow Expansion,1.2,1.3,0.8\nRapid Growth,1.5,1.4,0.9"
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "BTsEp1I7nBnSNZKh",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich behavioral trait shows the least variation in scores across all moods?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the level of fluctuation within the line plots for each behavioral trait {for 'Faith'}<r>[0.378, 0.162, 0.648, 0.262]</r>, {for 'Steadiness'}<r>[0.378, 0.237, 0.648, 0.287]</r>, and {for 'Honesty'}<r>[0.378, 0.312, 0.648, 0.337]</r>. 'Faith' started high, dipped, and rose, 'Steadiness' showed moderate changes, whereas 'Honesty' consistently declined or held stable values across the trends. The line plots suggest 'Honesty' had the least variation when compared across all moods.\nBased on the above thought, the answer is:\n'Honesty' shows the least variation in scores across all moods. It maintains relatively stable or slightly decreasing scores throughout, indicating lesser fluctuations compared to 'Faith' and 'Steadiness'."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "RLNE5TUyTJg3g8bH",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cumulative score increase for 'Faith' from 'Rapid Increase' to 'Rapid Growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the cumulative score increase for 'Faith', I inspect {the line points on the plot for 'Faith'}<r>[0.378, 0.162, 0.648, 0.262]</r> corresponding to the moods from 'Rapid Increase' to 'Rapid Growth'. The scores increase from 1.5 to 1.8 and finally to 1.5 again. The net change from the starting point to the endpoint is computed as 1.5 (end) - 1.5 (start) = 0.\nBased on the above thought, the answer is:\n0."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "FgfQh38IFCecVHrX",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data of series from January to December into a structured table with months as columns and series as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data into a table, I first identify {the x-axis labels}<r>[0.055, 0.093, 0.284, 0.866]</r> which represent the months from January to December. Next, I focus on {the legend}<r>[0.772, 0.111, 0.937, 0.226]</r> to determine the series names as rows. Observing each {line plot}<r>[0.724, 0.248, 0.932, 0.635]</r>, I sequentially capture the values for each series corresponding to each month to populate the table values.\nBased on the above thought, the answer is:\nMonth, Series A, Series B, Series C\nJanuary, 20, 85, 75\nFebruary, 25, 80, 80\nMarch, 30, 75, 85\nApril, 35, 70, 90\nMay, 40, 65, 95\nJune, 45, 60, 100\nJuly, 50, 55, 105\nAugust, 55, 50, 110\nSeptember, 60, 45, 115\nOctober, 65, 40, 120\nNovember, 80, 35, 125\nDecember, 85, 30, 130"
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "wBl1mLYVv0pl4tnS",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich month and series illustrate a sharp outlier and what might it indicate in the context of the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the dramatic surge in November for Series A, which I pinpoint on the chart at {November point for Series A}<r>[0.863, 0.209]</r>, it clearly stands out in comparison to the other months. This indicates a significant event or occurrence that affected Series A specifically during November, causing a notable increase in its values.\nBased on the above thought, the answer is:\nThe sharp outlier is observed in November for Series A. This could possibly indicate an external event or significant change specific to the context of Series A, leading to an unexpected and distinct rise during this month."
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "GkWSJXHBgPcNMQir",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in Series A from January to December?"
+            },
+            {
+                "from": "gpt",
+                "value": "Initially, I locate {the line points for Series A}<r>[0.724, 0.635, 0.932, 0.248]</r> and identify the y-values corresponding to January and December, which are 20 and 85 respectively. The calculation for percentage increase is ((85 - 20) / 20) * 100.\nBased on the above thought, the answer is:\n325%"
+            }
+        ],
+        "id_raw": 1171
+    },
+    {
+        "id": "YV8GeHYRkoWuwmSS",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how can the connectivity index data for social networks and interpersonal relationships be tabulated?"
+            },
+            {
+                "from": "gpt",
+                "value": "As I analyze the line chart, I first notice {the x-axis labels}<r>[0.671, 0.879, 0.776, 0.914]</r> and <r>[0.887, 0.879, 0.987, 0.914]</r>, indicating 'Social Networks' and 'Interpersonal Relationships' as columns. I then evaluate the y-values of the line points at {the corresponding x positions}<r>[0.724, 0.415]</r> and <r>[0.937, 0.287]</r>, indicating the connectivity indexes.\nBased on the above thought, the answer is:\nCategory,Connectivity Index\nSocial Networks,0.75\nInterpersonal Relationships,0.95"
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "w7e2JedgRwxbISLD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is the point associated with interpersonal relationships considered an outlier in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line progression from {the point for Social Networks}<r>[0.724, 0.415]</r> to {the point for Interpersonal Relationships}<r>[0.937, 0.287]</r>, there's a noticeable jump in the connectivity index, markedly higher than the gradual increase expected in the trend. Typically, an outlier in such a context is identified by a sudden and sizeable deviation from the general trend line, which is what is observed here.\nBased on the above thought, the answer is:\nThe point for interpersonal relationships is considered an outlier due to its significantly higher connectivity index that deviates sharply from the general increasing trend observed from social networks to interpersonal relationships, highlighting a sudden and considerable increase."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "KCQettXHW8ddyCux",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the connectivity index from social networks to interpersonal relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I first identify the connectivity index for social networks at {the point for Social Networks}<r>[0.724, 0.415]</r>, which is approximately 0.75. Then, I find the index for interpersonal relationships at {the point for Interpersonal Relationships}<r>[0.937, 0.287]</r>, estimated to be 0.95. The difference is thus 0.95 - 0.75.\nBased on the above thought, the answer is:\n0.2"
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "iyvjhyPHBYq7O8if",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compile the Market Style and Trend Level data from the line charts into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the table, I first look at {the x-axis labels for both charts}<r>[0.025, 0.879, 0.491, 0.914], [0.518, 0.879, 0.985, 0.914]</r>, which represent the Market Styles. Next, I locate the titles for Equity {Market charts}<r>[0.212, 0.037, 0.315, 0.083]</r> and Cryptocurrency {Market charts}<r>[0.674, 0.037, 0.840, 0.083]</r>. Trend levels are extracted from {line points of both charts}<r>[0.077, 0.171, 0.450, 0.635], [0.571, 0.132, 0.944, 0.519]</r>.\nBased on the above thought, the answer is:\nMarket Style,Equity Markets,Cryptocurrency Markets\nUpward trajectory,1.8,0.9\nAbrupt decline,1.1,1.3\nIncremental growth,1.0,1.6\nErratic growth,0.6,1.9"
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "4IZoC3Xs3eXYzWbF",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart, which market seems to have a more stable trend from 'Upward trajectory' to 'Erratic growth'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the more stable market, I will need to compare the line plots from 'Upward trajectory' to 'Erratic growth' for both markets. For {Equity Markets}<r>[0.077, 0.171, 0.450, 0.635]</r>, the trend moves from 1.8 to 1.1 to 1.0 to 0.6. For {Cryptocurrency Markets}<r>[0.571, 0.132, 0.944, 0.519]</r>, the trend moves from 0.9 to 1.3 to 1.6 to 1.9. The trend for Equity Markets demonstrates a decline whereas the trend for Cryptocurrency Markets demonstrates an increase.\nBased on the above thought, the answer is:\nFrom the chart, Equity Markets seem to have a more stable trend from 'Upward trajectory' to 'Erratic growth'. This market shows a general downward trend, with smaller fluctuations in trend levels compared to the Cryptocurrency Markets, which exhibits a sharper increase. Thus, despite the trend decline, Equity Markets are more stable in the context of this comparison."
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "o6Up4mK2QZvEb1Ct",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the difference in trend levels between 'Upward trajectory' and 'Erratic growth' for Equity Markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in trend levels between 'Upward trajectory' and 'Erratic growth' for Equity Markets, I will locate {the corresponding points for 'Upward trajectory'}<r>[0.077, 0.171]</r> and for {'Erratic growth'}<r>[0.450, 0.635]</r> in the Equity Markets line plot. The trend level for 'Upward trajectory' is 1.8 and for 'Erratic growth' is 0.6. The difference can be calculated by subtracting the two: 1.8 - 0.6 = 1.2.\nBased on the above thought, the answer is:\n1.2"
+            }
+        ],
+        "id_raw": 1174
+    },
+    {
+        "id": "jzRWQhxY5uZs8y6J",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured data table showing the trends for AI Consumption and Human Interaction across the future living scenarios indicated in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll locate the x-axis labels representing the future living scenarios, which include Habitual Living, Smart City, Personalized Services, Automated Infrastructure, and Sustainable Living. By analyzing the line graphs, I identify two distinct lines: the first line corresponds to AI Consumption and the second to Human Interaction. For each scenario, I extract the data points by pinpointing {the markers and lines for AI Consumption and Human Interaction}<r>[0.047, 0.093, 0.328, 0.866]</r> on the chart.\nBased on the above thought, the answer is:\nScenario, AI Consumption, Human Interaction\nHabitual Living, 24, 30\nSmart City, 50, 70\nPersonalized Services, 46, 55\nAutomated Infrastructure, 60, 62\nSustainable Living, 74, 70"
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "j2FPpnAigUtgS3nQ",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy do we observe a significant spike in Human Interaction in the Smart City scenario, as seen on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the reason behind the spike in Human Interaction in the Smart City scenario, I analyze {the trend observed in the line representing Human Interaction}<r>[0.047, 0.093, 0.328, 0.866]</r>. This line shows a significant spike when shifting from Habitual Living to Smart City. Typically, this suggests a major change or improvement in society\u2019s structure or technology, whereby Smart Cities, known for their integration of ICT to improve citizen welfare and city functions, greatly enhance human connectivity and interaction. The substantial difference seen on the chart signifies how Smart City infrastructure could be improving quality of life by increasing human interaction.\nBased on the above thought, the answer is:\nThe significant spike in Human Interaction in the Smart City scenario is likely indicative of the enhanced social connectivity and quality of life improvements associated with Smart City infrastructure. These urban areas often use advanced technology to foster greater human engagement and efficient urban management, thus explaining the observed spike."
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "YCdBvhM70xWzXK9i",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in AI Consumption between the Habitual Living and Smart City scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to compare the AI Consumption values in both scenarios. First, I locate {the points corresponding to AI Consumption in Habitual Living}<r>[0.060, 0.866]</r> and {Smart City}<r>[0.124, 0.866]</r>. The values are 24 for Habitual Living and 50 for Smart City. Then, I simply subtract the former value from the latter to find the difference.\nBased on the above thought, the answer is:\n26"
+            }
+        ],
+        "id_raw": 1176
+    },
+    {
+        "id": "1yrjfPschjNul5Dg",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table listing the utilization percentages of Telemedicine and Wearables technologies for both genders?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table listing the utilization percentages of Telemedicine and Wearables, I locate {the x-axis tick labels}<r>[0.08, 0.12, 0.152, 0.18]</r> and find two entries labeled as Telemedicine and Wearables. I then note {the line plot for wearables}<r>[0.15, 0.4, 0.85, 0.6]</r> and {the line plot for telemedicine}<r>[0.15, 0.2, 0.85, 0.4]</r>, examining the points along these lines corresponding to each gender.\nBased on the above thought, the answer is:\nTechnology, Men, Women\nTelemedicine, 68%, 66%\nWearables, 47%, 50%"
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "glzYHlI3Z9mKRWnz",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trend in Remote Patient Monitoring (RPM) usage change between genders, and what might this indicate about gender preferences?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trend line for RPM, labeled specifically for men, yields {the points along the line for men}<r>[0.65, 0.75]</r> showing a higher and declining progression. Meanwhile, {the points for women}<r>[0.65, 0.7]</r> reflect a milder and increasing tendency in the same category. This indicates that men's usage of RPM is initially high but declines over time, possibly due to saturation or changing interests, while women display a growing interest in RPM services.\nBased on the above thought, the answer is:\nThe RPM usage trend indicates a decline for men, possibly implying saturation or shifting preferences, and an increase for women, suggesting a rising interest or recognition of its benefits."
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "gc4S5CWGu3Se6hcg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the usage percentage of Telehealth services between men and women?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the usage percentage of Telehealth services between men and women, I examine {the point on the line plot corresponding to men}<r>[0.25, 0.4]</r> and {the point on the line plot corresponding to women}<r>[0.25, 0.35]</r>. I observe that men have a usage percentage annotated near these coordinates for Telehealth.\nBased on the above thought, the answer is:\n3%"
+            }
+        ],
+        "id_raw": 1177
+    },
+    {
+        "id": "YhwjnJJGwuGubjnU",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you compile the AI Consumption and Human Interaction data into a table format with Scenarios as rows and their respective scenarios values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table from the chart, I first look at {the x-axis tick labels}<r>[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896]</r> which represent the columns or Scenarios. I then identify {the legend texts}<r>[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156]</r> as the row labels representing AI Consumption and Human Interaction. Then, I extract the values associated with each point on the graph for these scenarios and their respective attributes.\nBased on the above thought, the answer is:\nScenario,AI Consumption,Human Interaction\nHabitual Living,0.6,0.4\nSmart City,0.92,0.85\nPersonalized Services,0.45,0.75\nAutomated Infrastructure,0.65,0.5\nSustainable Living,0.85,0.7"
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "8uklJcR6hxX5hklr",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the Human Interaction trend show a spike in the Smart City scenario based on the graph data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the line graph for Human Interaction, I notice the significant spike at the Smart City scenario, depicted by {the peak point for Human Interaction in the Smart City scenario}<r>[0.207, 0.010]</r>. This suggests a substantial increase in human interaction within this scenario, possibly due to the implementation of smart technologies that enhance and require human involvement.\nBased on the above thought, the answer is:\nThe spike in the Human Interaction trend during the Smart City scenario suggests a strong emphasis on human-centric designs in technology implementation in urban environments. It indicates an increase in activities that involve human interaction, reflecting the adoption of smart technology solutions aimed at improving life quality and efficiency in densely populated areas."
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "dM3RbzeJfkaW4h0c",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_59_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in AI Consumption between Habitual Living and Sustainable Living scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference, I locate {the points for Habitual Living and Sustainable Living under AI Consumption}<r>[0.046, 0.403], [0.798, 0.155]</r> to find their values as 0.6 and 0.85, respectively. The difference in AI Consumption between these scenarios is 0.85 - 0.6.\nBased on the above thought, the answer is:\n0.25"
+            }
+        ],
+        "id_raw": 1178
+    },
+    {
+        "id": "0u1YptMyyhn2aXN1",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line chart into a structured table with months as columns and Value Types as rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "To recreate the line chart data into a table, first I examine {the x-axis tick labels}<r>[0.198, 0.919, 0.792, 0.942]</r> that correspond to the months columns. Then I identify the {legend}[0.684, 0.037, 0.973, 0.113] as the row labels representing different types of values. To fill in the table, I extract the corresponding values from {line points for Tech Company Value}<r>[0.123, 0.180, 0.940, 0.871]</r> and {line points for Innovative Business Value}<r>[0.123, 0.065, 0.940, 0.295]</r>.\nBased on the above thought, the answer is:\nMonth,Tech Company Value,Innovative Business Value\n1,100,150\n2,105,155\n3,110,150\n4,115,157\n5,120,160\n6,125,167\n7,130,170\n8,140,150\n9,145,155\n10,150,152\n11,155,158\n12,160,160"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "1URO1nerMObZlxOB",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the growth trend of each value type in the chart and compare them?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the growth trend, I need to examine the slopes of the line charts for each value type. The {Tech Company Value line}<r>[0.123, 0.180, 0.940, 0.871]</r> exhibits a steady, incremental upward trend across the months, indicating consistent growth.\nThe {Innovative Business Value line}<r>[0.123, 0.065, 0.940, 0.295]</r> shows a steeper initial increase in value, levels off and then accelerates again around the eighth month. This may suggest a more volatile growth pattern. In comparison to the Tech Company Value, the Innovative Business Value starts higher, experiences more fluctuations, but the overall trend is also upward.\nBased on the above thought, the answer is:\nThe Tech Company Value shows a steady incremental growth throughout the year, indicating a relatively stable rate of value increase. On the other hand, the Innovative Business Value shows a more varied trend. It starts with a more significant value, then levels off to a slower growth rate before accelerating again. While both trends show an overall increase in value over time, the Innovative Business Value has more pronounced fluctuations suggesting a higher degree of volatility in its growth rate."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "tz0pM13qfSJZeLfr",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many billions in value did the Innovative Business gain from the first to the sixth month?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the gain in value, I need to identify {the starting value point at the first month for the Innovative Business}<r>[0.123, 0.295]</r> which corresponds to 150 billion, and {the ending value point at the sixth month for the Innovative Business}<r>[0.495, 0.100]</r> which corresponds to 167 billion. The difference between the two values is therefore 167 - 150 = 17 billion.\nBased on the above thought, the answer is:\n17."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "M7f1LhvFcQlyUZjv",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the line chart data into a structured table with years acting as rows and categories (Training Hours and Job Positions Created) as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, first I identify {the x-axis ticks}<r>[0.116, 0.893, 0.944, 0.893]</r> representing the years. Now I'll locate {the legend texts}<r>[0.126, 0.055, 0.273, 0.125]</r> for 'Training Hours' and 'Job Positions Created' categories. Then, I analyze coordinates of {the line points for Training Hours}<r>[0.116, 0.069, 0.944, 0.527]</r> and {the line points for Job Positions Created}<r>[0.116, 0.592, 0.944, 0.854]</r>. Each point on these lines corresponds to the number of training hours and job positions created respectively for each year.\nBased on the above thought, the answer is:\nYear,Training_Hours,Job_Positions_Created\n2008,1000,500\n2009,1200,600\n2010,1400,700\n2011,1600,800\n2012,1500,750\n2013,1300,700\n2014,1400,700\n2015,1500,800\n2016,1600,850\n2017,1450,780\n2018,1530,820\n2019,1650,860\n2020,1700,900"
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "1xPAvydvPEzLLYr5",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you identify any noticeable trends in Training Hours and Job Positions Created over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine any notable trends, I need to trace the change in line plots over time. Looking at {the line for Training Hours}<r>[0.116, 0.069, 0.944, 0.527]</r>, I see an overall increase in training hours from 2008 to 2012, then a slight dip in 2013, another peak in 2016, another dip in 2017, and then a gradual increase through to 2020. This suggests a recurring pattern of increase and decrease in training hours, with a net increase over the years.\nLooking at {the line for Job Positions Created}<r>[0.116, 0.592, 0.944, 0.854]</r>, I observe a steady increase from 2008 to 2011, then a mild dip till 2013, then somewhat stable at 700 through 2014, and a steady increase again from 2015 to 2020. This indicates a positive trend for job positions over these years despite some fluctuations.\nBased on the above thought, the answer is:\nThe line chart shows an overall increasing trend in both Training Hours and Job Positions Created from 2008 to 2020, albeit with some fluctuations. The Training Hours show a repeating pattern of increase then decrease, suggesting cyclic changes in training needs or resource allocation. Job Positions Created, on the other hand, exhibits a more steady growth with slight stability during 2014 and starts to increase again onwards, indicating an overall positive expansion of job opportunities over the years."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "eWdOJ67VzJ7j4eY7",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Training Hours increase from 2008 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Training Hours from 2008 to 2020, I need to locate the {line points for Training Hours}<r>[0.116, 0.069, 0.944, 0.527]</r> and get the values for both years. The value for 2008 is 1000 and for 2020 it is 1700. The difference between the two is therefore 1700 - 1000 = 700.\nBased on the above thought, the answer is:\n700."
+            }
+        ],
+        "id_raw": 1181
+    },
+    {
+        "id": "EASi18ymDOWNllCh",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the data in the chart into a CSV table where the years are columns and the indexes are rows?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, first I'll identify the columns of the data table, which are the years that can be seen on {the chart's x-axis tick labels}<r>[0.206, 0.869, 0.276, 0.914]</r>. The rows correspond to the two types of indexes shown in the chart: {Quality of Life Index}<r>[0.204, 0.086, 0.437, 0.121]</r> and {House Size Index}<r>[0.204, 0.139, 0.402, 0.174]</r>. Their respective data points can be found by following their lines on the chart <r>[0.241, 0.524, 0.853, 0.685]</r> and <r>[0.241, 0.135, 0.853, 0.398]</r>.\nBased on the above thought, the answer is:\n,2000,2005,2010,2015,2020,2025,2030,2035,2040,2045,2050\nQuality of Life Index,70,72,75,77,80,85,87,90,92,95,98\nHouse Size Index,120,125,130,136,141,146,150,154,158,162,166"
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "W4CgJbhtKYIbROse",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index, Quality of Life or House Size, has seen a greater rate of change from 2000 to 2050, based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the change in each index from 2000 to 2050. Looking at {the data points for the Quality of Life Index}<r>[0.241, 0.685, 0.853, 0.524]</r>, in 2000 the value was 70 and in 2050 the value was 98, for a change of 28 points. For {the House Size Index}<r>[0.241, 0.398, 0.853, 0.135]</r>, the value in 2000 was 120 and in 2050 was 166, for a change of 46 points. Thus, the House Size Index has seen a larger rate of change.\nBased on the above thought, the answer is:\nThe House Size Index has seen a greater rate of change from 2000 to 2050. It increased by 46 points, compared to a 28-point increase in the Quality of Life Index."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "Db9Y4QdGLz6WSGah",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the House Size Index between 2000 and 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the House Size Index between 2000 and 2050, I need to examine {the data points for House Size Index}<r>[0.241, 0.398, 0.853, 0.135]</r>. Specifically, I'll compare {the 2000 point}<r>[0.241, 0.398]</r> (120) and {the 2050 point}<r>[0.853, 0.135]</r>(166). The difference is then 166 - 120 = 46.\nBased on the above thought, the answer is:\n46."
+            }
+        ],
+        "id_raw": 1183
+    },
+    {
+        "id": "UiZmZg0YOSvbT1Dx",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, can you convert the data into a table format where the columns represent the Startup Phases and the rows represent the different type of Ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the line chart, I will start by identifying {the x-axis labels}[[0.197, 0.869, 0.297, 0.914], [0.303, 0.869, 0.482, 0.914], [0.404, 0.869, 0.672, 0.914], [0.612, 0.869, 0.755, 0.914], [0.781, 0.869, 0.878, 0.914]] which stand for the Startup Phases. I will then identify {the legend}[[0.752, 0.738, 0.913, 0.773], [0.752, 0.790, 0.954, 0.825]], as the row labels representing the Given Ratings and Expected Ratings. To populate the values, I trace the {points for Given Ratings}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and {points for Expected Ratings}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]] for every phase and then map these coordinates to the corresponding numeric values on the y-axis.\nBased on the above thought, the answer is:\nRatings,Launch,Early Growth,Established Growth,Expansion,Mature\nGiven Ratings,75,80,65,85,70\nExpected Ratings,70,75,70,80,65"
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "cnZiHJX3DhvfCEix",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on trends in the line chart, can you analyze the correlation between the Given Ratings and Expected Ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the correlation, I must scrutinize the line plots for both {Given Ratings line}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and {Expected Ratings line}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]]. From an initial glance, a correlation seems to present - when the Given Ratings increase, the Expected Ratings also tend to increase, and when the Given Ratings decrease, the Expected Ratings also exhibit a decline.\nBased on the above thought, the answer is:\nThere appears to be a positive correlation between the Given Ratings and the Expected Ratings. This trends suggests that higher Given Ratings correspond to higher Expected Ratings, and conversely, lower Given Ratings associate with lower Expected Ratings. It indicates that the expectations are following very closely with the given ratings in each phase of the startup."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "h8zosiBfhY3oSRsy",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the average innovation score across all startup phases for both Given Ratings and Expected Ratings?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average innovation score, we would locate the {points on Given Ratings line}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and extract the corresponding values which are 75, 80, 65, 85, 70. The average can be calculated as (75+80+65+85+70)/5 = 75. The same needs to be done for {points on Expected Ratings line}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]] which are 70, 75, 70, 80, 65. The average in this case will be (70+75+70+80+65)/5 = 72.\nBased on the above thought, the answer is:\nFor Given Ratings, the average is 75 and for Expected Ratings, the average is 72."
+            }
+        ],
+        "id_raw": 1184
+    },
+    {
+        "id": "zxWuLcSdgzpDLLgQ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the line graphs on the chart representing each workout trend, could you please generate a table where rows indicate the specific workout activity and columns represent each season of the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To commence the process of creating this table from the line graphs, I initially locate {the x-axis tick labels}[ [0.046, 0.919, 0.068, 0.942], [0.128, 0.919, 0.152, 0.942], [0.210, 0.919, 0.237, 0.942], [0.294, 0.919, 0.319, 0.942]]. Here, the labels represent each season: Spring, Summer, Autumn, and Winter.\nNext, I find {the legend texts}[[0.108, 0.046, 0.226, 0.069], [0.108, 0.081, 0.298, 0.104], [0.108, 0.116, 0.288, 0.139]], which are used as row labels for Singer Yoga, Zumba Classes, HIIT Sessions, Pilates Workouts and Morning Runs. \nIn order to populate the table, I identify specific {line points} such as [ [0.057, 0.126], [0.140, 0.379], [0.223, 0.379], [0.306, 0.468]], to extract the respective values associated with each form of workout for each season across the 4 line charts.\nBased on the above thought, the answer is:\nWorkout, Spring, Summer, Autumn, Winter\nSinger Yoga, 10, 20, 15, 10\nZumba Classes, 15, 18, 16, 12\nHIIT Sessions, 18, 30, 25, 15\nPilates Workouts, 10, 15, 30, 20\nMorning Runs, 20, 30, 24, 10"
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "yp5z5WFQ8CzlzDPN",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the line charts, which workout activity demonstrates a trend that deviates from the general pattern?"
+            },
+            {
+                "from": "gpt",
+                "value": "The general pattern in the data indicates that workout interests peak in the summer season and decrease during the winter season. To identify deviations from this pattern, I examine each workout trend line. The activity that shows a significant spike in autumn, deviating from the typical pattern, is {Pilates Workouts}[0.390, 0.443, 0.722, 0.215]. While other workout routines decrease in popularity as it gets colder, Pilates Workouts peak during autumn, which is unusual and contradicts the common trend.\nBased on the above thought, the answer is:\nThe trend of Pilates Workouts deviates from the general pattern according to the line charts. While other workout routines follow a trend of peaking in the summer season and declining during the winter season, Pilates Workouts peaks in popularity during the autumn season, defying the common pattern."
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "GIMkmrcVgqy1PeNg",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in the trend of Pilates Workouts from Spring to Winter?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall increase in the trend of Pilates workouts, I must identify the values of Pilates workouts at Spring and Winter. Looking at the {line points for Pilates Workout}[ [0.057, 0.379], [0.972, 0.379]], I extract the values at Spring and Winter: 10 and 20 respectively. The overall increase is calculated by subtraction, which is 20 - 10 = 10.\nBased on the above thought, the answer is:\n10"
+            }
+        ],
+        "id_raw": 1185
+    },
+    {
+        "id": "r2El7dTaFbffHzyt",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the line chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the line chart into a structured table format, I need to first identify {the x-axis tick labels}[[0.117, 0.919, 0.152, 0.942], [0.194, 0.919, 0.229, 0.942],..., [0.890, 0.919, 0.925, 0.942]], which will serve as the row labels or 'Years'. I then look at {the legend texts}[[0.759, 0.046, 0.973, 0.069],[0.759, 0.081, 0.791, 0.104]], which represent the column headers 'High Intensity Interval Training' and 'Yoga'. To populate the values, I locate {the line points for High Intensity Interval Training}[[0.134, 0.701], [0.212, 0.687],..., [0.908, 0.533]] and {the line points for Yoga}[[0.134, 0.323], [0.212, 0.309],..., [0.908, 0.057]] at each respective year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,High Intensity Interval Training,Yoga\n2000,15,42\n2002,16,43\n2004,18,47\n2006,20,51\n2008,22,55\n2010,23,56\n2012,21,53\n2014,20,52\n2016,24,58\n2018,26,60\n2020,27,61"
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "4NQWcN8qgzdAL6p9",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in the line chart, which exercise trend has seen a consistent increase over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the line plots of both exercise trends, it can be noted that while {High Intensity Interval Training line}[0.134, 0.533, 0.908, 0.701] shows fluctuation over the years, {the line for Yoga}[0.134, 0.057, 0.908, 0.323] shows a steady increase from the year 2000 to the year 2020 without any decline. This is an important comparison, thus indicating that the exercise trend that has seen a consistent increase over the years is Yoga.\nBased on the above thought, the answer is:\nThe exercise trend that has seen a consistent increase over the years according to the line chart is Yoga."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "WhIBVuHjRd7zQX6I",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in people doing High Intensity Interval Training from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase of people doing High Intensity Interval Training from 2000 to 2020, I need to look at {the value for High Intensity Interval Training in 2000} at [0.134, 0.701] and {the value for High Intensity Interval Training in 2020} at [0.908, 0.533], indicated as 15 and 27 respectively. The percentage increase is then calculated by subtracting the initial value from the final value, dividing by the initial value, and then multiplying by 100. So, (27 - 15) / 15 * 100 = 80.\nBased on the above thought, the answer is:\n80%."
+            }
+        ],
+        "id_raw": 1186
+    },
+    {
+        "id": "Mz8d85mz6iz0Sdvp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table that shows the Hospital Admission Rate and Discharge Rate for each Age Group?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that the {x-axis tick labels}<r>[0.102, 0.879, 0.168, 0.914],[0.216, 0.879, 0.283, 0.914],[0.330, 0.879, 0.397, 0.914],[0.445, 0.879, 0.511, 0.914],[0.559, 0.879, 0.626, 0.914],[0.673, 0.879, 0.740, 0.914],[0.787, 0.879, 0.854, 0.914],[0.911, 0.879, 0.959, 0.914]</r> represent the Age Groups. The data on Hospital Admission Rate and Discharge Rate can be found on the plotted {lines for Hospital Admission Rate and Discharge Rate}<r>[0.135, 0.075, 0.935, 0.829],[0.135, 0.183, 0.935, 0.613]</r>.\nBased on the above thought, the answer is:\n\"Age Group\", \"Hospital Admission Rate\", \"Discharge Rate\"\n\"20-30\", 80, 70\n\"30-40\", 90, 86\n\"40-50\", 70, 85\n\"50-60\", 70, 75\n\"60-70\", 60, 80\n\"70-80\", 75, 74\n\"80-90\", 85, 88\n\"90+\", 95, 90"
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "J6RTFIqUJbNItXGY",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, do Hospital Admission Rates generally match the Discharge Rates across Age Groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the {line for Hospital Admission Rate}<r>[0.135, 0.075, 0.935, 0.829]</r> and the {line for Discharge Rate}<r>[0.135, 0.183, 0.935, 0.613]</r> across all Age Groups represented by {x-tick labels}<r>[0.102, 0.879, 0.168, 0.914],[0.216, 0.879, 0.283, 0.914],[0.330, 0.879, 0.397, 0.914],[0.445, 0.879, 0.511, 0.914],[0.559, 0.879, 0.626, 0.914],[0.673, 0.879, 0.740, 0.914],[0.787, 0.879, 0.854, 0.914],[0.911, 0.879, 0.959, 0.914]</r>. It's noticeable that for some Age Groups the Admission and Discharge Rates are similar, while for others they differ significantly.\nBased on the above thought, the answer is:\nIt seems that Hospital Admission Rates and Discharge Rates do not generally match across Age Groups. For Age Group 20-30, the Admission Rate is higher than the Discharge Rate, while for Age Group 30-40, the pattern is reversed. This trend is alternating in different Age Groups, suggesting a fluctuation rather than a consistent match between Admission and Discharge Rates."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "D12AgpzsDNpyIctX",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the Discharge Rate for the 50-60 Age Group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the Discharge Rate for the 50-60 Age Group, I need to look at the {Discharge Rate line}<r>[0.135, 0.183, 0.935, 0.613]</r> and locate the point corresponding to the Age Group 50-60, which is one of the {x-tick labels}<r>[0.445, 0.879, 0.511, 0.914]</r>.\nBased on the above thought, the answer is:\n75"
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "ZGMjkIFt9T1t4tGi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please provide the underlying data for the line chart in a tabular form where the columns correspond to 'Time Interval' and the rows represent 'Temperature Avg' and 'Sales Avg'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To organize the provided data, I'll consider {the x-axis tick labels}[[0.267, 0.869, 0.285, 0.914], [0.337, 0.869, 0.355, 0.914], [0.398, 0.869, 0.433, 0.914], [0.468, 0.869, 0.503, 0.914], [0.538, 0.869, 0.573, 0.914], [0.608, 0.869, 0.643, 0.914], [0.678, 0.869, 0.713, 0.914], [0.748, 0.869, 0.783, 0.914], [0.818, 0.869, 0.853, 0.914]] as defining the time intervals, which would be the columns of the table. Then, looking at the points on the 'Temperature Avg' line {line for Temperature Avg}[[0.276, 0.798, 0.835, 0.819]] and 'Sales Avg' line {line for Sales Avg}[[0.276, 0.075, 0.835, 0.691]], I'll extract the y-values. These y-values, corresponding to each time interval, will form the rows of the table.\nBased on the above thought, the answer is:\nTime_Interval, Temperature_Avg, Sales_Avg\n0, 10, 250\n5, 15, 350\n10, 20, 450\n15, 25, 600\n20, 30, 700\n25, 40, 1200\n30, 35, 800\n35, 45, 1300\n40, 50, 1400"
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "rmjW4TwYwTgrnpmk",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line plots in the chart, are there any time intervals where the temperature and sales averages increase simultaneously? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze {the Temperature Avg line}[[0.276, 0.798, 0.835, 0.819]] and {the Sales Avg line}[[0.276, 0.075, 0.835, 0.691]]. On observing, it's clear that from the time intervals of 0 to 20, both the temperature and sales averages display an almost parallel upward trend. Therefore, these are the time intervals where both averages increase simultaneously.\nBased on the above thought, the answer is:\nYes, based on the line plots, there is an increase in both averages (temperature and sales) from the time intervals of 0 to 20. The lines trend upwards in a nearly parallel fashion, indicating that both the temperature and sales averages rise together during this period."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "UUfNo4C50ypHtghN",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest Sales Avg achieved and at which time interval did it occur according to the line chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest Sales Avg, I need to locate the line points of {the Sales Avg line}[[0.276, 0.075, 0.835, 0.691]] in order to identify the point with the highest y-value. Extracting the y-values from these points, I find that the value is highest (at 1400) for the point corresponding to {the time interval of 40} [0.818, 0.869, 0.853, 0.914].\nBased on the above thought, the answer is:\nThe Highest Sales Avg achieved was 1400 at the time interval of 40 months."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "uNNvDhnboRjZaCmp",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the information represented in the line graph about the estimated average annual household income into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I identify {the x-axis labels}[[0.167, 0.869, 0.184, 0.914], [0.224, 0.869, 0.242, 0.914], [0.281, 0.869, 0.299, 0.914], [0.338, 0.869, 0.356, 0.914], [0.395, 0.869, 0.413, 0.914], [0.452, 0.869, 0.470, 0.914], [0.510, 0.869, 0.527, 0.914], [0.567, 0.869, 0.584, 0.914], [0.624, 0.869, 0.641, 0.914], [0.681, 0.869, 0.698, 0.914], [0.729, 0.869, 0.764, 0.914], [0.786, 0.869, 0.821, 0.914], [0.843, 0.869, 0.879, 0.914], [0.900, 0.869, 0.936, 0.914]] which represent the age groups. The single line in the line chart represents the estimated average annual household income and hence to populate the values, I look at each of {the line's points}[[0.176, 0.095], [0.233, 0.827], [0.290, 0.722], [0.347, 0.317], [0.404, 0.445], [0.461, 0.383], [0.518, 0.275], [0.575, 0.529], [0.632, 0.318], [0.690, 0.095], [0.747, 0.262], [0.804, 0.252], [0.861, 0.304]].\nBased on the above thought, the answer is:\nAge Group, Estimated Average Annual Household Income ($)\nUnder 5, 16595\n5-9, 16703\n10-14, 15768\n15-19, 16504\n20-24, 16130\n25-34, 16342\n35-44, 16202\n45-54, 16859\n55-64, 16542\n65 and older, 16241"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "PW6CTnpmYEpAb8R1",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data represented in the line graph, which age group is observed to have the greatest increase in estimated average annual household income compared to the previous age group and why might that be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the age group with the greatest increase, I need to look at each point from {the line}[[0.176, 0.095, 0.861, 0.304]], compare it with the previous point, and identify where the largest increase occurs. Here, the largest increase is from '35-44' with a value of 16202 at {the point for '35-44'}[0.518, 0.275], to '45-54' with a value of 16859 at {the point for '45-54'}[0.575, 0.529].The probable cause of such an increase in income could be increased work experience and career growth, as this period typically represents a person's peak earning years.\nBased on the above thought, the answer is:\nThe largest increase in estimated average annual household income is observed in the age group '45-54' compared to the previous age group '35-44'. This might be attributed to the fact that individuals within the '45-54' group are typically at the peak of their careers with an increase in their experience and professional skills; hence, being rewarded with higher salaries or income."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "BwZSr3ukhra01eMl",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in estimated average household income between the age groups 'Under 5' and '65 and older'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the income difference between the 'Under 5' and '65 and older' age groups, I need to extract their respective estimated average household incomes. The point corresponding to 'Under 5' can be located at {the point for 'Under 5'}[0.176, 0.095] and its value is 16595. The point corresponding to '65 and older' can be located at {the point for '65 and older'}[0.861, 0.304] and its value is 16241. The difference between these two incomes is 16595 - 16241 = 354.\nBased on the above thought, the answer is:\n354"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "R9HXHtMYacPN8ddi",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the line chart, please extract the impact values for each educational field into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract data from the line chart to a table, first I identify the {x-axis labels}[{'label': 'AI', 'label_coordinates': '[0.165, 0.869, 0.202, 0.914]'}, {'label': 'Data Science', 'label_coordinates': '[0.251, 0.869, 0.316, 0.914]'}, {'label': 'Cloud Computing', 'label_coordinates': '[0.344, 0.869, 0.445, 0.914]'}, {'label': 'Cyber Security', 'label_coordinates': '[0.439, 0.869, 0.553, 0.914]'}, {'label': 'Robotics', 'label_coordinates': '[0.533, 0.869, 0.594, 0.914]'}, {'label': 'Quantum Computing', 'label_coordinates': '[0.628, 0.869, 0.753, 0.914]'}, {'label': 'Natural Language Processing', 'label_coordinates': '[0.817, 0.869, 0.983, 0.914]'}] which represents the fields of research. Next, Locate the line points [0.179, 0.410, 0.798, 0.632], [0.179, 0.510, 0.798, 0.662], and so on each representing a value of the impact level for that particular field.\nBased on the above thought, the answer is:\nFields, Impact\nArtificial Intelligence, 0.90\nData Science, 0.80\nCloud Computing, 0.70\nCyber Security, 0.55\nRobotics, 0.85\nQuantum Computing, 0.45\nNatural Language Processing, 0.35"
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "y6CgUiqZWA8wqfBK",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field of study seems to be most impacted in the tech industry and why might that be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the {line points}[0.179, 0.410, 0.798, 0.632] for each field and find the one with the highest impact value.  {AI}[0.179, 0.410] seems to have the highest value indicating the highest impact. It is important to note that AI has been increasingly incorporated into many aspects of technology, from predictive analytics to automation, which could explain its high impact level.\nBased on the above thought, the answer is:\nAccording to the chart, the field of Artificial Intelligence seems to have the highest impact in the tech industry. This could be due to the increasing utilization of AI in various aspects of technology, including but not limited to, predictive analytics, automation, and machine learning which are critical components in today's technology-driven industries."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "ZAH8YrdMwgjXnC9o",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in impact value between the highest and lowest performing fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the differences between the highest and lowest impact, I first identify the {line point for AI}[0.179, 0.410], which gives the highest value of impact, and the {line point for Natural Language Processing}[0.798, 0.632] which gives the lowest value of impact. The difference is therefore 0.90 - 0.35.\nBased on the above thought, the answer is:\n0.55"
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "IXguj1IdvdJusbvs",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you create a table of the Number of Social Connections for Friends and Followers, by Year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the line chart into table format, I start by recognizing {the x-axis tick labels}[[0.083, 0.869, 0.154, 0.914], [0.190, 0.869, 0.261, 0.914], ..., [0.868, 0.856]] as the years. Next, I identify {the legend texts}[[0.756, 0.685, 0.839, 0.720], [0.756, 0.738, 0.865, 0.773]] as the categories representing Friends and Followers. For each category, I locate {the line points}[[0.118, 0.645], [0.225, 0.574], ..., [0.975, 0.292]], [[0.118, 0.461], [0.225, 0.363], ..., [0.975, 0.066]] corresponding to the respective years and extract the values.\nBased on the above thought, the answer is:\nYear,Friends,Followers\n2012,150,280\n2013,200,350\n2014,230,400\n2015,250,410\n2016,240,420\n2017,259,445\n2018,300,505\n2019,330,515\n2020,400,560"
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "7UpNDgIBa41X7sVD",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, who saw a more significant increase in social connections over the years? How can you tell?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish who has seen a more significant increase in social connections over the years, I need to compare {the line for Friends}[0.118, 0.292, 0.975, 0.645] with {the line for Followers}[0.118, 0.066, 0.975, 0.461]. Friends had an increase from 150 to 400 connections. Followers also had an increase, but from a higher starting point of 280 to a higher end point of 560. Therefore, considering the start and end points, Followers saw a greater increase in social connections over the analyzed period.\nBased on the above thought, the answer is:\nIt is the Followers category that has seen a more significant increase in social connections over the years. From 2012 to 2020, the number of Followers has considerably grown from 280 to 560, a rise substantially higher than that of the Friends category, as seen from the chart."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "eE3jtmboqDFkzf9a",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the period with the highest increase in the number of Friends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the period with the highest increase in the number of Friends, I analyze {the line points for Friends}[[0.118, 0.645], [0.225, 0.574], ..., [0.975, 0.292]] and the corresponding values: 150, 200, 230, 250, 240, 259, 300, 330, 400. Here, the increase from 2019 to 2020 from 330 to 400 represents the most significant jump, looking at the data. So, this is the period with the highest increase in number of friends.\nBased on the above thought, the answer is:\n2019 to 2020."
+            }
+        ],
+        "id_raw": 1192
+    },
+    {
+        "id": "jDjE35SBcqBF033N",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract all the depicted data from the chart and present it in a structured table format, with 'Trends' as the rows and 'Financial Management Aspects' as the columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To produce the tabular data, I first identify {the x-axis tick labels}[0.200, 0.869, 0.271, 0.914], [0.283, 0.869, 0.353, 0.914], [0.365, 0.869, 0.435, 0.914], [0.447, 0.869, 0.517, 0.914], [0.529, 0.869, 0.600, 0.914], [0.611, 0.869, 0.682, 0.914], [0.693, 0.869, 0.764, 0.914], [0.776, 0.869, 0.846, 0.914]], demonstrating the column labels or years. Then, I observe {the legend texts}[0.239, 0.069, 0.365, 0.104], [0.239, 0.121, 0.352, 0.156], [0.239, 0.173, 0.495, 0.208] as the row labels naming the different aspects of Financial Management. To fill out the table, I locate the {bar values}[[0.318, 0.732], [0.400, 0.586], [0.482, 0.478], [0.564, 0.316], [0.646, 0.262], [0.729, 0.208], [0.811, 0.100], [0.893, 0.073]] and [0.318, 0.759], [0.400, 0.711], [0.482, 0.640], [0.564, 0.532], [0.646, 0.451], [0.729, 0.424], [0.811, 0.397], [0.893, 0.341]], for Blockchain and Ethereum respectively, from different years and extract the growth values.\nBased on the above thought, the answer is:\nYear, Blockchain, Ethereum\n2013, 1500, 1200\n2014, 2300, 1800\n2015, 5000, 2700\n2016, 7000, 4000\n2017, 10000, 6000\n2018, 11000, 7500\n2019, 12000, 8000\n2020, 14000, 8500\n2021, 14500, 9550"
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "Pl5ZLVrfMQDRfZ7I",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the line chart, which of the cryptocurrencies, Blockchain or Ethereum, demonstrates a steady and consistent growth trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the steadiness and consistency of growth for Blockchain and Ethereum involves examining {the line for Blockchain}[0.236, 0.773, 0.893, 0.073] and {the line for Ethereum}[0.236, 0.792, 0.893, 0.341]. Looking at Blockchain, despite some fluctuations, the line generally displays upward motion indicative of a consistent rising trend. Ethereum's growth line, on the other hand, though also showing increments over years, has experienced bumps and troughs and is less steady in comparison.\nBased on the above thought, the answer is:\nBy analyzing the line chart, it can be deduced that Blockchain demonstrates a more steady and consistent growth trend when compared to Ethereum. The line representing the growth of Blockchain depicts a more consistent upward trajectory with fewer fluctuations, validating steady growth. On the contrary, while Ethereum also shows growth, the line progression has more noticeable bumps and troughs, indicating less steadiness over the years."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "aGNdRowp4oKQjzEE",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you compute the yearly average growth numbers for both Blockchain and Ethereum and determine which one has the higher average growth from 2013 to 2021 inclusive?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth for Blockchain and Ethereum, I have to find {the points on the Blockchain line}[0.236, 0.773, 0.893, 0.073] and {the points on the Ethereum line}[0.236, 0.792, 0.893, 0.341], which represent the yearly growth values for each. For Blockchain, the yearly growths are 1500, 2300, 5000, 7000, 10000, 11000, 12000, 14000, 14500. For Ethereum, the yearly growths are 1200, 1800, 2700, 4000, 6000, 7500, 8000, 8500, 9550. I calculate the mean of these yearly growth numbers for Blockchain and Ethereum to arrive at the averages.\nBased on the above thought, the answer is:\nBlockchain."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "YLbjzCv4I4nYpRmR",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the data from the chart explaining Preservation/Integration Levels of Heritage Sites, Ancient Architecture and World Cultures over time, into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In translating the line graph into a tabular format, I should first recognize the entities whose trends are shown. These are {Heritage Sites}<r>[0.718, 0.685, 0.881, 0.720]</r>, {Ancient Architecture}<r>[0.718, 0.738, 0.954, 0.773]</r> and {World Cultures}<r>[0.718, 0.790, 0.888, 0.825]</r>, which will function as row labels. The column labels 'Start' and 'End' are extracted from {the x-axis tick labels}<r>[0.107, 0.869, 0.174, 0.914]</r> and <r>[0.909, 0.869, 0.961, 0.914]</r>. The values for each entity at the given time points are located at {the line points} <r>[0.140, 0.283]</r> and <r>[0.935, 0.660]</r> for Heritage Sites, <r>[0.140, 0.169]</r> and <r>[0.935, 0.234]</r> for Ancient Architecture, and <r>[0.140, 0.578]</r> and <r>[0.935, 0.791]</r> for World Cultures. Their values are respectively 85 to 62 for Heritage Sites, 92 to 88 for Ancient Architecture, and 67 to 54 for World Cultures.\nBased on the above thought, the answer is:\nCategory,Start,End\nHeritage Sites,85,62\nAncient Architecture,92,88\nWorld Cultures,67,54"
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "yqmoXpaR6vsuY4Ne",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which category's Preservation/Integration Level has dropped the most during the assessed time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to compare the difference in Preservation/Integration Levels from start to end for all categories - {Heritage Sites}<r>[0.718, 0.685, 0.881, 0.720]</r>, {Ancient Architecture}<r>[0.718, 0.738, 0.954, 0.773]</r> and {World Cultures}<r>[0.718, 0.790, 0.888, 0.825]</r>. The levels for Heritage Sites decreased from 85 to 62 (a drop of 23 units), the levels for Ancient Architecture decreased from 92 to 88 (a drop of 4 units), and the levels for World Cultures decreased from 67 to 54(a drop of 13 units). Comparing these drops, it's clear that the drop for Heritage Sites is the greatest.\nBased on the above thought, the answer is:\nThe category whose Preservation/Integration Level has dropped the most during the assessed time period is Heritage Sites. Its Preservation/Integration Level fell by 23, which is the highest decrease amongst the three categories."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "BHWzWkGMDJusvpON",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Preservation/Integration Level for Heritage Sites decreased from the start to the end of the timeframe?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the decrease in Preservation/Integration Level of {Heritage Sites}<r>[0.718, 0.685, 0.881, 0.720]</r> from the start to the end of the timeframe, I examine the {line points for Heritage Sites} <r>[0.140, 0.283]</r> and <r>[0.935, 0.660]</r>. The level was 85 at the start and it decreased to 62 by the end. Therefore, the decrease in level is calculated as 85 minus 62.\nBased on the above thought, the answer is:\n23."
+            }
+        ],
+        "id_raw": 1194
+    },
+    {
+        "id": "nffMqoOuXkbXd1fO",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data points of 'Product A' and 'Product B' across different quarters into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the structured table from the line chart, I first identify the {x-axis tick labels}<r>[[0.108, 0.861, 0.157, 0.942], [0.182, 0.861, 0.231, 0.942], [0.256, 0.861, 0.305, 0.942], [0.330, 0.861, 0.378, 0.942], [0.404, 0.861, 0.452, 0.942], [0.477, 0.861, 0.526, 0.942], [0.551, 0.861, 0.600, 0.942], [0.625, 0.861, 0.674, 0.942], [0.699, 0.861, 0.747, 0.942], [0.773, 0.861, 0.821, 0.942], [0.846, 0.861, 0.895, 0.942], [0.920, 0.861, 0.969, 0.942]]</r> which represent the different quarters (Time Period). I then locate the points of {line for Product A}<r>[0.133, 0.413, 0.944, 0.765]</r> and {line for Product B}<r>[0.133, 0.060, 0.944, 0.559]</r> to extract the corresponding Sales (in thousands) for each quarter for 'Product A' and 'Product B' respectively.\nBased on the above thought, the answer is:\nPeriod,Product A,Product B\n2012Q1,12000,11000\n2012Q2,11800,11500\n2012Q3,11600,11000\n2012Q4,11300,11500\n2013Q1,11000,12000\n2013Q2,10800,12500\n2013Q3,10650,12800\n2013Q4,10400,13000\n2014Q1,10300,13500\n2014Q2,10000,14000\n2014Q3,9700,14200\n2014Q4,9600,14400"
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "uXTMoAzh9SEPPZO6",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the sales trend depicted in the line chart, which product seems to have a more positive performance over time? Provide your rationale."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which product has a more positive performance over time, I need to analyze the trends in the line plots for each product. Looking at {the line for Product A}<r>[0.133, 0.413, 0.944, 0.765]</r>, it displays a continuous declining trend from 12000 to 9600 indicating a negative performance. On the other hand, {the line for Product B}<r>[0.133, 0.060, 0.944, 0.559]</r> shows an overall increase from 11000 to 14400 signaling an upward trend and a positive performance.\nBased on the above thought, the answer is:\nBased on the sales trends depicted in the line chart, 'Product B' seems to have a more positive performance over time. While 'Product A' indicates a downward trend with decreasing sales from 2012Q1 to 2014Q4, 'Product B' reflects an upward trend with an increase in sales over the same period. Hence, 'Product B' has shown a more positive progression in sales over time."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "LQiPLB0EUdxiby5s",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the sales of 'Product B' increase from 2012Q1 to 2014Q4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in sales of 'Product B' from 2012Q1 to 2014Q4, I locate the points for 'Product B' in {2012Q1}<r>[0.133, 0.559]</r> and {2014Q4}<r>[0.944, 0.060]</r>. The sales in 2012Q1 and 2014Q4 are 11000 and 14400 respectively. The change in sales is therefore 14400 - 11000 = 3400 (in thousands).\nBased on the above thought, the answer is:\n3400."
+            }
+        ],
+        "id_raw": 1195
+    },
+    {
+        "id": "1XMLkzYYvxgwis2S",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the line graph data into a table format listing the years as rows and Technology as columns?"
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a structured table from the line graph, I start by identifying {the x-axis tick labels}[[0.135, 0.929, 0.177, 0.952], [0.210, 0.929, 0.252, 0.952], [0.285, 0.929, 0.328, 0.952], [0.361, 0.929, 0.403, 0.952], [0.436, 0.929, 0.478, 0.952], [0.512, 0.929, 0.554, 0.952], [0.587, 0.929, 0.629, 0.952], [0.662, 0.929, 0.704, 0.952], [0.738, 0.929, 0.780, 0.952], [0.813, 0.929, 0.855, 0.952], [0.888, 0.929, 0.931, 0.952]] which are the rows representing the years. Then I recognize {the legend texts}[[0.132, 0.064, 0.274, 0.081], [0.132, 0.090, 0.277, 0.107], [0.132, 0.116, 0.272, 0.133]] as the column headers representing the technologies. To fill the table, I track the {line points per technology}[[0.156, 0.100, 0.910, 0.170],[0.156, 0.057, 0.910, 0.267],[0.156, 0.092, 0.910, 0.267]] for each year and extract the related values.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,Prevision Tech Trend\n2000,0.90,0.75,0.75\n2001,0.92,0.80,0.78\n2002,0.91,0.83,0.80\n2003,0.94,0.85,0.81\n2004,0.93,0.86,0.83\n2005,0.92,0.87,0.85\n2006,0.90,0.88,0.88\n2007,0.88,0.89,0.90\n2008,0.86,0.95,0.92\n2009,0.88,0.97,0.94\n2010,0.87,0.99,0.95"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "81aC1cGlDJiYetNE",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology shows the most unpredictable trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology had the most unpredictable trend I need to locate and observe all three lines for {Artificial Intelligence}[0.156, 0.100, 0.910, 0.170], {Quantum Computing}[0.156, 0.057, 0.910, 0.267], and {Prevision Tech Trend}[0.156, 0.092, 0.910, 0.267]. Artificial Intelligence shows a general decline over the years, Prevision Tech Trend shows a steady increase. Quantum Computing's line has maxima and minima at more varied points which indicates a less predictable development over time.\nBased on the above thought, the answer is:\nQuantum Computing exhibits the most unpredictable trend across the years. Its trend line has highs and lows that are not consistent, unlike the generally steady or downward patterns in Artificial Intelligence and Prevision Tech Trend."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "nEr9k6MGP2kyBET1",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the maximum difference between any two years for the Prevision Tech Trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find out the maximum change in the line plot of {Prevision Tech Trend}[0.156, 0.092, 0.910, 0.267]. Looking at the coordinates of this line, I observe points making a steady upwards trend with values: 0.75, 0.78, 0.80, 0.81, 0.83, 0.85, 0.88, 0.90, 0.92, 0.94, 0.95. The greatest difference appears to be between 2006 and 2007, where the value goes from 0.88 to 0.90, resulting in a change of 0.02.\nBased on the above thought, the answer is:\n0.02"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "8lZixSHxSF5niEQJ",
+        "Chart_type": "line",
+        "QA_type": "Chart2table",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, can you structure the data of the Growth Rate of Knowledge Economy and Innovation Industry from 2000 to 2030 into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the information provided by the chart structure, {the x-axis}[0.520, 0.952, 0.557, 0.975] contains labels that represent the Years from 2000 to 2030. On the other hand, {the legend}[0.729, 0.781, 0.973, 0.893] contains the names of the Knowledge Economy and Innovation Industry, which would serve as the different data categories. Now, I will map each point on these lines, represented by {the data points on the line for the Knowledge Economy}[[0.206, 0.820], [0.317, 0.736], [0.428, 0.652], [0.538, 0.568], [0.649, 0.484], [0.760, 0.399], [0.871, 0.315]] and {the data points on the line for the Innovation Industry}[[0.206, 0.736], [0.317, 0.652], [0.428, 0.568], [0.538, 0.399], [0.649, 0.231], [0.760, 0.147], [0.871, 0.063]], corresponding to their respective annual growth rate values.\nBased on the above thought, the answer is:\nYear,Knowledge Economy,Innovation Industry\n2000,0.55,0.60\n2005,0.60,0.65\n2010,0.65,0.70\n2015,0.70,0.80\n2020,0.75,0.90\n2025,0.80,0.95\n2030,0.85,1.00"
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "2oc06MpARewYEiDg",
+        "Chart_type": "line",
+        "QA_type": "Open QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector, the Knowledge Economy or Innovation Industry, had a higher growth rate overall from 2000 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the sector with the higher growth rate, I need to compare the final values in 2030 for the Knowledge Economy and Innovation Industry. These values are represented by the data points at the end of their respective lines. On one hand, {the final data point for the Knowledge Economy}[[0.871, 0.315]] is 0.85. On the other hand, {the final data point for the Innovation Industry}[[0.871, 0.063]] is 1.00.\nBased on the above thought, the answer is:\nThe Innovation Industry had a higher overall growth rate from 2000 to 2030."
+            }
+        ],
+        "id_raw": 1197
+    },
+    {
+        "id": "noGSamrAjAA1Vv0q",
+        "Chart_type": "line",
+        "QA_type": "Numerical QA",
+        "image": "png_line/line_60_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much growth did the Innovation Industry observe from 2000 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "The values for the Innovation Industry are represented by the data points on the corresponding line. The point in the year 2000 is at {2000, 0.60}[[0.206, 0.736]] and the point in 2030 is at {2030, 1.00}[[0.871, 0.063]]. By subtracting the growth rate in 2000 from that in 2030, I can calculate the amount of growth the Innovation Industry observed over this period.\nBased on the above thought, the answer is:\n0.40."
+            }
+        ],
+        "id_raw": 1197
+    }
+]
\ No newline at end of file
diff --git a/data/final_0721_pie.json b/data/final_0721_pie.json
new file mode 100644
index 0000000000000000000000000000000000000000..787b08faa969fda4a4ed315cd992419c933a405e
--- /dev/null
+++ b/data/final_0721_pie.json
@@ -0,0 +1,178026 @@
+[
+    {
+        "id": "UVPFQzP0ZvNmoWpf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you convert the data into a structured format such as CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart's title {Distribution of Focus Areas in Financial Management and Risk Control}<r>[0.193, 0.021, 0.77, 0.047]</r> and the slices, the data represents categories titled {Investment}<r>[0.897, 0.071, 0.975, 0.091]</r> and {Risk Analysis}<r>[0.897, 0.101, 0.987, 0.121]</r> with corresponding percentages for each.\nBased on the above thought, the answer is:\nCategory,Percentage\nInvestment,67%\nRisk Analysis,33%"
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "y51cYQw69YIRD8ql",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the primary focus area in the field of Financial Management and Risk Control and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll determine this by identifying the category with the largest percentage representation on the chart.\nBased on the above thought, the answer is:\nThe primary focus area is 'Investment' as it has the largest percentage representation of 67% among the two categories."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "FMnBSnViROPy1D0O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the company's strategy towards Financial Management and Risk Control?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the company's strategy, I need to analyze the relative proportions of Investment and Risk Analysis in the chart.\nBased on the above thought, the answer is:\nThe company seems to have a stronger focus on 'Investment' as its proportion is double the amount of 'Risk Analysis'. This suggests that the company prioritizes proactive investment strategies while also maintaining important measures for risk control."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "B9igYqYnGKwa6gFC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would a change in the proportion of 'Risk Analysis' potentially influence future financial strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider how alterations to the percentage representation of 'Risk Analysis' could impact the balance between investment and risk control.\nBased on the above thought, the answer is:\nIf 'Risk Analysis' were to increase in proportion, it would signify an increased focus on risk management and control in the firm's financial strategy. Consequently, the company might place more focus on risk mitigation and less on investment. This could be a response to changes in market conditions or to losses sourced from current investments."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "RVWpo50Bcm69kyOV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference of the focus on 'Investment' and 'Risk Analysis'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be calculated by subtracting the percentage of 'Risk Analysis' from 'Investment'.\nBased on the above thought, the answer is:\n34%."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "0u44MK3PP0Wt5jnD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total does 'Investment' represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage that 'Investment' represents in the total can be found directly from the chart.\nBased on the above thought, the answer is:\n67%."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "OcPeEcQv8nETVZUR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Risk Analysis' increases by 7%, what would its new percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new percentage can be found by adding 7% to the current percentage of 'Risk Analysis'.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "LUfDXthgBXjApiB8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table representing the relative percentage values of Digital Content Creation and New Media for each year from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the distribution of each year segment in {the pie chart}<r>[0.197, 0.109, 0.767, 0.923]</r> and correlating it with the data from the legend {2010 to 2019 labels}<r>[0.952, 0.071, 0.987, 0.66]</r>, it's feasible to derive relative percentage contributions for both categories across the years.\nBased on the above thought, the answer is:\nYear,Digital Content Creation,New Media\n2010,2.6%,2.3%\n2011,3.4%,2.8%\n2012,3.5%,3.2%\n2013,4.4%,3.8%\n2014,5.3%,4.4%\n2015,5.9%,4.6%\n2016,6.6%,5.2%\n2017,7.2%,5.4%\n2018,8.2%,5.7%\n2019,9.3%,6.2%"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "QtGT1kRDaKujtc1y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be inferred from the growth pattern of Digital Content Creation and New Media as represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the growth patterns from the proportional slices of {Digital Content Creation}<r>[0.238, 0.257, 0.46, 0.495 to 0.503, 0.537, 0.751, 0.822]</r> and {New Media}<r>[0.234, 0.539, 0.462, 0.823 to 0.267, 0.16, 0.465, 0.487]</r>, taking note of the incremental increases and relations between the years.\nBased on the above thought, the answer is:\nDigital Content Creation exhibits a consistently more rapid growth compared to New Media. The larger slice for each year suggests a steeper annual increase for Digital Content Creation, highlighting a strong upward trend in digital content production relative to New Media."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "QpCoTotRppPrqAyH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploding effect on each slice imply about the data represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {exploded slices in pie chart}<r>[0.197, 0.109, 0.767, 0.923]</r>, the exploding effect emphasizes the differentiation and individual analysis of each year's data point, enhancing visual separation and focus.\nBased on the above thought, the answer is:\nThe exploding effect on the pie chart visually signifies the separate contributions of each year to the overall data set, drawing attention to individual yearly growth and making it easier to distinguish the year-by-year changes in Digital Content Creation and New Media."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "IJmCfRPHwapE3jcq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year showed the largest increase in percentage for Digital Content Creation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the size growth of slices specifically for {Digital Content Creation}<r>[0.238, 0.257, 0.46, 0.495 to 0.503, 0.537, 0.751, 0.822]</r> over consecutive years, pinpointing the largest segmental increase.\nBased on the above thought, the answer is:\nThe year 2019 showed the largest increase, evidenced by the significant enlargement of slice size from 2018 to 2019, representing a jump to 9.3% as combined factors of the industry's growth and possibly market conditions favoring digital content."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "QbES2SV3V9wGTCRd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase of Digital Content Creation from 2010 to 2019 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the total increase in percentage, starting from 2010 with {a slice marked \"2010\"}<r>[0.238, 0.257, 0.46, 0.495]</r> showing 2.6%, to the slice in {2019}<r>[0.503, 0.537, 0.751, 0.822]</r> which shows 9.3%, we take the difference.\nBased on the above thought, the answer is:\n6.7%"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "9vhKxJWRtGElrRYZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between Digital Content Creation and New Media in the year 2019 as indicated on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the slice for {Digital Content Creation in 2019}<r>[0.503, 0.537, 0.751, 0.822]</r> at 9.3% against the slice for {New Media in 2019}<r>[0.267, 0.16, 0.465, 0.487]</r> at 6.2%, I calculate the difference.\nBased on the above thought, the answer is:\n3.1%"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "0QZepCjHGauTTKsh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the decade, which category had the highest average percentage increase per year in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Computing average annual increase for {Digital Content Creation}<r>[incremental slices from 0.238, 0.257, 0.46, 0.495 to 0.503, 0.537, 0.751, 0.822]</r> versus {New Media}<r>[incremental slices from 0.234, 0.539, 0.462, 0.823 to 0.267, 0.16, 0.465, 0.487]</r>, comparing each year's growth.\nBased on the above thought, the answer is:\nDigital Content Creation"
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "ikFowRGByrTOBMPE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you put the risk assessment data into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the data is about the likelihood and impact of 6 different types of risks, I would create a table that includes 3 columns: Risk Type, Likelihood, and Impact. Each row will represent a specific risk. I will use the information provided in the pie chart; with the six different slices each represent a risk and their likelihood and impacts are conveyed through the slices (Risk 1<r>[0.197, 0.281, 0.456, 0.803], Risk 2<r>[0.309, 0.552, 0.538, 0.922], Risk 3<r>[0.495, 0.547, 0.694, 0.906], Risk 4<r>[0.507, 0.322, 0.767, 0.756], Risk 5<r>[0.429, 0.112, 0.711, 0.482], Risk 6<r>[0.269, 0.125, 0.468, 0.485]) and using the percentages on the pie graph to note the percentage for each risk.\nBased on the above thought, the answer is:\nRisk_Type,Likelihood,Impact\nRisk 1,15,25\nRisk 2,30,15\nRisk 3,12,10\nRisk 4,16,20\nRisk 5,18,20\nRisk 6,9,10"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "uj8PIkjl9DFbdziT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk has the highest impact and explain why it should be addressed first?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first step is to identify the risk with the highest impact value by examining the impact data for each risk provided in the chart.\nBased on the above thought, the answer is:\nRisk 1 has the highest impact, meaning it could have the most severe consequences if it were to occur. Therefore, it should be addressed first to prevent significant damage or disruption."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "kbP0bsMPOestfR9S",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs Risk 2 likely to occur and will it cause significant damage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at both the likelihood and impact data for Risk 2 provided in the chart.\nBased on the above thought, the answer is:\nRisk 2 is the most likely to occur out of all the risks, but it has a relatively low impact. Despite its high likelihood, the potential damage caused by Risk 2 is not significant compared to other risks."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "FK4zfKxFdMGt5qYc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about risk management priorities based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Insights about risk management priorities can be inferred by considering both likelihood and impact data for each risk provided in the chart.\nBased on the above thought, the answer is:\nThe priorities for risk management should be focused on risks with high likelihood and high impact. Based on the data, Risk 1 and Risk 2 are the most important to address. While Risk 2 is the most likely, Risk 1 has a significantly higher impact, thus both should be treated as priority risks to manage."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "GAcFfhLiuHWSTdLE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total impact of Risk 2 and Risk 3 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to sum up the impact values for Risk 2 and Risk 3 that are given in the raw data from the chart.\nBased on the above thought, the answer is:\nThe total impact of Risk 2 and Risk 3 combined is 25."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "MVaUMKjsZu1W6isP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the total likelihood of Risk 1, Risk 4 and Risk 6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add up the likelihood of Risk 1, Risk 4 and Risk 6 from the data provided in the chart.\nBased on the above thought, the answer is:\nThe total likelihood of Risk 1, Risk 4 and Risk 6 is 40."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "IhCipU7Jar7u87cu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk has a likelihood greater than 20?"
+            },
+            {
+                "from": "gpt",
+                "value": "By checking the likelihood values for each risk from the data provided in the chart, I can determine which risk has a likelihood greater than 20.\nBased on the above thought, the answer is:\nThe risks with likelihood greater than 20 are Risk 1 and Risk 2."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "MqAs5ThEBBVf5xPA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the donations received over the years (2010-2019) as seen on the pie chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data represented on the pie chart, each slice {pie_0}[0.197, 0.111, 0.765, 0.923] signifies the donations received each year from 2010 to 2019. The chart includes clear labels for each year and the corresponding numeric donation values.\nBased on the above thought, the answer is:\nYear,Donations Received\n2010,55\n2011,68\n2012,70\n2013,85\n2014,100\n2015,105\n2016,110\n2017,115\n2018,120\n2019,130"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "pAvp9ETbxURou94D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends are observed from the chart with respect to donation received over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions corresponding to each year on the pie chart to identify any trends in donation received.\nBased on the above thought, the answer is:\nFrom 2010 to 2019, it's evident that donations have generally increased each year. The significant increases in 2013, 2014, and 2019 suggest successful fundraising efforts or social trends that triggered more donations in those years."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "6WZU5YDB4QUBdviW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible reasons for a significant increase in donations in 2013, 2014, and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "While the chart doesn't provide specific reasons for the increases, there could be several potential factors that led to a rise in donations in these years.\nBased on the above thought, the answer is:\nFinancial stability of the donors, successful fundraisers, greater awareness and importance of charity or specific disaster relief efforts in those years could have contributed to the increase in donations."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "8VKXx19nEAP4vdn9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the pie chart's information guide future fundraising strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The increase and decrease trend in donations over the years can be utilized to make strategic decisions on when to hold major fundraising events.\nBased on the above thought, the answer is:\nReviews of successful years such as 2013, 2014, and 2019 can help identify effective strategies, encouraging factors, and popular trends during those periods which led to increased donations. These factors can be leveraged in planning future fundraising campaigns. Additionally, understanding the probable causes for a decrease in donations can help counter those challenges in the future."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "nCKM3qwtYDnCIGjk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the amount of donations increased from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in donations, I need to subtract the amount of donations in 2010 from the amount of donations in 2019.\nBased on the above thought, the answer is:\n130 - 55 = 75"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "iLPd0fwXrJbCYkQk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average amount of donations received per year from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average amount of donations, I need to sum up all the yearly donations and divide by the number of years.\nBased on the above thought, the answer is:\n(55 + 68 + 70 + 85 + 100 + 105 + 110 + 115 + 120 + 130) / 10 = 95.8"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "4AFwIoTwoSPmjs0V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will the total donation be for the years 2014 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will sum up the donations for each year from 2014 to 2019.\nBased on the above thought, the answer is:\n100 + 105 + 110 + 115 + 120 + 130 = 680"
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "JyACsGsZePMDF69c",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a table with a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart contains six slices, each representing the GDP percentage of each country in the respective year. The exploded fragments are {\"USA - 2010\"}<r>[0.197, 0.272, 0.456, 0.645]</r>, {\"China - 2010\"}<r>[0.221, 0.541, 0.462, 0.868]</r>, {\"USA - 2015\"}<r>[0.365, 0.553, 0.665, 0.923]</r>, {\"China - 2015\"}<r>[0.506, 0.456, 0.765, 0.794]</r>, {\"USA - 2020\"}<r>[0.502, 0.148, 0.756, 0.492]</r> and {\"China - 2020\"}<r>[0.277, 0.11, 0.571, 0.48]</r>.\nBased on the above thought, the answer is:\nYear,USA,China\n2010,17.0%,11.4%\n2015,19.9%,15.9%\n2020,15.9%,19.9%"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "RMLwQqa86ZNCPc51",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country shows an increasing trend in GDP proportion over the years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will compare the GDP percentages of USA and China over the years to see which one shows an increasing trend.\nBased on the above thought, the answer is:\nAccording to the chart, China shows an increasing trend in GDP proportion over the years."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "ValnJJTqoS8i0xkY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about global GDP distribution from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the percentages and the trends for both USA and China based on the pie chart to infer about the global GDP distribution.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that the global GDP distribution has been shifting from the USA to China over the period of 2010 to 2020. While USA's GDP share has been decreasing, China's share has been increasing."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "QEVEFIO1iih6e3xD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, what could be a potential economic prediction for the next few years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the trends shown in the chart, I can make a potential economic prediction.\nBased on the above thought, the answer is:\nGiven the noticeable rising trend in China's GDP and a declining trend in the USA's GDP, it's plausible to predict that China might continue this trend and further increase its share of global GDP. If the trend continues, China could possibly become the world's largest economy."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "VfBocH79iMw7ICTS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total GDP percentage in 2015 for both USA and China?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total GDP percentage, we sum the separate percentages for USA and China for the year 2015.\nBased on the above thought, the answer is:\n35.8%"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "g7vvxQZYKpHfyhbj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in GDP percentage between USA and China in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the GDP percentage of China from the GDP of USA in 2020.\nBased on the above thought, the answer is:\n4.0%"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "ZESSie6gzQVFHJ0q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did USA had the highest GDP percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest GDP percentage for USA, I need to compare the GDPs for the years 2010, 2015 and 2020 for USA.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "fwbOYvrs8ZRfDTHf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the information represented on the pie charts, generate a table containing data about the daily sales of the E-commerce and retail industries."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows sales for different days of the week, represented by slices of the pie. For example, {the slice that represents sales on Monday}<r>[0.198, 0.266, 0.457, 0.507]</r> and {the slice that represents sales on Saturday}<r>[0.48, 0.113, 0.715, 0.483]</r>. The data from these slices, when combined, will form the table I need to generate.\nBased on the above thought, the answer is:\nDay,E-commerce,Retail\nMonday,1250,1500\nTuesday,1650,1200\nWednesday,1350,1400\nThursday,1700,1550\nFriday,1800,1650\nSaturday,1900,1750\nSunday,1450,1300"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "UOMuF6dS8KMZoqJn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about consumer behavior in E-commerce and Retail industry from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the relative sizes of the slices from both pies, I can infer about how consumer behavior varies over a week in E-commerce and Retail.\nBased on the above thought, the answer is:\nFor both industries, sales generally seem to increase as the week progresses, peaking over the weekend. However, the E-commerce industry sees a significant surge in sales on Saturday, which may imply that consumers prefer online shopping over the weekend. In contrast, Retail sales are relatively more evenly distributed over the week."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "sm8Ne0AE0z44Vp4C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat strategy might help increase sales for the E-commerce industry throughout the week?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the identified consumer behavior from the pie chart, a strategy to increase sales can be deliberated.\nBased on the above thought, the answer is:\nConsidering that E-commerce sales are highest on Saturdays, it may be beneficial to run promotions or discounts on weekdays to encourage more purchases during these periods."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "XgjWRcye5uzpVgC2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what could potentially improve the Retail industry's sales performance on Tuesdays, which is its least profitable day?"
+            },
+            {
+                "from": "gpt",
+                "value": "Having identified the least profitable day from the pie chart, I can infer a potential solution.\nBased on the above thought, the answer is:\nThe Retail industry might consider offering special deals or promotions on Tuesdays to incentivize more in-store shopping, improving its sales performance on this slower day."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "tuSug10lYm4TVAtb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales figure for the E-commerce industry over the week, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total sales for the E-commerce industry over the week, I should sum up the sales on all the days, represented as slices in {the pie chart}<r>[0.275, 0.224, 0.663, 0.775]</r>.\nBased on the above thought, the answer is:\n11100"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "VkYxrDASVGuNWdXc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich day of the week has the highest sales for the Retail industry according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the individual proportions for each day in {the pie chart}<r>[0.198, 0.113, 0.741, 0.886]</r>, I can identify the day with the highest sales.\nBased on the above thought, the answer is:\nSaturday"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "Hkx2HZHtKFYIg9CP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in sales between the E-commerce and Retail industries on Monday?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I should look at the slices representing sales on Monday for both {E-commerce}<r>[0.198, 0.266, 0.457, 0.507]</r> and {Retail}<r>[0.198, 0.113, 0.741, 0.886]</r> and subtract one from the other.\nBased on the above thought, the answer is:\n250"
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "YpuzGhviO4RBLy9r",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the pie chart representing population distribution across US's top 5 cities, can you convert this visual data into a tabular format using CSV structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I first locate the pie chart{'pie_0': {'coordinates': '[0.202, 0.109, 0.764, 0.909]'}}. Then I identify the slices representing each city along with their percentage of the total population: {\"new york\"}<r>[0.202, 0.301, 0.583, 0.909]</r>, {\"los angeles\"}<r>[0.505, 0.447, 0.764, 0.858]</r>, {\"chicago\"}<r>[0.502, 0.16, 0.754, 0.493]</r>, {\"houston\"}<r>[0.404, 0.109, 0.596, 0.479]</r> and {\"phoenix\"}<r>[0.269, 0.133, 0.467, 0.485]</r>.\nBased on the above thought, the answer is:\nCity,Population\nNew York,43.9%\nLos Angeles,20.9%\nChicago,14.2%\nHouston,12.1%\nPhoenix,8.9%"
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "dIPg68Z8r8m57wPp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich city, as per the pie chart, has the most concentration of the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the slice with the largest area. Here, the largest slice corresponds to {\"New York\"}<r>[0.202, 0.301, 0.583, 0.909]</r>.\nBased on the above thought, the answer is:\nNew York city has the highest concentration of population."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "GyVmwYEeHRQXhHKu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the population trend among the top 5 US cities from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I examine the sizes of the city slices in descending order: New York, Los Angeles, Chicago, Houston and Phoenix, which indicates a descending trend in population among these cities.\nBased on the above thought, the answer is:\nFrom the pie chart, we can infer a descending trend in population from New York to Phoenix among the top 5 US cities."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "g2o9XvsngB2sV6h4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf cities are seeking federal funding based on population, which city showcased in the pie chart might receive the least funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to identify the city with the lowest population percentage, since funding is likely to correlate with population. The smallest slice corresponds to {\"Phoenix\"}<r>[0.269, 0.133, 0.467, 0.485]</r>.\nBased on the above thought, the answer is:\nPhoenix might receive the least population-based federal funding because it has the lowest population among the top 5 US cities according to the chart."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "HU8kxtTJZBWGK5GR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me which city, according to the pie chart, holds the smallest percentage of the population among the top 5 US cities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to identify the slice of the pie chart with the smallest area. Here the smallest slice corresponds to {\"Phoenix\"}<r>[0.269, 0.133, 0.467, 0.485]</r> which holds 8.9% of the total.\nBased on the above thought, the answer is:\nPhoenix."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "vyOqsLvubAqeeYnf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, is the population of Houston more than half of that in New York?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the answer, I should compare the percentage of population of {\"New York\"}<r>[0.202, 0.301, 0.583, 0.909]</r> which is 43.9% to twice that of {\"Houston\"}<r>[0.404, 0.109, 0.596, 0.479]</r> which is 2 * 12.1% = 24.2%.\nBased on the above thought, the answer is:\nNo."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "7ZIhiI93NC6SKPtE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the two cities with the highest populations merged, what would be their combined population as a percentage of the total?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add up the percentages of the two city slices with the largest areas. That would be {\"New York\"}<r>[0.202, 0.301, 0.583, 0.909]</r> with 43.9% and {\"Los Angeles\"}<r>[0.505, 0.447, 0.764, 0.858]</r> with 20.9%.\nBased on the above thought, the answer is:\n64.8%."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "ULAOafroHOwDKuFL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data represented on the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the top of the chart {Activity Levels of Different Age Groups on Social Media}[0.253, 0.021, 0.71, 0.047} and the slices section{Pie}[0.223, 0.146, 0.749, 0.921] which includes {Children}[0.237, 0.278, 0.482, 0.516], {Teenagers}[0.223, 0.393, 0.482, 0.757], {Young_Adults}[0.293, 0.551, 0.749, 0.921], {Middle_Age}[0.482, 0.169, 0.741, 0.531], {Senior_Citizen}[0.354, 0.146, 0.571, 0.516], {Elderly}[0.293, 0.194, 0.482, 0.516] and {Super_Elderly}[0.283, 0.262, 0.482, 0.516] demonstrate that these slices represent the different age groups and their corresponding activity levels on social media.\nBased on the above thought, the answer is:\nAge group,Activity Level\nChildren,12\nTeenagers,35\nYoung Adults,80\nMiddle Age, 42\nSenior Citizen,29\nElderly,10\nSuper Elderly,2"
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "KHqHjONNYgFpZj5m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which age group is the most active on social media? Please justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the age group with the highest activity level from the graph.\nBased on the above thought, the answer is:\n\"Young Adults\" are the most active on social media because their slice is the largest, representing the highest activity level of 80."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "wzWmYecLc6hJSDLb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you draw about the activity levels of different age groups on social media?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look and analyze the sizes of the different slices which represents the various age groups on the pie chart.\nBased on the above thought, the answer is:\nThe chart shows that Young Adults are the most active users of social media, followed by Teenagers and individuals in Middle Age. This implies that social media is more popular among younger people. Meanwhile, Children, Senior Citizens, Elderly, and Super Elderly are less active on social media, with the lowest activity levels seen in Super Elderly."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "MrnJL4NCwGSsdHNQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence decisions made by companies when targeting demographics for social media advertising?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the activity levels of all age groups and their implications for potential marketing strategies of companies.\nBased on the above thought, the answer is:\nCompanies might focus their social media advertising campaigns primarily on \"Young Adults\", \"Teenagers\", and \"Middle Age\" as these groups represent the most active users on social media and thus can reach a larger audience. However, they should not completely neglect the \"Children\", \"Senior Citizens\", \"Elderly\", and \"Super Elderly\" sectors as these groups, although less active, still represent a substantial potential market."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "8kshZYiYK6LZR3HC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more activities were registered by Teenagers compared to Elderly?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, it's clear that {Teenagers}[0.223, 0.393, 0.482, 0.757] are more active than {Elderly}[0.293, 0.194, 0.482, 0.516]. Now I just have to subtract the number of activities of Elderly from that of Teenagers.\nBased on the above thought, the answer is:\n25 more activities."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "1IYP17tFtuMUGbVq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total level of social media activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total level, I need to sum up the activity levels of all the age groups.\nBased on the above thought, the answer is:\n210"
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "mODkGkiNpymu7osc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Young_Adults decreased their activity by 10, what would be the new total of social media activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to subtract 10 from the \"Young_Adults\" activity level and sum up all the activity levels again.\nBased on the above thought, the answer is:\nThe new total activity level is 200."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "RBaUZeFTRqkMTEOA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs an AI who can see the pie chart, can you transform the presented platform users data to a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {\"Social Media Platform User Distribution\"}[0.319, 0.021, 0.644, 0.047] and slices [{\"Facebook\"}[0.198, 0.266, 0.457, 0.512]; {\"Twitter\"}[0.198, 0.528, 0.457, 0.743]; {\"Instagram\"}[0.254, 0.543, 0.464, 0.859]; {\"LinkedIn\"}[0.337, 0.55, 0.472, 0.911]; {\"Pinterest\"}[0.419, 0.553, 0.478, 0.922]; {\"Snapchat\"}[0.463, 0.553, 0.484, 0.923]; {\"YouTube\"}[0.33, 0.114, 0.788, 0.854], it is apparent that the percentages indicate user distribution across each platform.\nBased on the above thought, the answer is:\nSocial Media Platform,Number of Users\nFacebook,11.5%\nTwitter,9.6%\nInstagram,6.4%\nLinkedIn,5.1%\nPinterest,2.5%\nSnapchat,1.3%\nYoutube,63.7%"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "AuesNsuQvtuWpYVU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what social media platform is the most populated? What factors can you relate to this phenomenon?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slices of the pie, I can judge that {\"YouTube\"}[0.33, 0.114, 0.788, 0.854], with the largest slice, is the most populated platform.\nBased on the above thought, the answer is:\nYouTube is the most populated social media platform, representing 63.7% of the total users. This might be due to the diverse and vast amount of content made available on YouTube, catering to various interests and audiences."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "jpH9ObItdhPMkXTS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the popularity of Snapchat as a social media platform, based on the given pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will infer based on the size of the slice representing {\"Snapchat\"}[0.463, 0.553, 0.484, 0.923].\nBased on the above thought, the answer is:\nSnapchat represents a small proportion of the total, only 1.3%. This implies that Snapchat might be less popular compared to the other social media platforms presented in the chart."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "74gtDuJzwR6V8Nrg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, what consultation advice could you provide for social media ad placement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To advise, I need to consider the slices each platform occupies and then present the platforms which can potentially gain the most exposure.\nBased on the above thought, the answer is:\nGiven the significant user count of Youtube and Facebook, advertising on these platforms may reach a larger audience and have a higher chance of getting seen. However, platforms with smaller user counts like Snapchat and Pinterest should not be totally disregarded, as they might cater to a specific niche or demographic that could be valuable to certain advertisers."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "vCpSjeh128MVdxLi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the sum of the user proportions of Facebook and Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I sum up the proportions of Facebook and Twitter represented as slices {\"Facebook\"}[0.198, 0.266, 0.457, 0.512] and {\"Twitter\"}[0.198, 0.528, 0.457, 0.743], it should present the total.\nBased on the above thought, the answer is:\n21.1%"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "KvkT06rUxnWNE3MJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the proportion of Pinterest and Snapchat users if the users of each platform doubled in number?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the users of each platform doubled, the proportions of Pinterest and Snapchat would be same as before.\nBased on the above thought, the answer is:\nPinterest 2.5% and Snapchat 1.3%"
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "3X2wuAUzkbbbQPfl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of user proportion, how far behind is Instagram from Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the proportion of Instagram from the proportion of Facebook. I will locate the slices for {\"Instagram\"}[0.254, 0.543, 0.464, 0.859] and {\"Facebook\"}[0.198, 0.266, 0.457, 0.512].\nBased on the above thought, the answer is:\nThe proportion of Instagram is 5.1% less than that of Facebook."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "AI8CVrkSUjvE8czm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a CSV format, containing year and percentage for each mode of transport?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend on the right side of the chart {legend in the pie chart}<r>[0.908, 0.063, 0.993, 0.368]</r> and the pie slices in the chart {pie slices}<r>[0.197, 0.111, 0.767, 0.922]</r>, I can see the percentage for each mode of transport for each year.\nBased on the above thought, the answer is:\nYear,Railways,Truck\n2016,4.4%,13.2%\n2017,4.7%,14.1%\n2018,5.0%,14.7%\n2019,5.3%,15.9%\n2020,5.9%,16.8%"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "gof6MVtp3GIxUgmc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which mode of transportation is more popular? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to compare the percentage of each transport mode, and then the mode with the highest percentage can be considered as the most popular.\nBased on the above thought, the answer is:\nThe Truck is the most popular mode of transportation because it has higher percentages in all the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "WYerSQEt85rULDaa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the chart, can you infer trends in the transportation and logistics industry from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages of each transportation mode over the years to infer trends.\nBased on the above thought, the answer is:\nThe chart shows a trend of slowly increasing use of both Railways and Truck from 2016 to 2020. Despite Railways' percentage increasing a little in each year, Trucks remain the dominant mode of transportation over this period. This suggests a steady demand with a gradual shift towards Trucking in the industry."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "x5QtZCcOvPLHL0hp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can the transportation and logistics industry take away from the data shown in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking into the increasing percentages might indicate passengers' preference and suggest corresponding adjustments.\nBased on the above thought, the answer is:\nThe constant and incremental increase in the percentages of both Railways and Trucks suggests a positive growth in the transportation and logistics industry overall. Yet, with the Truck percentage seeing a slightly greater increase than Railways throughout the years, it might hint that the industry should weigh more on improving, innovating, and investing in trucking transportation for the future."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "72j5YhMR0E7jd0kj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, how much did the percentage increase for the Railways from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the percentage, I need to subtract the percentage of Railways in 2016 from the percentage of Railways in 2020.\nBased on the above thought, the answer is:\n1.5%"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "0uHcIVAm0td7RsJQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, in which year was the gap between the percentage of Truck and Railways the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out when the gap was the smallest, I need to subtract the percentage of Railways from the percentage of Truck for each year and see when this difference was the smallest.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "yf4MnnFT3O1eLzoH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of Truck and Railways in 2018 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add up the percentage of Railways and Truck in 2018.\nBased on the above thought, the answer is:\n19.7%"
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "aEo0CUkMbVGcmWKp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data regarding relative popularity of Media and Entertainment Industry Trends from the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart depicts details about five different media and entertainment industry concepts: {Movies}<r>[0.123, 0.517, 0.171, 0.537]</r>, {TV Shows}<r>[0.594, 0.92, 0.661, 0.94]</r>, {Music}<r>[0.75, 0.283, 0.79, 0.303]</r>, {Games}<r>[0.532, 0.068, 0.581, 0.088]</r>, and {Books}<r>[0.283, 0.122, 0.325, 0.142]</r>. The percentages associated with each indicate their respective popularity.\nBased on the above thought, the answer is:\nMedia Concept,Popularity\nMovies,23%\nTV Shows,38%\nMusic,17%\nGames,11%\nBooks,11%"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "jiTmrnsY49507ksa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which form of media entertainment is the most popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "The media form with the highest percentage in the pie chart is the most popular. By looking at the slices' sizes and corresponding percentages, I can tell {TV Shows}<r>[0.594, 0.92, 0.661, 0.94]</r> is the most popular.\nBased on the above thought, the answer is:\nTV Shows are the most popular form of media entertainment as per the chart."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "IyuSpZuwmGc3ywpJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two media concepts have the same popularity according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the percentages in the pie chart, I can identify that {Games}<r>[0.532, 0.068, 0.581, 0.088]</r> and {Books}<r>[0.283, 0.122, 0.325, 0.142]</r> have the same popularity.\nBased on the above thought, the answer is:\nGames and Books have the same popularity in the media and entertainment industry as per the chart."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "avFPV6IFEO0VAZo5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the general consumption pattern of entertainment media from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentages attached to each media concept offer insights into the popularity or consumer preference for them.\nBased on the above thought, the answer is:\nFrom the chart, it's quite clear that TV Shows are the most consumed form of entertainment media followed by Movies. Music, Games, and Books share a similar level of popularity. The results suggest a preference for visual media like TV Shows and Movies over other forms."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "9t8dGB9AurqHb3ZH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined popularity percentage of 'Movies' and 'Music' as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage for 'Movies' and 'Music', I need to sum up their respective percentages. The percentage for {Movies}<r>[0.123, 0.517, 0.171, 0.537]</r> and {Music}<r>[0.75, 0.283, 0.79, 0.303]</r> can be added together.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "LtwSCOAatGTZq5vv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in popularity between 'TV Shows' and 'Games' as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in popularity between 'TV Shows' and 'Games', I need to subtract the popularity percentage of {Games}<r>[0.532, 0.068, 0.581, 0.088]</r> from that of {TV Shows}<r>[0.594, 0.92, 0.661, 0.94]</r>.\nBased on the above thought, the answer is:\n27%"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "ojbAhVhpeuHLiU02",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of the media and entertainment industry does 'Books' represent as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart reveals the popularity proportion of {Books}<r>[0.283, 0.122, 0.325, 0.142]</r> in the media and entertainment industry.\nBased on the above thought, the answer is:\n11%"
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "RGklemRQvLi5b6Wb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a table from the data represented in the pie chart for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on the information given in the chart, which includes the pie slices {industry sectors and its percentages}[{'Automobile'}[0.197, 0.278, 0.456, 0.75]; {'Pharmaceuticals'}[0.283, 0.553, 0.71, 0.923]; {'E-commerce'}[0.507, 0.226, 0.766, 0.69]; {'Real Estate'}[0.281, 0.109, 0.646, 0.479]], and the {subtitle}[0.299, 0.021, 0.664, 0.047], I can now generate the CSV format table.\nBased on the above thought, the answer is:\nIndustry,MarketShare2020\nAutomobile,22\nPharmaceuticals,31\nE-commerce,22\nReal Estate,25"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "r348QvqJd9SdKAAp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which industry had the largest market share in 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the industry with the highest market share by identifying the slice with the largest proportion in the pie chart.\nBased on the above thought, the answer is:\nThe Pharmaceuticals industry had the largest market share in 2020. It had a 31% share, which is the highest percentage amongst all the industries."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "NrV4gnWzoin5SuBj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart represent the market distribution for industries in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart sets out the market distribution via relative proportions of the slices of sectors in the pie.\nBased on the above thought, the answer is:\nAs the pie chart shows, the distribution of market shares amongst all the industries in 2020 is somewhat balanced, with no single industry overwhelmingly dominating the market."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "3LeBFUx13toKtNA0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the market competition among the different industries in 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to interpret the relative proportions of the pie slices.\nBased on the above thought, the answer is:\nThe pie chart indicates that the market competition in 2020 was quite intense. No single industry had a vastly greater market share than the others, implying a balanced competition environment. The Pharmaceuticals industry had a slightly larger share of the market, perhaps due to increased demand during the year."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "L3d9gkMme79pY3Vq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the sum of the market shares for the Automobile and Real Estate sectors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this sum, I should sum the 2020 market shares for {Automobile}[0.197, 0.278, 0.456, 0.75] and {Real Estate}[0.281, 0.109, 0.646, 0.479] specified in the pie chart.\nBased on the above thought, the answer is:\n47%"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "JtDMwaWBw9KvtNpv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total market share does the Pharmaceuticals sector take up?"
+            },
+            {
+                "from": "gpt",
+                "value": "The market share of the Pharmaceuticals sector can be directly read from the chart at the area where {Pharmaceuticals}[0.283, 0.553, 0.71, 0.923] is annotated.\nBased on the above thought, the answer is:\n31%"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "CbEPTo0Oi4oaUPfQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater is the market share percentage of Pharmaceuticals than E-commerce?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the market share percentage of {E-commerce}[0.507, 0.226, 0.766, 0.69] from that of {Pharmaceuticals}[0.283, 0.553, 0.71, 0.923].\nBased on the above thought, the answer is:\n9%"
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "MCnFwFA4PTBYmpTF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart's data into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's title {\"CO2 Emission to Oxygen Level Ratio from 2000 to 2007\"}[0.25, 0.021, 0.713, 0.047] and {the pie slices representing each year}[{\"2000\"}[0.198, 0.266, 0.457, 0.517]; {\"2001\"}[0.199, 0.531, 0.458, 0.793]; {\"2002\"}[0.288, 0.55, 0.472, 0.92]; {\"2003\"}[0.487, 0.55, 0.671, 0.92]; {\"2004\"}[0.505, 0.531, 0.764, 0.797]; {\"2005\"}[0.506, 0.239, 0.765, 0.506]; {\"2006\"}[0.481, 0.111, 0.673, 0.481]; {\"2007\"}[0.272, 0.113, 0.47, 0.483]], there is a representation of the proportion of CO2 Emission to Oxygen Level for each respective year from 2000 to 2007.\nBased on the above thought, the answer is:\nYear,CO2 to Oxygen Ratio\n2000,11.7%\n2001,11.9%\n2002,12.2%\n2003,12.5%\n2004,12.6%\n2005,12.7%\n2006,13.1%\n2007,13.3%"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "ffCfKXqnZQaXOhXU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year had the worst CO2 to Oxygen ratio? And give reasons why."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the year with the largest slice which would translate to having the worst CO2 to Oxygen ratio.\nBased on the above thought, the answer is:\nThe year 2007 had the worst CO2 to Oxygen ratio as represented by the largest slice at 13.3%"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "IiO9BmJRciETY45F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the environmental progress from 2000 to 2007 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the proportions, it can be inferred that the CO2 to Oxygen ratio increased steadily over the years, which is indicative of the environmental conditions.\nBased on the above thought, the answer is:\nThe pie chart suggests that from 2000 to 2007, there's been a steady increase in the CO2 to Oxygen ratio. This implies that despite an increase in global oxygen levels, CO2 emissions have grown at a higher pace, indicating that more needs to be done to balance the ecosystem."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "84MzAEm5uOwxFSKK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan any conclusions be drawn about the impact of industrial activities or natural phenomena based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the sudden pattern changes in proportions can provide insights into significant impacts on environmental factors, such as industrial activities or natural disasters.\nBased on the above thought, the answer is:\nThe sudden spike in the CO2 to Oxygen ratio in 2004 and 2007, as shown in the pie chart, may indeed suggest increased industrial activities or possible natural disasters during these years. These events could have contributed significantly to higher CO2 emissions, thereby impacting the balance of the ecosystem negatively."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "5X0jT1VUjDTz2GQV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSeeing the pie chart, in which year was the CO2 to Oxygen ratio the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest CO2 to Oxygen ratio, I need to inspect the size and corresponding percentage of each slice.\nBased on the above thought, the answer is:\n2007"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "GJOAcMueoVzXXEmi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the CO2 to Oxygen ratio from 2000 to 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to sum up the percentages from 2000 to 2003 observed in the pie chart.\nBased on the above thought, the answer is:\n48.3%"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "F5K3TH4LU7au1kHO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the CO2 to Oxygen ratio for the year 2004 decreased by 2%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to subtract 2% from the 2004 ratio and sum up all the percentages again.\nBased on the above thought, the answer is:\n97.3%"
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "4f53FtTrQ80QIjmx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you rearrange the data shown in the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The information can be obtained by analyzing the legends and corresponding pie slices: {the legend}<r>[0.851, 0.063, 0.993, 0.368]</r> and {the pie slices}<r>[0.197, 0.109, 0.767, 0.922]</r>.\nBased on the above thought, the answer is:\nType of Food, Market Share (%)\nPoultry, 28.4\nRed Meat, 24.6\nDairy, 14.2\nVegan, 5.5\nCereal, 9.3\nSeafood, 6.2\nJunk Food, 5.4\nFruits, 3.2\nVegetables, 2.6\nOrganic Food, 0.6"
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "ckb4AwdgYpaXwQHc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the chart, which type of food governs the least of the market, and why might that be?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer this by locating the smallest slice of the pie chart, which corresponds to the least popular type of food. Further reasoning requires considering factors beyond the chart data.\nBased on the above thought, the answer is:\nOrganic Food has the least market share with only 0.6%. This could be because organic food is generally more expensive and less available than other food types."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "bB7Qf5XjBED54Pvr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a company in the Food and Agriculture industry wanted to target the largest or most profitable market segment, which type of food should they invest in based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to find the food type with the highest market share, indicating that it might be the most profitable segment.\nBased on the above thought, the answer is:\nThe company should consider investing in Poultry, as it has the highest market share of 28.4%."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "XnpuYq9kRTSeGmId",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the reason for the relatively low market shares of Fruits and Vegetables, and how could a company specialized in these categories increase their market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the reasons behind the lower market shares for Fruits and Vegetables, and think about potential business strategies. Since the chart only provides market share data, reasoning about business strategies requires additional understanding of the industry beyond the chart.\nBased on the above thought, the answer is:\nThe low market shares of Fruits and Vegetables may be due to a variety of factors, including potentially higher spoilage rates, lack of consumer awareness about the benefits, or pricing issues. To increase their market share, a company could implement strategies such as improving storage and distribution methods to reduce spoilage, launching educational campaigns about the health benefits of these foods, or offering competitive pricing."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "pFxDwQB4qxyq8zpB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which type of food controls more market share - Dairy and Cereal combined or Poultry alone?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can solve this by comparing the market share for Poultry with the combined market share of Dairy and Cereal.\nBased on the above thought, the answer is:\nPoultry."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "BanrgHpfGri91aJN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much of the market share do Red Meat and Dairy combined control?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to sum up the market shares of Red Meat and Dairy.\nBased on the above thought, the answer is:\n38.8%."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "f1tddLZqPu8zAGJH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percent of the market share do Vegan and Organic Food together make up?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires the sum of the market share of Vegan and Organic Food.\nBased on the above thought, the answer is:\n6.1%."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "72bq0W4d3EUdR3Jj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a CSV table from the data observed in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {\"Funding for Scientific Research & Technological Innovation (2014-2020)\"}<r>[0.185, 0.021, 0.778, 0.047]</r> and the pie slices, each representing a year labeled and colored from {the legend}<r>[0.908, 0.063, 0.993, 0.279]</r>, I see that the necessary data to retrieve are the year and funding expressed in percentages.\nBased on the above thought, the answer is:\nYear,Percentage\n2014,11.0%\n2015,12.0%\n2016,14.4%\n2017,15.1%\n2018,15.4%\n2019,16.0%\n2020,16.1%"
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "DhHnwiuUo8z4g7Qo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's information, what can you infer about the changes in scientific research & technological innovation funding from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, I need to look at the pie slices' proportions to understand the distribution of fundings and the changes over these years.\nBased on the above thought, the answer is:\nThe funding for scientific research & technological innovation has increased from 2014 to 2020. The increase seems to be steady, with no dramatic jumps or drops in any given year. The largest allocation of funds was made in the year 2020."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "BtZq66oCMoonOgE7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan anything be concluded about the year 2016 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must look at the area corresponding to 2016 in the pie chart and compare it with the areas allocated to the other years.\nBased on the above thought, the answer is:\nIn 2016, there was a significant increase in funding for scientific research and technological innovation compared to previous years, marking it as a pivotal year for such investments."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "IODfVPkeT9Pi8wyC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could governmental funding bodies use the data portrayed in this chart for their future decision making?"
+            },
+            {
+                "from": "gpt",
+                "value": "With this type of question, I have to think about the implications of the data and how a governmental body may interpret it.\nBased on the above thought, the answer is:\nGovernmental bodies could use this data to evaluate the effectiveness of past funding decisions for scientific research & technological innovation. By focusing on years where funding increased, such as 6 (2016), they could investigate what led to those increases and apply those strategies in future budgeting. Conversely, learning from years like 7 (2020), where despite the highest funding, it was less than the previous year, could offer insights into causes for the slight drop."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "Dh4KjFyUBVRsZ2kG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the size of the smallest fraction, depicted in the chart in terms of Funding Percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest fraction or the least funding percent could be found by looking at each pie slice's percentage and finding the lowest one.\nBased on the above thought, the answer is:\n11.0%"
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "JP3n0EFdAmookAZz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 2020's funding was split evenly between 2020 and a new entry, 2021, what would be the new funding percentage for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the funding for 2020 is 16.1% and it is to be evenly divided between 2020 and 2021, I need to divide this value by 2.\nBased on the above thought, the answer is:\n8.05% for each."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "ZIgqiKF9evz9WqGB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total funding size of years where their funding exceeded the average?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I first need to calculate the funding mean, and then sum the slices' percentages that exceeded this average.\nBased on the above thought, the answer is:\n61.5%"
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "eano3DIF9eZWs2M5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data in the pie chart into a table format that includes the years and percentages for both sectors, Biotech and AI&ML?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the data from the pie chart, I would look at each slice from {the legend}<r>[0.908, 0.063, 0.993, 0.338]</r> and this will give me the year and the percentage that corresponds to that year. Using this information, I can create a table.\nBased on the above thought, the answer is:\nYear,Biotech,AI&ML\n2011,2.75%,3.75%\n2012,3.35%,3.65%\n2013,3.70%,4.10%\n2014,4.25%,4.25%\n2015,4.75%,4.05%\n2016,5.10%,3.5%\n2017,5.75%,3.2%\n2018,6.0%,3.0%\n2019,6.4%,2.8%"
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "ejs8RxMvbcPU0Ls3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector experienced the most growth throughout the years according to the information shown in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the total percentages from each year from the slices' labels in {the legend}<r>[0.908, 0.063, 0.993,0.338]</r>, it can be seen that the sector with the highest total percentage experienced the most growth.\nBased on the above thought, the answer is:\nThe Biotech sector experienced the most growth throughout the years."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "lIpLlP1q7yOZgMSo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you infer any trends about the growth in the Biotech and AI&ML sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the changes in the size of the slices from year to year in the pie chart with the information in {the legend}<r>[0.908, 0.063, 0.993, 0.338]</r>.\nBased on the above thought, the answer is:\nYes, it appears that while both sectors are growing, the rate of growth in the Biotech sector appears to be a bit steadier and consistent, whereas growth in the AI&ML sector started later but rapidly increased especially after 2014."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "qhxnYYXf8bjiyR4F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart's data, can you predict how these sectors might progress in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Predictions can be made based on the trends seen in the pie chart using the data in {the legend}<r>[0.908, 0.063, 0.993, 0.338]</r>.\nBased on the above thought, the answer is:\nOn the assumption that the trends continue, we can expect steady growth in the Biotech sector and potentially rapid growth in the AI&ML sector, although the latter may plateau as the market matures."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "vhxnIIylc6V2q7ZG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual growth rate of Biotech and AI&ML sectors in the period from 2011 to 2019, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate that, I would add all the percentages in each year for both sectors from {the legend}<r>[0.908, 0.063, 0.993, 0.338]</r> and then divide by the total number of years, which is 9.\nBased on the above thought, the answer is:\nThe average annual growth rate of Biotech and AI&ML sectors from 2011 to 2019 is 7.81%."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "BCHmDS71ZGWHNAX6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which sector has the highest percentage in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages from the pie chart's slice labels in {the legend}<r>[0.908, 0.063, 0.993, 0.338]</r> for the year 2019.\nBased on the above thought, the answer is:\nThe Biotech sector has the highest percentage in the year 2019."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "JBNdMN3GjPPFTFEv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of growth in both sectors in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to refer to the pie chart's slice labels from {the legend}<r>[0.908, 0.063, 0.993,0.338]</r> for the year 2014.\nBased on the above thought, the answer is:\nThe total percentage of growth in both sectors in 2014 was 8.5%."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "RcDnBo0tnfjG8nvQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're looking at, could you export the data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {\"Future Technological Development Prospects\"}[0.301, 0.021, 0.674, 0.047] alongside each pie slice pertaining to {\"Artificial Intelligence\"}[0.203, 0.19, 0.462, 0.77], {\"Blockchain\"}[0.298, 0.552, 0.598, 0.923], {\"Cloud Computing\"}[0.509, 0.536, 0.768, 0.868], {\"Machine Learning\"}[0.51, 0.169, 0.769, 0.513], and {\"Data Science\"}[0.357, 0.109, 0.607, 0.479], I gather that each slice showcases the respective relevance for the corresponding technology area.\nBased on the above thought, the answer is:\nTechnology Area,Relevance\nArtificial Intelligence,29\nBlockchain,20\nCloud Computing,17\nMachine Learning,18\nData Science,16"
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "I0vYnseBmwWSx9zb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, which technology area appears to have the highest relevance in future technological developments?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I ought to determine the pie slice that is the largest in order to find out the area with the highest future relevance.\nBased on the above thought, the answer is:\nArtificial Intelligence appears to have the highest relevance based on its largest share in the pie chart."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "3Q767xFnfvEbc3Lz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you infer about the future technological development trends from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferring from the pie slices, the larger the slice, the more relevant the technology area is expected to be, and vice versa.\nBased on the above thought, the answer is:\nThe chart suggests that Artificial Intelligence will play a significant role in future technological developments as it possesses the greatest relevance. Additionally, all five areas will have a considerable influence, indicating that technology development will likely encompass a broad spectrum of fields including AI, Blockchain, Cloud Computing, Machine Learning, and Data Science."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "QmOfOprRCRNZ5rEb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data presented in the chart influence strategic decisions in the technology industry moving forward?"
+            },
+            {
+                "from": "gpt",
+                "value": "To process this, I must take into consideration the impact of the relevance of each of these technology areas on decision-making within the industry.\nBased on the above thought, the answer is:\nThe data suggests that technology businesses should focus greater strategic attention on the development and integration of Artificial Intelligence, which holds the highest relevance. However, other areas like Blockchain, Machine Learning, Cloud Computing, and Data Science also hold significant relevance. Therefore, a balanced strategic focus across these areas might be crucial for comprehensive technological growth and staying competitive in the future market."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "rMfSoKaJ1NYWzc6n",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine which of the technology areas in the pie chart has the least relevance?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this task, I need to compare the sizes of the pie slices and determine the smallest one.\nBased on the above thought, the answer is:\nData Science."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "mSwLI9ZNAkFUkOHA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Cloud Computing increases its relevance by 5%, what will be the new total relevance of all technology areas combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "For calculating this, I need to add 5% to the relevance of Cloud Computing and then sum up the relevances of all technology areas.\nBased on the above thought, the answer is:\n101%"
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "mjNIjvw4ugNUleaV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mean relevance of the five technology areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I first need to sum the relevances of the five areas, and then divide by the number of areas.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "wOCzBBChXtJjgNoF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the pie chart's sector percentages for each technology into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do that, I will need to consider the labels provided for each sector of the pie chart along with the percentages provided. These labels and percentages can come from {the sector slice}[{'AI'}[0.201, 0.299, 0.537, 0.907]; {'IoT'}[0.498, 0.544, 0.731, 0.897]; {'ML'}[0.506, 0.153, 0.765, 0.666]; {'VR'}[0.321, 0.109, 0.563, 0.479]; {'Blockchain'}[0.265, 0.195, 0.464, 0.489]].\nBased on the above thought, the answer is:\nTechnology,Percentage\nAI,40.9%\nIoT,12.9%\nML,27.0%\nVR,15.7%\nBlockchain,3.5%"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "1WNVygaiF5HjZzvv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong these tech categories, which one seems to be leading in terms of innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be determined by the size of the sectors. The larger the slice, the more innovations in that field. By looking at the sizes of the sectors, it seems to be {AI sector}<r>[0.201, 0.299, 0.537, 0.907]</r>.\nBased on the above thought, the answer is:\nThe AI sector is leading in terms of innovations considering it holds the largest proportion of Intellectual Property counts, which suggests a strong innovation drive."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "OTjGjtPJEqyDvIeg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of IP counts, which tech category seems to be underrepresented and may require more focus for progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest slice can be noticed as the sector that is underrepresented in the field of Intellectual Property counts. From observing the slices, it seems to be {Blockchain sector}<r>[0.265, 0.195, 0.464, 0.489]</r>.\nBased on the above thought, the answer is:\nThe Blockchain technology appears to be underrepresented in terms of IP counts, indicating that additional focus and support might be beneficial to progress in that field."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "M7HqM31jQkCnFCDu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the distribution of IP counts across the tech fields reflect the general focus towards technological innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sizes of the sectors can indicate which fields had the most focus during this particular period. By looking at the observed proportions of each sector, we can infer the general focus towards technological innovation.\nBased on the above thought, the answer is:\nThe distribution of IP counts heavily favoring AI and ML indicates a significant focus toward these fields in terms of technological innovation. The smaller proportions of VR, IoT, and particularly Blockchain suggest less focus on these areas."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "AxsZWDsjEXmI97cw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of IP counts aggregate to AI and ML?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by locating the sectors for AI and ML in the pie chart and adding their percentages. The sectors are marked by their bounding box coordinates {AI sector}<r>[0.201, 0.299, 0.537, 0.907]</r> and {ML sector}<r>[0.506, 0.153, 0.765, 0.666]</r>.\nBased on the above thought, the answer is:\n67.9%"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "2piKGAVTlSjM4pkn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between the sector with the highest percentage (AI) and the sector with the lowest percentage (Blockchain)?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage of sectors can be found in their bounding box coordinates. This involves {AI sector}<r>[0.201, 0.299, 0.537, 0.907]</r> with the highest percentage and {Blockchain sector}<r>[0.265, 0.195, 0.464, 0.489]</r> with the lowest percentage.\nBased on the above thought, the answer is:\n37.4%"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "7AYPZW92dkqaFVIf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we disregard the Blockchain sector, what would be the new percentage of AI?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to consider the rest of the sections and calculate the relative weights. For this, I need to subtract {Blockchain sector}<r>[0.265, 0.195, 0.464, 0.489]</r> from the data and recalculate the proportion of {AI sector}<r>[0.201, 0.299, 0.537, 0.907]</r>.\nBased on the above thought, the answer is:\n42.4%"
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "StX5Y3Nwz4OFG4VV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data visualized in the pie chart into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "To extract the raw data, I need to look at each colored slice of the pie and match it with the color labeled for it in the legend. I can see the legend includes {\"Twitter\"}<r>[0.9, 0.071, 0.946, 0.091]</r>, {\"Facebook\"}<r>[0.9, 0.101, 0.965, 0.121]</r>, {\"Whatsapp\"}<r>[0.9, 0.131, 0.97, 0.151]</r>, {\"Instagram\"}<r>[0.9, 0.161, 0.97, 0.181]</r>, and {\"Snapchat\"}<r>[0.9, 0.191, 0.965, 0.211]</r>. % readouts for each slice are contained in the autotext for each sector slice.\nBased on the above thought, the answer is:\nPlatform,Usage(%)\nTwitter,12.6\nFacebook,20.4\nWhatsapp,23.7\nInstagram,24.9\nSnapchat,18.6"
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "tjj4i3zY8FWAjS8d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which social media platform had the highest usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will determine the highest usage by looking at every slice in the chart, the slice with the largest circumference has the highest usage. This can be confirmed by the % detailed in the autotext of each slice.\nBased on the above thought, the answer is:\nInstagram had the highest usage because it had the highest % of 24.9 as shown by the largest slice in the chart."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "jbsAznPZAzgSy2kt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make about the popularity of social media platforms based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity of each platform can be inferred from the size of each slice representing the relation of their usage % to the total 100% represented by the full pie.\nBased on the above thought, the answer is:\nAccording to the chart, Instagram was the most popular platform, followed closely by WhatsApp. Facebook and Snapchat had slightly lesser popularity, while Twitter was the least popular among the listed platforms."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "rggoh4zeVWLwIWMk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow may the depicted data influence a social media marketing strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must contemplate each slice's usage %, which represents the popularity of the associated social media platform. The larger the slice, the more popular the platform is, hence it might attract more marketing efforts.\nBased on the above thought, the answer is:\nBased on the data, Instagram and WhatsApp, being the most-used platforms, might be given more focus in a marketing strategy. More resources could be allocated to creating content for these platforms. Meanwhile, efforts on Twitter might be reduced due to its lesser popularity or a different strategy could be implemented to increase its usage and publicity."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "Igk9kHSigwP2FRbJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has the least usage % as visualized in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "During the observation of the pie chart, I see each labeled slice corresponds to a usage %. The platform with the smallest slice will have the least usage %. Comparing the sizes and labels of each slice will provide the answer.\nBased on the above thought, the answer is:\nTwitter."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "y7BTQE1n4MkebMz5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice representing the \"Snapchat\" platform indicate its usage % is?"
+            },
+            {
+                "from": "gpt",
+                "value": "The usage % is denoted in the slice's autotext. By looking at the Snapchat slice in the pie chart, I can find its corresponding autotext to know its usage %.\nBased on the above thought, the answer is:\n18.6%."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "tpxS0zL53e499gCi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Instagram's usage was to drop by 5%, what would be its new usage %?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate Instagram's new usage %, I must deduct 5 from the current usage % stated in the autotext of Instagram's slice.\nBased on the above thought, the answer is:\n19.9%."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "PRDPZlnMdL1Wo4ra",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data you examined into a structured CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the sublegend and the pie slices that the cultural categories and the proportion of consumption scores they represent. {sublegend}<r>[0.885, 0.063, 0.993, 0.219]</r> has the listings for categories: Italian, French, Chinese, Mexican, and Indian. Each corresponding pie slice in {the pie}<r>[0.197, 0.11, 0.764, 0.918]</r> segment represents a category and its score.\nBased on the above thought, the answer is:\nCuisine,Consumption Score\nItalian,68\nFrench,60\nChinese,75\nMexican,80\nIndian,70"
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "Kl0cGoxrtGS6iHvI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell you about the relative popularity of Italian and Indian cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to understand the relative popularity, I need to compare the slices corresponding to Italian and Indian cuisines in the pie chart.\nBased on the above thought, the answer is:\nThe Italian cuisine is slightly less popular than the Indian cuisine, as its consumption score is 68 compared to Indian's 70."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "q5crCYixEsV7vMWG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cuisines seem to be most and least preferred? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The sizes of pie slices are directly proportional to consumption scores. So, the largest slice will represent the most preferred cuisine and the smallest one will represent the least preferred.\nBased on the above thought, the answer is:\nFrom the chart, it appears that Mexican cuisine is the most preferred with a consumption score of 80, and French cuisine is the least preferred with a score of 60."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "Gxskdi9EkAjK9qab",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were a restaurant owner, what cuisine would you prefer to offer based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "As a restaurant owner looking at this chart, I would consider the cuisine with the highest consumption score because it seems to be the most preferred by customers.\nBased on the above thought, the answer is:\nBased on the chart, I would prefer to offer Mexican cuisine because it has the highest consumption score."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "WCFlxeCJOYNMYsUc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has the highest consumption score according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice on the pie chart represents one cuisine and its score. To find the cuisine with the highest score, I have to look at the largest slice on the pie chart.\nBased on the above thought, the answer is:\nMexican."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "hxW3BpVFPTPII6Xc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total consumption score of all cuisines according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To total the consumption score, I need to add the scores of each cuisine together.\nBased on the above thought, the answer is:\nThe total consumption score is 68 (Italian) + 60 (French) + 75 (Chinese) + 80 (Mexican) + 70 (Indian) = 353."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "PgzZxtysRolEX3yK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more popular is the Chinese cuisine compared to the French one?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to compare the Chinese cuisine to the French one, I have to subtract the French score from the Chinese score.\nBased on the above thought, the answer is:\nThe difference is 75 (Chinese) - 60 (French) = 15."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "brEHNLuwwevdJHYZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation, could you represent the data from the pie chart in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the legend at the side of the chart {\"AI in Healthcare\"}<r>[0.877, 0.071, 0.987, 0.091]</r> and {\"AI in Transport\"}<r>[0.877, 0.101, 0.977, 0.121]</r>, and the corresponding slices of the pie with their percentages {\"AI in Healthcare slice\"}<r>[0.208, 0.308, 0.692, 0.916]</r> and {\"AI in Transport slice\"}<r>[0.298, 0.115, 0.756, 0.669]</r>, I can see that each sector represents a percentage distribution in the pie chart.\nBased on the above thought, the answer is:\nSectors,AI in Healthcare,AI in Transport\nPercentages,52.8%,47.2%"
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "WUpcgInP2isVUvMT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which application of AI has more case studies? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages for the case studies of each application to find the application with the highest percentage.\nBased on the above thought, the answer is:\nThe application of AI in Healthcare has more case studies as it has a higher percentage of 52.8% compared to AI in Transport with 47.2%."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "qqXxY0cljddDRDIF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the focus of AI application case studies from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the percentages of the two sectors to make an inference about the focus.\nBased on the above thought, the answer is:\nIt can be inferred that more AI application case studies were conducted in the Healthcare sector than in the Transport sector from 2016 to 2021."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "qJxCJdEdyhCbbAbW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart guide future research directions in the field of AI?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider how the balance of case studies might influence future focus areas in AI research.\nBased on the above thought, the answer is:\nAs AI in Healthcare has a higher percentage of case studies, it suggests that this might be a well-researched area. Therefore, future research may either continue to explore this mature field deeper or direct more focus towards the AI in Transport, which might be relatively less explored, to balance the research efforts."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "H1AX0hKlSAEADkkN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the applications of AI in Healthcare and Transport sectors according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to subtract the percentage of \"AI in Transport\" from the percentage of \"AI in Healthcare\".\nBased on the above thought, the answer is:\nThe percentage difference is 52.8% (AI in Healthcare) - 47.2% (AI in Transport) = 5.6%."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "rcFqhEhsfgcP0Pns",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage would \"AI in Transport\" require to even match with the \"AI in Healthcare\" sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the required percentage, I need to find the result from Q1 which is the percentage difference between the two sectors.\nBased on the above thought, the answer is:\nThe \"AI in Transport\" sector would require an additional 5.6% to match with the \"AI in Healthcare\" sector."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "YfMYPK9KnLiKEe1V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of \"AI in Healthcare\" decreases by 3% in the next analysis, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract 3% from the current percentage of \"AI in Healthcare\".\nBased on the above thought, the answer is:\nThe new percentage for \"AI in Healthcare\" would be 52.8% - 3% = 49.8%."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "bWqpOLBVjAkfgiXC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, there are five slices representing Credit Risk, Market Risk, Operational Risk, Liquidity Risk, and Business Risk recorded in five different labels in the legend {legend}<r>[0.828, 0.063, 0.993, 0.219]</r>. The expenses associated with each risk type are displayed as percentages in autotexts spread around the pie chart {pie_0}<r>[0.197, 0.11, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nRisk Type,Expense(%)\nCredit Risk,25.1%\nMarket Risk,20.5%\nOperational Risk,15.7%\nLiquidity Risk,18.0%\nBusiness Risk,20.9%"
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "EyS6GoKebrRCVQEC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the pie chart, which risk type does the company focusing on the most and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to identify the slice with the largest percentage. That would indicate that the company is focusing more in that risk type.\nBased on the above thought, the answer is:\nThe company is focusing the most on Credit Risk. It consumes the a relative high proportion (25.1%) of the expenses in the risk management department."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "kSWozoR8nNaKvKma",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the distribution of the company's risk management expenses?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions of different risk types to evaluate the distribution of expenses.\nBased on the above thought, the answer is:\nAccording to the pie chart, the allocation of expenses in risk management is leaning towards Credit Risk and Business Risk, as they take up respectively 25.1% and 20.9% of the total expenses. Operational Risk seems to be having the least allocation with only 15.7% of the total risk budgets."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "au412RhRourrx61i",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the company wants to balance the expense on all risk types, what changes should be made?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to review the proportions of all risk types and suggest adjustments to make them approximately equal.\nBased on the above thought, the answer is:\nTo balance the expenses across all risk types, the company may need to divest some of its resources from Credit Risk, which currently accounts for 25.1% of the expenses, and invest more in Operational Risk, which only accounts for 15.7%. In addition, there might be need for slight adjustments in Market Risk, Liquidity Risk, and Business Risk expenses."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "pAE0Vv6cjWT9R98J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion of Market Risk and Liquidity Risk provided within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find both the Market Risk and Liquidity Risk slices {slice 1}<r>[0.316, 0.516, 0.626, 0.886]</r> and {slice 3}<r>[0.482, 0.181, 0.741, 0.516]</r> respectively to get their percentages and then add them together.\nBased on the above thought, the answer is:\n38.5%"
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "YxcQnhlnac779Gb4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 5% from Operational Risk gets redistributed to Credit Risk, what percentage would Credit Risk command?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of Credit Risk, I need to subtract 5% from Operational Risk's {slice 2}<r>[0.482, 0.515, 0.741, 0.823]</r> current percentage and add it to Credit Risk's current value {slice 0}<r>[0.197, 0.281, 0.456, 0.804]</r>.\nBased on the above thought, the answer is:\n30.1%"
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "OJISmjNjb0GAjdSQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which risk type commands less than 20% expense?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the risk type with an autotext showing less than 20%. This is found in the pie chart slice information {slice 2 and 3}<r>[0.482, 0.515, 0.741, 0.823]</r> and <r>[0.482, 0.181, 0.741, 0.516]</r> respectively.\nBased on the above thought, the answer is:\nOperational Risk and Liquidity Risk."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "UUFUhmJJJd51ETRd",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the information represented in the pie chart into a CSV structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart visualizes the potential for efficiency improvement. Each slice represents a different category, and the size of each slice is proportional to the quantity of cases in each category, according to the raw data. The categories are: 'High', 'Medium', 'Low', 'Unknown', 'Pending', 'New', 'Exceptional', and 'Discarded'.\nBased on the above thought, the answer is:\nPotential, Quantity\nHigh, 25\nMedium, 40\nLow, 11\nUknown, 13\nPending, 55\nNew, 5\nExceptional, 15\nDiscarded, 2"
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "SrnHBm7U3cMWgf5N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the least potential for efficiency improvement? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "The category with the least potential would be the one with the smallest number of cases.\nBased on the above thought, the answer is:\nThe 'Discarded' category has the least potential for efficiency improvement as it only has 2 cases, which is the smallest amount compared with any other category."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "VYDvf3ivqYJZpotx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about efficiency improvements from this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to observe the distribution of the cases to infer the efficiency improvements.\nBased on the above thought, the answer is:\nBased on the pie chart, a significant number of efficiency improvement cases are 'Pending', representing the highest potential for improvement. Meanwhile, 'Medium' level improvements are the second most prevalent, suggesting there is a reasonable degree of improvement potential. The 'High' and 'Exceptional' categories also indicate the existence of cases with significant improvement potential, although their quantities are less. A small number of cases are 'Discarded', indicating these may have been deemed ineffective or not feasible for improving efficiency."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "bFJwVaaSr0J92wJj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we want to focus on high-impact efficiency improvements, which categories should we pay attention to the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "The categories that have high potential for efficiency improvement are those with the largest number of cases.\nBased on the above thought, the answer is:\nWe should pay the most attention to the 'Pending' and 'Medium' categories, as they have the highest number of potential cases for efficiency improvement."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "1rRqn3qznAOWkirr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of efficiency improvement potential cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number of cases, I need to sum up the quantity of cases in each category.\nBased on the above thought, the answer is:\n166"
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "bFuy5ZTpY4CE8SVJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more potential cases are in the 'Pending' category than in the 'High' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the quantity of 'High' cases from the quantity of 'Pending' cases.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "9SFQkdH5ajqC7eic",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we discard all 'Discarded' cases, what would be the new total quantity of cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total quantity, I should subtract the quantity of 'Discarded' cases from the original total quantity.\nBased on the above thought, the answer is:\n164"
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "DAHZA7Xp9qvhQw85",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, can you convert the data of different medical and biotechnology industry trends into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In line with the subtitles at the top of the chart {\\\"Medical and Biotechnology Industry Trends\\\"}[0.303, 0.021, 0.66, 0.047] and the slices of sectors {Nanotechnology}[0.197, 0.282, 0.456, 0.818], {Biotech}[0.328, 0.553, 0.632, 0.923], {Pharmaceuticals}[0.505, 0.511, 0.764, 0.833], {Medical Device}[0.504, 0.186, 0.763, 0.497], {Healthcare}[0.423, 0.109, 0.626, 0.479] and {Life Sciences}[0.269, 0.125, 0.468, 0.485]; I can recognize that the pie sectors are representing distinct medical and biotech categories and the percentage each category holds.\nBased on the above thought, the answer is:\nIndustry, Percentage\nNanotechnology, 26\nBiotech, 20\nPharmaceuticals,16\nMedical Device, 15\nHealthcare, 13\nLife Sciences, 10"
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "oXMfMkryQdsPZzYL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which is the sector holding the largest share in the medical and biotechnology industry trends? And elaborate on why."
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I need to compare the sizes of the pie slices, with the largest slice indicating the sector with the most significant share.\nBased on the above thought, the answer is:\nNanotechnology holds the largest share in the medical and biotechnology industry trends as it has the widest pie slice indicating a growth rate of a whopping 26%."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "h7ETEPx9Zxd38Nn7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make an inference about the industry distributions from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Making an inference about the industry distributions requires looking at how much share each sector holds in the pie chart.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that Nanotechnology significantly dominates the medical and biotechnology industries, holding more than a quarter of the total growth. Meanwhile, sectors like Pharmaceuticals, Medical Device, Healthcare, and Life Sciences have a somewhat similar market share with a bit of variation, indicating a balanced growth among these sectors."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "aR6QynFqaKnKU2ES",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data in this pie chart impact investment decisions in the medical and biotechnology industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investment decisions are often influenced by the growth rates of different sectors, as represented by their shares in the pie chart.\nBased on the above thought, the answer is:\nConsidering the data, investors might put a substantial amount of their funds into Nanotechnology as it holds the highest growth rate. Biotech, being the second highest, could also be a good option for investment. For Pharmaceuticals, Medical Device, and Healthcare sectors, balancing the investment could be a good strategy as their growth rates are closely tied. On the other hand, Life Sciences might need a more careful examination before investing, seeing that it holds the least growth rate among all sectors."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "NR6p1KE1VQFzvi6U",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage of Pharmaceuticals and Medical Device industries in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the combined percentage, I need to add the percentages of both Pharmaceuticals and Medical Device industries.\nBased on the above thought, the answer is:\nThe combined percentage is 16% (Pharmaceuticals) + 15% (Medical Device) = 31%."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "zILLcMktJNb4opWx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Healthcare sector grows by 7%, what will be its new percentage in the industry trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add this growth to the existing percentage of the Healthcare sector.\nBased on the above thought, the answer is:\nThe new percentage for Healthcare will be 13% + 7% = 20%."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "0DalC7yJMggOv2zp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage does the smallest sector, Life Sciences, need to double its current size?"
+            },
+            {
+                "from": "gpt",
+                "value": "To double the size, the sector needs to increase by the same amount as its current percentage.\nBased on the above thought, the answer is:\nSo, Life Sciences needs to increase by another 10% to double its current size."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "rQC4OhFZEAno4W3N",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the pie chart's data into a readable CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {chart's title}<r>[0.274, 0.021, 0.689, 0.047]</r> and the {pie slices}<r> [{\"CEO\"}[0.197, 0.275, 0.456, 0.709]; {\"CFO\"}[0.256, 0.552, 0.603, 0.922]; {\"CTO\"}[0.504, 0.513, 0.763, 0.856]; {\"CMO\"}[0.5, 0.123, 0.759, 0.491]; {\"COO\"}[0.274, 0.111, 0.502, 0.482]]</r>, I can deduce that each slice represents the skillset level of a different management role in the organization.\nBased on the above thought, the answer is:\nRole, Percentage of Advanced Skillset\nCEO, 70%\nCFO, 85%\nCTO, 62%\nCMO, 78%\nCOO, 55%"
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "t9nPmjCSihAAM2Be",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which role's contribution to the advanced skillset is the least significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the role with the smallest pie slice, which symbolizes the lowest contribution.\nBased on the above thought, the answer is:\nThe COO role has the least contribution, as evident from its pie slice, which is the smallest at 15.7%."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "w5hUC0jEVFGpxlsz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the key skill requirements in the company's management roles based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the sizes of the pie slices which correspond to the percentage of advanced skills required for each role, I can infer the skill requirements.\nBased on the above thought, the answer is:\nIt can be inferred that the CFO role requires the maximum number of advanced skills, followed by the CMO. The CEO and CTO require somewhat similar levels of advanced skills, while the COO has the lowest advanced skill requirements."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "H5MkQgE8hk8jJnhF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this chart possibly guide promotions within the organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to think about how the chart, which shows the percentage of advanced skills required for each role, could influence decisions about promotions.\nBased on the above thought, the answer is:\nThe chart could influence promotions by helping to identify skill gaps between positions. If an employee in a lower role has a skillset matching or surpassing that of a higher role, they could be considered for promotion. For instance, if a CTO demonstrates advanced skills found in 85% of CFOs, they may be considered for the CFO role."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "liUpgwi2Ojnl9Hf6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the roles that accumulate to approximately half of the total percentage of advanced skills?"
+            },
+            {
+                "from": "gpt",
+                "value": "Calculating the percentages of each pie slice, I need to find a combination of roles that would accumulate to around 50%.\nBased on the above thought, the answer is:\nCFO and COO."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "RLi84sFQrAoUS7qE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich managerial position requires the highest level of advanced skills?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will compare the percentages of all the roles and determine the one with the highest percentage.\nBased on the above thought, the answer is:\nCFO."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "lSejkKu4dv1atano",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRoughly, what is the difference between the role requiring the highest level of advanced skills and the one requiring the least?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the smallest percentage from the largest one.\nBased on the above thought, the answer is:\nThe difference is about 30%."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "rISSGEwMpNT0MNdx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data in the chart you observed into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart with its informative title {\"Number of Patients: Heart Disease vs Diabetes (2013-2019)\"}<r>[0.232, 0.021, 0.731, 0.047]</r> and the {legend-data}<r>[0.845, 0.063, 0.993, 0.129]</r>, I can see the number of cases for each disease in respective years. The slices of the {pie-chart}<r>[0.198, 0.111, 0.766, 0.921]</r> represent each year from 2013 to 2019 where the text for each year is given on each slice while the autotext defines the proportion of the cases.\nBased on the above thought, the answer is:\nYear,Heart Disease,Diabetes\n2013,24,10\n2014,27,15\n2015,34,12\n2016,29,11\n2017,35,14\n2018,41,16\n2019,38,21"
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "zaN9EIWCvYdd1JWT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich disease showed a greater increase in the number of patients over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing the yearly count of patients for both diseases from {the pie chart}<r>[0.198, 0.111, 0.766, 0.921]</r>, I need to calculate the increase in the number of patients over the years for both diseases.\nBased on the above thought, the answer is:\nHeart Disease showed a greater increase in the number of patients over the years."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "x1CAzeldnzewIWqm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease indicate a year that could be considered an \"outlier\" for both diseases and explain your judgement."
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing {the pie chart}<r>[0.198, 0.111, 0.766, 0.921]</r>, I need to identify the year where there was a significant jump in the number of cases for both diseases from the previous year.\nBased on the above thought, the answer is:\nThe year 2015 can be considered an \"outlier\" for Heart Disease and 2019 for Diabetes, as during these years, there was a substantial increase in the total number of patients from their previous years."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "3lBUDBHilJYML8Oc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data presented in the chart be used by health authorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given my visual analysis of {the pie chart}<r>[0.198, 0.111, 0.766, 0.921]</r>, I need to hypothetically consider the factors health authorities could potentially focus on.\nBased on the above thought, the answer is:\nHealth authorities can use this data to identify the increase in the prevalence of Heart Disease and Diabetes over the years. They could then invest in more effective preventive measures, improve healthcare facilities and services, and create awareness campaigns focusing on these diseases. Special focus should be given to the \"outlier\" years (2015 for Heart Disease and 2019 for Diabetes) to investigate the potential causes behind the significant jump in cases."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "1I5tn7BT4CJdpA7G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of patients for both diseases in the year 2015."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the number of patients for both the diseases in 2015, I need to locate the labels and values from {2015 slice data}<r>[0.285, 0.551, 0.492, 0.921]</r>.\nBased on the above thought, the answer is:\nThe total number of patients in 2015 is 46."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "NwHEgCjD2p9e3ONg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the number of Heart Disease patients from 2013 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to subtract the number of cases in 2015 from 2013 from my observation of {2013 slice data}<r>[0.199, 0.265, 0.457, 0.503]</r> and {2015 slice data}<r>[0.285, 0.551, 0.492, 0.921]</r>.\nBased on the above thought, the answer is:\nThe increase in the number of Heart Disease patients from 2013 to 2015 is 10."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "GGwFrCnO3EwbE4Li",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of patients with Diabetes from 2013 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the numbers of Diabetes patients from 2013 to 2019 and then divide it by the number of years.\nBased on the above thought, the answer is:\nThe average number of Diabetes patients from 2013 to 2019 is 14."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "rZN7XDKr4AMVL64Q",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data on global CO2 emissions from 2015 to 2021 from the pie chart you have analyzed into a structured CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Global CO2 Emissions from 2015 to 2021}<r>[0.31, 0.021, 0.653, 0.047]</r> and the slices with their corresponding labels and percentages in the pie chart, I can see that the size of each slice represents the CO2 emissions of each year from 2015 to 2021.\nBased on the above thought, the answer is:\nYear,Global CO2 Emissions\n2015,14.3%\n2016,14.5%\n2017,14.8%\n2018,14.8%\n2019,14.7%\n2020,13.7%\n2021,13.2%"
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "x5ijVThBh8oxbNiQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, which year had the highest global CO2 emissions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the slices, which represent the percentages of CO2 emissions for each year, and find the largest slice.\nBased on the above thought, the answer is:\nThe year 2017 and 2018 had the highest global CO2 emissions, each accounting for 14.8% of the total emissions during the period 2015-2021."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "7toWuE5izv6fug5O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat were the significant changes in CO2 emissions over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the sizes and labels of the slices in the pie chart to understand the changes in CO2 emissions over time.\nBased on the above thought, the answer is:\nCO2 emissions rose steadily from 2015 through to 2018, after which they showed a slight decline. However, there was a noticeable drop in 2020, which can be attributed to the reduction in human activity due to the Covid-19 pandemic."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "3wf8csofCKonMKzB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this chart suggest about the global trend of CO2 emissions and how might this impact future policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must interpret the trend and implications of CO2 emissions data from the chart.\nBased on the above thought, the answer is:\nThe chart shows a worrying trend of increasing CO2 emissions, reaching a peak in 2018, and then a slight reduction. However, the sharp drop in 2020 is likely an anomaly due to the pandemic rather than a result of climate policies. This analysis suggests that effective policies and actions need to be implemented to reverse the rising trend of CO2 emissions and achieve long term, sustainable reductions."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "qJVkTbl7LSJRIGAi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell me what the average percentage of global CO2 emissions was between 2015 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I need to add up the percentages from each year and then divide by the number of years, which is 7.\nBased on the above thought, the answer is:\nThe sum of the percentages is 100%. The average percentage is 100% / 7 \u2248 14.28%."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "0w51dSOKZOITaitO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year shows a decline in CO2 emissions compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of CO2 emissions for each year and find where the percentage goes down compared to the previous year.\nBased on the above thought, the answer is:\nFrom 2019 to 2020, the percentage of CO2 emissions decreased."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "P1AdU8yrepX1awQv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what percentage of global CO2 emissions accumulated over the period 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the accumulated percentage of global CO2 emissions from 2018 to 2021, I need to add up the percentages of CO2 emissions from 2018, 2019, 2020, and 2021.\nBased on the above thought, the answer is:\nThe accumulated percentage of global CO2 emissions from 2018 to 2021 is 14.8% + 14.7% + 13.7% + 13.2% = 56.4%."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "32AQg5p1aNus0U4H",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tabulate the pie chart data shown, providing the year and corresponding market share details in a CSV-like format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title \"Building and Real Estate Market Trends from 2014 to 2020\"<r>[0.239, 0.021, 0.724, 0.047]</r> and the legend with labels for the years 2014 to 2020 <r>[0.908, 0.063, 0.993, 0.279]</r>, I can see the market shares for each year represented as pie slices<r>[0.201, 0.11, 0.765, 0.906]</r>. Each slice's percentage depicts the market share in the specified year.\nBased on the above thought, the answer is:\nYear,Market Share(%)\n2014,40\n2015,30\n2016,15\n2017,10\n2018,3\n2019,1\n2020,1"
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "2AFA7IFfHQfcL0Ku",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, during which period did the Building and Real Estate sector have the highest market share? And why might this be?"
+            },
+            {
+                "from": "gpt",
+                "value": "The period with the highest market share can be determined by noting the largest slice of the pie.\nBased on the above thought, the answer is:\nThe Building and Real Estate sector had the highest market share in 2014, as per the largest slice of pie <r>[0.201, 0.298, 0.522, 0.906]</r> labeled as \"2014\". It might be due to potential booms in the real estate market, successful projects, or favorable economic conditions during that year."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "kF0YwdFeaXHXcESD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the Building and Real Estate sector's market trends from 2014 to 2020 using the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the pie slices sizes and their change over the years to make inferences about market trends.\nBased on the above thought, the answer is:\nThe Building and Real Estate sector showed a significant decrease in market share from 2014 to 2020, shown by the decrease in size of the pie slices <r>[0.201, 0.11, 0.765, 0.906]</r> over the years. This indicates a decline in the sector's market position over this period."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "utIL4RyvcVAQe5FU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are potential business implications for the Building and Real Estate sector given these market share trends from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the business implications, I need to look at the trends in market shares over the years and consider their potential impact on business strategy.\nBased on the above thought, the answer is:\nThe significant decline in market share for the Building and Real Estate sector from 2014 to 2020 suggests an increasingly competitive market or possible challenges in the sector. This indicates a need for the company to review and adjust its strategy, possibly focusing more on competitiveness, market analysis, innovation, or customer retention."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "B3nlmFw7KJKI9FxT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the span of how many years was there a 50% decrease in market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to look for when the market share decreased from a higher percentage to half of that percentage.\nBased on the above thought, the answer is:\nIn 2014 the market share was 40%, and in 2017, it decreased to 20%, so in a span of 3 years, there was a 50% decrease."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "OT5CyU2742WIM23O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share for the period from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to sum up the market shares from the years 2016 to 2020.\nBased on the above thought, the answer is:\nThe total market share from 2016 to 2020 is 15% + 10% + 3% + 1% + 1% = 30%."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "wNO3dSisQ46GKlj4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the market share percentage decrease from 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the 2015 market share percentage from the 2014 market share percentage.\nBased on the above thought, the answer is:\nThe difference between the market shares in 2014 and 2015 is 40% - 30% = 10%."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "goGs7aHzDYStNdKU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the growth rate data for E-commerce and Social Media from 2012 to 2021 into a structured table from your analysis of the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing the bar chart, it's evident that the \"E-commerce\" and \"Social Media\" bars represent the growth for each year from 2012 to 2021. The bars comparably represent the growth rate for each data point, with labels to indicate the exact values. The percentage data of the pie cart {\"52.0%\" for E-commerce}<r>[0.21, 0.863, 0.297, 0.883]</r> and {\"48.0%\" for Social Media}<r>[0.666, 0.148, 0.754, 0.168]</r> confirmed that the data is accurate, being consistent with the raw data that was provided.\nBased on the above thought, the answer is:\nYear,E-commerce,Social Media\n2012,55,68\n2013,60,74\n2014,65,78\n2015,75,85\n2016,85,90\n2017,95,95\n2018,105,100\n2019,110,110\n2020,120,115\n2021,130,120"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "HT6e4CSI8dBvxnBL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry experienced a more pronounced growth from 2012 to 2021 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart displays how the growth rates of E-commerce and Social Media in 2021 compare. To determine the industry that showed more growth over the given time frame, I need to consider the growth rate in 2021.\nBased on the above thought, the answer is:\nThe E-commerce industry experienced a more pronounced growth from 2012 to 2021. It occupied 52% of the total growth in 2021, as indicated by its larger slice on the pie chart."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "1z8gXC9mclvm37SV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, can you predict the trend of growth rate in the two sectors for the upcoming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a prediction, I need to evaluate the trend of growth rates for E-commerce and Social Media from 2012 to 2021.\nBased on the above thought, the answer is:\nThe data indicates that both sectors have been growing consistently since 2012, with E-commerce showing a faster growth rate than Social Media. If the current trends continue, we can predict that growth in both sectors will persist, with E-commerce potentially outpacing Social Media in growth rate."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "KsJIjZnuZjycqolb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart analysis, which industry should a prospective investor consider investing in for high returns in the coming year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to evaluate the growth rate of both industries. The investor should ideally invest in the industry that exhibits a greater growth rate, thereby implying greater potential return on investment.\nBased on the above thought, the answer is:\nA prospective investor should consider investing in the E-commerce industry. According to the pie chart, it showed a greater growth rate of 52% compared to Social Media's 48% in the year 2021, suggesting a more robust upward trend."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "XAMNVzEbynuxZ5Ux",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total growth rate in 2021 for both E-commerce and Social Media as illustrated by the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total, I need to sum up the growth rates of both industries in 2021 according to the pie chart. I can assume the years are represented sequentially in order from 2012 to 2021.\nBased on the above thought, the answer is:\n250"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "uQ0zDwhjb7AsXlBs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the percentage of E-commerce's growth in 2021 compared to the total growth of both industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To acquire E-commerce's percentage growth, the proportion of its segment in the pie chart is to be taken into consideration. The legend confirms this percentage {\"52.0%\" for E-commerce}<r>[0.21, 0.863, 0.297, 0.883]</r>.\nBased on the above thought, the answer is:\n52%"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "zAMTyOVepNcOqOlW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the growth rate for E-commerce increase from 2019 to 2021 according to the bar chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in E-commerce's growth rate, I need to subtract its growth rate in 2019 from that of 2021 using the bar chart details.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "Oq83ujWZa0UQ8R0f",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data plotted in the chart and put it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the data from the pie chart into a structured format, I'll locate and quantify each slice of the pie. Each slice corresponds to a year between {2015}<r>[0.952, 0.071, 0.987, 0.091]</r> and {2021}<r>[0.952, 0.251, 0.987, 0.271]</r> and the size of the slice represents the degree of change for that year.\nBased on the above thought, the answer is:\nYear,Change in Social Value\n2015,10.9\n2016,12.7\n2017,15.5\n2018,13.7\n2019,16.9\n2020,18.3\n2021,12.0"
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "ihnPXaHNY1aJEe1q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, which year experienced the biggest change in social value? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The answer can be found by looking at the pie slice that is the largest. The size of each slice corresponds to the degree of change for a given year.\nBased on the above thought, the answer is:\nThe year 2020 experienced the biggest change in social value, as indicated by its slice, {2020}<r>[0.952, 0.221, 0.987, 0.241]</r>, being the largest in the pie chart."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "1RwGmA5ZT4ztPkuY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of social change over the period based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "An inference can be made by looking at the changes in the size of the slices over the years.\nBased on the above thought, the answer is:\nThe trend of social change over the period shows a general increase, with 2020 having the highest degree of change. However, 2021 varies from this trend by showing a decrease in social change, suggesting a possible shift in the trend."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "ITmAufE4B5w7mJsW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of planning could be done based on the social value changes indicated by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The planning suggestion can be made based on the trend demonstrated in the pie chart.\nBased on the above thought, the answer is:\nSince the pie chart shows an overall increase in social value changes through the years, reaching its highest in 2020, plans could have been made to maintain and capitalize on this upward trend. However, the drop in social value change in 2021 indicates that plans to counteract or understand this reduction might be needed."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "iZN6XX7vx8uTjIfu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage change in social value in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By checking the slice representing {2019}<r>[0.952, 0.191, 0.987, 0.211]</r> in the pie chart, I can see the degree of change for this year.\nBased on the above thought, the answer is:\n16.9%"
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Iqq94ynKwgQH58iS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average change in social value over the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to add the degrees of change for each year from 2015 through 2021 and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average change is (10.9 + 12.7 + 15.5 + 13.7 + 16.9 + 18.3 + 12.0) / 7 = 14.3%."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "m9VDiuUpKDRicWdZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the year 2020 had a 5% increase in its change in social value, what would be the total change for the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to adjust the social value degree for {2020}<r>[0.952, 0.221, 0.987, 0.241]</r> by adding 5% to the original value and then re-calculate the total.\nBased on the above thought, the answer is:\nThe total change would be 10.9% (2015) + 12.7% (2016) + 15.5% (2017) + 13.7% (2018) + 16.9% (2019) + 23.3% (adjusted 2020) + 12.0% (2021) = 105.0%."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "rK7gUJKsUEAibe2j",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data you analyzed, can you place the data in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the annotated distributions of each year in the pie chart, such as {2020}[0.206, 0.261, 0.458, 0.499], {2021}[0.197, 0.435, 0.456, 0.686], {2022}[0.235, 0.546, 0.466, 0.91], {2023}[0.447, 0.55, 0.708, 0.92], {2024}[0.533, 0.238, 0.792, 0.711] and {2025}[0.282, 0.109, 0.658, 0.479] can be extracted and arranged in a structured manner.\nBased on the above thought, the answer is:\nYear,Integration\n2020,30\n2021,45\n2022,60\n2023,75\n2024,90\n2025,105"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "9Vme2CDJuA51XFP8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you identify which year saw the most significant increase in the integration of future technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportion of each year in the pie chart. The year with the largest proportion represents the most significant increase. This can be seen in slices such as {2020}[0.206, 0.261, 0.458, 0.499], {2021}[0.197, 0.435, 0.456, 0.686], {2022}[0.235, 0.546, 0.466, 0.91], {2023}[0.447, 0.55, 0.708, 0.92], {2024}[0.533, 0.238, 0.792, 0.711] and {2025}[0.282, 0.109, 0.658, 0.479].\nBased on the above thought, the answer is:\nThe year 2025 saw the most significant increase in the integration of future technologies, as it is presented with the largest slice in the pie chart."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "2TNrGjYlBLO1nHlr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this chart, what inference can be made about the future integration of technologies into our daily lives?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will pull from the chart's visual elements and the depicted trends.\nBased on the above thought, the answer is:\nThe pie chart suggests a steady and significant growth in the integration of future technologies into daily life scenarios. Based on the increasing size of the slices from 2020 to 2025, it's evident that technology integration is predicted to become more prevalent over time. This highlights our increasing reliance on technological advancements."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "5IrhDWFK1GqzWHt2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you predict the pattern of future technology integration beyond the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can make a hypothetical prediction based on the ongoing trend spotted in the pie slices {2020}[0.206, 0.261, 0.458, 0.499], {2021}[0.197, 0.435, 0.456, 0.686], {2022}[0.235, 0.546, 0.466, 0.91], {2023}[0.447, 0.55, 0.708, 0.92], {2024}[0.533, 0.238, 0.792, 0.711] and {2025}[0.282, 0.109, 0.658, 0.479].\nBased on the above thought, the answer is:\nGiven the consistent upward trend in the integration of future technologies from 2020 to 2025 as represented in the pie chart, it's reasonable to predict that this tendency will probably continue beyond 2025. However, it's speculative and should be substantiated with more focused studies and data."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "bV8Qipn9tWNIUJet",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total predicted increase in the integration of future technologies from 2020 to 2025 based on the pie chart's slices' percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the provided percentages for each year seen in slices such as {2020}[0.206, 0.261, 0.458, 0.499], {2021}[0.197, 0.435, 0.456, 0.686], {2022}[0.235, 0.546, 0.466, 0.91], {2023}[0.447, 0.55, 0.708, 0.92], {2024}[0.533, 0.238, 0.792, 0.711] and {2025}[0.282, 0.109, 0.658, 0.479], I can calculate the total predicted increase by summing them up.\nBased on the above thought, the answer is:\n375"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "bhgW1VkMJGYP7ZYJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how much will the integration of future technologies increase in 2022 compared to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the value of integration in 2020 from the value in 2022 seen in slices {2020}[0.206, 0.261, 0.458, 0.499] and {2022}[0.235, 0.546, 0.466, 0.91].\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "CgDH8xcPErt6ivBE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mean rate of integration of future technologies each year from 2020 to 2025 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The mean rate would be the total increase in integration from 2020 to 2025 divided by the number of years. The values for each year can be found in slices such as {2020}[0.206, 0.261, 0.458, 0.499], {2021}[0.197, 0.435, 0.456, 0.686], {2022}[0.235, 0.546, 0.466, 0.91], {2023}[0.447, 0.55, 0.708, 0.92], {2024}[0.533, 0.238, 0.792, 0.711] and {2025}[0.282, 0.109, 0.658, 0.479].\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "yPTLzgHO7Fwzmilh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart observed, can you help me to translate the distribution of research activities into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "As labeled in the pie chart's legend, we have different research fields such as {Arts}<r>[0.881, 0.071, 0.909, 0.091]</r>, {Sciences}<r>[0.881, 0.101, 0.942, 0.121]</r>, {Social Sciences}<r>[0.881, 0.131, 0.987, 0.151]</r>, {Business}<r>[0.881, 0.161, 0.942, 0.181]</r>, and {Engineering}<r>[0.881, 0.191, 0.964, 0.211]</r>. The size of the parts that these labels correspond to in the pie map shows the number of research activities in these fields.\nBased on the above thought, the answer is:\nFields, Number_of_Projects\nArts, 40\nSciences, 200\nSocial Sciences, 150\nBusiness, 70\nEngineering, 280"
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "c4RdhNc3Tc91W6BY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field of education and research is more intensive in academic research activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, I would consider the sector with the largest slice as representing the most intensive research activity, which can be identified by its size and label.\nBased on the above thought, the answer is:\nThe most intensive activity is in the field of \"Engineering\"."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "jvrjEPlxuDG753fJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can be made regarding the distribution of academic research across different fields from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences, I need to consider both the size and distribution proportions of the sectors, as represented in the pie chart.\nBased on the above thought, the answer is:\nThe pie chart shows the most research activity is occurring in \"Engineering\", \"Sciences\", and \"Social Sciences\", suggesting these are currently the most favored areas of academic research. In contrast, \"Arts\" and \"Business\" have less activity, indicating these areas might be less popular or under-resourced."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "wSXi1zFEkGTdNZ7g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data in pie chart influence policy decision-making in terms of resource distribution in the next fiscal year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to think about the proportions and their potential influence on decision-making processes.\nBased on the above thought, the answer is:\nThe quantity of ongoing projects could inform funding allocation. Areas like \"Engineering\", \"Sciences\", and \"Social Sciences\" with high activity, might receive further funding to sustain the momentum. On the other hand, areas like \"Arts\" and \"Business\", with fewer ongoing projects, might attract attention for increased funding to promote diversity in research."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "QWUX3AZnoqsaXigj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find out the total number of research projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total number of research projects, I'll sum up the counts from each of the different research fields. These are identified by their respective labels and sizes in the pie chart's slices.\nBased on the above thought, the answer is:\n740"
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "6GAQeAXRUz4zfcAT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of Arts research projects in total research projects according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slice provides the percentage of the arts field. To find out the proportion of Arts, I should calculate the ratio of the quantity of Arts out of the total quantity of all sectors.\nBased on the above thought, the answer is:\nApproximately 5.4%"
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "WFybTFSw3iu5AmMs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the range of the projects that the Engineering field takes up in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the range, I need to locate the slice representing Engineering in the pie chart and observe the scope.\nBased on the above thought, the answer is:\nTheta range is from 364 to 500."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "lQUikguKU3NOc6qw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the pie charts available, could you please organize the cost differences of each stage of the value chain (in terms of percentage) between the Coffee Processor and the Off-season Supplier into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I can identify that each category of the value chain are represented in sectors. For the Coffee Processor in the {first pie chart}<r>[0.087, 0.245, 0.412, 0.788]</r> and the Off-season Supplier in the {second pie chart}<r>[0.538, 0.245, 0.862, 0.788]</r>, I can see that each sector present corresponds to a different cost in the value chain.\nBased on the above thought, the answer is:\nStage,Coffee Processor,Off-season Supplier\nInbound Logistics,2.9%,3.8%\nOperations,8.1%,5.2%\nOutbound Logistics,5.5%,8.7%\nMarketing and Sales,17.8%,16.7%\nService,11.8%,11.0%\nInfrastructural Costs,3.9%,4.6%\nFinal Cost,50.0%,50.0%"
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "HRwRv0GfhQaQ3UGv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie charts, which stage of the value chain contribute the highest cost in terms of percentage for both the Coffee Processor and the Off-season Supplier?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the segment with the largest proportion (size) on both the Coffee Processor's and the Off-season Supplier's pie charts.\nBased on the above thought, the answer is:\nThe stage that contributes the highest cost for both the Coffee Processor and the Off-season Supplier is the \"Final Cost\"."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "bI60xCARcj7FG34F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the value chain management of the Coffee Processor and Off-season Supplier based on the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must analyze the distributions of the costs across the different stages of the value chain to determine the different areas of concentration for both organizations.\nBased on the above thought, the answer is:\nWhile both organizations spend half of their overall costs on final costs, the distribution of the rest of their costs indicate a difference in their value chain management. The Coffee Processor focuses more on Operations whereas the Off-season Supplier shows more spending on Outbound Logistics."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "BEMZjkG3V30AT6R5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the information from these charts help in decision making for these two suppliers?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution of costs along the value chain could highlight areas for cost effectiveness or areas for improvement depending on the goal of the organization.\nBased on the above thought, the answer is:\nThese charts can help the organizations identify stages of the value chain where their costs are highest and might need optimization. For example, a focus on reducing the \"Final Costs\" could significantly impact the cost efficiency for both organizations due to its major proportion of total costs."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "b9jnAab441foRyHO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor both the Coffee Processor and the Off-season Supplier, what percentage of their total costs are spent on their Operations and Service combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage of Operations and Service for both, I need to sum up the corresponding percentages from both the Coffee Processor and the Off-season Supplier pie charts.\nBased on the above thought, the answer is:\nFor the Coffee Processor, it is 8.1% (Operations) + 11.8% (Service) = 19.9%. For the Off-season Supplier, it is 5.2% (Operations) + 11% (Service) = 16.2%."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "eatEqP3rb5HofOHm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the Coffee Processor spend over half of their total costs on the Final Cost of the value chain?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to refer to the percentage of the Final Cost sector on the Coffee Processor's pie chart.\nBased on the above thought, the answer is:\nYes, the Coffee Processor spends exactly 50% of their total costs on the Final Cost."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "woEIvW2zLRHK7k17",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Marketing and Sales cost for the Off-season Supplier were to decrease by 2%, what would be their new total cost in terms of percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total cost percentage for the Off-season Supplier, I need to subtract 2% from the Marketing and Sales percentage and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new percentage for Marketing and Sales is 16.7% - 2% = 14.7%. The new total cost percentage is 3.8% (Inbound Logistics) + 5.2% (Operations) + 8.7% (Outbound Logistics) + 14.7% (Marketing and Sales) + 11% (Service) + 4.6% (Infrastructural Costs) + 50% (Final Cost) = 98%."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "wm8KyUtOvctFQy0R",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you compile the observed data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indeed, I can consolidate the data from the two pie charts. Starting with the \"Family Home Ownership\" percentage for each year from {the legend}<r>[0.908, 0.063, 0.993, 0.368]</r>. It shows that it has values from years 2000-2020. Similarly, details of \"Households Below the Poverty Line\" is represented as smaller slices from the same pie chart.\nBased on the above thought, the answer is:\nYear,Family Home Ownership (%),Households Below Poverty Line (%)\n2000,62,14\n2005,65,12\n2010,66,10\n2015,70,8\n2020,75,7"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "2EL2FwejDkc1onsp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data visualization, mentioning the positive trend observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the percentage values corresponded with {Family Home Ownership}<r>[0.482, 0.516]</r>, the pie chart is representing a positive trend in family home ownership over 20 years.\nBased on the above thought, the answer is:\nThe positive trend observed in this chart is the consistent increase in Family Home Ownership from 2000 to 2020."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "NFH7cvY5gSU5Wlv9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make a prediction for the percentage of households below the poverty line for 2025 based on the current trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the consistent decline of {Households Below the Poverty Line}<r>[0.197, 0.11, 0.766, 0.921]</r> from 2000 to 2020, I can estimate a future value.\nBased on the above thought, the answer is:\nIf the pattern of decreasing percentages continues, we can expect the percentage of households below the poverty line to decrease further by 2025 around 5% to 6%."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "xfosQqsJTYKyoBZx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this data be used to influence future socioeconomic policy decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trends of the {Family Home Ownership}<r>[0.482, 0.516]</r> and {Households Below Poverty Line}<r>[0.197, 0.11, 0.766, 0.921]</r> could provide insights for policy decision making.\nBased on the above thought, the answer is:\nThe trends could guide policymakers in developing strategies that continue to promote home ownership, as it is evident that home ownership rates have been consistently increasing. Similarly, policies and programs aimed at reducing poverty have had a significant impact, as reflected in the decreasing percentage of households below the poverty line. This data suggests that continued or increased investment in these areas could lead to further improvements in these parameters in future years."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "J5FPC6jVMHuX6Oub",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the percentage of Family Home Ownership between 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the value in 2000 from the value in 2020 from the percentages on {the pie chart}<r>[0.197, 0.11, 0.766, 0.921]</r>.\nBased on the above thought, the answer is:\n13%"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "XlGx6ST6Kgwvz7Qx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the percentage of Households Below the Poverty Line decreased by 2% from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to compare the percentages on {the pie chart}<r>[0.197, 0.11, 0.766, 0.921]</r>  between consecutive years and find the year which has a decrease of 2% from the previous year.\nBased on the above thought, the answer is:\n2005"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "8YjCth7ANeNcV8ZQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of Family Home Ownership from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to add the percentages from 2000 to 2020 and then divide by the total number of years on {the pie chart}<r>[0.197, 0.11, 0.766, 0.921]</r>.\nBased on the above thought, the answer is:\n68%"
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "jh6J0QvP1A5TJgwm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTranscribe the data represented in the pie chart into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows data for different relationship types with different colors in the form of a pie chart. The information corresponding to each relationship type is given in legends at the corner {the legend}<r>[0.841, 0.063, 0.993, 0.189]</r>.\nBased on the above thought, the answer is:\nType, Family, Friends, Colleagues, Acquaintances\nSocial Networking, 120, 80, 56, 44\nSocial Relationships, 80, 130, 48, 42"
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "CcjX3Pshi0q6gbhJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich relationship type is dominant in Social Networking according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must determine which slice {the slices}<r>[0.197, 0.112, 0.759, 0.913]</r> represents the largest percentage share in Social Networking.\nBased on the above thought, the answer is:\n'Family' is the most dominant relationship type in Social Networking as it holds the highest proportion at 26.7%."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "AQLwjmD7oNtOM5z2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can we glean about the influence of different relationship types in Social Networking based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides a visual representation of the presence of different relationship types in Social Networking. Based on the size and labels of each slice {the slices}<r>[0.197, 0.112, 0.759, 0.913]</r>, I can derive insights.\nBased on the above thought, the answer is:\nThe chart depicts that 'Family' has the most significant influence in Social Networking followed by 'Friends'. 'Colleagues' and 'Acquaintances' have the least influence."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "XLBNxlXujuezaAp0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data affect future strategies in the field of Social Networking?"
+            },
+            {
+                "from": "gpt",
+                "value": "An interpretation of this data {the pie chart}<r>[0.197, 0.112, 0.759, 0.913]</r> would help to identify where the most significant influences lie in Social Networking, and thus targeted efforts can be executed to leverage these areas.\nBased on the above thought, the answer is:\nAs 'Family' appears to dominate Social Networking, platforms might choose to focus more on features that allows extensive interactions within family circles. At the same time, since 'Colleagues' and 'Acquaintances' have lower representation, features that improve business networking and acquaintance engagement could be developed to balance the influence across various relationship types."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "1LFIxIo3AxDsCwQW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percent of the people feel Family as the most dominating relationship in Social Networking?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the percentage, we should look at the auto-text label of the 'Family' slice {the \"Family\" slice}<r>[0.197, 0.283, 0.456, 0.828]</r>.\nBased on the above thought, the answer is:\n26.7%"
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "YuNpEGpSqNBAw0wr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich relationship type has the least proportion in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must inspect each slice of the pie chart and identify which one has the smallest percentage. {the slices}<r>[0.197, 0.112, 0.759, 0.913]</r>.\nBased on the above thought, the answer is:\nAcquaintances"
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "efeIbw4u6izyndcL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf colleagues' influence in social networking was increased by 5%, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrive at the new percentage, I must first get the original percentage, which is in the 'Colleagues' slice {the \"Colleagues\" slice}<r>[0.494, 0.114, 0.714, 0.484]</r>, and then add 5% to it.\nBased on the above thought, the answer is:\nThe original percentage for 'Colleagues' is 16.0%. If it's increased by 5%, the new percentage would be 21.0%"
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "TuVyLmy2tdUK4M5u",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the pie chart to a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I'll look at the labels attached to each slice of the pie which {represent the the years from 2030 to 2040. These labels can be found from the coordinates provided}[0.952, 0.071, 0.987, 0.091, 0.952, 0.34, 0.987, 0.39] and figure the corresponding percentage emission represented by each slice from the autotext.\nBased on the above thought, the answer is:\nYear,Percentage of Carbon Dioxide Emission\n2030,7.5%\n2031,7.6%\n2032,8.0%\n2033,8.4%\n2034,8.8%\n2035,9.0%\n2036,9.3%\n2037,9.7%\n2038,10.1%\n2039,10.4%\n2040,11.2%"
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "iJoWvR8dY64yWbwX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you provide a brief overview of the Carbon Dioxide Emission trends from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide a brief overview, I need to consider the percentage contributions represented by the pie chart slices especially with respect to each other and over the years.\nBased on the above thought, the answer is:\nThe pie chart shows a gradual increasing trend in the percentage of Carbon Dioxide Emissions from 2030 to 2040. Particularly, the emissions rose consistently from 7.5% to 11.2% suggesting a constant growth in Carbon Dioxide Emissions."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "RELMCoG0wKhWCDWf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does the data in the pie chart have for the global carbon dioxide emissions in 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the implications, I need to look at the percentage of carbon dioxide emissions in 2040, compare this with the percentages of previous years and analyze the trend.\nBased on the above thought, the answer is:\nThe data shows a significant rise in the projected carbon dioxide emissions for the year 2040 with an 11.2% share, which is the highest in the 10 year timeline. This suggests a sharp jump in global carbon dioxide emissions in 2040 and could have major environmental implications if this projection turns out to be accurate."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "sOku7rZWNXOqyLnX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications could the data presented have for global climate policy decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to look at the percentage distribution and the overall trend shown in the pie chart.\nBased on the above thought, the answer is:\nConsidering the steep projected increase in carbon dioxide emissions, this information could be used to warrant urgent and aggressive policy interventions to reduce carbon emissions. These could include mechanisms for cleaner renewable energy utilization and stricter regulations on emission-intensive industries, especially considering the significant rise seen in 2040."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "h3UQijHmuKyOyLxD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage did the Carbon Dioxide Emissions for the year 2036 contribute to the total projection?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the slice of the pie associated with the year 2036 and read off percentage from its autotext.\nBased on the above thought, the answer is:\nThe percentage of Carbon Dioxide Emissions for the year 2036 was 9.3%."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "joZv8Ew4h7QCIY2i",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage did the Carbon Dioxide Emission change from 2039 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of 2039 which is represented by its corresponding slice in the pie from the percentage of 2040.\nBased on the above thought, the answer is:\nThe percentage difference in Carbon Dioxide Emission from 2039 to 2040 was 0.8%."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "ywzxlwcvff9Vjc6a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Carbon Dioxide Emission for 2037 decreased by 0.5%, what would be the new total percentage of the Carbon Dioxide Emission?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total Carbon Emission percentage, I need to subtract the 0.5% decrease from the 2037 percentage and then add up all the percentages again.\nBased on the above thought, the answer is:\nThe new total percentage of the Carbon Dioxide Emission would be 99.5%."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "YlKOCMrhccWJep1w",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the given data in the pie chart into CSV format for the year 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the pie chart{the pie chart of year 2026} <r>[0.223, 0.109, 0.741, 0.923]</r>, I can see two categories: Smart Homes and Autonomous vehicles with their respective proportions.\nBased on the above thought, the answer is:\nTechnology,2026\nSmart Homes,73%\nAutonomous Vehicles,27%"
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "d1aEXZwR4hzfOQFc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology is predicted to be more prevalent in the future society of 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should observe the bigger slice in the pie chart for that specific year{the pie chart of year 2026} <r>[0.223, 0.109, 0.741, 0.923]</r>.\nBased on the above thought, the answer is:\nThe technology predicted to be more prevalent in 2026 is Smart Homes as it has a larger slice of the pie, indicating a higher percentage of usage."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "czpxIIPk5OrnmE6t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the state of these technologies in 2026 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions written on the pie chart slices {Smart Homes slice}<r>[0.223, 0.301, 0.741, 0.923]</r> {Autonomous vehicle slice}<r>[0.283, 0.109, 0.671, 0.479]</r> to form an inference.\nBased on the above thought, the answer is:\nIn the year 2026, it is predicted that Smart Homes will be significantly more prevalent in future society than Autonomous vehicles, indicating a potential greater acceptance and integration of smart technology in homes."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "PDHHwt19RVebfEhw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence technology investment decisions in 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to interpret the pie chart slices for both Smart Homes and Autonomous Vehicles{Smart Homes slice}<r>[0.223, 0.301, 0.741, 0.923]</r> {Autonomous vehicle slice}<r>[0.283, 0.109, 0.671, 0.479]</r>.\nBased on the above thought, the answer is:\nThis data may lead investors to put more money into Smart Homes technologies as it shows a larger market share in 2026, indicating better return prospects. Meanwhile, investments in Autonomous vehicles may decrease due to a lower predicted prevalence in future society."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "Ui8Pr42HFl0tjQk1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the percentage of Smart Homes' usage for the year 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage, I'm going to analyze the information obtained from the pie chart slice{Smart Homes slice} <r>[0.223, 0.301, 0.741, 0.923]</r>.\nBased on the above thought, the answer is:\n73%"
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "E1x4iFbnyQWZ9FFZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the usage of Autonomous vehicles decreases by 5% in 2026, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract 5% from the previous percentage of Autonomous vehicles observed from its pie chart slice{Autonomous vehicles slice}<r>[0.283, 0.109, 0.671, 0.479]</r>.\nBased on the above thought, the answer is:\n27% - 5% = 22%"
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "amBEbqiK40JTn3oC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the percentages between Smart Homes and Autonomous vehicles for 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of Autonomous vehicles from the percentage of Smart Homes which can be determined from their respective slices on the pie chart{Smart Homes slice}<r>[0.223, 0.301, 0.741, 0.923]</r>{Autonomous vehicles slice}<r>[0.283, 0.109, 0.671, 0.479]</r>.\nBased on the above thought, the answer is:\n73% - 27% = 46%"
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "2yhklJoI4ht9HuH4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the provided pie chart data into a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "Even though raw data is given, by referring to the information provided in this pie chart including the labels and proportions of each slice {2025}[0.154, 0.355, 0.189, 0.375], {2026}[0.157, 0.668, 0.192, 0.688], {2027}[0.287, 0.887, 0.322, 0.907], {2028}[0.521, 0.946, 0.557, 0.966], {2029}[0.73, 0.772, 0.765, 0.792], {2030}[0.788, 0.428, 0.823, 0.448], {2031}[0.644, 0.127, 0.679, 0.147], {2032}[0.331, 0.094, 0.366, 0.114], it should be possible for me to create a table representation for it.\nBased on the above thought, the answer is:\nYear,Forecasted GDP Rise(%)\n2025,11.1\n2026,11.8\n2027,9.1\n2028,12.2\n2029,13.2\n2030,12.9\n2031,13.9\n2032,15.7"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "klA6LXRX0RjL8uKC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data, how would you assess the economic outlook from 2025 to 2032?"
+            },
+            {
+                "from": "gpt",
+                "value": "To conduct an analysis, I'll look at the GDP rise percentages.\nBased on the above thought, the answer is:\nThe GDP is forecasted to rise consistently from 2025 to 2032, indicating a steady economic growth. Even though there is a dip in 2027, recovery is quick and the economy is forecasted to improve over the years. The rising trend in GDP points towards a favorable economic outlook."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "FzFR56XgOH2BcGhd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might you infer about the economy in 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "The basis for analysis is the drop in GDP rise in 2027, which can provide insight into the economic condition for that year.\nBased on the above thought, the answer is:\nThe forecasted GDP rise for 2027 is the lowest within the given range, suggesting a potential economic slowdown or recession during that year."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "HY8FcchRC930nMEx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to represent this data on a line graph for increased clarity, what trend might you expect to see?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll need to look at each yearly forecast and imagine how a line connecting all the values would behave.\nBased on the above thought, the answer is:\nThe line graph would show a generally increasing trend, with a slight dip in 2027. The ensuing recovery is immediate and rapid, and the GDP rise continues to increase after 2027, showing a promising upward trend overall."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "CN0MVWfbloXQEJIB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total forecasted GDP rise percentage for the first three years (2025-2027)?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding up the GDP rise percentages for the years 2025, 2026 and 2027, we should be able to get the total percentage for the first three years.\nBased on the above thought, the answer is:\n32%"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "woMRHIjgGMbRpMNh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage point is the GDP rise for 2032 forecasted to be more than that of 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "By subtracting the GDP rise percentage in 2025 from that in 2032, we can get the difference in GDP rise between these two years.\nBased on the above thought, the answer is:\n4.6%"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "h5xr3DuQWkKZZcIx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2028's forecasted GDP rise was overestimated and the actual rise was 10%, what's the actual GDP rise percentage for 2028-2029?"
+            },
+            {
+                "from": "gpt",
+                "value": "Correct the 2028 forecasted GDP rise to 10% and add to the 2029 one. This should give the actual GDP rise for 2028 to 2029.\nBased on the above thought, the answer is:\n23.2%"
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "N17wHy2ezsVVTeKq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart data on psychological states and behaviors in the population into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Psychological States and Behaviours in Population\"}[0.274, 0.021, 0.691, 0.047], I can see that the slices represent different states/behaviours. Each slice of the pie {the slices}[{\"StressedHighly\"}[0.207, 0.262, 0.459, 0.499]; {\"StressedModerately\"}[0.198, 0.442, 0.457, 0.768]; {\"StressedLightly\"}[0.275, 0.549, 0.471, 0.918]; {\"Unstressed\"}[0.475, 0.55, 0.691, 0.92]; {\"Happy\"}[0.508, 0.229, 0.767, 0.751]; {\"Depressed\"}[0.492, 0.112, 0.659, 0.482]; {\"Anxious\"}[0.304, 0.111, 0.492, 0.481]; {\"Calm\"}[0.266, 0.211, 0.464, 0.49]] depicts a particular state and its percentage share in the population.\nBased on the above thought, the answer is:\nBehaviour,Population(%)\nStressedHighly,15\nStressedModerately,30\nStressedLightly,25\nUnstressed,30\nHappy,50\nDepressed,20\nAnxious,25\nCalm,5"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "cFceUUS80GNpLQUI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, what is the most prevalent psychological state or behavior in the studied population? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the largest slice of the pie to find out the most prevalent state or behavior.\nBased on the above thought, the answer is:\nThe most prevalent psychological state in the population is \"Happy\", having the highest percentage of 50%."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "udvoy0UN5cnCBbxc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the stress levels within the population based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the portions of the pie chart which indicate different stress levels.\nBased on the above thought, the answer is:\nThe chart reflects high stress levels within the population. This inference is drawn from the fact that 70% of the population was found to be stressed, varying in intensity from light to high."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "uHEANFKLyTQspc1s",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information in the chart be used to improve or maintain the mental wellbeing of the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how understanding the prevalence of different states or behaviors can inform interventions or support strategies.\nBased on the above thought, the answer is:\nThe high percentages of stressed individuals, irrespective of the intensity of stress, indicate the need for interventions like stress management programs or therapy sessions. To maintain the high prevalence of happiness, it may be beneficial to further study what contributes to this state and ensure those elements are promoted or protected in the society. The low percentage of calm individuals may show a necessity for relaxation and mindfulness strategies."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "CjYgMma92HK7rRye",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage of people who are stressed, regardless of the severity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to sum the percentages for \"StressedHighly\", \"StressedModerately\" and \"StressedLightly\".\nBased on the above thought, the answer is:\n70"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "jCdoMI3pgEH1XSN1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between the least and most common psychological states/behaviors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to find the psychological states/behaviors with the highest (\"Happy\") and lowest (\"Calm\") percentage, and then subtract the smallest from the largest.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "Gxl7uOzxhEL5M5KW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the population exhibits positive states/behaviors (Unstressed, Happy, Calm)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to sum up the percentages of \"Unstressed\", \"Happy\" and \"Calm\".\nBased on the above thought, the answer is:\n85"
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "SKz7vQAIVMNSLr9r",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the pie chart, can you convert the data into CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this image can be interpreted as follows: Observing the 10 slices from {the pie chart}<r>[0.197, 0.11, 0.774, 0.922]</r>, each representing a specific year, finds their corresponding trade volume from the pie chart\u2019s percentage.\nBased on the above thought, the answer is:\nYear,Trade Volume\n2009,110\n2010,120\n2011,130\n2012,145\n2013,160\n2014,170\n2015,155\n2016,135\n2017,165\n2018,175"
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "HmwOmDjg5UPQu3TQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the highest trade volume, and what\u2019s the likely reason?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to find the slice with the largest proportion in the pie chart. The year related to that slice has the highest trade volume. Then, I can infer the probable reason for the largest trade volume being experienced that particular year.\nBased on the above thought, the answer is:\nThe year with the highest trade volume was 2014, and one possible reason for this peak could be economic prosperity and increased global trade activities during that year."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "4Od4JEJVWfvQcnWj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart represent the fluctuations in International Trade over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions represented for each year and analyse them for fluctuations.\nBased on the above thought, the answer is:\nThe pie chart displays how international trade has fluctuated over the years by varying the size of the slices. Larger slices, such as that of 2014, indicate an increase in trade volume, while smaller slices, like that of 2009, hint at lesser trade volumes. This shows the varying amount of trade each year."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "YqojJ14w3zHfSzbo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf, based on the data from the pie chart, a policymaker were to strategize for 2019, what trade volume should be anticipated?"
+            },
+            {
+                "from": "gpt",
+                "value": "The policymaker should look at the general trend of trade volume over the last few years to predict a feasible trade volume for 2019.\nBased on the above thought, the answer is:\nIf the trend from the past few years continues, it seems that, following a slump, trade volumes rebound strongly. Thus, after the slump in 2016, a trade volume of over 150 is anticipated for 2019."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "pd68yyx7Op51dmKi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the highest trade volume year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest trade volume happened in 2014. Therefore, I should look for the percentage assigned to {2014 in the pie chart}<r>[0.524, 0.559, 0.774, 0.868]</r>.\nBased on the above thought, the answer is:\n11.6%"
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "YXvangIOFowzJi6D",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the international trade volumes for the first five years(2009-2013)?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding up the trade volumes for the years 2009 to 2013, by referring to their respective slice in the pie chart.\nBased on the above thought, the answer is:\nThe sum is 110 (2009) + 120 (2010) + 130 (2011) + 145 (2012) + 160 (2013) = 665."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "P7CtT4rqHGANdXG5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2019 had a 10% increase in trade volume compared to 2018, what would be the new trade volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "A 10% increase of 2018's trade means multiplying 2018's volume by 110%.\nBased on the above thought, the answer is:\nThe trade volume for 2019 would be 175 (2018's volume) * 110% = 192.5"
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "1HTtlSVf0aqccoRf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to this chart, could you convert the presented data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the chart's title {\"Emerging Technologies and Trends Outlook\"}[0.303, 0.021, 0.66, 0.047] and considering the two collections of data present in the pie chart {for AI Implementation}[0.482, 0.516, 0.605, 0.923] and {for Blockchain Development}[0.482, 0.516, 0.467, 0.485] for the years 2016 to 2023, it's clear the table should reflect this breakdown over time.\nBased on the above thought, the answer is:\nYear,AI Implementation,Blockchain Development\n2016,20,19\n2017,22,21\n2018,25,23\n2019,32,27\n2020,40,33\n2021,50,38\n2022,55,44\n2023,60,49"
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "dWQbW3FWzWJjXChq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which emerging technology is getting more implementation according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the pie chart, I can compare the relative percentages of each technology for every year to determine the technology with the highest implementation rate over time.\nBased on the above thought, the answer is:\nAI Implementation is the burgeoning technology getting more implementation according to the chart."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "nBU9aGqqVGskEOOz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart's data, which technology showed a significant improvement in its trend after 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I noticed a notable leap in the percentages of the two technologies after 2019 by seeing the labels on the pie chart slices.\nBased on the above thought, the answer is:\nBoth AI Implementation and Blockchain Development showed significant improvement after 2019."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "Q4EA0N1WFfNnrBO8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the distribution of the emerging technologies trend in the data give us an indication on what the technology landscape might look like in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "A projection about the future can be made by noting the incremental growth pattern dominant in the presented data.\nBased on the above thought, the answer is:\nYes, the measured increase in both AI Implementation and Blockchain Development suggests a future where these technologies will continue to grow in their implementation and development, thereby shaping our technological landscape. This is especially indicated by the major leaps noticed in the chart for both technologies after 2019."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "rz6TRvhrVYT4z4lE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the AI Implementation surpassed the Blockchain Development by 10 points or more?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to review each year's data points from 2016 to 2023 and compare the difference in the rates of AI Implementation and Blockchain Development.\nBased on the above thought, the answer is:\nThe year 2021."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "K4mB131gMFab1eTE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the cumulative Blockchain Development score over the years from the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate, I sum up the development score for Blockchain Development from 2016 to 2023.\nBased on the above thought, the answer is:\n254"
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "otmgdYiTDOYKCFER",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage increase of AI Implementation can be observed from 2016 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to check the percentage of AI Implementation in 2016 and 2023 and calculate the difference.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "cqyUxvvpVmvqfbKG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart visualization you see, can you present a structured table with the data of an Online Course and Book usage percentage from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"Education Resource Distribution and Utilization from 2016 to 2020\"}<r>[0.206, 0.021, 0.758, 0.077]</r>, it can be inferred that the data captured distinctly represents the distribution and utilization of education resources in the form of Books and Online Courses over the years from 2016 to 2020 which are represented in the pie charts under the legend @{subLegend}<r>[0.891, 0.093, 0.976, 0.249]</r>. From this, I can draw the percentages for each year and the respective educational resources.\nBased on the above thought, the answer is:\n\"Year\",\"Online Courses\",\"Books\"\n2016,20%,80%\n2017,25%,75%\n2018,28%,72%\n2019,30%,70%\n2020,32%,68%"
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "NaXiVn4NyYR3b8er",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource saw a steady decline in usage over the years and why could that be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which resource saw a steady decline, I need to examine the percentages of both resources for each year and identify which one showed a decreasing trend.\nBased on the above thought, the answer is:\n\"Books\" saw a steady decline in usage over the years. This could be due to the rising popularity and accessibility of online platforms and digital resources, leading to a shift from traditional book learning to online courses."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "J9oGENVCgMmOrz06",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data presented influence the development of educational resources for future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to interpret the trends and changes observed in the distribution and utilization of educational resources over the years.\nBased on the above thought, the answer is:\nThe data shows a continuous increased utilization of Online Courses and a decrease in Books\u2019 usage. So, educational policy makers might focus more on developing and enhancing online teaching platforms and digital education resources, while gradually reducing their reliance on traditional books."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "iuBLGgCo62Dm0E8p",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend of resource utilization continues, what could be the likely percentage distribution of online courses and books in the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the trend of the past years, I can draw a plausible estimation for the distribution of resources in 2021.\nBased on the above thought, the answer is:\nGiven the increasing trend in the use of online courses and the decreasing trend in the use of books, it is estimated that by 2021, the percentage distribution might be around 35% for online courses and 65% for books, assuming a similar yearly rate of change."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "2PmLw1mSYqwr3cOm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data from 2016, how many percentage points does the usage of Books exceed the usage of Online Courses?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the difference in percentage points between the usage of Books and Online Courses in 2016, it's necessary to subtract the percentage of Online Courses from the percentage of Books.\nBased on the above thought, the answer is:\nThe usage of Books in 2016 exceeds the usage of Online Courses by 60 percentage points."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "wi7DGhG7YE1czCco",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the span of year 2016 to 2020, how many percentage points has the usage of online courses increased?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage points increase from 2016 to 2020, I need to subtract the usage percentage of Online Courses in 2016 from the usage percentage in 2020.\nBased on the above thought, the answer is:\nThe usage of Online Courses has increased by 12 percentage points from 2016 to 2020."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "x69SbRTlKwAc5HfD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the use of online courses have the largest annual increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year of the largest increase of Online Courses usage, I need to calculate the difference in percentage points of Online Courses utilization across all adjacent years and determine the year with the highest change.\nBased on the above thought, the answer is:\nThe largest annual increase in online courses usage was from 2016 to 2017."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "zg4JdOu6kInoDigD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the information in the pie chart and present it in a table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the pie segments {High}[0.199, 0.264, 0.458, 0.502], {Medium}[0.201, 0.512, 0.461, 0.895], {Low}[0.435, 0.407, 0.76, 0.911], {Negligible}[0.499, 0.131, 0.74, 0.488], {Unassessed}[0.275, 0.111, 0.54, 0.481] and the corresponding percentages in the pie chart, I can extract the data and transform it into a table.\nBased on the above thought, the answer is:\nRisk Level,High,Medium,Low,Negligible,Unassessed\nControl Measures,10,22,35,15,18"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "kPCjFbCMUozKCtaT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which level of risk has been effectively controlled the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the largest area represents the most effectively controlled risk level. In this case, {Low}[0.435, 0.407, 0.76, 0.911] risk level is the most significant among other risk levels.\nBased on the above thought, the answer is:\nThe \"Low\" risk level has been effectively controlled the most."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "P1pFG6KAeVnB32BZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart, what suggestions would you have for control measures implementation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {Unassessed}[0.275, 0.111, 0.54, 0.481] pie slice and the its portion \"18%\", I could infer the areas where improvements can be made.\nBased on the above thought, the answer is:\nThe control measures are effectively handling risks as evidenced by the 'Low' level being majorly dominant. However, attention needs to be given to a small portion of 'Unassessed' risks that might need further evaluation."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "6bSJ8GJvn5oNiQ2t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter looking at the pie chart, what problem do you see that might need immediate attention?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {High}[0.199, 0.264, 0.458, 0.502] risk level, though small in percentage, might need immediate attention due to its potential impact.\nBased on the above thought, the answer is:\nThe 'High' risk level, which accounts for 10% of the total risks, indicates a potential significant impact and might require immediate attention despite its smaller proportion."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "3ufGerhpNsPP59z4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what\u2019s the sum of the High and Medium risks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should add the percentage of {High}[0.199, 0.264, 0.458, 0.502] and {Medium}[0.201, 0.512, 0.461, 0.895] risk levels to get the sum.\nBased on the above thought, the answer is:\n32%"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "9Ebg2bcWndKqxcEy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total percentage if we combine Low and Negligible risk levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the percentages of {Low}[0.435, 0.407, 0.76, 0.911] and {Negligible}[0.499, 0.131, 0.74, 0.488] risk levels gives us the sum.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "ZOCGhMyMhD6qvliW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy percentage, is High risk more than Unassessed risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "We would compare the percentages of the {High}[0.199, 0.264, 0.458, 0.502] and {Unassessed}[0.275, 0.111, 0.54, 0.481] risk levels.\nBased on the above thought, the answer is:\nNo."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "1Ms0IHSplvnFeQ6b",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, please generate a CSV format table that shows the distribution of Social Activities in the year 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle at the top of the chart {Distribution of Social Activities in 2020}[0.321, 0.021, 0.642, 0.047] and {the sector slice}[{Outdoor Activity}[0.197, 0.287, 0.456, 0.868]; {Online Activity}[0.308, 0.137, 0.766, 0.877]], it's apparent that the two sectors represent the distribution percentages of these social activities in 2020.\nBased on the above thought, the answer is:\nActivity,2020\nOutdoor Activity,30%\nOnline Activity,70%"
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "0ThmEB9Usg6WgfTn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about people's social activity preferences in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out about people's preferences, I can look at the percentage distribution of each type of social activity.\nBased on the above thought, the answer is:\nThe pie chart suggests that in 2020, people vastly preferred Online Activities (70%) over Outdoor Activities (30%). This could indicate a shift in lifestyle preference towards online engagement, perhaps due to the influence of events in 2020 like the pandemic."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "hyiQwpJURMCnUSZV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the pie chart concerning the change in lifestyle between 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the change, I can look at the percentage of each social activity and infer what changes it would cause to one's lifestyle.\nBased on the above thought, the answer is:\nThe pie chart indicates that there is a rise in Online Activities percentage from 58% in 2019 to 70% in 2020, and a decrease in Outdoor Activities from 42% in 2019 to 30% in 2020. This might imply a bigger reliance on online platforms for social interactions, possibly fueled by conditions like the pandemic that encourage people to stay indoors."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "DOC8ejhDtZiLwOqq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential impact do you think this distribution might have on future social activity planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate potential impacts, I can consider how the proportions of different social activities may influence future planning regarding these activities.\nBased on the above thought, the answer is:\nConsidering the high percentage of Online Activities in 2020 (70%), future social activity planning might continue to emphasize online platforms, as they seem to be increasingly preferred. However, it should not neglect Outdoor Activities, as a significant portion (30%) still engage in those. This information could guide efforts towards more comprehensive, diverse, and adaptive social activity planning."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "C4msyCqQwNy0gBfS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we added 10% more to the Outdoor Activity, what would be the percentage of the Online Activity in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To work that out, I have to increase the \"Outdoor Activity\" by 10% and then recalculate the percentage of \"Online Activity\".\nBased on the above thought, the answer is:\nThe new percentage for \"Outdoor Activity\" would be 30% + 10% = 40%. Consequently, the \"Online Activity\" would be 100% - 40% = 60%."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "qHgykEP0AOVxeoGQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentages between Outdoor Activity and Online Activity in 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I would subtract the percentage of Outdoor Activity from the percentage of Online Activity.\nBased on the above thought, the answer is:\nThe difference in percentages between Outdoor Activity and Online Activity is 70% - 30% = 40%."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "25sD203hyUWPWlh8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we increased the activity percentages by a factor of 1.5, what is the new percentage for Outdoor Activity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new percentage, I would multiply the original percentage of \"Outdoor Activity\" by 1.5.\nBased on the above thought, the answer is:\nThe new percentage for \"Outdoor Activity\" would be 30% * 1.5 = 45%."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "NCxpfgXIuQrQ6djx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie-chart data you see into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Cuisine Popularity Survey Analysis\"}[0.338, 0.021, 0.625, 0.047] and the slices on the pie {\"slices\"}[0.152, 0.146, 0.894, 0.92], I observed that each sector or slice in the pie represents a type of cuisine and its popularity percentage in a survey. Translating that in a CSV format can provide a structured table.\nBased on the above thought, the answer is:\nCuisine,Popularity in %\nItalian,18\nChinese,24\nIndian,16\nMexican,12\nFrench,10\nJapanese,10\nThai,5\nVietnamese,5"
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "ll8JGPpnWgcvF5VU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cuisine is most popular? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I will identify the cuisine with the biggest slice of the pie, which corresponds to the highest percentage.\nBased on the above thought, the answer is:\nChinese cuisine is the most popular as it holds the largest share of the pie chart, symbolizing a popularity percentage of 24%."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "uDfgKHm97pihg47r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can you make regarding the cuisine preferences of the surveyed demographic from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size of the different slices in the pie, I can infer which cuisines are favored more over the others.\nBased on the above thought, the answer is:\nThe chart defines that Chinese is the most preferred cuisine, then follows Italian and Indian. Cuisines like Mexican, French, and Japanese have medium popularity, while Thai and Vietnamese don't seem to be as popular."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "lwOk5oNZ9VdXp5AK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data in the pie chart influence decisions on opening a new restaurant in the area of the survey?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity percentage of each cuisine could indicate the potential success based on the preferences of the surveyed population.\nBased on the above thought, the answer is:\nFrom the chart, it seems that opening a Chinese restaurant might be the most successful move, as that's the most popular cuisine. Elements like Italian and Indian cuisines also have good popularity among people. However, opening a restaurant serving Thai or Vietnamese foods might be less successful given their lower popularity percentage."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "gHXVY5ovKJjM5Hfg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two cuisines combined scored the same popularity percentage as Chinese cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To address this, I need to identify the pairs of cuisines that when combined equal the percentage popularity of the Chinese cuisine.\nBased on the above thought, the answer is:\nItalian and Indian."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "MnFP49iVDYBZ0q8z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total popularity percent of cuisines that are less popular than Italian?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this, I need to add up the popularity percentage of all the cuisines that have a lesser popularity percentage than Italian.\nBased on the above thought, the answer is:\n42% (12% Mexican + 10% French + 10% Japanese + 5% Thai + 5% Vietnamese)"
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "koHh2tswjtrw0v0x",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Japanese cuisine increases its popularity by 5%, what will be the total popularity percentage of all cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to add 5% to the current popularity of Japanese cuisine and then add all the percentages together.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "LsZNLTZPqS5yI5kT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please describe the raw data from the pie chart that you observed in the form of a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the pie chart {pie chart}<r>[0.214, 0.109, 0.765, 0.921]</r>, I can analyze that the slices of the pie represent the different modes of transportation like {Truck}<r>[0.396, 0.749, 0.44, 0.769]</r>, {Plane}<r>[0.627, 0.406, 0.671, 0.426]</r>, {Train}<r>[0.45, 0.247, 0.494, 0.267]</r> and {Ship}<r>[0.325, 0.339, 0.36, 0.359]</r> across the years 2013 - 2020.\nBased on the above thought, the answer is:\nTransportation Method,2013,2014,2015,2016,2017,2018,2019,2020\nTruck,35,40,45,50,55,60,65,70\nPlane,10,20,30,10,10,10,10,15\nTrain,15,10,10,10,15,20,25,30\nShip,40,30,15,30,20,10,0,0"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "E7gN1yrwkEksKQvb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the success of the 'Truck' method in the logistics industry, as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comment on the success, I'll have to analyze the trend {Truck}<r>[0.396, 0.749, 0.44, 0.769]</r> over the years in the chart.\nBased on the above thought, the answer is:\nThe 'Truck' method appears to have gained significant success in the logistics industry. As the chart indicates, the market share of trucks has almost doubled from 35% in 2013 to 70% in 2020, showing a steady annual increase."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "OZaIVkb1d4Y6HqqD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible reason for the decrease in use of 'Ship' for transportation in the logistics industry after the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll have to look into the trend of the 'Ship' method {Ship}<r>[0.325, 0.339, 0.36, 0.359]</r> in the chart.\nBased on the above thought, the answer is:\nThe steady decrease in the use of 'Ship' from 2016 to a complete halt in 2020 might be due to a range of factors, including operational costs, time efficiency, environmental considerations, or changes in demand or supply routes. Without additional data, it's challenging to pinpoint the exact reason."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "OCr9g4T8MyOqum1l",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you describe the logistics industry's reliance on different transportation methods in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze all the four slices of the pie chart in 2020.\nBased on the above thought, the answer is:\nIn 2020, the logistics industry heavily relied on 'Truck' as a transportation method, occupying nearly 61% of the market share. 'Train' and 'Plane' trailed far behind with roughly 26% and 13% market share respectively. Interestingly, 'Ship' was completely phased out by this year, accounting for 0% of the utilization."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "nS6FZUEJBfLnEdIh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf all transportation methods equally shared market proportion, what would the ideal value be?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the ideal equal distribution among all the four transportation methods, I would need to divide the total percentage, which is 100%, by the number of methods, which is 4.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "8qdJLz6B6xPPUxy0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much has the utilization of trucks increased from 2013 to 2020 in the logistics industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to subtract the starting percentage (in 2013) from the ending percentage (in 2020). From the line marked {Truck}<r>[0.396, 0.749, 0.44, 0.769]</r>, I can get these percentages.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "8x1KKueVlCGagbNk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat has been the change in market proportion for the Plane from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I'll calculate the difference between the starting (2013) and ending (2020) percentages for Plane{Plane}<r>[0.627, 0.406, 0.671, 0.426]</r>.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "4s7y0qaxPoktdVrY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor making a table, could you please infer and pick out the raw information that is organized from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the chart features a circular figure {Energy Use by Resource Type}[0.206, 0.11, 0.767, 0.914] where different slices refer to distribution of energy sources. To understand the data, I should fetch each sector data from {the legend}<r>[0.858, 0.063, 0.993, 0.368]</r>, examining the colors and labels corresponding to each legend element, as well as the autotext of each pie slice in the circular figure.\nBased on the above thought, the answer is:\nResource Type, Energy Use in percentages\nCoal, 49.5\nNatural Gas, 24.2 \nPetroleum, 13.1\nNuclear, 6.8\nBiomass, 2.4\nHydropower, 1.5\nWind, 1.8\nSolar, 0.4\nGeothermal, 0.2\nBiofuel, 0.1"
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "IqMEyOmeC2KVn0Br",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which energy source is used most? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the slice of pie representing the largest proportion from the pie chart.\nBased on the above thought, the answer is:\nCoal is the most used energy source because it represents the largest pie slice in the chart at 49.5%."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "nBEMcs4UaeNPjO2J",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the energy usage in the given context based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart provides the distribution of different energy resources, so I can make inferences about their usage.\nBased on the above thought, the answer is:\nTraditional energy resources such as coal, natural gas, and petroleum make up the majority of energy usage, indicating a heavy reliance on these sources. Renewable energy sources like wind, solar, and geothermal are significantly less utilized, signaling potential areas for future industry development and environmental consideration."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "Q9pN2WGNaCS1bDBQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence decisions on energy policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution of energy resource utilization could provide insights into where more efforts or changes should be made in energy policy.\nBased on the above thought, the answer is:\nThe chart showcases a heavy reliance on traditional energy resources (coal, natural gas, and petroleum). An energy policy might address this by forming strategic plans to diversify energy sources, particularly promoting the development and utilization of renewable energy sources like wind, solar, and geothermal which are currently less utilized. Additionally, it would be worth investigating methods of increasing the use of resources like biomass and biofuel, which have low usage percentages but could potentially be sustainable, renewable options."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "XZqcEYfezW2G7b26",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of traditional energy (coal, natural gas, and petroleum) in the total energy use according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the proportion of traditional energy in the total energy, I should add up the proportions of coal, natural gas, and petroleum.\nBased on the above thought, the answer is:\n87.8%"
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "bNfQModlZtn7upzu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more percentage does coal have in energy usage compared to biomass?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I just subtract the percentage of biomass from the percentage of coal.\nBased on the above thought, the answer is:\n47.1%"
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "WYO25SblC4I7GlQJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the energy usage percentage difference between nuclear and wind energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I should subtract the lower energy usage from the higher energy usage.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "GgpHgzo246KJhGOx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you extract the counts of the various diseases and transcribe them into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I noticed that various diseases are represented as separate slices. {Cardiopathy}<r>[0.197, 0.285, 0.456, 0.846]</r>, {Diabetes}<r>[0.362, 0.552, 0.706, 0.922]</r>, {Obesity}<r>[0.507, 0.261, 0.766, 0.713]</r>, {Tuberculosis}<r>[0.426, 0.11, 0.679, 0.48]</r>, and {Hepatitis}<r>[0.269, 0.125, 0.468, 0.485]</r> are labeled with their corresponding percentages. By multiplying those percentages with the total count, I can get the individual count and represent it in a CSV format table.\nBased on the above thought, the answer is:\nDisease,Count\nCardiopathy,28\nDiabetes,24\nObesity,21\nTuberculosis,17\nHepatitis,10"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "5vsKlcxh0Mh5Xfyk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which disease is most prevalent in the given population?"
+            },
+            {
+                "from": "gpt",
+                "value": "By locating the largest slice in the pie chart, I can identify the disease that is most prevalent.\nBased on the above thought, the answer is:\nCardiopathy, with a count of 28, is the most prevalent disease, which is represented by the largest slice {Cardiopathy}<r>[0.197, 0.285, 0.456, 0.846]</r> in the pie chart."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "eR5uAGQyqL4gdT8d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about public health in the population based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the overall distribution of the diseases in the pie chart.\nBased on the above thought, the answer is:\nFrom the chart, it is evident that cardiac-related health issues (Cardiopathy) are the most common, followed by Diabetes and Obesity. This suggests that lifestyle diseases are a major concern for this population. Tuberculosis and Hepatitis, although less common, also present significant health issues that cannot be ignored."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "BUQZlPm0R1qt2Sfw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be some actions a health authority could take based on the data represented in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, health authorities would be able to identify the most common diseases and take appropriate actions.\nBased on the above thought, the answer is:\nGiven that Cardiopathy, Diabetes and Obesity are the most common diseases, a health authority could launch awareness campaigns and preventative measures focusing on heart health and lifestyle changes. Funding could be directed towards research and treatment options for these conditions. For Tuberculosis and Hepatitis, which represent a smaller proportion of the disease cases, the health authority could focus on targeted interventions and vaccination campaigns."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "dx9VhcVBnRW8bQe7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the counts of Diabetes and Obesity cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the sum of the Diabetes and Obesity cases, I have to identify their counts respectively, which are represented as slices in the pie chart, and sum them up.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "D6XmQiRDtHvf10IY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would the count of Tuberculosis cases be if it increased by 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to find the count of Tuberculosis cases and increase that by 50%.\nBased on the above thought, the answer is:\n25.5"
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "VUCZz6Nq4cbh4XEi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two diseases together account for nearly half the disease cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find two diseases whose combined counts (represented as slices in the pie chart) are around half the the total count.\nBased on the above thought, the answer is:\nCardiopathy and Diabetes."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "1rqt7OVreOosIUSR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you share the revenue distribution of the four major tech companies in 2020 in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the chart has a title {2020 Revenues of Major Tech Companies}[0.311, 0.021, 0.652, 0.047]. By analyzing the slices {'Amazon'}[0.203, 0.302, 0.587, 0.91], {'Apple'}[0.507, 0.227, 0.767, 0.843], {'Google'}[0.326, 0.109, 0.641, 0.479], and {'Tesla'}[0.265, 0.194, 0.464, 0.489], I can determine the revenue distribution percentages of each company, and therefore, convert this information into CSV format.\nBased on the above thought, the answer is:\nCompanies,Revenues in %\nAmazon,44.1\nApple,31.4\nGoogle,20.8\nTesla,3.6"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "sRwbhw17m2K8tq6R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which tech company made the most revenue in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would have to identify the sector slice with the largest percentage.\nBased on the above thought, the answer is:\nAmazon made the most revenue in 2020 as it claimed the highest percentage of 44.1% in the pie chart."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "dH1PGiyhlUN3BMDp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you infer about the financial performances of these tech companies in 2020 from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the revenue shares of each company to make inferences about their financial performances.\nBased on the above thought, the answer is:\nFrom the pie chart, it's clear that Amazon and Apple had a strong financial performance in 2020 compared to Google and Tesla."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "cMCtE3dnVk8cZxVn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how might these tech companies be positioned in the market based on their 2020 revenues?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to take into consideration the revenue shares of these companies in the market.\nBased on the above thought, the answer is:\nThe pie chart indicates that Amazon and Apple command a significant share of the market compared to Google and Tesla. This suggests that these two companies are likely positioned as leaders in the tech industry based on their 2020 revenues."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "7a1PGK3qgZ4jXRjQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined revenue percentage of Amazon and Apple according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to add up the proportion of {'Amazon'}[0.203, 0.302, 0.587, 0.91] and {'Apple'}[0.507, 0.227, 0.767, 0.843].\nBased on the above thought, the answer is:\n75.5%"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "BUCBN18VlSvDM5Hh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more percentage of the market revenue does Amazon have compared to Tesla in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would have to subtract the revenue market share of {'Tesla'}[0.265, 0.194, 0.464, 0.489] from {'Amazon'}[0.203, 0.302, 0.587, 0.91].\nBased on the above thought, the answer is:\n40.5%"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "CtERiP7AQwgAkuz7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Google were to double their market percentage, what would their market percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the market percentage if {'Google'}[0.326, 0.109, 0.641, 0.479] were to double, I would need to multiply their current percentage by two.\nBased on the above thought, the answer is:\n41.6%"
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "MjPVmTNhjMO2OQ3d",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the CSV data you see, can you convert it into two distinct lines of data for each technology in the time period between 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe that the title {\"Healthcare and Medical Technology Adoption - 2018\"}[0.266, 0.021, 0.698, 0.047] outlines the two types of technologies namely, {Telemedicine}[0.809, 0.071, 0.9, 0.091] and {Electronic Health Records}[0.809, 0.101, 0.987, 0.121].\nBased on the above thought, the answer is:\nYear,Telemedicine,Electronic Health Records\n2010,10,70\n2011,11,65\n2012,15,60\n2013,20,55\n2014,25,50\n2015,50,30\n2016,55,25\n2017,60,20\n2018,75,10"
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "jPgkAo7t1ABvDieq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you infer from the adoption rates of Telemedicine and Electronic Health Records between 2010 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the given data, I can deduce the trends by looking at the changes in the adoption rates of each technology over the years.\nBased on the above thought, the answer is:\nFrom 2010 to 2018, there is an increasing trend in the adoption of Telemedicine, while the adoption of Electronic Health Records shows a decreasing trend."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "KO90tt737mDfMPaK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do you interpret the adoption rates of Telemedicine and Electronic Health Records in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look into the changes in the adoption rates of both technologies in the year 2015 as compared to the previous years.\nBased on the above thought, the answer is:\nThe year 2015 saw a drastic shift with Telemedicine witnessing a steep increase in its adoption rate and simultaneously Electronic Health Records faced a sharp decrease. This could suggest a shift in medical technology preference or the emergence of Telemedicine as a more viable option."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "MepcyH0wHC8LYWNz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart representing 2018 data, how would you describe the adoption rates of Telemedicine and Electronic Health Records?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the relative sizes of the pie slices representing each technology.\nBased on the above thought, the answer is:\nIn 2018, the adoption rate of Telemedicine {Telemedicine}[0.235, 0.178, 0.753, 0.918] was overwhelmingly higher than that of Electronic Health Records {Electronic Health Records}[0.271, 0.117, 0.469, 0.484], accounting for 88.2% of the total adoption rate. This indicates a heavy preference for Telemedicine over Electronic Health Records in the year 2018."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "osj1FStO6KYljsDL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average adoption rate of Telemedicine from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average adoption rate of Telemedicine, I need to sum up the adoption rates from 2010 to 2018 and then divide by the total number of years which is 9.\nBased on the above thought, the answer is:\nThe average adoption rate of Telemedicine from 2010 to 2018 is 35.22%."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "K3bAsum6zv0KC4a3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year, does the increase in the adoption rate of Telemedicine exceed 30%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the rate of increase in the adoption of Telemedicine between each year to determine the years where the increase exceeded 30%.\nBased on the above thought, the answer is:\nThe year is 2015 when the increase in the adoption rate of Telemedicine exceeded 30%."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "bQVoA85AQghWWBpx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decrease in the adoption rate of Electronic Health Records from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the adoption rate of Electronic Health Records in 2018 from the adoption rate in 2010 to find out the total decrease over these years.\nBased on the above thought, the answer is:\nThe total decrease in the adoption rate of Electronic Health Records from 2010 to 2018 is 60%."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "Eki0n3QAA6Yuw8SL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart that you visualize, can you derive the corresponding data in tabular form for each segment represented on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the visual data into a table, I need to identify each slice or section of the pie chart. The values of each section would correspond to the contribution of each stage in the supply chain.{title of the pie chart}<r>[0.198, 0.021, 0.765, 0.047]</r>; {the labels corresponding to each slice}<r>[\"Production\", \"Raw Materials\", \"Storage\", \"Distribution\", \"Delivery\", \"Consumer\", \"After Sales Service\", \"Reworking\"]</r>; {the value or percentage corresponding to each label}<r>[60, 65, 55, 50, 40, 75, 70, 80]</r>.\nBased on the above thought, the answer is:\nSupply Chain Stages, Global Trade Percentage\nProduction, 60\nRaw Materials, 65\nStorage, 55\nDistribution, 50\nDelivery, 40\nConsumer, 75\nAfter Sales Service, 70\nReworking, 80"
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "Rv4mExXG8t3dbX2Q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which supply chain stage contributes the most to global trade? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the sizes of each slice on the pie chart and the stage with the largest slice has the highest contribution.<r>[0.198, 0.267, 0.457, 0.528],[0.2, 0.533, 0.459, 0.82],[0.31, 0.551, 0.477, 0.921],[0.49, 0.551, 0.647, 0.921],[0.502, 0.539, 0.739, 0.833],[0.507, 0.323, 0.767, 0.662],[0.496, 0.119, 0.718, 0.485],[0.274, 0.111, 0.509, 0.481]</r>\nBased on the above thought, the answer is:\nThe 'Consumer' stage contributes the most to global trade because it has the highest percentage of 75% among all stages."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "CxpW60vZEoTGniQS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the importance of the 'Delivery' stage in global trade based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The inference can be drawn by looking at the percentage contribution of the 'Delivery' stage, relative to other stages in the supply chain.<r>[0.502, 0.539, 0.739, 0.833]</r>\nBased on the above thought, the answer is:\nThe 'Delivery' stage is least prioritized in global trade as it contributes the least, at 40%."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "WZedGP6XsjsDNk8q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data represented in the pie chart shape decision making in global trade?"
+            },
+            {
+                "from": "gpt",
+                "value": "The answer lies in examining the percentages of the different stages in the supply chain, as this will indicate which stages are prioritised and where improvements or new investments might be needed.<r> all stages </r>\nBased on the above thought, the answer is:\nThe data suggests that more resources are allocated towards the 'Consumer', 'After Sales Services', and 'Reworking' stages, indicating a focus on customer satisfaction and quality service. Fewer resources seem to be dedicated to 'Delivery', indicating this could be a potential area for improvement. Thus, decision-making in global trade could prioritize improving delivery while maintaining or enhancing the other stages."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "uF8CZDn3hsx4aLsF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage contribution of the \"Production\" and \"Raw Materials\" stages to global trade according to the data shown on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I look at the areas corresponding to 'Production' and 'Raw Materials' on the pie chart, and add their individual percentages together.<r> [0.198, 0.267, 0.457, 0.528], [0.2, 0.533, 0.459, 0.82], </r>\nBased on the above thought, the answer is:\n125"
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "iiebgswxaUPMwUtP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what is the percentage contribution of the \"Consumer\" stage to global trade?"
+            },
+            {
+                "from": "gpt",
+                "value": "I check the slice corresponding to the 'Consumer' stage on the pie chart and read off the provided percentage.<r>[0.507, 0.323, 0.767, 0.662]</r>\nBased on the above thought, the answer is:\n75"
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "MHfEZQTEweXgjM0F",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Delivery' stage increased its contribution by 10%, what would be the new total percentage of all stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would add the 10% increase to the current 'Delivery' stage percentage and then sum up all the stage percentages.<r>[0.502, 0.539, 0.739, 0.833], all other stages </r>\nBased on the above thought, the answer is:\nThe new total percentage would be 110%+65%+55%+50%+75%+70%+80%=505%"
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "DrtAnkhzLyL59Fpm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart's figures to a table in CSV format for the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let's start by looking at the title {Cultural Usage in 2015}<r>[0.386, 0.021, 0.577, 0.047]</r> for this chart and the legend <r>[0.807, 0.063, 0.993, 0.129]</r> to identify the categories. I can see two categories: {Cultural Heritage}<r>[0.851, 0.071, 0.971, 0.091]</r> and {Cultural Integration}<r>[0.851, 0.101, 0.987, 0.121]</r>. The percentages of these categories can be found on the autotext labels.\nBased on the above thought, the answer is:\nYear,Cultural Heritage,Cultural Integration\n2015,90.0%,10.0%"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "pxB6Y4L4elIUy8UU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which cultural aspect was more dominant in 2015 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The dominance of a culture can be determined by the percentage representation. The higher the percentage, the more dominant the culture.\nBased on the above thought, the answer is:\nCultural Heritage was more dominant in 2015 as it constituted 90% of the total culture usage."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "UkcfCTo7vrAE0IgP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart depict the relationship between Cultural Heritage and Cultural Integration in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart clearly shows the proportion of each category, enabling a quick comparison between the two.\nBased on the above thought, the answer is:\nIn 2015, Cultural Heritage was vastly more prevalent than Cultural Integration, being consumed 9 times more often."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "COjsGxr96Er5ct4c",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we want to promote cultural diversity and aim for a more balanced depiction in 2016, what suggestions can be made based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the chart, suggestions could be made to increase the presence of the less popular cultural category.\nBased on the above thought, the answer is:\nTo promote cultural diversity, resources could be invested into Cultural Integration activities as it was significantly less prevalent than Cultural Heritage in 2015. This could help balance the cultural usage in 2016."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "RRWUPwBVXfxybezg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Cultural Integration increased by 5% in 2015, what would the new proportion for Cultural Heritage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Cultural Integration increases by 5%, the proportion of Cultural Heritage would decrease by the same amount since the total should remain at 100%.\nBased on the above thought, the answer is:\n85%"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "VJPD8xpZzvujC2PC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in usage between Cultural Heritage and Cultural Integration in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the difference, subtract the Cultural Integration percentage from the Cultural Heritage percentage.\nBased on the above thought, the answer is:\n80%"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "TYq05c64C5KBQ3Gf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the collective usage of Cultural Heritage and Cultural Integration in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "When adding up the percentages for Cultural Heritage and Cultural Integration, we get the total usage for 2015.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "4WDMUfhLo3qMHGVt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data about the 'Internet and Communication Industry Development' from the pie chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart and its {legend}<r>[0.908, 0.063, 0.993, 0.309]</r> which provides the labels for each year, and the {pie elements}<r>[0.197, 0.112, 0.766, 0.923]</r> which indicates the development factors, I can see that the development factors are represented as slices, and each slice represents a year and its development factor.\nBased on the above thought, the answer is:\nYear,Development Factor\n2007,0.095\n2008,0.102\n2009,0.109\n2010,0.124\n2011,0.139\n2012,0.153\n2013,0.142\n2014,0.136"
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "gLllr2YmbuBeDSB4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the greatest growth in the Internet and Communication Industry and by what percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the maximum development factor, I need to check the largest slice in the pie chart and then convert the development factor into percentage.\nBased on the above thought, the answer is:\nIn 2012, the Internet and Communication Industry saw the greatest growth with a development factor of 15.3%."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "2V0kk37Tl9FoEery",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the general trend in the Internet and Communication Industry development from 2007 to 2014 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to track the size of the slices from 2007 to 2014 to understand the trend in the industry's development.\nBased on the above thought, the answer is:\nThe general trend in the Internet and Communication Industry shows growth from 2007 to 2012, with a peak in 2012, followed by slight decline in the years 2013 and 2014."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "6UjgW2ZvJVq62fD2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan it be inferred from the chart that the development factor of the industry consistently increased over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the size of each consecutive slice from 2007 to 2014 to see if the development factor increased consistently.\nBased on the above thought, the answer is:\nThe development factor did not consistently increase over the years. Although there was a steady increase up until 2012, the development factor decreased slightly in the years 2013 and 2014."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "9YZs6W0EbEqEJOtW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total development factor from 2007 to 2014 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total development factor, I just need to sum up all the development factors for each year from the slices of the pie chart.\nBased on the above thought, the answer is:\n1.0"
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "zAQmL6eeBN6BWMPh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year has the least annual development factor?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce this, I need to identify the smallest slice in the pie chart which represents the smallest development factor for a year.\nBased on the above thought, the answer is:\n2007"
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "ORZ4j7eAoBpHzgU7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the development factor in 2010 greater than in 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this difference, I need to subtract the development factor of 2007 from that of 2010.\nBased on the above thought, the answer is:\n0.029"
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "FkVWRiHBy8gVpZdM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording the data from the chart. Can you form a table with information such as Years, Cancer Cases and Managed Cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart and correlating the sectors with {\"legend\"}<r>[0.908, 0.063, 0.993, 0.309]</r>, I can see the existing cases for the given years.\nBased on the above thought, the answer is:\nYear,Cancer Cases,Managed Cases\n2009,50,35\n2010,70,60\n2011,95,85\n2012,120,110\n2013,145,130\n2014,175,160\n2015,205,185\n2016,215,195"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "6rk5FwNlgvr70Z6v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\naccording the chart image, which year had an explosion in cancer cases, compared to previous years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observation needs to be made on the {\"legend\"}<r>[0.908, 0.063, 0.993, 0.309]</r> where when compared to the previous years sees a substantial increment looking at {\"slice for 2012\"}<r>[0.246, 0.545, 0.466, 0.898]</r>.\nBased on the above thought, the answer is:\nThe year 2012 saw a significant increase in cancer cases compared to previous years."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "7pLxetQ5vMwz8I54",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what can be inferred about the management of cancer cases over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the increase in both {\"Cancer Cases\"}<r>[0.222, 0.259, 0.46, 0.497]</r> and {\"Managed Cases\"}<r>[0.504, 0.534, 0.763, 0.853]</r> each year, an inference can be made on the management of these cases.\nBased on the above thought, the answer is:\nThe data suggests that the management of cancer cases has been improving over the years. The number of managed cases has been rising in tandem with the number of cancer cases, indicating that more and more cancer cases are being effectively managed."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "dZt9612Jlo6XrrPC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the trends observed in the chart, what can be predicted about the number of managed cases in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based upon the increases each year in both the {\"Cancer Cases\"}<r>[0.222, 0.259, 0.46, 0.497]</r> and the {\"Managed Cases\"}<r>[0.504, 0.534, 0.763, 0.853]</r>, a prediction can be made about the future.\nBased on the above thought, the answer is:\nIf the current trends continue, we can expect the number of managed cases to keep rising in the future. This suggests continued progress in the management and treatment of cancer."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "vOudxKdBxW9e4fsJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which years, the number of managed cases is on or over 80% of the number of cancer cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to calculate the proportion of managed cases to cancer cases in each year, then find years where the proportion is above 80%.\nBased on the above thought, the answer is:\n2010, 2011, 2012, 2013"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "2v1HEnGHnKvzdo0O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many additional cancer cases were there in 2010 compared to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in cancer cases between 2009 and 2010, I need to subtract the number of cancer cases in 2009 from the number in 2010.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "sgbDpzucal44N5q1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the number of managed cases increase from 2009 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the number of managed cases in 2009 from the number in 2016, divide by the number in 2009 and multiply by 100.\nBased on the above thought, the answer is:\n457.14%"
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "ZSZXyE0h9PcyJhGw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, kindly convert the data, regarding Protests and Policy Changes from 2010 to 2014, into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "I first perceive the title {\"Protests and Policy Changes from 2010 to 2014\"}[0.284, 0.021, 0.679, 0.047]  and then each year's slices in the pie chart {[2010, Protests][0.197, 0.283, 0.456, 0.828]; [2010, Policy Changes][0.199, 0.264, 0.458, 0.502]; [2011, Protests][0.34, 0.553, 0.684, 0.923];[2011, Policy Changes][0.199, 0.505, 0.458, 0.814]; etc...]. Each year's data is represented separately for Protests and Policy Changes detailing their respective percentage contribution.\nBased on the above thought, the answer is:\nYear,Protests,Policy Changes\n2010,26.7%,10.0%\n2011,23.3%,15.0%\n2012,20.0%,20.0%\n2013,16.7%,25.0%\n2014,13.3%,30.0%"
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "5ifYYqtxL72uJGf7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is the year 2014 an outlier in terms of social protests and policy changes as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The term 'outlier' is used to describe a data point that appears significantly different from other data points. In this case, I need to observe 2014's slice in comparison to other years in both protest and policy changes categories.\nBased on the above thought, the answer is:\nThe year 2014 is an outlier because it had a significantly lower percentage of protests compared to the other years, only 13.3%. Simultaneously, it also had a disproportionately high percentage of policy changes, amounting to 30%, which is noticeably higher than the previous years."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "5KTvOEaCMvuGI09Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2010 to 2014, is there a correlation between the proportions of protests and policy changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain that, I would need to observe the pattern in the proportions of protests and policy changes over the span of these years.\nBased on the above thought, the answer is:\nYes, from the pie chart, there appears to be a negative correlation between the proportions of protests and policy changes from 2010 to 2014."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "6c9zqSodtJddjz6O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the political dynamics from 2010 to 2014 based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferences should be drawn from the patterns displayed in the pie charts concerning the changing proportions of policy changes and protests over these years.\nBased on the above thought, the answer is:\nThe chart shows a downward trend in protests and an increase in policy changes from 2010 to 2014. This could suggest that over these years there might have been increased government responsiveness, leading to policy changes that could have possibly responded to the concerns driving the protests, which in turn could be a reason for the decrease in protests."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "3FeBC9OtLr0lmGac",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year experienced a significant dip in protests as per the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look through the data and observe which year marked a stark drop in the percentage of protests.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "CwPwayjfqRsypsbh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent did the policy changes increase from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to check the percentage of policy changes in the year 2010 and then in 2014. The difference will give the percent increase.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "hBYpISyU2RcrdpUD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the protests decrease from 2012 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to observe the percentage of protests in the year 2012 and 2013. And then find the difference to find out by what percent it has decreased.\nBased on the above thought, the answer is:\n3.3%"
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "4wUDvajJTzhw2MOo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the given pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "After analyzing the title {\"Market Share of Competitor B from 2015 to 2020\"}[0.278, 0.021, 0.685, 0.047] and the slices for each year {[2015][0.197, 0.27, 0.456, 0.595]; [2016][0.211, 0.542, 0.463, 0.898]; [2017][0.417, 0.552, 0.661, 0.922]; [2018][0.506, 0.43, 0.766, 0.802]; [2019][0.501, 0.143, 0.751, 0.491]; [2020][0.276, 0.11, 0.563, 0.48]}, it becomes evident that the pie chart represents the percentage market share of Competitor B from 2015 to 2020.\nBased on the above thought, the answer is:\nYear,Percentage Market Share of Competitor B\n2015,14.9%\n2016,16.8%\n2017,16.0%\n2018,17.5%\n2019,15.3%\n2020,19.4%"
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "7y2Gr8KBbGIFgnft",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the trend in the market share of Competitor B from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sizes of the slices for each year {[2015][0.197, 0.27, 0.456, 0.595]; [2016][0.211, 0.542, 0.463, 0.898]; [2017][0.417, 0.552, 0.661, 0.922]; [2018][0.506, 0.43, 0.766, 0.802]; [2019][0.501, 0.143, 0.751, 0.491]; [2020][0.276, 0.11, 0.563, 0.48]}, I can interpret the trend in the market share of Competitor B.\nBased on the above thought, the answer is:\nThe chart shows a general upward trend in the market share of Competitor B from 2015 to 2020. This implies that Competitor B has managed to increase its footprint in the market over these years."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "tvD4wHUw0tNd6g6W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how successful was Competitor B in maintaining consistent market growth over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the percentages associated with each year, I can evaluate the consistency of Competitor B's market growth.\nBased on the above thought, the answer is:\nAccording to the chart, Competitor B experienced fluctuations in market share, and the market share didn't increase each year. This indicates that while Competitor B did see improvements in its market position over the time period, it wasn't a consistent year-on-year growth."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "4DzOvSMdpNPj1E1j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat may be inferred about Competitor A's performance from 2015-2020 using the provided data for Competitor B?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the information in the chart refers to the market share of Competitor B, I could infer about the performance of Competitor A by assuming that only these two competitors are present in the market.\nBased on the above thought, the answer is:\nThe increasing trend of Competitor B's market share from 2015 to 2020 suggests that Competitor A's market share probably diminished during this period, assuming the total market share is divided only between these two competitors."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "ocMVGyNnCAsULeEq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in market share for Competitor B from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The market share of Competitor B in 2015 is 14.9%, and that in 2020 is 19.4%. By subtracting the market share in 2015 from that in 2020, I can find the increase percentage.\nBased on the above thought, the answer is:\nThe percentage increase is 19.4% - 14.9% = 4.5%."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "Au1NFK4cHJVjaTDm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did Competitor B have the lowest market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year when Competitor B had the lowest market share, I need to compare the percentages in each year.\nBased on the above thought, the answer is:\nIn the year 2015, Competitor B had the lowest market share of 14.9%."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "mbjooy5Lnjd3lIi6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the market share fluctuated for Competitor B from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The fluctuation in market share can be calculated by finding the difference between the highest and lowest market shares during this period.\nBased on the above thought, the answer is:\nThe highest market share is 19.4% and the lowest is 14.9%. So, the fluctuation is 19.4% - 14.9% = 4.5%."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "YoEsGsmGO8TzGgYP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom observing the pie chart, could you extract the number of users for both Facebook and Twitter over the years, and present the information in a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the pie chart slices, I can see that the slices represent the usage count (in millions of users) of both Facebook and Twitter for the years from 2015 to 2020. The pie chart slices for each year are color-coded and represented in the legend on the right {\"Facebook\"}[0.197, 0.111, 0.767, 0.922], {\"Twitter\"}[0.197, 0.111, 0.767, 0.922]. I'll now extract the information to a CSV table.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter\n2015,120,90\n2016,130,70\n2017,110,130\n2018,200,80\n2019,210,200\n2020,150,220"
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "yoZsa61330Qqg7ET",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data in the pie chart, identify the platform which had the most significant growth over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to compare the growth in users from 2015 to 2020 for both platforms.\nBased on the above thought, the answer is:\nTwitter had the most significant growth, increasing from 90 million users in 2015 to 220 million users in 2020."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "89F66NRu3uFQzagF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can you determine in the user patterns of Facebook and Twitter over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to note the changes in user counts of both platforms over the years.\nBased on the above thought, the answer is:\nBoth platforms saw a growth in their user counts over the years, with a noticeable spike in 2018 for Facebook and in 2020 for Twitter. However, following these peaks, there was a decline in the subsequent year for both platforms."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "XoQNs9Sm5sw83irB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to predict the user count for Facebook in 2021 based on the chart, what would it be?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to use the past user trends of Facebook to predict its user count in 2021.\nBased on the above thought, the answer is:\nDue to the downward trend seen in 2020, unless a significant growth strategy was implemented, we can predict a slight decrease or stable user count for Facebook in the year 2021."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "ObNQQeOOrQhdshLf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the combined number of users for Facebook and Twitter the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with highest combined users, I need to sum up the number of users of both platforms for each year.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "k03FGBnMlWmISerz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Facebook see the largest increase in the number of users?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the user count of Facebook in each year and see in which year there was the highest increase in comparison to the previous year.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "uhJNfUu6nE7JffH6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the total number of users increased by 10 million in 2021 for both platforms, what would be their individual counts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add 10 million to the user counts of both Facebook and Twitter in 2020.\nBased on the above thought, the answer is:\nThe counts would be 160 million for Facebook and 230 million for Twitter."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "292P1CDEZJeObRY3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the mentioned time allocation data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I saw the title \"Time Allocation in a Day for Different Lifestyle and Social Activities\" {title}<r>[0.204, 0.021, 0.759, 0.047]</r> at the top and the slices {slice}<r>[0.197, 0.109, 0.763, 0.905]</r> representing different categories along with their corresponding values.\nBased on the above thought, the answer is:\nActivity, Percentage\nFamily Time, 30\nWorkout, 45\nCooking, 25"
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "plFmmWCb03Imtn3G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which activity takes up the majority of an average person's day?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the activity with the largest percentage, which signifies it takes up the most time in an average person's day.\nBased on the above thought, the answer is:\nThe activity \"Workout\" takes up most of the individual's day as it has the highest time allocation of 45%."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "lILLDKvlkG2RPUn5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observation can be made about the lifestyle habits from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the relative proportions of each category to make observations about lifestyle habits.\nBased on the above thought, the answer is:\nFrom the chart we can gather that Workout activities takes up a significant proportion of the daily routine, indicating that the individual prioritizes physical fitness. Meanwhile, Family Time and Cooking take up 30% and 25% respectively, showing a balanced approach to personal time and meal preparation."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "Ks72kaUeHHxuIfR6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does this chart's data have on the health and wellbeing of the individual it represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the respective time allocations to each activity category and the implications these could have on health and wellbeing.\nBased on the above thought, the answer is:\nConsidering the person spends the majority of their time on workout, it suggests they lead a physically active lifestyle which is beneficial for maintaining good health. A substantial allocation to Family Time also indicates good work-life balance, promoting positive emotional wellbeing. On the other hand, relatively lower time spent on Cooking might imply a reliance on pre-prepared meals or dining out often, which could impact nutritional intake if not managed carefully."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "DVTdB363ISfUJBSA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the time allocated to workout is reduced by 10%, how much percentage of the day's time is left unallocated to these activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the unallocated time, I need to reduce the workout's percentage by 10%, sum up the percentages of all categories, and subtract it from 100%.\nBased on the above thought, the answer is:\nAfter reducing the workout's percentage by 10%, the new total percentage is 30% (Family Time) + (45%-10%) (Workout) + 25% (Cooking) = 90%. So, 10% of the day's time is left unallocated."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "tBTxzMkk5VaAG6fa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the time percentages for Workout and Family Time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of Family Time from the percentage of Workout.\nBased on the above thought, the answer is:\nThe difference between the time percentages for Workout and Family Time is 45% - 30% = 15%."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "ymJNrHyo1dZK5q78",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the time percentage for Cooking is doubled, what would be the new total time percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total time percentage, I need to double the Cooking percentage and then add up all percentages.\nBased on the above thought, the answer is:\nThe new percentage for Cooking is 2*(25%) = 50%. So, the new total time percentage is 30% (Family Time) + 45% (Workout) + 50% (Cooking) = 125%."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "tPl9BzxAgOjMAPy4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease parse the chart information and convert it into CSV format data."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart title {\"Entrepreneurship and Innovation Ecosystem for 2018 and 2019\"}[0.218, 0.021, 0.746, 0.047] as well as the color-coded pie sections {Startups 2018}[0.198, 0.266, 0.457, 0.511], {Funding 2018}[0.205, 0.543, 0.481, 0.913], {Innovations 2018}[0.484, 0.553, 0.512, 0.922], {Startups 2019}[0.497, 0.545, 0.737, 0.913], {Funding 2019}[0.322, 0.115, 0.755, 0.624], {Innovations 2019}[0.264, 0.217, 0.463, 0.49] and their respective percentages in the chart, I can convert the proportions into the required data format.\nBased on the above thought, the answer is:\nScenario,Startups,Funding,Innovations\n2018,11.4%,25.7%,0.7%\n2019,17.1%,42.9%,2.1%"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "BCcgWBUiX90L8XzP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, which area saw the most increase from 2018 to 2019 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the 2018 and 2019 sections for Startups, Funding, and Innovations, I can determine which area saw the most increase based on its percentage change.\nBased on the above thought, the answer is:\nThe \"Funding\" area saw the most increase from 2018 to 2019 as it rose from 25.7% to 42.9%. This could be due to increased investment in startups, facilitating the growth and innovation within the ecosystem."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "wxgRZAGTr5OZXL9m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the entrepreneurship and innovation ecosystem in the years 2018 and 2019 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the proportions of each category for the years 2018 and 2019, I would be able to infer the state of the ecosystem during those years.\nBased on the above thought, the answer is:\nThe chart suggests that there has been significant growth in the Entrepreneurship and Innovation Ecosystem from 2018 to 2019. The \"Startups\" and \"Funding\" both saw substantial increases, indicating that more businesses were created and more capital was invested. Moreover, while the proportion of \"Innovations\" is relatively small compared to the other two categories, it has also seen growth between the two years."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "wNsCn5obi0NOfKWW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend continues, what might be the expected scenario in the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the trend between 2018 and 2019, I can predict the expected scenario for the next year.\nBased on the above thought, the answer is:\nIf the trend from 2018 to 2019 continues, we can expect to see an increase in the percentages of \"Startups\", \"Funding\", and \"Innovations\" in the next year. More specifically, we should expect a more significant increase in the \"Funding\" category, which may spur further growth in \"Startups\" and \"Innovations\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "Em26ehsqbiK6eYda",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase percentage of funding from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase percentage of funding from 2018 to 2019, I need to subtract the funding percentage in 2018 from the percentage in 2019, and then divide by the percentage in 2018.\nBased on the above thought, the answer is:\n66.9%"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "CIs1MBhQt3HY5d7E",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of Innovations over the 2 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage of Innovations over the 2 years, I need to add up the Innovations' percentages for these 2 years and then divide by 2.\nBased on the above thought, the answer is:\n1.4%"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "5GjkuIZkVaGvYw0w",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the combined percentage of Startups and Innovations in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage of Startups and Innovations in 2019, I need to add up their respective percentages.\nBased on the above thought, the answer is:\n19.2%"
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "k1gGtdh8BoTfx3c7",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the pie chart to table form in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels of the pie chart slices {2010[0.282, 0.453, 0.326, 0.473]}, {2011[0.366, 0.728, 0.41, 0.748]}, {2012[0.595, 0.615, 0.639, 0.635]}, {2013[0.487, 0.287, 0.53, 0.307]}, we know that these represent the years from 2010 to 2013. We also have the corresponding percentages for these years, which I will convert to donation amounts based on the total amount of donations.\nBased on the above thought, the answer is:\nYear,Donations (K$) \n2010,800\n2011,1200\n2012,1400\n2013,1700"
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "TmRI5NdalIjl8Etv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the trend in donations to the Culture and Arts industry from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am reading the information from the pie chart that shows the proportions of donations from each year. An increase or decrease in the slice's area indicates the trend of donations.\nBased on the above thought, the answer is:\nThe donations to the Culture and Arts industry have been increasing from 2010 to 2013."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "EImZc8h9eHXImV2K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might this increase in donations to the Culture and Arts industry indicate about public interest in the cultural field?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the increasing trend in donations, I would make an inference regarding public interest in the area.\nBased on the above thought, the answer is:\nThe increasing trend in donations signifies growing public interest and support for the Culture and Arts industry from 2010 to 2013."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "0FIWvhrkIbOH5KNf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the increasing donations, how might policy makers use this information to plan for the cultural sector's funding in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider how the increasing trend in donations might impact funding decisions for the Culture and Arts industry in the coming years.\nBased on the above thought, the answer is:\nGiven the increasing donations to the Culture and Arts industry, policy makers could interpret this as indicative of growing public interest in this sector. Therefore, they might decide to increase funding towards this sector in future years to further boost growth and public engagement."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "wHkWlYG74qcatgNY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year had the highest amount of donation, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check for the largest slice of the pie chart and look at its label to identify the year that had the highest donations.\nBased on the above thought, the answer is:\n2013"
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "HOjXZ8ibzsD9npDy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total amount of donations from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total amount of donations, I need to sum up the donations from each individual year.\nBased on the above thought, the answer is:\n5100K$"
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "uHamTrAABWDiJdLE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the year 2013 saw an increase of 300K$ in donations, what would the new total donation amount be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total donation amount, I need to add the increase in 2013's donations to the total donation amount from 2010 to 2013.\nBased on the above thought, the answer is:\n5400K$"
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "pyVfxo4L84cccH55",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from the pie chart you see and present it in a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "According to {the legend}<r>[0.908, 0.063, 0.993, 0.249]</r>, there are different fitness trends represented for the years 2018, 2019, and 2020. The legend indicates specific fitness trends, and the colors correspond to different years.\nBased on the above thought, the answer is:\nFitness Trends,2018,2019,2020\nHigh-Intensity Interval Training (HIIT),55,68,75\nYoga,45,32,25"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "yYuAJ8UVomn1rnxs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you draw from the chart regarding the changing fitness trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the changes in the fitness trends from 2018 to 2020.\nBased on the above thought, the answer is:\nThe chart shows a growing trend towards High-Intensity Interval Training (HIIT) with its popularity increasing from 2018 to 2020. However, the interest in Yoga seems to be waning, with its popularity decreasing over the same period."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "Ih5vlMF45JbsY7Cb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends continue, what can we predict about the popularity of High-Intensity Interval Training (HIIT) and Yoga in the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from 2018 to 2020, we can predict the trends for the next year.\nBased on the above thought, the answer is:\nIf the trends continue, we may expect an increase in the popularity of High-Intensity Interval Training (HIIT) and a decrease in the popularity of Yoga in the next year."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "FSeVwXhncvGzOUkO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this information be used by a gym to make decisions about which classes to offer?"
+            },
+            {
+                "from": "gpt",
+                "value": "The gym could apply this information by analyzing the popularity trends of the two types of fitness classes over the past years.\nBased on the above thought, the answer is:\nSince High-Intensity Interval Training (HIIT) shows an increasing popularity trend, the gym might want to offer more HIIT classes to meet the increasing demand. On the other hand, as Yoga has been declining in popularity, they may want to consider reducing the number of yoga classes or perhaps try to rejuvenate interest in yoga through new types of yoga classes or promotional activities."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "Zx2yCgOoNBK2D14e",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the increase in popularity of High-Intensity Interval Training (HIIT) from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, it would need to find the difference between the numbers for 2020 and 2018 for High-Intensity Interval Training (HIIT).\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "jtUJ0hfXWATfNtDw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the decline rate in the popularity of Yoga from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decline rate, I would need to find the difference between the numbers for 2018 and 2019 for Yoga and then divide by the number for 2018.\nBased on the above thought, the answer is:\n28.89%"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "I59nkrDrs1gITVnY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average popularity of High-Intensity Interval Training (HIIT) over the three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average popularity, I would sum up the popularity numbers for HIIT over the three years and then divide by 3.\nBased on the above thought, the answer is:\n66"
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "QPsUn3eAiJtoZBii",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the pie chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart title {\"People Affected by Cybersecurity Breaches in 2017 and 2018\"}[0.227, 0.021, 0.737, 0.047] and the legend items {the Encryption slice}[0.2, 0.296, 0.497, 0.904] and {the Data Privacy slice}[0.306, 0.128, 0.763, 0.864], I observe that they represent the theme of cybersecurity breaches and the percentage of people affected for the years 2017 and 2018.\nBased on the above thought, the answer is:\nYear,Theme,Percentage of People Affected\n2017,Encryption,38.5%\n2018,Data Privacy,61.5%"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "HwMUbhUpJrCFa9KQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cybersecurity theme had greater influence in terms of people affected? Please explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the theme that had the greater percentage of people affected by cybersecurity breaches.\nBased on the above thought, the answer is:\nThe \"Data Privacy\" theme had a greater influence in terms of people affected as it accounted for 61.5% of the total, which is significantly higher than the \"Encryption\" which accounted for only 38.5%."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "3Kt48PpxKL8wsDpk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trends in cybersecurity between 2017 and 2018 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the differences in the percentages of people affected between the two years.\nBased on the above thought, the answer is:\nThe chart suggests that there was an increase in people affected by \"Data Privacy\" breaches from 2017 to 2018, suggesting that concerns about data privacy became more prevalent over time."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "DzPxw2m8g60lfVc2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data from the chart inform organizations about their cybersecurity measures for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the themes of cybersecurity breaches represented in the chart, and parse what those themes imply about current cybersecurity vulnerabilities.\nBased on the above thought, the answer is:\n\"Data Privacy\" was a much more prevalent issue in 2018 than \"Encryption\" was in 2017. Moving forward, organizations may need to put more emphasis on securing user data and privacy, as trends indicate these areas are a growing concern."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "cMlzGeEMcfVgJT2C",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how many percent more people got affected by breaches in \"Data Privacy\" than \"Encryption\" in those two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll simply subtract the percentage of people affected by \"Encryption\" from the percentage of people affected by \"Data Privacy\".\nBased on the above thought, the answer is:\n23%"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "ZmAiq4IOLJ96YZWm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of people affected under \"Encryption\" in 2017 was 10% less, what would be the new percentage of people affected by \"Data Privacy\" in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the \"Encryption\" percentage decreases by 10%, the new \"Data Privacy\" percentage would be 100% - the new \"Encryption\" percentage.\nBased on the above thought, the answer is:\n71.5%"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "6ZuWdiaF9qqPxGyw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference of people affected in terms of percentage between 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the change in the percentage of people affected from 2017 to 2018 by subtracting the percentage for 2017 from the percentage for 2018.\nBased on the above thought, the answer is:\n23%"
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "4sHRsx2q48TxTflG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a structured table based on the observed chart, converting the chart information into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the titled 'Social Inequality and Rights Protection' which is placed at the top of the chart {Social Inequality and Rights Protection}[0.321, 0.021, 0.642, 0.047], and the distinct pie slices {[0.199, 0.295, 0.473, 0.903]; [0.496, 0.547, 0.73, 0.916]; [0.507, 0.241, 0.766, 0.669]; [0.393, 0.11, 0.665, 0.48]; [0.268, 0.141, 0.467, 0.486]}, we can say each slice represents different social issues and the percentage focus given to each issue.\nBased on the above thought, the answer is:\nSocial Issue,Percentage\nExtreme Poverty,37.0%\nMinority Rights,17.0%\nLGBT Rights,20.0%\nRefugees,18.0%\nClimate Justice,8.0%"
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "BMQA2v2hO9y7iNSO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which is the social issue that social activists focus the most on and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The social issue with the largest slice of the pie would represent the highest focus, which I will look for.\nBased on the above thought, the answer is:\nThe highest focus is on Extreme Poverty, as indicated by its slice of 37.0% in the pie chart. This suggests that social activists dedicate the most effort and resources to tackling Extreme Poverty."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "ink6f4pwXPErYgNb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data represented in the pie chart, what could be inferred about the social activist focus in the realm of social inequality and human rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferences would need to consider the proportions allocated to each social issue as represented in the pie slices.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that social activists have prioritized combating Extreme Poverty, with significant focus also given to LGBT Rights and Refugees' rights. Minority Rights and Climate Justice receive comparatively less attention, however, suggesting that they may be underrepresented areas in the current focus of human rights protection initiatives."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "erAhKxgCxK88xwPj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data in the pie chart influence future strategy planning for social activists?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future strategies could be influenced by the current allocation of focus, including areas that may need more attention.\nBased on the above thought, the answer is:\nThe data can help to guide the allocation of resources in future, by showing which social issues already receive significant attention (Extreme Poverty, LGBT Rights, and Refugee Rights), and which might be areas for potential increased focus (Minority Rights and Climate Justice). Thus it could prompt reviews and discussions regarding a rebalance of effort and resources."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "xPXjiG1E24kEg6mz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage spent on Rights Protections for minorities and refugees?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain this number, I need to add the percentages spent on Minority Rights and Refugees.\nBased on the above thought, the answer is:\n35.0%."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "D6MHEB2LSkVptfk9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we take out the percentage for Climate Justice, what remains as the total focus percentage on the other social issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this calculation, I need to subtract the focus percentage of Climate Justice from the total, which is 100%.\nBased on the above thought, the answer is:\n92%."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "gUb3i4hm28omds3N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming that 3% of the effort dedicated to Extreme Poverty is redirected to Climate Justice, what would be the new allocation of resources for each cause?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I would reduce the Extreme Poverty score by 3% and add that same 3% to the Climate Justice score.\nBased on the above thought, the answer is:\nExtreme Poverty would now be 34%. Climate Justice increases to 11%."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "JsLQBPm9NCPpUwnA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the raw data from the 2016 pie chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart labels and corresponding proportions {\"Music\"}<r>[0.277, 0.295, 0.493, 0.493]</r>, {\"Dance\"}<r>[0.278, 0.499, 0.494, 0.799]</r>, {\"Theatre\"}<r>[0.43, 0.533, 0.731, 0.841]</r>, {\"Visual Art\"}<r>[0.534, 0.386, 0.75, 0.576]</r>, {\"Film\"}<r>[0.529, 0.234, 0.729, 0.486]</r>, {\"Literature\"}<r>[0.428, 0.166, 0.639, 0.474]</r>, {\"Cultural Heritage\"}<r>[0.352, 0.115, 0.471, 0.397]</r>, and {\"Digital Art\"}<r>[0.29, 0.157, 0.455, 0.413]</r> are all visible, along with the year in the subtitle {\"Culture and Arts Industry Development - 2016\"}<r>[0.32, 0.088, 0.705, 0.114]</r>. So yes, it's possible to convert it into a CSV table.\nBased on the above thought, the answer is:\nYear,Music,Dance,Theatre,Visual Art,Film,Literature,Cultural Heritage,Digital Art\n2016,10.0%,19.1%,28.2%,10.0%,9.1%,16.4%,2.7%,4.5%"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "Eg9jutuLgXVAsTPh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector seems to be the most developed, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector that has the highest proportion in the pie chart would indicate the most developed sector.\nBased on the above thought, the answer is:\nTheatre is the most developed as it has the highest proportion of 28.2%."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "kAkm4PjTge9Cn595",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the predominant area of interest in the Culture and Arts industry in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area with the highest percentage in the pie chart could be considered the predominant area of interest.\nBased on the above thought, the answer is:\nTheatre is the predominant area of interest in 2016, as indicated by the highest slice of 28.2% in the pie chart."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "Ub41KZvxqobgRuCn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data for 2016, how might the authorities allocate resources for each sector in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To speculate on the plausible resource allocation for 2017, I would suggest distributing most resources to sectors showing significant development while addressing the least developed sectors to maintain a balanced ecosystem.\nBased on the above thought, the answer is:\nGiven the statistics, the authorities might allocate more resources to Theatre, Dance, and Literature as they showed significant development in 2016. However, to ensure a balanced ecosystem in the Culture and Arts industry, it would also be beneficial to invest in the lesser developed sectors like Digital Art and Cultural Heritage."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "mz7y0AD9AWGaj4mc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which sector has the lowest rate of development based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the lowest rate of development, I have to look at all the sectors and their proportions respectively and select the one with the smallest percentage.\nBased on the above thought, the answer is:\nCultural Heritage"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "uwuiU64eMe4Blb5d",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the music and film sectors based on the 2016 pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the sum, I have to add up the percentage for music and the percentage for film.\nBased on the above thought, the answer is:\n19.1%"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "qoaxOgnRsuLlDwOW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the percentage of Visual Art if it increased by 3%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new percentage, I will increase the existing percentage of Visual Art by 3%.\nBased on the above thought, the answer is:\n13%"
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "JTrycblgSpYmCeKX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the Population Distribution by Social Class chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart title {Population Distribution by Social Class}[0.322, 0.021, 0.641, 0.047] and the social classes represented as the slices logged in the legend {[0.845, 0.063, 0.993, 0.219], I can see that each class represents a portion of the total population. Obtaining data from pie slices {\"Upper Class\"}[0.197, 0.27, 0.456, 0.602], {\"Middle Class\"}[0.249, 0.193, 0.758, 0.913], {\"Working Class\"}[0.333, 0.109, 0.563, 0.479], {\"Retired\"}[0.28, 0.181, 0.465, 0.488], {\"Unemployed\"}[0.264, 0.233, 0.462, 0.491] and their proportions will give us the data to populate the table.\nBased on the above thought, the answer is:\nSocial Class,Population Share\nUpper Class,15.2\nMiddle Class,65.6\nWorking Class,14.8\nRetired,3.2\nUnemployed,1.2"
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "DGf57sutJjhhy535",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which social class is the least present in society?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of each slice of the pie chart, the smallest slice represents the least present class in society.\nBased on the above thought, the answer is:\nThe 'Unemployed' class is the least present with only 1.2% of the total population."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "e8vAfNssQIUS3WKm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart portray the economic condition of the society?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the population shares and what each category implies about the economic condition of the society.\nBased on the above thought, the answer is:\nThe pie chart shows that the 'Middle Class' makes up the majority of the society, which might suggest a relatively robust economy considering the large middle-class population. However, the significant differences observed among the classes indicate economic inequality."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "JnpMlnWxbAIBkYG4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat societal implications might one draw from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should consider the shares of each class and what they potentially convey about societal structures and tendencies.\nBased on the above thought, the answer is:\nThe large proportion of the Middle and Upper classes hints towards a society where many people have means to engage constructively in economic activities. The relatively small percentages of Retired and Unemployed classes could imply an efficient system of employment and retirement. The close percentages of Upper and Working classes might suggest either upward mobility for the working class or downward mobility for the upper class."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "oTFeKRp1Wmdr2vNw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the difference between the population shares of the 'Working Class' and the 'Upper Class'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to subtract the population share of the 'Upper Class' from the population share of the 'Working Class'.\nBased on the above thought, the answer is:\nThe difference is -0.4 percent."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "dDkw2g14WdT1J06t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much of the population is not part of either Middle Class or Retired population shares?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, first, I need to sum up the percentages of the 'Middle Class' and 'Retired'. Then, subtract the sum from 100%.\nBased on the above thought, the answer is:\n31.2% of the population."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "ZBSJdOoT22Ef39Uv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there was an equal distribution across all social class categories, what would be each category's population share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get this, I need to divide 100% by the number of categories.\nBased on the above thought, the answer is:\nEach category would have 20%."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "UGSpRYMdsyroRVOw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart's data into a structured CSV format table for each year."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart's title {\"Exports from 2012 to 2020\"}<r>[0.37, 0.021, 0.594, 0.047]</r>, and the separated slices for each year from the legend <r>[0.908, 0.063, 0.993, 0.338]</r>, each slice represents the percentage of exports for that year.\nBased on the above thought, the answer is:\nYear,Exports\n2012,8.9%\n2013,9.5%\n2014,10.4%\n2015,10.7%\n2016,11.1%\n2017,11.8%\n2018,12.6%\n2019,13.4%\n2020,11.7%"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "k8QxCEDF18IUPS1I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year was a turning point in terms of the export rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The turning point should be the year when a noticeable change occurred in the percentage. This could be reflected in the chart by a significant change in slice size.\nBased on the above thought, the answer is:\n2018 is the turning point since there was a significant increase in the export rate from the previous year."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "G0Nvnsfr4J9gcyJR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be drawn regarding the impact of the year 2020 on exports?"
+            },
+            {
+                "from": "gpt",
+                "value": "The impact of the year 2020 can be inferred by comparing its percentage with percentages of the preceding years.\nBased on the above thought, the answer is:\nThe year 2020 had a negative impact on exports, as it was the first year after 2012 to show a drop in the export percentage. This could be attributable to the global COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "2V0TGBIPpp51JeRA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nApart from the year 2020, has there been any year when the export rate dropped compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the percentage of each year with the percentage of its preceding year.\nBased on the above thought, the answer is:\nNo, apart from the year 2020, no other year has shown a drop in exports compared to the previous year. It has been an upward trend from 2012 to 2019."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "zZr7A1HPuyUDtouU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find which year had the highest proportion of exports?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the highest proportion is signified by the slice with the greatest size. I will determine this by examining the proportion displayed on each slice.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "5ULpYyZmq3GZoUcn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the least and the highest proportion of exports?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, first I need to identify which years had the least and the highest percentage, then subtract the least from the highest.\nBased on the above thought, the answer is:\n4.5%"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "4p5SPkipMQkh2WY0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in exports between 2012 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the percentage in 2012 from the percentage in 2018.\nBased on the above thought, the answer is:\n3.7%"
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "V6arRj62KDeHFe3K",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the details of the pie chart, please convert the data indicating the \"Distribution of Different Subculture Fashion Trends\" into CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the chart, one can see that the pie slices represent two categories, {Goth}<r>[0.225, 0.269, 0.743, 0.923]</r> and {Cyberpunk}<r>[0.281, 0.109, 0.646, 0.479]</r>. The numerical values belonging to each category can be seen in each of these slices.\nBased on the above thought, the answer is:\nSubculture,Fashion Trend\nGoth,75\nCyberpunk,25"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "vhMUWr4cb7dOQu3T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subculture fashion trend is more prevalent, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Clearly, the pie chart slice with the greater area indicates the more prevalent subculture fashion trend. Comparison of the areas of slices {Goth}<r>[0.225, 0.269, 0.743, 0.923]</r> and {Cyberpunk}<r>[0.281, 0.109, 0.646, 0.479]</r> can give us the answer.\nBased on the above thought, the answer is:\nGoth is more prevalent because, as seen in the pie chart, it caters to 75% of the fashion trend, which is 50% more than the Cyberpunk trend."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "OzupzVQsvtKKY8TB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven this data, how might you expect these trends to influence fashion retailers' stock decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "The comparison of the two trends and how strongly each trend is represented in the data can provide indicators for decision-making on stock allocation.\nBased on the above thought, the answer is:\nSince the Goth trend makes up a larger portion (75%) of the fashion scene, fashion retailers are likely to allocate more resources and shelf space to Goth items than Cyberpunk. However, they might still keep a fair amount of Cyberpunk items to cater to its popularity among 25% of the people."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "sUln7PZjatFqNVpD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the representation of these subcultures in the fashion market?"
+            },
+            {
+                "from": "gpt",
+                "value": "The respective sizes of {Goth}<r>[0.225, 0.269, 0.743, 0.923]</r> and {Cyberpunk}<r>[0.281, 0.109, 0.646, 0.479]</r> on the pie chart offer insights into how prevalent each trend is within the fashion market.\nBased on the above thought, the answer is:\nThe larger slice of the pie chart occupied by Goth indicates that it is more represented in the fashion market compared to Cyberpunk. This might suggest that brands and designers are paying more attention to the Goth trend, which is likely driving its widespread acceptance and popularity."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "MJAfsFyodxwZsIfH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Cyberpunk fashion trend gains more popularity and increases by 10%, what will be the new percentage of the Goth trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Cyberpunk increases by 10%, it becomes 35%. Since the total always has to be 100%, the new percentage for the Goth trend would be 100% - 35%.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "lThublAOPFOccPLG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the Goth trend exceed the Cyberpunk trend by?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of the Cyberpunk trend from that of the Goth trend.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "7gGNv0TCUnFWnZBU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Cyberpunk trend doubles in popularity, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "As the Cyberpunk trend is currently 25%, doubling it results in 25% times 2.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "7PNcxTTUqtDGGejK",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the pie chart data into structured table data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend area, I can see that there are seven continents listed: {Asia}<r>[0.903, 0.071, 0.932, 0.091]</r>, {Africa}<r>[0.903, 0.101, 0.943, 0.121]</r>, {Europe}<r>[0.903, 0.131, 0.952, 0.151]</r>, {America}<r>[0.903, 0.161, 0.96, 0.181]</r>, {Australia}<r>[0.903, 0.191, 0.964, 0.211]</r>, {Antarctica}<r>[0.903, 0.221, 0.974, 0.241]</r> and {Unidentified}<r>[0.903, 0.251, 0.987, 0.271]</r>. By reading the labels and corresponding text on the pie slices, I can determine the user projected projects in each continent.\nBased on the above thought, the answer is:\nContinent,User Projected\nAsia,20\nAfrica,35\nEurope,25\nAmerica,15\nAustralia,5\nAntarctica,0\nUnidentified,15"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "Mjd7iOY0KbHohUcf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which continent has been studied the most? Reason out your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of the pie slices and their corresponding percentages, I can identify the continent that has been studied the most.\nBased on the above thought, the answer is:\nAfrica has been studied the most because it has the largest pie slice, which represents 30.4% of all user projected projects."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "xyeRCo0bs18GDh2Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the user's geographical preference for conducting projects from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices and their corresponding percentages, I can make some inferences about the geographical preference of the users.\nBased on the above thought, the answer is:\nMost of the users seem to be from Africa and Asia, given the high percentage of projects from these regions. This suggests that users may prefer to conduct projects within their continents."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "6Gq1BrM5H7itPmkJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven that Antarctica has no projects, how can this information help future project planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the absence of projects in Antarctica, I can consider its implications for future project planning.\nBased on the above thought, the answer is:\nGiven that Antarctica has no projects, it might suggest a lack of interest or accessibility issues in this region. This could highlight a potential opportunity for future projects, as it is an underrepresented area. It may require additional resources and considerations such as environmental and logistic issues."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "Ogd54TN7CV1Cr5jV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of user projected projects across all continents?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the number of projected projects from different continents, I can determine the total number of user projected projects.\nBased on the above thought, the answer is:\n115"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "ZYw3cyD0VBWEmyVF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of projects from Africa out of the total projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the numbers of all projected projects in each continent, and then, take the number of projects in Africa to calculate its percentage of the total.\nBased on the above thought, the answer is:\n30.4%"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "8F1fFRrCISwc4IE6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the number of projects in Australia increases by 5, what will be the new total of user projected projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 5 projects to the existing number of projects in Australia and sum up the updated numbers of all projected projects in each continent to find out the new total.\nBased on the above thought, the answer is:\n120"
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "fvZ9yS4TfTRSUHJ3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you assist by converting the pie chart depicting migratory population trends into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I realize that multiple slices represent the population migration from 2000 to 2009. The key point is the percentage shown for each year, which helps in transforming this data into table format.\nBased on the above thought, the answer is:\nYear,Population Migration (%)\n2000,7.5\n2001,8.7\n2002,8.0\n2003,9.5\n2004,8.3\n2005,10.0\n2006,11.2\n2007,10.5\n2008,12.5\n2009,13.8"
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "CbwpSa4K2XXgM79r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest population migration according to the chart? What might this suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should identify the slice with the largest percentage and note the corresponding year.\nBased on the above thought, the answer is:\nThe year with the highest population migration was 2009 with 13.8%. This might suggest an increase in economic or political instability during that period leading to increased migration."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "GHi2WRIkXE8WM6EV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart depict population migration trends from 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should look at the size and percentage of each slice, which represents population migration for a specific year.\nBased on the above thought, the answer is:\nThe pie chart shows an increasing trend in population migration from 2000 to 2009. Each slice of the pie represents the percentage of total migrations for a given year, with the size of the slices increasing over time, indicating a rise in population migration."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "41zMQ3HfQSv0fdVq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we assume a direct link between population migration and economic instability, what could be inferred about economic conditions over the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the overall trend in the pie chart slices from 2000 to 2009.\nBased on the above thought, the answer is:\nIf we assume a direct link between population migration and economic instability, the increasing size of the pie slices from 2000 to 2009 suggests that economic instability might have grown over the decade, leading to higher rates of population migration."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "hVGEVJZFGKRFMcS7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum percentage of population migration from 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum percentage, I need to look at each slice of the pie chart and note the year that has the highest percentage of population migration.\nBased on the above thought, the answer is:\nThe maximum percentage is 13.8% occurring in 2009."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "TI1EriQEC2VuFvuO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of population migration from 2000 to 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrive at the total percentage, I calculate the sum of the individual percentages in the years 2000, 2001, 2002, and 2003.\nBased on the above thought, the answer is:\nThe total percentage from 2000 to 2003 is 7.5% (2000) + 8.7% (2001) + 8.0% (2002) + 9.5% (2003) = 33.7%."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "ZwWw1P0BDYEg26b2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would the percentage for the year 2004 be if it increased by 2.5% from its original value on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 2.5% to the existing value for the year 2004 to find the new value.\nBased on the above thought, the answer is:\nIf the value for 2004 increased by 2.5%, the new value would be 8.3% + 2.5% = 10.8%."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "84ojuYokkv8BkF2V",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a CSV formatted table? The chart represents a trend in the use of Education Technology from 2015 to 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I see different slices of the pie each representing use of Education Technology for a certain year from 2015 to 2020. The slices are differently colored and each color corresponds to a certain year, given in the legend {the legend}<r>[0.908, 0.063, 0.993, 0.249]</r>. Also, the percentages for each year are given inside the corresponding pie slices.\nBased on the above thought, the answer is:\nYear,Education Technology\n2015,10.3%\n2016,12.4%\n2017,13.4%\n2018,17.5%\n2019,20.6%\n2020,25.8%"
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "B5sceLCPlc2RV2Ly",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which year saw the highest utilization of Education Technology? Provide a reasoning based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the biggest pie slice can be considered the year with the highest utilization of Education Technology.\nBased on the above thought, the answer is:\nThe year 2020 saw the highest utilization of Education Technology, as it has the largest pie slice of 25.8% among all the years."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "Tp1Pq6efMO52GBAd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend in the usage of Education Technology from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to track the increase or decrease in the percentage of the pie slices from 2015 to 2020 to infer the trend.\nBased on the above thought, the answer is:\nFrom the chart, it is evident that the usage of Education Technology has been increasing progressively from 2015 to 2020, with the percentage each year bigger than the previous year signifying an increasing trend."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "t9H26Q0U4qZtSVbr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how might the data influence strategic decisions for the use of Education Technology for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The decision makers would look at the increasing trend of the usage of Education Technology over the years, and specifically the large increase in the year 2020.\nBased on the above thought, the answer is:\nGiven the steady rise in the usage of Education Technology from 2015 to 2020, with a significant surge in 2020, decision makers might consider increasing investment or focus on Educational Technology for the year 2021 anticipating a potentially greater demand or utilization."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "uVj3E4j14k177uwV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the given Education Technology trend, during what year does the percentage of usage surges to more than 15%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to locate the pie slice where the usage of Education Technology exceeds 15%.\nBased on the above thought, the answer is:\n2018."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "AF9lMLgvNcEyBEHe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two consecutive years show the smallest percentage difference in Education Technology usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to calculate the difference in Education Technology usage between each pair of consecutive years and find the smallest one.\nBased on the above thought, the answer is:\n2015 and 2016."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "R1a2ezEF0vh1RTln",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage usage of Education Technology in 2020 was increased by 2.2%, what would be the new total percentage covered by the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 2.2% to the percentage of the year 2020 and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new total percentage would be 102.2% as the original total percentage of all the years is 100%."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "wkS4KldlTSEUw4sm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the effectiveness of each policy type into a CSV format according to your analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Making use of the legend {sublegend}<r>[0.842, 0.063, 0.993, 0.249]</r>, I can identify each sector of the pie chart. With help of that, I can transform the into a CSV format listing policy types against their effectiveness.\nBased on the above thought, the answer is:\nPolicy Type, Effectiveness\nEnvironmental, 22\nLabor, 30\nHealth, 25\nImmigration, 8\nEconomic, 10\nEducation, 5"
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "vVOOwoMP3FlqgypP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart's depiction, which government policy would you say is the most effective, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most effective policy would be the one with the largest slice on the pie chart.\nBased on the above thought, the answer is:\nThe \"Labor\" policy is the most effective, having the largest segment in the pie chart. This implies that it has a higher effectiveness score of 30 compared to the other policies."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "rTTsFbBvhlX2DBBM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the effectiveness of the \"Education\" policy compared to the other policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the \"Education\" slice size in relation to the others, one can understand the effectiveness of this policy type.\nBased on the above thought, the answer is:\nThe \"Education\" policy appears to be the least effective, as evidenced by its segment being the smallest on the pie chart. This means it has a lower effectiveness score, in this case, 5, compared to the other policy types."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "GL9eLfb0PCPLpGbj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this chart affect government decisions in formulating policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Government decisions might be influenced by the effectiveness of each policy as represented in the pie chart.\nBased on the above thought, the answer is:\nBased on the chart, the government might consider improving the effectiveness of the \"Education\" and \"Immigration\" policies as these appear to be least effective. They might also maintain or further enhance the effectiveness of \"Labor\" policy, due to it being most effective among all."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "sZNWMSzZDRVO4C5o",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total effectiveness score of all policy types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total effectiveness score, I have to add up all the values from the CSV table of effectiveness that were interpreted from the chart.\nBased on the above thought, the answer is:\nThe total effectiveness is 100."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "7oQuIsFg089xHipx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the effectiveness score difference between the highest and the lowest policy types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference, I need to subtract the value of Education policy from the Labor policy.\nBased on the above thought, the answer is:\nThe difference in effectiveness score between the highest and the lowest policy types is 25."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "ywY8RkpI1qSJ2jCq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage does \"Economic\" policy contribute to the overall effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to obtain the percentage of the \"Economic\" policy, I should take its effectiveness value and divide it by the total effectiveness value, then multiply by 100.\nBased on the above thought, the answer is:\nThe \"Economic\" policy contributes to 10% of the overall effectiveness."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "PkQX3Ueyc4yEzTKa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you provide the data used for this chart in tabular form (CSV)?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title and pie slices, I can see it depicts the distribution of different development areas in the community. I can convert this information into CSV by observing the different categories in {the legend}<r>[0.83, 0.063, 0.993, 0.159]</r> and the respective percentages in the exploded pie slices {\"Public Spaces\"}<r>[0.204, 0.304, 0.63, 0.912]</r>, {\"Training Centers\"}<r>[0.507, 0.187, 0.766, 0.795]</r> and {\"Green Zones\"}<r>[0.278, 0.109, 0.601, 0.48]</r>.\nBased on the above thought, the answer is:\nCommunity Development Area,Public Spaces,Training Centers,Green Zones\n2018,47.3%,30.9%,21.8%"
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "L7F6AjWV6yVoDYBH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Community Development category had the largest allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest allocation would be the category with the highest percentage.\nBased on the above thought, the answer is:\nThe category with the largest allocation is \"Public Spaces\" as it has the highest percentage of 47.3%."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "jSwVxyWXwxeip1lg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow was the distribution of Green Zones resources compared to other areas of community development in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the distribution of Green Zones resources with other areas, I have to look at the percentages of each category.\nBased on the above thought, the answer is:\nThe pie chart shows that Green Zones had the least resources allocated in 2018 with a 21.8% share, which is lower than Public Spaces with 47.3% and Training Centers with 30.9%."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "ReAgtzqljqLEjYN4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about the community development focus in 2018 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can infer about the community development focus by analyzing the percentages of each sector.\nBased on the above thought, the answer is:\nThe chart implies a significant focus on developing Public Spaces in 2018 as they occupy 47.3% of the pie. The Training Centers and Green Zones received less emphasis, with 30.9% and 21.8% shares, respectively. It suggests that more resources were allocated to public space than other areas."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "PqZGgxkisHDDI7WS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage represented by the Training Centers and Green Zones?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add the percentages values of the two categories Training Centers and Green Zones.\nBased on the above thought, the answer is:\nThe total percentage for Training Centers and Green Zones is 30.9% + 21.8% = 52.7%."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "0gkxgLA6LsBS0hY5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Public Spaces increased by 5%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I have to add 5% to the existing percentage that represents Public Spaces and then calculate the total percentage.\nBased on the above thought, the answer is:\nThe new total percentage is 47.3% (Public Spaces) + 5% + 30.9% (Training Centers) + 21.8% (Green Zones) = 105.0%."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "tqBWSEilcuOKI2IB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average distribution of community developments across Public Spaces, Training Centers, and Green Zones?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average distribution can be calculated by adding up the percentages and dividing by the number of categories.\nBased on the above thought, the answer is:\nThe sum of the percentages is 47.3% (Public Spaces) + 30.9% (Training Centers) + 21.8% (Green Zones) = 100%. Therefore, the average distribution is 100% / 3 = 33.33%"
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "JHVD0CfALwIhO3zz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the chart, could you convert the data into a CSV format table including all the social activities and their frequencies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I can see that each section represents a different social activity, and the percentage of each activity can be calculated from the frequency given in the raw data.\nBased on the above thought, the answer is:\nSocial Activities,Frequency\nMovie Watching,104\nParty,89\nShopping,70\nHiking,75\nReading,112\nTraveling,130\nDining,115\nPicnic,120"
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "jugE6XU9T7UAluRu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the most and least popular social activities from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity of the social activities can be extracted from the size of each slice in the pie chart. The bigger the slice, the more popular is the activity.\nBased on the above thought, the answer is:\nThe most popular activity is \"Traveling\", and the least popular activity is \"Shopping\"."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "rdg9Nn51KzoCviXA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can you make from the chart about people's preferences towards social activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the distribution of the pie chart and compare the sizes of the slices.\nBased on the above thought, the answer is:\nFrom the pie chart, we can see that people prefer \"Traveling\" and \"Dining\". These activities account for the largest shares of the pie, indicating a high preference. On the contrary, people do not prefer \"Shopping\" that much, as it has the smallest share in the chart."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "kML0SsYVrPcmPKS0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this information be utilised by businesses to focus their promotional activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Businesses could look at the proportion of each social activity and target their promotions towards the activities that have the highest proportion.\nBased on the above thought, the answer is:\nBusinesses could focus their promotional efforts on \"Traveling\" and \"Dining\" as these are the most popular social activities. They could potentially encourage \"Shopping\", which is currently the least popular, by offering discounts or special promos to increase its relative popularity."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "QHJh6acR5LefiWX9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total frequency of all social activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the frequency of all the social activities to get the total frequency.\nBased on the above thought, the answer is:\n815"
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "YDkWRLSnzyDUWGBF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in frequency between the most and least common social activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the highest and lowest frequency among all the social activities and then subtract the smallest one from the biggest one.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "Bw5AR7XwpCmIBwKi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf shopping increased in frequency by 20, what would the new total frequency be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new total frequency, I need to add 20 to the current frequency of shopping and then add that to the current total frequency.\nBased on the above thought, the answer is:\n835"
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "z7ZnYkOOAy7JPsmD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease translate the data on the pie chart into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the graph, the pie chart represents recognition for various social activities and lifestyles as indicated by the title {\"Recognition for Various Social Activities and Lifestyles\"}[0.257, 0.021, 0.706, 0.047] and slices of the pie can be broken down into {\"Outdoors\"}[0.197, 0.272, 0.456, 0.645], {\"Internet\"}[0.229, 0.549, 0.54, 0.919], {\"Reading\"}[0.499, 0.543, 0.74, 0.9], {\"Sports\"}[0.507, 0.214, 0.766, 0.642], and {\"Entertainment\"}[0.28, 0.109, 0.638, 0.479]. Each activity corresponds to a specific recognition percentage.\nBased on the above thought, the answer is:\nActivities,Recognition\nOutdoors,35\nInternet,48\nReading,30\nSports,42\nEntertainment,50"
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "brmldelb8ctZG9nk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social activity or lifestyle is most recognized? And why is that so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The activity with the largest slice of the pie chart can be considered the most recognized.\nBased on the above thought, the answer is:\nThe most recognized social activity or lifestyle is \"Entertainment\" because it has the highest recognition rate of 50% according to the pie chart."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "JnuVO5JmUg9gOB3j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow are the recognition rates for different social activities and lifestyles distributed based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution can be inferred from the sizes of the pie slices for each activity or lifestyle.\nBased on the above thought, the answer is:\nBased on the pie chart, \"Entertainment\" has the highest recognition, followed by \"Internet\", \"Sports\", \"Outdoors\", and \"Reading\". This suggests that people highly recognize entertainment activities and lifestyle, while reading being the least recognized."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "8icKyJrHDM4C8MEj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this data, what could be inferred about the development or promotion of social activities and lifestyles in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The recognition rates from the data can provide insights into which activities or lifestyles are more likely to be developed or promoted.\nBased on the above thought, the answer is:\nThe data suggests that \"Entertainment\" and \"Internet\"-based activities or lifestyles are likely to be heavily developed or promoted as they have higher recognition rates. On the other hand, there might be a need to promote or enhance the attraction of \"Reading\"-related activities or lifestyles as they have the lowest recognition rate according to the pie chart."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "veQOllhuCMfLlYup",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest recognition rate noted in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest recognition rate would be the largest slice of the pie chart.\nBased on the above thought, the answer is:\nThe highest recognition rate is 50% for \"Entertainment\"."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "fQpEggaq24E6qReG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the recognition rate for the \"Internet\" sector of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The recognition rate is given by the percentage corresponding to the \"Internet\" slice of the pie chart.\nBased on the above thought, the answer is:\nThe recognition rate for \"Internet\" is 48%."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "rhuZFpYhxt9HMeqY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the \"Entertainment\" recognition rate exceed the \"Reading\" recognition rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference in recognition rates can be calculated by subtracting the \"Reading\" rate from the \"Entertainment\" rate.\nBased on the above thought, the answer is:\nThe \"Entertainment\" recognition rate exceeds \"Reading\" by 20%."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "U4zBmizcAqhoFoui",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the information from the pie chart regarding the usage rate of different social media platforms and represent it in a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart's slices labeled as {\"Facebook\"}<r>[0.198, 0.268, 0.457, 0.569]</r>, {\"Instagram\"}<r>[0.206, 0.54, 0.462, 0.886]</r>, {\"Twitter\"}<r>[0.392, 0.553, 0.596, 0.923]</r>, {\"LinkedIn\"}<r>[0.502, 0.539, 0.756, 0.872]</r>, {\"Snapchat\"}<r>[0.506, 0.243, 0.765, 0.577]</r>, {\"WeChat\"}<r>[0.493, 0.117, 0.676, 0.483]</r>, and {\"WhatsApp\"}<r>[0.274, 0.111, 0.51, 0.481]</r>, I can see that these slices represent the usage rates of the corresponding social media platforms.\nBased on the above thought, the answer is:\nSocial Media,Usage Rate\nFacebook,75\nInstagram,90\nTwitter,70\nLinkedIn,80\nSnapchat,85\nWeChat,55\nWhatsApp,88"
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "ieAq543ccFB03Q6c",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform would you consider the most popular, based on the chart? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The social media platform with the highest usage rate can be considered the most popular according to the chart.\nBased on the above thought, the answer is:\nInstagram is the most popular social media platform because it has the highest usage rate of 90%."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "uZnxQ2TPRXeXYzi4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an advertising company wants to reach the largest audience, on which social media platform should they focus their advertising?"
+            },
+            {
+                "from": "gpt",
+                "value": "For maximum reach, the company should focus on the platform with the highest usage rate.\nBased on the above thought, the answer is:\nThe company should focus their advertising on Instagram, as it has the highest usage rate of 90%."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "dCAQ0p1XMfTC8moF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given pie chart, what can be inferred about WeChat's popularity compared to other social media platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze and compare WeChat's usage rate to other platforms in the chart.\nBased on the above thought, the answer is:\nAs per the chart, WeChat's usage is much lower when compared to most other platforms, suggesting it is less popular."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "HTuDwrwEkbaiYeqE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which social media platform has the highest usage rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the usage rates provided on the pie slices and identify the one with the highest rate.\nBased on the above thought, the answer is:\nInstagram."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "SWHqNuCjkRkjGhUI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average usage rate of all the social media platforms presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the usage rates of all the platforms and divide it by the number of platforms.\nBased on the above thought, the answer is:\nThe average usage rate is 77.29%."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "4TLb0CkjzuKgRQbp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points lower is the usage rate of WeChat compared to the highest social media platform?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to find the social media platform with the highest usage rate, and then compare it with WeChat's usage rate.\nBased on the above thought, the answer is:\nThe usage rate of WeChat is 35 percentage points lower than that of Instagram."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "zJz43Ulj5I0dLt3F",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're observing, could you present the data in the form of a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, having observed the pie chart, with the title {\"Consumer Behavior in Emerging Markets\"}<r>[0.311, 0.021, 0.652, 0.047]</r> at the top and the slices representing different emerging markets {Brazil}<r>[0.197, 0.27, 0.456, 0.597]</r>, {China}<r>[0.233, 0.553, 0.742, 0.923]</r>, {India}<r>[0.502, 0.13, 0.761, 0.519]</r>, {Russia}<r>[0.34, 0.109, 0.527, 0.48]</r> and {South Africa}<r>[0.266, 0.174, 0.465, 0.488]</r> I'll render the data in a table.\nBased on the above thought, the answer is:\nEmerging Markets, Percentage\nBrazil, 15%\nChina, 45%\nIndia, 23%\nRussia,12%\nSouth Africa, 5%"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "TMprd4gP4B80O611",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we determine which emerging market was leading in Consumer Behavior in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I identify the largest slice of the pie chart as the leading emerging market.\nBased on the above thought, the answer is:\nYes, China was leading in Consumer Behavior as it has the largest slice <r>[0.233, 0.553, 0.742, 0.923]</r> in the pie chart representing a 45% stake of the emerging markets."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "7Vhcp79Z6aNsMCcQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make any assumptions about the global influence of these emerging markets in terms of consumer behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size of each slice of the pie, I can estimate the global influence of each emerging market.\nBased on the above thought, the answer is:\nThe chart suggests that the global influence in consumer behavior is strongly dominated by China and India, as they occupy the largest slices of the pie. Brazil and Russia have moderate influence, while South Africa has the least influence."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "x5vdekzj1UnHsqnF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the findings from this chart impact future business investment decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires understanding how the proportions of consumer behavior in each emerging market might influence future investment decisions.\nBased on the above thought, the answer is:\nConsidering the chart, businesses may be likely to invest more in China and India due to their dominant positions in the pie chart. Those wanting a broader spread risk might consider investing in Brazil and Russia as well. However, they may choose to invest less in South Africa due to its small share of the market."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "yW2O59Hb3GYQImTw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined proportion of the markets in South Africa and Russia?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to add up the percentages of both markets {South Africa}<r>[0.266, 0.174, 0.465, 0.488]</r> and {Russia}<r>[0.34, 0.109, 0.527, 0.48]</r>.\nBased on the above thought, the answer is:\n17%"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "Rv0ie7c25TomHicy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the emerging market does India represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "I locate the slice representing India in the pie chart {India}<r>[0.502, 0.13, 0.761, 0.519]</r>.\nBased on the above thought, the answer is:\n23%"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "Aut4P6jMZ8x7OQSF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf China's market dropped by 10%, what percentage of the emerging market would it then represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assuming China's percentage of the emerging market drops by 10%, I subtract 10% from current percentage represented by {China}<r>[0.233, 0.553, 0.742, 0.923]</r>.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "ZlOU1rPGUPjfZevN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented in the pie chart of the year 2019 and tabulate it in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data based on the pie chart, I need to look at the labels and the corresponding percentages in the chart. Here, the subtitles of the chart {subplot_0}[0.158, 0.053, 0.805, 0.979] provide the year, and the legend indicates the categories: Rural and Urban. The slices of the pie chart provide the precise percentages for each category in the year 2019.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2019,54,46"
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "1jtOer2WQw81EaIm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the 2019 chart, which area showed more development in 2019 - rural or urban?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine which area had more development by comparing the percentages in the pie chart slices.\nBased on the above thought, the answer is:\nIn 2019, more development was shown in rural areas as it constituted 54% of the total development, while urban development constituted 46%."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "00ISCA5ipCPWNsEx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart data, can we infer a specific development trend for rural and urban areas in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "We can infer the development trend by comparing the specific percentages of rural and urban development in the pie chart.\nBased on the above thought, the answer is:\nYes, we can infer that there is a trend of decreasing rural development and increasing urban development since the rural development percentage (54%) is only slightly higher than the urban development percentage (46%)."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "ikCNvZFpuGZibY3W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a development organization wanted to balance rural and urban development based on 2019 data, what could be their approach?"
+            },
+            {
+                "from": "gpt",
+                "value": "To balance development, an organization should aim to allocate resources such that the percentages in both rural and urban areas get close to equal.\nBased on the above thought, the answer is:\nThe organization could aim to increase their focus and resources allocated for urban development in the coming years. Since urban development is trailing by 8%, efforts could be made to increase urban development to match that of rural areas. At the same time, they would need to maintain or slightly decrease their efforts in rural areas to achieve a balanced development."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "KJHaVBzev8HKknVn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between the rural and urban development in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I should subtract the percentage of urban development from the rural development for the year 2019 represented by the slices in the pie chart.\nBased on the above thought, the answer is:\nThe difference is 8%."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "JN6lPdv0VjyBfNxO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of urban development increases by 7% in the year 2020, what would be the new percentage for rural development, assuming total development stays at 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of rural development, I need to subtract the increased percentage of urban development from 100%.\nBased on the above thought, the answer is:\nThe new percentage for rural development would be 47%."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "QVrctHSNggiwwpTA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the rural development shows a decreasing trend by 2% every year, what would be its percentage in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the rural development decreases by 2% each year, to find the percentage in 2021, I need to subtract 2% from its percentage in 2020.\nBased on the above thought, the answer is:\nThe percentage for rural development in 2021 would be 45%."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "RBNgGCblXQmJnWOR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart slices and the legend in the upper right corner, the Urban slice<{Urban}[0.198, 0.266, 0.457, 0.506]; [0.482, 0.304, 0.733, 0.516]> and the Rural slice<{Rural}[0.199, 0.53, 0.458, 0.788]; [0.482, 0.198, 0.694, 0.516]> I can see that the slices represent the distribution of infrastructure development in these area types each year from 2012 to 2016.\nBased on the above thought, the answer is:\nArea Type, 2012, 2013, 2014, 2015, 2016\nUrban, 480, 520, 570, 580, 630\nRural, 250, 290, 320, 310, 330"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "HqenLvxr2iDUTRP8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you determine which type of area experienced a stronger growth in infrastructure development from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the overall size increase of the Urban and Rural slices over the 5 year period, the type of area with a larger increase can be inferred as having stronger growth in infrastructure development.\nBased on the above thought, the answer is:\nUrban areas experienced stronger growth in infrastructure development from 2012 to 2016."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "2egOWYuKZjpM85TR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of infrastructure development in both urban and rural areas from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend of infrastructure development, I need to look at the changes in the Pie slices for both Urban and Rural from 2012 to 2016.\nBased on the above thought, the answer is:\nThe pie chart suggests a general trend of increasing infrastructure development in both urban and rural areas from 2012 to 2016. However, the growth in urban areas is visibly stronger compared to rural areas."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "lqp4kLuwAUXncipL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information in the chart, how could planning and investment be directed for the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to contemplate on the previous development trends and the potential strategies for balanced regional development.\nBased on the above thought, the answer is:\nBased on the chart, Urban areas have seen a strong growth trajectory from 2012 to 2016. While it's crucial to continue supporting urban areas due to their higher population densities, we can't ignore the rural areas. There is also a need for more infrastructure development in rural areas to bridge the developmental gap. So, for balanced development, the authorities may want to allocate a higher percentage of the infrastructure investment to rural areas in 2017."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "i4Ng7kOiNKTmuhS6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year experienced a decrease in infrastructure development for rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which year saw a decrease in infrastructure development for rural areas, I need to compare data of each consecutive year for rural areas.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "m9tqCe1aR4jbTnQm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the total infrastructure development in urban areas from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the total infrastructure development in urban areas from 2012 to 2016, I need to sum up the values in the Urban row data.\nBased on the above thought, the answer is:\n2780"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "CBl9y7QzMy216wPZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in infrastructure development between 2012 and 2016 for rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference in infrastructure development between 2012 and 2016 for rural areas, I need to subtract the 2012 value from the 2016 value in the Rural row data.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "h7nsuhjDQBtLgGYW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, could you convert the data about internet usage in the years 2000 and 2020 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By extracting the data from both pie charts {\"Internet Usage in 2000\"}[0.171, 0.011, 0.308, 0.037] and {\"Internet Usage in 2020\"}[0.65, 0.011, 0.786, 0.037] and taking into account the categories {Dial-Up}[0.127, 0.658, 0.158, 0.678], {DSL}[0.348, 0.548, 0.38, 0.568] and {Broadband}[0.233, 0.235, 0.264, 0.255], I can extract the percentages for each year.\nBased on the above thought, the answer is:\nYear,Dial-Up,DSL,Broadband\n2000,45%,25%,30%\n2020,3%,27%,70%"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "oAMToCkLxgZaZb4B",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the charts, which type of connection saw the most significant increase in use from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the two charts, it is apparent that one connection type has had a significant increase between the two years.\nBased on the above thought, the answer is:\nBroadband saw the most significant increase in use from 2000 to 2020, as its proportion increased from 30% to 70%."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "KgYH7PYnx0bLxwt7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich connection type experienced the most significant decrease in usage from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the differences in the slices between the two years, it's clear that one type of connection has decreased in use.\nBased on the above thought, the answer is:\nDial-Up experienced the most significant decrease from 2000 to 2020, with a decrease from 45% to 3%."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "zQvM1UB7ZKVwggt6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the major difference in internet usage between the year 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the pie charts, one can notice the shift in preference of internet connection types over the 20 years.\nBased on the above thought, the answer is:\nThe major difference in internet usage between 2000 and 2020 is the shift from Dial-Up to Broadband. In 2000, Dial-Up was the dominant type of connection with a share of 45%, while Broadband was only 30%. By 2020, Broadband became the major type of connection with 70%, while Dial-Up fell to only 3%."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "e4odmovKh6vKw4cP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage change for DSL from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage change for DSL, I need to compare the DSL percentages in the year 2000 and in the year 2020.\nBased on the above thought, the answer is:\n2%"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "haoIpLo0JS4Ld0IT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total decrease in percentage for Dial-Up from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total decrease in percentage for Dial-Up, I need to subtract the Dial-Up percentage in 2020 from the Dial-Up percentage in 2000.\nBased on the above thought, the answer is:\n42%"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "T9EUwcj1DXBVXqOp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of Dial-Up in both years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to add the percentages of Dial-Up in 2000 and 2020 and then divide it by 2.\nBased on the above thought, the answer is:\n24%"
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "sfj3UjbtEV3XnqIk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're looking at, could you convert the analyzed sustainability ratings for different ecological factors into a structured table, please?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {\"Ecological Environment Sustainability Assessment\"}<r>[0.273, 0.021, 0.69, 0.047]</r> and individual parts represented by the pie chart, I can see that the sectors correspond to different ecological elements, and their corresponding sustainability scores.\nBased on the above thought, the answer is:\nEcological Elements,Sustainability Score\nWater Quality,8\nAir Quality,4\nSoil Health,5\nBiodiversity,6\nClimate Change,3\nEnviromental Education,9"
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "FXnV6a58Cz1nzaju",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which ecological aspect fares the best in terms of sustainability? Give reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to locate the pie slice with the highest sustainability score and identify the corresponding ecological aspect.\nBased on the above thought, the answer is:\n\"Environmental Education\" fares best in terms of sustainability because it has the highest sustainability score of 9 among all ecological aspects."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "tzrFOcx9KPfstxSF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be drawn from the chart about the state of climate change?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the sustainability score for Climate Change and analyze it in comparison with scores of other ecological elements.\nBased on the above thought, the answer is:\nClimate Change is in a critical state as it has the lowest sustainability score of 3 among all the ecological elements, indicating urgent need for addressing this issue."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "QHFqvs6PQmgonlDX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would these sustainability scores potentially guide policy decisions to improve the ecological environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy decisions could take into account the sustainability scores of each ecological element to assess where improvement is needed and where current efforts are successful.\nBased on the above thought, the answer is:\nPolicies aimed at improving the ecological environment might focus on improving Air Quality and addressing Climate Change as these aspects have the lowest sustainability scores. Initiatives relating to Environmental Education already seem effective and could be continued or expanded upon given its leading sustainability score."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "2V5yXZYhWlrHpFbw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which ecological factor has a sustainability score of 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to look at the pie slices, identify the one whose sustainability score corresponds to 5, and then find the associated ecological factor.\nBased on the above thought, the answer is:\nSoil Health"
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "8bPZX4dp3mC9aM4T",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of sustainability scores for \"Water Quality\" and \"Climate Change\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to add the sustainability score for \"Water Quality\" to the sustainability score for \"Climate Change\".\nBased on the above thought, the answer is:\n8 + 3 = 11"
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "N6bd1FKPgaQulF4T",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the sustainability score for \"Air Quality\" increased by two points, what would be the new average Sustainability Score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new average, I would need to add two points to the \"Air Quality\" score, calculate the new sum, and then divide by the number of ecological elements.\nBased on the above thought, the answer is:\n(4 (original Air Quality score) + 2 (increase)) + 8 (Water Quality) + 5 (Soil Health) + 6 (Biodiversity) + 3 (Climate Change) + 9 (Environmental Education) = 37. The new average = 37/6 = 6.17, approximately 6.2"
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "MWsK96SAwmyxcpyo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you process the raw data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From understanding the legend {legend}<r>[0.908, 0.063, 0.993, 0.309]</r> and pie chart {pie chart}<r>[0.223, 0.146, 0.741, 0.923]</r>, I can see the year wise distribution of investments made in Venture Capital and Private Equity from 2012-2019.\nBased on the above thought, the answer is:\nYear,Venture Capital,Private Equity\n2012,450,375\n2013,570,700\n2014,755,820\n2015,680,600\n2016,890,800\n2017,775,1000\n2018,1000,820\n2019,1150,900"
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "CZGMiMG8dSzshDKQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend in the investment pattern for Venture Capital from 2012 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the slices from {2012}<r>[0.23, 0.278, 0.482, 0.516]</r> through {2019}<r>[0.952, 0.281, 0.987, 0.301]</r> on the pie chart to identify the trend.\nBased on the above thought, the answer is:\nThe trend for Venture Capital investments noticeably increases from 2012 to 2019, except for a slight dip in 2015. This reflects overall growth despite minor fluctuations."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "RiV6T9C2huMh45vw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the highest increase in the investment in the Private Equity market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year, I need to compare the increase in investment in the Private Equity market for each year.\nBased on the above thought, the answer is:\nThe biggest leap in investment in Private Equity market occured in the year 2013."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "99DROLLb5pkwG5mM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart visually emphasize the drop in Venture Capital Investments in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To explain the visual emphasis, I need to locate the slice for the year {2015} <r>[0.356, 0.553, 0.527, 0.923]</r> and explain its unique visual property.\nBased on the above thought, the answer is:\nThe chart uses an \"exploded\" pie slice for the year 2015. This visual element draws attention to the slice and emphasizes the drop in Venture Capital investment for that year."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "GM9Zg8bZyepHMaWj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total investment in Venture Capital in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the total investment in Venture Capital in 2019, I need to find the corresponding slice in the legend {2019}<r>[0.952, 0.281, 0.987, 0.301]</r>.\nBased on the above thought, the answer is:\n1150."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "CTL5VUg80JAqjTT0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in investment from 2018 to 2019 in the Venture Capital market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to locate the investment values in 2018 {2018}<r>[0.952, 0.251, 0.987, 0.271]</r> and 2019 {2019}<r>[0.952, 0.281, 0.987, 0.301]</r> for Venture Capital market and subtract.\nBased on the above thought, the answer is:\n150."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "k87IHqce8wecLMa9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average investment in Private Equity over the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the investments for Private Equity for every year and then divide by the number of years.\nBased on the above thought, the answer is:\n765."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "GbypExvDfTgr9Ge9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, please convert the data of market share from 2019 to 2021 for Apple and Samsung into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, we have different sections represented by {the pie slice for Apple}<r>[0.206, 0.307, 0.671, 0.915]</r> and {the pie slice for Samsung}<r>[0.299, 0.117, 0.757, 0.712]</r> specifying their market shares. Therefore, I can draw up a table to show the yearly market share for each company.\nBased on the above thought, the answer is:\nYear,Apple,Samsung\n2019,30,28\n2020,32,30\n2021,35,34"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "KcwBtA2UmBOm7e2p",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company had a larger market share in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of {the pie slice for Apple}<r>[0.206, 0.307, 0.671, 0.915]</r> and {the pie slice for Samsung}<r>[0.299, 0.117, 0.757, 0.712]</r> for 2021, I can determine which company had a larger market share.\nBased on the above thought, the answer is:\nApple had a larger market share in 2021."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "lnzd6Egy2NH4cQI2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the competition between Apple and Samsung based on their market shares from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the changes in market shares for both Apple and Samsung from 2019 to 2021, I can infer about their competition.\nBased on the above thought, the answer is:\nBoth Apple and Samsung have shown steady growth in their market shares during this period. However, Apple has slightly outperformed Samsung, suggesting a tougher competition for market dominance between these two companies."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "gQi6ZWvZgcoXRna0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on Samsung's market share trend, what could we expect for Samsung's market share in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze Samsung's market share trend from 2019 to 2021 to predict its potential market share in 2022.\nBased on the above thought, the answer is:\nBased on the observed trend, Samsung's market share has been growing steadily over the past years. If this trend continues, we can expect Samsung's market share to increase in 2022."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "bzU1yui12jBamRsl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the market share for Apple increase from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will find the market share of Apple for the years 2019 and 2021, then subtract the earlier from the later.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "asTbQ6prs9TUZ88G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share for Samsung over the three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add up the market shares for Samsung for the three years and divide by 3 to get the average.\nBased on the above thought, the answer is:\n31"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "MgXFZV72uZ8ZjbFI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Samsung's market share increased by 2% in 2022, what would be their new market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new market share, I need to add 2% to Samsung's market share in 2021.\nBased on the above thought, the answer is:\n36"
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "yTp9NCUuOe9v7uJu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the pie chart, please convert the corresponding data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart, {Medical and Biotech Industry Trends}[0.331, 0.021, 0.632, 0.047], and through the identification of the handle-color connection in the legend to the different pie slices {\"Cell Therapy\"}[0.306, 0.643, 0.35, 0.663], {\"Organoids\"}[0.604, 0.589, 0.648, 0.609], {\"CRISPR\"}[0.518, 0.3, 0.562, 0.32], {\"Bioinformatics\"}[0.377, 0.318, 0.421, 0.338], I can confidently produce the csv formatted data.\nBased on the above thought, the answer is:\nFields,Percentage\nCell Therapy,40\nOrganoids,30\nCRISPR,20\nBioinformatics,10"
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "C8f8S2x7B3TldgQP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which field in the medical and biotech industry is currently the most popular, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To conclude the most popular field, I need to identify the pie slice with the largest proportion.\nBased on the above thought, the answer is:\nCell Therapy is the most popular field in the medical and biotech industry because it has the largest slice in the pie chart with a percentage of 40%."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "HlnhB4gu1CQHGti8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the future development trend of the medical and biotech industry based on the data provided in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, the inference of future development trend of the industry should be made by comparing the proportions of different fields.\nBased on the above thought, the answer is:\nGiven the current data, it seems likely that the Cell Therapy field will continue to dominate, followed by Organoids. There may also be some growth in CRISPR, whereas Bioinformatics may need to catch up."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "ksuyh2dG7z2ONqMg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were an investor in the medical and biotech industry, how would you decide to allocate your investments based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In making investment decisions, I should consider factors such as the current dominance and potential future of each field in the industry.\nBased on the above thought, the answer is:\nAs an investor, given the current trends, it would be wise to invest most heavily in Cell Therapy because it is currently a predominant field. Also, investing in Organoids would also seem a good option as it holds a sizeable portion of the industry. Although CRISPR holds a smaller piece of the pie, upcoming trends could see a rise in this field, making it worth considering. Bioinformatics currently holds the smallest market share, an investor might hold back resources in this area, but it might be wise to keep a watchful eye on any forthcoming trends."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "HWgH5YATDUjQeu4k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, by what percentage does Cell Therapy exceed Bioinformatics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to subtract the percentage of Bioinformatics from the percentage of Cell Therapy.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "umWDvmp2OIwZPrzo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRoughly what percentage of the pie chart is represented by the fields of CRISPR and Bioinformatics together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage of CRISPR and Bioinformatics, I need to add the two percentages together.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "Qm9U9BdI9QYBkuCs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the proportions of all the fields represented, what percentage does Organoids contribute to the sum of Cell Therapy and CRISPR?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add the percentages of Cell Therapy and CRISPR and then calculate what proportion of that sum is made up by Organoids.\nBased on the above thought, the answer is:\n43%."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "iE4CtBshpa3GvAVS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you've assessed, could you please transcribe the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart and its title {\"Population Migration from 2010 to 2019\"}[0.316, 0.021, 0.647, 0.047] along with the {different slices}[{\"2010\"}[0.168, 0.308, 0.203, 0.328]; {\"2011\"}[0.136, 0.531, 0.171, 0.551]; {\"2012\"}[0.202, 0.78, 0.237, 0.8]; {\"2013\"}[0.379, 0.94, 0.414, 0.96]; {\"2014\"}[0.626, 0.899, 0.661, 0.919]; {\"2015\"}[0.762, 0.697, 0.797, 0.717]; {\"2016\"}[0.788, 0.435, 0.824, 0.455]; {\"2017\"}[0.703, 0.194, 0.738, 0.214]; {\"2018\"}[0.526, 0.066, 0.561, 0.086]; {\"2019\"}[0.289, 0.123, 0.324, 0.143]], I can comprehend that the slices signify different years and the size corresponds to the quantity of population migration in those years.\nBased on the above thought, the answer is:\nYear,Population_Migration\n2010,100000\n2011,120000\n2012,137000\n2013,155000\n2014,142000\n2015,130000\n2016,127000\n2017,135000\n2018,140000\n2019,145000"
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "jfdnXJTJEyiaO4ni",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you identify any trends in population migration over the decade from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "With respect to discerning any trends, I should study the changes in the sizes of the slices from 2010 to 2019.\nBased on the above thought, the answer is:\nObservably, population migration has generally increased over the years, starting from 100,000 in 2010, peaking at 155,000 in 2013, and concluding at 145,000 in 2019. However, there were some fluctuations in 2014, 2015, and 2016 where the number of migrations decreased."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "wpEdC3mhnFuDH4qx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you interpret from the sudden decrease in population migration in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to interpret this, I need to consider the decrease in the size of the slice from 2013 to 2014.\nBased on the above thought, the answer is:\nThe sudden decrease in population migration in 2014 may indicate a change in conditions that year. This could be due to various factors such as the economic condition, regulatory policy change, or social events influencing people's decisions to move."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "TgNqPzJ7iqQ7I9Sv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a similar trend continues, what can be forecasted for the population migration in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a forecast, I should consider the general trend over the years from 2010 to 2019 as seen in the chart.\nBased on the above thought, the answer is:\nIf a similar trend continues, it is likely that population migration would stay relatively high in 2020, possibly above 140,000, barring any significant changes or events."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "pag0Xf2lU512E4T9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin which year did we observe the highest number of population migrations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To locate which year witnessed the most population migration, I need to evaluate the sizes of the slices and identify the largest one.\nBased on the above thought, the answer is:\n2013."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "b5djx5ht94Dy7n7r",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did population migration decrease from 2013 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change in population migration between these two years, I will subtract the population migration value in 2014 from that in 2013.\nBased on the above thought, the answer is:\n13000."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "h3R3OBUlLacgdCFy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total population migration over the 10 years from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total population migration over these years, I need to sum up the values of population migration for all the years.\nBased on the above thought, the answer is:\n1331000."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "omN3Av5SOLRHiQh8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data represented on the given Pie Chart as a table in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the data, I can see that the chart title {\"Technological Innovation and Intellectual Property Over Time\"}[0.229, 0.021, 0.735, 0.047], and slice categories under the sublegend (appended to the right of the pie-chart) {\"Year 1\", \"Year 2\", \"Year 3\", \"Year 4\", \"Year 5\", \"Year 6\"} [0.9, 0.063, 0.993, 0.428]. Analyzing the pie slices, they represent the proportions of the two categories, \"technological innovation\" and \"intellectual property\" over the years.\nBased on the above thought, the answer is:\nYear,Technological Innovation,Intellectual Property\nYear 1,20,10\nYear 2,46,15\nYear 3,62,25\nYear 4,76,33\nYear 5,89,41\nYear 6,99,52"
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "W7wKlCWMM63AEDf7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you decipher any trends in technological innovation and intellectual property over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trends, I need to look at the pie slices representing the proportions of \"Technological Innovation\" and \"Intellectual Property\" year after year.\nBased on the above thought, the answer is:\nFor both technological innovation and intellectual property, the proportions have been increasing over the years, suggesting a growth in technological inventions and a simultaneous increase in awareness and protection of intellectual property."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "6a849P7vpAOk48jv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data provided in the chart, can we infer any relationship between technological innovation and intellectual property?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw inferences about the relationship between technological innovation and intellectual property, I need to look at the pie chart and consider how the two categories change over time.\nBased on the above thought, the answer is:\nThe data suggests that as the level of technological innovation increases, the awareness and protection of intellectual property also increases, albeit at a slower pace."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "IZQhTxYmu1RDveg9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence decision making in the field of technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the implications of growing technological innovation and intellectual property protection.\nBased on the above thought, the answer is:\nAs the chart shows a rise in technology innovations, companies might lean towards investing in research and development. Similarly, as intellectual property awareness grows, more emphasis might be placed on safeguarding innovations and legally protecting them from being replicated or stolen."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "st21ecuB5riWDKvu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the percentage difference between technological innovation and intellectual property the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the year with the largest percentage difference between technological innovation and intellectual property, there is a need to calculate the percentage difference for each year using data from the slices.\nBased on the above thought, the answer is:\nYear 6."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "1xlBJuWnAH7XinhV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the cumulative percentage of technological innovation and intellectual property the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest cumulative percentage of technological innovation and intellectual property, I need to sum the percentages for both categories for each year.\nBased on the above thought, the answer is:\nYear 6."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "kLCjxbco81UC6H7f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average percentage increase in Intellectual Property rights over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage increase, I need to sum up the yearly changes and divide by the number of years which equals to 6.\nBased on the above thought, the answer is:\n7%."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "TmTidfUgk2oztFaJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to extract the chart data and return it to table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the slice of pie attributed to Facebook}<r>[0.208, 0.309, 0.695, 0.917]</r> and {the slice of pie attributed to Twitter}<r>[0.283, 0.146, 0.741, 0.693]</r>, I can calculate the percentages attributed to each social media platform. I just need to keep in mind the chart title {Social Media Usage in 2019}<r>[0.367, 0.021, 0.596, 0.047]</r>.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter\n2019,53.2%,46.8%"
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "U3X7e5mKKKso1zzi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform had more users in 2019 according to the chart, and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentages of {Facebook's slice}<r>[0.208, 0.309, 0.695, 0.917]</r> and {Twitter's slice}<r>[0.283, 0.146, 0.741, 0.693]</r> in 2019 will reveal which one had more users.\nBased on the above thought, the answer is:\nFacebook had more users in 2019. The exact reason cannot be determined from the chart, but it could be due to various factors such as user interface, features, or the demographic of the user base."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "pqcQj4RmOgANPjez",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend about social media usage in 2019 does the chart reveal?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the percentages depicted by {Facebook's slice}<r>[0.208, 0.309, 0.695, 0.917]</r> and {Twitter's slice}<r>[0.283, 0.146, 0.741, 0.693]</r>, one can infer the trend.\nBased on the above thought, the answer is:\nThe chart reveals that Facebook had a slightly larger user base than Twitter in 2019. This suggests that while both platforms were popular, Facebook was marginally more so."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "YFm5zwa6oMe23Wqd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data presented in the chart impact future advertising strategies for companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the {user distribution between Facebook and Twitter in 2019}<r>[0.208, 0.309, 0.695, 0.917];[0.283, 0.146, 0.741, 0.693]</r>, one can predict potential impacts.\nBased on the above thought, the answer is:\nCompanies might allocate more of their advertising budget to Facebook since it had a slightly larger user base in 2019. However, Twitter also had a significant percentage of users, indicating that it would also be beneficial for companies to maintain or increase their advertising efforts on this platform."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "ATf24xhOT2zMIAme",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the difference in usage percentages between Facebook and Twitter in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing {Facebook's slice}<r>[0.208, 0.309, 0.695, 0.917]</r> and {Twitter's slice}<r>[0.283, 0.146, 0.741, 0.693]</r>, one can easily spot the difference.\nBased on the above thought, the answer is:\nThe difference is 6.4%."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "ceOD8kYcqxA0BRhA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Facebook managed to increase its user base by 5% in 2020, what would its new percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding 5% to the current {Facebook's percentage in 2019}<r>[0.208, 0.309, 0.695, 0.917]</r>, one can determine the new percentage.\nBased on the above thought, the answer is:\nThe new percentage would be 58.2%."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "kX63R9DBZvoC2Bly",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percent does Twitter need to gain to equal Facebook's user base in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By subtracting {Twitter's percentage in 2019}<r>[0.283, 0.146, 0.741, 0.693]</r> from {Facebook's percentage in 2019}<r>[0.208, 0.309, 0.695, 0.917]</r>, we can determine the required gain.\nBased on the above thought, the answer is:\nTwitter needs to gain 6.4% to equal Facebook's user base."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "u9r2VSDmCLiB1psy",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, how can the distribution of Start-ups and Venture Capital be represented as a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart, each slice corresponds to distribution of Start-ups and Venture Capital by year from 2017 to 2021. The colors of each slice also represent the category it belongs to.\nBased on the above thought, the answer is:\nYear,Start-ups,Venture Capital\n2017,75,20\n2018,90,30\n2019,100,40\n2020,105,45\n2021,110,50"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "A2SW6ucdY8Jp91gg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which element showed a more pronounced increase over the years, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, it is necessary to study and compare the increase in both \"Start-ups\" and \"Venture Capital\" over the years.\nBased on the above thought, the answer is:\nBoth \"Start-ups\" and \"Venture Capital\" showed an increase over the years. However, \"Start-ups\" has grown by 35 from 75 in 2017 to 110 in 2021, while \"Venture Capital\" has increased by 30 billion dollars from 20 billion dollars in 2017 to 50 billion dollars in 2021. So, \"Start-ups\" have experienced a more pronounced increase."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "gLvdOQRLLxym9QWJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the pie chart about the trend in the innovation and technology entrepreneurship ecosystem from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the distribution of \"Start-ups\" and \"Venture Capital\" for each year from 2017 to 2021, I can interpret the trend in the ecosystem.\nBased on the above thought, the answer is:\nThe increasing numbers of both start-ups and venture capital over the years suggest a positive trend in the innovation and technology entrepreneurship ecosystem. This increase could indicate a strengthening environment for start-ups, with more businesses being founded and more investment funding flowing into these new companies."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "jPsOdiE8xrlymMGn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the distribution presented in the pie chart, what could be a potential risk that investors might need to be aware of in the ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the potential implications of the trends observed in the \"Start-ups\" and \"Venture Capital\" sectors.\nBased on the above thought, the answer is:\nWhile the ecosystem shows an encouraging trend with the increasing number of start-ups, it could also imply increased competition and potentially higher risk rates. As the amount of venture capital has been increasing but not at the same pace as the number of start-ups, it could potentially lead to a shortage of funds for every start-up, indicating a riskier investment environment."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "jpdj36fKNLqPeWAi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the quantity of start-ups increase between 2017 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The start-ups are represented by slices of the pie chart for the respective years. The values are also given in the data. So, I need to find the difference between the quantity of start-ups in 2021 and 2017.\nBased on the above thought, the answer is:\n35"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "uBWG67a5ViaY4Ax2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year showed the highest growth in venture capital?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest growth in venture capital, I need to calculate the annual increase in venture capital and locate the year with the most significant rise.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "vGb8yUnnFHQCw4Tw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total amount of venture capital invested in these start-ups over the years 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total amount of venture capital in these years, I have to sum up the values of venture capital for each year from 2017 to 2021.\nBased on the above thought, the answer is:\n185 billion dollars"
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "9ZlYkaPjh2aD5k6P",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart, can you tabulate the data into CSV format with columns 'Region', 'Seed', 'Early Stage', 'Late Stage', 'Second Stage' and 'M&A'?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing and analyzing the diagram, I can note the relative percentages of each stage of investment represented by the {different shades}[<r>[0.892, 0.071, 0.924, 0.211]</r>] of the pie chart. In order to facilitate the presentation of percentages as numerical values, I convert the percentages to their respective values based on the overall sum of investments. However, the precise figures can only be provided if the raw data is known.\nBased on the above thought, the answer is:\nRegion,Seed,Early Stage,Late Stage,Second Stage,M&A\nUSA,15000000,45000000,65000000,55000000,75000000\nEUROPE,10000000,25000000,35000000,55000000,50000000"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "8YD6CZRFtj64DdOp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to the chart, which venture capital stage receives the most investment overall?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to combine the figures for \"Seed\", \"Early Stage\", \"Late Stage\", \"Second Stage\" and \"M&A\" stage investments from both regions and see which total is the greatest.\nBased on the above thought, the answer is:\nThe \"M&A\" stage receives the most investment overall."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "NA3X6k0WsFJpKi96",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications can be drawn from the chart about venture capital activity in the USA and Europe?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the different percentages in the different stages of the venture capital markets in the pie chart, I can infer some implications.\nBased on the above thought, the answer is:\nThe chart shows that both Europe and the USA invest significantly in all stages of the venture capital markets, but the USA invests noticeably more in every stage. It suggests that the USA had a more vibrant venture capital scene during the period covered by the data."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "gmX8X3OwfODezFoh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data presented in the chart guide future investment decisions in the venture capital markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "The relative proportions of each investment stage as given in the chart may influence future investment decisions in various ways.\nBased on the above thought, the answer is:\nSince M&A sees the most investment, future investors may consider it as a more lucrative stage for investing. However, early stage investments show significant potential as well, especially in the USA. Future investors may also want to explore opportunities in Europe, where the competition might be lower."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "442aCB1JDZjXe9ZL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, what is the combined seed investment by both regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined seed investment, I need to add the seed investment from the USA and Europe according to the CSV data.\nBased on the above thought, the answer is:\n25000000"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "EFmbBCwwqKCQ4GhZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the USA's M&A investment and Europe's M&A investment, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the USA's M&A investment and Europe's M&A investment, I need to subtract Europe's M&A investment from the USA's M&A investment according to the CSV data.\nBased on the above thought, the answer is:\n25000000"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "mpIxXwGAK9cJ17JX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which region has the largest total investment in all stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the region with the largest total investment, I need to sum up the investments in all the stages for each region and see which total is larger.\nBased on the above thought, the answer is:\nUSA"
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "4eU6dQ84WOOx4Jqm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the distribution of different fields in Education Technology in a CSV format table according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {the subtitle}<r>[0.256, 0.021, 0.707, 0.047]</r> indicates the distribution of different fields in Education Technology. Pie slices of {Education Tech}<r>[0.197, 0.274, 0.456, 0.689]</r>, {Online Courses}<r>[0.244, 0.55, 0.531, 0.92]</r>, {e-Learning}<r>[0.503, 0.502, 0.762, 0.897]</r>, {EdTech Companies}<r>[0.503, 0.176, 0.761, 0.496]</r> and {MOOCs}<r>[0.279, 0.109, 0.608, 0.479]</r> show the distribution.\nBased on the above thought, the answer is:\nField, Percentage\nEducation Tech,19.0%\nOnline Courses,20.8%\ne-Learning,22.8%\nEdTech Companies,15.0%\nMOOCs,22.3%"
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "cOheH5BXt7rwpG7u",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which field is leading in the Education Technology space?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector that covers the most area in the pie chart is the leading field in the Education Technology space.\nBased on the above thought, the answer is:\nThe leading field in the Education Technology space is \"e-Learning\", since it has a proportion of 22.8%, the highest among all the categories."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "4BWYLtkKnDFdoqMX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssess the competitiveness among various areas in Education Technology as represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to understand the proportions related to each category and compare them to gauge competitiveness.\nBased on the above thought, the answer is:\nThe chart indicates strong competition among different areas in Education Technology. \"e-Learning\" leads the sector, but \"MOOCs\" and \"Online Courses\" closely follow it. Despite \"EdTech Companies\" holding the smallest portion, the sector is ripe for growth, signaling opportunities for new companies."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "lDH5Wc2gQFPBtgFT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you draw about the role of 'Online Courses' in Education Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can interpret this by looking at the proportion that 'Online Courses' occupy in the pie chart.\nBased on the above thought, the answer is:\n\"Online Courses\" carry a significant role in Education Technology, holding a strong 20.8% portion of the sector. This indicates that despite the reigning popularity of \"e-Learning\" and \"MOOCs\", \"Online Courses\" remain a vital and prominent mode of delivering education technology."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "Pi10bY4WAH7S5NVt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of the 'Education Tech' and 'MOOCs' categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add the proportions of 'Education Tech' and 'MOOCs'.\nBased on the above thought, the answer is:\nThe total percentage is 19.0% (Education Tech) + 22.3% (MOOCs) = 41.3%."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "yoQCeHjquvRWdOua",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two categories' percentages sum up to approximately 43%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the sum of the proportions of each two categories and then judge which two sum up to approximately 43%.\nBased on the above thought, the answer is:\nThe categories 'Education Tech' and 'Online Courses' sum up to approximately 43% (19.0% + 24.0%)."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "TabLxuYvbNH9G8k2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"e-Learning\" decreased its proportion by 5%, what would be the new total percentage of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total percentage, I would reduce the \"e-Learning\" percentage by 5% and sum all the percentages again.\nBased on the above thought, the answer is:\nThe new value for \"e-Learning\" is 22.8% - 5% = 17.8%. The new total percentage is 19.0% (Education Tech) + 20.8% (Online Courses) + 17.8% (e-Learning) + 15.0% (EdTech Companies) + 22.3% (MOOCs) = 94.9%."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "RiyRkHW2qtA4I7eN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease transcribe the data from the pie chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the legend {Emails sent}<r>[0.731, 0.071, 0.811, 0.091]</r> and {Phone Calls}<r>[0.731, 0.101, 0.811, 0.121]</r> that the two slices of pie represent the proportion of each in the year 2019. Furthermore, both categories {Emails sent}<r>[0.407, 0.744, 0.451, 0.764]</r>(87.2% of total communications) and {Phone Calls}<r>[0.539, 0.302, 0.583, 0.322]</r>(12.8% of total communications) are labelled with their actual percentage values.\nBased on the above thought, the answer is:\nYear,Emails sent(%),Phone Calls(%)\n2019,87.2,12.8"
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "cQdXSnN0DobDmeZN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the preferred mode of communication in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the chart, I can compare the proportions of emails sent and phone calls made to determine the more prominent form of communication.\nBased on the above thought, the answer is:\nBased on the significant difference in proportions in the pie chart, Emails were the most preferred method of communication in 2019."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "uLQG8zjyLDx4kcNh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the reason behind the shift towards emails as the more preferred form of communication?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the chart does not provide explicit reasons, I can infer from the dataset and current trends.\nBased on the above thought, the answer is:\nThe shift towards emails as a preferred communication mode could be due to convenience, cost-effectiveness, and the increasing digitization of nearly all aspects of life including work, education, and even social engagements."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "nKIJ61oH1Fwrb7ZS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence future predictions about the mode of communication?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would have to look at the trend presented in the pie chart and compare the relative growth or reduction between emails and phone calls.\nBased on the above thought, the answer is:\nGiven the trend observed in the pie chart, it is likely that the preference for emails over phone calls will continue into the future, potentially expanding even further, given advancements in tech and increased internet accessibility."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "xDUOPHeinPLEKluO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio of emails sent to phone calls made in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ratio can be calculated by dividing the proportion of emails sent by the proportion of phone calls made according to the data provided in the Pie chart for the year 2019.\nBased on the above thought, the answer is:\nThe ratio is approximately 7:1."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "14qKwtB2SUHLF7xP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage are emails more to phone calls according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be calculated by subtracting the percentage of Phone Calls to the percentage of emails sent.\nBased on the above thought, the answer is:\nEmails are more than phone calls by 74.4%."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "kFWneqd9MvM73y94",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven that the total number of communications in the year 2019 was 1 billion, how many were emails sent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Multiply the total communications by the percentage of emails sent.\nBased on the above thought, the answer is:\nThe approximate emails sent were 872 million."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "NNzyXXR0KmXV5NFm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie graph shows the proportion of Social Values and Cultural Changes in the year 2020: {Social Values slice}<r>[0.233, 0.181, 0.751, 0.92]</r> and {Cultural Changes slice}<r>[0.273, 0.112, 0.49, 0.482]</r>. Thus, this information should be aggregated into two categories, each representing its respective value in the year 2020.\nBased on the above thought, the answer is:\nSocial Aspect, 2020\nSocial Values, 85%\nCultural Changes, 15%"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "PYmd6pWE3IT6hInt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart indicate about societal tendencies towards social values and cultural changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can observe the portions assigned to {Social Values}<r>[0.233, 0.181, 0.751, 0.92]</r> and {Cultural Changes}<r>[0.273, 0.112, 0.49, 0.482]</r>. These portions indicate their respective prevalence in 2020.\nBased on the above thought, the answer is:\nThe chart indicates a larger societal inclination towards social values, with an 85% representation as compared to cultural changes which stand at 15%."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "wrfFVJ6K76vuKrTr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data of the previous years, what trend can we predict for Social Values and Cultural Changes for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the raw data provided, I could note the increase in social values and the decrease in cultural changes year after year. This trend will help us predict for the next year.\nBased on the above thought, the answer is:\nIf the previous years' trend continues, we can predict an increase in Social Values and a decrease in Cultural Changes for the next year."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "1PY4EGx78GFdD1Ts",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat impact could this data have on policy-making regarding societal changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To consider the potential implications of this data on policy-making, I need to consider the proportions of Social Values and Cultural Changes in the year 2020 provided in the chart.\nBased on the above thought, the answer is:\nThis data could impact policy-making by indicating a need for policies promoting cultural evolution given the declining trend. Meanwhile, Social values, already being well represented, might require policies ensuring their sustenance."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "L56MYw6aLbc03AeV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, how many percent greater was the representation of Social Values than Cultural Changes according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the slices of the pie chart, {Social Values}<r>[0.233, 0.181, 0.751, 0.92]</r> is represented as 85% and {Cultural Changes}<r>[0.273, 0.112, 0.49, 0.482]</r> as 15%. I have to subtract the percentage of Cultural Changes from the percentage of Social Values to answer this question.\nBased on the above thought, the answer is:\n70%"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "js413rx7KupbKTrq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Social Values percentage increase from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The raw data provided outlines that the percentage of Social Values was 60% in 2015 and increased to 85% in 2020. I will subtract the percentage for 2015 from the percentage for 2020.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "Z2XxUUDv1jDLc9ah",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we combined the percentages of Social Values and Cultural Changes in 2020, would they total 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to add the percentages of Social Values {85%}<r>[0.233, 0.181, 0.751, 0.92]</r> and Cultural Changes {15%}<r>[0.273, 0.112, 0.49, 0.482]</r>.\nBased on the above thought, the answer is:\nYes"
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "UiyoaGDMOcfCUI5J",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the data from the pie chart and generate a structured table based on your observation?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels around the pie at the coordinates {2015}[0.321, 0.346, 0.356, 0.366], {2016}[0.310, 0.368, 0.345, 0.388], {2017}[0.294, 0.415, 0.329, 0.435], {2018}[0.283, 0.515, 0.318, 0.535], {2019}[0.336, 0.696, 0.380, 0.716], {2020}[0.571, 0.711, 0.615, 0.731], {2021}[0.524, 0.264, 0.568, 0.284] and from the percentage shown inside each slice at the coordinates {1.1%}[0.202, 0.232, 0.237, 0.252], {2.3%}[0.183, 0.270, 0.218, 0.290], {4.2%}[0.155, 0.351, 0.190, 0.371], {8.4%}[0.136, 0.522, 0.171, 0.542], {16.7%}[0.235, 0.832, 0.270, 0.852], {28.0%}[0.672, 0.857, 0.707, 0.877], {39.4%}[0.592, 0.091, 0.628, 0.111] respectively, we can see how much each application has been used over the years.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,1.1%\n2016,2.3%\n2017,4.2%\n2018,8.4%\n2019,16.7%\n2020,28.0%\n2021,39.4%"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "aOqBA2vMyr0yiyyB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which year witnessed the highest usage of these AI applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest usage, I need to check the size of each slice and the annotations inside them.\nBased on the above thought, the answer is:\n2021 has the highest usage with a percentage of 39.4%."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "hZx0RtoDMdZ8eVSU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend in AI application usage from 2015 to 2021, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend, I need to look at the changes in the size of the slices consecutively from 2015 to 2021.\nBased on the above thought, the answer is:\nThe chart shows a significant increase in AI application usage from 2015 to 2021, suggesting a consistent, rapid growth trend over these years."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "ABgLmluvnMCcVYpo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends from the past years continue, what might you expect for the usage of AI applications in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the trends visible in the pie chart data from 2015 to 2021, I need to extrapolate this trend to predict 2022.\nBased on the above thought, the answer is:\nIf the trends from the past years continue, we might expect an even higher usage of AI applications in 2022, possibly surpassing 40% if the usage continues to grow at the same rate."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "IPT4RGmNede9BWxM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the application usage for the years 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to look at the percentages of the slices for 2015 and 2016.\nBased on the above thought, the answer is:\n3.4%"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "tosdUOvnfgdyGXII",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add the percentages of all the slices from 2015 - 2021.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "QGhEThWoCWvj6NtP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf usage for 2021 had been 10% less, what would have been the percentage for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract 10% from the original 2021 value.\nBased on the above thought, the answer is:\n29.4%"
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "HW7pJ4zINyWBlY3K",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me convert the pie chart's data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Begin by looking at the pie chart title {\"Cybersecurity and Data Privacy Incidents\"}<r>[0.31, 0.021, 0.653, 0.047]</r> followed by the slices of the pie chart {\"Phishing\"}<r>[0.197, 0.281, 0.456, 0.803]</r>, {\"Hacking\"}<r>[0.324, 0.551, 0.743, 0.921]</r>, {\"Data Breach\"}<r>[0.506, 0.235, 0.765, 0.582]</r>, {\"Malware\"}<r>[0.375, 0.109, 0.663, 0.479]</r>, and {\"Spyware\"}<r>[0.267, 0.152, 0.466, 0.487]</r>. These represent different threats and their percentage prevalence.\nBased on the above thought, the answer is:\nThreats,Incidents(Percentage)\nPhishing,25.0%\nHacking,32.8%\nData Breach,16.4%\nMalware,19.0%\nSpyware,6.9%"
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "UZ7WDnC3NunTOfME",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what is the most common threat? Explain why this is the case."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most common threat, we should look at the threat with the highest percentage in the pie chart.\nBased on the above thought, the answer is:\nThe most common threat is \"Hacking\" as it constitutes the largest slice of the pie chart with a percentage of 32.8%."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "vpdYKvYzFk3HhxsR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be drawn about the prominence of different threats from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will evaluate the size of each category's segment in the pie chart to draw conclusions.\nBased on the above thought, the answer is:\nMost incidents of cybersecurity and data privacy threats are due to Hacking and Phishing, indicating that these forms of threats are more prevalent. On the other hand, Spyware incidents are relatively rare, suggesting that they're not as prominent."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "aUay6mHvmdZ4VGg7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what areas would need to increase their security measures to decrease the occurrence of these threats?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the proportion of each threat, areas suffering the most from each threat should increase their security measures.\nBased on the above thought, the answer is:\nAreas that need to increase their security measures are mainly those prone to \"Hacking\" and \"Phishing\" threats as these have the highest occurrence rates. The focus should also be put on \"Data Breach\" and \"Malware\" threats, even though they occur less frequently."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "rb9adkw8qysMzaC6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat threat category makes up a third of the incidents according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the threat category that makes up a third of the incidents, we need to look for the category with a percentage close to 33.33%.\nBased on the above thought, the answer is:\nHacking."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "XtjhxsxPPkuMvRgw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for the two least reported threats?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total percentage for the two least reported threats, we need to add the percentages for \"Spyware\" and \"Data Breach\".\nBased on the above thought, the answer is:\nThe total percentage for the two least reported threats is 16.4% (Data Breach) + 6.9% (Spyware) = 23.3%."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "CW0zZKhal0rgBcBY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"Phishing\" loses 5% of its proportion to \"Spyware\", what would be the new percentage for \"Spyware\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract 5% from the \"Phishing\" category and add this amount to the \"Spyware\" category.\nBased on the above thought, the answer is:\nThe new percentage for \"Spyware\" would be 6.9% + 5% = 11.9%."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "4xKMhZqQDnCOCinF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you see, can you convert the percentage usage of different social media channels in 2020 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {the subtitle}[0.367, 0.197, 0.596, 0.222] that suggests it's the data of 2020, and the sublegend {'items'}[0.774, 0.239, 0.895, 0.454] that includes the names of the social media channels, as well as the percentage usage data in each {'slices'}[0.251, 0.046, 0.712, 0.931] of the pie chart - which represents each channel - I can convert these data into a CSV format table.\nBased on the above thought, the answer is:\nSocial Media,2020\nFacebook,21.8%\nTwitter,16.1%\nInstagram,13.6%\nLinkedIn,14.2%\nPinterest,10.6%\nReddit,5.2%\nWhatsApp,18.5%"
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "zjkzbc1D43ZqK4js",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social media platform is used the least? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest slice of the pie chart reflects the least used social media platform, which is associated with the lowest percentage.\nBased on the above thought, the answer is:\nReddit is the least used platform. With an attributed percentage of 5.2%, it indicates that Reddit's user penetration is smaller than the other platforms. The reasons for this could be varied, such as the platform's user interface, it's demographic, or the nature of the content on the platform."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "6KqfCAG9bYoveCd1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the popularity of different social media channels in 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices give an indication of what percentage of the population uses each of these social media platforms in 2020. The larger the slice, the more popular the platform.\nBased on the above thought, the answer is:\nThe pie chart suggests that Facebook was the most popular social media platform in 2020, claiming nearly 22% of total usage. This is followed by WhatsApp and Twitter. Reddit seems to be the least popular with only a little over 5% usage."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "bY7lRKaRgAEWhcic",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data shown in the chart, how might this influence social media marketing strategies for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The majority of social media usage correlates directly with potential marketing opportunities. The more a platform is used, the larger the potential reach for marketing campaigns.\nBased on the above thought, the answer is:\nThe data suggest that marketers might lean more toward using Facebook, WhatsApp, and Twitter for their campaigns in 2021, considering their high user penetration. That said, Reddit's lower usage might present an untapped market with less advertising competition."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "lUBFzGvxPKuGmM69",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what social media channel had the smallest usage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to inspect the colorized slices of the pie and the corresponding legend for each channel's percentage. Then, it's just a matter of picking out the smallest number.\nBased on the above thought, the answer is:\nReddit."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Rc4OaOM5YhIqmJEU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you combine the percentages of LinkedIn and Pinterest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage between LinkedIn and Pinterest, I need to add their percentages together.\nBased on the above thought, the answer is:\nThe sum of the percentages is 14.2% (LinkedIn) + 10.6% (Pinterest) = 24.8%."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "bqYOtJRGDv7VNX8n",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Instagram increases by 5% in 2021, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of Instagram, I need to add 5% to its current percentage.\nBased on the above thought, the answer is:\nThe new percentage for Instagram is 13.6% + 5% = 18.6%."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "Qr7U0FjFCADKihhx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I've identified five technology categories {[\"AI\"][0.158, 0.475, 0.171, 0.495], [\"Blockchain\"][0.192, 0.828, 0.267, 0.848], [\"Quantum Computing\"][0.509, 0.948, 0.654, 0.968], [\"IoT\"][0.713, 0.802, 0.734, 0.822], and [\"Nanotechnology\"][0.666, 0.148, 0.779, 0.168]} with their corresponding development prospects. Let's convert these elements into a CSV format table.\nBased on the above thought, the answer is:\nTechnology,Development Prospect (%)\nAI,20\nBlockchain,8\nQuantum Computing,19\nIoT,5\nNanotechnology,48"
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "wjia0GJ6umJGSd6P",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the pie chart, which technology is predicated to have the highest future development prospects? And explain the reason."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine each technology's development prospect and identify the technology with the highest percentage.\nBased on the above thought, the answer is:\nThe technology predicted to have the highest future development prospects is \"Nanotechnology\" because it has the largest slice, with a development prospect of 48%."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "w3vDouORILNaFg4G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the future development prospects of technologies based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer the future development prospects by comparing the development prospects of each technology.\nBased on the above thought, the answer is:\nThe pie chart shows a clear predominance of Nanotechnology in the future development prospects with a 48% proportion. This is followed by AI and Quantum Computing, indicating a strong potential for their development. However, Blockchain and IoT lag with 8% and 5%, indicating comparatively slower growth in these areas."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "QjkfabiRwo9Xt3LJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can these technology development prospects influence future investment decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the future development prospects might affect the investors' decision-making process.\nBased on the above thought, the answer is:\nThe high development prospect for Nanotechnology indicates that it may receive a substantial amount of investment in the future. Moderate prospects for AI and Quantum Computing suggest that they may also attract considerable investments. Blockchain and IoT, however, due to their low development prospects, might struggle to attract the same level of investment unless there are changes in the market."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "Fd1epXj1xjiWsG60",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat two technologies combined have the smallest development prospect percentage according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given chart data, I need to compare and find the two technologies which combined present the smallest development prospect.\nBased on the above thought, the answer is:\nBlockchain and IoT."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "TVroerCaNURL3RpB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the development prospects of AI and Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the development prospects of AI and Quantum Computing, I need to add the development prospects of AI and Quantum Computing.\nBased on the above thought, the answer is:\nThe sum of the development prospects of AI and Quantum Computing is 20% + 19% = 39%."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "6SGptONf5oIKXgQI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the development prospect of IoT increases by 5%, what would be the total development prospect of all technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total development prospect, I need to add the increased percentage of IoT to the total current development prospect.\nBased on the above thought, the answer is:\nThe total development prospect if IoT increases by 5% would be 100% (current total) + 5% = 105%."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "L2jOBAYgXpybYuLq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the pie chart on \"External risks\" into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart and its legend of \"External risks\" {subtitle}<r>[0.199, 0.11, 0.278, 0.135]</r>, I can distinguish nine distinct categories each representing a different risk level. I can also see their corresponding percentages within {the slices}<r>[\"Risk Level 1\", \"Risk Level 2\", \"Risk Level 3\", \"Risk Level 4\", \"Risk Level 5\", \"Risk Level 6\", \"Risk Level 7\", \"Risk Level 8\", \"Risk Level 9\"]</r>.\nBased on the above thought, the answer is:\nRisk Level,Percentage\nRisk Level 1,9.3%\nRisk Level 2,10.3%\nRisk Level 3,11.3%\nRisk Level 4,12.4%\nRisk Level 5,13.4%\nRisk Level 6,12.4%\nRisk Level 7,11.3%\nRisk Level 8,10.3%\nRisk Level 9,9.3%"
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "S1G0lfqD5vE8rmbS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart on external risks, which risk level should be prioritized for management and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should determine which risk level has the highest percentage as this indicates the most prevalent risk.\nBased on the above thought, the answer is:\nRisk Level 5 should be prioritized for management, as it has the highest percentage (13.4%) among external risks, indicating that it is the most prevalent."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "eFtBeGB2aNdhbutZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn from the pie chart about the distribution of internal risks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will need to analyze the distribution of percentages across different risk levels in the pie chart.\nBased on the above thought, the answer is:\nThe internal risks are relatively evenly distributed across levels, with slight dips at Risk Level 1, 2, and 9, and a peak at Risk Level 5. This suggests that a larger portion of the internal risks is clustered in the mid-range levels."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "PbAUGCAAp2M5Hf2t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, what are the implications for risk mitigation strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate risk mitigations strategies, I should consider the prevalent risk levels in both the external and internal categories.\nBased on the above thought, the answer is:\nThe mitigation strategies should prioritize tackling the risk factors contributing to Risk Level 5 in both external and internal risks, as these are the highest in their respective categories. As the external risks show a trend of decreasing after Risk Level 5 and internal risks are more evenly spread, the strategies need to cover a broad array of internal risk factors while focusing more on higher-level risks externally."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "kip7am4RCaKu9Xnf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart of external risks, which risk level has the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will examine the percentages given for each risk level in the external risks chart to determine which level has the highest rate.\nBased on the above thought, the answer is:\nRisk Level 5"
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "BZYPWFQFlKUVqlpD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest percentage of risk level in the internal risks pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will have to locate and compare the percentages assigned to each risk level in the internal risks pie chart.\nBased on the above thought, the answer is:\nRisk Level 9"
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "NEFKb5vPzY1ubbzW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total percentage of Risk Level 5 and Risk Level 6 in external risks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the percentages of Risk Level 5 and Risk Level 6.\nBased on the above thought, the answer is:\nThe combined percentage for Risk Level 5 and Risk Level 6 is 13.4% + 12.4% = 25.8%"
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "hdNUFRcIumSYnzCL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented by the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can convert the data represented by the chart into a table format. The chart has a title {\"Technological Industry Development Trend from 2010 to 2014\"}[0.225, 0.021, 0.739, 0.047] and sections representing different years {2010, 2011, 2012, 2013, 2014}[0.223, 0.146, 0.766, 0.923]. Each sector of the pie chart has a percentage value associated with it, indicating the percentage growth from the previous year.\nBased on the above thought, the answer is:\nYear,Percentage\n2010,15\n2011,12\n2012,18\n2013,25\n2014,30"
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "cn3Vwx0tPtzHu51A",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is observed in the technological industry from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the changes in the percentage from year to year.\nBased on the above thought, the answer is:\nThe trend observed is one of overall growth in the technological industry from 2010 to 2014. Although there were variations year-to-year, with the lowest growth occurring in 2011 and the highest in 2014, the overall trajectory was upward."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "E9LvIaFXjacF1gww",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which years saw the most significant change in the technological industry's growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to look at the proportions in the chart to identify which years are represented by outliers.\nBased on the above thought, the answer is:\nThe most significant changes were observed in 2012 (an increase of 18%) and 2013 (an increase of 25%). These are larger than other years and are both depicted as exploded slices in the pie chart, signifying they are outliers of particular interest."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "X5WeZ3voDErtAoYl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the 'explode' feature in the chart contribute to the understanding of the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the 'explode' feature in the chart differentiates certain data points from others.\nBased on the above thought, the answer is:\nThe 'explode' feature in the chart highlights the years that saw the most significant growth in the technological industry (2012 & 2013). It brings attention to these outliers, making it easier to understand the dramatic increase in these years compared to others."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "6gEYLvGudHJjEmZM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the technological industry experience the lowest percentage growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I have to look at the percentages associated with each year and identify the smallest value.\nBased on the above thought, the answer is:\n2011."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "j399sA6yifmL1k4t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage growth in the technological industry from 2010-2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to add up the percentages associated with each year.\nBased on the above thought, the answer is:\nThe total percentage growth is 15 (2010) + 12 (2011) + 18 (2012) + 25 (2013) + 30 (2014) = 100%."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "b3aKbxHzJKtvva7l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage growth for 2015 is projected to be 20%, what would be the total percentage growth from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I have to add the projected value for 2015 to the previous total percentage growth.\nBased on the above thought, the answer is:\nThe total projected percentage growth from 2010 to 2015 would be 100 (2010-2014) + 20 (2015) = 120%."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "7iVM4szafteMFZZ4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the annotations spotted in the pie chart, can you tabulate the observed data into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into consideration the title {\"Global Trade Volume per Item\"}[0.358, 0.021, 0.605, 0.047] and pie slices {Electronics}[0.197, 0.28, 0.456, 0.786], {Apparel}[0.298, 0.552, 0.57, 0.922], {Automobile Parts}[0.501, 0.541, 0.751, 0.886], {Pharmaceuticals}[0.507, 0.357, 0.766, 0.607], {Oil & Petroleum}[0.311, 0.11, 0.722, 0.48] and {Agricultural Products}[0.264, 0.219, 0.463, 0.49]; I can identify the categories and their corresponding percentages which can then be arranged in a tabular form.\nBased on the above thought, the answer is:\nGlobal Trade Item,Trade Volume (in US$ Billion)\nElectronics,24.0%\nApparel,18.0%\nAutomobile Parts,15.0%\nPharmaceuticals,11.0%\nOil & Petroleum,30.0%\nAgricultural Products,2.0%"
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "IrS5XVBVmpdkHZ09",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell which trade item had the highest volume in terms of US dollars? Why so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportions of each trade item, I can determine that the item with the largest proportion is the one with the highest trade volume.\nBased on the above thought, the answer is:\nThe trade item with the highest volume is \"Oil & Petroleum\" as it takes up the largest segment with 30.0% of the total trade volume."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "l6uAraD6cCiphSi7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you derive about the global trade landscape from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the different proportions for each trade item, I can make assumptions about the global trade landscape.\nBased on the above thought, the answer is:\nThe chart shows that \"Oil & Petroleum\" takes up a significant proportion of the global trade volume, suggesting that energy resources are a major focus in global trade. Meanwhile, \"Electronics\" and \"Apparel\" also account for a considerable share, indicating a robust consumer electronics and fashion industry. On the least end, \"Agricultural Products\" represent just 2.0% of the volume, implying less global trading activity in this sector."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "Bb1pbxHZCt8RP9yW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be a potential impact on global trade strategies considering the data presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "An analysis of the proportions might give insights into how the global trade strategies could be modified.\nBased on the above thought, the answer is:\nGiven the large proportion accounted for by \"Oil & Petroleum\", global trade strategies might continue to focus on this industry due to its significance. However, there could be room for growth in industries such as \"Apparel\" and \"Automobile Parts\" which also have a substantial share. Furthermore, strategies could also aim to improve industries with comparably lower volumes like \"Agricultural Products\"."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "MPmy4zi4EzbsK3fR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, can you determine the two least prevalent items in terms of trade volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine all the pie slices and identify those with the lowest percentages. Those would represent the items with the least trade volume.\nBased on the above thought, the answer is:\nPharmaceuticals and Agricultural Products."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "LShJOIRnDBcQ4EIE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in trade volume between \"Electronics\" and \"Apparel\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage difference, I need to subtract the percentage for \"Apparel\" from the percentage for \"Electronics\".\nBased on the above thought, the answer is:\nThe percentage difference is 24.0% - 18.0% = 6.0%."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "zJ3IPgyWpJetPnxF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there is a 5% increase in the trade volume for \"Apparel\", what would be the total trade volume in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total trade volume in percentage, I would add 5% to \"Apparel\" and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new percentage for \"Apparel\" would be 18.0% + 5.0% = 23.0%. The total trade volume in percentage would then be 24.0% (Electronics) + 23.0% (Apparel) + 15.0% (Automobile Parts) + 11.0% (Pharmaceuticals) + 30.0% (Oil & Petroleum) + 2.0% (Agricultural Products) = 105.0%."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "Re7RjYNHyhBNZwIF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the chart, could you please convert the annual percentages of Online and Classroom Learning from 2018 to 2020 into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are six segments in the pie chart: three segments for each year. From the legend on the chart's side {Legend}[0.908, 0.063, 0.993, 0.249], I can observe the colours mapping to each year for both Online and Classroom learning. After identifying each segment, I match them with the correct year and type of learning to note the respective percentages.\nBased on the above thought, the answer is:\nYear,Online Learning,Classroom Learning\n2018,60,40\n2019,75,25\n2020,95,5"
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "gRJULdnFGVgLrP08",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what trend do you observe in the Education and Training industry from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I'll examine the trend each year for both Classroom Learning and Online Learning.\nBased on the above thought, the answer is:\nThe chart shows a consistent increase in Online Learning and a significant decrease in Classroom Learning over the three years. This suggests a major shift towards Online Learning in the Education and Training industry from 2018 to 2020."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "HfXeixOni2XcxaAx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy do you think there is such a drastic decrease in Classroom Learning over the years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can interpret the decreasing trend of Classroom Learning by looking at the shifting numerical values associated with each year.\nBased on the above thought, the answer is:\nAlthough the chart does not provide explicit reasons for the trend, it could be inferred that advancements in technology, increasing flexibility, or even events like the global COVID-19 pandemic could have contributed to a move away from traditional classroom learning towards online platforms."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "M4hi4tztjriN0hw2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this trend were to continue, what would you predict the percentages for Classroom Learning and Online Learning to be like for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the past trend, I assume the percentage of Online Learning would increase and the percentage of Classroom Learning would decrease.\nBased on the above thought, the answer is:\nIf the trends were to continue, we could expect an even higher percentage for Online Learning, possibly above 95%, and an even lower percentage for Classroom Learning, possibly less than 5%, for the year 2021."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "ls52G2pCJ139a5YG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year witnessed a percentage increase of 15% in Online Learning over the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of Online Learning in each year, as provided in the dataset, to find the year when the value increased by 15% from the previous year.\nBased on the above thought, the answer is:\n2019."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "kF7kCszUBeUxdNmC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2018 and 2020, by how much percentage did Classroom Learning decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to calculate the difference in the proportions of Classroom Learning between the years 2018 and 2020 according to the dataset.\nBased on the above thought, the answer is:\n35% decrease."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "Gk4CqNrMKwywJN5q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, what is the difference in percentage between the proportions of Online Learning and Classroom Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage difference between Online Learning and Classroom Learning in 2020, I need to fetch the percentage values of both types of learning for the year 2020.\nBased on the above thought, the answer is:\n90% difference."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "Q6Bw0bHog5GDoHGZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data from the pie chart into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can see there are six sectors represented here, namely {Home}<r>[0.899, 0.071, 0.94, 0.091]</r>, {Transport}<r>[0.899, 0.101, 0.964, 0.121]</r>, {Industry}<r>[0.899, 0.131, 0.956, 0.151]</r>, {Services}<r>[0.899, 0.161, 0.957, 0.181]</r>, {Construction}<r>[0.899, 0.191, 0.987, 0.211]</r> and {Mining}<r>[0.899, 0.221, 0.945, 0.241]</r> and they each represent different percentages of energy use.\nBased on the above thought, the answer is:\nSector,Energy Use\nHome,10\nTransport,15\nIndustry,20\nServices,25\nConstruction,20\nMining,10"
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "E3nP3m5kVhV1jubp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which sector is the largest consumer of energy? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "By counting the proportion of each sector, the sector with the highest proportion is the largest consumer of energy.\nBased on the above thought, the answer is:\nThe Services sector is the largest consumer of energy as it has the highest proportion at 25% among all the sectors."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "4pDiWFqS0xkpKeJk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom observing the chart, what can be inferred about energy consumption among the sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the proportions of energy consumption for each sector, I can deduce the relative energy consumption among the sectors.\nBased on the above thought, the answer is:\nFrom the pie chart, it is clear that Services is the largest consumer of energy, followed closely by Construction and Industry. Home and Transport have similar levels of energy consumption, and Mining consumes the least amount of energy."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "P1nzj4pK6boXQdVk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend in the chart continues, how might this influence future energy allocation among these sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the proportions of energy consumption per sector, I can speculate how future energy allocation may be influenced.\nBased on the above thought, the answer is:\nIf the trend continues, we might expect more energy to be allocated to the Services, Construction, and Industry sectors given their high energy consumption. Home, Transport, and Mining, with their relatively lower energy consumption, could see less energy allocation unless efforts are made to increase energy efficiency in these sectors."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "3GcflEeYKS805uCh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which two sectors consume 40% of total energy use?"
+            },
+            {
+                "from": "gpt",
+                "value": "To achieve 40% total energy use, I need to identify two sectors where the sum of their proportions equals 40%.\nBased on the above thought, the answer is:\nIndustry and Construction."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "IY5NebIIWpKpIflz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much energy in percentage is consumed by the top 3 energy consuming sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the amount of energy consumed by the top 3 sectors, I need to add the percentages of the top three sectors which are Services, Industry, and Construction.\nBased on the above thought, the answer is:\n20% (Industry) + 25% (Services) + 20% (Construction) = 65%."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "O8UnlZ7A5fw4ZAvI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Services sector reduces its energy consumption by 5%, what would be the new overall total percentage of energy consumed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new overall total energy consumption, I should subtract 5% from the Services sector and sum up all the sectors' percentages again.\nBased on the above thought, the answer is:\n20% (Industry) + 15% (new Services consumption) + 20% (Construction) + 15% (Transport) + 10% (Home) + 10% (Mining) = 90%."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "XIvvggonukfJY7yh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart's distribution over the years, could you convert these findings into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {title of the Pie chart}<r>[0.182, 0.021, 0.782, 0.047]</r> reads \"Growth of Technology Companies vs. Innovative Businesses (2010-2018).\" The data for each year is displayed in exploded rings. Therefore, to extract the data for the CSV file, I will reference each year's labeled slice, such as these for {2010}<r>[0.312, 0.365, 0.347, 0.385]</r>, {2011}<r>[0.291, 0.428, 0.326, 0.448]</r>, and {2012}<r>[0.283, 0.509, 0.318, 0.529]</r>, among others. When the percentages shown in the auto text, such as the one for {2010}<r>[0.186, 0.264, 0.221, 0.284]</r>, are pieced together with the raw data, we can establish the values for each year.\nBased on the above thought, the answer is:\nYear,Technology Companies,Innovative Businesses\n2010,800,200\n2011,950,250\n2012,1100,300\n2013,1450,550\n2014,1800,700\n2015,2100,1200\n2016,2300,1700\n2017,2700,2000\n2018,3000,2500"
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "smr9EMWTCJx2KRsw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity shows a more stable growth pattern over the years: Technology Companies or Innovative Businesses?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the size of the pie slices, the growth of Technology Companies seems more modest and steady, while the growth of Innovative Businesses appears to have larger increases in certain years, as indicated by the slices for {2013}<r>[0.164, 0.691, 0.199, 0.711]</r> and {2015}<r>[0.520, 0.947, 0.555, 0.967]</r>.\nBased on the above thought, the answer is:\nTechnology Companies show a more stable growth pattern."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "rr0gTp53u1Z5xFHU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart support the notion that Innovative Businesses have unpredictable growth trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To examine this, I would need to review the pie chart and the proportions displayed on the Innovative Businesses slices, which show larger variability in growth from year to year, in sharp contrast to the more steady Technology Companies.\nBased on the above thought, the answer is:\nThe chart shows that the proportions associated with Innovative Businesses fluctuate significantly from year to year. This includes sharp increases in certain years like {2015}<r>[0.520, 0.947, 0.555, 0.967]</r>, which illustrates the unpredictability in its growth trends."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "KykhyoE8n18PeGq5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity's growth is more dominant in the later years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to look at the area of the pie slices corresponding to the later years. The slices for the later years such as {2017}<r>[0.617, 0.378, 0.661, 0.398]</r> and {2018}<r>[0.424, 0.252, 0.468, 0.272]</r> show that the growth of Technology Companies became more dominant in the later years.\nBased on the above thought, the answer is:\nTechnology Companies' growth is more dominant in the later years."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "29mLd0U8FC7FMUuy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the combined growth of Technology Companies and Innovative Businesses come close to 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I can compare the growth percentages outlined in the autotext attached to each pie slice. Therefore, the answer would be the year with a growth percentage close to 10%, such as {2014}<r>[0.274, 0.876, 0.309, 0.896]</r>.\nBased on the above thought, the answer is:\n2014."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "Da6kRB2slylHBzsR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total growth of Innovative Businesses from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to sum up the growth from the raw data of Innovative Businesses from 2010 to 2016.\nBased on the above thought, the answer is:\n5200."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "UsM46YpqOBQ2kFoC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Technology Companies grow in 2018 compared to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate this by subtracting the growth of Technology Companies in 2012 from the growth in 2018.\nBased on the above thought, the answer is:\n1900."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "Wxdb524mg0JXEv1G",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you provide the percentage of design innovation for each year from 2012 to 2021 in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I need to identify the percentage of each year's share from the respective slices {2012}<r>[0.208, 0.261, 0.459, 0.499]</r>, {2013}<r>[0.223, 0.425, 0.482, 0.693]</r>, {2014}<r>[0.254, 0.516, 0.482, 0.835]</r>, {2015}<r>[0.35, 0.516, 0.482, 0.88]</r>, {2016}<r>[0.437, 0.516, 0.588, 0.886]</r>, {2017}<r>[0.503, 0.538, 0.76, 0.875]</r>, {2018}<r>[0.482, 0.311, 0.741, 0.553]</r>, {2019}<r>[0.482, 0.159, 0.697, 0.516]</r>, {2020}<r>[0.382, 0.146, 0.549, 0.516]</r>, and {2021}<r>[0.283, 0.174, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nYear,Design Disciplines Innovation (%)\n2012,7.1%\n2013,11.9%\n2014,8.6%\n2015,5.7%\n2016,9.5%\n2017,16.7%\n2018,11.0%\n2019,11.4%\n2020,10.5%\n2021,7.6%"
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "0IsYwmNprUwNvMA3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend in the chart, which year witnessed a sudden increase in design innovation, and what could that suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe that 2017's slice {2017}<r>[0.503, 0.538, 0.76, 0.875]</r> is significantly larger than the others, implying a surge in design innovation for that year.\nBased on the above thought, the answer is:\nIn 2017, the design innovation surged, as evidenced by its largest share of 16.7% in the pie chart. This could suggest a breakthrough in design disciplines or substantial investment in research and development in that year."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "mguuMzqImPeB59Cb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow was the distribution of design innovation over the years, and what does it imply about the field of design?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the pie chart slices from {2012}<r>[0.208, 0.261, 0.459, 0.499]</r> to {2021}<r>[0.283, 0.174, 0.482, 0.516]</r>, the distribution of design innovation varied from year to year.\nBased on the above thought, the answer is:\nThe innovation in design disciplines was not evenly distributed as it changed from year to year. This implies that the field of design is dynamic and subject to periodic shifts and changes, possibly driven by varying factors like technological advancements, societal trends, or funding levels."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "D7fQSRmZB7DXrdgT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe years 2015 and 2017 witnessed a drastic change in design innovation. Based on the chart, what can be inferred about these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Referring to the slices {2015}<r>[0.35, 0.516, 0.482, 0.88]</r> and {2017}<r>[0.503, 0.538, 0.76, 0.875]</r>, the low percentage in 2015 was followed by the highest percentage in 2017.\nBased on the above thought, the answer is:\nIn 2015, the least innovation was observed, suggesting possible challenges or reduced activity in the design industry that year. However, the subsequent drastic rise in 2017 indicates a significant rebound and progression in design disciplines. This suggests that the field might have undertaken effective measures or experienced conditions that favorably boosted design innovation dramatically."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "3nUKzw3a6BAZhLPa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest innovation percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the year with the lowest innovation by comparing the percentages of each year.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "vVu6sIx5rIaxG6Sd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the design innovation percentages for the years 2016 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the sum, I need to add the percentage values of 2016 and 2019.\nBased on the above thought, the answer is:\n20.9%"
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "8FPSz25dxC1qJHg2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher was the innovation percentage in 2017 compared to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the percentage value of 2021 from that of 2017.\nBased on the above thought, the answer is:\n9.1%"
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "xf0Y2IicWx8n7N80",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the pie chart data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, to do this, I'll note every platform and its corresponding use percentage, which should be easily readable from the pie chart {slices of pie chart}<r>[0.197, 0.11, 0.766, 0.92]</r> and then format this into a CSV style table.\nBased on the above thought, the answer is:\nPlatform,Usage\nFacebook,20.8%\nTwitter,16.7%\nYouTube,8.7%\nInstagram,10.4%\nLinkedIn,12.5%\nPinterest,6.2%\nWhatsApp,13.5%\nTikTok,11.1%"
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "VP3394oWVhjWPmoq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed pie chart, which social media platform appears to be more popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining every platform's usage percentage displayed on the pie chart {slices of pie chart}<r>[0.197, 0.11, 0.766, 0.92]</r>, I can determine which platform has the highest percentage hence proving its popularity.\nBased on the above thought, the answer is:\nFacebook appears to be the most popular social media platform as it has the highest percentage usage at 20.8% among Internet users."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "BRfmhw1HdstFJqUY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat thoughts come to mind regarding the distribution of social media usage among different platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to look at the usage percentages of each platform, and understand how usage is distributed among these platforms.\nBased on the above thought, the answer is:\nFacebook and Twitter appear to have the highest usage, indicating their significant influence among internet users. Instagram and LinkedIn share a similar usage level, indicating a robust and steady user base. Despite TikTok's rise in popularity, its user percentage is slightly less but comparative to YouTube, suggesting room for growth. On the other hand, Pinterest shows the least usage, indicating its relatively niche appeal or possibly untapped potential."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "yDZ0v404cTdBxp4N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this pie chart guide a digital marketing strategy for reaching a large audience?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decision-makers can use this chart to understand which platforms have a greater reach based on their user percentages.\nBased on the above thought, the answer is:\nWith this chart, a digital marketing strategy might prioritize Facebook and Twitter due to their large usage percentages, indicating a broad audience. Instagram and LinkedIn, having notable usage percentages, could be used strategically for targeted campaigns. Despite less utilization, platforms like TikTok and WhatsApp could be ideal for reaching niches or younger audiences. Platforms like Pinterest, while having a smaller percentage, could still be valuable depending on the targeted demographic and industry."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "lPAmk3SEg4XvF9Qj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by usage, which two platforms combined have approximately a third of all users?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll compare the usage percentages {slices of pie chart}<r>[0.197, 0.11, 0.766, 0.92]</r> of each platform and identify which two, when added together, total around 33% (a third).\nBased on the above thought, the answer is:\nWhatsApp and LinkedIn."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "mAp6jYwgkyMLllU2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the remaining percentage of users if the three most used platforms were removed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine that, I would need to identify the three platforms with the highest user percentages and subtract their percentages from 100%.\nBased on the above thought, the answer is:\nAfter removing the users from Facebook, Twitter, and WhatsApp, the remaining percentage would be 49%."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "TeN1d2H4m5rxJBoR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has half the user percentage as Facebook?"
+            },
+            {
+                "from": "gpt",
+                "value": "For that, I'll need to examine each platform's user percentage, looking for a platform that has about half the usage of Facebook.\nBased on the above thought, the answer is:\nYouTube has approximately half the user percentage of Facebook."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "BFevWYVSfXAiPjMo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, can you convert the revenue of the Culture and Arts industry from 2005 to 2014 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart's title {\"Culture and Arts Industry Revenue from 2005 to 2014\"}[0.258, 0.021, 0.705, 0.047], and {the legend\u2019s labels and handles}[{\"2005\"}[0.952, 0.071, 0.987, 0.091]; {\"2006\"}[0.952, 0.101, 0.987, 0.121]; {\"2007\"}[0.952, 0.131, 0.987, 0.151]; {\"2008\"}[0.952, 0.161, 0.987, 0.181]; {\"2009\"}[0.952, 0.191, 0.987, 0.211]; {\"2010\"}[0.952, 0.221, 0.987, 0.241]; {\"2011\"}[0.952, 0.251, 0.987, 0.271]; {\"2012\"}[0.952, 0.281, 0.987, 0.301]; {\"2013\"}[0.952, 0.311, 0.987, 0.331]; {\"2014\"}[0.952, 0.34, 0.987, 0.36]], respectively, it\u2019s clear that the revenues from different years are already stated.\nBased on the above thought, the answer is:\nYear,Revenue\n2005,10\n2006,15\n2007,18\n2008,22\n2009,32\n2010,35\n2011,40\n2012,60\n2013,70\n2014,80"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "w42633O9oK4b9lO2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year witnessed the highest increase in revenue? Explain how you determined this."
+            },
+            {
+                "from": "gpt",
+                "value": "The highest increase in revenue can be identified by comparing the revenue differences between consecutive years from 2005 to 2014.\nBased on the above thought, the answer is:\nThe year 2012 showed the highest increase in revenue. There was a jump from 40 million USD in 2011 to 60 million USD in 2012."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "7Qz6I2HaSe1TIYDQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart portray the growth of the Culture and Arts industry over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth of the Culture and Arts industry is represented by the different slices of the pie chart, with larger slices corresponding to a higher revenue.\nBased on the above thought, the answer is:\nThe pie chart shows a clear trend of revenue growth in the Culture and Arts industry from 2005 to 2014. The relative size of the pie slices increases over the years, indicating a steady growth in revenue."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "CeIVsqill8YCqU0l",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the Culture and Arts industry based on the chart data from 2005 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the overall growth, and note any years where there has been an unusual increase or decrease in revenue.\nBased on the above thought, the answer is:\nBased on the chart, the Culture and Arts industry has been steadily growing in terms of revenue from 2005 to 2014. However, 2012 stands out as an exceptional year where there was a significant increase in revenue, indicating possibly a surge in interest or government funding in this sector."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "Z21ksCVBJ11OmDma",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the revenue increase from 2005 to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the revenue increase, I need to subtract the revenue of 2005 from the revenue of 2010.\nBased on the above thought, the answer is:\n25 million USD"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "yOviZcAY46f4xFyO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest yearly revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest annual revenue, I need to compare all the revenues from 2005 to 2014.\nBased on the above thought, the answer is:\n80 million USD in 2014"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "2aorUvLDUZjpBpC9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total revenue from 2005 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total revenue, I need to sum up the revenues from 2005 to 2014.\nBased on the above thought, the answer is:\n400 million USD"
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "dKL79yqN76a0QApz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart data you observed into a table which contains the years, the improvement percentages for production and logistics respectively."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate this table, I need to extract the text and autotext from each slice of the pie. The \"text\"{\"s\"} of the slice represents the year while the \"autotext\"{\"s\"} represents the percentage of improvement. These were captured from each slice of the pie<r>[0.197, 0.109, 0.766, 0.922]</r>\nBased on the above thought, the answer is:\nYear,Production %,Logistics %\n2011,4.1%,3.1%\n2012,4.7%,3.7%\n2013,5.4%,4.3%\n2014,6.4%,5.6%\n2015,7.2%,6.2%\n2016,8.2%,7.0%\n2017,8.6%,7.8%\n2018,9.3%,8.4%"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "bjx5aNj0oHvW5080",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking from the pie chart, what was the trend of the efficiency improvement for the two areas from 2011 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to observe each year in sequence and assess whether the percentage values have generally increased over time. Since it's a pie chart, the size of the slices would appear to increase in clockwise order.\nBased on the above thought, the answer is:\nThe trend for both areas shown by the pie chart was that the potential for efficiency improvement increased from 2011 to 2018."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "AJKtkJJtVqIVVtMV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about the strategy for improvement in the logistics sector for the last year based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the 2018 slice for logistics and analyze its size compared to the slices for previous years.\nBased on the above thought, the answer is:\nThe logistics sector saw the biggest improvement in efficiency in 2018. This suggests that the strategies or actions undertaken in 2018 were particularly effective in increasing logistics efficiency."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "9mfKNBgwj9kiF2Dh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be implied about the performance of the two sectors in terms of efficiency improvement according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the progression of the two sectors over the years. The size of the slices for each sector from 2011 to 2018 can help to interpret their performance.\nBased on the above thought, the answer is:\nAccording to the pie chart, the production sector has been consistent in improving efficiency and even performed better than logistics in the last year. Logistics, however, had a steady increase in potential efficiency improvement but remained slightly behind production."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "RzTGmdxHEQChMrtt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOverall, which area saw a bigger improvement in efficiency between the years 2011 and 2018 according to the pie chart you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentages of 2011 from 2018 for both areas using the data from the slices. Then compare the difference to find which area saw a bigger improvement.\nBased on the above thought, the answer is:\nProduction"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "fpuwwhpNNugoSAIA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the logistics area see the biggest improvement in efficiency based on the pie chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the \"autotext\"{\"s\"} values from each slice that belongs to logistics (2011 - 2018 on the bottom part of the pie chart) and locate the year where the percentage was highest.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "uBa2oUjAi2TuglO9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a year between 2011 till 2018 when the improvements in efficiency in both areas are the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the \"autotext\"{\"s\"} values from the corresponding slices for each year (For example, compare logistics 2011 with production 2011, and so on). If there is a pair with the same percentage, then I found the answer.\nBased on the above thought, the answer is:\nNo"
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "XyeIUoRQ8a4nkVBZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data on Mutual Funds investment from 2012 to 2019 into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "I observed the subtitle {\"Mutual Funds Investment Distribution from 2012 to 2019\"}<r>[0.245, 0.021, 0.718, 0.047]</r>. Then, I will transform the slices on the pie chart denoted by each year and their corresponding percentages into a CSV table.\nBased on the above thought, the answer is:\nYear,Mutual Funds\n2012,20%\n2013,18%\n2014,23%\n2015,25%\n2016,30%\n2017,35%\n2018,40%\n2019,37%"
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "PoRj0iREiMimsCgc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how has the trend in Mutual Funds investments changed from 2012 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the slices}<r>[0.197, 0.111, 0.766, 0.922]</r> in the pie chart, I can see a gradual change in the Mutual Funds investment distribution over the years.\nBased on the above thought, the answer is:\nThe investment in Mutual Funds has generally increased from 2012 to 2019, with minor dips in between. The most significant increase occurred around 2018."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "e06dZi3uejjKn0Fe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the trend of Mutual Funds investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the investment distribution over the years, I can provide some insights into the behavior of investors.\nBased on the above thought, the answer is:\nInvestors seem to have increased their allocation to Mutual Funds over the years, suggesting growing confidence in these types of investments."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "uSbSjZfldRbX6mno",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the previously observed trends, what could be expected for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the increasing trend in Mutual Funds investment from 2012 to 2018 and a slight decrease in 2019, I can make a prediction for 2020.\nBased on the above thought, the answer is:\nThere might be a stabilization of the Mutual Funds investment in 2020 around the values observed in 2019, although a further increase could also be possible given the overall investment trend in Mutual Funds. However, it's necessary to consider other variables which might affect investment decisions."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "eH0UbbKLgG8jLvJd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year did the Mutual Funds investment percentage dip in comparison to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the correct legend to identify each year from {the slices}<r>[0.197, 0.111, 0.766, 0.922]</r> of the pie chart, I need to compare the percentage for each year to the previous year's percentage to find out when there was a dip.\nBased on the above thought, the answer is:\n2013."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "Rmj0Wm3pboqfT2Jc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the largest percentage of Mutual Funds investment of all the years and which year was it?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find out which slice of the pie chart represents the largest percentage and match the color of that {slice}<r>[0.498, 0.121, 0.744, 0.488]</r> with the corresponding year {in the legend}<r>[0.908, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\nThe largest percentage was 40% in 2018."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "rvwNoXzCUoPiOwdU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Mutual Funds investment surpass 30%?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the pie chart, I need to find out the first year the percentage is over 30% and match the color of that {slice}<r>[0.494, 0.548, 0.702, 0.916]</r> with the corresponding year {in the legend}<r>[0.908, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\n2016."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "Vqvd7E17wFGhfBFn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are analyzing, can you transform the data into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given chart information, I can observe that each slice of the pie chart{pie_0}[0.197, 0.112, 0.766, 0.922] represents a unique AI tool and its usage percentage over the course of a year. By classifying each tool with its corresponding usage percentage, I can clearly depict the data in a CSV format.\nBased on the above thought, the answer is:\n\"AI_Tool\",\"AI_Usage_Percent\"\n\"Neural Networks\",25\n\"Deep Learning\",15\n\"Machine Learning\",35\n\"Genetic Algorithms\",10\n\"Reinforcement Learning\",15"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "R4itvsJ9UxslYBEl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which AI and ML tool is used the most? Justify your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To distinguish the most widely used tool, I need to identify the largest slice in the pie chart{pie_0}[0.197, 0.112, 0.766, 0.922].\nBased on the above thought, the answer is:\n\"Machine Learning\" is used the most as it accounts for the largest slice, with a usage percentage of 35%, indicating its popularity among AI and ML tools."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "BvuNDJ9ifnqNYdXm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the information in the chart, can you infer the trend in the usage of these AI and ML tools?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart{pie_0}[0.197, 0.112, 0.766, 0.922] displays the popularity of various AI and ML tools, enabling me to interpret the trend in their usage over the past year.\nBased on the above thought, the answer is:\nBased on the chart, Machine Learning tools are the most popular, followed by Neural Networks. Deep Learning and Reinforcement Learning tools are also moderately popular, while Genetic Algorithms are the least used amongst all."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "M58yHckrPKIxjClu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this trend of AI and ML tool usage continued, what could you predict about the potential use of Neural Networks and Genetic Algorithms in the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the chart{pie_0}[0.197, 0.112, 0.766, 0.922] for current usage percentages of Neural Networks and Genetic Algorithms and then speculate on potential usage.\nBased on the above thought, the answer is:\nIf this trend continues, it could be anticipated that Neural Networks will remain highly preferred due to their relatively high current usage of 25%. Genetic Algorithms might continue to be less preferred given their current lowest usage of 10%. However, the actual usage would depend on advancements in these tools and changes in user needs."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "5JNqvLecv20d7w7X",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify which AI tool has the lowest usage percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To ascertain the tool with the lowest usage, I shall compare the percentages found in the slices of the pie chart{pie_0}[0.197, 0.112, 0.766, 0.922].\nBased on the above thought, the answer is:\nGenetic Algorithm"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "BKBtIpmIJSUetD48",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the usage of Genetic Algorithms doubles in the coming years, what will its new usage percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the new usage of Genetic Algorithms, I need to double its current usage percentage, which can be identified from the pie chart{pie_0}[0.197, 0.112, 0.766, 0.922].\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "RXF8UVCAVQ8jecMb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum usage percentage among all these AI and ML tools?"
+            },
+            {
+                "from": "gpt",
+                "value": "To elucidate the tool with the maximum usage, I should compare the percentages displayed in the slices of the pie chart{pie_0}[0.197, 0.112, 0.766, 0.922].\nBased on the above thought, the answer is:\n35% (Machine Learning)"
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "B2BbBhsMgU61bkDN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the details in the pie chart and organize them into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I can see from the legend<r>[0.823, 0.063, 0.993, 0.189]</r> and the pie slices <r>[0.201, 0.298, 0.522, 0.906]</r>, <r>[0.506, 0.334, 0.765, 0.889]</r>, <r>[0.482, 0.147, 0.701, 0.516]</r>, and <r>[0.283, 0.146, 0.5, 0.516]</r> associated with each factor of climate change and their corresponding impact percentage.\nBased on the above thought, the answer is:\nFactors,Percentage\nEcosystem,40\nWater_use,30\nHabitat_Loss,15\nCarbon_Footprint,15"
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "o2W3ANhb5PNWVacW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factor has the most impact on climate change and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the factor with the most impact, I need to compare the percentages of all the factors.\nBased on the above thought, the answer is:\nThe Ecosystem has the most impact on climate change because it has the highest percentage of 40% amongst the factors."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "QaysmH0jeQlIMNBo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what is the contribution of habitat loss and carbon footprint to climate change?"
+            },
+            {
+                "from": "gpt",
+                "value": "The contribution can be determined by looking at the percentages of habitat loss and carbon footprint.\nBased on the above thought, the answer is:\nBoth habitat loss and carbon footprint contribute 15% each to climate change, they together account for 30% of the contribution to climate change."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "wSvK7PSwUdAWn2uP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be an implication of the climate change impact distribution as represented in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implication could be deduced by considering the proportions of each factor.\nBased on the above thought, the answer is:\nAs per the data in the chart, most of the climate change impact can be attributed to Ecosystem changes accounting for 40% and Water usage at 30%. This suggests that efforts to mitigate climate change should focus more on these areas, while also addressing Habitat Loss and Carbon Footprint which together account for the remaining 30%."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "77bpekDdT5DQcs6A",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between the factors Ecosystem and Habitat Loss?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of Habitat Loss from the percentage of Ecosystem.\nBased on the above thought, the answer is:\nThe difference is 25%."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "sraCocJfgsDABpJ5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of Water use and Carbon Footprint?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add the percentage of Water use and Carbon Footprint.\nBased on the above thought, the answer is:\nThe total is 45%."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "ofXj1sOPXL3oenj7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the new total percentage if the percentage of Water use increases by 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to first add 5% to the current percentage of Water use, then add the new result to the percentages of the other factors.\nBased on the above thought, the answer is:\nThe new total percentage will be 105%."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "mvIvehnEtPC2iy0B",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transform the data from the pie chart into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart, the data represents the Value Change from 2000 to 2006. Each slice indicates a separate year and the pie chart value corresponds to the value change. The individual slices range from {2000}<r>[0.15, 0.371, 0.185, 0.391]</r> to {2006}<r>[0.336, 0.091, 0.371, 0.111]</r>.\nBased on the above thought, the answer is:\nYear,Value Change\n2000,12.4%\n2001,13.4%\n2002,13.7%\n2003,14.3%\n2004,14.7%\n2005,15.3%\n2006,16.2%"
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "VQqn2WmeArwl8vZu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you explain which year saw the greatest increase in societal values and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To explain this, I need to observe each year's slice in the pie chart. The largest slice would correspond to the greatest increase in societal values.\nBased on the above thought, the answer is:\nThe year 2006 saw the greatest increase in societal values as its slice of the pie chart is the largest, representing a 16.2% change."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "BuH0vnHlfu3616Ru",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell you about the change in societal values from 2000 to 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret this, I can consider the size of each slice, translating to the proportion of the value change for the given year.\nBased on the above thought, the answer is:\nThe chart suggests a consistent increase in societal values from 2000 to 2006. Each year saw an increase, signifying that the emphasis on societal values such as equality, freedom of speech, and environmental protection has grown over this period."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "d0KdjFvVvkjdcEwd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this chart influence societal development plans for the succeeding years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider how the increasing trend of societal value change might impact future societal development plans.\nBased on the above thought, the answer is:\nThis chart shows a steady increase in value change, suggesting that awareness and efforts toward societal values have been increasing year by year. Hence, for future development plans, this trend will likely continue, placing more emphasis on societal values considered in planning and executing societal development projects."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "5gnD8KoArepPH7zW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the value change by more than 14% according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to observe the year slices that have a value change greater than 14%.\nBased on the above thought, the answer is:\nIn the years 2003, 2004, 2005, and 2006, the value change was more than 14%."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "v641qyHLDnsTkWRt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average value change over the period from 2000 to 2006 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to add up each year's value change and divide by the total number of years.\nBased on the above thought, the answer is:\nThe average value change from 2000 to 2006 was 14.28%."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "kPgU7AQ0uO99KLUb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage change for the period from 2000 to 2006 based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total value change for this period, I can simply add up each year's percentage change.\nBased on the above thought, the answer is:\nThe total value change from 2000 to 2006 was 100%."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "eadBRQHlECSJV56t",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data, what are the changes in AI and Automation from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart and its legend {\"legend\"}[0.908, 0.063, 0.993, 0.368], I can infer that each slice of the pie represents a different year and gives a percentage that shows the proportion of that year's AI and Automation contributions compared to the outliers. By considering the percentage of each year, I can construct this raw data into a table.\nBased on the above thought, the answer is:\nYear,AI and Automation,Outliers\n2010,40,10\n2011,45,5\n2012,50,0\n2013,55,5\n2014,60,10\n2015,65,15\n2016,70,20\n2017,75,25\n2018,80,30\n2019,85,35"
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "sukH59eJlqlayySk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the contribution of AI and Automation progressed over the years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the progression of the pie chart slices from 2010 {\"2010\"}[0.952, 0.071, 0.987, 0.091] to 2019 {\"2019\"}[0.952, 0.34, 0.987, 0.36].\nBased on the above thought, the answer is:\nThe pie chart shows a steady increase in the contribution of AI and Automation. This indicates the growing reliance on and advancement of AI and Automation technologies from 2010 to 2019."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "cdA5zoxUBVbIVLsp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year shows the highest contribution to AI and Automation according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the pie chart slices, with larger slices indicating larger contributions.\nBased on the above thought, the answer is:\nThe pie chart slice representing 2019 has the largest size, hence 2019 shows the highest contribution to AI and Automation."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "ubdJ8jkQDao0zBgI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you observe from the pie chart regarding the progress of AI and Automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires me to analyse the continuous progress of AI and Automation from the year 2010 to 2019 based on pie chart data.\nBased on the above thought, the answer is:\nThe pie chart exhibits a clear upward trend for AI and Automation over the years. The proportion of AI and Automation steadily increase year after year, reflecting the rapid advancement and growing importance of these technologies."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "mLXSUVqz03eNv02h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how much percentage does AI and Automation contribution increase from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the percentage contribution of AI and Automation in 2010 from its contribution in 2019.\nBased on the above thought, the answer is:\n45%"
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "8wSS5vgKdOBJvGwx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we increase the Automation contribution by 5% in 2013, what percentage does it reach?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, we need to add 5% to the AI and Automation contribution in the year 2013.\nBased on the above thought, the answer is:\n60%."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "P8JE31euNwrYiVVl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data, what is the total percentage of AI and Automation in the year 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the total percentage for the years 2010 and 2019, we need to add the AI and Automation percentages for these two years.\nBased on the above thought, the answer is:\n125%."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "4w6yceuWIwZN2pKZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, can you arrange the yearly consumption data into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I confirm it from the CSV data provided and by using the chart pie segments {\"2000\"}<r>[0.203, 0.262, 0.458, 0.5]</r>; {\"2001\"}<r>[0.197, 0.456, 0.456, 0.657]</r>; {\"2002\"}<r>[0.221, 0.539, 0.461, 0.843]</r>; {\"2003\"}<r>[0.327, 0.551, 0.485, 0.922]</r>; {\"2004\"}<r>[0.491, 0.55, 0.661, 0.92]</r>; {\"2005\"}<r>[0.504, 0.533, 0.761, 0.813]</r>; {\"2006\"}<r>[0.507, 0.274, 0.766, 0.553]</r>; {\"2007\"}<r>[0.493, 0.113, 0.695, 0.483]</r>; {\"2008\"}<r>[0.273, 0.112, 0.48, 0.482]</r>.\nBased on the above thought, the answer is:\nYear,Consumption\n2000,52\n2001,55\n2002,58\n2003,64\n2004,67\n2005,72\n2006,80\n2007,85\n2008,90"
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "hueFjqT1kTQvrZYC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which year had the biggest change in consumption rate? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the biggest change in consumption rate, I need to analyze the data for each year and compare the percentage change in consumption from the previous year.\nBased on the above thought, the answer is:\nThe year 2006 had the biggest change in the consumption rate because it saw an 8% increase in consumption from the previous year, which is noticeably larger than the changes in other years."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "xY1kA8AbdlCkQCLe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed in consumption based on the analysis of the chart from 2000 to 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the figure and chart data, I can determine the trend of consumption over the years from 2000 to 2008.\nBased on the above thought, the answer is:\nThe chart depicts a general upward trend in the consumption of resources from 2000 to 2008, indicating an increasing demand for resources over time."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "gKuxwlddXIjraKr5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in consumption from 2002 to 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I'll compare the consumption in the year 2003 to the year 2002.\nBased on the above thought, the answer is:\nThe percentage increase in consumption from 2002 to 2003 was approximately 10.3%."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "HPrr9A19wUm5VS6c",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the total consumption of resources from 2000 to 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the consumption of resources each year from 2000 to 2008 based on the data.\nBased on the above thought, the answer is:\nThe total consumption from 2000 to 2008 is 673."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "aDIYVJlPIcGTbaZJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in what year did the consumption of resources increase by 8%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with an 8% consumption increase, I need to calculate the percentage change and find the year with an 8% increase.\nBased on the above thought, the answer is:\nThe consumption increased by 8% from 2005 to 2006."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "wFdk7AMNMSAoZde5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average consumption per year from 2000 to 2008 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average, I need to sum the consumption from 2000 to 2008 and divide that by the total number of years.\nBased on the above thought, the answer is:\nThe average consumption per year is approximately 74.8."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "LUXKNFpbQxCzhXAQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart about \"Social Cultural and Value Change\" into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart's {legend}<r>[0.76, 0.063, 0.993, 0.219]</r> has labels for five categories of cultural and social variations: \"Traditionalism Drop\", \"Religiosity Increase\", \"Gender Equality Rise\", \"Globalization Influence\", and \"Ecological Awareness Rise\". Each label corresponds with a color-coded pie slice having their specific percentage values, which are encapsulated in sequence {pie slice data}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nChange Type, Percentage\nTraditionalism Drop, 15%\nReligiosity Increase, 30%\nGender Equality Rise, 25%\nGlobalization Influence, 20%\nEcological Awareness Rise, 10%"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "Lt3Fwgt2uRJpFOwc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social change boasts the highest increase? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine each pie slice from the figure to see which one has the most significant value.\nBased on the above thought, the answer is:\nThe \"Religiosity Increase\" displays the highest boost with 30% of the pie chart share suggesting that this social value change is the most dominant one."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "6NIAKwHLsCqgqR9v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about societal shifts in terms of cultural values based on the data from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the proportion each category gets in the pie chart, the cultural shift can be inferred.\nBased on the above thought, the answer is:\nThe pie chart implies a major societal shift toward increased religiosity (30%) and attention to gender equality (25%). Although there's a significant drop in traditionalism (15%), the influence of globalization (20%) and ecological awareness (10%) are nonetheless considered essential."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "P3nUxO8eR0JxxVZD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information presented in the chart influence public awareness campaigns?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the largest and smallest values in the chart and discuss their implications on public awareness efforts.\nBased on the above thought, the answer is:\nThe information demonstrates that cultural transitions are gravitating towards greater religiosity and gender equality. Public awareness campaigns, therefore, could focus on these areas to resonate with societal shifts. However, the relatively lower interest shown in ecological awareness suggests more attention should be directed in this area to raise public consciousness."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "K5lzgRwM2LgkX16N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how much greater is the percentage of \"Religiosity Increase\" compared to \"Traditionalism Drop\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I need to subtract the percentage of the \"Traditionalism Drop\" from the percentage of the \"Religiosity Increase\".\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "lhNdhmUKOWu5oZ8q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf \"Ecological Awareness Rise\" increased its share by 9%, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new percentage can be computed by adding 9% to the current percentage of \"Ecological Awareness Rise\".\nBased on the above thought, the answer is:\n19%"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "fprgPxZFmS253Ig0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined rise in \"Gender Equality Rise\", \"Globalization Influence\", and \"Ecological Awareness Rise\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know this, we need to add the percentages of the \"Gender Equality Rise\", the \"Globalization Influence\", and the \"Ecological Awareness Rise\".\nBased on the above thought, the answer is:\n55%"
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "h6Bl3cyiv5gWyekj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart data into a csv structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, I see there are four slices of the pie with different color legends. By examining the chart title <r>[0.385, 0.021, 0.578, 0.047]</r>, the legends <r>[0.885, 0.063, 0.993, 0.189]</r>, and the element data for \"Children\" <r>[0.223, 0.278, 0.482, 0.712]</r>, \"Youth\" <r>[0.262, 0.516, 0.68, 0.886]</r>, \"Adults\" <r>[0.482, 0.147, 0.741, 0.754]</r>, and \"Seniors\" <r>[0.283, 0.146, 0.5, 0.516]</r>, I understand that the pie slices represent the demographics.\nBased on the above thought, the answer is:\nAge Bracket,Children,Youth,Adults,Seniors\nPercentage,20,30,35,15"
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "fi3vFGfsK5ltFtSC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which age group is the most represented and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size or the percentage of each segment in the pie chart, I can determine which segment has the largest share.\nBased on the above thought, the answer is:\nThe \"Adults\" age group is the most represented because it accounts for the highest percentage of 35% in the demographic research."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "ceUa6NSRJkkmnoJ7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the chart, what insights can we derive about the demographic representation of different age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the sizes and percentage values of each pie slice in the chart, I can infer the representation of each age group.\nBased on the above thought, the answer is:\nThis demographic representation shows a trend of higher participation from adults and youths as they hold proportionally larger percentages. Children and especially seniors are comparatively less represented in the distribution, indicating lesser engagement or smaller populations in these age brackets."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "hFG6jh67km9iS3P8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the representation in the demography chart impact social and community planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide some insight into this, I would have to contemplate on the potential implications of the demographic distribution displayed in the pie chart.\nBased on the above thought, the answer is:\nThe demographic distribution, which shows a large proportion of adults and youths, may suggest that social and community planning should be centered around these groups. This might involve structuring more adult-oriented and youth-friendly programs or policies. However, due attention should be given to children and seniors too, considering their subsections in the society are significant as well."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "8TMndRNB4lKm69BG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segment of population accounts for more than one-third of the total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the segment which accounts for over one-third or 33.33% I need to refer to the percentages of each group. Comparing each segment, it's apparent which group exceeds this threshold.\nBased on the above thought, the answer is:\nAdults"
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "yCrEAapSWZhIaYZc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points more do Youth represent compared to Children?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this, I need to subtract the percentage representation of \"Children\" from that of \"Youth\".\nBased on the above thought, the answer is:\n10 percentage points."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "pKmnCd8OrM7OIdMx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Youth and Adults collectively account for 65% of the population, what percentage is shared between Children and Seniors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the total percentage representation of \"Youth\" and \"Adults\" from 100%.\nBased on the above thought, the answer is:\n35%."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "AodGTzVzF67dLOgU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the data from the pie chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart's structure, the legend on the right reveals that each slice represents a different year from {2015}<r>[0.952, 0.071, 0.987, 0.091]</r> to {2021}<r>[0.952, 0.251, 0.987, 0.271]</r>. The percentages indicating AI and automation adoption can be extracted from within each pie slice.\nBased on the above thought, the answer is:\nYear, Adoption of AI and Automation (%)\n2015, 11.3\n2016, 12.2\n2017, 13.2\n2018, 14.1\n2019, 15.6\n2020, 16.6\n2021, 16.9"
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "oCO1cChfc302N8pF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, which year had the largest increase in organizations adopting AI and automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I have to compare the difference in percentages from year to year to see which leap is the greatest.\nBased on the above thought, the answer is:\nThe year 2020 had the largest increase, going from 15.6% in 2019 to 16.6% in 2020."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "FmOadRxNIOQTeOVw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend from 2015 to 2021 continues, what might you infer about AI and automation adoption in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to consider the consistent growth in AI and automation adoption over the years.\nBased on the above thought, the answer is:\nIf the trend from 2015 to 2021 continues, we can infer that the proportion of organizations adopting AI and automation would increase in 2022, potentially around 17.8% considering the average annual growth rate."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "U2DXLR2oj7jcEanC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications can be deduced for organizations lagging behind in AI and Automation adoption based on this trend from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the consistent increase in the adoption of AI and automation among organizations over the period, we can deduce potential consequences for organizations not adopting these technologies.\nBased on the above thought, the answer is:\nOrganizations that are not adopting AI and automation could find themselves at a competitive disadvantage given the increasing trend of adoption amongst their peers. The increasingly larger slices from 2015 to 2021 suggest that these technologies are becoming more critical to organizational success, hence, benign substantial in maintaining a level playing field."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "kjlVdvpJhWpMiEvz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, by how much has the proportion of organizations adopting AI and automation increased from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to subtract the percentage for 2015 from the percentage for 2021.\nBased on the above thought, the answer is:\n16.9% - 11.3% = 5.6%."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "hEcEnVpbxA3Xp109",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I need to subtract the percentage for 2019 from the percentage for 2020.\nBased on the above thought, the answer is:\n16.6% - 15.6% = 1.0%."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "ws6zl74k7tWgB5u8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual increase in the percentage of organizations adopting AI and automation from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average annual increase, I need to subtract the percentage for 2015 from the percentage for 2021 and divide by the number of years (2021 - 2015).\nBased on the above thought, the answer is:\n(16.9% - 11.3%) / 6 = 0.933%."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "NWbXAd2y9mNVxjbt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the observed pie chart into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noticing from the chart title {\"Efficiency Improvement Potential Assessment\"}[0.291, 0.021, 0.672, 0.047] and observing the various slices of the pie and their labeled values [{\"Energy Efficiency\"}[0.223, 0.278, 0.482, 0.799]; {\"Material Efficiency\"}[0.315, 0.32, 0.741, 0.886]; {\"System Efficiency\"}[0.482, 0.147, 0.701, 0.516]; {\"Process Efficiency\"}[0.283, 0.146, 0.5, 0.516]]}. I can determine that the slices represent different efficiency types and their associated potential for improvement.\nBased on the above thought, the answer is:\nCategories,Improvement Potential\nEnergy Efficiency,25%\nMaterial Efficiency,45%\nSystem Efficiency,15%\nProcess Efficiency,15%"
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "BNtpFi8zTqQnDbw6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which efficiency category has the highest potential for improvement? And why do you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, I need to inspect the improvement potential of each efficiency category to locate the one with the highest percentage.\nBased on the above thought, the answer is:\nThe category \"Material Efficiency\" holds the highest potential for improvement because it has a percentage of 45%, which is the largest value among all categories."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "3H3ZHshszNKqcnCq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you deduce what strides might be taken to increase overall efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the improvement potential percentages for each category, I can recommend areas where the focus should be placed to improve overall efficiency.\nBased on the above thought, the answer is:\nThe chart suggests that the most significant efficiency gains could be made in the \"Material Efficiency\" category, as it has the highest potential for improvement. Therefore, efforts should be dedicated here. Also, considerable benefits can be gained from improving \"Energy Efficiency\". While \"System Efficiency\" and \"Process Efficiency\" also offer room for improvement, the potential gains are comparatively smaller."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "oaRFAXa2h9Aq4Ffv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might these data guide the deployment of resources to improve overall efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should consider how the proportional improvement potential could guide resources allocation.\nBased on the above thought, the answer is:\nGiven that \"Material Efficiency\" shows the highest potential for improvement at 45%, it would be logical to allocate a larger portion of resources to developing methods for enhancing material efficiency. Similarly, \"Energy Efficiency\", with a potential of 25%, might also receive a considerable allocation. Categories like \"System Efficiency\" and \"Process Efficiency\", having the lowest potential, might receive lesser resources but still significant enough to extract the 15% improvement potential each."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "otK8yrhCh96bQyRr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the sum of the improvement potential of the categories \"Energy Efficiency\" and \"Process Efficiency\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the sum of the improvement potential, I must locate the respective percentages of \"Energy Efficiency\" and \"Process Efficiency\" and add them.\nBased on the above thought, the answer is:\nThe sum is 25% + 15% = 40%."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "QScQrFjgsfbizze4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween which two categories, does the improvement potential sum up to 60%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the pair that sums up to 60%, I need to individually sum up the potential improvements of every two categories and check if the sum equals to 60%.\nBased on the above thought, the answer is:\nMaterial Efficiency and System Efficiency."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "C33iDMXESxwQDv1K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"System Efficiency\" increases its potential by 5%, what would be the new total improvement potential of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the new total, I need to add 5% to the improvement potential for \"System Efficiency\" and then sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new potential for \"System Efficiency\" becomes 15% + 5% = 20%.  Hence, the new total potential is 25% (Energy Efficiency) + 45% (Material Efficiency) + 20% (System Efficiency) + 15% (Process Efficiency) = 105%."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "PMQ3qdRUIKcX6ci1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease create a structured table from the data given in the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "There are six significant slices in the chart: {Urban Electricity}<r>[0.223, 0.278, 0.482, 0.743]</r>, {Rural Electricity}<r>[0.277, 0.516, 0.497, 0.886]</r>, {Urban Drinking Water}<r>[0.482, 0.516, 0.736, 0.885]</r>, {Rural Drinking Water}<r>[0.482, 0.295, 0.741, 0.587]</r>, {Urban Paved Roads}<r>[0.401, 0.146, 0.689, 0.516]</r>, and {Rural Paved Roads}<r>[0.283, 0.164, 0.482, 0.516]</r>. These components help me understand the attribute each sector of the pie chart signifies and their corresponding percentages.\nBased on the above thought, the answer is:\nArea Type, Electricity Availability (%), Clean Drinking Water (%), Paved Roads (%)\nUrban, 98, 95, 90\nRural, 70, 60, 40"
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "xIlGJWiBUT13ID10",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInterpreting the chart, which type of area has a better development index? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of each of the three key factors(Electricity, Drinking Water, Paved Roads) in both urban and rural areas to ascertain the area having a better development index.\nBased on the above thought, the answer is:\nUrban areas have a better development index as it has higher availability percentages for all three key factors."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "vv8m7s5ZnzAfud0e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what conclusions can we draw about the availability of key factors in rural and urban areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the percentages of each key factor in the pie chart for both rural and urban areas to draw any conclusions about their availability.\nBased on the above thought, the answer is:\nThe chart shows a clear disparity in the availability of key factors between urban and rural areas. Urban areas have a distinct advantage, with higher availability percentages for all key factors, demonstrating a higher development index."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "MzV6SfvClvLQFSDL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what kind of measures should be taken to improve the development index of Rural areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "A detailed observation of the chart's for rural areas indicates the three key factors in need of improvement.\nBased on the above thought, the answer is:\nTo improve the development index of Rural areas, measures should be taken to increase the availability of Electricity, Clean Drinking Water, and Paved Roads, as these were the areas where rural regions lag behind urban ones. Increasing these percentages could narrow the gap between rural and urban areas."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "mDc4c0FmL2YqnuYj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the percentage of availability of Electricity between Urban and Rural areas according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of electricity availability in the rural areas from the urban areas.\nBased on the above thought, the answer is:\nThe difference is 28%."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "XQT5vpdQfbQLfFzl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the Development Index for Urban areas as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage of Urban Development Index, I need to sum the percentages of all three factors(Electricity, Drinking Water, and Paved Roads) for urban areas.\nBased on the above thought, the answer is:\nThe total percentage is 283%."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "iMhc9JlJMYZK6aPm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there is a 10% increase in the Electricity availability in the Rural areas, what would be the new percentage of availability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of Electricity availability in rural areas, I need to add 10% to the existing Electricity Percentage for Rural areas.\nBased on the above thought, the answer is:\nThe new percentage of Electricity availability in Rural areas is 80%."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "5WpfCiGc9NRsDcfs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, construct the CSV data reflecting the market share of the Entertainment and Gaming Industry from 2015 to 2019."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart's title{\"Entertainment and Gaming Industry Market Share from 2015 to 2019\"}[0.193, 0.021, 0.77, 0.047], we're looking at the yearly market share for these years. The specific slice of pie and corresponding legend item each represents a different year, and the autotext on the slice gives the precise percentage for that year.\nBased on the above thought, the answer is:\nYear,Market Share\n2015,20%\n2016,25%\n2017,30%\n2018,15%\n2019,10%"
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "aGI1Ac42txFAcnpu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in what year did the gaming and entertainment industry have the worst performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the worst performance can be identified by the smallest slice in the pie chart.\nBased on the above thought, the answer is:\nThe worst performance of the gaming and entertainment industry was in 2019 as it had the smallest market share of 10%."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "8mijCSE2A2uuiKAL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the gaming and entertainment industry from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the trend, I need to follow the years in order and observe the relative changes in the sizes of the corresponding slices.\nBased on the above thought, the answer is:\nFrom 2015 to 2017, the market share of the gaming and entertainment industry showed an increasing trend, reaching its peak in 2017. However, from 2017 to 2019, the market share showed a downwards trend."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "oZhbvt79th2oyGJn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications can one infer about the gaming and entertainment industry's performance in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about future performance, I would have to consider the observed trend in the pie chart.\nBased on the above thought, the answer is:\nBased on the observed trend, the performance of the gaming and entertainment industry declined significantly from 2017 to 2019. If this trend continues, it might suggest a declining performance in the coming years. However, it's also important to note that numerous factors can influence the industry's performance, and historical trends don't guarantee future results."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "0LZ0IK8LtXijCuED",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total market share of the gaming and entertainment industry in the years 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie slices for the years 2015 and 2016, I can see the corresponding market share percentage displayed on each slice.\nBased on the above thought, the answer is:\nThe total market share for 2015 and 2016 is 45%."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "ghJYw4XKvrUiTZSr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in market share between the years 2017 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in market share, I need to locate the slices that correspond to the years 2017 and 2019 and then subtract the smaller percentage from the larger one.\nBased on the above thought, the answer is:\nThe difference in market share between 2017 and 2019 is 20%."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "jJkTj5qId5Nwamew",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the market share of the gaming and entertainment industry reach its peak?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the highest market share, I need to look at the sizes of the individual pie slices and identify which one is the largest.\nBased on the above thought, the answer is:\nThe market share of the gaming and entertainment industry reached its peak in 2017."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "nogKvovCqbnHfQ3D",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart observed, could you extract the gaming profit data over the years and present it in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By using the legend {2005}[0.913, 0.073, 0.941, 0.087], {2006}[0.913, 0.103, 0.941, 0.117], {2007}[0.913, 0.133, 0.941, 0.147], {2008}[0.913, 0.163, 0.941, 0.177], {2009}[0.913, 0.193, 0.941, 0.207], and {2010}[0.913, 0.223, 0.941, 0.237], and the respective profit percentages marked on the pie chart, I can convert this information into a structured table.\nBased on the above thought, the answer is:\nYear,Profits (%)\n2005,11.8%\n2006,13.7%\n2007,15.7%\n2008,14.9%\n2009,20.4%\n2010,23.5%"
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "Ih0w0WcBgmXo60GB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the data, in which year did the gaming industry see the biggest jump in profits?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I can compare the profit percentages of the consecutive years to find out the year with the biggest jump.\nBased on the above thought, the answer is:\nThe biggest jump in profits was witnessed in 2009, as the profit percentage increased from 14.9% in 2008 to 20.4%."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "YUvsQXEegS5LkA4t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make a suggestion on the trend of the gaming industry's profit over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the change in profit percentages over the years to suggest a trend.\nBased on the above thought, the answer is:\nOver the years from 2005 to 2010, the profit percentage of the gaming industry has been steadily increasing, with only a minor drop in 2008. The significant increase in profit percentages in 2009 and 2010 could suggest a flourishing future for the industry."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "IMhYGBn9ulZsG7TN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer anything about the year 2008 from the chart in terms of the gaming industry's profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference, I should examine the profit percentage for the year 2008 in comparison with the previous and following years.\nBased on the above thought, the answer is:\nIn 2008, the profit percentage for the gaming industry slightly decreased from the previous year (2007). However, this dip was quickly followed by a substantial increase in 2009, suggesting a rapid recovery of the gaming industry in terms of profits."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "v6Z7DdQCzNsobRIs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the profit percentages of the gaming industry for the years 2005 and 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I will add the profit percentages of 2005 and 2008.\nBased on the above thought, the answer is:\n11.8% + 14.9% = 26.7%."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "csTyhii5F12uQKHV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the profit percentage increase from 2006 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the profit increase, I need to subtract the 2006 profit percentage from the 2009 profit percentage.\nBased on the above thought, the answer is:\n20.4% - 13.7% = 6.7%."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "Z0M5hHd5O9GVC3gZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the gaming industry's profit percentage for 2010 were increased by 2%, what would be the new profit percentage for that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new profit percentage, I need to add 2% to the 2010 profit percentage.\nBased on the above thought, the answer is:\n23.5% + 2% = 25.5%."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "SbEiYW8jLWXj8Csy",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data regarding the frequency of focus on various health conditions into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the title {Human Health and Disease Management}[0.311, 0.021, 0.652, 0.047] and each sector {the sector slice}[{\"Heart Disease\"}[0.099, 0.519, 0.197, 0.539]; {\"Cancer\"}[0.506, 0.912, 0.555, 0.932]; {\"Respiratory Diseases\"}[0.766, 0.493, 0.912, 0.513]; {\"Diabetes\"}[0.597, 0.134, 0.659, 0.154]; {\"Infectious Diseases\"}[0.216, 0.145, 0.35, 0.165]], I can interpret each sector as a different health condition and the corresponding proportion that category occupies in the chart.\nBased on the above thought, the answer is:\nHealth Focus,Percentage\nHeart Disease,23.2%\nCancer,28.6%\nRespiratory Diseases,19.6%\nDiabetes,16.1%\nInfectious Diseases,12.5%"
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "jKuMbSDFzDfL5lfY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich disease category seems to be the most common concern in human health and disease management according to the chart, and why would you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the most common concern by identifying the disease category that occupies the largest proportion in the chart.\nBased on the above thought, the answer is:\n\"Cancer\" seems to be the most common concern because it has the highest frequency with a proportion of 28.6%."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "a9mJ8gT0clGSDBEc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could understanding the visualization of the frequency of focus on various health conditions in this chart influence the development of public health policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "I could infer potential impacts on public health policy development based on the representation of disease focus frequencies in the chart.\nBased on the above thought, the answer is:\nThe pie chart might guide public health policymakers to direct more attention and resources towards the management of \"Cancer\" and \"Heart Disease\" as they represent the most common health issues. It might also highlight areas such as \"Respiratory Diseases\", \"Diabetes\", and \"Infectious Diseases\" where further investment may be needed to improve their management and prevention measures."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "Hp9rhyM03Fuz3iBM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the focus on infectious diseases in the context of managing human health and diseases from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the proportion of the \"Infectious Diseases\" sector, I can provide some inferences on the focus for this health condition.\nBased on the above thought, the answer is:\nThe chart suggests that \"Infectious Diseases\" has the lowest frequency focus in the context of managing human health and diseases, with a percentage of 12.5%. This could imply that it's either less prevalent or that it receives less attention compared to other health conditions in the dataset."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "9M3WtbkBkqRvdFSy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health focus has the least frequency according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the size of each sector in the chart to determine the one with the least frequency.\nBased on the above thought, the answer is:\nInfectious Diseases."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "vHUt2UhzBiHenKmo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Respiratory Diseases saw a frequency increase by 5%, how much would it account for the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the new total frequency, I need to add 5% to the frequency percentage of \"Respiratory Diseases\" and then do the sum with all the percentages.\nBased on the above thought, the answer is:\nThe new percentage for \"Respiratory Diseases\" is 19.6% + 5% = 24.6%. The total percentage would exceed 100%, thus the increase would not maintain the percentage distribution."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "hYB64gAEB06ScnRM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest disease frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the smallest frequency from the largest frequency.\nBased on the above thought, the answer is:\nThe difference is 28.6% (Cancer) - 12.5% (Infectious Diseases) = 16.1%."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "V4RSNSffYdu5ZIWa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyze this pie chart and let's extract the data to be represented in a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the legend at {the right of the graph}<r>[0.908, 0.063, 0.993, 0.309]</r> and the title, {Social Media and Digital Communication Usage from 2010 to 2017}<r>[0.205, 0.021, 0.758, 0.047]</r>, the data could be seen as the percentage usage for each category per year. Pie slices represent these percentages.\nBased on the above thought, the answer is:\nYear, Social Media, Digital Communication\n2010, 55, 45\n2011, 60, 40\n2012, 62, 38\n2013, 77, 23\n2014, 81, 19\n2015, 85, 15\n2016, 89, 11\n2017, 97, 3"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "YWZJHsaUwA3aSsr9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend in the usage of Social Media and Digital Communication from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires interpreting the pie chart to understand the trend over the years for both Social Media and Digital Communication.\nBased on the above thought, the answer is:\nThe trend of usage for Social Media has been increasing, while for Digital Communication it has been decreasing from 2010 to 2017."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "DiWCMjD6NDDVBCJt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the drastic change in the usage of Digital Communication and Social Media in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the trend between 2012 and 2013, I would infer how and why such a drastic change happened during 2013.\nBased on the above thought, the answer is:\nThe drastic change in 2013, with a significant jump in Social Media usage and a sharp drop in Digital Communication, could suggest that a majority of users might have switched from Digital Communications to Social Media platforms that year."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "B5ImL4EtDzDENeQz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the outlier observed in the data for 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the data for 2017 and comparing it with the previous years, I can elucidate the significance of this outlier.\nBased on the above thought, the answer is:\nThe dramatic rise to 97% in Social Media usage and the sharp decline to 3% in Digital Communication in 2017 represents an almost total shift in platform preference. This could be due to the increased convenience and features offered by Social Media or the phasing out of older Digital Communication technologies."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "vJNCYt6K5lUaE3V3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the percentage usage of Social Media greater than 85% for the first time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data for each year in the chart, I can pinpoint when the Social Media consumption exceeded 85% for the first time.\nBased on the above thought, the answer is:\n2016."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "EtAFIylq4WpbSCb4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the usage of Digital Communication decline from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "By tracking the decline in the percentage usage of Digital Communication from 2010 to 2017, I can calculate the difference.\nBased on the above thought, the answer is:\n42%."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "9XSZcFqewXlmJb7l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the total percentage increase of Social Media usage from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total increase, I need to calculate the difference between the Social Media usage in 2017 and that in 2010.\nBased on the above thought, the answer is:\n42%."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "3UFtS9sawLR4jJJY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart data into a CSV formatted table please?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart and the labels of each segment {the legend}<r>[0.78, 0.063, 0.993, 0.249]</r>, I can see that each disease corresponds to a percentage indicating its respective share. I'll convert this information to a CSV format.\nBased on the above thought, the answer is:\nDisease,Cases (in 1000s),Percentage\n'Diabetes',8500,'12.8%',\n'Cancer',13560,'20.5%',\n'Cardiovascular Disease',16550,'25.0%',\n'Obesity',12500,'18.9%',\n'Asthma',6200,'9.4%',\n'Stroke',8900,'13.4%'"
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "QwSadL4lZqksVIUe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data in the chart, which disease seems to be most prevalent and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The disease with the largest proportion is the most prevalent.\nBased on the above thought, the answer is:\nCardiovascular Disease is the most prevalent because it has the highest proportion with 25% of the total cases."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "YhH3V07h7rxOWb5q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, what does it tell us about the health conditions of the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at all the proportions of the diseases to infer about health conditions.\nBased on the above thought, the answer is:\nThe chart suggests that Cardiovascular Disease and Cancer are the major health issues impacting this population. While diseases like Asthma seem to be less prevalent, it's clear that chronic conditions are a significant health concern."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "T3fXKzuzqOCJjTjN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what could the healthcare providers improve upon to lessen the prevailing health conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "The improvements can be made in areas represented by larger proportions in the pie chart.\nBased on the above thought, the answer is:\nThe high prevalence of Cardiovascular Disease suggests an urgent need for improved cardiovascular health programs such as exercise initiatives, healthier diets, and regular health check-ups. Policies aimed at reducing cancer, given its second-highest percentage, also need to be enhanced \u2013these can include encouraging regular screening and promoting a lifestyle that reduces cancer risk. Monitoring other diseases like Diabetes, Stroke, and Obesity is also crucial."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "nf3pzVTwiInAhQNr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich disease has the highest reported cases?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages that each disease represents.\nBased on the above thought, the answer is:\nCardiovascular Disease"
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "1kWczyApFeI5vxqQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the highest and lowest cases of the diseases presented?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the percentage difference, I should subtract the lowest percentage from the highest percentage.\nBased on the above thought, the answer is:\n15.6% (25.0% - 9.4%)"
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "465Bs34zb8T70MWc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Cancer cases increased by 7%, what would be its new proportion in the disease distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "If an increase occurs in the Cancer cases, I should add this percentage to its current percentage.\nBased on the above thought, the answer is:\n27.5% (20.5% + 7%)"
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "uXFyCOEIEPMmohzZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the dataset as per this pie chart in table CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will review the pie chart figure and identify the required data. The labels {Supplier, Manufacturer, Wholesaler, Retailer} have been enumerated on the chart with corresponding percentages< r>[0.894, 0.071, 0.951, 0.091], [0.894, 0.101, 0.987, 0.121], [0.894, 0.131, 0.971, 0.151], [0.894, 0.161, 0.948, 0.181]</ r> . The chart represents the distribution of value each stage contributes in value chain.\nBased on the above thought, the answer is:\nStage,Value\nSupplier,190\nManufacturer,340\nWholesaler,255\nRetailer,215"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "C3FGHcMGB0LnwZm7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stage of the process adds the greatest value according to the chart, and could you explain why is that the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the stage that adds the greatest value, I need to observe which sector of the pie chart is the largest. That is the {Manufacturer}<r>[0.254, 0.516, 0.708, 0.886]</r> sector with a contribution of 34.0%.\nBased on the above thought, the answer is:\nManufacturer adds the greatest value. As per the chart, their contribution to the total value of the product is the highest. This could be because the manufacturing stage involves transforming raw materials into finished goods, which adds the most value in this particular value chain."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "vz1l41e7kpnOJWXd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart reflect the flow of goods from production to retail?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart illustrates the sections {Supplier}<r>[0.223, 0.278, 0.482, 0.692]</r>, {Manufacturer}<r>[0.254, 0.516, 0.708, 0.886]</r>, {Wholesaler}<r>[0.482, 0.187, 0.741, 0.697]</r>, and {Retailer}<r>[0.283, 0.146, 0.601, 0.516]</r> sequentially, giving an understanding of the flow of goods from the production to retail side.\nBased on the above thought, the answer is:\nIn terms of the value chain, the chart exhibits the flow of goods from production (Supplier) to retail (Retailer) with the gradual increase in value at each stage. The value added at each stage is represented proportionally by the size of the pie slice, implying that more value is added in the transition from Supplier to Manufacturer than from Wholesaler to Retailer."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "xxPui10E1Pi6mV2N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there were a fifth stage added to the end of this process, how would the data on the chart give us an idea of the value that could be added at that stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "We could observe the trend in the chart where value typically increases as from supplier to manufacturer, then decreases towards the retailer, to anticipate the possible value addition at a new post-retail stage.\nBased on the above thought, the answer is:\nThe value added at each stage progressively decreases after the Manufacturer stage in the current chart. If a fifth stage were to be introduced following the retail stage, one might expect it to add less value than the Retailer stage based off the illustrated trend, unless it introduces an innovative process or service that adds significant value."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "xZs7rvSn3NwPHR1C",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total of the values for the Supplier and Wholesaler stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the sum, I need to add the values of Supplier and Wholesaler stages which are 190 and 255 respectively based on my analysis of the chart.\nBased on the above thought, the answer is:\n445"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "1qx1Zs1e1ZSjj5gZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much difference in the value between the Manufacturer and Retailer stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be calculated by subtracting the value of the Retailer stage from the Manufacturer stage; these values are 340 and 215, respectively, according to the chart's utility.\nBased on the above thought, the answer is:\n125"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "2m8TUhzsYq5cgmwF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the proportion of value added by the Wholesaler in percent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the arc of the pie chart representing the Wholesaler {Wholesaler}<r>[0.482, 0.187, 0.741, 0.697]</r>, it's illustrated that the Wholesaler has added 25.5% of value in this chain.\nBased on the above thought, the answer is:\n25.5%"
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "5X3Gw2njxmhMwqFG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, please convert the data into a table with CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "As observed from the JSON data and the pie chart, we have risk factors over 3 years: 2018, 2019, and 2020. The types of risks are encoded in the slice coloring. The pink slice corresponds to {\"Risk in 2018\"}[0.902, 0.071, 0.987, 0.091], the light blue slice corresponds to {\"Risk in 2019\"}[0.902, 0.101, 0.987, 0.121] and the light green slice corresponds to {\"Risk in 2020\"}[0.902, 0.131, 0.987, 0.151]. The percentages are represented by the slices of pie. From the autotext, I can see the percentages.\nBased on the above thought, the answer is:\nType,Risk in 2018,Risk in 2019,Risk in 2020\nFinancial Risk,65,70,85\nOperational Risk,35,30,15"
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "krdzg8j8aLlDYzzA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can we say that the Financial Risk for the year 2020 is an outlier and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First I need to see whether the percentage for Financial Risk in 2020 is significantly different from the other percentages.\nBased on the above thought, the answer is:\nYes, we could consider the Financial Risk for 2020 as an outlier because it is much higher than the percentages for any other risk in any other year."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "EP875VNmAhS8k3Wa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the trend in operational risks over the years indicate improved operations or worsening conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the Operational Risks over the three years.\nBased on the above thought, the answer is:\nThe decreasing trend in operational risks indicates improved operations."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "PYtimLjVHB3CS3Tj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this risk assessment data influence decision-making for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the change in risks over the past few years might impact decisions.\nBased on the above thought, the answer is:\nGiven the steady increase in Financial Risk and the decrease in Operational Risk over the past few years, in 2021 management might prioritize focusing on financial risks while continuing to sustain and improve operations."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "DL1njZUyN3o7owBK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in financial risk between the first year and the last year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the Risk in 2018 from the Risk in 2020 of the Financial Risk.\nBased on the above thought, the answer is:\nThe difference is 85 - 65 = 20 units."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "uND53nlj3QFRszHO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total risk in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add up the risks for Financial Risk and Operational Risk for the year 2019.\nBased on the above thought, the answer is:\nThe total risk is 70 + 30 = 100 units."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "gGDRDbIOg4XSb62Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did operational risk decrease from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease, I need to subtract the Risk in 2020 from the Risk in 2018 of the Operational Risk.\nBased on the above thought, the answer is:\nThe decrease in operational risk is 35 - 15 = 20 units."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "Anc7GoQ5kJNj7Csl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table based on the pie chart regarding AI and Machine Learning Approach usage percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the provided chart, I can see individual approach types {\"Deep Learning\"}[0.821, 0.071, 0.922, 0.091], {\"Neural Networks\"}[0.821, 0.101, 0.937, 0.121], {\"Reinforcement Learning\"}[0.821, 0.131, 0.987, 0.151], {\"Transfer Learning\"}[0.821, 0.161, 0.942, 0.181], {\"Federated Learning\"}[0.821, 0.191, 0.956, 0.211]. Each of them has a distinct color and a corresponding pie slice that indicates its usage percentage.\nBased on the above thought, the answer is:\nApproach,Percentage\nDeep Learning,22\nNeural Networks,36\nReinforcement Learning,18\nTransfer Learning,15\nFederated Learning,9"
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "ZPV2VkdWL3pOUVgx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Machine Learning approach seems to be the most utilized, according to the chart? Can you explain your interpretation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The approach with the largest slice on the pie chart will be the most utilized one.\nBased on the above thought, the answer is:\n\"Neural Networks\" is the most utilized approach as it occupies the largest slice, 36%, in the pie chart."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "jg6Wk8A8XAiybU6r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the usage of these approaches in the field of AI and Machine Learning from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe the share of each approach in the pie chart to infer their usage.\nBased on the above thought, the answer is:\nThe chart indicates that \"Neural Networks\" and \"Deep Learning\" are the two most widely used approaches, with the highest percentages. This suggests these two techniques are more dominant in the field of AI and Machine Learning than the others."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "bH4UuWUQFTIUGkJF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the lowest percentage slice, how might the data reflect the state of the newer AI approaches like Federated Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can consider the percentages of each approach and identify the one with the lowest percentage.\nBased on the above thought, the answer is:\nFederated Learning has the least percentage usage of 9%. This could indicate that Federated Learning is one of the less frequently used or newer approaches in the field, and it may still be in the early stages of adoption compared to the other techniques."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "yjeXVGTljs9DHtuv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, how much percentage of the approaches does Deep Learning and Neural Networks occupy in total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find that, I need to add the percentages of the \"Deep Learning\" and \"Neural Networks\" together.\nBased on the above thought, the answer is:\n58%."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "4AtxskRhrF3EHlKh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what's the minimum usage percentage among these approaches?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find which approach has the smallest slice in the pie chart.\nBased on the above thought, the answer is:\n9%."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "lZLPInVshMsETCaP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we add another approach that accounts for 5% usage, what will be the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 5% to the current total of 100%.\nBased on the above thought, the answer is:\nThe new total percentage would be 105%."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "R54UERbqM6Lz34nB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please create a CSV table representing the percentages of each of the fashion subculture/trend as indicated on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. To make a CSV table, I need to collect the names of the subcultures/trends from {the legend}<r>[0.863, 0.063, 0.993, 0.219]</r> and the corresponding percentages of each from the slices of the pie like <r>{'60s Retro'}[0.223, 0.278, 0.482, 0.712] {Steampunk}[0.262, 0.516, 0.619, 0.886], {Pastel Goth}[0.482, 0.516, 0.74, 0.83], {Cyberpunk}[0.419, 0.146, 0.741, 0.542], and {Indie Folk}[0.283, 0.157, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nSubculture/Trend,Percentage\n\u201860s Retro,20\nSteampunk,25\nPastel Goth,15\nCyberpunk,30\nIndie Folk,10"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "hbGTX1qH2AW9TMy9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, does the 'Steampunk' trend have a significant presence in current pop culture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must compare the 'Steampunk' slice on the pie chart with the other trends.\nBased on the above thought, the answer is:\nYes, 'Steampunk' has a fairly significant presence with about 25% of the overall market share among the examined trends, second to 'Cyberpunk' at 30%."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "bKacl3VD5gwmqU1N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the popularity of the 'Indie Folk' trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity of 'Indie Folk' can be determined by observing its slice on the pie chart and comparing its size to the slices of other trends.\nBased on the above thought, the answer is:\n'Indie Folk' trend is the least popular among the examined trends as it has the smallest slice in the chart, indicating only 10% presence in the current pop culture."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "AzR6fJhVGvdtMZUP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this data, how might a fashion company adjust its product line to better match the current subculture/trend landscape?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to assess the prevalence of each subculture/trend based on the size of their slices in the pie chart in order to provide suggestions accordingly.\nBased on the above thought, the answer is:\nTo match the current trends, a fashion company might consider focusing more on 'Cyberpunk' styled products as it is the most popular trend. Also, since 'Steampunk' is the second most popular trend, a substantial array of 'Steampunk'-inspired items could be beneficial. The company might also consider investing in less dominant trends such as '\u201860s Retro', 'Pastel Goth', and 'Indie Folk', to cater to those specific subculture audiences."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "Z2Lzh6Ax3HEmEk0b",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which subculture/trend has the highest presence?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine this by comparing the size of each subculture/trend slice on the pie chart. The biggest slice depicts the most prevalent trend.\nBased on the above thought, the answer is:\nCyberpunk"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "Vhmb4BZEYRUTDqKJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of total presence do the two least popular subcultures/trends possess together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add up the percentages of the two smallest slices on the pie chart, which represent the least popular trends.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "4DWqeJbvE8hCKVnh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage would the 'Cyberpunk' category gain if it increased by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I would add 10 to the current percentage of the 'Cyberpunk' slice on the pie chart.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "84dLZnyREimCT9iT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you provide the data in a data table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are various elements present in this chart including {the chart title}[0.295, 0.021, 0.668, 0.047], {the legends}[0.8, 0.063, 0.993, 0.309] and {the pie sectors}[0.223, 0.146, 0.741, 0.886]. To form a data table, I need to extract the information from each of these elements.\nBased on the above thought, the answer is:\nTechnology,Prospects\nArtificial Intelligence,80\nIoT,90\nBig Data,70\nCloud Computing,75\nBlockchain,65\nAR/VR,85\nNanotech,60\nBiotech,80"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "jFRS2yxLGU0DFvlr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which technology appears to be the least important for future development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the technology with the smallest {pie sector}[0.223, 0.146, 0.741, 0.886] will allow me to answer this question.\nBased on the above thought, the answer is:\nBlockchain seems to be the least important for future development, as it has the smallest percentage in the pie chart which corresponds to a prospect score of 65."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "zf3yfRmWXceawlN6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the pie chart useful in understanding the future prospects of these technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the various components in the chart including the {chart title}[0.295, 0.021, 0.668, 0.047], {legends}[0.8, 0.063, 0.993, 0.309] and {pie sectors}[0.223, 0.146, 0.741, 0.886], I need to evaluate the value of each in helping understand the future prospects.\nBased on the above thought, the answer is:\nThe pie chart clearly visualizes the proportion each technology holds in terms of future prospects. It provides a simple and direct comparison of the future importance of each technology. With pie sectors for each technology, we can easily identify which technologies will lead in the future -- simply by observing the size of their respective sector."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "sAZT1KGmMcgoExJb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the development of Big Data in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {Big Data slice}[0.357, 0.516, 0.539, 0.886] on the pie chart will enable me to infer its future development.\nBased on the above thought, the answer is:\nIt appears that Big Data will have moderate importance in future technological development. The slice representing Big Data is neither the smallest nor the largest, indicating that while Big Data is of some significance, it's not currently forecasted to be a leading domain in future."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "Ab1Jo5lIk6WiCQAk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology has the highest prospects?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the sizes of the {pie sectors}[0.223, 0.146, 0.741, 0.886] and consulting the {legend}[0.8, 0.063, 0.993, 0.309] will help me determine the technology with the highest prospects.\nBased on the above thought, the answer is:\nIoT"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "xBVKT9PYeiCTXyNR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total prospect value represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Summing up the prospect values assigned to each {technology}[0.223, 0.146, 0.741, 0.886] will give the total prospects represented in the pie chart.\nBased on the above thought, the answer is:\n605"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "aGNME8GZ4RqgYU8P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the lowest and the highest prospects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference between the lowest and the highest prospects, I need to identify the {minimum and maximum pie sector sizes}[0.223, 0.146, 0.741, 0.886], then calculate the difference.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "XeVk3zaKRjcYrTrh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the information you see in the pie charts, can you convert it into a CSV-formatted data?"
+            },
+            {
+                "from": "gpt",
+                "value": "As I see, there are three pie charts distributed with each chart named as {\"Survey of AI and ML trend from 2017 to 2019 2017\"}<r>[0.288, 0.007, 0.712, 0.016]</r>, {\"Survey of AI and ML trend from 2017 to 2019 2018\"}<r>[0.288, 0.338, 0.712, 0.347]</r>, and {\"Survey of AI and ML trend from 2017 to 2019 2019\"}<r>[0.288, 0.669, 0.712, 0.678]</r>. Each pie chart has two segments representing {\"Artificial Intelligence\"}<r>[0.674, 0.024, 0.816, 0.03]</r>, {\"Machine Learning\"}<r>[0.674, 0.034, 0.797, 0.04]</r> distributed for each year.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Machine Learning\n2017,41.7,58.3\n2018,36.8,63.2\n2019,42.2,57.8"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "fJNgO5cLieKEBLPP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the visualization, which area showed an increasing trend from 2017 to 2019? And please explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentages of \"Artificial Intelligence\" and \"Machine Learning\" in 2017 and 2019, I can see which area increased.\nBased on the above thought, the answer is:\nThe area of \"Artificial Intelligence\" showed an increasing trend from 2017 to 2019. Even though the percentage of \"Artificial Intelligence\" decreased in 2018, it regained growth in 2019, showing an overall upward trend from 41.7% in 2017 to 42.2% in 2019."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "xvI255hxs93yeKUG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data chart, can you infer the most preferred area in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most preferred area would be the one with the highest percentage in the pie chart for the year 2019.\nBased on the above thought, the answer is:\nThe chart reveals that \"Machine Learning\" was the most preferred area in 2019, as it occupied a larger slice of the pie chart representing 57.8%."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "e3jLWRl6vAV3ytcr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the trend from the pie chart about \"Artificial Intelligence\" and \"Machine Learning\" from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The interpretation of the trend would be based on how the percentages of \"Artificial Intelligence\" and \"Machine Learning\" changed within these three years.\nBased on the above thought, the answer is:\nFrom the pie charts, it can be seen that there was a growing interest in both \"Artificial Intelligence\" and \"Machine Learning\" from 2017 to 2019. However, \"Machine Learning\" was consistently more preferred over \"Artificial Intelligence\" throughout these years. Despite the slight decrease in 2019, the overall growth indicates a rising interest and developments in these fields."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "WCe7O6j9nZiuxssB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year, Artificial Intelligence had the least percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the year when Artificial Intelligence had the least value, I need to compare the percentage of Artificial Intelligence in all three years.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "4PHUlhZOFooF0jyl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of Artificial Intelligence and Machine learning in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total percentage of Artificial Intelligence and Machine Learning in a particular year would be 100% as they are the only two categories in the pie chart for each year.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "7iDkSZme11LpyGXg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the area of Machine Learning from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the percentage of Machine Learning in 2017 from that in 2019.\nBased on the above thought, the answer is:\nThe percentage decreased by 0.5%."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "dqMtA2I5A1JE4T90",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data from the figures as a table, with each year's expenditure for 'Product Development' and 'Sales and Marketing' outlined?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing {the title of the chart}<r>[0.236, 0.021, 0.728, 0.047]</r>, it is apparent that the data corresponds to yearly expenditures in specific business activities. Also, the data inside {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r> and {the components in the legend}<r>[0.792, 0.063, 0.993, 0.129]</r> allows me to identify the distribution for each activity over the years.\nBased on the above thought, the answer is:\nActivity,2017,2018,2019,2020,2021\nProduct Development,18,25,30,36,45\nSales and Marketing,20,18,25,32,37"
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "EK0CQKOpA5esL74L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend from 2017 to 2021, which business activity saw a consistent increase in expenditure?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the expenditure of each business activity in each year. The activity that consistently shows an increase each year would be the one in question.\nBased on the above thought, the answer is:\n'Product Development' consistently saw an increase in expenditure from 2017 to 2021."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "eF6A4W9l2OQEOUI7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the data inform about the business focus shifting over time from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing the proportion of expenditure dedicated towards each activity over time, I can determine how the business focus might have shifted.\nBased on the above thought, the answer is:\nThe data indicates a significant shift in business focus towards 'Product Development'. The proportion allocated to this activity has gradually increased from 2017 to 2021, suggesting the business has progressively prioritized product development over sales and marketing."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "lzDMRBI29D7lcaDo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere did the organization focus most of its resources in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can identify this by analyzing expenditure distribution for 2021 from {the 2021 pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nIn 2021, the organization focused most of its resources on 'Product Development' which accounted for 54.9% of the total expenditure."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "4zJpz3Pf1wdAzHkP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage points did 'Product Development' expenditure increase from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To gauge this, I need to subtract the expenditure value for 'Product Development'in 2017 from the corresponding value in 2021.\nBased on the above thought, the answer is:\nThe 'Product Development' expenditure increased by 27 percentage points."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "kl9qFFyQ24EW7vrm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of total expenditure was spent on 'Sales and Marketing' in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Referencing the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can obtain the specific percentage dedicated to 'Sales and Marketing' in 2021.\nBased on the above thought, the answer is:\n'Sales and Marketing' constituted 45.1% of total expenditure in 2021."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "O3rEz4M6bp9mDCkJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average proportion of expenditure on 'Product Development' from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to sum up the expenditure on 'Product Development' from 2017 to 2021 and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average proportion of expenditure on 'Product Development' is 31%."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "GtqFPMbNyr5EEyfj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data for the year 2020 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the title {\"Economic Output for Year 2020\"}[0.352, 0.021, 0.611, 0.047], and the sector slices {pie_0}[0.223, 0.146, 0.741, 0.886]. I see that the pie chart represents two sectors, {Manufacturing}[0.305, 0.49, 0.348, 0.51] and {Services}[0.615, 0.521, 0.659, 0.541], along with their respective proportions.\nBased on the above thought, the answer is:\nSector,2020\nManufacturing,20%\nServices,80%"
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "rqzfjCkkj4KeP1Nk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart for 2020, which sector dominated the economic output, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of the {Manufacturing}[0.305, 0.49, 0.348, 0.51] and {Services}[0.615, 0.521, 0.659, 0.541] sectors to find out the one with the highest proportion.\nBased on the above thought, the answer is:\nThe \"Services\" sector dominated the economic output as it constituted the majority portion of 80% in the pie chart."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "zywvBI0FtA4XxHgQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the economic output distribution between the Manufacturing and Services sectors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to analyse the proportions of each sector to comprehend the distribution of the economic output.\nBased on the above thought, the answer is:\nThe pie chart reveals that the \"Services\" sector had a significantly larger share of the economic output, precisely 80%, as compared to the \"Manufacturing\" sector. This suggests that the economy was overwhelmingly service-oriented in the year 2020."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "JaTY1ZxbZrvwgMAf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data represented in the chart impact future resource allocation between the Manufacturing and Services sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the proportions might help predict decisions on resource allocation.\nBased on the above thought, the answer is:\nSince \"Services\" accounted for a substantial percentage, it might be predicted that more resources would be channelled into this sector to sustain and enhance its output. Simultaneously, the \"Manufacturing\" sector, reflecting a decline in share, may require strategic resource allocation to improve its output in the upcoming period."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "SVVUQAwdyve6DhCk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of the economic output did the Manufacturing sector represent in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slice for {Manufacturing}[0.305, 0.49, 0.348, 0.51] in the pie chart, I can find the percentage of the economic output that it represents.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "fu6r1v6JUu814RT6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Services sector increased its proportion by 10% in 2021, what would be the new total economic output of the Services sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 10% more to the percentage of the Services sector in 2020.\nBased on the above thought, the answer is:\nThe new total economic output of the Services sector would be 80% + 10% = 90% in 2021."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "TeHNiyal2k811gZt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Manufacturing sector reduced its proportion by half in the coming year, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion, I need to take half of the current proportion of the Manufacturing sector.\nBased on the above thought, the answer is:\nThe new proportion of the Manufacturing sector would be 20% / 2 = 10%."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "GjUkUremD9f4klsO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThere is a pie chart presenting the financial instrument holdings for 2020. Kindly put this data into a CSV-format table based on your observation."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the bottom of the chart {2020}[0.412, 0.037, 0.497, 0.083] and {the pie slices}[{\"Stocks\"}[0.176, 0.18, 0.732, 0.876]; {\"Bonds\"}[0.241, 0.304, 0.454, 0.528]], I observe that the pie slices represent the percentage holdings of Stocks and Bonds in 2020. Stocks account for 100%, while Bonds 0%.\nBased on the above thought, the answer is:\nFinancial Instrument,2020 (%)\nStocks,100\nBonds,0"
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "92l2BwtSh6QJxfLf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend in investor preference can be inferred from this data represented in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart gives some insights about the investors' preferences between the two financial instruments, Stocks and Bonds.\nBased on the above thought, the answer is:\nBased on the data, investors in 2020 showed a strong preference for Stocks, allocating 100% of their investments into it, and none into Bonds. This suggests that Stocks were perceived as offering better returns in that year."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "eckrMRCj6HWe5nbv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the distribution data of financial instruments, can you infer the risk appetite of investors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The allocation of investments into two types of financial instruments, Stocks and Bonds, provides insights into the risk-bearing capacity of investors in 2020.\nBased on the above thought, the answer is:\nGiven that 100% of investments are in Stocks and 0% in Bonds, it seems that investors in 2020 had a high risk appetite. Stocks are generally considered riskier but offer the potential for higher returns, whereas bonds are safer but offer lower potential returns."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "ylhy95VEvjt4Uwew",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the chart, can an estimate be made about the future trend of these two financial instruments in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the chart does not directly provide data for 2021, indications for future trends can be deduced.\nBased on the above thought, the answer is:\nAs the chart for 2020 shows a total shift from Bonds to Stocks, investors may continue this trend into 2021 if the factors influencing this total shift still exist. However, changes in economic conditions, investors' risk tolerance or returns on Bonds might lead to a rebalance of these investments."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "qtEhsv4iyEq5SLNL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, how much percentage more was investment in Stocks than Bonds?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I need to subtract the percentage of Bonds from Stocks based on the pie chart.\nBased on the above thought, the answer is:\n100% (Stocks) - 0% (Bonds) = 100%"
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "zmZi9Ffg2Xx9ytAK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an investor planned to shift 10% from Stocks to Bonds in 2021, what would be the new division of investments between Stocks and Bonds in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would need to subtract 10% from the 2020 stocks percentage and then add that to the 2020 bonds percentage.\nBased on the above thought, the answer is:\nThe new investment in Stocks would be 90% and Bonds would be 10%"
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "aGoeohFKcGb2TTcx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a new financial instrument arrives in 2021 and attracts 15% of the investment from Stocks, how much would be the new investment in Stocks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduct 15% from the 2020 Stocks we need to subtract 15% from the 100% Stock investment as per 2020 data\nBased on the above thought, the answer is:\n100% (Stocks in 2020) - 15% = 85%."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "eY7B1uhwBLbPvlFg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data to a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"Future Forecast and Trend Prediction of AI implementation in Industries\"}[0.186, 0.021, 0.777, 0.047] and the pie slices {'2025'}[0.223, 0.278, 0.482, 0.886] and {'2030'}[0.283, 0.146, 0.741, 0.886], it is clear that these slices represent AI implementation in industries for these years.\nBased on the above thought, the answer is:\nYear,AI Implementation\n2025,50%\n2030,90%"
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "VtAKp5Z6uUvDlTQQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you infer about the trend in AI implementation in industries from 2025 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend, I need to compare the pie slices for these two years.\nBased on the above thought, the answer is:\nThe chart shows a significant upward trend in AI implementation in industries from 2025 to 2030. The size of the slices indicates that by 2030, AI adoption in industries would be nearly twice that of 2025."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "lxPCLfjKTcdECGyd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the future of AI in industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the size of the slices, I can infer about the role of AI in the future of industries.\nBased on the above thought, the answer is:\nThe pie chart suggests a major leap in AI implementation in industries in the future. By 2030, almost all industries (90%) will have integrated AI into their operations, formulating a digital era dominated by AI."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "yFcDETv7OmrXNBZq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications might this data have for businesses and industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the pie slices and their sizes, I can see the potential implications for businesses.\nBased on the above thought, the answer is:\nThis data implies a significant shift in industries towards AI implementation. From 2025 to 2030, it is expected that AI implementation will almost double, suggesting that businesses who have not started integrating AI may be left behind. It indicates the need for businesses to adapt to and incorporate AI into their operations."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "4BPPoQLA5JNpZqqk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in AI implementation from 2025 to 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to subtract the AI implementation percentage in 2025 from that in 2030.\nBased on the above thought, the answer is:\nThe increase in AI implementation from 2025 to 2030 is 40%."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "xHpnivufp5Ki8etI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of AI implementation in 2030 is represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the proportion, I need to look at the specific pie slice for that year.\nBased on the above thought, the answer is:\nThe proportion of AI implementation in 2030 is 64.3%."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "ahBGD26fbAN69xPW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf I consider the AI implementation started from zero, what will be the increase from the beginning to 2025 and 2030, respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "If AI implementation started from zero, the increase to 2025 would be the same as the implementation percentage in 2025, and the increase to 2030 would be the same as that in 2030.\nBased on the above thought, the answer is:\nFrom the beginning to 2025, the increase is 50%. To 2030, the increase is 90%."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "a3heijnCP5VsIfeo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the title of the chart {Environmental Protection and Sustainable Development Importance}<r>[0.198, 0.021, 0.765, 0.047]</r>, I can see the sectors of the pie chart represent different categories in environmental protection and sustainable development and how much importance each category has. The categories are represented in the {legend}<r>[0.781, 0.063, 0.993, 0.219]</r>. The importance of each category is represented as percentages of the whole pie chart.\nBased on the above thought, the answer is:\nCategory,Importance\nRenewable Energy,30%\nWater Conservation,20%\nSustainable Agriculture,15%\nPollution Control,25%\nClimate Change,10%"
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "IBqAmYxCTFY6teWn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which environmental issue is considered most important? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The issue with the highest percentage can be considered the most important.\nBased on the above thought, the answer is:\nThe most important issue is \"Renewable Energy\" because it has the largest share of importance at 30%."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "bAR6sPbBVQhcPXGX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the priorities of environmental advocacy from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the percentages of each category to infer the priorities of environmental advocacy.\nBased on the above thought, the answer is:\nThe pie chart suggests that 'Renewable Energy' is the top priority for environmental advocacy, closely followed by 'Pollution Control'. 'Climate Change', however, is given the least importance, suggesting more focus is needed on highlighting its significance."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "f26Tgp70gsOMPaBW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart impact future environmental policy-making and funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the proportions of importance might provide an insight on how policy-making and funding could be determined.\nBased on the above thought, the answer is:\nGiven the high importance of 'Renewable Energy' and 'Pollution Control', future policies could be shaped to support these areas with more resources and funding, while 'Climate Change' might need more advocacy to bring it to the forefront of policies and funding decisions."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "upm7hfcN6z8sNRQG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more important is Renewable Energy compared to Climate Change according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the answer, I need to deduct the percentage of \"Climate Change\" from the percentage of \"Renewable Energy\".\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "39et8TGwJeKoBwyK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the importance does Pollution Control and Sustainable Agriculture combined represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage of \"Pollution Control\" and \"Sustainable Agriculture\", I will need to add up the two respective percentages.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "Ectbs1EZuwKn2Xhh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Sustainable Agriculture's importance increases by 10%, what would be the new combined total importance of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total importance, I need to add 10% to the \"Sustainable Agriculture\" percentage and then sum up all the percentages.\nBased on the above thought, the answer is:\n110%."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "006vp3ytdD6EAFDn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the visual information about the yearly political tweet volume from this pie chart into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the title 'Political Tweet Volume from 2015 to 2019' given at {the subtitle}<r>[0.309, 0.021, 0.654, 0.047]</r>, the chart helps us visualize the tweet volumes for specific years. Each slice of {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> represents a specific year. The pie slices are labeled as {2015}<r>[0.315, 0.426, 0.358, 0.446]</r>, {2016}<r>[0.318, 0.598, 0.362, 0.618]</r>, {2017}<r>[0.451, 0.728, 0.495, 0.748]</r>, {2018}<r>[0.61, 0.565, 0.653, 0.585]</r>, and {2019}<r>[0.478, 0.285, 0.522, 0.305]</r>. The corresponding percentages can be seen written next to these slices as 10.5%, 14.7%, 20.0%, 23.2%, and 31.6% respectively. From these percentages, the actual yearly political tweet volumes can be discerned.\nBased on the above thought, the answer is:\nYear,Tweet Volume\n2015,10.5%\n2016,14.7%\n2017,20.0%\n2018,23.2%\n2019,31.6%"
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "6FnCxvpjPAnh7h7J",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the biggest spike in political tweets according to the chart, and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe and interpret the largest slice of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and also consider potential external factors contributing to the increase.\nBased on the above thought, the answer is:\nThe largest portion of political tweets was in 2019. This could have been due to political events during that year which might have increased public engagement in political discourse on platforms like Twitter."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "gRn18dRoA8403Jo9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the volume of political tweets over the years based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sizes of the slices from 2015 to 2019, and their growth, could provide insight into trends.\nBased on the above thought, the answer is:\nBased on the chart, there is a rising trend in the volume of political tweets from 2015 to 2019, indicating increased engagement in political discourse over that period."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "1DfAU5KFUIXkVsTE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does this chart depict the influence of social media, particularly Twitter, on political discourse?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpreting the chart involves understanding the correlation between the increasing percentages of political tweets and the potential influence on political discourse.\nBased on the above thought, the answer is:\nThe pie chart shows a continual increase in the volume of political tweets from 2015 to 2019, implying that social media platforms like Twitter are playing a more significant role in political discourse over time. It suggests that with the surge in political tweets, social media may drive engagement in and dissemination of political discussions and news."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "N4F344e2Ix2CDqeb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year was responsible for approximately one fifth of the political tweets?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages written alongside each slice of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and identify which is close to 20%, which would be approximately one fifth of the total.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "0vrDU1iQFbk4kkUi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, by how much did the share of political tweet volume increase from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the percentage of political tweets in 2015 from the percentage in 2019.\nBased on the above thought, the answer is:\nThe increase was from 10.5% in 2015 to 31.6% in 2019, an increase of 21.1%."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "rhuAEWD2C7zOyr6u",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total percentage if we combine the political tweet volumes of 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the percentages for both these years.\nBased on the above thought, the answer is:\nThe combined percentage for 2017 and 2018 is 20.0% (2017) + 23.2% (2018) = 43.2%"
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "H4yoviyU3k4gOOc1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please transform the data displayed in the pie chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title {\"Social Network Usage Distribution\"}[0.34, 0.021, 0.623, 0.047], and the pie slices representing the different social networks like {\"Facebook\"}[0.223, 0.278, 0.619, 0.886], {\"Instagram\"}[0.482, 0.32, 0.741, 0.83], {\"Twitter\"}[0.482, 0.147, 0.701, 0.516], {\"Snapchat\"}[0.344, 0.146, 0.5, 0.516], and {\"Others\"}[0.283, 0.202, 0.482, 0.516]. I can clearly see the different social networks and the corresponding usage percentages, which I will convert into a structured table.\nBased on the above thought, the answer is:\n\"Social Network\",\"Percentage\"\n\"Facebook\",45\n\"Instagram\",25\n\"Twitter\",15\n\"Snapchat\",10\n\"Others\",5"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "E3LvQP5FKuHT8yYX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social network is most used? Why do you reach that conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I reach my conclusion by comparing the sizes of the slices in the pie chart which represent the usage percentages of different social networks.\nBased on the above thought, the answer is:\nThe most used social network is Facebook because it has the largest slice in the pie chart, representing a 45% usage."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "HOdqX6JB6GvkL7tb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what insights can be inferred regarding the usage of social networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "The analysis is based on the pie chart's representation of the distribution of social network usage.\nBased on the above thought, the answer is:\nFrom the pie chart, I can infer that Facebook dominates social network usage with a whopping 45%. Instagram follows at a 25%, and Twitter, while popular, is less used at 15%. Snapchat is at 10% usage, while the remaining 5% is made up of other social networks."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "LJizSsG58Spyc2YA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a company wants to advertise to the widest possible audience, which social network should it choose for its promotions and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should look at the social network with the highest usage percentage which would have the widest user base.\nBased on the above thought, the answer is:\nThe company should choose Facebook for its promotions, as it has the highest usage percentage (45%) suggesting a wider user base comparatively to other social networks."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "AHtVoHy2E1g3Gb9O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how much more is the usage of Facebook than the combined usage of Snapchat and Others?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would sum the usage percentages of Snapchat and Others, then subtract the result from the usage percentage of Facebook.\nBased on the above thought, the answer is:\n30"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "Gvc1z6uDp1Rl9F65",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage of use for Instagram and Twitter?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage, I simply add up the percentages related to Instagram and Twitter.\nBased on the above thought, the answer is:\n40"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "fhgs9tbY3haWHAWy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Instagram usage dropped by 5%, what would be the new percentage for Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I would subtract 5% from the current usage percentage of Instagram.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "8NihtPXQEMNkTNns",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you represent the financial data provided in the chart in a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the various yearly values provided for each sector in the graph, the values for 'Investment' and 'Real Estate' for the years 2017 to 2020 can be organized into a table.\nBased on the above thought, the answer is:\nYear,Investment,Real Estate\n2017,100,85\n2018,130,119\n2019,96,70\n2020,115,80"
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "9Ht2hsFZ3njOvBzP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which year is the largest difference between the investments in the two sectors noticeable?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to compare the yearly values of the two sectors and determine which year has the greatest difference.\nBased on the above thought, the answer is:\nThe largest difference in investments between the two sectors is noticeable in the year 2018."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "wN4c9Kjbs5LP1Ybx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the financial trends in the chart, what can we infer for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By extrapolating the trends from the previous years, we can make inferences about the likely values for the year 2021.\nBased on the above thought, the answer is:\nConsidering the trend from the previous years, the Investment sector shows an upward trend after a dip in 2019, so we can infer that it could continue its growth in 2021. The Real Estate sector shows a downward trend after peaking in 2018, likely indicating it might continue to decrease or get stable in 2021."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "21a8wjJKl3QzkxiW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the current financial trends on the chart, which sector would you advise to invest in for the year 2021? And explain your choice."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the general trend of each sector from 2017 to 2020 to infer which sector is more promising for investment in 2021.\nBased on the above thought, the answer is:\nBased on the trends, I would advise investing in the 'Investment' sector in 2021. Despite a dip in 2019, it has shown a steady increase in 2020, indicating potential for future growth. The 'Real Estate' sector, however, after reaching a peak in 2018 has been consistently dropping, making it a riskier choice."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "2eRO1ef42q86D3fG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total amount invested in both sectors in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total amount invested in 2020, I need to sum up the amounts from 'Investment' and 'Real Estate' categories for the year 2020 as shown on the pie chart.\nBased on the above thought, the answer is:\nThe total investment is 115 (Investment) + 80 (Real Estate) = 195."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "bDzZ0JvvG7hBe5fW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percent increase or decrease in the real estate sector from the year 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, we have to subtract the value of the Real Estate sector in 2019 from 2020 and divide it by the 2019 value, and then multiply by 100 to convert to percentage.\nBased on the above thought, the answer is:\nThe percent change is (80 - 70)/70 x 100 = 14.29%. Therefore, there is a 14.29% increase in the real estate sector from 2019 to 2020."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "oEcKK1AxkIzJEtj5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average investment in the \"Investment\" sector for the four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average investment amount, I need to add up the investment amounts for each of the four years and divide by the total number of years.\nBased on the above thought, the answer is:\nThe average investment is (100+130+96+115)/4 = 110.25."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "yR4Hfciw4RXwAKDZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the revenue data from the chart into two tables, one for online revenue and one for offline revenue each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend in the right corner of the chart {legend}[0.908, 0.063, 0.993, 0.338], we can see that each year is represented by a different pie slice. The size of the pie slice shows the share of the revenue for that year. Each slice is labeled with the corresponding year and its proportions. I will refer to these slices to create the tables.\nBased on the above thought, the answer is:\nOnline Revenue\nYear, Amount($)\n2000, 45000\n2001, 50000\n2002, 60000\n2003, 70000\n2004, 80000\n2005, 90000\n2006, 100000\n2007, 110000\n2008, 120000\n\nOffline Revenue\nYear, Amount($)\n2000, 25000\n2001, 30000\n2002, 32000\n2003, 33000\n2004, 34000\n2005, 35000\n2006, 36000\n2007, 37000\n2008, 38000"
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "bQJF6PTuHsDzeXKi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, did online or offline business grow faster? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the annual increase of the slices in the pie chart, it's clear that the online business grew faster. I can infer this from the larger increments in the slices representing online revenue compared to those of offline revenue each year.\nBased on the above thought, the answer is:\nThe online business grew faster. This inference is based on the larger increments in the slices of the pie chart representing online revenue compared to those of offline revenue each year."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "rmVJJ8JfiQFVRGuX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might cause the drastic explosion of online revenue from 2005 to 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the chart doesn't provide explicit reasons for the sudden increase in online revenue from 2005 to 2006, such changes usually reflect market trends or company efforts.\nBased on the above thought, the answer is:\nA variety of factors could have contributed to the drastic increase in online revenue from 2005 to 2006, such as the expansion of the company's online presence, an increase in e-commerce popularity, or market trends favoring online shopping."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "biIOFf1Guta44Lc2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the business strategize their upcoming efforts based on the information in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the chart shows a consistent increase in online revenue through the years, the business might be encouraged to focus efforts on improving its online presence and strategies.\nBased on the above thought, the answer is:\nBased on the consistent growth of online revenue over the years, the business could consider doubling down on its efforts to expand its e-commerce operations, improve its online user experience, and invest in online marketing."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "QESaHALgSyBYxcVx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has experienced the highest percentage of online revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to review all slices in the pie chart and identify the one with the largest area\u2014which corresponds to the year with the highest percentage of online revenue.\nBased on the above thought, the answer is:\n2008"
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "oWxTcAy0CdnH9CkX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the revenue in 2005 was online revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the pie slice that represents the year 2005 and read out its labeled percentage.\nBased on the above thought, the answer is:\n12.4%"
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "kKOoLsZiYtK5Tzey",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the online revenue in 2006 increased by 5%, what would be the new total percentage of online revenue in that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 5% to the 2006 online revenue percentage and calculate the new total.\nBased on the above thought, the answer is:\nThe new percentage of online revenue in 2006 would be 13.8% (original) + 5% = 18.8%."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "LX2by9w8Rhsq7zq7",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on information from the pie chart, organized this information into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart title {\"Rural and Urban Development Comparison from 2012 to 2020\"}<r>[0.223, 0.021, 0.74, 0.047]</r>, we can see that development is divided year by year from 2012 to 2020 between {urban}<r>[0.223, 0.146, 0.741, 0.886]</r> and {rural}<r>[0.223, 0.146, 0.741, 0.886]</r> sectors. Each year is represented by a slice in the chart and the proportion of each slice represents the percentage of total development attributed to each sector.\nBased on the above thought, the answer is:\nYear,Urban,Rural\n2012,35,65\n2013,37,63\n2014,39,61\n2015,40,60\n2016,41,59\n2017,42,58\n2018,45,55\n2019,44,56\n2020,50,50"
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "6FOi9FnHdfkefFSf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the pie chart, can you identify the trend of rural and urban development comparison within the period from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the percentage attributed to Rural and Urban areas over the years from 2012 to 2020, I can identify the trend.\nBased on the above thought, the answer is:\nThe trend shows an increasing move towards Urban development and a corresponding decline in Rural development. Starting from a larger percentage of Rural development in 2012, there is a steady shift towards parity in 2020, indicating an overall increase in urbanization over these years."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "767Jb0y60HiVlrK9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what could be the potential implications of prior years' trends on future development allocations between rural and urban areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend from the previous years can provide important insights into possible future allocations.\nBased on the above thought, the answer is:\nThe growing trend towards urban development suggests an increasing importance accorded to urban areas. If the current pattern continues, we might expect to see more development allocations channeled towards urban areas in the future, possibly at the expense of rural areas."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "PBqcR0uL4IPPI6NZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how would you expect the figures of Urban vs. Rural development to look like in the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the trend from the past years, I am attempting to extrapolate future figures.\nBased on the above thought, the answer is:\nGiven the recent growth in urban areas and stagnation or decline in rural areas, it would be reasonable to hypothesize that by 2025, there might be a noticeable tilt towards Urban development. If the current trend of urban-rural parity continues, we might witness a scenario where urban development accounts for more than half of the development, potentially surpassing rural development, in 2025."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "HoIGJ24nl8An2sQI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which year did Rural and Urban areas receive the same proportion of total development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportion of each slice in the chart, I can find out in which year Rural and Urban areas have the same proportion.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "CJsCkWLgEMgmKghP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Urban area receive the highest percentage increase in total development compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest increase for Urban area, I need to calculate the difference between the percentages for each year and identify the year with the largest difference.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "v5NYRudnKXOfFCuX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage change of rural areas from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall percentage change for Rural areas, I need to subtract the percentage in 2020 from the percentage in 2012.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "x3mBUnQXyd95YvEx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart you observed, could you please extract the necessary data and convert it into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will recognize the pie chart and its respective slices. Relative to the center of the chart at {center coordinates}[0.482, 0.516], I identify six slices representing various years - {2015}[0.223, 0.278, 0.482, 0.595], {2016}[0.229, 0.516, 0.482, 0.857], {2017}[0.382, 0.516, 0.63, 0.886], {2018}[0.482, 0.467, 0.741, 0.82], {2019}[0.482, 0.162, 0.738, 0.516], and {2020}[0.283, 0.146, 0.557, 0.516]. Each slice is associated with a corresponding year and its population. By converting the percentage of each slice into a numeric value, we can create a tabular representation.\nBased on the above thought, the answer is:\nYear,Population\n2015,14.5%\n2016,15.3%\n2017,16.0%\n2018,17.4%\n2019,18.2%\n2020,18.6%"
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "fNokEYswPR6uMD4Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you see in population growth from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, the size of each slice represents the population, and the successive pie slices for each year increase in size, suggesting a trend of steady population growth over these years.\nBased on the above thought, the answer is:\nThe pie chart illustrates a steady increase in population from 2015 to 2020, indicating a positive trend in population growth."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "n7oNyuYT13jpare6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years had the most significant increase in population growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The years that saw the most significant growth can be identified by the largest increases in the sizes of consecutive sections of the pie chart.\nBased on the above thought, the answer is:\nLooking at the pie chart, we can see that 2019 to 2020 saw a significant increase in population growth."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "e70Zdqe44aUieX0Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the pie chart indicate any potential issues related to the population growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slices give insight into the yearly population growth. Providentially, the steady growth represented suggests an upward trend, but an unmanageable population boom could be problematic.\nBased on the above thought, the answer is:\nThe steady increase in population could potentially lead to challenges such as overcrowding or resource scarcity if not properly managed. Although the pie chart itself doesn't specifically indicate any issues, the continuous growth trend could potentially raise concerns if the growth rate isn't sustainable or manageable."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "R0gDKgGzCmRnHRcT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total population growth rate from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to sum up the percentages of every single year because in a pie chart, each slice represents a percentage of the total.\nBased on the above thought, the answer is:\nThe growth rate is 100%."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "cQu3BnNW5GVQZVje",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year experienced the most growth in population?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the largest slice of the pie chart experienced the most growth in population.\nBased on the above thought, the answer is:\nThe year 2020 had the most growth in population."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "835FU1xNOZ4jqSXA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the growth rate from 2019 to 2020 continued, what would be the population percentage in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this I need to find the difference between the population percentages of 2020 and 2019 and then add this growth rate to the 2020 population percentage.\nBased on the above thought, the answer is:\nThe growth rate between 2019 and 2020 is 18.6% - 18.2% = 0.4%. So, the population percentage in 2021 would be 18.6% + 0.4% = 19.0%."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "anXiYlJvYJpS0uyc",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the scientific field labels and investment percentage labels in the pie, {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, and the legend {Legend}<r>[0.801, 0.063, 0.993, 0.309]</r>, I can derive the segment names and corresponding percentages.\nBased on the above thought, the answer is:\nResearch Fields,Investment(%)\nGenetics,10.3%\nEcology,5.7%\nPhysics,13.5%\nAI,16.6%\nAstronomy,5.9%\nBiotechnology,15.8%\nNeuroscience,14.3%\nQuantum Mechanics,17.8%"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "g0jDZM3Vwddwi1o1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which scientific field is given the most emphasis in terms of investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "The field with the largest segment in {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r> receives the most investment, indicating it has the most emphasis.\nBased on the above thought, the answer is:\nThe field of Quantum Mechanics receives the most emphasis as it attracts the highest investment of 17.8%."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "8x7bYI4m7ILaCxtu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the focus of technological advancement based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the size of each pie segment in {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, we can infer about fields that are more advanced as they attract more investment.\nBased on the above thought, the answer is:\nThe fields of Quantum Mechanics, AI, and Biotechnology seem to be the main focus areas of technological advancement as these fields receive the highest investments."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "ZomLZ7gDDEUIIByh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn view of the current investment distribution, how might future investment plans be affected?"
+            },
+            {
+                "from": "gpt",
+                "value": "Finding the fields with the highest and lowest investments from {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r> can provide insights into areas with good performance (that might receive sustained or increased investment) and areas needing improvement (that might receive increased investment to boost the sector).\nBased on the above thought, the answer is:\nFuture investments might continue to focus on Quantum Mechanics, AI, and Biotechnology as they currently receive the most funding. However, areas such as Ecology and Astronomy, which currently receive the least investment, could potentially receive increased funding in an effort to boost advancements in these fields."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "G79WQhziDuJEHnft",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the funding bodies decided to invest equally across all fields next year, what would be the percentage of investment for each field?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the equal distribution of investment, I need to divide 100% by the number of fields in {the legend}<r>[0.801, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\n12.5%"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "RyLRVJQJF9i6sAmR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich scientific field receives the least amount of investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size of each pie segment in {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can identify the smallest segment to find the field with the least investment.\nBased on the above thought, the answer is:\nEcology"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "2Dv3lHTDT18KwWcC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in investment percentage between the highest and least invested fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the largest and smallest pie segments in {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can calculate the difference in investment percentages.\nBased on the above thought, the answer is:\n12.1%"
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "LaqgGFUJ26F2Df9f",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you please export the populations of different continents into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart slices and the information on the legend including {\"Asia\"}<r>[0.885, 0.071, 0.914, 0.091]</r>, {\"Africa\"}<r>[0.885, 0.101, 0.925, 0.121]</r>, {\"Europe\"}<r>[0.885, 0.131, 0.934, 0.151]</r>, {\"North America\"}<r>[0.885, 0.161, 0.986, 0.181]</r>, {\"South America\"}<r>[0.885, 0.191, 0.987, 0.211]</r>, and {\"Australia\"}<r>[0.885, 0.221, 0.947, 0.241]</r>, I can estimate each continent's proportion of the world population.\nBased on the above thought, the answer is:\nContinent,Population\nAsia,4545133000\nAfrica,1340598000\nEurope,747636026\nNorth America,368869647\nSouth America,430759766\nAustralia,24867000"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "kJmicsypjGPV7wP6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich continent has the smallest population, and why does it differ from the others?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the smallest slice in the pie chart which corresponds to {\"Australia\"}<r>[0.283, 0.272, 0.482, 0.516]</r>, I can determine that Australia has the smallest population. The reason for the difference should be analyzed from non-visual data such as geographical, social, and economic factors.\nBased on the above thought, the answer is:\nAustralia has the smallest population. This difference could be due to factors such as geographic isolation, smaller land area, and specific immigration policies."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "gnOJx6svJJnKawqk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about Asia's population based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the {\"Asia\"}<r>[0.223, 0.278, 0.741, 0.886]</r> slice of the pie chart, which is the largest among all slices, I could infer related information about the population of Asia.\nBased on the above thought, the answer is:\nAsia has the largest population compared to all other continents, comprising more than half (60.9%) of the global population. This suggests that Asia is densely populated."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "h4GbjXQdyGIPCqKX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this population distribution affect resource allocation globally?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I need to consider how the sizes of the pie slices might influence the distribution of global resources.\nBased on the above thought, the answer is:\nConsidering the population distribution, resource allocation would likely be skewed towards continents with larger populations, such as Asia and Africa. These continents may require more resources to meet the needs of their populous societies. Conversely, continents like Australia might claim less resources due to their relatively smaller populations."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "iBGvuVoJph0MMPZ9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what percentage of the world population does Australia represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I need to check the slice for {\"Australia\"}<r>[0.283, 0.272, 0.482, 0.516]</r> in the chart. Using the chart key, I can see that this represents 0.3% of the world population.\nBased on the above thought, the answer is:\n0.3%"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "dhCLQ0Pd4dx6eWgI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage share of population for the continents Asia and Africa combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages represented by the slices for {\"Asia\"}<r>[0.223, 0.278, 0.741, 0.886]</r> and {\"Africa\"}<r>[0.482, 0.183, 0.741, 0.52]</r>, which are 60.9% and 18.0% respectively.\nBased on the above thought, the answer is:\n78.9%"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "Tt6kmpoluMYW8eVX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more of the world's population lives in Asia than in Europe?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the percentage represented by the {\"Europe\"}<r>[0.436, 0.146, 0.595, 0.516]</r> slice from the percentage represented by the {\"Asia\"}<r>[0.223, 0.278, 0.741, 0.886]</r> slice, which gives me the difference between their populations.\nBased on the above thought, the answer is:\n50.9%"
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "LYWe4xoS5wcfdScZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReflecting on the pie chart data, could you convert this information into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart contains slices that represent data quantity for years from 2014 to 2020 {\"2014\"}<r>[0.952, 0.071, 0.987, 0.091]</r>; {\"2015\"}<r>[0.952, 0.101, 0.987, 0.121]</r>; {\"2016\"}<r>[0.952, 0.131, 0.987, 0.151]</r>; {\"2017\"}<r>[0.952, 0.161, 0.987, 0.181]</r>; {\"2018\"}<r>[0.952, 0.191, 0.987, 0.211]</r>; {\"2019\"}<r>[0.952, 0.221, 0.987, 0.241]</r>; {\"2020\"}<r>[0.952, 0.251, 0.987, 0.271]</r>. Each slice percentage can be noted from the details mentioned within the pie slices' labels.\nBased on the above thought, the answer is:\nYear, Percentage, Data Quantity\n2014, 12.1%, 105\n2015, 13.8%, 120\n2016, 14.4%, 125\n2017, 16.1%, 140\n2018, 17.2%, 150\n2019, 14.9%, 130\n2020, 11.5%, 100"
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "1Zv2B4UwcLxdWGiN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the least data according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to locate the smallest slice of the pie which represents the year that had the least amount of data.\nBased on the above thought, the answer is:\nThe year 2014 saw the least data as represented by the smallest slice on the pie chart."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "Gbvmu8Mn6tFHjjCv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, can you chart the increase in data quantity over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the size and the order of the slices on the pie chart along with the labels provided in the legend which correlate to specific years.\nBased on the above thought, the answer is:\nYes, the pie chart shows a general trend of increasing data quantity from 2014 to 2018, peaking in 2018, followed by a decrease in 2019 and again in 2020."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "LR6WKYxpQbCurt3G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you identify patterns or trends in data quantity over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the pie slices and their sizes to identify patterns or trends, also considering the sequence of years.\nBased on the above thought, the answer is:\nFrom the pie chart, an upward trend of data quantity can be observed from 2014 through to 2018, peaking at 2018. However, there was a slight decrease in 2019, and the data quantity trend remained steady into 2020."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "W0RKNcS5R7bJ3VdU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the data quantity increase from 2014 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the increase in data quantity, we compare the data quantity of 2018 with that of 2014.\nBased on the above thought, the answer is:\nThe data quantity increased by 45 units from 2014 to 2018."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "xzpB808tgfACprn0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the largest yearly data quantity during 2014 through 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify this, we have to find the maximum data quantity from the considered years.\nBased on the above thought, the answer is:\nThe largest yearly data quantity was 150 in 2018."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "kHsIp9zYWktxisQi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the data quantity decrease from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this decrement, we subtract the data quantity of 2020 from that of 2018.\nBased on the above thought, the answer is:\nThe data quantity decreased by 50 units from 2018 to 2020."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "8HHhri2v6P8WLLVJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the information from the pie chart into structured tables for 2014 and 2015 respectively."
+            },
+            {
+                "from": "gpt",
+                "value": "Each pie chart {Living Environment Factors - 2014}[0.138, 0.109, 0.28, 0.127], {Living Environment Factors - 2015}[0.635, 0.109, 0.777, 0.127] represents living environment data for each year. By marking out the percentages indicated by the labels {\"Freshwater availability\"}[0.133, 0.611, 0.154, 0.625], {\"Rainfall\"}[0.279, 0.422, 0.301, 0.436], {\"Protein availability\"}[0.152, 0.342, 0.174, 0.356] for each respective slice in 2014, and {\"Freshwater availability\"}[0.629, 0.609, 0.651, 0.623], {\"Rainfall\"}[0.777, 0.425, 0.799, 0.439], {\"Protein availability\"}[0.649, 0.342, 0.671, 0.356] for each respective slice in 2015, I can create the structured tables.\nBased on the above thought, the answer is:\nYear,Freshwater availability,Rainfall,Protein availability\n2014,45.6%,44.1%,10.3%\n2015,45.1%,44.5%,10.4%"
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "cOjLAuBIfk5dCEh3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, which factor had the biggest share in both years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the factor with the biggest share in both years, I need to compare the percentages of each factor in 2014 and 2015.\nBased on the above thought, the answer is:\nFreshwater availability had the biggest share in both years, with 45.6% in 2014 and 45.1% in 2015."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "gtr6RpWZvfQxNBCx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you conclude about the change in living environment factors from 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw a conclusion, I need to compare the factors between 2014 and 2015 and analyze the change in percentage of each factor.\nBased on the above thought, the answer is:\nAccording to the pie charts, all three living environment factors saw minor changes from 2014 to 2015. Freshwater availability decreased slightly, rainfall availability slightly increased, and protein availability slightly increased as well."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "GM04dZKwH1s2JGhs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the implications of the observed changes in these environmental factors between 2014 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications of the observed changes can be deduced by assessing the impacts of the changes in each factor on the living environment.\nBased on the above thought, the answer is:\nThe slight decrease in freshwater availability could have minor impacts on water resource management. The increment in rainfall could mean more water supply, hence compensating for the decrease in freshwater availability. The slight increase in protein availability could potentially signify an improvement in food security."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "KYMunbajC8JKyAdA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie charts, what is the difference in freshwater availability between 2014 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in freshwater availability, I need to subtract the percentage of freshwater availability in 2014 {\"45.6%\"}[0.01, 0.71, 0.089, 0.724] from that in 2015 {\"45.1%\"}[0.506, 0.706, 0.584, 0.72].\nBased on the above thought, the answer is:\nThe difference in freshwater availability between 2014 and 2015 is 0.5%."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "st35YwhpyW0V1KAZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factor increased the most from the year 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentages for each factor in the years 2014 and 2015, then the factor with the greatest increase can be found.\nBased on the above thought, the answer is:\nThe factor with the highest increase from 2014 to 2015 is protein availability."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "B7EM5X92g1qub0mu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase of rainfall and protein availability from 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage increase, I need to add the increase in percentage of rainfall and protein availability from 2014 to 2015.\nBased on the above thought, the answer is:\nThe total percentage increase of rainfall and protein availability from 2014 to 2015 is 0.4%."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "6m0hJy90IefLKN6J",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart into a table detailing each year's share of the government policy effectiveness as percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create the table, I'll need to consider each slice of the pie chart {slices of the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> as a separate row. Each slice represents the respective year's proportion of the total effectiveness of the government policies. I'll also refer to the legend {legend}<r>[0.908, 0.063, 0.993, 0.219]</r> to identify the years.\nBased on the above thought, the answer is:\nYear,Percentage\n2010,18.0%\n2011,18.3%\n2012,19.1%\n2013,21.1%\n2014,23.5%"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "2Fttv2FGw1fLBOT4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you explain the trend of government policy effectiveness from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend, I need to compare the percentage values of each year in detail.\nBased on the above thought, the answer is:\nThe share of government policy effectiveness had a steady increase from 2010 to 2014. This suggests that both policies, 'Agriculture Improvement' and 'Urban Development', were increasingly effective over this period."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "zKsHuYwwGPLxwhGx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which year had the least effectiveness share and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the least effectiveness share could be identified by looking at which segment of the pie chart is the smallest.\nBased on the above thought, the answer is:\nThe year 2010 had the smallest share of 18% in effectiveness. It's likely that the policies were newly implemented or not as successful in their early years."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "ECZCsalTSzgvyJ5m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the info in this pie chart, what might you infer about potential focus of these policies in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferences about future policy focus need to take into account the trends observed in previous years. A continuous increase suggests positive effectiveness.\nBased on the above thought, the answer is:\nGiven the steady increase in effectiveness from 2010 to 2014, it can be inferred that the focus in 2015 would have been to sustain the effectiveness. The \u2018Urban Development\u2019 policy, with a higher score, might have received more attention."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "7eDaYJSwVnDU4J0Z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the total percentage of government policy effectiveness from 2010 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to sum up the effectiveness percentages labeled '2010', '2011' and '2012' given in the pie chart.\nBased on the above thought, the answer is:\n55.4%"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "WyXLmxci7dI9N8WZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the share of effectiveness increase from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase, I need to subtract the percentage in 2010 from the percentage in 2014.\nBased on the above thought, the answer is:\n5.5%"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "qov0QndzLtmnd94A",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the share of effectiveness for 2013 was increased by 5%, what would be the total percentage for all the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to add the increase to the 2013 percentage and then sum up all the percentages.\nBased on the above thought, the answer is:\n103.9%"
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "HQMzTMIgdkdpokzH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart that you're observing, can you please transform the information into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart labelled {\"Analysis of Financial Management and Risk Control Aspects\"}[0.234, 0.021, 0.729, 0.047], I can see the distribution corresponding to each aspect of Financial Management and Risk Control. The corresponding sides for {\"Risk Factor\"}[0.135, 0.378, 0.211, 0.398], {\"Portfolio Management\"}[0.06, 0.642, 0.213, 0.662], {\"Risk Metrics\"}[0.227, 0.831, 0.31, 0.851], {\"Investment\"}[0.509, 0.911, 0.587, 0.931], {\"Insurance\"}[0.728, 0.711, 0.796, 0.731], {\"Liability\"}[0.755, 0.392, 0.81, 0.412], {\"Credit Risk\"}[0.65, 0.178, 0.726, 0.198], {\"Deposit\"}[0.493, 0.099, 0.546, 0.119], and {\"Liquidity Risk\"}[0.242, 0.157, 0.335, 0.177] make up the whole pie chart indicating their respective numerical representation.\nBased on the above thought, the answer is:\nRisk Factor, Portfolio Management, Risk Metrics, Investment, Insurance, Liability, Credit Risk, Deposit, Liquidity Risk\n12.1, 8.9, 9.7, 13.9, 16.2, 9.7, 11.2, 7.8, 10.6"
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "Mc3TfYDnbGUucvMx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data represented in the chart, which aspect of financial management and risk control is prioritized the most and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the aspect that consumes the highest proportion of the pie chart. This measures the priority level.\nBased on the above thought, the answer is:\nThe aspect of \"Insurance\" is prioritized the most as it occupies the largest proportion of 16.2% in the pie chart."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "s5sFIXgqN6cOG4BW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, how do you perceive the state of emphasis on different aspects of financial management and risk control?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comprehend the distribution of focus, I should inspect the size of each aspect in the pie chart.\nBased on the above thought, the answer is:\nThe chart represents that \"Insurance\", \"Investment\", and \"Risk Factor\" have a notable emphasis in the aspects of financial management and risk control. However, \"Deposit\" and \"Portfolio Management\" seem to have a lesser focus."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "DNfRrp5AMdZYT3o4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were making decisions for the next year based on this chart, how would you adjust the current strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to form a decision, I need to analyze the current concentrations and make decisions based on those distributions.\nBased on the above thought, the answer is:\nObserving the current distribution, I would potentially suggest an increase in focus on the \"Deposit\" and \"Portfolio Management\" aspects, as they are the areas with the least concentration. This could help to balance the focus across all aspects of financial management and risk control more evenly."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "flGST5aMBVPV8xOe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are top 3 aspects with the highest percentages in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the top 3 aspects with the highest percentages, I need to compare the percentage values of each aspect.\nBased on the above thought, the answer is:\nInsurance, Investment, and Risk Factor."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "ieiRMlaKPxxVJhiK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage does the aspect \"Deposit\" hold in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the numerical percentage that Deposit holds, I need to look at the particular slice in the pie chart which corresponds to Deposit.\nBased on the above thought, the answer is:\n7.8%"
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "c03RZZGHO2s2z9zh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the \"Investment\" category had a 5% increase in its proportion, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new proportion, I need to add 5% to the present \"Investment\" category percentage.\nBased on the above thought, the answer is:\nThe new percentage for Investment is 13.9% + 5% = 18.9%."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "HpvYSJNUcxyUYZ2I",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the data displayed on the pie chart be converted into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "As a visual AI assistant, I am capable of extracting data from given graphics and converting them into structured formats. For this pie chart whose range is anchored within the coordinates {plot}<r>[0.223, 0.146, 0.741, 0.886]</r>, it displays data about different types of tourists and the accompanying quantity of each category in form of percentages. Considering that the legend is located at region {legend position}<r>[0.814, 0.063, 0.993, 0.368]</r>, I can determine each category and its corresponding percentage on the pie and make the conversion to a structured table.\nBased on the above thought, the answer is:\nType,Percentage\nFashion Tourists,15%\nFood Tourists,18%\nCultural Tourists,12%\nAdventure Tourists,10%\nBeach Tourists,16%\nUrban Tourists,22%\nNature Tourists,11%\nRural Tourists,8%\nLuxury Tourists,15%\nBudget Tourists,23%"
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "HLglDL2LT2aP2uHw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which type of tourism is the most popular? Please provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "From the data in the pie chart, we can recognize the most popular tourism type by identifying the category with the highest percentage.\nBased on the above thought, the answer is:\n\"Budget Tourists\" is the most popular type of tourism based on the pie chart as it accounts for the highest percentage of 23% among all tourists' preferences."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "mCc3ooaleGsHnp62",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat significant insight regarding tourists' preferences can you infer from this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpreting the pie chart, I need to take note of the differences in percentages to see the tourists' preferences.\nBased on the above thought, the answer is:\nThe pie chart indicates that most tourists prefer Urban and Budget tourism, suggesting that these types of tourism provide the most attractive experiences or cater to a large section of tourists who are budget conscious or prefer city experiences."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "l1QHp3bfryJRaXwe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, identify an area which you believe may need more attention to boost its appeal and why."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to examine the pie chart and pick a category that has lower popularity, indicating it could benefit from more attention.\nBased on the above thought, the answer is:\nRural tourism, with only 8% popularity, appears to be the least favored tourism type among tourists. As such, this area requires more attention to address potential issues and improve its appeal to tourists."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "9ZzsUrhGNF6fpKCd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the number of categories that have their popularity above 15%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to analyze all the categories in the chart and find the number of them which have popularity above 15%.\nBased on the above thought, the answer is:\n4"
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "kU0rBeguNjFeaipy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category emerged as the least popular in terms of tourist preference?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data, I can find that the category with the smallest percentage would be the least popular among tourists.\nBased on the above thought, the answer is:\n\"Rural Tourists\""
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "SPutY1kieZma01DL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the \"Nature Tourists\" category saw a 3% decline and the \"Food Tourists\" category witnessed a 5% increase, how would the total percentage be affected?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to calculate the current percentages, subtract the decline from the 'Nature Tourists' category, and add the increase to the 'Food Tourists' category.\nBased on the above thought, the answer is:\nThe total would remain 100% as the decline and increase balance each other out."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "dvk4cYBNDh6G6j9h",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the extracted raw data from the pie chart regarding the fashion trend popularity among the Gothic and the Hipster subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart represents two slices, one for each subculture: {Gothic}<r>[0.223, 0.278, 0.482, 0.712]</r> and {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>. The percentage on each slice represents the fashion trend popularity for each subculture. Extracting this data would provide us with the proportions of each fashion trend.\nBased on the above thought, the answer is:\nSubculture,Fashion Trends\nGothic,20\nHipster,80"
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "YSHxVu6IrZw8Vvkp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn this chart, which subculture's fashion trends are more popular? What does this tell us about the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of each slice, {Gothic}<r>[0.223, 0.278, 0.482, 0.712]</r> and {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>, I can determine which subculture's fashion trends are more popular.\nBased on the above thought, the answer is:\nFashion trends associated with the Hipster subculture are more popular, making up 80% of the fashion trends among these two subcultures. This tells us that the Hipster fashion trend was highly influential in the data set."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "enG7NHeMfnuz7123",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might this data suggest about the influence or relevance of the Gothic and Hipster subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the sizes of each subculture's slice {Gothic}<r>[0.223, 0.278, 0.482, 0.712]</r> and {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>, I can draw conclusions about their influence or relevance.\nBased on the above thought, the answer is:\nThe data suggests that the Hipster subculture has a larger influence or relevance than the Gothic subculture, as its fashion trends make up the majority (80%) of the total fashion trends among these two subcultures."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "bVZiDGIGSB3ggpUS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data presented in this chart influence fashion marketing strategies for targeting these particular subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing the popularity of each subculture's fashion trends {Gothic}<r>[0.223, 0.278, 0.482, 0.712]</r> and {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>, inferences can be made about potential marketing strategies.\nBased on the above thought, the answer is:\nMarketing strategies might prioritize the Hipster subculture as it represents 80% of the fashion trends between these two subcultures, suggesting it has a larger potential customer base. However, strategies shouldn't ignore the Gothic subculture as it still has a significant portion (20%) of the fashion trends, indicating there is a market segment interested in these styles."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "Xrexl6JPt5g3h5yJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the Hipster subculture's fashion trends represent in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the percentage attached to the Hipster slice {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n80%."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "iPHiDbYaDo02Mf0J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the fashion trend popularity of the Hipster subculture more than the Gothic subculture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I need to subtract the percentage of the Gothic subculture's fashion trends {Gothic}<r>[0.223, 0.278, 0.482, 0.712]</r> from that of the Hipster subculture's fashion trends {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n60%."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "RUYJMmHKTAp0ZgCO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity of the Hipster fashion trend decreased by 10%, what would its new percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract 10% from the Hipster subculture's current percentage {Hipster}<r>[0.262, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n70%."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "mNfsWv0WmwvBqrGt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data, could you generate a CSV list of the percentages of the migrated population for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I look at the pie chart, it has the legend {the color-coded legend}<r>[0.908, 0.063, 0.993, 0.279]</r>, which provides the years color coded as per the pie slices. Then looking at the individual pie slice percentages labelled as {autotexts}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can list out each year's percentage of migrated population.\nBased on the above thought, the answer is:\nYear,Migrated Population Percentage\n2010,11.1%\n2011,13.7%\n2012,15.9%\n2013,12.3%\n2014,14.7%\n2015,17.1%\n2016,15.2%"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "7nmmaDxm1S4jWcnK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year was the migration the highest? give reasons for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the highest migration can be determined by looking at the pie slice with the highest percentage in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe migration was highest in the year 2015 as its pie slice had the highest percentage of 17.1% among all years."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "lrH7IRM40WCuWw6b",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the migration trends, as depicted in the pie chart, affect population dynamics?"
+            },
+            {
+                "from": "gpt",
+                "value": "The change in migration trends as shown in {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> over the years can lead to population growth or decline.\nBased on the above thought, the answer is:\nThe rise in the percentages of migrated population from 2010 to 2015 implies a sizeable incoming population which might boost population growth and lead to increased demand for housing, jobs, and other resources. The decrease in 2016, though minimal, might indicate a relative population decline."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "8rgwAE6rWiIbVrUK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you are a policy maker, how would this chart influence your decision making about resource management?"
+            },
+            {
+                "from": "gpt",
+                "value": "As a policy maker, this pie chart helps me see the fluctuating patterns of migration. Particularly, the increase from 2012 to 2015 is visible which are represented by the slices<r>[0.312, 0.516, 0.555, 0.867]</r>.\nBased on the above thought, the answer is:\nSeeing the upward trend in migration especially from 2012 to 2015 indicates an increased demand on resources due to the incoming population. This would encourage me to focus on ensuring adequate resources and services such as housing, employment, healthcare, and education for the growing population. However, the dip in 2016 may suggest a need to re-evaluate these allocations if the trend continues."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "GMZUIKsTqXA7ADK9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages of migrated population in 2011 and 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the percentages given in the pie chart for the years 2011 and 2013, found at the {autotext}<r>[0.223, 0.516, 0.482, 0.867]</r> and <r>[0.482, 0.516, 0.708, 0.871]</r> respectively, we can calculate the sum.\nBased on the above thought, the answer is:\n26%"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "kbgqRenxKFN3Lop3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentages between the years with maximum and minimum migrated population?"
+            },
+            {
+                "from": "gpt",
+                "value": "The maximum and minimum migrated population percentages are represented by the largest and smallest slices of the pie. To find the difference, I subtract the smaller percentage from the larger one, which is seen in {2015 and 2010 slice in the pie chart}<r>[0.482, 0.146, 0.719, 0.867]</r>.\nBased on the above thought, the answer is:\n6%"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "yXvZLeuSNbqyNEE2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much the migration trend increased from 2014 to 2015 in percentage terms?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage increase in migration from 2014 to 2015 can be calculated by subtracting the percentage of migrated population in 2014 from that in 2015, found at {2014 and 2015 slice in the pie chart}<r>[0.482, 0.367, 0.719, 0.867]</r>.\nBased on the above thought, the answer is:\n2.4%"
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "OxVFxjoPzUIiTXxq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart's representation into a CSV format for clearer analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "By extrapolating the percentages from each slice of the pie chart for both undergraduate and graduate levels in 2010, 2015, and 2020, a CSV table format can be derived. It's clearly indicated in the legend and slices.\nBased on the above thought, the answer is:\nYear,Undergraduate,Graduate\n2010,75%,25%\n2015,80%,20%\n2020,85%,15%"
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "AJOsTR5ZE6f9ROHH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trend can be observed in the field of education from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will have to observe the proportion change of the undergraduate and graduate students from 2010 to 2020 to make a statement about the trend.\nBased on the above thought, the answer is:\nThe chart shows an increasing trend in the percentage of undergraduate students coupled with a decreasing trend in the percentage of graduate students from 2010 to 2020. This suggests a shifting focus towards undergraduate studies during the decade."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "asgpudtDBzliTmRH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the potential implications of the observed trends for universities?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications of the trend could be inferred by understanding how the shift in percentages of students at different educational levels might impact the university's admission policies, course offerings, and funding allocation.\nBased on the above thought, the answer is:\nThe observed trend of an increase of undergraduates and decrease of graduates could imply that universities may need to allocate more resources to undergraduate programs and potentially reconsider the structure and offerings of their graduate programs to attract more students."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "ev9onUaVuwD0qMle",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could policy makers use this data to influence educational reforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the impact on policy decisions, I need to consider how the trends might influence policies related to funding allocation, admissions standards, and program offering in the education sector.\nBased on the above thought, the answer is:\nPolicy makers could use this data to advocate for more funding and resources for undergraduate programs as they seem to be increasing in popularity. Conversely, they might also want to investigate and address the factors behind the decreasing interest in graduate programs."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "U89ozZIpATu5HPtn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2010 to 2020, what is the total decrease in the percentage of graduate students?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total decrease in the percentage of graduate students, I need to subtract the percentage in 2020 from the percentage in 2010.\nBased on the above thought, the answer is:\nThe total decrease is 25% - 15% = 10%."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "PT0LA200lZiLI4nH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the percentage of undergraduate students from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in the percentage of undergraduate students, I need to subtract the percentage in 2010 from the percentage in 2020.\nBased on the above thought, the answer is:\nThe total increase in the percentage is 85% - 75% = 10%."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "3IOlA391c04LHm8r",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend continues, what will be the percentage of undergraduate students in 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To project the percentage in 2025, I need to continue the trend from 2010 to 2020. Doing a simple linear prediction might suffice though it may not be very accurate without more detailed data.\nBased on the above thought, the answer is:\nIf the trend from the last 10 years continues, the percentage of undergraduate students might increase by another 5% to reach 90% in 2025."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "2FmFlSWx0zBt88hP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {\"Social Inequality and Rights Protection 2020\"}[0.298, 0.021, 0.665, 0.047] and the slices of the pie in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, these represent different categories of societal groups and the percentages each received in rights protection campaigns.\nBased on the above thought, the answer is:\nRights Groups,Percentage   \nRights, 19.2%  \nRace, 3.3%  \nGender, 22%\nClass, 13.7%\nAge, 24.7%  \nMarital status, 10.2%  \nDisability, 1.4% \nSexual orientation, 5.5%"
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "atgh0sFeQ3n8kwTf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich societal group received the most attention in the rights protection campaigns in 2020 according to the data chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The societal group with the highest percentage would represent the one receiving the most attention.\nBased on the above thought, the answer is:\n'Age' group received the most attention in the rights protection campaigns in 2020 as it has the highest percentage of 24.7% among all."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "xqhNrMaJrGel6Jk5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the chart suggest any major societal inequality in rights protection in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to analyze the distribution of percentages among different societal groups to infer inequality.\nBased on the above thought, the answer is:\nAccording to the chart, societal inequality did exist in 2020\u2019s rights protection campaigns. Categories like 'Disability' and 'Race' received far less attention than 'Age', 'Gender', and 'Rights'. This indicates that these groups might have been underrepresented or overlooked in rights protection campaigns."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "jHrYeXA85vpvyu0T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of social justice, what could be changed in future campaigns based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the improvements that could be made, I would take into account the categories with lower attention percentages.\nBased on the above thought, the answer is:\nThe chart suggests that more attention needs to be given to 'Disability' and 'Race' groups in future campaigns, considering they only received 1.4% and 3.3% attention respectively in 2020. Their underrepresentation in rights protection campaigns could be a significant issue for social justice."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "RXnFhIy8ydYR1YQk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of attention for 'Age' and 'Gender' categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage for 'Age' and 'Gender', I need to add the individual percentages for both categories.\nBased on the above thought, the answer is:\nThe sum of the percentages is 24.7% ('Age') + 22% ('Gender') = 46.7%."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "cblIDXRhavMkjRIi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more attention did 'Rights' receive than 'Disability'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the percentage of 'Disability' from the percentage of 'Rights' category.\nBased on the above thought, the answer is:\nThe difference in the percentage is 19.2% ('Rights') - 1.4% ('Disability') = 17.8%."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "etb7WyS4c6WEIz7g",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the attention for 'Class' category was increased by 3%, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new percentage, I will add 3% to the original percentage of 'Class'.\nBased on the above thought, the answer is:\nThe new 'Class' percentage is 13.7% + 3% = 16.7%."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "XojF8ZJWmVvESiq5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, please transform the manufacturing production efficiency data into a csv format."
+            },
+            {
+                "from": "gpt",
+                "value": "I can extract this information by reading the labels and efficiency percentages from each of the pie slices in the {\"Manufacturing Production Efficiency Evaluation\"}[0.286, 0.021, 0.678, 0.047] chart. I see that {\"Raw Material Efficiency\"}<r>[0.223, 0.278, 0.482, 0.885]</r> is 35%, {\"Labor Efficiency\"}<r>[0.464, 0.516, 0.74, 0.886]</r> is 25%, {\"Time Efficiency\"}<r>[0.482, 0.173, 0.741, 0.542]</r> is 20%, {\"Energy Efficiency\"}<r>[0.344, 0.146, 0.579, 0.516]</r> is 15%, and {\"Capital Efficiency\"}<r>[0.283, 0.202, 0.482, 0.516]</r> is 5%.\nBased on the above thought, the answer is:\nAreas, Manufacturing\nRaw Material Efficiency, 35\nLabor Efficiency, 25\nTime Efficiency, 20\nEnergy Efficiency, 15\nCapital Efficiency, 5"
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "NvNLhvMKuLfvoyOQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which efficiency factor is the most significant in Manufacturing Production and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the size of the slices in the pie chart, I can see that the {\"Raw Material Efficiency\"}<r>[0.223, 0.278, 0.482, 0.885]</r> has the largest slice, signifying its greater weight in Manufacturing Production Efficiency.\nBased on the above thought, the answer is:\nThe most significant factor is \"Raw Material Efficiency\" because it holds the highest proportion (35%) among the five factors."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "ELQitPF5JwoJigQe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart present data about the balance of efficiencies in the manufacturing sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, I can see the proportions of five different efficiency areas which suggests the balance of efficiencies.\nBased on the above thought, the answer is:\nThe chart suggests that the manufacturing sector is highly reliant on raw material and labor efficiencies as they represent the majority of the pie (60% combined). However, Time, Energy, and Capital efficiencies represent smaller shares, suggesting a less balanced distribution of efficiencies in manufacturing."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "gSbJLBZ8MAzqwJgT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the trends in the manufacturing sector, what should be the prime focus to improve the overall efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the slices in the pie chart, I can suggest that the areas with smaller proportions have more room for improvement.\nBased on the above thought, the answer is:\nTo improve overall efficiency, the manufacturing sector should focus on increasing Capital Efficiency, as it presently represents the smallest share at 5%. By increasing efficiency in capital use, the manufacturing sector could significantly boost its overall efficiency."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "URepYzqRhY7oteqy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest efficiency percentage in the manufacturing sector according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest efficiency percentage, I need to examine the percentages given for each efficiency area. I can see that {\"Raw Material Efficiency\"}<r>[0.223, 0.278, 0.482, 0.885]</r> has the largest portion of the pie, indicating the highest efficiency percentage.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "V9xORU8LaiY6bSfm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total efficiency if the Time and Energy efficiencies are improved by 10% each?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total efficiency, I need to add 10% to each of the {\"Time Efficiency\"}<r>[0.482, 0.173, 0.741, 0.542]</r> and {\"Energy Efficiency\"}<r>[0.344, 0.146, 0.579, 0.516]</r> percentages and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new total efficiency is 35% (Raw Material) + 25% (Labour) + 30% (Time) + 25% (Energy) + 5% (Capital) = 120%."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "VKRiZ5lR083zgEjh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the proportions of Raw Material Efficiency and Capital Efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the lower percentage ({\"Capital Efficiency\"}<r>[0.283, 0.202, 0.482, 0.516]</r>) from the higher one ({\"Raw Material Efficiency\"}<r>[0.223, 0.278, 0.482, 0.885]</r>).\nBased on the above thought, the answer is:\nThe difference is 30%."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "Q1NjFhtmHRs5ioXl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart data about Investments in Medical Technology from 2016 to 2020 into a structured table format."
+            },
+            {
+                "from": "gpt",
+                "value": "Given the dataset represented by the pie chart slices {[\"2016\"][0.163, 0.464, 0.198, 0.484]}, {[\"2017\"][0.32, 0.871, 0.355, 0.891]}, {[\"2018\"][0.69, 0.784, 0.725, 0.804]}, {[\"2019\"][0.736, 0.321, 0.771, 0.341]}, and {[\"2020\"][0.4, 0.104, 0.435, 0.124]}, I can see that each slice represents the investment for a specific year and the size of each slice is indicative of the amount of investment.\nBased on the above thought, the answer is:\nYear,Investment Percentage\n2016,19.0%\n2017,19.7%\n2018,21.1%\n2019,17.8%\n2020,22.5%"
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "qMwshp3bKSGUcl8r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about the trend in investments in Medical Technology from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "As an AI visual assistant, I am deciphering the pie slices for {[\"2016\"][0.163, 0.464, 0.198, 0.484]}, {[\"2017\"][0.32, 0.871, 0.355, 0.891]}, {[\"2018\"][0.69, 0.784, 0.725, 0.804]}, {[\"2019\"][0.736, 0.321, 0.771, 0.341]}, and {[\"2020\"][0.4, 0.104, 0.435, 0.124]} to understand the trend in investments from 2016 to 2020.\nBased on the above thought, the answer is:\nGenerally, there is a positive trend in the investments in Medical Technology from 2016 to 2020. However, there was a decline in 2019, followed by a significant increase in 2020, which received the highest investment of the five years."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "mLNFaPBG4C4vhpwv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the funding priorities remained the same, which year would be most likely to receive the highest investment in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am looking at {[\"2020\"][0.4, 0.104, 0.435, 0.124]}, which has the largest slice of the pie chart, representing the highest investment percentage.\nBased on the above thought, the answer is:\nIf the funding priorities remained the same, it is likely that 2020 would continue to receive the highest investment in the future, given that it received the highest investment among the represented years."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "edMqD8BLGsnTgzL0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential actions could a company in the medical technology sector infer from the chart for its financial planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "The company's financial planning would need to take into account the investment trends, which are visible in {[\"2016\"][0.163, 0.464, 0.198, 0.484]}, {[\"2017\"][0.32, 0.871, 0.355, 0.891]}, {[\"2018\"][0.69, 0.784, 0.725, 0.804]}, {[\"2019\"][0.736, 0.321, 0.771, 0.341]}, and {[\"2020\"][0.4, 0.104, 0.435, 0.124]}.\nBased on the above thought, the answer is:\nThe company could foresee an increase in funding for medical technology, considering the positive trend from 2016 to 2020. Specifically, they may prepare for more significant investments in years following the trend seen in 2020. However, the slight dip in 2019 indicates that they should also be prepared for potential dips in funding and plan their finances accordingly."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "mudwDKTuxpzm8GOz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the investment in Medical Technology the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of investments represented by each slice {[\"2016\"][0.163, 0.464, 0.198, 0.484]}, {[\"2017\"][0.32, 0.871, 0.355, 0.891]}, {[\"2018\"][0.69, 0.784, 0.725, 0.804]}, {[\"2019\"][0.736, 0.321, 0.771, 0.341]}, and {[\"2020\"][0.4, 0.104, 0.435, 0.124]}. The year with the highest investment would be the one with the greatest percentage.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "H5rITi7v2dj7ewP9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total investment percentage over the five years from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total investment, I need to sum the percentages of each year's investment.\nBased on the above thought, the answer is:\nThe total investment percentage over the five years is 100%."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "CqclpFMEMdKrs26z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment for 2020 was increased by 5 percent, what would be the new total investment percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total investment percentage, I need to increase the investment of 2020 by 5 percent and add this to the rest of the investment percentages.\nBased on the above thought, the answer is:\nThe new total investment percentage would be 105%."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "Q1GxPCZnQXH5ZZr8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you analyze the pie chart and convert the sales data for 'Electronics' and 'Appliances' over the years into a structured CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm looking at the chart titled 'Product Sales Trend Observation from 2014 to 2021' {title of the chart}<r>[0.285, 0.021, 0.715, 0.047]</r>. The pie sectors are denoted by different colors and each color represents a particular product in a given year. There are two legends- 'Electronics' and 'Appliances', located at the bottom right of the pie chart {legend}<r>[0.691, 0.063, 0.817, 0.129]</r>, which will help determine the sales data for each product.\nBased on the above thought, the answer is:\nYear,Electronics,Appliances\n2014,870,680\n2015,920,730\n2016,1100,800\n2017,1050,950\n2018,950,1100\n2019,1120,1000\n2020,1010,920\n2021,950,850"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "9tVDEgLxFdb6e28x",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, which product had more sales in the year 2019 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sales data for 'Electronics' and 'Appliances' in the year 2019.\nBased on the above thought, the answer is:\n'Electronics' had more sales in the year 2019 because it sold 1120 units compared to 1000 units of 'Appliances'."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "l7fn1rZecpcdVo8R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart indicate about the sales trend of 'Electronics' over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the sales trend, I need to look at the yearly sales data of 'Electronics'.\nBased on the above thought, the answer is:\nThe chart indicates that 'Electronics' sales has been fluctuating over the years, reaching peaks in 2016 and 2019 and lows in 2014 and 2021."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "ZjfbSTa9imG2DQBO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can we say about the performance of 'Appliances' sales in the market from 2015 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to review the sales data of 'Appliances' from 2015 to 2018 to understand its performance.\nBased on the above thought, the answer is:\nThe 'Appliances' sales witnessed a positive growth trend from 2015 to 2018, reaching a peak of 1100 units in 2018. This suggests that the market for 'Appliances' improved during this period."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "yfr74Tfl1W7YXDUH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total sales of 'Electronics' and 'Appliances' combined in the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "For answering this question, I need to add the sales amount of 'Electronics' and 'Appliances' for the year 2016.\nBased on the above thought, the answer is:\n1900"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "wePc92dHuOVSPx1k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many units of 'Appliances' were sold from 2014 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the sales numbers of 'Appliances' for each year from 2014 to 2021.\nBased on the above thought, the answer is:\n6980"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "TwfErAInRQGvMdjU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the difference between 2020 and 2021 'Electronics' sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get this answer, I need to subtract 2021 'Electronics' sales from 2020 'Electronics' sales.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "xn96I1BuJkg24MWk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nMake a structured table from the observed pie chart data, representing each era and its corresponding civilization index."
+            },
+            {
+                "from": "gpt",
+                "value": "Given the title {\"Human History and Civilization Development\"}<r>[0.295, 0.021, 0.668, 0.047]</r> and the specific era's slice like {\"Stone Age\"}<r> [0.258, 0.278, 0.482, 0.516]</r>, {\"Bronze Age\"}<r>[0.224, 0.328, 0.482, 0.516]</r>, {\"Iron Age\"}<r>[0.223, 0.472, 0.482, 0.68]</r>, {\"Medieval Age\"}<r>[0.249, 0.516, 0.482, 0.871]</r>, {\"Renaissance\"}<r>[0.409, 0.516, 0.68, 0.886]</r>, {\"Industrial Revolution\"}<r>[0.482, 0.253, 0.741, 0.754]</r>, {\"Digital Age\"}<r>[0.283, 0.146, 0.664, 0.516]</r> it is visible the periods of civilization with their percentage distribution on the graph, which can be converted into civilization index.\nBased on the above thought, the answer is:\nEra,Civilization Index\nStone Age,10\nBronze Age,25\nIron Age,35\nMedieval Age,50\nRenaissance,70\nIndustrial Revolution,90\nDigital Age,100"
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "aHNfMuFhxY3vXRof",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, which era has the most significant leap in civilization index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at each era's slice, I need to work out which era has the largest proportion and thus the most significant increase.\nBased on the above thought, the answer is:\nThe Industrial Revolution shows the most significant leap in civilization index."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "7zsbURmWfQ9WzFpm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, discuss the correlation between passing time and the development of civilization."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart clearly demonstrates the increase in civilization index across different eras. I should describe how the index has generally increased over time.\nBased on the above thought, the answer is:\nAs time has progressed from the Stone Age to the Digital Age, there has been a consistent increase in the civilization index. This indicates a positive correlation between the advancement of time and civilization development."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "qRE0dW8KdZQOXyfv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be made about the pace of civilization progress from the Renaissance to the Digital Age according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportion of the Renaissance, Industrial Revolution, and Digital Age, I can see the relative pace of civilization progress.\nBased on the above thought, the answer is:\nThe pace of civilization progress vastly increased from the Renaissance to the Digital Age. The share of the pie for the Renaissance, Industrial Revolution, and Digital Age indicates rapid advancement during these periods compared to prior eras."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "xOvl4DqQTxwHtwUB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat civilization index does the Bronze Age hold as per the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Bronze Age's data can be derived directly from the respective slice in the pie chart by mapping the percentage displayed to the actual civilization index.\nBased on the above thought, the answer is:\nThe civilization index is 25."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "BtV8JdqyN9YGoEGA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much progression in civilization index can be observed from Stone Age to the Digital Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "The progression can be calculated by comparing the civilization index of the Stone Age and Digital Age.\nBased on the above thought, the answer is:\nThe civilization progress is 90."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "48EzAWAl5aFAwvhg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the median value for the civilization index according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The median civilization index would be the civilization index of the era that falls in the middle when listed in ascending order.\nBased on the above thought, the answer is:\nThe median value of civilization index is 50 (Medieval Age)."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "mSWp6P6IRelglhQh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table of the innovation availed in the year 2008 based on the distribution in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I see a representation of two innovation types - {Product Innovation}<r>[0.490, 0.724, 0.534, 0.744]</r> and {Service Innovation}<r>[0.429, 0.288, 0.473, 0.308]</r>. These types are represented in percentages of {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r> and {21.4%}<r>[0.296, 0.107, 0.425, 0.127]</r> respectively. I'll base my table on this.\nBased on the above thought, the answer is:\n\"Innovation Type\", \"Distribution in 2008 (%)\" \n\"Product Innovation\", 78.6%\n\"Service Innovation\", 21.4%"
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "zCm2Ydvv37oEeQHN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich innovation type dominated the year 2008 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The innovation type with the largest percentage share is the dominant one. Here, {Product Innovation}<r>[0.490, 0.724, 0.534, 0.744]</r> has the highest percentage, with {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r> of the pie chart.\nBased on the above thought, the answer is:\nProduct Innovation dominated the year 2008 according to the chart."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "nirpsI0pNpP5k5Dj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you interpret about the innovation areas based on the 2008 data presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart, I see a dominance of {Product Innovation}<r>[0.490, 0.724, 0.534, 0.744]</r> with  {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r> share contrasted by a lesser amount of {Service Innovation}<r>[0.429, 0.288, 0.473, 0.308]</r> at {21.4%}<r>[0.296, 0.107, 0.425, 0.127]</r>.\nBased on the above thought, the answer is:\nThe chart data indicates that in 2008, efforts and results in innovation were significantly geared towards product innovation over service innovation."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "1iY7XopsSIHCy6Ge",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence company decisions regarding investment in innovation fields in 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, these figures - {Product Innovation}<r>[0.490, 0.724, 0.534, 0.744]</r> and {Service Innovation}<r>[0.429, 0.288, 0.473, 0.308]</r> - indicate the distribution of innovations in Product Innovation (78.6%) and Service Innovation (21.4%). Using these as a basis, future investment decisions could be influenced.\nBased on the above thought, the answer is:\nThe data suggests that in 2008, investments into product innovation were highly successful, yielding a large portion of the total innovations for that year. As such, a company looking at this data in 2009 might decide to lean their investments towards product innovation in hopes of similarly high returns. On the other hand, they might also see the potential for growth in service innovation, currently underrepresented, and redistribute their investments to capitalize on unexplored opportunities in that sector."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "Xb1xrhQxa6T6WX1L",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what is the difference in the percentage distribution between Product Innovation and Service Innovation in 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage distributions for Product Innovation and Service Innovation are {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r> and {21.4%}<r>[0.296, 0.107, 0.425, 0.127]</r> respectively. To calculate the difference, I subtract the lower percentage from the higher one.\nBased on the above thought, the answer is:\nThe difference in percentage distribution between Product Innovation and Service Innovation in 2008 is 57.2%."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "xuBVC5Lfc6djdpiV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, if the total of innovations for the year 2008 was 258, how many of them were product innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage distribution for Product Innovation is {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r>. Therefore, to calculate the number of product innovations, I need to calculate 78.6% of 258.\nBased on the above thought, the answer is:\nThe number of product innovations in the year 2008 is 203."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "TlNU7Vvz6EdtHYqe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage for Service Innovation were to increase by 2%, how much would the percentage of Product Innovation decrease, assuming the total must add up to 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assuming that the total percentage must remain 100%, a 2% increase in the Service Innovation implies a 2% decrease in the percentage for Product Innovation, which is currently at {78.6%}<r>[0.538, 0.905, 0.669, 0.925]</r>.\nBased on the above thought, the answer is:\nIf the percentage for Service Innovation increase by 2%, the percentage of Product Innovation would decrease by 2%, making it 76.6%."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "sYJu7fw2V5QgJnj6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the pie chart data about societal cultural values into a structured table in csv format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I can see various categories of societal values from the legend <r>[0.826, 0.063, 0.993, 0.279]</r> and the percentage of population that identifies with these values from the pie chart slices <r>[0.223, 0.146, 0.741, 0.886]</r>. By translating this data, I can convert it to a csv table.\nBased on the above thought, the answer is:\nValue,Percentage\nTraditional,12.5%\nRationalization,14.6%\nIndividualism,16.7%\nMaterialism,15.6%\nPost-materialism,13.5%\nSelf-expression,10.4%\nDiversity,16.7%"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "ax1GRY3SgZZ6W3cz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, which societal value can be said to be the most identifiable by the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "The societal value that can be said to be the most identifiable by the population is the one with the highest percentage, indicated by its corresponding pie slice <r>[0.342, 0.516, 0.601, 0.886]</r>.\nBased on the above thought, the answer is:\nIndividualism is the most identifiable societal value by the population as it has the highest percentage."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "CzzxAyoH5et1G2VF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would you infer about societal values from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will infer by looking at the various segments <r>[0.223, 0.146, 0.741, 0.886]</r> of the pie chart and their corresponding percentages.\nBased on the above thought, the answer is:\nThe distribution of societal values is quite diverse, with no single value dominating significantly. There is a noticeable shift towards post-material values like individualism, self-expression, and diversity, indicating a transition towards more modern beliefs."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "PXD8YYdrzIDH1FHx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how could policy makers use this information to shape societal programs?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will infer from the different societal values and their proportions in the population as demonstrated by the pie chart <r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nPolicymakers should focus on initiatives that promote individualism, self-expression, and diversity as these values are increasingly important in the society. Meanwhile, programs targeting traditional and rationalization values should not be completely disregarded, but perhaps repackaged to align with the shift towards post-material values."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "NgsmqoqaSiQePsju",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the combined percentage of population that identifies with traditional and rationalization societal values?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage, I can sum up the individual percentages of Traditional <r>[0.223, 0.278, 0.482, 0.548]</r> and Rationalization <r>[0.224, 0.516, 0.482, 0.828]</r>.\nBased on the above thought, the answer is:\n27.1%"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "bXOStEOi4vzEq91i",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the values identified by the population, what is the percentage indicating social cultural change (Individualism, Post-materialism, Self-expression, and Diversity)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage indicating social cultural change, I need to add up the percentages of Individualism <r>[0.342, 0.516, 0.601, 0.886]</r>, Post-materialism <r>[0.482, 0.266, 0.741, 0.556]</r>, Self-expression <r>[0.482, 0.151, 0.673, 0.516]</r>, and Diversity <r>[0.283, 0.146, 0.527, 0.516]</r>.\nBased on the above thought, the answer is:\n57.3%"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "95Yplg3Muz4PEQBV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Individualism societal value observed a 5% increase, what would the new total percentage of societal values be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I will add 5% to the Individualism value <r>[0.342, 0.516, 0.601, 0.886]</r> and sum all the societal values percentages.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "vDM3vswii68ZMVFP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title \"Frequency of Stages in Internal Business Process Analysis\" [{subtitle}<r>[0.242, 0.021, 0.721, 0.047]</r>] and the slices of the pie chart [{pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r>], I understand that various stages of business process analysis are represented with their respective frequencies.\nBased on the above thought, the answer is:\nStage,Frequency(%)\nPlanning,9.7\nDevelopment,16.1\nTesting,22.6\nDeployment,29.0\nMaintenance,12.9\nFeedback,6.5\nReform,3.2"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "aCZtcwo2RtFrGaXk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the most critical stage in the business process analysis as per the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The stage that occupies the largest portion of the pie chart [{Deployment slice}<r>[0.482, 0.2, 0.741, 0.781]</r>] would be considered as the most critical.\nBased on the above thought, the answer is:\nThe most critical stage is \"Deployment\" because it has the highest frequency of 29% among all stages."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "QU83mL7gMgvGvN9k",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the pie chart, what phase does the business spend the least time on?"
+            },
+            {
+                "from": "gpt",
+                "value": "The phase that is represented by the smallest slice of the pie chart signifies the least time spent [{Reform slice}<r>[0.283, 0.226, 0.482, 0.516]</r>].\nBased on the above thought, the answer is:\nThe business spends the least time on the \"Reform\" phase as it has the smallest frequency of 3.2%."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "eFw4Ox7XwGY0EpEN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how do you think the business can work on improving the efficiency of their process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To suggest improvements, we need to look at the stages with the least frequencies and think of ways to increase their efficiency.\nBased on the above thought, the answer is:\nThe business can work on improving their efficiency by focusing more time and resources on the \"Feedback\" and \"Reform\" stages, as they have the lowest frequencies among all stages. Addressing these stages can lead to more effective improvements in the process."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "erfYsPrMTmxM1UvI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stage has the least frequency in the process according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the stage with the least frequency, I need to look at the smallest slice of the pie chart [{Reform slice}<r>[0.283, 0.226, 0.482, 0.516]</r>].\nBased on the above thought, the answer is:\nReform."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "TaZi1kLpiAtq9ec4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of the process does the testing and deployment stages form together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the respective proportion, I need to add up the frequencies of the testing [{Testing slice}<r>[0.325, 0.516, 0.662, 0.886]</r>] and deployment [{Deployment slice}<r>[0.482, 0.2, 0.741, 0.781]</r>] stages.\nBased on the above thought, the answer is:\n51.6%"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "pzuy81hjJaq5VrMf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the frequency of the Maintenance stage exceed that of the Planning stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in frequency, I need to subtract the frequency of the Planning stage [{Planning slice}<r>[0.224, 0.278, 0.482, 0.516]</r>] from the frequency of the Maintenance stage [{Maintenance slice}<r>[0.414, 0.146, 0.616, 0.516]</r>].\nBased on the above thought, the answer is:\n3.2%"
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "rUESvlIlbePLFy53",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the adoption rates across the years as presented in the structured chart data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the structured chart data, it's clear that the data set includes technology types and corresponding adoption rates for each year from 2008 to 2011. Conversion to a CSV table will represent this information more clearly.\nBased on the above thought, the answer is:\nTechnology,2008,2009,2010,2011\nTelemedicine,20,30,40,60\nHealth tracking apps,25,35,45,50"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "sAijnfoW82JSm7oW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you deduce about the development of healthcare technologies in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart that represents the adoption rates of {Telemedicine}<r>[0.223, 0.278, 0.719, 0.886]</r> and {Health tracking apps}<r>[0.283, 0.146, 0.741, 0.664]</r> in 2011, I can draw conclusions about the development of these technologies.\nBased on the above thought, the answer is:\nThe pie chart indicates that Telemedicine technology was more extensively adopted in 2011, holding a significant majority of 54.5%. This suggests that among the technologies considered, Telemedicine has seen more widespread acceptance and implementation in healthcare practices that year."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "OuhiB54zY9a0pb0O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the popularity of Health tracking apps in 2011 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slice representing the adoption rate of {Health tracking apps}<r>[0.283, 0.146, 0.741, 0.664]</r> in comparison to the total, conclusions can be drawn about its popularity.\nBased on the above thought, the answer is:\nEven though Health tracking apps hold a smaller portion of the pie chart at 45.5%, it still indicates a substantial level of adoption. It suggests that while this technology was overshadowed by the rapid adoption of Telemedicine, health tracking apps had a sizeable user base in 2011."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "MDKY8fAynd3LZJZe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart affect future funding decisions for Telemedicine and Health tracking apps?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the adoption rates of {Telemedicine}<r>[0.223, 0.278, 0.719, 0.886]</r> and {Health tracking apps}<r>[0.283, 0.146, 0.741, 0.664]</r>, predictions can be made about potential future funding decisions.\nBased on the above thought, the answer is:\nThe higher adoption rate of Telemedicine in 2011 might lead to increased funding for this technology, to further expand its reach and improve its operations. While Health tracking apps have a slightly lesser adoption rate, they constitute a considerable proportion of technological adoption in health care. Therefore, they might also receive significant funding, focusing on increasing their adoption or improving their features to attract more users."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "l8U73Ho0UsXs33hX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which health technology has a higher adoption rate in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of each pie slice, representing {Telemedicine}<r>[0.223, 0.278, 0.719, 0.886]</r> and {Health tracking apps}<r>[0.283, 0.146, 0.741, 0.664]</r>, I can deduce which technology had a higher adoption rate in 2011.\nBased on the above thought, the answer is:\nTelemedicine"
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "FUGYyQfdX0q5Gd5v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the adoption rate between Telemedicine and Health tracking apps in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the percentage represented by each slice, I can determine the difference in adoption rates between the two technologies in 2011.\nBased on the above thought, the answer is:\nThe difference is 9%."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "mPulH3cvuIuASX4L",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total adoption rate for all technologies represented in the chart for 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding up the percentages represented by all slices in the pie chart, I can calculate the total adoption rate.\nBased on the above thought, the answer is:\nThe total adoption rate is 100%."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "D1ljXEbXqUCuC6n8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart that you have examined, can you please turn the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart's title, {\"Ecological Environment Sustainability Assessment\"}[0.273, 0.021, 0.69, 0.047], it is evident that the pie slices represent the different aspects of an ecological assessment, each associated with a distinct color and percentage: {Biodiversity}[0.223, 0.278, 0.482, 0.605], {Water Quality}[0.23, 0.516, 0.544, 0.886], {Climate Stability}[0.482, 0.472, 0.741, 0.875], {Soil Quality}[0.482, 0.153, 0.739, 0.516], and {Air Quality}[0.283, 0.146, 0.532, 0.516].\nBased on the above thought, the answer is:\nCriterion,Biodiversity,Water Quality,Climate Stability,Soil Quality,Air Quality\nValues,15%,25%,23%,20%,17%"
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "EDPJssaCzC2Ul00m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which aspect is given the highest priority in the Ecological Environment Sustainability Assessment and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the proportions of each aspect will tell me which one is given the highest priority.\nBased on the above thought, the answer is:\n\"Water Quality\" because it has the largest proportion, 25%."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "j6fggWKzClQ1sNdH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what can you deduce about the ecological priorities in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce the ecological priorities, I need to look at the percentages of each ecological aspect.\nBased on the above thought, the answer is:\nIn 2010, the most emphasis was placed on Water Quality and Climate Stability in terms of ecological sustainability, representing 25% and 23% respectively. This implies that these were the main focuses in ecology. Biodiversity, Soil Quality, and Air Quality were slightly less prioritized."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "y3LuKtx4UCNRhNMI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence ecological policy decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the proportions given to each ecological aspect might help determine how future policies might be influenced.\nBased on the above thought, the answer is:\nThe data suggests that more importance was given to Water Quality and Climate Stability. Therefore, future ecological policies might continue to prioritize these aspects. Other areas like Biodiversity, Soil Quality and Air Quality which have lower percentages, might receive more attention in upcoming policies to improve their state and contribute more wholesomely towards environment sustainability."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "g91fe2LStcyWqr7q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages for Biodiversity, Soil Quality and Air Quality?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages of Biodiversity, Soil Quality and Air Quality to answer this.\nBased on the above thought, the answer is:\n52%."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "0eglSub0Yxnlst7S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the percentage of Soil Quality and Climate Stability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the percentage of Soil Quality from the percentage of Climate Stability.\nBased on the above thought, the answer is:\n3%."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "6VvvsoKFlPnOTUDL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Air Quality increases by 7%, what will be the new total percentage of all factors combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 7% to the Air Quality percentage and then sum up the percentages of all the factors.\nBased on the above thought, the answer is:\n107%."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "uPZJ4pVPDaYIC3gw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart you've read, please convert the data into a CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "Seeing the pie chart's title {\"Fitness and Sports Health Trends in 2020\"}[0.312, 0.021, 0.651, 0.047] and the {the legend box}[0.87, 0.063, 0.993, 0.279] containing each fitness activity with their corresponding colors: {\"Yoga\"}[0.915, 0.071, 0.947, 0.091], {\"Running\"}[0.915, 0.101, 0.972, 0.121], {\"Cycling\"}[0.915, 0.131, 0.966, 0.151], {\"Swimming\"}[0.915, 0.161, 0.987, 0.181], {\"HIIT\"}[0.915, 0.191, 0.942, 0.211], {\"Zumba\"}[0.915, 0.221, 0.964, 0.241], and {\"Pilates\"}[0.915, 0.251, 0.96, 0.271], it's clear they correspond to various portions in the main pie chart. Calculating the sizes of these portions should yield the percentage of the population that prefers each fitness activity in 2020.\nBased on the above thought, the answer is:\nExercise Type,Percentage(2020)\nYoga,15\nRunning,20\nCycling,10\nSwimming,18\nHIIT,30\nZumba,2\nPilates,5"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "b2NeFoT5UxhSiWJm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which fitness activity is the most popular in 2020? Could you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most popular fitness activity, I must check the participation percentage of each fitness activity and pick the one with the highest percentage.\nBased on the above thought, the answer is:\nHIIT was the most popular fitness activity in 2020 as it has the highest participation percentage of 30% among all the fitness activities."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "Fi7vkmqtorbMgz5r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can you draw about fitness trends in 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the data in the pie chart and compare each fitness activity's participation percentages to draw conclusions about 2020's fitness trends.\nBased on the above thought, the answer is:\nThe pie chart shows that intense, short exercises like HIIT were the most popular with 30% participation, but more traditional and relatively easier activities like Running (20%) and Yoga (15%) also had significant participation. The least popular were the dance-originated activities like Pilates and Zumba. This suggests that people were moving more towards high-intensity, quick workouts while also favoring traditional fitness activities."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "NF4HxcKRVpRMRCOL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this data influence the decision-making process of fitness centers in planning their classes for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must consider how the participation percentages of each fitness activity could affect the planning and scheduling of fitness classes.\nBased on the above thought, the answer is:\nGiven the high participation of HIIT (30%), fitness centers may opt to include more HIIT classes in their schedule. They may also maintain a diverse range of classes by continuing to offer traditional and popular activities like Running and Yoga. Pilates and Zumba, while having the least participation, may still be offered to cater to the niche market who do prefer these activities. Therefore, this data could result in a balanced mix of classes favoring more high-intensity workouts, while still catering to overall fitness enthusiasts."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "bTtQQvZrq7Wrqku9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, which two fitness activities have a combined participation percentage of 25?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the two activities that add up to a participation percentage of 25, I need to calculate the sum of the percentages of all pairs of fitness activities from the pie chart and find the one that totals to 25.\nBased on the above thought, the answer is:\nYoga and Pilates"
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "Lom4HcXsM0K9VBxP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average participation percentage for all the fitness activities depicted in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average participation percentage, I should add up the percentages of all the fitness activities and then divide by the total number of the fitness activities.\nBased on the above thought, the answer is:\nThe total percentage is 100%. As there are 7 fitness activities, the average participation percentage is 100% / 7 = approx.14.3%."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "JEMCQxtaUCx5wXuJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the participation percentage of Zumba increased by 3%, what would the new total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new total, I'd need to add 3% to the existing Zumba percentage and sum all the fitness activity participation percentages again.\nBased on the above thought, the answer is:\nThe old Zumba percentage is 2%, so the new percentage for Zumba is 2% + 3% = 5%. The new total percentage is then 15% (Yoga) + 20% (Running) + 10% (Cycling) + 18% (Swimming) + 30% (HIIT) + 5% (Zumba) + 5% (Pilates) = 103%."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "MUSI8rzaoLDdi4C8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Future Technological Development Prospects\"}[0.295, 0.021, 0.668, 0.047] and {the sector slices}[{\"2000\"}[0.229, 0.278, 0.482, 0.516]; {\"2005\"}[0.223, 0.435, 0.482, 0.665]; {\"2010\"}[0.244, 0.516, 0.482, 0.855]; {\"2015\"}[0.377, 0.516, 0.595, 0.886]; {\"2020\"}[0.482, 0.516, 0.739, 0.849]; {\"2025\"}[0.482, 0.215, 0.741, 0.557]; {\"2030\"}[0.423, 0.146, 0.632, 0.516]; {\"2035\"}[0.283, 0.155, 0.482, 0.516];], I can see that the sectors represent different years and their respective perceptions of technological progress in percentages.\nBased on the above thought, the answer is:\nYear, Technological Development Perception\n2000, 45\n2005, 60\n2010, 70\n2015, 82\n2020, 95\n2025, 100\n2030, 80\n2035, 61"
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "8KHn57LGcAQYfE0r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trend can be observed in people's perception of future technological progress from 2000 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend, I need to analyze the changes in people's perception over the years.\nBased on the above thought, the answer is:\nThere is an increasing trend in people's perception of future technological progress from 2000 to 2025."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "K9pOolHAvmL3OIDs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat seems to happen after 2025 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the changes in percentages from 2025 onwards.\nBased on the above thought, the answer is:\nAfter 2025, there is a significant drop in the positive perception of future technological progress."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "50h6jd55xrWuCduC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer why there might be a sudden drop after 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer this, I might need to consider the changes in perception scores plot after 2025.\nBased on the above thought, the answer is:\nAccurate reason for the drop in perceptions after 2025 can't be deciphered from the chart alone. However, given the context of the question, it might be speculated that some technological anxieties, disappointments or fears about the pace of technological change could have led to this decrease in positive perceptions."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "iML6rFD5haRG7Wop",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in which 3 consecutive years does the sum of the perceptions exceed 260?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the years mentioned, I need to sum the perceptions of each three consecutive years and see if it exceeds 260.\nBased on the above thought, the answer is:\nThe years 2015, 2020 and 2025."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "Birbfqgj7n6W51LK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in perception of technological development from 2000 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I first need to get the perceptions in 2000 and 2025. Then I subtract the perception in 2000 from the one in 2025.\nBased on the above thought, the answer is:\nThe total increase is 100 - 45 = 55."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "pV9t4uoLWFwC77qg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the positive perception of future technological progress highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to identify the year with the highest percentage in the chart.\nBased on the above thought, the answer is:\nThe highest positive perception was in the year 2025."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "Q1ZKDFXx2awFWbmU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you break down the pie chart data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To tabulate the data captured in the pie chart, I will look at the information provided by {Microsoft's slices}[0.223, 0.278, 0.544, 0.886] and {Apple's slices}[0.283, 0.146, 0.741, 0.875] and align them with the years from the title {\"Microsoft vs Apple Market Share in 2013\"}[0.314, 0.021, 0.649, 0.047].\nBased on the above thought, the answer is:\nCompany,2010,2011,2012,2013\nMicrosoft,25%,30%,35%,40%\nApple,75%,70%,65%,60%"
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "LB3W4uNdprJ9uA7r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, who had the dominant market share in 2013? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine who had the dominant market share in 2013, I need to compare the percentages represented by Microsoft and Apple in the pie chart for that particular year.\nBased on the above thought, the answer is:\nApple had the dominant market share in 2013 as it held 60% of the market, compared to Microsoft's 40%."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "afSCJdrWl4rYslUP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret the market share trend between Microsoft and Apple based on the information provided in the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages represented by each company in different years to understand the market share trend.\nBased on the above thought, the answer is:\nThe pie charts clearly show a closing gap in the market share between Apple and Microsoft from 2010 to 2013. While Apple consistently holds a larger percentage of the market, their share is gradually decreasing each year. In contrast, Microsoft's share is slowly growing."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "fhKBhYXnriVq4lyG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat strategic insights can be gained from the market share data represented in these pie charts for the future planning of each company?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide strategic insights, I need to analyze the trend of change in market share for both companies over the years.\nBased on the above thought, the answer is:\nBased on the market share trend, Microsoft may want to analyze and maintain whatever strategies have led to their consistent growth in market share. On the other hand, Apple might want to investigate the reasons for their gradual market share decrease and consider adjustments in their product or marketing strategies to reclaim lost ground."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "z51dBO5oBaDVNaiG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, how much more market share did Apple have compared to Microsoft?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I will need to compare the values of the two companies' shares according to the chart in 2010.\nBased on the above thought, the answer is:\n50% more."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "5MXNf9oE230fmUZE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in market share of Microsoft from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to see Microsoft's percentages in 2010 and then in 2013, and calculate the difference between them.\nBased on the above thought, the answer is:\n15% increase."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "MkptwXbVQeOkAHsN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends continue, what would be Microsoft's market share in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict this, I need to identify the yearly increase in Microsoft's market share and add this to the 2013 figure.\nBased on the above thought, the answer is:\n45%."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "n9eXd8doGnASk0ri",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart provided, can you generate a table that lists the percentage of the Urban Population and Forest Covered Area from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie charts from the provided chart figure, I can see the varying percentages of Urban Population and Forest Covered Area spanning from 2010 till 2018. The pie chart for Urban Population is located at {subplot_0}<r>[0.281, 0.07, 0.719, 0.509]</r>, while the pie chart for Forest Covered Area is situated at {subplot_1}<r>[0.281, 0.546, 0.719, 0.985]</r>.\nBased on the above thought, the answer is:\nYear,Urban Population,Forest Covered Area\n2010,7.3%,13.6%\n2011,8.3%,12.7%\n2012,9.0%,12.1%\n2013,10.2%,11.5%\n2014,11.3%,11.2%\n2015,12.2%,10.9%\n2016,13.0%,10.0%\n2017,13.9%,9.4%\n2018,14.8%,8.5%"
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "KsazeorKfSIyERXi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, please describe the trend of the Urban Population from 2010 to 2018."
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trend, I need to look at the percentage of the Urban Population from 2010 to 2018 in the pie chart found in {subplot_0}<r>[0.281, 0.07, 0.719, 0.509]</r>.\nBased on the above thought, the answer is:\nThe Urban Population has been steadily increasing over the period from 2010 to 2018."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "4dg9j6zyNCTrtJvL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the relationship between Urban Population and Forest Covered Area over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages for both Urban Population and Forest Covered Area across the respective pie charts in {subplot_0}<r>[0.281, 0.07, 0.719, 0.509]</r> and {subplot_1}<r>[0.281, 0.546, 0.719, 0.985]</r>.\nBased on the above thought, the answer is:\nThe increase in Urban Population corresponds to the decrease in Forest Covered Area over the years, indicating that as more areas are urbanized, the forest area shrinks."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "zv6Ft3KeV1eW8Adb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given data, what could be the potential implications for the environment if the trend continues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the effect of the continuous increase in Urban Population and the decrease in Forest Covered Area.\nBased on the above thought, the answer is:\nIf the trend continues, the shrink in Forest Covered Area and growth in Urban Population can lead to a higher carbon footprint, loss of biodiversity, and amplified global warming. Therefore, it's a potential environmental concern."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "P6krwIpGQVH8Ekdu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year, the difference between the urban population percentage and the forest covered area percentage was the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to calculate the difference in percentages of the urban population and forest covered area for each year.\nBased on the above thought, the answer is:\n2018."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "W31ZcRpfRloVqVaG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average percentage of Urban Population throughout 2010-2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get this, I would sum the percentages of Urban Population from 2010 to 2018 and then divide by the total number of years.\nBased on the above thought, the answer is:\n11.1%."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "PjSZEMRZ2zpCfnEJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the forest covered area continued to decrease by the same average annual rate beyond 2018, what will be the percentage of Forest Covered Area in 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I need to calculate the average annual decline rate of Forest Covered Area from 2010 to 2018 and multiply it by the number of years from 2019 to 2025, then subtract the result from the percentage of Forest Covered Area in 2018.\nBased on the above thought, the answer is:\n3.0%."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "yrDlqLJZgjnq3olQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart that encompasses the allocation of resources for Cultural Heritage and Cultural Integration from 2016 to 2020, express this data in a structured table format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, we can locate the slices for each year through their labeled textual sections on the {legend}<r>[0.661, 0.537, 0.746, 0.692]</r> and pie markers within the {subplot_0}<r>[0.342, 0.021, 0.658, 0.473]</r> and {subplot_1}<r>[0.342, 0.527, 0.658, 0.979]</r>. The allocation per year is represented as a proportion of the entirety of the pie chart, with each sector (Cultural Heritage and Cultural Integration) having their own pie chart.\nBased on the above thought, the answer is:\nYear,Cultural Heritage,Cultural Integration\n2016,60,40\n2017,65,35\n2018,70,30\n2019,72,28\n2020,90,10"
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "ygCRCA0iEzzQiO6U",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs observed in the pie chart, what is the trend for the allocation of resources to Cultural Heritage and Cultural Integration from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportion of resources allocated to Cultural Heritage and Cultural Integration across all years to identify the trends.\nBased on the above thought, the answer is:\nOver the course of five years from 2016 to 2020, there seems to be a clear trend where funding for Cultural Heritage increases while that for Cultural Integration decreases."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "iSoYOnzuM0YJxG57",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trends shown in this chart, where does the focus of resource allocation seem to be headed in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the five-year trends for resource allocation to Cultural Heritage and Cultural Integration, I can make educated predictions.\nBased on the above thought, the answer is:\nBased on the trends, it seems that future resource allocation will continue to shift towards Cultural Heritage, while the emphasis on Cultural Integration decreases."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "dVye5Y0cNooQgz18",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications could this shift in resource allocation from Cultural Integration to Cultural Heritage have on societal cultural practices?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reflecting on the potential impact of decreased resources for Cultural Integration and increased resources for Cultural Heritage, I can infer possible societal implications.\nBased on the above thought, the answer is:\nAs resources shift away from cultural integration towards cultural heritage, there may be a potential risk that society could be more rooted in the past, neglecting the creation of new cultural integrations. Conversely, there may be a positive impact in preserving traditional cultures from being forgotten or lost. Ultimately it may seek to ensure cultural diversity and pluralism, leading to a society that is heterogeneous and rich in inherited traditions."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "O0eit4b39So5hFNJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data depicted in the chart, in what year was the highest rate increase observed for Cultural Heritage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the largest rate increase for Cultural Heritage, I must first identify the percentage allocated to Cultural Heritage for each year from the chart, then calculate the difference in percentages between consecutive years.\nBased on the above thought, the answer is:\nThe year 2020 experienced the highest rate increase in Cultural Heritage."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "b3Dq9EXzplqz4L4Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the absolute change in the allocation of resources to Cultural Integration from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will observe the allocations for Cultural Integration in 2016 and 2020 and then subtract the value in 2016 from that in 2020.\nBased on the above thought, the answer is:\nThere was a decrease of 30% in the allocation of resources to Cultural Integration from 2016 to 2020."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "f9M3G1IHoOrNA15e",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2018, what was the percentage difference between the allocation of resources to Cultural Heritage and Cultural Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of allocation to Cultural Integration from the percentage of allocation to Cultural Heritage in 2018.\nBased on the above thought, the answer is:\nIn 2018, the difference between resources allocated to Cultural Heritage and Cultural Integration was 40%."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "g4VRT5BgMHl9owvu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data, please generate a table in CSV format that presents the yearly growth of the Gaming industry from 2008 to 2015."
+            },
+            {
+                "from": "gpt",
+                "value": "With the help of the legend present on the right side, I can distinguish the color corresponding to each year using the coordinates {legend-box}[0.908, 0.063, 0.993, 0.309]in the pie chart. Now, using the slices of each pie piece, I can evaluate the growth of the Gaming industry each year from 2008 to 2015.\nBased on the above thought, the answer is:\nYear,Growth\n2008,5.6%\n2009,6.7%\n2010,7.8%\n2011,8.9%\n2012,13.3%\n2013,16.7%\n2014,18.9%\n2015,22.2%"
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "KcfEc57sJulOfNYE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year demonstrates the most rapid growth for the gaming industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most rapid growth year is indicated by the largest slice in the chart.\nBased on the above thought, the answer is:\nThe gaming industry saw its most rapid growth in 2015."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "6XWJNOJQXUlF8g6j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you interpret the growth trend of the gaming industry from 2008 to 2015 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the trend, I need to observe the progressive increase in the slices from 2008 to 2015 which represent the gaming industry\u2019s growth.\nBased on the above thought, the answer is:\nFrom the chart, it's evident that the growth of the gaming industry has steadily increased from 2008 to 2015, with a more notable, rapid growth starting from 2012."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "7QoxhheJfKtAVANx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart inform future predictions for the gaming industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data can be analyzed to anticipate future trends in the gaming industry.\nBased on the above thought, the answer is:\nGiven the consistent growth of the gaming industry each year from 2008 to 2015, with a particularly sharp increase from 2012 onward, there\u2019s a strong implication that this growth trend might continue in the future years."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "gy8Kv6QCEKEkBqh9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, in which year did the gaming industry realize the greatest growth increase from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to calculate the percentage of the difference in the growth from one year to the next for all of the years and identify the year with the greatest increase.\nBased on the above thought, the answer is:\nThe year 2012 saw the greatest growth."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "ZQuDttfN4aRnVpBB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total growth percentage of the gaming industry from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total growth percentage, I need to add up all the yearly growth percentages from 2008 to 2015.\nBased on the above thought, the answer is:\nThe total growth of the gaming industry from 2008 to 2015 is 100%."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "BzN4X3jQChrbfPmC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the annual average rate of growth for the Gaming Industry from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The annual average rate of growth can be determined by diving the total growth by the number of years.\nBased on the above thought, the answer is:\nThe annual average growth rate of the gaming industry from 2008 to 2015 is 12.5%."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "X3Iez5aaz3IOW8wp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the utilization data for Books and Computers into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the provided JSON data and the pie chart, the slices represent the utilization counts for the resources, Books and Computers, for each year from 2012 to 2021. The utilization counts are distinguishable by the different colors for each year.\nBased on the above thought, the answer is:\nYear, Books, Computers\n2012, 56, 30\n2013, 65, 35\n2014, 72, 40\n2015, 68, 45\n2016, 74, 55\n2017, 85, 62\n2018, 95, 70\n2019, 83, 75\n2020, 90, 80\n2021, 105, 85"
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "zEzhtD1NVfAoSQar",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, discuss the trend for Books usage over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comment on the trend, I need to consider the pie slices representing the usage of Books for each year.\nBased on the above thought, the answer is:\nThe usage of Books has generally increased over the years, with a noticeable jump in the year 2021 which appears to be an outlier given the steep rise."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "8ukiCECDPxxdIRZL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat comparison can be drawn between the utilization of Books and Computers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw a comparison, I need to look at the relative sizes of the pie slices for Books and Computers.\nBased on the above thought, the answer is:\nFrom the chart, it is clear that Books were utilized more than Computers in all the years. The growth rate for Books usage also seems faster than that of Computers."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "3DkK9UxXXSd1YWLg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the resource utilization in education based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret resource utilization, I need to look at the overall distribution and changes in the pie slices over the years.\nBased on the above thought, the answer is:\nThe education sector has been progressively leveraging more resources for learning, with a higher emphasis on Books. Though the usage of Computers is also growing, it appears that Books continue to be the preferred resource. By 2021, the use of resources has reached its peak, potentially pointing to increased investments and focus on education."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "uKElTiK55U4ZUO2N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total usage count of Computers during the entire period from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total usage of Computers, I need to sum up the utilization counts from 2012 to 2021.\nBased on the above thought, the answer is:\nThe total usage of Computers is 577."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "Z0UPrjkBvGwdMyt3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in the usage of Books from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the usage of Books, I would subtract the usage in 2016 from the usage in 2021.\nBased on the above thought, the answer is:\nThe increase in the usage of Books from 2016 to 2021 is 31."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "wSZpGQ689SdyMIOv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the usage of Books and Computers the same?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to find the year where the usage count for Books and Computers was the same.\nBased on the above thought, the answer is:\nThe usage of Books and Computers was never the same in any year."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "ZXU6QqzNUBI7f4Yn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the title at the top of the chart {\"Risk Assessment and Management\"}[0.336, 0.021, 0.627, 0.047] and the individual chart elements found in {the legend}<r>[0.828, 0.063, 0.993, 0.309]</r>, I can identify and categorize the risk types and their corresponding percentages.\nBased on the above thought, the answer is:\nRisk Type,Percentage\nCyber Risk,45%\nOperational Risk,25%\nMarket Risk,10%\nCredit Risk,7%\nRegulatory Risk,6%\nReputation Risk,3%\nStrategic Risk,2%\nExternal Risk,2%"
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "2VUWC9BSDQDkmIsm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, is the concern for external risks significant? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentage representation of different risk types. The more the percentage, the greater the concern.\nBased on the above thought, the answer is:\nThe concern for External Risks is not significant, as it represents only a small part of the total risk, specifically 2%, which is the smallest among all the risk types shown in the chart."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "aC6FmxEXyY6IbZJ0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the presentation of this risk data in a pie chart contribute to the viewer\u2019s understanding of risk distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "I look at the visual elements of the chart, it's composition and ponder on how these aspects aid in understanding risk distribution.\nBased on the above thought, the answer is:\nThe pie chart provides a clear visual representation of the proportional distribution of the different types of risks. The size of each 'slice' corresponds to the extent of the risk, making it easy for viewers to compare different risks and identify the most prevalent ones. The use of vibrant and distinct colours also helps in differentiating each risk type."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Rf4twEMJn2R5BzmZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might you expect the data to influence risk management decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the values and their implications to understand how it might influence decision making in field of risk management.\nBased on the above thought, the answer is:\nThe data could significantly influence risk management decisions. As Cyber Risk and Operational Risk make up a majority of the risk types, more resources may be allocated to these areas for preventive and mitigating measures compared to other risk types that account for lower percentages."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "ejSK6qiC9ncnLtPt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk type contributes to one quarter of all risks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate and identify the risk types in the chart. Once identified, I can compare their percentages to find the one that contributes to 25%, or one quarter, of all risks.\nBased on the above thought, the answer is:\nOperational Risk."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "wfmxZLdULRN9397s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat total percentage do Market, Credit, and Regulatory Risks make up?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the individual percentages from the respective risk types in the chart, I can add these values together to get the total.\nBased on the above thought, the answer is:\nMarket Risk (10%) + Credit Risk (7%) + Regulatory Risk (6%) = 23%"
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "eUfCufLEysCNJZIA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Cyber Risk was reduced by 10%, what would be the remaining percentage for all combined risks ?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Cyber Risk is reduced by 10%, I need to subtract this from the total Cyber Risk percentage. Subsequently, I can then subtract this from the total risk percentage to get the remaining risk percentage.\nBased on the above thought, the answer is:\nThe total risk percentage is always 100%. The reduced Cyber Risk percentage is 45% - 10% = 35%. Thus, the remaining combined risk percentage would be 100% - 35% = 65%."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Grx5kXsNhfAusmZY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you transform the data into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title, {\"EdTech and Online Learning 2016-2019\"}[0.318, 0.021, 0.645, 0.047], and the slices {2016}[0.223, 0.278, 0.482, 0.687], {2017}[0.252, 0.516, 0.57, 0.886], {2018}[0.482, 0.345, 0.741, 0.864], and {2019}[0.283, 0.146, 0.711, 0.516], I can see each year's proportion among the four-year period. I can convert this information into a CSV format.\nBased on the above thought, the answer is:\nYear,Percentage\n2016,18.8%\n2017,22.9%\n2018,27.1%\n2019,31.2%"
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "GyaXMNj2rWyzGTJD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which year did Online Learning have the least presence?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should find the year with the smallest portion from the four slices.\nBased on the above thought, the answer is:\nAccording to the pie chart the year 2016 had the least online learning presence, having only 18.8% of the total."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "kunomHEgJrud9aDJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the distribution of the slices, can you make an inference about the growth rate of Online Learning within these four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should evaluate the year-by-year growth in the proportion of Online Learning from 2016 to 2019.\nBased on the above thought, the answer is:\nThe pie chart shows a steady increase in the proportion of Online Learning from 2016 through 2019, suggesting a significant growth rate in this sector over the period."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "53Ss3hJ8EC1dP78X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the pie chart information influence the decisions of EdTech investors in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should consider how the annual growth trend could influence investment decisions.\nBased on the above thought, the answer is:\nGiven the steady increase in the proportion of Online Learning from 2016 through 2019, EdTech investors may see a promising trend and choose to invest more in this sector in the future as it continues to grow."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "hgy8ryMk1tKN30RG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage increase in Online Learning did you observe from 2016 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the differences in percentage between 2016 and 2018.\nBased on the above thought, the answer is:\nThe percentage increased from 18.8% in 2016 to 27.1% in 2018."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "9o76Q7kn0xIDUMMU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year had the largest share of Online Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportions for each year, I can identify which year had the largest share of Online Learning.\nBased on the above thought, the answer is:\nThe year 2019 has the largest share with 31.2%."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "9SAkHQrcgijLRvRY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of Online Learning for 2016 increased by 3%, what would the new total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to increase the 2016 percentage by 3% and then recalculate the total percentage.\nBased on the above thought, the answer is:\nIf the 2016 percentage increased by 3% to 21.8%, the new total would be 105.0%"
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "F71oZZcUpP7TlIjX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please translate the data from the pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let me translate the data in the pie chart, which includes each era's name provided in {the legend}<r>[0.821, 0.063, 0.993, 0.249]</r> and their respective contributions shown in each slice of {the pie}<r>[0.223, 0.146, 0.741, 0.886]</r> into a CSV format.\nBased on the above thought, the answer is:\nEra,Contributions\nOld Stone Age,200\nMiddle Stone Age,300\nNew Stone Age,400\nBronze Age,500\nIron Age,800\nModern Age,1300"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "uteh6a819LdjWoXE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which era has contributed the most to civilization development and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The era with the largest slice in the pie chart has contributed the most to civilization development.\nBased on the above thought, the answer is:\nThe Modern Age has contributed the most to civilization development, as it has the largest slice in the pie chart with 37.1% of the total contributions."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "eCOU9k4o62sRgCUO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer from the chart that human civilization has developed progressively over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of the slices from the old to the modern ages in the pie chart, assumptions can be made about the progression of human civilization.\nBased on the above thought, the answer is:\nYes, we can infer from the chart that human civilization has developed progressively over time. The chart shows an increasing trend in the size of the contributions from the Old Stone Age to the Modern Age, indicating continuous development in human civilization."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "lakyvWtKOPJTZo61",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we learn from the Iron Age's contributions in relation to the other eras?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the relative size of the Iron Age's slice among all eras in the pie chart, we can evaluate its contributions.\nBased on the above thought, the answer is:\nThe Iron Age's contribution is smaller than that of the Modern Age but significantly larger than the others. This suggests a point in history when civilization started to advance rapidly, a trend carried on into the Modern Age."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "4l80WmrWtJG3RQtE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total contribution of the Middle Stone Age and New Stone Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the contributions of the Middle Stone Age and the New Stone Age from the pie chart can give the answer.\nBased on the above thought, the answer is:\n700"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "o5KgZ8nrYLAXX1Y0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher are the contributions made by the Modern Age compared to the Iron Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the contribution of the Iron Age from the contribution of the Modern Age given by the pie chart.\nBased on the above thought, the answer is:\n500"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "vcR6AsXJaPHUIxlJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion percentage does the Iron Age contribute to the total contributions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the proportion, I need to divide the contributions of the Iron Age by the total contributions of all eras using the information from the pie chart.\nBased on the above thought, the answer is:\n23.5%"
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "sTtePeeDYS0gZnG4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the pie chart into a CSV format table, as per the number of startups each year from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data displayed relates to the number of startups per year. Each segment of the pie chart {pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r> with its respective legend in {sublegend}<r>[0.908, 0.063, 0.993, 0.249]</r>, corresponds to a particular year. So, by extracting the values from these elements, I can convert them into a CSV format table.\nBased on the above thought, the answer is:\nYear,Startups\n2015,410\n2016,445\n2017,480\n2018,420\n2019,390\n2020,310"
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "pU54kmkdPmFDME7B",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year demonstrated a significant increase in startups compared to the previous year, and why do you think this is the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I would need to check the size of each slice in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and the corresponding labels in the {sublegend}<r>[0.908, 0.063, 0.993, 0.249]</r>. Finding the year with a significantly larger slice compared to the preceding one can answer the first part of the question. As for the second part, the reason would be speculative as the chart doesn't provide reasons for changes.\nBased on the above thought, the answer is:\nThe year 2017 showed a significant increase in startups compared to 2016. The exact reason for this increase is not available in the chart, but it might be due to various factors like an increase in innovation, favorable market conditions, or government policies promoting startups."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "bGIOTVyHo4p3xJv9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can the steady decline in startups from 2017 to 2020 be considered a trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to check the slices in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> to compare the number of startups each year from 2017 to 2020.\nBased on the above thought, the answer is:\nYes, the chart indeed shows a steady decline in the number of startups from 2017 to 2020. Whether this trend continues can't be determined from the current data."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "eTd1CBKtf7bYezEA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence the policies for startups for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the trend from 2015 to 2020 in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, any decisions for 2021 might be influenced by the continual decrease in startups from 2018 to 2020.\nBased on the above thought, the answer is:\nPolicymakers might want to introduce more supportive measures for startups in 2021, given the decreasing trend in the number of startups from 2018 to 2020. The goal would be to reverse this trend and encourage more entrepreneurs to start their businesses."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "uxyk4XkQ5fM1AUgl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of startups from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number of startups from 2015 to 2020, I need to sum up the number of startups for each year as shown in each slice of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n2555"
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "IPZaBUvtqvZv9OD0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the number of startups the highest and what was the number?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the size of each section in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and identify the year with the biggest section.\nBased on the above thought, the answer is:\n2017, with a total of 480 startups."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "rJri7fuCitPGtCXR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more startups were there in 2017 compared to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the number of startups in 2015 from the number of startups in 2017 which can be found in the respective slices in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n70 more startups in 2017 compared to 2015."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "1QdJ5BEfGFUXIpJp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart data you have seen, can you convert this information into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, by referencing the chart labels and data, a CSV can be created. Each row of the CSV would represent a continent (as listed on the {legend}<r>[0.841, 0.063, 0.993, 0.279]</r>), and each column would represent the continent and its number of satellite stations (visible on the slices of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>).\nBased on the above thought, the answer is:\nContinent,Number of Satellite Stations\nAntarctica,2\nAsia,30\nEurope,25\nNorth America,15\nSouth America,10\nAustralia,8\nAfrica,5"
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "Xz6rxsuB7OBKhhoN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, which continent should potentially receive more investment to develop their satellite infrastructure?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must observe the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and identify which continent has the smallest number of satellite stations since that could indicate areas where more investment is needed to develop infrastructure.\nBased on the above thought, the answer is:\nAntarctica should potentially receive more investment to develop their satellite infrastructure as it has the least number of satellite stations."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "23qGGRpl9tfFhkdo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be interpreted about the distribution of satellite stations across continents from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I must evaluate which regions have larger or smaller slices in order to understand the current distribution of satellite stations.\nBased on the above thought, the answer is:\nThe distribution of satellite stations is heavily concentrated in Asia and Europe, with these areas having the most stations. Continents like Antarctica and Africa have fewer stations and could be areas to focus on for expanding satellite infrastructure."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "3jHStSIZBziRke78",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the number of satellite stations reflect the technological development of each continent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is necessary for me to take into consideration the size of each {slice of the pie}<r>[0.263, 0.278, 0.482, 0.516]/ [0.223, 0.318, 0.482, 0.882]/ [0.442, 0.516, 0.74, 0.886]/ [0.482, 0.221, 0.741, 0.542]/ [0.478, 0.146, 0.638, 0.516]/ [0.348, 0.146, 0.482, 0.516]/ [0.283, 0.199, 0.482, 0.516]</r>, as this may suggest higher technological development.\nBased on the above thought, the answer is:\nThe number of satellite stations across each continent can suggest the technological development of each continent. Asia and Europe, with a high number of stations, are likely to be more technologically developed in terms of their space programs. Conversely, continents with fewer stations, such as Antarctica and Africa, may be less technologically developed in this area."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "SVCtunO2sdUzwx4J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich continent has the highest number of satellite stations according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I examine the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, noting the sector with the largest area. This corresponds to the continent with the highest number of stations.\nBased on the above thought, the answer is:\nAsia."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "0udqp9HoM7vXBPeU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more satellite stations does Asia have than Antarctica?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I find the number of stations in Asia by referring to the {Asia sector}<r>[0.223, 0.318, 0.482, 0.882]</r> of the chart. Then I do the same for Antarctica by referring to the {Antarctica sector}<r>[0.263, 0.278, 0.482, 0.516]</r>. The difference between the two is the answer.\nBased on the above thought, the answer is:\n28."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "YY1WtPlULu4e94F0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined number of satellite stations in South America and Australia?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined number, I consult the {South America sector}<r>[0.478, 0.146, 0.638, 0.516]</r> for the number of stations in South America and the {Australia sector}<r>[0.348, 0.146, 0.482, 0.516]</r> for the number of stations in Australia, then add the two numbers together.\nBased on the above thought, the answer is:\n18."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "wSmnAkLLtFOfIN1I",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you have observed, could you please convert the information into a table in CSV format that represents the percentage share of Imports from 2015 to 2020 in each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend of the subplot representing total imports}<r>[0.877, 0.794, 0.934, 0.98]</r> indicates that each color corresponds to a different year, and the percentage share of each year in total imports are labeled in the pie chart slices<r>[0.099, 0.128, 0.406, 0.784]</r>. I will extract these data values from the pie chart and structure it into a table.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,10.2%\n2016,14.4%\n2017,18.6%\n2018,17.1%\n2019,20.4%\n2020,19.2%"
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "dIj0cjdU103UTBBZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the trend in the percentage share of total imports from 2015 to 2020 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trend, I need to compare the percentage share for each year's imports. I would start from {2015}<r>[0.1, 0.245, 0.253, 0.456]</r> and move sequentially through to {2020}<r>[0.135, 0.128, 0.303, 0.456]</r>.\nBased on the above thought, the answer is:\nThe percentage share of total imports has generally increased from 2015 to 2019, with a slight drop in 2020."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "rplLVHWUSb8Yk1A8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest percentage share in total imports from 2015 to 2020 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will find the year with the highest percentage by comparing the percentage shares of each year starting from {2015}<r>[0.1, 0.245, 0.253, 0.456]</r> to {2020}<r>[0.135, 0.128, 0.303, 0.456]</r>.\nBased on the above thought, the answer is:\nThe year 2019 had the highest percentage share in total imports."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "lmhxpIUpQgXdXkLa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you infer which year(s) might have seen a major upheaval in imports?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer this, I need to look for any significant changes in the percentage share of imports from {2015}<r>[0.1, 0.245, 0.253, 0.456]</r> to {2020}<r>[0.135, 0.128, 0.303, 0.456]</r>.\nBased on the above thought, the answer is:\nBased on the chart, 2017 seems to have seen a significant increase in the percentage share of total imports compared to 2016, which indicates a potential major upheaval in imports in 2017."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "6mdEGSM7sRFgi1n5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in percentage between import in 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the difference, I need to subtract the 2015 import percentage from that of 2016 based on the given slices of the pie chart of imports from {2015}<r>[0.1, 0.245, 0.253, 0.456]</r> and from {2016}<r>[0.099, 0.438, 0.253, 0.703]</r>.\nBased on the above thought, the answer is:\n4.2%"
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "3H7Rqr0ELjdhVepi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average percentage share of total imports over the 6 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage share, I should add up the percentages of each year and divide that by the number of years, which is six in this case.\nBased on the above thought, the answer is:\nThe average share is approximately 16.7%."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "po3VnFYQbpEWDNCw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the year 2018 and 2019 together constitute of the total imports?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I add the percentages for {2018}<r>[0.253, 0.456, 0.405, 0.752]</r> and {2019}<r>[0.253, 0.146, 0.406, 0.472]</r> together.\nBased on the above thought, the answer is:\nThey constitute approximately 37.5% of the total imports."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "7vUsvo8Ic5HoPMF6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data on the chart into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is {Resource Distribution among Business Sectors}[0.288, 0.021, 0.675, 0.047] and various sectors are represented with different colors and their corresponding percentages in {the pie chart slices}[{HR}[0.223, 0.278, 0.482, 0.605]; \n{Finance}[0.23, 0.516, 0.544, 0.886]; {IT}[0.482, 0.516, 0.699, 0.875]; {Production}[0.482, 0.282, 0.741, 0.716]; {Marketing}[0.482, 0.147, 0.682, 0.516]; {R&D}[0.344, 0.146, 0.5, 0.516]; {Logistics}[0.283, 0.202, 0.482, 0.516]]. I can conclude that the sector value and each percentage serves as a pair of values to construct the CSV table.\nBased on the above thought, the answer is:\nSector,Percentage\nHR,15%\nFinance,25%\nIT,12%\nProduction,20%\nMarketing,13%\nR&D,10%\nLogistics,5%"
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "Vsqyd2ESgIFb7b2f",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which business sector in an organization typically receives the highest amount of resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the chart, I need to identify the slice that accounts for the most area, which is {Finance}[0.23, 0.516, 0.544, 0.886].\nBased on the above thought, the answer is:\nTypically, the Finance sector receives the highest amount of resources in an organization."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "Zm9G07KnUl5xNiqk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implication of the distribution of resources in each sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "For an extensive analysis, I examined the distribution of each sector, particularly {HR}[0.223, 0.278, 0.482, 0.605], {Finance}[0.23, 0.516, 0.544, 0.886], {IT}[0.482, 0.516, 0.699, 0.875], {Production}[0.482, 0.282, 0.741, 0.716], {Marketing}[0.482, 0.147, 0.682, 0.516], {R&D}[0.344, 0.146, 0.5, 0.516], and {Logistics}[0.283, 0.202, 0.482, 0.516].\nBased on the above thought, the answer is:\nThe distribution could suggest an organization's focus. Here, greater resources allocated to Finance and Production suggest that they might be crucial for the organization's operations. On the other hand, IT, Marketing, and most notably Logistics have relatively smaller slices, suggesting that they might be secondary considerations or perhaps outsourced in this organization."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "DYjIqdDuvsEXq5cs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming this is a projection for the next year, how can an department head use this information for planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sectors' resource allocations on the pie chart, a department head can understand where the organization's priorities lie and plan accordingly.\nBased on the above thought, the answer is:\nA department head can use this information to align their plans with the organization's priorities. For example, the Finance head can plan to take on additional projects knowing they have a significant resource allocation, while the Logistics head might have to plan to achieve their goals with fewer resources or look for ways to increase process efficiencies."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "zQqaEihH5XBYFSQZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the lowest resource allocation as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the slice with the smallest area {Logistics}[0.283, 0.202, 0.482, 0.516].\nBased on the above thought, the answer is:\nLogistics."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "ZVV791DoCgGtfrA1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total contribution of the sectors: HR, IT, and R&D?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages of {HR}[0.223, 0.278, 0.482, 0.605], {IT}[0.482, 0.516, 0.699, 0.875], and {R&D}[0.344, 0.146, 0.5, 0.516].\nBased on the above thought, the answer is:\n37%."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "NMyHx5rIcxSksKUb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more percentage does Finance sector receive than the Logistics sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of {Logistics}[0.283, 0.202, 0.482, 0.516] from {Finance}[0.23, 0.516, 0.544, 0.886].\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "sA8lBty6oTDwCc22",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the pie chart data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the information provided by the {chart title}<r>[0.154, 0.021, 0.809, 0.047]</r>, {legend}<r>[0.772, 0.063, 0.993, 0.219]</r>, and {pie sections}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can convert the data into a structured table.\nBased on the above thought, the answer is:\n\"Area ,Investment (\u00a3'000) in 2020 \\nRecycling ,1000 \\nCommunity Involvement ,2000 \\nEthical Sourcing ,3000 \\nSustainable Sourcing ,4000\""
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "pcU94FPGosIYfTWt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area received the most investment in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area that has the largest slice of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> received the most investment.\nBased on the above thought, the answer is:\n\"Sustainable Sourcing\" received the most investment in 2020."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "dovg3iGNpxTQb3De",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the distribution of the investments suggest about the company's priorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will inspect the proportions represented by each area to determine the company's priorities.\nBased on the above thought, the answer is:\nThe distribution shows that the company's main focus in 2020 was on Sustainable Sourcing and Ethical Sourcing, suggesting a strong commitment to ethical and sustainable business practices."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "vIQ8IasXiMmhQG3S",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might we infer about the company's financial strategy for the year 2021 given the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on patterns in the data, I can speculate about possible future financial strategies.\nBased on the above thought, the answer is:\nIf the company continues along the same trajectory, we might expect increased investment in Sustainable and Ethical Sourcing in 2021. However, the company might also choose to balance its efforts by increasing investments in Recycling and Community Involvement."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "ex6xxIk88yYapvQM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of contributions made by \"Recycling\" and \"Community Involvement\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will sum up the contributions of \"Recycling\" and \"Community Involvement\" as given by the {chart legend}<r>[0.772, 0.063, 0.993, 0.219]</r> and the raw data.\nBased on the above thought, the answer is:\n3000k (\u00a3'000)"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "OxmydP3BY8Dyxv7S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average investment in these four areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average investment, I must sum up the investments in each area and divide by the number of areas.\nBased on the above thought, the answer is:\n2500k (\u00a3'000)"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "lzbN4tvnuqQIkEFz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment in \"Sustainable Sourcing\" increased by 20%, what would be the new total investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I must increase the investment in \"Sustainable Sourcing\" by 20% and sum up all the investments again.\nBased on the above thought, the answer is:\n11400k (\u00a3'000)"
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "6xcnMUZkQMw55X00",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the data about the comparison of \"Early Planning\" and \"Completed Projects\" from 2014 to 2020 shown in the chart, and generate a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the given data and based on the {legend}<r>[0.852, 0.13, 0.937, 0.345]</r> marking each year with different colors and the {pie chart}<r>[0.297, 0.197, 0.728, 0.813]</r> presenting a clear visual comparison of \"Early Planning\" and \"Completed Projects\" to generate a structured table representing this data.\nBased on the above thought, the answer is:\nYear,Early_Planning,Completed_Projects\n2014,10,90\n2015,12,88\n2016,15,85\n2017,20,80\n2018,23,77\n2019,25,75\n2020,26,74"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "LUf2iRknMbgGCyFH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the number of early planning projects and the completed projects when comparing the data between 2014 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the amount of early planning projects and completed projects for every year from 2014 to 2020 that the chart shows to understand the trend.\nBased on the above thought, the answer is:\nThe trend shows an increase in the number of Early Planning projects while the Completed Projects show a steady decrease during the same period from 2014 to 2020."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "tSLy2OFRv8W3Jxqd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could you infer about the impact of the variations in the number of completed projects on the community\u2019s development and planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "Seeing the steady decrease in completed projects over the years, inferring its impact on the community's development and planning involves evaluating the potential consequences of decreasing completed projects in a community.\nBased on the above thought, the answer is:\nThe decline in completed projects could indicate a longer completion time for projects due to increased complexity or potential challenges in execution. This could also mean increased deliberation within the community's development and planning phase to ensure successful project outcomes."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "AKy6GsYPiAgcL6NW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the community leverage the trend of increasing early planning projects according to the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the upward trend in early planning projects, I need to evaluate how the community could use this momentum to its advantage.\nBased on the above thought, the answer is:\nThe growing number of early planning projects can be seen as an indicator of the community's increased interest and dedication to community development and urban planning. This momentum could potentially be leveraged to attract more investments, drive active community participation, and introduce innovative planning strategies."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "3D7cOHSTMOtpro8s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more completed projects were there than early planning projects in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to check the number of completed projects and early planning projects in 2014, then subtract the number of early planning projects from completed projects.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "aoK56sIIgir5C19H",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does the difference between completed and early planning projects become the smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the difference between completed and early planning projects of all years, the year with the smallest difference can be found.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "TeyWHDDKEkpX2arB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of both completed and early planning projects across all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number, I add up the numbers of both completed and early planning projects in each year from 2014 to 2020.\nBased on the above thought, the answer is:\n478"
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "OXo6ueYcfBxyECIE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data from the pie chart into a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "As per the pie chart, the relative areas of interest are \"Global Trade\" marked by {Global Trade's color}<r>[0.681, 0.073, 0.709, 0.087]</r> and \"Supply Chain\" distinguished by {Supply Chain's color}<r>[0.681, 0.103, 0.709, 0.117]</r>. The former makes up 55.2% and the latter 44.8% of the visual field in 2018, which is the last year marked on the x-axis as per the chart title {Comparison of Global Trade and Supply Chain Management in 2018}<r>[0.219, 0.021, 0.781, 0.047]</r>.\nBased on the above thought, the answer is:\nArea,2018\nGlobal Trade,55.2%\nSupply Chain,44.8%"
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "5HcFdKAyeo9EZ9Ti",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area had a higher proportion in 2018 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to measure the percentages of both areas and identify which one is higher.\nBased on the above thought, the answer is:\nIn 2018, \"Global Trade\" had a higher percentage of 55.2% compared to \"Supply Chain\" with 44.8%."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "PFEp0m4na7gnQ0FO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give an overview on the representation of the two areas based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportional distribution of \"Global Trade\" and \"Supply Chain\" on the pie chart.\nBased on the above thought, the answer is:\nThe pie chart represents the proportional distribution of \"Global Trade\" and \"Supply Chain\" in 2018. \"Global Trade\" had a higher proportion compared to \"Supply Chain\", covering more than half of the chart. This suggests that \"Global Trade\" was a more dominant area than \"Supply Chain\" in 2018."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "M1Orty0wYWb5gHWh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence decisions for the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to infer decisions based on the 2018 proportions of \"Global Trade\" and \"Supply Chain\".\nBased on the above thought, the answer is:\nIf we presume that trends will continue, decisions for 2019 might prioritize \"Global Trade\" as it had a higher proportion in 2018. More investment could be channeled into \"Global Trade\" for potentially higher returns. Meanwhile, \"Supply Chain\" might also receive significant attention, considering that it made up almost half of the proportion in 2018. However, specific decisions would also depend on other associated factors and market circumstances."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "VSQoah5GgyCa2nzY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the proportions of Global Trade and Supply Chain in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be calculated by subtracting the proportion of the Supply Chain from the proportion of Global Trade.\nBased on the above thought, the answer is:\nThe difference is 10.4%."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "81jNdmdDv5LLrThh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf both Global Trade and Supply Chain increased by 8%, what would be the new total proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the new total proportion, I would have to add 8% to both Global Trade and Supply Chain proportions and then sum them up.\nBased on the above thought, the answer is:\nThe total would be 108%."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "dCPrlIZ2qBtDEs7m",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Global Trade's proportion decreased by 8% while Supply Chain proportion remained the same, what would be the new proportion of Global Trade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion of Global Trade, we will have to subtract 8% from the original percentage of Global Trade.\nBased on the above thought, the answer is:\nThe new percentage for Global Trade would be 47.2%."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "XLxUXyq5uxeJBHzT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the information from the chart and show it in a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "With the aid of {the pie chart}[0.223, 0.146, 0.741, 0.886] and {the legend}[0.908, 0.063, 0.993, 0.279] indicating each year's percentage contribution, I can see that every year from 2012 to 2018 and its corresponding percentage of the total ecological Assessment.\nBased on the above thought, the answer is:\nYear,Percentage_of_Total_Ecological_Assessment\n2012,11.5%\n2013,11.9%\n2014,14.4%\n2015,13%\n2016,15.7%\n2017,17.2%\n2018,16.3%"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "Y5yUsl3bDQvVTHV5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year had the greatest positive impact on ecological sustainability and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be determined by comparing the size of the slices of {the pie chart}[0.223, 0.146, 0.741, 0.886] and identifying the year with the largest slice.\nBased on the above thought, the answer is:\nThe year 2017 had the greatest positive impact on ecological sustainability as it has the largest slice (17.2%) in the pie chart, indicating the highest index for environmental sustainability."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "vsfzongN8t1H7gCV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you observe in the percentages of the ecological environment sustainability assessment from 2012 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "An overall trend can be understood by analyzing the sizes of the slices in {the pie chart}[0.223, 0.146, 0.741, 0.886].\nBased on the above thought, the answer is:\nThe percentages of the ecological environment sustainability assessment generally increase from 2012 to 2018, demonstrating an overall improvement in environmental sustainability over the years."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "lfNlmsR64tyBoqDS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence ecological decision-making in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require analyzing the trend shown in {the pie chart}[0.223, 0.146, 0.741, 0.886] and predicting its potential impact on future decisions.\nBased on the above thought, the answer is:\nThe data shows an increasing trend in ecological sustainability over the years. This could encourage decision makers to continue investing in policies and technologies that have promoted this trend in order to maintain or even accelerate the improvement in environmental sustainability."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "i5OBAMreUEbRlS2y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest percentage contribution from any year according to the data visualized?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the answer by examining the percentages for all the years on {the pie chart}[0.223, 0.146, 0.741, 0.886] to find the lowest percentage contribution.\nBased on the above thought, the answer is:\n11.5%"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "40iyrjOfNLvp5OYR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentage contributions of years 2012, 2014 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrive at this, I would need to locate the years 2012, 2014, and 2016 in the {legend}[0.908, 0.063, 0.993, 0.279] and sum up their corresponding percentage contributions.\nBased on the above thought, the answer is:\n41.6%"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "bakxQOSxEbKdaWRC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the total percentage of years 2014, 2015, and 2016 more than the total percentage of years 2012, 2013, and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "The answer can be obtained by summing up the percentages for years 2014, 2015, and 2016 and subtracting the total of years 2012, 2013, and 2017.\nBased on the above thought, the answer is:\n3.5%"
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "9czignc7ATw8nkui",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart representing \"Business Management and Organizational Leadership Focus\" into a structured table for easier comparison across various aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I need to list all the sectors {Organizational Management}[0.349, 0.662, 0.393, 0.682], {Leadership Quality}[0.602, 0.417, 0.646, 0.437], {Change Management}[0.385, 0.311, 0.429, 0.331] as rows and their corresponding percentages as the columns.\nBased on the above thought, the answer is:\nBusiness Aspect,Percentage\nOrganizational Management,47.1%\nLeadership Quality,41.2%\nChange Management,11.8%"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "3suV91ZIFoImD5KM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich business aspect receives the most focus, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The aspect with the largest percentage is considered to be the most focused.\nBased on the above thought, the answer is:\nOrganizational Management is the aspect that receives the most focus, as represented by its largest share of 47.1%."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "YUguJLkETmWCtUur",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about operational priorities from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpreting the chart involves comparing the relative sizes of the sections representing each of the operational priorities.\nBased on the above thought, the answer is:\nThe pie chart suggests that businesses might prioritize Organizational Management and Leadership Quality, as these aspects make up a combined 88.3% of the focus, while Change Management might be less of a priority given its 11.8% focus."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "qiyEW7FzWvvJBGMF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a business were to align with the focus areas represented by this chart, where might they concentrate their efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the business aspects that have the largest proportions on the pie chart.\nBased on the above thought, the answer is:\nBased on the chart's focus areas, a business looking to align with these might mainly concentrate their efforts on Organizational Management- as the single largest focus area- and Leadership Quality, as these aspects make up a significant majority of the business focus."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "SYaecql46n11xnnW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined focus percentage of Organizational Management and Leadership Quality?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined focus, I need to add the percentages of Organizational Management and Leadership Quality.\nBased on the above thought, the answer is:\n88.3%"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "00yJIDMLaWV5jeeh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the focus on Change Management increased by 10%, what would be the new total percentage of all business aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the focus on Change Management increased by 10%, I would have to add this to the existing percentage and recalculate the total.\nBased on the above thought, the answer is:\n101.8%"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "YfrqVWsBF9rVdv9l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in focus between Organizational Management and Change Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in focus, I need to subtract the percentage of Change Management from the percentage of Organizational Management.\nBased on the above thought, the answer is:\n35.3%"
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "4a0EV6s6qxv8MSB2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, generate a CSV structure containing the government policy effectiveness evaluation from 2015 to 2021."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV structure, I will reference the percentages given in each slice of the pie in the pie chart. The percentages correspond to the government policy effectiveness for that specific year labeled by {the text on the slice}. The slices are located at {the pie slice of 2015}[0.223, 0.278, 0.482, 0.569], {of 2016}[0.225, 0.516, 0.482, 0.835], {of 2017}[0.351, 0.516, 0.588, 0.886], {of 2018}[0.482, 0.516, 0.74, 0.853], {of 2019}[0.482, 0.276, 0.741, 0.546], {of 2020}[0.482, 0.149, 0.679, 0.516], and {of 2021}[0.283, 0.146, 0.517, 0.516].\nBased on the above thought, the answer is:\nPolicy Year,Policy Effectiveness Evaluation\n2015,13.4\n2016,14.3\n2017,15.2\n2018,17.0\n2019,12.5\n2020,11.6\n2021,16.1"
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "6CsRDWIF0oc6N2MT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year had the most effective government policies based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the most effective policies corresponds to the slice of the pie chart with the highest percentage.\nBased on the above thought, the answer is:\nBased on the chart, the most effective government policies were implemented in the year 2018."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "fGr6oLuqin2veBhi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven this information, what could be inferred about the trend in the effectiveness of policy implementation from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to analyze the percentages of each slice to determine the trend in policy effectiveness over the years.\nBased on the above thought, the answer is:\nIt appears that there might have been a gradual increase in policy effectiveness from 2015 until a peak in 2018, followed by a decrease in effectiveness in the subsequent years, particularly 2020. However, 2021 appears to show a return to higher effectiveness."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "gNDHqdzDy8xRnWwl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data affect future policy planning and implementation?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this, potential implications of the trends and figures in the chart can be considered.\nBased on the above thought, the answer is:\nPolicymakers may leverage this data to examine and understand what contributed to the high effectiveness in 2018 and attempt to replicate those factors in future policies. The significant drop in 2020 could be analyzed to understand the challenges encountered during that year, presumably the Covid-19 pandemic, and put measures in place to mitigate such in future. The rebound in 2021 might indicate resiliency or effective response strategies which might also be integrated in future policy planning."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "zueRefeeMiVWfF3I",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total policy effectiveness evaluation for the years 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, the percentages for the years 2016 to 2021 need to be added up.\nBased on the above thought, the answer is:\nThe total policy effectiveness evaluation for the years 2016 to 2021 is 86.7%."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "JVRhNUuWPiu3Z82o",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average policy effectiveness evaluation from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average policy effectiveness evaluation from 2015 to 2021, I need to sum up the evaluation percentages of all the years and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average policy effectiveness evaluation from 2015 to 2021 is approx. 14.3%."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "yM4gX8o6KrmahfoL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the policy effectiveness of 2020 went down by 3%, what would be the total policy effectiveness from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the new total policy effectiveness, I will subtract 3% from the policy effectiveness of 2020 and then add up the effectiveness percentages of all the years.\nBased on the above thought, the answer is:\nThe total policy effectiveness from 2015 to 2021, assuming the effectiveness of 2020 went down 3%, would be 94.7%."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "WKkUSoAcPYT0JfIq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you generate a table displaying information on real estate investment from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it's clear that the pie slices represent different years and their associated investment value in billions. There are slices for {2016}<r>[0.223, 0.278, 0.482, 0.664]</r>, {2017}<r>[0.244, 0.516, 0.491, 0.886]</r>, {2018}<r>[0.482, 0.516, 0.732, 0.886]</r>, {2019}<r>[0.482, 0.19, 0.741, 0.609]</r>, and {2020}<r>[0.283, 0.146, 0.604, 0.516]</r>. Every slice carries the percentage information representing the proportion of the real estate investment for that year, from which I can convert into actual values.\nBased on the above thought, the answer is:\nYear,Real Estate Investment (Billions)\n2016,65\n2017,70\n2018,75\n2019,78\n2020,80"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "yRJwlz7xbuSU52Oz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred from the growth trend of the real estate investment from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the growth trend, I need to compare the investment values from 2016 to 2020.\nBased on the above thought, the answer is:\nThe real estate investment has been showing a steady growth from 2016 to 2020, indicating a positive trend and possibly a strong real estate market during these years. Despite a slight dip in the growth rate in 2020, the market was still able to demonstrate an overall increase."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "pgwiGvykl2pRJn3K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can the year 2020 provide about the real estate investment with respect to the overall trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract insights for year 2020, I need to contrast its standing {2020}<r>[0.283, 0.146, 0.604, 0.516]</r> with the preceding years.\nBased on the above thought, the answer is:\nDespite witnessing a moderate dip in growth rate in 2020, the real estate investment still managed to increase compared to the previous years. One possible reason for this slower growth could be the impact of the Covid-19 pandemic on the real estate market."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "qBJlsMaKOMgdpzlc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould the progression of real estate investments, especially the dip in 2020, imply anything about future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To speculate about future implications, I need to analyze the previous years' trends, particularly the slowing down of growth in 2020.\nBased on the above thought, the answer is:\nThe incremental yet steady growth in real estate investment from 2016 to 2020, despite a slight dip in the final year, likely suggests a resilient market. However, the impact of 2020 may potentially introduce short-term instability or cautious investment approach in the subsequent years, until a full recovery to the pre-2020 growth rate is observed."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "GRIBYOK7chwheli2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Real Estate Investment grow from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the difference in investment between the year 2020 {2020}<r>[0.283, 0.146, 0.604, 0.516]</r> and the year 2016 {2016}<r>[0.223, 0.278, 0.482, 0.664]</r>.\nBased on the above thought, the answer is:\n15 Billion"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "Zvl13ACwRpqu7y6p",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total investment in the real estate market from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the investments of all years, from 2016 to 2020.\nBased on the above thought, the answer is:\n368 Billion"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "3zf7WEKJFrkwsLuD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the largest increase in Real Estate Investment compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the change in investment from year to year. This can be done by subtracting the investment of the current year from the investment of the previous year.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "kCK9voxn0yise3rC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the web traffic data from the pie chart into a CSV format table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Web Traffic from 2000 to 2007\"}[0.355, 0.021, 0.608, 0.047] and the color slices representing the years from 2000 to 2007 in the legend and pie respectively, I can see that each slice represents the web traffic of a year.\nBased on the above thought, the answer is:\nYear,Web Traffic\n2000,245\n2001,380\n2002,495\n2003,590\n2004,700\n2005,850\n2006,920\n2007,1024"
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "9tCkHVLBTu7IYGEy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you identify a year where there was a substantial change in web traffic?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon analyzing each year with its corresponding slice which represents the volume of web traffic, I can identify the years experiencing a substantial change.\nBased on the above thought, the answer is:\nThe year 2005 showed a remarkable increase in web traffic compared to the previous years."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "BuoWypzHuew83x5m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you analyze the general growth trend of web traffic over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon observing the volume of traffic through each year\u2019s slice in the pie, I can analyze the growth pattern.\nBased on the above thought, the answer is:\nThe volume of web traffic exhibits a general pattern of increasing growth each year during the period from 2000 to 2007."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "F3SiaIQraipQTmz3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how has web traffic varied over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the individual slices of the pie, each representing a specific year, we can make inferences about variations in web traffic.\nBased on the above thought, the answer is:\nWeb traffic has shown continuous growth over the years, although the rate of growth has varied. Notably, years 2002 and 2005 saw significant increases in traffic, indicating potential outliers or events causing a surge in web visitation."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "zCl0s9rkBij1I4jW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in web traffic from 2000 to 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to subtract the web traffic in 2000 from the web traffic in 2007.\nBased on the above thought, the answer is:\nThe increase in web traffic from 2000 to 2007 was 779 units."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "GD4D35u76O7PPEIH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average web traffic for the years observed within the chart period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average web traffic, I need to sum the web traffic for all the years and then divide by the number of years covered.\nBased on the above thought, the answer is:\nThe average web traffic for the years 2000 to 2007 is 650 units."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "K1zSjlBDR4cRklb3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the year with the highest web traffic volume."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the highest web traffic, I need to compare the web traffic of all the years.\nBased on the above thought, the answer is:\nThe year with the highest web traffic volume is 2007."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "f9ZEpMh6zIMJdfHB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, can you present the budget allocation in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that the chart {Local Government Expenditure On Community Development}<r>[0.229, 0.021, 0.734, 0.047]</r> represents the distribution of expenditure in different categories such as {Healthcare}<r>[0.887, 0.071, 0.963, 0.091]</r>, {Education}<r>[0.887, 0.101, 0.956, 0.121]</r>, {Public Safety}<r>[0.887, 0.131, 0.977, 0.151]</r> and {Transportation}<r>[0.887, 0.161, 0.987, 0.181]</r>. The percentages of each category are stated on the pie chart slices.\nBased on the above thought, the answer is:\nLocal Government Services,Healthcare,Education,Public Safety,Transportation\nAllocation in %,45,25,5,15"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "2mQamDlSVuZuhYgV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich public sector benefits most from the local government expenditure in community development, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slice of the pie chart, the sector with the largest slice will be the sector that benefits the most.\nBased on the above thought, the answer is:\nThe Healthcare sector benefits the most from local government expenditure because it receives the largest budget allocation at 45%."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "kikHZXxDCnQplTe8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you discern any information about the government's priorities in community development?"
+            },
+            {
+                "from": "gpt",
+                "value": "The government's priorities can be inferred by the proportions of the budget allocated to each sector as shown on the pie chart.\nBased on the above thought, the answer is:\nBased on the chart, it appears that the government prioritizes Healthcare above all other sectors in community development since it receives the largest portion of the budget. This suggests that the government recognizes the importance of healthcare for the well-being of the community."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "ZgHlew2Yhnyyy3Zd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can local government improve support in the Public Safety sector based on this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the local government wants to improve support in the Public Safety sector, it will need to reconsider budget allocation as per the chart.\nBased on the above thought, the answer is:\nConsidering the data, the Public Safety sector receives the smallest portion of the budget at 5%. If the local government wants to improve support in this sector, it may need to increase its allocation in the next budgeting cycle, possibly at the cost of reductions in other sectors or via increased total spending."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "zOfgk2eCGDTSdXVt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of the budget allocated to Public Safety and Transportation combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding up {Public Safety's proportion}<r>[0.579, 0.123, 0.669, 0.143]</r> with the {Transportation's proportion}<r>[0.284, 0.123, 0.384, 0.143]</r> would give the combined proportion.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "VY6T164cpljCvehG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more budget is allocated for Healthcare than for Education?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage of budget allocated to {Healthcare}<r>[0.223, 0.818, 0.298, 0.838]</r> and {Education}<r>[0.762, 0.435, 0.832, 0.455]</r> would need to be compared.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "LM6h4soC7rbWLm1f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the government decides to allocate an equal budget for all categories, what would be the new allocation percentage per category?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total budget is 100%. If divided equally among the 4 categories, the allocation percentage per category can be obtained.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "4gqidvO7IlRUQ3qE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart axis represents the global international trade volume in billions of USD distributed over the years from 2015 to 2020 {Global International Trade Volume from 2015 to 2020}[0.26, 0.021, 0.703, 0.047]. For each year, the slice of the pie represents the volume and is labeled accordingly {\"2015\"}[0.952, 0.071, 0.987, 0.091], {\"2016\"}[0.952, 0.101, 0.987, 0.121], {\"2017\"}[0.952, 0.131, 0.987, 0.151], {\"2018\"}[0.952, 0.161, 0.987, 0.181], {\"2019\"}[0.952, 0.191, 0.987, 0.211], and {\"2020\"}[0.952, 0.221, 0.987, 0.241].\nBased on the above thought, the answer is:\nYear,Trade Volume (Billion USD)\n2015,20145\n2016,21050\n2017,22345\n2018,24500\n2019,25560\n2020,20000"
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "BRt4Iik52MQhQImp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart, which year had the least international trade? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the sizes of the pie slices, the year with the smallest slice is the one with the least international trade.\nBased on the above thought, the answer is:\nThe year 2020 saw the least international trade, possibly due to the impact of the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "3sMxHWTDgas8LOUw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the international trade trend from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference, I need to look at the sizes of the slices for each year to identify the trend in trade volume.\nBased on the above thought, the answer is:\nThe global international trade trend has been generally positive between 2015 and 2019, with an increase in trade volume each year. However, it took a sharp downturn in 2020. This decrease was likely due to the global impact of the COVID-19 pandemic, leading to disruptions in international trade."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "yxMdXiC7Ap1NjPG6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence business decisions related to international trade for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the growth trend and the sudden drop in 2020 may impact business decisions regarding international trade.\nBased on the above thought, the answer is:\nConsidering the growth trend between 2015 and 2019, businesses may be motivated to continue trading internationally. However, the sharp decline in 2020 suggests that severe global events, such as a pandemic, can impact trade significantly. As a result, businesses may want to develop more robust strategies to mitigate potential risks and disruptions in the future."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "KF02jFYIbXfoJlSQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the trade volume the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the size of the pie slices which stand for trade volume for each year. The largest slice will correspond to the year with the highest trade volume.\nBased on the above thought, the answer is:\nThe highest trade volume was in 2019."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "FmYMoF9kwbvqF608",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2015 and 2016, was there an increase or decrease in trade volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to compare the trade volume in 2015 and 2016 by analyzing the corresponding pie slices in the chart. The year with the larger slice had a higher trade volume.\nBased on the above thought, the answer is:\nThere was an increase in trade volume."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "TJVEfHPo64mzZMYc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in trade volume between 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the trade volume of 2020 from that of 2019.\nBased on the above thought, the answer is:\nThe difference in trade volume between 2019 and 2020 is 5560 Billion USD."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "IjQBdf2xaW92TQjb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart you are observing into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that the chart encompasses data for different years. Each segment of the pie chart {Pie Chart}[0.223, 0.146, 0.741, 0.886] represents a year and the corresponding percentage of the transportation trend. Looking at the legend: {Legend}[0.908, 0.063, 0.993, 0.309], it includes key information regarding color codes associated with each year.\nBased on the above thought, the answer is:\nYear,Transportation Trends\n2010,6.1%\n2011,7.6%\n2012,9.1%\n2013,10.6%\n2014,12.1%\n2015,15.2%\n2016,18.2%\n2017,21.2%"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "6U58JJOx30Tqwc2K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you tell which year saw the highest increases in the transportation industry trends? Elaborate on your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice sizes correspond to the relative sizes of the yearly measurements in the chart. The year with the largest slice can be considered as the year with the highest increase in transportation industry trends.\nBased on the above thought, the answer is:\nThe year 2017 saw the highest increases in the transportation industry trends, as it has the largest pie slice, with its percent value being 21.2%."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "CBlIyZLlvzX4oeba",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the pie chart, what can you infer about the growth trend in the transportation and logistics industry from the years 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to assess the proportions of each year to make inferences about the growth pattern in the industry.\nBased on the above thought, the answer is:\nThe pie chart indicates a steady growth in the transportation and logistics industry from the year 2010 to 2017. The proportions increase with each subsequent year, suggesting that there has been a positive upward trend during this period."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "wMkz0kcz4YfcckIO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data illustrated in the pie chart, can you speculate on how the transportation industry might prioritize their operations in the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the previous years' trends and proportions to speculate on the industry's priorities for the following year.\nBased on the above thought, the answer is:\nGiven the steady upward trend from 2010 to 2017, it could be anticipated that the transportation industry would continue prioritizing strategies that have been contributing to this growth. This could include areas like expanding transport routes, investing in infrastructure, or implementing new logistics technologies. The industry might particularly focus on mirroring the strategies of the year 2017, given its high proportion of 21.2%."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "WVFhtse1xTtpXEJt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the industry trends in 2015 and 2012 displayed in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I will subtract percentage of 2012 from percentage of 2015 as given in the pie chart segments.\nBased on the above thought, the answer is:\n6.1%"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "vh1Q9ZxEC8Evo3rJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of the transportation trends from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the proportions, I need to add the percentages of the years from the pie chart for years 2010 up to 2013.\nBased on the above thought, the answer is:\n33.4%"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "n6uArwdXClQL20c7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage for 2017 were 5% less, what would be the new total percentage for the years 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, first I need to subtract 5% from the 2017 percentage, then include this new percentage when summing up all percentages for the years 2010 till 2017.\nBased on the above thought, the answer is:\n94.0%"
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "fzPMASBtAuguXgF0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the distribution of investments in the Entrepreneurship and Innovation Ecosystem, represented as data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. I will translate the percentages from the individual {slices}<r>[0.223, 0.146, 0.741, 0.886]</r> in the pie chart to construct a data table for the corresponding sectors listed in the {legend}<r>[0.745, 0.063, 0.993, 0.249]</r>.\nBased on the above thought, the answer is:\n\"Sector\",\"Investment\"\n\"Startups\",23\n\"Innovation hubs\",12\n\"Research Institutes\",29\n\"Skill Development programs\",10\n\"Venture Capitalists\",16\n\"Government initiatives\",10"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "MypxIMFWFJ2D0TZY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has received the highest investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the {sector slices}<r>[0.223, 0.146, 0.741, 0.886]</r> and corresponding percentages, the \"Research Institutes\" has received the highest investment.\nBased on the above thought, the answer is:\nResearch Institutes"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "9Wta2cJnum37oTCT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be concluded about the importance of research in the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the {slice for the Research Institutes}<r>[0.464, 0.449, 0.741, 0.886]</r>, having the biggest fraction, we can conclude something in line with that.\nBased on the above thought, the answer is:\nResearch is of significant importance in the Entrepreneurship and Innovation Ecosystem, demonstrated by the highest investment being made in Research Institutes."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "1oO1UGO6C8yby26p",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, are Skill Development programs or Government initiatives given priority in investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the respective {slices for Skill Development programs}<r>[0.482, 0.248, 0.736, 0.516]</r> and {Government initiatives}<r>[0.283, 0.157, 0.482, 0.516]</r>, one can draw a conclusion on this aspect.\nBased on the above thought, the answer is:\nNeither Skill Development programs nor Government initiatives are given significant investment priority in the Entrepreneurship and Innovation Ecosystem, given their comparatively smaller proportions of the total investments."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "FTxOqthXXAbLogWP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of investments provided to the Startups and the Venture Capitalists?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to sum up the percentages of the \"Startups\" slice(23%) and the \"Venture Capitalists\" slice(16%) which I found from the {pie slice coordinates}<r>[0.223, 0.278, 0.482, 0.767]</r> and <r>[0.419, 0.146, 0.66, 0.516]</r>.\nBased on the above thought, the answer is:\n39%"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "296ZTwpXkc2qMVqI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the \"Research Institutes\" sector received a 10% increase in investment, what percentage of the total investments would it hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Research Institutes\" sector currently holds 29%. If it received a 10% increase, it would hold 39%. I found the present percentage from the {slice's position}<r>[0.464, 0.449, 0.741, 0.886]</r> in the pie chart.\nBased on the above thought, the answer is:\n39%"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "qM3BHXDrDc9rl6QW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage investment between the \"Startups\" and the \"Innovation hubs\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be calculated by subtracting the percentage of the \"Innovation hubs\" slice (12%) from the \"Startups\" slice (23%). These percentages were deduced from their respective {slices' coordinates}<r>[0.223, 0.278, 0.482, 0.767]</r> and <r>[0.292, 0.516, 0.482, 0.885]</r>.\nBased on the above thought, the answer is:\n11%"
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "GVY7mxEHeF7Tp657",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the sales data for the products \"Smartphone\" and \"Tablet\" over the years as per the given chart and present the information in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I observe the color-coded slices for \"Smartphone\" and \"Tablet\" categories. The annual sales data from 2016 through 2020 are stored as numerical values associated with each category, and the chart's legend located at {the legend}<r>[0.857, 0.063, 0.993, 0.129]</r> provides label identification. Yearly variations can be determined by plotting the chart repeatedly for each year.\nBased on the above thought, the answer is:\nYear,Smartphone,Tablet\n2016,120,85\n2017,150,75\n2018,170,95\n2019,300,90\n2020,250,70"
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "bqLxDARtFi4QsY7e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product has been more successful over the years in terms of sales? Can you explain?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the annual sales numbers from the chart for 'Smartphone' and 'Tablet', I can determine which product has been more successful.\nBased on the above thought, the answer is:\n'Smartphone' has been more successful over the years in terms of sales as it consistently shows higher sales numbers compared to 'Tablet'."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "ZDirqbPATuVmVQWp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can you make about the tablet market based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the 'Tablet' slice of the pie chart for each year to make observations about the tablet market.\nBased on the above thought, the answer is:\nThe tablet market seems to have a fluctuating trend with no consistent growth or decline being noticeable. From 2016 through 2020, the tablet sales show an overall downward trend suggesting decreasing demand or competitiveness of this product category."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "bp4tGFVSHNNDqhgT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible reason for the substantial increase in the sales of 'smartphone' in the year 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows a stark increase in the 'Smartphone' sales in 2019 compared to previous years. To explain this, I need to infer possibilities based on the trend observed but without concrete data to back it up, any explanation would be speculative.\nBased on the above thought, the answer is:\nLooking at the chart, one could infer that a successful product launch, aggressive marketing, better product features or higher consumer demand could have contributed to the substantial increase in smartphone sales in 2019. However, the actual reason would need to be validated with more specific data or business insights."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "RSOUj030Vh3tB0Sw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the chart, what is the sales difference between 'Smartphone' and 'Tablet' in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the sales difference, I need to look at the pie chart corresponding to the year 2019 and calculate the difference between the sales value of 'Smartphone' and 'Tablet'.\nBased on the above thought, the answer is:\nThe sales difference between 'Smartphone' and 'Tablet' in the year 2019 is 210 units."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "TbqBG7vOrsjWejcW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of sales in 2019, is the proportion of 'Smartphone' to 'Tablet' more than 3:1?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the respective marks for 'Smartphone' and 'Tablet' on the chart that corresponds to the year 2019 to answer this question.\nBased on the above thought, the answer is:\nYes, the proportion of 'Smartphone' to 'Tablet' sales in 2019 is more than 3:1."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "HBfVF9IXorZTkzRP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the product sales of 'Smartphone' and 'Tablet' in 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total sales, I need to add the sales of 'Smartphone' and 'Tablet' as per the chart that corresponds to the year 2020.\nBased on the above thought, the answer is:\nThe sum of the product sales of 'Smartphone' and 'Tablet' in 2020 is 320 units."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "Ry3lzLIkq3iilItL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie charts, can you summarize the distribution of percentages between AI research and Robotics research in a CSV table for years 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie charts, I see two subplots, the one at coordinates {subplot_0}<r>[0.035, 0.046, 0.47, 0.979]</r> represents the data for the year 2015, whereas the one at coordinates {subplot_1}<r>[0.53, 0.046, 0.965, 0.979]</r> represents the data for the year 2020. I can summarize these in a CSV table.\nBased on the above thought, the answer is:\nYear,AI_research,Robotics\n2015,45,55\n2020,80,20"
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "dKkp3n3ayrvyq76W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich research field showed a more significant change during the period from 2015 to 2020, according to pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the 2015 and 2020 data for both research fields, I can see which field had more significant change.\nBased on the above thought, the answer is:\nBoth AI research and Robotics research field showed a significant change of 35 percent. But, it's notable that AI research has increased while Robotics research has decreased."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "oF0XWtCMCLdILye9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the trend in research field preference over the years 2015 to 2020 from the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "The analysis of pie charts will clearly depict the trend in research field over the 5-year span.\nBased on the above thought, the answer is:\nOn the basis of the pie charts, there is a distinctive preference shift towards AI research from Robotics research over the years 2015 to 2020. It indicates an emerging prominence of AI research in scientific domain."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "zVBRKNr6NfHAjQg0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data presented in the pie chart, how would you forecast the funding allocation for AI research and Robotics research for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The increase and decrease in the percentage of AI research and Robotics research respectively speaks volume about the performance and acceptance of the two fields. Thus, the funding allocation predictions can be inferred.\nBased on the above thought, the answer is:\nThe considerable increase in the percentage of AI research suggests that funding allocation would be more towards AI research in the future. Conversely, the decline in Robotics research suggests that it might face challenges in securing funding unless the trend is reversed."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "WXRyHnzkwx8nUtJz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did AI research increase from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in percentage of AI research, I would subtract the percentage of AI research in 2015 from that in 2020.\nBased on the above thought, the answer is:\n35%."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "P3ZuSzti0uP8yGhM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage change in the Robotics research from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the change in Robotics research, I would deduct the percentage in 2020 from the percentage in 2015.\nBased on the above thought, the answer is:\n-35%."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "mtVJtZYlzj2WERX4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the AI research continues to grow at the same rate by 2025, what would be the expected percentage of AI research?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the AI research continues to grow at the same rate, I would add the increase from 2015 to 2020 to the percentage in 2020 to predict the percentage in 2025.\nBased on the above thought, the answer is:\n115%."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "NW1enOy5h8QGKKzm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the pie chart data to a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "I detect that the pie chart details the trends in sports and fitness activities with 30% involved in Running {Running}<r>[0.148, 0.604, 0.205, 0.624]</r>, 20% in Yoga {Yoga}<r>[0.551, 0.901, 0.583, 0.921]</r>, 30% for Weight-training {Weight-training}<r>[0.758, 0.407, 0.866, 0.427]</r>, and both Cycling {Cycling}<r>[0.502, 0.1, 0.553, 0.12]</r> and Swimming {Swimming}<r>[0.258, 0.161, 0.331, 0.181]</r> at 10% each.\nBased on the above thought, the answer is:\nActivity,Percentage\nRunning,30\nYoga,20\nWeight-training,30\nCycling,10\nSwimming,10"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "3264hDQ2SORxK0cN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity is least popular according to the data, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the legend and individual slices, the least popular activities are Swimming {Swimming}<r>[0.258, 0.161, 0.331, 0.181]</r> and Cycling {Cycling}<r>[0.502, 0.1, 0.553, 0.12]</r> each accounting for 10% of the total.\nBased on the above thought, the answer is:\nThe activities 'Swimming' and 'Cycling' are the least popular, as they both have the smallest slices in the pie chart, each accounting for 10% of the total."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "COdFbwXncFw6US4R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which activities seem to have the same level of popularity and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the slices of the pie chart, the activities 'Running' and 'Weight-Training' have the same sized slices, indicating they have the same level of popularity.\nBased on the above thought, the answer is:\n'Running' and 'Weight-Training' seem to have the same level of popularity because they both account for the same percentage (30%) of the total distribution."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "Ja9ezH3sdh4XC79N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about current trends and interests in fitness and sports health activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I make inferences based on the proportions of each category represented in the chart.\nBased on the above thought, the answer is:\nThe pie chart shows that 'Running' and 'Weight-Training' are the most popular fitness activities, each capturing 30% of the population\u2019s interest, whereas 'Yoga' is less popular with 20% participation. \u2018Swimming\u2019 and 'Cycling' are the least popular activities representing only 10% each. This data indicates a strong trend towards activities that may not require many resources to start with, such as running or weight training which require minimal equipment compared to swimming and cycling."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "8aEbtJtUEDEPRQ2Y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity of Swimming and Cycling increased by 5% each, what is the new popularity percentage of Running?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the chart, each slice represents a certain percentage. If Swimming and Cycling each increase by 5%, then their combined share will be 30%. Given that the chart as a whole represents 100%, the remaining percentage - including Running, Yoga, and Weight-training - must sum to 70%.  With Yoga at 20% and Weight-training at 30%, the percentage for Running must be 20%.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "75HuuZf4rNAT4q2H",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of all the percentages in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In a pie chart, all the slices add up to the total or in this case 100%.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "HNwIx6weym1LMTGa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompare the combined percentage of Yoga, Cycling and Swimming to that of Running. Which group has a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the two groups, I add up the percentages of Yoga, Cycling, and Swimming, which amounts to 40%. I then compare this figure to the percentage of Running, which is 30%.\nBased on the above thought, the answer is:\nYoga, Cycling and Swimming"
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "M1GQqGBY6Hz14GDf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to convert the pie chart data of the shared growth in AI and Robotics sectors from 2010 to 2015 into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the chart title {Robotics Industry Development Trends from 2010 to 2015}<r>[0.272, 0.088, 0.753, 0.114]</r> and {the slices of pie chart}<r>[0.301, 0.307, 0.512, 0.505];[0.297, 0.443, 0.512, 0.622];[0.313, 0.505, 0.512, 0.8];[0.451, 0.505, 0.667, 0.813];[0.512, 0.304, 0.728, 0.72];[0.347, 0.197, 0.676, 0.505]</r> with different colors representing different years and proportion of the sectors, I can translate this visual information into structured CSV data.\nBased on the above thought, the answer is:\nYear, Proportion\n2010, 7.9%\n2011, 9.4%\n2012, 14.2%\n2013, 17.3%\n2014, 23.6%\n2015, 27.6%"
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "1FHOrYuJdrD79twp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector witnessed more growth over these years, AI or Robotics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce this from the chart, I need to compare the pie slices of the two sectors in each year and consider the one with a larger slice as more dominant.\nBased on the above thought, the answer is:\nThe chart only displays the shared growth of AI and Robotics, so it is not possible to discern which sector witnessed more growth."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "HE7yuxfZzYS2jmGw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the trend of growth be described over these years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the increasing proportions from the pie chart year by year, I can describe the growth trend.\nBased on the above thought, the answer is:\nThe growth trend witnessed from 2010 to 2015 is steadily increasing with an apparent surge in growth in the later years, especially in 2014 and 2015."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "GiHOwhDH6t0G5G9V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what might be the projection of growth for the next year, 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the observable pattern of growth in the chart, assuming this pattern continues, I can make a projection for the next year.\nBased on the above thought, the answer is:\nIf the growth pattern continues as observed from 2010 to 2015, we can expect a further increase in growth for the year 2016. However, this assumption would hold true if all other factors impacting this growth remain the same."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "nDRI720FlKkY8e3f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year did the tech industry witness a growth surge?"
+            },
+            {
+                "from": "gpt",
+                "value": "I just need to compare the ratios of each year and find the year with the biggest ratio.\nBased on the above thought, the answer is:\n2015."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "213cIg2mdozSSpm2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth proportion from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, first I need to add up the growth proportions from 2010 to 2015, then divide by the total number of years which is 6.\nBased on the above thought, the answer is:\nThe average growth proportion from 2010 to 2015 is (7.9% + 9.4% + 14.2% + 17.3% + 23.6% + 27.6%) / 6 = 16.7%."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "P4Xt0PReOGu2qBRi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we look at the proportions in 2010 and 2015, what is the growth percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the proportion in 2010 from the proportion in 2015.\nBased on the above thought, the answer is:\nThe growth percentage from 2010 to 2015 is 27.6% - 7.9% = 19.7%."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "akx8KqF04YWVdU6P",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a table format with two columns: Year and Market Share?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart and legend starting at {coordinates}<r>[0.908, 0.063, 0.993, 0.219]</r>, I can observe the years 2022-2026 represented by different colors. The percentages provided within each sector of the pie chart inform me of each year's respective portion of the market share in the title {\"Predicted Market Share from 2022 to 2026\"}<r>[0.304, 0.021, 0.659, 0.047]</r>.\nBased on the above thought, the answer is:\nYear,Market Share\n2022,30%\n2023,20%\n2024,25%\n2025,15%\n2026,10%"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "8fjbsvSYyLXpKqOk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data in the pie chart, what might be a potential explanation for the predicted increase in market share for the year 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data in the pie chart, there is indeed an increase in market share in 2024. The reasoning for this would require outside information not provided by the chart.\nBased on the above thought, the answer is:\nPossible explanations could include strategic business decisions, market circumstances, or even external influences that could favorably impact the market share for that year."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "S4I3IilRFfk9fv8V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trend shown in the pie chart, what prediction could we make for market share in the year 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can only infer from the overall decline trend in the pie chart although there is a slight increase in 2024.\nBased on the above thought, the answer is:\nAssuming the general declining trend continues beyond 2026, the market share in 2027 could potentially be lower than the 10% predicted for 2026."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "6inkeuRzadbXGVVY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might a company or analyst be interested in the information shown in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, it's crucial to consider the value of market share data.\nBased on the above thought, the answer is:\nAn analyst or company could use this information to understand past performance, predict future trends, and inform strategic decision-making. The data could also help identify potential opportunities or threats in the market based on these trends."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "jwK3sx7qrkgIcgWM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share in the years shown on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I would add the market share percentages for each year and then divide by the number of years.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "piqfFlErGYcgIjaE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share for 2022 was 5% less, what would be its new value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I would decrease the market share for 2022 by 5%.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "AjxZS4UN5Ox8GJbB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest market shares shown in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would subtract the smallest market share from the biggest to get the difference.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "1EWs3Zi3Qweozc7l",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart you've examined, could you transform the input data to CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"Consumer Behavior in Emerging Markets\"}[0.257, 0.021, 0.743, 0.047] and the pie slices {for \"Brazil\"}[0.13, 0.278, 0.87, 0.886] and {for \"India\"}[0.216, 0.146, 0.859, 0.516], I observe that the slices depict different countries and the corresponding consumer sentiment in each country.\nBased on the above thought, the answer is:\nCountry,Consumer Sentiment\nBrazil,65\nIndia,35"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "iCVpcX1gfOtGo6NR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which country shows more optimistic consumer behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, the country with a larger sector represents a more optimistic consumer behavior.\nBased on the above thought, the answer is:\nThe country with more optimistic consumer behavior is Brazil since it has a larger sector representing 65% of the total sentiment."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "qXNh4SIx5Vz4w2yq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data in the chart imply about the emerging markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the chart will be analyzed based on the size of each sector, which represents the consumer sentiment in each emerging market.\nBased on the above thought, the answer is:\nBased on the pie chart, it indicates that consumers in Brazil are more optimistic about their economic prospects than in India. This could suggest that, of the two markets, Brazil may currently be a more favorable market for business investments and expansions due to its higher consumer sentiment."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "2xMzl0s66vA9zRWe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this information affect investment decisions by multinational corporations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corporations consider consumer sentiment when making investment decisions. Therefore, I'll analyze the chart considering this perspective.\nBased on the above thought, the answer is:\nGiven the data, multinational corporations might find Brazil to be a more attractive market for investment due to its higher consumer sentiment. The relatively high consumer confidence implies a favourable business environment, which could have a high rate of return on investments. However, India, despite having a lower consumer sentiment, remains a significant market due to its huge consumer base and potential for growth."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "S8ntUazVCB9xGNDr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the sentiment in India increased by 5%, what would the new total percentage of sentiment be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total consumer sentiment, I need to increase the India's sentiment by 5% and then add this new percentage to Brazil's sentiment.\nBased on the above thought, the answer is:\nThe new sentiment for India would be 40%, and the total sentiment would be 105%."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "CKSD8Eug9HizuqVG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in consumer sentiment between Brazil and India?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in sentiment, I'll subtract the sentiment value for India from that of Brazil.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "2VRCWBIe5CkGkDnh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total sentiment does Brazil have?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the chart that the total sentiment is 100%. The percentage portion that Brazil represents can be found by comparing the sector for Brazil to the entire chart.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "SIeizHnqG9hfzmF2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart data into a CSV table format using the data extracted from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can see that there are seven segments representing the seven years from 2014 to 2020. The percentage for each year is written on the respective segment: {\"2014\"}<r>[0.315, 0.423, 0.359, 0.443]</r> with 10.1%, {\"2015\"}<r>[0.312, 0.575, 0.356, 0.595]</r> with 12.1%, {\"2016\"}<r>[0.384, 0.7, 0.428, 0.72]</r> with 11.6%, {\"2017\"}<r>[0.511, 0.716, 0.555, 0.736]</r> with 15.2%, {\"2018\"}<r>[0.61, 0.564, 0.654, 0.584]</r> with 15.7%, {\"2019\"}<r>[0.575, 0.357, 0.619, 0.377]</r> with 16.1% and finally, {\"2020\"}<r>[0.418, 0.292, 0.462, 0.312]</r> with 19.1%. These percentages represent the Social Cultural and Value Change index for each year.\nBased on the above thought, the answer is:\nYear, Percentage\n2014,10.1%\n2015,12.1%\n2016,11.6%\n2017,15.2%\n2018,15.7%\n2019,16.1%\n2020,19.1%"
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "0MGs0tllP5sd3eOd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year demonstrated the most significant social cultural and value change?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the most significant social cultural and value modification should correspond to the largest segment. The largest segment is {\"2020\"}<r>[0.418, 0.292, 0.462, 0.312]</r> with a percentage of 19.1%.\nBased on the above thought, the answer is:\nThe most significant social cultural and value change was observed in 2020."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "1H5U6X77cCUkAITX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the pie chart, what can we infer about the evolution of social cultural and value change over the analyzed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the change in social culture and values over time by representing each year with a segment. By comparing segment sizes/percentages, we can infer a general trend. The size of the segment for {\"2020\"}<r>[0.418, 0.292, 0.462, 0.312]</r> appears considerably larger than the segment for {\"2014\"}<r>[0.315, 0.423, 0.359, 0.443]</r>, indicating an increased rate of social cultural and value change over the period.\nBased on the above thought, the answer is:\nThe pie chart suggests a trend of increasing social cultural and value change from 2014 to 2020."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "O8GcLYG1w2YP2UEE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, how might we predict the social cultural and value change index for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "If we assume the trend of increasing social cultural and value change seen from 2014 to 2020 will continue, {\"2021\"} may likely witness an index higher than {\"2020\"} which is <r>[0.418, 0.292, 0.462, 0.312]</r> with the highest proportion of 19.1%.\nBased on the above thought, the answer is:\nBased on the observed trend, we can expect the social cultural and value change index to continue to rise in 2021, however this is a prediction and actual results will depend on several other factors that might come into play during the year."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "eY6mQ75KA8z3CB6y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the combined percentage of the social cultural and value change index for the years 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage, I need to sum up the percentages of 2017 and 2018. The segment corresponding to 2017<r>[0.511, 0.716, 0.555, 0.736]</r> is 15.2%, and that of 2018<r>[0.61, 0.564, 0.654, 0.584]</r> is 15.7%.\nBased on the above thought, the answer is:\n30.9%"
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "DoQLIqsjGesNBDC2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Social Cultural and Value Change index reach its highest level, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest index level, I need to look for the segment with the largest percentage. The segment indicating 2020<r>[0.418, 0.292, 0.462, 0.312]</r> has the highest percentage of 19.1% among all years.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "8GQUrbTccLDgmRP8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which three years do you see the total percentage surpassing 40%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to identify three segments whose combined percentages surpass 40%. The segments corresponding to 2019<r>[0.575, 0.357, 0.619, 0.377]</r>, 2020<r>[0.418, 0.292, 0.462, 0.312]</r>, and 2018<r>[0.61, 0.564, 0.654, 0.584]</r> have percentages of 16.1%, 19.1%, and 15.7% respectively, whose total is over 40%.\nBased on the above thought, the answer is:\n2018, 2019, and 2020"
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "58dfhGm4zjzhfMvG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please pull out the yearly data for Bonds and Equities and present it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two segments in the pie chart which represent {Bonds}<r>[0.932, 0.071, 0.975, 0.091]</r> and {Equities}<r>[0.932, 0.101, 0.987, 0.121]</r> originally from a column chart. The title of the chart which is {\"Investment Distribution in 2021\"}<r>[0.349, 0.021, 0.614, 0.047]</r> indicates the distribution in year 2021. Hence, we can utilize the raw data provided to generate a table representation of the annual data for each type of investment in CSV format.\nBased on the above thought, the answer is:\nYear,Bonds,Equities\n2013,20,32\n2014,25,35\n2015,28,30\n2016,24,36\n2017,29,32\n2018,27,33\n2019,23,36\n2020,30,34\n2021,32,31"
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "dTweOVm5xbDbxjRO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart of 2021, what is the primary investment type? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "The slices of the pie chart directly illustrate the market share of each investment type in 2021: {Bonds}<r>[0.223, 0.278, 0.688, 0.886]</r> and {Equities}<r>[0.283, 0.146, 0.741, 0.739]</r>. I can determine the primary investment type by the size of the slice.\nBased on the above thought, the answer is:\nThe primary investment type in 2021 is Bonds because it accounts for a larger portion of the pie chart (50.8%)."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "CMVMkLmUz9xtA9fJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how does the financial industry's preference towards Bonds and Equities change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the changes in preference towards Bonds and Equities, we can look at the sizes of their slices and their growth trends over the years based on the raw data.\nBased on the above thought, the answer is:\nOver the years, the financial industry has shown a growing preference towards Bonds, while the preference for Equities has been fluctuating."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "oJXIiBIavKymzfI5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the current trend in the distribution of investment types, what could be the potential strategy for a new investor in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To come up with an investment strategy, we can analyze the sizes of the investment types in the pie chart and their trends over the years as presented in the raw data.\nBased on the above thought, the answer is:\nGiven the current trend, a new investor in 2022 might consider investing more in Bonds as they seem to have a stable and growing trend, while keeping a portion in Equities to manage risk due to its volatility."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "KkdEJF8GdnOyR3xz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the original chart data, how much did investment in Equities increase from 2015 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I'd need to subtract the amount of Equities invested in 2015 from that in 2016, which can be obtained from the raw data.\nBased on the above thought, the answer is:\n6"
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "qstmwaoJ006vmGwL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this year's distribution continues, what will be the investment in Bonds in 2026, given that the average annual growth rate of bonds from 2013 to 2021 continues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I will first need to calculate the average annual growth rate of investment in Bonds from 2013 to 2021, then apply this rate to the 2021 figure to predict the investment in 2026.\nBased on the above thought, the answer is:\nThe answer depends on the exact value of the average annual growth rate."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "T7lF8rJpJ0I3OLPx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more was invested in Bonds than in Equities in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I'll subtract the value of Equities invested in 2020 from the value of Bonds invested in the same year based on the raw data.\nBased on the above thought, the answer is:\n-4"
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "ix9atWbTyh8XoKrJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data observed, can you illustrate the data in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart {Media Influence and Public Opinion Guidance} [0.294, 0.021, 0.669, 0.047] and its slices with respective percentages help me identify the media channels and their influence index. I can format this data as follows.\nBased on the above thought, the answer is:\nMedia Channels,Media Influence Index\nTelevision,10.8\nRadio,7.2\nNewspaper,6.3\nInternet,15.3\nEmail,13.5\nMobile Applications,14.4\nSocial Media,17.1\nOutdoor Advertisements,6.3\nPodcasts,9.0"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "eFqtG0BX40vZc8Ws",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Media channel has the highest influence on public opinion according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to identify the biggest slice in the pie chart.\nBased on the above thought, the answer is:\nSocial Media has the highest influence on public opinion with an index of 17.1."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "7CWdL0KKvp0ZneDD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart indicate about the role of traditional and digital media in influencing public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the Media Influence Index of traditional media (Television, Radio, Newspaper) with digital media (Internet, Email, Mobile Applications, Social Media, Podcasts).\nBased on the above thought, the answer is:\nThe pie chart indicates that digital media channels have a greater influence on public opinion compared to traditional media channels."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "mtD90UAMBNiPDJFm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the data influence marketing strategies based on the target audience's media consumption pattern?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this, I need to consider the Media Influence Index of each media channel.\nBased on the above thought, the answer is:\nBased on the pie chart, marketing strategies would need to prioritize digital media channels for audience engagement. More resources might be directed towards Social Media, Internet, and Email. However, traditional media such as Television should not be ignored due to its considerable influence. Lesser emphasis might be placed on mediums such as Newspapers, Radios and Outdoor Advertisements based on their relatively lower Media Influence Index."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "pJdWQzCR2axZSi57",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Media Influence Index of Digital media channels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add up the Media Influence Index of Internet, Email, Mobile Applications, Social Media and Podcasts.\nBased on the above thought, the answer is:\n69.3"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "862c3kWiM0OqeNTO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich four media channels have the highest combined influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the four highest values of Media Influence Index and then locate their corresponding media channels.\nBased on the above thought, the answer is:\nInternet, Email, Mobile Applications, Social Media"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "1qBMc8C0qUcAamq9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Podcasts doubled their influence, what would be the new total Media Influence Index?"
+            },
+            {
+                "from": "gpt",
+                "value": "Doubling the Podcasts influence and adding it to the total will give the new total Media Influence Index.\nBased on the above thought, the answer is:\n100.0 + 9.0 = 109"
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "tWa3P59rEvWSJMlZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, could you extract and transform the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the {chart's subtitle}<r>[0.236, 0.021, 0.727, 0.047]</r> and {the slices of the pie}<r> [{'2016'} [0.223, 0.278, 0.482, 0.516], {'2017'} [0.223, 0.494, 0.482, 0.684], {'2018'} [0.251, 0.516, 0.482, 0.837], {'2019'} [0.353, 0.516, 0.489, 0.886], {'2020'} [0.482, 0.516, 0.661, 0.886], {'2021'} [0.482, 0.516, 0.729, 0.783], {'2022'} [0.482, 0.405, 0.741, 0.627], {'2023'} [0.482, 0.199, 0.729, 0.516], {'2024'} [0.447, 0.146, 0.615, 0.516], {'2025'} [0.283, 0.149, 0.482, 0.516]]</r>, I can ascertain the data by calculating the percentages for each year.\nBased on the above thought, the answer is:\nYear,Income%\n2016,10.2%\n2017,8.4%\n2018,9.2%\n2019,8.7%\n2020,11.8%\n2021,7.9%\n2022,9.7%\n2023,11.5%\n2024,10.7%\n2025,11.8%"
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "Pl5sgIJDmwpuuvkg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year did the Education and Training Industry have the highest share of the total income, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing {the pie slices}<r> [{'2016'} [0.223, 0.278, 0.482, 0.516], {'2017'} [0.223, 0.494, 0.482, 0.684], {'2018'} [0.251, 0.516, 0.482, 0.837], {'2019'} [0.353, 0.516, 0.489, 0.886], {'2020'} [0.482, 0.516, 0.661, 0.886], {'2021'} [0.482, 0.516, 0.729, 0.783], {'2022'} [0.482, 0.405, 0.741, 0.627], {'2023'} [0.482, 0.199, 0.729, 0.516], {'2024'} [0.447, 0.146, 0.615, 0.516], {'2025'} [0.283, 0.149, 0.482, 0.516]]</r>, and determine the slice with the highest percentage.\nBased on the above thought, the answer is:\nThe year 2020 had the highest share of the total income in the Education and Training Industry."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "zfVOkqkwHr7W8pGF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this data, what can you infer about the Education and Training Industry's income trend from 2016 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend can be deduced by looking at the comparison of {the pie slices}<r> [{'2016'} [0.223, 0.278, 0.482, 0.516], {'2017'} [0.223, 0.494, 0.482, 0.684], {'2018'} [0.251, 0.516, 0.482, 0.837], {'2019'} [0.353, 0.516, 0.489, 0.886], {'2020'} [0.482, 0.516, 0.661, 0.886], {'2021'} [0.482, 0.516, 0.729, 0.783], {'2022'} [0.482, 0.405, 0.741, 0.627], {'2023'} [0.482, 0.199, 0.729, 0.516], {'2024'} [0.447, 0.146, 0.615, 0.516], {'2025'} [0.283, 0.149, 0.482, 0.516]]</r>.\nBased on the above thought, the answer is:\nThere is a fluctuating trend in the Education and Training Industry's income from 2016 to 2025. It seems the industry experienced a significant increase in income in 2020; however, there was a drop in 2021. This was followed by a general increasing trend from 2022 to 2025."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "1y9g1xfcB9fUXnm6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how might the Education and Training Industry plan for the years following 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze {the pie slices}<r> [{'2016'} [0.223, 0.278, 0.482, 0.516], {'2017'} [0.223, 0.494, 0.482, 0.684], {'2018'} [0.251, 0.516, 0.482, 0.837], {'2019'} [0.353, 0.516, 0.489, 0.886], {'2020'} [0.482, 0.516, 0.661, 0.886], {'2021'} [0.482, 0.516, 0.729, 0.783], {'2022'} [0.482, 0.405, 0.741, 0.627], {'2023'} [0.482, 0.199, 0.729, 0.516], {'2024'} [0.447, 0.146, 0.615, 0.516], {'2025'} [0.283, 0.149, 0.482, 0.516]]</r> to investigate the income trends and make inferences about possible strategies for future years.\nBased on the above thought, the answer is:\nObserving the overall trend from 2016 to 2025, it can be seen that there are fluctuations in the income of the Education and Training Industry. The industry might consider investigating the factors that led to the significant increase in 2020 for repetition. Also, understanding the reasons behind the sudden drop in income in 2021 could help in mitigating such occurrences in the future. A further increase in income seems feasible for the years following 2025 based on the positive trend observed from 2022 to 2025."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "6XYbQ1q3DNCYARDT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year has the smallest share of the industry's total income?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the pie slices}<r> [{'2016'} [0.223, 0.278, 0.482, 0.516], {'2017'} [0.223, 0.494, 0.482, 0.684], {'2018'} [0.251, 0.516, 0.482, 0.837], {'2019'} [0.353, 0.516, 0.489, 0.886], {'2020'} [0.482, 0.516, 0.661, 0.886], {'2021'} [0.482, 0.516, 0.729, 0.783], {'2022'} [0.482, 0.405, 0.741, 0.627], {'2023'} [0.482, 0.199, 0.729, 0.516], {'2024'} [0.447, 0.146, 0.615, 0.516], {'2025'} [0.283, 0.149, 0.482, 0.516]]</r>, by comparing the percentage of each year, I can confirm which is the smallest.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "hxMAhFE3dFoY6mnj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two consecutive years did the industry income increase the most, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the yearly differences in percentage. The two consecutive years that show the biggest percentage increase will be the years during which the industry income increased most significantly.\nBased on the above thought, the answer is:\n2019 - 2020"
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "O7sY27hGoa3lssad",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the industry income in 2021 increased by 2% compared to the original calculation, what would the percentage for 2021 be?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate this, I should add 2% to the original 2021 percentage.\nBased on the above thought, the answer is:\nThe new percentage for 2021 would be 7.9% + 2% = 9.9%"
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "7biIYU52wlmOqD8L",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table of the budget allocation percentages for the different services according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see various slices in the pie chart, each corresponding to a different service {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, and each labeled with their respective percentages. They are indicated in the legend {the legend}<r>[0.839, 0.063, 0.993, 0.279]</r>. I will illustrate these percentages in a row of a table along with their corresponding services.\nBased on the above thought, the answer is:\nServices,Police,Fire,Healthcare,Education,Transportation,Parks,Social Services\nSpending,%15,%16,%20,%25,%10,%7,%7"
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "uT1bpJpaRODAmb6F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, which service does the city invest in the most, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the section of the pie that is the largest.\nBased on the above thought, the answer is:\nThe service that the city invests the most in is Education, as it occupies the largest portion of the pie chart at 25%, suggesting that improving Education is a priority for them."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "FiVvuFPDpo7rDnVC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which areas might require increased investment in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The areas with the least allocation are those that might require increased investment in the future.\nBased on the above thought, the answer is:\nFrom the pie chart, the areas of Parks and Social Services, each with only 7% allocation, might require increased investment in the future to improve public services in these sectors."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "FOIzmF8hn44nBm4i",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the city's priorities in public service spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "The areas with the most funding likely represent the city's spending priorities.\nBased on the above thought, the answer is:\nThe pie chart suggests that the city's priority in public service spending lies primarily in Education and Healthcare, indicating a focus on the public's wellbeing and development."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "sUn5M3m1ae6WcDVp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the total budget spent on sectors other than Education?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of the budget spent on Education from the total budget.\nBased on the above thought, the answer is:\nThe total percentage of the budget spent on sectors other than Education is 100% - 25% = 75%."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "QrcAGVdCGIxnYlc6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more of the budget is allocated to Healthcare than to Police?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of the budget allocated to Police from that allocated to Healthcare.\nBased on the above thought, the answer is:\nThe percentage of the budget allocated to Healthcare is 5% higher than that allocated to Police."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "4SoGf95IcTCX9kxF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two services together make up 17% of the total budget?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find these two services, I need to find which two services add up to 17% of the total budget.\nBased on the above thought, the answer is:\nParks and Social Services together make up 17% of the total budget."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "QigmknUoUA4YRHtV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart figure depicting the user percentages of different social media platforms in 2011 into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing slices in the pie chart and each color representation in the legend {legend}<r>[0.816, 0.13, 0.937, 0.285]</r>, it represents different social media platforms and the corresponding user percentages.\nBased on the above thought, the answer is:\nPlatform,Percentage\nFacebook,19.0%\nTwitter,20.4%\nInstagram,18.5%\nLinkedIn,19.8%\nYouTube,22.3%"
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "kvGEmYf3NkUYPmHt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which social media platform was the least popular in 2011 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The least popular social media platform is the one with the smallest slice {slice}<r>[0.512, 0.505, 0.724, 0.806]</r> in the pie chart.\nBased on the above thought, the answer is:\nInstagram was the least popular social media platform in 2011, as it had the smallest user percentage of 18.5%."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "NwnZf0aVDD92TYpa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about the social media usage pattern in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer, I need to compare the proportions of each social media platform in the chart.\nBased on the above thought, the answer is:\nFrom the pie chart it is inferred that in 2011, YouTube was the most popular social media platform, followed closely by Twitter, LinkedIn and Facebook, respectively. Instagram was the least used among the considered platforms."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "LtC0EtnToJkyFDxU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence social media marketing strategies for the year 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the user percentages of each social media platform based on the pie chart will aid in determining how the marketing strategies can be influenced.\nBased on the above thought, the answer is:\nGiven that YouTube had the highest user percentage in 2011, marketers might consider allocating more resources to YouTube video ads for their 2012 campaigns. Instagram, which had the least user percentage, might need focus with engaging strategies to increase user engagement. Therefore, the information from the pie chart can be used to allocate marketing resources based on the popularity of the different platforms."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "7DRpPPMbCCCuQQge",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which social media platform has the maximum user percentage in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "By scanning each slice of the pie chart, it is clear that the one with the highest user percentage is the maximum.\nBased on the above thought, the answer is:\nYouTube."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "dwyivNbrnlltvP0V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of users on Facebook and Instagram for the year 2011 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the user percentage slices of Facebook {slice}<r>[0.297, 0.307, 0.512, 0.652]</r> and Instagram {slice}<r>[0.512, 0.505, 0.724, 0.806]</r>.\nBased on the above thought, the answer is:\nThe total percentage is 37.5%."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "m52X95leIozKFBDB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you compute the difference in user percentages between Twitter and YouTube for 2011 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference in user percentages is computed by subtracting the Twitter slice {slice}<r>[0.323, 0.505, 0.557, 0.813]</r> from the YouTube slice {slice}<r>[0.347, 0.197, 0.621, 0.505]</r>.\nBased on the above thought, the answer is:\nThe difference is 1.9%."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "tDsvk4kqLRVqzIcF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart you observed, can you please convert the specific Inbound logistics data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the Subtitle at the center of the chart {Inbound and Outbound Logistics from 2015 to 2020}<r>[0.267, 0.021, 0.696, 0.047]</r>, and from the pie slices for Inbound logistics{2015 to 2020}<r>[\"[0.227, 0.278, 0.482, 0.516]\",\"[0.223, 0.446, 0.482, 0.644]\",\"[0.239, 0.516, 0.482, 0.802]\", \"[0.317, 0.516, 0.482, 0.884]\", \"[0.451, 0.516, 0.607, 0.886]\",\"[0.482, 0.516, 0.724, 0.84]\"]</r>, I can see the percentage each year represents, and thus the values.\nBased on the above thought, the answer is:\nYear,Inbound logistics\n2015,65%\n2016,70%\n2017,68%\n2018,73%\n2019,80%\n2020,90%"
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "3rA1h32uBq56joP5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which logistical operation showed a more consistent growth trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the sizes of the slices for Inbound and Outbound logistics for each year.\nBased on the above thought, the answer is:\nOutbound logistics showed a more consistent growth trend over the years."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "RDAYxu6Uh7kyRqSi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the company's focus based on the changes in Inbound and Outbound logistics from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The inference will require comparison of the growth rates for inbound and outbound logistics during this time period.\nBased on the above thought, the answer is:\nThe company has increasingly focused more on outbound logistics, with a significant boost in 2019. This could be attributed to a strategic switch to satisfy customer demand efficiently and potentially grow its market share."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "k56wWtF5seLIMqzA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might these trends in Inbound and Outbound logistics impact decision making for the company's operations in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The predictions about future decisions should be based on historic trends of logistics activities.\nBased on the above thought, the answer is:\nGiven the increasing trend in Outbound logistics, the company might continue to prioritize this area. This could involve efforts to further improve delivery times and customer service, expand logistics networks, or invest in infrastructure. The steady rise in Inbound logistics indicates an ongoing focus on improving supplier relations and upstream processes."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "PCdjkTEUK4SRdx4H",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2019, what was the percentage difference between Inbound and Outbound logistics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to compare the slices of the pie chart that represent outbound and inbound logistics in 2019.\nBased on the above thought, the answer is:\nThe difference was 20%."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "s9lLQVdXiJiJwkIv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the largest increase in outbound logistics from its previous year, and what was the increase in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the year with the largest increase in outbound logistics, I need to compare the percentage for each year with the percentage of the previous year and determine where the difference is the highest.\nBased on the above thought, the answer is:\nThe year 2019 saw the biggest increase with 20%."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "34ujHzBnhx4ZQNvw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did Inbound logistics increase from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in percentage, I would subtract the percentage in 2015 from the percentage in 2020.\nBased on the above thought, the answer is:\nInbound logistics increased by 25% from 2015 to 2020."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "kd5zN9TycVBnf44e",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your understanding of the pie chart, can you convert the data into table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can extract the data by referring to the title {\"Tourism and Hospitality Industry Data Analysis from 2012 to 2021\"}<r>[0.207, 0.021, 0.756, 0.047]</r> and the segment labels with corresponding percentages from {the legend}<r>[0.908, 0.063, 0.993, 0.368]</r>. Each segment represents a year, and the size represents the proportion of tourists for that year.\nBased on the above thought, the answer is:\nYear, Percentage of Tourists\n    2012, 9.9%\n    2013, 11.1%\n    2014, 8.7%\n    2015, 9.3%\n    2016, 12.4%\n    2017, 12.9%\n    2018, 14.3%\n    2019, 14.8%\n    2020, 3.7%\n    2021, 3.0%"
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "AnPXvWBsfLZHTzTa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat were the changes in the Tourism and Hospitality Industry from 2012 to 2021 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the size of the pie chart slices to infer an analysis. This is based on the pie chart slices and coordinating colors displayed within {the plot}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe pie chart shows a general growth trend in the tourism and hospitality industry from 2012 to 2019, indicating an increasing interest in tourism. However, there's a drastic drop in 2020 and 2021, likely due to the impact of COVID-19."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "WRyv1HTFmdTjGu8c",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the most significant contribution to the tourism sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will identify the pie slice with the largest percentage which represents the year with the most significant contribution.\nBased on the above thought, the answer is:\nThe year 2019 has the most significant contribution to the tourism sector with 14.8% of total tourists."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "CDDbKw7rpAQtDJB1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can the government draw from this data when planning for the recovery of the tourism sector post-COVID-19?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to carefully consider the drastic drop in 2020 and 2021 and the potential for recovery.\nBased on the above thought, the answer is:\nThe government can understand from this chart that there was a steady increase in the number of tourists visiting the country before the pandemic. However, the significant drops in 2020 and 2021 point to the severe impact of COVID-19 on the sector. Thus, when planning for recovery, the government may need to take into account the time it might take for tourist numbers to reach pre-COVID-19 levels, and strategize accordingly to boost the travel confidence amongst potential tourists."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "Wwri9ZZeOJTuXuom",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you find the total percentage of tourists from 2012 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages in the slices for the years 2012 to 2015 in {the pie plot}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe total percentage of tourists from 2012 to 2015 is 39%."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "A1icbqLObSmznCnR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of tourists per year from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage of tourists, I should add up the percentages of the ten years from the pie chart slices and divide by ten.\nBased on the above thought, the answer is:\nThe average percentage of tourists per year from 2012 to 2021 is 10%."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "Yrv6rLXma5GzWEKD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage did the count of tourists drop from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in percentage from 2019 to 2020, I need to subtract the 2020 slice percentage from the 2019 slice percentage found within {the pie plot}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe count of tourists dropped by 11.1% from 2019 to 2020."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "O4QJhrLQvjafs2zK",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you tabulate the distribution of investment in Stocks and Bonds over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data, I can see each year represented as a slice in the pie chart for the years {2015}<r>[0.913, 0.073, 0.941, 0.087]</r>, {2016}<r>[0.913, 0.103, 0.941, 0.117]</r>, {2017}<r>[0.913, 0.133, 0.941, 0.147]</r>, {2018}<r>[0.913, 0.163, 0.941, 0.177]</r>, and {2019}<r>[0.913, 0.193, 0.941, 0.207]</r>. The percentage of investments for each segment of the pie indicates the distribution of investments in Stocks and Bonds for that specific year.\nBased on the above thought, the answer is:\nYear,Stocks,Bonds\n2015,78,22\n2016,85,15\n2017,91,9\n2018,88,12\n2019,82,18"
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "PxDVZPrirpXE4qjY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, were investors more inclined towards Stocks or Bonds over the years? Explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should examine the sizes of the pie slices for Stocks and Bonds over the course of the years.\nBased on the above thought, the answer is:\nInvestors were more inclined towards Stocks, as it consistently had a higher percentage of investment compared to Bonds each year."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "It6MTCki8EflpDru",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the investment distribution from 2015 to 2019, which direction do you see the investment trend leaning towards?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the sizes of the pie chart slices over the years, I can discern the direction of the investment trend.\nBased on the above thought, the answer is:\nThe investment trend is leaning towards Bonds as the size of the Bonds segment noted a notable increase in 2018 and 2019."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "wxEncynQRwp0sbkx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can investors use this chart for future investment decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "This involves interpreting the trends seen in the pie chart and applying them to decision making.\nBased on the above thought, the answer is:\nInvestors can use this chart to track historical trends in investments distribution. By seeing that Stocks initially had a higher allocation and noting the rising trend in Bonds, investors could consider diversifying their portfolio by investing more in Bonds to capitalize on this trend in the future years."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "XsMV7rmFqoSXmIP8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year had the highest percentage of investment in Stocks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must review the sizes and explode rates of each respective slice on the pie chart to determine which year had the highest investment percentage.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "l1XX4pBbl7KK0k8b",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the overall average percentage of investment in Stocks over the span of these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will need to add up the percentages of investment in Stocks for all the years and then divide by the number of years to find the average.\nBased on the above thought, the answer is:\n84.8%."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "Aw5tHSdpAkC3NbA0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf I had investments in Bonds that increased by 5% in 2015, what would be the new percentage of investment in Bonds for that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I simply add 5% to the figure from 2015.\nBased on the above thought, the answer is:\n27%."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "X5tDq5MHDRxoYKXM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the resource distribution data for the years from 2015 to 2020 into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the different wedges indicated by colors in the pie chart, and the legend right to the pie chart {the legend}<r>[0.811, 0.063, 0.993, 0.129]</r>, each slice is representing the corresponding percentage.  The slices with different colors represent each category in different years. The legend right to the pie chart indicates that the peach color represents 'Physical Resources', the light blue color represents 'Digital Resources'.\nBased on the above thought, the answer is:\n\"Year\",\"Physical Resources\",\"Digital Resources\"\n\"2015\",25,75\n\"2016\",27,73\n\"2017\",33,67\n\"2018\",30,70\n\"2019\",38,62\n\"2020\",40,60"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Ezi879bF56X2w6T5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trend have you observed in the utilization of physical resources from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slices consecutively from 2015 to 2020, particularly the peach-colored slices, I can observe if the slices have been getting larger or smaller over time.\nBased on the above thought, the answer is:\nIt seems that the utilization of physical resources has been gradually increasing from 2015 to 2020."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "LFruWceGd2dwzMJH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data suggest about the preference between physical and digital resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to comapre two things: the overall magnitude of each resource type's percentage and the trend change of each resource type's percentage.\nBased on the above thought, the answer is:\nThe data suggests a stronger preference for digital resources over physical resources from 2015 to 2020, as digital resources consistently held the majority share. However, there was a slight increase in preference for physical resources towards 2020."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "peMuqvb9F9nIkRZz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart inform decision making for resource allocation in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the recent trends in the distribution and utilization of resources.\nBased on the above thought, the answer is:\nThe trend of increasing utilization of physical resources suggests a growing interest in this area and might inform a decision to increase allocation for these resources in 2021. However, since the digital resources still hold the majority share, it should still receive significant investment."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "EuQifQrt7hEOvF8j",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf digital resources utilization decreased by 5% in 2017, what would be the corresponding physical resources utilization percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the total utilization of resources should be 100%, the physical resources utilization would increase by the same percentage that the digital resources utilization decreased.\nBased on the above thought, the answer is:\nThe physical resources utilization percentage would be 38%."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "R63yjP1IcuAaP0ZL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average utilization percentage of digital resources from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I would add up the utilization percentages for the digital resources across the six years and divide by 6.\nBased on the above thought, the answer is:\n67.83%"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "pcb1SDOjodBQ6910",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the six years, what is the difference between the maximum and minimum percentages of physical resources utilized?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to find the maximum and minimum utilized percentages for physical resources over the six years and subtract the minimum from the maximum.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "4IdZjowiGePz2BFr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart observed, can you convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart {\"Big Data Integration Overview\"}[0.356, 0.021, 0.607, 0.047], I can see that the pie chart represents categories labelled as {Data Mining}[0.848, 0.071, 0.932, 0.091], {Analytics}[0.848, 0.101, 0.911, 0.121], and {Systems Integration}[0.848, 0.131, 0.987, 0.151]. Understanding that these categories represent percentages from their 'pie slices', I can convert this into data in tabular form.\nBased on the above thought, the answer is:\nCategory,Percentage\nData Mining,55%\nAnalytics,30%\nSystems Integration,15%"
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "V4MOFTSJO89QRawH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's data, which field seems to be the most crucial area in Big Data Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll check the proportions for each area in the chart to determine the area with the highest proportion, which likely indicates its importance.\nBased on the above thought, the answer is:\nThe most crucial area appears to be 'Data Mining' since it has the highest percentage (55%) among the three areas shown."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "TGUjk2j2ig980hu7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the Big Data Integration landscape from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the chart, I'll look at the proportions of each field, which indicate the weight or importance of each in the Big Data Integration landscape.\nBased on the above thought, the answer is:\nThe chart indicates that 'Data Mining' is the most prominent field within Big Data Integration, taking up over half of the landscape. 'Analytics' also represents a notable percentage, while 'Systems Integration' has the smallest portion. This suggests that data extraction processes and analytical operations are particularly significant in Big Data Integration practices."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "R3VF0SKFV6gJylZc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications might this chart's data have on resource allocation within a Big Data company?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the chart data, I should consider how the proportions of different categories might influence company decisions, particularly around resource allocation.\nBased on the above thought, the answer is:\nThe data might influence a company to allocate more resources towards 'Data Mining', given it constitutes a significant proportion of big data integration. 'Analytics' might also receive a substantial allocation, while 'Systems Integration', being the smallest sector, could see less resource allocation. However, considering 'Systems Integration's' smaller piece of the pie, it could indeed be an area where a company might want to allocate more resources to increase its share."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "Ww9qhWQVIJye0P20",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find out which two categories' sum is roughly equal to Data Mining's percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the two categories whose sum is roughly equal to the Data Mining's percentage,  I need to compute the sum of percentages of every possible pair of categories excluding Data Mining.\nBased on the above thought, the answer is:\nAnalytics and Systems Integration."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "o2Fy5bHoZquVAtNQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the average proportion of the three categories presented?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to sum up the proportions of the three categories presented and then divide by the total number of categories.\nBased on the above thought, the answer is:\nSum of the percentages is 55% (Data Mining) + 30% (Analytics) + 15% (Systems Integration) = 100%. The average percentage is, therefore, 100% / 3, which is approximately 33.3%."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "4butYaPO3PG1F8aI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of Systems Integration were to increase by 10%, what would be the combined total percentage for all fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total, I have to add the 10% increase to Systems Integration's current percentage and then sum up all percentages again.\nBased on the above thought, the answer is:\nThe increase makes Systems Integration 25%. Adding all fields together gives a new total of 55% (Data Mining) + 30% (Analytics) + 25% (Systems Integration) = 110%."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "FNQ4hZVZjxOlKjlL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the depicted pie chart, convert the annual expenditure data on Healthcare and Education sectors into a comprehensible CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the pie chart data into a CSV format, I need to derive the allocations from the colors associated with each year and their corresponding percentage values from the pie slices {\"2010\"}[0.223, 0.278, 0.482, 0.549], {\"2011\"}[0.224, 0.516, 0.482, 0.808], {\"2012\"}[0.323, 0.516, 0.532, 0.886], {\"2013\"}[0.482, 0.516, 0.71, 0.879], {\"2014\"}[0.482, 0.367, 0.741, 0.691], {\"2015\"}[0.482, 0.151, 0.719, 0.516], {\"2016\"}[0.283, 0.146, 0.526, 0.516], and the legend markers.\nBased on the above thought, the answer is:\nYear,Healthcare,Education\n2010,12.5%,7.5%\n2011,13.0%,8.0%\n2012,13.6%,8.4%\n2013,14.1%,8.9%\n2014,14.4%,9.0%\n2015,15.7%,10.0%\n2016,16.6%,11.0%"
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "POuUMu5xTYWs25rG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, from 2010 to 2016, which sector saw a consistently increasing expenditure?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of both Healthcare and Education sectors over the years.\nBased on the above thought, the answer is:\nAccording to the pie chart, both Healthcare and Education sectors saw consistently increasing expenditure over the years. However, the Healthcare sector saw a slightly higher increase in its expenditure."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "jrW2RocIxUubHruh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan any inference be made about the policy priorities of the country from 2010 to 2016 regarding expenditure on Healthcare and Education sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the changes in the proportion of expenditure for both sectors over the years shown on the chart.\nBased on the above thought, the answer is:\nYes, the pie chart suggests that the policy priority of the country leaned towards Healthcare as the expenditure on it consistently increased from 2010 to 2016, taking up a larger share each year. However, Education also saw an increase, although at a slower rate suggesting it was also considered significant."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "AYRACgPShKl8jLX9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the trend in expenditure influence the sectors' budgeting future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to extrapolate the trend observed on the chart into the future.\nBased on the above thought, the answer is:\nIf the trend in expenditure observed from 2010 to 2016 continues, we can expect the proportion of budget allocated to Healthcare to consistently inflate, potentially allowing for advancements and improvements in this sector. Education, while also seeing an increase, might experience a slower growth which could restrict its expansion to some extent."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "lu7OMkKo5p1jXMwm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase in expenditure on Healthcare from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to add up the percentages represented on the pie chart for each year for the Healthcare sector.\nBased on the above thought, the answer is:\nIt's an increase of 4.1%. This is calculated from 16.6% in 2016 minus 12.5% in 2010."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "PLkvbPk0qPhnYl0r",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage expenditure on Education in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the pie slice corresponding to the year 2015.\nBased on the above thought, the answer is:\nThe expenditure on Education in the year 2015 was 10.0%."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "1SXhl2pu3Vkucu1v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher was the expenditure on Healthcare than Education in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover this, I need to find the difference between the Healthcare and Education percentages in 2013.\nBased on the above thought, the answer is:\nThe expenditure on Healthcare was 5.2% higher than Education in 2013."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "d9NMypHFpviDzJkF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a structured table-based representation of the data in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the data from the pie chart into a table, I need to first identify the metrics represented in the chart {legend}<r>[0.855, 0.063, 0.993, 0.309]</r>. Also, I will gather percentages that each slice occupies on the pie as seen {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nMetrics,Percentage\nLeadership,6.9%\nStrategy,12.4%\nTeamwork,15.5%\nBudget,18.0%\nBrand,13.8%\nTalent,7.8%\nTraining,9.1%\nPerformance,16.4%"
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "eQDEU6CxVjypZVFp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich metric has the highest importance according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the most important metric, I need to locate the one with the highest proportion on the pie chart.\nBased on the above thought, the answer is:\nThe most important metric according to the chart is Budget because it represents the highest percentage of 18.0% among all the metrics."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "ssFvxavqLVQUCP4I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart illustrate the organization's priorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart reflects organization's priorities by showing the proportion of each metric, a larger slice indicates a higher priority.\nBased on the above thought, the answer is:\nThe chart illustrates the organization's priorities by showing a higher emphasis on Budget, and Performance and Teamwork to some extent. In contrast, Leadership, Talent and Training have smaller proportions, indicating they may require more focus for improvement."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "IwRVY5Z9KGadgv5f",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat suggestions would you make for the organization to improve its metrics distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "The suggestions for improvement would need to be based on the proportion of each metric in comparison to others.\nBased on the above thought, the answer is:\nGiven the chart, the organization may want to allocate more resources and attention to Leadership, Talent, and Training as their sizes in the chart are notably smaller. While Budget already has a high percentage, the organization should ensure this allocation is benefiting other areas effectively."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "iZwWkPNzjNFD0Tzj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich three metrics combined make up over 40% of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to identify three metrics whose combined percentage is over 40%.\nBased on the above thought, the answer is:\nTeamwork, Budget, and Performance."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "y8KEiXCHocpTlrDt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum metric percentage in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to identify the pie slice with the largest percentage to answer this question.\nBased on the above thought, the answer is:\n18.0%"
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "5EjUL0fe0NZBY11u",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percentage points does Budget exceed Teamwork?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in percentages between Budget and Teamwork, I need to subtract the percentage of Teamwork from the percentage of Budget.\nBased on the above thought, the answer is:\n2.5%"
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "6gT4feDGLywVV9i0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a CSV format table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart [\"E-commerce and Retail Industry Analysis from 2018 to 2020\"](0.231, 0.021, 0.732, 0.047) is divided into six sectors that indicate the market shares of the e-commerce and physical retail industries from 2018 to 2020. Each sector has a label that directly links to the category it represents, such as {E-commerce 2018}<r>[0.847, 0.071, 0.974, 0.091]</r>, {E-commerce 2019}<r>[0.847, 0.101, 0.974, 0.121]</r>, {E-commerce 2020}<r>[0.847, 0.131, 0.974, 0.151]</r>, {Physical Retail 2018}<r>[0.847, 0.161, 0.987, 0.181]</r>, {Physical Retail 2019}<r>[0.847, 0.191, 0.987, 0.211]</r> and {Physical Retail 2020}<r>[0.847, 0.221, 0.987, 0.241]</r>. Processing this, I can extract the structured table data.\nBased on the above thought, the answer is:\nIndustry,2018,2019,2020\nE-commerce,75,85,90\nPhysical Retail,25,15,10"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "mU6a7u6ROX5wa3PC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trends in the e-commerce and physical retail industries based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart clearly shows the market shares of e-commerce and physical retail from 2018 to 2020. The slices representing e-commerce and physical retail provide a clear indication of the trends.\nBased on the above thought, the answer is:\nThe e-commerce industry has shown a consistent growth trend over the years, with its market share increasing from 75% in 2018 to 90% in 2020. On the contrary, the physical retail industry has shown a declining trend, with its market share decreasing from 25% in 2018 to 10% in 2020. This indicates a growing consumer preference for e-commerce over physical retail."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "DygcWSNqTARl7fcn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry \u2014 e-commerce or physical retail \u2014 presents a promising prospect for investment? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question requires considering the trends and market shares depicted by the pie slices for e-commerce and physical retail.\nBased on the above thought, the answer is:\nBased on the chart, the e-commerce industry presents a more promising prospect for investment. The industry has shown a consistent growth trend over the past three years, with its market share increasing from 75% in 2018 to 90% in 2020, indicating its popularity and potential for further growth."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "6hLpMMv1Q2KcaBJP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the physical retail industry improve its performance based on the chart's data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart presents the declining market share for the physical retail industry, providing a basis to address this question.\nBased on the above thought, the answer is:\nThe physical retail industry has been experiencing a decrease in market share from 25% in 2018 to 10% in 2020. This indicates that consumers are moving towards e-commerce platforms. To improve its performance, the physical retail industry could adopt strategies to enhance the shopping experience, invest in advertising to attract customers, and leverage technology to compete with e-commerce platforms."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "joba8GQhwxBuQH61",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total percentage of the physical retail industry in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe in the chart that the slice {Physical Retail 2020}<r>[0.847, 0.221, 0.987, 0.241]</r> represents the market share of the physical retail industry in 2020.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "bhLFfutlrjTbjQUr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share of the e-commerce industry over these three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate that, I would sum up the percentages for e-commerce for each year, which are depicted by the slices {E-commerce 2018}<r>[0.847, 0.071, 0.974, 0.091]</r>, {E-commerce 2019}<r>[0.847, 0.101, 0.974, 0.121]</r>, and {E-commerce 2020}<r>[0.847, 0.131, 0.974, 0.151]</r> in the pie chart.\nBased on the above thought, the answer is:\n250%"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "feLQYZ9aEaK2qSBW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the market share for the physical retail industry decrease from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to get the market share of the physical retail industry in 2018 from the slice {Physical Retail 2018}<r>[0.847, 0.161, 0.987, 0.181]</r>, and the market share in 2020 from the slice {Physical Retail 2020}<r>[0.847, 0.221, 0.987, 0.241]</r>, and then calculate the decrease.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "Uqdb1nOMaRo3eSIs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart and its data, I noticed that the chart {\"Government System Changes from 1990 to 2015\"}[0.277, 0.021, 0.686, 0.047] represents the changes in governmental systems for the years {1990}<r>[0.952, 0.071, 0.987, 0.091]</r>, {1995}<r>[0.952, 0.101, 0.987, 0.121]</r>, {2000}<r>[0.952, 0.131, 0.987, 0.151]</r>, {2005}<r>[0.952, 0.161, 0.987, 0.181]</r>, {2010}<r>[0.952, 0.191, 0.987, 0.211]</r>, and {2015}<r>[0.952, 0.221, 0.987, 0.241]</r>.\nBased on the above thought, the answer is:\nYear,Percentage\n1990,20\n1995,25\n2000,10\n2005,30\n2010,10\n2015,5"
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "7hrFmxy9Is8yutK6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the year with the most changes in the governmental system based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A closer look at the segments of the pie chart would reveal that the year with the largest segment would have the most changes in the governmental system.\nBased on the above thought, the answer is:\nThe year with the most changes in the governmental system is 2005, as it occupies the largest segment of the pie chart."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "Z22aE2Y1Vn1uV5jy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of change can be inferred from 1995 to 2000 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the segments for the years 1995 and 2000, one can infer the kind of change that occurred within that span.\nBased on the above thought, the answer is:\nFrom 1995 to 2000, there seems to be a significant decrease in changes in the governmental system."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "w08GThMzr60hwx6g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what can one predict for the changes in the governmental system in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the pie chart's pattern of fluctuating changes in governmental systems every five years, one might estimate the scenario in 2020 based on 2015 by using the difference between 2015 and the preceding 10 years.\nBased on the above thought, the answer is:\nThe pie chart shows a cycle of increases and decreases in every 10 and 5 years respectively. So, based on the past trend for 2010 (10%) and 2015 (5%), one can predict that the changes in the governmental system would increase to 10% in 2020."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "kONci4nvUZKzZ2DC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage difference between the year 1995 and the year 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage difference, I need to subtract the percentage of the year 1995 from that of the year 2005.\nBased on the above thought, the answer is:\nThe percentage difference between the year 1995 and 2005 is 5%."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "JMZccFwLr9DiTlaT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the pattern continued, what would the percentage be in 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the pattern of a cycle of increasing and decreasing every 10 and 5 years respectively, I could say the pattern will be repeated once more in 2025.\nBased on the above thought, the answer is:\nThe percentage in 2025 would be 10%."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "AeRVQBuOiVpLUazs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category 2000 increased its proportion by 5%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 5% to the \"2000\" percentage and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new percentage for 2000 is 10% + 5% = 15%. The new total percentage would be 105%."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "wUQiJHVL885O4sdO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, could you extract the data and arrange it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the chart legend {the legend}<r>[0.908, 0.063, 0.993, 0.189]</r> that there are four years represented: 2017, 2018, 2019, and 2020. This suggests that those are the years that should be the rows in the table. I can also see two sectors represented in the pie slices, \"Computer Science\" and \"Artificial Intelligence\". These should be the columns of the table. I should get the values in each cell by looking at the percentage each sector has for each year.\nBased on the above thought, the answer is:\nYear,Computer Science,Artificial Intelligence\n2017,300,200\n2018,350,250\n2019,400,325\n2020,550,500"
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "iM0diS0xU223d15d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, which sector saw a more substantial increase in interest over the period from 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which sector saw a more substantial increase, I need to compare the growth in student numbers of the \"Computer Science\" and \"Artificial Intelligence\" sectors from 2017 to 2020.\nBased on the above thought, the answer is:\nThe \"Artificial Intelligence\" sector saw a more substantial increase in interest over the period from 2017 to 2020 as it had a growth of 300 students compared to 250 in the \"Computer Science\" sector."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "Z5C4k7ECOCguL1Kz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you conclude about the trend in education and training industry for these two sectors over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the values for each sector for every year, we can draw conclusions on trends in the education and training industry.\nBased on the above thought, the answer is:\nBoth sectors, \"Computer Science\" and \"Artificial Intelligence\", saw an increase in the number of students. The \"Artificial Intelligence\" sector, however, saw a more significant increase, particularly in 2019 and 2020, showing a sudden peak in interest in the field."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "cHA6kgVp6FaeCfXG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could these trends potentially impact planning for the education and training industry in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to look at the growth rates of both sectors and consider how that might affect planning in terms of course offerings, faculty hiring, and resource allocation.\nBased on the above thought, the answer is:\nThe substantial increase in interest in the \"Artificial Intelligence\" field might lead the industry to invest more resources into AI-related courses and training programs. They may increase the number of AI classes offered, hire more faculty specialized in AI, and allocate more funding to AI research. Given the steady growth in the \"Computer Science\" sector, continued support and resources allocation for computer science programs would likely also be beneficial."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "S033ZnKDc3zEbKFY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you calculate the total number of students across both sectors for the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the data provided, there were 400 students in the \"Computer Science\" sector and 325 in the \"Artificial Intelligence\" sector in the year 2019. Thus, I can obtain the total number of students in both sectors for the year 2019 by adding these two values together.\nBased on the above thought, the answer is:\n725"
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "CXABLqFZmg1qj5I8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the \"Artificial Intelligence\" sector grown from 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth of the \"Artificial Intelligence\" sector from 2017 to 2020 can be calculated by subtracting the number of students in 2017 from the number in 2020.\nBased on the above thought, the answer is:\n300"
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "N8JRprwLe122SBzV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the percentage growth of the \"Computer Science\" sector from 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the percentage growth of the \"Computer Science\" sector from 2017 to 2020 by subtracting the number of students in 2017 from the number in 2020, dividing by the number in 2017, and then multiplying the result by 100%.\nBased on the above thought, the answer is:\n83.33%"
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "TGil7kYsEdxHhlCx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you extract and format the data as a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Energy Use and Resource Consumption - 2019\"}[0.288, 0.021, 0.675, 0.047] and {the slices of the pie}[{\"Nuclear\"}[0.328, 0.399, 0.363, 0.419], {\"Coal\"}[0.321, 0.605, 0.364, 0.625], {\"Natural Gas\"}[0.497, 0.721, 0.541, 0.741], {\"Hydroelectric\"}[0.6, 0.613, 0.635, 0.633], {\"Solar\"}[0.617, 0.543, 0.652, 0.563], {\"Wind\"}[0.619, 0.489, 0.654, 0.509], {\"Geothermal\"}[0.613, 0.441, 0.648, 0.461], {\"Biofuels\"}[0.605, 0.411, 0.64, 0.431], {\"Oil\"}[0.474, 0.285, 0.518, 0.305]], I can see that each slice represents a different energy source and the size of each segment corresponds to its consumption amount.\nBased on the above thought, the answer is:\nEnergy Source,Consumption\nNuclear,2000\nCoal,8000\nNatural Gas,6000\nHydroelectric,2500\nSolar,1000\nWind,1500\nGeothermal,800\nBiofuels,700\nOil,10000"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "9itwbgSy9kjzEr69",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which energy source had the highest consumption in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The energy source with the highest consumption can be determined by looking at the size of the pie chart segments; the largest segment represents the most consumed energy source.\nBased on the above thought, the answer is:\nThe energy source with the highest consumption in 2019 was Oil."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "6BpuFJxutmr0dAaC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the consumption data in the chart suggest about the energy use pattern in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart indicates that Oil, Coal, and Natural Gas were the predominantly consumed energy sources, representing non-renewable major sources of energy, while the alternative sources remained considerably underutilized.\nBased on the above thought, the answer is:\nThe data suggests that non-renewable energy sources were heavily relied upon in 2019; while renewable energy sources such as Solar, Wind, and Biofuels had significantly lesser consumption, indicating a potential underutilization of renewable energy."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "VirYLMzuVERfaFze",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this chart data help to inform future decisions on energy allocation or promotion?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data can show which energy sources are currently dominating the energy market, and where there may be potential for growth or need for reduction.\nBased on the above thought, the answer is:\nGiven the heavy reliance on non-renewable energy sources shown in this 2019 data, entities may decide to promote more sustainable and renewable energy options like Solar, Wind, and Biofuels, in future energy allocation decisions."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "UXEFL0cHGOo9UYTj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source depicted in the pie chart had the 2nd highest consumption in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the second highest energy consumed, I should check the sizes of all the slices and find out which one is the second biggest.\nBased on the above thought, the answer is:\nCoal"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "FRVyfBQBzWbUj3Ov",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the total consumption of all the energy sources in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total consumption, I should add up the consumption amounts of all the energy sources from the chart.\nBased on the above thought, the answer is:\n34500"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "Zd34xVkKh8lFYPeD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the consumption of Nuclear energy increased by 1000, what would be the new consumption value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new consumption value, I should first determine the current value of nuclear energy from the chart and then add 1000 to it.\nBased on the above thought, the answer is:\n3000"
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "3frOFAbzcoYR4KRj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the pie chart and transform it into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The data for each year is represented by a unique color slice on the pie chart. The chart has a clear legend which {lists 5 different years}<r>[0.891, 0.063, 0.976, 0.219]</r> and corresponds with the pie slices colors. I notice that each slice also contains labels with the percentage of resources used for each year. By taking all this information into account, I can construct a data table.\nBased on the above thought, the answer is:\nYear,Textbooks (%),Online Resources (%)\n2016,14.2,0.0\n2017,16.5,0.0\n2018,15.6,0.0\n2019,25.5,0.0\n2020,28.3,0.0"
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "LZrtX5IKR6D5w80B",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the use of textbooks change in terms of the proportion over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the distribution of the pie slices, one can derive how the use of textbooks has varied over the studied years. As the percentage and years are labelled on each of the slices, an analysis can be drawn.\nBased on the above thought, the answer is:\nThe use of textbooks apparently shows an increasing trend over the years, however, there are few non-consistent jumps. From 2016 to 2017, the use increased, but then a slight decrease was witnessed in 2018. The use surged in 2019 and continued to increase in 2020."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "R4xmFeWH6fzVXuFC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich period observed the steepest increase in the use of textbooks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the difference in the percentage of textbook use between consecutive years and find out the period that has the most significant increase.\nBased on the above thought, the answer is:\nThe period between 2018 and 2020 saw the steepest increase in the use of textbooks. Specifically, the rise from 2018 (15.6%) to 2019 (25.5%) shows a dramatic increase."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "Iv14eC59Q1b4NJZn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you comment on the hypothesis that online learning resources were not extensively used until 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the pie chart provides clear information about the proportion of textbook use over the years, the pie slices don't represent any percentage of online resource use. Hence, any conclusion regarding online resources cannot be drawn from this chart.\nBased on the above thought, the answer is:\nThe pie chart does not contain any information about the use of online resources until 2020 or any other year. Therefore, it does not support or counter the hypothesis."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "Am94qZ7oRZQCCe3C",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year had the highest use of textbooks?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest use of textbooks can be identified by finding the year with the largest slice in the pie chart. Each year is marked with a different color, and the size of the slice represents the percentage of textbook use for that year.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "Uklmxrv6bxCK7p4a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage of textbook use in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage of textbook use in 2017, I will have to look at the pie slice that corresponds to 2017 in the chart's legend. The label on the slice gives the percentage.\nBased on the above thought, the answer is:\n16.5%."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "aPNSkTpdvFnR5OuA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the average percentage of textbooks use from 2016 to 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the percentages of textbook use for each year from 2016 to 2020 and divide by the number of years, which is five in this case.\nBased on the above thought, the answer is:\nThe sum of the percentages is 14.2% + 16.5% + 15.6% + 25.5% + 28.3% = 100.1%. So, the average percentage is 100.1% / 5 = 20.02%."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "sixstyXRMkyjlwVQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, which represents the use of various innovations in education and training technology in 2020. Can you convert the data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the chart's legend {Innovation Usage in 2020}<r>[0.406, 0.088, 0.619, 0.114]</r>, I can see the representation of each sector. The sectors correspond to the different innovations being measured and by observing the chart and the data within it, such as the percentage represented by each sector, It can be converted into a CSV table. Sectors are following: {\"Webinars\"}<r>[0.297, 0.307, 0.512, 0.649]</r>, {\"Online Courses\"}<r>[0.322, 0.505, 0.6, 0.813]</r>, {\"Virtual Reality\"}<r>[0.512, 0.505, 0.68, 0.786]</r>, {\"Augmented Reality\"}<r>[0.512, 0.505, 0.72, 0.698]</r>, {\"Gamification\"}<r>[0.512, 0.279, 0.728, 0.591]</r>, {\"Microlearning\"}<r>[0.486, 0.197, 0.659, 0.505]</r>, {\"Mobile Learning\"}<r>[0.347, 0.199, 0.512, 0.505]</r>\nBased on the above thought, the answer is:\nInnovation, Usage in 2020\nWebinars, 18.9%\nOnline Courses, 23.9%\nVirtual Reality, 7.5%\nAugmented Reality, 6.3%\nGamification, 17.6%\nMicrolearning, 13.8%\nMobile Learning, 11.9%"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "uesV9mxFdjIDDFur",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which innovation in education and training had the highest usage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of each sector in the pie chart, the sector with the largest area represents the most used innovation in 2020.\nBased on the above thought, the answer is:\nOnline Courses had the highest usage in 2020 as it has the largest sector in the chart."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "gEVFIYw7rBgbwJFf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can you see in the use of various innovations in education and training technology from the chart for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the different sectors of the pie chart, I can draw some conclusions regarding the usage trends of different innovations.\nBased on the above thought, the answer is:\nThe chart indicates that in 2020, Online Courses were the most popular innovation in education and training technology, taking up almost a quarter of the total use. Following Online Courses, other popular modalities were Webinars and Gamification. Meanwhile, technologies like Virtual Reality and Augmented Reality had relatively less usage, indicating possibly that they were emerging technologies in that year."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "X8gSbis4pyk7QfvO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data from 2020 influence future investment strategies in educational technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data can provide valuable insights into where to invest for growth in the field of education and training technology.\nBased on the above thought, the answer is:\nConsidering the data from 2020, future strategies might prioritize investment in Online Courses, Webinars, and Gamification technologies as they were the most popular innovations. It might also be worthwhile to invest in Virtual Reality and Augmented Reality technologies as they are emerging modalities despite their lower usage. For Mobile Learning, though it had less usage, the growth potential could be significant, given the increasing prevalence of mobile devices."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "pIbHtSnvMYsR9oWe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich innovation had the least usage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking over the pie chart, I can determine which sector is the smallest, which represents the innovation with the least usage in 2020.\nBased on the above thought, the answer is:\nAugmented Reality"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "32i2RrT0Wh8C2bxl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total usage of Augmented Reality, Gamification, Microlearning, and Mobile Learning in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total usage, I add the percentages of Augmented Reality, Gamification, Microlearning, and Mobile Learning in 2020.\nBased on the above thought, the answer is:\nThe total usage is 6.3% (Augmented Reality) + 17.6% (Gamification) + 13.8% (Microlearning) + 11.9% (Mobile Learning) = 49.6%."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "IsGcri2RHfK7nllO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the usage of Mobile Learning increased by 3.5% in 2020, what would the new total percentage usage of all innovations be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total percentage, I add the increased percentage of Mobile Learning to the current total percentage.\nBased on the above thought, the answer is:\nThe new total percentage will be 100% + 3.5% = 103.5%"
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "ipL9fNaMDwgUlXDh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you arrange the data of big data adoption rate from 2011 to 2020 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's no explicit numerical data visible in the chart. However, the labels in the {legend}<r>[0.908, 0.063, 0.993, 0.368]</r> and the sizes of the {pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r> indicate the year and its associated big data adoption rate. Therefore, I can arrange this data into a CSV format.\nBased on the above thought, the answer is:\nYear,Adoption Rate\n2011,5%\n2012,5.5%\n2013,6.3%\n2014,8.3%\n2015,9.2%\n2016,11.1%\n2017,12.4%\n2018,13.1%\n2019,14.4%\n2020,14.8%"
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "6zbVWYSuOQJyZxcu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two years saw the biggest increase in big data adoption rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I need to identify which two consecutive years' slices show the largest difference in size, as that would correspond to the years with the biggest increase in big data adoption.\nBased on the above thought, the answer is:\nThe biggest increase in big data adoption rate appears to occur between the years 2013 and 2014."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "9sa8YDqj8xu4dWSn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trend in big data adoption can you identify over the years from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the general increase in size of the pie slices from the {2011 slice}<r>[0.242, 0.278, 0.482, 0.516]</r> to the {2020 slice}<r>[0.283, 0.146, 0.496, 0.516]</r>, I can infer the trend in big data adoption over the years.\nBased on the above thought, the answer is:\nThe chart shows that the rate of big data adoption has steadily been increasing from 2011 to 2020."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "1TPoeuH85DsWvuGu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend of big data adoption continues as depicted in the chart, what might we expect for the adoption rate in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I can infer future trends by observing the progression rate in the slices from {2011}<r>[0.242, 0.278, 0.482, 0.516]</r> to {2020}<r>[0.283, 0.146, 0.496, 0.516]</r>.\nBased on the above thought, the answer is:\nIf the trend continues as depicted in the chart, we might expect the rate of big data adoption to continue rising in the coming years, though perhaps at a slowing pace as the rate of adoption seems to be plateauing in recent years."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "LsCCcMIYDD23WI23",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the approximate big data adoption rate in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the big data adoption rate for each year as different slices in the pie chart. Let's find the {slice labeled '2016'} <r> [0.913, 0.223, 0.941, 0.237] </r>\nBased on the above thought, the answer is:\nThe 2016 Big Data adoption rate was approximately 11.1%."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "fFNrmzQgtRGbvtlI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the big data adoption rate increase between the years 2013 and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to locate the {2013 slice}<r>[0.223, 0.502, 0.482, 0.645]</r> in the pie chart, and the {2014 slice}<r>[0.239, 0.516, 0.482, 0.801]</r>. Then I need to subtract the percentage of 2013 from that of 2014.\nBased on the above thought, the answer is:\nThe adoption rate increased by 2% between 2013 and 2014."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "iuzmdlnhHhUqjvFt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in big data adoption rates between the first recorded year and the last year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the {2011 slice}<r>[0.242, 0.278, 0.482, 0.516]</r> and the {2020 slice}<r>[0.283, 0.146, 0.496, 0.516]</r> on the pie chart and subtract the 2011 rate from the 2020 rate.\nBased on the above thought, the answer is:\nThe difference in adoption rate from 2011 to 2020 was approximately 9.8%."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "blDnMKhk0Izxk3wH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the data in the pie chart you observed into a CSV chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let's go by what is visible in the {chart title}<r>[0.295, 0.021, 0.668, 0.047]</r>, and the sectors in the legend. {AI Development slice}<r>[0.241, 0.278, 0.482, 0.516]</r>, {Blockchain Adoption slice}<r>[0.223, 0.377, 0.482, 0.799]</r>, {Quantum Computing slice}<r>[0.315, 0.516, 0.482, 0.885]</r>, and the {Space Exploration slice}<r>[0.283, 0.146, 0.741, 0.886]</r>. I can create a CSV structured format based on these labels and percentage distribution.\nBased on the above thought, the answer is:\nTechnology,Percentage\nAI Development,5\nBlockchain Adoption,20\nQuantum Computing,10\nSpace Exploration,65"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "2jCz8jOmK5ZXS3do",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology area do most respondents believe will experience future development according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would have to look at the slice with the highest percentage.\nBased on the above thought, the answer is:\nSpace Exploration is the area in which most of the respondents believe will experience future development. This is evident since it has the highest proportion, amounting to 65%, in the pie chart."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "BLOfOptIJQgyV7UW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer from the responses that AI Development is not as promising as the other technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer this, I would have to look at the smallest slice of the pie chart.\nBased on the above thought, the answer is:\nYes, we can infer that AI Development is not considered as promising as the other technologies, as it has the smallest proportion, only 5%, in the pie chart compared to the other technologies."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "zWNSXWaOyg8nv37t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart reflect the relative importance of Quantum Computing compared to other areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I would have to calculate the proportion of responses for Quantum Computing in relation to the other areas.\nBased on the above thought, the answer is:\nAccording to the pie chart, Quantum Computing is of lesser relative importance to the future technological development compared to Space Exploration and Blockchain Adoption. Although it outranks AI Development, it only accounts for 10% of the responses, indicating limited impact according to the surveyed experts."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "4TQNhtBMjz0F4SX6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of responses indicate the development of technologies other than space exploration?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find this out, I would need to subtract the percentage of responses showing interest in space exploration from the total responses.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "SV5JDPPBSze4BS7q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the combined response percentage for AI Development and Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I will need to take the sum of the percentages for AI Development and Quantum Computing.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "CJ8M5yvxIkK97tZF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater is the interest in space exploration compared to the combined interest in all other fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this, I need to sum the percentages of AI Development, Blockchain Adoption, and Quantum Computing, then subtract from the percentage for Space Exploration.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "7Vgr37KwirtkeWVc",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a table for the pie chart, including 'Process' and 'Percentage' fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the pie chart's slices in sequence: {'Research'}<r>[0.223, 0.278, 0.482, 0.605]</r>,{'Design'}<r>[0.23, 0.516, 0.482, 0.839]</r>, {'Development'}<r>[0.355, 0.516, 0.658, 0.886]</r>, {'Sourcing'}<r>[0.482, 0.516, 0.74, 0.788]</r>, {'Testing'}<r>[0.482, 0.147, 0.741, 0.542]</r>, {'Review'}<r>[0.419, 0.146, 0.5, 0.516]</r> and {'Sales'}<r>[0.283, 0.157, 0.482, 0.516]</r>, they correspond to different business processes. Similarly, the 'autotext' attached to each slice represents its percentage value.\nBased on the above thought, the answer is:\nProcess,Percentage\nResearch,15\nDesign,13\nDevelopment,20\nSourcing,12\nTesting,25\nReview,5\nSales,10"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "Pc2tbi1N0OsGZfnk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs 'Sourcing' given more time than 'Design' in the organization's workflow?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to evaluate the sizes of the 'Sourcing'<r>[0.482, 0.516, 0.74, 0.788]</r> and 'Design'<r>[0.23, 0.516, 0.482, 0.839]</r> slices in the pie chart, or alternatively compare their percentage values.\nBased on the above thought, the answer is:\nNo, 'Sourcing' is given less time than 'Design' in the organization's workflow."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "OvsBCCBzA708lXzg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the organization's focus in its workflow?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the sizes of the pie slices: 'Research'<r>[0.223, 0.278, 0.482, 0.605]</r>, 'Design'<r>[0.23, 0.516, 0.482, 0.839]</r>, 'Development'<r>[0.355, 0.516, 0.658, 0.886]</r>, 'Sourcing'<r>[0.482, 0.516, 0.74, 0.788]</r>, 'Testing'<r>[0.482, 0.147, 0.741, 0.542]</r>, 'Review'<r>[0.419, 0.146, 0.5, 0.516]</r> and 'Sales'<r>[0.283, 0.157, 0.482, 0.516]</r>, especially, the slices that are significantly larger or smaller than others, I can infer the organization's focus in its workflow.\nBased on the above thought, the answer is:\nThe chart suggests that the organization places its major focus on 'Testing' and 'Development', as they have the largest proportions. Additionally, 'Review' appears to be less prioritized, given its smaller proportion."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "3SDQwhxTSmskvnup",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might we infer about the effects of giving significantly less allocated time to 'Review' in the business process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I should consider the impacts of the minimal share of time given to 'Review'<r>[0.419, 0.146, 0.5, 0.516]</r> in comparison with the other processes.\nBased on the above thought, the answer is:\nThe significantly less time allocated to 'Review' could imply a few things. It might suggest that the organization highly values efficiency and aims to streamline processes to produce results faster. However, it might also point to a potential risk, as less time for review could lead to lower quality control, overlooked mistakes, or less opportunity for refinement and improvement before goods or services reach the sales stage."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "RErWLyLLfKfxA07l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong all business processes, which process occupies the smallest percentage of time?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing each business process present in the pie slices: {'Research'}<r>[0.223, 0.278, 0.482, 0.605]</r>,{'Design'}<r>[0.23, 0.516, 0.482, 0.839]</r>, {'Development'}<r>[0.355, 0.516, 0.658, 0.886]</r>, {'Sourcing'}<r>[0.482, 0.516, 0.74, 0.788]</r>, {'Testing'}<r>[0.482, 0.147, 0.741, 0.542]</r>, {'Review'}<r>[0.419, 0.146, 0.5, 0.516]</r> and {'Sales'}<r>[0.283, 0.157, 0.482, 0.516]</r>, I can find the one that accounts for the smallest slice which is equivalent to the smallest percentage of time.\nBased on the above thought, the answer is:\nReview"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "dR4tjERhF38wd3uf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentage of time spent on 'Development' and 'Testing'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, find the location and size of 'Development'<r>[0.355, 0.516, 0.658, 0.886]</r> and 'Testing'<r>[0.482, 0.147, 0.741, 0.542]</r> in the pie chart. Then, sum up their percentages.\nBased on the above thought, the answer is:\n45"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "WiGZ1seIXPs2Eglo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process occupies exactly 1/10 of the time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to inspect all processes and find one whose percentage of time is equal to 10. That means I will locate and examine the 'autotext' section of each slice: 'Research'<r>[0.223, 0.278, 0.482, 0.605]</r>, 'Design'<r>[0.23, 0.516, 0.482, 0.839]</r>, 'Development'<r>[0.355, 0.516, 0.658, 0.886]</r>, 'Sourcing'<r>[0.482, 0.516, 0.74, 0.788]</r>, 'Testing'<r>[0.482, 0.147, 0.741, 0.542]</r>, 'Review'<r>[0.419, 0.146, 0.5, 0.516]</r> and 'Sales'<r>[0.283, 0.157, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nSales"
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "CPll3uwkx1Z0ySsL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data from the pie chart you studied into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart showing {\"Invoice Generation per Weekday\"}[0.346, 0.021, 0.617, 0.047] and the concentrations of each day slice[ranging from '[0.223, 0.278, 0.482, 0.684]' to '[0.283, 0.238, 0.482, 0.516]'] with their labels, I can sort the daily data for Invoice Generation and convert it into a CSV format table.\nBased on the above thought, the answer is:\nDay,Percentage\nMonday,18.6%\nTuesday,21.7% \nWednesday,12.2%\nThursday,22.8%\nFriday,19.5%\nSaturday,2.7%\nSunday,2.4%"
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "aJ2kyOsz8XAxRWHt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich weekday has the highest rate of invoice generation according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the percentages for each weekday to identify the one with the highest value.\nBased on the above thought, the answer is:\nThursday has the highest rate of invoice generation which is 22.8%."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "u7fO5wyxz63miVWR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the pie chart, which days are considered as the busiest for invoice generation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Days with high percentages would be recognized as the busiest days. Hence, I need to identify those days.\nBased on the above thought, the answer is:\nBased on the data, Thursday, Tuesday, and Friday are considered as the busiest days for invoice generation."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "WvDOZCH8iGOuu2v1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can we conclude that invoice generation significantly slows down during the weekends?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll derive this conclusion from the percentages for Saturday and Sunday compared to weekdays.\nBased on the above thought, the answer is:\nYes, we can say that invoice generation significantly slows down during the weekends as Saturday and Sunday combined contribute to only 5.1% of the total weekly Invoice Generation."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "UGvRUQtN2SxJtGgG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the total percentage of Invoice Generation on weekdays based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I should sum the percentage for each weekday: Monday, Tuesday, Wednesday, Thursday, and Friday.\nBased on the above thought, the answer is:\nThe total percentage of Invoice Generation on weekdays is 94.8%."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "kVTTLOKPOfGmZBoU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any day that contributes to more than a quarter of the weekly Invoice Generation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check each day's percentage and see if any is above 25%.\nBased on the above thought, the answer is:\nNo, there isn't any single day that contributes more than a quarter to the weekly Invoice Generation."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "TGQJNCfEqJVK9GfZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the weekend contribute to the total weekly Invoice Generation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add the percentages for Saturday and Sunday.\nBased on the above thought, the answer is:\nThe percentage of the weekend contributes is 5.1%."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "XZrb5t2AwgcZ6lKR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the distribution of the budget for cancer research from 2016 to 2020 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from {the pie chart\u2019s center}[0.482, 0.516] and distribution of each slice [2016[0.223, 0.278, 0.482, 0.658]; 2017[0.243, 0.516, 0.482, 0.886]; 2018[0.468, 0.516, 0.722, 0.886]; 2019[0.482, 0.21, 0.741, 0.655]; 2020[0.283, 0.146, 0.628, 0.516]], I understand that this pie chart represents the percentage distribution of the cancer research budget from 2016 to 2020.\nBased on the above thought, the answer is:\nYear,Percentage\n2016,17.4%\n2017,17.9%\n2018,19.7%\n2019,21.6%\n2020,23.4%"
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "sVsnyJpV4G8qMPxx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can the chart tell you about the budget trend for cancer research from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportions from the year 2016[0.223, 0.278, 0.482, 0.658] to 2020[0.283, 0.146, 0.628, 0.516], I can deduce the budget trend.\nBased on the above thought, the answer is:\nThe chart shows a growing trend in the budget allocated for cancer research from 2016 to 2020, with a notable increase from 2018 onwards."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "6xvXL0UZxmtk6OMc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest budget allocation for cancer research and what could be the reason?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the smallest portion indicates the year with the smallest budget allocation. In this instance, the smallest slice belongs to the year 2016[0.223, 0.278, 0.482, 0.658].\nBased on the above thought, the answer is:\nThe year with the lowest budget allocation for cancer research was 2016, which could be due to less emphasis on cancer research during that period, or other areas might have been allocated more funding."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "j68SZWiOE1EIIXVe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming that the budget for 2021 will continue the trend seen in the chart, what can you infer about the budget allocation for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the increasing budget trend displayed in {the pie chart}[0.223, 0.146, 0.741, 0.886] from 2016 to 2020, I can make an inference for 2021.\nBased on the above thought, the answer is:\nIf the budget for 2021 continues with the same upward trend, it's likely that the allocation for cancer research will exceed the 23.4% allocated in 2020, indicating a higher budget and increased importance and investment towards cancer research."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "zD6V3ngCQ0uGcG70",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two consecutive years, the sum of their budget proportions exceed 40%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find two consecutive years when the sum of their budget proportions exceeds 40%.\nBased on the above thought, the answer is:\n2019 and 2020."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "taZYrCtixEAjj7y5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in budget proportion from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the overall increase, I need to subtract the budget proportion of 2016 from that of 2020.\nBased on the above thought, the answer is:\nThe increase is 23.4% - 17.4% = 6%."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "z9M1LqsdPjCsYjfH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf  the proportion of the budget for the year 2017 increased by 2%, what would be the new total budget proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 2% to the budget proportion of 2017 and recalculate the total percentage.\nBased on the above thought, the answer is:\nThe new proportion for 2017 is 17.9% + 2% = 19.9%. The new total budget proportion is 17.4% (2016) + 19.9% (2017) + 19.7% (2018) + 21.6% (2019) + 23.4% (2020) = 102%."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "7V1dCxP5xCSX37GO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you have seen, could you convert the number of clients data for each year into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, based on the pie chart, I will convert the slices of each year into a CSV format. The pie slices represent different years - {2016}<r>[0.223, 0.278, 0.482, 0.582]</r>, {2017}<r>[0.227, 0.516, 0.482, 0.847]</r>, {2018}<r>[0.366, 0.516, 0.619, 0.886]</r>, {2019}<r>[0.482, 0.49, 0.741, 0.83]</r>, {2020}<r>[0.482, 0.17, 0.74, 0.516]</r>, {2021}<r>[0.283, 0.146, 0.574, 0.516]</r>.\nBased on the above thought, the answer is:\nYear,Number of Clients\n2016,85\n2017,90\n2018,99\n2019,105\n2020,110\n2021,120"
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "6Eslc5UZLqV0cHmW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's data, can you determine which year saw the lowest number of clients?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to identify the smallest slice in the pie chart which represents the year with the lowest number of clients.\nBased on the above thought, the answer is:\n2016 had the lowest number of clients."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "CqfmEcRyQE8PbSWD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, how can you describe the growth trend of the company's clients from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth trend can be described by looking at the size of each pie slice and comparing them in chronological order.\nBased on the above thought, the answer is:\nThe company saw a steady growth in the number of clients each year from 2016 to 2021."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "cBPbKfT5zcZDqiMn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the company wants to achieve a 20% increase in clients in 2022 compared to 2021, how many clients would it need to reach its goal?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to calculate 20% of the number of clients in 2021 and add it to the number of clients in the same year.\nBased on the above thought, the answer is:\nThe company would need to acquire 144 clients in 2022 to achieve its goal."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "9169AHpNnha0MKxl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how many clients did the company have in total from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer that, I will sum up the numbers of clients for every year represented by the slices of the pie.\nBased on the above thought, the answer is:\n609."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "wspobcAreh2nqNMj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the highest increase in the number of clients?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the difference in the number of clients each year from the previous year.\nBased on the above thought, the answer is:\n2021."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "9YAtnVZy29DrrxWb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average number of clients the company had in these 6 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total number of clients over these years is 609. To calculate the average, this number should be divided by the number of years, which is 6 in this case.\nBased on the above thought, the answer is:\nApproximately 101.5."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "gzj3dUIIqDq6KqlA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart for venture capital investments, can you construct a table showing the percentage of each investment type?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will take the text labels and percentages for each type of investment from the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r> and use them to construct the table.\nBased on the above thought, the answer is:\nInvestment Type, Percentage\nSeed, 5.0%\nPre-Series A, 8.0%\nSeries A, 12.0%\nSeries B, 17.0%\nSeries C, 20.0%\nSeries D, 18.0%\nSeries E, 12.0%\nPost-Series, 8.0%"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "dFyKJepxkWfo49Yq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which investment stage receives the most funding, and why do you think this is the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the slice with the largest percentage in the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r>. This will indicate the stage at which most investments are made.\nBased on the above thought, the answer is:\nThe Series C stage receives the most investment, accounting for 20.0% of total venture capital investments. This could be due in part to such companies demonstrating a proven business model and substantial growth prospects, attracting higher funding than the other stages."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "IF9GEgeOm1ewaV39",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the distribution of investments change over time according to the data in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing the percentages associated with each investment type in the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r>, I can infer about the changing distribution of investments over time.\nBased on the above thought, the answer is:\nThe venture capital investments start off moderately high during the Seed stage, increase through to the Series C stage, and then decrease progressively. This pattern likely reflects the typical investment lifecycle where a larger number of startups receive initial funding but only a few mature to the later stages and require larger sums of capital."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "6ceFaTqvVBbjYjy0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Trends in investments continue the same way according to the charts, what could it suggest about the future of these investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To supply this reasoning, I need to consider the investment trajectory exhibited in the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r>.\nBased on the above thought, the answer is:\nIf the current trends in investments remain the same, it can be projected that early to mid-stage investments such as Pre-Series A to Series C will continue to dominate the venture capital market. Startups that demonstrate substantial growth potential and a viable business model are likely to attract more attention. However, fewer companies are expected to navigate effectively past these stages and sustain investment growth, reflected in the decreasing percentage from Series D onwards."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "2MOAw2Wlky9Nq3xR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total value of all investments from Series A to Series E?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total value of all investments from Series A to Series E, I need to add their corresponding percentages from the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r>.\nBased on the above thought, the answer is:\n79.0%"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "XHtV3nsmWmFGSPxq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 3 new investments are added to the Seed stage, how much percentage would it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage for the Seed stage, I need to add 3 to its current value and recalculate the percentage as a proportion of the total investments.\nBased on the above thought, the answer is:\n5.7%"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "KtHXPs5T22JKMbJ2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentifying the investment type which has the double percentage of the Seed stage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the investment type with double the percentage of the Seed stage, I have to look through the pie chart {subplot_0}<r>[0.158, 0.053, 0.805, 0.979]</r> and compare each slice's percentage.\nBased on the above thought, the answer is:\nSeries B"
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "pWWcgniFZtQNGiZB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the trend of Education Innovation from 2015 to 2021 as shown on the chart. Provide your answer in a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate a table from this chart, I need to extract the data from the legend {legend}<r>[0.908, 0.063, 0.993, 0.279]</r>. Based on the colours, labels, and proportions for each slice, I can get the corresponding percentages for each year and convert it into a table mentioning the innovations in Online Learning and AI Tutoring each year.\nBased on the above thought, the answer is:\nYear,Online Learning,AI Tutoring\n2015,65,35\n2016,78,42\n2017,83,49\n2018,92,58\n2019,101,71\n2020,109,87\n2021,117,103"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "xhAPmTLozWjxLUx6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which innovation area had more focus from the year 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the numbers of innovations mentioned for the two categories, {Online Learning} and {AI Tutoring}, I can compare and tell which category was focused more.\nBased on the above thought, the answer is:\nThe category of Online Learning had more focus considering the number of innovations from the year 2015 to 2021."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "i7lq4ly6ld4falJx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of AI Tutoring innovations from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the numbers of AI Tutoring innovations per year, I can infer the trend.\nBased on the above thought, the answer is:\nThe AI Tutoring innovations have been continuously increasing over the years from 2015 to 2021, with a significant leap from 2018 onwards."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "cRXTqWPu3aLk6ByW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the innovations in Online Learning and AI Tutoring influence the Education and Training Technology sector in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the constant increase in both Online Learning and AI Tutoring innovations, I'll think about their potential impact on the future of the Education and Training Technology sector.\nBased on the above thought, the answer is:\nThe consistent increase in both Online Learning and AI Tutoring innovations indicates a strong shift towards digitalized and personalized learning methods. This trend will likely continue in the future, influencing the sector by possibly creating a higher demand for AI-based learning systems and user-friendly online learning platforms."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "aeIbLQv5OxySrH5i",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increase in AI Tutoring innovations is observed from the year 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in AI Tutoring innovations, I need to subtract the number of innovations in the year 2018 from those in the year 2021.\nBased on the above thought, the answer is:\n103 - 58 = 45"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "qT5ZHMKL8vbKrqe4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined total of Online Learning and AI Tutoring innovations in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined total, I need to add the number of Online Learning and AI Tutoring innovations for the year 2017.\nBased on the above thought, the answer is:\n83 (Online Learning) + 49 (AI Tutoring) = 132"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "9WNvU4BnXvOGkTtt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the rate of increase of Online Learning innovations from the year 2015 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of increase, I need to subtract the number of innovations in the year 2015 from those in the year 2016 and divide by the number in 2015, then multiply by 100% for percentage.\nBased on the above thought, the answer is:\n[(78 - 65) / 65] * 100% = 20%"
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "XkDkRZqJI33hMIvl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the costs of each phase of the innovation project in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will observe the labels in {the legend}<r>[0.805, 0.063, 0.993, 0.309]</r> and their corresponding colors. Then, match these colors to the sectors in the chart at <r>[0.223, 0.146, 0.741, 0.886]</r> and read the autotext percentage values to obtain the cost distribution.\nBased on the above thought, the answer is:\nProject Phase, Cost\nPlanning, 40000\nDevelopment, 150000\nTesting, 60000\nPost Launch Review, 20000\nMarket Research, 30000\nImplementation, 120000\nProduction, 95000\nDeployment, 80000"
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Yrp5fVuASHAxkYud",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which project phase required the most investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would observe the slices of pie in <r>[0.223, 0.146, 0.741, 0.886]</r>. The sector covering the largest area would correspond to the project phase with the highest cost, which I would find in {the legend}<r>[0.805, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\nThe Development phase required the most investment as its associated cost was the highest among all the project phases."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Rf5Qu6zchfB6JUSk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the project's cost distribution from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the different segments' sizes and corresponding percentages, we can get an overview of how the project's total cost was distributed among different phases.\nBased on the above thought, the answer is:\nThe cost distribution in the innovation project was quite uneven. Majority of the project budget was spent on the Development and Implementation phases, suggesting these were the most resource-intensive parts of the project. Post Launch Review, on the other hand, used the smallest portion of the budget."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "I8nvTHs5SEpE1uTG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this cost distribution information be used by management for future project planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to look at the proportions of costs for each phase and consider how a better balance of funds might be achieved.\nBased on the above thought, the answer is:\nManagement could use this information to plan more strategically for future projects. They could look for methods to control costs in Development and Implementation phases since these areas consumed the majority of resources. Alternatively, they might invest more in Market Research or Planning stages to better anticipate problems and therefore reduce costs later on, particularly in the Development phase."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "QmxAddocyzKeGnAm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cost of the phase that accounts for around 20% of the total project cost?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the slice of pie in <r>[0.223, 0.146, 0.741, 0.886]</r>, which represents about 20% of the total project cost, and then link the color of this slice back to the respective project phase in {the legend}<r>[0.805, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\nImplementation."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "4PNilvEB7CG4tAyM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total cost of the project?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total cost of the project, I need to add up the values of each phase's cost presented in the structured table generated from the chart.\nBased on the above thought, the answer is:\n605000."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "siiqS3DNzt0EPTbA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the cost for the \"Development\" phase is increased by 50000, what will be the new total cost of the project?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total cost, I need to add 50000 to the \"Development\" phase cost and then calculate the total costs of all project phases.\nBased on the above thought, the answer is:\n655000."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "xDQIhbzxvPuiXLQW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that reflects the distribution of funding for Scientific Research and Technological Innovation from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the legend located at {the right side of the chart}<r>[0.771, 0.063, 0.993, 0.129]</r> and looking at {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can see that the pie slices represent the funding percentages for Scientific Research and Technological Innovation.\nBased on the above thought, the answer is:\nType,2016,2017,2018,2019,2020\nScientific Research,45,50,54,59,65\nTechnological Innovation,55,50,46,41,35"
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "vecN0ArUAgC9fdVa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the allocation of funding over the years, which type of research (Scientific Research or Technological Innovation) has been prioritized recently and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the trend of funding distribution over the past few years.\nBased on the above thought, the answer is:\nScientific Research has been recently prioritized because its funding has been increasing for the last few years."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "D07nejiHIwS4wbkE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, what inference can you make about the possible trend of funding allocation for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the funding trend of the previous years, I can make a reasonable inference about the allocation for 2021.\nBased on the above thought, the answer is:\nThe data suggests that the trend of increasing funding for Scientific Research and decreasing funding for Technological Innovation will likely continue in 2021."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "OxQT65vy5O7alHKg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence policy decisions towards funding for research?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the trend of funding distribution over the past few years may impact decisions about future funding allocation.\nBased on the above thought, the answer is:\nThe trend of increasing funding for Scientific Research suggests a recognition of its growing importance. Therefore, policy decisions might continue to prioritize funding for Scientific Research. With the decrease in funding for Technological Innovation, policymakers might want to review and consider whether this area needs more support to prevent it from falling behind."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "C9MYei1GBfpTW9k8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the funding percentage for Scientific Research from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the funding percentage, I need to subtract the percentage in 2016 from that in 2020.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "3Ol7tCXyUmeFkceA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the funding percentage for Technological Innovation decrease in 2020 compared to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in the funding percentage, I need to subtract the percentage in 2020 from that in 2016.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "pwXt2ab1kPMijCYq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the funding for Scientific Research increases by 5% in 2021, what would be the new funding percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new funding percentage, I need to add 5% to the 2020 percentage.\nBased on the above thought, the answer is:\n70%."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "X7ZigUiBawY7BuDv",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract and present the data represented in the pie chart as a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sectors {Healthcare}<r>[0.819, 0.071, 0.895, 0.091]</r>{Social Security}<r>[0.819, 0.101, 0.922, 0.121]</r>{Education}<r>[0.819, 0.131, 0.889, 0.151]</r>{Public Transportation}<r>[0.819, 0.161, 0.966, 0.181]</r>{Housing}<r>[0.819, 0.191, 0.876, 0.211]</r>{Unemployment Benefits}<r>[0.819, 0.221, 0.987, 0.241]</r>{Public Safety}<r>[0.819, 0.251, 0.91, 0.271]</r>. The slices of these sectors are coded with different colors and have certain percentages indicated in them to signify their representation in the budget allocation.\nBased on the above thought, the answer is:\nSector,Percentage\nHealthcare,18%\nSocial Security,22%\nEducation,16%\nPublic Transportation,14%\nHousing,6%\nUnemployment Benefits,15%\nPublic Safety,9%"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "kG43qhxn76UpfdU3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had the highest allocation? How is this significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slice of each sector on the pie chart, I can determine which sector received the highest budget allocation.\nBased on the above thought, the answer is:\nSocial Security had the highest allocation with 22%. This implies it was considered the most crucial sector in terms of social welfare and public service requirements."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "DGigKJfXYP0fJTeZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn regarding the budget allocation for social welfare and public services?"
+            },
+            {
+                "from": "gpt",
+                "value": "The understanding of the budget allocation to different sectors would be based on the size of each sector's slice in the pie chart.\nBased on the above thought, the answer is:\nThe pie chart demonstrates that Social Security and Healthcare are prioritized in the budget allocation, indicating crucial requirements in these areas. In contrast, Housing received the least budget allocation, suggesting the lesser requirements in Housing as compared to other sectors."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "Bhp2VGmY8AkKBxTM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow will the allocation influence decisions regarding future budget distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "The decision on future budget distribution would be influenced by understanding the existing resource allocation portrayed in the pie chart.\nBased on the above thought, the answer is:\nSectors like Social Security, which received the highest allocation, might continue receiving attention in future budget decisions. Meanwhile, sectors like Housing, which had the least allocation, might either continue to receive a lesser budget if the lower requirement persists or may demand prioritization if there comes a rise in requirements. It will depend on the changing requirements of the constituents."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "xP5w8rKFDq1VEd8K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage of the overall budget is shared by the following trio: Social Security, Education, and Housing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add the percentages of Social Security, Education, and Housing.\nBased on the above thought, the answer is:\n44%"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "pwIytceRi6K0ieuu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much the allocation to Housing is lesser than Education?"
+            },
+            {
+                "from": "gpt",
+                "value": "I just have to subtract the percentage of Housing from the percentage of Education.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "OuOWtJTEvxQwkwCy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the budget allocation for Public Safety was increased by 3%, what would be its new percentage allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage for Public Safety, I have to add this 3% increase to the existing percentage.\nBased on the above thought, the answer is:\n12%"
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "aM1hVOg5whD1tntz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide the distribution of the national budget for each technology as seen in the pie chart, in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart includes distinct slices each representing a different technology: {Artificial Intelligence (AI)}<r>[0.227, 0.278, 0.486, 0.537]</r>, {Blockchain}<r>[0.227, 0.516, 0.486, 0.827]</r>, {5G Network}<r>[0.346, 0.516, 0.736, 0.886]</r>, {Quantum Computing}<r>[0.486, 0.232, 0.745, 0.61]</r>, and {Digital Twins}<r>[0.288, 0.146, 0.653, 0.516]</r>. Each slice has a corresponding percentage indicating the share of national budget allocation.\nBased on the above thought, the answer is:\nTechnologies,National Budget Allocated (%)\nArtificial Intelligence (AI),12\nBlockchain,15\n5G Network,30\nQuantum Computing,18\nDigital Twins,25"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "CCNy3yyC32r94JF4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, which technology appears to be the most prioritized in terms of budget allocation? Kindly explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the most prioritized technology is achieved by observing which slice of the pie is the largest, indicating the most amount of allocated funding.\nBased on the above thought, the answer is:\nThe \"5G Network\" technology appears to be the most prioritized. It has the largest slice in the pie chart which represents the highest budget allocation compared to the other technologies."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "WDyoLfv6OnZmfmC9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom how the budget is distributed among these technologies, what could be inferred about the country's technology development focus for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to infer about the country's technology development focus for the future, I need to evaluate how much budget is allocated to each technology.\nBased on the above thought, the answer is:\nThe chart suggests that the country's primary focus for future technology development is likely to be \"5G Network\", as it accounts for the biggest slice of the budget. \"Digital Twins\" and \"Quantum Computing\" also receive significant attention. \"Artificial Intelligence (AI)\" and \"Blockchain\" are less focused areas judging by the allocation of budget."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "IkxdZieptgumkAEl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, how might this budget allocation affect the advancement of AI technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To estimate how this budget allocation might affect the advancement of AI technology, I need to assess the amount of budget allocated to AI.\nBased on the above thought, the answer is:\nGiven that AI receives the least share of the budget, this could potentially slow its advancement compared to that of other technologies like \"5G Network\" or \"Digital Twins\" which are receiving larger portions of the budget. It could affect the speed and quality of research, development, and implementation of AI technologies."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "iX3R0rv9dpgPOBqt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you inform me which technology preserved the largest proportion of the national budget?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology preserved the largest proportion of the national budget, I need to assess the size of each slice in the chart.\nBased on the above thought, the answer is:\n5G Network"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "wfjqDlEjE2oA7AHh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total of the national budget allocation shared among AI, Blockchain, and Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total of the national budget allocation among AI, Blockchain, and Quantum Computing, I need to add the percentages associated with these technologies.\nBased on the above thought, the answer is:\n12 + 15 + 18 = 45"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "DWPONWQzXFhSFRt1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Digital Twins received a 5% budget increase, deducting it equally from each of the other technologies, what would be the new percentage of Blockchain?"
+            },
+            {
+                "from": "gpt",
+                "value": "If 5% is equally deducted from each of the other four technologies: AI, Blockchain, 5G Network, and Quantum Computing, I need to deduct 1.25% from current Blockchain percentage.\nBased on the above thought, the answer is:\n15% - 1.25% = 13.75%"
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "jhWlPwhyYj7MDKut",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart on Environmental and Climate Change Impact in 2020 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the top section of the chart, {there's the title, \"Environmental and Climate Change Impact 2020\"}<r>[0.279, 0.021, 0.684, 0.047]</r>. The pie chart slice {for \"Air Pollution\"}<r>[0.223, 0.219, 0.741, 0.886]</r> represents the percentage impact on Air Pollution in 2020. Similarly, the slice {\"for Ocean Acidification\"}<r>[0.283, 0.146, 0.637, 0.516]</r> represents the corresponding impact in 2020 on Ocean Acidification.\nBased on the above thought, the answer is:\nImpact Area, 2020\nAir Pollution,75.9%\nOcean Acidification,24.1%"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "1rocZc012eC6ZSpm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which area has taken the largest environmental and climate change impact in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area with the largest slice in the pie chart would be the one having the largest impact.\nBased on the above thought, the answer is:\n\"Air Pollution\" has taken the largest environmental and climate change impact in 2020 because it constitutes 75.9% of the pie chart."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "JoTesKJ1mvyeABW8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the pie chart about the comparison in harm to the environment and climate made by Air Pollution and Ocean Acidification in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of the pie slices representing \"Air Pollution\" and \"Ocean Acidification\", I can infer the comparative harm done by both in 2020.\nBased on the above thought, the answer is:\nThe pie chart shows that Air Pollution has done significantly more harm than Ocean Acidification in 2020. Air Pollution constituted 75.9% of the impact as opposed to Ocean Acidification that only accounted for 24.1%. It suggests that action should be taken to reduce air pollution further in the upcoming years."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "q3ZQ39FcrrZ8DJo2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the data in the chart highlight the areas that should receive more focus in future environmental preservation efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analysis of the pie chart will provide insight into what areas should be focused on for future environmental preservation.\nBased on the above thought, the answer is:\nThe data highlights that while both Air Pollution and Ocean Acidification are areas of concern, Air Pollution, which accounted for 75.9% of the impact in 2020, should receive more focus in future environmental preservation efforts. At the same time, given the increasing trend in Ocean Acidification, it should not be neglected."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "E3Z9JyVjb5SUyOnC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined environmental and climatic impact for the \"Air Pollution\" and \"Ocean Acidification\" according to the pie chart for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I just need to add the impact of \"Air Pollution\" and \"Ocean Acidification\" in 2020.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "cF2UGBVoGZ3buMlg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage impact from air pollution to ocean acidification for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the impact of \"Ocean Acidification\" from \"Air Pollution\" for the year 2020.\nBased on the above thought, the answer is:\n51.8%"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "HLlxN4lcWx3VKQSY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the impact on \"Air Pollution\" was 10% less in 2020, what would be the new percentage for  \"Air Pollution\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract 10% from the original \"Air Pollution\" impact.\nBased on the above thought, the answer is:\n65.9%"
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "SPbq2Fv8jbyfZYeW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data represented in the pie chart be organized into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart represents the community resident growth rate over different years from 2010 to 2019. We actually have a pie slice for each year like  {2010}[0.265, 0.278, 0.482, 0.516], {2011}[0.242, 0.313, 0.482, 0.516] and so on up to {2019}[0.283, 0.146, 0.561, 0.516]. This data could be clearly arranged in tabular format.\nBased on the above thought, the answer is:\n\"Year\", \"Residents Growth Rate (%)\"\n\"2010\", 1.9\n\"2011\", 3.0\n\"2012\", 5.3\n\"2013\", 6.8\n\"2014\", 9.4\n\"2015\", 10.6\n\"2016\", 13.2\n\"2017\", 14.7\n\"2018\", 16.2\n\"2019\", 18.9"
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "S8pPOtAgxHAf21aX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the resident growth trend in the community based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the sizes of the pie slices from 2010 to 2019, where each year is a separate pie slice like {2010}[0.265, 0.278, 0.482, 0.516] up to {2019}[0.283, 0.146, 0.561, 0.516]. Then, I can infer the trend in resident growth.\nBased on the above thought, the answer is:\nThe resident growth rate in the community has been increasing every year from 2010 to 2019. This means the community is continuously growing and attracting more residents each year."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "zKUCB4GOSZtPwgiJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the community development from the growth rate trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the yearly resident growth rates in the pie chart, starting from {2010}[0.265, 0.278, 0.482, 0.516] to {2019}[0.283, 0.146, 0.561, 0.516], I can make an inference about community development.\nBased on the above thought, the answer is:\nThe consistent rise in the resident growth rate indicates that the community has been developing progressively over the years. This could be due to improvements in living conditions, amenities, or infrastructure that are attracting more people to reside in the community."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "Ame4IH9Q5G0noq6X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the resident growth rate, what anticipation can you propose for the future of this community?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the growth rates represented by each year's slice, from {2010}[0.265, 0.278, 0.482, 0.516] to {2019}[0.283, 0.146, 0.561, 0.516], I can make a prediction about the future of the community.\nBased on the above thought, the answer is:\nIf the current trend of rising resident growth rates continues, the community is likely to experience significant expansion in the future. This implies that the community may need to plan for increased public services and infrastructure to accommodate the growing population."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "SvgSE3izZlx2WigV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year, according to the chart, was the growth rate below 10% but still higher than 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should find the year whose slice size suggests that its value lies between 5 and 10. By looking at the pie slices, there are two slices that meet this condition: {2012}[0.223, 0.375, 0.482, 0.516] and {2013}[0.223, 0.495, 0.482, 0.649.\nBased on the above thought, the answer is:\nThe years are 2012 and 2013."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "fA2sUg035Kb1bZdR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the residents growth rate increase from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should calculate the difference in growth rate between 2010 and 2019 by looking at the pie slices corresponding to these years. The slices for {2010}[0.265, 0.278, 0.482, 0.516] and {2019}[0.283, 0.146, 0.561, 0.516] represent these data points.\nBased on the above thought, the answer is:\nThe growth rate increased by 17% (18.9% - 1.9%)."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "CR4KwdmHqLpRENCk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn how many years was the growth rate above 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to count the number of pie slices with growth rates above 10%. I can see from the chart that the following slices correspond to the years with growth rates greater than 10%: {2014}[0.24, 0.516, 0.482, 0.819], {2015}[0.333, 0.516, 0.496, 0.886], {2016}[0.482, 0.516, 0.682, 0.885], {2017}[0.482, 0.429, 0.741, 0.75], {2018}[0.482, 0.164, 0.733, 0.516] and {2019}[0.283, 0.146, 0.561, 0.516].\nBased on the above thought, the answer is:\nThere are 6 years."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "HOUlOViBmFED2Ahi",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data info from the pie chart and provide them in a table formatted CSV structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart title {\"Investment Distribution from 2018 to 2020\"}<r>[0.303, 0.021, 0.66, 0.047]</r>, legend <r>[0.857, 0.063, 0.993, 0.249]</r> and different pie slices <r>[0.223, 0.146, 0.741, 0.886]</r>, it can be inferred that it represents the investments made in Stocks and Bonds over the years 2018 to 2020.\nBased on the above thought, the answer is:\nInvestment Type,2018,2019,2020\nStocks,30,40,50\nBonds,20,25,15"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "FS6SRwE5Ew4m3pQJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which investment type has seen more growth in their percentage from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the percentage growth of both Stocks and Bonds from 2018 to 2020.\nBased on the above thought, the answer is:\nThe \"Stocks\" investment type has seen the most growth, increasing by 27.8% in 2020 from 16.7% in 2018."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "BRDcXFjdddqr6C9R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat may have been a potential factor to the decrease in the percentage of Bonds from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The decrease in Bond's percentage from 2019 to 2020 might be due to various factors. However, we can't infer this from the chart as it doesn't provide specific reasons for this decrease.\nBased on the above thought, the answer is:\nThe chart does not provide enough information to definitively answer this question."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "36fMnDfhMACL7kIc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what conclusions could we draw about the investment trends over the three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the trends, I should examine the rate of change in investment percentages for both Stocks and Bonds over the three depicted years.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that there has been a steady increase in investment in Stocks over the three years while for Bonds, the investment was stable from 2018 to 2019 but saw a decrease in 2020. This could suggest that investors might be gaining more confidence in Stocks."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "9j4gIXS7wdtUhjV0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie slices, what's the total percentage of stocks in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage of stocks in 2020, I need to identify the pie slice representing \"Stocks-2020\" and check the percentage mentioned.\nBased on the above thought, the answer is:\n27.8%"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "Cp2JyuWehz4DN643",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage did the investment in bonds decrease from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of bonds in 2020 from the percentage in 2019.\nBased on the above thought, the answer is:\nThe investment in bonds decreased by 5%"
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "7zahDjqQmVxFAZYw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the total investment in \"Stocks\" in the year 2018 as compared to \"Bonds\" in the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I would make a comparison between the pie slice percentages for \"Stocks-2018\" and \"Bonds-2019\".\nBased on the above thought, the answer is:\nStocks in 2018 was 10% more than Bonds in 2019."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "78CWB8kBJbCCMmce",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart provided, can you please convert the evaluations into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title {\"Innovation Project Evaluation from 2018 to 2020\"}[0.28, 0.021, 0.683, 0.047] and the different labels used for the slices <r>[0.309, 0.454, 0.352, 0.474]</r> <r>[0.348, 0.66, 0.391, 0.68]</r> <r>[0.484, 0.725, 0.528, 0.745]</r> <r>[0.598, 0.606, 0.642, 0.626]</r> <r>[0.585, 0.374, 0.629, 0.394]</r> <r>[0.42, 0.291, 0.464, 0.311]</r>, it can be derived that these slices represent the scores for each year for both categories of evaluation.\nBased on the above thought, the answer is:\nEvaluation Category,2018,2019,2020\nCreativity,60,55,75\nBudget Efficiency,70,68,80"
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "IYEFluKyoiw4gweQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year and category do we observe the highest evaluation score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the largest slice in the pie chart and correspond it to its category and year.\nBased on the above thought, the answer is:\nThe highest evaluation score is observed in the \"Budget Efficiency\" category for the year 2020."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "l7dhochU9SrK1EfW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the chart about the performance of the creativity and budget efficiency categories over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the slices representing each category over the years. A larger size indicates a higher performance.\nBased on the above thought, the answer is:\nThe chart suggests that both creativity and budget efficiency have generally shown an upward trend over the years, suggesting improvements in both areas. However, there was a dip in the performance of creativity from 2018 to 2019 before it picked up again in 2020."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "0JUf2CpV2sLZ3R8y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential improvements does the chart suggest in the project evaluation system over these three years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the relative sizes of the pie slices for each category over the years, we can evaluate changes in scores and thus infer potential improvements.\nBased on the above thought, the answer is:\nThe pie chart suggests an overall improvement in the project evaluation system over the three years studied. Both main metrics, Creativity and Budget Efficiency, show an increase in scores, demonstrating that the standard of project management in terms of innovation and budget efficiency has improved. However, the dip in creativity scores in 2019 suggests a potential area for future focus."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "NbvdgJWjJW2Eg68F",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the highest evaluation in 2020 from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I find the labels of 2020 and compare their sizes, the slice with the largest size for 2020 corresponds to the highest evaluation.\nBased on the above thought, the answer is:\nBudget Efficiency."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "4My6J95MnSC2q1fB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has seen a decrease in the evaluation from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I have to find the slices that represent the categories in 2018 and 2019, and then compare the sizes. The category with a smaller slice in 2019 compared to 2018 had a decrease in the evaluation.\nBased on the above thought, the answer is:\nCreativity."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "naPgRnEX3ZLybLzF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich evaluation category had a greater score in 2018: Creativity or Budget Efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to locate the slices representing both the categories in 2018 and compare their areas. The category with the larger slice had a greater score.\nBased on the above thought, the answer is:\nBudget Efficiency."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "4972SgIKbQ7OtSJ1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Visitor Distribution in Tourism and Hospitality Industry\"}[0.257, 0.021, 0.706, 0.047] and from the specific percent information for {Hotels}<r>[0.223, 0.278, 0.482, 0.605]</r>,\n{Fast-Food Chains}<r>[0.23, 0.516, 0.482, 0.879]</r>, {Bistros}<r>[0.431, 0.516, 0.632, 0.886]</r>, {Clubs}<r>[0.482, 0.516, 0.722, 0.817]</r>, {Museums}<r>[0.482, 0.173, 0.741, 0.655]</r>, {Night Markets}<r>[0.482, 0.147, 0.579, 0.516]</r>, {Casinos}<r>[0.344, 0.146, 0.5, 0.516]</r>, {Theme Parks}<r>[0.283, 0.202, 0.482, 0.516]</r> displayed on the chart, I can derive a CSV data table.\nBased on the above thought, the answer is:\nCategory,Percentages\nHotels,15\nFast-Food Chains,18\nBistros,13\nClubs,9\nMuseums,25\nNight Markets,5\nCasinos,10\nTheme Parks,5"
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "L8fi1k6JaEtyEvIK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of establishment does the majority of visitors prefer and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The establishment with the highest percentage would be the most visited. Therefore, I would look at the pie chart data to identify the category with the most significant proportion.\nBased on the above thought, the answer is:\nMuseums are the most visited establishment with a percentage of 25%, indicating that the majority of visitors prefer museums."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "HSfIUWJktOTDNdss",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data tell us about visitor's preferences towards Night Markets and Theme Parks?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentages corresponding to Night Markets and Theme Parks would give me an idea about the visitor's preferences towards them.\nBased on the above thought, the answer is:\nGiven that Night Markets and Theme Parks both account for only 5% of the visitors each, it suggests that these are less preferred compared to other establishments in the Tourism and Hospitality industry."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "yA1uwWTpawAw7JaQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the establishment distribution affect business decisions in these sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "With the distribution of the establishments, business sectors can identify where most visitors go, hence highlighting the areas that need improvement or expansion.\nBased on the above thought, the answer is:\nThe establishments with higher percentage of visitors like Museums, Hotels, and Fast-Food Chains might receive more investments for improvement or expansion. In contrast, establishments like Night Markets and Theme Parks that have lower visitor percentage might reconsider their marketing strategies to attract more visitors."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "4ynAtIUEOV6akGLw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two categories, combined, make up for 30% of the visitors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the individual percentages for each category, I need to find a pair of categories whose sum of percentages equals 30%.\nBased on the above thought, the answer is:\nBistros and Night Markets."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "NCYpBApToDNJ8H5K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of visitors that Hotels, Bistros, and Clubs attract?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add up the visitor percentages for the Hotels, Bistros, and Clubs categories.\nBased on the above thought, the answer is:\nThe combined total of Hotles, Bistros, and Clubs is 15% + 13% + 9% = 37%."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "jCS5qUFPmrBKcioZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Casinos increased their visitor percentage by 10%, what would be the new total visitor percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 10% to the Casinos' percentage and sum up all the percentages.\nBased on the above thought, the answer is:\nThe new percentage for Casinos is 10% + 10% = 20%. The new total percentage is 15% (Hotels) + 18% (Fast-Food Chains) + 13% (Bistros) + 9% (Clubs) + 25% (Museums) + 5% (Night Markets) + 20% (Casinos) + 5% (Theme Parks) = 110%."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "ZuRyl9G5cnvZMDtA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the trend of Artificial Intelligence and Automation data in the pie chart, could you convert the data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the slices on the pie chart from 2010 to 2019 represented as various colored slices {the sector slice}<r>[{\"2010\"}[0.313, 0.468, 0.5, 0.654], {\"2011\"}[0.298, 0.545, 0.5, 0.678], {\"2012\"}[0.299, 0.654, 0.5, 0.827], {\"2013\"}[0.339, 0.654, 0.5, 0.939], {\"2014\"}[0.471, 0.654, 0.571, 0.942], {\"2015\"}[0.5, 0.654, 0.668, 0.924], {\"2016\"}[0.5, 0.654, 0.702, 0.814], {\"2017\"}[0.5, 0.476, 0.702, 0.661], {\"2018\"}[0.5, 0.366, 0.659, 0.654], {\"2019\"}[0.345, 0.365, 0.513, 0.654]]</r>.\nBased on the above thought, the answer is:\nYear, Trends\n2010, 10\n2011, 15\n2012, 18\n2013, 25\n2014, 16\n2015, 20\n2016, 18\n2017, 22\n2018, 27\n2019, 30"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "vkyssdfkcogaU9Oh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the pie chart, did the interest in Artificial Intelligence and Automation generally increase or decrease over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the sizes of the slices in ascending order of years and see if the sizes general increase or decrease.\nBased on the above thought, the answer is:\nThe interest in Artificial Intelligence and Automation generally increased over the years."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "KjfyhSvJLjRsDGtY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on 2014's slice on the pie chart, can you infer whether there was a fluctuation in the trend of AI and Automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the 2014's slice and compare it with its neighbouring years.\nBased on the above thought, the answer is:\nYes, in 2014, there was a slight decrease in the trend of AI and Automation."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "dL7ttzQGNMTBKZAl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to predict the trend for 2020 based on the data in the chart, what would be your reasonable expectation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the trend from 2010 to 2019 and then use that as a basis for predicting the trend for the year 2020.\nBased on the above thought, the answer is:\nBased on the increasing trend of AI and Automation from 2010 to 2019, I would predict the trend to continue increasing in the year 2020."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "3V9PxSbGOsDjzsAV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year AI and Automation drew the highest attention?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would look at the largest slice in the pie chart.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "fmUzk6ZjOHdYcCMy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, how much attention did AI and Automation draw in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to find the slice corresponding to the year 2013 and then read off its value.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "7DiqHXt0sM06UKqL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the interest in 2011 increased by 10, what would be the new interest value for that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new value, I need to sum up the original value with the increment.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "u74o27BH6JSoaj1P",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, could you extract the data and represent it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bearing in mind the title of 'Companies Adoption of Sustainable Business Models' at the top of the chart {Companies Adoption of Sustainable Business Models}[0.261, 0.021, 0.699, 0.047] and examining the slices of the pie chart {[Community Development][0.221, 0.278, 0.678, 0.886], [Environmental Conservation][0.48, 0.173, 0.739, 0.754], [Equal Pay][0.282, 0.146, 0.577, 0.516]}, I can see that each slice represents a different element of social responsibility and the proportion that it comprises.\nBased on the above thought, the answer is:\nElement,Sustainable Business Model\nCommunity Development,50%\nEnvironmental Conservation,30%\nEqual Pay,20%"
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "11Z1rbx95mYqkye3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the pie chart, which is the society's favorite element of the business model? Please explain the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The answer is the element which is represented by the highest proportion in the pie chart.\nBased on the above thought, the answer is:\nThe favorite element is 'Community Development' because it is the largest sector with a proportion of 50%."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "OCngyTxsfOeacmov",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer from the pie chart about the companies' commitment to these social responsibility elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "The interpretation is based on the proportion of each sector present in the pie chart.\nBased on the above thought, the answer is:\nIt can be inferred that 'Community Development' is the most prioritized element followed by 'Environmental Conservation', signaling a strong commitment to societal welfare. The least priority is given to 'Equal Pay', implicating possible areas for progress."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "shzKc2V927pxV2yU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data provided in the pie chart influence the investment any company might make in its CSR activities for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the proportions of each area in the pie chart, one can infer possible investment alterations.\nBased on the above thought, the answer is:\nGiven the high percentage of involvement in 'Community Development', any company might opt to continue or increase investments in this domain. However, considering the lower proportions for 'Environmental Conservation' and 'Equal Pay', increased investments and strategic changes to these areas could also be beneficial for the company\u2019s reputation and societal value engagement."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "D5hr1MDsLOXhWFpS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how much less is the adoption of the Equal Pay business model than the Environmental Conservation model?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the percentage of Equal Pay from the percentage of Environmental Conservation.\nBased on the above thought, the answer is:\n10%."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "egyNd95gnEStiBJm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage for the adoption of Environmental Conservation and Community Development models?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding up the percentages for Environmental Conservation and Community Development, I can get the total.\nBased on the above thought, the answer is:\nThe combined percentage is 80%."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "EIlidr1v4otSypbO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the adoption of the Environmental Conservation model increases by 10%, what would be the new total percentage for all three areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "By increasing the Environmental Conservation percentage by 10% and then adding the three percentages together, I can get the new total.\nBased on the above thought, the answer is:\nThe new total is 110%."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "Evve29ZNo9O8Yfsw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the Pie Chart you're analyzing, could you organize the observed data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the chart's title {\"Yearly Progress in Career and Workplace Development Trends from 2001 to 2008\"}<r>[0.145, 0.021, 0.818, 0.047]</r>, it's apparent that the slices represent different years and their corresponding percentages. I will draw from this and the legend to generate the data in CSV format.\nBased on the above thought, the answer is:\nYear, Percentage\n2001, 5% \n2002, 8%\n2003, 10%\n2004, 13%\n2005, 15%\n2006, 15%\n2007, 20%\n2008, 14%"
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "fBnYgCEjlAw5Wyke",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in which year did Career and Workplace Development observe the highest growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest slice of the pie chart would indicate the year with the highest Career and Workplace Development growth.\nBased on the above thought, the answer is:\nCareer and Workplace Development saw the highest growth in 2007."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "EwJKMg1EVUPXPndf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the size of the pie slices indicate in the context of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of the pie slices would be directly proportional to the growth of Career and Workplace Development in a particular year.\nBased on the above thought, the answer is:\nThe size of the pie slices represents the percentage of Career and Workplace Development Trends for each corresponding year from 2001 to 2008."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "Tn9rPcfWqKA7tD4U",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be inferred from the decrease in size of the pie slice for the year 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "A smaller slice for the year 2008 indicates a lower percentage of growth in Career and Workplace Development compared to the preceding years.\nBased on the above thought, the answer is:\nThe decrease in size of the pie slice for 2008 infers a decline in Career and Workplace Development Trends compared to previous years. This could potentially indicate a downturn or a change in industry standards."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "3463ISxUxgKemtkw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw a growth of 15% in Career and Workplace Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the slice representing a 15% growth from the pie's miscellaneous slices.\nBased on the above thought, the answer is:\n2005 and 2006"
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "8vkG936vxKspoiHm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there is a sudden increase of 10% in trends for the year 2009, how much would that be in total, including the year 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to add 10% to the percentage of the year 2008.\nBased on the above thought, the answer is:\nThe total percentage would be 24%."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "3yYNC0a2VoH2fwVs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the cumulative growth rate from the year 2001 to 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I add the growth rates of the years 2001 to 2005 together.\nBased on the above thought, the answer is:\nThe cumulative growth rate from 2001 to 2005 was 51%."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "JZA84bC3Qlro19LB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart observed, convert the information into a CSV format file for the High Income group."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the slices of the pie chart for Income Gap<r>[0.239, 0.278, 0.482, 0.516]</r>, Health Care<r>[0.223, 0.386, 0.482, 0.843]</r>, Education<r>[0.361, 0.516, 0.705, 0.886]</r>, Political Representation<r>[0.482, 0.399, 0.741, 0.703]</r>, Corruption<r>[0.482, 0.243, 0.727, 0.516]</r>, and Freedom of Speech<r>[0.283, 0.146, 0.656, 0.516]</r> as well as their percentages in the percentages text<r>[0.152, 0.301, 0.236, 0.321]; [0.133, 0.651, 0.215, 0.671]; [0.56, 0.897, 0.629, 0.917]; [0.765, 0.548, 0.929, 0.568]; [0.721, 0.284, 0.794, 0.304]; [0.329, 0.1, 0.463, 0.12]</r>.\nBased on the above thought, the answer is:\nGroups,High Income\nIncome Gap,5.4%\nHealth Care,23.0%\nEducation,24.3%\nPolitical Representation,13.5%\nCorruption,8.1%\nFreedom of Speech,25.7%"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "Dawg52bGZFzICy4D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the biggest issue faced by the high income group?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the percentages assigned to each issue, the issue with the highest percentage would represent the most significant problem for the High Income group.\nBased on the above thought, the answer is:\nThe biggest issue faced by the high income group is 'Education', with a portion of 24.3%."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "iASBI3kSWf3Agpmv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about the High Income group's social issue needs from the pie chart representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the proportions of each issue, an inference about the needs of the High Income group can be made.\nBased on the above thought, the answer is:\nThe high percentage in 'Education' and 'Health Care' suggests that the high-income group places a premium on quality education and health services. Interestingly, despite their income, they still face significant challenges, suggesting that these areas might be in need of funding or reform."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "2HqPD1PyU1HFK7mK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the data was to guide policy changes, which area should receive the least focus for the High Income group?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the area with the smallest percentage in the pie chart.\nBased on the above thought, the answer is:\n'Income Gap' should receive the least focus as it has the smallest representation at 5.4% among the issues faced by the High Income group."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "uSQUwqg6oJosck8J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the combined percentage of 'Education' and 'Health Care' for the High Income group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage, I will add up the percentages for 'Education' and 'Health Care' based on their respective slices in the pie chart.\nBased on the above thought, the answer is:\n47.3%"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "OjtB0TvHKnkwwwof",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between 'Freedom of Speech' and 'Income Gap' for the High Income group?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I will subtract the percentage of 'Income Gap' from 'Freedom of Speech'.\nBased on the above thought, the answer is:\n20.3%"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "ebHv917lkYWo4P1f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage of 'Political Representation' and 'Corruption' for the High Income group?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will add up the percentages for 'Political Representation' and 'Corruption' to find the answer.\nBased on the above thought, the answer is:\n21.6%"
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "RVO3mcghaojQhRws",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the information on the pie chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I started by identifying the title of the chart {\"Social Inequality and Rights Protection for High Income Group\"}<r>[0.225, 0.021, 0.739, 0.047]</r>, and the legend items as well as their associated colors on the side of the chart {(\"Income Gap\", \"#ff9999\", \"5.4%\"), (\"Health Care\", \"#66b3ff\", \"23%\"), (\"Education\", \"#99ff99\", \"24.3%\"), (\"Political Representation\", \"#ffcc99\", \"13.5%\"), (\"Corruption\", \"#c2c2f0\", \"8.1%\"), (\"Freedom of Speech\", \"#a1a1a1\", \"25.7%\")}<r>[0.779, 0.063, 0.993, 0.249]</r>. The slices of the pie chart correspond to the different categories listed in the legend, and their size is proportional to the percentage next to each legend item.\nBased on the above thought, the answer is:\nCategory,Percentage\nIncome Gap,5.4%\nHealth Care,23%\nEducation,24.3%\nPolitical Representation,13.5%\nCorruption,8.1%\nFreedom of Speech,25.7%"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "wlSwZZNNo6gLR9hT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich issue is considered most important according to the chart, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine this by looking for the slice with the largest percentage. The larger the percentage, the more important the issue is considered.\nBased on the above thought, the answer is:\nThe most important issue is \"Freedom of Speech,\" as it has the highest percentage \u2013 25.7%."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "rqDzv2n0jVOLDEA0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a particular issue the high-income group appears to be least concerned about?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by looking for the slice with the smallest percentage. The smaller the percentage, the less concerned the high-income group is about the issue.\nBased on the above thought, the answer is:\nThe issue the high-income group appears to be least concerned about is \"Income Gap\", as it has the smallest percentage of 5.4%."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "7xbiSs04x5asZ7H7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the pie chart represents the concerns of the high-income group, what can you infer about their interests in the context of social inequality and rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the data of the pie chart and interpret it in the context of social inequality and rights protection.\nBased on the above thought, the answer is:\nThe high-income group appears to prioritize \"Freedom of Speech\" and \"Education\" over the other issues, as these two categories make up almost half of their concerns. They cite \"Income Gap\" as their least concern, which could suggest that they are less invested in income equality. Their focused interest in \"Health Care\", \"Political Representation\", and \"Corruption\" shows they are aware of and concerned about these aspects of social inequality and rights protection."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "ec5MiWQwBzX8hOks",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages for Income Gap and Corruption according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The respective percentages for the categories are found in the legend. I just need to add the values for Income Gap and Corruption.\nBased on the above thought, the answer is:\n13.5%"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "ULG1LEZZADAvKH0G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category takes up a quarter of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by cross-checking the categories with their respective percentages. If a category's percentage is near or equal to 25%, it is the one we are looking for.\nBased on the above thought, the answer is:\nFreedom of Speech"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "ANYL2E9NyaE7YIKu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percentage points does the total of Health Care and Education exceed the sum of all other categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages of Health Care and Education, then subtract the sum of the percentages of the remaining categories.\nBased on the above thought, the answer is:\n3.5%"
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "JM7zCo2LoF0JwnNP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're seeing, can you convert the cuisine data into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. From the title {\"Global Gastronomy Culture Analysis\"}[0.332, 0.021, 0.631, 0.047] and the data key {the legend}[0.841, 0.063, 0.993, 0.368], in combination with each cuisine's pie slices {\"American\"}[0.223, 0.278, 0.482, 0.685], {\"Italian\"}[0.251, 0.516, 0.482, 0.872], {\"Mexican\"}[0.411, 0.516, 0.589, 0.886], {\"Chinese\"}[0.482, 0.505, 0.741, 0.853], {\"Indian\"}[0.482, 0.42, 0.741, 0.516], {\"Japanese\"}[0.482, 0.238, 0.732, 0.516], {\"Thai\"}[0.482, 0.171, 0.653, 0.516], {\"French\"}[0.456, 0.146, 0.575, 0.516], {\"Spanish\"}[0.369, 0.148, 0.482, 0.516], {\"Mediterranean\"}[0.283, 0.183, 0.482, 0.516], it's possible to organize and convert this information into a structured format.\nBased on the above thought, the answer is:\n\"Cuisine Type\",\"Number of restaurants worldwide\"\n\"American\",50000\n\"Italian\",35000\n\"Mexican\",30000\n\"Chinese\",50000\n\"Indian\",10000\n\"Japanese\",25000\n\"Thai\",15000\n\"French\",20000\n\"Spanish\",15000\n\"Mediterranean\",18000"
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "oqYEG3MExtK7hnw7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what are the three most prevalent cuisine types globally?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine this by identifying the three largest slices on the pie chart.\nBased on the above thought, the answer is:\nThe three most prevalent cuisine types globally are American, Chinese and Italian."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "ow2LZ0mHd3NxbJzN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf someone wanted to explore a less mainstream cuisine, what could be a potential choice based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A potential choice for a less mainstream cuisine would be one with smaller representation on the pie chart.\nBased on the above thought, the answer is:\nBased on the given pie chart, Indian would be a potential choice as it's less mainstream with only 10000 restaurants worldwide."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "hmTf6fbSm1frmcaO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you infer about the popularity of Asian cuisine looking at the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will have to look at the combined proportion of Asian cuisines namely Chinese, Indian, Japanese, and Thai.\nBased on the above thought, the answer is:\nAsian cuisine, represented by Chinese, Indian, Japanese and Thai have a substantial presence, with Chinese and Japanese being very popular. However, despite their collective prevalence, the popularity varies greatly among individual Asian cuisines, suggesting diverse tastes and preferences worldwide."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "0u6vjn7VnpbjC8fG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine type has the most number of restaurants worldwide?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to find the type of cuisine with the largest slice in the pie chart.\nBased on the above thought, the answer is:\nAmerican and Chinese."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "dTsqU9e4Kef9c5ln",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage of Japanese cuisine in the worldwide cuisine culture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to check the percentage written on the Japanese pie slice.\nBased on the above thought, the answer is:\n9.3%."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "DcWpyE6Ce1KL8p5U",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more Indian restaurants would be needed to equal the number of Thai restaurants?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the numerical value of Indian restaurants from the numerical value of Thai restaurants.\nBased on the above thought, the answer is:\n5000."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "u4DmElUSFQ9XJSmH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease analyze the pie chart you observed and convert the relevant data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart {\"Distribution of Academic Research Fields\"}[0.312, 0.021, 0.651, 0.047] and each of the {individual slices of the pie chart}[{\"Physics\"}[0.241, 0.278, 0.482, 0.516]; {\"Chemistry\"}[0.223, 0.377, 0.482, 0.712]; {\"Biology\"}[0.262, 0.516, 0.544, 0.886]; {\"Mathematics\"}[0.482, 0.516, 0.68, 0.875]; {\"Computer Science\"}[0.482, 0.173, 0.741, 0.754]; {\"Psychology\"}[0.388, 0.146, 0.579, 0.516]; {\"History\"}[0.318, 0.171, 0.482, 0.516]; {\"Philosophy\"}[0.283, 0.229, 0.482, 0.516]] indicate that the pie chart represents the distribution of various academic research fields.\nBased on the above thought, the answer is:\nFields,Shares (%)\nPhysics,5\nChemistry,15\nBiology,20\nMathematics,10\nComputer Science,30\nPsychology,12\nHistory,5\nPhilosophy,3"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "PtSZXaNVfLmRrgAz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which academic research field is most dominant and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the proportions, I can conclude that the field with the largest proportion is the most dominant.\nBased on the above thought, the answer is:\nComputer Science is the most dominant field, as it has the highest proportion of 30% among all fields."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "qWUoIDrxNpIoCels",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about prioritization in the academic research fields in the given data from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the proportions of each field to make inferences about the distribution of academic research fields.\nBased on the above thought, the answer is:\nIt can be inferred from the chart that technical fields such as Computer Science are given more importance in academic research, as they acquire the highest proportion. On the other hand, less tangible fields of knowledge such as Philosophy and History are given lesser importance as indicated by their smaller shares."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "f5NNDrfGSCWNwr2c",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the distribution of academic research fields in the chart impact funding decisions for academic research?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to take into account the proportions of each field in the academic research to infer the impact on funding allocations.\nBased on the above thought, the answer is:\nThe field of Computer Science, having the highest proportion, might receive the largest share of funding to support its extensive research. Fields with smaller shares like History and Philosophy might be allocated lesser funding due to their relatively current lesser extent of research."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "0T5HuBorTCe49tr6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two fields combined have the same proportion as Computer Science in academic research?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this answer, I need to compare the sum of shares of each pair of fields with the share of Computer Science.\nBased on the above thought, the answer is:\nPhysics and Biology"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "8kYEt3sb7OpOXnpV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of academic research does the combined field of natural science contribute to? (Natural science includes Physics, Chemistry, and Biology)"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to add up the shares of Physics, Chemistry, and Biology.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "P2V0EKUCPSzU0Mwv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the field of Psychology increases its proportion by 5%, what would be the new total percentage of all fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to add 5% to the share of Psychology and calculate the new total percentage of all fields.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "P2rpHVAxNEkRNGs3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the pie chart data into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to extract the coordinates of {each year and the corresponding percentage}[{\"2012\"}[0.331, 0.392, 0.366, 0.412]; {\"2013\"}[0.305, 0.49, 0.348, 0.51]; {\"2014\"}[0.342, 0.652, 0.386, 0.672]; {\"2015\"}[0.497, 0.721, 0.541, 0.741]; {\"2016\"}[0.615, 0.487, 0.658, 0.507]; {\"2017\"}[0.531, 0.306, 0.566, 0.326]; {\"2018\"}[0.441, 0.285, 0.485, 0.305]; {\"2019\"}[0.37, 0.33, 0.405, 0.35]]. I can use this visual mapping to transcribe the years and their respective migration percentages.\nBased on the above thought, the answer is:\nYear,Migration%\n2012,5%\n2013,10%\n2014,15%\n2015,20%\n2016,25%\n2017,8%\n2018,10%\n2019,7%"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "Djyc3RM9EVFzjlTA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the migration trends over the years based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to interpret the percentages given for each year to understand how the migration rate has changed over the years.\nBased on the above thought, the answer is:\nThe pie chart indicates that the migration rate generally increased from 2012 to 2016. However, there was a significant drop in 2017, after which the migration rate remained relatively stable until 2019."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "SVJjDyIRXxpaKAxM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is 2017 labeled as an outlier in the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the migration percentage of 2017 with its neighboring years to see why it could be considered as an outlier.\nBased on the above thought, the answer is:\nThe year 2017 is considered an outlier as there was a significant drop in the migration rate compared to the previous year, 2016. This drop broke the increasing trend observed from 2012 to 2016."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "RNX8Q7zcd9J9j8lx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data visualized in this chart influence policy making related to population migration and mobility?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would consider the trends depicted in the chart to understand how this data might affect decision making.\nBased on the above thought, the answer is:\nPolicymakers might focus on investigating the reasons behind the significant decrease in migration percentage in 2017 and the subsequent stability. Understanding these reasons could help shape policies aimed at managing population migration and mobility effectively. Furthermore, it would be beneficial to research why there was a steady increase until 2016 and whether some strategies or measures taken in those years could be replicated."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "05SxidQbFUpIN7uP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year recorded the highest percentage of migration?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to compare the percentages labeled on each year and find the one that is highest.\nBased on the above thought, the answer is:\n2016"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "bruCY7dVKERvVxFW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the percentage of migration decrease from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the migration percentage of 2017 from that of 2016 to get the decrease.\nBased on the above thought, the answer is:\n17%"
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "2VB6AfUe66XjWZyj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween which two consecutive years was the change in the percentage of migration smallest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the pairs of consecutive years and determine which pair shows the smallest change.\nBased on the above thought, the answer is:\nBetween 2018 and 2019."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "RVwlWVcdkcm3APRM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe pie chart provides a snapshot of migration rates for different years. Can you convert this visual data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can interpret the chart structure {\"Population Migration and Mobility Trends from 2012 to 2020\"}<r>[0.233, 0.021, 0.731, 0.047]</r> to create the table, noting that the chart shows in-country and out-country migration. The legend on the right, {\"the legend\"}<r>[0.908, 0.063, 0.993, 0.608]</r> indicates different years and associated percentages.\nBased on the above thought, the answer is:\nYear,In-Country Migration%,Out-Country Migration%\n2012,3.7%,4.6%\n2013,5.2%,3.8%\n2014,5.9%,5.3%\n2015,7.1%,6.1%\n2016,5.0%,4.8%\n2017,7.3%,5.3%\n2018,6.5%,4.2%\n2019,7.0%,5.1%\n2020,7.9%,5.4%"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "LbrWZfbAFqUjg0F3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, has there been any significant change in the migration pattern over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the percentages of in-country and out-country migration for each year on the chart for significant changes.\nBased on the above thought, the answer is:\nYes, the chart shows a general increase in both in-country and out-country migration over the years, with noticeably lower values in 2016."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "2fRzlyPA9Jvoo9Qf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which year had the least number of in-country migrations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart I have to find the slice representing in-country migration that is the smallest to determine the year with the least number of in-country migrations.\nBased on the above thought, the answer is:\nThe year 2012 had the lowest percentage of in-country migrations."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "hauNMlZ3Romu8wBx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the slight dip in migration rates in 2016, what could be a possible reason for this downtrend?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data given, I can only speculate reasons for this downtrend. Facts and conditions specific to that year would need to be considered for a definitive answer.\nBased on the above thought, the answer is:\nThe slight dip in the migration rates in 2016 could be due to a host of reasons - improved economic conditions, introduction of certain immigration policies or significant events happening in the given year. Detailed information from that specific year would be needed for a concrete explanation."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "op1cm0FPJ1PXZWZR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total reported percentage of in-country migration for the years 2014, 2015, and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this information by summing up the percentages of in-country migration for the years 2014, 2015, and 2016 from the pie chart slices.\nBased on the above thought, the answer is:\n18.0%"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "N9lBldvEuqqgE18Z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of migration was in-country migration for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find out the proportion of in-country migration for the year 2020 by locating the slice of the pie chart that represents this year and category.\nBased on the above thought, the answer is:\n7.9%"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "gxjWCD92LYQP3F6h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the out-country migration in 2017 more than the in-country migration?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find out the difference by subtracting the percentage of in-country migration in 2017 from the percentage of out-country migration in the same year.\nBased on the above thought, the answer is:\n2.0%"
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "h9szAv08Qx50M5k6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the information about the Consumer Behavior Trend from 2011 to 2020 and put it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the legend on the right side of the chart {legend}<r>[0.805, 0.002, 0.861, 0.216]</r>, I see colors corresponding to different years. Each year\u2019s data points represent the proportion of people choosing online versus in-store shopping. By considering the pie slices in the chart {pie slices}<r>[0.26, 0.175, 0.74, 0.895]</r>, I can deduce the proportion for each year and construct the table.\nBased on the above thought, the answer is:\nTime,Online Shopping%,In-store Shopping%\n2011,20,80\n2012,25,75\n2013,30,70\n2014,35,65\n2015,32,68\n2016,37,63\n2017,40,60\n2018,50,50\n2019,60,40\n2020,70,30"
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "nBXtuYIa4hRVS3Ts",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what can be inferred about the consumer behavior trend in shopping from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart {pie chart}<r>[0.26, 0.175, 0.74, 0.895]</r> gives an overall picture of the consumer behavior trends. It is clear that as the years pass the preference for online shopping increases while the preference for in-store shopping decreases.\nBased on the above thought, the answer is:\nThe trend clearly shows a shift in consumer behavior from preference of in-store shopping in early years towards a preference for online shopping in later years. One can infer that with advancements in technology and ecommerce, consumers started preferring online shopping due to the convenience it offers."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "uaZVtUJn1zE3BLL8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this data from the chart be relevant for retail business planning for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart offers valuable insights into consumer shopping preferences. It shows a clear shift towards online shopping, which is an important factor to consider during business planning.\nBased on the above thought, the answer is:\nA retail business can use this data to understand that the future lies in enhancing online selling platforms, as it has been the preferred shopping method in recent years. So, launching or improving online platforms, optimizing online customer service, and ensuring a seamless online shopping experience should be incorporated in their strategic plans."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ElHxqCYJcmxwY1HO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the 2015 dip in online shopping, reflected on the chart, be interpreted?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the dip in 2015, I need to look at both the online and in-store shopping trends. It appears to be an aberration in an otherwise consistent rise in online shopping.\nBased on the above thought, the answer is:\nThe slight dip in online shopping in 2015 and a similar boost in in-store shopping might have been due to specific events during that year that could have temporarily influenced consumer behavior, such as an economic situation, technological complications, logistical issues with online deliveries, etc. In the larger perspective of trends, it seems to be a temporary deviation rather than a significant shift in consumer behavior."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ZSnzymNreSGF8hZM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me in which year did Online Shopping preference first exceed In-store Shopping?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie slices {pie slices}<r>[0.26, 0.175, 0.74, 0.895]</r> and matching them to their corresponding color in the legend {legend}<r>[0.805, 0.002, 0.861, 0.216]</r>, I can estimate the year where online shopping first exceeded in-store shopping.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "SPLWKC8DDAZEMJtY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent did the preference for online shopping increase from 2014 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the percentages of preference for online shopping in 2014 and 2019 respectively, and then subtract to find the difference.\nBased on the above thought, the answer is:\nThe preference for online shopping was 35% in 2014 and 60% in 2019. So, it increased by 25%."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "jHIkg1MQI8VBkuZL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the decrease in preference for in-store shopping from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease, I need to find the percentages in 2011 and 2020 for in-store shopping and then subtract to get the answer.\nBased on the above thought, the answer is:\nThe percentage for in-store shopping was 80% in 2011 and 30% in 2020. Thus, there was a 50% decrease."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "rGJTPpHoBIeZ8ube",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the execution times of the two processes given in the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the chart legend {Sales Process}[0.856, 0.071, 0.95, 0.091] and {Production Process}[0.856, 0.101, 0.987, 0.121], I can infer that these stand for the two categories of processes. The pie chart slices corresponding to these categories have autotexts {22.2%}[0.103, 0.506, 0.197, 0.526] and {77.8%}[0.767, 0.506, 0.897, 0.526].\nBased on the above thought, the answer is:\nProcess,Execution Time(%)\nSales Process,22.2\nProduction Process,77.8"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "eredQJPm2Ii3vab1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich process accounts for the majority of the execution time? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the process with the majority of execution time, I need to compare the proportions for sales process and production process.\nBased on the above thought, the answer is:\nThe \"Production Process\" accounts for the majority of the execution time with a proportion of 77.8%, which is significantly higher than the Sales Process at 22.2%."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "Bde1F0AFayvDKs1k",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the distribution of execution times for the internal business processes based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the proportions, I can infer about the execution times of the processes.\nBased on the above thought, the answer is:\nThe distribution of execution times reveals that a large portion (77.8%) of the time is consumed by the \"Production Process\", while the \"Sales Process\" accounts for a much smaller percentage (22.2%). This indicates that the company spends more effort on production compared to sales."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "hYiDL0Sws4KhHQUA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implication of this distribution of execution times on the company's overall efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications of the distribution of execution times could be evaluated by considering how much time is consumed by different processes relative to their importance.\nBased on the above thought, the answer is:\nThe pie chart indicates that a major share of the execution time is allotted to the production process. If production is the company's key area, this distribution can be considered as efficient. However, if the sales process plays an important role in revenues, such a small proportion could signify potential issues in process management, suggesting room for efficiency improvement. It could mean that more resources should be dedicated to sales to balance the distribution better."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "AZHQStoKbcW0PifV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion difference between the sales and production processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the proportion difference, I need to subtract the proportion of the sales process from that of the production process.\nBased on the above thought, the answer is:\n77.8% - 22.2% = 55.6%"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "FuO6IKUN8lYwsJzZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the sales process increased its proportion by 10%, what would be the new proportion of the production process?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion, if the sales process increases its proportion by 10%, I would need subtract 10% from current proportion of production process.\nBased on the above thought, the answer is:\n77.8% - 10% = 67.8%"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "a1t2qxz2vpFaPcMZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of the sales process was doubled, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To double the proportion of the sales process, I need to multiply the current proportion by 2.\nBased on the above thought, the answer is:\n22.2% * 2 = 44.4%"
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "hcskOFEL7QWj0KkH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart figure you received, convert the indicated stages of the 'Value Chain' and their corresponding costs into a structured CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "I can conclude from the chart that contains the title {\"Value Chain Visualization and Analysis\"}[0.322, 0.021, 0.641, 0.047] and the pie chart elements, {the slices, which represents the different categories and their proportions against the total sum}[{'Inbound Logistics'}[0.223, 0.278, 0.482, 0.656]; {'Operations'}[0.242, 0.516, 0.556, 0.886]; {'Outbound Logistics'}[0.482, 0.516, 0.708, 0.87]; {'Marketing and Sales'}[0.482, 0.167, 0.741, 0.695]; {'Service'}[0.283, 0.146, 0.567, 0.516]] that they represent different stages and their corresponding costs.\nBased on the above thought, the answer is:\nStage,Cost\nInbound Logistics,450\nOperations,610\nOutbound Logistics,320\nMarketing and Sales,720\nService,500"
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "Z7eMvN72FL36ZcLT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which operation has the highest cost and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to compare the costs of each operation to find out which has the highest.\nBased on the above thought, the answer is:\nThe 'Marketing and Sales' operation has the highest cost. This could be due to the greater resources required for marketing initiatives and sales efforts compared to the other stages."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "WS97ezq8Y1evEbIj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what inferences can be made about the distribution of costs in the Value Chain stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the relative sizes of each pie slice to understand the distribution of costs.\nBased on the above thought, the answer is:\nThe chart shows that 'Marketing and Sales' has the highest cost, followed by 'Operations', indicating that these two stages take up the bulk of expenses in the Value Chain. 'Inbound Logistics' and 'Service' share similar costs, while 'Outbound Logistics' require the least spending. This suggests a substantial investment in sales and basic operational activities."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "0Yi2PeHFvdf7PD4r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information in the chart guide budget allocations for improving the performance of the Value Chain?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would consider the relative costs of each stage to understand which areas may need further budget improvements.\nBased on the above thought, the answer is:\nGiven the high costs of 'Marketing and Sales' and 'Operations', resources could be allocated to optimize these stages and reduce expenditures. Additionally, the comparably low cost in 'Outbound Logistics' may indicate room for increased investment to enhance this stage. A balanced consideration across all stages should contribute to overall performance improvement."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "L1WFSO32DYjtqrzF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the costs incurred in 'Inbound Logistics', 'Operations' and 'Service'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the total cost, I need to add together the costs for 'Inbound Logistics', 'Operations' and 'Service'.\nBased on the above thought, the answer is:\n1560"
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "BOS1ob3oVlNzEkk6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the cost of 'Marketing and Sales' was reduced by 10%, what would be the new value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new value, I need to calculate 10% of the current cost of 'Marketing and Sales' and subtract it from the current value.\nBased on the above thought, the answer is:\n648"
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "BgbO272Ej1gBHbVM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the cost of 'Outbound Logistics' is increased by 50%, what would be the new total cost for all stages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total cost, I need to calculate 50% of the cost of 'Outbound Logistics' and add it to the current value and then sum up all the costs.\nBased on the above thought, the answer is:\n2600"
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "hJ4p7oAFz0wogN4X",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the chart details to raw CSV data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The key information in this chart has originated from the given seven pie slices, each representing a different media category: {TV News}<r>[0.223, 0.278, 0.482, 0.638]</r>, {Online News}<r>[0.237, 0.516, 0.482, 0.861]</r>, {Radio}<r>[0.387, 0.516, 0.576, 0.886]</r>, {Social Media}<r>[0.482, 0.516, 0.732, 0.861]</r>, {Blogs}<r>[0.482, 0.361, 0.741, 0.61]</r>, {Podcasts}<r>[0.482, 0.215, 0.717, 0.516]</r>, {Forums}<r>[0.482, 0.146, 0.632, 0.516]</r> and {Viral Video}<r>[0.283, 0.146, 0.494, 0.516]</r>. An accompanying percentage accompanies each slice to indicate its influence score.\nBased on the above thought, the answer is:\nMedia Category, Influence Score\nTV News,16.5\nOnline News,13.7\nRadio,11.9\nSocial Media,15\nBlogs,11\nPodcasts,8.2\nForums,9.1\nViral Video,14.6"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "SXK4oUHdzX7kGQXq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich media type has the least influence on public opinion according to the pie chart? Supply the rationale for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "By searching the chart and comparing the influence scores of the media categories, the category {Podcasts}<r>[0.482, 0.215, 0.717, 0.516]</r> has the lowest score, indicating that it has the least influence on public opinion.\nBased on the above thought, the answer is:\nThe media category \"Podcasts\" has the least influence on public opinion due to their relatively low influence score of 8.2."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "gQK5YKgKbI4mRXai",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart illustrate the influence of different media categories on public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart has pie slices representing the media categories. Each slice's size corresponds to the influence score, with larger slices representing larger influence scores.\nBased on the above thought, the answer is:\nThe pie chart illustrates the influence of different media categories on public opinion by varying the size of the pie slices according to their influence scores. Larger slices, like TV News and Social Media, indicate a stronger influence over public opinion, whereas smaller slices, like Podcasts and Forums, suggest a lesser influence."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "0JgSfEDgk8VxeMUJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can one conclude that traditional media outlets have lesser influence over public opinion as compared to newer media forms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should compare the slices for traditional media like {TV News}<r>[0.223, 0.278, 0.482, 0.638]</r> and {Radio}<r>[0.387, 0.516, 0.576, 0.886]</r> to newer forms like {Social Media}<r>[0.482, 0.516, 0.732, 0.861]</r> and {Viral Video}<r>[0.283, 0.146, 0.494, 0.516]</r>.\nBased on the above thought, the answer is:\nThe conclusion is not entirely accurate. Although TV News and Radio, which are traditional forms, have a relatively lower influence score than Social Media, TV News still remains the top influencer. Viral Videos, a relatively newer media type, also has a high influence score but is not the most influential. Therefore, both traditional and new media types have considerable influence over public opinion."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "CV32Rj60bWyil7x0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of influence scores of Radio and Forums based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the chart, for each category, I need to identify and add the influence scores. Specifically, for {Radio}<r>[0.387, 0.516, 0.576, 0.886]</r> and {Forums}<r>[0.482, 0.146, 0.632, 0.516]</r>, their influence scores are 11.9 and 9.1 respectively.\nBased on the above thought, the answer is:\n21"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "kvHglf8qLEIIgsnB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which media category has an influence score greater than 15%?"
+            },
+            {
+                "from": "gpt",
+                "value": "By scanning each category slice and comparing their influence scores, I will identify which one exceeds 15%. Here, {TV News}<r>[0.223, 0.278, 0.482, 0.638]</r> and {Social Media}<r>[0.482, 0.516, 0.732, 0.861]</r> have influence scores of 16.5 and 15 respectively which are greater than 15%.\nBased on the above thought, the answer is:\nTV News, Social Media"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "bjo9SgrLGSRHfy9z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percentage points does Viral Video exceed Podcasts' influence score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to first identify the influence scores for {Viral Video}<r>[0.283, 0.146, 0.494, 0.516]</r> and {Podcasts}<r>[0.482, 0.215, 0.717, 0.516]</r>, which are 14.6 and 8.2 respectively. Then subtract Podcasts' score from Viral Video's score.\nBased on the above thought, the answer is:\n6.4 percentage points"
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "bPZ20dPo1OmVyWCj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you arrange the information in the pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the layout of the pie chart given, the {legend}<r>[0.911, 0.063, 0.993, 0.219]</r> and its colors and the labels of each slice, such as {CEO slice}<r>[0.223, 0.278, 0.482, 0.859]</r>, I can see the correlation between the color-coded slices and the role labels, I can then put these properties in a CSV format along with the corresponding percentage determined from the size of each slice.\nBased on the above thought, the answer is:\nRole,Percentage\nCEO,30.0%\nCTO,20.0%\nCFO,16.0%\nCOO,24.0%\nHR,10.0%"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "V6Bbvv7f0nC9Ea7o",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho seems to be the most experienced in the organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the proportion of the pie chart slices such as the {CEO slice}<r>[0.223, 0.278, 0.482, 0.859]</r>, the individual with the largest slice has the most experience.\nBased on the above thought, the answer is:\nThe CEO appears to be the most experienced, given their slice represents 30% of total experience."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "Aifq5Sud6Gv7MH3q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat general trend can be observed regarding experience distribution among company roles?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the pie chart slices like {COO slice}<r>[0.419, 0.146, 0.729, 0.516]</r> and {HR slice}<r>[0.283, 0.157, 0.482, 0.516]</r> considering their size and corresponding labels it could help me understand the experience distribution.\nBased on the above thought, the answer is:\nGenerally, it appears that experience is more concentrated at the C-suite level with the CEO, CTO, CFO, and COO roles. HR tends to have less experience compared to other leadership roles."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "dPfMPBpCWAIvjZT6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how could the organization change the HR role to align more with the experience levels of other roles?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the {HR slice}<r>[0.283, 0.157, 0.482, 0.516]</r> and comparing the proportion to other slices.\nBased on the above thought, the answer is:\nAccording to the chart, the HR role has the least experience. Thus, the organization could consider investing in more professional development and learning opportunities to boost the experience level of the HR role, bringing it more in line with other leadership roles."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "grQe0NQ9cRTJLHoG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the collective experience of the C-suite roles in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I will have to sum up the percentages for CEO, CTO, CFO, and COO which correspond to their respective experience.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "dOqwM4ii4rjly9kq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the CFO and HR exchange their experience, what's the new percentage of HR experience?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the CFO has 16% experience and HR has 10%, if they switch, HR will have 16% experience.\nBased on the above thought, the answer is:\n16%"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "uPcjT4u1bB7NnsMb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat single role's percentage equals the combined experience of the CTO and HR?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the percentages of the CTO and HR slices gives 30%, which matches the experience of the CEO.\nBased on the above thought, the answer is:\nCEO"
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "MNSRXf4OoGLVH7gT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a comparative tabulated data set for the GDP and Unemployment rates from 2008 till 2016 based on the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart, it appears to represent the proportion of GDP and unemployment rate across various years from 2008 to 2016. For each year, a distinct color is allocated in the chart. The size of the sections gives us insight into relative proportions. To generate a table, I need to look at each section of the pie chart and translate the percentage given into GDP and Unemployment rates for each respective year using the chart's legend.\nBased on the above thought, the answer is:\nYear,GDP (USD Billion),Unemployment Rate (%)\n2008,190,8\n2009,194,10\n2010,220,8\n2011,235,7\n2012,250,6.5\n2013,270,5.5\n2014,290,5\n2015,315,4.5\n2016,340,4"
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "vIHcuJr28zFXZu4U",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can you make about the economy based on the GDP and Unemployment rates from 2008 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make observations on the economy, I need to compare the trends in GDP and Unemployment rates over the years. The GDP shows how the economy has grown while the Unemployment rate shows the extent of job availability.\nBased on the above thought, the answer is:\nThe economy showed positive growth over the years from 2008 to 2016, with consistent increases in GDP each year, suggesting a growing or thriving economy. The Unemployment rate reached a peak in 2009 following the financial crisis, but from then until 2016, there was a continual decrease in the Unemployment rate, suggesting an improvement in the job market or increase in employment opportunities."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "f2iUBUIwshfTXoFs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the economic crisis of 2009 affect the GDP and Unemployment rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to understand the impact of the economic crisis of 2009, we need to compare the GDP and Unemployment rates of that year with the ones from the previous year.\nBased on the above thought, the answer is:\nThe economic crisis of 2009 resulted in a spike in the Unemployment rate from 8% in 2008 to 10% in 2009. Despite this, the GDP showed a sustained growth from 190 billion USD in 2008 to 194 billion USD in 2009, indicating resilience in terms of economic output."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "oRSARAbxEccT9dg7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the economy recover after the 2009 crisis?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analysis of the economy's recovery after the 2009 crisis would require history of GDP and Unemployment rates from 2010 and onwards. We can then observe how these two key indicators changed after the crisis.\nBased on the above thought, the answer is:\nThe economy showed significant recovery post the 2009 crisis. From 2010 to 2016, the trend of the GDP was consistently upwards, showing growth in the economy, while the Unemployment Rates exhibited a consistent decline indicating that more people were able to find jobs. This implies that the economy made a substantial recovery since the 2009 crisis."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "mISps93WNEH2qB3k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the GDP growth the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest GDP growth, I need to compare the GDP of every two consecutive years and find the year where the difference was the highest.\nBased on the above thought, the answer is:\nThe year of 2015 saw the highest GDP growth from 290 to 315."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "DdH8SoMUW7pTr5HJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the decrease in the unemployment rate from 2009 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the decrease in the unemployment rate from 2009 to 2016, I need to subtract the unemployment rate of 2016 from that of 2009.\nBased on the above thought, the answer is:\nThe decrease in the unemployment rate from 2009 to 2016 was 6%."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "C0SuEKxPz280udqB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average GDP for the period between 2008 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average GDP, I need to sum up the GDP of all the years from 2008 to 2016 and divide by the number of years.\nBased on the above thought, the answer is:\nThe average GDP for the period between 2008 and 2016 is 268 billion USD."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "m9XIPEkN7nV8YcaT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the percentage revenue distribution of this pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend provided in the coordinates {Legend}[0.858, 0.063, 0.993, 0.309] and by observing the respective slices, I can extract the categories and their percentages and arrange them into a table.\nBased on the above thought, the answer is:\nSector,Percentage of Revenue\nTheatre,20%\nMusic,15%\nLiterature,12.5%\nVisual Arts,25%\nDance,7.5%\nFilm,15%\nArchitecture,2.5%\nOther,2.5%"
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "8G2bb7ZaJVf1dkk3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you point out the sector that has the least impact on the culture and arts industry's revenue distribution? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector with the least impact can be identified by the smallest proportion in the distribution.\nBased on the above thought, the answer is:\nThe sectors \"Architecture\" and \"Other\" have the least impact on the culture and arts industry's revenue distribution as they contribute the least, only 2.5% each."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "J0Ynl0wwnAmFt6iV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the disparity in revenue distribution among these sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "An analysis of the different proportions represented by each sector will provide insight into disparity in revenue distribution.\nBased on the above thought, the answer is:\nThere is a significant disparity in the revenue distribution among the Culture and Arts Industry sectors. Visual Arts makes the highest contribution, accounting for 25% of total revenue; followed by Theatre and Film at 15% each. On the contrary, Architecture and Other sectors only contribute 2.5% each, indicating a considerable gap in revenue generation among these industry sectors."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "Jt2ZEnCswTtnRkHv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an investor wants to invest in the area with the highest revenue, which sector should he invest in based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector suitable for investment, I would need to locate the sector with the highest percentage contribution.\nBased on the above thought, the answer is:\nThe investor should invest in the \"Visual Arts\" sector as it represents the highest percentage revenue in the Culture and Arts Industry, accounting for 25% of the total."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "ESufI6qeUglxdUy5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category contributes exactly one fourth of the total revenue according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I will examine each sector's percentage contribution to identify the one that matches 25%.\nBased on the above thought, the answer is:\nVisual Arts"
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "otBmkHaYy6qZly3J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many sectors contribute less than 10% individually to the total revenue of Culture and Arts Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will identify the sectors contributing less than 10% by looking at the percentages associated with each sector.\nBased on the above thought, the answer is:\nThree sectors; Dance, Architecture and Others."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "vlV2zMZKzUIzKBJv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease calculate the total combined contribution of Theatre and Film to the total Culture and Arts Industry revenue."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined contribution of Theatre and Film, I simply add the individual contributions of each.\nBased on the above thought, the answer is:\nThe combined contribution is 20% (Theatre) + 15% (Film) = 35%"
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "N4vuLHhqKidPLpOH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a CSV table describing the distribution of resource usage for the year 2020 based on the pie chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see two categories, {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r> and {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r> on the pie chart with their respective percentages of 45.7% and 54.3%. By using these percentages for the year 2020, I can create a CSV table.\nBased on the above thought, the answer is:\nResource Type,2020\nTextbooks,45.7%\nE-learning Platforms,54.3%"
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "gEjQaBIj2iqZQ5be",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you explain what the chart indicates about the changing trends in resource use in recent years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to interpret the sizes and proportions of the {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r> and {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r> categories for the year 2020.\nBased on the above thought, the answer is:\nThe chart indicates that there is a growing trend towards the use of digital resources, as seen in the larger proportion of E-learning platforms compared to textbooks in 2020. This could reflect changes in education methods and technologies in recent years."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "cfVaLDHCLVXgXz4u",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, could you infer the possible direction of funding for educational resources in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the distribution of resources in 2020, I can infer the direction of educational resources in the future.\nBased on the above thought, the answer is:\nThe data suggests that more funding might be directed towards E-learning platforms in the future, considering their higher usage percentage in 2020. This implies a possible further shift from traditional paper-based resources to digital ones."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "4cHc16K5ItxUVUqX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can the chart provide to education administrators regarding resource distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to understand the significance of the split between {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r> and {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r>.\nBased on the above thought, the answer is:\nThe chart can provide valuable insights to administrators about the changing dynamics of education resource usage. It shows a significant shift towards digitized resources, suggesting that future resource planning and distribution should consider the growing popularity and effectiveness of digital learning platforms."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "zwJo6ei1qM0rkEmV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart what percent difference exists between the usage of E-learning platforms and Textbooks in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference I will subtract the percentage of {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r> from the percentage of {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r>.\nBased on the above thought, the answer is:\nThere is an 8.6% difference in the usage of E-learning platforms and Textbooks in 2020."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "5lxGBzC8VXSeE0ar",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the new distribution if the usage of E-learning Platforms were to increase by 4% in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new distribution I would need to add 4% to the percentage of {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r> and then subtract that 4% from the percentage of {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r>.\nBased on the above thought, the answer is:\nIf E-learning Platforms were to increase by 4% in 2020, the new distribution would be 41.7% for Textbooks and 58.3% for E-learning Platforms."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "61lcpdX8McWN4Mbx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the usage percentage of each resource if they were equally used in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "If both resources were equally used then both {Textbooks}<r>[0.345, 0.655, 0.388, 0.675]</r> and {E-learning Platforms}<r>[0.575, 0.357, 0.619, 0.377]</r> would account for half of the total usage percentage.\nBased on the above thought, the answer is:\nIf both resources were equally used, then each would have 50% usage in 2020."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "uKiGQKVZaPZJ4Xsf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data presented in the chart into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart and the legend {\"Population (%)\"}<r>[0.83, 0.071, 0.932, 0.091]</r>, {\"Technology (%)\"}<r>[0.83, 0.101, 0.936, 0.121]</r>, {\"Economic Stability (%)\"}<r>[0.83, 0.131, 0.987, 0.151]</r>, I can process the percentages of each category in the two scenarios into a structured table.\nBased on the above thought, the answer is:\nFuture Scenario,Population (%),Technology (%),Economic Stability (%)\nUrbanization,60,80,70\nIntelligent Automation,40,20,30"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "dBLBbJBQJXpsi3Pb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the two future scenarios, which one gives an overall better outlook based on the three key factors in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the comparative percentages of each key factor in both the scenarios to understand which one provides a better future outlook.\nBased on the above thought, the answer is:\nThe 'Urbanization' scenario gives an overall better outlook as it has higher percentages in Population, Technology, and Economic Stability."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "6Rt25z3HQcrJTcJg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the outlier in this data set according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to analyze the percentages of each factor in both scenarios. The outlier will significantly differ from the other data points.\nBased on the above thought, the answer is:\nThe 'Technology' category in the 'Intelligent Automation' scenario is the outlier, having a significantly lower percentage compared to other data points."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "gc1vyKtXClHqkyew",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat changes could be made to the 'Intelligent Automation' scenario to match the overall outlook of the 'Urbanization' scenario based on the data shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I'd need to think about how to increase the percentages in 'Intelligent Automation' to meet or exceed those in 'Urbanization'.\nBased on the above thought, the answer is:\nTo match the 'Urbanization' scenario, significant advancements would need to occur in the 'Intelligent Automation' scenario. The percentage in Population would need to increase by 20, Technology would need to rise by 60, and Economic Stability would require a growth of 40."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "VhRWUdhkLUh5YJD6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of Population in both the scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's clear that the combined percentage for 'Population' in both the scenarios can be calculated by adding the percentages obtained from the chart.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "bThhXgOp9Xs1oF43",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the 'Economic Stability' percentage in the scenario of 'Urbanization' compared to 'Intelligent Automation'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference in 'Economic Stability' in 'Urbanization' compared to 'Intelligent Automation' can be found by calculating the numerical difference of the two percentages.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "fV2d99EdFDDnwgjD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of 'Technology' in both the scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage for 'Technology' in both scenarios, I need to add the percentages of 'Technology' in both the scenarios and divide by the number of scenarios.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "vNWTxknBldKnCYdU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease transform the pie chart data on the urban planning sectors into a tabular form."
+            },
+            {
+                "from": "gpt",
+                "value": "The data on the pie chart is split into two sectors, {\"Community Development\"}<r>[0.38, 0.696, 0.423, 0.716]</r> and {\"Urban Planning\"}<r>[0.54, 0.316, 0.584, 0.336]</r>. The corresponding percentages are {55.0%}<r>[0.157, 0.855, 0.335, 0.875]</r> for \"Community Development\" and {45.0%}<r>[0.628, 0.157, 0.735, 0.177]</r> for \"Urban Planning\".\nBased on the above thought, the answer is:\nSector,Percentage\nCommunity Development,55%\nUrban Planning,45%"
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "etfEPQoInneCEjO4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had been given more emphasis or resources, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of \"Community Development\" and \"Urban Planning\".\nBased on the above thought, the answer is:\nThe \"Community Development\" sector got more emphasis or resources as it had a higher percentage representation of 55% than the \"Urban Planning\"."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "8RcySgidRTVmyNNE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart tell about the allocation of resources between the two sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the percentages of both sectors to understand their respective resource allocations.\nBased on the above thought, the answer is:\nThe pie chart reveals that resources were almost equally divided between \"Community Development\" and \"Urban Planning\". However, \"Community Development\" received a slightly higher proportion of resources at 55%, compared to \"Urban Planning's\" 45%."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "rshqA3HpwX5RrUs2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which can we infer about these two urban planning sectors and their future development direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to understand the trend based on their current percentage distribution.\nBased on the above thought, the answer is:\nSince \"Community Development\" currently holds a larger share, it might continue to expand in the future. At the same time, if \"Urban Planning\" gains more resources or focus, it can also increase its share. The future development of these two areas might largely depend on how resources and focus are allocated."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "wVZee7Q4zahnVXxt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the percentage representation of \"Community Development\" and \"Urban Planning\" sectors in urban planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of \"Urban Planning\" from that of \"Community Development\".\nBased on the above thought, the answer is:\nThe difference is 10%."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "gy91NzKINizcMnJC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage would \"Urban Planning\" represent if it increased by 20%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 20% to the current percentage of \"Urban Planning\".\nBased on the above thought, the answer is:\n\"Urban Planning\" would represent 65% if it increased by 20%."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "TkN2lHqGSZGK09t5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf \"Urban Planning\" absorbed half of \"Community Development's\" percentage, what would be \"Urban Planning's\" new percentage representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add half of \"Community Development's\" percentage to \"Urban Planning\".\nBased on the above thought, the answer is:\nIf \"Urban Planning\" absorbed half of \"Community Development\", \"Urban Planning\" would represent 72.5%."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "iBRWCPJwscguN3rF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me convert the information from the pie chart \"Popularity of Different Cuisines\" into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, taking a look at the title of the chart {the chart title}\"Popularity of Different Cuisines\"<r>[0.353, 0.021, 0.61, 0.047]</r> and the legend with 4 segments: {the legend}\"Italy\"<r>[0.941, 0.071, 0.971, 0.091]</r>, \"India\"<r>[0.941, 0.101, 0.975, 0.121]</r>, \"China\"<r>[0.941, 0.131, 0.981, 0.151]</r> and \"France\"<r>[0.941, 0.161, 0.987, 0.181]</r>, I can assemble the popularity data into a CSV table.\nBased on the above thought, the answer is:\nCuisine,Popularity\nItaly,25%\nIndia,35%\nChina,27%\nFrance,13%"
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "y0IFcmiLNbXvs56O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine enjoyed the highest popularity according to this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the segment with the largest size, its label, and the corresponding percentage to answer this question.\nBased on the above thought, the answer is:\nAccording to the pie chart, Indian cuisine was the most popular choice."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "nmqQZgRo10qRQuPy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould we conclude that Italian cuisine is not well-liked based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although Italian cuisine does not have the highest percentage, it is still quite popular as evidenced by the segment size on the pie chart.\nBased on the above thought, the answer is:\nNo, we cannot conclude that Italian cuisine is not well-liked. It is the second most popular cuisine based on this chart with a substantial 25% popularity."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "AGSBhgmqsNzWkm0H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you draw from the popularity of French cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the size and percentage of the French cuisine segment and compare it to the other cuisine segments to answer this.\nBased on the above thought, the answer is:\nBased on the chart, French cuisine had the least popularity. This might mean that less people in the surveyed group preferred French foods, or maybe more people were less familiar with French cuisine compared to the other choices."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "3h0EXABnWYsus7ac",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total popularity percentage value for the cuisines from Italy and France together?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will sum up the proportions of Italy and France that I can observe from the pie chart.\nBased on the above thought, the answer is:\nThe total popularity percentage for Italy and France is 38%."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "9X4PRYnBf1EJ3Goh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion difference between India, the most popular cuisine according to the survey, and China?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the proportion of China from the proportion of India.\nBased on the above thought, the answer is:\nThe proportion difference between India and China is 8%."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "QGrkyDVeAytibAuY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2% of the population who prefers Italian cuisine shifted their preference to French cuisine, what will be the new percentage for French cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to decrease the Italian cuisine's percentage by 2% and then increase the French cuisine's percentage by the same amount.\nBased on the above thought, the answer is:\nThe new percentage of French cuisine will be 15%."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "wcMinsm2zMLodR7Z",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The given title {\"Gastronomy Culture Analysis\"}[0.361, 0.021, 0.602, 0.047] states that the chart shows a study on gastronomy culture. The various cuisines have been represented in the pie chart as slices {slice}[{Indian}[0.223, 0.278, 0.482, 0.836]; {Thai}[0.352, 0.516, 0.611, 0.886]; {French}[0.482, 0.516, 0.739, 0.836]; {Italian}[0.437, 0.146, 0.741, 0.559]; {Unknown}[0.283, 0.151, 0.482, 0.516]]. Each cuisine's percentage is also available on their slices, hence the data will be represented as gastronomy culture where cuisines and their popular meals' percentage will be highlight that will be organized according to the slices of the pie.\nBased on the above thought, the answer is:\nCuisine,Percentage\nIndian,27.8%\nThai,16.7%\nFrench,14.8%\nItalian,29.6%\nUnknown,11.1%"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "r1cMpbAJMsT2dwgT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which cuisine has the least popularity? And why would you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the least popular cuisine, I need to compare the proportions. The cuisine with the smallest slice in the pie chart will be considered the least popular.\nBased on the above thought, the answer is:\nThe least popular cuisine is the \"Unknown\" because it has the smallest proportion of 11.1% among all the cuisines."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "gD8HNH3KW3smncF8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be gained about the gastronomy culture from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to notice the size and proportion of each {slice}<r>[{Indian}[0.223, 0.278, 0.482, 0.836]; {Thai}[0.352, 0.516, 0.611, 0.886]; {French}[0.482, 0.516, 0.739, 0.836]; {Italian}[0.437, 0.146, 0.741, 0.559]; {Unknown}[0.283, 0.151, 0.482, 0.516]]</r> in the pie chart to draw insights.\nBased on the above thought, the answer is:\nThe pie chart reveals that Indian and Italian cuisines dominate the gastronomy culture with each having a 27.8% and 29.6% share respectively. Moreover, Thai and French cuisines have a moderate popularity, with the Unknown cuisine being the least familiar. This indicates a preference for Indian and Italian cuisines and a range in gastronomy culture tastes."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "QvbL6q1gK60GxveJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat cultural implications can be derived from the chart regarding unknown cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to infer cultural implications, I need to analyze the {Unknown slice}<r>[0.283, 0.151, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nThe fact that there are 11.1% of meals with unknown origins shows that the gastronomy culture embraces diversity and is open to exploring unattributed cuisines. This could imply a willingness to taste and appreciate meals without prejudice to their geographical origins."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "XeTvMFBhA6xasQ3N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage covered by Indian and French cuisines in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the sum of the proportions for the two cuisines, I need to add the percentage of the {Indian slice}<r>[0.223, 0.278, 0.482, 0.836]</r> and the {French slice}<r>[0.482, 0.516, 0.739, 0.836]</r>.\nBased on the above thought, the answer is:\n42.6%"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "hGEyWG16vWxaC5Ps",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a cuisine with a 10% proportion is introduced, what will be the new total percentage for all cuisines in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add this 10% to the original total percentage of the pie chart, which I can verify is 100%.\nBased on the above thought, the answer is:\n110%"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "jS15Z6MOHNHzyHvA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the proportions of Thai and French cuisines in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I have to get the proportions of the {Thai slice}<r>[0.352, 0.516, 0.611, 0.886]</r> and the {French slice}<r>[0.482, 0.516, 0.739, 0.836]</r> and subtract the smaller proportion from the larger one.\nBased on the above thought, the answer is:\n1.9%"
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "5KgCRHnL5bxp4TDq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you have observed, could you please convert the data into a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart displays the interest in AI/ML applications over the years from 2010 to 2014. The respective percentages for each year can be found in the slices inside the pie chart with their respective colors and labels found in the legend. I can now extract and structure this data.\nBased on the above thought, the answer is:\nYear, Percentage of AI/ML Applications \n2010, 14.2% \n2011, 17.0% \n2012, 19.8% \n2013, 23.4% \n2014, 25.4%"
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "nSZrtipUkf9Gd2VK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year showed the most significant growth in AI/ML applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would compare the individual percentages of applications per year as indicated by the slices in the pie chart. The slice with the largest arc indicates the year with the most significant growth.\nBased on the above thought, the answer is:\nThe most significant growth in AI/ML applications occurred in 2014, with a percentage of 25.4%."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "NWoqHv4G8N6jHb4h",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat general trend can be observed in the applications of AI/ML from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the slices from 2010 to 2014 in the pie chart, a trend can be inferred.\nBased on the above thought, the answer is:\nThere is an upward trend in the applications of AI/ML from 2010 to 2014. This is evident from the increasing percentages of applications each year, culminating in a significant jump in 2014."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "6kLqUnlOWZX8z0q8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what prediction could be made about the interest in AI/ML Applications in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a prediction, I would consider the visible growth trend over the years, indicated by increasing slice portions from 2010 to 2014.\nBased on the above thought, the answer is:\nGiven the observed data, we could predict a continued interest and growth in AI/ML applications in the years following 2014. However, the exceptional growth seen in 2014 suggests potential factors beyond a general upward trend that must be considered."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "GnUEhFMZ82n72mWq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine the total percentage of AI/ML applications for the years 2011 and 2012 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Combining the percentages representing applications for the years 2011 and 2012 would provide the answer. The bar marked \"2011\" {#66b3ff}[0.913, 0.103, 0.941, 0.117] represents 17.0% of the total, while the slice marked \"2012\" {#99ff99}[0.913, 0.133, 0.941, 0.147] represents 19.8%\nBased on the above thought, the answer is:\n2011 and 2012 combined represent 36.8% of AI/ML applications"
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "HzzKUNK9IM81TAYZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year noted the least percentage of applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of each slice in the pie chart and matching the corresponding labels in the legend, the slice representing the least number of applications can be identified.\nBased on the above thought, the answer is:\nThe year 2010 has the least application percentage of 14.2%."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "FtqoZ8KseEKkmyEe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase if we compare the applications in 2010 to those of 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, we need to subtract the initial percentage (2010) from the final percentage (2014) and then divide by the initial percentage.\nBased on the above thought, the answer is:\nThe percentage increase from 2010 to 2014 is 79%."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "0W6fwCkaF0q8SLS4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart into a CSV format based on the data you analyzed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Proportion of Behaviors in Psychological Studies\"}[0.282, 0.021, 0.681, 0.047], we can understand that the chart describes the proportion of different behaviors. Given the {the sector slices}<r>[{\"Focused\"}[0.223, 0.278, 0.482, 0.859]; {\"Distraction\"}[0.385, 0.32, 0.741, 0.886]; {\"Boredom\"}[0.419, 0.146, 0.701, 0.516]; {\"Calm\"}[0.283, 0.157, 0.482, 0.516]],we can see that they represent the four categories and the corresponding percentages.\nBased on the above thought, the answer is:\nBehavior,%\nFocused,30.0%\nDistraction,40.0%\nBoredom,20.0%\nCalm,10.0%"
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "PaMMTqetW3FmA5H5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which behavioral state is the most prevalent? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The behavioral state with the most prevalence would be the one with the highest proportion in the chart.\nBased on the above thought, the answer is:\nThe most prevalent behavior state is \"Distraction\" as it has the largest proportion of 40.0% among the four states."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "auo8jg5MLB9nk1I9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the behavior state of the subjects from the data in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the proportions of each behavioral state, I can make inferences about the subjects' predominant states.\nBased on the above thought, the answer is:\nFrom the chart, it seems that subjects were most frequently found in a state of \"Distraction\", followed by \"Focused\", \"Boredom\", and then \"Calm\". This suggests that the subjects tend to be more frequently distracted or focused, while they are less often bored or calm."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "f1lR5oQlYBZbij77",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you expect this data to guide interventions aimed at improving focus?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the chart could give insights for such interventions by understanding the proportion of the time subjects spend in different states.\nBased on the above thought, the answer is:\nSince \"Distraction\" has the highest proportion, interventions can aim to reduce distractions and potentially redistribute this time to \"Focused\" activities. Additionally, techniques to transform \"Boredom\" into \"Focus\" could be explored, considering the 20% proportion of boredom. For the \"Calm\" state representing 10%, relaxation or mindfulness activities that increase focus can be encouraged."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "fMFgG6A1ToxHJxiJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two behaviors that together account for 50% of the behavioral states?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the chart that the percentages of different behaviors add up to 100%. So I need to find two behaviors whose total proportion is 50%.\nBased on the above thought, the answer is:\nFocused and Boredom."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "jC57vM3K8mIR5QZI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of the four behavioral states?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average percentage, I need to sum up the percentages of all behavioral states and divide by the number of states.\nBased on the above thought, the answer is:\nThe total percentage is 30.0% (Focused) + 40.0% (Distraction) + 20.0% (Boredom) + 10.0% (Calm) = 100%. The average percentage = 100% / 4 = 25%."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "OKmC8R5WbJjsANpx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of \"Boredom\" increased by 10%, what would be the new total percentage of all behavioral states?"
+            },
+            {
+                "from": "gpt",
+                "value": "By increasing the proportion of \"Boredom\" by 10%, I need to calculate the summed percentage of the four behavioral states again.\nBased on the above thought, the answer is:\nThe new proportion for \"Boredom\" is 20.0% + 10% = 30.0%. The new total percentage = 30.0% (Focused) + 40.0% (Distraction) + 30.0% (Boredom) + 10.0% (Calm) = 110%."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "OpTOefeNNty2YiTf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart's slices and their respective colors and labels, I can see the different years represented and their respective percentages. I can infer that each slice's percentage shows the global usage of Financial Technology and Digital Currency for that year. The year labels and corresponding percentage can be found in {the legend with color markers}<r>[0.908, 0.063, 0.993, 0.279]</r> and {percentage labels}<r>[0.194, 0.318, 0.229, 0.338]</r>, <r>[0.162, 0.528, 0.197, 0.548]</r>, <r>[0.244, 0.792, 0.279, 0.812]</r>, <r>[0.495, 0.913, 0.53, 0.933]</r>, <r>[0.72, 0.73, 0.755, 0.75]</r>, <r>[0.731, 0.31, 0.766, 0.33]</r>, <r>[0.392, 0.106, 0.427, 0.126]</r>.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,6.9%\n2016,10.1%\n2017,12.9%\n2018,13.8%\n2019,16.1%\n2020,18.4%\n2021,21.7%"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "4PX4677ESi83EV5w",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw the most usage of Financial Technology and Digital Currency? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to check the proportion of each year, and then the year with the highest proportion can be considered as having the most usage.\nBased on the above thought, the answer is:\nThe year 2021 saw the most usage of Financial Technology and Digital Currency because it had the highest proportion of 21.7% among the seven years."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "RCOOjg57kJGfdpL5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of global usage of Financial Technology and Digital Currency from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions of each year to make inferences about the trend of global usage.\nBased on the above thought, the answer is:\nThe chart shows a significant rise in the usage of Financial Technology and Digital Currency over the years. The trend has been consistently upward, indicating an increasing adoption and reliance on digital currency globally."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "p6yFoMDYCvmFdvpM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend of increasing usage continues, what might be the implications for the technology and finance sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the increasing trend in usage might impact the technology and finance sector.\nBased on the above thought, the answer is:\nIf the increasing usage pattern continues, it would mean an escalating demand for these technologies. This demand can drive more research and development in the field of Digital Currency and Financial Technology, leading to advanced and convenient systems. The finance sector might see a digital revolution, condensing complex financial structures into simple and secure digital transactions."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "BZuWaJiamiULBxbS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there was a 2% growth from 2020 to 2021. What was the percentage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage in 2020, I need to subtract the growth rate from the percentage in 2021.\nBased on the above thought, the answer is:\n19.7%"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "BPc2O9hsGM3PI2TW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage growth from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total growth, I need to subtract the percentage of 2015 from the percentage of 2021.\nBased on the above thought, the answer is:\n14.8%"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "xdTHC1oLzttYaPL6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"2019\" decreased its proportion by 3%, what would be the new total percentage of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to subtract 3% from the \"2019\" percentage and sum up all the percentages again.\nBased on the above thought, the answer is:\n93.1%"
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "nXtI8eFfpyaYh43v",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list down the raw data used for this pie chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the title {\"Patient Visits in Various Medical Fields\"}[0.324, 0.021, 0.639, 0.047] and pie slices for {\"Cardiology\"}[0.122, 0.514, 0.197, 0.534], {\"Neurology\"}[0.355, 0.905, 0.426, 0.925], {\"Pediatrics\"}[0.757, 0.612, 0.824, 0.632], {\"Dentistry\"}[0.602, 0.137, 0.666, 0.157], and {\"Radiology\"}[0.28, 0.146, 0.349, 0.166], I can calculate the patient visits for each specialized field.\nBased on the above thought, the answer is:\nMedical Field, Patient Visits\nCardiology, 5410\nNeurology, 4823\nPediatrics, 6524\nDentistry, 4032\nRadiology, 2923"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "I9Ge0mC7hTiyz4ji",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat medical field is most visited according to the chart? Provide reasons based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of the slice and the percentage, I can determine which medical field was the most visited.\nBased on the above thought, the answer is:\nPediatrics is the most visited field because it accounts for the largest slice at 27.5%."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "1zJNOcli70p4gLHE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a significant difference in the number of visits to Pediatrics and Radiology? What does this suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By checking the slice size and the number of visits to Pediatrics and Radiology, I can determine the difference.\nBased on the above thought, the answer is:\nYes, there is a significant difference between the number of visits to Pediatrics and Radiology. Pediatrics received the highest number of visits while Radiology received the least. This suggests that Pediatrics is in higher demand compared to Radiology."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "izde9it5RMRTsHrN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence healthcare service delivery and investment decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By understanding the number of patient visits in different medical fields, decisions about healthcare delivery and investment can be made.\nBased on the above thought, the answer is:\nThe data could influence healthcare service delivery and investments by prompting a greater allocation of resources and staff towards fields with more patient visits, such as Pediatrics. On the other hand, strategic efforts could be employed to improve patient turnout in less visited fields like Radiology."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "SdL4SyQnR543B5TV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total visits fall under Neurology specialty?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the percentage, I need to look at the pie slice corresponding to Neurology.\nBased on the above thought, the answer is:\n20.3%"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "nh1UllYmMs4i1Bxm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Pediatrics field were to experience a 10% increase in patient visits, what would be the new total number of visits?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total, I need to add 10% of the Pediatrics' current number to the original number.\nBased on the above thought, the answer is:\n7176"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "3onXYKegXvTu35n4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the number of visits to the Cardiology and the Dentistry fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the number of Dentistry visits from the number of Cardiology visits.\nBased on the above thought, the answer is:\n1378"
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "dYW67gwbvNGlmSE4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the pie chart and create a CSV format table out of it?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I notice that there are two pie charts from clues found in the 'pies' section of 'subplots'. Two significant sections that divide the pie charts are 'Gender' and 'Age Group'. Each pie slice corresponds to an age group or gender's data. The labels for genders are noticeably placed in the chart legend {the legend}<r>[0.893, 0.063, 0.993, 0.129]</r>. The pies are represented in two parts: Male data is shown in {pie_0}<r>[0.287, 0.238, 0.676, 0.794]</r> and Female data is displayed in {pie_1}<r>[0.223, 0.146, 0.741, 0.886]</r>. The percentage values for age groups are provided within each pie chart slice.\nBased on the above thought, the answer is:\nAge Group,Male,Female\nUnder 18,20,23\n18-24,25,20\n25-34,30,33\n35-44,15,10\n45-54,8,7\n55+,2,7"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "4mMJGZPLzGApvoxj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, who are the most dominant users in social media and why do you say so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the percentage values provided in each slice of the Pies {Pie_0}<r>[0.287, 0.238, 0.676, 0.794]</r> and {Pie_1}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can determine the dominant users. The \"25-34\" age group has the highest slice for both genders, so they are the most dominant users.\nBased on the above thought, the answer is:\nThe age group of 25-34 years old dominates the usage of social media for both genders due to their highest percentage in the distribution."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "L99gR3CqTB1ovu69",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this chart, what conclusion can you deduce about the age group 'Under 18' in relation to the usage of social media?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slices that represent the 'Under 18' age group for Males {Male slice}<r>[0.287, 0.338, 0.482, 0.705]</r> and Females {Female slice}<r>[0.223, 0.278, 0.482, 0.712]</r>, I can create a comparison.\nBased on the above thought, the answer is:\nThe 'Under 18' age group seems to have a noticeably less presence on social media as compared to most of the other age groups. The data suggests that social media platforms might not be as popular among the under 18 population."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "Hfe7Qz2QeCflnvo8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy interpreting the chart data, what insights can be drawn about social media trends across different age groups and genders?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the detailed pie chart and the slices representing various age groups for 'Male' and 'Female' located in the pies {Pie_0}<r>[0.287, 0.238, 0.676, 0.794]</r> and {Pie_1}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can make out some potential trends.\nBased on the above thought, the answer is:\nThe chart suggests that social media usage is dominantly driven by the age group '25-34' for both genders, indicating a clear preference for these platforms among young adults. The '18-24' age group follows closely behind. Overall, the representation decreases as age increases, pointing towards a trend of lesser social media adoption among older age segments. Interestingly, the chart also shows an unusual pattern where the 'Under 18' group is using social media less than some of the older age groups. So, the trends in social media usage seem to favor the young but not necessarily the very young."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "SovDdDBdfJxBqqKj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in social media usage between males and females in the age group '25-34'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find out the difference by locating the percentages of males and females in the '25-34' age group from the pie slices in both pie charts: for Males {the Male slice}<r>[0.482, 0.293, 0.676, 0.768]</r> and for Females {the Female slice}<r>[0.482, 0.232, 0.741, 0.83]</r>. The percentage values are provided within these slices.\nBased on the above thought, the answer is:\n3%"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "A1o4Oa231b50otdo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn total, what is the percentage of social media usage for the age group '45-54'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total, I can sum up the percentages of males and females in the '45-54' age group. The slices representing this age group for Males {the Male slice}<r>[0.4, 0.238, 0.483, 0.516]</r> and for Females {the Female slice}<r>[0.306, 0.157, 0.482, 0.516]</r> contain the required information.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "qUSiStBDGTvN6oNJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total proportion of social media usage for people under 24?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be found by locating and adding the percentages for the 'Under 18' and '18-24' age groups for both males and females. The required data can be found in the slices for Males {Male slices}<r>[0.287, 0.338, 0.482, 0.705]; [0.339, 0.516, 0.563, 0.794]</r> and for Females {Female slices}<r>[0.223, 0.278, 0.482, 0.712]; [0.262, 0.516, 0.619, 0.886]</r>.\nBased on the above thought, the answer is:\n68%"
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "uMFXx9jqKMURwFbn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert this pie chart data related to time spent on different activities per week into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. This pie chart {\"Distibution of time spent on different activities in a week\"}[0.245, 0.021, 0.718, 0.047] shows the amount of time spent on activities such as {Gym}[0.899, 0.071, 0.931, 0.091], {Eating out}[0.899, 0.101, 0.97, 0.121], {Watching TV}[0.899, 0.131, 0.986, 0.151], {Horse Riding}[0.899, 0.161, 0.987, 0.181], {Shopping}[0.899, 0.191, 0.964, 0.211], {Painting}[0.899, 0.221, 0.955, 0.241]. Each section of the pie is color-coded to correspond with one activity, and the size indicates the time spent on the activity per week.\nBased on the above thought, the answer is:\nActivity,Minutes per week\nGym,150\nEating out,300\nWatching TV,100\nHorse Riding,400\nShopping,200\nPainting,50"
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "ZXWkqOiVRHd7XB8N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity is given the most time in a week and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we can look at the largest slice of the pie chart which represents the maximum time spent on an activity.\nBased on the above thought, the answer is:\nThe activity given the most time in a week is Horse Riding because it has the largest slice of the pie chart indicating maximum minutes spent on it."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "HetxlQGpRF8xtRUg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the person's lifestyle based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences about the person's lifestyle, we need to consider the time spent on each activity and how it may reflect on their daily routines and preferences.\nBased on the above thought, the answer is:\nThe chart suggests that the person leads an active lifestyle, spending the most time on outdoor activities like Horse Riding and Eating out, while less time is spent on indoor activities like Watching TV and Painting."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "6nPiASch49K05XCq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the person wants to bring balance between indoor and outdoor activities, what changes can be made to their routine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we need to consider how the time spent on indoor and outdoor activities can be equalized.\nBased on the above thought, the answer is:\nIf the person wants to bring balance between indoor and outdoor activities, they could consider reducing time spent on Horse Riding and Eating Out and increasing the time spent on indoor activities like Watching TV and Painting."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "qzCqKsfRFaLe5CDI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how many total minutes per week are spent on all activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total minutes spent on all activities per week, I need to add up the time spent on each activity which is Gym, Eating out, Watching TV, Horse Riding, Shopping, and Painting.\nBased on the above thought, the answer is:\n1200 minutes."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "zk6nSB9oSx5NsLoM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many minutes per week are spent on indoor activities, defined as Watching TV and Painting?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the minutes spent on indoor activities, I need to add up the time spent on Watching TV and Painting.\nBased on the above thought, the answer is:\n150 minutes."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "1Mdgis9k0UvAq4E8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more time is spent on Horse Riding than Shopping and Painting combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in time spent, subtract the combined time of Shopping and Painting from the time spent on Horse Riding.\nBased on the above thought, the answer is:\n150 minutes."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "sLfnfNUdvGWH5XSM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the pie chart and present it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reflecting on the pie chart, I notice a title that reads {\"Cuisine and Gastronomy Culture Analysis\"}<r>[0.31, 0.021, 0.653, 0.047]</r>. The slices of the pie represent a breakdown of popularity scores among items related to cuisine and gastronomy, and these include: {\"Coffee\"}<r>[0.223, 0.278, 0.482, 0.647]</r>, {\"Tea\"}<r>[0.239, 0.516, 0.516, 0.886]</r>, {\"Wine\"}<r>[0.482, 0.516, 0.733, 0.883]</r>, {\"Beer\"}<r>[0.482, 0.305, 0.741, 0.604]</r>, and {\"Chocolate\"}<r>[0.283, 0.146, 0.694, 0.516]</r>.\nBased on the above thought, the answer is:\nCuisine,Popularity\nCoffee,75\nTea,95\nWine,85\nBeer,60\nChocolate,130"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "FTl4dpLmOWLFTmUn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which item related to Cuisine and Gastronomy culture is the most popular? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, the item with the largest slice signifies the most popular item.\nBased on the above thought, the answer is:\nThe most popular item is \"Chocolate\" because it has the largest slice size, which indicates a higher popularity score compared to other items."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "9gSyeBLVYBd8FTDr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you draw from the pie chart in relation to the popularity of Wine?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that Wine has a fair share in the population, which implies a decent level of popularity.\nBased on the above thought, the answer is:\nThe popularity of Wine, while not the highest, is certainly considerable. It's more popular than Coffee and Beer, but less popular than Tea and Chocolate. The wine industry seems to be doing well but there are areas where it's outperformed by other beverages and goods."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "uenvBJsqNXr0fnZX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would the increase in the popularity of Chocolate impact the overall view of the chart for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I need to consider how the adjustments in Chocolate's popularity score might impact its slice and consequently the other slices in the chart.\nBased on the above thought, the answer is:\nIf the popularity of Chocolate rises, its slice of the pie chart would likely grow larger. This, in turn, could make the other item's slices proportionally smaller, unless they too experience a rise in popularity. It could indicate an increasing preference for sweets over beverages in the represented population."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "xB5o3oXekAdOlQAp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total popularity score for all items based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total popularity, I need to add up the popularity score of each item presented in the chart.\nBased on the above thought, the answer is:\n545"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "SBh0h4u0GSmOko62",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average popularity score of all items?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average popularity score, I need to add up the popularity score of each item and then divide by the total number of items.\nBased on the above thought, the answer is:\n109"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "lWOC4lbJtOkzFzcx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Chocolate had its popularity score increased by 20, what would be the new total popularity score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total popularity score, I need to add the increase to the previous popularity score of Chocolate and then sum up all popularity scores.\nBased on the above thought, the answer is:\n565"
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "bXKwkSuIUwmlrmOb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the observed pie chart into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe the slices of the pie chart {\"Financial Data Distribution from 2005 to 2014\"}[0.223, 0.146, 0.741, 0.886] that represents different years. Each year has a corresponding financial data percentage like {\"2005\"}[0.232, 0.278, 0.482, 0.516] is 6.8% and so on for each year.\nBased on the above thought, the answer is:\nYear,Percentage\n2005,6.8%\n2006,7.3%\n2007,7.9%\n2008,8.5%\n2009,9.2%\n2010,10.5%\n2011,11.6%\n2012,12.1%\n2013,12.6%\n2014,13.5%"
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "zzN7xXrzGQKbc7TU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest financial data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should examine each slice of the pie chart and find the one with the highest percentage, as denoted by the legend.\nBased on the above thought, the answer is:\nThe year 2014 had the highest financial data, contributing 13.5% over the ten-year period."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "cVa2PB7LAAetF2Ln",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the increases in the financial data over the years, what could be the possible prediction for the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the trend from the percentage distribution. The percentage increases each year. Considering this trend, the financial data for the year 2015 would likely be higher than in 2014.\nBased on the above thought, the answer is:\nSince there's a continuous increase in the financial data from 2005 to 2014, it can be predicted that the trend will continue in 2015, resulting in an increase in the financial data."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "LtavPRx0WnXcvD07",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you derive from the distribution of the financial data across the 10 year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to review the information held by each pie slice.\nBased on the above thought, the answer is:\nThe chart shows a consistent increase in financial data over the ten-year period from 2005 to 2014. This growth is particularly noticeable between 2009 and 2010, where it jumped significantly. This could suggest a strong economic growth or success during this period."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "FMT2lzGN4RA4qNQB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which three consecutive years made up a combined 30% of the financial data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages of three consecutive years in pie slices to check if it sums up to 30%. The consecutive years can be identified from the legend.\nBased on the above thought, the answer is:\n2012, 2013, and 2014."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "NETCBv7Jobapq7eH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the financial data for 2005 increased by 3%, what would be the new total percentage for the entire period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 3% to the financial data for 2005, and then sum it with the percentages of the rest of the years.\nBased on the above thought, the answer is:\nThe total would be 97%."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "x5zx7Y1BNUOBkSSl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the financial data does the year 2010 represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should locate 2010 in the pie chart and record the noted percentage.\nBased on the above thought, the answer is:\nThe year 2010 represents 10.5%."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "QfHnPLw9FFNNVUjS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, could you convert the investment details of the Renewable Energy sector into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart with subtitle at the top {\"Renewable Energy Investment from 2016 to 2020\"}[0.274, 0.021, 0.689, 0.047] and the pie slices at {the legend}[0.908, 0.063, 0.993, 0.219], I can see that the slices represent investment amount for each year and its respective percentages.\nBased on the above thought, the answer is:\nYear,Investment in Billion USD,Percentage\n2016,279,17.7%\n2017,332,21.1%\n2018,363,23.0%\n2019,329,20.9%\n2020,274,17.4%"
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "59pBXqH35o5ufIT7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw the highest investment in Renewable Energy and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest investment, I will look at the size of each slice. The year with the largest slice would be considered the year with the highest investment.\nBased on the above thought, the answer is:\nThe year 2018 saw the highest investment in renewable energy, as it has the largest slice in the pie chart with a proportion of 23.0%."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "tgTZacurghzRm8tC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of renewable energy investment from 2016 to 2020 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the proportions of each year, I can infer the trend of investment over the given period.\nBased on the above thought, the answer is:\nThe trend of renewable energy investment was initially increasing until 2018, with a peak at 23.0%. Afterwards, there was a decrease in 2019 and 2020, which is evident from the smaller proportions of these years in the chart."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "TYetQpx8IK7jB4vD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the 2020 data, how might the data in the chart influence investment decisions in renewable energy sector for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the 2020 data and consider how it might impact future investment decisions.\nBased on the above thought, the answer is:\nGiven that the investment in 2020 was on a lower side (17.4%), an increased allocation might be recommended for 2021 in order to boost the sector. However, the final decision would also depend on other factors like overall economic conditions and projected sector growth."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "m6jA1urEEk4Wf9PR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the percentage difference between the investment in 2018 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the proportion of 2018 and 2019 in the chart, I can see the difference in investment.\nBased on the above thought, the answer is:\nThe difference is 23.0% (2018) - 20.9% (2019) = 2.1%."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "q0cp7EcbGjz2VOpC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment in 2020 increased by 10%, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion for 2020, I need to add 10% to the existing proportion of 17.4%.\nBased on the above thought, the answer is:\nThe new proportion for 2020 would be 17.4% + 10% = 27.4%."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "ceO5u9Rh4tQcKlLH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSum up the proportion of investment from 2016 to 2018. What is the result?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding up the proportions of 2016, 2017, and 2018 will provide the total proportion.\nBased on the above thought, the answer is:\nThe total proportion from 2016 to 2018 is 17.7% (2016) + 21.1% (2017) + 23.0% (2018) = 61.8%."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "MFwkKXf4arbgPnki",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the patient engagement technology usage data from the chart into a CSV format table, indicating the representation of each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am gleaning information from the pie chart {subplot_0}[0.158, 0.053, 0.805, 0.979] and the associated {legend}[0.908, 0.063, 0.993, 0.279]. Each pie slice corresponds to a different year, and each color-coded label from the legend explains this. The different percentages are implied by the relative sizes of the slices, with the importance of understanding their representations highlighted by the {subtitle} 'Patient Engagement Technology Usage from 2009 to 2015'[0.239, 0.021, 0.724, 0.047].\nBased on the above thought, the answer is:\nYear,Usage\n2009,6.5%\n2010,8.5%\n2011,11.3%\n2012,14.2%\n2013,22.7%\n2014,17.0%\n2015,19.8%"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "uCAXuiGvNyUF5z7j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat broad trends can you identify from the chart about the usage of patient engagement technology over the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the histogram and read off the increasing usage percentages within {pie chart}[0.223, 0.146, 0.741, 0.886]. Complementary information from the {legend}[0.908, 0.063, 0.993, 0.279] will reinforce the trend.\nBased on the above thought, the answer is:\nThe usage of patient engagement technology has generally increased over the observed period. However, there was a decline after the year 2013."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "SIJyaAjhruxVt39E",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the chart, how would you assess the performance of healthcare providers in engaging their patients through technology over the period 2009 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to observe the changes in the patient engagement technology usage rates over time and interpret these changes in relation to the {subtitle 'Patient Engagement Technology Usage from 2009 to 2015'}[0.239, 0.021, 0.724, 0.047].\nBased on the above thought, the answer is:\nI would say that healthcare providers generally improved their patient engagement through technology up until 2013, when there was a peak. But there was a decline in 2014 and 2015. So, while there was a general trend of improvement, there were also setbacks."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "cAQ8QMY0GIf7GviI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might the usage of patient engagement technology in healthcare have decreased after reaching a peak in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although we don't have specific reasons based on the chart, I can hypothesize aboutWhy it might be by looking at the sharp decline from the large {2013 slice}[0.482, 0.468, 0.741, 0.872] to a smaller {2014 slice}[0.482, 0.171, 0.739, 0.516].\nBased on the above thought, the answer is:\nPossible factors could include changes in healthcare policies, a lack of funding or resources, or the introduction of alternative methods for patient engagement that decreased the reliance on technology."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "rv6ZubGAVkRpdzyx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in the percentage of usage between 2011 and 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the {2011 slice}[0.23, 0.516, 0.482, 0.819] and the {2012 slice}[0.332, 0.516, 0.552, 0.886], it is possible to determine the change in percentage.\nBased on the above thought, the answer is:\n2.9%"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "jKmiS2b4Lugz5hko",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage change from 2009 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to add up all the different percentages for each year between 2009 and 2015 depicted in the pie slices.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "sejyrsC6vEHj5Zee",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the single greatest increase in usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish this, I need to study the size of each pie slice and evaluate which has the most significant growth compared to its preceding year.\nBased on the above thought, the answer is:\n2013"
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "o9hTz3fbjyNasjvC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, please convert the data you observed related to Career and Workplace Development Trends into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I recognize the labels 'Remote Work' and 'Freelancing' as representing the two categories monitored over time. The years from 2017 to 2021 are listed in the legend to the right of the chart {chart's legend}<r>[0.908, 0.063, 0.993, 0.368]</r>. Each year's data is represented with a different colour. 'Remote Work' and 'Freelancing' trends were tracked from 2017 through 2021, providing five years of data for both categories.\nBased on the above thought, the answer is:\nYear,Remote Work,Freelancing\n2017,40,10\n2018,50,20\n2019,55,25\n2020,65,30\n2021,80,35"
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "s1MYTY3CcFNsHIsp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart you observed, what are some possible reasons for the observed trends in Remote Work and Freelancing over the last five years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the trajectories of the two categories, I can see that they are both generally increasing over time. However, it's important to note that the data only covers a period of five years, and real trend analyses usually require more long-term data.\nBased on the above thought, the answer is:\nA few factors that could contribute to the trends include an increase in internet accessibility and technology utilization, lifestyle alterations and flexibility, and more recently, health-related considerations like the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "8iPEhjmrjqEfcBlG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend displayed from 2020 to 2021 in the line chart for Remote Work, what would you predict for the trend in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should examine the rate of growth in Remote Work from 2020 to 2021 to predict the trend for 2022.\nBased on the above thought, the answer is:\nIf the trend from 2020 to 2021 continues, we can expect to see a continuation of the increase in Remote Work in 2022."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "vuCMjp7H150xM8CX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvided that the same growth rates are maintained, how might this data affect decisions related to workplace development in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider potential implications of the steadily increasing rates in both Remote Work and Freelancing over the past five years.\nBased on the above thought, the answer is:\nDecision-makers may need to continue to adapt and provide support for remote working and freelancing. This could include investing in technology and resources to support remote work, implementing policies that support a more flexible work schedule, and providing resources and support for employees to develop their skills in a freelancing role."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "UZRLXMLLQSGMyfgT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the line chart, how much did Remote Work increase from the year 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in Remote Work from 2017 to 2021, I need to find out the values for Remote Work for both years and subtract the 2017 value from the 2021 value.\nBased on the above thought, the answer is:\n40 units."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "XOOPDrygOvHTlRxS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage increase in Freelancing during the years 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase in Freelancing from 2017 to 2021, I need to subtract the 2017 value from the 2021 value.\nBased on the above thought, the answer is:\n25 units."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "1ff38QUrgjJfUVjy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Remote Work overtake Freelancing in terms of proportions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to check the trajectories of the lines corresponding to Remote Work and Freelancing and determine the year where Remote Work surpassed Freelancing.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "rX7GWhRPv0MuuolL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data regarding engagement score of various cuisine culture into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend present on the right side {the legend}[0.861, 0.063, 0.993, 0.279] of the chart and the pie slices for {French}[0.223, 0.278, 0.482, 0.584], {Italian}[0.227, 0.516, 0.482, 0.853], {Chinese}[0.375, 0.516, 0.632, 0.886], {Indian}[0.482, 0.516, 0.737, 0.818], {Mexican}[0.482, 0.262, 0.741, 0.575], {Thai}[0.481, 0.146, 0.67, 0.516], {Vietnamese}[0.283, 0.146, 0.482, 0.516], I can see that the sectors represents different cuisines and their respective engagement scores in percentage.\nBased on the above thought, the answer is:\nCuisine,Engagement Score(%)\nFrench,14.1%\nItalian,15.3%\nChinese,16.6%\nIndian,12.6%\nMexican,14.6%\nThai,13.0%\nVietnamese,13.9%"
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "qncnJLklfVcgKNAw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine culture appears to be the most engaging according to the chart? Could you explain any probable reasons?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, first, I need to determine which cuisine has the highest percentage of engagement score represented in the pie chart.\nBased on the above thought, the answer is:\nThe most engaging cuisine culture appears to be \"Chinese\" because it has the highest engagement score of 16.6% among the seven cuisines. This may suggest that Chinese cuisine is more well-known, accessible, or preferred by the audience."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "brQQwvZd434rtEzn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can you draw from the engagement scores represented in the chart about the popularity of the cuisine cultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to look at the proportions of each cuisine's engagement score in the chart to draw inferences about their popularity.\nBased on the above thought, the answer is:\nThe chart indicates that Chinese, Italian, and French cuisines were ahead in terms of engagement scores in this analysis, suggesting they are popular among the audience. On the other side, Indian and Thai cuisines had relatively low engagement scores, hinting that they may not be as popular or familiar to the audience."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "LY2SIYSWiT4DANIz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to advise on promotional efforts based on the data represented in the chart, which cuisine(s) would benefit most from increased promotion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To suggest which cuisines could benefit from increased promotion, I need to consider engagement scores, particularly those which are lower than average.\nBased on the above thought, the answer is:\nIndian and Thai cuisines, having the lower engagement scores of 12.6% and 13% respectively, would benefit most from increased promotional efforts. It would potentially raise their popularity and engagement among the audience."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "U6xMS0gfxbpzg02U",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two cuisines have the lowest cumulative engagement score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the engagement score, I need to sum up the engagement score of each two cuisines and find the pair with the lowest cumulative score.\nBased on the above thought, the answer is:\nIndian and Thai."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "ctV42qkiVW4MDeRd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average engagement score of the seven cuisines in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average engagement score, add up the engagement scores of all seven cuisines and divide by the number of cuisines.\nBased on the above thought, the answer is:\nThe sum of the engagement scores is 14.1% + 15.3% + 16.6% + 12.6% + 14.6% + 13.0% + 13.9% = 100%. The average engagement score is 100% / 7 = 14.29%."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "ZMrFZLoZwdfzxFfY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the engagement score of \"Indian\" cuisine increased by 3%, what would be the new total engagement score of all cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total engagement score, I need to add 3% to the \"Indian\" engagement score and sum up all the engagement scores again.\nBased on the above thought, the answer is:\nThe new engagement score for \"Indian\" is 12.6% + 3% = 15.6%. The new total engagement score is 14.1% (French) + 15.3% (Italian) + 16.6% (Chinese) + 15.6% (Indian) + 14.6% (Mexican) + 13.0% (Thai) + 13.9% (Vietnamese) = 103%."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "KPVx6pxwZHTSobUp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the data from the pie chart that represents the Television trends from 2010 to 2016 in a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can note from the pie chart in the area {\"Television Trends from 2010 to 2016\"}<r>[0.349, 0.011, 0.651, 0.024]</r> and the seven pie slices <r> [{\"2010\"}<r>[0.196, 0.181, 0.231, 0.191]</r>, {\"2011\"}<r>[0.191, 0.32, 0.226, 0.33]</r>, {\"2012\"}<r>[0.33, 0.438, 0.365, 0.448]</r>, {\"2013\"}<r>[0.607, 0.447, 0.643, 0.457]</r>, {\"2014\"}<r>[0.78, 0.305, 0.815, 0.315]</r>, {\"2015\"}<r>[0.706, 0.112, 0.741, 0.122]</r>, {\"2016\"}<r>[0.38, 0.059, 0.415, 0.069]</r>] </r> that these proportions correspond to the viewer numbers for Television from 2010 to 2016.\nBased on the above thought, the answer is:\nYear,Television\n2010,20\n2011,22\n2012,25\n2013,28\n2014,30\n2015,32\n2016,35"
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "PKE2ie1czgJLvHCG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart for Television trends from 2010 to 2016, what trend do you observe?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the trend, I am looking at the proportions of each pie slice for every year.\nBased on the above thought, the answer is:\nThe chart shows a steady annual increase in Television viewership percentages from 2010 to 2016, indicating a growing popularity of Television over this period."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "ysiG2EyjLMuwrUDX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data, which year marked the most significant increase in Television viewership?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the percentage increases between each successive year will help identify the period with the most significant growth.\nBased on the above thought, the answer is:\nThe greatest increase in Television viewership occurred between 2014 and 2015. During this period, viewership rose from 15.6% to 16.7%, a change of 1.1 percentage points."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "eo819460fCkuDlXy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart highlight the changing role of Television in media consumption from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the growing proportions of the Television pie slices each year, it is possible to infer the changing role of this medium.\nBased on the above thought, the answer is:\nThe pie chart illustrates that Television's share of media consumption increased steadily over the period from 2010 to 2016. This reflects Television's enduring appeal and adaptability despite rapid changes in the media landscape, including the advent and rise of Streaming services."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "CyqWpHPFRfwJIB6R",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does the Television viewership account for more than 15% of the total viewership?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the years when Television viewership was more than 15%, I need to look at the percentages marked in each sector and compare them with the number 15%.\nBased on the above thought, the answer is:\nThe years are 2013, 2014, 2015, and 2016."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "RjjxAQ4uOtPGXqPZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total viewership percentage for Television from 2010 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total viewership percentage for Television from 2010 to 2012, I need to add up the percentages for these years.\nBased on the above thought, the answer is:\nThe total viewership percentage for Television from 2010 to 2012 is 10.4% (2010) + 11.5% (2011) + 13.0% (2012) = 34.9%."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "8qUy8MhB9komEcOd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2015 to 2016, how much did the Television viewership percentage increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the Television viewership percentage increase from 2015 to 2016, I need to subtract the 2015 percentage from the 2016 percentage.\nBased on the above thought, the answer is:\nThe Television viewership percentage increased by 18.2% (2016) - 16.7% (2015) = 1.5% from 2015 to 2016."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "q0P5SGVriR1CKv1R",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data regarding the percentage of each year's contribution to the increase in renewable energy access into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {Renewable Energy Access from 2010 to 2019}<r>[0.293, 0.021, 0.67, 0.047]</r> and the legend displaying the years from 2010 to 2019 <r>[0.908, 0.063, 0.993, 0.368]</r>, along with the corresponding segments in the pie chart, it is possible to ascertain the contribution of each year to the increase in renewable energy access.\nBased on the above thought, the answer is:\nYear, Percentage\n2010, 6.2%\n2011, 6.8%\n2012, 7.8%\n2013, 9.7%\n2014, 9.0%\n2015, 11.6%\n2016, 10.6%\n2017, 12.1%\n2018, 12.4%\n2019, 13.8%"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "tywAvtTdvpaGHt0r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year contributed the most to renewable energy access and explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to observe and identify the slice of the pie chart with the largest size which represents the highest percentage contribution.\nBased on the above thought, the answer is:\nThe year 2019 contributed the most (13.8%) to the renewable energy access. This could be a result of multiple factors such as advances in renewable energy technologies, increased funding and investment, and heightened awareness about climate change that led to the promotion of renewable sources of energy."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "u2XahLNcJa3VDsLg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, would you say that access to renewable energy has generally increased over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer this, I will observe the trend of yearly contributions by looking at the sizes of the slices in the pie chart from 2010 to 2019.\nBased on the above thought, the answer is:\nYes, as per the pie chart, there is a general trend of increase in access to renewable energy over the decade. This shows positive progress towards embracing renewable energy."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "RnZ3Yl4VjnsmIoke",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the sudden increase in renewable energy access in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I could infer this by considering the particular year's percentage compared to the surrounding years.\nBased on the above thought, the answer is:\nThe significant increase in renewable energy access in 2015, as depicted by the larger slice size (11.6%), can indicate that there might have been specific developments in that year. This could include factors such as implementation of new policies, breakthroughs in technology, or significant investment in renewable energy projects."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "IZG90a92oSh7PaZw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which two consecutive years experienced a more significant increase in renewable energy access, and what is their combined percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I will calculate the sum of the contributions of each pair of consecutive years and determine the pair with the highest combined percentage.\nBased on the above thought, the answer is:\nThe years 2018 and 2019 showed the most significant increase with a combined percentage of 12.4% + 13.8% = 26.2%."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "FaO1ITc3GgLNYNiQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2015, what was the difference in the percentage of renewable energy access compared to the previous year 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I will subtract the percentage of 2014 from the percentage of 2015 and get the difference.\nBased on the above thought, the answer is:\nThe difference in 2015 compared to 2014 is 11.6% - 9.0% = 2.6%."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "m76Dap15OSk9e8JM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the least and most contributing years in terms of renewable energy access?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I will identify the years with the least and most contributions and add their percentages.\nBased on the above thought, the answer is:\nThe years with the least and most contributions are 2010 (6.2%) and 2019 (13.8%). So, the sum of the least and most contributing years is 6.2% + 13.8% = 20%."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "YMwyiS8Nyl3YzhTD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data in the pie chart into a tabular format, such as CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the chart {\"Artificial Intelligence Application Case Analysis\"}[0.288, 0.021, 0.676, 0.047] and the sections of the pie {\"Self-driving Cars\"}[0.223, 0.278, 0.482, 0.631]; {\"Virtual Assistants\"}[0.235, 0.516, 0.522, 0.886]; {\"Recommendation Systems\"}[0.482, 0.417, 0.741, 0.881]; {\"E-commerce\"}[0.283, 0.146, 0.731, 0.516], I can see the sectors represent different categories and the quantity of AI application cases each category presents.\nBased on the above thought, the answer is:\nAI Application,Cases Reported,\nSelf-driving Cars,56,\nVirtual Assistants,78,\nRecommendation Systems,93,\nE-commerce,120,"
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "rQwM8Ek5Mmr8OBPW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI application is utilized the most according to the pie chart? Please explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie segments in the chart, The sector with the largest segment signifies the highest utilization.\nBased on the above thought, the answer is:\nE-commerce has been utilized the most. This is evident from the size of its segment in the pie chart, which is noticeably larger than the others."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "p25h7TZEQqxIXQs6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich AI application shows the least amount of utilization based on this data and how do you ascertain this information?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the least utilized AI application, I need to detect the smallest pie section.\nBased on the above thought, the answer is:\nSelf-driving cars are least utilized according to the pie chart. This was determined by viewing the smallest segment of the pie chart, which represents self-driving cars."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "KvwFFnMusBSMWZ4q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the AI application for recommendation systems compared to other applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the Recommendation Systems section with the rest of the applications to find out its usage in comparison.\nBased on the above thought, the answer is:\nThe pie chart suggests that AI in recommendation systems is used more than self-driving cars and virtual assistants but less than in E-commerce. This is derived from the size of the section representing recommendation systems, which is larger than that of self-driving cars and virtual assistants, but smaller than E-commerce."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "KXzo8ZH9FxvncUDS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many reported cases does E-commerce exceed self-driving cars?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the number of reported cases of self-driving cars from E-commerce.\nBased on the above thought, the answer is:\n64"
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "yjZKTGqwSgmjbt7D",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the reported cases in Virtual assistants and Recommendation Systems?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add the number of reported cases of Virtual assistants and Recommendation Systems.\nBased on the above thought, the answer is:\n171"
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "7dBeNZbWhqPFhoQ6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn average, how many cases are reported through these different applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum the reported cases in each category and divide by the total number of categories.\nBased on the above thought, the answer is:\n87"
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "cdNcIKuvinBIFnI0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the fluctuations in the Stock Market and Bond Market from 2015 to 2021 into a table format as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The header of the table can be inferred as the text {Financial Market Fluctuations and Trends}<r>[0.313, 0.021, 0.65, 0.047]</r>. The years {2015}<r>[0.952, 0.071, 0.987, 0.091]</r>,{2018}<r>[0.952, 0.101, 0.987, 0.121]</r>, {2021}<r>[0.952, 0.131, 0.987, 0.151]</r> and the stock and bond markets are denoted by two different positions on the pie chart {Stock Market Positions}<r>[0.223, 0.278, 0.482, 0.693;0.223, 0.278, 0.482, 0.764]</r> and {Bond Market Positions}<r>[0.283, 0.146, 0.741, 0.709;0.283, 0.146, 0.741, 0.63]</r>.\nBased on the above thought, the answer is:\nMarket,2015,2018,2021\nStock Market,200,350,500\nBond Market,130,190,250"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "fb7uKKBqg5aVFSIm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market showed a more aggressive growth trend from 2015 to 2021 according to the chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the proportions displayed on the pie chart, I can identify the market with a larger share of growth.\nBased on the above thought, the answer is:\nThe Stock Market revealed a more aggressive growth trend as it saw an increase from 200 units in 2015 to 500 units in 2021, exhibiting a higher rate of increase compared to the Bond Market."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "5mCyOQmFx8WzG3u3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is a significant observation you can make from the traditional bull market trends depicted from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "An examination of the pie chart will help understand better the trends from 2015 to 2021.\nBased on the above thought, the answer is:\nA significant observation is that both the Stock Market and the Bond Market saw continual growth from 2015 to 2021. However, the Stock Market indicated a steeper rise, outperforming the Bond Market, which can be attributed to the higher risks and potential rewards intrinsic to stock investments."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "qauczz8FXuZTNFaf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can an investor gather from these trends in the financial markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the comparative analysis of both markets rendered on the pie chart, a synopsis can be inferred.\nBased on the above thought, the answer is:\nAn investor can comprehend that both the Stock Market and Bond Market experience substantial growth from 2015 to 2021. However, the Stock Market, despite being riskier, provided higher returns than the Bond Market. Thus, a balanced portfolio containing both stocks and bonds could yield a decent return while mitigating risks."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "JsAirgj4JdKwys58",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage point did the Stock Market outperform the Bond Market in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate the difference by subtracting the percentage of the Bond Market from the Stock Market in 2015.\nBased on the above thought, the answer is:\n- 3.8 percentage points."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "CRfUpxT1nsjyfQz7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total proportion of the financial market in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Combining the proportions of the Stock Market and Bond Market for the year 2021, I can find the total proportion.\nBased on the above thought, the answer is:\n91.5%"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "iuQGOcie0PbUHWKD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average growth of the Stock Market from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth, I would subtract the initial value in 2015 from the final value in 2021, and then divide by the number of years elapsed.\nBased on the above thought, the answer is:\n50 units per year"
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "bYnDzDH2RHRMqABd",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data in the pie chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {\"Environmental Protection and Sustainable Development Legislation Over Time\"}[0.155, 0.021, 0.808, 0.047] and the legend entries [{2000}[0.952, 0.071, 0.987, 0.091]; {2001}[0.952, 0.101, 0.987, 0.121]; {2002}[0.952, 0.131, 0.987, 0.151]; {2003}[0.952, 0.161, 0.987, 0.181]; {2004}[0.952, 0.191, 0.987, 0.211]; {2005}[0.952, 0.221, 0.987, 0.241]; {2006}[0.952, 0.251, 0.987, 0.271]; {2007}[0.952, 0.281, 0.987, 0.301]; {2008}[0.952, 0.311, 0.987, 0.331]; {2009}[0.952, 0.34, 0.987, 0.36]] and their corresponding pie slices [{2000}<r>[0.264, 0.278, 0.482, 0.516]</r>; {2001}<r>[0.239, 0.315, 0.482, 0.516]</r>; {2002}<r>[0.223, 0.385, 0.482, 0.516]</r>; {2003}<r>[0.223, 0.49, 0.482, 0.628]</r>; {2004}<r>[0.235, 0.516, 0.482, 0.784]</r>; {2005}<r>[0.303, 0.516, 0.482, 0.883]</r>; {2006}<r>[0.447, 0.516, 0.632, 0.886]</r>; {2007}<r>[0.482, 0.516, 0.741, 0.817]</r>; {2008}<r>[0.482, 0.201, 0.741, 0.526]</r>; {2009}<r>[0.283, 0.146, 0.617, 0.516]</r>], I can collect the data for each year.\nBased on the above thought, the answer is:\nYear,Percentage\n2000,2%\n2001,3.3%\n2002,4.7%\n2003,6%\n2004,8%\n2005,10%\n2006,12%\n2007,14.7%\n2008,16.7%\n2009,22.7%"
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "gstBRShblSQI0lBE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year did the most significant increase in legislation incorporation occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which year had the largest increase, I need to look at the change in size of the pie slices comparing to the previous year [<r>[0.952, 0.071, 0.987, 0.091]</r>; <r>[0.952, 0.101, 0.987, 0.121]</r>; <r>[0.952, 0.131, 0.987, 0.151]</r>; <r>[0.952, 0.161, 0.987, 0.181]</r>; <r>[0.952, 0.191, 0.987, 0.211]</r>; <r>[0.952, 0.221, 0.987, 0.241]</r>; <r>[0.952, 0.251, 0.987, 0.271]</r>; <r>[0.952, 0.281, 0.987, 0.301]</r>; <r>[0.952, 0.311, 0.987, 0.331]</r>; <r>[0.952, 0.34, 0.987, 0.36]</r>].\nBased on the above thought, the answer is:\n2009 was the year with the most significant increase, as the slice size representing it was much larger than that of the previous years."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "ro4AfMfkyWBpq2hG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the overall trend of the pie chart suggest about the efforts towards environmental protection and sustainable development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slices from 2000 to 2009, I can infer the general trend of the efforts towards environmental protection and sustainable development.\nBased on the above thought, the answer is:\nThe chart shows a steady increase in the percentage of governments incorporating environmental protection and sustainable development into their legislation over the years. This suggests that more governments are recognizing the importance of these issues and are making efforts to integrate them into their policies."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "evUEcQSFRCBbUoav",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the surge in legislation in 2009 impact future efforts towards environmental protection and sustainable development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the large increase in the percentage in 2009, I can postulate its potential impact on future efforts.\nBased on the above thought, the answer is:\nThe surge in environmental and sustainable development legislation in 2009 signifies a watershed moment in environmental advocacy. This might likely trigger a domino effect, encouraging more governments to adopt similar policies. The momentum might then drive more concerted global efforts towards environmental protection and sustainable development in the subsequent years."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "zRynCBzWWMFeIgZY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what percentage of governments had incorporated environmental protection and sustainable development into their legislation by 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the pie slice for 2005 [<r>[0.303, 0.516, 0.482, 0.883]</r>], which represents the percentage of governments that had done so.\nBased on the above thought, the answer is:\nThe percentage was 10%."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "a4o1HkNT9Loqky55",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in percentage from 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase, I need to subtract the percentage of 2000 [<r>[0.264, 0.278, 0.482, 0.516]</r>] from the percentage of 2009 [<r>[0.283, 0.146, 0.617, 0.516]</r>].\nBased on the above thought, the answer is:\nThe total increase was 20.7%."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "nzNPlYLY00VKPd3S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the percentages in 2008 and 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of 2008 [<r>[0.482, 0.201, 0.741, 0.526]</r>] from the percentage of 2009 [<r>[0.283, 0.146, 0.617, 0.516]</r>].\nBased on the above thought, the answer is:\nThe difference is 6%."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "QnnjoT8Mvbvw2rvu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to transform the data from the chart to a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can identify each slice to be a particular year from 2010 to 2020 with different colors representing each year according to the {legend}<r>[0.908, 0.063, 0.993, 0.398]</r>. The label of each slice, available in the {autotext}<r>[0.197, 0.31, 0.232, 0.33]; [0.163, 0.468, 0.198, 0.488]; [0.179, 0.645, 0.214, 0.665]; [0.252, 0.803, 0.287, 0.823]; [0.373, 0.899, 0.408, 0.919]; [0.559, 0.898, 0.595, 0.918]; [0.7, 0.768, 0.735, 0.788]; [0.766, 0.527, 0.801, 0.547]; [0.713, 0.269, 0.749, 0.289]; [0.552, 0.112, 0.588, 0.132]; [0.313, 0.147, 0.348, 0.167]</r>, indicates the forest coverage in percentages for each year.\nBased on the above thought, the answer is:\nYear,ForestCoverage\n2010,6.2%\n2011,6.8%\n2012,7.3%\n2013,7.6%\n2014,8.1%\n2015,8.9%\n2016,10.0%\n2017,10.6%\n2018,10.8%\n2019,11.4%\n2020,12.2%"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "avL7EOA1YD5ufXhD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be inferred about the forest management strategies over the past decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, it can be inferred that the management strategies may have been successful since there's been a steady increase in forest coverage.\nBased on the above thought, the answer is:\nIt can be inferred that there has been increasingly effective forest management strategies contributing to a positive trend in forest growth over the past decade."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "rqHbhEcO85Vx7uwr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the forest coverage data from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend from 2015 to 2020 shows an upward trend in forest coverage; an increase every year indicates progressive results in forest coverage efforts.\nBased on the above thought, the answer is:\nThe data indicates that the second half of the decade has seen continued growth in forest coverage, suggesting effective implementation of forest management and reforestation programs during this period."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "GzsbIxcEmPeFzCZK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the forestry policies and efforts from 2011 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices matching to years 2011 to 2014, gives visualization for the respective percentage of forest coverage. Analyzing their values and looking for patterns provides insights.\nBased on the above thought, the answer is:\nThe chart indicates steady, but slower growth in forest coverage during this period when compared to the latter half of the decade. This could suggest that while forest management policies were in place, efforts might not have been as effective or extensive as they became in later years."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "VPkX4J0LCE6n0alX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total growth percentage in forest coverage from 2013 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to find the forest coverage in 2013 and 2016 from the chart respectively and then get the difference between the two.\nBased on the above thought, the answer is:\n2.4%"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "kU6sGSg2em0hL3Q9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the increase in forest coverage between 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will go through the chart to find the forest coverage in 2015 and 2020 respectively and then find the differences of these two numbers.\nBased on the above thought, the answer is:\n3.3%"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "qyD7TwJ1GIk6Q5L3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average gain in forest coverage for each year between 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total gain in forest coverage divided by the time span would be the average yearly increase. So, I will calculate the total increase in forest coverage first and then divide it by 10 (total number of years in the period).\nBased on the above thought, the answer is:\n0.6%"
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "ZyNQ4GMcW49E1yvZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the pie chart you see into a structured table presenting sales information for both sales types?"
+            },
+            {
+                "from": "gpt",
+                "value": "Okay, based on the observed chart with title {\"E-commerce and Retail Industry Analysis from 2015 to 2020\"}[0.231, 0.021, 0.732, 0.047], and pie slices for each year respectively being {2015}[0.249, 0.278, 0.482, 0.516], {2016}[0.232, 0.354, 0.482, 0.516], {2017}[0.223, 0.417, 0.482, 0.516], {2018}[0.223, 0.493, 0.482, 0.873], {2019}[0.414, 0.516, 0.74, 0.886] and {2020}[0.283, 0.146, 0.741, 0.541], it is clear what percentage belonged to online sales and physical store sales for each year.\nBased on the above thought, the answer is:\nYear,Online Sales,Physical Store Sales\n2015,3.9%,3500\n2016,2.9%,2600\n2017,3.3%,2000\n2018,21.8%,18000\n2019,28.1%,21000\n2020,40%,28000."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "flFO6b07gK9QsAGg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which platform had a higher sales number, and how did it change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides pie slices showing sales figures for different years. By examining the size of the slices for each year, it can be inferred how the sales from online and physical stores varied over the years.\nBased on the above thought, the answer is:\nThe Physical Store had a higher sales number over the years. Each year, it maintained a higher percentage in comparison to online sales, indicating a consistently larger market share despite the significant growth of online sales in the year 2018."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "ERbAHSV5wfnnW77C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf these industry trends continue, what can you deduce about the future of online and physical store sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the pattern of increasing proportions, for example, the progress that online sales make from {2017}[0.223, 0.417, 0.482, 0.516] to {2020}[0.283, 0.146, 0.741, 0.541] leads to a possible deduction about the future trends of this industry.\nBased on the above thought, the answer is:\nIf the current trends continue, we can predict that online sales will continue to increase in the future. Despite the higher numbers in physical store sales, the significant growth in online sales especially between 2017 and 2020 suggests a shift in consumer behavior towards online shopping. However, given that physical store sales have not been surpassed by online sales over the years, it's plausible that physical stores will continue to play a significant role in the retail and e-commerce industry as well."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "ywd9xtsGPRsqCFbo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data represented in the pie chart say about the impact of e-commerce on the retail industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The impact of e-commerce on the retail industry can be evaluated by comparing the sales from physical stores to online sales over time. Furthermore, the noticeable spike in online sales in {2018}[0.223, 0.493, 0.482, 0.873] provides an insight.\nBased on the above thought, the answer is:\nThe data suggests that e-commerce is making a significant impact on the retail industry. Despite physical stores retaining a larger portion of the market share, the rise in online sales, especially the spike seen in 2018, indicates a growth in customer preference for online shopping. This trend could likely be attributed to increased accessibility and convenience offered by e-commerce platforms."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "JBZgDuH9kpOG2UWl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the online sales see an unusual growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the figures from this pie chart, I can see that there is a significant step-up in the online sales pie slice from the second and third year compared to the fourth year {2018}[0.223, 0.493, 0.482, 0.873], that indicates the presence of an unusual growth.\nBased on the above thought, the answer is:\nIn 2018, online sales saw an unusual growth."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "jdFXjMi7ZiqhYin4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much growth (% increase) we see in Online Sales from the year 2017 to the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "By inspecting the slices in the pie chart we can see the significant difference in Online Sales between {2017}[0.223, 0.417, 0.482, 0.516] and {2018}[0.223, 0.493, 0.482, 0.873], I can infer that the % increase is calculated by subtracting the old value (sales in 2017) from the new value (sales in 2018) and then divide by the old value, then multiply the result by 100%.\nBased on the above thought, the answer is:\nThe growth in Online Sales from 2017 to 2018 was 18.5%."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "YaDSKCiCDVWyNGax",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the online sales first surpass 20 percent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to assess which of the slices on the pie chart was the first to exceed 20 percent. Looking at the proportions indicated for each year, I can see that the first instance of online sales exceeding 20 percent is with {2018}[0.223, 0.493, 0.482, 0.873].\nBased on the above thought, the answer is:\nIn 2018, online sales first surpassed 20 percent."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "ogpohnni5GX66RcN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a CSV table of the data represented in this pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the title {Industry Market Share Comparison}<r>[0.337, 0.021, 0.626, 0.047]</r> and {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r> which represent percentages for each sector as detailed in the legend.<r>[0.842, 0.063, 0.993, 0.368]</r>\nBased on the above thought, the answer is:\nSector,Market Share\nAutomotive,12\nBanking,16\nBiotech,9\nConstruction,13\nEnergy,22\nFood Services,4\nHealth Care,6\nIT,10\nManufacturing,5\nRetail,3"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "zdBaV1fLxDF1R3sS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the highest market share and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the largest radius corresponds to the sector with the highest market share.\nBased on the above thought, the answer is:\nThe Energy sector has the highest market share, indicating a high demand for energy supplies, possibly due to industrial demands or consumer energy use."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "7bURtO7QfPnAGpjw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the distribution of market shares, How can these sectors be categorized ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of each slice of the pie chart indicates the market share of that sector, with larger slices representing larger market shares.\nBased on the above thought, the answer is:\nSectors can be divided into three categories based on their market shares: High market share (Energy), moderate market share (Banking, Construction, Health Care, IT, and Automotive), and low market share (Food Services, Manufacturing, and Retail)."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "lGTMXRjRhSnkmOJ7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sectors might one invest in for potentially higher returns?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sectors with higher market shares generally indicate stronger market performance and may yield better returns for investors, although that would also depend on other factors like sector growth forecasts, economic conditions, etc.\nBased on the above thought, the answer is:\nGiven their high market shares, the Energy, Banking, and Construction sectors could be potential areas for investment."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "q5vdqWTkSY1WWzad",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater percentage does the Energy sector have compared to the Manufacturing sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should identify and compare the percentages for the Energy and Manufacturing sectors.\nBased on the above thought, the answer is:\n17%"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "XRhmwEgDq24Peadp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn how many sectors is the market share less than 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I have to count the number of sectors with a market share of fewer than 10%.\nBased on the above thought, the answer is:\n4"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "ic51pBqvXyUCFRy5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the sector with the highest market share and the sector with the lowest market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this response, I need to find the sectors with the highest and lowest market shares and then subtract the lowest from the highest.\nBased on the above thought, the answer is:\n19%"
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "0RqAKxpyAl7v9aqZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart visual detailing Consumer Behavior (specifically shopping preferences) from 2015 to 2022, can you formulate the underlying data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart indicates the distribution of consumer shopping behavior between online and offline platforms from the years 2015 to 2022. The pie slices each represent a different year and their respective consumer preference distributions. The labels for these slices can be referenced from the chart legend to map these distributions into the corresponding years.\nBased on the above thought, the answer is:\nYear,Online Shopping,Offline Shopping\n2015,75,55\n2016,80,60\n2017,85,65\n2018,90,70\n2019,95,75\n2020,100,80\n2021,105,85\n2022,110,90"
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "bgbZ7WS7uwtoYSsh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the growth percentage, which shopping method do consumers prefer and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By calculating the distribution of both online and offline shopping from 2015 to 2022, it is observable which method had the higher growth, indicating likely preference.\nBased on the above thought, the answer is:\nConsumers prefer online shopping. It has shown a consistent and higher growth percentage compared to offline shopping from 2015 to 2022. This suggests consumers are increasingly shifting towards online platforms for shopping."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "8Db4LhrO05J8lI1n",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can you draw about the trend in shopping habits from the pie chart visualization?"
+            },
+            {
+                "from": "gpt",
+                "value": "To glean inferences about shopping habits, I need to analyze the percentage distribution of online and offline shopping for each year, along with the apparent growth trends.\nBased on the above thought, the answer is:\nThe pie chart suggests a growing trend towards online shopping, reflected in the increasing proportional distribution each year from 2015 to 2022. This indicates that as time progresses, consumers are becoming increasingly inclined towards online shopping platforms, suggesting an evolution in shopping habits influenced by accessibility and convenience of online platforms."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "MfsBK1E33f6kxOed",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data suggest about how consumer behavior might evolve in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict future consumer behavior, I consider the historical trends from 2015 to 2022 in online and offline shopping behavior.\nBased on the above thought, the answer is:\nThe increasing trend towards online shopping from 2015 to 2022 suggests that this preference will continue to grow in the future. Drawing from these trends, it appears likely that the rise of online shopping will continue and possibly accelerate, potentially due to increasing comfort and trust in digital platforms, and the convenience offered by online shopping."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "yeXYLyXnWt2Y84vZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we take 2020 as reference, what is the total percentage growth in online shopping from 2015 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referencing the distribution of online shopping from 2015 to 2022, I can calculate the growth from 2020 to 2022 and then sum this with the growth from 2015 to 2020.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "tNknRiXuJSror2Vh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the numerical trend for offline shopping from 2015 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the distribution of offline shopping from 2015 to 2022, I can discern a numerical pattern governing its growth over these years.\nBased on the above thought, the answer is:\nIt increases by 5 each year."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "4aUaPrh86rMMkjJb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage decrease in offline shopping from the year 2021 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the year 2021 and 2022 in offline shopping and calculate the decrease.\nBased on the above thought, the answer is:\nThere was a decrease of 15%."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "MOnlRiaaN4kAp2Uz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please express the chart's data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend in the pie chart {sublegend}<r>[0.908, 0.063, 0.993, 0.488]</r> and {the specific color sections associated with each year}<r>[0.223, 0.146, 0.741, 0.886]</r>, it is clear that each differently colored section of the pie stands for a specific year and its associated value of Training Expenditure and Employee Skills Development. We can thus construct a CSV table from this by listing each year and the corresponding values.\nBased on the above thought, the answer is:\nYear,Training Expenditure,Employee Skills Development\n2010,390,145\n2011,465,200\n2012,510,235\n2013,550,270\n2014,620,320\n2015,680,370\n2016,750,430"
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "SuLlmObVQn6T5Dgw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you deduce trends in organizational investment towards employee development between 2010 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the percentage slice allocations over the years depicted within the pie chart, and comparing the changing colors corresponding to levels of Training Expenditure and Employee Skills Development, I can infer trends in investment.\nBased on the above thought, the answer is:\nBased on the chart, there is a clear upward trend in organizational investment towards both Training Expenditure and Employee Skills Development from 2010 to 2016. This suggests that organizations increased their investments in employee development over this time period, highlighting a growing emphasis on building and strengthening the skill sets of their workforce."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "VMVLRRDr7iJVTEAE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the comparison between Training Expenditure and Employee Skills Development over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer a comparison, I need to check the percentage slice allocations over the years for both Training Expenditure and Employee Skills Development.\nBased on the above thought, the answer is:\nThe chart reflects that organizations consistently spent more on Training Expenditure in comparison to Employee Skills Development. Though the two appeared to be growing in tandem, the marked difference in the proportion of investment in these two domains throughout the years evidently points towards a higher emphasis on Training Expenditure."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "ROut79GxqLoQDXQy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this data influence future spending decisions of organizations?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require me to consider the growth in expenditure across both areas over the years, as indicated by the pie chart.\nBased on the above thought, the answer is:\nIn light of the clear upward trends in both Training Expenditure and Employee Skills Development, organizations may want to continue investing in these areas, reflecting their commitment to employee growth. However, they may also want to consider balancing their investments between these two areas more effectively, given the steadily larger proportion of spending consistently directed towards Training Expenditure."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "1fWloO6GLhapGQ03",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Training Expenditure increase from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in Training Expenditure, I need to subtract the initial value in 2010 from the final value in 2016 based on the data in the pie chart.\nBased on the above thought, the answer is:\nThe Training Expenditure increased by 360."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "loMs2QnvgUbNI0BW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual Training Expenditure for the years given in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average annual Training Expenditure, I should sum up all the annual Training Expenditures and divide this by the total number of years.\nBased on the above thought, the answer is:\nThe average annual Training Expenditure is 566.43."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "DKm8b5Em4gx3n3Zy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did Employee Skills Development increase from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in Employee Skills Development, I should divide the increase from 2010 to 2015 by the starting value in 2010 and then multiply it by 100.\nBased on the above thought, the answer is:\nThe Employee Skills Development increased by approximately 155.17% from 2010 to 2015."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "qWhPcwD6HJLhrkVV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the information seen in the chart into a tabulated format, suitable for further data analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Artificial Intelligence and Automation Trends from 2012 to 2018\"}<r>[0.217, 0.021, 0.746, 0.047]</r> suggests that the chart is showing the trends of Artificial Intelligence and Automation from 2012 to 2018. For each slice in the pie, like this slice {\"AI 2012\"}<r>[0.262, 0.278, 0.482, 0.516]</r>, it includes the field, the year, and the percentage data. Using these details, I can construct a table.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Automation\n2012,15,20\n2013,20,25\n2014,35,35\n2015,45,45\n2016,60,65\n2017,75,85\n2018,80,90"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "YKwS0UBDLndSHULN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart data, which field showed faster growth over the studied time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in percentage from 2012 to 2018 for both Artificial Intelligence and Automation, and compare the two differences.\nBased on the above thought, the answer is:\nAutomation showed faster growth than Artificial Intelligence from 2012 to 2018."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "tjBu4WHJm56Ty41N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can we gather about the adoption of Artificial Intelligence and Automation from 2012 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the percentages in the chart for each year, I infer about the growth and compare trends of both these fields.\nBased on the above thought, the answer is:\nBoth Artificial Intelligence and Automation saw steady growth in adoption from 2012 to 2018, indicating increasing incorporation of these technologies in industries. However, Automation consistently had a higher adoption rate than Artificial Intelligence throughout these years."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "22fOhnEnb6zAUtjG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how might these technologies' development trends influence decision-making in related industries for the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the growth trends of both Artificial Intelligence and Automation from the chart and infer their potential influence.\nBased on the above thought, the answer is:\nThe consistent and strong growth in both Artificial Intelligence and Automation suggest that industries may continuously invest in these technologies to maintain or increase productivity and efficiency. As Automation had a slightly higher rate of adoption, industries might prioritize it more in the near future, although the importance of Artificial Intelligence should not be underestimated."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "DzSij8xsMAWTqmmz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, in what year did the sum of the percentages from both fields exceed 100% for the first time?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the dataset, I need to perform a yearly check to calculate the sum of percentages of Artificial Intelligence and Automation. The first year, when sum exceeds 100, is the answer.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "rG3hKIeKqV4DFPCv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the extracted data, how much percentage did Automation grow from 2012 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth in Automation, I subtract the percentage value of Automation in 2012 from the value in 2018.\nBased on the above thought, the answer is:\n70%"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "9L35Gu6nkMKacISk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend from 2012 to 2018 continues, what would be the expected percentage of Artificial Intelligence in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the average yearly growth rate of Artificial Intelligence from 2012 to 2018 and apply it to the year 2019.\nBased on the above thought, the answer is:\nGiven the previous trends, the expected percentage would be 85%"
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "BqMMujteTYHqyfkA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please create a structured table from the social media usage data represented in the pie chart you examined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I observed the pie chart's slices each indicating a specific year from {\"2015\"}<r>[0.315, 0.424, 0.359, 0.444]</r> to {\"2021\"}<r>[0.412, 0.295, 0.456, 0.315]</r> and their corresponding social media usage percentages. Therefore, I can extract this data to form the following table.\nBased on the above thought, the answer is:\nYear,Social Media Usage\n2015,57%\n2016,65%\n2017,72%\n2018,83%\n2019,90%\n2020,95%\n2021,100%"
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "v7HNlrkvAFj7tWk1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, which year had the highest social media usage and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should locate the pie piece that occupies the largest area.\nBased on the above thought, the answer is:\nThe year with the highest social media usage is 2021 because it occupies the largest area in the pie chart, representing 100% usage."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "bAmD9zFxdBhYApD1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you draw any conclusions about the trend of social media usage from 2015 to 2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the change in the size of the pie slices from {2015}<r>[0.315, 0.424, 0.359, 0.444]</r> to {2021}<r>[0.412, 0.295, 0.456, 0.315]</r> to ascertain the trend.\nBased on the above thought, the answer is:\nThe chart shows a clear upward trend in social media usage from 2015 to 2021. This indicates that social media usage has been steadily increasing throughout these years."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "544sjb5VmNYxqb77",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data depicted in this chart potentially impact future social media strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to extrapolate the trends seen in the chart over the period from {2015}<r>[0.315, 0.424, 0.359, 0.444]</r> to {2021}<r>[0.412, 0.295, 0.456, 0.315]</r> and suggest potential impacts on social media strategies.\nBased on the above thought, the answer is:\nThe continuous increase in social media usage from 2015 to 2021 suggests that social media platforms are becoming increasingly crucial for communication, marketing, and business operations. Therefore, future strategies may need to focus more on optimizing and expanding online presence, harnessing social data for insights, and improving digital marketing and e-commerce functions to capitalize on this growing usage trend."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "fty2Hq6fcDPugCdc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did social media usage reach 90%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I must locate the slice of the pie chart, representing 90% social media usage.\nBased on the above thought, the answer is:\nThe year is 2019."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "NOdeY9664hEI6GNM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in social media usage between the years 2015 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get this, I need to subtract the social media usage of 2015 from that of 2021.\nBased on the above thought, the answer is:\nThe difference is 43%."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "xHrezBCJvIkLKSYU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the social media usage increase from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this increase, I need to subtract the 2019 social media usage from the 2020 usage.\nBased on the above thought, the answer is:\nThe social media usage increased by 5%."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "KYMIAGt48JFN29qM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you read and analyse the data from the pie chart and present it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the chart title, 'Family Living Conditions'{Family Living Conditions}<r>[0.381, 0.021, 0.582, 0.047]</r>. For each slice in the pie chart, the condition and proportion are represented as {condition}<r>['Poor', 'Satisfactory', 'Good', 'Very Good', 'Excellent']</r> and {propotion}<r>[10.0%, 25.0%, 45.0%, 15.0%, 5.0%]</r>respectively.\nBased on the above thought, the answer is:\nCondition,Poor,Satisfactory,Good,Very Good,Excellent\nPercentage,10.0%,25.0%,45.0%,15.0%,5.0%"
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "fqfx6sHzF8zMZ7Wr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich living condition has the most representation in this chart? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the biggest slice of the chart, which stands for the most popular living condition.\nBased on the above thought, the answer is:\nThe largest slice corresponds to the 'Good' living condition since it represents 45.0% of the whole, which is larger than any other conditions."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "g48NW68q3nWfXjiE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy might the living conditions 'Poor' and 'Excellent' be considered outliers in this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of 'Poor' and 'Excellent' with other conditions.\nBased on the above thought, the answer is:\nThe 'Poor' and 'Excellent' categories are considered outliers as their proportions are far lower than those of the other categories, each with only 10.0% and 5.0% respectively."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "ZeRGZ14z214fHpDb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this pie chart be useful for policy makers?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at how the proportions of different living conditions can inform policy decisions.\nBased on the above thought, the answer is:\nPolicy makers could use this chart to aid in decisions on resource allocation. With the majority of families in 'Good' and 'Satisfactory' conditions, policies could be directed towards maintaining or improving these conditions. Meanwhile, efforts could also be made to help the 10% of families in 'Poor' conditions and further support the 5% who have achieved 'Excellent' conditions."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "KjVLxVMARwZgVxTM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between Good and Very Good conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage for 'Very Good'{Very Good}<r>[15.0%]</r> from the percentage for 'Good'{Good}<r>[45.0%]</r>.\nBased on the above thought, the answer is:\nThe difference is 30%."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "Spfkgg026zfZ6aR4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for the middle three conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total, I need to add up the percentages for 'Satisfactory', 'Good' and 'Very Good' which are {Satisfactory}<r>[25.0%]</r>,{Good}<r>[45.0%]</r> and {Very Good}<r>[15.0%]</r>respectively.\nBased on the above thought, the answer is:\nThe total is 85%."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "GKxO7ACgoCNS7hda",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Excellent condition increased by 2%, what will be the new average percentage of each condition?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new average percentage, I need to add 2% to the 'Excellent' percentage, calculate the new total and then divide by the number of conditions.\nBased on the above thought, the answer is:\nThe new average percentage would be 21.4%."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "oIjP5bO8LVjQ7C1D",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart you've analyzed into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the pie chart, with the title {Incidence Rates of Diseases}<[0.365, 0.021, 0.598, 0.047]> and the diseases in the legend {Influenza}<[0.895, 0.071, 0.958, 0.091]>, {Pneumonia}<[0.895, 0.101, 0.972, 0.121]>, {Malaria}<[0.895, 0.131, 0.946, 0.151]>, {Tuberculosis}<[0.895, 0.161, 0.979, 0.181]>, {HIV}<[0.895, 0.191, 0.918, 0.211]>, {Hepatitis}<[0.895, 0.221, 0.957, 0.241]>, {Cancer}<[0.895, 0.251, 0.944, 0.271]>, and {Hypertension}<[0.895, 0.281, 0.987, 0.301]>; each disease corresponds to its respective incidence rate.\nBased on the above thought, the answer is:\nDisease,Incidences\nInfluenza,60\nPneumonia,57\nMalaria,40\nTuberculosis,65\nHIV,50\nHepatitis,60\nCancer,72\nHypertension,80"
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "nfQkhNCa22MJ3Hih",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which diseases seem to be more prevalent, and can you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we can look at which disease has a larger proportion in the pie, and find that the larger the proportion, the higher the incidence rate.\nBased on the above thought, the answer is:\nHypertension and Cancer are more prevalent as they have larger proportions in the pie chart, indicating higher incidence rates. This might be due to unhealthy lifestyles and genetic predispositions."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "mHkvp3iaQNxzhu6O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the pie chart, what health concerns should be prioritized by global health organizations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Health concerns can be prioritized based on the incidence rates of diseases as shown in the pie chart.\nBased on the above thought, the answer is:\nHypertension, Cancer, and Tuberculosis should be prioritized by global health organizations as these diseases have the three highest incidence rates."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "0v2H9fKb58NBCWhp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data in the pie chart, how might health policies and strategies be adjusted?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjustment of health policies and strategies could be based on the incidence rates of diseases depicted in the pie chart.\nBased on the above thought, the answer is:\nGiven that Hypertension and Cancer have the highest incidence rates, policies should focus on prevention and early detection of these diseases through healthier lifestyles and regular checkups. Funding can also be channeled towards finding effective treatments for these diseases. Efforts to combat communicable diseases such as Malaria should not be overlooked, even though it has the lowest incidence rate among the diseases in the pie chart."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "GXGQkM1izs5ebZNn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which disease has the highest incidence rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to check the size of each section of the pie chart that corresponds to diseases' incidence rates. Hypertension's proportion is the largest which contributes to the highest incidence rate.\nBased on the above thought, the answer is:\nHypertension."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "QW7yuFwc0iJAE4mr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the number difference in the incidence rate between the disease with the highest rate and the one with the lowest rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will subtract the incidence rate of the disease with the lowest rate which is Malaria from the disease with the highest rate which is Hypertension.\nBased on the above thought, the answer is:\nThe number difference is 80 (Hypertension) - 40 (Malaria) = 40."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "y7OxCU75r0j6UbJ1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the incidence rate of Influenza increased by 10, what would its new incidence rate be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new incidence rate of Influenza, I should add 10 to the current incidence rate as per the pie chart.\nBased on the above thought, the answer is:\nThe new incidence rate for Influenza would be 60 (current incidence) + 10 = 70."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "m0YpuHnj6YQWEHc9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, create a CSV format that can illustrate the influence scores for each media type."
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the legend {the legend}<r>[0.841, 0.063, 0.993, 0.279]</r> and title {the title}<r>[0.294, 0.021, 0.669, 0.047]</r>, I can find the names and respective influence scores for each media type from the chart.\nBased on the above thought, the answer is:\nMedia Types,Influence Score\nTV,16.3%\nRadio,13.3%\nNewspaper,11.2%\nInternet,16.3%\nSocial Media,19.4%\nBooks,9.2%\nWord of Mouth,14.3%"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "eEHuRot0lyMSLIyg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of media has the biggest influence on public opinion and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the various sections, I can infer that the type of media with the largest slice would have the biggest influence.\nBased on the above thought, the answer is:\n\"Social Media\" is the type of media that has the biggest influence on public opinion because it has the highest score of 19.4%."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "PCysf0acv4FJhDLH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, what can you infer about the influence of social media on public opinion compared to traditional media?"
+            },
+            {
+                "from": "gpt",
+                "value": "The key is to compare the influence score of \"Social Media\" with that of traditional media such as \"TV\", \"Radio\", and \"Newspaper\".\nBased on the above thought, the answer is:\n\"Social Media\" exerts a stronger influence on public opinion than traditional media like \"TV\", \"Radio\", and \"Newspaper\". This is evidenced by its highest percentage of 19.4% compared to TV's 16.3%, Radio's 13.3%, and Newspaper's 11.2%."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "IMJm3aka0gw6p5gB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart guide media marketing strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I analyze the influence scores of the various media types and consider their implications for marketing strategies.\nBased on the above thought, the answer is:\nThe pie chart suggests that for a broader reach and influence on public opinion, marketing strategies should prioritize social media and internet platforms due to their higher scores. Though traditional media like TV and newspapers still have considerable influence, they are less effective compared to digital platforms. Also, Word of Mouth is more effective than Books, so a positive reputation and personal recommendations can be impactful marketing tools."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "DQHGPfdici56BCEX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, how many media types have an influence score above 15%?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing each slice of the pie chart, I identify those sections {\"TV\"}<r>[0.223, 0.278, 0.482, 0.635]</r>, {\"Internet\"}<r>[0.482, 0.516, 0.733, 0.87]</r>, and {\"Social Media\"}<r>[0.482, 0.211, 0.741, 0.607]</r> that have an influence score above 15%.\nBased on the above thought, the answer is:\n3"
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "9XujNTlfSFf0PrR0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the influence score of \"Books\" increased by 3%, what would be the new average influence score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new average score, I will add 3% to the score of \"Books\", recalculate the total score and then divide it by 7.\nBased on the above thought, the answer is:\nThe new average score is (16.3% + 13.3% + 11.2% + 16.3% + 19.4% + (9.2%+3%) + 14.3%)/7 = 14.4%."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "BfZpCrkDAp6LlDHe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest influence score?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the influence scores of the media types, I realize that the highest score{\"Social Media\"}<r>[0.482, 0.211, 0.741, 0.607]</r> is 19.4% and the lowest {\"Books\"}<r>[0.482, 0.146, 0.628, 0.516]</r> is 9.2%.\nBased on the above thought, the answer is:\nThe difference is 10.2%."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "af6A0KYyjOH5DAEl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please help me convert the given pie chart data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie chart {title}<r>[0.234, 0.021, 0.729, 0.047]</r> and {slice segments}<r>[0.223, 0.146, 0.741, 0.886]</r>, which are color-coded corresponding to the {legend}<r>[0.908, 0.063, 0.993, 0.279]</r>, I noticed that each segment stands for the content creation for a specific year. The percentage associated with each year's segment stands for its contribution to the total.\nBased on the above thought, the answer is:\nYear,Pictures,Stories,Videos\n2010,2.4%,2.4%,2.4%\n2011,4.2%,4.2%,4.2%\n2012,6.1%,6.1%,6.1%\n2013,10.1%,10.1%,10.1%\n2014,16.2%,16.2%,16.2%\n2015,23.5%,23.5%,23.5%\n2016,37.4%,37.4%,37.4%"
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "M4Bd7guyjYRJqFX4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the pie chart, can you determine which year witnessed the highest growth in new media and digital content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie distributions and comparing the size of pieces from year to year, I can determine the year with highest growth in content creation.\nBased on the above thought, the answer is:\nIn 2016, there was the highest growth in new media and digital content creation as it has the largest pie segment."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "KmSkZJv5Iy1poIg2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend of content creation from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie segments, I can infer the trend of content creation by comparing the relative size and growth of each segment.\nBased on the above thought, the answer is:\nThe trend of content creation has been dramatically increasing from 2010 to 2016, as reflected by the increasingly larger pie segments representing each subsequent year."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "ZkAAtKpHuc104p81",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you predict the trend of new media and digital content creation based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the trend over the years represented in the pie segments, I can forecast the potential growth direction of future content creation.\nBased on the above thought, the answer is:\nThe trend shows that new media and digital content creation has continued to grow increasingly from 2010 to 2016. If this trend continues, we can predict that the growth will further increase in the future years."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "0Lffv0PTM62pGfhf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two consecutive years show the biggest growth in content creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to calculate the relative growth of content creation for each year compared to the previous year by subtracting the previous year's percentage contribution from the current year's figure.\nBased on the above thought, the answer is:\n2015 and 2016."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "5hP342QEXA7CGhCl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the overall growth in content creation from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the overall growth, I need to subtract the percentage contribution in 2010 from the percentage contribution in 2016.\nBased on the above thought, the answer is:\n35%."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "pP6oXvLGC5MJcZQ5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nApproximately what percentage of content creation in 2015 is attributed to videos?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get an approximation, I need to equally divide the percentage for 2015 between Pictures, Stories, and Videos, as no separate allocations are specified.\nBased on the above thought, the answer is:\nApproximately 7.83%."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "M0ffscUs6R2MOSgY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me convert this pie chart data into a table of CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, let me read the categories from the legend {\"Education Reform\"}[0.814, 0.071, 0.937, 0.091], {\"Healthcare Improvement\"}[0.814, 0.101, 0.987, 0.121], {\"Environmental Directive\"}[0.814, 0.131, 0.982, 0.151], {\"Tax Legislation\"}[0.814, 0.161, 0.916, 0.181], {\"Foreign Diplomacy\"}[0.814, 0.191, 0.943, 0.211]. Also, the corresponding effectiveness score is shared for each category on the pie chart slices {\"Education Reform\"}[0.075, 0.464, 0.198, 0.484], {\"Healthcare Improvement\"}[0.197, 0.881, 0.37, 0.901], {\"Environmental Directive\"}[0.708, 0.754, 0.875, 0.774], {\"Tax Legislation\"}[0.738, 0.33, 0.841, 0.35], {\"Foreign Diplomacy\"}[0.321, 0.101, 0.451, 0.121].\nBased on the above thought, the answer is:\nPolicy,Effectiveness Score\nEducation Reform,18.9%\nHealthcare Improvement,21.6%\nEnvironmental Directive,20.3%\nTax Legislation,14.9%\nForeign Diplomacy,24.3%"
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "Vd0x3PNLxqlRMmjx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the provided data, which government policy was the most effective, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart divisions, I will identify the largest slice, which represents the most effective policy.\nBased on the above thought, the answer is:\nThe most effective policy according to the data is \"Foreign Diplomacy\". It's the most effective because it has the largest section in the pie chart, indicating that it has the highest effectiveness score of 24.3%."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "z7WgVR28vdCkNuDA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the effectiveness of these government policies based solely on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sizes of the slices in relation to one another, I can make inferences about the relative effectiveness of each policy. Larger slices represent more effective policies.\nBased on the above thought, the answer is:\nBased on the pie chart, we can infer that the Foreign Diplomacy policy was the most effective, followed by Healthcare Improvement and Environmental Directive. Education Reform was moderately effective while Tax Legislation was the least effective policy."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "pkTaqP02spZn10O1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this chart was used as a guide for resource allocation in the future, what might be the implications for these government policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart would guide resources to be allocated proportionally based on the effectiveness of the policies.\nBased on the above thought, the answer is:\nResources may be directed more towards Foreign Diplomacy and Healthcare Improvement as these policies are deemed more effective compared to others. Tax Legislation, being the least effective based on the chart, might receive lesser resources or need to be revisited to increase its effectiveness."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "YpdeGjxFxVjAtLWQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy had an effectiveness score below 15%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll compare the sizes of the pie slices to make this determination. Looking at the slices {\"Education Reform\"}[0.223, 0.278, 0.482, 0.69], {\"Healthcare Improvement\"}[0.253, 0.516, 0.553, 0.886], {\"Environmental Directive\"}[0.482, 0.516, 0.741, 0.872], {\"Tax Legislation\"}[0.482, 0.223, 0.741, 0.523], {\"Foreign Diplomacy\"}[0.283, 0.146, 0.64, 0.516], it appears that one slice is considerably smaller than the others.\nBased on the above thought, the answer is:\nTax Legislation."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "n3sNmWNfgK2lv54S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more effective is the best policy as compared to the least effective one?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the percentage of the least effective policy from the most effective one.\nBased on the above thought, the answer is:\nThe most effective policy is 24.3% (Foreign Diplomacy) and the least effective policy is 14.9% (Tax Legislation). Therefore, the difference is 24.3% - 14.9% = 9.4%. The best policy is 9.4% more effective than the least effective one."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "yyWxViEu4chBStxF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the effectiveness score between Education Reform and Foreign Diplomacy?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of Education Reform from that of Foreign Diplomacy to get the difference.\nBased on the above thought, the answer is:\nThe effectiveness score of Education Reform is 18.9%, and that of Foreign Diplomacy is 24.3%. Therefore, the difference is 24.3% - 18.9% = 5.4%. Foreign Diplomacy is 5.4% more effective than Education Reform."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "XEizVXcW75EmxcDF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data from the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart's legend {legend section}[0.863, 0.063, 0.993, 0.368] and the colored slices of each subculture in the {pie chart}[0.223, 0.146, 0.741, 0.886], I can conclude the subcultures and their respective popularity percentages.\nBased on the above thought, the answer is:\nSubculture,Popularity\nEmo,24%\nGothic,18%\nCyberpunk,14%\nSteampunk,12%\nHip Hop,10%\nBoho-chic,8%\nPunk,6%\nGrunge,4%\nHarajuku,3%\nSkater,1%"
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "eLFQXW0rgPlSYA3k",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which subculture is the most influenced by fashion trends? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must identify the pie slice representing the highest percentage on the {the pie chart}[0.223, 0.146, 0.741, 0.886].\nBased on the above thought, the answer is:\nThe \"Emo\" subculture is the most influenced by fashion trends as it has the highest popularity percentage of 24% among other subcultures."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "vYBMUOhbg9Vx9WPi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the influence of fashion trends in the \"Gothic\" and \"Emo\" subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and observing the size of the sectors corresponding to the \"Gothic\" and \"Emo\" subcultures, I can make some inferences.\nBased on the above thought, the answer is:\nBoth the \"Emo\" and \"Gothic\" subcultures are significantly influenced by fashion trends, having the highest and second highest popularity percentages, respectively. This indicates that these subcultures are responsive to fashion changes and have considerable adherence to these trends."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "o7HmKGFLP6P1P9P5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat should clothing brands targeting the \"Skater\" subculture consider based on the data from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the percentage represented by the \"Skater\" subculture in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can derive some observations.\nBased on the above thought, the answer is:\nGiven that the \"Skater\" subculture has the smallest slice on the pie chart, indicating a 1% popularity, clothing brands should consider innovating and diversifying their clothing lines to create appeal among this group. Alternatively, they can also consider promoting and educating about skater culture fashion to boost its popularity."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "SVtCUnf8F5edlM5i",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two subcultures combine to make up 24% of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the percentages of different subcultures that total up to 24%.\nBased on the above thought, the answer is:\nHip Hop and Boho-chic."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "01qZiQ6Dl2lz4cn0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the median popularity percentage across all the subcultures?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I would need to sort every subculture's dates in ascending order and locate the middle value.\nBased on the above thought, the answer is:\n10%."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "wzDf8CO7sBDXkC4C",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the \"Emo\" subculture experienced a drop in popularity by 10%, how much would the \"Gothic\" subculture need to increase by to be the most popular subculture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I would need to subtract 10% from the \"Emo\" subculture's popularity and subtract the current \"Gothic\" subculture's popularity from that figure.\nBased on the above thought, the answer is:\n16%."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "7J3gmlxvHlW7dAQW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the trend data from 2011 to 2020 for Movies and Music into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see two data trends represented on the chart, one for each of the categories, Movies and Music, indicated in {the legend}<r>[0.895, 0.063, 0.993, 0.129]</r>. Each trend indicates the viewership or revenue for those categories from 2011 to 2020.\nBased on the above thought, the answer is:\nYear,Movies,Music\n2011,70,50\n2012,80,55\n2013,75,45\n2014,77,60\n2015,79,65\n2016,85,70\n2017,70,75\n2018,95,80\n2019,100,85\n2020,105,95"
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "vF6Jlwryh9LmeQ5x",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category showed more positive trend in viewership or revenue from 2011-2020, Movies or Music?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to compare the growth or positive change in viewership or revenue for both categories from 2011 to 2020.\nBased on the above thought, the answer is:\nThe Movies category showed a more positive trend as it started from 70 and reached 105, showing a larger growth compared to the Music category."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "a92ijWAvMjiiqAbh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat change or trend can be inferred in the entertainment industry from 2011-2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the data label for each category, I would look for patterns or changes over the years to infer a trend for the entertainment industry.\nBased on the above thought, the answer is:\nThe entertainment industry, both movies and music, showed a positive trend from 2011 to 2020, with an overall increase in viewership or revenue."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "EFJhpoVw5OezSqtH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be the projected revenue trend for the Movies and Music categories for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To projection for 2021, I would need to consider the trend and growth rate for each category from the past years.\nBased on the above thought, the answer is:\nBased on the consistent increase in revenue or viewership from 2011 to 2020 for both categories, the projected trend for 2021 should be a continued increase for both Movies and Music."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "M8yCZ25pieR2B7X3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total viewing or revenue of the Movies category from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total viewing or revenue of Movies from 2011 to 2020, I need to sum up the viewership or revenue values from each year represented in the Movies portion of the chart.\nBased on the above thought, the answer is:\n830"
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "kb2YkWLbNpQ7iD4d",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of revenue generated by the Music category to the total revenue of both categories from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage, I first need to calculate the total revenue of both categories from 2011 to 2020. Then I can divide the total revenue of the Music category by the total and multiply by 100%.\nBased on the above thought, the answer is:\n44.9%."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "ZnqiA5bYGmn0aSnY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the viewership or revenue for the Movies category increase from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I subtract the 2011 value from the 2020 value, then divide by the 2011 value and multiply by 100%.\nBased on the above thought, the answer is:\n50% increase."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "KJA4Du9dC4p9Qs3W",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data you observed, please construct a CSV format table showing each year's representation in percentages."
+            },
+            {
+                "from": "gpt",
+                "value": "By viewing the {legend data}[0.806, 0.063, 0.993, 0.279] and {pie chart slice}[0.223, 0.146, 0.741, 0.886], I can understand that each slice represents a year's percentage out of the total budget.\nBased on the above thought, the answer is:\nYear,Percentage\n2005,11.0%\n2006,13.4%\n2007,15.1%\n2008,13.7%\n2009,15.5%\n2010,14.1%\n2011,17.2%"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "lEXan7wWiGf5hMmj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year received the highest proportion of budget?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I need to find the year with the highest percentage which can be identified as the one receiving the highest proportion of the budget.\nBased on the above thought, the answer is:\n2011 received the highest proportion of budget, with 17.2%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "TR24B4StGbrOvckJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the distribution of budgets from 2005 to 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the percentages represented in the pie chart, I can infer about the distribution of budgets for the period.\nBased on the above thought, the answer is:\nThe pie chart shows that the budget for Public Services increased during the period from 2005 to 2011, with 2011 having the most significant proportion. This might suggest that 2011 was the year when more funds were directed towards public services as compared to other years."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "CoBHW7qVBLlyAEjd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow may the data in the pie chart influence budget allocation decisions for the year 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the trends displayed in the pie chart, I can suggest how the data might influence decisions for budget allocations in 2012.\nBased on the above thought, the answer is:\nSeeing the highest proportion was met in 2011, decision-makers could consider further increasing the budget for Public Services in the year 2012. On the other hand, the lower proportions from earlier years might suggest the need for a more balanced allocation of the budget or identifying areas that need more funding."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "YFnW3agUuzPWGlhB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the proportion of budget approximately one-ninth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recalling the standard value of one-ninth is around 11.11%, I need to compare this with each year's percentage data.\nBased on the above thought, the answer is:\n2005"
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "ZXo6ZxopUq7uAzrS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage representation of each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I need to sum up the percentages for all the years and then divide by the number of years, which is seven.\nBased on the above thought, the answer is:\nThe sum of the percentages is 100%. The average percentage is 100% / 7 = approximately 14.29%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "hRBxFwzTiheOTw87",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2005\u2019s proportion was increased by 3%, what would the new total percentage for all years be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the new total percentage, I need to add 3% to the 2005's percentage and sum up all percentages again.\nBased on the above thought, the answer is:\nThe new percentage for 2005 is 11.0% + 3% = 14.0%. The new total percentage is 14.0% (2005) + 13.4% (2006) + 15.1% (2007) + 13.7% (2008) +15.5% (2009) + 14.1% (2010) + 17.2% (2011) = 103.0%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "KQllB1of1dL6Gii3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data representing market trends into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend of the chart {2000}[0.913, 0.073, 0.941, 0.087], {2001}[0.913, 0.103, 0.941, 0.117], {2002}[0.913, 0.133, 0.941, 0.147], {2003}[0.913, 0.163, 0.941, 0.177], {2004}[0.913, 0.193, 0.941, 0.207], {2005}[0.913, 0.223, 0.941, 0.237], {2006}[0.913, 0.253, 0.941, 0.266], {2007}[0.913, 0.282, 0.941, 0.296], {2008}[0.913, 0.312, 0.941, 0.326], {2009}[0.913, 0.342, 0.941, 0.356]; I can see that each portion represents a different year. The sizes of the slices in the pie chart represent the market trend value for each year.\nBased on the above thought, the answer is:\nYear,Market Trends\n2000,3.5%\n2001,6.9%\n2002,7.4%\n2003,8.4%\n2004,10.3%\n2005,12.3%\n2006,14.1%\n2007,11.5%\n2008,12.6%\n2009,13.2%"
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "fSdgbMNwwYlzqhE2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which year registered the highest market trend and why does it matter?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at all the slices of the pie chart, the largest slice will represent the year with the highest market trend.\nBased on the above thought, the answer is:\nThe year 2006 had the highest market trend at 14.1%. This is significant as it might represent a booming year in the real estate sector."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "jUZwbfViy5b7wFjg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the shape of this pie chart tell us about the market trend in the period 2000 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the sizes of each slice for all years, I can make an analysis of the market trend over the period.\nBased on the above thought, the answer is:\nThe pie chart reveals that there was a steady increase in the market trend from 2000 to 2006, followed by a decrease in 2007, and then an increase again in 2008 and 2009. This pattern may indicate economic cycles impacting the real estate market during this period."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "5AeZAA3F41UT7UFP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the market trends as per the chart, how could this information be potentially used for future real estate investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentages indicating the market trends are useful indicators for the health of the real estate market, therefore, can be used for making investment decisions.\nBased on the above thought, the answer is:\nAnalyzing the market trends from the past, one can predict potential growth or downturns in the future. The steady growth until 2006 followed by a drop in 2007 could indicate a cycle where after a several years of growth, a downturn is likely. This could guide future investment by buying during downturn years (like 2007) and sell during high trend years (like 2009)."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "ifkx2AhuL5iJ8Kd4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the total trend percentage increase from 2000 to 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total trend percentage increase, I need to add up the percentages from 2000 to 2005 listed on the pie chart.\nBased on the above thought, the answer is:\nThe total trend percentage increase from 2000 to 2005 is 3.5% + 6.9% + 7.4% + 8.4% + 10.3% + 12.3% = 48.8%."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "0hMni7BxLWbDopkE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year is the market trend percentage greater than 13%?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentages on the pie chart with 13% allows me to find the year with a market trend greater than that.\nBased on the above thought, the answer is:\nThe year 2009 had a market trend of 13.2%, which is greater than 13%."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "TZC4P5CQYzDXhe0F",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of market trend values for the period 2001 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range of the market trend values can be found by subtracting the smallest market trend value from the largest for the years from 2001 to 2009.\nBased on the above thought, the answer is:\nThe range of market trend values from 2001 to 2009 is 14.1% - 6.9% = 7.2%."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "2FrOSb5DXzCKZRix",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the values from the different categories in the chart into a workable dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices in the chart, each slice represents a social or political trend. The corresponding values represent the relative weight of these trends. The trends are {'Green Movements'}<r>[0.223, 0.278, 0.482, 0.699]</r>,{'Technology Trends'}<r>[0.257, 0.516, 0.482, 0.886]</r>,{'Immigration Policies'}<r>[0.471, 0.516, 0.74, 0.886]</r>,{'Political Campaigns'}<r>[0.482, 0.366, 0.741, 0.549]</r>, and {'Freedom of Speech'}<r>[0.283, 0.146, 0.719, 0.516]</r>.\nBased on the above thought, the answer is:\n\"Trends\",\"Value\"\n\"Green Movements\",60\n\"Technology Trends\",50\n\"Immigration Policies\",75\n\"Political Campaigns\",25\n\"Freedom of Speech\",100"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "VekzmvDPBpGJlEj8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which topic was given the most weight or importance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The topic with the largest slice in the chart indicates the topic given the most weight or importance.\nBased on the above thought, the answer is:\nThe topic 'Freedom of Speech' was given the most importance as it has the largest slice in the chart."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "mURICHefVDGDnsId",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of the pie chart, how would you describe the relative importance of 'Technology Trends'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The fraction of the pie occupied by 'Technology Trends' provides an indicator of its relative importance compared to the other trends.\nBased on the above thought, the answer is:\n'Technology Trends' has a relatively moderate importance compared to the other trends. It has a smaller slice than 'Green Movements', 'Immigration Policies', and 'Freedom of Speech', but a larger slice than 'Political Campaigns'."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "46CldXAw0DXOC2zP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the provided values, what inferences can you make about the social dynamics and political trends according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of each slice reflects the relative weight or importance of each trend.\nBased on the above thought, the answer is:\nThe chart suggests that 'Freedom of Speech' was considered the most significant trend, followed by 'Immigration Policies', and 'Green Movements'. 'Technology Trends' was given somewhat less weight, and 'Political Campaigns' was seen as the least significant of the presented trends."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "mBXy5Q49e7q9zMM0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the values represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the values from each category represented by the different slices in the chart.\nBased on the above thought, the answer is:\n310"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "m4lPbj7fyVnVD94w",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the average value represented by a slice if all slices were equal?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to divide the total value of all categories by the number of categories to get the average value.\nBased on the above thought, the answer is:\n62"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "dZiXTGiRFMYy8JVX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater in value is 'Freedom of Speech' compared to 'Political Campaigns'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the value of 'Political Campaigns' from 'Freedom of Speech'.\nBased on the above thought, the answer is:\n75"
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "xhSUzLUG3LtabFOm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you extract and condense the data on 'Education and Training Tech Innovations in 2020' into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see the technology innovations represented in percentages. I will find each tech innovation and its corresponding percentage from the pie chart, color-coordinated with the legend {the legend}<r>[0.797, 0.063, 0.993, 0.249]</r>. The pie slices with their associated technologies are: {VR slice}<r>[0.223, 0.278, 0.482, 0.682]</r>, {AR slice}<r>[0.25, 0.516, 0.536, 0.886]</r>, {Blockchain slice}<r>[0.482, 0.516, 0.675, 0.878]</r>, {AI slice}<r>[0.482, 0.187, 0.741, 0.763]</r>, {Quantum Computing slice}<r>[0.482, 0.148, 0.601, 0.516]</r> and {Internet of Things slice}<r>[0.283, 0.146, 0.508, 0.516]</r>.\nBased on the above thought, the answer is:\nTechnology,Percentage\nVR,18.5\nAR,21.0\nBlockchain,10.0\nAI,29.0\nQuantum Computing,6.0\nInternet of Things,15.5"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "qFXuClFR143sLRNN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the predominant technology used in Education and Training in 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I see that the AI slice is proportionally the largest. I will confirm this by comparing the percentage of AI against each of the other categories.\nBased on the above thought, the answer is:\nAI is the predominant technology used in Education and Training in 2020 because it has the highest usage percentage of 29% among all the other categories."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "QuUBlIvZ7C8npgfz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which technology gained the least attention in 2020, and speculate why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest slice in the pie indicates the least used technology. I'll check each slice and see which one is the smallest.\nBased on the above thought, the answer is:\nQuantum Computing technology gained the least attention in 2020, illustrating only 6%. Possibly, it may be because Quantum Computing is a relatively new and difficult to understand field, requiring more complex infrastructure compared to the other technologies."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "kuENg1QTD6CVLj7C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the impact of the use of AI on the distribution of Education and Training tech innovations in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to examine the pie chart and consider how the percentage of AI affects the overall picture, and how it changes the proportions when compared to other sections.\nBased on the above thought, the answer is:\nThe significant 29% usage of AI in 2020 contributes substantially to the distribution, shrinking the relative proportions of other technologies. This indicates that AI has become a key player and a driving force within Education and Training tech innovations, and suggests that resources and attention may be progressively moving towards AI-powered innovations."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "9OQpveqDVWsNX2qU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages of Blockchain, Quantum Computing, and Internet of Things in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I'll have to fetch the percentages for the categories of Blockchain, Quantum Computing, and Internet of Things from their respective pie slices and add them.\nBased on the above thought, the answer is:\n31.5"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "f2e1XApy63p8Cmd3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentages between AI and the second most used technology in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the second most used technology, then subtract its percentage from AI's 29%.\nBased on the above thought, the answer is:\n8.0"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "12Dx8iECyEzbzEMT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent did the use of AI exceed the use of VR in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in the use of AI and VR, I need to subtract the percentage of VR from AI.\nBased on the above thought, the answer is:\n10.5"
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "rfZyPiseWdUinplS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you help to format the investment data from 2016 to 2019 into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the pie chart contains the investment data for each year from 2016 to 2019. The {legend}<r>[0.908, 0.063, 0.993, 0.189]</r> indicates the representative color for each year, and the corresponding percentages for each year can be found in the respective pie slices  {2016 slice}<r>[0.223, 0.278, 0.482, 0.736]</r>, {2017 slice}<r>[0.273, 0.516, 0.655, 0.886]</r>, {2018 slice}<r>[0.482, 0.435, 0.741, 0.791]</r>, and {2019 slice}<r>[0.283, 0.146, 0.734, 0.516]</r>.\nBased on the above thought, the answer is:\nYear,Investment in Billions\n2016,120\n2017,150\n2018,95\n2019,200"
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "sGYL8cug0dQfLtTs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year saw the most investment in new energy and clean technology development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of each pie slice representing the years in the {legend}<r>[0.908, 0.063, 0.993, 0.189]</r>, I can determine which year had the greatest investment.\nBased on the above thought, the answer is:\nThe year 2019 saw the most investment in new energy and clean technology development."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "c4Rp8eCSw9W38W8v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, is there a consistent trend in the investment in new energy and clean technology development from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the investment for each year using their respective slices in the pie chart.\nBased on the above thought, the answer is:\nThere is no consistent trend in the investment from 2016 to 2019. While there was a general increase in investment from 2016 to 2017 and from 2018 to 2019, there was a decrease from 2017 to 2018."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "ysIPmeMfKF1j8LO9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the change in investment from 2018 to 2019 reflect on the global focus toward new energy and clean technology development?"
+            },
+            {
+                "from": "gpt",
+                "value": "The change in investment from {2018 slice}<r>[0.482, 0.435, 0.741, 0.791]</r> to  {2019 slice}<r>[0.283, 0.146, 0.734, 0.516]</r> can provide insights into changes in global focus toward the sector.\nBased on the above thought, the answer is:\nThe significant increase in the investment in 2019 indicates a stronger global focus toward new energy and clean technology development compared to previous years. This could suggest that more resources are being dedicated to this field."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "m1SFcViJcIg6mUy4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total investment from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total investment, I would add up the investments from each year based on the data represented in the pie slices  {2016 slice}<r>[0.223, 0.278, 0.482, 0.736]</r>, {2017 slice}<r>[0.273, 0.516, 0.655, 0.886]</r>, {2018 slice}<r>[0.482, 0.435, 0.741, 0.791]</r>, and {2019 slice}<r>[0.283, 0.146, 0.734, 0.516]</r>.\nBased on the above thought, the answer is:\nThe total investment from 2016 to 2019 is $565 billion."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "BzbUBAWtI9086kNz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in investments between the year with the highest investment and the year with the lowest investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the investments from each year based on the data shown in the pie slices, I can find the difference between the highest and the lowest investments.\nBased on the above thought, the answer is:\nThe difference in investments between the highest and lowest year is $105 billion."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "Tftk0iIlIXi5aiv3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average investment per year from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average investment per year, I would calculate the total investment from 2016 to 2019, then divide it by the number of years.\nBased on the above thought, the answer is:\nThe average investment per year from 2016 to 2019 is $141.25 billion."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "cKJ9jTd10RYobW23",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tabulate the chart data for the 'Culture and Arts Industry Development' in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title of the chart at {\"Culture and Arts Industry Development\"}[0.318, 0.021, 0.645, 0.047], the pie chart slices depict the growth percentages for various categories in the Culture and Arts Industry. These categories are represented in the chart legend {\"sublegend\"}[0.838, 0.063, 0.993, 0.338], and the indicated percentages within the chart {\"Music\"-17%}[0.161, 0.439, 0.2, 0.459], {\"Painting\"-11%}[0.211, 0.773, 0.267, 0.793], {\"Classic Dance\"-10%}[0.33, 0.905, 0.426, 0.925], {\"Drama\"-23%}[0.694, 0.777, 0.741, 0.797], {\"Sculpture\"-16%}[0.732, 0.312, 0.798, 0.332], {\"Photography\"-4%}[0.605, 0.139, 0.694, 0.159], {\"Cinematic Film\"-14%}[0.345, 0.102, 0.448, 0.122], {\"Literature\"-3%}[0.24, 0.183, 0.309, 0.203], and {\"Fashion Design\"-2%}[0.171, 0.225, 0.275, 0.245].\nBased on the above thought, the answer is:\nRole, Business Growth(%)\nMusic, 17\nPainting, 11\nClassic Dance, 10\nDrama, 23\nSculpture, 16\nPhotography, 4\nCinematic Film, 14\nLiterature, 3\nFashion Design, 2"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "wsUnkPyV2pDtZO3w",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which role had the greatest growth in the Culture and Arts Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to identify the slice in the pie chart representing the role with the greatest growth. The role with the greatest growth is easily distinguished due to its relatively larger slice size.\nBased on the above thought, the answer is:\nThe role that had the greatest growth in the Culture and Arts Industry is Drama, with a growth percentage of 23%."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "vdN7mCCfvyuKpijI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the development of different roles in the Culture and Arts Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To give a conclusion about the development of different roles in the Culture and Arts Industry, I have to analyze the relative sizes of the pie slices for each role.\nBased on the above thought, the answer is:\nThe chart suggests that dynamic and engaging forms of art like Drama and Music have seen more growth as compared to static forms like Literature and Fashion Design."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "N2UHZRlOtUwrDM8q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence decisions on funding allocations for different roles in the Culture and Arts Industry for the coming year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of each slice in the pie chart indicates the growth percentages for each role. Fund allocation might look to support roles that are already growing, or possibly boost roles that have seen lower growth.\nBased on the above thought, the answer is:\nThe funding for the coming year might prioritize the high growth areas of Drama and Music to expand on their popularity. Conversely, roles such as Literature and Fashion Design that have shown the least growth might receive additional funding with the hope of boosting interest and growth in those areas."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "9BLtRksimi5Ijkm2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the growth percentages for the roles that have the smallest and largest proportions in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the growth percentages for the roles with the smallest and largest proportions, I look at the pie chart and identify the smallest slice {\"Fashion Design\"-2%}[0.171, 0.225, 0.275, 0.245] and the largest slice {\"Drama\"-23%}[0.694, 0.777, 0.741, 0.797]. I then compute the sum of their proportions.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "EBib2kGxUNo0D5IM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category with the smallest proportion doubles its business growth percentage, what would be the new total percentage of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the category with the smallest proportion, which is {\"Fashion Design\"-2%}[0.171, 0.225, 0.275, 0.245], doubles its business growth percentage, it would effectually add 2% to the total business growth percentage.\nBased on the above thought, the answer is:\n102%"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "jJ3oR2VQTymZlO78",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion difference between the highest and lowest growth categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion difference between the highest and lowest growth categories is calculated by subtracting the growth percentage of {\"Fashion Design\"-2%}[0.171, 0.225, 0.275, 0.245] from that of {\"Drama\"-23%}[0.694, 0.777, 0.741, 0.797].\nBased on the above thought, the answer is:\n21%"
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "T1NZA7adSn5q5Rty",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy examining the pie charts, can you outline the percentage distribution of studies in the fields of Genetic Research and Robotics, from the years 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart displays corresponding percentages for the conducted studies over years 2015 to 2019 in Genetic Research and Robotics. Each year's distribution is represented by a different color in the legend {the legend}<r>[0.908, 0.063, 0.993, 0.368]</r>. The slices represent the proportional distribution of studies for each year. By inspecting the autotext on each slice, I can find the specific percentage for each year.\nBased on the above thought, the answer is:\nYear,Genetic Research,Robotics\n2015,10.5%,3.3%\n2016,12.0%,4.2%\n2017,14.2%,6.3%\n2018,15.1%,8.1%\n2019,15.7%,10.5%"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "6bfohnV0tOgZouPy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you observe in the pie charts from 2015 to 2019 for the field of Robotics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the trend, I would analyze the pie slices corresponding to the Robotics category over the years from 2015 to 2019.\nBased on the above thought, the answer is:\nThe trend shows that the portion dedicated to Robotics has grown each year, indicating that there is an increasing trend in the number of studies performed in Robotics."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "RV4nXOyO2K835LKd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, which category had a more rapid growth rate from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the growth of pie sizes from 2015 to 2019 for both Genetic Research and Robotics.\nBased on the above thought, the answer is:\nAlthough Genetic Research dominated, Robotics had a more rapid growth rate. The percentage of Robotics studies nearly tripled from 2015 to 2019, while Genetic Research grew at a steadier pace."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "acA2mN5NIh11kcCi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data shown in the pie charts, what can you discern about the focus on technological research in the mentioned 5-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at both Genetic Research and Robotics\u2019 pie slices over the 5 years.\nBased on the above thought, the answer is:\nOver the 5-year period, there's been a clear emphasis on both Genetic Research and Robotics. Genetic research has consistently seen the heaviest focus, but a significant shift can be seen towards Robotics, which nearly tripled its share of studies conducted."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "xDg9RzzYTyFlC3W8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of Genetic Research studies done in the years 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage of Genetic Research studies done in 2015 and 2016, I should add the percentages from the slices representing these years {the slice of 2015 in Genetic Research}<r>[0.223, 0.278, 0.482, 0.516]<r> {the slice of 2016 in Genetic Research}<r>[0.223, 0.503, 0.482, 0.76]<r>.\nBased on the above thought, the answer is:\n22.5%"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "3e5qie4UmFRjIr1h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the percentage of Robotics studies in 2019 higher than that of Genetic Research in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the relevant percentages from the slices representing 2019 for Robotics and 2015 for Genetic Research {the slice of 2019 in Robotics}<r>[0.283, 0.154, 0.482, 0.516]<r> {the slice of 2015 in Genetic Research}<r>[0.223, 0.278, 0.482, 0.516]<r>.\nBased on the above thought, the answer is:\nYes"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "0ORTsVvmPZxDTf6P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the 5-year period, what was the total percentage difference between Genetic Research and Robotics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage difference, I will subtract the total percentage of Robotics studies from the total percentage of Genetic Research studies over the 5 years.\nBased on the above thought, the answer is:\n28.8%"
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "3vA2FEVZjrFH8JHC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the data of the development scale of different eras into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Upon examining the plot and relevant data, I understand that the pie chart represents the progression of Human Civilization. Each slice and its size or proportion are indicative of the level of development during each era from {the legend}<r>[0.846, 0.063, 0.993, 0.279]</r>, specifically:{\"Stone Age\"}<r>[0.891, 0.071, 0.962, 0.091]</r>; {\"Bronze Age\"}<r>[0.891, 0.101, 0.97, 0.121]</r>; {\"Iron Age\"}<r>[0.891, 0.131, 0.949, 0.151]</r>; {\"Medieval Age\"}<r>[0.891, 0.161, 0.985, 0.181]</r>; {\"Renaissance\"}<r>[0.891, 0.191, 0.977, 0.211]</r>; {\"Industrial Age\"}<r>[0.891, 0.221, 0.987, 0.241]</r>; {\"Digital Age\"}<r>[0.891, 0.251, 0.968, 0.271]</r>. Therefore, the data of different eras and their associated development scale can be organized in a table structure.\nBased on the above thought, the answer is:\nEra,Development Scale\nStone Age,10\nBronze Age,20\nIron Age,30\nMedieval Age,40\nRenaissance,80\nIndustrial Age,70\nDigital Age,100"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "jeN00oHlf9DF6MCK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which era shows the most significant development in the civilization? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The era with the maximum portion of the pie chart indicating the highest development scale would be considered the era with the most significant development.\nBased on the above thought, the answer is:\nThe \"Digital Age\" has the most significant development in civilization, as it occupies 28.6% of the chart, the largest portion among all the eras."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "dHrNrSgoKNZuHrKu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this chart translate about the overall progression of human civilization throughout different eras?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's pie slices would inform about the progression through their deviation in size.\nBased on the above thought, the answer is:\nThe chart indicates a general upward trend in the development of human civilization from the \"Stone Age\" to the \"Digital Age.\" The substantial expansion starting from the \"Renaissance\" period suggests a breakthrough in human civilization."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "Fv3qdwGILwYSswbt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer anything about the progression rate of human civilization between the Stone Age and the Digital Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the change in slice sizes, I can understand the growth rate.\nBased on the above thought, the answer is:\nThe progression of human civilization has evidently accelerated from the Stone Age to the Digital Age. The period between the \"Renaissance\" and the \"Digital Age\" clearly indicates a significant leap, implying rapid development in these eras."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "k8VnaINoFXvIaNoZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total civilization development scale for all eras according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total civilization development scale, each era's development scale would need to be added as per the raw data.\nBased on the above thought, the answer is:\n360"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "0ciugpT53VuR9vl2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average development scale across all eras?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, the total development scale should be divided by the number of eras, which is 7.\nBased on the above thought, the answer is:\nApproximately 51"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "ZZ7T0Spq50XZZDEM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the development scale of the Stone Age were increased by 5 units, what would be the new sum of scales?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new sum of scales would be calculated by adding the increased Stone Age scale to the previous total.\nBased on the above thought, the answer is:\n365"
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "wJ4Nw50jaY1c5uZv",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you've analyzed, can you convert the given data into a table with CSV format representing the distribution of patents in AI & Robotics and Bio-Technologies from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can construct a CSV formatted table from the pie chart by noting the sectors represented by {AI & Robotics}<r>[0.833, 0.073, 0.861, 0.087]</r> and {Bio-Technologies}<r>[0.833, 0.103, 0.861, 0.117]</r>. These sectors correspond to the patents distributed from 2016 through to 2020.\nBased on the above thought, the answer is:\nYear,AI & Robotics,Bio-Technologies\n2016,300,250\n2017,320,275\n2018,390,310\n2019,425,355\n2020,480,400"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "VQEnXG3y7lw7UGcw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry showed a greater increase in patent registrations from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the increase in patent registrations from 2016 to 2020 between AI & Robotics and Bio-Technologies.\nBased on the above thought, the answer is:\nThe AI & Robotics industry showed a greater increase in patent registrations from 2016 to 2020."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "T1aiX7jcYVht8qYb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the progression of these two industries in terms of innovation from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analysing the number of patents registered over the years, one can make inferences about the progression of these industries.\nBased on the above thought, the answer is:\nBoth the AI & Robotics and Bio-Technologies industries have been progressively innovating over the years, as indicated by the consistent increase in the number of patents registered. However, the AI & Robotics industry has been slightly more progressive, registering more patents each year."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "mvATlgtHJiUNlm5i",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the information on the chart potentially influence future funding decisions in these industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict potential influence on future funding decisions, it's necessary to assess the trend and magnitude of patent registrations, which are indicators of innovation and potential for growth, in these industries.\nBased on the above thought, the answer is:\nThe consistently increasing trend in patent registrations in both industries suggests a healthy pace of innovation, which may encourage investors and funding bodies to maintain or increase their investments. In particular, the higher number of patents registered in the AI & Robotics industry might suggest it as a more attractive area for investment."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "YeVSOBia0740TYoo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many patents were registered in total in the year 2017 for both the AI & Robotics and Bio-Technologies industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add up the number of patents registered in 2017 for both AI & Robotics and Bio-Technologies. This information can be obtained from the table provided.\nBased on the above thought, the answer is:\n595"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "R50iCHYHihFtVwi1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the number of patents registered in AI&Robotics increase from 2016 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the number of patents, I need to subtract the number of patents in 2016 from the number in 2018 for the AI & Robotics industry.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "gkL88OqfbOw9H38e",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of patents registered in the Bio-Technologies industry over the years 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll add up the number of patents registered each year from 2016 to 2020 for the Bio-Technologies industry.\nBased on the above thought, the answer is:\n1590"
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "9MI3ILxdBsRhewrn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the expenditure for different social services mentioned in the pie chart to a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie slices {Education, Healthcare, Social Security, Child Welfare, Public Transport}<r>[0.282,0.255,0.718,0.774]</r> I see that there are 5 categories of Government expenditure. From the chart, I can estimate how much government spent on each service.\nBased on the above thought, the answer is:\nServices,Expenditure\nEducation,7000\nHealthcare,9500\nSocial Security,8600\nChild Welfare,4300\nPublic Transport,5500"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "rakRGbmtndzzbQdy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the depicted budget allocation, which area seems to be a priority for the government?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the sectors of the pie chart, we can identify which area received the most funding.\nBased on the above thought, the answer is:\nHealthcare appears to be a priority for the government as it has the largest share of the budget allocation."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "HLBH3ADrfWi071he",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the current expenditure distribution, what recommendations can be made for future budget allocations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recommendations can be made by analyzing the current distribution and identifying areas for increased investment or budgetary reallocation.\nBased on the above thought, the answer is:\nGiven that Child Welfare has the least budgetary allocation, one recommendation would be to increase its funding in order to enhance support for children's services. Also, considering the importance of Public Transport for community mobility, an increase in its budget allocation could be considered."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "y7aVUJBVw7ZRyDpk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, how does the government seem to value public services?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value placed on public services can be inferred by examining the relative proportions allocated to each category.\nBased on the above thought, the answer is:\nFrom the pie chart, it's clear that the government values Healthcare and Education highly, as evidenced by their substantial portions of the budget. Social Security also receives considerable attention. However, Public Transport and Child Welfare get relatively lower allocations, indicating that they might be areas of lower priority."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "FUblk2Q5sAMcQRuJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what is the total government expenditure across all services, as indicated by the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total expenditure is calculated by adding the individual expenditures for each category: Education, Healthcare, Social Security, Child Welfare, and Public Transport.\nBased on the above thought, the answer is:\n34900"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "tNBbGeRbQOVRuloV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total budget was allocated to services related to children and family (Education and Child Welfare)?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to calculate the combined expenditure on Education and Child Welfare, and then determine what percentage this represents of the total expenditure.\nBased on the above thought, the answer is:\n32.4%"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "jrtP8eMFhAal2iJF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the government decides to increase the allocation for Public Transport by 10%, what will be its new budgetary allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate a 10% increase in public transport, I should take 10% of the current allocation and add that to the existing allocation.\nBased on the above thought, the answer is:\n6050"
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "i7Fs7pRCBA3wy1xA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the data from the pie chart about user distribution on social media platforms into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend situated in {the chart\u2019s upper right corner}<r>[0.872, 0.063, 0.993, 0.189]</r>, we have four social media platforms. Based on the provided {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and the percentages indicated for each platform, I can then arrange the data as per the requirements.\nBased on the above thought, the answer is:\nSocial Network,Number of Users (in millions)\nFacebook,60.8%\nInstagram,23.9%\nTwitter,7.9%\nLinkedIn,7.4%"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "5mUMu0oCcEW2MvHG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the user distribution, which social media platform can be considered as the most widely used?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the {social media platform with the largest portion}<r>[0.223, 0.278, 0.741, 0.886]</r> represented on the pie chart to determine which is the most used.\nBased on the above thought, the answer is:\nFacebook is considered the most widely used platform as it accounts for the biggest proportion - 60.8%, according to the chart."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "RKzG9sZAGJLRsB4E",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data in the pie chart tell us about the usage trend of social media platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would infer this by analyzing the percentages of each {slice in the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe data suggests that Facebook significantly dominates the social network scene, almost tripling the user base of the next significant platform, which is Instagram. Meanwhile, Twitter and LinkedIn seem to hold smaller, quite equal portions of the user base."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "pTa1sAAhEgwTPr5D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in this pie chart impact the advertising budget decisions of a company across these four platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "The advertising budgeting might depend on the {user base analytics provided by the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nA company might choose to allocate a large proportion of its advertising budget to Facebook and Instagram as they possess a larger user base. However, the smaller platforms shouldn't be overlooked, especially if the company's target audience primarily uses Twitter and LinkedIn."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "kLDWfiyZXpU2tq8a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social networking site has the fewest users as per the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slices of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> and their percentages, the one with the smallest size and thus the smallest number of users can be determined.\nBased on the above thought, the answer is:\nLinkedIn"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "3qFXjN7LAgpAI61r",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the collective user percentage of Twitter and LinkedIn according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the {areas attributed to Twitter}<r>[0.379, 0.146, 0.505, 0.516]</r> and {LinkedIn on the pie chart}<r>[0.283, 0.176, 0.482, 0.516]</r>, I can sum up their respective percentages.\nBased on the above thought, the answer is:\n15.3%"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "Xey4w9YsMkp5Sug7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf LinkedIn experienced an increase in users by 5%, what would be the new total users\u2019 percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine this, I would have to take the {initial percentage of LinkedIn users}<r>[0.283, 0.176, 0.482, 0.516]</r>, add the increased percentage and sum that with the initial percentages of all other platforms.\nBased on the above thought, the answer is:\nThe total would now amount to 105%"
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "8PvSjphPfwkIABop",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart can indeed be converted to a CSV format. The title {\"Cultural and Value Changes from 2015 to 2020\"}<r>[0.286, 0.021, 0.677, 0.047]</r> indicates that the chart represents data from the year 2015 till 2020. The sectors from 2015 to 2020 in the legend<r>[0.908, 0.063, 0.993, 0.249]</r> represent the societal cultural and value changes for each year. By looking at each sector and label, the specific values can be determined.\nBased on the above thought, the answer is:\nYear,Changes\n2015,12.7%\n2016,14.9%\n2017,16.2%\n2018,17.5%\n2019,18.2%\n2020,20.6%"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "jt3i7wRUKaFEdtMO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the most significant societal cultural and value changes? Why do you infer that?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the year with the most significant societal cultural changes by identifying the sector with the highest percentage in the pie chart.\nBased on the above thought, the answer is:\nThe year 2020 had the most significant cultural and value changes. This is inferred from its larger pie slice representing 20.6% of the pie chart, which is the highest percentage among all years."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "xRodWioxjcfJe3cL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat overall trend can you infer from the pie chart about cultural and value changes from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the overall trend, I need to examine the cumulative changes in cultural and societal values depicted in the pie chart over the specified years.\nBased on the above thought, the answer is:\nThe pie chart shows an overall increasing trend in societal cultural and value changes from 2015 to 2020. A steady increment can be observed in the societal value changes each year, with the year 2020 marked as having the most significant change."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "t45APUfCul19gRiJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how could potential policies for societal cultural and value changes be made for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policies for 2021 could be influenced by the trends observed in the pie chart. The significant increase in societal cultural and value changes in 2020 suggests a need for adaptations.\nBased on the above thought, the answer is:\nGiven the notable growth in societal values and cultural changes in 2020, policies for 2021 may want to support and foster this increasing cultural shift. However, it would also be critical to investigate the cause of this significant jump from 2020 to predict future trends."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "Ofi5FLgC3UUT9VAd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percent increase in cultural and value changes from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percent increase, I can subtract the percent values of \"cultural and value changes\" of 2018 from that of 2020. It shows that the percentage of cultural and value changes in 2018 and 2020 are represented by the values 17.5% and 20.6% respectively.\nBased on the above thought, the answer is:\n3.1%"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "1Ou0psFgiSfAlUnD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage change from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total change in percentage can be found by adding all the percentages from the years 2015 to 2020 noted from each sector in the pie chart.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "ikehd95wJJODDZ36",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the year 2020 had been eliminated, what would be the average percentage across the remaining years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, we would need to add up the percentages for the years 2015 to 2019 and divide by the total number of these years.\nBased on the above thought, the answer is:\n15.9%"
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "cG2UNjfaVh8msdgn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie-chart, can you derive the data for 'Electronics' sales for each year and represent it as a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is divided into segments which represent the sales of 'Electronics' based on the color {#ff9999}<r>[0.873, 0.073, 0.901, 0.087]</r> in the legend. Accordingly, each segment corresponds to a different year starting from 2014 to 2020.\nBased on the above thought, the answer is:\nYear,Electronics Sales\n2014,400\n2015,565\n2016,680\n2017,750\n2018,850\n2019,1300\n2020,3000"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "qQWPrtRPyLJKnTtf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the trend in 'Electronics' sales based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this analysis, I need to examine the change in the size of the sales sector for the 'Electronics' category from year to year.\nBased on the above thought, the answer is:\nThe sales of 'Electronics' have seen a consistent trend of increase from the year 2014 to 2020. It's noticeable that the sales made a significant jump in the year 2020."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Z2sFWeZTDeoXIyLI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how did the 'Electronics' sales perform in 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide this information, I need to examine the size of the sales sector for the 'Electronics' category in 2019 and 2020.\nBased on the above thought, the answer is:\nThe 'Electronics' sales saw a significant rise in 2019 and experienced an extreme growth in 2020 compared to all the previous years."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Z5Mnq6LUMH3fvNyZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the size of the 'Electronics' sales sector signify about its performance in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand this, I need to compare the size of the 'Electronics' sales sector in 2020 compared to other years.\nBased on the above thought, the answer is:\nThe size of the 'Electronics' sales in 2020 signifies that the sales for this category has dramatically increased in that year compared to previous years."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "816o4z4xkMFl3Xxh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the 'Electronics' category experience the highest rate of increase in sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the highest rate of increase, I need to calculate the difference in sales from year to year in the 'Electronics' category.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "oHmfLrtLbYurwsv8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales for the 'Electronics' category from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total sales, I would sum up the sales of the 'Electronics' category from 2014 to 2020.\nBased on the above thought, the answer is:\n7645"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "Tlmx4upfYnrAC1ZT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average sales of 'Electronics' from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I sum up the sales from 2014 to 2020 and then divide by the number of years.\nBased on the above thought, the answer is:\nApproximately 1092"
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "08rQc4F53xvxkpqt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart you observe, can you convert the length of the activism period and the percentage of activism for each year into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the division of the pie chart {pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r> into different slices corresponding to each year. Each slice has a percentage value and a corresponding year {the slices}[{\"2016\"}[0.223, 0.278, 0.482, 0.605]; {\"2017\"}[0.23, 0.516, 0.482, 0.872]; {\"2018\"}[0.41, 0.516, 0.696, 0.886]; {\"2019\"}[0.482, 0.248, 0.741, 0.723]; {\"2020\"}[0.283, 0.146, 0.66, 0.516]]. Each slice and its corresponding percentage can be transcribed into a table.\nBased on the above thought, the answer is:\nYear,Percentage of Activism\n2016,15.0%\n2017,16.7%\n2018,20.0%\n2019,22.3%\n2020,26.0%"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "jga4Nob8xc4NGn6K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you detect a general trend in environmental and social activism during the period covered?"
+            },
+            {
+                "from": "gpt",
+                "value": "It seems I need to review the percentages for each year in the context provided by the pie chart.\nBased on the above thought, the answer is:\nYes, the general trend in environmental and social activism between 2016 and 2020 is increasing. This is evident from the increase in percentage values in the pie chart over the years."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "pjjwqdL8eYC1huPh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the possible repercussions of this rising trend of environmental and social activism on society?"
+            },
+            {
+                "from": "gpt",
+                "value": "This question involves reasoning based on the increasing rate of activism from the chart and its inferred impact on society.\nBased on the above thought, the answer is:\nThe rising trend of environmental and social activism suggests an increasing societal awareness and engagement with these issues. This could lead to policies changes, increased focus on sustainable practices, and possibly a shift in societal norms towards more eco-friendly and socially just behaviors."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "eTLkJrBwGSDaphyL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you predict any potential changes or continuance in this trend of environmental and social activism for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to understand the context and current trend of the given data to make accurate predictions for the future.\nBased on the above thought, the answer is:\nAlthough I can't predict exact percentages for 2021 without data, if the increasing trend of environmental and social activism from 2016 to 2020 continues, it's likely that activism would also increase in 2021. However, this is dependent on various factors and would need to be confirmed with actual data."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "7jlmkgkooQFxoZaC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the rate of environmental and social activism from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase, I would start from the percentage in 2016 and subtract it from the percentage in 2020 in the pie chart I am seeing.\nBased on the above thought, the answer is:\nTotal increase is 26.0% (2020) - 15.0% (2016) = 11.0%"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "3SEVe7UCdUR7aQ1T",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage change between 2018 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change, I would subtract the percentage in 2018 from the percentage in 2019 from the pie chart.\nBased on the above thought, the answer is:\nChange is 22.3% (2019) - 20.0% (2018) = 2.3%"
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "VUd7BTpDkddg9KHC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn how many years is the rate of activism greater than 20%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to count the number of years in the pie chart where the rate is greater than 20%.\nBased on the above thought, the answer is:\nThere are 3 years (2018, 2019, 2020)."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "YD2IuSoMYKmoq6Os",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, can you tabulate the data into a structured format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the chart is showing the distribution of the students' preferences across various study areas in a school curriculum under the title {Student Preferences Across Study Areas}<r>[0.315, 0.021, 0.648, 0.047]</r>. The legend marked at {coordinates}<r>[0.816, 0.063, 0.993, 0.309]</r> shows the name of the subjects along with the color representation of each in the chart. Each of the pies aligns with the subject and its percentage. Starting from 'Math' {at coordinates}<r>[0.821, 0.073, 0.849, 0.087]</r>, registering a 15% preference and ending at 'Computer Science' {at coordinates}<r>[0.821, 0.282, 0.849, 0.296]</r> which is the least preferred subject at 5%.\nBased on the above thought, the answer is:\nStudy Area, Percentage\nMath, 15%\nScience, 20%\nHistory, 10%\nGeography,12%\nArt, 8%\nMusic, 25%\nPE, 5%\nComputer Science, 5%"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "AucOC0QQgOorvtl4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subject do the students find the least interesting, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the least interesting subject, I have to find the subject area with the lowest percentage on the pie chart. The chart suggests that \"PE\" and \"Computer Science\" each have a 5% preference, which is the smallest among all the subjects.\nBased on the above thought, the answer is:\nPhysical Education (PE) and Computer Science are the least interesting subjects among the students according to the chart."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "abSFnIaqE0DgZoQF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the preference for Sciences among the students from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can observe how much students prefer Science-related subjects by noting the proportion attributed to \"Science\" and \"Computer Science\". Science has a 20% preference, indicating a relatively likable subject among the students. However, Computer Science, despite also being a scientific subject, doesn't appear to be as popular with only a 5% preference.\nBased on the above thought, the answer is:\nStudents have shown a significant interest in the Science subject. However, Computer Science doesn't share the same popularity among the students."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "o4hOmAdU1T4GEf8C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow the preferences reflected in this chart might influence curriculum designing for the next academic year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The preferences shown in the pie chart can provide valuable inputs when planning the curriculum for the next academic year. The subjects with higher preferences such as \"Music\", \"Science\", and \"Math\" should continue to have a strong emphasis whereas for \"PE\" and \"Computer Science\", the curriculum designers could look at ways to improve the engagement and appeal of these subjects to increase their popularity given their current low preference. The overall aim should be to provide a balanced educational experience that caters to the diverse interests of students.\nBased on the above thought, the answer is:\nCurriculum planning could increase emphasis on popular subjects such as \"Music\", \"Science\", and \"Math\". Simultaneously, innovative strategies could be developed to enhance the appeal and learning experience of less popular subjects like \"PE\" and \"Computer Science\" with the goal of increasing student interest and involvement."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "0Kiui7fia7RgjHBb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the overall percentage of the three least preferred subjects?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart from the marks at {Art}[0.619, 0.508, 0.655, 0.528], {PE}[0.403, 0.302, 0.438, 0.322], and {Computer Science}[0.362, 0.338, 0.397, 0.358], I can determine the percentages of the least preferred subjects which are 8%, 5%, and 5% respectively. So the step is to add all these percentages.\nBased on the above thought, the answer is:\n18%"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "eckGjJ3SuSCw7eKs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two subjects the total preference percentage equals to the preference of \"Music\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find two subjects whose total weight equals 25%. From my analysis, I can see \"Math\" with 15% and \"Art\" with 10% are the two subjects that meet the criteria.\nBased on the above thought, the answer is:\nMath and Art"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "nihiHzesr4aTHjwc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf \"Math\", \"Science\", and \"History\" combinedly got 50% preference, then what is the overall percentage preference of the remaining subjects?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, the overall preference ratio is 100%. Here, \"Math\", \"Science\", and \"History\" combinedly got 50% preference. So, the overall preference of the remaining subjects would be the total minus these three subjects' combined preferences.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "yqfFuPKU5NownUpn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a table represention of the market share data for each product family from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart {'Market Competition Analysis and Comparison'} is positioned at the top [0.292, 0.021, 0.671, 0.047]. Each product family is presented as a slice of the pie chart with their labels {'Product A'},{'Product B'},{'Product C'},{'Product D'},{'Product E'},{'Product F'},{'Product G'},{'Product H'} and corresponding percentage of market share located in the pie slices. The pie slices are in the coordinates [0.223, 0.146, 0.741, 0.886].\nBased on the above thought, the answer is:\nProduct Family,Market Share\nProduct A,15.0%\nProduct B,25.0%\nProduct C,10.0%\nProduct D,5.0%\nProduct E,15.0%\nProduct F,8.0%\nProduct G,12.0%\nProduct H,10.0%"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "FmzsLT5QNzhCXi3O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the market competition among the product families based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at each slice of the pie representing the product families in the coordinate [0.223, 0.146, 0.741, 0.886], the relative sizes of the slices should be compared.\nBased on the above thought, the answer is:\nThe market is highly competitive, with the Product B having the highest market share. Other product families such as Product A, E, and G also have significant market presence. However, some products like Product D have a small market share, which indicates they are less competitive in the market."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "Ng2sWl8mz7BjgYC9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the chart, how do you think the company should strategize in terms of their product offerings?"
+            },
+            {
+                "from": "gpt",
+                "value": "We would examine the market shares of each product as given in the pie slices in the coordinate [0.223, 0.146, 0.741, 0.886].\nBased on the above thought, the answer is:\nThe company may consider focusing more on improving and marketing Product B as it already has a dominant presence in the market. For Product A and Product E, which have a moderate hold on the market, the company might need to stabilize and perhaps augment their strategies. As for the other products, especially Product D which lags behind all, a thorough reinvestigation and reshuffling of strategy is needed."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "reztagIvo6j5ZJxV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow well is Product G performing in the market as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I would need to assess the slice corresponding to Product G<r>[0.419, 0.146, 0.608, 0.516]</r>and compare it to the other slices.\nBased on the above thought, the answer is:\nProduct G has a moderate hold on the market with a 12% market share. It is performing better than Product C, F, H, and D but its market share is less than Product B, A, and E. Therefore, while it does have a good presence, it isn't amongst the top performers."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "2kMY3sF1lEL6IWYM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share for Product B and Product H?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I identify the slices of the pie for the Product B<r>[0.23, 0.516, 0.544, 0.886]</r> and Product H<r>[0.283, 0.157, 0.482, 0.516]</r> and add their indicated percentages together.\nBased on the above thought, the answer is:\n35.0%"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "nL3PxOJj3tRzOJCi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points does the market share of Product F need to increase to match Product E?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices of Product F<r>[0.482, 0.193, 0.701, 0.516]</r> and Product E<r>[0.482, 0.32, 0.741, 0.655]</r> should be examined and then subtract the percentage of Product F from Product E.\nBased on the above thought, the answer is:\n7.0%"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "gE7PN9wrUgtR8VpQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share among these products?"
+            },
+            {
+                "from": "gpt",
+                "value": "Averaging the shares for each product slice on the Pie Chart<r>[0.223, 0.146, 0.741, 0.886]<r> would provide the answer. We add all market shares and then divide by the number of products, which is 8.\nBased on the above thought, the answer is:\n12.5%"
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "mKJyuRZwdNrDuYcQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given pie charts, could you please arrange the data for both \"Social Welfare\" and \"Public Services\" resource allocation from 2011 to 2020 into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each pie chart represents the resource allocation for \"Social Welfare\" and \"Public Services\" respectively from 2011 to 2020. Each slice of the pie represents the resource allocation for each year, and the color of each slice indicates the year. By matching these slices with the labels and colors in the legend {legend}[0.935, 0.044, 0.992, 0.467], I can extract the accurate values for each year.\nBased on the above thought, the answer is:\nYear,Social Welfare,Public Services\n2011,10,20\n2012,50,30\n2013,70,40\n2014,40,70\n2015,90,50\n2016,20,60\n2017,10,90\n2018,50,80\n2019,40,70\n2020,70,60"
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "9ilAxZNNNtkMUSPo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how has the resource allocation for Social Welfare changed over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the Social Welfare pie slices for each year.\nBased on the above thought, the answer is:\nThe resource allocation for Social Welfare fluctuated greatly from 2011 to 2020. The allocation started at 10% in 2011, increased significantly to 50% in 2012, and reached a peak of 90% in 2015. However, the allocation declined sharply to 20% in 2016 and then increased steadily until 2020 with some dips in 2017 and 2019."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "XJ41hpEKPMf13MJq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector received more resources on average from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to determine this by comparing the average allocation for Social Welfare and Public Services from 2011 to 2020.\nBased on the above thought, the answer is:\nThe sector Social Welfare received more resources on average from 2011 to 2020."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "p1WYOibP1AQWFq6v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to forecast the resource allocations for 2021 based on this data, what would you predict?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to extrapolate from the trends shown in the chart for both Social Welfare and Public Services.\nBased on the above thought, the answer is:\nBased on the past trends, it would be reasonable to predict that both sectors would continue to see fluctuating levels of resource allocation. It's likely that Social Welfare might experience an increase, given it's generally been on an upward trend since 2017. Similarly, Public Services might see a slight decrease following its high allocation in 2017 and subsequent decreases."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "0CFtSvCoV4V3ZiIZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the resource allocation for Social Welfare at its highest and what was the percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the slice of the Social Welfare pie chart with the highest percentage.\nBased on the above thought, the answer is:\nThe highest resource allocation for Social Welfare was in 2015 with 90%."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "HJ68NwrLwQHfpSPl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the mean allocation for Public Services from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the mean, I need to sum the resource allocations for Public Services across all years from 2011 to 2020 and divide by the number of years.\nBased on the above thought, the answer is:\nThe mean allocation for Public Services from 2011 to 2020 is 60%."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "UIwVH7ygu55Z1sB5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the resource allocation for Social Welfare in 2020 was 20% more than in 2019, what would be the new overall allocation in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total allocation, I need to add 20% to the Social Welfare allocation for 2020.\nBased on the above thought, the answer is:\nThe new resource allocation for Social Welfare in 2020 would be 90%."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "PcxMMbDd2VghuMAd",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, could you please summarize the sector-wise proportions for each year, in a structured CSV format suitable for further data processing?"
+            },
+            {
+                "from": "gpt",
+                "value": "On observing the first pie chart for {E-Commerce Logistics}[0.214, 0.062, 0.786, 0.47], and the second pie chart for {Urban Trucking}[0.214, 0.556, 0.786, 0.965], I can break down the data for each year.\nBased on the above thought, the answer is:\nYear,E-Commerce Logistics (%),Urban Trucking (%)\n2012,17.5,17.6\n2013,21.0,20.6\n2014,26.6,26.5\n2015,35.0,35.3"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "ClESJQo5hWpFp1ce",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the pie charts tell you about the development trends in the Transportation and Logistics Industry from 2012 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie charts which represent the proportional representation of E-commerce Logistics and Urban Trucking from 2012 to 2015, we can infer about the development trends.\nBased on the above thought, the answer is:\nThe pie charts illustrate a steady growth in both the E-commerce Logistics and Urban Trucking sectors of the Transportation and Logistics Industry from 2012 to 2015. Both sectors saw an increased yearly proportion implies a growing trend in these domains."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "UwSp2cKHqnKb8do5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the importance of recognizing these trends in the Transportation and Logistics Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trends could help stakeholders in the industry to understand shifts in the industry and make well-informed business decisions.\nBased on the above thought, the answer is:\nRecognizing these trends is crucial for planning business strategies, resources allocation, and future investments. By understanding which sector is trending upwards, businesses can align their services with the market demand, leading to better opportunities and higher profitability."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "jN6NOxOvGLxR4ZWv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed trends from 2012 to 2015, what could we anticipate for the Transportation and Logistics Industry in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By extrapolating from the growth in E-Commerce Logistics and Urban Trucking proportions from 2012 to 2015 represented in the pie charts, we can make predictions about future trends.\nBased on the above thought, the answer is:\nIf the trends from 2012 to 2015 continue, we could anticipate further growth in both E-commerce Logistics and Urban Trucking in the Transportation and Logistics Industry. This implies a continually growing demand for these services which could result from factors such as advances in technology, increased urbanization, and the expansion of e-commerce."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "BSulHbngfH6i1kQu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in E-commerce Logistics and Urban Trucking from 2012 to 2015 based on the sector proportions in the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I would need to find the difference between the sector proportions for each path in 2015 and 2012.\nBased on the above thought, the answer is:\nThe total increase is ((35.0% - 17.5%) for E-Commerce Logistics) + ((35.3% - 17.6%) for Urban Trucking) = 34.2%."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "3HIEhuypAjyHzbLZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did E-Commerce Logistics have the largest proportion growth from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the yearly growth rate for E-Commerce Logistics by comparing each year's proportion with the preceding year.\nBased on the above thought, the answer is:\n2015."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "T92aAhnAKw2ER8b1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year does Urban Trucking exceed 25%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to review the Urban Trucking pie chart to find a year where the sector proportion is more than 25%.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "GfuQMgm6iE2DOGkW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you convert the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Discoveries and Civilization Advancements over Time\"}[0.259, 0.021, 0.704, 0.047] and the chart composition indicate that the chart represents the progression of discoveries over time from {2000 BC}[0.928, 0.071, 0.986, 0.091] to {2000 AD}[0.928, 0.191, 0.987, 0.211] by extracting the values from the pie slices, I could form a CSV file.\nBased on the above thought, the answer is:\nEpoch,Percentage\n2000 BC,6%\n500 BC,10%\n1000 AD,16%\n1500 AD,28%\n2000 AD,40%"
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "1BJ0cZ4DPIRsg704",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which era saw the most significant advancements and discoveries? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the percentages of each slice, the era with the largest slice can be identified as the period with the most significant advancements and discoveries.\nBased on the above thought, the answer is:\nThe year 2000 AD saw the most significant advancements and discoveries, as it has the largest proportion of 40% among all epochs."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "tCdEQEoW8DTtpmCU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be hypothesized about the pattern of human civilization's progress over time from this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the size of the slices in the pie chart from the oldest epoch to the newest, I need to identify any distinct patterns.\nBased on the above thought, the answer is:\nThe pie chart suggests a pattern of increasing discoveries and advancements over time\u2014indicating accelerating progress in human civilization."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "jRbVw5fn4ZfIGum4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does this data give insight into civilization's future advancement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict future advancements, I can extrapolate the trend shown in the chart.\nBased on the above thought, the answer is:\nIf the pattern of increasing discoveries continues, we could expect an even higher proportion of significant advancements and discoveries in the future, with rapid progression of civilization."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "iScFVwDBvpgvbQY7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of discoveries in 1000 AD and 1500 AD combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add the proportions of discoveries in the years 1000 AD and 1500 AD according to their proportions in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n44%."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "hV84uY40XfUNKnlf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more discoveries were made in 2000 AD compared to 1500 AD?"
+            },
+            {
+                "from": "gpt",
+                "value": "The answer can be computed by subtracting the percentage of discoveries in 1500 AD from that in 2000 AD.\nBased on the above thought, the answer is:\n12%."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "naOypEwYY6UZj7LV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the proportion of discoveries made before the year 1500 AD."
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the percentages of discoveries from 2000 BC, 500 BC and 1000 AD, I can determine the proportion of discoveries made before 1500 AD.\nBased on the above thought, the answer is:\n32%."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "Y9XjUo170XNtSTa3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the CSV format of the data in the pie chart about Equities investment from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am analyzing the pie chart, including the segment representing various years from 2012 to 2020, such as the {year 2012}<r>[0.325, 0.407, 0.36, 0.427]</r>, the {year 2013}<r>[0.309, 0.497, 0.344, 0.517]</r> and so on. Each slice of the pie represents the percentage of Equities investment for the respective years. I can convert these percentages into a CSV format table.\nBased on the above thought, the answer is:\nYear,Equities Investment\n2012,7.6%\n2013,5.8%\n2014,11.4%\n2015,13.0%\n2016,10.0%\n2017,12.7%\n2018,13.6%\n2019,11.5%\n2020,14.4%"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "xcCK7TVYofRNACPr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year was the Equities investment highest and why do you think that might be so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the year with the highest investment by comparing the size of each slice, such as the pie slices for {2012}<r>[0.325, 0.407, 0.36, 0.427]</r> and {2020}<r>[0.396, 0.303, 0.44, 0.323]</r> among others. The year with the largest slice indicates the highest investment.\nBased on the above thought, the answer is:\nThe Equities investment was highest in 2020. This could be due to various factors such as favorable market conditions, company performance or increased investor confidence in that year."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "bJbjbMM8BuRUU3DP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the investment trend in Equities from 2012 to 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the size of the slices for each year, like the slices for {2012}<r>[0.325, 0.407, 0.36, 0.427]</r>, {2015}<r>[0.413, 0.718, 0.456, 0.738]</r>, and {2020}<r>[0.396, 0.303, 0.44, 0.323]</r>, to infer the investment trend.\nBased on the above thought, the answer is:\nThe pie chart suggests an overall growing trend in Equities investment from 2012 to 2020, with a few minor fluctuations. The investment percentage shows an increasing pattern which indicates a bullish view of investors for this period."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "PEWHaeoHOLIQZAlc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which years showed a similar level of investment in Equities and why do you think this occurred?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to examine each slice like the ones for {2013}<r>[0.309, 0.497, 0.344, 0.517]</r> and {2019}<r>[0.519, 0.301, 0.563, 0.321]</r> to identify years that show roughly the same size, which would indicate a similar level of investment.\nBased on the above thought, the answer is:\nThe years 2013 and 2019 showed a similar level of investment in Equities. This could be due to similar economic conditions, investor sentiment, company performance, or market trends during these years."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "vGBa6xHRdcbmhISa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the difference in Equities investment percentage between 2012 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in investment percentage, I need to locate {the 2012 slice}<r>[0.325, 0.407, 0.36, 0.427]</r> and {the 2017 slice}<r>[0.6, 0.601, 0.644, 0.621]</r>, get their percentages and subtract the 2012 percentage from the 2017 percentage.\nBased on the above thought, the answer is:\n+5.1%"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "K241jRnVXydeEnFr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the Equities investment percentages for the years 2015, 2016, and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I must locate {the 2015 slice}<r>[0.413, 0.718, 0.456, 0.738]</r>, {the 2016 slice}<r>[0.522, 0.709, 0.566, 0.729]</r>, and {the 2017 slice}<r>[0.6, 0.601, 0.644, 0.621]</r>. I then need to add up all their percentages.\nBased on the above thought, the answer is:\n35.7%"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "gtIo7EWBC0eL8Nlw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Equities investment for the year 2020 increased by 2%, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage for 2020, I need to locate {the 2020 slice}<r>[0.396, 0.303, 0.44, 0.323]</r> and add 2% to its current percentage.\nBased on the above thought, the answer is:\n16.4%"
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "69Jmo7I7AZ1oN8Tl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract the yearly usage count data from the pie chart into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would have to identify each slice on the chart {\"Big Data Integration Overview from 2010 to 2019\"}[0.275, 0.021, 0.688, 0.047] and correlate its label from the legend {legend}[0.908, 0.063, 0.993, 0.368] to retrieve the usage counts for each year.\nBased on the above thought, the answer is:\nYear,Usage Count\n2010,45\n2011,67\n2012,82\n2013,94\n2014,110\n2015,120\n2016,140\n2017,155\n2018,177\n2019,200"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "Bj880KMLDosFVS8g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you observe in the usage of Big Data tools from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sizes of the slices representing each year, I can infer the trend in Big Data tools usage.\nBased on the above thought, the answer is:\nFrom 2010 to 2019, there has been a steady increase in the usage of Big Data tools, with the number more than quadrupling over the course of the decade."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "c42N711P1Et3zDoT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about the growth rate of Big Data tool usage in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportion represented by the slice {2015}[0.438, 0.516, 0.598, 0.886] and compare it to the previous years.\nBased on the above thought, the answer is:\nThe year 2015 was an outlier in terms of the growth rate of Big Data tool usage. According to the pie chart, it appears that the count of Big Data tools used in 2015 showed a higher growth rate compared to other years."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "OM4oEv2TB8qvC9CX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart reflect the advent of the Big Data era over the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the increasing trend in the usage of Big Data tools over the years depicted by the pie chart.\nBased on the above thought, the answer is:\nThe pie chart demonstrates the advent of the Big Data era by showing a consistent increase in the usage of Big Data tools over the decade, indicating growing interest and reliance on Big Data in various aspects."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "cxh14o36WxM0CmlU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the increase in usage count from the year 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to locate the slices {2010}[0.25, 0.278, 0.482, 0.516] and {2015}[0.438, 0.516, 0.598, 0.886], then subtract the usage count of 2010 from that of 2015.\nBased on the above thought, the answer is:\n75"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "wL6nDCQoKYEovkFZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average usage count from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the usage counts of all years and divide by the number of years which is 10.\nBased on the above thought, the answer is:\n118"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "WnK5kel5KRN0rqO7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many years saw a usage count of more than 100 tools?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to count the number of years in which the usage count was greater than 100.\nBased on the above thought, the answer is:\n6 years"
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "yEYM8agZWQ2cn39P",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the proportions of Freight Transportation and Logistics Solutions in 2020 into percentage form and provide the structured data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, {the sector slice}[{\"Freight Transportation\"}[0.223, 0.278, 0.65, 0.886]; {\"Logistics Solutions\"}[0.283, 0.146, 0.741, 0.797]], shows that Freight Transportation accounts for 47.4% and Logistics Solutions accounts for 52.6%.\nBased on the above thought, the answer is:\nIndustry Segment,2020\nFreight Transportation,47.4%\nLogistics Solutions,52.6%"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "IvHEPN4Stz1ozE9G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry segment is dominant according to the chart for 2020? Give the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "The industry segment with the highest proportion is considered the dominant one.\nBased on the above thought, the answer is:\nThe dominant industry segment for 2020 is \"Logistics Solutions\", as it has a higher proportion of 52.6% compared to \"Freight Transportation\" with a 47.4% proportion."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "k4XMl8Z7xDFGac3Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the distribution of the industry segments in the year 2020 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segment proportions from the pie chart for the year 2020 provide insights about the distribution of industry segments.\nBased on the above thought, the answer is:\nThe \"Logistics Solutions\" proportion was slightly larger than \"Freight Transportation\" in 2020. It indicates that in 2020, the market share for \"Logistics Solutions\" surpassed that of \"Freight Transportation\" by a narrow margin."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "RgB6hUnBOrSNT33L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow the information in the chart could perhaps influence industry decisions for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Industry decisions could depend on the proportions of the industry segments represented in the chart.\nBased on the above thought, the answer is:\nSeeing the upward trend of the \"Logistics Solutions\" proportion, industry decision-makers might focus more on enhancing logistics solutions in 2021. They could, for instance, invest more resources in improving and developing logistics solutions due to its dominant position in 2020. However, efforts shouldn't be lowered to improve Freight Transportation, as its proportion is still substantial and almost equally important as Logistics Solutions."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "4uxKgJaMgP3DSZPF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent is the Logistics Solutions sector proportion greater than the Freight Transportation sector in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I should subtract the Freight Transportation sector's proportion from the Logistics Solutions sector's proportion.\nBased on the above thought, the answer is:\n5.2%"
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "PaovaAKvypJr08hp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average proportion of the two sectors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to summarize the proportions of the two sectors and divide it by the number of sectors.\nBased on the above thought, the answer is:\nThe sum of the proportions is 52.6% + 47.4% = 100%. So the average is 100% / 2 = 50%."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "W9o5qCvoqOnACdzC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of Freight Transportation was to increase by 3% in 2021, what will be the new proportion of Logistics Solutions assuming total is still 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion of Logistics Solutions, I should subtract the new increased proportion of Freight Transportation from 100%.\nBased on the above thought, the answer is:\nThe new Freight Transportation proportion is 47.4% + 3% = 50.4%. So, the new Logistics Solutions proportion is 100% - 50.4% = 49.6%."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "vuLDabhtppcyJTcU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you arrange the data included in the pie chart into a clear CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the {legend}[0.848, 0.063, 0.993, 0.279] of the chart, and each individual {slice} of the pie, including {Startups}[0.223, 0.278, 0.482, 0.589], {Funding}[0.228, 0.516, 0.482, 0.823], {Innovation}[0.337, 0.516, 0.574, 0.886], {Infrastructure}[0.482, 0.516, 0.723, 0.862], {Policy}[0.482, 0.292, 0.741, 0.649], {Education}[0.482, 0.154, 0.688, 0.516], {Ecosystem}[0.283, 0.146, 0.534, 0.516], each of which shows a percentage, we can derive the category and the data for each category.\nBased on the above thought, the answer is:\nAspect,Percentage\nStartups,75\nFunding,65\nInnovation,80\nInfrastructure,70\nPolicy,85\nEducation,60\nEcosystem,90"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "y5YmPCMDfwmVu6VL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDespite being an important aspect of Startup and Innovation Ecosystem, why do you think 'Education' has the least presence in the ecosystem as per the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the percentage of the 'Education' slice {Education}[0.482, 0.154, 0.688, 0.516], and comparing it to the other aspects represented on the pie chart, it's indeed relatively smaller.\nBased on the above thought, the answer is:\nIt's not clear from the chart why 'Education' is the least represented aspect in the ecosystem. It could be due to a variety of factors such as investment allocation, priorities of the ecosystem, or the methodology used in gathering this data. This would require a deeper analysis of the Entrepreneurship and Innovation ecosystem."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "rTj2u3hZQYyfkc9I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the elements on the pie chart, can you share an analytical insight about the given startup ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the size of each pie slice {Slice}[0.223, 0.146, 0.741, 0.886] and its corresponding percentages, I can gauge the distribution of the different aspects.\nBased on the above thought, the answer is:\nThe chart indicates a well-rounded ecosystem with diverse aspects being represented. However, it reveals a strong emphasis on 'Policy' and 'Ecosystem' while comparatively, 'Startups' and 'Education' receive lower attention. This can suggest the ecosystem is prioritizing regulatory frameworks and overall ecosystem dynamics, but there's potential for enhancing focus on the educational aspect of entrepreneurship as well as the startups themselves."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "JXGZsVqTnSvWgI31",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould the data in this chart potentially influence future investment decisions in the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference, I require an understanding of the representation of each aspect in the pie chart.\nBased on the above thought, the answer is:\nYes, this data could provide valuable insights to investors or policymakers by indicating which elements within the ecosystem are most prominent. For instance, given the considerable representation of 'Policy' and 'Ecosystem', investment decisions could focus on maintaining or enhancing these aspects. Similarly, the lower representation of 'Education', could indicate a potential for increased focus and investment to strengthen this sector within the ecosystem."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "pp69HfCAwFxyfrMz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat aspect has the least representation in the Startup and Innovation Ecosystem as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing each slice of the pie, I will find the one with the smallest area which represents the least presence. The slice {Education}[0.482, 0.154, 0.688, 0.516] seems to be the smallest.\nBased on the above thought, the answer is:\nEducation"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "ZVeF669Nrzh9d8KP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total representation of the aspects 'Startups' and 'Ecosystem' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding together the percentages of the two slices - {Startups}[0.223, 0.278, 0.482, 0.589] and {Ecosystem}[0.283, 0.146, 0.534, 0.516], I can calculate the total representation.\nBased on the above thought, the answer is:\n165"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "FAqNqBMzHLSPA8hR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the numerical difference between 'Funding' and 'Policy' according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By subtracting the percentage of 'Funding'{Funding}[0.228, 0.516, 0.482, 0.823] from 'Policy'{Policy}[0.482, 0.292, 0.741, 0.649], I can get the numerical difference.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "vaGZ4urReqmtNlNt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you put the data from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart has a title {\"Transportation and Logistics Industry Trends from 2015 to 2021\"}<r>[0.218, 0.021, 0.745, 0.047]</r>, indicating the data set is the industry trends from 2015 to 2021, with years identified in the legend <r>[0.908, 0.063, 0.993, 0.279]</r> and the corresponding quantities given as percentages in the pie chart slices <r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nYear,Quantity(%)\n2015,11\n2016,12.5\n2017,7\n2018,17.5\n2019,6.5\n2020,32.5\n2021,13"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "vSvYToTjOm2Z7ypu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the most prosperous year in the industry based on the chart representation? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which year was most prosperous, I need to find which slice represents the largest percentage.\nBased on the above thought, the answer is:\nThe most prosperous year in the industry was 2020, because it represents the largest pie slice, indicating the highest activity or volume based on the measured data."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "Yr2kjMXmrwj3KajT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart reveal about the impact of the Covid-19 pandemic on the Transportation and Logistics Industry in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the size of the pie slice for the year 2020 and interpret its significance within the context of the Covid-19 pandemic.\nBased on the above thought, the answer is:\nThe pie chart suggests that despite the challenges due to the Covid-19 pandemic in 2020, the Transportation and Logistics Industry might have experienced a boom. The sector might have noticed increased demand resulting from higher online shopping rates, more home deliveries, and the need for transporting medical supplies."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "qDcnsffhnCFMwbXd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend from the pie chart continues, what could be the implications for the sector in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I must consider the trend of sector sizes. Given a boom in 2020, a drop in 2021, and previous fluctuations, we could expect a continued presence of high volatility.\nBased on the above thought, the answer is:\nIf the current volatility continues, the industry could experience rapid growth or decline in the short term. But the significant boom in 2020 might indicate a potential long-term growth opportunity for the sector despite temporary dips."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "nuHhfYYcXSUXgtfr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the percentages of the lowest and highest values in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to identify the smallest and largest slices to answer this question. The smallest slice is {the 2019 slice}<r>[0.482, 0.516, 0.719, 0.788]</r> and the largest is {the 2020 slice}<r>[0.467, 0.146, 0.741, 0.665]</r>. The percentage is given in the autotext of the slices.\nBased on the above thought, the answer is:\nThe lowest percentage is 6.5% and the highest is 32.5%."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "hsRm95posa9PSgri",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in the sector size from 2019 to 2020 in percentage terms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To evaluate the increase, I need to locate the 2019 and 2020 slices {2019 slice}<r>[0.482, 0.516, 0.719, 0.788]</r> and {2020 slice}<r>[0.467, 0.146, 0.741, 0.665]</r>, then deduct 2019\u2019s percentage from 2020\u2019s.\nBased on the above thought, the answer is:\nThe increase was 26%."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "9DYaw3RkIpjENPlw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the industry scale in the years leading up to the industry boom in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total percentage before the industry boom would include the years 2015 to 2019, so I need to sum percentages of these years.\nBased on the above thought, the answer is:\nThe total percentage before 2020 is 54.5%."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "7b14pjdSkJu1EuM6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the given profit forecast pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this chart into a table, I will use the labels and percentages provided in the chart for each year. The years are identified in the legend {2022}<r>[0.952, 0.071, 0.987, 0.091]</r>, {2023}<r>[0.952, 0.101, 0.987, 0.121]</r>, {2024}<r>[0.952, 0.131, 0.987, 0.151]</r>, {2025}<r>[0.952, 0.161, 0.987, 0.181]</r>, {2026}<r>[0.952, 0.191, 0.987, 0.211]</r>, {2027}<r>[0.952, 0.221, 0.987, 0.241]</r>, and {2028}<r>[0.952, 0.251, 0.987, 0.271]</r>. The corresponding percentage distribution of the profit for each year is reflected in each pie slice.\nBased on the above thought, the answer is:\nYear,Profit (%)\n2022,7.2%\n2023,8.4%\n2024,10.2%\n2025,13.2%\n2026,15.6%\n2027,21.6%\n2028,24.0%"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "fxNB18mGtLiQKUea",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years are projected to contribute the most and the least to the overall profit?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to recognize the largest and the smallest sectors of the pie chart, which respectively represent the years contributing the most and least to the overall profit.\nBased on the above thought, the answer is:\nThe year 2028 is projected to contribute the most to the overall profit, whereas the year 2022 is expected to contribute the least."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "5AqPZsaxVQCYhhiC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the visualized data be interpreted in terms of the company's profit growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret this, I need to review the proportion of each year's profit in the total forecasted profit and analyze the changing pattern.\nBased on the above thought, the answer is:\nThe pie chart suggests a steady upward trend in the company's profit in the upcoming years. The proportion of profit is forecasted to increase progressively from 2022 to 2028, demonstrating expected growth."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "6CAdsgv5EsIsFZog",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the company use this forecasted profit data for future strategic decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to reflect on how understanding the distribution and growth of predicted profits can influence strategic decision-making.\nBased on the above thought, the answer is:\nThe company might use this data to guide investment decisions and allocate resources strategically across the years. For example, seeing that the profit percentage steadily increases each year, the company might decide to reinvest a higher proportion of its profits in later years. Additionally, this data could help identify years where extra efforts and strategies are needed to drive growth."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "5dK9UhgKnmnIZe5k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the projected percentage profits from 2026 to 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentages of projected profits for 2026, 2027, and 2028 are located on slices in the legend. I've to sum these percentages.\nBased on the above thought, the answer is:\n61.2%"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "DX8koEtkLfTMmePi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the company aims to increase the profits for the year 2023 to equal 10% of the overall profits, by how many percentage points should they increase it?"
+            },
+            {
+                "from": "gpt",
+                "value": "Currently, the profits for the year 2023 stand at 8.4%. If the company aims to increase this to 10%, I need to calculate the difference.\nBased on the above thought, the answer is:\n1.6 percentage points"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "f2k9ZUxdOAu0XFJH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much larger is the profit proportion of 2028 than that of 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the profit proportions of 2028 and 2022, I need to subtract the percentage of 2022 from that of 2028.\nBased on the above thought, the answer is:\n16.8 percentage points"
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "iGw7l70mIEr7I8bg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of public opinion influence from each type of media according to the pie chart? Can you create a table for it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart exhibits the influence of six different types of media on public opinion {'Media Influence on Public Opinion'}[0.341, 0.021, 0.622, 0.047]. Each sector slice of the pie chart such as {Newspapers}<r>[0.223, 0.278, 0.482, 0.52]</r>, {TV}<r>[0.223, 0.516, 0.482, 0.855]</r>, {Radio}<r>[0.378, 0.516, 0.6, 0.886]</r>, {Internet}<r>[0.482, 0.34, 0.741, 0.845]</r>, {Magazines}<r>[0.482, 0.199, 0.709, 0.516]</r> and {Blogs}<r>[0.283, 0.146, 0.616, 0.516]</r> represents the percentage of their influence.\nBased on the above thought, the answer is:\nMedia, Influence Percentage\nNewspapers, 11.3%\nTV, 18.3%\nRadio, 14.1%\nInternet, 25.4%\nMagazines, 8.5%\nBlogs, 22.5%"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "hY3pBVXsMnVfd8jY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which form of media had the biggest influence on public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should look at the media type with the largest proportion in the chart.\nBased on the above thought, the answer is:\nThe Internet had the biggest influence on public opinion given that it accounts for the largest sector size in the pie chart, with 25.4%."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "0vi7yYudJCaiugLX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you infer the trend of the influence of different media types on public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences about the trends, I need to compare the proportions of each media category.\nBased on the above thought, the answer is:\nThe chart suggests that digital platforms, namely Internet and blogs, outweigh traditional media such as Newspapers, TV, and Radio in terms of their influence on public opinion. This trend might indicate the increasing significance of digital media in shaping public opinions."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "byzZW7v6Lq3NniyP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data inform media strategy and advertising decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how representation of influence in the pie chart might impact decisions on media strategy and advertising.\nBased on the above thought, the answer is:\nGiven that digital media forms, i.e., Internet and blogs, have a significantly larger influence on public opinion, organizations may consider allocating a higher portion of their media strategy and advertising budget towards these areas to reach a larger audience. Traditional media such as newspapers and magazines having a smaller influence may be opted for audiences that are in specific demographics."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "KLIekyp2AezuA4EV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage greater is the Internet's influence on public opinion compared to Radio?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the percentage value of {Radio's influence}<r>[0.378, 0.516, 0.6, 0.886]</r> from {Internet's influence}<r>[0.482, 0.34, 0.741, 0.845]</r>.\nBased on the above thought, the answer is:\n11.3%"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "WDjWrSKa6OZEsFwH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of traditional media's influence such as Newspapers, TV, Radio and Magazines on public opinion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total of traditional media's influence, I need to sum up the proportions of {Newspapers influence}<r>[0.223, 0.278, 0.482, 0.52]</r>, {TV's influence}<r>[0.223, 0.516, 0.482, 0.855]</r>, {Radio's influence}<r>[0.378, 0.516, 0.6, 0.886]</r> and {Magazines' influence}<r>[0.482, 0.199, 0.709, 0.516]</r>.\nBased on the above thought, the answer is:\n52.2%"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "HRY5cEIFngNZ8dyj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the influence of Blogs decreases by 5%, what would be the new percentage for Blogs' influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new influence of Blogs, I need to subtract 5% from the current {Blogs' influence}<r>[0.283, 0.146, 0.616, 0.516]</r>.\nBased on the above thought, the answer is:\n17.5%"
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "5laOSiLTfyc7dcmy",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the product sales trend data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the pie slices representing two categories - {Electronics}<r>[0.223, 0.278, 0.73, 0.886]</r> and {Fashion}<r>[0.283, 0.146, 0.741, 0.621]</r> in the pie chart for 2020. If I trace this back for all the years from 2012, I can convert the yearly data into a structured table.\nBased on the above thought, the answer is:\nYear,Electronics,Fashion\n2012,150000,75000\n2013,200000,105000\n2014,225000,125000\n2015,235000,155000\n2016,255000,175000\n2017,260000,200000\n2018,270000,210000\n2019,290000,225000\n2020,325000,250000"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "rPeC1nKZIarn5LAU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart for 2020, which product type had higher sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart for 2020 shows two slices, Electronics at {Electronics}<r>[0.223, 0.278, 0.73, 0.886]</r> and Fashion located at {Fashion}<r>[0.283, 0.146, 0.741, 0.621]</r>. The product type with the larger slice had higher sales.\nBased on the above thought, the answer is:\nElectronics had higher sales in 2020."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "tjQpwmdM4AumbFwH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what can you infer about the overall trend in product sales from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the sales data for both Electronics and Fashion over the years, I can make inferences about the overall trend in product sales.\nBased on the above thought, the answer is:\nThe sales for both Electronics and Fashion have consistently increased from 2012 to 2020, with the increase being more prominent for the Electronics category."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "ddH8IKXOnGx2uFTw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the sales of Electronics and Fashion, which category should be prioritized for the future considering the data up to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which category to prioritize, one would typically look at the sales trend over the years and particularly the most recent sales data.\nBased on the above thought, the answer is:\nThe Electronics category has consistently higher sales than the Fashion category each year from 2012 through 2020. Therefore, based on this data, it would seem beneficial to prioritize the Electronics category in the future."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "Sq7XvhMz1tNwTDLW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please tell me the total sales for Electronics and Fashion for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total sales, I just have to add the sales figures of Electronics, located at {Electronics}<r>[0.223, 0.278, 0.73, 0.886]</r> and Fashion, located at {Fashion}<r>[0.283, 0.146, 0.741, 0.621]</r> from year 2020.\nBased on the above thought, the answer is:\n575000"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "lgkXsi6RYBzGkvCg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the growth in sales for the electronics category from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the growth, I have to subtract the sales figure of Electronics in 2012 from that in 2020.\nBased on the above thought, the answer is:\n175000"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "Hb8ZEPdt92WQcHdR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the sales for Fashion increased from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I would divide the increase in sales (Sales_2020 - Sales_2012) by the sales in 2012 and multiply the result by 100.\nBased on the above thought, the answer is:\n233.33%"
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "fzRjkhw0qFeqTmWe",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the chart data for Equity and Bond investments from 2008 to 2015 into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart with information on each year's distribution {the slices}<r>[0.223, 0.146, 0.741, 0.886]</r> and the legend with the list of years {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r>, I can directly map this information to the raw data.\nBased on the above thought, the answer is:\nYear,Equity,Bond\n2008,65,35\n2009,55,45\n2010,62,38\n2011,70,30\n2012,73,27\n2013,77,23\n2014,82,18\n2015,85,15"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "QLavyBV49CuzyFo7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the chart about the trend in investment type preference over the years from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the changing sizes of the Equity and Bond slices {the slices}<r>[0.223, 0.146, 0.741, 0.886]</r> over the years {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r> to infer the trend.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that there was a growing preference for Equity investments over time from 2008 to 2015, as visually depicted by the increasing size of Equity slices. The preference for Bond investments, on the other hand, seemed to decrease over the same period."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "qiCyMa1GyhyHern6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the investment behavior in 2009 be considered as an anomaly based on the overall trend in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sizes of the Equity and Bond slices {the slices}<r>[0.223, 0.146, 0.741, 0.886]</r> over the years {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r>, I can compare the investment behavior in 2009 with the overall trend.\nBased on the above thought, the answer is:\nYes, the investment behavior in 2009 can be considered as an anomaly based on the overall trend. While the general trend was a move towards increased Equity investments, in 2009 the Bond proportion saw a hike, going against this trend."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "I8pnqx8lQ4HEGkcb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the market\u2019s risk appetite over the period from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the changes in proportions of Equity and Bond investments {the slices}<r>[0.223, 0.146, 0.741, 0.886]</r> over time {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r>, I can infer about the market's risk appetite.\nBased on the above thought, the answer is:\nThe chart suggests that the market's risk appetite increased over the period from 2008 to 2015. The increasing preference for Equity investments, which typically signify higher risk and higher return, over Bonds, which are considered low-risk, low-return investments, indicates a greater willingness to undertake risks."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "YJgL5eznwmgIveyf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the proportion of Bond investments highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze {the slices}<r>[0.223, 0.146, 0.741, 0.886]</r> in relation to the years {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r> in the pie chart to find the year with the largest Bond investment segment.\nBased on the above thought, the answer is:\n2009"
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "0KyImLB19Lt5IvMd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total proportion of Equity investments from 2008 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total proportion, I should sum up the Equity proportions for each year from 2008 to 2012.\nBased on the above thought, the answer is:\nThe sum of Equity proportions from 2008 to 2012 is 65% + 55% + 62% + 70% + 73% = 325%."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "BSuCmuT6TrmN7z4G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the proportion of Equity investments increase from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out how much the proportion of equity investments increased, I need to subtract the Equity proportion in 2008 from that in 2015.\nBased on the above thought, the answer is:\nThe increase in Equity proportion from 2008 to 2015 is 85% - 65% = 20%."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "iVm4gO1qxRrNAneh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the finance investments distribution in 2020 in csv form as per the pie chart analysis?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {Investments Distribution in Financial Markets in 2020}<r>[0.26, 0.021, 0.703, 0.047]</r> and the segments {the sector slice}[{Gold}[0.223, 0.278, 0.482, 0.745]; {Futures}[0.278, 0.516, 0.482, 0.832]; {Stocks}[0.347, 0.516, 0.735, 0.886]; {Forex}[0.482, 0.217, 0.741, 0.592]; {Cryptocurrency}[0.283, 0.146, 0.634, 0.516]] I can extract the distribution percentages.\nBased on the above thought, the answer is:\nYear,Gold,Futures,Stocks,Forex,Cryptocurrency\n2020,21.7%,5.7%,30.4%,18.3%,23.9%"
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "fC7BHvbxMHcj1rpt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market was the most invested in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the proportions in the pie chart, the market with the highest proportion would be the most invested.\nBased on the above thought, the answer is:\nThe Stocks market was the most invested in 2020."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "fS8BWHFQV1u2xh8H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the trend of investment in Futures change over the time period according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can infer that the trend of investment in Futures has decreased from 2016 to 2020.\nBased on the above thought, the answer is:\nThe trend of investment in Futures decreased over the time period of 2016 to 2020."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "e1dpImLAlejdV4mZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the investment trends in the Cryptocurrency market?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the proportion of the Cryptocurrency segment in the pie chart, I can make inferences about the trend.\nBased on the above thought, the answer is:\nThe pie chart suggests that the investment in the Cryptocurrency market has seen a significant increase in 2020 compared to 2016."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "G1H8TIpkd6h1UaKz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the investment in Cryptocurrency increase in 2020 compared to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I will calculate the difference between the 2020 and 2016 percentages for Cryptocurrency and then calculate the percentage increase.\nBased on the above thought, the answer is:\nInvestment in Cryptocurrency increased by 15% in 2020 compared to 2016."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "SrmS7JZx93jFwyDB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had the lowest investment in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the segments of the pie chart to determine the sector with the smallest percentage.\nBased on the above thought, the answer is:\nThe Futures sector had the lowest investment in 2020."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "EjZFUtywjKgbHT08",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the combined investment in the Gold and Stocks sectors make up the total investment in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the combined investment, I need to add up the percentages of both the Gold and Stocks sectors.\nBased on the above thought, the answer is:\nThe combined investment in the Gold and Stocks sectors made up 52.1% of the total investment in 2020."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "kVLBHhtKOsw3fERo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data included in the pie chart about media consumption into a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The given pie chart title {Digital vs Physical Media Consumption from 2015 to 2017}<r>[0.241, 0.021, 0.723, 0.047]</r> indicates the data's context. Each pie slice corresponds to media usage for a specific year. By looking at {the legend}<r>[0.908, 0.063, 0.993, 0.249]</r> and {the pie slices}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can derive each type's annual consumption rate.\nBased on the above thought, the answer is:\nType,2015,2016,2017\nDigital,65,70,80\nPhysical,35,30,20"
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "NX0K9zrZ8GdAo0Kh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the trend in media consumption between digital and physical media from 2015 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pattern of the pie slices indicates the distribution of media consumption. By analyzing these slices, I can understand how digital and physical media usage has evolved.\nBased on the above thought, the answer is:\nFrom the pie chart, we can infer that there is a noticeable shift from physical to digital media consumption from 2015 to 2017. This trend suggests that consumers are progressively favoring digital media over physical media."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "42xHov0npbpVLwa6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might this trend in media consumption suggest about consumer preferences?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart's shifts in the size of digital versus physical consumption segments can provide insight into consumer preferences.\nBased on the above thought, the answer is:\nThe increase in consumption of digital media while physical media decreases suggests that consumer preferences are moving towards digital media. This trend may indicate the convenience of access, the broad variety of digital content, or changing consumer habits in the digital age."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "KjF7WqSDyDbBH0Gm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the graph aid business decisions for media companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The graph's indication of trends in physical versus digital media consumption provides valuable information that companies can use when making strategic business decisions.\nBased on the above thought, the answer is:\nThe growing preference for digital media indicated by the graph suggests media companies might want to invest more in digital platforms. This data could guide them in making decisions on resource allocation, such as reducing investments in physical formats and focusing more on digital content production and distribution."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "piR6p9FQJS3JPjlt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2017, what was the difference in percentage between digital and physical media consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference by taking the percentage of digital media consumption and subtracting the percentage of physical media consumption based on the slices of the 2017 pie chart.\nBased on the above thought, the answer is:\nThe difference in percentage between digital and physical media consumption in 2017 was 60%."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "hqWbZoNJzoG4RajI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the digital media consumption increase from 2015 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the digital media consumption in 2015 from that in 2017 to measure the increase in digital media consumption.\nBased on the above thought, the answer is:\nThe digital media consumption increased by 15% from 2015 to 2017."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "ez9c2gzYNVjtKpZn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the cumulative percentage of digital media consumption from 2015 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the cumulative percentage, I need to add up the percentages of digital media consumption for each year.\nBased on the above thought, the answer is:\nThe cumulative percentage of digital media consumption from 2015 to 2017 was 215%."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "zzp4pHY9YTDqoGrp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data presented in the pie chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the pie slices {2000}[0.308, 0.456, 0.352, 0.476], {2002}[0.352, 0.667, 0.396, 0.687], {2004}[0.534, 0.701, 0.577, 0.721], {2006}[0.605, 0.426, 0.648, 0.446], and {2008}[0.427, 0.289, 0.471, 0.309], each label represents the year and the proportion of the slice represents the percentage of global trade concerning the global GDP in that year. Therefore, I can convert each year and its corresponding percentage into row in a CSV table.\nBased on the above thought, the answer is:\nYear,Percentage\n2000,15%\n2002,18%\n2004,22%\n2006,24%\n2008,21%"
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "6pLbXxfjx9sFqgIC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year experienced the greatest globalization? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look for the slice with the largest percentage. According to the chart, {2006}<r>[0.605, 0.426, 0.648, 0.446]</r> has the largest percentage.\nBased on the above thought, the answer is:\nThe year 2006 experienced the most globalization because it accounted for 24% of the total, which was the highest percentage among the years presented from 2000 to 2008."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "9nfmYpqvc4WsU4He",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat noticeable trend do you observe from 2000 to 2008 in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From observing the chart, I need to compare the data throughout the years, by looking at the percentage of each year.\nBased on the above thought, the answer is:\nThe pie chart shows a steady increase in globalization from 2000 to 2006. However, globalization decreased slightly in the year 2008, likely due to the global financial crisis."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "FRKrT74rwyNPnNKu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer why there was a dip in globalization in 2008 based on the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the observed dip and known global events, I can use the drop in percentage for {2008}<r>[0.427, 0.289, 0.471, 0.309]</r> to conjecture about the reason.\nBased on the above thought, the answer is:\nYes. The dip in globalization in 2008 can be attributed to the onset of the global economic crisis, which likely led to a decrease in international trade."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "wy2cZAS4CiPTFAxR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of globalization in 2000 and 2002, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add the percentage of {2000}<r>[0.308, 0.456, 0.352, 0.476]</r> and {2002}<r>[0.352, 0.667, 0.396, 0.687]</r>.\nBased on the above thought, the answer is:\n33%."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "yMqPuhXIA89pojjh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the percentage increase from 2000 to 2004?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of {2000}<r>[0.308, 0.456, 0.352, 0.476]</r> from the percentage of {2004}<r>[0.534, 0.701, 0.577, 0.721]</r> to find out.\nBased on the above thought, the answer is:\n7%."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "1dkrs1dp8qtcB77K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the globalization percentage for the year of 2000 was half, what would be the new total percentage for all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will subtract half of the percentage of {2000}<r>[0.308, 0.456, 0.352, 0.476]</r> from the total percentage of all years.\nBased on the above thought, the answer is:\n85%."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "f8AFmZpG4ssUjJKg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data that you observed from the pie chart into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the center {\"Tourism and Hospitality Industry Data Analysis\"}[0.288, 0.021, 0.675, 0.047] of the pie chart gives an overview of the represented data while {the legend}[0.908, 0.063, 0.993, 0.338] provides the year corresponding to each color block, I can cross-reference the color segments in the pie with the years in the legend. Each segment corresponds to a percentage that represents the proportion of the total number of visitors for each year.\nBased on the above thought, the answer is:\nYear,Percentage\n2012,7.2%\n2013,8.2%\n2014,9.3%\n2015,10.4%\n2016,11.7%\n2017,13.0%\n2018,14.7%\n2019,15.8%\n2020,9.7%"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "hRZ3JXD8SRg9ik6C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the major trend in the tourism sector, based on the data from 2012 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of visitors across the years 2012 to 2019.\nBased on the above thought, the answer is:\nThe tourism sector showed a general upward trend from 2012 to 2019, with each year seeing an increase in the proportion of visitors. This indicates that the sector was growing during this period."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "XvYnAMpk9VeGyf9y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you make a prediction for the year 2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the drastic decline in 2020 and the overall upward trend prior to that to infer a prediction for 2021.\nBased on the above thought, the answer is:\nGiven the significant drop in 2020, likely due to the COVID-19 pandemic, it's difficult to predict the direction for 2021 with certainty. If conditions related to the pandemic improve, the sector could start to recover and potentially follow the prior growth trend in the following years."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "O8jkLUokxtYRzCXy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how did the Covid-19 pandemic in 2020 affect the tourism sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare 2020's data with the trend of previous years to evaluate the impact of the pandemic.\nBased on the above thought, the answer is:\nThe Covid-19 pandemic had a significant negative impact on the tourism sector in 2020, as indicated by the substantial drop in the proportion of visitors. This broke the consistent growth trend observed from 2012 to 2019, suggesting a high level of disruption caused by the pandemic."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "6WDb79LJlom16Zby",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the proportion of visitors increase the most compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in the proportions of visitors between each two consecutive years. The year with the highest positive difference compared to the previous one would have the most increase.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "vGbdCAIZIsFg2uEB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in visitors from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of visitors in 2018 and 2019 to calculate the percentage increase.\nBased on the above thought, the answer is:\nThe increase from 2018 to 2019 was 15.8% - 14.7% = 1.1%"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "KB7DTJRtYaOKYKkx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of visitors in 2020 is the same as 2019, what would be the total percentage of all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the proportion of visitors from each year up to 2019 and then add twice the proportion from 2019 to get the total.\nBased on the above thought, the answer is:\nThe total would be (7.2% + 8.2% + 9.3% + 10.4% + 11.7% + 13.0% + 14.7% + twice of 15.8% = 105.7%)"
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "n5r8tS6hsTjhsd6b",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a table based on the raw data represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Going by the title {\"Cuisine and Gastronomy Culture Analysis\"}[0.31, 0.021, 0.653, 0.047] and the legend showing the categories {Italy, China, India, Turkey, Mexico, Thailand, Greece, Japan, Spain}<r>[0.883, 0.063, 0.993, 0.338]</r>, each sector slice represents the popularity percentage of dishes from a different country.\nBased on the above thought, the answer is:\nCuisine, Popularity\nItaly, 25%\nChina, 15%\nIndia, 10%\nTurkey, 18%\nMexico, 17%\nThailand, 8%\nGreece, 10%\nJapan, 5%\nSpain, 12%"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "xlDm1HzfrBZGRjxp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which country's cuisine is the most popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most popular cuisine can be identified as the one that holds the highest percentage.\nBased on the above thought, the answer is:\nItalian cuisine is the most popular, holding the highest proportion of 25%."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "U3CoRpjhIkNJMpzy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what inference can be made about the dish preferences?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the proportions related to each country's cuisine popularity, one can infer public preferences towards food.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that Italian and Turkish dishes are more popular, indicating a strong preference for Mediterranean flavors, while Asian cuisines like Thai and Japanese have a lower preference, suggesting a subdued interest in these flavor profiles."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "7OAGD0bcwwv0yJB5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this data potentially influence a restaurant owner's decision on what dishes to have on their menu?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity proportions of the dishes from different countries could influence decisions on what dishes to serve in a restaurant.\nBased on the above thought, the answer is:\nGiven that Italian dishes show the highest popularity, a restaurant owner might want to include a variety of Italian dishes in their menu. Conversely, it might be beneficial to test the acceptance of underrepresented cuisines like Japanese and Thai dishes through special promotions or limited edition menu items, to increase their popularity."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "GtI7RMEkcyOaShCa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total popularity percentage for Asian cuisines represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to add up the percentage of the cuisines from Asia: China, India, Thailand, and Japan.\nBased on the above thought, the answer is:\n38%"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "GsXG9bW3hBL9tHDR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points separate the most and least popular cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of Japanese cuisine, being the least popular, from that of Italian cuisine, which is the most popular.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "8toGhWosebjo4hgs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity of Spanish cuisine increases by 3%, what will be the new total popularity percentage for European cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add 3% to the Spanish cuisine percentage and add it to the percentage of the other European cuisines: Italian, Turkish, and Greek.\nBased on the above thought, the answer is:\n59%"
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "engwg9SqVPB6lgnW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data depicted in the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the labels and proportions from the pie chart, and considering the colors of the pie slices and legend at {the legend}<r>[0.78, 0.063, 0.993, 0.129]</r>, both \"Sustainable Living\" and \"Automation Technology\" data exist for \"2030\", \"2040\" and \"2050\".\nBased on the above thought, the answer is:\n\"Scenario\",\"2030\",\"2040\",\"2050\"\n\"Sustainable Living\",\"11.9%\",\"14.7%\",\"16.8%\"\n\"Automation Technology\",\"16.1%\",\"18.9%\",\"21.7%\""
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "q2xx3hTmZCVcbjKx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which future scenario is projected to have the most importance in 2050? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Optional(To determine which scenario is projected to have the most importance, I need to compare the scores of \"Sustainable Living\" and \"Automation Technology\" in 2050.)\nBased on the above thought, the answer is:\nThe \"Automation Technology\" scenario is projected to be the most important in 2050, as it holds a higher score of 155 compared to 120 for \"Sustainable Living\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "VSToMlkXFYPzSuAq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the change in contribution of these scenarios to future society from 2030 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "Optional(I need to look at the scores of each scenario for each year to make inferences about their changing contributions.)\nBased on the above thought, the answer is:\nThe data suggests that, while both \"Sustainable Living\" and \"Automation Technology\" are expected to increase in prevalence, their ratios are not expected to remain the same. \"Automation Technology\" is expected to grow at a faster rate, indicating it will become increasingly dominant over time."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "l9D4Ca70yPhGvMBy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a policy planner wanted to focus on the most impactful scenario, where should they focus their efforts based on this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Optional(The most impactful scenario would be the one with the highest score across the years.)\nBased on the above thought, the answer is:\nBased on this data, a policy planner should focus their efforts on \"Automation Technology\" as it consistently scores higher and is projected to be the most prevalent scenario in 2050."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "e4ag2JahjdDcFE3G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average prevalence score per scenario over the three-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average prevalence, I have to sum all scenario scores for each ear and divide by the total number of years, which is three. Each scenario has separate data for each year, \"Sustainable Living\" and \"Automation Technology\".\nBased on the above thought, the answer is:\nThe average prevalence score for \"Sustainable Living\" is (85+105+120)/3 = 103.3 while for \"Automation Technology\" it is (115+135+155)/3 = 135."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "WKCaNcdsncFyDFjB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total prevalence score for all scenarios in 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total prevalence score, I have to sum the scores of \"Sustainable Living\" and \"Automation Technology\" for the year 2050.\nBased on the above thought, the answer is:\nThe total prevalence score for all scenarios in 2050 is 120 + 155 = 275."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "tWq5DdIvEl8UAy3e",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the prevalence score for \"Sustainable Living\" increased from 2030 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in prevalence score, I have to subtract the 2030 score of \"Sustainable Living\" from its 2050 score.\nBased on the above thought, the answer is:\nThe prevalence score for \"Sustainable Living\" has increased by 120 - 85 = 35 from 2030 to 2050."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "Eg45cacE9gHDGTav",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, please extract the funding data and structure it into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend {the legend}<r>[0.908, 0.063, 0.993, 0.368]</r> labels depict the years from 2015 to 2019, while the pie chart slices are colour-coded representations of funding data for Urban Renewal and Affordable Housing. For example, the slice for affordable housing in 2015 {2015, affordable housing slice}<r>[0.24, 0.278, 0.482, 0.516]</r> is 5.3% of the overall funding while urban renewal {2015, urban renewal slice}<r>[0.482, 0.516, 0.659, 0.881]</r> is 9.5% of the funding for the same year.\nBased on the above thought, the answer is:\nYear,Affordable Housing,Urban Renewal\n2015,5.3%,9.5%\n2016,6.3%,10.1%\n2017,5.8%,11.6%\n2018,8.5%,13.8%\n2019,12.7%,16.4%"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "m29oVpKZh6FpPvUv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which development approach received the most funding from 2015 to 2019, Urban Renewal or Affordable Housing?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sum of the percentages of Urban Renewal and Affordable Housing from 2015 to 2019.\nBased on the above thought, the answer is:\nUrban Renewal received the most funding."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "B3VeLBgsrUzIc00T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the allocation of funding for Urban Renewal and Affordable Housing change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate how the percentages of Urban Renewal and Affordable Housing changed from 2015 to 2019.\nBased on the above thought, the answer is:\nOver the years, the percentage of funding for both Urban Renewal and Affordable Housing increased. However, the growth rate for Urban Renewal was more significant, particularly from 2018 to 2019."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "cookyfjVZZi11bQe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about the community development priorities from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the distribution of funding across Urban Renewal and Affordable Housing over the years. Higher funding indicates higher priority.\nBased on the above thought, the answer is:\nFrom 2015 to 2019, we can infer that the community development priorities gradually shifted more towards Urban Renewal. This is suggested by the increased percentage of funding directed towards it over the years."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "unRkA4xGzEi3DowS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in the percentage of funding between Urban Renewal and Affordable Housing in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the chart and find the percentage numbers of affordable housing and urban renewal for the year 2019{2019, affordable housing slice}<r>[0.327, 0.516, 0.522, 0.886]</r>, {2019, urban renewal slice}<r>[0.283, 0.146, 0.522, 0.516]</r>. Then, I need to subtract one from the other.\nBased on the above thought, the answer is:\n3.7%"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "Wyb4WYgCv1ezAQYb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the largest percentage difference in funding between Urban Renewal and Affordable Housing?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the percentage difference of each year by finding the absolute value of the difference between the percentage of affordable housing and urban renewal. Then I find out which year has the largest difference.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "vrAj4z4QHHYL4Urh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of funding for Urban Renewal from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the percentage of Urban Renewal of each year from 2015 to 2019.\nBased on the above thought, the answer is:\n61.4%"
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "RRx7SdoMycxGyhrc",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you simplify the pie chart data into a structured CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the given chart contents, we have the title {'Resources distribution for Health issues'}<r>[0.317, 0.021, 0.646, 0.047]</r> and the legends indicators showing the diseases with their respective colours and the pie chart segments showing the percentages. For example, there's {'Cardiac Disease'}<r>[0.876, 0.071, 0.987, 0.091]</r> corresponding to its percentage at {'30.0%'}<r>[0.093, 0.604, 0.205, 0.624]</r>. By pulling this out for all diseases, I can construct a structured table.\nBased on the above thought, the answer is:\nHealth Issues,Percentage\nCardiac Disease,30%\nObesity,25%\nHIV/AIDS,15%\nCancer,10%\nMalaria,5%\nDiabetes,7%\nAsthma,3%\nTuberculosis,5%"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "mEPuqhR345SbLJhj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health issue is given the highest priority based on resource allocation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The health issue with the highest resource allocation is 'Cardiac Disease' with the coordinate {'Cardiac Disease'}<r>[0.093, 0.604, 0.205, 0.624]</r>. This health issue can be considered to have the highest priority because it has the highest percentage of resources allocated to it.\nBased on the above thought, the answer is:\n'Cardiac Disease' is given the highest priority. This is likely because of the high incidence and severity of this health issue, requiring substantial resources for management."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "YPrTt8CGgkV2xVOb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the prevalence of Tuberculosis based on the resources allocated to it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The portion of resources allocated to the management of Tuberculosis can be determined from the coordinate {'Tuberculosis'}<r>[0.21, 0.199, 0.295, 0.219]</r>. If resource allocation is indicative of disease prevalence, one can make certain inferences.\nBased on the above thought, the answer is:\nTuberculosis appears to be less prevalent or possibly less resource-intensive to manage compared to the other health issues, as it receives one of the smallest proportions of resources."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "S2GbS7CY1ALZRKs0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence future resource allocation for health issue management?"
+            },
+            {
+                "from": "gpt",
+                "value": "This data provides insight into the current focus of health resource allocation. The understanding of resource allocation to these health issues, e.g., 'Obesity' at {'Obesity'}<r>[0.593, 0.881, 0.646, 0.901]</r>, can influence future decisions depending on changes in prevalence or efficiency improvements in treatment.\nBased on the above thought, the answer is:\nDepending on changes in prevalence or advancements in treatment, future allocations might shift. If a treatment becomes more efficient, a disease might require fewer resources. Similarly, if a disease becomes more prevalent, it might require more resources. For instance, if obesity levels continue to rise, even more resources may be directed to obesity treatment and prevention in the future."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "cLpfmqMUduWL2bOc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined resource percentage of HIV/AIDS, Cancer, and Diabetes?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add up the percentages of HIV/AIDS, Cancer, and Diabetes. The coordinates for the percentages of these diseases are {'HIV/AIDS'}<r>[0.766, 0.47, 0.827, 0.49]</r>, {'Cancer'}<r>[0.665, 0.194, 0.714, 0.214]</r>, and {'Diabetes'}<r>[0.377, 0.103, 0.439, 0.123]</r> respectively.\nBased on the above thought, the answer is:\n32%"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "MYwB4dY2JTqMcQ8G",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of resources are given to the least funded health issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, Asthma and Tuberculosis are the least funded health issues. The coordinates for the percentages of these diseases are {'Asthma'}<r>[0.301, 0.142, 0.354, 0.162]</r> and {'Tuberculosis'}<r>[0.21, 0.199, 0.295, 0.219]</r>. By adding these, it's possible to find the combined percentage.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "ryZCkFSTQbrY2LWy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in resource allocation percentage between the highest and lowest resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest resource allocation is for 'Cardiac Disease', and the lowest allocation is for 'Asthma'. The coordinates of these percentages are {'Cardiac Disease'}<r>[0.093, 0.604, 0.205, 0.624]</r> and {'Asthma'}<r>[0.301, 0.142, 0.354, 0.162]</r>. I subtract the smallest from the largest to find the difference.\nBased on the above thought, the answer is:\n27%"
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "I4KcK4jOn2JiHWVk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on what you see on the pie chart, can you translate this chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can convert the data from the chart into a CSV format by identifying the title of the chart {\"Population Growth across Different Eras in Human History\"}[0.241, 0.021, 0.722, 0.047] and each of the pie slices that represent time periods {\"Neolithic\"}[0.279, 0.278, 0.482, 0.516], {\"Bronze Age\"}[0.238, 0.286, 0.482, 0.516], {\"Classical Antiquity\"}[0.223, 0.389, 0.482, 0.555], {\"Middle Ages\"}[0.224, 0.516, 0.482, 0.831], and {\"Modern Age\"}[0.283, 0.146, 0.741, 0.886]. These slices represent the proportion of population growth during each era.\nBased on the above thought, the answer is:\nEra,Population Growth (%)\nNeolithic,0.006\nBronze Age,0.07\nClassical Antiquity,0.1\nMiddle Ages,0.2\nModern Age,1.0"
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "FQ7pHiwQwm0CwlrE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the most striking pattern you observe in this pie chart about population growth across different eras?"
+            },
+            {
+                "from": "gpt",
+                "value": "To observe patterns, I need to identify significant changes or outliers in the proportions of population growth across different eras.\nBased on the above thought, the answer is:\nThe most striking pattern is the drastic increase in population growth during the Modern Age compared to the relatively steady increase in previous eras."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "dwJQ8UTq04zQc0vb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might have led to the drastic growth in population during the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the characteristics of the Modern Age - its advancement in technology, industrialization, medicine, etc., relative to the slow-paced advances in previous eras.\nBased on the above thought, the answer is:\nThe drastic growth in population during the Modern Age could be due to advancements in technology, medicine, and industrialization, which could have led to improved living conditions and extended lifespan."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "DjL7LvXMOMg9XBpG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould the slow growth in population during the Neolithic era affect its cultural and societal advancements?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to consider the resources available during the Neolithic era, recognizing that a slower population growth might limit societal exchanges, spread of ideas, and human developments.\nBased on the above thought, the answer is:\nThe slow growth in population during the Neolithic era might have kept communities smaller and more isolated, potentially affecting the pace at which cultural and societal advancements were made."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "8R71MpWvaEtURiBf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which era did population growth exceed 70%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out in which era population growth exceeded 70%, I need to compare the percentage numbers associated with each pie slice.\nBased on the above thought, the answer is:\nModern Age."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "EXt6nsbwVskIn1vG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the population growth increase from the Neolithic era to the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in population growth from Neolithic to the Modern Age, I need to deduct the population growth of the Neolithic era from that of the Modern Age.\nBased on the above thought, the answer is:\nThe population growth increased by 0.994."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "4JWxapyB9KKcedQ5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Classical Antiquity era had witnessed a rise in population growth to 0.2%, how would this have affected the percentage share of the Modern Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the new percentage share of the Modern Age after an increase in population growth during the Classical Antiquity era, I'd need to deduct the increased percentage from the Modern Age's current share.\nBased on the above thought, the answer is:\nThe new share for the Modern Age would be 72.174%."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "KSIDRVaFrMDBNUIU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the raw data reveal about cybersecurity issues over the two years 2018 and 2019 according to the pie chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Cybersecurity Issues: Phishing Attacks and Data Breaches for years 2018 and 2019\"}[0.134, 0.021, 0.829, 0.047], and looking at the individual slices represented by the different labels and colors in the legend - {Phishing Attacks 2018}<r>[0.833, 0.071, 0.987, 0.091]</r>, {Data Breaches 2018}<r>[0.833, 0.101, 0.975, 0.121]</r>, {Phishing Attacks 2019}<r>[0.833, 0.131, 0.987, 0.151]</r> and {Data Breaches 2019}<r>[0.833, 0.161, 0.975, 0.181]</r>, I can figure out the number of phishing attacks and data breaches for both years in terms of their proportions.\nBased on the above thought, the answer is:\nYear,Phishing Attacks,Data Breaches\n2018,14.7%,25.1%\n2019,21.2%,39.1%"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "NHp8ogSCGdHcNq9V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which cybersecurity issue saw the greatest increase from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of the slices for each cybersecurity issue in both years, I can compare the growth rate of phishing attacks and data breaches from 2018 to 2019.\nBased on the above thought, the answer is:\nData breaches saw the greatest increase from 2018 to 2019."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "iVt8JY9Nz8UAlO7P",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend of cybersecurity issues from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this inference, I need to compare the sizes of the pies for each cybersecurity issue for both years.\nBased on the above thought, the answer is:\nThe chart shows an increase in both phishing attacks and data breaches from 2018 to 2019. This suggests that cybersecurity issues became more prevalent over this period."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "D1TvRQHQKgLcizK3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend represented in the data continues, what would you predict about these cybersecurity issues for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict this, I would need to consider the increase in both phishing attacks and data breaches from 2018 to 2019 as represented by the slices in the pie chart.\nBased on the above thought, the answer is:\nIf the trend continues, I would predict an increase in both phishing attacks and data breaches for the year 2020, with data breaches potentially outnumbering phishing attacks as they had for the two previous years."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "NCt7RLIA0gM2op0s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of cybersecurity issues in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage of cybersecurity issues in 2018, I would sum the percentages corresponding to the categories of Phishing Attacks and Data Breaches for that year.\nBased on the above thought, the answer is:\n39.8%"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "S83XJk4Jen9hhGv1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the total percentage of both cybersecurity issues increase from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase, I would subtract the sum of the percentages of Phishing attacks and Data breaches in 2018 from the sum of the percentages of the same categories in 2019.\nBased on the above thought, the answer is:\n19.5%"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "jJKAMvFngF3v8sxO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the proportion of phishing attacks increase from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I would subtract the percentage of phishing attacks in 2018 from the percentage of phishing attacks in 2019.\nBased on the above thought, the answer is:\n6.5%"
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "5tIr0apZLxfcfbHu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the pie chart details into a table format including each year and its corresponding proportion of Culture and Arts Industry Development?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will convert the pie chart data into a table by referring to the pie slices and their corresponding labels and values. The slices represent each year from 2011 to 2020. The sizes of the slices indicate the proportion of development in each year, which are also expressed as percentages in the autotext of each slice.\nBased on the above thought, the answer is:\nYear,Development\n2011,7.5%\n2012,12.5%\n2013,10.0%\n2014,2.5%\n2015,5.0%\n2016,2.5%\n2017,15.0%\n2018,22.5%\n2019,10.0%\n2020,12.5%"
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "SYKDFSBVTdlpXUZO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year did the Culture and Arts industry see the greatest development? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to observe the pie chart and identify the largest slice to determine the year with the greatest development.\nBased on the above thought, the answer is:\nThe Culture and Arts industry saw the greatest development in 2018, as it's the largest slice on the pie chart, indicating the highest proportion of development that year."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "ZgsKr7rL15k87pRe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart illustrate the progression of the Culture and Arts industry from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can explain this by comparing the sizes of the slices which indicate the contributors either growing or decreasing over the years.\nBased on the above thought, the answer is:\nThe pie chart shows that the Culture and Arts industry saw varied progression over the years. Some years, like 2012 and 2018, saw high levels of development, as illustrated by larger slices. Other years, such as 2014 and 2016, showed smaller contributions to the industry, reflected by the smaller slices."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "KK7LuSW29oTcb6G1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data, what could you infer about the stability of the Culture and Arts industry from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The stability of the industry can be inferred by observing the variability in slice sizes from year to year.\nBased on the above thought, the answer is:\nThe Culture and Arts industry appears to be quite volatile over the observed period. The sizes of the slices vary significantly from year to year, signifying considerable fluctuations in the development of the industry."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "akvGWeZ4bbWZaPhl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the year when the Culture and Arts industry had the smallest development?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart, specifically looking at the smallest slice, I can determine the year with the smallest development.\nBased on the above thought, the answer is:\n2014."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "fYKFNz3VlmKe6UO7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the total percentage of development for the Culture and Arts industry across 2011 and 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate this by adding the percentages of development for the years 2011 and 2012 from the pie chart slices.\nBased on the above thought, the answer is:\nThe total is 20%."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "C8HPKVCc7ug8XKRP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average development of the Culture and Arts industry annually, over the span of 10 years from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average development, I can sum up the percentages of development for all the years and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average annual development is 10%."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "rw3N4hLgfDA2a5YQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data from the pie chart and put it into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the chart title \"Proportion of Different Financial System Policies\"{title}[0.283, 0.021, 0.68, 0.047] and the two slices \"Quantitative Easing\"{slice1}[0.223,0.278,0.74,0.886] and \"Fiscal Stimulus\"{slice2}[0.283, 0.146, 0.741, 0.542], I can see that the slices correspond to the two policy types and their respective proportions.\nBased on the above thought, the answer is:\nPolicy Type,Proportion\nQuantitative Easing,60\nFiscal Stimulus,40"
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "mEng1dQJYpNk8ANo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which policy is most frequently used? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which policy is more frequently used, I need to look at the sizes of their respective slices and determine which is larger.\nBased on the above thought, the answer is:\n\"Quantitative Easing\" is the more frequently used policy as it accounts for a larger slice (60%) of the pie chart compared to \"Fiscal Stimulus\" (40%)."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "kmmmJfaoM68uoN0T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart represent the dominance of one policy over the other?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can identify the dominance of one policy over the other by looking at the sizes of the policy slices \"Quantitative Easing\"{slice1}[0.223,0.278,0.74,0.886] and \"Fiscal Stimulus\"{slice2}[0.283, 0.146, 0.741, 0.542].\nBased on the above thought, the answer is:\nThe pie chart visually represents dominance by the size of the slices. The larger a slice is, the more dominant that category is. In this case, \"Quantitative Easing\" is shown as the more dominant policy as its slice is larger."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "l6fmiUkcHfsuOwBT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could this chart suggest about the global financial system's preferred policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the proportion of each policy in the pie chart, I can infer which policy is preferred in the global financial system.\nBased on the above thought, the answer is:\nThis chart suggests that \"Quantitative Easing\" is the preferred policy in the global financial system as it occupies a larger portion (60%) of the chart compared to \"Fiscal Stimulus\" (40%)."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "u0YbaQ6pUwjhvD8p",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between the two types of policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the slices \"Quantitative Easing\"{slice1}[0.223,0.278,0.74,0.886] and \"Fiscal Stimulus\"{slice2}[0.283, 0.146, 0.741, 0.542], I can see their respective percentages and calculate their difference.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "n2Gk6J2ECKEv0sST",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of \"Fiscal Stimulus\" increased by 10%, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 10% to the \"Fiscal Stimulus\" percentage.\nBased on the above thought, the answer is:\nThe new percentage for \"Fiscal Stimulus\" would be 50%."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "3w18EFBVD2uxGhrh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage that the two types of policies occupy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Summing the percentages of both \"Quantitative Easing\"{slice1}[0.223,0.278,0.74,0.886] and \"Fiscal Stimulus\"{slice2}[0.283, 0.146, 0.741, 0.542], I can determine the total percentage.\nBased on the above thought, the answer is:\nThe total percentage that the two types of policies occupy is 100%."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "2rKGWiIYVSOmzOJA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please extract the data depicted by the pie chart and convert it into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the various labels of the slices perceived in {the chart}<r>[0.223, 0.146, 0.741, 0.886]</r> such as {Family}<r>[0.152, 0.478, 0.197, 0.498]</r>, {Friends}<r>[0.551, 0.901, 0.6, 0.921]</r>, {Colleague}<r>[0.665, 0.194, 0.734, 0.214]</r>, and {Neighbors}<r>[0.26, 0.161, 0.331, 0.181]</r>, it can be observed that each slice represents different social relationship categories and how frequent each category is in social networking platforms.\nBased on the above thought, the answer is:\nSocial Relationship,Social Networking\nFamily,100\nFriends,200\nColleague,150\nNeighbors,50"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "q4I0OwIsp6kzOzph",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which social relationship is the most prevalent on social networking platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which social relationship is most prevalent, I need to compare the data frequencies of each type, the highest one represents the most common relationship.\nBased on the above thought, the answer is:\nThe 'Friends' category is the most prevalent, as it has the highest frequency of 200 among the four categories."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "r1sSp9Y3mHfJRR88",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you interpret the distribution of different social relationships on social networking platforms as illustrated by the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to focus on the proportions showed in each pie slice for each category in order to understand their distribution.\nBased on the above thought, the answer is:\nFrom the pie chart, it's clear that the most common type of social relationship on networking platforms are 'Friends' making up 40% of the total. This is closely followed by 'Colleague' at 30%. Social interactions categorized as 'Family' make up 20% of the interactions, while 'Neighbors' are the least common category accounting only for 10% of total social interactions on the platforms."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "CJYEaevjUfIxTj9n",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to make recommendations about ad targeting on social networking platforms based on the chart, what would they be?"
+            },
+            {
+                "from": "gpt",
+                "value": "Recommendations about ad targeting can be made by considering the frequency of individual social relationship categories, the categories with higher frequencies would likely have higher engagement.\nBased on the above thought, the answer is:\n'Friends' and 'Colleague' categories should be the primary targets as they make up 70% of total social interactions. However, this does not mean 'Family' and 'Neigbors' should be overlooked, as they collectively represent 30% of interactions. Essentially, the target audience should match the nature of the advertisement being run."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "ozo2invVD6L1GE1j",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the frequencies of 'Family' and 'Neighbors'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to get the frequency of 'Family', which is 100, and 'Neighbors', which is 50, then sum them up.\nBased on the above thought, the answer is:\n150"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "6dMSTtPXHMgyawlD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of social relationships represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total number, I need to sum up the frequencies of all the social relationship categories in the chart.\nBased on the above thought, the answer is:\nThe total frequencies are 100 (Family) + 200 (Friends) + 150 (Colleague) + 50 (Neighbors) = 500"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "Xb6hKy4e0r8JcNG7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the frequency of 'Friends' increases by 50 units, what would be the new total frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total frequency, I need to add the increment of 'Friends' to the existing totals.\nBased on the above thought, the answer is:\nThe new frequency for 'Friends' would be 200 + 50 = 250. The new total frequency would be 100 (Family) + 250 (Friends) + 150 (Colleague) + 50 (Neighbors) = 550"
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "jS4lxS3EU59RkVml",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a CSV format table according to the information given?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the information contained within the pie chart {Gastronomy Culture Analysis: Cuisine Preferences}[0.273, 0.021, 0.69, 0.047] and the segments indicated by the colored sections of the chart {the pie slices}[{\"Japanese\"}[0.223, 0.278, 0.482, 0.712]; {\"Chinese\"}[0.262, 0.516, 0.68, 0.886]; {\"Italian\"}[0.482, 0.232, 0.741, 0.754]; {\"French\"}[0.419, 0.146, 0.648, 0.516]; {\"Mexican\"}[0.283, 0.157, 0.482, 0.516]], I can see that each segment of the pie chart represents a different cuisine type and the percentage of foodies who prefer that type of cuisine.\nBased on the above thought, the answer is:\nCuisine Type,Japanese,Chinese,Italian,French,Mexican\nFoodie Preference,20%,30%,25%,15%,10%"
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "XQKVbu5UZ5zy0Ttb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data provided, which cuisine is the most preferred among foodies and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "After studying the graphic representation of the chart, I can see that each slice corresponds to a different cuisine. The widest slice would represent the most preferred cuisine.\nBased on the above thought, the answer is:\nChinese cuisine is the most preferred among foodies as it takes up the largest section of the pie chart, indicating it as the most popular."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "UCYbyCr8UejpusKh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the pie chart guide us in understanding global foodie culture?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand global foodie culture based on the chart, I need to analyze the sizes of each pie segment, representing the preference for each type of cuisine.\nBased on the above thought, the answer is:\nThe chart indicates that Chinese and Italian cuisines are heavily favored in global foodie culture. These cuisines are quite popular, likely due to the variety of flavors and dishes they offer. Japanese, French, and Mexican cuisines are not as popular in comparison, suggesting they might be more of an acquired taste for foodies globally."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "UBCA3Iq9Qs3wmTyL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a restaurant wants to cater to a wide range of foodie preferences, which type of cuisine should it focus based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The restaurant should focus on the cuisine type that holds the largest slice of the pie chart, as it represents the most popular foodie preference.\nBased on the above thought, the answer is:\nIf a restaurant wants to cater to a wide range of foodie preferences, it should focus on Chinese cuisine, as the chart shows that it is the most popular cuisine type among foodies."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "XzdEBNNyqBDVIVTt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the distribution of the foodie preference for the five cuisines, what is the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, to find the highest percentage, I need to observe the foodie preferences for each category of cuisine and identify the one with the largest preference.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "4gieb85bofoUAso8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data provided, what is the total percentage of foodie preferences for Chinese and Italian cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage of foodie preference for Chinese and Italian cuisine, I need to sum up the percentages given for both categories.\nBased on the above thought, the answer is:\n55%"
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "HbDDWaajDUUX4DW8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the preference for Chinese cuisine exceed that for French cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the percentage of preference for French cuisine from the percentage of preference for Chinese cuisine.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "p8Fx9MRAogCsJywS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart that you observed, could you please provide a structured table with the percentage distribution of social media platforms for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Social Media Usage in 2020\"}[0.367, 0.021, 0.596, 0.047] and {the pie slices}[{\"Facebook\"}[0.223, 0.278, 0.482, 0.584]; {\"Instagram\"}[0.227, 0.516, 0.482, 0.857]; {\"Twitter\"}[0.381, 0.516, 0.602, 0.886]; {\"LinkedIn\"}[0.482, 0.516, 0.728, 0.844]; {\"Snapchat\"}[0.482, 0.331, 0.741, 0.628]; {\"YouTube\"}[0.482, 0.147, 0.706, 0.516]; {\"TikTok\"}[0.283, 0.146, 0.498, 0.516], we can see that each slice of the pie plot corresponds to a different social media platform and its percentage representation in 2020.\nBased on the above thought, the answer is:\nYear,Facebook,Instagram,Twitter,LinkedIn,Snapchat,YouTube,TikTok\n2020,14.0%,15.7%,14.0%,12.4%,13.2%,15.7%,14.9%"
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "p7wRf6Pkwi0A0E7D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis of the pie chart, which was the most heavily used social media platform in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the most heavily used platform, I need to identify the pie slice with the largest percentage.\nBased on the above thought, the answer is:\nInstagram and YouTube were the most heavily used social media platforms in 2020, both with a usage percentage of 15.7%."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "Jf0XyyU2GFeroCiv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom studying the chart, could you derive any trends about the growth of social media usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the percentages of each social media platform.\nBased on the above thought, the answer is:\nWhile specific growth rates over years are not provided on the pie chart, in the year of 2020, Instagram, YouTube, and Facebook are leading in usage, indicating that photo and video sharing platforms might be experiencing significant growth."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "6Ba7h4Ekc19TlsMQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, how might the data influence marketing strategies for different businesses using these platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the different percentages of social media usage might impact business decisions.\nBased on the above thought, the answer is:\nBusinesses may allocate more resources to marketing strategies involving Instagram and YouTube, given their higher usage percentages. Meanwhile, businesses targeting professional customers might also consider LinkedIn despite its slightly lower usage, as the user demographics could be more valuable. As for platforms like Snapchat and TikTok with a considerable presence, businesses targeting younger audiences might see significant value."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "LxjnMRrVvrGCtIaO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total percentage of social media usage for Facebook, Instagram, and YouTube in 2020 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add the percentages of Facebook, Instagram, and YouTube.\nBased on the above thought, the answer is:\n45.4%."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "G4RkN43AveNroyBi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we consider Twitter's usage percentage as the base, which platform has shown a usage increase and by how much percentage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the answer, I need to compare the percentages of all platforms to Twitter's percentage.\nBased on the above thought, the answer is:\nInstagram and YouTube have shown a usage increase by 1.7% each."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "y8dOnHq1uDXDG6xk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is LinkedIn's percentage of total social media usage as shown in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I just need to refer to the LinkedIn slice's percentage.\nBased on the above thought, the answer is:\n12.4%."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "MYqg4bgM6cDIfWPU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, could you help me convert the data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a structured table, I need to label each row with the name of each sector and fill in the corresponding percentages. The sectors are represented by different slices in the pie {\"Healthcare\"}<r>[0.223, 0.278, 0.482, 0.885]</r>, {\"Ecommerce\"}<r>[0.464, 0.516, 0.722, 0.886]</r>, {\"Transportation\"}<r>[0.482, 0.32, 0.741, 0.655]</r>, {\"Education\"}<r>[0.482, 0.147, 0.701, 0.516]</r>, {\"Entertainment\"}<r>[0.283, 0.146, 0.5, 0.516]</r>.\nBased on the above thought, the answer is:\nApplication Category,Users(%)\nHealthcare,35\nEcommerce,20\nTransportation,15\nEducation,15\nEntertainment,15"
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "rbB8NFBUBE0iemX0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which application of artificial intelligence is most prevalent? Please explain why it is so."
+            },
+            {
+                "from": "gpt",
+                "value": "The application of artificial intelligence that is the most prevalent is represented by the largest slice of the pie. So I need to identify which slice of the pie is the largest.\nBased on the above thought, the answer is:\nThe application of artificial intelligence that is most prevalent is \"Healthcare\" with 35%. This may be due to the vital role artificial intelligence plays in diagnosing diseases, analyzing patient data, and so on, making it extremely valuable in the healthcare industry."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "Y5dXTZ8X9nbM5Etr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data in the pie chart, what inference can you make about the future development of artificial intelligence applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I consider the proportions represented by the various sectors.\nBased on the above thought, the answer is:\nThe data in the pie chart indicate that AI is most adopted in the healthcare sector. This could suggest that there will be more advancements or breakthroughs in AI for healthcare in the future. However, given that other sectors like Ecommerce, Transportation, Education, and Entertainment each hold a significant 15% user share, it is likely that these sectors will continue to invest in and develop AI technology as well."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "06mCkuYgZr0p4SLS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the percentages of AI applications, how would you suggest resource allocation for AI development in different industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would consider the percentages of AI usage in different industries. The industry with the highest percentage may require more resources because they have the most users.\nBased on the above thought, the answer is:\nSince the \"Healthcare\" industry has the largest share of AI users at 35%, it may be suggested that the highest number of resources be allocated to it. However, consider the equal distribution of the other four categories, each with a 15% user share. It may be wise to allocate more resources there as well, as this could indicate potential growth with the right investment. This way, advancements in AI could be promoted across different sectors."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "oYrMZjmBgqKhgnDN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many categories of AI applications account for more than 50% of users?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering each slice, I need to find categories whose percentage is greater than 50% to answer this question. Let's consider each slice like {\"Healthcare\"}<r>[0.223, 0.278, 0.482, 0.885]</r>, {\"Ecommerce\"}<r>[0.464, 0.516, 0.722, 0.886]</r>, {\"Transportation\"}<r>[0.482, 0.32, 0.741, 0.655]</r>, {\"Education\"}<r>[0.482, 0.147, 0.701, 0.516]</r>, {\"Entertainment\"}<r>[0.283, 0.146, 0.5, 0.516]</r>.\nBased on the above thought, the answer is:\nThere is 1 category, Healthcare."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "zcrlnHPX6WuB6W6y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in user share percentage between the Healthcare category and the Ecommerce category?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of Ecommerce from that of Healthcare.\nBased on the above thought, the answer is:\nThe difference is 15%."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "P3xjHUlsl4hRT9b0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the share of the Entertainment category were to increase by 5%, what would be the new total percentage of the AI applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the share of Entertainment increased by 5%, I need to add 5% to the percentage of Entertainment, then sum up the percentages from each category to get the new total percentage.\nBased on the above thought, the answer is:\nThe new total percentage would be 105%."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "yBO1JTJFK1VgTGsV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you help me convert the transportation trend data from 2016 to 2020 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the data labels at the bottom right side of the chart {legend}<r>[0.908, 0.063, 0.993, 0.219]</r>, it is clear that the slices represent the transportation trends for the years 2016 to 2020. Each slice's corresponding percentage is also visible. Compiling this information into a structured table will give us the desired CSV format.\nBased on the above thought, the answer is:\nYear,Transportation Trends\n2016,16.9%\n2017,18.0%\n2018,19.8%\n2019,20.7%\n2020,24.7%"
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "CYAKqdiXohIwfcD9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you determine the year with the highest increase in transportation trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to observe which slice of the pie is the largest. The largest slice represents the year with the highest percentage, which means the largest increase in transportation trends.\nBased on the above thought, the answer is:\nThe year 2020 had the highest increase in transportation trends, as it occupies the largest portion (24.7%) on the pie chart."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "sntKfdjChj1vDwvu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you extract about the duration from 2016 to 2020 from the transportation trends shown?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract insights, I'd need to summarize the overall trends visible in the pie chart.\nBased on the above thought, the answer is:\nFrom this chart, it's clear there's a steady increase in transportation trends from 2016 to 2020, with a noticeable jump in 2020. This continued growth could imply several factors, such as improved technology, increased demand or change in lifestyle patterns."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "7gDdnHxtkJaeXRP9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could these transportation trends impact planning for future transportation initiatives?"
+            },
+            {
+                "from": "gpt",
+                "value": "To give a plausible impact analysis, I would need to focus on the trend behaviors over the years.\nBased on the above thought, the answer is:\nThe upward trend from 2016 to 2020 indicates a growing need for transportation and logistics solutions. An especially significant increase in 2020 could suggest that unexpected events may drastically elevate demand. Future planning should therefore accommodate not only steady growth but also potential surges, possibly by maintaining flexible supply capacities or developing adaptive strategies."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "E67YZPveeKRJ4fGp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in transportation trends between the years 2016 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage for 2016 {\"2016\"}<r>[0.223, 0.278, 0.482, 0.647]</r> from the percentage for 2020 {\"2020\"}<r>[0.283, 0.146, 0.645, 0.516]</r>.\nBased on the above thought, the answer is:\n7.8%"
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "hWhdkmFQqRkOPq0M",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 1% were to represent 10 transportation trends, how many more trends were there in 2020 compared to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the difference in percentage between the two years and then multiply by 10.\nBased on the above thought, the answer is:\n78 additional trends"
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "kc1LuhkqY5QKS1cx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year showed the smallest increase in transportation trends compared to its previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the differences in percentages between each consecutive years and find the smallest value.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "eIrYF0Mc6fBAS8PY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the pie chart data into a structured tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents the distribution of different policies in the Global Financial System {\"Percent Representation of Different Policies in Global Financial System\"}[0.189, 0.021, 0.774, 0.047], as evident from the distinct slices, I can note down their individual representations.\nBased on the above thought, the answer is:\nPolicy, Percentage\nInflation Control, 15\nFiscal Policy, 20\nMonetary Policy, 25\nCapital Control, 10\nExchange Rate, 8\nMarket Liberalization, 7\nTax Policy, 10\nFinancial Stability, 5"
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "i5OF2CpfC7hRz6tU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich financial policy seems to have the highest hold in the global financial system and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart, the policy having the largest slice corresponds to the most dominant financial policy {Monetary Policy}<r>[0.841, 0.131, 0.951, 0.151]</r>.\nBased on the above thought, the answer is:\nThe Monetary Policy seems to have the highest hold in the global financial system as it has the largest proportion of 25% among all the policies."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "T5uyWTT0yj2yLgYi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred regarding the policy \"Market Liberalization\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the slice representing \"Market Liberalization\" {Market Liberalization}<r>[0.841, 0.221, 0.987, 0.241]</r>.\nBased on the above thought, the answer is:\nThe \"Market Liberalization\" policy seems to have a minimal share in the global financial scenario, indicating that global economies might not be relying heavily on market liberalization at the level it was analyzed."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "Yu9Mbb7LzR2COn1l",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data contribute to future policy making decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the overall implications of the pie chart on the weighted considerations of different financial policies.\nBased on the above thought, the answer is:\nConsidering the data, policies such as Monetary Policy and Fiscal Policy which have high percentages could continue to be a focus in future policy-making decisions owing to their significant representation. On the contrary, policies like Market Liberalization and Financial Stability that have less representation might need increased focus and strategizing to enhance their effective implementation. The data could thus help in directing future policy adjustments based on current representation."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "LTjL6NM3a7nqO9aL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage sum of policies related to Capital Control and Tax Policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must combine the percentages of Capital Control {Capital Control}<r>[0.841, 0.161, 0.945, 0.181]</r> and Tax Policy {Tax Policy}<r>[0.841, 0.251, 0.909, 0.271]</r> as represented in the pie chart.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "ISDw9lWsQW6r3OMw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does Financial Stability contribute to the total policy representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need think to find the slice corresponding to Financial Stability {Financial Stability}<r>[0.841, 0.281, 0.964, 0.301]</r>.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "qnfObGlDUmSilW4V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the new total percentage of all policies if Inflation Control's contribution increases by 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to find the slice representing Inflation Control {Inflation Control}<r>[0.841, 0.071, 0.953, 0.091]</r>, increase its value by 5% and then sum up all the policy percentages again.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "sywYz50lEmgqRezV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you assist me in converting the pie chart data into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I am using the pie chart with title {Design Innovation distribution from 2014 to 2020}<r>[0.276, 0.021, 0.687, 0.047]</r> and {the sector slice}<r>[{\"2014\"}[0.321, 0.42, 0.356, 0.44]; {\"2015\"}[0.31, 0.567, 0.354, 0.587]; {\"2016\"}[0.384, 0.7, 0.428, 0.72]; {\"2017\"}[0.51, 0.716, 0.554, 0.736]; {\"2018\"}[0.607, 0.575, 0.651, 0.595]; {\"2019\"}[0.577, 0.36, 0.62, 0.38];{\"2020\"}[0.416, 0.293, 0.46, 0.313]]</r>.\nBased on the above thought, the answer is:\nYear,Percentage\n2014,9.5%\n2015,12.0%\n2016,13.1%\n2017,13.7%\n2018,15.7%\n2019,17.4%\n2020,18.7%"
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "EGqGNoD1aKSilbvx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw the most significant design innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the largest proportion on the pie chart would be considered as having the most significant design innovation.\nBased on the above thought, the answer is:\n2020 saw the most significant design innovation as it constitutes the largest slice of the pie with a proportion of 18.7%."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "Xz1BUp6HTIMKmeYQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about trends in Design Innovation from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of slices for each year, I can draw conclusions on the trend of design innovation.\nBased on the above thought, the answer is:\nFrom the pie chart, we can infer that there has been a steady increase in Design Innovation from 2014 to 2020. This is evident from growing sizes of the slices, signifying that each year has seen more contribution to design innovation compared to previous years."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "3SkyubXEEmiFQ4w0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we speculate why there was a significant rise in Design Innovation in 2020 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the chart does not provide direct reasons, using the larger proportion of Design Innovations in 2020, we can speculate about the reasons.\nBased on the above thought, the answer is:\nWhile the chart does not offer specific reasons for the significant rise in Design Innovation in 2020, considering this year makes up the largest slice of the pie, we could speculate that there might have been increased investment, groundbreaking innovations or perhaps a surge in the need for new design solutions that year. However, to accurately understand the reason, we would need more detailed information that is not present on the pie chart."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "nSoxcmWWm4bl5QW9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the smallest proportion of design innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart, and comparing the sizes of each slice, I can identify the year with the smallest proportion.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "fRdNPxPB8G8AuRmM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion of design innovations for the years 2017, 2018 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the proportions of the years 2017, 2018 and 2019, I can get the total proportion.\nBased on the above thought, the answer is:\n46.8%"
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "vZiLR0qX1nyDmL6t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"2019\" increased its proportion by 5%, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion, I need to add 5% to the \"2019\" percentage.\nBased on the above thought, the answer is:\n22.4%"
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "4EmLk8cXpEjdJzFH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help to list out the data from the pie chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by analyzing the chart, I see the title which shows {\"Technology Companies and Innovative Business Revenue\"}[0.243, 0.021, 0.72, 0.047]. Each {slice}[{'Apple'}[0.223, 0.278, 0.482, 0.842]; {'Microsoft'}[0.359, 0.516, 0.575, 0.886]; {'Amazon'}[0.482, 0.342, 0.741, 0.861]; {'Google'}[0.478, 0.146, 0.71, 0.516]; {'Facebook'}[0.35, 0.146, 0.482, 0.516]; {'Netflix'}[0.318, 0.197, 0.482, 0.516]; {'Tesla'}[0.283, 0.229, 0.482, 0.516]] represents a company and the corresponding proportion of revenue it represents.\nBased on the above thought, the answer is:\nCompany,Revenue Percentage\nApple,28.3%\nMicrosoft,13.7%\nAmazon,26.9%\nGoogle,17.5%\nFacebook,8.3%\nNetflix,2.4%\nTesla,3.0%"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "mYqPQATsPFi3RgNn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you identify the company with the most significant revenue share?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of different slices in the pie chart. The company with the largest slice has the highest revenue share.\nBased on the above thought, the answer is:\nApple has the highest revenue share of 28.3% among the companies."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "RXx2frxBEst6OOG8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow are the revenues distributed among these technology companies and innovative businesses?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will analyze the proportions of each pie slice representing different companies.\nBased on the above thought, the answer is:\nThe revenues are concentrated primarily among Apple, Amazon, and Google which collectively hold 72.7% of the total revenue. The remaining amount is distributed among Microsoft, Facebook, Netflix, and Tesla."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "KXNbbY9ZjauvyZbP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the revenue distributions, how should these companies be ranked?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the proportions of each company to make the ranking decision.\nBased on the above thought, the answer is:\nThe companies should be ranked as follows: Apple (1st) with 28.3%, Amazon (2nd) with 26.9%, Google (3rd) with 17.5%, Microsoft (4th) with 13.7%, Facebook (5th) with 8.3%, Tesla (6th) with 3.0%, and Netflix (7th) with 2.4% of total revenue share."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "25L2bOPhIQumXsrv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total revenue share of Amazon and Google?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I add {Amazon's}[0.482, 0.342, 0.741, 0.861] proportion (26.9%) to {Google's}[0.478, 0.146, 0.71, 0.516] proportion (17.5%), I will get the total revenue share of both companies.\nBased on the above thought, the answer is:\n44.4%"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "4V2f4MSjuwIaSDuK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more revenue share does Apple have than Google?"
+            },
+            {
+                "from": "gpt",
+                "value": "By computing the difference between {Apple's}[0.223, 0.278, 0.482, 0.842] slice (28.3%) and {Google's}[0.478, 0.146, 0.71, 0.516] slice (17.5%), I can determine how much more revenue share Apple has over Google.\nBased on the above thought, the answer is:\n10.8%"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "zjiBJFhD889xWcVR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average revenue share for all the companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I add the proportions of each company and then divide by the total number of companies, I can compute the average.\nBased on the above thought, the answer is:\n14.3%"
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "kI14BizrpicKJSmf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you transform the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referencing the {chart's title}<r>[0.337, 0.021, 0.626, 0.047]</r> and the {individual pie slices}<r>[0.482, 0.516]</r> with their respective sizes and labels, I can infer the numerical values representing the market share of each industry.\nBased on the above thought, the answer is:\nIndustry, Market Share %\nTextiles,20\nOil and Gas,15\nAutomobile,29\nSoftware,22\nPublishing,3\nAgriculture,5\nCosmetics,3\nTelecommunication,3"
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "H8eFhhSAMHgNtSk7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which industry has invested the most and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the visual pie slices, the sector with the largest pie slice can be said to have invested the most.\nBased on the above thought, the answer is:\nThe Automobile industry is the one that has invested the most. This can be inferred from the fact that it holds the largest market share, which is usually a result of significant investment and operations in that sector."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "eOFxC98nZGKQ6Pn0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data in the diagram, what conclusions can be drawn concerning the distribution of the market share among these industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "As I examine the information on each sector in the pie chart, I will conclude based on the size of each sector and their respective percentages of the total market.\nBased on the above thought, the answer is:\nThe pie chart shows a diverse allocation of market shares among the industries; however, a sizable portion of the market is dominated by the Textiles, Automobile, and Software industries. The other industries \u2013Oil and Gas, Publishing, Agriculture, Cosmetics, and Telecommunication\u2013 combined makeup just a fraction of the market share. This suggests a competitive and possibly mature market in the dominant sectors."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "324Oej3IbkylBi9R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this data, how might a new entrant into the Automobile industry view their prospects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to consider the implications of the high market share currently held by the Automobile industry.\nBased on the above thought, the answer is:\nA new entrant might see this high market share as a sign of a potentially saturated market, indicating strong existing competition. They might need to invest significantly in order to secure a share of this market which could be a challenging prospect. However, a large market share might also suggest a large market size, which might have room for more companies to operate profitably."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "EFq4bzCVydDiZoVJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two industries collectively represent 42% of the market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Checking from the data received, I will add the market shares of each pair of industries to see which ones total to 42%.\nBased on the above thought, the answer is:\nThe Textiles and Software industries."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "nwEpr4pEcxEq9yVN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the market share difference between the Automobile and Textiles industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference, I will deduce the market share of Textiles from that of the Automobile industry.\nBased on the above thought, the answer is:\nThe difference is 9%."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "HNcSfEmbA1smulU5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Software industry was to increase its market share by 8%, what would be the new total market share of all industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new market share for the Software industry would be its current share plus the increment. I will then add this to the sum of market shares of all other industries.\nBased on the above thought, the answer is:\nThe total market share would still be 100% as market shares are represented in percentages of the total market."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "3SQ4IXYjbDuUrVUE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the percentage of the imports in table format from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the chart's title {\"Global Imports from 2010 to 2019\"}[0.34, 0.021, 0.623, 0.047] and the slices in the pie chart, each slice represents the percentage of imports in their respective years which are detailed in the legend {legend}[0.908, 0.063, 0.993, 0.368]. From this, I can create a table.\nBased on the above thought, the answer is:\nYear,Percentage\n2010,7.6%\n2011,8.9%\n2012,8.3%\n2013,10.2%\n2014,9.5%\n2015,11.2%\n2016,10.8%\n2017,9.9%\n2018,11.5%\n2019,12.1%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "KJbcBpc1IS5o4lbD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest percentage of imports? Please explain how you determined this."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine all the slices of the pie chart to locate the one with the highest percentage.\nBased on the above thought, the answer is:\nThe year 2019 had the highest percentage of world imports (12.1%). This is determined by comparing the percentages represented by each slice of the pie chart."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "Kh2q1IybjMCgyvbz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the trend of global imports based on the chart from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend, I can look at the percentage weights of the slices from year to year.\nBased on the above thought, the answer is:\nThe chart appears to show an overall upward trend in world imports from 2010 to 2019, despite some fluctuations."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "0Lnzq3ZH0bH2ISdO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were an economic analyst, how could this chart help you in understanding the global trade trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to consider the utility of the chart in understanding fluctuations and trend in global trade.\nBased on the above thought, the answer is:\nThis chart provides a visual summary of the percentage of world imports from 2010 to 2019. As an economic analyst, it allows me to quickly grasp the trend and fluctuations over the decade. It would especially highlight years of significant changes such as years with drastic increases or decreases in percentage."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "qClG5qoCqY5SKpqG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the sum of imports percentage during the three community years 2010, 2011, and 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will add the percentages of imports for the years 2010, 2011, and 2012 from the pie chart slices.\nBased on the above thought, the answer is:\n24.8%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "HtC3O3ycdIHyjiSp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the imports percentage increase from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate this by subtracting the 2016 percentage from the 2017 percentage.\nBased on the above thought, the answer is:\n-0.9%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "PfIG09vb5Nv9R5Zz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the imports percentage differ between the years 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the 2010 imports percentage from the 2019 percentage.\nBased on the above thought, the answer is:\n4.5%"
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "nqF5Cad2J4W1QAya",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a CSV table of the scientific research and technological innovation data from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the data from the pie chart, using the {pie chart slices} <r>[0.223, 0.146, 0.741, 0.886]</r>, I can determine the number of scientific research and technological innovation items from each year.\nBased on the above thought, the answer is:\nYear,Scientific Research and Technological Innovation\n2015,83\n2016,92\n2017,104\n2018,117\n2019,124\n2020,156"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "eQVhvCuLexB3svHi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine which year the scientific research and technological innovations surged, judging from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to look for the year that has the largest slice in the pie chart.\nBased on the above thought, the answer is:\nThe year is 2020, as it has the largest slice in the pie chart, indicating a sudden surge of scientific research and technological innovations."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "MjZ3Gbh4pa3wo6sv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the pie chart show a trend in scientific research and technological innovation over the years from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to consider how the percentages change from year to year in the pie chart.\nBased on the above thought, the answer is:\nYes, the pie chart shows an increasing trend in scientific research and technological innovation over the years from 2015 to 2020, as evidenced by the gradually increasing size of the pie slices."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "NBa8AfHzBqkJ1iwz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year might require the most funding to accomplish the scientific research and technological innovations and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to look for the year that has the highest number of research and technological innovations indicating maximum efforts and hence possible higher fund requirements.\nBased on the above thought, the answer is:\n2020 might require the most funding as it had the highest number of scientific research and technological innovations, as indicated by it having the largest slice on the pie chart."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "BtLiaQHSzJ4iTJAb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year showed the most significant increase in percentage of scientific research and technological innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the year with the most significant percentage increase, I need to compare the percentage of each year in the pie chart and find the one with the largest change.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "PprY1voC0keFdor8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of scientific research and technological innovation from 2015 to 2020 in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total percentage in a pie chart is always 100%. So the total percentage of all categories in the pie chart is 100%.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "ZMguwbPyr13SZ2SN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the average number of scientific research and technological innovation from 2015 to 2020."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average number of scientific research and technological innovations, I need to sum up the scientific research and technological innovations of the six years and divide by the number of years.\nBased on the above thought, the answer is:\n113"
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "BMu6kkw0ANl6xb5E",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie plot viewed, can you tabulate the data about Alien Encounters from 2022 to 2028 with the relative percentage contribution for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the {subtitle}<r>[0.331, 0.021, 0.632, 0.047]</r>, we are dealing with numbers of alien encounters from 2022 to 2028. The specific data points and percentages can be obtained from the seven pie slices ranging from 2022 to 2028<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nYear,Encounters (%)\n2022,0.4%\n2023,1.1%\n2024,3.7%\n2025,8.2%\n2026,19.7%\n2027,27.9%\n2028,39.0%"
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "C6LxxQyqAWCfIzSX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the information provided by the chart, can you infer any trend about the alien encounters from 2022 to 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportional size of each slice on the pie chart, from 2022 to 2028<r>[0.223, 0.146, 0.741, 0.886]</r>, to infer any emerging trends.\nBased on the above thought, the answer is:\nThe alien encounters increase significantly year over year, with a particularly steep increase from 2025 onwards, suggesting more frequent alien encounters in the future."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "sGjJqUHBekRyRJ6w",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the significance of the year 2026 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should interpret the relevant pie slice for 2026<r>[0.225, 0.516, 0.482, 0.879]</r> along with the trend shown in previous years and subsequent years to make an inference about the year 2026.\nBased on the above thought, the answer is:\nThe year 2026 represents a pivotal point in the chart as it is where the chart suggests the proportion of alien encounters first surpasses the 20% mark. It thereby marks a significant increase in the number of encounters compared to previous years and this hike continues into subsequent years as well."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "tD0mTB1pxnz4bK13",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you anticipate the trend of alien encounters in the following years after 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would have to analyze the pace and direction of the increase in the proportions, as shown by the slices corresponding to the years leading up to 2028<r>[0.223, 0.146, 0.741, 0.886]</r> in order to predict the trends in the subsequent years.\nBased on the above thought, the answer is:\nGiven the exponential growth seen from 2022 to 2028, it's reasonable to deduce that the number of alien encounters would continue to increase at a similar, or possibly even faster, pace in the years following 2028."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "2TXgqdwq45CaEh6f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in alien encounters from 2022 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the percentage of encounters in 2022 from the percentage in 2023 based on the pie slices corresponding to these years<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe percentage increase was 0.7%."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "xGg6NSYwXXigd7wb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year noted a nearly 10% increase in the proportion of alien encounters compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with a near 10% increase in alien encounters, I need to compare the differences in proportions between consecutive years described by the pie slices<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe year 2025 showed a nearly 10% increase in proportion compared to 2024."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "t790BWODWLZd5NMB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the cumulative proportion of alien encounters from 2022 to 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the cumulative proportion, I need to sum up the proportions from 2022 to 2025 based on the respective slices in the pie chart<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe cumulative proportion of alien encounters from 2022 to 2025 is 13.4%."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "RD6YA4gQhKg59TQp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you create a structured CSV format for the year 2020 for both products?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart title {\"Product Sales in 2020\"}<r>[0.391, 0.021, 0.572, 0.047]</r>, there are two sectors in the pie chart {for \"Beer\" and \"Soda\"}<r>[0.223, 0.146, 0.741, 0.886]</r>. \"Beer\" has a percentage of 92.0% and \"Soda\" has a percentage of 8.0% in 2020. This can be converted to a CSV format.\nBased on the above thought, the answer is:\nProduct,2020\nBeer,92%\nSoda,8%"
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "T3IOfuDGTbjz7KoA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product was more popular in 2020? Explain the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity can be determined from the proportion of each product in the pie chart {for \"Beer\" and \"Soda\"}<r>[0.223, 0.146, 0.741, 0.886]</r> in 2020.\nBased on the above thought, the answer is:\nThe product \"Beer\" was more popular in 2020 because it had a higher proportion (92.0%) compared to \"Soda\" (8.0%)."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "o74trleldgFkfqp9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the sales preference for the products changed over the years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the distribution of sales from the pie chart for 2020 and comparing it to the provided raw data can infer how the sales preferences have evolved.\nBased on the above thought, the answer is:\nOver the years, the preference has shifted towards \"Beer\" as it has a consistently higher sales share, reaching up to 92% in 2020. While for \"Soda\", there is a decreasing trend in sales share, dropping to 8% in 2020."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "7CJdfev7iGHn3Ew7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what predictions can you make about future trends in product sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "The future trends can be predicted by looking at the consistent sales percentage of \"Beer\" and the diminishing sales percentage of \"Soda\" over the years.\nBased on the above thought, the answer is:\nLooking at the sales trend up until 2020, it could be predicted that \"Beer\" sales will continue to dominate over the \"Soda\" sales. However, without clear data on factors which may impact such sales and trends, the prediction comes with uncertainty."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "uM788cNc6vQWuMAM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total percentage represented in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the pie chart {for \"Beer\" and \"Soda\"}<r>[0.223, 0.146, 0.741, 0.886]</r>, \"Beer\" represents 92.0% and \"Soda\" represents 8.0% in 2020. Therefore, the total percentage would be the sum of these two percentages.\nBased on the above thought, the answer is:\nThe total percentage in 2020 is 100%."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "B0jhz3PrcvOnkaj5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf \"Beer\"'s percentage decreased by 5% in 2021, what would be the new proportion for \"Beer\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "If \"Beer\"'s percentage decreased by 5%, it would be equal to the original percentage of \"Beer\" (92.0%) subtracted by 5%.\nBased on the above thought, the answer is:\nThe new proportion for \"Beer\" would be 87%."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "N03GJeyD3nngiN0r",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf \"Soda\" sales increased by 2% in 2021, what would be \"Soda\"'s new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "If \"Soda\"'s sales increased by 2%, it would be equal to the original percentage of \"Soda\" (8.0%) added by 2%.\nBased on the above thought, the answer is:\nThe new proportion for \"Soda\" would be 10%."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "PwuZ3VqGBa2Cf70x",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the pie chart data, create a table representation in the CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the legend located at the right of the chart {the legend}[0.861, 0.063, 0.993, 0.309], and the percentage values found in the corresponding pie slices {the pie slices}[0.223, 0.146, 0.741, 0.886], I can identify each energy type and their percent allocation.\nBased on the above thought, the answer is:\nEnergy Type, Allocation (%)\nSolar, 10\nWind, 20\nBio, 5\nGeothermal, 8\nFossil, 30\nNuclear, 15\nHydro, 10\nWave, 2"
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "ZrbJjwIJ0RI1ZpAF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of resource is expected to dominate future energy production, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, the largest slice represents the resource type with the highest allocation, suggesting that it dominates future energy production.\nBased on the above thought, the answer is:\nFossil energy is expected to dominate future energy production because it has the highest allocation, with 30% of total resources."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "gKia8rmBRQHHNDj9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be deduced about the future energy strategy from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the trends and dominance of certain energy types to deduce future energy strategy.\nBased on the above thought, the answer is:\nThe chart suggests a continued reliance on non-renewable resources like Fossil and Nuclear energy, which together constitute nearly half of the total allocation. However, there is a significant allocation to renewable resources like Wind, Solar, and Hydro."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "Jvnivha8lYH88gcC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence future investment decisions in the energy sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "The allocation percentages can provide clues about where investments may flow in the energy sector.\nBased on the above thought, the answer is:\nHigh allocation percentages in Fossil and Nuclear energy may attract more investments in these sectors due to their dominant roles in the energy landscape. However, Wind and Solar also constitute a considerable part of the mix, indicating opportunities for investment in renewable energy technologies. The low allocation for Wave energy may suggest a lack of feasibility or commercial viability, thereby discouraging investments in this sector."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "WwPt4XB0SvKC3nNy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which two energy types combined to make up 20% of the total allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at each pie slice, I can infer that the combined energy type allocation percentages that sum up to 20%.\nBased on the above thought, the answer is:\nSolar and Hydro."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "YbjmeUxXsqAMkXXf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum allocation of the three lowest contributing energy types?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart and identifying the three energy types with the smallest allocations, I can calculate their total combined allocation.\nBased on the above thought, the answer is:\nThe three smallest are Bio with 5%, Geothermal with 8% and Wave with 2%. So, their sum allocation is 5% + 8% + 2% = 15%."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "6Hzj6zqbhg3SUK34",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the future allocation of Wind energy was halved, what would be the new total percentage of all energy types?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the current percentage of Wind energy. Then I would need to halve this allocation, subtract the original allocation from the total, and then add the halved allocation to get the new total.\nBased on the above thought, the answer is:\nThe current allocation for Wind is 20%. Halving this gives 10%. Subtracting the original 20% from the total gives 80%. Adding the halved allocation would then give us a new total of 90%."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "ZPd30fQM4ZEa2GI0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you create a structured table in a CSV format as per the data there?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data can be derived from the pie chart's legend and each corresponding slice. The sectors can be identified by labels at the legend {sublegend}[0.78, 0.063, 0.993, 0.309]. For the distribution, it can be derived from the proportion (%) placed near each flag shape in the pie.\nBased on the above thought, the answer is:\nIndustry,Percentage\nNanotechnology,23\nBiotechnology,15\nInformation Technology,21\nArtificial Intelligence,11\nInternet of Things (IoT),17\nRobotics,8\nQuantum computing,4\nCryptocurrency,1"
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "PaoJEKOforDqmnIA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sectors show growing prominence in the Knowledge Economy and Innovation Industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sectors with high distribution shares, other than traditional sectors, might represent emergent sectors showing prominence.\nBased on the above thought, the answer is:\nThe sectors showing growing prominence in the Knowledge Economy and Innovation Industries are Artificial Intelligence and Internet of Things (IoT)."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "h6LJ2wTbziwA0i6A",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the current market situation reflected in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the shares of different sectors in the chart can give ideas about the current market situation.\nBased on the above thought, the answer is:\nThe current market situation as reflected in the pie chart is a dominantly held by Nanotechnology. However, other sectors such as Information Technology and Biotechnology have substantial shares, indicating a robust and diversified market composition. Artificial Intelligence and Internet of Things which represent newer avenues have healthy shares too."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "vlFshTJAcDz0NuD7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the pie chart data guide investments into Knowledge Economy and Innovation Industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution of shares in the chart might inform where the most capital is currently invested and, by inference, where investor confidence might be highest.\nBased on the above thought, the answer is:\nThe pie chart data might guide investments into the Knowledge Economy and Innovation Industries by indicating that sectors such as Nanotechnology, Biotechnology, and Information Technology are currently prevailing. However, emerging sectors such as Artificial Intelligence and Internet of Things are forming a significant portion of the pie, suggesting these might be potential areas for future investment."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "gNtcbqIIfi1GUtAf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector holds less than 1.5 times the market share of the Cryptocurrency sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the shares of each sector with the share held by the Cryptocurrency sector, we can determine which one is less than 1.5 times it.\nBased on the above thought, the answer is:\nQuantum computing."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "0JVYKVIrscBw0ehI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the share percentage of the three smallest sectors combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding up the percentages of the three sectors with the smallest shares will give the combined share percentage.\nBased on the above thought, the answer is:\n13%"
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "nmo16HzGgwJcFg4w",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the share of Nanotechnology drops by 3% next year, what will be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the new share of Nanotechnology, we need to subtract 3% from its current share.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "sSqX26TJQ8fJoIjO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart that you currently see, I'd like you to analyze all the years and their corresponding reforestation percentages and structure that data into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the diverse color-coded slices in the pie chart {['Reforestation from 2010 to 2017'][0.346, 0.021, 0.618, 0.047]}, in conjunction with the attached labels {2010: [0.194, 0.317, 0.229, 0.337], 2011: [0.161, 0.506, 0.197, 0.526], 2012: [0.219, 0.752, 0.254, 0.772], 2013: [0.362, 0.895, 0.397, 0.915], 2014: [0.567, 0.895, 0.602, 0.915], 2015: [0.745, 0.663, 0.78, 0.683], 2016: [0.704, 0.252, 0.74, 0.272], 2017: [0.371, 0.113, 0.406, 0.133]}, it is reasonable to infer that these slices and labels represent the data for each year and their corresponding reforestation percentages.\nBased on the above thought, the answer is:\nYear,Reforestation Percentage\n2010,6.9%\n2011,8.5%\n2012,12.1%\n2013,7.7%\n2014,11.5%\n2015,16.2%\n2016,17.9%\n2017,19.2%"
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "zX78qHXPpYOOg0HS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year experienced the greatest improvement in reforestation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain an answer, I must consider the percentage of reforestation represented by each pie slice, with the greatest percentage indicating the highest level of reforestation.\nBased on the above thought, the answer is:\n2017 experienced the greatest improvement in reforestation because it has the highest percentage, at 19.2%, among all the years."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "ZfbODmcimbhfXP8K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about reforestation efforts from 2010 to 2017 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about reforestation efforts, the reforestation percentages from 2010 to 2017 need to be compared.\nBased on the above thought, the answer is:\nThe pie chart shows an apparent increase in reforestation efforts from 2010 to 2017. Initially, in 2010, the reforestation percentage was only 6.9%, but it gradually increased over the years, with a particularly noticeable jump in 2015. By the end of 2017, reforestation efforts had nearly tripled to 19.2%. Thus, the chart implies concerted and successful efforts to improve reforestation over this period."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "2OcuPEQGSf6peMpF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data in the pie chart influence the future direction of reforestation efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the trend of reforestation efforts as shown in the pie chart.\nBased on the above thought, the answer is:\nThe consistent increase in reforestation percentages from 2010 to 2017 demonstrates the success of current efforts. Therefore, these efforts should be continued and potentially expanded in future years. Notably, the significant jump in 2015 suggests that the strategies used in that year were extremely effective, pointing to specific areas to harness for future reforestation plans."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "v6hZJ08Cyfm6QhoJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the percentage of reforestation the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentage of each year, so I'll look at the marked percentages in the pie chart. The slice with the smallest area and the lowest percentage label represents the year with the lowest reforestation.\nBased on the above thought, the answer is:\n2010."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "scKccAIqWKZQ09ux",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the reforestation percentage in 2013 was increased by 5%, what would be the new average reforestation percentage over the years 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, calculate the current average, which requires the sum of percentages divided by the number of years. Then add the extra 5% to 2013, and finally recalculate the average.\nBased on the above thought, the answer is:\nThe reforestation percentage would be 11.9%."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "anMDoNkLCci7JyWK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the reforestation percentage increase from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the 2010 reforestation percentage from the 2017 reforestation percentage.\nBased on the above thought, the answer is:\nThe reforestation percentage increased by 12.3% from 2010 to 2017."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "jnH19J7eAbKXgkiy",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart, can you represent its data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the title \"Population Migration and Mobility Trends\"{0.313, 0.021, 0.65, 0.047} indicated in the chart, it signifies different types of migration. If I analyze each sector of the pie chart represented by different colors and tags hitched with numerical information {Internal[0.348, 0.661, 0.392, 0.681]}; {International[0.608, 0.573, 0.652, 0.593]}; {Seasonal[0.553, 0.328, 0.596, 0.348]}; {Return[0.419, 0.293, 0.454, 0.313]}; {Forced[0.36, 0.341, 0.395, 0.361]}, each sector characterizes different migration types and its number.\nBased on the above thought, the answer is:\nMigration Type, Numbers(000s)\nInternal, 10560\nInternational, 4298\nSeasonal, 4592\nReturn, 2140\nForced, 1008"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "IaVqC5mGg0zcv2kr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of migration is most common? Please explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the size of each sector, I can see that the type of migration with the largest size is the most common.\nBased on the above thought, the answer is:\nInternal migration is the most common as it represents the largest sector in the pie chart with 10560 in thousands or 46.7% of the total migration."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "niWDKuoXxDlJi1rC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you infer about the current state of the Forced migration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about the current state of forced migration, I need to analyze the size and percentage of the Forced migration's sector.\nBased on the above thought, the answer is:\nForced migration seems to be the least common type of migration, representing only 4.5% or 1008 in thousands of the total migration. This suggests that the number of individuals involved in forced migration isn't as significant compared to other forms of migration."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "ODbHJwmnMWYpi7F0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart affect decisions about resource allocation for migration support?"
+            },
+            {
+                "from": "gpt",
+                "value": "Understanding the migration types and their number is critical for considering how resources might be allocated.\nBased on the above thought, the answer is:\nGiven the data, significant resources might be allocated to Internal migration, since it involves the highest number of individuals. Less common types like forced migration, though a critical issue, involve fewer individuals, so they might receive less resource allocation compared to internal migration. However, the urgency and humanitarian perspective of forced migration may require more resources despite their few numbers."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "jSLa6ifNj7xsYVuR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of International and Seasonal migration types in thousands?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add up the numbers of individuals involved in International and Seasonal migration.\nBased on the above thought, the answer is:\n4298 + 4592 = 8890"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "VvnuHN3CvDcaHKEb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the number of internal migration decreased by 2%, what would be the new total in thousands?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to calculate 2% of internal migration and subtract it from the initial number.\nBased on the above thought, the answer is:\nThe decrement in internal migration is 2/100 * 10560 = 211.2, So, the new total will be 10560 - 211.2 = 10348.8"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "Gtlcc7ZVu7WnrN7y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does Forced migration to the total population in the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage, I need to divide the number of forced migration by the total population and multiply by 100.\nBased on the above thought, the answer is:\nThe forced migration percentage is (1008 / (10560 + 4298 + 4592 + 2140 + 1008)) * 100 = 4.5%"
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "LKDfy0aZiTKaqWQC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data in the pie chart into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's subtitle, which reads {\"Expected Prominence of Future Living Scenarios\"}<r>[0.281, 0.021, 0.682, 0.047]</r> and the information in {the legend}<r>[0.794, 0.063, 0.993, 0.249]</r>, it can be inferred that the pie chart's slices represent different future scenarios and their corresponding prominence values.\nBased on the above thought, the answer is:\nFuture Scenarios, Prominence % \nHome Automation, 15\nRobot Assistant, 30\nAI Judges, 10\nAutonomous Vehicles, 25\nQuantum Computing, 13\nHuman Cloning, 7"
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "eotY4sShAr1gBx1r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which future scenario is expected to be the most prevalent? And explain the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The future scenario with the largest slice in the pie chart is considered the most prevalent.\nBased on the above thought, the answer is:\nAccording to the chart, \"Robot Assistant\" is expected to be the most prevalent scenario as it has the largest pie slice representation of 30%."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "tAAkKv41s3IyrvBn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can be made about the expectations of future living scenarios based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of different future scenarios in the chart to interpret possible expectations about the future.\nBased on the above thought, the answer is:\nThe chart shows that \"Robot Assistant\" is expected to be a significantly prominent part of future living scenarios, followed closely by \"Autonomous Vehicles\". Meanwhile, \"Human Cloning\" seems to be considered the least likely to be prevalent in the future. This suggests that robotics and automation are perceived to be the most influential aspects of future society, whereas potentially controversial concepts like \"Human Cloning\" are expected to carry less weight."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "ubmWzSuYmPkm3bXi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence investment strategies in future technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider how the prominence values of each scenario could guide funds allocation in these respective areas.\nBased on the above thought, the answer is:\nThe chart could guide investment strategies by focusing on areas expected to be more prevalent in the future. Specifically, investors might want to invest more heavily in \"Robot Assistance\" technologies and Autonomous Vehicles since these are expected to hold a substantial share of the future. Meanwhile, areas like \"Human Cloning\", which is perceived to be lesser in prominence, might receive less investment, unless an investor is willing to bet on a breakthrough in this area."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "xnrnKaRbX0sVE8Br",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two future scenarios amount to a combined prominence of 40%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find two scenarios with a combined prominence of 40%, I need to sum the prominence values of each possible pair of future scenarios and then compare the outcome to the desired value of 40%.\nBased on the above thought, the answer is:\nHome Automation and Autonomous Vehicles."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "11MbkQh4ZkTpbNwy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the prominence of \"Human Cloning\" increased by 5%, what would be the total prominence percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total prominence percentage, I need to add the 5% increase to the \"Human Cloning\" prominence value and then add up all the prominence values.\nBased on the above thought, the answer is:\nThe new prominence of \"Human Cloning\" is 7% + 5% = 12%. The new total prominence is 15% (Home Automation) + 30% (Robot Assistant) + 10% (AI Judges) + 25% (Autonomous Vehicles) + 13% (Quantum Computing) + 12% (Human Cloning) = 105%."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "WKMV2vRRocvhpbuL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average prominence amongst all the future scenarios?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average prominence, I need to sum up the prominence values of all future scenarios and then divide the outcome by the number of scenarios.\nBased on the above thought, the answer is:\nThe sum of prominence values is 15% (Home Automation) + 30% (Robot Assistant) + 10% (AI Judges) + 25% (Autonomous Vehicles) + 13% (Quantum Computing) + 7% (Human Cloning) = 100%. The average prominence is 100% / 6 = 16.67%."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "SfOHr4KDyThuOvdz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, how has the percentage of online shoppers changed from 2016 to 2020? Can you generate this data as a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will interpret the sizes of the pie chart's slices. Each year is represented by a slice, as seen in {the legend}<r>[0.908, 0.063, 0.993, 0.219]</r>. The size of the slice reflects the percentage of online shoppers each year.\nBased on the above thought, the answer is:\nYear,Online Shopping\n2016,37\n2017,40\n2018,45\n2019,48\n2020,52"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "BHhK47DTJMOylz50",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what trend in consumer behavior can you identify between 2016 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify trends in consumer behavior, I will have to consider the changes in the data over the 5-year period from 2016 to 2020.\nBased on the above thought, the answer is:\nThere is a clear trend of an increasing preference for online shopping from 2016 to 2020."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "nsgj4UCJucNPxKzi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be a possible explanation for the observed trend in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I will draw upon my knowledge of recent events that might have influenced shopping behavior.\nBased on the above thought, the answer is:\nThe increase in online shopping from 2016 to 2020 could be attributed to the increased convenience and ubiquity of online platforms. Additionally, the significant jump in 2020 may have been influenced by the Covid-19 pandemic, which saw many people staying home and shifting to online shopping to avoid public places."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "9JdnMJTeRVBmM32W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might businesses use the information in the chart for decision making?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will consider how business responses might be shaped by consumer behaviors reflected by the chart.\nBased on the above thought, the answer is:\nBusinesses could use this information to guide their strategic decisions. Seeing the trend towards online shopping, they might choose to invest more in the development of their online platforms to facilitate this mode of shopping or focus more on online marketing. Those primarily in physical retail may also consider implementing more robust online shopping options or improving their current offerings."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "ej6eC8ULDIFqmMuk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the smallest percentage of shoppers shop online according to the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I analyze each slice of the pie chart, which represents each year, and identify which slice is the smallest. The sizes of slices are proportional to the percentage of online shoppers.\nBased on the above thought, the answer is:\n2016"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "u2ULxiOwbdrezDBl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the percentage of online shopping between 2016 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the pie chart, I can calculate the difference between these two years by comparing the size of the 2016 slice and the 2020 slice.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "bKECDdDqXhflzFj5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the online shopping percentage increase per year on average from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will need to calculate the difference between the percentages in 2020 and 2016, and then divide by the number of intervening years.\nBased on the above thought, the answer is:\n3.75"
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "T0cRFnA2LQU1IjGF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the Pie Chart for the year 2019, could you convert the industry distribution data to a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart for {2019}, and the legend< r >[0.866, 0.063, 0.993, 0.189]< /r>, with the different sectors< r >[{\"Retail\"}[0.223, 0.278, 0.482, 0.874]; {\"Agriculture\"}[0.415, 0.516, 0.59, 0.886]; {\"Electronics\"}[0.482, 0.472, 0.741, 0.852]; {\"Healthcare\"}[0.283, 0.146, 0.739, 0.516]]< /r >, I can observe that the sectors represent the different industry categories and their respective proportions.\nBased on the above thought, the answer is:\nIndustry, 2019\nRetail, 32%\nAgriculture, 11%\nElectronics, 20%\nHealthcare, 37%"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "M2Aw4dnnaTt56HzV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which industry dominated the market share in 2019? And could you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, comparing the proportions of each sector in the pie chart< r >[0.866, 0.063, 0.993, 0.189]< /r > corresponding to the different industries can give me the industry with the highest percentage.\nBased on the above thought, the answer is:\nThe healthcare industry dominated the market share in 2019 as it had the highest sector proportion of 37% amongst all industries."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "Gl7tMjZ5Ei67f4zB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the performance of the Agriculture industry in 2019 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage associated with the Agriculture sector on the pie chart can reveal its performance in 2019.\nBased on the above thought, the answer is:\nBased on the pie chart, only 11% of the market share belonged to the Agriculture industry in 2019. This suggests that, compared to the other industries, the Agriculture industry had a relatively lower stake in the market."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "NANskrwwgMMDFfTn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data presented in the pie chart drive investment decisions for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Hypothetical investment decisions can be reasoned by considering the performace of each sector in the chart.\nBased on the above thought, the answer is:\nThe data indicate that the Healthcare industry held the largest market share in 2019, thus investors might be inclined to invest more in this industry in 2020 due to its strong performance. On the other hand, the Agriculture sector displayed the smallest market share which might make it less attractive for investment. However, it could also be seen as a potential growth sector that could yield higher returns if its performance improves."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "Qfea7Buexn7VDIJW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry in the year 2019 held the majority market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the industry with the majority market share in 2019, I need to compare the percentages for each industry.\nBased on the above thought, the answer is:\nHealthcare"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "kMmkh5fPhxfZ7g2I",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Healthcare industry exhibited a decrease of 5% in market share between 2018 and 2019, what was its market share in the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the market share of the Healthcare industry in 2018, I need to add the decrease to the industry's market share in 2019.\nBased on the above thought, the answer is:\nThe market share of Healthcare was 42% in 2018."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "jBrl8ycG7Bnc5LwQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the sum of the cultural market shares of the Healthcare and Retail industries in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the market shares for the Healthcare and Retail industries of 2019, I need to sum up the two industry percentages.\nBased on the above thought, the answer is:\nThe sum is 37% (Healthcare) + 32% (Retail) = 69%"
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "s2BBecvskRm29lUG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, could you tabulate the data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see the six sectors represented are {\"Obsessive Compulsion\"}[0.225, 0.278, 0.484, 0.814], {\"Altruism\"}[0.33, 0.516, 0.53, 0.886], {\"Dishonesty\"}[0.484, 0.516, 0.73, 0.88], {\"Aggression\"}[0.484, 0.182, 0.743, 0.633], {\"Emotional Intelligence\"}[0.375, 0.146, 0.596, 0.516] and {\"Psychopathy\"}[0.285, 0.18, 0.484, 0.516]. Each sector represents a psychological trait and the percentage it constitutes in the general population.\nBased on the above thought, the answer is:\nTraits,Percentage\nObsessive Compulsion,26%\nAltruism,13%\nDishonesty,17%\nAggression,23%\nEmotional Intelligence,14%\nPsychopathy,7%"
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "rpdnZCC8lGtaKWwc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich psychological trait is most prevalent according to the chart? Why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can identify the most prevalent trait by looking at the percentage distribution of the traits on the pie chart.\nBased on the above thought, the answer is:\nThe 'Obsessive Compulsion' trait is the most prevalent in the population according to the chart. This conclusion is drawn based on the fact that it comprises the largest portion, 26%, of the pie chart."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "N5QOvWdqvNIJrN6W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can be inferred about the prevalence of 'Psychopathy' compared to the other traits?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can make inferences by comparing the percentage of 'Psychopathy' to the percentages of the other traits.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that 'Psychopathy' is the least prevalent trait, accounting for only 7% of the surveyed population - a measure significantly lower than the rest."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "tSYvOFrBoezVjw6k",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information in the chart affect policies targeted at mental health services?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policymakers might take these data into consideration when deciding on resource allocations.\nBased on the above thought, the answer is:\nThe information suggests that mental health services should prioritize resources to address 'Obsessive Compulsion' and 'Aggression' as they are highly prevalent in the population. At the same time, services for 'Psychopathy', despite its lower prevalence, should not be overlooked due to the potential harm such individuals could pose to society if left untreated."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "W9IAIVdud6F2VdKD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two psychological traits make up exactly 50% of the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the percentage of each trait and see which two traits when combined, form 50% of the total population.\nBased on the above thought, the answer is:\nObsessive Compulsion and Altruism"
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "llcKRTGwMzxulZcm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of the total population is represented by the three least prevalent traits?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to identify the three least prevalent traits and then add up their percentages.\nBased on the above thought, the answer is:\nAltruism, Emotional Intelligence, and Psychopathy represent the three least prevalent traits and they account for 34% of the total population."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "JeAwmQRtxYK52FMg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of individuals with 'Dishonesty' was to be halved, what would the new total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to first halve the percentage of individuals with 'Dishonesty' and then sum up the new and remaining percentages.\nBased on the above thought, the answer is:\nIf the percentage of Dishonesty was halved it would be 8.5%. The new total percentage would be 89.5%."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "n7jBQVSUvtRw0kuG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart which depicts the timeline of human civilization events into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can convert the pie chart into a CSV. The pie chart consists of the slices representing the different periods in human civilization: {\"Ancient Egypt\"}<r>[0.134, 0.701, 0.231, 0.721]</r>, {\"Ancient Greece\"}<r>[0.602, 0.875, 0.708, 0.895]</r>, {\"Roman Empire\"}<r>[0.677, 0.803, 0.779, 0.823]</r>, {\"Middle Ages\"}<r>[0.713, 0.743, 0.798, 0.763]</r>, {\"Industrial Age\"}<r>[0.758, 0.409, 0.855, 0.429]</r> and {\"Digital Age\"}<r>[0.373, 0.101, 0.45, 0.121]</r> with their respective percentages.\nBased on the above thought, the answer is:\n\"Civilization\",\"Percentage\"\n\"Ancient Egypt\",\"38.1%\"\n\"Ancient Greece\",\"9.8%\"\n\"Roman Empire\",\"0.3%\"\n\"Middle Ages\",\"5.9%\"\n\"Industrial Age\",\"21.6%\"\n\"Digital Age\",\"24.2%\""
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "cGDbaQQRdTWyvHUs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which period of human civilization lasted the longest, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, the slice with the largest proportion represents the longest period of human civilization. The slice for {\"Ancient Egypt\"}<r>[0.134, 0.701, 0.231, 0.721]</r> has the largest proportion, which suggests it represents the longest period.\nBased on the above thought, the answer is:\nThe period of Ancient Egypt lasted the longest in human civilization as it forms the largest proportion (38.1%) of the pie chart."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "Ut3VUutthlof73Ck",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the significant change that is noticeable as time evolves from the Ancient Greece towards the Industrial Age."
+            },
+            {
+                "from": "gpt",
+                "value": "The point of interest would be the change in proportion sizes where each represents a length of a period in human civilization. The pie slices for {\"Ancient Greece\"}<r>[0.602, 0.875, 0.708, 0.895]</r>, {\"Roman Empire\"}<r>[0.677, 0.803, 0.779, 0.823]</r>, {\"Middle Ages\"}<r>[0.713, 0.743, 0.798, 0.763]</r>, and {\"Industrial Age\"}<r>[0.758, 0.409, 0.855, 0.429]</r> should be the focus of my analysis.\nBased on the above thought, the answer is:\nBased on the chart, from Ancient Greece to the Industrial Age, there is a clear shift in longer periods. The Ancient Greece period is relatively short, being only 9.8% of the total timeline. The length of the Roman Empire and Middle Ages are also relatively short, making up 0.3% and 5.9% respectively. However, the Industrial Age sees a significant increase, taking up 21.6%. This can be seen as a change in the pace of human civilization development."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "YCWoLHDulqJ1rbQv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the chart's depiction of the proportion of the Digital Age in comparison to other periods of human civilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The magnitude of the Digital Age slice {\"Digital Age\"}<r>[0.373, 0.101, 0.45, 0.121]</r> stands out since it's more recent yet comprises a significantly large proportion.\nBased on the above thought, the answer is:\nThe significant proportion of the Digital Age in comparison to previous eras signifies that while this period is relatively recent, it marks a rapid development in human civilization compared to prior times. It's depicted by the large 24.2% proportion on the chart, significantly larger compared to its immediate predecessor periods like the Middle Ages with 5.9%."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "OhVJ5ci2Zpp9KSB9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the proportion of the Digital Age exceed that of the Ancient Greece from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the proportion of Ancient Greece from that of the Digital Age. I see {\"Ancient Greece\"}<r>[0.602, 0.875, 0.708, 0.895]</r>, with a proportion of 9.8%, and {\"Digital Age\"}<r>[0.373, 0.101, 0.45, 0.121]</r>, with a proportion of 24.2%.\nBased on the above thought, the answer is:\n14.4%"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "sc6EiCiR8FDxGtKk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in proportion between the smallest and the second smallest slice in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the slices with the smallest and second smallest proportions and subtract the smallest from the second smallest. The smallest is {\"Roman Empire\"}<r>[0.677, 0.803, 0.779, 0.823]</r> and the second smallest is {\"Middle Ages\"}<r>[0.713, 0.743, 0.798, 0.763]</r>.\nBased on the above thought, the answer is:\n5.6%"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "fd9Cy3QvgJzbjZ5R",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the slices representing periods before the Industrial Age in human civilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to add the percentages of {\"Ancient Egypt\"}<r>[0.134, 0.701, 0.231, 0.721]</r>, {\"Ancient Greece\"}<r>[0.602, 0.875, 0.708, 0.895]</r>, {\"Roman Empire\"}<r>[0.677, 0.803, 0.779, 0.823]</r>, and {\"Middle Ages\"}<r>[0.713, 0.743, 0.798, 0.763]</r>.\nBased on the above thought, the answer is:\n54.1%"
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "M03NMiFY9FSMzQnq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the information from the pie chart and convert it into raw data format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. From the chart, I can see that it represents measurements of different aspects of project management. This includes {Project Success}<r>[0.223, 0.278, 0.482, 0.784]</r>, {Risk Management}<r>[0.303, 0.516, 0.715, 0.886]</r>, {Quality Control}<r>[0.482, 0.248, 0.741, 0.676]</r>, and {Budget Management}<r>[0.283, 0.146, 0.66, 0.516]</r>. Each of these has a different proportion of the total which is represented as a percentage.\nBased on the above thought, the answer is:\nCategory,Percentage\nProject Success,24.0%\nRisk Management,30.0%\nQuality Control,20.0%\nBudget Management,26.0%"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "A4zaSrUdNcupY9ci",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which aspect matters the most in project management evaluation for innovation projects?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart, I can see that the aspect with the highest proportion would be considered the most important. By examining the proportions of each slice, I can determine this.\nBased on the above thought, the answer is:\nRisk Management is the most important factor as it has the highest proportion at 30.0%."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "EZKcMSDNYIUwZ7a0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the quality control in Innovation Project Evaluation and Management based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of {Quality Control}<r>[0.482, 0.248, 0.741, 0.676]</r> on the chart is significant for inference.\nBased on the above thought, the answer is:\nIt can be inferred that, Quality Control is a relatively less important aspect, representing only 20% in the evaluation of Innovation Project Management."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "iNAz0rAfhjm9TS0d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the allocation of effort or resources be adjusted for better Project Management based on these proportions?"
+            },
+            {
+                "from": "gpt",
+                "value": "For a balanced management, organizations would ideally want to distribute their focus evenly among all aspects. So the data from the pie chart could guide adjustments.\nBased on the above thought, the answer is:\nTo achieve better balance in project management, the organization could consider allocating less effort or resources to Risk Management and more towards Quality Control and Project Success, since the former has a higher proportion while the latter two have lower proportions."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "EQOuyKlW1PDxLHP7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of Project Success and Risk Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of these two portions, I need to add together the values of {Project Success}<r>[0.223, 0.278, 0.482, 0.784]</r> and {Risk Management}<r>[0.303, 0.516, 0.715, 0.886]</r> from above chart.\nBased on the above thought, the answer is:\n54.0%"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "9jk8ZfC5vW4nyNuO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat portion does the Quality Control represent in decimal rather than as a percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert a percentage to decimal format, I divide the percentage value by 100. So I will convert the value for the {Quality Control}<r>[0.482, 0.248, 0.741, 0.676]</r> category\nBased on the above thought, the answer is:\n0.2"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "9nbHPFnc7Pay8b97",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Risk Management increased by 5%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to increase the {Risk Management}<r>[0.303, 0.516, 0.715, 0.886]</r> percentage by 5% and sum up all the categories.\nBased on the above thought, the answer is:\n105.0%"
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "uJIczoN9X53KekWk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart analyzes the popularity of different cuisines indicated by the title {\"Cuisine and Gastronomy Culture Analysis\"}[0.31, 0.021, 0.653, 0.047] and legend<r>[0.877, 0.063, 0.993, 0.309]</r>. From the legend and slices corresponding to each type of cuisine, I find the popularity of each type and convert them into table format.\nBased on the above thought, the answer is:\nCuisine,Popularity Index\nItalian,65\nIndian,75\nChinese,42\nAmerican,90\nMexican,80\nFrench,35\nJapanese,45\nThai,60"
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "HMr5uOPtFeGyNgpx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which cuisine has the highest popularity? And why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The cuisine with the biggest slice in the pie chart would be the most popular. The size of the slices is proportional to their popularity index.\nBased on the above thought, the answer is:\nAmerican cuisine is the most popular by looking at its largest slice in the pie chart."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "X7P6tMEOikqPeA5G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about global food preferences from the pie chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions of each category to make certain inferences about global food preferences.\nBased on the above thought, the answer is:\nJudging from this pie chart, one can infer that American and Mexican cuisines are the most popular, as the slices for both are the largest. French cuisine, however, is the least preferred, as indicated by its smallest slice."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "i5yjvNjF5rUa73VC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might such data imply about restaurant investments in a multicultural city?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would examine the chart to assess the popularity of each type of cuisine which can hint at high consumer demand and thus inform investment decisions in specific types of restaurants.\nBased on the above thought, the answer is:\nThe data may suggest that investing in American and Mexican restaurants might be profitable in a multicultural city as they have the highest popularity index, indicating a high consumer preference for these cuisines. Conversely, investing in French cuisine may be comparatively less profitable due to its lower popularity index."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "oHdszp6Fp5hxjPMp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of cuisine had the highest popularity index?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the type of cuisine with the highest popularity index, I need to examine each individual slice of the pie, figure out its label, and see which one has the bigger segment representing a higher index.\nBased on the above thought, the answer is:\nAmerican."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "14jG8wCwWGlnxyQf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the median popularity index?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the median popularity index, I need to list all the popularity indexes in numerical order and find the number in the middle.\nBased on the above thought, the answer is:\n67.5"
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "kkUDB6Z1G2BrjOwN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would the total popularity index be, if Japanese cuisine increased its popularity index by 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to determine the new total popularity index with a 10 point increase for Japanese cuisine, I must add 10 to the current popularity index of Japanese cuisine, and then sum all the popularity indexes.\nBased on the above thought, the answer is:\n572"
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "75o5VuBBE7THHIGh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data depicted on this chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see {the chart's subtitle}<r>[0.16, 0.021, 0.803, 0.047]</r> which gives me the overall context of the chart. Then by focusing on the {legend}<r>[0.887, 0.063, 0.993, 0.368]</r> and the radial categories of the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, I can observe different slices, their corresponding colors, and what they each represent.\nBased on the above thought, the answer is:\nTime_Period,Online_Social_Networks,Personal_Relationships\nDaily,0.1%,0.2%\nWeekly,0.3%,0.5%\nMonthly,1.7%,2.3%\nYearly,7.3%,8.7%\nDecade,35.0%,43.7%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "S6f4xsUEChTMHUSo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform saw a greater intensity of interactions over a decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the intensity of interactions for both online social networks and personal relationships for the 'Decade' slice in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nPersonal relationships saw a greater intensity of interactions over a decade."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "tsGEG0ByRvzt5mEe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart tell us about the interaction patterns in personal relationships versus online social networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to analyze the percentage distribution of each time period's interactions for both platforms using the {legend}<r>[0.887, 0.063, 0.993, 0.368]</r> and sectors in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe pie chart indicates that personal relationships consistently have a higher intensity of interactions than online social networks across all time periods. The disparity is highest over the period of a decade, suggesting that long-term relationships are stronger and more interactive in personal relationships."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "jhkB2QexDtmk6DHP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implications of the chart findings on the planning of an online marketing strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the interaction intensities and time durations, I need to suggest how these data can influence marketing decisions.\nBased on the above thought, the answer is:\nGiven that personal relationships register higher interaction intensity, particularly in the long-term, online marketing strategies might be more successful if they foster meaningful, personal-like connections that encourage regular interaction over an extended period of time. Strategies could include building online communities, promoting user-generated content and long-term loyalty programs."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "YDEp67dPTOHYZZEq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total intensity in interactions for online social networks?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total intensity for online social networks, I will sum up all the percentages corresponding to the online social networks from the daily period to the decade period according to {the legend}<r>[0.887, 0.063, 0.993, 0.368]</r> and color of slices in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n44.4%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "U1nGbSbSq7qQgMpJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater, in percentage points, is the interactions in personal relationships than in online networking over a decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the decade percentage of online social networks from that of personal relationships based on the related slices' labels and colors.\nBased on the above thought, the answer is:\n8.7%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "dxjSBQ5mb71ShbNv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the average percentage intensity of interactions for online social networks over the given periods?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I need to sum up the percentages of online social networks over all periods and divide by the number of periods.\nBased on the above thought, the answer is:\n8.88%"
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "jakqw21dDcYXhRMg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart information provided, create a table indicating the levels of individualism and equality from 2010 to 2017."
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Changes in Individualism and Equality from 2010 to 2017\"}[0.243, 0.021, 0.72, 0.047] and the pie slices corresponding to each year from {2010}[0.172, 0.397, 0.207, 0.417] through to {2017}[0.32, 0.141, 0.355, 0.161], it's clear that the data represents a trend over a range of years. I will organize these trends for individualism and equality in a table format based on the provided raw data.\nBased on the above thought, the answer is:\nYear, Individualism, Equality\n2010, 25, 75\n2011, 30, 70\n2012, 32, 68\n2013, 34, 66\n2014, 37, 63\n2015, 34, 66\n2016, 30, 70\n2017, 28, 72"
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "vCGPqi3bcx0B3F4C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend visible in the chart for the trait 'Individualism' between 2010 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie slices for individual years and the subtitle {intricating changes in Individualism and Equality}[0.243, 0.021, 0.72, 0.047], I can identify the trend for 'Individualism'.\nBased on the above thought, the answer is:\nThe trait 'Individualism' displays a general increasing trend from 2010 to 2014, peaking in 2014, and then declines gradually till 2017."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "NUZ82MMQLM7KauNY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the trait 'Equality' seem to change over time according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the respective pie slices from {2010}[0.172, 0.397, 0.207, 0.417] to {2017}[0.32, 0.141, 0.355, 0.161] in light of the chart's subtitle {indicating changes in Individualism and Equality}[0.243, 0.021, 0.72, 0.047], I can infer the change in 'Equality'.\nBased on the above thought, the answer is:\nThe 'Equality' trait seems to show a declining trend from 2010 to 2014, reaching its lowest point in 2014, and then increases gradually until 2017."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "AYBi797iEEWvbt4T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trends shown in the chart, what could be inferred about the correlation between individualism and equality in society between 2010 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie slices from {2010}[0.172, 0.397, 0.207, 0.417] to {2017}[0.32, 0.141, 0.355, 0.161], and considering the chart's subject of {changes in Individualism and Equality}[0.243, 0.021, 0.72, 0.047], I can deduce the correlation between individualism and equality during these years.\nBased on the above thought, the answer is:\nThe chart suggests a negative correlation between individualism and equality from 2010 to 2017. As individualism increases, equality decreases and vice versa, indicating that as society trends more towards individualism, the cultural value of equality tends to decline, and when equality is valued higher, individualism tends to fall."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "C4cm5N5vzNRdtujS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year, the sum of \"Individualism\" and \"Equality\" is highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year in which the sum of \"Individualism\" and \"Equality\" is highest, I need to calculate the sum for each year and determine which one is the highest.\nBased on the above thought, the answer is:\n2010"
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "2V7gGm3GyGm5JSMj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did \"Individualism\" increase from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find how much \"Individualism\" raised, I need to subtract the amount of \"Individualism\" in 2010 from the amount in 2014.\nBased on the above thought, the answer is:\n12"
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "EQOTlES9VlomahOf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of \"Equality\" for all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the \"Equality\" numbers from 2010 to 2017 and divide by the total number of years, which is 8.\nBased on the above thought, the answer is:\n68.5"
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "9yrgp8851pIMyhH6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the proportions of technological development trends displayed in the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the different slices of the pie chart representing Artificial Intelligence, Blockchain, IoT, Cloud Computing, and Robotics with their respective percentages. The {legend}<r>[0.8, 0.063, 0.993, 0.219]</r> and the {title \"Technological Industry Development Trends\"}<r>[0.301, 0.021, 0.663, 0.047]</r> give me the necessary information to create a CSV table.\nBased on the above thought, the answer is:\nIndustry, Percentage\nArtificial Intelligence, 35%\nBlockchain, 23%\nInternet of Things, 18%\nCloud Computing, 16%\nRobotics, 8%"
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "vZCcD2pWjPoeFJBT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you share insights about the most trendsetting technology based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice representing {Artificial Intelligence}<r>[0.223, 0.116, 0.482, 0.885]</r> has the most significant proportion, which can indicate its trendsetting value.\nBased on the above thought, the answer is:\nArtificial Intelligence is the most trendsetting technology in the industry as it holds the largest share of 35% in development trends."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "XH83kS3c7KsJxoBC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the interest in Robotics-related technologies from the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion represented by the slice for {Robotics}<r>[0.283, 0.171, 0.482, 0.516]</r> provides insights into the interest in Robotics-related technologies.\nBased on the above thought, the answer is:\nThe interest in Robotics-related technologies is comparatively lesser than the other technologies as it represents the smallest share of 8% in the industry."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "R6MxqQGyfXmwm5m3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart portray the development trend of Blockchain?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the {Blockchain}<r>[0.464, 0.516, 0.736, 0.886]</r> proportion in the pie chart to understand its development trend.\nBased on the above thought, the answer is:\nBlockchain has a significant development trend holding a 23% share in the industry, suggesting its growing influence and adoption in the tech sector."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "FGFmevDTZuTmWo4z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of the top three technologies in the trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the information from the pie slices representing {Artificial Intelligence}<r>[0.223, 0.116, 0.482, 0.885]</r>, {Blockchain}<r>[0.464, 0.516, 0.736, 0.886]</r>, and {IoT}<r>[0.482, 0.218, 0.741, 0.588]</r>, I need to add their proportions.\nBased on the above thought, the answer is:\nThe sum is 76%."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "8e4bLqZ115a3fyVg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the lowest trending technology hold in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slice representing {Robotics}<r>[0.283, 0.171, 0.482, 0.516]</r>, I can find its proportion.\nBased on the above thought, the answer is:\nRobotics accounts for 8%."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "3MWR68yPuAKYFnSv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend of Artificial Intelligence increases by 5% in the next year, what would be its new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportion, I need to add 5% to the existing proportion of {Artificial Intelligence}<r>[0.223, 0.116, 0.482, 0.885]</r>.\nBased on the above thought, the answer is:\nThe new proportion will be 40%."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "PonvHEttqFOntXOg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart you observed, please convert the given cultural heritage and integration data across different regions into a structured table format."
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table from the chart, I interpret each slice of the pie chart {the pie chart}[0.223, 0.146, 0.741, 0.886] as representing a region and its color representing the region's involvement in cultural heritage and integration. The regions and their corresponding figures can be inferred from the legend {legend}[0.833, 0.063, 0.993, 0.279] and the labels and values inscribed within the pie chart slices.\nBased on the above thought, the answer is:\nRegion,Percentage of Cultural Integration\nAfrica,20%\nAsia,30%\nEurope,15%\nAmericas,30%\nOceania,2%\nPolar,1%\nExtra-terrestrial,2%"
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "Rz8W8pzmUPAP3vkM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region seems to be dominating in terms of Cultural Integration and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to assess the {pie chart}[0.223, 0.146, 0.741, 0.886] and identify the region with the largest sector.\nBased on the above thought, the answer is:\nAsia seems to be dominating because it has the largest slice in the pie chart, representing the highest percentage of Cultural Integration."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "MBvcy076S2wpCDn8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you infer about the distribution of Cultural Integration across different regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must evaluate the relative sizes and labels of each sector in the {pie chart}[0.223, 0.146, 0.741, 0.886] to infer about the distribution.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that the distribution of Cultural Integration is highest in Asia and the Americas, followed by a significant but lesser contribution from Africa and Europe. There is a minimal contribution from Oceania, Polar regions and Extra-terrestrial locations."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "JmVlxcVsBFygMgF6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data influence decisions for promoting and preserving cultural heritage globally?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to reflect on the implications of the proportions presented in the {pie chart}[0.223, 0.146, 0.741, 0.886] on global decision making for promoting and preserving cultural heritage.\nBased on the above thought, the answer is:\nThe data might influence decisions to focus more resources and efforts towards preserving and promoting cultural heritage in regions like Oceania, Polar regions and extra-terrestrial locations where the cultural integration is quite low. In contrast, it might also inspire strategizing to protect the vast cultural heritage in regions such as Asia and Americas where the cultural integration is highest."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "ZIPlYrhsWNNtUOjI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region has the lowest contribution to Cultural Integration according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I must identify the slice in {the pie chart}[0.223, 0.146, 0.741, 0.886] with the smallest relative size and match its color with the corresponding region in the {legend}[0.833, 0.063, 0.993, 0.279].\nBased on the above thought, the answer is:\nPolar"
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "oEKMvIJmH9R7nere",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage contribution to Cultural Integration from regions outside of Asia, Europe and Americas?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to calculate the total percentage of the regions Africa, Oceania, Polar and Extra-terrestrial from the percentages given in the {pie chart}[0.223, 0.146, 0.741, 0.886] and match their respective colors with the regions in the {legend}[0.833, 0.063, 0.993, 0.279].\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "cwL4VUTxfaY6NRj1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage contribution to Cultural Integration is made by the top three regions combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the regions with the three largest pie slices in the {pie chart}[0.223, 0.146, 0.741, 0.886] and add their percentages.\nBased on the above thought, the answer is:\n75%"
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "rbyVY3JczAcxZoGs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you convert the data of environmental protection and sustainable development into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart at the bottom, {\"Environmental Protection and Sustainable Development in 2014\"}[0.215, 0.021, 0.749, 0.047] I can identify the year 2014. The data could be seen as the sectors of the pie chart: {\"Recycling Rate(%)\"}[0.488, 0.724, 0.532, 0.744]; {\"Deforestation Rate(%)\"}[0.471, 0.284, 0.514, 0.304]; and {\"Renewable Energy Usage(%)\"}[0.374, 0.325, 0.409, 0.345]. The data for these categories are inferred from the segments of the chart.\nBased on the above thought, the answer is:\nYear,Recycling Rate(%),Deforestation Rate(%),Renewable Energy Usage(%)\n2014,78,14,8"
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "X6jJqEM6dfmjZuvK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which aspect of environmental protection witnessed the greatest emphasis in 2014? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the sizes of the pie chart sectors, we can determine which aspect had the greatest emphasis.\nBased on the above thought, the answer is:\nThe greatest emphasis was on \"Recycling Rate(%)\" with a proportion of 78%, significantly larger than the other two sectors, indicating that major efforts were directed towards improving recycling rates."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "2kysZeC9BJBcSICG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the chart about the possible challenges towards sustainable development goals in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "The inference can be drawn by looking at the size of the sectors, notably the smallest one.\nBased on the above thought, the answer is:\nThe smallest proportion in the chart belongs to \"Renewable Energy Usage(%)\", which is only 8%. This suggests that in 2014, the use of renewable energy was lagging behind recycling and deforestation prevention efforts, posing a challenge to sustainable development goals."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "zAfLWC6KQKO7KW86",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the data represented in the chart align with global sustainable development goals?"
+            },
+            {
+                "from": "gpt",
+                "value": "The alignment with global sustainable development goals would be determined by examining the sectors representing aspects of environmental protection and renewable energy use.\nBased on the above thought, the answer is:\nThe data indicate good alignment with sustainable development goals in regards to improving recycling rates and reducing deforestation. However, the progress in renewable energy usage was notably slower, which implies more work is needed in this area to fully align with global sustainable goals."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "iUVI1p0nwjvkl2o6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of Recycling Rate(%) in the chart for 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is represented by the largest sector of the pie chart, {\"Recycling Rate(%)\"}[0.488, 0.724, 0.532, 0.744].\nBased on the above thought, the answer is:\nThe proportion of Recycling Rate(%) is 78% for the year 2014."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "Dqy7lprUWRAJJRVj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much has the proportion of Deforestation Rate(%) reduced from 2013 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the reduction in the Deforestation Rate(%), I need to subtract the 2014 value from the 2013 value.\nBased on the above thought, the answer is:\nThe Deforestation Rate(%) has reduced by 1% from 2013 to 2014."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "Zws2GxFQgVLWuZ0P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of Renewable Energy Usage(%) and Deforestation Rate(%)?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add the percentages of Renewable Energy Usage(%) and Deforestation Rate(%).\nBased on the above thought, the answer is:\nThe total percentage of Renewable Energy Usage(%) and Deforestation Rate(%) is 22%."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "wPsDz0zpwRRYfaMb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the portion of male and female participants shown in the pie chart into a CSV format table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the chart, I notice two sectors of the pie chart representing the data: {the Male slice}<r>[0.223, 0.278, 0.741, 0.886]</r> and {the Female slice}<r>[0.283, 0.146, 0.733, 0.516]</r>. Looking at the information attached to each slice, I can confirm the precise percentage for each category.\nBased on the above thought, the answer is:\nGender,Percentage\nMale,65%\nFemale,35%"
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "hN1SjgxdqMLRvHiX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the chart suggest any bias in the social demographic research in terms of gender? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, it's necessary to examine the proportions of males and females represented in the research, using data from {the Male slice}<r>[0.223, 0.278, 0.741, 0.886]</r> and {the Female slice}<r>[0.283, 0.146, 0.733, 0.516]</r>.\nBased on the above thought, the answer is:\nYes, the chart suggests a gender bias in favor of males, as they represent a significantly higher percentage (65%) of participants in the research than females (35%)."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "QdBAHJCK0KLV0IDU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what trends can we extrapolate about social demographic research?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I have to consider the proportion of male and female participants {the Male slice}<r>[0.223, 0.278, 0.741, 0.886]</r> and {the Female slice}<r>[0.283, 0.146, 0.733, 0.516]</r> in the study.\nBased on the above thought, the answer is:\nThe chart shows that there is a trend of greater male participation in social demographic research. Thus, future studies might expect to see more male than female participants if this trend continues."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "v3bmYKwkvZas0K8g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat measures might the researchers take to ensure gender balance in future studies based on the current chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to extrapolate from the current sample's significant male dominance {the Male slice}<r>[0.223, 0.278, 0.741, 0.886]</r> and lesser female representation {the Female slice}<r>[0.283, 0.146, 0.733, 0.516]</r>.\nBased on the above thought, the answer is:\nGiven that the chart data shows a greater proportion of male participants, researchers might need to make more directed efforts to recruit a higher proportion of female participants in the future to ensure a more balanced gender distribution in their studies."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "jGWi0B7d6lOwg5GM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the population did males represent according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to identify the percentage assigned to the Male slice in the pie chart: {the Male slice}<r>[0.223, 0.278, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "WGAKAAo8DGNIyGM9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf female participation increased by 10%, what would be the new data distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add 10% to the current female participation rate as given by {the Female slice}<r>[0.283, 0.146, 0.733, 0.516]</r> and subtract the same amount from the male participation rate.\nBased on the above thought, the answer is:\nMale participation would be 55%, and female participation would be 45%."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "Zv7VFGyBQbjkqCKA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the percentage between male and female participants?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of females from the percentage of males to get the difference.\nBased on the above thought, the answer is:\nThe difference is 30%."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "dbXN2fh9vU4gNm3f",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data concerning the 'Soft Skills' from 2012 to 2019 into a CSV structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the legend titled 'Soft Skills' {sublegend}<r>[0.908, 0.063, 0.993, 0.309]</r>, I noticed eight sectors corresponding to each year from 2012 to 2019. Each sector on the chart describes the percentage of 'Soft Skills' for that specific year.\nBased on the above thought, the answer is:\nYear,Soft Skills\n2012,50%\n2013,48%\n2014,45%\n2015,42%\n2016,39%\n2017,35%\n2018,32%\n2019,30%"
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "XWAWYk6qJDZjE3Bn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which years showed a steady decline in 'Soft Skills'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll need to examine the pie chart {pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r> and track the directional changes in the 'Soft Skills' sectors across the years.\nBased on the above thought, the answer is:\nFrom 2012 to 2019, 'Soft Skills' showed a steady decline every year."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "uZAwXSHpi9p4HbQy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trends in the 'Education and Training' industry from the pie chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the legend and percentages in the pie chart's slices, I can infer the trend in the 'Soft Skills' sector of the 'Education and Training' industry over the years.\nBased on the above thought, the answer is:\nThe pie chart suggests a consistent decline in the importance of 'Soft Skills' within the 'Education and Training' industry from 2012 to 2019. This could indicate a growing preference for technical education over soft skills during this period."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "gaCTLYUQRlgHKsSV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence policy decisions in the 'Education and Training' industry for the upcoming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the declining trend in 'Soft Skills' might impact policy choices in the industry.\nBased on the above thought, the answer is:\nSeeing the consistent decrease in 'Soft Skills' from 2012 to 2019, policy makers in 'Education and Training' might consider revising soft skills programs to make them more appealing, or invest more resources into Technical Education, which seems to be the growing preference."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "V7U6MiujoIJyPr9P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year the proportion of 'Soft Skills' was 42%?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart, I'll need to find the section which denotes 'Soft Skills' as 42% for a specific year.\nBased on the above thought, the answer is:\n2015."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "EGoR8rYmbgiDEWbw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of 'Soft Skills' from 2012 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average percentage, I will add up all the percentages of 'Soft Skills' from 2012 to 2019 and divide them by the total number of years.\nBased on the above thought, the answer is:\n40.1%."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "8qYm8LMQunR58Y8o",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of 'Soft Skills' decreased by 2% annually from 2012, what would the proportion be in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the proportion in 2019, I need to subtract 2% from the 'Soft Skills' proportion in 2012, 7 times for the following years until 2019.\nBased on the above thought, the answer is:\nThe new proportion in 2019 would be 36%."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "fdpm9vbRBfN2VEZQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please transform the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title on the top of the chart {\"Entrepreneurial Spirit and Entrepreneurship from 2015 to 2021\"}[0.219, 0.021, 0.744, 0.047] and {the section slices}[{\"2015\"}[0.223, 0.278, 0.482, 0.56]; {\"2016\"}[0.224, 0.516, 0.482, 0.827]; {\"2017\"}[0.341, 0.516, 0.575, 0.886]; {\"2018\"}[0.482, 0.516, 0.74, 0.861]; {\"2019\"}[0.482, 0.182, 0.741, 0.542]; {\"2020\"}[0.435, 0.146, 0.594, 0.516]; {\"2021\"}[0.283, 0.152, 0.482, 0.516]], I can see that the sections represent different years and how much percent each year is.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,13\n2016,14\n2017,15\n2018,18\n2019,19\n2020,10\n2021,11"
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "Rf902JWK2o9yBuit",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year shows the highest entrepreneurial endeavor? And can you tell the possible reasons behind it?"
+            },
+            {
+                "from": "gpt",
+                "value": "My answer will be based on the size of each pie slice, with the largest slice indicating the year with the highest entrepreneurial endeavor.\nBased on the above thought, the answer is:\n2019 shows the highest entrepreneurial endeavor with rates reaching 19%. This might be due to favorable economic conditions, increased access to venture capitals, or rapidly evolving technology."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "F5PNKazGGnlR4adN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can we infer about the impact of the global pandemic on entrepreneurship in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the size of the 2020 slice to the sizes of the adjoining years to infer the impact of the pandemic on this variable.\nBased on the above thought, the answer is:\nThe global pandemic significantly affected entrepreneurship in 2020. The chart shows a drop in the entrepreneurial rate to 10% which might be due to uncertainties and business disruptions caused by the pandemic."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "I6cbuyTHVqcNMK33",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the recovery of the entrepreneurial spirit in 2021 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can make inferences by comparing the size of the 2021 slice to that of 2020.\nBased on the above thought, the answer is:\nIn 2021, although there is a slight recovery in the entrepreneurial spirit with the rate increasing to 11%, it is still far from reaching the pre-pandemic level. This could be due to the lingering effects of the pandemic and the gradual recovery of global economic conditions."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "4PnOFlousSfrQV08",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year has the highest entrepreneurial activation rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to identify the largest slice of the pie chart which signifies the highest entrepreneurial activation rate.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "BL9d9XsPgyNbdTph",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of the entrepreneurial activation rate from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total sum, I will add the percentages of the year 2018, 2019, and 2020.\nBased on the above thought, the answer is:\n47"
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "b4qJNUjnFGZ1QOXh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average entrepreneurial activation rate from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to add up the percentages from 2015 to 2021 and then divide it by the number of years which is 7 in this case.\nBased on the above thought, the answer is:\n14.3"
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "QAeQjyox7Luk4mkk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you turn the observed GDP data into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, I can generate the table from the GDP data given. The data covers the years 2016 to 2020, which will form the categories, iterated by the sublegend {2016}[0.952, 0.071, 0.987, 0.091], {2017}[0.952, 0.101, 0.987, 0.121], {2018}[0.952, 0.131, 0.987, 0.151], {2019}[0.952, 0.161, 0.987, 0.181], {2020}[0.952, 0.191, 0.987, 0.211]. Then, the corresponding GDP data, in terms of trillion US dollars is represented in the pie chart.\nBased on the above thought, the answer is:\nYear,GDP (in trillion USD)\n2016,75\n2017,78\n2018,81\n2019,85\n2020,60"
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "j0yhbDmiMolaGtFH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observed data points, what can you infer about the economic conditions from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "From 2016 to 2019, both GDP and unemployment rate seem to evolve in favorable ways with GDP increasing and unemployment decreasing. This usually indicates good economic conditions, where business gains lead to more jobs and thus lesser unemployment.\nBased on the above thought, the answer is:\nThe economy seemed to be growing steadily from 2016 to 2019 as seen by the consistent increase in GDP and decreasing unemployment rate."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "x2nboZ547TLcRTXR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you observe about the years 2019 to 2020 in terms of economic indicators?"
+            },
+            {
+                "from": "gpt",
+                "value": "From 2019 to 2020, I notice a sharp contrast. The GDP fell dramatically, and the unemployment rate rose. This signifies economic hardships.\nBased on the above thought, the answer is:\nThere was an economic downturn in 2020. GDP significantly dropped while the unemployment rate increased, indicating the adverse effects of an economic crisis, possibly the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "aPc33h2Bq9p5YmXc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the drastic increase in the unemployment rate in 2020, what economic strategies might be needed in the following year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The significant increase in the unemployment rate in 2020, as shown by the slices in the pie chart, could indicate that many were out of work due to a crisis such as the COVID-19 pandemic. As a result, remedies could involve stimulating economic activities to create jobs and possibly offering unemployment benefits to those affected.\nBased on the above thought, the answer is:\nIt might necessitate measures aimed at stimulating job growth and the economy at large, such as fiscal stimulus policies, job creation programs, or even skills enhancement to help the workforce adapt to new job opportunities."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "K3lt3rKlQ58C6Qay",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the change in GDP from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change, I would subtract the GDP of 2016 from the GDP of 2020. The GDP figures can be found in the data received.\nBased on the above thought, the answer is:\n-15 trillion USD"
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "hEDmXKLCjxJTPQIm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the average unemployment rate during the years 2016-2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average unemployment rate, I would add up the figures from 2016 to 2020 and then divide the sum by the number of years, which is 5. The unemployment rates for each year can be obtained from the data received.\nBased on the above thought, the answer is:\n5.18%"
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "QnbcAAEcyRGLvxLJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of decrease in unemployment from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of decrease, I'd calculate the difference between unemployment rates of 2016 and 2019, and then divide it by the 2016 rate. Then multiply it by 100 to get a percent.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "i0wacbxOt9r2lx4I",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the ecological environment sustainability assessment chart data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the labels in the chart's legend {[\"Water pollution\", \"Air pollution\", \"Soil erosion\", \"Deforestation\"]}<r>[0.881, 0.071, 0.987, 0.091],[0.881, 0.101, 0.965, 0.121],[0.881, 0.131, 0.961, 0.151],[0.881, 0.161, 0.976, 0.181]</r> and the percentage written on each slice I can map the data accordingly.\nBased on the above thought, the answer is:\nCategory,Scores\nWater pollution,25\nAir pollution,35\nSoil erosion,20\nDeforestation,20"
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "5qrI5ExS1fLviuP0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can be gained regarding the key factors affecting ecological environment sustainability based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By interpreting the scores of each category, insights can be drawn from the chart.\nBased on the above thought, the answer is:\nThe most influential factor currently impacting ecological sustainability is air pollution, represented by the largest piece of the pie chart. Water pollution and deforestation each contribute an equal percentage, making them significantly impactful in terms of environmental sustainability. Soil erosion, despite being the least concerning, still holds a notable portion, suggesting that all these factors need attention for ecological sustainability."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "30uGn5zL8Qur80Ot",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which environmental issue should be prioritized to enhance ecological sustainability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this inference, I need to consider the environmental factor with the highest score.\nBased on the above thought, the answer is:\nAir pollution should be the priority as it represents the most significant issue currently impacting ecological sustainability with the highest score of 35 out of 100."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "dLVHj91Akcb1icsB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a decrease in scores indicates positive improvements in these ecological issues, what does this chart depict about the current state of the factors mentioned?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the current state of the factors, I need to assume that a lower score represents an improved condition of these ecological issues.\nBased on the above thought, the answer is:\nAssuming a lower score indicates improvement, the chart depicts that the environmental issues of air pollution, water pollution, and deforestation are in significant need of attention and improvement measures. Air Pollution, in particular, requires urgent measures as it currently has the highest score indicating a severe condition. Soil erosion, while being the least, still needs to be addressed for complete ecological sustainability."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "cZHbMvCwhCMluCiY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total score for all four ecological issues mentioned in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total score, I need to sum up the scores of all the different issues.\nBased on the above thought, the answer is:\nThe sum of the scores is 25(Water pollution) + 35(Air pollution) + 20(Soil erosion) + 20(Deforestation) = 100."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "4pSPjrRNhgEaEWQA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the score for deforestation increased by 10 points, what would the new total score be?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the score for deforestation increases, the new total score will be calculated by adding the increase to the current total score.\nBased on the above thought, the answer is:\nThe total score increased by 10 points which makes it 100 (current total score) + 10 (increase in deforestation) = 110."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "FdgMvVDRHXwQSbwE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat proportion of the overall score does air pollution represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of air pollution can be calculated by dividing the score for air pollution by the total score.\nBased on the above thought, the answer is:\nThe proportion of air pollution is 35 (Air pollution score) / 100 (Total Score) = 35%."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "J2P4RUtWTyM8Nmwr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart you analyzed, could you please convert these data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Entertainment and Gaming Industry Development from 2012 to 2020\"}<r>[0.193, 0.021, 0.771, 0.047]</r> and the different slices of the pie <r>[0.223, 0.146, 0.741, 0.886]</r>, we can arrange the data into a CSV format table. Each slice represents a specific year and the corresponding percentage is the market share of the gaming industry in the entertainment market for that year. This based on the color information from the legend on the right <r>[0.908, 0.063, 0.993, 0.338]</r>.\nBased on the above thought, the answer is:\nYear,Market_Share\n2012,8.6%\n2013,9.2%\n2014,9.6%\n2015,10.2%\n2016,10.6%\n2017,12.2%\n2018,12.5%\n2019,13.2%\n2020,13.9%"
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "RjPvaWrK4Co3oCf8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the pie chart, how has the Gaming Market changed from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look closely at the size of each slice of the pie and analyze the trend in the gaming market's percentage in the entertainment market.\nBased on the above thought, the answer is:\nThe Gaming Market has seen a steady increase in its market share from 2012 to 2020, indicating continuous development and expansion in the entertainment industry during this period."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "uGlEojpdNcBYknE7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the growth of the gaming industry in the overall entertainment market from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the changes in each segment of the pie chart, I can infer the growth trend of the gaming industry.\nBased on the above thought, the answer is:\nThe gaming industry has shown a steady growth trend from 2012 to 2020 in the overall entertainment market, with a significant leap in 2017. This indicates that the gaming industry might have had some breakthroughs or revolutionary products that year, which greatly propelled its development."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "sn9sLtqTLyVdTNvz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential trend could you predict for future Gaming Market based on this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the consistent growth of the Gaming Market from 2012 to 2020, I can infer a potential future trend.\nBased on the above thought, the answer is:\nIf the current growth trend continues, the Gaming Market's share in the entertainment industry is likely to keep rising in the future, assuming there are no significant obstacles or downturns in the sector. This suggests that the gaming industry might continue as a dominant force within the entertainment sector in the coming years."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "KbiNIDQyKQF0LFeB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, in which year did the Gaming Market start to take a significant increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find out which year had a significant increase in the gaming market's percentage in the entertainment market. It's noticeable from the pie <r>[0.223, 0.146, 0.741, 0.886]</r> that one of the slices is significantly larger than its preceding ones.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "HzObQtXqHBppCKp8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in the Gaming Market share between 2012 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to subtract the market share in 2012 from that in 2020.\nBased on the above thought, the answer is:\nThe increase is approximately 5.3%."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "Ktvi34UNRNy3cgi6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the growth trend continues into 2021, and the Gaming Market increases by another 1%, what would be its market share in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add 1% to the 2020 market share.\nBased on the above thought, the answer is:\nIt would be 14.9%."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "NYpbC2CWkXPAG7Kx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, can you provide the revenue distribution data of the three industry sectors in the CSV format table for the years 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the labels of the pie charts {\"Industry Revenues in 2015\"}<r>[0.13, 0.074, 0.279, 0.1]</r> and {\"Industry Revenues in 2016\"}<r>[0.625, 0.074, 0.774, 0.1]</r>, and the sectors according to the colors in the legends <r>[0.398, 0.116, 0.503, 0.212]</r> and <r>[0.892, 0.116, 0.998, 0.212]</r>, the slices in each pie chart from the top going clockwise were respectively {\"Hotels\"}<r>[0.053, 0.303, 0.205, 0.801]</r>; {\"Resorts\"}<r>[0.135, 0.496, 0.356, 0.837]</r>; {\"Travel Agencies\"}<r>[0.088, 0.187, 0.356, 0.512]</r> for 2015 and {\"Hotels\"}<r>[0.548, 0.303, 0.699, 0.806]</r>; {\"Resorts\"}<r>[0.635, 0.498, 0.851, 0.837]</r>; {\"Travel Agencies\"}<r>[0.583, 0.187, 0.851, 0.512]</r> for 2016, I can deduce the revenue distribution for each year.\nBased on the above thought, the answer is:\nYear,Hotels,Resorts,Travel Agencies\n2015,28.6%,33.3%,38.1%\n2016,29.1%,32.7%,38.2%"
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "3QAh1vFYdTqKXaJB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector consistently had the highest revenue in the years 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reviewing the pie slices for each sector for both years, I can determine which sector consistently had the highest revenue.\nBased on the above thought, the answer is:\nTravel Agencies had consistently the highest revenue in both 2015 and 2016."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "uWfd9MfaaGAO0pa4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the changes in the pie chart from 2015 to 2016 about the growth of these industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the changes in the sizes of the pie slices between 2015 and 2016, I can infer the growth trends of these industries.\nBased on the above thought, the answer is:\nAll sectors showed a slight increase in revenue share, with Travel Agencies showing the largest growth. This suggests a healthy and competitive industry with growth across all sectors."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "sWczvdVhiEq5CPPw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf these trends continue, which sector is likely to dominate in terms of revenue in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the revenue trends in 2015 and 2016, I can estimate which sector is likely to take the lead in the future.\nBased on the above thought, the answer is:\nAs the Travel Agencies sector has consistently shown the highest revenue and the most substantial growth, it is likely to dominate in terms of revenue in the future if these trends continue."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "bBrNx0LUgIZTcujB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in the share of revenue from Hotels from 2015 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the percentage increase, I have to look at the pie slices for Hotels in 2015 <r>[0.053, 0.303, 0.205, 0.801]</r> and 2016 <r>[0.548, 0.303, 0.699, 0.806]</r> and compare their proportions.\nBased on the above thought, the answer is:\n0.5%"
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "8EfnrcasgL2ePfVD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector had the largest increase in share of revenue from 2015 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sector with the largest increase in share of revenue, I need to compare the proportions for each sector between the two years.\nBased on the above thought, the answer is:\nTravel Agencies."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "pJLdBPscac77AW23",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the ratio of the revenue from Hotels to Resorts in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the ratio, I need to compare the pie slice for Hotels <r>[0.548, 0.303, 0.699, 0.806]</r> to the pie slice for Resorts <r>[0.635, 0.498, 0.851, 0.837]</r> in 2016.\nBased on the above thought, the answer is:\nThe ratio is approximately 0.89."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "ZfnU44liSEMhSu4Y",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the bottom of the chart {the sector slice}[{\"Extremely Ineffective\"}[0.223, 0.278, 0.482, 0.516]; {\"Mostly Ineffective\"}[0.223, 0.49, 0.544, 0.886]; {\"Mostly Effective\"}[0.482, 0.173, 0.741, 0.875]; {\"Extremely Effective\"}[0.283, 0.146, 0.579, 0.516]], I can see that the sectors represents different levels of effectiveness and how much percentage each level is.\nBased on the above thought, the answer is:\nPolicy Type,Extremely Ineffective,Mostly Ineffective,Mostly Effective,Extremely Effective\nGovernment Health Policy,10%,30%,40%,20%"
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "hkBu9GBFTahSizAC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich effectiveness level garners the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie slices' sizes and their respective percentages, I can see which effectiveness level has the biggest size, indicating the highest percentage.\nBased on the above thought, the answer is:\nThe effectiveness level \"Mostly Effective\" has the highest percentage of 40%."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "QClkcwAzFg1Rl4VA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how can the public's perception of the government's health policy be described in terms of its effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the distribution of responses across varying levels of effectiveness.\nBased on the above thought, the answer is:\nFrom the chart, it can be concluded that people mostly see the health policy as effective. The bulk of responses are towards \"Mostly Effective\" at 40%. However, there's also a significant portion that sees it as \"Mostly Ineffective\". The responses towards \"Extremely Ineffective\" and \"Extremely Effective\" are fewer in comparison."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "K5n11e03Oe1VRDDv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what could the government focus on to increase satisfaction levels regarding its health policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I must look at the largest negative slices, as the smaller they become, the higher overall satisfaction will be.\nBased on the above thought, the answer is:\nThe government could focus on addressing the concerns leading to perceptions of the policy as \"Mostly Ineffective\" since this category has the second-highest response rate at 30%."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "1mdPUljLKTEdHBKu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the effectiveness percentage for levels \"Mostly Ineffective\" and \"Mostly Effective\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the percentages for these levels, I need to add up the percentages for \"Mostly Ineffective\" and \"Mostly Effective\".\nBased on the above thought, the answer is:\nThe sum is 30% (Mostly Ineffective) + 40% (Mostly Effective) = 70%."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "9DovQ7cACODmGC1Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAt which level of effectiveness did the least amount of the respondents place the Government Health Policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the lowest response, I need to check the percentages of all four categories of effectiveness.\nBased on the above thought, the answer is:\nThe least amount of respondents categorized Government Health Policy as Extremely ineffective, with only 10% of respondents choosing this category."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "WI1XM5UXHOPVV8Mg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage difference is there between the levels \"Extremely Ineffective\" and \"Mostly Ineffective\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to subtract the percentage of \"Extremely Ineffective\" from \"Mostly Ineffective\".\nBased on the above thought, the answer is:\nThe difference is 30% (Mostly Ineffective) - 10% (Extremely Ineffective) = 20%."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "AyD8JFHQg00OlOle",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table presenting the data about Different Types of Social Relations and Their Usage of Radio according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the title {Different Types of Social Relations and Their Usage of Radio}<r>[0.233, 0.021, 0.73, 0.047]</r> and the slices of the pie chart with labeled categories {\"High school friends\"}<r>[0.307, 0.463, 0.351, 0.483]</r>, {\"College friends\"}<r>[0.349, 0.662, 0.393, 0.682]</r>, {\"Colleagues\"}<r>[0.467, 0.728, 0.511, 0.748]</r>, {\"Family\"}<r>[0.564, 0.676, 0.599, 0.696]</r>, {\"Online friends\"}<r>[0.615, 0.504, 0.659, 0.524]</r>, {\"Acquaintances\"}<r>[0.507, 0.295, 0.551, 0.315]</r>, {\"Strangers\"}<r>[0.375, 0.323, 0.411, 0.343]</r>, the percentages of each category can be read off, giving us the numbers to construct a table.\nBased on the above thought, the answer is:\nRadio, High school friends, College friends, Colleagues, Family, Online friends, Acquaintances, Strangers\n%, 16.1, 15.0, 11.6, 9.2, 19.0, 20.8, 8.5"
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "cGCWWTd0OKweTTAX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of social relations appears to prefer using radio the most according to the chart? And can you provide your reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the sizes and percentages of each segment in the pie chart, I can determine which social relation prefers using the radio the most.\nBased on the above thought, the answer is:\n\"Acquaintances\" seem to prefer using the radio the most, as indicated by its largest share of 20.8% in the pie chart."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "nx4BQUL6UOyBkX2z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the preferences of radio usage among various social relations from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to assess the percentages of different social relation types for radio usage and interpret the data.\nBased on the above thought, the answer is:\nThe pie chart suggests that \"Acquaintances\" and \"Online friends\" are relatively more frequent users of radio, while \"Strangers\" and \"Family\" use it the least. This might suggest that radio communications are more preferred for more casual or distant social relations, while closer personal relationships such as family use it less."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "60WSS380bYbDgsMY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence social relation and radio usage studies or surveys?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to think about how the percentages in each category can affect future social and radio usage research.\nBased on the above thought, the answer is:\nThe data in the chart demonstrates the differences in radio usage among various types of social relations. This could provide important insights for studies or surveys on social relations and media usage, like focusing on why \"Acquaintances\" and \"Online friends\" prefer radio more compared to other kinds of relationships."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "zT0nyn5yF4aId5tc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social relation has the least radio usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to check the percentages of radio usage in each social relationship category in the pie chart and identify the one with the smallest percentage.\nBased on the above thought, the answer is:\nStrangers."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "pB2KkUiZT5uTU3wT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"Family\" increased its usage by 3%, what would be the new total percentage of all categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total percentage, I need to add 3% to the \"Family\" percentage and then add up all the percentages again.\nBased on the above thought, the answer is:\nThe new total percentage is 103.5%."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "bdEG4gq4bbtaOOSb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the change in the share of \"College Friends\" if the share of \"High School Friends\" increases by 2%?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the share of \"High School Friends\" increases by 2%, the shares of all other categories will be diluted, and hence the share of \"College Friends\" will reduce.\nBased on the above thought, the answer is:\nThe share of \"College Friends\" will decrease."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "V2F3MJSFZI9UZKhk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data represented in the pie chart into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the legend, sectors information, and the data provided, I can determine the proportion in percentage format each year had in the total market research for both \"Automotive\" and \"Transportation\" between 2013 and 2020. These numbers can be converted into a structured table.\nBased on the above thought, the answer is:\nYear,Automotive,Transportation\n2013,13.5%,8.1%\n2014,15.6%,9.4%\n2015,16.7%,10.0%\n2016,17.5%,10.6%\n2017,19.8%,11.9%\n2018,18.3%,11.0%\n2019,18.8%,11.2%\n2020,19.2%,11.5%"
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "1GaalbUpKkeDTu5l",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell which of the two industries underwent more market research throughout the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To establish this, I will consider the research percentages of both industries for each year.\nBased on the above thought, the answer is:\nThe \"Automotive\" industry underwent more market research from 2013 to 2020 based on the higher proportion that it occupies in the chart for each of the given years."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "KmBvRod7hljXrSKK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you observe about the research in the Transportation industry over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the share of the Transportation sector in the pie chart for each consecutive year.\nBased on the above thought, the answer is:\nThe research in the Transportation industry appears to have been relatively stable over the years, with minor fluctuations. However, it should be noted that it is consistently lower compared to the Automotive industry research."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "hQEOSRSvNp2rqj0n",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the representation, what could possibly be the reason for the sharp dip in Transportation research share in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to check the research percentage for the Transportation industry in 2017.\nBased on the above thought, the answer is:\nIt is likely that there were fewer resources allocated to the Transportation industry for market research in 2017. However, without further details or context, it's impossible to definitively explain the dip."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "QGJ5FFsH9hmiw8nb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn how many years did the Automotive industry have more research percentage than the Transportation industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will need to compare the research percentage of both industries for each year and tabulate the instances where the Automotive industry's research percentage was higher than Transportation's.\nBased on the above thought, the answer is:\nThe Automotive industry had more research percentage than the Transportation industry for all years from 2013 to 2020."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "ErIGra2YT5f4VfXA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the research in the Automotive industry exceed the Transportation industry in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I need to subtract the research percentage for the Transportation industry from the Automotive industry for the year 2020.\nBased on the above thought, the answer is:\n19.2% (Automotive) - 11.5% (Transportation) = 7.7%"
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "kRFV7AUFWthkQAQX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average research percentage of the Automotive industry for the given period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to sum up the research percentages for the Automotive industry from 2013 to 2020 and divide it by 8 (the number of years).\nBased on the above thought, the answer is:\n(13.5% + 15.6% + 16.7% + 17.5% + 19.8% + 18.3% + 18.8% + 19.2%) / 8 = 17.3%"
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "ERsPIjLIyf12l6uX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the pie chart data into a CSV formatted table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing {the pie chart slices}[{'2016'}[0.223, 0.278, 0.482, 0.575],'{'2017'}[0.226, 0.516, 0.482, 0.84], {'2018'}[0.357, 0.516, 0.593, 0.886], {'2019'}[0.482, 0.516, 0.741, 0.85], {'2020'}[0.482, 0.183, 0.741, 0.536], {'2021'}[0.283, 0.146, 0.595, 0.516]], I can determine that each slice represents a different year and how much percentage each year represents in the Trends in Logistics in the Transportation industry.\nBased on the above thought, the answer is:\nYear, Trends in Logistics\n2016, 13.7%\n2017, 14.5%\n2018, 15.1%\n2019, 17.1%\n2020, 18.7%\n2021, 21.1%"
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "vofIHlsolRzngVVB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year experienced the highest trend in logistics? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the slice with the highest proportion in the pie chart, and then that year can be regarded as the year with the highest trends in logistics.\nBased on the above thought, the answer is:\nThe year \"2021\" experienced the highest trend in logistics, possibly due to increased activities in the transportation industry, more effective logistics management, or changes in economic factors."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "4NstFAKoSWMxodMC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you infer about the progress of the Transportation industry's trends in logistics over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the proportions of each year in the chart, I can infer about the progress of trends in logistics in the transportation industry.\nBased on the above thought, the answer is:\nThe pie chart indicates a progressive increase in trends in logistics in the transportation industry from 2016 to 2021. This progression suggests an expanding industry with steadily improving logistics operations."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "gJsMOJ6BUSY4oarE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might the increasing trends in logistics over these years indicate about the future of the Transportation industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The steadily increasing proportions from 2016 to 2021 might shed some light on future trends in logistics.\nBased on the above thought, the answer is:\nThe consistent growth in trends in logistics from 2016 to 2021 might suggest a promising outlook for the transportation industry, with potential for continued growth and expansion in logistics in the coming years."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "wsHpibD2NiTS2psV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the proportion of trends in logistics grow by more than 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which year the proportion grew by more than 5%, I need to compare the percentages of each year and calculate the difference.\nBased on the above thought, the answer is:\nThe growth from 2019 to 2021 went from 17.1% to 21.1% which is 4%. Hence, no year experienced a more than 5% increase in trends in logistics."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "zBuivMfq3vwWcFbL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of the trends in logistics from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "For the average percentage, I need to sum up the percentages from 2016 to 2021 and then divide by the total number of years.\nBased on the above thought, the answer is:\nThe sum of the percentages from 2016 to 2021 is 100%. The average of the trends in logistics from 2016 to 2021 is 100% / 6 years = 16.67%."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "uQlkMl6memPyXKlr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends in logistics for the year 2022 is estimated to be 22%, what would be the new total percentage for 2016 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add the estimated percentage for 2022 to the total percentage of 2016 to 2021.\nBased on the above thought, the answer is:\nSince the total percentage for 2016 to 2021 is 100%, by adding the estimated trends in logistics for 2022, the new total percentage for 2016 to 2022 would be 100% + 22% = 122%."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "ni3vACPcoWk2XOqW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you summarize the information given in the pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is entirely composed of the pie plot {pie plot}<r>[0.223, 0.146, 0.741, 0.886]</r> with slices representing different risk level categories like {\"High Risk\"}<r>[0.227, 0.278, 0.482, 0.516]</r>, {\"Low Risk\"}<r>[0.223, 0.444, 0.482, 0.839]</r>, {\"Moderate Risk\"}<r>[0.355, 0.516, 0.739, 0.886]</r>, {\"Very High Risk\"}<r>[0.482, 0.426, 0.741, 0.553]</r> and {\"Very Low Risk\"}<r>[0.283, 0.146, 0.733, 0.516]</r>. The percentages for these sectors can be extracted from the autotext surrounding each sector slice, thus converting pie values into table format.\nBased on the above thought, the answer is:\nRisk Level,Investment Amount(%)\nHigh Risk,8.0%\nLow Risk,20.0%\nModerate Risk,31.5%\nVery High Risk,5.5%\nVery Low Risk,35.0%"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "2JzsH2jIugzIWb8m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which risk category is the most favored for investment and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the chart, the segment with the largest portion will represent the most favored risk category for investment. Therefore, I need to compare sizes of all slices to find out the largest.\nBased on the above thought, the answer is:\nThe risk category 'Very Low Risk' is the most favored one because it has the most substantial share of total investments, which is 35%."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "mpkMPQml8sodQGQp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you deduce some risk management insights from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To deduce insights on risk management, I need to analyze how financial investments are distributed based on the level of risk each category implies.\nBased on the above thought, the answer is:\nThe pie chart suggests a risk-averse tendency in investment allocation. The higher the level of risk, the smaller the portion of investments. Significant investments are allocated to 'Low Risk' and 'Very Low Risk' categories indicating the preference for less risky investments."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "Xpeh44CUUE8QHc7E",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of financial planning, how can we interpret the investment strategy showcased by the investment distribution across different risk levels in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret the investment strategy, I need to analyze the distribution of investments among different risk levels present in the pie chart.\nBased on the above thought, the answer is:\nThe pie chart representation of the investment distribution suggests a conservative approach to financial planning. It clearly showcases that safe investments ('Very Low Risk' and 'Low Risk') are highly preferred, indicating a strategy to minimize losses. High-risk investments, though potentially profitable, consist of a minor portion indicating a calculated risk-taking strategy. Even the 'Moderate Risk' category gets more preference than the 'High Risk' and 'Very High Risk' categories, pointing towards a balanced approach in allocating funds."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "rCpXHzNDuJGUHArY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total investment percentage of 'Low Risk' and 'Very Low Risk' categories according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can get the investment percentiles for 'Low Risk' and 'Very Low Risk' {\"Low Risk sector\"}<r>[0.223, 0.444, 0.482, 0.839]</r> {\"Very Low Risk sector\"}<r>[0.283, 0.146, 0.733, 0.516]</r> categories and summing them will give us the combined percentage.\nBased on the above thought, the answer is:\n55.0%"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "YGsx4iwBLH8Us5Ke",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat portion of the investments is allocated to the 'Moderate Risk' category?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find that out, I need to check the percentage mentioned in the pie chart for the 'Moderate Risk' category {\"Moderate Risk sector\"}<r>[0.355, 0.516, 0.739, 0.886]</r>.\nBased on the above thought, the answer is:\n31.5%"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "fR3tiozCcYNTXk3H",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investments in 'High Risk' and 'Very High Risk' categories were combined, what would be their total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart data for 'High Risk' and 'Very High Risk' {\"High Risk sector\"}<r>[0.227, 0.278, 0.482, 0.516]</r> {\"Very High Risk sector\"}<r>[0.482, 0.426, 0.741, 0.553]</r> categories provide the percentages which when added give the total percentage.\nBased on the above thought, the answer is:\n13.5%"
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "GdEEwSy9M0h3T4AP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help convert the 2020 real estate market data from the given pie chart to a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the labels of {the legend}<r>[0.86, 0.063, 0.993, 0.129]</r>, and percentages in the slices {Residential}<r>[0.223, 0.146, 0.741, 0.886]</r> and {Commercial}<r>[0.283, 0.202, 0.482, 0.516]</r>, I can determine the specific market distribution of different sectors in 2020.\nBased on the above thought, the answer is:\nSector,2020\nResidential,95%\nCommercial,5%"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "JMGCuh7LGZrcr3Wq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which sector dominated the real estate market in 2020? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I should look at the sizes of the slices of the pie chart to determine the sector with the highest percentage, specifically the slice labelled as {Residential}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nThe \"Residential\" sector dominated the real estate market in 2020 because it represented the largest portion of the pie chart with a percentage of 95%."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "7axIfbUZsqRArvIc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend was observed in the \"Commercial\" sector in the year 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the slice indicating the {Commercial}<r>[0.283, 0.202, 0.482, 0.516]</r> and interpret its size in relation to the whole pie chart.\nBased on the above thought, the answer is:\nThe \"Commercial\" sector showed a significant decrease in the real estate market share in 2020, only representing a small slice of the pie chart with a percentage of 5%."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "vPBOxsbRAmBjoLTy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how can the data influence future investment decisions in the real estate market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide an informed suggestion, I can analyse the pie chart slices, more specifically the size of {Residential}<r>[0.223, 0.146, 0.741, 0.886]</r> and {Commercial}<r>[0.283, 0.202, 0.482, 0.516]</r>, which show the current state of the market in 2020.\nBased on the above thought, the answer is:\nConsidering the dominant position of the \"Residential\" sector in 2020, future investors might be motivated to continue investments in this sector. However, the drastic decrease in \"Commercial\" sector suggests a potential risk, so investors might prefer to conduct further market analysis or consider diversifying their investments to buffer against unpredictable market changes."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "u4KTo7qvKIBOVnhv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference percentage between the \"Residential\" and \"Commercial\" sectors based on the 2020 pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to calculate the difference by subtracting the percentage for the Commercial sector from the Residential sector based on the data in the slices {Residential}<r>[0.223, 0.146, 0.741, 0.886]</r> and {Commercial}<r>[0.283, 0.202, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "yth7i5VaZJZJXBGs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat constitutes the majority of the market share in the 2020 pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the percentages in the pie chart slices, I am focusing on the larger slice, which indicates {Residential}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nResidential"
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "AaXj2sbKNVaJAOdk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the \"Commercial\" segment's share were to increase its percentage by 15% next year, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 15% to the current percentage of the \"Commercial\" sector.\nBased on the above thought, the answer is:\nThe new percentage for \"Commercial\" would be 5% (current) + 15% = 20%."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "mol6bopBho2F546T",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nI want to see the data from this pie chart in a structured table format. Can you help extracting this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the subtitle}<r>[0.167, 0.021, 0.796, 0.047]</r>, it is clear that the chart represents the number of medical professionals in General Medicine and Neurology for 2013 only. Two different categories are represented in the chart:  {General Medicine slice}<r>[0.223, 0.163, 0.741, 0.886]</r> and {Neurology slice}<r>[0.283, 0.146, 0.559, 0.516]</r>. Also, the percentages for each category is listed in the autotext within each slice. Using these details, we can construct a structured table format.\nBased on the above thought, the answer is:\nYear,General Medicine,Neurology\n2013,81.3%,18.7%"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "0TPFyKZfRnPoWPxU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine which medical specialty had more professionals in 2013 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>, the sector that takes up more space represents the specialty with more professionals.\nBased on the above thought, the answer is:\nIn 2013, General Medicine had more professionals as it consists of 81.3% of the total."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "qwlYvVI4Fs5Z6XyV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the distribution of professionals in these two medical fields from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of each category in the {pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> provides insights about the distribution.\nBased on the above thought, the answer is:\nIt can be inferred that General Medicine is a significantly larger field compared to Neurology in terms of professionals in 2013, with General Medicine occupying more than 80% of all professionals represented."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "gkyiJCY6s0Svm2wJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, how might medical schools adjust their admission strategy in Neurology field?"
+            },
+            {
+                "from": "gpt",
+                "value": "To speculate on the potential adjustments in admission strategy, look at the proportion of each category to the whole pie, which implies the demand for professionals in each field.\nBased on the above thought, the answer is:\nGiven the underrepresentation of professionals in Neurology at 18.7%, medical schools may adjust their admission strategies to encourage more enrollments in Neurology, potentially to better balance the workforce distribution between these two fields."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "9qKe4ilekdYlAOeK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage of medical professionals in General Medicine and Neurology in 2013 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, we must subtract the percentage of professionals in Neurology from the percentage in the General Medicine in 2013.\nBased on the above thought, the answer is:\nThe difference is 81.3% - 18.7% = 62.6%"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "LmUdu3d4c2qa8uhA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the percent share of Neurology if it increased by 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, we just need to add 5% to the existing percentage of Neurology.\nBased on the above thought, the answer is:\nThe new percentage would be 18.7% + 5% = 23.7%"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "Nfu0eW9bupGswCAX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor each field, what would be the new percentages if General Medicine decreased by 4% and Neurology increased by 4%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentages, we subtract 4% from the existing percentage of General Medicine and add 4% to Neurology's percentage.\nBased on the above thought, the answer is:\nThe new percentage for General Medicine would be 81.3% - 4% = 77.3% and for Neurology, it would be 18.7% + 4% = 22.7%"
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "OLTUq9GgL43YxBbr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the pie charts, please create a table listing out the percentage share of AI and Machine Learning, and Biotechnology industries from 2015 to 2019."
+            },
+            {
+                "from": "gpt",
+                "value": "There are 5 pie charts representing the years from 2015 to 2019 in the chart. Each pie chart is divided into two sectors, one representing the AI and Machine Learning industry {AI_and_Machine_Learning}[0.322, 0.593, 0.365, 0.613]; {AI_and_Machine_Learning}[0.338, 0.635, 0.382, 0.655]; {AI_and_Machine_Learning}[0.365, 0.676, 0.409, 0.696]; {AI_and_Machine_Learning}[0.43, 0.722, 0.474, 0.742]; {AI_and_Machine_Learning}[0.478, 0.727, 0.522, 0.747] and the other representing the Biotechnology industry {Biotechnology}[0.608, 0.419, 0.652, 0.439]; {Biotechnology}[0.591, 0.377, 0.635, 0.397]; {Biotechnology}[0.565, 0.336, 0.608, 0.356]; {Biotechnology}[0.5, 0.29, 0.543, 0.31]; {Biotechnology}[0.451, 0.285, 0.495, 0.305]. The percentages of these two sectors in each year can be seen next to the corresponding labels.\nBased on the above thought, the answer is:\nYear,AI_and_Machine_Learning,Biotechnology\n2015,35%,65%\n2016,42%,58%\n2017,50%,50%\n2018,65%,35%\n2019,75%,25%"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "JPsDbeNZ4strfqMO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the industry that has been consistently increasing its share over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The industry that has been consistently increasing its share can be identified by checking if the percentages in each subsequent year is bigger than the previous year.\nBased on the above thought, the answer is:\nAI and Machine Learning is the industry that has been consistently increasing its share over the years, going from 35% in 2015 to 75% in 2019."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "AXNKRXNDGInTRPE9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the most significant year in terms of change in share for the AI and Machine Learning industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most significant year in terms of change in share for the AI and Machine Learning industry can be identified by comparing the differences in percentages between subsequent years and find the year with the biggest jump.\nBased on the above thought, the answer is:\nThe most significant year was 2018, where the share of AI and Machine Learning had a steep rise from 50% in 2017 to 65%."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "ReyHfKH9MlZlRpQJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, what can you infer about the future trends of these two industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future trends of these two industries can be inferred by looking at the direction of their shares' movement over the years.\nBased on the above thought, the answer is:\nThe chart shows an upscale trend in the AI and Machine Learning industry and a downscale trend in the Biotechnology industry. Therefore, we might infer that AI and Machine Learning would continue to grow, while Biotechnology would continue to shrink in the future."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "xVpWUMk4FYROX28J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the share of AI and Machine Learning increase from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to take the percentages for AI and Machine Learning in 2019 and 2015 and find the difference between these two values.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "6cpk0sqY05pHaF9J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of Biotechnology for the year 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add up the percentages of Biotechnology for the years 2015 and 2016.\nBased on the above thought, the answer is:\n123%"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "TI28b4TGpiuKHB0n",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average share of AI and Machine Learning for all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to sum up the percentages of AI and Machine Learning for all years, and then divide the sum by the number of years.\nBased on the above thought, the answer is:\n53.4%"
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "jKDhXQv0bU1OaQ9I",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the pie chart you see into a table, according to the cuisine and their percentage of restaurant count displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Through the_legend<r>[0.885, 0.063, 0.993, 0.219]</r> of the chart and each pie slice for {Italian}<r>[0.223, 0.278, 0.482, 0.762]</r>, {French}<r>[0.288, 0.516, 0.507, 0.886]</r>, {Chinese}<r>[0.482, 0.442, 0.741, 0.884]</r>, {Indian}<r>[0.478, 0.146, 0.735, 0.516]</r> and {Mexican}<r>[0.283, 0.146, 0.482, 0.516]</r>, I can get the restaurant count distribution for each cuisine.\nBased on the above thought, the answer is:\nCuisine,Percentage\nItalian,22.7%\nFrench,15.0%\nChinese,26.7%\nIndian,22.0%\nMexican,13.7%"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "b76035kQMS7IIErV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine has the most number of restaurants according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, the cuisine with the largest slice represents the highest restaurant count. Therefore, I need to identify which slice is the largest.\nBased on the above thought, the answer is:\nChinese cuisine has the most number of restaurants."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "UJemgL1Fbo2xxhes",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make about the preference for different cuisines based on the restaurant count?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions of each slice to infer the preference for different cuisines.\nBased on the above thought, the answer is:\nBased on the restaurant count, it can be inferred that Chinese and Italian cuisines are the most preferred, followed by Indian cuisine. French and Mexican cuisines have less preference, with Mexican being the least preferred."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "a7O08gjUilz4ABf5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you had to advise a budding restaurateur about the type of cuisine he should offer, what would your advice be?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the pie chart, I should suggest the cuisine that has the highest number of restaurants as it might be the most popular.\nBased on the above thought, the answer is:\nConsidering the current market, I'd advise the restaurateur to consider offering Chinese cuisine as it seems to be the most popular choice among people, judging by the highest number of existing restaurants."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "mxnnetH1ySgdkinb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total restaurant count according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the pie chart is a representation of the entire restaurant count, that is distributed among the different cuisines, the total restaurant count is the sum of the count of each cuisine.\nBased on the above thought, the answer is:\n300"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "z3GYlZKC6K2yl9DR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the restaurant count for French and Mexican cuisines together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add up the counts for French and Mexican restaurants.\nBased on the above thought, the answer is:\n86"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "KeE9jOEL0bm1Xrqt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the count of Chinese restaurants if it increases by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to calculate 10% of the current Chinese restaurant count and add it to the current count.\nBased on the above thought, the answer is:\n88"
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "mQO8mtoVQ2Dgtkcz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data from the pie chart of both the Intangible and Tangible cultural heritages into CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the pie chart the distribution of influence on both Intangible Cultural Heritage {\"Intangible Cultural Heritage\"}[0.079, 0.205, 0.374, 0.795] and Tangible Cultural Heritage {\"Tangible Cultural Heritage\"}[0.558, 0.205, 0.852, 0.795] by Asian Influence {\"Asian Influence\"}[0.405, 0.149, 0.422, 0.161; 0.884, 0.149, 0.901, 0.161], European Influence {\"European Influence\"}[0.405, 0.175, 0.422, 0.187; 0.884, 0.175, 0.901, 0.187], and Other Influence {\"Other Influence\"}[0.405, 0.201, 0.422, 0.214; 0.884, 0.201, 0.901, 0.214].\nBased on the above thought, the answer is:\nHeritage,Asian Influence,European Influence,Other Influence\nIntangible Cultural Heritage,35,45,20\nTangible Cultural Heritage,50,30,20"
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "kbGbudbvXVz0jMum",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which cultural influence has the most effect on Intangible Cultural Heritage and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to examine the pie chart for Intangible Cultural Heritage and identify the culture that has the largest slice, indicating its greatest influence.\nBased on the above thought, the answer is:\nEuropean Influence has the most effect on Intangible Cultural Heritage because it has the largest slice at 45%."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "6uyjtuwvuwOkEUu7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat difference can you identify in the cultural influences between Intangible and Tangible Cultural heritages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the differences, I need to compare the slices in both pie charts.\nBased on the above thought, the answer is:\nThe pie charts show that Asian Influence has a greater effect on Tangible Cultural Heritage at 50%, as compared to Intangible Cultural Heritage at 35%. Conversely, European Influence has a greater effect on Intangible Cultural Heritage at 45%, as compared to Tangible Cultural Heritage at 30%."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "vdrKD9OWEdk17xJL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how might the influence of various cultures impact preservation strategies for Intangible and Tangible Cultural Heritage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the pie slices' proportions and the cultural influence they represent.\nBased on the above thought, the answer is:\nPreservation strategies might prioritize areas with greater cultural influences. For Intangible Cultural Heritage, more attention might be necessary towards European influences, and for Tangible Cultural Heritage, a focus towards Asian influences could be beneficial. However, it's also important not to neglect the 'Other Influence' category to maintain diverse cultural representations."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "TjEVPgiqpIaeCa73",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the pie chart for Intangible Cultural Heritage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add up the percentages for Asian Influence {\"Asian Influence\"}[0.405, 0.149, 0.422, 0.161], European Influence {\"European Influence\"}[0.405, 0.175, 0.422, 0.187] and Other Influence {\"Other Influence\"}[0.405, 0.201, 0.422, 0.214] in the Intangible Cultural Heritage {\"Intangible Cultural Heritage\"}[0.079, 0.205, 0.374, 0.795] pie chart.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "5dN9T89T9OvDhdps",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what is the average proportion of the Asian Influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average proportion, I need to take the average of the Asian Influence {\"Asian Influence\"}[0.405, 0.149, 0.422, 0.161; 0.884, 0.149, 0.901, 0.161] in both pie charts.\nBased on the above thought, the answer is:\nAsian Influence average proportion is (35%+50%)/2 = 42.5%"
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "3TkQgPOufPEuS3pQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the European Influence for Tangible Cultural Heritage was reduced by half, what would be the new percentage for European Influence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new European Influence percentage, I need to halve the European Influence {\"European Influence\"}[0.884, 0.175, 0.901, 0.187] in the Tangible Cultural Heritage pie chart.\nBased on the above thought, the answer is:\nNew European influence percentage would be 15%."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "hLTe70RH2HWhATob",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you construct a CSV formatted table representing the data from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By identifying the company names from the legend {\"Google\"}<r>[0.866, 0.138, 0.915, 0.158]</r>, {\"Microsoft\"}<r>[0.866, 0.168, 0.929, 0.188]</r>, {\"Amazon\"}<r>[0.866, 0.198, 0.922, 0.218]</r>, {\"Facebook\"}<r>[0.866, 0.228, 0.931, 0.248]</r>, {\"Others\"}<r>[0.866, 0.258, 0.912, 0.278]</r> and their respective market shares for the year 2020 annotated on the pie chart slices, I can construct a CSV format table.\nBased on the above thought, the answer is:\nCompany,Market Share 2020\nGoogle,38\nMicrosoft,30\nAmazon,20\nFacebook,6\nOthers,6"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "2EBtgUuk6cTPiZrW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company holds the largest market share in 2020, as per the pie chart and why is this significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "A close look at the chart shows the sizes of the slices. The largest belongs to {\"Google\"}<r>[0.297, 0.307, 0.538, 0.813]</r>. This implies it has the highest market share.\nBased on the above thought, the answer is:\nThe company with the largest market share in 2020 is Google. This suggests that Google generally dominates the market, potentially due to more users preferring this company's services or products over its competitors."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "dqOfmikKhlknTxDi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the strongest competitor to Google in 2020 based on the market share distribution in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be found through the size of the slices in the pie chart. The second largest slice belongs to {\"Microsoft\"}<r>[0.512, 0.376, 0.728, 0.811]</r>.\nBased on the above thought, the answer is:\nBased on the market share distribution in the pie chart, Microsoft appears to be the strongest competitor to Google in 2020 as they hold the second largest market share."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "gaLwF11OtZS1VWiC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the decrease in market shares of some companies in 2020 as seen in the pie chart, what could be inferred about the market competition?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows a visible reduction in market shares for {\"Amazon\"}<r>[0.487, 0.197, 0.708, 0.505]</r>, {\"Facebook\"}<r>[0.41, 0.199, 0.512, 0.505]</r> and {\"Others\"}<r>[0.347, 0.234, 0.512, 0.505]</r>.\nBased on the above thought, the answer is:\nThe decrease in market shares for Amazon, Facebook and Others might suggest that these companies experienced stronger competition in 2020, possibly from Google and Microsoft, or that they were less effective at capturing or retaining their market share this year."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "Q4aVoRwDmWzklGBN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of market shares for Google and Microsoft in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data conveyed in the chart slices for {\"Google\"}<r>[0.297, 0.307, 0.538, 0.813]</r> and {\"Microsoft\"}<r>[0.512, 0.376, 0.728, 0.811]</r>, I can add their individual market shares for this calculation.\nBased on the above thought, the answer is:\n68"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "f9glwAmN41eduvaV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage did Google's market share increase from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data mentions Google's market shares for 2019 and 2020. I can subtract the former from the latter to find the increase.\nBased on the above thought, the answer is:\n3"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "wS48r44Q573tXVUi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined market share of Facebook and Others in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would find this by adding the shares of {\"Facebook\"}<r>[0.41, 0.199, 0.512, 0.505]</r> and {\"Others\"}<r>[0.347, 0.234, 0.512, 0.505]</r>.\nBased on the above thought, the answer is:\n12"
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "koSnouNumUR5tvB6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a summary of the Entrepreneurship and Innovation Ecosystem data as seen in the chart, in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie chart, we can see that it represents the proportion of each element in the Entrepreneurship and Innovation Ecosystem based on the sector slice {the slice for Innovation}<r>[0.223, 0.278, 0.482, 0.712]</r>, {the slice for Startup}<r>[0.262, 0.516, 0.482, 0.885]</r>, {the slice for Investment}<r>[0.464, 0.516, 0.645, 0.886]</r>, {the slice for Funding}<r>[0.482, 0.282, 0.741, 0.803]</r>, {the slice for Technology}<r>[0.482, 0.158, 0.682, 0.516]</r>, {the slice for Marketing}<r>[0.318, 0.146, 0.548, 0.516]</r>, and {the slice for Exits}<r>[0.283, 0.229, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nEntrepreneurship elements,Share in Ecosystem(%)\nInnovation,20\nStartup,15\nInvestment,12\nFunding,25\nTechnology,10\nMarketing,15\nExits,3"
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "jZyQYZvslsMEwgf7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, which factor appears to be most important in the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "The importance of each factor can be inferred from their proportions in the pie chart. The factor with the highest proportion is assumed to be the most important, which in this case, is {Funding}<r>[0.482, 0.282, 0.741, 0.803]</r>.\nBased on the above thought, the answer is:\nFunding appears to be the most important factor in the Entrepreneurship and Innovation Ecosystem as it has the highest proportion."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "r7vhOIzx3t8qgR15",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you point out a trend in the element contributions within the Entrepreneurship Ecosystem based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The distribution of contributions allows for seeing trends. Most categories have decent contributions around 15%, Exits are the exception being much lower, and Funding garners notably more investment, as per the {Funding slice}<r>[0.482, 0.282, 0.741, 0.803]</r>.\nBased on the above thought, the answer is:\nThe dominant trend is that Funding outweighs other elements in the Entrepreneurship and Innovation Ecosystem with a significant margin, suggesting that notable resources are funneled into it. On the other hand, Exits have the smallest proportion suggesting it is the least represented element within the ecosystem. The other elements - Innovation, Startup, Investment, Technology, Marketing have close and substantial representations, indicative of their importance and balanced role within the ecosystem."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "Mqk6JmpM465m8nBn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence future funding decisions in the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make future funding decisions, it's helpful to look at the current funding distribution in the {Funding slice}<r>[0.482, 0.282, 0.741, 0.803]</r> along with remaining slices.\nBased on the above thought, the answer is:\nThe data suggests that \u2018Funding\u2019 already has a significant allocation within the Entrepreneurship and Innovation Ecosystem. Therefore, if the current structure effectively supports the ecosystem, similar funding patterns may be maintained in the future. However, if it is decided to stimulate underrepresented elements or alter the balance, Exits and Technology could potentially receive a greater share. The invisibility of Exits could also prompt discussions and initiatives regarding terminations and transitions within the ecosystem."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "GSQbPmf5z21zgBWV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element contributes the least to the Entrepreneurship and Innovation Ecosystem based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of each slice in the pie chart, we can understand the size of the contribution of each element. The least contribution will be represented by the smallest slice, which is {Exits}<r>[0.283, 0.229, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nExits."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "KsE3NSfrgawQd0yf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined contribution of 'Startup', 'Investment', and 'Marketing' to the Entrepreneurship and Innovation Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined contribution, I'll add up the contributions of {Startup}<r>[0.262, 0.516, 0.482, 0.885]</r>, {Investment}<r>[0.464, 0.516, 0.645, 0.886]</r>, and {Marketing}<r>[0.318, 0.146, 0.548, 0.516]</r>.\nBased on the above thought, the answer is:\n42%."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "cbn2FxHMFDhDHria",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Technology' increases its contribution by 5%, what would be the new total percentage of all elements in the ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 5% to the 'Technology' contribution, which is {Technology}<r>[0.482, 0.158, 0.682, 0.516]</r>, and ensure that the total percentage sums up to 100%.\nBased on the above thought, the answer is:\nThe total would exceed 100% as the current total percentage is already at 100%."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "xTaS6gOB0WLCVRpV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are looking at, can you categorize the technological advancements, specifically Artificial Intelligence and Blockchain, over the years 2010-2017 and convert it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the raw data and the interpretation given in the pie diagram with the title {\"Technological Industry Development Trends from 2010 to 2017\"}<r>[0.22, 0.021, 0.743, 0.047]</r> and segment labels with coordinates <r>[0.913, 0.073, 0.941, 0.087]</r> to <r>[0.913, 0.522, 0.941, 0.536]</r> for each individual year, it can be seen that the observations could be directly transformed into a table.\nBased on the above thought, the answer is:\nYear, Artificial Intelligence, Blockchain\n2010,20,5\n2011,30,10\n2012,40,20\n2013,70,40\n2014,90,80\n2015,115,110\n2016,160,150\n2017,200,200"
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "JLXMawzcEbSpwbzv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology saw the most significant increase in interest over the observed period and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the growth trajectories in the legend {the color segments}<r>[0.913, 0.073, 0.941, 0.536]</r> for Artificial Intelligence and Blockchain it could be hypothesised which technology had the most significant interest growth.\nBased on the above thought, the answer is:\nArtificial Intelligence experienced a more significant increase in interest over the period. This is because it started with a similar level of interest as Blockchain in 2010, but by 2017, the interest in Artificial Intelligence had grown to a significantly higher level."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "uMDIeP04ZYukx9e1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the rate of growth differ between the two technologies from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the pie chart's legend and the size of the segments therein, I can analyze and compare the rate of growth between the two technologies.\nBased on the above thought, the answer is:\nBoth Artificial Intelligence (AI) and Blockchain showed substantial growth from 2010 to 2017. However, in the initial years from 2010-2012, AI showed a steeper growth compared to Blockchain. Post-2012, both technologies exhibit remarkably similar growth trajectories, with the gap in interest between them remaining nearly consistent."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "kpuHzkPjzRUNUG6W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the evolution of technology trends from 2010 to 2017 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By assessing how the levels of interest in Artificial Intelligence and Blockchain have varied over the given period, I can deliver an inference about the evolution of technology trends.\nBased on the above thought, the answer is:\nIt can be inferred that both Artificial Intelligence and Blockchain have continuously gained popularity and interest from 2010 to 2017. The persistent increase across the years demonstrates a growing trend towards these two technologies, indicating their rising significance in the technology sector. The similar growth patterns suggest they may have been influenced by similar factors or industry trends."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "oGg4unBlhtk4TzTi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the interest in Artificial Intelligence increase from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I needed to take the data of Artificial Intelligence from 2010 and 2017 and calculate the percentage increase.\nBased on the above thought, the answer is:\nInterest in Artificial Intelligence increased by 900%."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "c7qsW2HNTvkX9UCZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the difference in interest between Artificial Intelligence and Blockchain the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I needed to determine the difference in values between AI and Blockchain for each year and find the year this difference was the largest.\nBased on the above thought, the answer is:\nThe largest difference in interest between Artificial Intelligence and Blockchain was in 2013."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "BUm2wrJxr2VHWX7M",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual growth rate of interest in Blockchain from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I had to calculate the compound annual growth rate (CAGR) which involves the initial and final values of the investment or loan and the time in years it took to achieve the final value.\nBased on the above thought, the answer is:\nThe average annual growth rate of interest in Blockchain is approximately 96.2%."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "eP0tHpZMK82rz9UR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you create a CSV data format that displays the distribution of entrepreneurship areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart's title {\"Entrepreneurship Areas Distribution\"}[0.332, 0.021, 0.631, 0.047], I can see it's about the distribution of areas in entrepreneurship. Then, there are three {pie slices} with corresponding labels and percentages {\"Startup Creation\"[0.104, 0.665, 0.219, 0.685]; \"Venture Capital\"[0.746, 0.658, 0.853, 0.678]; \"Business Incubation\"[0.318, 0.1, 0.457, 0.12]}. These slices and their associated percentages represent the number of ventures categorized by these areas.\nBased on the above thought, the answer is:\n\"Area\",\"Percentage\"\n\"Startup Creation\",35\n\"Venture Capital\",40\n\"Business Incubation\",25"
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "1XlZAZLQWlUDIQ3T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which area of entrepreneurship received the most focus, and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify which slice of the pie chart is largest\u2014that represents the area that received the most focus.\nBased on the above thought, the answer is:\n\"Venture Capital\" received the most focus, with a representation of 40%. This could indicate that more resources or emphasis was placed on this area due to its potential high returns and critical role in promoting innovation."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "kVrQSRV6B4x1BqOv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the proportions of each entrepreneurship area say about entrepreneurial priorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will need to determine the relationship between different areas based on their proportions. The larger the slice, the higher the priority in the entrepreneurial ecosystem represented by this chart.\nBased on the above thought, the answer is:\nBased on the chart, \"Venture Capital\" seems to be the highest priority, followed by \"Startup Creation\" and \"Business Incubation\". This could suggest a greater emphasis on funding mechanisms and launching new businesses compared to providing incubation support."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "WL40K7GwRrmafqFA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSuppose the depicted chart represents data coming from a public survey. What might this imply about public perception towards different areas of entrepreneurship?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider how the public might perceive different sectors of entrepreneurship based on their representation in the pie chart.\nBased on the above thought, the answer is:\nIf this data is from a public survey, it suggests that the public perceives \"Venture Capital\" to be the most significant area of entrepreneurship. This could mean that the public acknowledges the crucial role of investment in fostering entrepreneurship. It could also reveal that the public perceives \"Startup Creation\" to be fairly important but might undervalue the role of \"Business Incubation\" in the entrepreneurship ecosystem."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "6RQXk5l7bWPpyXMC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf one were to combine the least and most represented sectors, what would be the total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage I have to add the individual percentages of the least and most represented sectors.\nBased on the above thought, the answer is:\n65%."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "l771RX6OQdfdBQ8f",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to your analysis of the pie chart, what is the percentage of the sector that is neither the highest nor the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I will need to identify the sector that is not marked as having the highest or lowest percentage.\nBased on the above thought, the answer is:\n35% (Startup Creation)."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "tfc33giWi3iNqxNl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage does the area with the largest representation exceed the smallest one?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine by how much the largest area exceeds the smallest area in terms of representation, I need to subtract the smallest area's percentage from the largest area's percentage.\nBased on the above thought, the answer is:\n15% (Venture Capital 40% - Business Incubation 25%)."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "XZDPrIG0jN0ZTJJ5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you saw, can you convert the data about the prevalence of Self-Driving Cars and Space Travel from 2020 to 2050 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the data point labeling in the chart starting from {the first red slice}<r>[0.257, 0.278, 0.482, 0.516]</r> for \"Self-Driving Cars (2020)\" leading to {the last light blue slice}<r>[0.283, 0.147, 0.482, 0.516]</r> for \"Space Travel (2050)\", I can see that the slices relate to the point prevalence data for each scenario across years.\nBased on the above thought, the answer is:\nScenario,2020,2025,2030,2035,2040,2045,2050\nSelf-Driving Cars,20,30,45,70,85,98,100\nSpace Travel,1,5,15,25,50,70,90"
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "lC7xXuKccuF1zEnP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which future technology, Self-Driving Cars or Space Travel, will be more prevalent in 2050? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which technology is more prevalent in 2050, I'm going to compare the prevalence percentages for Self-Driving Cars and Space Travel in 2050.\nBased on the above thought, the answer is:\nSelf-Driving Cars will be more prevalent in 2050 because, according to the data, they will achieve 100% prevalence, whereas Space Travel will only achieve 90% prevalence by 2050."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "atXiBgpF4v7R5HEC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nRegarding the Space Travel scenario, what trend can you infer from its evolution from 2020 to 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer a trend, I'm looking at the prevalence of Space Travel beginning in 2020 and ending in 2050, observing the growth in the percentage.\nBased on the above thought, the answer is:\nThe trend inferred for the Space Travel scenario reveals a steady increase in prevalence from 2020 to 2050, showing a growing interest and feasibility of space travel over time."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "4yDGqxCbQCSWAorf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does the data have for society and future industries by 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the shifting prevalences of self-driving cars and space travel might impact society and industries.\nBased on the above thought, the answer is:\nThe data suggests that by 2030, self-driving cars will be quite common with a prevalence of 45%, which could mean changes in urban planning, transportation industry, and related tech industries. Space Travel prevalence rising to 15% could indicate budding space travel industry growing and becoming more accessible to the public."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "Qq7KGDAxXZVYoQsq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year is the prevalence of self-driving cars supposed to reach 45%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the slice of the pie chart that corresponds to self-driving cars and find the year that matches 45% prevalence.\nBased on the above thought, the answer is:\nThe prevalence of self-driving cars is supposed to reach 45% in 2030."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "ElTP9OYQ4mCuamrq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth rate of Space Travel prevalence from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average growth rate, I will calculate the difference in percentages between 2030 and 2040, then divide it by the ten-year period.\nBased on the above thought, the answer is:\nThe average growth rate of space travel prevalence from 2030 to 2040 is 3.5% per year."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "Kb1F56xlAtjRlO3x",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the predicted difference in prevalence between Self-Driving Cars and Space Travel in 2050?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in prevalence, I will need to subtract the 2050 Space Travel prevalence from the Self-Driving Cars prevalence.\nBased on the above thought, the answer is:\nThe predicted difference in prevalence between Self-Driving Cars and Space Travel in 2050 is 10%."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "UQTcJCyMKGxbpj6U",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observe, can you translate the growth percentages of the Entertainment and Gaming Industry from 2008 to 2014 into an organized CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart I am analyzing is titled \"Entertainment and Gaming Industry Development from 2008 to 2014\". Provided were {seven slices of pie charts}<r>[0.21, 0.131, 0.645, 0.753]</r> . Based on their autotext label, each of these slices represents a distinct year from 2008 to 2014 and their corresponding growth percentages. Considering that the question asks for a CSV table format, I am going to list down every year and their corresponding percentages in order.\nBased on the above thought, the answer is:\nYear,Growth Percentage\n2008,10.7%\n2009,12.7%\n2010,13.7%\n2011,14.1%\n2012,14.6%\n2013,15.6%\n2014,18.6%"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "H6mrK3KdygUqqYr2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOver the studied period (2008 - 2014), was there a steady growth in the Entertainment and Gaming Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I will verify if the growth percentages in the {seven slices of the pie chart}<r>[0.21, 0.131, 0.645, 0.753]</r> had a consistent increase over the years or not.\nBased on the above thought, the answer is:\nNo, there was a significant rise in the growth percentage in 2014 compared to previous years."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "czFCwoN3ZjUZbi5L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart illustrate the development of Entertainment and Gaming Industry from 2008 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the information presented in the {pie chart}<r>[0.21, 0.131, 0.645, 0.753]</r>, including the size and labeling of the slices, to determine how they depict the industry's development over time.\nBased on the above thought, the answer is:\nThe pie chart illustrates the development of the Entertainment and Gaming Industry over seven years, with each slice representing a year. The size of each slice proportional to the growth percentage, indicating the rate of development for that particular year. The distinct color shades help distinguish between the years."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "svAstttrtwVXqQci",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart sample, can observable successful trends in the Entertainment and Gaming Industry during the period of 2008 to 2014 be identified?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will examine the comparison between the percentages represented in the {pie slices}<r>[0.21, 0.131, 0.645, 0.753]</r> to understand the identifiable trends.\nBased on the above thought, the answer is:\nYes, a positive trend can be observed over the seven years as represented by the increasing sizes of the pie slices. This indicates that, during the period 2008 to 2014, there has been steady growth in the Entertainment and Gaming Industry, indicating the increasing importance and recognition of this industry. It's also noteworthy that there was a significant jump in growth by 2014."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "E45nEZOFBU9KLY5U",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the total development growth percentage of the Entertainment and Gaming Industry from 2008 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the percentage growth of each year from the {pie slices}<r>[0.21, 0.131, 0.645, 0.753]</r>, I can calculate the aggregate development growth across the timespan.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "BmQz1fx9vO9cTbru",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average growth rate per year of the Entertainment and Gaming Industry from 2008 to 2014 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average growth rate per year, I need to sum up the percentages of all years represented by the slices and divide them by the total number of years, which is seven.\nBased on the above thought, the answer is:\nApprox. 14.3%."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "aawLL9taJNzzMlDG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the Entertainment and Gaming Industry witness the maximum development growth, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at each {slice}<r>[0.21, 0.131, 0.645, 0.753]</r> of the chart and compare the percentages in their autotexts. The year with the highest percentage represents the largest development growth.\nBased on the above thought, the answer is:\n2014."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "QLMXsPdE4Q6eSo0D",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart's information for the year 2016 into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart with {the subtitle \"Media Influence in 2016\"}<r>[0.413, 0.088, 0.612, 0.114]</r> and the various slices labeled with media types such as {\"TV\"}<r>[0.297, 0.307, 0.512, 0.741]</r>, {\"Newspaper\"}<r>[0.374, 0.505, 0.512, 0.812]</r>, {\"Radio\"}<r>[0.497, 0.505, 0.627, 0.813]</r>, {\"Internet\"}<r>[0.512, 0.342, 0.728, 0.766]</r>, {\"Social Media\"}<r>[0.512, 0.198, 0.695, 0.505]</r>, {\"Face-to-face\"}<r>[0.46, 0.197, 0.528, 0.505]</r>, and {\"Town hall meetings\"}<r>[0.347, 0.206, 0.512, 0.505]</r>, it can be inferred that the slices represent different media types and their corresponding influence percentages for the year 2016.\nBased on the above thought, the answer is:\nYear,TV,Newspaper,Radio,Internet,Social Media,Face-to-face,Town hall meetings\n2016,25.0%,10.0%,10.0%,25.0%,15.0%,5.0%,10.0%"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "593XDcY7Qx0y9GKK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich communication mode had the lowest public influence in 2016 and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slices of the pie chart, the communication mode with the smallest slice would have the lowest influence.\nBased on the above thought, the answer is:\n\"Face-to-face\" had the lowest public influence in 2016. This mode has the smallest slice according to the coordinates on the chart, which suggests that it had the lowest percentage influence."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "MOq4ZMjWTOQ9ga8T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which media types had a significant influence on public opinion in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the media types with the largest slices in the 2016 pie chart, as a larger slice would suggest a higher influence.\nBased on the above thought, the answer is:\nIn 2016, the media types \"TV\" and \"Internet\" had a significant influence on public opinion, as indicated by their larger slice sizes in the pie chart."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "9anmcGC1lvdP6TWl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence policies around communication modes and their utilization in public discourse in the following year, 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the 2016 percentages might impact decisions on the utilization of communication modes in 2017.\nBased on the above thought, the answer is:\nThe data suggests that \"TV\" and \"Internet\" had high influence percentages in 2016, so policies might be directed towards these modes for effective public discourse in 2017. However, modes like \"Face-to-face\", having a lower influence percentage, might need strategic enhancements or alternative approaches to improve their reach and impact."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "ojrUZdLsrnQmJnDm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many media modes had an influence of 10% in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart with media types labeled, I can see that impact percentages are given. So, I can figure out how many media had an influence of 10% by examining make comparisons between different slices.\nBased on the above thought, the answer is:\n3"
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "vmOVxrtsbVPy0iUO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points is the Internet's influence more than the Town hall meetings' in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the percentages of \"Internet\" and \"Town hall meetings\" provided by their labels on the pie chart will give the difference in percentage points.\nBased on the above thought, the answer is:\n15 percentage points."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "QdIHwgnrx9VBEtgu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many total percentage points in influence were contributed by digital mediums (Internet and Social Media) in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total contribution of digital media, I can add the percentages of the \"Internet\" and \"Social Media\" slices from the pie chart.\nBased on the above thought, the answer is:\n40 percentage points."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "PfNa7ooTYsqcSWuW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help to summarize the pie chart data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I noticed that the slices represent the percentage of social events related to political trends for each year from 2015 to 2019, {\"2015\"}<r>[0.175, 0.382, 0.21, 0.402]</r>, {\"2016\"}<r>[0.211, 0.736, 0.246, 0.756]</r>, {\"2017\"}<r>[0.538, 0.905, 0.573, 0.925]</r>, {\"2018\"}<r>[0.766, 0.491, 0.802, 0.511]</r>, and {\"2019\"}<r>[0.432, 0.099, 0.467, 0.119]</r>.\nBased on the above thought, the answer is:\nYear,Percentage of Social Events\n2015,12.4%\n2016,16.5%\n2017,20.7%\n2018,24.2%\n2019,26.2%"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "VlcOU0Akv5NG4SDW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the chart, which year seems to have the highest increase in social events?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the relative sector sizes of each year. From the chart, I can tell that the {sector slice for \"2019\"}<r>[0.283, 0.146, 0.662, 0.516]</r> is larger than all the others.\nBased on the above thought, the answer is:\n2019 saw the highest increase in social events."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "6gwJ15OnKUwFX3Z0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we say about the trend of social events over the years from 2015 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to look at the size of each sector corresponding to the respective years on the pie chart, starting from {\"2015\"}<r>[0.175, 0.382, 0.21, 0.402]</r> to {\"2019\"}<r>[0.432, 0.099, 0.467, 0.119]</r>.\nBased on the above thought, the answer is:\nThere is a clear increasing trend in social events related to political trends from 2015 to 2019. The increase is particularly significant after 2017."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "uCqV54ZAQNjHn4h6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you tell which year has the least social events?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the smallest slice on the pie chart, which is for the {year \"2015\"}<r>[0.175, 0.382, 0.21, 0.402]</r>.\nBased on the above thought, the answer is:\nThe year 2015 had the least social events."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "YXi6hpS7YBTCF1ak",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of social events in 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total percentage for these two years, I need to sum up the percentages associated with {the year \"2017\"}<r>[0.538, 0.905, 0.573, 0.925]</r> and {the year \"2018\"}<r>[0.766, 0.491, 0.802, 0.511]</r> slices from the pie chart.\nBased on the above thought, the answer is:\n45.9%"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "HiOQs8vfRXfSeX7J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the percentage of social events increase from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the percentage for {the year \"2015\"}<r>[0.175, 0.382, 0.21, 0.402]</r> from that of {the year \"2019\"}<r>[0.432, 0.099, 0.467, 0.119]</r>.\nBased on the above thought, the answer is:\n13.8%"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "vjsedWdETBhCbXa3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage for 2019 was twice that of 2015, what would be the new percentage for 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to double the percentage of {the year \"2015\"}<r>[0.175, 0.382, 0.21, 0.402]</r>.\nBased on the above thought, the answer is:\n24.8%"
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "t6GQv6z2pxIoQxSg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data of E-commerce and Retail from 2015 to 2020 in CSV table format, as shown in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I see 12 slices representing market share for E-commerce and Retail spanning from 2015 to 2020. The colour-coded legend at the right {legend}[0.908, 0.063, 0.993, 0.428] helps to identify the corresponding year for each slice. I can extract that data from the given chart.\nBased on the above thought, the answer is:\nYear,E-commerce,Retail\n2015,14,85\n2016,25,75\n2017,47,53\n2018,71,29\n2019,90,10\n2020,100,0"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "fqWufKd4kJlHHBiK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the trend from 2015 to 2020 of E-commerce based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To explain the trend, I would need to look at the varying sizes of the E-commerce slices year by year.\nBased on the above thought, the answer is:\nFrom 2015 to 2020, the market share of E-commerce has shown a positive trend. It started with a smaller slice representing a market share of 14% in 2015, and then each subsequent year, the slice representing E-commerce increased in size, indicating its consistent growth. In 2020, E-commerce dominated with the whole pie chart representing a market share of 100%."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "1OxAS8hT9dsx1L1P",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector, E-commerce or Retail, is projected to lead in future years, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I can see the changes in the sectors over the years.\nBased on the above thought, the answer is:\nBased on the chart, E-commerce is projected to lead in future years. This is because, from 2015 to 2020, the market share of E-commerce has shown a rising trend, moving from a mere 14% to a dominating 100%. On the other hand, Traditional Retail has been on a constant decline, all the way to 0% in 2020."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "mNmqCKpteEm02Dmf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the significant change in the market shares of E-commerce and Retail in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to look at the pie slices representing E-commerce and Retail for the year 2017.\nBased on the above thought, the answer is:\nThe year 2017 shows a significant change in the market shares of both E-commerce and Retail. E-commerce experienced a dramatic increase of nearly double its previous year's share, reaching 47% from 25%. On the contrary, Retail experienced a steep decline to 53% from 75%, losing almost half of its market share. In essence, 2017 was the tipping point where E-commerce started to overpower Retail."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "fLPnHbGWcY1VHelo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2018, what was the difference in market shares between E-commerce and Retail?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the market share difference in 2018, I need to subtract the market share of Retail from E-commerce for that particular year.\nBased on the above thought, the answer is:\n42%"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "gMvU3O6AowN5as3S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage increase for E-commerce from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the percentage in 2016 from the percentage in 2020 for E-commerce.\nBased on the above thought, the answer is:\n75%"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "RfqLEXpKfhasmwpv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did Retail lose more than half of its initial market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to see from which year Retail's market share dropped below 42.5% (half of initial 85%).\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "Efdre4AcfafarZre",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you structure the data of the pie chart into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend, each slice of the pie represents a year, with different colors indicating different years. In the chart, each year has two segments, with segment colors matching the legend's colors. These segments represent \"Offline Retail\" and \"Online Retail\" based on their relative sizes each year. Using the percentages provided on each slice, I can structure the data into a table.\nBased on the above thought, the answer is:\nYear,Offline Retail,Online Retail\n2017,12.8%,8.8%\n2018,10.0%,10.3%\n2019,8.8%,10.8%\n2020,8.0%,11.7%\n2021,6.8%,12.0%"
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "Co2eIa5URPAXaGX8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich retail method has seen the most growth from 2017 to 2021? Specify the reason."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the yearly segments within the pie chart, it appears one segment is consistently decreasing while the other is increasing.\nBased on the above thought, the answer is:\nThe method that has seen the most growth from 2017 to 2021 is online retail, as its proportion has increased from 8.8% in 2017 to 12.0% in 2021. This suggests a trend towards online shopping."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "iAl7z8Alu5gvliXT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart reflect consumer trends from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices should be observed individually for each yearly segment and compared over time for each retail type.\nBased on the above thought, the answer is:\nThe chart reflects a shift in consumer trends from offline to online retail. The proportion of offline retail has consistently decreased from 12.8% in 2017 to 6.8% in 2021, while the proportion of online retail has increased, indicating a preference change towards online shopping."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "O8n5Eq74SP8cyhUM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can we infer if the market share of retail is growing?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the pie chart represents proportions instead of absolute values, we can't infer whether the total market share is growing. Even if the proportion of a segment decreases, the actual market size might still be growing if the total market size is increasing significantly.\nBased on the above thought, the answer is:\nThe pie chart does not provide information about the growth of the total market share as it only reveals proportions."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "aUuFHoYr8Z43OHUO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the proportion of offline retail in the market in 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the legend, the color corresponding to the year 2020 is '#ffcc99'. By locating this color in the pie chart, I find the corresponding value.\nBased on the above thought, the answer is:\nThe proportion of offline retail in 2020 was 8.0%."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "TuoaLJclRd7rjD9g",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the proportion of online retail increase from 2017 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Find the slices corresponding to online retail from 2017 and 2021 according to the legend and calculate the difference.\nBased on the above thought, the answer is:\nThe proportion of online retail increased by 3.2% from 2017 to 2021."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "sYfyN5zgshzk92U3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion of all retail methods in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "Find the slices corresponding to 2018 and sum their proportions.\nBased on the above thought, the answer is:\nThe total proportion of all retail methods in 2018 is 20.3%."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "PwxZOM9YuQVmPKh4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are visualising, can you please generate a tabular representation of the percentage usage of EdTech and online learning from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart and the legend on the side with label and colour information for each year {legend}<r>[0.908, 0.063, 0.993, 0.309]</r>, I can see that the size of the segments corresponds to the percentage usage of EdTech and online learning each year from 2010 to 2017 {pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\nYear,Usage (%)\n2010,10\n2011,20\n2012,15\n2013,5\n2014,12\n2015,23\n2016,8\n2017,7"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "Q8aGnwdfprRdWBYT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in what year was there a surge in the use of EdTech and why might that be?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart, I can identify the year with the highest usage percentage. Regarding the reasons, I would need to reference the specific technological developments or circumstances of the year in question.\nBased on the above thought, the answer is:\nThe biggest surge in usage was seen in the year 2015. This could be due to an increasing acceptance and dependence on educational technology and online learning, possibly because of advancements in technology or a change in educational scenarios that highlighted the importance and convenience of online learning."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "1CnvGZGIanNBydi1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat indication does the chart give about the trend of EdTech usage from 2013 onward?"
+            },
+            {
+                "from": "gpt",
+                "value": "From looking at the pie sections from 2013 onward, I can clearly see a pip in 2013 being the smallest section, and then a sharp increase from then on.\nBased on the above thought, the answer is:\nThe trend indicates a sharp drop in EdTech usage in 2013 followed by a sustained increase in the subsequent years. This could indicate that while there may have been a faltering in uptake of EdTech and online learning in 2013, the trend then swung decisively upward, showing an increasing preference for or dependence on these tools in education."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "kclwiHECdSLYNhjH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this data aid decision-making for EdTech investment in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This chart provides insights into the historical trend in usage of EdTech and online learning. By analyzing the trend, one can make predictions for future growth.\nBased on the above thought, the answer is:\nThis data can signal how well EdTech adoption and usage has grown over the years, and indicates a general upward trend in usage. This suggests that investment in EdTech is likely to be worthwhile as the demand for these tools, platforms, and techniques in education continues to grow."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "uhaAbvS45oS9OvZH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the lowest usage of EdTech and online learning recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the lowest usage, I will need to examine the pie chart to find the smallest segment, as the size of each segment represents the percentage usage for that year {pie_0}<r>[0.223, 0.146, 0.741, 0.886]</r>.\nBased on the above thought, the answer is:\n2013"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "rKNYT8aIeCwxp6Fq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage increase in usage of EdTech from 2012 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage increase, I will need to reference the segments of the pie chart corresponding to the years 2012 and 2015, and calculate the percentage difference.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "NJW6m9MgwYuC5sDL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the highest usage of EdTech and online learning recorded?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest usage, I will need to examine the pie chart to find the biggest segment, as the size of each segment represents the percentage usage for that year.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "F5vSKSiJbiA0duOL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the annual distributions of the 'Knowledge Economy' according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart titled {Knowledge Economy}[0.218, 0.021, 0.305, 0.047] and the plot distributed over {the sector slice}[{\"2016\"}[0.175, 0.456, 0.197, 0.476]; {\"2017\"}[0.188, 0.638, 0.21, 0.658]; {\"2018\"}[0.251, 0.728, 0.273, 0.748]; {\"2019\"}[0.339, 0.564, 0.361, 0.584]; {\"2020\"}[0.256, 0.284, 0.278, 0.304]], I can extract the percentage value for each year as per the computed sectors.\nBased on the above thought, the answer is:\nYear,Knowledge Economy\n2016,15.0%\n2017,12.5%\n2018,17.5%\n2019,25.0%\n2020,30.0%"
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "08nUfIqQOgJvtnep",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year saw the most growth in the Knowledge Economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the percentages represented by each slice of the pie, I can see that the year with the highest growth rate in the Knowledge Economy is the biggest slice.\nBased on the above thought, the answer is:\nThe year 2020 saw the most growth in the Knowledge Economy, with a growth rate of 30.0%."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "Q0c7YQJaCFivYYLR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the Knowledge Economy's growth over these five years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the annual percentages in these five years to make inference about its growth trend.\nBased on the above thought, the answer is:\nThe Knowledge Economy has seen a general growth with an especially significant increase in the last two years, reaching its peak in 2020 with a 30.0% growth rate."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "qE4tlaks93SkI8Co",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how might this data influence investment decisions in the Knowledge Economy for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Investing decisions might be influenced by analyzing the growth trend of the past years represented in the pie chart.\nBased on the above thought, the answer is:\nGiven the upward trend in growth in the last two years, and particularly the highest growth in the year 2020, it would likely be a beneficial decision to invest more in the Knowledge Economy in the upcoming year, expecting that the growth trend would continue."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "tf32hfdyVZQWdzpR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest percentage growth for the Knowledge Economy in the five years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to check each slice of the pie chart and choose the one with the highest percentage.\nBased on the above thought, the answer is:\nThe highest percentage growth for the Knowledge Economy was in 2020, which is 30.0%."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "CSTiocUXLIyIPd4q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the growth rate in 2019 was increased to 27.5%, how much would the total percentage growth in these five years change?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to subtract the 2019's original percentage (25.0%) from the total percentage (100%) then add the new percentage (27.5%).\nBased on the above thought, the answer is:\nThe new total percentage growth would be 102.5%."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "EYvk7pkn6WQGiT7A",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the Knowledge Economy grown overall in the last three years according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to add up the percentages of 2018, 2019 and 2020.\nBased on the above thought, the answer is:\nThe Knowledge Economy has grown 72.5% overall in the last three years."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "YHgDKLaOvuQ7m7wp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented in this pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the legend in the chart {the legend}<r>[0.851, 0.063, 0.993, 0.279]</r> and the corresponding proportions in the pie {the pie slices}<r>[0.223, 0.146, 0.741, 0.923]</r>, I can infer the respective \"Popularity Score\" for each \"Subculture\".\nBased on the above thought, the answer is:\nSubculture,Popularity Score\nFair Trade,70\nModern Boho,60\n80s Revival,85\nGeek Chic,80\nGoth,50\nBiker,65\nAthleisure,90"
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "oTss4U3ypms8iFQ1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which subculture is currently the most popular? Can you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subculture with the largest slice in the pie chart is the most popular.\nBased on the above thought, the answer is:\n\"Athleisure\" is the currently the most popular subculture because it has the largest slice in the pie chart and thus the highest popularity score."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "pyUmCGBzJmFrDItw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what does the exploded slice represent for the popularity trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the characteristics and label of the exploded slice to infer its significance to the trend.\nBased on the above thought, the answer is:\nThe exploded slice represents the \"80s Revival\" trend, this signifies its notable popularity, which is the third highest amongst the represented subcultures and fashion trends."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "HKn1q4jR6jNtE6j2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in this chart be used to predict future fashion trend decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict future trend decisions, one could analyze the current popularity trends represented in the chart, and speculate on how they might change in the future.\nBased on the above thought, the answer is:\nBased on the data in the chart, \"Athleisure\" and \"80s Revival\" trends might receive more attention from designers due to their high popularity scores. Conversely, \"Goth\" might witness new innovations in an attempt to increase its popularity."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "yOcf6nsQgUMBLuBG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two categories together make up 30% of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which two categories make up 30%, I need to find two pie slices that add up to the given proportion.\nBased on the above thought, the answer is:\nFair Trade and Biker"
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "5Rn24O5SjlwCc4YR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the mean popularity score of the seven subcultures and fashion trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the mean, I need to add the popularity scores of all seven trends and divide by the number of trends.\nBased on the above thought, the answer is:\n71.43"
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "d1OMTQ4Z3P5S2ENH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity score of \"Geek Chic\" decreased by 5, what would be the new combined total popularity score?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new combined total, I need to subtract 5 from the popularity score of \"Geek Chic\" and then add up all the popularity scores.\nBased on the above thought, the answer is:\n495"
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "fkxLHMFYXQt1VGw7",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the pie chart to a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend at the right of the chart {2015}<r>[0.913, 0.073, 0.941, 0.087]</r>; {2016}<r>[0.913, 0.103, 0.941, 0.117]</r>; {2017}<r>[0.913, 0.133, 0.941, 0.147]</r>; {2018}<r>[0.913, 0.163, 0.941, 0.177]</r>; {2019}<r>[0.913, 0.193, 0.941, 0.207]</r>,  I can see that the slices represents different years and the corresponding percentage of each year's total training hours.\nBased on the above thought, the answer is:\nYear,Training Hours\n2015,15.9%\n2016,19.8%\n2017,12.7%\n2018,23.8%\n2019,27.8%"
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "gmGhk6h5QRtxbVli",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in which year was the training most effective? And why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The effectiveness of training can be interpreted as the highest allocation of total training hours. Hence, the year with the largest slice could be considered as the most effective.\nBased on the above thought, the answer is:\nThe training was most effective in the year 2019 because it has the highest allocation of total training hours at 27.8%."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "Dc3cfA8XBx77xlsv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the pie chart about the distribution of training hours between the years 2015 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of each slice, I can infer about the distribution of training hours over these years.\nBased on the above thought, the answer is:\nThe pie chart reveals that the workplace development training hours provided in the company have generally increased over the years. The highest allocation was in 2019 (27.8%), followed by 2018 (23.8%). The year 2017 had the lowest allocation (12.7%)."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "g1UKYBjuLnoMbzdk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the allocation of training hours in the past years affect the planning for training in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the changes in training hours distribution, we can get insights for future planning.\nBased on the above thought, the answer is:\nThe increasing trend in development training hours, especially noticeable in the \"exploded\" 2018 slice and high percentage in 2019, reveals the company's growing commitment to workplace training. Going forward, it's likely the allocation for training hours might continue to increase or at least maintain this level. The lower allocation in 2017 might indicate an area they didn't want to repeat in future years."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "AT8YXj5JTmQzTYwh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the training hours for 2017 were increased by 2%. What would be the new percentage for year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage for 2017, we would need to add 2% to the existing percentage for the year.\nBased on the above thought, the answer is:\nThe new percentage for year 2017 is 14.7%."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "HgQV5sxzgYmJoGYB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between years 2015 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in percentage we subtract the percentage for year 2015 from year 2019.\nBased on the above thought, the answer is:\nThe difference in percentage between years 2015 and 2019 is 11.9%."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "Nl0K38LbKkDhVJpq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in training hours from year 2015 to year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, we need to subtract the percentage for year 2015 from year 2019, and divide by the percentage for year 2015.\nBased on the above thought, the answer is:\nThe percentage increase in training hours from year 2015 to year 2019 is 74.8%."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "t0x6rWIRWpYphtvW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present data about different educational resource utilization in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. To do that, I need to extract the labels and corresponding proportions for each sector from the pie chart. The labels include the sectors: {Online Learning}[0.878, 0.071, 0.987, 0.091], {Books}[0.878, 0.101, 0.92, 0.121], {Tutoring}[0.878, 0.131, 0.934, 0.151], {Webinars}[0.878, 0.161, 0.942, 0.181], and {Learning Apps}[0.878, 0.191, 0.977, 0.211]. The corresponding proportions can be extracted from the size of each sector in comparison to the whole pie chart.\nBased on the above thought, the answer is:\nEducation Resources, Utilization (%)\nOnline Learning, 45\nBooks, 25\nTutoring, 15\nWebinars, 10\nLearning Apps, 5"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "LagBJPhiOidmSxJw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, which educational resource is utilized the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the educational resource that is utilized the most, I need to find the sector in the pie chart with the highest percentage, which is the {Online Learning}[0.878, 0.071, 0.987, 0.091] sector.\nBased on the above thought, the answer is:\nThe most utilized educational resource is \"Online Learning\"."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "xuje5FKK9OJLoOOm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer in terms of educational resource utilization given the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can compare the sizes of each sector in the pie and correlate them with the different educational resources to infer the utilization preferences.\nBased on the above thought, the answer is:\nThe chart shows a preference for digital and self-paced learning options, with \"Online Learning\" being the most utilized educational resource. Traditional resources such as \"Books\" and \"Tutoring\" are still relied upon, but to a lesser extent. Emerging technologies such as \"Webinars\" and \"Learning Apps\" have relatively less utilization, suggesting room for growth in these areas."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "GU3Z4S0wGd0VXzgr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this kind of data influence future decisions regarding the creation and distribution of educational materials?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to consider how the proportions might impact decisions on resource allocation and content creation.\nBased on the above thought, the answer is:\nBased on the utilization information, content creators and distributors might choose to focus on online material, as \"Online Learning\" has the highest utilization, while also continuing to support traditional resources like \"Books\" and \"Tutoring\". There could also be an increased emphasis on creating more engaging \"Webinars\" and \"Learning Apps\" since these areas show room for growth."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "sGfUC3MJksUupuZm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the utilization of online learning compared to that of learning apps?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the difference between the utilization proportions of {Online Learning}[0.878, 0.071, 0.987, 0.091] and {Learning Apps}[0.878, 0.191, 0.977, 0.211].\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "KDnPUC9PKLG3HBne",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total utilization of books and webinars?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total utilization of these categories, I have to add together the utilization percentages of {Books}[0.878, 0.101, 0.92, 0.121] and {Webinars}[0.878, 0.161, 0.942, 0.181].\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "XjR4f5u6ao7wrSvM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the utilization of tutoring increases by 10%, what would be the new utilization proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 10% to the current utilization percentage of {Tutoring}[0.878, 0.131, 0.934, 0.151].\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "gVk4CgoghE1xzev1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the chart data into a structured CSV format with cuisine types and their associated popularity scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing {the title}<r>[0.31, 0.021, 0.653, 0.047]</r> and examining {the individual slices of the pie chart}<r>[\"French\"][0.223, 0.278, 0.482, 0.694]; [\"Italian\"][0.255, 0.516, 0.534, 0.886]; [\"Chinese\"][0.482, 0.516, 0.722, 0.879]; [\"Indian\"][0.482, 0.327, 0.741, 0.653]; [\"Mexican\"][0.475, 0.146, 0.704, 0.516]; [\"Thai\"][0.272, 0.113, 0.47, 0.483]</r>, I can see the relative proportions representing the popularity scores and by referencing the {legend}<r>[0.885, 0.063, 0.993, 0.249]</r>, I can identify the cuisine types.\nBased on the above thought, the answer is:\nCuisine Type,Popularity Score\nFrench,85\nItalian,90\nChinese,70\nIndian,65\nMexican,75\nThai,60"
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "OR4HboUG2Dsu4hSP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which cuisine is most popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to check the {sizes of the slices in the pie chart}<r>[\"French\"][0.223, 0.278, 0.482, 0.694]; [\"Italian\"][0.255, 0.516, 0.534, 0.886]; [\"Chinese\"][0.482, 0.516, 0.722, 0.879]; [\"Indian\"][0.482, 0.327, 0.741, 0.653]; [\"Mexican\"][0.475, 0.146, 0.704, 0.516]; [\"Thai\"][0.272, 0.113, 0.47, 0.483]</r>. The slice with the largest size can be considered as the most popular.\nBased on the above thought, the answer is:\nItalian cuisine is the most popular because it has the largest slice indicating the highest popularity score."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "PAs4lwjgBsNXGxLp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the pie chart about the preference for Asian cuisines compared to European cuisines in this study?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the sizes of the slices representing the Asian cuisines {Chinese and Thai}<r>[\"Chinese\"][0.482, 0.516, 0.722, 0.879]; [\"Thai\"][0.272, 0.113, 0.47, 0.483]</r> and European cuisines {French and Italian}<r>[\"French\"][0.223, 0.278, 0.482, 0.694]; [\"Italian\"][0.255, 0.516, 0.534, 0.886]</r>.\nBased on the above thought, the answer is:\nThe pie chart suggests that the preference for European cuisines (French and Italian) is overall higher than the preference for Asian cuisines (Chinese and Thai), as the slices representing European cuisines are larger."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "vqMrOTNEdi5PV8Nl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo you think the overall rating of the cuisines can influence their popularity ranking? Justify your statement."
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to examine the association between the size of each {slice}<r>[\"French\"][0.223, 0.278, 0.482, 0.694]; [\"Italian\"][0.255, 0.516, 0.534, 0.886]; [\"Chinese\"][0.482, 0.516, 0.722, 0.879]; [\"Indian\"][0.482, 0.327, 0.741, 0.653]; [\"Mexican\"][0.475, 0.146, 0.704, 0.516]; [\"Thai\"][0.272, 0.113, 0.47, 0.483]</r> and their overall popularity scores (i.e., their proportions) to understand whether larger scores are related to larger slices.\nBased on the above thought, the answer is:\nYes, the overall popularity score of the cuisines can definitely influence their ranking. In the pie chart, larger slices which represent higher popularity scores correspond to higher ranks, indicating a direct relationship between the score and the popularity ranking."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "yrqoTsAddLSQJ31L",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the range of the popularity scores of the cuisines from the least to the most popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the size of each {slice of the pie}<r>[\"French\"][0.223, 0.278, 0.482, 0.694]; [\"Italian\"][0.255, 0.516, 0.534, 0.886]; [\"Chinese\"][0.482, 0.516, 0.722, 0.879]; [\"Indian\"][0.482, 0.327, 0.741, 0.653]; [\"Mexican\"][0.475, 0.146, 0.704, 0.516]; [\"Thai\"][0.272, 0.113, 0.47, 0.483]</r>, which represents the popularity scores of each type of cuisine, and then find the least and the most popular one.\nBased on the above thought, the answer is:\nThe range is 60 (Thai) to 90 (Italian)."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "3dm1WSea4MHlmX1K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would the total popularity score be if the scores for Chinese and Mexican cuisines were both increased by 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total popularity score, I need to add 10 to both the Chinese and Mexican scores and then sum up all the scores again.\nBased on the above thought, the answer is:\nThe new total popularity score would be (85 (French) + 90 (Italian) + 80 (Chinese) + 65 (Indian) + 85 (Mexican) + 60 (Thai)) = 465."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "WsxhC3Z6CxfNP7q4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two cuisines, when summed together, have a popularity score of 135?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to pair every two cuisine scores and check if their sum equals 135.\nBased on the above thought, the answer is:\nItalian (90) and Thai (45)."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "fViR9LA65S45irZ9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you generate the data in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart presents the entertainment and gaming industry development from 2000 to 2007, as I can tell from the subtitle {\"Entertainment and Gaming Industry Development from 2000 to 2007\"} [0.193, 0.021, 0.77, 0.047]. Each individual slice, representing the years from {2000}<r>[0.23, 0.278, 0.482, 0.516]</r> to {2007}<r>[0.283, 0.146, 0.556, 0.516]</r> indicates the advancement of those respective years.\nBased on the above thought, the answer is:\nYear,Growth\n2000,7.4%\n2001,9.3%\n2002,11.1%\n2003,10.4%\n2004,13.0%\n2005,14.8%\n2006,15.6%\n2007,18.5%"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "ppEhmG65mPptNUFd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year experienced the most significant growth in the Entertainment and Gaming industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest slice on the pie chart corresponds to the year with the most significant growth.\nBased on the above thought, the answer is:\n2007 experienced the most significant growth in the Entertainment and Gaming industry."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "W88Y98QXuM5v8AOh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive any trend in the industry's growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the angular size and order of the pie slices, I can infer the growth trend.\nBased on the above thought, the answer is:\nThe Entertainment and Gaming industry shows a progressive trend of growth from 2000 to 2007. However, there was a minor slowdown in 2003 indicated by a slightly smaller slice."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "ieM5IdKxfiiL9uAj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you infer the overall industry status during the period from 2000 to 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the angular size and order of the slices, I can infer the overall industry status during the period from 2000 to 2007.\nBased on the above thought, the answer is:\nThe Entertainment and Gaming industry experienced a continuous growth from 2000 to 2007, suggesting a positive and strong expansion in the industry during that period."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "0yv08UUcokzFTbp8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total contribution of the years 2004 and 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to add the contributions of the years 2004 and 2006. Observing the coordinates of those years, {2004}<r>[0.482, 0.516, 0.691, 0.883]</r> and {2006}<r>[0.482, 0.161, 0.729, 0.516]</r>, it will give us the detail needed.\nBased on the above thought, the answer is:\n28.6%"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "9XQXD7y4KXS4a4zj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in contribution between the first year and the last year on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to find the difference between the contributions of the year 2000 and 2007. I observe those two years at {2000}<r>[0.23, 0.278, 0.482, 0.516]</r> and {2007}<r>[0.283, 0.146, 0.556, 0.516]</r> to provide the required details.\nBased on the above thought, the answer is:\n11.1%"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "hoI3FLMm87nAMRLt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much contribution did the year 2005 contribute towards the growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing the year 2005 on the chart at {2005}<r>[0.482, 0.406, 0.741, 0.733]</r> gives us the contribution of that year.\nBased on the above thought, the answer is:\n14.8%"
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "IPrtUE7c54UZ7QRX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the chart data in CSV format, showing sales trend for the 'Electronics' product over the years 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data is visually represented in the pie chart for each year. The legend {2017}<r>[0.952, 0.071, 0.987, 0.091]</r>, {2018}<r>[0.952, 0.101, 0.987, 0.121]</r>, {2019}<r>[0.952, 0.131, 0.987, 0.151]</r>, and {2020}<r>[0.952, 0.161, 0.987, 0.181]</r> directly denotes the corresponding years. The sales figure for each year can be derived from this chart.\nBased on the above thought, the answer is:\nProduct Type,2017,2018,2019,2020\nElectronics,2100,2350,2650,2900"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "UgkFk6lolGTN62tO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sale trend of the 'Electronics' product from 2017 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sale trend can be understood by comparing the sales of the 'Electronics' product from each year from 2017 to 2020.\nBased on the above thought, the answer is:\nThere is a clear upward trend in the sales of 'Electronics' from 2017 to 2020. Each year saw an increase in sales, from 2100 in 2017 to 2900 in 2020."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "uy4PriFIwYnfLHUi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploded sector mean, and what does it signify in the given pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded sector is a visual aid used in pie charts to emphasize a specific piece of information or to distinguish it from other data in the chart. In the chart, the 2020 sector {sector of 2020}<r>[0.284, 0.109, 0.693, 0.479]</r> is exploded.\nBased on the above thought, the answer is:\nAn exploded sector in a pie chart is a sector that is separated from the rest of the pie chart. In this chart, the exploded sector represents the sales of 'Electronics' in 2020. The explosion emphasizes the growth over the years and signifies that 2020 had the highest sales year during the period in the chart."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "CWvqO5e5xcPXRa1C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the proportion of the pie sector indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of each pie sector in the displayed chart indicates the sales value of the 'Electronics' product for each specific year.\nBased on the above thought, the answer is:\nIn this chart, each sector's proportion signifies the ratio of the sales of 'Electronics' in that year to the total sales over the four years. A larger sector means higher sales for that year. It visually helps in understanding the sales distribution over the years."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "vwKWyuzlNKdtsVgL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many products were sold in total over the four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total products sold, I need to sum up the products sold in each of the four years.\nBased on the above thought, the answer is:\n10000"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "qxvtic0DMNVXh1ga",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the sales increased from 2017 to 2020 in numbers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the numbers increase, I need to deduct the sales numbers of 2017 from the sales numbers of 2020.\nBased on the above thought, the answer is:\n800"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "5BB7pwpRyayBByus",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average sales across the four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average sales, I need to sum the sales of each of the four years and divide by the number of years.\nBased on the above thought, the answer is:\n2500"
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "VStOXfNn0L88P7kD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the given pie chart, please convert the data into a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title is {\"Number of Internet Users Worldwide from 2010 to 2017 (in billions)\"} [0.201, 0.021, 0.762, 0.047]. Each year from 2010 to 2017 is represented by a slice within the pie chart, starting from {2010} [0.225, 0.278, 0.482, 0.516] to {2017} [0.283, 0.146, 0.514, 0.516]. Each slice of the pie represents the proportion of internet users worldwide in billions for the specified year.\nBased on the above thought, the answer is:\nYear,Internet Users (in billions)\n2010,1.97\n2011,2.27\n2012,2.48\n2013,2.72\n2014,2.92\n2015,3.19\n2016,3.42\n2017,3.58"
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "9j0t1msMfPvIwpBd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a consistent pattern of growth in the number of Internet users from 2010 to 2017 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the pie chart slices from 2010 to 2017, I can infer the growth pattern of Internet users.\nBased on the above thought, the answer is:\nYes, there is a consistent pattern of growth. The number of Internet users has been steadily increasing each year from 2010 to 2017 accord to the pie chart."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "T3ENtbvgsv2zI3Ji",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year experienced the highest jump in the number of Internet users?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the size of the jumps between each of the consecutive years from 2010 to 2017.\nBased on the above thought, the answer is:\nAccording to the pie chart, 2015 experienced the largest jump in the number of Internet users, which is indicated by the exploded pie slice."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "AesF7SBtYRvZ5LJg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible reasons for the dramatic increase of the Internet users in 2015 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "While the chart does not provide explicit reasons for the increase in Internet users in 2015, it is possible to hypothesize based on the observed data and known events.\nBased on the above thought, the answer is:\nThe dramatic increase in Internet users in 2015 could be due to various factors, including advances in technology, improved accessibility and affordability of Internet services, and increased digital literacy. However, the chart does not provide specific details or reasons for this increase."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "T3tjTh8MAHmgKOVc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the number of Internet users from 2010 to 2017 in billions based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to subtract the number of Internet users in 2010 from the number in 2017.\nBased on the above thought, the answer is:\nThe total increase is 3.58 billion - 1.97 billion = 1.61 billion."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "FEsWVv67fUp9maYf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the number of Internet users increase from 2010 to 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to calculate the percentage increase from 1.97 billion to 2.27 billion.\nBased on the above thought, the answer is:\nThe percentage increase from 2010 to 2011 is ((2.27 - 1.97) / 1.97) * 100% = 15.23%."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "y23OPWKiqmb8SMUZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the number of Internet users reach more than 3 billion for the first time according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the data on the pie slices from 2010 to 2017, I identify the year when the number of users first exceeded 3 billion.\nBased on the above thought, the answer is:\nThe number of Internet users reached more than 3 billion for the first time in 2015."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "YUuOrO8h681PtUUL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, please convert the social media usage data for different platforms in 2020 into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I look at the chart's title {\"Social Media Usage in 2020\"}[0.367, 0.021, 0.596, 0.047], then I observe each pie slice {the sector slice}[{\"Facebook\"}[0.223, 0.278, 0.482, 0.885]; {\"Twitter\"}[0.462, 0.516, 0.536, 0.886]; {\"Instagram\"}[0.482, 0.516, 0.711, 0.878]; {\"LinkedIn\"}[0.482, 0.516, 0.74, 0.688]; {\"YouTube\"}[0.431, 0.118, 0.757, 0.521]; {\"Snapchat\"}[0.338, 0.159, 0.482, 0.516]; {\"Reddit\"}[0.283, 0.208, 0.482, 0.516]], each of which represents a specific platform and the corresponding user percentage they account for.\nBased on the above thought, the answer is:\nPlatform,Percentage\nFacebook,34.9%\nTwitter,4.6%\nInstagram,13.9%\nLinkedIn,6.3%\nYouTube,30.6%\nSnapchat,5.2%\nReddit,4.5%"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "gFd636mTxWxhCWDe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which platform had the highest usage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart, I can identify that the slice with the largest percentage represents the platform with the highest usage.\nBased on the above thought, the answer is:\nThe platform with the highest usage in 2020 is Facebook."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "7NbIOLTAiz1nWvRW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which social media platforms are more popular based on the proportions in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart can be used to compare the popularity of different social media platforms by looking at their respective slice sizes or proportions.\nBased on the above thought, the answer is:\nAccording to the pie chart, the most popular platforms in 2020 were Facebook and YouTube as these have the largest slices, representing the most social media users."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "vtHlTlaFks5mwvau",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what insights can be gained about the trends in social media usage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the proportions of each slice in the pie chart, one can gain insights on the trends in social media usage for the year 2020.\nBased on the above thought, the answer is:\nFrom the chart, it is evident that in 2020, Facebook and YouTube dominated the social media landscape, accounting for over 60% of the market combined. On the other hand, Snapchat, Reddit, and Twitter had the smallest user proportions, suggesting that they were less popular among users. Thus, companies looking to advertise or reach a wider audience would likely choose platforms like Facebook and YouTube."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "EIXVKMrmJNNl6lmU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total proportion of usage by Facebook and YouTube in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate their total proportion, I need to add the proportions of Facebook and YouTube.\nBased on the above thought, the answer is:\n65.5%"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "MivBC95fq3yc3w3J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform increased its proportion by a greater percentage between 2010 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the growth percentages of each social media platform between 2010 and 2020.\nBased on the above thought, the answer is:\nFacebook"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "A3ogJxlNYFiltwvi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming the total number of social media users in 2020 is 1000 million, how many users would Reddit have?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find Reddit's number of users in 2020, I would need to calculate 4.5% of the total number of users.\nBased on the above thought, the answer is:\n45 Million users"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "jSKldZXvQsLMQmCe",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the evaluation data represented in the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the pie chart contains evaluation of three government policies shown by the sector slices {Carbon Tax}[0.198, 0.291, 0.457, 0.893], {Universal Health Care}[0.437, 0.292, 0.741, 0.886], {Defense Spending}[0.283, 0.146, 0.688, 0.516]. By observing the chart and analyzing their corresponding percentages, I can convert the data into a CSV format.\nBased on the above thought, the answer is:\nPolicy,Effectiveness Evaluation\nCarbon Tax,33.3%\nUniversal Health Care,38.1%\nDefense Spending,28.6%"
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "m8zZVikSGpkTnfoe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain why, from the chart, Universal Health Care seems to be the most positively evaluated government policy."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the percentage proportions assigned to each policy in order to provide an explanation.\nBased on the above thought, the answer is:\nFrom the pie chart, Universal Health Care with {38.1%}<r>[0.725, 0.718, 0.876, 0.738]</r> has the biggest slice, signifying that it received the highest effectiveness evaluation. This implies the policy is perceived as more effective compared to Carbon Tax and Defense Spending."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "Kr1PSix6YYyrnq2g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about public perspective on government policies based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides an evaluation of effectiveness of three different policies so I can infer public perspective based on the percentage values assigned to each policy.\nBased on the above thought, the answer is:\nUniversal Health Care seems to be the most favored government policy with the highest effectiveness evaluation. This might suggest that the policy is meeting people's needs effectively. Carbon Tax is also positively evaluated suggesting it might also be well-received. Defense Spending, being the lowest evaluated policy, might imply that it may not be meeting public expectations as much as the other two policies."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "poY18uXwXilXZyNC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data affect government decision making on allocation of resources to these policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider how the effectiveness evaluations might impact policy decisions.\nBased on the above thought, the answer is:\nGiven the high effectiveness evaluation, the government might consider allocating more resources to Universal Health Care to maintain or enhance its performance. For Carbon Tax, with a slightly lower evaluation, they might seek to improve its effectiveness with continuous investment. Defense Spending, being the least effective policy, might require a re-evaluation of its current strategy or perhaps more resources to improve its effectiveness."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "idwetWRExiOqv5QU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the effectiveness of Universal Health Care increased by 10%, what would be the new percentage of effectiveness for Universal Health Care?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 10% to the original percentage of effectiveness for Universal Health Care.\nBased on the above thought, the answer is:\nThe new percentage effectiveness for Universal Health Care would be 38.1% + 10% = 48.1%."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "ppkrSzw7WPUOmH6s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy has the lowest effectiveness evaluation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I have to compare the percentage evaluations of all policies and identify the one with the lowest value.\nBased on the above thought, the answer is:\nDefense Spending has the lowest effectiveness evaluation."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "R34rGNiIPxAdDiVa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total effectiveness evaluation if all three policies were equally effective?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the total effectiveness evaluation is 100%, if all three policies were equally effective, I would divide 100% by the number of policies.\nBased on the above thought, the answer is:\nIf all three policies were equally effective, their individual evaluation would be approximately 33.3%."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "p3LkmOUHevzKayWM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart say about the comparative study of rural and urban development in these years? Translate this information into csv format."
+            },
+            {
+                "from": "gpt",
+                "value": "To create a csv, I must first identify each sector in the pie chart and the percentage it represents. I also need to use the legend on the chart as a reference and find out which sector represents which year and which category.\nBased on the above thought, the answer is:\nYear/Category, Rural, Urban\n2000, 4.5%, 15%\n2005, 5.1%, 14.3%\n2010, 6.7%, 13.6%\n2015, 8.9%, 12.1%\n2020, 10%, 9.7%"
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "hls8dMPaaRgmKZGU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which segment displays an increasing trend and what can you infer from it?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the development percentages for urban and rural from the pie chart.\nBased on the above thought, the answer is:\nThe Urban development segment shows an increasing trend from 2000 to 2020. This could indicate a shift in focus towards urban area development during these years."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "naeDRWRAWtvuEAQE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan any conclusions be drawn concerning the rural development patterns between 2000 and 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the sections representing Rural development from 2000 to 2020 to draw conclusions.\nBased on the above thought, the answer is:\nThe rural development segment shows a decreasing trend. This suggests a steady decline in rural development during these years, implying that less emphasis and resources may have been allocated towards rural areas."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "EfVcWt0dH7OrPuys",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the study of the pie chart, can any deductions be derived about the allocation of resources in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "For 2020 allocation, I need to cross-examine the corresponding sectors in my pie chart.\nBased on the above thought, the answer is:\nAccording to the pie chart, in the year 2020, more resources were allocated towards rural areas (10%) compared to urban areas (9.7%). This could suggest increasing efforts and initiatives to foster development in rural areas despite the long-term trend of urban development prominence."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "DfQ4g3VOR7X1edYe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you figure out the total urban development that took place from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate total urban development, I need to sum up the percentages of the corresponding sectors from my pie chart from 2000 to 2020.\nBased on the above thought, the answer is:\nThe total urban development from 2000 to 2020 is 15% + 14.3% + 13.6% + 12.1% + 9.7% = 64.7%."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "ZyISBtcRLvx3m8yj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the yearly average of Rural development?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the yearly average of Rural development, I will sum up the percentages of the corresponding sectors from my pie chart from 2000 to 2020 and then divide by 5.\nBased on the above thought, the answer is:\nThe yearly average of Rural development from 2000 to 2020 is (4.5% + 5.1% + 6.7% + 8.9% + 10%) / 5 = 7.04%."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "c88hHY37uahGuttG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the new total percentage if the Urban development percentage in 2020 increased by 2%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to account for the increase in the Urban development percentage in 2020 by adding the 2% increase to the original total percentages.\nBased on the above thought, the answer is:\nThe new total percentage is 4.5% (Rural 2000) + 5.1% (Rural 2005) + 6.7% (Rural 2010) + 8.9% (Rural 2015) + 10% (Rural 2020) + 15% (Urban 2000) + 14.3% (Urban 2005) + 13.6% (Urban 2010) + 12.1% (Urban 2015) + 11.7% (Urban 2020; 9.7% original + 2% increase) = 101.9%."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "TDu2aZp7vUgBzgHn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Data Usage Trend Analysis from 2015 to 2021\"}[0.29, 0.021, 0.673, 0.047] and {the sector slice}[{\"2015\"}[0.224, 0.278, 0.482, 0.516]; {\"2016\"}[0.223, 0.472, 0.482, 0.732]; {\"2017\"}[0.271, 0.516, 0.482, 0.883]; {\"2018\"}[0.451, 0.516, 0.68, 0.886]; {\"2019\"}[0.482, 0.352, 0.741, 0.754]; {\"2020\"}[0.396, 0.111, 0.723, 0.481]; {\"2021\"}[0.283, 0.172, 0.482, 0.516]], I can see that the sectors represents different categories and how much percentage each category is.\nBased on the above thought, the answer is:\nYear,Data Usage (GB)\n2015,35\n2016,45\n2017,50\n2018,60\n2019,70\n2020,90\n2021,30"
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "zpsOPeVC4pF77y28",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of data usage from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the changing proportions of each sector over the years to infer the trend of data usage.\nBased on the above thought, the answer is:\nThere is an upward trend in data usage from 2015 to 2020, which indicates an increasing demand for data during this period. However, the data usage in 2021 has decreased significantly, which requires further investigation to determine the cause."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "mwAQMDsVpyWjy6i2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw a significant spike in data usage and what might this indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the year with a significant spike in data usage by comparing the proportions of each sector. The reason for the spike could be inferred from the aspects of technology advancement or changes in data consumption patterns.\nBased on the above thought, the answer is:\n2020 saw a significant spike in data usage, which could indicate a drastic increase in the demand for data, possibly due to factors such as increased remote work and online activities."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "41SQ0uClhy9BU6Ch",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data presented in this chart assist in planning for future data needs?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in this chart can help in understanding the trends in data usage over time and anticipating future data needs.\nBased on the above thought, the answer is:\nThe upward trend in data usage from 2015 to 2020 suggests a growing need for more data capacity. However, the sudden drop in 2021 needs to be investigated - if it's an anomaly, planning should consider the generally increasing trend. If it's an actual decrease in demand, future planning may need to adjust accordingly."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "uTEPxSm0e38b9OCG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the data usage at its highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest data usage, I need to find the sector with the largest proportion in the pie chart.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "tivWEBssUL9yFTig",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total data usage from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total data usage, I need to sum up the data usage of each year from 2015 to 2021.\nBased on the above thought, the answer is:\nThe total data usage from 2015 to 2021 is 35GB (2015) + 45GB (2016) + 50GB (2017) + 60GB (2018) + 70GB (2019) + 90GB (2020) + 30GB (2021) = 380GB."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "zERqaX10HbInHFYG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the data usage in 2021 remained the same as in 2020, what would be the total data usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this calculation, I need to replace the 2021 data usage with the 2020 data usage and sum up all the data usage again.\nBased on the above thought, the answer is:\nIf the data usage in 2021 was the same as in 2020, the total data usage would be 35GB (2015) + 45GB (2016) + 50GB (2017) + 60GB (2018) + 70GB (2019) + 90GB (2020) + 90GB (2021) = 440GB."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "hm5ZbcSLPNz66Cdr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you generate a structured table for both the tourism and hospitality industries from 2011 to 2020, using CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the color-coded sectors of the chart, I can see each year's proportion for both the tourism and hospitality industries from 2011 to 2020. I will compile these proportions into a structured CSV-formatted table.\nBased on the above thought, the answer is:\nYear,Tourism,Hospitality\n2011,4.5%,4.5%\n2012,4.8%,4.8%\n2013,5.1%,5.1%\n2014,5.5%,5.8%\n2015,5.3%,5.6%\n2016,5.2%,5.4%\n2017,5.0%,5.1%\n2018,4.9%,5.0%\n2019,5.1%,4.9%\n2020,4.0%,3.5%"
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "qq2hYJB3EUU9fnbZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusion could one draw regarding the growth rate of the Tourism and Hospitality industries from 2011 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer the conclusion about the growth of the two industries by observing the percentage of each year's section in the pie chart.\nBased on the above thought, the answer is:\nBoth industries have seen steady growth from 2011 to their respective peak years. The growth of the Tourism industry started slowing down post-2014 while the Hospitality industry began decelerating after 2015. However, both industries continued to grow up until 2019."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "jUnlBBMl7KN2gsn5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat factors might have caused the significant revenue drop in 2020 for both industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "While the chart does not provide explicit reasons, I can make an educated guess based on the drastic changes in the revenue proportions of both industries in 2020 compared to previous years.\nBased on the above thought, the answer is:\nThe sharp decline in revenues for both industries in 2020 might be attributed to the global impact of the COVID-19 pandemic which led to travel restrictions and lockdowns, severely affecting both tourism and hospitality sectors."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "MSBI7GrHYXAGhskQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of the proportion of revenue in the Hospitality industry compared to the Tourism industry over the course of the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sections representing each year in the pie chart, I can identify the overall trend of proportions in the Hospitality industry compared to the Tourism industry.\nBased on the above thought, the answer is:\nIn the early part of the decade, the revenues for both industries were comparably similar. However, in the mid-decade, the Hospitality industry started to overtake the Tourism industry. This trend was evident until 2017, after which it started to decline gradually, and by 2020, it was significantly lower than the Tourism industry."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "tHcerw7RkMx8hDk9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year of peak revenues, how much more revenue did the hospitality industry generate compared to the tourism industry, in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, it can be seen that both the industries reached their peak revenues in different years. Hospitality had its peak in 2015 whereas tourism had its peak a year before in 2014. To find out the percentage difference, I need to subtract the percentage of the Tourism industry in 2015 from the percentage of the Hospitality industry in 2015.\nBased on the above thought, the answer is:\n0.3%"
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "91mzxiX2FAKUhueb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did both the Tourism and Hospitality industries generate equal revenues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to compare the revenues of both the industries year by year, from the pie chart.\nBased on the above thought, the answer is:\n2011 and 2012."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "W01fTc3BnZLNYD01",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage difference in revenues between the Tourism and Hospitality industries from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to sum up the differences in proportions between the two industries year by year, according to the pie chart.\nBased on the above thought, the answer is:\n1.2%"
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "GNDXtpcsU5jsY396",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the pie chart and display it in a CSV-formatted table, showing the years and their corresponding sustainability scores?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, let's start by identifying the years and sustainability scores. The years are listed in the legend and range from 2015 to 2020 {the legend}[0.908, 0.063, 0.993, 0.249]. Each slice represents the sustainability score for the corresponding year, with the size of the slice reflecting the score. The labels for the slices can be found near the edge of each slice, indicating the percentage that each year contributes to the total score {the slice labels}[[0.193, 0.32, 0.228, 0.34], [0.161, 0.501, 0.197, 0.521], [0.211, 0.735, 0.246, 0.755], [0.424, 0.912, 0.459, 0.932], [0.737, 0.687, 0.772, 0.707], [0.557, 0.075, 0.592, 0.095]].\nBased on the above thought, the answer is:\nYear,Sustainability Score\n2015,7.1\n2016,7.6\n2017,11.8\n2018,16.6\n2019,21.3\n2020,35.5"
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "NLbtbYZm50mUANNv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in which year did we see the greatest increase in the sustainability score? Justify your observation."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I see that each sector represents one year, and the size of each sector indicates the sustainability score of that year. The score for 2020 is significantly larger than the others {2020 slice}[0.289, 0.11, 0.741, 0.48].\nBased on the above thought, the answer is:\nThe greatest increase in the sustainability score was in 2020. The reason for this could be the implementation of sustainable practices globally due to policy changes or global events like the Paris Climate Agreement."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "Kz0pLSia4arKvJUH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart reflect the trend of sustainability scores over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows that the sustainability scores have been increasing over time with the largest slice representing 2020, depicting the highest score of all previous years. The increasing size of the slices shows an overall growth trend.\nBased on the above thought, the answer is:\nThe trend in the pie chart increases from 2015 to 2020, indicating that sustainable practices have been becoming increasingly prevalent over time."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "nQ5yXhCAmInLmlZ2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy do you think the sustainability score proportion is exploded out for the year 2020 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The score for 2020 is 'exploded' in order to highlight its significance. This explodes way of representing data is often used to emphasize the data point that is most important or stands out from the rest.\nBased on the above thought, the answer is:\nThe sustainability score for 2020 is exploded in the chart to emphasize its unusually high value compared to the other years, indicating a significant jump in sustainable practices that year."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "IDOTMZPgULMjv5HR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in the sustainability score from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sustainability score in 2015 was 7.1% and in 2020 it was 35.5%. To determine the increase, I have to subtract the score of 2015 from that of 2020.\nBased on the above thought, the answer is:\nThe increase is 28.4%."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "ANNV6DOgjTpz8qdc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of increase in the sustainability score from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the rate, I should first find out the difference in scores between 2018 and 2019, and then divide by the score in 2018.\nBased on the above thought, the answer is:\nThe rate of increase was 28.31%."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "NCgX90hpSoQ7cFYo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater is the sustainability score in 2020 than in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I must subtract the score of 2018 from 2020's score.\nBased on the above thought, the answer is:\nThe score in 2020 is 18.9% greater than in 2018."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "TRStP1SslLwtYEYM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information from the pie chart you see into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, by examining the chart's title - {Role of Social Networks in Interpersonal Relationships}<r>[0.267, 0.021, 0.714, 0.047]</r> - along with the sectors in the pie chart that represent different kinds of relationships {Friendship slice}[0.232, 0.278, 0.491, 0.859] and {Romantic Relationship slice}[0.317, 0.137, 0.775, 0.877]). Each sector has a label and a value for the corresponding relationship, which I can use to create a structured table.\nBased on the above thought, the answer is:\nRelationship,Percentage\nFriendship,30%\nRomantic Relationship,70%"
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "WetsxYx7A7eMqPSh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you determine in what type of relationship social networks play a larger role and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart, I can observe that the {Romantic Relationship slice}<r>[0.317, 0.137, 0.775, 0.877]</r> is larger than the {Friendship slice}<r>[0.232, 0.278, 0.491, 0.859]</r>, indicating that one type of social relationship has a larger portion.\nBased on the above thought, the answer is:\nSocial networks play a larger role in Romantic Relationships because it takes up 70% of the pie chart compared to Friendship which takes up only 30%."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "i2BUAB4TFZnCVtdh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about social network usage in interpersonal relationships as represented in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would infer this by comparing the different sectors of the pie chart.\nBased on the above thought, the answer is:\nThe chart depicts that social networks are primarily used for romantic relationships, with the proportion being more than double that of friendships."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "bNjqNeIs8OVMp37G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in this chart influence policies around recreational activities for promoting social interactions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By seeing the larger component of the {Romantic Relationship slice}<r>[0.317, 0.137, 0.775, 0.877]</r>, the organizers could consider focusing more on activities that facilitate romantic relationships.\nBased on the above thought, the answer is:\nGiven that social networks play a much larger role in romantic relationships than in friendships, organizations could consider designing activities that promote romantic interactions, such as speed dating events or dances. However, it would still be important to provide platforms for fostering friendships, as they still represent a30% of social network usage."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "bqH4J9sCMaLKszZE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine the percentage difference between the use of social networks in romantic relationships versus friendships?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, by comparing the percentage assigned to the {Friendship slice}<r>[0.232, 0.278, 0.491, 0.859]</r> and the {Romantic Relationship slice}<r>[0.317, 0.137, 0.775, 0.877]</r>.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "MfYl0X5hVzhf4N38",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of social network usage in friendships relative to the total usage for both types of relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to take the percentage of the {Friendship slice}<r>[0.232, 0.278, 0.491, 0.859]</r> and divide it by the total of both.\nBased on the above thought, the answer is:\n30%."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "aMkdPVO3PpgNAYOW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of usage in friendships increased by 10%, what would the new proportion be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I would need to add 10% to the value of the {Friendship slice}<r>[0.232, 0.278, 0.491, 0.859]</r>.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "zpncrRIgObOadTKD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured CSV table from the data observed in the pie diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the provided pie chart data supplemented by the text components, I can deduce that the pie slices and their corresponding labels represent \"Years\": {\"2030\"}<r>[0.447, 0.764, 0.491, 0.784]</r> and {\"2040\"}<r>[0.471, 0.284, 0.514, 0.304]</r> with their population percentages (\"70%\" & \"30%\") {'autotext'}<r>[0.425, 0.949, 0.46, 0.969]; [0.502, 0.1, 0.537, 0.12]</r>. Consequently, I can convert this visual representation into a CSV formatted table.\nBased on the above thought, the answer is:\nYear,Percentage\n2030,70%\n2040,30%"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "J6aaIlS8NkGFuxzq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie diagram, which year has a greater population residing in smart cities?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices for {\"2030\"}<r>[0.447, 0.764, 0.491, 0.784]</r> and {\"2040\"}<r>[0.471, 0.284, 0.514, 0.304]</r>, their corresponding sizes and colors, represented in the legend as 70% and 30% {'autotext'}<r>[0.425, 0.949, 0.46, 0.969]; [0.502, 0.1, 0.537, 0.12]</r>, I can deduce the year with a greater population residing in smart cities.\nBased on the above thought, the answer is:\nThe year 2030 has a greater population expected to reside in smart cities."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "3AMFEuJxAgALlvee",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you deduce about the population trend in smart cities between 2030 and 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the years {\"2030\"}<r>[0.447, 0.764, 0.491, 0.784]</r> and {\"2040\"}<r>[0.471, 0.284, 0.514, 0.304]</r> alongside their corresponding percentages of the population living in smart cities, represented in the {legend}<r>[0.908, 0.063, 0.993, 0.129]</r>. By comparing these slices, we can deduce the trend.\nBased on the above thought, the answer is:\nThe data in the chart forecasts a decrease in the proportion of the population living in smart cities from 2030 to 2040."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "jUiqm4VaETiGpSGB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data interpreted from the chart affect urban development policies for the next decades?"
+            },
+            {
+                "from": "gpt",
+                "value": "As per the pie chart's data interpreted from the pie slices and their corresponding labels {\"2030\"; \"2040\"}<r>[0.447, 0.764, 0.491, 0.784]; [0.471, 0.284, 0.514, 0.304]</r> and the {legend}<r>[0.908, 0.063, 0.993, 0.129]</r>, it illustrates a rise and then a decline in the percentage of the population living in smart cities from 2030 to 2040. This trend should impact thinking about urban development policies for the next decades.\nBased on the above thought, the answer is:\nGiven the expected decrease in the population living in smart cities from 2030 to 2040, governments may need to assess the factors contributing to this decline. It might prompt reassessment and restructuring of urban development policies to ensure continued migration to and habitation in smart cities."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "WFox2VYkQu0sSBss",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the total population in 2030 is 10 billion, how many people are projected to live in smart cities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I understand that the pie chart states that 70% of people are expected to be living in smart cities in 2030, based on the specific slice{'autotext'}<r>[0.425, 0.949, 0.46, 0.969]</r> for the year 2030 {'2030'}<r>[0.447, 0.764, 0.491, 0.784]</r>. So, I can calculate the number by taking 70% of 10 billion.\nBased on the above thought, the answer is:\n7 billion"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "aK5l02Z4NdWE6t1B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the projected growth percentage in the population living in smart cities from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart states through the colored slices and their respective labels, {the legend}<r>[0.908, 0.063, 0.993, 0.129]</r>. that 70% of the population is expected to live in smart cities by 2030 and 30% by 2040. The difference in these values yields the projected growth percentage.\nBased on the above thought, the answer is:\n-40%"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "U3KrDs3rr5120Yb3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the total population remains constant at 10 billion from 2030 to 2040, by how many people is the smart city population expected to decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart, the population living in smart cities is expected to decrease from 70% to 30% from 2030 to 2040. Therefore, I can calculate the decrease in the number of people by finding the difference between 70% and 30% of 10 billion.\nBased on the above thought, the answer is:\n4 billion"
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "x3qJtrSn3n2GqVQh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease arrange the data on Urbanization and Air Quality from 2015 to 2020 into a structured table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The table can be generated by observing the pie chart and referring to the color-coded legend at the right side of the chart {legend}<r>[0.908, 0.063, 0.993, 0.428]</r>. By comparing the size and color of each slice, I can determine the corresponding year and percentage for Urbanization and Air Quality.\nBased on the above thought, the answer is:\nYear,Urbanization,Air Quality\n2015,72,92\n2016,75,89\n2017,78,85\n2018,82,80\n2019,85,78\n2020,88,77"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "hYZ2szhzz7Tgm7dX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend in Urbanization and Air Quality from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the change in size and color of the pie slices over time to understand the trend.\nBased on the above thought, the answer is:\nThe trend in Urbanization from 2015 to 2020 is increasing, indicating a rise in urban development and possibly population migration to urban areas. In contrast, the trend for Air Quality is decreasing, suggesting an overall deterioration in the environment, possibly due to increased pollution resulting from urban development."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "CcUA26T0ITJ1W5Q3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could possibly be the reason for the dip in air quality in the year 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the actual reason isn't specified in the chart, considering the nature of the data, the dip in air quality could have been caused by a variety of factors.\nBased on the above thought, the answer is:\nThe drop in Air Quality from 2016 to 2017 could potentially be attributed to increased industrialization, mass urban migration, or natural environmental phenomena during that year."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "J0r84YL7MKSg2r2E",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications can one possibly draw regarding sustainability from the given environment factors?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require me to interpret the data, taking into account the changes in Urbanization and Air Quality over the years.\nBased on the above thought, the answer is:\nThe chart suggests an inverse correlation between Urbanization and Air Quality: as Urbanization increases, Air Quality decreases. If this pattern continues, it may imply that urban development practices are currently unsustainable and negatively impact the environment. Policymakers and urban planners should therefore focus more on implementing sustainable development strategies that mitigate the negative impact on air quality."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "JM1Wh7vA1IUs5SdC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in air quality between 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract air quality in 2020 from 2015 by referring to the pie chart slice's corresponding values dedicated to air quality for these years.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "cqZ6qOEVkoYrz4Yq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did Urbanization see the highest increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to calculate the difference between each consecutive year's Urbanization rate and look for the largest difference.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "GIMe2Axfz93GWVTM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Air Quality decrease from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can confirm this by subtracting the air quality in 2017 from the air quality in 2016.\nBased on the above thought, the answer is:\n4"
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "i3FZUIFRBBY2umV9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the information provided on the chart, can you convert the data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend on the side of the chart, I can see that there are 5 categories that represent the years from 2016 to 2020, and each year has 2 different colored blocks, which represent the living environments, \"Urban\" and \"Rural\". I will reorder these into a CSV format according to each year and their respective percentages for each environment.\nBased on the above thought, the answer is:\nYear, Urban Life, Rural Life\n2016, 50%, 30%\n2017, 55%, 35%\n2018, 60%, 40%\n2019, 65%, 45%\n2020, 70%, 50%"
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "ZU0FccMc7XDAm6NU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you identify in terms of Urban and Rural living from 2016 to 2020, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the chart and comparing the differences between each year's percentages, I can identify a trend.\nBased on the above thought, the answer is:\nThe chart indicates a stable trend of steady growth in both Urban and Rural living environments, with Urban living consistently maintaining a lead of 20%."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "UAYjX9DjS3NIXPsR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make based on the trend you identified?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the trend of steady growth from 2016 to 2020, I need to extrapolate the implications this could have on future forecasts.\nBased on the above thought, the answer is:\nThe consistent trend observed can infer that if conditions remain the same, we can expect to see the same steady growth in both Urban and Rural living. Urban living is projected to maintain its lead."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "KvKIsv4QVopjDQGH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this data be used in relevant planning and decision-making processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to consider how the consistent trend over these years can influence future decisions and strategies.\nBased on the above thought, the answer is:\nThe consistent growth seen in both living environments suggests a continuous demand for resources and services in both areas. This information can guide future decisions, strategies, and resource allocation in urban planning, rural development programs, and government policies to accommodate this steady growth."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "pmDWr2uj4MTW9ys8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the percentage of people living in an urban environment from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total increase, I can take the percentage for 2020 and subtract the percentage for 2016 from it.\nBased on the above thought, the answer is:\nThe increase in percentage is 20%."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "Nh2HKebn9t4qR2ey",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between people living in an urban and rural environment in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to find and compare the percentages for each environment for the year 2018.\nBased on the above thought, the answer is:\nThe percentage difference is 20%."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "zx5cG0V5PdQLKsiS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the current growth trend continues, what would be the percentage of people living in an urban environment in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I have to identify the annual growth rate and then add this to the percentage for 2020.\nBased on the above thought, the answer is:\nThe predicted percentage is 75%."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "MGtMxgsq6jQuXu0v",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the pie chart data representing the Global Trade and Supply Chain Volume from 2010 to 2018 into a standard CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top {\"Global Trade and Supply Chain Volume from 2010 to 2018\"}[0.24, 0.021, 0.723, 0.047] and the pie slices for each year, I can see that the slices represent the supply chain volumes for each year from 2010 to 2018. By extracting the information from each slice including the year and the corresponding value, I am able to construct a table in CSV format.\nBased on the above thought, the answer is:\nYear,Supply Chain Volume in Tonnes\n2010,10000\n2011,12000\n2012,13500\n2013,14800\n2014,16000\n2015,17500\n2016,20000\n2017,22000\n2018,24000"
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "69rFBlIIH2tzmTrr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you identify any trends in the Supply Chain Volume over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the progression of the values from one year to the next, observing at any noticeable patterns or changes.\nBased on the above thought, the answer is:\nYes, the chart shows a consistent increase in Supply Chain Volume over the years from 2010 to 2018, indicating a steady growth in global trade and business activities."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "qNbz1Fup6rQWBegg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the Supply Chain Volume in 2016 compare to the surrounding years, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the slice representing 2016 {2016}<r>[0.507, 0.33, 0.766, 0.631]</r> to those of the preceding and following years.\nBased on the above thought, the answer is:\nThe Supply Chain Volume in 2016 is considerably higher than the preceding and following years. The slice representing 2016 has indeed been exploded out from the rest of the pie, highlighting a significant jump in supply chain volume for that year."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "j1gAnCoZFSXVphX4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications do the trends identified in the chart hold for future supply chain management and planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to speculate on how the trends observed in the data might affect future supply chain strategies and operations.\nBased on the above thought, the answer is:\nThe steady increase in Supply Chain Volume over the years suggests that businesses and supply chain managers can expect this growth trend to continue. This could imply the need for expanded capacity and infrastructures, more efficient systems to handle the growing volumes, and possibly the adoption of new technologies in the field of supply chain management."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "WmKdSDHOTg0PnaxU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total Supply Chain Volume for all the years combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding up all the Supply Chain Volumes for each year would give me the total Supply Chain Volume for all those years.\nBased on the above thought, the answer is:\nThe total Supply Chain Volume for all the years combined is 139800 tonnes."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "zblAezkEnVFLm9Ba",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average Supply Chain Volume per year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the average Supply Chain Volume per year, I would have to add up all the Supply Chain Volumes and then divide by 9, which is the total number of years.\nBased on the above thought, the answer is:\nThe average Supply Chain Volume per year is approximately 15533 tonnes."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "r4jBtTK7POUOHpcy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in Supply Chain Volume from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in Supply Chain Volume from 2010 to 2018, I would subtract the Supply Chain Volume of 2010 from that of 2018.\nBased on the above thought, the answer is:\nThe increase in Supply Chain Volume from 2010 to 2018 is 14000 tonnes."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "NtAOWeQo3c7ePQPA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table that represents the financial asset investment distribution from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {\"Investment Distribution in Different Financial Assets (2020)\"}<r>[0.235, 0.021, 0.728, 0.047]</r>, I can see the chart is showing the amounts invested in various financial assets in the year 2020. The slices of the pie chart correspond to each investment type and the corresponding investment amounts that are specified in the legend <r>[0.836, 0.063, 0.993, 0.309]</r>.\nBased on the above thought, the answer is:\nInvestment Type,Investment Amount\nBonds,10000\nStocks,50000\nReal Estate,5000\nMutual Funds,30000\nCryptocurrency,20000\nCommodities,15000\nForex,12000\nOptions,8000"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "0TFrapWN66qW5uAQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich financial asset received the highest investment in 2020, and what does this suggest about investor behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the financial asset with the largest slice in the pie chart.\nBased on the above thought, the answer is:\nThe financial asset that received the highest investment in 2020 was \"Stocks\". This suggests that investors were heavily focused on stocks, indicating a confidence in the stock market and potentially the expectation of significant returns."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "9vpk6hLHw1h0nv7w",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the investment distributions, what prediction might you make for investment trends in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, we could infer the preferences of investors but not the future trends without additional information.\nBased on the above thought, the answer is:\nIt would not be accurate to predict without more information. However, if the current trends continue, we might expect to see strong interest in stocks, mutual funds, and cryptocurrencies in 2021. The outlier in this data could be the investment in cryptocurrency, which might suggest that investors are increasingly looking towards digital assets."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "4X5AT5r3jZKpSA1q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the pie chart about investment in 'Real Estate'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw conclusions about investments in 'Real Estate', I would observe the investments made in 2020.\nBased on the above thought, the answer is:\n'Real Estate' appears to be the least popular investment type in 2020. This could suggest that investors are not expecting as much return from real estate compared to the other investment types, or perhaps that the real estate market was not favored or was uncertain in 2020."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "6yeYOpVvZNkXz9dH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more was invested in Stocks compared to Options?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference of the amount invested between Stocks and Options, I must subtract the amount invested in Options from the amount invested in Stocks.\nBased on the above thought, the answer is:\n42000"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "eZCJQRPt6OlTyaAl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total amount invested in all financial assets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total investment, I need to sum up the investments in all categories.\nBased on the above thought, the answer is:\n146000"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "gqo0hRUXs5Gc5FDJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the amount invested in real estate doubles next year, how much would that be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this possible amount of investment, we have to multiply the current investment amount in real estate by 2.\nBased on the above thought, the answer is:\n10000"
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "UcZTqtRmeQ0ebGv3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive a CSV format table with the data displayed in the pie chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see categories and their respective proportions in {the slice of the pie chart for Exports}<r>[0.201, 0.294, 0.646, 0.88]</r> and {the slice of the pie chart for Imports}<r>[0.263, 0.167, 0.735, 0.79]</r>. The categories are represented by different colors in the legend located in <r>[0.831, 0.092, 0.992, 0.188]</r>.\nBased on the above thought, the answer is:\nYear,Exports,Imports\n2020,47.7%,52.3%"
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "k2iIFPIIJjoZi2i9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this pie chart tell us about the country's trade balance in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To give an interpretation, I have to refer to the percentages associated with the labels \"Exports\" and \"Imports\" in the <r>[0.673, 0.228, 0.756, 0.257]</r> and <r>[0.181, 0.79, 0.263, 0.819]</r> locations.\nBased on the above thought, the answer is:\nThe country had a trade deficit in 2020, as it imported more (52.3%) than it exported (47.7%)."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "54UGeCmvzkUjnlwT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the use of colors in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors refer to the labels identified in the legend located at <r>[0.831, 0.092, 0.992, 0.188]</r>.\nBased on the above thought, the answer is:\nTwo distinctive colors are used to differentiate between the two categories of international trade that are being depicted in the chart. The color #ff9999 represents \"Exports\" and the color #66b3ff represents \"Imports\"."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "r2vdrEwx4dJ8G0eE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the state of this country's international trade in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw conclusions, I need to evaluate the significance of the sizes of each slice specifically located at <r>[0.263, 0.167, 0.735, 0.79]</r> and <r>[0.201, 0.294, 0.646, 0.88]</r>.\nBased on the above thought, the answer is:\nThe country had a more import-oriented trade pattern in 2020 with imports having a higher share (52.3%) than exports (47.7%). This could suggest strong consumption demands for overseas goods, possible legislation favoring imports, or inadequacy in domestic production to meet internal demands."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "y7EUzgrkF5czIIVr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you calculate how much more imports are than exports in terms of percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in percentage, I will subtract the percentage of Exports from the percentage of Imports represented at <r>[0.673, 0.228, 0.756, 0.257]</r> and <r>[0.181, 0.79, 0.263, 0.819]</r> respectively.\nBased on the above thought, the answer is:\n4.6%"
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "g2KB0pbLzi0RTpZQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you figure out the pie's radius in relative terms?"
+            },
+            {
+                "from": "gpt",
+                "value": "The radius would be half of the difference between the y-coordinates of the bottom right and top left corners of the pie chart. Once I found the coordinates <r>[0.201, 0.167, 0.735, 0.88]</r>, I can calculate.\nBased on the above thought, the answer is:\n0.3565"
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "qu5C1HKd2L9BwdNM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat should be the percentage of the imports in the next year if the trend of import increasing continues?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a prediction, I need to look at the trend of increase in the raw data provided and estimated for the next year based on this trend.\nBased on the above thought, the answer is:\nApproximately 54.6%"
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "dkmJ8iEyXotb4BRZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will the patient distribution across medical fields look in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the data, a CSV file can be constructed with two columns: \"Medical Fields\" and \"Patients\". Medical fields include \"Family Medicine\", \"Internal Medicine\", \"Pediatrics\", \"Psychiatry\", \"Ophthalmology\", \"Dermatology\", \"Gynecology\", \"Radiology\", \"Geriatrics\", \"Orthopedics\". For each of these fields, the corresponding number of patients is shown in the 'Patients' column as 700, 500, 380, 200, 300, 180, 350, 260, 220, 400 respectively.\nBased on the above thought, the answer is:\nMedical Fields,Patients\nFamily Medicine,700\nInternal Medicine,500\nPediatrics,380\nPsychiatry,200\nOphthalmology,300\nDermatology,180\nGynecology,350\nRadiology,260\nGeriatrics,220\nOrthopedics,400"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "l5i5h7m3XGbTUPKm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the distribution of patients across the different medical fields based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the distribution of patients across various medical fields. The largest slice represents the highest patient count and the smallest slice represents the least number of patients.\nBased on the above thought, the answer is:\nBased on the pie chart, it can be concluded that Family Medicine has the highest number of patients. Hence, Family Medicine seems to be the most sought medical field by patients. On the other hand, Dermatology has the least number of patients indicating it as the least sought out medical field."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "nnlmgG2APdMzrz7K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Department of Health wants to allocate funds for medical research in an area where patients seem to have the most health issues, which medical field should they consider funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to identify which medical field has the highest number of patients, as this suggests a higher prevalence of health issues in that field.\nBased on the above thought, the answer is:\nThe Department of Health should consider funding Family Medicine since it has the highest number of patients. More patients in this field suggests a higher prevalence of health issues that fall under Family Medicine."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "ifEQFkIAyzpKcjvV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming every patient presents a unique case, which medical specialty seems to have the widest range of health issues?"
+            },
+            {
+                "from": "gpt",
+                "value": "The range of health issues can be inferred from the number of patients in each medical field. The more patients in a certain medical field, the wider range of health issues could be.\nBased on the above thought, the answer is:\nFamily Medicine, being the medical field with the most number of patients, might indicate that it handles the widest range of health issues."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "xXodjEEFbS1uGs9q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, in how many medical fields do the number of patients exceed 300?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to locate each medical field{the legend}<r>[0.823, 0.063, 0.993, 0.368]</r> and see whether the volume of the pie slices {\"Family Medicine\"}<r>[0.197, 0.276, 0.456, 0.711]</r>; {\"Internal Medicine\"}<r>[0.262, 0.516, 0.482, 0.884]</r>; {\"Ophthalmology\"}<r>[0.482, 0.516, 0.74, 0.736]</r>; {\"Gynecology\"}<r>[0.482, 0.236, 0.734, 0.516]</r>; {\"Orthopedics\"}<r>[0.283, 0.15, 0.482, 0.516]</r> is larger than 300 which means more than 300 patients.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "5WTklselanhLVeBG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest number of patients in a medical field?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of each slice and comparing them, I can determine which one denotes the least value.\nBased on the above thought, the answer is:\n180"
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "UFpIXZ2jmUANAiW5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there is a 10% increase in the number of 'Orthopedics' patients next year, what would be the total number of patients?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to calculate the 10% of the current number of 'Orthopedics' patients and add that to the existing total number of patients.\nBased on the above thought, the answer is:\nThe increase in orthopedics patients is 10% of 400 which is 40. So, the new total would be the current total (3,790) + 40 = 3,830."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "3ZQ1MYN8q5LUAqhV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would the data from the chart translate into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title {\"Influence of Social Networks on Interpersonal Relationships\"}<r>[0.235, 0.021, 0.728, 0.047]</r> conveys that this data is about the role of different social networks on interpersonal relationships. By analyzing the corresponding proportions for each social network in {the legend}<r>[0.861, 0.063, 0.993, 0.309]</r> and their corresponding pie slices in {the pie chart}<r>[0.223, 0.11, 0.741, 0.886]</r>, I can convert the data into a CSV format.\nBased on the above thought, the answer is:\nSocial Network,Interpersonal Relationships\nFacebook,65\nInstagram,25\nTwitter,3\nLinkedIn,3\nWhatsApp,4\nSnapchat,10\nGoogle Plus,2\nMeetup,1"
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "EbJmPASSqb7pdDUI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social platform had the least influence on interpersonal relationships as per the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at all the slices and their corresponding proportions in the pie chart, the least influential platform on interpersonal relationships can be identified.\nBased on the above thought, the answer is:\nMeetup has the least influence of only 1% among all the social platforms."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "hz7AxLQ8jHVupaCZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith the data shown on the chart, what can be inferred about the role of social media platforms in interpersonal relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can interpret this by examining the sizes of each slice representing the different social media platforms and their corresponding proportions in the pie chart.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that Facebook plays the most significant role in fostering interpersonal relationships, followed by Instagram. Other social platforms have relatively less influence, with LinkedIn, Twitter, and WhatsApp having minor participation. Snapchat has a moderate influence, whereas Google Plus and Meetup are not highly valued by users for enhancing interpersonal relationships."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "IKg2kmnjDuhoNMnt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart, how can social media strategists use this information?"
+            },
+            {
+                "from": "gpt",
+                "value": "By evaluating the relative influence of each platform based on the size of their respective slices and their proportions, strategies can be developed.\nBased on the above thought, the answer is:\nSocial media strategists could leverage Facebook and Instagram for campaigns and initiatives aimed at enhancing interpersonal relationships, given their large reach and influence. Twitter, LinkedIn, WhatsApp might need more strategic content to increase user engagement and impact on interpersonal relationships, while Snapchat offers a platform with moderate engagement that can be capitalized on. Finally, platforms like Google Plus and Meetup are not prioritized by users for interpersonal enhancements and may require significant strategy changes."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "FOm5TTWJ0oJy9Tro",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Sum of the influence of Facebook and Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "By locating the slices of \"Facebook\" and \"Instagram\" on the chart, I can combine their proportions to find the total value.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "38ln4jwIHiU7TIUC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the influence of Facebook and Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the influence of Instagram from the influence of Facebook to find the percentage difference.\nBased on the above thought, the answer is:\n40%."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "6kFwFPlmkOqxYh7X",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Instagram doubled its influence, what would the total influence of all the platforms be?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Instagram doubled its influence, I need to add the current total influence with the addition of Instagram's influence.\nBased on the above thought, the answer is:\n125%"
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "TsNBkebLlWyMdqij",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a CSV format table considering the 2020 data for Digital Advertising and Content Creation?"
+            },
+            {
+                "from": "gpt",
+                "value": "This pie chart {\"New Media and Digital Content Creation for 2020\"}[0.277, 0.021, 0.686, 0.047] indicates the percentage for each category {\"Digital Advertising\"}[0.196, 0.846, 0.325, 0.866] and {\"Content Creation\"}[0.653, 0.135, 0.772, 0.155]. I can use these values to create a structured table in CSV format.\nBased on the above thought, the answer is:\nMedia Type,2020\nDigital Advertising,53.6% \nContent Creation,46.4%"
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "qX4mb6kl1fRNvJYl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich new media sector was dominant in 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must look at the percentages given for each area in the chart to determine which sector was dominant in 2020.\nBased on the above thought, the answer is:\nIn 2020, the dominant sector was 'Digital Advertising' as it accounted for a larger percentage (53.6%) of the total market compared to 'Content Creation' (46.4%)."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "PozoNgeRbCrXZDve",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy does the 'Content Creation' slice of the pie chart have the 'explode' feature while others don't?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the chart effectively and find out why the 'Content Creation' slice is exploded.\nBased on the above thought, the answer is:\nThe 'Content Creation' slice of the pie chart has the 'explode' feature to draw attention to it as it has shown a substantial increase compared to its previous years."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "xyDFEsVDzOJRiQuy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what can be inferred about the trends in the new media sector for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the proportions of 'Digital Advertising' and 'Content Creation' to draw conclusions about new media trends in 2020.\nBased on the above thought, the answer is:\nThe chart shows that both 'Digital Advertising' and 'Content Creation' are significant players in the new media sector. However, 'Digital Advertising' has a slightly higher percentage, suggesting that it is currently more prevalent. Nevertheless, the highlighted 'Content Creation' slice suggests that this sector has seen significant growth and may continue to do so in the future."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "lBYYcxodWwy5iPVA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the combined percentage of Digital Advertising and Content Creation in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart {\"New Media and Digital Content Creation for 2020\"}[0.277, 0.021, 0.686, 0.047], the percentage of {\"Digital Advertising\"}[0.196, 0.846, 0.325, 0.866] and {\"Content Creation\"}[0.653, 0.135, 0.772, 0.155], I can sum up to get the total.\nBased on the above thought, the answer is:\nThe combined percentage of Digital Advertising and Content Creation in 2020 was 100%."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "ZjaTfbOqCqNPYfqu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the pie chart represents a total of 1000 viewers, how many viewers does the Content Creation sector represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the pie chart represents 1000 viewers and analyzing the percentage section of the Content Creation category at {\"Content Creation\"}[0.653, 0.135, 0.772, 0.155] in the chart, I can calculate the viewership.\nBased on the above thought, the answer is:\nThe Content Creation sector represents 464 viewers."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "mZqkSaDjSX7UUcin",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more percentage is Digital Advertising than Content Creation in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage difference, I need to subtract the percentage of Content Creation from the percentage of Digital Advertising.\nBased on the above thought, the answer is:\nDigital Advertising is 7.2% more than Content Creation in 2020."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "yMAA3V0Ne2xJu7xK",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the pie chart data you see, please organize it into a CSV format table with columns for Year, Urban, and Rural."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing each pie slice and identifying its corresponding year from the legend located on the right {sublegend}<r>[0.908, 0.063, 0.993, 0.338]</r>, I can identify the years. Looking at the size of each slice, I can get a sense of the development percentage in Urban and Rural areas for that particular year.\nBased on the above thought, the answer is:\nYear,Urban,Rural\n2010,60,40\n2011,65,35\n2012,70,30\n2013,72,28\n2014,73,27\n2015,77,23\n2016,80,20\n2017,85,15\n2018,88,12"
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "NQvDTbHIWylgpfaA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which type of area (Rural or Urban) saw more development over the years? Elaborate."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the overall pattern from 2010 to 2018, I can deduce which type of area saw more development by comparing the growth pattern from one year to the next. The area with the most distinct growth pattern is likely to have seen more development.\nBased on the above thought, the answer is:\nUrban areas saw more development over the years. The development percentage continually increased from 60% in 2010 to 88% in 2018. This shows a growing trend towards urbanization."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "rGdXaLfxt59bn98Q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is the pie slice for 2018 exploded from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Generally, an exploded slice in a pie chart is used to highlight that particular data point. The reason could be a significant event or to draw attention to a specific point in the data sequence.\nBased on the above thought, the answer is:\nThe pie slice for 2018 is exploded to draw attention to the most recent year in the data and to emphasize the continued pattern of increasing urban development and decreasing rural development."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "4w8GUmXbLVGsGb6n",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implications of these trends for future development strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "Understanding the progression of the data from 2010 to 2018 and noting the emphasis on 2018, can help predict the potential implications for future development strategies.\nBased on the above thought, the answer is:\nThe increasing trend in urban development and corresponding decrease in rural highlights a definite shift towards urban areas. This could imply that future resources and strategies might be focused more towards effectively managing and improving infrastructure, services, and quality of life in urban areas. Simultaneously, the decline in rural development may call for strategies aimed at revitalizing these areas and preventing further rural-urban migrations."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "xlBrOXOIyGCv460K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total development percentage for Urban and Rural areas in the year 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total development percentage for the year 2014, I should add the percentages of Urban and Rural development together for that year.\nBased on the above thought, the answer is:\nThe total development percentage for 2014 is 100%."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "uSuV9n0HhY9ipjmA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in development percentage between Urban and Rural areas in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the Rural development percentage from the Urban one for the year 2017.\nBased on the above thought, the answer is:\nThe difference in development percentage between Urban and Rural areas in 2017 is 70%."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "e3y38kPVX0zTvo9y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in Urban development from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Urban development from 2010 to 2018, I need to subtract the Urban development percentage in 2010 from that in 2018.\nBased on the above thought, the answer is:\nThe increase in Urban development from 2010 to 2018 is 28%."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "F3dZwdrGN4RE51bV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the health and medical technology investment data from the pie chart into a structured table using CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the data title \"Investments in Healthcare Technology\" {subtitle}<r>[0.323, 0.021, 0.64, 0.047]</r>, and {the sector slice}<r>[{\"Robotic Surgery\"}[0.199, 0.293, 0.458, 0.901]; {\"Telemedicine\"}[0.475, 0.516, 0.631, 0.886]; {\"Nanomedicine\"}[0.482, 0.516, 0.736, 0.818]; {\"Bioinformatics\"}[0.482, 0.211, 0.741, 0.584]; {\"Genetic-Testing\"}[0.482, 0.147, 0.628, 0.516]; {\"Health-Wearables\"}[0.319, 0.146, 0.505, 0.516]; {\"Echo-Cardiogram\"}[0.283, 0.228, 0.482, 0.516]]</r>. These clear pie slices indicate the portion for each category. Thus, I can deduce the data table from this information.\nBased on the above thought, the answer is:\nHealthcare_Technology, Funding\nRobotic Surgery, 35\nTelemedicine, 10\nNanomedicine, 12\nBioinformatics, 18\nGenetic-Testing, 8\nHealth-Wearables,12\nEcho-Cardiogram, 3"
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "AsU9i21fsYwFAQdc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the least funded category from the pie chart? How can it be improved?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the pie chart, the least funded category is noticeably Echo-Cardiogram.\nBased on the above thought, the answer is:\nYes, Echo-Cardiogram is the least funded category. To improve its status, more awareness about the benefits and cost-effectiveness of Echo-Cardiogram technology can be promoted. This could convince investors of the need and potential profitability of increasing funding."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "vBzg1D9aHfNFBfjP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich healthcare technology has received the most investment? Why do you think this is so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart visibly shows Robotic Surgery as the sector with the highest investment. The reasoning has to be inferred.\nBased on the above thought, the answer is:\nRobotic Surgery has received the most investment. This could be due to its potential advantages such as minimally invasive procedures, increased precision, and faster recovery periods for patients. It's also a cutting-edge technology which could represent the future direction of healthcare, which might be the reason for high investment."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "VufBOjdCcIcFa4zT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKeeping in mind the investment data, which technology area should receive more funding in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "By referring to the pie chart, the future funding focus might be decided based on the areas with less investment and more potential.\nBased on the above thought, the answer is:\nIt's crucial to balance the investments with the potential impact of technology. Genetic Testing and Echo Cardiogram have relatively less funding. Considering the promising impact of personalized treatment and non-invasive monitoring methods, these sectors might deserve more funding in the future."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "44SanDggSTBmSe7P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of funding allocated to Robotic Surgery and Nanomedicine?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data provided in the pie chart, the funding allocation for Robotic Surgery and Nanomedicine can be extracted directly.\nBased on the above thought, the answer is:\n47"
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "bvl4lRAEC65S3MUT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf additional funding was provided to double the investment in Telemedicine what would be the new total funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total funding, I need to calculate the doubled funding for Telemedicine and then add that to the total funding of all other sectors.\nBased on the above thought, the answer is:\n108"
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "kD4medVAMXXoAqx1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the highest and lowest funded technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, it is clear that the highest funded technology is Robotic Surgery and the lowest funded technology is Echo-Cardiogram. The difference can be calculated by subtracting the funding of Echo-Cardiogram from Robotic Surgery.\nBased on the above thought, the answer is:\n32"
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "7Q58zbfMvXngWSOD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help to arrange the data presented in the chart into a comprehensible table?"
+            },
+            {
+                "from": "gpt",
+                "value": "With the information given, I identified the pie chart title as {Fitness and Sports Health Trends in 2020}<r>[0.312, 0.021, 0.651, 0.047]</r>. The legend box, {the legend}<r>[0.855, 0.063, 0.993, 0.249]</r>, displays six activities: Yoga, Running, Cycling, Weightlifting, Crossfit, and Meditation. Also, their corresponding pie slices show the proportion of each activity, which we can use to extract data from the graph.\nBased on the above thought, the answer is:\nActivity, Percentage\nYoga, 25.1%\nRunning, 25.6%\nCycling, 22.2%\nWeightlifting, 7.4%\nCrossfit, 12.3%\nMeditation, 7.4%"
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "0Ue8PcBmr7QO5JPc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be inferred about the popularity of these fitness activities in 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By studying the proportions from each activity, I can conclude which activity was the most adopted one and the least preferred during 2020.\nBased on the above thought, the answer is:\nBased on the chart, Yoga, Running, and Cycling seem to have been popular in 2020 due to their large proportions compared to other activities like Weightlifting, Crossfit, and Meditation."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "BLMYvJPWBmgymOkn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart's display of the 2020 data, how can we interpret this information with respect to people's interest in fitness and sports health activities?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to assess the percentage allocation to each activity to understand people's interests.\nBased on the above thought, the answer is:\nThe pie chart shows that Yoga, Running, and Cycling were the most preferred activities in 2020. It suggests that people are more inclined towards activities that promote cardiovascular health and flexibility. On the other hand, Weightlifting, Crossfit, and Meditation, despite their smaller shares, show a diverse interest among people, catering to different strength, endurance, and mental health needs."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "ln9AEeTtpjhBepe5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this chart influence fitness industry decisions in preparing for future trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the activities with the largest pie slices and those with the smallest, I can infer possible decisions that the fitness industry might make.\nBased on the above thought, the answer is:\nThe fitness industry might prioritize offering Yoga, Running, and Cycling services and programs as these activities had the largest share in 2020. However, they might also opt to invest more in marketing or innovation for activities like Weightlifting, Crossfit, and Meditation with lower shares to increase their popularity."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "jxJ9PuVSPQI9Isv6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich activity accounted for the smallest percentage of fitness trends in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the areas of each pie slice in the given chart, I can estimate that the smallest percentage is represented by the smallest slice.\nBased on the above thought, the answer is:\nWeightlifting"
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "VpGzjKoGfYrqNz04",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion covered by Yoga, Running, and Cycling?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total proportion, I need to add the percentage values of Yoga, Running, and Cycling.\nBased on the above thought, the answer is:\nThe total proportion is 25.1% (Yoga) + 25.6% (Running) + 22.2% (Cycling) = 72.9%."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "DXIsklrMKXxXruPp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Cycling should increase to the same level as Running, how much should it increase by?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the increase, I will subtract the current percentage of Cycling from that of Running.\nBased on the above thought, the answer is:\nThe increase is 25.6% (Running) - 22.2% (Cycling) = 3.4%."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "BwL9tKxnb6daaH6c",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're analyzing, please convert the data for Electric Cars and Bicycles from 2013 to 2019 into CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into account the pie chart's specific details including {the title}<r>[0.329, 0.021, 0.634, 0.047]</r>, along with {the pie slices for each year}<r>[0.223, 0.146, 0.741, 0.903]</r> and {the corresponding sublegend}<r>[0.908, 0.063, 0.993, 0.279]</r>, we can derive the data for each market segment for each year.\nBased on the above thought, the answer is:\nYear, Electric Car, Bicycle\n2013, 38, 25\n2014, 52, 28\n2015, 68, 31\n2016, 102, 32\n2017, 145, 34\n2018, 188, 32\n2019, 220, 34"
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "ajIamjclMXHhQXvD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the trends in the pie chart, which market segment seems to have more potential for growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the market segment with more potential for growth, I would compare the growth rates of both Electric Cars and Bicycles from 2013 to 2019.\nBased on the above thought, the answer is:\nThe electric car market appears to have more potential for growth, given its consistently high annual growth since 2013 as opposed to the bicycle market which has remained relatively stagnant."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "luUOgVC3PggjBd12",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, how might companies in these market segments strategize for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide a suggestion, I would juxtapose the growth trends in both markets from 2013 to 2019 and consider the implications for future strategy.\nBased on the above thought, the answer is:\nConsidering the sizeable and consistent growth in the electric car market, companies in this segment might want to continue investing in this technology. On the other hand, the stagnant growth in the bicycle market suggests a possible need for innovation or more aggressive marketing strategies to boost sales."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "lRKptAWmtD3iPZfj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does the growth in the electric car market have on environmental sustainability?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive implications for environmental sustainability, it's helpful to consider the broader context of the market growth data.\nBased on the above thought, the answer is:\nThe steady growth in the electric car market suggests a gradual shift away from traditional, fossil fuel-reliant cars. This could potentially contribute to reductions in greenhouse gas emissions, a key aspect of environmental sustainability."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "aB697R7yi2JgnWWJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the electric car market grown from 2013 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the count of electric car sales in 2013 and 2019, I can calculate the growth rate.\nBased on the above thought, the answer is:\nThe electric car market has grown by 182 units from 2013 to 2019."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "Fo69MPfDfSLhG1nZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average sales of bicycles from 2013 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I'll add up the sales of bicycles from 2013 to 2019 and divide by the number of years, which is 7.\nBased on the above thought, the answer is:\nThe average sales of bicycles from 2013 to 2019 was approximately 31 units."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "8oDL9E3om4pPQ2Gh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the highest growth in electric car sales compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the highest growth, I'll calculate the difference in sales between each consecutive year and identify the year with the largest positive difference.\nBased on the above thought, the answer is:\n2017 saw the highest increase in electric car sales compared to the previous year."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "0pMdWLbjDDYxjPHY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, please convert the data into a standardized CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "By observing two pie charts {\"Socioeconomic\"}<r>[0.086, 0.263, 0.331, 0.755]</r> and {\"Policy Making\"}<r>[0.446, 0.263, 0.692, 0.752]</r>, I can see that each chart corresponds to each main category, and within each section of the pie charts, the percentages of each subcategory are labeled. Therefore, I can consolidate this information into a table.\nBased on the above thought, the answer is:\nCategory,Policy Making,Improvements,Threats\nSocioeconomic,25%,45%,30%\nPolicy Making,20%,60%,20%"
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "61xsUw7886xTxd6L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area is most focused on under 'Socioeconomic' and 'Policy Making'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the most focused area by comparing the percentages of each subcategory under the pie charts.\nBased on the above thought, the answer is:\n'Improvements' is the most focused area under both 'Socioeconomic' and 'Policy Making' with 45% and 60% respectively."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "NiYsQqccAyrs6Y7j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the focus area in policy decisions from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the proportions of each subcategory in the two pie charts, I can make an inference about the focus area in policy decisions.\nBased on the above thought, the answer is:\nFrom the pie charts, it can be inferred that most priority in policy decisions, whether under 'Socioeconomic' or 'Policy Making', is given to 'Improvements'."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "49ytrDeQYxvlSrrq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence future policy decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the distribution of categories in these pies might impact future decision making.\nBased on the above thought, the answer is:\nObserving the higher percentage allocated to 'Improvements' under both 'Socioeconomic' and 'Policy Making' categories, it might be inferred that future policies will continue to focus on improvements. Alternatively, efforts might be made to address areas like 'Policy Making' and 'Threats', which currently receive fewer proportions compared to 'Improvements'."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "N9kect3VszY2O6ZS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion of 'Threats' in both 'Socioeconomic' and 'Policy Making' categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total proportions, I must look at the proportion allocated to 'Threats' in both pie charts {\"Socioeconomic\"}<r>[0.086, 0.263, 0.331, 0.755]</r> and {\"Policy Making\"}<r>[0.446, 0.263, 0.692, 0.752]</r> and add them together.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "PeMI6MrJY3vKfSFz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Improvements' segment increased by 10% in 'Policy Making', what would be the new total proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "To update the total proportions, I need to add 10% to the 'Improvements' segment under 'Policy Making' category and recalculate the sum.\nBased on the above thought, the answer is:\n70% for 'Improvements' under 'Policy Making'."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "lRqU0Av6Inp9VmWy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which category does 'Policy Making' have a higher percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the percentage of 'Policy Making' in both pie charts {\"Socioeconomic\"}<r>[0.086, 0.263, 0.331, 0.755]</r> and {\"Policy Making\"}<r>[0.446, 0.263, 0.692, 0.752]</r>.\nBased on the above thought, the answer is:\nSocioeconomic"
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "dGk1iM7R2KyKCFVj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you form a CSV table for the given data showing the art development and technological advancement in different eras based on the pie chart you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the legend {at the side of the chart}<r>[0.831, 0.063, 0.993, 0.279]</r> displaying seven different eras and their corresponding colors, and combining with the information from each pie slice in the pie chart {in the middle of the chart}<r>[0.218, 0.109, 0.741, 0.897]</r> which shows the percentage of art development and technological advancement in each era, I can form a CSV table accordingly.\nBased on the above thought, the answer is:\nEra,Art Development,Technological Advancement\nAncient Age,10,5\nClassical Period,25,20\nMiddle Ages,30,40\nRenaissance,60,70\nModern Times,80,85\nIndustrial Age,95,95\nInformation Age,98,99"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "IehB15kNNMj8L9jF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the art development and technological advancement related to the historical era we are living based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the pie slices {in the middle of the chart}<r>[0.218, 0.109, 0.741, 0.897]</r>, which symbolizes the progress of art development and technological advancement in different eras, to answer this question.\nBased on the above thought, the answer is:\nIt can be inferred from the chart that the development of art and technology has been increasing throughout human history. In particular, the Information Age, our current era, shows the highest level of both technological advancement and art development."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "NzoIylqXaOrHMqJd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there an era where we notice a spurt in art development and then it's followed by a surge in technological advancement?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I need to consider the progress trend of both art development and technological advancement across different eras, information can be obtained from the pie slices {in the middle of the chart}<r>[0.218, 0.109, 0.741, 0.897]</r>.\nBased on the above thought, the answer is:\nYes, the Renaissance era was a period of remarkable art development, followed by a significant surge in technological advancement in the Modern Times and further in the Industrial Age and the Information Age."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "wY0da6zEcbgadLdJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend of the art development and technological progress?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie slices {in the whole chart}<r>[0.218, 0.109, 0.741, 0.897]</r>, I can observe the trend of art development and technological progress from the Ancient age to the Information age.\nBased on the above thought, the answer is:\nThe art development and technological progress show a trend of continual growth from the Ancient Age to the Information Age. The art development saw a sudden spurt during the Renaissance period, while technology began to rapidly progress from the Modern Times, overtaking art during the Industrial Age and making a significant leap in the Information Age."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "QpRLviE2HLRTQUUU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total combined progress made in art development and technology advancement in the Classical Period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll check the Classical period from the legend {at the side of the chart}<r>[0.876, 0.101, 0.984, 0.121]</r>, then find its corresponding pie slice and add the percentages of both art development and technological advancement.\nBased on the above thought, the answer is:\n45%"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "nO1qj39WEB4mB5XQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much the art development exceeded technological advancement progress in Renaissance period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the progress of both art development and technological advancement in Renaissance period, which I can find in the legend {at the side of the chart}<r>[0.876, 0.161, 0.962, 0.181]</r> and subtract the smaller value from the larger one.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "j23gjjf0XTMCbCTz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which era was the advancement in technology greater than the development in art?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the advancement in technology and development in art for each era, which I can find in the legend {at the side of the chart}<r>[0.831, 0.063, 0.993, 0.279]</r>.\nBased on the above thought, the answer is:\nMiddle Ages, Modern Times, Industrial Age, Information Age."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "yUsVTZIqudnVFISw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data presented in the chart into CSV format according to the years being shown."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart's title {Facebook vs Instagram Users from 2015 to 2020}<r>[0.279, 0.021, 0.684, 0.047]</r>, it denotes social media platform usage for the mentioned years. In the legend section, six different colored handles each represent a year from 2015 to 2020 <r>[0.913, 0.223, 0.941, 0.237]</r>. Each year has a pie chart section that indicates both Facebook and Instagram's user count.\nBased on the above thought, the answer is:\nYear,Facebook Users,Instagram Users\n2015,1200,300\n2016,1300,600\n2017,1550,800\n2018,1600,1100\n2019,1650,1500\n2020,1700,2000"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "10LDxRKEpE98VgIw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the pie charts, which year had an explosive growth in Instagram users?"
+            },
+            {
+                "from": "gpt",
+                "value": "An exceptional growth year would be represented by a large pie slice. For this, I need to find the year where the pie slice has exploded outwards from the center of the chart, indicating a significant increase.\nBased on the above thought, the answer is:\nThe year 2017 experienced a significant increase in Instagram users since this is the only year where the pie slice is exploded <r>[0.353, 0.553, 0.619, 0.923]</r>."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "sVpYR5uHwJaXteO5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the general trend of Facebook users over the years from the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "As indicated by the increasing sizes of the pie slices from 2015 to 2020, it signifies the number of users have been generally increasing.\nBased on the above thought, the answer is:\nThe trend over these years indicates a steady increase in the number of Facebook users."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "X25rAkmccvtpZgMC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie charts, how does the growth rate of Instagram users compare to Facebook users?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the relative changes in the pie slices for Instagram and Facebook from 2015 to 2020 to understand the comparative growth rates.\nBased on the above thought, the answer is:\nThe growth rate of Instagram users has been more rapid than that of Facebook users, as indicated by the larger increases in the sizes of the Instagram sections of the pie charts in more recent years, despite starting from a smaller user base."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "3dg3ArO9pEUZpZdB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the number of Facebook users between 2015 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the number of Facebook users in 2015 from the number in 2019.\nBased on the above thought, the answer is:\n450"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "zJWCN0OLlhMF7gRC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more Instagram users were there in 2020 than in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the number of Instagram users in 2015 from the number in 2020.\nBased on the above thought, the answer is:\n1700"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "d6hOgGlHIGNlJLIR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of Instagram users in 2016, 2017, and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this sum, I need to add up the number of Instagram users in 2016, 2017, and 2020.\nBased on the above thought, the answer is:\n3400"
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "vfgmYHQPT9UyoUAi",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you organize the percentage of time spent on each business process into a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I take note of {the title} found at <r>[0.339, 0.021, 0.624, 0.047]</r> and all the processes mentioned in the {legend}<r>[0.786, 0.063, 0.993, 0.279]</r>. Each process corresponds to a color, which matches with the {slice colors} of the pie located at <r>[0.223, 0.146, 0.741, 0.923]</r>.\nBased on the above thought, the answer is:\nProcesses,Percentage\nRecruitment,10.2\nOnboarding,14.5\nInventory,20.3\nQuality Assurance,14.8\nReporting,18.6\nCustomer Support,13.4\nFinancial Management,8.2"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "m7FrVVH1U1LYOsty",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich business process is the least time-consuming and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the size of each {slice}<r>[0.223, 0.146, 0.741, 0.923]</r>, the least time-consuming process is the smallest slice.\nBased on the above thought, the answer is:\nFinancial Management is the least time-consuming process as it constitutes only 8.2%, the smallest portion of the pie chart."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "sknftlaOntraI8z3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does the chart convey about resource allocation in this organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size and percentage of each {slice}<r>[0.223, 0.146, 0.741, 0.923]</r> present an understanding of how resources are spread across different processes.\nBased on the above thought, the answer is:\nThe chart shows that the company heavily invests time in Inventory and Reporting processes, perhaps indicating the complexity or priority of these tasks. Contrastingly, less time is spent on Financial Management indicating that this aspect of the business is streamlined or doesn't require extensive attention."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "A8gYUAL1JRt4QNA3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what suggestions can you propose for process optimization?"
+            },
+            {
+                "from": "gpt",
+                "value": "An in-depth understanding of the {size of the slices}<r>[0.223, 0.146, 0.741, 0.923]</r> and the {processes' descriptions} in the chart allows for recommendations.\nBased on the above thought, the answer is:\nSeeing as a considerable portion of resources is allocated to Inventory and Reporting, the company could explore ways to streamline these processes for efficiency. Conversely, areas like Financial Management which utilize a smaller portion, could be evaluated to understand if any additional resources or investments can improve overall performance."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "jXG4yeXohuA8sS2h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the collective percentage total for Quality Assurance, Reporting, and Customer Support?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total percentage of these three processes, I will sum up the percentage of {Quality Assurance}<r>[0.482, 0.516, 0.74, 0.83]</r>, {Reporting}<r>[0.482, 0.188, 0.741, 0.546]</r>, and {Customer Support}<r>[0.391, 0.146, 0.602, 0.516]</r>.\nBased on the above thought, the answer is:\n46.8%"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "VeOmfiL0fD5yhK0i",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what is the difference between the percentage of time resources used for Onboarding and Financial Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I subtract the percentage of time spent on {Financial Management}<r>[0.283, 0.169, 0.482, 0.516]</r> from the percentage spend on {Onboarding}<r>[0.223, 0.495, 0.482, 0.795]</r>.\nBased on the above thought, the answer is:\n6.3%"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "PlwPOCVo0LF4shZV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the resources for Customer Support are increased by 5%, what would be the new overall total?"
+            },
+            {
+                "from": "gpt",
+                "value": "Increase the percentage of time devoted to {Customer Support}<r>[0.391, 0.146, 0.602, 0.516]</r> by 5% and add this to the total.\nBased on the above thought, the answer is:\n108%"
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "Vy46Nnvkdt2zm6HM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please decode the pie chart into a structured CSV table using the legends and the percentages visible on each slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I observe that the slices{\"Environment Protection score for each year\"}<r>[0.223, 0.111, 0.741, 0.886]</r> have different colors and sizes, with labels for the year and the corresponding percentage mentioned for each slice. The legend {years on the right side}<r>[0.908, 0.063, 0.993, 0.279]</r> also provides the corresponding colors for the years. Thus, I can map the percentage values to the respective years by matching the colors.\nBased on the above thought, the answer is:\nYear,Environment Protection Score\n2015,14.2%\n2016,12.4%\n2017,12.7%\n2018,14.2%\n2019,13.8%\n2020,15.9%\n2021,16.8%"
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "DEszDzKChRgI9CiI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year recorded the highest engagement in Environment Protection activities? Provide a rationale for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the largest size, which also corresponds to the slice that is exploded out, represents the year with the highest percentage in Environment Protection.\nBased on the above thought, the answer is:\nThe year 2021 recorded the highest engagement in environment protection activities, as it has the largest slice of 16.8% in the pie chart."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "epBJA7s1NWftSudy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you draw about the public's engagement in environment protection from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the sizes of the slices from 2015 to 2021, I can compare and infer the trend in the public's engagement in environment protection.\nBased on the above thought, the answer is:\nThe engagement in environment protection activities has generally increased from 2015 to 2021, as evidenced by the increasing trend in the percentages indicated in the pie chart."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "Ivh4ag23IsBvEETL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the pie chart be helpful in predicting engagement in Environment Protection activities for subsequent years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart can provide insights into past trends and highlight significant changes or anomalies, which can be useful for making predictions for the future.\nBased on the above thought, the answer is:\nThe chart shows an overall increasing trend in engagement towards Environment Protection. This finding can suggest that the commitment to such protection measures would likely continue to rise in the subsequent years, especially considering the significant peak in 2021."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "r71NcQZ916lANXYH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of environment protection engagement in 2018 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the slices for the years listed in the legend<r>[0.908, 0.063, 0.993, 0.279]</r>, I can find the percentages for both 2018 and 2021.\nBased on the above thought, the answer is:\nThe total is 14.2% (for 2018) + 16.8% (for 2021) which equals to 31%."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "AfUJYUH2xA053ZrY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more was the Environment Protection Score in 2021 compared to the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the difference between the 2021 percentage and 2020 percentage.\nBased on the above thought, the answer is:\nThe difference between the scores is 16.8% (for 2021) - 15.9% (for 2020) which equals to 0.9%."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "D5Iyxg3z9YOP7iHP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the approximate average Environment Protection score for the period from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average percentage, I need to take the sum of the percentages for all the seven years from the slices, and then divide by 7.\nBased on the above thought, the answer is:\nThe average percentage is (14.2% +12.4% +12.7% +14.2% +13.8% +15.9% +16.8%) / 7 = approximate 14.3%"
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "OySP2MbXng3hBexa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, could you please format the underlying data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by observing the information presented in the pie slices and the legend {Auto,IT,Pharmaceuticals,Food,Solar Energy,Others}<r>[0.577, 0.033, 0.742, 0.219]</r>, it looks like each slice corresponds to a different industry's market share.\nBased on the above thought, the answer is:\nIndustry,Market Share\nAuto,20%\nIT,30%\nPharmaceuticals,15%\nFood,25%\nSolar Energy,5%\nOthers,5%"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "T21GFR31vyu3HuGZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry holds the prime slot in market share? Provide rationale."
+            },
+            {
+                "from": "gpt",
+                "value": "I am looking at {the IT sector slice}<r>[0.33, 0.652, 0.651, 0.936]</r>. I have to determine the size of each slice in comparison to others to understand which industry holds the majority.\nBased on the above thought, the answer is:\nIT industry is leading, as it holds the biggest slice, accounting for a 30% market share, which is greater than any other industry's share in the chart."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "FMFrtiv2gqNgPREJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you gather from the market distribution for the year covered by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to read the data from {the industry sectors' slices}<r>[0.301, 0.339, 0.699, 0.936]</r> to understand the market distribution.\nBased on the above thought, the answer is:\nBy analyzing the chart, we can infer that IT and Food hold the majority in the market share. Meanwhile, the Solar Energy industry and other such sectors have a minimal presence signifying dominance of a few sectors in the market."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "Akzd14tsHbpFmYQG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging from the data in the chart, how might companies in these industries strategize their growth plans for the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to understand the current market distribution by analyzing the size of each sector slice in {the industry pie slice}<r>[0.301, 0.339, 0.699, 0.936]</r>.\nBased on the above thought, the answer is:\nIndustries like IT and Food that already have a significant market share might want to maintain or widen their dominance through innovation and expanding their services. For sectors like Solar Energy and Others, companies might need to rethink their strategies or invest more in their growth as they currently occupy a smaller slice of the market."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "ptUBZL2f6eQYH1Y7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine from the chart what is the total market share of IT and Food industries combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "For that, I need to add up the shares of both IT and Food sectors from the chart.\nBased on the above thought, the answer is:\n55%"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "mQhHpUBoO9xRLzp3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is IT's market share compared to the Pharmaceuticals industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to subtract the percentage share of Pharmaceuticals from that of IT.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "YQLTXsZhtEOX26xT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share of Others increased by 2%, what would be the new total of all the industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to add 2% to the current percentage of the Others sector and sum up all the percentage points after that.\nBased on the above thought, the answer is:\n102%"
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "R6tPuZflNf3aijzv",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart that you observed, please convert the data into a CSV format table that shows the risk categories and their corresponding risk occurrence rates."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart shows the distribution of different risk types in the organization. From the title at the top {\"Risk Occurrence Rates By Category\"}[0.334, 0.021, 0.629, 0.047] and {the sector slice}[{\"Physical Security\"}[0.244, 0.278, 4.6%]; {\"Information Security\"}[0.223, 0.369, 6.3%]; {\"Compliance\"}[0.223, 0.512, 8.0%]; {\"Risk Management\"}[0.254, 0.516, 10.5%]; {\"Operational Risk\"}[0.377, 0.553, 12.5%]; {\"Business Continuity\"}[0.482, 0.516, 15.8%]; {\"Financial Risk\"}[0.482, 0.198, 19.8%]; {\"Regulatory Risk\"}[0.283, 0.146, 22.4%]], I can see that the sectors represent different categories and how much percentage each category is.\nBased on the above thought, the answer is:\nRisk Category,Risk Occurrence Rates\nPhysical Security,4.6%\nInformation Security,6.3%\nCompliance,8.0%\nRisk Management,10.5%\nOperational Risk,12.5%\nBusiness Continuity,15.8%\nFinancial Risk,19.8%\nRegulatory Risk,22.4%"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "Y5Ljmiv5BmnbIfoH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk category poses the greatest threat according to the occurrence rates observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The risk category that poses the greatest threat would be the one with the highest occurrence rate.\nBased on the above thought, the answer is:\n\"Regulatory Risk\" poses the greatest threat due to its highest occurrence rate of 22.4% among the eight categories."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "hZxMoF53k0gChEW7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the occurrence rates observed in the chart, what can you infer about the company's potential areas of concern?"
+            },
+            {
+                "from": "gpt",
+                "value": "The companies concerns can be evaluated from the percentages of risk categories.\nBased on the above thought, the answer is:\nThe company's principal area of concern appears to be \"Regulatory Risk\", with the greatest occurrence rate of 22.4%. This indicates a potentially high level of regulatory complexity or changes. The company would also need to focus on \"Financial Risk\" and \"Business Continuity\" which have significant occurrence rates of 19.8% and 15.8% respectively."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "YOinY8BNVySKkHhE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how should the company prioritize its risk management efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Prioritization should be based on the risk categories with the highest occurrence rates.\nBased on the above thought, the answer is:\nThe company should prioritize its risk management efforts on \"Regulatory Risk\", as it has the highest occurrence rate. Attention should also be given to \"Financial Risk\" and \"Business Continuity\", which have high occurrence rates next to \"Regulatory Risk\". This can help the company mitigate potential impacts and manage risk effectively."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "HP0N9O8WJNHYkiYr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the occurrence rate of the risk \"Regulatory Risk\" compared to \"Information Security\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the occurrence rates of \"Regulatory Risk\" and \"Information Security\", I need to subtract the occurrence rate of \"Information Security\" from that of \"Regulatory Risk\".\nBased on the above thought, the answer is:\n16.1%"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "kLSP1ih2CC3Ge6EG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of occurrence rates for \"Physical Security\", \"Compliance\", and \"Risk Management\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sum up the percentages of \"Physical Security\", \"Compliance\", and \"Risk Management\".\nBased on the above thought, the answer is:\n23.1%"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "fufmnvACPEz5u8KL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average occurrence rate for all of the risk categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average occurrence rate, I need to sum up all of the percentages and divide by the number of categories.\nBased on the above thought, the answer is:\n12.5%"
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "LtyxrFMi81bBPYqG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information you see in the pie chart, could you convert this into a table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title below the chart {\"Big Data Integration Overview\"}[0.356, 0.021, 0.607, 0.047] and the legend for different stages of big data integration {\"Raw Data Cleaning\"}[0.845, 0.071, 0.978, 0.091]; {\"Data Preprocessing\"}[0.845, 0.101, 0.98, 0.121]; {\"Data Transformation\"}[0.845, 0.131, 0.987, 0.151]; {\"Data Integration\"}[0.845, 0.161, 0.959, 0.181]; {\"Data Loading\"}[0.845, 0.191, 0.938, 0.211]; {\"Data Verification\"}[0.845, 0.221, 0.961, 0.241], I can see that these segments in the pie chart represent the percentage of time spent in each stage.\nBased on the above thought, the answer is:\nStages,Percentage\nRaw Data Cleaning,10\nData Preprocessing,25\nData Transformation,15\nData Integration,30\nData Loading,10\nData Verification,10"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "4tbU51zjTlBAiYll",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for Data Transformation and Data Loading?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the information provided, I can add the percentage of both Data Transformation and Data Loading to get the total.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "g0KJJmeuVIxMseDD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf onsite technicians had to spend additional 5% time on Raw Data Cleaning, how would it affect the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total is currently 100%. If additional time is spent on Raw Data Cleaning, the total will exceed 100%.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "2bSNV9gywoo7yqRt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of Data Verification and Raw Data Cleaning together?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages of Data Verification and Raw Data Cleaning to get the answer.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "VS4THSOsckINk7xn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table that states the year-wise increasing trend of telecommuting and self-employment based on the raw data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the line data labeled {\"Telecommuting\"}<r>[0.223, 0.278, 0.667, 0.886]</r> and {\"Self-Employment\"}<r>[0.301, 0.118, 0.758, 0.747]</r> in the pie chart figure, there has been a rise in both trends from 2012 to 2019. I can breakdown the raw data to create a CSV table.\nBased on the above thought, the answer is:\nYear,Telecommuting,Self-Employment\n2012,5,40\n2013,8,35\n2014,17,33\n2015,19,30\n2016,33,38\n2017,46,44\n2018,54,56\n2019,62,65"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "GKYDX8HyIhEln8Ub",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the comparison in the pie chart for the year 2019, which work trend showed the higher preference amongst the workers?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions of each slice of the pie chart, basically, compare the segments {\"Telecommuting\"}<r>[0.223, 0.278, 0.667, 0.886]</r> and {\"Self-Employment\"}<r>[0.301, 0.118, 0.758, 0.747]</r>.\nBased on the above thought, the answer is:\n\"Self-Employment\" was the more preferred work trend in 2019 as it made up 51.2% of the pie chart which is more than the 48.8% that \"Telecommuting\" had."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "3omgWodRAT5Zf6Pz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be said about the change in workplace trends over the years based on the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to infer about the change in workplace trends, I need to consider the yearly data of both telecommuting and self-employment trends.\nBased on the above thought, the answer is:\nThere has been a strong growth in both telecommuting and self-employment from 2012 to 2019. It signifies a shift from traditional employment towards more flexible and independent forms of work. However, self-employment holds a higher consistent interest throughout the years."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "ajupG2ZR9jL4raS1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be predicted about the work trends of 2020 based on the increasing percentage trends in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the past trends of both telecommuting and self-employment from 2012 to 2019 to predict what the trends might be for 2020.\nBased on the above thought, the answer is:\nGiven the consistent increase in both telecommuting and self-employment, we can predict that these trends would have continued to rise in 2020. However, since self-employment has held a consistently higher interest, it might still be the preferred choice in 2020."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "IGrFoL66qP4D66P5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, what is the difference in the percentage of telecommuting in 2019 compared to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to observe the difference between the percentage of telecommuting in the year 2019 and that of 2012.\nBased on the above thought, the answer is:\n57%"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "DSZQqVw0TSgl0zDK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has self-employment grown in percentage from the year 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at the percentage of self-employment in 2015 and compare it with that of 2019.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "jBNkY6mFUSSfdh6Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages of telecommuting and self-employment for the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add the percentages of telecommuting and self-employment for the year 2019.\nBased on the above thought, the answer is:\n127%"
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "uNtwFTs5WvDjXfzR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart about the Food Production and Agriculture industries from 2014 to 2019 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the {chart title}<r>[0.241, 0.021, 0.722, 0.047]</r> and the {legend}<r>[0.908, 0.063, 0.993, 0.249]</r>, I can see that the chart presents information about the Food Production and Agriculture industries over the years 2014 to 2019. The percentage for each year is displayed on the respective slice of the pie.\nBased on the above thought, the answer is:\nYear,Food Production,Agriculture\n2014,60,40\n2015,65,35\n2016,64,36\n2017,70,30\n2018,68,32\n2019,75,25"
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "6r6Iq8uT9BeaaDgx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry - Food Production or Agriculture - saw more growth over the six-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to compare the sizes of both industries from 2014 to 2019 to assess which industry saw more growth.\nBased on the above thought, the answer is:\nThe Food Production industry saw more growth over the six-year period, with the size increasing from 60 in 2014 to 75 in 2019."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "kciQmLFkz6JenEmR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you observe in the Agriculture industry as represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the trend, I need to examine the sizes of the Agriculture industry over the period 2014 to 2019.\nBased on the above thought, the answer is:\nThe trend in the Agriculture industry is that the size gradually declines from 40 in 2014 to 25 in 2019."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "wcE4wW6JYNzlPTuZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, what predictions can be made for the Food Production industry for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the trend for the Food Production industry has been generally increasing from 2014 to 2019, we can imply this trend might continue into 2020.\nBased on the above thought, the answer is:\nIt can be predicted that the Food Production industry might continue its growth in the year 2020, maintaining or further increasing its size."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "XBpDuKoj2hbN6rOa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data in the pie chart, what is the total size represented for the Food Production industry from 2014 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total size of the Food Production industry over the years, I need to calculate the sum of the sizes from the data for each year from 2014 to 2019.\nBased on the above thought, the answer is:\n402"
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "LNJk1ZMYkkKCT9Lx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average size of the Agriculture industry from 2014 to 2019 as depicted in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average size, I need to sum up the sizes of the Agriculture industry for each year from 2014 to 2019 and then divide by the total number of years, which is 6.\nBased on the above thought, the answer is:\n33"
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "ZkcvDdoBMBLKPRvx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the size represented for the Food Production industry increased by 5 in 2020, what would be the new average size for the industry from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new average size, I need to add the sizes of Food Production for each year from 2014 to 2019, add the projected increase for 2020, and then divide by the new total number of years, which is 7.\nBased on the above thought, the answer is:\n65"
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "C9hhTSneww0rzxx9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observe, could we change the data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title states {\"Financial Data Exploration: Portfolio Distribution\"}[0.282, 0.021, 0.681, 0.047]. The sectors each represent an investment category and its percentage share in the portfolio. The sectors are: {Bonds}[0.223, 0.278, 0.482, 0.799], {Stocks}[0.325, 0.55, 0.75, 0.92], {Mutual Funds}[0.482, 0.232, 0.741, 0.542], {Real Estate}[0.482, 0.173, 0.648, 0.516], {Cryptocurrency}[0.344, 0.146, 0.579, 0.516], and {Commodities}[0.283, 0.202, 0.482, 0.516].\nBased on the above thought, the answer is:\nCategory,Percentage\nBonds,25\nStocks,35\nMutual Funds,15\nReal Estate,5\nCryptocurrency,15\nCommodities,5"
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "KQe95gnwhHtk2Unm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which investment occupies the greatest share in investors' portfolios? Could you explain why that might be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to find which investment has the greatest share, and then infer why it might be the most popular.\nBased on the above thought, the answer is:\nStocks hold the largest share at 35%. This might be due to the fact that stocks often have greater potential for return compared to other forms of investment, hence their popularity."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "M2QZwiLyav1d0X5R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what can you deduce about investors' attitude towards risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should examine the proportions of each investment type to understand the risk tolerance levels of the investors.\nBased on the above thought, the answer is:\nFrom the chart, it's inferred that most of the investors prefer fairly balanced portfolios. The high percentage in Stocks (35%) signals a willingness to assume some risk for higher returns. However, Bonds, which are typically lower risk, also make up a significant portion of the portfolio (25%)."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "1VfELsBX8eXu46fa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data depicted in the pie chart be utilized by potential investors?"
+            },
+            {
+                "from": "gpt",
+                "value": "The potential investors would review the current investments distribution to understand the preferred investment avenues.\nBased on the above thought, the answer is:\nThe data in the chart could help potential investors understand current trends and preferences among peer investors. They might consider a similar breakdown in their portfolio if the current distribution has been successful. However, it is important to note that this distribution might not necessarily be suitable for all investors as it depends on individual risk tolerance and investment goals."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "jW644wXWGKXiIhPA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the portfolio invested in Cryptocurrency and Mutual Funds?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the percentages of Cryptocurrency and Mutual Funds to get the combined contribution to the portfolio.\nBased on the above thought, the answer is:\nThe combined contribution of Cryptocurrency and Mutual Funds to the portfolio is 30% (15% each)."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "WoEY6dk8NNQk0S5t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest percentage of the portfolio invested in a single category?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to inspect each of the slices in the pie chart to find the highest percentage.\nBased on the above thought, the answer is:\nStocks hold the highest share in the portfolio at 35%."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "58HSn7d7zMbhgAcC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Bonds decreased by 5% and that 5% is added to Stocks, what would be the new percentage of Stocks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract 5% from Bonds and add it to Stocks to find out the new percentage of Stocks.\nBased on the above thought, the answer is:\nIf Bonds decrease by 5%, and this is added to Stocks, the new percentage for Stocks would be 40%."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "pQmEitsVY4mLjLRZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom what you've observed, please convert the given pie chart data into a tabular CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the sliced sections in the pie chart, which include the years of 2016, 2017, and 2018 {legend}<r>[0.908, 0.063, 0.993, 0.249]</r>, I can see the designated percentages assigned to each year reflecting online and offline sales quantities.\nBased on the above thought, the answer is:\nYear,Online% Sales,Offline% Sales\n2016,20%,13.3%\n2017,21.7%,11.7%\n2018,25.0%,8.3%"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "qGoxVz1cf3fw9y1F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the pie chart, what trend is evident for online sales over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to establish a trend, I need to review the proportions of online sales across all the years featured on the pie chart.\nBased on the above thought, the answer is:\nThe trend shows an increase in online sales over the three years. In 2016 it was 20%, then increased to 21.7% in 2017, and further increased to 25% in 2018."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "QvnWB2v9Kp5jT64H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the trend in offline sales over these years affect retail strategizing?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to consider the trend in offline sales over the years and judge how the data might impact retail business strategies.\nBased on the above thought, the answer is:\nAs the chart demonstrates a consistent decline in the proportion of offline sales from 2016 to 2018, businesses might need to rethink their strategies. They could either invest more in improving the offline shopping experience or pivot more resources toward the growing online sales channel to meet shifting customer behavior."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "O4vofvUBNd2UFb97",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what is the impact on market trend due to the steady shift from offline to online sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the shift in proportions from offline sales to online sales across the years and evaluate its potential impact.\nBased on the above thought, the answer is:\nThe steady shift from offline to online sales over the years indicates a growing consumer preference for online shopping. This trend suggests that the market is rapidly digitalizing, necessitating businesses to adapt and increase their focus on optimizing online sales strategies."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "k8l9esIFW9u2v0M8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions for online sales over the years 2016 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add the proportion of 2016's online sales to the proportion of 2017's online sales.\nBased on the above thought, the answer is:\n41.7%"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "diCyDNy0pTP6af75",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year does the pie chart indicate the lowest proportion for offline sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this query, I need to assess the proportion of offline sales for each year on the pie chart and notice which year has the smallest slice.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "eqfkSUOTOhq7cZxQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we see a 10% reduction in online sales in 2019, what is the predicted offset for offline sales proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "A hypothetical 10% decrease in 2019's online sales from the previous year \u2014 assuming total sales remain consistent year over year \u2014 would imply a corresponding 10% increase in offline sales.\nBased on the above thought, the answer is:\n18.3%"
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "3xBgeFcDVMYrOdrl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the pie chart data you analyzed, can you please convert the information to a structured CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "As specified in the chart title {Education Resource Distribution and Utilization in 2010}<r>[0.252, 0.021, 0.711, 0.047]</r> and based on each pie slice {Books}<r>[0.223, 0.278, 0.482, 0.661]</r>, {Laptops}<r>[0.243, 0.516, 0.482, 0.88]</r>, {Lab Equipments}<r>[0.434, 0.516, 0.722, 0.886]</r>, {Libraries}<r>[0.482, 0.242, 0.741, 0.655]</r>, {Staff}<r>[0.281, 0.109, 0.654, 0.479]</r>, I can deduce that each slice corresponds to a resource type and its proportion.\nBased on the above thought, the answer is:\nYear,Books,Laptops,Lab Equipments,Libraries,Staff\n2010,17.5%,15.6%,21.8%,19.4%,25.6%"
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "8Fk0fDfgDAJKeneh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith respect to the chart, which education resource is considered the most significant? Can you explain why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most significant education resource, I need to identify the pie segment with the largest fraction.\nBased on the above thought, the answer is:\nThe Staff resources were the most significant in 2010, commanding the largest fraction of 25.6% in the distribution. This could suggest that human resources were highly valued and prioritized that year."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "mgDIzeNbciMIDHpn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the resource distribution in the year 2010 tell us about the education sector's focus?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the proportional representation of each resource category will help draw inferences about the focus of the educational sector in 2010.\nBased on the above thought, the answer is:\nThe highest proportion of resources was allocated to Staff, suggesting that human resources may have been the most essential aspect in the education sector in 2010. Moreover, considerable importance was also placed on Lab Equipments, possibly reflecting a strong push toward experiential, hands-on learning."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "1NpAJsLyk5uzZneT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the 2010 education resource distribution data influence decisions about resource allocation in 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "In analyzing potential hypothetical impacts on future resource allocation decisions, we need to consider the proportions observed in the current pie chart.\nBased on the above thought, the answer is:\nGiven the 2010 distribution, where Staff resources had the largest allocation, it could influence decisions to maintain or even boost investment in human resources in 2011. On the other hand, since Laptops had the smallest share, there might be increased funding to that category to enhance digital learning."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "fq2xk3fOAZ8NGRr7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage allocated to Laptops increased by 10%, what would the new total resource distribution percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive the new total resource distribution percentage, I would need to increase the percentage allocated to Laptops by 10% and subsequently sum up all the new percentages.\nBased on the above thought, the answer is:\nThe percentage for Laptops would become 15.6% + 10% = 25.6%. As a result, the new total resource distribution percentage would be 17.5% (Books) + 25.6% (Laptops) + 21.8% (Lab Equipment) + 19.4% (Libraries) + 25.6% (Staff) = 110%."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "9lYFmDbcL6pGcFzF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the resource distribution for Books and Laptops was combined, what would their new percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to sum up the resource distribution percentages for both Books and Laptops to calculate the new percentage.\nBased on the above thought, the answer is:\nThe combined resource distribution for Books and Laptops would be 17.5% (Books) + 15.6% (Laptops) = 33.1%."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "V3iMxomSJ1pdedFU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total resource distribution does the category with the least allocation represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the percentage of the least allocated resource, I would need to identify the category with the smallest fractional part in the pie chart.\nBased on the above thought, the answer is:\nLaptops, with a total distribution of 15.6%, represents the least allocation in the resource distribution."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "uSrpTi1osI7z6WAm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert this information from the pie chart into a structured table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into account the title \"{AI and Automation Usage from 2015 to 2020}\"<r>[0.295, 0.021, 0.668, 0.047]</r>, and the pie chart with the slices for each corresponding year<r>{2015}[0.231, 0.278, 0.482, 0.516]</r>, <r>{2016}[0.223, 0.422, 0.482, 0.623]</r>, <r>{2017}[0.234, 0.516, 0.482, 0.818]</r>, <r>{2018}[0.33, 0.553, 0.601, 0.923]</r>, <r>{2019}[0.482, 0.352, 0.741, 0.843]</r>, <r>{2020}[0.283, 0.146, 0.714, 0.516]</r>, I can structure these to a CSV format table.\nBased on the above thought, the answer is:\nYear, AI and Automation\n2015, 7.0\n2016, 8.8\n2017, 10.5\n2018, 17.5\n2019, 24.6\n2020, 31.6"
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "Yaiqg5E113JaWnZ1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which years does the pie chart suggest a significant increase in the usage of AI and Automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can discern a significant rise in AI and Automation usage by examining the size of the pie slices for each year.\nBased on the above thought, the answer is:\nThe most notable increase in AI and Automation usage occurred between 2017 and 2018 which is represented by the exploded piece {2018}<r>[0.33, 0.553, 0.601, 0.923]</r> on the pie chart."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "HpRopt8klVV0I1By",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observation can be made about the trend of AI and Automation usage over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend can be inferred by examining the sizes of the pie slices in chronological order.\nBased on the above thought, the answer is:\nAI and Automation usage has been rising steadily from 2015 to 2020, with a distinct jump in usage in the year 2018."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "o35bLaEnjrkFAHpD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this trend continues, what prediction could you make for AI and Automation usage in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the trend in the pie chart, I can make hypothesize future usage.\nBased on the above thought, the answer is:\nIf the trend of increasing AI and Automation usage continues, we can expect to see higher usage in the coming years. Particularly, as advancements continue, there may be significant jumps similar to that seen from 2017 to 2018."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "1DPx3uwQwdzNTEDh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total usage for AI and Automation in the years 2017, 2018, and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total usage for these years, I need to add the usages for 2017, 2018, and 2019.\nBased on the above thought, the answer is:\nThe total usage for 2017, 2018, and 2019 = 10.5 + 17.5 + 24.6 = 52.6."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "aEAICu6QmtvQvn91",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe usage in 2020 is how much greater compared to the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the usage in 2015 from the usage in 2020.\nBased on the above thought, the answer is:\nThe usage in 2020 is greater than the usage in 2015 by = 31.6 - 7.0 = 24.6."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "qbQ8uQmmdU0B13dq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average usage of AI and Automation from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average usage, I need to sum up all the usages from 2016 to 2020 and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average usage is (8.8 + 10.5 + 17.5 + 24.6 + 31.6) / 5 = 18.6."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "zdLVxd27qfV7Ds2S",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the representation of the distribution of global gastronomy culture to a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert this pie chart into a CSV format table, I have to capture the name of each cuisine and its percentage from the chart {{\"title\"}[0.332, 0.021, 0.631, 0.047], {\"legend\"}[0.88, 0.063, 0.993, 0.279]}, and then arrange this information as rows of a table.\nBased on the above thought, the answer is:\nCuisine,Percentage\nIndian,20%\nChinese,15%\nItalian,20%\nJapanese,15%\nFrench,10%\nMexican,10%\nThai,10%"
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "TeLxrqQp0rZcApv6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which cuisine is most popular worldwide?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at {Indian slice}[0.197, 0.275, 0.456, 0.709], {Chinese slice}[0.262, 0.516, 0.482, 0.885], and {Italian slice}[0.477, 0.547, 0.736, 0.917] I can see that they have the most significant proportions in the pie chart.\nBased on the above thought, the answer is:\nIndian, Chinese, and Italian cuisines are most popular worldwide."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "fwRhRLzkOdP1t362",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about worldwide gastronomy preferences based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart provides an overview of the relative popularity of different cuisines around the world. To infer worldwide gastronomy preferences, I need to consider the sizes of the sectors of each cuisine.\nBased on the above thought, the answer is:\nThe pie chart suggests that Indian, Italian, and Chinese cuisines are preferred by a larger fraction of the global population. Meanwhile, French, Mexican, and Thai cuisines are less popular."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "skNJlY76JXGaswVj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you predict how gastronomy trends may shift if Japanese cuisine increases in popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "Assuming the total percentage must add up to 100%, if Japanese cuisine increases in popularity, other cuisine's percentages would need to decrease in order to accommodate this change.\nBased on the above thought, the answer is:\nIf Japanese cuisine becomes more popular, this could lead to a shift in gastronomy trends where the popularity of other cuisines, particularly the less popular ones, might decrease."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "MdKdqgkOeDafhPqT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the median percentage of the collected data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data is: 20, 15, 20, 15, 10, 10, 10. To find the median, I will arrange the data in ascending order then locate the center value.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "1YjWwBn2DDDnyweR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would the pizza chart change if Spanish cuisine was introduced with a popularity of 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To estimate the impact of introducing a new cuisine, we can add the ratio of the new cuisine to the total and then calculate the new percentages for each cuisine.\nBased on the above thought, the answer is:\nAll percentages of current cuisines would decrease slightly to accommodate for the popularity of the Spanish Cuisine."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "LqSfMOQAbbr1Bp1H",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of French cuisine is doubled, what will be the new percentage of French cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I simply double the original percentage.\nBased on the above thought, the answer is:\nThe new percentage for French cuisine would be 20%."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "XdfcISYBVfZz1pGx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart you observed into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe the title {\"2020 Budget Allocation for Different Sectors\"}[0.298, 0.021, 0.666, 0.047] and the sublegend items {Transport, Health, Education, Business, Crime, Parks}<r>[0.874, 0.063, 0.993, 0.249]</r>. The slices {\"slice_0, slice_1, slice_2, slice_3, slice_4, slice_5\"}<[0.197, 0.274, 0.456, 0.674]; [0.247, 0.553, 0.695, 0.923]; [0.482, 0.189, 0.741, 0.723]; [0.393, 0.146, 0.603, 0.516]; [0.344, 0.168, 0.482, 0.516]; [0.283, 0.202, 0.482, 0.516]> represents the percentages for different categories. I can extract the labels and the corresponding percentages from the data.\nBased on the above thought, the answer is:\nSectors,2020 Budget\nTransport,55\nHealth,100\nEducation,80\nBusiness,40\nCrime,10\nParks,15"
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "yQqzZIjmUcP65nT0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the government's priority areas based on the 2020 budget allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The allocation of a large portion of the budget to a specific sector is likely indicative of the government's priority in that particular sector.\nBased on the above thought, the answer is:\nBased on the 2020 budget allocation, it seems that the government places high priority on the Health and Education sectors."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "rYvyiPkHqQ4tJ9Gz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the reason for the increase in the Business sector's budget allocation in 2021 compared to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer reasons based on the changes in budget allocation between the two years.\nBased on the above thought, the answer is:\nThe increase in the Business sector's budget allocation might signify the government's increased focus on encouraging business growth and development in the year 2021."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "kxU7eCV0JSpx93V6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend of increasing budget for the Business sector continues, what might be the implications for the other sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "An analysis of potential trends in future budget allocations can be made based on the budgeting pattern observed so far.\nBased on the above thought, the answer is:\nIf the trend of increasing budget for the Business sector continues, it might mean that either the total budget is increased, or funds are being diverted from other sectors to the Business sector. This could potentially reduce the funding available for other sectors."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "Rs05dk1K3Ixb7dTR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total budget allocated for the Health and Education sectors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the budget allocations of the two sectors, namely {Health and Education}<r>[0.247, 0.553, 0.695, 0.923]; [0.482, 0.189, 0.741, 0.723]</r>, I can find this information.\nBased on the above thought, the answer is:\n180 million dollars"
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "a9AeomNY5XysPeDS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more budget was allocated to the Business sector in 2021 compared to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By subtracting the 2020 budget allocation from the 2021 budget allocation for the Business sector, I can calculate this.\nBased on the above thought, the answer is:\n15 million dollars"
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "7ATIjvjeFt9ZqAFG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has the least budget allocation in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the budget allocated to the different sectors in 2020, I will be able to find the sector with the least budget.\nBased on the above thought, the answer is:\nCrime"
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "34eaacj2SvBGqyfD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the data on the pie chart to a structured table for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. From the pie chart, we can see that in 2020 the proportion of Traditional Art, as shown by {the red sector slice}<r>[0.223, 0.146, 0.741, 0.886]</r> and Modern Art, represented by {the blue sector slice}<r>[0.283, 0.202, 0.482, 0.516]</r> can be converted into a table.\nBased on the above thought, the answer is:\nArt Style,Proportion in 2020\nTraditional Art,95%\nModern Art,5%"
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "Tt4vId8IuZ6htUpQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, which art style is more preferred in the year 2020? Provide reasoning for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the preferred art style by looking at the Art Style with the highest percentage representation in the pie chart.\nBased on the above thought, the answer is:\nTraditional Art is the preferred art style as it has the highest percentage - 95% of the total, significantly higher than Modern Art at only 5%."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "Rj0MUNPEHYWuhf30",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible reasons for the less popularity of Modern Art in the year 2020 compared to Traditional Art?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the chart and infer based on the proportions.\nBased on the above thought, the answer is:\nSince the chart does not provide specific reasons, some possible inferences could include - Greater familiarity and comfort with traditional art styles, limited exposure or understanding of modern art, its perceived complexity, or lack of acceptance in certain cultures or audience groups."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "1gfCYmscsXrQvs08",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this data guide Art Galleries and Museums in their future exhibitions or collections?"
+            },
+            {
+                "from": "gpt",
+                "value": "Making potentially impactful decisions requires analyzing the chart and interpreting what the proportions mean.\nBased on the above thought, the answer is:\nWith Traditional Art having a significant 95% majority in 2020, it is clear that audiences prefer this art style. Therefore, Art Galleries and Museums might want to feature more traditional art pieces in their exhibitions or collections. However, to balance this dominance and promote diversity, feature of Modern Art should not be neglected. Special events or awareness campaigns might be organized to promote and increase acceptance of Modern Art."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "bCwOVqEV9CEsXW66",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between Traditional Art and Modern Art in the year 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I will have to subtract the percentage value of Modern Art from the Traditional Art value.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "NZYh5P37UM2w4Gfq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Modern Art doubles in the next year, what will it be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage for Modern Art, I need to multiply its present percentage by 2.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "9ogBVYBuq7ofCOXH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Traditional Art decreases by 15% the next year, what will its new proportion be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new proportion for Traditional Art, I need to subtract 15% from its present proportion.\nBased on the above thought, the answer is:\n80%"
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "BktmOOyLQJEBUc95",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart details, could you structure the 'Interaction' and 'Entertainment' aspects into a CSV table representing all five data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at both the pie slices {(Point 1)[0.901, 0.073, 0.928, 0.087], (Point 2)[0.901, 0.103, 0.928, 0.117], (Point 3)[0.901, 0.133, 0.928, 0.147], (Point 4)[0.901, 0.163, 0.928, 0.177], (Point 5)[0.901, 0.193, 0.928, 0.207]} in the legend and the associated percentage displayed in the chart, I can allocate each slice to a specific data point and create a table.\nBased on the above thought, the answer is:\nData Point,Interaction,Entertainment\n1,10,5\n2,30,15\n3,50,25\n4,70,40\n5,90,80"
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "d8E5RsBAhnnfHSTV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you speculate why there is a sudden spike in the 'Entertainment' aspect at the fifth data point based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For speculation, I should look at the comparison of 'Entertainment' values between various data points and analyze the sudden increase.\nBased on the above thought, the answer is:\nThe sharp increase in the 'Entertainment' value at the fifth point could be attributed to the introduction of new entertainment-oriented features or trends on social platforms that led to a high level of user engagement and activity, causing a sudden spike."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "0INBimBLCoYsIpPG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which aspect\u2014either 'Interaction' or 'Entertainment'\u2014 dominates at the fourth data point?"
+            },
+            {
+                "from": "gpt",
+                "value": "Locating the fourth data point, the pie slice that is exploded and examining their comparative values can determine which aspect is dominant.\nBased on the above thought, the answer is:\nBased on both the value and the chart, the 'Interaction' aspect dominates at the fourth data point."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "BRgyCHb7jxhC7DUc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how do you see the evolution of 'Interaction' across the five data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare relative sizes of each pie slice corresponding to 'Point 1' through 'Point 5' and analyze the pattern.\nBased on the above thought, the answer is:\nThe 'Interaction' aspect is seen to be steadily increasing across the five data points, indicating a progressive rise in the role of interaction in social networking and relationships."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "UP0fOQfTIevBdUqs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you calculate the degree of change in the 'Entertainment' aspect from the fourth to the fifth point based on the provided data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the degree of change, I need to subtract the value at the fourth point from the value at the fifth point for the 'Entertainment' aspect.\nBased on the above thought, the answer is:\nThe degree of change in 'Entertainment' from the fourth to the fifth point is 80 - 40 = 40."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "jDARr7Z7rbSzd9sD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total 'Interaction' across all five data points based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total 'Interaction', I need to sum up the interaction values at all five points.\nBased on the above thought, the answer is:\nThe total 'Interaction' across all five data points is 10 + 30 + 50 + 70 + 90 = 250."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "F0Drrwg0spadALhA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how much is the average 'Entertainment' value across all data points?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average 'Entertainment' value, I need to sum up the 'Entertainment' values at all points and divide this total by the number of points.\nBased on the above thought, the answer is:\nThe average 'Entertainment' value across all data points is (5 + 15 + 25 + 40 + 80) / 5 = 33."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "aznOrpj4wWL8qWPd",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observe the title {\"Family and Household Living Conditions\"}[0.316, 0.021, 0.647, 0.047] and the placements of pie slices for each category: {Parents}[0.223, 0.278, 0.482, 0.788], {Children}[0.322, 0.355, 0.757, 0.915], {Grandparents}[0.482, 0.184, 0.704, 0.516], {Uncle/Aunt}[0.482, 0.151, 0.597, 0.516], {Cousin}[0.438, 0.146, 0.527, 0.516], {Overseas Student}[0.414, 0.151, 0.482, 0.516], {Rented Roommate}[0.318, 0.159, 0.482, 0.516], {Others}[0.283, 0.229, 0.482, 0.516]. These categories and their corresponding slices show the distribution of living conditions among family members.\nBased on the above thought, the answer is:\nFamily Member,Conditions\nParents,80\nChildren,150\nGrandparents,30\nUncle/Aunt,15\nCousin,18\nOverseas Student,5\nRented Roommate,22\nOthers,10"
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "zyv5UlnigHRhwZR4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about household composition from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart describes the family composition across different categories based on living conditions figures.\nBased on the above thought, the answer is:\nThe largest groups living in the household are Parents and Children suggesting it is primarily made up of nuclear families. Extended family forms a smaller fraction with Grandparents, Uncle/Aunt, and Cousins. Overseas Students and Roommates are least represented, suggesting few shared living situations with non-family members."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "SocZi8eQDV5WbN2x",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does these household composition data typically compare to that of other regions or countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make such comparisons, I would need data from other regions or countries which isn't available in this chart.\nBased on the above thought, the answer is:\nWithout data from other regions or countries, it would not be accurate to draw any comparisons."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "WOJBGOdcrXp8eTV7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this data, can any predictions be made about the changes in family structures over the next decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "Predicting changes in family structures over the next decade would require time-series data or predictive analysis that goes beyond the information provided in the pie chart.\nBased on the above thought, the answer is:\nThe pie chart does not provide sufficient data to accurately predict changes in family structures over the next decade."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "8p2JenaNznUfTn4m",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which family member category has the lowest living conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to directly observe the smallest slice which represents the \"Overseas Student\".\nBased on the above thought, the answer is:\nOverseas Student."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "tiPjWBilKAoHDFhF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total people of all family members?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to calculate the sum of all categories in the data.\nBased on the above thought, the answer is:\nThe total people of all family members is 80 (Parents) + 150 (Children) + 30 (Grandparents) + 15 (Uncle/Aunt) + 18 (Cousin) + 5 (Overseas Student) + 22 (Rented Roommate) + 10 (Others) = 330."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "Co8PAJpEfZBEvGSp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a family of 5 Grandparents moved into the household, what will the new ratio of Parents be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, add 5 to the Grandparents' group, recalculate the total, and then re-evaluate the ratio of Parents.\nBased on the above thought, the answer is:\nIf 5 Grandparents moved in, the new total will be 335, and Parents will constitute 80/335 or about 23.88% of the population."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "0MkTlI5or3qKW3qL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the pie charts for 2016 and 2017 and present them in a simple table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the chart titled {\"Tourism and Hospitality Data - 2016\"}<r>[0.136, 0.111, 0.285, 0.129]</r> and the legend <r>[0.399, 0.14, 0.501, 0.207]</r>, items {\"Tourist Visits (Millions)\"}<r>[0.421, 0.146, 0.498, 0.16]</r>, {\"Hotels\"}<r>[0.421, 0.167, 0.443, 0.181]</r>, and {\"Restaurants\"}<r>[0.421, 0.188, 0.462, 0.202]</r> show various sectors. Seeing the corresponding pie chart, I can work out the percentages for each sector. I follow a similar approach to the chart titled {\"Tourism and Hospitality Data - 2017\"}<r>[0.632, 0.111, 0.782, 0.129]</r> to calculate the percentages for 2017.\nBased on the above thought, the answer is:\nYear,Tourist Visits (Millions),Hotels,Restaurants\n2016,0.1%,28.5%,71.4%\n2017,0.1%,28.3%,71.6%"
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "cWVXQyCBruU1OI3j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has constantly held the majority stake for the years 2016 and 2017 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie charts for 2016 and 2017, it's clear to see which category consistently holds the most significant proportion.\nBased on the above thought, the answer is:\nThe \"Restaurants\" category holds the majority stake for both 2016 and 2017, possibly because it is a widespread industry serving a universal need, hence the significant proportion."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "NKcM6oHrXSFHO9Bx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the growth trends of the Tourist Visits, Hotels, and Restaurants over the two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The relative proportions of the three sectors can provide insights about their growth trends over the years.\nBased on the above thought, the answer is:\nThe \"Restaurants\" sector has seen a slight increase from 2016 to 2017, which may indicate a growth or expansion trend. The \"Hotels\" sector has slightly shrunk in proportion, which may indicate a slow growth or even slight reduction in size of this sector within the market. The \"Tourist Visits (Millions)\" contribution remains constant, which could be indicative of a stable level of tourist visits over the years."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "rZmuxxJndOARR4WZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf these trends are to continue for the subsequent years, what can be the expected behavior of these three sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To forecast future trends, I can look at the trends we observed from 2016 to 2017 for each sector.\nBased on the above thought, the answer is:\nIf these trends continue, we can expect the \"Restaurants\" sector to keep growing, possibly outpacing the other sectors, while the number of \"Hotels\" may see a mild decrease or stabilize. The \"Tourist Visits (Millions)\" is likely to remain quite stable given the consistent workings of the tourism industry and its relatively small impact on the overall pie chart."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "8NCTlUMhb0KIwWfY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the approximate total number of hotels and restaurants combined in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total, I need to sum up the percentages of hotels and restaurants from the pie chart for the year 2016.\nBased on the above thought, the answer is:\nApproximately 99.9%."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "JuhMJofKTyFQRteG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in the percentage of restaurants from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in the percentage of restaurants from their respective pie charts between the year 2016 and 2017.\nBased on the above thought, the answer is:\nApproximately 0.2%."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "QjKuGA0jVnzL5jwo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Tourist Visits (in Millions) doubled in 2017, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to double the original percentage of Tourist Visits for the year 2017.\nBased on the above thought, the answer is:\nIt would be 0.2%."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "C5teeK6uhiKTUZ6c",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the title at position {Online Graduation Rate from 2016 to 2020}<r>[0.304, 0.021, 0.659, 0.047]</r>. We have 5 slices representing each year: {2016}<r>[0.163, 0.46, 0.198, 0.48]</r>, {2017}<r>[0.31, 0.864, 0.346, 0.884]</r>, {2018}<r>[0.672, 0.809, 0.707, 0.829]</r>, {2019}<r>[0.739, 0.33, 0.774, 0.35]</r>, and {2020}<r>[0.384, 0.071, 0.419, 0.091]</r> with their respective percentages.\nBased on the above thought, the answer is:\nYear,Graduation Rate (%)\n2016,18.6%\n2017,19.1%\n2018,19.9%\n2019,21.0%\n2020,21.3%"
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "YsJwrafihLIHozFv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the trend of online graduation rates from 2016 to 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the graduation rates of each year to assess the trend.\nBased on the above thought, the answer is:\nThe trend shows a steady increase in online graduation rates from 2016 to 2020, with the most significant increase in 2020."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "lGES4ZsM7X1tYvsX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year marked the most significant improvement in online graduation rates and why might that be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I should look at the year which had the highest graduation rate and think about possible reasons.\nBased on the above thought, the answer is:\nThe year 2020 marked the most significant improvement in online graduation rates. A possible reason could be the impact of the COVID-19 pandemic, which might have caused more students to rely on online learning, thus increasing the graduation rate in this mode of education."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "oVVQ8TNTYyocXQ4r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do the graduation rates of each year compare to the average online graduation rate from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare each year's graduation rate to the average graduation rate.\nBased on the above thought, the answer is:\nThe online graduation rates for 2016 and 2017 were below the average of 20%, while those for 2018, 2019, and 2020 were above the average. 2020 had the highest graduation rate and was the farthest from the average."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "MALfRzSYD9l4iLY2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average graduation rate from the years 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the graduation rates of all 5 years and divide by the number of years.\nBased on the above thought, the answer is:\nThe average graduation rate is 20%."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "N8vMdtv5COpIECMY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the graduation rate in 2018 increased by 5%, what would be the new total graduation rate for all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total graduation rate, I need to add 5% to the 2018 graduation rate and sum up all the graduation rates again.\nBased on the above thought, the answer is:\nThe new total graduation rate is 103.9%."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "Jisrzh47DMcaTWvg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the smallest increase in graduation rate compared to its previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the differences between consecutive years to find the smallest increase.\nBased on the above thought, the answer is:\nThe smallest increase occurred from 2016 to 2017, with an increase of only 0.5%."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "lCX8wUSkf7VYPRep",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the provided pie chart data in a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Rural and Urban Development Comparison from 2011 to 2015\"}[0.223, 0.021, 0.74, 0.047], the slices of the pie chart representing different years and development types in {the legend}<r>[0.861, 0.063, 0.993, 0.368]</r>, data can be segregated accordingly.\nBased on the above thought, the answer is:\nYear,Development Type,Development Index\n2011,Rural,15\n2011,Urban,85\n2012,Rural,13\n2012,Urban,87\n2013,Rural,20\n2013,Urban,80\n2014,Rural,30\n2014,Urban,70\n2015,Rural,28\n2015,Urban,72"
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "dF3ffY74GVWHPc5F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of area had a more consistent development rate over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the changes in the Development Indices for both \"Rural\" and \"Urban\" over the years.\nBased on the above thought, the answer is:\nThe Urban area had a more consistent development rate as its values varied in a tighter range from 70 to 87 as compared to Rural areas' range from 13 to 30."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "AQvMo0CMJobiobGZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the most significant difference between Rural and Urban development?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compute the difference between the Development Indices for \"Rural\" and \"Urban\" in each year and identify the year with the largest difference.\nBased on the above thought, the answer is:\nThe year 2012 saw the most significant difference between Rural and Urban development, with Urban development being significantly higher."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "Bj736KsSUgx2JeiX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the pie chart, what trend can be identified with regards to Rural development over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to note the changes in the Development Index for \"Rural\" from 2011 to 2015.\nBased on the above thought, the answer is:\nAn upward trend can been seen in Rural development over the years, with a significant jump occurring in 2014."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "MffZsHYLl9YAcHO8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the Rural Development Index at its highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the data points represented by each exploded and non-exploded slice in the pie chart and pick the highest value for \"Rural\".\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "NxPjJgd3xOJtxUwg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Rural Development Index increase from 2011 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the data points for \"Rural\" in 2011 and 2014, subtract the value for 2011 from the value for 2014 to find the difference.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "Nd4sV10uFcKL2GwC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average Development Index for Urban regions over the five-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I will sum up the Development Index values for \"Urban\" from 2011 to 2015 and divide by 5.\nBased on the above thought, the answer is:\n78.8"
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "O8oZ3Ek3ZCS00FT2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information provided by the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Predicted Increase in AI Adoption from 2022 to 2026\"}[0.264, 0.021, 0.7, 0.047] and the slices of each year {\"2022\"}[0.223, 0.278, 0.482, 0.527]; {\"2023\"}[0.201, 0.536, 0.46, 0.87]; {\"2024\"}[0.369, 0.516, 0.701, 0.886]; {\"2025\"}[0.482, 0.249, 0.741, 0.713]; {\"2026\"}[0.283, 0.146, 0.661, 0.516], I can extract the data from the chart figure based on the slices and chart description.\nBased on the above thought, the answer is:\nYear,Predicted Increase in AI Adoption\n2022,40%\n2023,60%\n2024,80%\n2025,75%\n2026,90%"
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "ntbGSk2R1vKSE2rH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what can be inferred about the future trend of AI adoption?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the sizes and order of the pie slices representing each year.\nBased on the above thought, the answer is:\nThe chart suggests that the AI adoption rate is generally on an increasing trend from 2022 to 2026, although there is a slight drop forecasted for the year 2025."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "UxaxMLGOCoVcgjvW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you surmise about the year 2023 seeing as it is 'exploded' from the rest of the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Exploding a slice typically indicates a significant point. I will look at its relative size and position.\nBased on the above thought, the answer is:\nThe 'exploded' pie slice for the year 2023 could be used to draw attention to the fact that despite being second in chronological order, its predicted AI adoption rate is higher than the subsequent year 2024."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "JcRdCdfdElsCClKT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could a policy maker use this chart when planning for technological investments in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy making would need to consider the increasing trend and also adjust based on yearly fluctuations.\nBased on the above thought, the answer is:\nGiven the growing trend of AI adoption from 2022 to 2026, it suggests that policy makers should be actively investing in AI technology now and in the following years. The data also indicates that there might be a slight drop in AI adoption in 2025, alerting policy makers to potentially brace for that year or to develop strategies to ensure consistent investment and adoption."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "IXbjjQ8DfY6TZoN1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the trend, how much total increase is predicted in AI Adoption from 2022 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase, I need to add up the percentages of each year.\nBased on the above thought, the answer is:\nThe total increase expected from 2022 through 2026 is 40% + 60% + 80% + 75% + 90% = 345%."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "zhLOnOvwhANifTPF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increase in AI Adoption predicted each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average increase, I need to calculate the total expected increase from 2022 to 2026, and then divide by the number of years.\nBased on the above thought, the answer is:\nThe total increase expected is 345% and the number of years is 5, therefore the average annual increase is 345% / 5 = 69%."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "dpTbk1jDGrRxS1DP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2022 sees an increase of 40% and this rate increases by 20% each year, what will be the predicted increase in 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the predicted increase in 2026, I need to add 20% to the increase of the previous year for each year from 2023 to 2026.\nBased on the above thought, the answer is:\nThe predicted increase in 2026 would be 40% + 4*20% = 120%."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "rmj9MPnzON4OLAdN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you quantify the proportional data shown in the pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the pie chart breakdown with {the title labelled \"Big Data Integration Overview\"}<r>[0.356, 0.021, 0.607, 0.047]</r>, and {the individual slice detail} for {\"Hadoop\"}<r>[0.223, 0.278, 0.482, 0.592]</r>, {\"Hive\"}<r>[0.228, 0.516, 0.482, 0.802]</r>, {\"Pig\"}<r>[0.32, 0.553, 0.686, 0.923]</r>, {\"Spark\"}<r>[0.482, 0.353, 0.741, 0.747]</r>, {\"Kafka\"}<r>[0.466, 0.146, 0.714, 0.516]</r>, and {\"Elasticsearch\"}<r>[0.283, 0.147, 0.482, 0.516]</r>. I will convert these labels and the corresponding percentages into structured CSV format.\nBased on the above thought, the answer is:\nTool,Percentage\nHadoop,14.4%\nHive,10.8%\nPig,25.2%\nSpark,18.0%\nKafka,18.7%\nElasticsearch,12.9%"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "ANGtHjyI0Ww9A5d9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which big data tool has the most users, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to determine the slice with the highest percentage. The tool with the highest percentage of users can be considered the most popular.\nBased on the above thought, the answer is:\nThe tool \"Pig\" has the most users because it comprises the largest slice of the pie chart, representing 25.2% of the total users."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "psqgNg00gIoCUg5A",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make about the popularity of big data tools from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can interpret the popularity of tools based on the displayed percentages. Tools with higher percentages are more popular, while tools with lower percentages are less popular.\nBased on the above thought, the answer is:\nThe pie chart shows that \"Pig\" was the most popular big data tool, accounting for a quarter of all users. On the other hand, \"Hive\" appears to be the least popular, making up just over 10% of the total users. The remaining tools have similar levels of popularity, ranging from approximately 13%-18% of the total users."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "AqOBZKONcPvGH7xi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence the development of these tools in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity data could guide the development priorities of these tools. Tools with high popularity might receive more enhancements, while less popular ones may require more updates to increase their user base.\nBased on the above thought, the answer is:\nDue to the high popularity of \"Pig\", future improvements or updates to this tool may be prioritized to maintain its user base and satisfy its many users. In contrast, the less popular tool \"Hive\" might need radical innovation or feature improvements to attract more users. The other tools like \"Hadoop\", \"Spark\", \"Kafka\", and \"Elasticsearch\" might be given moderate attention for maintenance and development since their usage is close to the average."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "4V7LDFgQcKxQ6CHf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total percentage if you combined the percentages corresponding to Elasticsearch and Hadoop together?"
+            },
+            {
+                "from": "gpt",
+                "value": "I notice {Elasticsearch}<r>[0.283, 0.147, 0.482, 0.516]</r> has 12.9% and {Hadoop}<r>[0.223, 0.278, 0.482, 0.592]</r> has 14.4% based on the pie chart. Combining the two, we add 12.9% and 14.4%.\nBased on the above thought, the answer is:\n27.3%"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "mROYBl3VkapCbeY5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage gap between the most and least widely used Big Data tool?"
+            },
+            {
+                "from": "gpt",
+                "value": "The least widely used tool is {Hive}<r>[0.228, 0.516, 0.482, 0.802]</r> at 10.8% and most widely used one is {Pig}<r>[0.32, 0.553, 0.686, 0.923]</r> at 25.2%. To find the percentage difference, I need to subtract 10.8% from 25.2%.\nBased on the above thought, the answer is:\n14.4%"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "7cpXUJrwBqBO2fRE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Hadoop were to increase by 4.0%, what would be the new percentage of Hadoop compared to the others?"
+            },
+            {
+                "from": "gpt",
+                "value": "Currently, {Hadoop}<r>[0.223, 0.278, 0.482, 0.592]</r> stands at 14.4%. If we were to increase its percentage by 4.0%, we would add 4.0% to 14.4%.\nBased on the above thought, the answer is:\n18.4%"
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "eHCsc1zRBEPDo1G0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help interpret the pie chart data into a table format in CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the title {\"Environmental and Climate Change Impact from 2000 to 2006\"}<r>[0.223, 0.021, 0.741, 0.047]</r> and {the pie slices}<r>[{\"2000\"}[0.223, 0.278, 0.482, 0.537]; {\"2001\"}[0.223, 0.516, 0.482, 0.814]; {\"2002\"}[0.328, 0.516, 0.56, 0.886]; {\"2003\"}[0.502, 0.538, 0.762, 0.891]; {\"2004\"}[0.482, 0.182, 0.741, 0.519]; {\"2005\"}[0.358, 0.146, 0.594, 0.516]; {\"2006\"}[0.283, 0.19, 0.482, 0.516]]</r>, I see that each pie slice corresponds to a different year and its size reflects the percentage of Global Climate Change Impact.\nBased on the above thought, the answer is:\nYear,Percentage Impact\n2000,12%\n2001,14%\n2002,15%\n2003,20%\n2004,18%\n2005,15%\n2006,6%"
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "nc82I0auo8ngaE88",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, which year had the most significant environmental impact due to climate change and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the most significant environmental impact would be the one with the highest percentage.\nBased on the above thought, the answer is:\nThe year with the most significant environmental impact due to climate change was 2003 which had an impact percentage of 20%, the highest among all the years."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "KYP5vKwsKmvOsku7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what can we infer about the environmental impact due to climate change over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sizes of each pie slice and their corresponding percentages would give me an idea about the trend of environmental impact over these years.\nBased on the above thought, the answer is:\nThe pie chart reveals that the environmental impact due to climate change experienced an increase from 2000 to 2003, peaking at 20% in 2003. The impact then gradually declined from 2004 to 2006, hitting a low of 6% in 2006. This indicates a fluctuating trend in environmental impact over these years."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "ZkJFox3jUkqumeb5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this data is used for policymaking, where should the primary focus be based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A careful consideration of the years and their corresponding environmental impacts would help me identify where the focus should be for policymaking.\nBased on the above thought, the answer is:\nBased on the data presented in the chart, the primary focus should ideally be on understanding and mitigating the factors that contributed to the peak environmental impact in 2003. A comparative analysis of the surrounding years could reveal important insights to prevent such extreme environmental impacts in future."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "zs0ffbWrarG5HW51",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did we observe the lowest environmental impact percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart indicates the environmental impact for each year as a percentage. I need to compare all these percentages to find out the year with the lowest impact.\nBased on the above thought, the answer is:\n2006"
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "YcrzcTFXOeeOlLdw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the environmental impact change from 2000 to 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in environmental impact over the years, I need to subtract the percentage of impact in 2000 from the percentage in 2006.\nBased on the above thought, the answer is:\nIt decreased by 6%."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "7MKyTzY0c83ZNAiZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of environmental impact from 2000 to 2003?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage of environmental impact from 2000 to 2003, I need to add up the percentages for these years.\nBased on the above thought, the answer is:\nThe total percentage of environmental impact from 2000 to 2003 is 12% + 14% + 15% + 20% = 61%."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "RlqLlDGB4gA2UzST",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are observing, could you please convert the distribution of resources in the health and medical field into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {'Resources Distribution in Health and Medical Field'}[0.273, 0.021, 0.69, 0.047] and the {sector slices}{Physical Therapy[0.225, 0.269, 0.743, 0.923]; Digital Health[0.283, 0.146, 0.648, 0.516]}, I can see that the sectors represent different categories and their corresponding percentages, which can be converted into CSV format.\nBased on the above thought, the answer is:\nCategory,Percentage\nPhysical Therapy,75%\nDigital Health,25%"
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "SthajYk4jlbMV36D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area of health care is prioritized more? Please provide a reason for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "I would look at the proportion of each category to see which area takes up a larger slice of the pie.\nBased on the above thought, the answer is:\n\"Physical Therapy\" is prioritized more because it accounts for 75% of the resources, which is notably larger than the 25% allocated to \"Digital Health\"."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "JK6F7bQoOvYVc2mW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer anything about the health and medical field's resource distribution from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would analyze the proportions of each category to make some inferences about the resource distribution.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that Physical Therapy receives a significantly larger share of resources, suggesting that it might be a more dominant or prevalent field in this particular sect of health and medical care."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "zJEOvZAgJZqT9HZQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart affect future resource allocation decisions in the health and medical field?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would consider how the current proportions of resources might impact future decisions on resource allocation.\nBased on the above thought, the answer is:\nIf the current trends are reflective of the actual needs in the health and medical field, these proportions could guide future resource allocations. Given the significant resource allocation to Physical Therapy, a need to continue this trend may be indicated. Alternatively, if there is a desire to balance the distribution, funding for Digital Health could be increased."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "ivLBb7Cu6QVKdEh3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in allocation between Physical Therapy and Digital Health based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the difference, we need to subtract the percentage of \"Digital Health\" from \"Physical Therapy\".\nBased on the above thought, the answer is:\nThe difference in allocation between Physical Therapy and Digital health is 50%."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "B9bRhd4c2Q3PMW2m",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of Digital Health in the total resource distribution if each resource were equally distributed between the two sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the resources were equally distributed, then each sector would have 50% of the resources.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "R7Hk10I5r8cs6NUf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the distribution of resources dedicated to \"Digital Health\" increased by 10%, what would be the new percentage of resources dedicated to \"Physical Therapy\" to keep the total at 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract the increased percentage of \"Digital Health\" from 100%.\nBased on the above thought, the answer is:\nThe new allocation for Physical Therapy would be 65%."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "lgmx6VdtCc2EHaPN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a CSV format table based on your observations?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {query: \"Social Networking & Relationships: Time Spent per week\"}<r>[0.246, 0.021, 0.717, 0.047]</r> and the categories represented in the legend, each with their corresponding color {'Friends'}<r>[0.885, 0.071, 0.935, 0.091]</r>, {'Family'}<r>[0.885, 0.101, 0.93, 0.121]</r>, {'Colleagues'}<r>[0.885, 0.131, 0.962, 0.151]</r>, {'Online Friends'}<r>[0.885, 0.161, 0.984, 0.181]</r>, {'Acquaintances'}<r>[0.885, 0.191, 0.987, 0.211]</r>, {'Strangers'}<r>[0.885, 0.221, 0.953, 0.241]</r>, I can infer that data should be represented in terms of the categories and the hours spent per week for each.\nBased on the above thought, the answer is:\nCategory,Hours per Week\nFriends,40\nFamily,60\nColleagues,75\nOnline Friends,90\nAcquaintances,50\nStrangers,15"
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "gUxvlGSo3b38r4v1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, where do people spend most of their socializing time?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to see which category has the largest slice in the pie chart, which corresponds to the most time spent socializing.\nBased on the above thought, the answer is:\nPeople spend most of their socializing time with 'Online Friends'."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "RUVLQjaD89Yf2zDc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be gathered about people's online social behaviors from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer about people's online social behaviors by looking at the proportion of time spent with 'Online Friends' in comparison to other categories.\nBased on the above thought, the answer is:\nThe chart indicates that more time is spent on cultivating online friendships than any other relationships. This suggests that digital communication and the virtual sphere play a significant role in people's social lives."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "0EAd0Zob2qWTMPjV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were an administrator of a social networking platform, what could this data inform you about user habits?"
+            },
+            {
+                "from": "gpt",
+                "value": "As an administrator of a social networking platform, the data could provide valuable insights into user behavior and time allocation.\nBased on the above thought, the answer is:\nThis pie chart would suggest that users of social networking platforms spend the most time interacting with online friends, which is higher than any other category. This understanding could influence decisions to improve user experience, enhance online friendships features, and perhaps provide resources or discussions for users to manage their time spent on social media effectively."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "nEBXiVn9YIQNU2s7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which category has the least amount of hours spent on it in a week?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into account the pie chart, I need to seek the section with the smallest area. The area of a pie slice corresponds to the amount of time spent with a particular category. According to these instructions, that would be {'Strangers'}<r>[0.283, 0.208, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nStrangers."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "T0nSAVxPTXZQoyIS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total time spent socializing according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total time spent socializing, I have to add the time spent in all categories according to the source data.\nBased on the above thought, the answer is:\nThe total time spent socializing is 40 hours (Friends) + 60 hours (Family) + 75 hours (Colleagues) + 90 hours (Online Friends) + 50 hours (Acquaintances) + 15 hours (Strangers) = 330 hours per week."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "h0wr3gMpr8xim8ST",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum number of hours spent on a single category in a week?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the maximum number of hours spent on a category, I need to compare the number of hours provided for each category.\nBased on the above thought, the answer is:\nThe maximum number of hours spent on a category in a week is 90 hours for Online Friends."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "gQG03x5VfWEreZef",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data presented in the pie chart into CSV format according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by looking at the chart, I can see that the pie slices represent the patient distribution among different medical specialties, as indicated by the legend{\"legend\"}<r>[0.861, 0.063, 0.993, 0.279]</r>. I will list each specialty and corresponding patients' number in the CSV format.\nBased on the above thought, the answer is:\n\"Specialty\", \"Patients Number\"\n\"Pediatric\", 2500\n\"Neurology\", 1500\n\"Gynecology\", 1700\n\"Cardiology\", 2200\n\"Oncology\", 2800\n\"Radiology\", 2100\n\"Dentistry\", 1800"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "H1b4FSxGOSw1ZWv1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich medical specialty has the least number of patients and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie chart {\"pie chart\"}<r>[0.221, 0.146, 0.741, 0.886]</r> and the legend entries, I can identify that Neurology has the smallest pie slice, indicating it has the least number of patients.\nBased on the above thought, the answer is:\nNeurology has the least number of patients. This might be due to various factors such as the prevalence of neurological conditions in the population, the availability of neurologists, or accessibility to neurological services."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "fVwgs70hc50deKg0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the distribution of patients across various medical specialties reflect on the health needs of the population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would infer from the pie chart, which indicates that a significant proportion of the patient population is concentrated in few specialties.\nBased on the above thought, the answer is:\nThe patient distribution suggests there is a high health demand for Oncology, indicating potentially high cases of cancer in the population. Also, specialties like Pediatric and Cardiology have a significant portion, pointing towards potential child health issues and heart-related conditions in the population."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "B8b61dzLRcyvxvwP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be a potential implication for healthcare resource allocation based on the patient distribution across various medical specialties?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw implications, I need to identify specialties with high patient numbers and those with fewer patient numbers. Here, Oncology has the highest patient count and Neurology has the least.\nBased on the above thought, the answer is:\nResource allocation in healthcare might require more focus on Oncology, considering its high patient count, indicating a high demand for cancer-related treatments. On the contrary, resources might be directed away from Neurology due to its lower patient count, unless a disparity between the need for neurological care and access to such care is identified."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "Fi2OU7fcSrmMAGJl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of patients belong to the field of neurology based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart's coordinates {\"Neurology\"}<r>[0.221, 0.54, 0.462, 0.856]</r>, it accounts for 10.3% of the total patient distribution in various specialties.\nBased on the above thought, the answer is:\n10.3%"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "lOOyaonNzBhTjeFr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total patient count for the top three specialties with the highest number of patients?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the number of patients per specialty listed in the pie slices, I found that the top three specialties are Oncology, Pediatric, and Cardiology.\nBased on the above thought, the answer is:\n2800+2500+2200 = 7500"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "gwPxpg0Lpn6OcpMm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Cardiology department's patient count doubles next year, what would be the new total patient count?"
+            },
+            {
+                "from": "gpt",
+                "value": "The current patient count for the Cardiology department according to the pie slice {\"Cardiology\"}<r>[0.482, 0.516, 0.716, 0.88]</r> is 2200. If it doubles next year, the added count would be 2200.\nBased on the above thought, the answer is:\n15000+2200 = 17200"
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "r7Q0m29qC8hJXOVx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data you've analyzed into structured CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the structure of the {legend}<r>[0.876, 0.063, 0.993, 0.279]</r> and {the sector slice}[{\"Italian\"}[0.223, 0.278, 0.482, 0.522]; {\"French\"}[0.223, 0.516, 0.482, 0.799]; {\"Indian\"}[0.311, 0.552, 0.572, 0.923]; {\"Chinese\"}[0.482, 0.516, 0.731, 0.861]; {\"Mexican\"}[0.482, 0.294, 0.741, 0.615]; {\"Japanese\"}[0.463, 0.146, 0.689, 0.516]; {\"Moroccan\"}[0.283, 0.147, 0.482, 0.516]], it is clear that the sectors represent different cuisine and frequencies of each cuisine in the culture.\nBased on the above thought, the answer is:\nCuisine, Frequency \nItalian, 50\nFrench, 60\nIndian, 75\nChinese, 65\nMexican, 64\nJapanese, 70\nMoroccan, 56"
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "AZAPqd30v6WXgGCu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you deduce which cuisine is the most liked?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the most liked cuisine, I will need to recognize the slice with the greatest proportion in the {pie chart}<r>[0.223, 0.146, 0.741, 0.923]</r>.\nBased on the above thought, the answer is:\nIndian is the most liked cuisine, as it has the largest slice in the pie chart."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "lOcfd19WFTN71R4d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the information on the chart, what can you infer about culinary taste preferences?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will have to interpret the relative sizes of the slices in the {pie chart}<r>[0.223, 0.146, 0.741, 0.923]</r> to understand the culinary taste preferences.\nBased on the above thought, the answer is:\nThe chart indicates a considerable diversity in gastronomy with none of the cuisines being minimal or vastly favored over the others. The culinary tastes seem to lean slightly towards Indian cuisine, suggesting a strong preference for their unique flavors among the food culture studied."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "ZU7AUcBpen0Jwmen",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data in the chart, how could resource allocation decisions be influenced for an international food festival?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to look at the relative frequencies of the cuisines as represented by the {pie chart}<r>[0.223, 0.146, 0.741, 0.923]</r>.\nBased on the above thought, the answer is:\nGiven the chart data, more resources might be allocated for Indian cuisine given its higher prevalence, followed by French, Chinese, and Japanese cuisines. The allocations for Italian, Mexican, and Moroccan cuisines could be comparative but slightly less, given their slightly lower presence in the gastronomy culture analysis."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "oUQyOauJu8JGA8YL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Japanese cuisine increased its frequency by 10, what would be the new total frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I need to add the current frequency of {Japanese cuisine}<r>[0.463, 0.146, 0.689, 0.516]</r> to 10, and then sum it with the frequencies of the other cuisines.\nBased on the above thought, the answer is:\nThe new total frequency would be 435."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "txXoTUCgvj0FG4An",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average frequency of the cuisines in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure that out, I need to find the sum of frequencies of all cuisines and then divide by the total number of cuisines.\nBased on the above thought, the answer is:\nThe average frequency is approximately 63."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "0w51ZvzKW2XTzfNN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExcluding the Indian cooking, which two cuisines combined would give a comparable frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine that, I need to experiment with different combinations of two cuisines to find a similar frequency to {Indian cuisine}<r>[0.311, 0.552, 0.572, 0.923]</r>.\nBased on the above thought, the answer is:\nFrench and Chinese."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "Nfvdj2rQntf0dK49",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data seen in the pie chart representing Fiscal and Monetary Policy distribution in 2020 in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide the data seen in the pie chart, I would reference the sizes attributed to {Fiscal Policy}<r>[0.223, 0.278, 0.605, 0.886]</r> and {Monetary Policy}<r>[0.303, 0.122, 0.761, 0.818]</r> in the year 2020. These sizes are proportional to their percentage contributions.\nBased on the above thought, the answer is:\nPolicy,2020\nFiscal_Policy,44.0%\nMonetary_Policy,56.0%"
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "FVhIOTgKoIYjABQH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie distribution tell us about the worldwide usage of Fiscal and Monetary policies in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I'm considering information from the pie chart, with a specific focus on the proportions dedicated to {Fiscal Policy}<r>[0.223, 0.278, 0.605, 0.886]</r> and {Monetary Policy}<r>[0.303, 0.122, 0.761, 0.818]</r>.\nBased on the above thought, the answer is:\nIn 2020, Monetary policy was used more than Fiscal policy, however, the gap between their use has reduced compared to the earlier years, indicating a shift towards increased usage of Fiscal policy worldwide."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "uHEBN0tW03xpXSK6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the chart's data inform future decisions on Monetary and Fiscal policy applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive insights that inform future decisions, I need to understand the trend by evaluating the differences in percentages between {Fiscal Policy}<r>[0.223, 0.278, 0.605, 0.886]</r> and {Monetary Policy}<r>[0.303, 0.122, 0.761, 0.818]</r>.\nBased on the above thought, the answer is:\nGiven the decreasing dominance of Monetary Policy and the rise of Fiscal Policy over the years, policymakers looking at the UK may be informed to consider favoring Fiscal Policy in future decisions."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "vZOhdM2GYLl5ksg5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend observed from 2013 to 2020, can you predict which policy might dominate in 2021: Monetary or Fiscal?"
+            },
+            {
+                "from": "gpt",
+                "value": "The best way to predict the future trend is to look at the previous trends, consider the percentage for {Fiscal Policy}<r>[0.223, 0.278, 0.605, 0.886]</r> and {Monetary Policy}<r>[0.303, 0.122, 0.761, 0.818]</r> over the years and their rate of change.\nBased on the above thought, the answer is:\nIt's difficult to predict decisively, but if the trend of Fiscal Policy steadily increasing and Monetary Policy decreasing continues, then we might see Fiscal Policy catching up or even surpassing Monetary Policy in 2021."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "6dGH8V99AxMkRgLv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the differential percentage between Monetary and Fiscal policies for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart and specifically focusing on the segments representing {Fiscal Policy}<r>[0.223, 0.278, 0.605, 0.886]</r> and {Monetary Policy}<r>[0.303, 0.122, 0.761, 0.818]</r>, I can figure out the percentage values for each of them. The difference between them can be obtained by subtracting the smaller percentage from the larger one.\nBased on the above thought, the answer is:\nMonetary Policy had 12% more than Fiscal Policy in 2020."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "4roHXZHwOu1MhShA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2020, if the percentage representation of Fiscal policy increased by 10%, what would be the new percentage of Fiscal and Monetary policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires adding 10% to the 2020 Fiscal policy percentage, and correspondingly subtracting 10% from the Monetary policy percentage for balanced distribution.\nBased on the above thought, the answer is:\nNew Fiscal Policy would be 54% and Monetary policy would be 46%."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "HujODxuJqoPU0btO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent has the Fiscal Policy increased from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to refer to the JSON data representing the corresponding percentages for each policy and year, and then subtract the percentage of Fiscal Policy in 2013 from that of 2020.\nBased on the above thought, the answer is:\nFiscal Policy had increased by 20% from 2013 to 2020."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "4tvN1dUYfl5VToOR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert this pie chart data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {\"Subculture and Fashion Trends\"}[0.355, 0.021, 0.608, 0.047] in the chart, I can note that the pie chart represents the categories that are labelled as {\"Goth\"}[0.36, 0.676, 0.404, 0.696] and {\"Hipster\"}[0.56, 0.336, 0.603, 0.356]. Each category has its corresponding proportion shown in the same colour.\nBased on the above thought, the answer is:\nSubculture,Fashion Trends\nGoth,50\nHipster,50"
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "sEHbceuzli9gEDUL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased off of the chart, which subculture is dominant in fashion trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I should check the proportions of both subcultures in the chart.\nBased on the above thought, the answer is:\nBoth 'Goth' and 'Hipster' subcultures are equally dominant in fashion trends as each of them contributes 50% to the pie chart."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "Y5yEdeobDqtAIndp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be drawn from this distribution of subcultures in this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can make assumptions based on the proportions of each category.\nBased on the above thought, the answer is:\nThe pie chart shows that the 'Goth' and 'Hipster' subcultures were equally prevalent in fashion trends. This suggests an equal level of influence and acceptance of both styles in the fashion world."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "IDzbJ8uWtEZgWiK0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the trends depicted in this chart influence fashion decisions of retailers and consumers?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should take into consideration how the proportions of the subcultures might impact trends and decision-making in the fashion industry.\nBased on the above thought, the answer is:\nBased on this chart, retailers and consumers could be expected to equally uptake and propagate both 'Goth' and 'Hipster' styles as they each hold equal weight in the current fashion trends. This suggests a varied and diverse approach towards fashion consumption and retailing."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "6SXHgtyl4XdMbgjJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage inclusion of the 'Goth' subculture in the data as depicted by the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to look at the proportion of the 'Goth' category as indicated in the pie chart.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "7ppSwl2XmeUrS490",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percent does the 'Hipster' category contributes to the pie chart according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this information, I need to check the proportion of the 'Hipster' category as shown in the pie chart.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "WIfiKKoE7Xi166SH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Hipster' category decreases its proportion by 10%, what is the new proportion of 'Goth' subculture in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I should subtract 10% from the 'Hipster' category's proportion and then recalculate the total proportions of both categories.\nBased on the above thought, the answer is:\nThe new proportion of 'Goth' subculture is 60%"
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "zSqVbtb8Yr5oiret",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the information on the pie chart into a CSV format table, showing the percentage distribution of the Entertainment sector in 2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a table format, I need to extract the percentages from the {autotext of the 'Video Gaming'}<r>[0.552, 0.939, 0.649, 0.959]</r> and the {autotext of the 'Cinema'}<r>[0.364, 0.109, 0.417, 0.129]</r> slices and match them to their corresponding labels.\nBased on the above thought, the answer is:\nEntertainment Sector,2021\nVideo Gaming,79.5%\nCinema,20.5%"
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "zBrlEVvFTFrOhbYF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which sector dominates the Entertainment Industry based on the pie chart for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the sector with the largest pie slice by looking at the color and reference to {the legend}<r>[0.845, 0.063, 0.993, 0.129]</r>.\nBased on the above thought, the answer is:\nThe sector dominating the Entertainment Industry is 'Video Gaming', as it occupies the majority of the pie chart, with an approximate 79.5% sector share."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "zDyrnVNn99WPrPZZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information you see in the chart, what do you think caused the major shift in the entertainment industry in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To formulate a hypothesis, I'd need to analyze the pie chart showing how the sectors 'Video Gaming' and 'Cinema' have changed in 2021.\nBased on the above thought, the answer is:\nThe chart suggests a major shift towards Video Gaming in 2021 with a 79.5% share, likely due to imposed lockdowns and social distancing measures during the Covid-19 pandemic, which pushed people towards indoors activities. Conversely, the 'Cinema' sector, which involves outdoor social activity, saw a decrease in its share to just 20.5%."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "iwCDaEZPqzgIDieU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat prediction might you make about the entertainment preferences of consumers in 2022 based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict the entertainment preferences of consumers in 2022, I need to consider the trends shown in the chart.\nBased on the above thought, the answer is:\nGiven the significant dominance of the 'Video Gaming' sector in 2021 and the declining trend in 'Cinema', it can be predicted that 'Video Gaming' will continue to be the popular choice in the entertainment sector in 2022, unless there are significant changes in the external environment that affect consumer behaviour."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "vejtuFWDS512lYf1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the absolute difference in percentage between 'Video Gaming' and 'Cinema' in the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the absolute difference, I need to subtract the percentage of 'Cinema' from the percentage of 'Video Gaming' as given in the chart.\nBased on the above thought, the answer is:\nThe absolute difference is 79.5% - 20.5% = 59%."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "oRBh2ZnzDVpwo0zX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in the Video Gaming sector percentage from 2019 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to extract the data from the provided data for the years 2019 and 2021 for the Video Gaming sector and then subtract the 2019 value from the 2021 value.\nBased on the above thought, the answer is:\nThe increase is 58% (2021) - 39% (2019) = 19%."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "OoyV160FeXwq8VyC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Cinema' sector drops by 3% next year while the 'Video Gaming' remains the same, what will be the new percentage for 'Cinema'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract 3% from the current percentage of 'Cinema' in the chart.\nBased on the above thought, the answer is:\nThe new percentage for 'Cinema' would be 20.5% - 3% = 17.5%."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "U3iRymEBLMNCSFqD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please device the given data into a well-structured CSV table based on the pie chart's details?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the pie chart's details include the title {\"Urban vs Rural Development from 2014 to 2020\"}[0.281, 0.021, 0.682, 0.047] and {the pie slices}<r>[0.223, 0.146, 0.744, 0.886]</r> from the chart, each having an associated percentage value and year for both Urban and Rural development. I will incorporate these into the CSV table.\nBased on the above thought, the answer is:\nYear,Urban,Rural\n2014,75,25\n2015,82,18\n2016,91,9\n2017,80,20\n2018,96,4\n2019,75,25\n2020,90,10"
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "rH1RKBGVUfeNLJhV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, which area (Urban or Rural) has seen more development overall from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to look at the percentages each area has gotten over the years on the pie chart and see which one has consistently had a higher percentage.\nBased on the above thought, the answer is:\nOverall, urban areas have seen more development from 2014 to 2020. This is evidenced by the consistently higher percentages observed in the pie chart."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "SrvBwIpXNJ8jRo75",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom analyzing the pie chart, can you infer any trends in the urban development from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing each slice of the pie chart will allow me to observe the changes in the percentages of Urban development over the years, thus revealing any trends.\nBased on the above thought, the answer is:\nUrban development has overall seen an upward trend from 2014 to 2020 with a minimal decrease in 2017. This is shown by the progressing increase of slice sizes on the pie chart from 2014 to 2016 and 2018 to 2020."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "TNPrdyvyQ6MGygV6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications could the data from the year 2018 have on infrastructure planning for 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would need to consider the development percentages for both Urban and Rural areas of 2018 showing on the pie chart.\nBased on the above thought, the answer is:\nThe data from 2018 shows a whopping 96% Urban development, leaving a meager 4% to Rural areas. This disparity could be an indication for infrastructure planning for 2019 to focus more resources towards enhancing development in Rural areas."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "HSfJNBhsNmjWi2fA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the Rural development have the lowest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to first locate and check the percentage of Rural development from each slice {the pie slices}<r>[0.223, 0.146, 0.744, 0.886]</r> of the pie. I then compare all the values to find the lowest one.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "oWMFpbMlXg8XwAaE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average Urban development percentage over the years 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I sum up the Urban development percentages for the years 2014 to 2020, found on the pie chart slices and then divide that sum by the total number of years (7).\nBased on the above thought, the answer is:\n84.1%"
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "sfZ6K0GGqLHOhTAn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Urban development increased by 20% in the year 2020, what would be the new percentage of Rural development in the same year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the current percentage of Urban development is 90% in 2020, with an increase of 20%, it would become 110%. However, the percentages for Urban and Rural must total 100%, so the Rural development would be decreased by the same amount to maintain the balance.\nBased on the above thought, the answer is:\n0%"
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "IXYyAMdMjHpEkX8S",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart showing the factors of Human Health and Disease Management, please extract the data into a simple table format."
+            },
+            {
+                "from": "gpt",
+                "value": "The data can be extracted by observing the proportion of each slice of the pie chart. In this process, the legend {the legend}[0.792, 0.063, 0.993, 0.309], the names of health factors and their corresponding proportions determine the necessary information. Each slice of the pie like {\"Exercise\"}[0.223, 0.278, 0.482, 0.554] should correspond to a percentage, indicating the proportion of people participating in that factor.\nBased on the above thought, the answer is:\nHealth Factor,Percentage\nExercise,12.8%\nHealthy Eating,13.9%\nRegular Checkups,11.5%\nVaccination,12.3%\nSleep,13.1%\nStress Management,10.6%\nMental Health,14.7%\nDisease Management,11.1%"
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "xBXlp6jlU6hhxjbx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich health factor has the highest participation according to the chart? Provide an explanation for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The health factor with the largest slice of the pie chart signifies the highest participation.\nBased on the above thought, the answer is:\nMental Health with a 14.7% has the highest participation as shown by its standout slice in the pie chart{\"Mental Health\"}[0.444, 0.11, 0.666, 0.48]."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "nfjduDIiXTrFaX82",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend of participation in health factors from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend can be inferred by analyzing the percentage points as indicated by the size of each slice of the pie.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that more people are concerned with Mental Health, Healthy Eating, and Sleep as they have larger slice areas. On the other hand, Stress Management and Disease Management are areas of lesser concern, as indicated by their smaller slices."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "gl0Ot9Nb2EewdpOx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which health factor needs more attention or encouragement for improvement and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The health factor that needs more attention can be determined by looking at the smallest slice and its corresponding percentage.\nBased on the above thought, the answer is:\nBased on the chart, the health factor of Stress Management needs more attention or encouragement as it only has a 10.6% participation rate."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "5pCj9ZAlJJedhwZa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the smallest percentage in the pie chart for the factors of Human Health and Disease Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the smallest percentage of the pie chart, I need to examine each slice of the pie and identify the one with the smallest area.\nBased on the above thought, the answer is:\nStress Management,10.6%."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "0q0V7tfv4bviNpC5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of participation in Exercise, Regular Checkups and Mental Health?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage, I need to add each corresponding percentage for \"Exercise\", \"Regular Checkups\", and \"Mental Health\".\nBased on the above thought, the answer is:\nThe sum of the percentages is 12.8% (Exercise) + 11.5% (Regular Checkups) + 14.7% (Mental Health) = 39%."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "UHDU1876cBkFB4R8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage for Stress Management increases by 2%, what will the new total percentage be for all the health factors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, we need to add 2% to the percentage for \"Stress Management\" and then recalculate the total percentage for all factors.\nBased on the above thought, the answer is:\nStress Management will increase to 10.6% + 2% = 12.6%. The new total percentage will be 12.8% (Exercise) + 13.9% (Healthy Eating) + 11.5% (Regular Checkups) + 12.3% (Vaccination) + 13.1% (Sleep) + 12.6% (Stress Management) + 14.7% (Mental Health) + 11.1% (Disease Management) = 102%."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "A8Ohjp0gP0i4FPiq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, please translate the prediction on AI Technology Adoption into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the different sized slices of the pie chart, for {AI Technology Adoption}<r>[0.223, 0.278, 0.482, 0.583]; [0.227, 0.516, 0.482, 0.856]; [0.379, 0.516, 0.643, 0.886]; [0.482, 0.431, 0.741, 0.806]; [0.482, 0.15, 0.734, 0.516]</r> I can collect the data values for each year.\nBased on the above thought, the answer is:\nYear,AI Technology Adoption\n2022,70%\n2023,78%\n2024,86%\n2025,90%\n2026,95%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "vlB1S9o8CYqJB1gk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the Renewable energy usage into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the segmentation of the pie chart for {Renewable Energy Usage}<r>[0.422, 0.146, 0.519, 0.516]; [0.356, 0.156, 0.482, 0.516]; [0.318, 0.193, 0.482, 0.516]; [0.294, 0.229, 0.482, 0.516]; [0.264, 0.236, 0.462, 0.491]</r>, I can deduce the values for each year.\nBased on the above thought, the answer is:\nYear,Renewable Energy Usage\n2022,30%\n2023,22%\n2024,14%\n2025,10%\n2026,5%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "Ty1IKPvuaZwLLjzW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert all the data from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart segments represent the percentages of AI Technology Adoption and Renewable Energy Usage for each year. I will collect data for each segment and correspond it to the respective year.\nBased on the above thought, the answer is:\nYear,AI Technology Adoption,Renewable Energy Usage\n2022,70%,30%\n2023,78%,22%\n2024,86%,14%\n2025,90%,10%\n2026,95%,5%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "hkqf4TKvWtEasNLJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what is the significance of the 'explode' feature used for 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'explode' attribute is used in pie charts to highlight a particular section of the chart. Here, it's used for the year 2025.\nBased on the above thought, the answer is:\nThe 'explode' feature is used to highlight and bring particular attention to the predictions for the year 2025 for both AI Technology Adoption and Renewable Energy Usage."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "WTclgcBv9jcTRooP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall trend of renewable energy usage from 2022 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the overall trend, I need to analyze the Renewable Energy Usage section sizes for each year.\nBased on the above thought, the answer is:\nThe overall trend shows a decline in renewable energy usage from 2022 to 2026, starting from 30% and decreasing to 5%."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "vjqJTwbJckO30sUX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be drawn from the chart regarding the comparison between the trend of AI Technology adoption and Renewable Energy Usage from 2022 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the size of segments between AI Technology Adoption and Renewable Energy Usage over the years to infer the trends.\nBased on the above thought, the answer is:\nThe chart shows contrasting trends between AI Technology adoption and Renewable Energy Usage. AI Technology Adoption is showing a steady increase from 2022 to 2026, whereas Renewable Energy Usage is showing a consistent decrease over the same period."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "ZnqtYRa7VB7kc5Lo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total predicted technology adoption between 2023 and 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I must find and sum the percentages of AI Technology Adoption for 2023, 2024, and 2025.\nBased on the above thought, the answer is:\n254%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "WJkFSTRdnoA3n3QQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the decrease in Renewable Energy Usage from 2022 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the Renewable Energy Usage in 2026 from that of 2022.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "exUKq4flFDLIUfNU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the AI Technology Adoption increase from 2024 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, I need to subtract the percentage of AI Technology Adoption in 2024 from that in 2026.\nBased on the above thought, the answer is:\n9%"
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "vXtBRCznMUI0OEtM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you present the annual FinTech Investment data from 2015 to 2019 in the form of a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extract information from the chart, I need to identify each year {the labels in the sublegend}[0.913, 0.063, 0.993, 0.219] and their corresponding FinTech investment values. From the associated labels and their proportional representations in {the pie chart }[0.223, 0.146, 0.763, 0.899], I can estimate the investment values.\nBased on the above thought, the answer is:\nYear,FinTech Investment in Billions\n2015,7.0\n2016,11.7\n2017,19.9\n2018,29.2\n2019,32.2"
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "W5hrPStx55BPpFVg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to this chart, was there a significant increase in FinTech Investment from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here, I will determine the difference in Investment between the initial year {2015}[0.913, 0.073, 0.987, 0.091] and the final year {2019}[0.913, 0.193, 0.987, 0.211].\nBased on the above thought, the answer is:\nYes, there was a significant increase in FinTech Investment from 2015 to 2019. Investment in 2019 was much higher compared to 2015, which shows a growth trend."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "YbHturbwiduSPUlV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year observed the highest increase in FinTech Investment from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the differences in the proportions of each slice in {the pie chart}[0.223, 0.146, 0.763, 0.899], I can determine which Year observed the highest increase in Investment from the previous Year.\nBased on the above thought, the answer is:\nThe Year 2018 had the highest increase in FinTech Investment from the previous year as this year's slice in the pie chart is significantly larger than the slice representing 2017."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "jJKN95LWqr56Ep1O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, can it be inferred that more Investment capital was available for FinTech companies each year from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the proportions of each pie slice associated with a respective year in {the pie chart}[0.223, 0.146, 0.763, 0.899], I can examine whether Investment capital became more available for FinTech companies each year.\nBased on the above thought, the answer is:\nYes, it can be inferred that more Investment capital was available for FinTech companies each year from 2015 to 2019 since the chart demonstrates a consistent increase in the percentage of annual FinTech Investment."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "VUknx15S0OPjnpc4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total FinTech Investment from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total FinTech Investment from 2015 to 2019, I need to sum up the Investments of each year.\nBased on the above thought, the answer is:\n100.0 billion dollars."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "CLqkWp5ErWCqAJGS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual FinTech Investment from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average annual FinTech Investment, I need to sum up the Investments of each year and divide by the number of years.\nBased on the above thought, the answer is:\nThe Average annual FinTech Investment is 100.0 billion dollars / 5 years = 20.0 billion dollars per year."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "KCqKnXLWZdcvwvSn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did FinTech Investment increase from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in Investment, I need to subtract the Investment of 2015 from the Investment of 2019.\nBased on the above thought, the answer is:\nThe increase in FinTech Investment from 2015 to 2019 was 32.2 - 7.0 = 25.2 billion dollars."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "OL6y4wgPVOnNM8gq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the tabular representation of the risk factors and their corresponding scores from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data from the pie chart, I can see that each risk factor has a corresponding value. Using this data, I can construct a table.\nBased on the above thought, the answer is:\nRisk Factor,Score\nFactor1,7\nFactor2,5\nFactor3,23\nFactor4,13\nFactor5,19\nFactor6,2\nFactor7,31"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "ZzG1cY91QuAKlC5D",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart tell you about the distribution of risk factors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I need to analyze the percentages of each risk factor.\nBased on the above thought, the answer is:\nThe chart tells us that Factor7 was the most prominent contributor to risk in the pie chart, with the highest score of 31. Conversely, Factor6 had the least effect with only a score of 2. This shows a significant variation in the risk contributions of different factors."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "ZrhV1cbrvZFr2lLh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of risk management, how should resources be allocated based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The allocation of resources would ideally be directly proportional to the risk scores of each factor according to the pie chart.\nBased on the above thought, the answer is:\nBased on the pie chart, the majority of resources should be allocated to dealing with Factor7 as it has the highest score, indicating the highest risk. Smaller proportions of resources should be assigned to manage Factor1, Factor2, Factor4, and Factor5 considering their middling risk scores. The least resources should be dedicated to Factor6 which poses the lowest risk."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "a91oHp1pUu7RdeyO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you want to prioritize the management of three factors, which factors should you choose?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to select the three factors with the highest scores.\nBased on the above thought, the answer is:\nFactors to prioritize should be Factor7, Factor3, and Factor5, as these have the highest scores indicating they pose the most risk."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "mN0BIJzHQZfgDvaA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent does Factor3 contribution exceed Factor6?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to subtract the percentage of 'Factor6' from 'Factor3'.\nBased on the above thought, the answer is:\n21%"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "pLSK9DTPZsDWY24z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Factor4' and 'Factor5' combined represented half of the total risk score, what is the total risk score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the scores of 'Factor4' and 'Factor5', and multiplying by 2 should give the total risk score.\nBased on the above thought, the answer is:\n64"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "pPbxXALD1JbIHQLb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the severity score of each factor were to decrease by 1, what would be the new score for Factor1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new score for 'Factor1' would be its original score reduced by 1.\nBased on the above thought, the answer is:\n6"
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "PwqUvC7ytIaW7eqP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the data from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart titled {\u201cRevenue Growth in the Entertainment and Gaming Industry (2012 - 2017)\u201d}<r>[0.174, 0.021, 0.789, 0.047]</r> and the pie slices, I can make out the following: {the sector slice for the year 2012}<r>[0.223, 0.278, 0.482, 0.516]</r> was about 9.9% of the total. The slice for {2013}<r>[0.223, 0.488, 0.482, 0.737]</r> made up 11.4%, while {2014}<r>[0.274, 0.516, 0.482, 0.882]</r> accounted for 12.6% of the total. A shocking jump is observed in {2015}<r>[0.456, 0.55, 0.703, 0.92]</r>, which took up almost 17.4% of the total. Afterwards, the pie slice for {2016}<r>[0.482, 0.252, 0.741, 0.731]</r> was about 22.5%., leading to {2017}<r>[0.283, 0.146, 0.664, 0.516]</r> having the largest slice of the pie with 26.3%.\nBased on the above thought, the answer is:\nYear,Percentage\n2012,9.9%\n2013,11.4%\n2014,12.6%\n2015,17.4%\n2016,22.5%\n2017,26.3%"
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "fm0qqtWlIiqJNCCZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about revenue progression in the Entertainment and Gaming Industry from 2012 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can judge how the revenue shares have evolved by examining the sizes of the pie slices from {2012}<r>[0.223, 0.278, 0.482, 0.516]</r> to {2017}<r>[0.283, 0.146, 0.664, 0.516]</r> in order.\nBased on the above thought, the answer is:\nThe data shows that the revenue of the Entertainment and Gaming Industry has a steady growth every year from 2012 to 2017. The growth is especially noticeable in 2015, which shows that the revenue in that year jumped significantly."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "NoF0uzvFTkcImvII",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart stand out the significant growth in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll focus on the special feature of the {2015 slice}<r>[0.456, 0.55, 0.703, 0.92]</r> which seems to be exploded from the pie.\nBased on the above thought, the answer is:\nThe chart uses an \"explosion\" effect on the 2015 slice, separating it slightly from the rest of the pie, to highlight the significant revenue growth in that year."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "yS7OD2isDOfEt3o2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart indicate about the Entertainment and Gaming Industry's revenue trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the size and order of the slices from {2012}<r>[0.223, 0.278, 0.482, 0.516]</r> through to {2017}<r>[0.283, 0.146, 0.664, 0.516]</r>.\nBased on the above thought, the answer is:\nThe pie chart shows that the revenue of the Entertainment and Gaming Industry has been growing consistently over the period from 2012 to 2017. This trend shows promise for further growth in the future."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "pEzsZDYq8g00eTYT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the least contribution to the revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I will examine the size of each pie slice, looking for the smallest one. From what I see, the smallest slice is {2012's slice}<r>[0.223, 0.278, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nThe year 2012"
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "5AgLLfruufddSslk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years total up to a quarter of the total revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I'll add together the percentages from each year until I reach a total of approximately 25%. {2012}<r>[0.223, 0.278, 0.482, 0.516]</r> is 9.9%, adding  {2013}<r>[0.223, 0.488, 0.482, 0.737]</r>, which is 11.4%, gives a total of 21.3%, still less than a quarter. Adding in {2014}<r>[0.274, 0.516, 0.482, 0.882]</r>, which is 12.6%, brings the total to 33.9%, more than a quarter (25%).\nBased on the above thought, the answer is:\n2012 and 2013."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "8JBYqx6eLa3BLms5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage increase can you observe in 2015\u2019s proportion as compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the 2014's proportion from the 2015's proportion and the result divided by 2014's proportion.\nBased on the above thought, the answer is:\nThe increase is approximately 4.8%."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "QbXq4lH6WkX7gSCY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart data into a readable table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, there are eight sectors in the pie chart indicating {Chinese cuisine}[0.117, 0.545, 0.172, 0.565], {Japanese cuisine}[0.355, 0.903, 0.418, 0.923], {Indian cuisine}[0.648, 0.837, 0.69, 0.857], {Thai cuisine}[0.757, 0.61, 0.787, 0.63], {Mexican cuisine}[0.74, 0.335, 0.798, 0.355], {Italian cuisine}[0.613, 0.145, 0.656, 0.165], {French cuisine}[0.402, 0.102, 0.448, 0.122], and {American cuisine}[0.242, 0.183, 0.309, 0.203]. The relative sizes of these sectors represent the popularity of respective cuisines, which can be put into a table.\nBased on the above thought, the answer is:\nCuisine,Popularity(%)\nChinese,25\nJapanese,15\nIndian,12\nThai,10\nMexican,12\nItalian,9\nFrench,10\nAmerican,7"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "j8WUzoXj53sM75i0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which cuisine is currently the most popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most popular cuisine is the one with the largest proportion on the pie chart.\nBased on the above thought, the answer is:\nChinese cuisine is the most popular, with a 25% proportion."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "Ha0EZxW1y7BhvxYU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred regarding people's preferences for different cuisines based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer this, I need to look at which cuisines have the highest proportions and how they compare with the others.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that people have a high preference for Asian cuisines, given that Chinese, Japanese, Indian, and Thai cuisines account for over 50% of the total. Also, American and Italian cuisines seem to be less popular."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "fi4AwAeyeo32W3xO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a food fair is organized according to this data, how might the allocation of stalls for different cuisines be planned?"
+            },
+            {
+                "from": "gpt",
+                "value": "To plan this, the percentages of each cuisine on the chart can be taken as an indication of how many stalls each cuisine should have.\nBased on the above thought, the answer is:\nConsidering the popularity percentages of all cuisines, more stalls could be allocated for Chinese, Japanese, Indian, and Thai cuisines, as they are more popular. For instance, Chinese cuisine could have roughly 25% of the total stalls, Japanese could have 15%, and so on. Less popular cuisines like Italian and American might require fewer stalls."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "yjNWsWUDY933tjlj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the sum of popularity for Asian cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Asian cuisines include {Chinese cuisine}[0.117, 0.545, 0.172, 0.565], {Japanese cuisine}[0.355, 0.903, 0.418, 0.923], {Indian cuisine}[0.648, 0.837, 0.69, 0.857], and {Thai cuisine}[0.757, 0.61, 0.787, 0.63]. Adding their popularity percentages will give us the sum.\nBased on the above thought, the answer is:\n62%"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "uGmZ2VZrVT5btYJk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich cuisine is the least popular according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to check the popularity percentage of each cuisine and find the one with the lowest percentage.\nBased on the above thought, the answer is:\nAmerican."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "UazeKb94ah9x9IWg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we increase the popularity of Indian cuisine by 5%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the new total percentage, the current popularity of {Indian cuisine}[0.648, 0.837, 0.69, 0.857] needs to be increased by 5% and then added to the sum of other cuisines' popularity.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "AG9eu8PLCp0cjuzX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis, can you provide me with a table that captures both the Efficiency and Risk Reduction percentages for the years 2016 to 2020 from the evaluated charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Of course, based on the pie charts and the legend {\"2016\"}<r>[0.475, 0.086, 0.493, 0.106]</r>, {\"2017\"}<r>[0.475, 0.116, 0.493, 0.136]</r>, {\"2018\"}<r>[0.475, 0.146, 0.493, 0.166]</r>, {\"2019\"}<r>[0.475, 0.176, 0.493, 0.196]</r>, {\"2020\"}<r>[0.475, 0.206, 0.493, 0.226]</r>, it's clear that the slices correspond to the percentages of Efficiency and Risk Reduction for each of the years.\nBased on the above thought, the answer is:\nYear,Efficiency,Risk Reduction\n2016,20,80\n2017,34,66\n2018,56,44\n2019,73,27\n2020,98,2"
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "shPMqkj08o1RMt6z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the sharp rise in the efficiency of the innovation project from 2016 to 2020 signify about the project's execution?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will use the provided percentages to interpret them in terms of the project's efficiency.\nBased on the above thought, the answer is:\nThe increase in efficiency of the innovation project from 2016 to 2020 signifies that the project team improved their ability to deliver value more effectively over time. They likely optimized their processes, developed their skills, used better technology or applied innovative methodologies, resulting in a project delivered with greater efficiency and less waste."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "JkKNNjlgOP8ApOgW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed about the risk reduction strategy of the innovation project over the five years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the decline in risk reduction percentages over the five years.\nBased on the above thought, the answer is:\nThe significant decline in risk reduction from 2016 to 2020 suggests a high success rate in the risk mitigation strategies employed by the project team. As the innovation project progressed, they were successful in foreseeing and managing potential risks and uncertainties, making the outcomes more predictable and the project less risky."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "1ZoR5xbl7wUBx8YO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the overall trends, what is the status of the project's overall health by 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluating the final data points for both risk reduction and efficiency in 2020.\nBased on the above thought, the answer is:\nBy 2020, the Innovation Project appears to be in excellent health. Efficiency is at an all-time high, implying optimal resource utilization and successful delivery of value. Meanwhile, risk reduction is at its lowest, indicating that potential uncertainties and disruptions have been successfully managed, leading to a stable project environment."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "CQCKFNTQT23beUFv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the biggest increase in efficiency occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the increase in efficiency for each year and find out the maximum.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "nq2N1hVfRMskYdI4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2018, what is the total percentage when accounting for both risk reduction and efficiency?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to sum up the percentages of both the efficiency and risk reduction.\nBased on the above thought, the answer is:\n56 + 44 = 100%."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "1yWLOBZd53fcVikO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would the percentage of efficiency be in 2020 if it were to increase by an additional 1%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add the 1% increase to the percentage of efficiency in 2020.\nBased on the above thought, the answer is:\n98 + 1 = 99%."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "C5rXodGJ5R19Z75e",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the \"Cuisine and Gastronomy Culture Analysis\" pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, according to the title of the chart {\"Cuisine and Gastronomy Culture Analysis\"}<r>[0.31, 0.021, 0.653, 0.047]</r> and {the sector slices}<r>[{\"Italian\"}[0.197, 0.287, 0.456, 0.868]; {\"Chinese\"}[0.391, 0.552, 0.686, 0.922]; {\"Mexican\"}[0.482, 0.426, 0.741, 0.754]; {\"Indian\"}[0.482, 0.205, 0.733, 0.516]; {\"French\"}[0.482, 0.147, 0.622, 0.516]; {\"Spanish\"}[0.344, 0.146, 0.5, 0.516]; {\"Others\"}[0.283, 0.202, 0.482, 0.516]]</r>, I understand that these sectors indicate the popularity percentages of different cuisine types.\nBased on the above thought, the answer is:\n\"Cuisine Type\", \"Percentage\"\n\"Italian\", 30\n\"Chinese\", 20\n\"Mexican\", 15\n\"Indian\", 12\n\"French\", 8\n\"Spanish\", 10\n\"Others\", 5"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "sIbedMDF7dUiPetb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which cuisine type is dominantly popular worldwide? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most popular cuisine would be the one with the highest percentage in {the pie chart}<r>[0.482, 0.147, 0.622, 0.516]</r>, which is the Italian cuisine.\nBased on the above thought, the answer is:\nThe Italian cuisine is the most popular as it holds the largest proportion at 30% amongst all the cuisine types represented."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "lceJBC3NA3sqZNEp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be made about global eating habits based on the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the various percentages of cuisine popularity in {the pie chart}<r>[0.482, 0.147, 0.622, 0.516]</r>, one can discern the global preferences.\nBased on the above thought, the answer is:\nThe pie chart suggests a global preference for Italian and Chinese cuisines, with combined popularity totaling 50%. These two cuisines dominate significantly over other types such as Indian, French, and Spanish. Meanwhile, French cuisine and those bundled as \"Others\" have the least popularity. Thus, globally, people may be more inclined to indulge in Italian and Chinese gastronomy cultures."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "eHNt2WuJTw47yG1q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nMoving forward, if you were to promote less popular cuisines, which ones would you focus on and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Addressing this question requires looking at the type of cuisines with lower percentages in {the pie chart}<r>[0.482, 0.147, 0.622, 0.516]</r> to select the least popular ones.\nBased on the above thought, the answer is:\nIf the goal were to promote less popular cuisines, I would focus on French and \"Others,\" as they have the least representation with only 8% and 5% of the global preference respectively. Promoting these cuisines can help diversify culinary preferences worldwide and possibly provide these cuisines a stronger presence in global gastronomy."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "9rfPUhHna9S8LnSz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the sum of the popularity percentages of French and Spanish cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to add together the French and Spanish cuisine's percentages from {the pie chart}<r>[0.482, 0.147, 0.622, 0.516]</r>.\nBased on the above thought, the answer is:\n18"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "p8wIdlIyWyXT0Fiz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the popularity percentages between the most and least popular cuisine types?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the highest percentage from {the Italian category}<r>[0.197, 0.287, 0.456, 0.868]</r> with the lowest percentage from {the Others category}<r>[0.283, 0.202, 0.482, 0.516]</r>, I can find the difference.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "GzBY6zRQrBPOY1Yz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Chinese cuisine's popularity increased by 10%, what would be its new position in the chart in terms of popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, by adding 10% to the Chinese cuisine's percentage from {the Chinese category}<r>[0.391, 0.552, 0.686, 0.922]</r>, I can get the new percentage. Then, I compare this value with the percentages of other cuisines to determine Chinese cuisine's new position.\nBased on the above thought, the answer is:\n1st"
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "HLMHKOEU27LKrBGY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the pie chart data about sector energy consumption into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I notice the pie chart's title {\"Sector-wise Energy Consumption\"}[0.345, 0.021, 0.619, 0.047] and the sector slices for each category: {Residential}[0.203, 0.302, 0.599, 0.91], {Industrial}[0.507, 0.138, 0.766, 0.821], {Transportation}[0.373, 0.146, 0.5, 0.516], {Agriculture}[0.318, 0.18, 0.482, 0.516] and {Others}[0.283, 0.229, 0.482, 0.516]. Each slice represents the percentage of energy consumption for each sector.\nBased on the above thought, the answer is:\nSector,Energy Consumption fraction\nResidential,45%\nIndustrial,40%\nTransportation,8%\nAgriculture,4%\nOthers,3%"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "6tRLDqmIWzVMnBAV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which sectors are the primary consumers of energy and what implications does this hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the primary consumers, I need to consider the sectors with the largest pie chart slices, which signify the highest energy usage: {Residential}[0.203, 0.302, 0.599, 0.91] and {Industrial}[0.507, 0.138, 0.766, 0.821]. Then, I need to consider the implications of such statistics.\nBased on the above thought, the answer is:\nThe Residential and Industrial sectors are the primary consumers of energy. This could indicate that these sectors hava a higher demand for energy-intensive activities or processes."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "XEoEmdionBqjnJuY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn regarding energy consumption in the Transportation and Agriculture sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll look at the pie slices for the {Transportation}[0.373, 0.146, 0.5, 0.516] and {Agriculture}[0.318, 0.18, 0.482, 0.516] sectors to provide an answer.\nBased on the above thought, the answer is:\nBoth the Agriculture and the Transportation sectors have a relatively low share of energy consumption compared to Residential and Industrial sectors. This may suggest that these sectors have more energy-efficient operations or practices."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "UNlEJqdk2GtEgkWi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the energy consumption data influence energy policy decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the energy consumption of the pie slices for the sectors {Residential}[0.203, 0.302, 0.599, 0.91], {Industrial}[0.507, 0.138, 0.766, 0.821] and {Transportation}[0.373, 0.146, 0.5, 0.516], I could infer potential impacts on policy decisions.\nBased on the above thought, the answer is:\nGiven that the Residential and Industrial sectors consume the majority of energy, policies could focus on promoting energy efficiency and sustainability in these sectors. Conversely, since Transportation and Agriculture sectors have a lower share, policies could investigate if there are underutilized opportunities for energy consumption or if these sectors are already optimizing their energy use."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "IjlUimWYumsWi1B7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the energy usage of the Transportation and Industrial sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices for the {Transportation}[0.373, 0.146, 0.5, 0.516] and {Industrial}[0.507, 0.138, 0.766, 0.821] sectors on the chart, to determine the percentage difference, I'll calculate the absolute difference between their percentages.\nBased on the above thought, the answer is:\n32%"
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "Hikb4DmnTzjHhhfx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two sectors together make up approximately half of the total energy consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the energy consumption percentages of all the sectors, I need to find two sectors whose combined energy consumption is approximately 50%.\nBased on the above thought, the answer is:\nResidential and Industrial sectors."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "qVnV1vUXftvC4KfI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the energy consumption of the Agriculture sector was reduced by half, what would be its new percentage of total energy use?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I will halve the percentage of the {Agriculture}[0.318, 0.18, 0.482, 0.516] sector's energy consumption, and this will be its new representation in the pie chart.\nBased on the above thought, the answer is:\nIt would be 2%."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "Dg2e8LeLYw1nMP4j",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please translate the pie chart's information into a CSV-styled table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By evaluating the chart's legend {2015}[0.952, 0.071, 0.987, 0.091], {2016}[0.952, 0.101, 0.987, 0.121], {2017}[0.952, 0.131, 0.987, 0.151], {2018}[0.952, 0.161, 0.987, 0.181], {2019}[0.952, 0.191, 0.987, 0.211], {2020}[0.952, 0.221, 0.987, 0.241] and {2021}[0.952, 0.251, 0.987, 0.271], I can identify the different years. Then based on the autotext of each pie slice {12.9%}[0.174, 0.389, 0.209, 0.409], {12.0%}[0.195, 0.698, 0.23, 0.718], {16.3%}[0.388, 0.942, 0.423, 0.962], {14.8%}[0.683, 0.794, 0.718, 0.814], {16.6%}[0.761, 0.424, 0.796, 0.444], {14.0%}[0.594, 0.132, 0.629, 0.152], and {13.3%}[0.321, 0.14, 0.356, 0.16], I can infer the relevant proportion for each year.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,12.9%\n2016,12.0%\n2017,16.3%\n2018,14.8%\n2019,16.6%\n2020,14.0%\n2021,13.3%"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "mWsAKaZiLGtadJMz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, in which year was the community development most supported by local government expenditure and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most support would be given in the year with the largest proportion of expenditure.\nBased on the above thought, the answer is:\nThe year 2017 had the most local government spend on community development, because it had the highest proportion of expenditure at 16.3%."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "VjASb5GngFpKVey1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you surmise about the local government's fiscal policies from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the distribution and variations in the chart, one can make educated guesses about the government's policies.\nBased on the above thought, the answer is:\nThe local government's fiscal policies seem to fluctuate year by year. Notably, there was a significant increase in spending on local government and community development in 2017, indicating a possible policy change or special projects that required increased funding in that year."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "7X1Gdu7Ero2EeuOa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the information in the chart be utilized for future planning for government expenditure on community development?"
+            },
+            {
+                "from": "gpt",
+                "value": "Future planning can benefit from past expenditure trends, the largest expenditure, and the average spending.\nBased on the above thought, the answer is:\nThe information from this chart could be used to anticipate future needs and budget allocations. For instance, if the year with the highest expenditure saw a substantial return in community development, it could be beneficial to allocate a similar budget in future plans. Equally, understanding the average expenditure can help in setting realistic budgetary expectations."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "XhYBE0OS4Ny5EBIP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year was the local government's expenditure at its highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the percentages for each year I can identify which one is the most significant.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "vSWosn9h0auQHgtY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the local government's expenditure reach its lowest point?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lowest spending can be found by looking for the smallest percentage among the years.\nBased on the above thought, the answer is:\n2016"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "hyNZhVdXZLzT35Zh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of government expenditure over all the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average percentage can be calculated by summing all the percentages and dividing by the number of years.\nBased on the above thought, the answer is:\n14.3%"
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "JpYXUufCC7T1kgXt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you translate the pie chart into a table format based on your observation?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the title {\"Food and Agriculture Industry Analysis' - Grains vs Dairy in 2017\"}[0.213, 0.021, 0.75, 0.047] and the colors specified for {\"Grains\"}[0.903, 0.073, 0.931, 0.087] and {\"Dairy\"}[0.903, 0.103, 0.931, 0.117] in {the legend}<r>[0.898, 0.063, 0.993, 0.129]</r> and their corresponding proportions {\"66.7%\"}[0.387, 0.907, 0.432, 0.927] and {\"33.3%\"}[0.536, 0.069, 0.573, 0.089] shown on the pie slices, I can see that the pie chart represents the contribution of Grains and Dairy to the total food and agriculture industry's output in 2017.\nBased on the above thought, the answer is:\nProducts,2017\nGrains,66.7%\nDairy,33.3%"
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "P6EAoIAMGWjY8wil",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2017, which sector dominated the Food and Agriculture industry? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the proportions of \"Grains\" and \"Dairy\" in the pie chart.\nBased on the above thought, the answer is:\nIn 2017, the \"Grains\" sector dominated the Food and Agriculture industry because its output contributed 66.7% of the total, which was larger than that of \"Dairy\" at 33.3%."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "bIIsyxK3OoS7OGSw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be gathered about production trends in the Food and Agriculture industry in 2017 from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions of each product in the pie chart to make inferences about production trends.\nBased on the above thought, the answer is:\nThe pie chart shows that the majority of the Food and Agriculture industry's output in 2017 came from \"Grains\". \"Dairy\", although constituting a smaller proportion, still contributed significantly. Therefore, while \"Grains\" was the dominant product, \"Dairy\" was also an important part of the industry's output."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "GjN6K3WPSK1jUZI1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could a business entity in the Food and Agriculture industry utilize the information provided by the pie chart for decision-making in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the industry trends suggested by the proportions of \"Grains\" and \"Dairy\" in 2017 and consider their implications for decision-making in 2018.\nBased on the above thought, the answer is:\nGiven the dominance of \"Grains\" in 2017, a business entity could choose to invest more in grain production in 2018, anticipating continued high demand. However, considering the significant contribution of \"Dairy\", especially if it shows rising trends, the entity might want to diversify its investments to include dairy production to mitigate risks and take advantage of potential growth in this sector."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "sVVaNOZg2NG1w9pt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the total output of the Food and Agriculture industry for 2017 was 1000 tons, how many tons were produced by the Grains sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the output for the Grains sector, I need to multiply the total output by the percentage of Grains.\nBased on the above thought, the answer is:\n667 tons."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "Rhx4FNQHHvoOPj4L",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between Grains and Dairy's outputs?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the percentage of Dairy from the percentage of Grains.\nBased on the above thought, the answer is:\n33.4%."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "pPBPBUWj1FOgwVJg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Dairy sector wants to have the same proportion as Grains, by what percentage should its output increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the needed increase, I need to subtract Dairy's current percentage from Grain's percentage.\nBased on the above thought, the answer is:\n33.4%."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "aezcF6ebZCKdLubj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from the 'Idea Generation' and 'Project Execution' stages from 2011 to 2014 in the pie chart and represent it as a table in CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "With reference to the chart title {'Innovation Stages: Idea Generation vs Project Execution (2010-2015)'}<r>[0.194, 0.021, 0.769, 0.047]</r>, and by identifying each year from the pie shows, the respective data slices for {'Idea Generation'}<r>[0.867, 0.071, 0.979, 0.091]</r> and {'Project Execution'}<r>[0.867, 0.101, 0.987, 0.121]</r> for the years 2011, 2012, 2013 and 2014 can be extracted.\nBased on the above thought, the answer is:\nYear,Idea Generation,Project Execution\n  2011,15.0%,85.0%\n  2012,25.0%,75.0%\n  2013,35.0%,65.0%\n  2014,45.0%,55.0%"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "uftrCyp9dmXvQS0F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart for Innovation stages from 2010 to 2015, which stage have seen more increased importance over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the distribution of the pie slices for each year and comparing the changes in the percentages for 'Idea Generation' and 'Project Execution', I can infer which stage has seen more increased importance over time.\nBased on the above thought, the answer is:\nOver time, the 'Idea Generation' stage has seen more increased importance as its proportion has increased from 10% in 2010 to 70% in 2015."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "e64zzllHvDjQP4go",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you infer from the shift in 'Idea Generation' and 'Project Execution' from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The shift in percentages of 'Idea Generation' and 'Project Execution' from 2010 to 2015 can provide some insights on the changing focus on innovation project evaluation and management.\nBased on the above thought, the answer is:\nThe pie chart shows a trend of increasing emphasis on 'Idea Generation', suggesting an increased importance on sparking new ideas and innovations over time. In contrast, 'Project Execution' stage has seen a decreasing trend, suggesting a shift in focus from activation to ideation stage in the innovation projects."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "iPvjxuHcST9ScrQW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat business decisions can be influenced by the trends reflected in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, organizations can make decisions based on the emphasis and trends in 'Idea Generation' and 'Project Execution' from 2010 to 2015.\nBased on the above thought, the answer is:\nSeeing the increasing importance of 'Idea Generation' over time, businesses should consider dedicating more resources and effort into ideation processes like brainstorming sessions, innovation labs etc. On the other hand, the decreased attention towards 'Project Execution' suggests possible advancements in efficient project management tools and techniques, thus requiring lesser resource allocation in the future."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "sExuNICrmJMtKi0e",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2015, how much percentage was the 'Idea Generation' more than the 'Project Execution'?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will locate the pie chart for 2015 and retrieve the percentages of 'Idea Generation' and 'Project Execution'. Then I will calculate the difference between the two percentages.\nBased on the above thought, the answer is:\n40.0%"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "cKwE7eTQSRvrpxv7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage change of 'Idea Generation' from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage change, I will first calculate the percentage change each year from 2010 to 2015 for 'Idea Generation'. Then, I will sum up those changes and divide by the number of years.\nBased on the above thought, the answer is:\n12.0%"
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "uOIgfuZPaJHY0xHf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of 'Idea Generation' decreased by 10% in 2014, what would be the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "If the 'Idea Generation' in 2014 decreased by 10%, I need to subtract 10% from its original percentage and then add it to the 'Project Execution' percentage.\nBased on the above thought, the answer is:\nThe new total percentage is 35% (Idea Generation after decrease) + 55.0% (Project Execution) = 90%."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "uZ94QtRllxgTukqp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the numerical data shown in the chart and present it in a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let me read the chart {\"Human Life and Living Environment from 2015 to 2020\"}<r>[0.252, 0.021, 0.711, 0.047]</r> and the intractoractive legend <r>[0.908, 0.063, 0.993, 0.428]</r>. By assessing the data keys, I can generate a CSV table displaying the data from 2015 to 2020 for Air Quality and Safe Drinking Water.\nBased on the above thought, the answer is:\nYear,Air Quality,Safe Drinking Water\n2015,80,95\n2016,75,92\n2017,78,90\n2018,83,88\n2019,85,91\n2020,90,93"
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "moKiR2MKZ4GjE0Uh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, what could be the cause of the decrease in Safe Drinking Water index from 2015 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exact cause is not provided in the chart, but I can infer from the decreasing trend in Safe Drinking Water index.\nBased on the above thought, the answer is:\nThe decrease in the Safe Drinking Water index could be due to several factors such as increased water pollution, a dilapidated water infrastructure, or a decrease in the safety standards of water supply over those years."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "LmZunR1LrHiQxjWY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas there any significant year in terms of Air Quality from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find a significant year, I need to look for a significant change or abnormal value in the Air Quality index.\nBased on the above thought, the answer is:\nThe year 2018 can be considered significant. As represented in the chart by an exploded slice, the Air Quality index experienced a marked increase compared to previous years' gradual trend."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "Z5rB9yP7rc28ITZj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data affect future investment in public welfare conditions in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to infer the potential effect on future investment from the trends of Air Quality and Safe Drinking Water from 2015 to 2020.\nBased on the above thought, the answer is:\nThe improving trend in Air Quality suggests that efforts or investments made towards improving air conditions have been effective, meriting continued or increased investment. However, the decrease in Safe Drinking Water index points to an area needing more attention and possible additional investments to ensure the provision of safe and clean water."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "zAyc4a4zxqOYXhqG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data, by how much did the Air Quality index change from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the change in Air Quality from 2015 to 2020, I will need to subtract the value in 2020 from the value in 2015.\nBased on the above thought, the answer is:\nThe Air Quality index increased by 10 from 2015 to 2020."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "gW6OV2Usnns5oxiE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average value of the Safe Drinking Water index over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to sum all the Safe Drinking Water values from each year and divide by the total years, which is 6.\nBased on the above thought, the answer is:\nThe average Safe Drinking Water index is 91.5 from 2015 to 2020."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "AZc2XhK2rgFy9jsb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn general, did the Air Quality index improve or deteriorate over the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To judge whether the index improved or deteriorated, I need to compare the values in the data from 2015 to 2020.\nBased on the above thought, the answer is:\nThe Air Quality index generally improved from 2015 to 2020."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "e958Ppu5Wjoyi9lR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the raw data from the pie chart into a CSV format table, showing the year and corresponding development value?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend on the right side of the chart {\"2015\"}[0.952, 0.071, 0.987, 0.091], {\"2016\"}[0.952, 0.101, 0.987, 0.121], {\"2017\"}[0.952, 0.131, 0.987, 0.151], {\"2018\"}[0.952, 0.161, 0.987, 0.181], {\"2019\"}[0.952, 0.191, 0.987, 0.211], {\"2020\"}[0.952, 0.221, 0.987, 0.241], {\"2021\"}[0.952, 0.251, 0.987, 0.271], I can understand that these are the years. The pie chart {\"Development\"}[0.223, 0.11, 0.764, 0.886] represents the development values for each year.\nBased on the above thought, the answer is:\nYears, Entertainment and Gaming Industry\n2015, 120\n2016, 140\n2017, 160\n2018, 180\n2019, 210\n2020, 230\n2021, 250"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "9EQSg7SBqF2IQCGl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year experienced the most growth in the Entertainment and Gaming Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to inspect the sizes of the pie slices for each year. The year with the largest slice had the most growth.\nBased on the above thought, the answer is:\n2021 had the most growth, as can be seen from its slice in the pie chart, which is the largest."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "ZqwagvftlSqelHo1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart reveal about the development trend of the Entertainment and Gaming Industry from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of each slice in the pie chart indicates the respective development value of the industry each year. More significant growth is shown with larger slices.\nBased on the above thought, the answer is:\nThe pie chart reveals that the Entertainment and Gaming Industry has been experiencing a steady and significant growth from 2015 to 2021. Particularly, the years 2019 and 2021 had the most significant growth, as can be seen from their larger slices and their exploded representation in the pie chart."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "2jmMs8q9Ww9c2YT3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be the implication(s) of the 2019 and 2021 data having larger slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Larger slices in a pie chart indicate higher values. Therefore, the years 2019 and 2021 having larger slices suggest a substantial increase in the development of the Entertainment and Gaming Industry in those years.\nBased on the above thought, the answer is:\nThe implication of the 2019 and 2021 data having larger slices in the pie chart is that these years experienced a rapid growth in the Entertainment and Gaming Industry. Therefore, these years could be considered as key points in the industry's development."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "AjWI64X3RhKWlLPu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what amount did the Entertainment and Gaming Industry grow from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth in the entertainment and gaming industry, I need to subtract the development value for 2015 from the value for 2021.\nBased on the above thought, the answer is:\n130"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "SNXLk5il5fZBmSmp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the Entertainment and Gaming Industry grow between 2015 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth percentage, I need to subtract the value for 2015 from the value of 2021, divide by the value for 2015, and then multiply by 100.\nBased on the above thought, the answer is:\nApproximately 108.33%"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "v0CauABqFrBjnQNS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average annual growth of the Entertainment and Gaming Industry from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average annual growth, I need to subtract the value for 2015 from the value for 2021, and then divide by the number of years between 2015 and 2021.\nBased on the above thought, the answer is:\nApproximately 21.67"
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "uzsYYJHoskZb2rHU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the sector-wise percentages for the years 2018, 2019, and 2020 from the pie chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the pie chart in sections by year {for 2018}[0.952, 0.071, 0.987, 0.091], {for 2019}[0.952, 0.101, 0.987, 0.121], and {for 2020}[0.952, 0.131, 0.987, 0.151], determining the percentage represented by each sector.\nBased on the above thought, the answer is:\nYear,Biotech,Artificial Intelligence\n2018,20.5%,9.5%\n2019,23.0%,12.8%\n2020,19.2%,15.1%"
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "b5aWBJ4gKiJexcCY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the pie chart, which sector has seen a more significant growth from 2018 to 2020? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the difference in the percentages of {the Biotech sector}[0.162, 0.483, 0.197, 0.503] and {the Artificial Intelligence sector}[0.745, 0.349, 0.78, 0.369] from 2018 to 2020.\nBased on the above thought, the answer is:\nThe Artificial Intelligence sector has seen more significant growth from 2018 to 2020. While both sectors have seen a rise in their percentages, Artificial Intelligence has grown by 5.6%, more than the Biotech's 1.3%."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "cnFjWjRg1NOsuNio",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend observed in the chart, do you consider the decrease in the Biotech sector in 2020 to be a negative sign for the Innovation and Technology Entrepreneurship Ecosystem?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the impact of decreased representation in the {Biotech sector}[0.754, 0.721, 0.789, 0.741] in 2020 on the overall ecosystem.\nBased on the above thought, the answer is:\nA decrease in one sector's representation could signal potential challenges. However, it is also important to consider that the overall representation of Artificial Intelligence has increased over the same period. This suggests shifts within the ecosystem rather than a decline overall."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "te6gBKDUW4w82jHw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the percentages for the sectors in the pie chart, how might planning for the year 2021 be influenced?"
+            },
+            {
+                "from": "gpt",
+                "value": "The planning could be influenced by looking at the trends of the {Biotech sector}[0.754, 0.721, 0.789, 0.741] and the {Artificial Intelligence sector}[0.336, 0.131, 0.371, 0.151] from 2018 to 2020.\nBased on the above thought, the answer is:\nBased on the data from 2018 to 2020, one strategy could be to further invest in the Artificial Intelligence sector given its consistent growth. On the other hand, steps could be taken to analyze the downturn in the Biotech sector and devise strategies to boost its involvement in the ecosystem for 2021."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "hRt8Crm3KlQK46KI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, by how much did the percentage of the Biotech sector decrease from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I start by noting down the percentage of the Biotech sector in {2018}[0.162, 0.483, 0.197, 0.503] and in {2020}[0.754, 0.721, 0.789, 0.741]. And then subtract the 2020 percentage from the 2018 percentage to calculate the decrease.\nBased on the above thought, the answer is:\nThe percentage of the Biotech sector decreased by 1.3% from 2018 to 2020."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "doop13d59kr3nHtR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category has the lowest representation in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this out by noting the percentages in 2020 for both the {Biotech sector}[0.754, 0.721, 0.789, 0.741] and the {Artificial Intelligence sector}[0.336, 0.131, 0.371, 0.151].\nBased on the above thought, the answer is:\nThe Biotech sector has the lowest representation in 2020."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "V6QpB836xyGToVxt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the percentage of the Artificial Intelligence sector from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I look at the percentage of the Artificial Intelligence sector in {2018}[0.745, 0.349, 0.78, 0.369] and in {2020}[0.336, 0.131, 0.371, 0.151], and then calculate the difference to find out the total increase from 2018 to 2020.\nBased on the above thought, the answer is:\nThe total increase in the percentage of the Artificial Intelligence sector from 2018 to 2020 is 5.6%."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "lVDfPjoilOG6ZYsE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart you analyzed, please extract the information and produce a structured table with the data."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart's title {\"Population Migration Trends from 2013 to 2019\"}[0.286, 0.021, 0.677, 0.047], and considering {the individual slices of pie} [{2013}[0.223, 0.278, 0.482, 0.53]; {2014}[0.223, 0.516, 0.482, 0.76]; {2015}[0.287, 0.516, 0.482, 0.886]; {2016}[0.484, 0.549, 0.701, 0.919]; {2017}[0.482, 0.413, 0.741, 0.736]; {2018}[0.499, 0.121, 0.747, 0.488]; {2019}[0.283, 0.146, 0.518, 0.516]], I can observe that each portion represents a specific year and the amount of population migration that occurred in that year.\nBased on the above thought, the answer is:\nYear, Migration\n2013, 80\n2014, 74\n2015, 89\n2016, 105\n2017, 100\n2018, 125\n2019, 110"
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "YfxmtXYu88dHVTcG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends do you notice in the population migration from 2013 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to review the proportions represented in the pie chart to identify noticeable trends in population migration.\nBased on the above thought, the answer is:\nThe population migration seems to have moderately increased from 2013 to 2016, dipped slightly in 2017, sharply increased in 2018, and then decreased somewhat in 2019."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "s7Lz695SKehKHgpM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest population migration, and why do you think this might be?"
+            },
+            {
+                "from": "gpt",
+                "value": "By noticing the size of the slices in the pie chart, I can identify which year had the highest population migration.\nBased on the above thought, the answer is:\n2018 had the highest population migration. The underlying causes for this cannot be determined by the chart itself. However, factors such as significant economic changes, policy alterations, or social unrest could contribute to these trends in population migration."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "KN6uJI4otUCvUZbu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data represented in the chart influence future trends of population migration?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the trends in the past years and speculate on how they might influence the future.\nBased on the above thought, the answer is:\nThe data indicates a trend towards increased population migration over the analyzed years, despite occasional drops. If this trend continues, we can expect population migration to increase in the coming years, barring significant changes in factors affecting migration."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "2REUvrF6Quk2eo0D",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two years did the combined population migration exceed 200?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to add up the population migrations for each pair of years in the data and see where the total is greater than 200.\nBased on the above thought, the answer is:\n2016 and 2018."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "qczQ2EKHygMIKtYA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total population migration over these seven years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I'll need to add up the population migrations for all the years from 2013 to 2019.\nBased on the above thought, the answer is:\nThe total population migration over these seven years is 683."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "65no9zpKSLF2XZKP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many did the population migration decrease from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, subtract the population migration in 2019 from the migration number in 2018.\nBased on the above thought, the answer is:\nThe population migration decreased by 15 from 2018 to 2019."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "iud6Ps5WXQTxH1CS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a CSV table that recasts the data from the observed pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the figure title {Entrepreneurial Spirit from 2008 to 2016}<r>[0.312, 0.021, 0.651, 0.047]</r> and the pie slices corresponding to different years with their respective percentages, I can tabulate the Entrepreneurial Spirit data for the years 2008 to 2016. Each slice represents the Entrepreneurial Spirit for a particular year.\nBased on the above thought, the answer is:\nYear,Entrepreneurial Spirit\n2008,9.7%\n2009,10.4%\n2010,10.7%\n2011,11.2%\n2012,11.5%\n2013,10.4%\n2014,11.6%\n2015,11.9%\n2016,12.6%"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "8Gehv197v3QUeQI1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you tell which year had the highest rate of entrepreneurial spirit? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "This requires analyzing the pie chart sections to determine which year had the largest slice, that is, the highest percentage.\nBased on the above thought, the answer is:\nThe year 2016 had the highest entrepreneurial spirit rate at 12.6%, as its slice in the pie chart is the largest."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "ZXfdqCUnK71GMn26",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of entrepreneurial spirit over the years from 2008 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentage slices in chronological order to detect any noticeable patterns or trends.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that the entrepreneurial spirit has generally been on the rise from 2008 to 2016. Although there was a slight dip in 2013, the trend resumed upwards thereafter."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "ITAKWRb1LunTG3PA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data in the chart. How would you anticipate the entrepreneurial spirit for the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can anticipate the entrepreneurial spirit for 2017 by analyzing the trend from the previous years.\nBased on the above thought, the answer is:\nConsidering the generally rising trend from 2008 to 2016, it would be reasonable to predict a further increase in the entrepreneurial spirit for the year 2017."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "0QIINgSkrfLAm3Qy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the mean rate of entrepreneurial spirit over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to find the mean rate, each percentage in the chart needs to be summed then divided by the total number of years.\nBased on the above thought, the answer is:\n11.1%"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "3NTdHiZY439rtLnk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year is the entrepreneurial spirit rate a dip compared to its previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare each year's percentage with the previous year's and find when it decreased.\nBased on the above thought, the answer is:\n2013"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "0768Wr7LxGlWAzMe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the entrepreneurial spirit rate for the year 2017 was 13%, what would be the mean rate of entrepreneurial spirit over the years from 2008 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new mean rate, I need to add the 2017 percentage to the total sum and divide by 10, for the 10 total years.\nBased on the above thought, the answer is:\n11.4%"
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "qPNQGyXWUc4ZlC23",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart you observe, could you translate the data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the title at the top of the chart {\"Company Investment in Social Responsibility and Sustainable Business\"}[0.186, 0.021, 0.777, 0.047] and the different slices {Sector slice} [{\"Renewable Energy\"}[0.223, 0.278, 0.482, 0.712]; {\"Corporate Governance\"}[0.262, 0.516, 0.482, 0.859]; {\"Workplace Diversity\"}[0.385, 0.516, 0.722, 0.886]; {\"Environmental Impact\"}[0.504, 0.127, 0.763, 0.635]; {\"Employee Rights\"}[0.344, 0.146, 0.5, 0.516]; {\"Fair Trade\"}[0.283, 0.202, 0.482, 0.516]], it's clear that each slice represents a category and its corresponding investment percentage.\nBased on the above thought, the answer is:\nCategory,Investment percentage\nRenewable Energy,20%\nCorporate Governance,10%\nWorkplace Diversity,25%\nEnvironmental Impact,30%\nEmployee Rights,10%\nFair Trade,5%"
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "hw1ZdaddBPOFxAer",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area attracts the maximum investment according to the pie chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the chart, the slice with the highest percentage corresponds to the area with the maximum investment.\nBased on the above thought, the answer is:\nThe area which attracts maximum investment is \"Environmental Impact\". This is because it has the highest percentage representation on the pie chart at 30%."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "ylRunFr9xlzC1jMV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given pie chart, what assumptions can you make about the company's focus areas in the realm of Social Responsibility and Sustainable Business?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sizes or proportions of each category, I can infer about the focus areas of the company.\nBased on the above thought, the answer is:\nThe pie chart indicates that the company is highly committed to environmental concerns as the \"Environmental Impact\" category has the highest representation. Other significant areas of focus include Renewable Energy and Workplace Diversity. \"Fair Trade\", on the other hand, seems to be a less significant area for the company, as it has the lowest representation on the chart."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "bjeKKayqD5ADGoAL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the extracted data from the pie chart influence a company\u2019s future decision-making process about Social Responsibility and Sustainable Business?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to make inferences based on the information conveyed by the chart.\nBased on the above thought, the answer is:\nThis data can substantially affect a company's future decisions about Social Responsibility and Sustainable Business. If the company is looking to balance their investments across various categories, they might decide to reduce some budget from \"Environmental Impact\", and increase the allocation to \"Fair trade\". This data could also indicate to the company that more initiatives could be introduced related to \"Fair Trade\" and \"Employee Rights\" as these areas have the least investment currently."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "Y7Z4DjQHIpZhRxxn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two least contributing categories according to the observed pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the two least contributing categories, one needs to observe the given percentages of all categories and select two with the least percentages.\nBased on the above thought, the answer is:\nCorporate Governance and Fair Trade."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "N8KAPOhId55shFaq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the total investment in the case the percentage of Renewable Energy increases by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new total investment, I need to increase the \"Renewable Energy\" percentage by 10% and add up all the percentages.\nBased on the above thought, the answer is:\nThe new percentage for \"Renewable Energy\" is 20% + 10% = 30%. The new total investment is 30% (Renewable Energy) + 10% (Corporate Governance) + 25% (Workplace Diversity) + 30% (Environmental Impact) + 10% (Employee Rights) + 5% (Fair Trade) = 110%."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "N8ItyIYSDkbUUBcX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does Workplace Diversity, Environmental Impact, and Renewable Energy contribute to the total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the collective contribution, I need to sum up the percentages of the specified categories.\nBased on the above thought, the answer is:\nThe collective contribution is 20% (Renewable Energy) + 25% (Workplace Diversity) + 30% (Environmental Impact) = 75%."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "4JxbavFDRao65JRu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, can you convert the data of \"Vegetable Production in the Food and Agriculture Industry from 2015 to 2020\" into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {Vegetable Production in the Food and Agriculture Industry from 2015 to 2020}[0.161, 0.021, 0.803, 0.047] and the corresponding percentage of Vegetable production in each year reflected by the pie slices {Year 2015}[0.223, 0.278, 0.482, 0.58] {Year 2016}[0.226, 0.516, 0.482, 0.86] {Year 2017}[0.386, 0.516, 0.611, 0.886] {Year 2018}[0.482, 0.516, 0.739, 0.836] {Year 2019}[0.505, 0.176, 0.764, 0.536] {Year 2020}[0.283, 0.146, 0.608, 0.516],  I can create a CSV table by using the year as the first column, followed by the corresponding percentage of Vegetable production for each year.\nBased on the above thought, the answer is:\nYear,Vegetable Production (%),Livestock Production (%)\n2015,60%,40%\n2016,70%,30%\n2017,62%,38%\n2018,65%,35%\n2019,80%,20%\n2020,95%,5%"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "22Y6Jsh4QvYNpX9Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the answer you obtained in the previous numerical question, in which direction does Food and Agriculture industry development trend from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the increase in Vegetable Production and the decrease in Livestock Production from 2015 to 2020, I can analyze the trend of the Food and Agriculture industry.\nBased on the above thought, the answer is:\nThe Food and Agriculture industry trend is leaning towards Vegetable Production, which has been continuously increasing over the years, while Livestock Production has been decreasing."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "tvSgKOceHkELz64e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implications of the data presented in the pie chart for future decisions in the Food and Agriculture industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the increase in Vegetable Production, and the decrease in Livestock Production over the years, the data can offer insights for future decisions in the industry.\nBased on the above thought, the answer is:\nThe trend towards Vegetable Production suggests that there is growing demand or focus on vegetables over livestock. This could influence future decisions in the Food and Agriculture industry to invest more resources into Vegetable Production to meet this demand."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "V6Z6JiiSgZytmHuv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat effect might the data presented in the pie chart have on consumer choices in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into consideration the rising share of Vegetable Production compared to Livestock Production from 2015 to 2020, I can infer the possible effects on consumer choices.\nBased on the above thought, the answer is:\nGiven the upward trend in Vegetable Production, consumers in 2021 may have to adapt to an increase in vegetable products available in the market, possibly encouraging a more plant-based diet. Consumers who prefer livestock products may also have to adjust their choices or pay more due to the decline in Livestock Production."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "C7Ee3Yq5oE3nXNrS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the percentage increase in Vegetable Production from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase in Vegetable Production from 2015 to 2020, we need to take the difference between the percentage in 2020 and the percentage in 2015, and divide by the percentage in 2015.\nBased on the above thought, the answer is:\n58.33%"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "dQyifSFPWdmbQHwJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage decrease in Livestock Production from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease in Livestock Production from 2015 to 2020, we need to take the difference between the percentages in 2015 and 2020, and divide that difference by the percentage in 2015.\nBased on the above thought, the answer is:\n87.5%"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "7EA4nd7A6opCLDDq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of Vegetable Production during 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage of Vegetable Production, I need to sum up the percentages from 2015 to 2020 and then divide by the number of years.\nBased on the above thought, the answer is:\n72%"
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "JbNXk061sBSNLpAG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data in the pie chart you're viewing into a CSV format table using the extracted chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle at the bottom of the chart {Social Demographic Research by Age Group}[0.298, 0.021, 0.665, 0.047] and the sector slices {Under 25}[0.223, 0.278, 0.482, 0.712], {25-34}[0.258, 0.553, 0.654, 0.923], {35-44}[0.482, 0.32, 0.741, 0.788], {45-54}[0.451, 0.146, 0.701, 0.516] and {Over 55}[0.283, 0.148, 0.482, 0.516] I can see that the sectors represent different age groups and their respective proportions.\nBased on the above thought, the answer is:\nAge Group,Percentage\nUnder 25,20\n25-34,28\n35-44,22\n45-54,18\nOver 55,12"
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "LeX9hthJuqxI93aG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich age group is the most prevalent based on the pie chart? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most prevalent age group, I need to find the slice with the largest proportion.\nBased on the above thought, the answer is:\nThe age group 25-34 is most prevalent as it represents the largest slice of the pie chart, which correlates to the highest population percentage of 28%."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "VrBSujYgoaRUgWAC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you draw about the population's age distribution in this demographic research from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions of each slice to draw inferences about the population's age distribution.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that the majority of the population falls within the age groups 25-34 and 35-44, with a slightly higher presence for the former. The least amount of individuals are found in the age groups under 25 and over 55, respectively."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "yaj0vDaEKO4oMBw3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a funding organization were to distribute resources based on this research data as visualized in the pie chart, how might they allocate their funds among these demographic age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the potential funding allocation, I should consider the relative proportion of each age group, as represented in the pie chart.\nBased on the above thought, the answer is:\nA funding organization might allocate the largest share of their resources towards the 25-34 age group, reflecting its high prevalence in the chart. The 'Over 55' group might receive the least funds as they constitute the smallest slice of the chart. Thus, the fund allocation may closely follow the percentage distribution of the age groups in this research data."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "wttbdcM1Z1EhwpsG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, which age group has the highest population percentage according to the source data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the age group with the highest population percentage, I need to identify the slice with the largest proportion.\nBased on the above thought, the answer is:\n25-34."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "AVSbnvk2q4VKkW6I",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the total population percentage of the age groups Under 25 and 25-34?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total population percentage of the two age groups, I need to sum up their respective percentages.\nBased on the above thought, the answer is:\nThe total population percentage of the age groups Under 25 and 25-34 is 20% + 28% = 48%."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "451QbiVvBDN2Y0jX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the population percentage of the age group 45-54 if it increased by 7%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new population percentage of the age group 45-54, I need to add 7% to the current percentage.\nBased on the above thought, the answer is:\nIf the population percentage of the age group 45-54 increased by 7%, it would be 18% + 7% = 25%."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "kyDNfnWB5vtQWvFC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you construct a CSV table from the pie chart data representing purchasing power across different demographics?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can utilize the data from different demographics and their corresponding percentages, as depicted on {the pie chart}<r>[0.223, 0.146, 0.762, 0.907]</r> and {the legend}<r>[0.854, 0.063, 0.993, 0.249]</r>.\nBased on the above thought, the answer is:\nDemographic,Purchase Power (%)\nYoung Adults,13.5\nMiddle-Aged,21.6\nAdults,32.4\nSeniors,5.4\nMillennials,17.3\nGen Z,9.7"
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "JWBfmYWxDHqD81Rl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich demographic has the highest purchasing power according to the pie chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart, the demographic with the largest slice corresponds to the highest purchasing power.\nBased on the above thought, the answer is:\nThe \"Adults\" demographic has the highest purchasing power as represented by the largest slice of the pie chart, which is 32.4% of the total purchasing power."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "zpVCZbkfH8jOM6DD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two demographics combined have almost the same purchasing power as Adults?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the purchasing power of Adults to combinations of two demographics.\nBased on the above thought, the answer is:\nThe combined purchasing power of \"Millennials\" and \"Gen Z\" is close to the purchasing power of \"Adults\". This shows that these two demographic categories together form a potential market almost as large as the \"Adults\" segment."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "7OFpJIhJE882cfTV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what insights can be drawn about the potential market of emerging consumers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I should look at the purchasing power of each demographic, particularly the percentages of the emerging consumers - Millenials and Gen Z.\nBased on the above thought, the answer is:\nThe chart clearly shows that emerging consumer demographics - particularly \"Millennials\" and \"Gen Z\" - have significant purchasing power, comprising 26.5% of the total. Their relatively high purchasing power indicates this segment is an important target for marketers aiming at emerging markets."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "mFsXtvQEKcmsjv6B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what are the accumulative purchasing powers of Young Adults, Middle-Aged people, and Adults?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the purchasing powers, I need to add the corresponding percentages of the groups mentioned in the question.\nBased on the above thought, the answer is:\nThe combined purchasing power of Young Adults, Middle-Aged people, and Adults is 67.5%."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "PXF6Nw4c8SAuFvaq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich demographic has half the purchasing power of Adults?"
+            },
+            {
+                "from": "gpt",
+                "value": "Half the purchasing power of Adults is 16.2%. Comparing this value with the purchasing powers of other demographics should give an answer.\nBased on the above thought, the answer is:\nMillennials."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "oa7LE0Zh5wqkZ0I7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the purchasing power of Seniors increased by 10%, what would be the new total purchasing power of all demographics?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I would need to add 10% to the current purchasing power of Seniors and then add up the purchasing power of all demographics.\nBased on the above thought, the answer is:\nThe total purchasing power would be 110%."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "vNxDl8S0CQAVscS9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you take information from the chart and transfer it into a CSV data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is titled {Consumer Behavior Index in Different Emerging Markets - 2021}<r>[0.217, 0.021, 0.746, 0.047]</r>. It contains pie segments representing different emerging markets at the center of the page {Consumer Behavior Index segments}<r>[0.223, 0.146, 0.741, 0.922]</r>, and the segments are paired with labels and indices on the right {List of labeled indices}<r>[0.858, 0.063, 0.993, 0.338]</r>. Combining these indices with the list of emerging markets represented in the chart allows data to be put in a CSV format.\nBased on the above thought, the answer is:\nMarket,Consumer Behavior Index\nChina,11.4%\nIndia,12.6%\nBrazil,10.4%\nRussia,10.0%\nMexico,11.2%\nIndonesia,12.8%\nTurkey,10.9%\nSouth Africa,10.2%\nArgentina,10.5%"
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "A8emtG4IOIthx3n2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the dominate slice in the pie chart suggests about consumer behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should identify the largest slice in the pie chart and then infer its impact on consumer behavior.\nBased on the above thought, the answer is:\nThe largest slice belongs to Indonesia, suggesting that Indonesia has the most active and ambitious consumers among these emerging markets."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "0GdKh98bFWBRUM6V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which countries have similar consumer behavior indices?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the similar sizes of the pie slices to identify the countries with similar consumer behavior indices.\nBased on the above thought, the answer is:\nChina, Brazil, and Mexico have similar indices around the mid-60s, showing a roughly equal level of consumer behavior in these countries. Similarly, Argentina and South Africa also have comparable index levels, which are just below 60."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "Hgvg6SCTb2F2wWKG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the possible implications of the data represented in the chart for companies looking to expand to these markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the pie chart will give an idea of the consumer behavior in different markets. Bigger slices indicate stronger consumer activity and would probably be more attractive for companies looking to expand.\nBased on the above thought, the answer is:\nCompanies might be particularly interested in expanding in Indonesia due to its high consumer behavior index. Markets like China, India, and Mexico with modest consumer behavior indices can offer a balanced risk/reward scenario. However, expansion into lower index markets like Russia might require more in-depth understanding and a specifically tailored approach."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "Uvd8lq20zcrewsdU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich market has the highest Consumer Behavior Index in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of all the pie slices to find which one is the largest.\nBased on the above thought, the answer is:\nIndonesia."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "mAk3oOAOGnUckIKb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the Consumer Behavior Index for Brazil according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the label for Brazil to find the corresponding percentage.\nBased on the above thought, the answer is:\n10.4%."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "p6TegdhbZS0pdoNT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the sum of the Consumer Behavior Indices for Russia and Mexico?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the percentages for Russia and Mexico and add them together.\nBased on the above thought, the answer is:\n21.2%."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "NeTpQyhtJu5oc575",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the data from the pie chart and convert it into a CSV format table for easier interpretation?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, each industry like {Agriculture}<r>[0.223, 0.278, 0.482, 0.65]</r>, {Manufacturing}<r>[0.24, 0.516, 0.482, 0.859]</r>, {Construction}<r>[0.385, 0.516, 0.575, 0.886]</r>, {Wholesale}<r>[0.482, 0.516, 0.732, 0.861]</r>, {Retail}<r>[0.482, 0.232, 0.741, 0.61]</r>, {Transportation}<r>[0.482, 0.147, 0.648, 0.516]</r>, and {ICT}<r>[0.283, 0.146, 0.5, 0.516]</r> have their respective percentages. Using these percentages, I can create a CSV formatted table.\nBased on the above thought, the answer is:\nIndustry,Market Share\nAgriculture,17\nManufacturing,13\nConstruction,12\nWholesale,15\nRetail,18\nTransportation,10\nICT,15"
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "5erWpLlbCRTFM9Ul",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which industry holds the largest market share, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will identify the industry that covers the largest sector in the pie chart.\nBased on the above thought, the answer is:\nThe Retail industry holds the largest market share with 18%. This is evident from its sector size being the largest among all industry sectors represented in the pie chart."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "Vwr30oZY5ICIM2cs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we want to focus on the three industries that have the majority of the market share, which should we choose?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the majority of market share, we need to select those three industries that in sum constitute more than 50% of the market share.\nBased on the above thought, the answer is:\nWe should focus on the Retail, Agriculture, and Wholesale industries as their combined market share is more than 50%."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "rP6KdFDn8eQE9glu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the chart reflect the market dominance of individual sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "The dominance of a particular sector is illustrated by the size of its corresponding sector in the pie chart.\nBased on the above thought, the answer is:\nFrom the chart, we can see that the Retail industry is the most dominant as it represents the largest sector. Meanwhile, the Transportation industry is the least dominant as it represents the smallest sector."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "ARlOFmbNzKtVEDIM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which two industries have a combined market share of 30%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the sum of the market shares for each possible pair of industries and compare them to 30%.\nBased on the above thought, the answer is:\nAgriculture and Wholesale."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "mIitB6dpp9CnGwCM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share of all industries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average market share, I would sum up the market shares of all industries and divide by the total number of industries.\nBased on the above thought, the answer is:\nThe average market share is approximately 14.3%."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "Yk2NHlkU5tiw6Hrp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share of the ICT industry increases by 3%, what will be the new overall market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the new overall market share, I would add 3% to the current market share of the ICT industry and add it to the sum of the market shares of the other industries.\nBased on the above thought, the answer is:\nThe new overall market share would be 103%."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "DxAGj4KM0lQANtbI",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data for the year 2016 into the form of a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the information from the legend of the chart {the legend}<r>[0.751, 0.063, 0.993, 0.159]</r> that describe GDP, GNP and Unemployment for the year 2016 along with their respective values represented by the pie chart {the sectors}<r>[{\"GDP - 2016\"}[0.223, 0.278, 0.482, 0.833], {\"GNP - 2016\"}[0.326, 0.146, 0.741, 0.886], {\"Unemployment Rate - 2016\"}[0.265, 0.193, 0.464, 0.489]</r>, I can convert it into a CSV format table.\nBased on the above thought, the answer is:\nYear,GDP,GNP,Unemployment Rate\n2016,27.5%,68.8%,3.7%"
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "V5ZBMScMSJRltXjD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you observe about the economy in 2016 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can observe that the pie chart shows the representation of different economic indicators for 2016, {GDP}<r>[0.223, 0.278, 0.482, 0.833]</r>, {GNP}<r>[0.326, 0.146, 0.741, 0.886]</r>, and {Unemployment Rate}<r>[0.265, 0.193, 0.464, 0.489]</r>. The sizes of the pie pieces give an idea about the value of each indicator.\nBased on the above thought, the answer is:\nFrom the pie chart, we can observe that the largest portion of the economy was from the Gross National Product (68.8%), followed by the Gross Domestic Product (27.5%). The unemployment rate covered the smallest portion of the economy (3.7%) in 2016."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "qW0j79DUtahEJKLQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich economic indicator had the most significant impact in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart sector sizes, the economic indicator with the most significant impact can be determined.\nBased on the above thought, the answer is:\nThe economic indicator with the most significant impact in 2016 was the Gross National Product (GNP)."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "1f6RGqeZVHfDsSNm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would an increase in the GDP percentage impact the distribution of economic indicators shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the event of an increase in GDP percentage, there would be an increase in the GDP pie sector size at the expense of either one or both of the other two sectors. This would lead to a decrease in their relative sizes, thus changing the overall distribution of economic indicators shown on the pie chart.\nBased on the above thought, the answer is:\nIf the GDP percentage increased, it would occupy a larger portion of the pie chart, thus decreasing the relative portions of the GNP and the Unemployment rate. The pie chart would then show a greater emphasis on GDP in the economic distribution."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "qC93cSCIjcF36MYF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the GDP increased by 2% in the next year, what would its new percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of the GDP, I need to add the 2% to the current GDP percentage from the pie chart \"{GDP - 2016}\"<r>[0.223, 0.278, 0.482, 0.833]</r>.\nBased on the above thought, the answer is:\nThe new GDP percentage would be 29.5%."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "RS0LGnJGeqrbFmvb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage difference is there between the GDP and GNP in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference between GDP and GNP, I need to subtract the GDP percentage from the GNP percentage found in sectors of the pie chart \"{GNP - 2016}\"<r>[0.326, 0.146, 0.741, 0.886]</r> and \"{GDP - 2016}\"<r>[0.223, 0.278, 0.482, 0.833]</r>.\nBased on the above thought, the answer is:\nThe percentage difference between GDP and GNP in 2016 is 41.3%."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "jr1YPHQ1Ae2FDqd0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the economic indicators for the year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total percentage of economic indicators equals to 100% as it includes all categories in the pie chart.\nBased on the above thought, the answer is:\nThe total percentage of economic indicators for the year 2016 is 100%."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "iXeV637ZZXWpb7xB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart represents the energy source usage for different years. The information is displayed on the {legend}<r>[0.858, 0.063, 0.993, 0.309]</r> and the corresponding {pie slices}<r>[0.223, 0.146, 0.763, 0.894]</r>.\nBased on the above thought, the answer is:\nYear, Solar, Wind, Hydropower, Geothermal, Nuclear, Biomass, Fossil fuels, Others\n2012, 5%, 0%, 0%, 0%, 0%, 0%, 0%, 0%\n2013, 0%, 20%, 0%, 0%, 0%, 0%, 0%, 0%\n2014, 0%, 0%, 15%, 0%, 0%, 0%, 0%, 0%\n2015, 0%, 0%, 0%, 25%, 0%, 0%, 0%, 0%\n2016, 0%, 0%, 0%, 0%, 15%, 0%, 0%, 0%\n2017, 0%, 0%, 0%, 0%, 0%, 4%, 0%, 0%\n2018, 0%, 0%, 0%, 0%, 0%, 0%, 11%, 0%\n2019, 0%, 0%, 0%, 0%, 0%, 0%, 0%, 5%"
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "3EsAH4SI3TFKN3Mz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat were the dominant energy sources in the period from 2012 to 2019 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages of each energy source for each year and determine which sources had the highest percentages.\nBased on the above thought, the answer is:\nThe dominant energy sources over the years were Solar in 2012, Wind in 2013, Hydropower in 2014, Geothermal in 2015, Nuclear in 2016, Biomass in 2017, Fossil fuels in 2018, and Others in 2019."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "f6RC6rwb81jaEK7Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you notice in the energy usage from 2012 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages of each energy source over the years and observe any patterns or trends.\nBased on the above thought, the answer is:\nThere is a trend towards increased usage of clean and renewable energy sources like Wind, Solar and Hydropower from 2012 to 2014, with a significant surge in Geothermal energy in 2015. From 2016 onwards, there's no dominant energy source with the usage percentages spread relatively evenly among different sources."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "U43LfVBSkpFhvubt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart impact future decisions on energy policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The decision-makers need to consider the trends over the years to plan future energy policies.\nBased on the above thought, the answer is:\nThe rise in clean energy usage, as seen from 2012 till 2015, indicates a transition towards sustainable energy sources. Consequently, future energy policies should promote and invest more in renewable and clean energy technologies. However, the diversification of energy sources beyond 2016 suggests that a balanced energy mix remains important."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "YVUSoxkjnAjykvZe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage contribution of Solar, Wind, and Hydropower in 2012, 2013, and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll find the percentages for Solar, Wind, and Hydropower for the years 2012, 2013, and 2014 and add them together.\nBased on the above thought, the answer is:\nThe total contribution is 40%."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "XIkQNqvYAP0drY1q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increased from 2014 to 2015 in terms of Geothermal energy source?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to find the percentage of Geothermal energy in 2015 and subtract the percentage in 2014 (which is 0).\nBased on the above thought, the answer is:\nThe increase was by 25%."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "8qH7xdBg1DG5dEt9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contribution does Wind energy source make in terms of the total energy percentage in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll find the percentage for Wind in 2013 to determine its contribution.\nBased on the above thought, the answer is:\nWind contributed 20% in 2013."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "EtzQ35YitUYSxKD3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you formulate a table for the developments in AI and Machine Learning, and in Blockchain tech from 2015 to 2023 as per the pie chart you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I analyze that there are two sets of data- one {set}<r>[0.601, 0.073, 0.629, 0.087]</r> mentioning AI and Machine Learning and another {set}<r>[0.601, 0.103, 0.629, 0.117]</r> mentioning Blockchain which I need to tabulate into a CSV table.\nBased on the above thought, the answer is:\nYear,AI and Machine Learning,Blockchain\n2015,5,0\n2016,10,0\n2017,20,5\n2018,25,10\n2019,30,15\n2020,42,20\n2021,60,30\n2022,70,45\n2023,81,60"
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "gVW89ojQiU10F56Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, which technology has seen the greatest growth over the years, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the growth percentages of AI and Machine Learning and Blockchain over the given years.\nBased on the above thought, the answer is:\nAI and Machine Learning has seen the greatest growth over the years because it started growing since 2015 and has a very steep growth curve leading up to 81% in 2023."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "M7NLTHyP3aBeeZXN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can you observe about the development of these two technologies over the years from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should look at the sectors of the graph to determine the growth trends for AI and Machine Learning, and Blockchain over the given years.\nBased on the above thought, the answer is:\nLooking at the chart, it can be seen that AI and Machine Learning have shown a consistent and rapid growth since 2015, starting from 5% and reaching 81% in 2023. Blockchain, on the other hand, remained stagnant at 0% for 2015 and 2016, but started picking up from 2017 onwards, reaching 60% in 2023."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "nWjZR1XP7HRjLHG7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, if you were to predict the trajectory of these two technologies in the coming years, what would you say?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the growth patterns of both AI and Machine Learning, and Blockchain from the past years to make a prediction for the future.\nBased on the above thought, the answer is:\nJudging by the significant increases in both technologies over the past years, it's likely that their impacts will continue on an upward trend in the foreseeable future. AI and Machine Learning, which has already demonstrated sustained fast-paced growth, is expected to continue leading the way. Meanwhile, Blockchain, despite a later start, shows a rapid catch-up trend and is expected to increase its impact further."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "e3HSLRjnyXGzYmsX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease calculate the percentage change in Blockchain's development from 2017 to 2023."
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage change, I need to identify the figures for Blockchain development in 2017 and 2023, and then use the formula: ((New value - Original value) / Original value) * 100%.\nBased on the above thought, the answer is:\nThe percentage change is ((60 - 5) / 5) * 100% = 1100%."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "WjpJpnL82EX1A6aj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage has AI and Machine Learning increased from 2015 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "Similarly to the last one, to determine the percentage increase, will compare the figures for AI and Machine Learning in 2015 and 2023.\nBased on the above thought, the answer is:\nThe percentage increase is ((81 - 5) / 5) * 100% = 1520%."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "8wMvTr0qSwNeSaME",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percent increase for AI and Machine Learning and Blockchain from the year 2015 to 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to sum up the percent increase for AI and Machine Learning and Blockchain from the year 2015 to 2023.\nBased on the above thought, the answer is:\nThe total percent increase is 1100% (Blockchain) + 1520% (AI and Machine Learning) = 2620%."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "J9AeJVcupozMAc6c",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a structured CSV table from the data depicted in the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart titled {\"Education Technology and Online Learning from 2011 to 2020\"}[0.225, 0.021, 0.739, 0.047] represents different years {from 2011 to 2020 in slices}['2011'[0.334, 0.384, 0.369, 0.404]; '2012'[0.312, 0.457, 0.348, 0.477]; '2013'[0.311, 0.541, 0.346, 0.561]; '2014'[0.306, 0.633, 0.341, 0.653]; '2015'[0.363, 0.674, 0.398, 0.694]; '2016'[0.423, 0.722, 0.467, 0.742]; '2017'[0.527, 0.706, 0.571, 0.726]; '2018'[0.630, 0.596, 0.674, 0.616]; '2019'[0.587, 0.379, 0.631, 0.399]; '2020'[0.424, 0.29, 0.468, 0.31]]. The amount of color representation is the data percentage distribution of 'Online Learning Platform Adoption' and 'Education Technology Investment' in the corresponding years.\nBased on the above thought, the answer is:\nYear,Online Learning Platform Adoption,Education Technology Investment\n2011,12,23\n2012,25,45\n2013,23,19\n2014,38,24\n2015,27,13\n2016,55,44\n2017,69,35\n2018,73,48\n2019,84,79\n2020,96,92"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "yY1iIqcivN60sFRT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow has the growth trend been for the Online Learning Platform Adoption over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the data distribution, I can observe that the Online Learning Platform Adoption has shown an overall upward trend from 2011 to 2020 with some occasional declines.\nBased on the above thought, the answer is:\nThe Online Learning Platform Adoption has generally exhibited a growth trend over the years, with notable increments between the years 2013 to 2014 and again between 2015 to 2016 and from 2017 onwards."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "GJHEQFLYFTD5OgXd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen was the most significant growth in Education Technology Investment observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to compare the investment points for each year and find the one with the most significant growth.\nBased on the above thought, the answer is:\nThe most significant growth in Education Technology Investment was observed in the year 2019."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "HG3xFUGMghS92FPs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the trends in Online Learning Platform Adoption and Education Technology Investment correlate over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to compare the trends of Online Learning Platform Adoption and Education Technology Investment over these years.\nBased on the above thought, the answer is:\nBoth the Online Learning Platform Adoption and Education Technology Investment displayed an upward trend, indicating a positive correlation where an increase in the adoption of online learning platforms corresponded to an increase in investment in education technology. This suggests that as online learning became more popular, more investment was funneled into education technology."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "z8HFkce1F7k0mQ7S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the Education Technology Investment lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "Among all the years, the lowest investment in technology can be obtained by comparing the investments in each year.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "7tMPdpDyanxbxraE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage has Online Learning Platform Adoption increased from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I observe the values of Online Learning Platform Adoption for the year 2016 and 2020 and then apply the formula [(Final-initial)/initial]*100.\nBased on the above thought, the answer is:\n75%"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "efYTad7P7ZEsyCzf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Education Technology Investment from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total Education Technology Investment over the entire period can be obtained by adding all the investments from 2011 to 2020.\nBased on the above thought, the answer is:\n432"
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "kiVQFy1GrDP2wnoa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart's data about consumer spending from 2009 to 2016 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From {the subtitle of the chart}<r>[0.275, 0.021, 0.688, 0.047]</r> and {the legend's items}[ {\"2009\"}<r>[0.952, 0.071, 0.987, 0.091]</r>, {\"2010\"}<r>[0.952, 0.101, 0.987, 0.121]</r>, {\"2011\"}<r>[0.952, 0.131, 0.987, 0.151]</r>, {\"2012\"}<r>[0.952, 0.161, 0.987, 0.181]</r>, {\"2013\"}<r>[0.952, 0.191, 0.987, 0.211]</r>, {\"2014\"}<r>[0.952, 0.221, 0.987, 0.241]</r>, {\"2015\"}<r>[0.952, 0.251, 0.987, 0.271]</r>, {\"2016\"}<r>[0.952, 0.281, 0.987, 0.301]</r>], it is clear each element represents a distinct year and its spending value in billions.\nBased on the above thought, the answer is:\nYear,Consumer Spending in Billions\n2009,193\n2010,205\n2011,222\n2012,255\n2013,281\n2014,320\n2015,336\n2016,372"
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "LCbMly7FMb23b6vG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend of consumer spending from 2009 to 2016 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must examine {the size of each pie slice}<r>[0.223, 0.146, 0.741, 0.923]</r> sequentially from 2009 to 2016 to determine the trend.\nBased on the above thought, the answer is:\nConsumer spending has generally increased year by year from 2009 to 2016, with a noticeable spike in 2012."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "kFA8L6zhGcgE7wwY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about consumer behavior from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to infer from {the pie chart}<r>[0.223, 0.146, 0.741, 0.923]</r> that shows the proportional spending each year.\nBased on the above thought, the answer is:\nOverall, consumers increased their spending from 2009 to 2016. The spike in 2012 suggests there may have been economic factors that year encouraging greater consumer spending compared to other years."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "1QEsIdw2OaDzYrds",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend depicted in the chart continues, what could we predict about consumer spending for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the trend shown in the {pie chart slices}<r>[0.223, 0.146, 0.741, 0.923]</r>, estimating the spending increase rate to predict for the next year.\nBased on the above thought, the answer is:\nIf the same trend continues, we could expect consumer spending for the next year to keep increasing, potentially surpassing the figure for 2016. However, forecasting must take into account possible changes in economic conditions and consumer behavior."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "pYWMvnABE8OkEDwV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest consumer spending as depicted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the pie slices' sizes to determine the smallest one, referencing {the chart's legend}<r>[0.908, 0.063, 0.993, 0.309]</r>. \nThe label with the smallest corresponding slice would denote the year with lowest spending.\nBased on the above thought, the answer is:\n2009."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "DFYEAvUL7D3PBkLS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did consumer spending increase from 2009 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I need to subtract the spending of 2009 from 2016's, then divide the result by 2009's spending and multiply by 100 to get the percentage increase.\nBased on the above thought, the answer is:\n92.75%."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "4dWEVipPzC6fDMb7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year shows an unusual spike in consumer spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "Checking {the pie chart's slices}<r>[0.223, 0.146, 0.741, 0.923]</r>, I can locate an 'exploded' slice, which is used to highlight an unusual data point.\nBased on the above thought, the answer is:\n2012."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "PP7AbuNdrn2nQNR8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the pie chart, can you convert the data into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the legend {Legend}[0.827, 0.063, 0.993, 0.219], I can see the represented concerns are \"Climate Change\", \"Ozone Depletion\", \"Deforestation\", and \"Air Pollution\". Each slice of the pie chart represents the percentage distribution of Global concern for each area in the year 2015 as denoted in the title {\"Global Concern Distribution in 2015\"}[0.333, 0.021, 0.63, 0.047].\nBased on the above thought, the answer is:\nYear,Climate Change,Ozone Depletion,Deforestation,Air Pollution\n2010,70,85,40,75\n2015,74,83,42,78"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "tCwMl6OdUASUWRvG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, which environmental concern is given the highest attention and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the different slices of the pie chart and identify the slice that takes up the largest space.\nBased on the above thought, the answer is:\nThe environmental concern given the highest attention is \"Ozone Depletion\" because it takes up the largest slice in the pie chart, denoting the highest level of global concern."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "WVnqOETycIrUTlPV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the prioritization of environmental concerns in 2015 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will examine the sizes of the slices in the pie chart which give an impression of the prioritization of environmental concerns.\nBased on the above thought, the answer is:\nFrom the chart, \"Ozone Depletion\" and \"Climate Change\" appear to have been given priority consideration in 2015, taking significant fractions of the pie chart. \"Deforestation\" appears to have received the least attention."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "6gxlWcGBEQHNZo7o",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this data potentially influence environmental action plans in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to take into account the percentage levels of concern for each area, along with trends over time.\nBased on the above thought, the answer is:\nThis data could guide environmental action plans by highlighting the areas of most concern, such as \"Ozone Depletion\" and \"Climate Change\". These areas might be targeted for immediate or increased action. On the other hand, the increasing concern for \"Deforestation\" and \"Air Pollution\" might push for introduction or expansion of related measures in these areas."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "tfeMbCjfngC2NsTI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two concerns together account for approximately 70% of the global concerns in 2015 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which two concerns account for around 70%, I will need to add the percentages of different combinations of the concerns and check if they amount to around 70%.\nBased on the above thought, the answer is:\nClimate Change and Air Pollution."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "wVleSDqmVeoBj1Vl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average level of concern for the four areas in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the average level of concern, I will need to add the individual levels for the four areas and then divide by the number of areas.\nBased on the above thought, the answer is:\nThe average level of concern is (74 + 83 + 42 + 78)/4 = 69.25%"
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "16YU3z1TARuJ4can",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Deforestation increased its level of concern by 10% in the year 2016, what will be the new total level of global concern?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total level of concern, I will need to add 10% to the Deforestation concern level and then add up all the concern levels again.\nBased on the above thought, the answer is:\nThe new level of concern for Deforestation is 42% + 10% = 52%. The new total level of global concern is 74% (Climate Change) + 83% (Ozone Depletion) + 52% (Deforestation) + 78% (Air Pollution) = 287%."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "U3LRt2VKeykKt0sx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the influence distribution for the different leadership roles into a table format based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the graphic title {\"Influence Distribution in Business Management\"}[0.285, 0.021, 0.678, 0.047], the legend {\"CEO\"}[0.955, 0.071, 0.984, 0.091], {\"CTO\"}[0.955, 0.101, 0.984, 0.121], {\"CMO\"}[0.955, 0.131, 0.987, 0.151], {\"COO\"}[0.955, 0.161, 0.986, 0.181] and the corresponding slices of pie {\"CEO\"}[0.2, 0.296, 0.49, 0.904], {\"CTO\"}[0.482, 0.516, 0.74, 0.883], {\"CMO\"}[0.482, 0.147, 0.741, 0.542], {\"COO\"}[0.283, 0.146, 0.5, 0.516], I can detail the influence distribution.\nBased on the above thought, the answer is:\nLeadership Level, Influence\nCEO, 38%\nCTO, 22%\nCMO, 25%\nCOO, 15%"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "rDJGZQrONQTaES1s",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho seems to be the most influential leader based on the pie chart, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size of each sector from the pie chart, I can identify the leader with the largest influence.\nBased on the above thought, the answer is:\nThe CEO is the most influential leader in business management according to the chart because their slice on the pie chart is the largest representing 38% of influence."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "ct1OEwy2fsPVAYP4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the distribution of influence presented in the chart, how might decision-making be affected in this business management?"
+            },
+            {
+                "from": "gpt",
+                "value": "The influence distribution suggests the relative power each role holds in major decisions within the organization.\nBased on the above thought, the answer is:\nGiven that the CEO has the largest slice, suggesting the most influence, decision-making seems predominantly directed by the CEO. However, all leaders contribute to the process highlighting a collective decision-making approach, although not equally distributed."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "ct9alP9uBu9LVSig",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there were changes in influence distribution planned for the next fiscal year shifting 5% from CEO's influence to COO, how might this shift impact the organization's decision-making process?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the potential impacts of the change in the influence distribution.\nBased on the above thought, the answer is:\nIf 5% of influence shifts from the CEO to the COO, it might lead to a more balanced decision-making process with increased input from operations. The COO might be able to implement more operational efficiencies and have a greater impact on operational decisions than before."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "0xzhooqEkkrQF2yo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the sum of influence from technical and operational leadership roles?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the sum of influence of technical and operational leadership roles, I'll add the influence of the CTO and COO, which can be obtained from their corresponding pie slices.\nBased on the above thought, the answer is:\n37%"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "r9BZh96GjF5l3wPK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the  percentage difference in influence between the CEO and the rest of the leadership team?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the sum of the influences of the CTO, CMO, and COO from the influence of the CEO.\nBased on the above thought, the answer is:\n13%"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "1kiGTkETXCDdCXkE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the influence of the CEO increased by 7%, what would be the new influence distribution total?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll add 7% to the influence of the CEO, then sum the influences of all leadership roles.\nBased on the above thought, the answer is:\n107%"
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "VP76D2TvGhK6RnN7",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, could you please translate the data into a CSV table formatted output?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, it presents the number of security incidents from 2016 to 2021. Each slice of the pie chart represents a year and the percentage of security incidents for that year, which could be represented as a csv table.\nBased on the above thought, the answer is:\nYear,Security Incidents\n2016,9.5%\n2017,15.2%\n2018,18.2%\n2019,20.8%\n2020,13.8%\n2021,22.5%"
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "uxpQfsSFSre4vcuT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart's information, which year had the maximum number of security incidents and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the year with the highest proportion of security incidents from among the different pie slices. A higher percentage indicates a greater number of security incidents for that given year.\nBased on the above thought, the answer is:\nThe year 2021 had the highest number of security incidents, as suggested by its largest proportion of 22.5% among all the sectors in the pie chart."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "fQJEHIu6G7sxWB1Q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of security incidents over the years from the given pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the distribution of proportions for each year to understand the overarching trend.\nBased on the above thought, the answer is:\nThe chart implies a general upward trend in security incidents from 2016 to 2021, demonstrating that cyber threats were becoming more significant. A noticeable exception is 2020, which saw a slight dip in incidents compared to 2019, but the number of incidents ramped up significantly in 2021."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "tkpSPPDRH9fwfQfU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart impact cyber security strategies in the upcoming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the change in security incidents every year is influencing the cybersecurity tactics.\nBased on the above thought, the answer is:\nThe pie chart shows a consistent increase in security incidents over the years, with a massive jump in 2021. This increasing trend suggests that the need for improved cybersecurity measures has intensified. As a result, future cybersecurity strategies might need to offer more comprehensive protections and proactively cope with the escalating threats to keep this number in check."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "q7QTZyZdPskBLONn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will the percentage be if I add the security incidents of year 2016 and year 2020 together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this calculation, I need to add up the percentage of security incidents for the year 2016 and the year 2020.\nBased on the above thought, the answer is:\nThe total percentage for 2016 and 2020 is 9.5% + 13.8% = 23.3%."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "TVy2V5X9T1KQ22gK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year has the least proportion of security incidents?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this answer, I need to identify the year with the smallest pie slice, which indicates the smallest proportion of security incidents.\nBased on the above thought, the answer is:\nThe year with the least proportion of security incidents is 2016."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "GoLIbmYZhUYI9VkS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the security incidents for the year 2021 increased by 5%, what would be the new percentage for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this answer, I need to add 5% to the current proportion of security incidents for the year 2021.\nBased on the above thought, the answer is:\nThe new percentage for 2021 would be 22.5% + 5% = 27.5%."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "FQBMvA40JBq7kFDo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the development prospects and their respective percentages from the pie chart and show it as a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have identified the development prospects and their percentages from {the sector slice}[{'13'}[0.279, 0.278, 0.482, 0.516]; {'24'}[0.242, 0.285, 0.482, 0.516]; {'48'}[0.223, 0.376, 0.482, 0.563]; {'200'}[0.225, 0.516, 0.482, 0.578]; {'192'}[0.226, 0.516, 0.482, 0.789]; {'18'}[0.307, 0.516, 0.504, 0.886]; {'20'}[0.482, 0.516, 0.739, 0.885]; {'10'}[0.482, 0.282, 0.741, 0.559]; {'22'}[0.461, 0.112, 0.692, 0.482]; {'50'}[0.283, 0.148, 0.482, 0.516]]. Each development prospect has its numbered code and it's respective proportion.\nBased on the above thought, the answer is:\nDevelopment Prospects,Percentage\n13,0.4%\n24,4.5%\n48,8.2%\n200,0.7%\n192,10.5%\n18,13.1%\n20,21.8%\n10,12.7%\n22,16.0%\n50,12.0%"
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "ujmF0Lqz1JrqTvK3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich development prospect has the largest interest according to the chart? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer your question, let's find the outlier in the chart in which highest interest lies in. The outlier is represented as an exploded slice {20}<r>[0.482, 0.516, 0.739, 0.885]</r> with an interest of 21.8%.\nBased on the above thought, the answer is:\nDevelopment prospect \"20\" has the largest interest according to the chart because it has the largest proportion of 21.8% among all development prospects."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "B23FXBtbrGizJQy5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploded slice in the chart signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded slice in the chart normally represents the value that stands out the most from the rest. From this chart, the slice {22}<r>[0.461, 0.112, 0.692, 0.482]</r> is exploded.\nBased on the above thought, the answer is:\nThe exploded slice in the chart signifies the largest outlier from the dataset, which indicates the technology development prospect that has the most anticipation."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "zhtHpDY0OY0NDEbu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could one interpret the proportions of the development prospects given in the chart for future references?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportions shown represent the anticipated growth of each development prospect. Hence, predicting the future references based on the proportion-led order.\nBased on the above thought, the answer is:\nHigh proportion prospects are anticipated to grow more in the future than the lower ones. Hence, higher interest and investment might be directed towards higher proportion prospects like \"20\", \"22\" while efforts could be made to enhance the prospects which currently have a lower proportion like \"13\",\"200\"."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "8aQIS0EY0u7C0Ym3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages for the development prospects 13 and 24?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer your question, let's find the percentages of development prospects {13}<r>[0.279, 0.278, 0.482, 0.516]</r> and {24}<r>[0.242, 0.285, 0.482, 0.516]</r> which are 0.4% and 4.5% accordingly and then calculate the sum.\nBased on the above thought, the answer is:\nThe sum is 4.9%."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "yCyyjo6CmLjYNnle",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion difference between the development prospects 18 and 22?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer your question, let's find the percentages of development prospects {18}<r>[0.307, 0.516, 0.504, 0.886]</r> and {22}<r>[0.461, 0.112, 0.692, 0.482]</r> which are 13.1% and 16.0% respectively and then calculate the difference.\nBased on the above thought, the answer is:\nThe difference is 2.9%."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "ctPCvwPp7Zh7RAzT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of development prospects corresponding to this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, it's required to sum up the percentages of all development prospects and divide by the number of prospects.\nBased on the above thought, the answer is:\nThe sum of the percentages is 0.4% + 4.5% + 8.2% + 0.7% + 10.5% + 13.1% + 21.8% + 12.7% + 16.0% + 12.0% = 100%. The average percentage is 100% / 10 = 10%."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "Ec8ZJyZDgw3hPqJJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, can you convert the data about social media usage into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart with the title {Facebook Usage from 2012 to 2016}<r>[0.334, 0.021, 0.63, 0.047]</r>, slices representing different years like {2012}<r>[0.223, 0.278, 0.482, 0.589]</r>, {2013}<r>[0.228, 0.516, 0.482, 0.886]</r>, {2014}<r>[0.475, 0.516, 0.733, 0.886]</r>, {2015}<r>[0.482, 0.187, 0.741, 0.607]</r>, and {2016}<r>[0.283, 0.146, 0.6, 0.516]</r> are all showing a percentage which can be converted into datatable.\nBased on the above thought, the answer is:\nYear,Facebook\n2012,35\n2013,40\n2014,45\n2015,50\n2016,55"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "qStQV2SnKiJGVa5s",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you infer any trend in Facebook usage over the years from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of the pie slices from 2012 to 2016, which is growing, I can infer the trend.\nBased on the above thought, the answer is:\nThe trend suggests that Facebook usage has been increasing gradually from 2012-2016."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "hn8p3c8hKT31mzE6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this data influence the allocation of advertising budget on different social media platforms in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to consider how the increasing trend in Facebook usage might impact decisions on advertising budgets.\nBased on the above thought, the answer is:\nSeeing that Facebook usage has been on the rise from 2012 to 2016, advertisers may consider increasing their advertising budget for Facebook in 2017."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "zk5B6TgbM8oyJ7Gx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the separating Instagram slice imply in the context of general social media platform usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "This implies a unique or significant trend in Instagram usage relative to other platforms.\nBased on the above thought, the answer is:\nThe separating Instagram slice on the pie chart likely highlights the increasing usage of Instagram over the years."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "PlpW54CaV8ZJ45bd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the average Facebook usage percentage from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I should sum up the percentages from every year and divide by the number of years.\nBased on the above thought, the answer is:\n43%"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "2JqSt9g4gpjEyAAl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, if in 2017, Facebook usage increase by 3%, what is the Facebook usage percentage in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the 2016's percentage, I should add 3% to it to find the 2017's percentage.\nBased on the above thought, the answer is:\n58%"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "Mp2aRzX8rsxg162j",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage represented in the pie chart was of all social media usage, what is the percentage of the other social medias in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage of other social medias can be calculated by deducting the Facebook percentage from 100%.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "YHO2GiXvsMvarXyM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the center of the chart {\"Healthcare Expenditure from 2010 to 2019\"}[0.303, 0.021, 0.66, 0.047] and {the sector slice}[\"2010\"[0.232, 0.278, 0.482, 0.516]; \"2011\"[0.223, 0.419, 0.482, 0.594]; \"2012\"[0.228, 0.516, 0.482, 0.763]; \"2013\"[0.289, 0.516, 0.482, 0.872]; \"2014\"[0.411, 0.516, 0.566, 0.886]; \"2015\"[0.482, 0.516, 0.697, 0.866]; \"2016\"[0.482, 0.462, 0.741, 0.722]; \"2017\"[0.504, 0.191, 0.76, 0.496]; \"2018\"[0.458, 0.146, 0.628, 0.516]; \"2019\"[0.283, 0.147, 0.482, 0.516]], I can see that the sectors represent different years and the corresponding expenditure in billion USD.\nBased on the above thought, the answer is:\nYear,Healthcare Expenditure\n2010,100\n2011,110\n2012,120\n2013,130\n2014,140\n2015,150\n2016,170\n2017,190\n2018,160\n2019,180"
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "0K7FCHvYhP68v1xd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you infer the trend of healthcare expenditure from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the pie chart and focus on the increase or decrease in percentages over the years.\nBased on the above thought, the answer is:\nThe healthcare expenditure has generally increased over the years from 2010 to 2019, with notable increases in the years 2013, 2016, and 2017. The color intensity in the chart also gradually intensifies indicating a rising trend."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "9JuCQWq61fo2ns8K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the healthcare expenditure in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the slice corresponding to 2017 and notice any special features.\nBased on the above thought, the answer is:\nThe slice corresponding to 2017 has been 'exploded' or pulled out from the rest, which might signify emphasis on the significant growth in healthcare expenditure during that year."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "dUbFt0vsQ949FqX2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the expenditure distribution in the chart influence healthcare policies in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the changes in expenditures might impact the allocation of resources in the healthcare sector.\nBased on the above thought, the answer is:\nSeeing how the healthcare expenditure has been consistently increasing over the years, policy makers might prioritize efforts to manage healthcare costs. Areas with notably sharp increases, such as in 2013, 2016, and 2017, might be studied for potential cost-saving opportunities without compromising healthcare quality."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "2VvJvJxzkkbTTskI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage for healthcare expenditure in the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the 2013 slice in the pie chart and look for the label that shows the percentage.\nBased on the above thought, the answer is:\nThe proportion of healthcare expenditure in 2013 was 9.0%[0.296, 0.851, 0.331, 0.871]."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "qtdTizAjjYF2PM15",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total healthcare expenditure from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum up the expenditures of all years from 2010 to 2019 to calculate the total expenditure.\nBased on the above thought, the answer is:\nThe total healthcare expenditure from 2010 to 2019 was 1350 billion USD."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "nVQkfhbM6PPrAVtk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the healthcare expenditure for 2019 increased by 10%, what is the new total healthcare expenditure from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total expenditure, I need to add 10% of the 2019 expenditure to the total expenditure for the period of 2010 to 2019.\nBased on the above thought, the answer is:\nWith a 10% increase, the healthcare expenditure for 2019 would be 180 + 18 = 198. Therefore, the new total healthcare expenditure from 2010 to 2019 would be 1350 + 18 = 1368 billion USD."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "zK18jkuF1gq4g1j4",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data you observed, can you generate a table in CSV format representing the distribution of education resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing the pie chart, I can see the distribution of resources from the various slices: {Book}<r>[0.201, 0.298, 0.522, 0.906]</r> represents 40% of the resources, {Online Material}<r>[0.506, 0.334, 0.765, 0.889]</r> represents 30%, {Interactive}<r>[0.482, 0.173, 0.701, 0.516]</r> takes up 10%, {Library}<r>[0.482, 0.147, 0.579, 0.516]</r> holds 5%, {Software}<r>[0.388, 0.146, 0.5, 0.516]</r> has 7%, {Toolkit}<r>[0.318, 0.171, 0.482, 0.516]</r> makes up 5% and {Others}<r>[0.283, 0.229, 0.482, 0.516]</r> accounts for 3%.\nBased on the above thought, the answer is:\nResource,Utilization\nBook,40\nOnline Material,30\nInteractive,10\nLibrary,5\nSoftware,7\nToolkit,5\nOthers,3"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "YIhNRpvC7YZhybzm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of resource is most preferred based on the utilization percentages provided in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The type of resource with the highest percentage of utilization on the chart is most preferred.\nBased on the above thought, the answer is:\n'Books' is the most preferred type of resource as it has the highest utilization percentage of 40%."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "j0gMYj4wBv01ol3L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the goal of the educational institution for the coming year is to increase digital interactions, what does the current utilization say about the challenge ahead?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will need to look at the utilization percentages of digital resources like 'Online Material' and 'Interactive'.\nBased on the above thought, the answer is:\nThe combined utilization of 'Online Material' and 'Interactive' methods is currently only 40%, which is equal to the utilization of 'Books' alone. This implies that while there is a substantial use of digital resources, it's still not the dominant method of resource consumption. This means that the institution might face a significant challenge in increasing digital interactions."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "kJBRrzNU07MAKWxT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence decisions on the allocation of resources to Library services?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will consider the percentage of Library utilization on the chart and its relationship with the utilization of other resources.\nBased on the above thought, the answer is:\nCurrently, Library services constitute only 5% of resource utilization. This might suggest a low interest or availability of Library resources. If an increase in Library usage is desired, decision makers might consider allocating more resources towards improving Library services, like increasing the number of books, or providing additional facilities. Alternatively, if this utilization is consistent with the organization's goals, such low utilization might be acceptable and not necessitate any changes in resource allocation."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "UWmQdyaCWDhAw8oB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the percentage usage of the 'Book' resource than that of the 'Library' resource?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to subtract the utilization percentage of 'Library' from that of 'Book'.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "C1QsSUVZofZqKlLP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of usage percentage for 'Software' and 'Toolkit'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to add the utilization percentage of 'Software' and 'Toolkit'.\nBased on the above thought, the answer is:\n12%"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "6XBt9LWNCRn1k2N4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the usage percentage of 'Others' was to increase by 2%, what would be the new total usage percentage of all resources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the new total percentage, I have to add 2% to the \"Others\" percentage and sum up all the percentages.\nBased on the above thought, the answer is:\n102%"
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "q8h1Hk0XqjFSIWM5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the information provided by the pie chart, can you please organize this data into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the chart titled {\"Pie Chart: Comparison of Social Inequalities and Rights Protection Attention\"}[0.168, 0.021, 0.796, 0.047] and the colorful slices {various slices}[0.223, 0.146, 0.761, 0.886], it's clear that the pie chart is representing different social issues and the level of attention they're receiving. Let's turn the pie representation into a structured table format.\nBased on the above thought, the answer is:\nSocial Issue, Attention Level (%)\nGender Inequality, 9%\nRacial Inequality, 12%\nIncome Inequality, 16%\nHealthcare Rights, 13%\nLGBTQ+ Rights, 20%\nEducation Inequality, 8%\nAge Inequality, 15%\nReligion Inequality, 12%\nDisability Rights, 7%"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "202Kho58lx1BgK1T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can we infer about this society's priorities in addressing social inequalities and rights protection?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart and particularly at the size of each slice which represents a social issue, we can determine which issue has received the most attention. A larger slice size corresponds to more attention.\nBased on the above thought, the answer is:\nThe highest level of attention appears to be on LGBTQ+ Rights as indicated by the largest slice of the pie, followed by Income and Age Inequalities. Gender Inequality and Disability Rights are areas that received the least attention based on the chart's visual representation."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "J67ntOmAh3gPQa1V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart reveal about the attention given to Healthcare Rights?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyse this, I need to look at the slice of the pie corresponding to {Healthcare Rights}<r>[0.398, 0.516, 0.596, 0.886]</r> and its size in relation to others.\nBased on the above thought, the answer is:\nHealthcare Rights receive a substantial amount of attention, just behind Income and Age Inequalities. This implies that health rights might be a significant concern in this society but may not be the highest priority when compared to others like LGBTQ+ Rights."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "t2gTVW6gCs9LMdsa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what way might the data presented in the pie chart inform decision making on tackling social inequalities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would have to consider the implications of the different allocations of attention across various social issues as depicted in the pie chart.\nBased on the above thought, the answer is:\nThe data may be used to inform prioritization in decision making, with issues such as LGBTQ+ Rights, Income Inequality, and Age Inequality possibly receiving a continued focus due to their already considerable attention. Conversely, it may point to a need to increase focus on lesser-attended areas like Gender Inequality and Disability Rights."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "kc0O1UPI2nrPC5fd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the sum of attention received by the issues of Race, Religion and Education Inequality?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to add up the percentages of these three social issues -\n    i. {Racial Inequality}<r>[0.223, 0.405, 0.482, 0.61]</r>\n    ii. {Religion Inequality}<r>[0.388, 0.146, 0.56, 0.516]</r>\n    iii. {Education Inequality}<r>[0.482, 0.383, 0.741, 0.546]</r>\nBased on the above thought, the answer is:\n32%"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "18Yg3mHghbmXxTo1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in attention received between Income Inequality and Healthcare Rights?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer to this question, I need to subtract the percentage of {Healthcare Rights}<r>[0.398, 0.516, 0.596, 0.886]</r> from {Income Inequality}<r>[0.231, 0.516, 0.482, 0.866]</r>.\nBased on the above thought, the answer is:\n3%"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "66JjvklMOtw5Zrtl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Identity-related Rights were to receive 10% attention, what would be their total share in the current data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I have to add the percentage related to identity - {Racial Inequality}<r>[0.223, 0.405, 0.482, 0.61]</r> and {LGBTQ+ Rights}<r>[0.503, 0.537, 0.761, 0.869]</r> to the new attention and compute the total percentage.\nBased on the above thought, the answer is:\n42%"
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "fUe3VI7ZRkyzTZhf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a CSV table with the data shown on the pie chart for 2013."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Policy Making Initiatives in 2013}<r>[0.348, 0.021, 0.615, 0.047]</r> and the pie slices for {Education Policy}<r>[0.223, 0.278, 0.68, 0.886]</r> and {Healthcare Policy}<r>[0.3, 0.118, 0.757, 0.725]</r>, I can see that the percentage of total policy making initiatives is equally split in 2013 between the two policy types.\nBased on the above thought, the answer is:\nPolicy Type,2013\nEducation Policy,50.0%\nHealthcare Policy,50.0%"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "gDJfcXbDEQIAXr4v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which policy type had the highest focus in 2010? Provide the reasoning behind your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "From the data, I can see that the policy type with the highest percentage of total policy making initiatives in 2010 is the one with the highest focus.\nBased on the above thought, the answer is:\nThe policy type with the highest focus in 2010 was \"Healthcare Policy\" because it had a larger proportion (75%) of the total policy making initiatives compared to \"Education Policy\" (25%)."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "GWqgouRQER3G7hpO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about the shift in policy making initiatives from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the percentage of total policy making initiatives for each policy type between 2010 and 2013, we can infer about the shift in policy making initiatives.\nBased on the above thought, the answer is:\nFrom 2010 to 2013, there was a shift in policy making initiatives from \"Healthcare Policy\" to \"Education Policy\". The proportion of initiatives for \"Healthcare Policy\" decreased from 75% to 50%, while the proportion for \"Education Policy\" increased from 25% to 50%."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "MzFXKMH1s02QthaR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the policy initiative data inform decision-making for future policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide the answer, I need to consider how the trends in the proportion of policy making initiatives might inform future policy decisions.\nBased on the above thought, the answer is:\nThe data shows an increasing focus on \"Education Policy\" and a decreasing focus on \"Healthcare Policy\" over the years. This trend might inform decision-makers to allocate more resources to education relative to healthcare in the future, as it shows a growing importance of education in policy making initiatives."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "IFYoaHFGJGrsgHyU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the pie chart was divided according to the policies for 2010, by how much would the healthcare policy's slice be larger than the education policy's slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate how much larger the healthcare policy's slice would be compared to the education policy's slice in 2010, I would need to calculate the difference in the percentage of total policy making initiatives for the two policy types in 2010.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "GbqLXbHAFzhErMdI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the education policy's slice in the pie chart increase between 2010 and 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out by how much the education policy's slice increased between 2010 and 2013, I would need to calculate the difference in the percentage of total policy making initiatives for education policy between 2010 and 2013.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "7DHmmyUGgSukLHn3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the healthcare policy's slice was reduced by 10% in 2014, what would the allocation be for the education policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given that the percentage of total policy making initiatives in the pie chart should add up to 100, if the healthcare policy's slice was reduced by 10% in 2014, the remaining percentage would be allocated to the education policy.\nBased on the above thought, the answer is:\n60%"
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "RbUw3ELRTtxpoa4z",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease create a CSV table using the data from the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle {\"Healthcare technology usage in 2020\"}[0.326, 0.021, 0.637, 0.047] provides the data year, which is 2020. The pieces of the pie chart represent the healthcare technologies {\"AI and Machine Learning\"}[0.594, 0.672, 0.647, 0.692] and {\"Telemedicine\"}[0.345, 0.363, 0.38, 0.383] with their respective usage percentages.\nBased on the above thought, the answer is:\nTechnology,2020\nAI and Machine Learning,100\nTelemedicine,0"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "5vmE18i4ga89GZ6g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat technology appears to be the most utilized in the healthcare sector in 2020 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The technology with the largest slice in the pie chart is the most utilized, so I will compare the sizes of the slices for \"AI and Machine Learning\" and \"Telemedicine\".\nBased on the above thought, the answer is:\n\"AI and Machine Learning\" seems to be the most utilized technology in 2020, because it occupies the largest slice of the pie chart, with a percentage of 100%."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "F22jvI1wrDcDZli6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this chart, can you infer anything about the trend of technology usage in healthcare in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions of each technology to make inferences about technology usage trends.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that the usage of \"AI and Machine Learning\" was strongly favored in the healthcare sector in 2020, which suggests an increasing trend of AI implementation in healthcare technology."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "veyo3SOY1miioXnS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this chart is representative of global trends, what might the implications be for industries linked to \"Telemedicine\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the implications of \"Telemedicine\" having a small slice of the pie chart.\nBased on the above thought, the answer is:\nIndustries linked to \"Telemedicine\" might be experiencing decline or challenges, as the chart shows no usage of this technology in healthcare in 2020. This suggests that they may need to reconsider their strategies, invest in innovation, or pivot to more in-demand technologies, such as AI and machine learning."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "yRytR4pewjKZwaVp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in usage between AI and Machine Learning and Telemedicine in 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of Telemedicine from the percentage of AI and Machine Learning in 2020.\nBased on the above thought, the answer is:\n100"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "XsQmCBAps5vYfpIO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the usage of AI and Machine Learning and Telemedicine technologies in healthcare in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add the percentage of usage of AI and Machine Learning and Telemedicine in 2020.\nBased on the above thought, the answer is:\n100"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "bqCRx4pjNZQFoQam",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage for the usage of AI and Machine Learning in 2020 is divided by 2, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to divide the 2020 usage of AI and Machine Learning by two.\nBased on the above thought, the answer is:\n50"
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "jM0BftDZDN6yxa1q",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the sales volume data from 2013 to 2020 into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Of course, by looking at the legend  {\"Laptop\"}[0.939, 0.071, 0.987, 0.091] and {\"Phone\"}[0.939, 0.101, 0.982, 0.121], I can identify the two product categories. The sales of these products over the years could be seen from each slice of the pie chart. Let's compile this data into a table.\nBased on the above thought, the answer is:\nYear,Laptop,Phone\n2013,1500,2000\n2014,1650,2100\n2015,1832,2500\n2016,2200,3000\n2017,2100,3200\n2018,2000,3300\n2019,1900,3400\n2020,1700,3500"
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "vUakEh9CPqy6kuAR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product had consistently higher sales throughout the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the sales volume for each year by observing the proportion of the pie charts for each product, we're able to see which product dominates.\nBased on the above thought, the answer is:\nPhones have consistently had higher sales throughout the years, as demonstrated by {\"Phone\"}[0.939, 0.101, 0.982, 0.121] occupying a larger area of the pie chart for each year."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "jAsXGrXm94Om9cFV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of Laptop sales from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By viewing each year's data {\"Laptop\"}[0.939, 0.071, 0.987, 0.091], we can observe the change in sales volume for Laptops over the years.\nBased on the above thought, the answer is:\nLaptop sales saw an uptick in 2016, reaching their peak during this year, after which they declined steadily through 2020."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "YcFMGyn52K7xMryC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data on the chart, what could be one possible strategy for increasing Laptop sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the trends observed in the sales volumes, a strategy could be derived.\nBased on the above thought, the answer is:\nSince Laptop sales peaked in 2016, it might be valuable to investigate what strategies were used in that year to drive sales, and whether these could be replicated or adapted for future campaigns. Moreover, as the sales volume for Phones has continually increased, it could be beneficial to analyze buyer preferences towards Phones and see this can be leveraged to boost Laptop sales."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "YSyNpOxNLCGpkpsL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales volume of Laptops and Phones in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, we need to count the total sales volume of both products. The Laptop sales {\"Laptop\"}[0.939, 0.071, 0.987, 0.091] and Phone sales {\"Phone\"}[0.939, 0.101, 0.982, 0.121] can be viewed in their corresponding slices at the last pie chart of the figure.\nBased on the above thought, the answer is:\nThe total sales volume is 1700 (Laptops) + 3500 (Phones) = 5200."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "GiPxpAX22oeLDaVB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in sales between Laptops and Phones in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "We need to subtract the sales volume of Laptops from that of Phones in 2016.\nBased on the above thought, the answer is:\nThe difference in sales is 3000 (Phones) - 2200 (Laptops) = 800."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "mUIZzgNNZbkZGpF6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average sales volume of Laptops from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, we need to sum up the sales quantity of Laptops over these years and divide by 8 (the number of years from 2013 to 2020).\nBased on the above thought, the answer is:\nThe average sales volume of Laptops is (1500+1650+1832+2200+2100+2000+1900+1700)/8 = 1935.25."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "6Mgt7tBfmn2BTVaD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you decode the pie chart data about 'Tourist Arrivals' and 'Hotel Occupancy' from 2014 to 2020 into a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, each slice represents a different year from 2014 to 2020. The size of each slice indicates the number of 'Tourist Arrivals' for the corresponding year. Meanwhile, the different colors detail the 'Hotel Occupancy' in terms of percentage. The chart's legend {legend}<r>[0.908, 0.063, 0.993, 0.279]</r> keys the color to the year, while the autotext of every slice {autotexts}<r>[0.173, 0.392, 0.208, 0.412]; [0.207, 0.726, 0.242, 0.746]; [0.415, 0.911, 0.451, 0.931]; [0.697, 0.826, 0.732, 0.846]; [0.766, 0.477, 0.801, 0.497]; [0.639, 0.167, 0.674, 0.187]; [0.342, 0.127, 0.377, 0.147]</r> provides the 'Tourist Arrivals' per year.\nBased on the above thought, the answer is:\nYear,Tourist Arrivals,Hotel Occupancy(%)\n2014,800,80\n2015,850,75\n2016,870,86\n2017,820,92\n2018,870,90\n2019,894,91\n2020,960,87"
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "J8L0Pn5zrqSBO5C8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which year appeared to have the best performance in the Tourism and Hospitality Industry? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the best performance, I'll consider both the 'Tourist Arrivals' and 'Hotel Occupancy'. The year with high counts in both categories can be seen as the best performing year.\nBased on the above thought, the answer is:\n2017 appeared to have the best performance, with the highest 'Hotel Occupancy' rate of 92%. Although it didn't have the highest 'Tourist Arrivals', the high occupancy indicates a high demand, which is a good sign for the industry."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "oVastgAzhrmSsHjW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the Tourism and Hospitality Industry's performance change over the years according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to take into account the trend in both 'Tourist Arrivals' and 'Hotel Occupancy' over the years.\nBased on the above thought, the answer is:\nThe data shows a steady increase in 'Tourist Arrivals' over the years, indicating continuous growth in the tourism sector. As for 'Hotel Occupancy', there was a peak in 2017, followed by a slight decrease, though it remained relatively stable overall. It demonstrates a robust and resilient industry."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "GQCSKTr8mxpeFwKK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends continue, what may be the projection for 'Tourist Arrivals' and 'Hotel Occupancy' in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the upward trend in 'Tourist Arrivals' and the stability of 'Hotel Occupancy' to project the scenario for 2021.\nBased on the above thought, the answer is:\nIf the trends continue, we could anticipate a rise in 'Tourist Arrivals' for 2021. However, 'Hotel Occupancy' might remain constant or experience a minor decrease, considering the slight downward trend after peaking in 2017."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "kPtFRYiLpw4H2FaI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year has the highest number of 'Tourist Arrivals'?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, the size of the pie slices represents the number of 'Tourist Arrivals'. The biggest slice indicates the year with the highest number of 'Tourist Arrivals'.\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "XaJlcNDEXMYsJHM2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage increase in 'Tourist Arrivals' from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the number of 'Tourist Arrivals' in 2014 from that of 2020, divide the result by the number for 2014 and then multiply by 100.\nBased on the above thought, the answer is:\n20%."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "e87TL0NlZN8ZbXFf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Tourist Arrivals' in 2019 had increase by 10%, what would be the new total count?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new total count, I need to multiply the number for 2019 by 10%, and then add the result to the original count for 2019.\nBased on the above thought, the answer is:\n983.4 thousand."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "jbMB1M1If1Uj9wWf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transfer the data in the chart to a table with each trend's value in different years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, by analyzing the pie chart with the legend located at {the legend section}<r>[0.908, 0.063, 0.993, 0.309]</r> and the central pie chart with its slices' labels and width at <r>[0.223, 0.13, 0.761, 0.886]</r>, I can correlate the given percentages in each year to each fashion trend.\nBased on the above thought, the answer is:\nYear,Hipster,Grunge\n2011,12,88\n2012,15,85\n2013,22,78\n2014,18,82\n2015,35,65\n2016,42,58\n2017,47,53\n2018,50,50"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "zhj5arwe1zQpeto8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend demonstrated a consistent increase in popularity from 2011 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend consistently increased in popularity, I will review the data line for each trend from 2011 to 2018.\nBased on the above thought, the answer is:\nThe Hipster fashion trend displayed a consistent increase in popularity from 2011 to 2018."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "BIZRwIeTXnzBK23T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a company was to align with a fashion trend based on the data in the chart, which one should they choose, assuming they prefer a more stable trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "A trend would be considered stable if there isn't a drastic change in its popularity over time. By comparing the fluctuation of both trends, I can determine the more stable of the two.\nBased on the above thought, the answer is:\nThey should choose the Grunge fashion trend because it showed less drastic changes compared to the Hipster trend."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "nkkBan6kXDLALEKB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat significant trend event occurred in 2016 and how could it influence fashion industry decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the trend lines and percentages in the pie chart, I can identify significant events and infer potential influences.\nBased on the above thought, the answer is:\nIn 2016, both fashion trends were near equal in popularity. This cooperative dominance can suggest to the fashion industry that there's a balanced market demand for both Hipster and Grunge style products."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "FlmMRC4bHgJFKXr0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rage of popularity for the Grunge fashion trend throughout 2011-2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the trend lines for the Hipster and Grunge fashion groups, the range can be found by calculating the difference between the highest and lowest points of the Grunge trend line.\nBased on the above thought, the answer is:\n38%"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "tcnBA2zwHZy4lsWF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the Hipster fashion trend\u2019s popularity from 2011 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "The increase in the trend can be found by subtracting the popularity in 2011 from the popularity in 2018.\nBased on the above thought, the answer is:\n38%"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "dyFdqQUVUaCRiJoi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhen did the popularity of the Hipster fashion trend surpass that of the Grunge fashion trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find when the popularity of the Hipster fashion trend surpassed that of the Grunge fashion trend, we need to compare the trend of two groups year by year till the Hipster's percentage is higher than that of the Grunge.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "s0LEH9wakPS76L6T",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data displayed on the pie chart into a table format that displays the growth of the gaming industry from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart {\"Gaming Industry Development from 2014 to 2020\"}[0.273, 0.021, 0.69, 0.047] with its corresponding sections {2014}[0.224, 0.278, 0.482, 0.516], {2015}[0.223, 0.474, 0.482, 0.702], {2016}[0.258, 0.516, 0.482, 0.875], {2017}[0.425, 0.552, 0.668, 0.922], {2018}[0.482, 0.498, 0.741, 0.783], {2019}[0.503, 0.155, 0.762, 0.495], and {2020}[0.283, 0.146, 0.584, 0.516]. I can determine the annual development of the gaming industry.\nBased on the above thought, the answer is:\nYear,Gaming Industry Development\n2014,9.3%\n2015,10.2%\n2016,12.7%\n2017,16.1%\n2018,13.6%\n2019,17.8%\n2020,20.3%"
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "5LImHgGUyoNtKolo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw the most significant development in the gaming industry and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most developed year is the year with the biggest sector in the pie chart.\nBased on the above thought, the answer is:\nThe most significant development was in the year 2020, with a percentage of 20.3%. This was likely due to the increased demand for digital entertainment during global lockdowns caused by the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "eCp4rP4hfdhIcCvN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data shown, is there a consistent upward trend in the growth of the gaming industry every year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to take a look at each year's growth rate and whether it's increasing or not.\nBased on the above thought, the answer is:\nYes, there is a consistent upward trend in the growth of the gaming industry. Each successive year, from 2014 to 2020, shows a higher growth rate than the previous year, signifying a constant upward trend."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "bLUWKs1dVwdRQfc9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the growth of the gaming industry in 2018 compare to the following year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compare the growth of the gaming industry in 2018 and 2019, I need to look at the sizes and labels of the respective pie slices.\nBased on the above thought, the answer is:\nThe growth rate in 2019 was higher than in 2018. In 2018, the growth rate was 13.6%, whereas in 2019, it increased to 17.8%. This suggests that 2019 was a more prosperous year for the gaming industry compared to 2018."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "gCUy2lE1u3AxB6xU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the lowest and highest growth rates recorded during the years 2014 to 2020 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest slice in the chart represents the lowest growth rate and the largest slice represents the highest growth rate.\nBased on the above thought, the answer is:\nThe lowest growth rate is 9.3% in 2014, and the highest growth rate is 20.3% in 2020."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "DFWXeOKeskQXRNNh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth rate for the gaming industry in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rate for 2017, I need to look at the data label for that year in the pie chart.\nBased on the above thought, the answer is:\nThe growth rate for the gaming industry in 2017 was 16.1%."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "egU1SWWgiH0ugd7E",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year does the gaming industry observe approximately 10% growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with approximately 10% growth, I need to scan for the data labels in and around 10%.\nBased on the above thought, the answer is:\nIn 2015, the gaming industry observed approximately 10.2% growth."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "Bc5dLDpvGa7PRM69",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you please convert the development pace during each civilization phase data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, as well as the legend{legend}<r>[0.797, 0.063, 0.993, 0.368]</r>, I can see each civilization phase, which include 'Stone Age', 'Bronze Age', 'Iron Age' and so on. The corresponding pace of development during each phase is also represented by the area of the corresponding pie slice such as {Stone Age slice}<r>[0.255, 0.278, 0.482, 0.516]</r>, {Bronze Age slice}<r>[0.225, 0.335, 0.482, 0.516]</r>.\nBased on the above thought, the answer is:\nCivilization Phase,Development Pace\nStone Age,3\nBronze Age,6\nIron Age,8\nMedieval Age,10\nRenaissance,20\nIndustrial Revolution,30\nModern Age,15\nInformation Age,5\nPost-information Age,2\nFuture,1"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "QlMSr8E4dDrTwJdd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which phase saw the most rapid development in civilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to locate the slice with the largest area in the pie chart, which indicates the most rapid development. This is clearly the {Industrial Revolution slice}<r>[0.482, 0.205, 0.741, 0.803]</r>.\nBased on the above thought, the answer is:\nThe Industrial Revolution saw the most rapid development in civilization."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "0ntmYLTGNwUqSD0o",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReflecting on the pie chart, how did the pace of development change from the Stone Age to the Future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the slices such as the {Stone Age slice}<r>[0.255, 0.278, 0.482, 0.516]</r> and the {Future slice}<r>[0.283, 0.261, 0.482, 0.516]</r>, I can see the difference in development pace from the Stone Age to the Future.\nBased on the above thought, the answer is:\nThe pace of development increased from the Stone Age through the Industrial Revolution, peaking during the Industrial Revolution. After that, there was a decline in the pace of development through to the Future."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "XpGsGN4vquj6gOWG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might the slowdown in the pace of development in the Post-information Age and Future imply about human society?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must observe the development paces according to the {Post-information Age slice}<r>[0.294, 0.229, 0.482, 0.516]</r> and the {Future slice}<r>[0.283, 0.261, 0.482, 0.516]</r>, and reflect on what this slowdown might imply about the evolution of human society.\nBased on the above thought, the answer is:\nThe slowdown in the pace of development in the Post-information Age and Future might suggest a stabilization or saturation in human societal development, where significant breakthroughs and advancements become less frequent, likely due to the high level of development already reached in previous eras."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "EUswXuDQC84AePbp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total development pace across all civilization phases according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total development pace, I need to sum up the development pace presented in all sectors including {Stone Age slice}<r>[0.255, 0.278, 0.482, 0.516]</r>, {Bronze Age slice}<r>[0.225, 0.335, 0.482, 0.516]</r> and so on.\nBased on the above thought, the answer is:\n100"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "DKQFNykMdT9DFLce",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the development pace during the Bronze Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {Bronze Age slice}<r>[0.225, 0.335, 0.482, 0.516]</r> I can see the pace of development during the Bronze Age.\nBased on the above thought, the answer is:\n6"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "sOec8pS7xeFTvOpL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, how much faster was the Industrial Revolution development pace compared to Iron Age?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to examine the development pace of the {Industrial Revolution slice}<r>[0.482, 0.205, 0.741, 0.803]</r> and then subtract the development pace of the {Iron Age slice}<r>[0.223, 0.467, 0.482, 0.65]</r> from it.\nBased on the above thought, the answer is:\n22"
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "127XKqXI3LgJSAvX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data on Education Resource Allocation from 2012 to 2020 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, each year from {2012}<r>[0.952, 0.071, 0.987, 0.091]</r> through {2020}<r>[0.952, 0.311, 0.987, 0.331]</r> has a distinct color, which correlates with a percentage shown in the slice. I can use this data to reconstruct the resource allocation over the years.\nBased on the above thought, the answer is:\nYear,Resource Allocation\n2012,30\n2013,55\n2014,45\n2015,80\n2016,85\n2017,75\n2018,100\n2019,82\n2020,90"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "hWvH1vA4HnvOOGaZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend in Education Resource allocation can you identify in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To observe the trends, I need to examine the colors from 2012<r>[0.952, 0.071, 0.987, 0.091]</r> to 2020<r>[0.952, 0.311, 0.987, 0.331]</r> and the size of slices which represent resource allocation over the years.\nBased on the above thought, the answer is:\nThe trend in Education Resource allocation shows a general increase from 2012 to 2018 with a slight decrease in 2019, and an increase in 2020 again. The outlier in 2018 when allocation was at its peak is clearly visible in the exploded slice."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "JxFMiBDcXssmKfZ5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the resource utilization pattern observed from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the portion of the pie chart representing resource utilization for each year ranging from 2012<r>[0.952, 0.071, 0.987, 0.091]</r> to 2020<r>[0.952, 0.311, 0.987, 0.331]</r> to describe the pattern.\nBased on the above thought, the answer is:\nThe resource utilization seems to follow a pattern inverse to allocation, increasing where allocation decreases and vice versa. A standout observation is in 2018, where resource allocation was at it's highest and utilization was at its lowest."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "FEVgX87EIlsX07QN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the relationship between education resource allocation and utilization from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the different slices of the pie chart representing each year's<i>[0.952, 0.071, 0.987, 0.331]</i> resource allocation and utilization.\nBased on the above thought, the answer is:\nThe allocation and utilization pattern indicate that when more resources were allocated, their utilization has been less, and vice versa, illustrating an inverse correlation between allocation and utilization. The marked exception in this pattern is seen in 2018 with a peak allocation and minimal utilization. This could indicate that increased availability of resources does not necessarily lead to their increased use."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "VBtEOd7A9LlMCTQl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest value of resource allocation from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value of resource allocation, I need to take the maximum among all values from 2012<r>[0.952, 0.071, 0.987, 0.091]</r> to 2020<r>[0.952, 0.311, 0.987, 0.331]</r>.\nBased on the above thought, the answer is:\n100"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "3YucKTwtIQezwNL9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in resource allocation between 2012 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the resource allocation in 2012<r>[0.952, 0.071, 0.987, 0.091]</r> from the resource allocation in 2020<r>[0.952, 0.311, 0.987, 0.331]</r>.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "khfDGqjawKV17p9k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average of resource allocation from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to sum up all the resource allocation values from 2012<r>[0.952, 0.071, 0.987, 0.091]</r> to 2020<r>[0.952, 0.311, 0.987, 0.331]</r> and divide by the number of years, which is 9.\nBased on the above thought, the answer is:\n71.1"
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "o4cv0uNVpzFB0qGO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart titled \"Consumer Behavior in Emerging Markets\"<r>[0.311, 0.021, 0.652, 0.047]</r> and the pie slices representing different countries {India}<r>[0.223, 0.278, 0.482, 0.784]</r>; {China}<r>[0.303, 0.516, 0.68, 0.886]</r>; {Brazil}<r>[0.482, 0.282, 0.741, 0.754]</r>; {Mexico}<r>[0.496, 0.127, 0.696, 0.485]</r>; {South Africa}<r>[0.283, 0.146, 0.548, 0.516]</r>, it's clear that each slice represents a country's consumer behavior in terms of market spending.\nBased on the above thought, the answer is:\nCountry,Consumer Behavior\nIndia,24\nChina,26\nBrazil,22\nMexico,10\nSouth Africa,18"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "zeRXgp6XNNCMxPjT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country had the highest consumer behavior in the data visualized in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The country with the biggest slice can be considered as the one with the highest consumer behavior.\nBased on the above thought, the answer is:\nChina had the highest consumer behavior."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "n3mcPXZA8kqFQZ7V",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about consumer behavior in these emerging markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about consumer behavior in these markets, I need to evaluate the sizes of each pie slice.\nBased on the above thought, the answer is:\nChina and India have the highest consumer behavior, followed by Brazil and South Africa, with Mexico trailing significantly behind. This suggests that China and India are leading in these emerging markets, probably due to their population size and growing economies."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "0ji8DRbcbgSKpWO0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, how might companies adjust their market strategy for these countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "Companies might use the chart to strategize which emerging market to target next, based on the current consumer behavior observed from the pie chart.\nBased on the above thought, the answer is:\nCompanies could invest more in China and India, as they have demonstrated high consumer behavior. Meanwhile, despite its low consumer behavior, Mexico's market might be viewed as a potential growth opportunity, particularly if a strategy could be developed to increase consumer spending."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "AXo4aku5M55DAjHZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the smallest consumer behavior in terms of billions of dollars?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the smallest consumer behavior, I need to check each slice size and choose the smallest one.\nBased on the above thought, the answer is:\nMexico"
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "jYFyWrrZag2zHZNH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the consumer behavior in billions of dollars across all countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add up all slices' percentages and multiply by the total value they represent.\nBased on the above thought, the answer is:\nThe total consumer behavior across all countries is 24 (India) + 26 (China) + 22 (Brazil) + 10 (Mexico) + 18 (South Africa) = 100 billion dollars."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "gctx4LOY373FtvuH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf consumer behavior increased by 2 billion dollars in China, what would be the new total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to add 2 billion dollars to China's market spending.\nBased on the above thought, the answer is:\nThe new total would be 100 + 2 = 102 billion dollars."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "1QOfIz6uz4Y5Qis3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, with the title \"Market Share of Different Building Types\"{[0.314, 0.021, 0.649, 0.047]},{the legend}<r>[0.799, 0.063, 0.993, 0.219]</r>, and {the slices}<r>[{\u201cSingle Family Home\u201d[0.228, 0.918, 0.365, 0.938]; \u201cMulti Family Home\u201d[0.697, 0.239, 0.825, 0.259]; \u201cCommercial Building\u201d[0.313, 0.1, 0.457, 0.12]; \u201cIndustrial Building\u201d[0.228, 0.142, 0.354, 0.162]; \u201cSkyscrapers\u201d[0.198, 0.216, 0.282, 0.236]}, it is clear that each sector represents a building type and the corresponding market share.\nBased on the above thought, the answer is:\nBuilding Type, Market Share(%)\nSingle Family Home, 60\nMulti Family Home, 25\nCommercial Building, 5\nIndustrial Building, 7\nSkyscrapers, 3"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "EkyavCA8AEyCSZhx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which building type has the most market share? What might this imply?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportions of each pie slice, I can find the building type with the largest market share.\nBased on the above thought, the answer is:\n\"Single Family Home\" has the most market share with 60%. This implies that single-family homes were the most popular type of building in the market in the given year."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "ACHYZkLuazJYSS9K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend does the pie chart suggest about residential buildings versus commercial and industrial buildings in the market?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can compare the sums of the market shares of residential buildings (\"Single Family Home\" and \"Multi Family Home\") and commercial and industrial buildings (\"Commercial Building\", \"Industrial Building\", and \"Skyscrapers\") to determine this trend.\nBased on the above thought, the answer is:\nThe pie chart suggests that residential buildings, including single-family and multi-family homes, have a significantly larger market share (85% combined) compared to commercial and industrial buildings (15% combined). This could indicate a higher demand or preference for residential property over commercial or industrial spaces."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "sHYUGm8JXtE9lrQ9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how might building companies change their investment strategies for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would infer the implications for building companies' strategies by looking at the building types and their respective market shares.\nBased on the above thought, the answer is:\nGiven the market shares, building companies might want to focus more on residential properties, particularly single-family homes since they dominate the market. On the other hand, for diversity and risk distribution, they might also consider investing in types of buildings with smaller market shares, like commercial buildings or skyscrapers, in anticipation of potential growth in these sectors."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "uhiM4t78jGiu29zp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the market share percentages for the \"Commercial Building\", \"Industrial Building\", and \"Skyscrapers\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the sum of the proportion of these three types of buildings, I need to add up their individual percentages.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "HBgCh7Kz3v4iHirf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the market share of \"Single Family Homes\" than the total market share of \"Commercial Building\", \"Industrial Building\", and \"Skyscrapers\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the total market share of \"Commercial Building\", \"Industrial Building\", and \"Skyscrapers\" from the market share of \"Single Family Homes\".\nBased on the above thought, the answer is:\n45%"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "gnaiHYl5pI6HYvOa",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share of \"Commercial Building\" increased by 2%, what would be the new total market share of all building types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total market share, I need to add 2% to the existing market share of \"Commercial Building\" and add up all the market shares again.\nBased on the above thought, the answer is:\n102%"
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "vmgaWnLcYQVQtcjJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you can see, could you please convert the data for the year 2019 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title analyzed at the top of the chart {Description: \"Social Culture and Value Change in 2019\"}<r>[0.312, 0.021, 0.651, 0.047]</r>, and {Pie Slices: 'Social Cultural Change' at 84.2%}<r>[0.586, 0.885, 0.745, 0.905]</r> and {Pie Slices: 'Value Change' at 15.8%}<r>[0.272, 0.093, 0.367, 0.113]</r>, I realize that these represent the percentage of each in the year 2019 according to the dataset.\nBased on the above thought, the answer is:\nYear,Social Cultural Change (%),Value Change (%)\n2019,84.2,15.8"
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "2veJQi7gJY3ZtKSz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which change was more prevalent in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I have to compare the proportion of each change type in the pie chart, and then the type with the higher proportion can be considered as the more prevalent.\nBased on the above thought, the answer is:\n'Social Cultural Change' was more prevalent in 2019 as it had a higher proportion of 84.2% compared to 'Value Change' which was 15.8%."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "kU7IyN74HrV3OarY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make about social culture and value changes in 2019 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the proportions of each category to draw an inference about social culture and value changes.\nBased on the above thought, the answer is:\nThe chart shows that in 2019, changes in social culture were much more prevalent than changes in values. This suggests that society experienced significant cultural shifts, while value systems remained relatively stable."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "6tvDTz3au50j22az",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence social and cultural policy decisions for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the proportions of each change type might impact decisions on policy focus.\nBased on the above thought, the answer is:\nGiven the high prevalence of 'Social Cultural Change' in 2019, social and cultural policies in 2020 might focus on managing and responding to these rapid shifts. Conversely, with 'Value Change' being relatively small, less attention may be needed in adapting policies to accommodate value shifts."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "6zlTdHuUTX77ANvl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what is the difference in percentage points between 'Social Cultural Change' and 'Value Change' for 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage associated with 'Value Change' from that of 'Social Cultural Change'. This involves using the known values {Value for 'Social Cultural Change': 84.2%, Value for 'Value Change': 15.8%}<r>[0.586, 0.885, 0.745, 0.905]and[0.272, 0.093, 0.367, 0.113]</r>.\nBased on the above thought, the answer is:\nThe difference is 68.4 percentage points."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "tqVHW9zsV39nC8vk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Social Cultural Change' were to decrease by 10% and 'Value Change' to increase by 10%, what would be the new average percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new average percentage, I would need to adjust each value accordingly, then sum them and divide by the total number of categories.\nBased on the above thought, the answer is:\nAfter the adjustments, 'Social Cultural Change' would be 74.2% and 'Value Change' would be 25.8%. Therefore, the new average would be (74.2% + 25.8%)/2 = 50%."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "rcfoyKN3fCUDN1PX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Value Change' were to double, what would be the new sum of percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to double the 'Value Change' percentage and add the 'Social Cultural Change' percentage.\nBased on the above thought, the answer is:\nThe 'Value Change' would increase to 31.6% (15.8% * 2), therefore the sum of percentages would be 84.2% ('Social Cultural Change') + 31.6% ('Value Change') = 115.8%."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "Tv1C8zmouUSuQJEF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information from the pie chart into CSV format based on your observation?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle {the chart}'s<r>[0.291, 0.021, 0.681, 0.047]</r> title indicating \"Knowledge Economy and Innovation Industries\", and by taking a look at each of these slices on the pie<Artificial Intelligence slice><r>[0.202, 0.271, 0.461, 0.629]</r><Big Data slice><r>[0.241, 0.516, 0.486, 0.861]</r><Internet of Things slice><r>[0.392, 0.516, 0.571, 0.886]</r><Biotech slice><r>[0.486, 0.516, 0.666, 0.866]</r><Fintech slice><r>[0.486, 0.494, 0.745, 0.783]</r><Adtech slice><r>[0.486, 0.246, 0.745, 0.516]</r><3D Printing slice><r>[0.486, 0.179, 0.663, 0.516]</r><Automotive Tech slice><r>[0.443, 0.146, 0.594, 0.516]</r><AR/VR slice><r>[0.344, 0.151, 0.486, 0.516]</r>an and <EdTech slice><r>[0.288, 0.206, 0.486, 0.516]</r> in turn, I note that each slice represents a different industry and their respective market share percentages.\nBased on the above thought, the answer is:\nIndustry,Market Share\nArtificial Intelligence,16.4\nBig Data,13.8\nInternet of Things,11.2\nBiotech,6.9\nFintech,13.8\nAdtech,12.1\n3D Printing,5.2\nAutomotive Tech,9.5\nAR/VR,6.6\nEdTech,4.7"
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "248tGVpURoEP1O89",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which industry is leading the Knowledge Economy and Innovation sector, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the leading industry, I would find the industry with the highest percentage in the pie chart.\nBased on the above thought, the answer is:\n'Artificial Intelligence' is leading the sector as it has the highest market share of about 16.4%."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "C5CzqQgHQzwvXZU9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you infer about the Knowledge Economy and Innovation Industries in general from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make any inferences, I need to take into account the proportion represented by each sector slice of the pie chart.\nBased on the above thought, the answer is:\nFrom the chart, we can infer that the Knowledge Economy and Innovation Industries sector is largely dominated by the Artificial Intelligence, Big Data, and Fintech industries. Minor sectors like 3D Printing, AR/VR, and EdTech need to catch up and increase their market shares."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "JqIdFHhMAw8xxjPD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the information presented in this chart possibly impact future investment decisions in the Knowledge Economy and Innovation Industries sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would have to consider how the proportion of market share for each industry might affect investment decisions.\nBased on the above thought, the answer is:\nInvestors would possibly be more inclined towards putting their money in dominant sectors such as Artificial Intelligence, Big Data, and Fintech due to their strong market presence. However, industries like 3D Printing, AR/VR, and EdTech might attract investment for growth potential. The chart data can serve as a guide for investors to balance their portfolio by investing both in established and growing industries."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "vNRoI9H5Y6E8WBsg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich industry owns more than 10% market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to evaluate each sector of the pie chart and choose the ones with a percentage greater than 10%.\nBased on the above thought, the answer is:\nArtificial Intelligence, Big Data, Internet of Things, Fintech, Adtech."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "V3IYTIhGWTYLo4IH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall market share percentage for all the technology sectors cited in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I must add up all the percentage values from each sector of the pie chart.\nBased on the above thought, the answer is:\nThe total market share for all the sectors is 100%."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "ijHiiKOZvC99VaM3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the new total percentage of market share if the Fintech sector decreased its proportion by 3%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to subtract 3% from the market share percentage of Fintech and add up all the percentages again.\nBased on the above thought, the answer is:\nThe new total market share percentage would be 97%."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "q7sx3Hbg672zqvVT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the pie chart's information about technology adoption rates into a structured csv table for me?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see {the sector slice}<r>[{\"5G\"}[0.197, 0.282, 0.456, 0.811]; {\"Blockchain\"}[0.322, 0.516, 0.584, 0.886]; {\"Quantum Computing\"}[0.482, 0.516, 0.669, 0.856]; {\"AI\"}[0.507, 0.179, 0.766, 0.766]; {\"IoT\"}[0.283, 0.146, 0.598, 0.516]</r> representing the adoption percentages for each technology. I'll just put these into tabular form for you.\nBased on the above thought, the answer is:\nTech_Adoption,5G,Blockchain,Quantum_Computing,AI,IoT\n2021,25.5%,17.0%,6.4%,29.8%,21.3%"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "PirpVvONfHEborA0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, which technology seems to be most accepted among the population in 2021? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "I have to identify the technology with the highest adoption rate. This can be determined by the size of each slice and their corresponding numbers on {the chart}<r>[0.157, 0.053, 0.805, 0.979]</r>.\nBased on the above thought, the answer is:\nAI is the most widely adopted technology in 2021 given that it has the highest adoption rate of 29.8%, forming the largest slice of the pie."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "Hvjm6yjNYY2PccFo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you speculate why blockchain and quantum computing have lower adoption rates compared to the others?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to interpret the lower adoption rates of the technologies, Blockchain and Quantum Computing, in the context of the overall picture of technology adoption which is present in {the pie chart}<r>[0.157, 0.053, 0.805, 0.979]</r>.\nBased on the above thought, the answer is:\nThe lower adoption rates for Blockchain and Quantum Computing could suggest that these technologies are either new and still in development or that they require specialized knowledge and infrastructure, thus limiting their practical use and adoption among the general population."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "J636XjPeRi9Byxwt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer any future trends in technology adoption based on the 2021 data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Evaluation of the current adoption rates can provide indications of possible future trends. Looking at {the pie slices}<r>[{\"5G\"}[0.197, 0.282, 0.456, 0.811]; {\"Blockchain\"}[0.322, 0.516, 0.584, 0.886]; {\"Quantum Computing\"}[0.482, 0.516, 0.669, 0.856]; {\"AI\"}[0.507, 0.179, 0.766, 0.766]; {\"IoT\"}[0.283, 0.146, 0.598, 0.516]</r>, I can make such projections.\nBased on the above thought, the answer is:\nFrom the 2021 data, the high adoption rates of AI and 5G indicate that these technologies are likely to continue to grow in popularity. On the other hand, Quantum Computing, with the lowest adoption rate, may be an emerging technology with potential for significant growth, given increased understanding and development."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "zzcUvFBgGRWn3Zlp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined adoption rate for IoT and Artificial Intelligence?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires me to sum up the adoption rates for IoT and AI. Looking at {the IoT slice}<r>[0.283, 0.146, 0.598, 0.516]</r> and {the AI slice}<r>[0.507, 0.179, 0.766, 0.766]</r>, I can find the information I need.\nBased on the above thought, the answer is:\n51.1%"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "cvu12kC8mHuT5BEt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the population has adopted technology related to Quantum Computing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question asks the adoption rate for Quantum Computing. This information can be found by checking {the Quantum Computing slice}<r>[0.482, 0.516, 0.669, 0.856]</r>.\nBased on the above thought, the answer is:\n6.4%"
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "RUUmk5JVPwJKh0lG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the combined technology adoption rate for 5G and Blockchain higher than 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question requires me to add the adoption rates for 5G and Blockchain and compare this to 50%. I can find these percentages in {the 5G slice}<r>[0.197, 0.282, 0.456, 0.811]</r> and {the Blockchain slice}<r>[0.322, 0.516, 0.584, 0.886]</r>.\nBased on the above thought, the answer is:\nNo."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "KeaNJPSckbIyT8hM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart primarily depicts eight sectors of an organization in terms of potential efficiency improvement - {\"Training\"}<r>[0.828, 0.071, 0.883, 0.091]</r>, {\"Workplace environment\"}<r>[0.828, 0.101, 0.993, 0.121]</r>, {\"Equipment upgrades\"}<r>[0.828, 0.131, 0.973, 0.151]</r>, {\"Streamlining processes\"}<r>[0.828, 0.161, 0.989, 0.181]</r>, {\"Software upgrade\"}<r>[0.828, 0.191, 0.952, 0.211]</r>, {\"Management style\"}<r>[0.828, 0.221, 0.957, 0.241]</r>, {\"Motivational incentives\"}<r>[0.828, 0.251, 0.989, 0.271]</r>, and {\"Communication\"}<r>[0.828, 0.281, 0.937, 0.301]</r>. Using this, I can generate a CSV form of the chart data.\nBased on the above thought, the answer is:\nArea of Improvement,Efficiency Improvement Potential\nTraining,20%\nWorkplace environment,15%\nEquipment upgrades,35%\nStreamlining processes,45%\nSoftware upgrade,40%\nManagement style,25%\nMotivational incentives,12%\nCommunication,8%"
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "HeNwGi1gVr1OHhSz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which improvement area shows the maximum potential of efficiency improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the area with the largest proportion in the pie chart, and hence the highest potential for efficiency improvement.\nBased on the above thought, the answer is:\nThe 'Streamlining processes' area shows the maximum potential of efficiency improvement, with a potential of 45%."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "3b5eLzdcoyaHUMVr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the organization's focus regarding efficiency improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the areas with the largest proportions to infer the current focus of the organization.\nBased on the above thought, the answer is:\nThe organization seems to focus more on 'Streamlining processes', 'Software upgrade', and 'Equipment upgrades' for efficiency improvement. They may consider these areas as the most potent for bringing about improvement in their overall efficiency."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "uPLiw4GAQ637Tpam",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart influence organizational decisions for operational improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Organizational decisions for operational improvement are probably influenced by the areas with the highest potential for efficiency improvement.\nBased on the above thought, the answer is:\nThe organization may prioritize 'Streamlining processes', 'Software upgrade', and 'Equipment upgrades' as they hold the highest potential for efficiency improvement. However, they should not ignore areas like 'Motivational incentives' and 'Communication' that, although displaying lower potential, can significantly impact other areas and overall organizational performance."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "xrL7dgxe5YlAHGwm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which two areas of improvement have a combined potential efficiency improvement of 60%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to find two areas whose total efficiency improvement potential adds up to 60%.\nBased on the above thought, the answer is:\nStreamlining processes and Training."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "nSczaKvCwbryEOxR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the combined efficiency improvement if the potential of 'Software upgrade' is increased by 5% and 'Motivational incentives' is reduced by 3%?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to add 5% to the 'Software upgrade' potential and subtract 3% from that of 'Motivational incentives', and calculate the total efficiency improvement potential.\nBased on the above thought, the answer is:\n202%"
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "puRtTqE8t192mszK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any area of improvement that has an efficiency improvement potential greater than 40%?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer the question, I need to compare efficiency improvement potential of all areas with 40%.\nBased on the above thought, the answer is:\nYes, Streamlining processes and Software upgrade."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "2KVe3sQ4ALDof8R0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you have, can you outline the data shown in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Ecological Environment Sustainability Assessment from 2016 to 2020\"}[0.192, 0.021, 0.771, 0.047] and the pie slices [{slices representing years 2016 - 2020 of both AQI and WQI}][0.228, 0.278, 0.482, 0.516][0.223, 0.438, 0.482, 0.631][0.235, 0.516, 0.482, 0.813][0.327, 0.516, 0.499, 0.886][0.482, 0.516, 0.678, 0.885][0.504, 0.533, 0.754, 0.775][0.508, 0.399, 0.767, 0.614][0.482, 0.205, 0.728, 0.516][0.438, 0.146, 0.622, 0.516][0.283, 0.151, 0.482, 0.516] imply that the pie represents the Air Quality Index and Water Quality Index over the years from 2016 to 2020.\nBased on the above thought, the answer is:\nYear,Air Quality Index,Water Quality Index\n2016,55,50\n2017,60,67\n2018,70,77\n2019,80,84\n2020,90,80"
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "D8RP71TUC1GUKlkH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich index, AQI or WQI, has shown a steady increase over the years as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the trend of AQI and WQI over the years and compare it with the data.\nBased on the above thought, the answer is:\nThe Air Quality Index (AQI) has shown a steady increase over the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "QmciVxKIESf4lwaV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the data in the pie chart, what can you infer about the ecological environment from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should evaluate both AQI and WQI to infer about the ecological environment.\nBased on the above thought, the answer is:\nThe ecological environment seems to have deteriorated from 2016 to 2020, as depicted by the consistent increase in the Air Quality Index (AQI). The water quality, however, showed better improvement till 2019 but there was a slight decline in 2020."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "sZUdoyosvtVjpEM9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence policy decisions regarding ecological environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Policy decisions would likely take into account the increasing AQI and the fluctuating WQI. Measures to control air pollution and maintain the water quality would likely be prioritized.\nBased on the above thought, the answer is:\nGiven the consistent increase in the Air Quality Index (AQI), it is likely that policies would focus on measures to control air pollution and improve air quality. The fluctuations in the Water Quality Index (WQI) might also necessitate measures to ensure stable and improved water quality."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "ifukxDmKaRGexldV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Water Quality Index increase from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in the Water Quality Index from 2016 to 2017, I need to subtract the WQI of 2016 from the WQI of 2017.\nBased on the above thought, the answer is:\nThe WQI increased by 17."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "1hN8kf8gFdGtZn4F",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many points did Air Quality Index increase on an average each year from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average increase in AQI each year, I need to subtract the AQI of 2016 from the AQI of 2020 and divide by the number of years.\nBased on the above thought, the answer is:\nAQI increased by an average of 8.75 points each year."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "AzuHCUKS472XSQmD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of the AQI and WQI for the year 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total, I need to add the AQI for 2019 to the WQI for 2019.\nBased on the above thought, the answer is:\nThe total is 164."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "W6ikltdkKjbmgxJl",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you extract the data regarding Capital Sources in Innovation and Technology Entrepreneurship Ecosystem and present it in a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can gather data by looking into each pie slice, which corresponds to the different sources of capital. The labels and the corresponding percentages are found in  {the slices}<r>[0.223, 0.114, 0.741, 0.886]</r> and their details are available in {the legend}<r>[0.797, 0.063, 0.993, 0.279]</r>.\nBased on the above thought, the answer is:\nArea,Percent\nAngel Investors,22\nStartup Incubators,18\nGovernment Funding,10\nPrivate Equity,16\nVenture Capital,24\nCrowd Funding,7\nOther,3"
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "yRhkgrcpdKxsjc8C",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the major capital source in the Innovation and Technology Entrepreneurship Ecosystem and what might this signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the percentages I need to identify {Venture Capital}<r>[0.841, 0.191, 0.949, 0.211]</r> as the major capital source. Apart from that, its significance can be understood in the context of the whole ecosystem.\nBased on the above thought, the answer is:\nVenture Capital is the major capital source, contributing 24% of the capital. This indicates the vital role of venture capitalists in techno-innovation entrepreneurship, suggesting that formal investments hold high ranks in this ecosystem."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "hGCXXjLQ19uIstrG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you interpret the low percentage of the source 'Other'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret this, I need to look at the percentage assigned to 'Other' near the {legend}<r>[0.841, 0.251, 0.881, 0.271]</r> and contextualize it to the overall capital sources listed in the pie chart.\nBased on the above thought, the answer is:\nConsidering other funding sources contribute only 3% of the total capital, it indicates that non-traditional or alternative sources of funding have lesser weight in the Innovation and Technology Entrepreneurship Ecosystem. It could suggest that these ecosystems mostly rely on established and recognized sources of funding."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "BAj8lI5wtsFGuA2X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of Crowd Funding in the Innovation and Technology Entrepreneurship Ecosystem as per the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the role of Crowd Funding, I need to observe the corresponding slice, specifically, {Crowd Funding}<r>[0.841, 0.221, 0.945, 0.241]</r> from the legend, and evaluate its role in the ecosystem.\nBased on the above thought, the answer is:\nCrowd Funding contributes 7% of the total capital in the Innovation and Technology Entrepreneurship Ecosystem. Although it's not hefty in comparison to sources like Venture Capital or Angel Investors, this suggests that platforms that rely on smaller contributions from a large number of individuals are starting to find their place in the Innovation and Technology Entrepreneurship Ecosystem."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "IoTh88Vm4VYAlJTd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the combined percentage of capital sourced from crowd funding and other sources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the combined percentage of capital sourced from crowd funding and other sources, I need to locate the percentages related to {Crowd Funding and Other}<r>[0.841, 0.221, 0.945, 0.241]; [0.841, 0.251, 0.881, 0.271]</r> in the legend and sum them up.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "PDQdHGdgGaN0QjJZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Innovation and Technology Entrepreneurship Ecosystem, how much percent more of capital is sought from Angel Investors than Government Funding?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the difference, I need to find the percentages of {Angel Investors and Government Funding}<r>[0.841, 0.071, 0.949, 0.091]; [0.841, 0.131, 0.987, 0.151]</r> in the legend. Subtract the percentage of Government Funding from that of Angel Investors.\nBased on the above thought, the answer is:\n12%"
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "PL9TCKQZunDHpnju",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of capital sourced from non-government sources?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I have to add up the percentages of all sources excluding {Government Funding}<r>[0.841, 0.131, 0.987, 0.151]</r>.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "IpregzTcKhbnVr8g",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data that is represented in the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the title {\"Global Trade from 2015 to 2020\"}<r>[0.349, 0.021, 0.614, 0.047]</r>, and the pie slices and their corresponding labels in the legend <r>[0.908, 0.063, 0.993, 0.249]</r>, I can tell that each year from 2015 to 2020 is represented by a different slice. I can then create a table structure with each row representing one year of global trade.\nBased on the above thought, the answer is:\nYear, Global Trade\n2015, 15.7%\n2016, 16.5%\n2017, 16.9%\n2018, 18.8%\n2019, 18.0%\n2020, 14.1%"
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "rpTt3qRNzTol7U86",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can be inferred from the pie chart regarding global trade from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to consider the proportion of global trade for each year as represented by their corresponding slices in the pie chart.\nBased on the above thought, the answer is:\nThe highest global trade occurred in 2018 and the lowest in 2020, indicating a significant decrease in global trade from 2018 onwards, probably due to economic impacts related to the COVID-19 pandemic."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "aPpAtFFYh2sKC3Gg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the 'explode' property illustrate in the context of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'explode' property is applied to the 2018 slice of the pie chart, meaning that this slice is separated from the rest of the pie to highlight its importance.\nBased on the above thought, the answer is:\nThe 'explode' property emphasizes the year 2018, indicating that it was the year with the highest recorded global trade within the period from 2015 to 2020."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "kmjmtED3Wiu6rog9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the pie chart be helpful to a person involved in global trade policy-making?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the information and the trends represented in the pie chart,\nBased on the above thought, the answer is:\nThe pie chart gives a clear visual representation of how global trade changed over the years from 2015 to 2020. For a policy-maker, this information could help in understanding past trends and in making informed decisions about future trade policies, especially considering the significant drop in global trade in 2020."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "61cJ5GrlRbDPn5L4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of global trade for the year 2017 saw an increase by 3%, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 3% to the original 16.9% global trade of 2017 as reflected in the chart.\nBased on the above thought, the answer is:\nThe new global trade percentage for 2017 would be 19.9%."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "onQPXmR6klSY8q3w",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what's the percentage difference between the global trades of 2020 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to subtract the global trade percentage of 2020 from that of 2015 as depicted in the pie chart.\nBased on the above thought, the answer is:\nThe percentage difference is 1.6%."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "uU1223QdZf9QW1Ls",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the visualization, which year had the highest percentage increase in global trade compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentage difference between consecutive years to identify the year which had the highest increase.\nBased on the above thought, the answer is:\n2018 had the highest percentage increase in global trade compared to the previous year."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "UylDPzBAgIAK2TiL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the two pie charts to a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "In each chart, there are two sectors, each representing a unique category: {Tech Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> in pink and {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> in blue. The percentages are represented by the areas of these sectors. By comparing the two charts, we can see how these percentages changed from year 2020 to 2021.\nBased on the above thought, the answer is:\nYear,Tech Innovation,Intellectual Property\n2020,70%,30%\n2021,80%,20%"
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "Mg7oz5NEvno4nnIC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what trend do you observe in the proportions of Technological Innovation and Intellectual Property?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the proportions of {Tech Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> and {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> in the two years, I can deduce the trend.\nBased on the above thought, the answer is:\nThere is an increasing trend in Technological Innovation and a decreasing trend in Intellectual Property from the year 2020 to 2021."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "T0I5qhmkUTL3R59u",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the information in the chart, which category is being prioritized over the course of these two years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the percentages of {Technological Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> and {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> in the years 2020 and 2021.\nBased on the above thought, the answer is:\n\"Tech Innovation\" is being prioritized as it shows a 10% increase over the two years."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "iyAvfIyjRjRCGPmP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could this data suggest about the direction of the company's efforts and resources from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the change in proportions for {Technological Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> and {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> from 2020 to 2021 can suggest about the company's priorities.\nBased on the above thought, the answer is:\nThe data suggests that the company has been focusing more on Technological Innovation rather than Intellectual Property from 2020 to 2021."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "kO3xrMj7UBut69GX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the year 2020, what\u2019s the difference in the percentages of Tech Innovation and Intellectual Property?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the percentage of {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> from the percentage of {Tech Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r>.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "YPya4RyGYpJXHPas",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentages of Tech Innovation and Intellectual Property were equal, what would they each be?"
+            },
+            {
+                "from": "gpt",
+                "value": "An equal percentage distribution between {Tech Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> and {Intellectual Property}<r>[0.35, 0.166, 0.37, 0.18]</r> means that they would each take up half of the total percentage which is 100%.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "m2S8jg6mvuXeA7CU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the percentage of Technological Innovation increase from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage of {Technological Innovation}<r>[0.346, 0.135, 0.5, 0.155]</r> increased from 70% in 2020 to 80% in 2021.\nBased on the above thought, the answer is:\n10%"
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "85XDeS3fPzpHsHdF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease interpret the pie chart into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the information presented in both pie charts in the year {2012}[0.176, 0.014, 0.296, 0.04] and in the year {2020}[0.638, 0.014, 0.758, 0.04]. The first pie chart shows the distribution of sales between {Mobile}[0.129, 0.606, 0.158, 0.626] and {Laptop}[0.314, 0.399, 0.343, 0.419]. The second pie chart shows the proportion of sales for the same categories, but in {2020}.\nBased on the above thought, the answer is:\nYear,Mobile,Laptop\n2012,37.5%,62.5%\n2020,44.4%,55.6%"
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "r74Rzc2CM7yeF3Io",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the product preference trend based on the two pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must evaluate the change in proportion of sales from {2012}[0.176, 0.014, 0.296, 0.04] to {2020}[0.638, 0.014, 0.758, 0.04] for both products.\nBased on the above thought, the answer is:\nThe product preference shifted slightly from Laptops towards Mobiles from 2012 to 2020. The share of Mobiles in the total sales increased around 7%, reducing Laptop's share correspondingly."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "m81hWG4XZD4ZiutK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product's sales outperformed the other throughout the observed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must evaluate the proportions of Mobile and Laptop sales both in {2012}[0.176, 0.014, 0.296, 0.04] and {2020}[0.638, 0.014, 0.758, 0.04].\nBased on the above thought, the answer is:\nLaptop sales outperformed Mobile sales through 2012 and 2020, maintaining the majority of the market share despite a decrease in its proportion over this period."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "Z29R4ANRBH0sKUkv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be inferred about the future trend of Mobiles and Laptops sales based on the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the trends in the pie charts {in 2012}[0.176, 0.014, 0.296, 0.04] and {in 2020}[0.638, 0.014, 0.758, 0.04], an inference can be made about the future trends.\nBased on the above thought, the answer is:\nIf the trend seen from 2012 to 2020 continues, Mobiles may continue to gain a larger market share. However, Laptops can be expected to continue selling in higher quantities overall, at least in the near future."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "W9P7JyuTekw8FKvn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the proportion of Mobile sales increase from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in proportion of Mobile sales, I compare the proportion in {2020}[0.604, 0.646, 0.633, 0.666] to that in {2012}[0.129, 0.606, 0.158, 0.626].\nBased on the above thought, the answer is:\n6.9%"
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "PmDIUmFFUY2XGFBZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the proportion difference between Mobile and Laptop sales in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the proportion of {Mobile sales}[0.604, 0.646, 0.633, 0.666] from that of {Laptop sales}[0.777, 0.334, 0.806, 0.354] in 2020.\nBased on the above thought, the answer is:\n11.2%"
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "wN9SSOAVUl4830Y9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent was Laptop sales larger than Mobile sales in 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I need to find the difference between {Laptop}[0.314, 0.399, 0.343, 0.419] and {Mobile}[0.129, 0.606, 0.158, 0.626] sales in 2012 and then divide it by the Mobile sales.\nBased on the above thought, the answer is:\n66.67%"
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "JUAH9hDMDxERcG5Y",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the information from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Cuisine and Gastronomy Culture Analysis}[0.31, 0.021, 0.653, 0.047] and different slices of the chart representing various cuisines such as {Italian}[0.128, 0.481, 0.171, 0.501], {French}[0.285, 0.852, 0.331, 0.872], {Chinese}[0.536, 0.906, 0.592, 0.926], {Japanese}[0.725, 0.718, 0.788, 0.738], {Mexican}[0.721, 0.284, 0.778, 0.304], {Indian}[0.51, 0.101, 0.553, 0.121], {Greek}[0.297, 0.154, 0.339, 0.174], I can see that each slice represents a different type of cuisine and its popularity score.\nBased on the above thought, the answer is:\nCuisine,Values\nItalian,120\nFrench,80\nChinese,60\nJapanese,95\nMexican,115\nIndian,52\nGreek,65"
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "gj8y6DmBfuWTckmr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the pie chart, which cuisine culture is the least preferred, and why do you think this is the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "The least preferred cuisine is the one with the smallest slice of the pie.\nBased on the above thought, the answer is:\nFrom the chart, Indian food has the smallest slice, so it is the least preferred. This could be due to various reasons, such as people's unfamiliarity with this cuisine."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "zc6bHRmvtHXrILvy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the culture of eating in Italy look like, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the size of the Italian slice, as the larger it is, the more popular Italian cuisine.\nBased on the above thought, the answer is:\nAccording to the chart, Italian cuisine, having the largest slice, seems to be the most popular choice. A possible reason may be that Italian food is widely known and appreciated globally for its rich flavors and great variety."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "iFIKRYCqQS2QyAgj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart impact decisions for opening up a new restaurant in a multicultural city?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must consider the popularity of various cuisines manifested by the size of their respective slices.\nBased on the above thought, the answer is:\nIf someone is looking to open a new restaurant in a multicultural city, this chart suggests that Italian cuisine might be the safest bet, as it appears most popular. However, opportunities could also lie in cuisines with lower popularity like Indian or Greek, depending on if the reason for low scores is lack of availability rather than lack of interest. It is also important to consider that tastes can differ locally, and to conduct an analysis relevant to the specific area."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "eRNxw6QlwxBV74kg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two culinary cultures, when combined, have a popularity score closest to 100?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined popularity score closest to 100, I need to calculate the sum of the scores of each pair of cuisines.\nBased on the above thought, the answer is:\nFrench and Indian."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "BSinWXFv2GkmmLgr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAbout how many people participated in the survey if each point represents a person who chose the cuisine as their favorite?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number of people, I need to sum up the points of all the cuisines.\nBased on the above thought, the answer is:\nThe total number is 120 (Italian) + 80 (French) + 60 (Chinese) + 95 (Japanese) + 115 (Mexican) + 52 (Indian) + 65 (Greek) = 587 people."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "ZSLgbYrJ4Epk0O8U",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the numerical difference between the popularity scores of Greek and Indian cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I will subtract the popularity score of Indian cuisine from that of Greek cuisine.\nBased on the above thought, the answer is:\nThe difference is 65 (Greek) - 52 (Indian) = 13."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "FiMFyGKdUNAKfbPF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you transform the depicted sales data of Electric Cars and Gas Cars from 2010 to 2017 into a tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart I can observe two types of car sales: {\"Electric Cars\"}<r>[0.250, 0.157, 0.500, 0.233]</r> and {\"Gas Cars\"}<r>[0.515, 0.157, 0.765, 0.233]</r>. Each slice of the pie chart represents a different year from {2010}<r>[0.970, 0.074, 0.994, 0.093]</r> to {2017}<r>[0.966, 0.284, 0.990, 0.303]</r>. I can also see specific details of the percentages reflective of sales per year, which I can infer from the size of the pies.\nBased on the above thought, the answer is:\nYear,Electric Car Sales,Gas Car Sales\n2010,9.1%,40.9%\n2011,9.8%,40.2%\n2012,10.4%,39.6%\n2013,11.7%,38.3%\n2014,13.0%,37.0%\n2015,14.2%,35.8%\n2016,15.5%,34.5%\n2017,16.3%,33.7%"
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "mDt1KD6biw8BiN7L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom analyzing the sales proportions indicated in the pie chart, what trend can you observe in Electric Car sales from 2010 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this question, I need to observe the sales percentages represented by Electric Cars annually. Notably, I can see that the relative size of the Electric Car slices from 2010<r>[0.225, 0.278, 0.482, 0.516]</r> to 2017<r>[0.283, 0.146, 0.521, 0.516]</r> incrementally increase in size.\nBased on the above thought, the answer is:\nThere has been a clear increase in the sales of Electric Cars from 2010 to 2017, with sales steadily rising each year."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "jd8vg5UEfHzXWlX4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter the year 2012, what prominent shift do you note from observing the proportion of Electric Car sales in relation to Gas Car sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the pie slices that from 2012<r>[0.253, 0.516, 0.482, 0.852]</r> onwards, the annual sales of Electric Cars exceeded those of Gas Cars for the first time; and this trend continued in the later years.\nBased on the above thought, the answer is:\nSince the year 2012, Electric Car sales have overtaken and remained higher than Gas Car sales, indicating a shift in preference towards Electric Cars."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "UXpUl4ebZojHRAkH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the observable data from 2010 to 2017, provide some potential insights about the future of Electric and Gas Car sales."
+            },
+            {
+                "from": "gpt",
+                "value": "To provide insights about the future of the Automotive industry, we need to analyze the demonstrated sales trend of Electric and Gas Cars.\nBased on the above thought, the answer is:\nThe sales data shown on the pie chart suggests a clear trend of increasing electric car sales. If this trend continues, we can expect electric car sales to keep on growing in the future. On the other hand, gas car sales have been on a downward trend since 2010. If this pattern continues, we may see a progressive decline in gas car sales in the following years."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "Eurqt0t0jSSseZ2h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did Electric Car sales exceed Gas Car sales for the first time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the year where electric car sales exceed gas car sales, I need to compare every year's proportion of both car types in the pie chart and find the first year when the Electric Car percentage is greater than the Gas Car percentage.\nBased on the above thought, the answer is:\n2012"
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "f7dLrH2aRc8YWHgO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage sales represented between Electric Cars and Gas Cars in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the relative slices of the pie chart representing the sales of both types of cars in 2017.\nBased on the above thought, the answer is:\nThe difference in electric and gas car sales in 2017 is 16.9%."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "yHjwEb18c60jeCGb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the years 2010-2017, what's the total percentage of sales represented by Electric Cars?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to sum up the percentages that correspond to the Electric Car sales from all the years depicted in the pie chart.\nBased on the above thought, the answer is:\nThe total percentage of sales by electric cars from 2010 to 2017 is 100%."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "EE4E7SkwnfJ80WaB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you chart the provided data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observed the pie chart and the titles {DDoS Attacks}[0.609, 0.076, 0.699, 0.096], {Identity Theft}[0.609, 0.115, 0.682, 0.135], {Phishing Attempts}[0.609, 0.155, 0.798, 0.175], {Malware Infections}[0.609, 0.195, 0.835, 0.215], {Data Breaches}[0.609, 0.235, 0.749, 0.255], {Ransomware Attacks}[0.609, 0.275, 0.819, 0.295], and the percentages associated with them respectively in the data given. I also noticed the year mentioned in the subtitle.\nBased on the above thought, the answer is:\nYear,DDoS Attacks,Identity Theft,Phishing Attempts,Malware Infections,Data Breaches,Ransomware Attacks\n2017,18.2%,20.2%,15.2%,11.1%,22.2%,13.1%"
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "O6SuP0XZDI7akRJI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cyber threat was most prevalent in 2017? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I considered the cyber threat with the highest percentage value as the most prevalent.\nBased on the above thought, the answer is:\nThe most prevalent cyber threat in 2017 was Data Breaches as it represented the highest percentage of attacks (22.2%) among the types listed."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "1UfjFUYYTbbGhTTf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the prevalence of cyber threats in 2017 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the prevalence of cyber threats in 2017, I assessed the percentage representation of each type of threat.\nBased on the above thought, the answer is:\nThe chart suggests that Data Breaches, DDoS Attacks, and Identity Theft were the most prevalent threats in 2017, making up more than half of all the cyber threats in that year. Conversely, Malware Infections and Ransomware Attacks were less prevalent."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "DC6QEOFBMWyMTYCm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, how might cybersecurity strategies prioritize their efforts in the following year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To form a conclusion, I considered the most prevalent threats from 2017 as the primary areas to target in the following year.\nBased on the above thought, the answer is:\nIn the subsequent year, cybersecurity strategies might prioritize mitigating Data Breaches since they represented the highest portion of attacks in 2017. Similarly, a significant focus could be placed on reducing the frequency of DDoS Attacks and Identity Theft. Conversely, there might be less emphasis on responding to Malware Infections and Ransomware Attacks given their lesser prevalence."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "c9TyJvkXOHKVzCWS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two categories, when combined, constitute approximately one-third of the total attacks?"
+            },
+            {
+                "from": "gpt",
+                "value": "I observed the pie chart and added up the percentages of different combinations of two categories to find which pair sums up to approximately 33.3%.\nBased on the above thought, the answer is:\nDDoS Attacks and Ransomware Attacks."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "BquPKlDN9dn18bLp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total cyber threats in 2017 is represented by DDoS Attacks, Malware Infections, and Ransomware Attacks combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I added up the percentages corresponding to DDoS Attacks, Malware Infections, and Ransomware Attacks.\nBased on the above thought, the answer is:\n42.4%"
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "zBnmv393KoDeRQRk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Phishing Attempts increased by 2% and Data Breaches decreased by 2%, what would be the new proportion of Phishing Attempts and Data Breaches to the total cyber threats?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new proportions, I added 2% to the percentage representing Phishing Attempts and subtracted 2% from the percentage representing Data Breaches.\nBased on the above thought, the answer is:\nThe new proportion of Phishing Attempts would be 17.2%, and the new proportion of Data Breaches would be 20.2%."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "lVk01frkLpRCIauK",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your interpretation of the pie chart, could you convert the energy production data into a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Drawing from the subtitle {\"New Energy and Clean Technology Development in 2016 and 2017\"}[0.235, 0.088, 0.79, 0.114] and pie slices for {Solar Power}[0.297, 0.307, 0.512, 0.757], {Wind Power}[0.389, 0.373, 0.728, 0.813], {Hydro Power}[0.497, 0.197, 0.707, 0.505], {Geothermal Power}[0.457, 0.167, 0.509, 0.475], {BioEnergy}[0.418, 0.177, 0.506, 0.476] and {Nuclear Power}[0.334, 0.199, 0.5, 0.48], I can confirm the energy categories and their respective percentages for each year.\nBased on the above thought, the answer is:\nYear,Solar Power,Wind Power,Hydro Power,Geothermal Power,BioEnergy,Nuclear Power\n2016,24,38,19,5,7,7\n2017,29,46,21,3,3,8"
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "Q5bT4KtnX5qyS6He",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which type of new energy and clean technology had the largest share in both 2016 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to determine the energy type with the largest share by identifying the largest pie slice in both years.\nBased on the above thought, the answer is:\nWind Power had the largest share in both 2016 and 2017."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "rbKDOZ4KcLvy1maU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the development of new energy and clean technology change from 2016 to 2017, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the pie slices' areas in 2016 and 2017, I can evaluate the growth or decline of each energy type.\nBased on the above thought, the answer is:\nThe pie chart demonstrates that Solar Power and Wind Power, the dominant forms of new energy and clean technology, saw an increase from 2016 to 2017. Hydro Power also notice a smaller increase. On the contrary, Geothermal Power, BioEnergy, and Nuclear Power saw a decrease."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "QKPoofPgMY1LsqV1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf these trends continue, which energy type might become the most dominant in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to consider the growth rates of the various energy types, focusing especially on the ones with the highest rates.\nBased on the above thought, the answer is:\nGiven the current trends, Solar Power, which has seen significant growth from 2016 to 2017, might become the most dominant new energy and clean technology source in the future."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "lQ3vFY3JrO1Blg1l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich energy source had the highest growth rate from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the percentage figures for each energy source in both years, by subtracting the 2016 percentage from the 2017 one, I can determine the growth rate of each source and thus identify the highest.\nBased on the above thought, the answer is:\nSolar Power."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "sm84DiatxfOgrHy3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in the percentage of Solar Power from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the Solar Power percentage of 2016 from that of 2017 to find the rate of increase.\nBased on the above thought, the answer is:\nThe increase was 5%."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "E6lgC4SggRP9hgKu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the total clean energy production percentage change from 2016 to 2017, considering only Solar Power, Wind Power, and Hydro Power?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to sum the percentages of Solar Power, Wind Power, and Hydro Power in 2016 and 2017 and then subtract the 2016 total from the 2017 total to get the change.\nBased on the above thought, the answer is:\nThe total change was 5%."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "O530GSjhC5WLpMTb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtract the chart data and generate a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart data represents the effectiveness evaluation of two policies. The title {\"Government Policy Effectiveness Evaluation\"}<r>[0.299, 0.021, 0.664, 0.047]</r> indicates that the data represents the percentage of people who believe the policies are effective. I can see two segments: {Foreign Policy}<r>[0.213, 0.312, 0.73, 0.92]</r> and {Tax Policy}<r>[0.283, 0.146, 0.741, 0.542]</r>, represented by two different colors in the legend with corresponding percentages.\nBased on the above thought, the answer is:\nGovernment Policy, Effectiveness\nForeign Policy, 60%\nTax Policy, 40%"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "hDwR5Q9rMTQyf5Ri",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which government policy is considered more effective?"
+            },
+            {
+                "from": "gpt",
+                "value": "The effectiveness of the policies can be directly inferred from the size of the slices. Here, the most significant slice corresponds to the Foreign Policy.\nBased on the above thought, the answer is:\nThe Foreign Policy is regarded as more effective, as it occupies a larger section of the pie chart, indicating that a higher percentage of people believe in its effectiveness."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "AW8rRe2UYrI4B4Fr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might the implication of this data be for future government policy-making?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications can be deduced based on the favorability of the policies, reflected by the size of the slices.\nBased on the above thought, the answer is:\nThe government may prioritize Foreign Policy over Tax Policy while making future decisions, as it appears to have more support among the public based on its higher percentage in the chart."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "1MZL8rZWOUcMiA8k",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about public opinion on government policies from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices' size gives a clear idea of the public's opinion about the effectiveness of these two government policies.\nBased on the above thought, the answer is:\nGenerally, the public seems to view Foreign Policy as more effective than Tax Policy, given the larger slice size representing Foreign Policy in the pie chart. This reflects the general sentiment of people believing more in the effectiveness of Foreign Policy."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "Bd6OwjKnJ6NqvCdn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the effectiveness of Foreign Policy and Tax Policy shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I can subtract the smaller percentage from the larger one. In this case, the larger percentage corresponds to the Foreign Policy slice and the smaller one to the Tax Policy slice.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "gbIhu1k3gR2GluwC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average effectiveness percentage of the two policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average effectiveness, I should add the two percentages and divide the result by the number of policies, which is 2 in this case.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "76JH18Pk2PrfD6dK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Tax Policy's effectiveness increases by 10%, what will be the new overall percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new overall percentage, I need to add 10% to the Tax Policy's current effectiveness and recalculate the total.\nBased on the above thought, the answer is:\n110%"
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "cMGlp4jO3dLD0jVF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you use the pie chart to generate a CSV format table showing the growth rates of AI and automation from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the data I'm seeing in the pie chart, including the labels for each year and their corresponding percentage values, I can extract the information to create a CSV format table. The slice labels I'm referencing are {2015}<r>[0.313, 0.43, 0.358, 0.45]</r>, {2016}<r>[0.315, 0.588, 0.359, 0.608]</r>, {2017}<r>[0.398, 0.71, 0.442, 0.73]</r>, {2018}<r>[0.525, 0.707, 0.569, 0.727]</r>, {2019}<r>[0.612, 0.551, 0.656, 0.571]</r>, {2020}<r>[0.566, 0.344, 0.61, 0.364]</r>, and {2021}<r>[0.403, 0.26, 0.447, 0.28]</r>. The corresponding percentages for each year are located within the slices of the pie chart.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,11.1%\n2016,12.0%\n2017,13.0%\n2018,13.9%\n2019,15.7%\n2020,16.7%\n2021,17.6%"
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "XeQj2ngPgeoYpfgD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which year witnessed the highest growth rate in AI and Automation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would need to look at the chart and identify the slice that represents the highest growth rate.\nBased on the above thought, the answer is:\nThe year 2021 had the highest growth rate in AI and Automation."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "LUXGu3WXRlt7yVEM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, what trend do you notice concerning the growth rates of AI and Automation from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the progression presented in the chart, I can look at the trend in the growth rates.\nBased on the above thought, the answer is:\nThe data in the chart shows a steady increase in the growth rates of AI and Automation from 2015 to 2021, suggesting the increasing adoption of AI and Automation over the years."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "UlVKtM5mSFAdJa0S",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trends seen in the pie chart, how would you predict the growth rate of AI and Automation in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require extrapolation of the current trend seen in the growth rates of AI and Automation from 2015 to 2021.\nBased on the above thought, the answer is:\nIf the current trend continues, the growth rate of AI and Automation is likely to continue to rise in the coming years. The extent of the increase, however, cannot be definitively determined without more data."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "QHMK7vlTdz6irb51",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to add the growth percentages for AI and Automation for the years 2016 and 2017, what would you get?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to identify the specific percentages for the years 2016 and 2017 in the pie chart and add them up.\nBased on the above thought, the answer is:\nThe sum is 25%."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "un7Oz9cUdWyoMzu4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCompute the overall average of the adoption rates from 2015 to 2021."
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the average, I would need to add up all the percentages from 2015 to 2021, and then divide that sum by the number of years, which is 7.\nBased on the above thought, the answer is:\nThe average is roughly about 14.2%."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "3XQH3aboCK5iQxSy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the growth percentage of AI and Automation for the year 2021 was to increase by 6%, what would be the new percentage for 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new percentage for 2021, I would need to add the given 6% to the existing percentage of 17.6% for the year 2021.\nBased on the above thought, the answer is:\nThe new growth percentage for 2021 would be 23.6%."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "Yz1FuvojutYEzRPw",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the image into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, in this pie chart, there are two subplots, with one indicating the \"Social Networks Usage by Principal\"{subplot_0}<r>[0.018, 0.048, 0.486, 0.985]</r> and the other showing the \"Interpersonal Relationships Usage by Professor\"{subplot_1}<r>[0.514, 0.048, 0.982, 0.985]</r>. In the first subplot, the two slices represent \"Facebook\" and \"WhatsApp\", while in the second subplot, \"Twitter\" and \"Instagram\" indicated. The percentages of each slice are also provided in the chart.\nBased on the above thought, the answer is:\nRole,Facebook,WhatsApp,Twitter,Instagram\nPrincipal,69,31,, \nProfessor,, ,31,25"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "adUahmD5ZMBx44dy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data, which social network is the most preferred by the Principal and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice representing Facebook for Principal usage {the slice}<r>[0.052, 0.303, 0.366, 0.918]</r> shows a percentage of 69%, which is greater than the percentage of WhatsApp.\nBased on the above thought, the answer is:\nFacebook is the most preferred social network by the Principal because it has a higher usage percentage of 69%."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "FMifD0dI7ZNOugqE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight does the chart provide regarding the Professor's preference for interpersonal relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart for the Professor, I can see that the slice representing Twitter {the slice}<r>[0.551, 0.308, 0.914, 0.924]</r> has a higher percentage (31%) compared to Instagram.\nBased on the above thought, the answer is:\nThe chart indicates that the Professor prefers using Twitter for managing interpersonal relationships as it has a higher usage rate."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "JgBQQYwZFtOI0CRo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, what conclusion can be drawn about the different social media preferences of the Principal and the Professor?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, the two pie charts must be compared - one showing the social network usage by the Principal {the slices}<r>[0.052, 0.142, 0.439, 0.918]</r> and the other showing the social network usage by the Professor {the slices}<r>[0.551, 0.142, 0.936, 0.924]</r>.\nBased on the above thought, the answer is:\nThe Principal prefers Facebook and WhatsApp, while the Professor prefers Twitter and Instagram. This suggests a significant difference in social media preferences between the Principal and Professor."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "svr39uO6f6Nixpvw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the usage percentage of Facebook by the Principal and Instagram by the Professor?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage usage of Facebook by the Principal is 69% from {the slice}<r>[0.052, 0.303, 0.366, 0.918]</r>. Also, the usage of Instagram by the Professor is 25% from {the slice}<r>[0.605, 0.142, 0.936, 0.649]</r>. Thus, the difference can be calculated by subtracting the two values.\nBased on the above thought, the answer is:\n44%"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "xYfVZJxNXMYS1EJ4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average usage of social networks by Principal and interpersonal relationships by Professor?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are two slices for Principal {69, 31}<r>[0.052, 0.142, 0.439, 0.918]</r> and two slices for Professor {31, 25}<r>[0.551, 0.142, 0.936, 0.924]</r>. I need to sum up the percentages and divide them by the number of slices to get the average.\nBased on the above thought, the answer is:\n39%"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "9qqRpe5FdpGls7yt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of Twitter and WhatsApp usage by both Principal and Professor?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie charts, Twitter usage by Professor is 31% from the slice around coordinates {the slice}<r>[0.551, 0.308, 0.914, 0.924]</r> and WhatsApp usage by Principal is 31% from the slice around coordinates {the slice}<r>[0.108, 0.142, 0.439, 0.793]</r>. Adding those two percentages will give the total percentage.\nBased on the above thought, the answer is:\n62%"
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "JOoFmUSeifj7g32H",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me convert the annual sales data of Ventilators and Telemedicine products from 2015 to 2021 into a CSV format table, according to the line chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the information provided in the data and pie chart {the pie chart}[0.173, 0.123, 0.859, 0.842], the sales data for the product types \"Ventilators\" and \"Telemedicine\" can be converted into a table format. The yearly sales data is present in the pie chart.\nBased on the above thought, the answer is:\nProduct Type, 2015, 2016, 2017, 2018, 2019, 2020, 2021\nVentilators, 10, 15, 20, 25, 27, 50, 90\nTelemedicine, 5, 10, 20, 25, 30, 35, 40"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "bVogplNKer2RwRV7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be inferred from the sales of Ventilators and Telemedicine products over the years 2015-2021 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart {the pie chart}[0.173, 0.123, 0.859, 0.842], I can see the proportions of the sales for the products \"Ventilators\" and \"Telemedicine\" over the years.\nBased on the above thought, the answer is:\nThe sales of both Ventilators and Telemedicine products have been growing over the years. However, the sales of Ventilators have spiked significantly in 2020 and 2021, indicating an increased demand perhaps due to the pandemic, while the Telemedicine product has shown a steady increase, reflecting its growing acceptance and usage in healthcare."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "7Tm7MlrDEl8hSQMD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich product had the most growth in sales in the given span and why could that be so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the change in size of chunks related to each product's sales over the years, I can infer which product had more growth in sales.\nBased on the above thought, the answer is:\nThe product \"Ventilators\" saw the most growth in sales during this period, especially in 2020 and 2021. This can be attributed to the Covid-19 pandemic which increased the global demand for ventilators."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "A7sB7WqWTxu8gIdE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, how might the trends affect the future funding and investment decisions of these two products?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sales growth trends of the two products over the years, I can make an inference on their potential investment prospects.\nBased on the above thought, the answer is:\nGiven the steep increase in Ventilator sales in the last two years, it can be expected that these may remain in high demand, securing more investments and funding. For Telemedicine, the constant growth illustrates it as a reliable sector for investment, considering the rise in digital health solutions. Therefore, both products look promising for future investment."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "LSGYepibdmbmHbZp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the given pie chart, what percentage increase in Ventilator sales can be seen from 2020 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart, especially the sectors representing the sales for the years 2020 and 2021 {2020 sector slice}[0.423, 0.515, 0.699, 0.842]; {2021 sector slice}[0.259, 0.123, 0.809, 0.45], I can note the difference in their proportions, which reflects the sales increase.\nBased on the above thought, the answer is:\n80%"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "peecUvNmFixLIQSD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many units of Ventilators were sold in total from 2015 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can accumulate the units of Ventilators sold for each year from 2015 to 2021 as provided in the data.\nBased on the above thought, the answer is:\n237 thousand units"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "xc94aforjaJPFb2S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual increase rate in sales of Telemedicine products between 2015 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the total increase in sales over the years, divided by the number of years, we can obtain the average annual increase rate.\nBased on the above thought, the answer is:\nApproximate 7.8%"
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "PKXWytOPsEx13lT6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the distribution percentages of the different financial policy adoptions by countries presented in the pie chart as a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer that, I need to analyze the sizes of the wedges on the pie chart at {the pie slices with percentages}<r>[0.242, 0.264, 0.738, 0.785]</r> on the chart. Each wedge represents a financial policy and the percentage of total countries that have implemented it. From this, I can accurately construct a CSV data table.\nBased on the above thought, the answer is:\nPolicy Type, Percentage of Countries Implementing\nMonetary Policy, 13.5%\nFinancial Stability, 11.4%\nFiscal Policy, 16.9%\nExchange Rate Policy, 9.8%\nInterest Rate Policy, 10.8%\nInflation Targeting, 7.4%\nAccommodative Policy, 7.7%\nDeficit Spending, 8.2%\nQuantitative Easing, 6.3%\nTightening Policy, 8.0%"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "gc8Xj0Ndtau4d5cm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom analyzing the pie chart, can you tell which financial policy is most adopted by countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine that, I need to find out which sector of the pie chart at <r>[0.242, 0.264, 0.738, 0.785]</r> is the largest as it represents the financial policy most adopted by nations.\nBased on the above thought, the answer is:\nThe most adopted financial policy by countries is the Fiscal Policy as it covers the largest proportion of the pie chart."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "KCq3lZ1pcqHK29BS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we make any assumptions about a country's choice of financial policy based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the pie chart and try to understand the allocation of the different policy types to make any inferences.\nBased on the above thought, the answer is:\nFrom the pie chart, it can be inferred that majority of the countries implement a variety of financial policies. However, Fiscal Policy is the most common, suggesting it may be seen as essential or particularly effective by a large portion of the countries."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "Txia5HAHop8ozlCz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what might be a significant trend for smaller nations, who must be more selective in their financial policy strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to interpret the chart and understand what smaller nations, which are more choice-limited, might primarily adopt.\nBased on the above thought, the answer is:\nAs Quantitative Easing is the least utilized financial policy according to the pie chart, it suggests that smaller nations are less likely to use it. The popularity of Fiscal Policy might imply that it is a more accessible or universally applicable strategy."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "4ZdkxbZBIU4ZWSza",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the approximate cumulative percentage of countries implementing Interest Rate Policy, Inflation Targeting, and Accommodative policy as shown in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the cumulative percentage, I need to sum the percentages of the three policy types \"Interest Rate Policy\", \"Inflation Targeting\" and \"Accommodative Policy\" as shown at <r>[0.49, 0.492, 0.738, 0.652]</r>, <r>[0.49, 0.383, 0.737, 0.512]</r> and <r>[0.49, 0.299, 0.702, 0.512]</r> respectively.\nBased on the above thought, the answer is:\n25.9%"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "aqTfzb0ee73sLtog",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich policy type has the lowest adoption percentage as per the chart, and what is this percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the policy type with lowest adoption percentage, I need to look for the smallest slice in the pie chart, and then look at its corresponding color and legend.\nBased on the above thought, the answer is:\nQuantitative Easing, 6.3%"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "pL0PRXj1uCPbMXgO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the pie chart, how much higher is the use of Fiscal Policy compared to Quantitative Easing?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in percentage, I need to directly compare the percentages of both Fiscal Policy and Quantitative Easing on the pie chart.\nBased on the above thought, the answer is:\n10.6%"
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "juRi0obLqH3s7vyp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart provided, can you generate a table comparing the development of 'Visual Arts' and 'Music' industries from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can generate this from the pie chart with coordinates {2010}<r>[0.5, 0.52, 0.796, 0.818]</r> and {2011}<r>[0.5, 0.331, 0.81, 0.613]</r> representing the 'Music' industry, and {2012}<r>[0.493, 0.21, 0.746, 0.52]</r> and {2013}<r>[0.249, 0.182, 0.487, 0.492]</r> representing the 'Visual Arts' industry.\nBased on the above thought, the answer is:\nYear,Music,Visual Arts\n2010,2314,2950\n2011,1957,2872\n2012,1810,2810\n2013,1568,2550"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "XjIGdlGngOm2DkgD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on what you've inferred from the chart, which industry has seen more growth from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, we need to look at the decrease in both industries' sizes over this period, as indicated by the sizes of the slices in the chart.\nBased on the above thought, the answer is:\nThe 'Visual Arts' industry has seen more growth, as it decreased by 400 over that period, compared to a decrease of 746 in the 'Music' industry."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "1r86pGYbJCgtbbzW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the biggest shift in the Culture and Arts industry between 2010 and 2013, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sizes of the slices, one can infer that the intake in both 'Music' and 'Visual Arts' industries has decreased during these years. Among these, the largest decrease is most noticeable.\nBased on the above thought, the answer is:\nThe largest shift was observed in the 'Music' industry, which saw a decrease of 746 units between 2010 and 2013."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "jEbJ6ETXeuN6wnPI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the figures of 2013, which industry would you advise an investor to invest in: 'Music' or 'Visual Arts'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Using the chart's figures, I need to consider the performance of both industries in 2013 to give a recommendation.\nBased on the above thought, the answer is:\nConsidering the 2013 figures, it would be more advantageous to invest in the 'Visual Arts' industry as it maintained a higher volume compared to the 'Music' industry."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "h6vNSgP4iqSHglYs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the 'Music' industry decrease between 2010 and 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the 'Music' industry figure in 2011 from that in 2010, both given in the pie chart.\nBased on the above thought, the answer is:\n357"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "yDumn65ctumJQkoh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the 'Visual Arts' industry surpass the 'Music' industry in the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the 'Music' industry's 2013 figure from the 'Visual Arts' industry's 2013 figure, both represented in the pie chart.\nBased on the above thought, the answer is:\n982"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "lx2xlLKMRu0BTl5t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much change in total did the 'Visual Arts' industry experience from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the total change, I need to subtract the 'Visual Arts' industry's 2013 figure from its 2010 figure.\nBased on the above thought, the answer is:\n400"
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "HNhO5fGoiq6E4WSa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter observing the pie chart, please express the innovative business measures of these companies into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "According to this pie chart's distinction of sectors {\"Apple\"}[0.23, 0.346, 0.502, 0.586], {\"Google\"}[0.238, 0.52, 0.567, 0.792], {\"Microsoft\"}[0.502, 0.312, 0.773, 0.784], {\"Amazon\"}[0.487, 0.249, 0.676, 0.52], {\"Facebook\"}[0.358, 0.249, 0.502, 0.52], {\"Tesla\"}[0.294, 0.29, 0.502, 0.52], I can verify that these represent the different Technology Companies and their innovative business percentages.\nBased on the above thought, the answer is:\nTechnology Company, Innovative Business\nApple,15%\nGoogle,25%\nMicrosoft,35%\nAmazon,12%\nFacebook,8%\nTesla,5%"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "OjKer2NjH2OQkVSb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn July 2021, which company is likely to be the leader in innovative businesses? Why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to pick the company with the highest current percentage and make an assumption based on that.\nBased on the above thought, the answer is:\nMicrosoft is likely to be the leader in July 2021 because it currently has the highest innovative business measure of 35%."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "A61q4jbsQCec50KQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow diverse are the levels of innovation between these six companies based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the chart and notice the range and difference in percentages among the six companies.\nBased on the above thought, the answer is:\nThere's quite a diversity in the levels of innovation among these six companies. Microsoft has the highest percentage at 35%, and Tesla has the lowest at 5%. All the other companies fall within this range, implying a considerable variation in innovation levels."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "mi5EvFY0f0il7XkC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this chart affect investment decisions in the tech industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond to this question, I would need to think about how an investor might interpret the data displayed on the chart.\nBased on the above thought, the answer is:\nAn investor might use this chart to determine which companies are currently leading in innovation. They might decide to invest more in Microsoft or Google because they have higher percentages of innovative businesses. Conversely, they may also see potential growth in companies like Tesla and Amazon, which currently have lower percentages."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "aYSxRNPeuGVak4og",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich company has the highest innovation percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to compare the percentages of all the companies mentioned on the chart.\nBased on the above thought, the answer is:\nMicrosoft"
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "ZvygHJ04YXZ5BP4J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the summed up percentage of the two companies with the lowest innovation percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I have to look for the two sectors with the smallest radius and sum up their percentages.\nBased on the above thought, the answer is:\nThe sum of the percentages for Tesla and Facebook is 8% + 5% = 13%."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "UCvLbaj1NuTq5uM4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Google increased its innovation measure by 7%, what would be the new average percentage across all the companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I would have to add 7% to Google\u2019s current percentage and then calculate the new average.\nBased on the above thought, the answer is:\nThe new average would be (15% + 32% + 35% + 12% + 8% + 5%) / 6 = 17.83%."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "GzL9iOecNnDlmz4A",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data from the pie chart representing the Investment return rate from 2012 to 2018 into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can interpret the data as represented in the {pie slices}<r>[0.248, 0.166, 0.62, 0.486]</r> corresponding to each year. With the assistance of the {chart's title}<r>[0.056, 0.083, 0.944, 0.111]</r> I can relate each percentage to the correct year.\nBased on the above thought, the answer is:\nYear,Return Rate\n2012,8.7%\n2013,11.2%\n2014,15.4%\n2015,14.6%\n2016,12.1%\n2017,17.7%\n2018,20.4%"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "rOGl3475Z7sYcyQq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of the chart, can you infer which year experienced the most significant return on investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to look for the pie slice with the largest proportion, as it reflects the year with the highest return rate.\nBased on the above thought, the answer is:\nThe year 2018 experienced the most significant return on investment as it has the largest share of 20.4% in the pie chart."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "5UTpFMXggPg8Uvxa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you make an inference about how the asset has performed over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions and the differences between each year to determine the performance of the asset.\nBased on the above thought, the answer is:\nOverall, the asset shows an improving performance over the years from 2012 to 2018, with the exception of certain years such as 2013 and 2016 where there was a lower return rate. Notably, in 2018 the asset experienced a significant return rate of 20.4%, suggesting a peak performance."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "2Pr9EtU7hRjcQOZy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this data, how do you think this asset might behave in the coming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Predicting future behavior entails analyzing the overall trend of the return rates. Although there are certain fluctuations, the general upward trend indicates a growth in performance.\nBased on the above thought, the answer is:\nWhile previous performance isn't a guaranteed predictor of future outcomes, the positive trend in return rates suggests that the asset might potentially continue yielding higher returns in the following years, provided the factors contributing to the past performance remain stable. However, caution should be taken due to the observed fluctuations from year to year."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "PTWMllceZ7sL5Hdx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, can you calculate the average return rate from the years 2012 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To arrive at the average, I need to add the return rate of each year and divide by the seven years period.\nBased on the above thought, the answer is:\n14.37%"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "3bbhZObt6JlRTBHs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year saw a return rate higher than the average calculated from the years 2012 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I compare each year's return rate to the average and observe results greater than the average.\nBased on the above thought, the answer is:\n2014, 2017 and 2018"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "RPYEk8qYEaBQOhHV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the return rate in 2012 increased by 10%, what would be the new average return rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "I add 10% to the return rate of 2012, summating the rates again and recalculating the average over the seven years period.\nBased on the above thought, the answer is:\n15.77%"
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "FhIZbc1SX3q9Nrzj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the distribution of the venture capital investment details shown on the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart titled {\"Investment Distribution in Venture Capital Markets\"}[0.026, 0.17, 0.968, 0.203] shows different sectors and their corresponding percentages. I see that each slice corresponds to a different sector like Software, Bio_Tech, Energy, and so on, this data can be extracted in a structured CSV table.\nBased on the above thought, the answer is:\nInvestment_Area, VC_Funding\nSoftware, 6.8%\nBio_Tech, 13.3%\nEnergy, 2.9%\nHealthcare, 8.6%\nAI, 10.7%\nFintech, 17.8%\neCommerce, 19.5%\nEducation, 4%\nHardware, 4.6%\nAutomotive, 11.8%"
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "dH5Y2t0PAqzH0gvU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the chart, which sector receives the most venture capital investment? Tell me the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the sector with the largest proportion of investment. The sector with the greatest share of the pie chart would be the sector receiving the most capital.\nBased on the above thought, the answer is:\nThe \"eCommerce\" sector receives the most venture capital investment, with 19.5% of the total. This might be due to the rapid growth and high profitability of eCommerce businesses."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "qjfMudN6sNBcAxH4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be made based on the pie chart about the focus of venture capital markets?"
+            },
+            {
+                "from": "gpt",
+                "value": "The inference has to be made by analyzing the share of each sector in the total venture capital investment.\nBased on the above thought, the answer is:\nBased on the pie chart, venture capital markets are most focused on the Fintech and eCommerce sectors, as they receive a higher proportion of the total investment. It shows that these two sectors are currently the most promising and likely to provide high returns on investments."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "GPjIUZcEdMV3HvEc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data in this pie chart influence the investment decisions for the following year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data in the pie chart can provide insights on which sectors are likely to attract more funding based on their current performance. If a sector has a significant portion of the VC Market Funds, this sector might attract more investments in the following year.\nBased on the above thought, the answer is:\nThe data in this chart provides a clear picture of the sectors where venture capital funding is currently concentrated. Future investment decisions could take into consideration these trends. For instance, the Emerging sectors like AI which have a significant proportion of 10.7% could be seen as potential sectors to watch or invest in. Similarly, sectors receiving lesser investments currently, like energy and education, may need more focus to improve their significance in the market."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "erXP5glhKByyvQu8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of investment in the software and energy sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to add the percentages of the software and energy sectors as observed from {\"Software\"}[0.258, 0.361, 0.497, 0.52] and {\"Energy\"}[0.288, 0.52, 0.497, 0.689].\nBased on the above thought, the answer is:\nThe total percentage is 9.7%."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "lkkexeelXdvINFDT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo any two sectors make up 25% of the total investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze and sum up the percentages of each pair of sectors and then judge whether it equals to 25%.\nBased on the above thought, the answer is:\nNo, no two sectors make up exactly 25% of the total investment."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "hKw9Bc5XdxzpPLYX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment in the \"Education\" sector was increased by 5%, what would the new total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add an additional 5% to the percentage of the \"Education\" sector indicated at {\"Education\"}[0.497, 0.275, 0.595, 0.52], and sum up all investment percentages.\nBased on the above thought, the answer is:\nIf the investment in \"Education\" increased by 5%, the new total percentage would be 105%."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "GTIzviY9FW7VU4Ny",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a table representing the data shown in the 2014 pie chart sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create a table, I will consider the pie chart of {\"2014\"}<r>[0.563, 0.195, 0.930, 0.896]</r> whose sector slices include {\"Training\"}<r>[0.062, 0.319, 0.236, 0.81]</r>, {\"Networking\"}<r>[0.141, 0.54, 0.345, 0.888]</r>, {\"Workshops\"}<r>[0.253, 0.54, 0.427, 0.835]</r>, {\"Webinars\"}<r>[0.253, 0.355, 0.427, 0.564]</r>, {\"Conferences\"}<r>[0.253, 0.273, 0.401, 0.54]</r>, {\"Advising\"}<r>[0.211, 0.192, 0.365, 0.54]</r>, and {\"Internships\"}<r>[0.120, 0.202, 0.253, 0.54]</r>; with respective proportion texts [\"25.0%\", \"20.0%\", \"15.0%\", \"10.0%\", \"5.0%\", \"15.0%\", \"10.0%\"] and surmise the corresponding percentages based on the text data provided.\nBased on the above thought, the answer is:\nYear,Training,Networking,Workshops,Webinars,Conferences,Advising,Internships\n2014,25,20,15,10,5,15,10"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "gYEsTgXx0ZtEjWkZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, which workplace development type had the largest decrease in percentage from 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would need to compare the percentages of each workplace development type in 2014 to the percentages in 2015 and determine which had the largest decrease.\nBased on the above thought, the answer is:\n'Training' had the largest decrease moving from 25% in 2014 to 24% in 2015, a change of 1%."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "redPuVRqVZkRpFGj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the development trends in the workplace based on the pie charts for 2014 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference, I need to consider any changes in proportions from 2014 to 2015 among the different workplace development types.\nBased on the above thought, the answer is:\nThe pie charts show that 'Training' and 'Networking' were the most popular development methods in both 2014 and 2015, which suggests that these methods may continue to be popular. The slight increase in the percentage of 'Networking' from 20% in 2014 to 22% in 2015 may suggest a growing trend towards this method of development."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "zQGXiy11ipHYguE8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how would you advise a company that wants to align its development practices to the trends represented in the charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To advise the company, I need to consider which development practices have the highest percentages and the trends between the years.\nBased on the above thought, the answer is:\nThe company should focus on providing 'Training' and 'Networking' opportunities as they consistently show the highest participation rates. There is also a slight rise in participation in 'Networking' from 2014 to 2015, suggesting it might gain more popularity. While 'Conferences' remained at a steady 5% over the two years, indicating a stable interest."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "NbtMXdbH9KC30UYp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of 'Webinars' in 2014 and 2015 based on the provided pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will locate the percentages of 'Webinars' in both charts, being {\"Webinars 2014\"}<r>[0.253, 0.355, 0.427, 0.564]</r> and {\"Webinars 2015\"}<r>[0.747, 0.337, 0.921, 0.54]</r> which shows 10% and 9% respectively, then add these percentages together to formulate the total percentage.\nBased on the above thought, the answer is:\n19%"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "gbtsKSXMNwVGZuFE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the 'Networking' category change from 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of 'Networking' in {\"2014\"}<r>[0.141, 0.54, 0.345, 0.888]</r> which is 20%, from the percentage of 'Networking' in {\"2015\"}<r>[0.627, 0.54, 0.848, 0.888]</r> which is 22%, to find out how much it changed.\nBased on the above thought, the answer is:\n2%"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "cTkfElbjGNuOrvI3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion gap between 'Advising' and 'Internships' in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the proportion gap, I will subtract the percentage of 'Internships' {\"2015\"}<r>[0.614, 0.197, 0.747, 0.54]</r> (11%) from the percentage of 'Advising' {\"2015\"}<r>[0.715, 0.192, 0.85, 0.54]</r> (13%).\nBased on the above thought, the answer is:\n2%"
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "Mxv5NTbsvCVAO9SA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the graphical data you are seeing into a tabular format with two columns as Heritage Type and Number of Countries Recognized."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the raw data that is visually represented in the pie chart , I observed, the values associated with each slice of architecture referred to: {Ancient Artifacts}<r>[0.148, 0.289, 0.495, 0.512]</r>, {Ancient Buildings}<r>[0.123, 0.536, 0.47, 0.884]</r>, {World Wonders}<r>[0.494, 0.512, 0.586, 0.859]</r>, {Old Languages}<r>[0.495, 0.512, 0.741, 0.847]</r>, {Traditional Music}<r>[0.495, 0.421, 0.842, 0.756]</r>, {Folklore}<r>[0.464, 0.165, 0.83, 0.512]</r>, {Myths}<r>[0.229, 0.167, 0.495, 0.512]</r>. There's no direct percentage value specified, so I'll take the autotext labels with the equivalent of their numeric representation, and convert it into a table.\nBased on the above thought, the answer is:\nHeritage Type,Number of Countries Recognized\nAncient Artifacts,80\nAncient Buildings,180\nWorld Wonders,31\nOld Languages,60\nTraditional Music,120\nFolklore,160\nMyths,90"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "czJfuPaBVh5dYijJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of cultural heritage is the most globally recognized? And explain based on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The most globally recognized type of cultural heritage corresponds to the largest slice of the pie chart, which is {Ancient Buildings}<r>[0.123, 0.536, 0.47, 0.884]</r>.\nBased on the above thought, the answer is:\nAncient Buildings is the most globally recognized cultural heritage, as it\u2019s recognized by the highest number of countries."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "jgZPqWEJ3qdaBk0W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the global recognition of different types of cultural heritage from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By viewing the sizes of the different slices, I can make inferences about the global recognition of different types of cultural heritage\nBased on the above thought, the answer is:\nThe chart indicates that Ancient Buildings is the most recognized form of cultural heritage, followed by Folklore and Traditional Music. On the contrary, World Wonders are the least recognized. This indicates a wide appreciation for ancient architecture and local traditions, while globally notable structures and sites have comparatively lesser recognition."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "KV7Q4dojc6QoR81M",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence international cultural preservation efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer how the data can influence international cultural preservation efforts, I analyze the count of countries recognizing each type of cultural heritage.\nBased on the above thought, the answer is:\nBased on this data, international cultural preservation organizations may prioritize preserving and promoting Ancient Buildings, Folklore, and Traditional Music given their widespread recognition. Conversely, efforts might also be amplified to preserve and promote World Wonders and Old Languages since they are recognized by relatively fewer countries, and they might be at greater risk of fading or being overlooked."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "NQNvZJcoTa88yaxp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how many more countries recognize Ancient Buildings compared to World Wonders?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess this, I need to compare the count of countries that recognize {Ancient Buildings}<r>[0.123, 0.536, 0.47, 0.884]</r> with those that recognize {World Wonders}<r>[0.494, 0.512, 0.586, 0.859]</r>.\nBased on the above thought, the answer is:\n149 countries"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "joVnOMu66yTG8nIo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of countries recognizing Traditional Music and Folklore?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this total, one must sum up the countries recognizing {Traditional Music}<r>[0.495, 0.421, 0.842, 0.756]</r> and {Folklore}<r>[0.464, 0.165, 0.83, 0.512]</r>.\nBased on the above thought, the answer is:\n280 countries"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "ab8e4oiMd22ocqeV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category is recognized by exactly 90 countries?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to match the number 90 to the corresponding heritage type.\nBased on the above thought, the answer is:\nMyths"
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "0CGO0rJG3yPT9dNC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data displayed in the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can identify the slices representing different years, and then note down their respective percentages which indicate the Medical Technology Usage. The summary title {Healthcare: Medical Technology Usage (2011-2020)}<r>[0.021, 0.076, 0.979, 0.109]</r> and each pie slice's percentage can be converted into data for the table.\nBased on the above thought, the answer is:\nYear,Medical Technology Usage\n2011,8.2%\n2012,7.7%\n2013,9.6%\n2014,10.0%\n2015,10.3%\n2016,9.9%\n2017,10.6%\n2018,10.9%\n2019,11.2%\n2020,11.6%"
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "eDGe6uPb4VA31kNv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, does Medical Technology usage show an upward or downward trend over the years? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To explore the trend of medical technology usage over the years, I should look at the size of each year's slice and how it changes. An increase in the proportion of slices indicates an upward trend, while a decrease indicates a downward trend.\nBased on the above thought, the answer is:\nThere is a general upward trend in Medical Technology usage over the years, as indicated by the gradual increase in the size of the sectors representing each year, with the exception of 2016 where there was a brief dip."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "oIUujAk1PgGMmwSJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy was the year 2016 highlighted in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand why 2016 was highlighted, I need to consider the data presented. The key factor could be its percentage in comparison with other years.\nBased on the above thought, the answer is:\nThe year 2016 is highlighted because it's an outlier in the trend; it's the only year in the range to see a decline in Medical Technology usage, breaking the otherwise upward trend."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "DtR26PDoivFJr7k5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the development of Medical Technology in healthcare over these years based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the change in the proportions over time to make an inference about the development of Medical Technology in healthcare.\nBased on the above thought, the answer is:\nThe chart suggests a substantial growth in the use of Medical Technology in healthcare over these years. The general upward trend indicates a progressive adoption and implementation of medical technology, which suggests ongoing development and advances in this sector."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "TiWHg2oiCR3EuVDI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two consecutive years was there the biggest increase in Medical Technology usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the two consecutive years with the biggest increase, I should first identify the difference in usage between each pair of consecutive years, and then choose the pair with the largest difference.\nBased on the above thought, the answer is:\n2012 to 2013."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "6BLeWVtDZZwsaFbZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in percentage between 2016 and 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the percentages of Medical Technology usage for 2016 and 2017 from the pie chart, and then subtract the 2016 percentage from the 2017 percentage to find the change.\nBased on the above thought, the answer is:\nThe percentage increased by 0.7%."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "wKOGsdUVdYdGPElf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Medical Technology usage in 2021 continues the average growth rate of the past ten years, what is the expected percentage in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the expected percentage in 2021, I should first calculate the average growth rate of the previous ten years, and then add this growth rate to the 2020 percentage.\nBased on the above thought, the answer is:\nThe expected percentage is 12.3%."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "B4oiuVuvmhRmghnx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTransform the given demographic data surveying gender into a CSV table form, can you do that?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can summarize the JSON data by finding the participants in the research, their genders are represented by this binary data, 0 indicates absence and 1 indicates presence. The given value is {\"Male, Female\"}<r>[0.192, 0.216, 0.801, 0.855]</r> for each value in sequence {\"John, Mike, Linda, Mary, James, Lucy, Bob\"}<r>[0.22, 0.099, 0.774, 0.133]</r>.\nBased on the above thought, the answer is:\nParticipant,Male,Female\nJohn,1,0\nMike,1,0\nLinda,0,1\nMary,0,1\nJames,1,0\nLucy,0,1\nBob,1,0"
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "MrKtV7dTrhv4Bjk5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we conclude from the chart that the research was gender-balanced?"
+            },
+            {
+                "from": "gpt",
+                "value": "Let's inspect the {\"Male, Female\"}<r>[0.192, 0.216, 0.801, 0.855]</r> categories. The gender balance can be inferred by comparing the number of male participants with the number of female participants.\nBased on the above thought, the answer is:\nYes, the research was fairly balanced as there were 4 males and 3 females."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "qX82BXwAi9JPJWQS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on these results, how balanced was the gender distribution in the dataset?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure this out, I need to compare the number of males and females. The binary representation in the chart helps with this, where 1 represents the presence of that gender.\nBased on the above thought, the answer is:\nThe gender distribution in the dataset was slightly skewed towards males, with 4 males and 3 females."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "Dy59GCcBGPocqeOE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould we infer from the chart that there could be any potential bias in this research based on gender?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the number of males to females. If one gender is significantly higher than the other, it could suggest potential bias.\nBased on the above thought, the answer is:\nThere is a slight skew towards males with 57% males participation, but overall this appears to be quite a balanced research sample and it is unlikely there would be considerable gender bias in the results."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "8sgq5WAkVlcHM0K4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total number of individuals involved in the research?"
+            },
+            {
+                "from": "gpt",
+                "value": "To know the total number of participants, I will count the number of names found in the available data.\nBased on the above thought, the answer is:\n7."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "XM4w9jpzRN8jkY5a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many males participated in the survey?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will examine the Male section of the pie chart and count the number of 1's marked in the Male row.\nBased on the above thought, the answer is:\n4."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "pu1MIOt2QIENcvgE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage of females that took part in this research?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage of females in the research, I have to divide the number of females by the total number of participants and multiply the answer by 100.\nBased on the above thought, the answer is:\n42.86%."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "vvqVsYFEcYGywLkV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the distribution of risk levels based on their frequency of occurrence from the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart titled {Distribution of Risk Levels Based on Frequency}[0.067, 0.139, 0.936, 0.172] and the pie sectors {\"Extreme\"}[0.246, 0.329, 0.479, 0.504]; {\"High\"}[0.229, 0.38, 0.501, 0.586]; {\"Medium\"}[0.237, 0.52, 0.754, 0.793]; {\"Low\"}[0.501, 0.267, 0.774, 0.622]; {\"Very Low\"}[0.501, 0.248, 0.604, 0.52]; {\"None\"}[0.477, 0.22, 0.52, 0.493]; {\"Unknown\"}[0.293, 0.249, 0.501, 0.52] show the risk levels and their frequencies in percentages.\nBased on the above thought, the answer is:\nRisk Level,Percentage Frequency\nExtreme,2.5%\nHigh,12.5%\nMedium,40.0%\nLow,25.0%\nVery Low,5.0%\nNone,2.5%\nUnknown,12.5%"
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "UtOinkQInUjdn2JH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which risk level occurs most frequently? Provide justification for your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "The risk level that occurs most frequently can be identified by spotting the largest sector on the pie chart.\nBased on the above thought, the answer is:\nThe risk level that occurs most frequently is 'Medium'. This is determined by the largest sector in the pie chart representing 'Medium'."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "a3Lom7nYC3w8zWcW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the prevalence of 'Low' risk events in comparison to 'Very Low' and 'High' risk events?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the frequency of 'Low' risk events with the frequencies of 'Very Low' and 'High' risk events.\nBased on the above thought, the answer is:\nThe chart suggests that 'Low' risk events occur more frequently than 'Very Low' risk events but are less common than 'High' risk events. This is evidenced by the larger sector for 'Low' risk compared to 'Very Low' and smaller sector compared to 'High' risk."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "333Hyi2EdUBqeLhV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might a risk manager use the information from this chart in their risk mitigation strategy formulation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would need to consider the potential implications of risk frequency for risk mitigation strategies.\nBased on the above thought, the answer is:\nThe risk manager might prioritize targeting 'Medium' and 'Low' risks as they occur more frequently, thus posing a higher overall risk. Mitigation strategies for 'High' risks might also be important due to their significant frequency, even if they occur less often than 'Medium' and 'Low' risks. 'Extreme' and 'None' risk events, while infrequent, should not be ignored due to their potential implications. 'Very Low' risks might require lower priority but still should be addressed to ensure comprehensive risk mitigation. The 'Unknown' risks might need further investigation to better understand and categorize their level of risk for more effective mitigation strategy formulation."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "d9TCnhMcE9O2UuFS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points do Medium risk events exceed Low risk events by?"
+            },
+            {
+                "from": "gpt",
+                "value": "To come up with the answer, I need to subtract the percentage frequency of Low risk events from the percentage frequency of Medium risk events.\nBased on the above thought, the answer is:\n15.0%."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "SV78wGxsTORZ6079",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the combined percentage frequency of Extreme, None, and Very Low risk events?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I would add the percentage frequencies of Extreme, None, and Very Low risk events.\nBased on the above thought, the answer is:\n10.0%."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "4RqPxUGy6KuB3Fcl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the combined percentage frequencies of Extreme, High, Medium risk levels and Low, Very Low, None, Unknown risk levels?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get this, I sum up the respective percentages of the first three and last four risk levels and then find the difference.\nBased on the above thought, the answer is:\n0.0%."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "TRX2jzKSIUKCP1K6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data provided in the pie chart, can you provide the data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sections of the pie chart, I note there are two distinct behaviors {impulsive buying}<r>[0.45, 0.469, 0.499, 0.511]</r> and {mindful spending}<r>[0.499, 0.472, 0.547, 0.536]</r> depicted over three observations {\"obs1\"<r>[0.451, 0.5, 0.499, 0.529]</r>,\"obs2\"<r>[0.46, 0.5, 0.499, 0.541]</r>,\"obs3\"<r>[0.462, 0.447, 0.539, 0.495]</r>] in different-coloured slices.\nBased on the above thought, the answer is:\nBehavior Type,Observation 1,Observation 2,Observation 3\nImpulsive Buying,150,70,55\nMindful Spending,220,240,300"
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "BPPC2s5Ol8AsRCfs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is evident in people's behavior when reviewing the data points presented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart and comparing the data points for impulsive buying and mindful spending, a trend can be discerned.\nBased on the above thought, the answer is:\nThe trend that becomes apparent is that impulsive buying is declining while mindful spending is increasing."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "b8T2Y05TIJMtoBs0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the implications of this trend for businesses and marketers?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications would depend on how businesses and marketers respond to these changing trends in consumer behavior.\nBased on the above thought, the answer is:\nThis trend could mean a need for businesses and marketers to adapt their strategies. Since impulsive buying is decreasing, businesses may need to shift their focus from high-pressure sales tactics to more informative and conscientious marketing approaches that encourage mindful spending."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "rSNLLf1xNEeWsxuu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWould the outlier in the second observation for impulsive buying affect the overall trend observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the outlier value and then comparing with the overall trends would reveal if there is a significant impact.\nBased on the above thought, the answer is:\nWhile the drop in impulsive buying in the second observation is noted, it does not significantly sway the overall trend of a decrease in impulsive buying and an increase in mindful spending."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "QPjX406V87Badf7q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has mindful spending increased from observation 1 to observation 3?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the values of mindful spending at observation 1 and observation 3 would give the increase in mindful spending.\nBased on the above thought, the answer is:\n80."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "PVMwEQWiKxPy84ou",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of all observations for both impulsive buying and mindful spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adding the observations for impulsive buying and mindful spending would give the total.\nBased on the above thought, the answer is:\n1035."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "9xMmerYuSh3dAE6K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the maximum and minimum observations for impulsive buying?"
+            },
+            {
+                "from": "gpt",
+                "value": "Finding the maximum and minimum values amongst the observations for impulsive buying and calculating the difference would give the answer.\nBased on the above thought, the answer is:\n95."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "YhSOCSuQoO4279JT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you analyze the pie chart and extract the yearly data on the development of the 'Culture and Arts' industry between 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart {\"Culture and Arts Industry Development 2015 - 2020\"}[0.137, 0.076, 0.863, 0.101] represents the development in the culture industry differentiated by year. Each sector slice represents a different year with the radian of the sector representing the development for that year. This can be converted to structured data using the labels and percentage annotations on each slice.\nBased on the above thought, the answer is:\nYear,Development\n2015,10.7%\n2016,12.8%\n2017,14.9%\n2018,17.4%\n2019,17.4%\n2020,26.9%"
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "ROuZFF0GJ1hbe2DL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an overview of the development in the Culture and Arts industry from 2015 to 2020, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart {\"Culture and Arts Industry Development 2015 - 2020\"}[0.137, 0.076, 0.863, 0.101], I can observe that the development of the Culture and Arts industry has been increasing year by year from 2015 to 2019. The development in 2020 experienced a significant increase compared to the previous years.\nBased on the above thought, the answer is:\nThe development of the Culture and Arts industry has seen a gradual growth from 10.7% in 2015 to 17.4% in 2019. However, there was a drastic increase in 2020 which became an outlier with a contribution of 26.9%."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "rdX3JZYCm3HkuxcS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend from 2015 to 2019 continues, what can you infer about the distribution of development in the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart {\"Culture and Arts Industry Development 2015-2020\"}[0.137, 0.076, 0.863, 0.101], I can analyze the steady increase in development from 2015 to 2019. Using this trend, I can make predictions regarding future development.\nBased on the above thought, the answer is:\nIf the trend from 2015 to 2019 continues, we can expect a steady increase in the development of the Culture and Arts industry. However, the big leap in 2020 should be treated as an outlier and not a norm for trend prediction."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "c30aCxXqbc7c0oQO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart data, if a similar surge occurs in the future as in 2020, how would it affect the industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze this, I need to consider the impact of the surge in 2020 represented as the exploded slice in the pie chart {\"Culture and Arts Industry Development 2015-2020\"}[0.137, 0.076, 0.863, 0.101].\nBased on the above thought, the answer is:\nA similar surge as in 2020 would evidently increase the rate of development within the Culture and Arts industry and could also drastically shift resources and attention towards this sector. However, the sustainability of such a surge would need to be evaluated and monitored due to potential instability."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "C6lK6e6DOQg9NUoA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two consecutive years have a combined proportion over 30%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I first need to examine the percentages associated with each year on the chart. Then I can calculate the pairs of consecutive years until I find a pair that sums to over 30%.\nBased on the above thought, the answer is:\n2018 and 2019."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "Yuo29KjgVDOmMb7t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage has the Arts and Culture Industry development grown from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute this, I need to find the proportion of the Arts industry development in 2020 and subtract the proportion in 2015.\nBased on the above thought, the answer is:\nThe growth is 26.9% (in 2020) - 10.7% (in 2015) = 16.2%."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "XYIywp4dN7WWhLuv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 2020 was not an outlier, can you estimate the average yearly industry growth, listing the growth percentages in ascending order?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average yearly industry growth excluding 2020, I have to add the growth percentages from 2015 to 2019 and then divide by the number of years (5). The numbers should also be listed in an ascending order.\nBased on the above thought, the answer is:\nThe average yearly growth is (10.7% + 12.8% + 14.9% + 17.4% + 17.4%) / 5 = 14.64%. The growth percentages in ascending order are: 10.7%, 12.8%, 14.9%, 17.4%, 17.4%."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "qQ9NxgqCIHrkJoeq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are studying, can you transform the information on international trade trends in form of a CSV formatted table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"International Trade and Globalization Trends from 2014 to 2020\"}[0.057, 0.076, 0.943, 0.101] and pie slices {\"2014\"}[0.189, 0.305, 0.5, 0.515],\"2015\"[0.173, 0.415, 0.5, 0.551],\"2016\"[0.175, 0.515, 0.5, 0.695],\"2017\"[0.227, 0.515, 0.5, 0.804],\"2018\"[0.346, 0.515, 0.521, 0.842],\"2019\"[0.5, 0.515, 0.701, 0.842],\"2020\"[0.526, 0.535, 0.823, 0.794], I can infer that each slice represents different years and how much percents they took up.\nBased on the above thought, the answer is:\nYear, International Trade\n2014, 6.2%\n2015, 6.7%\n2016, 7.5%\n2017, 8.0%\n2018, 8.8%\n2019, 9.5%\n2020, 7.7%"
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "KpQWaFxpLbsMi68L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you tell me the year with the largest International Trade proportion and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare all the percentages of the pie slices and identify the largest one.\nBased on the above thought, the answer is:\nThe year with the largest International Trade proportion is 2019 because it has the highest percentage of 9.5%."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "3kvb0yJqwAnDdppx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about International Trade trends over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the changes in percentages over the years to understand the trend.\nBased on the above thought, the answer is:\nThe pie chart shows an overall upward trend in International Trade from 2014 to 2019, indicating an increase in inter-country trade. However, there was a slight drop in 2020, which could be attributed to global events such as the COVID-19 pandemic that disrupted trade."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "vnGHwZjNlrlvsKGw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trends were to continue post 2020, what might we expect for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyse the trend pattern from 2014 to 2020, see if there is any anomaly and then predict for the year 2021.\nBased on the above thought, the answer is:\nGiven the continued growth from 2014 to 2019 and discounting 2020 as an anomaly due to global pandemic circumstances, we might expect the International Trade percentage to rise again in 2021 if the pre-2020 trend resumes."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "YS0ej6I04cwTnPuh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage of International Trade in 2014 and 2015 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the combined percentage, I need to add the percentage of International Trade in 2014 and 2015.\nBased on the above thought, the answer is:\nThe combined percentage of International Trade in 2014 and 2015 is 12.9%."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "qbFEm8mv6p9TTwSh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of International Trade in 2020 increased by 2%, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 2% to the 2020 percentage.\nBased on the above thought, the answer is:\nThe new percentage for International Trade in 2020 is 9.7%."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "lEcsKrvTfmOhu0gn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages of International Trade from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the percentages, I need to add the percentages of International Trade from 2016 to 2019.\nBased on the above thought, the answer is:\nThe sum of the percentages of International Trade from 2016 to 2019 is 33.8%."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "ka54XdJHsXKyv5Ws",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the chart, can you list the Efficiency Areas and their corresponding Percent Improvement Potential in column-separated CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can do that by examining the color-coded sections of the pie chart {chart sectors}[0.416, 0.415, 0.636, 0.625]. Each sector represents an Efficiency Area and its corresponding Percent Improvement Potential. These details can be converted into a column-separated CSV format.\nBased on the above thought, the answer is:\nEfficiency Areas,Percent Improvement Potential\nGreen Building,30\nHVAC Optimization,20\nInsulation Improvement,20\nEnergy Efficient Lighting,10\nRenewable Energy,10\nEnergy Management Systems,10"
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "Mu8fpF1MrcfCrYVr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which area has the highest potential for efficiency improvement? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the section of the pie chart that represents the largest percentage.\nBased on the above thought, the answer is:\nThe area with the highest potential for efficiency improvement is the \"Green Building\" sector. This is because it makes up the largest section of the pie chart, indicating a potential improvement of 30%."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "BSTyjbjkepTmiiaP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can you make about the focus areas for efficiency improvement in terms of their potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can make inferences about the potential of different focus areas by comparing the sizes of their respective sections on the pie chart.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that the primary focus for efficiency improvement should be the \"Green Building\" sector, given its high potential of 30%. Areas such as \"HVAC Optimization\" and \"Insulation Improvement\" may be secondary targets as they reflect 20% improvement potential each. Sectors like \"Energy Efficient Lighting\", \"Renewable Energy\", and \"Energy Management Systems\" have equal but the lowest improvement potential, suggesting they can be tertiary targets for efficiency improvement."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "8RDjKDDY1a31Ifrj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information presented in the chart guide future decisions on energy efficiency improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decision-making can be influenced by the percentages that represent the potentials of improvement in all the efficiency areas as shown in the pie chart.\nBased on the above thought, the answer is:\nGiven that \"Green Building\" shows the highest potential for improvement, it would make sense to allocate more resources and focus towards this area to achieve maximum efficiency gains. Other areas like \"HVAC Optimization\" and \"Insulation Improvement\", which show a moderate level of potential, would also require attention. However, sectors with lower improvement potentials such as \"Energy Efficient Lighting\", \"Renewable Energy\", and \"Energy Management Systems\" should not be ignored. Instead, they might be areas where innovative solutions or changes in strategy can lead to greater efficiency gains."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "lAb4j5Z5aqFbm4j7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe sum of efficiency improvement potential for which two areas is equal to 30%?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the size of the sections for each Efficiency Area, I can determine the ones that sum to 30% improvement potential.\nBased on the above thought, the answer is:\nHVAC Optimization and Insulation Improvement."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "nmb6Ibsr2gePuOVi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total improvement potential does Green Building represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can identify this by observing the proportion of the Green Building section in the pie chart.\nBased on the above thought, the answer is:\nGreen Building represents 30% of the total improvement potential."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "xcuQeQZWwpwQbLjo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the efficiency improvement potential of Energy Efficient Lighting increased by 5%, what would be the new total improvement potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new total improvement potential can be obtained by adding 5% to the current percentage of Energy Efficient Lighting and totaling all the percentages again.\nBased on the above thought, the answer is:\nThe new total improvement potential would be 105%."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "DxHEyGeQQn7fiKAp",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you generate a table which shows the trend of fitness and sports health from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the color-coded {pie slices}<r>[0.163, 0.165, 0.837, 0.861]</r> and the legend, I can determine the value of fitness trends for each year from 2012 to 2021.\nBased on the above thought, the answer is:\nYear, Fitness Trends\n2012, 15\n2013, 35\n2014, 45\n2015, 60\n2016, 50\n2017, 80\n2018, 65\n2019, 75\n2020, 85\n2021, 95"
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "62pbzElTDmqYfW1G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year witnessed the highest growth in fitness and sports health trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the {largest pie slice}<r>[0.195, 0.53, 0.481, 0.861]</r>, I can determine the year with the highest growth.\nBased on the above thought, the answer is:\nThe year 2021 experienced the highest growth in fitness and sports health trends."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "NYEGlxUC9KwSav44",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the information in the chart be beneficial for fitness equipment retailers?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to consider how the trend rates might impact retail sales and the stocking up of fitness equipment.\nBased on the above thought, the answer is:\nThe chart shows an overall increase in fitness trends over the years. Retailers can use this information to anticipate a growing demand for fitness equipment and thus stock up accordingly. Specifically, increased attention can be given to the years with higher growth rates such as 2017 and 2021."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "xnSmSFZXe4WHgATg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer from the chart when there was a decline in fitness trend from the prior year?"
+            },
+            {
+                "from": "gpt",
+                "value": "A decline can be identified when a smaller pie slice follows a larger one.\nBased on the above thought, the answer is:\nThe year 2016 saw a decline in fitness trend from the previous year 2015, Additionally, there was also a decline in 2018 from the year 2017."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "dYTHSI33qjikuyQh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two consecutive years, the sum of the fitness trends is 135?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the sum of fitness trends of two consecutive years and then check if it equals 135.\nBased on the above thought, the answer is:\n2020 and 2021"
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "XRYURK637OJ81UHr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average trend value for the 10 years period from 2012 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average trend value, I need to sum up the trend values of all years and divide by the number of years which is 10.\nBased on the above thought, the answer is:\nThe average trend value is 61"
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "t2JJ2ZWlYe1YTD1v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the fitness trend for 2014 increased by 15, what would be the new total fitness trends for all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to add 15 to the 2014 value and then sum up all the values again.\nBased on the above thought, the answer is:\nThe new total fitness trends for all years is 635."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "xXqhUJLMTualfe3H",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart showing education trends from 2011 to 2016 into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can see each segment represents education trends for a different year of comparison between Distance Education and Classroom Learning from the slices of the pie {pie_0} [0.245, 0.23, 0.755, 0.775] and {pie_1} [0.136, 0.156, 0.892, 0.884].\nBased on the above thought, the answer is:\nYear,Distance Education, Classroom Learning\n2011,14.1%,14.1%\n2012,14.9%,14.9%\n2013,16.0%,16.0%\n2014,17.3%,17.3%\n2015,18.3%,18.3%\n2016,19.4%,19.4%"
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "XPKKVllIgTYaDT5Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, which education form is becoming more popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie charts {pie_0} [0.245, 0.23, 0.755, 0.775] and {pie_1} [0.136, 0.156, 0.892, 0.884]) provide a visual representation of the popularity of both distance education and classroom learning from 2011 to 2016. By examining the sizes of the slices for each year, we can infer the popularity trend.\nBased on the above thought, the answer is:\nDistance Education is becoming more popular. This is evident as the percentage increases every year from 2011 to 2016, which indicates a growing trend in popularity."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "jzHxbC5J8yUblR3Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference could be drawn about the shift in education preference over the years 2011 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am examining the pie charts {pie_0} [0.245, 0.23, 0.755, 0.775] and {pie_1} [0.136, 0.156, 0.892, 0.884] to identify the prevailing preference for each year over the six-year period.\nBased on the above thought, the answer is:\nThere is a clear shift in education preference over the years 2011 to 2016, from Classroom Learning to Distance Education. The proportion of Distance Education has consistently grown, while that of Classroom Learning has been decreasing, indicating a clear preference for Distance Education over time."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "ZL3wd96rywviEJQn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the observed trends in the chart affect decisions about educational resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "On observing the data represented in the pie charts, {Distance Learning pie}<r>[0.245, 0.23, 0.755, 0.775]</r> and {Classroom Learning pie}<r>[0.136, 0.156, 0.892, 0.884]</r>, I can analyze the transformation in education trends from 2011 to 2016, which could subsequently impact decisions regarding the allocation of educational resources.\nBased on the above thought, the answer is:\nAs Distance Education has shown a consecutive increase in its proportion, resources might be gradually reframed towards the development of improved online learning platforms, tech-friendly environments, and efficient digital teaching methods. Further, resources can be directed towards enhancing hybriding learning to bridge the gap between both forms of education."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "a5r2uHmtVp6KqcWj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest proportion of Distance Education?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking closely at the pie chart segments of {Distance Learning pie}<r>[0.245, 0.23, 0.755, 0.775]</r>, it appears that the largest slice is for the year 2016.\nBased on the above thought, the answer is:\n2016."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "ds1PboiU6aozbB1Y",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the slices of the Classroom Learning pie, what is the decrease in the proportion from 2011 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the decrease in the proportion, I need to subtract the Classroom Learning proportion in 2016 from that in 2011, which can be read from the Classroom Learning pie slices in the coordinates {Classroom Learning pie} <r>[0.136, 0.156, 0.892, 0.884]</r>.\nBased on the above thought, the answer is:\nThe Classroom Learning decreased by 14.1% - 19.4% = -5.3%."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "z2P1LiJ4TQ8DtF3v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy approximately how much percentage did Distance Education increase from 2011 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the Distance Education pie slices in the coordinates {Distance Learning pie}<r>[0.245, 0.23, 0.755, 0.775]</r>, I can see the specific percentages of Distance Education for years 2011 and 2014. The increase can be calculated as the displacement from 2011 to 2014.\nBased on the above thought, the answer is:\nDistance Education increased by approximately 17.3% - 14.1% = 3.2% from 2011 to 2014."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "8MNTUAqiAN1hGwLt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the data represented on the pie chart to a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart titled {Importance of Organizational Roles}<r>[0.22, 0.123, 0.874, 0.156]</r>, different colors and slice sizes represent the importance rates of each role in the organization. These roles are {CEO}<r>[0.256, 0.36, 0.817, 0.822]</r>, {CTO}<r>[0.547, 0.254, 0.828, 0.534]</r>, {CFO}<r>[0.398, 0.234, 0.652, 0.515]</r>, and {HR Manager}<r>[0.332, 0.277, 0.547, 0.515]</r>.\nBased on the above thought, the answer is:\nRole,Importance_in_Organization\nCEO,60\nCTO,20\nCFO,15\nHR Manager,5"
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "CHQnst1KodpmI0Ua",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the most critical role in an organization according to the chart. Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "The role with the largest slice of the pie represents the most critical role in an organization.\nBased on the above thought, the answer is:\nThe most critical role is the \"CEO\", because it has the largest slice of the pie chart, indicating that it has the highest importance rate at 60% in the organization."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "61VsupXTaEJMXPQg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the relative significance of these roles in an organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the different slices in the pie chart to determine the relative significance of the roles.\nBased on the above thought, the answer is:\nThe chart suggests that the CEO role is given the most importance in an organization, accounting for 60% of the overall significance. Following this, the CTO role is the next most important with a 20% share. The CFO role holds 15% importance while the HR Manager is considered least significant at 5%."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "mBR3Lqn3BX4XhAwT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications does this chart have for decision making within an organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart can inform decision making by revealing the relative importance of different roles.\nBased on the above thought, the answer is:\nThis chart implies that decisions within an organization are likely to be heavily influenced by the CEO, as it is deemed the most important role. On the other hand, HR Manager, being the least significant role, might have lesser influence on major decisions."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "OJhWWC3RYHFSbi6P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what's the percentage of the roles CEO and CTO combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the combined percentage of CEO and CTO, I add the percentage values of the CEO and CTO separately.\nBased on the above thought, the answer is:\n80%"
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "zyUhK4QFeaaZbLHp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much less significant is the HR Manager role compared to the CEO role as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I subtract the percentage of the HR Manager role from the CEO role to find the difference in significance levels.\nBased on the above thought, the answer is:\nThe CEO role is 55% more significant than the HR Manager role."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "sBNx23r2B7sEgUE1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more significant are the roles of CEO and CTO together, compared to the combined significance of CFO and HR Manager?"
+            },
+            {
+                "from": "gpt",
+                "value": "I add the percentage values of CEO and CTO, and subtract the sum of the percentages for CFO and HR Manager.\nBased on the above thought, the answer is:\nThe CEO and CTO roles are together 60% more significant."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "F5Q4z7kfNJ5RKzju",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart you observed into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the title in the middle of the chart {\"Living Environment Preference\"}[0.219, 0.108, 0.789, 0.141] and the pie slices {the sector slice}[{\"Nature\"}[0.206, 0.329, 0.504, 0.736]; {\"Urban\"}[0.276, 0.223, 0.802, 0.818]], it's evident that the pie sectors represent different living environment preferences and the percentage each type of environment is preferred.\nBased on the above thought, the answer is:\nLiving Environment,Percentage\nNature,24.0%\nUrban,76.0%"
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "viiFYKRhCvFZPVcc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the pie chart, which type of environment do most people prefer to live in? Could you explain the reason for your conclusion?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the sizes of each pie slice, the living environment with the larger slice represents the environment most people prefer to live in.\nBased on the above thought, the answer is:\nMost people prefer to live in Urban environments because it makes up 76% of the pie chart, which is significantly larger than the 24% that prefer Nature."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "rYBKwMFwJIYP1zgF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about people's living environment preference in general from the data in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a general inference about living environment preference, I need to compare the percentage of each environment type.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that the majority of people prefer urban environments compared to nature, indicating a possible preference for the convenience and amenities provided by Urban living."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "HYeOzMhaaOWGWB7g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the findings in this chart influence policy decisions on urbanization and natural conservation?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to influence policy decisions, the larger percentage shown for urban environments might suggest a greater emphasis on urban initiatives, whereas the smaller percentage shown for nature might highlight the need for natural conservation strategies.\nBased on the above thought, the answer is:\nGiven the majority preference for Urban environments, policy makers might place more emphasis on urbanization projects such as public transportation, housing, and urban infrastructure. However, the significant proportion of people who prefer Nature suggests a need for effective natural conservation policies to cater to their preferences, balancing the development with environmental protection."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "Yvff4gDp5uIY6AsY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much greater is the Urban preference than the Nature preference, in percentage points?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in percentage points between Urban and Nature, I need to subtract the percentage for Nature from the percentage for Urban.\nBased on the above thought, the answer is:\nThe difference is 76.0% - 24.0% = 52%."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "opuoH6rtKnWkKZYO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the groups that prefer Nature and Urban environments were merged, what would be the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Because the two groups cover all the possibilities, the total percentage should be 100%.\nBased on the above thought, the answer is:\n100%."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "ghGyILipon7W6dx1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the new percentage for Urban if Urban's preference decreased by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new percentage for Urban, I need to subtract 10% from the current percentage for Urban.\nBased on the above thought, the answer is:\nThe new percentage for Urban would be 76.0% - 10% = 66.0%."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "mBg1RTQZEeCbmJT3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the prevalence of different areas of technological innovations in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can see that the chart lists eight different areas of technological innovation: {Innovation}[0.27, 0.453, 0.27, 0.453], {Patents}[0.289, 0.627, 0.289, 0.627], {Technology Transfer}[0.381, 0.723, 0.381, 0.723], {Open Source}[0.525, 0.754, 0.525, 0.754], {Virtual Reality}[0.686, 0.67, 0.686, 0.67], {Augmented Reality}[0.742, 0.504, 0.742, 0.504], {Artificial Intelligence}[0.653, 0.329, 0.653, 0.329], and {Quantum Computing}[0.414, 0.269, 0.414, 0.269]. Let me list these in a csv format.\nBased on the above thought, the answer is:\nArea, Prevalence\nInnovation, 70\nPatents, 50\nTechnology Transfer, 40\nOpen Source, 60\nVirtual Reality, 65\nAugmented Reality, 55\nArtificial Intelligence, 80\nQuantum Computing, 85"
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "DuiNTAeSUOj3CKU7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does growing prevalence of Quantum Computing relative to other areas signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the prevalence of Quantum Computing compared to other technological areas.\nBased on the above thought, the answer is:\nThe growing prevalence of Quantum Computing relative to other areas could signify that it is currently a highly trending research and development area. This could be due to increased investments, advancements in technology or a greater demand for Quantum Computing in various industries."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "CuqHdEx9bKyT0Hia",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area appears to need the most improvement in terms of prevalence and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the area with the lowest prevalence.\nBased on the above thought, the answer is:\nThe area of Technology Transfer seems to need the most improvement as it has the lowest prevalence of 40. This could be due to various factors, such as lack of resources, insufficient awareness or low demand in the industry."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "QZuuF2Kdfr4H5ph1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer any trend in technological innovation from the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer a trend, I should look at the prevalence of all the areas and how they compare to each other.\nBased on the above thought, the answer is:\nOne trend we might infer from the chart is that recent and advanced technologies such as Quantum Computing and Artificial Intelligence have higher prevalence values, indicating heightened focus and interest in these areas. On the other hand, the relatively lower prevalence of Patents and Technology Transfer might suggest a need for increased attention to these more traditional yet crucial aspects of technological innovation."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "HkCRImKi8rS6DHDP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total prevalence for all eight technological areas combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total prevalence value, I need to sum the values for all eight technological areas.\nBased on the above thought, the answer is:\nThe total prevalence value is 555."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "Um0SIEvtxzcBs99h",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average prevalence for each area?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I should divide the total prevalence value by the number of areas.\nBased on the above thought, the answer is:\nThe average prevalence is 69.375 for each area."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "0bdJbAOGKXucUfOt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the prevalence of Quantum Computing higher than that of Innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I should subtract the prevalence value of Innovation from that of Quantum Computing.\nBased on the above thought, the answer is:\nQuantum Computing is higher than Innovation by 15."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "C45rg86QTnCnqZyK",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data provided, could you generate a .csv table listing each risk type and its respective percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Noting the title {Risk Assessment and Management}[0.173, 0.233, 0.821, 0.266] and each pie slice {[Cybersecurity][0.317, 0.393, 0.497, 0.52]; [Data Breach][0.299, 0.439, 0.497, 0.571]; [Physical Security][0.306, 0.52, 0.497, 0.663]; [Natural Disaster][0.347, 0.535, 0.484, 0.695]; [Health & Safety][0.38, 0.52, 0.534, 0.718]; [Compliance][0.497, 0.52, 0.609, 0.715]; [Strategic Risk][0.497, 0.52, 0.691, 0.684]; [Operational Risk][0.497, 0.351, 0.695, 0.561]; [Reputational Risk][0.434, 0.323, 0.6, 0.52] and [Financial Risk][0.345, 0.333, 0.497, 0.52]}, the information can be translated into raw data for a CSV table.\nBased on the above thought, the answer is:\nRisk Type, Risk Factor\nCybersecurity, 10\nData Breach, 25\nPhysical Security, 20\nNatural Disaster, 5\nHealth & Safety, 30\nCompliance, 15\nStrategic Risk, 28\nOperational Risk, 45\nReputational Risk, 32\nFinancial Risk, 20"
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "bMPMjc5bONjkV1NE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich risk type has the biggest impact in terms of risk management based on the pie chart observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice segment with the largest area will represent the biggest impact. By comparing the areas of slices, we can make an inference.\nBased on the above thought, the answer is:\nOperational Risk is the highest contributor making it the risk type with the biggest impact in terms of risk management."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "6sBZJrpeun9yeW5H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of approach to risk management does this pie chart suggest an organization is taking?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to make a reasoning, I need to look into the proportions of each risk type. It will give me an idea about the prioritization of the organization in terms of risk management.\nBased on the above thought, the answer is:\nThe organization seems to be prioritizing Operational Risks, indicating they're likely focused on daily business and processes. This might mean they're emphasizing on having robust operational procedures."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "UMzNgS1GPr8VdU4F",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would an increase in Physical Security risk by 10% possibly affect the overall risk distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the effect of a 10% increase in Physical Security risk, I need to consider the reshaped proportions of each risk type and their impact on the entire risk model.\nBased on the above thought, the answer is:\nAn increase in Physical Security risk by 10% would make it one of the top 5 risk area. This would require the organization to assign more resources for managing Physical Security, potentially influencing overall risk management strategies."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "xjVYDWqqmeMbDa4x",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me based on the risk percentages, which risk type(s) contribute(s) to one fourth of the total risk factor?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to calculate which risk type(s) contribute to a total of 25% risk factor, I must add up individual percentages from the slice segments.\nBased on the above thought, the answer is:\nData Breach and Physical Security."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "GLbwed3QITCuEYTF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a new risk type is introduced and its risk factor is the average of all the current factors, what would be its value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average risk factor of the current categories, I need to add up all the risk factors and then divide by the number of categories.\nBased on the above thought, the answer is:\nThe sum of the risk factors is 230. The average risk factor is 230/10 = 23."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "TozIrCCt6O03n0ED",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOut of 100%, how much does the top three risk types contribute?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the contribution of the top three risk types, I need to add up the risk factors of Operational Risk, Reputational Risk and Health & Safety.\nBased on the above thought, the answer is:\nThe three risk types contribute 107% to the total risk factor."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "tg3KOMO1LoqByumc",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart you analyzed, could you please convert the data related to the Food and Agriculture Industry Analysis in 2018 into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the top of the chart is {\"Food and Agriculture Industry Analysis in 2018\"}[0.048, 0.262, 0.917, 0.295], and various sectors {\"Beef Production\"}[0.292, 0.403, 0.466, 0.529], {\"Poultry Production\"}[0.309, 0.52, 0.482, 0.685], {\"Vegetable Production\"}[0.427, 0.52, 0.626, 0.694], {\"Fruit Production\"}[0.482, 0.437, 0.657, 0.619], {\"Seafood Production\"}[0.482, 0.348, 0.635, 0.52] and {\"Grain Production\"}[0.349, 0.346, 0.511, 0.52] are represented in the chart. Each of this represents different categories and their proportion of the output in the industry for the year 2018.\nBased on the above thought, the answer is:\nIndustry,Beef Production,Poultry Production,Vegetable Production,Fruit Production,Seafood Production,Grain Production\n2018,12.4%,18.6%,20.6%,17.5%,14.4%,16.5%"
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "tmHHvpn1q0gzhqRp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which sector saw the highest production in 2018? Please provide the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to find which sector slice in the chart is largest as it represents the highest production.\nBased on the above thought, the answer is:\nVegetable Production saw the highest production in 2018 as it occupies the largest slice, signifying 20.6% of total production."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "sUSvZdJW6BL7Jb45",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significant trend displayed by the production of different sectors in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the size of slices to infer any significant trends in the production outputs.\nBased on the above thought, the answer is:\nThe chart indicates that vegetable production was prioritized, as it was the highest, while beef production was given less importance, as suggested by its smallest slice."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "zcKCsuIld8YmojiY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of resource allocation for 2019, what can we infer from the chart data about how resources might be distributed across sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the production proportions of the sectors in this pie chart can offer insights about future resource allocations.\nBased on the above thought, the answer is:\nFor the year 2019, resource allocation might lean more toward sectors such as vegetables, poultry, and grains, which had substantial production in 2018. Conversely, sectors such as beef and seafood production, which had a relatively lower output, might receive less allocation or might require strategies to improve their productivity."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "HxBmJt05lcjceCun",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector contributes to less than 15% of the total food and agriculture production for that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify which sector contributes to less than 15%, I need to look at the percentage listed inside each sector slice.\nBased on the above thought, the answer is:\nBeef production and Seafood Production."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "sRhT4HRuZgkb69xo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two sectors add up to approximately 33% of the total food and agriculture production?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the answer, I have to find the two sectors whose sum of percentages is approximately equal to 33%.\nBased on the above thought, the answer is:\nBeef Production and Fruit Production."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "QhiJnOc1lmhykyPu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the total food and agriculture production percentage if Poultry Production falls by 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the new total production, I would have to subtract 5% from the Poultry Production and add up the percentages for all the sectors.\nBased on the above thought, the answer is:\nThe new total percentage will be 95.5%."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "P8BEvRf2AlX1O7VW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart's data, could you convert the data into a table with each sector's improvement potential percentage in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that the pie slices at {IT}[0.464, 0.277, 0.701, 0.495], {Industrial Processes}[0.427, 0.515, 0.624, 0.733], {Transport}[0.342, 0.304, 0.509, 0.515], {Education}[0.509, 0.4, 0.726, 0.53], {Renewable Energy}[0.344, 0.515, 0.509, 0.717], {Residential Buildings}[0.509, 0.515, 0.695, 0.68], {Commercial Buildings}[0.509, 0.515, 0.726, 0.627], {Agriculture}[0.509, 0.515, 0.651, 0.7], {Retail}[0.291, 0.375, 0.509, 0.515], {Healthcare}[0.291, 0.5, 0.509, 0.658] each represent different sectors.\nBased on the above thought, the answer is:\nSector,Potential(%)\nTransport,20\nResidential Buildings,25\nCommercial Buildings,15\nIndustrial Processes,30\nAgriculture,5\nRetail,10\nHealthcare,15\nEducation,20\nIT,40\nRenewable Energy,20"
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "CtQ9f03mNM7wRDzC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the pie chart, which area shows the highest potential for efficiency improvements?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look for the sector with the highest percentage.\nBased on the above thought, the answer is:\nThe IT sector has the highest potential for efficiency improvements, with a percentage of 40%."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "TiYJUgTUsuU3x3Dt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's data, what could be implied about the sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the proportions of each sector, I can make interpretations on the efficiency improvement potential across them.\nBased on the above thought, the answer is:\nThe chart suggests that the IT sector has the most significant potential for efficiency improvement, followed by sectors such as Industrial Processes and Transport. Sectors like Agriculture and Retail show less potential, indicating perhaps less scope for improvement or already high levels of efficiency."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "Inm4BU24WEgQBaFm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the efficiency improvement potential data influence policy or resource allocation decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to infer from the proportion of each sector and think about its effect on policy decisions.\nBased on the above thought, the answer is:\nThe chart suggests prioritizing resource allocation for the IT, Industrial Processes, Transport, and Education sectors due to their high improvement potential. Simultaneously, sectors like Agriculture and Retail, with lower potential percentages, might need to be examined for existing efficiency measures or for potential overlooked areas of improvement."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "keDOA070BIN21zPA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the Pie Chart view, which two sectors have a total improvement potential of 40%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To check this, I need to find the two sectors where the sum of their percentage is equal to 40%.\nBased on the above thought, the answer is:\nIndustrial Processes and Transport."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "sjuTibJZfl8kPq9k",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of all the sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add up all the sector percentages and divide by the total number of sectors.\nBased on the above thought, the answer is:\nThe average percentage is 21%."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "8NtoWxvEa0gzc80I",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssuming the \"IT\" sector increased its potential by 10%, what would be the new total percentage of all sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get that, I have to increase the initial \"IT\" sector percentage by 10% and sum up all the percentages again.\nBased on the above thought, the answer is:\nThe new percentage for the \"IT\" sector is 40% + 10% = 50%. Hence the new total percentage is 110%."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "Th7OKlQCqygPzfU2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the given pie chart's data into table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure! The pie chart represents information about the usage of different digital currencies. There are ten types of digital currencies included: Bitcoin, Ethereum, Ripple, Litecoin, Dash, Cardano, Binance Coin, Monero, Tron, and Dogecoin. Each of them is represented by a slice in the pie chart and their sizes indicate the usage percentage of each currency. The usage percentages can be read off from the autotext which is located within the slices: Bitcoin<r>[0.292, 0.718, 0.292, 0.718]</r>, Ethereum<r>[0.776, 0.624, 0.776, 0.624]</r>, Ripple<r>[0.728, 0.339, 0.728, 0.339]</r>, Litecoin<r>[0.556, 0.246, 0.556, 0.246]</r>, Dash<r>[0.444, 0.254, 0.444, 0.254]</r>, Cardano<r>[0.373, 0.286, 0.373, 0.286]</r>, Binance Coin<r>[0.331, 0.317, 0.331, 0.317]</r>, Monero<r>[0.316, 0.333, 0.316, 0.333]</r>, Tron<r>[0.311, 0.338, 0.311, 0.338]</r>, Dogecoin<r>[0.309, 0.341, 0.309, 0.341]</r>.\nBased on the above thought, the answer is:\nCurrency,Usage\nBitcoin,45.0%\nEthereum,20.0%\nRipple,15.0%\nLitecoin,8.0%\nDash,5.0%\nCardano,4.0%\nBinance Coin,2.0%\nMonero,0.5%\nTron,0.3%\nDogecoin,0.2%"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "nmXJAlnDlbtHgGHA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich digital currency is most widely used in financial technology, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie chart, I can see that the largest slice belongs to Bitcoin.\nBased on the above thought, the answer is:\nBitcoin is the most widely used digital currency in financial technology."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "F5JqTXeyrijBCNWi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart tell us about the usage distribution of these digital currencies in financial technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the sizes of the slices for each digital currency.\nBased on the above thought, the answer is:\nThe pie chart indicates that the usage of digital currencies in the financial technology sector is highly concentrated, with Bitcoin, Ethereum, and Ripple making up the majority of the usage. The rest of the cryptocurrencies, including Litecoin, Dash, Cardano, Binance Coin, Monero, Tron, and Dogecoin, make up a smaller portion of the usage."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "UtE8k8wJx5uydCST",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing their usage percentages, which currencies might be considered the minor players in this market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand this, I need to look at the smallest slices in the pie chart that represent the lowest usage percentages.\nBased on the above thought, the answer is:\nBased on their low usage percentages, Monero, Tron, and Dogecoin can be considered the minor players in this digital currency market."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "O2Xg0HGDJdhM5JD2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total usage percentage of Ethereum and Ripple combined, according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the combined usage percentage of Ethereum and Ripple, I can simply add their individual usage percentages.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "HST9IbYzjf3XOCOq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more in percentage terms is Bitcoin\u2019s usage than Ethereum's usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the difference in usage between Bitcoin and Ethereum, I need to subtract the usage percentage of Ethereum from that of Bitcoin.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "chk1AEXMxUZMtNFM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total usage does the combined usage of Litecoin, Dash, Cardano, Binance Coin, Monero, Tron, and Dogecoin represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I sum the individual usage percentages of Litecoin, Dash, Cardano, Binance Coin, Monero, Tron, and Dogecoin.\nBased on the above thought, the answer is:\n20%"
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "ML943DevxhleXHpg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart that shows the effectiveness of different government policies into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the chart titled \"Effectiveness of Different Government Policies\"<r>[0.097, 0.173, 0.96, 0.207]</r> that I see. The slices of the pie<r>[0.262, 0.275, 0.774, 0.778]</r> represent different policies and their relative effectiveness.\nBased on the above thought, the answer is:\nPolicy Type,Effectiveness\nEnvironmental,40%\nEconomic,25%\nSocial,35%"
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "l1xiUNQ8JvZxBUQC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich government policy appears to be the most effective according to the pie chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the pie slices representing each government policy and determine which one is largest.\nBased on the above thought, the answer is:\nThe Environmental policy appears to be the most effective as it occupies the largest portion of the pie chart, representing 40% effectiveness."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "SlgW6Lj4R6HoIGbx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the effectiveness of the government policies from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe and interpret the percentage data shown for each policy in the chart.\nBased on the above thought, the answer is:\nThe pie chart suggests that Environmental policies were the most effective, accounting for 40% of the total effectiveness. Social policies were slightly less effective at 35%, and Economic policies were the least effective, contributing 25% to the total effectiveness."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "m0XiKabAfeWepfS3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how could the government adjust its policies to increase overall effectiveness?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the percentages of effectiveness associated with each policy in the pie chart.\nBased on the above thought, the answer is:\nThe government could aim to improve the effectiveness of Economic policies, which were the least effective according to the chart. By focusing on the areas that made Environmental policies successful, they could potentially increase the overall effectiveness of their policies."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "weKTmaiqF4BXqRUk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much less effective is the Economic policy compared to the Environmental policy as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at both the section on Environmental policy and that on Economic policy within the pie chart and compute the difference in their effectiveness.\nBased on the above thought, the answer is:\nThe Economic policy is 15% less effective compared to the Environmental policy."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "vrcdOnFGc9lDM4mD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Environmental and Social policy had the same effectiveness, what would their common percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'd need to sum up the effectiveness of both policies and divide it by 2 to get the average.\nBased on the above thought, the answer is:\nTheir common percentage would be 37.5%."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "PKNPfGBxNqFym5lx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total percentage of the effectiveness of the three policies represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the percentages from the three policy sections of the pie chart together.\nBased on the above thought, the answer is:\nThe three policies combined make up 100% effectiveness."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "XxlvBdAGzsyaHjh6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart's details that you have viewed convert the values into a structured table with CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is representing the investment in different Financial Technologies and Digital Currencies. The sectors including {Blockchain}<r>[0.294, 0.362, 0.54, 0.52]</r>, {Cryptocurrency}<r>[0.293, 0.509, 0.54, 0.683]</r>, {ICO}<r>[0.355, 0.52, 0.54, 0.752]</r>, {DEX}<r>[0.455, 0.52, 0.658, 0.767]</r>, {Bitcoin}<r>[0.56, 0.534, 0.804, 0.75]</r>, {Ethereum}<r>[0.54, 0.311, 0.786, 0.554]</r>, {Ripple}<r>[0.49, 0.274, 0.67, 0.52]</r>, and {Polkadot}<r>[0.351, 0.279, 0.54, 0.52]</r> are representing each technology and the size of each sector shows the amount of investment made. Hence, I can structure the data into a CSV format.\nBased on the above thought, the answer is:\nTech,Investment\nBlockchain,68\nCryptocurrency,80\nICO,52\nDEX,89\nBitcoin,120\nEthereum,79\nRipple,70\nPolkadot,64"
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "BEHGktmQWrPQPAxV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the area of interest of investors in 2010 based on the data from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing and comparing the sizes of the pie sectors including {Blockchain}<r>[0.294, 0.362, 0.54, 0.52]</r>, {Cryptocurrency}<r>[0.293, 0.509, 0.54, 0.683]</r>, {ICO}<r>[0.355, 0.52, 0.54, 0.752]</r>, {DEX}<r>[0.455, 0.52, 0.658, 0.767]</r>, {Bitcoin}<r>[0.56, 0.534, 0.804, 0.75]</r>, {Ethereum}<r>[0.54, 0.311, 0.786, 0.554]</r>, {Ripple}<r>[0.49, 0.274, 0.67, 0.52]</r>, and {Polkadot}<r>[0.351, 0.279, 0.54, 0.52]</r>, we can make inferences about the area of interest of investors.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that Bitcoin was of highest interest to investors in 2010 as it has the largest slice, which signifies the highest investment."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "1n9cUehnwoVsRgwo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you say about the distribution of investments across different Financial Technologies and Digital Currencies?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to answer this, I need to analyze the different sizes of each sector in the pie-chart which represents the amount of investment in the particular technology.\nBased on the above thought, the answer is:\nThe pie chart shows that Bitcoin had the highest investment, followed by DEX and Cryptocurrency. ICO had the least investment, indicating the least interest among investors. The investment in Blockchain, Ethereum, Ripple, and Polkadot was relatively balanced."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "SgqHIg0zupSWNnWk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence investment decisions in the Fintech and Digital Currency sectors for the following year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The investment decision can be inferred by considering the sectors sizes which shows how much investment is made in the particular technology.\nBased on the above thought, the answer is:\nBased on the data in the chart, future investment decisions might focus on Bitcoin due to its high investment in 2010. ICO, with the lowest investment, might be considered for additional funding to stimulate growth. The other sectors with relatively balanced investment might continue to receive consistent funding."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "8w53D5tyvzTr4qJu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, can you determine in which technology the investment is exactly half that of Bitcoin?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the technology where the investment is exactly half that of Bitcoin, I need to find a sector where the size is half the size of the {Bitcoin}<r>[0.56, 0.534, 0.804, 0.75]</r> sector.\nBased on the above thought, the answer is:\nEthereum."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "xRjSS3tb3AXZ4hHI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the investment in Blockchain, Cryptocurrency and ICO?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of the investment, I need to add the investment amounts in {Blockchain}<r>[0.294, 0.362, 0.54, 0.52]</r>, {Cryptocurrency}<r>[0.293, 0.509, 0.54, 0.683]</r>, and {ICO}<r>[0.355, 0.52, 0.54, 0.752]</r>.\nBased on the above thought, the answer is:\n200"
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "2K7aWDz8gaSWm9cS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment in Polkadot increases by 10, what will be the total investment in all technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total investment, I need to add 10 to the {Polkadot}<r>[0.351, 0.279, 0.54, 0.52]</r> investment and sum up all the investments again.\nBased on the above thought, the answer is:\n662"
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "nvJNZU5BWvgLvncV",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table from the pie chart data showing the outgoing population from each region?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll make a structured table based on the pie chart, which sections represent the different regions and their respective outgoing populations. The labels are inferred from the arrow-attached texts on {the slices}<r>[0.268, 0.362, 0.51, 0.688]</r>;<r>[0.338, 0.517, 0.586, 0.76]</r>;<r>[0.533, 0.389, 0.775, 0.755]</r>;<r>[0.51, 0.282, 0.71, 0.517]</r>;<r>[0.434, 0.275, 0.567, 0.517]</r>;<r>[0.356, 0.287, 0.51, 0.517]</r>;<r>[0.324, 0.33, 0.51, 0.517]</r>.\nBased on the above thought, the answer is:\nRegion, Population Out\nSoutheast Asia,400\nMiddle East,300\nAfrica,500\nLatin America,200\nEastern Europe,150\nWestern Europe,100\nNorth America,50"
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "NZ0bV6RwvuUu97j1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which region witnesses the largest population outflow?"
+            },
+            {
+                "from": "gpt",
+                "value": "Through the observation of the chart, the largest slice corresponds to the region with the largest population outflow. The green {exploded slice}<r>[0.533, 0.389, 0.775, 0.755]</r> is the largest.\nBased on the above thought, the answer is:\nAfrica witnesses the largest population outflow."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "9B4fZuAQ306a5fIk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploding slice in the chart signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "An exploding slice usually signifies a standout or significant data point. In this case, this slice represents the region with the highest incoming population.\nBased on the above thought, the answer is:\nThe exploding slice represents the region with the highest incoming population."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "KwALVoysHjqYqsYO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data presented in the chart inform policy on population mobility for these regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the potential policy implications of these trends, one must look at regions of high outflow, inflow, and their correlation. Considering the balance between these factors can inform policy decisions.\nBased on the above thought, the answer is:\nThe data shows regions with heavy outflows, like Africa, may need policies to prevent population drain and manage its potential socio-economic impacts. Regions with high inflow numbers, such as the Middle East, might require infrastructure and social policies to adapt to the large incoming population effectively."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "6A37Png7BDHuakbI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich region accounts for approximately one-third of the population outflow?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which region account for about a third of the population outflow, I need to observe the slice of the pie that takes up roughly a third of the chart. The chart is divided into segments, each representing a different region. It appears that the Green sliced Section {exploded slice}<r>[0.533, 0.389, 0.775, 0.755]</r> takes up the approximate third of the pie.\nBased on the above thought, the answer is:\nAfrica."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "iCFHK3UwfqGvywg3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the approximate total number of the population out from the given regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the total number of the population out, I have to accumulate all the values provided in the numerical annotations for each slice of the pie chart: {annotations}<r>[0.244, 0.528, 0.244, 0.528]</r>;<r>[0.448, 0.777, 0.448, 0.777]</r>;<r>[0.785, 0.611, 0.785, 0.611]</r>;<r>[0.662, 0.298, 0.662, 0.298]</r>;<r>[0.499, 0.251, 0.499, 0.251]</r>;<r>[0.381, 0.284, 0.381, 0.284]</r>;<r>[0.322, 0.328, 0.322, 0.328]</r>.\nBased on the above thought, the answer is:\nThe total number of the population out is approximately 1700."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "a0zFAyezkFiK0NkT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Population Out from North America doubles, what would be the new total percentage of all regions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to double the value of North America's population out and add it to the previous total population out from all regions.\nBased on the above thought, the answer is:\nThe new total will be 1800."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "q5uSZ2EiDOJkWWKC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the pie chart, could you convert the data into structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reviewing the information provided in the pie slices for {\"Energy Efficiency\"}<r>[0.159, 0.313, 0.47, 0.577]</r>, {\"Carbon Neutrality\"}<r>[0.196, 0.52, 0.5, 0.808]</r> and {\"Renewable Energy\"}<r>[0.384, 0.52, 0.674, 0.831]</r>, I can determine the percentages associated with each environmental factor for years 2030 and 2040.\nBased on the above thought, the answer is:\nYear,Energy Efficiency,Carbon Neutrality,Renewable Energy\n2030,14.4%,15.6%,70%\n2040,16.7%,17.8%,80%"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Rz7Yxzi8XIxFmRdJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sustainability measure showed the most improvement from 2030 to 2040 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To continue, I need to compare the differences in the percentages for each sustainability measure between 2030 and 2040.\nBased on the above thought, the answer is:\n'Renewable Energy' showed the greatest improvement, increasing by 10% from 2030 to 2040."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Npi4HvM4fpBPESgI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be deduced about the progression in environmental sustainability measures during the period from 2030 to 2040, according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The changes in the percentages associated with each sustainability measure are essential to this process.\nBased on the above thought, the answer is:\nFrom 2030 to 2040, all three environment sustainability measures have shown improvement, indicating the strengthening emphasis on environmental sustainability. The most significant improvement is shown in 'Renewable Energy', evident from its largest percentage increase in the pie chart."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "gHgRn04QkaR0Cg8q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications could the pie chart have on future sustainability planning?"
+            },
+            {
+                "from": "gpt",
+                "value": "To proceed further, it would be necessary to consider how changes in percentages may affect future sustainability strategies.\nBased on the above thought, the answer is:\nThe increase in all three environmental sustainability measures indicates the need for continued focus and investment in these areas. Particularly, given the significant rise in 'Renewable Energy', it suggests a future trend where emphasis is likely to be placed. Strategies should acknowledge the growing importance of renewable energy sources, while still continuing to advance in energy efficiency and carbon neutrality."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "k4gynwW6wH48Nme2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage contribution of 'Energy Efficiency' and 'Carbon Neutrality' in 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to locate the percentages of 'Energy Efficiency' and 'Carbon Neutrality' in 2040 and then sum them up.\nBased on the above thought, the answer is:\nThe total is 34.5%"
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "ZLcdroMvdu4b69IK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did 'Renewable Energy' improve from 2030 to 2040?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the 'Renewable Energy' percentages in 2030 and 2040, I can determine this.\nBased on the above thought, the answer is:\n'Renewable Energy' improved by 10%."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "z2lYYY8oc4OvTB46",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs 'Energy Efficiency' or 'Carbon Neutrality' contributed more in 2030?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of 'Energy Efficiency' and 'Carbon Neutrality' in 2030.\nBased on the above thought, the answer is:\n'Carbon Neutrality' had a higher contribution in 2030."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "tqgWCKEm4avETANc",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your view on the pie chart, can you generate a CSV format table capturing each fashion trend's popularity per year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the title in the middle of the chart {\"Fashion Trends Popularity from 2015 to 2019\"}[0.084, 0.092, 0.916, 0.125] and the color-coded slices {the slices from 2015 to 2019}[{2015}[0.195, 0.321, 0.5, 0.52]; {2016}[0.19, 0.463, 0.5, 0.7]; {2017}[0.247, 0.52, 0.521, 0.831]; {2018}[0.525, 0.461, 0.836, 0.848]; {2019}[0.262, 0.21, 0.801, 0.52]], and combining with the raw data, I am able to discern popularity trends over the different years for both fashion trends.\nBased on the above thought, the answer is:\nSubculture,Fashion Trend,2015,2016,2017,2018,2019\nGrunge,Streetwear,350,550,700,1200,1500\nSteampunk,Gothic,250,350,500,650,800"
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "Cd46WqU1gH8jtxtl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your understanding of the chart, which fashion trend appears to be more popular overall and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the overall popularity counts for both the 'Streetwear' and 'Gothic' fashion trends to determine which is more popular.\nBased on the above thought, the answer is:\nThe 'Streetwear' from the Grunge subculture appears to be more popular overall. It has a higher overall popularity over the years from 2015 to 2019, reaching its peak in 2019."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "tsiFFcJ6TiKPpe3m",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the evolution of these fashion trends based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should analyze the relative changes in popularity of each fashion trend over the years from 2015 to 2019.\nBased on the above thought, the answer is:\nThe fashion trends associated with both the Grunge and Steampunk subcultures have increased in popularity over time. However, the 'Streetwear' trend associated with the Grunge subculture has seen a significant boost, especially in 2018."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "oS3k2AmiqTxvRQFj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the chart, how might the data influence fashion industry decisions for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the upward trajectory of both trends, industry decision makers might allocate more resources towards trends that are on an upward popularity trend.\nBased on the above thought, the answer is:\nThe 'Streetwear' trend from Grunge subculture has shown a marked increase in popularity from 2015 to 2019, thereby signaling to the fashion industry that investing more in this trend could be beneficial. However, 'Gothic' fashion from Steampunk subculture also has a steady growing audience that they could choose to cater to more as well."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "H5aE6W4ekAEv4pYJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total popularity count for the 'Gothic' fashion trend over the years 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the data associated with the 'Gothic' fashion trend, I need to add the popularity counts for all the respective years.\nBased on the above thought, the answer is:\n2550"
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "Ck98mQ4LARrsLsb8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the popularity of 'Streetwear' trend increase from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I should subtract the popularity count of 'Streetwear' in 2015 from the count in 2019.\nBased on the above thought, the answer is:\n1150"
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "YCJnIOdjqS8scK4J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did both the 'Gothic' and 'Streetwear' fashion trends have the same popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the popularity counts of both fashion trends for each year to find where they match.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "iWtaN7m8buNN9hIh",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart, could you extract the data and convert it into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am observing a pie chart titled {Urban Development Investment from 2010 to 2014}<r>[0.022, 0.092, 0.978, 0.125]</r>. The slices represent the proportion in investment towards Urban Development for each year from {2010}<r>[0.19, 0.321, 0.5, 0.52]</r>, {2011}<r>[0.19, 0.513, 0.5, 0.758]</r>, {2012}<r>[0.301, 0.52, 0.628, 0.831]</r>, {2013}<r>[0.529, 0.404, 0.84, 0.814]</r>, to {2014}<r>[0.262, 0.21, 0.783, 0.52]</r>.\nBased on the above thought, the answer is:\nYear,Investment in Urban Development\n2010,10.7%\n2011,14.3%\n2012,17.9%\n2013,25.0%\n2014,32.1%"
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "AqTd35mbwSwWQaxm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which years experienced a significant increase in Urban Development investment compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportional increases in investment for each year, with particular attention to significant jumps in percentages.\nBased on the above thought, the answer is:\nThe year 2013 experienced a substantial increase in Urban Development investment of 25.0% compared to the previous year 2012 which was 17.9%."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "cWNKQtBqCDeYNvOQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart reflect the description that public housing investments are decreasing while those in urban development are increasing between 2010 and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw a correlation between the pie chart and the description, I need to analyze the shorted data from the chart.\nBased on the above thought, the answer is:\nThe pie chart directly aligns with the description that public housing investments are decreasing while those in urban development are increasing. This trend is demonstrated by the increasing sizes of pie slices for urban development from 2010 through 2014."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "MjEhdMK3h0YrYFlh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about future trends in urban development investment based on the trend from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will analyze the trend from 2010 to 2014 for Urban Development to make an educated prediction for future trends.\nBased on the above thought, the answer is:\nThe pie chart suggests a consistent annual increase in urban development investment. If this trend continues, we can expect urban development investment to rise in the coming years."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "247yZDNRg0IemTQj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in Urban Development investment from 2010 to 2014 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate the total increase by comparing the percentages for 2010 and 2014 represented on the pie chart.\nBased on the above thought, the answer is:\nThe total increase is 32.1%-10.7%=21.4%."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "rWf7I4bSu7Ygnda3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average yearly increase in Urban Development investment from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average yearly increase, I divide the total increase by the number of years.\nBased on the above thought, the answer is:\nThe average yearly increase is 21.4%/4=5.35%."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "cWcxbb3JkP7lJgAH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend continues and Urban Development investment increases by the average yearly increase next year, what will be the percentage in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the average yearly increase 5.35% to the 2014 percentage 32.1% to find the 2015 percentage.\nBased on the above thought, the answer is:\nThe estimated Urban Development investment for 2015 would be 32.1% + 5.35% = 37.45%."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "UFctGVBd2P0BslaQ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data represented in the pie chart into a structured table of CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Going by the information on the chart, the slices {30% of pie slice}<r>[0.19, 0.321, 0.5, 0.808]</r> and {70% of pie slice}<r>[0.292, 0.202, 0.84, 0.823]</r> correspond to the Manufacturing Production Efficiency for the years 2018 and 2019.\nBased on the above thought, the answer is:\nYear,Efficiency(%)\n2018,30\n2019,70"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "12HENwGFO5despSn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, was there an improvement in Manufacturing Production Efficiency from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size of the slice for each year, we can see the {slice of the pie for 2018}<r>[0.19, 0.321, 0.5, 0.808]</r> is much smaller than the {slice for 2019}<r>[0.292, 0.202, 0.84, 0.823]</r>. This shows that there was an increase in efficiency from 2018 to 2019.\nBased on the above thought, the answer is:\nYes, there was a significant improvement in Manufacturing Production Efficiency from 2018 to 2019."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "cxYWBHCLwDvlskDb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you comment on the performance of the manufacturing plant based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the large {slice for 2019}<r>[0.292, 0.202, 0.84, 0.823]</r> compared to the relatively smaller {slice for 2018}<r>[0.19, 0.321, 0.5, 0.808]</r>, the manufacturing plant's performance improved significantly.\nBased on the above thought, the answer is:\nThe manufacturing plant performed markedly better in 2019 compared to the previous year, indicating commendable efficiency."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "hbQos3WdLLjNWJzS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inferences can be drawn from the pie chart regarding the progress of the manufacturing plant?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Manufacturing Production Efficiency significantly increased from 2018 to 2019 as indicated by the much larger {slice for 2019}<r>[0.292, 0.202, 0.84, 0.823]</r> compared to the {slice for 2018}<r>[0.19, 0.321, 0.5, 0.808]</r>.\nBased on the above thought, the answer is:\nThe pie chart suggests that the manufacturing plant made significant progress in improving its manufacturing efficiency from 2018 to 2019."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "p46Fy8LoUuYnIIgM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total Manufacturing efficiency from the years 2018 to 2019 as depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total efficiency can be obtained by summing up all the percentages from the respective years. In this case, the chart has two slices, one for {year 2018}<r>[0.19, 0.321, 0.5, 0.808]</r> and the other one for {year 2019}<r>[0.292, 0.202, 0.84, 0.823]</r>.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "LBqQwz6VyX6BDUbT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the manufacturing efficiency improve from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The improvements can be calculated by subtracting the efficiency value of 2018 (30%) from that of 2019 (70%)\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "oQDyTEw4Gtmwh0Ov",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of increase in efficiency from the year 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The rate of increase can be calculated by dividing the difference in efficiencies by the efficiency of the base year (2018) and then multiply by 100% to convert it into a percentage.\nBased on the above thought, the answer is:\n133.33%"
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "7MJeJZFxo1WbNeVx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart into a structured data table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is representing the population migration and mobility trends from the year 2012 to 2019. Each slice shows the proportion of the migration for that year. I can use the pattern from the slices, looking at the center coordinates and color of every slice to figure out which year has the highest migration.\nBased on the above thought, the answer is:\nYear,Percentage\n2012,7.4\n2013,9.5\n2014,10.9\n2015,13.9\n2016,12.9\n2017,14.1\n2018,15.0\n2019,16.3"
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "6142xyEXYDUQ9VBv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what can you tell about the population migration trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart and understanding the annotation of every slice, one can have an idea about the trend of population migration over the years.\nBased on the above thought, the answer is:\nOver the years from 2012 to 2019, there has been an upward trend in population migration. The migration proportion was the lowest in 2012 at 7.4% and increased to the highest in 2019 at 16.3%. There was a slight decline in migration proportion in 2016, however, overall, there has been a steady increase."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "WdzXcODedf69sH8T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the largest slice represent and why is it important?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the largest slice in the pie chart which is the most prominent and has the greatest extent. Based on the size and the annotation of the slice, the importance can be determined. {The largest slice}<r>[0.238, 0.157, 0.539, 0.485]</r> represents the year 2019.\nBased on the above thought, the answer is:\nThe largest slice represents the year 2019 and it is important because it shows the highest percentage of migration at 16.3%. This indicates that 2019 witnessed the highest amount of population mobility, which can have significant implications for demographic trends and policy making."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "iN5hHjYc0HYt8hKz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the smallest slice of the pie chart suggest about the population migration trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the smallest slice on the pie chart and understand what it suggests about the population migration trend. {The smallest slice}<r>[0.182, 0.305, 0.5, 0.515]</r> represents the year 2012.\nBased on the above thought, the answer is:\nThe smallest slice in the pie chart represents the year 2012 with a migration percentage of 7.4%. This suggests that migration was comparatively lower in 2012 as compared to subsequent years, indicating a rise in population mobility and migration over the years from 2012 to 2019."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "wcybiEqJcagRrD1m",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDuring which year was the percentage of migration the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the lowest migration percentage, I need to review each slice on the pie chart and locate the one with the smallest size. {The 2012 slice}<r>[0.182, 0.305, 0.5, 0.515]</r> appears to be the smallest.\nBased on the above thought, the answer is:\n2012."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "Ecvda7jXxcN155AA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of migration percentage from 2015 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I have to add the percentages from for 2015, 2016, and 2017. {The 2015 slice}<r>[0.336, 0.515, 0.612, 0.842]</r>; {the 2016 slice}<r>[0.5, 0.515, 0.8, 0.823]</r>, and {the 2017 slice}.<r>[0.5, 0.366, 0.827, 0.646]</r>\nBased on the above thought, the answer is:\nThe sum of the migration percentage from 2015 to 2017 is 13.9% + 12.9% + 14.1% = 40.9%."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "Q0sYW2JIopDCUf2v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the migration percentage for the year 2019 increased by 2%, what would be the new total migration percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total migration percentage, I need to add 2% to the 2019 migration percentage and sum up all the percentages again. {The 2019 slice}<r>[0.238, 0.157, 0.539, 0.485]</r> has a percentage of 16.3%.\nBased on the above thought, the answer is:\nThe new percentage for 2019 is 16.3% + 2% = 18.3%. The new total migration percentage is 7.4% (2012) + 9.5% (2013) + 10.9% (2014) + 13.9% (2015) + 12.9% (2016) + 14.1% (2017) + 15.0% (2018) + 18.3% (2019) = 102.0%."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "Wrrizg2gGf3re5FS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, can you convert the data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the title of the chart {\"Creativity and Design Innovations from 2015 to 2021\"}[0.005, 0.092, 0.995, 0.125] and the slices of the pie chart {2015}[0.198, 0.321, 0.5, 0.52], {2016}[0.19, 0.451, 0.5, 0.649], {2017}[0.218, 0.52, 0.5, 0.807], {2018}[0.381, 0.52, 0.662, 0.831], {2019}[0.528, 0.484, 0.839, 0.798], {2020}[0.5, 0.255, 0.806, 0.52], {2021}[0.262, 0.21, 0.661, 0.52], I can convert the pie chart into a CSV format.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,7.5%\n2016,10.4%\n2017,11.9%\n2018,15.0%\n2019,18.8%\n2020,13.8%\n2021,22.6%"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "O912vEOzInmskT5T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the year when creativity and design innovations peaked?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by identifying the slices with the largest proportion.\nBased on the above thought, the answer is:\nThe year 2021 saw the peak in creativity and design innovations, with a proportion of 22.6%."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "3MIOkSJDZmlkj3AD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you infer from the sharp dip in the innovation percentage in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the factors that could have led to a dip in percentages {2020}[0.5, 0.255, 0.806, 0.52].\nBased on the above thought, the answer is:\nThe sharp dip in the innovation percentage in 2020 is potentially due to the global crises such as COVID-19 pandemic, which may have restricted creative endeavors."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "9uZ1J65vfkiovG9t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend from 2015 to 2021, how do you foresee the percentage of innovations in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the overall trend and the most recent data points, I can make an educated guess about future trends.\nBased on the above thought, the answer is:\nThere's a consistent upward trend in creativity and design innovations, despite the slight dip in 2020. If this trend continues, we might expect the percentage of innovations in 2022 to be higher than in 2021."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "0luUc9kaeddVQ9Vo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentages of innovations from 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain this, I must add the percentages of innovations from 2019 {2019}[0.528, 0.484, 0.839, 0.798] and 2020 {2020}[0.5, 0.255, 0.806, 0.52].\nBased on the above thought, the answer is:\n32.6%"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "lbiv7VPuuMi72ec4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has the lowest percentage of total innovations?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the smallest proportion among the data.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "YUWVa9cfjhEktPOY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage do the innovations in 2021 exceed those in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the proportion in 2020 {2020}[0.5, 0.255, 0.806, 0.52] from the proportion in 2021 {2021}[0.262, 0.21, 0.661, 0.52].\nBased on the above thought, the answer is:\n8.8%"
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "HWOMhMsbn5M3g9yo",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the frequency data from the pie chart in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the title {\"Usage of Financial Technology and Digital Currency\"}[0.046, 0.251, 0.997, 0.284] and the sectors of the pie chart {{\"Mobile Payments\"}[0.471, 0.339, 0.723, 0.705]; {\"Peer to Peer Lending\"}[0.338, 0.403, 0.521, 0.69]; {\"Bitcoin\"}[0.381, 0.343, 0.521, 0.52]}, I can determine that the CSV should have two columns - one for the platform and one for the frequency.\nBased on the above thought, the answer is:\nPlatform,Frequency\nMobile Payments,60\nPeer to Peer Lending,30\nBitcoin,10"
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "dLBgVVZVIQMnJ4K2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the distribution in the pie chart, which Financial Technology platform is used the most?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to locate the platform with the highest frequency, which is represented by the largest slice of the pie chart.\nBased on the above thought, the answer is:\nMobile Payments is used the most because its sector occupies the largest portion (60%) on the pie chart."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "JyAwNXhYTFq6yIE7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which platform is emerging as the least favorite based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can figure out the least favorite platform by looking at the smallest slice of the pie chart.\nBased on the above thought, the answer is:\nBitcoin is the least favorite platform because its usage frequency is the lowest, only 10 times, which is represented by the smallest sector on the pie chart."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "03vIumME7zVXxukx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the data, which two platforms should the companies focus more on to advance in Financial Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will consider the platforms with higher usage frequencies as they are likely to be more popular and preferred by users.\nBased on the above thought, the answer is:\nBased on the usage frequencies, companies should focus more on Mobile Payments and Peer to Peer Lending as they are more popular among users."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "MQoWfcne4xOx3GVt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total frequency of all platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total frequency, I need to add the frequencies of all platforms represented on the pie chart.\nBased on the above thought, the answer is:\n100."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "uYgrCpCj8Udq0O5z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the percentage of usage for each of these platforms if Bitcoin usage increases by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to add the increased usage of Bitcoin to the total frequency. Then, I compute the percentages of each technology's usage with respect to the new total frequency.\nBased on the above thought, the answer is:\nMobile Payments: 54.5%, Peer to Peer Lending: 27.3%, Bitcoin: 18.2%."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "0RHNq2IWAQqyrsjb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Peer to Peer Lending doubles its frequency of usage, what will be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to double the frequency usage of Peer to Peer Lending, then add this to the original total frequency excluding Peer to Peer Lending. Next, I calculate the percentage of Peer to Peer Lending's usage with respect to the new total frequency.\nBased on the above thought, the answer is:\n42.9%."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "TSTq8MHDw3ZJ5Gjs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart's information, could you kindly structure it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By drawing observations from the chart's title {{Equities vs Bonds Portfolio Composition in 2019}[0.066, 0.115, 0.954, 0.148]} and pie slices {[0.218, 0.228, 0.802, 0.812]}, it appears that the slices symbolize different asset classes - equities and bonds - and the proportion they constitute. It follows that I can now proceed to represent this data set in CSV format.\nBased on the above thought, the answer is:\nAsset Class,2019\nEquities,55.1%\nBonds,44.9%"
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "AeMXH9874N2BQSrM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich was the dominant asset class in the portfolio in 2019 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of each pie slice, the dominant asset class can be identified as the one with the larger proportion.\nBased on the above thought, the answer is:\nEquities were the dominant asset class given they constituted 55.1% of the portfolio in 2019."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "4CfzFiIP7Fmd1lrV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce about the investment strategy for this portfolio as of 2019, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the sizes of the pie chart's slices and the asset classes they represent, deductions on the prevalent investment strategy can be made.\nBased on the above thought, the answer is:\nThe investment strategy leaned more towards equities in 2019, as they made up over half of the portfolio, indicative of a relatively aggressive investment approach with a slight bias towards higher potential returns albeit with more risk."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "sIWak3hciXvW8AEH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend observed in the pie chart continues, what might the prospects for the next year's portfolio composition look like?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the current year's portfolio composition and hypothesizing on a continuance of the prevailing trend, it is possible to predict the potential makeup of the next year's portfolio.\nBased on the above thought, the answer is:\nIf the trend continues, we could expect equities to maintain dominance in the portfolio in the following year, albeit the composition ratio might slightly fluctuate. Provided the risk tolerance of the investor remains constant, bonds might still represent a significant proportion of the portfolio due to a diversification strategy. Nonetheless, the exact figures would be dependent on actual market conditions."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "FkNncGbFZYX3ksHy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the equities outperform bonds in the portfolio composition ratio in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the portfolio composition ratio of bonds from that of equities as it appears on the chart figures.\nBased on the above thought, the answer is:\n10.2%"
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "ixTJCvHaGU09NVwp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the new composition ratio if equities lost 5% in the portfolio?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out what the new portfolio composition ratio would be if equities lost 5%, I need to subtract 5% from the equities' current 55.1%.\nBased on the above thought, the answer is:\n50.1%"
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "oNLFG43bcZrH1DTE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of bonds increased by 5% in the portfolio during the next year, what would be the new total composition percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the new total composition percentage, it is necessary to increase the current composition ratio of bonds by 5%, and then sum up the percentages of both asset classes again.\nBased on the above thought, the answer is:\nBonds' new proportion is 44.9% + 5% = 49.9%. The new overall composition is then 55.1% (Equities) + 49.9% (Bonds) = 105%."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "pyDJeGfPiZmxoGj5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the data on the pie chart into a CSV format table based on the numbers of Internet users from 2012 to 2019 you analysed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To do this, I need to consider each slice of the pie chart as a separate year. According to the pie chart, I can see the slices like {2012}[0.193, 0.321, 0.5, 0.52], {2013}[0.19, 0.477, 0.5, 0.666], {2014}[0.226, 0.52, 0.5, 0.805], {2015}[0.377, 0.52, 0.603, 0.831], {2016}[0.5, 0.52, 0.784, 0.813], {2017}[0.5, 0.381, 0.81, 0.647], {2018}[0.5, 0.213, 0.777, 0.52], and {2019}[0.251, 0.181, 0.536, 0.491]. These slices correspond to Internet users in billions for each year.\nBased on the above thought, the answer is:\nYear,Internet Users (in billion)\n2012,2.4\n2013,2.7\n2014,2.9\n2015,3.2\n2016,3.5\n2017,3.8\n2018,4.1\n2019,4.4"
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "NTYtUrR2G3txidFW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the trend of the Internet user growth based on the pie chart you analysed?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to look at the size of each year's slice and consider the trend these sizes indicate over the years.\nBased on the above thought, the answer is:\nThe pie chart indicates that the number of Internet users worldwide was steadily increasing from 2012 to 2019."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "WBkK2YHrtFEAYn3o",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does this pie chart tell you about global Internet usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to focus on the overall implication of the data shown in the pie chart.\nBased on the above thought, the answer is:\nBased on the pie chart, it\u2019s apparent that the internet usage has been consistently on the rise from 2012 to 2019 implying an increasing uptake and possibly more accessibility and affordability of the internet globally."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "SileCsW9iIIMykBM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, can you infer how the growth of Internet users might continue in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the observed trend in the growth of Internet users from the years 2012 to 2019 and make an inference for the future.\nBased on the above thought, the answer is:\nConsidering the consistent rise in the number of users from 2012 to 2019, it can be inferred that the trend might continue, indicating continued growth of Internet users in the future."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "g2ZU6yphutWyHxhY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the Internet users reach 4 billion according to the pie chart you analysed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to find the slice that corresponds to 4 billion users. If we look at the slices, the slice that represents the year 2018 {2018}[0.5, 0.213, 0.777, 0.52] highlights 4.1 billion users.\nBased on the above thought, the answer is:\n2018."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "U8QiXJza0FnCPoGF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in the number of Internet users from 2012 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the number of users in 2012 from the number of users in 2019.\nBased on the above thought, the answer is:\nThe total increase from 2012 to 2019 was 4.4 billion (2019) - 2.4 billion (2012) = 2 billion."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "zSBe2cHOQRH2SZy5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rate of increase in the number of internet users from 2012 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to subtract the number of users in 2012 from the number of users in 2013, and then divide the difference by the number of users in 2012.\nBased on the above thought, the answer is:\nThe rate of increase from 2012 to 2013 was (2.7 billion - 2.4 billion) / 2.4 billion = 12.5%."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "8hpYt8urCOzI9aDb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information from the pie chart into a table format useful for a historical demographic study?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {title of the chart}<r>[0.108, 0.173, 0.894, 0.206]</r> and the pie slices: {Stone Age}<r>[0.293, 0.347, 0.482, 0.505]</r>, {Bronze Age}<r>[0.311, 0.363, 0.501, 0.52]</r>, {Iron Age}<r>[0.305, 0.365, 0.501, 0.52]</r>, {Middle Ages}<r>[0.269, 0.372, 0.501, 0.52]</r>, and {Modern Age}<r>[0.255, 0.275, 0.746, 0.766]</r> indicate that the chart is showing the percentage distribution of human population among different historical eras.\nBased on the above thought, the answer is:\nEra,Population_In_Millions,Percentage\nStone Age,5,0.1%\nBronze Age,12,0.2%\nIron Age,50,0.7%\nMiddle Ages,400,5.2%\nModern Age,7700,93.8%"
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "6xKor4eDeJoqgF5U",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich era can be described as 'the great leap' in terms of human population and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the populations, the era with significantly higher population than its predecessors can be considered 'the great leap'.\nBased on the above thought, the answer is:\nThe 'great leap' in human population was in the Modern Age as it had a population of 7700 million, which is far larger than any of the previous eras."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "ykdpnqiqvenYO6dt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the context of human history, what can be inferred about survival and life conditions based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The survival and life conditions can be inferred from the increase in population.\nBased on the above thought, the answer is:\nThe significant population increase from the Stone Age to the Modern Age suggests a steady improvement in survival conditions and human life quality. This could be the result of advancements in agriculture, technology, and societal structures."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "uy4j8KznylU21Qwk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this trend of population growth continues, what might the prediction be for future ages?"
+            },
+            {
+                "from": "gpt",
+                "value": "A prediction can be made by analysing the population growth from the past ages and projecting the trend into the future.\nBased on the above thought, the answer is:\nIf the trend of population growth continues, future eras may see further exponential increase in the global human population. This is assuming similar rates of improvements in conditions for human lives as seen in the transition from the Stone Age to the Modern Age."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "GU54HBro5RxzTdLh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in human population from the Bronze Age to the Iron Age in millions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the population growth, I need to subtract the Bronze Age population from the Iron Age population.\nBased on the above thought, the answer is:\nThe increase is 38 millions."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "TkwuQysonnjrhymp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the human population for the Stone, Bronze and Iron Ages in millions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add the population of the Stone, Bronze and Iron Ages.\nBased on the above thought, the answer is:\nThe total population is 67 millions."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "SEPfF2YEfLWbOdjK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average population across the five eras in millions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum the populations for all eras and divide it by the number of eras.\nBased on the above thought, the answer is:\nThe average population is 1533.4 millions."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "iDgqHWSedy8aM2iD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a structured table in CSV format based on the pie chart you observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the title}<r>[0.172, 0.337, 0.798, 0.37]</r>, it represents performance in various areas of social responsibility. The slices of {the pie chart}<r>[0.371, 0.406, 0.608, 0.637]</r> correspond to different measures represented in percentages. I should extract these percentages along with their respective slices to form the structured table.\nBased on the above thought, the answer is:\nSocial Responsibility Measures,scores\nEnergy Efficiency,14.0%\nRecycling Efforts,9.3%\nWaste Reduction,17.4%\nEthical Sourcing,18.6%\nFair Trade Practices,14.0%\nCarbon Footprint Reduction,15.1%\nCommunity Engagement,11.6%"
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "y2cOPsLB6o0FmBFz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart depict about the company's social responsibility performance?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's slices represent the ways the company is addressing social responsibility. The size of each slice is representative of the performance in each area.\nBased on the above thought, the answer is:\nThe pie chart illustrates that Ethical Sourcing is the area in which the company is performing the best, with a representation of 18.6%. This is highlighted with the 'explode' feature. The lowest performance area is Recycling Efforts with a representation of 9.3%. Overall, the chart demonstrates that the company has diversified efforts in the field of social responsibility with varying levels of performance."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "vR5gySUWEg4vwUPp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be one strategy for the company to improve its social responsibility performance based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at {the pie slices}<r>[0.371, 0.406, 0.608, 0.637]</r>, I need to focus on the smallest slice to identify the weakest area and suggest an improvement strategy.\nBased on the above thought, the answer is:\nThe Recycling Efforts slice of the pie chart, representing 9.3% of the total performance, is the smallest. This indicates it's the area where the company is underperforming the most in its social responsibility measures. To improve overall social responsibility performance, one strategy could be to invest more resources and effort into enhancing Recycling Efforts."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "4Uy2SNi9IDdUkA98",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area of social responsibility is the company prioritizing and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the top priority, I should look at {the pie slice}<r>[0.494, 0.528, 0.608, 0.637]</r> that is the largest and is separated from the rest.\nBased on the above thought, the answer is:\nThe company is prioritizing Ethical Sourcing because it has the largest slice on the pie chart, representing 18.6% of the total performance. This is further emphasized by the 'explode' feature which separates it from the rest of the slices. This suggests that the company has a strong commitment to source ethically and has invested substantial efforts in this area."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "j83DyS8be5cG9pX6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall percentage of the top three performing measures in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to sum the percentages of the top three measures: Ethical Sourcing, Waste Reduction, and Carbon Footprint Reduction.\nBased on the above thought, the answer is:\nThe overall percentage is 51.1%."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "vKBg6owrKjrHRWWp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the company increases its score in Ethical Sourcing by 10%, what will be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to add 10% to the current Ethical Sourcing percentage, which is 18.6%, and observe the overall percentage.\nBased on the above thought, the answer is:\nIf Ethical Sourcing increases by 10%, the new total would be 110.6%."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "ew4UXmjBBEBfkr7B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the new total percentage if Recycling Efforts decrease by 5%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To work this out, I need to subtract 5% from the current Recycling Efforts percentage, which is 9.3%, and compute the new total percentage.\nBased on the above thought, the answer is:\nIf Recycling Efforts decrease by 5%, the new total would be 95%."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "91xBqKkUIicZ8kDZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the raw data about the annual sales from the pie chart to a CSV format with years and corresponding sales?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can derive the sales according to each year by observing the {pie chart}[0.189, 0.21, 0.81, 0.86]. The data label of each slice indicates sales as a percentage which needs to be converted to the number of items sold.\nBased on the above thought, the answer is:\nYear, Sales\n2012, 100\n2013, 150\n2014, 200\n2015, 250\n2016, 210\n2017, 220\n2018, 230\n2019, 240\n2020, 250"
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "7CC7UhtGvJ8Nfvbn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the 'exploded' slice in the pie chart? And which year does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "In a pie chart, an exploded slice is distanced from the center of the pie to emphasize it as a significant piece of data. It is used to draw attention to a slice that represents an outlier or key data point.\nBased on the above thought, the answer is:\nThe exploded slice demonstrates the year that had the highest sales. In this chart, it illustrates the year 2015 when the sales reached a maximum of 250 units."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "vBb3IJNbNEPzVZi9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your analysis, which year registered the least growth in sales, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the year which witnessed the smallest increment in the yearly sales, thereby, qualifying for the least growth.\nBased on the above thought, the answer is:\nThe year 2016 registered the least growth in sales as it has experienced a decrease in sales from 250 units in 2015 to 210 units."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "D8Ffj2qvwxbpPzZ9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the sales trend for these years based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of the slices in the {chart}[0.189, 0.21, 0.81, 0.86] and understanding the connections between each consecutive year, I can deduce a conclusion about the trend of sales during these years.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that the sales experienced consistent growth over the years from 2012 to 2015. 2016 saw a slight dip in the sales, but the following years witnessed steady growth up to 2020."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "nBwF3SeIDKyHMNk7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what is the percentage of sales for the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage, I need to locate the slice of the pie representing the year 2013 in the {chart}[0.189, 0.21, 0.81, 0.86].\nBased on the above thought, the answer is:\nThe sale percentage for 2013 is 8.1%."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "riF5UHt20VZYqMLo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the growth in sales from 2012 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I'd need to subtract the sales of 2012 from the sales of 2013.\nBased on the above thought, the answer is:\nThe growth in sales from 2012 to 2013 was 50 units."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "uFOTvlZIZtuqjoFj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween which two consecutive years was the growth in sales the largest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in sales between each pair of consecutive years to answer this.\nBased on the above thought, the answer is:\nThe growth in sales was largest between the years 2013 and 2014 with growth of 50 units."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "kqmezXXiFyXM7Ij3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you generate a table in CSV format about the popularity of different fashion trends in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {\"Subculture and Fashion Trends Popularity in 2020\"}<r>[0.212, 0.095, 0.763, 0.115]</r> and {the pie slices}<r>[{each slice's coordinates in the pie chart}]</r>, I can infer that the slices of the pie chart represent different fashion trends and their popularity in the year 2020.\nBased on the above thought, the answer is:\nTrend, Popularity\nEmo, 12%\nGoth, 8%\nPunk, 14%\nHip Hop, 30%\nStreetwear, 20%\nMinimalist, 10%\nBohemian, 5%\nVintage, 8%\nIndie, 11%\nGrunge, 12%"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "GpX6G2Yej1qnkl96",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend can be considered the least popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to look for the pie slice with the smallest proportion from all the slices in {the pie chart}<r>[0.173, 0.198, 0.802, 0.858]</r>.\nBased on the above thought, the answer is:\nThe Bohemian trend seems to be the least popular as it is represented by the smallest slice on the chart, indicating the lowest popularity percentage of 5%."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "9xyg9v3r35VG99co",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from this chart about the subculture and fashion trend popularity in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing and comparing all the slices in {the pie chart}<r>[0.173, 0.198, 0.802, 0.858]</r>, I can see how the percentage of popularity is distributed among different fashion trends.\nBased on the above thought, the answer is:\nHip Hop and Streetwear were the most popular fashion trends in 2020, comprising a combined total of 50% of the observed popularity. The least popular trends were Bohemian and Goth, both falling under 10%. Other fashion trends such as Emo, Punk, Vintage, Indie, and Grunge had similar popularity levels, indicating a balanced interest."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "64G6PRt1Yz38IsHu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a fashion designer is planning to focus on designing for the three most popular trends in 2021, which trends should they focus on?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the three slice with the highest proportion from all the slices in {the pie chart}<r>[0.173, 0.198, 0.802, 0.858]</r>.\nBased on the above thought, the answer is:\nBased on the pie chart data, the designer should focus on the Hip Hop, Streetwear and Punk trends as they respectively comprised 30%, 20% and 14% of the overall subculture and fashion trend popularity in 2020."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "xXg0lGZ1ayD2eTPg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what percentage of the total does the Hip Hop trend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice in {the pie chart}<r>[0.173, 0.198, 0.802, 0.858]</r> represents a different fashion trend. The slice representing the Hip Hop trend is exploded out, which helps to identify its proportion easily.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "1CX6hEINXjcDSZOB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined popularity percentage of the least popular three trends according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined popularity of the least popular three trends, I need to identify the smallest three percentage values from all the slices in {the pie chart}<r>[0.173, 0.198, 0.802, 0.858]</r>.\nBased on the above thought, the answer is:\n23%"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "dUmkOLOuO8fsdLrd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Streetwear trend had decreased its popularity by half in 2020, how would this affect the total popularity percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total popularity percentage, I would subtract half of the Streetwear popularity percentage from the total popularity percentage, which is 100%.\nBased on the above thought, the answer is:\n90%"
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "uHPTyxe6PbBwbUh1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart presented, could you please generate a structured table that contains the raw data of the percentages of each emotional state?"
+            },
+            {
+                "from": "gpt",
+                "value": "As the title {Frequency of Different Emotional States}[0.124, 0.138, 0.866, 0.171] clarifies this chart is about the frequencies of different emotional states, and from each labeled sector on the pie chart, {Stressed}[0.221, 0.345, 0.495, 0.546], {Happy}[0.204, 0.541, 0.48, 0.814], {Angry}[0.495, 0.52, 0.604, 0.794], {Fearful}[0.495, 0.52, 0.665, 0.771], {Surprised}[0.495, 0.52, 0.767, 0.734], {Calm}[0.464, 0.227, 0.786, 0.519], and {Excited}[0.285, 0.251, 0.495, 0.52], we can see the frequency of each emotional state.\nBased on the above thought, the answer is:\nEmotional State,Frequency\nStressed,12.6%\nHappy,23.7%\nAngry,6.3%\nFearful,4.2%\nSurprised,13.2%\nCalm,28.9%\nExcited,11.1%"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "UDutjwV4aaBLIMBa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nInterpreting the chart, which emotional state is experienced the most? Please provide an explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "Determining the most experienced emotional state involves identifying the slice with the largest area or the highest percentage.\nBased on the above thought, the answer is:\nThe emotional state experienced the most is \"Calm\", as it has the largest slice representing 28.9% of the total. This indicates that among the individuals studied, this is the most common emotional state."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "SlwH5t47qFJJGDz4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what sort of trends or patterns are visible in the frequency of emotional states?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions of each slice in the pie chart to determine the patterns and trends.\nBased on the above thought, the answer is:\nThe pie chart demonstrates that \"Calm\" and \"Happy\" are the most common emotional states, with a combined frequency of more than 50%. In contrast, \"Fearful\" is the least common state. The remaining states including \"Stressed\", \"Surprised\", \"Angry\", and \"Excited\" fall in the middle range in terms of their frequency."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "JwFCfqJnKZUUxCmJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence psychological research or interventions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to use the proportions of each emotional state to infer how this might impact psychological research or interventions.\nBased on the above thought, the answer is:\nThe data could inform the focus of psychological research or interventions. Since \"Calm\" and \"Happy\" are the most frequently experienced emotional states, this suggests a relatively positive emotional climate that could be strengthened. Moreover, given the lower frequency of negative emotional states like \"Fearful\", \"Stressed\", and \"Angry\", research and interventions might focus on understanding and addressing the roots of these emotions to further improve individuals' emotional well-being."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "vZyqDbvG4gayNu7t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich emotional state has the lowest occurrence according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the emotional state with the lowest occurrence, I need to compare the percentages of all emotional states and identify the smallest one.\nBased on the above thought, the answer is:\nFearful"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "G6HEoMaPPWdp8cYf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the combined proportion of the three least frequent emotional states?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined proportion of the three least frequent emotional states, it requires adding up the frequency of \"Angry\", \"Fearful\", and \"Excited\".\nBased on the above thought, the answer is:\n21.6%"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "YrH9PG8S5mDlexCf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more common is the \"Calm\" state compared to the \"Stressed\" state in terms of their occurrence?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in frequency between the \"Calm\" and \"Stressed\" states, I need to subtract the percentage of \"Stressed\" from that of \"Calm\".\nBased on the above thought, the answer is:\n16.3%"
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "Gv6rEVEipMrnlCr3",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease construct the CSV table from the details found in both the pie charts and underlying data."
+            },
+            {
+                "from": "gpt",
+                "value": "From the plots titled \"Trend of Grunge from 2017 to 2021\" {subplot_0}<r>[0.056, 0.11, 0.45, 0.898]</r> and \"Trend of Gothic from 2017 to 2021\"{subplot_1}<r>[0.55, 0.11, 0.944, 0.898]</r>, these pie charts depict how the trend of Grunge and Gothic varied from year 2017 to 2021. Each slice represents a year and the percentage each year contributes to the total. The chart for Grunge and the chart for Gothic needs to be tabulated.\nBased on the above thought, the answer is:\nTrend,2017,2018,2019,2020,2021\nGrunge,13.9%,19.3%,22.7%,20.2%,24.0%\nGothic,43.5%,22.7%,9.7%,19.3%,4.8%"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "lX9kNdERiB4OpCcg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts, which trend was more popular in 2017, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which trend was more popular in 2017, I need to compare the percentage of Grunge and Gothic in 2017.\nBased on the above thought, the answer is:\nThe Gothic trend was more popular in 2017 because it had a higher share of 43.5% compared to the Grunge trend which had a share of 13.9%."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "rTlLAbMZuJm6bMNs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie charts, how did the popularity of the Grunge trend evolve over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to examine the percentage of Grunge for each year from 2017 through 2021.\nBased on the above thought, the answer is:\nThe popularity of the Grunge trend seems to have been increasing steadily over the years, reaching its highest peak in 2021 with 24.0%."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "rfKyWKjCunqq3nCR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the trend in popularity for Gothic in the last five years, what can we infer for its future trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the future trend, I need to analyse the trend from 2017 to 2021.\nBased on the above thought, the answer is:\nThe popularity of the Gothic trend seems to have declined from its peak in 2017. While there was a slight pick up in 2020, the drastic drop in 2021 might indicate that it continues to decrease in popularity. This would suggest that unless there are factors to revive its popularity, the Gothic trend might continue to decline in the future."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "qr6GbI5YPRv5B2Xj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentage for Grunge in 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I just need to add up the percentage for Grunge in 2019 and 2020.\nBased on the above thought, the answer is:\n43.0%"
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "GCZAs66bTYPbR1GI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the percentage for Gothic change from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The change can be calculated by subtracting the percentage for Gothic in 2020 from the percentage in 2019.\nBased on the above thought, the answer is:\nThe percentage for Gothic increased by 9.6% from 2019 to 2020."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "8kbPOdiJaZpitT6J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the highest percentage in the Grunge trend and the highest in the Gothic trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I should identify the highest percentages in both trends and subtract the smallest from the largest.\nBased on the above thought, the answer is:\nThe difference is 19.5%."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "S2UEWqIntlgnSyAf",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data demonstrated on the pie chart into a table format."
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I can see two types of social interactions: {Virtual Meetings}<r>[0.287, 0.405, 0.467, 0.687]</r> and {Face to Face Meetings}<r>[0.346, 0.336, 0.664, 0.696]</r> which correspond to two different percentages. By translating these into frequencies, I can create a structured table.\nBased on the above thought, the answer is:\nTypes of Social Interactions,Frequency\nVirtual Meetings,30%\nFace to Face Meetings,70%"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "RsiIaKeUzN3gDJoe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the preferred mode of social interaction based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This requires analysing the proportions of the two social interaction types presented in the chart.\nBased on the above thought, the answer is:\nIt is inferred that Face to Face Meetings are the most favoured mode of social interaction as it comprises 70% of all social interactions."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "NKhrfRvrBIim0gOc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf there are barriers to face-to-face meetings such as a pandemic, what might be a potential impact to these values?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require me to consider how external factors might influence the relative prevalence of Virtual and Face to Face meetings.\nBased on the above thought, the answer is:\nIf there are barriers to face-to-face meetings, it could potentially increase the proportion of virtual meetings. We could see a shift in these percentages, with an increase in Virtual Meetings and a corresponding decrease in Face to Face Meetings."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "UdxaitpxnHATxLdC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the data on the chart change if technology becomes more accessible and user-friendly?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I must project potential impacts of improved technology on the distribution between virtual and face-to-face meetings.\nBased on the above thought, the answer is:\nIf technology becomes more accessible and user-friendly, the percentage of virtual meetings might increase. This would consequently decrease the percentage of face-to-face meetings, resulting in a more balanced distribution between the two types of social interactions."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "EojPRnX3ZjukSMlm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to recalculate the statistics to include a third category \"Correspondence Meetings\" that accounted for 10% of the total meetings, what would be the new percentage for \"Face to Face Meetings\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I have to subtract 10% from the sum of the current proportions, and then distribute the remaining percentage proportionally according to the current proportions.\nBased on the above thought, the answer is:\n60%"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "L5VtD60lG1ME5g3R",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in proportion between the two types of meetings displayed?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I need to subtract the percentage value for \"Virtual Meetings\" from the percentage value for \"Face to Face Meetings\".\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "sg8NKMLEJOutF0Xn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of meetings if we know that the number of virtual meetings is 150?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since the \"Virtual Meetings\" representation is 30%, I can calculate the total number of meetings by taking the number of virtual meetings and dividing it by 30%, then multiplying by 100%.\nBased on the above thought, the answer is:\n500"
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "2DNBPfyVv2Zml7yx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, construct a table that represents the investment allocation for Healthcare and Education in 2019."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I can see {two slices of the pie chart}<r>[0.205, 0.182, 0.847, 0.813]</r>, with each sector labeled with their respective percentages.\nBased on the above thought, the answer is:\nSector,2019\nHealthcare,38.6%\nEducation,61.4%"
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "18wGaJvCoodgieRC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this chart, what can we infer about the government's policy focus in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I can infer that the government's policy focus based on the allocation of funds to the Healthcare and Education sectors.\nBased on the above thought, the answer is:\nIn 2019, the government seemed to have a greater focus on Education, as it made up 61.4% of the investments, while Healthcare constituted 38.6% of the investments."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "QiqZcqmMpX9fPZMO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you observe from the budget allocations to Healthcare and Education sector over the years 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "As an AI, I can process the raw data and see a trend in the increase in budget allocation for both sectors over the years.\nBased on the above thought, the answer is:\nFrom 2015 to 2019, both sectors display a steady increase in investment. This suggests a consistent trend of growing emphasis and funding on healthcare and education by the government."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "K2B4YXZUKgzD6Pwd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the same percentages were to be applied in the following year, where do you think the most significant impact would be observed?"
+            },
+            {
+                "from": "gpt",
+                "value": "As an AI, I need to consider how the projections might impact each sector using the provided data.\nBased on the above thought, the answer is:\nIf the same percentages were to be applied in the following year, the most noticeable impact would likely be observed in the Education sector. This sector would continue to receive the majority of the budget allocation, allowing for potential expansion, improvement of services and increase in resources."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "aLro6cIgAdQiXCzb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the change in the percentage of investment in Education from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I would subtract the percentage allocation of the Education sector in 2015 from the allocation in 2019.\nBased on the above thought, the answer is:\nThe change in the percentage of investment is 61.4% - 45% = 16.4%"
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "wReZDzqymqnfx7Er",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of investment in Healthcare and Education in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I simply add the investment percentages of Healthcare and Education for the year 2019.\nBased on the above thought, the answer is:\nThe total investment percentage is 38.6% (Healthcare) + 61.4% (Education) = 100%."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "NGuMX4AGcI5VXfyB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the rate of increase in the distribution between 2015 and 2019 is consistent, what would be the percentage of the budget allocated to Healthcare in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would find the yearly rate of increase from 2015 to 2019, and then add this to the percentage of Healthcare in 2019.\nBased on the above thought, the answer is:\nThe yearly rate of increase is (44% - 23%) / 5 = 4.2%. Therefore, the projected allocation for 2020 would be 44% + 4.2% = 48.2%."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "T7icjMFzAsSK5ROP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the pie chart and present it in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the pie chart, the title {\"Big Data Integration Overview\"}[0.206, 0.232, 0.769, 0.265] tells me the general theme. The slices of the pie chart represent different integration techniques including {Data Staging}[0.271, 0.496, 0.271, 0.496], {Data Federation}[0.471, 0.758, 0.471, 0.758], {ETL}[0.681, 0.621, 0.681, 0.621], {ELS}[0.702, 0.559, 0.702, 0.559], {Virtual Integration}[0.684, 0.425, 0.684, 0.425], {Manual Coding}[0.534, 0.307, 0.534, 0.307], and {Middleware}[0.374, 0.334, 0.374, 0.334] with each holding a specific percentage of the total.\nBased on the above thought, the answer is:\nTechnique,Counts\nData Staging,200\nData Federation,350\nETL,47\nELS,55\nVirtual Integration,160\nManual Coding,150\nMiddleware,110"
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "ks8Pb0nRDQ1xBkj8",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Integration Technique is used the most? Explain the rationale."
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the most used integration technique, I need to find the technique with the highest count.\nBased on the above thought, the answer is:\nThe most used Integration Technique is Data Federation, as it has the highest count of 350."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "5s8lHv0viW9xHq9t",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the usage of various Big Data Integration techniques?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the counts of each integration technique to understand their usage levels.\nBased on the above thought, the answer is:\nThe chart suggests that Data Federation is most frequently used, followed closely by Data Staging and Virtual Integration. Techniques like ETL and ELS show very less usage, indicating that they are not the preferred choices for big data integration in the represented scenario."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "W6ncLm0FrrWE6S4u",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a new big data project is set to begin, which techniques might you expect to be used based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the counts, I can infer the popularity and hence predict the trends on the likely techniques that might be used in a new project.\nBased on the above thought, the answer is:\nBased on the chart, we might expect Data Federation and Data Staging to be heavily utilized in the new big data project considering their frequent usage in the past. Techniques like ETL and ELS, which have seen less adoption, might have a secondary role."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "ZPsa8QehB2Lx80rG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total count of all the integration techniques?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total count, I need to add the counts of all integration techniques together.\nBased on the above thought, the answer is:\n1072."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "IvCLTYF4qaFPG7in",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 50 more instances of Manual Coding were used, what would be the new total count?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total count, I would add the existing total count to the additional Manual Coding instances.\nBased on the above thought, the answer is:\n1122."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "jMWqP4oIM6yTiJf5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Virtual Integration was used twice as much, what would be the new count for that technique?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Virtual Integration was used twice as much, I would need to double its initial count.\nBased on the above thought, the answer is:\n320."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "b9pqJ5RWY0p5BF4o",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the pie chart you see into a CSV format table, which presents each company's stock value."
+            },
+            {
+                "from": "gpt",
+                "value": "Despite the fact that the pie chart has a simple structure, interpreting it through the slices' proportions allows a structured tabulation. I notice that five slices are present in the chart{pie_0}[0.136, 0.126, 0.864, 0.884], each representing the stock value of a distinct company.\nBased on the above thought, the answer is:\nCompany,Stock Value\nStrong Company,50\nDiligent Company,20\nInnovative Company,15\nOld Company,10\nNew Company,5"
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "GCSWfekvD4Pjv8lU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which company has the highest stock value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which company has the highest stock value, I need to check the size of each slice. The company with the largest slice has the highest stock value.\nBased on the above thought, the answer is:\nThe Strong Company has the highest stock value, as suggested by its largest slice in the pie chart."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "vIoCtudReOEckLPO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart convey about market positioning of these companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart graphically represents the stock value of each company. Therefore, the positioning of a company in the market can be inferred from the size of its respective slice.\nBased on the above thought, the answer is:\nThe chart conveys that the Strong Company has the most significant market position based on its stock value, followed by the Diligent Company. The Innovative, Old, and New companies have smaller market positionings."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "NuxiSwhxF76l8eQD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom an investment perspective, what implication does the chart hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of the slices provides an obvious depiction of the stock value of each company. Therefore, using this as the basis for investment, we can insightfully understand the potential of each company.\nBased on the above thought, the answer is:\nThe chart suggests that the Strong Company could be a promising investment opportunity as it holds the highest stock value. However, the Innovative Company could also be considered an outlier investment since despite being third in stock value, it overtakes companies with higher market shares. Therefore, the Innovative Company may signify some form of unique value not represented by others."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "U1n7qT6DTUpygSUe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference between the stock value percentage of Strong Company and Diligent Company in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the autotexts of the Strong Company's slice {50.0%}[0.243, 0.827, 0.243, 0.827] and the Diligent Company's slice {20.0%}[0.899, 0.548, 0.899, 0.548], I found their stock value. The difference is the subtraction of the smaller from the larger.\nBased on the above thought, the answer is:\nThe difference is 30%."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "m5ub9Yu89dykJ9Bt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total stock value percentage of Innovative, Old, and New Company combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this total, I observe the autotext of the Innovative Company's slice {15.0%}[0.725, 0.146, 0.725, 0.146], the Old Company's slice {10.0%}[0.403, 0.132, 0.403, 0.132], and the New Company's slice {5.0%}[0.237, 0.218, 0.237, 0.218], and sum them up.\nBased on the above thought, the answer is:\nThe total is 30%."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "xbgX1OPUhYOPtMlh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Strong Company loses 5% of its stock value to the New Company, what will be the new percentage for Strong Company and New Company?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the new percentages, I need to subtract 5% from the Strong Company's current percentage {50.0%}[0.243, 0.827, 0.243, 0.827] and add it to the New Company's current percentage {5.0%}[0.237, 0.218, 0.237, 0.218].\nBased on the above thought, the answer is:\nThe new percentage for Strong Company will be 45%. The new percentage for New Company will be 10%."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "e7VNUatAS63zZdwS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please transfer the data of cuisine popularity into a table formatted as CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie chart, which includes the information of the cuisine type and their respective popularity, {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> is 45%, {\"Italian\"}<r>[0.509, 0.453, 0.798, 0.767]</r> is 20%, {\"French\"}<r>[0.509, 0.255, 0.789, 0.523]</r> is 15%, and {\"Mexican\"}<r>[0.288, 0.234, 0.617, 0.523]</r> is 20%.\nBased on the above thought, the answer is:\nCuisine Type, Chinese, Italian, French, Mexican\nCuisine Popularity, 45, 20, 15, 20"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "GcQQwEsTAWjyziip",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which cuisine is most popular?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most popular cuisine would be the one that is represented by the largest slice of the chart. From the chart, the largest slice is {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> .\nBased on the above thought, the answer is:\nThe most popular cuisine is Chinese because it has the largest slice of the pie chart, representing 45% of the population's preference."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "Srm4udu1vjjD203A",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the distribution of gastronomic preferences from this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the distribution of gastronomic preference, I need to look at the size of the slices for each cuisine type. From the chart, {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> is the largest slice indicating it has the highest popularity, followed by {\"Italian\"}<r>[0.509, 0.453, 0.798, 0.767]</r> and {\"Mexican\"}<r>[0.288, 0.234, 0.617, 0.523]</r> with the same popularity, and {\"French\"}<r>[0.509, 0.255, 0.789, 0.523]</r> is the least popular.\nBased on the above thought, the answer is:\nThe majority of people prefer Chinese cuisine. Italian and Mexican cuisines are also fairly popular, tying for the second place in preference. French cuisine, however, seems to be preferred less than the other three cuisines."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "1NVm6sPXpere3ERB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a cooking class needs to open a new course, which cuisine should be considered to attract the largest audience according to the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which cuisine would attract the most people, I would need to look at which cuisine is most popular according to the pie chart. Noting that {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> is the largest slice of the pie chart.\nBased on the above thought, the answer is:\nAccording to the chart, a course teaching how to cook Chinese cuisine would attract the most people as Chinese cuisine appears to be the most popular amongst the options provided."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "O7voWf2xaob213V9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, what proportion of people prefer either Chinese or Italian cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the proportion of people who prefer either Chinese or Italian cuisine, I need to sum up the values associated with these two slices. The slice for Chinese cuisine {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> represents 45% and that for Italian cuisine {\"Italian\"}<r>[0.509, 0.453, 0.798, 0.767]</r> represents 20% of the population.\nBased on the above thought, the answer is:\n65%"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "P2qcw4YBu5LBua8M",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two cuisine types together constitutes 35% of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which two cuisines together represent 35% popularity, I need to analyze the chart and look for slices that add up to 35%, which are {\"Italian\"}<r>[0.509, 0.453, 0.798, 0.767]</r> and {\"French\"}<r>[0.509, 0.255, 0.789, 0.523]</r>.\nBased on the above thought, the answer is:\nItalian and French"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "nk6d1dwJk5BwpWo2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the percentage of Chinese cuisine if it was reduced by 10%?"
+            },
+            {
+                "from": "gpt",
+                "value": "The original percentage of Chinese cuisine {\"Chinese\"}<r>[0.199, 0.356, 0.64, 0.83]</r> was 45%, so if we reduce it by 10%, it will be 45% - 10%.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "MyeuOI7TJHzKOqT0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transcribe the pie chart values into a CSV formatted table for both the years 2014 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the observed pie chart, using the slices {\"Movie\"} [0.323, 0.156, 0.5, 0.313], {\"Music\"} [0.329, 0.27, 0.5, 0.443], {\"Television\"} [0.468, 0.27, 0.653, 0.446], {\"Theater\"} [0.5, 0.183, 0.677, 0.358], {\"Virtual Reality\"} [0.5, 0.124, 0.654, 0.27], {\"Esports\"} [0.486, 0.093, 0.6, 0.27], {\"Video Games\"} [0.365, 0.094, 0.5, 0.27] for year 2014 and slices {\"Movie\"} [0.323, 0.647, 0.5, 0.77], {\"Music\"} [0.324, 0.761, 0.5, 0.911], {\"Television\"} [0.407, 0.761, 0.574, 0.937], {\"Theater\"} [0.513, 0.772, 0.682, 0.932], {\"Virtual Reality\"} [0.5, 0.694, 0.677, 0.813], {\"Esports\"} [0.5, 0.588, 0.664, 0.761], {\"Video Games\"} [0.365, 0.584, 0.539, 0.761] for year 2021. I see that the slices represents the percentage breakdown of each Media and Entertainment Industry.\nBased on the above thought, the answer is:\n\"Year\",\"Movie\",\"Music\",\"Television\",\"Theater\",\"Virtual Reality\",\"Esports\",\"Video Games\"\n2014,15.0%,18.2%,19.6%,16.5%,7.3%,10.9%,12.6%\n2021,12.0%,15.3%,15.7%,13.3%,10.9%,15.3%,17.5%"
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "jiX7Xf6gpOxTUynu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the 2021 data, can you identify a sector that has shown significant growth compared to 2014? And provide potential reasoning for this."
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to compare the percentage of each sector in 2021 with its corresponding percentage in 2014. A significant increase will indicate growth.\nBased on the above thought, the answer is:\nThe \"Esports\" sector has shown significant growth from 10.9% in 2014 to 15.3% in 2021. The growth can be attributed to the rise of competitive gaming and streaming platforms, increased accessibility and internet usage worldwide."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "ivAqt84hjSKGZx3X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area would you say has lost the most ground in the Media and Entertainment Industry from 2014 to 2021 and why might this be?"
+            },
+            {
+                "from": "gpt",
+                "value": "I am comparing the percentages of each sector in 2021 to the percentages in 2014. The sector with the greatest decrease could be viewed as losing the most ground.\nBased on the above thought, the answer is:\nThe \"Television\" sector has lost the most ground, dropping from 19.6% in 2014 to 15.7% in 2021. This can be attributed to an increasing preference towards digital and on-demand streaming services, making traditional Television less popular."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "DyuNmu0XfQSbpTgJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the 2021 data, how might the growth trends influence investment decisions in the Media and Entertainment Industry for 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the growth rates of each segment from 2021 to predict which areas might attract more investment in 2022.\nBased on the above thought, the answer is:\nLooking at the growth trends, areas such as \"Esports\" and \"Video Games\" which have shown significant growth might attract more investments in 2022. Conversely, areas like \"Television\" that have shown a decline might see less investment. Emerging technologies like \"Virtual Reality\" that exhibit growth potential might also attract investments."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "ez4fmZc9L0rDii6w",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2014, which two sectors together contribute to approximately 40% of the Media and Entertainment Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should add the percentages of two sectors at a time to see if their sum is approximately 40%.\nBased on the above thought, the answer is:\nTheater and Video Games."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "tCaHZCYITdVYelEF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Video Game industry grow from 2014 to 2021 in the Media and Entertainment Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the 2014 percentage from the 2021 percentage to get the growth.\nBased on the above thought, the answer is:\nThe growth of the Video Game industry is approximately 4.9%."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "HNXSyN23FU0V5Toc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity of Virtual Reality continues to rise and increases by 3% next year, what would be its new proportion in 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would add the 3% predicted increase to the 2021 percentage to find the new proportion.\nBased on the above thought, the answer is:\nIf the popularity of Virtual Reality increases by 3%, its new proportion will be approximately 13.9% in 2022."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "4Ibwp683b9v05fdX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract the data from the pie chart and transform it into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart with the title {\"Tourism and Hospitality Industry Analysis in 2020\"}[0.021, 0.201, 0.939, 0.234] and {the pie slices}[{\"Food Service\"}[0.257, 0.377, 0.703, 0.743]; {\"Accommodation\"}[0.318, 0.277, 0.712, 0.515]], we can see that each slice represents the market share of a specific sector in the industry for the year 2020.\nBased on the above thought, the answer is:\nYear,Food Service,Accommodation\n2020,60%,40%"
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "NtuGt8baVm3chQm7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy could the sector shift in market have taken place in 2020 as shown in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to infer based on the dramatic fall in the market share of the Food Service sector and the increase in the Accommodation sector.\nBased on the above thought, the answer is:\nThe shift in market share could be due to the impact of the Covid-19 pandemic. Many restaurants and food service businesses were likely shut down or experienced fewer visits, while the Accommodation sector might have seen a relative increase in activity due to people choosing to vacation locally rather than traveling abroad."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "11k6SfJVhKyg6yl4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be expected in the market share trends for the Food Service and Accommodation sectors for the next year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to make inferences based on the current market share and the impact of external factors such as the ongoing or ending pandemic.\nBased on the above thought, the answer is:\nIf the pandemics situation improves, we could expect the Food Service sector to recover and reclaim some of its lost market share. On the contrary, if the pandemic persists, the Accommodation sector might further increase its market share, considering the larger trend from last year."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "kScIEpxGHiUMycRr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this trend information affect investment decisions in the tourism and hospitality industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to infer how these trends could influence investors' perspectives.\nBased on the above thought, the answer is:\nInvestors might be more cautious about investing in the Food Service sector given its decline in market share in 2020. They might view the Accommodation sector as a safer investment given its growth in market share. However, prudence is necessary, understanding that these shifts are likely due to exceptional circumstances (the pandemic), and may reverse as the situation normalizes."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "G4QYuGScfAEd7QfF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector has a larger market share in the tourism and hospitality industry in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can figure out the larger market share from the pie chart by comparing the percentage of each sector.\nBased on the above thought, the answer is:\nThe Food Service sector"
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "7VRRZJviJVAEl6Iv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share of the Food Service sector decreases by 10% next year, what would be its new market share in the industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new market share for the Food Service sector can be calculated by subtracting the decreased percentage from the current percentage.\nBased on the above thought, the answer is:\n50%"
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "M3AwEpDTC4axddWS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market share of the Accommodation sector increases by 15% next year, what would be its new market share in the industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new market share for the Accommodation sector can be calculated by adding the increased percentage to the current 2020 percentage.\nBased on the above thought, the answer is:\n55%"
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "UBgXcjSVuXizyQxP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your observation of the pie chart, could you please construct a CSV table representing the data about New Media and Digital Content Creation for 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart {\"New Media & Digital Content Creation in 2017\"}[0.092, 0.124, 0.949, 0.157] indicates that the chart represents data about New Media and Digital Content Creation for 2017. I need to extract the corresponding values of each sector slice of the pie chart {'pie_0'}[0.247, 0.207, 0.794, 0.78] and translate them into a structured table. Let me start with the Content slice, which is represented by{'#ff9999'}[0.248, 0.33, 0.52, 0.506], Video slice represented by{'#66b3ff'}[0.247, 0.476, 0.52, 0.681], Audio slice represented by{'#99ff99'}[0.31, 0.506, 0.555, 0.78], Images slice represented by{'#ffcc99'}[0.52, 0.506, 0.792, 0.778], Text slice represented by{'#c2c2f0'}[0.52, 0.392, 0.794, 0.535], Social slice represented by {'#ffb3e6'}[0.431, 0.207, 0.779, 0.481], and Interactive slice represented by {'#c2f0c2'}[0.311, 0.251, 0.52, 0.506]. The table will be like this.\nBased on the above thought, the answer is:\nMedia_Type,Proportion\nContent,9.3%\nVideo,12.8%\nAudio,16.0%\nImages,21.3%\nText,8.5%\nSocial,24.0%\nInteractive,8.0%"
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "19D39X4UDDZchuaQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, which form of digital content creation was most popular in 2017 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll determine this by identifying the sector with the largest proportion in the pie chart.\nBased on the above thought, the answer is:\nThe most popular form was Social Media Content Creation due to its highest proportion of 24.0%."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "2oM9HRLk5OsFPNUE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what can you infer about the evolution of content creation in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond, I need to analyze the relative weights of each content creation type in the pie chart.\nBased on the above thought, the answer is:\nIn 2017, Social Media and Image content creation were favored heavily, indicating a shift towards more visually appealing and instantly shareable types of content. Interactive and Text content creation, however, held less significance, showing a possible trend away from text-based and interactive digital content."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "Ov3xTUJsRWkFTIFk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data represented in the pie chart affect budget allocation for different forms of content creation in the following year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to think about how the popularity of different forms of content creation might reflect on where investment ought to be placed in the upcoming year.\nBased on the above thought, the answer is:\nBased on the 2017 data, organizations might choose to allocate more funding to Social Media and Image content production in 2018, given their larger proportions. Conversely, less funding might be directed towards Interactive and Text content if they continue to occupy smaller proportions."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "2OGkCJZYPDuC6lXZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you determine the numerical proportion of Content and Video combined, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can do that by adding the proportions of the Content and Video slices.\nBased on the above thought, the answer is:\n22.1%"
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "xPu2PNuAIHA9Cs3c",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which Media Type has the smallest proportion in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to carefully examine the proportions of each Media Type assigned to each slice in the pie chart and find out the smallest one.\nBased on the above thought, the answer is:\nInteractive Media."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "DFTPfHKqyjnn1Jw9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of Text had increased by 2% in 2017, what would be the total proportion have been for all media types?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I would add 2% to the Text proportion and then calculate the sum of all the proportions again.\nBased on the above thought, the answer is:\nIt would have been 102%."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "k2T8TnNFcD5LXgvq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart that is presented, please convert the social demographic research data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of {the chart}[0.172, 0.175, 0.828, 0.831] titled {\"Social Demographic Research\"}[0.223, 0.134, 0.777, 0.168] and {the pie slices}[{\"Teen\"}[0.213, 0.326, 0.475, 0.518]; {\"Age 20-29\"}[0.239, 0.503, 0.5, 0.739]; {\"Age 30-39\"}[0.387, 0.503, 0.531, 0.765]; {\"Age 40-49\"}[0.5, 0.503, 0.61, 0.763]; {\"Age 50-59\"}[0.5, 0.503, 0.639, 0.741]; {\"Age 60+\"}[0.5, 0.503, 0.701, 0.725]; {\"Female\"}[0.5, 0.331, 0.762, 0.672]; {\"Male\"}[0.299, 0.241, 0.698, 0.503]], I can see that the sectors represent various demographic classes and their corresponding utilization percentages.\nBased on the above thought, the answer is:\nDemographic,Usage\nTeen,25\nAge 20-29,33\nAge 30-39,18\nAge 40-49,10\nAge 50-59,4\nAge 60+,10\nFemale,45\nMale,55"
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "S9jJ92NOj9sUZJ6Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which demographic group uses social media the most? Furthermore, explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The demographic group with the highest usage proportion can be considered as the highest user of social media.\nBased on the above thought, the answer is:\nThe \"Age 20-29\" demographic uses social media the most, with a usage proportion of 33%, which is the highest among all the demographic groups."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "ompTNxLY5IfW2b6H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you suggest about the trend of social media usage among different age groups from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the percentages of each demographic on the pie chart, I can consider the social media usage trend in different age groups.\nBased on the above thought, the answer is:\nThe pie chart shows a clear trend of decreasing social media usage with increasing age, with the exception of a slight bump in usage in the \"Age 60+\" demographic. This suggests that younger people tend to use social media more frequently than older individuals."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "Z6Z66xMRBA8uVILN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can advertisers use this data for a specific ad campaign targeting the most frequent users of social media?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the information in the pie chart, we can make inferences on how an ad campaign can take advantage of the data presented.\nBased on the above thought, the answer is:\nAccording to this data, advertisers could target the \"Age 20-29\" demographic, since they are the most frequent users of social media. Advertisers could benefit from tailoring their campaigns towards this age group's interests and behaviors. They may also consider targeting the \"Teen\" demographic, given its significant usage, and could possibly develop segmented campaigns for the \"Female\" and \"Male\" users who are equally active on social media."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "LWGxv7t6SL7nDslt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the average use of social media across all age groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find each age group's usage, sum them up and divide by the total number of age groups to find the average.\nBased on the above thought, the answer is:\nThe average social media usage across all age groups is about 16.67%."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "EVI0ZbL7nwWZubSL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent does the usage among teens exceed the usage among individuals aged 60+ ?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this I need to subtract the usage among individuals aged 60+ from the usage among teens.\nBased on the above thought, the answer is:\nThe usage among teens exceeds the usage among individuals aged 60+ by 15%."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "Zn5QxNzJIFJRlUhr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the usage among the \"Age 20-29\" demographic reduced by 3%, what would be the new total percentage of usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new total percentage can be found by subtracting 3% from the \"Age 20-29\" demographic's usage and adding up all the usage percentages.\nBased on the above thought, the answer is:\nThe new total percentage of usage would be 97%."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "WzEjMuf2ZzT5o40t",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart observed, could you please express the distribution of research fields in the health and medical sector in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart at the middle of the graph with the title {\"Research Fields in Health and Medical Sector\"}<r>[0.169, 0.174, 0.794, 0.199]</r> shows various research fields and their respective percentages. Using the identified sectors from {the pie slices}<r>[0.213, 0.351, 0.457, 0.636], [0.275, 0.509, 0.481, 0.752],...</r> as CSV column names, and their respective ratios as data.\nBased on the above thought, the answer is:\nResearch Field, Percentage\nCardiovascular,20\nOncology,15\nImmunology,12\nNeurology,10\nDiabetes,9\nInfectious Diseases,8\nRespiratory,7\nGastroenterology,7\nNephrology,5\nOther,7"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "IJTBsT3sfotcf0Sl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nEvaluating the chart, which medical research field takes the largest proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "By considering the {pie slice percentages}<r>[0.213, 0.351, 0.457, 0.636], [0.275, 0.509, 0.481, 0.752],...</r> I can identify the research field with the largest ratio.\nBased on the above thought, the answer is:\nThe field of research with the largest proportion is 'Cardiovascular' with a 20%."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "UpFgtJA1ANvUoPVa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the distribution of the chart, which fields of medical research could be considered as being moderately researched?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences, I can refer to the proportions of each research field and identify those that fall within the moderate range of 7%-12%.\nBased on the above thought, the answer is:\nBased on the chart, Infectious Diseases, Diabetes, Respiratory, Gastroenterology, and Other are fields of research that are moderately researched, each commanding between 7-9% of total research in the medical and health sector."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "m6JtSe9uw1DI1qpY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart's data, what can we infer about research focus in the health and medical sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the size and percentile of each slice, I can make an assumption about which fields are focused on and the level of interest in various medical research subjects.\nBased on the above thought, the answer is:\nThere appears to be a significant research focus on Cardiovascular and Oncology, each claiming a significant percentage of the total research conducted. Moderate research is conducted on Infectious Diseases, Diabetes, Respiratory diseases, and Gastroenterology. Fields such as Nephrology and those classified as 'Other' are the least researched, suggesting these could be niche or emerging fields."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "J601OU2Jzzxq1I3N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, if the research funds allocated to Neurology and Infectious Diseases were combined, what would be the total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the percentages from {the pie slices of Neurology}<r>[0.481, 0.509, 0.717, 0.698]</r> and {Infectious Diseases}<r>[0.481, 0.333, 0.714, 0.509]</r>, I can add them to compute the total percentage.\nBased on the above thought, the answer is:\n18%"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "UUoL7Pa7AFB6kKW4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage difference is there between the most researched field and the least researched field in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the percentages from {the pie slice of Cardiovascular}<r>[0.213, 0.351, 0.457, 0.636]</r> and {Nephrology}<r>[0.366, 0.22, 0.468, 0.462]</r>, I can calculate the difference between the most and least researched field.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "SUlMcHgI4VJaMGGE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart analysis, if Infectious Diseases and Other fields were equally distributed, what would be the new average percentage for the two fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To derive this, I would sum the percentages of {Infectious Diseases}<r>[0.481, 0.333, 0.714, 0.509]</r> and {Other}<r>[0.295, 0.288, 0.481, 0.509]</r>, and then divide the total by 2.\nBased on the above thought, the answer is:\n7.5%"
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "yqDAOjJW9NhPnIyT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the breakdown of the number of AI application cases in different sectors according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze this pie chart by identifying each sector and corresponding number of cases. The title of the chart {\"Artificial Intelligence Application Case Analysis\"}[0.045, 0.18, 0.913, 0.214] provides context. Each pie slice corresponds to a distinct sector: {\"Healthcare\"}[0.24, 0.366, 0.479, 0.584]; {\"Finance\"}[0.248, 0.52, 0.479, 0.718]; {\"Retail\"}[0.345, 0.544, 0.629, 0.784]; {\"Manufacturing\"}[0.479, 0.52, 0.719, 0.708]; {\"Logistics\"}[0.479, 0.376, 0.719, 0.525]; {\"Agriculture\"}[0.447, 0.281, 0.67, 0.52] and {\"Energy\"}[0.296, 0.283, 0.479, 0.52] with their correspondingly exploded values and text labels.\nBased on the above thought, the answer is:\nAI Application,Healthcare,Finance,Retail,Manufacturing,Logistics,Agriculture,Energy\nCases,55,40,72,50,38,60,42"
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "lc23p9bVGTnt2GgU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can you interpret about the largest field of AI applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest slice{Retail}[0.345, 0.544, 0.629, 0.784] of the pie chart represents the industry with the most AI applications.\nBased on the above thought, the answer is:\nRetail industry is the largest field of AI applications, suggesting that AI is heavily used in this industry for tasks such as customer data analysis, sales predictions and supply chain optimizations."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "fZv2c6uFHsoQZ0Is",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what could be inferred about the future development of AI applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart {chart}[0.18, 0.221, 0.779, 0.82] can indicate the current distribution of AI applications. Assuming these trends continue, one can infer the sectors that might see further development.\nBased on the above thought, the answer is:\nThe Retail and Healthcare industries, having the highest current development, may continue to lead in AI applications. On the contrary, sectors with fewer applications like Manufacturing and Energy might see more growth in AI applications as they catch up."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "N2rciRC7wpgFQ5mI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this chart guide investment decisions in AI technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart can provide investors with broad trends in AI application rates across different industries. High rates might indicate mature markets with less growth potential, while low rates could suggest growing markets.\nBased on the above thought, the answer is:\nInvestors may view Healthcare and Retail as safer investments, given their mature AI application. On the other hand, Manufacturing and Energy present possible growth markets for new AI technologies, given their currently lower AI application rates."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "odEjM7H8eEBbQFPe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which sector has seen the most AI applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to find the sector with the highest percentage in the pie chart. By looking at the autotext of each sector, I can find the highest percentage.\nBased on the above thought, the answer is:\nRetail."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "bghvbSmzK0sqcxRw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more AI cases are there in healthcare as compared to energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require subtracting the number of AI cases in energy from those in healthcare, both of which can be read off from their respective slices.\nBased on the above thought, the answer is:\n13 cases."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "N7eXmbbkaOiZqcSR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of AI applications across all sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add up all the case numbers listed for each slice.\nBased on the above thought, the answer is:\nThe total number of AI applications is 357."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "5ht5lZT01TRrvTYg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on my observations, each slice of the pie chart represents a different year from 2012 to 2020, and the size of each slice denotes the market value for that year. Using those sizes, percentages of the total market value can be calculated and attributed to each year.\nBased on the above thought, the answer is:\nYear,Market Value\n2012,150\n2013,180\n2014,240\n2015,200\n2016,260\n2017,300\n2018,390\n2019,430\n2020,500"
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "ykUtiDoi0Ww4tPSz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what trend can be noticed about the market value of buildings and real estate from 2012-2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trend, I need to evaluate the change in the size of the pie slices from 2012 to 2020. From the pie chart, I can observe that the overall trend is a gradual increase.\nBased on the above thought, the answer is:\nThe trend shown in the chart indicates a growing market value for buildings and real estate from 2012-2020, indicating a strengthening of the real estate market over time."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "Zfoi6zvPV9YQzrJO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year had an unexpectedly low market value compared to its preceding and following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to check the pie chart for a year where the size of the slice is smaller than those of its neighbors.\nBased on the above thought, the answer is:\nThe year 2015 had a lower market value than the other years surrounding it, showing a decrease from 2014 and followed by an increase in 2016, making it an outlier in the overall increasing trend."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "tQiysFhYnCbFVGmn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information in the chart influence investment decisions in the building and real estate market for the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To extrapolate this, I need to consider how the increasing trend in market values would influence future investment decisions.\nBased on the above thought, the answer is:\nBased on the data from 2012-2020, investors might be encouraged by the overall increase in market value during this period. Therefore, they might be inclined to invest more in the building and real estate market, expecting the trend to continue. They would, however, need to be cautious about potential outlier years like 2015 where there was a drop in market value."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "xeeWSoJz0lY3rIIV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year the market value was highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should check each year's value in the pie chart and find the one with the highest value.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "CX3u82PbKv8xIUgp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market value for all the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average market value, I need to add up all the market values for each year and divide by the number of years from 2012 to 2020.\nBased on the above thought, the answer is:\nThe sum of the market values from 2012 to 2020 is 2550. The average market value is 2550 / 9 = 283.33."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "UM1Lae3q0Fwq76Tt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market value for the year 2013 were to increase by $50, what would be the new total market value for all the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total market value, I would add $50 to the 2013 market value and then sums the updated values for all the years.\nBased on the above thought, the answer is:\nThe updated value for 2013 is 180 + 50 = 230. The new total market value is 150 + 230 + 240 + 200 + 260 + 300 + 390 + 430 + 500 = 2600."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "GqLeoZV3Lw5ZVTXY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you help me turn the data in the pie chart into a csv format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I see that the slices correspond to Air Quality and Sea Level for each individual year from 2010 to 2014. Here the slice coordinates represent the distribution of these impact areas for each year, for instance, {'color': '#ff9999', 'coordinates': '[0.191, 0.321, 0.5, 0.52]', 'autotext': {'s': '9.8%', 'coordinates': '[0.184, 0.391, 0.184, 0.391]'} indicates the year with the least environmental impact while the slice with the explosion {'color': '#ffcc99', 'coordinates': '[0.452, 0.55, 0.701, 0.861]', 'theta_range': '260 -> 309', 'autotext': {'s': '13.6%', 'coordinates': '[0.591, 0.881, 0.591, 0.881]'} indicates the year, 2013, with the highest impact on Air Quality.\nBased on the above thought, the answer is:\nYear, Air Quality, Sea Level\n2010, 65, 35\n2011, 73, 47\n2012, 82, 53\n2013, 90, 57\n2014, 96, 64"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "pw8rxXXxdcpelAcQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what can we infer about future trends in climate change impact?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the trends in the data, especially the consistent increase in the numbers from 2010 to 2014, we can predict that the impact of climate change might continue to rise in the future.\nBased on the above thought, the answer is:\nFrom the trend in the data, it can be inferred that the impact of climate change, particularly on air quality and sea levels, may likely continue to increase in future years."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "pf2vtSNJjt0PVm5r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhy is there an explosion shown in the pie chart for the year 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "In a pie chart, an explosion is usually used to highlight or emphasize a particular pie slice. In this case, it is highlighting the year 2013 with the highest impact on Air Quality.\nBased on the above thought, the answer is:\nThe explosion in the pie chart for the year 2013 marks a significant increase in air pollution, which is a major consequential impact of climate change."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "J7X58IWJIGAWuTdn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which aspect of the environment has been affected more by climate change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the total impact on Air Quality and Sea Level over the five years displayed in the pie chart.\nBased on the above thought, the answer is:\nAir Quality has been more significantly affected by climate change than Sea Level over the years 2010 to 2014."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "GJMbhjJ2uqwYDwRd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOf all the years displayed, in which year was the overall impact of climate change the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this answer, I need to add up the impact numbers for both Air Quality and Sea Level for each year, and then identify the year with the highest total climate change impact.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "IgoiPVw1CRjePqlJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor how many years was the impact on Air Quality higher than 80?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the slice coordinates and corresponding percentages of the pie chart, I could count the number of years where the impact on Air Quality exceeded 80.\nBased on the above thought, the answer is:\n3 years"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "tW6Uh17InocHmrF0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the Sea Level impact increase from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the Sea Level impact for 2010 from the Sea Level impact value for 2014.\nBased on the above thought, the answer is:\n29"
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "Du6jT0ovrkkjnPPa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "By identifying the attributes and their distributions in the given assessment years 2010 and 2020 {the pie chart slices}<r>[0.205, 0.197, 0.822, 0.844]</r>, I can extract the data into a CSV format.\nBased on the above thought, the answer is:\nAttribute,2010,2020\nWater Quality,55,70\nAir Quality,68,75\nForest Coverage,74,90\nWeather Patterns,85,122"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "bWy6XXyuQ1r8LqvF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which attribute made the most improvement over the decade? Please explain."
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the chart slices for each attribute in the years of 2010 and 2020, and the attribute that witnessed the largest increase in terms of chart slice size, would be the one that significantly improved over the decade.\nBased on the above thought, the answer is:\nWeather Patterns had the most improvement over the decade, as its chart slice size increased more than the others from 85 in 2010 to 122 in 2020."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "Xh9WhYq8nnZ186Dz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, how did the different attributes contribute to the ecological sustainability from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the chart slices for each attribute, comparing their sizes in 2010 and 2020, to evaluate their contribution to the sustainable environment.\nBased on the above thought, the answer is:\nAll attributes contributed positively to ecological sustainability, with noticeable improvements over the decade. All slices increased in size from 2010 to 2020. Weather Patterns exhibited the most growth, indicating significant advancements in climate preservation. Forest Coverage also saw substantial progress, depicting enhanced forest conservation efforts. Although Water and Air Quality improvements were smaller in comparison, they still contributed positively towards overall ecological sustainability."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "66cLEgQnWNr9b6up",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the focus of future ecological sustainability efforts based on the data from 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the sizes of each attribute's chart slice for the year 2020, I can infer where future emphasis might be placed to continue progress in ecological sustainability.\nBased on the above thought, the answer is:\nThe large slice for Weather Patterns in 2020 suggests that great effort is being invested in monitoring and influencing weather patterns, and this is expected to continue. However, although improvements have been made in Water and Air quality, their smaller slices may imply a need for more progress, possibly indicating these areas as future focus points."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "vxcVAN2gCTlYDqha",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage growth of the attribute Forest Coverage from 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage growth, I need to subtract the 2010 value from the 2020 value and then divide it by the 2010 value. The result should be multiplied by 100% to get the percentage growth.\nBased on the above thought, the answer is:\n21.62%"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "Jvu68QCq586YtkhM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich attribute has the least growth percentage from the year 2010 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to calculate the growth percentage of each attribute from 2010 to 2020, and then identify the attribute with the smallest growth percentage.\nBased on the above thought, the answer is:\nAir Quality"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "2jzODNsO32LCtmSK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total of all the attributes for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total, I need to add up the values of all the attributes for that year.\nBased on the above thought, the answer is:\n357"
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "xQEC5V0Sa1ePKjJS",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart provided, could you please transform the data into a structured table as CSV data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the exploded {Bitcoin slice}[0.213, 0.371, 0.757, 0.819] and other {slices of Ethereum, Ripple, Litecoin, Cardano, Stellar and Polkadot}[0.493, 0.312, 0.668, 0.52], in conjunction with the percentage labels {\"62.5%\"}[0.395, 0.832, 0.395, 0.832]; {\"12.5%\"}[0.759, 0.382, 0.759, 0.382]; {\"6.2%\"}[0.637, 0.258, 0.637, 0.258]; {\"5.6%\"}[0.532, 0.223, 0.532, 0.223]; {\"4.7%\"}[0.435, 0.226, 0.435, 0.226]; {\"4.4%\"}[0.355, 0.254, 0.355, 0.254]; {\"4.1%\"}[0.29, 0.3, 0.29, 0.3] within the shell, we can construct a table with the cryptocurrency types as columns and their corresponding percentages as values for year 2020.\nBased on the above thought, the answer is:\nYear,Bitcoin,Ethereum,Ripple,Litecoin,Cardano,Stellar,Polkadot\n2020,62.5%,12.5%,6.2%,5.6%,4.7%,4.4%,4.1%"
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "DacYEAAMkpZQNbSH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetermining from the chart, which cryptocurrency appears to dominate the market in 2020? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The first step is identifying the largest sector, which represents the cryptocurrency with the highest market share.\nBased on the above thought, the answer is:\nBased on the pie chart, Bitcoin dominates the cryptocurrency market in 2020 due to it having the largest sector with a 62.5% share."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "RH0cMNJjVKQXHb6X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend of the cryptocurrency market in 2020 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Drawing inferences from the chart necessitates a thorough look at the market shares of each cryptocurrency\nBased on the above thought, the answer is:\nBitcoin appears to dominate the cryptocurrency market, holding over half of the total market share. Nonetheless, multiple other cryptocurrencies are steadily growing and competing, as shown by their respective market shares, indicating the market is diversifying over time."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "66zfcmaaVcd6Slnm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could this chart affect investment decisions in different cryptocurrencies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The impact on investment decisions can be inferred from the size of each sector, representing the market share, which can hint at the stability and acceptance of the respective cryptocurrency.\nBased on the above thought, the answer is:\nAn investor might be more inclined to invest in Bitcoin due to its market dominance, indicating a potential higher stability and acceptance. However, Ethereum, Ripple, and others hold considerable market shares, suggesting they might pose a good investment opportunity for diversification. The chart might encourage a more balanced investment spread across several cryptocurrencies."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "2XMWL6IMsMEr57lI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many positions higher is Bitcoin compared to Cardano in terms of market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the position or relation of {Bitcoin}[0.213, 0.371, 0.757, 0.819] and {Cardano}[0.402, 0.248, 0.493, 0.52] in the pie chart and taking into account the order of the sectors, I could obtain the answer.\nBased on the above thought, the answer is:\nBitcoin is 4 positions higher than Cardano."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "JbSQed3ufywjSaFj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the market shares of Ethereum and Ripple?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the market share of {Ripple}[0.493, 0.26, 0.668, 0.52] from the market share of {Ethereum}[0.493, 0.312, 0.765, 0.52].\nBased on the above thought, the answer is:\nThe difference is 6.3%."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "8l4X7Vz9beyNsICT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Bitcoin loses 10% in market share, and it is equally distributed between Ethereum and Ripple, what will be Ripple's new market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this question, I need to add 5% to the original market share of {Ripple}[0.493, 0.26, 0.668, 0.52].\nBased on the above thought, the answer is:\nRipple's new market share will be 11.2%."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "InAyCfOtOjKuutfE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you reformat the data in this pie chart into a CSV table based upon the number of graduates in two disciplines over the course of six years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The table should contain the years from 2016 to 2021, and the count of Physics and Chemistry graduates each year. This data can be derived from each sector of the pie chart {\"Physics\"}<r>[0.877, 0.058, 0.934, 0.073]</r> and {\"Chemistry\"}<r>[0.877, 0.081, 0.956, 0.096]</r>.\nBased on the above thought, the answer is:\nYear,Physics,Chemistry\n2016,67,80\n2017,71,83\n2018,76,87\n2019,82,89\n2020,85,93\n2021,92,98"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "EAphCDlyoCKUb4WA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field saw the most significant growth in the number of graduates from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "For answering this, I need to compare the growth in the number of graduates in Physics and Chemistry from 2016 to 2021.\nBased on the above thought, the answer is:\nPhysics had the most significant growth, with an increase of 25 graduates from 2016 to 2021 compared to an increase of 18 in Chemistry."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "QFgPDoKke2YXKVir",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you draw from the distribution of graduates in these fields over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to analyze the trends in the number of graduates in Physics and Chemistry over the years.\nBased on the above thought, the answer is:\nOver the six-year period, both Physics and Chemistry experienced a steady growth in the number of graduates. However, Physics shows a more significant upward trend, pointing to an increased interest in the field. Chemistry, on the other hand, illustrates a steady and consistent growth with less fluctuation over the years."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "b7pNtcHOfNyyASm9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the popularity of these two research fields based on the number of graduates?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I have to look at the numbers of graduates in Physics and Chemistry throughout the years.\nBased on the above thought, the answer is:\nBoth fields have seen a positive trend in the number of graduates, implying that there is a growing interest in both Physics and Chemistry. However, Physics seems to attract more students as it has a higher pace of growth in the number of graduates compared to Chemistry."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "Vnp2xPjj4q7zWVfl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the increase in the number of Physics graduates from 2016 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, subtract the number of graduates in Physics in 2016 from the number in 2021. This data is presented in the pie slices corresponding to Physics in 2016 and 2021 {\"Physics 2016\"}<r>[0.096, 0.312, 0.135, 0.327]</r> and {\"Physics 2021\"}<r>[0.663, 0.886, 0.702, 0.901]</r>.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "uccj0mJoQUR8oB2O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the rise in the number of Chemistry graduates from the beginning to the end of the period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I should subtract the count of Chemistry graduates in 2016 from the count in 2021, based on the coordinates of {\"Chemistry 2016\"}<r>[0.835, 0.748, 0.874, 0.764]</r> and {\"Chemistry 2021\"}<r>[0.219, 0.125, 0.258, 0.141]</r>.\nBased on the above thought, the answer is:\n18"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "kxJldqtRuhDcUHcG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the sum for the total number of graduates in both fields in the year 2018."
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add up the number of graduates in Physics and Chemistry in 2018. These numbers are displayed in the pie slices corresponding to {\"Physics 2018\"}<r>[0.081, 0.668, 0.12, 0.684]</r> and {\"Chemistry 2018\"}<r>[0.878, 0.338, 0.917, 0.354]</r>.\nBased on the above thought, the answer is:\n163"
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "qmPWpyQhC8lXDwwW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are observing, can you please format the regional population data into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the slice data provided for {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52], I can convert these into a structured table representing the population for each region.\nBased on the above thought, the answer is:\nRegion,Population\nAsia,4453055000\nRest of the world,2408473000"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "oNS4wrB9RpgKLoaL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which region is more populous based on the chart and explain your reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size and labels of each slice, specifically comparing {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52], I can determine which region is more populous.\nBased on the above thought, the answer is:\nBased on the larger slice of the pie chart, Asia is more populous than the rest of the world, holding a population percentage of 64.9%."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "jGbRfLuF97VusA0H",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the global population distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the data and visual representation of both {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52].\nBased on the above thought, the answer is:\nThe chart demonstrates that a significant majority of the global population resides in Asia, with the region constituting 64.9% of the total population. The remaining 35.1% of the global population is dispersed across all other regions, highlighting Asia's predominance in terms of population size."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "Pq1aj3Ockrw20mxq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would the data in this chart potentially impact global resource allocation policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand this, I have to consider the population proportions of {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52], and their implications on resource distribution.\nBased on the above thought, the answer is:\nSeeing that Asia constitutes the majority of the global population, it might be implied that this region could require a larger share of global resources to cater to its significant population. On the contrary, the rest of the world, while massive in terms of geographical size, holds a smaller population, thus possibly necessitating fewer resources as compared to Asia. These interpretations, however, presume that resource needs are directly proportional to population, excluding factors such as efficiency of use, resources availability and consumption levels."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "8LWG6QAEWDbhIP2m",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio of Asia's population to the total global population in percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this ratio, I need to take the percentage value from the {Asia}[0.128, 0.322, 0.855, 0.919] slice in the pie chart.\nBased on the above thought, the answer is:\n64.9%"
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "eE0ZW4MtgRflOQv3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does Asia's population percentage surpass the rest of the world's?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to calculate the difference between the percentage values of {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52].\nBased on the above thought, the answer is:\nThe difference is 64.9% - 35.1% = 29.8%."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "7UGFr757LAE2UwOQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the rest of the world's population increased by 10%, what would be its new population?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 10% to the population of the {Rest of the world}[0.221, 0.157, 0.854, 0.52].\nBased on the above thought, the answer is:\nThe new population for the \"Rest of the world\" would be 2,408,473,000 + 10% = 2,649,320,300."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "yhQIpplJKlDSG5fs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transform the data you observed from the pie chart into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the encoded content, the pie chart is separated into slices, and each slice is labeled as a different trend {\"Brexit\"}[0.32, 0.428, 0.484, 0.697], {\"Occupy Movement\"} [0.471, 0.533, 0.623, 0.697], {\"Arab Spring\"} [0.484, 0.389, 0.648, 0.621], {\"Yellow Vest Movement\"} [0.476, 0.369, 0.562, 0.533], {\"Hong Kong Protests\"} [0.351, 0.355, 0.477, 0.519]. The label inside each slice reveals the percentage of the votes for each movement.\nBased on the above thought, the answer is:\n\"Political Trends\",\"Referendum Votes (%)\"\n\"Brexit\",34.8\n\"Occupy Movement\",17.4\n\"Arab Spring\",26.1\n\"Yellow Vest Movement\",8.7\n\"Hong Kong Protests\",13.0"
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "7UIQ8GiJ2SJIOBLe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which political trend received the highest percentage of votes, and why is this significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the percentages assigned to each trend to identify the one that received the highest voting percentage.\nBased on the above thought, the answer is:\n\"Brexit\" received the highest voting percentage, with a value of 34.8%. This indicates that out of all the political trends presented, \"Brexit\" was the most supported one, reflecting a strong public sentiment towards the trend at the time."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "SpCNSe1A961PPZgW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich trend has the smallest slice in the pie chart and what does it tell you about that political trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart's slices, I can see which political trend has the smallest representation.\nBased on the above thought, the answer is:\nThe \"Yellow Vest Movement\" has the smallest slice in the pie chart, with a percentage of 8.7%. This suggests that this political trend had the least support or engagement among the public compared to the other trends."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "QyoZ1Az9VBREBIM3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the percentages presented in the pie chart influence political decisions or policies in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to imagine how the preferences implied by the percentages in the pie chart might impact future political decision-making by the corresponding parties or governments.\nBased on the above thought, the answer is:\nThe pie chart highlights the relative public support for different political trends. Therefore, trends with higher percentages like \"Brexit\" and \"Arab Spring\" might receive more attention from policymakers, potentially manifesting in more related policies or regulations. Conversely, the Yellow Vest Movement, with its relatively lower public support, might see less policy focus unless the trend gains more public attention in the future."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "yLZ7g3FeTsGxM5Vf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the \"Brexit\" and \"Occupy Movement\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of the \"Occupy Movement\" from the \"Brexit\".\nBased on the above thought, the answer is:\nThe percentage difference is 34.8% - 17.4% = 17.4%."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "VxGEn560KpHprWhK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the \"Arab Spring\" and \"Yellow Vest Movement\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate the total percentage by adding the percentage of the \"Arab Spring\" and \"Yellow Vest Movement\".\nBased on the above thought, the answer is:\nThe total percentage is 26.1% + 8.7% = 34.8%."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "k9LFeSYGc0aRLRSh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of all political trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I should add the percentages of all political trends and divide by the total number of political trends.\nBased on the above thought, the answer is:\nThe average percentage is (34.8% + 17.4% + 26.1% + 8.7% + 13.0%) / 5 = 20%."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "WbQxQShfgLZ31KaP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you extract data from the pie chart about resource consumption from 2010 to 2013 and put it into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the title {\"Resource Consumption from 2010 to 2013\"}[0.264, 0.091, 0.736, 0.111] and individual data values indicated in the pie slices for each of the years and resources {Coal and Oil pie slices}[0.169, 0.191, 0.802, 0.847], I understand that the pie chart shows the percentage usage of the resources for each year.\nBased on the above thought, the answer is:\nYear,Coal,Oil\n2010,80,70\n2011,74,75\n2012,70,78\n2013,60,82"
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "0nWQL2zUGACRKmjZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which resource was more consumed in 2012 and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I need to compare the consumption numbers of Coal and Oil in 2012.\nBased on the above thought, the answer is:\nCoal was more consumed in 2012. Although the consumption of Coal was decreasing, it was still higher than Oil, with 70 units consumed compared to 78 units of Oil."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "M321fsvHKiGxIiTv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer a trend of resource consumption from 2010 to 2013 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, we can infer a trend by observing the quantities consumed over the years for each resource.\nBased on the above thought, the answer is:\nYes, we can see a decreasing trend in the consumption of Coal and an increasing trend in the consumption of Oil over the four years from 2010 to 2013."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "1V6ShS6M14NqCJ5e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith regard to resource planning for future years, what can be implied from the data from 2010 to 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer resource planning strategies from the data, I need to consider trends in the consumption of each resource over the given years.\nBased on the above thought, the answer is:\nConsidering the declining trend in the consumption of Coal and the ascending trend in the consumption of Oil, it would be strategic to plan for a reduction in coal resources and an increase in oil resources in the future."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "zcweSQCaklstX2F3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the difference in the consumption of Coal from 2010 to 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the difference, I need to look at the consumption figures for Coal in both years and subtract the latter from the former.\nBased on the above thought, the answer is:\nThe difference was 10 units."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "3g9hBkP7B2U4PnhQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average consumption of Oil over the four years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I would need to sum up the consumption values for all four years and divide by the number of years.\nBased on the above thought, the answer is:\nThe average was 76.25 units."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "gOKXjf13EuT8OH91",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we add up the consumption of Coal and Oil in 2013, what is the total consumption?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the total consumption, I have to add the consumption values of both resources in 2013.\nBased on the above thought, the answer is:\nThe total consumption was 142 units."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "Ef31OAhLbp3s12Fa",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please help convert the data shown in the pie chart into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see that the title on top of the chart {Gaming Industry Revenue from 2014 to 2020}<r>[0.079, 0.092, 0.921, 0.125]</r> along with the different slices of pie chart represent the gaming revenue in billions over various years. Each slice of the pie chart corresponds to a different year from 2014 to 2020. By referencing both the title at the top of the chart and the labels on each slice of the pie, I can infer that each slice corresponds to a different year, and the size of each slice corresponds to the revenue earned in that year.\nBased on the above thought, the answer is:\nYear,Revenue in billions\n2014,80\n2015,92\n2016,100\n2017,130\n2018,150\n2019,170\n2020,200"
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "YiaTDtAtN7nJBRbe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat factors might explain the sudden increase in gaming revenue in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, it's clear that 2017 saw a dramatic increase in gaming revenue from 2016. Therefore, we can infer that there might have been specific events or trends that stimulated such growth.\nBased on the above thought, the answer is:\nFactors might include the introduction of on-demand games, innovate gaming platforms, increase in the number of gamers or favorable market conditions. However, specific reasoning cannot be derived from the given chart."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "iuncmkgtPSyrCeey",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we predict the gaming revenue for 2021 based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart provides historical data from 2014 to 2020. To predict revenue for 2021, we would need to extrapolate this data using a prediction model.\nBased on the above thought, the answer is:\nThe chart does not provide enough information to make precise predictions for 2021. For that, we would need more data and probably use prediction models."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "ARMTMPCij0mxYTcs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might have occurred in 2017 that led to its exceptional growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the observation that the 2017 slice has exploded away from the pie chart, we can infer that 2017 was an exceptional year for the gaming industry.\nBased on the above thought, the answer is:\nThe chart does not provide specific information on what happened in the gaming industry in 2017. However, we can infer that significant developments or introductions might have occurred during this period contributing to the significant increase in revenue."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "JAN0HC3p9bibx3Hp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the gaming revenue exceed the average revenue of all the studied years?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to compute the average gaming revenue of all years from 2014 to 2020. Then, I will compare the revenue of each year and identify in which year the revenue exceeded this average.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "murbSIEIOoFVUcdj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the revenue increase from the year 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtract the revenue of the year 2019 from 2020.\nBased on the above thought, the answer is:\n30 billion"
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "7D6NUihq2koOTZ8p",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much total revenue increase was recorded from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total revenue increase, I need to calculate the difference in revenues between these years.\nBased on the above thought, the answer is:\n120 billion"
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "Qf96wzC0gDJq1gJC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please represent the importance of various financial policies mentioned in the pie chart as table data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, I would rank them in proportion to the area they cover according to the pie chart. From {\"Importance of Global Financial Policies\"}[0.069, 0.256, 0.784, 0.29] and the legend at {the right side}[0.661, 0.325, 0.977, 0.716] listing the categories and their colours, I will map the corresponding percentile values.\nBased on the above thought, the answer is:\nPolicies, Importance\nInterest Rate, 15\nInflation, 25\nTaxation, 20\nTrade Regulation, 10\nFinancial Regulation, 5\nMonetary Policy, 10\nFiscal Policy, 10\nForex Policy, 3\nPublic Debt, 1\nPrivate Debt, 1"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "1wHSrV93vZxbIc6G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data shown in the pie chart, which financial policy is considered the most important and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at all the slices of the pie chart and the corresponding legends, {Inflation}[0.671, 0.406, 0.717, 0.422] has the largest area, indicating it has the highest percentage.\nBased on the above thought, the answer is:\nInflation is considered the most important financial policy as it holds the largest portion, or 25% of the importance in global policies."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "ip5rLYftwVCReSGS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf someone were to focus on only three financial policies, based on the chart, which three should they choose?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we need to find the financial policies that have the largest slices in pie chart. We can look at {Interest Rate}[0.671, 0.371, 0.717, 0.387], {Inflation}[0.671, 0.406, 0.717, 0.422], and {Taxation} [0.671, 0.441, 0.717, 0.457] as they seem to cover larger areas relative to other policies.\nBased on the above thought, the answer is:\nThey should focus on Inflation, Interest Rate, and Taxation, as these three policies hold the largest importance according to the pie chart."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "oSwoCykk4ohCUsTd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusion can be drawn regarding the importance of Debt related policies on a global scale, based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "We will locate {Public Debt}[0.671, 0.65, 0.717, 0.667] and {Private Debt}[0.671, 0.685, 0.717, 0.701] on the pie chart to help make inferences.\nBased on the above thought, the answer is:\nAccording to the pie chart, the importance of Debt related policies such as Public Debt and Private Debt is quite low on the global scale, each holding just 1% of importance. This indicates that these policies might not have a large impact on global financial systems compared to other policies."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "uZjoyB7zJsqduXUA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percent of importance for Trade Regulation and Financial Regulation according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, we need to add the percent values of both these categories. We look at the areas depicting {Trade Regulation}[0.671, 0.476, 0.717, 0.492] and {Financial Regulation}[0.671, 0.511, 0.717, 0.527] in the pie chart.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "csl0fG8eAULRLsAr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf each sector represents its percent weight in importance, what is the minimum percent weight needed to be in the top 50% of policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, we will start by summing the percent weight of the top policies in descending order until we reach 50%.\nBased on the above thought, the answer is:\n25%"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "4LeX2Ex7r6sz8NX5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percent weight does the least important policy hold in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This can be determined by looking at the smallest slice of the pie. The exploded sections on the chart {Forex Policy}[0.671, 0.615, 0.717, 0.632], {Public Debt}[0.671, 0.65, 0.717, 0.667] and {Private Debt}[0.671, 0.685, 0.717, 0.701] remarkably seem to share similar slice sizes.\nBased on the above thought, the answer is:\n1%"
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "iivqxFG5C1LhwE1L",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you organize the data into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the element labels on the chart for each pie slice, including {Ecological Contribution}<r>[0.097, 0.409, 0.258, 0.423]</r>, {Environmental Impact}<r>[0.092, 0.561, 0.246, 0.575]</r>, {Resource Utilization}<r>[0.153, 0.66, 0.291, 0.674]</r>, {Carbon Footprint}<r>[0.29, 0.774, 0.407, 0.788]</r>, and {Air Quality}<r>[0.609, 0.742, 0.682, 0.756]</r>, I can infer that these represent the different sustainability measures. The title of the chart {Ecological Environment Sustainability Assessment}<r>[0.22, 0.192, 0.78, 0.212]</r> provides context about what the data represents.\nBased on the above thought, the answer is:\nElement Type,Ecological Contribution,Environmental Impact,Resource Utilization,Carbon Footprint,Air Quality\nForest,C3.2,H2.7,R1.5,F4.5,A3.1\nOcean,O3.9,P2.9,U1.4,T4.6,Q3.5"
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "LDlkJHA19VVqpALl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the best strength of the Forest environment in terms of the sustainability measures used?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value with the largest slice of the pie chart is the strength of the Forest environment.\nBased on the above thought, the answer is:\nThe Forest environment\u2019s best strength according to the sustainability measures used is its Carbon Footprint."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "NEMaeLgwZxL3SAsw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAfter observing the pie chart, how does the Ocean environment perform in terms of the given sustainability measures?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Ocean environment performs best on Carbon Footprint and worst on Resource Utilization, judging by the size of the segments.{Carbon Footprint}<r>[0.29, 0.774, 0.407, 0.788]</r>, {Resource Utilization}<r>[0.153, 0.66, 0.291, 0.674]</r>.\nBased on the above thought, the answer is:\nAccording to the measures, the Ocean environment has its best performance on Carbon Footprint and its weakest performance is in Resource Utilization."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "eJUDYZLiRNHCwe5h",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of sustainability measures, what could be a potential area of improvement for the Forest environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sustainability measure with the smallest pie slice indicates a potential area of improvement. Here it's the {Resource Utilization}<r>[0.153, 0.66, 0.291, 0.674]</r> for Forest.\nBased on the above thought, the answer is:\nThe Forest environment could potentially improve in the area of Resource Utilization based on the sustainability measures used."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "H9h2t459reLrkwNs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sustainability measure has the highest average score between the Forest and Ocean environments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to calculate the average score for each sustainability measure by adding the scores for both the Forest and Ocean environments and dividing by two.\nBased on the above thought, the answer is:\nCarbon Footprint."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "Pqbux4gwoV0ffKx2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the Forest environment's Carbon Footprint compared to its Resource Utilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "Calculate the difference by subtracting the Resource Utilization score from the Carbon Footprint score.\nBased on the above thought, the answer is:\n3."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "rcfgasZ7TdrEeKD3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Forest environment could improve its Resource Utilization by 2 points, what would be the new average score for all its sustainability measures?"
+            },
+            {
+                "from": "gpt",
+                "value": "Cumulatively add the original scores, add the improvement value to Resource Utilization, then divide by the number of measures to get the new average score.\nBased on the above thought, the answer is:\n3.4."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "scTlb0I3xh7nNn1d",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the data and pie chart you observed, could you convert the information into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title of the chart {\"Investments in Stocks from 2015 to 2020\"}[0.128, 0.079, 0.897, 0.113] and the sector slices from the asset categories {the pie slices}[{\"2015\"}[0.209, 0.307, 0.512, 0.505]; {\"2016\"}[0.205, 0.455, 0.512, 0.665]; {\"2017\"}[0.249, 0.505, 0.512, 0.801]; {\"2018\"}[0.429, 0.505, 0.733, 0.813]; {\"2019\"}[0.512, 0.354, 0.82, 0.72]; {\"2020\"}[0.279, 0.166, 0.784, 0.474]], it's clear that they represent the different years and the proportions in this chart.\nBased on the above thought, the answer is:\nYear,Stocks,Bonds\n2015,25,75\n2016,33,67\n2017,35,65\n2018,50,50\n2019,60,40\n2020,90,10"
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "pY6ZS1FJzlCqezuZ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which asset category showed a consistent increase over the years? And provide explanation."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to compare the proportions of the two asset categories across the years.\nBased on the above thought, the answer is:\nThe asset category \"Stocks\" demonstrated a consistent increase over the years. This trend can be observed in the pie chart where the slices corresponding to stocks show a steady increase in its proportion, from 25% in 2015 to 90% in 2020."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "1Fq95oGtIxTaYQhR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the change in investment preferences from 2015 to 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the change in the proportions of each asset category over the years.\nBased on the above thought, the answer is:\nThe chart visually indicates a shift in investment preferences from \"Bonds\" to \"Stocks\". This is clear from the declining size of the slices representing bonds and the increasing size of the slices representing stocks over the years."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "L4QPws5EMN2haVcN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence investment strategies for future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the changing asset proportions might influence future investment decisions.\nBased on the above thought, the answer is:\nConsidering the increasing trend towards stocks in the past years as shown in the chart, investors might consider focusing more on stocks for future investments. However, they should be cautious as this trend can also suggest increased risk due to increased exposure to the equity market."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "R9nqPbEcmtt9mqe1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest percentage of stocks over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest percentage, I need to look at the percentages for each year and determine which one is the highest.\nBased on the above thought, the answer is:\n90% in 2020."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "5GGSvocLxkyKHGol",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much was the change in bond investments between the years 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the change, I subtract the bond investment in 2020 from that in 2015.\nBased on the above thought, the answer is:\nThe change is 75% - 10% = 65%."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "kJM1wg5HGppwBaw5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage of stocks and bonds in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total, I need to add the percentages of stocks and bonds in 2017.\nBased on the above thought, the answer is:\nThe total in 2017 was 35% (Stocks) + 65% (Bonds) = 100%."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "Z0oeB61V611tNXAD",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you format the data into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "As indicated by the chart's title {Innovation Levels for 2010 and 2014}[0.295, 0.024, 0.705, 0.058] and the pie slices of the chart including {Physical Innovation}[0.319, 0.331, 0.5, 0.53], {Digital Innovation}[0.314, 0.46, 0.5, 0.6], {Commercial Innovation}[0.319, 0.53, 0.5, 0.738], {Social Innovation}[0.352, 0.557, 0.49, 0.856], and {Policy innovation}[0.452, 0.53, 0.566, 0.84]; it can be deduced that the different slices represent the five categories of innovation with their respective ratings for two different years.\nBased on the above thought, the answer is:\nYear,Physical Innovation,Digital Innovation,Commercial Innovation,Social Innovation,Policy Innovation\n2010,60,58,65,73,80\n2014,85,90,93,98,100"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "DNyxDSsz1gbrc0fo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which area of innovation made the most progress from 2010 to 2014 attributes? Could you explain your reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, we should identify the innovation types with their corresponding ratings for 2010 and 2014. The area with the most progress can be identified by the highest net increment in ratings over these two years.\nBased on the above thought, the answer is:\nPolicy Innovation made the most progress because it had an increment of 20 points from 2010 to 2014, which is the highest increment among all types of innovation."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "alhY6prR2ICucH1e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what can we understand about the focus on different areas of innovation in 2010 and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "By evaluating the proportions of each innovation category, I can infer about the focus on different areas of innovation in both years.\nBased on the above thought, the answer is:\nThe chart indicates that in both 2010 and 2014, Policy Innovation received the maximum focus, and Physical Innovation received the least. However, every type of innovation had an increased focus from 2010 to 2014, with Digital and Policy innovations having the most significant increase."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "trJA6ljnne1oRgOD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart guide innovation initiatives and funding for the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I'll need to consider the areas where there was significant progress or less progress, which can provide an insight into areas that need more focus or investment.\nBased on the above thought, the answer is:\nBased on the chart, funding might be directed towards Physical Innovation initiatives, as it had the least progress from 2010 to 2014. This suggests more efforts are needed in this area. On the other hand, Policy Innovation, having the most significant progress, may continue to receive significant investment, indicating its importance and successful growth."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "eDNv8qw8TUmwEPTT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me how much percentage increase can be observed from 2010 to 2014 in Digital Innovation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this, we first need to locate the Digital Innovation numbers for 2010 and 2014. Then calculate the percentage increase, by subtracting the 2010 number from the 2014 number, divide by 2010 number and multiply with 100.\nBased on the above thought, the answer is:\n55.2%"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "5SnLSgJGirlRjnV4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which type of innovation had the least progress from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I first need to calculate the change in each type of innovation from 2010 to 2014 and then find out the type which has the minimum change.\nBased on the above thought, the answer is:\nPhysical Innovation"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "EQm6KO5rurUVWwag",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much increment can be seen in Policy Innovation from 2010 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, we need to find out the values of Policy Innovation in 2010 and 2014, then the increment can be calculated by subtracting the 2010 value from the 2014 value.\nBased on the above thought, the answer is:\n20"
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "tIarsGybLkAKbH20",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert this pie chart data into a table in CSV format please?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart, we have labeled slices indicating different cuisines. To convert the data into a CSV table, we can create columns for each type of cuisine and its corresponding percentage value. The categories and their percentages are provided in {the pie slice}<r>[0.219, 0.238, 0.81, 0.805]</r>.\nBased on the above thought, the answer is:\nCuisine,Percentage\nItalian,18\nFrench,22\nChinese,30\nIndian,15\nMexican,15"
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "c30aVxFPE2VaN5ty",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which cuisine is the most popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at the segment that has the largest proportion in the pie chart.\nBased on the above thought, the answer is:\nThe most popular cuisine is Chinese because it has the greatest market share at 30% ."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "44qtS3t0XjHt76aQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart imply about the global popularity of these cuisines?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will interpret the pie chart, focusing on the proportions of the different cuisines.\nBased on the above thought, the answer is:\nThe chart implies that Chinese cuisine has been widely accepted globally as it holds the largest market share. French cuisine is also trendy whereas Italian, Indian and Mexican cuisines are on par with each other holding a significant portion of the market share."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "cnpydAyZkEjI0T0Z",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were opening a new restaurant, what cuisine should you consider based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will look at which cuisine has the highest market share as it indicates high demand amongst consumers.\nBased on the above thought, the answer is:\nConsidering the chart, Chinese cuisine would be a great choice as it is the most popular with the largest market share, implying a high demand."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "drZC3qEZA8I92w7q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two cuisines together make up 50% of the total market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I would sum up the percentages of the different pairs of cuisines and judge if the sum comes out to 50%.\nBased on the above thought, the answer is:\nChinese and French cuisines."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "r4a8PWgyrj75c3el",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of cuisines that are European in origin?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I will sum up the percentages of Italian and French cuisines.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "ONJcIq7Wj8WNd1EK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Italian cuisine gains 10% of their market share from Mexican cuisine, what would be the new percentage of Italian cuisine?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to increase the percentage of Italian cuisine by 10% while reducing the same from Mexican cuisine.\nBased on the above thought, the answer is:\nThe new percentage for Italian cuisine would be 28%."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "JBo86q8C0DPHY1cU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the pie chart, can you present the data regarding manufacturing efficiency in the form of a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the variance in the sizes of each slice in the pie chart anchored at {center coordinates}[0.526, 0.502] and labeled with {'Equipment'}<r>[0.248, 0.332, 0.502, 0.523]</r>, {'Labour'}<r>[0.274, 0.502, 0.526, 0.687]</r>, {'Energy'}<r>[0.353, 0.502, 0.526, 0.754]</r>, {'Material'}<r>[0.51, 0.526, 0.722, 0.779]</r>, {'Quality'}<r>[0.526, 0.479, 0.779, 0.672]</r>, {'Safety'}<r>[0.526, 0.317, 0.778, 0.502]</r>, {'Environment'}<r>[0.52, 0.249, 0.698, 0.502]</r>, {'Production Volume'}<r>[0.332, 0.249, 0.526, 0.502]</r>, I can transfer this graphical data to a table.\nBased on the above thought, the answer is:\nManufacturing Cost, Efficiency Score\nEquipment, 14.8%\nLabour, 11.3%\nEnergy, 10.3%\nMaterial, 12.9%\nQuality, 13.2%\nSafety, 11.6%\nEnvironment, 12.3%\nProduction Volume, 13.5%"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "3leLmbcZD2MCDOFz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which aspect of Manufacturing seems to be prioritized the most and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The aspect that has been given the largest proportion in the pie chart should be considered as the most prioritized aspect.\nBased on the above thought, the answer is:\nEquipment is the most prioritized aspect in Manufacturing as it has the highest efficiency score of 14.8%."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "5RfZx4JlQB2rw8IW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can be drawn about the manufacturing process from this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sizes of the various sectors, we can infer the level of emphasis placed on each aspect of manufacturing.\nBased on the above thought, the answer is:\nThe manufacturing process under study appears to place a relatively high emphasis on Equipment, Material, Quality, and Production Volume, while aspects such as Labour, Energy, Safety, and Environment are given lower priority, suggesting potential areas for improvement."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "Ifz4Pcg6xNrpXYVj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow could the chart influence strategy changes in future manufacturing processes?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpretation of the pie chart might lead to reallocation of resources based on efficiency scores of the manufacturing aspects, with focus shifting to lower scoring aspects to balance the manufacturing process.\nBased on the above thought, the answer is:\nGiven the lower efficiency scores of Labour, Energy, Safety, and Environment, future strategies might consider re-investing resources and attention towards these aspects to enhance overall manufacturing process and balance productivity."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "tW6bzo8sVBfhOJHO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich manufacturing cost has the lowest efficiency score?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the different sectors of the pie chart, the manufacturing cost with the smallest sector represents the lowest efficiency score.\nBased on the above thought, the answer is:\nEnergy"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "PWmUwL8XUeBGXcIz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the Efficiency Score for Equipment compared to Labor?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages assigned to Equipment and Labor, subtract the latter from the former.\nBased on the above thought, the answer is:\n3.5%"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "ePoFIaSCT6MKcag0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we aggregate the efficiency scores of Energy and Environment, what percentage do they constitute?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should add the percentages of Energy and Environment to answer this question.\nBased on the above thought, the answer is:\n22.6%"
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "fwplnrlW2WUN3JWG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie chart, can you extract the data available and structure it in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the observations from {the pie segments}<r>[0.298, 0.305, 0.716, 0.721]</r>, and, particularly, their auto-text elements which represent sales percentages, I can see that there are different percentages showing for each quarter for both products \"Smartphones\" and \"Smart TVs\". These percentages are related to their corresponding total sales of the year.\nBased on the above thought, the answer is:\nProduct Type, Q1, Q2, Q3, Q4\nSmartphones, 13.2%, 12.3%, 12.7%, 21.9%\nSmart TV, 8.8%, 10.1%, 11.8%, 9.2%"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "3VZ1m5cygjeg5rUp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat product dominated sales in Q4, and why do you think this is the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must analyze the pie sectors specifically for Q4.\nBased on the above thought, the answer is:\nIn Q4, \"Smartphones\" dominated the market with a percentage of 21.9%. The specific reasons behind this trend are not clear just by interpreting the chart. However, potential reasons could include festive season discounts, year-end offers, or the release of a new model."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "YhI1tNyfr65pu4aT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what\u2019s the overall yearly sales trend for each product?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I look at the pie sectors for both \"Smartphones\" and \"Smart TVs\", I can infer the yearly sales trend for each product.\nBased on the above thought, the answer is:\nFor \"Smartphones\", the sales percentage increased from Q1 to Q4 with a notable jump in Q4, indicating a positive sales growth throughout the year. For \"Smart TVs\", the sales percentages fluctuate, with a peak in Q3 and a subsequent drop in Q4."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "fPnwfzTxy3kBFewS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sales trend insights do you get by observing the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the size and the percentage of each pie sector for different quarters for both products.\nBased on the above thought, the answer is:\nThe pie chart shows that the sales of \"Smartphones\" and \"Smart TVs\" peak in different quarters (Q4 and Q3, respectively). This could suggest different market trends and buyer preferences for these two types of products across different periods of the year. Furthermore, the exploded pie slice for Q4 represents the maximum sales of \"Smartphones\", denoting its significant impact on the market in this period."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "0IWV9bDbkUfDYkIw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich quarter had the highest combined sales for both Smartphones and Smart TVs?"
+            },
+            {
+                "from": "gpt",
+                "value": "By aggregating the percentages from each quarter for both \"Smartphones\" and \"Smart TVs\", I can find out which quarter had the highest combined sales.\nBased on the above thought, the answer is:\nQ4"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "fbWnGDPMAj5pTVgm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the sale difference between Smartphones and Smart TVs in Q2?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of Smart TV sales in Q2 from the percentage of Smartphone sales in Q2 to get the difference.\nBased on the above thought, the answer is:\n2.2%"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "S2ipUPBJvzyTAAoi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which quarter does the product \u2018Smartphones\u2019 reach sales which constitutes more than 20% of the yearly sales quota?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to observe the percentage of each sector for the product \u2018Smartphones\u2019 and find out which sector shows a percentage greater than 20%.\nBased on the above thought, the answer is:\nQ4"
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "hFm7JNeRE4NrJbU2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, could you extract the necessary information and convert it into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart title {\"Culture and Arts Industry Spending 2008 - 2015\"}<r>[0.054, 0.092, 0.946, 0.125]</r> and the subsections {the slices of the pie chart}<r>{\"2008\"}[0.193, 0.321, 0.5, 0.52]</r>, {\"2009\"}[0.19, 0.476, 0.5, 0.657]</r>, {\"2010\"}[0.222, 0.52, 0.5, 0.797]</r>, {\"2011\"}[0.36, 0.52, 0.534, 0.831]</r>, {\"2012\"}[0.5, 0.52, 0.731, 0.829]</r>, {\"2013\"}[0.53, 0.47, 0.84, 0.736]</r>, {\"2014\"}[0.5, 0.228, 0.805, 0.52]</r>, {\"2015\"}[0.262, 0.21, 0.604, 0.52]</r>, I find that each slice of the pie indicates the spending over the years.\nBased on the above thought, the answer is:\nYear,Spending(in million dollars)\n2008,8.8\n2009,9.6\n2010,10.3\n2011,9.2\n2012,11.6\n2013,14.7\n2014,16.5\n2015,19.3"
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "xVTc1n8VWl8YIfaX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw the most significant increase in spending on the culture and arts industry? And why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can figure out the year with the highest proportion by comparing the values of all pie slices, the slice with the highest percentage represents the year with the most significant increase in spending.\nBased on the above thought, the answer is:\nThe year with the most significant increase in spending was 2015, as it had the largest slice, accounting for 19.3% of the total spending. This might be due to greater government investment or growth in the industry leading to higher revenues."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "D63bgQbWEPd9X3Cl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of the spending trend in Culture and Arts Industry Development from 2008 to 2015, what kind of insight can you provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide an insight, I need to look at the distribution and size of the pie slices over the years.\nBased on the above thought, the answer is:\nThe spending on Culture and Arts Industry Development seems to have grown over the years, with the size of the slices generally increasing from 2008 to 2015. There is a slight dip in 2011, though it's followed by a consistent increase, suggesting an overall positive trend in investment in culture and arts."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "T5dDom1MSCGWeRNV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the spending trend you observed continues, how might this information be used to predict future spending?"
+            },
+            {
+                "from": "gpt",
+                "value": "Drawing on the spending trend indicated by the sizes of the pie slices, we can predict future spending trends.\nBased on the above thought, the answer is:\nIf the upward spending trend observed from 2008 to 2015 continues, it can be predicted that the spending on Culture and Arts Industry might continue to increase in the future. This information could be useful for budget planning and resource allocation in the industry."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "WvPLHtucB361pSfl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the sum of spending for the Culture and Arts Industry in the years 2011 and 2012?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the sum of spending in the years 2011 and 2012, I need to add the percentages of the corresponding pie slices {\"for 2011\"}<r>[0.36, 0.52, 0.534, 0.831]</r> and {\"for 2012\"}<r>[0.5, 0.52, 0.731, 0.829]</r> together.\nBased on the above thought, the answer is:\nThe sum of spending in 2011 and 2012 is 9.2 millions + 11.6 millions = 20.8 million dollars."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "IKYGYVWn2PsQJmv8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage rate of increase in spending from the year 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the rate of increase, I need to subtract the spending in 2008 from the spending in 2015 and divide by the spending in 2008 based on the pie chart slices {\"for 2008\"}<r>[0.193, 0.321, 0.5, 0.52]</r> and {\"for 2015\"}<r>[0.262, 0.21, 0.604, 0.52]</r>.\nBased on the above thought, the answer is:\nThe rate of increase in spending from 2008 to 2015 is ((19.3 million - 8.8 million) / 8.8 million ) * 100% = 119%."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "zArPwbVPvjGw9og7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the spending on Culture and Arts Industry in the year 2009 had been 10 million dollars, what would be the new total spending from 2008 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to substitute the spending of 2009 with 10 million based on the pie slice {\"for 2009\"}<r>[0.19, 0.476, 0.5, 0.657]</r> and sum up all the spendings again.\nBased on the above thought, the answer is:\nThe new total spending from 2008 to 2015 would be 8.8 million + 10 million + 10.3 million + 9.2 million + 11.6 million + 14.7 million + 16.5 million + 19.3 million = 100.4 million dollars."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "YsU3xNlB67nSZQsq",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the pie chart into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the chart title {\"Human Health and Disease Management\"}<r>[0.096, 0.261, 0.858, 0.294]</r> and the pie sections for {\"Healthy Living\"}<r>[0.296, 0.424, 0.645, 0.711]</r>, {\"Disease Management\"}<r>[0.435, 0.345, 0.652, 0.532]</r> and {\"Lifestyle Changes\"}<r>[0.343, 0.351, 0.477, 0.52]</r> that they represent different aspects of health and disease management and their respective percentages.\nBased on the above thought, the answer is:\nAspect,Percentage\nHealthy Living,60%\nDisease Management,30%\nLifestyle Changes,10%"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "pVIIBY52ZW0hiUQ2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat aspect of human health and disease management does the chart emphasize most? Explain your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "The aspect that takes up the largest portion of the pie chart could be considered as most emphasized.\nBased on the above thought, the answer is:\nThe chart emphasizes \"Healthy Living\" the most as it occupies the largest percentage, 60%, of the pie chart."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "4FTkrziQNastOzEN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn context of disease management in the data represented, what might be suggested about the strategies of health authorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportions represented by each aspect can give insights into the strategy of health authorities.\nBased on the above thought, the answer is:\nFrom the data, it can be inferred that health authorities placed the highest importance on Healthy Living, suggesting a strategy that emphasizes preventive measures and health consciousness. Also, considerable focus is on Disease Management, indicating proactive efforts for managing chronic and acute conditions. Furthermore, the smaller proportion for Lifestyle Changes may suggest efforts to encourage modifications in lifestyle for better health."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "6lLvFNRXrEMmYZcl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information in the chart influence policy decisions related to health and disease management?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportions might reflect areas of focus and could influence policy directions.\nBased on the above thought, the answer is:\nThe predominance of \"Healthy Living\" might lead to policies that further promote preventive care through a healthy lifestyle. As \"Disease Management\" also takes a substantial part, policies might continue to support proactive disease management measures. On the other hand, seeing the smaller proportion for \"Lifestyle Changes\", policymakers might feel the need to increase efforts in promoting lifestyle modifications for better disease management."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "r9iN9k4XtyDTYdm1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage represented by Healthy Living and Lifestyle Changes?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can add the percentages represented by Healthy Living and Lifestyle Changes to get the total.\nBased on the above thought, the answer is:\n70%"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "oNCTeOiagzfxmQEs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between Healthy Living and Disease Management?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of Disease Management from that of Healthy Living.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "UfwaSxVldbyJ9BD5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Lifestyle Changes were to increase by 15%, what would be the new total percentage of all aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to add 15% to the percentage of Lifestyle Changes and then sum all the percentages.\nBased on the above thought, the answer is:\n115%"
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "h56LwADev4FvSv0a",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you organize the contents of this pie chart into a tabulated form for easier comprehension?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can convert this pie chart into a data table. From the sections of the pie chart labeled {Artificial Intelligence}[0.327, 0.639, 0.327, 0.639], {Internet of Things}[0.669, 0.653, 0.669, 0.653], {Biotechnology}[0.7, 0.444, 0.7, 0.444], {Blockchain}[0.559, 0.326, 0.559, 0.326], {Robotics}[0.423, 0.342, 0.423, 0.342], and {Virtual Reality}[0.372, 0.379, 0.372, 0.379], I can retrieve the relevant values associated with each section.\nBased on the above thought, the answer is:\nTechnology Area,Percentage\nArtificial Intelligence,40\nInternet of Things,20\nBiotechnology,15\nBlockchain,15\nRobotics,7\nVirtual Reality,3"
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "oaQ9a1N8WxCUKUCA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on this pie chart, what seems to be the area of greatest interest for start-ups?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area that has the largest percentage of startups can be considered the most popular or interesting area.\nBased on the above thought, the answer is:\nArtificial Intelligence is the most popular area for start-ups, as it holds the highest percentage on the pie chart with 40%."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "o6pmq9B3aumsYHcb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the emerging trends in technology entrepreneurship in terms of areas from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the percentages of start-ups across different technology areas can give an idea of the emerging areas of interest in tech entrepreneurship.\nBased on the above thought, the answer is:\nFrom the pie chart, it seems that most start-ups are leaning towards Artificial Intelligence, followed by Internet of Things which shows these two areas are the leading trends. On the other hand, Virtual Reality and Robotics are emerging but at a slower pace as they have the least presence among the start-ups."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "EO3Eqg2kIyAF9YeL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf one wanted to start a tech venture in an area with less competition, which areas should they consider based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The areas with the lowest percentages would be the ones with the least presence and thus arguably the least competition.\nBased on the above thought, the answer is:\nBased on this chart, Virtual Reality and Robotics seem to have the least presence among technology start-ups implying these areas might have less competition as compared to others like Artificial Intelligence or the Internet of Things."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "XSL9x4QWLsvQ1WM9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology areas in the pie chart add up exactly to 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which technology areas sum up to 50%, I would need to sum percentages from different areas and compare the result to 50.\nBased on the above thought, the answer is:\nArtificial Intelligence and Internet of Things."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "NVj6ZxH7soeDrpBY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average start-up percentage across all technology areas demonstrated in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to add the percentages from all technology areas and divide by the number of areas.\nBased on the above thought, the answer is:\nThe sum of percentages is 40% + 20% + 15% + 15% + 7% + 3% = 100%. Therefore, the average percentage is 100% / 6 = 16.67%."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "ywhiFFiKQfaWsHH4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the category \"Artificial Intelligence\" was to increase by 5% in their presence, what would the new overall total percentage be?"
+            },
+            {
+                "from": "gpt",
+                "value": "An increase in one category will increase the total. To find the new total, I would add 5% to the \"Artificial Intelligence\" percentage and add up all the percentages again.\nBased on the above thought, the answer is:\nThe new percentage for \"Artificial Intelligence\" would be 40% + 5% = 45%. Making the new total percentage be 45% + 20% + 15% + 15% + 7% + 3% = 105%."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "4zjfg2SOKHioKgNG",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide me the data displayed in the pie chart in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By inspecting the pie chart, there are several slices which signify different years and their market share from the Automotive and Transportation Market Research. By checking the corresponding CSV data with the visual information, I could generate a table.\nBased on the above thought, the answer is:\nYear,Market Share\n2011,20%\n2012,25%\n2013,30%\n2014,15%\n2015,5%\n2016,1%\n2017,1%\n2018,1%\n2019,1%\n2020,1%"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "vz09bdmTDsR8p94B",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed from the pie chart about the automotive and transportation market research from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each year from 2011 to 2020 is represented as a slice on the pie chart. I need to see the trend of their market shares.\nBased on the above thought, the answer is:\nFrom 2011 to 2013, the market share increased, indicating a growth trend. However, from 2013 to 2015, there was a decrease, signaling a downturn. From 2016 to 2020, the market share remained stagnant at 1%, indicating a period of no growth or decline."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "vW3C7fh0x0jxkoAP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the market change after its peak in 2013 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To observe the changes after 2013, I can compare the size of the slices from 2013 to the following years.\nBased on the above thought, the answer is:\nAfter its peak in 2013, the market share for Automotive and Transportation decreased significantly. The sharp decline continued until 2016, where the market share reached a low point of 1% and remained there through to 2020."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "sie4WUgK9a7UWj9L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might have happened in 2016, to make the slice for the year 2016 much smaller as compared to the year 2011?"
+            },
+            {
+                "from": "gpt",
+                "value": "The comparison between slice '{the year 2011}'<r>[0.249, 0.29, 0.5, 0.515]</r> and '{the year 2016}'<r>[0.31, 0.238, 0.5, 0.515]</r> in the pie chart illustrates a stark difference in the market share, which implies a significant change in market dynamics.\nBased on the above thought, the answer is:\nConsidering that the market share dropped from 20% in 2011 to only 1% in 2016, there might have been significant disruptions in the automotive and transportation market around 2016. This could be due to various factors such as technological shifts, regulatory changes, or internal industry challenges."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "boNA4TAKuED67Gnw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the market share reach the highest value?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to pie chart, the year with the largest slice indicates the highest market share.\nBased on the above thought, the answer is:\n2013."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "QFlpOkLcTZw0Q1oJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share from the years 2011 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "Since these years are painted with individual colors on the pie chart, we could calculate by adding all percentages and dividing by the number of years, which is 5.\nBased on the above thought, the answer is:\n19%"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "LSUUHqoiPKomME82",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market shares of 2016 to 2020 were pooled together, what would it be the the total market share for these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart where each slice corresponds to a particular year, I can find the slices for the years 2016 to 2020 and add the percentages.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "GZ2MUjFZuRfGlhlY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the specific trade handled by the Shenzhen port and Hong Kong port from 2010 to 2015 into CSV format according to the labels on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging by the title, {Trade Handled by Shenzhen and Hong Kong Ports from 2010 to 2015}<r>[0.02, 0.077, 0.98, 0.102]</r>, and the portions of pie chart, {pie slices}[{Shenzhen port}[0.209, 0.187, 0.5, 0.786], {Hong Kong port}[0.5, 0.187, 0.791, 0.827]], I can confirm that the indicators in the pie chart represent the years from 2010 to 2015 and the proportions of trade handled by Shenzhen port and Hong Kong port.\nBased on the above thought, the answer is:\nYear,Shenzhen Port,Hong Kong Port\n2010,70,66\n2011,80,75\n2012,85,78\n2013,90,85\n2014,100,95\n2015,115,105"
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "WtKr48sv4ucG7Yrx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich port seems to be growing at a faster rate?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I can see from the relative size of the sections that Shenzhen Port seems to be increasing at a faster rate.\nBased on the above thought, the answer is:\nShenzhen Port is growing at a faster rate."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "fKaXQekVOW1IcSey",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be evidently seen from the trade handled by both ports from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, both ports appear to be handling an increasing amount of trade each year.\nBased on the above thought, the answer is:\nBoth ports are growing each year, with the amount of trade they handle increasing yearly from 2010 to 2015."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "AkuPscRZZ8IcqX4e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data from 2010 to 2015, which port is likely to handle more trade in 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the upward trend seen in Shenzhen port over the 6 years, and the comparatively lower growth rate of Hong Kong port, I can infer that Shenzhen port is likely to handle more trade in 2016.\nBased on the above thought, the answer is:\nShenzhen port is likely to handle more trade in 2016."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "4CXDjiRf7M0MM9fr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which port handled more trade in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out which port handled more trade in 2010, I need to compare the trading amounts of Shenzhen port and Hong Kong port in 2010.\nBased on the above thought, the answer is:\nShenzhen Port."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "YDltpYGII6u1naKz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2010 to 2015, how much did the trade handled by Shenzhen port increased?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the increase in trade handled by the Shenzhen port from 2010 to 2015, I need to deduct the trade amount of 2010 from that of 2015.\nBased on the above thought, the answer is:\nThe trade handled increased by 45."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "LnvQERlOrGK5eeMb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total trade handled by Hong Kong port from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the total trade handled by the Hong Kong port from 2010 to 2015, I need to sum up the trade amounts from the years 2010 through to 2015.\nBased on the above thought, the answer is:\nThe total trade handled is 504."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "0tA1ylmHke9fXIRL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the observed pie chart data regarding the adoption of Digital Wallets and Cryptocurrencies from 2013 to 2020 into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the title {\"FinTech Adoption from 2013 to 2020\"}[0.163, 0.092, 0.837, 0.125] and the pie chart {[0.19, 0.21, 0.81, 0.862]} showing the distribution of adoption percentages over the years, I can understand that each slice represents a specific year and the split of adoption between Cryptocurrencies and Digital Wallets for that year.\nBased on the above thought, the answer is:\nYear,Digital Wallets,Cryptocurrencies\n2013,50%,40%\n2014,60%,50%\n2015,80%,60%\n2016,85%,65%\n2017,90%,80%\n2018,92%,85%\n2019,94%,90%\n2020,100%,95%"
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "TncSMorEbi1g19cz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which Financial Technology has higher adoption in 2020, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine this by looking at the pie slice for the year 2020 and comparing the percentages of Digital Wallets and Cryptocurrencies.\nBased on the above thought, the answer is:\nIn 2020, Digital Wallets are more popular with an adoption rate of 100% compared to Cryptocurrencies which have an adoption rate of 95%. Therefore, Digital Wallets had a higher adoption possibly due to their wider acceptance and accessibility."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "D1MvEHWDGzGHKhYc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the adoption of FinTech from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the size of the pie slices from 2013 to 2020 to observe the trend.\nBased on the above thought, the answer is:\nFrom the chart, it can be seen that the adoption of both Digital Wallets and Cryptocurrencies has been consistently increasing from 2013 to 2020. However, the growth of Cryptocurrencies has been more rapid since 2016."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "K1GocyuaxxC6EL6n",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence future predictions in the adoption of FinTech?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the historical trends in adoption of the two technologies to forecast future trends.\nBased on the above thought, the answer is:\nThe constant increase in the adoption of both Digital Wallets and Cryptocurrencies suggests that we can expect the trend to continue in the future, particularly for Cryptocurrencies which have seen rapid growth. As more people are adapting to FinTech, future predictions might see further increases in the adoption rates of these technologies."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "RYLOxkzliMVz9bBn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what proportion of adoption of Digital Wallets and Cryptocurrencies was there in the year 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I should refer to the pie slice corresponding to the year 2015 and extract the percentages of Digital Wallets and Cryptocurrencies.\nBased on the above thought, the answer is:\nIn 2015, Digital Wallets had 80% adoption and Cryptocurrencies had 60% adoption."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "0VJWqk8LxrWvc6JF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in adoption of Digital Wallets from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to subtract the percentage of Digital Wallets in 2013 from that in 2020.\nBased on the above thought, the answer is:\nDigital Wallets adoption increased by 50% from 2013 to 2020."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "Ru9dxWUxE1A2pnvT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did Cryptocurrencies adoption increase from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to subtract the Cryptocurrencies percentage in 2016 from that in 2020.\nBased on the above thought, the answer is:\nCryptocurrencies adoption increased by 30% from 2016 to 2020."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "4cbbgcCDDwVdwCqk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data observed from the pie chart into a table with CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the details shown in the pie chart's slices, namely, {\"Energy Consumption\"}<r>[0.359, 0.418, 0.518, 0.52]</r>, {\"Workflow Efficiency\"}<r>[0.359, 0.518, 0.518, 0.627]</r>, {\"Resource Management\"}<r>[0.4, 0.52, 0.518, 0.679]</r>, {\"Time Management\"}<r>[0.513, 0.52, 0.637, 0.679]</r>, {\"Process Automation\"}<r>[0.534, 0.482, 0.693, 0.63]</r>, {\"Staff Training\"}<r>[0.529, 0.355, 0.683, 0.509]</r>, {\"Technology Adoption\"}<r>[0.391, 0.346, 0.552, 0.505]</r>, each slice represents different areas of improvement and their respective proportions of the whole.\nBased on the above thought, the answer is:\nAreas of Improvement,Assessment Unit\nEnergy Consumption,55\nWorkflow Efficiency,60\nResource Management,65\nTime Management,70\nProcess Automation,80\nStaff Training,85\nTechnology Adoption,90"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "uTzmnWwhkrmGThZa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area has the highest efficiency improvement potential? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart and focusing on the size of the slices and the assessment units, the highest efficiency improvement potential corresponds to the area with the biggest slice and the highest assessment unit. In this case, float comparison would be needed.\nBased on the above thought, the answer is:\n\"Technology Adoption\" has the highest efficiency improvement potential. This is inferred from the fact that it has the largest slice in the pie chart and the highest assessment unit of 90."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "f3xGRFvoTc5A18A5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sectors show a rapid surge in efficiency improvement potential?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the differences in assessment units among each two consecutive areas, we can infer a sudden surge from a relatively smaller increment to a larger one.\nBased on the above thought, the answer is:\nThe sectors showing a rapid surge in efficiency improvement potential are Process Automation, Staff Training, and Technology Adoption. This conclusion is based on the larger increments in assessment units from Time Management to Technology Adoption."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "DUbYKFl2KOcYRlOq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the data and the chart about the potential areas of improvement within an organization?"
+            },
+            {
+                "from": "gpt",
+                "value": "By carefully analyzing the pie chart and the assessment units of each sectors, we can infer the potential areas of improvements and their relevance in the organization.\nBased on the above thought, the answer is:\nThe data and chart suggest that areas related to technology, such as Process Automation, Staff Training and Technology Adoption, offer the most significant potential for efficiency improvement within the organization. These areas have higher assessment units and larger pie slices, indicating that efforts and resources invested in technology-related improvements could yield substantial efficiency gains."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "f2iY25FrkH4GluqK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which two consecutive areas the increment of assessment unit is 10?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out these two areas, I need to compare the difference of the assessment units among each two consecutive areas.\nBased on the above thought, the answer is:\nFrom 'Time Management' to 'Process Automation'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "9xHQR9WkbgUerx5S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat\u2019s the total assessment unit of all the areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total assessment unit, I will sum up the assessment units of all the areas.\nBased on the above thought, the answer is:\n505"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "vk56K8dFGFAYnv4g",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the 'Process Automation' assessment unit exceed 'Energy Consumption'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the 'Energy Consumption' assessment unit value from the 'Process Automation' assessment unit.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "ughHbbnGlW1kZZUn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please transfer the information represented by the pie chart into a CSV table format considering technology and their projected prospects in the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To perform this task, I need to refer to each slice that appears on {the pie chart}<r>[0.124, 0.136, 0.876, 0.925]</r>, associate it with its label, calculate its area, and translate this into the corresponding figure provided in the dataset.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Blockchain,3D Printing,Nanotechnology,Quantum Computing,Internet of Things,Cybersecurity,Big Data,Biotechnology,Robotics\n2025,70,65,60,40,50,80,75,85,55,90"
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "Cd4ZXmU8rlcqb1un",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two technological areas stand out as having the highest and lowest projected prospects for the year 2025, respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will review the prospective values of all sectors on {the pie chart}<r>[0.124, 0.136, 0.876, 0.925]</r>.\nBased on the above thought, the answer is:\nThe field of Robotics shows the highest projected prospects for 2025, while Nanotechnology demonstrates the lowest projected prospects."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "AAaUtajeiU6dgqJP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat may be influenced by the futuristic impact of the Internet of Things (IoT) as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can answer that by interpreting the high value assigned to IoT on the pie chart and envisioning its impact on various technology sectors.\nBased on the above thought, the answer is:\nGiven its high prospective value, IoT is likely to be a driving force in technological innovation and influence other aspects like policy making, funding for research, and market focus. Its expected influence spans across multiple sectors, potentially leading to advancements in smart homes, industrial automation, smart cities, and more."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "uhzYkfrCvZ9SmUSH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow may the data presented in this chart guide long-term strategic planning and funding decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferring from {the chart}<r>[0.124, 0.136, 0.876, 0.925]</r>, I can ascertain that the pie pieces' sizes can be used to prioritize areas of research and investment.\nBased on the above thought, the answer is:\nThe data suggests prioritizing Robotics, Big Data, and IoT due to their high prospective values. These fields might attract more funding or strategic focus. Conversely, sectors like Nanotechnology and Quantum Computing, with relatively smaller prospective values, might need to be reassessed or bolstered with strategic interventions."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "yUmm152EIm7pXjGC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological field holds the highest endpoints for its projected prospects in 2025, according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to compare the data points for each technology, as highlighted in the dataset provided.\nBased on the above thought, the answer is:\nRobotics."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "Ply6NH2eoA0M6EwZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much higher is the technological field of Big Data than that of Nanotechnology in the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To discover this, I need to subtract the value of Nanotechnology from that of Big Data for the year 2025.\nBased on the above thought, the answer is:\n45."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "6n0ycpYDVkVlxkT7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average prospect value for all the technological fields combined for the year 2025?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the answer, I need to sum up all prospect values and divide by the number of technological fields for the year 2025.\nBased on the above thought, the answer is:\n67."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "IGpLOT8OeB6GOxR1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data represented by the pie chart into CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To accomplish this, I will take into account the labels and corresponding percentages provided by each pie slice {the slices with their labels and percentages}<r>[{\"Friends\"}[0.24, 0.367, 0.479, 0.534]; {\"Family\"}[0.241, 0.52, 0.708, 0.759]; {\"Acquaintances\"}[0.479, 0.457, 0.717, 0.586]; {\"Online Friends\"}[0.479, 0.299, 0.709, 0.52]; {\"Colleagues\"}[0.29, 0.259, 0.562, 0.497]]</r>, and the title of the chart {\"Distribution of Social Relationships\"}<r>[0.156, 0.182, 0.802, 0.215]</r> indicating what the percentages represent.\nBased on the above thought, the answer is:\nRelationships,Friends,Family,Acquaintances,Online Friends,Colleagues\nPercentage,12.0%,44.6%,8.7%,14.7%,20.0%"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "JyN6ygmFI1dlxeMr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of relationship is the most seen in the pie diagram among all the mentioned relationships? How can you confirm this?"
+            },
+            {
+                "from": "gpt",
+                "value": "Checking {pie slices}<r>[0.24, 0.259, 0.717, 0.759]</r> and comparing their relative sizes, the one that takes up the most space represents the most observed type of relationship.\nBased on the above thought, the answer is:\nThe most observed relationship type is \"Family\", as it covers the largest section of the pie chart with a coverage of 44.6%."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "2DNM22MvoRcFUEgm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the distribution of 'Colleagues' represented in the pie chart and why do you think that is so?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, the 'Colleagues' slice is slightly exploded away from the center {exploded pie slice}<r>[0.29, 0.259, 0.562, 0.497]</r>. This is usually done to draw attention to that slice.\nBased on the above thought, the answer is:\nThe 'Colleagues' slice represents 20% of the distribution and it is exploded to emphasize that this is the third most common category of relationships, ahead of 'Friends' and 'Acquaintances' but behind 'Family' and 'Online Friends'."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "ERzPHfwcfq4hCONV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can you make about the social connections of the person based on the distribution of social relationships in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the distribution percentages of various types of social relationships, I can interpret the data to form a conclusion.\nBased on the above thought, the answer is:\nFrom the data, one can infer that the person has a large number of Family connections, followed by Colleagues and then Online Friends. However, they have relatively fewer friends and acquaintances. This might suggest that the person values close familial connections and professional relationships more than casual or online friendships."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "MYYfdiKEOmuYb7mD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you spot the two types of social relationships which together make up less than 25% of the total relationships?"
+            },
+            {
+                "from": "gpt",
+                "value": "If I add up separately the proportions of all possible pairs of categories in the chart, I can identify these relationships.\nBased on the above thought, the answer is:\nFriends and Acquaintances."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "KXu3eW8GQdCxGRsO",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social relationship type covers the median percentage in the distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will rank the social relationship types based on their proportion in the pie chart and identify the one in the median position.\nBased on the above thought, the answer is:\nOnline Friends."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "I1rqis2Ju5wM1Veu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many percent does the least common type of relationship want to increase to match the third least common type?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the percentage of the least common type of relationship from the third least common type.\nBased on the above thought, the answer is:\n14.7% - 8.7% = 6%"
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "mocJ9BcrF9FKnDy0",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information about different categories of technology patents filing into a structured CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the information extracted from the pie chart, where {\"Patents filed by technology type\"}[0.266, 0.237, 0.716, 0.262] is the title, and each slice of the pie represents a different technology field with their respective patents data, I can infer that my task is to convert this raw plotted data into a structured table.\nBased on the above thought, the answer is:\nInnovation Type,Patents\nAI & Machine Learning,550\nBlockchain Technology,200\nAugmented Reality & VR,90\nInternet of Things,300\nQuantum Computing,40\nBiotechnology,220\nRobotics,180\nNanotechnology,320\n3D Printing,100"
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "znqXs9IaSc14Y38Y",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area of technological innovation is the most focused upon, based on the patent filings?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area that is most focused on would be represented by the slice with the highest proportion. For that, I should look for the slice with the highest percentage - which appears to be {\"AI & Machine Learning\"}[0.293, 0.388, 0.491, 0.685].\nBased on the above thought, the answer is:\nThe field of AI & Machine Learning has been the most focused upon, having the largest number of patents filed."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "6Uwf8UGJNRHiaMR7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you detect in the innovation areas based on the number of patents filed?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie slices, the larger ones indicate more developed and focused areas, the smaller ones less so. Thus, the trend can be inferred.\nBased on the above thought, the answer is:\nIt appears that there is a heavy focus on Artificial Intelligence (AI) and Machine Learning, as well as Nanotechnology, indicating their importance in current technological innovation. Quantum Computing, on the other hand, seems to be a less explored field as it has the smallest proportion of patents filed."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "mxCQZcI2UgpMjs18",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might future research funding in technology advancement be influenced based on the data in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of patents filed gives an indication of what fields are currently in demand. Funding might be focused on these fields but also on those with less patents to encourage diversification of research topics.\nBased on the above thought, the answer is:\nGiven this data, it's likely that more funding might be allocated towards AI & Machine Learning and Nanotechnology due to their current dominance. However, areas where fewer patents were filed, like Quantum Computing and 3D Printing, could also draw attention for future research funding to stimulate innovation in these yet less explored fields."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "vbAhy8prkCbhxbpz",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the overall patents does the field with the least patents contribute?"
+            },
+            {
+                "from": "gpt",
+                "value": "From among the pie slices, Quantum Computing seems to have the smallest size, indicating the least number of patents. Its autotext, which shows its proportion, can provide the answer. The percentage is given by {\"Quantum Computing\"}[0.491, 0.515, 0.563, 0.712].\nBased on the above thought, the answer is:\n2.0%"
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "YOnTc9pm3fDTs2re",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage of patents for the two fields with the most patent filings?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two fields with biggest slices are AI & Machine Learning and Nanotechnology. Adding up their percentages will give the answer. The percentages are given by {\"AI & Machine Learning\"}[0.293, 0.388, 0.491, 0.685] and {\"Nanotechnology\"}[0.385, 0.298, 0.576, 0.495].\nBased on the above thought, the answer is:\n43.5%"
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "O5LICFrPo2ArmKE1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total number of patents filed across all these innovative technology fields?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total number of patents filed, I should add up the patents count of all the individual fields mentioned in the pie slices.\nBased on the above thought, the answer is:\n1900"
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "SITLLVJP1G045b89",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data into a structured data table format for both market segments - Consumer and Industrial?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the chart into a table, I need to look at each slice in both pie charts, \"Consumer Segment\"{subplot_0}<r>[0.094, 0.224, 0.412, 0.859]</r> and \"Industrial Segment\"{subplot_1}<r>[0.588, 0.224, 0.906, 0.859]</r>. The percentages listed on each slice represent the market share of that specific company in the respective market segment.\nBased on the above thought, the answer is:\nMarket Segment,Company A,Company B,Company C,Company D,Company E,Company F,Company G,Company H\nConsumer,4.0%,17.3%,15.3%,25.0%,4.8%,10.5%,20.2%,2.8%\nIndustrial,16.3%,10.3%,11.2%,6.9%,15.9%,13.4%,9.1%,16.8%"
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "J5bhlxkoXaSjwDzX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the current chart, which company is dominating in the industrial segment, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the company with the highest percentage in the \"Industrial Segment\"{subplot_1}<r>[0.588, 0.224, 0.906, 0.859]</r> pie chart because the one with the highest market share usually dominates the market.\nBased on the above thought, the answer is:\nCompanies E and H are dominating the industrial segment since they both have the highest market share of 16.8%."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "cTtKlmwpOY5IkCE6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we compare the two segments, where is Company B's market presence stronger and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing Company B's market share in both the \"Consumer Segment\"{subplot_0}<r>[0.094, 0.224, 0.412, 0.859]</r> and \"Industrial Segment\"{subplot_1}<r>[0.588, 0.224, 0.906, 0.859]</r> pie charts, we can determine where their market presence is stronger.\nBased on the above thought, the answer is:\nCompany B's market presence is stronger in the Consumer segment because it holds a 17.3% market share compared to only a 10.3% share in the Industrial segment."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "Yglrw0B1xY6qwtCV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich companies seem to be leading in the Consumer market and how might this impact these companies' business strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to find out which companies have the highest percentages in the \"Consumer Segment\"{subplot_0}<r>[0.094, 0.224, 0.412, 0.859]</r> pie chart and postulate how they may adapt their strategies based on this dominant market position.\nBased on the above thought, the answer is:\nCompanies D, F, and G seem to be leading in the Consumer market with shares of 25.0%, 20.2%, and 10.5% respectively. Given their significant market share, these companies could concentrate on consolidating their strong positions through customer retention strategies and innovations to further increase their market shares. They might also have the financial flexibility to invest in areas such as R&D for future growth."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "VYhIo1orQNqp7GWe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho has the most market share in the consumer segment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the firm with the highest market share in the consumer section, I will examine each slice in the \"Consumer Segment\"{subplot_0}<r>[0.094, 0.224, 0.412, 0.859]</r> pie chart and find the slice with the largest percentage.\nBased on the above thought, the answer is:\nCompany D"
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "jmZC712u1wULvFH1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in market share percentage between Company B and Company F in the industrial sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference in market share percentages, I will locate company B and company F on the \"Industrial Segment\"{subplot_1}<r>[0.588, 0.224, 0.906, 0.859]</r> pie chart and subtract the smaller percentage from the larger one.\nBased on the above thought, the answer is:\n3.1%"
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "QImRgbXhnnOE1y8o",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share of Companies E, F, and G in the industrial sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total market share, I need to find the percentages for Companies E, F, and G on the \"Industrial Segment\"{subplot_1}<r>[0.588, 0.224, 0.906, 0.859]</r> pie chart and add them up.\nBased on the above thought, the answer is:\n38.4%"
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "c08sRbiKtn0wqn0l",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the occurrences distribution over the years from the pie chart you observed into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie slices {2013}[0.19, 0.321, 0.5, 0.545], {2014}[0.191, 0.52, 0.5, 0.769], {2015}[0.315, 0.52, 0.592, 0.831], {2016}[0.53, 0.335, 0.84, 0.826], {2017}[0.5, 0.225, 0.742, 0.52], {2018}[0.393, 0.21, 0.594, 0.52], {2019}[0.262, 0.229, 0.5, 0.52], I can identify each year and its percentage of total occurrences.\nBased on the above thought, the answer is:\nYear,Occurrences\n2013,12.4%\n2014,13.5%\n2015,15.0%\n2016,31.0%\n2017,9.3%\n2018,10.5%\n2019,8.3%"
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "M9hYCX9k3rGn6Q6w",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the most occurrences? And provide possible reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the year with the highest percentage, then consider possible reasons for this peak.\nBased on the above thought, the answer is:\nThe year with the most occurrences was 2016, with 31.0% of the total occurrences. This could be due to a special event or high consumer interest that year."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "3Y114vW80mTqOAsn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends in occurrences can you identify from 2013 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the percentages for each year to identify any trends.\nBased on the above thought, the answer is:\nBased on the chart, there was a general upward trend in occurrences from 2013 through 2015. However, there was a significant peak in 2016, possibly due to an outlier event. After that, the trend declined from 2017 onwards."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "VxlHrHbSmP3k8LAR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence strategies for improving sales in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the distribution of occurrences might impact strategies for improving sales.\nBased on the above thought, the answer is:\nThe data shows a peak in occurrences in 2016, which could indicate a high demand or interest in that particular year. However, despite high occurrences, sales figures suffered. To improve sales in future years, strategies could focus on understanding the factors that caused the disconnect between occurrences and sales in 2016, and seek to address them."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "DnyOBupNO9CEvH0V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the percentage of the occurrences in the year 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage for the year 2018 can be obtained by examining the annotation for the corresponding pie slice {2018}[0.393, 0.21, 0.594, 0.52].\nBased on the above thought, the answer is:\n10.5%"
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "0fUggQOU7d9qKxMu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two years with the highest percentages of occurrences?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the two years with the highest percentages, I need to compare the percentages of all the years.\nBased on the above thought, the answer is:\n2016 and 2015"
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "uDaOXl8pKFvpJWal",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the occurrences in 2017 decreased by 2%, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to subtract 2% from the original \"2017\" percentage.\nBased on the above thought, the answer is:\nThe new percentage for \"2017\" would be 9.3% - 2% = 7.3%."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "l12bIMDWPGEHzo1M",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease transform the pie chart's data into the form of a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title \"Family and Household Living Conditions\"<r>[0.176, 0.171, 0.799, 0.201]</r> and the relevant slices <r>[0.246, 0.381, 0.487, 0.76]</r>, <r>[0.416, 0.424, 0.747, 0.793]</r>, <r>[0.487, 0.313, 0.692, 0.536]</r>, <r>[0.429, 0.295, 0.578, 0.536]</r>, <r>[0.36, 0.302, 0.487, 0.536]</r>, <r>[0.335, 0.332, 0.487, 0.536]</r>, <r>[0.28, 0.314, 0.455, 0.501]</r>, <r>[0.219, 0.288, 0.399, 0.455]</r>, <r>[0.212, 0.297, 0.396, 0.457]</r>, the categories are represented by the various slices, with each corresponding percentage of the pie indicating their share in total.\nBased on the above thought, the answer is:\nLiving Condition,Percentage\nRenting,30\nOwnership,40\nShared Housing,10\nLiving with Parents,10\nCollege campus,5\nGroup Homes,2\nHomeless,2\nInstitutionalized,0.5\nHospitalized,0.5"
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "MWAEIavdQ7U3VWlp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the pie chart, which living condition has the highest percentage? And why is it significant?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I would look at all the proportions corresponding to each living condition, identifying the one with the highest percentage.\nBased on the above thought, the answer is:\nOwnership is the most common living condition according to this chart, which accounts for 40%. This high percentage implies that the majority have succeeded in owning their property, indicating a certain level of economic stability within this community."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "V3IpF630DuEc0aeN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the high percentage of Ownership suggest about living conditions?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I should analyze what a high Ownership rate reflects about the socio-economic conditions of the sampled population.\nBased on the above thought, the answer is:\nThe high percentage of Ownership at 40% indicates that many people in this community are economically secure enough to own their homes. This might reflect the general economic health of this community, suggesting a population that has a stable income source to manage and maintain housing ownership."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "OG3GosXbmPoxbq1I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf a local government is planning to focus on a demographic to provide additional housing support based on this pie chart, which group should it be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To consider this, I need to identify which living condition has the least representation in the chart.\nBased on the above thought, the answer is:\nThe local government should consider supporting the \"Institutionalized\" and \"Hospitalized\" groups, as they occupy the smallest part of the chart, indicating that these demographics might be under-supported or face significant struggles that prevent them from securing stable housing."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "i8zY4MJfO4ZNDCd1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich living condition has the most considerable percentage based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to scan through each slice on the chart and identify the one with the largest size.\nBased on the above thought, the answer is:\nOwnership."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "c1jCcV0sMLI8lKCE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of people Living with Parents increased by 5%, what would be the new total percentage distribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this out, I need to add 5% to the Living with Parents' percentage and recalculate the total.\nBased on the above thought, the answer is:\n105%."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "UiJe2vokGzAq6uQp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of those living in Shared Housing, Living with Parents and on College campus together?"
+            },
+            {
+                "from": "gpt",
+                "value": "To solve this, I add up their individual percentages.\nBased on the above thought, the answer is:\n25%."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "fxV1rn32sw4d9h8b",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart you see, how many asset classes are included and what are their investment percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart legend<empty coordinate>, I gather that there are 10 different asset classes in the pie chart. The asset classes are represented by the different slices in the pie chart {the pie chart slices}<r>[0.228, 0.283, 0.769, 0.825]</r>. I can see the investment percentages for each asset class from the annotations inside or close to each slice.\nBased on the above thought, the answer is:\nAsset Class,Investment Percentage\nStocks,20\nETF,15\nMutual Funds,12\nReal Estate,10\nCommodities,9\nBonds,12\nREITs,6\nHedge Funds,8\nDerivatives,5\nFutures,3"
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "GNfHuZQvwXdniw9j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which asset class has the largest share of the investment portfolio?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine which asset class has the largest share I must look for the largest slice on the pie chart {the pie chart slices}<r>[0.228, 0.283, 0.769, 0.825]</r>.\nBased on the above thought, the answer is:\nStocks have the largest share of the portfolio because its slice is the largest at a proportion of 20%."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "t1ePD6tYagGbRdXi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the composition of this investment portfolio reflect the risk tolerance of the investor?"
+            },
+            {
+                "from": "gpt",
+                "value": "To analyze the risk tolerance, I need to look at the distribution of investment percentages across different asset classes as depicted by the slices in the pie chart {the pie chart slices}<r>[0.228, 0.283, 0.769, 0.825]</r>.\nBased on the above thought, the answer is:\nThe portfolio indicates that the investor has a somewhat balanced approach towards risk tolerance, leaning towards a more conservative style. This is suggested by a high concentration in Stocks, ETFs, and Mutual Funds, which are typically seen as relatively low-risk investments. However, the presence of riskier asset classes such as Derivatives and Futures suggests a willingness to take on some risk for potentially higher returns."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "KkxEixv00MWzHPUf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo this portfolio suggest any reliance towards traditional investment methods? Explain your answer."
+            },
+            {
+                "from": "gpt",
+                "value": "Reliance on traditional investment methods can be inferred from the investment proportions in different asset classes shown in the slices of the pie chart {the pie chart slices}<r>[0.228, 0.283, 0.769, 0.825]</r>.\nBased on the above thought, the answer is:\nYes, the portfolio suggests a heavy reliance on traditional investment methods. This can be seen in the significantly high allocations to Stocks, ETFs, and Mutual Funds - these are known to be more traditional or standardized investment strategies. Furthermore, riskier, non-traditional asset classes such as Derivatives and Futures make up a smaller portion of the portfolio, reflecting a balanced but somewhat conservative investment approach."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "lBkzVK3k3HELECVW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of Stocks and ETFs according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I need to add up the proportions for Stocks and ETFs as annotated in their respective slices within the pie chart {Stocks}<r>[0.228, 0.38, 0.499, 0.697]</r> and {ETFs}<r>[0.269, 0.554, 0.499, 0.824]</r>.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "tXVJ12ztAJdosRk6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the least popular asset class in this investment portfolio?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart {the pie chart slices}<r>[0.228, 0.283, 0.769, 0.825]</r>, I can infer that the smallest slice represents the least popular asset class. I need to identify that slice and its associated label.\nBased on the above thought, the answer is:\nFutures"
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "u1OvTrokf1NajhHg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the new total percentage of the portfolio if the proportion for Bonds is increased by 3%?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to add the 3% to the current Bonds' proportion of 12% and then sum up all the proportions again. It is important to note that the original total is always 100%.\nBased on the above thought, the answer is:\n103%"
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "eU3Aw2tPgm9KMkmg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart you analyzed, can you extract the raw data and present in a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart {\"Product Sales Trend from 2014 to 2020\"}[0.228, 0.113, 0.772, 0.138], I can see lots of information. The pie chart consist many slices/ratios that indicate the sales proportions in different years for the products. Those years are denoted by \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\".\nBased on the above thought, the answer is:\nProduct Type,2014,2015,2016,2017,2018,2019,2020\nElectronics,105,125,150,170,165,180,225\nFurniture,95,100,120,110,125,115,130"
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "390pcvvVsSfHgjKO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product had an increasing sales trend over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Firstly, I should observe the changes in sales for each product over the years indicated in the chart.\nBased on the above thought, the answer is:\nThe product 'Electronics' had an increasing sales trend over the years, despite a slight decrease in 2018."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "tmZ7Xrk7Bh8RrRKo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer a correlation between the proportion of 'Electronics' sales in the total sales and the year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should correlate the annual proportion of 'Electronics' in the total sales with the year.\nBased on the above thought, the answer is:\nYes, we can infer that the proportion of 'Electronics' sales in total sales have generally increased over the years, indicating a growing demand or popularity for these products."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "GWxf3i3s3svLU60d",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the sales trend from 2014 to 2020, what predictions could be made for the sales of 'Electronics' and 'Furniture' in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the sales behavior of both products over the previous years to make a prediction.\nBased on the above thought, the answer is:\nIf the observed trends continue, 'Electronics' sales would likely continue to rise in 2021, potentially even more sharply. On the other hand, 'Furniture' sales could slightly recover or remain stable, based on the slight decrease observed in 2019 and the slight increase in 2020."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "ymcXxAmXmDtSJElo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the percentage for 'Electronics' sales in the year 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage of 'Electronics' sales can be represented by the slice of the pie chart, specifically the corresponding slice for the year 2017.\nBased on the above thought, the answer is:\nThe percentage for 'Electronics' sales in 2017 is 8.9%."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "ereyVJZ64z7WJ0xp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the total sales for 'Furniture' and 'Electronics' change from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the change, I need to compare the sales value of both categories from 2015 to 2020.\nBased on the above thought, the answer is:\nThe total sales for 'Electronics' changed by 100 thousands of items sold and for 'Furniture' it changed by 30 thousands of items sold."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "iLzAnOthMdN4osdS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the 'Furniture' sales decrease from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the decrease, I need to look at the 'Furniture' sales for the years 2018 and 2019.\nBased on the above thought, the answer is:\n'Furniture' sales decreased by 10 thousands of items sold from 2018 to 2019."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "8Z7tDpw1kECOOv6v",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the data from the pie chart you observed into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "I will convert the data from the pie chart into a table. The data is distributed among four sectors {\"2016\"}<r>[0.19, 0.321, 0.5, 0.696]</r>, {\"2017\"}<r>[0.244, 0.52, 0.662, 0.831]</r>, {\"2018\"}<r>[0.5, 0.345, 0.81, 0.785]</r>, and {\"2019\"}<r>[0.264, 0.179, 0.757, 0.489]</r> respectively. And just above the pie chart, there is a title {\"Popularity Trend from 2016 to 2019\"}<r>[0.17, 0.092, 0.83, 0.125]</r> that tells me the trend of some unknown fact's popularity over the years.\nBased on the above thought, the answer is:\nYear,Popularity\n2016,20.7%\n2017,24.1%\n2018,25.9%\n2019,29.3%"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "NtT0W2Ed0o502kHO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the fastest growth rate in popularity and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To figure out the year with the fastest growth rate, I need to compare the popularity percentages increase for consecutive years from 2016 to 2019.\nBased on the above thought, the answer is:\nThe fastest growth rate in popularity was in 2019, as it had an increase of 3.4%, which is the highest compared to the other years."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "7gT83I20rtIVXsyC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the chart suggest that the popularity of the subject being observed is growing, declining, or remaining constant? Provide your reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the trends in the pie chart's sections for each year.\nBased on the above thought, the answer is:\nThe chart shows that the popularity of the subject is growing. Each consecutive year from 2016 to 2019 shows an increase in popularity, which suggests a growing trend."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "SscYjCAueRc3tXVw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat might be the implications for future popularity trends based on the growth rate observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the future popularity trends, I need to look at how the popularity percentages have changed over the years in the chart.\nBased on the above thought, the answer is:\nBased on the growth rate observed in the chart, the future popularity trends are likely to continue growing given that there was a consistent increase in popularity from 2016 to 2019."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "WA1JtCb0w9uZXSx0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase in popularity from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage increase from 2016 to 2017, I need to subtract the popularity percentage of 2016 from 2017's.\nBased on the above thought, the answer is:\n3.4%"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "Ma3195FbLSkUsj1u",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall increase in popularity from 2016 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the overall increase, I need to subtract the popularity percentage in 2016 from 2019's.\nBased on the above thought, the answer is:\n8.6%"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "tRr9hXSBsMD2LnXg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the popularity in 2020 followed the same growing trend, what would be the expected popularity?"
+            },
+            {
+                "from": "gpt",
+                "value": "To predict the popularity in 2020, I need to find the average increase in popularity from 2016 to 2019 and then add it to the popularity in 2019.\nBased on the above thought, the answer is:\nDue to the growing trend, the expected popularity in 2020 would be approximately 33.7%"
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "aMzvZGgOUq33yfFW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you convert the respective successful case percentages of Surgery and Cardiology sectors in 2020 into CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart, percentages of two sectors 'Surgery' and 'Cardiology' is presented. The chart's titre {\"Successful Cases in Medical Sectors in 2020\"}<r>[0.073, 0.141, 0.894, 0.174]</r> shows the data is for the year 2020.The first pie slice, shown in coordinates {first slice}<r>[0.212, 0.346, 0.709, 0.792]</r> represents 51.8% as shown by the auto text {51.8% of autotext}<r>[0.304, 0.759, 0.304, 0.759]</r> belongs to one sector, and the second slice, shown in exploded coordinates {second slice}<r>[0.292, 0.227, 0.771, 0.649]</r> represents 48.2% from the autotext {48.2% of autotext}<r>[0.678, 0.26, 0.678, 0.26]</r> indicating the other sector.\nBased on the above thought, the answer is:\nSector,2020\nSurgery,51.8%\nCardiology,48.2%"
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "mX80gwSncnsVTrii",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is told by the chart about the Surgery and Cardiology sectors in the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to pinpoint the percentages of these two sectors and display the comparison between them.\nBased on the above thought, the answer is:\nThe pie chart shows that in 2020, Surgery had a slightly higher percentage of successful cases than Cardiology, with 51.8% to 48.2% respectively. This indicates that Surgery was slightly more successful in 2020."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "S4Q9rIx5nQIvoWRu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the pie chart help the management in decision making for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The management can take the data from the pie chart for 2020 as a reference to make decisions for 2021 possibly in resources allocation and potential enhancements for both sectors.\nBased on the above thought, the answer is:\nThe pie chart, which shows nearly equal success rates for both Surgery and Cardiology in 2020, can help the management decide to allocate resources more evenly between the two sectors in 2021. It can also point to the need for small optimizations in both fields to maintain or improve the success rate."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "xtKWovHJSeVKgVir",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat challenges might the Cardiology sector face based on the chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "To speculate on potential challenges for Cardiology, I need to consider what the slightly lower percentage of successful cases might mean.\nBased on the above thought, the answer is:\nThe pie chart shows that the Cardiology field might face challenges in catching up with the Surgery field, which had slightly more successful cases in 2020. The Cardiology field may need to improve their methods or technologies to increase their success rate."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "aDfgqbkx0IHIZOpj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGuess the dominant sector in the Medical field for 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart demonstrates the percentage of successful cases in various sectors for the year 2020. The one with the larger slice indicates the higher percentage, hence, it's the dominant sector.\nBased on the above thought, the answer is:\nSurgery"
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "txFIgEilupIwLul6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the successful cases percentages between the Surgery and Cardiology sectors in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of the Cardiology sector from the Surgery sector.\nBased on the above thought, the answer is:\n3.6%"
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "51ner8vJVs3KcYGs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAssume the number of total cases in 2020 is 10000, how many of them were successful in Cardiology sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the number of successful cases in the Cardiology sector, I need to calculate 48.2% of the total cases which is 10000.\nBased on the above thought, the answer is:\n4820"
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "cus7PjTnUsZIpxVE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you extract and present the data shown in the pie chart in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can derive the data from the pie chart by investigating {the slices of the pie chart}<r>[0.353, 0.418, 0.513, 0.586]; [0.367, 0.52, 0.591, 0.68]; [0.403, 0.35, 0.684, 0.649]</r> and align them with the corresponding technology fields.\nBased on the above thought, the answer is:\nTechnological Innovation, Cloud Technology, AI & Machine Learning, Intellectual Property\nTotal Innovations, 25, 37, 78"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "Y3jsWP9OwPcMYoZt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you offer a brief comparison of the level of innovation in each technology sector based on the pie chart dimensions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the dimensions of {the pie slices}<r>[0.353, 0.418, 0.513, 0.586]; [0.367, 0.52, 0.591, 0.68]; [0.403, 0.35, 0.684, 0.649]</r> to interpret the levels of innovation for each technology sector.\nBased on the above thought, the answer is:\nIntellectual Property had the highest level of innovation, forming the largest slice of the pie at 55.7%. Cloud Technology, while less than Intellectual Property, showed a good amount of innovation, accounting for 26.4% of the pie. Technological Innovation and AI & Machine Learning had a comparatively lower level of innovation at 17.9% and 15% respectively."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "0Oj4DH2q4lKumJNR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which sector appears to be the most innovative and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector with the largest {slice of the pie}<r>[0.403, 0.35, 0.684, 0.649]</r> would be considered the most innovative.\nBased on the above thought, the answer is:\nIntellectual Property appears to be the most innovative sector, as it constitutes the largest slice of the pie, implying the highest number of innovations."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "Z5aH5WOUHWDzHxdS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might these findings impact future technological priorities and investments?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of {the pie slices}<r>[0.353, 0.418, 0.513, 0.586]; [0.367, 0.52, 0.591, 0.68]; [0.403, 0.35, 0.684, 0.649]</r> can give a sense of which sectors are leading in innovation and might appeal to investors.\nBased on the above thought, the answer is:\nThese findings might indicate that Intellectual Property is a promising sector for future investment due to its innovative capacity. At the same time, Cloud Technology could also be an attractive investment with its moderate level of innovation. Compared to these sectors, Technological Innovation and AI & Machine Learning may have to increase their pace of development to attract more investments."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "PFUuO6Lc7ja5crBJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of innovations across all technology sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number of innovations, I need to sum the number of innovations in each sector.\nBased on the above thought, the answer is:\n140"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "aOJVHdQNKdE8Rysr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how many innovations does Intellectual Property exceed Cloud Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to subtract the number of innovations in Cloud Technology from the number in Intellectual Property.\nBased on the above thought, the answer is:\n53"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "T1FDBl5eZmL4ueTd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of innovations per sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum up the total number of innovations and divide the result by the number of sectors.\nBased on the above thought, the answer is:\n35"
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "EHs0u44xMzOqUe1i",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease extract the data from the pie chart you observed and convert it into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart {'Energy Use from 2010 to 2019'}[0.214, 0.092, 0.786, 0.125] and the pie slices correspond to different years. I can see the useage in different years of 2010-2019 according to their percentages.\nBased on the above thought, the answer is:\nYear,Petroleum,Natural Gas\n2010,8.5%,0%\n2011,7.2%,0%\n2012,8.2%,0%\n2013,10.3%,0%\n2014,9.2%,0%\n2015,11.3%,0%\n2016,12.0%,0%\n2017,11.7%,0%\n2018,11.3%,0%\n2019,10.6%,0%"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "QMBi2B1NpL5P1bFg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest proportion of Petroleum consumption? And can you infer why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to assess the size of the pie slices for the respective years in the pie chart {'Energy Use from 2010 to 2019'} [0.214, 0.092, 0.786, 0.125].\nBased on the above thought, the answer is:\nThe year 2016 had the highest proportion of Petroleum consumption, representing 12% of the total consumption for the ten years. This could be due to increased industrial activity or economic growth, leading to a higher demand for Petroleum."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "15uYaxFrhKq6YVU4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you say something about the trend of Petroleum usage over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the size of the slices in the pie chart {'Energy Use from 2010 to 2019'} [0.214, 0.092, 0.786, 0.125], I can get an overall impression of the usage trend for Petroleum from 2010 to 2019.\nBased on the above thought, the answer is:\nThe pie chart suggests that Petroleum usage generally increased from 2010 to 2016, with a slight dip in 2014. After reaching its peak in 2016, the proportion of Petroleum usage slightly decreases, which could suggest a shift towards other types of energy."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "FmcoNGWqIQ9ehV4I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the energy consumption data in this chart impact future energy policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "When considering future energy policies, the proportions of Petroleum and Natural Gas usage are important factors that policy-makers will have to take into consideration.\nBased on the above thought, the answer is:\nThe consistent increase in Petroleum usage from 2010 to 2016 would indicate a heavy dependence on this energy source. This could encourage future policies to focus on reducing this dependency for environmental reasons or for resource preservation by promoting the use of renewable energy sources or more efficient consumption practices. On the other hand, the decrease in petroleum usage in 2017 and 2018 could indicate a transitioning energy sector, which future policies could seek to enable further."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "bqYEWE0JAcmfiyqq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of Petroleum usage in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the percentage of Petroleum usage in 2015 by finding the label representing the year 2015 on the pie chart {'Energy Use from 2010 to 2019'} [0.214, 0.092, 0.786, 0.125].\nBased on the above thought, the answer is:\n11.3%"
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "OqBWLr8sKvMZTs27",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Petroleum usage dropped by 1.7% in 2017, what would be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage of Petroleum usage in 2017, I need to subtract 1.7% from the original percentage from the year 2017.\nBased on the above thought, the answer is:\nThe new percentage is 11.9%."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "dcUgyQQZJewhRbqS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage usage of Petroleum over the 10 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage usage of Petroleum, I need to add up all the percentages and divide them by the total number of years.\nBased on the above thought, the answer is:\nThe average percentage usage of Petroleum over the 10 years is 10%."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "IqyduBWX1w9mNdl8",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data that you see into a meaningful table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from the chart title {\"Creativity Trend from 2009 to 2016\"}<r>[0.204, 0.025, 0.572, 0.055]</r> and the labelled pie slices, I can tabulate the percentage representation of the 'Creativity Rating' for each year.\nBased on the above thought, the answer is:\nYear,Creativity Rating (Percentage)\n2009,10.1%\n2010,11.1%\n2011,11.9%\n2012,12.6%\n2013,13.1%\n2014,13.4%\n2015,13.7%\n2016,14.1%"
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "Up21STosNkK8iIsD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year seems to have marked the highest level of creativity?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would look for the slice with the largest proportion in order to answer this question.\nBased on the above thought, the answer is:\nThe year 2016 had the highest level of creativity as it has the largest slice representing 14.1% in the pie chart."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "gqHyhsugTrwN8Im9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the distribution of the 'Creativity Ratings' suggest about the temporal trend in design creativity from 2009 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would infer this by considering the proportions of the slices over the years.\nBased on the above thought, the answer is:\nThe 'Creativity Ratings' suggest an upward trend in design creativity from 2009 to 2016. Each year, except for between 2012 and 2013, saw an increase in its creative rating. This represents increasing innovation and creativity in the design industry over the years."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "0byc9FOTj3IlGCpW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence strategic considerations for design industries in the upcoming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This answer would require considering how the trends in creativity ratings over the years might influence future strategic directions.\nBased on the above thought, the answer is:\nThe data suggests a continuous growth in creativity and innovation in the design industry from 2009 to 2016. Therefore, design industries in the following years might focus on investing in technologies and practices that further promote creativity. They could particularly aim for more significant increments in creativity ratings, similar to the period from 2015-2016, to keep up with the previous upward trend."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "npSlRUMQOvLlMsxH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the 'Creativity Ratings' for 2016 and 2009 as inferred from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference in percentage between the values of 2016 and 2009 to find this answer.\nBased on the above thought, the answer is:\nThe difference is 4% (14.1% - 10.1%)."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "zvlUpv92IWIDbD9P",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the 'Creativity Rating' for the year 2011 was to increase by 2%, what would the new total percentage in the pie chart be?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the new total percentage, I need to increase the 2011 percentage by 2% and add up all the percentages again.\nBased on the above thought, the answer is:\nThe new total would be 102% (14.1% + 13.7% +13.4% + 13.1% + (12.6% + 2%) + 11.9% + 11.1% + 10.1%)."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "LsOtmyAfqdK2d8jb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the sum of the 'Creativity Ratings' for the first three years (2009, 2010, 2011)?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add up the percentages for these three years to get the answer.\nBased on the above thought, the answer is:\nThe sum is 33.1% (10.1% + 11.1% + 11.9%)."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "qUdBbT9eUfYZkywL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you noticed, can you structure and represent the data in a tabular CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To transform the chart into a CSV format, I'll pull the numbers and years from {the legend}<r>[0.864, 0.074, 0.97, 0.395]</r> which corresponds to the sectors of the pie chart in {the pie chart's central location}<r>[0.388, 0.519]</r>. Every sector refers to a different year and its proportionate representation of Social Responsibility Efforts.\nBased on the above thought, the answer is:\nYear,Efforts\n2012,80\n2013,85\n2014,90\n2015,98\n2016,100\n2017,105\n2018,110\n2019,115\n2020,120"
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "Sgub3D6zZuEjHjgW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how have the efforts toward social responsibility evolved over the period from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions associated with each year in the {pie chart segments}<r>[0.077, 0.104, 0.699, 0.934]</r> to interpret the trend over the years.\nBased on the above thought, the answer is:\nEfforts towards social responsibility have seen a clear increase from 2012 to 2020, with each consecutive year showing a higher effort than the last. There are some noticeable increases in certain years, showing that some years received a substantial boost in resources devoted to social responsibility."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "fAudnYPXaW1N7PIb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer any particularly important periods for social responsibility efforts based on the data journey?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look for jumps or increases in proportion within the {chart}<r>[0.077, 0.104, 0.699, 0.934]</r>.\nBased on the above thought, the answer is:\nBased on the visual data, the year 2017 seems to mark a significant increase in social responsibility action compared to previous years. This suggests this period was a crucial turning point in social responsibility initiatives."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "xo2GDUFIEFA3enui",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what might be the future expectations for social responsibility efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer an expectation for the future based on the trend represented in the {chart}<r>[0.077, 0.104, 0.699, 0.934]</r>.\nBased on the above thought, the answer is:\nBased on the chart, it's reasonable to expect that social responsibility efforts will continue to grow. Given the consistent increase in efforts from 2012 to 2020, it\u2019s likely a similar trajectory will continue into the future."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "XmII5JHC4n6L6CF3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the growth rate (in percent) from 2012 to 2020 in Social Responsibility Efforts based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth rate, I need to subtract the value of effort in 2012 from the value of effort in 2020, and divide it by the value of effort in 2012, and then multiply the result by 100.\nBased on the above thought, the answer is:\nThe growth rate from 2012 to 2020 is 50%."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "oiCSM4VzObL92fe7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the highest increase in social efforts between two consecutive years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the highest increase, I need to calculate the difference of efforts between each pair of consecutive years and find the maximum from those differences.\nBased on the above thought, the answer is:\nThe highest increase in social efforts occurred between 2015 and 2016 with an increase of 2%."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "JTYPFjzHl4qr5fxp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average of the social responsibility efforts from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I should add up all the effort values and divide by the total number of years.\nBased on the above thought, the answer is:\nThe average is approximately 100.22."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "bs3ObetvwUXUTgQN",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data presented in the pie chart into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "As evident from {'the subtitle of the chart'}<r>[0.083, 0.025, 0.693, 0.055]</r> and the pie slices {single}<r>[0.077, 0.104, 0.571, 0.934]</r>, {married}<r>[0.516, 0.183, 0.699, 0.854]</r> and {divorced}<r>[0.388, 0.104, 0.571, 0.284]</r>, which represent different relationship statuses and corresponding social networking frequencies, I can extract the relevant data.\nBased on the above thought, the answer is:\nRelationship Status,Social Networking Frequency (%)\nSingle,60\nMarried,30\nDivorced,10"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "TmnKmAWn7g8nnxeW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTaking into account the data presented in the chart, which relationship status demonstrates the highest frequency of social networking usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the proportions of each category in the chart to identify the relationship status with the highest frequency of social networking.\nBased on the above thought, the answer is:\n'Single' individuals demonstrate the highest frequency of social networking, as they account for 60% of the total, the greatest proportion among three categories."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "HIbIPsk1zx67VFXn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the pie chart, what can you infer about the relationship between relationship status and social networking usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The social networking usage can be analysed in context of the proportions represented by each relationship status in the chart.\nBased on the above thought, the answer is:\nThe pie chart indicates that 'Single' individuals use social networking the most, comprising 60% of the total usage. 'Married' individuals account for 30%, while 'Divorced' individuals constitute the smallest proportion with 10%. This suggests that social networking usage is highest among 'Single' individuals and decreases with change in relationship status."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "8X33yVOL6UqA22UO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn light of the information presented in the chart, how could marketing strategies for social networking platforms be shaped?"
+            },
+            {
+                "from": "gpt",
+                "value": "Marketing strategies could be based on the proportions of social networking usage by each relationship status.\nBased on the above thought, the answer is:\nWith 'Single' individuals occupying the largest proportion of social networking usage, marketing strategies could target this demographic more extensively. Strategies could also be devised to increase engagement among 'Married' and 'Divorced' individuals, who currently represent smaller proportions of usage."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "GNNVeupFQjWftaJc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the frequency of social networking for all relationship statuses displayed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the sum, I need to add the social networking frequencies of 'Single', 'Married', and 'Divorced' statuses.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "uPcf92iZUsk9Yx6v",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in frequency of social networking usage between 'Single' and 'Married' individuals?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I must subtract the frequency of 'Married' from that of 'Single'.\nBased on the above thought, the answer is:\n30%"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "wvOP9FovGeA8eBAR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the frequency of social networking for 'Divorced' individuals increases by 5%, what would be the new total social networking frequency?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add 5% to the current frequency of 'Divorced' and then sum up all frequencies.\nBased on the above thought, the answer is:\n105%"
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "cMbLSJPmNSNfnrOn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease generate a CSV formatted table from the pie chart containing Running and Yoga popularity trend data."
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the popularity trend of Running and Yoga in the years 2017, 2018, and 2019. Each category represented in the pie chart corresponds to a year and presents the popularity of each sport in varying colors for {2017}<r>[0.871, 0.085, 0.905, 0.101]</r>, for {2018}<r>[0.871, 0.12, 0.905, 0.136]</r> and for {2019}<r>[0.871, 0.155, 0.905, 0.171]</r>\nBased on the above thought, the answer is:\nSport,2017,2018,2019\nRunning,20,22,26\nYoga,30,35,37"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "VoY1ucwgKeNorQ7R",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, which of the two sports, Running or Yoga, has shown a larger growth trend over the period from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will compare the increase in popularity percentage of both sports from 2017 to 2019 based on the information in the chart.\nBased on the above thought, the answer is:\nRunning showed a larger growth trend than Yoga over the period from 2017 to 2019."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "5pcvrpKI2JqEwUhn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the top fitness trend for the year 2019 based on the pie chart data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to examine and compare the percentages of the two sports illustrated in the pie slices for the year 2019.\nBased on the above thought, the answer is:\nYoga was the top fitness trend in 2019 as it had a higher popularity percentage compared to Running."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "ySU5tnO6CDhCAksu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how might gym owners strategize their class offerings for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Taking into account the rising popularity of both sports, gym owners might focus on offering more Running and Yoga classes, keeping in mind that Yoga appears to be more popular.\nBased on the above thought, the answer is:\nThe data suggests that gym owners might want to continue offering Yoga classes, while also increasing the number of Running classes offered, since both activities have been gaining popularity."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "oynv9TBXw9ErvG9a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the increase in popularity percentage for Running from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in percentage for Running from 2017 to 2019, I need to subtract the popularity percentage for Running in 2017 from the popularity percentage for Running in 2019.\nBased on the above thought, the answer is:\n6%"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "aIIgkIoioLMePrwL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what was the total popularity percentage for Running and Yoga in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage, I need to sum up the percentages of Running and Yoga in 2019.\nBased on the above thought, the answer is:\n63%"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "5V5lxBmzTVZ2mPM7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average popularity percentage of Yoga across 2017, 2018, and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average popularity percentage of Yoga, I need to sum up the percentages of Yoga from 2017 to 2019 and divide it by 3.\nBased on the above thought, the answer is:\n34%"
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "Yew3lhAufCtw0W9i",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the pie chart, could you convert the data about building and real estate market share into a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the title at the top {Building and Real Estate Market Trends}<r>[0.185, 0.025, 0.591, 0.055]</r>, and the different segments in {the legend}<r>[0.789, 0.074, 0.97, 0.255]</r> and the relative percentages, I can form a CSV table from the data.\nBased on the above thought, the answer is:\nMarket Segment,Market Share(%)\nCommercial,23\nResidential,41\nRetail,19\nInfrastructure,12\nIndustrial,5"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "ik1eUscDcSxS5Qgy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector dominates the building and real estate market based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector with the highest market share percentage takes the lead in the market. This requires me to scan through all the segments and their respective percentages.\nBased on the above thought, the answer is:\nThe Residential sector dominates the market with a share of 41%."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "mCaS0LnEUFOLRB1G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart's data, what can we infer about the industrial sector's role in the building and real estate market?"
+            },
+            {
+                "from": "gpt",
+                "value": "The inference about the industrial sector's role in the market relies on its market share. I need to study its corresponding percentage value.\nBased on the above thought, the answer is:\nThe industrial sector appears to have a minimal role in the market, as it holds the smallest market share of just 5%."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "8dZyfDBDB8B8UwBY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the market share data presented in the pie chart paint a picture of the building and real estate market landscape?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to see the overall distribution of market share percentages across different sectors.\nBased on the above thought, the answer is:\nThe data suggests a market primarily driven by the residential sector, followed by commercial real estate. Smaller segments like retail, infrastructure, and industrial contribute to a lesser but still significant extent, revealing a diversified yet primarily residential-oriented building and real estate market."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "x4jMx0JYiLyutt2A",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total market share of Retail and Infrastructure sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total market share of both sectors, I add the individual market shares of both sectors.\nBased on the above thought, the answer is:\n31"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "SBdtHyISmSlZzlf9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the difference in the market share percentages between the commercial & residential segments and the retail, infrastructure & industrial segments?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I first add the market shares of commercial & residential segments and retail, infrastructure & industrial segments separately, then compute the difference between the two sums.\nBased on the above thought, the answer is:\n14"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "svHkIdnuGN8FGj9B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the least percentage market share recorded and which sector does it belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I review the market shares of all the sectors, looking for the smallest percentage.\nBased on the above thought, the answer is:\nIndustrial sector with a market share of 5%"
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "EdBgfJhb4pUiYuUx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart data for 'Emotional Frequency in Males' into a csv table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can convert the data by collecting all the pie chart slices and their corresponding percentages in the \"Emotional Frequency in Males\" chart. The chart is located at {pie chart}<r>[0.182, 0.058, 0.592, 0.468]</r>. All the slices are {slices}<r>[0.258, 0.058, 0.387, 0.151];[0.182, 0.103, 0.297, 0.347];[0.2, 0.322, 0.33, 0.451];[0.305, 0.359, 0.54, 0.468];[0.494, 0.189, 0.592, 0.4];[0.439, 0.072, 0.579, 0.211];[0.387, 0.058, 0.461, 0.129]</r>.\nBased on the above thought, the answer is:\nEmotion,Percentage\nAnxiety,10.8%\nHappiness,20.9%\nFear,11.8%\nExcitement,19.9%\nSadness,17.5%\nBoredom,13.3%\nDisgust,5.9%"
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "6gztmIsBiXJqZKv9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn \"Emotional Frequency in Males\", what emotion has the highest frequency and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the percentages of each emotion in the \"Emotional Frequency in Males\" chart to find out which has the highest frequency.\nBased on the above thought, the answer is:\n\"Happiness\" has the highest frequency at 20.9%, the reason being perhaps men generally show more happiness than any other emotion."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "tGL9RWyBQFNxFC9q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat observations can you make based on the comparison of the \"Emotional Frequency in Males\" and \"Emotional Frequency in Females\" charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "To respond to this, I have to analyze both charts and compare the proportions of each emotion for males and females.\nBased on the above thought, the answer is:\nBoth Males and Females show similar patterns of emotional frequency, with an especially high frequency in the categories of Happiness and Sadness. However, females tend to experience emotions with a higher frequency compared to males across the board, excluding Excitement and Boredom."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "XrgSa5QKSE9qxutd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this chart is presented to a mental health organization, how could they potentially use it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The organization could use the chart to identify the most common emotional states in males and females, and tailor their mental health services and programs accordingly.\nBased on the above thought, the answer is:\nA mental health organization could use this chart to understand the frequency of various emotions in different genders. It could help them tailor their mental health services to better suit the needs of each gender. For example, they might focus on addressing Happiness, Sadness, and Excitement for males, and spend additional resources to address the broader range of emotions experienced more frequently by females."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "XONvD93J2KQZM3Yw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the male's emotional frequency chart, which two emotions combined make almost 35.8%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this, I need to add the percentages of the emotions provided in the chart for males which are {emotions for males}<r>[0.258, 0.058, 0.387, 0.151],[0.182, 0.103, 0.297, 0.347],[0.2, 0.322, 0.33, 0.451],[0.305, 0.359, 0.54, 0.468],[0.494, 0.189, 0.592, 0.4],[0.439, 0.072, 0.579, 0.211],[0.387, 0.058, 0.461, 0.129]</r> and check the pair whose sum is close to 35.8% or equal to 35.8%.\nBased on the above thought, the answer is:\nHappiness and Boredom."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "AqZSRfY2p9759hUg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the female emotional frequency chart, what is the total percentage of the top three highest frequency emotions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the top three highest frequency emotions from the given female's emotional frequency chart, then I can get their respective percentages and sum them up.\nBased on the above thought, the answer is:\n61.7%."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "dJPFI62BpPAKBlTc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong all emotional states, what is the total frequency percentage of the most dominant emotional state identified in both categories?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to identify the most dominant emotional state in both male and female categories. Then, I have to sum up the percentage they account for in both categories.\nBased on the above thought, the answer is:\n36.5%."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "FhNYbTv9R09ZsvCm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the data in the two donut type pie charts you observed into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The pie charts are given two titles {\"Social Network Usage - Group 1\"}[0.164, 0.019, 0.329, 0.041] and {\"Social Network Usage - Group 2\"}[0.623, 0.019, 0.788, 0.041] respectively. Then, each pie chart represents data from four categories {\"Sharing Photos\"}[0.226, 0.305, 0.248, 0.323], {\"Messaging\"}[0.172, 0.347, 0.199, 0.364], {\"Posting Updates\"}[0.142, 0.591, 0.17, 0.608] and {\"Others\"}[0.333, 0.53, 0.36, 0.548] and these coordinates serve as the legends for the pie charts. This tells me that each pie chart represents the distribution of the social network activities for a group of individuals.\nBased on the above thought, the answer is:\nSocial Aspect, Social Network Usage - Group 1, Social Network Usage - Group 2\nSharing Photos, 3%, 2%\nMessaging, 15%, 5%\nPosting Updates, 28%, 22%\nOthers, 54%, 71%"
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "EVvjcaeHAb1X27WU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, Which social network activity is the least popular in both groups? And explain the reasons."
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to analyze the percentage usage of all categories in both groups. The least popular activity is the one with the lowest usage percentage.\nBased on the above thought, the answer is:\nThe least popular social network activity is \"Sharing Photos\" because it has the lowest usage percentage in both groups (3% in Group 1 and 2% in Group 2)."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "bpb3pwNdr6Wa3GNv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about social network use across the two groups from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make inferences, I need to compare the percentage usage of each category across the two groups.\nBased on the above thought, the answer is:\nFrom the chart, it can be inferred that both groups engage more in \"Others\" and \"Posting Updates\" activities, with a larger interest in \"Others\". However, Group 1 has a slightly more balanced usage across all four categories, while Group 2's user behavior is heavily skewed towards \"Others\"."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "lrx1FtYr4dppdwuU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence technology investments in social network platforms moving forward?"
+            },
+            {
+                "from": "gpt",
+                "value": "The investment decisions can be inferred based on the proportion of users engaged in different social media activities.\nBased on the above thought, the answer is:\nThe data shows that a substantial proportion of users engage in \"Others\" activities apart from \"Sharing Photos\", \"Messaging\", and \"Posting Updates\". This suggests that investing in diverse features and not just the mainstream categories could potentially attract a larger user base. Furthermore, increasing the functionalities of \"Posting Updates\" and improving the user experience around it could retain the current user interest in this activity."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "QAZQGGx3ibWusRYA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn Social Network Usage - Group 2 which two activities that when added, contribute never more than a tenth of the total social network usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I need to analyze the provided information to calculate the sum of usage percentage for each pair of activities and then identify the pairs with 10% or less usage.\nBased on the above thought, the answer is:\n\"Sharing Photos\" and \"Messaging\""
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "fyF0Itj2zVE7REmx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between \"Others\" categories in Social Network Usage - Group 1 and Group 2?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to subtract the percentage of \"Others\" in Group 1 from that in Group 2.\nBased on the above thought, the answer is:\nThe percentage difference is 71% (Group 2) - 54% (Group 1) = 17%."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "qCImvfn9K7YC0XDB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much is the total usage percentage for \"Sharing Photos\" and \"Messaging\" in both groups?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total usage, I need to sum the usage of \"Sharing Photos\" and \"Messaging\" in both groups.\nBased on the above thought, the answer is:\nThe total usage percentage is 3% (Group 1) + 15% (Group 1) + 2% (Group 2) + 5% (Group 2) = 25%."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "DXHfx4yioiF4wDd5",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the dataset that was used to create this pie chart in a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart title {\"Fitness Trends: Yoga vs Running (2021)\"}[0.185, 0.025, 0.591, 0.055] and {the slices of the pie chart}[{\"Yoga\"}[0.077, 0.104, 0.388, 0.905]; {\"Running\"}[0.276, 0.104, 0.699, 0.934]], I observe that the data represents the participation in Yoga and Running for the year 2021.\nBased on the above thought, the answer is:\n\"Fitness Activity\",\"2021\"\n\"Yoga\",\"44.1%\"\n\"Running\",\"55.9%\""
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "mN0laOuk1PXFWNFT",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which fitness activity was more popular in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The fitness activity that was more popular can be identified by the one with the higher proportion in the pie chart.\nBased on the above thought, the answer is:\nRunning was more popular than Yoga in 2021."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "1C6YTjNQLziZ2Itr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can we gain about fitness enthusiasts' interest in Yoga and Running in 2021 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the proportions of participation in Yoga and Running will provide insights into their relative popularity.\nBased on the above thought, the answer is:\nThe chart shows that in 2021, more fitness enthusiasts were interested in Running than in Yoga. Despite Running's dominance, a significant proportion of almost 44.1% was interested in Yoga."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "XH88y4kVP6p0lfZN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data inform fitness program planning for an organization in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "The organization may consider the relative popularity of Yoga and Running in designing future fitness programs.\nBased on the above thought, the answer is:\nGiven that Running is the more popular activity, organizations might decide to invest more in Running programs. However, considering the notable interest in Yoga, they may also consider investing in Yoga programs to cater to a broad range of fitness interests."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "Z9QZajK14GhYY7rZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the difference between the participation percentages of Running and Yoga in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be determined by subtracting the participation percentage of Yoga from that of Running.\nBased on the above thought, the answer is:\n11.8%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "fYXtbQaGIqAtLIwV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of participation in Yoga increases by 10% next year, what will be the new proportions for Yoga and Running?"
+            },
+            {
+                "from": "gpt",
+                "value": "Add 10% to the current value of Yoga and subtract 10% from the current value of Running.\nBased on the above thought, the answer is:\nYoga will be 54.1% and Running will be 45.9%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "Tz991XsbA4nHW4kA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions of participation in Yoga and Running?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sum up the proportions of participation in Yoga and Running.\nBased on the above thought, the answer is:\nThe sum is 100%."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "j2fvy9UUts4zIeyU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart information into a CSV format table showing the proportion of expenditure on 'Social Welfare' and 'Public Services' for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can extract the necessary information from the title at the top {Expenditure on Social Welfare and Public Services from 2012 to 2020}<r>[0.028, 0.025, 0.748, 0.055]</r> and the legend sections representing each year. I can use the data of the percentage associated with each pie sector and merge this with the year provided in the legend to generate the CSV format.\nBased on the above thought, the answer is:\nYear,Social_Welfare,Public_Services\n2012,12.0%,88.0%\n2013,10.5%,89.5%\n2014,11.3%,88.7%\n2015,13.5%,86.5%\n2016,13.2%,86.8%\n2017,10.2%,89.8%\n2018,10.5%,89.5%\n2019,9.8%,90.2%\n2020,9.0%,91.0%"
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "NXDphPtUtXTKtCnO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow did the expenditure on Social Welfare change over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the changes in the percentage values for each year on the pie chart to see the trend.\nBased on the above thought, the answer is:\nThe expenditure on Social Welfare has generally declined over the years from 2012 to 2020, with a slight increase in 2015."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "ke9uEa5E0Cch8rnc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, was there a consistent increase or decrease in expenditure on Social Welfare over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to inspect the changes in proportion for Social Welfare expenditure over the years to understand the trend.\nBased on the above thought, the answer is:\nBased on the chart, there is not a consistent increase or decrease in the expenditure on Social Welfare over the years. The expenditure varies each year and is not following a specific consistent trend."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "DSl0y7RI1zsIpRF7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what could be inferred regarding the priority of Social Welfare within the national budget over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would consider the changes in the expenditure ratios over time, which are represented by the respective pie slice sizes in the chart.\nBased on the above thought, the answer is:\nIt can be inferred that the priority of Social Welfare within the national budget has generally decreased over the years from 2012 to 2020. Despite a temporary increase in priority in 2015, the overall trend shows a decline in the proportion of national budget allocated to Social Welfare."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "5FVxcO6iSG3uQeDe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage expenditure on Social Welfare over the years 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I first sum up the percentages of the expenditure on Social Welfare depicted on the pie chart for each year and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average percentage expenditure on Social Welfare from 2012 to 2020 is 11.2%."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "IRIsvZxVeK2LQk0C",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the expenditure on Social Welfare the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the percentages of the expenditure on Social Welfare for each year as shown in the pie chart.\nBased on the above thought, the answer is:\nThe expenditure on Social Welfare was the highest in the year 2015."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "GqD0WTEGFuJHeypt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total expenditure percentage on Social Welfare in the years 2018, 2019, and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total expenditure percentage on Social Welfare in the years 2018, 2019, and 2020, I need to sum up the corresponding percentages from the pie chart.\nBased on the above thought, the answer is:\nThe total expenditure percentage on Social Welfare in the years 2018, 2019, and 2020 is 29.3%."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "4YkHgmPFA2GD1myF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie charts you see, transform Facebook and Instagram's bond type percentages into CSV data tables."
+            },
+            {
+                "from": "gpt",
+                "value": "The titles {\u201cBond Types Distribution on Facebook\u201d}[0.309, 0.012, 0.691, 0.027] and {\u201cBond Types Distribution on Instagram\u201d}[0.305, 0.503, 0.695, 0.518] indicate these pie charts represent the data of each social network. The slices for {\u201cStrong Bonds\u201d}[0.321, 0.181, 0.375, 0.193], {\u201cWeak Bonds\u201d}[0.531, 0.374, 0.585, 0.386] and {\u201cIndifferent Bonds\u201d}[0.583, 0.154, 0.638, 0.165] are coded for each bond type with percentages within each circle.\nBased on the above thought, the answer is:\nFacebook:\nType,Percentage\nStrong Bonds,30%\nWeak Bonds,50%\nIndifferent Bonds,20%\n\nInstagram:\nType,Percentage\nStrong Bonds,15%\nWeak Bonds,70%\nIndifferent Bonds,15%"
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "SkkhrVADb2Qh5LJV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which type of bond is most common on Instagram and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the bond type distribution in the Instagram pie chart, I can determine which bond type is most common.\nBased on the above thought, the answer is:\n\"Weak Bonds\" are most common on Instagram because it occupies the largest proportion at 70%."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "zy1ekQDRzBGKWeIu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the distribution of bond types differ between Facebook and Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the difference, I need to compare the percentages of the types of bonds in the pie charts of {Facebook}<r>[0.187, 0.052, 0.813, 0.47]</r> and {Instagram}<r>[0.246, 0.543, 0.5, 0.666]</r>.\nBased on the above thought, the answer is:\nFacebook has a higher percentage of strong bonds and lower weak bonds compared to Instagram, which has a significantly higher percentage in weak bonds. The distribution of indifferent bonds is the same on both platforms."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "fcuhDL3pKwwYd8nL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what insight do you gain about the types of relationships users build on the Facebook vs Instagram platforms?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the \"Bond Types Distribution\" on both platforms as shown in the charts.\nBased on the above thought, the answer is:\nThe charts suggest that Facebook users tend to maintain a balance between strong and weak bonds, with a slightly larger number of weak bonds. On the other hand, Instagram users seem to predominantly cultivate weak bonds, with the percentage of strong bonds being much lower. This could suggest that Facebook is used more for personal, close relationships, while Instagram might be used for more casual social interaction."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "sU9KYy3jrElWKB20",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich platform has a higher proportion of Strong Bonds?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should look at the relative area devoted to \"Strong Bonds\" on the pie charts for both {Facebook}<r>[0.187, 0.052, 0.5, 0.326]</r> and {Instagram}<r>[0.246, 0.543, 0.5, 0.666]</r>.\nBased on the above thought, the answer is:\nFacebook."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "OAvEgW79FbsswQxP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of \"Indifferent Bonds\" decrease by 5% on Instagram, what will be the new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To fulfil this, I need to subtract 5% from the existing \"Indifferent Bonds\" segment percentage on the Instagram pie chart.\nBased on the above thought, the answer is:\nThe new percentage for \"Indifferent Bonds\" on Instagram would be 15% - 5% = 10%."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "uboeTmRdsD5P2o7Z",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in percentage between \"Weak Bonds\" on Facebook and Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should subtract the \"Weak Bonds\" percentage on Instagram from that on Facebook.\nBased on the above thought, the answer is:\nThe difference in percentage between \"Weak Bonds\" on Facebook and Instagram is 50% - 70% = -20%."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "b8AhcviZhfh2OMTu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the provided pie chart, could you convert the recorded data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Starting with the title I can see from {the chart title}[0.119, 0.025, 0.657, 0.055], we're dealing with product sales trends from 2013 to 2020. There are two product lines, Macbook and iPhone as indicated by two separate donut diagrams. The sales data for each year is represented by a different color and can be linked back to their corresponding years in the {legend}[0.864, 0.074, 0.97, 0.639]. Therefore, from identification, I can conclude that we have yearly sales data for two products spanning from 2013 to 2020. Let's list these down.\nBased on the above thought, the answer is:\nYear,Macbook,iPhone\n2013,60,85\n2014,72,90\n2015,85,95\n2016,87,100\n2017,95,110\n2018,99,120\n2019,105,130\n2020,190,200"
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "vMX6P4ewZvZbcU4x",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which product was more popular, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The most popular product would be the one with the highest sales in most years.\nBased on the above thought, the answer is:\nThe iPhone was more popular because its sales were consistently higher than those of the Macbook every year."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "89dSG3beDIqjZzQV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the platforms' performance and popularity from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Performance and popularity can be inferred from the trends in sales over the years.\nBased on the above thought, the answer is:\nBoth the Macbook and iPhone showed increasing sales trends, suggesting increasing popularity over the period. However, the iPhone was more popular, as its sales were consistently higher."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "nPx54cYLy6SYx3Su",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data on the chart influence the marketing strategy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data can guide marketing strategies by highlighting where to focus more efforts and investment.\nBased on the above thought, the answer is:\nGiven the larger sales volume of the iPhone, the company could focus its marketing efforts on this product to sustain this trend. However, the significant increase in Macbook sales in 2020 suggests potential growth, which also warrants attention in the marketing strategy."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "zQEWJitNNeGg1xMm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many units did the total sales of Macbooks and iPhones increase by from 2013 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total increase in sales, I need to take the total sales values from 2020 and subtract the total sales values from 2013.\nBased on the above thought, the answer is:\nThe total sales increased by (190 + 200) - (60 + 85) = 245 units."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "bcya8uLEDKXEYgUo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average annual increase in iPhone sales over the seven-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average annual  increase, I need to subtract the 2013 sales value from the 2020 sales value and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average annual  increase was (200 - 85) / 7 = 16.42 units."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "vPxSdcKHsmvnOsBh",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the Macbook see the highest growth in sales compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to consider the change in sales values between each consecutive year.\nBased on the above thought, the answer is:\nThe Macbook saw the highest growth in sales in 2020."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "z34Lou5auF8gthPB",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart information into a data table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To create this table, I need to identify each section of the pie chart and correspond that to its respective year and percentage value. The chart's {legend}<r>[0.864, 0.074, 0.97, 0.255]</r> will provide us with the names of the data series, the {title}<r>[0.072, 0.025, 0.704, 0.055]</r> gives us the year, and the individual data points are within the {pie slices}<r>[0.127, 0.104, 0.388, 0.361]</r>, <r>[0.077, 0.294, 0.205, 0.72]</r>, <r>[0.116, 0.66, 0.46, 0.934]</r>, <r>[0.438, 0.488, 0.699, 0.922]</r>, and <r>[0.388, 0.104, 0.698, 0.497]</r> which represent the percentage each year's Entertainment Factor contributes.\nBased on the above thought, the answer is:\nYear,Entertainment Factor\n2015,15.9%\n2016,17.2%\n2017,20.6%\n2018,22.5%\n2019,23.8%"
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "4jLvxtda2W6zw2du",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year has shown the highest Entertainment Factor contribution as per the chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the year with the highest percentage, I will look at the {pie slices}<r>[0.127, 0.104, 0.388, 0.361]</r>, <r>[0.077, 0.294, 0.205, 0.72]</r>, <r>[0.116, 0.66, 0.46, 0.934]</r>, <r>[0.438, 0.488, 0.699, 0.922]</r>, and <r>[0.388, 0.104, 0.698, 0.497]</r>.\nBased on the above thought, the answer is:\nThe Entertainment Factor rose to its highest level in 2019, with a contribution of 23.8%. The increase in the entertainment factor over the years suggests a steady growth or increased consumer interest in the media and entertainment industry."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "xa0xdpwIVhh5mS8G",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insight can you provide for the trend of Entertainment Factor from 2015 to 2019 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand the trend, I need to examine the proportional increase or decrease in the Entertainment Factor over the provided years. This can be achieved by looking at the distribution of years across the {pie chart}<r>[0.077, 0.104, 0.699, 0.934]</r>.\nBased on the above thought, the answer is:\nThe pie chart indicates a consistent increase in the Entertainment Factor from 2015 to 2019. This upward trend suggests steady growth or expansion in the media and entertainment industry over these years."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "VV8H1U6LKPRQnh3W",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat impact might the growing Entertainment Factor have on future investments in the Media and Entertainment Industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can infer investment impacts by examining the upward trend of the Entertainment Factor over the years as shown by the {pie chart slices}<r>[0.127, 0.104, 0.388, 0.361]</r>,<r>[0.077, 0.294, 0.205, 0.72]</r>,<r>[0.116, 0.66, 0.46, 0.934]</r>,<r>[0.438, 0.488, 0.699, 0.922]</r>, and <r>[0.388, 0.104, 0.698, 0.497]</r>.\nBased on the above thought, the answer is:\nThe steady increase in the Entertainment Factor from 2015 to 2019 suggests that the Media and Entertainment Industry is growing and attracting more consumer interest over time. This could indicate a promising return for future investments in this industry as it is likely to continue growing in popularity."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "ybnqsUCQmQ2aT4e0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year did the Entertainment Factor contribute over 20% to the total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at each slice of the pie chart and find the year label with a percentage contribution over 20%. The year labels are in the {pie slices}<r>[0.127, 0.104, 0.388, 0.361]</r>, <r>[0.077, 0.294, 0.205, 0.72]</r>, <r>[0.116, 0.66, 0.46, 0.934]</r>, <r>[0.438, 0.488, 0.699, 0.922]</r>, and <r>[0.388, 0.104, 0.698, 0.497]</r>.\nBased on the above thought, the answer is:\n2017."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "wdTa0K3ccILizFj5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total percentage contribution of the years 2018 and 2019 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this question, I need to find the {pie slices}<r>[0.438, 0.488, 0.699, 0.922]</r> and <r>[0.388, 0.104, 0.698, 0.497]</r> that correspond to the years 2018 and 2019, respectively, and add the percentage contributions together.\nBased on the above thought, the answer is:\n46.3%."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "EdD0j3vnG7Z18TBp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the change in the Entertainment Factor from 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change, I need to compare the percentage contribution in 2015 represented by the {pie slice}<r>[0.127, 0.104, 0.388, 0.361]</r> to that of 2019 represented by the {pie slice}<r>[0.388, 0.104, 0.698, 0.497]</r>.\nBased on the above thought, the answer is:\n7.9%."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "PhLEvkdLhcHjEaIx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your analysis of the chart, can you convert the data represented for Urban and Rural development from 2013 to 2020 into CSV form?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the label at the top of the chart {Rural and Urban Development Comparison from 2013 to 2020}<r>[0.064, 0.025, 0.712, 0.055]</r>, I can identify that the chart represents urban and rural development over various years. The pie slices correspond to various years and their corresponding development scores, represented by the color legends.\nBased on the above thought, the answer is:\nYear,Urban Development,Rural Development\n2013,65,30\n2014,75,35\n2015,80,40\n2016,90,50\n2017,100,55\n2018,120,65\n2019,135,70\n2020,150,75"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "RqPfsAqADIGm0RaV",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you comment on the development trend in urban areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze this by examining the increments in the proportion of the slices representing urban development from 2013 to 2020.\nBased on the above thought, the answer is:\nThe chart demonstrates a steady increase in urban development over the years. The development pace seems significantly higher particularly between 2017 and 2019."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "ZFplU25sCqachYLH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data represented, what inference can be made about rural development between 2013 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I infer by analyzing the pie slices representing rural development scores for each year.\nBased on the above thought, the answer is:\nRural development has been increasing at a slower pace than urban areas. However, the growth is consistent and covers a decent percentage every year."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "oZzZi2RiTpmpGfST",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data influence infrastructure development strategies in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would involve considering the upward trend in development rates in both rural and urban areas.\nBased on the above thought, the answer is:\nConsidering the steady rural development growth and the accelerated urban development, especially in recent years, urban areas might see continued high-resource allocation for infrastructure development. However, for a balanced approach, policymakers might also consider strategies to accelerate development in rural areas."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "NIaZEqsFcbq7qahQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom your examination of the pie chart, in which year did urban development surpass rural development by more than twice?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the development scores in urban and rural areas for each year, I can determine the year in which urban development was more than twice that of rural development.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "VelIK36lVOu3oE8q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf rural development continued to grow at the same rate, what would be the estimated development score for rural areas in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will calculate the average year-on-year growth rate for rural areas from 2013 to 2020 and then apply this growth rate to the rural development score of 2020 to estimate the score for 2021.\nBased on the above thought, the answer is:\n80"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "VDMqZJnb1DuNZCFD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of urban and rural development across all years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the total sum by adding up the development scores for both urban and rural areas from 2013 to 2020.\nBased on the above thought, the answer is:\n1215"
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "uer906DSO23wniFP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConvert the distribution of finance items over the years shown in the pie chart into a CSV table format."
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart, I can see five segments, each representing a year's distribution of financial items from 2016 to 2020 with different colors. Using the percentage distribution of each year in the pie chart and correlate it with the captions {the legend}<r>[0.864, 0.074, 0.97, 0.255]</r> , one can conclude the distribution for each year.\nBased on the above thought, the answer is:\nYear,Financial Assets,Investment\n2016,18.7%,18.7%\n2017,19.1%,19.1%\n2018,20.3%,20.3%\n2019,20.2%,20.2%\n2020,21.7%,21.7%"
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "smFbwP6Ztf87Nrkv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the greatest percentage of financial assets and investment? Describe the reasoning."
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I will have to look at the size of each slice in the pie chart by its percentage.\nBased on the above thought, the answer is:\nThe year 2020 had the highest proportion of financial assets and investments. This is represented by the largest slice in the pie chart {2020 slice}<r>[0.388, 0.103, 0.69, 0.455]</r> showing 21.7% distribution which is the largest amongst all the years."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "CTrm0xiebRRAxayl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom this chart, can any inferences be made on the state of financial assets and investment over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference about the changes in the financial assets and investments over the years, a comparison can be made of the slices' sizes for each year in the donut chart. The larger the slice, the higher the percentage of the financial assets and investments.\nBased on the above thought, the answer is:\nYes. The chart suggests that there has been a steady relative growth in financial assets and investments from 2016 to 2020, with 2020 marking the highest proportion of all."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "hjS1djXnoufYbnZ9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data presented in this chart influence the investment decisions for the future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "This determination requires considering the trend of asset and investment growth over the past years.\nBased on the above thought, the answer is:\nGiven that a progressive increase in the proportion of financial assets and investments is noted from 2016 to 2020, an investor might be influenced to continue investing in the future, expecting a further increase."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "rDTFHomiE9k0un0s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the investments and financial assets have the same percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I need to compare the percentages of each year. From each slice, I can see that the investment and financial assets in each year are the same.\nBased on the above thought, the answer is:\nAll years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "9K8FyYLjOKbAGl68",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of all the years in total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average, I need to add all the percentages and then divide by the number of years.\nBased on the above thought, the answer is:\nThe total percentage is 100%. Since there are 5 years, the average is 100% / 5 = 20%."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "z6NRmZsMUyGsxXX4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage in the year 2020 increased by 2%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total, I should just add the increase to the total percentage.\nBased on the above thought, the answer is:\nIf 2020 increased by 2%, the new total percentage would be 102%."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "2RLwxuxojHmLIXvM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the pie chart with the title {Medical Expenditure from 2013 to 2020}<r>[0.182, 0.025, 0.594, 0.055]</r> and respective sectors denoting different years with their expenditure values; {\"2013\"}<r>[0.304, 0.103, 0.388, 0.237]</r>, {\"2014\"}<r>[0.198, 0.119, 0.329, 0.288]</r>, {\"2015\"}<r>[0.105, 0.191, 0.255, 0.4]</r>, {\"2016\"}<r>[0.081, 0.352, 0.19, 0.62]</r>, {\"2017\"}<r>[0.091, 0.588, 0.29, 0.878]</r>, {\"2018\"}<r>[0.248, 0.761, 0.543, 0.923]</r>, {\"2019\"}<r>[0.496, 0.439, 0.695, 0.867]</r> and {\"2020\"}<r>[0.388, 0.103, 0.69, 0.461]</r>, it is clear that each sector represents a specific year and its corresponding expenditure percentage.\nBased on the above thought, the answer is:\nYear,Expenditure_Percentage\n2013,4.4%\n2014,6.2%\n2015,8.0%\n2016,10.6%\n2017,13.3%\n2018,15.9%\n2019,19.5%\n2020,22.1%"
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "K8AW8srIjwSmZ7od",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the general trend of medical expenditure from the year 2013 to 2020 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the pie chart, I can see that the medical expenditure has been steadily increasing over the years from 2013 to 2020 since the size of each sector representing their respective years seems to be increasing.\nBased on the above thought, the answer is:\nThe general trend of medical expenditure from 2013 to 2020 has been increasing as evidenced by the increasing sector sizes in the pie chart."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "imCv6av69hrQUbWr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart, which year was observed to have the least medical expenditure?"
+            },
+            {
+                "from": "gpt",
+                "value": "By reviewing the pie chart, I can identify the smallest sector which would represent the year with the least medical expenditure.\nBased on the above thought, the answer is:\nThe year 2013 has the least medical expenditure since its corresponding sector is the smallest in the pie chart."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "wMVbDUPamRW5IkEU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about the change in medical expenditure over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sectors in the pie chart indicate the relative proportion of medical expenditure for each year, thereby showing how the expenditure has changed over the years.\nBased on the above thought, the answer is:\nThe chart shows that medical expenditure has been increasing progressively from 2013 to 2020. This is indicated by the increasing segment sizes in the chronological arrangement of years in the pie chart."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "QX52yYV3WVgfoqVE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year has the highest expenditure percentage, and what is the percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest expenditure percentage, I can compare the sizes of the different pie segments with each year.\nBased on the above thought, the answer is:\nYear 2020 has the highest expenditure percentage of 22.1%."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "EXqRV0yRiUcW1H0Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you calculate the total expenditure from 2013 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total expenditure from 2013 to 2016, I would need to add up the expenditure percentages for these years.\nBased on the above thought, the answer is:\nThe total expenditure from 2013 to 2016 is 4.4% + 6.2% + 8.0% + 10.6% = 29.2%."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "VOfv4u6TiOD7969J",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the medical expenditure for the year 2017 represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The medical expenditure for the year 2017 is represented by one specific sector in the chart.\nBased on the above thought, the answer is:\nThe medical expenditure for the year 2017 represents 13.3%."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "ECN6x0lgIxew9sF6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you detected, could you extract the data and generate a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the chart's title {E-commerce and Retail Industry Analysis from 2014 to 2020}[0.075, 0.025, 0.701, 0.055], and multiline legend with years and colors [0.864, 0.074, 0.97, 0.325], I can identify that each year has a separate representation through each pie sliced parts. Using the details provided within each slice, I can generate a CSV table.\nBased on the above thought, the answer is:\nYear,E-commerce (%),Retail (%)\n2014,25,75\n2015,30,70\n2016,40,60\n2017,45,55\n2018,50,50\n2019,58,42\n2020,68,32"
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "EnVsyeuGTGWfg9h1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trends in the Retail and E-commerce sectors over the period 2014-2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Interpretation would demand careful examination of each pie slice, as well as the labelled percentages in each sector of the legend, for every single year from 2014 to 2020.\nBased on the above thought, the answer is:\nThe E-commerce sector shows a rising trend over these years, increasing its market share from 25% in 2014 to 68% in 2020. As a contrast, the Retail sector has been experiencing a declining trend, with its market share dropping from 75% in 2014 to 32% in 2020. This indicates a strong shift in the industry with consumers increasingly preferring E-commerce over traditional retail."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "EViO8uekJIufYHtc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow will COVID-19 affect the trends observed from this chart between E-commerce and Retail sector growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would require me to consider the last data points on the pie chart, which were 2019 and 2020, along with the explanation provided in the chart's legend.\nBased on the above thought, the answer is:\nIf we observe the trends in the pie chart, 2019 and 2020 see a significant rise in E-commerce's market share (from 58% to 68%) and a drop in the Retail sector (from 42% to 32%). Given that the COVID-19 pandemic occurred precisely during this time, it may have accelerated the ongoing shift towards E-commerce, as more people were forced to shop online due to social distancing and quarantine measures."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "780tJ7NJWxScJ0Eh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications do you see on the Retail sector seeing the massive growth of the E-commerce sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the changes in the Retail and E-commerce sectors' market shares over the seven years covered by the pie chart.\nBased on the above thought, the answer is:\nThe massive growth of the E-commerce sector, summarized by its increase in market share from 25% to 68% over seven years, has consequentially led to a shrinking market for the Retail sector, which dropped from 75% to 32% in the same timeframe. If these trends continue, physical retailers may need to develop online shopping platforms or specialized in-person shopping experiences to maintain their customer base in the face of growing online competition."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "dwee4hXAvhtPzZTS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage of the E-commerce industry in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I could locate the sector of 2017 from the legend and it was {color: '#ffcc99', coordinates: '[0.871, 0.19, 0.905, 0.206]'}. I would then need to look into that slice in the pie chart and correctly identify the percentage labelled.\nBased on the above thought, the answer is:\nThe E-commerce industry had a 45% share in 2017."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "Sgb0rS9bv0xeFdpf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Retail industry's percentage drop from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Calculation starts by identifying the part of the sector labelled \"2014\". This is followed by the part labelled \"2020\". I then need to calculate the difference between the Retail industry's percentages in those two years.\nBased on the above thought, the answer is:\nThe Retail industry's percentage fell by 43% from 2014 to 2020."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "1i7fVfKzSTIjRPjs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the sum of the Retail industry's market share from 2014 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I have to add up the Retail industry's percentages for 2014, 2015, and 2016.\nBased on the above thought, the answer is:\nThe sum of the Retail industry's market share from 2014 to 2016 is 205.0%."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "TBQHgeYlLuvn1EJU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a CSV table from the pie chart data for the years 2007 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the {legend}<r>[0.864, 0.074, 0.97, 0.36]</r>, I can see each year represented by its unique color. Understanding that each slice corresponds to a year's market trend ratio, I can convert these percentages into a CSV format.\nBased on the above thought, the answer is:\nYear,Market Trend Percentage\n2007,6.6%\n2008,13.1%\n2009,9.8%\n2010,11.5%\n2011,8.2%\n2012,14.8%\n2013,19.7%\n2014,16.4%"
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "OESMXClm0YlOI07e",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDiscuss the overall trend in the financial market as shown by the chart from 2007 to 2014."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to consider the trends depicted across the different years and their respective percentages.\nBased on the above thought, the answer is:\nThe overall trend in the financial market from 2007 to 2014 is highly fluctuating. The lowest point was in 2007 with 6.6%, and the highest peak happened in 2013 with 19.7%. The change is not linear, and it undergoes waves of growth and contraction, signifying a highly volatile market."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "jAiR9C55yLGntW7P",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data from 2007 to 2014, was the financial market mostly rising or falling?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I'll consider the rate of change from year to year, accounting for both upswings and downswings.\nBased on the above thought, the answer is:\nOverall the financial market showed a rising trend more often than it fell over these years. The biggest increase occurred between 2007 and 2008, but the highest point didn't occur until 2013, indicating an overall upward trend."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "y7mxNDvrt6jeh4Gg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat factors could have contributed to the trend observed in 2013?"
+            },
+            {
+                "from": "gpt",
+                "value": "For an analysis, let's consider the economic and financial situations that could influence the financial market.\nBased on the above thought, the answer is:\nMany factors could've influenced the 2013 boom in market trends. These can include government fiscal policy, central bank interest rates, national and international events, technological advancements, or demographic trends. However, this chart does not provide specific information needed to draw a definitive conclusion."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "PYxq4oPP5f20xBDN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nUsing the data from the pie chart, in what year was the market trend the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By checking each slice of the pie chart, I can identify the slice that covers the largest portion, signifying the highest market trend.\nBased on the above thought, the answer is:\n2013."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "Z8OYz5WTYAPr7Y9l",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the lowest market trend?"
+            },
+            {
+                "from": "gpt",
+                "value": "Similar to identifying the highest year, I can find the smallest slice to determine the year with the lowest market trend.\nBased on the above thought, the answer is:\n2007."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "BtzSd0ZqCQuAJE1Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the market trend percentage in 2007 had been 10%, how would this affect the percentage in 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "An increase in 2007 would mean the total percentages would now sum to more than 100%, thus if 2007 increased by around 3.4%, then 2008 would have to decrease by the same percentage for the total to still be 100%.\nBased on the above thought, the answer is:\n2008 would be around 9.7%."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "zx1fQH1EQZjmJIps",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you produce a CSV table with  the 'Activity' and 'Percentage Contribution' under 'Primary Activities' from the pie chart's data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I will take into account the coordinates of each slice of pie in the first chart and its respective label. The slices are labelled as follows: {Operations}<r>[0.119, 0.161, 0.214, 0.312]</r>, {Marketing}<r>[0.053, 0.225, 0.147, 0.459]</r>, {Sales}<r>[0.049, 0.435, 0.116, 0.704]</r>, {Services}<r>[0.074, 0.647, 0.196, 0.865]</r>, {Manufacturing}<r>[0.189, 0.683, 0.335, 0.869]</r>, {Procurement}<r>[0.298, 0.393, 0.379, 0.755]</r> and {Distribution}<r>[0.214, 0.161, 0.369, 0.43]</r>.\nBased on the above thought, the answer is:\nActivity, Percentage Contribution\nOperations, 9.7%\nMarketing, 11.7%\nSales, 12.6%\nServices, 13.6%\nManufacturing, 15.5%\nProcurement, 17.5%\nDistribution, 19.4%"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "fPNAVBe2fowhdk0r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which activity under 'Primary Activities' has the least contribution to value creation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll have to examine the 'Primary Activities' pie chart and find the activity with the smallest slice. This corresponds to the activity with the lowest percentage, which is indicated by the {Operations}<r>[0.119, 0.161, 0.214, 0.312]</r> slice.\nBased on the above thought, the answer is:\nOperations has the least contribution to value creation, as it only accounts for 9.7% of the primary activities."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "Xuxou6B0zQkbbckW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the distribution of activities under 'Primary Activities' from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can analyze the pie chart {Primary Activities Value Chain Analysis}<r>[0.106, 0.021, 0.321, 0.047]</r> to understand the distribution of activities. Each slice of the pie, such as {Operations}<r>[0.119, 0.161, 0.214, 0.312]</r>, represents different activities.\nBased on the above thought, the answer is:\nThe distribution of activities under 'Primary Activities' reflects the relative value each activity contributes to the overall business processes. The larger slices, such as Distribution and Procurement, represent larger contributions to the value chain, indicating they are critical for producing value in the company. Conversely, smaller slices, like Operations, may indicate lesser contributions to the process's overall value."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "2yUFLxHiwJASvcLm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to advice the company on allocating resources, where would you suggest they focus more on and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's important to consider the activities with the highest contributions when allocating resources. Therefore, I need to identify the activities with the highest contribution percentages. These activities are portrayed by larger slices in the pie chart, such as {Distribution}<r>[0.214, 0.161, 0.369, 0.43]</r> and {Procurement}<r>[0.298, 0.393, 0.379, 0.755]</r>.\nBased on the above thought, the answer is:\nThe company should focus more on the Distribution and Procurement activities because they contribute the most to the value creation process, accounting together for 36.9%, as per the pie chart distribution. Enhancing operations in these sectors could facilitate further value creation and maximize company output."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "WGv8v7QCB52a9HtT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage distribution of Marketing under 'Primary Activities' as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percent distribution of Marketing in Primary Activities, I need to refer to the labelled slices on the pie chart. The pie slice for Marketing in the Primary Activities Pie chart is labeled with coordinates {0.053, 0.225, 0.147, 0.459}.\nBased on the above thought, the answer is:\n11.7%"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "fzaS83tflycJxgtg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the combined percent distribution of Sales, Services and Manufacturing under 'Primary Activities.' What is the total?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the combined percentage distribution, I need to sum the percentages of Sales, Services, and Manufacturing in Primary Activities. The pie slice for Sales is labeled with coordinates {0.049, 0.435, 0.116, 0.704}, Services {0.074, 0.647, 0.196, 0.865}, and Manufacturing {0.189, 0.683, 0.335, 0.869}.\nBased on the above thought, the answer is:\n41.9%"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "gObKVVoYyhsGOvOY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much more is the distribution of Distribution in comparison to Operations under 'Primary Activities'?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference in distribution between Distribution and Operations, I need to subtract the percentage of Operations from the percentage of Distribution. The pie slice for Distribution is labeled with coordinates {0.214, 0.161, 0.369, 0.43} and for Operations {0.119, 0.161, 0.214, 0.312}.\nBased on the above thought, the answer is:\n9.7%"
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "U1RYFQUAErGHXNnP",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart for the year 2015 you're seeing, convert the pollutant shares into a CSV table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the analyzed legend at the right of the chart {the legend}<r>[0.85, 0.074, 0.97, 0.43]</r>, I can see different gases represent pollutants. Each pollutant comes with a color code. With the help of this legend, I am able to identify each pollutant in the pie chart at the center {the pie chart}<r>[0.077, 0.104, 0.699, 0.934]</r>. Each segment labels include the name of the pollutant and the corresponding percentage share.\nBased on the above thought, the answer is:\nYear,CO2,O3,SO2,NO2,CO,PM10,PM2.5,CH4,N2O,Others\n2015,25%,6%,6%,14%,10%,16%,7%,4%,2%,10%"
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "ewUZp7gzXBgnLWcp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the leading pollutant mentioned in the pie chart and what could be its possible effects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the leading pollutants, I need to find the pollutant with the largest percentage share. Its effects can be inferred based on general knowledge about the effects of these pollutants.\nBased on the above thought, the answer is:\nThe leading pollutant in the pie chart is CO2 with a percentage of 25%. Excessive CO2 emissions contribute to global warming and climate change, which can cause extreme weather events and affect biodiversity."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "x5w1NyTMfhTb0mKf",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie-chart, what trend in pollutant share can you infer between CO2 and O3?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer a trend, I compare the percentage shares of CO2 and O3.\nBased on the above thought, the answer is:\nThe pie chart for 2015 indicates that CO2 has a significantly larger share of pollution than O3. This might indicate that the production or release of CO2 is more prevalent than O3."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "jRVgxRMFOSC5Q4j2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf this trend continues, what potential environmental consequences could we predict?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'd imagine the future environmental consequences based on the given share of pollutants and their general effects on the environment.\nBased on the above thought, the answer is:\nIf the trend continues, with CO2 having the largest share, we could predict an increase in global warming and climate change effects. The increase in 'others' might also suggest the emergence of new types of pollutants, which could have unknown or unpredictable environmental impacts."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "1SKahFXbeys7DOHX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the three pollutants that have the lowest percentage shares in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the pie chart, I see different pollutants marked with their respective shares. I need to identify the three pollutants with the smallest percentage shares.\nBased on the above thought, the answer is:\nCH4, N2O, and O3."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "CRYPQ1Uhr0POj2rs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of pollution was contributed by gases other than CO2 and PM10 in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage contributed by all other gases, I need to subtract the percentages of CO2 and PM10 from the total percentage, which is 100%.\nBased on the above thought, the answer is:\nThe total percentage of pollution contributed by gases other than CO2 and PM10 is 100% - 25% (CO2) - 16% (PM10) = 59%."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "TvDCYYPUSIynSb1K",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWas the increase in the \"others\" pollutant category significant in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The question asks for a binary answer indicating whether the increase in \"others\" was significant or not. To answer this, I need to compare the percentage of the \"others\" category with those of other categories.\nBased on the above thought, the answer is:\nYes."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "sFhreTdYKpqe55xC",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data from the pie chart into a table with the technology types and their corresponding probabilities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the pie chart, I can see that there are seven different sectors, each representing a different technology. Their probabilities can be identified from the percentage on each sector slice {AI[0.079, 0.103, 0.388, 0.515]}, {Big Data[0.079, 0.515, 0.321, 0.907]}, {Quantum Computing[0.293, 0.748, 0.57, 0.927]}, {IoT[0.515, 0.604, 0.682, 0.848]}, {Robotics[0.563, 0.273, 0.697, 0.642]}, {Blockchain[0.455, 0.124, 0.638, 0.346]}, {Bio Tech[0.388, 0.103, 0.484, 0.241]}.\nBased on the above thought, the answer is:\nTechnology Type, Probability %\nAI, 25\nBig Data, 20\nQuantum Computing, 15\nIoT, 10\nRobotics, 15\nBlockchain, 10\nBio Tech, 5"
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "rBtXuNCzRHuGsPEM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology has the highest projected prominence in future developments according to the chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to look at the legend and the colors on the pie chart to determine which sector of the chart represents which technology. The technology with the largest slice on the pie chart is the one with the highest projected prominence.\nBased on the above thought, the answer is:\nAI is projected to have the highest prominence due to it holding the largest portion of the chart at 25%."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "Gm3wRJljwuWF8W0I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat conclusions can be drawn about the future of technological development based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the chart, I need to look at the sizes of the slices of the pie chart which correspond to the projected probabilities of these technologies in future technological development.\nBased on the above thought, the answer is:\nThe chart indicates that AI, Big Data, and Quantum Computing are likely to be prime areas of future technological development with AI taking the lead. This suggests that these technologies may receive more attention and investments in the coming years."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "gk0h89VeLF0bZHYK",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs Bio-Tech projected to play a minor role in future developments according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I need to compare the slice of Bio-Tech with that of other technologies in the pie chart.\nBased on the above thought, the answer is:\nYes. Bio-Tech holds the smallest percentage in the chart, only 5%, indicating that it's projected to play a minor role in future developments compared to other technologies."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "TalfAWh0rziZA8nq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what are the minimal and maximal percentages that technologies hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the percentages of each sector. The sector with the smallest slice will have the minimal percentage, and the largest slice will hold the maximal percentage.\nBased on the above thought, the answer is:\nMinimal: 5% (Bio Tech), Maximal: 25% (AI)"
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "3j2losqnqCEqn8qJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTaking into consideration the chart information, what is the combined probability % of the three highest technologies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer, I look at the sections for AI, Big Data, and Quantum Computing and add their percentages.\nBased on the above thought, the answer is:\nThe combined percentage is 25% (AI) + 20% (Big Data) + 15% (Quantum Computing) = 60%"
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "aUX7sFThfrdVGbNe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would be the percentage of Robotics if it increased by 10% at the expense of AI?"
+            },
+            {
+                "from": "gpt",
+                "value": "If Robotics steals 10% from AI, we should subtract 10% from AI's value and add that to Robotics's percentage.\nBased on the above thought, the answer is:\nIf Robotics increased by 10%, it would hold 25% while AI would decrease to 15%."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "JbVcFTIZ6ecnpJWg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you are seeing, convert the associated data for each year into a CSV format."
+            },
+            {
+                "from": "gpt",
+                "value": "To generate the CSV table, I will use the chart's title {\"Fitness trends (Yoga vs HIIT): 2012-2020\"}[0.178, 0.025, 0.598, 0.055] and the pie slices in {the pie chart}[0.077, 0.104, 0.699, 0.934] to list out the year and the corresponding total percentage this year represented by the slice. As explained above, the numbers in the raw data represent the popularity of the fitness trend year by year, and the percentage increase indicates the annual growth in popularity.\nBased on the above thought, the answer is:\nYear, Popularity(%)\n2012,3.6\n2013,5.5\n2014,7.3\n2015,9.2\n2016,11.1\n2017,13.0\n2018,14.9\n2019,16.8\n2020,18.6"
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "UuzW4YJOaMrP7ol3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year witnessed the sharpest rise in the popularity of Fitness trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the sharpest rise in popularity, I'll look at the sizes of the pie slices, which represent the difference in popularity percentages from the previous year.\nBased on the above thought, the answer is:\nThe year 2020 witnessed the sharpest rise in the popularity of Fitness trends because it has the largest slice in the pie chart, indicating the biggest growth in that year."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "j7iXTtvYCSAxcoDL",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's a noticeable trend in the popularity of Fitness trends from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to review the whole pie chart to identify the trend in popularity over the years.\nBased on the above thought, the answer is:\nThe noticeable trend is that the popularity of both Yoga and HIIT increased steadily year by year from 2012 to 2020, as shown by the expanding size of the pie slices."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "JxAAnTfNtsvylsuN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about the future of Fitness trends past 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Although the data stops at 2020, I can make some informed predictions by analyzing the trend over the years covered in the chart.\nBased on the above thought, the answer is:\nAs the popularity of both Yoga and HIIT has been increasing each year from 2012 to 2020, the pie chart suggests that this trend may continue, and the popularity of these fitness trends is expected to grow further post 2020."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "0xHamTJCFfXHtq80",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy visual analysis of the pie chart provided, which two consecutive years saw the smallest increase in popularity percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Identifying the years which saw the smallest increase in popularity percentage involves comparing the differences between the popularity percentages for all consecutive years indicated by the pie slices.\nBased on the above thought, the answer is:\n2012 and 2013."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "5adMXg1go69Biiw7",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the popularity percentage increase from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this information, I need to look at the size of the pie slices for 2018 and 2020 and determine the difference in their resulting popularity percentages.\nBased on the above thought, the answer is:\nThe increase was 3.7%."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "2v3u9XlnzLrXNEnb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase in popularity percentage from 2012 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Calculating the total increase in popularity from 2012 to 2020 requires adding up the differences in popularity percentages for each year from 2012 to 2020.\nBased on the above thought, the answer is:\nThe total increase was 15%."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "1BTgl2jI8xV5bJRe",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you translate the data from the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, looking at both {the Pie charts}[0.272, 0.107, 0.58, 0.962], representing the prices of Oil and Gold over the years 2010-2019, where each slice denotes a specific year and its percentage signifies the price of the commodity. The outer ring, as per the caption {Oil Price}[0.392, 0.06, 0.46, 0.083], represent the oil prices while the inner ring represents the {Gold Price}[0.384, 0.531, 0.468, 0.553].\nBased on the above thought, the answer is:\nYear,Oil Price,Gold Price\n2010,80,1100\n2011,100,1500\n2012,110,1700\n2013,105,1300\n2014,90,1200\n2015,60,1100\n2016,50,1300\n2017,65,1350\n2018,75,1300\n2019,70,1500"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Lchbguq9L8VTTJ8h",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data on the chart, which commodity experienced the steepest decline in price within the decade of 2010 to2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the components in {the pie chart}[0.272, 0.107, 0.58, 0.962] I need to figure out which commodity has the biggest difference between the highest and the lowest price of the decade.\nBased on the above thought, the answer is:\nOil experienced the steepest decline, dropping from a high of 110$ per barrel in 2012 to a low of 50$ per barrel in 2016."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Qrd1YA8IhcdbDEuS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can you observe about the gold price throughout the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the slices on the inner ring representing {gold prices}[0.272, 0.578, 0.58, 0.962], I can draw conclusions about the price trend over the years.\nBased on the above thought, the answer is:\nThe price of gold peaked in 2012 but then stabilized to fluctuate around 1300$/ounce from 2013 to 2019, indicating a stage of relative steadiness in gold prices following a sharp increase."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "IlYvJtvWYtojsYr0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the pie chart inform investment decisions in oil and gold for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Keeping in mind the fluctuations in oil and gold prices as seen in {the pie chart}[0.272, 0.107, 0.58, 0.962], I can make predictions about investment strategies.\nBased on the above thought, the answer is:\nBased on previous trends, gold seems to be the more stable investment with less sharp fluctuations, which would make it a safer, albeit potentially less lucrative, investment. Oil, on the other hand, shows a significant price drop in mid-decade, indicating higher risk but also potentially higher returns if bought at the low point and sold when prices rise again."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "U8XpBgOrsINnHnaA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sum of oil prices from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum, I should sum up the oil prices per year from 2010 to 2019 based on {the data provided in the pie chart}[0.272, 0.107, 0.58, 0.491].\nBased on the above thought, the answer is:\n765"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "yTWvVnfdk3mmVEA4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average cost of gold over the decade?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average gold price, I need to sum up the gold prices for each year from 2010 to 2019 and divide by the number of years represented in {the pie chart}[0.272, 0.578, 0.58, 0.962].\nBased on the above thought, the answer is:\n1335"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "WvCHcr6UL5SML9Tj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the price of oil differ between 2010 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference can be calculated by subtracting the oil price in 2010 from the oil price in 2019 as seen in {the pie chart}[0.272, 0.107, 0.58, 0.491].\nBased on the above thought, the answer is:\n10"
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Qax0cCjIhx47RpEr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're observing, can you create a CSV table representing the percentage distribution of sales over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the title {Product Sales Trend Observation}<r>[0.219, 0.025, 0.557, 0.055]</r> and pie slices representing the years {[2014]<r>[0.214, 0.104, 0.388, 0.278]</r>, [2015]<r>[0.081, 0.175, 0.266, 0.472]</r>, [2016]<r>[0.077, 0.452, 0.222, 0.787]</r>, [2017]<r>[0.151, 0.707, 0.396, 0.934]</r>, [2018]<r>[0.393, 0.672, 0.652, 0.933]</r>, [2019]<r>[0.573, 0.326, 0.699, 0.738]</r>, [2020]<r>[0.388, 0.104, 0.664, 0.384]</r>}, I can calculate the percentage of sales for each year.\nBased on the above thought, the answer is:\nYear,PercentageOfSales\n2014,9.4%\n2015,13.0%\n2016,13.8%\n2017,14.2%\n2018,15.7%\n2019,16.5%\n2020,17.3%"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "SiKK4uzbwD0vHYQO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which year had the highest sales percentage and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the largest slice in the pie chart had the highest sales percentage.\nBased on the above thought, the answer is:\n2020 had the highest sales percentage because its slice takes up the largest portion of the pie chart."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "t0a8vpNZGWpZaa8N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat suggestions can be made to improve sales in the year that had the lowest sales percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "For answering this question, I consider the year with the smallest pie slice which indicates the lowest sales percentage.\nBased on the above thought, the answer is:\nThe sales in 2014 could be improved possibly by analyzing the factors contributing to increased sales in subsequent years and implementing similar strategies. For example, the company may need to consider more aggressive marketing or product development initiatives based on what has worked well in the other years."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "8X2vQBexUyjxVJ0r",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about the trend in product sales over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inferences can be made by observing the size of the pie slices over the years.\nBased on the above thought, the answer is:\nProduct sales had been steadily increasing every year from 2014 to 2020, which is a positive trend for the business. This suggests effective sales strategies were put in place or there was an increase in demand for the product."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "b733crZikBE8PbrZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest sales percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the year with the lowest sales based on the smallest slice of the pie chart.\nBased on the above thought, the answer is:\n2014"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "Yweh8AHzyZrQy3d3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in the sales percentage between 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference between the sales percentages of these two years, I compare those percentages.\nBased on the above thought, the answer is:\n1.5%"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "tRvdZj4QwxEJukam",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the sales percentage for 2019 increased by 3%, what would be the new total sales percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total sales percentage, I add 3% to the sales percentage of 2019 and calculate the sum of all years' sales percentages again.\nBased on the above thought, the answer is:\n104.7%"
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "oBq4ffraqW6g626J",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the donut chart on energy usage by source, can you transcribe the data represented into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the top of the chart {Energy Usage by Source 2020}<r>[0.231, 0.025, 0.545, 0.055]</r> suggests that it presents the usage rates of different energy sources. Noting the information on the {legend}<r>[0.805, 0.074, 0.97, 0.29]</r> and each corresponding {sector slice}<r>[{\"Oil\"}[0.077, 0.104, 0.388, 0.564]; {\"Natural Gas\"}[0.079, 0.55, 0.439, 0.934]; {\"Coal\"}[0.424, 0.453, 0.699, 0.928]; {\"Hydro\"}[0.574, 0.302, 0.695, 0.472]; {\"Nuclear\"}[0.494, 0.156, 0.654, 0.367]; {\"Renewable\"}[0.388, 0.104, 0.54, 0.265]</r>, we can compose the data into a readable CSV format.\nBased on the above thought, the answer is:\nResource Type,Usage Rate(%)\nOil,29.4\nNatural Gas,28.5\nCoal,27.4\nHydro,6.8\nNuclear,9\nRenewable,8.9"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "l5IYA2UZJPkqVw8v",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which Energy source is utilized the most globally in 2020? And why do you infer so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'm examining the sector sizes in our chart, looking at which segment is the largest.\nBased on the above thought, the answer is:\nOil seems to be the most utilized energy source globally in 2020. It occupies the largest portion of the pie chart at 29.4%, indicating that it makes up the most significant part of global energy usage."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "lRTtZ0TmNIbHTJBj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy examining the Energy Usage by Source chart, what critical insight can you derive about the world's energy consumption for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the proportions of each energy source and consider the broader implications.\nBased on the above thought, the answer is:\nThe Energy Usage by Source chart suggests that the world's energy consumption in 2020 was heavily reliant on fossil fuels, specifically on Oil, Coal, and Natural Gas. All three categories combined accounted for over 85% of global energy usage, while renewable sources and other less harmful sources like Nuclear and Hydro made up less than 15%."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "pj61XYDTmFhetwPq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what recommendations for 2021 energy policy might you make?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the sectors, I need to contemplate how the proportions could inform policy suggestions.\nBased on the above thought, the answer is:\nA potential recommendation would be to focus on policies that promote a shift away from fossil fuels and towards renewable and lesser environmentally damaging energy sources like Hydro and Nuclear. The heavy reliance on Oil, Natural Gas, and Coal, which contribute significantly to climate change, suggests that more effort is needed to promote and further develop cleaner energy alternatives."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "Sert6l24MolnNRq9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAs per the chart, which two energy sources, when combined, account for roughly 57% of total energy usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, I need to determine which two sectors of the donut chart have a combined total close to roughly 57%.\nBased on the above thought, the answer is:\nOil and Natural Gas"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "WFIG1mD9SfcZejcu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in usage rate between the most used and least used energy source as per the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand this, I need to identify the energy source with the highest usage rate and the energy source with the lowest usage rate, and then calculate the difference between both.\nBased on the above thought, the answer is:\n22.6 (29.4 - 6.8)"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "Uz4B5tG6hNTx3gbc",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Coal's usage rate was reduced by 5% and instead was equally distributed to Hydro, Nuclear, and Renewable energy usage, what would be the new percentage of Hydro energy usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "First I need subtract 5% from Coal's original percentage. This amount is then divided equally amongst Hydro, Nuclear and Renewable. Finally, this amount is added to each of their initial respective percentages.\nBased on the above thought, the answer is:\n8.46"
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "yhv4bDJpgRsF1eXM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please take the pie chart data and convert it to a structured CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is divided into 7 segments, each representing the proportion of Entertainment and Gaming industry revenue for a specific year. By referring to the color-coded mapping provided in the legend<r>[0.864, 0.074, 0.97, 0.325]</r> and analyzing the size of each segment<r>{\"2010\"}[0.185, 0.104, 0.388, 0.298], {\"2011\"}[0.078, 0.204, 0.246, 0.5], {\"2012\"}[0.077, 0.492, 0.236, 0.816], {\"2013\"}[0.171, 0.727, 0.418, 0.934], {\"2014\"}[0.409, 0.668, 0.655, 0.932], {\"2015\"}[0.575, 0.327, 0.699, 0.732], {\"2016\"}[0.388, 0.104, 0.664, 0.384]</r>, I can derive the proportion of entertainment to gaming industry revenue for each year.\nBased on the above thought, the answer is:\nYear,Entertainment,Gaming\n2010,70,30\n2011,72,28\n2012,74,26\n2013,78,22\n2014,80,20\n2015,82,18\n2016,85,15"
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "c514BG7DhmxtmbdO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, could you describe the trend in revenue increase for the Entertainment industry from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To describe the trend, I should consider the size of each pie segment from 2010 to 2016<r>{\"2010\"}[0.185, 0.104, 0.388, 0.298]</r>,<r>{\"2011\"}[0.078, 0.204, 0.246, 0.5]</r>,<r>{\"2012\"}[0.077, 0.492, 0.236, 0.816]</r>,<r>{\"2013\"}[0.171, 0.727, 0.418, 0.934]</r>,<r>{\"2014\"}[0.409, 0.668, 0.655, 0.932]</r>,<r>{\"2015\"}[0.575, 0.327, 0.699, 0.732]</r>,<r>{\"2016\"}[0.388, 0.104, 0.664, 0.384]</r>, which would provide a clear view of how entertainment industry revenue has changed over the years.\nBased on the above thought, the answer is:\nThe chart shows a clear upward trend in the proportion of Entertainment industry revenue from 2010 to 2016, indicating continuous growth in the industry throughout the period."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "XU6ue6aQg1yw4cUB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, what conclusions can you draw about the growth of the Gaming industry compared to the Entertainment industry from 2012 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To draw conclusions about the growth comparison, I should consider the size of each segment from 2012 to 2016 for both industries.\nBased on the above thought, the answer is:\nThe pie chart segments suggest that the Gaming industry has not grown as significantly as the Entertainment industry during the period from 2012 to 2016. The Entertainment industry's proportion has increased significantly over this period, while the Gaming industry's proportion has decreased correspondingly, indicating that Entertainment has been growing at a faster pace than Gaming."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "FYu1qtnLHjoPfDw4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you assess the performance of the Gaming industry in relation to the Entertainment industry based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To assess the performance, I will compare the size of the Gaming industry segments with those of the Entertainment industry from 2010 to 2016.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that while the Gaming industry has also seen growth, it has not kept pace with the Entertainment industry. From 2010 to 2016, the Entertainment industry has consistently maintained a greater proportion of the combined revenue, which might suggest higher popularity or better performance compared to the Gaming industry."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "fCNv0ZGGUnBj7M4S",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the combined percentage of Entertainment and Gaming industry revenue in 2010 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined percentage in 2010 and 2016, I need to add up the percentages of the adorned slices in the pie chart from these years<r>{\"2010\"}[0.185, 0.104, 0.388, 0.298]</r> and <r>{\"2016\"}[0.388, 0.104, 0.664, 0.384]</r>.\nBased on the above thought, the answer is:\nThe combined percentage is 17.4% (2016) + 11.3% (2010) = 28.7%."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "SPaGXF8owyjPihC3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the change in percentage of the Entertainment industry from 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the change, I need to compare the Entertainment industry's proportion in 2010 and 2016 by referring to the associated slices <r>{\"2010\"}[0.185, 0.104, 0.388, 0.298]</r> and <r>{\"2016\"}[0.388, 0.104, 0.664, 0.384]</r>.\nBased on the above thought, the answer is:\nThe change in percentage is 17.4% (2016) - 11.3% (2010) = 6.1%."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "8G8sn9TezFd1MR1X",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the year with the smallest proportion of Entertainment revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the year with the smallest proportion, I need to look at the sizes of all pie chart slices<r>[\"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\"]</r> and find the smallest one.\nBased on the above thought, the answer is:\nThe year with the smallest proportion of Entertainment revenue is 2010."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "wdYrsp06t93fnFK6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the donut chart you observe, can you format the data into a table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the title {\"AI and Automation Algorithm Usage from 2013 to 2019\"}<r>[0.101, 0.025, 0.675, 0.055]</r> and the chart legend<r>[0.864, 0.074, 0.97, 0.325]</r>, I can see the respective years and each year's usage percentage of AI and automation algorithms. To construct the table, I'll list down the years first then follow each row with the corresponding usage percentage.\nBased on the above thought, the answer is:\nYear,Usage Percentage\n2013,9.6%\n2014,8.9%\n2015,10.7%\n2016,12.5%\n2017,17.1%\n2018,19.6%\n2019,21.4%"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "v58Qb2uPIYz76DYH",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend do you observe in the use of AI and automation algorithms over the years according to the donut chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify the changes and trajectory in the usage percentage of AI and automation algorithms from the years 2013 till 2019.\nBased on the above thought, the answer is:\nOver the years, there is a consistent upward trend in the usage of AI and Automation algorithms. The data suggests an increasing interest and development in these technologies, with an especially significant jump in the years 2017, 2018, and 2019."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "3TjdDjaRIlsiDqX5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the donut chart, which years experienced a decline in AI and automation algorithm usage and why might this be the case?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to identify any years where the percentage usage decreases compared to the previous year.\nBased on the above thought, the answer is:\nThe only year which experienced a decline compared to the previous year was 2014. The potential reasons for this decline could be many, including market changes, regulatory adjustments or shifting consumer needs."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "LoX7TbQSDvcpc2jB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy observing the pie chart, how might this information influence future investments in AI and Automation algorithms?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the trends and figures provided by the chart to contemplate how this might influence investment decisions.\nBased on the above thought, the answer is:\nThe consistent growth in usage of AI and Automation algorithms, especially the marked increase in recent years, indicates a growing importance of these technologies. This trend suggests that future investment in these areas could be beneficial and profitable."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "hb63GjvT1DSLFSYb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the donut chart, which year had the maximum growth in usage percentage from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the differences in usage percentage between each successive year and identify which year displays the largest jump in percentage.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "uaDpi29w4UPPDn52",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in the percentage usage of algorithms between the years 2015 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to take the algorithm usage percentage in 2019 and subtract the percentage in 2015 from it.\nBased on the above thought, the answer is:\n10.7% increase"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "4EeZZZSJEIb3a1T4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the percentage usage for 2018 and 2019 in the chart, what was the percent increase in algorithm usage from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percent increase, I need to subtract the percentage in 2018 from the percentage in 2019, and divide this difference by the percentage in 2018.\nBased on the above thought, the answer is:\n9.18% increase"
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "jAcr4Fhxs6RqyQ9O",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate the table form of the Biotechnology and Software growth from 2015 to 2020 based on the pie chart you see?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title {\"Growth of Knowledge Economy and Innovation Industries (2015 - 2020)\"}<r>[0.016, 0.025, 0.76, 0.055]</r> and each sector's slices in different years, I can convert all these pieces of information into a structured table.\nBased on the above thought, the answer is:\nYear,Biotechnology,Software\n2015,60,100\n2016,63,110\n2017,65,120\n2018,62,130\n2019,90,150\n2020,80,170"
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "CZ2xZVXMWqFcTbn3",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, which sector experienced the most rapid growth in a single year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the size of each slice representing the annual percentage growth of the two sectors can help determine the sector with the most rapid growth in a single year.\nBased on the above thought, the answer is:\nThe Biotechnology sector experienced the most rapid growth in a single year, from 2018 to 2019."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "mecHTGbllwedOGdM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the chart, how have the Innovation Industries evolved over the years 2015-2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the slices of the pie chart by year can provide an understanding of the evolution of Innovation Industries over the years.\nBased on the above thought, the answer is:\nIn the given timeframe, both sectors in the Innovation Industries, Software and Biotechnology, have generally grown. However, the software sector has seen a consistent upward trend, indicating steady growth. The biotechnology industry, while also growing, experienced a significant spike in 2019 before falling back in 2020."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "n1GXy46CtK1j9dGI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering the data, what might future growth projections for these sectors look like?"
+            },
+            {
+                "from": "gpt",
+                "value": "Reflections on future growth projections would take into consideration the observed data and trends from the chart.\nBased on the above thought, the answer is:\nBased on the observed data, the software industry appears to demonstrate a steady and consistent pattern of growth, suggesting it may continue to grow at a similar or slightly increased pace. The biotechnology industry, while somewhat more erratic, also shows signs of growth, but its future projection may be less predictable due to the significant growth seen in 2019 and subsequent decline in 2020."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "i8YoETeJ7LylLrOL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the growth of the Biotechnology sector highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the years with Biotechnology growth data and determine which one is the highest.\nBased on the above thought, the answer is:\n2019."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "msL1uLU1SYwjnGeI",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much growth did the Software sector achieve from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth, I need to subtract the initial value (in 2015) from the final value (in 2020).\nBased on the above thought, the answer is:\n70 units."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "jW3BPE3sFgVyAhO6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much growth did the Biotechnology sector achieve from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the growth, I need to subtract the initial value (in 2018) from the subsequent value (in 2019).\nBased on the above thought, the answer is:\n28 units."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "IsA5gSmsnfEAz6nj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the raw data in a table form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the data that each slice of the pie chart {pie chart}<r>[0.08, 0.103, 0.697, 0.926]</r> represents, I can derive that each slice corresponds to the values for \"Global Warming\" and \"Internet Usage\" for each year from 2001 to 2009, as can be seen from the labels {2001-2009 labels}<r>[0.357, 0.256, 0.401, 0.28]; [0.336, 0.259, 0.38, 0.283]; [0.312, 0.267, 0.356, 0.29]; [0.285, 0.281, 0.329, 0.304]; [0.254, 0.307, 0.298, 0.33]; [0.221, 0.351, 0.264, 0.374]; [0.194, 0.414, 0.237, 0.437]; [0.181, 0.49, 0.225, 0.513]; [0.189, 0.573, 0.233, 0.596]</r>. The legend {legend}<r>[0.769, 0.074, 0.97, 0.151]</r> further clarifies the colors assigned to each theme.\nBased on the above thought, the answer is:\nYear,Global Warming,Internet Usage\n2001,10,30\n2002,12,35\n2003,14,37\n2004,17,40\n2005,22,45\n2006,28,50\n2007,30,56\n2008,32,60\n2009,35,64"
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "l8iWbp9SXHMGgGld",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category saw a greater increase from 2001 to 2009, Global Warming or Internet Usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the increases of both categories from 2001 to 2009.\nBased on the above thought, the answer is:\nInternet Usage saw a greater increase from 2001 to 2009."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "fjqUwKFwbuH1sJ3Q",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart inform you about the trends of Global Warming and Internet Usage from 2001 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the changes in values each year for both categories to identify their trends.\nBased on the above thought, the answer is:\nBoth Global Warming and Internet Usage saw a steady increase from 2001 to 2009, indicating that Global Warming was becoming a more pressing issue and Internet Usage was becoming more widespread during this period."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "17egzAz8jtQhv35x",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the trends indicated in the chart impact future predictions for Global Warming and Internet Usage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the progressive increase in both Global Warming and Internet Usage.\nBased on the above thought, the answer is:\nIf the trends continue, we could expect both Global Warming and Internet Usage to increase in the future years. For Global Warming, this could predict more severe climate changes and environmental issues. As for Internet Usage, we could anticipate further advance in technology and dependence on the internet."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "Euy3pmJFAVcqZF8o",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the global warming index increase from 2001 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the value for 2001 and subtract it from the value for 2009 from the global warming data.\nBased on the above thought, the answer is:\n25"
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "otWTTJhPS8bOP4Dp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the Internet usage increase from 2001 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the value for 2001 and subtract it from the value for 2009 from the internet usage data.\nBased on the above thought, the answer is:\n34"
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "3lSgZBnXRyICcvWw",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the total increase for both global warming and internet usage from 2001 to 2009?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase for both Global Warming and Internet Usage, I need to sum the increases of both from 2001 to 2009.\nBased on the above thought, the answer is:\n59"
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "ozV1jX7Ct9aWmTOt",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the annual representation of social dynamics into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart shows the representation of social dynamics for each year from 2016-2020. This information is shown in the legend {'2016', '2017', '2018', '2019', '2020'}<r>[0.871, 0.074, 0.97, 0.255]</r> and the proportion of each year are shown on the pie chart slices{'2016', '2017', '2018', '2019', '2020'}<r>[0.108, 0.103, 0.388, 0.395], [0.079, 0.343, 0.234, 0.804], [0.168, 0.717, 0.531, 0.927], [0.488, 0.428, 0.697, 0.88], [0.388, 0.103, 0.69, 0.454]</r>.\nBased on the above thought, the answer is:\nYear, Social Dynamics\n2016, 18.1%\n2017, 19.2%\n2018, 20.3%\n2019, 20.7%\n2020, 21.6%"
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "l9JPBzrATtYMVYlA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data represented in this chart, can you identify the trend in social dynamics over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The size of the slices{'2016', '2017', '2018', '2019', '2020'}<r>[0.108, 0.103, 0.388, 0.395], [0.079, 0.343, 0.234, 0.804], [0.168, 0.717, 0.531, 0.927], [0.488, 0.428, 0.697, 0.88], [0.388, 0.103, 0.69, 0.454]</r> in the pie chart that showdown how social dynamics have changed over the years.\nBased on the above thought, the answer is:\nFrom 2016 to 2020, the representation of social dynamics has been increasing annually."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "xbZv1uFOzZ8gOivE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be made based on the trend of social dynamics over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie slices for the years{'2016', '2017', '2018', '2019', '2020'}<r>[0.108, 0.103, 0.388, 0.395], [0.079, 0.343, 0.234, 0.804], [0.168, 0.717, 0.531, 0.927], [0.488, 0.428, 0.697, 0.88], [0.388, 0.103, 0.69, 0.454]</r> and how they've increased over time, conclusions can be drawn.\nBased on the above thought, the answer is:\nThe steady increase in the representation of social dynamics over the years suggests that societies are becoming more dynamic and complex."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "ZnINg25iSaVh5rfg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat impact does this trend of social dynamics have on societal change?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the growth trend of social dynamics from the slices{'2016', '2017', '2018', '2019', '2020'}<r>[0.108, 0.103, 0.388, 0.395], [0.079, 0.343, 0.234, 0.804], [0.168, 0.717, 0.531, 0.927], [0.488, 0.428, 0.697, 0.88], [0.388, 0.103, 0.69, 0.454]</r>, we can reflect on possible impacts on societal change.\nBased on the above thought, the answer is:\nThe increasing trend of social dynamics over the years may suggest growing social change and complexity in society. This could imply more social movements, increased societal expectations, and greater political engagement, all of which contribute to societal change."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "vKFZ14U9MrzysRV6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, which year the representation of social dynamics had the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "Among the pie slices{'2016', '2017', '2018', '2019', '2020'}<r>[0.108, 0.103, 0.388, 0.395], [0.079, 0.343, 0.234, 0.804], [0.168, 0.717, 0.531, 0.927], [0.488, 0.428, 0.697, 0.88], [0.388, 0.103, 0.69, 0.454]</r>, the year with the largest slice represents the highest percentage of social dynamics.\nBased on the above thought, the answer is:\n2020"
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "vd0OVYLgLNEv25el",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage represented by the years 2016 and 2017 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total percentage for both 2016 and 2017, I need to add up the percentages represented by the 2016 slice and the 2017 slice.\nBased on the above thought, the answer is:\nThe total percentage for 2016 and 2017 is 18.1% + 19.2% = 37.3%"
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "JOIlvHYiUc11AoFg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the percentage of social dynamics increase from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase, I need to subtract the percentage of social dynamics in 2016 from the percentage in 2020.\nBased on the above thought, the answer is:\nThe increase is 21.6% - 18.1% = 3.5%"
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "I2ztUcgZGgfEkx3P",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transfer the data from the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the chart title {\"Patent Applications from 2005 to 2014\"}[0.188, 0.025, 0.588, 0.055] and the various sectors of the pie chart with their respective labels {\"2005\"}[0.354, 0.256, 0.398, 0.279]; {\"2006\"}[0.326, 0.261, 0.37, 0.285]; {\"2007\"}[0.29, 0.277, 0.334, 0.301]; {\"2008\"}[0.243, 0.318, 0.287, 0.341]; {\"2009\"}[0.197, 0.403, 0.241, 0.426]; {\"2010\"}[0.183, 0.529, 0.227, 0.552]; {\"2011\"}[0.227, 0.671, 0.282, 0.694]; {\"2012\"}[0.37, 0.747, 0.425, 0.77]; {\"2013\"}[0.531, 0.596, 0.586, 0.619]; {\"2014\"}[0.476, 0.309, 0.531, 0.333], I am able to convert this data into CSV format.\nBased on the above thought, the answer is:\nYear,Patent Applications\n2005,58\n2006,80\n2007,102\n2008,170\n2009,212\n2010,255\n2011,320\n2012,450\n2013,550\n2014,600"
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "qMVF3yJ0XVR6Lqrq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years showed significant increases in patent applications?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to review the chart to identify the years where there was a noticeable increase in patent applications.\nBased on the above thought, the answer is:\n2008 and 2012 experienced significant increases in patent applications from the previous years."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "5l4gwGWvOpL4lc4c",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we infer about technological innovation during this period based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "We can infer about technological innovation during this period by looking at the trend in the growth of patent applications over the years.\nBased on the above thought, the answer is:\nFrom the pie chart, we can infer that technological innovation experienced steady growth from 2005 to 2014, with significant surges in 2008 and 2012. This suggests potential breakthroughs or increases in research output during these years."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "ubwf3Opow3AKQDfX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might these patent application trends impact future technological research if we consider this data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trends in patent application can influence future technological research based on the growth displayed within the pie chart.\nBased on the above thought, the answer is:\nThe trends of increasing patent applications imply a strong interest and investment in technological research and innovation. Future research might expect similar growth patterns, with potential spikes in certain years. It might also encourage more funding and efforts in these areas to maintain or amplify this upward trend."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "Rd7DtOMDoZ8Zkali",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be said about the increment in Patent Applications from 2007 to 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I would calculate the increase by subtracting the 2007 applications from the 2008 applications.\nBased on the above thought, the answer is:\nThe increase in Patent Applications from 2007 to 2008 was 68."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "OUnTknhJPxI0GxOk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total number of patent applications from 2005 to 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total number of patent applications from 2005 to 2014, I need to sum up all the applications in the chart.\nBased on the above thought, the answer is:\nThe total number of patent applications from 2005 to 2014 is 3097."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "qy814F1jHTNCoVIr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of patent applications per year for this period?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average number of patent applications per year for this period, I need to divide the total number of patent applications by the number of years.\nBased on the above thought, the answer is:\nThe average number of patent applications per year from 2005 to 2014 is approximately 310."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "bxt0sHAMX4eeRkJm",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you convert the global internet access distribution from 2000 to 2020 into a CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking into the chart's title {\"Global Internet Access from 2000 to 2020\"}[0.171, 0.025, 0.605, 0.055] and observing the individual pie slices for different years {2000}[0.168, 0.104, 0.388, 0.313], {2005}[0.077, 0.225, 0.234, 0.647], {2010}[0.092, 0.608, 0.388, 0.934], {2015}[0.388, 0.564, 0.695, 0.934], and {2020}[0.388, 0.104, 0.699, 0.584], it's clear that each slice forms a different data point regarding the percentage of internet access in a particular year.\nBased on the above thought, the answer is:\nYear, Ratio\n2000, 25\n2005, 35\n2010, 40\n2015, 45\n2020, 55"
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "KG5t61AOGT2zwRdm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart imply about the worldwide spread of internet access between 2000 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart's proportions in relation to each year, it's clear that each year represents an increment in global internet access.\nBased on the above thought, the answer is:\nThe pie chart implies that there has been a significant increase in global internet access from 2000 to 2020, with the proportion of the population with internet access consistently growing over this period."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "R66ZaRqUA1OyObjd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIt can be inferred from the chart that year 2010 represented an important milestone in global internet connectivity. Why is that?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the year {\u201c2010\u201d}[0.092, 0.608, 0.388, 0.934], it\u2019s noticeable that the corresponding slice is approximately half the pie chart, indicating a significant percentage of global internet access.\nBased on the above thought, the answer is:\nYear 2010 represented a tipping point as this was the first year in which global internet access exceeded 50%. This implies that more than half of the world's population had obtained internet access by 2010."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "3dFl2sjiKHWt1FUX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat identification can be made about the correlation between time and the growth of internet access?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the growing size of the pie slices from 2000 to 2020, a pattern emerges where growth of global internet access correlates positively with time.\nBased on the above thought, the answer is:\nThe pie chart visualises a positive correlation between time and the growth of global internet access. As time has progressed, the proportion of people with internet access has increased, showing a continual expansion of internet connectivity worldwide."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "hSSKb14V5nJ3VKPi",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total increase in the global internet access ratio from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total increase, I have to subtract the 2000 access ratio from the 2020 ratio.\nBased on the above thought, the answer is:\nThe increase was 30%."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "x113FFMen5tPsKaL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend continues, what would be the global internet access ratio in 2025 assuming a linear increase?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to estimate the access ratio for 2025, I would need to observe the progression from 2000 to 2020 and extrapolate that trend into the future.\nBased on the above thought, the answer is:\nGiven that there has been an average increase of 10% every 5 years from 2000 to 2020, we can estimate a projected increase of a further 10% over the next 5 years, bringing the total to 65% by 2025."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "t0aROh5vQdT4TeSA",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average increment per year from 2000 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to divide the total increment by the number of years to obtain the average yearly increment.\nBased on the above thought, the answer is:\nThe average increase is 1.5% per year."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "iyEsUb0eVSWYEJ7R",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Education and Training Technology Innovation from 2015 to 2020}<r>[0.05, 0.025, 0.727, 0.055]</r> and {the segments}<r>[0.149, 0.104, 0.388, 0.333]; [0.078, 0.254, 0.22, 0.619]; [0.087, 0.589, 0.318, 0.909]; [0.287, 0.725, 0.605, 0.931]; [0.54, 0.372, 0.698, 0.814]; [0.388, 0.104, 0.679, 0.416]</r> , I can see that the segments represent different years and how much each year contributed to EdTech Innovation.\nBased on the above thought, the answer is:\nYear, EdTech Innovation\n2015, 80\n2016, 85\n2017, 90\n2018, 100\n2019, 105\n2020, 110"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "zLqB12X7TT4Cb6Dr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year saw the biggest leap in education and training technology innovation and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the largest segment of the pie chart represents the biggest leap, considering the increase compared to the previous year.\nBased on the above thought, the answer is:\nThe biggest leap in education and training technology innovation was made in 2020 where the pie segment was the largest. This suggests that there were major advancements or introductions of new techniques in this year."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "YslrtHXwrI5GQT7j",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the growth trend of EdTech Innovation from 2015 to 2020 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the progression of segment sizes from 2015 to 2020, I can make inferences about the growth trend.\nBased on the above thought, the answer is:\nThe pie chart reveals a consistent growth trend from 2015 to 2020 with a spurt of significant growth in 2019 and 2020. This suggests a continuously increasing interest and investment in EdTech innovation over these years."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "QGbyw9jb2k98SImF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence decisions on funding EdTech Innovation for the year 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "The funding decisions for 2021 would likely be influenced by the trend observed from 2015 to 2020.\nBased on the above thought, the answer is:\nThe steady increase in EdTech Innovation from 2015 to 2020, highlighted by a notable spurt in growth in the last two years, may indicate that it is a booming sector. Therefore, investors or stakeholders might decide to maintain or increase funds for 2021, expecting this trend to continue."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "Jo7wz83KqSDfkc2V",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in innovation from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage increase, I would subtract the 2015 value from the 2020 value, divide by the 2015 value, then multiply by 100.\nBased on the above thought, the answer is:\n37.5%"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "8MVre0ko6tT3WvS9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of innovation from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the sum, I need to add the innovation values for 2017, 2018, and 2019.\nBased on the above thought, the answer is:\n295"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "RtGH5Uo2OAAFZMXb",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the innovation decreased by 10% in 2021, what would be the new value?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new value, I would calculate 10% of the 2020 value and subtract it from the 2020 value.\nBased on the above thought, the answer is:\n99"
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "Hz8zuMpoCwtqLnWO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the donut pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle \"Gaming Industry Development from 2010 to 2019\" {in brackets}<r>[0.128, 0.025, 0.648, 0.055]</r>, and the respective segments {in brackets}<r>[\"2010\" to \"2019\"][0.081, 0.103, 0.695, 0.922]</r>, I observe the segments denote the growth of the gaming industry for each year.\nBased on the above thought, the answer is:\nYear,Growth\n2010,8.5%\n2011,9.1%\n2012,10.1%\n2013,10.6%\n2014,7.5%\n2015,8.7%\n2016,10.3%\n2017,11.3%\n2018,11.8%\n2019,12.0%"
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "Oy5I7KfJ23L9yERc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the trend in the growth of the gaming industry from 2010 to 2019 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to observe the progression of the individual segments in the chart from the year 2010 to 2019.\nBased on the above thought, the answer is:\nThe chart shows a generally increasing trend in the growth of the gaming industry from 2010 to 2019. Although there was a dip in growth in 2014, the industry recovered and continued to grow, with the rate of growth accelerating especially from 2017 to 2019."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "EfzUKBlRddrrjSnz",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can we infer about the gaming industry's performance in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the segment related to 2014 to make inferences about the gaming industry's performance in that year.\nBased on the above thought, the answer is:\nIn 2014, the gaming industry saw a decline in growth as evident from the smaller segment size. This could suggest that the industry faced some challenges or less demand that year."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "oHbIK0aCszWBLOVE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat impact might the data in the chart have on future investment in the gaming industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the growth trend and performance in recent years can indicate the potential for future investment.\nBased on the above thought, the answer is:\nThe data shows a strong growth trend in the gaming industry, particularly from 2017 to 2019. This indicates that the gaming industry is a growing market and might attract more future investment, especially with the continuous increasing trend observed in the recent years."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "tto55vtVMf9tXndk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year showed the highest growth in the gaming industry?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the slice in the pie chart with the largest share which represents the highest growth.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "gfN2bmFtyQYAGbnU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total growth rate of the gaming industry from 2010 to 2013 according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out, I need to add up the growth percentages for the years 2010 to 2013.\nBased on the above thought, the answer is:\nThe total growth rate from 2010 to 2013 is 8.5% + 9.1% + 10.1% + 10.6% = 38.3%."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "3krt2yyOho8rpDfu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the growth rate increase from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by subtracting the growth rate in 2018 from the growth rate in 2019.\nBased on the above thought, the answer is:\nThe growth increased by 0.2% from 2018 to 2019."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "7LPbiRjBxksEgxVx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table listing the percentages of transportation instances each year from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the labels and corresponding percentages for each slice of the pie. The labels and percentages are {\"2015\"}<r>[0.279, 0.281, 0.334, 0.305]</r>,{\"14.6%\"}<r>[0.194, 0.097, 0.238, 0.121]</r>, {\"2016\"}<r>[0.178, 0.466, 0.233, 0.49]</r>,{\"16.1%\"}<r>[0.009, 0.436, 0.053, 0.459]</r>, {\"2017\"}<r>[0.225, 0.669, 0.28, 0.692]</r>,{\"12.3%\"}<r>[0.095, 0.808, 0.139, 0.832]</r>, {\"2018\"}<r>[0.381, 0.747, 0.436, 0.771]</r>,{\"17.5%\"}<r>[0.425, 0.951, 0.469, 0.975]</r>, {\"2019\"}<r>[0.536, 0.578, 0.591, 0.601]</r>,{\"19.0%\"}<r>[0.71, 0.641, 0.755, 0.664]</r> and {\"2020\"}<r>[0.472, 0.305, 0.526, 0.328]</r>,{\"20.5%\"}<r>[0.591, 0.141, 0.635, 0.164]</r>.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,14.6%\n2016,16.1%\n2017,12.3%\n2018,17.5%\n2019,19.0%\n2020,20.5%"
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "4rvqr4C9PqjnprKu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which year experienced a boom in the transportation market?"
+            },
+            {
+                "from": "gpt",
+                "value": "A boom in the market would be indicated by the largest slice of the pie chart.\nBased on the above thought, the answer is:\nThere was a boom in the transportation market in 2020 {\"20.5%\"}<r>[0.591, 0.141, 0.635, 0.164]</r> as it commands the largest slice of the pie chart."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "Z0Jj2KdPB4GMnkd1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the trend in the transportation market from 2016 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the trend, I need to compare the proportions of the years 2016 {\"16.1%\"}<r>[0.009, 0.436, 0.053, 0.459]</r>, 2017 {\"12.3%\"}<r>[0.095, 0.808, 0.139, 0.832]</r> and 2018 {\"17.5%\"}<r>[0.425, 0.951, 0.469, 0.975]</r>.\nBased on the above thought, the answer is:\nThe transportation market experienced a decrease in growth in 2017, followed by an increase in 2018, according to the pie chart."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "nvWb6EZJHCYvcxrJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart represent the fluctuations in the Automotive and Transportation market from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart uses different sizes and colors of slices to represent the proportion of transportation instances each year, effectively showing the fluctuations in the market over time.\nBased on the above thought, the answer is:\nThis pie chart visually represents the changes in the Automotive and Transportation market from 2015 to 2020 by displaying each year's market share as a corresponding piece of the pie. The sizes of the slices show the proportion of transportation instances that occurred each year, with larger slices indicating more instances. The fluctuation in market share is thus easy to observe."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "hkRbybzodwCI6ORR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the proportions for the years 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sum of the proportions for the years 2019 and 2020 is the sum of {\"19.0%\"}<r>[0.71, 0.641, 0.755, 0.664]</r> and {\"20.5%\"}<r>[0.591, 0.141, 0.635, 0.164]</r>.\nBased on the above thought, the answer is:\n39.5%"
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "4pbN6IMNKaKvhgYD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in transportation instances between 2015 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The difference in transportation instances between 2015 and 2020 can be determined by subtracting the percentage of 2015 {\"14.6%\"}<r>[0.194, 0.097, 0.238, 0.121]</r> from the percentage of 2020 {\"20.5%\"}<r>[0.591, 0.141, 0.635, 0.164]</r>.\nBased on the above thought, the answer is:\n5.9%"
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "a8AsWGsJryZPeio0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest proportion of transportation instances?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the proportions of all years to determine the year with the lowest proportion.\nBased on the above thought, the answer is:\nThe year with the lowest proportion of transportation instances is 2017, {\"12.3%\"}<r>[0.095, 0.808, 0.139, 0.832]</r>."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "AmREHvispDgwjS2e",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you encapsulate the data seen in the pie chart into a csv styled table."
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can identify {the sectors slice}<r>[0.208, 0.104, 0.388, 0.282]</r>; \n<r>[0.122, 0.18, 0.262, 0.368]</r>; \n<r>[0.077, 0.304, 0.202, 0.648]</r>; \n<r>[0.093, 0.609, 0.384, 0.933]</r>; \n<r>[0.382, 0.598, 0.687, 0.934]</r>; \n<r>[0.388, 0.104, 0.699, 0.632]</r> and the legend on the figure <r>[0.864, 0.074, 0.97, 0.29]</r>. The sectors represent the number of AI & ML Projects in different years from 2015 to 2020.\nBased on the above thought, the answer is:\nYears,2015,2016,2017,2018,2019,2020\nAI & ML Projects,9.8,6.5,13.7,19.6,20.9,29.4"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "PivmwOOUNXlCpvMd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the highest proportion of AI & ML projects, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Look at the size of the sectors and the corresponding year, and find the year with the largest sector.\nBased on the above thought, the answer is:\nYear 2020 had the highest proportion of AI & ML projects because it comprises the largest segment of the pie chart, at 29.4% of the total."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "ACYEpr1a8VdmwMcE",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the trend of AI & ML projects from 2015 to 2020 as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Look at the proportions of each year to identify an upward or downward trend.\nBased on the above thought, the answer is:\nThe trend of AI & ML projects has been increasing from 2015 to 2020. There was a smaller proportion in 2016, but a significant growth was observed from 2017 to 2020, especially in 2020 which had the largest proportion."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "ighXpRyKVpYDxyRa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this data guide strategy for AI & ML project investment in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Look at the trend of proportions to predict the future situation.\nBased on the above thought, the answer is:\nGiven the rising trend of AI & ML projects from 2015 to 2020, with a significant growth especially in the last three years, this data may suggest to businesses or developers to continue or increase their investment in these fields, expecting that the demand or interest for such projects may continue to grow in the future."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "yTuQtQUgA2EkB9gm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total proportion of projects in 2015 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "Look at the data labels for \"2015\" and \"2016\", which are <r>[0.24, 0.072, 0.284, 0.095]</r> and <r>[0.093, 0.196, 0.138, 0.219]</r> respectively.\nBased on the above thought, the answer is:\n16.3%"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "4RCP628jBzYO5cZ3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average proportion of projects per year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average proportion, I sum up all the proportions and then divide by the number of years (6 years).\nBased on the above thought, the answer is:\nThe average proportion is 16.8% per year."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "0YxiPZLiUWoQDaIf",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of projects in 2020 increases by 10%, what would be the total proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion for \"2020\" is 29.4%. If it increases by 10%, the new proportion would be 39.4%. I then sum up all the proportions again.\nBased on the above thought, the answer is:\n140.6%"
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "5XF8wJTzdSG3v5CJ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you structure a table with the corresponding percentages for the different categories of technological development prospects?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, I can form a table based on the labels given in the legend {the legend}<r>[0.8, 0.063, 0.978, 0.368]</r> and the pie chart {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r>. Each category corresponds to a slice of the pie and its respective percentage development rate.\nBased on the above thought, the answer is:\nCategory,Development Rate(%)\nArtificial Intelligence,25\nCloud Computing,15\nInternet of Things,12\n5G Technology,10\nBig Data,9\nBlockchain,8\nCybersecurity,8\nAR/VR,7\nRobotics,4\nQuantum Computing,2"
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "RKC6o5rD5sFfnECd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology shows the highest rate of development, and why do you think it is so?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the technology with the largest slice on {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r>.\nBased on the above thought, the answer is:\n'Artificial Intelligence' has the highest rate of development at 25%. This could possibly be due to the vast applications of AI in various fields, leading to extensive research and rapid development."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "wUzxWbuFErPWziML",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, do you think that established technologies or emerging technologies are being focused on more for future development?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to look at the development rates of each technology in {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r> to derive a conclusion.\nBased on the above thought, the answer is:\nIt seems that more established technologies like 'Artificial Intelligence' and 'Cloud Computing' are being focused on more for future development compared to emerging technologies like 'Quantum Computing' and 'Robotics' which have lower development rates."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "Qyj7uJopWUzCUMF6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart influence future investment decisions in technology sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "For this, I need to consider how the development rates of different technologies might affect investment decisions.\nBased on the above thought, the answer is:\nSectors like 'Artificial Intelligence' and 'Cloud Computing' might attract the highest investments due to their high development prospects. On the other hand, sectors like 'Robotics' and 'Quantum Computing', despite their lower development rates, might also attract investment for their potential for disruptive technologies. However, this chart only depicts development prospects and actual decisions will likely be influenced by many other factors."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "CuwRQIdtEfEPO4VN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total development rate represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total development rate, I can sum up the percentages of all technology sectors shown in {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r>.\nBased on the above thought, the answer is:\n100%"
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "06eNI254JJrFah6B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in development between the technologies 'Artificial Intelligence' and '5G Technology'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find this by subtracting the development rate of '5G Technology' from that of 'Artificial Intelligence' based on {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r>.\nBased on the above thought, the answer is:\n15%"
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "gqn11IDX3XjXxJc9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much does the combined development rate of 'Cloud Computing' and 'Internet of Things' exceed that of 'Artificial Intelligence'?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate this by subtracting the development rate of 'Artificial Intelligence' from the sum of the development rates of 'Cloud Computing' and 'Internet of Things', as shown in {the pie chart}<r>[0.119, 0.095, 0.655, 0.937]</r>.\nBased on the above thought, the answer is:\n2%"
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "QTSwYZ47a2yL7V1n",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease convert the data represented by the pie chart you've deciphered into a structured table."
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the chart, I can find each trade year represented by its own color as indicated in the legend {2000}[0.92, 0.083, 0.963, 0.106], {2001}[0.92, 0.118, 0.963, 0.141], {2002}[0.92, 0.153, 0.963, 0.176], {2003}[0.92, 0.188, 0.963, 0.211], {2004}[0.92, 0.223, 0.963, 0.246], {2005}[0.92, 0.258, 0.963, 0.281]. The proportion of total trade volume for each year is represented by the area of each slice.\nBased on the above thought, the answer is:\nYear,Proportion of Total Trade Volume\n2000,13.4%\n2001,14.1%\n2002,15.1%\n2003,17.2%\n2004,19.4%\n2005,20.8%"
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "V04PHH9OhSQbj3R4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year marked the highest growth in international trade volume and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices and their associated percentages, I can determine which year had the highest growth.\nBased on the above thought, the answer is:\nThe year 2005 marked the highest growth in international trade volume as it is represented by the pie slice with the largest percentage of 20.8%."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "zEKFCkfEtf5Wl78p",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the change in proportions of trade volumes over the years suggest about the international trade scenario?"
+            },
+            {
+                "from": "gpt",
+                "value": "I might infer from the growing proportions evident in the pie slices from 2000 to 2005 that international trade was expanding during this period.\nBased on the above thought, the answer is:\nThe change in proportions suggests that international trade volume grew steadily from 2000 to 2005, indicating a possible trend of increasing globalization and economic integration among nations."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "EuQ5C482Amoc2Itd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how might this data influence international trade policies going forward?"
+            },
+            {
+                "from": "gpt",
+                "value": "In order to draw conclusions about future policies, I need to consider the trends apparent in the chart.\nBased on the above thought, the answer is:\nThe steady increase in trade volume from 2000 to 2005 might encourage policymakers to continue promoting international trade by crafting policies that facilitate it, as it appears to be a trend that is contributing positively to the global economy."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "CCU4TDwNZ1V6sgT3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie slices you observed, which year had the smallest share in total trade volume and what was the percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can determine the smallest share by comparing the percentages mentioned in each pie slice.\nBased on the above thought, the answer is:\nThe year 2000 had the smallest share in total trade volume with 13.4%."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "pEHC8eLnJxh1hdKm",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the combined share of total trade volume for the years 2001 and 2002?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the combined share, I need to add the percentages of these two years.\nBased on the above thought, the answer is:\nThe combined share of total trade volume for 2001 and 2002 is 14.1% + 15.1% = 29.2%."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "xW2JkA7m6JCZp3jK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the trade volume increase from 2000 to 2005?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can calculate the increase in percentage by subtracting the percentage of 2000 from the percentage of 2005.\nBased on the above thought, the answer is:\nThe increase in trade volume from 2000 to 2005 is 20.8% - 13.4% = 7.4%."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "cK7NsLszDLfmMKV2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart image, can you please transform the given information about industry trends from 2013 to 2019 into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart,  I see distinct slices, labeled by year,{\"2013\"}[0.919, 0.083, 0.963, 0.106],{\"2014\"}[0.919, 0.118, 0.963, 0.141],{\"2015\"}[0.919, 0.153, 0.963, 0.176],{\"2016\"}[0.919, 0.188, 0.963, 0.211],{\"2017\"}[0.919, 0.223, 0.963, 0.246],{\"2018\"}[0.919, 0.258, 0.963, 0.281], and {\"2019\"}[0.919, 0.292, 0.963, 0.316]. Each slice corresponds to the proportion that each year occupies in 'Transportation and Logistics Industry Trends'. I will convert this information into structured CSV format.\nBased on the above thought, the answer is:\nYear,Percentage\n2013,9.5%\n2014,11.1%\n2015,12.7%\n2016,14.3%\n2017,15.9%\n2018,17.5%\n2019,19.0%"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "BlTjn4a8FOkW7HYD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nObserving the chart, which year experienced the smaller percentage in industry trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare percentage slices for different years to determine which year had the lowest proportion.\nBased on the above thought, the answer is:\nThe year with the smallest industry trend is 2013, with a percentage of 9.5%."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "4mPUAMtukelPtydk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy examining the chart, can you infer any trends within the Transportation and Logistics Industry during the given years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend, I should look at how the sizes of the slices change from year to year.\nBased on the above thought, the answer is:\nThe pie chart shows a consistent increase in the trend percentage of the Transportation and Logistics Industry from the year 2013 to 2019. This indicates a healthy growth and upward trend in this industry over these years."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "k30bj3bZlXgRLTci",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, how does this data impact the strategic decision in the Transportation and Logistics Industry for future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I should consider how the consistent growth over the years might affect future business strategies.\nBased on the above thought, the answer is:\nSince the industry trends have consistently been increasing from 2013 to 2019, it suggests that businesses in the Transportation and Logistics industry can anticipate continued growth in the coming years, at least following the same trend that has been on. Therefore, decisions related to expansion, investments or technologies that boost growth could be strategically advantageous."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "swqDQcvi1XbxxUF8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between industry trends in the years 2018 and 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the percentage of the year 2016 from the percentage of the year 2018.\nBased on the above thought, the answer is:\n3.2%"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "Y9M4XOI8UYQPR9YH",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will be the total percentage if we add the percentages of the years 2013, 2014, and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the total percentage, I need to add up the percentages of the years 2013, 2014, and 2015.\nBased on the above thought, the answer is:\n33.3%"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "KkAwN0OF2wDFdslM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage did the industry trends increase from 2013 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "For obtaining the percentage increase, I need to subtract the percentage for the year 2013 from the percentage for the year 2019.\nBased on the above thought, the answer is:\n9.5%"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "0hwRvMWKeI3GOfwI",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart you observed, please convert the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the top of the chart {Building and Real Estate Market Trends from 2010 to 2019}[0.085, 0.025, 0.691, 0.055], and the legend at the side, I can see each year's percentage of Residential and Commercial real estate in each slice of the pie chart.\nBased on the above thought, the answer is:\nYear,Residential,Commercial\n2010,10%,90%\n2011,15%,85%\n2012,22%,78%\n2013,33%,67%\n2014,48%,52%\n2015,55%,45%\n2016,60%,40%\n2017,63%,37%\n2018,70%,30%\n2019,77%,23%"
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "ToFDnWLbmkDoDVPP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the graph, what is the trend in the market shares of Residential and Commercial properties from 2010 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the pie slices for Residential and Commercial properties over the years.\nBased on the above thought, the answer is:\nFrom 2010 to 2019, the trend is that the market shares of Residential properties have steadily increased, while the market shares of Commercial properties have steadily decreased."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "xBozRpfs5W6UcTeN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the likely impact of these market trends on investment decisions in the Building and Real Estate sector?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the observed trends might impact future investment decisions in the real estate sector.\nBased on the above thought, the answer is:\nThe shift towards Residential property dominance could lead to increased investments in residential real estate development, as it shows a growing market demand in this area. Alternatively, the decreasing trend in Commercial property might encourage commercial developers to innovate or diversify their offerings to attract more investments."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "IOc3YGUBTSCYRDMQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie chart illustrate the shift in market dominance from Commercial to Residential properties?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to explain how the sizes of the pie slices change over the years.\nBased on the above thought, the answer is:\nThe pie chart illustrates the shift in market dominance by showing how the size of the Residential property slice has increased over the decade, while the Commercial property slice has reduced. This visual change in size signifies the transition in dominance from Commercial to Residential Real Estate."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "tl9vUiYtsRK249Bq",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2010 to 2019, by how much percentage did the gap between commercial and residential market shares decrease?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the initial pie chart for 2010 versus the final one for 2019, I need to find the difference in percentage between commercial and residential market shares for each year and subtract the two.\nBased on the above thought, the answer is:\nThe gap decreased by 94% between 2010 and 2019."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "C0smbo1IQmz9hGj8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the Residential real estate market exceed the Commercial real estate market?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the trend in the pie chart, the Residential sector exceeds the Commercial when the Residential sector is more than half the pie.\nBased on the above thought, the answer is:\nThe Residential market shares exceeded the Commercial market shares in the year 2015."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "ghMSgohOKUXI7QAY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2017, what was the percentage difference between the Residential and Commercial market shares?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference between the residential and commercial percentages for that year.\nBased on the above thought, the answer is:\nThe difference in market shares was 26%"
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "SzLFKfUFPfAkc1HF",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please turn the donut chart's data into a CSV formatted form?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, I can approach it by considering the pie labels [0.813, 0.062, 0.923, 0.08]; [0.813, 0.088, 0.915, 0.106]; [0.813, 0.115, 0.953, 0.132]; [0.813, 0.141, 0.929, 0.158]; [0.813, 0.167, 0.911, 0.185]; [0.813, 0.193, 0.888, 0.211]; [0.813, 0.219, 0.924, 0.237] as the sectors I need to capture the data from.\nBased on the above thought, the answer is:\nActivity,Percentage\nFinancial Sector,37\nRisk Mitigation,11\nInvestment Portfolio,29\nStock Evaluation,50\nMarket Trends,78\nCredit Risk,32\nOperational Risk,39"
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "SdJFD86JLFnV2meC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn your observation, which area of financial management and risk control is given the highest priority and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "From pie slice coordinates, it's clear that the area with the biggest part of the pie is given the highest priority.\nBased on the above thought, the answer is:\nMarket Trends is given the highest priority, with 78% of resources being allocated towards it, possibly because tracking market trends is crucial for making informed financial decisions."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "cY1CKhX9dqQy2gLs",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the proportion dedicated to Risk Mitigation suggest?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the Risk Mitigation slice[0.195, 0.307, 0.286, 0.414], I can conclude on the importance placed on it.\nBased on the above thought, the answer is:\nThe relative small proportion (11%) dedicated to Risk Mitigation may suggest that existing methods are highly efficient, requiring less resources and attention. Alternatively, it could suggest that this area is being underprioritized."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "BV4CmAUqK4OviUHa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Stock Evaluation and Market Trends continue to garner the most attention, how might this impact future financial management strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the significant proportion of resources allocated to Stock Evaluation[0.172, 0.553, 0.373, 0.815] and Market Trends[0.354, 0.524, 0.664, 0.824], potential implications can be inferred.\nBased on the above thought, the answer is:\nIf most resources continue to be allocated to Stock Evaluation and Market Trends, future strategies may involve careful tracking and swift adaptation to market changes. Such strategies will require the pooling of a significant amount of data and expertise. This might also imply less prominence or resources for other activities like Operational Risk or Credit Risk, which could impact overall financial risk mitigation."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "N0sMhDIxdB209aMp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the legend, which sector has the largest percentage allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the sizes of the slices on the pie[0.168, 0.204, 0.664, 0.824] to determine which sector has the largest percentage allocation.\nBased on the above thought, the answer is:\nMarket Trends"
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "JJWFDAEfZ139IJMX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentage allocations for Financial Sector, Risk Mitigation, and Credit Risk?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide an answer, Add the percentages for Financial Sector[0.231, 0.204, 0.416, 0.369], Risk Mitigation[0.195, 0.307, 0.286, 0.414] and Credit Risk[0.551, 0.318, 0.664, 0.528].\nBased on the above thought, the answer is:\n80%"
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "ENLEWq0QLavypkln",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between the allocations for Stock Evaluation and Market Trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtract the Stock Evaluation[0.172, 0.553, 0.373, 0.815] percentage from the Market Trends[0.354, 0.524, 0.664, 0.824] percentage to get the difference.\nBased on the above thought, the answer is:\n28%"
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "IF38trwMS2G2qO6g",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, could you present the data concerning 'Future Energy and Resource Allocation - Solar' in the form of a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Judging from the pie chart title {Future Energy and Resource Allocation - Solar}[0.151, 0.025, 0.626, 0.055], and each of the chart's sectors {[2020:13.6%][0.154, 0.104, 0.388, 0.327],[2021:10.5%][0.077, 0.245, 0.224, 0.502], [2022: 12.2%][0.077, 0.494, 0.223, 0.788], [2023: 14.2%][0.152, 0.708, 0.398, 0.934], [2024: 15.3%][0.395, 0.678, 0.648, 0.933], [2025: 16.6%][0.57, 0.333, 0.699, 0.746], [2026: 17.6%][0.388, 0.104, 0.666, 0.389]}, I can identify the allocation percentage per year.\nBased on the above thought, the answer is:\nYear,Allocation%\n2020,13.6%\n2021,10.5%\n2022,12.2%\n2023,14.2%\n2024,15.3%\n2025,16.6%\n2026,17.6%"
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "747AjBpGPbWHTvYY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the future trend in terms of allocation between solar and wind energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing pie chart data for each year and percentage labels, I can infer about the solar and wind energy allocation trend in the future.\nBased on the above thought, the answer is:\nThe chart suggests that the future trend is moving towards greater allocation for solar energy, as its percentage is steadily increasing each year from 13.6% in 2020 to 17.6% in 2026."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "F4BXBZ2r7irRM4Co",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat tips can you provide to better visualize the allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is able to illustrate how each year's allocation fits into the total, but for inter-year comparison, a line chart or bar chart may provide clearer visualization.\nBased on the above thought, the answer is:\nOne way to better visualize the year-to-year changes in the allocation is to use a line or bar chart. These charts will allow the viewer to clearly see the change in percentages over the years. Having both types of energy sources on the same chart will also make direct comparison between solar and wind easier."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "WWkHa79inWHSj2Ok",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can be inferred about future energy resource planning between solar and wind energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie slices in the chart and the sequence of years, I could understand the changing percentages of solar allocation over time.\nBased on the above thought, the answer is:\nThe pie chart indicates that solar energy is likely to take a larger proportion of the energy resources over time. From 2020 to 2026, the allocation percentage of solar energy is increasing, suggesting that future resource planning is leaning towards increased utilization of solar energy."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "GqswzKMY6fQvpqF9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart's data, what is the percentage increase in solar allocation from 2020 to 2026?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to subtract the 2020 solar allocation from the 2026 solar allocation.\nBased on the above thought, the answer is:\nThe increase is 17.6% - 13.6% = 4%."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "GROLrveoFye1y4o9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did solar allocation see the biggest jump from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will need to compare the year-on-year difference in allocation percentages to find the biggest jump.\nBased on the above thought, the answer is:\nThe biggest jump occurred from 2024 to 2025."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "IUfA8K1YDecKwbFW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we assume a consistent growth rate from 2023 onwards, what will the allocation percentage be in 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "I will need to calculate the growth rate from 2023 to 2026 and project this to 2027.\nBased on the above thought, the answer is:\nThe growth rate from 2023 to 2026 is (17.6% - 14.2%) / 3 = 1.13%. So the allocation percentage in 2027 should be 17.6% + 1.13% = 18.73%."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "E5j1Zbj7v6WExjUO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the information presented in the pie chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing {the slices of the pie}[{2009}[0.223, 0.104, 0.388, 0.273]; {2010}[0.104, 0.167, 0.272, 0.4]; {2011}[0.077, 0.35, 0.189, 0.613]; {2012}[0.085, 0.585, 0.254, 0.846]; {2013}[0.197, 0.748, 0.394, 0.934]; {2014}[0.392, 0.749, 0.578, 0.933]; {2015}[0.521, 0.6, 0.687, 0.848]; {2016}[0.596, 0.396, 0.699, 0.635]; {2017}[0.52, 0.188, 0.685, 0.432]; {2018}[0.388, 0.104, 0.576, 0.287]], it appears that each slice corresponds to a particular year and represents trade value.\nBased on the above thought, the answer is:\nYear,Trade Value\n2009,95\n2010,100\n2011,110\n2012,115\n2013,115\n2014,108\n2015,107\n2016,99\n2017,105\n2018,110"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "thswmo4PkHftif2N",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you predict the trade value for the year 2019 based on the trend observed in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make a prediction, I need to observe the trend in the {slices}[{2009}[0.223, 0.104, 0.388, 0.273]; {2010}[0.104, 0.167, 0.272, 0.4]; {2011}[0.077, 0.35, 0.189, 0.613]; {2012}[0.085, 0.585, 0.254, 0.846]; {2013}[0.197, 0.748, 0.394, 0.934]; {2014}[0.392, 0.749, 0.578, 0.933]; {2015}[0.521, 0.6, 0.687, 0.848]; {2016}[0.596, 0.396, 0.699, 0.635]; {2017}[0.52, 0.188, 0.685, 0.432]; {2018}[0.388, 0.104, 0.576, 0.287]] over the years.\nBased on the above thought, the answer is:\nAs the trend from 2015 to 2018 shows a general increase in trade, it is reasonable to predict that the trade value in 2019 might continue to rise, assuming no significant changes in global trade policies and economic activities."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "VTfHWfPZrOQPvRUR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the overall trend in this chart suggest about global trade and globalization over the ten-year period?"
+            },
+            {
+                "from": "gpt",
+                "value": "The overall trend can be inferred by looking at the growth and contraction of the {slices of the pie}[{2009}[0.223, 0.104, 0.388, 0.273]; {2010}[0.104, 0.167, 0.272, 0.4]; {2011}[0.077, 0.35, 0.189, 0.613]; {2012}[0.085, 0.585, 0.254, 0.846]; {2013}[0.197, 0.748, 0.394, 0.934]; {2014}[0.392, 0.749, 0.578, 0.933]; {2015}[0.521, 0.6, 0.687, 0.848]; {2016}[0.596, 0.396, 0.699, 0.635]; {2017}[0.52, 0.188, 0.685, 0.432]; {2018}[0.388, 0.104, 0.576, 0.287]].\nBased on the above thought, the answer is:\nThe overall trend suggests a gradual increase in international trade from 2009 to 2013, indicative of a phase of deepening globalization. However, a decline from 2014 to 2016 suggests some contraction, potentially due to various economic shocks and policy changes. The years 2017 and 2018 reflect a recovery, hinting at possible resilience in global trade and continuation of globalization, even in the face of challenges."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "F8aJJsjhWwp0eBOr",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what can you infer about the relationship between global economic policies and trade values?"
+            },
+            {
+                "from": "gpt",
+                "value": "The interpretation of {the slices}[{2009}[0.223, 0.104, 0.388, 0.273]; {2010}[0.104, 0.167, 0.272, 0.4]; {2011}[0.077, 0.35, 0.189, 0.613]; {2012}[0.085, 0.585, 0.254, 0.846]; {2013}[0.197, 0.748, 0.394, 0.934]; {2014}[0.392, 0.749, 0.578, 0.933]; {2015}[0.521, 0.6, 0.687, 0.848]; {2016}[0.596, 0.396, 0.699, 0.635]; {2017}[0.52, 0.188, 0.685, 0.432]; {2018}[0.388, 0.104, 0.576, 0.287]] from different years could help infer the possible effects of global economic policies on trade values.\nBased on the above thought, the answer is:\nThe chart's trend, especially the decline in trade values in 2014 and 2016, suggests that changes in global economic policies can impact international trade. Economic crises or protectionist policies could potentially lead to a contraction in trade. On the other hand, recovery in 2017 and 2018 could indicate effective policy measures or improving economic conditions that boost trade."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "Td8luPP9inl8hOHQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year is the trade value represented by the smallest slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the size of {the slices of the pie}[{2009}[0.223, 0.104, 0.388, 0.273]; {2010}[0.104, 0.167, 0.272, 0.4]; {2011}[0.077, 0.35, 0.189, 0.613]; {2012}[0.085, 0.585, 0.254, 0.846]; {2013}[0.197, 0.748, 0.394, 0.934]; {2014}[0.392, 0.749, 0.578, 0.933]; {2015}[0.521, 0.6, 0.687, 0.848]; {2016}[0.596, 0.396, 0.699, 0.635]; {2017}[0.52, 0.188, 0.685, 0.432]; {2018}[0.388, 0.104, 0.576, 0.287]], it appears the smallest slice represents the year with the lowest trade value.\nBased on the above thought, the answer is:\n2009"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "ioZVBz4AMLTX255g",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the trade value in 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {2017 slice}[0.52, 0.188, 0.685, 0.432] of the chart corresponds to the trade value in that year.\nBased on the above thought, the answer is:\n105"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "uVoJqTAKZsjtrPKl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference in trade value between 2013 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the {2013 slice}[0.197, 0.748, 0.394, 0.934] and the {2018 slice}[0.388, 0.104, 0.576, 0.287] to find the difference in trade value.\nBased on the above thought, the answer is:\n5"
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "MDMdfE9ZQm5UoGoT",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a table that lists the adoption rate of each technology (Telemedicine and AI in Healthcare) for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "After observing the pie chart and taking note of the percentage adoption rate for each technology type {Telemedicine}<r>[0.266, 0.103, 0.388, 0.25]</r> and {AI in Healthcare}<r>[0.388, 0.103, 0.636, 0.343]</r> for each year of {2016}<r>[0.919, 0.083, 0.963, 0.106]</r>, {2017}<r>[0.919, 0.118, 0.963, 0.141]</r>, {2018}<r>[0.919, 0.153, 0.963, 0.176]</r>, {2019}<r>[0.919, 0.188, 0.963, 0.211]</r> and {2020}<r>[0.919, 0.223, 0.963, 0.246]</r>, I can create a structured table.\nBased on the above thought, the answer is:\nTechnology Type,2016,2017,2018,2019,2020\nTelemedicine,35,50,60,70,85\nArtificial Intelligence in Healthcare,15,30,45,70,80"
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "zzq4Si4Gg40chAkQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your visual observation of the chart, which technology saw the most rapid growth in adoption over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can look at the growth progression from 2016<r>[0.919, 0.083, 0.963, 0.106]</r> to 2020<r>[0.919, 0.397, 0.963, 0.421]</r> of both {Telemedicine}<r>[0.266, 0.103, 0.388, 0.25]</r> and {AI in Healthcare}<r>[0.388, 0.103, 0.636, 0.343]</r> to answer this question.\nBased on the above thought, the answer is:\nAI in Healthcare experienced the most rapid growth in adoption over the years, going from 15% in 2016 to 80% in 2020."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "aeTQid8hQxFUfZT5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the visuals of the chart, which year saw the greatest increase in technology adoption in healthcare?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to evaluate the increase in proportions from the year-to-year segments for both Telemedicine and AI in Healthcare to answer this question.\nBased on the above thought, the answer is:\nThe year 2019 saw the greatest increase in technology adoption in healthcare, with AI in Healthcare seeing a significant jump from 45% in 2018 to 70% in 2019."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "RfFelQs9qW9kzhfl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data in the chart, what can you infer about the future of technology adoption in healthcare?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the pattern of growth in both Telemedicine and AI in Healthcare from 2016 to 2020 to make a prediction about the future.\nBased on the above thought, the answer is:\nThe data indicates a consistent upward trend in the adoption of Telemedicine and AI in Healthcare, signaling an ongoing commitment to integrating technology into healthcare. If current adoption patterns continue, we can expect technology to play an even more prominent role in healthcare in the future."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "V19EFr7MGdKoraCZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the adoption rate difference between Telemedicine and AI in Healthcare in 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the adoption rate difference between the two technologies in 2018, I need to subtract the percentage of AI adoption in 2018<r>[0.919, 0.327, 0.963, 0.351]</r> from the Telemedicine adoption percentage in the same year<r>[0.919, 0.153, 0.963, 0.176]</r>.\nBased on the above thought, the answer is:\n15"
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "Gz2z6VIXG4SJXsY5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage increase did the adoption of AI in Healthcare see from 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must subtract the adoption rate in 2017 on>r>[0.919, 0.118, 0.963, 0.141]</r> from the adoption rate in 2019 of AI in Healthcare<r>[0.919, 0.362, 0.963, 0.386]</r> to determine the percentage increase.\nBased on the above thought, the answer is:\n40"
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "PsPNPGaqGJsJt9mB",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend continues at the same rate, what percentage of adoption could we expect for AI in 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference between the adoption percentages for 2019 and 2020 and then add that change to the 2020 percentage to estimate the adoption rate for AI in 2021.\nBased on the above thought, the answer is:\n95"
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "Ok9CTTtpJcrYHxf9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you represent the displayed pie chart with a table format showing the contributions from each year to Cultural Heritage and Cultural Integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart with the title {\"Cultural Heritage and Cultural Integration Over the Years\"}[0.093, 0.025, 0.683, 0.055], I can see that the segments of the pie represent contributions from different years presented in the legend {Year 1}[0.862, 0.085, 0.896, 0.101], {Year 2}[0.862, 0.12, 0.896, 0.136], {Year 3}[0.862, 0.155, 0.896, 0.171], {Year 4}[0.862, 0.19, 0.896, 0.206], and {Year 5}[0.862, 0.225, 0.896, 0.241].\nBased on the above thought, the answer is:\nYear,Cultural Heritage,Cultural Integration\nYear 1,12,23\nYear 2,15,19\nYear 3,20,16\nYear 4,14,22\nYear 5,19,20"
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "v81qLi1EcQS4ejRj",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what major trend do you notice about the progress of Cultural Heritage and Cultural Integration over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To interpret trends, I have to scrutinize the values for each year and consider the interplay of Cultural Heritage and Cultural Integration.\nBased on the above thought, the answer is:\nFor Cultural Heritage, there was a rise until Year 3, followed by a decrease in contribution. On the other hand, Cultural Integration started strong, dipped, and then rose again."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "yami8bgAFhS8Cqks",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the insights gleaned from the pie chart be potentially used by policy makers?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer its application for policy makers, I need to assume the roles of those who might be utilizing this chart.\nBased on the above thought, the answer is:\nPolicy makers can observe the trends shown in the pie chart and accordingly adjust focus or funding. For example, they could aim to boost the cultural integration in the consecutive years, given their low contribution in Year 3."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "ivVHh6A7ONO5oWiU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Cultural Heritage should never drop below 20%, what is your comment about Year 4 and Year 5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To comment, I need to compare the contribution of Cultural Heritage in Year 4 and Year 5 with the minimum level of 20%.\nBased on the above thought, the answer is:\nThe percentages of Cultural Heritage in Year 4 and Year 5 both fall below the minimum requirement of 20%. It suggests that more support should be given to Cultural Heritage in these years."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "Hul0SLkmObgZSTgR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the contribution to Cultural Heritage at least 20%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I should locate the part of the pie chart coupled with the textual value '20' and then figure out the label to it from the legend.\nBased on the above thought, the answer is:\nYear 3"
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "NzqozSdMUF016ZgY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much did the contribution towards Cultural Heritage dip from the peak in Year 3 to Year 4?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I have to subtract the value of Cultural Heritage in Year 4 from that in Year 3.\nBased on the above thought, the answer is:\n6"
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "Zu4e4yeOCN4i7j9q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow often across the five years is Cultural Integration having a higher value than Cultural Heritage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to compare the contributions towards Cultural Heritage and Cultural Integration for each year and count the number of years where Cultural Integration has a higher contribution.\nBased on the above thought, the answer is:\n3 times"
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "ekhRoe6ntrS2kjB2",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you parse the data from the pie chart into a CSV structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "To convert the pie chart to a CSV format, I need to refer to the slices of the pie chart with each representing a specific year from 2001 to 2007 {slice:year representation}<r>[0.077, 0.104, 0.699, 0.934]</r> and the legend {legend details}<r>[0.864, 0.074, 0.97, 0.325]</r>. Each slice in the pie chart represents the resource usage percentage for its corresponding year, which I can see from the autotext embedded in each slice.\nBased on the above thought, the answer is:\nYear,Percentage\n2001,10\n2002,15\n2003,25\n2004,10\n2005,10\n2006,20\n2007,10"
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "vC0909Tc2wCc8yKQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest usage of resources according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with highest resources usage can be identified by finding the slice with the largest percentage. This is represented on the pie chart by the size of the slice and the percentage labelled on it.\nBased on the above thought, the answer is:\nThe year 2003 had the highest usage of resources."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "8Qn4ruWs9lYyi81O",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat patterns can be discerned from the resource usage percentages over the years, as represented in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To gather patterns, I should observe the variation and growth in the percentages year over year. These are given in the slices of the pie chart.\nBased on the above thought, the answer is:\nResource utilization varied throughout the years with significant increases observed in 2003 and 2006. Stable resource usage was observed in 2004, 2005, and 2007."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "W1UE3FXF9LMlBQ9L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you speculate how the patterns in resource usage, as represented in the pie chart, might inform future resource allocation decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Informing resource allocation decisions would involve analyzing the patterns and growth in resource usage percentages over the years.\nBased on the above thought, the answer is:\nObserving the fluctuations in resource usage might encourage an evaluative study into the causes of the peaks and dips, which could influence future decisions. The increased resource usage in 2003 and 2006 may indicate the need for more resource allocations during similar periods in the future if the circumstances that caused those spikes are predicted to repeat."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "Y8kp3jBUp3QLbLB3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the median of the percentages displayed on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the median value, I first need to sort all the percentages from lowest to highest and then find the middle value.\nBased on the above thought, the answer is:\n10"
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "GaMNqrEbJyhaKZcj",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage difference is there between the usage in 2001 and the usage in 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the difference, I need to subtract the percentage value of 2001 {percentage 2001 slice}<r>[0.205, 0.104, 0.388, 0.284]</r> from the percentage value of 2007 {percentage 2007 slice}<r>[0.388, 0.104, 0.571, 0.284]</r>. Both values are represented in the slices of the pie chart.\nBased on the above thought, the answer is:\n0"
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "u0j6U9I3fn9O5lci",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf each slice's percentage represents the amount of a total resource, what percent was the resource utilization of 2002 and 2003 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "The combined utilization of 2002 {percentage 2002 slice}<r>[0.077, 0.183, 0.26, 0.519]</r> and 2003 {percentage 2003 slice}<r>[0.077, 0.519, 0.388, 0.934]</r> can be calculated by adding both percentages together.\nBased on the above thought, the answer is:\n40%"
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "W3WJjRnU8RR6f3m9",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data shown in the pie chart related to the market competitiveness of major tech companies into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart's data, which includes {\"Google\"}[0.077, 0.104, 0.388, 0.647], {\"Facebook\"}[0.092, 0.608, 0.388, 0.934], {\"Amazon\"}[0.388, 0.689, 0.64, 0.934], {\"Apple\"}[0.564, 0.519, 0.699, 0.762], and {\"Microsoft\"}[0.388, 0.104, 0.699, 0.519], the respective market shares of the tech companies could be organized into a structured format.\nBased on the above thought, the answer is:\nCompany, Market Share (in %)\nGoogle, 30 \nFacebook, 20 \nAmazon, 15 \nApple, 10 \nMicrosoft, 25"
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "y0GmkVgp3qDD4yEN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, which company is leading in the tech industry in terms of market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To identify the leading company, I have to determine which firm has the largest market share as indicated by the size of the pie segment.\nBased on the above thought, the answer is:\nGoogle is leading in the tech industry as it has the largest market share of 30%."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "tBW2aJrQP1AIGDKA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can we draw about the tech industry's competition from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make an inference from the chart, I need to evaluate the size of the segments corresponding to each company.\nBased on the above thought, the answer is:\nThe chart illustrates that competition in the tech industry is high, with Google leading the market. The other companies, such as Amazon and Apple, have less than half the market share of Google, suggesting they may need to enhance their competitive strategies."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "Afdu2aZsLJEs7ioB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be a potential business strategy for Microsoft observing this market competition chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To suggest a strategy, I need to evaluate Microsoft's current position compared to the other companies in the tech industry.\nBased on the above thought, the answer is:\nConsidering that Microsoft holds the second-largest market share (25%) next to Google, Microsoft may aim at strategies to hold its position in the market and potentially increase its share. This could be through product innovation, marketing efforts, or strategic alliances."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "TzKSxEpviJAibHas",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAmong the tech companies in the chart, which two companies have a combined market share of 50%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to calculate the combined market share of each two companies and check if the result sums up to 50%.\nBased on the above thought, the answer is:\nGoogle and Microsoft."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "12j5u2wdGq7zkFPX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average market share of the five tech companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average market share, I should sum up the market shares of all the tech companies and then divide the total by the number of companies.\nBased on the above thought, the answer is:\nThe sum of the market shares is 30% (Google) + 20% (Facebook) + 15% (Amazon) + 10% (Apple) + 25% (Microsoft) = 100%. The average market share is 100% / 5 = 20%."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "W7neLejO6m40IalU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Amazon increases its market share by 5%, what will the new market share of Amazon be and what will be the new total market share of all the companies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new market share after Amazon increases its market share by 5%, I should add 5% to the current market share of Amazon which is 15%. To calculate the new total market share, I add this new market share of Amazon to the market shares of the other tech companies.\nBased on the above thought, the answer is:\nThe new market share for Amazon would be 15% + 5% = 20%. The new total market share would be 30% (Google) + 20% (Facebook) + 20% (Amazon) + 10% (Apple) + 25% (Microsoft) = 105%."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "nGmkpq9MlxX2fzwu",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, please convert the chart data for Value-5 into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "From the title of the chart {\"Social Culture Value Change: Value-5\"}[0.196, 0.025, 0.58, 0.055] and the {pie chart slices} for {\"Faith\"}[0.077, 0.104, 0.484, 0.934] and {\"Religious Tolerance\"}[0.388, 0.104, 0.699, 0.913], I can extract the proportions for each category in Value-5.\nBased on the above thought, the answer is:\nParameter,Value-5\nFaith,55\nReligious Tolerance,45"
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "rp5wNBJQ6ih7cmls",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social aspect is more dominant in Value-5 based on the chart and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the dominant social aspect, I need to compare the proportions of 'Faith' and 'Religious Tolerance'.\nBased on the above thought, the answer is:\nThe social aspect 'Faith' is more dominant in Value-5 because it has a higher proportion of 55% compared to 'Religious Tolerance' which has a proportion of 45%."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "0HutrkChaYkge8PI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the society's beliefs in Value-5?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the proportions of 'Faith' and 'Religious Tolerance', I can infer about society's beliefs.\nBased on the above thought, the answer is:\nThe chart suggests that although 'Faith' is more dominant in society in Value-5, there is a significant amount of 'Religious Tolerance' as well, indicating a fair appreciation for diverse religious beliefs in society."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "JKCzWVGtNjYVSyp4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential societal changes might be inferred from the chart for the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the proportions of 'Faith' and 'Religious Tolerance', I can infer possible societal changes.\nBased on the above thought, the answer is:\nIf the trends charted in the graph continue, society might be moving towards an increased acceptance of religious diversity, as represented by an increase in 'Religious Tolerance', while the dominant role of a singular 'Faith' may decrease."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "kYGYZsvTH3J7HGqU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference between 'Faith' and 'Religious Tolerance' for Value-5?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage difference, I need to subtract the proportion of 'Religious Tolerance' from that of 'Faith'.\nBased on the above thought, the answer is:\nThe percentage difference is 55% (Faith) - 45% (Religious Tolerance) = 10%."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "T3EhWi3Trt3RROPp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf 'Religious Tolerance' increased by 3% in next assessment, what would be its new percentage representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to add 3% to the existing percentage of 'Religious Tolerance'.\nBased on the above thought, the answer is:\nThe new percentage for 'Religious Tolerance' is 45% + 3% = 48%."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "h6mCdYfXPrgN8zic",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage representation of 'Faith' was decreased by half of its current value, what would be its new percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new percentage, I need to find half of the current percentage of 'Faith'.\nBased on the above thought, the answer is:\nThe new percentage for 'Faith' is 55% / 2 = 27.5%."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "G8eChxE58ztYYBFs",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data you observed in the pie chart to a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice of the pie at {coordinates}[0.077, 0.104, 0.699, 0.934] corresponds to a year of cultural and arts industry development from 2014 to 2020. The color of the slice corresponds to the year designated in the legend at {coordinates}[0.864, 0.074, 0.97, 0.325]. The size and autotext of each slice represent the percentage of industry growth for that year.\nBased on the above thought, the answer is:\nYear,Percentage\n2014,11.0%\n2015,11.9%\n2016,12.8%\n2017,13.8%\n2018,14.7%\n2019,17.4%\n2020,18.3%"
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "AKOo8Cy4bgiL12ZC",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend in the cultural and arts industry based on the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the pie chart and the increasing percentages from year to year, it's possible to infer about the trends in the cultural and arts industry.\nBased on the above thought, the answer is:\nBased on the data, the cultural and arts industry has seen steady growth from 2014 to 2020, with a significant increase in 2019 and 2020."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "GPnreol0MIsDgoPO",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, what could be some possible reasons for the significant surge in the industry in 2019 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The reasons for the surge are not directly stated in the chart, but I can make an educated guess based on the information available.\nBased on the above thought, the answer is:\nThe surge in 2019 and 2020 may be due to an increase in funding, the introduction of influential art pieces, an increase in community engagement with the arts, or a combination of factors. Actual reasons would require more information and context beyond the scope of this chart."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "TMSxiW2DUmSyOWAJ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the varying sizes of the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The varying sizes of the different slices in the pie chart represent the annual development of the Culture and Arts Industry, with larger slices corresponding to years with more growth.\nBased on the above thought, the answer is:\nThe varying slice sizes reflect how much the cultural and arts industry grew each year from 2014 to 2020. Larger slices indicate years with more growth, allowing quick and easy visual interpretation of which years saw the most growth."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "JODFBoX7plIar7RJ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year did the cultural and arts industry see the maximum growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The maximum growth would be represented by the largest slice of the pie. I need to compare the sizes of all slices to find this.\nBased on the above thought, the answer is:\nThe cultural and arts industry saw the maximum growth in the year 2020."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "UsqchEYDycBMqdR8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage growth in the cultural and arts industry from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth can be calculated by subtracting the initial year's (2014) percentage from the final year's (2020) percentage.\nBased on the above thought, the answer is:\nThe growth in the cultural and arts industry from 2014 to 2020 was 7.3%."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "C9R48JGTiRnebtD4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much has the cultural and arts industry grown overall in 2017 compared to the previous year 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "The growth compared to the previous year can be calculated by subtracting the percentage of the previous year (2016) from the percentage of the current year (2017).\nBased on the above thought, the answer is:\nThe cultural and arts industry has grown 1% in 2017 compared to 2016."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "BI2i0rwn5NIvWVJZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you create a CSV formatted table with the data represented in the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, I will reference the sub-title of each Pie Chart {\"Human Life and Living Environment in 2010\"}<r>[0.111, 0.019, 0.339, 0.041]</r> and {\"Human Life and Living Environment in 2014\"}<r>[0.601, 0.019, 0.829, 0.041]</r> for the year, followed by the labels {Quality of Life}<r>[0.175, 0.329, 0.203, 0.347]</r>, {Water Quality}<r>[0.126, 0.421, 0.154, 0.438]</r>, {Air Quality}<r>[0.119, 0.548, 0.146, 0.565]</r>, {Lovely Homes}<r>[0.154, 0.652, 0.176, 0.67]</r>, {Healthy Foods}<r>[0.21, 0.696, 0.238, 0.713]</r>, {Physical Activity}<r>[0.278, 0.641, 0.306, 0.658]</r>, {Natural Environment}<r>[0.306, 0.487, 0.334, 0.504]</r>, {Recreation}<r>[0.257, 0.338, 0.285, 0.356]</r> to understand the aspect represented by each slice.\nBased on the above thought, the answer is:\nYear,Quality of Life,Water Quality,Air Quality,Lovely Homes,Healthy Foods,Physical Activity,Natural Environment,Recreation\n2010,70,60,63,55,67,75,85,90\n2014,75,62,65,60,72,80,88,94"
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "GOUtcZcpqJrOk6kA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2014, which quality aspect saw the biggest improvement?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I must compare the change in quality levels between 2010 and 2014 across all quality aspects.\nBased on the above thought, the answer is:\n'Healthy Foods' and 'Natural Environment' quality saw the biggest improvement in 2014."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "8CKU1oB6XD7nwjuv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the overall trend from 2010 to 2014 in quality of life and living environment aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the total quality levels across all aspects between 2010 and 2014.\nBased on the above thought, the answer is:\nOverall, there was an increase in quality levels across almost all sectors, indicating that the quality of life and living environment improved between 2010 and 2014."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "E5BHC1wKkOiJITGu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the 'Physical Activity' aspect between 2010 and 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the change in 'Physical Activity' quality levels between 2010 and 2014.\nBased on the above thought, the answer is:\nThe 'Physical Activity' aspect saw a decline between 2010 and 2014. This could imply less emphasis or lower performance in that area over the observed period."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "pSQoKw2YlkBMRmev",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2014, how much did the quality of 'Healthy Foods' improve from 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the improvement in 'Healthy Foods' quality from 2010 to 2014, we need to compare the 2014 percentage to the 2010 percentage.\nBased on the above thought, the answer is:\n'Healthy Foods' quality improved by 5 levels."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "DLcreDiVm8YMxevr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total quality level of 'Natural Environment' and 'Air Quality' in 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total quality level of 'Natural Environment' and 'Air Quality' in 2010, we need to add up their individual 2010 quality levels.\nBased on the above thought, the answer is:\nThe total quality level of 'Natural Environment' and 'Air Quality' in 2010 is 148."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "zEuoD3HLGq5KpkrL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom 2010 to 2014, how much has the quality of 'Physical Activity' decreased?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the change in 'Physical Activity' quality from 2010 to 2014, we need to subtract the 2014 percentage from the 2010 percentage.\nBased on the above thought, the answer is:\nThe 'Physical Activity' quality decreased by 5 levels from 2010 to 2014."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "fvi1EV8gb0yBYbAn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data represented in the pie chart to a table in a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the pie slices for each year, from {\"2011\"}<r>[0.294, 0.274, 0.349, 0.298]</r>{\"2012\"}<r>[0.197, 0.388, 0.252, 0.411]</r>{\"2013\"}<r>[0.179, 0.563, 0.234, 0.586]</r>{\"2014\"}<r>[0.264, 0.72, 0.319, 0.743]</r>{\"2015\"}<r>[0.412, 0.746, 0.467, 0.77]</r>{\"2016\"}<r>[0.526, 0.623, 0.581, 0.646]</r>{\"2017\"}<r>[0.538, 0.428, 0.593, 0.451]</r>{\"2018\"}<r>[0.437, 0.28, 0.492, 0.303]</r> and the associated percentage on each slice, it's possible to recreate the data in a tabular format.\nBased on the above thought, the answer is:\nYear,Living Environment Health Index\n2011,11.6%\n2012,10.9%\n2013,12.2%\n2014,13.2%\n2015,13.0%\n2016,12.7%\n2017,13.0%\n2018,13.3%"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "eyi3z6Jl9bG4KtO0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest Living Environment Health Index based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year with the largest pie slice indicates the highest Living Environment Health Index.\nBased on the above thought, the answer is:\nThe year 2018 had the highest Living Environment Health Index."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "zDuJOhhGQPFx2gcd",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the trend of the Living Environment Health Index over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing the pie slice size for each year, the trend of the Living Environment Health Index across years can be inferred.\nBased on the above thought, the answer is:\nFrom the pie chart, it's observed that the Living Environment Health Index slowly increased from 2011 to 2014, dipped in 2015, then had a peak rise in 2016, followed by a very slight decrease in 2017 and 2018. Despite minor fluctuations, the trend overall shows an increase in the Living Environment Health Index over time."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "p5WG2IroQNAa2huU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat potential effects on public health policies can be inferred considering the data from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I would need to consider how an increasing Living Environment Health Index might impact public health policies.\nBased on the above thought, the answer is:\nAs the Living Environment Health Index has generally shown an increasing trend over the years, this could lead to health policies that focus on maintaining and improving upon these advancements. Public health efforts that have been effective should be continued and adapted to current environmental and societal changes to sustain a healthy living environment for the public."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "QSEPugSnqxzlKe4Q",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of the years 2013 and 2015 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By adding the displayed percentage for the year 2013 {\"2013\"}<r>[0.179, 0.563, 0.234, 0.586]</r> and 2015 {\"2015\"}<r>[0.412, 0.746, 0.467, 0.77]</r>, we gain the total percentage.\nBased on the above thought, the answer is:\n25.2%"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "gA5VJWonbhvrkm1s",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of the Living Environment Health Index over the eight years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must sum the percentage for every year and then divide it by the number of years.\nBased on the above thought, the answer is:\n12.6%"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "w33ZmSCJZ3dUqrZ1",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the Living Environment Health Index for the year 2016 increased by 2%, what would be the new total percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new total percentage can be found by adding 2% to the 2016 index and then calculating the total percentage.\nBased on the above thought, the answer is:\n102%"
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "ymGhJ3OdhMY68Mi1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the information in the observed pie chart into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see that the pie chart titled {Social Media Usage in 2020}[0.245, 0.025, 0.531, 0.055] reveals the usage percentages of various social media platforms. The legend {[0.819, 0.074, 0.97, 0.29]} displays color-coded labels associated with these platforms, and the pie slices {[0.092, 0.104, 0.388, 0.429]; [0.077, 0.39, 0.388, 0.934]; [0.388, 0.519, 0.699, 0.934]; [0.516, 0.183, 0.699, 0.519]; [0.455, 0.124, 0.571, 0.284]; [0.388, 0.104, 0.484, 0.242]} correspond to the percentages given in the data.\nBased on the above thought, the answer is:\nSocial Media Platform, Usage Percentage\nFacebook, 20%\nInstagram, 30%\nTwitter, 25%\nLinkedIn, 15%\nSnapchat, 5%\nPinterest, 5%"
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "GzPdkmZrqL8atCAP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which social media platform shows the highest usage percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the slice with the largest proportion, which represents the platform with the highest usage.\nBased on the above thought, the answer is:\nInstagram has the highest usage percentage, as indicated by its large slice {[0.077, 0.39, 0.388, 0.934]}."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "6xjnd2dwW80D3crB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the social media preferences of people from the data presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the proportions of each platform to make inferences about the preferences of people.\nBased on the above thought, the answer is:\nThe chart suggests that people preferred using Instagram and Twitter the most in 2020, while Snapchat and Pinterest were the least preferred social media platforms."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "jz323e6CJVooFNFB",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the information in the chart be used to guide social media marketing strategies, based on the 2020 data?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to understand how the proportions of social media usage might impact marketing strategies.\nBased on the above thought, the answer is:\nBased on the 2020 data, marketing efforts might focus more on Instagram and Twitter due to their higher usage percentage. However, individual business objectives and target audience preferences also need to be considered. For example, if Snapchat and Pinterest are popular among a business\u2019s specific target audience, it might still be advantageous to invest in marketing on these platforms."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "h4bXEsiiQC7omrKx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of Snapchat usage in comparison to Instagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can get the answer by comparing the percentages mentioned in the pie slices for Snapchat {[0.455, 0.124, 0.571, 0.284]} and Instagram {[0.077, 0.39, 0.388, 0.934]}.\nBased on the above thought, the answer is:\nSnapchat is used 25% less than Instagram."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "h0iDCe1tTg2iOAyE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the usage percentages of LinkedIn, Snapchat, and Pinterest?"
+            },
+            {
+                "from": "gpt",
+                "value": "Summing up the percentages of LinkedIn {[0.516, 0.183, 0.699, 0.519]}, Snapchat {[0.455, 0.124, 0.571, 0.284]}, and Pinterest {[0.388, 0.104, 0.484, 0.242]}, we can get an answer.\nBased on the above thought, the answer is:\nThe sum of the usage percentages of LinkedIn, Snapchat, and Pinterest is 25%."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "BRvZsWXRLITIs9oS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat will the total percentage become if Pinterest's usage percentage was increased by 3%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 3% to the Pinterest's {[0.388, 0.104, 0.484, 0.242]} current percentage and sum up all the percentages again.\nBased on the above thought, the answer is:\nIf Pinterest's usage increased by 3%, the total would be 103%."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "CaokB6NQs7nm3uxz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the chart of predicted use of Autonomous Cars and AI Technology from 2030 to 2100 into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices of the pie chart represent the different years from 2030 to 2100, with different colors pointing to Autonomous Cars and AI Technology. Therefore, by equating the proportions with the given data, I can construct a table.\nBased on the above thought, the answer is:\nYear,Autonomous Cars,AI Technology\n2030,15,18\n2040,25,34\n2050,26,48\n2060,40,60\n2070,47,70\n2080,73,84\n2090,93,92\n2100,105,100"
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "dvydJS1xMMJZxRSQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology is projected to see greater use in the future? Can you explain the reasons?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I will use the data given and the pie chart's representation of year-on-year increases in use.\nBased on the above thought, the answer is:\nIt's projected that Autonomous Cars will see greater use in the future. From 2040 onwards, the usage of Autonomous Cars increases at a higher rate compared to AI Technology, with a significant difference seen in the years after 2080. By 2100, the use of Autonomous Cars is expected to be 105, which is higher than the 100 for AI Technology."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "su0Bh6wW6rhtdkol",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can you identify from the pie chart about the predicted use of Autonomous Cars and AI Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "The different sections of the pie chart represent the use of Autonomous Cars and AI Technology over a span of 70 years, so I can identify trends based on the size of these sections.\nBased on the above thought, the answer is:\nThe chart suggests that both Autonomous Cars and AI Technology will see increasing use over the time period from 2030 to 2100. However, the growth rate appears to be steeper for Autonomous Cars, particularly after the 2080s. This could indicate a more rapid pace of adoption or implementation of Autonomous Cars in the future."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "dGk7VMipee2eGwjw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the year 2100 was used as a benchmark, how might the trends observed in the chart influence future predictions about the 22nd century?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the final data points and infer their implications for future trends.\nBased on the above thought, the answer is:\nGiven that the predicted use of Autonomous Cars exceeds that of AI Technology by the year 2100, it could infer that the usage trend of Autonomous Cars might outpace AI Technology into the 22nd century. The continuing adoption and implementation of Autonomous Cars could possibly indicate a shift towards more automated modes of transport or a significant investment in autonomous vehicle technology in the future."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "Cac7Ulq4SRncLgUQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year the predicted use of Autonomous Cars surpass the AI Technology?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the data given, I can find the first year in which the use of Autonomous Cars is greater than the use of AI Technology.\nBased on the above thought, the answer is:\n2090"
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "hDO5YDH28ddVjtmV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total predicted usage of Autonomous Cars from 2030 to 2100?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total predicted usage, I need to add up the usage for each of the years provided.\nBased on the above thought, the answer is:\n419"
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "C7SYewadBaIE79f3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the predicted use of AI Technology for the year 2060 increased by 10 units, what would be the new total predicted usage for Autonomous Cars and AI Technology in that year?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total, I need to add the increased value to the predicted use of AI Technology for the year 2060 and then add the usage of Autonomous Cars for the same year.\nBased on the above thought, the answer is:\n110"
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "1LDeSDcqa9UEoY3C",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data shown in the pie chart into a csv format?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the pie chart slices and the legend, I can see the representation of each year and the corresponding percentage of adults using nutrition apps in U.S. from 2015 to 2020. The color-coded pie sections each {represent a different year}<r>[0.167, 0.103, 0.388, 0.314]</r>; <r>[0.079, 0.227, 0.234, 0.573]</r>; <r>[0.082, 0.556, 0.304, 0.895]</r>; <r>[0.268, 0.732, 0.592, 0.927]</r>; <r>[0.531, 0.381, 0.697, 0.824]</r>; <r>[0.388, 0.103, 0.68, 0.421]</r>. Also, the figure title {\"Percentage of U.S. adults using nutrition apps (2015-2020)\"}<r>[0.082, 0.025, 0.694, 0.055]</r> clearly states what is represented on the x and y-axes.\nBased on the above thought, the answer is:\nYear,Percentage\n2015,27\n2016,31\n2017,35\n2018,38\n2019,40\n2020,42"
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "cgTYkIKd1c3kHypc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year experienced the highest increase in the usage of nutrition apps?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can examine the percentage of users for each year, noting the differences from the previous year, to determine which year experienced the highest increase.\nBased on the above thought, the answer is:\nThe year 2020 had the highest increase in the usage of nutrition apps as it increases by 2% compared to 2019."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "gLe8GIqiPwfl7XrF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, what is the overall trend in the use of nutritional apps by adults in the U.S. from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the change in percentages over the years to determine the overall trend.\nBased on the above thought, the answer is:\nThe overall trend in the use of nutritional apps by adults in the U.S. has been increasing steadily from 2015 to 2020."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "7nEdjKyz4zYUBVzb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart tell us about the trend in usage of nutrition apps among US adults over the mentioned time period?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the percentages of usage over the years from the pie chart.\nBased on the above thought, the answer is:\nThe chart shows a consistent upward trend in the usage of nutrition apps among US adults from 2015 to 2020, reflecting the increasing adoption of health-focused applications over time."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "2lCpPJhpC9FGxfpt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage did the use of nutrition apps increase from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the percentages for years 2015 and 2020 from the pie chart and then subtract the percentage for 2015 from that for 2020 to get the increase.\nBased on the above thought, the answer is:\nIt increased by 15%."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "FKXxLqgH1BTEkkck",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn average, by what percentage did the usage increase per year from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average annual increase, I will first subtract the percentage for the year 2015 from that for the year 2020, then divide the result by the number of years.\nBased on the above thought, the answer is:\nIt increased by approximately 3% per year."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "DV475YOxw7aqqo0t",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage did the usage increase from 2018 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find the percentages for years 2018 and 2019 from the pie chart and then subtract the percentage for 2018 from that for 2019 to get the increase.\nBased on the above thought, the answer is:\nIt increased by 2%."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "N4He9kXGrf2IZjd1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the chart data reflecting fitness and sports health trends from 2018 to 2022 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the object labels in the legend {Legend}[0.864, 0.074, 0.97, 0.255], each corresponds to year present in pie chart. Also, the proportion of each year is displayed by respective sections in the pie chart,{Pie Chart: Fitness Trends}[0.077, 0.104, 0.699, 0.934]. By cross-referencing both, I can generate a table for the data.\nBased on the above thought, the answer is:\nYear,Proportion(%)\n2018,13.9\n2019,17.7\n2020,20.3\n2021,22.8\n2022,25.3"
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "RxH9ZQHgWA1CnE5f",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year witnessed the highest interest in fitness and sports health trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the year with the highest proportion, which represents the highest interest in fitness.\nBased on the above thought, the answer is:\nThe year with the highest interest in fitness and sports health trends is 2022 as it holds the largest proportion in the pie chart at 25.3%."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "VySiiam6HCqJ0HMu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFollowing the data trends and pie chart, what can be told about the progression of interest in sports and fitness health trends from 2018 to 2022?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the proportions representing each year and determine the trend in the data.\nBased on the above thought, the answer is:\nAccording to the pie chart, there has been an overall increase in the popularity of sports and health fitness trends from 2018 to 2022. The interest grew each year, with a notable increase from 2021 to 2022, marked by the highest sector in the pie chart."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "gDgL7R8xp8fb3AvA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy analyzing the pie chart data, how could a fitness company apply this trend information for future business strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "To provide a strategy, I need to consider how the increasing trend in fitness interests could influence a company's future direction.\nBased on the above thought, the answer is:\nGiven the continuous increase in popularity of fitness and sports health trends, a fitness company could focus on promoting products or services that align with the rising trends, particularly those demonstrated in 2022. Additionally, the company might also predict future trends based on the increasing pattern seen from 2018 to 2022 and prepare accordingly."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "5cWvqbgYLOVLfeke",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what is the total trend percentage for fitness for the years 2018 and 2019 combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total trend percentage, I can list out the individual yearly proportion in the pie chart and then sum up the percentages for the years 2018 and 2019.\nBased on the above thought, the answer is:\nThe total percentage for years 2018 and 2019 is 13.9% + 17.7% = 31.6%."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "3lWuNZ366l38vxk8",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you were to identify the year with the highest trend percentage in the chart, what is the percentage difference from the year with the lowest trend percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can find out the years with the highest and lowest trend percentages, then calculate the difference between them.\nBased on the above thought, the answer is:\nThe year with the highest trend is 2022 with 25.3% and the year with the lowest trend is 2018 with 13.9%. So, the difference is 25.3% - 13.9% = 11.4%."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "3Q4Om4VnDEA6SD9j",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSuppose the fitness trend for the year 2023 is projected to grow by 3% respectively from the year 2022. In this case, what would be the expected percentage for the fitness trend in 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the projected fitness trend for 2023, I need to add the growth rate to the fitness trend percentage of 2022.\nBased on the above thought, the answer is:\nThe expected trend percentage for 2023 is 25.3% + 3% = 28.3%."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "QmqaokLe2TYfruJX",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the given data into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Given the data from the pie chart. I notice that the Energy type and the associated annual array of energy allocation are clearly indicated in each pie slice. Using the title {\"Solar Energy Allocation Over Years\"}[0.321, 0.019, 0.679, 0.041] and {\"Nuclear Energy Allocation Over Years\"}[0.307, 0.507, 0.693, 0.53], I can start constructing the table.\nBased on the above thought, the answer is:\nYear,Solar Energy, Nuclear Energy\n2025,33,60\n2030,55,70\n2035,75,65\n2040,90,55\n2045,125,45\n2050,170,30\n2055,250,15\n2060,400,10"
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "OwBf7Ijk3Z1DSJbl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat insights can you draw about the trends in energy allocation presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the allocation trends for solar and nuclear energy. The solar slices {specific pie slices of Solar Energy}<r>[0.299, 0.067, 0.701, 0.468]</r> shows a steady increase over the years, while the nuclear slices {specific pie slices of Nuclear Energy}<r>[0.299, 0.555, 0.701, 0.957]</r> show a steady decrease.\nBased on the above thought, the answer is:\nThe chart indicates a shift in energy allocation from nuclear to solar over the course of about 35 years. The allocation for solar energy is shown to increase significantly, while the allocation for nuclear energy decreases. This may be indicative of a shift toward more sustainable energy practices."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "3P0T6ICm0xNQNeIQ",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the future of Solar Energy and Nuclear Energy?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie slices for solar {specific pie slices of Solar Energy}<r>[0.299, 0.067, 0.701, 0.468]</r> and nuclear energy {specific pie slices of Nuclear Energy}<r>[0.299, 0.555, 0.701, 0.957]</r>, I can interpret the future of these energy sources.\nBased on the above thought, the answer is:\nThe graph suggests that solar energy use will increase dramatically in the future while nuclear energy use will decrease. This could be due to the rise in the demand for more sustainable energy sources and the advantages of solar energy over nuclear energy, such as safety and sustainability."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "uWksWNRVYePIlHQt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might this chart influence future decisions in energy resource allocation?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the slices showing the increasing trend for solar {specific pie slices of Solar Energy}<r>[0.299, 0.067, 0.701, 0.468]</r> and decreasing trend for nuclear energy {specific pie slices of Nuclear Energy}<r>[0.299, 0.555, 0.701, 0.957]</r>, I can imagine how this chart might impact future decisions.\nBased on the above thought, the answer is:\nGiven the increasing emphasis on solar energy, policymakers and investors might decide to allocate more resources towards the development and advancement of solar technologies. On the other hand, the decrease in nuclear energy allocation might lead to less new investment in the nuclear energy sector and a redistribution of these resources to more sustainable energy sources."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "V181AmQsnBPeWTdZ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the increase in Solar Energy allocation from 2025 to 2060?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the allocation of Solar Energy in 2025 from that of 2060.\nBased on the above thought, the answer is:\n367 units"
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "xTvjDP7wAuGFsZg0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the decrease in Nuclear Energy allocation from 2025 to 2060?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the allocation of Nuclear Energy in 2060 from that of 2025.\nBased on the above thought, the answer is:\n50 units"
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "AfZo3LkNBMcuvmXu",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many times does the Solar Energy allocation increase from 2025 to 2060?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to divide the allocation of Solar Energy in 2060 by that of 2025.\nBased on the above thought, the answer is:\nIt increases around 12.1 times"
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "2rOtwfGvjKXuj5cx",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you turn the information in the graph about the distribution of different characteristics in Medical Field in 2006 into a CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart {Characteristics of Medical Field in 2006}[0.185, 0.025, 0.592, 0.055] and the sectors {Medical Device}[0.109, 0.103, 0.388, 0.392], {Drugs}[0.079, 0.34, 0.261, 0.848], {Hospital Beds}[0.207, 0.685, 0.638, 0.927], and {Workforce}[0.388, 0.103, 0.697, 0.757] indicate that these sectors represent the distribution in 2006.\nBased on the above thought, the answer is:\nYear,Medical Device,Drugs,Hospital Beds,Workforce\n2006,18%,22%,25%,35%"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "F9RpCfmJtoH2uw72",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which characteristic had the highest percentage in 2006? Can you explain the reasoning?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the proportions for each category, with the highest proportion indicating the characteristic with the highest percentage.\nBased on the above thought, the answer is:\nWorkforce had the highest percentage in 2006 as it had the highest proportion of 35% among the four aspects in the medical field."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "XIPnsXWIa0XM3Zny",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the distribution data of the medical field characteristics in 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportions of the categories need to be analyzed to infer from the distribution data.\nBased on the above thought, the answer is:\nThe chart clearly shows that Workforce was the most dominant characteristic in the medical field in 2006, followed by Hospital Beds, Drugs, and Medical Device. This may suggest a significant requirement for workforce in this sector while maintaining good availability of hospital beds and drugs and a growing utilization of medical devices."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "98OxC2OC0tU5czhW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the chart data affect decisions for resource allocation in the medical field in 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, it's necessary to analyze how the proportions may influence decisions regarding resource allocation.\nBased on the above thought, the answer is:\nGiven that Workforce had the highest proportion in 2006, it's likely that resource allocation in 2007 would continue prioritizing the workforce. Similarly, an increasing emphasis might be placed on Drugs and Medical Devices due to their increased proportions. As for hospital beds, even though there was a decrease, its proportion is still competitive, hence, resources allocation would still be considerable."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "BTS1F8PiiUHAyfWN",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the total does Drugs and Hospital Beds combined total in 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add together the percentages of Drugs and Hospital Beds.\nBased on the above thought, the answer is:\n47%"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "jKb0RPBoVdFzBIQ3",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we consider Workforce and Drugs together, what would be their combined proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should sum up the proportions of Workforce and Drugs.\nBased on the above thought, the answer is:\n57%"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "eNXbgm99B9rIJ4ez",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Medical Device doubles in 2007, what would be the new total percentage of all aspects?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine this, I need to calculate the double of Medical Device's proportion and add this to the total percentage.\nBased on the above thought, the answer is:\n136%"
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "doI7U3zl0cBzkubL",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the donut chart, could you convert the data about the investment return rates into a CSV table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the labels and the segments of the chart,{\"Investment Return Rate Comparison from 2016 to 2020\"}[0.098, 0.025, 0.678, 0.055]. Also, we have the detailed data for each year from the legend of the chart {the legend}[0.864, 0.074, 0.97, 0.43]. The different colours correspond to different years, and the percentage next to each colour indicates the investment return rate for that year. Therefore, combining all the figures, we can form a table.\nBased on the above thought, the answer is:\nInvestment Type,2016,2017,2018,2019,2020\nReal Estate,15,17.5,19.3,14.2,16.7\nTechnology Stocks,17,30.2,23.8,25,21"
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "oZVCVjkKj5rDZqeo",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the trend of Technology Stocks' return rates based on the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the patterns in the data, specifically the Technology Stocks' return rates from 2016 to 2020.\nBased on the above thought, the answer is:\nTechnology Stocks started at a return rate of 17% in 2016, and saw a substantial increase in 2017 to a peak of 30.2%. Following 2017, the return rates gradually decreased but remained above 20%. This suggests that although there was considerable fluctuation, Technology Stocks generally offered a higher return rate compared to Real Estate."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "N6835qzyfZMOylKG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, do you think Real Estate is a stable investment option?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to assess the trends in return rates of Real Estate from 2016 to 2020.\nBased on the above thought, the answer is:\nYes, Real Estate can be considered a relatively stable investment option as the fluctuation in its return rates from 2016 to 2020 was minimal, ranging from 14.2% to 19.3%."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "cZyen2CaDvf3FsqD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nComparing the Real Estate and Technology Stocks as investment options, which one seems more risky and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Risk in investment is usually associated with volatility or the degree of variation in return rates. By comparing the fluctuations in the return rates of both Real Estate and Technology Stocks, I can determine which is more risky.\nBased on the above thought, the answer is:\nTechnology Stocks appear to be a more risky investment option compared to Real Estate. This is because the return rates of Technology Stocks have shown larger fluctuations over the same period, with a peak of 30.2% in 2017 and a decrease to 21% in 2020."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "ZpGEMjT2rLdNXoeP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year, the total return rate of Real Estate and Technology Stocks was the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the year with the highest total return rate, I need to calculate the sum of the return rates of Real Estate and Technology Stocks for each year, and then find out the year with the highest sum.\nBased on the above thought, the answer is:\n2017"
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "l7sPj90OB9mCt7vF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the overall difference in return rates between Real Estate and Technology Stocks from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the difference in return rates, we need to calculate the sum of return rates for both Real Estate and Technology Stocks from 2016 to 2020 and then find the difference.\nBased on the above thought, the answer is:\n8.1"
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "LIHwn5w47fkyro5d",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the lowest annual return rate for Real Estate and in which year did this occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the lowest annual return rate for Real Estate, I need to check each year's return rate for Real Estate from 2016 to 2020 and find the smallest number.\nBased on the above thought, the answer is:\nThe lowest annual return rate was 14.2 in the year 2019."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "loAwPDiekQ8JC04U",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the pie chart, can you provide the deforestation area data represented there in a CSV table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "First, considering the title of the chart {Deforestation Area (sq.km) from 2010 to 2016}<r>[0.147, 0.025, 0.629, 0.055]</r> and observing the legend showing respective years<r>[0.864, 0.074, 0.97, 0.325]</r>, I see that the pie chart represents the deforestation area in square kilometers for each year from 2010 to 2016. The chart is divided into slices for each year, the size and color of which correlate to the deforestation area in that particular year.\nBased on the above thought, the answer is:\nYear, Deforestation Area (sq.km)\n2010, 5000\n2011, 5170\n2012, 5420\n2013, 4800\n2014, 4600\n2015, 5472\n2016, 6310"
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "6y0tPvKr4zs6WiZF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the trend of deforestation from 2010 to 2016 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the trend, I need to look at the changes in the sizes of the slices from 2010 to 2016.\nBased on the above thought, the answer is:\nThe trend of deforestation from 2010 to 2016 shows an overall increase. Although there was a short drop in 2013 and 2014, the deforestation area increased again in the following years."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "b8hNgjl0ieTQ3apg",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the deforestation in the year 2014 based on the data provided in the pie-chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the size of the segment representing the year 2014, I can infer the details about the deforestation area in that year.\nBased on the above thought, the answer is:\nThe deforestation area in the year 2014 decreased when compared to the previous years. It was an outlier year in the data with a relatively lower deforestation area."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "dZlY5GD5FAAfiwWM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what are the implications for forest conservation efforts?"
+            },
+            {
+                "from": "gpt",
+                "value": "The implications for forest conservation efforts can be determined by the general trend of deforestation area represented in the chart.\nBased on the above thought, the answer is:\nThe chart shows a general increase in deforestation area from 2010 to 2016, indicating that forest conservation efforts during this period might not have been effective in significantly reducing deforestation."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "k1XzZHYarMwRScjR",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the chart, what is the percentage increase in deforestation from the year 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the percentage increase in deforestation from 2010 to 2016, I need to find out the difference between deforestation area of the two years and then divide it by the deforestation area of the year 2010. Then I need to multiply the resultant by 100 to convert it into percentage.\nBased on the above thought, the answer is:\nThe increase in deforestation area from 2010 to 2016 is 26.2%."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "pfBhwTwLRFW6OOTp",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year observed the lowest deforestation area?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, I can identify the smallest slice which corresponds to the lowest deforestation area.\nBased on the above thought, the answer is:\nThe year with the smallest deforestation area is 2010."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "HVnHj6VymiLtHefS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much square kilometers has the deforestation area increased from the year 2010 to 2016?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the increase in deforestation area in sq.km, from 2010 to 2016, I just need to subtract the deforestation area of 2010 from that of 2016 which are represented as different segments in the pie chart.\nBased on the above thought, the answer is:\nThe deforestation area has increased by 1310 sq.km from 2010 to 2016."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "gKJL5cI04NMzSDZR",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a CSV table representing the distribution of research papers in both Biology and Physics over the years from 2015 to 2020 according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see the categories corresponding to different years labeled in the legend {legend}<r>[0.864, 0.074, 0.97, 0.29]</r> with different colors, and these colors are used in the pie chart to represent the proportion of research papers published in those specific years. Let's look at the pie chart now {the pie chart}<r>[0.081, 0.103, 0.696, 0.923]</r>. Each color slice represents the percentage of papers published in a year, so the data can be summarized into a structured table.\nBased on the above thought, the answer is:\nField of Study,2015,2016,2017,2018,2019,2020\nBiology,12,15,13,14,17,25\nPhysics,24,22,20,25,27,36"
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "eTIIAD1GreYwyFPi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be observed in the number of Physics research papers over the years from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the trajectory of the pie slices for Physics, noting any visible increase or decrease over the years.\nBased on the above thought, the answer is:\nOver the years, the number of Physics research papers has generally increased, as seen by the growing size of corresponding pie slices."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "F16czfDj86QFOd4K",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer which field between Physics and Biology might have a larger academic research community based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the total percentages for both fields. The field with more research papers likely has a larger academic community.\nBased on the above thought, the answer is:\nPhysics likely has a larger academic research community as it consistently has more research papers across the years compared to Biology."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "WGz3464Su2B3Ldfp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would the chart influence resource allocation for research in the forthcoming years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the research paper distribution might impact decisions on funding allocations.\nBased on the above thought, the answer is:\nGiven the increasing number of research papers in both fields, with a relatively higher surge in Physics, more resources might be shifted towards Physics. However, to promote diverse research, a significant amount of resources would still need to be allocated to Biology."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "6YvvDLjV08aJhPzg",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn how many years did the number of Physics research papers exceeded the Biology research papers according to the Pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the pie slices for each year in both fields, and note the sizes of the slices. The field with the larger slice has the higher number of papers in that year.\nBased on the above thought, the answer is:\n6 years."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "7qhbIePZuax5sAA6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the year 2019, were there more Biology or Physics research papers?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the 2019 slice in both pie charts and determine which one is larger to answer this question.\nBased on the above thought, the answer is:\nPhysics."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "4c2v8wAacNAdzgTs",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nApproximately how much percentage does Physics' 2020 research papers contribute to the total research papers of Physics from 2015 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the 2020 slice for Physics, I need to note down its percentage and sum up the percentages of all the Physics slices from 2015 to 2020. With the total and 2020's percentage, I can find out its contribution to the total.\nBased on the above thought, the answer is:\n23.4%."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "sx6hwE5mHrlOub4G",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you observed, could you extract the subculture-fashion trend data and present it in tabular form?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the title at the chart {Popularity of fashion trends by Subculture}[0.252, 0.019, 0.6, 0.041] and the segments of the pie chart {Subcultures and Trends}[0.088, 0.089, 0.764, 0.934], it is clear that the segments represent subcultures and their associated popularity. Each subculture\u2019s popularity is represented both by the size of the segment and the percentage denoted on or beside the segment.\nBased on the above thought, the answer is:\nSubculture,Fashion Trends\nGothic,5.3%\nHipster,11.0%\nHip-hop,26.4%\nPunk,6.6%\nCyberpunk,5.7%\nSteampunk,6.8%\nGrungers,4.8%\nEmo,11.6%\nBikers,7.9%\nIndie,13.8%"
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "hRPLow1HCT8BSoUn",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which subculture's fashion trend is the most popular and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify the subculture with the largest percentage representation.\nBased on the above thought, the answer is:\nThe most popular fashion trend is related to the \"Hip-hop\" subculture because it has the largest segment in the pie chart, representing 26.4% of the popularity."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "WVUnZlXY2u6K4FTG",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from this chart about the distribution of popularity among subculture fashion trends?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sizes of the segments in the chart, I can comment on the popularity distribution among the subcultures.\nBased on the above thought, the answer is:\nThe popularity of fashion trends among subcultures is uneven with the Hip-hop trend being the dominant, followed by Indie, Emo, and Hipster. The least popular trends are associated with Grungers, Cyberpunk, and Gothic, each having less than 6% of total popularity."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "NZ6YLxRB4dgWDNF1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the distribution of popularity among these subculture fashion trends impact the fashion industry's product lines and marketing strategies?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the fashion industry might respond to the popularity distribution among the subculture fashion trends.\nBased on the above thought, the answer is:\nWith \"Hip-hop\" fashion being the most popular trend, the fashion industry might place more focus on producing and marketing hip-hop-inspired clothing lines. Whereas, trends associated with \"Hipster\", \"Emo\" and \"Indie\" may also get a fair share of attention due to their considerable popularity. The least popular trends like \"Grungers\", \"Cyberpunk\", and \"Gothic\" might be catered to in niche markets or dedicated brands and stores."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "mlwRTEy8kKKf91jM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich three subcultures comprise approximately 50% of the fashion trends according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find which three subcultures make up approximately 50% of the fashion trends, I need to find combinations of three subcultures whose combined percentages add up to approximately 50%.\nBased on the above thought, the answer is:\nPunk, Cyberpunk, and Steampunk."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "ELishvbKfhYUeksD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage difference in popularity between the most popular and least popular fashion trend according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the least popular subculture's percentage from the most popular subculture's percentage.\nBased on the above thought, the answer is:\nThe difference is 26.4% (Hip-hop) - 4.8% (Grungers) = 21.6%."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "SjDcxpsXnDoiu6BL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of the fashion trends is covered by the top four most popular subcultures in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to sum up the percentages of the top four most popular subcultures.\nBased on the above thought, the answer is:\nThe percentage covered by the top four subcultures is 26.4% (Hip-hop) + 13.8% (Indie) + 11.6% (Emo) + 11.0% (Hipster) = 62.8%."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "neX5saM1PBtUpqJU",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart you're looking at, can you represent the percentage of innovative startups from 2013 to 2021 in the form of a table?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see from the chart that each segment corresponds to a year starting from 2013 to 2021 {the legend}<r>[0.864, 0.074, 0.97, 0.395]</r>. Each slice of the pie chart {the pie}<r>[0.077, 0.104, 0.699, 0.934]</r> corresponds to a certain percentage which is representative of the innovative startups for the year.\nBased on the above thought, the answer is:\nYear,Percentage\n2013,20\n2014,13\n2015,21\n2016,18\n2017,10\n2018,8\n2019,5\n2020,3\n2021,2"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "eSHjLg4y9YisoN7i",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the trend of the pie chart, what can you infer about the innovativeness of start-ups over time?"
+            },
+            {
+                "from": "gpt",
+                "value": "To understand this, I need to observe how the size of the pie slices change over time. A smaller slice indicates a lower percentage of innovative start-ups.\nBased on the above thought, the answer is:\nThe trend suggests that the percentage of innovative start-ups has been decreasing over time, indicating that there may possibly be a decrease in the innovativeness of these start-ups."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "aOPpOElrujTulQ0T",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend depicted in the pie chart continues, what type of start-ups might you expect to see more in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to project the trend observed in the pie chart, which shows that the percentage of innovative start-ups is generally decreasing.\nBased on the above thought, the answer is:\nIf the trend continues, we may see more start-ups that are not classified as innovative. This might be due to the increasing saturation and competition in the market, which makes it difficult for companies to deliver unique offerings, or perhaps the criteria for judging innovation has risen."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "m1UplipfCmLA848i",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat message should entrepreneurs take from this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Analyzing this requires me to interpret the data presented, and consider how it would impact someone considering starting a business.\nBased on the above thought, the answer is:\nEntrepreneurs should note the apparent decrease in innovative start-ups over time. This could suggest that producing truly innovative offerings is becoming more difficult, or that there is increased competition and saturation within the market. Therefore, they may need to invest more in research and development or think more creatively and strategically to differentiate their start-up as innovative."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "I5tn0hV4hoUFTXLG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of innovative start-ups from 2018 to 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage, I need to add the percentages of the years 2018, 2019, 2020, and 2021 together.\nBased on the above thought, the answer is:\n18%"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "hksxzkCNWMJzoQgG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the percentage of innovative start-ups the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to find the largest pie slice, which represents the year with the highest percentage.\nBased on the above thought, the answer is:\n2015"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "KLZISrFxv9sLSZck",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the percentage of innovative start-ups in the year 2017 and 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the sum of these percentages, I need to add the percentages for 2017 and 2018.\nBased on the above thought, the answer is:\n18%"
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "9YaEiZ6OSyJHlmfW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on your observation of the chart, can you convert the data about the Fluctuations and Trends of the Financial Market from 2016 to 2021 into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "To recast the chart data into a structured table, I have to inspect each sector slice of the pie chart. The color of each sector corresponds to a specific year, written in the legend {\"2016\"}<r>[0.919, 0.083, 0.963, 0.106]</r>; {\"2017\"}<r>[0.919, 0.118, 0.963, 0.141]</r>; {\"2018\"}<r>[0.919, 0.153, 0.963, 0.176]</r>; {\"2019\"}<r>[0.919, 0.188, 0.963, 0.211]</r>; {\"2020\"}<r>[0.919, 0.223, 0.963, 0.246]</r>; {\"2021\"}<r>[0.919, 0.258, 0.963, 0.281]</r>. The proportion of each section represents the proportion of investment in that year relative to the others.\nBased on the above thought, the answer is:\nYear,Investment\n2016,80\n2017,120\n2018,95\n2019,85\n2020,100\n2021,130"
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "x8bO6yjrdpYCEmX6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, which year had the most investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the most investment, I need to look at the size of each section of the pie chart.\nBased on the above thought, the answer is:\nThe year 2021 had the most investment, as indicated by the largest section of the pie chart {\"2021\"}<r>[0.388, 0.104, 0.691, 0.452]</r>."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "uTbnPh8bR2Kq34Cp",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred about trends in financial market investments from 2016 to 2021, based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the changes in the size of the pie sections over the years, I can infer the trend in investments.\nBased on the above thought, the answer is:\nThe chart shows that after a major investment in 2017, there was a two-year decline, a stabilization in 2020, and then a notable increase in 2021. This could suggest a cycle of investment activity over the years."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "RPg5M1hciC0DQWu1",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the data in this chart potentially assist financial planners or investors?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the investment trends represented in the pie chart can aid in financial decision-making.\nBased on the above thought, the answer is:\nThis chart can provide key insights into past financial market trends and fluctuations. It could help planners or investors predict future trends, develop portfolio strategies, and make investment decisions. For instance, noting the steady increase in investment from 2020 to 2021 might encourage more aggressive investment strategies."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "7QMqpkGyJSjlQmIY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the highest level of investment during the years 2016-2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out the year with the highest investment, I need to compare the investment amounts for all the years.\nBased on the above thought, the answer is:\nThe highest level of investment was in 2021 with 130 units."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "6hpyY6mujRMfqDYo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the investment in 2020 increased by 20%, what would be the new value for investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new value of investment, I need to calculate 20% of the investment in 2020 and add it to the original investment.\nBased on the above thought, the answer is:\nThe new investment in 2020 would be 120 units."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "MLud5CHuVVxGYfO4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average investment for the years 2016-2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the average investment, I need to sum up the investments for each year and then divide by the total number of years.\nBased on the above thought, the answer is:\nThe average investment for the years 2016-2021 is about 101.67 units."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "PKUZOkpRjJeGveRn",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the observed pie chart, can you extract and convey the data in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "I got the title of the plot as \"Online Learning Usage from 2011 to 2020\"{'params': {'label': 'Online Learning Usage from 2011 to 2020', 'color': 'black', 'rotation': 0.0}, 'coordinates': '[0.17, 0.025, 0.606, 0.055]'} from the chart. By studying each pie slice together with the corresponding legend{sublegend: {'params': {'title': '', 'ncol': 1, 'facecolor': '#ffffff'}, 'coordinates': '[0.864, 0.074, 0.97, 0.43]'}}, the value of \"Online Learning Usage\" can be assigned to each year.\nBased on the above thought, the answer is:\nYear,Online Learning Usage\n2011,25\n2012,30\n2013,35\n2014,40\n2015,45\n2016,55\n2017,60\n2018,65\n2019,73\n2020,85"
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "vgRWETl01RofRJHm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you infer the trend of online learning usage from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the pie chart, I need to analyze the size of each slice and the rise in the percentages from 2011 to 2020.\nBased on the above thought, the answer is:\nThe trend of online learning usage was continuously increasing from the year 2011 to 2020. This indicates that more and more people were using online learning platforms in this period."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "N1Z1wGgdNseztVUe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the online learning usage increased to 90% in 2021, what could be the possible inference for that year compared with 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to compare the hypothetical percentage for 2021 with the given percentage for 2020 in the chart.\nBased on the above thought, the answer is:\nIf the online learning usage increased to 90% in 2021, it can be inferred that the use of online learning platforms continues to rise, growing by 5% from the previous year 2020."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "Qkf7XIFAyR56Sqo9",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn reflexion, how does the representation of the data using a pie chart enhance our understanding of the usage of online learning from 2011 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the pie chart provided, I need to explain how it has enabled us to easily visualize and understand the trends and differences in percentage values across the years.\nBased on the above thought, the answer is:\nThe use of a pie chart visually emphasizes the increase in online learning usage over the years. It allows for a clear comparison of each year's online learning usage as a distinct portion of the whole, making it easy to see the trend of steady growth over the decade. The varying sizes and colors of the pie slices represent each year and allow for a quick visual understanding of the data and its trends."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "0FdkS31LeQy31nc4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which year saw around a 10% increase in online learning usage compared to the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the size of the pie slice and the corresponding percentage on each of the slices, I need to locate a slice that represents a roughly 10% growth compared to the previous year's slice.\nBased on the above thought, the answer is:\nThe year 2016 saw a substantial increase in online learning usage from the previous year, nearly 10%."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "dl2cGKnrY0AM2e15",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what was the usage of online learning in 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to examine the pie slice that corresponds to the year 2015 and the corresponding percentage given.\nBased on the above thought, the answer is:\nThe usage of online learning in 2015 was 45%."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "pssahueAy8W9ibT0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which year experienced the largest growth from the previous year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By examining the sizes and the percentages marked on each slice, I need to identify the slice that has made the most growth from the slice before it.\nBased on the above thought, the answer is:\nThe year 2020 experienced the highest growth from its previous year in online learning usage."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "1w8ZWHD531bWpGho",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you list the sales volumes for each year from 2001 to 2008 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can first assess the pie chart utilizing its given sections labelled {\"2001\"}<r>[0.92, 0.083, 0.963, 0.106]</r>, {\"2002\"}<r>[0.92, 0.118, 0.963, 0.141]</r>, {\"2003\"}<r>[0.92, 0.153, 0.963, 0.176]</r>, {\"2004\"}<r>[0.92, 0.188, 0.963, 0.211]</r>, {\"2005\"}<r>[0.92, 0.223, 0.963, 0.246]</r>, {\"2006\"}<r>[0.92, 0.258, 0.963, 0.281]</r>, {\"2007\"}<r>[0.92, 0.292, 0.963, 0.316]</r>, and {\"2008\"}<r>[0.92, 0.327, 0.963, 0.351]</r>, respectively. Each section is proportional to the sales volume for that respective year.\nBased on the above thought, the answer is:\nYear,Sales Volume\n2001,60\n2002,65\n2003,75\n2004,85\n2005,70\n2006,90\n2007,80\n2008,105"
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "wZegrRGzhOg5KOLy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which year had the best sales performance? And explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to compare the contributions of all years and identify the year that contributed the most to the total sales volume.\nBased on the above thought, the answer is:\nThe year 2008 had the best sales performance because it had the highest sales volume of 105 among all the years."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "25OWcxWw4CHa6b42",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the sales trend from 2001 to 2008 from the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer the sales trend, I need to look at the proportions of each year's sales volume in chronological order.\nBased on the above thought, the answer is:\nThe general sales trend from 2001 to 2008 was increasing, despite some fluctuations, as the sizes of the slices generally got larger over time."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "RE69qgJBe0rCnwPI",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the 2005 sales volume drop affect the company\u2019s sales strategy in 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider how the drop in sales volume in 2005 might have inspired changes in the company's sales strategy for the following year.\nBased on the above thought, the answer is:\nThe sales volume drop in 2005 possibly enforced the company to innovate their sales strategies or focus on more aggressive marketing in 2006, as suggested by the increased sales volume in 2006. The change in strategy seems to have been successful as the sales volume rose again."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "S0l0YouVWytXlxm5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the year with the highest sales volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the sizes of the pie slices, I can determine that the year with the largest sales volume would have the largest slice. From my analysis, the largest slice is labeled {\"2008\"}<r>[0.92, 0.327, 0.963, 0.351]</r>.\nBased on the above thought, the answer is:\n2008"
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "6tm3b8NcZgzcvmYo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total sales volume from 2001 to 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart represents total sales from 2001 to 2008. By summing all the sales volumes from each year, I can find the total sales volume.\nBased on the above thought, the answer is:\n630"
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "V5hwksQzoLY0xGA2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the sales volume in 2003 was doubled, what would be the new total sales volume?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to add the original sales volume of 2003 to the current total sales volume.\nBased on the above thought, the answer is:\n705"
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "3GPNPWznL7hqMJqj",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you derive a table representation of the pie chart values in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The CSV data in the pie chart comprises of five main components of Big Data Integration which are clearly positioned in {the legend}<r>[0.837, 0.074, 0.97, 0.255]</r>. Each component corresponds to a slice of the pie chart located at <r>[0.079, 0.103, 0.697, 0.927]</r> and has an autotext specifying its percentage.\nBased on the above thought, the answer is:\nComponent,Percentage\nVolume,22.2%\nVariety,16.1%\nVelocity,18.6%\nVeracity,22.7%\nValue,20.4%"
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "zLzHhxtbfndS3fSt",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the Big Data components are below the average?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average needs to be calculated by adding up all the percentages and dividing them by the components number. Any component with a percentage lower than this average is then identified.\nBased on the above thought, the answer is:\nVariety and Velocity are below the average."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "u6kKaAXkKOqYRWrD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any components which share a similar percentage and what does that indicate about those integrations?"
+            },
+            {
+                "from": "gpt",
+                "value": "By identifying the components with similar percentages from the pie chart information at <r>[0.079, 0.103, 0.697, 0.927]</r>, I can draw conclusions on their shared characteristics.\nBased on the above thought, the answer is:\nVolume and Veracity share similar percentages, indicating that the size and quality of the data are given almost the same importance in big data integration."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "RY9ex28znJZ1lUvx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the distribution in the pie chart reflect the strategic importance of each Big Data component in terms of data integration?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the sizes of each slice in the pie chart at <r>[0.079, 0.103, 0.697, 0.927]</r>, I can deduce their strategic importance.\nBased on the above thought, the answer is:\nThe chart illustrates that Volume and Veracity are given the highest importance in Big Data Integration. This might be because the size and quality of data are critical in data integration. Meanwhile, components like Value, Velocity, and Variety come, not far behind, similar importance highlighting the quick data generation and different forms of data and their value extraction in the process. Hence, the chart indicates a balanced integration strategy with slight emphasis on the size and quality of data."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "oswTRBbtNEJFaolK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of Volume and Velocity combined in the Pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By summarizing the proportions of 'Volume' and 'Velocity' from the pie chart mentioned at <r>[0.084, 0.103, 0.388, 0.464]</r> and <r>[0.181, 0.728, 0.518, 0.927]</r> respectively, the combined percentage can be calculated.\nBased on the above thought, the answer is:\n40.8%."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "ivEX95ITJyKo7gC2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the percentage values of Veracity and Variety?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of 'Variety' from that of 'Veracity' which are located at <r>[0.079, 0.443, 0.243, 0.82]</r> and <r>[0.479, 0.397, 0.697, 0.888]</r> respectively.\nBased on the above thought, the answer is:\n6.6%."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "JXFnLNywv872K9Tr",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much is the component with lowest presence less than the average?"
+            },
+            {
+                "from": "gpt",
+                "value": "The component with the lowest presence is 'Variety'. The average needs to be calculated by adding up all the percentages of the components divided by the number of components. Then I subtract the 'Variety' percentage from the average.\nBased on the above thought, the answer is:\n4.4%."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "9GQKSZbU5JETNfsr",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the data represented in the pie chart into a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the pie chart and it's title {Workplace Training Program attendance from 2016 to 2020}<r>[0.08, 0.025, 0.697, 0.055]</r>, I identified distinct pie slices representing each year {2016}<r>[0.109, 0.103, 0.388, 0.394]</r>{2017}<r>[0.081, 0.343, 0.255, 0.835]</r>{2018}<r>[0.198, 0.738, 0.558, 0.923]</r>{2019}<r>[0.507, 0.427, 0.695, 0.855]</r>{2020}<r>[0.388, 0.103, 0.689, 0.453]</r> and their associated percentages of the total. This data can be represented in a CSV format table.\nBased on the above thought, the answer is:\nYear, Percentage\n2016,18.2%\n2017,21.2%\n2018,19.9%\n2019,19.0%\n2020,21.6%"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "Iy9EXwvVPXb9kdCF",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, which year saw the highest participation in the Workplace Training Program? Explain why."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to identify the year with the largest slice in the pie chart.\nBased on the above thought, the answer is:\nThe year with the highest participation in the Workplace Training Program was 2017, as it represents the largest slice of the pie chart with 21.2% of the total."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "aUk4WAtDnlnLYaoN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the distribution of the pie chart tell you about the Workplace Training Program's attendance from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the size and distribution of the pie slices, I can infer trends in the attendance of the Workplace Training Program.\nBased on the above thought, the answer is:\nFrom 2016 to 2020, attendance at the Workplace Training Program has been fairly consistent, with no single year dramatically out-performing or under-performing others. The distribution implies that the program has reliably attracted learners over the observed period."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "OCdRQEYVneIGMTz2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the information illustrated in the pie chart affect future planning for the Workplace Training Program?"
+            },
+            {
+                "from": "gpt",
+                "value": "Examining the distribution and changes in annual attendance percentage can help infer how future planning can be affected.\nBased on the above thought, the answer is:\nThe year 2017 saw the highest attendance, which may suggest a strong program lineup or effective marketing, thus practices from this year could be replicated in the planning for future years. The dip in attendance in 2019 may suggest a need for improvement or change in certain aspects of the program. Alongside, the uptick in 2020 could be investigated to retain positive changes made that year."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "RrF3AmW27grQpX3O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what was the least attended year for the Workplace Training Program?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the least attendance, I need to compare the percentages of all years in the chart.\nBased on the above thought, the answer is:\n2019"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "ABzmLmVYJqzok6C5",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference between the proportions of the Workplace Training Program's highest and lowest attended years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the difference, I need to subtract the percentage of the least attended year from the most attended year.\nBased on the above thought, the answer is:\n2.2%"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "rtmEamuEseHoHMB9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the attendance in 2020 increased by 5%, what would be the total attendance percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the new total percentage, I need to add 5% to the 2020 attendance percentage and sum up all the percentages again.\nBased on the above thought, the answer is:\n105.8%"
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "Jhcad1QYxtYynPGk",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you convert the pie chart data into two separate tables for the years 2010 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the two pie charts in the figure titled {\"Fitness and Sports Health Trends, 2010\"}[0.159, 0.005, 0.362, 0.027] and {\"Fitness and Sports Health Trends, 2015\"}[0.646, 0.005, 0.849, 0.027], I can see ten sectors representing different fitness activities. The sectors represent the percentage participation of the population in the given activity, each for the years 2010 and 2015.\nBased on the above thought, the answer is:\nYear,Bodybuilding,Yoga,Cardio,Kickboxing,Zumba,CrossFit,Marathon,Pilates,HIIT,Cycling\n2010,10%,15%,25%,30%,20%,35%,12%,18%,5%,45%\n2015,20%,18%,30%,35%,25%,40%,17%,24%,7%,40%"
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "XZWs5ntRXW4pdW7I",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fitness activity saw the most participation in 2010 and 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the most participated activity in each year, I need to identify the sector with the highest percentage in each pie chart.\nBased on the above thought, the answer is:\nIn 2010, Cycling had the most participation with 45%. In 2015, this was the case as well, with Cycling still having the most participation at 40%."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "81c8S1RCIH2j7egi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the data, which activity saw a decline in participation from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the percentages for each activity in the year 2010 and 2015, I can identify which activity saw a drop in participation.\nBased on the above thought, the answer is:\nZumba saw a decline in participation from 2010 to 2015, moving from 20% to 25%."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "Opz99EUX4h5LuL3U",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the two pie charts, what could you infer about the trend of fitness and sports health activities from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "From analysing the charts, besides counting the proportion of each fitness activity in both years and compare their changes, I also need to consider the potential tendency of public's fitness choice.\nBased on the above thought, the answer is:\nFrom the data, it can be inferred that the trend from 2010 to 2015 saw a broadening interest in a range of activities. While activities like Cycling remained highly popular throughout, there was an increase in activities such as Bodybuilding, Yoga and particularly Pilates, suggesting an increased willingness amongst the public to engage in diverse forms of fitness. The slight decline in Zumba and CrossFit could indicate shifting preferences over the five years."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "SyE6aQoWFPgYUdwl",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn 2010, what was the combined percentage participation in Bodybuilding and Cardio?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to add the percentages of Bodybuilding and Cardio from the pie chart for 2010.\nBased on the above thought, the answer is:\n35%"
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "vvrOJ8PwfLENBuBW",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage decrease in Zumba participation from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the percentage decrease, I need to subtract the percentage of Zumba in 2015 from its percentage in 2010, then divide by the percentage in 2010, and finally multiply by 100.\nBased on the above thought, the answer is:\n5%"
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "MDIpBLizinFENwLX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage increase in Pilates participation from 2010 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage increase can be found by subtracting the percentage of Pilates in 2010 from its percentage in 2015, dividing by the percentage in 2010, and then multiplying by 100.\nBased on the above thought, the answer is:\n33.33%"
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "9IEwUGjZSWYOJxQg",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you help me read this chart and convert the data into a structured table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly, from the chart title {\"Rural and Urban Development Comparison from 2000 to 2008\"}[0.065, 0.025, 0.711, 0.055], I gather that this is a comparison across different years. Further, the data is represented in a pie chart divided into sections {slices}[{\"2000\"}[0.232, 0.104, 0.388, 0.267]; {\"2001\"}[0.11, 0.16, 0.279, 0.388]; {\"2002\"}[0.077, 0.332, 0.193, 0.578]; {\"2003\"}[0.08, 0.56, 0.241, 0.824]; {\"2004\"}[0.178, 0.733, 0.382, 0.933]; {\"2005\"}[0.38, 0.739, 0.591, 0.934]; {\"2006\"}[0.53, 0.542, 0.698, 0.833]; {\"2007\"}[0.549, 0.239, 0.699, 0.552]; {\"2008\"}[0.388, 0.104, 0.618, 0.323]]. So after inspecting all the objects in the chart, we can convert these into a CSV format.\nBased on the above thought, the answer is:\nYear,Urban Development(%),Rural Development(%)\n2000,8.4,91.6\n2001,9.2,90.8\n2002,9.7,90.3\n2003,10.9,89.1\n2004,11.4,88.6\n2005,11.7,88.3\n2006,12.4,87.6\n2007,13.1,86.9\n2008,13.2,86.8"
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "RqYy3pwF9fvr0dxh",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan we infer a trend in urban and rural development from the chart? Please explain."
+            },
+            {
+                "from": "gpt",
+                "value": "To infer a trend, I should look at the changes in urban and rural development percentages over the years.\nBased on the above thought, the answer is:\nYes, the chart shows a rising trend in urban development and a declining trend in rural development from 2000 to 2008. This may be due to the migration from rural areas to urban areas or due to the expansion of urban areas."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "uw0PNTwk51zBfc1g",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be the reason for the large increase in urban development in 2003 and 2006?"
+            },
+            {
+                "from": "gpt",
+                "value": "While the exact reason isn't given in the chart, it could be due to various factors such as government policies, infrastructure development, economic growth, and more.\nBased on the above thought, the answer is:\nA possible reason for the sudden increase in urban development in 2003 and 2006 could be due to increased infrastructure development or significant economic growth causing a surge in urbanization. However, to know the exact reason, further detailed data or information would be needed."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "Jd0lmj0hwDyBfy72",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can this pie chart be useful for urban planning authorities?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this I have to consider how the changes in urbanization over the years could be used for informed decision-making.\nBased on the above thought, the answer is:\nThe pie chart provides data on urban development over the years, which could assist urban planning authorities in understanding historical trends in urbanization. By studying these trends, they could predict future patterns and formulate policies accordingly. For instance, recognizing the declining rural areas might prompt initiatives for balanced regional development."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "R8rWPg2ni8uivAQ0",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the pie chart show the highest urban development percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the year with the highest urban development percentage, I need to look at each section in the pie chart and compare the percentages.\nBased on the above thought, the answer is:\n2008"
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "Ej19WRiUd6isK768",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percent has the urban development grown from 2000 to 2008?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the year 2000's urban development percentage from the year 2008's.\nBased on the above thought, the answer is:\n4.8%"
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "2BmsW1F0Elo2HAPe",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much does the urban development percentage increased from 2005 to 2007?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to subtract the urban development percentage of the year 2005 from that of the year 2007.\nBased on the above thought, the answer is:\n1.4%"
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "uVvq3rC2LlhnUDlO",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you convert the pie chart's data you observed into a CSV format table, including the percentages for each year for Hydroelectric and Solar energy respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, from {the chart title}<r>[0.109, 0.025, 0.667, 0.055]</r> and all the slices in {the pie chart}<r>[0.08, 0.103, 0.696, 0.924]</r>, I can deduce that the slices and their respective colors stand for different years and energy resource allocations. I can refer to the corresponding color-guide provided in {the legend}<r>[0.864, 0.074, 0.97, 0.43]</r>.\nBased on the above thought, the answer is:\nYear, Resource Type, Allocation Percentage \n2014, Hydroelectric, 40% \n2015, Hydroelectric, 35%\n2016, Hydroelectric, 30%\n2017, Hydroelectric, 28%\n2018, Hydroelectric, 24%\n2014, Solar, 15%\n2015, Solar, 25%\n2016, Solar, 35%\n2017, Solar, 48%\n2018, Solar, 58%"
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "OYuISkh9TP1brydP",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide an interpretation of the energy resource allocation based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the allocation percentages for Hydroelectric and Solar energy to make inferences about the changes in energy resource allocation.\nBased on the above thought, the answer is:\nThe chart shows a decrease in the reliance on Hydroelectric energy from 2014 to 2018, with a steady increase in Solar energy usage over the same period. This suggests a strategic shift towards Solar energy resources."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "cOfnXkzK2QG6QPBa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trends can be observed about the changes in resource allocation based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Understanding the trends requires analyzing the allocation percentages for the two types of resources along the five-year timeline.\nBased on the above thought, the answer is:\nAn observable trend is the growing preference for Solar energy as its allocation significantly increases every year. In contrast, there is a consistent reduction in Hydroelectric energy allocation."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "PspnF3myAjemTPsa",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications could this allocation trend have on future energy resource decisions?"
+            },
+            {
+                "from": "gpt",
+                "value": "Predicting future implications requires understanding the observed trends and considering their likely impact on future energy resource planning and decision-making.\nBased on the above thought, the answer is:\nThis trend could suggest a continued growth in the use of Solar energy in the future due to its increasing allocation. Businesses and policymakers may choose to invest more in solar technology considering this trend. Conversely, there may be less investment in Hydroelectric resources unless there is a change in this decline."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "qhCCaDMeVkAgvs15",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCalculate the total difference between the percentages of Hydroelectric and Solar energy from 2014 to 2018."
+            },
+            {
+                "from": "gpt",
+                "value": "To obtain the total difference, I need to compute the difference in allocation percentage per year from 2014 to 2018, and then add up these differences.\nBased on the above thought, the answer is:\nThe total difference is 20%."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "etsEWlThIm9Ya8GK",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average allocation percentage for Solar energy from 2014 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to sum the allocation percentages of Solar energy from 2014 to 2018 and then divide by the number of years.\nBased on the above thought, the answer is:\nThe average allocation percentage is 34%."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "6p7lzk8s9Yum0nNS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy how much percentage did the allocation for Solar increase from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "To compute the increase, I need to subtract the 2016 allocation from the 2017 allocation.\nBased on the above thought, the answer is:\nThe increase in allocation for Solar energy from 2016 to 2017 is 13%."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "rvMV3QFEpVaVAhd1",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you transform the data in the chart into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the legend on the right side {the legend}<r>[0.864, 0.074, 0.97, 0.395]</r> and the title on top {\"Future Technological Development Prospects from 2020 to 2028\"}<r>[0.055, 0.025, 0.722, 0.055]</r>, I can identify that every slice of the pie represents the technological development prospects for each year from 2020 to 2028.\nBased on the above thought, the answer is:\nYear,Prospects\n2020,8.6\n2021,9.2\n2022,9.8\n2023,10.8\n2024,11.3\n2025,11.5\n2026,12.1\n2027,12.9\n2028,13.7"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "mFPAJB2TWtXI1IvU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, in which year was the peak of technological development prospects predicted to occur?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to search through all the slices of the pie chart and find the slice with the highest percentage which represents the year when technological development prospects will peak.\nBased on the above thought, the answer is:\nThe peak of technological development prospects is predicted to occur in 2028, as it has the biggest slice in the pie chart."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "5B3wpjnBnSeDzj51",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data portrayed in the chart, could you speculate on the anticipated trend of technological development over these years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should evaluate all of the slices and their sizes in comparison to each other to determine the anticipated trend.\nBased on the above thought, the answer is:\nAccording to the chart, there is a consistent increase in the technological development prospects from the year 2020 to 2028. This suggests a trend of progressive growth in technological advancements over these years."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "3M67BJdwv5nbPRbl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the given chart, what can be inferred about the acceleration in technological innovation in future years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To infer about the acceleration, I need to compare the sizes of the slices year by year to understand the pace of growth.\nBased on the above thought, the answer is:\nBased on the pie chart, it is evident that technological innovation is gradually accelerating over the years. It might be inferred that there are several advancements or breakthroughs expected to occur in the later years, especially in 2027 and 2028, as they have notably larger slices compared to the previous years."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "4X30JHDhCd5BQmE9",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many years experienced more than 10% in technological development prospects?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie chart, I need to identify each slice with its related autotext percentage in the legend, and then count how many of them exceed 10%.\nBased on the above thought, the answer is:\n5 years"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "rYRTDTzC5qKgJ6m2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percent does the technological development prospect increase from 2020 to 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find out this increase, I need to subtract the percentage of the development prospect in 2020 from that in 2028.\nBased on the above thought, the answer is:\n5.1%"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "qzZAsI2U3qH5rRhM",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average development prospect over the 9 years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To get the average, I would add up all the percentages and divide them by the total number of years.\nBased on the above thought, the answer is:\nAround 10.8%"
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "HEFccUQOVUWHK7OZ",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the specialist distribution data from 2018 to 2020 into a structured table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. The key data I can see comes from the legends for the years {2018}<r>[0.919, 0.083, 0.963, 0.106]</r>, {2019}<r>[0.919, 0.118, 0.963, 0.141]</r>, and {2020}<r>[0.919, 0.153, 0.963, 0.176]</r>, and the data points on the pie chart slices for the cardiologists and neurologists for each year.\nBased on the above thought, the answer is:\nMedical Object,2018,2019,2020\nCardiologists,28.2%,34.5%,37.3%\nNeurologists,26.7%,35.1%,38.2%"
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "BmRhIGZL9FKghb9X",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend is observed in the distribution of specialists in the hospital from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I must review the proportions for each specialty over the three years to infer the trend.\nBased on the above thought, the answer is:\nBoth Cardiologists and Neurologists show increasing trends from 2018 to 2020, reflecting a growing emphasis on these medical specialties in the hospital."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "tsiUA545WexjQ2dD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich specialty appears to be the hospital's focus, as inferred from the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "I have to compare the proportions of Cardiologists and Neurologists in 2020 to infer the hospital's focus.\nBased on the above thought, the answer is:\nThe hospital seems to equally focus on both specialties, with a marginal higher proportion for Neurologists."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "YT1VHcqsL8ZwucAD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data influence the hospital's hiring decisions for the subsequent years?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the growth trends of both specialties and their current proportions to infer potential hiring decisions.\nBased on the above thought, the answer is:\nGiven the growing trend for both specialties, the hospital might continue to hire more Cardiologists and Neurologists. As neurology had a marginally higher proportion in 2020, they might consider hiring slightly more Neurologists to maintain this emphasis."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "pirrldoZtKg3KbXo",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2018 and 2020, for which medical specialty has the proportion grown more?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll need to calculate the difference in proportions for Cardiologists and Neurologists between 2018 and 2020.\nBased on the above thought, the answer is:\nCardiologists."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "CBzQJ5oAaRPsuGYx",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average percentage of both specialists in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "To manage the average, I need to sum the proportions for Cardiologists and Neurologists in 2019 and divide by the number of specialties.\nBased on the above thought, the answer is:\n34.8%."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "nE3na4eLguP19YG4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the proportion of Neurologists increased by 4% in 2021, what would be the new total proportion of all specialties?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the new total proportion, I need to add 4% to the 2020 percentage for Neurologists and add that to the 2020 percentage for Cardiologists.\nBased on the above thought, the answer is:\n79.5%."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "5x5Y24O1oBlhIM71",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, could you convert the data reflecting the progression from 2016 to 2020 into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the titled color-coded sectors {2016}<r>[0.096, 0.103, 0.388, 0.425]</r>, {2017}<r>[0.081, 0.388, 0.271, 0.856]</r>, {2018}<r>[0.22, 0.753, 0.556, 0.922]</r>, {2019}<r>[0.506, 0.429, 0.695, 0.856]</r>, {2020}<r>[0.388, 0.103, 0.689, 0.454]</r>, supplemental indicators correspond to each year. Also, I see the trended count in percentages for each year in the pie chart.\nBased on the above thought, the answer is:\nYear,Influencer Power,Political Incidence\n2016,60,40\n2017,62,38\n2018,55,45\n2019,57,43\n2020,65,35"
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "68fRUkfXbbkqW3GX",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart, can you elaborate on the relationship between Social Dynamics and Political Trends from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to analyze the data by comparing the proportion size of each year and tracing the trends over time.\nBased on the above thought, the answer is:\nThe pie chart shows that from 2016 to 2020, there was a general increase in the influence and power of social movements, as reflected in the expanding proportion of the corresponding sectors over these years. This trend points to a rising significance of social dynamics over the period. At the same time, the relative incidence and power of political events decreased, suggesting a decreasing influence of political forces within the same timeframe."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "zA4rpl3z96FV65eu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart imply about the shifting power dynamics from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze the changes in the proportions of influencer power over the years to interpret the shifting dynamics.\nBased on the above thought, the answer is:\nThe chart implies a shift towards more socially influenced power dynamics from 2016 to 2020. It indicates that social movements have gained substantial influence and power over time, while the impact of political incidences has somewhat diminished during the same period."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "W4QPQGqJ3freUvnS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan the chart's data be used to predict future years, and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "To make this determination, I need to analyze the five-year trend shown in the chart.\nBased on the above thought, the answer is:\nThe chart's data could potentially be used as a basis for predicting future years' trends, as they reflect a consistent five-year trend. However, because social and political dynamics are influenced by a wide array of factors and events that can change rapidly, predictions based purely on past data should be used with caution."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "nMj1pyDg24RolIiy",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the highest Influencer Power and in what percentage did it reach?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find this, I need to determine the slice of the pie that accounts for the most significant proportion and match it to the year.\nBased on the above thought, the answer is:\nThe year 2020 had the highest Influencer Power, reaching a percentage of 21.7%."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "nRmeobZXP7sVxTmX",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage for Social Dynamics trend from 2018 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the total percentage, I need to add up the percentage of the Social Dynamics Inference in 2018, 2019, and 2020.\nBased on the above thought, the answer is:\nThe total percentage for the Social Dynamics trend from 2018 to 2020 is 18.4% (2018) + 19.1% (2019) + 21.7% (2020) = 59.2%."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "T4UtzAx11q308FMT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many percentage points did the Influencer Power change from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract the Influencer Power of 2016 from that of the year 2020 to find the change in percentage points.\nBased on the above thought, the answer is:\nThe Influencer Power increased by 21.7% (2020) - 20.1% (2016) = 1.6 percentage points from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "FCK9MKp5J4VW1ykM",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please extract the data from the pie chart you have observed and represent it in a table format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title at the bottom position of the chart {\"Market Shares of Company X and Company Y from 2020 to 2025\"}[0.052, 0.025, 0.724, 0.055] and sector slices and colours {sector slices and representatives of \"Company X\"[0.815, 0.085, 0.849, 0.101] and \"Company Y\"}[0.815, 0.12, 0.849, 0.136] suggest that they represent market shares of the two companies over the course of 6 years from 2020 to 2025 respectively.\nBased on the above thought, the answer is:\nYear,Company X,Company Y\n2020,12%,20%\n2021,20%,30%\n2022,35%,20%\n2023,25%,22%\n2024,36%,33%\n2025,40%,42%"
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "Nu1usiH1qvA5MUNY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the chart, what can you infer about the growth experience by Company X and Company Y?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can regard the size of each segment in relation to each company as the growth rate, the larger the segment the more the growth.\nBased on the above thought, the answer is:\nBoth Company X and Company Y experienced ups and downs in their growth from 2020 to 2025. Overall, Company X shows a significant rise in its market share over the years. Even though Company Y had a stronger start in 2020 and 2021, its market share took a dip in 2022 but later showed a steady recovery."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "BQCGtkGboJjWDDY2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can the chart influence market strategies of both companies for the following years?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the trends over the past years would help infer the market strategies for the coming years.\nBased on the above thought, the answer is:\nThe chart indicates that Company X has a positive growth trend, despite some fluctuations. This could lead to strategies focused on maintaining and reinforcing this growth. Company Y, on the other hand, after experiencing a slump in 2022, may need to investigate the causes and adapt their strategies to prevent a recurring pattern in the future."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "O56P6uWs3CfDOMkS",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart reveal about the competitiveness of the market environment?"
+            },
+            {
+                "from": "gpt",
+                "value": "Observing the size shift of segments related to each company, I evaluate the competitive nature of the market environment.\nBased on the above thought, the answer is:\nThe pie chart reveals a dynamic and highly competitive market environment. Both companies have periods of gain and loss, which points to ongoing competition and market shifts. It suggests that both companies need to consistently adapt to stay competitive."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "taA3ae3lRSgIVdpv",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what was the total market share of Company X in 2023 and 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total share of Company X in 2023 and 2024 could be found by adding the shares for the two years.\nBased on the above thought, the answer is:\n61%"
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "vm7tsJBq901OvcQ4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Company X increased its market share by 5% in 2025, what would be the new proportion?"
+            },
+            {
+                "from": "gpt",
+                "value": "The new market share can be calculated by adding 5% to the existing market share of Company X in 2025.\nBased on the above thought, the answer is:\n45%"
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "epNvv9LDhLohrHPt",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2020 and 2025, in which year did Company Y experience the poorest performance in terms of market share?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the answer, I should look for the lowest market share percentage of Company Y during the concerned years.\nBased on the above thought, the answer is:\n2022"
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "M5nOwm9rJUD5MhPY",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the chart you just viewed, please transform the data into a CSV format table."
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the cookies in the pie chart, each segment of the pie represents a year from 2010 to 2019 and the corresponding number of people using Educational Technology and Online Learning in that year. {\"2010\"}<r>[0.919, 0.083, 0.963, 0.106]</r>, {\"2011\"}<r>[0.919, 0.118, 0.963, 0.141]</r>, {\"2012\"}<r>[0.919, 0.153, 0.963, 0.176]</r>, {\"2013\"}<r>[0.919, 0.188, 0.963, 0.211]</r>, {\"2014\"}<r>[0.919, 0.223, 0.963, 0.246]</r>, {\"2015\"}<r>[0.919, 0.258, 0.963, 0.281]</r>, {\"2016\"}<r>[0.919, 0.292, 0.963, 0.316]</r>, {\"2017\"}<r>[0.919, 0.327, 0.963, 0.351]</r>, {\"2018\"}<r>[0.919, 0.362, 0.963, 0.386]</r>, {\"2019\"}<r>[0.919, 0.397, 0.963, 0.421]</r>.\nBased on the above thought, the answer is:\nYear,Education Technology and Online Learning\n2010,20\n2011,30\n2012,35\n2013,50\n2014,40\n2015,60\n2016,70\n2017,80\n2018,90\n2019,105"
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "12i5V6StPuUsOheD",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, what do you think caused the drop in people using Education Technology and Online Learning in 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "To consider possible reasons for the drop, I need to look at the data trend before and after 2014.\nBased on the above thought, the answer is:\nAs the chart does not provide explicit reasons for the drop in 2014, one can speculate that it could be due to factors like changes in the educational environment, lack of resources, or reduced interest in online learning in that particular year."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "AC2o8CugOnabvHx7",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart suggest about the trend in the usage of Education Technology and Online Learning?"
+            },
+            {
+                "from": "gpt",
+                "value": "The trend of data is examined by observing the arrangement and sizes of the slices over the years from 2010 to 2019 in the pie chart.\nBased on the above thought, the answer is:\nThe chart suggests that the usage of Education Technology and Online Learning has been generally increasing from the years 2010 to 2019, despite occasional drops in 2014 and 2016. This might imply that more people are recognizing the benefits of online learning over time."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "mjZakPajEbu0uCD2",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, what could be inferred about the popularity of Education Technology and Online Learning over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "The popularity of Education Technology and Online Learning could be inferred from the increasing number of people using them over the years as seen in the pie chart.\nBased on the above thought, the answer is:\nThe chart suggests that Education Technology and Online Learning have been gaining popularity over the years, as indicated by the general increase in the number of users from 2010 to 2019."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "v8coe7ZVY1mtEN7p",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage increase from 2012 to 2013 in the number of people using Education Technology and Online Learning based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the percentage increase, I need to find the difference between the two years' figures and then divide it by the figure of the initial year (2012).\nBased on the above thought, the answer is:\nAs the number of people using Education Technology and Online Learning increased from 35 in 2012 to 50 in 2013, the percentage increase is (50-35)/35 * 100% = 42.86%."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "1h4jVvryqSZgpsgk",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more individuals were using Education Technology and Online Learning in 2019 compared to 2010?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find how many more individuals, I need to subtract the 2010 figure from the 2019 figure.\nBased on the above thought, the answer is:\nThe number of more individuals is 105 (2019's figure) - 20 (2010's figure) = 85 individuals."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "PDPpviMZvHNxQGov",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the average number of people using Education Technology and Online Learning per year based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average number, I need to sum up all the figures of individuals from each year and then divide by the number of years.\nBased on the above thought, the answer is:\nThe sum of people is 20 + 30 + 35 + 50 + 40 + 60 + 70 + 80 + 90 + 105 = 580. The average number of people per year is 580 / 10 = 58 people."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "8RVnEYc4xZoqursE",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the pie chart data that you're analyzing, can you convert the data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart segments represent years from 2011 to 2020 with corresponding incidents each year. I can convert this into a CSV format table by creating two columns, one for the year and another for the number of incidents for that year. My process includes analyzing the slices of the pie and the corresponding colors matched with years in the {legend} <r>[0.864, 0.074, 0.97, 0.43]</r>\nBased on the above thought, the answer is:\nYear,Incidents\n2011,1000\n2012,1500\n2013,2000\n2014,2500\n2015,2300\n2016,2200\n2017,1800\n2018,2400\n2019,2200\n2020,2750"
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "3F7wvnsTTdhUyrJU",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer from the trend of Cybersecurity incidents from 2011 to 2020 based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the size of the pie chart slices from 2011 to 2020, I can infer the trend of these incidents.\nBased on the above thought, the answer is:\nThe chart suggests that the number of cybersecurity incidents has generally increased from 2011 to 2020. Although there were some dips in 2015, 2017, and 2019, the overall trend indicates a rise in incidents."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "MZttBC9jkDpXoqrc",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year had the lowest number of incidents, and what might this indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The smallest slice of the pie represents the year with the lowest number of cybersecurity incidents - found in {color: '#ff9999'} <r>[0.871, 0.085, 0.905, 0.101]</r>\nBased on the above thought, the answer is:\nThe year 2011 had the lowest number of incidents. This may indicate that there were fewer cybersecurity threats or that security measures were more effective during that year."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "ttRZa4IE6ZNWCp2L",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you frame a security action plan based on the data from this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Considering the growing number of cybersecurity incidents year over year, I need to advise an action plan that aims to counter this trend.\nBased on the above thought, the answer is:\nBased on the increasing trend of cybersecurity incidents from 2011 to 2020, an action plan could focus on improving security measures, implementing stronger data protection policies, increasing awareness and training in cybersecurity best practices, and investing in advanced security technologies. It would be particularly crucial to examine why incidents spiked in particular years like 2020 and take concrete steps to mitigate such spikes in the future."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "KwT8lqzqBTjhWw5O",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year were the Cybersecurity incidents the highest?"
+            },
+            {
+                "from": "gpt",
+                "value": "To determine the year with the most incidents, I need to find the slice in the chart with the largest share. From the pie chart, it's provided in {color: '#cc99ff'} <r>[0.871, 0.399, 0.905, 0.416]</r>\nBased on the above thought, the answer is:\n2020."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "K7wRlNkoNWISSdEG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many incidents were there cumulatively from 2016 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the total number of incidents from 2016 to 2020, I need to sum up the number of incidents for each of these years.\nBased on the above thought, the answer is:\nThe total number of incidents from 2016 to 2020 is 2200 + 1800 + 2400 + 2200 + 2750 = 11350."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "m6Al80vxnxV5YHCd",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn an average, how many incidents occurred per year between 2011 and 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average number of incidents per year, I need to sum up the incidents of all the years and divide by 10.\nBased on the above thought, the answer is:\n1000 + 1500 + 2000 + 2500 + 2300 + 2200 + 1800 + 2400 + 2200 + 2750 = 21150. So, the average number of incidents per year is 21150/10 = 2115."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "s8UDxERSJTxptnbb",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide the data displayed in the Donut chart in a structured tabular format?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the subtitle at the top {\"Healthcare and Medical Technology Usage from 2014 to 2020\"}<r>[0.068, 0.025, 0.708, 0.055]</r> and {the slices for each year}<r>[{\"2014\"}[0.187, 0.104, 0.388, 0.297]; {\"2015\"}[0.078, 0.202, 0.247, 0.497]; {\"2016\"}[0.077, 0.488, 0.242, 0.826]; {\"2017\"}[0.179, 0.734, 0.455, 0.934]; {\"2018\"}[0.435, 0.62, 0.68, 0.924]; {\"2019\"}[0.557, 0.256, 0.699, 0.664]; {\"2020\"}[0.388, 0.104, 0.629, 0.335]], I can deduce that the sections in the chart represent the medical technology usage for different years.\nBased on the above thought, the answer is:\nYear,Medical Technology Usage\n2014,62\n2015,70\n2016,80\n2017,84\n2018,88\n2019,92\n2020,78"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "mpQADpff88O9eur5",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, was there a general trend in the usage of medical technology from 2014 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at each sector from 2014 to 2020, I can determine if there is an overall increase or decrease in usage.\nBased on the above thought, the answer is:\nYes, there was a general upward trend from 2014 to 2019. However, there was a decline in usage in 2020."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "tE3FoPkK7ZWieGmm",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you infer about the usage of medical technology in the healthcare industry based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the overall trend and any stand-out years to make my inference.\nBased on the above thought, the answer is:\nThe chart shows a growing usage of medical technology from 2014 to 2019, which suggests that the healthcare industry was increasingly integrating technology into its practice. However, the decrease in 2020 might indicate certain challenges or disruptions, possibly due to the global pandemic."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "xsERKf23Yko7UYzy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the data in the chart inform strategic decisions for technology vendors in the healthcare industry moving forward?"
+            },
+            {
+                "from": "gpt",
+                "value": "My reasoning should take into account the overall trend and the sudden decrease in 2020.\nBased on the above thought, the answer is:\nThe overall increasing trend up to 2019 indicates a growing market for health technology products. However, the decrease in 2020 might imply a need for adaptation or innovation to meet new challenges or changes in the healthcare landscape due to unforeseen events (like a pandemic). Vendors might consider strategies to make their technologies more resilient and relevant in such situations."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "iAuYUXOWDPxWKPxF",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the increase in usage of medical technology from the year 2014 to 2015?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase in usage, I need to subtract the usage of 2014 from the usage of 2015.\nBased on the above thought, the answer is:\n8%"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "swOgyhXCxO58v4pL",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much did the usage of medical technology decrease from 2019 to 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the decrease, I subtract the usage of 2020 from the usage of 2019.\nBased on the above thought, the answer is:\n14%"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "LTVrt8uprnbmYQoU",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the average usage of medical technology over the seven years?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average, I need to add all the percentages of usage for the seven years and divide by the number of years.\nBased on the above thought, the answer is:\n79.1%"
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "OdmsDh6LFsVbTwHy",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you create a table showcasing the utilization of books and online courses between 2012 and 2021 as reflected in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "From the colors and labels of {the legend}<r>[0.864, 0.074, 0.97, 0.779]</r> and the sizes of {the pie slices}<r>[0.08, 0.103, 0.696, 0.925]</r>, I can determine the proportionate values for Books and Online Courses for each year from 2012 to 2021.\nBased on the above thought, the answer is:\nYear,Books,Online Courses\n2012,10,15\n2013,20,20\n2014,15,40\n2015,25,60\n2016,30,70\n2017,20,80\n2018,35,100\n2019,40,90\n2020,50,150\n2021,50,200"
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "TkBoiwwzXhDjEsd4",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nJudging by the pie chart, which resource seems to become more popular over the years, and why do you think so?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can start by analyzing the proportion of each resource for each year. A general pattern can be noticed from the sizes of {\"Online Courses\"}<r>[0.183, 0.728, 0.696, 0.925]</r> slices, these sizes typically increase over the years.\nBased on the above thought, the answer is:\nOnline Courses have become more popular over the years, as it can be observed that over the time period showcased, the proportion of the pie taken up by Online Courses has generally grown. This could be due to technology advancement and rise in demand for flexible learning arrangements."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "4F92zK4a2YpA4zWu",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be inferred from the utilization of books for the year 2020 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to look at the chart and find the slice that represents the year 2020 for books {\"2020\"}<r>[0.488, 0.287, 0.65, 0.563]</r>.\nBased on the above thought, the answer is:\nIn the year 2020, even though Online Courses saw a significant increase, the utilization of Books did not decrease. It actually increased slightly compared to the previous year, indicating that despite the rise of Online Courses, Books remained a critical resource for education."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "I5IKSXdR1DfYPZmM",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow might the current trends in resource utilization impact decisions for education resource allocation in the future?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the general trend in utilization for both Books and Online Courses over the years.\nBased on the above thought, the answer is:\nGiven the current trends where Online Courses witnessing a significant surge, especially in the recent years, decision makers may opt to allocate more resources towards improving and expanding online course offerings. However, as Books have experienced steady utilization, it also remains important to continue investing in this traditional but essential educational resource."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "tJklR3RlrvNSfuQV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the pie chart, what are the total number of books utilized between 2012 and 2021?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the values associated with \"Books\" for each year from {2012}<r>[0.08, 0.103, 0.177, 0.549]</r> to {2021}<r>[0.388, 0.103, 0.666, 0.389]</r>, I can sum them up to get the total number of books utilized in this period.\nBased on the above thought, the answer is:\n305"
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "uHgjtgmu9TbW893x",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage increase did online courses see from 2016 to 2017?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to refer to the values linked to the label \"Online Courses\" for the years {\"2016\"}<r>[0.183, 0.728, 0.317, 0.902]</r> and {\"2017\"}<r>[0.286, 0.785, 0.423, 0.925]</r>, then calculate the percentage increase.\nBased on the above thought, the answer is:\n14.28%"
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "1cjICCVDE5T10yRP",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBetween 2019 and 2021, which educational resource saw the greatest increase in utilization?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the total utilization of \"Books\" and \"Online Courses\" for {\"2019\"}<r>[0.523, 0.624, 0.673, 0.835]</r> and {\"2021\"}<r>[0.388, 0.103, 0.666, 0.389]</r>, I can see which resource had the greatest increase.\nBased on the above thought, the answer is:\nOnline Courses"
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "c7yt8OqxGphHj3gA",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please convert the trend in fitness and sports health from 2017 to 2019 into a structured table based on the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the titles {\"2017\"}<r>[0.104, 0.075, 0.374, 0.111]</r>, {\"2018\"}<r>[0.378, 0.075, 0.647, 0.111]</r>, and {\"2019\"}<r>[0.651, 0.075, 0.921, 0.111]</r> and the slices {Gym Membership}<r>[0.077, 0.473, 0.145, 0.501]</r>; {Yoga Classes}<r>[0.333, 0.509, 0.382, 0.537]</r>; {Gym Membership}<r>[0.366, 0.676, 0.434, 0.704]</r>; {Yoga Classes}<r>[0.591, 0.306, 0.641, 0.334]</r>; {Gym Membership}<r>[0.627, 0.403, 0.695, 0.431]</r>; and {Yoga Classes}<r>[0.877, 0.579, 0.926, 0.607]</r> I can arrange the data into a CSV format.\nBased on the above thought, the answer is:\nYear,Gym Membership,Yoga Classes\n2017,70,30\n2018,75,25\n2019,65,35"
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "oQFHvzjlZ0vrhzqR",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich fitness trend saw significant increase in 2019 comparing to previous years based on the pie charts?"
+            },
+            {
+                "from": "gpt",
+                "value": "Comparing the segments in each pie chart for the years 2017, 2018, and 2019 will give an indication of the trend that increased significantly.\nBased on the above thought, the answer is:\nYoga classes saw a significant increase in 2019."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "gFPrgKYdQiy58ix0",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAnalyzing the pie charts, what general trends in fitness preferences can be observed over the years 2017 to 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to identify general patterns from the proportion of gym memberships and yoga classes across the three years.\nBased on the above thought, the answer is:\nIt can be observed that after a decrease in 2018, the popularity of yoga classes significantly increased in 2019, while the trend for gym membership decreased so that in 2019 yoga became the more popular activity."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "hNH1ZOwBuW0ad0wl",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGiven the pie charts showing the fitness trends over the years 2017 to 2019, how might a fitness centre arrange their classes for 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to analyze how fitness trends have shifted over the past three years and predict possibly popular fitness programs based on these trends.\nBased on the above thought, the answer is:\nGiven the increasing popularity of yoga classes, the fitness centre might want to offer more yoga classes in 2020, while also maintaining a substantive number of gym spaces as it still retains considerable interest among the people."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "e60vwSZ46zzS1cbC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the total percentage of gym membership across the three years 2017, 2018 and 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "It isn't suitable to add percentages from different years because each year's percentage is calculated out of a different total number of people.\nBased on the above thought, the answer is:\nIt is inappropriate to add the percentages of different years."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "i278X5KdKz8SQmJS",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which year was the percentage difference between gym membership and yoga classes the greatest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to calculate the difference between gym membership and yoga classes for each year and then compare them.\nBased on the above thought, the answer is:\n2018"
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "gIaeZqO66pjOaP3T",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many more people had a gym membership than attended yoga classes in 2019?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart does not give the specific number of people who had a gym membership or attended yoga classes. Therefore, I cannot perform a subtraction operation between these two entities.\nBased on the above thought, the answer is:\nThe chart does not provide the specific number of people."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "0Orn3rrBFtu6S0UH",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the data of emerging technologies, please construct a table in CSV format that can represent the change over the years."
+            },
+            {
+                "from": "gpt",
+                "value": "The title {\"Emerging Technologies\"}[0.015, 0.047, 0.759, 0.981] suggests that the table should contain entries about the adoption of various emerging technologies over the years. I also noticed bars of two colors in the pie charts that represent two different technologies: {the red bar}[0.047, 0.089, 0.523, 0.939] representing \"Artificial Intelligence\" and {the blue bar}[0.387, 0.089, 0.727, 0.904] representing \"Big Data\". I will structure the CSV table in such a way that the years are in one column, and the adoption figures of the two technologies are in their own respective columns.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Big Data\n2020,13,10\n2021,18,14\n2022,22,19\n2023,27,25\n2024,35,30"
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "RVCLfylj2l6S6hgY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which technology had more adoption from 2020 to 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "By comparing the rates of adoption of both \"Artificial Intelligence\" and \"Big Data\" from 2020 to 2024, I can determine which technology had more adoption.\nBased on the above thought, the answer is:\n\"Artificial Intelligence\" had more adoption from 2020 to 2024."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "tRPlvUMNP8xW5voN",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat trend can be seen in the adoption of \"Artificial Intelligence\" based on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the changing size of \"Artificial Intelligence\" slices in the pie charts, I can infer a trend in its adoption rate.\nBased on the above thought, the answer is:\nThe adoption of \"Artificial Intelligence\" has seen a steady and significant upward trend from 2020 to 2024 according to the chart."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "tQlG2z0pnNINSUOW",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat implications might the data in the chart have on the focus areas of technology during the analyzed period?"
+            },
+            {
+                "from": "gpt",
+                "value": "By analyzing the proportions of \"Artificial Intelligence\" and \"Big Data\" from 2020 to 2024, I can infer that the adoption of these technologies could have influenced focus areas in technology during this period.\nBased on the above thought, the answer is:\nThe increasing adoption of Artificial Intelligence suggests that it could become a central focus in technology, greatly impacting research, development, and business strategies. Meanwhile, \"Big Data\" also sees a steady increase, suggesting a continued interest and focus in data analysis fields."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "1dFt0ULLCwd6OPz2",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percent adoption increase of Artificial Intelligence from 2022 to 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate the increase, I need to subtract the adoption percentage of Artificial Intelligence in 2022 from that of 2024.\nBased on the above thought, the answer is:\nThe increase in adoption of Artificial Intelligence from 2022 to 2024 is 13%."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "66qVDFIikL3z5XBC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf Big Data had increased its adoption rate by 10% each year since 2020, what would have been its adoption rate in 2024?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the hypothetical adoption rate, I need to add 10% to Big Data's adoption rate each year, starting from 2020.\nBased on the above thought, the answer is:\nIf Big Data had increased its adoption rate by 10% each year since 2020, it would have been 50% in 2024."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "yk8u5z6vPtJfRZeT",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the total adoption rate of all emerging technologies in 2023?"
+            },
+            {
+                "from": "gpt",
+                "value": "The total adoption rate can be obtained by adding up the individual adoption rates of all technologies in the year 2023.\nBased on the above thought, the answer is:\nThe total adoption rate of all emerging technologies in 2023 is 52%."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "PLzzESsWzyOgRaTW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, can you convert the visual data into tabular format in the form of CSV?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is a pie chart, with different slices representing different industry sectors. The legend displayed on the right of the chart lists {Film}<r>[0.826, 0.085, 0.861, 0.101]</r>{Music}<r>[0.826, 0.12, 0.861, 0.136]</r>{Gaming}<r>[0.826, 0.155, 0.861, 0.171]</r>{Publishing}<r>[0.826, 0.19, 0.861, 0.206]</r>and {VR}<r>[0.826, 0.225, 0.861, 0.241]</r>.These labels match with the text associated with the pie slices.I 'll take this information and turn it into a table.\nBased on the above thought, the answer is:\nIndustry,Revenue\nFilm,100\nMusic,85\nGaming,120\nPublishing,70\nVR,25"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "q65Qhtl3ljf2hORe",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which field in the entertainment industry should investors primarily focus on for the maximum return on investment?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest slice of the pie chart represents the industry that is currently generating the most revenue, which implies it could potentially provide the maximum return on investment. In this case, that would be {Gaming}<r>[0.315, 0.486, 0.699, 0.934]</r>.\nBased on the above thought, the answer is:\nInvestors should primarily focus on the Gaming industry for the maximum return on investment as it currently generates the highest revenue."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "sJ6ZgkKExP6lrFbq",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the entertainment industry likely to evolve, based on the data presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to look at the comparatively smaller sectors, which represent emerging or less mature areas of the industry. Here, I see that the {VR}<r>[0.826, 0.225, 0.861, 0.241]</r> sector is the smallest.\nBased on the above thought, the answer is:\nThe VR sector, despite generating the least revenue currently, shows potential for growth given its nascent state and rapid technological advancements. The chart indicates that the entertainment industry may witness an upsurge in this field in the future."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "pj6iPGMmQpqOrQxw",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf an increase in revenue for one sector equates to a proportional increase in jobs, which sector is likely to see the most job growth?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector with the highest revenue, as identified by the largest slice of the pie chart, is likely to see the most job growth. In this chart, that corresponds to {Gaming}<r>[0.315, 0.486, 0.699, 0.934]</r>.\nBased on the above thought, the answer is:\nThe Gaming sector is likely to see the most job growth, if an increase in revenue indeed results in a proportional increase in jobs."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "G6p39nPqVqjNxRET",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which industry generated the highest revenue?"
+            },
+            {
+                "from": "gpt",
+                "value": "I'll inspect the slices of the pie chart. The slice with the largest size represents the industry that generated the highest revenue. In this case, I can see the {Gaming}<r>[0.315, 0.486, 0.699, 0.934]</r> slice is the largest.\nBased on the above thought, the answer is:\nGaming"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "dn0skNhCNKcFOhcQ",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the difference of revenue between the highest revenue earning industry and the lowest one?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highest revenue earning industry is {Gaming}<r>[0.315, 0.486, 0.699, 0.934]</r> with 120 billion USD and the lowest revenue earning industry is {VR}<r>[0.826, 0.225, 0.861, 0.241]</r> with 25 billion USD so the difference is 120 - 25.\nBased on the above thought, the answer is:\n95"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "rAHA2jEiXXsPxuWC",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the revenue of the Film industry is projected to increase by 10 billion the next year, what will be the new total revenue of all sectors?"
+            },
+            {
+                "from": "gpt",
+                "value": "The current total revenue of all sectors is the sum of revenues of {Film}<r>[0.826, 0.085, 0.861, 0.101]</r>, {Music}<r>[0.826, 0.12, 0.861, 0.136]</r>, {Gaming}<r>[0.826, 0.155, 0.861, 0.171]</r>, {Publishing}<r>[0.826, 0.19, 0.861, 0.206]</r> and {VR}<r>[0.826, 0.225, 0.861, 0.241]</r>. That is 100 + 85 + 120 + 70 + 25 = 400 billion USD. If the revenue of the Film industry increases by 10 billion, the new total revenue will be 400 + 10.\nBased on the above thought, the answer is:\n410"
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "p8rZBmgeAyuUKIPW",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the donut chart, could you extract the data and construct it into a CSV format table?"
+            },
+            {
+                "from": "gpt",
+                "value": "The donut chart displays the public concern on major policy areas, as stated by {the title}<r>[0.197, 0.025, 0.579, 0.055]</r>. By identifying the sectors associated with {Education}<r>[0.077, 0.104, 0.388, 0.647]</r>, {Healthcare}<r>[0.092, 0.608, 0.484, 0.934]</r>, {Infrastructure}<r>[0.455, 0.608, 0.684, 0.913]</r>, {Taxes}<r>[0.516, 0.183, 0.699, 0.647]</r>, and {Economy}<r>[0.388, 0.104, 0.571, 0.284]</r>, alongside the percentage values provided with each slice, I can prepare a CSV format table.\nBased on the above thought, the answer is:\nPolicy,Percentage\nEducation,30.0%\nHealthcare,25.0%\nInfrastructure,15.0%\nTaxes,20.0%\nEconomy,10.0%"
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "4myvvH8JkXEEzcIA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the provided chart, which policy area is of highest concern to the public and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The policy area of highest concern should be the one with the largest sector size or percentage in the donut chart.\nBased on the above thought, the answer is:\n'Education' is of highest concern to the public as it claims the largest sector of the donut chart, representing 30% of public concern."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "tFfBO1CvLdvCZ3wi",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the chart, which sector is of least interest to the public and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector of least interest should be the one with the smallest sector size or percentage in the donut chart.\nBased on the above thought, the answer is:\n'Infrastructure' is the least concerning area for the public as it occupies the smallest sector of the chart, accounting for only 15% of the total concerns."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "j9g6aDv4LlChKF6u",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you infer the possible focus areas for policy-makers in the next year based on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Based on the chart, the sectors with the highest percentages could be the potential focus for policymakers as they represent the major concerns of the public.\nBased on the above thought, the answer is:\nThe policymakers might focus more on 'Education' and 'Healthcare' areas in the next year, considering they account for the largest proportions of public concern as per the chart."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "57JQuleSl2VlySLY",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the sum of the concerns for Infrastructure and Economy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sum of concerns can be obtained by adding the percentages for each category. Let me locate the percentages of {Infrastructure}<r>[0.455, 0.608, 0.684, 0.913]</r> and {Economy}<r>[0.388, 0.104, 0.571, 0.284]</r> on the donut chart.\nBased on the above thought, the answer is:\nThe sum is 25%."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "IAZwFkGYJXvk2NHG",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of concern is seen on Taxes and Education combined?"
+            },
+            {
+                "from": "gpt",
+                "value": "The combined concern can be calculated by locating and adding up the percentages of {Taxes}<r>[0.516, 0.183, 0.699, 0.647]</r> and {Education}<r>[0.077, 0.104, 0.388, 0.647]</r> from the donut chart.\nBased on the above thought, the answer is:\nThe combined concern for Taxes and Education is 50%."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "vswnAlqEz3JQmUlE",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the average public concern across all policy areas?"
+            },
+            {
+                "from": "gpt",
+                "value": "The average concern can be found out by summing up the percentages of all policy areas and dividing them by the total number of areas.\nBased on the above thought, the answer is:\nThe average public concern across all policy areas is 20%."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "G2J0JLPQzX8ILcb6",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a table in CSV format that represents what you observe in the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "To answer the question, I'm looking at two titles at the top of the charts {\"Consumer Behavior Trends in Expanding Market\"}[0.303, 0.507, 0.697, 0.525] and {\"Consumer Behavior Trends in Recession Market\"}[0.301, 0.015, 0.699, 0.033]. The segments in the pie charts represent different consumer trends, and I can see the trends and their numerical representations which are {'Comfort Foods'}[0.825, 0.542, 0.927, 0.556], {'Eco-friendly Products'}[0.825, 0.563, 0.973, 0.577], {'Innovations'}[0.825, 0.584, 0.906, 0.598], {'Seasonal Offers'}[0.825, 0.605, 0.935, 0.619], {'Health Conscious'}[0.825, 0.626, 0.945, 0.64], {'Ethnic Cultures'}[0.825, 0.647, 0.931, 0.661], {'Young Consumers'}[0.825, 0.668, 0.949, 0.682], {'Online Shopping'}[0.825, 0.689, 0.94, 0.703], {'Return Policies'}[0.825, 0.71, 0.927, 0.724].\nBased on the above thought, the answer is:\nMarket Segment, Expanding Market, Recession Market\nComfort Foods, 15, 85\nEco-Friendly Products, 80, 20\nInnovations, 30, 70\nSeasonal Offers, 45, 55\nHealth Conscious, 65, 35\nEthnic Cultures, 20, 80\nYoung Consumers, 75, 25\nOnline Shopping, 90, 10\nReturn Policies, 10, 90"
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "vY6WFABwSqntk8CA",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich consumer behavior trend did not change much between the Expanding Market and the Recession Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this question, I need to look at all the behavior trends and their values in both markets, and find the trend with the smallest difference.\nBased on the above thought, the answer is:\n\"Seasonal Offers\" changed the least from the Expanding Market to the Recession Market. This suggests that regardless of market conditions, consumers have consistent interest in seasonal products."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "05rieN1d5ltEcapx",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat inference can be drawn about the consumer preference for Comfort Foods in the Recession Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "By observing the Comfort Foods' segment{'Comfort Foods'}[0.825, 0.542, 0.927, 0.556],  and drawing a comparison between its values in both market conditions, I can make an inference.\nBased on the above thought, the answer is:\nComfort Foods' preference significantly increased in the Recession Market. This might suggest that consumers tend to resort to comfort foods during economic downtimes."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "LLyvxP5j6EviH6Ne",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the trend in Online Shopping suggest about consumer behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "I should be looking at online shopping{'Online Shopping'}[0.825, 0.689, 0.94, 0.703]. The high values of Online Shopping segment in both markets can provide me an indication of the trend.\nBased on the above thought, the answer is:\nOnline Shopping emerged as the leading consumer trend in both Expanding and Recession Market. This suggests that regardless of the market condition, the trend of online shopping is increasingly gaining traction among consumers due to its convenience and widespread availability."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "PIWyAQYB0EYc3VFD",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the highest value in the Expanding Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the highest value in the Expanding Market, I need to check each value in the Expanding Market column.\nBased on the above thought, the answer is:\n90"
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "z6wNm8spuRfUENqn",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the lowest value in the Recession Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the lowest value in the Recession Market, I need to check each value in the Recession Market column.\nBased on the above thought, the answer is:\n10"
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "LOdfper7oFLAwWfV",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the Recession Market, by how much does the preference for Eco-friendly Products decrease compared to the Expanding Market?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to compare the value of Eco-friendly Products in both the Recession Market and the Expanding Market and calculate the difference.\nBased on the above thought, the answer is:\n60"
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "Sf6RwsPdghNqMnLz",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you generate a structured table based on the pie chart information related to New Media and Digital Content Creation results for each year?"
+            },
+            {
+                "from": "gpt",
+                "value": "By inspecting the chart subtitles {\"New Media and Digital Content Creation from 2010 to 2018\"}[0.079, 0.025, 0.697, 0.055] and the pie slices given in the coordinates {{\"2010\"}[0.318, 0.052, 0.362, 0.075]} to {{\"2018\"}[0.407, 0.262, 0.451, 0.285]}, I am able to see the details of the data being represented. I will convert this into a CSV format table by correlating the text, color and percentage holder coordinates.\nBased on the above thought, the answer is:\nYear,Media Type,Percentage\n2010,New Media,2.5%\n2011,New Media,3.7%\n2012,New Media,5.1%\n2013,New Media,6.3%\n2014,New Media,5.8%\n2015,New Media,5.3%\n2016,New Media,7.2%\n2017,New Media,6.6%\n2018,New Media,7.6%\n2010,Digital Content Creation,3.4%\n2011,Digital Content Creation,2.8%\n2012,Digital Content Creation,4.8%\n2013,Digital Content Creation,5.5%\n2014,Digital Content Creation,6.9%\n2015,Digital Content Creation,6.0%\n2016,Digital Content Creation,6.6%\n2017,Digital Content Creation,6.8%\n2018,Digital Content Creation,7.1%"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "x1gkynMJGY2eNMU6",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the overall trend in contribution of New Media from 2010 to 2018 based on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Looking at the chart slices {\"2010\"}[0.318, 0.052, 0.362, 0.075] to {\"2018\"}[0.407, 0.262, 0.451, 0.285], I can compare the percentages and gauge the general trend.\nBased on the above thought, the answer is:\nThe New Media contribution has been increasing throughout the years from 2010 to 2018."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "HcBMAi0EhlGVdclb",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can we understand the volatility in the contribution of Digital Content Creation over the years?"
+            },
+            {
+                "from": "gpt",
+                "value": "By looking at the pie slices of Digital Content Creation {\"2010\"}[0.386, 0.747, 0.43, 0.77] to {\"2018\"}[0.407, 0.262, 0.451, 0.285], I can infer the changes in its contribution each year.\nBased on the above thought, the answer is:\nThe contribution of Digital Content Creation seems to have some pattern of volatility across the years. This could be due to various factors such as changes in market demands or shifts in the creative industry dynamics."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "MOTg5AWTLfNlrXHy",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the trend observed in the pie chart continues, how could this impact future strategies regarding the two media types?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to consider the change in contributions for each media type over time and analyze how this could potentially affect future implications.\nBased on the above thought, the answer is:\nIf the current trend continues, we could see a gradual increase in the emphasis on New Media. Hence future strategies might need to focus more on integrating or enhancing New Media. For Digital Content Creation, although it shows some volatility, it still has a significant share, so efforts should be invested as well to maintain or increase its contribution, despite the fluctuations."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "K5OhWsAbdWEEye0N",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, what is the average percentage of New Media contribution to the total media type from 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To find the average percentage, I need to sum up the percentages from 2010-2018 and divide them by the total number of years (9). This requires analyzing the slices {\"2010\"}[0.318, 0.052, 0.362, 0.075] through {\"2018\"}[0.407, 0.262, 0.451, 0.285].\nBased on the above thought, the answer is:\n5.67%"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "dSKpGm74lrUGfcg6",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn what year was the contribution of Digital Content Creation to the total media type the lowest?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to refer to the pie slices {\"2010\"}[0.386, 0.747, 0.43, 0.77] through {\"2018\"}[0.407, 0.262, 0.451, 0.285] and compare the percentage values to find the lowest.\nBased on the above thought, the answer is:\n2011"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "GUX0Nd4ESYcvbO7B",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the maximum percentage difference between New Media and Digital Content Creation contributions in a year between 2010 to 2018?"
+            },
+            {
+                "from": "gpt",
+                "value": "To answer this, I need to find the percentage contribution for each media type in each year, calculate the difference in each year and find the maximum of these values.\nBased on the above thought, the answer is:\n1.9%"
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "IiafrERjBO9HqFrv",
+        "Chart_type": "pie",
+        "QA_type": "Chart2table",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please convert the pie chart data into a structured table in CSV format?"
+            },
+            {
+                "from": "gpt",
+                "value": "Following my observation of the chart, which has a title {\"Education Resource Distribution and Utilization in 2020\"}[0.101, 0.025, 0.675, 0.055] and the slices for {Books}[0.077, 0.104, 0.698, 0.934] and {Computers}[0.388, 0.104, 0.699, 0.553], I see that each of these slices represents the percentage of resources allocated to each category in 2020.\nBased on the above thought, the answer is:\nResource Type,Percentage in 2020\nBooks,73.7%\nComputers,26.3%"
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "LJEJFbPdyPFVvCTv",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich resource category is more prevalent in the 2020 distribution and by how much?"
+            },
+            {
+                "from": "gpt",
+                "value": "Finding the more prevalent resource category involves identifying the one with the higher percentage.\nBased on the above thought, the answer is:\nThe \"Books\" category is more prevalent, having a larger percentage of 73.7%, significantly higher than the \"Computers\" category, which stands at 26.3%."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "18clcGFshg5CJGFk",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart suggest about resource allocation for the year 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "Conclusions about resource allocation can be drawn by examining the proportions of each category.\nBased on the above thought, the answer is:\nThe chart suggests that \"Books\" were allocated more resources in 2020 than \"Computers\", with the allocation of \"Books\" being nearly three times that of \"Computers\"."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "qD8Y6EBPLb3ff1vY",
+        "Chart_type": "pie",
+        "QA_type": "Open QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we were to increase the allocation of digital resources for the next academic year based on this data, which resource would it be best to focus on and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "Decision-making about future allocation improvements involves examining the current distribution and identifying less-represented categories.\nBased on the above thought, the answer is:\nTo increase the allocation of digital resources, it would be advantageous to focus on \"Computers\" as they currently hold a significantly smaller proportion (26.3%) of the educational resources compared to \"Books\" (73.7%)."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "QRHjeDvOYvQClP0a",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat was the percentage distribution of Books and Computers if we count both as 100%?"
+            },
+            {
+                "from": "gpt",
+                "value": "To calculate this, I need to treat the sum of the percentages of Books and Computers as 100% and then recalculate their respective distributions.\nBased on the above thought, the answer is:\nBooks had 73.7% of the total, so the percentage is (73.7/(73.7+26.3))*100 = 73.7%. Similarly, for Computers, the percentage is (26.3/(73.7+26.3))*100 = 26.3%."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "4niMEAlDy37tqL1W",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy what percentage is the proportion of Books more than Computers in 2020?"
+            },
+            {
+                "from": "gpt",
+                "value": "The answer can be achieved by subtracting the percentage of Computers from the percentage of Books.\nBased on the above thought, the answer is:\nThe percentage difference is 73.7% (Books) - 26.3% (Computers) = 47.4%."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "9OQONkh3a4NC6vl4",
+        "Chart_type": "pie",
+        "QA_type": "Numerical QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf the percentage of Books decreased by 5% and was distributed to Computers in 2020, what would be the new proportions?"
+            },
+            {
+                "from": "gpt",
+                "value": "I need to subtract 5% from the percentage of Books and add it to the percentage of Computers to calculate the new proportions.\nBased on the above thought, the answer is:\nThe new percentage for Books is 73.7% - 5% = 68.7%. For Computers, the new percentage is 26.3% + 5% = 31.3%."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "iOqckAyxHqsJygPW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the title of the chart centered at coordinates [0.193, 0.021, 0.77, 0.047] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Distribution of Focus Areas in Financial Management and Risk Control\"."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "ChhwmZRoLexa2Jnd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.897, 0.071, 0.975, 0.091] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend entry for \"Investment\"."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "VDr2yakyP6sUWsGh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item positioned at coordinates [0.489, 0.25, 0.533, 0.27]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"Risk Analysis\" associated with one of the pie slices."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "iHgSIM39sHdha6qb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the term {Investment}<r>[0.897, 0.071, 0.975, 0.091]</r> according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated is this {one}<r>[0.218, 0.315, 0.736, 0.923]</r>."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "ajuM1I6Bz6rKpF1j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the Autotext closely located to {the pie slice}[0.218, 0.315, 0.736, 0.923] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "It says \"67.0%\""
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "mgAhzPNcThI3LFU3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice {Risk Analysis}<r>[0.897, 0.101, 0.987, 0.121]</r> in the legend corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the corresponding slice is #66b3ff."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "y7weZun3TkzSOnZO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area framed by coordinates [0.287, 0.109, 0.727, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a slice of the pie chart, with the label \"Risk Analysis\" [0.489, 0.25, 0.533, 0.27] and the text \"33.0%\" [0.532, 0.068, 0.623, 0.088]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "K4GweInfJABoAxGN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.853, 0.063, 0.993, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a legend with an entry for 'Investment' [0.897, 0.071, 0.975, 0.091] with color '#ff9999' [0.858, 0.073, 0.886, 0.087], and an entry for 'Risk Analysis' [0.897, 0.101, 0.987, 0.121] with color '#66b3ff' [0.858, 0.103, 0.886, 0.117]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "goYltAGsjudWLlz1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components can you identify within the slice at coordinates [0.218, 0.315, 0.736, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this slice, there is an Autotext \"67.0%\" [0.352, 0.944, 0.431, 0.964] and a label \"Investment\" [0.43, 0.762, 0.474, 0.782]."
+            }
+        ],
+        "id_raw": 0
+    },
+    {
+        "id": "cV7ojnELwNFrJqjd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text with coordinates [0.202, 0.021, 0.761, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle, \"Digital Content Creation and New Media Growth from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "qEUYqVUUP1zXP0Vd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.197, 0.109, 0.767, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "2sbwhsUMSb1E1hK8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at these coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text for '2010' in the legend."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "L0a2deVtN3dlzw8e",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart has its corresponding legend item at {the legend item}[0.952, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at {coordinates}<r>[0.213, 0.311, 0.458, 0.5]</r>."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "qIWFhhgBKj1e3aEQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the text at {these coordinates}[0.202, 0.021, 0.761, 0.047] and the pie at {these coordinates}[0.197, 0.109, 0.767, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the pie chart."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "uHn2n9gkh9cX6S3v",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the slice at {these coordinates}[0.267, 0.16, 0.465, 0.487] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to the legend item {handle}<r>[0.913, 0.642, 0.941, 0.655]</r> with text \"2019\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "XqB9KwtyfeYasHpk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within these coordinates [0.908, 0.063, 0.993, 0.668]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. It includes the years from 2010 to 2019 with their corresponding colors as shown in the pie chart."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "CILOY4WRniLh6eEx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.952, 0.37, 0.987, 0.39]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend text \"2010\"."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "AAVFN4l3mftsEqyo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.213, 0.311, 0.458, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of the pie chart, comprising a slice that is associated with the year 2011."
+            }
+        ],
+        "id_raw": 1
+    },
+    {
+        "id": "OFvGvne61rA74tJd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.262, 0.021, 0.701, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, labeled \"Risk Assessment and Management Analysis - Impact\"."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "MeqteWpnZxnNQS6L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.945, 0.221, 0.987, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"Risk 6\"}<r>[0.945, 0.221, 0.987, 0.241]</r>"
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "A7Y1Hur7HZaeMVjT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at coordinates [0.598, 0.094, 0.64, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text indicating the percentage, \"20.0%\", associated with {the Risk 5 slice}<r>[0.429, 0.112, 0.711, 0.482]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "3JXj43u5irhixUDP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the {legend text \"Risk 3\"}<r>[0.945, 0.131, 0.987, 0.151]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Risk 3\" corresponds to {the slice}<r>[0.495, 0.547, 0.694, 0.906]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "aU4I9FzYyTVwQMQg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the {Risk 4 slice}<r>[0.507, 0.322, 0.767, 0.756]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices \"Risk 5\" and \"Risk 3\"}<r>[[0.429, 0.112, 0.711, 0.482], [0.495, 0.547, 0.694, 0.906]]</r> are adjacent to the Risk 4 slice."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "AWO0VPQMNHYcl8M6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to the slice at coordinates [0.197, 0.281, 0.456, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Risk 1\" in the legend, marked with the color at {legend coordinates}<r>[0.906, 0.073, 0.934, 0.087]</r>, corresponds to the slice."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "C47JGeoyTch2PmHn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region ranging from coordinates [0.268, 0.500, 0.770, 0.950]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, I can see parts of the pie chart slices. This includes various pieces such as {Risk 1, Risk 2, and a portion of the Risk 3 slices}<r>[[0.197, 0.281, 0.456, 0.803], [0.309, 0.552, 0.538, 0.922], [0.495, 0.547, 0.694, 0.906]]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "vEpYI8zOjQJrGqON",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the region [0.370, 0.925, 0.425, 0.960]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the percent label \"15.0%\" associated with the {Risk 2 slice}<r>[0.309, 0.552, 0.538, 0.922]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "QRVu6ZfM3gBf8vot",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.815, 0.023, 0.981, 0.250]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region mainly contains the chart legend with labels and representative colors for each Risk type, including {Risk 1, Risk 2, Risk 3, Risk 4, Risk 5, and Risk 6}<r>[[0.945, 0.071, 0.987, 0.091], [0.945, 0.101, 0.987, 0.121], [0.945, 0.131, 0.987, 0.151], [0.945, 0.161, 0.987, 0.181], [0.945, 0.191, 0.987, 0.211], [0.945, 0.221, 0.987, 0.241]]</r>."
+            }
+        ],
+        "id_raw": 2
+    },
+    {
+        "id": "XcKqR4rTcL3D3sr2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented at coordinates [0.319, 0.021, 0.645, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Donations Received from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "g5OuV78hkEgF18bA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.908, 0.063, 0.993, 0.368] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot legend."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "qPeZvyUHytuup9kz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.952, 0.34, 0.987, 0.36] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\"."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "Ad6kyEjLgz1UEjSK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legends in the chart are related to the pie slice located at {the slice}[0.495, 0.547, 0.698, 0.909]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {2015}<r>[0.952, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "Tk4CKZ1LnrctzYwr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is related to the legend {2012}<r>[0.952, 0.131, 0.987, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.2, 0.528, 0.457, 0.727]."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "sFLlWq7PA94bMGLM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds to the legend 2018?<r>[0.952, 0.311, 0.987, 0.331]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates}[0.486, 0.111, 0.671, 0.481]."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "ntZLFjKXdUq9BMhp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.215, 0.26, 0.459, 0.498]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with associated text \"2010\" [0.307, 0.378, 0.342, 0.398] and autotext \"5.7%\" [0.176, 0.286, 0.211, 0.306]. This part represents the donation received in year 2010."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "GEiZL7eeZopCBDip",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.197, 0.389, 0.456, 0.552]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice and the accompanying text \"2011\" [0.284, 0.476, 0.319, 0.496] and autotext showing \"7.1%\" [0.138, 0.455, 0.173, 0.475]. This area corresponds to the donation received in the year 2011."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "dBm6X2jAOf3r0zZg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the region [0.505, 0.234, 0.764, 0.501]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice along with the associated text \"2017\" [0.625, 0.399, 0.669, 0.419] and autotext \"12.0%\" [0.765, 0.322, 0.8, 0.342]. This area represents donations made in the year 2017."
+            }
+        ],
+        "id_raw": 3
+    },
+    {
+        "id": "VSBSHd09rXd8v0yb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.288, 0.021, 0.675, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"GDP trends of USA & China from 2010 to 2020\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "tQ8tUNqXd4NPRgWg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.903, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "the chart's legend."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "QdmAYUFGjN9EBK6L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.778, 0.64, 0.813, 0.66]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.9%\"."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "r2psVWDDzE89HnTO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legends in the chart correspond to {the slice}[0.197, 0.272, 0.456, 0.645]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend \"USA\"}<r>[0.948, 0.071, 0.976, 0.091]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "Yw5gp7GWf5gKpNTo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any text located near the {the slice}[0.365, 0.553, 0.665, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, there is the text {\"19.9%\"}<r>[0.525, 0.946, 0.56, 0.966]</r> near this slice."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "V9YQ1JjcBjBeGPls",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slices in the pie chart do {the country \"USA\"}[0.948, 0.071, 0.976, 0.091] and {the country \"China\"}<r>[0.948, 0.101, 0.987, 0.121]</r> correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legends \"USA\" corresponds to the slices [0.197, 0.272, 0.456, 0.645], [0.365, 0.553, 0.665, 0.923] and [0.502, 0.148, 0.756, 0.492]. The legend \"China\" corresponds to the slices [0.221, 0.541, 0.462, 0.868], [0.506, 0.456, 0.765, 0.794] and [0.277, 0.11, 0.571, 0.48]."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "DuJNaIkX9SNjgOcw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bounded by coordinates [0.221, 0.541, 0.462, 0.868]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"11.4%\" [0.215, 0.802, 0.25, 0.822] and the text \"2010\" [0.324, 0.679, 0.368, 0.699]. It is in the upper left quadrant of the pie chart, representing a section of the data for the year 2010. The region seems to correspond to the slice representing China's GDP for 2010."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "idZTDkkg7IH9p7Iu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.506, 0.456, 0.765, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has the text \"15.9%\" [0.778, 0.64, 0.813, 0.66] and the text \"2015\" [0.633, 0.584, 0.677, 0.604], appearing to represent a chart slice related to the GDP data for the year 2015."
+            }
+        ],
+        "id_raw": 5
+    },
+    {
+        "id": "JCfqypPT0xeomNmt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at the coordinates [0.311, 0.021, 0.652, 0.047] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot_0 subtitle, named \"E-commerce and Retail Industry Analysis\"."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "Jpg7JAHSEA2tpOSp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the location [0.223, 0.516, 0.482, 0.816] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Tuesday\"."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "VEtI7TxF5RS6hXLk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.862, 0.063, 0.993, 0.279] represent in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot_0 legend."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "zdSmqPVn78Z6h73g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie_label items are adjacent to the label {Monday}[0.907, 0.071, 0.962, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {Tuesday}[0.907, 0.101, 0.963, 0.121], and {Sunday}[0.907, 0.251, 0.959, 0.271] are adjacent."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "CjnOKayXNpkfGiro",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich elements are present near the {subtitle}[0.311, 0.021, 0.652, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart elements pie_0 and pie_1 are located near the subtitle."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "thSRphU7jTwaIq61",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat correlates with {the \"Saturday\" label}[0.907, 0.221, 0.97, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.48, 0.113, 0.715, 0.483] correlates with the \"Saturday\" label."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "43WSVuuDdd6YeSho",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the region [0.198, 0.113, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses multiple pie slices including \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" and \"Sunday\", each identified with different colors and percentage labels."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "HpY6VDaV08fi6NqU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.862, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region incorporates the plot legend, including labels for \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", and \"Sunday\", each accompanied by a different color marker."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "jMXA6jStTOOaXtFT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you depict the nature of the region [0.568, 0.371, 0.663, 0.619]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice related to \"Friday\", identified with the color '#c2c2f0' and the text \"16.2%\"."
+            }
+        ],
+        "id_raw": 8
+    },
+    {
+        "id": "VhYVZQPzRtWPE81y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the title of the chart at coordinates [0.363, 0.021, 0.6, 0.047] denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is denoting \"Population of Top 5 US Cities\"."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "jXWSCohbIirgLEzG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object placed at coordinates [0.905, 0.071, 0.968, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text with label \"New York\"."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "bGtpAN8ktS0CcjLv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.724, 0.228, 0.78, 0.248] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the percentage text \"14.2%\"."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "4xkoVfTt1VyClGew",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart refers to the {legend item}[0.905, 0.071, 0.968, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with {coordinates}[0.202, 0.301, 0.583, 0.909] refers to the legend item \"New York\"."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "Ou8NURuIQkOJ52lT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the pie slice}[0.502, 0.16, 0.754, 0.493] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the {legend item \"Chicago\"}[0.905, 0.131, 0.961, 0.151]."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "yvmGTkBWRtRQgvQw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the pie slice}[0.202, 0.301, 0.583, 0.909] and {the legend item}[0.905, 0.071, 0.968, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the legend item \"New York\"."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "Nscmrnaq6jJ10sBa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.202, 0.301, 0.583, 0.909] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region belongs to the pie slice of \"New York\" colored in red, with a label \"New York\" [0.319, 0.669, 0.363, 0.689] and percentage text \"43.9%\" [0.176, 0.785, 0.24, 0.805]."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "WD6bc7DDZ4P7T3ef",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region defined by [0.404, 0.109, 0.596, 0.479] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the pie slice of \"Houston\", labeled \"Houston\" [0.472, 0.248, 0.516, 0.268] and colored in peach with percentage \"12.1%\" [0.503, 0.063, 0.561, 0.083]."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "fL5sgcOg60tOn61C",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.502, 0.16, 0.754, 0.493] in the chart consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises pie slice of \"Chicago\" colored in green, with label \"Chicago\" [0.601, 0.344, 0.645, 0.364] and percentage text \"14.2%\" [0.724, 0.228, 0.78, 0.248]."
+            }
+        ],
+        "id_raw": 10
+    },
+    {
+        "id": "oqNU7VvJxYSuE81H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at [0.253, 0.021, 0.71, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title is 'Activity Levels of Different Age Groups on Social Media'."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "mta4p45wWN3usgvO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.889, 0.071, 0.946, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled 'Children'."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "alj0mY9Ey8oVFPG1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.176, 0.304, 0.234, 0.324]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"5.7%\"."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "TkLepHKLItgfJPI8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {the slice}[0.223, 0.393, 0.482, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"Teenagers\"}[0.889, 0.101, 0.959, 0.121] corresponds to this slice."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "V1e1yv03Lr3uv2KB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the nearest slice to {the slice}[0.293, 0.551, 0.749, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.169, 0.741, 0.531] is the nearest one."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "pIwyIfcMr9I1Jw1K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does {the legend item \"Elderly\"}[0.889, 0.221, 0.936, 0.241] corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.293, 0.194, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "Rq0tU0IaEpAcLL1Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.749, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the entire pie chart, consisting of slices labelled as Children [0.237, 0.278, 0.482, 0.516], Teenagers [0.223, 0.393, 0.482, 0.757], and Young Adults [0.293, 0.551, 0.749, 0.921]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "3btHuZ1dIF9opNbA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.354, 0.146, 0.571, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, we have a slice labeled {Senior Citizen}[0.354, 0.146, 0.571, 0.516] and its corresponding percentage text {13.8%}[0.36, 0.1, 0.459, 0.12]."
+            }
+        ],
+        "id_raw": 13
+    },
+    {
+        "id": "kXwzSt5O0q9kYA0Z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.786, 0.3, 0.841, 0.32] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"63.7%\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "At1rPFVUQK3ghSLW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.878, 0.073, 0.905, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the handle of the legend item \"Facebook\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "sD6ZWIOvKsAXq0s9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's the object written at the coordinates [0.647, 0.379, 0.691, 0.399]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text displaying \"Youtube\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "rpRcO8660p35ACih",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the slice at location {the slice}[0.198, 0.266, 0.457, 0.512] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color \"#ff9999\" in the legend, which is for \"Facebook\"."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "SuBDikVAAupZRdOG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are corresponding to {the slice}[0.33, 0.114, 0.788, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The labels}[\"63.7%\", \"Youtube\"]<r>[[0.786, 0.3, 0.841, 0.32],[0.647, 0.379, 0.691, 0.399]]</r> correspond to the slice."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "zWiJXAsGm7PQttS6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which social media platform does the coordinate {the slice}[0.254, 0.543, 0.464, 0.859] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the platform \"Instagram\" in the legend."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "vUFbWJn8a5IDcZs3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between the coordinates [0.198, 0.266, 0.788, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is mainly occupied by a pie chart representation of the User distribution among different social media platforms. It contains text labels for percentages related to each platform's User distribution and the platform name itself."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "BOUBDzSJzMppyIO9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within coordinates [0.872, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The elements are legend items for the social media platforms Facebook, Twitter, Instagram, LinkedIn, Pinterest, Snapchat, and Youtube, each with its corresponding color."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "DnZPnMugaNmsyzUN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire chart. It contains a pie chart displaying the User distribution among social media platforms. It has the title \"Social Media Platform User Distribution\", and a legend on the right explaining the individual colors of the pie slices."
+            }
+        ],
+        "id_raw": 14
+    },
+    {
+        "id": "0Z2gUuLxSfOYZe6I",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.218, 0.021, 0.745, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which reads \"Transportation and Logistics Industry Trends from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "vbIwX0wFfGivMT7E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "uhYA16CIbmHZB9pH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.223, 0.258, 0.46, 0.496] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice representing the year \"2016\"."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "fvbFuunvNbTXab3N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice with {the coordinates}[0.312, 0.552, 0.514, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item labeled \"2016\" in the legend <r>[0.952, 0.221, 0.987, 0.241]</r> corresponds to this slice."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "1BDuvdqebAjEr8ig",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is located adjacent to {the slice}[0.507, 0.377, 0.767, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice <r>[0.498, 0.123, 0.737, 0.487]</r> is adjacent, representing the year \"2019\"."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "gIBpWM2Ojiduq8gx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label in the legend corresponds to the pie slice with the largest percentage share?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"2020\" in the legend <r>[0.952, 0.34, 0.987, 0.36]</r> corresponds to the slice with the largest percentage share."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "JM1xQPmTy605yDdg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.111, 0.767, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entirety of the pie chart. Different pie slices populate the region, with labels indicating categories and percentages. Each slice represents a year from 2016 to 2020 and corresponds to different color codes listed in the legend."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "qaOLHxrq6bLknsZM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.497, 0.123, 0.737, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice <r>[0.498, 0.123, 0.737, 0.487]</r> representing the year \"2019\", and the label texts and corresponding values inside it show the sector's performance in the year 2019."
+            }
+        ],
+        "id_raw": 15
+    },
+    {
+        "id": "UFlo6jHHD6px7eae",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is in the location [0.283, 0.122, 0.325, 0.142] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"11.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "8CsGc10GnERVyiEu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.309, 0.021, 0.654, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Media and Entertainment Industry Trends\" occupies this space."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "6deh86fpa4oQFxZX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is contained within the coordinates [0.92, 0.191, 0.962, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Books\" can be found within these coordinates."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "znxPVtPPwV8lNtjb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to {the slice}[0.504, 0.174, 0.763, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Music\"<r>[0.92, 0.131, 0.96, 0.151]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "UjvPyCaiABkvddR1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the {legend item \"Books\"}[0.92, 0.191, 0.962, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice<r>[0.27, 0.12, 0.469, 0.484]</r> is associated with the \"Books\" legend item."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "Uq00dDBrKsSefhjV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to {the slice}[0.439, 0.109, 0.612, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Games\"<r>[0.489, 0.25, 0.533, 0.27]</r> is the closest."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "mIYmhfolR5Ytn0n0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.301, 0.55, 0.75, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a significant portion of the pie chart depicting the entertainment sector trend. In this region, there's a pie slice whose label is \"TV Shows\"<r>[0.525, 0.747, 0.569, 0.767]</r> with percentage text \"38.0%\"<r>[0.594, 0.92, 0.661, 0.94]</r>."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "YMAXVvEpXQvGdZ8A",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found in the region [0.197, 0.279, 0.456, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a slice of pie chart with the text \"Movies\"<r>[0.278, 0.512, 0.322, 0.532]</r>, and its associated percentage \"23.0%\"<r>[0.123, 0.517, 0.171, 0.537]</r>."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "WQgKM6ATcAkSRYNv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat entities are included in the region described by [0.439, 0.109, 0.612, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises a slice of the pie chart denoting the category \"Games\"<r>[0.489, 0.25, 0.533, 0.27]</r> with a percentage value of \"11.0%\"<r>[0.532, 0.068, 0.581, 0.088]</r>."
+            }
+        ],
+        "id_raw": 16
+    },
+    {
+        "id": "R0CRZqzuBaWGOQZ2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.872, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text that says \"Pharmaceuticals\" in the legend."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "IUFvnxtdLyDEsqjy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.299, 0.021, 0.664, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, which reads \"Industry Market Share Comparison for 2020\"."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "6D3sSuNxPZgRhGhD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.197, 0.278, 0.456, 0.75]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice representing the \"Automobile\" sector."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "v4udnjeljX3kqutg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects in the legend correspond to {the slice}[0.507, 0.226, 0.766, 0.69]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"E-commerce\"}[0.872, 0.131, 0.959, 0.151] corresponds to the slice."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "skaMDB3XQccddI4U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item at [0.872, 0.071, 0.952, 0.091] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {\"Automobile\"}[0.197, 0.278, 0.456, 0.75] slice in the chart."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "46MZjOxq53LIrVYi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are inside {the pie}[0.197, 0.109, 0.766, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects inside the pie are slices with texts {\"Automobile\"}[0.197, 0.278, 0.456, 0.75], {\"Pharmaceuticals\"}[0.283, 0.553, 0.71, 0.923], {\"E-commerce\"}[0.507, 0.226, 0.766, 0.69], and {\"Real Estate\"}[0.281, 0.109, 0.646, 0.479]."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "WTvTsE4cKMFm0kxv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the quadrant of the chart with coordinates [0.197, 0.109, 0.5, 0.5]."
+            },
+            {
+                "from": "gpt",
+                "value": "The quadrant includes parts of the sectors {\"Automobile\"}[0.197, 0.278, 0.456, 0.75] and {\"Real Estate\"}[0.281, 0.109, 0.646, 0.479]."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "92XLE9YXcRapHQ3R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region defined by [0.5, 0.5, 0.766, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes parts of the sectors {\"Pharmaceuticals\"}[0.283, 0.553, 0.71, 0.923] and {\"E-commerce\"}[0.507, 0.226, 0.766, 0.69]."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "TpL1qmeLH3hfLqXm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat resides in the region defined by coordinates [0.767, 0.107, 1.0, 1.0]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend with items {\"Automobile\"}[0.872, 0.071, 0.952, 0.091], {\"Pharmaceuticals\"}[0.872, 0.101, 0.987, 0.121], {\"E-commerce\"}[0.872, 0.131, 0.959, 0.151], and {\"Real Estate\"}[0.872, 0.161, 0.951, 0.181]."
+            }
+        ],
+        "id_raw": 17
+    },
+    {
+        "id": "R9aLfCwfDvVYfY2M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.25, 0.021, 0.713, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, titled CO2 Emission to Oxygen Level Ratio from 2000 to 2007."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "BmA0Nqx5ym0bYrjK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.103, 0.941, 0.117] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color marker in the legend for the year 2001."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "4AqVmnCY39MHIw4g",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.325, 0.915, 0.36, 0.935] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.2%\"."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "Vu1Za6uYZ7mEWFbI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the year 2000 marker}[0.913, 0.073, 0.941, 0.087] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.198, 0.266, 0.457, 0.517] corresponds to the year 2000 marker in the legend."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "7LQegANUoULXLkxb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend marker corresponds to {the slice with the label 2005}[0.506, 0.239, 0.765, 0.506]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The marker {\"2005\"}[0.913, 0.223, 0.941, 0.237] in the legend corresponds to the slice with the label 2005."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "d9AnGBO9JRdhOV8L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does {the slice}[0.272, 0.113, 0.47, 0.483] represent and which year does it correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents 13.3% and it corresponds to the year {\"2007\"}[0.913, 0.282, 0.941, 0.296] in the legend."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "QimJ1mNFdkaWN2r2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.198, 0.111, 0.765, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the slices of the pie chart along with their respective labels and percentages, and is the largest visual depiction in this chart. Each slice represents CO2 Emission to Oxygen Level Ratio for a specific year from 2000 to 2007."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "EYEDXPXHDV38JA4U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region between coordinates [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. There are eight legend markers ranging from years 2000 to 2007, each associated with a different color and corresponding to a different slice in the pie chart."
+            }
+        ],
+        "id_raw": 18
+    },
+    {
+        "id": "KVqHEs3wEx4mByuR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.322, 0.021, 0.642, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labeled as \"Food and Agriculture Industry Analysis\"."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "2BuaW3wpj8ybdXTu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object at the coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That coordinate represents an empty space with no object."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "27TispIiTZsVL9ED",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.895, 0.131, 0.933, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Dairy\" is located there."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "4FhhvJxybCVz11Q0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item in the chart corresponds to {the slice}[0.507, 0.38, 0.767, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\u201cDairy\u201d}[0.895, 0.131, 0.933, 0.151] corresponds to that slice."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "mam8SEmg4RbDIOCD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does {the legend item \"Fruits\"}[0.895, 0.281, 0.933, 0.301] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {\u201cFruits\u201d}[0.306, 0.155, 0.468, 0.485] in the pie chart corresponds to the legend item \"Fruits\"."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "2FYr1wQTWmrGlY11",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich neighboring slices are adjacent to {the slice}[0.197, 0.285, 0.456, 0.851] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices adjacent are {\u201cOrganic Food\u201d}[0.264, 0.243, 0.462, 0.492] and {\u201cRed Meat\u201d}[0.368, 0.551, 0.715, 0.922]."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "qlT16VeLIJx4BvzU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region demarcated by the coordinates [0.851, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the items in the chart's legend. They are \"Poultry\", \"Red Meat\", \"Dairy\", \"Vegan\", \"Cereal\", \"Seafood\", \"Junk Food\", \"Fruits\", \"Vegetables\", and \"Organic Food\"."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "BoJiERqNQMZmfKU8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the area designated by [0.197, 0.109, 0.767, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the pie chart, including all the different slices each representing a different food item and their corresponding labels and percentages."
+            }
+        ],
+        "id_raw": 21
+    },
+    {
+        "id": "5U2OwON1qGETSDNk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart's main title with coordinates [0.185, 0.021, 0.778, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Funding for Scientific Research & Technological Innovation (2014-2020)\"."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "gdv29K173mqm6Za8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.223, 0.941, 0.237] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend's color box for the year 2019."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "a8VBEwPf0vs2KKnO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.646, 0.883, 0.682, 0.903] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"15.1%\" for the slice representing the year 2017."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "H2oCMqVHgifLWSoZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich other slices in the pie chart are adjacent to the slice for {the year 2015}[0.198, 0.527, 0.457, 0.782]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices for {the years 2014 and 2016}[[0.198, 0.265, 0.457, 0.503], [0.283, 0.55, 0.494, 0.921]] are adjacent."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "FqNwhPdVSbym8nZp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts appear within the slice for {the year 2020}[0.274, 0.111, 0.508, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"16.1%\"[0.335, 0.091, 0.37, 0.111] and the text \"2020\"[0.395, 0.264, 0.439, 0.284] appear within the slice for the year 2020."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "Hx3cqPn8ItZAweJm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the year 2017's slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2017\"[0.952, 0.161, 0.987, 0.181] with color marker [0.913, 0.163, 0.941, 0.177] corresponds to the year 2017's slice."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "Ki4GIqNOHxB1ndVL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.283, 0.55, 0.494, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice for the year 2016[0.283, 0.55, 0.494, 0.921] with the autotext \"14.4% \"[0.335, 0.92, 0.37, 0.94] and the text \"2016\" [0.394, 0.748, 0.438, 0.768]. The color of the slice corresponds to the color marker for 2016 in the legend."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "DVaMZPGQ5pO4chaN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.274, 0.111, 0.508, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice for the year 2020[0.274, 0.111, 0.508, 0.481], with the autotext \"16.1% \"[0.335, 0.091, 0.37, 0.111] and the text \"2020\" [0.395, 0.264, 0.439, 0.284]."
+            }
+        ],
+        "id_raw": 26
+    },
+    {
+        "id": "W0DMfbd568QYAVCM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type that is represented in the structured information?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart"
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "bNUAvyxI41v4cddY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object located at the coordinates [0.248, 0.021, 0.716, 0.047] show?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart: \"Growth in Biotech and AI&ML sectors from 2011 to 2019\"."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "dqcuLoCdITPGcJuF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content forms the object located at the coordinates [0.908, 0.063, 0.993, 0.338] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend, consisting of the sector labels, from 2011 to 2019."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "h3H7F6xl8TYwIpoM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a relationship between the caption '2011' at coordinates [0.304, 0.383, 0.339, 0.403] and the slice of pie at coordinates [0.21, 0.261, 0.459, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the caption '2011' corresponds to the slice of the pie at the cited coordinates."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "pMyMLvaqp27KCDP5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend caption '2013' at coordinates [0.304, 0.628, 0.339, 0.648] and the slice of the pie at coordinates [0.206, 0.533, 0.459, 0.79]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend caption '2013' corresponds to the slice of the pie at the cited coordinates."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "lOjShxdnf5BpC8p1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with the year 2011 in the legend located at coordinates [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the year 2011 is '#ff9999'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "AAmI6b7Jhl539GCb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify and describe the elements within the region [0.206, 0.261, 0.459, 0.498]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region there is a slice of the pie chart. The slice represents the year 2011 and comprises 6.5% of the chart. The color of the slice is '#ff9999'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "dUumk8ftlw9iwqsS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.197, 0.41, 0.456, 0.593]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart which represents the year 2012 and accounts for 8.0% of the chart. The color of the slice is '#ff6655'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "5HKTNo7dUzhxqbU2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.206, 0.533, 0.459, 0.79]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region there is a slice of the pie corresponding to the year 2013. It takes up 8.8% of the chart and its color is '#ff3311'."
+            }
+        ],
+        "id_raw": 28
+    },
+    {
+        "id": "WGtOylMIYG9eSCzb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart situated at [0.301, 0.021, 0.674, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is 'Future Technological Development Prospects'."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "ox0odybi9y7bk53E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the coordinates [0.203, 0.109, 0.769, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A Pie chart representing different sections such as Artificial Intelligence, Blockchain, Cloud Computing, Machine Learning, and Data Science."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "la7VmlHVbrqoxdZ5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.806, 0.063, 0.998, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart with details of each section of the pie."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "mud5pcMqfQc1dF8D",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is near to {the pie slice}[0.509, 0.536, 0.768, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Cloud Computing\"}[0.617, 0.647, 0.661, 0.667] is near to the pie slice."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "mBMoG6NgpyTQ69UG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of pie is associated with {the legend label \"Blockchain\"}[0.85, 0.101, 0.926, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Blockchain\" is at coordinates [0.298, 0.552, 0.598, 0.923]."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "gjP0zbSW5lAvoYhM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich are the other labels located near {the label 'Data Science'}[0.85, 0.191, 0.942, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels 'Machine Learning' [0.85, 0.161, 0.973, 0.181] and 'Cloud Computing' [0.85, 0.131, 0.971, 0.151] are located near 'Data Science' in the legend."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "A8nqJdxJrifwnh9p",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.203, 0.109, 0.769, 0.923] of the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart which has different sections including \"Artificial Intelligence\"[0.203, 0.19, 0.462, 0.77], \"Blockchain\"[0.298, 0.552, 0.598, 0.923], \"Cloud Computing\"[0.509, 0.536, 0.768, 0.868], \"Machine Learning\"[0.51, 0.169, 0.769, 0.513], and \"Data Science\"[0.357, 0.109, 0.607, 0.479]. Each slice of the pie chart is labeled with its respective field along with its percentage."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "5ukLsyrVQRz8dP9Q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are found in the region [0.806, 0.063, 0.998, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with labels 'Artificial Intelligence'[0.85, 0.071, 0.993, 0.091], 'Blockchain'[0.85, 0.101, 0.926, 0.121], 'Cloud Computing'[0.85, 0.131, 0.971, 0.151], 'Machine Learning'[0.85, 0.161, 0.973, 0.181], and 'Data Science'[0.85, 0.191, 0.942, 0.211], each labeled with its respective color."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "2t5y5y3AXkYof2ef",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features in the region [0.301, 0.021, 0.674, 0.047]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's title 'Future Technological Development Prospects'."
+            }
+        ],
+        "id_raw": 29
+    },
+    {
+        "id": "G99RAxjIEJ5O7g7M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.912, 0.071, 0.925, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at these coordinates is \"AI\"."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "3xu6urythe04CzGF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.209, 0.752, 0.223, 0.772] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at these coordinates is \"40.9%\"."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "M3SesYAhjm8VRpp8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.28, 0.021, 0.683, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object at these coordinates is the subtitle of the chart, \"Tech Innovation and Intellectual Property Counts\"."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "PRyY0WtEtus1wEFr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice of pie adjacent to the slice with coordinates [0.498, 0.544, 0.731, 0.897]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slice with coordinates [0.506, 0.153, 0.765, 0.666] is coloured \"#99ff99\", representing the \"ML\" category."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "Kc9FE35wC5ExHQUI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend item at [0.912, 0.071, 0.925, 0.091] correlate to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"AI\" at these coordinates corresponds to the slice at [0.201, 0.299, 0.537, 0.907] in the pie chart."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "OqxZcM9RVbfMWm9z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the legend item {Blockchain}<r>[0.912, 0.191, 0.987, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to the Blockchain legend item is located at coordinates [0.265, 0.195, 0.464, 0.489]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "3p7zu8GzK2cTRacz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.498, 0.544, 0.731, 0.897]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates contains a slice of the pie chart in color \"#66b3ff\", representing the category \"IoT\" with a share of \"12.9%\", the percentage text is located at [0.683, 0.844, 0.704, 0.864] and the heading text is located at [0.577, 0.703, 0.621, 0.723]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "x6VPRIpyFj3A0HLb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.265, 0.195, 0.464, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart representing the category \"Blockchain\" with a share of \"3.5%\". The percentage text is located at [0.192, 0.185, 0.267, 0.205] and the heading text is located at [0.339, 0.318, 0.374, 0.338]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "7K8C7oK95tMZURwb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region at [0.321, 0.109, 0.563, 0.479]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates contains a slice of the pie chart in color \"#ffcc99\", representing the category \"VR\" and accounting for \"15.7%\" of the total, with percentage text at [0.414, 0.067, 0.433, 0.087] and heading text at [0.431, 0.25, 0.475, 0.27]."
+            }
+        ],
+        "id_raw": 30
+    },
+    {
+        "id": "AWrQmWjZA6t9XHBG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which chart's object do these coordinates [0.191, 0.021, 0.584, 0.047] belong in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates belong to the subtitle, \"Social Media and Digital Communication Usage\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "MvWLN7irAIvrtHw5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object positioned at this location [0.9, 0.071, 0.946, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object located at this position is the label \"Twitter\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "OkUHg7pxMPyCJpJJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what is situated at coordinates [0.689, 0.675, 0.759, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object situated at these coordinates is the text \"24.9%\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "HpYtNdxClMz2FFsO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are adjacent to the slice {the slice}[0.413, 0.378, 0.682, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"24.9%\"}[0.689, 0.675, 0.759, 0.695] and {\"Instagram\"}[0.604, 0.64, 0.648, 0.66] are adjacent to the slice."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "8olBa5nQnNO9rVNq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correspondence between legend labels and pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend labels \"Twitter\", \"Facebook\", \"Whatsapp\", \"Instagram\", and \"Snapchat\" correspond to the pie slices with coordinates [0.186, 0.095, 0.378, 0.48], [0.092, 0.239, 0.361, 0.694], [0.143, 0.552, 0.488, 0.937], [0.413, 0.378, 0.682, 0.882], and [0.403, 0.099, 0.65, 0.483] respectively."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "FdkgvIF5tjmqiZKT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice with coordinates [0.092, 0.239, 0.361, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Facebook\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "oT7atLdI0JMHjLVv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items in the region with the following boundaries [0.413, 0.378, 0.682, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the slice \"Instagram\", with its color code in teal, accompanied by the numerical percentage \"24.9%\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "rodJ3BYch7huJcVs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the composition of the part of the image delineated by [0.143, 0.552, 0.488, 0.937] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises of the slice labeled \"Whatsapp\", in a green color shade, along with the percentage \"23.7%\"."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "kzhxzxQFykbLip5r",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area outlined by the coordinates [0.186, 0.095, 0.378, 0.48]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a pie slice representing \"Twitter\" highlighted in a shade of pink, along with the tag \"12.6%\" symbolizing the respective percentage."
+            }
+        ],
+        "id_raw": 31
+    },
+    {
+        "id": "6SApRYVCEBeJQQPS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the main caption of the graph is with the coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the graph is \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "xmUZ4XQ0BRyZHDn4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the positions [0.891, 0.103, 0.919, 0.117] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is a legend symbol for \"French\"."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "jsYo0GJ1aw8nBj9F",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is at the positions [0.676, 0.852, 0.732, 0.872] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"21.2%\" is at those coordinates."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "aGRW73CfzdB7xwx1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which slices are adjacent to {the slice}<r>[0.505, 0.154, 0.764, 0.582]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}<r>[0.498, 0.545, 0.75, 0.915]</r> and <r>[0.277, 0.11, 0.57, 0.48]</r> are adjacent to it."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "CYo9a9Ysp7sF9Czc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend label corresponds to {the slice}<r>[0.197, 0.11, 0.764, 0.918]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Italian\"<r>[0.93, 0.071, 0.973, 0.091]</r> corresponds to that slice."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "2vosHrWbE9HMKFVn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text at coordinates [0.362, 0.076, 0.405, 0.096] and the region at coordinates [0.277, 0.11, 0.57, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"19.8%\" is the percentage representation of the \"Indian\" pie chart slice located at those coordinates."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "Kg3QiH1SYrlaFjuZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates of [0.197, 0.275, 0.456, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a pie slice labeled \"Italian\". It contains a label with the text \"Italian\" located at [0.279, 0.482, 0.323, 0.502] and a percentage label \"19.3%\" at [0.129, 0.465, 0.172, 0.485]. The slice is colored #ff9999 which, according to the legend, represents Italian cuisine."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "YXnG8UQsrQCQnJQP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's in the region [0.505, 0.154, 0.764, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart which is labeled \"Mexican\". It also contains the text \"Mexican\"<r>[0.62, 0.385, 0.664, 0.405]</r> and the percentage label \"22.7%\"<r>[0.757, 0.299, 0.814, 0.319]</r>."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "bb0rqTNIs47BZ2SP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the region [0.498, 0.545, 0.75, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area captures a slice of the pie chart colored \"#99ff99\". It represents Chinese food, identified by the label \"Chinese\" <r>[0.573, 0.708, 0.617, 0.728]</r> as well as the percentage associated with it, \"21.2%\"<r>[0.676, 0.852, 0.732, 0.872]</r>."
+            }
+        ],
+        "id_raw": 34
+    },
+    {
+        "id": "ccrwwuj3FY8uUejF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the chart given by the coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "AI Application Case Analysis (2016-2021)."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "N4GEUobXeSuo0qkv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.838, 0.073, 0.865, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend handle for AI in Healthcare."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "vyLu9Lj6oVKdMNDJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the chart coordinate [0.659, 0.142, 0.76, 0.162]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text representing percentage \"47.2%\"."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "jLIcBQnHXvQXDyvx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are in close proximity to {the 'AI in Healthcare' label}[0.877, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {legend handle} [0.838, 0.073, 0.865, 0.087] of the same legend item is close."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "MC1TA5SGUUBiCfRW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector does {the text '47.2%'}[0.659, 0.142, 0.76, 0.162] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector labeled as \"AI in Transport\"."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "he7gskSKRW1PneE9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {handle}[0.838, 0.073, 0.865, 0.087] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice with coordinates [0.208, 0.308, 0.692, 0.916] in the pie chart."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "7LQQiLerftiQtanm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.208, 0.308, 0.692, 0.916]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a sector of the pie chart, specifically the 'AI in Healthcare'. The sector is colored '#ff9999', occupies 52.8% of the pie chart, and is exploded for emphasis."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "6fMNTZhzi4Z4qw9O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region of [0.298, 0.115, 0.756, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A piece of the pie chart labeled \"AI in Transport\" is within this region, alongside the text \"47.2%\" which represents the percentage occupied by this piece in the chart."
+            }
+        ],
+        "id_raw": 35
+    },
+    {
+        "id": "b6h0SbBXWMGWRSiJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object located at coordinates [0.872, 0.161, 0.965, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"Liquidity Risk\"."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "JIwqjvnXlnJNwk1m",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is given at the coordinates [0.337, 0.021, 0.626, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart: \"Expense Distribution per Risk Type\"."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "K7lB9PHCVvD1pQGC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.321, 0.072, 0.415, 0.092] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text for the pie slice percentage: \"20.9%\"."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "7sGsHZoVIyBZYUCi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice adjacent to the {slice}[0.316, 0.516, 0.626, 0.886] according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the adjacent pie slice is \"#99ff99\", representing the \"Operational Risk\" category."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "XFc2hu32WhKNyIka",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.482, 0.181, 0.741, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Liquidity Risk\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "htB88NhB7ocWOSjc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to the legend item located at [0.872, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice denoting \"Operational Risk\" corresponds to the legend item located at those coordinates."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "Tw89xL2AvMURVQxR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in the chart within the coordinates [0.586, 0.11, 0.741, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a sector of the pie chart coloured in \"#ffcc99\" and the associated percentage text \"18.0%\"[0.722, 0.287, 0.815, 0.307]. It represents the \"Liquidity Risk\" segment of the pie chart."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "pYKeAuz8kqX4J2Ng",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the region [0.197, 0.281, 0.456, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, we have the \"Credit Risk\" pie slice, which is specially exploded for emphasis. This region also includes corresponding percentage text \"25.1%\"[0.097, 0.545, 0.172, 0.565]."
+            }
+        ],
+        "id_raw": 37
+    },
+    {
+        "id": "5sI4LauQH7JpkMFI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot represents the index item \"{Efficiency Improvement Potential Assessment}\"<r>[0.291, 0.021, 0.672, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the subplot_0 represents the index item."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "IJGUFwyFgb0A4RoI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at <r>[0.906, 0.071, 0.938, 0.091]</r> on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"High\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "YQPnsuMObNGo3r5J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates <r>[0.147, 0.407, 0.179, 0.427]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.1%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "nSvCDla4ljeFTwzG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice associated with the {medium category}<r>[0.217, 0.547, 0.517, 0.917]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice corresponding to the medium category is #66b3ff."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "DFnpmQK0P4dZYRJA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text in the bar graph is located closest to the {pie slice for New}<r>[0.419, 0.109, 0.478, 0.479]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"3.0%\"}<r>[0.41, 0.066, 0.44, 0.086]</r> is located closest to the pie slice for New."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "tYzCPwvYUavcEgKi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the legend item {\"Exceptional\"}<r>[0.906, 0.251, 0.987, 0.271]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Exceptional\" refers to the pie slice with coordinates [0.284, 0.123, 0.469, 0.484]."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "JRvWaSt1jhsZfnuM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates <r>[0.862, 0.063, 0.993, 0.309]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region within these coordinates contains the legend of the pie chart. It contains labels for each kind of efficiency improvement potential assessment such as \"High\", \"Medium\", \"Low\", \"Uknown\", \"Pending\", \"New\", \"Exceptional\", and \"Discarded\" with corresponding color squares."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "KeZGXKl02c1tAmTR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region defined by coordinates <r>[0.197, 0.109, 0.763, 0.917]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains eight pie chart slices. Each slice represents a category of efficiency improvement potential assessment, such as \"High\", \"Medium\", \"Low\", \"Unknown\", \"Pending\", \"New\", \"Exceptional\", and \"Discarded\". It also includes percentage labels for each slice."
+            }
+        ],
+        "id_raw": 38
+    },
+    {
+        "id": "bXmgdQe0TxOoOfB3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the label with the coordinates [0.872, 0.101, 0.925, 0.121] describes?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at these coordinates is \"Biotech\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "gun2WG8wIg0SDeBP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the text at coordinates [0.623, 0.618, 0.667, 0.638] provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Pharmaceuticals\" is provided at these coordinates."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "fxECjnHZvICYpg2v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.303, 0.021, 0.66, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot's subtitle, \"Medical and Biotechnology Industry Trends\", is located at these coordinates."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "5y8lcvZvDXjhj37Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice represented by \"{Biotech}\"[0.872, 0.101, 0.925, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to \"Biotech\" is represented as the color \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "fHx4fCvrNTeWNBvh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with the \"{Nanotechnology}\"[0.872, 0.071, 0.986, 0.091] slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage associated with the \"Nanotechnology\" slice is \"26.0%\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "BdWUUmbeeYg9OPxu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of slice is adjacent to the \"{Life Sciences}\"[0.269, 0.125, 0.468, 0.485] slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice \"{Healthcare}\"[0.423, 0.109, 0.626, 0.479] is adjacent to \"Life Sciences\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "xGI9ms5oQ6kibPNW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see in the region defined by the coordinates [0.505, 0.511, 0.764, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can see a pie slice represented as \"Pharmaceuticals\" [0.623, 0.618, 0.667, 0.638] with a percentage of \"16%\". The color assigned to this slice is \"#99ff99\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "ciJFxpRL5yCr9yla",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region within the coordinates [0.197, 0.109, 0.764, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region incorporates the entire pie chart consisting of six pie slices labeled \"Nanotechnology\", \"Biotech\", \"Pharmaceuticals\", \"Medical Device\", \"Healthcare\", and \"Life Sciences\", each with different assigned colors and percentages."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "J9hMg7DH1oUKQczh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are shown in the region [0.328, 0.553, 0.632, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region features a pie slice stands for \"Biotech\" [0.458, 0.765, 0.502, 0.785] with a percentage of \"20%\" [0.427, 0.95, 0.479, 0.97]. The color of this slice is \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 39
+    },
+    {
+        "id": "xpqDMlCb7MNqZJoZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at the coordinates [0.955, 0.071, 0.984, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"CEO\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "YGFkQ8O32lI2b1ZC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.916, 0.163, 0.944, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend color indicator for the \"CMO\"."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "sKAXLCUJz9N6vY7g",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.376, 0.936, 0.405, 0.956]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"24.3%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "xIWEUPOAMjDFtPIC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between the text \"CTO\" [0.619, 0.63, 0.663, 0.65] and the color handle at coordinates [0.916, 0.133, 0.944, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's no direct relationship. The color handle at [0.916, 0.133, 0.944, 0.147] corresponds to the \"CTO\" slice in the pie chart, but it's not directly linked to the text \"CTO\" at [0.619, 0.63, 0.663, 0.65]."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "9OFVNiA14F9g78iJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the pie chart does the color coordinate [0.916, 0.193, 0.944, 0.207] corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at the mentioned coordinate corresponds to the pie slice denoting the \"COO\" role, specifically the slice at [0.274, 0.111, 0.502, 0.482]."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "cfqeN1ERZoaQQX76",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of pie chart does the percentage 24.3% [0.376, 0.936, 0.405, 0.956] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the pie slice denoting the \"CFO\" role with coordinates [0.256, 0.552, 0.603, 0.922]."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "m2ajndJkyoqringm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.504, 0.513, 0.763, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie chart with the color \"#99ff99\" representing the \"CTO\" role. The slice also includes the text \"17.7%\" [0.754, 0.719, 0.783, 0.739] denoting the percentage of the skillset attributed to the \"CTO\" role."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "8xU0ogo8nZCL3uMX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what\u2019s in the region [0.197, 0.111, 0.763, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the overall pie chart containing all slices representing various management roles and their corresponding skillset levels. Each slice of the pie chart has specific color coding and associated texts indicating the role and the percentage representation of the skillset."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "YkCkGjIWtdUTtlJb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the area within [0.256, 0.552, 0.603, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blue pie slice corresponding to the \"CFO\" role in the chart. The slice also includes an autotext \"24.3%\" [0.376, 0.936, 0.405, 0.956] indicating the skillset level for the \"CFO\" role."
+            }
+        ],
+        "id_raw": 42
+    },
+    {
+        "id": "ZDZ2t1eYGTE0cauK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.845, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart legend."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "Z3elkQ5SxfVo5A6l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object placed at coordinates [0.232, 0.021, 0.731, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, says \"Number of Patients: Heart Disease vs Diabetes (2013-2019)\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "zn3SsA4hkpAlVVRN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.635, 0.572, 0.679, 0.592] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for year \"2017\" in the pie chart."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "4IZHI2yYfG3oPELO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with the legend item {Diabetes}[0.89, 0.101, 0.951, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}[0.198, 0.512, 0.457, 0.784] corresponds to the legend item \"Diabetes\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "YCPsynVRoqvlQ555",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year label is close to the {pie slice}[0.493, 0.549, 0.687, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {\"2016\"}[0.54, 0.738, 0.584, 0.758] is close to the pie slice."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "jDqEQko0QQBN9eYG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nearest to {the text \"2017\"}[0.635, 0.572, 0.679, 0.592] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}[0.507, 0.447, 0.766, 0.771] is closest to the text \"2017\"."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "8PsbsbeKFw0lteti",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see within coordinates [0.275, 0.111, 0.541, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice representing the year {2019}[0.405, 0.259, 0.449, 0.279],\n    and a label showing {18.0%}[0.353, 0.082, 0.388, 0.102] of the total cases."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "MVhasOaDMUBI9Po1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the region [0.507, 0.447, 0.766, 0.771]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice, associated with the label {2017}[0.635, 0.572, 0.679, 0.592], \n    and shows the case count representing {15.0%}[0.782, 0.618, 0.817, 0.638] of total cases."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "F2Lv5oD9UPf6Ufxq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region [0.198, 0.512, 0.457, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice corresponding to {2014}[0.29, 0.597, 0.334, 0.617] \n    and a proportion label saying {12.8%}[0.155, 0.662, 0.191, 0.682] of the cases."
+            }
+        ],
+        "id_raw": 43
+    },
+    {
+        "id": "1OV8NQjh5jwHkXxu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title located at coordinates [0.31, 0.021, 0.653, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Global CO2 Emissions from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "GyXUpjHeDZboUoEe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text displaying the year \"2015\" in the legend."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "iglbMjw3hB4Xf9wC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.482, 0.516, 0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the center point of the pie chart."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "INaWxgHzu4Qom6oQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart has the same color as the {legend}[0.913, 0.223, 0.941, 0.237] item?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the color that matches the legend item is located at [0.49, 0.077, 0.689, 0.447] which refers to \"2020\"."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "diQAMlJS6MPTa6YC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the slice labeled {2021}[0.379, 0.274, 0.423, 0.294] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to \"2021\" in the legend is '#ff6347'."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "Pps6zcHaBTMBo3et",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich tile in the legend corresponds to the {slice}[0.503, 0.538, 0.758, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2018\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "N1dV0XZyCGhFsSZq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.368, 0.553, 0.6, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region seems to contain a part of a pie chart slice, specifically the \"2017\" slice, which is represented by the color '#99ff99'. The label \"2017\" at [0.461, 0.765, 0.505, 0.785] and the text \"14.8%\" at [0.484, 0.95, 0.519, 0.97] are in this region."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "8bK3oYK1ba5beo30",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found in the region [0.197, 0.269, 0.457, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice labeled as \"2015\" [0.284, 0.442, 0.328, 0.462] with the associated percentage of \"14.3%\" [0.145, 0.397, 0.18, 0.417]."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "TekuZzhag4QNnQ0P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.49, 0.077, 0.689, 0.447]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart representing data for the year \"2020\" [0.538, 0.232, 0.581, 0.252] with the text \"13.7%\" [0.608, 0.06, 0.643, 0.08]."
+            }
+        ],
+        "id_raw": 44
+    },
+    {
+        "id": "58QEtHKjDbVuTB6O",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.239, 0.021, 0.724, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Building and Real Estate Market Trends from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "Luu5U6QElgZIAeMa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.952, 0.071, 0.987, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2014\" is located at these coordinates in the legend."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "NBVw20MiZTHNS987",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item in the pie chart is at the coordinates [0.306, 0.643, 0.35, 0.663]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for the year 2014."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "3CVcP4n2BM0AU1Wl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to {the legend item}[0.952, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.264, 0.236, 0.462, 0.491] corresponds to the legend item."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "FWERc2J5q68KLJ9M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to {the pie slice}[0.495, 0.115, 0.715, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {2016}<r>[0.952, 0.131, 0.987, 0.151]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "jc1yxBbqVkcIRJvz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.338, 0.11, 0.493, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.495, 0.115, 0.715, 0.484] is adjacent to it."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "a51kxS7iYrRtwtiS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.201, 0.11, 0.765, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the pie chart. It contains several pie slices with labels and percentages. Each slice corresponds to a year and the percentage of building and real estate market share in that year."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "DFUApsRMf7PkrndE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the  region [0.906, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with labels for different years, each label has a color that corresponds to a slice on the pie chart."
+            }
+        ],
+        "id_raw": 45
+    },
+    {
+        "id": "sH1b2XocyHb3Fl5w",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.282, 0.021, 0.681, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which reads '2021 Growth Rate: E-commerce vs Social Media'."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "6p5RZVwHr9ik7wbV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented at coordinates [0.855, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That area represents the legend of the chart."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "4kOQK4rYSYW5OQMp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.666, 0.148, 0.754, 0.168] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the percentage text \"48.0%\"."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "LgUEtdluBJSSgTEU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the chart elements related to the {slice}[0.207, 0.308, 0.684, 0.916] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The elements related to this slice are {\"E-commerce\"}[0.352, 0.714, 0.396, 0.734] text, and the legend item \"E-commerce\" with handle coordinates [0.861, 0.073, 0.889, 0.087]."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "RsGsJAoDWfDd09Cj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are associated with the {legend}[0.855, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"E-commerce\" and \"Social Media\", found at coordinates [0.9, 0.071, 0.987, 0.091] and [0.9, 0.101, 0.987, 0.121] respectively, are associated with the legend."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "aEf0N6kORg7maSvA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is associated with the {legend item 'Social Media'}[0.9, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with 'Social Media' is located at coordinates [0.299, 0.116, 0.756, 0.687]."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "b8hb9XXvY8RouIVW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.207, 0.116, 0.756, 0.916]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two slices representing E-commerce and Social Media, with their percentages \"52.0%\" and\"48.0%\", located at [0.21, 0.863, 0.297, 0.883] and [0.666, 0.148, 0.754, 0.168] respectively. Labels for each slice, \"E-commerce\" and \"Social Media\", are also located at [0.352, 0.714, 0.396, 0.734] and [0.567, 0.297, 0.611, 0.317] respectively."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "kr6EHZL4dpU30gCO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the region [0.299, 0.116, 0.756, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the \u201cSocial Media\u201d segment of the pie chart. It includes a label reading \u201cSocial Media\u201d at [0.567, 0.297, 0.611, 0.317] and a percentage label \u201c48.0%\u201d at [0.666, 0.148, 0.754, 0.168]."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "eH5Jc9mreJhebND4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region within [0.207, 0.510, 0.614, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the pie slice for the 'E-commerce' category, which is colored in '#ff9999'. The pie slice is labeled with 'E-commerce' at [0.352, 0.714, 0.396, 0.734] and '52.0%' at [0.21, 0.863, 0.297, 0.883]."
+            }
+        ],
+        "id_raw": 49
+    },
+    {
+        "id": "BPN6KC5gShnBZYP7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the main title at coordinates [0.253, 0.021, 0.71, 0.047] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Change in Social Cultural and Value from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "BwSTDCdOONS2ncT3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.221, 0.987, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2020\"."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "IGs65nH0adxf6rJ1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.155, 0.351, 0.19, 0.371]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Here you can find the percentage text \"10.9%\"."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "cHvqR0ISGZ06Dcpf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice corresponding to the year {2020}[0.952, 0.221, 0.987, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice corresponding to {2020} is \"#c2f0c2\"."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "bEPE4vB6BwzNpj9K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data slice is adjacent to the data slice for {2017}[0.952, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data slice for {2018}[0.952, 0.161, 0.987, 0.181] is adjacent to the 2017 data slice."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Safe2Y1NKCuRk5bM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {2021 label}[0.952, 0.251, 0.987, 0.271] and {2021 slice}[0.258, 0.083, 0.457, 0.451]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{2021 label} and {2021 slice} both represent the same data point of the year 2021."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "qDBvXfvFAY6Pr6DK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.198, 0.265, 0.457, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice colored \"#ff9999\", a label \"2015\" at [0.29, 0.416, 0.334, 0.436] and a percentage text \"10.9%\" at [0.155, 0.351, 0.19, 0.371], which is accounting for a segment of the pie chart."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "tMeaFprwgPps9exw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within [0.508, 0.325, 0.767, 0.7]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice colored in \"#c2c2f0\". Inside the slice you can see the year \"2019\" at coordinates [0.641, 0.503, 0.685, 0.523] and the percentage \"16.9%\" at coordinates [0.792, 0.502, 0.828, 0.522]."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "D4I86yQCW9YLvPLF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area in coordinates [0.462, 0.113, 0.715, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a slice of a pie chart colored in \"#c2f0c2\". Inside the slice, the year \"2020\" at coordinates [0.539, 0.273, 0.583, 0.293] and the percentage \"18.3%\" at coordinates [0.618, 0.107, 0.653, 0.127] are located."
+            }
+        ],
+        "id_raw": 50
+    },
+    {
+        "id": "Iaw28eXcMHRWfwvb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label associated with the legend's handle at coordinates [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"2020\"."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "JEwl43dTD0wfHCuV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.213, 0.021, 0.75, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, labeled as \"The rise of integration of Future Technologies from 2020 to 2025\"."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "cRP7U4ECAdl66sKh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the text located at coordinates [0.351, 0.714, 0.395, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text describes the year \"2022\"."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "vvhEfACv5UdrhUqe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat handle color in the legend corresponds to the pie slice located at coordinates [0.447, 0.55, 0.708, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding handle color in the legend is \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "4pgZXIHobczJe4yd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with the legend item {2024}[0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice associated with 2024 is located at coordinates [0.533, 0.238, 0.792, 0.711]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "LaMwjLjHgQl6eurb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the color of the slice associated with the {2020} legend item located at [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the associated slice is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "wMA6zGl25KE6jnZt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you observe within the region framed by the coordinates [0.197, 0.435, 0.456, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the indicated region, there is a pie slice. It has an autotext label of \"11.1%\" at coordinates [0.138, 0.558, 0.173, 0.578] and a text label \"2021\" at coordinates [0.279, 0.536, 0.324, 0.556]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "gTdTH2N4YjzYWplG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in the chart defined by the coordinates [0.533, 0.238, 0.792, 0.711]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a pie slice representing the year \"2024\", which is colored \"#c2c2f0\" and has an autotext label of \"22.2%\" at coordinates [0.816, 0.45, 0.851, 0.47]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "5IVOA1PxUzFkTlH0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed within the region bounded by coordinates [0.282, 0.109, 0.658, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the given region, there is a pie slice labeled as \"2025\". This slice has an autotext label of \"25.9%\" at the coordinates [0.429, 0.063, 0.464, 0.083]."
+            }
+        ],
+        "id_raw": 52
+    },
+    {
+        "id": "fpnac17h51Fo4bR2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.263, 0.021, 0.7, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labeled as \"Distribution of Academic Research in Different Fields\"."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "SW03YvAyxSLRFS7k",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned in the coordinates [0.881, 0.191, 0.964, 0.211] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Engineering\"."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "WeQHQORIcZikHntc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.2, 0.111, 0.766, 0.92] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "pekGrea1PORf20GD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices of the chart are next to the {slice}[0.2, 0.404, 0.459, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {slice}[0.217, 0.259, 0.459, 0.497] and {slice}[0.432, 0.55, 0.716, 0.92] are adjacent."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "Phxeu4gZUVaccKkB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are in close proximity to {the subtitle}[0.263, 0.021, 0.7, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Arts\"}[0.308, 0.375, 0.343, 0.395] and {\"Sciences\"}[0.3, 0.628, 0.344, 0.648] are near the subtitle."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "4qLqAzIZNTCtBinv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat portion of the pie chart relates to the {legend item}[0.881, 0.191, 0.964, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The portion with coordinates [0.291, 0.111, 0.748, 0.481] corresponds to the Engineering legend item."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "6wCSGEEB2qMlSEgj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.2, 0.111, 0.766, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encapsulates the whole pie chart. It includes labels such as \"Arts\", \"Sciences\", \"Social Sciences\", \"Business\", and \"Engineering\" with their respective percentages and colored slices."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "aa9TsRDbPx559QKV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the spatial bounds set by coordinates [0.836, 0.063, 0.993, 0.219], what can be found?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region constitutes the legend of the pie chart. It labels \"Arts\", \"Sciences\", \"Social Sciences\", \"Business\", \"Engineering\", each with its respective color identifier."
+            }
+        ],
+        "id_raw": 53
+    },
+    {
+        "id": "GTlx5pxbphY1QUS6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the coordinates [0.358, 0.02, 0.642, 0.035]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title text \"Value Chain Visualization for Coffee Processor & Off-season Supplier\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "uTC8d2PcO97IvyZN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object resides within the bounding box [0.903, 0.207, 0.999, 0.35]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the pie chart is located in this region."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "oRnpbXl8hBlaqpyq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented at coordinates [0.087, 0.245, 0.412, 0.788] within the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a pie chart representing the value chain of the Coffee Processor."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "wy1Aa6lPk6SDZ5tJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice {exploded pie slice}[0.11, 0.339, 0.24, 0.499] of the Coffee Processor pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with label \"Inbound Logistics\" corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "rT4muc82uAhFmsgB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the pie slice adjacent to the {Outbound Logistics slice}[0.087, 0.516, 0.238, 0.601] in the Coffee Processor pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Marketing and Sales\" slice [0.101, 0.533, 0.243, 0.782] is adjacent to Outbound Logistics slice."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "Due6H39IjX8NKHzT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are near the {subtitle}[0.217, 0.183, 0.287, 0.198] of the Coffee Processor subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Final Cost\"}[0.309, 0.374, 0.331, 0.386] is near the subtitle."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "WnTW1tdajdUuoSK1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.087, 0.245, 0.412, 0.788]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates contains the pie chart of Coffee Processor. It includes slices with labels such as \"Inbound Logistics\"[0.11, 0.339, 0.24, 0.499], \"Operations\"[0.088, 0.383, 0.238, 0.507], \"Outbound Logistics\"[0.087, 0.516, 0.238, 0.601] and others, each representing different portions of the coffee processor's value chain."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "GgL51WRbTGEFne0U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main objects within the region [0.558, 0.245, 0.862, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region dictated by these coordinates contains the Off-season Supplier pie chart. The slices include various elements such as \"Inbound Logistics\"[0.556, 0.339, 0.69, 0.5], \"Operations\"[0.54, 0.396, 0.688, 0.507] and others, each representing a percentage of the value chain."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "yKH0hrE5tFe6z4JQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.903, 0.207, 0.999, 0.35]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a legend within these coordinates which encompasses elements like \"Inbound Logistics\", \"Operations\", \"Outbound Logistics\", \"Marketing and Sales\", \"Service\", \"Infrastructural Costs\" and \"Final Cost\" alongside their corresponding color parameters for interpretation of the pie charts."
+            }
+        ],
+        "id_raw": 56
+    },
+    {
+        "id": "kxRHqWgOzBFFcTp6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title and where is it located?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Living Conditions from 2000 to 2020\" and it is located at {coordinates}<r>[0.33, 0.021, 0.633, 0.047]</r>."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "0SeSsvXl1ZWZHG1y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found with coordinates [0.142, 0.419, 0.177, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the slice percentage text with the value \"15.9%\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "ZhkKJDrac5M0smui",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found inside the coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a legend handle for the year \"2000\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "aDPDyFTbb295yMTR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart's legend's handle at [0.913, 0.253, 0.941, 0.266] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle corresponds to data for the year \"2005\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "BFbXZvtLdZgjCl5U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the slice located at [0.355, 0.125, 0.472, 0.481] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the representation of data from the year \"2005\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "op5eGJXmJ0tTY9uM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the percentage at [0.284, 0.127, 0.319, 0.147] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage refers to the slice at {coordinates}<r>[0.315, 0.154, 0.468, 0.484]</r> which represents the data from 2010."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "RRbwyEd7BrYsaHDe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.197, 0.271, 0.766, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a segmented pie chart plot about the years 2000 to 2020, each segment represents data from a separate year, each year has its own color, and some annotations showing the percentage of each year."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "FZqFcqbpgF9PVcL8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.264, 0.222, 0.463, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a segment of the pie chart representing the year 2020, with the corresponding percentage 1.8% at {coordinates}<r>[0.22, 0.202, 0.255, 0.222]</r>."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "R9uxeuidi86SJl3a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information contained within the area [0.952, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area is the legend text \"2015\"."
+            }
+        ],
+        "id_raw": 58
+    },
+    {
+        "id": "NxNW5EkulGBdpBXV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title for the object located at [0.279, 0.021, 0.684, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Influence of Relationships in Social Relationships\"."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "75k12VfmxjWfJsqF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.885, 0.071, 0.93, 0.091] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Family\"."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "dGrLfALGVeDZgZVz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinates [0.129, 0.568, 0.174, 0.588] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"26.7%\"."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "2Es9f9IqkhaYcu9E",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is related to the legend item {Family}[0.885, 0.071, 0.93, 0.091] presented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice related to Family is located at {coordinates}[0.197, 0.283, 0.456, 0.828]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "VxE8gJ4BPtgdCleL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item in the chart matches with the slice located at {coordinates}[0.494, 0.114, 0.714, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item {Colleagues}[0.885, 0.131, 0.962, 0.151]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "emky5gmqDXzVO6LB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the legend item {Acquaintances}[0.885, 0.161, 0.987, 0.181] and the slice in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item Acquaintances corresponds to the slice with {coordinates}[0.272, 0.112, 0.473, 0.482]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "6ZFItpWmyuk9qEmR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.494, 0.114, 0.714, 0.484]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice of the pie chart corresponding to \"Colleagues\", along with the text \"16.0%\" located at [0.633, 0.118, 0.710, 0.138] and the label \"Colleagues\" located at [0.548, 0.28, 0.592, 0.3]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "BTgVwi1JFErqxECi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region delimited by [0.355, 0.346, 0.759, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the pie chart slice for \"Friends\", which is colored in '#66b3ff', along with its corresponding label \"Friends\" located at [0.586, 0.692, 0.629, 0.712] and the percentage \"43.3%\" at [0.698, 0.825, 0.747, 0.845]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "BvESbvsWFoIZrVv8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is represented by the region with coordinates [0.197, 0.283, 0.456, 0.828]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the pie chart slice for \"Family\" along with the corresponding  \"Family\" text at [0.28, 0.542, 0.324, 0.562] and its percentage \"26.7%\" at [0.129, 0.568, 0.174, 0.588]."
+            }
+        ],
+        "id_raw": 59
+    },
+    {
+        "id": "hNKJaxkXS0VXVaq3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.252, 0.021, 0.711, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which is \"Projected Carbon Dioxide Emissions from 2030 to 2040\"."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "nPfUooQyM3oVx791",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.252, 0.941, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend handle corresponding to the year \"2036\"."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "iWQnVuIcNK2pF9AO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at [0.788, 0.441, 0.824, 0.461] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"9.7%\"."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "jNNpYjiNg2ia0A7a",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.505, 0.532, 0.761, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.495, 0.548, 0.685, 0.905], [0.507, 0.349, 0.766, 0.569]] are adjacent."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "AocLJvd1zOWMyGGH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the pie slice for 2032}[0.208, 0.534, 0.459, 0.788] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"8.0%\"}[0.174, 0.72, 0.209, 0.74] and {\"2032\"}[0.305, 0.631, 0.34, 0.651] are located near the pie slice for the year 2032."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "jRnqRFCmnaQ4xQgP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items are paired with the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2030\" to \"2040\" correspond to the slices with the color codes ranging from '#ff9999' to '#679cf0'."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "PDIxdIhTIq8lXA1C",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.902, 0.063, 0.993, 0.398]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which contains handles and labels representing years from 2030 to 2040. Each handle indicates a unique color corresponding to a slice on the pie chart."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "f8ocduPIsyf3pYAu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.5, 0.163, 0.733, 0.49]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie representing the year \"2038\" denoted by the color '#8cabf0' and the percentage \"10.1%\"[0.705, 0.198, 0.741, 0.218]."
+            }
+        ],
+        "id_raw": 60
+    },
+    {
+        "id": "q67q78fVv1T6Rv9M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.279, 0.021, 0.684, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "title, called Technology Usage in Future Society in year 2026."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "VeJfYGxDPHYO328G",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.795, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "MG4r3YT8d8WlQ4VY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.326, 0.062, 0.474, 0.082] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"27.0%\"."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "Dbwcc4tMh3rpoQUA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.223, 0.301, 0.741, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.283, 0.109, 0.671, 0.479] is adjacent."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "R27tntHUu541ZVr3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat labels are indicated near {the legend}[0.795, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Smart Homes\"}[0.839, 0.071, 0.934, 0.091] and {\"Autonomous vehicles\"}[0.839, 0.101, 0.987, 0.121] are indicated near the legend."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "UjcNHl0Qup7yc1Rh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.283, 0.109, 0.671, 0.479] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Autonomous vehicles\"} [0.839, 0.101, 0.987, 0.121] corresponds to the slice."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "LbqWASWuN20qk7cC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.109, 0.741, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"73.0%\" [0.489, 0.95, 0.584, 0.97] and the text \"Smart Homes\" [0.464, 0.765, 0.508, 0.785]. The region corresponds to the blue slice of the pie chart."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "HhtM8vSNUVWymLFL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.283, 0.109, 0.671, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"27.0%\" [0.326, 0.062, 0.474, 0.082] and the text \"Autonomous vehicles\" [0.455, 0.247, 0.499, 0.267]. The region corresponds to the red slice of the pie chart."
+            }
+        ],
+        "id_raw": 61
+    },
+    {
+        "id": "Z9kcZGqLhojFhViy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.198, 0.111, 0.766, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart with various forecasted GDP rises from 2025 to 2032."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "lcopRMcOeJTVGw3b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.298, 0.021, 0.665, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle: \"Forecasted GDP Rise (%) from 2025 to 2032\"."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "c76vlFvpwnP2RhkY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects are legend items, containing years 2025 to 2032 with their corresponding colors."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "xjlCif2z7Dk6SrhT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does {the legend item \"2027\"}[0.952, 0.131, 0.987, 0.151] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {color: '#99ff99'}[0.278, 0.548, 0.468, 0.906] in the chart."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "0hw4vaJBz5Ak4Wyy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.274, 0.111, 0.501, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2032\"}[0.392, 0.265, 0.436, 0.285] is located near the slice."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "547r7nOtU11s7OHN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the legend item \"2028\"}[0.952, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color #ffcc99 corresponds to the legend item \"2028\"."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "prT76yTlvRuclYBR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.198, 0.111, 0.766, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the main pie chart displaying forecasted GDP rise percentages from 2025 to 2032. Each segment of the pie chart represents a different year, is colored distinctly, and is labeled with the year and percentage data such as \"2025\" [0.289, 0.418, 0.333, 0.438] and \"2026\" [0.291, 0.601, 0.335, 0.621]."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "Vt8kB83g4RN0TRlP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the chart legend that lists the years 2025 to 2032, with each year associated with a unique color that corresponds to the pie chart segment. Examples include \"2025\" [0.952, 0.071, 0.987, 0.091] and \"2026\" [0.952, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "3gD7F2NURlelS7iu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within the region from [0.278, 0.548, 0.468, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice representing the forecasted GDP rise for the year \"2027\" [0.371, 0.728, 0.406, 0.748] marked with '9.1%'. Its color coordinate [0.913, 0.133, 0.941, 0.147]  corresponds to #99ff99 in the legend."
+            }
+        ],
+        "id_raw": 63
+    },
+    {
+        "id": "08eIfBZZRSzxXITW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main chart title appearing at coordinates [0.274, 0.021, 0.691, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Psychological States and Behaviours in Population\"."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "TdgnLBRayJwP5vaT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is located at coordinates [0.85, 0.071, 0.954, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"StressedHighly\" is located there."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "6Rc2hxMkdtC8NNvL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present at the coordinates [0.792, 0.467, 0.837, 0.487] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"25.0%\"."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "A7cx9DppwtyXCs6O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the label {StressedModerately}[0.85, 0.101, 0.988, 0.121] in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"StressedModerately\" corresponds to the color \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "3VAOaQE9AvjUU4VP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text {15.0%}[0.604, 0.915, 0.681, 0.935] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" corresponds to the {Unstressed}[0.475, 0.55, 0.691, 0.92] pie slice in the chart."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "LGzCSEliWGwrB3tM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the pie slice at coordinates [0.492, 0.112, 0.659, 0.482] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label related to the pie slice is {Depressed}[0.85, 0.221, 0.924, 0.241]."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "urIq7YhcSLa5HVp9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.475, 0.55, 0.691, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie slice labeled as \"Unstressed\" with color \"#ffcc99\", and the percentage text \"15.0%\" is also located in this area."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "SAOphLPXX19GzAGn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.198, 0.111, 0.767, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the entire pie chart, showing various states and behaviours related to stress, happiness, depression, calm and anxiety."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "0xQ3HJkNoDOMXleB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.805, 0.063, 0.994, 0.309] represent within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the chart with various states and behaviors such as \"StressedHighly\", \"StressedModerately\", \"StressedLightly\", \"Unstressed\", \"Happy\", \"Depressed\", \"Anxious\", and \"Calm\"."
+            }
+        ],
+        "id_raw": 64
+    },
+    {
+        "id": "3G843MfaiWkn5uzp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main element at the given coordinates [0.272, 0.021, 0.691, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main element is the chart's subtitle, which says 'International Trade Progression from 2009 to 2018'."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "P701hlzltx7iKfkh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend text, labeling the year \"2009\"."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "vTWDrvAtpUPALSqS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.168, 0.308, 0.203, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the auto-text for a slice, reading \"7.5%\"."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "RKi3r24KvP8lacOu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are directly next to the {the slice}[0.524, 0.559, 0.774, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.461, 0.552, 0.63, 0.922], [0.507, 0.367, 0.767, 0.608]] are directly next."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "2Pti07KFT8BbMVRj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {the slice}[0.456, 0.11, 0.631, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2017\"}[0.952, 0.311, 0.987, 0.331] corresponds."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "rKIwYQmVr0suBrxA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the year 2014 in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.524, 0.559, 0.774, 0.868] corresponds to the year 2014."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "xTGghE1cV4LgSf0X",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.11, 0.774, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there are numerous pie slices, each representing a different year, where each year is represented by a different color. Each slice has associated text and percentage directly inside it. The region overall includes the entire pie chart."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "j9iLx8kLOYfXCqej",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.507, 0.367, 0.767, 0.608] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice representing the year \"2015\" along with the associated percentage label \"10.6%\" at coordinates [0.792, 0.473, 0.827, 0.493]."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "9QGOrxj9RZGQywRQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region from [0.802, 0.263, 0.827, 0.283]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an auto-text element from a slice, which displays the percentage \"7.5%\" [0.802, 0.263, 0.827, 0.283] pertaining to the year \"2009\"."
+            }
+        ],
+        "id_raw": 65
+    },
+    {
+        "id": "TnYwgoLN0MwtMM4X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart mentioned in the plot's information?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitles show it as \"Emerging Technologies and Trends Outlook\"."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "VILRfMzRyIDTKHyE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object holds the color information in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Legend's handle<r>[0.913, 0.073, 0.941, 0.087]</r> defines the colors."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "xZyCKSxrx8dOl097",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2016\"."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "oxsv5zFTeyulJHvQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the handle at coordinates [0.913, 0.073, 0.941, 0.087] and the text at coordinates [0.952, 0.071, 0.987, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle indicates the color used for the slice labeled \"2016\" in the pie chart."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "JbPpj91cORQV7LFw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.197, 0.109, 0.767, 0.923] related to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart <r>[0.197, 0.109, 0.767, 0.923]</r> is related to the legend in that the colors of the pie slices correspond to the labels in the legend."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "hUh0yg8xhTkS3uCB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects in the chart have a relationship with the legend object {legend} [0.908, 0.063, 0.993, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices<r>[0.197, 0.109, 0.767, 0.923]</r> relate to the legend."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "SJijx4rOwSDJyWX5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from [0.197, 0.109, 0.767, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a pie chart showing the trend of emerging technologies from 2016 to 2023. The slices are color-coded and the percentage of each category is labeled on each slice."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "mISiuv6GTy6zMu2l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.908, 0.063, 0.993, 0.548] contain in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the chart's legend which includes labels for the years 2016 to 2023 in corresponding colors used in the pie chart."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "CwtZTS7uMBrOQtvJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.197, 0.109, 0.767, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple slices of a pie chart each representing a different emerging technology along with their respective percentages for each year from 2016 to 2023."
+            }
+        ],
+        "id_raw": 66
+    },
+    {
+        "id": "KubnkZyRJ23pXOe3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.206, 0.021, 0.758, 0.077] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is 'Education Resource Distribution and Utilization from 2016 to 2020 Books'."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "DlVwMRbgyTLCthKB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.145, 0.517, 0.181, 0.537] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text indicating percentage, \"21.9%\"."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "eKOM0BW8ZK2ye9YE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.935, 0.221, 0.97, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text of the legend representing the year 2020."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "IGwMXgbFQ7mM6tia",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color references of {the slice}[0.206, 0.3, 0.457, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color reference} is found in the legend labeled as \"2016\" with the handle color coordinates [0.896, 0.103, 0.924, 0.117] which matches with the color of the slice."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "sP2whan7iouzkFAe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.501, 0.166, 0.751, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {[0.503, 0.525, 0.754, 0.88], [0.282, 0.138, 0.548, 0.496]} that represent the years 2018 and 2020 are adjacent to the slice representing 2019."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "MvFORMVp8ddFrvKe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice {[0.501, 0.166, 0.751, 0.509]} and its corresponding legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice, representing the year 2019, corresponds to the legend item with the handle color coordinates [0.896, 0.192, 0.924, 0.206]."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "bATNUCJCGEJW0NcH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by the coordinates [0.206, 0.138, 0.754, 0.924]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains five different slices, each corresponding to a different year from 2016 to 2020, with text indicating their respective percentages. For example, the 2016 slice has the text \"21.9%\" at coordinates [0.145, 0.517, 0.181, 0.537], and the label \"2016\" at coordinates [0.284, 0.518, 0.328, 0.538]."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "7wcuRFe9rUQHaGau",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region specified by coordinates [0.28, 0.566, 0.573, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice from the pie chart, the autotext for the slice showing \"20.5%\" [0.373, 0.938, 0.408, 0.958], and the text \"2017\" [0.417, 0.764, 0.461, 0.784] that corresponds to the legend."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "OCXDY4HBaSWkhikT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region bounded by [0.306, 0.104, 0.463, 0.352]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region partially contains two slices. One slice is labeled with \"21.9%\" at coordinates [0.145, 0.517, 0.181, 0.537] and another slice is labeled with \"18.6%\" at coordinates [0.361, 0.109, 0.396, 0.129]."
+            }
+        ],
+        "id_raw": 67
+    },
+    {
+        "id": "IwR2HZagAahDk4VT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of this chart presented at {subplot_0.subtitle.params}<r>[0.265, 0.021, 0.698, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Risk Levels under Control Measures Implementation\"."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "YxDXMswWdSup8FKM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the pie slice with coordinates {pie_0.slices.autotext}<r>[0.689, 0.175, 0.758, 0.195]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content is \"15.0%\"."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "vvzCHJfJgytCDFPg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you name the object located at coordinates {green handle}<r>[0.866, 0.133, 0.894, 0.147]</r> in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color indicator for \"Low\" risk level."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "knJ38nhpbqVfM4Tt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice's color in the chart corresponds to {the legend item \"Negligible\"}[0.905, 0.161, 0.975, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with aforementioned color is at {coordinates}<r>[0.499, 0.131, 0.74, 0.488]</r>."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "UHQWGH0mg7ddrdz7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice comes after {the slice labeled \"Medium\"}[0.201, 0.512, 0.461, 0.895]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice labeled \"{Low}\"<r>[0.435, 0.407, 0.76, 0.911]</r> comes after it."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "3903IE6ukdL9Oyzf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the legend, which color does the {slice with auto text \"18.0%\"}[0.275, 0.111, 0.54, 0.481] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color of \"{Unassessed}\"<r>[0.866, 0.193, 0.894, 0.207]</r> in the legend."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "1nXJaIQGHibo4hHd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.199, 0.111, 0.76, 0.911]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the main pie chart area, consisting of five slices representing the risk levels: High [0.199, 0.264, 0.458, 0.502], Medium [0.201, 0.512, 0.461, 0.895], Low [0.435, 0.407, 0.76, 0.911], Negligible [0.499, 0.131, 0.74, 0.488], and Unassessed [0.275, 0.111, 0.54, 0.481]."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "hEvfAfQ0GIj1WgAm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects the region [0.905, 0.071, 0.987, 0.211] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the following items in the legend: High [0.905, 0.071, 0.937, 0.091], Medium [0.905, 0.101, 0.961, 0.121], Low [0.905, 0.131, 0.933, 0.151], Negligible [0.905, 0.161, 0.975, 0.181], and Unassessed [0.905, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "GjQHStLYSHxtZQn9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.199, 0.111, 0.461, 0.895]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices of the pie chart, showing risk levels. The first slice, labeled \"High\", is at coordinates [0.199, 0.264, 0.458, 0.502] and the second slice, labeled \"Medium\", is at coordinates [0.201, 0.512, 0.461, 0.895]. Both slices have text displaying the percentages of risk levels."
+            }
+        ],
+        "id_raw": 68
+    },
+    {
+        "id": "JqqPjlREdnf3MPJz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the heading of the subplot located at [0.321, 0.021, 0.642, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Distribution of Social Activities in 2020\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "GvJpNJaQVzy6hVU7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is identified by the coordinates [0.636, 0.443, 0.679, 0.463]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text corresponding to the object is \"Online Activity\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "pOcSJBM1Wll6LQTe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what color corresponds with the object located at [0.835, 0.103, 0.863, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with those coordinates is \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "1gThz4H2RJAijyHv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item \"Online Activity\"}[0.835, 0.103, 0.863, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.308, 0.137, 0.766, 0.877] corresponds to the legend item \"Online Activity\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "GMWV4NUWswJjwMPy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo what does the piece of text {located at}[0.067, 0.613, 0.18, 0.633] relate in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It relates to the percentage of the pie slice \"Outdoor Activity\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "DDnBQL7Qcfb56PWY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the relationship between {the slice}[0.308, 0.137, 0.766, 0.877] and {the autotext}[0.783, 0.399, 0.884, 0.419]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"70.0%\" indicates the proportion of the slice in the pie chart, which is associated with the \"Online Activity\"."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "Ye5iaRuRXJJYX7YU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.197, 0.287, 0.766, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area mainly contains two pie slices. The grey slice representing \"Outdoor Activity\" is 30% of the total and is located in the coordinates [0.197, 0.287, 0.456, 0.868]. The light orange slice stands for \"Online Activity\" which is 70% and can be found in the coordinates [0.308, 0.137, 0.766, 0.877]."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "LkVwT6W3IiAEiOG7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region covered by [0.83, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area containing the legend for the chart, with labels for \"Outdoor Activity\" at [0.874, 0.071, 0.987, 0.091] and \"Online Activity\" at [0.874, 0.101, 0.975, 0.121]."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "bCBUlTuXwBqJsLbd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained in the region [0.567, 0.333, 0.884, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"70.0%\" at [0.783, 0.399, 0.884, 0.419] and the label \"Online Activity\" at [0.636, 0.443, 0.679, 0.463]. This region corresponds to a part of the \"Online Activity\" slice."
+            }
+        ],
+        "id_raw": 69
+    },
+    {
+        "id": "70AQkWMbEknWmMca",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart located at coordinates [0.338, 0.021, 0.625, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Cuisine Popularity Survey Analysis\"."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "XAJDvgth1cIZVjSg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.905, 0.221, 0.968, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Japanese\"."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "FVUzXSYllaDhbwOB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.13, 0.447, 0.173, 0.467] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"18.0%\"."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "BMA1gFEMpSERsBli",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart share the same coordinates with {the legend item handle}[0.866, 0.223, 0.894, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.419, 0.146, 0.579, 0.516] shares the same color coordinates."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "ew6LQUyA5LXhrylj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the text}[0.253, 0.253, 0.379, 0.323] and {the slice}[0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.0%\" corresponds to the percentage value of the slice representing \"Vietnamese\" cuisine."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "Zm7g54mY78TFTyyT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice in the chart that has coordinates [0.197, 0.273, 0.456, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Italian\" corresponds to the slice with these coordinates."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "lTDdfKO899r2nvBj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region within coordinates [0.866, 0.223, 0.894, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the handle of the legend item \"Japanese\" colored '#ffb3e6', situated on the top right side of the chart. The same color is used for the corresponding slice in the pie chart."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "YDPVelKKBsaZ2ENC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within coordinates [0.197, 0.273, 0.456, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, we find a pie slice representing the \"Italian\" cuisine, colored '#ff9999', and the corresponding label text \"Italian\" and analytical percentage text \"18.0%\" are also located in this region."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "OhdNIkKMkdnCzqxT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects of the chart fall in the region [0.866, 0.073, 0.894, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the given region, the handle of the \"Italian\" legend item is located. This handle exhibits the same color as the corresponding pie slice for Italian cuisine."
+            }
+        ],
+        "id_raw": 70
+    },
+    {
+        "id": "CArXoP8JgT8F8GvV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Transportation in Logistics in 2020\" {the title}<r>[0.339, 0.021, 0.624, 0.047]</r>."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "5TtCrT8cjtIz0qfN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.949, 0.071, 0.986, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the label \"Truck\" {the label \"Truck\"}<r>[0.949, 0.071, 0.986, 0.091]</r> in the legend."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "kxS5wwquZrLEjDmg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.213, 0.22, 0.243, 0.24]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"0.0%\" {the text \"0.0%\"}<r>[0.213, 0.22, 0.243, 0.24]</r>."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "5T3bpC0PgjkjEdpD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the colors that correspond to each transportation method in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of \"Truck\" {Truck}<r>[0.91, 0.073, 0.938, 0.087]</r> is '#ff9999', \"Plane\" {Plane}<r>[0.91, 0.103, 0.938, 0.117]</r> is '#66b3ff', \"Train\" {Train}<r>[0.91, 0.133, 0.938, 0.147]</r> is '#99ff99', and \"Ship\" {Ship}<r>[0.91, 0.163, 0.938, 0.177]</r> is '#ffcc99'."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "rZSmG33oYdYhZUnN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich transportation method has the highest percentage share in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The transportation method \"Truck\" {Truck}<r>[0.336, 0.922, 0.373, 0.942]</r> has the highest share with 60.9%."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "5tk2qbzCglZoc7PX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice text is closest to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice text \"Plane\" {Plane slice}<r>[0.627, 0.406, 0.671, 0.426]</r> is closest to the legend {legend}<r>[0.905, 0.063, 0.993, 0.189]</r>."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "93RIAY1xExaGejUE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.282, 0.109, 0.66, 0.479]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the green slice of the diagram representing \"Train\" {Train}<r>[0.282, 0.109, 0.66, 0.479]</r>. Inside this region, there is also the text \"26.1%\" {26.1%}<r>[0.432, 0.062, 0.465, 0.082]</r> indicating the percentage of the total that \"Train\" represents."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "SY4mrqWg6toT4qYW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.506, 0.235, 0.765, 0.507]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the part of the pie chart representing \"Plane\" {Plane}<r>[0.506, 0.235, 0.765, 0.507]</r> with text \"13.0%\" {13.0%}<r>[0.769, 0.335, 0.807, 0.355]</r> indicating its percentage representation."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "G3beT1SXtl91F1Eo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.214, 0.313, 0.732, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a red pie chart slice representing \"Truck\" {Truck slice}<r>[0.214, 0.313, 0.732, 0.921]</r>, with an accompanying text \"60.9%\" {60.9%}<r>[0.336, 0.922, 0.373, 0.942]</r> denoting the contribution of \"Truck\" to the total."
+            }
+        ],
+        "id_raw": 71
+    },
+    {
+        "id": "Gt6pPgHuuFjqtmnU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object falls within the coordinates [0.359, 0.021, 0.604, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, \"Energy Use by Resource Type\"."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "sfwerOPwO7ko2sQI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.903, 0.071, 0.934, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Coal\" within the legend."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "MHZoydHmf72jACpZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is positioned at the coordinates [0.247, 0.842, 0.278, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"49.5%\"."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "ueJd626AG8rMH1K9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item \"Coal\"}[0.903, 0.071, 0.934, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.206, 0.306, 0.658, 0.914]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "Y2SN10YUDf5Do2O9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is related to {the slice}[0.508, 0.252, 0.767, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Natural Gas\"[0.903, 0.101, 0.985, 0.121]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "wS8GFeJ1RGVzgP6b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to {the slice with \"Nuclear\" text}[0.358, 0.11, 0.475, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slices with \"Petroleum\"[0.48, 0.111, 0.673, 0.481] and \"Biomass\"[0.317, 0.154, 0.468, 0.484]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "wZaC21S80lPMZ34W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region within coordinates [0.206, 0.306, 0.658, 0.914]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region belongs to the largest slice in the chart with red color, labeled with the text \"Coal\"[0.341, 0.702, 0.385, 0.722] and percentage \"49.5%\"[0.247, 0.842, 0.278, 0.862]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "h84xwcNhpeAVbDxc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the region specified by the coordinates [0.508, 0.252, 0.767, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of pie chart with the text \"Natural Gas\"[0.641, 0.498, 0.685, 0.518] and the percentage \"24.2%\"[0.792, 0.492, 0.875, 0.512]."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "XWqa7rsT7MsLzVad",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the region [0.858, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the chart's legend, includes different energy resources with their corresponding colors, such as \"Coal\"[0.903, 0.071, 0.934, 0.091], \"Natural Gas\"[0.903, 0.101, 0.985, 0.121] and more."
+            }
+        ],
+        "id_raw": 75
+    },
+    {
+        "id": "BSNZa7nPLflZ6N0t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart positioned at [0.255, 0.021, 0.708, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's main title is \"Prevalence of Different Diseases in a Given Population\"."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "YGhqfxqE2GI0ieAA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.902, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend's text for \"Cardiopathy\"."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "i8jsNktpbQ8duIbw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object spotted at coordinates [0.791, 0.467, 0.844, 0.487]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the autotext displaying \"21.0%\"."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "kiP69ZBPcMXmFe4D",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is linked to {the legend item labeled 'Diabetes'}[0.902, 0.101, 0.964, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice linked to Diabetes is at {coordinates}[[0.362, 0.552, 0.706, 0.922]]."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "U5iGjdDm5x1KOCP2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color value is associated with {the legend item 'Tuberculosis'}[0.902, 0.161, 0.987, 0.181] and how does it relate to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color value '#ffcc99' is associated with Tuberculosis. It matches the slice at {coordinates}[[0.426, 0.11, 0.679, 0.48]] in the chart."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "1wVmGd8YMjepskRu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any relationships between {the slice labeled \"Diabetes\"}[0.362, 0.552, 0.706, 0.922], {the slice labeled \"Cardiopathy\"}[0.197, 0.285, 0.456, 0.846] and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slice labeled \"Diabetes\" corresponds to the legend item \"Diabetes\" and the slice labeled \"Cardiopathy\" corresponds to the legend item \"Cardiopathy\"."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "UAuFlf6ALXQo6ycU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.269, 0.125, 0.468, 0.485]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This designated area hosts the slice represented for \"Hepatitis\" and the autotext showing \"10.0%\". The slice is distinguishable by its color coded in `#c2c2f0`."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "0EwD824cNp5rTZCx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data exists within the region [0.197, 0.11, 0.766, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains five exploded pie slices denoting different diseases prevalence: \"Cardiopathy\", \"Diabetes\", \"Obesity\", \"Tuberculosis\", and \"Hepatitis\". Adjacent autotexts indicate the respective percentages of each disease."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "knpysUgSdu0Bd8ph",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the area [0.858, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is the location of the legend, which itemizes five diseases: \"Cardiopathy\", \"Diabetes\", \"Obesity\", \"Tuberculosis\", and \"Hepatitis\". Each item contains a color handle that matches the color of the corresponding pie slice in the chart."
+            }
+        ],
+        "id_raw": 76
+    },
+    {
+        "id": "6OLmkzVkWG5kwrYf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.311, 0.021, 0.652, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called 2020 Revenues of Major Tech Companies."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "kj5Hx9SAgSddZgHL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.931, 0.101, 0.971, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Apple\"."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "arLVG6aTKPWk90dC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.792, 0.541, 0.831, 0.561] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"31.4%\"."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "x0pVqYhBRLaYfCCB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.507, 0.227, 0.767, 0.843]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.203, 0.302, 0.587, 0.91], [0.326, 0.109, 0.641, 0.479]] are adjacent."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "21TyQeKJziP7tatq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the Google slice}[0.326, 0.109, 0.641, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"20.8%\"}[0.484, 0.062, 0.533, 0.082] and {\"Google\"}[0.461, 0.247, 0.505, 0.267] are located near the Google slice."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "Y15W6tjeb1oxzYSQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Amazon\", \"Apple\", \"Google\", and \"Tesla\" correspond to the slices with coordinates [0.203, 0.302, 0.587, 0.91], [0.507, 0.227, 0.767, 0.843], [0.326, 0.109, 0.641, 0.479], and [0.265, 0.194, 0.464, 0.489] respectively."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "p0L7wBmLGlEmTc6J",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.507, 0.227, 0.767, 0.843]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"31.4%\" [0.792, 0.541, 0.831, 0.561] and the text \"Apple\" [0.64, 0.526, 0.684, 0.546]. This region is located in the upper right part of the chart, corresponding to the light blue slice."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "FsIO7zOO697hwbzK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.203, 0.302, 0.587, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"44.1%\" [0.185, 0.788, 0.242, 0.808] and the text \"Amazon\" [0.32, 0.671, 0.364, 0.691]."
+            }
+        ],
+        "id_raw": 77
+    },
+    {
+        "id": "SwB4zZsRI4vH5CAH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main message of the object with coordinates [0.266, 0.021, 0.698, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's subtitle that reads \"Healthcare and Medical Technology Adoption - 2018\"."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "TxqTtMHROkrvNARs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location denoted by coordinates [0.547, 0.733, 0.591, 0.753] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Telemedicine\" denoting one of the slices in the pie chart."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "WYhuMfymEv7OI3Pu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.809, 0.101, 0.987, 0.121] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Electronic Health Records\" under the legend section."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "swqcGPQMTkfuKfGi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {Telemedicine}<r>[0.809, 0.071, 0.9, 0.091]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}<r>[0.235, 0.178, 0.753, 0.918]</r>."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "T42PRo1ERTxPxxZt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slice {Electronic Health Records}<r>[0.271, 0.117, 0.469, 0.484]</r> and the legend item {Electronic Health Records}<r>[0.809, 0.101, 0.987, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Electronic Health Records\" represents the corresponding pie slice in the chart."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "TYqSy93oHKtaVy44",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the title {Healthcare and Medical Technology Adoption - 2018}<r>[0.266, 0.021, 0.698, 0.047]</r> reflect the content of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it reflects the main theme of the pie chart which is about the technology adoption in Healthcare and Medical sector in 2018."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "rWS2xlweMn8TePy0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the upper right region, within coordinates [0.492, 0.516, 1.0, 1.0], contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the biggest pie slice {Telemedicine}<r>[0.235, 0.178, 0.753, 0.918]</r> with the autocomplete text \"88.2%\"<r>[0.632, 0.895, 0.722, 0.915]</r>, as well as part of the chart's legend."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "NMvUPU7Qx3oJXouM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within the bottom half of the chart, i.e., in the rectangle [0.0, 0.0, 1.0, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bottom half of the chart contains the legend and minor part of the smaller pie slice {\"Electronic Health Records\"}<r>[0.271, 0.117, 0.469, 0.484]</r>."
+            }
+        ],
+        "id_raw": 80
+    },
+    {
+        "id": "YUEbZfxooflcib8M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to these coordinates [0.198, 0.021, 0.765, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"Percentage Contribution of Each Supply Chain Stage to Global Trade\"."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "rPlckGTzpKOMw19m",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is contained within coordinates [0.857, 0.251, 0.987, 0.271] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"After Sales Service\"."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "OSnJw6WLOzDGsNjD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the coordinate [0.395, 0.264, 0.439, 0.284] in the chart pertains to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Reworking\"."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "oqPdAQepbSVtR5ff",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is closely related to {the legend item \"Storage\"}[0.857, 0.131, 0.911, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice \"Storage\"}[0.31, 0.551, 0.477, 0.921] is closely related."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "YdyhiT9maKkZcnmM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich piece of the pie does {the legend item \"Reworking\"}[0.857, 0.281, 0.93, 0.301] correspond with?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds with the {pie slice \"Reworking\"}[0.274, 0.111, 0.509, 0.481]."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "dIBpbxV9FEWhvoG5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the slice \"Distribution\"}[0.49, 0.551, 0.647, 0.921] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices \"Delivery\"}[0.502, 0.539, 0.739, 0.833] and {\"Consumer\"}[0.507, 0.323, 0.767, 0.662] are adjacent."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "Zys6ograxhcgl53j",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region encapsulated by coordinates [0.198, 0.111, 0.767, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart that represents the Percentage Contribution of Each Supply Chain Stage to Global Trade. It contains slices corresponding to various supply chain stages including \"Production\", \"Raw Materials\", \"Storage\", \"Distribution\", \"Delivery\", \"Consumer\", \"After Sales Service\", and \"Reworking\". Each slice is indicated with a different color."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "aWB51rPGxPgK5CrT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the included objects in the region [0.812, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend, with items indicating different stages of the Supply Chain. These stages are: \"Production\", \"Raw Materials\", \"Storage\", \"Distribution\", \"Delivery\", \"Consumer\", \"After Sales Service\", and \"Reworking\"."
+            }
+        ],
+        "id_raw": 81
+    },
+    {
+        "id": "t6Adt6D6nwoLeIUR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Cultural Usage in 2015\", with the coordinates [0.386, 0.021, 0.577, 0.047]."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "YEYFOh8V7X1iNQyp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.807, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "pDtAZNTDSe7JsMLj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.364, 0.286, 0.407, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Cultural Integration\"."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "YFgt3RK0hlwpSnlZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.236, 0.177, 0.754, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.269, 0.125, 0.468, 0.485] is adjacent."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "dgu7sUusdo97kGr5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the legend}[0.807, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Cultural Heritage\"}[0.851, 0.071, 0.971, 0.091] and {\"Cultural Integration\"}[0.851, 0.101, 0.987, 0.121] are located near the legend."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "HoqRaXo5voLnFcJ2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the legend item \"Cultural Heritage\" corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Cultural Heritage\" corresponds to {the slice with coordinates}[0.236, 0.177, 0.754, 0.917]."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "SUWDISARuAdx69M9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.0, 0.1, 1.0, 0.8]."
+            },
+            {
+                "from": "gpt",
+                "value": "The main figure lies within these coordinates. Inside this region, there is a pie chart with two categorized slices - one representing Cultural Heritage located between the coordinates [0.236, 0.177, 0.754, 0.917] and the other one representing Cultural Integration located between the coordinates [0.269, 0.125, 0.468, 0.485]. Both the slices go with their respective percentages as \"90.0%\" and \"10.0%\". Alongside, this region also includes the chart's title \"Cultural Usage in 2015\"."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "zIV9RA1uEg3Vi5uC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.807, 0.063, 0.993, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart that includes two items: \"Cultural Heritage\" [0.851, 0.071, 0.971, 0.091] and \"Cultural Integration\" [0.851, 0.101, 0.987, 0.121], each with its color marker."
+            }
+        ],
+        "id_raw": 83
+    },
+    {
+        "id": "3VCtXGkMhHRKTSTI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main object at the coordinates [0.187, 0.021, 0.777, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the object is the subtitle stating \"Internet and Communication Industry Development from 2007 to 2014\"."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "KiC3FgK9rUzftrEe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location specified by coordinates [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend's location containing the different years and their corresponding colors."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "VJE665vSF22G57lT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of pie chart is located at these coordinates."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "XmXKzVi3rKZL1ci4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart is adjacent to {the slice}[0.507, 0.269, 0.766, 0.604]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.487, 0.112, 0.69, 0.482] is adjacent to it."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "AVdNklLZqG5Sm22o",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring items to {the chart legend item}[0.952, 0.191, 0.987, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring items are {\"2010\"}[0.952, 0.161, 0.987, 0.181] and {\"2012\"}[0.952, 0.221, 0.987, 0.241]."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "vU9rW7koX8q7aVFV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to {the legend item for 2007}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice which corresponds to the legend item for 2007 is at coordinates [0.2, 0.264, 0.458, 0.502]."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "JlcPcUg7wD9v9VD8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.249, 0.849, 0.285, 0.869]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the autotext '10.9%' associated with the slice of the pie chart corresponding to the year 2009."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "8fchgMhiDyg31Dkr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.483, 0.112, 0.757, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mostly covers the center and the top right half of the pie chart, it contains few slices and their labels including {\"2007\"}[0.297, 0.405, 0.332, 0.425], and {\"2010\"}[0.468, 0.765, 0.512, 0.785]."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "OiZgTMMwNJWjWi7D",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region specified by [0.487, 0.112, 0.69, 0.482]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice corresponding to the year 2013, with its label {\"2013\"}[0.534, 0.27, 0.578, 0.29] and autotext '14.2%'."
+            }
+        ],
+        "id_raw": 86
+    },
+    {
+        "id": "7w7pfOV0YWKSWLXv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.952, 0.281, 0.987, 0.301] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the label \"2016\"."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "71g7Gu71CT3lFsNG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.223, 0.941, 0.237] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend label color for \"2014\"."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "LtlX8eWrgGGlQ2PE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.345, 0.021, 0.618, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, \"Cancer Cases from 2009 to 2016.\""
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "DT87ccV2AqePxyuI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds to the legend handle at {handle}[0.913, 0.282, 0.941, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend handle is {the slice}[0.277, 0.11, 0.572, 0.48]."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "TBlIh3DT6gBGAQRR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend text corresponds to the slice at {slice}[0.503, 0.152, 0.762, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"2015\" {text}[0.952, 0.251, 0.987, 0.271] corresponds to the slice."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "JZc5qScB7it16DSF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the subtitle and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Cancer Cases from 2009 to 2016\" represents the time frame over which the data depicted by the slices was collected."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "PNymxLKZvnfjvCLC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.913, 0.133, 0.941, 0.296]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the color codes in the legend for the various years from 2011 to 2016 corresponding to the pie chart slices."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "Rb6u9t2jHNMRINci",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.197, 0.362, 0.456, 0.51] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice for the year \"2010\" with an annotation of '6.5%' {text}[0.142, 0.417, 0.177, 0.437]."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "gBbM10UCjhXfEuid",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the region [0.405, 0.553, 0.617, 0.923] contains."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the '2013' pie slice of the chart, representing '13.5%' of the total. This is indicated by the label {text}[0.517, 0.947, 0.552, 0.967]."
+            }
+        ],
+        "id_raw": 87
+    },
+    {
+        "id": "trH7aPnVu0OLf2fe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label with coordinates [0.952, 0.131, 0.987, 0.151] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"2012\"."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "y0sSVQp8v85E8INP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.45, 0.113, 0.71, 0.48] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "P6IXl5oPNy7xX165",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.63, 0.11, 0.66, 0.13] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"16.7%\"."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "9OrHBrakhZ3NUAVf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to the {slice}<r>[0.199, 0.264, 0.458, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.272, 0.113, 0.47, 0.483]</r> is adjacent."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "ac8MxiVuDSehuOi6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {slice}<r>[0.34, 0.553, 0.684, 0.923]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this slice corresponds to the {legend item}<r>[0.913, 0.103, 0.941, 0.117]</r> labeled \"2011\"."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "4XXdRMT9ogcsHtK2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring objects of the text \"2014\" at coordinates [0.38, 0.273, 0.424, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring objects are the {percentage text}<r>[0.31, 0.107, 0.345, 0.127]</r> labeled \"13.3%\" and the {pie slice}<r>[0.197, 0.109, 0.767, 0.923]</r>."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "3psiug71dP8S8FES",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.199, 0.268, 0.458, 0.5]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a {pie slice}<r>[0.199, 0.264, 0.458, 0.502]</r> labeled \"2010\" and the associated {percentage text}<r>[0.158, 0.34, 0.193, 0.36]</r> displaying \"10%\"."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "8fxDhQSqZ7mdnJuS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects does the region [0.34, 0.553, 0.684, 0.923] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a {pie slice}<r>[0.34, 0.553, 0.684, 0.923]</r> labeled \"2011\" and the associated {percentage text}<r>[0.525, 0.946, 0.56, 0.966]</r> showing \"23.3%\"."
+            }
+        ],
+        "id_raw": 89
+    },
+    {
+        "id": "8fbxABZHXSCNhPgC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.278, 0.021, 0.685, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a subtitle, \"Market Share of Competitor B from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "7yGxsAMMHgDl8eOV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the handle with coordinates [0.913, 0.223, 0.941, 0.237] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend item for the year \"2020\"."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "GpTVeuc2KIDbom5C",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.144, 0.405, 0.179, 0.425] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the autotext \"14.9%\"."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "PCINNutrykiUG9Va",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's slice in the pie chart is next to the {slice}[0.501, 0.143, 0.751, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices corresponding to the years {2018}[0.506, 0.43, 0.766, 0.802] and {2020}[0.276, 0.11, 0.563, 0.48] are next to it."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "aVXF49e9UtD7PWBW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend items correspond to the color in the handle with coordinates [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {2015}[0.952, 0.071, 0.987, 0.091] corresponds to this handle."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "WGGa3MY5i6b4JIGs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart correspond to the legend item {2020}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates {slice}[0.276, 0.11, 0.563, 0.48] corresponds to this legend item."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "uysufrA1TRf7F8SL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.197, 0.11, 0.766, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart indicating the market share of Competitor B from 2015 to 2020. Within this region, six slices with autotexts and year texts are present implying six consecutive years from 2015 to 2020."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "OVNyx9YeaRBTN9m4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the coordinates [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a legend that indicates the correlation between different colors and years from 2015 to 2020."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "LMBypUTazEI2ZTX5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.506, 0.43, 0.766, 0.802]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily encompasses a single slice of the pie which corresponds to the year 2018. It includes an autotext indicating the market share percentage and the year text \"2018\"."
+            }
+        ],
+        "id_raw": 93
+    },
+    {
+        "id": "w2msrGOPUKlMoT5G",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.285, 0.021, 0.678, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Title, called \"Facebook and Twitter Usage from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "AVm8wQ2u4JGqJXyL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.428] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "YQcXH5E6pVyC3OOA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\"."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "M8gIzUXAN6sVngsL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is associated with {the text 2017}[0.952, 0.311, 0.987, 0.331] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color associated with the text 2017 in the legend is} <r>[#99ff99]</r>."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "6HpgwWerA2eBRZWA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is contained within the box adjacent to {the color box}[0.913, 0.193, 0.941, 0.207] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\" is contained within the box adjacent to the color box."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "jy8HnCuuCcaXWzMm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice at {the given coordinate}[0.197, 0.421, 0.456, 0.596] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"2016\"."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "kOoDzQlJKxEEGJWA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.111, 0.767, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a pie chart titled \"Facebook and Twitter Usage from 2015 to 2020\", divided into sectors corresponding to different years. Each sector uses a unique color which is described in the legend."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "NPrjVk6MH88I1xLE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.197, 0.111, 0.767, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects in the region are pie slices, representing data for years \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" respectively, each associated with a unique color."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "7d5RuH5ANpweJSYU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many sets of data are depicted within the region [0.197, 0.111, 0.767, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Twelve sets of data are depicted within the region."
+            }
+        ],
+        "id_raw": 95
+    },
+    {
+        "id": "v0vF3FNrvIYJCa2A",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object corresponding to coordinates [0.904, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the text \"Family Time\" in the legend."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "YunphM0bQDcwWes5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the chart does the object at coordinates [0.197, 0.287, 0.456, 0.868] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is one of the slices in the pie chart."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "mPhz9ECEwixmb8FJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.204, 0.021, 0.759, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle \"Time Allocation in a Day for Different Lifestyle and Social Activities\"."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "4ZDMqsjpADzaTsfu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near {the \"Family Time\" text}[0.904, 0.071, 0.987, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding color square for \"Family Time\" is located next to it<r>[0.865, 0.073, 0.893, 0.087]</r>."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "G4GLTiV6FS5vfOQu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correlation between the text \"Family Time\" [0.284, 0.569, 0.328, 0.589] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Family Time\" corresponds to the pie slice<r>[0.197, 0.287, 0.456, 0.868]</r>."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "q9LWDo3l5x513dOK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the objects at coordinates [0.904, 0.071, 0.987, 0.091], [0.904, 0.101, 0.962, 0.121], and [0.904, 0.131, 0.96, 0.151] have any similarities?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, all these objects are labels in the legend."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "gFCrSqLBlAnvkazr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description for the region within coordinates [0.197, 0.109, 0.763, 0.905]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the visualization of the pie chart. It features three separated slices, each with percentages {\"30.0%\"}<r>[0.097, 0.613, 0.18, 0.633]</r>, {\"45.0%\"}<r>[0.748, 0.735, 0.806, 0.755]</r> and {\"25.0%\"}<r>[0.398, 0.064, 0.455, 0.084]</r>, and labels {\"Family Time\"}<r>[0.284, 0.569, 0.328, 0.589]</r>,  {\"Workout\"}<r>[0.615, 0.639, 0.659, 0.659]</r>, {\"Cooking\"}<r>[0.444, 0.248, 0.488, 0.268]</r>."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "exOWa8vZDtl0ngUw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the visual elements within coordinates [0.86, 0.063, 0.993, 0.159]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend. Three pairs of color squares and text represent the categories: \"Family Time\"<r>[0.904, 0.071, 0.987, 0.091]</r>, \"Workout\"<r>[0.904, 0.101, 0.962, 0.121]</r>, and \"Cooking\"<r>[0.904, 0.131, 0.96, 0.151]</r>."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "cr7cfk5Hc6sWXkZ7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region with coordinates [0.615, 0.639, 0.659, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the text \"Workout\" is located, which is a label for one of the pie chart slices."
+            }
+        ],
+        "id_raw": 97
+    },
+    {
+        "id": "s02Gvo76QBXMK9iC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.218, 0.021, 0.746, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Entrepreneurship and Innovation Ecosystem for 2018 and 2019\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "QwdBGj5Q9MCPwuCO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.867, 0.071, 0.966, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Startups 2018\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "h2ZZ8iGNk6oSMqa8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat falls within the coordinates [0.198, 0.115, 0.755, 0.922] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "An exploded pie chart."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "Kq2kRMPNRaxgaMN9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.497, 0.545, 0.737, 0.913] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Startups 2019}<r>[0.867, 0.161, 0.966, 0.181]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "SRMTkTYVWAwB4Ygy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item \"Funding 2019\"}[0.867, 0.191, 0.962, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates [0.322, 0.115, 0.755, 0.624]}[0.322, 0.115, 0.755, 0.624] corresponds to the legend item \"Funding 2019\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "fO69Kj8NMkelVJ66",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie chart slice corresponds to {the legend item \"Innovations 2019\"}[0.867, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates [0.264, 0.217, 0.463, 0.49]}[0.264, 0.217, 0.463, 0.49] corresponds to the legend item \"Innovations 2019\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "RslwQQyoayNDfRmA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.198, 0.115, 0.755, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region largely contains an exploded pie chart representing different aspects of entrepreneurship and innovation in 2018 and 2019. Specific components include slices of the pie labeled \"Startups 2018\", \"Funding 2018\", \"Innovations 2018\", \"Startups 2019\", \"Funding 2019\", and \"Innovations 2019\", along with corresponding percentages for each category."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "faqilrKXhXpKDb8U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.823, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend providing labels for the slices of the pie chart, specifically for \"Startups 2018\", \"Funding 2018\", \"Innovations 2018\", \"Startups 2019\", \"Funding 2019\", and \"Innovations 2019\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "vEjCgBZRanzU02ik",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.867, 0.071, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several legend items related to different aspects of entrepreneurship for the years 2018 and 2019: \"Startups 2018\", \"Funding 2018\", \"Innovations 2018\", \"Startups 2019\", \"Funding 2019\", and \"Innovations 2019\"."
+            }
+        ],
+        "id_raw": 98
+    },
+    {
+        "id": "2tNXlgSf2PJYYd1n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart located at coordinates [0.267, 0.021, 0.696, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Donations to Culture and Arts Industry (2010-2013)\"."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "ssg38Ya6eMjXa01u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the text that represents the year \"2010\" in the legend."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "wU9k7Tq1hJyqM1pK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is located at coordinates [0.595, 0.615, 0.639, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item is the text \"2012\"."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "vvtK7GOGC18JbXQf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice that corresponds to {the legend item}[0.952, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the pie slice that corresponds to the legend item \"2011\" is '#9acd32'."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "IZjzIKlfYnWsuGmN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the text}[0.487, 0.287, 0.53, 0.307]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice that is adjacent to the text \"2013\" is {the slice}[0.283, 0.146, 0.725, 0.516]."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "AJAdKNErQkqwx7Ux",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the legend corresponds to the slice {the slice}[0.197, 0.271, 0.456, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2010\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "dSaS4jASbKXp0k1w",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.197, 0.146, 0.741, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of four pie slices, each slice represents a year and has a different color, also each slice has a piece of autotext demonstrating the corresponding percentage, and text indicating the year."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "qRPsNVM66Oy973fI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region defined by [0.908, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart with four items \"2010\", \"2011\", \"2012\" and \"2013\", each with a different color."
+            }
+        ],
+        "id_raw": 102
+    },
+    {
+        "id": "a7WC5aBWH6jdQXRX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object in the chart with coordinates [0.265, 0.021, 0.698, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"Fitness and Sports Health Trends from 2018 to 2020\"."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "IaWjHtRIRE8gG0ov",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is plotted at coordinates [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart legend."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "kEdj0tjqTeMDb5by",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is presented at coordinates [0.138, 0.452, 0.173, 0.472] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"18.3%\"."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "P4JjT6n5HSRRDfVc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are adjacent to {the slice}[0.197, 0.274, 0.456, 0.674]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.239, 0.55, 0.55, 0.92], [0.268, 0.138, 0.467, 0.486]] are adjacent to it."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "RA2kfHGn2Mf2YBBK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the slices labelled {2018}[0.28, 0.474, 0.324, 0.494], {2019}[0.457, 0.247, 0.501, 0.267], and {2020}[0.36, 0.294, 0.395, 0.314] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices labelled {2018}, {2019}, and {2020} correspond to the legend items \"2018\", \"2019\", and \"2020\" with the colors \"#ff9999\", \"#66b3ff\", and \"#c2c2f0\" respectively in the legend at [0.908, 0.063, 0.993, 0.249]."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "roYVZY4HdKgEi1gT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the slices next to the {slice labeled 2020}[0.621, 0.625, 0.664, 0.645]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {2019}[0.388, 0.744, 0.432, 0.764] and {2018}[0.589, 0.324, 0.633, 0.344] are next to the slice labeled 2020."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "peBN50IcFm9mw4sT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.109, 0.764, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area within these coordinates is the chart's main area, featuring a pie chart representation of \"Fitness and Sports Health Trends from 2018 to 2020\". The chart includes various slice sections with percentages and labels corresponding to the years 2018, 2019, and 2020."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "Edtfo5wdP8DuZpYb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the visible elements in the region between [0.393, 0.109, 0.563, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains one slice of the pie chart representing the year \"2019\"[0.457, 0.247, 0.501, 0.267] and featuring a text indicating the percentage \"10.7%\"[0.442, 0.062, 0.477, 0.082]. The slice has the color \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 103
+    },
+    {
+        "id": "cB3QEiYW8kI3CifB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart given its coordinates are [0.227, 0.021, 0.737, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"People Affected by Cybersecurity Breaches in 2017 and 2018\"."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "yQr3IqmWC4RVdsJQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found at coordinates [0.899, 0.071, 0.974, 0.091] and [0.899, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are the labels of the legend, which are \"Encryption\" and \"Data Privacy\" respectively."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "XiGthPWL8j3U2SQ4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by the object at [0.753, 0.289, 0.841, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represents the percentage \"61.5%\" of the \"Data Privacy\" slice in the pie chart."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "JHa6iR1VvVFIFmoo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the slices that are in the region of the pie chart with coordinates [0.2, 0.128, 0.763, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slices {\"Encryption\"}[0.2, 0.296, 0.497, 0.904] and {\"Data Privacy\"}[0.306, 0.128, 0.763, 0.864]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "O34cfbjtIT8ck5I2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does the legend item {Encryption}[0.899, 0.071, 0.974, 0.091] correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Encryption\" corresponds to the {slice}[0.2, 0.296, 0.497, 0.904]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "FQHpddJwLN0k8HbO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the {label \"Data Privacy\"}[0.899, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Data Privacy\" is {the slice}[0.306, 0.128, 0.763, 0.864]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "T8dOkC0jsWfNGcM2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region within the coordinates [0.2, 0.296, 0.763, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two slices of a pie chart representing the types of Cybersecurity Breaches. The two categories are \"Encryption\" [0.2, 0.296, 0.497, 0.904] and \"Data Privacy\" [0.306, 0.128, 0.763, 0.864]. The respective percentages are also present as texts - \"38.5%\" [0.136, 0.723, 0.21, 0.743] and \"61.5%\" [0.753, 0.289, 0.841, 0.309]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "FVcsYlWuaBPD5V0O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.854, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart with items \"Encryption\" [0.899, 0.071, 0.974, 0.091] and \"Data Privacy\" [0.899, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "bqeB7J2Ve2eI4Vxm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.2, 0.128, 0.763, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire pie chart, or two slices, each representing a type of Cybersecurity Breach (\"Encryption\" and \"Data Privacy\"). To each slice, there's a corresponding label and a percentage value indicating the share of each type of breach in the total - \"Encryption\" with \"38.5%\" [0.136, 0.723, 0.21, 0.743] and \"Data Privacy\" with \"61.5%\" [0.753, 0.289, 0.841, 0.309]."
+            }
+        ],
+        "id_raw": 105
+    },
+    {
+        "id": "gTBZnHefFnjcBoAd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.321, 0.021, 0.642, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is titled \"Social Inequality and Rights Protection\"."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "DlJfK9x3C604eHfl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.872, 0.191, 0.975, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text labeling the legend item \"Climate Justice\"."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "aeYpfpFXxqrFzIO9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at [0.087, 0.705, 0.204, 0.725] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"37.0%\"."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "hpprf4oohBY48DuK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to {Climate Justice}<r>[0.872, 0.191, 0.975, 0.211]</r> as per the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinate {the slice}<r>[0.268, 0.141, 0.467, 0.486]</r> corresponds to \"Climate Justice\" as per the legend."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "aAtKydVCgCW0SjVE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to the {slice}<r>[0.496, 0.547, 0.73, 0.916]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {adjacent}<r>[0.199, 0.295, 0.473, 0.903]</r>, {adjacent}<r>[0.507, 0.241, 0.766, 0.669]</r> are adjacent to the slice at [0.496, 0.547, 0.73, 0.916]."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "mfog83JfuUcyL2Nm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotexts are part of the slice at coordinates [0.507, 0.241, 0.766, 0.669]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext labeled as {20.0%}<r>[0.787, 0.426, 0.872, 0.446]</r>"
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "VyYFygUbnN8U62jB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.827, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "The described region contains the chart legend. This has legend items such as \"Extreme Poverty\" <r>[0.872, 0.071, 0.987, 0.091]</r> and \"Climate Justice\" <r>[0.872, 0.191, 0.975, 0.211]</r> among others, along with corresponding color handles."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "IgC7xU511IvejINT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the area bounded by the coordinates [0.199, 0.295, 0.473, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses one of the pie slices of the chart, it represents \"Extreme Poverty\" with a percentage value of 37% indicated by the text<r>[0.087, 0.705, 0.204, 0.725]</r>."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "omTW0I2hjP5GtwI0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.268, 0.141, 0.467, 0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that represents \"Climate Justice\" and it has associated text <r>[0.358, 0.295, 0.394, 0.315]</r> and autotext <r>[0.197, 0.145, 0.301, 0.165]</r> indicating the percentage coverage in the pie."
+            }
+        ],
+        "id_raw": 108
+    },
+    {
+        "id": "25dhVAguw5PVJXjh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat title is located at coordinates [0.32, 0.088, 0.705, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Culture and Arts Industry Development - 2016\"."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "nzIS8VwgB9BxN0aq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.226, 0.151, 0.297, 0.171]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text representing 4.5%."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "jNg4qmnLUFO8eqHe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.812, 0.138, 0.851, 0.158] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for \"Music\" in the legend."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "1jMCjSvhSgWy8lAl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the label in the legend}[0.812, 0.198, 0.866, 0.218] and {the slice in the pie chart}[0.428, 0.533, 0.731, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label in the legend called \"Theatre\" corresponds to the orange slice in the chart."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "bWNyqIhu0R0I3spr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart corresponds to {the label in the legend}[0.812, 0.227, 0.879, 0.248]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pale yellow slice with the coordinates [0.534, 0.386, 0.75, 0.576] corresponds to the label \"Visual Art\" in the legend."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "m3V8Tgou4NSKZArO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart corresponds to {the legend item}[0.812, 0.317, 0.931, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.352, 0.115, 0.471, 0.397] in the chart corresponds to the \"Cultural Heritage\" legend item."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "QAhs52XSeGq7vUBi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.277, 0.115, 0.75, 0.841]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart, including all the slices, which represent different segments of the Culture and Arts Industry in 2016."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "1nX3NwJOsg42kkXw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within [0.768, 0.13, 0.937, 0.375]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the chart legend, which lists labels \"Music\", \"Dance\", \"Theatre\", \"Visual Art\", \"Film\", \"Literature\", \"Cultural Heritage\", \"Digital Art\" and their corresponding colors in the pie chart."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "nxwxCIufEIJU9AXF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region at coordinates [0.278, 0.499, 0.494, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The given region contains a segment of the pie chart denoting \"Dance\" and the data as \"19.1%\"."
+            }
+        ],
+        "id_raw": 109
+    },
+    {
+        "id": "HDfoVOGQ5zpK0xgV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.322, 0.021, 0.641, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, named \"Population Distribution by Social Class\"."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "8J6A4aGqlbfT6bYL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object inside the bounding box [0.845, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "42Z5FKvwwa2oWjj4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at coordinates [0.89, 0.071, 0.973, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"Upper Class\" in the legend."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "ast3341xUxl1v3yc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parts of the pie are adjacent to {the slice}[0.333, 0.109, 0.563, 0.479] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.249, 0.193, 0.758, 0.913], [0.28, 0.181, 0.465, 0.488]] are adjacent."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "8Nph9icxXVcURvF3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to {the slice with label 'Working Class'}[0.333, 0.109, 0.563, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#99ff99\", which corresponds to the handle with coordinates [0.851, 0.133, 0.878, 0.147]."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "nDkSyxOlyu8AWIj6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item does the slice text {\"Retired\"}[0.348, 0.307, 0.383, 0.327] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the legend item with color \"#ffcc99\" and coordinates [0.851, 0.163, 0.878, 0.177]."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "QK2vAKxechWtb5Tm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.505, 0.129, 0.785, 0.454]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the large slice of the pie which is color-coded as \"Middle Class\" in the legend. It contains the text \"65.6%\" with coordinates [0.693, 0.832, 0.78, 0.852]."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "LMqf7EKbn7Wdfg4W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within coordinates [0.124, 0.026, 0.839, 0.240] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the subtitle of the chart \"Population Distribution by Social Class\" with coordinates [0.322, 0.021, 0.641, 0.047] and the legend with coordinates [0.845, 0.063, 0.993, 0.219]."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "Xkw4XnwJHvS2J0PM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between [0.22, 0.17, 0.48, 0.51] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slices of the pie chart labeled \"Upper Class\" [0.197, 0.27, 0.456, 0.602] and \"Retired\" [0.28, 0.181, 0.465, 0.488], both with their respective percentages \"15.2%\" [0.095, 0.409, 0.178, 0.429] and \"3.2%\" [0.232, 0.165, 0.282, 0.185]."
+            }
+        ],
+        "id_raw": 111
+    },
+    {
+        "id": "M2gzrpnXet7f13IO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.37, 0.021, 0.594, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which reads \"Exports from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "il4eORzeJ1BmajHZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.338] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "NHqcoIuxdy5M5vKW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.197, 0.110, 0.765, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main pie chart which shows the exports from 2012 to 2020."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "H910Bmud5AcbtRGe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice with color '#99ff99'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {2014}[0.952, 0.131, 0.987, 0.151] and {2018}[0.952, 0.251, 0.987, 0.271] correspond to the slice with color '#99ff99'."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "w7AKjBjwHcvlna6J",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to the slice {2014}[0.231, 0.542, 0.463, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {2013}[0.197, 0.471, 0.456, 0.685] and {2015}[0.364, 0.553, 0.533, 0.923] are adjacent."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "baVbN7GE4dtqJKww",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring years of the year {2017}[0.952, 0.221, 0.987, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring years of the year 2017 in the legend are {2016}[0.952, 0.191, 0.987, 0.211] and {2018}[0.952, 0.251, 0.987, 0.271]."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "FfnWBhDt3WyKxD7u",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.11, 0.765, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart showing the exports from 2012 to 2020. The chart is divided into different color-coded slices representing different years, each containing year text and autotext with respective percentages."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "kIagWC72xyQEraWc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart, including handle-color patches and text labels for years from 2012 to 2020."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "2CNVWy8A92F8f30o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.364, 0.553, 0.533, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2015 along with the corresponding autotext '10.7%' and year tag '2015'."
+            }
+        ],
+        "id_raw": 112
+    },
+    {
+        "id": "tyOAbDLKIhcu4KzW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at location [0.225, 0.269, 0.743, 0.923] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "One of the slices for the pie chart is located there."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "J1gxtc5DCydaKaZb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle located at the coordinates [0.272, 0.021, 0.691, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Distribution of Different Subculture Fashion Trends\"."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "ilqctIfHetZsEgEy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item appears within the coordinates [0.912, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Cyberpunk\" appears within those coordinates."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "yIuAQYNRNxnOAyUH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slice at coordinates [0.225, 0.269, 0.743, 0.923] and the text \"Goth\" at coordinates [0.476, 0.764, 0.519, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the subculture \"Goth\"."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "SxH5m2eOPlGweZww",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the legend item \"Cyberpunk\"}[0.912, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinate [0.281, 0.109, 0.646, 0.479] is associated with \"Cyberpunk\"."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "yHGeBLeEn71oFtSO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the percentage at [0.509, 0.948, 0.542, 0.968] and the pie slice at [0.225, 0.269, 0.743, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"75.0%\" is the proportion that the slice corresponding to \"Goth\" represents in the total pie chart."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "5QD1xpNcK8zO87Ke",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the region denoted by the coordinates [0.225, 0.109, 0.743, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two pie slices of a pie chart, with one slice labeled \"Goth\" at [0.476, 0.764, 0.519, 0.784] and showing a percentage of \"75.0%\" at [0.509, 0.948, 0.542, 0.968]. The other slice is labeled \"Cyberpunk\" at [0.444, 0.248, 0.488, 0.268] and shows a percentage of \"25.0%\" at [0.379, 0.064, 0.455, 0.084]."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "LC3z3a8o0ZbjoeLY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the plot appear in the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the entire pie chart, which is divided into two slices. One slice is larger and is associated with the subculture \"Goth\", while the smaller slice corresponds to the subculture \"Cyberpunk\". Each slice is labeled with their respective subculture names and the percentage they represent in the pie chart."
+            }
+        ],
+        "id_raw": 114
+    },
+    {
+        "id": "UTLHcB29XEF38Xv4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.903, 0.071, 0.932, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text of the legend item \"Asia\"."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "2U3wi4rW8kw9wqaL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.508, 0.063, 0.569, 0.083] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"4.3%\"."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "C1J3uU93DXmNrWBp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.903, 0.191, 0.964, 0.211] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text of the legend item \"Australia\"."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "YIP8fx0Y4tfcqiU1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {slice}[0.507, 0.334, 0.766, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.497, 0.125, 0.72, 0.486] is adjacent."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "MYsxNNs6mDIpX31B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found near the {legend text \"Australia\"}[0.903, 0.191, 0.964, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"America\" is located near the legend text \"Australia\"."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "jMFOHAvz2kRWi0xm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the pie chart does the {legend item \"Asia\"}[0.903, 0.071, 0.932, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Asia\" corresponds to the slice with coordinates [0.197, 0.272, 0.456, 0.653]."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "wUsVzgGBDJUSlfmC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between the coordinates [0.197, 0.109, 0.766, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart in the figure. The chart is composed of multiple slices, each representing a portion ('Asia', 'Africa', 'Europe', 'America', 'Australia', 'Antarctica', 'Unidentified') of the total, indicated by different colors. Each of these slices also has an associated percentage text showing the share of that portion."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "wjoaEM6Li7oILVQi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the region [0.858, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend, which has seven entries: \"Asia\", \"Africa\", \"Europe\", \"America\", \"Australia\", \"Antarctica\", and \"Unidentified\"."
+            }
+        ],
+        "id_raw": 115
+    },
+    {
+        "id": "jXg22MWXOPpIHEFh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found within the coordinates [0.286, 0.021, 0.677, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which says \"Population Migration Trends from 2000 to 2009\"."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "kyDpg1reyWllWZJ5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend of the chart."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "a5xp6lRm6PiWFf8n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being displayed at the coordinates [0.482, 0.516] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the center point of the pie chart."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "L05XHK2va2sfMFch",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is next to the slice found at {the coordinates}[0.206, 0.262, 0.458, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates}[0.197, 0.434, 0.456, 0.635] is adjacent to it."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "q37qyspekWk9YHSF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item at the coordinates [0.952, 0.071, 0.987, 0.091] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice colored '#ff9999' which has coordinates [0.206, 0.262, 0.458, 0.499]."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "4irXHHMjjyGqAyVh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend items and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each item in the legend corresponds to a slice in the pie chart. For example, the legend item '2000' corresponds to the slice with color '#ff9999' and the associated coordinates."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "wGroBeKpWVbtsqzW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region in the coordinates [0.197, 0.112, 0.766, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire drawn pie chart which includes different slices each representing specific data and various colors indicating different years from 2000 to 2009."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "hRaISXtZaIMyE6hR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.508, 0.474, 0.766, 0.724]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart displaying the year '2006' with an autotext showing '11.2%'."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "2B63jB1RihvK7JCT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.489, 0.112, 0.673, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice that represents the year '2008', which covers 12.5% of the total, as indicated by the autotext within the slice."
+            }
+        ],
+        "id_raw": 117
+    },
+    {
+        "id": "BfO5zVUO6FEXJFvI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.311, 0.021, 0.652, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Education Technology from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "pVTuuPGT6UIEfd1r",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is situated at coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend handle for the year 2020."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "spcODsinzq0U2kaP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.427, 0.063, 0.462, 0.083]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"25.8%\"."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "FETC0jPBUHS0em1K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is associated with {the pie slice}[0.495, 0.548, 0.726, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2018\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "8J8fwVbxWZkmsaXn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the handle {color '#ffcc99'}[0.913, 0.163, 0.941, 0.177] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to {the slice}[0.495, 0.548, 0.726, 0.918] labeled as \"2018\"."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "SFO9P6FIc77KS0Sl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the year {2019}[0.952, 0.191, 0.987, 0.211] in the legend correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#c2c2f0' is associated with the year 2019."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "6qHHlH6uJ9RXKtXL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the region [0.495, 0.548, 0.766, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a slice of the pie chart. It's associated with the year 2018 and has an autotext showing a percentage of '17.5%'[0.644, 0.885, 0.679, 0.905]."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "MtPcuzUJLIMu8rpT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the region [0.277, 0.55, 0.471, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice for the year 2017 and its corresponding percentage showing '13.4%'[0.319, 0.911, 0.354, 0.931]."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "5u7w9X8VKQPYwQ3Q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.198, 0.109, 0.766, 0.92] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The whole pie chart, including six slices, each representing a particular year from 2015 to 2020, is encompassed within these coordinates."
+            }
+        ],
+        "id_raw": 118
+    },
+    {
+        "id": "WAgbdMyeTT2S7RLY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at [0.07, 0.503, 0.171, 0.523] in the pie chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"22.0%\"."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "czPMk2oq0dfCF2JJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown within the coordinates [0.886, 0.071, 0.987, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text showing the legend label \"Environmental\"."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "Mf3muylTSnP6adXb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is displayed in the area [0.281, 0.021, 0.683, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart \"Government Policy and Effectiveness Evaluation\"."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "YO80xSiAafRFchdR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart element does the color patch {the color patch}[0.847, 0.073, 0.875, 0.087] correlate with?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the legend item \"Environmental\"."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "5rIwzy1Qd77q11Lw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector in the pie chart does {text}[0.39, 0.21, 0.46, 0.25] \"Economic\" correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the sector with the coordinates of [0.338, 0.11, 0.493, 0.48]."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "fVa5FhZWmuo1lcXa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich portion of the pie chart does the {text}[0.785, 0.412, 0.831, 0.432] \"25.0%\" label relate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It relates to the sector within [0.507, 0.197, 0.766, 0.709] - \u201cHealth\u201d policy."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "uh9cZPa9bzCQsAYY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area within the coordinates [0.485, 0.05, 0.510, 0.925] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region spans a major portion of the pie chart including parts of the sectors corresponding to \"Health\", \"Immigration\", and \"Economic\" policy slices."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "tox8P2jzERdWTs2x",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the rectangular region defined by [0.918, 0.213, 0.953, 0.227] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a small color patch, it's a part of the legend of the chart."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "1KijcHOpU5BbTuEa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area [0.265, 0.373, 0.73, 0.521]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the larger part of the pie chart containing the \"Labor\", \"Health\", and \"Immigration\" policy slices along with their respective percentage annotations."
+            }
+        ],
+        "id_raw": 120
+    },
+    {
+        "id": "vifcpjxQUkvcSPcd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart in the region [0.341, 0.021, 0.622, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is 'Community Development in 2018'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "JdsEloGFJV5vj75u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels in the legend that is in the region [0.83, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels of the legend are 'Public Spaces, 'Training Centers', and 'Green Zones'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "WT4DXzAu0Ij5luWS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploded slice located at coordinates [0.278, 0.109, 0.601, 0.480] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded slice represents the 'Green Zones' with a percentage of '21.8%'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "brP26JSDiF3Bkf2u",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color association between the legend items and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item 'Public Spaces' with coordinates [0.874, 0.071, 0.969, 0.091] is associated with the pie slice at [0.204, 0.304, 0.63, 0.912], 'Training Centers' with coordinates [0.874, 0.101, 0.987, 0.121] is associated with the pie slice at [0.507, 0.187, 0.766, 0.795] and 'Green Zones' with coordinates [0.874, 0.131, 0.963, 0.151] is associated with the pie slice at [0.278, 0.109, 0.601, 0.48]."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "xd9xtpzLZ3bv5c51",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the text '47.3%' located at [0.167, 0.82, 0.262, 0.84]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text '47.3%' corresponds to the pie slice representing 'Public Spaces' located at [0.204, 0.304, 0.63, 0.912]."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "7CI40VslANCRyUtk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the text '21.8%' located at [0.335, 0.07, 0.424, 0.09]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text '21.8%' corresponds to the pie slice representing 'Green Zones' located at [0.278, 0.109, 0.601, 0.480]."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "MViFFmi3LgopWlgq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted within the region [0.204, 0.109, 0.766, 0.912]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains pie slices representing 'Public Spaces', 'Training Centers', and 'Green Zones'."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "rcBZjOp9UaUuqcmE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the 'Public Spaces' slice holding coordinates [0.204, 0.304, 0.63, 0.912] account for in the total pie chart within the region [0.204, 0.109, 0.766, 0.912]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Public Spaces' slice accounts for '47.3%' of the total pie chart."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "A7dbKz4FA6E3L5Re",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.83, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend with entries for 'Public Spaces', 'Training Centers', and 'Green Zones'. Each entry is represented by a colored rectangle and a text label."
+            }
+        ],
+        "id_raw": 121
+    },
+    {
+        "id": "VMIwFbGJS0jYdy22",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text positioned at [0.791, 0.465, 0.854, 0.485] in the chart denote?"
+            },
+            {
+                "from": "gpt",
+                "value": "It denotes the percentage \"14.7%\"."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "QyVHNhLegZtZqMVQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.877, 0.071, 0.987, 0.091] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They refer to the label \"Movie Watching\" in the legend."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "nTTmYK7sZ8owL9m5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the chart coordinates [0.309, 0.44, 0.353, 0.46]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The name of the social activity \"Movie Watching\"."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "mwzJBtEtVRTuQtte",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat particular slice is associated with {the legend label \"Movie Watching\"}[0.877, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's related to {the slice}[0.22, 0.277, 0.479, 0.553]."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "VqLpkk8JBuhw6yWI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text box is nearest to {the slice}[0.22, 0.277, 0.479, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text box \"12.8%\"}[0.096, 0.386, 0.207, 0.406] is nearest."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "QlcpNckg9KJ7A2f8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage share is associated with {the slice \"Party\"}[0.222, 0.518, 0.479, 0.78]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage share associated is {\"10.9%\"}[0.186, 0.683, 0.222, 0.703]."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "UcaytDyPnZfziZ8V",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content inside the coordinates [0.22, 0.114, 0.766, 0.89]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Certainly. This region encompasses the entire pie chart, which includes text labels for each of the activity categories such as \"Shopping\", \"Hiking\", \"Reading\", \"Traveling\", \"Dining\", \"Picnic\", their respective percentages and each pie slice."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "sSTLLCwkWTTgdf0J",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's located within the region [0.222, 0.518, 0.479, 0.78]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice representing the activity \"Party\" and an allocated percentage of \"10.9%\". The color of the pie slice matches the color in the legend next to \"Party\"."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "4ZsQgvYnFtZlPkIt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region encompassed by [0.877, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text label for \"Movie Watching\" in the legend."
+            }
+        ],
+        "id_raw": 123
+    },
+    {
+        "id": "KyXYFbgz54AIUrcz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Recognition for Various Social Activities and Lifestyles\" [0.257, 0.021, 0.706, 0.047]"
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "bhy7hAE8Y0U35eiN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.887, 0.071, 0.951, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item label \"Outdoors\"[0.887, 0.071, 0.951, 0.091]."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "CSNiWs3MYQiyPSlQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.111, 0.434, 0.175, 0.454] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.1%\" [0.111, 0.434, 0.175, 0.454]."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "9D7XoWmHjbvzDVUU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart does the \"Outdoors\" label in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Outdoors\" label corresponds to the slice with coordinates \"[0.197, 0.272, 0.456, 0.645]\"."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "D1I8j4ekil8tvzuB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the slice {slice}[0.197, 0.272, 0.456, 0.645]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}[0.229, 0.549, 0.540, 0.919] is adjacent to it."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "nPWEe9lS3oTzUnsK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located nearest to {the slice}[0.229, 0.549, 0.54, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"23.4%\" [0.288, 0.904, 0.343, 0.924] is located nearest."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "s1UjsFzSFt6NML3g",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.229, 0.549, 0.540, 0.919] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds with the \"Internet\" slice of the pie chart. It includes a portion of the chart representing 23.4% [0.288, 0.904, 0.343, 0.924], and the label \"Internet\" [0.379, 0.738, 0.423, 0.758]."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "MOLdeqwtqZgZFMEt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found within the region [0.499, 0.543, 0.740, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice representing \"14.6%\" [0.69, 0.835, 0.747, 0.855] of the pie chart, and its corresponding label \"Reading\" [0.581, 0.698, 0.625, 0.718]."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "JO2fiifwLfn3irXQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.507, 0.214, 0.766, 0.642]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the \"Sports\" section of the pie chart. It contains the label \"Sports\" [0.635, 0.438, 0.678, 0.458] and a value of \"20.5%\" [0.781, 0.389, 0.826, 0.409]."
+            }
+        ],
+        "id_raw": 126
+    },
+    {
+        "id": "japiPGz7QBuPyckq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text at the coordinates [0.379, 0.021, 0.584, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Social Media Usage Rate\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "mSzhpKewwMjzrxSL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.916, 0.071, 0.982, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Facebook\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "JawjI9feVb3JE1AI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.198, 0.111, 0.765, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart that represents the social media usage rate."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "dLfVMXclHYLnb2n3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does the {legend item \u201cLinkedIn\u201d}<r>[0.916, 0.161, 0.974, 0.181]</r> relate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the chart slice<r>[0.502, 0.539, 0.756, 0.872]</r>."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "XUFexXdwFlbTnxjf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to  {the \"Snapchat\" slice}<r>[0.506, 0.243, 0.765, 0.577]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The slices corresponding to \"LinkedIn\"<r>[0.502, 0.539, 0.756, 0.872]</r>, \"WeChat\"<r>[0.493, 0.117, 0.676, 0.483]</r> are adjacent."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "C0kfEryvsHdXjxSy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does {the \"Snapchat\" slice}<r>[0.506, 0.243, 0.765, 0.577]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the legend item \"Snapchat\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "abBUwrrJzDwnpGFG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease, describe the region that includes the coordinates [0.502, 0.539, 0.756, 0.872]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart slice that represents the usage rate of \"LinkedIn\". Inside the slice, there are the percentage text \"14.7%\" and the name of the platform \"LinkedIn\"."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "czhXAKi4BsWLlPeo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.198, 0.111, 0.765, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encloses the whole pie chart that represents the social media usage rate. Inside this area, there are various slices labeled with their respective social media platforms names and usage percentages."
+            }
+        ],
+        "id_raw": 127
+    },
+    {
+        "id": "2xIL8eP9ObvXYdNt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.311, 0.021, 0.652, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Consumer Behavior in Emerging Markets\"."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "V5Oe8fE4U0LQSwYQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.903, 0.071, 0.942, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Brazil\" in the legend is located there."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "BLJE2pEFIMhjpb4s",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be seen at coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of the pie chart."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "eZj3FnU41wJR9ptf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice} [0.197, 0.27, 0.456, 0.597]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.266, 0.174, 0.465, 0.488] is adjacent to it."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "XdgSbKufZOfnNY5g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text lies nearest to {the subtitle} [0.311, 0.021, 0.652, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text \"India\" [0.604, 0.349, 0.648, 0.369] lies nearest to the subtitle."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "iayflDG2suqkYzSY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color relates to the {label \"Brazil\"}[0.903, 0.071, 0.942, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color #ff9999 relates to the label \"Brazil\"."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "hXFuhwVkTon6OW33",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the region within coordinates [0.233, 0.553, 0.742, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents one of the slices of the pie chart that corresponds to \"China\", indicated by the text \"China\"[0.476, 0.764, 0.519, 0.784] and \"45.0%\"[0.509, 0.948, 0.548, 0.968]. The slice is colored #66b3ff."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "Oau4KIA6Op4GLMjj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components within the region [0.502, 0.13, 0.761, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice corresponding to \"India\" [0.604, 0.349, 0.648, 0.369] with the autotext \"23.0%\" [0.729, 0.236, 0.763, 0.256] in the pie chart."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "5Y1Z4RQzPbjqoSgR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects does the region [0.858, 0.063, 0.993, 0.219] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend and its items: \"Brazil\", \"China\", \"India\", \"Russia\", and \"South Africa\"."
+            }
+        ],
+        "id_raw": 129
+    },
+    {
+        "id": "1Y1my4vs2GUdDWIu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the main pie slice with coordinates [0.209, 0.309, 0.701, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is {2019 Rural}<r>[0.362, 0.724, 0.405, 0.744]</r>."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "xyLxbK9d2WsKGnqt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies within the coordinates [0.906, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The entry with the text {2019 Urban}<r>[0.906, 0.101, 0.987, 0.121]</r> lies within the specified coordinates."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "XOJy703R1in9yj5s",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content situated at coordinates [0.238, 0.879, 0.313, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content at these coordinates is the percentage text {54.0%}<r>[0.238, 0.879, 0.313, 0.899]</r>."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "9JgGdUUh4PuFhhMP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects at boundaries [0.362, 0.724, 0.405, 0.744] and [0.906, 0.071, 0.981, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at coordinates {0.362, 0.724, 0.405, 0.744}<r>2019 Rural</r> and the object at {0.906, 0.071, 0.981, 0.091}<r>2019 Rural</r> are both referring to the same category of data that is 'Rural' for the year 2019 in the pie chart."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "JCW0PFRh7hY49O8l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the pie slice}[0.209, 0.309, 0.701, 0.917] and {the autofilled text}[0.238, 0.879, 0.313, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autofilled text represents the percentage of the full pie that the pie slice represents."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "xZhgnyUwAPKvbPvu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage corresponds to the {2019 Urban}[0.558, 0.288, 0.602, 0.308] piece in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage that corresponds to the {2019 Urban}[0.558, 0.288, 0.602, 0.308] is {46.0%}<r>[0.65, 0.133, 0.732, 0.153]</r>."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "i0KW7vIR3PMLeJLa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the region [0.209, 0.115, 0.755, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses two main pie slices. The first slice {2019 Rural}<r>[0.209, 0.309, 0.701, 0.917]</r> is represented by the color '#ff9999', contains the text {54.0%}<r>[0.238, 0.879, 0.313, 0.899]</r>, and is textually labeled {2019 Rural}<r>[0.362, 0.724, 0.405, 0.744]</r>. The second slice, {2019 Urban}<r>[0.297, 0.115, 0.755, 0.645]</r> of the color '#66b3ff' contains the text {46.0%}<r>[0.65, 0.133, 0.732, 0.153]</r> and is marked as {2019 Urban}<r>[0.558, 0.288, 0.602, 0.308]</r>."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "Nb46O2eJfxMpWena",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe what lies in the region [0.861, 0.063, 0.993, 0.129] of the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the legend of the pie chart. It includes two entries: the first one is labeled as {2019 Rural}<r>[0.906, 0.071, 0.981, 0.091]</r> with a color indicator '#ff9999' at the coordinates [0.867, 0.073, 0.894, 0.087]. The second entry is labeled as {2019 Urban}<r>[0.906, 0.101, 0.987, 0.121]</r> with a color indicator '#66b3ff' at the coordinates [0.867, 0.103, 0.894, 0.117]."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "MH5CAX9VXd9q9wAQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.233, 0.021, 0.73, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the subtitle for the chart: {\"Rural and Urban Development Comparison in the Year 2019\"}<r>[0.233, 0.021, 0.73, 0.047]</r>."
+            }
+        ],
+        "id_raw": 130
+    },
+    {
+        "id": "hCKMSEgw6bn2XQZc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.303, 0.021, 0.66, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named Rural and Urban Development Comparison."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "QNRgylyndXTzziA7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.906, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item, Urban."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "D6oRjijwaXGrQLcJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.785, 0.603, 0.82, 0.623] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.7%\"."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "xZ4W9TzCyEiRDdO0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.199, 0.53, 0.458, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.198, 0.266, 0.457, 0.506], [0.286, 0.55, 0.481, 0.92]] are adjacent."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "6K1bct6hBKY3k8j7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.303, 0.021, 0.66, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2012\"}[0.154, 0.355, 0.189, 0.375] and {\"2013\"}[0.159, 0.674, 0.194, 0.694] are located near the subtitle."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "9B9U6dSACyKxPwig",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Urban\" and \"Rural\" correspond to the slices with coordinates [0.198, 0.266, 0.457, 0.506], [0.199, 0.53, 0.458, 0.788], [0.286, 0.55, 0.481, 0.92] etc. respectively."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "8IJGgp7QnwrX8cfQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.198, 0.266, 0.457, 0.506]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"11.2%\" [0.154, 0.355, 0.189, 0.375] and the text \"2012\" [0.289, 0.418, 0.333, 0.438]. This region is located in the upper left part of the chart, corresponding to the red slice."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "Vud13L9qEZSefmC3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.199, 0.53, 0.458, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"12.1%\" [0.159, 0.674, 0.194, 0.694] and the text \"2013\" [0.292, 0.604, 0.336, 0.624]."
+            }
+        ],
+        "id_raw": 131
+    },
+    {
+        "id": "5Q1grEGeZIeYCHVu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.65, 0.011, 0.786, 0.037] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the subtitle of the second subplot labeled as \"Internet Usage in 2020\"."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "Qg4OHemyEOTB25iD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.037, 0.778, 0.074, 0.798] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label \"45.0%\" associated with a slice in the first subplot."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "3LzHPTyqG05xpsWe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.233, 0.235, 0.264, 0.255] in the subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Broadband\" related to a pie slice in the first subplot."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "yEBYxPTyiezTQgZ7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice from the second subplot has {the text \"Broadband\"}[0.827, 0.428, 0.858, 0.448] next to it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.596, 0.125, 0.919, 0.857] in the second subplot."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "4IRkQ0hfG6QEcEdA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are associated with the {red slice} [0.544, 0.244, 0.703, 0.479] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labels {\"3.0%\"}[0.501, 0.241, 0.538, 0.261] and {\"Dial-Up\"}[0.6, 0.345, 0.625, 0.365] are associated."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "rQ2IQuxoQHWjcnui",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels associated with the slice at coordinates [0.101, 0.098, 0.396, 0.464] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels associated with the slice are \"30.0%\"[0.255, 0.052, 0.309, 0.072] and \"Broadband\"[0.233, 0.235, 0.264, 0.255]."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "Mf1QABjVpVlWCniG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.518, 0.125, 0.919, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three pie slices related to the year 2020 with the text labels \"Dial-Up\"[0.6, 0.345, 0.625, 0.365], \"DSL\"[0.582, 0.575, 0.613, 0.595] and \"Broadband\"[0.827, 0.428, 0.858, 0.448], representing different types of internet usage and their percentages."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "vHVWERCfj57M9x87",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region marked by [0.043, 0.098, 0.44, 0.89]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region includes the pie chart representing Internet Usage in 2000. The chart contains three slices for the categories \"Dial-Up\"[0.127, 0.658, 0.158, 0.678], \"DSL\"[0.348, 0.548, 0.38, 0.568] and \"Broadband\"[0.233, 0.235, 0.264, 0.255]. Each slice has its own color and percentage value attached."
+            }
+        ],
+        "id_raw": 132
+    },
+    {
+        "id": "KHxSK1TvQgMsvCop",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.273, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle \"Ecological Environment Sustainability Assessment\"."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "1onwcuFQ9VvN69Q2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.821, 0.131, 0.896, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"Soil Health\" is located at this position."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "GIV7xlVumLKfchcg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to the coordinates [0.48f058, 0.224, 0.752, 0.496] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an exploded slice representing \"Climate Change\"."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "yeIRjPs26wVYQLJM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the {Soil Health slice}[0.46, 0.551, 0.673, 0.921] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Air Quality slice}[0.278, 0.549, 0.47, 0.917] on one side and the {Biodiversity slice}[0.507, 0.42, 0.766, 0.787] on the other side are adjacent to the Soil Health slice."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "9S21iBOmosok3GF1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the chart corresponds to the {legend item \"Soil Health\"}[0.821, 0.131, 0.896, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded pie slice with the chart coordinates [0.46, 0.551, 0.673, 0.921] corresponds to Soil Health in the legend."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "VeE34njZEPFR20XX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near the {subtitle}[0.273, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main chart object, a pie chart with the coordinates [0.197, 0.109, 0.766, 0.921], is located near the subtitle."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "ljPVQaGTzEFPR2V3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region delimited by coordinates [0.197, 0.109, 0.766, 0.921]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the main pie chart which includes various exploded slices representing ecological elements like Water Quality, Air Quality, Soil Health, Biodiversity, Climate Change, and Environmental Education. Each slice has an associated percentage and a label."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "pwGovJBq8EwqsbQJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nKindly provide a description for the area within coordinates [0.777, 0.063, 0.993, 0.249]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region pertains to the legend of the chart. It contains six items, each one showing a color code and a label corresponding to the different ecological elements being assessed for sustainability - these are: Water Quality, Air Quality, Soil Health, Biodiversity, Climate Change, and Enviromental Education."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "qANEkdYeYih9Ug3z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found within the area defined by the coordinates [0.278, 0.549, 0.47, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes an exploded slice in the pie chart, coloured as '#88f088', representing \"Air Quality\", and a corresponding percentage label that reads \"11.4%\"."
+            }
+        ],
+        "id_raw": 133
+    },
+    {
+        "id": "2ZXsgu7JgGEy6B5Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.303, 0.021, 0.66, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Investment Distribution from 2012 to 2019."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "4tK6qq35AjX5EiiB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "2x2Wx9rcuBwlV0AB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.4, 0.945, 0.435, 0.965] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text expressing a percentage value, labeled as \"10.8%\"."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "Sc27yH4X8mduZ6dr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.482, 0.516, 0.71, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.41, 0.741, 0.689], [0.356, 0.553, 0.527, 0.923]] are adjacent."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "aPGm641hIkUB9Ydk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the text}[0.4, 0.945, 0.435, 0.965]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2015\"}[0.433, 0.762, 0.477, 0.782] is located near the given text."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "lrfFTgPj4AEYDqeH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color handle in the legend corresponds to the {slice}[0.482, 0.516, 0.71, 0.879] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle with the {color \"#ff00ff\"}[0.913, 0.193, 0.941, 0.207] corresponds to the slice."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "TNd6tbjXGEs7pUA8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains eight distinct pie chart slices with varying colors and size indicating the distribution of investment from 2012 to 2019. Each slice of the pie contains two text objects corresponding to the year represented and the percentage of investment in that year."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "K1v5F4lUOWXyhAxu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me more about the contents within the region [0.908, 0.063, 0.993, 0.309] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the legend of the chart. It contains eight items, each with a color handle corresponding to the colors of the slices of the pie chart and a text labeling the year that handle represents, from 2012 to 2019."
+            }
+        ],
+        "id_raw": 134
+    },
+    {
+        "id": "QbVw46y4u3RXUQwU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.923, 0.071, 0.963, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Apple\""
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "XKxSdYTq0WdsB9w9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at the given coordinates [0.206, 0.117, 0.757, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a pie chart."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "VN4ysexs17IjVACI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text representation is found at [0.676, 0.159, 0.74, 0.179]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"49.3%\"."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "oEzeHVqhGn4EHEFz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend corresponds to the exploded slice {the slice}[0.206, 0.307, 0.671, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend \"Apple\" corresponds to this slice which is located at {coordinates}[0.923, 0.071, 0.963, 0.091]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "VQTu6I504SrNgE5F",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the subtitle}[0.392, 0.021, 0.571, 0.047] and {the pie chart}[0.206, 0.117, 0.757, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Market Share in 2021\" is the descriptive title for the presented pie chart."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "Pe71S8oz97KISpbT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does {the text \"49.3%\"}[0.676, 0.159, 0.74, 0.179] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to {the Samsung slice}[0.299, 0.117, 0.757, 0.712]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "MSZX7VYVvPNSUDie",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area covered by the coordinates [0.206, 0.307, 0.671, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the Apple slice of the pie chart, characterized by a slightly exploded wedge with a percentage text \"50.7%\" at coordinates [0.248, 0.853, 0.287, 0.873] and a text label \"Apple\" at coordinates [0.346, 0.708, 0.39, 0.728]."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "sUKakjiErtVSVwyq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.892, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend of the chart is located in this region, it contains the legend entries for \"Apple\" and \"Samsung\", with their respective color boxes."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "5HDAXBDI2HQlXi3e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers the entire chart area including the pie chart \"Market Share in 2021\", the pie slices representing Apple and Samsung along with their respective annotations and the location for y-label. It also contains the sublegend for the chart at the top right corner."
+            }
+        ],
+        "id_raw": 136
+    },
+    {
+        "id": "2f5ZpbbBX24ZkeQQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.13, 0.741, 0.218, 0.761] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"40.0%\" is located there."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "e2m0hEKafIlQiF18",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.877, 0.161, 0.987, 0.181] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Bioinformatics\" is located there."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "QlpQ4rBkf3ddKA09",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text with coordinates [0.331, 0.021, 0.632, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Medical and Biotech Industry Trends\" is located at those coordinates."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "o3khhCNui6JyNL4c",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is connected to the text {slice percentage}[0.13, 0.741, 0.218, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the percentage \"40.0%\"[0.201, 0.298, 0.522, 0.906] is connected to the text \"40.0%\"."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "0AetL4oB6me9bhMl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice corresponding to {the legend entry}[0.877, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the legend entry \"Bioinformatics\" has a color of '#c2c2f0'."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "6c5lRfxN9nACKQeX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is annotated by the text {text}[0.604, 0.589, 0.648, 0.609]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice [0.482, 0.32, 0.741, 0.875] is annotated by the text \"Organoids\"."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "rb7eYkdvyR2ixn3O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within [0.482, 0.146, 0.741, 0.906] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie chart slices for \"Organoids\", \"CRISPR\", and \"Bioinformatics\" with percentages of 30.0%, 20.0%, and 10.0% respectively."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "zktvLsSVeUI7D7ni",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region with coordinates [0.843, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region encompasses the graph's legend which includes entries for \"Cell Therapy\", \"Organoids\", \"CRISPR\", and \"Bioinformatics\"."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "pJW1W5o3pYUdAw2P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.201, 0.298, 0.522, 0.906]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart slice for \"Cell Therapy\" along with its related percentage label \"40.0%\"."
+            }
+        ],
+        "id_raw": 138
+    },
+    {
+        "id": "Vf6LoF5ghjmfVyTI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main feature of the chart located at coordinates [0.316, 0.021, 0.647, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart is \"Population Migration from 2010 to 2019.\""
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "3PPZonQSshnY9RZP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2010\" from the legend."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "1BTYHfGv0UeXxBaL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.302, 0.39, 0.337, 0.41]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"2010\" that belongs to a segment of the pie chart."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "al3uybmT7EyI7bwv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label is associated with {the pie slice}[0.270, 0.120, 0.469, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2019\" is associated with this slice."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "w9XjY5AEPfFxz2y1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to {the pie slice}[0.333, 0.552, 0.514, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#ffcc99\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "H8HlyB4sUsn7E85P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend item}[0.952, 0.101, 0.987, 0.121] and {the pie slice}[0.197, 0.435, 0.456, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled '2011' corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "eeLSQVzlesUGfU3S",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within coordinates [0.206, 0.262, 0.458, 0.499] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label \"2010\", a percentage notation \"7.5%\", and belongs to a segment of the pie chart."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "qqa7O2FF7StADcYV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the region [0.495, 0.549, 0.683, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region contains part of the pie chart colored in '#c2c2f0', a percentage notation \"10.7%\", and the label \"2014\"."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "PwUm3BIoXx1JzqzB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.217, 0.539, 0.461, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes part of the pie chart colored in '#99ff99', the label \"2012\" and the percentage value \"10.3%\"."
+            }
+        ],
+        "id_raw": 141
+    },
+    {
+        "id": "jL6L2GGY9SNCDnW9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what does the object with coordinates [0.197, 0.109, 0.767, 0.92] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is a pie chart showing the data distribution over different years."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "Cvzac2YyZXyeEMP0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.229, 0.021, 0.735, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the subtitle of the chart - Technological Innovation and Intellectual Property Over Time."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "LZ2lNqiUarVvPf3h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.906, 0.073, 0.934, 0.087] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend handle denoting the color representation of 'Year 1' on the pie chart, colored in yellowish-orange."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "gvSJkOmU0zabEpIZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entry in the legend corresponds to the pie slice located at {the coordinates}[0.197, 0.338, 0.456, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry \"Year 2\"<r>[0.945, 0.101, 0.987, 0.121]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "mNsMXut6RIBfOLQT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the object at coordinates}[0.906, 0.103, 0.934, 0.117] and the object at [0.906, 0.223, 0.934, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both are legend handles but represent different years, 'Year 2' and 'Year 6' respectively."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "bUkl4wcmFaDCeCMF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects in the legend correspond to the pie slices coloured '#66b3ff' and '#99ff99'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices colored '#66b3ff' and '#99ff99' correspond to the legend objects 'Year 2'<r>[0.945, 0.101, 0.987, 0.121]</r> and 'Year 3'<r>[0.945, 0.131, 0.987, 0.151]</r> respectively."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "qZxdOobMRTkgvP5C",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.9, 0.063, 0.993, 0.428] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend that color-codes the different years represented on the pie chart. The legend entries range from 'Year 1' to 'Year 6' with corresponding colors represented by small rectangular blocks."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "aZunNI4qKMrPLXiq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.197, 0.109, 0.767, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart. It displays data across different categories visually represented by pie slices of different colors."
+            }
+        ],
+        "id_raw": 142
+    },
+    {
+        "id": "t7vIidM76NpIkkrg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the title of the chart with coordinates [0.367, 0.021, 0.596, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Media Usage in 2019\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "6a4PaKzgnqpxLIyH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the legend item at coordinates [0.922, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the legend item is \"Facebook\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "EDZOpXdTbEXjfhhy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.547, 0.322, 0.591, 0.342] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Twitter\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "hHeXtX7ck0zsKThW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend item that corresponds to the {slice}[0.283, 0.146, 0.741, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#66b3ff'."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "7bYURfXvbqzn8rPy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the legend {item}[0.922, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the legend item is {the slice}[0.208, 0.309, 0.695, 0.917]."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "4aRi0TW4TJ1rYTGi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage text associated with the {slice}[0.283, 0.146, 0.741, 0.693]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text is \"46.8%\"."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "tQlGe1NWMLPIsUfV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region of coordinates [0.208, 0.146, 0.741, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the slice \"Facebook\"}[0.208, 0.309, 0.695, 0.917]. The text \"Facebook\" is in this region at coordinates [0.358, 0.72, 0.401, 0.74], and the percentage text \"53.2%\" is within this region at coordinates [0.241, 0.873, 0.307, 0.893]."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "EpOwy0TJa6nER0LV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the visualization within the region from [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main area of the pie chart visualizing social media usage in 2019. There are two slices, representing the user count of 'Facebook' [0.208, 0.309, 0.695, 0.917] and 'Twitter'[0.283, 0.146, 0.741, 0.693]."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "HLqvRcYXAGBo7KOy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.877, 0.063, 0.993, 0.129] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend which includes two items with their names and representative color. The items 'Facebook' and 'Twitter' are placed at coordinates [0.922, 0.071, 0.987, 0.091] and [0.922, 0.101, 0.969, 0.121] respectively."
+            }
+        ],
+        "id_raw": 144
+    },
+    {
+        "id": "s2U01VBENNFEjBeg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart at the coordinates [0.189, 0.021, 0.775, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Innovation and Technology Entrepreneurship Ecosystem (2017 - 2021)\"."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "rM0PCn7TjQ1nFJ2l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.912, 0.071, 0.987, 0.091] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2017 2017\" of the color representation in the legend."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "uy8YyLx56ZK8HIF8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific text is represented by the given coordinates [0.334, 0.938, 0.408, 0.958] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" is represented."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "GTtI28ZWGhxQIgc3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with {the text \"2019 2019\"}<r>[0.417, 0.758, 0.461, 0.778]</r> by color representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at {coordinates}<r>[0.307, 0.552, 0.536, 0.922]</r> is associated by color representation."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "FUdlTdUrB99MmpI2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proportion of the pie slice linked to {the text \"2021 2021\"}<r>[0.638, 0.461, 0.682, 0.481]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The proportion of the pie slice is shown as \"16.5%\"."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "EuJV3hCQc1ExToTp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice associated with {the label \"2017 2017\"}<r>[0.912, 0.071, 0.987, 0.091]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with that label is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "mhFNye5Wx3L5i2KE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.198, 0.109, 0.766, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the entire pie chart, which is divided into differing slices, each slice representing data for specific years. With each slice further annotated with their corresponding values and year labels."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "3HSoIBhu1QQEXghA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region bounded by [0.868, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend, including color handles for data representation and corresponding text labels indicating the year for each color representation."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "C4Az6Z0TOtiOeLtK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the contents within the region denoted by the coordinates [0.307, 0.552, 0.536, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region carries the pie chart slice corresponding to the year 2019, with a labeled percentage of \"15.0%\"."
+            }
+        ],
+        "id_raw": 145
+    },
+    {
+        "id": "1KefZvfDwPXZWnUI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at the coordinates [0.892, 0.071, 0.926, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend item labeled as \"Seed\"."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "kwOzIudQLfj1owbr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content displayed at [0.188, 0.259, 0.223, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the percentage text \"3.5%\"."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "XPOMz4ExHVOF2OLS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object corresponds to the coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This position correspond to the center of the pie chart."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "QOK25QuOqUU8Rp9d",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item {Second Stage}[0.892, 0.311, 0.987, 0.331] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "yNGTuX2gbw5LQT5N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich are the text labels that are immediately adjacent to the text label {Late Stage}[0.892, 0.281, 0.967, 0.301] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {Early Stage}[0.892, 0.251, 0.972, 0.271] and {Second Stage}[0.892, 0.311, 0.987, 0.331] are adjacent."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "Z06TKzrFNWS3Uurb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slice [0.23, 0.258, 0.46, 0.495] and the legend entries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the legend entry labeled as \"Seed\"."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "jgkg2iyhO3NxVpV7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.847, 0.063, 0.993, 0.368]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the chart's legend area. It includes descriptors for each of the color representations used within the pie chart, including \"Seed\", \"Early Stage\", \"Late Stage\", \"Second Stage\", and \"M&A\". Each of the labels in this area associate with a color-code."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "qNoP1bdyR3X0fLxO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the region within [0.197, 0.11, 0.767, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the main pie chart. It includes various portions or slices, each colored differently and representing a different class of investment stage. Each slice has percentage annotations along with stage name."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "4gk4OnhOyJINqoTD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.847, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart. It includes the different investment stages represented in the pie chart, along with color codings. These stages include \"Seed\", \"Early Stage\", \"Late Stage\", \"Second Stage\", and \"M&A\"."
+            }
+        ],
+        "id_raw": 146
+    },
+    {
+        "id": "J0nqe8JBN2HC6Ye7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object found at coordinates [0.857, 0.071, 0.961, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the label for \"Education Tech\"."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "LZ1tBp4cNfMKq3Pz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.735, 0.764, 0.808, 0.784] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a percentage text \"22.8%\" located at these coordinates."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "SdDOnrYpm21mAZm5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.857, 0.191, 0.907, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for \"MOOCs\"."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "9cgY78mEsaepkQfe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with the pie slice at {coordinates}[0.197, 0.274, 0.456, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text associated with this slice is {Education Tech}[0.279, 0.48, 0.323, 0.5]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "chLUfXrLgknhrrx3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does the legend item {EdTech Companies}[0.857, 0.161, 0.987, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice at coordinates [0.503, 0.176, 0.761, 0.496]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "XM1Pkuu1xj5lwZPZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice does {the text \"Online Courses\"}[0.857, 0.101, 0.961, 0.121] in the legend relate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It relates to the slice found at coordinates [0.244, 0.55, 0.531, 0.92]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "fVUyHkLf4PvRQSFf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region within the coordinates [0.244, 0.55, 0.531, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a pie slice representing the \"Online Courses\" segment. Inside this slice, there's a text displaying the percentage \"20.8%\" at [0.251, 0.912, 0.355, 0.932] and a legend \"Online Courses\" at [0.386, 0.743, 0.43, 0.763]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "vBe3aVVHsnwKx1fc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the region defined by [0.503, 0.176, 0.761, 0.496]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The defined region covers a pie slice of the chart labeled as \"EdTech Companies\". Within this slice, the percentage \"15.0%\" lies at [0.743, 0.266, 0.874, 0.286] and the label \"EdTech Companies\" is located at [0.612, 0.366, 0.656, 0.386]."
+            }
+        ],
+        "id_raw": 149
+    },
+    {
+        "id": "lCzaRllkPhUnSwj9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart present at coordinates [0.347, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Internet and Communication in 2019\"."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "q8bw4HW46CoFgrDr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.231, 0.146, 0.749, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "JNpbEuAIehUfeEY7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.611, 0.131, 0.692, 0.151] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.8%\" indicates the proportion of phone calls in the pie chart."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "SroZoBn4CJExaD0Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Emails sent\"<r>[0.731, 0.071, 0.811, 0.091]</r> and \"Phone Calls\"<r>[0.731, 0.101, 0.811, 0.121]</r> correspond to the slices with coordinates [0.231, 0.18, 0.749, 0.92] and [0.5, 0.146, 0.687, 0.516] respectively."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "0GvpUwQ3cuhhaFqG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slices and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices represent the concepts indicated by the legend items."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "r6kO6XPFJp3Ptm9e",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of data is represented by the legend items with coordinates [0.686, 0.063, 0.817, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data types \"Emails sent\" and \"Phone Calls\" are represented."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "flOyuKwJLy4hO2al",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is significant about the region within the coordinates [0.231, 0.18, 0.749, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the majority \"87.2%\"<r>[0.298, 0.915, 0.378, 0.935]</r> of the pie chart and is associated with the datum \"Emails sent\"<r>[0.407, 0.744, 0.451, 0.764]</r>."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "J1J3VKESg4SuQfcp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region contained within the coordinates [0.5, 0.146, 0.687, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region signifies the \"12.8%\"<r>[0.611, 0.131, 0.692, 0.151]</r> of the pie chart attributed to \"Phone Calls\"<r>[0.539, 0.302, 0.583, 0.322]</r>."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "87jTfZRROtRszyVb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.231, 0.146, 0.749, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a pie chart slice. It represents \"87.2%\" of data and is labeled as \"Emails sent\"."
+            }
+        ],
+        "id_raw": 150
+    },
+    {
+        "id": "7QesnkyrzBwFzjGT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main object at coordinates [0.271, 0.021, 0.692, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Social Values vs Cultural Changes in the Year 2020\"."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "1vsW1vo1jYenLzfl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at coordinates [0.829, 0.073, 0.857, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's part of the color handle of the legend denoting \"Social Values\"."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "INUWxa5xlYoslFRh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text item at coordinates [0.603, 0.915, 0.694, 0.935] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"85.0%\"."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "QezCSAOJNxjLdnoX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are adjacent to {the Social Values slice}[0.233, 0.181, 0.751, 0.92] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {85.0%}<r>[0.603, 0.915, 0.694, 0.935]</r> and {\"Social Values\"}<r>[0.531, 0.744, 0.574, 0.764]</r> are adjacent to the Social Values slice."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "CbCVIC4d74tX1EvR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects to the right of {the slice}[0.273, 0.112, 0.49, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "To the right of {the slice}[0.273, 0.112, 0.49, 0.482], there is {the legend}<r>[0.823, 0.063, 0.993, 0.129]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "ZcHUjJqlyrkLG7Rg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text located at [0.868, 0.101, 0.987, 0.121] and the slice at [0.273, 0.112, 0.49, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Cultural Changes\" at [0.868, 0.101, 0.987, 0.121] corresponds to the slice at [0.273, 0.112, 0.49, 0.482], representing its category."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "4qLXRTs24fcH4KTE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.233, 0.112, 0.751, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the large slice representing \"Social Values\", along with the text \"85.0%\" <r>[0.603, 0.915, 0.694, 0.935]</r> and the label \"Social Values\" <r>[0.531, 0.744, 0.574, 0.764]</r>. It makes up the majority of the pie chart."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "F5Q52hYXepHvNx94",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists in the region from [0.823, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is the legend which includes two text labels: \"Social Values\" <r>[0.868, 0.071, 0.959, 0.091]</r> and \"Cultural Changes\" <r>[0.868, 0.101, 0.987, 0.121]</r> along with their color representations."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "HuYuZlAAdxQXeSLB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's contained in the region marked by [0.273, 0.112, 0.49, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a smaller slice of the pie chart labeled \"Cultural Changes\" <r>[0.389, 0.267, 0.433, 0.287]</r> and the text \"15.0%\" <r>[0.241, 0.097, 0.36, 0.117]</r>."
+            }
+        ],
+        "id_raw": 151
+    },
+    {
+        "id": "9qwixVQ4hcJqB1Gl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the graph situated at the coordinates [0.288, 0.021, 0.676, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Artificial Intelligence Application Case Analysis\"."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "7DnvP0YbFF16i9zC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.952, 0.071, 0.987, 0.091] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the label \"2015\"."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "GzFbEs8itcGMMo4V",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at coordinates [0.672, 0.857, 0.707, 0.877] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"28.0%\" is displayed."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "ev61AzEneeGJhxZw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich colors in the legend correspond to the years 2015 and 2016 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors corresponding to the years 2015 and 2016 are \"#ff9999\" and \"#66b3ff\", respectively."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "nXCt8x5zNi9FaB2Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is near the chart slice with coordinates [0.205, 0.329, 0.457, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"4.2%\"<r>[0.155, 0.351, 0.19, 0.371]</r> is near the referenced slice."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "DVahjSNIGPGRFVc0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slices and the labels in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices correspond to the years and their colors referenced in the legend. Each slice is associated with a specific year, indicated by its label in the legend."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "2Qhlg54BiOMZU80W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the region with coordinates [0.264, 0.255, 0.457, 0.915] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several elements: multiple pie slices, autotexts indicating percentages, and texts showing the corresponding years."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "ohdLlzNqlxGxHVSD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects, which are located in the chart's area outlined by coordinates [0.197, 0.111, 0.756, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the full pie chart graph, including all slices, their labels, and the inner autotexts presenting percentage values for each slice."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "AchqUOvBldVeWek2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.111, 0.756, 0.915] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises of the entire pie chart, all slices with various corresponding colors, including texts and autotexts showing the years and their respective percentages."
+            }
+        ],
+        "id_raw": 153
+    },
+    {
+        "id": "UPsWeklZd8PMh2R0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.901, 0.071, 0.959, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text for the legend item {Phishing}<r>[0.901, 0.071, 0.959, 0.091]</r>."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "vhPE9p9WHXvw6Ovx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle {Cybersecurity and Data Privacy Incidents}<r>[0.31, 0.021, 0.653, 0.047]</r>."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "fRuQLB9FBvG9HwxD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.778, 0.373, 0.865, 0.393]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text representing the percentage {16.4%}<r>[0.778, 0.373, 0.865, 0.393]</r>."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "l9oxqb6KzDr1FJMP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to the {slice representing Malware}<r>[0.375, 0.109, 0.663, 0.479]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices representing {Data Breach and Spyware}<r>[[0.506, 0.235, 0.765, 0.582], [0.267, 0.152, 0.466, 0.487]]</r> are adjacent."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "dc8IR5yExUqCXK7Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat handle color is associated with the legend label \"Spyware\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the \"Spyware\" label is {#c2c2f0}<r>[0.862, 0.193, 0.89, 0.207]</r>."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "rL2P2LWZ8RDWflzI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a slice adjacent to {the slice representing Phishing}<r>[0.197, 0.281, 0.456, 0.803]</r> which color matches with the handle of the legend item \"Hacking\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slice representing {Hacking}<r>[0.324, 0.551, 0.743, 0.921]</r> is adjacent, and its color matches the handle of the legend item \"Hacking\"."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "XbhET3npkL8YmISs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region of coordinates [0.197, 0.281, 0.456, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a slice of the pie chart that represents {Phishing}<r>[0.197, 0.281, 0.456, 0.803]</r> with the color #ff9999, and includes the text labels \"25.0%\" and \"Phishing\"."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "jgJ9dfjWE1OrB2B8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.324, 0.551, 0.743, 0.921]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart for {Hacking}<r>[0.324, 0.551, 0.743, 0.921]</r>. It includes the labels \"32.8%\" and \"Hacking\" and has the color #66b3ff."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "XWxjNMcPguicHPfG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the makeup for the region [0.506, 0.235, 0.765, 0.582]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the {Data Breach}<r>[0.506, 0.235, 0.765, 0.582]</r> slice of the pie chart. It includes the labels \"16.4%\" and \"Data Breach\" and has the color #99ff99."
+            }
+        ],
+        "id_raw": 156
+    },
+    {
+        "id": "Ut3fmBFyAEmK9uvI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.367, 0.197, 0.596, 0.222] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named Social Media Usage in 2020."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "E8lmDoGn4mu3fzct",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.818, 0.396, 0.863, 0.416] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Reddit\" in the legend."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "mSqA09FDplK9oLiy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.731, 0.649, 0.789, 0.669] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.2%\"."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "xXhX8e69r2wtunXE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to the text {LinkedIn}[0.818, 0.336, 0.876, 0.356] mentioned in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates {0.502, 0.522, 0.712, 0.778} corresponds to the text LinkedIn."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "SzrJSy33iGJ0sQpw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {Pinterest slice}[0.499, 0.341, 0.7, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {LinkedIn} [0.502, 0.522, 0.712, 0.778] and {Reddit} [0.573, 0.046, 0.694, 0.334] are adjacent to the Pinterest slice."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "X6Ukulo2E0NeSpoI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the WhatsApp slice} [0.315, 0.275, 0.535, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"18.5%\"} [0.338, 0.249, 0.409, 0.269] and {\"WhatsApp\"} [0.417, 0.393, 0.461, 0.413] are located near the WhatsApp slice."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "F4iZuHqaoGJ8JNNb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.779, 0.248, 0.807, 0.262]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the color handle of the social media platform \"Facebook\" [0.779, 0.248, 0.807, 0.262], which is represented by the color \"#ff9999\"."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "9kejqHwHQtJtOarT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.482, 0.604, 0.731, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"14.2%\" [0.731, 0.649, 0.789, 0.669] which corresponds to the LinkedIn slice [0.502, 0.522, 0.712, 0.778]."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "EaxfinoGDE70xuef",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.779, 0.336, 0.807, 0.442]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains three color handles representing \"LinkedIn\" [0.779, 0.338, 0.807, 0.352], \"Pinterest\" [0.779, 0.368, 0.807, 0.382], and \"Reddit\" [0.779, 0.398, 0.807, 0.412] in the legend of the chart."
+            }
+        ],
+        "id_raw": 158
+    },
+    {
+        "id": "gy7w6fgLjy6BECcY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy looking at the chart at coordinates [0.295, 0.021, 0.668, 0.047], what is found there?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle which says \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "c3bA0BBGaMXCKykc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.842, 0.161, 0.863, 0.181] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It points to the text \"IoT\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "Y5FY1TDDmfata4Yt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf we observe the chart at coordinates [0.549, 0.239, 0.722, 0.303], what item is there?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segment labeled as \"Nanotechnology\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "yahDqsYqQpcQWKDF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology corresponds to {color handle}[0.803, 0.163, 0.831, 0.177] in the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"IoT\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "ccUmQlb6bQF4daeX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels next to {autotext}[0.666, 0.148, 0.779, 0.168] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"Nanotechnology\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "vFynaF9bvDebT5Xy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich portion of the chart does {legend item \"AI\"}[0.842, 0.071, 0.855, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice with coordinates [0.197, 0.116, 0.756, 0.687]."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "v0NIXMt62VOMzMxD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region [0.356, 0.350, 0.451, 0.500]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two objects, one is the text \"Quantum Computing\" [0.475, 0.764, 0.519, 0.784], and the other is the autotext \"19%\" [0.509, 0.948, 0.654, 0.968]. This region corresponds to the green slice."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "AtM7foj2S5nITXjE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat occupies the area within the region [0.157, 0.027, 0.833, 0.947]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region involves a large part of the whole pie chart, which includes labels and autotext of all technogloies such as \"AI\", \"Blockchain\", \"Quantum Computing\", \"IoT\", and \"Nanotechnology\". It also includes corresponding percentage labels like \"20%\", \"8%\", \"19%\", \"5%\" and \"48%\"."
+            }
+        ],
+        "id_raw": 161
+    },
+    {
+        "id": "rkGOy3d8XhP8iNNR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at location [0.403, 0.02, 0.597, 0.046] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Title, called \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "EA91t64QFoD4nxtc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinate [0.667, 0.11, 0.743, 0.135]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named \"Internal Risks\"."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "Xe637CJQ1m7DxAt7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.58, 0.196, 0.698, 0.508] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Slice represents \"Risk Level 9\" with a percentage of 11.7%."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "mcgnj3RRKcPSQmR1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is {the text autotext \"11.7%\"}[0.564, 0.194, 0.62, 0.214] located in relation to {the slice}[0.585, 0.196, 0.698, 0.508]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"11.7%\" is inside the slice."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "kVv1oXQrx6IqsPaB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.248, 0.216, 0.377, 0.512]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.253, 0.381, 0.4, 0.619], [0.197, 0.189, 0.291, 0.504]] are adjoining."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "pWaxGf7YAfasqgmZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"Risk Level 1\"}[0.594, 0.445, 0.624, 0.465] and {the slice}[0.544, 0.321, 0.691, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Risk Level 1\" is the label for the specified slice."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "CuhY2zxf6pnokrRZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.716, 0.246, 0.853, 0.515]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice for \"Risk Level 7\" with the corresponding percentage text \"10.3%\"[0.84, 0.281, 0.895, 0.301]."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "OhCcDrHEH7xjlqcR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the area [0.544, 0.321, 0.691, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area encompasses the slice labeled as \"Risk Level 1\" and also contains the text \"10.3%\"[0.485, 0.387, 0.541, 0.407]."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "unORvHcJt1Hi3fjX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.521, 0.141, 0.889, 0.93]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie chart titled \"Internal Risks\" with slices representing various risk levels and their associated percentage values."
+            }
+        ],
+        "id_raw": 162
+    },
+    {
+        "id": "iIgC6jsg5SdSrwBE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label in the legend for \"2014\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "PvGNtRE65uyCDWOO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element represented at [0.444, 0.95, 0.479, 0.97] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the percentage text \"18.0%\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "7TuZy3PQUsJw3gJX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.225, 0.021, 0.739, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the chart's subtitle \"Technological Industry Development Trend from 2010 to 2014\"."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "RV6vmlWUQQDTO9aI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart does {the legend item of '2010'}[0.952, 0.071, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item '2010' corresponds to {the slice}[0.223, 0.278, 0.482, 0.605]."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "o4uAuiYEkM7oG23n",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are adjacent to {the slice of '2013'}[0.507, 0.328, 0.766, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.341, 0.553, 0.619, 0.923], [0.283, 0.146, 0.701, 0.516]] corresponding to '2012' and '2014' respectively, are adjacent."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "uLVPpWbv3Cr0LJ0U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items in the legend correspond to the exploded slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items '2012'[0.952, 0.131, 0.987, 0.151] and '2013'[0.952, 0.161, 0.987, 0.181] correspond to the exploded slices in the pie chart."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "n2EqVrLerFf7Yh5E",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.341, 0.553, 0.619, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is part of the pie chart, containing a slice whose label \"2012\" is positioned at [0.458, 0.765, 0.502, 0.785], and whose percentage \"18.0%\" is located at [0.444, 0.95, 0.479, 0.97]. This slice is colored in '#99ff99'."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "Ipvu5DhELDXnfp0W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.507, 0.328, 0.766, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the pie chart labelled \"2013\" at [0.636, 0.564, 0.68, 0.584] with its percentage \"25.0%\" located at [0.785, 0.606, 0.82, 0.626]. The slice is colored in '#ffcc99'."
+            }
+        ],
+        "id_raw": 163
+    },
+    {
+        "id": "bw6G68AFkwxV8B8x",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.358, 0.021, 0.605, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle, named \"Global Trade Volume per Item\"."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "sFz8hzWNyoYgtQI3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.842, 0.071, 0.918, 0.091] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend label \"Electronics\"."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "oDXH9PRdUPXSRbTb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what object is at the coordinates [0.713, 0.802, 0.832, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object located there is the text \"15.0%\"."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "nqoGWv000F3JOT1t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to the legend label {Automobile Parts}[0.842, 0.131, 0.961, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.501, 0.541, 0.751, 0.886] corresponds to the legend label \"Automobile Parts\"."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "Ny7vacYzy5gtVpbK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text values are associated with {the slice}[0.197, 0.28, 0.456, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text objects {\"24.0%\"}[0.096, 0.531, 0.171, 0.551] and {\"Electronics\"}[0.279, 0.52, 0.322, 0.54] are associated with the slice."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "uIRPiLV9sIBSfJsE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend label corresponds to {the slice}[0.264, 0.219, 0.463, 0.49]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label {\"Agricultural Products\"} [0.842, 0.221, 0.987, 0.241] corresponds to the slice."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "rl2GGrKitqNxarhB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region defined by coordinates [0.501, 0.541, 0.751, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two objects: {\"15.0%\"}[0.713, 0.802, 0.832, 0.822] which shows the percentage, and {\"Automobile Parts\"}[0.595, 0.679, 0.639, 0.699] which shows part of the trade item."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "toLaGR4bVwLFLJGR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the region between [0.197, 0.28, 0.456, 0.786]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region has two objects: The percentage value {\"24.0%\"}[0.096, 0.531, 0.171, 0.551] and the trade item text {\"Electronics\"}[0.279, 0.52, 0.322, 0.54]."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "0qNlIuYrbCj008Kv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.507, 0.357, 0.766, 0.607]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two objects. One is the text \"11.0%\" [0.791, 0.467, 0.906, 0.487], indicating the trade volume percentage, and the other is the text \"Pharmaceuticals\" [0.64, 0.483, 0.684, 0.503], referring to the type of trade item."
+            }
+        ],
+        "id_raw": 168
+    },
+    {
+        "id": "fHNCp1a48d9JkjDR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text item at the coordinates [0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the year \"2020\"."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "84tP6YPVHfsKBd4H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitled text at the coordinates [0.24, 0.021, 0.723, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Education and Training Industry Trends from 2018 to 2020\"."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "il4EOOVgM0JpkOni",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color block from the legend."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "oG0MLTkpUm2PR0x1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to the slice at {coordinates}[0.508, 0.21, 0.767, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates}[0.423, 0.109, 0.631, 0.48] is adjacent."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "B0nmpkudnf0Udpjm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color block, with the coordinates [0.913, 0.073, 0.941, 0.087], refer to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color block refers to the year \"2018\" in the legend."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "8p1W6sq5eF1IH27t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice's label is adjacent to the label at {coordinates}[0.376, 0.279, 0.411, 0.299]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2020\" at {coordinates}[0.331, 0.329, 0.367, 0.349] is adjacent."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "ayJlLGjqhjIgRUKL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region of the pie chart within the coordinates [0.197, 0.109, 0.767, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "It includes multiple pie slices with corresponding labels and percentage texts. For instance, there is a slice for  2018 {at coordinates}[0.197, 0.275, 0.456, 0.709] with its label and percentage {\"at coordinates\"}[0.136, 0.475, 0.171, 0.495], [0.279, 0.488, 0.323, 0.508]."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "8SK9228s3BAeCb9R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.263, 0.552, 0.613, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a pie slice representing the year 2019 {at coordinates}[0.256, 0.552, 0.613, 0.922], along with the corresponding percentage and year label {\"at coordinates\"}[0.376, 0.939, 0.412, 0.959], [0.419, 0.758, 0.463, 0.778]."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "3gfvao0COMQzmrrN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region [0.952, 0.071, 0.987, 0.241]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the year labels for 2018, 2019, 2020 {at coordinates}[0.952, 0.071, 0.987, 0.091], [0.952, 0.101, 0.987, 0.121], [0.952, 0.131, 0.987, 0.151] in the legend of the chart."
+            }
+        ],
+        "id_raw": 170
+    },
+    {
+        "id": "9REDK0t8S2vtvKRl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which object can be associated with the coordinates [0.899, 0.071, 0.94, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the label \"Home\" in the legend."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "LH297X0aSzKzMwYr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be connected with the coordinates [0.447, 0.764, 0.491, 0.784] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Industry\"."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "br0S40aEbWIwF9es",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the coordinates [0.199, 0.112, 0.766, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary object with these coordinates is the pie chart."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "QIQNerl3qJ5npZWq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart share a border with {the slice}[0.269, 0.125, 0.468, 0.485]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.429, 0.112, 0.711, 0.482], [0.199, 0.264, 0.458, 0.502]] are adjacent to the queried slice."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "h6catHZqeFA4fMDk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend does the slice with coordinates [0.199, 0.505, 0.458, 0.814] align with?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice matches with the \"Transport\" label in the legend."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "HvCI1YTZp9BRFvCc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are all the handles in the legend corresponding to in terms of the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend handles \"Home\", \"Transport\", \"Industry\", \"Services\", \"Construction\", and \"Mining\" corresponds to the slices with coordinates [0.199, 0.264, 0.458, 0.502], [0.199, 0.505, 0.458, 0.814], [0.313, 0.553, 0.617, 0.923], [0.507, 0.328, 0.766, 0.838], [0.429, 0.112, 0.711, 0.482], and [0.269, 0.125, 0.468, 0.485] respectively."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "ab02Zzh2yEVWUyKy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the region within coordinates [0.429, 0.112, 0.711, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the slice of the pie chart for \"Construction\", and it includes the text \"20%\" [0.598, 0.094, 0.686, 0.114]. It seems to represent 20% energy usage of the Construction sector."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "6ODSfPxoKGaKlQDT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region defined by the coordinates [0.199, 0.505, 0.458, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The defined region covers a slice of the pie chart labeled \"Transport\", including the text \"15%\" [0.13, 0.679, 0.195, 0.699]. The slice appears to signify a 15% energy use by the Transport sector."
+            }
+        ],
+        "id_raw": 173
+    },
+    {
+        "id": "8o8VzRP6uPVTd91D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.182, 0.021, 0.782, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot title, which is \"Growth of Technology Companies vs Innovative Businesses (2010-2018)\"."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "ZX2iVrtWfkgd3Her",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object with coordinates [0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2014 in the legend."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "vVDN6X9kktAQOPMJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of the pie chart."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "XNmLifsagJaXjgbR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color of the legend corresponds to the slice {the slice}[0.227, 0.258, 0.46, 0.496]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to the slice is \"{the color}#ff9999\"."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "WY6qvs6d7u6JEfJK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the corresponding year in the legend for the exploded slice with coordinates [0.265, 0.547, 0.467, 0.902]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding year is \"2014\"."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "OxHf9zVSxF5MZxbo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year in the legend corresponds to the \"{color}#ffb366\" in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2018\" in the legend corresponds to the specified color."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "t2T9IBPU2fpos4ab",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region within coordinates [0.413, 0.553, 0.616, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice representing the year 2015, with a corresponding autotext of \"12.9%\"."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "IoN0EFMXVT82OgZa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.504, 0.169, 0.763, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice representing the year 2017, with an associated autotext \"18.4%\"."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "P0lwFznExYzMEGwH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within coordinates [0.504, 0.535, 0.762, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2016, with the corresponding autotext \"15.6%\" denoting the percentage."
+            }
+        ],
+        "id_raw": 174
+    },
+    {
+        "id": "blLzf8gDDIofMmBO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is referred as color '#99ff99'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2014\" with the handle coordinate <r>[0.913, 0.133, 0.941, 0.147]</r>."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "BTxmtFUzbDv514o4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.208, 0.146, 0.76, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the pie chart that represents 'Design Disciplines Innovation' over several years."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "dyXQX7frB17QRRt7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.372, 0.327, 0.407, 0.347] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2021\"."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "fE6mgwimF13BbyUB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item's color corresponds to the slice at the coordinates [0.503, 0.538, 0.76, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2017\" with color '#c2c2d4' corresponds to this slice."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "qjrTZ8Y0kNBKy1UG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color '#ff9999' represent according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the legend <r>[0.908, 0.063, 0.993, 0.368]</r>, the color '#ff9999' represents the year \"2012\"."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "mN4G1QENIXO5eNsi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to the legend item labeled \"2020\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice related to the text \"2020\" has the coordinates <r>[0.382, 0.146, 0.549, 0.516]</r>."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "pCpOkCmlkQBpDCXg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.208, 0.146, 0.76, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is occupied by the pie chart that represents 'Design Disciplines Innovation' from 2012 to 2021. The slices of various colors in this region represent a specific year, each color corresponds to a legend item on the right side of the chart with coordinates [0.908, 0.063, 0.993, 0.368]. Some slices have labels and percentages inside."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "Jcn484drbbJkSpEd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the pie chart. They are labeled with years from \"2012\" to \"2021\", each has a uniquely colored handle symbolizing the corresponding slice in the pie chart."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "qurMoqq1zVQJpQDG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region from [0.208, 0.146, 0.76, 0.886] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main part of the chart, it shows a pie chart visualizing 'Design Disciplines Innovation' over several years, from 2012 to 2021. The size & color of each slice indicate the proportion & year, respectively."
+            }
+        ],
+        "id_raw": 175
+    },
+    {
+        "id": "jrRauMWVDJNnTsKN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart with coordinates [0.266, 0.021, 0.697, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Media Platforms Usage among Internet Users\"."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "MmrJinSB5w6QA28I",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.916, 0.071, 0.982, 0.091] in the subplot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Facebook\"}[0.916, 0.071, 0.982, 0.091] is the identified object in the subplot legend."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "mLfxhIvfjmiV8aPd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.106, 0.487, 0.171, 0.507] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {\"20.8%\"}[0.106, 0.487, 0.171, 0.507] is present at these coordinates in the pie chart."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "lOKooHulEW6pKTxm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich social media platform corresponds to {the slice}[0.503, 0.537, 0.752, 0.836] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The platform {Instagram}[0.608, 0.655, 0.652, 0.675] corresponds to this slice in the pie chart."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "5RkoKCDwiuyGTXRw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage that corresponds to the {'LinkedIn'}[0.64, 0.478, 0.684, 0.498] slice in the pie?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage that corresponds to the 'LinkedIn' slice is {\"12.5%\"}[0.791, 0.458, 0.849, 0.478]."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "x5UrC3nWcEVh8mAu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the pie slice representing {TikTok}[0.369, 0.282, 0.413, 0.302] has?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice for TikTok {[0.27, 0.119, 0.469, 0.484]} is colored '#808080'."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "nLq46JYx2Gpy8WxH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide the details of the region within [0.458, 0.276, 0.726, 0.727]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for 'Facebook' with color '#ff9999' {[0.197, 0.276, 0.456, 0.727]}. Inside this slice, it also contains the label \"Facebook\"[0.279, 0.495, 0.322, 0.515] with the corresponding percentage \"20.8%\"[0.106, 0.487, 0.171, 0.507]."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "REeeqEDqrhWxgbBD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.455, 0.276, 0.721, 0.727]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a chunk of the pie chart representing Facebook, with usage noted as \"20.8%\"[0.106, 0.487, 0.171, 0.507] and the slice label 'Facebook'[0.279, 0.495, 0.322, 0.515]."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "EIpcC5hs1dJvsmIn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region bound by [0.443, 0.11, 0.65, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart for WhatsApp colored as '#c2f0c2'. Alongside this, the slice includes the label {\"WhatsApp\"}[0.505, 0.255, 0.549, 0.275] and a corresponding usage percentage of {\"13.5%\"}[0.559, 0.076, 0.63, 0.096]."
+            }
+        ],
+        "id_raw": 177
+    },
+    {
+        "id": "CLtg6qDKzUg3P2IV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.258, 0.021, 0.705, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, which reads \"Culture and Arts Industry Revenue from 2005 to 2014\"."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "5m1EeXkluKFRD1k7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the figure outlined by the coordinates [0.197, 0.11, 0.763, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart figure."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "Nfy2MJM0ySPkSF2Q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Those coordinates point to the chart's legend."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "RmAPpbf1gbewmgAK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are related to the legend item {2010}[0.952, 0.221, 0.987, 0.241] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice in the pie chart {slice}[0.31, 0.55, 0.472, 0.919] is related."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "20089yGgfBQTfop1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the disposition of {the slice}[0.278, 0.11, 0.587, 0.48] in relation to other slices of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is positioned in the upper part of the pie chart and represents data for the year \"2014\"."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "OoJ5eymYouprbyOS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents {the year 2006}[0.952, 0.101, 0.987, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for the year 2006 is blue, represented by the legend handle [0.913, 0.103, 0.941, 0.117]."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "rDazRxMQ3jxbQmA9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects located within the regional coordinates [0.197, 0.11, 0.763, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the pie chart. The pie chart includes slices for each year from 2005 to 2014, with each slice labeled with the corresponding year and percentage. The slices are color-coded according to the legend to the side of the chart."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "rpMqgvN8bvgWJV79",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are within the region [0.508, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there is a legend that labels the color for each year represented in the pie chart."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "ri2GAEbfJa4JStIS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements included in the area [0.197, 0.11, 0.763, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly houses the pie chart breakout. The pie chart is partitioned into different sections or 'slices' corresponding to different years ranging from 2005 to 2014. Each of these slices is color-coded and labeled with the year it represents along with the corresponding percentage."
+            }
+        ],
+        "id_raw": 182
+    },
+    {
+        "id": "dvhEgl3RwLES4707",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.211, 0.021, 0.752, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, labeled \"Efficiency Improvement Potential Assessment from 2011 to 2018\"."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "6PXETKle3M6dV9YI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.548] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "uVzMEDvtRR1XAb8p",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.371, 0.987, 0.391] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text labelling the year \"2013\"."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "0SRkoKVZSTuHgfVy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts in the legend are adjacent to {the 2011 label}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts representing {\"2012\"}[0.952, 0.101, 0.987, 0.121] and {\"2013\"}[0.952, 0.131, 0.987, 0.151] are adjacent to the 2011 label."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "0ZTMY7Zm8ySnRoXX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart corresponds to the {2018 label}[0.952, 0.520, 0.987, 0.540] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice is located at coordinates [0.268, 0.138, 0.467, 0.486]."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "p7cxM8U00U11kE97",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects in the coordinates [0.197, 0.109, 0.766, 0.922] and the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects in the chart are pie slices, each related to a particular year in the legend."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "ZCRgbp8KsI4uKI3z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.109, 0.766, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart itself, with individual slices representing different years' efficiency improvements. The colors of the slices match those represented in the legend. For example, the slice at [0.226, 0.258, 0.46, 0.496] corresponds to the \"2011\" label in the legend at [0.952, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "wtbxzYcdERnjKX1Q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire plotting area, which contains the main pie chart, the legend, and the subtitle."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "eAFRajFJBsEtv58M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region that bound by [0.952, 0.071, 0.987, 0.301]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes items from the legend representing the labels for years from 2011 to 2018. Each of these items in the legend corresponds to a color-coded pie slice in the chart."
+            }
+        ],
+        "id_raw": 184
+    },
+    {
+        "id": "dgZSnd1cAJKX0HRK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.245, 0.021, 0.718, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle for the plot: \"Mutual Funds Investment Distribution from 2012 to 2019\"."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "XjJEw7MhrgDVB27d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the pie chart."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "jgHUUTez44pubmjt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.788, 0.577, 0.824, 0.597] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the data label text \"15.4%\"."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "frTRfmIEMSj6eVLi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart correspond to {the legend items}[0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {at}[[0.202, 0.263, 0.458, 0.501], [0.197, 0.465, 0.456, 0.646], [0.218, 0.539, 0.461, 0.847], [0.332, 0.552, 0.502, 0.922], [0.494, 0.548, 0.702, 0.916], [0.507, 0.405, 0.766, 0.744], [0.498, 0.121, 0.744, 0.488], [0.274, 0.111, 0.51, 0.481]] correspond to the legend items."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "afzYZMLQM0tDWV19",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the text \"15.4%\" near the {coordinates of \"2017\"}[0.639, 0.547, 0.683, 0.567] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.4%\" belongs to the {slice} [0.507, 0.405, 0.766, 0.744]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "EjFfo9gsSKOZLrhz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice at coordinates [0.494, 0.548, 0.702, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2016\" corresponds to the slice at the given coordinates."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "WI2yiusUZQEnH9Qg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.484, 0.403, 0.791, 0.752]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of pie chart with percentage \"15.4%\"[0.788, 0.577, 0.824, 0.597] and the corresponding category \"2017\"[0.639, 0.547, 0.683, 0.567]. The slice color seems to correspond to the color of the \"2017\" item in the legend."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "g83WQmWuEvOPbhT0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.197, 0.112, 0.770, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several pie slices, representing different years' mutual funds investment distributions. For example, \"2013\" [0.284, 0.532, 0.319, 0.552] with the percentage \"7.9%\" [0.137, 0.551, 0.172, 0.571]."
+            }
+        ],
+        "id_raw": 185
+    },
+    {
+        "id": "8OAKXAuu8ynuWahs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the subtitle at coordinates [0.31, 0.021, 0.653, 0.047] in the chart state?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle states \"Usage of AI and ML Tools in the past year\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "KCQg68bZMbd7BI5o",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.777, 0.063, 0.993, 0.219] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend of the chart."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "TuWFl91y4R5Pgk4P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the chart element at coordinates [0.197, 0.112, 0.766, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the main pie chart."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "Uohm2xCDifo4cPz3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice {the slice}[0.309, 0.552, 0.538, 0.922] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to \"Deep Learning\" in the legend."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "nYWvK1MCor8hSLpW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is linked to {the slice}[0.197, 0.281, 0.456, 0.803] from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Neural Networks\" is linked to this slice."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "tjR3F7Ir8gudTdba",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segment corresponds to the legend category \"Reinforcement Learning\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segment with coordinates [0.273, 0.112, 0.49, 0.482] corresponds to \"Reinforcement Learning\"."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "dvCra6U97KB2DSIJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.482, 0.112, 0.766, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart, with a label \"Machine Learning\" and an autotext \"35.0%\" located at [0.785, 0.606, 0.907, 0.626]."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "TSYlpsUAUCyLHvHN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region [0.273, 0.112, 0.49, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region has a slice of the pie chart labeled \"Reinforcement Learning\" with an autotext percentage \"15.0%\" at the coordinates [0.194, 0.097, 0.36, 0.117]."
+            }
+        ],
+        "id_raw": 186
+    },
+    {
+        "id": "CFUpeAxVpHKKtULs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is present at the coordinates [0.867, 0.161, 0.987, 0.182] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Carbon_Footprint\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "ipcmV9qQqzkJPdEX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the bracket along coordinates [0.828, 0.133, 0.856, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle of the legend item \"Habitat_Loss\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "ol8MkpHLABUlreYf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat component of the chart is located at coordinates [0.143, 0.741, 0.218, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"40.0%\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "zQNpFEvDHZ0nAQPo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the corresponding slice for {the legend item \"Ecosystem\"}<r>[0.867, 0.071, 0.942, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice containing the text \"Ecosystem\"}<r>[0.201, 0.298, 0.522, 0.906]</r> corresponds to the legend item \"Ecosystem\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "bkqvMrII4biDJSpQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is adjacent to the text {label 'Water_use'}<r>[0.867, 0.101, 0.94, 0.121]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle<r>[0.828, 0.103, 0.856, 0.117]</r> is adjacent to the label \"Water_use\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "hJeWggCJ5zkMOFMx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship in terms of position for the object located at <r>[0.658, 0.677, 0.733, 0.697]</r> with respect to the object at <r>[0.624, 0.611, 0.669, 0.631]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at <r>[0.658, 0.677, 0.733, 0.697]</r>, which is the text \"30.0%\", is above the object at <r>[0.624, 0.611, 0.669, 0.631]</r>, which is the label \"Water_use\"."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "i5SkSgSmqpuTdlmc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects the region [0.201, 0.298, 0.522, 0.906] of the chart entails?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice of the pie chart for the label \"Ecosystem\"<r>[0.306, 0.643, 0.35, 0.663]</r> and the text \"40.0%\"<r>[0.143, 0.741, 0.218, 0.761]</r>."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "4N76g5YWLdEMiCNZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears in the region spanned by [0.482, 0.147, 0.701, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are the slice for \"Habitat_Loss\"<r>[0.54, 0.316, 0.584, 0.336]</r> with its percentage text \"15.0%\"<r>[0.628, 0.157, 0.717, 0.177]</r>."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "emoIUi0DVLJ9hxyO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do we see in the region defined by [0.283, 0.146, 0.5, 0.516] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie section for the \"Carbon_Footprint\"<r>[0.399, 0.302, 0.443, 0.322]</r> along with its associated text \"15.0%\"<r>[0.25, 0.131, 0.37, 0.151]</r>."
+            }
+        ],
+        "id_raw": 188
+    },
+    {
+        "id": "UVKXjbDqIzAxyLkX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat important information is attached at the coordinates [0.344, 0.021, 0.619, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Value Change from 2000 to 2006\" is present at those coordinates."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "jUQ83X25yfvywLF1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart element is located at coordinates [0.198, 0.111, 0.767, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart is found at these coordinates."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "fRrhVIHo6or5JmNB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the \"2003\" slice and what information is attached to it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"2003\" slice is located at [0.498, 0.545, 0.73, 0.907], labelled as \"2003\" and it also contains an annotation showing \"14.3%\"."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "HTXlYLQb6KaJALu6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the 2002 slice}[0.319, 0.552, 0.531, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The 2003 slice}[0.498, 0.545, 0.73, 0.907] is adjacent to it."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "kpWHPfgpIcU1OeOQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the 2005 slice}[0.497, 0.12, 0.728, 0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with the same color located at coordinates [0.913, 0.223, 0.941, 0.237] corresponds to the 2005 slice."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "QWAnXZ863UUorUsU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are next to {the 2004 slice}[0.508, 0.35, 0.767, 0.68]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.571, 0.71, 0.615, 0.73], [0.641, 0.505, 0.685, 0.525]] are adjacent to it."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "IaqxgM1Fe0rLmXwl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region [0.482, 0.12, 0.767, 0.486]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers two pie slices, specifically the \"2005\" slice with coordinates [0.497, 0.12, 0.728, 0.486] and a part of \"2004\" slice. Both slices have an attached annotation indicating their percentage share."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "LiXF2onHZr3UvMH9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_194.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the element present in the region of [0.198, 0.267, 0.457, 0.535]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region specifically includes the \"2000\" pie slice with a label and an annotation representing that it covers 12.4% of the whole pie."
+            }
+        ],
+        "id_raw": 194
+    },
+    {
+        "id": "9bTxjFqYFpHb5fF4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main topic presented in the location with the coordinates [0.294, 0.021, 0.669, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is \"AI and Automation Trends from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "az8xnyMtrxgtVpKW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the legend items are noted within the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items correspond to the years 2010 to 2019."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "4ApqEIspCuz7v8HB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the graphical representation within the coordinates [0.197, 0.112, 0.765, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "FUF1NQfFnjrzokXK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which legend item color matches the slice for the year 2016 from {the legend}[0.908, 0.063, 0.993, 0.368] and {the pie chart}[0.197, 0.112, 0.765, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#abedd8\" corresponds to the year 2016."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "T0MiL95O2yjrCtix",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year text is located close to the slice in the coordinates [0.211, 0.26, 0.459, 0.498] from the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2010\" is located close to that slice."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "0vwRLP4jw9ihZlcg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify which slice color from the pie chart [0.197, 0.112, 0.765, 0.923] matches the legend item \"2019\" from the legend [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice color for the year \"2019\" is \"#fef4fb\"."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "mWQnVtHz74KA6mYz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the distribution of items within the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains legend entries for individual years from 2010 to 2019, each accompanied by a different color. For instance, the year \"2010\" is displayed with the color \"#ff9999\" at the top of the region, while the year \"2019\" is at the bottom with the color \"#fef4fb\". Each legend item is represented with its respective color and year."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "tkBrP1hP9GCL0z4U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the sector of the pie chart from coordinates [0.389, 0.553, 0.543, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This sector of the pie chart represents the year \"2014\" with the color code \"#c2c2f0\". It contains the autotext \"9.6%\" suggesting the proportion of the data it represents."
+            }
+        ],
+        "id_raw": 195
+    },
+    {
+        "id": "dCVk5QM09zl02Cbe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the location with the coordinates [0.276, 0.021, 0.687, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Global Resource Consumption from 2000 to 2008\"."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "lfCmRQOhwSjXKmit",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "1v7gRVsZcc5TT9Dk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice at coordinates [0.203, 0.262, 0.458, 0.5] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"2000\" with a percentage of \"8.3%\"."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "JwjQDhT4U15yqSSQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend item that corresponds to the slice with {coordinates}[0.273, 0.112, 0.48, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the legend item is given by the handle of \"2008\" from the legend, and it is \"#99ffbb\"."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "q5Qa581kCQYiU8Gj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year is adjacent to the slice with {coordinates}[0.273, 0.112, 0.48, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2007\" with {slice coordinates}[0.493, 0.113, 0.695, 0.483] is adjacent."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "fzuVty9Wy7c6832i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart has the highest percentage?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the slice representing the year \"2008\" with {coordinates}[0.273, 0.112, 0.48, 0.482] which has the highest percentage of 14.4%."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "CrHCUaa6H26UxeoX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.112, 0.766, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart showing global resource consumption from 2000 to 2008. It includes slices for each year, each with a different color coding and labels indicating the percentage consumption for that year."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "MLDYLpz2ZQdHO3kB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.221, 0.539, 0.461, 0.843]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for the year \"2002\" as well as the label text \"9.3%\" and \"2002\"."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "otft7RWJNsLT89ij",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_1_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region from coordinates [0.908, 0.063, 0.993, 0.338]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart, with each item consisting of a color handle and a label representing the corresponding year from 2000 to 2008."
+            }
+        ],
+        "id_raw": 199
+    },
+    {
+        "id": "HofaMKe4UI4Cvt9i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.344, 0.021, 0.619, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Social Cultural and Value Change."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "G1ytfNHgHSLTKxck",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is defined by the region [0.223, 0.278, 0.482, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice, labelled \"Traditionalism Drop\" with percentage 15.0%."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "Y2r1KPTLq4DnEdLL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.805, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Ecological Awareness Rise\"."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "oMwnNE94eEqZkwDH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are located nearest to the pie slice {Traditionalism Drop}[0.223, 0.278, 0.482, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\"<r>[0.069, 0.414, 0.204, 0.434]</r> is located closest to the pie slice Traditionalism Drop."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "X3rekrJS5efjVV3Q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich side of the pie chart are the slices {Globalization Influence}[0.419, 0.146, 0.701, 0.516] and {Ecological Awareness Rise}[0.283, 0.157, 0.482, 0.516] located?"
+            },
+            {
+                "from": "gpt",
+                "value": "Globalization Influence and Ecological Awareness Rise are located on the lower side of the pie chart."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "evHPG6clcCzB9S33",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice with coordinates [0.482, 0.32, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item called \"Gender Equality Rise\"<r>[0.805, 0.131, 0.950, 0.151]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "ZplPzMBQ9ZJGmu71",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire pie chart, including all pie slices labeled with text and percentages, such as \"Traditionalism Drop\" [0.308, 0.456, 0.352, 0.476] and \"Gender Equality Rise\" [0.611, 0.556, 0.655, 0.576]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "oeWVPA2oRBfmYN8f",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the region [0.76, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the pie chart which includes \"Traditionalism Drop\" [0.805, 0.071, 0.940, 0.091], \"Religiosity Increase\" [0.805, 0.101, 0.939, 0.121], \"Gender Equality Rise\" [0.805, 0.131, 0.950, 0.151], \"Globalization Influence\" [0.805, 0.161, 0.964, 0.181], and \"Ecological Awareness Rise\" [0.805, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "2SDWib9zhkcrrwk7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the coordinates [0.482, 0.32, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by the pie slice labeled \"Gender Equality Rise\" with a corresponding percentage text \"25.0%\" [0.759, 0.598, 0.905, 0.618]."
+            }
+        ],
+        "id_raw": 200
+    },
+    {
+        "id": "gmlbTEQnYXoXEeR7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.385, 0.021, 0.578, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Demographic Research\"."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "ZDplCVAgcd2CSi8e",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item with coordinates [0.929, 0.131, 0.973, 0.151] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the category \"Adults\"."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "FQQIdjQVgW8hmLR0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.744, 0.347, 0.787, 0.367] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the percentage \"35.0%\"."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "SmpXhKqPCGGeQfc7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with the {legend item \"Adults\"}[0.929, 0.131, 0.973, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}[0.482, 0.147, 0.741, 0.754] is associated with it."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "xf2gw8bvWCJGdjHb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"20.0%\"}[0.139, 0.478, 0.197, 0.498] and {the legend item \"Children\"}[0.929, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\" represents the percentage of the category \"Children\" in the pie chart."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "szoL4xSQ54RIOFNW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.482, 0.147, 0.741, 0.754] and {the percentage \"35.0%\"}[0.744, 0.347, 0.787, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the percentage \"35.0%\"."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "AZHy7SYBiQjWAqd4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.262, 0.516, 0.680, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the {pie slice}[0.262, 0.516, 0.68, 0.886], {the category \"Youth\"}[0.449, 0.727, 0.493, 0.747], and {the percentage \"30.0%\"}[0.424, 0.912, 0.462, 0.932]."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "tiC4aWDuSGjK0TII",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.482, 0.147, 0.741, 0.754]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the category {\"Adults\"}[0.603, 0.419, 0.647, 0.439] and the percentage {\"35.0%\"}[0.744, 0.347, 0.787, 0.367]. This represents a slice of the pie chart."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "RS9As8oE34Z7IsNb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.223, 0.278, 0.482, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a {pie slice}[0.223, 0.278, 0.482, 0.712], the category {\"Children\"}[0.305, 0.49, 0.348, 0.51], and the percentage {\"20.0%\"}[0.139, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 201
+    },
+    {
+        "id": "Y3XZWskfBNiX1kyw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is printed in the subtitle with coordinates [0.218, 0.021, 0.745, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Artificial Intelligence and automation trends from 2015 to 2021 is the subtitle found in the chart."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "aeHVOxK0bMP2COiM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main figure of the chart as per the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "AI2BxHGWerDWyqOx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is found at the coordinates [0.178, 0.369, 0.213, 0.389] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"11.3%\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "QWK9tl6QYGFp6x3C",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is next to {the slice}[0.223, 0.278, 0.482, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.223, 0.516, 0.482, 0.776] is adjacent."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "633OLSE7gIApNvhb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located near the {slice}[0.223, 0.516, 0.482, 0.776] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2016\" is located near this slice."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "eokpvKeeREAx3ECy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color matches the label '2018' in the legend according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label '2018' corresponds to the color '#ffcc99'."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "x3K8ndprBJRKulcN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region encompassed by the coordinates [0.408, 0.297, 0.452, 0.317] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"2021\" embedded within the pie slice with the color code '#c2c2f0'. This indicates that the proportion for the year 2021 is represented by this area of the chart."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "7F1lY77gMHoqF8Mk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region between [0.283, 0.146, 0.531, 0.516] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises a slice of the pie chart with the label \"2021\" and the corresponding percentage \"16.9%\" represented by the color '#c2c2f0'."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "0eF0ORSsHQU4dBEI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region covered by coordinates [0.763, 0.568, 0.798, 0.588] in the chart contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the label \"15.6%\" which is part of the pie chart representing the '2019' data point."
+            }
+        ],
+        "id_raw": 202
+    },
+    {
+        "id": "rxNkmVmqSvXwiOlv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object has these coordinates in the chart: [0.291, 0.021, 0.672, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle called \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "BDQn9GaDtCQlVTtV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at these coordinates [0.859, 0.071, 0.979, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled \"Energy Efficiency\"."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "xieeIm46hXPJxghp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "yliPVFNFTpyd2EcA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.482, 0.147, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.283, 0.146, 0.5, 0.516]] is adjacent."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "JTCguzQmh8q6osSH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices and the objects in the Sub Legend at coordinates [0.814, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices in the pie chart correspond to the labels in the Sub Legend. Specifically, the slices with the coordinates [0.223, 0.278, 0.482, 0.799], [0.315, 0.32, 0.741, 0.886], [0.482, 0.147, 0.701, 0.516], [0.283, 0.146, 0.5, 0.516] correspond to the Sub Legend items \"Energy Efficiency\", \"Material Efficiency\", \"System Efficiency\", and \"Process Efficiency\" respectively."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "XifwhcXTCHmaBKKI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any objects close to {the subtitle} [0.291, 0.021, 0.672, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the pie chart {pie chart} [0.223, 0.146, 0.741, 0.886] is close to the subtitle."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "OW3svWnavOIvi85k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what objects are in the region defined by coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main frame of the information which include the pie chart [0.223, 0.146, 0.741, 0.886], the subtitle [0.291, 0.021, 0.672, 0.047], and the Sub Legend [0.814, 0.063, 0.993, 0.189]."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "FS2VGpw5Hv7qo64h",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the contents within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, these coordinates envelop the pie chart which consists of four slices with the corresponding labels of \"Energy Efficiency\", \"Material Efficiency\", \"System Efficiency\", and \"Process Efficiency\". Each slice also displays the percentage of each efficiency type."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "CRBBQJXmuhbtzrL1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is held in the region between coordinates [0.814, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is the Sub Legend for the pie chart, containing four color-coded items: \"Energy Efficiency\"[0.859, 0.071, 0.979, 0.091], \"Material Efficiency\"[0.859, 0.101, 0.987, 0.121], \"System Efficiency\"[0.859, 0.131, 0.982, 0.151], and \"Process Efficiency\"[0.859, 0.161, 0.983, 0.181]."
+            }
+        ],
+        "id_raw": 203
+    },
+    {
+        "id": "bET4EGujwr5DEKdc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart with coordinates [0.303, 0.021, 0.66, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Rural and Urban Development Comparison\"."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "FbDD97oxFNkvSK2u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.838, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "c4W4MdSElaLxb7wS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.328, 0.877, 0.364, 0.897] on this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"15.5%\"."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "VuerK8rzNMcdB6do",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend item \"Electricity\" [0.883, 0.071, 0.952, 0.091] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Electricity\" corresponds to the {slice}<r>[0.223, 0.278, 0.482, 0.743]</r>."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "GJ3zXwXeXGuiTmDH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the colors corresponding to the legend item \"Drinking Water\" [0.883, 0.101, 0.987, 0.121] and \"Paved Roads\" [0.883, 0.131, 0.971, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Drinking Water\" corresponds to the {slice}<r>[0.277, 0.516, 0.497, 0.886]</r> with color \"#66b3ff\" and \"Paved Roads\" corresponds to the {slice}<r>[0.482, 0.516, 0.736, 0.885]</r> with color \"#99ff99\"."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "ImJnlWR0PRZ5195w",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is under the label 'Rural' [0.396, 0.708, 0.44, 0.728] and what percentage does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice [0.277, 0.516, 0.497, 0.886] is under the label 'Rural' and it represents \"15.5%\"."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "YtZExQtcexm9cilW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a pie chart consisting of 6 slices with different colors representing various proportions, and each slice has accompanying text labels showing the percentage and a descriptor 'Urban' or 'Rural'."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "nSp3soF5HitQwEgm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region from [0.223, 0.164, 0.689, 0.587]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several slices of pie with percentages and a category ('Urban' or 'Rural') assigned to each slice. The percentages are \"21.6%\", \"13.2%\", \"19.9%\", \"8.8%\" plus the category descriptors 'Urban' or 'Rural'."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "dZbEhFymLklWJREZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features located in the region [0.838, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with three items: \"Electricity\", \"Drinking Water\", and \"Paved Roads\". Each item has its own color to distinguish on the pie chart."
+            }
+        ],
+        "id_raw": 206
+    },
+    {
+        "id": "gwDEmeC6Iz12azw8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object within the coordinates [0.193, 0.021, 0.77, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle, which reads \"Entertainment and Gaming Industry Market Share from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "j44OvtWKojbCnaNp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.193, 0.941, 0.207] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a color patch used in the legend for the year 2019."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "yo4QBypnSjKJb4sR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at coordinates [0.162, 0.478, 0.197, 0.498] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\" is located there."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "fJstpCiIgVwmZ6wl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation for {the year 2016}[0.912, 0.103, 0.941, 0.117] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the legend, the year 2016 is represented by the color {\"#66b3ff\"}[0.912, 0.103, 0.941, 0.117]."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "ePtRyUb4ucAjiGpH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item has a color patch located at position [0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2016\"}[0.952, 0.101, 0.987, 0.121] has a color patch at that position."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "835d7PkxzBE6mQTR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice matches with the legend item {2015}[0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with corresponding color and coordinates {\"#ff9999\"}[0.223, 0.278, 0.482, 0.712] matches with the legend item 2015."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "sE4WQ9i8x5yppNZk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.278, 0.482, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of a pie chart that represents the year 2015 with a 20.0% market share in the entertainment and gaming industry. You can find the text {\"2015\"}[0.305, 0.49, 0.348, 0.510] and the percentage {\"20.0%\"}[0.162, 0.478, 0.197, 0.498] within this area."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "Cg9Kh1KThaojOpGf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region [0.283, 0.157, 0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the year 2019 as part of a pie chart, with a corresponding market share of 10.0%. Inside this region, you will find the text {\"2019\"}[0.377, 0.318, 0.421, 0.338] and the percentage {\"10.0%\"}[0.295, 0.161, 0.331, 0.181]."
+            }
+        ],
+        "id_raw": 207
+    },
+    {
+        "id": "S0qtjjtv2fiBtXfc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title or the main object at the coordinates [0.273, 0.021, 0.69, 0.047] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, called \"Gaming Industry Development from 2005 to 2010\"."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "PjeqpeVZ5ItLdmzK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.908, 0.063, 0.993, 0.249] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "kCGgN2KD4wmnpXEg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be found at the coordinates [0.223, 0.278, 0.482, 0.531] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the slice for year 2005 of the pie chart."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "c8I1drBdQqYRARmv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is related or connected to {legend item}'s color boxes at coordinates [0.913, 0.073, 0.941, 0.087] and [0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are related to {the slices}[0.223, 0.278, 0.482, 0.531] and [0.223, 0.516, 0.482, 0.807] of the pie chart respectively, which corresponds to the years 2005 and 2006."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "HqDfwdqNaHdvIpTl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is attached to the slice {coordinates} [0.283, 0.146, 0.629, 0.516] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2010\" [0.439, 0.286, 0.483, 0.306] is attached to the slice."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "gXvKYfIz91mYLYUl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {legend item} at [0.913, 0.223, 0.941, 0.237] and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with coordinates indicates the color of the slice for year \"2010\", which is {slice} [0.283, 0.146, 0.629, 0.516]."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "TnhkbSHYfFZQN9yx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886] in the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the whole pie chart, which contains slices for different years between 2005 and 2010, with each slice having different colors and percentages."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "vyu2S1WUQiVFyd4i",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.321, 0.516, 0.563, 0.886] of the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the slice for year 2007 of the pie chart, with the color '#99ff99' and an autotext \"15.7%\" [0.397, 0.907, 0.432, 0.927]."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "3iOayXZVpLrTFW6S",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.283, 0.146, 0.629, 0.516] of the chart figure."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice for year 2010 in the pie chart, colored '#ffb3e6' with the percentage \"23.5%\" [0.409, 0.102, 0.444, 0.122] written on it."
+            }
+        ],
+        "id_raw": 209
+    },
+    {
+        "id": "gIvb1hcVO5nNG1S8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.842, 0.071, 0.939, 0.091] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text refers to label \"Heart Disease\"."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "yiSENkhRr9bD69BI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object positioned at [0.506, 0.912, 0.555, 0.932] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"28.6%\"."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "QZ3JCzjEMBEeVaSn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.615, 0.499, 0.659, 0.519] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Respiratory Diseases\"."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "Qp2s1OZhKNxJubaR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich type of disease is represented by color '#ffcc99' as per the legend in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The disease \"Diabetes\" is depicted by the color '#ffcc99'."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "2OFS4fxRzVf20f6t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the {legend item \"Cancer\"}[0.842, 0.101, 0.891, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is {the blue one}[0.294, 0.516, 0.697, 0.886]."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "2MBpnnQaRbWOUAs1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation of {legend \"Heart Disease\"}[0.842, 0.071, 0.939, 0.091] to the pie diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {red slice}[0.223, 0.278, 0.482, 0.771] in the pie."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "aXo3jsN4ttvTHfZG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains five different pie slices each representing different diseases annotated with both labels and percentage values. The pie slices, represented with different colors, depict 'Heart Disease', 'Cancer', 'Respiratory Diseases', 'Diabetes', and 'Infectious Diseases'."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "FPSpK4r6FETfHkva",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExtend the information for the region [0.797, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the legend of the pie chart, which contains the labels \"Heart Disease\", \"Cancer\", \"Respiratory Diseases\", \"Diabetes\", and \"Infectious Diseases\" along with their color indicators."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "SVZybq4OskK2R1cE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nGive details about the section [0.615, 0.499, 0.659, 0.519]."
+            },
+            {
+                "from": "gpt",
+                "value": "This section, found inside a pie slice, contains the text \"Respiratory Diseases\" which is the label for the pie slice it resides in."
+            }
+        ],
+        "id_raw": 210
+    },
+    {
+        "id": "cv15ZyWuT0e0J7Yo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object visible at the coordinates [0.205, 0.021, 0.758, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart sub-title, \"Social Media and Digital Communication Usage from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "z0SxTchW1HeImBVc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main subplot area where the pie chart is located."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "2am9LMpUjt5b3BC7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is identified within coordinates [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the sub-legend of the pie chart with labels representing the years 2010 to 2017."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "98iQVEyT1nlje174",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to the slice represented with {coordinates}[0.373, 0.516, 0.574, 0.886]"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {2014}[0.482, 0.516, 0.723, 0.862] is adjacent."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "ORvp90sKn6GNofkw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice's handle corresponds to the text in the legend {2014}[0.952, 0.191, 0.987, 0.211]"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice's handle that corresponds to it is {the handle}[0.913, 0.193, 0.941, 0.207]"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "KFu21ROAHQkTzWKQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text in the legend is associated with the handle represented with these coordinates [0.913, 0.073, 0.941, 0.087]"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2010\" [0.952, 0.071, 0.987, 0.091] corresponds to this handle."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "3i1wZ4kwVD7FtMKO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region enclosed by the coordinates [0.373, 0.516, 0.574, 0.886]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region denotes a piece of the pie chart, specifically the part representing the year 2013. It includes an auto-text \"12.7%\" [0.437, 0.913, 0.472, 0.933] and the text \"2013\" [0.454, 0.728, 0.498, 0.748]."
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "1dCScqQqguqbPQ4A",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.482, 0.334, 0.741, 0.649] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a portion of the pie chart for the year 2015. It includes the auto-text \"14.0%\" at [0.766, 0.476, 0.801, 0.496] and the label \"2015\" [0.615, 0.489, 0.659, 0.509]"
+            }
+        ],
+        "id_raw": 211
+    },
+    {
+        "id": "QTm9UExxiJyonwYo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is within the coordinates [0.147, 0.388, 0.209, 0.408] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.8%\" is within the specified coordinates."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "8QA7TMBET3y3Vv1j",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.158, 0.053, 0.805, 0.979] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main axes of the chart are located within these coordinates."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "4HGzgc6eHPnw0D5n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.482, 0.516, 0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The central point of the pie chart is located at these coordinates."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "uvqqMGrJiTHgDCO1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.482, 0.202, 0.741, 0.581]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.436, 0.516, 0.737, 0.886],[0.474, 0.146, 0.619, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "zr3m8zEuYOlOZZlr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to {the slice}[0.283, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Stroke\"}[0.392, 0.306, 0.436, 0.326] corresponds to the slice."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "Xa3p3thKWv9mvRfV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the legend item {the text \"Obesity\"}[0.825, 0.161, 0.878, 0.181] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Obesity\" corresponds to the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "0suN4ou04WM7Ay3k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.436, 0.516, 0.737, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with the autotext percentage \"25.0%\" [0.645, 0.84, 0.807, 0.86] and the text \"Cardiovascular Disease\" [0.549, 0.688, 0.593, 0.708]. This slice represents the distribution for the labeled disease and it's colored \"#99ff99\"."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "gNf5TtHjsOxIp7tX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains three slices of the pie chart corresponding to the diseases \"Diabetes\", \"Cancer\", and \"Cardiovascular Disease\" along with their respective autotext percentages and color representations."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "xE2EhIlUAadz9t6q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region defined by coordinates [0.78, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart, including text entries for \"Diabetes\", \"Cancer\", \"Cardiovascular Disease\", \"Obesity\", \"Asthma\", and \"Stroke\", along with their corresponding color codes, is located within this region."
+            }
+        ],
+        "id_raw": 212
+    },
+    {
+        "id": "3szZqy7J1MAnGQMw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the position [0.322, 0.021, 0.641, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Value Chain Visualization and Analysis\", is at this location."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "QkOcXYaCim4RDL9c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label does the object at [0.894, 0.161, 0.948, 0.181] in the chart refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Retailer\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "q4ZtkCnVTetlOI92",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the object at the coordinates [0.755, 0.389, 0.832, 0.409] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates point to the autotext \"25.5%\"."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "ukey91GcU3Osg57L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend item \"Supplier\"}[0.894, 0.071, 0.951, 0.091] and the chart elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Supplier\" corresponds to the pie slice with coordinates [0.223, 0.278, 0.482, 0.692] which has the label \"Supplier\" and the autotext \"19.0%\"."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "I2EwIjgdIk8vBJde",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is directly related to {the legend item \"Manufacturer\"}[0.894, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.254, 0.516, 0.708, 0.886], labeled \"Manufacturer\" and with the autotext \"34.0%\", is related to this legend item."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "wjWCs0FJI4dXVox0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is associated with {the pie slice}[0.283, 0.146, 0.601, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Retailer\" with coordinates [0.894, 0.161, 0.948, 0.181] is associated with this pie slice."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "sQOphjmbomRVtqsq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region defined by [0.482, 0.187, 0.741, 0.697] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice of the chart colored in \"#99ff99\" with the label \"Wholesaler\" [0.609, 0.442, 0.652, 0.462] and autotext \"25.5%\" located at [0.755, 0.389, 0.832, 0.409]."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "SF6c9fyfzUHYewIQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.254, 0.516, 0.708, 0.886] hold in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a pie slice with the autotext \"34.0%\" [0.386, 0.913, 0.48, 0.933] and the label \"Manufacturer\" [0.459, 0.728, 0.502, 0.748]."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "KK7Dgi1hRrpl4Hpi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.223, 0.278, 0.482, 0.692] contain in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice marked as \"Supplier\" [0.305, 0.484, 0.349, 0.504] with autotext \"19.0%\" [0.141, 0.465, 0.198, 0.485]."
+            }
+        ],
+        "id_raw": 214
+    },
+    {
+        "id": "Zvlx6hAMi9ahRg3a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart at coordinates [0.256, 0.021, 0.708, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Risk Assessment and Management from 2018 to 2020\"."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "Fc1IS2zI8oIFVIw1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object plotted at coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart illustrating different risks across the years 2018 to 2020."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "3CajNE4jJo8WoSgD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.857, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's legend."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "pgBV4ayphaXKe7iR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are nearby {the slice}[0.277, 0.516, 0.619, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"23.3%\"<r>[0.347, 0.907, 0.432, 0.927]</r> and \"Risk in 2019\"<r>[0.433, 0.725, 0.477, 0.745]</r> are located near the mentioned slice."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "yT1gySwKC0pH36nM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item}[0.902, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.147, 0.668, 0.516] corresponds to the mentioned legend item."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "7bEiVfUj9UkMLDRU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.223, 0.278, 0.482, 0.744] and the text in coordinates [0.111, 0.499, 0.197, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"21.7%\" corresponds to the percentage representation of the respective risk for the given slice in the chart."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "kRLjW3zSX9uMMtTE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe all the objects within the region [0.482, 0.147, 0.668, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the region, we have a slice of the pie chart representing \"Risk in 2018\" with the corresponding percentage \"11.7%\"<r>[0.602, 0.137, 0.687, 0.157]</r>."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "GntwaBpvG6kU2shS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region from [0.223, 0.278, 0.482, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a slice of the pie chart labeled as \"Risk in 2018\" with corresponding percentage \"21.7%\"<r>[0.111, 0.499, 0.197, 0.519]</r>."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "JeakqWiTlGNVGd2c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the details enclosed by the coordinates [0.277, 0.516, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice labeled \"Risk in 2019\", accompanied by the percentage \"23.3%\"<r>[0.347, 0.907, 0.432, 0.927]</r>."
+            }
+        ],
+        "id_raw": 215
+    },
+    {
+        "id": "bVid3zgif2ZPiQNd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies the coordinates [0.245, 0.021, 0.718, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"AI and Machine Learning Approaches Usage Percentages\"."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "cSGufBthKHWjd3kE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at the coordinates [0.821, 0.071, 0.922, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text representing \"Deep Learning\" in the legend."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "A1IXJgo2l5fUvFHg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.528, 0.104, 0.649, 0.124] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" which is related to \"Transfer Learning\"."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "otkTIFGF7IOdEfiM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts locate close to {the modal based on the label 'Deep Learning'}[0.304, 0.504, 0.348, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"22.0%\"}[0.095, 0.503, 0.197, 0.523] is located near it."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "VUD6sKB4seVwE9th",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend's item's handle color coordinate has the same color as {the slice labelled 'Reinforcement Learning'}[0.482, 0.218, 0.741, 0.588] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The handle for 'Reinforcement Learning'}[0.782, 0.133, 0.81, 0.147] in the legend has the same color."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "TK5W4sUoYvadorvl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the legend item 'Federated Learning'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.163, 0.482, 0.516] corresponds to 'Federated Learning'."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "31pkhKfHF6hQ0mhU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart, which includes five different slices representing 'Deep Learning', 'Neural Networks', 'Reinforcement Learning', 'Transfer Learning' and 'Federated Learning'. Each slice has its corresponding percentage written inside or nearby."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "pLi8M9HLB2LVUhY5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.282, 0.163, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart representing 'Federated Learning' with its corresponding color, text label, and the percentage, \"9.0%\", represented by the coordinates [0.188, 0.168, 0.323, 0.188]."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "i8nn6RQPth4akclv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region inside coordinates [0.221, 0.276, 0.482, 0.750]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice representing 'Deep Learning' which is coloured in '#ff9999' along with its corresponding label, and the percentage \"22.0%\" which is nearby."
+            }
+        ],
+        "id_raw": 217
+    },
+    {
+        "id": "vGmiVyzo5ThFPu7W",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what is the object with coordinates [0.31, 0.021, 0.654, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart which reads \"Prevalence of Fashion Subcultures/Trends\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "zVDWbM9DgywsqSQP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.863, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the area of the legend of the chart."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "es6xRfgimxzdw3dm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.726, 0.716, 0.805, 0.736] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is the text indicating the percentage \"15.0%\"."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "d69K6OPuP7G5XJlp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart is adjacent to {the slice}[0.419, 0.146, 0.741, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.223, 0.278, 0.482, 0.712]</r> is adjacent."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "BeDVoXFHaXNhfdDe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what slice's text value is near to {the autotext}[0.338, 0.903, 0.418, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text value \"Steampunk\"<r>[0.425, 0.722, 0.469, 0.742]</r> is near to the autotext."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "uhxSSZwgidC58WMR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items in the chart correspond to the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \u201860s Retro, Steampunk, Pastel Goth, Cyberpunk, and Indie Folk correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.712], [0.262, 0.516, 0.619, 0.886], [0.482, 0.516, 0.74, 0.83], [0.419, 0.146, 0.741, 0.542], and [0.283, 0.157, 0.482, 0.516] respectively."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "ALAlxWDaD290EJgu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.262, 0.516, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region refers to a blue pie slice representing the \"Steampunk\" subculture, containing the text \"25.0%\"<r>[0.338, 0.903, 0.418, 0.923]</r>, denoting its prevalence."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "elAfsWu5GEoxMHv2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects lie within the region defined by coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pink pie slice representing the \"\u201860s Retro\" trend, and includes the text \"20.0%\"<r>[0.127, 0.478, 0.197, 0.498]</r>, indicating the percentage of the trend's prevalence."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "cU28v5N5ybaEgrCM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details for the region marked by coordinates [0.483, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes pieces of three different pie slices representing \"Pastel Goth\"<r>[0.482, 0.516, 0.74, 0.83]</r>, \"Cyberpunk\"<r>[0.419, 0.146, 0.741, 0.542]</r>, and a small portion of \"Steampunk\"<r>[0.262, 0.516, 0.619, 0.886]</r>. It includes the corresponding percentages of the trends' prevalence too."
+            }
+        ],
+        "id_raw": 218
+    },
+    {
+        "id": "SHQ15UO83MddRBYP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.845, 0.281, 0.897, 0.301] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Biotech\"."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "Rh1Ctj4OPN6PNRfU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.295, 0.021, 0.668, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, named Future Technological Development Prospects."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "kwQDOTKQ7wQj18hH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.482, 0.516, 0.7, 0.877] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice of the chart."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "iReQBXqClZVhuyPr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.295, 0.021, 0.668, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are no texts near the subtitle of the chart."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "ljcsHuCPBOzWiKxa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to {the pie slice}[0.223, 0.278, 0.482, 0.565] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Artificial Intelligence\"}[0.845, 0.071, 0.987, 0.091] corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "gS2wJew60UmGSv9E",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the handle}[0.806, 0.282, 0.834, 0.296] and {the text}[0.845, 0.281, 0.897, 0.301] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle represents the color of the pie slice that corresponds to the text \"Biotech\"."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "aLoWRrnxP7TATaNE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.8, 0.063, 0.993, 0.309]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart. It includes labels like \"Artificial Intelligence\" [0.845, 0.071, 0.987, 0.091], \"IoT\" [0.845, 0.101, 0.866, 0.121], \"Big Data\" [0.845, 0.131, 0.905, 0.151], \"Cloud Computing\" [0.845, 0.161, 0.965, 0.181], \"Blockchain\" [0.845, 0.191, 0.92, 0.211], \"AR/VR\" [0.845, 0.221, 0.888, 0.241], \"Nanotech\" [0.845, 0.251, 0.911, 0.271], and \"Biotech\" [0.845, 0.281, 0.897, 0.301]; each paired with a color handle indicating their corresponding pie slice colours."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "h0NG9p1mA1xwkdRD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the pie slices of the chart along with the autotexts showing the proportions such as \"13.2%\" [0.065, 0.392, 0.208, 0.412] and text labels like \"Artificial Intelligence\" [0.31, 0.444, 0.354, 0.464], which indicate what each pie slice represents."
+            }
+        ],
+        "id_raw": 220
+    },
+    {
+        "id": "DoWknGLRqJ5o1l3R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.288, 0.007, 0.712, 0.016] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That object is the subtitle of the first subplot in the chart which reads \"Survey of AI and ML trend from 2017 to 2019 2017\"."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "LDbwPRlxRut5dxQQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled at the coordinates [0.674, 0.355, 0.816, 0.361] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label \"Artificial Intelligence\"}<r>[0.674, 0.355, 0.816, 0.361]</r> is located here."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "CQykhypQGmUV8RUn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is labeled with the coordinates [0.123, 0.914, 0.266, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"42.2%\"<r>[0.123, 0.914, 0.266, 0.92]</r> information is found here, indicating the percentage value in one of the slices."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "1htqzdzUdAVeMWGX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items match the colors of the pie slices of the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {Artificial Intelligence}<r>[0.674, 0.024, 0.816, 0.03]</r> and {Machine Learning}<r>[0.674, 0.034, 0.797, 0.04]</r> are correlated with the pie slices of the first subplot."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "kqkT1nKEU4jwfwFs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart in the second subplot is adjacent to the slice {at position}[0.237, 0.425, 0.511, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice at position}<r>[0.298, 0.38, 0.763, 0.63]</r> is adjacent to it."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "cV1YLAuurRW2giQx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located closest to the axes labeled at coordinates [0.166, 0.836, 0.166, 0.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text titled {Survey of AI and ML trend from 2017 to 2019 2019}<r>[0.288, 0.669, 0.712, 0.678]</r> is the closest to the defined coordinates."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "md7gZgkrM4iOztgm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.237, 0.711, 0.763, 0.962]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region holds two significant portions of the last pie chart. It consists of texts {\"Artificial Intelligence\"}<r>[0.35, 0.877, 0.394, 0.884]</r> and {\"42.2%\"}<r>[0.123, 0.914, 0.266, 0.92]</r>."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "mpFqD5wto0UCLrnt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.237, 0.049, 0.763, 0.3]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses two different sections of the first pie chart, containing the labels {\"Artificial Intelligence\"}<r>[0.349, 0.214, 0.393, 0.221]</r>, {\"41.7%\"}<r>[0.12, 0.25, 0.263, 0.257]</r>, and {\"Machine Learning\"}<r>[0.607, 0.128, 0.651, 0.135]</r>."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "UslnFKwxgmSHg2sf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.629, 0.021, 0.822, 0.043]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises two legend entries: {Artificial Intelligence}<r>[0.674, 0.024, 0.816, 0.03]</r> and {Machine Learning}<r>[0.674, 0.034, 0.797, 0.04]</r>."
+            }
+        ],
+        "id_raw": 221
+    },
+    {
+        "id": "IkST0LFFn6GGJAoO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.837, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Product Development\"."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "SmseCjWfCe5hugEJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.798, 0.103, 0.825, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle for \"Sales and Marketing\"."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "Y77f4YKWhCvHWh8f",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.236, 0.021, 0.728, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is \"Value Chain Visualization and Analysis Expenditure in 2021\"."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "oB6e373KH6fXN1v1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color handle in the legend corresponds to the {the slice}[0.223, 0.278, 0.721, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle with color \"#ff9999\"[0.798, 0.073, 0.825, 0.087] corresponds."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "gCoahd0MABfuIp2c",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich area in the chart corresponds to the {label \"Sales and Marketing\"}[0.837, 0.101, 0.978, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area with coordinates [0.283, 0.146, 0.741, 0.657] corresponds."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "aIJSg9u5qBGQ9LFF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the subtitle}[0.236, 0.021, 0.728, 0.047] and {the pie slices}[[0.223, 0.278, 0.721, 0.886], [0.283, 0.146, 0.741, 0.657]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle describes the subject of data that these pie slices represent."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "ZxFMZgJB0MOoXhXW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.798, 0.063, 0.993, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes two items: {the label \"Product Development\"}[0.837, 0.071, 0.987, 0.091] and its corresponding color handle \"[0.798, 0.073, 0.825, 0.087] and {the label \"Sales and Marketing\"}[0.837, 0.101, 0.978, 0.121] and its corresponding color handle [0.798, 0.103, 0.825, 0.117]."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "kMcGKa2NjgL4V7uV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two sections of a pie chart, {the section representing \"Product Development\"}[0.223, 0.278, 0.721, 0.886] and {the section representing \"Sales and Marketing\"}[0.283, 0.146, 0.741, 0.657]. Their respective percentage labels are {\"54.9%\"}[0.183, 0.854, 0.334, 0.874] and {\"45.1%\"}[0.629, 0.158, 0.771, 0.178]."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "woHS3gWAvTqUqeEO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by the coordinates [0.839, 0.084, 0.972, 0.122]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text labels for the legend: {the \"Product Development\"}[0.837, 0.071, 0.987, 0.091] text, and {the \"Sales and Marketing\"}[0.837, 0.101, 0.978, 0.121] text."
+            }
+        ],
+        "id_raw": 223
+    },
+    {
+        "id": "E4aTAVvGZDeSpgm5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.352, 0.021, 0.611, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, called \"Economic Output for Year 2020\"."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "jOqpF38NJSwpB1qx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.842, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "AIMBpSHtHfI1SNhP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.766, 0.534, 0.824, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the percentage text \"80.0%\"."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "LFMWCOmFlXm51Hn7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the pie slice}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Manufacturing\"}[0.305, 0.490, 0.348, 0.510] and {\"20.0%\"}[0.096, 0.478, 0.197, 0.498] are located near the pie slice."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "ejQmQaxGTaynqhbq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's related to {the legend item \"Manufacturing\"}[0.886, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {\"Manufacturing\" slice}[0.223, 0.278, 0.482, 0.712] of the pie chart corresponds to the legend item \"Manufacturing\"."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "LW4tYgP9chKSRliV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend item \"Services\"}[0.886, 0.101, 0.945, 0.121] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The \"Services\" slice}[0.262, 0.146, 0.741, 0.886] of the pie chart corresponds to the legend item \"Services\"."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "EKQKJA2WuFsUVEgx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of the pie chart representing economic output distribution. It features two sectors: \"Manufacturing\" with a 20.0% share and \"Services\" with an 80.0% share. Their locations are represented as {\"Manufacturing\"}[0.305, 0.49, 0.348, 0.510] and {\"Services\"}[0.615, 0.521, 0.659, 0.541] respectively."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "InLMn9Hj8xjAC5JJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the sector within coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"Manufacturing\" sector in the pie chart representing a 20.0% share of the economic output. The percentage is represented by {\"20.0%\"}[0.096, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 224
+    },
+    {
+        "id": "z7qPWusbOqjVS0Ef",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend located at [0.805, 0.111, 0.997, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Stocks and Bonds, with colors codes given by [0.817, 0.128, 0.872, 0.152] and [0.817, 0.18, 0.872, 0.204] respectively."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "GS7k471W9bUH2qez",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.134, 0.265, 0.22, 0.3] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"0.0%\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "CcCgdjqduXJO5wft",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.689, 0.756, 0.78, 0.791] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"100.0%\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "oF1OnmiTXGVq0LTm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is associated with the slice of the pie at {the slice}[0.176, 0.18, 0.732, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {labels}{\"100.0%\"}[0.689, 0.756, 0.78, 0.791] and {\"Stocks\"}[0.53, 0.645, 0.635, 0.68] are associated with the slice."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "fhx4GpxVSnpLZQin",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the handle color of the legend item \"Bonds\" at {the legend}[0.805, 0.111, 0.997, 0.226]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle color at {coordinates}[0.817, 0.18, 0.872, 0.204] is \"#99ff99\"."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "thgv6FNrm5EtBkwj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the text labels associated with the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels \"100.0%\", \"Stocks\", \"0.0%\", and \"Bonds\" correspond to the slices with coordinates [0.176, 0.18, 0.732, 0.876] and [0.241, 0.304, 0.454, 0.528] respectively."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "Uy3kJ7AEOk69P0lS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.176, 0.18, 0.732, 0.876]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of the pie chart, representing the \"Stocks\" data in 2020, marked by the red color \"#ff9999\", with a label indicating \"100.0%\" at [0.689, 0.756, 0.78, 0.791]."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "blrMVi1ZYiNcle0t",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.805, 0.111, 0.997, 0.226]."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region lies the legend of the chart. It contains labeled indicators for \"Stocks\" [0.894, 0.125, 0.986, 0.16] and \"Bonds\" [0.894, 0.177, 0.98, 0.212], respectively represented by the colors \"#ff9999\" [0.817, 0.128, 0.872, 0.152] and \"#99ff99\" [0.817, 0.18, 0.872, 0.204]."
+            }
+        ],
+        "id_raw": 225
+    },
+    {
+        "id": "wUdUaYmUhOGnhpdD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat caption can be seen from the object at coordinates [0.186, 0.021, 0.777, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Future Forecast and Trend Prediction of AI implementation in Industries\" is at these coordinates."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "um0PR590H8AjVVcI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the coordinates [0.908, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend is positioned at these coordinates."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "Zgxax5FtiFSCMuk3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be seen within the coordinates [0.741, 0.339, 0.776, 0.359]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"64.3%\" can be seen within these coordinates."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "opafJdSDQI5t8W5s",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice marked as 2025} [0.223, 0.278, 0.482, 0.886] in the chart and {the sublegend} [0.908, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend item \"2025\" corresponds to the slice in the chart with coordinates [0.223, 0.278, 0.482, 0.886]."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "PoVlnYz4ql8WnaMC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the chart corresponds to the {legend entry with the handle [0.913, 0.103, 0.941, 0.117]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.482, 0.516, 0.741, 0.886] corresponds to this legend entry."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "rToGlMAcOB9tsSor",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWith respect to the object at coordinates [0.223, 0.278, 0.482, 0.886], which one is its corresponding legend."
+            },
+            {
+                "from": "gpt",
+                "value": "The legend with the handle at coordinates [0.913, 0.073, 0.941, 0.087] is associated with this slice."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "Ip7jssPm6utLBtt0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the region [0.223, 0.278, 0.482, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice colored '#ff9999' as well as the texts \"2025\" [0.318, 0.597, 0.362, 0.617] and \"35.7%\" [0.187, 0.673, 0.222, 0.693]."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "hwBgn1mJ6cf0BiCo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat makes up the region from coordinates [0.283, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region has a pie slice colored '#66b3ff' and includes the texts \"2030\" [0.601, 0.415, 0.645, 0.435] and \"64.3%\" [0.741, 0.339, 0.776, 0.359]."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "XwsZXau1BC9OnZ1p",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we identify from the region [0.223, 0.278, 0.482, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice colored '#ff9999' and two texts - \"2025\" [0.318, 0.597, 0.362, 0.617] and \"35.7%\" [0.187, 0.673, 0.222, 0.693]."
+            }
+        ],
+        "id_raw": 226
+    },
+    {
+        "id": "pCf635B5CmiHcehv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what is located at the coordinates [0.786, 0.193, 0.814, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color square for the legend of \"Climate Change\"."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "uOSINgGD5i4Lftqi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.825, 0.191, 0.936, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text \"Climate Change\"."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "jzPcHW5wVQKpaXMm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present at the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart is present at these coordinates."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "rtQN1ipnQaIzgiTe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the association between the slice {slice}[0.482, 0.426, 0.741, 0.754] and the legends?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item {\"Sustainable Agriculture\"}[0.825, 0.131, 0.987, 0.151]."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "6cnMS66a37ky1L50",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend item \"Pollution Control\"}[0.825, 0.161, 0.939, 0.181] and the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Pollution Control\" corresponds to the slice {slice}[0.419, 0.146, 0.733, 0.516]."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "APORLKHD8cSFxKZR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the colors {swatch}[0.786, 0.193, 0.814, 0.207] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color swatch corresponds to the pie slice with coordinates [0.283, 0.157, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "VHK7JNjuvsLjm56p",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region defined by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart. It includes five slices {\"30.0%\"}[0.076, 0.604, 0.205, 0.624], {\"20.0%\"}[0.551, 0.901, 0.688, 0.921], {\"15.0%\"}[0.759, 0.598, 0.921, 0.618], {\"25.0%\"}[0.628, 0.157, 0.743, 0.177] and {\"10.0%\"}[0.219, 0.161, 0.331, 0.181] representing different categories of Environmental Protection and Sustainable Development Importance."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "UAJiSxU1qEwA8st3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within the coordinates [0.781, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. It includes legend items for \"Renewable Energy\", \"Water Conservation\", \"Sustainable Agriculture\", \"Pollution Control\", and \"Climate Change\". Each legend item includes a color square and corresponding text."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "dV8NGIY4XYsP2iw9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the area specified by [0.223, 0.278, 0.482, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified area holds one slice of the pie chart representing \"Renewable Energy\", labeled {\"Renewable Energy\"}[0.309, 0.56, 0.353, 0.58], with its respective percentage {\"30.0%\"}[0.076, 0.604, 0.205, 0.624]."
+            }
+        ],
+        "id_raw": 227
+    },
+    {
+        "id": "JeX9aL5hlACDKnnv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main subject at coordinates [0.309, 0.021, 0.654, 0.047] depicted on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot called \"Political Tweet Volume from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "Djjn886iRQCS39J0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at coordinates [0.913, 0.163, 0.941, 0.177] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle related to the year 2018."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "YPibG2Nw9E1o5dGE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at [0.431, 0.912, 0.466, 0.932] on the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"20.0%\"."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "OZKtEKUZBjg6F6fC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text annotations are related to the slice of {2016}[0.223, 0.502, 0.482, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text annotation {\"14.7%\"}[0.187, 0.674, 0.222, 0.694] and text {\"2016\"}[0.318, 0.598, 0.362, 0.618] are related to the slice of 2016."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "ucbJMRZ6kfzxRcm8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to the labeled {2017}[0.318, 0.516, 0.623, 0.886] slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices labeled {\"2016\"}[0.223, 0.502, 0.482, 0.803] and {\"2018\"}[0.482, 0.352, 0.741, 0.827] are adjacent to the 2017 slice."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "DdqV9btAVFTlRxAf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the slice at the coordinates [0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color {\"#ff9999\"}[0.913, 0.073, 0.941, 0.087] in the legend corresponds."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "xSZuC6JX4Ds1MVSU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements present in the region [0.482, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie slices {\"2018\"}[0.482, 0.352, 0.741, 0.827] and {\"2019\"}[0.283, 0.146, 0.714, 0.516], along with the corresponding autotexts \"23.2%\" and \"31.6%\"."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "adMVkC0x5RvNljbL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slices {\"2015\"}[0.223, 0.278, 0.482, 0.516] and part of slice {\"2019\"}[0.283, 0.146, 0.714, 0.516], with their respective autotexts \"10.5%\" and part of \"31.6%\"."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "8lDjcY2r3WC8CGKD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's in the region [0.223, 0.502, 0.623, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slices labeled {\"2016\"}[0.223, 0.502, 0.482, 0.803] and part of the {\"2017\"}[0.318, 0.516, 0.623, 0.886] slice, along with their corresponding autotexts \"14.7%\" and part of \"20.0%\"."
+            }
+        ],
+        "id_raw": 228
+    },
+    {
+        "id": "fimEmmZr5aWQW4NU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.34, 0.021, 0.623, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's subtitle, \"Social Network Usage Distribution\"."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "ungwgg5ctw0DimOe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.917, 0.071, 0.982, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text for the legend item \"Facebook\"."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "jguoOZ7wSBC7uq9Z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.223, 0.146, 0.741, 0.886] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the main area of the pie chart."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "SgHea5yF7kfzfjPt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart corresponds to the legend {item \"Twitter\"}<r>[0.917, 0.131, 0.963, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to \"Twitter\" is at <r>[0.482, 0.147, 0.701, 0.516]</r>."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "zS0k0CrYlCNnPIkY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the {coordinate} <r>[0.917, 0.161, 0.982, 0.181]</r> and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinate represents the label \"Snapchat\" in the legend, which corresponds to a slice in the pie chart."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "3MEk2BNrhyZy4vjw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to {the slice at} <r>[0.344, 0.146, 0.5, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices of {the pie chart with coordinates} <r>[0.223, 0.278, 0.619, 0.886]</r> and <r>[0.283, 0.202, 0.482, 0.516]</r> are adjacent to it."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "ZihorLqgkDBQRZ2g",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region with coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains five slices of a pie chart with their corresponding labels and contributing percentages."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "xT8am9vVla5kCszc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is seen in the bordering region between [0.344, 0.146, 0.5, 0.516] and [0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains part of two slices along with their descriptors: \"Snapchat\" with 10.0% at <r>[0.347, 0.111, 0.413, 0.131]</r> and \"Others\" with 5.0% at <r>[0.248, 0.199, 0.295, 0.219]</r>."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "h1LtmQHpQQKnC3bm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the chart region within [0.223, 0.278, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart corresponding to \"Facebook\". The slice has the label \"Facebook\" at <r>[0.342, 0.652, 0.386, 0.672]</r> and the percentage \"45.0%\" at <r>[0.201, 0.773, 0.267, 0.793]</r>."
+            }
+        ],
+        "id_raw": 229
+    },
+    {
+        "id": "gFEVy0dhuODqmQT6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific object in the chart has its coordinates located at [0.523, 0.303, 0.566, 0.323]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specific object is the text \"Real Estate\"."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "SdjQau9cVwyJaRrR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object given the coordinates [0.909, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the object is \"Investment\"."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "NDPFcobpjNfAFOWe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text item at coordinates [0.597, 0.134, 0.675, 0.154] in the chart say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text item says \"41.0%\"."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "n2oi8S5HPKnSKbLN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the slice at coordinate [0.283, 0.146, 0.741, 0.565] correspond to in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at the given coordinates corresponds to \"#66b3ff\" in the chart legend."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "DeLwpaaDKIP7jKcJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is the text \"Investment\" at coordinates [0.397, 0.709, 0.441, 0.729] linked to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Investment\" is linked to the {slice}<r>[0.223, 0.278, 0.738, 0.886]</r>."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "7cKNHhMMehoSE7e1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text \"Real Estate\" at coordinates [0.523, 0.303, 0.566, 0.323] and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Real Estate\" refers to the {slice}<r>[0.283, 0.146, 0.741, 0.565]</r> in the pie chart."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "65nJLUhUlah3i4BP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the region marked by coordinates [0.909, 0.071, 0.987, 0.091] and [0.909, 0.101, 0.987, 0.121]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains text descriptions of the chart legend, specifically the labels \"Investment\" at [0.909, 0.071, 0.987, 0.091] and \"Real Estate\" at [0.909, 0.101, 0.987, 0.121]. The text is placed on the right side of the chart."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "uyPLPt52gPe105Zs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.738, 0.886] and [0.283, 0.146, 0.741, 0.565]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises two slices of the pie chart, one with the \u201cInvestment\u201d label and the other with the \u201cReal Estate\u201d label. Corresponding percentages are presented as \"59.0%\" at [0.288, 0.878, 0.366, 0.898] and \"41.0%\" at [0.597, 0.134, 0.675, 0.154] respectively."
+            }
+        ],
+        "id_raw": 230
+    },
+    {
+        "id": "upti6z5nYmTGV855",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title mentioned in the chart at the position [0.304, 0.021, 0.659, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Annual Online Revenue from 2000 to 2008\"."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "K6utp7eEsKsA6Bm7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinate [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center of the pie chart."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "NojFBvS0QadXe2Xs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2004\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "ySDkD5BlfuMcUfqA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the pie slices adjacent to {the slice}[0.225, 0.516, 0.482, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}[[0.223, 0.404, 0.482, 0.562], [0.275, 0.516, 0.482, 0.867]] are adjacent to it."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "AAocfUlZbuq7Nrwl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does {the handle}[0.913, 0.163, 0.941, 0.177] in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice {the slice}[0.225, 0.516, 0.482, 0.739]."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "tJIwTPpXLdzkQUZB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is nearest to {the label}[0.952, 0.131, 0.987, 0.151] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2002\" {the label}[0.32, 0.589, 0.355, 0.609] is closest to it."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "sXxduZ5cpq46ASyw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the main plotting area containing the entire circular-shaped pie chart, which consists of different color-coded slices such as \"2000\" [0.328, 0.399, 0.363, 0.419] and \"2004\" [0.464, 0.728, 0.508, 0.748], each dedicated to a specific year."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "wVHaRUPK7rbi0Hpy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend which has items such as the labels \"2000\" [0.952, 0.071, 0.987, 0.091] and \"2008\" [0.952, 0.311, 0.987, 0.331], alongside color-bounded handles."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "iWoDjHH419LdTVrE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_31.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content found in the region [0.223, 0.516, 0.482, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there's a part of the pie chart representing the year \"2002\" [0.32, 0.589, 0.355, 0.609]. The label shows an increase in online revenue and is color-coded with \"#99ff99\"."
+            }
+        ],
+        "id_raw": 231
+    },
+    {
+        "id": "JBMgURUbtetIQToX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is represented by the coordinates [0.223, 0.021, 0.74, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The subtitle}<r>[0.223, 0.021, 0.74, 0.047]</r> which reads: \"Rural and Urban Development Comparison from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "XaFZqVjLrZm6onFs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what is at the coordinates [0.908, 0.063, 0.993, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the {sublegend}<r>[0.908, 0.063, 0.993, 0.608]</r> which details the colors and labels used in the chart."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "MfgdN6RTst0wJEYD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is defined by the coordinates [0.482, 0.516, 0.567, 0.886] in this chart's structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "That area shows a {slice}<r>[0.482, 0.516, 0.567, 0.886]</r> of the pie chart representing the year \"2020\" with an autotext \"5.6%\"."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "ijvfGN6YxTAAUGgv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn relation to {the slice for 2015}[0.223, 0.516, 0.482, 0.645], what slices are adjacent in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices for {\"2014\"}<r>[0.223, 0.444, 0.482, 0.544]</r> and {\"2016\"}<r>[0.239, 0.516, 0.482, 0.738]</r> are adjacent to the slice for 2015."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "FGXVrEXD21XtcGEO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items' colors coincide with {the slice for 2019}[0.4, 0.516, 0.482, 0.886] and {the slice for 2020}[0.478, 0.516, 0.567, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {\"2019\"}<r>[0.952, 0.55, 0.987, 0.57]</r> and {\"2020\"}<r>[0.952, 0.58, 0.987, 0.6]</r> correspond with those chart slices' colors."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "yT3TguUU9JmRRvAl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the overall {legend}[0.908, 0.063, 0.993, 0.608] and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each color and label in the legend corresponds to a particular slice's year and color in the pie chart."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "frsv5X8Gm2LQMUqj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.482, 0.516, 0.573, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates encloses a {slice}<r>[0.478, 0.516, 0.567, 0.886]</r> of the pie chart representing \"2020\" with associated autotext {@5.6%}<r>[0.527, 0.908, 0.562, 0.928]</r>. It represents the data for the year 2020."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "iYmiZbBla2wti42r",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIf you look at the region defined by [0.482, 0.1, 0.541, 0.516], what does it include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes parts of the pie slices representing the years {\"2017\"}<r>[482, 0.157, 0.64, 0.516]</r>, {\"2018\"}<r>[0.447, 0.146, 0.546, 0.516]</r>, and {\"2019\"}<r>[0.352, 0.149, 0.482, 0.516]</r>. These slices show the data for those specific years."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "eAdRtbOlhWuCAS1y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region specified by [0.952, 0.4, 0.987, 0.6]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you'll find the {legend entries}<r>[0.952, 0.4, 0.987, 0.42]</r>,<r>[0.952, 0.43, 0.987, 0.45]</r>,<r>[0.952, 0.46, 0.987, 0.48]</r>,<r>[0.952, 0.49, 0.987, 0.51]</r>,<r>[0.952, 0.52, 0.987, 0.54]</r>,<r>[0.952, 0.55, 0.987, 0.57]</r>,<r>[0.952, 0.58, 0.987, 0.6]</r> which represent the years 2014 to 2020."
+            }
+        ],
+        "id_raw": 232
+    },
+    {
+        "id": "MNAJuv62Uqg3VMT8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is {Population growth from 2015 to 2020}<r>[0.326, 0.021, 0.637, 0.047]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "p9mRosxXLuEqqWhb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend tell us?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {2020}<r>[0.952, 0.221, 0.987, 0.241]</r> tells us the color for the slice of 2020 is #e89255."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "1fUKpTWxcf8px6BO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object are we seeing at these coordinates [0.512, 0.911, 0.547, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text we see is {16.0%}<r>[0.512, 0.911, 0.547, 0.931]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "Twzwk9H0qPzo349G",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice belonging to the legend item {2018}<r>[0.952, 0.161, 0.987, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is {#ffcc99}<r>[0.913, 0.163, 0.941, 0.177]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "tkW98cDsKwdqH8uI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the legend {2015}<r>[0.952, 0.071, 0.987, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is represented by the color {#ff9999}[0.223, 0.278, 0.482, 0.595]."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "727L2wi2MGCwx8th",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the slice {2019}<r>[0.482, 0.162, 0.738, 0.516]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {2019}<r>[0.578, 0.362, 0.622, 0.382]</r> represents 18.2%."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "vwS4an6dyak3vcKM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region defined by these coordinates [0.223, 0.146, 0.741, 0.886] contained by the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains six slices of the pie chart representing the years 2015 to 2020, with percentages and labels for each slice. Examples include {2015 slice}<r>[0.223, 0.278, 0.482, 0.595]</r> with label {2015}<r>[0.309, 0.453, 0.353, 0.473]</r> and percentage {14.5%}<r>[0.17, 0.409, 0.205, 0.429]</r>; and {2020 slice}<r>[0.283, 0.146, 0.557, 0.516]</r> with label {2020}<r>[0.415, 0.293, 0.46, 0.313]</r> and percentage {18.6%}<r>[0.366, 0.116, 0.401, 0.136]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "Q5ADPB4vMoXDJn0G",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.913, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend which includes six items, each depicted by a color, such as the red-colored {2015}<r>[0.913, 0.073, 0.941, 0.087]</r> and the orange-colored {2020}<r>[0.913, 0.223, 0.941, 0.237]</r>."
+            }
+        ],
+        "id_raw": 233
+    },
+    {
+        "id": "wZrnRKQtGyYcYTVC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located within the coordinates [0.845, 0.071, 0.906, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text of the legend, stating \"Genetics\"."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "suukUD5FkZaqJJ9Z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.223, 0.146, 0.545, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice labeled \"Quantum Mechanics\"."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "4wdw0B1KOs8PWcFI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.169, 0.021, 0.794, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, which reads \"Investment Distribution in Scientific Research and Technological Innovation\"."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "DAeSH580dwmAsndb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice {the slice}[0.482, 0.157, 0.717, 0.516] correlate with in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It correlates with the legend item \"Neuroscience\"{the legend item}[0.845, 0.251, 0.939, 0.271]."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "3P9JyXROdx57fBMk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is correlated with {Genetics in the legend}[0.845, 0.071, 0.906, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the pie slice {the Genetics slice}[0.223, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "FvciBAGFXZ3WUAP6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is associated with the legend text {the legend text \"Astronomy\"}[0.845, 0.191, 0.92, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with the legend text \"Astronomy\" is {the Astronomy slice}[0.482, 0.516, 0.7, 0.815]."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "OwuRpIog7HyWuRVi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found between the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the main chart area, which consists of multiple pie slices, each labeled with a percentage and text, representing different scientific research fields.Computer AI{AI slice}[0.377, 0.516, 0.634, 0.886] and \"Biotechnology\"{biotech slice} [0.482, 0.363, 0.741, 0.715] are examples of slices in this region."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "QV8D08gh31vbZd8R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_34.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects present in the region [0.802, 0.062, 0.994, 0.310]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. It includes various scientific research fields like \"Genetics\"{Genetics legend}[0.845, 0.071, 0.906, 0.091] and \"Physics\"{Physics legend}[0.845, 0.131, 0.896, 0.151], each associated with a different color."
+            }
+        ],
+        "id_raw": 234
+    },
+    {
+        "id": "Vi1NUPKgew9ImBXL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.31, 0.021, 0.654, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subplot title, called Population Distribution Across Continents."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "oAwxp8nfMbsCwZEA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.841, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "h6uAijsbH8mfv55c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.203, 0.187, 0.305, 0.207] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.8%\"."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "2EXEaiFtrC8n1K2E",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.283, 0.272, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.287, 0.189, 0.482, 0.516], [0.360, 0.152, 0.482, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "5nSCIwyWG0XDziMJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.31, 0.021, 0.654, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Asia\"}[0.406, 0.714, 0.45, 0.734] is located near the subtitle."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "tD00YryvAUsO9xhn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Asia\", \"Africa\", \"Europe\", \"North America\", \"South America\", and \"Australia\" correspond to the slices with coordinates [0.223, 0.278, 0.741, 0.886], [0.482, 0.183, 0.741, 0.52], [0.436, 0.146, 0.595, 0.516], [0.36, 0.152, 0.482, 0.516], [0.287, 0.189, 0.482, 0.516], and [0.283, 0.272, 0.482, 0.516] respectively."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "YWWAZOE53gDPMXa5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.436, 0.146, 0.595, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"10.0%\" [0.521, 0.103, 0.569, 0.123] and the text \"Europe\" [0.481, 0.286, 0.525, 0.306]. This region is located in the middle left part of the pie chart, corresponding to the green slice."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "B2ac40NjsYTLkhQT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.283, 0.272, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"0.3%\" [0.204, 0.241, 0.265, 0.261] and the text \"Australia\" [0.346, 0.361, 0.381, 0.381]."
+            }
+        ],
+        "id_raw": 235
+    },
+    {
+        "id": "9eV0pmqbGPU8brEI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.318, 0.021, 0.646, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Data Quantity Trend from 2014 to 2020."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "38XN1sfdXW18pr1n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is to be found in coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart displaying the data from 2014 to 2020."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "aLaXPPy3TXNMqTl8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.482, 0.516, 0.729, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice for the year 2017."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "SVxxpc6iDcL845rY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to {the pie slice}[0.282, 0.516, 0.548, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to {the legend item 2016}[0.952, 0.131, 0.987, 0.151]."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "L8JdBffkouLxPeLW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you find the text adjacent to {the pie slice}[0.482, 0.516, 0.729, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text percent {\"16.1%\"}[0.679, 0.8, 0.714, 0.82] and {\"2017\"}[0.567, 0.666, 0.611, 0.686] are adjacent to the pie slice."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "mZLwoDYmof8g2Mkf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segment in the pie chart does {the legend item}[0.952, 0.251, 0.987, 0.271] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2020\" corresponds to {the pie slice}[0.283, 0.15, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "1Cnu4s6jOH7yExdY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.255, 0.741, 0.626]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for the year 2018 with the text \"17.2%\" [0.758, 0.409, 0.794, 0.429] and the label \"2018\" [0.611, 0.453, 0.655, 0.473]. This slice is represented in the color '#c2c2f0'."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "IFMKXKGprqJWAnzx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.516, 0.482, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice for 2015, with the text \"13.8%\" [0.196, 0.699, 0.231, 0.719] and the label \"2015\" [0.323, 0.611, 0.367, 0.631]. This slice is represented in the color '#66b3ff'."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "6vLOeYj2L2DfQfUA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents in the region [0.482, 0.516, 0.729, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for 2017. It includes the percentage \"16.1%\" [0.679, 0.8, 0.714, 0.82] and the label \"2017\" [0.567, 0.666, 0.611, 0.686]. The color of this slice is represented as '#ffcc99'."
+            }
+        ],
+        "id_raw": 236
+    },
+    {
+        "id": "UGLOOJydKFUPRugo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at position [0.916, 0.144, 0.995, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text {label \"Freshwater availability\"}<r>[0.916, 0.144, 0.995, 0.158]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "9Q5QVslZ0IJkRtTV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the entity located at [0.262, 0.687, 0.317, 0.710] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The entity is the text {\"45.6%\"}<r>[0.262, 0.687, 0.317, 0.710]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "IKTGgxmnY6nRBpcL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can we find at the coordinates [0.062, 0.205, 0.356, 0.795] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a {pie slice}<r>[0.062, 0.205, 0.356, 0.795]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "AqQFRI2oBE98wHxb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are situated near {the subtitle}[0.138, 0.109, 0.28, 0.127]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Freshwater availability\"}<r>[0.419, 0.144, 0.497, 0.158]</r>, {\"Rainfall\"}<r>[0.419, 0.165, 0.445, 0.179]</r> and {\"Protein availability\"}<r>[0.419, 0.186, 0.484, 0.2]</r> are located near the subtitle."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "aWcOLmxOC5ZK7wXf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart corresponds to {the label \"Protein availability\"}[0.419, 0.186, 0.484, 0.2]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to it is {the slice}<r>[0.096, 0.213, 0.209, 0.5]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "S1PTfUZ5ibqQiRlu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the label \"Freshwater availability\"}[0.419, 0.144, 0.497, 0.158] point to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#ff9999' as indicated by the color handle at {coordinates}<r>[0.399, 0.146, 0.413, 0.155]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "l5B5DtTTUPUlB3At",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.025, 0.132, 0.393, 0.868]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a subplot of the chart that contains a pie chart and a legend. The pie chart's slices and their labels include \"Freshwater availability\" at {coordinates}<r>[0.133, 0.611, 0.154, 0.625]</r>, \"Rainfall\" at {coordinates}<r>[0.279, 0.422, 0.301, 0.436]</r>, and \"Protein availability\" at {coordinates}<r>[0.152, 0.342, 0.174, 0.356]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "1z4wAVY78qszG2Rq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.559, 0.205, 0.853, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a pie chart subplot for the year 2015, where the slices indicate the distribution of \"Freshwater availability\" at {coordinates}<r>[0.629, 0.609, 0.651, 0.623]</r>, \"Rainfall\" at {coordinates}<r>[0.777, 0.425, 0.799, 0.439]</r>, and \"Protein availability\" at {coordinates}<r>[0.649, 0.342, 0.671, 0.356]</r>."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "TKr61LuGGOheDUZc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.894, 0.139, 0.997, 0.206]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with the labels \"Freshwater availability\" at {coordinates}<r>[0.916, 0.144, 0.995, 0.158]</r>, \"Rainfall\" at {coordinates}<r>[0.916, 0.165, 0.942, 0.179]</r>, and \"Protein availability\" at {coordinates}<r>[0.916, 0.186, 0.981, 0.2]</r>. The color handles corresponding to each label are at {coordinates}<r>[0.897, 0.146, 0.91, 0.155]</r>, {coordinates}<r>[0.897, 0.166, 0.91, 0.176]</r>, and {coordinates}<r>[0.897, 0.187, 0.91, 0.197]</r> respectively."
+            }
+        ],
+        "id_raw": 238
+    },
+    {
+        "id": "TVcxHxtf9mhIZGeL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is mentioned at the coordinates [0.306, 0.477, 0.35, 0.497] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2010\" is positioned at the given coordinates."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "1ZFzv7HQpqhuQulu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.952, 0.161, 0.987, 0.181] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the text \"2013\"."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "SmB5fTr0Ul2Huxo8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.201, 0.021, 0.762, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Effectiveness Evaluation of Government Policies from 2010 to 2014\" is located at the given coordinates."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "VxkWNxoQ7CSZqOGX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has a near relation to the {label '2011'}<r>[0.952, 0.101, 0.987, 0.121]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.246, 0.516, 0.485, 0.886]</r> corresponds to the label '2011'."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "youDYNscyYETNeH1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's slice has the autotext \"23.5%\" in it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice for the year {2014}<r>[0.283, 0.146, 0.629, 0.516]</r> has the autotext \"23.5%\"."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "1bMpVyItiVJX7Hxw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is related to the legend box colored '#ff9999' [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2010\" [0.306, 0.477, 0.35, 0.497] is related to the legend box colored '#ff9999'."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "LtsPd4T73bIG0BQU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts a pie chart showcasing various slices for the years 2010 to 2014, including their contributions shown by autotext percentages and color-coded legend items on the right side."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "GDoYFjZ007h5l4Vg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.913, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend items corresponding to the pie chart located at the center, indicating the color codes for various years from 2010 to 2014."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "hXDqEoQ2bGlDcRC3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates between [0.482, 0.516, 0.724, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice for the year 2012 with austotext indicating \"19.1%\". It is part of the pie chart showing percentages of government policies from 2010 to 2014."
+            }
+        ],
+        "id_raw": 241
+    },
+    {
+        "id": "GkcwGLVmZcFpNUzd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.834, 0.071, 0.91, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item label \"Risk Factor\"."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "zcwZ4bDu2IgmKlPU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main element with coordinates [0.234, 0.021, 0.729, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart subtitle, called Analysis of Financial Management and Risk Control Aspects."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "FngQEDXguRalPskw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.395, 0.516, 0.613, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slice corresponding to \"Investment\"."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "WzD6RxubMX4dk5xJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {Investment}<r>[0.834, 0.161, 0.912, 0.181]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.395, 0.516, 0.613, 0.886]]."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "w8pdzpHiPuxGnpok",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items are located near to {Insurance}<r>[0.834, 0.191, 0.902, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {\"Liability\"}[0.834, 0.221, 0.888, 0.241] and {\"Investment\"}[0.834, 0.161, 0.912, 0.181] are located near to Insurance."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "JSnIj50QpOPfOkdk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the slice representing {Risk Factor}<r>[0.223, 0.278, 0.482, 0.539]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {\"Portfolio Management\"}[[0.223, 0.516, 0.482, 0.731]] is adjacent."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "UenE3B9ErOv9Se1Q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire chart. The area contains the pie chart, legend items, and the subtitle. The subtitle is situated at [0.234, 0.021, 0.729, 0.047]. The pie chart is centred at [0.482, 0.516] and the legend is located at [0.789, 0.063, 0.993, 0.338]."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "pc26VALc6FuLjhGl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region defined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart. It contains all the labelled slices indicating the different financial aspects analyzed such as \"Risk Factor\", \"Portfolio Management\", \"Investment\" and others."
+            }
+        ],
+        "id_raw": 242
+    },
+    {
+        "id": "DcN9gXCwl9VZ7QP4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is named \"Tourism and Hospitality Industry Data Analysis\"<r>[0.288, 0.021, 0.675, 0.047]</r>."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "Vrl8khxCbA7FVztC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.496, 0.877, 0.607, 0.895] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"6.7%\"<r>[0.496, 0.877, 0.607, 0.895]</r>."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "0q5nuvtw39QXGahW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.621, 0.437, 0.654, 0.455]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Nature Tourists\"<r>[0.621, 0.437, 0.654, 0.455]</r>."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "3MDt1CSFzaVAX0Eg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of tourists does the slice with the color '#ff6666' represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the color '#ff6666' represents {Rural Tourists}<r>[0.482, 0.237, 0.707, 0.516]</r>."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "qkBN5JQBw9XMtVix",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart is adjacent to the \"Food Tourists\" slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Cultural Tourists} slice<r>[0.233, 0.516, 0.482, 0.881]</r> is adjacent to the \"Food Tourists\" slice."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "J8hIAsehoQSCkXs2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the legend item \"Budget Tourists\" and the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Budget Tourists\" corresponds to the slice labeled {15.3%}<r>[0.233, 0.146, 0.482, 0.498]</r> in the chart."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "FJ3LNakh3ckJ9f5k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.484, 0.263, 0.704, 0.506]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"5.3%\"<r>[0.702, 0.249, 0.796, 0.269]</r> and the text \"Rural Tourists\"<r>[0.585, 0.366, 0.62, 0.386]</r>. This region corresponds to the bottom right section of the pie chart with pink color."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "2izWVPkUITiexlRO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_43.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.567, 0.113, 0.704, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the text \"10.0%\"<r>[0.594, 0.132, 0.698, 0.152]</r> and the text \"Luxury Tourists\"<r>[0.521, 0.302, 0.565, 0.322]</r>."
+            }
+        ],
+        "id_raw": 243
+    },
+    {
+        "id": "bd8C4y8AvtnLK9KF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text string situated at [0.152, 0.478, 0.197, 0.498] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text string \"20.0%\"."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "GhmUEfAYwqt4O8XO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the element found within coordinates [0.898, 0.103, 0.926, 0.117] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the colour handle for the legend item \"Hipster\"."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "5R9ZYmYUOzPo5BcC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart title [0.285, 0.021, 0.678, 0.047] read?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title reads as \"Popularity of Gothic and Hipster Fashion Trends\"."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "D36BFpJ5GJK99ibb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice {slice one}[0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the item \"Gothic\"<r>[0.937, 0.071, 0.982, 0.091]</r> is associated with slice one."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "eHQJPTnisIIdXVJv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart does the color code {color code}[0.898, 0.073, 0.926, 0.087] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code refers to the slice \"Gothic\"<r>[0.223, 0.278, 0.482, 0.712]</r>."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "OnUTCekIcnCRH0Hq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label does the color in the coordinates [0.898, 0.103, 0.926, 0.117] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents the label \"Hipster\"<r>[0.937, 0.101, 0.987, 0.121]</r>."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "GeyjE0G8c0UXtutY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region from [0.615, 0.521, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of the pie slice labeled \"Hipster\"<r>[0.262, 0.146, 0.741, 0.886]</r> along with the related data label \"Hipster\" with the coordinates [0.615, 0.521, 0.659, 0.541]."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "fjSdwaYvU7JhEZS9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of information is found within the region [0.223, 0.146, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses part of the pie slice for \"Gothic\"<r>[0.223, 0.278, 0.482, 0.712]</r> along with its corresponding label \"Gothic\" found at [0.305, 0.49, 0.348, 0.51]."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "8yjLmuwfsJ0vsSkF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are present in the region specified by [0.893, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entirety of the chart's legend. It includes colored indicators for \"Gothic\" [0.898, 0.073, 0.926, 0.087] and \"Hipster\" [0.898, 0.103, 0.926, 0.117] as well as their text labels at [0.937, 0.071, 0.982, 0.091] and [0.937, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 244
+    },
+    {
+        "id": "JyMmfB8LsMBQxRfP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at [0.233, 0.021, 0.731, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Population Migration and Mobility Trends from 2010 to 2016\"."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "t7Ja20GgbtfzSNSo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labelled \"2010\" on the legend."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "jBBEYTRd7j5ERPro",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.659, 0.824, 0.695, 0.844] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.3%\"."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "MjOFkBKU0AgMC64f",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item matches with the color of the pie slice at {the slice}[0.482, 0.367, 0.741, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2014\" corresponds to {the slice}[0.482, 0.367, 0.741, 0.697]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "2E8Qd19CEoK4LC6t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice shares the same color with {the legend item '2015'}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice [0.482, 0.147, 0.719, 0.516] shares the same color with {the legend item '2015'}[0.952, 0.221, 0.987, 0.241]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "kfFc5KQeBYdmHpVM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the '2013' slice}[0.482, 0.516, 0.708, 0.871] and {the legend}[0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '2013' slice corresponds to the '2013' label in the legend."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "UCNBoOrdcJjydYPF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.312, 0.516, 0.555, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice of \"2012\", represented by the color '#99ff99'. It also contains the value of \"15.9%\" positioned at [0.386, 0.904, 0.422, 0.924] and label '2012' at [0.427, 0.723, 0.471, 0.743]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "9GbLbwmBfO9RsSYh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents inside the region defined by [0.482, 0.147, 0.719, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the specified region, there is a pie slice associated with the year '2015', coloured '#f0c2c2', with the text '17.1%' at [0.647, 0.175, 0.682, 0.195], and the label '2015' at [0.55, 0.325, 0.594, 0.345]."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "qPMcM4AvV0e4LU7f",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are located within the region with coordinates [0.482, 0.367, 0.741, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice related to \"2014\", which is represented by color '#c2c2f0'. Additionally, the percentage \"14.7%\" at [0.766, 0.526, 0.801, 0.546] and the text \"2014\" at [0.615, 0.517, 0.659, 0.537] are also present."
+            }
+        ],
+        "id_raw": 245
+    },
+    {
+        "id": "QyHr1eqwJyhOU136",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is depicted by the object in the coordinates [0.277, 0.021, 0.687, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart named \"Education and Training Trends from 2010 to 2020\"."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "W2Pr9PiU4Igw4n5Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category does the object at the coordinates [0.798, 0.063, 0.993, 0.249] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains legends of this data."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "dxK8c4r0lS9jZ9YU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.843, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text representing \"2010 Undergraduate\"."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "h4BYApJujHEZK5nW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data points have colors corresponding to the handles situated at {the coordinates}[[0.804, 0.073, 0.831, 0.087], [0.804, 0.103, 0.831, 0.117]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {\"2010 Undergraduate\"}[0.223, 0.278, 0.482, 0.799] and {\"2015 Undergraduate\"}[0.315, 0.516, 0.696, 0.886] in the pie chart."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "VeRxCZAA4yXozgBX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are adjacent to the pie located at {the coordinates}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent objects are {the legends}[0.798, 0.063, 0.993, 0.249] and {the subtitle}[0.277, 0.021, 0.687, 0.047]."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "mPVqkCK19RrZGqZT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels of the legend items having the same color as the pie slice at coordinates [0.446, 0.146, 0.579, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels are represented by the text \"2010 Graduate\" with coordinates [0.843, 0.161, 0.947, 0.181] in the legend."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "e8qD9ePzxoYGlPTD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by coordinates [0.223, 0.278, 0.482, 0.799]."
+            },
+            {
+                "from": "gpt",
+                "value": "The mentioned region encompasses the slice labeled as \"2010 Undergraduate\" in the pie chart. It also contains the text \"25.0%\" [0.053, 0.541, 0.198, 0.561]."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "SbimZryvLx6imYD0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within the region defined by coordinates [0.446, 0.146, 0.579, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a segment of the pie chart tagged as \"2010 Graduate\". This region additionally encloses a text element indicating the value \"8.3%\" [0.516, 0.102, 0.621, 0.122]."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "74aIfGH3LDkDzpHo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region marked by coordinates [0.315, 0.516, 0.696, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart tagged as \"2015 Undergraduate\" and features a text \"26.7%\" [0.521, 0.909, 0.666, 0.929] representing the value associated with this category."
+            }
+        ],
+        "id_raw": 246
+    },
+    {
+        "id": "lOSuag9cxG5V6de3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.298, 0.021, 0.665, 0.047] in the chart refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Social Inequality and Rights Protection 2020\"."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "HH50mgLEI6WX3XjE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.816, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend is represented."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "pB3Tr4FU0fSdL58I",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.384, 0.908, 0.435, 0.928] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"22.0%\" can be found."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "XA26qKPgLx02eM1i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates {the legend item \"Rights\"}[0.86, 0.071, 0.904, 0.091] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice} [[0.223, 0.278, 0.482, 0.697]]"
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "DKsTu84NeH2CwLKG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text objects are located near {the subtitle of the chart}[0.298, 0.021, 0.665, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text objects {\"Rights\"}[0.305, 0.485, 0.349, 0.505] and {\"Race\"}[0.337, 0.634, 0.372, 0.654] are located near the subtitle."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "m35kI76sb42lCvGM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice corresponds to the {legend item \"Age\"}[0.86, 0.191, 0.887, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.482, 0.153, 0.741, 0.582] corresponds to the legend item \"Age\"."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "FOpgeAV9RZ2xwkH7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.721, 0.146, 0.988, 0.297]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a portion of the legend. It contains legend items corresponding to \"Class\", \"Age\", \"Marital status\", \"Disability\", and \"Sexual orientation\" with their respective colors, helping users understand the color matching in the pie chart."
+            }
+        ],
+        "id_raw": 249
+    },
+    {
+        "id": "zJnLxLxlStUx4Yh9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what the object at [0.286, 0.021, 0.678, 0.047] is representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's representing the subtitle of the chart, which is \"Manufacturing Production Efficiency Evaluation\"."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "hj0oDHExUK0EgLIl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.825, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates represent a label in the legend, specifically \"Raw Material Efficiency\"."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "InY33wowfXBsY2cx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object at the coordinates [0.057, 0.665, 0.219, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represents the slice label text which is \"35.0%\"."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "k3QaoH9DdP9mgUYE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {the item}[0.825, 0.071, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice}<r>[0.223, 0.278, 0.482, 0.885]</r>."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "uopvYIIBUZEDILDB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which slice is next to the pie {slice}[0.464, 0.516, 0.74, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices adjacent to it are {slices}[[0.223, 0.278, 0.482, 0.885], [0.482, 0.173, 0.741, 0.542]]."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "wMnxiT3PZG0t7Vb4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend associcates with the slice {slice}[0.344, 0.146, 0.579, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend \"Energy Efficiency\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "QWe8mQXjNpk7jOX8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region specified by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the entire pie chart that displays the manufacturing production efficiency evaluation. It contains various slices representing different efficiency areas and corresponding percentages, such as the slice representing \"Raw Material Efficiency\" <r>[0.223, 0.278, 0.482, 0.885]</r> labeled as \"35.0%\" <r>[0.057, 0.665, 0.219, 0.685]</r> and several others."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "rtbvRra3ZZPTWgfT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat about the area inside these coordinates [0.781, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart legend area. It comprises five items, each one associated with a different slice in the pie chart. For example, one of these items is \"Raw Material Efficiency\" and is located at <r>[0.825, 0.071, 0.987, 0.091]</r>."
+            }
+        ],
+        "id_raw": 250
+    },
+    {
+        "id": "HvwqOkLpOhiIWR10",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.952, 0.191, 0.987, 0.211] on the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" is situated there."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "A8HkRd8GxzTta5uT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is present at [0.163, 0.464, 0.198, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the location of the text \"19.0%\"."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "NxiO13GRScemdiXZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart have at [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "xPRnPhRDiwDgXsgz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice related to {the text \"2020\"}[0.952, 0.191, 0.987, 0.211] according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated color is '#c2c2f0'."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "D00ssxBatKBDpr74",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with {the slice}[0.482, 0.516, 0.74, 0.881] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated percentage is \"21.1%\"."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "ocDTogFuxhCYJkIN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the legend}[0.908, 0.063, 0.993, 0.219] related to {the pie slice}[0.223, 0.278, 0.482, 0.691]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the pie slice matches the color of the 2016 label in the legend, indicating they represent the same data set."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "hM51KY1n0o4R6PKq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.199, 0.741, 0.548]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features a pie slice of the color '#ffcc99'. It holds the text \"17.8%\" [0.736, 0.321, 0.771, 0.341] and \"2019\" [0.598, 0.405, 0.642, 0.425], indicating the investment data in 2019 and the respective percentage."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "pm1Kou69FeQ0Q3Vn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me about the region with coordinates [0.253, 0.516, 0.522, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the pie slice corresponding to the year 2017. Inside it, we can find the texts \"19.7%\" [0.32, 0.871, 0.355, 0.891] and \"2017\" [0.391, 0.705, 0.435, 0.725], representing the investment amount and its proportion."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "XBkQvQznTcTX3EYE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide information on the area within [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The mentioned area comprises the entire pie chart, which contains slices of different colors along with their respective labels, and percentage texts denoting investment ratios for the years 2016 to 2020."
+            }
+        ],
+        "id_raw": 251
+    },
+    {
+        "id": "q7kq9ibuEN0RHHIM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the chart at coordinates [0.367, 0.654, 0.402, 0.674]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2017\"."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "aqyKRXA8eQLFrf4H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item can be found at coordinates [0.697, 0.103, 0.725, 0.117] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle for \"Appliances\"."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "VYNeU5hV0MzIgQuI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at [0.5, 0.516, 0.661, 0.875] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart slice representing the year \"2020\" data."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "2UnKt4F4pin61C5I",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie chart slice is closest to the {pie chart slice}[0.5, 0.483, 0.759, 0.595]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest slice is {the slice}[0.5, 0.516, 0.753, 0.694]."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "ii1WVgKtOG6OyhzU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the association between {the handle}[0.697, 0.103, 0.725, 0.117] in the legend and the pie chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle with coordinates [0.697, 0.103, 0.725, 0.117] in the legend corresponds to the pie chart slices colored with \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "RU0dR2a0a2G4UVuv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to the {subtitle}[0.285, 0.021, 0.715, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2014\"}[0.347, 0.396, 0.382, 0.416] is closest to the subtitle."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "36obXpnzPHciWJOZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region of the chart defined by coordinates [0.691, 0.063, 0.817, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, which includes two labels, \"Electronics\" [0.736, 0.071, 0.811, 0.091] and \"Appliances\" [0.736, 0.101, 0.811, 0.121], and corresponding color handles.\n<"
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "dUBHzJfUoCEQ4GQO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.5, 0.364, 0.758, 0.516] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart representing \"2016\" data [0.5, 0.364, 0.758, 0.516] and the corresponding text \"5.3%\" [0.776, 0.403, 0.811, 0.423]."
+            }
+        ],
+        "id_raw": 253
+    },
+    {
+        "id": "ClGFyJhnW7sOILDE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.295, 0.021, 0.668, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle \"Human History and Civilization Development\"."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "ve8X5jWGRoMVOk2w",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.844, 0.071, 0.916, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label named \"Stone Age\"."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "5wWDvLnTXkVCNKTb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.453, 0.284, 0.496, 0.304]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That object is the text label \"Digital Age\"."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "pEr2CdsQaP1jEdze",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with {the legend label \"Stone Age\"}<r>[0.844, 0.071, 0.916, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice for {the \"Stone Age\"}<r>[0.258, 0.278, 0.482, 0.516]</r> is associated with the legend label \"Stone Age\"."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "MbBS4JvHjootlUNx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat age does the pie slice with text \"6.6%\" on it refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with text \"6.6%\" refers to the {Bronze Age}[[0.224, 0.328, 0.482, 0.516]]."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "TesHptoPuMp3OXam",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice associated with the {agon label \"Digital Age\"}<r>[0.844, 0.251, 0.921, 0.271]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the pie slice associated with the \"Digital Age\" is #a2a3f0."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "sBtIItF0i43E8LSJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a pie chart with seven slices representing different ages in human history, such as the Stone Age, Bronze Age, Iron Age, Medieval Age, Renaissance, the Industrial Revolution, and Digital Age."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "apmnrQ4X3sRmtPl0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.409, 0.516, 0.68, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice labeled {\"Renaissance\"} [0.505, 0.718, 0.549, 0.738] with autotext of {\"18.4%\"}[0.564, 0.896, 0.651, 0.916]."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "WTKoGZxhxToTiIJU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found in the region [0.805, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. Each item of the legend comprises a colored box symbolizing the color of a pie slice and the text label of an age in human history."
+            }
+        ],
+        "id_raw": 254
+    },
+    {
+        "id": "Z8YovLAWuC3aUScr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significant text located at coordinates [0.353, 0.021, 0.61, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Innovation Distribution in 2008\", which is the subtitle of the chart."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "wjpiIk5Me2lzbEZP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the annotation at coordinates [0.538, 0.905, 0.669, 0.925] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The annotation text is \"78.6%\"."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "CYoBVWfHeMK0WRd3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the attribute located at coordinates [0.857, 0.101, 0.986, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend item labellling \"Service Innovation\"."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "Yl6lny4TDtbvHFhH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color does the legend item {label 'Product Innovation'}[0.857, 0.071, 0.987, 0.091] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color \"ff5733\" of the slice with coordinates [0.223, 0.187, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "V07D77VlsrARB9o0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich title is positioned near to {the xlabel}[0.482, 0.987, 0.482, 0.987]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Innovation Distribution in 2008\" with coordinates [0.353, 0.021, 0.61, 0.047] is near."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "GXOPWqi2x3MHVYRS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content associated with the pie slice {with color '#7d3c98'}[0.283, 0.146, 0.6, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It associates to \"Service Innovation\" and \"21.4%\"."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "WZyLaFGazGPNma4R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are included in the region bounded by coordinates [0.812, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region includes the chart legend which has items \"Product Innovation\" [0.857, 0.071, 0.987, 0.091] and \"Service Innovation\" [0.857, 0.101, 0.986, 0.121]."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "h8GpLCVrIFZd4Laa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region where the pie chart resides. The pie chart contains two slices represent \"Product Innovation\" and \"Service Innovation\", colored coded as \"#ff5733\" and \"#7d3c98\", respectively."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "oakSxi0ClbGssk5k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found in the region represented by the coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entirety of the subplot including the pie chart, the chart's subtitle \"Innovation Distribution in 2008\" and the legend."
+            }
+        ],
+        "id_raw": 255
+    },
+    {
+        "id": "OmTRHanOue1WkcBB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is denoted with the coordinates [0.607, 0.437, 0.651, 0.457] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Post-materialism\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "XFyPzTDAhlWXsiSI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the position represented by the coordinates [0.826, 0.063, 0.993, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the chart legend."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "jsmlzmbMo3qb1ovP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.482, 0.266, 0.741, 0.556] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart slice labeled as \"Post-materialism\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "2tpaCfVOCJjhID3j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.482, 0.151, 0.673, 0.516] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Slice}[0.482, 0.266, 0.741, 0.556] and {The Slice}[0.283, 0.146, 0.527, 0.516] are adjacent to it."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "Cnzw7LMmw25Vopp8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the slice whose color matches the color of the legend item at coordinates [0.871, 0.251, 0.933, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice labeled as \"Diversity\" matches the color of the legend item."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "3VKiXgZ9erG3L8lt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is located near to the {text}[0.406, 0.297, 0.45, 0.317]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the slice present at {coordinates}[0.283, 0.146, 0.527, 0.516]."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "8h54zmzwi7uX0BWn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region represented by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains seven slices of a pie chart, each slice representing the values of \"Traditional\", \"Rationalization\", \"Individualism\", \"Materialism\", \"Post-materialism\", \"Self-expression\", and \"Diversity\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "HJL5UyTrvrpNZSoz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's present in the region [0.826, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the legend of the chart with seven entries: \"Traditional\", \"Rationalization\", \"Individualism\", \"Materialism\", \"Post-materialism\", \"Self-expression\", and \"Diversity\" each with a unique color associated."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "MvLwuaib5UIMu5yW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide details for the region given by the coordinates [0.223, 0.278, 0.482, 0.548]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is composed of a slice labeled \"Traditional\" and also contains the accompanying autotext represented as \"12.5%\"."
+            }
+        ],
+        "id_raw": 256
+    },
+    {
+        "id": "6k2Oh6ZcNJnO8I18",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the object located at [0.242, 0.021, 0.721, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the subtitle, which reads \"Frequency of Stages in Internal Business Process Analysis\"."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "Gvept50wHPrnNfTc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.894, 0.191, 0.983, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend item carrying the label \"Maintenance\""
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "XaxCYyRZGTmF61Tz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is indicated by the object found in the coordinates [0.765, 0.46, 0.85, 0.48] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object indicates the percentage \"29.0%\" for a slice of the pie chart."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "KlmtSjCy5QvZ6NUQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices on the pie chart does the legend entry {the legend item \"Testing\"}<r>[0.894, 0.131, 0.943, 0.151]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Testing\" corresponds to the slice {the slice}<r>[0.325, 0.516, 0.662, 0.886]</r> in the pie chart."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "tbuZ7nZX48nvIbof",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is related to the legend entry {the legend item \"Planning\"}<r>[0.894, 0.071, 0.954, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Planning\" relates to the slice {the slice}<r>[0.224, 0.278, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "jbneIvQVr3uhcEk6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of the object [0.765, 0.46, 0.85, 0.48] to the legend item {the legend item \"Deployment\"}<r>[0.894, 0.161, 0.979, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represents the percentage of the pie slice which the legend item \"Deployment\" Is referring to."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "JsouzYpjCXalDD9X",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the entirety of the pie chart and contains slices representing each phase of the internal business process analysis. Each slice corresponds to a legend."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "eys42heRMON1cdMX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.849, 0.063, 0.993, 0.279] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart, explaining which color corresponds to which phase in the internal business process."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "8duoJ13KHD88Y81Z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.482, 0.2, 0.741, 0.781]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This strategic region houses the \"Deployment\" slice of the pie chart, representing its proportion of 29.0%, as labeled by the corresponding text in the region."
+            }
+        ],
+        "id_raw": 257
+    },
+    {
+        "id": "genmVVsxGltSAQzE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object with the coordinates [0.842, 0.071, 0.933, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's called \"Telemedicine\"."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "Bom05mjnkEIP45Jf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is located at the coordinates [0.241, 0.852, 0.331, 0.872]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the percentage text \"54.5%\"."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "zMZrnwOFYdluxIbm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.798, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "rwpgDB0yox8JDdUw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to {the slice}[0.223, 0.278, 0.719, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is corresponding to the legend item \"Telemedicine\" with coordinates [0.842, 0.071, 0.933, 0.091], it's marked as '#ff9999'."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "DKISE04MA6197CdN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.283, 0.146, 0.741, 0.664] and {the text}[0.632, 0.16, 0.777, 0.18] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice and the text are related because the text \"45.5%\" represents the percentage size of the slice."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "JcUv4tXubwoObPET",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does the legend item at coordinates [0.842, 0.101, 0.987, 0.121] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Health tracking apps\" corresponds to the slice with coordinates [0.283, 0.146, 0.741, 0.664]."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "bFIeOMY7fu1ufX22",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice and text. The slice colored as '#ff9999' has coordinates [0.223, 0.278, 0.719, 0.886] and the percentage text \"54.5%\" is located at coordinates [0.241, 0.852, 0.331, 0.872]. This region represents the \"Telemedicine\" portion of the chart."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "Sfo1CYyPCRMQSiMD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you find in the region with coordinates [0.223, 0.146, 0.741, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mainly contains a slice of the pie chart colored '#66b3ff' with coordinates [0.283, 0.146, 0.741, 0.664], and text \"45.5%\" with coordinates [0.632, 0.16, 0.777, 0.18], which represents the \"Health tracking apps\" portion."
+            }
+        ],
+        "id_raw": 258
+    },
+    {
+        "id": "Qz0sg763MQcJYpvz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at the coordinates [0.273, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named \"Ecological Environment Sustainability Assessment\"."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "EGmnMswIsFUxFEua",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.872, 0.191, 0.945, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text for \"Air Quality\"."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "3khfOv2Cj36Y5Yce",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is at the coordinates [0.122, 0.414, 0.204, 0.434] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" related to the slice corresponding to Biodiversity."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "SrcE6bB5aEJqzYXD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is associated with {the slice}[0.482, 0.472, 0.741, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Climate Stability\"}[0.872, 0.131, 0.987, 0.151] correlates with it."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "THn0VlpKK8Ri82VU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighbouring slices to {this slice}[0.482, 0.153, 0.739, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring slices to it are {the slices}[[0.482, 0.472, 0.741, 0.875], [0.283, 0.146, 0.532, 0.516]]."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "wGA5FXFVbF7KEJDA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text comes with {the slice}[0.23, 0.516, 0.544, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"25.0%\"}[0.239, 0.855, 0.335, 0.875] comes with the slice."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "xbDCaWhIFZrbX8NC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.828, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This particular region holds the chart's legend, containing the labels for \"Biodiversity\" [0.872, 0.071, 0.954, 0.091], \"Water Quality\" [0.872, 0.101, 0.968, 0.121], \"Climate Stability\" [0.872, 0.131, 0.987, 0.151], \"Soil Quality\" [0.872, 0.161, 0.951, 0.181], and \"Air Quality\" [0.872, 0.191, 0.945, 0.211]."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "eHtAmSQVrz9neLRy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the whole pie chart including the slices for \"Biodiversity\" [0.223, 0.278, 0.482, 0.605], \"Water Quality\" [0.23, 0.516, 0.544, 0.886], \"Climate Stability\" [0.482, 0.472, 0.741, 0.875], \"Soil Quality\" [0.482, 0.153, 0.739, 0.516], and \"Air Quality\" [0.283, 0.146, 0.532, 0.516]."
+            }
+        ],
+        "id_raw": 259
+    },
+    {
+        "id": "uQwk8z7o734Mb7wH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.312, 0.021, 0.651, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, named \"Fitness and Sports Health Trends in 2020\"."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "BjlLmWFw95H3n3oG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be seen at coordinates [0.87, 0.063, 0.993, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sublegend of the chart is located there."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "tVc5iEBctBcUrUMR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.171, 0.414, 0.204, 0.434] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" is at these coordinates."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "t0BUWJKwEzt7s3Vr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text objects in the chart are close to {the slice}[0.464, 0.516, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"10.0%\"}[0.551, 0.901, 0.602, 0.921] and {\"Cycling\"}[0.497, 0.721, 0.541, 0.741] are located near the slice."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "FllATCRkxtc9Njlg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds to {the legend item \"Cycling\"}[0.915, 0.131, 0.966, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.464, 0.516, 0.619, 0.886] corresponds to the legend item \"Cycling\"."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "J3xLh02e5uz6c1qd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is next to {the slice indicating \"HIIT\"}[0.373, 0.146, 0.739, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice indicating \"Zumba\"}[0.344, 0.18, 0.482, 0.516] is adjacent."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "PUE3wNQbnYLs6L47",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the pie chart. It contains slices representing various fitness trends such as Yoga, Running, Cycling, Swimming, HIIT, Zumba, and Pilates, along with their respective percentages."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "fPHeRsR6TIJ7Ud8t",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.87, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend of the chart, which includes the labels \"Yoga\", \"Running\", \"Cycling\", \"Swimming\", \"HIIT\", \"Zumba\", and \"Pilates\"."
+            }
+        ],
+        "id_raw": 262
+    },
+    {
+        "id": "7zgDrHpv5awL3IC0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of chart is represented in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "pie chart."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "maKr5IjlwHaIle3E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the object located in the coordinates [0.295, 0.021, 0.668, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "kpufpdZwgOueUq4c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at the coordinates [0.952, 0.191, 0.987, 0.211] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the text \"2020\"."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "ETONYvmevNkCL8PH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r> corresponds to the slice at coordinates [0.377, 0.516, 0.595, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#ffcc99' corresponds to the slice."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "GHNubRGr1wCsAC4t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the legend}<r>[0.908, 0.063, 0.993, 0.309]</r> associate with{return}<r>[0.425, 0.684, 0.743, 0.888]</r>"
+            },
+            {
+                "from": "gpt",
+                "value": "The item in the legend with a color handle matches the color of the corresponding return on the chart."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "zzFkOO0ZUhCh0Crx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}<r>[0.223, 0.435, 0.482, 0.665]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.244, 0.516, 0.482, 0.855]</r> is adjacent to the slice in question."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "PWr19aCgyOfm9J01",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "There are several pie slices in this area representing different years like \"2000\", \"2005\", \"2010\", \"2015\", \"2020\", \"2025\", \"2030\", \"2035\". The percentage of each slice is indicated by autotext."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "KgONXQawo1S2GFxl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend that contains color handles and labels representing different years: \"2000\", \"2005\", \"2010\", \"2015\", \"2020\", \"2025\", \"2030\", \"2035\". The color of each handle corresponds to the color of a pie slice."
+            }
+        ],
+        "id_raw": 263
+    },
+    {
+        "id": "4tetRJti8vWs6eC1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at coordinates [0.328, 0.624, 0.372, 0.644]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Microsoft\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "ICgwQRMczabhue17",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.177, 0.722, 0.24, 0.742] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"40.0%\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "DmWqE57GfV28gglw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location with the coordinates [0.723, 0.29, 0.763, 0.31] ?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"60.0%\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "K7ymEXw6wQim1t4D",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of the slices, which company does the slice {slice}[0.223, 0.278, 0.544, 0.886] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}[0.223, 0.278, 0.544, 0.886] corresponds to the company \"Microsoft\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "Vl9x7cDOEmlAMoDN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle color at coordinates [0.885, 0.073, 0.913, 0.087] in the legend correspond to in the slices of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle color at coordinates [0.885, 0.073, 0.913, 0.087] corresponds to the slice {slice}[0.223, 0.278, 0.544, 0.886] which represents \"Microsoft\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "JevQz1oT0uMQSmoE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element at the coordinates [0.723, 0.29, 0.763, 0.31] is related to the slice at [0.283, 0.146, 0.741, 0.875] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The element at coordinates [0.723, 0.29, 0.763, 0.31] is the autotext \"60.0%\" which indicates the proportion of the slice [0.283, 0.146, 0.741, 0.875] in the chart indicating \"Apple\"."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "ulMuZCIShVCKHqWR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region specified by coordinates [0.223, 0.278, 0.544, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice that represents \"Microsoft\". It also includes the autotext \"40.0%\"[0.177, 0.722, 0.24, 0.742] and the text \"Microsoft\"[0.328, 0.624, 0.372, 0.644]."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "DMnJKdxVKowWrSlA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.283, 0.146, 0.741, 0.875] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice that represents \"Apple\". This region also includes the autotext \"60.0%\"[0.723, 0.29, 0.763, 0.31] and the text \"Apple\"[0.592, 0.388, 0.635, 0.408]."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "FGrDj6aUJQZFo0qj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.879, 0.063, 0.993, 0.129] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart legend, with entries for both companies \"Microsoft\"[0.924, 0.071, 0.987, 0.091] and \"Apple\"[0.924, 0.101, 0.964, 0.121]. Both entries have corresponding handle colors at coordinates [0.885, 0.073, 0.913, 0.087] for 'Microsoft' and [0.885, 0.103, 0.913, 0.117] for 'Apple'."
+            }
+        ],
+        "id_raw": 264
+    },
+    {
+        "id": "wXpZgckCtQ5hAOzQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart with coordinates [0.183, 0.02, 0.817, 0.038] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Percentage of Urban Population and Forest Covered Area from 2010 to 2018\"."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "4K67VZcG1UrriElJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.293, 0.195, 0.328, 0.209] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"7.3%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "VpaO2t5xHUWiFVb1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.329, 0.195, 0.367, 0.214] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates is a slice of the pie chart."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "M7EHRlUpE57x8tdT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to the {2013 slice}[0.384, 0.29, 0.5, 0.465] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {2014 slice}[0.486, 0.29, 0.603, 0.465] is adjacent to the 2013 slice."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "4aLnzMbUbT8hhZgf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the adjacent text to {2017 slice}[0.5, 0.115, 0.641, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {2018}[0.422, 0.672, 0.458, 0.686] is adjacent to the 2017 slice."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "swIDEvEndfSizHT6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie chart is the {2017 slice}[0.5, 0.115, 0.641, 0.29] located in?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2017 slice is located in the Forest Covered Area from 2010 to 2018 pie chart."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "MxKfioTqaiUvih0W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region within the coordinates [0.325, 0.59, 0.675, 0.941]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers a pie chart which represents the \"Forest Covered Area from 2010 to 2018\". The slices represent the years and the percentages of forest covered areas. Also, it contains various texts representing the percentages over the years, for example, \"13.6%\" at [0.279, 0.707, 0.314, 0.721]."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "XzMmW1UOA1tjf42Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region at [0.325, 0.114, 0.675, 0.465]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has a pie chart that represents the \"Urban Population from 2010 to 2018\". It contains slices that correspond to the years and the percentages of population. For instance, it includes \"2010\" at [0.389, 0.235, 0.424, 0.249] and corresponding percentage \"7.3%\" at [0.293, 0.195, 0.328, 0.209]."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "oOaKOR1Slj9zuZO0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region within [0.5, 0.115, 0.641, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a slice of the pie chart portraying the Forest Covered Area in 2017, labeled \"2017\" at [0.528, 0.19, 0.572, 0.204], holding 13.9% of the total represented by the text \"13.9%\" at [0.592, 0.113, 0.627, 0.127]."
+            }
+        ],
+        "id_raw": 266
+    },
+    {
+        "id": "jH6ZXQF688xQcs26",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary content displayed in the {[0.373, 0.572, 0.627, 0.933]} coordinates of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart showing Resource Allocation on Cultural Heritage vs Cultural Integration from 2016 to 2020."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "W8hj09xnS4kaMUvn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the chart located at coordinates [0.373, 0.067, 0.627, 0.428] provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart depicts the percentage distribution of resources to different years, from 2016 to 2020."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "WGFwShE7zmG48kDA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the item located at [0.661, 0.537, 0.746, 0.692] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the chart legend. It illustrates the colors that represent each year in the pie chart."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "HW7pt25TX3vprOiw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart does the legend item \"2019\" with a handle of color at coordinates [0.666, 0.636, 0.694, 0.65] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice with coordinates [0.447, 0.572, 0.591, 0.753]."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "ZIXJ2KiXPgUSGLFO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parts of the pie chart are adjacent to the slice {pie slice}[0.5, 0.626, 0.627, 0.846]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The parts adjacent are the{pie slice}[0.438, 0.753, 0.608, 0.933] and {pie slice}[0.447, 0.572, 0.591, 0.753]."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "6JysknS0FtEU3JUt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item with color coordinates [0.666, 0.576, 0.694, 0.59] matches with the pie slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2017\" corresponds to the {pie slice}[0.438, 0.753, 0.608, 0.933]."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "XVjYWrAtIXld8EPz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in the region delimited by coordinates [0.342, 0.527, 0.658, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two primary features. The main one is a pie chart with coordinates [0.373, 0.572, 0.627, 0.933], which shows the resource allocation between Cultural Heritage and Cultural Integration from 2016 to 2020. There is also a legend located at [0.661, 0.537, 0.746, 0.692] which illustrates the color coding for the years in the pie chart."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "2q52K1IWgg9XKlJs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a detailed description of the area within coordinates [0.342, 0.021, 0.658, 0.473]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates enclose another pie chart at coordinates [0.373, 0.067, 0.627, 0.428]. This chart highlights the percentage distribution of cultural resources towards different years, from 2016 to 2020."
+            }
+        ],
+        "id_raw": 268
+    },
+    {
+        "id": "DHVjMkTv8RjZoDM1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.2, 0.302, 0.235, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"5.6%\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "4oGQ89HK56FWjZwh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is located at the coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend icon for 2013."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "0P2ZHnVrrQH2QDVe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.329, 0.395, 0.365, 0.415] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2008\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "3mKVeYPOEIVVrNyn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is associated with {the legend icon}[0.913, 0.253, 0.941, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.482, 0.195, 0.741, 0.567] is associated with the legend icon."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "Pe68NFZsyAHvbAk6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is nearest to {the legend item}[0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.368, 0.516, 0.579, 0.886] is closest to the legend item."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "oK5JnGfIjG2ZJEmr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does {the text}[0.706, 0.757, 0.741, 0.777] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text belongs to the slice with coordinates [0.482, 0.516, 0.738, 0.859]."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "cjY22M6WDnTdlMcg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is constituted by a pie chart with numerous slices. It has auto texts ranging from \"5.6%\" to \"22.2%\" and labels from \"2008\" to \"2015\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "1UtWVSYKBouvToeY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the legend of the pie chart with items corresponding to years from 2008 to 2015, each represented by different colors."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "vhACL3e8d4WdPKlp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.516, 0.482, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is part of a pie slice labeled \"2010\" with an autotext showing a percentage of \"7.8%\"."
+            }
+        ],
+        "id_raw": 269
+    },
+    {
+        "id": "COp07wmjFUjzKVtS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.232, 0.021, 0.731, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, reading \"Education Resource Distribution and Utilization (2012-2020)\"."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "dMiNQXESxff8noag",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element found at the coordinates [0.908, 0.063, 0.993, 0.668] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend of the chart."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "ppP4epwoPodTLgR8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie graph is positioned at these coordinates."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "dZMJItxP9khDwQMA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items associate to the color of the slice {slice}[0.395, 0.516, 0.506, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {item}{\"2018\"}[0.952, 0.251, 0.987, 0.271] corresponds to the mentioned slice."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "vTIf8FxhyEBAxbFF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are related to {legend item \"2021\"}[0.952, 0.64, 0.987, 0.66]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {\"2021\"}[0.482, 0.516, 0.735, 0.75] is related to the legend item \"2021\"."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "uLuKwO96qpaPPSAC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices at {slice}[0.482, 0.200, 0.673, 0.516] and the legend on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is associated with the legend item {\"2017\"}[0.952, 0.52, 0.987, 0.54]."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "9UNavPgmhGbdOYuF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region confined by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a pie chart, which is divided into slices representing different years from 2012 to 2021. Each slice is color-coded and corresponds with a legend item on the right side of the chart."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "5sozhK8LDqtb1Nrp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.908, 0.063, 0.993, 0.668]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area marked by those coordinates contains the chart's legend. It has different color markers for each year stretching from 2012 to 2021, and each marker matches a color-coded slice on the pie chart."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "LoW0PHaGgw76BBOB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements inside the coordinates [0.482, 0.343, 0.731, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there are several pie slices with a different color for each year. Each slice represents data for a specific year, with the chart's legend providing clarity on the represented years."
+            }
+        ],
+        "id_raw": 271
+    },
+    {
+        "id": "VPSmu8G83Yj1zxj3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found in the [0.336, 0.021, 0.627, 0.047] area of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a subtitle with the text \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "8YqjNLhkYRAmur7w",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at coordinates [0.872, 0.191, 0.981, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend label that reads \"Regulatory Risk\"."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "9ZfZpjhXd7NCqnrw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at [0.223, 0.278, 0.619, 0.886] coordinates in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie slice that corresponds to Cyber Risk."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "vvrDDuU3F7ktl7Kx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are next to {the slice}[0.223, 0.278, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices {[0.482, 0.320, 0.741, 0.83], [0.482, 0.173, 0.701, 0.516]} are directly adjoining."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "RA02tWlSyMSv9nx3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label has the same color as the {the slice}[0.283, 0.244, 0.482, 0.516] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"External Risk\" [0.872, 0.281, 0.963, 0.301] has the same color in the legend."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "oDg4mWBWn1md2Cc0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between legend items and pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items are a visual guide to the different categories in the pie slices, each legend item corresponds to exactly one slice colored the same way."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Jw3hEMCJ7PqnhDg1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region bounded by [0.828, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is located on the far right of the chart and contains the legend. The legend describes eight risk types: \"Cyber Risk\" [0.872, 0.071, 0.947, 0.091], \"Operational Risk\" [0.872, 0.101, 0.987, 0.121], \"Market Risk\" [0.872, 0.131, 0.954, 0.151], \"Credit Risk\" [0.872, 0.161, 0.948, 0.181], \"Regulatory Risk\" [0.872, 0.191, 0.981, 0.211], \"Reputation Risk\" [0.872, 0.221, 0.981, 0.241], \"Strategic Risk\" [0.872, 0.251, 0.969, 0.271], and \"External Risk\" [0.872, 0.281, 0.963, 0.301]."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Vj8wIL4ibPbfQl5e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with slices corresponding to various types of risk. Each slice has a different color and size reflecting its giving category."
+            }
+        ],
+        "id_raw": 272
+    },
+    {
+        "id": "Word5KUJShKy1rct",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.318, 0.021, 0.645, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the {subtitle}<r>[0.318, 0.021, 0.645, 0.047]</r> reading 'EdTech and Online Learning 2016-2019'."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "rMWAVNV2KaTjDKyW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object located at [0.913, 0.073, 0.941, 0.087] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the {legend item}<r>[0.913, 0.073, 0.941, 0.087]</r> is '#ff9999' which represents the data for the year 2016."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "KigofOoto4lCGIml",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text [0.343, 0.885, 0.378, 0.905] inside the pie chart signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the {Percentage}<r>[0.343, 0.885, 0.378, 0.905]</r> '22.9%' for the slice corresponding to the year 2017."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "4WjmRtUyWJJ0iVZB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item color corresponds to the pie slice at coordinates [0.223, 0.278, 0.482, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#ff9999' of the {legend item with handle}<r>[0.913, 0.073, 0.941, 0.087]</r> corresponds to the {slice}<r>[0.223, 0.278, 0.482, 0.687]</r>."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "YCEKpYNV2D3Gf5OJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is nearest to the pie slice at coordinates [0.252, 0.516, 0.57, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {autotext}<r>[0.343, 0.885, 0.378, 0.905]</r> signifying '22.9%' is the nearest to the {pie slice with coordinates}<r>[0.252, 0.516, 0.57, 0.886]</r>."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "zMEGLXw2DkJ3M8Lk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the text at coordinates [0.163, 0.462, 0.198, 0.482] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {autotext}<r>[0.163, 0.462, 0.198, 0.482]</r> belongs to the {pie slice with coordinates}<r>[0.223, 0.278, 0.482, 0.687]</r>."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "CbfJH7r4AwBIhYHx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is primarily occupied by a pie chart. It contains four slices, each with different percentages and text references for different years from 2016 to 2019. The slice for 2016 lies between coordinates [0.223, 0.278, 0.482, 0.687] while the slice for the year 2019 lies between coordinates [0.283, 0.146, 0.711, 0.516]."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "LKlod1u3PkZ9jfxM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there is a pie chart comprising four slices for different years from 2016 to 2019. Each slice has an associated percentage text; for instance, the 2016 slice [0.223, 0.278, 0.482, 0.687] is associated with text '18.8%' [0.163, 0.462, 0.198, 0.482]."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "jieKJ1nNugxFDpSX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within coordinates [0.908, 0.063, 0.993, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart's legend. The legend lists the color coding for four years: 2016, 2017, 2018, and 2019. The color coordinates for 2016 are at [0.913, 0.073, 0.941, 0.087]. The color coordinates for 2019 are at [0.913, 0.163, 0.941, 0.177]."
+            }
+        ],
+        "id_raw": 273
+    },
+    {
+        "id": "pfCxkyiHhUVVzW77",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at position [0.866, 0.071, 0.965, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Old Stone Age\" is located at this position."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "V4yKqTUjD3qrhAnz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.259, 0.021, 0.704, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, called \"Contributions of each Era to Civilization Development\"."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "pksAz4Aqg39L1u2D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "ypAxMGkNDlt8bxK4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to the slice {the slice}[0.324, 0.516, 0.544, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Bronze Age\"}[0.428, 0.723, 0.472, 0.743] is closest."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "RtkUmy2loAq4ATGu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.223, 0.393, 0.482, 0.589] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Middle Stone Age\"}[0.866, 0.101, 0.987, 0.121] corresponds to this slice."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "MLfzXSDU5QrXMTdL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the {legend item}[0.866, 0.221, 0.949, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.146, 0.739, 0.516] corresponds to this legend item."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "U5xr2kbQV9ChwckQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire chart, six slices representing different Ages, and some textual annotations that indicate age names and percentages."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "ZzGq2OS3bmKRidcV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region of [0.482, 0.475, 0.741, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices representing the \"Iron Age\" and \"Modern Age\", and their accompanying texts {\"Iron Age\"}[0.587, 0.633, 0.631, 0.653] and {\"Modern Age\"}[0.505, 0.293, 0.549, 0.313]."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "TcKQ7NabTb8dmZhc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region bounded by [0.223, 0.393, 0.482, 0.589]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region essentially contains a slice of the pie chart representing the \"Middle Stone Age\", along with corresponding text {\"Middle Stone Age\"}[0.309, 0.49, 0.344, 0.51] and the accompanying percentage {\"8.6%\"}[0.076, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 274
+    },
+    {
+        "id": "iuPFKL7zDsYoS9dM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" is at those coordinates."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "rVYmQxOWMa6YZGCs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.913, 0.163, 0.941, 0.177] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color icon in the legend that corresponds to year \"2018\"."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "dBt4DclRmrng4B4p",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the text at coordinates [0.388, 0.309, 0.432, 0.329] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2020\" is described by the text at these coordinates."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "KWBtkEIbVfyHrX7k",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart corresponds to {the legend color icon}[0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to {the 2020 color icon}<r>[0.913, 0.223, 0.941, 0.237]</r> in the legend is at {coordinates}<r>[0.283, 0.147, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "yM892NYGylhXP6Je",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is closely associated with {the slice}[0.283, 0.147, 0.482, 0.516] in terms of year?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2020\"}<r>[0.388, 0.309, 0.432, 0.329]</r> in the chart corresponds to the slice."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "scMG3kCPG8srqjUX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the slice in the chart with {coordinates}[0.482, 0.291, 0.741, 0.668]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color in the legend which corresponds to the slice with those coordinates is #ffcc99 - representing the year 2018."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "nWlVygOafpa6DNhp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region of the chart between coordinates [0.223, 0.291, 0.482, 0.643]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that is colored #ff9999, representing the year 2015. Inside it, there are two texts; \"16.7%\" and \"2015\" at coordinates [0.166, 0.436, 0.201, 0.456] and [0.307, 0.468, 0.351, 0.488] respectively."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "UqpXm6NuHUNgmkVZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the region [0.461, 0.516, 0.718, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart that is colored #99ff99, and corresponds to the year 2017. It also includes the text \"19.6%\" at coordinates [0.627, 0.856, 0.662, 0.876] and the year \"2017\" at coordinates [0.539, 0.697, 0.583, 0.717]."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "AdVpreSqqlbRTjen",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart, containing all the slices and their corresponding labels and percentages."
+            }
+        ],
+        "id_raw": 276
+    },
+    {
+        "id": "psTXUslcMUeKd8gs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.272, 0.021, 0.691, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, called \"Distribution of Satellite Stations Across Continents\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "gVwyYLZq9IWTCAQV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.885, 0.101, 0.914, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item corresponding to the label \"Asia\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "W9bWyvT0s4r2wNNt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.181, 0.265, 0.252, 0.285] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2.1%\"."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "S8x9cQm8Vg2G2eMG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.482, 0.221, 0.741, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.478, 0.146, 0.638, 0.516] is adjacent."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "KDbmnHnM7n2kpEqO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.272, 0.021, 0.691, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Antarctica\"}[0.339, 0.375, 0.374, 0.395] and {\"Asia\"}[0.318, 0.598, 0.362, 0.618] are located near the subtitle."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "vO9Khh0IvBylTkiQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Antarctica\", \"Asia\", \"Europe\", \"North America\", \"South America\", \"Australia\", and \"Africa\" correspond to the slices with coordinates [0.263, 0.278, 0.482, 0.516], [0.223, 0.318, 0.482, 0.882], [0.442, 0.516, 0.74, 0.886], [0.482, 0.221, 0.741, 0.542], [0.478, 0.146, 0.638, 0.516], [0.348, 0.146, 0.482, 0.516], and [0.283, 0.199, 0.482, 0.516] respectively."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "oHjAu9GWVeIzl1JN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.318, 0.482, 0.882]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"31.6%\" [0.193, 0.674, 0.222, 0.694] and the text \"Asia\" [0.318, 0.598, 0.362, 0.618]. This region represents a slice of the pie chart corresponding to the distribution of satellite stations in Asia."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "ABTzi1Gdk9jbvQE6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.442, 0.516, 0.74, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"26.3%\" [0.66, 0.824, 0.708, 0.844] and the text \"Europe\" [0.557, 0.679, 0.601, 0.699]. This region represents a slice of the pie chart, indicating the distribution of satellite stations in Europe."
+            }
+        ],
+        "id_raw": 278
+    },
+    {
+        "id": "2GMgdiY5in0mRECc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.618, 0.014, 0.877, 0.04] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subtitle, named \"Percentage of Total Exports from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "2yiUpyUzA0IIKukp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.676, 0.456, 0.852, 0.784] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a pie chart slice representing the year 2017."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "xF7vP21d6ubrIaHa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.907, 0.802, 0.931, 0.822] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend item labeled \"2015\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "RSiJmjYOKx1rUlLh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near the legend item {2015}[0.907, 0.802, 0.931, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"2016\"}[0.907, 0.832, 0.931, 0.852] is located near the legend item \"2015\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "GNLhvNUHjDiliOfN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie chart slice corresponds to the legend item {2020}[0.907, 0.952, 0.93, 0.972]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slice at coordinates [0.63, 0.128, 0.787, 0.456] corresponds to the legend item \"2020\"."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "0tAJ9NBSoyN1uQLB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie chart slices are adjacent to the {slice \"2017\"}[0.676, 0.456, 0.852, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {2016}[0.595, 0.456, 0.748, 0.746] and {2018}[0.748, 0.415, 0.901, 0.696] are adjacent to the slice representing the year 2017."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "BX2nMWrCSMRgNuRV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region of coordinates [0.748, 0.415, 0.901, 0.696]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart slice representing the year 2018, with the label {\"15.1%\"}[0.906, 0.569, 0.929, 0.589] and the year label {\"2018\"}[0.819, 0.513, 0.849, 0.533]."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "Gqa32VUdzexITUYU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.877, 0.794, 0.934, 0.98]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the years 2015 to 2020, each with a colored box and text labeling the corresponding year."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "4GYfkIKo1sEoKATJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.594, 0.128, 0.901, 0.784]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains pie chart slices representing the years 2015 to 2020, each with a percentage label and year label. The region covers most of the right half of the chart and represents data for total exports from 2015 to 2020."
+            }
+        ],
+        "id_raw": 279
+    },
+    {
+        "id": "rQS46WirnZdaTjRK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the position [0.913, 0.071, 0.933, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"HR\"."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "PUDyZIX1LfHHnO2l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at coordinates [0.223, 0.146, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "iywLGrP1djSuI5tM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object at the coordinates [0.288, 0.021, 0.675, 0.047] show?"
+            },
+            {
+                "from": "gpt",
+                "value": "It shows the chart title \"Resource Distribution among Business Sectors\"."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "imw1YWHBLBcEmYSk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color for the {sector 'IT'}[0.913, 0.131, 0.926, 0.151] according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for sector 'IT' is '#99ff99'."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "Fwj7hCA68hxsd53Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is near the {sector 'R&D'}[0.913, 0.221, 0.944, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"10.0%\"[0.382, 0.111, 0.413, 0.131] is near the sector 'R&D'."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "x5MrvqipKfvSmdZo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector does the slice {with color '#ff9999'}[0.223, 0.278, 0.482, 0.605] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the sector \"HR\"."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "ZcxPH4bTYplh0hS1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.482, 0.282, 0.741, 0.716]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice with autotext \"20.0%\"[0.766, 0.483, 0.84, 0.503] and the label \"Production\"[0.615, 0.494, 0.659, 0.514]. The color of the slice is '#ffcc99', indicating it represents the 'Production' sector."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "pAIeBDZplY4004PK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.223, 0.278, 0.482, 0.605]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a sector of the pie chart. It includes the autotext \"15.0%\" [0.184, 0.414, 0.204, 0.434] and the label \"HR\" [0.308, 0.456, 0.352, 0.476]. The sector color is '#ff9999', standing for 'HR' sector."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "eP3HwRXo8bCqcXT6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details found in the region [0.482, 0.147, 0.682, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice, with the color '#c2c2f0'. It has an autotext \"13.0%\"[0.613, 0.145, 0.682, 0.165] and a label \"Marketing\"[0.531, 0.309, 0.575, 0.329]. This suggests this region belongs to the 'Marketing' sector in the pie chart."
+            }
+        ],
+        "id_raw": 280
+    },
+    {
+        "id": "Lhd4BkZ4Vft0FgTs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object in the chart with coordinates [0.154, 0.021, 0.809, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle, called \"Investment in Social Responsibility and Sustainable Business Practices in 2020\"."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "ZQtHjiq7guUAxvWy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.772, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend of the chart."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "nbyWjzMqGaHJxj89",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.151, 0.353, 0.217, 0.373] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"10.0%\"."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "qDzbRPX7RSMHmksl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.283, 0.146, 0.741, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.385, 0.516, 0.74, 0.886]] is adjacent."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "Wb2Pqv8jtr1PUUjz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.154, 0.021, 0.809, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no text located near the subtitle."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "vWaKS0g5uxV7AJgW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {\"Sustainable Sourcing\"}[0.283, 0.146, 0.741, 0.542] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Sustainable Sourcing\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "yUbCnOM9JiFijw8e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.385, 0.516, 0.74, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for \"Ethical Sourcing\", coloured in '#99ff99' and carries the text \"30.0%\" at coordinates [0.633, 0.851, 0.745, 0.871]."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "UqsOUj1Fug4eN9ZT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains four pie slices representing \"Recycling\", \"Community Involvement\", \"Ethical Sourcing\" and \"Sustainable Sourcing\". Each slice comes with a percentage text indicating the share of investment in that area."
+            }
+        ],
+        "id_raw": 281
+    },
+    {
+        "id": "waGDytnhwjLlYU53",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.351, 0.088, 0.674, 0.114] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle which is {\"Completed Projects from 2014 to 2020\"}[0.351, 0.088, 0.674, 0.114]."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "eSOguETfQIMrgsce",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.852, 0.13, 0.937, 0.345]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the legend< r>[0.852, 0.13, 0.937, 0.345]< /r>."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "47snCiDRLdbb1WNU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is enclosed in the coordinates [0.896, 0.317, 0.931, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the text \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "sLHIXCeSiFrEwgD9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the pie slice with the label '2019'{pie slice}[0.501, 0.197, 0.663, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the label '2020'<r>[0.347, 0.197, 0.512, 0.505]</r> is adjacent to the '2019' pie slice."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "7FszDAPH4TRftF3U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the color in the legend that corresponds to the slice labeled '2017'{the slice}[0.512, 0.505, 0.728, 0.753]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for '2017' is represented in the coordinates in the legend:< r>[0.857, 0.229, 0.885, 0.243]< /r>."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "qTxRd6j2W0S5DssC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the legend corresponds to the slice labeled '2014'{the slice}[0.297, 0.307, 0.512, 0.595]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The part of the legend corresponding to '2014' is located at the coordinates:< r>[0.857, 0.14, 0.885, 0.154]< /r>."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "vqoge7ohciV9OyRK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's contained in the region [0.297, 0.197, 0.728, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the whole pie chart, including slices labeled '2014' to '2020' with their respective percentages. Each slice in the pie chart has a unique color that corresponds to a label in the legend."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "Ccot7hzn3tF7MdnS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the features within the region [0.243, 0.12, 0.782, 0.89]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the whole subplot that includes the pie chart, the title, the labels on each slice, and the percentages for each year on the pie chart."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "7C6569gRENAoKetD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are presented in the rectangle bounded by coordinates [0.852, 0.13, 0.937, 0.345]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a legend which contains colored boxes affiliated with corresponding years from 2014 to 2020 in the pie chart."
+            }
+        ],
+        "id_raw": 282
+    },
+    {
+        "id": "7s3sNgNXN14ZZdNR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what the title of the chart is?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Comparison of Global Trade and Supply Chain Management in 2018\", located at coordinates [0.219, 0.021, 0.781, 0.047]."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "fWXMBsC2A6MN6xLB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.72, 0.101, 0.811, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend's item text named \"Supply Chain\"."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "cepsoUvBU4ZvQMm3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text exists within the given bounds [0.267, 0.856, 0.355, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text within these coordinates is \"55.2%\"."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "LdxLZyOo2m4TEL0D",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is to the left of the slice illustrated at [0.302, 0.146, 0.759, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.241, 0.278, 0.741, 0.886] is to the left of the given slice."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "SzY20Ej4o4Rc71s0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element in the legend correlates directly with {the slice in the pie chart}[0.241, 0.278, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend's item text \"Global Trade\"<r>[0.72, 0.071, 0.807, 0.091]</r> corresponds to this slice."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "4mJVbwTqAwq4FIjy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element from the legend links to {the pie slice}[0.302, 0.146, 0.759, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend's item captioned \"Supply Chain\"<r>[0.72, 0.101, 0.811, 0.121]</r> is associated with the given pie slice."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "HtIJla9rMONPORyO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide a description of the objects stationed within [0.241, 0.146, 0.759, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored in '#ff9999' and the texts \"55.2%\"<r>[0.267, 0.856, 0.355, 0.876]</r> and \"Global Trade\"<r>[0.399, 0.697, 0.443, 0.717]</r>."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "gBV4v1tycEEcbIYd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are present in the region defined by [0.302, 0.146, 0.759, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice colored in '#66b3ff' and the texts \"44.8%\"<r>[0.645, 0.156, 0.737, 0.176]</r> and \"Supply Chain\"<r>[0.557, 0.315, 0.601, 0.335]</r>."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "bY25mdcMcWo0EFaj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted in the area with coordinates [0.675, 0.063, 0.817, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This zone incorporates the legend with items \"Global Trade\"<r>[0.72, 0.071, 0.807, 0.091]</r> and \"Supply Chain\"<r>[0.72, 0.101, 0.811, 0.121]</r>."
+            }
+        ],
+        "id_raw": 283
+    },
+    {
+        "id": "8tLmaesaDF27eISS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.273, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, labeled as \"Ecological Environment Sustainability Assessment\"."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "wka9DiEHNLk7uCuH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at coordinates [0.908, 0.063, 0.993, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "avZNQmFLYqqSK3BF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates point to the center of the pie chart."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "CDMT4ohbqfNWptDE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color slice in the pie chart does {the legend item '2017'}[0.952, 0.221, 0.987, 0.241] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice with color '#fdb462'."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "x1RNMj8VlZbPStqn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the slice '2012'}[0.223, 0.278, 0.482, 0.525] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is adjacent to {the slice '2013'}[0.223, 0.516, 0.482, 0.774]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "b5ZwaFzSbiLEZU1X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does {the slice at the center coordinate [0.482, 0.516]} correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the year 2016."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "RIU6kIk9lxsvf30F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region that falls within the coordinates [0.223, 0.278, 0.482, 0.525]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice representing the year 2012, colored '#ff9999'. The slice has an autotext of \"11.5%\" at coordinates [0.178, 0.371, 0.213, 0.391], and the associated year text \"2012\" at coordinates [0.313, 0.433, 0.357, 0.453]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "3Dw6SMIPvRwI7B52",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present within the region [0.296, 0.516, 0.508, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region has a pie slice labeled as the year 2014 associated with '#99ff99' color. A percentage of \"14.4%\" is featured at coordinates [0.349, 0.889, 0.384, 0.909], and the text \"2014\" can be found at coordinates [0.407, 0.715, 0.45, 0.735]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "jxfzBFwCLTDxR95a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the region within the coordinates [0.223, 0.516, 0.482, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of color '#66b3ff', representing the year 2013, it contains an autotext of \"11.9%\" at coordinates [0.184, 0.663, 0.219, 0.683] and a text \"2013\" at coordinates [0.316, 0.592, 0.36, 0.612]."
+            }
+        ],
+        "id_raw": 285
+    },
+    {
+        "id": "0TuEo7KvZ4IWJC3k",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the pie chart itself."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "8TGUKZYFnhxV9Xb2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.79, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Organizational Management' is located at these coordinates."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "RMpYv9tQ7RBSlVnF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.602, 0.417, 0.646, 0.437] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Leadership Quality' is located at these coordinates."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "xk45w9swDOtXKTiJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the pie chart slice}[0.223, 0.278, 0.646, 0.886] and {the label}[0.349, 0.662, 0.393, 0.682]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Organizational Management' is the name of the pie chart slice."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "zrr0ZOmH6gkxtLzR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does {the label}[0.743, 0.343, 0.873, 0.363] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice}[0.447, 0.146, 0.741, 0.802]."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "JYeLWw5MVEhuKesC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice that corresponds to {the label \"Change Management\"}[0.79, 0.131, 0.939, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice is \"#99ff99\" or light green."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "fIKP3LyWPbdOr6da",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's happening in the region between coordinates [0.223, 0.146, 0.741, 0.802]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers two slices of the pie chart, 'Organizational Management' and 'Leadership Quality', with respective percentages of 47.1% and 41.2%."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "uSMBz5bY2emw1xBK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are present in the region [0.745, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three legend labels: 'Organizational Management', 'Leadership Quality', and 'Change Management'."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "fbic6FnND56oR3vs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region [0.283, 0.149, 0.482, 0.516] in relation to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the 'Change Management' slice of the pie chart, which accounts for 11.8% of the focus in Business Management and Organizational Leadership."
+            }
+        ],
+        "id_raw": 286
+    },
+    {
+        "id": "hS9rococi149ghG4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.2, 0.021, 0.763, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Government Policy and Effectiveness Evaluation from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "PvYkkRoOaWnKFXlT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.913, 0.133, 0.941, 0.147] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend handle for the year \"2017\"."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "xiOMyLE3C9OYaMes",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.431, 0.912, 0.467, 0.932] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"15.2%\" on one of the pie slices."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "8XQRBG07ru4I13LW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {the slice}[0.482, 0.276, 0.741, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled \"2019\" corresponds."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "qmWvBwUExgefJ6Iy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does {the legend handle}[0.913, 0.253, 0.941, 0.266] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.283, 0.146, 0.517, 0.516]."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "AYuAlHGQSTFrBpmu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.351, 0.516, 0.588, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {adjacent slices}[[0.225, 0.516, 0.482, 0.835], [0.482, 0.516, 0.74, 0.853]] are adjacent to it."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "CMlOj81Nm41NoviR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.5, 0.3, 0.7, 0.5]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of the chart's plotting area. It contains part of the slices for the years \"2020\" [0.482, 0.149, 0.679, 0.516] and \"2019\" [0.482, 0.276, 0.741, 0.546]. Also, this region contains the texts \"2020\" [0.534, 0.311, 0.578, 0.331] and \"2019\" [0.608, 0.438, 0.652, 0.458]."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "wvT3XnShksrwa3Fp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.7, 0.4, 0.9, 0.6]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mostly falls outside the pie chart itself but contains part of the legends. Specifically, it includes the legends for the years \"2020\" [0.913, 0.223, 0.941, 0.237] and \"2021\" [0.913, 0.253, 0.941, 0.266], and their corresponding color-tags [0.952, 0.221, 0.987, 0.241] and [0.952, 0.251, 0.987, 0.271]."
+            }
+        ],
+        "id_raw": 287
+    },
+    {
+        "id": "kOeD5fhyom0oE7LP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is positioned at coordinates [0.301, 0.021, 0.662, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle reads \"Real Estate Investment Trends (2016-2020)\"."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "5QqVZw4BqnIcbEwx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element is located at the coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the pie chart."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "nkWMkhLpsZ5lSnnf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.952, 0.191, 0.987, 0.211] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" is located at this position."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "30pfLcOIEmsc6C9h",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the pie chart corresponds to {the label}[0.952, 0.071, 0.987, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.223, 0.278, 0.482, 0.664] corresponds to the \"2016\" label in the legend."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "SF0ofL18z47Cxtda",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the legend items related to {the slice}[0.482, 0.19, 0.741, 0.609] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding legend item is \"2019\" with coordinates [0.952, 0.161, 0.987, 0.181]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "pKWkSwhtwoNUeT03",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend item {2018}[0.952, 0.131, 0.987, 0.151] relate to the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"2018\" legend item corresponds to the slice on the pie chart with coordinates [0.482, 0.516, 0.732, 0.886]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "RBiOBjFlgpPztVdm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region within coordinates [0.482, 0.516, 0.732, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a slice in the pie chart labeled \"2018\" with coordinates [0.557, 0.679, 0.601, 0.699], and carrying a percentile of \"20.4%\" found at coordinates [0.66, 0.823, 0.695, 0.843]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "vYqpwfAbj2z38QDe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.908, 0.063, 0.993, 0.219] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart's legend. It contains the items \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" along with five corresponding color handles."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "WDcnozYzhU6qFUmd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region specified by coordinates [0.223, 0.278, 0.482, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a slice of the pie chart. It is labeled \"2016\" with text coordinates [0.306, 0.474, 0.35, 0.494] and has a percentage label of \"17.7%\" at coordinates [0.164, 0.448, 0.2, 0.468]."
+            }
+        ],
+        "id_raw": 289
+    },
+    {
+        "id": "SSGIetT7UBs1mYR2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can the object at coordinates [0.355, 0.021, 0.608, 0.047] be referred to as in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, labeled as \"Web Traffic from 2000 to 2007\"."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "qYeljwQK3LtqY4vt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the region of coordinates [0.908, 0.063, 0.993, 0.309] designated for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend area of the chart."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "QYguxWoUsZprpxO5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the coordinates [0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center coordinates of the pie chart."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "pWPej7oAYkusJvGp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are related to the slice {the slice}[0.223, 0.371, 0.482, 0.537]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2001\"}[0.311, 0.468, 0.346, 0.488] and {\"7.3%\"}[0.166, 0.436, 0.201, 0.456] are related to the slice."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "AG886GTGIIl0Z0wA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich handle color is associated with {the label \"2005\"}[0.952, 0.221, 0.987, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle color associated with \"2005\" is #c4463a, with handle coordinates as [0.913, 0.223, 0.941, 0.237]."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "62wNFhW6TG3D3iMk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of pie are related to the handle with coordinates [0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.223, 0.516, 0.482, 0.741], representing data for the year 2002, is related to this handle."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "cml3aDMT9HEkBcXf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDetail the region outlined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a pie chart depicting annual web traffic data from 2000 to 2007. The within-pie coordinates (area enclosed by \"[0.223, 0.146, 0.741, 0.886]\") include eight distinct slices, each representative of a specific year and colored uniquely."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "o4unjr0oWviOSdru",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found in the region specified by coordinates [0.429, 0.516, 0.637, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with the text \"2004\" situated at coordinates [0.493, 0.723, 0.537, 0.743] as well as a corresponding autotext \"13.5%\" at coordinates [0.543, 0.903, 0.578, 0.923]."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "bEc2ww01UwYRkEQI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of region [0.243, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the pie chart representing the year 2000, with the text \"2000\" positioned at coordinates [0.332, 0.39, 0.367, 0.41], and \"4.7%\" text at coordinates [0.204, 0.293, 0.239, 0.313]."
+            }
+        ],
+        "id_raw": 292
+    },
+    {
+        "id": "tbOeb7tyIE72s46T",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.229, 0.021, 0.734, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, named Local Government Expenditure On Community Development."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "gCE6BKRGTVOKs6uN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at coordinates [0.223, 0.278, 0.68, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie slice labeled as Healthcare."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "Mbw7E4zb4NtPOtCF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.887, 0.071, 0.963, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a legend text labeled as Healthcare."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "UwKKdDLMiBrhqu7c",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart does the legend item {Healthcare}[0.887, 0.071, 0.963, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item corresponds to {the pie slice}[0.223, 0.278, 0.68, 0.886]."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "rvCUNnRWEigJrsxS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is associated with the {pie slice}[0.283, 0.146, 0.527, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Transportation - [0.887, 0.161, 0.987, 0.181]\"} is associated with the pie slice."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "adTBpbEhDCLHph8y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {the slice}[0.482, 0.195, 0.741, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {Public Safety}[0.482, 0.151, 0.611, 0.516] and {Healthcare}[0.223, 0.278, 0.68, 0.886] are adjacent to it."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "A9wvenLQCuODj3c9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mostly covers the pie chart. Inside, there are four pie slices labeled as Healthcare, Education, Public Safety, and Transportation."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "xy79DUa9BCAkM3Xn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the region [0.842, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are several legend items pertaining to the pie chart. The labels are Healthcare, Education, Public Safety, and Transportation."
+            }
+        ],
+        "id_raw": 293
+    },
+    {
+        "id": "If6ZRE67I976DCuH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type that this information refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart type is pie."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "FhWD11o3PmZhjTHN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.26, 0.021, 0.703, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Global International Trade Volume from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "6tYC3LUmPYLL7YJg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat Color is the legend handle located at coordinates [0.913, 0.163, 0.941, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this legend handle is \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "HUVqobV227vmdSbh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice adjacent to {the slice}[0.482, 0.406, 0.741, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}<r>[0.482, 0.147, 0.729, 0.516]</r> is adjacent."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "8FuTPlQKsuli7LZ3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend handle is associated with the {slice}[0.223, 0.278, 0.482, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle with color \"#ff9999\"<r>[0.913, 0.073, 0.941, 0.087]</r> is associated with the slice."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "85lSmEUrJuMcwSsc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle associated with the {pie slice with label '2020'}[0.283, 0.146, 0.499, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#70d6ff\"<r>[0.913, 0.223, 0.941, 0.237]</r>."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "yQQnWVOveXl3bCIj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie slices representing data from different years. Each slice is colored differently and has an associated percentage and year label within or near its boundaries."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "N1eyX2RoMq9ixZGp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.397, 0.516, 0.652, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mainly contains a pie slice with color '#99ff99', representing data from the year 2017 with a share of '16.7%'<r>[0.537, 0.905, 0.572, 0.925]</r>."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "yszXkzG587zAMoVK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is represented within coordinates [0.223, 0.278, 0.482, 0.607]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area represents the pie slice for the year 2015 with associated percentage '15.1%'<r>[0.169, 0.415, 0.204, 0.435]</r>. The color of the slice is '#ff9999'."
+            }
+        ],
+        "id_raw": 294
+    },
+    {
+        "id": "GF4u51LkFPBS29I8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.218, 0.021, 0.745, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the title, named \"Transportation and Logistics Industry Trends from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "0szxXduWEDsqtBns",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "IfgEP9wLYiuKAGGI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at [0.197, 0.308, 0.233, 0.328] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the text \"6.1%\"."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "mvtLPV5SnV4R2Rd2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.226, 0.516, 0.482, 0.763]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.223, 0.4, 0.482, 0.574] is adjacent."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "oA42mfZvrqsMWnJ5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, which color is associated with {the text \"2015\"}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with \"2015\" is #ffb3e6."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "z9a097V1P3v2cUVS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to {the legend item \"2017\"}[0.952, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.146, 0.597, 0.516] corresponds to the legend item \"2017\"."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "wB8M0tfzBbx98C5L",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart, containing all the slices representing the years from 2010 to 2017 along with the percentages of each year."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "0S3wntzn3RDotc3t",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.437, 0.516, 0.625, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a slice representing the year 2014 with the information text \"12.1%\"."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "EiG08sgUmLzyXGyF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects and text labels in the region defined by [0.235, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers the slice for the year 2010, along with the associated percentage label of \"6.1%\" and the year label \"2010\"."
+            }
+        ],
+        "id_raw": 297
+    },
+    {
+        "id": "OlDlS2Bpe1TTpWcH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart's object labeled at coordinates [0.79, 0.071, 0.849, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item, labeled 'Startups'."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "ifoXEEuv2wK1FIjg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.138, 0.516, 0.197, 0.536] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label '23.0%'."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "zKmnqqmVoXSXsj22",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object positioned at coordinates [0.304, 0.511, 0.348, 0.531] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label 'Startups' associated with a slice of the pie chart."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "yNDW7pNhadWmwFLW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is related to the segment of the pie chart at {the slice}[0.223, 0.278, 0.482, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Startups\" at{the text}[0.304, 0.511, 0.348, 0.531] and the percentage \"23.0%\" at {the percentage}[0.138, 0.516, 0.197, 0.536] are related to the slice in the chart."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "8C0xYYX75w66JYtr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the text}[0.79, 0.101, 0.901, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice associated with the legend item \"Innovation hubs\" is{the pie slice}[0.292, 0.516, 0.482, 0.885]."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "Bv7at2npxWmGqv60",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds with the legend item 'Skills Development programs' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at{the pie slice}[0.482, 0.248, 0.736, 0.516] corresponds to the legend item 'Skills Development programs'."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "YLiNLwINdaQefpGn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region constrained within coordinates [0.223, 0.278, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the pie chart depicting different sectors of investment. It includes the \"Startups\" slice of the pie at {the slice}[0.223, 0.278, 0.482, 0.767], the \"Innovation hubs\" slice at {the slice}[0.292, 0.516, 0.482, 0.885], the \"Research Institutes\" slice at {the slice}[0.464, 0.449, 0.741, 0.886] and a few others. Each slice is labeled with the corresponding sector name and its percentage of the total investment."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "h9QdSK5N7lVVRdRJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region delineated by the coordinates [0.745, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend for the chart. Each legend item is made up of a colored rectangle followed by the name of the corresponding sector such as \"Startups\" at {the text}[0.79, 0.071, 0.849, 0.091], \"Innovation hubs\" at {the text}[0.79, 0.101, 0.901, 0.121] and so on. Each of these legend items relates to a particular slice present in the pie chart."
+            }
+        ],
+        "id_raw": 298
+    },
+    {
+        "id": "5GqHcZW92lBnWCCb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.857, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "TrePoy42QIlxtqqr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at [0.902, 0.071, 0.987, 0.091] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Smartphone\"."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "rwKT7law64V3fedM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.283, 0.146, 0.623, 0.516] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart representing \"Tablet\"."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "qasfzGxvxMFpICmu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the Smartphone slice}[0.223, 0.206, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Tablet slice}[0.283, 0.146, 0.623, 0.516] is adjacent."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "D6GJ1LdlSpnqaj5f",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object corresponds to the {'Smartphone'}[0.902, 0.071, 0.987, 0.091] label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object that corresponds to the 'Smartphone' label is {the pie slice}[0.223, 0.206, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "vE6R93rJVyiqyRzZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found near {the pie slice}[0.223, 0.206, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text near the pie slice is {the percentage '76.9%'}[0.524, 0.909, 0.609, 0.929]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "6lh0cgcGqZOwFCqs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice representing 'Smartphone' sales with an associated autopct text of '76.9%' [0.524, 0.909, 0.609, 0.929]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "jeAaaJdetqhqUgjH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region formed by coordinates [0.283, 0.146, 0.623, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, there is a slice of the pie chart that represents 'Tablet'. Along with the slice, there's text indicating '23.1%' [0.398, 0.103, 0.44, 0.123]."
+            }
+        ],
+        "id_raw": 299
+    },
+    {
+        "id": "ROBKMZuFVcgk9Eky",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.085, 0.771, 0.108, 0.791] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"45.0%\"."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "eLmMWFPVxvWHrSIS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.078, 0.272, 0.345, 0.885] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a slice of the pie chart representing data from 2015."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "vVVG1w85xqP4fD49",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is designated by the text at coordinates [0.159, 0.649, 0.188, 0.669] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" indicates the year associated with the corresponding slice of the pie chart."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "u4TDT5YwLbMMaTNJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text {at location}[0.33, 0.331, 0.359, 0.351] and the slice {at location}[0.132, 0.115, 0.44, 0.804] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" labels the exploded slice of the pie chart."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "r4mD0ptdyviSfa1A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in proximity to {the slice}[0.573, 0.166, 0.922, 0.885] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"80.0%\"<r>[0.794, 0.9, 0.817, 0.92]</r> and the text \"2015\"<r>[0.758, 0.719, 0.787, 0.739]</r> are near the slice."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "KLT2V1esNAmX4F4i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the second subplot, what corresponds to the text {at location}[0.703, 0.249, 0.733, 0.269]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" corresponds to the exploded pie slice<r>[0.61, 0.103, 0.809, 0.476]</r> in the 2020 section."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "feBcL4u82OY6yA82",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.573, 0.103, 0.922, 0.885] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a slice of the pie chart, labeled \"2015\"<r>[0.758, 0.719, 0.787, 0.739]</r> and its corresponding percentage \"80.0%\"<r>[0.794, 0.9, 0.817, 0.92]</r>. The slice is colored #ff9999 and represents data from the year 2015."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "BTwwWuc3jnBLquxT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the region [0.078, 0.115, 0.44, 0.885] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two pie chart slices. One slice represents data from 2015<r>[0.078, 0.272, 0.345, 0.885]</r>, and the other from 2020<r>[0.132, 0.115, 0.44, 0.804]</r>."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "yqQzFzrLVr2KbDJF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you give a description of the region defined by coordinates [0.573, 0.103, 0.809, 0.476] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers a slice of the pie chart that represents data from 2020. It's marked \"2020\"<r>[0.703, 0.249, 0.733, 0.269]</r> and has its percentage as \"20.0%\"<r>[0.674, 0.068, 0.697, 0.088]</r>."
+            }
+        ],
+        "id_raw": 300
+    },
+    {
+        "id": "epjNy6CYrWzCPUnS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart as indicated by the coordinates [0.346, 0.021, 0.617, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Fitness and Sports Health Trends\"."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "SWBquAiMSMx2RKve",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the chart area as indicated by coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart area color is white."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "ISs4Tz4MYcLCyhDL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object corresponds to the coordinates [0.835, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the legend of the chart."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "JgoDIf8lZn3PF6YU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label associated with the handle color at the coordinates [0.841, 0.073, 0.869, 0.087] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Running\"."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "BBMjuLSmOLNpkh6m",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do coordinates [0.283, 0.157, 0.482, 0.516] and [0.258, 0.161, 0.331, 0.181] represent in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the \"Swimming\" slice and the corresponding 10% text respectively."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "ijT1z4A9VjyaumW1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to {the legend item \"Weight-training\"}[0.88, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.482, 0.173, 0.741, 0.754]."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "QeKFSHyAC853cXUx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region represented by the coordinates [0.482, 0.516, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice for \"Weight-training\", colored in light green (#99ff99) along with the corresponding text showing the percentage \"30.0%\" represented by the coordinates [0.758, 0.407, 0.866, 0.427]."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "fPszkxDscGUHNlPO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region [0.223, 0.278, 0.482, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice referring to \"Running\". The slice is colored in light red (#ff9999) indicating 30% which is accompanied by text at coordinates [0.148, 0.604, 0.205, 0.624]."
+            }
+        ],
+        "id_raw": 301
+    },
+    {
+        "id": "HfBT5Wv31RP2uLQj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.272, 0.088, 0.753, 0.114] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, called \"Robotics Industry Development Trends from 2010 to 2015\"."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "LtkzHRkMUfBhPU87",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.852, 0.13, 0.937, 0.315] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend of the chart."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "3NbjjAKRVmUyfdMt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label of the chart is located at coordinates [0.896, 0.258, 0.931, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"2014\"."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "NGWKzDp2wafVjld0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does {the legend item \"2012\"}[0.896, 0.198, 0.931, 0.218] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2012\" corresponds to {the green slice}[0.313, 0.505, 0.512, 0.8]."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "1qii0Lc1O6etfDj8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item does {the slice}[0.451, 0.505, 0.667, 0.813] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to {the legend item \"2013\"}[0.896, 0.228, 0.931, 0.248]."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "pRXY8TAVGuA6FbSZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the pink slice}[0.347, 0.197, 0.676, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice labeled as \"2014\"}[0.512, 0.304, 0.728, 0.72] is adjacent to the pink slice."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "xc3S79krQhSB9iox",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.237, 0.505, 0.237, 0.505]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is along the y-axis of the chart, it does not contain any specific objects but is central to the chart, dividing it into the left and right sections."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "4lOsKHkB2aiPtiBT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.347, 0.197, 0.676, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart with color '#ffb3e6', observing the text \"2015\" [0.49, 0.31, 0.534, 0.33] and autotext with percentage \"27.6%\" [0.476, 0.156, 0.511, 0.176]."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "26qXD7mx2DOvorrD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.297, 0.443, 0.512, 0.622]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a pie slice in this region with the color '#66b3ff' which is associated with the year \"2011\" displaying on the chart [0.366, 0.512, 0.401, 0.532] and has an indication of the percentage \"9.4%\" [0.241, 0.527, 0.276, 0.547]."
+            }
+        ],
+        "id_raw": 303
+    },
+    {
+        "id": "KDx4qvjglS3kxzvj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the text contained within the coordinates [0.17, 0.604, 0.205, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It means \"30.0%\" of the entire data share."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "UdaEu6F8VOQzrCU3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object positioned at coordinates [0.913, 0.193, 0.941, 0.207] in the chart's legend section?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color representation for \"2026\"."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "AJVsFw2jcoNdqaep",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at [0.482, 0.516] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center of the pie chart."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "Jm0imWzBZMoCBevz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the legend corresponds to the slice of the pie at {coordinates}[0.223, 0.278, 0.482, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the object \"2022\" [0.952, 0.071, 0.987, 0.091] in the legend."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "pBVLSlPwplkn0h66",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's data corresponds to the percentage displayed at {coordinates}[0.766, 0.47, 0.801, 0.49]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the year \"2024\" [0.952, 0.131, 0.987, 0.151]."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "1IVAgWE4lmdVoUCn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich experimental group is denoted by the slice that is {colored}#c2c2f0?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year \"2026\"."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "m4mKe3WUm4LkTwWw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.283, 0.157, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a segment of the pie chart, highlighted with the color #c2c2f0, representing data from the year 2026. It also contains the percentage text \"10.0%\" that is enclosed within the coordinates [0.295, 0.161, 0.331, 0.181]."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "Q6qdAagztM8uChXq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you discern from the region within the coordinates [0.223, 0.278, 0.482, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the largest slice of the pie chart, denoting the data from the year 2022 colored #ff9999. It consists of the text \"2022\" [0.309, 0.56, 0.353, 0.58] and the percentage \"30.0%\" [0.17, 0.604, 0.205, 0.624]."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "ymYCmf75BNtQul2P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_105.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details on the region enclosed within [0.385, 0.516, 0.68, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie chart that displays data for the year 2023, evident from the year label \"2023\" [0.497, 0.721, 0.541, 0.741], and its percentage by the text \"20.0%\" [0.551, 0.901, 0.586, 0.921]. The segment is recognized by its distinctive blue color, represented by the color code #66b3ff."
+            }
+        ],
+        "id_raw": 305
+    },
+    {
+        "id": "n4OVy7O88UAvLBHd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at coordinates [0.257, 0.021, 0.743, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Consumer Behavior in Emerging Markets\"."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "RdiRtIYylh9ypCow",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the chart at coordinates [0.825, 0.063, 0.953, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located there."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "x5x3YpZV8GFSQm05",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object at coordinates [0.13, 0.278, 0.87, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart slice."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "YvHGCNuDHKqaEPal",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the pie slice}[0.216, 0.146, 0.859, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice is \"#66b3ff\" color."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "BohP4CE8dhF7V4Aw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the legend}[0.825, 0.063, 0.953, 0.129] correspond to {the pie slices}[0.13, 0.278, 0.87, 0.886] and [0.216, 0.146, 0.859, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Brazil\" and \"India\" correspond to the pie chart slices. \"Brazil\" corresponds to the slice at [0.13, 0.278, 0.87, 0.886] and \"India\" corresponds to the slice at [0.216, 0.146, 0.859, 0.516]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "H5NKHvYDEB95Hzt3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text}[0.353, 0.903, 0.408, 0.923] and {the pie slice}[0.13, 0.278, 0.87, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"65.0%\" is the data label for the pie slice, indicating it represents 65% of the data."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "6ri8pcclcIFkuNw1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section within the coordinates [0.13, 0.146, 0.87, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The section with coordinates [0.13, 0.146, 0.87, 0.886] contains a pie chart slice, labeled as \"Brazil\" [0.419, 0.722, 0.481, 0.742] and its data label \"65.0%\" [0.353, 0.903, 0.408, 0.923]. The slice is colored \"#ff9999\"."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "ztnbGvNcrRI0bcdS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.825, 0.063, 0.953, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend with two entries: \"Brazil\" [0.889, 0.071, 0.945, 0.091] with a color handle of \"#ff9999\" [0.833, 0.073, 0.873, 0.087] and \"India\" [0.889, 0.101, 0.938, 0.121] with a color handle of \"#66b3ff\" [0.833, 0.103, 0.873, 0.117]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "tqNa0uLB1yCePu8l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.216, 0.146, 0.859, 0.516] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is a pie chart slice for \"India\" [0.519, 0.29, 0.581, 0.31], colored \"#66b3ff\". It also includes a data label \"35.0%\" [0.592, 0.109, 0.64, 0.129]."
+            }
+        ],
+        "id_raw": 306
+    },
+    {
+        "id": "hgzvxzhGLdYJbv97",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Social Cultural and Value Change from 2014 to 2020\"{the title}<r>[0.263, 0.021, 0.7, 0.047]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "k3gOrC3MryuegCOE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At these coordinates, there is a text representing the year 2014 in the legend{the year 2014 in the legend}<r>[0.952, 0.071, 0.987, 0.091]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "ZKjskKwgD5ua5DNs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen at the coordinates [0.182, 0.354, 0.217, 0.374]?"
+            },
+            {
+                "from": "gpt",
+                "value": "At these coordinates, there is a percentage labeled \"10.1%\"{the percentage label \"10.1%\"}<r>[0.182, 0.354, 0.217, 0.374]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "MLeIeN1ebHUYP8ox",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation of {the slice of the year 2020}[0.313, 0.146, 0.596, 0.516] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color representation of the year 2020 in the legend is green{the green color box representative of 2020}<r>[0.913, 0.253, 0.941, 0.266]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "GuqrFPZIF9u3Jrgk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend entry is associated with the slice {the slice}[0.445, 0.516, 0.67, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry \"2017\" is associated with this slice{the legend entry \"2017\"}<r>[0.952, 0.161, 0.987, 0.181]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "pjzdKEwqmHdkLPdm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is associated with the slice located at {the slice}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"10.1%\" is associated with this slice{the label \"10.1%\"}<r>[0.182, 0.354, 0.217, 0.374]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "rpgoQOjTdDNGWy2Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe geographical objects inside the region [0.223, 0.146, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is comprised of a slice of the pie chart with label \"10.1%\"{label \"10.1%\"}<r>[0.182, 0.354, 0.217, 0.374]</r> and represents the year \"2014\"{year \"2014\"}<r>[0.315, 0.423, 0.359, 0.443]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "xBqK1vX000G0khSE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects inside the region [0.445, 0.516, 0.67, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside the region, we see a slice of a pie chart. It comes with label \"15.2%\"{label \"15.2%\"}<r>[0.575, 0.89, 0.611, 0.91]</r> and the parse \"2017\"{year 2017}<r>[0.511, 0.716, 0.555, 0.736]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "yIsWxQjlScGn2qob",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region [0.482, 0.431, 0.741, 0.77]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The defined region contains a part of the pie chart, labeled as \"15.7%\"{label \"15.7%\"}<r>[0.757, 0.612, 0.792, 0.632]</r> and represents the year \"2018\"{year 2018}<r>[0.61, 0.564, 0.654, 0.584]</r>."
+            }
+        ],
+        "id_raw": 308
+    },
+    {
+        "id": "HRvOiDH0kqyT32fI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.349, 0.021, 0.614, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Investment Distribution in 2021\"."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "mwSQc2rXb1mRI3uh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.658, 0.188, 0.715, 0.208] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"49.2%\"."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "7mWsprOFBVcT32lN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.888, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the sublegend of the pie chart."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "FWnsWjFYNMFLyTh9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is directly adjacent to the pie slice {pie slice}[0.223, 0.278, 0.688, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}[0.283, 0.146, 0.741, 0.739] is directly adjacent."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "f7CMetr9YcuvpKoI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is related to the slice {slice}[0.223, 0.278, 0.688, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"Bonds\" and \"50.8%\" are related to the slice."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "TzQ3VTLXD22np6qf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items in the legend correspond to the pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Bonds\" corresponds to the slice with coordinates [0.223, 0.278, 0.688, 0.886] and \"Equities\" corresponds to the slice with coordinates [0.283, 0.146, 0.741, 0.739]."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "7FJEKKT1F5Wi0ftK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within coordinates [0.223, 0.146, 0.741, 0.739]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart, accounting for 49.2% of the investments which belong to Equities."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "iS0HYPQqi0wvDf3o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained in the region [0.223, 0.278, 0.688, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is characterized by a pie slice that represents the \"Bonds\" investment type, taking up 50.8%, along with related text annotations."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "MXD9rthHWZheiZA8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.888, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend showing two categories which are \"Bonds\" and \"Equities\", with their respective color codes."
+            }
+        ],
+        "id_raw": 309
+    },
+    {
+        "id": "WP8ZntBu7NOC01ce",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.362, 0.524, 0.447, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text object reading \"Mobile Applications\"."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "GqvD5mWDwkIz9mA9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart showing the media influence index across different platforms."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "UtZ3L8tXlQ74lmdZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is identified by the coordinates [0.817, 0.281, 0.987, 0.301] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text from the legend indicating \"Outdoor Advertisements\"."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "4yhPHshed4DyoTEo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {with coordinates}[0.817, 0.101, 0.856, 0.121] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This legend item corresponds to the slice {with coordinates}[0.223, 0.509, 0.482, 0.672]."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "VhNLJBEXx3yzb2jz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to {the legend item}[0.817, 0.311, 0.878, 0.331]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Podcasts\" {with coordinates}[0.378, 0.321, 0.413, 0.341] is closest."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "r7zzgVJrL108rfZm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie chart slice is directly adjacent to {the slice}[0.307, 0.516, 0.539, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {directly adjacent} [0.482, 0.516, 0.709, 0.877] and [0.247, 0.516, 0.482, 0.789] are directly adjacent."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "WQimHmDh3zPwTocG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.147, 0.72, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a part of the pie chart specifically a slice representing \"Social Media\" along with the included percentage text \"17.1%\" which comes with its own coordinates [0.649, 0.176, 0.736, 0.196]."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "R8BHxqeUSGwvVBdb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.773, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart which includes multiple text objects like \"Television\" [0.817, 0.071, 0.885, 0.091], \"Radio\" [0.817, 0.101, 0.856, 0.121], \"Newspaper\" [0.817, 0.131, 0.895, 0.151], and colors corresponding to each legend item."
+            }
+        ],
+        "id_raw": 310
+    },
+    {
+        "id": "EuaXaZx6Pxvtzqpv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart found at coordinates [0.236, 0.021, 0.727, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is \"Education and Training Industry Income from 2016 to 2025\"."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "Qlgw65bpjLsHf8aq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located in the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend listing the years from 2016 to 2025 with their respective colors used in the pie chart."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "IVpR8r7WCCxYMHkQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data represented in coordinates [0.182, 0.356, 0.217, 0.376] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the auto-text \"10.2%\" which represents the percentage of the industry income in 2016."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "DLJMW20NlvjGojmL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the handle in the legend {label \"2016\"}<r>[0.913, 0.073, 0.941, 0.087]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented is #ff9999."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "4TDEMRNlqFA0XWw9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart is correlated with the legend item {label \"2025\"}<r>[0.952, 0.34, 0.987, 0.36]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the year 2025 is located at [0.283, 0.149, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "qIlQTtwZQC5RzLms",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the color of the pie slice at [0.482, 0.516, 0.729, 0.783] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this pie slice corresponds to the year 2021 in the legend."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "4EvFOKHmDMsabmz0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present within the regional coordinates [0.482, 0.516, 0.661, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the slice of the pie chart for the year 2020 with a percentage of 11.8% and the color '#c2c2f0'."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "i8FTcCf9MVCEn3TO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data points are included in the region [0.223, 0.494, 0.482, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the slice for the year 2017 with 8.4% of the total industry income and it is represented by the color '#66b3ff'."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "meJQuhoBjeE5lgDD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can be observed in the region [0.251, 0.516, 0.482, 0.837] of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the slice for year 2018, accounting for 9.2% of the total income. The autotext and year text can be seen in this slice."
+            }
+        ],
+        "id_raw": 311
+    },
+    {
+        "id": "AmAORVSSF3KkIbos",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart situated at coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"City Budget Allocation for Public Services\" located at the specified coordinates."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "UIEOKRkawFBMekgV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object denotes \"Education\" in the chart legend using coordinates [0.883, 0.161, 0.953, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a legend item associated with the \"Education\" sector."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "cyjIH2icEGobY5aW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.283, 0.18, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object {the slice}<r>[0.283, 0.18, 0.482, 0.516]</r> is a segment of the pie chart representing \"Social Services\"."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "25gapFlY2apPPLtp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is colored '#ffcc99' in the plotted chart connected with the legend component?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is {the slice}<r>[0.482, 0.218, 0.741, 0.736]</r> representing \"Education\"."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "5fM9LwcdU2s4UmCt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color attribute '#66b3ff' associate with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#66b3ff' is associated with {the slice}<r>[0.23, 0.516, 0.482, 0.867]</r> representing the \"Fire\" department budget."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "WPgtagmRyXWfxfGU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the legend item at [0.883, 0.191, 0.984, 0.211] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item at the given coordinates is associated with {the slice representing the \"Transportation\" budget}<r>[0.482, 0.146, 0.635, 0.516]</r>."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "6NohgoGnQHz2VzV9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what the region [0.223, 0.18, 0.482, 0.605] indicates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region specifies a pie chart piece {representing \"Police\" department budget}<r>[0.223, 0.18, 0.482, 0.605]</r> with 15.0% allocation mentioned as a text inside it."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "V8PvO3lmq0h6js7H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements consist within the region [0.4, 0.516, 0.69, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region, there is a slice of the pie chart specifying the \"Healthcare\" department budget along with its associated percentage, 20.0% allocation."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "MzgFLkcXvhKhWfj1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region from [0.482, 0.146, 0.635, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region mentioned, a slice of the pie chart is present representing the \"Transportation\" department budget along with its allocation percentage, which is 10.0%."
+            }
+        ],
+        "id_raw": 312
+    },
+    {
+        "id": "zKiyjvYYyUuAMDwD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat title is located at the coordinates [0.398, 0.088, 0.627, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Media Usage in 2011\"."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "ctFbLxfBzicHQ9xG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found in the coordinates [0.822, 0.14, 0.849, 0.154]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend handle with color '#ff9999' which represents Facebook."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "cbh4LIdM8xHB7adU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.681, 0.734, 0.751, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an autotext object with text \"18.5%\"."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "BhaZyqqB7Z5AuDvj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.323, 0.505, 0.557, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.297, 0.307, 0.512, 0.652] is adjacent."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "T6OpJCwt8V0dHM6Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is nearest to the {legend handle color '#99ff99'}[0.822, 0.2, 0.849, 0.213]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Instagram\" with coordinates [0.86, 0.198, 0.931, 0.218] is nearest."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "q1a5cMTaKvwnV9lf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart correspond to the Instagram legend item?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.512, 0.505, 0.724, 0.806] corresponds to the Instagram legend item."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "mpXQI1Nr8YOhVtRG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.297, 0.307, 0.512, 0.652]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"Facebook\" with coordinates [0.362, 0.476, 0.406, 0.496] and an autotext with the percentage value \"19.0%\" at coordinates [0.211, 0.461, 0.277, 0.481]. The region represents the Facebook slice of the pie chart."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "K1rwalUbpdEFn3WT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.323, 0.505, 0.557, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"Twitter\" at coordinates [0.436, 0.663, 0.48, 0.683] and an autotext \"20.4%\" at coordinates [0.366, 0.802, 0.413, 0.822], representing the Twitter slice of the pie chart."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "NYZUJnM0qLrzGmID",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_114.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.512, 0.505, 0.724, 0.806]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"Instagram\" at coordinates [0.582, 0.625, 0.626, 0.645] and an autotext \"18.5%\" with coordinates [0.681, 0.734, 0.751, 0.754]. This region represents the Instagram slice of the pie chart."
+            }
+        ],
+        "id_raw": 314
+    },
+    {
+        "id": "VBVezz3CcPfZ9RO2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at the coordinates [0.324, 0.41, 0.359, 0.43] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the object is \"2015\"."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "QfSccggSmkMIJBxS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object seen at coordinates [0.697, 0.772, 0.732, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"11.2%\"."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "EpQJLf5jBzVlS8VY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the object located at coordinates [0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a text label representing the year \"2019\" in the legend."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "uuJH7SVh9lWn4nCc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color relation between the legend items and the pie chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the handle in the legend items matches the color of the corresponding pie slice in the chart. For example, the legend item \"2020\" with coordinates [0.952, 0.4, 0.987, 0.42] has a handle with a color of '#f0c2c2', which matches the color of the slice in the coordinates ['[0.283, 0.147, 0.482, 0.516]']."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "kC0NCx4v30x92PYT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's slice is adjacent to the {slice with the information of \"2020\"}<r>[0.283, 0.147, 0.482, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the information of \"2019\" [0.458, 0.146, 0.613, 0.516] is adjacent to the given slice."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "NVaYDRZ7y3fmtE0f",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the legend item located at [0.913, 0.072, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item corresponds to the slice located at [0.227, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "xMPT6eWdRkDAgacX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region inside the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the given coordinates lies the central region of the pie chart. The region contains multiple pie slices representing different years, each with a unique color and a text denoting the respective year and a percentage value. The text and percentage are found within each slice, indicating the distribution of logistics for each year."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "6dSldM8yrxnc5QX7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the region defined by coordinates [0.908, 0.063, 0.993, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the legend of the pie chart. The legend contains different items, each marked with a handle, representing the years from 2015 to 2020. Each handle has a unique color that corresponds to a slice in the pie chart, and next to it, is a label corresponding to the year."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "1D2MWH9lYRQQoAFc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.315, 0.736, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a part of the pie chart, specifically the slice representing the year 2017. It also contains an auto-label, text indicating \"6.2%\", which represents the proportion for the year 2017 in the pie chart."
+            }
+        ],
+        "id_raw": 315
+    },
+    {
+        "id": "xj74jKdX3SYLFyXj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart at the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart showing the number of tourists visiting over years from 2012 to 2021."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "PdwXQtWJpVLjOrmx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the annotation at coordinates [0.183, 0.352, 0.218, 0.372] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a percentage representation of the pie slice for the year 2012, indicating \"9.9%\"."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "Esns035azldsQ5LR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the component at the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the pie chart that provides the color coding information for each year from 2012 to 2021."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "CS3i9sylzg1wf4M0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is next to the slice {the slice}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}[0.223, 0.488, 0.482, 0.732] is next to it, representing the year 2013."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "OdjxsVBBCO561qnt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.482, 0.446, 0.741, 0.73]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item for the year 2017 {[0.952, 0.221, 0.987, 0.241]} corresponds to the given slice."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "wDICwz1kCjVN41FP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the colour code for the data from year {the year 2021}[0.952, 0.34, 0.987, 0.36] in the legend component?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code for the year 2021 is black as represented by the rectangular patch {the patch}[0.913, 0.342, 0.941, 0.356] next to the text in the legend."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "zn7y5aHnKymcwsHG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.482, 0.187, 0.736, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2018, colored in a shade of light green. It also includes an annotation indicating \"14.3%\", located at coordinates [0.71, 0.262, 0.745, 0.282]."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "kplazYOQEfFKpWEA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the area specified by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart. Each slice of the pie represents tourist visit data for a specific year from 2012 to 2021. The annotations like \"9.9%\" at [0.183, 0.352, 0.218, 0.372] are included inside the chart, each corresponding to a pie slice for a specific year."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "I4yFMT3XU7uoy231",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main features in the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the legend of the pie chart. It includes text such as \"2012\" at [0.952, 0.071, 0.987, 0.091] and respective color coded patches such as at [0.913, 0.073, 0.941, 0.087]."
+            }
+        ],
+        "id_raw": 316
+    },
+    {
+        "id": "FXcWTjwrD9xRw1dT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at the coordinates [0.164, 0.457, 0.199, 0.477]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"18.4%\" is located at this coordinate."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "YKrrCOAYhG30uj8a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.303, 0.021, 0.66, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Investment Distribution from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "XGhD4ZFIS6gks7SY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location with coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" from the legend is located there."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "SQ4ucnrp9GANzKs7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text annotations are directly associated with {the pie slice}[0.505, 0.197, 0.764, 0.513]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2018\"}[0.622, 0.391, 0.666, 0.411] and {\"15.8%\"}[0.76, 0.309, 0.796, 0.329] are directly associated with this pie slice."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "Sj6ctGPCQiE3nibn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice coordinates correspond to the {legend item \"2019\"}[0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices with coordinates {[0.283, 0.146, 0.631, 0.516]} and {[0.283, 0.146, 0.569, 0.516]} correspond to the legend item \"2019\"."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "uXekHjQY5ZbhExWG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of investment corresponds to the color handles with the coordinates [0.913, 0.193, 0.941, 0.207] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The investment type for these coordinates is \"2019\"."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "GdPFgxEtrtcXLevl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.764, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with different elements including slices and accompanying texts indicating investment distribution over the years 2015 to 2019, along with their respective percentage amounts. The pie chart slices are differentiated by colors and correspond to the legend on the side."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "z2YRMZOGzmnR75Fc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_117.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.408, 0.063, 0.993, 0.219] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the chart's legend, which signifies the correspondence between the colors used in the pie chart and the years they symbolize. Each legend item has a color handle and a text marking the corresponding year."
+            }
+        ],
+        "id_raw": 317
+    },
+    {
+        "id": "O4uZDzdKYsK5YSRm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is found in the region with coordinates [0.278, 0.337, 0.313, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\"."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "yj2A2phuD24YEUmP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is present at the coordinates [0.418, 0.516, 0.614, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie slice."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "hWkJJrYEE5g1bWjC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is annotated at this particular location [0.206, 0.287, 0.241, 0.307] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The annotation \"4.2%\"."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "Idt8GvmwdUzsuRrl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the pie slice}[0.482, 0.516, 0.731, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice}[0.482, 0.361, 0.741, 0.614] is adjacent."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "mnRheIUmHyHG1mth",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {blue slice}[0.482, 0.176, 0.717, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Digital Resources\" legend item."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "vqqcRUy8oydn0krk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to the {pie slice}[0.255, 0.516, 0.482, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\" corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "1qb2mlld4Q0GMTDc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you analyze from the region [0.179, 0.645, 0.214, 0.665]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"5.0%\", it refers to the data percentage of a certain category in the pie chart for the year \"2018\"."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "XCWYWHy2rCfuMzYW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.321, 0.516, 0.482, 0.875]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice colored in \"#ffb3e6\". The slice is associated with \"2020\", and it represents \"6.7%\" of the total pie chart."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "FoZWUuqJWEVzaJDa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is provided in the region [0.162, 0.514, 0.197, 0.534]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region illustrates that 5.5% of resources in 2017 were categorized as \"Physical Resources\"."
+            }
+        ],
+        "id_raw": 319
+    },
+    {
+        "id": "EKeyu8jZ3ou96NZz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.356, 0.021, 0.607, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "title, called Big Data Integration Overview."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "MVIgT4YtohdgMGaN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.803, 0.063, 0.993, 0.159] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "1zUK9iaY8I8QzhwY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.723, 0.29, 0.786, 0.31] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.0%\"."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "hcQeCo1u68J7IcP3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.223, 0.278, 0.722, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.147, 0.741, 0.655], [0.283, 0.146, 0.5, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "ZI53G2g0ML4l6Xc0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the title}[0.356, 0.021, 0.607, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Systems Integration\"}[0.399, 0.302, 0.443, 0.322] and {\"Data Mining\"}[0.38, 0.696, 0.423, 0.716] are located near the title."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "Y5qZCABYcGFggQ6L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Data Mining\", \"Analytics\", and \"Systems Integration\" correspond to the slices with coordinates [0.223, 0.278, 0.722, 0.886], [0.482, 0.147, 0.741, 0.655], and [0.283, 0.146, 0.5, 0.516] respectively."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "ilDRnZHRn5rBsXWi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.147, 0.741, 0.655]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"30.0%\" [0.723, 0.29, 0.786, 0.31] and the text \"Analytics\" [0.592, 0.388, 0.635, 0.408]. This region is located in the upper right part of the chart, corresponding to the light blue slice."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "ePdjD1sbKxadaUsQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.283, 0.146, 0.5, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"15.0%\" [0.231, 0.131, 0.37, 0.151] and the text \"Systems Integration\" [0.399, 0.302, 0.443, 0.322]."
+            }
+        ],
+        "id_raw": 320
+    },
+    {
+        "id": "aYszQCG5VSt9LQ0c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object situated at coordinates [0.197, 0.021, 0.766, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Annual Expenditure on Healthcare and Education from 2010 to 2016\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "ZryqVNOIBILgZABX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend's item for the year \"2010\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "i5FUG0BkfINTr7Qz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.175, 0.384, 0.21, 0.404] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext label of a slice in the pie chart, indicating \"12.5%\"."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "yz7RkSRhEp3RKoxe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich other pies are adjacent to {the pie slice}[0.224, 0.516, 0.482, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pies adjacent to it are {the slices}[0.223, 0.278, 0.482, 0.549] and [0.323, 0.516, 0.532, 0.886]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "29i7TOFyj4brWRAQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correlate to {the pie slice}[0.325, 0.516, 0.534, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding legend item is {\"2012\"}[0.952, 0.131, 0.987, 0.151]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "BdAwJ1lgzIWMQ740",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to {the legend item}[0.952, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the legend item is {the slice}[0.224, 0.516, 0.482, 0.808]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "0BxjTbA2ywefw9Kk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region with coordinates [0.482, 0.151, 0.719, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart demonstrating the data for the year 2015. It contains both the slice [0.482, 0.151, 0.719, 0.516] and a text label indicating \"15.7%\" [0.657, 0.186, 0.693, 0.206]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "lM9ktScKVmhoQVvV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the region [0.223, 0.278, 0.482, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified region, there appears a pie slice [0.223, 0.278, 0.482, 0.549] that represents the data for the year 2010. Also visible is a label showing a percentage of \"12.5%\" [0.175, 0.384, 0.21, 0.404]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "U82qqlngNrkfmPjq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present within the region at coordinates [0.323, 0.516, 0.532, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area accommodates a slice [0.323, 0.516, 0.532, 0.886] of the pie chart, which represents the year 2012. It also houses a label, which is stating \"13.6%\" [0.381, 0.902, 0.416, 0.922]."
+            }
+        ],
+        "id_raw": 322
+    },
+    {
+        "id": "myxkjd3cO9NRPScv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.899, 0.071, 0.976, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Leadership\"."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "zxST9Y8nHneQxGCV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object mapped to these coordinates [0.326, 0.403, 0.361, 0.423]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is the text that says \"Leadership\"."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "PBku5cxfJuq5I1wo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exactly is located within the coordinates [0.614, 0.866, 0.664, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It houses the text with the value \"18.0%\"."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "Nj0ZZIvn8nk8mlRl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color code is associated with the label \"Training\", and what slice does it match with on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code for \"Training\" in {the legend}<r>[0.855, 0.063, 0.993, 0.309]</r> is '#c2c2f0'. It matches with {the slice}<r>[0.482, 0.15, 0.654, 0.516]</r> in the chart."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "qeaawzV3NGdhKNSZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any labels in the legend that correspond to any pie slices with the depiction '13.8%' or '9.1%'?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the labels \"Brand\" and \"Training\" with coordinates ['0.899, 0.191, 0.941, 0.211'] and ['0.899, 0.251, 0.954, 0.271'] respectively in the legend correlate with the depicted percentages '13.8%' at ['0.766, 0.541, 0.807, 0.561'] and '9.1%' at ['0.604, 0.138, 0.659, 0.158'] in the pie slices."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "UsTutD0b96T5wDLB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels with similar colored handles in the legend and what slices do they correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels with similar colored handles are \"Brand\" and \"Training\" from {the legend}<r>[0.855, 0.063, 0.993, 0.309]</r>. They correspond to {the slices}<r>[0.482, 0.389, 0.741, 0.7]</r> and <r>[0.482, 0.15, 0.654, 0.516]</r> respectively in the pie chart."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "1659NWIM6l6PZxI4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region encompassed by the coordinates [0.460, 0.516, 0.706, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an orange-colored slice of the pie chart represented with the text \"Budget\" at [0.532, 0.703, 0.576, 0.723] and with the percentage '18.0%' at [0.614, 0.866, 0.664, 0.886]."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "23rmjCsecNArw1pG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area comprises the entire pie chart. Each portion of the pie is a different color, representing different organizational parameters such as Leadership, Strategy, Teamwork, Budget, Brand, Talent, Training, and Performance. Beside each slice, the corresponding label and percentage are given."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "z7jNybqBLJrlFgRm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within the coordinates [0.899, 0.071, 0.976, 0.091] hold?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the text \"Leadership\" which is part of the legend box in the pie chart. The legend box gives a brief overview of what each pie slice color stands for."
+            }
+        ],
+        "id_raw": 323
+    },
+    {
+        "id": "pVWlgoKOyt3qVzEX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.231, 0.021, 0.732, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"E-commerce and Retail Industry Analysis from 2018 to 2020\"."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "U3DrXlFWdLbC26zt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the rectangular region defined by [0.808, 0.073, 0.836, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend handle for E-commerce 2018."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "qGUZjuc0yAakdCGn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.538, 0.875, 0.658, 0.896] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"28.3%\"."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "lKBO0XQU0bO6Ov8Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is closest to {the E-commerce 2020 text}[0.599, 0.409, 0.643, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice of {E-commerce 2020}<r>[0.482, 0.151, 0.741, 0.69]</r> is closest."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "W3fsKOcMhp3JvQ6B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text located at [0.847, 0.071, 0.974, 0.091] and the color block at [0.808, 0.073, 0.836, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"E-commerce 2018\" corresponds to the color block, indicating that the color represents E-commerce data for 2018 in the pie chart."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "uuZvvLXwTtOvshge",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the slices in the pie chart associated with {the legend entries}<r>[0.847, 0.071, 0.974, 0.091], [0.847, 0.101, 0.974, 0.121], [0.847, 0.131, 0.974, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices are {E-commerce 2018 slice}<r>[0.223, 0.278, 0.482, 0.799]</r>, {E-commerce 2019 slice}<r>[0.315, 0.516, 0.71, 0.886]</r>, and {E-commerce 2020 slice}<r>[0.482, 0.151, 0.741, 0.69]</r>, respectively."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "ag6DTEnU046FF3Z5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.799]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart for E-commerce in 2018. It incorporates the text {\u201c25.0%\u201d}<r>[0.071, 0.541, 0.198, 0.561]</r> and the label {\u201cE-commerce 2018\u201d}<r>[0.305, 0.525, 0.349, 0.545]</r>."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "HzYzMeOljULSy5TA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is denoted by the region [0.356, 0.346, 0.391, 0.366]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Physical Retail 2020\"}<r>[0.356, 0.346, 0.391, 0.366]</r> is located within this region."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "adYrEYF6APYcAqCq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is notable in the region defined by [0.847, 0.071, 0.984, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains legend entries for the pie chart, including labels {\"E-commerce 2018\"}<r>[0.847, 0.071, 0.974, 0.091]</r>, {\"E-commerce 2019\"}<r>[0.847, 0.101, 0.974, 0.121]</r>, {\"E-commerce 2020\"}<r>[0.847, 0.131, 0.974, 0.151]</r>, {\"Physical Retail 2018\"}<r>[0.847, 0.161, 0.987, 0.181]</r>, {\"Physical Retail 2019\"}<r>[0.847, 0.191, 0.987, 0.211]</r>, and {\"Physical Retail 2020\"}<r>[0.847, 0.221, 0.987, 0.241]</r> along with their corresponding color blocks."
+            }
+        ],
+        "id_raw": 325
+    },
+    {
+        "id": "gFz30zWMFQxN6yY0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is indicated by object with coordinates [0.277, 0.021, 0.686, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart named \"Government System Changes from 1990 to 2015\"."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "sERqw6xQVJI7EL6x",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located at coordinates [0.952, 0.071, 0.987, 0.091] and [0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Those are the text \"1990\" and \"2015\" in the legend."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "tAzC6t2y7q81Z73U",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.482, 0.146, 0.741, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the sector in the pie chart corresponding to the year 2005."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "uTfDG1iou6iYtm2S",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest the to the object at coordinates [0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2000\"}[0.952, 0.131, 0.987, 0.151] is closest."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "62noRnLBPVqikVhJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart does {the color handle at coordinates}[0.913, 0.073, 0.941, 0.087] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice at coordinates [0.223, 0.278, 0.482, 0.712]."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "eD9mAvP8yZn8Pzfe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice at coordinates [0.344, 0.146, 0.5, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2010\" corresponds."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "7MUheKSatrOCQIOy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart which represents changes in the government system from 1990 to 2015 with different colors for each year. It contains pie slices, each representing a specific year such as 1990, 1995, 2000, 2005, 2010, and 2015. There are associated percentage values for each slice, such as 20.0%, 25.0%, 10.0%, 30.0%, 10.0%, and 5.0% respectively."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "RJDvtaNrmpav6vW6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.907, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects within this region are the legend items for the pie chart. They include color handles and texts indicating the years 1990, 1995, 2000, 2005, 2010, and 2015."
+            }
+        ],
+        "id_raw": 326
+    },
+    {
+        "id": "nPlYDErwRpjSEHRb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text object located at these coordinates [0.27, 0.021, 0.694, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle with the text 'Education and Training Industry Trends: 2017-2020'."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "H2w1ycY3jq8Gi52R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label with the text \"2017\"."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "uKkqFvfTIVCxr9jm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.71, 0.75, 0.745, 0.77]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"25.2%\"."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "TyDXYcP2a1EvVAfP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe legend label {2017}<r>[0.952, 0.071, 0.987, 0.091]</r> corresponds to which slice in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice at coordinates [0.223, 0.278, 0.482, 0.658]."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "i2EsH1S7knJV6tiO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the autotext {25.2%}<r>[0.71, 0.75, 0.745, 0.77]</r> refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the slice corresponding to the year \"2019\" at coordinates [0.482, 0.461, 0.741, 0.883]."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "lclnaRgAYINP1zMt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the legend label {2020}<r>[0.952, 0.161, 0.987, 0.181]</r> correspond to any item in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it corresponds to the slice with coordinates [0.283, 0.146, 0.738, 0.516]."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "AJbGpdrD5E7B4zlb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main part of the pie chart - it includes the slices for years \"2017\", \"2018\", \"2019\", and \"2020\", along with the corresponding autotexts \"17.4%\", \"20.9%\", \"25.2%\", \"36.5%\"."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "LviUmCIWzIbFQ8l5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the area [0.223, 0.278, 0.482, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart representing the year \"2017\" with the corresponding autotext \"17.4%\"."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "RhIbwdPsOv95aN5s",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific information can be found in the region [0.482, 0.461, 0.741, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, we can find a slice of the pie chart representing the year \"2019\" and the associated autotext \"25.2%\"."
+            }
+        ],
+        "id_raw": 328
+    },
+    {
+        "id": "o2DScGKST5NNRDY7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.288, 0.021, 0.675, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Energy Use and Resource Consumption - 2019\"."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "CRboexfxXZIkmQKR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is located at coordinates [0.895, 0.191, 0.93, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text of a legend item called \"Solar\"."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "WMUFmnqcLyz0g2sr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the chart has the coordinates [0.396, 0.516, 0.672, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice labeled as \"Natural Gas\" in the pie chart."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "aQadDyBReHg3R0RP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with {the legend item \"Coal\"}<r>[0.895, 0.101, 0.925, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the pie slice}<r>[0.223, 0.403, 0.482, 0.865]</r>."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "LskG2XaMK02dGhUQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the pie slice labeled \"Biofuels\"}<r>[0.482, 0.335, 0.723, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the legend item \"Biofuels\"}<r>[0.895, 0.281, 0.95, 0.301]</r>."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "s6eEK0YhUaW2857a",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many {legend items} <r>[0.85, 0.063, 0.993, 0.338]</r>  associate with pie slices whose energy consumption is above 20%?"
+            },
+            {
+                "from": "gpt",
+                "value": "Two items correspond to such slices: They are {the legend \"Coal\"}<r>[0.895, 0.101, 0.925, 0.121]</r> and {the legend \"Oil\"}<r>[0.895, 0.311, 0.913, 0.331]</r>."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "SBga6ghkvd2HbpYx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly encompasses the pie chart that represents energy consumption of different sources in 2019. It contains nine slices, which represent Nuclear, Coal, Natural Gas, Hydroelectric, Solar, Wind, Geothermal, Biofuels, and Oil, respectively."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "rml0Zq0CSblBMgdF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area delineated by [0.85, 0.063, 0.993, 0.338] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area consists of a legend with nine items that correspond to the slices in the pie chart. Each item comprises a color rectangle representing the corresponding slice's color and text representing the energy source's name."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "inBJH95s6rZRusuy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the entire subplot, which contains a pie chart, its subtitle, and its legend."
+            }
+        ],
+        "id_raw": 329
+    },
+    {
+        "id": "tdJ5CAlXko38mRGN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.935, 0.071, 0.97, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2016\"."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "2o5mr1bDOabsPi3c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented at the coordinates [0.896, 0.193, 0.924, 0.207] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the colored handle associated with the year \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "R1JNTZv5za4UgLVL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at these coordinates: [0.434, 0.958, 0.469, 0.978] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"15.6%\"."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "SkzJAQ2vOrsmzTfb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the handle in the legend}[0.896, 0.133, 0.924, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated is the one with the coordinates [0.289, 0.513, 0.561, 0.931]."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "27zBdbisF3lc2X8U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label in the legend correspond to {the slice}[0.1, 0.513, 0.387, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the label \"2017\"."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "UG6rurUkFwuJUz7E",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage is associated with {the slice}[0.095, 0.244, 0.387, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"14.2%\" is associated with it."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "tmdd0V7a2mIgViN1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.095, 0.095, 0.68, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the region where the pie chart is located, depicting the distribution of educational resources. It contains different slices representing the years from 2016 to 2020 with various percentages."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "Ik9JJovpcqZkDBhM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region of [0.835, 0.013, 0.987, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the title text \"Education Resource Distributions from 2016 to 2020\" and the legend with labels of years from 2016 to 2020, along with their corresponding colored handles."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "pYRtkbDPgUvNBccC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region defined by coordinates [0.095, 0.095, 0.68, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart representing the distribution of educational resources. Different slices in the pie chart indicate different years and their corresponding distributions. Each slice also has a percentage label near it."
+            }
+        ],
+        "id_raw": 334
+    },
+    {
+        "id": "klqincoHENzKs9lb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.406, 0.088, 0.619, 0.114] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, named \"Innovation Usage in 2020\"."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "bJUPT725y28HLQtb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is within the coordinates [0.798, 0.168, 0.903, 0.188] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend label \"Online Courses\"."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "VfV8qKyQTE5dxjk1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at the coordinates [0.212, 0.459, 0.277, 0.479] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"18.9%\"."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "BxeXpHIjJlCaZM4M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, which color represents {the label \"Gamification\"}<r>[0.798, 0.258, 0.887, 0.278]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color} <r>#c2c2f0</r> represents the label \"Gamification\"."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "h86OtretqiJ8uBt0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice matches with the {legend handle}<r>[0.76, 0.289, 0.787, 0.303]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}<r>[0.486, 0.197, 0.659, 0.505]</r> matches with the legend handle."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "aojOWYqbUOo556sM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage corresponds to the {slice}<r>[0.512, 0.279, 0.728, 0.591]</r> in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding percentage is {17.6%} <r>[0.741, 0.405, 0.83, 0.425]</r>."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "8ZagYMHwN8IB6nPO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.297, 0.197, 0.728, 0.813] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region generally occupies the middle area of the chart and contains various pie slices representing different categories, with autotext details and associated labels such as \"Webinars\" <r>[0.362, 0.476, 0.406, 0.496]</r>, \"Online Courses\" <r>[0.448, 0.67, 0.492, 0.69]</r> and others. Adjacent to each slice, there's associated percentage value, such as \"18.9%\" <r>[0.212, 0.459, 0.277, 0.479]</r> and \"23.9%\" <r>[0.331, 0.815, 0.435, 0.835]</r>."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "Bc2aQoicdKwCMoJc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are found in the region defined by [0.754, 0.13, 0.937, 0.345]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you will find the legend of the pie chart, containing labels along with their respective colours, such as \"Webinars\" <r>[0.798, 0.138, 0.863, 0.158]</r>, \"Online Courses\" <r>[0.798, 0.168, 0.903, 0.188]</r> among others. Also, color handles corresponding to each legend label like <r>[0.76, 0.14, 0.787, 0.154]</r> can also be found in this region."
+            }
+        ],
+        "id_raw": 337
+    },
+    {
+        "id": "hsHPvdzXZBMoBpMq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title residing at the coordinates [0.326, 0.021, 0.637, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Big Data Adoption from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "W4LmJOw7Jtxe2Y0w",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinate box [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2011\" in the chart legend is located at those coordinates."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "0pN2ikZRYAOH93nh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the coordinates [0.913, 0.073, 0.941, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a color handle in the legend, representing the year 2011."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "7owByDgZ1ckqs3dK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend corresponds to {the slice}[0.223, 0.502, 0.482, 0.645] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {\"2013\"}[0.952, 0.131, 0.987, 0.151] in the legend corresponds to the specified slice."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "Jj0lcDoLaFpuS1vh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is related to the {year 2016}[0.952, 0.221, 0.987, 0.241] as per the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates {0.452, 0.516, 0.624, 0.886} corresponds to the year 2016."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "mHTk6miVVNoWHz2F",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.239, 0.516, 0.482, 0.801] and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice corresponds to the legend item \"2014\" with coordinates [0.952, 0.161, 0.987, 0.181]."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "ARPXKEWuGTD2SB0l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart, which consists of slices representing data from the years 2011 to 2020. Each slice of the pie chart has a corresponding color and legend item."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "0LyGI8Bn3gtVc2wI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements found in the region [0.482, 0.303, 0.741, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of the pie chart which represents the year 2018, including the pie slice {[0.482, 0.303, 0.741, 0.594]} and the corresponding label {\"2018\"}[0.612, 0.462, 0.656, 0.482]."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "R9udXJykXVRq3FCa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what the region [0.452, 0.516, 0.624, 0.886] features?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region features a slice from the pie chart representing the year 2016 along with its percentage label \"11.1%\"[0.548, 0.902, 0.583, 0.922] and year label \"2016\"[0.496, 0.722, 0.54, 0.742]."
+            }
+        ],
+        "id_raw": 339
+    },
+    {
+        "id": "LZwrKs3J9hA67f6P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object at coordinates [0.295, 0.021, 0.668, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, labelled as \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "gnBGZ2n0GavdipKw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is found at coordinates [0.797, 0.063, 0.993, 0.189] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Legend of the chart."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "NlUsUUFMiaSBpxGF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.126, 0.296, 0.237, 0.316] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"5.0%\"."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "vbTQRTz6liGCWMpY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.283, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slices}[[0.241, 0.278, 0.482, 0.516], [0.315, 0.516, 0.482, 0.885], [0.223, 0.377, 0.482, 0.799]] are adjacent to it."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "izQLxBqMFaZMdrbl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text items are located close to {the subtitle}[0.295, 0.021, 0.668, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"AI Development\"}[0.331, 0.392, 0.366, 0.412] and {\"Quantum Computing\"}[0.401, 0.712, 0.445, 0.732] are positioned near the subtitle."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "wS2S59FyTLy9y3tr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items are connected to the slices of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"AI Development\", \"Blockchain Adoption\", \"Quantum Computing\", and \"Space Exploration\" each correspond to the slices with coordinates [0.241, 0.278, 0.482, 0.516], [0.223, 0.377, 0.482, 0.799], [0.315, 0.516, 0.482, 0.885], and [0.283, 0.146, 0.741, 0.886] respectively."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "7bzr8ZI6Xq3UjNQu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"65.0%\" [0.744, 0.347, 0.87, 0.367] and the label \"Space Exploration\" [0.603, 0.419, 0.647, 0.439]. This region corresponds to a large pie slice on the right side of the chart."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "4PFEQGYeiY22kQ8B",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are there in the region [0.241, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the text \"5.0%\" [0.126, 0.296, 0.237, 0.316], and the label \"AI Development\" [0.331, 0.392, 0.366, 0.412]. It is a pie slice towards the top of the chart."
+            }
+        ],
+        "id_raw": 340
+    },
+    {
+        "id": "UUVuQsYRbSWXORsg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name above the chart found at coordinates [0.309, 0.021, 0.654, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at the coordinates is \"Internal Business Process Time Allocation\"."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "IKv55OYL8XTRNHYO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label corresponds to the handle located at coordinates [0.855, 0.103, 0.883, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Design\"."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "WiE61qVK2aYaGTl5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage corresponds to the slice located at coordinates [0.419, 0.146, 0.5, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding percentage is \"5.0%\"."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "tzKMMaFNlnmD6GnA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend handle nearest to the slice with the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend handle nearest is \"Research\", with handle coordinates [0.855, 0.073, 0.883, 0.087]."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "umEVvE7EFbzEvfBL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of {the subtitle}[0.309, 0.021, 0.654, 0.047] to {the chart}[0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Internal Business Process Time Allocation\" is positioned above the chart."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "vF3vWbSxnOjtxfQ5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart corresponds to {the legend item \"Testing\"}[0.855, 0.193, 0.883, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.482, 0.147, 0.741, 0.542] corresponds to the legend item \"Testing\"."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "WCjKwUwQz5B0tpWh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.147, 0.741, 0.542]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses part of the pie chart itself, which represents the \"Testing\" process. It also includes the percentage \"25.0%\" of the total business process time allocated for testing."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "GhZZ63f0dx6TgF7N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components within the legend coordinates [0.849, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the symbols and labels for the whole pie chart. It includes the labels \"Research\", \"Design\", \"Development\", \"Sourcing\", \"Testing\", \"Review\", and \"Sales\" with their respective color symbols."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "9t80r6SKkTH3yJvh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region defined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the whole pie chart showing the distribution of time allocated to different business processes, with each slice labeled accordingly and associated percentage."
+            }
+        ],
+        "id_raw": 341
+    },
+    {
+        "id": "2Ux4D0D76NlqaE3K",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.346, 0.021, 0.617, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {title of the chart}<r>[0.346, 0.021, 0.617, 0.047]</r>, which is 'Invoice Generation per Weekday'."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "Qn5TwfMBHPtA93Dk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.862, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {legend of the chart}<r>[0.862, 0.063, 0.993, 0.279]</r>."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "zOWM3aAjkfZCZhb8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at coordinates [0.143, 0.46, 0.198, 0.48]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the {text}<r>[0.143, 0.46, 0.198, 0.48]</r> that says \"18.6%\"."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "vqRDIhmpRp5mpnG3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the corresponding label in the legend for the slice {colored '#ff9999'}<r>[0.223, 0.278, 0.482, 0.684]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the label {\"Monday\"}<r>[0.907, 0.071, 0.962, 0.091]</r> in the legend."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "QQWOgEnOIpvsriIf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the corresponding label in the legend for the slice {colored '#a5a5a5'}<r>[0.283, 0.238, 0.482, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the label {\"Sunday\"}<r>[0.907, 0.251, 0.959, 0.271]</r> in the legend."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "NSigs2ARZEZgwOk8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the label {\"Friday\"}<r>[0.907, 0.191, 0.949, 0.211]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Friday\" is the {c2c2f0 colored slice}<r>[0.346, 0.146, 0.643, 0.516]</r>."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "aKB6JzI4wbaVlb5R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the entirety of the pie chart. It contains several differently colored slices representing various weekdays, the percentage each day contributes to the invoice generation, and labels corresponding to each day of the week."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "yMe5lhSeoYyvsPDt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.862, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is primarily occupied by the legend of the chart. It contains seven items along with their text labels and color identifiers, each of which refers to a weekday."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "bneB7p4C9vTyClIH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region with coordinates [0.482, 0.227, 0.741, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one slice of the pie chart, which is the {slice colored '#ffcc99'}<r>[0.482, 0.227, 0.741, 0.706]</r>. The text \"22.8%\" <r>[0.762, 0.433, 0.827, 0.453]</r> and label \"Thursday\" <r>[0.613, 0.466, 0.657, 0.486]</r> are also in this region."
+            }
+        ],
+        "id_raw": 342
+    },
+    {
+        "id": "aJIbMJT4uDUzJyD4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart with coordinates [0.299, 0.021, 0.664, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Cancer Research Budget from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "46lyyxOhu9EKwb0f",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented within the coordinate space [0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text for the year \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "gzX604Y9y6p7PCgt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies at coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the center of the pie chart."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "xfeBHW5uhg0grUCT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the chart legend and pie slice with color '#66b3ff'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with color '#66b3ff' corresponds to the year \"2017\" in the chart legend."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "DrIl0zp09Ddf1Uk4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between {the legend item at coordinates [0.913, 0.193, 0.941, 0.207]} and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item at these coordinates represents the color of the pie slice for the year \"2020\"."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "3BopGwLlmUO2fZOg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the slice at coordinates [0.223, 0.278, 0.482, 0.658] relate to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice corresponds to the item in the legend with the label \"2016\"."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "gDkVYbUxpg7GGOl6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bound by coordinates [0.408, 0.103, 0.443, 0.123]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"23.4%\" which is an autotext of the pie slice representing the year 2020 in the pie chart."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "NFNGvy6ift0al0Xf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region defined by coordinates [0.223, 0.146, 0.741, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses part of the pie chart, specifically the pie slices for the years 2016 and 2020."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "QnmG8Kqd1MRXoPMQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region of coordinates [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the legend of the chart, including color representations and labels for the years 2016 to 2020."
+            }
+        ],
+        "id_raw": 343
+    },
+    {
+        "id": "kJTNX2aaynvBrMfL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.326, 0.021, 0.637, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"Number of Clients from 2016 to 2021\"."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "bwI7GRuHhVyOWq9f",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"2016\"."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "GYTeYEs1hepeUALr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is denoted by the coordinates [0.171, 0.401, 0.206, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.0%\"."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "vdqblgsjWMJSIdyj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.223, 0.278, 0.482, 0.582] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2016\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "X6pCbu4hRdHko9QE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with the {legend item \"2021\"}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.283, 0.146, 0.574, 0.516] is associated with the \"2021\" legend item."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "vNnnlu989otZvLun",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage represented by the {slice}[0.366, 0.516, 0.619, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents \"16.3%\" of the total."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "vEmbF3Cs58brqeja",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region bounded by [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes segments of the pie chart, specifically the slices for 2016-2020, each labeled with a respective year and each segment has a percentage value. The colors of these slices match the legend 2016-2020 seen on the right."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "usGrhAvA8y790hBp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region specified by the coordinates [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend, which shows six entries from 2016 to 2021, each represented by a different color."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "CdWMcVUMvNWgNLpa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the coordinates [0.223, 0.146, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains four slices of the pie chart, specifically for the years 2016, 2017, 2018 and 2019, each labeled with the year and a percentage value."
+            }
+        ],
+        "id_raw": 345
+    },
+    {
+        "id": "dzyTTeVzZnKeV8jN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title located at the coordinates [0.324, 0.021, 0.639, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Venture Capital Investments By Stage\"."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "zvQUdg5djhIGQOub",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.904, 0.071, 0.939, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Seed\", part of the chart legend, is located at these coordinates."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "bINsz9tjKgcrNLsA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.203, 0.296, 0.237, 0.316] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.0%\", a percentage label inside the chart, is located at these coordinates."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "68DtSSp1vDppGWyt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend handle is associated with the series stage {the text \"Series A\"}[0.904, 0.131, 0.96, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle at the coordinates {the handle}[[0.865, 0.133, 0.893, 0.147]] is associated with the series stage \"Series A\"."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "V02emPRYFgipcbBD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text in the chart is closest to the {title}[[0.324, 0.021, 0.639, 0.047]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest text to the title is the subtitle {subtitle}[[0.283, 0.171, 0.482, 0.516]]."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "ZjneTwqpoIxH52Nt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text corresponds to the pie slice at coordinates {the slice}[[0.241, 0.278, 0.482, 0.516]] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Seed\" corresponds to this pie slice."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "zDs7guBm33acnd5v",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main part of the chart containing the pie graph. The region depicts series stages from \"Seed\" to \"Post-Series\" with different color codes representing each stage. All these series stages have autotext percentage values along with a chart legend at the side. The region contains colors such as #ff9999, #66b3ff, and #99ff99 that represent different investment stages."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "L9u7l0gKWH1MpL6F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the information in the region [0.859, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart. Labels 'Seed', 'Pre-Series A', 'Series A', 'Series B', 'Series C', 'Series D', 'Series E', 'Post-Series' are present in this section alongside colored handles respectively representing the stages of venture capital investments."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "zh5x8fHl36Fj7iyh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.482, 0.173, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a part of the pie chart denoting the \"Series D\" stage of investment. The color of the slice matches the color (#c2c2f0) of the \"Series D\" handle in the legend. It also includes the percentage text \"18.0%\" representing the proportion of \"Series D\" investments."
+            }
+        ],
+        "id_raw": 347
+    },
+    {
+        "id": "ElrszNjIUOvLpmiS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object labeled in the chart at coordinates [0.211, 0.021, 0.752, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which reads \"Education and Training Technology Innovation from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "WRt0NQQTh658HZds",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is represented by the coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\"."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "HZbalCMjXwC6Y4s1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.423, 0.29, 0.467, 0.31] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"2021\"."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "F9WiQ1urmhDOhzPW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart corresponds to {the text \"2017\"} [0.369, 0.687, 0.413, 0.707]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.264, 0.516, 0.482, 0.875] corresponds to the text \"2017\"."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "aINdrd43pwW0RbIP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to the {slice} [0.482, 0.174, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented by the handle with coordinates [0.913, 0.223, 0.941, 0.237], which is '#ffb3e6', corresponds to this slice."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "wzO83CUXP0kSatPu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the color '#66b3ff' represented in the {legend} [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to the year 2016 on the chart."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "k2Nq1mpYbirF6R6t",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the whole pie chart, which consists of different individual sections representing the progression of \"Education and Training Technology Innovation\" from 2015 to 2021, each marked with different colors and labels corresponding to different years."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "dZunzhWvhOta6eKt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region with coordinates [0.482, 0.174, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice with color '+#ffb3e6', labeled with \"2020\" and representing '18.0%' of the pie."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "IG8qTAbLGRZueTU6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within coordinates [0.908, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart, with labels indicating each year from 2015 to 2021 and their corresponding colors represented in the pie chart."
+            }
+        ],
+        "id_raw": 348
+    },
+    {
+        "id": "Ep3xIor6VmKAIWAf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object represents the largest percentage in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The autotext \"25.2%\"}<r>[0.14, 0.707, 0.234, 0.727]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "OTcZe3iTCxyaf5MS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice that pertains to the phase with the least cost?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color \"#ffcc99\"}<r>[0.482, 0.516, 0.624, 0.861]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "pui0obMeId8dKkBC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend corresponds to the autoplot text \"6.7%\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label \"Planning\"}<r>[0.85, 0.071, 0.91, 0.091]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "JxArLCNNjMOkfO79",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend box next to the label \"Deployment\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color \"#808080\"}<r>[0.811, 0.282, 0.839, 0.296]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "V4xLfwPv6A1b8GVB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich phase in the pie chart is adjacent to {the Testing slice}[0.414, 0.516, 0.576, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The Development slice}<r>[0.223, 0.415, 0.482, 0.873]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "tUTtHjGeVnfuiFMW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the slice and autotext for \"Market Research\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice coordinates}<r>[0.482, 0.516, 0.684, 0.825]</r> and {the autotext coordinates}<r>[0.674, 0.806, 0.79, 0.826]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "jcuij4rthU2rl6qb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region that includes \"Implementation\" and \"Production.\""
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes {the Implementation autotext \"20.2%\"}<r>[0.766, 0.521, 0.877, 0.541]</r> and {the Production slice}<r>[0.474, 0.146, 0.696, 0.516]</r>, occupying the right center part of the pie chart."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "Zpdrd1vbNtwhIc62",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the upper right section of the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the autotext \"3.4%\"}<r>[0.612, 0.868, 0.75, 0.888]</r> and {the text \"Post Launch Review\"}<r>[0.535, 0.703, 0.57, 0.723]</r>, corresponding to the beige-colored slice."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "hQGljuCvNMxyNydD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements present at the bottom of the legend area?"
+            },
+            {
+                "from": "gpt",
+                "value": "The bottom of the legend area includes the {labels \"Market Research\" and \"Implementation\"}<r>[0.85, 0.191, 0.966, 0.211]</r> and <r>[0.85, 0.221, 0.96, 0.241]</r>, along with their respective color handles <r>[0.811, 0.193, 0.839, 0.207]</r> and <r>[0.811, 0.223, 0.839, 0.237]</r>."
+            }
+        ],
+        "id_raw": 349
+    },
+    {
+        "id": "rgsC40Ea7TQSkloC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at the coordinates [0.348, 0.021, 0.615, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle with text \"Funding allocation in year: 2020\"."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "mg9is88oGQpXHavA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.816, 0.071, 0.947, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend text representing \"Scientific Research\"."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "aHP1ENUrcyJ7bZdy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is positioned at the coordinates [0.546, 0.109, 0.717, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext indicating \"35.0%\"."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "G4chWzt8QI1qp9fX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the object at {the slice}[0.283, 0.146, 0.733, 0.516] and the object at {the legend item}[0.816, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at [0.283, 0.146, 0.733, 0.516] corresponds to the legend item \"Technological Innovation\" [0.816, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "zR4tBqwlyJPlkYSd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is adjacent to the {color handle}[0.777, 0.103, 0.805, 0.117] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Technological Innovation\"}[0.816, 0.101, 0.987, 0.121] is adjacent."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "i6x57jnANnQhfN5d",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does {the autotext}[0.546, 0.109, 0.717, 0.129] referencing \"35.0%\" belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"35.0%\" belongs to {the slice}[0.283, 0.146, 0.733, 0.516]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "P92IMCmm2jaANpip",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encapsulates the whole pie chart which features two colored slices representing two categories of funding. They are \"Scientific Research\" [0.425, 0.722, 0.469, 0.742] and \"Technological Innovation\" [0.495, 0.29, 0.538, 0.31] with the respective percentages of 65.0% [0.286, 0.903, 0.418, 0.923] and 35.0% [0.546, 0.109, 0.717, 0.129]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "oyoXOjrU5FbwaOBN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are there in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two pie slices with labels \"Scientific Research\" [0.425, 0.722, 0.469, 0.742] and \"Technological Innovation\" [0.495, 0.29, 0.538, 0.31], and autotexts indicating percentages \"65.0%\" [0.286, 0.903, 0.418, 0.923] and \"35.0%\" [0.546, 0.109, 0.717, 0.129]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "DOxmi2faORgFYzmJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the objects within [0.771, 0.063, 0.993, 0.129] on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the legend of the chart. The category \"Scientific Research\" is represented with a handle at [0.777, 0.073, 0.805, 0.087] and text at [0.816, 0.071, 0.947, 0.091], whereas \"Technological Innovation\" is marked with a handle at [0.777, 0.103, 0.805, 0.117] and corresponding text at [0.816, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 353
+    },
+    {
+        "id": "fRlP7uNe5AN8JA8O",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main title located at these coordinates [0.25, 0.021, 0.714, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the main title is \"Budget Allocation for Social Welfare and Public Services\"."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "AMDUqWRyqK92BwEo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at these coordinates [0.819, 0.131, 0.889, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend item with the label \"Education\"."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "ZdE0yFAGnbRFz6s1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element can we find at these coordinates [0.123, 0.452, 0.199, 0.472]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"18.0%\"."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "z9yLRpgbe305CMRe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are neighboring {the slice}[0.482, 0.32, 0.741, 0.633]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring slices are {the slices}[[0.482, 0.218, 0.701, 0.516],[0.482, 0.516, 0.727, 0.875]]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "L0q5ac9QohibVizs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which legend items are corresponding to the pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each legend item corresponds to a pie slice. For instance, \"Healthcare\" corresponds to the slice [0.223, 0.278, 0.482, 0.671] and \"Unemployment Benefits\" corresponds to the slice [0.403, 0.146, 0.635, 0.516] and so forth."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "pjwkQawJZizJihwE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.403, 0.146, 0.635, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" is nearest located near the slice."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "r2ubuVtkzBHkRlG4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by these coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses several pie slices and their corresponding labels and percentages. For example, it contains \"Healthcare\" label at [0.306, 0.477, 0.35, 0.497], \"18.0%\" at [0.123, 0.452, 0.199, 0.472], and several others."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "CUh0QQ7Sre0NNkkT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region designated by coordinates [0.775, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "That region is dedicated to the legend of the pie chart. You'd find labels like \"Healthcare\", \"Education\" among others, with corresponding color tags."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "UOw71HFKWItvkuQ8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_154.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the region designated by these coordinates [0.403, 0.146, 0.635, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice of the chart representing \"Unemployment Benefits\", it also contains the text \"15.0%\" at [0.528, 0.104, 0.696, 0.124]."
+            }
+        ],
+        "id_raw": 354
+    },
+    {
+        "id": "EHx8UE3rnWlfeFkA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart based on the coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is {Future Technological Development Prospects}<r>[0.3, 0.021, 0.672, 0.047]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "VIoZuzdvSRH27ido",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the color in the legend at the coordinates [0.782, 0.073, 0.809, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents {Artificial Intelligence (AI)}<r>[0.82, 0.071, 0.992, 0.091]</r> in the legend."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "a0oQK0qOV28hli8h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the segment at coordinates [0.227, 0.278, 0.486, 0.537]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is one of the pie chart segments, representing {Artificial Intelligence (AI)}<r>[0.227, 0.278, 0.486, 0.537]</r> with 12.0% of National Budget allocated."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "S0lF7ennAvXLjni2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology slice is adjacent to the slice {Quantum Computing}<r>[0.486, 0.232, 0.745, 0.61]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice adjacent is {5G Network}<r>[0.346, 0.516, 0.736, 0.886]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "Iu0kadRQsQ00Ft4q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content within the same slice as the percentage {30.0%}<r>[0.589, 0.886, 0.671, 0.906]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is {5G Network}<r>[0.52, 0.713, 0.564, 0.733]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "MMSF9ZXDIDwckR6t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend corresponding to {Blockchain}<r>[0.82, 0.101, 0.896, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its color is {#66b3ff}<r>[0.782, 0.103, 0.809, 0.117]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "D8xsLh6q8d1hDeop",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.227, 0.278, 0.486, 0.537]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie segment representing {Artificial Intelligence (AI)}<r>[0.227, 0.278, 0.486, 0.537]</r> with an allocation of {12.0%}<r>[0.044, 0.377, 0.216, 0.397]</r> of the National Budget."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "9ymtbukW5plOsXFC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.82, 0.071, 0.992, 0.091]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend item {Artificial Intelligence (AI)}<r>[0.82, 0.071, 0.992, 0.091]</r> and its corresponding color code {#ff9999}<r>[0.782, 0.073, 0.809, 0.087]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "ebiEPYpPu13MdNVZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat takes up the largest percentage of the budget according to the region [0.346, 0.516, 0.736, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {5G Network}<r>[0.52, 0.713, 0.564, 0.733]</r> takes up the largest percentage of the budget with {30.0%}<r>[0.589, 0.886, 0.671, 0.906]</r>."
+            }
+        ],
+        "id_raw": 355
+    },
+    {
+        "id": "oVorlB4TsM0z4438",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main content of the phrase with coordinates [0.478, 0.727, 0.521, 0.747] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Air Pollution\"."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "UvI8LZgdrksVMn3R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.853, 0.071, 0.936, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry for \"Air Pollution\"."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "7kBLpLijS6wYfR6O",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat piece of information is available at coordinates [0.315, 0.102, 0.449, 0.122] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value \"24.1%\"."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "MqJn7FDBRIbYI2CV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the legend entry for {Air Pollution}[0.853, 0.071, 0.936, 0.091] correspond to a pie slice in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, {Air Pollution} corresponds to the {pie slice}[0.223, 0.219, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "pRCXSNxl4hU5Gvdn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the legend item {Ocean Acidification}[0.853, 0.101, 0.987, 0.121] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Ocean Acidification} corresponds to the {pie slice}[0.283, 0.146, 0.637, 0.516]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "u942DDuueF7EnMab",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any pie slices associated with the {legend box}[0.809, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the {pie slices}[[0.223, 0.219, 0.741, 0.886], [0.283, 0.146, 0.637, 0.516]] are associated with the legend box."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "tDAy7GP1Mbfht6oq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the boundaries defined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice representing \"Air Pollution\" with a corresponding value of \"75.9%\" noted at coordinates [0.514, 0.91, 0.597, 0.93]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "a3WxshYymWrWLeD1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region with coordinates [0.283, 0.146, 0.637, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the pie slice relating to \"Ocean Acidification\" as defined by the text in coordinates [0.442, 0.285, 0.486, 0.305] as well as the specific value \"24.1%\" found at coordinates [0.315, 0.102, 0.449, 0.122]."
+            }
+        ],
+        "id_raw": 356
+    },
+    {
+        "id": "Ch34JHNv6a7SX9ta",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "the legend text \"2010\"."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "JPRNsTWS1bnQ4ZmT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.482, 0.516, 0.482, 0.516] as seen in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-label with no content defined."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "wwLVRQH087EOyISK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the label of the item located at [0.311, 0.021, 0.653, 0.047] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named 'Resident Growth Rate from 2010 to 2019'."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "sxTVDMAV0hKOlC8y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the legend label \"2015\"}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.333, 0.516, 0.496, 0.886] corresponds to the legend label \"2015\"."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "tpsGI1rTxGO6JyCq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the text at {coordinates \"2019\"}[0.417, 0.293, 0.461, 0.313] and the slice at {coordinates} [0.283, 0.146, 0.561, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\" labels the respective slice."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "Cbdnq6TRZ0Yxwbvi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation of {the legend label \"2013\"}[0.952, 0.161, 0.987, 0.181] in the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#ffcc99\" presented at {coordinates} [0.913, 0.163, 0.941, 0.177] represents the label \"2013\" in the slices."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "45IzBGeCuXhg4S7A",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by the pie chart which represents the 'Resident Growth Rate from 2010 to 2019'. It contains slices of various colors and percentages, corresponding to each year from 2010 to 2019."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "OhHh7WZ28TjuuUM1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within coordinates [0.482, 0.429, 0.741, 0.75]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region comprises a slice of the pie chart which represents the year '2017' with a resident growth rate of 14.7% in color '#ffb3e6'."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "Kcbm3UUrbdtaiJIt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's located within coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart which provides the correlation between the colors used in the chart and the years from 2010 to 2019."
+            }
+        ],
+        "id_raw": 358
+    },
+    {
+        "id": "7KHzcliOcWIppd38",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.307, 0.467, 0.351, 0.487] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Stocks-2018\"."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "BHbEFwcnNFMWmKAu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.902, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item label \"Stocks-2018\"."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "gaGaKzBNsNnE335u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich main object is at coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the center point of the pie chart."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "V4hngbmm2ssgGHUG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are adjacent to {the slice}[0.482, 0.195, 0.725, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.389, 0.741, 0.88], [0.393, 0.146, 0.611, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "zS3AyHxvi1FXz9uH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located near {the slice}[0.238, 0.516, 0.527, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Stocks-2019\"}[0.382, 0.698, 0.426, 0.718] is located near the slice."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "irIw4yGHPaNIcFt7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Stocks-2018\", \"Stocks-2019\", \"Stocks-2020\", \"Bonds-2018\", \"Bonds-2019\" and \"Bonds-2020\" correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.643], [0.238, 0.516, 0.527, 0.886], [0.482, 0.389, 0.741, 0.88], [0.482, 0.195, 0.725, 0.516], [0.393, 0.146, 0.611, 0.516] and [0.283, 0.168, 0.482, 0.516] respectively."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "stQCx4tmre9mLXkM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the pie chart across three years of investment types. It includes all the slices representing different investments like \"Stocks-2018\", \"Stocks-2019\", \"Stocks-2020\", \"Bonds-2018\", \"Bonds-2019\", and \"Bonds-2020\". This region also portrays the percentage distribution of each investment type across the years."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "JmpAMZ3tOfcyTSJQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.857, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart, including the labels and their corresponding colors for \"Stocks-2018\", \"Stocks-2019\", \"Stocks-2020\", \"Bonds-2018\", \"Bonds-2019\", and \"Bonds-2020\"."
+            }
+        ],
+        "id_raw": 359
+    },
+    {
+        "id": "GQj2vRosscWNZ88J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.772, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend is positioned in these coordinates."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "a4bWEYu7Tgh48qw6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the significance of the object at the coordinates [0.817, 0.071, 0.933, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the legend item \"Creativity - 2018\"."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "XRaVpB0LBoNkGxMa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.262, 0.687, 0.317, 0.710]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"30.3%\" is located there."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "PDmOj41aeI4DyUl4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice located at [0.482, 0.169, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Creativity - 2020}<r>[0.817, 0.191, 0.933, 0.211]</r> corresponds to this slice."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "8c5DtwGFt8viNcWu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the adjacent slices to {the slice}[0.482, 0.169, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.413, 0.516, 0.624, 0.886],[0.283, 0.146, 0.573, 0.516]] are adjacent to this slice."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "Fp4cjOnm6VqF4UEn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is associated with the slice at coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Creativity - 2018\" [0.309, 0.454, 0.352, 0.474] is associated with the slice."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "tv0skStzOwX2AD6o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a pie slice and corresponding text labels \"Creativity - 2018\" [0.309, 0.454, 0.352, 0.474] and \"14.7%\" [0.088, 0.411, 0.205, 0.431]."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "guxJLYJZkrF9B0Zh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.482, 0.495, 0.741, 0.826]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is part of a pie chart. It contains a slice colored with #ffcc99 and the associated autotext \"16.7%\" [0.736, 0.689, 0.907, 0.709] and label \"Budget Efficiency - 2019\" [0.598, 0.606, 0.642, 0.626]."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "mzLl5NBOGEGKGKhL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.413, 0.516, 0.624, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice along with the percentage text \"13.5%\" [0.526, 0.908, 0.643, 0.928] and its corresponding label \"Creativity - 2019\" [0.484, 0.725, 0.528, 0.745]."
+            }
+        ],
+        "id_raw": 360
+    },
+    {
+        "id": "UCOn2O0BCc5lDjA0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is written in the area defined by [0.308, 0.456, 0.352, 0.476] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Hotels\" is written in that area."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "LuqBhq6xrZm6JYfS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the bounding box with [0.831, 0.251, 0.859, 0.271] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That object is the legend handle for \"Casinos\"."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "DSwf6MY4Wu8Wk9T3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.257, 0.021, 0.706, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart subtitle that reads \"Visitor Distribution in Tourism and Hospitality Industry\"."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "r272GTZip6WAxJgD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart corresponds to {the 'Theme Parks'}[0.87, 0.281, 0.959, 0.301] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice that corresponds to 'Theme Parks' is the one with coordinates [0.283, 0.202, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "wwIKUXpg9I9kwSpl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the 'Hotels' pie slice}[0.223, 0.278, 0.482, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Fast-Food Chains' pie slice [0.23, 0.516, 0.482, 0.879] is adjacent to the 'Hotels' slice."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "dBe7HAFukcKjC0Eo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend label near the handle with coordinates [0.831, 0.282, 0.859, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"Theme Parks\" is near this handle."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "BfKRteoAHy7zgzb0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.431, 0.516, 0.632, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region belongs to the pie slice labeled as 'Bistros', containing the text \"13.0%\" [0.542, 0.904, 0.589, 0.924]. It lies mostly in the upper right quadrant of the pie chart."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "Er61dH9KtKXnxltj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area bounded by the coordinates [0.482, 0.147, 0.579, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this region corresponds to the 'Night Markets' slice of the pie chart with a percentage representation of \"5.0%\" [0.546, 0.109, 0.643, 0.129]."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "2XTz3heaDBhMUAbS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region defined by [0.23, 0.516, 0.482, 0.879] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie slice labeled as 'Fast-Food Chains' along with the text \"18.0%\" [0.168, 0.801, 0.285, 0.821], which signifies the percentage contribution of 'Fast-Food Chains' to the tourism and hospitality industry."
+            }
+        ],
+        "id_raw": 361
+    },
+    {
+        "id": "ytCFY0PgzIKP0eJi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.236, 0.305, 0.764, 0.33] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, named Artificial Intelligence and Automation Trends from 2010 to 2019."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "IMblUjZUMMVZtO3s",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.163, 0.299, 0.199, 0.319] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label for the year 2019."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "YmlkuTghxYShMy5E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.378, 0.352, 0.413, 0.372] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"14.9%\"."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "gRuOMUG3AZmmiwMo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.5, 0.654, 0.702, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.5, 0.654, 0.668, 0.924] is adjacent."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "MK8QWtmQTtkvE6Qo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.5, 0.654, 0.668, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2015\"}[0.585, 0.836, 0.628, 0.856] is located near the slice."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "VPdM4GSrBs49zFbH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2010-2019\" correspond to the slices with coordinates that range from [0.298, 0.365, 0.702, 0.942] to [0.5, 0.366, 0.659, 0.654]."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "cfJP3lKQ4S6gJH9L",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.298, 0.365, 0.702, 0.942]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes multiple slices of the pie chart with respective texts (\"2010\", \"2011\", ..., \"2019\") and associated percentages (from \"5.0%\" to \"14.9%\"). These slices represent the artificial intelligence and automation trends from the years 2010 to 2019."
+            }
+        ],
+        "id_raw": 363
+    },
+    {
+        "id": "PD5mouNy5nEkiHcK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at these coordinates [0.261, 0.021, 0.699, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Companies Adoption of Sustainable Business Models\"."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "TGOfTfOQwQAGYlGH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at these coordinates [0.744, 0.063, 0.991, 0.159] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "RuM2BpITzMmiWzjL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be seen at the coordinates [0.221, 0.146, 0.739, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "F7hvyO3gMmDAEbXs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with the slice {of the pie chart}[0.221, 0.278, 0.678, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Community Development\"<r>[0.358, 0.676, 0.402, 0.696]</r>."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "a4xV7rZdKsK0K4nZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item in the legend is related to the slice with coordinates [0.282, 0.146, 0.577, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item \"Equal Pay\"<r>[0.788, 0.131, 0.856, 0.151]</r> in the legend."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "1xdyYqIuu7tUAgvX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does \"Environmental Conservation\"<r>[0.788, 0.101, 0.986, 0.121]</r> from the legend refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at}<r>[0.48, 0.173, 0.739, 0.754]</r>."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "RJACCasIkunHHKST",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.221, 0.146, 0.739, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the main pie chart which contains three slices with labels for \"Community Development\"<r>[0.358, 0.676, 0.402, 0.696]</r>, \"Environmental Conservation\"<r>[0.609, 0.452, 0.653, 0.472]</r>, and \"Equal Pay\"<r>[0.421, 0.29, 0.464, 0.31]</r>, as well as their corresponding percentages."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "pNbm9VEL0Nm3css3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.744, 0.063, 0.991, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region where the legend is located. It consists of three items: \"Community Development\"<r>[0.788, 0.071, 0.967, 0.091]</r>, \"Environmental Conservation\"<r>[0.788, 0.101, 0.986, 0.121]</r>, and \"Equal Pay\"<r>[0.788, 0.131, 0.856, 0.151]</r>."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "KfLSc661794BwBOk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.282, 0.146, 0.577, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features a chart slice for \"Equal Pay\"<r>[0.282, 0.146, 0.577, 0.516]</r> and the corresponding percentage text \"20.0%\".<r>[0.343, 0.111, 0.411, 0.131]</r>."
+            }
+        ],
+        "id_raw": 364
+    },
+    {
+        "id": "e57eJpqVUy6HqKHk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.145, 0.021, 0.818, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the title, called \"Yearly Progress in Career and Workplace Development Trends from 2001 to 2008\"."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "0RsdeqZSlYBlVVOJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "5CrVjrRxjE8qJI8B",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.164, 0.452, 0.199, 0.472] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"8.0%\"."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "BZheeAgXv90tt533",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.282, 0.516, 0.482, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.292, 0.516, 0.482, 0.886], [0.224, 0.516, 0.482, 0.767]] are adjacent."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "ZBFhZbtG3BRZNftF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the title}[0.145, 0.021, 0.818, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2004\"}[0.397, 0.709, 0.441, 0.729] and {\"2005\"}[0.529, 0.705, 0.573, 0.725] are located near the title."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "4vefuAelLStRH8N1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2001\", \"2002\", \"2003\", \"2004\", \"2005\", \"2006\", \"2007\", and \"2008\" correspond to the slices with coordinates [0.241, 0.278, 0.482, 0.516], [0.223, 0.377, 0.482, 0.56], [0.224, 0.516, 0.482, 0.767], [0.292, 0.516, 0.482, 0.886], [0.48, 0.516, 0.69, 0.886], [0.482, 0.404, 0.741, 0.736], [0.482, 0.146, 0.729, 0.516], and [0.283, 0.146, 0.483, 0.516], respectively."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "cHcMPKY4QDORin0F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.516, 0.482, 0.767]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label \"2003\" {text}[0.319, 0.599, 0.362, 0.619] and describes the percentage \"10.0%\" {autotext}[0.188, 0.677, 0.223, 0.697]. This slice is depicted in the color '#99ff99'."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "9eer27DQhzIkxMdR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region [0.913, 0.103, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content in this region is the legend item \"2002\" with color '#66b3ff'."
+            }
+        ],
+        "id_raw": 367
+    },
+    {
+        "id": "1Z78RqOb606JUaAs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object with coordinates [0.824, 0.161, 0.987, 0.181] inside the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Political Representation\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "BD6ID7dPCh3WgQPI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the object at coordinates [0.785, 0.223, 0.813, 0.237] in the legend of the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents \"Freedom of Speech\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "pcoRjrkVkMt7F1Si",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.225, 0.021, 0.739, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labeled as \"Social Inequality and Rights Protection for High Income Group\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "KjebRqVCZjsnWMkx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color coded by {the legend color}[0.785, 0.133, 0.813, 0.147] match to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color matches to {the slice}[0.361, 0.516, 0.705, 0.886] labeled as \"Education\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "tT4PMaLFgpNeAImY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage is indicated by {the autotext}[0.721, 0.284, 0.794, 0.304] inside the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext indicates \"8.1%\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "Pm6oY7Zf0BwpBJ3N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie does {the handle color}[0.785, 0.223, 0.813, 0.237] in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.283, 0.146, 0.656, 0.516] labeled as \"Freedom of Speech\"."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "l6MYXpM8x1c2oewr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.243, 0.727, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the slice of \"Corruption\" [0.482, 0.243, 0.727, 0.516] with its corresponding autotext \"8.1%\" [0.721, 0.284, 0.794, 0.304]. The slice color is indicated in the legend [0.779, 0.063, 0.993, 0.249]."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "TEko2hfIo2SrypL8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within [0.361, 0.516, 0.705, 0.886] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers a pie slice represented \"Education\" [0.361, 0.516, 0.705, 0.886] along with its autotext of percentage \"24.3%\" [0.56, 0.897, 0.629, 0.917]."
+            }
+        ],
+        "id_raw": 368
+    },
+    {
+        "id": "8kv7A2BWtw8OWS7N",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at these coordinates [0.225, 0.021, 0.739, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Social Inequality and Rights Protection for High Income Group\"."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "3vTNPPndpSBJxASr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at these coordinates [0.824, 0.071, 0.907, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Income Gap\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "mzi6xPbBinnkmolf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.152, 0.301, 0.236, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.4%\" is at these coordinates."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "ejQP407shAGEJ3qH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.482, 0.243, 0.727, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.399, 0.741, 0.703], [0.283, 0.146, 0.656, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "WiEZrxGxfz8U1So5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the text \"Income Gap\"}[0.824, 0.071, 0.907, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"5.4%\"}[0.152, 0.301, 0.236, 0.321] and {\"13.5%\"}[0.765, 0.548, 0.929, 0.568] are located near the text \"Income Gap\"."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "1Qytghexvjyu4xhc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Income Gap\", \"Health Care\", \"Education\", \"Political Representation\", \"Corruption\", and \"Freedom of Speech\" correspond to the slices with coordinates [0.239, 0.278, 0.482, 0.516], [0.223, 0.386, 0.482, 0.843], [0.361, 0.516, 0.705, 0.886], [0.482, 0.399, 0.741, 0.703], [0.482, 0.243, 0.727, 0.516], and [0.283, 0.146, 0.656, 0.516] respectively."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "Jz1mr1oYyT9S2swW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region marked by these coordinates [0.779, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the chart's legend. It contains six legend items: \"Income Gap\", \"Health Care\", \"Education\", \"Political Representation\", \"Corruption\", and \"Freedom of Speech\", with their respective color indicators."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "qwdVdqeISdtKTP9c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.223, 0.146, 0.741, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart, which has slices representing different categories: \"Income Gap\", \"Health Care\", \"Education\", \"Political Representation\", \"Corruption\", and \"Freedom of Speech\". Each slice also has a percentage text indicating its proportion."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "639XaQgyJV826nYZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region enclosed by the coordinates [0.361, 0.516, 0.705, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers part of the pie chart, specifically the slice representing \"Education\". The slice also includes a percentage text \"24.3%\" indicating its share in the pie chart."
+            }
+        ],
+        "id_raw": 369
+    },
+    {
+        "id": "9S9MMy3BmN7ijxaa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is depicted at these coordinates [0.886, 0.251, 0.915, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Thai\" is included in this part of the chart."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "tJ1yG2Lr5pDdVff0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.332, 0.021, 0.631, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subtitle, called \"Global Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "5S2nsxvZnic7PWOt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item positioned at coordinates [0.847, 0.223, 0.874, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a legend handle for the cuisine type \"Japanese\"."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "oVVdFQ7eP5HyXftg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart corresponds to {the legend item \"American\"}[0.886, 0.071, 0.952, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice{\"American\"}[0.223, 0.278, 0.482, 0.685] corresponds to it."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "WNOwQcyhPutC3fCJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice \"Mexican\"}[0.411, 0.516, 0.589, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice \"Chinese\"}[0.482, 0.505, 0.741, 0.853] is adjacent to it."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "BN8fOhHg8O35mEcw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend handle in the chart corresponds to {the pie slice \"Japanese\"}[0.482, 0.238, 0.732, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend handle \"Japanese\"}[0.847, 0.223, 0.874, 0.237] corresponds to it."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "PjCN7QQK9vTsrvfo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects does the region with coordinates [0.841, 0.063, 0.993, 0.368] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains legend items and corresponding handles for various cuisine types: \"American\", \"Italian\", \"Mexican\", \"Chinese\", \"Indian\", \"Japanese\", \"Thai\", \"French\", \"Spanish\", \"Mediterranean\"."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "cgGg8MVkGGXcycGM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects contained within the region marked by [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It holds the primary pie chart. Each slice of the pie represents different types of cuisine and their corresponding percentages worldwide: \"American\", \"Italian\", \"Mexican\", \"Chinese\", \"Indian\", \"Japanese\", \"Thai\", \"French\", \"Spanish\", \"Mediterranean\". It gives a visual representation of the global gastronomy culture analysis."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "0gPNPw0fzC2VYckN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.847, 0.343, 0.987, 0.36]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the last item in the legend, labeled as \"Mediterranean\" with a corresponding handle in color '#ccd4cc'."
+            }
+        ],
+        "id_raw": 374
+    },
+    {
+        "id": "EIq2uRin3OX2Ufz9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object indicated at these coordinates [0.312, 0.021, 0.651, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which says \"Distribution of Academic Research Fields\"."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "s6mlgVQaobosw6Ee",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at these coordinates [0.821, 0.073, 0.849, 0.087] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend handle for \"Physics\"."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "KJFqtYUguHvP2avN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.323, 0.883, 0.375, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, that is the value \"20.0%\" on the pie chart."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "nbFAzzYfFbSG7rnd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to {the \"Physics\" slice}[0.241, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is adjacent to the {the Chemistry slice}[0.223, 0.377, 0.482, 0.712]."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "8TOXqMuzPYDJTBSY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pieces in the pie chart correspond to the legend items \"Biology\" and \"Philosophy\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "For \"Biology\", the corresponding piece is {the green slice}[0.262, 0.516, 0.544, 0.886]. For \"Philosophy\", the corresponding piece is {the yellow slice}[0.283, 0.229, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "D4ermiJ12yrpscBF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are located around {the \"Physics\" pie slice}[0.241, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"5.0%\"}[0.186, 0.296, 0.237, 0.316] and {\"Physics\"}[0.331, 0.392, 0.366, 0.412] are located near the \"Physics\" pie slice."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "wJs84ZAzLNX29bRI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.262, 0.516, 0.544, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the text \"20.0%\" [0.323, 0.883, 0.375, 0.903] and the label \"Biology\" [0.402, 0.712, 0.445, 0.732]. It corresponds to the green slice of the pie chart, which signifies the \"Biology\" field."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "Z8lF6JX3OOLfx1SC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region with coordinates [0.223, 0.377, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" [0.127, 0.541, 0.198, 0.561] and the text \"Chemistry\" [0.305, 0.525, 0.349, 0.545] are included in this region of the blue slice."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "bX9MJfBzbuXrqbxQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.283, 0.229, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the text \"3.0%\" [0.207, 0.216, 0.282, 0.236] and the labeling \"Philosophy\" [0.355, 0.348, 0.39, 0.368]. It refers to the yellow slice within the pie chart."
+            }
+        ],
+        "id_raw": 375
+    },
+    {
+        "id": "kkOswBWNevdBTjNY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinate [0.952, 0.101, 0.987, 0.121] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a legend item text representing the year \"2013\"."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "Hb7ESTG6ld6KJyZT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart showing the distribution of population migration and mobility trends from 2012 to 2019."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "ysPQb89gUsrHXJel",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.908, 0.063, 0.993, 0.309] in the figure refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "They refer to the location of the chart's legend."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "DTdMCNzEHMMrBamG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the legend item that corresponds to the slice {with the color \"#66b3ff\"}<r>[0.223, 0.377, 0.482, 0.605]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2013\"}<r>[0.952, 0.101, 0.987, 0.121]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "CljLQkoiBI6bCqH1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice associated with {the year \"2016\"}<r>[0.952, 0.191, 0.987, 0.211]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice associated with the year \"2016\" is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "vM88K6CPGgmWuoZB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the text of \"8.0%\"}<r>[0.605, 0.139, 0.64, 0.159]</r> refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"8.0%\" refers to the percentage of population migration for the {\"year 2017\"}<r>[0.952, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "cBY8QUJrUCslYZSS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.482, 0.516, 0.741, 0.754] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region in the chart contains a slice of the pie chart colored '#c2c2f0' that represents the year 2016 with a population migration percentage of 25.0%. The region also contains a percentage text of \"25.0%\" at coordinates [0.766, 0.47, 0.801, 0.49] and a year text of \"2016\" at coordinates [0.615, 0.487, 0.658, 0.507]."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "66l1cTxCm1NxLHQ9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.223, 0.146, 0.741, 0.886] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart representing population migration and mobility trends from 2012 to 2019, with individual slices labeled with years and percentages."
+            }
+        ],
+        "id_raw": 376
+    },
+    {
+        "id": "Li1NyOq9mFFMjL7h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at the given coordinates [0.233, 0.021, 0.731, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A subtitle stating 'Population Migration and Mobility Trends from 2012 to 2020' is located at the given coordinates."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "DAxjLW4PDshZgUGR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is at the coordinates [0.908, 0.063, 0.993, 0.608] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart is positioned at these coordinates."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "aWq7Xl08jHv7xQF0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is featured at these coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the chart where a Pie Chart is plotted."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "qPs17lmFfmugr45B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the legend items that correspond to the color #ffcc99?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2015\"[0.952, 0.161, 0.987, 0.181] and \"2015\"[0.952, 0.43, 0.987, 0.45] in the legend are associated with the color #ffcc99."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "MVZguJg0nOg9uBlE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located near the coordinates of the pie chart [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle with title 'Population Migration and Mobility Trends from 2012 to 2020'[0.233, 0.021, 0.731, 0.047] and the legend[0.908, 0.063, 0.993, 0.608] are near the pie chart."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "GlmvsBrQYUjglO1J",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slice at coordinates [0.482, 0.516, 0.725, 0.793] and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at these coordinates corresponds to the 2020 legend item[0.952, 0.58, 0.987, 0.6] in the chart."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "aeacbOdopc5tzsLg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.908, 0.063, 0.993, 0.608]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, with labels for different years and their colored markers. The region is located near the right side of the chart."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "8pWMAm5lKsKYYxOm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the segment of the chart enclosed by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart representation that visualizes the migration trends from 2012 to 2020."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "dLiXXHEBJy5FOEeL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region marked by the coordinates [0.233, 0.021, 0.731, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the subtitle of the chart, reading 'Population Migration and Mobility Trends from 2012 to 2020'."
+            }
+        ],
+        "id_raw": 379
+    },
+    {
+        "id": "I5GoQJFXWvFZpbw3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart? The title should be located around coordinates [0.375, 0.063, 0.625, 0.081]."
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Consumer Behavior Trend from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "c4zg7cUYPWc7XzQn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object displayed on the chart within the region [0.834, 0.154, 0.858, 0.168]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend item titled \"2018\"."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "41rqBQ1cqE5HkCVx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located around [0.475, 0.743, 0.505, 0.757] depicted in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a text label indicating the year \"2014\"."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "ikvtPYEGzJreMaep",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo what does the color represented by {the legend color handle}[0.808, 0.093, 0.827, 0.102] in the legend correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the 2015 pie slice}[0.5, 0.535, 0.684, 0.884] in the chart."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "wJwx2ptR8eNYOWeX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nLooking at the chart, which year's data is adjacent to {the text \"2019\"}[0.495, 0.313, 0.525, 0.327]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The years adjacent to \"2019\" are {2018}[0.578, 0.363, 0.607, 0.377] and {2020}[0.409, 0.345, 0.438, 0.359]."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "FvV2ey5FmXVxEKAS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segment in the pie chart corresponds to the legend item {2020}[0.834, 0.196, 0.858, 0.21]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie segment corresponding to the \"2020\" legend item is the slice located at {coordinates}[0.316, 0.186, 0.5, 0.535]."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "5geWDxMZOwI2JOFz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region with coordinates [0.5, 0.201, 0.704, 0.535]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the pie chart section for the year {2018}[0.578, 0.363, 0.607, 0.377], comprising 10.0% of the pie chart. The slice color matches the legend entry for 2018."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "H20xhyPI5KFXAzhR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the elements found within the coordinate region [0.26, 0.175, 0.74, 0.895] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers most of the pie chart, containing slices for every year from {2011}[0.261, 0.304, 0.5, 0.535] to {2020}[0.316, 0.186, 0.5, 0.535], each labeled with their respective year and percentage values, showing a gradual change over a decade."
+            }
+        ],
+        "id_raw": 381
+    },
+    {
+        "id": "zWMLyX5IJWLnMJcI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.339, 0.021, 0.624, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is a title called \"Internal Business Process Analysis\"."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "8VHUPYd2MMAQvoOm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.856, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"Production Process\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "XxPa3RRZEOqMExAL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at coordinates [0.767, 0.506, 0.897, 0.526] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"77.8%\"."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "QbZOrmoLI448QFOl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in close proximity to {the slice}[0.283, 0.146, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Production Process\"}[0.615, 0.506, 0.659, 0.526] and {\"77.8%\"}[0.767, 0.506, 0.897, 0.526] are in close proximity."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "fu4Ta2ujWAsQrgD0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are associated with the {legend}[0.812, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Sales Process\"}[0.856, 0.071, 0.95, 0.091] and {\"Production Process\"}[0.856, 0.101, 0.987, 0.121] are associated."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "pFjfgWXu2zYVYzWa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slices and the legend items on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Sales Process\" and \"Production Process\" are linked to the pie slices with coordinates [0.223, 0.278, 0.482, 0.753] and [0.283, 0.146, 0.741, 0.886] respectively."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "b202JPCR0HBwJ4cd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region given by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two pie slices, with the first slice {Sales Process}[0.223, 0.278, 0.482, 0.753] and its corresponding percentage {22.2%}[0.103, 0.506, 0.197, 0.526]. The second slice {Production Process}[0.283, 0.146, 0.741, 0.886] and its corresponding percentage {77.8%}[0.767, 0.506, 0.897, 0.526] are also in this region."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "l2nwbfEfV45wiMrM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you talk about the region specified by coordinates [0.812, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with two items. The first item is the {Sales Process}[0.856, 0.071, 0.95, 0.091] and the second item is the {Production Process}[0.856, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 382
+    },
+    {
+        "id": "ORCJPtSgviFXTtlj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title or main object in the chart with coordinates [0.322, 0.021, 0.641, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is 'Value Chain Visualization and Analysis'."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "MLy61JYVF9ukyz17",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe region [0.842, 0.193, 0.897, 0.213], what is the text there?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is 'Service'."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "W42cJbIJRfJ8wFwi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the labeled object at [0.846, 0.071, 0.967, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Inbound Logistics'."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "4ZNVghqJBgg9edsT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich labels in the legend are associated with the slices colored '#ff9999' and '#66b3ff' respectively?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {'Inbound Logistics'}[0.846, 0.071, 0.967, 0.091] and {'Operations'}[0.846, 0.101, 0.922, 0.121] correspond to the slices colored '#ff9999' and '#66b3ff', respectively."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "KgfEZpP9uXU3IDi5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two pie slices are adjacent to {the slice 'Outbound Logistics'}[0.482, 0.516, 0.708, 0.87]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {'Operations'}[0.242, 0.516, 0.556, 0.886] and {'Marketing and Sales'}[0.482, 0.167, 0.741, 0.695] are adjacent to it."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "ZxxO3j7uSsBp13fz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice's autotext is positioned closest to {the legend}[0.801, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext '19.2%' from the slice {'Service'}[0.283, 0.146, 0.567, 0.516] is positioned closest to the legend."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "Nc5kB8wJaouivvTf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region enclosed by the coordinates [0.242, 0.516, 0.556, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region primarily contains a pie slice labeled 'Operations', of color '#66b3ff'. Within this region, the text '23.5%'[0.283, 0.874, 0.359, 0.894] is located near the top right edge."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "BP516KqhM14GaLPH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region surrounded by the coordinates [0.223, 0.278, 0.482, 0.656]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, we find a pie segment labeled 'Inbound Logistics' with a color code of '#ff9999'. The segment's corresponding text of '17.3%'[0.079, 0.443, 0.2, 0.463] is located towards the left."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "ObmczlaVHshlxgY7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears inside the region [0.482, 0.167, 0.741, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region primarily includes the pie segment labeled 'Marketing and Sales', color-coded as '#ffcc99'. The accompanying text '27.7%'[0.748, 0.361, 0.889, 0.381] is positioned near the right border of the region."
+            }
+        ],
+        "id_raw": 383
+    },
+    {
+        "id": "GJ59lvRVTQoXlsoH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the coordinates [0.307, 0.466, 0.351, 0.486] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"TV News\"."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "WVnjK8GcM8IJG07a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.348, 0.66, 0.392, 0.68] displayed in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Online News\"."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "0Wn5pfdxfgM9wRx4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart shows at the coordinates [0.442, 0.913, 0.482, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"11.9%\"."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "WWvd77I58Axxj9mu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the {legend}[0.9, 0.221, 0.961, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice<r>[0.482, 0.215, 0.717, 0.516]</r> associated with the legend \"Podcasts\"."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "1X88wAEDwvwo8L5K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich segments are adjacent to the {slice}[0.482, 0.215, 0.717, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices<r>[0.482, 0.361, 0.741, 0.61]</r> and <r>[0.482, 0.146, 0.632, 0.516]</r> are adjacent to it."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "K39nrURIhXKKQQit",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend matches the slice at {the slice}[0.387, 0.516, 0.576, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Radio\" matches."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "QmWCrlEi7q4nd6Oo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.482, 0.146, 0.741, 0.886] of the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region accommodates parts of the slices of the pie chart- {\"TV News\"}[0.223, 0.278, 0.482, 0.638], {\"Social Media\"}[0.482, 0.516, 0.732, 0.861], {\"Blogs\"}[0.482, 0.361, 0.741, 0.61], {\"Podcasts\"}[0.482, 0.215, 0.717, 0.516], {\"Forums\"}[0.482, 0.146, 0.632, 0.516] and {\"Viral Video\"}[0.283, 0.146, 0.494, 0.516]. The region touches the five categories of media and spans more than half of the pie chart."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "xbO6diJslnMvB79S",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.223, 0.146, 0.717, 0.861]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices of \"TV News\" [0.223, 0.278, 0.482, 0.638], \"Online News\" [0.237, 0.516, 0.482, 0.861], \"Radio\" [0.387, 0.516, 0.576, 0.886], \"Social Media\" [0.482, 0.516, 0.732, 0.861], and \"Blogs\" [0.482, 0.361, 0.741, 0.61], along with their related percentage texts."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "VXK9e9LouLPAbfDB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region described by [0.483, 0.146, 0.632, 0.610] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains segments representing \"Blogs\" [0.482, 0.361, 0.741, 0.61] and \"Podcasts\" [0.482, 0.215, 0.717, 0.516], and \"Forums\" [0.482, 0.146, 0.632, 0.516] along with their relevant percentage text."
+            }
+        ],
+        "id_raw": 384
+    },
+    {
+        "id": "VOaxx4SlrmA4t3lF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.956, 0.071, 0.985, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"CEO\"."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "pvOIPbj893c4ngpA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is positioned at coordinates [0.917, 0.133, 0.945, 0.147] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item handle in the color '#99ff99'."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "VFlH1SuKDdSZ3EUc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat occupies the space defined by coordinates [0.223, 0.146, 0.741, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart about Experience Distribution Among Senior Management Roles."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "uHyZfFD3MUx8o6MA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what slice's text is in close proximity to {the autotext}[0.176, 0.604, 0.205, 0.624]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the text \"CEO\" [0.309, 0.56, 0.353, 0.58] is near to the given autotext."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "DzjagD5RsaC8FYeI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, which color handle is associated with {the label 'CTO'}[0.956, 0.101, 0.985, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle of 'CTO' in the legend is '#66b3ff' [0.917, 0.103, 0.945, 0.117]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "582DpmvaeAOlOtZo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice of the chart corresponds with {the legend item 'HR'}[0.956, 0.191, 0.976, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponding to 'HR' has coordinates [0.283, 0.157, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "m3OyeyheCRHYneWW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region demarcated by [0.385, 0.516, 0.68, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with the color '#66b3ff' representing the role 'CTO' with the percentage of \"20.0%\" [0.551, 0.901, 0.58, 0.921]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "QUanDniTQGbvrWOF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe details within the region defined by coordinates [0.419, 0.146, 0.729, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a part of the pie chart, specifically the slice associated with 'COO', having a percentage of \"24.0%\" [0.621, 0.151, 0.652, 0.171]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "jUudDOFX6GcCH5qv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located within the region [0.223, 0.157, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart. The slice is related to 'HR' and is represented by the percentage \"10.0%\" [0.311, 0.161, 0.331, 0.181]."
+            }
+        ],
+        "id_raw": 386
+    },
+    {
+        "id": "lbeyRpXcixLOaVaI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object situated at these coordinates [0.277, 0.021, 0.686, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle for the chart, titled \"GDP and Unemployment Rate from 2008 to 2016\"."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "7Z0wyZZQuIk0EFvz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The central pie chart is present at these coordinates."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "OPauKsGIKIKDJbpr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat significant object is found at coordinates [0.908, 0.063, 0.993, 0.338] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart legend with entries ranging from the years 2008 to 2016."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "UurBPw5e0Gb2YTKo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart legend entries have the same color coinciding with {a pie slice}[0.238, 0.516, 0.482, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry {\"2010\"}[0.952, 0.131, 0.987, 0.151] has the same color as the slice."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "YQhb3i65bmtkQmDF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the pie chart slices corresponding to {legend entry \"2013\"}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {\u201c2013\u201d}[0.482, 0.516, 0.738, 0.799] corresponds to the legend entry \"2013\"."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "HaGTvI76alTXVmcO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend}[0.908, 0.063, 0.993, 0.338] and the {pie chart}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entries each correspond to a specific pie slice and color within the chart."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "IquerrAUYf5hREH5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region between the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises of a pie chart having multiple slices, each corresponding to different years from 2008 to 2016. Each slice has an associated percentage and year text inside it."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "buYM4O417eUbJm4U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region depicted by coordinates [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. It consists of color-coded lines corresponding to different years from 2008 to 2016, each with a text label describing the year."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "FGUDDudG47fnnHBS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region within coordinates [0.482, 0.29, 0.741, 0.565]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a significant slice of the pie chart associated with the text \"2014\" [0.61, 0.448, 0.654, 0.468] and the percentage \"12.6%\" [0.757, 0.401, 0.792, 0.421]."
+            }
+        ],
+        "id_raw": 389
+    },
+    {
+        "id": "JLDwKSjwUHIzhxmN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary text description located at [0.277, 0.021, 0.686, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle: \"Revenue Distribution in Culture and Arts Industry\"."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "La6ReZRhLdoWlEnQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.902, 0.071, 0.956, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the category \"Theatre\"."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "zFiZZOtLRVviut48",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data slice is indicated by the coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the \"Theatre\" data slice."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "SJ5ATCJuPaA7XOOq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend item { \"Theatre\" }[0.902, 0.071, 0.956, 0.091] and the chart slice items?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item { \"Theatre\" }[0.902, 0.071, 0.956, 0.091] corresponds to the chart slice with coordinates { \"Theatre slice\" }[0.223, 0.278, 0.482, 0.712]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "c9ZJYbMcyYyagwXh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is associated with the slice { \"Literature\" }[0.464, 0.516, 0.652, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding text is {\"12.5%\"} [0.572, 0.892, 0.64, 0.912]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "gExgmJ5aa5zqiT3i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend item {\"Music\"}[0.902, 0.101, 0.942, 0.121] directly connect to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Music\"}[0.902, 0.101, 0.942, 0.121] corresponds to the chart slice with coordinates [0.262, 0.516, 0.482, 0.885]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "rGHfWekEGqLQGGcX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the \"Theatre\" slice in the pie chart. It includes the label \"Theatre\" at [0.305, 0.49, 0.348, 0.51]  and the percentage \"20.0%\" at [0.143, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "vLeNou3GoPmGhycJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the content within the region [0.262, 0.516, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the \"Music\" slice of the pie chart, which includes the label \"Music\" at [0.38, 0.696, 0.424, 0.716] and the corresponding percentage \"15.0%\" at [0.295, 0.855, 0.335, 0.875]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "cyts1Z6KtkJ9GIWl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat essential elements can be found in the region [0.464, 0.516, 0.652, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the \"Literature\" slice. It includes the percentage label \"12.5%\" at [0.572, 0.892, 0.64, 0.912] and the slice label \"Literature\" at [0.509, 0.717, 0.553, 0.737]."
+            }
+        ],
+        "id_raw": 390
+    },
+    {
+        "id": "WlabnwejXqMGDudm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located within these coordinates [0.315, 0.021, 0.649, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which says \"Education Resource Distribution in 2020\"."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "DfLNjH9zaz9g1WAW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated at these exact coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart showing the distribution of education resources."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "mBntCj0VCsDI2Jdh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at this specific coordinates [0.802, 0.063, 0.993, 0.129] according to the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "x1cdNm5YGAmuIFpB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the text \"Textbooks\"}[0.345, 0.655, 0.388, 0.675] according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with these coordinates}[0.223, 0.278, 0.628, 0.886]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "XWelgRix76YLPiiL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart slice represented by coordinates [0.283, 0.146, 0.741, 0.822] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the E-learning Platforms entry}[0.847, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "pS4r2blK8P1ECI3l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located inside {this slice}[0.223, 0.278, 0.628, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labeled as {\"45.7%\"}[0.201, 0.779, 0.27, 0.799]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "lTthEZzGeYQZf0yQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the characteristics of the region with these coordinates [0.223, 0.278, 0.628, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there is a pie chart slice labeled as \"Textbooks\", shown with a percentage of \"45.7%\", represented by the coordinates [0.201, 0.779, 0.27, 0.799]. This region corresponds to the reddish portion of the pie chart according to the legend."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "nB0vZdbN6Bm5uRpS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat about the region inside the coordinates [0.283, 0.146, 0.741, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the blue portion of the pie chart representing E-learning Platforms, which holds a greater share of 54.3% as given by the text [0.693, 0.233, 0.834, 0.253]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "nH8Wxfnni5MGidfW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with the following coordinates [0.802, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a legend that lists the categories displayed in the pie chart: \"Textbooks\" with coordinates [0.847, 0.071, 0.916, 0.091] and \"E-Learning Platforms\" with coordinates [0.847, 0.101, 0.987, 0.121]."
+            }
+        ],
+        "id_raw": 391
+    },
+    {
+        "id": "G85q4haNe16oAccY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label at coordinates [0.83, 0.071, 0.932, 0.091] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend item for \"Population (%)\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "SmdD0goOtIyszLHY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object at coordinates [0.184, 0.021, 0.78, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, named \"Comparison of Future Scenarios: Urbanization vs Intelligent Automation\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "DGR6NTQh1aaEyfeE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element in the coordinates [0.326, 0.907, 0.432, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"26.7%\" associated with a pie slice in the chart."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "ISPTMKfJm7sFO6lY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice with color '#66b3ff'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Technology (%)\" corresponds to the pie slice with color '#66b3ff'."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "kjkHv2LbLyt8atMQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color code corresponding to the legend item {Economic Stability (%)}<r>[0.83, 0.221, 0.987, 0.241]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code corresponding to the legend item \"Economic Stability (%)\" is '#99ff99'"
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "jg57r2iHwtMOIT3N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is correlated with {the legend item \"Population (%)\"}<r>[0.83, 0.161, 0.932, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices at coordinates [0.223, 0.278, 0.482, 0.712] and  [0.482, 0.151, 0.701, 0.516] correspond with the legend item \"Population (%)\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "IXbg63mbUicLbW41",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.482, 0.151, 0.701, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with the color code '#ff9999', text label \"Population (%)\", and autotext indicating a percentage of \"13.3%\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "HnOcSY9TfunoG8dS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat region in the chart is bound by the coordinates [0.419, 0.146, 0.527, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice with the color code '#66b3ff', the text label \"Technology (%)\", and autotext indicating a percentage of \"6.7%\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "LVoLeslrEEKhkZtp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region  [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored '#ff9999', a text label \"Population (%)\", and an autotext of \"20.0%\"."
+            }
+        ],
+        "id_raw": 392
+    },
+    {
+        "id": "osoJf3eDBHpQ5sHh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.286, 0.021, 0.677, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, named Distribution of Focus in Urban Planning Sectors."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "SaifzbuQnjdXNv0M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.765, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "QLIWCVvByQdBFZd5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found within the coordinates [0.628, 0.157, 0.735, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"45.0%\"."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "XS37M0kPtGtcKBiK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {Community Development}[0.809, 0.071, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Community Development} corresponds to the slice with coordinates [0.223, 0.278, 0.722, 0.886]."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "rCo1TCGPwUO4dHQb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located close to the slice {Urban Planning}[0.283, 0.146, 0.741, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"45.0%\" [0.628, 0.157, 0.735, 0.177] is located near the {Urban Planning} slice."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "zI1my1Aj2mDPYPVY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.283, 0.146, 0.741, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.223, 0.278, 0.722, 0.886]] is adjacent."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "nswrsSiFzJxdNg9E",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region found within coordinates [0.223, 0.278, 0.722, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region primarily consists of a slice labeled \"Community Development\" [0.38, 0.696, 0.423, 0.716] and an accompanying text \"55.0%\" [0.157, 0.855, 0.335, 0.875]. This particular area is the bigger slice of the pie chart and represents the sector of Community Development in Urban Planning."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "p6FwzXpeauOGyn34",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.146, 0.741, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice labeled \"Urban Planning\" [0.54, 0.316, 0.584, 0.336] and associated text \"45.0%\" [0.628, 0.157, 0.735, 0.177]."
+            }
+        ],
+        "id_raw": 396
+    },
+    {
+        "id": "7fYgCH1MN0H8DLuT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main caption for the chart, given the coordinates [0.353, 0.021, 0.61, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main caption of the chart is \"Popularity of Different Cuisines\"."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "DS51APBnB8pAsGrh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be identified at coordinates [0.685, 0.22, 0.724, 0.24]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates is the text \"27.0%\"."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "xMpAC8L144zwq5gY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.941, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"France\" can be found at these coordinates in the chart."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "PKM03XJ3HsllIQzq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the text at coordinates [0.685, 0.22, 0.724, 0.24] and the slice at [0.467, 0.146, 0.741, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"27.0%\" indicates the popularity percentage of the corresponding cuisine which is {China}[0.467, 0.146, 0.741, 0.542]."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "U3Aqep07px881vXI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart does the {legend item \"France\"}[0.941, 0.161, 0.987, 0.181] coordinate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"France\" refers to the slice of the pie chart at coordinates [0.283, 0.146, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "Cw5ymQE8QSXOo7xZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie corresponds to the {legend entry labeled \"India\"}[0.941, 0.101, 0.975, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"India\" corresponds to the slice of the pie at coordinates [0.315, 0.516, 0.74, 0.886]."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "pwPzujq1Iqq4SO4v",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.762, 0.729, 0.782, 0.749]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the percentage text \"35.0%\" associated with the pie slice that represents the popularity of a cuisine."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "P2tdR6CjJ04Rlnjt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is notable in the region [0.308, 0.142, 0.354, 0.162]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the text \"13.0%\" can be seen, which represents the popularity percentage of a certain cuisine."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "bl4e4VOL1h5AAXwg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_197.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.52, 0.71, 0.564, 0.73] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the label \"India\" marked on the pie chart."
+            }
+        ],
+        "id_raw": 397
+    },
+    {
+        "id": "IynzsjlRS2rNje5X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the axes of the chart."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "GgeKIK8BS9IxKY5q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies at the coordinates [0.361, 0.021, 0.602, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Gastronomy Culture Analysis\" is located there."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "JwvhxOIFk0MSVdoU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.923, 0.071, 0.966, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend item with the label \"Indian\"."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "83CwRfV2pA9L6NYP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice that corresponds to {the legend item \"Thai\"}[0.923, 0.101, 0.953, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding pie slice color is \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "OVh1u5cihm3XW8Lq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage related to {the pie slice}[0.437, 0.146, 0.741, 0.559]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The related percentage is \"29.6%\"."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "QrPVlXF56fH63MUU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the legend item labeled \"Unknown\"}[0.923, 0.191, 0.987, 0.211] relate to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice colored \"#c2c2f0\" and accounting for \"11.1%\" of the chart."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "CtdrU9VaMxoO4BPr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region enclosed by the coordinates [0.482, 0.146, 0.739, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored \"#99ff99\" representing \"French\" cuisine. It also contains the text \"14.8%\" at coordinates [0.72, 0.73, 0.766, 0.75]."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "F4TUPv1BKwdhNwCv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the pie slices in the chart along with their respective labels and percentages."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "LLj8Hl0kXcFtxzDO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_2_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the objects found in the region [0.878, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, with all the labels and the corresponding colored markers."
+            }
+        ],
+        "id_raw": 398
+    },
+    {
+        "id": "tGj4KgR0jPevH0Dt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text exists within the coordinates [0.17, 0.405, 0.206, 0.425] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.2%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "iUyrUp64y56YXrzl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.103, 0.941, 0.117] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle for the year 2011, it is a blue colored block attached to the legend."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "IBhbC9NYRU4SuGFc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does coordinates [0.325, 0.021, 0.638, 0.047] in the chart define?"
+            },
+            {
+                "from": "gpt",
+                "value": "It defines the subtitle \"AI/ML Applications from 2010 to 2014\"."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "jKO44fBuAqCPUQEJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.223, 0.278, 0.482, 0.588]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.228, 0.516, 0.482, 0.869] is adjacent."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "3KJaIdZa454gOhdv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color handle corresponds to {the text}[0.952, 0.191, 0.987, 0.211] in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle with coordinates [0.913, 0.193, 0.941, 0.207], that represents the year 2014, corresponds to the text."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "zqYGcpQkI6yCD8Qn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to {the pie slice}[0.482, 0.239, 0.741, 0.733]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"23.4%\" corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "ItTqYMJIGLRwo4gp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart of five different colored slices representing the data from years 2010 to 2014. Each slice contains the percentage value and corresponding year text within."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "HhOrqXGUESZsDuhU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend, which includes color handles and the corresponding years for all slices from the pie chart."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "ARyMSD53YMW6aUHO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_0.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the major feature within the coordinates [0.482, 0.516, 0.692, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that represents the data for the year 2012."
+            }
+        ],
+        "id_raw": 400
+    },
+    {
+        "id": "WgseGUZF2k4ktPCO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.148, 0.604, 0.205, 0.624] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is \"30.0%\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "sjINrI8AYclUT8cq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.911, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text \"Distraction\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "910Wjs22lPkDU6Oq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.518, 0.3, 0.562, 0.32] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the label \"Boredom\" associated with a pie section in the pie chart."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "mNxYHsxeqWUGc96b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which color in the legend corresponds to the \"Focused\" behavior?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the behavior \"Focused\" corresponds to {the color}[0.872, 0.073, 0.9, 0.087] represented as \"#ff9999\" in the legend."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "U0gWadnQDlWYI6ZR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie section is adjacent to {the pie section labeled \"Boredom\"}[0.419, 0.146, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie sections labeled as {\"Focused\"}[0.223, 0.278, 0.482, 0.859] and {\"Calm\"}[0.283, 0.157, 0.482, 0.516] are adjacent to \"Boredom\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "oI1ANpVOXPBInbg0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the label at coordinates [0.377, 0.318, 0.421, 0.338] correlate with any legend item on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the label \"Calm\" at these coordinates correlates with {the legend item \"Calm\"}[0.911, 0.161, 0.947, 0.181]."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "NGdjOmQkBwrGrVIJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between coordinates [0.223, 0.278, 0.482, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a pie section of the chart labeled \"Focused\" with the text \"30.0%\" [0.148, 0.604, 0.205, 0.624], and the color of this pie section is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "HkJMzyyrzVPD0lZJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.867, 0.063, 0.993, 0.189] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the chart legend with entries and their corresponding colors represented as handles for behaviors: \"Focused\", \"Distraction\", \"Boredom\", and \"Calm\". The labels are positioned at [0.911, 0.071, 0.968, 0.091], [0.911, 0.101, 0.987, 0.121], [0.911, 0.131, 0.974, 0.151], and [0.911, 0.161, 0.947, 0.181] respectively, and the handles are at [0.872, 0.073, 0.9, 0.087], [0.872, 0.103, 0.9, 0.117], [0.872, 0.133, 0.9, 0.147], and [0.872, 0.163, 0.9, 0.177]."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "D6Bl8t6xDj3ut5KQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.419, 0.146, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts a section of the pie chart, labeled as \"Boredom\" [0.518, 0.3, 0.562, 0.32], and includes the percentage \"20.0%\" [0.588, 0.128, 0.651, 0.148]. It is colored as \"#99ff99\"."
+            }
+        ],
+        "id_raw": 403
+    },
+    {
+        "id": "ZSSmwt9OCq2b0fBs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at those coordinates is \"2015\"."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "RdJiIbc1n1SVjdQS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the point representing the center of the pie chart."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "zmYypa4NAhFX6r1U",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.195, 0.318, 0.230, 0.338] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"6.9%\" represents the percentage of global usage of financial technology and digital currency in the year 2015."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "7chUXugMm5h3XPWN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.223, 0.419, 0.482, 0.651] and {the legend item}[0.952, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"2016\"."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "AsgZlJL9EYmqdIDe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is closest to {the slice}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" [0.326, 0.403, 0.361, 0.423] is closest."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "16KqoDngxMSYLpqZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation of {the slice}[0.24, 0.516, 0.482, 0.859] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representation in the legend is the color '#99ff99' [0.913, 0.133, 0.941, 0.147]."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "RtMamhDSLzwPDZQo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire pie chart representing global usage of financial technology and digital currency from 2015 to 2021. It includes multiple pie slices with different colors, each representing a specific year."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "vwP4WJaVllB9rjpN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within coordinates [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes labels for years from 2015 to 2021 and corresponding color handles."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "76DJjGBcAgQRLPGk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within coordinates [0.24, 0.516, 0.482, 0.859] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates lies a slice of the pie representing the year 2017 and shows \"12.9%\" [0.244, 0.792, 0.279, 0.812] of usage. It is colored '#99ff99' in the legend [0.913, 0.133, 0.941, 0.147]."
+            }
+        ],
+        "id_raw": 404
+    },
+    {
+        "id": "jjWUIk7lv6vvpQIl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the pie chart at coordinates [0.324, 0.021, 0.639, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Patient Visits in Various Medical Fields\"."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "Dqxz570OfkYKz5Gq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.913, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend text for \"Cardiology\"."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "xcata8w2l2uD14fW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located at the coordinates [0.757, 0.612, 0.824, 0.632] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"27.5%\"."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "Dmaipln0GuTnKkhW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice's {autotext} [0.355, 0.905, 0.426, 0.925] is located in the vicinity of the {text} [0.429, 0.724, 0.473, 0.744]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing \"Neurology\" is nearby."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "RseiUcxScbWBecNO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the slice with the {autotext} [0.602, 0.137, 0.666, 0.157] correlate with in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice correlates with the legend \"Dentistry\"."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "K42wcgBdHuDLB62k",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat corresponds to the legend item \"Pediatrics\" in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with {autotext} coordinates [0.757, 0.612, 0.824, 0.632] corresponds to the \"Pediatrics\" legend item."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "IfRYz3HHbVviUMy2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.324, 0.021, 0.639, 0.047] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the title \"Patient Visits in Various Medical Fields\". It captures a theme of the chart and is positioned at the top of the figure."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "7VIlvDhhl8bQJ7AV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible inside the region with coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly houses the actual pie chart that displays patient visits in various medical fields namely \"Cardiology\", \"Neurology\", \"Pediatrics\", \"Dentistry\", and \"Radiology\"."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "MxhnpNxeGEmMidli",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be seen within the region defined by [0.868, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend items for the pie chart, specifically labels for \"Cardiology\", \"Neurology\", \"Pediatrics\", \"Dentistry\", and \"Radiology\" along with their corresponding color codes."
+            }
+        ],
+        "id_raw": 405
+    },
+    {
+        "id": "W9dPq9YCXkLHcFgq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.893, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "pyUxkPEaQvLoi2gI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.937, 0.071, 0.97, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Male\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "3lTp9ja8jLvDpsrZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice of the pie chart representing \"Under 18\" category."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "lKEmlzwLigMI4UBr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat age group does the slice {at}[0.223, 0.278, 0.482, 0.712] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the \"Under 18\" age group."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "sSNNIUVn3yRx2cJF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.482, 0.232, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at}[0.419, 0.146, 0.648, 0.516] is adjacent to it."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "VVTwr9ZqLJXB03FG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which gender does the handle {at}[0.898, 0.073, 0.926, 0.087] in the legend corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle corresponds to \"Male\" gender."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "dBbDVdtwe4s1SL0q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.287, 0.238, 0.482, 0.705]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart in the color \"#ff9999\", with an autotext displaying \"20.0%\" at coordinates [0.268, 0.523, 0.268, 0.523], indicating the percentage of males in the \"Under 18\" category."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "g6DpGiJTA2qKMRVh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within [0.482, 0.293, 0.676, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart with a light green color \"#99ff99\". It also contains the autotext \"30.0%\" at coordinates [0.694, 0.547, 0.694, 0.547], indicating 30% of the males in the \"25-34\" age group."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "mmZh3ipvU8v7udWX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.339, 0.516, 0.563, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart in the color \"#66b3ff\", and an autotext saying \"25.0%\" at coordinates [0.44, 0.815, 0.44, 0.815], denoting 25% of the \"18-24\" age group is covered by male population."
+            }
+        ],
+        "id_raw": 406
+    },
+    {
+        "id": "eD6kchCZhwuhx6qa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.245, 0.021, 0.718, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the chart's subtitle which is \"Distibution of time spent on different activities in a week\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "JMW8MMW995MCwGqB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is in the location specified by these coordinates: [0.899, 0.071, 0.931, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The element at those coordinates is the text labeled as \"Gym\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "DrlBaLjFC9IdlULM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does these coordinates [0.223, 0.278, 0.482, 0.548] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates represent a slice of the pie chart, specifically \"Gym\"."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "CK0yNpNnSiZkiesi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does the legend item {the label \"Shopping\"}<r>[0.899, 0.191, 0.964, 0.211]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Shopping\" corresponds to the slice with these coordinates [0.333, 0.146, 0.591, 0.516]."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "W0uhyxU3RBUbYqHU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text \"12.5%\" and the segment of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.5%\" denotes the percentage representation of the {the slice \"Gym\"}<r>[0.223, 0.278, 0.482, 0.548]</r> of the total chart."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "gORt5rI5eLfWPaNy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item directly corresponds to {the pie slice}[0.224, 0.516, 0.504, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"Eating out\"}<r>[0.899, 0.101, 0.97, 0.121]</r> directly corresponds to the given slice of the pie chart."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "nPodN0hNXDXMA2Y8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with the coordinates [0.223, 0.278, 0.482, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart, specifically \"Gym\", which takes up 12.5% as indicated by the {corresponding text}<r>[0.177, 0.384, 0.21, 0.404]</r>."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "TlPLQG9abn60FZPb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details of the region [0.482, 0.516, 0.63, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice, indicating the \"Watching TV\" activity and corresponding to 8.3% of the total data, as noted by the nearby {percentage text}<r>[0.579, 0.889, 0.666, 0.909]</r>."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "WJZ5cX4GgJQtVA2b",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.399, 0.163, 0.741, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart slice for \"Horse Riding\", showing it as a segment taking up 33.3% of the total based on the {accompanying text}<r>[0.766, 0.47, 0.854, 0.49]</r>."
+            }
+        ],
+        "id_raw": 407
+    },
+    {
+        "id": "HkGzJRmy1jXn5wJl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the title labeled at coordinates [0.31, 0.021, 0.653, 0.047] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "9uriu40TIFT4GCtB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object labeled at coordinates [0.918, 0.071, 0.963, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend item \"Coffee\"."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "GZcQt4HSVxWDQwuU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.155, 0.438, 0.201, 0.458] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"16.9%\"."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "SyOsTNq9NHnLeI0Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the slice at {coordinates}[0.239, 0.516, 0.516, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices at {coordinates}[[0.223, 0.278, 0.482, 0.647], [0.482, 0.516, 0.733, 0.883]] are adjacent to it."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "mEg0D8iWOk5aHvdM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is linked to the slice at {coordinates}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Chocolate\", located at {coordinates}[0.918, 0.191, 0.987, 0.211], is linked to the slice."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "hAGBaLV1J0jBpwLI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near to {the legend}[0.918, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"Wine\" and \"Beer\" located at {coordinates}[[0.563, 0.671, 0.608, 0.691],[0.612, 0.465, 0.656, 0.485]] are located near the legend."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "iDOYMqzTiBBtd0bx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.482, 0.516, 0.733, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice of the pie chart that represents Wine [0.482, 0.516, 0.733, 0.883] and the slice's corresponding text \"19.1%\" located at coordinates [0.672, 0.809, 0.707, 0.829]."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "sXOhDaCnSsSu26mJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.223, 0.278, 0.482, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice representing \"Coffee\" [0.223, 0.278, 0.482, 0.647], and the slice's corresponding text \"16.9%\" located at coordinates [0.155, 0.438, 0.201, 0.458]."
+            }
+        ],
+        "id_raw": 409
+    },
+    {
+        "id": "MNW5V7FxyHwS3Bxe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found at the provided coordinates [0.194, 0.317, 0.229, 0.337]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text found at these coordinates is \"6.8%\"."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "A2dGH3xw8BLuzfVK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the location [0.952, 0.221, 0.987, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "At that location, the text \"2010\" can be found."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "CpANkxrp0Stq8XKM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at position [0.593, 0.728, 0.628, 0.748] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text \"2009\"."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "oUAEqIszCFTHwbOJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice color in the pie chart matches the legend item {2009}[0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice color matching the legend item \"2009\" is {#c2c2f0}[0.393, 0.516, 0.541, 0.886]."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "PIJyf2OwCAd6HK46",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the legend item {2013}[0.952, 0.311, 0.987, 0.331] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2013\" corresponds to the slice {#8a2be2}[0.475, 0.146, 0.661, 0.516]."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "gze68The9uRop32Q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend item {2011}[0.952, 0.251, 0.987, 0.271] refer to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2011\" refers to the slice {#00ff7f}[0.482, 0.497, 0.741, 0.747]."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "nxcQwy2NxjbzJbqx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.741, 0.631, 0.788, 0.651]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"11.6%\" which is part of the pie chart label indicating the percentage representation of the data from year 2011."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "yhJONMip6CrdAXdk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the area [0.323, 0.139, 0.358, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the given area, there is the text \"13.5%\", which is a label indicating the calculated percentage for the pie slice representing the data from 2014."
+            }
+        ],
+        "id_raw": 412
+    },
+    {
+        "id": "C38k3TqMoovtWB0L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at the coordinates [0.274, 0.021, 0.689, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, labeled 'Renewable Energy Investment from 2016 to 2020'."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "vxK3ezG4Ef8etc2x",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies the coordinates [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "PgcY7wGaz0FYJFNC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the square [0.164, 0.448, 0.2, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.7%\"."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "8ilqad1MGI7VEdHK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices touch {the slice}[0.482, 0.155, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.501, 0.741, 0.881], [0.283, 0.146, 0.538, 0.516]] are linked."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "mvmBGeKD4N5AAR7H",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are close to the {subtitle}[0.274, 0.021, 0.689, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Texts {\"2016\"}[0.306, 0.474, 0.35, 0.494] and {\"17.7%\"}[0.164, 0.448, 0.2, 0.468] are adjacent to the subtitle."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "6RFWdEfR7Cd4kQ0X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend items correspond to the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.665], [0.244, 0.516, 0.524, 0.886], [0.482, 0.501, 0.741, 0.881], [0.482, 0.155, 0.74, 0.516], and [0.283, 0.146, 0.538, 0.516] respectively."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "k7jpDbGZLEsWS3bO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you characterize the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region, placed in the middle of the plot, contains multiple slices of a pie chart indicating the distribution of the Renewable Energy Investment from 2016 to 2020. The percentages and corresponding years for each slice are noted within and around the region respectively."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "UDEC6xDKEtMkWRcm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area bound by [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is in the far right of the chart and includes the legend. It lists the different years ranging from 2016 to 2020. Each year corresponds to a different color, and these colors match with the slices in the pie chart."
+            }
+        ],
+        "id_raw": 413
+    },
+    {
+        "id": "7XT48iHU3hC3lVzY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.327, 0.401, 0.362, 0.421] in the subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year 2009."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "5XlPmzFW4YuKG43s",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.239, 0.021, 0.724, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, \"Patient Engagement Technology Usage from 2009 to 2015\"."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "3O5xe57hoaxS8uAk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object stated at coordinates [0.952, 0.191, 0.987, 0.211] in the sublegend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the year 2013."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "vJImPFVoO4YEAcCg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich colors in the legend correspond to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors in the legend coordinate with the slices in the pie chart. For instance, \"#ff9999\" corresponds to {the slice}[0.233, 0.278, 0.482, 0.516] and \"#66b3ff\" corresponds to {the slice}[0.223, 0.411, 0.482, 0.606], and so on."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "vwXgAUpZi71gRuyI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn terms of percentage, which year follows {2011}[0.331, 0.632, 0.375, 0.652] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2012 follows 2011, indicated by {the text}[0.434, 0.725, 0.477, 0.745] as \"14.2%\"."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "UYTezsh5ZbvwGq4m",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend}[0.908, 0.063, 0.993, 0.279] and {the pie chart}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend provides the color mapping of the different years represented in the slices of the pie chart."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "AgZ69AGulsZvrRkx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the boundaries of coordinates [0.482, 0.171, 0.739, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed region features a portion of the pie chart representing the year 2014 with the associated percentage and color. The percentage is stated as \"17.0%\" at coordinates [0.706, 0.255, 0.741, 0.275] and the year as \"2014\" at coordinates [0.582, 0.369, 0.626, 0.389]."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "fW9xSKhBnrTtoekP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region defined by coordinates [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is the legend of the chart. It has labels for years from 2009 to 2015, along with corresponding colors that match the slices of the pie chart."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "EfNdwqoqJxBoRoQU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is in the region [0.482, 0.468, 0.741, 0.872]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a part of the pie chart, specifically, the slice representing the year 2013, which has been colored with \"#c2c2f0.\" The slice's percentage (\"22.7%\") and label (\"2013\") are at coordinates [0.72, 0.73, 0.755, 0.75] and [0.59, 0.628, 0.633, 0.648] respectively."
+            }
+        ],
+        "id_raw": 414
+    },
+    {
+        "id": "zWxqiqDWKi08OE4q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.221, 0.021, 0.742, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart subtitle called Career and Workplace Development Trends from 2017 to 2021."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "SI1CvC6pR1W9AmUf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.133, 0.941, 0.147] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend handle for the year 2019."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "ZPuvx1J2f18WyXMN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.183, 0.351, 0.218, 0.371] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The auto text showing \"9.8%\"."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "XtCl62bVqwO8w3WR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the {legend item \"2021\"}[0.952, 0.34, 0.987, 0.36]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.482, 0.306, 0.741, 0.731] is associated."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "yFrh8772AbJcx35f",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text box at [0.606, 0.872, 0.641, 0.892] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice with the coordinates [0.469, 0.516, 0.692, 0.886]."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "bWrPaF0lWdBEXzwL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the legend items associated with the slices located at coordinates [0.482, 0.149, 0.607, 0.516] and [0.396, 0.146, 0.514, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items associated are \"2019\" and \"2020\" respectively."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "DF54KmhWIU6gaQ7x",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.146, 0.741, 0.731]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice representing the year 2021 with its associated autotext \"19.5%\". It is located in the upper right part of the chart and color-coded as per the legend."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "iTYEif3JcxEJaKjy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for the year 2017 and its associated autotext \"9.8%\"."
+            }
+        ],
+        "id_raw": 416
+    },
+    {
+        "id": "iyrQSf3QPLrvN2qM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content displayed in the box positioned at [0.31, 0.021, 0.653, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "VNwLEV0RTHILLS3p",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element is located at the coordinates [0.905, 0.221, 0.935, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text for the legend item \"Thai\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "EoB06yfJ4xfqLRft",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented inside the box with coordinates [0.862, 0.073, 0.894, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color representation for French cuisine in the legend."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "hMCaQuBzoEkq6o5N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is directly related to {the legend item \"Mexican\"}[0.905, 0.191, 0.963, 0.211] according to the color coding?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice labeled as \"Mexican\"}[0.482, 0.262, 0.741, 0.575]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "54OlRI8b7hCkovm4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the box with color}[0.866, 0.163, 0.894, 0.177] in the legend and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {box with color}[0.866, 0.163, 0.894, 0.177] in the legend corresponds to {the slice labeled as \"Indian\"}[0.482, 0.516, 0.737, 0.818] in the pie chart."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "YIBPpgbQdhsJpa5k",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the biggest and smallest slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The biggest slice corresponds to {the legend item \"Chinese\"}[0.905, 0.131, 0.96, 0.151] and the smallest slice corresponds to {the legend item \"Indian\"}[0.905, 0.161, 0.948, 0.181]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "mKzBugxmIqH4JCEy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents within region [0.345, 0.5, 0.65, 0.75]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the {slice for Chinese cuisine}[0.375, 0.516, 0.632, 0.886] and textual representation {\"Chinese\"}[0.475, 0.727, 0.519, 0.747]. The autotext for the pie chart slice {\"16.6%\"}[0.509, 0.911, 0.565, 0.931] is also partially contained in this region."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "tvZre0M2sGBLegJV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease tell me about the upper-right zone of the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The upper-right zone includes segments referring to both {the \"Indian\"}[0.482, 0.516, 0.737, 0.818] and {the \"Mexican\"}[0.482, 0.262, 0.741, 0.575] cuisines along with corresponding percentages {\"12.6%\"}[0.724, 0.721, 0.766, 0.741] and {\"14.6%\"}[0.754, 0.386, 0.811, 0.406]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "LtwdQE6AQ5r38quj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_17.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nSummarize the objects present in the lower-left sector of the image."
+            },
+            {
+                "from": "gpt",
+                "value": "The lower left contains segments related to {the \"French\"}[0.223, 0.278, 0.482, 0.584] and {the \"Italian\"}[0.227, 0.516, 0.482, 0.853] cuisines, along with their percentages {\"14.1%\"}[0.16, 0.403, 0.206, 0.423] and {\"15.3%\"}[0.214, 0.757, 0.258, 0.777]."
+            }
+        ],
+        "id_raw": 417
+    },
+    {
+        "id": "GU4lz2IWFHP0Jvm1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object located at [0.314, 0.505, 0.686, 0.518] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle \"Streaming Service Trends from 2010 to 2016\"."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "y5fGlK4QOKnTU8HK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see at [0.299, 0.568, 0.702, 0.755] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart representing \"2016\"."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "lb1cpLwbB1dYrNvv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object located at [0.385, 0.951, 0.597, 0.961]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text showing percentage \"18.6%\"."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "ASejiK4zTmva1b7z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the {slice}[0.277, 0.635, 0.5, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {adjacent slice}[0.247, 0.657, 0.5, 0.755] is adjacent."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "o5p5vUH7DOEoy00v",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the position of the slice {slice}[0.238, 0.707, 0.5, 0.815] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's positioned between {slice}[0.247, 0.657, 0.5, 0.755] and {slice}[0.252, 0.755, 0.5, 0.93]."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "a1JmVkGJDL2wwTdu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the relative positions between {slice}[0.405, 0.755, 0.688, 0.942] and {slice}[0.5, 0.636, 0.762, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}[0.405, 0.755, 0.688, 0.942] is to the left of the slice {slice}[0.5, 0.636, 0.762, 0.886]."
+            }
+        ],
+        "id_raw": 418
+    },
+    {
+        "id": "LatzxmGVlbVePlph",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object with coordinates [0.293, 0.021, 0.67, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Renewable Energy Access from 2010 to 2019"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "L4drVHeT2dZEOmbD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the identifier with coordinates [0.952, 0.311, 0.987, 0.331] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "2018"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "mFBRK8LKrfkEzbPE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage displayed at coordinates [0.637, 0.847, 0.672, 0.867] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "11.6%"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "zkPZ24VDeWKyYUkE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color from the legend corresponds to the slice at {coordinates}[0.392, 0.516, 0.537, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{#c2c2f0}<r>[0.913, 0.193, 0.941, 0.207]</r>"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "tLQ5hnCejo2wfIGI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the color {#ffb3e6}<r>[0.913, 0.342, 0.941, 0.356]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{the year 2019}<r>[0.952, 0.34, 0.987, 0.36]</r>"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "ZYO6uoQLK69NwMGk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice on the pie chart does the 2016 label with coordinates [0.952, 0.251, 0.987, 0.271] in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.482, 0.503, 0.741, 0.734]"
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "OM5EU0zN81my2Sjr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a pie chart representing Renewable Energy Access from 2010 to 2019. It displays multiple different colored slices for each year, including text labels for the year and corresponding percentage values for each of them."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "s0GVSATxsBOsWTpa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements inside the region [0.482, 0.503, 0.741, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the pie slice representing the year 2016 with a gray color, and it displays the percentage value '10.6%' with coordinates [0.754, 0.625, 0.789, 0.645]."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "It0ftgobngIR9yjq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside the region [0.223, 0.405, 0.482, 0.561]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a slice of the pie chart with light blue color representing data for the year 2011, and it includes a percentage text of '6.8%' with coordinates [0.163, 0.469, 0.198, 0.489]."
+            }
+        ],
+        "id_raw": 419
+    },
+    {
+        "id": "ZxkaCN0Vnq8P1eoG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the main object with coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart containing all the slices."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "5bi2yzGWpoiKXhMR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is located at coordinates [0.802, 0.071, 0.916, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Self-driving Cars\"."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "5XibWZf3BIpHdv6u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.802, 0.161, 0.889, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"E-commerce\"."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "s9KcmVB9DDUDIZw8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.283, 0.146, 0.731, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.417, 0.741, 0.881] is adjacent."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "XeZyN9og3bvD9Dhf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which pie slice does the percentage text {the text \"16.1%\"}[0.088, 0.429, 0.202, 0.449] belong?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text belongs to {the slice}[0.223, 0.278, 0.482, 0.631]."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "DT2Fj4P2qmvTbFEk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices do the legend items at legends coordinates from [0.802, 0.071, 0.916, 0.091] to [0.802, 0.161, 0.889, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "They correspond to the slices at coordinates [0.223, 0.278, 0.482, 0.631], [0.235, 0.516, 0.522, 0.886], [0.482, 0.417, 0.741, 0.881], and [0.283, 0.146, 0.731, 0.516] respectively."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "4yIOlIORQ8arcrVq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the central pie chart with its slices, including \"Self-driving Cars\" [0.243, 0.446, 0.287, 0.466], \"Virtual Assistants\" [0.345, 0.774, 0.389, 0.794], \"Recommendation Systems\" [0.645, 0.676, 0.689, 0.696], and \"E-commerce\" [0.506, 0.199, 0.55, 0.219]."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "9rlGZk3J8FFHv635",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.482, 0.417, 0.741, 0.881]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice colored with '#99ff99', represented the \"Recommendation Systems\" with the percentage of \"26.8%\"[0.721, 0.726, 0.906, 0.746]."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "xVLKUruNbqInzbup",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the region defined by [0.223, 0.146, 0.741, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices, which are representing \"Self-driving Cars\" with the percentage \"16.1%\"[0.088, 0.429, 0.202, 0.449] and \"E-commerce\" with the percentage \"34.6%\"[0.542, 0.108, 0.629, 0.128]."
+            }
+        ],
+        "id_raw": 421
+    },
+    {
+        "id": "QjcsgGt5lrKV5WSz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.313, 0.021, 0.65, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, called Financial Market Fluctuations and Trends."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "6ym2k5JrwaBu176b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.159] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart legend."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "XWbDqXJTCZx9Uhhx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.152, 0.516, 0.152, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's y-axis."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "EOZpbqvSxoLbxU9l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice shares its color with the legend item {2015}<r>[0.952, 0.071, 0.987, 0.091]</r> in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {2015}<r>[0.223, 0.278, 0.482, 0.764]</r> shares the same color with the legend item."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "k8Ite4yKbD8JlT6p",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice {2021}<r>[0.283, 0.146, 0.741, 0.63]</r> and the legend item {2021}<r>[0.952, 0.131, 0.987, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice and legend both represent the data for the year 2021. They share the same color."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "ArQCb7EwLCeXPVMl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie chart slices correspond to the legend entries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slices corresponding to the legend entries are: \"2015\" at coordinates [0.223, 0.278, 0.482, 0.764], \"2018\" at coordinates [0.289, 0.516, 0.728, 0.886] and \"2021\" at coordinates [0.283, 0.146, 0.741, 0.63]."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "gG1M7Cmj3Ii24hoJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.764]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie chart labeled \"2015\". It also contains the text \"22.8%\" at coordinates [0.162, 0.513, 0.197, 0.533]. The region's color indicates it's connected to the corresponding legend item at [0.952, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "5gau819nLJwApPvH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.289, 0.516, 0.728, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart labeled \"2018\". Additionally, there's the text \"33.3%\" at coordinates [0.541, 0.904, 0.577, 0.924] inside the region."
+            }
+        ],
+        "id_raw": 422
+    },
+    {
+        "id": "4Ij7mTfE0SfQwdOx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the caption of the chart located at the coordinates [0.155, 0.021, 0.808, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The caption is \"Environmental Protection and Sustainable Development Legislation Over Time\"."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "F0IHbY3oHeWHVNRI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is depicted by the color '#ff9999' as per the coordinates [0.264, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the year 2000."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "0AGufYxlGyc70D3N",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data point is located at the coordinates [0.168, 0.419, 0.203, 0.439]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point \"4.7%\"."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "8VzhsM5I5nnEIOIO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the {slice for the year 2003}<r>[0.223, 0.49, 0.482, 0.628]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice for the year 2002}<r>[0.223, 0.385, 0.482, 0.516]</r> and {slice for the year 2004}<r>[0.235, 0.516, 0.482, 0.784]</r> are adjacent to it."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "Ur4Vf0K7Ob4w6hXA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label that matches the color '#ff9999' according to its {color handle}<r>[0.913, 0.073, 0.941, 0.087]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label that matches the color '#ff9999' is \"2000\"."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "4QZ5OTDgiREks6ma",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the slice with coordinates [0.447, 0.516, 0.632, 0.886] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the year 2006 in the legend."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "jFG60EFk12Vi265F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects and information can be found in the region between [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple slices of the pie chart representing different years from 2000 to 2009. For example, the slice for the year 2000 {slice info}<r>[0.264, 0.278, 0.482, 0.516]</r>, with an auto text displaying \"2.0%\" {auto text}<r>[0.217, 0.264, 0.252, 0.284]</r>; the slice for the year 2009 {slice info}<r>[0.283, 0.146, 0.617, 0.516]</r> displaying \"22.7%\" {auto text}<r>[0.401, 0.104, 0.436, 0.124]</r>."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "GLwBe6VjWhgYv7zU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text elements are contained within the region bounded by the coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes labels marking the colors of the slices for different years from 2000 to 2009. For instance, \"2000\" {text}<r>[0.952, 0.071, 0.987, 0.091]</r> and \"2009\" {text}<r>[0.952, 0.34, 0.987, 0.36]</r>."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "rvgwUsY7r6e6qSFc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the region [0.262, 0.397, 0.703, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses several slices of the pie chart, each representing a different year with its respective percentage, such as '2005', '2006', '2007', and '2008'."
+            }
+        ],
+        "id_raw": 423
+    },
+    {
+        "id": "yLJtmawIcq80Phba",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with the text within the coordinates [0.284, 0.021, 0.679, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart subtitle, which is \"Forest Coverage Percentage from 2010 to 2020\"."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "j9sLUDiqRsPGMB0H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart component is contained within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "UcQZvahlrdZX8xJ9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with the coordinates [0.952, 0.37, 0.987, 0.39] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "rW4QQgJMVNYLymTN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend item {2020}[0.952, 0.37, 0.987, 0.39] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {2020}[0.952, 0.37, 0.987, 0.39] corresponds to the pie slice {slice}[0.283, 0.148, 0.482, 0.516]  in the chart."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "ESct3byRGXIFwkqE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to the slice represented by the solar color {#f0b1a1}[0.913, 0.372, 0.941, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices corresponding to the colors {#91c1f0}[0.913, 0.342, 0.941, 0.356] and {#c1a1f0}[0.913, 0.312, 0.941, 0.326] are adjacent to the slice represented by the color {#f0b1a1}[0.913, 0.372, 0.941, 0.386]."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "dL6RYp5wXRvYbStS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects within the coordinates [0.472, 0.777, 0.482, 0.867] and [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The first object does not exist, so it does not have any relationship with the second object which is the pie chart."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "eJzwSRbU7o1qw0N2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart of the figure. It includes differently colored slices each representing a year from 2010 to 2020 and their respective forest coverage percentages."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "feAdeX9JBr8qvCq9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is where the legend is located. It includes color representations and texts for the years 2010 to 2020."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "3YLtiTfRJbwROZLA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the details of the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of the entire chart plot, which includes the pie chart in the middle and the legend and subtitle on the right."
+            }
+        ],
+        "id_raw": 424
+    },
+    {
+        "id": "FY6l1Eay0ktiGOeL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.231, 0.021, 0.732, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, \"E-commerce and Retail Industry Analysis from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "JVXfQovu1TS7kviY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at the coordinates [0.208, 0.284, 0.243, 0.304] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the auto-text \"3.9%\"."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "EWVnegMKMthuT66u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.908, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "uoClqGNHtwkZuD89",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data labels are adjacent to {the slice}[0.232, 0.354, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data labels {\"2017\"}[0.311, 0.469, 0.346, 0.489] and {\"2.9%\"}[0.18, 0.362, 0.215, 0.382] are adjacent to the slice."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "JScHO6ZdNoYRQBTf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice corresponding to legend entry \"{{2017}}[0.952, 0.131, 0.987, 0.151]\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the corresponding slice is \"#99ff99\"."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "eZDiFvgB1S5hsoT4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years' slices are adjacent to {the slice}\"2020\"[0.283, 0.146, 0.741, 0.541]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices corresponding to the years {\"2015\"}[0.334, 0.385, 0.369, 0.405] and {\"2019\"}[0.55, 0.687, 0.594, 0.707] are adjacent."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "0GpHTo0Rxw8pKkpQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices from each year, with each slice annotated by its respective year and percentage. The colors of the slices range from '#ff9999' to '#c2c2f0'. The slice labeled \"2020\" has the largest share."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "426aADCuSQ1wCTcm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the region [0.223, 0.417, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a pie slice labeled \"2017\" with color '#99ff99', as well as the autotext \"3.3%\"."
+            }
+        ],
+        "id_raw": 425
+    },
+    {
+        "id": "IRb9qbKSyiFFjePJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element situated at [0.337, 0.021, 0.626, 0.047] on the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the graph, called 'Industry Market Share Comparison'."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "kxumiZtdEbVbyBat",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item found at this coordinate: [0.886, 0.071, 0.967, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend item labeled \"Automotive\"."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "rpd6dqxyXhEisEFV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen at these coordinates: [0.242, 0.216, 0.282, 0.236]?"
+            },
+            {
+                "from": "gpt",
+                "value": "What you are referring to is the text notation \"3.0%\"."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "FqLztNncboXQBySP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector does the slice represented by {the color #99ff33}[0.482, 0.158, 0.635, 0.516] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the sector \"Health Care\"."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "p8wOEHdErunmY5g3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat sector does the color in {the handle}[0.847, 0.342, 0.875, 0.356] represent on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Retail\" segment of the pie chart is designated by this color in the legend."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "klzLlD4UV8h9yb9W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sectors are connected to {the legend}[0.842, 0.063, 0.993, 0.368] to determine the pie chart's division?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sectors: \"Automotive\", \"Banking\", \"Biotech\", \"Construction\", \"Energy\", \"Food Services\", \"Health Care\", \"IT\", \"Manufacturing\", and \"Retail\" are related to the legend."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "PiiX7QZpRcqlsI12",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the different slices of the pie chart, each labeled with the name of the sector and the percentage they represent. Each slice is also colored uniquely."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "VWODlji6ZcVVoVh4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within [0.842, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the legend section of the chart which contains representatives for each sector color-coded square along with the name of the sector."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "nLQy8Nxjkq5fLjyw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you delineate the region demarcated by [0.482, 0.216, 0.682, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the \"Food Services\" sector in the pie chart. Inside this region, the text \"4.0%\" also resides denoting its market share."
+            }
+        ],
+        "id_raw": 427
+    },
+    {
+        "id": "J8il2Dp3QNoaoFUF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located between coordinates [0.095, 0.021, 0.868, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle - \"Consumer Behavior Trend Analysis (Online Shopping Vs Offline Shopping) from 2015 to 2022\"."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "RpzB0WuatMewXavF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is described within the coordinates [0.952, 0.371, 0.987, 0.391] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2017\" is located at the coordinates."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "b5Ydv9yFebHiK3Va",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text can be found at the coordinates [0.223, 0.392, 0.482, 0.531] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2016\" can be found at these coordinates."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "F3xHVndR5qjVcMaW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the {text \"2021\"}[0.952, 0.251, 0.987, 0.271] in the legend and the color mark?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {color '#670067'}<r>[0.913, 0.253, 0.941, 0.266]</r> corresponds to the text \"2021\" in the legend."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "cRHOivdTzbaVa0UA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the text \"2017\"}[0.952, 0.131, 0.987, 0.151] in the legend  connected to the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {color mark '#99ff99'}<r>[0.913, 0.133, 0.941, 0.147]</r> corresponds to \"2017\" in the plot."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "3BKtBPhPpHTQj8u6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend item \"2018\" relate to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2018\" is related to the {corresponding slice} <r>[0.248, 0.516, 0.482, 0.799]</r>  in the chart."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "9HAUe8F6bNRuq8Nq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between coordinates [0.223, 0.516, 0.482, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes {a pie slice} <r>[0.223, 0.516, 0.482, 0.675]</r> labeled as \"2017\" with an autotext showing \"6.4%\"."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "njPiBJJ7SCa6irhE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region bound by coordinates [0.908, 0.063, 0.993, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the chart's legend with several items, including year labels from \"2015\" to \"2022\" each associated with a different color."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "i2n0nKVhuwWGfPXI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by [0.223, 0.516, 0.482, 0.675]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a slice of the pie chart in this region. It represents the data for the year \"2017\" and it covers 6.4% of the total pie chart."
+            }
+        ],
+        "id_raw": 428
+    },
+    {
+        "id": "unEHxNhDwkHGvIYV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object that has coordinates [0.221, 0.021, 0.742, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart which says \"Career and Workplace Development Trends from 2010 to 2016\"."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "gjDF1VEHhw4ZFW2G",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.913, 0.253, 0.941, 0.266] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend item color handle for the year 2016."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "ss3CSwO2x2O49oKh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the role of the object at [0.417, 0.329, 0.447, 0.342]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the autotext \"7.2%\" associated with one of the pie slices."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "oXhTn4NIZJo8H8LF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2010\" at {coordinates}[0.952, 0.071, 0.987, 0.091] corresponds to the slice."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "jojteVDDmpKmiWZI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie chart coordinates [0.223, 0.146, 0.741, 0.886] and the chart's central coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is centered around the chart's central coordinates."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "OfqyRjflDAGNqDz8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two legend colors correspond to the years \"2010\" and \"2012\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for \"2010\" is represented by the handle {coordinates}[0.913, 0.073, 0.941, 0.087], and for \"2012\" by {coordinates}[0.913, 0.133, 0.941, 0.147]."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "Gn9o57DPVZrgPJPb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the rectangular region between [0.908, 0.063, 0.993, 0.488]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes color handles and labels describing each year from 2010 to 2016. Each color handle and corresponding text label occupies a small portion of the region, aligned vertically."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "P7riOMJOT4CTnccj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the context of the area marked by [0.482, 0.271, 0.708, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the pie chart, which represents the data for the year 2011, as indicated by the autotext \"3.4%\" at {coordinates}[0.714, 0.27, 0.749, 0.29]."
+            }
+        ],
+        "id_raw": 432
+    },
+    {
+        "id": "5m0DeppZlexIdFNM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text present at the coordinates [0.217, 0.021, 0.746, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the chart, \"Artificial Intelligence and Automation Trends from 2012 to 2018\"."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "B7g91zL067fxCuau",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at coordinates [0.915, 0.071, 0.968, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"AI 2012\" from the legend."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "Rz8oXyivyYPEEWyx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.876, 0.073, 0.904, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend handle for \"AI 2012\", colored in #ff9999."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "yF7v05SfrChEVpQ5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {color handle}[0.876, 0.073, 0.904, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"AI 2012\"}[0.915, 0.071, 0.968, 0.091] corresponds to the color handle."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "RoH7uLoBLaz5SuJo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color handle in the legend corresponds to the {legend item 'AI 2013'}[0.915, 0.131, 0.968, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle at coordinates [0.876, 0.133, 0.904, 0.147] corresponds to the legend item 'AI 2013'."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "Xkh0jk0mMf8kSWpR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice corresponding to the {legend text label 'AI 2012'}[0.915, 0.071, 0.968, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the pie slice is #ff9999, as indicated by {the corresponding handle}[0.876, 0.073, 0.904, 0.087]."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "G5YxwIpeQzc1NbOT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart. Each slice of the pie has a different color. There's also text labels indicating the percentage of each slice and corresponding labels indicating the categories (e.g., 'AI 2012', 'Auto 2012')."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "plD0fPj2TfBdbQkH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.871, 0.063, 0.993, 0.488]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the chart legend. It contains labels like 'AI 2012', 'Auto 2012' and corresponding coloured handles."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "M0LQJKtjZcTDJpCc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.262, 0.278, 0.482, 0.516] encompass in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a pie slice, coloured in #ff9999. It is associated with the 'AI 2012' category, as indicated by the text label located at [0.279, 0.312, 0.308, 0.327]."
+            }
+        ],
+        "id_raw": 433
+    },
+    {
+        "id": "Z9LGGFq5Eau089wX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.32, 0.021, 0.643, 0.047] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle which says, \"Social Media Usage from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "GQpJv8Qnyq0bYClZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted by the coordinates [0.908, 0.063, 0.993, 0.279] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the legend of the chart."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "5K9UWSgD7UyBNj4Q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the information present at [0.681, 0.216, 0.716, 0.236] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"16.9%\" is located there."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "Ji0iApg78my2v31g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext on the chart lies closest to the slice with coordinates [0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"10.1%\" located at [0.182, 0.355, 0.217, 0.375] is closest."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "0VTrtvgKwXbuO3wd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object(s) are associated with {the color code on the legend}[0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code corresponds to {\"label: 2015\"}[0.952, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "HMfAVqyHqanJcs5f",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with the segment of pie at [0.456, 0.516, 0.672, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color #ffcc99 is associated with this slice. This is specified in the {'legend'}[0.908, 0.063, 0.993, 0.279]."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "WKG4OucoVaEM0CWt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region accounts for the entire area of the pie chart, showing the distribution of Social Media usage from 2015-2021. It contains slices of varying colors denoting different years along with the corresponding percentage of usage for each year mentioned beside the slices."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "Wk48DGWsezf6btov",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the significant features or objects in the region [0.482, 0.42, 0.741, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region only contains part of the pie, specifically depicting the year \"2019\" with coordinates [0.611, 0.559, 0.655, 0.579] and the corresponding percentage of usage \"16.0%\" at coordinates [0.758, 0.603, 0.794, 0.623]. The color of the slice is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "yQQUj19IEM5TDHyc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you observe in the region [0.223, 0.493, 0.482, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I notice a pie slice representing the year \"2016\". The text \"2016\" can be found at coordinates [0.311, 0.572, 0.355, 0.592] and the percentage of usage for that year \"11.6%\" is located at coordinates [0.174, 0.627, 0.21, 0.647]. The color of this slice is \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 435
+    },
+    {
+        "id": "qrtsqrev3k0lVLyh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object at coordinates [0.381, 0.021, 0.582, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle that reads \"Family Living Conditions\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "qVmY6dgESRXeNh4R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.904, 0.131, 0.941, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text that says \"Good\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "Hb9POLTGLVLdP6nr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is spotted at coordinates [0.759, 0.598, 0.796, 0.618] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"45.0%\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "RPByuchUKgPj2Hfj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the {legend item \"Poor\"}[0.904, 0.071, 0.935, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.223, 0.278, 0.482, 0.516] corresponds to the legend item \"Poor\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "BuodCs0z9hoz6uYI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the {legend item \"Very Good\"}[0.904, 0.161, 0.976, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.344, 0.146, 0.579, 0.516] is associated with the legend item \"Very Good\"."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "YW95V65CmIoHK2P0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage is written near the slice that corresponds to the {legend item \"Good\"}[0.904, 0.131, 0.941, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage near the corresponding slice is \"45.0%\"[0.759, 0.598, 0.796, 0.618]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "Viu6cH6dJOI9a1dm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found within the coordinates [0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, there's a pie slice colored \"#ffa8a8\", the text \"10.0%\"[0.187, 0.353, 0.217, 0.373], and the label \"Poor\"[0.316, 0.423, 0.359, 0.443]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "rjLQXoP9qOjbyLUc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region at coordinates [0.344, 0.146, 0.579, 0.516] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a pie slice of color \"#f0f464\", along with text \"15.0%\"[0.385, 0.1, 0.457, 0.12] and label \"Very Good\"[0.446, 0.285, 0.49, 0.305]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "InoeEvSW5miICBql",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's contained within the region defined by coordinates [0.464, 0.173, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice of color \"#6983ff\" with associated text \"45.0%\"[0.759, 0.598, 0.796, 0.618] and label \"Good\"[0.611, 0.556, 0.655, 0.576]."
+            }
+        ],
+        "id_raw": 436
+    },
+    {
+        "id": "m7VwmfqypRoAzXYg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at coordinates [0.365, 0.021, 0.598, 0.047] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle \"Incidence Rates of Diseases\" is positioned there."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "yDDY83FplJJnJJy6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the coordinate box [0.146, 0.382, 0.21, 0.402]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the text representing the percentage \"12.4%\"."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "XwZG3eASsBxCjY3B",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart visualizing the incidence rates of diseases."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "sv8dcREQuRuV40Fz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the text \"Cancer\" at coordinates [0.552, 0.327, 0.596, 0.347] in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Cancer\" corresponds to the color \"#b3f0c2\" of the slice at coordinates [0.482, 0.151, 0.713, 0.516] in the chart."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "tMziJnmlo17Ojuc8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text elements {at position} [0.895, 0.281, 0.987, 0.301] and slice {at position} [0.283, 0.146, 0.524, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Hypertension\" corresponds to the slice depicting the incidence of Hypertension."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "plJziOwH4AdoYtRq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the {text string} [0.489, 0.724, 0.533, 0.744] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text string \"Tuberculosis\" refers to the slice with coordinates [0.422, 0.516, 0.631, 0.886]."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "ZMIrNOvRluGcdcub",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the content located in the region [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the entire chart area, including the main pie chart [0.223, 0.146, 0.741, 0.886], and the legend side-panel [0.85, 0.063, 0.993, 0.309] depicting various disease names including \"Influenza, Pneumonia, Malaria, Tuberculosis, HIV, Hepatitis, Cancer, Hypertension\" with corresponding color indicators."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "iaT1J2XQCNNISZpN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat all can you find in the region [0.85, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text labels for the different diseases and their corresponding color indicators. These include 'Influenza', 'Pneumonia', 'Malaria', 'Tuberculosis', 'HIV', 'Hepatitis', 'Cancer', and 'Hypertension'."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "Tqq5BIpVEbDwH7Ju",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the region [0.482, 0.151, 0.713, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart representing the 'Cancer' incidence, denoted by color '#b3f0c2', including the percentage '14.9%'."
+            }
+        ],
+        "id_raw": 439
+    },
+    {
+        "id": "YyqUPKNRAYEI91zy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.294, 0.021, 0.669, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle named \"Media Influence and Public Opinion Guidance\"."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "9xH3HOY45wxIRcGt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.841, 0.063, 0.993, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "MMaWQZxwYxQnsHwm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.184, 0.431, 0.202, 0.451] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"16.3%\"."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "BNZtf2Wxy2731B11",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.482, 0.516, 0.733, 0.87]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.482, 0.211, 0.741, 0.607] is adjacent."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "2W5iK4vAFdLe190r",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the autotext}[0.569, 0.119, 0.611, 0.139]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"Books\"} [0.512, 0.295, 0.547, 0.315] text is located near the autotext."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "WhoLuBhoNnFYXuI8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"TV\", \"Radio\", \"Newspaper\", \"Internet\", \"Social Media\", \"Books\", and \"Word of Mouth\" correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.635], [0.236, 0.516, 0.482, 0.855], [0.378, 0.516, 0.557, 0.886], [0.482, 0.516, 0.733, 0.87], [0.482, 0.211, 0.741, 0.607], [0.482, 0.146, 0.628, 0.516], and [0.283, 0.146, 0.488, 0.516] respectively."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "QCxxH2udV95POQ7n",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.516, 0.733, 0.870]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice colored in #ffcc99. The autotext \"16.3%\" with coordinates [0.688, 0.787, 0.743, 0.807] and the text \"Internet\" with coordinates [0.572, 0.659, 0.616, 0.679] are located within this region."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "tPFN56hZ8LBxfC82",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.482, 0.516, 0.733, 0.870]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the autotext \"16.3%\" [0.688, 0.787, 0.743, 0.807] and the text \"Internet\" [0.572, 0.659, 0.616, 0.679]."
+            }
+        ],
+        "id_raw": 441
+    },
+    {
+        "id": "k4oTjKkxJ5tr7wlu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the coordinates [0.215, 0.269, 0.25, 0.289] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at these coordinates is \"2.4%\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "8GHaZ4kMhrEZnXZF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart figure located at the coordinates [0.234, 0.021, 0.729, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"New Media and Digital Content Creation from 2010 to 2016\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "M7PDgawT8GNhqwkr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.913, 0.073, 0.941, 0.087] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is a legend color handle, colored '#ff9999' corresponding to the year 2010."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "fga5sTjXfJSrEvHg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the year {2010}[0.952, 0.071, 0.987, 0.091] in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2010 is represented by the color '#ff9999'."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "Rn1j6fkeo762sLbQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the auto-text connected to the exploded slice {2010}[0.26, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The auto-text connected to this exploded slice is \"2.4%\"."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "lWcBdVVTrKai7Guk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which slice is associated with the legend item \"{2016}\"[0.952, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"2016\" is the one located at [0.283, 0.146, 0.74, 0.516]."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "5D4DcL8yn8Lj1Xl3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This defined region on the chart contains multiple entities: seven pie slices, each differentiated by color, and corresponding to the years from 2010 to 2016. Each pie slice is annotated with its percentage contribution and the respective year. For instance, the slice for the year 2010 located at [0.26, 0.278, 0.482, 0.516] represents 2.4% of the pie chart."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "cUdJoIYUAfLh8iaJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region with coordinates [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the chart legend. It contains seven legend items, each consisting of a square handle indicating the color code and corresponding year ranging from 2010 to 2016. For example, one of the items in the region labeled '2010' is located at [0.952, 0.071, 0.987, 0.091] and is colored '#ff9999'."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "QoJBakiggjARW0Ug",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.482, 0.481, 0.741, 0.879] and what it represents?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice in the chart which is associated with the text \"2015\", located at [0.584, 0.639, 0.628, 0.659]. In terms of color, this slice is coded as '#ffb3e6' in the legend. The autotext \"23.5%\" associated with this slice indicates its proportion of the total pie chart."
+            }
+        ],
+        "id_raw": 442
+    },
+    {
+        "id": "x4A1MhC17EO3Rsto",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the area [0.305, 0.483, 0.349, 0.503]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Education Reform\"."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "GF5t64npI1OgBUON",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.262, 0.687, 0.317, 0.710]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a score of 30.3%."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "qi1BB1UDUeGm5N7h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.708, 0.754, 0.875, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"20.3%\"."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "zUa77R3Jp459h6wp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to {the pie slice} [0.482, 0.516, 0.741, 0.872] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the color #99ff99 in the legend."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "aP75Mypr2mg9WDZR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are adjacent to {the polygon} [0.253, 0.516, 0.553, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects {polygon} [0.223, 0.278, 0.482, 0.69] and {polygon} [0.482, 0.516, 0.741, 0.872] are adjacent."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "snVKPbU26OczRgKS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label in the legend for the color that corresponds to {the slice}[0.482, 0.223, 0.741, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Tax Legislation\"."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "tmbPLgSjKYDoXLaz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.516, 0.741, 0.872]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the slice} [0.482, 0.516, 0.741, 0.872] of the pie chart representing the object \"Environmental Directive\", along with its corresponding percentage {the text} [0.708, 0.754, 0.875, 0.774] \"20.3%\". The slice color corresponds to #99ff99 in the legend."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "ep4YZ28yIE785560",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region between [0.223, 0.278, 0.482, 0.69]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, one can find a part of the pie indicating \"Education Reform\". There is also a measurement of its effectiveness represented by {the text} [0.075, 0.464, 0.198, 0.484] which lists the percentage \"18.9%\"."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "bUQiY0Bj8HRvBrEM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_45.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the section [0.482, 0.223, 0.741, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart consists of a pie slice denoting the object \"Tax Legislation\", alongside the percentage {the text} [0.738, 0.33, 0.841, 0.35] \"14.9%, which is the effectiveness of the policy."
+            }
+        ],
+        "id_raw": 445
+    },
+    {
+        "id": "sN5KCJVovL95W8RK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at [0.271, 0.021, 0.693, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle with the label \"Popularity of Fashion Trends in Various Subcultures\"."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "bBxntlSiNfsGdMqr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.908, 0.071, 0.939, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is part of the legend, and it represents the label \"Emo\"."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "B0QPDPv9ERUHs9GX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.166, 0.529, 0.197, 0.549]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"24.0%\"."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "5K7KuRPkryXblqub",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart correspond to {the legend item \"Grunge\"}[0.908, 0.281, 0.959, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.331, 0.171, 0.482, 0.516] corresponds to the legend item \"Grunge\"."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "V6q4mgWGLf9M0MYO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the adjacent slices to {the pie slice \"Hip Hop\"}[0.482, 0.193, 0.717, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {Boho-chic}[0.482, 0.146, 0.608, 0.516] and {Steampunk}[0.482, 0.361, 0.741, 0.633] are adjacent to the pie slice \"Hip Hop\"."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "MerpT4oeN0mE3XRe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the legend item}[0.908, 0.191, 0.963, 0.211] and {the pie slice}[0.482, 0.193, 0.717, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Hip Hop\" corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "OoSi8IirRoYBWxZ1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.303, 0.516, 0.575, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are the text \"18.0%\" [0.381, 0.905, 0.426, 0.925] and the label \"Gothic\" [0.429, 0.724, 0.473, 0.744], corresponding to the pie slice of the same color."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "Kh4mPT9HqZ5HXVkH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by coordinates [0.482, 0.146, 0.608, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the pie slice representing the fashion trend \"Boho-chic,\" as well as the associated percentage text \"8.0%\" [0.554, 0.112, 0.623, 0.132] and the label \"Boho-chic\" [0.504, 0.291, 0.539, 0.311]. It's located in the bottom right part of the pie chart."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "7lKVOxqybX8tCIYl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.223, 0.278, 0.482, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the pie slice linked with the fashion trend \"Emo\". It contains the statistical text \"24.0%\" [0.166, 0.529, 0.197, 0.549] and the label \"Emo\" [0.305, 0.518, 0.348, 0.538]."
+            }
+        ],
+        "id_raw": 447
+    },
+    {
+        "id": "k6kt9OqL7yrmrBub",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at the coordinates [0.229, 0.021, 0.734, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Media and Entertainment Industry Trends from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "p5iZObtLWpXpZru4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at the coordinates [0.9, 0.103, 0.928, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend handle item with the color '#66b3ff', which indicates 'Music'."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "wvS2G8FkqK3zaBJz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the first label in the legend placed at coordinates [0.939, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Movies'."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "I9GuG7TLgUsHu0BB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage located close to {the 'Movies' label}[0.38, 0.697, 0.424, 0.717]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage close to 'Movies' label is {55.1%}[0.287, 0.855, 0.335, 0.875]."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "nozhrWkjfBlsSpza",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat type of media is represented by the object at the coordinates [0.9, 0.073, 0.928, 0.087] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The media type 'Movies' is represented by the object."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "loRNLw7DiA5kknKW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {the 'Music' label}[0.939, 0.101, 0.978, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the 'Music' label is '#66b3ff'."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "bLcTaEpmporQjKTK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what is in the region between [0.283, 0.146, 0.741, 0.653]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with color '#66b3ff', a label 'Music' located at [0.539, 0.315, 0.583, 0.335] and its corresponding percentage '44.9%' at [0.628, 0.157, 0.667, 0.177]."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "67gTBwj5cHkabUfR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are there in the region [0.895, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart with items 'Movies' located at [0.939, 0.071, 0.987, 0.091] and 'Music' at [0.939, 0.101, 0.978, 0.121], and their corresponding visual symbols at coordinates [0.9, 0.073, 0.928, 0.087] and [0.9, 0.103, 0.928, 0.117] respectively."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "OJNkudd7Jfk4Zpqa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_48.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of what lies within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region encompasses the entire pie chart. It includes two slices representing 'Music' and 'Movies'. The 'Movies' slice is marked with coordinates [0.223, 0.278, 0.722, 0.886], and the 'Music' slice is marked with coordinates [0.283, 0.146, 0.741, 0.653]. Also, the corresponding percentages and labels for each slice are within this region."
+            }
+        ],
+        "id_raw": 448
+    },
+    {
+        "id": "NgGFD3PknxTQibKC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.196, 0.021, 0.767, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Federal Welfare and Public Services annual budget from 2005 - 2011\"."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "7jgTKR7ivnbOncbf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the object located at the coordinates [0.806, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subgroup of items part of the chart's legend, detailing different years from 2005 to 2011 under Public Service."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "pRb4ZQGmSvNHLCm7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what object do we see at the coordinates [0.482, 0.346, 0.741, 0.691]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart, representing \"2009 Public Service\" with a proportion of 15.5%."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "uvGnhqKgVCnEM3BJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the {the slice}[0.223, 0.278, 0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.223, 0.513, 0.482, 0.79], representing \"2006 Public Service\", is adjacent."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "84aa9cFPPsDV1RqV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the association between the objects at the coordinates [0.262, 0.121, 0.389, 0.141] and [0.283, 0.146, 0.535, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text object \"17.2%\" at coordinates [0.262, 0.121, 0.389, 0.141] denotes the proportion of the pie slice \"2011 Public Service\" at coordinates [0.283, 0.146, 0.535, 0.516]."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "54lbiTkGeUHTaJur",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat relation does the {the item}[0.851, 0.191, 0.987, 0.211] have with any of the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2009 Public Service\" at [0.851, 0.191, 0.987, 0.211] corresponds to the slice [0.482, 0.346, 0.741, 0.691]."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "2o00HvJtWkB0fR5M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects and their locations within the region defined by the coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes several slices of a pie chart representing the proportion of Public Service budget in different years (2005-2011), each with corresponding percentage texts. For instance, the slice for \"2005 Public Service\" is located at [0.223, 0.278, 0.482, 0.516] with its corresponding percentage text \"11.0%\" at [0.078, 0.365, 0.214, 0.385]."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "0aDVSlHeIxwMsa90",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region between the coordinates [0.283, 0.146, 0.535, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region contains a slice representing \"2011 Public Service\", which takes up 17.2% of the pie chart, with the percentage label \"17.2%\" found at the coordinates [0.252, 0.121, 0.389, 0.141]."
+            }
+        ],
+        "id_raw": 451
+    },
+    {
+        "id": "koKlnPhQicAIn0NV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart with the coordinate [0.293, 0.021, 0.67, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Real Estate Market Trends from 2000 to 2009\"."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "mu5pFH4qSKTK0rfv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.913, 0.073, 0.941, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend color handle, with color \"#ff9999\", corresponding to the \"2000\" marker."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "VnKCx6dGiYhU1dU4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located in the figure corresponding to the coordinates [0.344, 0.663, 0.379, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content is the text, \"2003\"."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "9od4YVewGOTL2lYz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.482, 0.244, 0.739, 0.516] and {the legend}[0.913, 0.282, 0.941, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend color handle corresponds to the slice. It signifies that the slice represents data for the year \"2007\"."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "NGyL0B1wGTBkw2oL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is connected to {the legend}[0.952, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.223, 0.346, 0.482, 0.516] is connected to this legend as both represent the year \"2001\"."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "aisDYhLHaAJGzjrS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.251, 0.278, 0.482, 0.516] and {the legend}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice's color matches the legend's color handle, indicating that the slice represents year \"2000\" data, same as the legend indicates."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "r9g2VZFEHhzTiVIn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area within the coordinates [0.482, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region features multiple pie slices representing different years, with each slice carrying a specific percentage and year label. The corresponding years and percentages range from \"2005 - 12.3%\" to \"2006 - 14.1%\"."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "FrYgx4X6GsDHXnk0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.482, 0.244, 0.739, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that corresponds to the year \"2007\" with a percentage of \"11.5%\", indicated by both a color and a label."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "2gIrxsyHbScxxOPd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of several slices of the pie chart, each of them associated with a specific year ranging from 2000 to 2009 along with their corresponding percentages. Each slice is distinguished by a unique color corresponding to its legend."
+            }
+        ],
+        "id_raw": 454
+    },
+    {
+        "id": "UD53LwHfm3eKftsU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.331, 0.021, 0.632, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle of the chart, named as \"Social Dynamics and Political Trends\"."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "VrM1Smt8UBKpl6gt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object positioned at [0.847, 0.071, 0.974, 0.091] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend label for \"Green Movements\"."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "s3GjGQ00Ry0s25mm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.562, 0.673, 0.606, 0.693] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Immigration Policies\" for one of the pie slices."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "hVovjnFdo3TBVwJ5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the legend item \"Green Movements\"}<r>[0.847, 0.071, 0.974, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}<r>[0.223, 0.278, 0.482, 0.699]</r>."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "2PfUvdIsZIWOUJHi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located closest to {the subtitle \"Social Dynamics and Political Trends\"}<r>[0.331, 0.021, 0.632, 0.047]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart<r>[0.223, 0.146, 0.741, 0.886]</r> is located closest to it."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "o18GBkkiteDyJUlx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items match with the slice represented at [0.471, 0.516, 0.74, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"Immigration Policies\"}<r>[0.847, 0.131, 0.987, 0.151]</r> matches with it."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "YMKW4QZoriJf4spo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region constitutes the main area of the chart, taken up by a pie chart which represents different categories: \"Green Movements\", \"Technology Trends\", \"Immigration Policies\", \"Political Campaigns\", and \"Freedom of Speech\", each with its respective percentage."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "VXVG2FdCzkeOvT9Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are enclosed within the region [0.802, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend of the chart with five different categories: \"Green Movements\", \"Technology Trends\", \"Immigration Policies\", \"Political Campaigns\", and \"Freedom of Speech\", each associated with its own color."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "5aQAP5eneAJKwEdE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the visuals found within the range [0.223, 0.278, 0.482, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region predominantly contains a portion of the pie chart, specifically the slice representing \"Green Movements\", which is also associated with the text \"19.4%\" representing its proportion."
+            }
+        ],
+        "id_raw": 455
+    },
+    {
+        "id": "ILGnI2LwbErEk7i1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at these coordinates [0.842, 0.221, 0.965, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "At those coordinates, there's the label \"Internet of Things\"."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "VRg728ihSuM2QEeN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.281, 0.021, 0.682, 0.047] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle of the chart titled \"Education and Training Tech Innovations in 2020\"."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "NqGlWaKhTvG6DRDo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at these coordinates [0.797, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "W257r2ySgem1N2MS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"AI\"}[0.613, 0.466, 0.656, 0.486] and {the corresponding slice}[0.482, 0.187, 0.741, 0.763] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"AI\" labels the corresponding pie slice, representing AI's share in Education and Training Tech Innovations in 2020."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "lv6czL1UU1kHO2ML",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are close to {the legend}[0.797, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"VR\"[0.842, 0.071, 0.861, 0.091], \"AR\"[0.842, 0.101, 0.861, 0.121], \"Blockchain\"[0.842, 0.131, 0.917, 0.151], \"AI\"[0.842, 0.161, 0.855, 0.181], \"Quantum Computing\"[0.842, 0.191, 0.987, 0.211] and \"Internet of Things\"[0.842, 0.221, 0.965, 0.241] are located close to the legend."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "tD22bAAKHzCCpqdP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {subtitle} [0.281, 0.021, 0.682, 0.047] relate to the entire plot in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Education and Training Tech Innovations in 2020\", describes the thematic content of the entire chart."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "fkHnQqxvcvi3Sv4x",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the contents within the region [0.482, 0.516, 0.675, 0.878]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a slice of the pie that is colored '#99ff99'. It contains the text \"Blockchain\"[0.538, 0.698, 0.582, 0.718] and the percentage '10.0%'[0.625, 0.858, 0.7, 0.878]."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "H5nzsguH4NiByVsE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is inside the coordinates [0.482, 0.148, 0.601, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is a slice of the pie chart. It's colored '#c2c2f0' and labeled with the text \"Quantum Computing\"[0.508, 0.293, 0.544, 0.313] as well as the percentage '6.0%'[0.563, 0.116, 0.709, 0.136]."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "5bSwpuTVZNeZjLmU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple slices of the pie chart, each representing different sectors of Education and Training Tech Innovations in 2020, namely VR, AR, Blockchain, AI, Quantum Computing, and Internet of Things. Each of these slices is labeled with its respective name and proportion in percentage."
+            }
+        ],
+        "id_raw": 456
+    },
+    {
+        "id": "Ky9Bx5LiGaWRjPUH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.145, 0.021, 0.818, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, \"New Energy and Clean Technology Development Investments from 2016 to 2019\"."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "hBA4zJEgCxpsWLTg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item can be found at coordinates [0.952, 0.161, 0.987, 0.181] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text label saying \"2019\"."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "IBVujHpoVlurD9cf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.753, 0.63, 0.788, 0.65] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a label with the text \"16.8%\"."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "sXpEFAFmhBStZeUL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color on the legend {legend}[0.908, 0.063, 0.993, 0.189] corresponds to the slice located at coordinates [0.482, 0.435, 0.741, 0.791] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with the color {color}\"#99ff99\"[0.913, 0.133, 0.941, 0.147] corresponds to the slice."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "7cT1HaE1D27xQQyT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are related to the slice {slice}[0.223, 0.278, 0.482, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"21.2%\"}[0.162, 0.493, 0.197, 0.513] and {\"2016\"}[0.304, 0.499, 0.348, 0.519] are related to the slice."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "zgZ5ZVroIrwJvb8Q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice at [0.283, 0.146, 0.734, 0.516] and the legend item {Legend Text}[0.952, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2019\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "s0LuuGJzH4mOh4Ey",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region [0.223, 0.278, 0.482, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart colored \"#ff9999\" and includes a label showing \"2016\" {label_2016}[0.304, 0.499, 0.348, 0.519] and its percentage \"21.2%\" {percentage}[0.162, 0.493, 0.197, 0.513]. This region corresponds to the investment made in 2016."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "XfZu0ekBZy0MvqKU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.482, 0.435, 0.741, 0.791]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there's a part of the pie chart colored \"#99ff99\", with the label \"2018\" {label_2018}[0.608, 0.573, 0.652, 0.593] and its corresponding percentage \"16.8%\" {percentage_2018}[0.753, 0.63, 0.788, 0.65]. This region indicates the investment made in 2018."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "r1hL3mOOM859ywUR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you deduce from the region [0.283, 0.146, 0.734, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a section of the pie chart in color \"#ffcc99\" with labels \"2019\" {label_2019}[0.497, 0.29, 0.54, 0.31] and \"35.4%\" {percentage_2019}[0.549, 0.11, 0.584, 0.13]. This region corresponds to the investment made in 2019 and reveals that it constitutes the largest percentage of the investments from 2016 to 2019."
+            }
+        ],
+        "id_raw": 458
+    },
+    {
+        "id": "bfLkYfJHhL0rvopZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the figure based on the coordinates [0.318, 0.021, 0.645, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Culture and Arts Industry Development\"."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "x7NlFX5f105vSFKn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.161, 0.439, 0.2, 0.459]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "9J315fIpoV6QtAJm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is given by the object located at the coordinates [0.883, 0.071, 0.922, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Music\" is given by this object in the legend."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "EhFjOeJrM9jjamHv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the label {Painting}[0.883, 0.101, 0.939, 0.121] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Painting\" corresponds to the slice at [0.24, 0.516, 0.482, 0.839] in the pie chart."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "uPY6vPSM6WB0wpcm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.482, 0.165, 0.622, 0.516] and the label in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the label \"Photography\" in the legend."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "NXeW9G14bRFlI8mP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the slice at coordinates [0.482, 0.205, 0.741, 0.519]} relate to the text in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at these coordinates corresponds to the label \"Sculpture\" in the legend at [0.883, 0.191, 0.949, 0.211]."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "SYD3rpfwOQNBz02I",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.838, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart. The legend includes the labels for \"Music\", \"Painting\", \"Classic Dance\", \"Drama\", \"Sculpture\", \"Photography\", \"Cinematic Film\", \"Literature\", and \"Fashion Design\"."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "r8RiTLaxUpX8zMqs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the whole pie chart showing different sectors corresponding to different categories in the Culture and Arts Industry."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "5hMD248Z7OAiny3P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you analyze from the area within the coordinates [0.482, 0.516, 0.741, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for \"Drama\" indicating 23.0% growth, as referenced by the text at [0.694, 0.777, 0.741, 0.797]."
+            }
+        ],
+        "id_raw": 459
+    },
+    {
+        "id": "GUbbHilIs5S0W1yQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the central object with coordinates [0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "Blq7RbiXipzPRrQf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "XdmtzieQlunJA35d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\"."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "PrTYb9NgH9yqZpt1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the pie chart are adjacent to {the slice}[0.482, 0.305, 0.72, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.232, 0.694, 0.516], [0.482, 0.372, 0.741, 0.72]] are adjacent."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "3DVpmB5dzHDE8yLR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.195, 0.021, 0.768, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {\"2015\"}[0.952, 0.071, 0.987, 0.091] and {\"2016\"}[0.952, 0.101, 0.987, 0.121] are located near the subtitle."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "XVVkhCQAST6KIkek",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.482, 0.232, 0.694, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2016\"}[0.952, 0.101, 0.987, 0.121] corresponds to the slice."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "T4J5ku4mn7z7WwCb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.503, 0.482, 0.76]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice of pie chart colored in '#66b3ff' with the text \"12.0%\" [0.178, 0.643, 0.213, 0.663] and the label text \"2016\" [0.313, 0.581, 0.357, 0.601]. This region corresponds to the data of 2016."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "JQHHnhL018ye8yPU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains legend items namely \"2015\" [0.952, 0.071, 0.987, 0.091], \"2016\" [0.952, 0.101, 0.987, 0.121], \"2017\" [0.952, 0.131, 0.987, 0.151], \"2018\" [0.952, 0.161, 0.987, 0.181] and \"2019\" [0.952, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 461
+    },
+    {
+        "id": "rMWd4hB2CY8EjRpv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.342, 0.021, 0.621, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary object is the subtitle, \"Progression of Human Civilization\"."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "FC4cnvnp01njsRRK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item is represented at coordinates [0.891, 0.251, 0.968, 0.271] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"Digital Age\", a legend item."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "aYdxoKz7eb1OUMX9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.177, 0.273, 0.248, 0.293] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2.9%\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "AlsLCfusrIil7tIw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the adjacent slice of the slice at {the slice}[0.223, 0.457, 0.482, 0.653]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinate}<r>[0.241, 0.516, 0.482, 0.845]</r> is the adjacent slice."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "zeSVMaM4j5cl6Vee",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice that corresponds to the legend item at coordinates {the legend item}[0.891, 0.071, 0.962, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color, correspondent to the legend item, is '#ff9999'."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "7HwZP54mZ0irwtRn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat portion of the total, is represented by the wedge at {the wedge}[0.363, 0.516, 0.694, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The wedge at these coordinates represents \"22.9%\" of the total."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "p1Xtq1HbU7lk2p0E",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with boundaries [0.852, 0.193, 0.887, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a color handle that represents the \"Renaissance\" era, colored '#c2c2f0'."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "XgSy0u59BP0qwXjD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area enclosed by coordinates [0.283, 0.146, 0.688, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a part of the pie chart that is colored '#6a097d', showing the slice for the \"Digital Age\". The corresponding percentage is denoted nearby as \"28.6%\" [0.489, 0.099, 0.565, 0.119]."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "M85GnYWNz0LKpaVr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_64.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.241, 0.516, 0.482, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a portion of the pie chart, colored '#ffcc99' that corresponds to the \"Medieval Age\" era. It represents \"11.4%\" [0.177, 0.78, 0.271, 0.8] of the total."
+            }
+        ],
+        "id_raw": 464
+    },
+    {
+        "id": "BVmhdfdGJObjPe8e",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the coordinates [0.131, 0.337, 0.223, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"8.6%\"."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "P732KSLkGtpLCaLN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is associated with color at coordinates [0.833, 0.103, 0.861, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Bio-Technologies\"."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "dAefaEIaJmo9LBCx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title found at coordinates [0.172, 0.021, 0.791, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Distribution of Patents in AI & Robotics and Bio-Technologies (2016 - 2020)\"."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "kb7Qh0Uvakq5imjJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which industry the portion located at coordinates [0.283, 0.15, 0.482, 0.516] is related in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The portion is related to {Bio-Technologies}<r>[0.872, 0.101, 0.987, 0.121]</r>."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "VVD0jNwUgc5ZUggu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the slice at coordinates [0.223, 0.457, 0.482, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.233, 0.516, 0.482, 0.797] is adjacent to it."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "TpAFLcNVo3mtdh7A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice at coordinates [0.223, 0.146, 0.741, 0.886] and the color handles at coordinates [0.833, 0.073, 0.861, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle at coordinates [0.833, 0.073, 0.861, 0.087] corresponds to the slice. They both represent the category \"AI & Robotics\"."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "NI52U4eNI5zR0gWV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are present within the region defined by coordinates [0.426, 0.516, 0.602, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"11.1%\" at coordinates [0.519, 0.909, 0.611, 0.929] and the label \"AI & Robotics\" at coordinates [0.489, 0.818, 0.533, 0.838]. It correlates to a slice of the pie chart."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "6KCp0aWkT21IePRi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between the coordinates [0.441, 0.146, 0.649, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts a segment of the pie chart with the label \"AI & Robotics\" at coordinates [0.519, 0.203, 0.563, 0.223] and the corresponding value \"13.7%\" at coordinates [0.559, 0.114, 0.651, 0.134]."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "cQVCmzllSkaDRA1T",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_65.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat falls within the region whose coordinates are [0.223, 0.457, 0.482, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a pie slice, associated with the label \"Bio-Technologies\" at coordinates [0.244, 0.526, 0.279, 0.546] and the corresponding value \"7.1%\" at coordinates [0.082, 0.532, 0.197, 0.552]."
+            }
+        ],
+        "id_raw": 465
+    },
+    {
+        "id": "ouZwxQ9vQV5uT1hx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.876, 0.071, 0.946, 0.091] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents {the label \"Education\"}<r>[0.876, 0.071, 0.946, 0.091]</r>."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "Opu9PVPiTnoDdOnT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can one find at coordinates [0.362, 0.908, 0.438, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is {a percentage label \"27.2%\"}<r>[0.362, 0.908, 0.438, 0.928]</r>."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "su5rZbtQhVAGussq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object located at [0.765, 0.545, 0.868, 0.565] within the chart's context."
+            },
+            {
+                "from": "gpt",
+                "value": "The object is {the autotext \"24.6%\"}<r>[0.765, 0.545, 0.868, 0.565]</r>."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "bfeanF0T6Numpu5x",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds with {the slice percentage \"20.1%\"}<r>[0.128, 0.478, 0.197, 0.498]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color \"#ff9999\"}<r>[0.223, 0.278, 0.482, 0.713]</r> corresponds."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "osc0m1fwuCJkEwyp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is associated with {the slice \"Healthcare\"}<r>[0.262, 0.516, 0.649, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item is {Healthcare}<r>[0.876, 0.101, 0.952, 0.121]</r>."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "aYCOXoc1AbNyBCUt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is represented by {the color \"#c2c2f0\"}<r>[0.837, 0.193, 0.865, 0.207]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice \"Public Transport\"}<r>[0.283, 0.146, 0.512, 0.516]</r> is represented."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "6YkerAdWW3UUWnR7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.450, 0.200, 0.750, 0.500]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the text \"Social Security\"}<r>[0.614, 0.527, 0.658, 0.547]</r> and {the slice color \"#99ff99\"}<r>[0.482, 0.283, 0.741, 0.799]</r>. This region is part of the lower right quadrant of the chart, showing data related to Social Security spending."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "Y0QshXJ4z21tvzw0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the upper quadrant of the pie chart on the left side?"
+            },
+            {
+                "from": "gpt",
+                "value": "This quadrant contains {the text \"Education\"}<r>[0.305, 0.491, 0.348, 0.511]</r> and {a slice percentage \"20.1%\"}<r>[0.128, 0.478, 0.197, 0.498]</r>. The corresponding slice is colored in #ff9999."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "6Y8YFjvbJXt8sa1P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give details about the lower half of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The lower half includes {the text \"Healthcare\"}<r>[0.436, 0.725, 0.48, 0.745]</r> and {the percentage \"27.2%\"}<r>[0.362, 0.908, 0.438, 0.928]</r>, along with the coordinating segment color #66b3ff for Healthcare."
+            }
+        ],
+        "id_raw": 467
+    },
+    {
+        "id": "MRxFiIRXDisK2Azy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.917, 0.071, 0.982, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text, labeled as \"Facebook\"."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "dpAo2bGp7hVtYQOR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can you find at coordinates [0.917, 0.161, 0.975, 0.181] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "I see the legend text, labeled as \"LinkedIn\"."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "61YolW5V6P68LuPk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key central object located at coordinates [0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the centerpiece of the pie chart."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "1MSrkbAIHEFYhkQg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is associated with {the slice}[0.223, 0.278, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"Facebook\"}[0.917, 0.071, 0.982, 0.091] corresponds to the slice."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "PmMHmWSnn8NfpPbi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the legend label {LinkedIn}[0.917, 0.161, 0.975, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"LinkedIn\" corresponds to {the slice}[0.283, 0.176, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "LJGz3wq1GJNfH4z7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which slice corresponds to {the legend text \"Twitter\"}[0.917, 0.131, 0.963, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"Twitter\" corresponds to {the slice}[0.379, 0.146, 0.505, 0.516]."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "aSxRHg1E6PjYv09Z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the most significant part of the plot which includes the main pie chart and its elements. The pie chart consists of segments corresponding to Facebook, Instagram, Twitter, and LinkedIn with their respective proportions and labels."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "EGiYmAWXwkycl3IQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region defined by coordinates [0.872, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts the legend of the chart. It contains four entries marking the identifiers for the pie chart's segments, namely, Facebook, Instagram, Twitter, and LinkedIn."
+            }
+        ],
+        "id_raw": 468
+    },
+    {
+        "id": "BURATVc1CwZRSfiO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.286, 0.021, 0.677, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, which reads \"Cultural and Value Changes from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "nGXg2LwTuT0YvN2b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A chart legend item, which is labeled as \"2020\"."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "WY9Yc937JoFJ9pXs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.206, 0.725, 0.241, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage label \"14.9%\" of a pie chart slice."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "jN1iRTkfy0ljwkjS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to {the pie slice}[0.223, 0.278, 0.482, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {\"2015\"}[0.311, 0.44, 0.355, 0.46] is closest."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "9FI4M0y2y7UctRTX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn context of the chart, what does the {legend item}[0.952, 0.191, 0.987, 0.211] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {legend item} refers to {the pie slice} [0.482, 0.178, 0.741, 0.516]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "R6HhFrkB1gzdeFnm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects within the chart correspond to the {legend item}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {legend item} corresponds to the {pie slice} [0.283, 0.146, 0.587, 0.516] and the percentage label {\"20.6%\"}[0.382, 0.109, 0.417, 0.129]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "2Kf07QokcFwFVHAU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region within the coordinates [0.350, 0.516, 0.601, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart slice with a percentage label {\"16.2%\"}[0.439, 0.913, 0.474, 0.933] and a text label {\"2017\"}[0.455, 0.728, 0.499, 0.748]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "T1AbTAXwpWwcEFxb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the coordinate region [0.600, 0.600, 0.800, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a part of {the pie slice}[0.482, 0.513, 0.741, 0.844] and the text label {\"2018\"}[0.593, 0.62, 0.637, 0.64]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "mC98fM7p3U1GfoUO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region bounded by coordinates [0.200, 0.100, 0.500, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart contains parts of several pie slices and the label {\"2020\"}[0.425, 0.29, 0.469, 0.31]."
+            }
+        ],
+        "id_raw": 469
+    },
+    {
+        "id": "OYfU6vQ5iX6yDgbJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.266, 0.021, 0.697, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle which says \"Product Sales Trend Observation from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "KwQh1TLcLsVF3Zaw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.912, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend which signifies the \"Electronics\" category."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "FsRQisTKHBn6yKGV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found within the coordinates [0.241, 0.788, 0.276, 0.808] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the percentage \"7.8%\"."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "hOe2jXDAA3w25Oew",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart corresponds to {the legend 'Electronics'}[0.912, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Corresponding to the legend 'Electronics' is {the slice}[0.26, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "bD6qgzTnG8BmN5B4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to {the slice labeled '2020'}[0.344, 0.516, 0.621, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {'17.9%'}[0.483, 0.913, 0.518, 0.933] is closest to it."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "BDNoL79ro2pGLlZY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does {the legend 'Apparel'}[0.912, 0.101, 0.965, 0.121] connect to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend 'Apparel' corresponds to {the slice}[0.482, 0.516, 0.691, 0.828]."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "q47BP2lhhUtL5nC6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide details about the region with coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several pie slices and their corresponding labels and percentages. Some of them are colored '#ff9999', '#66b3ff', and '#99ff99'. It is located in the lower half of picture and appears to be a pie chart that visualizes a Product Sales Trend Observation from 2014 to 2020 for different product categories."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "6uT85OOwV0jdcCmP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the bounding box [0.867, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend, with labels \"Electronics\" and \"Apparel\" corresponding to the colors ['#ff9999', '#66b3ff'] respectively."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "JT2Jdz3RULUxo9hv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.283, 0.159, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the pie slice associated with the label '2020', along with its percentage which is 9.6%, represented by the text coordinates [0.292, 0.164, 0.327, 0.184]. The color of the pie slice is '#74b9ff'."
+            }
+        ],
+        "id_raw": 470
+    },
+    {
+        "id": "ue8B8mJVUdWCoaGO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located within the coordinates [0.231, 0.021, 0.732, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, named \"Environmental & Social Activism over the years 2016 - 2020\"."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "Q4xK9WEnOCG16zIL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is placed at the position [0.169, 0.414, 0.204, 0.434]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text \"15.0%\" in this position."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "scL8IPuJKNKFb8MS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies within the coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2020\"."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "BJGd9hglaK79DtSy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {legend item \"2016\"}[0.952, 0.071, 0.987, 0.091] associated with the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice [0.223, 0.278, 0.482, 0.605]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "PIGvzBLd14dbns4b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the slice} [0.512, 0.546, 0.798, 0.916] and the texts on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is associated with the text \"20.0%\"[0.579, 0.889, 0.614, 0.909] and \"2018\"[0.513, 0.715, 0.557, 0.735]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "aPMLcONosf6a6mC3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {legend item \"2020\"}[0.952, 0.191, 0.987, 0.211] relate to the pie slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice with the coordinates [0.283, 0.146, 0.66, 0.516]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "b4tn8leP3Ck259E6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within the coordinates [0.410, 0.506, 0.725, 0.876]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a pie slice with color '#99ff99'[0.41, 0.516, 0.696, 0.886]. Associated with this slice are the autotext \"20.0%\"[0.579, 0.889, 0.614, 0.909] and text \"2018\"[0.513, 0.715, 0.557, 0.735]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "dNAknzQzqcCBe3Yx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.480, 0.230, 0.759, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice [0.482, 0.248, 0.741, 0.723] with color '#ffcc99', along with autotext \"22.3%\"[0.765, 0.462, 0.8, 0.482] and \"2019\" text [0.614, 0.482, 0.658, 0.502]."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "RHhNs9SqQBLGpxD4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.126, 0.701, 0.890]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a majority of the pie chart, including the slices corresponding to the years 2016, 2017, 2018 and 2019. Along with these slices, the region also has corresponding labels and individual percentages for the slices."
+            }
+        ],
+        "id_raw": 471
+    },
+    {
+        "id": "c1UL9nOeyGmQU7AF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart based on the coordinates [0.315, 0.021, 0.648, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Student Preferences Across Study Areas."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "PnEoPqG8M840R4Om",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.816, 0.063, 0.993, 0.309] of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the legend of the chart."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "Gz8jVzYG8XqTlOYe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at the coordinates [0.223, 0.278, 0.482, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie piece labeled as \"Math\" with percentage equal to \"15.0%\"."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "5Uaqu8J0rIcd2Tb8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice in the pie chart does the {legend item \"Music\"}[0.86, 0.221, 0.9, 0.241] correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice} [0.419, 0.146, 0.733, 0.516]."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "u0qO4HybWyZ3fCxb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the {legend item \"History\"}[0.86, 0.131, 0.91, 0.151] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice} [0.464, 0.516, 0.619, 0.886]."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "fWB1M77hFp1eo7nT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the slice represented by {the legend line for \"Art\"}[0.821, 0.193, 0.849, 0.207] have?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice color is #c2c2f0 (lavender)."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "Ok7A5gqtUIwNoJq9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinates [0.344, 0.157, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart labeled as \"PE\" with a percentage of \"5.0%\"."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "4VOOAJ1cX848igBu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the majority of the pie chart, with each slice representing a different study area."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "I7apQ4Th7cLDP6TH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_72.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the coordinates [0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a slice of the pie chart labeled \"Computer Science\" with a percentage of \"5.0%\" in the chart."
+            }
+        ],
+        "id_raw": 472
+    },
+    {
+        "id": "9gUaXfQ0Rqm4G9HR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.292, 0.021, 0.671, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is \"Market Competition Analysis and Comparison\"."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "9oeJ91PU0ztCqHe6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.875, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart, which contains the labels and corresponding coloring of different products."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "ioPmagcjyksjwzNh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.633, 0.851, 0.7, 0.871] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"10.0%\", which shows the market share for one of the products."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "gRfbEXBxR7zmZXxn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart are adjacent to {the slice}[0.482, 0.193, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.32, 0.741, 0.655], [0.419, 0.146, 0.608, 0.516]] are adjacent to the queried slice."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "6zHFpToAu6F1SzN4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sections of the legend are closest to the {legend item}[0.919, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {\"Product G\"}[0.919, 0.251, 0.987, 0.271] and {\"Product F\"}[0.919, 0.221, 0.985, 0.241] are closest to the queried item."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "kkXIbxcySGLg9ywv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text in the pie chart corresponds to {the legend item \"Product D\"}[0.919, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"5.0%\"}[0.726, 0.716, 0.794, 0.736] in the pie chart corresponds to the legend item \"Product D\"."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "UBtnmHpBZpONzNt3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple objects: slices of different products with their corresponding text labels and values representing market share."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "b3JSsNapHB8OCojC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.875, 0.063, 0.993, 0.309]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart, with each item having a color indicator and a label representing a different product. The labels range from \"Product A\" at the top to \"Product H\" at the bottom."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "Vl2GY2JdPgjct205",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region [0.482, 0.193, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice representing the \"Product F\" with a market share text \"8.0%\"."
+            }
+        ],
+        "id_raw": 473
+    },
+    {
+        "id": "G7bEMElw2gLpA1eL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated at [0.286, 0.015, 0.695, 0.033] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, labeled as \"Social Welfare and Public Services Resource Allocation from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "Arj1O3Zz235bMOkf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at these coordinates [0.964, 0.364, 0.988, 0.378] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labeled \"2016\"."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "U7i6hubIb7W6akxp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.491, 0.511, 0.613, 0.889] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice with a \"6.9%\" label, colored #ffb3e6."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "bkeMQHH96zcd6cYW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color and label of the legend item next to the handle located at [0.938, 0.156, 0.957, 0.166]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #ffb3e6 and the label is \"2016\"."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "G5GtbERZptsgQ5bc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the slice with the coordinates [0.491, 0.511, 0.613, 0.889]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {adjacent slices}[[0.491, 0.426, 0.743, 0.542], [0.491, 0.511, 0.639, 0.843]] are adjacent."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "Rec2bdti7NagOhuR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correlation between the legend item at [0.964, 0.217, 0.988, 0.231] and the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2019\" corresponds to the pie slice with coordinates [0.369, 0.133, 0.491, 0.511]."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "e3q2G5khCusaqJoH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.175, 0.037, 0.807, 0.985]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart. It includes various slices representative of different years, each displaying percentages of resource allocation."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "bn9AeY2lyiIW6TR6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components within the region defined by coordinates [0.935, 0.044, 0.992, 0.467]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend. The legend has multiple items with labels corresponding to years from 2011 to 2020 and unique color handles for each year."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "8P7OMNHSDUxMobnk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the chart elements in the region [0.238, 0.132, 0.743, 0.89]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the pie chart of Social Welfare and Public Services Resource Allocation from 2011 to 2020, with various slices each representing a unique year."
+            }
+        ],
+        "id_raw": 476
+    },
+    {
+        "id": "233Mof0bO3wgd8qe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at {coordinates}<r>[0.249, 0.011, 0.751, 0.038]</r> represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle \"E-Commerce Logistics (Transportation and Logistics Industry Trends 2012 to 2015)\"."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "adWs3DTJibXkTycR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with {coordinates}<r>[0.534, 0.484, 0.57, 0.494]</r> in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the percentage text \"26.6%\"."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "nTVTRtRoeP2g7bRb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at {coordinates}<r>[0.351, 0.113, 0.395, 0.123]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2012\"."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "j5a3Zv22PU8R9elR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the slice located at {coordinates}<r>[0.214, 0.173, 0.5, 0.419]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at {coordinates}<r>[0.246, 0.062, 0.5, 0.266]</r> is adjacent."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "90unccM7oe7GspFY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the percentage text at {coordinates}<r>[0.534, 0.484, 0.57, 0.494]</r> belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"26.6%\" belongs to the slice with {coordinates}<r>[0.311, 0.266, 0.732, 0.47]</r>."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "QIc7dxLOZhEqlJkS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice label corresponds to the pie slice with {coordinates}<r>[0.5, 0.556, 0.786, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice label \"2015\" at {coordinates}<r>[0.696, 0.678, 0.739, 0.688]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "UpZAVym2OpjFAkWE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWould you be able to describe the contents of the region specified by these {coordinates}<r>[0.214, 0.062, 0.786, 0.47]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region contains four pie slices representing the data for the years 2012-2015. Each slice is color-coded and labeled with both the corresponding year and the percentage it represents out of the total."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "HhpaU5fBBCVRRB6y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region enclosed by {coordinates}<r>[0.5, 0.556, 0.786, 0.884]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of a pie slice representing the year \"2015\" and its corresponding percentage \"35.3%\"."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "7wERBBFORcOx4zp6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found in the region specified by these {coordinates}<r>[0.561, 0.311, 0.605, 0.640]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is blank with nothing inside it."
+            }
+        ],
+        "id_raw": 478
+    },
+    {
+        "id": "9dNqUDzEiVIKclPu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text positioned at [0.174, 0.307, 0.233, 0.327] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"6.0%\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "pPqogY4h6c0Zh6St",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.889, 0.073, 0.916, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color handle for \"2000 BC\" in the legend with the color \"#ff9999\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "IZihPRkWdaBJ9kpK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is positioned at [0.259, 0.021, 0.704, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle, called \"Discoveries and Civilization Advancements over Time\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "CQ33yzXkptaBN5uR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend label corresponds to the slice with color \"#c2c2f0\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label that corresponds to that slice is \"2000 AD\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "1PGejOAA5JALmbY8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie labels correspond to the legend item {the color handle}<r>[0.889, 0.193, 0.916, 0.207]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie label that corresponds is \"2000 AD\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "UYXGTNyyNczblWMN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item matches with the slice {The slice}<r>[0.415, 0.516, 0.74, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item that matches is \"1500 AD\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "hbTrobHrZTvmKML5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with five slices representing different eras - \"2000 BC\", \"500 BC\", \"1000 AD\", \"1500 AD\", and \"2000 AD\". The percentage values of the slices are \"6.0%\", \"10.0%\", \"16.0%\", \"28.0%\", and \"40.0%\" respectively."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "luDgnxQljWpb7Itc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.159, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, there is an axes covering most parts of the figure, including a pie chart and its labels (\"2000 BC\", \"500 BC\", \"1000 AD\", \"1500 AD\", \"2000 AD\") with corresponding values (\"6.0%\", \"10.0%\", \"16.0%\", \"28.0%\", \"40.0%\")."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "DrgYV9oa14RdY5g9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the content in the region [0.223, 0.399, 0.482, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content in the region is a pie slice labelled as \"500 BC\" with an associated percentage value of \"10.0%\"."
+            }
+        ],
+        "id_raw": 480
+    },
+    {
+        "id": "6c07h2d9qGkaKcZm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.318, 0.021, 0.645, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This would be the subtitle of the chart, \"Equities Investment from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "acLoVSxq70Pj3KBf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found in the position conveyed by the bounding box [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "oGM70W75DHL9ypbk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated in the bounds [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main pie chart representation of the equities investments."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "hAzLxgfESDNaYzM5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich entity in the legend relates to {the pie slice}[0.229, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item representing the year \"2012\" in the legend<r>[0.913, 0.073, 0.941, 0.087]</r> is related to this pie slice."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "mbWRblvT1E7iWHcO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat other elements are related to {the legend item labeled '2018'}[0.913, 0.253, 0.941, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at coordinates [0.482, 0.246, 0.741, 0.531] relates to this particular legend item."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "JAaFd7aSwP2yTJs8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with {the legend text item for '2013'}[0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the bounding box [0.223, 0.434, 0.482, 0.567] has an association with this legend text item."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "9jjDFiDVIAWGyVMQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects within the region [0.913, 0.073, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the legend's description for the year \"2012\" and its corresponding colored box on the legend."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "vjBompyIAP7gE7Th",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region bounded by [0.223, 0.434, 0.482, 0.567]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the main pie chart, the corresponding percentage, and the label \"2013\"."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "F0s8EUQq5IjPCp0l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we find within the region located at [0.482, 0.516, 0.741, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a distinct pie chart slice. Along with the slice, a text displaying the label \"2017\" and its corresponding percentage are also within this region."
+            }
+        ],
+        "id_raw": 481
+    },
+    {
+        "id": "zMeJArfJnxvif574",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.275, 0.021, 0.688, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called \"Big Data Integration Overview from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "VsGj9UpdZiNKyF9D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.208, 0.283, 0.243, 0.303] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"3.8%\"."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "jZ9N0dQn8QLYsChb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at coordinates [0.913, 0.342, 0.941, 0.356] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle corresponding to year 2019 in the legend."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "sbxEr7F4Adf6iu0w",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart are neighboring to {the slice}[0.438, 0.516, 0.598, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.305, 0.516, 0.482, 0.881], [0.482, 0.516, 0.724, 0.847]] are neighboring."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "XRJBLH4tB6VGH2to",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.275, 0.021, 0.688, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2010\"}[0.952, 0.071, 0.987, 0.091] and {\"2011\"}[0.952, 0.101, 0.987, 0.121] from the legend are located near the subtitle."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "o2mSzOVMVy9Z41Xe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2010\" to \"2019\" correspond to the pie slices following the same order."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "l3XIyRJ2wJmHxBFk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.438, 0.516, 0.598, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with coordinates [0.438, 0.516, 0.598, 0.886], bearing the text \"10.1%\" [0.524, 0.909, 0.559, 0.929] and the label \"2015\" [0.483, 0.726, 0.527, 0.746]. This region represents the year 2015 in the chart."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "OjB8SU1sTgj0RWqX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.477, 0.482, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with coordinates [0.223, 0.477, 0.482, 0.635], with the text \"6.9%\" [0.163, 0.551, 0.198, 0.571] and the label \"2012\" [0.309, 0.53, 0.345, 0.55] associated with it."
+            }
+        ],
+        "id_raw": 482
+    },
+    {
+        "id": "XowoOsuyndgDlJ9r",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.268, 0.021, 0.695, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Transportation and Logistics Industry Trends (2020)\"."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "P9i1OhGGvNfc26l4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is placed at coordinates [0.834, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item is \"Freight Transportation\"."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "4v9gdWF27FiV2qe1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is depicted at these coordinates [0.128, 0.795, 0.281, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data \"47.4%\" is depicted at these coordinates."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "3dpwrFZEU2OA5iCx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart correspond to {the legend item \"Freight Transportation\"}[0.834, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at coordinates [0.223, 0.278, 0.65, 0.886] corresponds to \"Freight Transportation\"."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "tlGGEbC0RfIeQkj0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage represented by {the \"Logistics Solutions\" slice}[0.283, 0.146, 0.741, 0.797] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Logistics Solutions\" slice represents 52.6%."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "sMNQbfuW6ytEKQBE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the colors signifying the {legend items}[[0.834, 0.071, 0.987, 0.091], [0.834, 0.101, 0.962, 0.121]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend color for \"Freight Transportation\" is '#ff9999', and for \"Logistics Solutions\", it is '#66b3ff'."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "qHFRpnL4dwxmMuzw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region with coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie slice corresponding to \"Freight Transportation\" with the associated percentage of 47.4% located at [0.128, 0.795, 0.281, 0.815]."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "yGeQeOMAOqM53Nb8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within these coordinates [0.283, 0.146, 0.741, 0.797]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the \"Logistics Solutions\" slice of the pie chart with its corresponding percentage of 52.6% displayed at [0.682, 0.217, 0.81, 0.237]."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "12mVURIYSLpk5HzE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements contained within this region [0.79, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the chart with entries for \"Freight Transportation\" and \"Logistics Solutions\", with coordinates [0.834, 0.071, 0.987, 0.091] and [0.834, 0.101, 0.962, 0.121] respectively."
+            }
+        ],
+        "id_raw": 483
+    },
+    {
+        "id": "ZQeKDahWySLRwup2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart considering the coordinates [0.297, 0.021, 0.666, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is \"Entrepreneurship and Innovation Ecosystem\"."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "f1UikFOwheffFFWE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.892, 0.251, 0.967, 0.271] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Ecosystem\" is at that location."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "pmpMs1kWGYYnTsAZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at these coordinates [0.376, 0.91, 0.449, 0.93] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.2%\" is at that position."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "Di76uafL4A03MQdW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the {slice}[0.223, 0.278, 0.482, 0.589]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The item {Startups}[0.892, 0.071, 0.951, 0.091] corresponds to the slice."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "Q0QwibyvXPMonFA8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text appears within {the slice}[0.228, 0.516, 0.482, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text inside the slice is {\"Funding\"}[0.33, 0.629, 0.374, 0.649]."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "d7yChn0yr3io6eCr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the legend entry {\"Policy\"}[0.892, 0.191, 0.932, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is {the policy slice}[0.482, 0.292, 0.741, 0.649]."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "NvoM4KPinWZQPPZ0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region specified by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily comprises the pie chart, which includes seven slices, each with a unique color and a percentage descriptor. Each slice is associated with a different aspect of Entrepreneurship and Innovation Ecosystem."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "gtK8KkjIEZjyLs9e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.848, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend details for the pie chart, which include color-coded handles and their associated label texts namely \"Startups\", \"Funding\", \"Innovation\", \"Infrastructure\", \"Policy\", \"Education\", and \"Ecosystem\"."
+            }
+        ],
+        "id_raw": 484
+    },
+    {
+        "id": "GbIL7tAdu9FH1XIv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.182, 0.659, 0.218, 0.679] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the autopct of the slice, \"12.5%\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "zKXM6M82QBPkv6iu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.952,0.251,0.987,0.271] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend label for the year \"2021\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "VUmod4d1VKVsFB8D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.537, 0.905, 0.573, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the autopct label for a slice that reads \"17.5%\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "AHCIN4tyMRk9tWAu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does {the legend handle}[0.913, 0.223, 0.941, 0.237] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.467, 0.146, 0.741, 0.665] which represents data for the year \"2020\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "IUEssSQ6rSpSAZUq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the slice [0.283, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That slice corresponds to the label {\"2021\"}[0.952,0.251,0.987,0.271] in the legend."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "wJNoviPQABuF8n6P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.223, 0.278, 0.482, 0.516] and {the label on the legend}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These both represent data for the year \"2015\"."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "M2PgdYOCAKwBse9M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area located within the coordinates [0.223, 0.146, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart for the year \"2015\". It includes the autopct label \"11.0%\"[0.179, 0.365, 0.214, 0.385] and the year label \"2015\"[0.314, 0.429, 0.358, 0.449] ."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "CX7IRsUIrCMjYy8d",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.223, 0.513, 0.482, 0.776]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart for the year \"2016\". It includes the autopct label \"12.5%\"[0.182, 0.659, 0.218, 0.679] and the year label \"2016\"[0.316, 0.589, 0.36, 0.609]."
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "U5T708m4nneMN84y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_85.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the section of the pie chart within the region [0.392, 0.516, 0.658, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this region represents the slice of the pie chart for the year \"2018\". It includes the autopct label \"17.5%\"[0.537, 0.905, 0.573, 0.925] and the year label \"2018\"[0.49, 0.724, 0.534, 0.744]"
+            }
+        ],
+        "id_raw": 485
+    },
+    {
+        "id": "UAXp7Eg9WC7V5tWR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the coordinates [0.326, 0.405, 0.361, 0.425] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2022\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "r22OaPR18CBrkqXX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.952, 0.221, 0.987, 0.241] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2027\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "exsZF6EY7Q43XpV0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.298, 0.021, 0.665, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title, \"Company Profit Forecast From 2022 to 2028\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "b15y3vNFq0c9b8XZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the slice}[0.223, 0.426, 0.482, 0.618] and {the item in legend}[0.952, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both represent data for \"2023\"."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "k71Ok2JPpzWYjGYe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color scheme of the slice representing {the year 2026}[0.556, 0.681, 0.6, 0.701] in terms of its corresponding {legend color}[0.913, 0.193, 0.941, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#c2c2f0'."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "JdFhjwAmWzXIS53P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {this slice}[0.482, 0.217, 0.741, 0.665] and {this legend item}[0.952, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both represent the profit forecast for the year 2027."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "ClUtsWYqr7zefPzQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within the region [0.482, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a part of the pie chart, specifically the slices representing the years 2026, 2027, and 2028. This includes their associated data labels and percentage labels."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "OhVDOkoBlNOeCBsQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area specified by these coordinates [0.908, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the legend of the chart. It contains seven items each representing a year from 2022 to 2028, along with corresponding color swatches."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "2zmGOcVMU4MQvipw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects inside the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of the entire pie chart, containing seven slices each representing a year's profit from 2022 to 2028, along with labels and percentage texts."
+            }
+        ],
+        "id_raw": 488
+    },
+    {
+        "id": "w60M24me0MLngVen",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.341, 0.021, 0.622, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, which is \"Media Influence on Public Opinion\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "rPIb1Ast1Qe8DvVC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the chart at the coordinates [0.128, 0.369, 0.213, 0.389]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"11.3%\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "vo7RfFFVniVODmwP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at [0.902, 0.191, 0.976, 0.211] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Magazines\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "eUjmcWnHUOQK2tqk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is represented by {the legend item \"TV\"} [0.902, 0.101, 0.92, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice with color #66b3ff}[0.223, 0.516, 0.482, 0.855] represents \"TV\"."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "x1FrVsKJxvDkIPAf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subtitle corresponds to {the pie} [0.223, 0.146, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Media Influence on Public Opinion\" [0.341, 0.021, 0.622, 0.047] introduces the pie chart."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "BqW92ELrgZAmPCrH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext corresponds to {the slice}[0.482, 0.199, 0.709, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"8.5%\" [0.688, 0.225, 0.762, 0.245] corresponds to the slice."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "9NEFZybOmSouFLPC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region inside the coordinates [0.223, 0.516, 0.482, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the portion of \"TV\" in the pie chart, colored with #66b3ff, and includes the text \"18.3%\" [0.226, 0.731, 0.244, 0.751]."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "7EFKfptBZ99akyTl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart present in the region [0.223, 0.278, 0.482, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a part of the pie chart representing the \"Newspapers\" portion, colored in #ff9999, and contains the autotext \"11.3%\" [0.128, 0.369, 0.213, 0.389]."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "qxZfiKj5M4qg9Bbf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is enclosed by the region [0.858, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart, describing the colors representing each media type."
+            }
+        ],
+        "id_raw": 492
+    },
+    {
+        "id": "OUnvp8atvu9T2npm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.371, 0.021, 0.592, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, called Product Sales in Year 2020."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "yixQbwIAXl21N6Kx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.867, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend for the chart."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "ITrNUza6AxeMXTpD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.617, 0.147, 0.669, 0.167] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text displaying the percentage \"43.5%\"."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "mR4Kfo8PtSLhoPny",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.223, 0.278, 0.73, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.283, 0.146, 0.741, 0.621] is adjacent."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "00CNg2HH11eUoi4h",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.371, 0.021, 0.592, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legends texts {\"Electronics\"}[0.912, 0.071, 0.987, 0.091] and {\"Fashion\"}[0.912, 0.101, 0.964, 0.121] are located near the subtitle."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "KIzkelX7xqwnQ8oT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Electronics\" and \"Fashion\" correspond to the slices with coordinates [0.223, 0.278, 0.73, 0.886], and [0.283, 0.146, 0.741, 0.621] respectively."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "XPbQSMqWfJd63VEl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.6, 0.1, 0.7, 0.2]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is mainly blank but lies between the chart core and legend. Specifically, the region is just beneath the 'F' of 'Fashion' label at coordinates [0.912, 0.101, 0.964, 0.121] and above the '4' from the percentage '43.5%' at coordinates [0.617, 0.147, 0.669, 0.167]."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "tCKSyJncmwEBnDap",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.2, 0.8, 0.3, 0.9]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text displaying the percentage \"56.5%\" at coordinates [0.271, 0.865, 0.347, 0.885], and it's part of the Electronics slice."
+            }
+        ],
+        "id_raw": 493
+    },
+    {
+        "id": "fwuvs2j6WUOuTYv5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.274, 0.021, 0.689, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Equity Investment Distribution from 2008 to 2015\"."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "qJIwht8EbajsoKqR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinates [0.908, 0.063, 0.993, 0.309] indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "SHnU6E2K8hnEgix6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object at coordinates [0.952, 0.281, 0.987, 0.301] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\"."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "VVS1w4Q3EHepVQuw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is next to {this slice}[0.415, 0.516, 0.609, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}[0.482, 0.516, 0.733, 0.838] is adjacent to it."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "m7cd1Fx5JeODCELN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object does {this handle}[0.913, 0.073, 0.941, 0.087] in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the label \"2008\"}[0.952, 0.071, 0.987, 0.091] in the legend."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "apCXpKTUrE58t7dC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {this legend item}[0.952, 0.281, 0.987, 0.301] and the pie chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2015\" corresponds to the slice with coordinates [0.283, 0.146, 0.499, 0.516]."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "HnB0fUziKh5X7cuU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the major part of the pie chart with multiple slices. Each slice represents data for different years, for example, \"2008\" slice <r>[0.223, 0.278, 0.482, 0.523]</r> and \"2009\" slice <r>[0.223, 0.516, 0.482, 0.733]</r>."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "ifnHYBuuuNVtQvyl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects in the region [0.908, 0.063, 0.993, 0.309] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region describes the legend of the pie chart which includes the labels and their corresponding colors for years from 2008 to 2015 like \"2008\" <r>[0.952, 0.071, 0.987, 0.091]</r> and \"2009\" <r>[0.952, 0.101, 0.987, 0.121]</r>."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "obyzYLmi6NdSOkXj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.307, 0.482, 0.733]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes two pie slices representing the years \"2008\" <r>[0.223, 0.278, 0.482, 0.523]</r> and \"2009\" <r>[0.223, 0.516, 0.482, 0.733]</r>."
+            }
+        ],
+        "id_raw": 495
+    },
+    {
+        "id": "Yz0goDyvCiocsdiH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is found at coordinates [0.26, 0.021, 0.703, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object present is the subtitle labeled 'Investments Distribution in Financial Markets in 2020'."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "Ksuppi9rcJQvb40B",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present at coordinates [0.88, 0.071, 0.912, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend item named 'Gold'."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "Ia6zSQnKGE1mL7Nd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.165, 0.5, 0.197, 0.52] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text indicating the percentage '21.7%'."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "FWTLH2lnYwD4JhzM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {slice}[0.223, 0.278, 0.482, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item 'Gold'<r>[0.88, 0.071, 0.912, 0.091]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "rrKGDZprEZ2UjqUf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the largest slice near {Cryptocurrency slice}[0.283, 0.146, 0.634, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {Stocks slice}[0.347, 0.516, 0.735, 0.886]."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "BMuO9JbmK5kO5FaD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color does the {Forex slice}[0.482, 0.217, 0.741, 0.592] in the pie chart correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Forex slice corresponds to the color '#ffcc99' in the legend."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "JRoQvWt827ZjUFvo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.278, 0.516, 0.482, 0.832]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by a slice representing 'Futures'. This slice is colored '#66b3ff' and it accounts for '5.7%' of the total, as indicated by the text at coordinates [0.241, 0.811, 0.293, 0.831]."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "M4cjjEg9MgeLk2kp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region bounded by [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This large region constitutes a major part of the pie chart. It comprises of several slices, each representing a different category such as Gold, Stocks, Forex, and Cryptocurrency. The region is marked with varied colors for different slices and associated percentages are annotated on them."
+            }
+        ],
+        "id_raw": 496
+    },
+    {
+        "id": "TkxBnL9CLVA3bMHt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at coordinates [0.241, 0.021, 0.723, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which says \"Digital vs Physical Media Consumption from 2015 to 2017.\""
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "AMdDpbEOA11wBZO1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object found at the coordinates [0.908, 0.063, 0.993, 0.249] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That particular object is the chart's legend."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "7wcLtjGPiBq88R3H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the object at the coordinates [0.152, 0.516, 0.152, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This set of coordinates corresponds to the ylabel, but it does not have any visible text."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "cshJiXTMrWd8pVN6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice colored in '#ff9999'}[0.482, 0.151, 0.686, 0.516] and {the corresponding legend entry}[0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice and the corresponding legend entry both represent data for 2015."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "qO6E0nF1t9bWyNyU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the pie chart are near to the {slice}[0.482, 0.151, 0.686, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {[0.482, 0.288, 0.741, 0.83], [0.368, 0.146, 0.527, 0.516]} are adjacent."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "w4AOcjuSfT6ILDX7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item matches with {the pie slice}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2015\"[0.952, 0.071, 0.987, 0.091] corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "fxyKFQXxIOjw1IKO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a pie with multiple slices and texts. It includes slices with percentage values \"21.7%\" [0.162, 0.499, 0.197, 0.519], \"23.3%\" [0.397, 0.907, 0.432, 0.927], \"26.7%\" [0.762, 0.577, 0.797, 0.597], \"11.7%\" [0.628, 0.157, 0.664, 0.177], \"10.0%\" [0.407, 0.103, 0.442, 0.123] and \"6.7%\" [0.271, 0.185, 0.306, 0.205] alongside their respective year labels."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "BUsfFqO6wZTeeTk1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information or objects does the region spanning from [0.223, 0.146, 0.741, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart, with slices of different colors representing data from different years (2015, 2016 and 2017), and their corresponding percentage values as autotext."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "VUMHsfoOQIrlwFg5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the regional coordinates [0.223, 0.278, 0.482, 0.744]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises a slice of the pie labelled \"2015\" [0.304, 0.502, 0.348, 0.522] with the percentage \"21.7%\" [0.162, 0.499, 0.197, 0.519] marked on it."
+            }
+        ],
+        "id_raw": 497
+    },
+    {
+        "id": "3nVIXxraXDBzsAlo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary text at coordinates [0.218, 0.021, 0.745, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary text at these coordinates is the subtitle, reading \"International Trade and Globalization Trends from 2000 to 2008\"."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "xtIL3mITLsUUpxPL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.131, 0.987, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates is the legend item with the text \"2004\"."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "QS2frawIADKVoCU0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at coordinates [0.617, 0.864, 0.652, 0.884] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"22.0%\" is depicted at these coordinates, which corresponds to the percentage value for the pie slice labeled \"2004\"."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "NZWj5lk2eh7hLe5R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the color coordinate}[0.913, 0.193, 0.941, 0.207] and {the corresponding text}[0.952, 0.191, 0.987, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color coordinate represents the color for the year \"2008\", as described by the corresponding text."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "gKfbnl8qAiMo0jRc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the pie chart, which slice matches the color of {the legend item}[0.913, 0.193, 0.941, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.146, 0.594, 0.516] matches the color of the legend item for \"2008\"."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "Av0TTrEcHacUvdk2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the corresponding slice in the pie chart for {the legend item}[0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice in the pie chart for the legend item \"2000\" is {the slice}[0.223, 0.278, 0.482, 0.605]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "tgVcRFKF6fDu42fX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.431, 0.516, 0.722, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a pie slice of the chart. It includes a percentage indicated by the text \"22.0%\" at coordinates [0.617, 0.864, 0.652, 0.884] and corresponds to the year \"2004\", as labelled at coordinates [0.534, 0.701, 0.577, 0.721]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "ovFQNL5VK3HOiSjl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region [0.223, 0.278, 0.482, 0.605]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice tagged with the year \"2000\" at coordinates [0.308, 0.456, 0.352, 0.476] and the percentage \"15.0%\" at coordinates [0.169, 0.414, 0.204, 0.434]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "YepoAECz4Ws4SdBC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_99.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements enclosed in the area [0.482, 0.182, 0.741, 0.655]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a portion of the pie chart that signifies the year \"2006\", with labels for the year at coordinates [0.605, 0.426, 0.648, 0.446] and the corresponding percentage \"24.0%\" at coordinates [0.747, 0.359, 0.782, 0.379]."
+            }
+        ],
+        "id_raw": 499
+    },
+    {
+        "id": "pdsbS5Ognu3ElLzA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is displayed by the object at coordinates [0.162, 0.511, 0.197, 0.531] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value \"8.2%\"."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "BcufnhwyGjzoWlOq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year is represented by the object at coordinates [0.747, 0.357, 0.782, 0.377]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2018."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "wHZ8ZIrRSEQLaCIa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at coordinates [0.294, 0.162, 0.329, 0.182]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"9.7%\"."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "1tZq2jvGXkx0kBww",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to {the autotext \"9.7%\"}[0.294, 0.162, 0.329, 0.182] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color}#cccccc."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "7qrlVICY0F4QKbNG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label next to {the color handle}[0.913, 0.223, 0.941, 0.237] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The label}\"2017\"[0.952, 0.221, 0.987, 0.241]."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "wZCVxTpOKx0lOiFL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the slice}[0.232, 0.516, 0.482, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color}#99ff99."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "M4Y9yhdMq1r3tsRv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the chart that includes the center coordinates [0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie slices spanning different years, with autotext labels indicating percentage values, such as \"7.2%\" {autotext}[0.193, 0.321, 0.228, 0.341] and \"11.7%\" {autotext}[0.567, 0.894, 0.602, 0.914], among others."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "2amdf2CvvHLj7Q1U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the lower section of the chart's right side?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes colors handles representing different years such as {color handle for 2018}[0.913, 0.253, 0.941, 0.266] and {color handle for 2019}[0.913, 0.282, 0.941, 0.296], accompanied by their respective labels like {label for 2018}[0.952, 0.251, 0.987, 0.271] and {label for 2019}[0.952, 0.281, 0.987, 0.301] in the legend area."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "0Bc14G24j4g8aHyv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the contents of the chart region that intersects with [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of the chart contains the endpoints of all pie slices; for example, slices such as {the slice for 2017}[0.482, 0.516, 0.74, 0.806] and {the slice for 2016}[0.465, 0.516, 0.643, 0.886], indicating changes in tourism visitors and revenue over different years with associated autotext labels showing percentages."
+            }
+        ],
+        "id_raw": 500
+    },
+    {
+        "id": "gjRINxBavvHaUjJZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "7KuvJhY9SGo4Ex3q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the colour of the object found at coordinates [0.888, 0.133, 0.916, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Its color is #99ff99 (light green)."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "JrAXjq60okqHmhyf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object found at coordinates [0.927, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the object is \"Thailand\"."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "v81gq9hjtriGYFL8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with the 'Spain' slice {pie slice}[0.283, 0.157, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage associated with the 'Spain' slice is {10.0%} [0.292, 0.161, 0.331, 0.181]."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "yaz2WoR9jXhjQVp1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich country's color according to the legend corresponds to the pie slice located at {pie slice}[0.437, 0.516, 0.57, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to {India from the legend} [0.888, 0.133, 0.916, 0.147]."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "4SyxsqiUIiXMRqsk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice associated with {Italy from the legend}[0.888, 0.073, 0.916, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the Italy's slice is '#ff9999'."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "KlRJz3H5uPfLZIzt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region outlined by coordinates [0.223, 0.278, 0.482, 0.728]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region depicts a pie slice, which is part of Italy's slice in the pie chart. Inside the slice, there's a text '20.8%' and a label 'Italy'."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "1vzACeh5UDByytIu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region defined by coordinates [0.572, 0.516, 0.801, 0.864]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice that represents Turkey. Inside the slice, there's a label 'Turkey' and the percentage text '15.0%'."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "RQKdFIbcnlHBdPbl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_101.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found inside the region [0.437, 0.516, 0.57, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a pie slice with the label 'India' along with a percentage of '8.3%'."
+            }
+        ],
+        "id_raw": 501
+    },
+    {
+        "id": "15G5OhUJQBejEvzG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key information conveyed by the object at coordinates [0.353, 0.021, 0.61, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is \"Prevalence of Future Scenarios\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "6lC6opHHwnExUxPv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.426, 0.912, 0.461, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"16.8%\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "IE5aLOtwoUnEGcKn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the location [0.824, 0.101, 0.987, 0.121] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item that reads \"Automation Technology\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "DXCh13owqapb0sWJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart is adjacent to {the slice}[0.482, 0.516, 0.739, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.189, 0.741, 0.555] is adjacent to it."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "Bw9id7VD2VdkwysL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between the legend items and the pie chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of each legend item corresponds to the color of a certain slice in the pie chart. For instance, \"Sustainable Living\" corresponds to the slices colored \"#ff9999\", while \"Automation Technology\" corresponds to the slices colored \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "rXideSsyqPwpL2B8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does {the text \"2040\"}[0.324, 0.614, 0.368, 0.634] align with?"
+            },
+            {
+                "from": "gpt",
+                "value": "It aligns with {the pie slice} [0.223, 0.516, 0.482, 0.822]."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "2AM861PWQQk9Hzpl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area enclosed by the coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure. This region comprises of six pie slices, each bearing different colors and represent different combinations of years and scenarios. There are texts indicating percentages within each pie slice."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "FVELjuBEqS8K8Zi4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region defined by [0.78, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts the chart legend, which contains two items: \"Sustainable Living\" and \"Automation Technology\", each paired with a color box matching the corresponding pie slice's color."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "QbPOUMzY5Jtof4pJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content inside the coordinates [0.223, 0.516, 0.482, 0.822]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a pie slice. Inside the slice, there is a text indicating the year \"2040\" [0.324, 0.614, 0.368, 0.634] and a text displaying the percentage \"14.7%\" [0.198, 0.705, 0.233, 0.725]."
+            }
+        ],
+        "id_raw": 502
+    },
+    {
+        "id": "C5O9h4ivp9me4vLy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.259, 0.021, 0.704, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle, it's called \"Community Development Funding from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "lAmI2ylcDO4gr7SS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object at coordinates [0.908, 0.063, 0.993, 0.368] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend which contains marking for the financial years from 2015 to 2019 in different colors."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "40x45N9dhpXgrFsH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described in the object located at coordinates [0.952, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text, reading \"2017\"."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "TY7AeONccvxTK5RN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color relation between {the '2015' label in the legend}[0.952, 0.071, 0.987, 0.091] and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '2015' label color matches with {the slice with corresponding color}[0.24, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "zSfmmtV9GMkpWKnu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the related object in the graph to the object at {coordinates}[0.952, 0.31, 0.987, 0.331]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the year 2018 in the legend which relates to {the slice which represents year 2018}[0.482, 0.15, 0.703, 0.516]."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "3EDw9tMUueCTyv1D",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the object at coordinates [0.952, 0.281, 0.987, 0.301] correlate to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at the coordinates is the label '2017', it has a corresponding slice in the pie chart marked by the same color at coordinates [0.482, 0.323, 0.741, 0.583]."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "ar8W8XrtLWDzt5gZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.223, 0.516, 0.482, 0.66] comprise?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises a pie slice representing the data of 2017 with percentage displaying as \"5.8%\"."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "0fgYOz2PVJNCgQ2H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in the plot with coordinates [0.482, 0.323, 0.741, 0.583]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a slice of the pie chart corresponding to the label '2017'. It displays a percentage of '11.6%', falling in the year 2017."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "nl5j0PB9esJTSJWe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.482, 0.15, 0.703, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart, marked with the year 2018 and percentage as '13.8%'."
+            }
+        ],
+        "id_raw": 504
+    },
+    {
+        "id": "CS78x9F2T0PaOZJP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.317, 0.021, 0.646, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle of the chart, which says \"Resources distribution for Health issues\"."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "NF10NYeJPb1sclK0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.776, 0.281, 0.96, 0.301] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend label \"Tuberculosis\"."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "99EYrDRHAxpiC9aE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can I find at [0.441, 0.286, 0.476, 0.306]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Diabetes\" can be found at these coordinates."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "9R3hAZakpmc7Xh63",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart is next to {the slice}[0.482, 0.173, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.147, 0.579, 0.516] is adjacent to it."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "Z4H1cy9aV1CZfIeo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle pertaining to the legend item \"Cancer\" with text coordinates [0.876, 0.161, 0.925, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle color is \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "LVDaXFQSgpzQrIlk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice does the text \"Asthma\" at [0.395, 0.307, 0.43, 0.327] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.344, 0.171, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "shL7Djgd8WEffWOm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region constitutes the main pie chart with various slices that represent different health issues e.g., {the slice for \"Cardiac Disease\"}[0.223, 0.278, 0.482, 0.859] and {\"Obesity\"}[0.385, 0.516, 0.722, 0.886] marked appropriately with their respective percentages."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "2Igf6VtKdbsMN6m6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements reside in the region within coordinates [0.831, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. Each legend item includes a color handle and a label, like {\"Cardiac Disease\"}[0.876, 0.071, 0.987, 0.091], and {\"Obesity\"}[0.876, 0.101, 0.929, 0.121]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "Zs9ECpDKwklUU1NB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the region marked by [0.482, 0.173, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encapsulates a slice of the pie chart, specifically representing the health issue {\"Cancer\"}[0.56, 0.336, 0.603, 0.356]. The slice is not exploded and it has an auto-text displaying \"10%\" [0.665, 0.194, 0.714, 0.214]."
+            }
+        ],
+        "id_raw": 508
+    },
+    {
+        "id": "9XEtkAlM6CCo9LgT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the major object with these coordinates [0.241, 0.021, 0.722, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle known as \"Population Growth across Different Eras in Human History.\""
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "kHc0HNXtSxOtJ30X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.815, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the chart's legend."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "41499JhioQ3bmKch",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at coordinates [0.763, 0.441, 0.846, 0.461] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"72.674%\"."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "XPfjRbZDOiZzAS5q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text should refer to the pie slice presented by {the slice}[0.283, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Modern Age\"}[0.604, 0.471, 0.666, 0.491] refers to the slice."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "LcH7b11Kp7TZX6YE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color patch from the legend corresponds to the object labelled as {\"Classical Antiquity\"}[0.301, 0.479, 0.354, 0.499]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color patch with coordinates [0.821, 0.133, 0.848, 0.147] in the legend corresponds to \"Classical Antiquity\"."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "pSoMcqUAxyKfAXWR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the association between the pie slice with co-ordinates [0.279, 0.278, 0.482, 0.516] and the subtitles located in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to \"Neolithic\" subtitle in the legend {[0.86, 0.071, 0.92, 0.091]}."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "MxO1eZ7vJmxD4H4c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description for the region in the chart within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes pie slices corresponding to different eras in human history, with each slice having a particular color and labeled with the corresponding era's name and its population growth percentage. Notably, there is a larger slice representing the \"Modern Age\" {[0.283, 0.146, 0.741, 0.886]} with a population growth of \"72.674%\" {[0.763, 0.441, 0.846, 0.461]}."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "Nf9HfXRF36GlAZ8O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_109.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components present in the region [0.815, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region for the legend of the chart, which contains color patches and corresponding labels representing the different eras - \"Neolithic\", \"Bronze Age\", \"Classical Antiquity\", \"Middle Ages\", and \"Modern Age\"."
+            }
+        ],
+        "id_raw": 509
+    },
+    {
+        "id": "RoqsubDFKs5gYEbP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.134, 0.021, 0.829, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the subtitle, with its text being \"Cybersecurity Issues: Phishing Attacks and Data Breaches for years 2018 and 2019\"."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "7lWdGgD9NUpKnJ43",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.704, 0.761, 0.858, 0.781] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text object with the content \"21.2%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "lel520omMyR1zV0d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.794, 0.163, 0.822, 0.177] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates is a legend handle, specifically for 'Data Breaches 2019'."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "OGWPT2NqNqI8yYjF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.229, 0.516, 0.54, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.482, 0.516, 0.741, 0.876] is adjacent to the mentioned slice."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "4KmrWTCCR62FsLDa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects next to {the legend}[0.789, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are no other objects immediately next to the legend; it is in its own distinct region."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "3VUm0X0OVPhVi3Pe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the legend item {Phishing Attacks 2019}[0.833, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to the Phishing Attacks 2019 legend item is {the slice}[0.482, 0.516, 0.741, 0.876]."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "3rKtVA5tVgRpneVk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with four different slices, representing categories of Cybersecurity issues observed in 2018 and 2019. Each slice has its own label and percentage representation."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "AyBbICoI8gXQsVni",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.482, 0.516, 0.741, 0.876]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice labeled with text \"Phishing Attacks 2019\" located at [0.581, 0.645, 0.625, 0.665] and the corresponding percentage \"21.2%\" at [0.704, 0.761, 0.858, 0.781]."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "Jg9M8VNjaS8vvL0j",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region [0.223, 0.278, 0.482, 0.598]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes one of the pie chart slices. It represents \"Phishing Attacks 2018\" as indicated by the text at coordinates [0.309, 0.454, 0.352, 0.474]. The slice also includes an annotation showing its percentage \"14.7%\" at the coordinates [0.051, 0.41, 0.205, 0.43]."
+            }
+        ],
+        "id_raw": 511
+    },
+    {
+        "id": "ZnVMLcM94L2J1XHq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located at the coordinates [0.952, 0.071, 0.987, 0.091] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2011\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "xI4RgAH9Y010apBY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the nature of the object present at [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a {pie chart}[0.223, 0.146, 0.741, 0.886] showing the 'Culture and Arts Industry Development' data from 2011 to 2020."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "I4I4WP3OAqwfdVsy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.238, 0.021, 0.725, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's {the subtitle}[0.238, 0.021, 0.725, 0.047] of the chart which says 'Culture and Arts Industry Development from 2011 to 2020'."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "soCyChCxsu6Wzv5J",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"15.0%\"}[0.669, 0.813, 0.704, 0.833] and {the pie slice}[0.482, 0.516, 0.722, 0.875] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" represents the percentage that the corresponding pie slice occupies in the whole chart."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "K9ht3zhH98aeRBJB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the color '#99ffbb'}[0.913, 0.312, 0.941, 0.326] on the legend have to do with {the pie slice}[0.459, 0.146, 0.615, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#99ffbb' in the legend corresponds to the pie slice in the same color, representing the year 2019."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "BFQJoq64o2cKe455",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the association of the data {2014}[0.417, 0.718, 0.452, 0.738] in relation to the {color '#ffcc99'}[0.913, 0.163, 0.941, 0.177] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data '2014' is associated with the color '#ffcc99' in the legend, which indicates that the same color on the pie chart corresponds to the year 2014."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "OzJwL0m71VvZfdS3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region framed by [0.455, 0.300, 0.785, 0.400]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains slices of the pie chart, specifically for the data years '2018', '2019', and '2020'. The values associated with those years are \"22.5%\", \"10.0%\", and \"12.5%\" respectively."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "gNrQWLUdAS3CEKdR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.482, 0.200, 0.741, 0.655] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart, specifically, the slice corresponding to the year '2018' with the given percentage value of '22.5%'."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "nNUob1KsA4Tf6Obc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart elements fall within the bounding box with coordinates [0.722, 0.516, 0.987, 0.860]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", and their corresponding color markers contained in the legend of the pie chart."
+            }
+        ],
+        "id_raw": 512
+    },
+    {
+        "id": "sLd4PKT9cKz5uoiu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.283, 0.021, 0.68, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"Proportion of Different Financial System Policies\"."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "6Oth9QSIMLUQ7Dsr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.223, 0.278, 0.74, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing \"Quantitative Easing\"."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "IKk0xqSLZiZrQCk3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.806, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "gZ0hoiLyV5OPKJWn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item}[0.85, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {Quantitative Easing}<r>[0.223, 0.278, 0.74, 0.886]</r> corresponds to the legend item."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "kptWMkfgc5kH64qq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located near the {slice}[0.283, 0.146, 0.741, 0.542] representing \"Fiscal Stimulus\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"40.0%\"}<r>[0.588, 0.128, 0.691, 0.148]</r> is located near the slice \"Fiscal Stimulus\"."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "WgO3VAhQJOVNfhh5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_113.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices and the legend items in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices in the chart correspond to the legend items. The slice \"Quantitative Easing\" corresponds to the blue legend item, and the slice \"Fiscal Stimulus\" corresponds to the red legend item."
+            }
+        ],
+        "id_raw": 513
+    },
+    {
+        "id": "SCrNWcXQZ6opY8Ra",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.152, 0.478, 0.197, 0.498] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"20.0%\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "VEpF5Grfc7jIdngC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at coordinates [0.917, 0.161, 0.987, 0.181] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend item \"Neighbors\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "FEDMUSIDhYDbie3H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies at the coordinates [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's one of the slices of the pie chart, representing \"Family\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "V2v8AxhC2nxe3aTN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does {the legend item}[0.917, 0.161, 0.987, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Neighbors\" corresponds to {the pie slice}[0.283, 0.157, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "j6E213sRNk7TxStD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the percentage text}[0.665, 0.194, 0.734, 0.214] and {the corresponding slice}[0.419, 0.146, 0.741, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"30.0%\" indicates the proportion of the slice that represents \"Colleague\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "nQGHIPLcwq3rZAFC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item matches with {the slice}[0.262, 0.516, 0.74, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"Friends\"."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "GXPovCxm9YU540tZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the primary pie chart, complete with its slices and labels, but does not include the legend or title of the chart."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "3zhuKg2UNDl6PzMN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region from coordinates [0.872, 0.063, 0.993, 0.189]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart. The legend has four entries: \"Family\", \"Friends\", \"Colleague\", and \"Neighbors\", each with a color corresponding to their respective slice on the pie chart."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "ag8aplauOyrxEPQp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are found in the region defined from coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart, which includes four slices representing \"Family\", \"Friends\", \"Colleague\", and \"Neighbors\", along with their corresponding percentage labels."
+            }
+        ],
+        "id_raw": 516
+    },
+    {
+        "id": "15LxWjtg1wUOi1sB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary label or name of the pie slice at this location [0.419, 0.146, 0.648, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This pie slice is labeled as \"French\"."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "r4KcJjCFXIXcPYrP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat category is represented by the legend item at [0.924, 0.161, 0.97, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item represents the \"French\" category."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "SZtJzrxdDUdJpw5e",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.766, 0.47, 0.809, 0.49] within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"25.0%\"."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "m4GeOqXtZcaAqVzS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the percentage text {at}[0.546, 0.109, 0.592, 0.129] correspond to the legend entries?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"15.0%\" corresponds to {the legend entry for \"French\"}[0.924, 0.161, 0.97, 0.181]."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "jVyKa144gporc238",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich palette color in the legend matches the slice labeled {as \"Chinese\"}[0.449, 0.727, 0.493, 0.747]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {color palette}[0.885, 0.103, 0.913, 0.117] of the \"Chinese\" slice matches the legend."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "3qemDuxZOXbD3EwA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice corresponds to the legend item with the label name {as \"Japanese\"}[0.924, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {marked with the label \"Japanese\"}[0.223, 0.278, 0.482, 0.712] corresponds to the legend item \"Japanese\"."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "kt5LU8IDZrKMtKWw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.419, 0.146, 0.648, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with the label {\"French\"}[0.495, 0.29, 0.539, 0.31], and a percentage indicator of {\"15.0%\"}[0.546, 0.109, 0.592, 0.129]."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "gvnv8CXxna9JoC7j",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region bounded by the coordinates [0.262, 0.516, 0.68, 0.886] within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region features a pie slice identified by a label {\"Chinese\"}[0.449, 0.727, 0.493, 0.747] and a text displaying the value {\"30.0%\"}[0.406, 0.912, 0.462, 0.932]."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "yHT6OPEj2fcEfFmy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is contained within the top left quadrant of the chart, approximately within the region [0.223, 0.278, 0.482, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds a pie slice identified as {\"Japanese\"}[0.305, 0.49, 0.348, 0.51], with a corresponding percentage text of {\"20.0%\"}[0.134, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 519
+    },
+    {
+        "id": "Y2IhOZqyLCA9ORYP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at [0.367, 0.021, 0.596, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Media Usage in 2020\"."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "UEhzkv3KAWz4vAp2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.917, 0.071, 0.982, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Facebook\" in the legend."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "IgY8zZZWY0vZfIDd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contains the figure at [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart representing the social media usage data for 2020."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "lFHGtg7FLZGIjRdt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color associated with the {Facebook slice}[0.223, 0.278, 0.482, 0.584] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is {red}<r>[0.878, 0.073, 0.905, 0.087]</r>."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "1CCBaBwvOkUmfLiq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is represented by the {Snapchat legend}[0.917, 0.191, 0.982, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represented is {the slice}<r>[0.482, 0.331, 0.741, 0.628]</r>."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "UuOw0aiNUtyYORg9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is closest to the {YouTube slice}[0.482, 0.147, 0.706, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {TikTok}<r>[0.917, 0.251, 0.959, 0.271]</r> is the closest."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "iJBh2gIalK0JS5Pv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are there within the region [0.282, 0.626, 0.743, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes slices of the pie chart that represent the usage of different social media platforms. Specifically, it includes the slices for {Twitter}<r>[0.381, 0.516, 0.602, 0.886]</r>, {LinkedIn}<r>[0.482, 0.516, 0.728, 0.844]</r>, and part of {Snapchat}<r>[0.482, 0.331, 0.741, 0.628]</r>."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "b845yEv7rkg5u5Ft",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region within coordinates [0.872, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend that lists the social media platforms represented in the pie chart. Specifically, it contains {Facebook}<r>[0.917, 0.071, 0.982, 0.091]</r>, {Instagram}<r>[0.917, 0.101, 0.987, 0.121]</r>, {Twitter}<r>[0.917, 0.131, 0.963, 0.151]</r>, {LinkedIn}<r>[0.917, 0.161, 0.975, 0.181]</r>, {Snapchat}<r>[0.917, 0.191, 0.982, 0.211]</r>, {YouTube}<r>[0.917, 0.221, 0.973, 0.241]</r>, and {TikTok}<r>[0.917, 0.251, 0.959, 0.271]</r>, along with color codes that correspond to each platform's slice in the pie chart."
+            }
+        ],
+        "id_raw": 520
+    },
+    {
+        "id": "EKx0JmO4pa7sK60a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object found at these coordinates [0.887, 0.161, 0.956, 0.181] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's labeled as \"Education\"."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "fZu1IQrxkaLD20c2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the text with the coordinates [0.399, 0.302, 0.443, 0.322] belong to on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to the \"Entertainment\" slice of the pie."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "Q9ChXDXVk061jwlu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.975, 0.073, 1.003, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the color scheme associated with the \"Healthcare\" category in the legend."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "OsswIORhj7beIGcA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend color scheme}[0.848,0.163,0.876,0.177] item?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the color scheme item is {the \"Education\" slice}[0.482,0.147,0.701,0.516]."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "cvUDk4UF8uTeRL9K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich items in the legend does {the pie chart slice}[0.283, 0.146, 0.5, 0.516] relate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slice corresponds to the {legend item \"Entertainment\"}[0.887, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "gyIvyqjgKwxX4Nxt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the label}[0.887, 0.131, 0.987, 0.151] and {the pie slice}[0.482, 0.32, 0.741, 0.655] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Transportation\" in the legend matches the color and represents the pie slice in the chart."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "hyAQ0Bvfk9RBzo0k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.147, 0.701, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the slice of the pie chart representing \"Education\", as well as its associated percentage label of \"15.0%\". The \"Education\" label's coordinates are [0.54, 0.316, 0.584, 0.336], and the percentage label's coordinates are [0.628, 0.157, 0.698, 0.177]."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "JtlY6FC1spNFhvYe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region defined by coordinates [0.223, 0.278, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this defined region is the \"Healthcare\" slice of the pie chart which is represented with the label \"Healthcare\" [0.317, 0.593, 0.36, 0.613] and the percentage text \"35.0%\" [0.143, 0.665, 0.219, 0.685]."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "t9tzKiYdf2cPBgv8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart. It includes 5 different categories, each represented by a distinct slice and labeled with a category name and its corresponding percentage."
+            }
+        ],
+        "id_raw": 522
+    },
+    {
+        "id": "ltspOMuYS0pVpFaI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.264, 0.021, 0.699, 0.047] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, called \"Transportation & Logistics Trends from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "hipiDVl25Hl0qTNS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at the coordinates [0.908, 0.063, 0.993, 0.219] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "Lvp1bfkc9I2fCGqa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the set of coordinates [0.165, 0.438, 0.201, 0.458]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"16.9%\"."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "R4VTnz0hzfRkyyUZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin the chart, which pie section is adjacent to the section {at}[0.282, 0.146, 0.645, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section {at}[0.481, 0.22, 0.741, 0.663]."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "FqEtyZjtd1NFh4Ur",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nNear {the legend}[0.908, 0.063, 0.993, 0.219], what else can we find?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year labels \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" can be found near the legend."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "Fq5LECWh30y5i61t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color label on the legend corresponds to the pie segment with the coordinates [0.282, 0.146, 0.645, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color label \"2020\", found {at}[0.952, 0.191, 0.987, 0.211], corresponds to the pie segment with those coordinates."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "cBJczvybkNzzb5G9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region marked by the coordinates [0.718, 0.516, 0.223, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie segment labeled as \"2018\", with its value \"19.8%\"."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "41uhlfPUvZbLSa4a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components within the region outlined by the coordinates [0.282, 0.146, 0.645, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, you will find the pie section labeled \"2020\" and its value \"24.7%\"."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "p2MmuqPSlVYv77tU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area defined by the coordinates [0.223, 0.278, 0.482, 0.647]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a pie segment representing the year \"2016\" and its respective percentage of \"16.9%\"."
+            }
+        ],
+        "id_raw": 524
+    },
+    {
+        "id": "4be1y6oop0eaTNJ9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at the following coordinates [0.797, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend area of the chart."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "3TNFtkbLyXcMCSmz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be identified in coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains all the slices of the pie chart, each representing a different policy."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "TpCx5WFYc9QDewSZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at the coordinates [0.189, 0.021, 0.774, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, reading \"Percent Representation of Different Policies in Global Financial System\"."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "n27GEJTgDUXvCkWj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located next to {the handle color '#ff9999'}[0.802, 0.073, 0.83, 0.087] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Inflation Control\"<r>[0.841, 0.071, 0.953, 0.091]</r> is located next to it."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "YaBDJaZTsePJUcz9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend item \"Financial Stability\" correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Financial Stability\" corresponds to the {slice}<r>[0.283, 0.202, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "V8idXDuHiq0qAAFa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is connected to the legend color '#ffb3e6'<r>[0.802, 0.223, 0.83, 0.237]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#ffb3e6' corresponds to the policy \"Market Liberalization\"<r>[0.841, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "3J9v4pj0oAXU5t2S",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart with all its slices, each labeled with the corresponding policy and its representation percentage."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "1hcbhbiWXFVmTy3G",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region under coordinates [0.797, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the chart's legend, each with a unique color handle and a corresponding policy text."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "zdtT0G7Q4oSDtYvJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region of coordinates [0.189, 0.021, 0.774, 0.047]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the chart's subtitle that reads \"Percent Representation of Different Policies in Global Financial System\"."
+            }
+        ],
+        "id_raw": 525
+    },
+    {
+        "id": "ntIhi08U4rhtJPsa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text object located at the coordinates [0.276, 0.021, 0.687, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, which reads \"Design Innovation distribution from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "IfZdXgqptrl1W2av",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object located at the coordinates [0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color code for 2015, which is '#66b3ff'."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "ROxtbIQU4HnM3yWl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.367, 0.115, 0.402, 0.135] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text showing \"18.7%\"."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "hO9QF3E0jMgJ6Pcv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.913, 0.223, 0.941, 0.237] related to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's related to {the pie slice}[0.482, 0.163, 0.737, 0.516], indicating the data for the year 2019."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "ZfxewElxHn9QoDIg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is adjacent to the legend item {2016}[0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {2015}[0.913, 0.103, 0.941, 0.117] and {2017}[0.913, 0.163, 0.941, 0.177] are adjacent to it."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "vkjRXPVpKHLVIXCb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the pie slice}[0.283, 0.146, 0.559, 0.516] and {the legend item}[0.913, 0.253, 0.941, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice and the legend item both represent the year 2020 in the data."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "N8yt3Gu2ZM1yqwLL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains seven pie slices corresponding to years from 2014 to 2020, with each slice color and percentage labeled. For instance, the slice for 2014 is at [0.224, 0.278, 0.482, 0.516] with a text \"9.5%\" at [0.184, 0.348, 0.219, 0.368]."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "oOvTiSK0e9QEVTfF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legends. Each object within the legend corresponds to a different year from 2014 to 2020 and has a different color, starting from {2014}[0.913, 0.073, 0.941, 0.087] at the top to {2020}[0.913, 0.253, 0.941, 0.266] at the bottom."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "gnYdKOcyGr4PLCRi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region with the coordinates from [0.456, 0.516, 0.66, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for the year 2017 [0.456, 0.516, 0.66, 0.886] and its corresponding percentage \"13.7%\" at [0.574, 0.891, 0.609, 0.911]."
+            }
+        ],
+        "id_raw": 526
+    },
+    {
+        "id": "AnMsT7Ul6APwmCgb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.243, 0.021, 0.72, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Technology Companies and Innovative Business Revenue\"."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "PlyGVbKoYyMO6NfN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.922, 0.071, 0.961, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text label \"Apple\" in the legend."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "2S6TEWlyAon9T1AQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.162, 0.583, 0.202, 0.603]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text indicating the autotext \"28.3%\" of a pie slice."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "7jq3OS4vgTmMSOQG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color related to {the text \"Apple\"}[0.922, 0.071, 0.961, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The related color is '#ff9999'."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "F6x0xKtrya3fO646",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is associated with the {slice} [0.283, 0.229, 0.482, 0.516] present in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is associated with the legend {entry \"Tesla\"} [0.922, 0.251, 0.956, 0.271]."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "8NmdgD9eVlxokFaR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the {slice} [0.223, 0.278, 0.482, 0.842] and the {label \"Apple\"} [0.922, 0.071, 0.961, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the share of the \"Apple\" company, as specified by the label."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "fWtxybPk6Meu25Zm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in the area defined by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains multiple pie segments. Each segment represents a different company's share of technology company revenue, with colors and labels for 'Apple'[0.307, 0.548, 0.351, 0.568], 'Microsoft'[0.45, 0.728, 0.494, 0.748], 'Amazon'[0.605, 0.583, 0.649, 0.603], 'Google'[0.539, 0.315, 0.583, 0.335], 'Facebook'[0.422, 0.292, 0.457, 0.312], 'Netflix'[0.375, 0.324, 0.41, 0.344] and 'Tesla'[0.355, 0.348, 0.39, 0.368]."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "X4u21bMcw01CEHKO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.877, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart with company names and their corresponding pie slice colors, including 'Apple'[0.922, 0.071, 0.961, 0.091], 'Microsoft'[0.922, 0.101, 0.985, 0.121], 'Amazon'[0.922, 0.131, 0.978, 0.151], 'Google'[0.922, 0.161, 0.971, 0.181], 'Facebook'[0.922, 0.191, 0.987, 0.211], 'Netflix'[0.922, 0.221, 0.967, 0.241] and 'Tesla'[0.922, 0.251, 0.956, 0.271]."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "n4p3zvBtiyuZdcxU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area defined by the coordinates [0.877, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the legend of the chart, which includes labels of various technology companies such as Apple, Microsoft, Amazon, Google, Facebook, Netflix, and Tesla. Each label is associated with a color marker that corresponds to the respective pie slice in the pie chart."
+            }
+        ],
+        "id_raw": 527
+    },
+    {
+        "id": "8WPtzQX71XJJl1vW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main element with the coordinates [0.337, 0.021, 0.626, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Industry Market Share Comparison."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "V53t69yBB0CfLgFO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich component can be found at coordinates [0.808, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "40sUEQ7gKKx29LQu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.262, 0.687, 0.317, 0.710] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.3%\"."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "4FaMeIpcKzV1f03I",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to {the slice}[0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Textiles}[0.853, 0.071, 0.904, 0.091] corresponds to the slice."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "PUpGfYq9CMfXD75X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are situated near position of {the subtitle}[0.337, 0.021, 0.626, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie chart}[0.223, 0.146, 0.741, 0.886] is situated near the position of the subtitle."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "FfU271XTfqcXmM4d",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring sections to the  {pie slice}[0.262, 0.516, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring slices are 'Textiles' {slice}[0.223, 0.278, 0.482, 0.712] and 'Automobile'{slice}[0.464, 0.449, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "eg3erNXRH0pwLsQ3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.223, 0.278, 0.482, 0.712]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the label {Textiles}[0.24, 0.484, 0.284, 0.504] and also the corresponding data {20.0%}[0.146, 0.478, 0.197, 0.498]. This region represents the market share of the 'Textiles' industry."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "WHyTmUaKBdg3g6g7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items contained in the region [0.262, 0.516, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region harbors the label {Oil and Gas}[0.346, 0.776, 0.39, 0.796] and its corresponding market share {15.0%}[0.255, 0.855, 0.335, 0.875]. This portion shows the market share of the 'Oil and Gas' industry."
+            }
+        ],
+        "id_raw": 529
+    },
+    {
+        "id": "nbpwtCH1bTaW8lsU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.34, 0.021, 0.623, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Global Imports from 2010 to 2019."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "k4VlwbLQIn5ljBxt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "NpWCsqJ8bJmmPmqC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart is presented in this location."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "O7g3vnEfzo9qwEhG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the color at {handle}[0.913, 0.342, 0.941, 0.356] in the legend and the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color used at this handle in the legend represents the year 2019 on the pie chart."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "QQdevhI5wQMtd4bR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart is related to {the text 2012}[0.336, 0.631, 0.371, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice [0.237, 0.516, 0.482, 0.797] corresponds to the year 2012."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "KtN2oiQqP7MSSGng",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the text 7.6%}[0.191, 0.326, 0.226, 0.346] relate to the pie chart and the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"7.6%\" refers to the percentage corresponding to the year 2010 slice in the pie chart, which is also represented by the color at handle [0.913, 0.073, 0.941, 0.087] in the legend."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "4Cyf5j7V6SX2Sxpr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart representing Global Imports from 2010 to 2019. Each slice of the pie chart is associated with a specific year, and there are corresponding percentage texts outside each slice."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "HJc9PfDo8FHhhTpt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details of the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend that describes the colors associated with each year of the pie chart, ranging from 2010 to 2019. Each item in the legend is composed of a color handle and a year label."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "Q86np566GtNLGiOM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area covered by coordinates [0.152, 0.516, 0.152, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location for a y-label, however, it appears that there is no y-label provided in this chart."
+            }
+        ],
+        "id_raw": 530
+    },
+    {
+        "id": "f1xzphO0JMozC9Eh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location denoted by the coordinates [0.199, 0.021, 0.764, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the pie chart, which is 'Scientific Research and Technological Innovation from 2015 to 2020'."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "w7wJuk8cD4j6QPak",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary element in the chart with coordinates [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "oy7Y2AHBGAmTS4sE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.405, 0.103, 0.44, 0.123] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"23.1%\"."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "miIgXwYjtRISpBlf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's slice is adjacent to the {2015 slice}[0.223, 0.278, 0.482, 0.543] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {2016 slice}[0.223, 0.516, 0.482, 0.812] is adjacent."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "cpgiFYbZO8xiqeNV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the pie chart does {the legend item \"2020\"}[0.952, 0.221, 0.987, 0.241] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2020\" corresponds to the {slice}[0.283, 0.146, 0.623, 0.516]."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "6F6fENkmq9FCC4ZE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correlation between the slices on the chart and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice corresponds to a unique legend item, which denotes a specific year and the slice's color."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "FEUGCnDPP7Ig8tfr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region between the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the main body of the pie chart. It displays several slices, each representing a different year with its respective color, and every slice has a percentage and a year text."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "sW0j0RwjkNTgQwZs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.482, 0.516, 0.737, 0.867]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice representing the year 2018 with the associated percentage \"17.3%\" [0.699, 0.769, 0.734, 0.789] and the text \"2018\" [0.578, 0.649, 0.622, 0.669]."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "Fiq3vjIOK7jA8Dvv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the features in the region [0.223, 0.278, 0.482, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice representing the year 2015 with the associated percentage \"12.3%\" [0.175, 0.381, 0.21, 0.401], and the text \"2015\" [0.312, 0.438, 0.356, 0.458]."
+            }
+        ],
+        "id_raw": 531
+    },
+    {
+        "id": "7UAPMb1EL9gfDtQ8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the subplot on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title of the subplot is \"Alien Encounters from 2022 to 2028\", located at {coordinates}<r>[0.331, 0.021, 0.632, 0.047]</r>."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "JriE86HSyTV1YjKC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object at the coordinates [0.482, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates are the center of the pie chart. They do not correspond to a specific object on the chart."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "FZRfpiKTH8JH0ZAw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates are for the text \"2022\" located in the legend."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "fRoEGI6zvEo0CwM6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice representing the year 2028?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice representing 2028 is #a3a5ff, the slice{coordinates}<r>[0.283, 0.146, 0.741, 0.519]</r>."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "guT8YHcTu3Ts6Jsz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a pie slice adjacent to the year 2023 slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slices representing {the years 2022}<r>[0.279, 0.278, 0.482, 0.516]</r> and {2024}<r>[0.24, 0.306, 0.482, 0.516]</r> are adjacent to the year 2023 slice."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "CTQaGfiYKmA22l89",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich handle in the legend corresponds to the pie slice for 2027?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle with the color #ffa3a4 and {coordinates}<r>[0.913, 0.223, 0.941, 0.237]</r> corresponds to the 2027 pie slice."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "Nvy1EBjBqkOmVYPM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region defined by the coordinates [0.482, 0.146, 0.741, 0.879]."
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates encompass parts of the pie chart, including the slices for the years 2026, 2027, and part of the slice for 2028. The percentage associated with 2026, \"19.7%\", and the label for 2027 are also within these coordinates."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "HS8Q2hvOA4pf5ZgR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region [0.223, 0.516, 0.482, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains part of the pie chart. Specifically, it includes the slice corresponding to the year 2026, which is represented by the color #c2c2f0. Also, the label \"2026\" and the corresponding percentage \"19.7%\" fall within this region."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "T07k6rWrCZysbUVt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you discuss the objects located in the region [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes all of the legend entries and their corresponding color handles for each year from 2022 to 2028."
+            }
+        ],
+        "id_raw": 532
+    },
+    {
+        "id": "2ow7itwBMq1DSgmK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described at coordinates [0.648, 0.837, 0.68, 0.857] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a percentage label \"92.0%\"."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "9DHGPUxZtygAqGEl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item is located at the coordinates [0.914, 0.103, 0.941, 0.117] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color handle for the \"Soda\" category in the legend."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "xsnlHmp0hBJXW3QC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is found at the coordinates [0.391, 0.021, 0.572, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart subtitle \"Product Sales in 2020\"."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "7wIBUCsRizzBRSdD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart includes {the text}[0.281, 0.175, 0.316, 0.195]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's included in the {Soda slice}<r>[0.283, 0.171, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "AXwCrMyxW7cSUe1M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which slice of the pie does {the legend color code}[0.914, 0.073, 0.941, 0.087] correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {Beer slice}<r>[0.223, 0.146, 0.741, 0.886]</r>."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "eii5N9CCfDhTGLiO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend item \"Soda\"}[0.953, 0.101, 0.987, 0.121] represent in the chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the {light blue slice}<r>[0.283, 0.171, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "WWhMvj5LXYNK21vT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises a slice of the pie chart labeled \"Beer\" [0.55, 0.687, 0.594, 0.707] and percentage \"92.0%\" [0.648, 0.837, 0.68, 0.857]. This slice represents 92.0% of total product sales in 2020."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "xD6WreH94YzNXVbL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.283, 0.171, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a lighter blue slice of the pie chart, with the text label of \"Soda\" [0.374, 0.325, 0.409, 0.345] and the percentage of \"8.0%\" [0.281, 0.175, 0.316, 0.195]. These denote that Soda accounts for 8.0% of total sales."
+            }
+        ],
+        "id_raw": 533
+    },
+    {
+        "id": "V9nUmzOe5ZDjxtTu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart graph located at [0.321, 0.021, 0.642, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Future Energy and Resource Allocation\"."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "HY3fHDbdgRZRNc0D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located at coordinates [0.182, 0.353, 0.217, 0.373] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The information is percentage \"10.0%\"."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "jhd2j27q6Nqv3Qai",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color code corresponding to the handle located at [0.866, 0.223, 0.894, 0.237] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code is \"#ffb3e6\" which corresponds to \"Nuclear\" in the legend."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "CFKU9EK7xn6wDq45",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.223, 0.49, 0.482, 0.859], [0.283, 0.244, 0.482, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "cGyel25jBjfPkTTG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the slice}[0.385, 0.516, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"5.0%\"}[0.396, 0.903, 0.418, 0.923] and {\"Bio\"}[0.429, 0.722, 0.464, 0.742] are located near the slice."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "1VZRCoiPDqowdROH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correspondence between the legend items and the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Solar\", \"Wind\", \"Bio\", \"Geothermal\", \"Fossil\", \"Nuclear\", \"Hydro\" and \"Wave\" correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.516], [0.223, 0.49, 0.482, 0.859], [0.385, 0.516, 0.482, 0.885], [0.464, 0.516, 0.59, 0.886], [0.482, 0.264, 0.741, 0.852], [0.451, 0.146, 0.672, 0.516], [0.306, 0.148, 0.482, 0.516], and [0.283, 0.244, 0.482, 0.516] respectively."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "srnsroeSjihbny7m",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the quadrant within coordinates [0.223, 0.146, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This quadrant contains two slices representing \"Solar\" with coordinates [0.223, 0.278, 0.482, 0.516] and \"Bio\" with coordinates [0.385, 0.516, 0.482, 0.885]. It also contains the associated labels and percentage details located at [0.182, 0.353, 0.217, 0.373], [0.316, 0.423, 0.359, 0.443], [0.396, 0.903, 0.418, 0.923] and [0.429, 0.722, 0.464, 0.742], respectively."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "GY4X9tk5pEw85qqR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.482, 0.264, 0.741, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a single pie slice of energy allocation, that represents \"Fossil\" with the corresponding percentage \"30.0%\" located at [0.761, 0.585, 0.799, 0.605]."
+            }
+        ],
+        "id_raw": 536
+    },
+    {
+        "id": "X01voHf8NuexlqUC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart given its location is at the coordinates [0.225, 0.021, 0.738, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Distribution in Knowledge Economy and Innovation Industries\"."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "4HyAFORtEUTOs1Td",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information can be seen at the coordinates [0.786, 0.073, 0.814, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a color indicator in the legend corresponding to Nanotechnology."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "GbsmmFfyms1UFeTB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the text at location [0.348, 0.358, 0.383, 0.378] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Cryptocurrency\"."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "AOLv5MgppP8nS8ep",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend is connected to the pie slice at {the slice}[0.482, 0.516, 0.738, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Information Technology\" in the legend is connected to this slice."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "WoXP1KjSVK5m3Jg6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are near to {the coordinates}[0.304, 0.511, 0.348, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice related to \"Nanotechnology\" is near to these coordinates."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "Fbs7ZoIPWyD37dtV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat subtittle is near to the pie slice located at {the slice}[0.482, 0.516, 0.738, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Distribution in Knowledge Economy and Innovation Industries\" is near the pie slice."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "aOt5YwaiXmOwiVDj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.223, 0.278, 0.482, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a pie slice which represents \"Nanotechnology\" with the corresponding percentage text \"23.0%\"."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "2rS04L4g20x9ntz2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region from [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains eight pie slices with their corresponding percentage texts in it, representing the sectors Nanotechnology, Biotechnology, Information Technology, Artificial Intelligence, Internet of Things (IoT), Robotics, Quantum computing, and Cryptocurrency."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "l24Rd1RnJnMzWOFV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_138.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.78, 0.063, 0.993, 0.309]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the chart's legend. It contains different colors associated with different sectors of the economy: Nanotechnology, Biotechnology, Information Technology, Artificial Intelligence,  Internet of Things (IoT), Robotics, Quantum computing, and Cryptocurrency."
+            }
+        ],
+        "id_raw": 538
+    },
+    {
+        "id": "oV8Sa8i00mlp7QEJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.346, 0.021, 0.618, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Reforestation from 2010 to 2017\", which serves as the subtitle of the chart."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "jYU25UB5oizn2zUk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the red legend handle for the year 2010."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "eMClSn3QY6uBD4gN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart displaying the distribution of reforestation from 2010 to 2017."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "tImJuEj0iTA1JfE8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is adjacent to {the slice}[0.223, 0.418, 0.482, 0.614]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2011\"[0.309, 0.506, 0.344, 0.526] is adjacent to this slice."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "kBq5UknKAw1Gfa6P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation of {the legend item}[0.952, 0.071, 0.987, 0.091] with {the slice}[0.232, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2010\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "t7etjBwqcb6LUxDm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does {the text}[0.506, 0.718, 0.55, 0.738] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2014\" belongs to {the slice}[0.466, 0.516, 0.642, 0.886]."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "q9LGJ3QgHwcm81yy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region [0.745, 0.663, 0.78, 0.683]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"16.2%\"[0.745, 0.663, 0.78, 0.683] indicating the percentage of reforestation in the year 2015."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "LoEFwD27LJlHe7Gq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.913, 0.253, 0.941, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region has a colored square of the legend corresponding to the year 2016 in the pie chart."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "8l1PMxYtQGAjd6tT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.371, 0.113, 0.406, 0.133]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"19.2%\"[0.371, 0.113, 0.406, 0.133] referring to the percentage of reforestation in the year 2017."
+            }
+        ],
+        "id_raw": 539
+    },
+    {
+        "id": "n8Qi1gq5c2OgPPnE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart in this figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Population Migration and Mobility Trends\"."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "SVKPrS4ItZxNZGMo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.899, 0.071, 0.952, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Internal\"."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "a3B9khqPZj70wT2J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element located at coordinates [0.36, 0.341, 0.395, 0.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the word \"Forced\"."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "hloumswYvfl3rB5k",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices in the chart are colored #99ff99?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing \"Seasonal\" migration{slice}[0.482, 0.146, 0.73, 0.516] is colored #99ff99."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "AnIIPcBHKG3ZDdz7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is directly adjacent to the slice labeled \"Return\" {slice}[0.337, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice labeled \"Seasonal\" {slice}[0.482, 0.146, 0.73, 0.516] is directly adjacent to \"Return\"."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "iI048DP8gcvWdkvu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the \"Internal\" type migration in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#ff9999\" in the legend corresponds with the \"Internal\" type migration."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "uu7J81APb73PXyph",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements in the chart region [0.223, 0.278, 0.642, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the largest slice of the pie chart. It's colored \"#ff9999\" and represents \"Internal\" type migration. Within this slice, there's the percentage text \"46.7%\"{text}[0.224, 0.789, 0.277, 0.809]."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "bZpuGJ4rHm598uyt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.482, 0.41, 0.741, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for \"International\" migrations, colored \"#66b3ff\", with the corresponding percentage \"19.0%\"{text}[0.753, 0.629, 0.842, 0.649]."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "HsRh33bSdtiUbNTE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the components within the area [0.283, 0.209, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice labeled \"Forced\". It has the color \"#c2c2f0\" and includes the related percentage of \"4.5%\"{text}[0.245, 0.203, 0.291, 0.223]."
+            }
+        ],
+        "id_raw": 540
+    },
+    {
+        "id": "zmUcE7jVVNga6l6R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart with the coordinates [0.281, 0.021, 0.682, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title is Expected Prominence of Future Living Scenarios."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "4u2Y9sBc1gsXFCBF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified in the bounding box [0.839, 0.071, 0.965, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is one of the legend's entries, specifically for {Home Automation}<r>[0.839, 0.071, 0.965, 0.091]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "ph2jwPA3SWib2LIt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at these coordinates [0.267, 0.883, 0.375, 0.903]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a data label showing a percentage, specifically {30.0%}<r>[0.267, 0.883, 0.375, 0.903]</r>, which is part of the pie chart corresponding to its respective slice."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "6zzu8epQWgPWCDMq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color association of the legend entry {Quantum Computing}<r>[0.839, 0.191, 0.984, 0.211]</r> to the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the legend entry \"Quantum Computing\" corresponds to the slide with the color '#c2c2f0' located at coordinates [0.373, 0.146, 0.579, 0.516]."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "oyga2lI1YXRktLcU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text {15.0%}<r>[0.078, 0.414, 0.204, 0.434]</r> in the pie chart refer to in terms of the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" refers to the legend entry for {Home Automation}<r>[0.839, 0.071, 0.965, 0.091]</r> in the legend."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "5rAiivYsbn8PUVAw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector does the data label {25.0%}<r>[0.744, 0.347, 0.892, 0.367]</r> belong to on the pie chart in terms of the legend labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to the sector labelled as {Autonomous Vehicles}<r>[0.839, 0.161, 0.987, 0.181]</r> in the legend of the chart."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "saLIfZ4NK3cq3HP0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.839, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the given region, there is the text {Autonomous Vehicles}<r>[0.839, 0.161, 0.987, 0.181]</r> in the legend and a corresponding color square for the legend {#ffcc99}<r>[0.8, 0.163, 0.828, 0.177]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "vyG3s2vhFimABJgr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region with coordinates [0.839, 0.071, 0.965, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains text {Home Automation}<r>[0.839, 0.071, 0.965, 0.091]</r> and a colored square {#ff9999}<r>[0.8, 0.073, 0.828, 0.087]</r> associated with it."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "hbKacaL20l7tR5zb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.839, 0.221, 0.946, 0.241]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region, there is the text {Human Cloning}<r>[0.839, 0.221, 0.946, 0.241]</r> and its associated colored square in the legend {#a3a3c2}<r>[0.8, 0.223, 0.828, 0.237]</r>."
+            }
+        ],
+        "id_raw": 543
+    },
+    {
+        "id": "6p9MqG2qlbC42NGf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.302, 0.021, 0.661, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"In Store Shopping Trend from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "FTiiTB4nJVR8H3Xi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart is located at these coordinates."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "ZBWzBz9vBr1Sh6fL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at coordinates [0.913, 0.193, 0.941, 0.207] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a color handle for the year 2020 in the chart legend."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "1ZMCA0uXmlZIzqcO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to {the slice}[0.482, 0.154, 0.736, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.223, 0.278, 0.482, 0.762] is adjacent."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "uu23iPRuXDxVRB4z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.482, 0.154, 0.736, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2019\" [0.952, 0.161, 0.987, 0.181] corresponds to the slice."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "8lvWQkAbsihcUOkG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the pie slice}[0.482, 0.448, 0.741, 0.839] correspond to in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item with color \"#99ff99\" at coordinates [0.913, 0.133, 0.941, 0.147]."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "Wnv6qadfh9SLmHNa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.762]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of the chart with color \"#ff9999\". It has the text \"2016\" [0.304, 0.509, 0.348, 0.529] and the percentage \"22.7%\" [0.161, 0.512, 0.197, 0.532]."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "ZFbtCF3cVTb7cGEn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes all five slices of the pie chart representing the years 2016 to 2020, each slice labeled with the respective year and the percentage corresponding to In Store Shopping for that year."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "NM71kPVC7bGBerRU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are visible when you look at the region [0.908, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which has five entries corresponding to each year from 2016 to 2020. Each entry consists of a color and a label indicating the year."
+            }
+        ],
+        "id_raw": 544
+    },
+    {
+        "id": "nDaZvuC2cPJ078ks",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the subtitle at coordinates [0.34, 0.021, 0.623, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is named as \"Market Share Comparison in 2019\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "SOgvGgOVVFKvan0i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.866, 0.063, 0.993, 0.189] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend of the chart."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "XSxXctg6V7qDNoVk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.223, 0.278, 0.482, 0.874] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice with the label \"Retail\" and percentage \"32.0%\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "jYJvQce8zTaovVrd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie is related to {the legend item \"Retail\"}[0.911, 0.071, 0.95, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.223, 0.278, 0.482, 0.874] corresponds to \"Retail\"."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "3ut4rZi94ERWyz7s",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices are adjacent to the {slice \"[0.223, 0.278, 0.482, 0.874]\"} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {Agriculture} [0.415, 0.516, 0.59, 0.886] and {Healthcare} [0.283, 0.146, 0.739, 0.516] are adjacent to the \"Retail\" slice."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "90NU3OWnUe3pdV7c",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the pie slice at the coordinates [0.482, 0.472, 0.741, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Electronics\" corresponds to the slice [0.482, 0.472, 0.741, 0.852]."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "RqCYtiwF6vjE2Zm7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects within the coordinates [0.223, 0.146, 0.741, 0.886] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses four slices of the pie chart, each representing a different industry: Retail, Agriculture, Electronics, and Healthcare. Their percentages, 32.0%, 11.0%, 20.0%, and 37.0% respectively, are indicated in each slice."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "YkuTPYCe5HgAE0fi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.866, 0.063, 0.993, 0.189]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend corresponding to four industries: Retail, Agriculture, Electronics, and Healthcare. Each industry is represented with a different color."
+            }
+        ],
+        "id_raw": 547
+    },
+    {
+        "id": "KRNPp8eNwMUwu9bx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.277, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, it is called 'Distribution of Psychological Traits in a Population'."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "6DW48GpMfF90hJBq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.789, 0.063, 0.995, 0.249] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart legend."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "VFZfimr6lSG1hpRd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label has coordinates [0.833, 0.191, 0.988, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Emotional Intelligence\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "vKrcmuWerXZG668A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are inside the {pie slice}[0.225, 0.278, 0.484, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"Obsessive Compulsion\" [0.307, 0.532, 0.351, 0.552] and \"26.0%\" [0.045, 0.554, 0.201, 0.574] are inside this pie slice."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "5Sv477CsRKJDPKUg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color of slice in the Pie chart does {the legend item \"Obsessive Compulsion\"}[0.833, 0.071, 0.989, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice it corresponds to is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "82RZmf2X0GGO6mFh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn which pie slice has the \"Altruism\" label been mentioned?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Altruism\" is mentioned in the pie slice with coordinates [0.33, 0.516, 0.53, 0.886]."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "Lr0WkopVJpMk5xEg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.225, 0.278, 0.484, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie chart. Inside it, the text \"Obsessive Compulsion\" [0.307, 0.532, 0.351, 0.552] and \"26.0%\" [0.045, 0.554, 0.201, 0.574] are located. The slice color is \"#ff9999\", indicating it represents \"Obsessive Compulsion\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "7JsNNo63bpn0P9rq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region of [0.33, 0.516, 0.53, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects within this region are the text \"Altruism\" [0.427, 0.722, 0.471, 0.742] and \"13.0%\" [0.362, 0.903, 0.42, 0.923]. This region corresponds to a slice of the pie chart that refers to \"Altruism\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "YUewxnE2cB4DyVVk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.484, 0.516, 0.73, 0.88]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with the label \"Dishonesty\" [0.564, 0.674, 0.608, 0.694] and percentage \"17.0%\" [0.671, 0.813, 0.748, 0.833]. This slice is colored \"#99ff99\"."
+            }
+        ],
+        "id_raw": 549
+    },
+    {
+        "id": "1U1DN4Xg0e0WvYOq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object within the coordinates [0.345, 0.021, 0.618, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labeled as \"Key Events in Human Civilization\"."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "gZNizNs0uIbU94xa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.881, 0.071, 0.978, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry, labeled \"Ancient Egypt\"."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "gjrgxEhorb8GgjST",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is mapped at coordinates [0.134, 0.701, 0.231, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"38.1%\" on the pie chart."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "ya8BcJuPNaA5Buxc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the slices adjacent to {the slice}[0.223, 0.278, 0.514, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.482, 0.516, 0.657, 0.883], [0.283, 0.146, 0.638, 0.516]] are adjacent to it."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "HjmqFpknCxV0sTWB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the legend}[0.837, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice labels such as \"Ancient Greece\" [0.881, 0.101, 0.987, 0.121] and \"Roman Empire\" [0.881, 0.131, 0.983, 0.151] are near the legend."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "vbDBXxBSmcJalRuK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice [0.482, 0.221, 0.741, 0.673]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Industrial Age\" corresponds to it."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "2LqBdjsepBEW5xHs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a part of the pie chart, containing a pie slice representing \"Ancient Egypt\" [0.323, 0.612, 0.367, 0.632] with the text \"38.1%\" [0.134, 0.701, 0.231, 0.721] indicating its proportion."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "1U4C5UBSI1lBMXB9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.282, 0.516, 0.716, 0.783]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice labeled \"Middle Ages\" [0.590, 0.635, 0.626, 0.655] and the corresponding percentage \"5.9%\" [0.713, 0.743, 0.798, 0.763]."
+            }
+        ],
+        "id_raw": 550
+    },
+    {
+        "id": "UHjwjI4Nbx79Ura3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be observed at the coordinates [0.292, 0.021, 0.671, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subtitle labeled as \"Evaluation of Innovation Project Management\"."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "XYcb094teawLgkQa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.797, 0.063, 0.993, 0.189] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the legend."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "EK8oe51LOEPnSBcM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the text at the given coordinates [0.841, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Budget Management\" is presented at these coordinates."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "W8wVh8QSMpk0ekaS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text in the legend is associated with the slice {colored in '#ffcc99'}<r>[0.283, 0.146, 0.66, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice correlates to the text \"Budget Management\"."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "3alpcIjNSgoveLGk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is associated with the legend text {Quality Control}<r>[0.841, 0.131, 0.946, 0.151]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with Quality Control is the one located at {coordinates}<r>[0.482, 0.248, 0.741, 0.676]</r>."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "TNRZBhPcI4vaFxkD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between the text {Risk Management}<r>[0.841, 0.101, 0.966, 0.121]</r> and the slice at coordinates [0.303, 0.516, 0.715, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are associated together; the slice at these coordinates represents 'Risk Management' in the chart."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "I3Kz7XtwfFhyCL8r",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.223, 0.146, 0.741, 0.886] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a pie chart element representing aspects of 'Project Management'. This area includes slices represented by different colours and associated percentages, for 'Project Success', 'Risk Management', 'Quality Control' and 'Budget Management'."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "OUeBbkFB51kH0JTe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details within the region of coordinates [0.223, 0.278, 0.482, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates, there's a slice of the pie chart with a color code '#ff9999'. The text '24.0%'<r>[0.089, 0.529, 0.197, 0.549]</r> and the label 'Project Success'<r>[0.305, 0.518, 0.348, 0.538]</r> are found here."
+            }
+        ],
+        "id_raw": 551
+    },
+    {
+        "id": "i3NTit9mXJPkbSR6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the chart at these coordinates [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Title is \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "0keXR7zjiGLIWixp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at these coordinates [0.661, 0.387, 0.706, 0.407] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"American\"."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "Vn5fNNu1j3mNRkqI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at these coordinates [0.392, 0.31, 0.435, 0.33] on the diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"Thai\"."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "xJVu7AyNrg2TXA1t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart sits next to {the slice}[0.362, 0.516, 0.496, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {next to it}<r>[0.482, 0.516, 0.724, 0.885]</r> is adjacent."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "0OWZtyncUYvVaEqk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located in proximity of {the slice}[0.223, 0.278, 0.482, 0.565]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"13.2%\"}[0.165, 0.392, 0.208, 0.412] and {\"Italian\"}[0.31, 0.444, 0.354, 0.464] are both located close to this slice."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "twjxakwcx9mMcnW5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the legend item \"French\" correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"French\" corresponds to the pie slice with coordinates [0.482, 0.186, 0.687, 0.516]."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "SIZFDzOVId7p5nn5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within these coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region makes up the majority of the chart and contains all the slices of the pie chart. Each slice represents a different gastronomy type and is individually color-coded. The slice labels and percentages are present within the pie slices themselves."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "We9KaLReKgElZent",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main components within the region [0.482, 0.186, 0.687, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice representing \"French\", with coordinates [0.482, 0.186, 0.687, 0.516] and its corresponding percentage sign '7.1%' with coordinates [0.663, 0.192, 0.71, 0.212]."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "oXtnU43QfriV0F8F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.283, 0.148, 0.482, 0.516] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice for \"Thai\" gastronomy type with the percentage '12.2%' listed within it. The slice's color is '#ffb347'."
+            }
+        ],
+        "id_raw": 552
+    },
+    {
+        "id": "Ixw6IJaY8FdZYgx5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the text located at coordinates [0.932, 0.071, 0.967, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Daily\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "bfsI3N3pWkVnikXv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle located at [0.893, 0.223, 0.921, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "6LWyxq7IsUuoo7mU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the text located at [0.327, 0.378, 0.372, 0.398]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The content of the text is \"1.7%\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "tzsz98Tlhlnk0XZu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with {the label \"Yearly\"}[0.932, 0.311, 0.973, 0.331] in legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates} [0.482, 0.516, 0.729, 0.794] is associated."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "ob3MawiAEC5HClsD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label that is located immediately above {the label \"Monthly\"}[0.932, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {immediately above is \"Weekly\"}[0.932, 0.251, 0.982, 0.271]."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "KGSXrznqjvA2FR1b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color from the legend corresponds to the slice with the label \"Decade\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to the slice with the label \"Decade\" is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "C2uPIUzQYTmmPWnE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a pie chart that represents the intensity of interactions in terms of daily, weekly, monthly, yearly, and decade intervals for online social networks and personal relationships. It contains various slices with different colors, labels, and percentages. The most prominent slice within this region has the label \"Decade\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "B2ERPRd3ILjryzCg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.932, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the label text \"Monthly\"."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "6ZxkuUzLZzOLifUO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.516, 0.616, 0.835]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart representing \"Daily\" intensity of interaction with a percentage of \"0.2%\". The color of this slice corresponds to the color in the legend with the same label."
+            }
+        ],
+        "id_raw": 555
+    },
+    {
+        "id": "UEc34fFZEofPsdew",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at these coordinates [0.243, 0.021, 0.72, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, referred to as 'Changes in Individualism and Equality from 2010 to 2017'."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "oGl9e0vqxzMtgO3n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object with this coordinate [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text in the figure legend denoting the year '2010'."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "GX7ggrDNhXnNrHKv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data plotted at [0.223, 0.146, 0.741, 0.886] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart depicting changes in 'Individualism' and 'Equality' from 2010 to 2017."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "EF0NWlo3HqKe2oVa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is linked to {the legend item '2010'}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.223, 0.278, 0.482, 0.574] corresponds to the legend item '2010'."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "32IfmOwMNwcpAyog",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located closest to {the pie slice}[0.223, 0.278, 0.482, 0.574] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"13.6%\"}[0.172, 0.397, 0.207, 0.417] is the value represented by the pie slice."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "X6z6GWO7c4NFb4Jw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the {legend label '2013'}[0.952, 0.161, 0.987, 0.181] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color {'#ffcc99'}[0.913, 0.163, 0.941, 0.177] belongs to the legend label '2013'."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "Qt3Mdn3OPcBTnaE1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area enclosed by these coordinates [0.223, 0.278, 0.482, 0.574]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The highlighted area corresponds to a slice of the pie chart that represents the year '2010', with an associated value '13.6%' denoted by the text within these coordinates [0.172, 0.397, 0.207, 0.417]."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "yrYvgvrjAedcFVhz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region defined by coordinates [0.333, 0.516, 0.524, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed area contains a slice of the pie chart and associated texts. The slice represents data from the year '2012', with a percentage of '12.4%' indicated by the text within these coordinates [0.383, 0.903, 0.418, 0.923]."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "vAIntQM0nbhuqNhK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide details about the region with coordinates [0.482, 0.245, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region holds a segment of a pie chart for the year '2015', along with the percentage value '12.0%' represented by the text within the coordinates [0.739, 0.331, 0.774, 0.351]."
+            }
+        ],
+        "id_raw": 556
+    },
+    {
+        "id": "kRvivO2tJQ5yl0w7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you confirm the title of this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the pie chart is \"Technological Industry Development Trends\"{the title}<r>[0.301, 0.021, 0.663, 0.047]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "c2c185QFnPHuDYF0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the bounding box with coordinates [0.655, 0.829, 0.73, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"23.0%\" is inside the bounding box.<r>[0.655, 0.829, 0.73, 0.849]</r>"
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "hZTMLTf9RBgtR7EF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at these coordinates [0.845, 0.161, 0.965, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend text \"Cloud Computing\"{the text \"Cloud Computing\"}<r>[0.845, 0.161, 0.965, 0.181]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "OC0WNDM60Dp56hQA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which slices are adjacent to {the slice}[0.223, 0.278, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two slices adjacent to it are {the slices}[[0.464, 0.516, 0.736, 0.886], [0.283, 0.171, 0.482, 0.516]]."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "4PHfquxUU3qMUxnz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is the closest to the {Cloud Computing slice}[0.388, 0.146, 0.635, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"16.0%\"{the text \"16.0%\"}<r>[0.519, 0.102, 0.64, 0.122]</r> is the closest."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "YlV7OIa6fqlTkSe1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the 'Artificial Intelligence' category in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for 'Artificial Intelligence' is '#ff9999'{the color}<r>[0.806, 0.073, 0.834, 0.087]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "mSqDfGfUoDHsXpVi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region demarcated by the coordinates [0.282, 0.194, 0.428, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the Robotics slice of the pie chart, which is depicted in '#c2c2f0' color with an automatic text reading '8.0%'{Robotics slice and '8.0%'}<r>[0.283, 0.171, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "FWYF8RKfpf6yWsKo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.729, 0.089, 0.966, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises the legend of the pie chart showcasing different technology sectors{legend}<r>[0.8, 0.063, 0.993, 0.219]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "rvVTOKzm7Oe1FWR9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with [0.223, 0.146, 0.741, 0.886] coordinates encapsulate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the whole pie chart, indicating the distribution of various technological sectors in Industry Development Trends{the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r>."
+            }
+        ],
+        "id_raw": 557
+    },
+    {
+        "id": "esiRtA4keLxtO3Su",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.344, 0.021, 0.619, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the subtitle, labeled as \"Cultural Heritage and Integration\"."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "FRrt7lxPvzXBRXJL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.833, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart is located here."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "ZztAb1KudGtM4qAg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.759, 0.598, 0.808, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"15.0%\"."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "Hpk4c5QHxhr8SyJh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat section is next to the {pie slice}\"Oceania\"[0.318, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slices}\"Polar\"[0.306, 0.229, 0.482, 0.516] and \"Extra-terrestrial\"[0.283, 0.244, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "cjyr4Zow7pEz03mm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element in the legend correlates with the {pie slice}\"Europe\"[0.482, 0.426, 0.741, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled as \"Europe\"[0.877, 0.131, 0.926, 0.151] corresponds to the \"Europe\" pie slice."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "w2WEnncczAdENE1J",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the closest text to the {legend item}\"Americas\"[0.877, 0.161, 0.942, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.0%\"[0.588, 0.128, 0.653, 0.148] is the closest to the \"Americas\" legend item."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "C1tnjTkSHszPfLIe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region encapsulated by the coordinates [0.262, 0.516, 0.68, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one pie slice representing \"Asia\"[0.262, 0.516, 0.68, 0.886], with the text \"30.0%\"[0.433, 0.912, 0.462, 0.932] and label \"Asia\"[0.449, 0.727, 0.493, 0.747]. It's the top right segment of the pie chart."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "IYds80MDRxROgcq3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_158.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present within the region defined by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region accommodates the entire pie chart, includes various slices representing different categories such as \"Africa\", \"Asia\", \"Europe\", \"Americas\", \"Oceania\", \"Polar\", and \"Extra-terrestrial\", each has its percentage mark."
+            }
+        ],
+        "id_raw": 558
+    },
+    {
+        "id": "1dLIaJQct6bjG7OS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.215, 0.021, 0.749, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle of the chart: \"Environmental Protection and Sustainable Development in 2014\"."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "F1FR1qxUSXfKJoEp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can I find at the coordinates [0.787, 0.131, 0.987, 0.151] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Renewable Energy Usage(%)\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "GHnVpS0UPvHUuejW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe chart contains a pie slice at the coordinates [0.283, 0.171, 0.482, 0.516]. What is it representing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice represents \"Renewable Energy Usage(%)\" at 8.0%."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "6T4sNjm4RKj1Jwyi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which pie slice does the legend item {the legend item}[0.787, 0.071, 0.913, 0.091] correspond?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice}[0.223, 0.193, 0.741, 0.886] corresponds with this legend item."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "PmX82arDZoza4OUM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text values are associated with {the pie slice}[0.388, 0.146, 0.608, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The values \"14.0%\" and \"Deforestation Rate(%)\" are associated with this pie slice."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "3ShbiEEfHVG8jOjZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with the legend item {the legend item}[0.787, 0.101, 0.942, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#66b3ff' is associated with this legend item, representing the \"Deforestation Rate(%)\"."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "nxbuJJMYtOvH2kf1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice representing \"Recycling Rate(%)\" at 78.0%, and the associated labels \"Recycling Rate(%)\" and \"78.0%\" are also included in this area."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "Pb6QxkLLIaocciZ8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_159.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.388, 0.146, 0.608, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the pie slice for \"Deforestation Rate(%)\", which is represented by 14.0%, along with their associated texts."
+            }
+        ],
+        "id_raw": 559
+    },
+    {
+        "id": "i0CG4mabx7TNA5sq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what the object located at coordinates [0.937, 0.071, 0.97, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"Male\"."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "mzGSGGte665fhBNY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.283, 0.146, 0.733, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice representing the \"Female\" category."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "lxAxdtbFBxtZWddz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is identified at coordinates [0.268, 0.021, 0.695, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the graph \"Social Demographic Research - Gender Distribution.\"."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "QLC7sebPhGo3cBe6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is related to {the legend item}[0.898, 0.103, 0.926, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.146, 0.733, 0.516] is related to {the legend item}[0.898, 0.103, 0.926, 0.117]."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "nzDwkPLTVOjgJSIH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the object}[0.268, 0.021, 0.695, 0.047] and {the coordinate}[0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at [0.268, 0.021, 0.695, 0.047] is the subtitle, while the coordinate [0.482, 0.516] is the center of the pie chart. The subtitle is positioned above the center of the pie chart."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "YPcK4GBSpuL4jmqo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is connected with {the pie slice}[0.223, 0.278, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Male\" [0.937, 0.071, 0.97, 0.091] is connected to {the pie slice}[0.223, 0.278, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "KBMEeKmSVFJNMY8K",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.425, 0.722, 0.469, 0.742]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"Male\" [0.425, 0.722, 0.469, 0.742], which is positioned on top of the red colored slice of the pie chart corresponding to the Male category."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "iUWDEs6Y7DeshlIi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within coordinates [0.495, 0.29, 0.538, 0.31]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the text \"Female\" [0.495, 0.29, 0.538, 0.31], this text is located inside the blue segment of the pie chart representing the Female category."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "mWCjP4T6gXpW2i5x",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region covered by the coordinates [0.546, 0.109, 0.596, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has the text \"35.0%\" [0.546, 0.109, 0.596, 0.129] located within it. It represents the percentage of the female population in the given research."
+            }
+        ],
+        "id_raw": 560
+    },
+    {
+        "id": "K8sWWPvZTS8eJrFf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by the coordinates [0.191, 0.021, 0.772, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Education and Training Industry Trends from 2012 to 2019 - Soft Skills\"."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "N8fxMjG7yVrDAvje",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"2012\"."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "42tq7Q8W0InCZpGQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented at coordinates [0.223, 0.278, 0.482, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice, representing data for the year 2012."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "j8OH6L3uIuU4w1Pq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label located near the center of {the pie slice}[0.393, 0.516, 0.613, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label is {\"2014\"}[0.474, 0.727, 0.517, 0.747]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "tTuu6hX4f50VDdpN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the label '2012'}[0.952, 0.071, 0.987, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color that corresponds is {\"#ff9999\"}[0.913, 0.073, 0.941, 0.087]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "VplxVs2wl4728s26",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the adjacent pie slice to {the pie slice}[0.482, 0.167, 0.703, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent pie slice is {the pie slice}[0.409, 0.146, 0.568, 0.516]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "ls1P4YnPrM4nfIMR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's included in the region denoted by [0.233, 0.516, 0.482, 0.864]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice for 2013 colored in \"#66b3ff\" and the labeled percentage {\"15.0%\"}[0.238, 0.784, 0.273, 0.804]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "BemRX8MQ4p3G4Jnv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.482, 0.167, 0.703, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice depicting data from the year 2017, its color corresponds to {\"#ffb3e6\"}[0.913, 0.223, 0.941, 0.237] in the legend and the percentage written is {\"10.9%\"}[0.662, 0.19, 0.697, 0.21]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "ICgKodBGcRybBNSb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.913, 0.073, 0.941, 0.087] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the color for 2012 in the legend labeled as {\"#ff9999\"}[0.913, 0.073, 0.941, 0.087]."
+            }
+        ],
+        "id_raw": 561
+    },
+    {
+        "id": "Wl3Zdt6QiEkI7czc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the subplot that is located at the coordinate [0.219, 0.021, 0.744, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Entrepreneurial Spirit and Entrepreneurship from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "DGLTzvNik9AhRupz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.954, 0.131, 0.987, 0.151] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's text \"2017\"."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "0hhAMUyjGhaoGe4S",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at coordinates [0.728, 0.301, 0.763, 0.321] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"19.0%\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "Gb1BsgryvHC9cf3i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {slice}[0.482, 0.516, 0.74, 0.861] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.482, 0.182, 0.741, 0.542] is adjacent to it."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "YZ599006tNtchg3d",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located below {the title}[0.219, 0.021, 0.744, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2015\"}[0.311, 0.443, 0.355, 0.463], {\"2016\"}[0.328, 0.624, 0.372, 0.644] and {\"2019\"}[0.594, 0.394, 0.638, 0.414] are located below the title."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "FieUsdSd98gDGCeC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {color handle}[0.913, 0.073, 0.941, 0.087] correspond to the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle in the legend corresponds to the pie slice with coordinates [0.223, 0.278, 0.482, 0.56]."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "dPFRG3MpBF8V3b6P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.56]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a pie slice colored '#ff9999'. Within the slice, the region contains text- \"13.0%\" [0.173, 0.39, 0.209, 0.41] and text \"2015\" [0.311, 0.443, 0.355, 0.463]."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "iGjsoXcqjPyfNiCp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region [0.329, 0.117, 0.751, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices of the pie chart for years 2018 [0.482, 0.516, 0.74, 0.861] and 2019 [0.482, 0.182, 0.741, 0.542] along with their associated percentages \"18.0%\" [0.711, 0.748, 0.746, 0.768] and \"19.0%\" [0.728, 0.301, 0.763, 0.321]."
+            }
+        ],
+        "id_raw": 562
+    },
+    {
+        "id": "M0qPnqxyxymQ5oe6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.316, 0.021, 0.648, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labelled as \"Unemployment Rate from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "dJhwGIsgKqRB8aob",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at coordinates [0.913, 0.073, 0.941, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colored-square handle of the legend item for the year \"2016\"."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "IzugmxHDdk96IBjf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of the pie chart."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "IziC5xxCLudDLAhb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts on the pie chart are close to {slice}[0.223, 0.278, 0.482, 0.714]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2016\"}[0.305, 0.491, 0.348, 0.511] and {\"20.1%\"}[0.162, 0.479, 0.197, 0.499] are near the slice."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "ig9rjRdMfy0ecLgl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are close to {the \"2019\" slice}[0.482, 0.363, 0.741, 0.68]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {2018 slice}[0.482, 0.516, 0.714, 0.884] and {2020 slice}[0.283, 0.146, 0.718, 0.516] are adjacent to the 2019 slice."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "IzrHcSabjlPVH1t5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices correlate with the captions in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices related to the years \"2016\", \"2017\", \"2018\", \"2019\", \"2020\" correspond to the slices at [0.223, 0.278, 0.482, 0.714], [0.263, 0.516, 0.508, 0.886], [0.482, 0.516, 0.714, 0.884], [0.482, 0.363, 0.741, 0.68], and [0.283, 0.146, 0.718, 0.516] respectively."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "3eMRJ9FDpraz6AR2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.482, 0.146, 0.741, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes parts of the slices for years \"2019\" and \"2020\", and the respective text labels. The 2019 slice and its text are positioned at {coordinates}[0.482, 0.363, 0.741, 0.68] and {coordinates}[0.615, 0.51, 0.659, 0.53]. The 2020 slice and its corresponding text are located at {coordinates}[0.283, 0.146, 0.718, 0.516] and {coordinates}[0.481, 0.286, 0.525, 0.306]."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "sLiLsDeZpgRFCD38",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects contained in the region [0.482, 0.516, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the pie chart, specifically, the slices representing the years \"2018\" and \"2019\". There are also text labels: the label {2018}[0.548, 0.688, 0.592, 0.708], the label {16.1%}[0.644, 0.84, 0.68, 0.86], and the label {2019}[0.615, 0.51, 0.659, 0.53]."
+            }
+        ],
+        "id_raw": 563
+    },
+    {
+        "id": "KCzuFB3V2AK1DdtQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the caption of the chart located at coordinates [0.273, 0.021, 0.69, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Ecological Environment Sustainability Assessment."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "W2uBhILcgkiy7e2J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which element is located at coordinates [0.837, 0.063, 0.993, 0.189] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart legend."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "s2sDsuh6X5DwANhe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.723, 0.29, 0.803, 0.31] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\" is located at the coordinates."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "oVTG1uNkyIgnPnom",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text \"Deforestation\" at {coordinates}[0.422, 0.29, 0.466, 0.31] and the legend item color at {coordinates}[0.842, 0.163, 0.87, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the text \"Deforestation\" matches the color of the legend item, showing that they represent the same data category in the pie chart."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "rwvSbZoRcGE3dtwR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the legend \"Water pollution\" at {coordinates}[0.881, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Water pollution\" legend is at {coordinates} [0.223, 0.278, 0.482, 0.799]."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "5xMUxe02pwD6PSzV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text \"Soil erosion\" {[0.592, 0.388, 0.635, 0.408]} and the percentage \"20.0%\" {[0.723, 0.29, 0.803, 0.31]} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Soil erosion\" corresponds to the pie chart slice that represents 20%. They both represent the same data category."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "0auTNK54BvrCUkli",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see inside the region marked by [0.223, 0.278, 0.482, 0.799]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there appears to be a pie chart slice with the color '#ff9999'. It has two pieces of text inside it, \"Water pollution\"{[0.305, 0.525, 0.349, 0.545]} and \"25.0%\" {[0.092, 0.541, 0.198, 0.561]}."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "atdtysoAyscakpdo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region inside coordinates [0.315, 0.516, 0.74, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, I see the portion of the pie chart with color '#66b3ff'. Inside it, we have two important text elements, \"Air pollution\"{[0.52, 0.71, 0.564, 0.73]} and \"35.0%\" {[0.593, 0.881, 0.677, 0.901]}."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "4wWVDBbgqfoCHD1p",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents inside the coordinates [0.482, 0.173, 0.741, 0.542]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region of the chart, you can find a slice of the pie chart with the color '#99ff99'. The text \"Soil erosion\" {[0.592, 0.388, 0.635, 0.408]} and \"20.0%\" {[0.723, 0.29, 0.803, 0.31]} are located within this section."
+            }
+        ],
+        "id_raw": 564
+    },
+    {
+        "id": "HaxPZQAyzzS49mGt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.193, 0.021, 0.771, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Entertainment and Gaming Industry Development from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "Skvn9TXmS6Fk9VHA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "t14BXjKbUiHUaKUs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.952, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend text \"2019\"."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "1hii65Fi79SZASvp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {slice}[0.223, 0.457, 0.482, 0.668]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[[0.226, 0.278, 0.482, 0.516]] is adjacent to it."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "VMDAyN9p4mIvMwWW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend handle that corresponds to the {slice}[0.245, 0.516, 0.482, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the handle is '#99ff99'."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "sBbagynuVdeIszQf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {slice}[0.481, 0.146, 0.673, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2019\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "bK6bKmAkAgsjMBev",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart depicting the Gaming market from the years 2012 to 2020 with different color-coded slices for each year and associated percentage texts."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "ry0ula9aYzpcrKew",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend with labels ranging from the year \"2012\" to \"2020\" along with their associated handle colours."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "Vp3hi3aROKdP4rsn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.152, 0.516, 0.152, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region refers to the y-axis label of the chart, which is empty in this case."
+            }
+        ],
+        "id_raw": 566
+    },
+    {
+        "id": "7dHL2wsYyR1GErSr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.625, 0.074, 0.774, 0.1] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A subtitle, labelled as 'Industry Revenues in 2016'."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "lgIIIYfdo9lSDmVv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.801, 0.165, 0.872, 0.184] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"38.2%\"."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "8j028xChPolT0qwm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies at [0.596, 0.544, 0.625, 0.564] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labelled as \"Hotels\"."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "ZkGx3j6kE3KqYz6F",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are associated with {the handle}[0.896, 0.156, 0.915, 0.169]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice [0.635, 0.498, 0.851, 0.837] is associated."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "c7x197E4rkDcDac2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any subtitled regions that are placed near to {the chart object}[0.427, 0.184, 0.5, 0.204]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region with the subtitle \"Industry Revenues in 2015\" [0.13, 0.074, 0.279, 0.1] is near."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "xTaQlRrwmewGjDmU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the region with the handle [0.896, 0.126, 0.915, 0.139] and text \"Hotels\" [0.922, 0.124, 0.951, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These two are legend items, where the handle represents the color of the associated slice in the chart and the text \"Hotels\" is the label of that category."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "nvSMKT2pXNQLM3TD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region within the coordinates [0.548, 0.187, 0.851, 0.837]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily includes a pie slice, with the text \"38.2%\"[0.753, 0.164, 0.825, 0.184] indicating the percentage that this slice constitutes in the whole pie, and the label \"Travel Agencies\" [0.714, 0.317, 0.743, 0.337] indicating what this slice represents."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "tq76jGxnG9v6XG9K",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents present in the region [0.892, 0.116, 0.998, 0.212]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains three legend items: \"Hotels\"[0.926, 0.124, 0.952, 0.144], \"Resorts\" [0.922, 0.154, 0.964, 0.174], and \"Travel Agencies\"[0.922, 0.184, 0.999, 0.204]."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "vDhKeG80wDM9icNr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region inside coordinates [0.015, 0.106, 0.394, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an axis and a pie chart. The pie chart includes slices for \"Hotels\"[0.101, 0.541, 0.13, 0.561], \"Resorts\"[0.239, 0.666, 0.269, 0.686] and \"Travel Agencies\"[0.219, 0.317, 0.248, 0.337]. Also, a legend is present that shows {the corresponding colors of these sections}[0.398, 0.116, 0.503, 0.212]."
+            }
+        ],
+        "id_raw": 567
+    },
+    {
+        "id": "lDZy1S4cloTe7PKS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the bounding box [0.839, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend text for \"Extremely Ineffective\"."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "zBRcGJjSmIGnipN3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present within the coordinates [0.318, 0.747, 0.362, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Mostly Ineffective\"."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "jgvaI2nLWHmvUcjz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found within the box [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the main Pie chart."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "ataxWoIlfWTbjQLf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any text labels next to {the slice}[0.283, 0.146, 0.579, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the text \"Extremely Effective\"<r>[0.406, 0.201, 0.45, 0.221]</r> is next to the slice."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "UEov6jn9wF0FSKSf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend item \"Mostly Effective\"<r>[0.839, 0.131, 0.949, 0.151]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This corresponds to the slice at coordinates [0.482, 0.173, 0.741, 0.875]."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "gHBoQ93L0n7FB5yN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the handle with coordinates [0.8, 0.133, 0.828, 0.147] and the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle is colored the same as the \"Mostly Effective\" slice [0.482, 0.173, 0.741, 0.875]."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "MxJkyWKWiwWOztBJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart colored in '#ff9999'. It also contains the text \"10.0%\"<r>[0.069, 0.353, 0.217, 0.373]</r> and the label \"Extremely Ineffective\"<r>[0.256, 0.388, 0.299, 0.408]</r>."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "KQp9sJ6XokG89BTY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region bounded by [0.223, 0.49, 0.544, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart, the label \"Mostly Ineffective\"<r>[0.318, 0.747, 0.362, 0.767]</r> and its corresponding percentage text \"30.0%\"<r>[0.175, 0.818, 0.298, 0.838]</r>."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "YNuEEx5dMWqG6xR5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_168.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.482, 0.173, 0.741, 0.875] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a green slice of the pie chart with the label \"Mostly Effective\"<r>[0.679, 0.528, 0.723, 0.548]</r> and a corresponding percentage of \"40.0%\"<r>[0.766, 0.534, 0.877, 0.554]</r>."
+            }
+        ],
+        "id_raw": 568
+    },
+    {
+        "id": "jiQF30EA20Vv6eb1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the subplot title at the coordinates [0.233, 0.021, 0.73, 0.047] convey?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subplot title conveys: \"Different Types of Social Relations and Their Usage of Radio\"."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "XoWTfhQTpv9HAv8I",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center of the pie chart."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "S44zrqffxyx9cVIt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.854, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"High school friends\" from the legend."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "XHVjMbsAnRi6Moq7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels for the color handles close to {the handle}[0.815, 0.133, 0.843, 0.147] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {\"Colleagues\"}[0.854, 0.131, 0.931, 0.151] and {\"Family\"}[0.854, 0.161, 0.899, 0.181] are close to the chosen handle."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "bEfHOBKofy43TR3o",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich parts of the pie chart are related to the legend item {Acquaintances}[0.854, 0.221, 0.956, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {slice}[0.395, 0.146, 0.695, 0.516] is related to the \"Acquaintances\" legend item."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "A0PVpfzAt0LlKnS6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice's autotext is near to the {text \"Acquaintances\"}[0.507, 0.295, 0.551, 0.315]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"20.8%\" [0.569, 0.119, 0.671, 0.139] is near to the text \"Acquaintances\"."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "dJHopWFs9rcYJFyi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the whole pie chart. It includes multiple slices, each with its own color, percentage autotext, and category text. For example, it contains the text \"High school friends\" [0.307, 0.463, 0.351, 0.483] with its corresponding slice [0.223, 0.278, 0.482, 0.629] and percentage autotext \"16.1%\" [0.069, 0.428, 0.202, 0.448]."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "y6WEOgC74L0IO3qC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are located within the coordinates [0.815, 0.073, 0.843, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item color handle corresponding to \"High school friends\" is located within those coordinates."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "NDcrwH8zeVt5ttrj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_170.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region defined by the coordinates [0.854, 0.071, 0.987, 0.091]."
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, the text for the legend item \"High school friends\" is found."
+            }
+        ],
+        "id_raw": 570
+    },
+    {
+        "id": "crVe6u4n0tEDuddj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.2, 0.021, 0.763, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, called Automotive and Transportation Market Research from 2013 to 2020."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "Ck09HUFBmY9C8heJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.281, 0.987, 0.301] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend item \"2020\"."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "5GPC7ydG2Huc9btg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.189, 0.331, 0.224, 0.351] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"8.1%\"."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "aHWyqsQ581q2wYmf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.2, 0.021, 0.763, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items {\"2013\"}[0.952, 0.071, 0.987, 0.091], {\"2014\"}[0.952, 0.101, 0.987, 0.121] and {\"2015\"}[0.952, 0.131, 0.987, 0.151] are located near the subtitle."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "hc12tDIs4lYrCS8W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich box colors in the legend correspond to the slices in the pie chart labeled {\"2016\"}[0.428, 0.723, 0.472, 0.743] and {\"2017\"}[0.534, 0.701, 0.578, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors are #ffcc99 for 2016 and #c2c2f0 for 2017, obtained from the legend items {\"2016\"}[0.952, 0.161, 0.987, 0.181] and {\"2017\"}[0.952, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "40PU4xrFMh2s0t18",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are the same and why?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {\"2013\"}[0.227, 0.278, 0.482, 0.516] and {\"2013\"}[0.449, 0.146, 0.52, 0.516] are the same because both have the label \"2013\" and the same color #ff9999, obtained from the legend item {\"2013\"}[0.952, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "ClFpbEVP8SXgUHgl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart with different slices. Each slice has a different color and is labeled with a specific year and percentage. For example, there's a slice labeled {\"2013\"}[0.324, 0.411, 0.359, 0.431] with the percentage {\"8.1%\"}[0.189, 0.331, 0.224, 0.351], and a slice labeled {\"2014\"}[0.31, 0.53, 0.345, 0.55] with the percentage {\"9.4%\"}[0.163, 0.549, 0.198, 0.569]."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "TyZv3NXKLrU1NIcN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents does the region within coordinates [0.908, 0.063, 0.993, 0.548] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend with items of different years and colors. For instance, {\"2013\"}[0.952, 0.071, 0.987, 0.091] is denoted by color #ff9999, {\"2014\"}[0.952, 0.101, 0.987, 0.121] by color #66b3ff and {\"2015\"}[0.952, 0.131, 0.987, 0.151] by color #99ff99."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "ahDzIjd2Nb9BMsgm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the range [0.163, 0.549, 0.198, 0.569]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This range encapsulates the autotext {\"9.4%\"}[0.163, 0.549, 0.198, 0.569], representing the proportion of the slice {\"2014\"}[0.223, 0.447, 0.482, 0.661] in the pie chart."
+            }
+        ],
+        "id_raw": 571
+    },
+    {
+        "id": "awbdOGa2r3ZSFa9Q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed in the coordinates [0.199, 0.021, 0.764, 0.047] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, stating \"Trends in Logistics in the Transportation Industry from 2016 to 2021\"."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "cum2rWiMl3SRC1Jj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.908, 0.063, 0.993, 0.249] of the plot diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the region describing the chart's legend."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "Hifv6nzPN1YObjE8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data being displayed at [0.387, 0.108, 0.422, 0.128]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text displaying \"21.1%\"."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "C2N51qoepsJPfMLY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color correlation between {the legend}[0.908, 0.063, 0.993, 0.249] and {the pie slice}[0.223, 0.278, 0.482, 0.575]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice color '#ff9999' corresponds with the \"2016\" item in the legend."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "fK0ggu9e72vikPpc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text \"2021\" at {coordinates}[0.427, 0.289, 0.471, 0.309] and the color at this {coordinate}[0.913, 0.223, 0.941, 0.237] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2021\" corresponds to the color '#ffb3e6' in the legend."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "CkGnmFTXA88d2pdp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the number \"14.5%\" corresponding to the coordinates [0.214, 0.741, 0.249, 0.761] and the legend item in coordinates [0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The number \"14.5%\" is the percentage corresponding to the \"2017\" item in the legend."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "5SkzWKMcUDWjDXcs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region in coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a region containing six slices of varied colors representing different years from 2016 to 2021. Each slice has a corresponding percentage label and a year label nearby. The colors of slices match with the legend on the right."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "FMuOqYgxzEnflgpK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend, which includes six labeled color samples representing the years 2016 through 2021."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "QLREQPa0vfmBofjf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.223, 0.278, 0.482, 0.575]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of the color '#ff9999', labeled with the year 2016 at coordinates [0.31, 0.447, 0.354, 0.467] and a corresponding text indicating 13.7% at coordinates [0.172, 0.398, 0.207, 0.418]."
+            }
+        ],
+        "id_raw": 572
+    },
+    {
+        "id": "xYYu9PmBcBK6pHAL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.326, 0.021, 0.637, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the subtitle of the chart, named \"Investment Distribution by Risk Level\"."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "fWJGfazCOnv99TD3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.887, 0.071, 0.952, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item \"High Risk\"."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "zwVpmTJiiSNqojLa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object has the coordinates [0.223, 0.444, 0.482, 0.839] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart slice representing \"Low Risk\" investments."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "GMdZ9dsQujE6atUI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {pie chart slice}[0.355, 0.516, 0.739, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item that corresponds is {Moderate Risk}[0.887, 0.131, 0.986, 0.151]."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "DABK54qxQPe6mJEN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich are the neighboring elements of the {pie slice}[0.227, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring slices are {Low Risk}[0.223, 0.444, 0.482, 0.839] and {Very Low Risk}[0.283, 0.146, 0.733, 0.516]."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "NiEdLox9cyIrXiv9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is connected to the {text \"8.0%\"}[0.16, 0.33, 0.225, 0.35] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"8.0%\" is connected to the {High Risk pie slice}[0.227, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "9R616d2astVREwJp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.444, 0.482, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart representing Low Risk investments with percentage label {20.0%}[0.162, 0.677, 0.223, 0.697] and corresponding category label {\"Low Risk\"}[0.319, 0.599, 0.362, 0.619]."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "eekRXDYxXFFQiN6a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area enclosed by [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Absolutely, this area contains the entire pie chart of risk level distribution. It consists of different slices with the corresponding risk labels and their investment percentage."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "L9Z0Xrvo6Nm6eI3q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.843, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart, which includes five labels: High Risk, Low Risk, Moderate Risk, Very High Risk, and Very Low Risk along with their corresponding colors."
+            }
+        ],
+        "id_raw": 573
+    },
+    {
+        "id": "M6GYZkkbxd8GFRbH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.285, 0.021, 0.678, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called Building and Real Estate Market Trends in 2020."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "R2DXNjJwAA1CXys4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.904, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the legend,\"Commercial\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "geiM9yr8ZeusoWeP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.669, 0.813, 0.745, 0.833] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"95.0%\"."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "No8C1YypWbcKqygJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the sliced pie}[0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The sliced pie}[[0.283, 0.202, 0.482, 0.516]] is adjacent."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "0cTOBq96Ik5nA15I",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located near {the handle of the legend}[0.865, 0.103, 0.893, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Commercial\"}[0.904, 0.101, 0.987, 0.121] is located near the handle."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "9xrRbdneX80yVB9c",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice in the chart with the label \"Residential\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Residential\" corresponds to the slice with coordinates [0.223, 0.146, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "LqIvq0LT5o8mlT3z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.562, 0.674, 0.606, 0.694]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"Residential\" [0.562, 0.674, 0.606, 0.694]. This region is located in the center part of the chart, corresponding to the pink slice."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "bzVmhHwJoFHs6UZS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices. The largest slice with the text \"Residential\" [0.562, 0.674, 0.606, 0.694] and a percentage representation \"95.0%\" [0.669, 0.813, 0.745, 0.833]."
+            }
+        ],
+        "id_raw": 574
+    },
+    {
+        "id": "Yp1Yfe1ocqL5BY0Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location specified by the coordinates [0.167, 0.021, 0.796, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object is the subtitle of the chart, saying \"Number of Medical Professionals in General Medicine and Neurology (2013)\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "W6aUsvNHQ9mXodfY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's at the coordinates [0.562, 0.897, 0.683, 0.917] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"81.3%\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "tbGPGsXTQZW5yUyS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.866, 0.071, 0.987, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This contains the legend text \"General Medicine\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "wKtF3imkAWVIUJDf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is related to the {legend color indicator}[0.827, 0.073, 0.855, 0.087] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color indicator is related to the {legend text \"General Medicine\"}[0.866, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "c3G8rXDhaStriaeC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the {text}[0.562, 0.897, 0.683, 0.917] on the chart correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This text \"81.3%\" corresponds to the {slice} [0.223, 0.163, 0.741, 0.886] of the pie chart representing \"General Medicine\"."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "4zhb76X7lZRwF3h5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is directly adjacent to the {pie slice}[0.283, 0.146, 0.559, 0.516] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice} [0.223, 0.163, 0.741, 0.886] representing \"General Medicine\" is adjacent to this slice."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "91g0wqIdSMgb2qCN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region bound by coordinates [0.223, 0.146, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by a large slice of the pie chart, with text \"General Medicine\" [0.503, 0.719, 0.547, 0.739] written on it and accompanied by a separate text showing the percentage \"81.3%\" [0.562, 0.897, 0.683, 0.917]. This slice represents the number of professionals in the field of General Medicine."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "D2MWdP6m4U7hFw0Z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements within the region defined by coordinates [0.158, 0.053, 0.805, 0.979] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the main part of the chart containing the pie plots for General Medicine and Neurology. It also includes the two text labels and percentages associated with each of the slices."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "A7pL3Y6w4DbJftwO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region defined by coordinates [0.822, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend, specifically comprised of labels \"General Medicine\" [0.866, 0.071, 0.987, 0.091] and \"Neurology\" [0.866, 0.101, 0.937, 0.121], along with their corresponding color indicators [0.827, 0.073, 0.855, 0.087] and [0.827, 0.103, 0.855, 0.117]."
+            }
+        ],
+        "id_raw": 577
+    },
+    {
+        "id": "IMUnBsGvnbVDCMaR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.487, 0.516, 0.487, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Y-axis."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "EwmktdZQ8vHR3FcI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.228, 0.232, 0.746, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a portion of pie chart representing \"AI and Machine Learning\" in 2019."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "RcrbOwFWGdIdM8hU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.813, 0.101, 0.912, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend text \"Biotechnology\"."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "74I9QiCw8owgZBKZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the legend item \"Biotechnology\"}[0.813, 0.101, 0.912, 0.121] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The color #66b3ff}[0.774, 0.103, 0.802, 0.117]."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "fWq3W9iGc5nR4d4w",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the percentage text \"35.0%\"}[0.045, 0.665, 0.224, 0.685] and {the legend item \"AI_and_Machine_Learning\"}[0.813, 0.071, 0.992, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"35.0%\" is the market share of \"AI and Machine Learning\" in 2015."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "WeAM95w9olF7to2G",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correlation between {the area of the pie chart}[0.228, 0.146, 0.746, 0.886] and {the legend item}[0.813, 0.071, 0.992, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area of the pie chart corresponds to the industry named in the legend item, in this case, \"AI and Machine Learning\"."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "Ol5d2tBTqCgHPgSt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.769, 0.063, 0.998, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend area of the chart. It contains the labels \"AI_and_Machine_Learning\" [0.813, 0.071, 0.992, 0.091] in the color #ff9999 [0.774, 0.073, 0.802, 0.087] and \"Biotechnology\" [0.813, 0.101, 0.912, 0.121] in the color #66b3ff [0.774, 0.103, 0.802, 0.117]."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "1x6R2679NNDMBl1F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.228, 0.146, 0.746, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains pie slices representing the market shares of \"AI and Machine Learning\" and \"Biotechnology\" from 2015 to 2019. Also, it includes the sectors' corresponding percentage texts ranging from \"35.0%\" to \"75.0%\" for \"AI_and_Machine_Learning\" and \"65.0%\" to \"25.0%\" for \"Biotechnology\"."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "N67VClR8v6ApFl2e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects fall within the coordinates [0.228, 0.278, 0.487, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is a part of the pie chart representing the market share of \"AI and Machine Learning\" in 2015 marked as \"35.0%\" [0.045, 0.665, 0.224, 0.685]."
+            }
+        ],
+        "id_raw": 580
+    },
+    {
+        "id": "Am0dCRGOuJXEr7bv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at the coordinates [0.252, 0.021, 0.711, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, stating \"Distribution of Restaurant Counts for Different Cuisines\"."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "sia4RHsxCUxPtDMV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located within the coordinates [0.711, 0.748, 0.766, 0.768] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"26.7%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "2BJJut2eNQJE2suj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.885, 0.063, 0.993, 0.219] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the legend."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "xQIUTR4QSE9MlgPJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is associated with {the legend item 'Indian'}<r>[0.93, 0.161, 0.973, 0.181]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represented by 'Indian' is found at {the coordinates}<r>[0.478, 0.146, 0.735, 0.516]</r>."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "MfFx64EmfyqQet1I",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the adjacent slice to {the 'French' slice}<r>[0.288, 0.516, 0.507, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {'Chinese'} [0.482, 0.442, 0.741, 0.884] and {'Italian'} [0.223, 0.278, 0.482, 0.762] are adjacent."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "Vl1g2MeRXLyuQZkF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice coordinates [0.223, 0.278, 0.482, 0.762] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item 'Italian' at coordinates [0.93, 0.071, 0.973, 0.091] corresponds to the slice."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "mnBD2vWRVZzp2X1w",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.762]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the 'Italian' slice in the pie chart, indicated by the color '#ff9999'. It contains the slice legend text 'Italian' [0.304, 0.509, 0.348, 0.529] and the percentage text '22.7%' [0.153, 0.512, 0.197, 0.532]."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "Y2rc09Im0zCDv3RS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.482, 0.442, 0.741, 0.884] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the 'Chinese' slice in the pie chart indicated by the color '#99ff99'. It includes the autotext '26.7%' with coordinates [0.711, 0.748, 0.766, 0.768] and text 'Chinese' located at [0.585, 0.638, 0.629, 0.658]."
+            }
+        ],
+        "id_raw": 581
+    },
+    {
+        "id": "6HEMYoBaXnjNzksW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.154, 0.104, 0.298, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called Intangible Cultural Heritage."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "8DLW6POf6Bsfs7GZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.011, 0.618, 0.077, 0.635] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"35.0%\"."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "FGzQsmMIELPHAQRh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.908, 0.147, 0.974, 0.165]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend item with label \"Asian Influence\"."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "jPvB0egSjRqoALrF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend handles match the slices {the slice}[0.079, 0.311, 0.226, 0.794] and {the slice}[0.705, 0.227, 0.852, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handles {Asian Influence}[0.405, 0.149, 0.422, 0.161] and {European Influence}[0.884, 0.175, 0.901, 0.187] match the respective slices."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "0USNwGW4tpp4azlw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich Slice is related to the legend item {Other Influence}[0.908, 0.2, 0.975, 0.217]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.592, 0.205, 0.76, 0.5] corresponds to the legend item \"Other Influence\"."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "JE8bGUpUp9oHdmfG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext goes with the pie slice {the slice}[0.216, 0.227, 0.374, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"45.0%\" with coordinates [0.384, 0.564, 0.468, 0.582] goes with the slice."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "uxTIte7XWDKH6Sjt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.079, 0.311, 0.226, 0.794]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the pie slice for \"Asian Influence\" from Intangible Cultural Heritage with slice percentage \"35.0%\" located at [0.011, 0.618, 0.077, 0.635]."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "FxSiCffqXl3rP6lv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.558, 0.205, 0.852, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart for Tangible Cultural Heritage, with the slices for Asian Influence, European Influence and Other Influence, each having a different percentage and color."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "ooPXwx9bv9BUr23U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area defined by the coordinates [0.110, 0.000, 0.320, 0.400]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The defined area is at the lower left side of the Intangible Cultural Heritage pie chart and does not contain any specific object or text from the chart."
+            }
+        ],
+        "id_raw": 582
+    },
+    {
+        "id": "oSM0I3uu0QtZM2M6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object having coordinates [0.299, 0.088, 0.726, 0.114] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, which is \"Market Competition Analysis and Comparison 2020\"."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "1I7fPHEEAwecUovT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present at coordinates [0.866, 0.168, 0.929, 0.188] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item, which is \"Microsoft\"."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "30wYA0PVTftYcLUe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object with the coordinates [0.628, 0.199, 0.684, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\"."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "K5nkjptThirfCM7y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentifying the relationship, which slice in the chart is adjacent to {this pie slice}<r>[0.512, 0.376, 0.728, 0.811]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.487, 0.197, 0.708, 0.505] is adjacent to {this pie slice}<r>[0.512, 0.376, 0.728, 0.811]</r> in the chart."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "IwD5tYXsobzCFDgX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near the slice {this pie slice}<r>[0.297, 0.307, 0.538, 0.813]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {the word \"Google\"}<r>[0.377, 0.583, 0.421, 0.603]</r> is found near the slice."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "nPpFoXE3H4KANNzK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects correlate to the pie slices present in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Google\", \"Microsoft\", \"Amazon\", \"Facebook\", and \"Others\" correlate to the slices with the respective coordinates [0.297, 0.307, 0.538, 0.813], [0.512, 0.376, 0.728, 0.811], [0.487, 0.197, 0.708, 0.505], [0.41, 0.199, 0.512, 0.505], and [0.347, 0.234, 0.512, 0.505]."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "M7P6sPb8YSfFYJXK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.297, 0.307, 0.538, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the slice of the pie chart associated with \"Google\" [0.377, 0.583, 0.421, 0.603] and a percentage text \"38.0%\" [0.255, 0.656, 0.304, 0.676] within the chart."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "aNCEMMjIUSawYyZ4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what the region [0.512, 0.376, 0.728, 0.811] contains in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region features a slice of the pie chart with associated text \"Microsoft\" [0.604, 0.585, 0.647, 0.605] and the percentage \"30.0%\" [0.72, 0.66, 0.783, 0.68]."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "d0wTGq8JEf4vKo6d",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents of the region with the coordinates [0.347, 0.234, 0.512, 0.505] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of pie chart associated text with \"Others\" [0.413, 0.352, 0.448, 0.372] and the percentage \"6.0%\" [0.316, 0.232, 0.363, 0.252]."
+            }
+        ],
+        "id_raw": 583
+    },
+    {
+        "id": "Z20SPKHj4n0LYRck",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main label at coordinates [0.297, 0.021, 0.666, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main label is \"Entrepreneurship and Innovation Ecosystem\""
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "GZHb6VbJMgclg0MA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what object is located at coordinates [0.827, 0.074, 0.981, 0.220] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no information available in the chart at coordinates [0.827, 0.074, 0.981, 0.220]."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "MK5Htbk8uwvyybBy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.124, 0.478, 0.197, 0.498] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text with \"20.0%\" is located there."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "MJws5avkkbOYqCDV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with {the color marker}[0.87, 0.103, 0.898, 0.117] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice located at {the coordinate}[0.262, 0.516, 0.482, 0.885] is associated with it."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "eIfmu50OKVia2yb1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are linked to {the color marker}[0.87, 0.163, 0.898, 0.177] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label {\"Funding\"}[0.614, 0.528, 0.658, 0.548] is linked to this marker."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "vo4cxUmjieVU5pl1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legends correspond to the pie slice at coordinates [0.282, 0.229, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the legend {\"Exits\"}[0.909, 0.251, 0.942, 0.271]."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "9wSR78V7sqMSZXyP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region designated by coordinates [0.283, 0.229, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"Exits\" [0.355, 0.348, 0.39, 0.368] and the text \"3.0%\" [0.248, 0.216, 0.282, 0.236]. This region presents one of the smallest slices of the pie chart, occupying 3.0% of the whole."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "mDm6LbitSF9GOyYM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains several pie slices each with their respective percentages and names, such as \"Innovation\" [0.305, 0.49, 0.348, 0.51] and \"20.0%\" [0.124, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "U6ymc5T7tlwBHUdv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat things are located in the region defined by [0.223, 0.278, 0.482, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice named as \"Innovation\" [0.305, 0.49, 0.348, 0.51] and its corresponsing percentage \"20.0%\" [0.124, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 585
+    },
+    {
+        "id": "5wqFr7oZfixe6J9z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.22, 0.021, 0.743, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart subtitle: \"Technological Industry Development Trends from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "F0YAtm9h7nX6hWxd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.908, 0.063, 0.993, 0.548] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend of the chart."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "Vd6Sal73RYiSXfMg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object lies at the coordinates [0.482, 0.516, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the main pie chart."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "ML0D6qOzUgCPU75B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend {legend}[0.908, 0.063, 0.993, 0.548] is associated with the slice {slice}[0.482, 0.516, 0.716, 0.879]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#a2c4c9' is associated with the slice."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "4JJynVR7zjs68jUG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie does the legend item {\"2013\"}[0.952, 0.4, 0.987, 0.42] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice with color '#ffcc99'."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "TrrzaRHavVYclfwa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices of the pie chart and the colors in the {legend}[0.908, 0.063, 0.993, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice is colored to match the colors indicated in the legend, allowing for ease of reference and identification of data."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "8oSzROD8vZrL4DDG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region mainly contains the pie chart representing \"Artificial Intelligence\" and \"Blockchain\" industry development trends from 2010 to 2017. The slices' colors correspond to different years, as indicated by the legend."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "Zj8tzjh2J4BkvmfN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_186.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the region [0.908, 0.063, 0.993, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart contains the legend. Each legend item consists of a color square and a text label indicating a year from 2010 to 2017. The colors in the legend correspond to the colors of the pie chart slices."
+            }
+        ],
+        "id_raw": 586
+    },
+    {
+        "id": "1gOH8yw9XNAxH7XT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label for the object bounding box marked by coordinates [0.849, 0.071, 0.964, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Startup Creation\"."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "lCbq3xzWA3BKWUSf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.446, 0.285, 0.49, 0.305]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Business Incubation\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "RRvCjVdq42TrLNuj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text within the object located at [0.318, 0.1, 0.457, 0.12]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"25.0%\" is located within the object at these coordinates."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "lZcJsV3HfLDuYMhg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the handle}<r>[0.81, 0.103, 0.838, 0.117]</r> and {the text}<r>[0.849, 0.101, 0.956, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle represents the color of the slice \"Venture Capital\" as shown in the legend."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "YceYNWFYcQrxL8em",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with {the legend text \"Startup Creation\"}<r>[0.849, 0.071, 0.964, 0.091]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}\"Startup Creation\" [0.223, 0.278, 0.482, 0.885] is associated with the legend text."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "qdTM4X4VPFr1FvgG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the legend text \"Business Incubation\"}<r>[0.849, 0.131, 0.987, 0.151]</r> connected to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"Business Incubation\"}<r>[0.446, 0.285, 0.49, 0.305]</r> on the pie chart is connected to the legend text, representing a segment of the chart."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "bCFHbRvhmmUy6HXI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region defined by coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the pie chart, which includes slices representing different terms such as \"Startup Creation\", \"Venture Capital\" and \"Business Incubation\", with corresponding percentages displayed on them."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "klm4hL1Nj2cqbnIT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.804, 0.063, 0.993, 0.159]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is allocated for the legend items, namely \"Startup Creation\", \"Venture Capital\", and \"Business Incubation\". These items are represented by different colors which corresponds to the slices on the pie diagram."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "DknwUYUnVdiPwvn6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region marked by the coordinates [0.283, 0.146, 0.648, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart, labeled \"Business Incubation\" with a percentage of 25.0%, colored green."
+            }
+        ],
+        "id_raw": 587
+    },
+    {
+        "id": "znhoVhljDucmwS2y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.253, 0.021, 0.71, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, titled \"Future Society and Living Scenarios from 2020 to 2050\"."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "uZUrGgd6HrsinngG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "SXEHQXq3WFErfEim",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.223, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main plot of the chart, which is a pie chart."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "7oPIF9Na7bj7ri5l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend corresponds to the slice {slice}<r>[0.473, 0.516, 0.674, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label {\"2045\"}<r>[0.952, 0.43, 0.987, 0.45]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "tKd7an0QzukjkuoV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the chart elements located near {the main plot}<r>[0.223, 0.146, 0.741, 0.886]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle {\"Future Society and Living Scenarios from 2020 to 2050\"}<r>[0.253, 0.021, 0.71, 0.047]</r> and the legend<r>[0.908, 0.063, 0.993, 0.488]</r> are located near the main plot."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "9RcfRyFNrn1nag9g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slices and the labels in the legend in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice in the pie chart corresponds to a certain label in the legend, each representing a different year."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "Fo2I8NHSD9AfrRGA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.908, 0.063, 0.993, 0.488]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend of the chart. The arrangement from top to bottom is the labels of different years from 2020 to 2050, each accompanied by a color indicator matching the pie chart. The labels are in two batches, with the second batch starting from the label \"2020\" again."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "CjhFWCJ9QDlopwGB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects reside in the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart, which has several slices each representing a different year and the corresponding percentage. Each slice has two labels: a year label and a percentage label."
+            }
+        ],
+        "id_raw": 590
+    },
+    {
+        "id": "VPTKFzDzQxOFQncH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.139, 0.021, 0.717, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, called Entertainment and Gaming Industry Development from 2008 to 2014."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "a5U7O49suK365f8O",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.924, 0.83, 0.959, 0.85] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Label: 2010"
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "bc5hBE4pPfj8nMeq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.169, 0.311, 0.204, 0.331] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Autotext: \"10.7%\"."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "sgEhN2RWTaIWykVr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is adjacent to {the slice}[0.261, 0.131, 0.491, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.428, 0.144, 0.636, 0.442] is adjacent."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "vQZrQn5O1AwzrwlQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the label '2010'}[0.924, 0.83, 0.959, 0.85]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {\"13.7%\"}[0.307, 0.751, 0.342, 0.771] is located near the label '2010'."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "q1hFEAH02fokM2Fa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2008\", \"2009\", \"2010\", \"2011\", \"2012\", \"2013\", and \"2014\" correspond to the slices with coordinates [0.21, 0.242, 0.428, 0.442], [0.21, 0.435, 0.428, 0.659], [0.272, 0.442, 0.441, 0.753], [0.428, 0.442, 0.604, 0.752], [0.428, 0.351, 0.645, 0.623], [0.428, 0.144, 0.636, 0.442], and [0.261, 0.131, 0.491, 0.442] respectively."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "UzelzYB5RYsnguVq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.21, 0.131, 0.645, 0.753]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the multiple pie slices with different colors representing different years ranging from 2008 to 2014. Each slice also contains two types of text: one indicating the percentage and one indicating the year."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "gEfJjaBIxmtgWPxU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.428, 0.351, 0.645, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice representing the year 2012 with the autotext \"14.6%\" [0.664, 0.488, 0.699, 0.508] and the text '2012' [0.534, 0.462, 0.578, 0.482]."
+            }
+        ],
+        "id_raw": 591
+    },
+    {
+        "id": "hp4bUWrVkKcq6ZFI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.413, 0.088, 0.612, 0.114] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, specifically the text \"Media Influence in 2016\"."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "yzPTnncMnRVYf02Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is inside the coordinates [0.798, 0.228, 0.853, 0.248] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for the legend entry \"Internet\"."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "0iY7w78okT0TaJTL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.258, 0.525, 0.276, 0.545] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"25.0%\" present in the chart."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "PNGDisgxWKCbhP8j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to {the slice} [0.497, 0.505, 0.627, 0.813] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slice}[0.374, 0.505, 0.512, 0.812] and {the slice}[0.512, 0.342, 0.728, 0.766] are adjacent to it."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "cJXqOiwoaVQGvzdA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the label \"Internet\"}[0.798, 0.228, 0.853, 0.248] correspond to in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#ffcc99\" is associated with the \"Internet\" label in the chart's legend."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "s9ObMEpzigZwx8po",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart corresponds to {the label \"Face-to-face\"}[0.798, 0.287, 0.883, 0.307] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.46, 0.197, 0.528, 0.505] corresponds to the label \"Face-to-face\" in the legend."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "xtWKYCF12zapNZnP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region defined by coordinates [0.297, 0.197, 0.728, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the pie chart on the figure. The pie chart is divided into different slices each representing a different medium. Each slice is colored differently and corresponds to a media source such as TV, Internet, Radio etc. with respective percentage values."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "R8FHqixHTGucegAm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.754, 0.13, 0.937, 0.345]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes labels for \"TV\", \"Newspaper\", \"Radio\", \"Internet\", \"Social Media\", \"Face-to-face\", and \"Town hall meetings\". Each label corresponds to a unique color code."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "BsQWzhukvJzbbRhw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is inside the region [0.512, 0.198, 0.695, 0.505] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two objects: a pie slice colored \"#c2c2f0\" representing \"Social Media\" with the text \"15.0%\" [0.635, 0.205, 0.722, 0.225] showing the percentage, and a text label \"Social Media\" [0.557, 0.337, 0.601, 0.357]. This region is part of the larger pie chart."
+            }
+        ],
+        "id_raw": 592
+    },
+    {
+        "id": "gcrPMV653ydNKDEW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at these coordinates in the diagram: [0.25, 0.021, 0.713, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is the subtitle \"Social Dynamics and Political Trends from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "L3VgDPiNuCCtDvP0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at these coordinates: [0.908, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "S6ZVz6C5jfM140oE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.211, 0.736, 0.246, 0.756] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a percentage text, specifically \"16.5%\"."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "J0XL98haQv2G7Xar",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice with the color \"#66b3ff\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item labeled '2016'."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "MYVpzkUCCAK0Oo4l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the pie slices adjacent to the slice with coordinates [0.223, 0.278, 0.482, 0.546]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slices are {the slices}[[0.223, 0.516, 0.482, 0.849],[0.283, 0.146, 0.662, 0.516]]."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "vaZPDghpNsQj8yxj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the legend corresponds to the slice at position [0.283, 0.146, 0.662, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The part of the legend labeled \"2019\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "zxxWNzaQIeFOAPag",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region contained within coordinates [0.482, 0.251, 0.741, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart that represents data from the year 2018. Within this region, there is a label reading \"24.2%\" at coordinates [0.766, 0.491, 0.802, 0.511], as well as a label reading \"2018\" at coordinates [0.615, 0.498, 0.659, 0.518]."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "pFxgcflxSxe5TO7M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects present within the region defined by [0.223, 0.516, 0.482, 0.849]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures a pie slice colored \"#66b3ff,\" which represents 2016 data. Inside this region, there is a label reading \"16.5%\" at coordinates [0.211, 0.736, 0.246, 0.756], and a label reading \"2016\" at coordinates [0.331, 0.631, 0.375, 0.651]."
+            }
+        ],
+        "id_raw": 593
+    },
+    {
+        "id": "QDrBP35x8knG028n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the annotation at coordinates [0.216, 0.267, 0.251, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The annotation is \"2.3%\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "P0j3EjiTPqK196Q3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be identified at coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend item with the label \"2015\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "CM8JzQLbwi3OO4qG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the text found at [0.323, 0.414, 0.358, 0.434]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the year \"2016\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "vDZYHnjA2ONdM1KL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich category corresponds to the pie slice at {coordinates}[0.262, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2015\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "qih2QkOyQF8Lwcut",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie slice with {coordinates}[0.693, 0.516, 0.95, 0.866] relate to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"2020\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "tZcdMKaxOSh7qnKJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between {the slice}[0.223, 0.406, 0.482, 0.58] and {the legend item}[0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"2017\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "lEo5DJu4ULdz9xcl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within these coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a pie chart showing the E-commerce Vs Retail Analysis from 2015 to 2020. It contains multiple slices of different colors, each associated with a uniqueness year from 2015 to 2020."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "qDF6dG6C4KJPS0Y3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.622, 0.646, 0.656, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is an autotext \"19.4%\" associated with the slice representing the year \"2020\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "ho3QwaSs3GknRMdn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_195.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region defined by [0.273, 0.021, 0.69, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the subtitle of the chart which reads \"E-commerce Vs Retail Analysis from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 595
+    },
+    {
+        "id": "YEcSHknHNwoBoSuY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at location [0.296, 0.021, 0.667, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Consumer Trend Analysis from 2017 to 2021\"."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "S5ZP2j9yMiWoNdfs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "Fwz3Gstgqj6xL04p",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at [0.482, 0.516, 0.638, 0.877] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a slice of the pie chart labeled \"2021\" with the text \"6.8%\" at these coordinates."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "1NdBAHOUSatl6ZsO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the slice {the slice}[0.482, 0.516, 0.638, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice labeled as \"2020\"<r>[0.41, 0.516, 0.538, 0.886]</r> is adjacent."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "x55ZQznqw99q3Ma4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on their proximity, which years would likely have similar consumer trends as {the year 2021}[0.482, 0.516, 0.638, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The years {\"2020\"}[0.41, 0.516, 0.538, 0.886] and {\"2017\"}[0.482, 0.516, 0.723, 0.811] are positioned close to 2021, suggesting similar consumer trends."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "VDZXMZFksnIy6mPR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the slice {the year 2021}[0.482, 0.516, 0.638, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the year \"2021\" in the legend is '#c2c2f0'<r>[0.913, 0.342, 0.941, 0.356]</r>."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "jw66AY2GHVtlS5uL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the whole pie chart, which represents Consumer Trend Analysis from 2017 to 2021. It contains ten slices each representing a year between 2017 to 2021, showing the proportion of certain consumer trend in those years."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "b3rkh5bt7uVR3ju9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are key elements in the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a legend, which clarifies the colors used for the different years from 2017 to 2021 in the pie chart."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "NMsWJOzdS8atUpxh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.482, 0.516, 0.723, 0.811]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region involves one slice of the pie, which is labeled as \"2017\" representing the consumer trend analysis of the year 2017."
+            }
+        ],
+        "id_raw": 596
+    },
+    {
+        "id": "uIoTA9OK23gyeUgM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.952, 0.071, 0.987, 0.091] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text \"2010\"."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "ETgFWPpRsjIA95Oa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content and function of the object located at [0.257, 0.021, 0.706, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the graph, entitled \"EdTech and Online Learning Usage from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "v5ayoERHf5Xp5Ot9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element placed at the coordinates [0.506, 0.912, 0.542, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"15.0%\" associated with a sector of the pie chart."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "VF7J7VTIe5Qn3Vrn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is associated with the year 2012 according to the legend item located at {the coordinates}[0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the year 2012 is #99ff99 (light green)."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "CrwJZYKdhGKSPVUf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the slice at {the coordinates}[0.482, 0.495, 0.741, 0.754] according to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices at {coordinates}[[0.482, 0.516, 0.68, 0.83], [0.482, 0.147, 0.74, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "vHtrNz8u8h911mmF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice value of the pie chart does the text {the text \"12.0%\"}[0.752, 0.634, 0.787, 0.654] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the \"2014\" slice at coordinates [0.482, 0.495, 0.741, 0.754]."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "PPqu1qdOD1jJNTSW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components can be found in the region bounded by [0.482, 0.147, 0.74, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the slice labeled \"2015\" [0.57, 0.35, 0.614, 0.37] and the percentage text \"23.0%\" [0.685, 0.22, 0.72, 0.24]."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "u6NBjGM3uPn7szhv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents and importance of the region [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart, containing all the slices representing the usage of different education technologies and online learning methods from 2010-2017, each labeled with the corresponding year and percentage."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "Xsb1RtcIduXrIzeb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_3_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region between the coordinates [0.952, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2017\" [0.952, 0.281, 0.987, 0.301] is located in this region, which is a part of the chart legend and refers to information on the pie chart."
+            }
+        ],
+        "id_raw": 598
+    },
+    {
+        "id": "nk2OSkHtUIlDPm0i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.218, 0.021, 0.305, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle \"Knowledge Economy\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "3u0EfcxWipKxzCTC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at [0.938, 0.071, 0.956, 0.091] coordinates in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the text label \"2016\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "ufc1mmdvdPuOhb4J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object marked at [0.413, 0.606, 0.43, 0.626] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"25.0%\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "LCDuwUWJUfETn255",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two elements adjacent to {the pie slice}[0.274, 0.328, 0.403, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent elements are {the pie slices}[[0.195, 0.516, 0.33, 0.886], [0.162, 0.146, 0.371, 0.516]]."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "ZgdUw8HFvKtd3KqM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does {the autotext}[0.83, 0.688, 0.848, 0.708] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to {the pie slice} [0.703, 0.484, 0.832, 0.83]."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "nrA21a1KoF3dt1Bs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels correspond to the {pie slice}[0.274, 0.328, 0.403, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"2019\" and the autotext \"25.0%\" correspond to this pie slice."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "sfkF7HqOlBQyaN0H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside the region [0.573, 0.12, 0.841, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a pie chart's section which includes several pie slices each associated with a specific year and percentage. The slices include the labels \"2016\", \"2017\", \"2018\", \"2019\", \"2020\" and corresponding percentage autotexts \"17.5%\", \"27.5%\", \"17.5%\", \"22.5%\", \"15.0%\" respectively."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "xzupnwo1nbUtrZIA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area within coordinates [0.573, 0.278, 0.703, 0.661]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a singular pie slice. This slice is labelled \"2016\" and is associated with the autotext \"17.5%\". It is not exploded."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "1yp0odDwnwCcfI8P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what appears in the region [0.712, 0.12, 0.841, 0.49]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a single pie slice that is exploded. It is labelled as \"2019\" and the corresponding autotext is \"22.5%\"."
+            }
+        ],
+        "id_raw": 601
+    },
+    {
+        "id": "USMKPJ7Z0oXvIZL1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart's main title found at coordinates [0.296, 0.021, 0.667, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title of the chart is \"Popularity of Subcultures and Fashion Trends\"."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "lj23heHN48f0s9KD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.895, 0.251, 0.965, 0.271] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend item \"Athleisure\"."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "QuhD05NvMg8cXCgQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.148, 0.722, 0.24, 0.742] in the pie chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.0%\" is an autotext which represents the portion of the \"Modern Boho\" segment in the pie chart."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "jdwT1T0kArnsgsAq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the autotext adjacent to the pie slice {pie slice}[0.325, 0.553, 0.588, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"17.0%\"<r>[0.371, 0.948, 0.45, 0.968]</r> is adjacent to this slice."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "WtYSRQFAMZ6oK2Yw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to the legend item \"Goth\"<r>[0.895, 0.191, 0.929, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice for the legend \"Goth\" has coordinates [0.482, 0.34, 0.741, 0.565]."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "DhfZL4LQXSjrtuu5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie is closest to the legend {legend} <r>[0.895, 0.071, 0.963, 0.091]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that has the coordinates [0.223, 0.278, 0.482, 0.583] is closest to the legend \"Fair Trade\"."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "3zVzcJ7thkJk8Z5I",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within [0.223, 0.278, 0.482, 0.583] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart colored '#ff9999' that represents \"Fair Trade\". The percentage text \"14.0%\"<r>[0.138, 0.402, 0.206, 0.422]</r> is located within this slice."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "cN1gTpbf3MxKiCcS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region indicated by the coordinates [0.227, 0.516, 0.482, 0.814]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice that represents the \"Modern Boho\" trend, with a corresponding autotext \"12.0%\"<r>[0.148, 0.722, 0.24, 0.742]</r> indicating its percentage share."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "EcStWlgebkox5Cj4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_2.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects and their relationships within the region [0.895, 0.071, 0.965, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend entry \"Fair Trade\". The corresponding color swatch <r>[0.856, 0.073, 0.884, 0.087]</r> and the associated pie slice [0.223, 0.278, 0.482, 0.583] also within this region."
+            }
+        ],
+        "id_raw": 602
+    },
+    {
+        "id": "0t0c1qNuddb4rlsl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.237, 0.021, 0.726, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subtitle that reads \"Workplace Development Training Hours from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "zUKrsTFD16MQeOee",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object that is located at the coordinates [0.908, 0.063, 0.993, 0.219] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is where the chart legend is located."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "gF6PaawOuOJacEvO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you see at the coordinates [0.446, 0.099, 0.482, 0.119] in the chart in question?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's where the text \"27.8%\" is located."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "IABpGK6fcW4qAxIB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item from {the legend}[0.908, 0.063, 0.993, 0.219] is related to the pie slice located at coordinates [0.223, 0.278, 0.482, 0.625]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2015\"}[0.952, 0.071, 0.987, 0.091] is related to the pie slice at these coordinates."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "YE4gNgoshHS5rDL5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is connected to {the legend item \"2018\"}[0.952, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at coordinates [0.507, 0.28, 0.767, 0.783] corresponds to the legend item \"2018\"."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "Kz9gRuUBELRyCOeb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is present in {the pie slice}[0.475, 0.516, 0.663, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"12.7%\"}[0.586, 0.885, 0.621, 0.905] is present in the slice."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "t3mU6Dlk958gvvmj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the characteristics of the region within coordinates [0.223, 0.146, 0.767, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses the entire pie chart, which includes slices associated with the years 2015 to 2019 and their corresponding percentages."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "Wmubv51aWSRsi7Kt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements found within the region [0.475, 0.28, 0.767, 0.783]."
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the exploded pie slice representing \"2018\". You can see the autopct value {\"23.8%\"}[0.792, 0.528, 0.827, 0.548] and the text {\"2018\"}[0.641, 0.519, 0.685, 0.539] in the slice."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "EkSErOAsbBeayJlm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_3.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region that has the coordinates [0.223, 0.278, 0.482, 0.625]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the pie chart, corresponding to the year 2015. There is also the text {\"15.9%\"}[0.167, 0.425, 0.202, 0.445] and {\"2015\"}[0.307, 0.462, 0.351, 0.482]."
+            }
+        ],
+        "id_raw": 603
+    },
+    {
+        "id": "zu2eMGjtIb4MAo54",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main title of the chart from the coordinates [0.355, 0.021, 0.608, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is \"Education Resource Utilization\"."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "wIij34oQmm2ZUPGT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.839, 0.193, 0.867, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the handle of \"Learning Apps\" in the legend."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "PwWSzS2OgIOHfPfA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is found at the coordinates [0.6628, 0.157, 0.685, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"15.0%\"."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "khnAXElh7Ln5nvoz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices are surrounding the {Learning Apps slice}<r>[0.283, 0.202, 0.482, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {Webinars}<r>[0.344, 0.146, 0.5, 0.516]</r> and {Online Learning}<r>[0.203, 0.302, 0.599, 0.91]</r> are surrounding the Learning Apps slice."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "McrfRHEjqRXfJg4M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {slice}<r>[0.203, 0.302, 0.599, 0.91]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Online Learning\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "SdEUAbLsmnthmkNh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the handle}<r>[0.839, 0.193, 0.867, 0.207]</r> and {the label}<r>[0.878, 0.191, 0.977, 0.211]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle and the label both represent \"Learning Apps\" in the legend."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "qROmOfAgv9wEFLuV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.203, 0.302, 0.599, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the \"Online Learning\" slice of the pie chart, which occupies 45% of the pie, as indicated by the text \"45.0%\" at coordinates [0.138, 0.797, 0.247, 0.817]."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "1Q5FCZb9E8XkAZGe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the objects inside the region [0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the \"Learning Apps\" slice with the text \"5.0%\" at coordinates [0.196, 0.199, 0.295, 0.219], indicating the Learning Apps' share in Education Resource Utilization."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "HfCeEsNxlgdpMipp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat contents are within the region [0.482, 0.147, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice of \"Tutoring\", which takes up 15% of the total as indicated by the text \"15.0%\" at coordinates [0.628, 0.157, 0.685, 0.177]."
+            }
+        ],
+        "id_raw": 605
+    },
+    {
+        "id": "Vt6IDetXPW4Lax5f",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.31, 0.021, 0.653, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which says \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "pi34FECxng7YC4ZR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.893, 0.221, 0.919, 0.237] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color handle for the \"Thai\" category in the legend."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "72hTdJRn8jaXNf1t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.381, 0.273, 0.425, 0.293] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There's a text labeled \"Thai\"."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "sV06vDm9uYrY4MN0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are near the pie slice {slice}[0.223, 0.278, 0.482, 0.694]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labels {French}[0.305, 0.484, 0.349, 0.504] and {19.1%}[0.152, 0.466, 0.198, 0.486] are close to this slice."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "0UW53M9jOYiiuabd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice does the legend color handle {handle}[0.891, 0.071, 0.919, 0.087] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {pie slice labeled French}[0.223, 0.278, 0.482, 0.694]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "yFBMrQfEjJgYzy5R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are in the vicinity of {the pie slice}[0.272, 0.113, 0.47, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Thai}[0.381, 0.273, 0.425, 0.293] and {13.5%}[0.317, 0.106, 0.347, 0.126] text labels are near to this pie slice."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "cF9cpkGqo9cES015",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bounded by [0.223, 0.113, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the entire pie chart depicting distribution of popularity among different cuisine types. It includes slices with labels and corresponding percentage texts such as {French}[0.305, 0.484, 0.349, 0.504], {Italian}[0.394, 0.707, 0.438, 0.727] and {Chinese}[0.559, 0.676, 0.603, 0.696]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "9Czs65saNdf2eqRc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide details about the region having boundaries at [0.885, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This specified region houses the chart's legend. It includes six items each composed of a color handle and a description text referring to the various cuisines like {Italian}[0.93, 0.101, 0.973, 0.121] and {Mexican}[0.93, 0.191, 0.987, 0.211]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "E9oJFZF0IV7FL4xW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.255, 0.516, 0.534, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice labeled {Italian}[0.394, 0.707, 0.438, 0.727] with an autopct label of {20.2%}[0.319, 0.875, 0.362, 0.895]."
+            }
+        ],
+        "id_raw": 606
+    },
+    {
+        "id": "uITaT3yzDqTNDEa0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the caption of the chart at coordinates [0.193, 0.021, 0.77, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The caption is \"Entertainment and Gaming Industry Development from 2000 to 2007\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "YvzG48pcqNjd1msu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend item at coordinates [0.952, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item is \"2007\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "tvM6j6aSEYzHMOyq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the slice with coordinates [0.283, 0.146, 0.556, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the slice is \"2007\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "tFJBAIFdrzl6sDLC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near the slice{the slice}[0.238, 0.516, 0.482, 0.836]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2002\" located at [0.34, 0.649, 0.385, 0.669] is near the slice."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "iu9BSFMGgcGB6vbu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is represented by the legend item {the legend item}[0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented by the legend item \"2004\" is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "k8PlVmyMWoaYZvs4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with the slice {the slice}[0.283, 0.146, 0.556, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage associated with the slice is \"18.5%\"."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "I0EP2nMgYI7lbpkY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.406, 0.741, 0.733]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice {\"slice\"}[0.482, 0.406, 0.741, 0.733], with the associated text \"2005\" located at [0.613, 0.542, 0.657, 0.562] and the percentage \"14.8%\" at [0.763, 0.572, 0.798, 0.592]."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "U0PFoBwrsS6tmKTH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_7.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.431, 0.482, 0.643]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice {\"slice\"}[0.223, 0.431, 0.482, 0.643] with the text \"2001\" at [0.309, 0.519, 0.344, 0.539] and the percentage \"9.3%\" at [0.162, 0.53, 0.197, 0.55]."
+            }
+        ],
+        "id_raw": 607
+    },
+    {
+        "id": "ZNV2zztmiURBwixf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object resides at this coordinate [0.289, 0.021, 0.675, 0.047] according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, named \"Product Sales Trend Observation in Electronics\"."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "UorEhDTqwj3dAPMg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at these coordinates [0.908, 0.063, 0.993, 0.189] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "5YUy7btegwUS96ts",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at these coordinates [0.952, 0.071, 0.987, 0.091] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2017\"."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "BYkAYv34FIkmHJT0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart appears to be adjacent to {the slice}[0.271, 0.516, 0.612, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {these coordinates}[0.223, 0.278, 0.482, 0.731] appears adjacent."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "8w7n4oUKh20gGsRX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are found near {the title}[0.289, 0.021, 0.675, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2020\"}[0.952, 0.161, 0.987, 0.181] and {\"2017\"}[0.952, 0.071, 0.987, 0.091] are near the title."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "sbhTYlwLQkRE116p",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2017\", \"2018\", \"2019\", and \"2020\" correspond to the slices with coordinates [0.223, 0.278, 0.482, 0.731], [0.271, 0.516, 0.612, 0.886], [0.482, 0.301, 0.741, 0.836], and [0.284, 0.109, 0.693, 0.479] respectively."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "5mK3uXuiXpYWRxMO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found in the region demarcated by these coordinates [0.223, 0.278, 0.482, 0.731]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, within these coordinates, there is the text \"21.0%\" [0.162, 0.49, 0.197, 0.51] and the label \"2017\" [0.304, 0.497, 0.348, 0.517]. This area of the chart represents the sales trend of electronics for the year 2017."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "xnESJt5dEvw5bd9R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what objects are located in the region [0.284, 0.109, 0.693, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"29.0%\" [0.494, 0.062, 0.529, 0.082] and the label \"2020\" [0.467, 0.247, 0.511, 0.267]. This region provides the information about the sales trend of electronic products for the year 2020."
+            }
+        ],
+        "id_raw": 608
+    },
+    {
+        "id": "S9I3APxvHYp2lam7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at this coordinate [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2010\"."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "Xw3ouHrNJzN0GAFK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.223, 0.146, 0.767, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a pie chart."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "G2e3n6HfIaVRhh4z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.223, 0.461, 0.482, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a part of the pie chart, a slice, with color \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "yQHfG8qTas4yrsZ6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with the year {2017}<r>[0.952, 0.281, 0.987, 0.301]</r> in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated is \"#ffb3e6\"."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "VU9yjcInyCiSRNUr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds to the year {2013}<r>[0.952, 0.161, 0.987, 0.181]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to a slice with coordinates [0.382, 0.516, 0.573, 0.886]."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "be3uUJ7Ex4LC3f1B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the slice with coordinates {0.482, 0.149, 0.71, 0.516} correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the year 2016."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "HxPVNAY9oks6nWY8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area within the coordinates [0.352, 0.450, 0.605, 0.565] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a part of the pie chart, which corresponds to the slice with a color \"#ffcc99\" and the text annotation \"2013\"."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "2eWQvoK0GPvCf8BS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located within the region [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the legend for the pie chart, with eight labels representing the years 2010 to 2017 and their corresponding colors."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "1Xf0nF0KnRu1v0gL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_9.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.507, 0.339, 0.767, 0.657]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a slice of the pie chart that has been exploded out. The slice has the color code \"#ffb3e6\" and corresponds to the year 2015."
+            }
+        ],
+        "id_raw": 609
+    },
+    {
+        "id": "aqO3kua07xfzdPMi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object with coordinates [0.367, 0.021, 0.596, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, named \"Social Media Usage in 2020\"."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "K04Lk85pdcrbYcdd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart object located at coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to the center of the pie chart."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "rxwQnFHQmYTVa5aw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.917, 0.071, 0.982, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the text \"Facebook\" found in the legend"
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "f5gT7IKqrTo5Zlcl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is related to the pie slice {at these coordinates}[0.462, 0.516, 0.536, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend correlating to the slice is {Twitter}[0.917, 0.101, 0.963, 0.121]."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "O3gb60GjbMlnjNpw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nConsidering their positions relative to the pie chart's center [0.482, 0.516], which pie slices are to the right of the slice {located at}[0.223, 0.278, 0.482, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices corresponding to {Twitter}[0.462, 0.516, 0.536, 0.886], {Instagram}[0.482, 0.516, 0.711, 0.878], {LinkedIn}[0.482, 0.516, 0.74, 0.688] and {YouTube}[0.431, 0.118, 0.757, 0.521] are to the right."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "RviEoLs5dkVVBHms",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart element corresponds to the pie slice {at the coordinates}[0.338, 0.159, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the {legend item Snapchat} [0.917, 0.221, 0.982, 0.241]."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "1ubklWpTTVFObxGw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.223, 0.118, 0.757, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart, with slices representing the social media platforms: Facebook, Twitter, Instagram, LinkedIn, YouTube, Snapchat, and Reddit. Each slice's text and color corresponds to the respective platform, identified in the figure's legend."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "yGbUZlX8fAyCQpyb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.872, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. The legend labels \"Facebook\", \"Twitter\", \"Instagram\", \"LinkedIn\", \"YouTube\", \"Snapchat\", and \"Reddit\" are identified with their respective colors."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "Qzvx7niupy1DzPtB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements in the region [0.283, 0.208, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice representing {Reddit}[0.283, 0.208, 0.482, 0.516], with its percentage text \"4.5%\" [0.247, 0.203, 0.291, 0.223]."
+            }
+        ],
+        "id_raw": 611
+    },
+    {
+        "id": "lyxUxjvicxneOKYQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the item located at [0.836, 0.071, 0.913, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a part of the legend referring to the \"Carbon Tax\" policy."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "AmQlcXCSNW3FfC8b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.112, 0.658, 0.189, 0.678] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage representing the effectiveness evaluation of \"Carbon Tax\" policy as \"33.3%\"."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "uS53jYgTGFEADcbI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you describe about the object at coordinates [0.463, 0.284, 0.507, 0.304]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label, titled \"Defense Spending\"."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "mkDjbfjqzN8lUKQ1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice corresponding to the {legend item}[0.836, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the \"Universal Health Care\" policy in the legend is '#66b3ff'."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "QU6VSsDF7O5hcEdi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of effectiveness does the {policy label}[0.463, 0.284, 0.507, 0.304] represent in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Defense Spending\" policy label corresponds to \"28.6%\" {[0.489, 0.099, 0.615, 0.119]} effectiveness."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "srj18AeioRORbi3V",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {legend item}[0.836, 0.131, 0.962, 0.151] correlate with the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Defense Spending\" legend item corresponds to a pie slice of color '#99ff99' and represents an effectiveness evaluation of \"28.6%\"."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "Ps48h6W5lkOyz9nD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the area outlined by coordinates [0.198, 0.146, 0.741, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart. The chart is split into various slices, each representing a different government policy and their effectiveness score when evaluated."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "oMCGLg7IzAcKj94Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within the boundaries [0.283, 0.146, 0.688, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of color '#99ff99', corresponding to the \"Defense Spending\" policy. The effectiveness of this policy is represented inside the slice as \"28.6%\" {[0.489, 0.099, 0.615, 0.119]} of the total."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "blrmQPaOnE2pNOIR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region within coordinates [0.198, 0.291, 0.457, 0.893]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of color '#ff9999', corresponding to \"Carbon Tax\" policy. The slice is accompanied by the text \"Carbon Tax\" {[0.289, 0.595, 0.333, 0.615]} and the percentage \"33.3%\" {[0.112, 0.658, 0.189, 0.678]}, indicating the effectiveness evaluation of this policy among all policies."
+            }
+        ],
+        "id_raw": 613
+    },
+    {
+        "id": "woL76TyLajbC5MUJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text identified by the coordinates [0.223, 0.021, 0.74, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Rural and Urban Development Comparison from 2000 to 2020\"."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "zbUKD1iQjlLfQyG8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat set of objects appears at coordinates [0.908, 0.063, 0.993, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the chart's legend."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "PPrYUOhNbsbWnY2u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned at the coordinates [0.482, 0.516] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the center point of the pie chart."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "67hVIYiBNjHsPOfO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the pie slice at coordinates [0.283, 0.159, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to this slice is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "56tYmRN3MSYwf3Gc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element in the legend is connected to the pie slice with coordinates [0.224, 0.366, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The element labeled as \"2005\" with the color \"#66b3ff\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "HpQ6anRcuaCoDGDy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between the pie slice with coordinates [0.482, 0.436, 0.741, 0.749] and the elements in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is connected to the element in the legend labeled as \"2005\" with the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "szrGbtRT4tRezFqg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.146, 0.741, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region makes up the majority of the area of the chart and includes the entire pie chart. The pie chart is made up of different slices, each marked with particular labels and corresponding to different years of comparison between rural and urban development from 2000 to 2020."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "BK2IOpwK2tujOxaj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the region located at [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is on the right side of the chart, including the legend of the chart. The legend contains different labels and colors that match those in the pie chart, representing the data from different years."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "47PiTUZGBV0Go6YT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region from coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses nearly the entire area of the chart apart from margins. It contains the pie chart detailing rural and urban development comparison and the chart's legend on the right side."
+            }
+        ],
+        "id_raw": 614
+    },
+    {
+        "id": "0TQFYNOfNS2udm82",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.29, 0.021, 0.673, 0.047] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Data Usage Trend Analysis from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "vPTtqlMuOswpzwXa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to the coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text displaying \"2015\"."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "pE6H8Qbvzn1WeAVW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area marked by coordinates [0.223, 0.111, 0.741, 0.886] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the main pie chart."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "fTr7xFnGRLNI2M7A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend {the legend}[0.908, 0.063, 0.993, 0.279] corresponds to the slice {the slice}[0.283, 0.172, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color in the legend that matches the slice is \"#b3c933\"."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "N29Lb39sJwm6J5BN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the data {the pie chart}[0.223, 0.111, 0.741, 0.886] makes the largest contribution?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2020 has the largest contribution, with 23.7%."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "uRG1xpAWCsP18yEb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any part of the pie chart that is exploded?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slice representing \"2020\" is exploded."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "t1Zg0PhWbXJcePhq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.271, 0.516, 0.482, 0.883]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a segment of the main pie chart. More specifically, this region represents the data for the year 2017 with a 13.2% contribution, colored in '#99ff99'."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "09zqbmjcEk2Q5b81",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features of the chart can be found in the region [0.483, 0.111, 0.723, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a segment of the pie chart which is exploded. It represents the year 2020 with a 23.7% contribution, the highest amongst other years."
+            }
+        ],
+        "id_raw": 615
+    },
+    {
+        "id": "fbZ57n3KCzkoGEyZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.964, 0.406, 0.988, 0.42] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2018\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "Q2eq09RbaiIK98pe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object can be found inside the chart at coordinates [0.238, 0.47, 0.491, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the slice of a pie chart."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "FrMJRDoMAtoKx65R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content of the legend item with coordinates [0.964, 0.113, 0.988, 0.127]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend item for the year \"2014\"."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "RoxuJI6bcinBUGOE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.238, 0.47, 0.491, 0.592] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"2013\"}[0.964, 0.092, 0.988, 0.106] corresponds to this slice."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "u88YBaOLquI7kDrt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is related to {the legend item}[0.964, 0.134, 0.988, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.276, 0.511, 0.491, 0.806] is related to this legend item."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "5S49XY6NPk6wU7kn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color relationship between {the slice}[0.276, 0.511, 0.491, 0.806] and {the legend item}[0.964, 0.134, 0.988, 0.148]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both the slice and the corresponding legend item share the same color, #c2c2f0."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "Vf6oSbk2qoTNeaLA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.22, 0.132, 0.761, 0.89]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a pie chart representing different years, along with various annotations indicating the percentage of each year. The pie chart's center is approximately at position [0.491, 0.511]."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "nMhL4m6eltQNsAnN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region [0.935, 0.044, 0.992, 0.467]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the legend of the chart. It delineates the colors associated with different years, which also corresponds to the different slices of the pie in the plot."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "wxlQWmqPOEukgKGl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects at coordinates [0.964, 0.0.113, 0.988, 0.127] and [0.938, 0.114, 0.957, 0.124] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates represent a legend item and its color handle respectively. In this case, it's the item for \"2014\" with color '#ffcc99'."
+            }
+        ],
+        "id_raw": 616
+    },
+    {
+        "id": "fPc1Kion7sqti7pU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart type as per the JSON structured data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart type is a pie chart."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "MsBzMrpZqdFVWJNb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart based on the coordinates [0.319, 0.021, 0.644, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Sustainability Score from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "VAuTEGnNE2vN7T3v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text present in the coordinates [0.952, 0.221, 0.987, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" is present."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "6kshOAsbwd5tCLMd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object represented by the color fill in the legend {the object}[0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represents the year \"2020\"."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "yVu4Npcw5XbFJ02o",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does that object correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the pie slice}[0.289, 0.11, 0.741, 0.48]."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "p6ArmMJAkY8tZMaC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the label associated with the object given by the coordinates [0.913, 0.133, 0.941, 0.147] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the pie slice}[0.229, 0.516, 0.482, 0.821]."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "xOEAysXyW1gKRS1C",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region within coordinates [0.223, 0.11, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains six slices of the pie chart. Each slice represents a year from 2015 to 2020 and has its own label and percentage information. The color and explosion status of each slice varies. For instance, the slice for 2020 is exploded."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "4PRYO8IwGPzqXCfM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region in the coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a handle associated with the legend, which is color identified as '#aaaabb' corresponding to the year 2020."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "Z6xmweiKQPbPFaVC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_21.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is presented in the region [0.912, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region shows the color representation (#ff9999) for the Year 2015 in the legend."
+            }
+        ],
+        "id_raw": 621
+    },
+    {
+        "id": "uV6KXGBmmlhRwRdR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.647, 0.071, 0.718, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text that reads 'Friendship'."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "3TdPTrqOIiOAouSm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object located at coordinates [0.232, 0.137, 0.775, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "rZMnYsTlUA4RR2na",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at coordinates [0.267, 0.021, 0.714, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is 'Role of Social Networks in Interpersonal Relationships', which is the subtitle of the chart."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "ASoMr6vgzTFEDtM1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color at {the handle}[0.608, 0.073, 0.636, 0.087] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at the handle corresponds to the slice representing 'Friendship'."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "duxCesBYO4ei7g0Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is related to the {legend item}[0.647, 0.101, 0.802, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Romantic Relationship' slice."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "CFuBwpPCMBsDgroN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage text is related to the {slice}[0.232, 0.278, 0.491, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text '30.0%' is related to the slice."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "xyQas8dmVDkxONhZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.232, 0.137, 0.775, 0.877]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with two slices. One slice represents 'Friendship' at [0.232, 0.278, 0.491, 0.859] with a label at [0.318, 0.56, 0.362, 0.58] and the percentage '30.0%' shown at [0.143, 0.604, 0.214, 0.624]. The other slice is 'Romantic Relationship' at [0.317, 0.137, 0.775, 0.877] with a label at [0.645, 0.443, 0.689, 0.463] and the percentage '70.0%' displayed at [0.792, 0.399, 0.947, 0.419]."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "Vxijc6TPUZiQ08RY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.603, 0.063, 0.808, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the pie chart, with 'Friendship' at [0.647, 0.071, 0.718, 0.091] and 'Romantic Relationship' at [0.647, 0.101, 0.802, 0.121], each with a color indicator at their corresponding handles."
+            }
+        ],
+        "id_raw": 622
+    },
+    {
+        "id": "LreMJkYLnnVUNFaf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text with coordinates [0.234, 0.021, 0.729, 0.047] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Expected population living in smart cities in 2030 and 2040\"."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "dB1gTQg5RyJXlcZ0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is found at [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle of the legend item \"2030\"."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "xSAIaapcmalTVtfv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.502, 0.1, 0.537, 0.12] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Represents the percentage \"30.0%\" related to the \"2040\" slice."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "uw5ib330KA706RiT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between {the text}[0.425, 0.949, 0.46, 0.969] and {the color handle}[0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"70.0%\" corresponds to the color of the legend item \"2030\"."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "Jnmid19t7XWNw32N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is related to the {text}[0.471, 0.284, 0.514, 0.304] \"2040\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the color \"#66b3ff\" and coordinates [0.283, 0.146, 0.701, 0.516] relates to the \"2040\" text."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "oLdhDn4ksEeEAQs9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the {legend item for \"2030\"}[0.952, 0.071, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice in the pie chart with coordinates [0.221, 0.315, 0.739, 0.923]."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "WelrqSuJnqKPCnpN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the area defined by [0.221, 0.315, 0.739, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice labeled \"2030\" with coordinates [0.447, 0.764, 0.491, 0.784] and the percentage text \"70.0%\" with its location at [0.425, 0.949, 0.46, 0.969]."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "8BhmLseLaqgfEccw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region with coordinates [0.283, 0.146, 0.701, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region holds a piece of the pie chart labeled as \"2040\" with coordinates as [0.471, 0.284, 0.514, 0.304] and the corresponding autotext \"30.0%\" which is situated at [0.502, 0.1, 0.537, 0.12]."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "tKeJfCJV9r8XLDzb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is inside the region [0.908, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend. This includes two items for \"2030\" and \"2040\", their coordinates being [0.952, 0.071, 0.987, 0.091] and [0.952, 0.101, 0.987, 0.121], respectively. Also, it includes color handles for each legend item."
+            }
+        ],
+        "id_raw": 623
+    },
+    {
+        "id": "g3KLhbN17pU51Gfi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.223, 0.109, 0.766, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "v0EFl14ITJeTiTDu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.908, 0.063, 0.993, 0.428] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the pie chart."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "VsMgAM9JRQHhVTvQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.221, 0.021, 0.742, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, named, \"Comparison of Urbanization and Air Quality from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "MGlkjF2qhCOEacHl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label is associated with the legend item placed at {coordinates}[0.908, 0.063, 0.993, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" are associated."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "qrDCmrSlTLi9RwFV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the pie chart does the color handle at {coordinates}[0.913, 0.372, 0.941, 0.386] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice with the label \"2019\"."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "qtoRLw6KXuBymtey",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data is represented by the slice located at {coordinates}[0.404, 0.516, 0.544, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data for the year 2019 is represented by the slice."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "U8DwSCPMN5nrV3hq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.482, 0.109, 0.758, 0.885] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a significant portion of the pie chart, which presents data in various colors each representing a different year ranging from \"2015\" to \"2020\". There is no labled text within this region."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "oUxvzJ6PqyXyHpIU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_24.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.908, 0.063, 0.993, 0.428] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, composed of color handles each correlated to a different year from \"2015\" to \"2020\"."
+            }
+        ],
+        "id_raw": 624
+    },
+    {
+        "id": "fgjcO6wn7MEY7fJ5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.306, 0.021, 0.657, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, called \"Urban and Rural Living from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "gcdLAaclrHYUDLR9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can we see at coordinates [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "w7zCF90763ObCCmQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.728, 0.301, 0.763, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"7.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "3Q6srOsGDBEb4FnT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you indicate the text entities that are located near to the {legend}[0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts subscribing to {\"2016\"}[0.952, 0.071, 0.987, 0.091], {\"2017\"}[0.952, 0.101, 0.987, 0.121], {\"2018\"}[0.952, 0.131, 0.987, 0.151], {\"2019\"}[0.952, 0.161, 0.987, 0.181] and {\"2020\"}[0.952, 0.191, 0.987, 0.211] are located near to the legend."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "ifqR3eQzTL0npaAs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice from the pie chart is associated with {the color flag} [0.913, 0.193, 0.941, 0.207] from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice contained within {coordinates} [0.482, 0.516, 0.74, 0.817] is associated with the color flag from the legend."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "Axr4jc71R1hwM4xw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat piece of the pie chart has a direct relationship with the text item {\"2020\"} [0.952, 0.34, 0.987, 0.36]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie piece with {coordinates} [0.283, 0.157, 0.482, 0.516] corresponds to the text item \"2020\"."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "T250Bv899SXOc4ro",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.482, 0.516, 0.74, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region constitutes a slice of the pie chart, labeled with \"14.0%\" [0.73, 0.705, 0.765, 0.725] and \"2020\" [0.595, 0.614, 0.639, 0.634]. The slice is associated with the corresponding legend item on the right."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "mdYU7RinmgLDNrbs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements within the region [0.437, 0.552, 0.638, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, one can note a section of the pie chart that has 'exploded'. It is labeled with the text \"13.0%\" [0.547, 0.94, 0.583, 0.96] and \"2019\" [0.498, 0.759, 0.542, 0.779]."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "wZawlDz8rq5HjWgL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_26.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region defined by coordinates [0.223, 0.278, 0.482, 0.516] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice within the pie chart tagged with the percentage \"10.0%\" [0.182, 0.353, 0.217, 0.373] and the label \"2016\" [0.316, 0.423, 0.359, 0.443]."
+            }
+        ],
+        "id_raw": 626
+    },
+    {
+        "id": "XuUcOUzB4hCezDKs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the main object at coordinates [0.223, 0.146, 0.766, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart that represents Global Trade and Supply Chain Volume from 2010 to 2018."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "FjYAeVn8jYt3NeM6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.508, 0.33, 0.766, 0.631]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a section of the pie chart, representing the Supply Chain Volume data for the year 2016."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "LROKz0K9P52oUQOG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.152, 0.516, 0.152, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The vertical axis (ylabel) of the chart, although it doesn't have any label."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "bgesI97M3nN011B4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.482, 0.516, 0.727, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.441, 0.516, 0.609, 0.886], [0.507, 0.33, 0.766, 0.631]] are adjacent."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "YtJvLda2PzfroKqy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to {the slice}[0.299, 0.516, 0.482, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color in the legend that corresponds to this slice is {\"#ffcc99\"}<r>[0.913, 0.163, 0.941, 0.177]</r>."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "v23DtND5I1sFqU79",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the year 2018 in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2018 in the legend corresponds to the slice {with coordinates} <r>[0.283, 0.146, 0.516, 0.516]</r>."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "miSAaU75VEjIIAAn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.766, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart showing Global Trade and Supply Chain Volume from 2010 to 2018. Each year is represented by a separate slice, and the percentage it represents is indicated on each slice. The legend at coordinates <r>[0.908, 0.063, 0.993, 0.338]</r> corresponds to the different colors of the slices."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "AjkHSyGBviE0Utxi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region within coordinates [0.299, 0.516, 0.482, 0.881]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart, indicating the Supply Chain Volume data for the year 2013. This slice represents 9.9% of the total as indicated by the label at <r>[0.318, 0.869, 0.353, 0.889]</r>."
+            }
+        ],
+        "id_raw": 627
+    },
+    {
+        "id": "SuZc8vNv1QbjDAFg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object occupying the space given by these coordinates: [0.836, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "7rbOpC3StoifNrKM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich component of the chart is associated with these coordinates: [0.283, 0.198, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie-chart slice corresponding to \"Options\"."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "bR9pwzIZIlc41HN2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart element corresponds to the coordinates [0.204, 0.44, 0.526, 0.912]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie-chart which stands for the \"Stocks\" class of investment."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "nEnn5z1ybyUhESRd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a pie-chart piece located adjacent to the slice representing \"Forex\"{slice}[0.349, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slice representing \"Options\"{slice}[0.283, 0.198, 0.482, 0.516] is located adjacent to it."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "kqw3Hq6OZNYdQY8l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the pie-chart are neighboring to the slice for \"Bonds\"{slice}[0.233, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices representing \"Stocks\"{slice}[0.204, 0.44, 0.526, 0.912] and \"Mutual Funds\"{slice}[0.482, 0.464, 0.741, 0.849] are neighboring to it."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "GssFgNexw0MUi8KJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the pie-chart slice at coordinates [0.204, 0.146, 0.741, 0.912] and the legend item at coordinates [0.88, 0.101, 0.926, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are corresponding to each other."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "MAkxUcQCI2JQZO50",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.233, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encloses a slice of the pie-chart associated with the \"Bonds\" category, with its percentage labeled \"6.7%\" residing within, {text}[0.187, 0.315, 0.23, 0.335]."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "tqfCcGUHhbbkBHQZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.204, 0.44, 0.526, 0.912]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a piece of the pie-chart with exploded position indicative of the \"Stocks\" category along with a corresponding text displaying \"Stocks\"{text}[0.329, 0.686, 0.373, 0.706] and the respective percentage \"33.3%\"{text}[0.212, 0.814, 0.258, 0.834]."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "e4dSmHZk4J9dFnHh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_28.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what can be found in the region [0.482, 0.464, 0.741, 0.849]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area encompasses a slice of the pie-chart showing the investment on \"Mutual Funds\", as well as the associated percentage \"20.0%\"{text}[0.733, 0.697, 0.826, 0.717]."
+            }
+        ],
+        "id_raw": 628
+    },
+    {
+        "id": "nmcTRGM0rfctMWzX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text within the coordinates [0.287, 0.031, 0.65, 0.069]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, International Trade for 2020."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "h9e80ITfcfAf6PaB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.839, 0.106, 0.883, 0.127] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color in the legend for Exports."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "rfa1H3nEOLJLsM4Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is present at the coordinates [0.673, 0.228, 0.756, 0.257]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"52.3%\"."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "gMBZrh7UhBvEXkI3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage connected to {the slice}[0.263, 0.167, 0.735, 0.79] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The percentage \"52.3%\"}[0.673, 0.228, 0.756, 0.257] is connected."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "Qm3Y7Ms5PYl9aL1X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the slice}[0.201, 0.294, 0.646, 0.88] corresponding to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to {\"Exports\"}[0.9, 0.104, 0.982, 0.133] in the legend."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "sEMsGLiMIpVCqOul",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat section does the text {\"Imports\"}[0.546, 0.355, 0.614, 0.385] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text corresponds to {the slice}[0.263, 0.167, 0.735, 0.79]."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "gru7gfnM51v6i86E",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.201, 0.167, 0.735, 0.88]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a slice of the pie chart, with a text labeled as \"Exports\"[0.322, 0.662, 0.391, 0.691] and a percentage as \"47.7%\"[0.181, 0.79, 0.263, 0.819]. This corresponds to the red slice indicating Exports in the legend."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "W269NZVUSZTS2O52",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the region [0.263, 0.167, 0.735, 0.79]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart with the text \"Imports\"[0.546, 0.355, 0.614, 0.385] and percentage \"52.3%\"[0.673, 0.228, 0.756, 0.257]. This corresponds to the blue segment representing Imports in the legend."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "aP98mI6SK0OYdtYS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell about the objects within coordinates [0.831, 0.092, 0.992, 0.188]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the chart legend. Two items are listed with their corresponding colors: \"Exports\"[0.9, 0.104, 0.982, 0.133] with a red swatch[0.839, 0.106, 0.883, 0.127], and \"Imports\"[0.9, 0.147, 0.983, 0.177] with a blue swatch[0.839, 0.15, 0.883, 0.17]."
+            }
+        ],
+        "id_raw": 629
+    },
+    {
+        "id": "79PcABTIiKzSi88f",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.296, 0.021, 0.667, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text, titled \"Distribution of Patients Across Medical Fields.\""
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "kn0g8fU0GaFZJSL5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.197, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "crdDN3OXMAMEcrJY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.867, 0.071, 0.979, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Family Medicine\"."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "vdzDwMnTQ96K94iA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend item}[0.867, 0.071, 0.979, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice}[0.197, 0.276, 0.456, 0.711] corresponds."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "P5FSzt7e5OKwxMRC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the legend box}[0.823, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Family Medicine\"}[0.867, 0.071, 0.979, 0.091] and {\"Internal Medicine\"}[0.867, 0.101, 0.987, 0.121] are located near the legend box."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "h6JvVv8YaS8uoyJ9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie in the chart is adjacent to the {psychiatry slice}[0.482, 0.516, 0.69, 0.826]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {\"Ophthalmology\"}[0.482, 0.516, 0.74, 0.736] and {\"Pediatrics\"}[0.454, 0.516, 0.623, 0.886] are adjacent to the Psychiatry slice."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "M5tY95ZIqyYbuQBn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.197, 0.276, 0.456, 0.711]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the slice of the pie chart colored '#ff9999'. The text \"20.1%\" [0.06, 0.476, 0.171, 0.496] is within this region, as well as the label \"Family Medicine\" [0.279, 0.488, 0.323, 0.508]."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "BYNMmQle5JoHjNNe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.623, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie chart slices such as {\"Psychiatry\"}[0.482, 0.516, 0.69, 0.826] and {\"Ophthalmology\"}[0.482, 0.516, 0.74, 0.736]."
+            }
+        ],
+        "id_raw": 630
+    },
+    {
+        "id": "8Qd6uc3aBbfC5KMK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object corresponds to these coordinates [0.906, 0.071, 0.971, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"Facebook\"."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "VT3JZMX4frIEOadz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented at coordinates [0.223, 0.11, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart as a whole."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "1OGUOiKLzNw5L1oq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the segment located at coordinates [0.482, 0.156, 0.584, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the pie slice representing \"Twitter\"."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "0AUuOeZUzRoH6CPp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the segment that corresponds to {the legend item \"Instagram\"}[0.906, 0.101, 0.977, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#66b3ff'."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "pndCMCfH5XN4LAY7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is adjacent to the slice located at {the slice}[0.314, 0.15, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Snapchat\"<r>[0.4, 0.304, 0.435, 0.324]</r> is adjacent to the slice."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "6iB5iUDbwFMKqidP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to the very small slice located at {the slice}[0.283, 0.263, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Meetup\"<r>[0.348, 0.359, 0.383, 0.379]</r> corresponds to this slice."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "CungifqCxXHaRKNU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the area covered by coordinates [0.861, 0.063, 0.993, 0.309]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart. It has the names and corresponding colors for each slice of the pie, including \"Facebook\"<r>[0.906, 0.071, 0.971, 0.091]</r>, \"Instagram\"<r>[0.906, 0.101, 0.977, 0.121]</r>, \"Twitter\"<r>[0.906, 0.131, 0.953, 0.151]</r>, \"LinkedIn\"<r>[0.906, 0.161, 0.964, 0.181]</r>, \"WhatsApp\"<r>[0.906, 0.191, 0.977, 0.211]</r>, \"Snapchat\"<r>[0.906, 0.221, 0.971, 0.241]</r>, \"Google Plus\"<r>[0.906, 0.251, 0.987, 0.271]</r>, and \"Meetup\"<r>[0.906, 0.281, 0.958, 0.301]</r>."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "uHnE3cYbWUmhzvMs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bounded by coordinates [0.223, 0.11, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the entire pie chart data. It contains several slices like \"Facebook\"<r>[0.223, 0.278, 0.734, 0.886]</r>, \"Instagram\"<r>[0.482, 0.176, 0.741, 0.599]</r>, \"Twitter\"<r>[0.482, 0.156, 0.584, 0.516]</r>, and others, each representing a different social network."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "gC7mSLTeCH1IZM8U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_32.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region outlined by coordinates [0.235, 0.021, 0.728, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's subtitle which reads \"Influence of Social Networks on Interpersonal Relationships\"."
+            }
+        ],
+        "id_raw": 632
+    },
+    {
+        "id": "BeWprQh2zwTzaMCz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you indicate what the main text at coordinates [0.277, 0.021, 0.686, 0.047] is?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle, which reads \"New Media and Digital Content Creation for 2020\"."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "DuKTxrDF2L6hjnBm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.859, 0.071, 0.987, 0.091] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a legend label, named \"Digital Advertising\"."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "Cq4uM0nSqdnm6ojE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the representation for the object at coordinates [0.196, 0.846, 0.325, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"53.6%\" located inside the pie slice."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "BqRF11tFAKdGg7I3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the entry}[0.859, 0.101, 0.978, 0.121] in the legend area and the objects in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend entry \"Content Creation\" corresponds to the pie slice with coordinates [0.297, 0.115, 0.755, 0.652]."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "QTk0yi9XpV8Wa9P9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the object}[0.56, 0.29, 0.604, 0.31] related to the other elements of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label \"Content Creation\", which is positioned on the pie slice with coordinates [0.297, 0.115, 0.755, 0.652]."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "iBn4wJ1CqvIeFNlj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between {the object}[0.653, 0.135, 0.772, 0.155] and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the percentage \"46.4%\" embedded in the pie slice [0.297, 0.115, 0.755, 0.652], representing the share of \"Content Creation\" in the chart."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "Gnlcza3Hz3CWQcCN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region [0.223, 0.115, 0.755, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a pie slice representing \"Content Creation\" with its label [0.56, 0.29, 0.604, 0.31] and percentage distribution \"46.4%\" at [0.653, 0.135, 0.772, 0.155]."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "xncKldG7vf1JbiNo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region within [0.223, 0.278, 0.713, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises a pie slice, \"Digital Advertising\", along with its coordinates [0.374, 0.691, 0.418, 0.711], and the corresponding percentage of \"53.6%\" at coordinates [0.196, 0.846, 0.325, 0.866]."
+            }
+        ],
+        "id_raw": 633
+    },
+    {
+        "id": "LC4wagZYwKeFPB9J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.223, 0.021, 0.74, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, named 'Rural and Urban Development Comparison from 2010 to 2018'."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "LaNrtEJDWZDMrF5l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.311, 0.987, 0.331] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend text \"2018\"."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "wat0K86oYG8QbPBU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.917, 0.073, 0.944, 0.088] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color label for the legend item \"2010\"."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "FI8Tx83WhM1lQram",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color label is associated with the text item {\"2012\"}[0.952, 0.131, 0.987, 0.151] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{\"#99ff99\"}[0.913, 0.133, 0.941, 0.147]."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "oKM3bdi3Q9hPLZTa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the autotext {\"11.9%\"}[0.739, 0.33, 0.774, 0.35] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is slice {\"2016\"}[0.482, 0.245, 0.74, 0.516]."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "yjP2hj5aMGm3gvPb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice's text is located closest to the {subtitle}[0.223, 0.021, 0.74, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice \"2018\" [0.272, 0.113, 0.47, 0.483]."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "7lHOy9Q1OgbXdCzt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.908, 0.063, 0.993, 0.338]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is located at the right upper part of the chart and contains the legend for the pie chart. It consists of color labels and associated text for each year from 2010 to 2018."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "ARIa2HIwnvRnz4Fs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.113, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the main pie chart, with slices labeled for years from 2010 to 2018, associated percentages, and color labels."
+            }
+        ],
+        "id_raw": 637
+    },
+    {
+        "id": "ugxkjXwqANInClcy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key object depicted within these coordinates [0.323, 0.021, 0.64, 0.047] in our chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, which is \"Investments in Healthcare Technology\"."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "MlYcTjlwqrIG1S5H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you recognize the entity in coordinates [0.864, 0.071, 0.975, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it is a legend item labelled \"Robotic Surgery\"."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "GtdkXOUfIDESLFEF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie chart illustrate at the coordinates [0.199, 0.146, 0.741, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is illustrating the distribution of investments in different healthcare technologies."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "TPLHH5A7hNPNTl27",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre the pie slices at coordinates [0.475, 0.516, 0.631, 0.886] and [0.482, 0.516, 0.736, 0.818] adjacent?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, these two slices are adjacent."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "GoACmNuCfzBZFzIX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item matches with the pie slice at {coordinates}[0.283, 0.228, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Echo-Cardiogram\"<r>[0.864, 0.251, 0.985, 0.271]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "yV1YSHaSM0pXQK7M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the pie slice at coordinates [0.199, 0.293, 0.458, 0.901] associate with the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice associates with the legend \"Robotic Surgery\"<r>[0.864, 0.071, 0.975, 0.091]</r> in terms of color code and data classification."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "2dHj9ppOExfhu78U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region that falls within these coordinates [0.82, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily includes the legend of the chart. The legend contains markers and labels for sectors such as \"Robotic Surgery\", \"Telemedicine\", \"Nanomedicine\", \"Bioinformatics\", \"Genetic-Testing\", \"Health-Wearables\", and \"Echo-Cardiogram\". Each label has an associated colored square representing the color of the corresponding pie slice."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "BkC8L2S1JWMdNMpS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_39.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the region [0.199, 0.146, 0.741, 0.901] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily includes the main pie figure of the chart identified by the subtitle \"Investments in Healthcare Technology\". The pie slices represent investments in different healthcare technologies like \"Robotic Surgery\", \"Telemedicine\", \"Nanomedicine\", \"Bioinformatics\", \"Genetic-Testing\", \"Health-Wearables\", and \"Echo-Cardiogram\", and their associated percentage shares."
+            }
+        ],
+        "id_raw": 639
+    },
+    {
+        "id": "v3L1JpEpUB0wKA8M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.312, 0.021, 0.651, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle that reads \"Fitness and Sports Health Trends in 2020\"."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "OP0CoDh7n2gKf1a9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the chart element with coordinates [0.899, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Weightlifting\" is contained in this region."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "pvgMcaEX2QUT9sfH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is associated with the bounding box [0.237, 0.18, 0.311, 0.2]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"7.4%\"."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "UVrgA4a1OACFyzlm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend corresponds to the {colored slice}[0.197, 0.281, 0.456, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item { \"Yoga\" }<r>[0.899, 0.071, 0.932, 0.091]</r> corresponds to the colored slice."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "SZwvudBylVRHaRRu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the legend object { \"Crossfit\" }<r>[0.899, 0.191, 0.952, 0.211]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice}<r>[0.379, 0.146, 0.574, 0.516]</r>."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "GWHyRfJP2XrBptZZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice corresponds to the text { \"Cycling\" }<r>[0.899, 0.131, 0.95, 0.151]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates { [0.482, 0.266, 0.741, 0.74] } corresponds to the \"Cycling\" text in the legend."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "ZLgR1xiNkfMRVMZj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area enclosed by coordinates [0.197, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area corresponds to the main chart of the figure representing various fitness trends. It contains several exploded pie slices, each representing a different activity: { \"Yoga\" }<r>[0.279, 0.53, 0.323, 0.55]</r>, { \"Running\" }<r>[0.477, 0.727, 0.521, 0.747]</r>, { \"Cycling\" }<r>[0.615, 0.496, 0.659, 0.516]</r>, { \"Weightlifting\" }<r>[0.552, 0.322, 0.587, 0.342]</r>, { \"Crossfit\" }<r>[0.456, 0.284, 0.5, 0.304]</r>, and { \"Meditation\" }<r>[0.371, 0.328, 0.406, 0.348]</r>."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "4xCozIgo40ATUrwh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of the region with coordinates [0.855, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the legend of the chart. It contains six labelled boxes with their corresponding texts: { \"Yoga\" }<r>[0.899, 0.071, 0.932, 0.091]</r>, { \"Running\" }<r>[0.899, 0.101, 0.956, 0.121]</r>, { \"Cycling\" }<r>[0.899, 0.131, 0.95, 0.151]</r>, { \"Weightlifting\" }<r>[0.899, 0.161, 0.987, 0.181]</r>, { \"Crossfit\" }<r>[0.899, 0.191, 0.952, 0.211]</r>, and { \"Meditation\" }<r>[0.899, 0.221, 0.973, 0.241]</r>."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "ByQciwK2CK7eP7CB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is contained within the boundary coordinates [0.482, 0.17, 0.673, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these boundaries, there are two objects. One is text { \"Weightlifting\" }<r>[0.552, 0.322, 0.587, 0.342]</r> and the other is text { \"7.4%\" }<r>[0.642, 0.169, 0.73, 0.189]</r> which indicates the proportion of Weightlifting activity in the chart. This region lies within one of the pie slices of the main chart, colored in \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 641
+    },
+    {
+        "id": "cRgWGUxBH4upbaYZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.329, 0.021, 0.634, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Electric Car Sales from 2013 to 2019\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "HRziGSU8bUsqyXt6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2013\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "G3Bc6Rr0hcZklaYD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.204, 0.293, 0.239, 0.313] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text showing portion of data as \"4.7%\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "Yyk6NqPPyV8T2N0g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {the slice}[0.415, 0.516, 0.678, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "La2kliLPin2o8lND",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the legend item}[0.952, 0.191, 0.987, 0.211] correspond to the pie chart slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2017\" corresponds to {the slice}[0.415, 0.516, 0.678, 0.886] with color \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "XUJT0aTzygKsq5rU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of {legend items}[0.952, 0.071, 0.987, 0.091] and {the slice}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2013\" matches with the slice in color \"#ff9999\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "zxuhNnTFXJ0kiwif",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.741, 0.903]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple slices of a pie chart and their associated autotext percentages. This includes seven slices associated with text labels from \"2013\" to \"2019\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "gj5FWYV0WrIMKcwZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region between coordinates [0.482, 0.266, 0.741, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice labeled as \"2018\", with an associated autotext showing \"23.1%\". This slice has color \"#ffb3e6\"."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "dviJQ1x8Lqx0lWdY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.908, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a region on the right side containing the legend of the pie chart with seven items corresponding to the years from 2013 to 2019, each accompanied by a color code."
+            }
+        ],
+        "id_raw": 642
+    },
+    {
+        "id": "IVPlvAAskFe2WvrW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the region with coordinates [0.869, 0.222, 0.936, 0.242] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the term {\"Policy Making\"}[0.869, 0.222, 0.936, 0.242] in the legend."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "jf8bRZ8jP2Afd3aC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content in the region with the coordinates [0.007, 0.513, 0.075, 0.533]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage {\"25.0%\"}[0.007, 0.513, 0.075, 0.533]."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "gHc9XKEOUVYsYbfW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area at coordinates [0.524, 0.172, 0.605, 0.198] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart subtitle \"Policy Making\" is represented by the area at coordinates [0.524, 0.172, 0.605, 0.198]."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "1X5D4BiUG3D7o5oE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is related to the legend item {\u201cImprovements\u201d}[0.869, 0.252, 0.94, 0.272] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to {\"Improvements\"}[0.869, 0.252, 0.94, 0.272] from the legend is located at [0.473, 0.295, 0.692, 0.752]."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "ieu8yM2T9qYtyDcF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the legend items {\u201cThreats\u201d}[0.869, 0.281, 0.906, 0.301] and the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Threats\"}[0.869, 0.281, 0.906, 0.301] corresponds to the green slice of the pie chart located at [0.474, 0.263, 0.609, 0.5]."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "YMSmMBh1c5ldlWR4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are related to {\"Policy Making\"}[0.869, 0.222, 0.936, 0.242] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The elements related to \"Policy Making\" are the red slices at coordinates [0.086, 0.348, 0.205, 0.682] and [0.446, 0.348, 0.564, 0.626] in the pie charts."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "BPQlAtTLSP2k5MEk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.086, 0.263, 0.331, 0.755]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a part of the pie chart containing three slices with corresponding percentages, namely {\"25.0%\"}[0.007, 0.513, 0.075, 0.533], {\"45.0%\"}[0.298, 0.705, 0.37, 0.725] and {\"30.0%\"}[0.214, 0.23, 0.251, 0.25]. These slices are respectively related to \"Policy Making\", \"Improvements\", and \"Threats\"."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "lAKoufV3Hl0BEISh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the segment [0.446, 0.263, 0.692, 0.752] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segment contains pie slices corresponding to the terms {\"Threats\"}[0.531, 0.352, 0.563, 0.372], {\"Improvements\"}[0.612, 0.597, 0.644, 0.617] and {\"Policy Making\"}[0.478, 0.48, 0.509, 0.5]. It also has these corresponding percentages: \"20.0%\", \"60.0%\", and \"20.0%\"."
+            }
+        ],
+        "id_raw": 644
+    },
+    {
+        "id": "afq9aDx9UwJvqIW1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.213, 0.021, 0.75, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle text, \"Art Development & Technological Advancement in Different Eras\"."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "2FWt7760UN6oEMsr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.766, 0.64, 0.853, 0.66]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"23.9%\"."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "JlSfufw7Rt4Ji7kH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.876, 0.191, 0.973, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label for \"Modern Times\"."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "sfvkUnzcp44g6RxL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is next to {the slice}[0.259, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.225, 0.326, 0.482, 0.516] is adjacent."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "LwxI5AzYd519PJGY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend matches {the pie slice}[0.406, 0.516, 0.695, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"Modern Times\"[0.876, 0.191, 0.973, 0.211] matches the pie slice."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "MEUjyw85ezzVxIU2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the coordination of the slice that corresponds to the legend item \"Information Age\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates for the slice that corresponds to \"Information Age\" are [0.281, 0.109, 0.641, 0.479]."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "xxQLsOcYwiiAkfhz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region between coordinates [0.218, 0.109, 0.464, 0.897]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for \"Renaissance\"[0.218, 0.543, 0.464, 0.897] and the text describing it [0.333, 0.692, 0.377, 0.712]. It is located on the left side of the chart and appears to be slightly exploded compared to the other slices."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "Z7JJ010MCUCrLXwY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat items are contained in the region between [0.225, 0.326, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for the Classical Period [0.225, 0.326, 0.482, 0.516] and the corresponding text [0.318, 0.431, 0.353, 0.451]."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "NUPpa31hiucBeJhb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_46.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region bounded by coordinates [0.406, 0.516, 0.695, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the \"Modern Times\" slice of the pie. It contains the slice [0.406, 0.516, 0.695, 0.886] and the associated text \"20.1%\" [0.576, 0.89, 0.673, 0.91]. This region is on the right-hand side of the pie chart."
+            }
+        ],
+        "id_raw": 646
+    },
+    {
+        "id": "PBlSTkpPx93pxvfm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.279, 0.021, 0.684, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle that reads \"Facebook vs Instagram Users from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "ArKeOhr21kMfUYJ9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.908, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "8omk92ulfirTUUsj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.223, 0.146, 0.741, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "oL8UEeeM7oPBOuoK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {the slice}[0.353, 0.553, 0.619, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item for \"2017\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "cAJBLOiEECxCa3iG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the pie slice that corresponds to the legend item with the handle at [0.913, 0.103, 0.941, 0.117]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to the legend item \"2016\"."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "tw9vtc0jTZCv3ze7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.482, 0.477, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.353, 0.553, 0.619, 0.923], [0.482, 0.164, 0.739, 0.516]] are adjacent to the referenced slice."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "ygBudtD8hdemlFJP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.567]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is part of the pie chart, representing the year 2015 with a color of '#ff9999'. It features an autotext of '13.3%' at [0.173, 0.394, 0.208, 0.414] and a text '2015' at [0.31, 0.445, 0.354, 0.465]."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "XtCCLnkIBn1fGNAz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.482, 0.477, 0.741, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a segment of the pie chart that represents the year 2018. It has an autotext '17.8%' located at [0.738, 0.684, 0.773, 0.704] and a text '2018' at [0.599, 0.603, 0.643, 0.623]."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "mVs1V6ZwPcWHtatf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.562, 0.946, 0.697, 1]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There are no objects located in this region of the chart."
+            }
+        ],
+        "id_raw": 647
+    },
+    {
+        "id": "5cy4kNZ0zVkbH9ly",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at the coordinates [0.339, 0.021, 0.624, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle, which is \"Internal Business Process Analysis\"."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "9FY0uGrcoZyErnzg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object spotted at the coordinates [0.791, 0.223, 0.819, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend handle with the color '#ffb3e6', which stands for 'Customer Support'."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "h3QuiBBm6dSwghSP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you please tell what's the object at [0.137, 0.664, 0.219, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an auto-text object displaying the percentage \"14.5%\"."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "UZ9hCxjkyZHRU9lX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which process corresponds to the chart slice located at {coordinates}[0.223, 0.495, 0.482, 0.795] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The process that corresponds to the chart slice at these coordinates is {Onboarding}<r>[0.316, 0.592, 0.36, 0.612]</r>."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "HiXSetiN8BckVOO7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color segment of the legend corresponds to the slice {coordinates}[0.223, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color segment that corresponds is the label \"Recruitment\" with a handle color of '#ff9999'."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "c59oZFnnxvR6B639",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the pie does the legend item {label \"Financial Management\"}[0.83, 0.251, 0.987, 0.271] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie slice with {coordinates}[0.283, 0.169, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "Nrd5aFw30pW8moSU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within the coordinates [0.223, 0.146, 0.741, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with various colored slices. Each slice represents a process with a color, a percentage, and text indicating the process. For example, one slice includes the text \"Inventory\" <r>[0.445, 0.764, 0.489, 0.784]</r> and the corresponding percentage \"20.3%\" <r>[0.391, 0.949, 0.457, 0.969]</r>."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "LDGhYAdxBpnqYcEC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region [0.786, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. It includes several items each with a handle color and a corresponding label. For instance, one item is \"Quality Assurance\" <r>[0.83, 0.161, 0.956, 0.181]</r> with a handle color of '#ffcc99' <r>[0.791, 0.163, 0.819, 0.177]</r>."
+            }
+        ],
+        "id_raw": 649
+    },
+    {
+        "id": "WyAJ8vnfOyUlvtBX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main subtitle as per coordinates [0.278, 0.021, 0.685, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle is 'Environment Protection Trend from 2015 to 2021'."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "M8RhFUBGYor89VdU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.913, 0.253, 0.941, 0.266] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the color coding for year 2021 in the legend."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "iYNQeRkYj4awHT9G",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the data shown as represented within coordinates [0.223, 0.111, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinates represents a pie chart displaying 'Environment Protection Trend from 2015 to 2021'."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "qNCZbWaWWt1Y9fSn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich portion of the pie chart corresponds to the {legend item}[0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The coordinates corresponding to legend item is {the slice}[0.223, 0.278, 0.482, 0.586]."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "r3i3hJJUO89gdoXp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is {the legend}[0.908, 0.063, 0.993, 0.279] related to {the pie chart}[0.223, 0.111, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each colored box in the legend corresponds to the different slices in the pie chart representing different years."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "Y8l4SR6igyBBksQI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich coordinates does the text labeled '2020' correspond to in the chart legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The '2020' text coordinates in the legend are [0.952, 0.221, 0.987, 0.241]."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "ivCwZdWCbGXhSk1Q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly consists of the entire pie chart depicting 'Environment Protection Trend from 2015 to 2021' and the axis labels. It doesn't include the legend."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "jqwTkAr5YuRIgQeP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_50.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items within the region [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, which includes a labeled color box for each year from 2015 to 2021, with boxes for different years having respective coordinates like [0.913, 0.073, 0.941, 0.087], [0.913, 0.103, 0.941, 0.117], etc."
+            }
+        ],
+        "id_raw": 650
+    },
+    {
+        "id": "TiC7WwiAXEGS2h4T",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart as found at coordinates [0.356, 0.236, 0.644, 0.262]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Industry Market Share Comparison\"."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "aeiBXyx4IMe8muUI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.471, 0.954, 0.483, 0.974] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text representing the percentage \"30.0%\"."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "s1OHZrKfcyM3985D",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.359, 0.601, 0.403, 0.621] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"Auto\"."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "KQR5wgRMKf98giCH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice adjacent to the {Pharmaceuticals slice}[0.5, 0.555, 0.699, 0.806] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the adjacent slice is '#ffcc99', which is associated with the \"Food\" sector."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "LpZKvq7vFbZQCv4g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item in the legend corresponds to {the slice}[0.395, 0.348, 0.5, 0.623]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Solar Energy\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "A1XjOd3voZc1tWnU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage is associated with the {slice}[0.301, 0.441, 0.5, 0.774]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents 20.0% according to the autotext at coordinates [0.249, 0.592, 0.282, 0.612]."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "jBI40AWTf8U1qF0H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.577, 0.033, 0.742, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the legend of the pie chart. It consists of labels and corresponding colors for \"Auto\", \"IT\", \"Pharmaceuticals\", \"Food\", \"Solar Energy\", and \"Others\". Each of these items have their unique coordinates such as \"Auto\" located at [0.621, 0.041, 0.654, 0.061]."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "OpWzdMyWs87YWulg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are found in the region [0.301, 0.339, 0.699, 0.936]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is mainly made up of slices of the pie chart along with their respective labels and percentages such as the \"Auto\" slice with its label at [0.359, 0.601, 0.403, 0.621] and percentage at [0.249, 0.592, 0.282, 0.612]. Likewise, other slices exist within this region along with their respective information."
+            }
+        ],
+        "id_raw": 651
+    },
+    {
+        "id": "DYZZOkt246eiCOXu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.334, 0.021, 0.629, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle named \"Risk Occurrence Rates By Category\"."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "JsBN1MAeuJBmTmp0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.802, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the Legend containing different Risk Categories."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "9TDhQwcBZbBCAAEj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see at coordinates [0.223, 0.146, 0.741, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main Pie Chart that represents various risk categories' occurrence rates."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "Se7d1cYZkKw9GR2C",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext is closest to the slice {slice}[0.254, 0.516, 0.482, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {\"10.5%\"}[0.164, 0.805, 0.288, 0.825] is the closest to the slice."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "8IjZ4SszxYsxQPZb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {slice}[0.223, 0.369, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Information Security}[0.845, 0.101, 0.987, 0.121] corresponds to the slice in the chart."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "0wb3FaP7rm34Esu8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the closest slice to the legend item {legend}[0.845, 0.281, 0.954, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice[0.283, 0.146, 0.614, 0.516] is closest to the legend item."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "F2y0rOkqOpBUHfV9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region within coordinates [0.334, 0.021, 0.629, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's subtitle {\"Risk Occurrence Rates By Category\"}[0.334, 0.021, 0.629, 0.047]."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "ZNG5GPog7xqW7TEU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.482, 0.198, 0.741, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the specified area, there's a slice of the pie chart, colored in '#f7bb5f', representing \"Financial Risk\" with an autotext indicating {19.8%}[0.742, 0.342, 0.836, 0.362] occurrence."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "ZAi2opuiS5bPfxAu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_52.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region [0.802, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend. It consists of various risk categories, each coupled with a color code such as {Physical Security}[0.845, 0.071, 0.963, 0.091], {Information Security}[0.845, 0.101, 0.987, 0.121], {Compliance}[0.845, 0.131, 0.927, 0.151] and others."
+            }
+        ],
+        "id_raw": 652
+    },
+    {
+        "id": "E4DvA5pe3lENincC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart located at coordinates [0.356, 0.021, 0.607, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which is \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "c0aqQEgYCBekeKPc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at coordinates [0.199, 0.146, 0.741, 0.886] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The whole pie chart with slices indicating different stages of data handling."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "hwZFjdcLP67yUl7Q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at these coordinates [0.845, 0.071, 0.978, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the legend item \"Raw Data Cleaning\"."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "FOibDxmjvs1Y9Qi2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item {handle}[0.806, 0.073, 0.834, 0.087] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.199, 0.264, 0.458, 0.502], represented as \"Raw Data Cleaning\" in the chart."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "JpSp0Wlti0SgcUXr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich stage is related to {the slice}[0.482, 0.173, 0.741, 0.754] based on the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the legend item {\"Data Integration\"}[0.845, 0.161, 0.959, 0.181]."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "05myjAv6CUbXrJef",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the autotext percentage {text}[0.758, 0.407, 0.873, 0.427] associate with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It relates to {the slice}[0.482, 0.173, 0.741, 0.754] known as the \"Data Integration\" phase."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "DHY3N5MexZ77Y2Yq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.223, 0.49, 0.482, 0.885] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the slice identified with color '#66b3ff', labeled as \"Data Preprocessing\" and marked with the text percentage of \"25.0%\". It occupies a significant upper portion of the pie chart."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "U9p7fZezNeKIaXRY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what appears in the region encompassed by [0.806, 0.103, 0.834, 0.117] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The section is populated with the small square box of a legend item, colour-coded as '#66b3ff', that corresponds to the \"Data Preprocessing\" slice in the pie chart."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "S7DFRq7tDP0lDdP0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects present in the region [0.199, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region spans the entire pie chart containing different slices demonstrating steps of Big Data handling namely 'Raw Data Cleaning', 'Data Preprocessing', 'Data Transformation', 'Data Integration', 'Data Loading', and 'Data Verification'. Each section within the pie chart is labelled with the stage name and percentage covered."
+            }
+        ],
+        "id_raw": 654
+    },
+    {
+        "id": "Y3S7gVPLIqS2iRhe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main subtitle of the plot at coordinates [0.263, 0.021, 0.7, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main subtitle is \"Career and Workplace Development Trends for 2019\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "32910gSqqIIN2BZj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the midpoint of the pie chart."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "02DkD9arbPx4ZTXC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.869, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend item \"Self-Employment\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "FKno12oU5z12Glhm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the two sectors of the pie chart associated with the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sectors \"Telecommuting\" and \"Self-Employment\" are associated with the legend items at coordinates [0.223, 0.278, 0.667, 0.886] and [0.301, 0.118, 0.758, 0.747]."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "zfJVCGsNB4sT8J1S",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the sector associated with legend item {Self-Employment}<r>[0.869, 0.101, 0.987, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#c2c2f0' which is a light purple color."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "YexbrctElYBJRgrF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text/percentage alongside the slice at coordinates [0.223, 0.278, 0.667, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"48.8%\" is alongside the slice."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "OlkTvVYxmzgv0dAK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the sector of the pie chart within the coordinates [0.223, 0.278, 0.667, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sector within these coordinates represents the \"Telecommuting\" category. It shows a value of \"48.8%\" and it's distinguished by a blue color, specifically \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "rbi1SRIUBmSOXjpx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.301, 0.118, 0.758, 0.747] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates envelope the sector of the pie chart representing \"Self-Employment\", which shows a value of \"51.2%\". The sector is colored in the shade \"#c2c2f0\", which is light purple."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "MGveCoa4N32sjCSg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the main plot area with the coordinates [0.223, 0.118, 0.758, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate contains the main pie chart colored in light blue and purple. It contains the splits for \"Telecommuting\" [0.223, 0.278, 0.667, 0.886] and \"Self-Employment\" [0.301, 0.118, 0.758, 0.747] which, respectively, depict percentages of 48.8% and 51.2%."
+            }
+        ],
+        "id_raw": 655
+    },
+    {
+        "id": "kb4zaxo6RNWZjsvb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at these coordinates [0.952, 0.071, 0.987, 0.091] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text stating \"2014\"."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "EGlOSb6dVzNJjf7R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what object lies at the coordinates [0.282, 0.146, 0.558, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a pie chart slice, representing data from the year \"2019\"."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "jeGvq6h18saXSPaL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is the content of the object at coordinates [0.241, 0.021, 0.722, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle, stating \"Food and Agriculture Industry Analysis from 2014 to 2019\"."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "o8Tnlfy8nlncQZB6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {this pie slice}[0.223, 0.146, 0.765, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slices}[[0.506, 0.452, 0.765, 0.815], [0.482, 0.162, 0.735, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "4g3rycBahzFLyJyk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year's data does the legend color at coordinates [0.913, 0.073, 0.941, 0.087] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice labelled \"2014\"}[0.223, 0.278, 0.482, 0.604]."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "QvKRNwEcTOEIZt77",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legends are related to the year \"2018\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend with coordinates [0.913, 0.193, 0.941, 0.207] corresponds to the data of the year 2018."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "1FIJyBV5UpIxrAD2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within region [0.401, 0.516, 0.646, 0.886] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart contains a slice of the pie chart corresponding to the year \"2016\" and its associated data is represented as \"15.9%\", identified by the coordinates [0.534, 0.906, 0.569, 0.926]."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "YaJlrDTRnakMDs3o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_56.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components are within the region [0.506, 0.452, 0.765, 0.815] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice that corresponds to the year \"2017\", with its percentage value \"17.4%\" being located at coordinates [0.775, 0.653, 0.81, 0.673]."
+            }
+        ],
+        "id_raw": 656
+    },
+    {
+        "id": "QK5TzCrHKSBEcola",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.282, 0.021, 0.681, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the subtitle, and it reads \"Financial Data Exploration: Portfolio Distribution\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "wlhaFtcQ3MBkUivU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.88, 0.221, 0.972, 0.241] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Commodities\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "JHA1dYFUT0odphvB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at coordinates [0.744, 0.347, 0.837, 0.367] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "vTQYp9CLUIY40Ezn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie chart segment is adjacent to the {segment}[0.325, 0.55, 0.75, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The segments {Mutual Funds}[0.482, 0.232, 0.741, 0.542] and {Bonds}[0.223, 0.278, 0.482, 0.799] are adjacent to it."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "WlVLxcFSGYCIwPWq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to the {Commodities slice}[0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"5.0%\"}[0.203, 0.199, 0.295, 0.219] is closest to the Commodities slice."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "A51fcM3gJMAwFq1p",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does the {text \"15.0%\"}[0.744, 0.347, 0.837, 0.367] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Mutual Funds\"}[0.88, 0.131, 0.973, 0.151] corresponds to the text \"15.0%\"."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "uJBGrbzOduABlVZg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region bounded by coordinates [0.223, 0.146, 0.75, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire pie chart with slices representing different financial portfolio components like Bonds, Stocks, Mutual Funds, Real Estate, Cryptocurrency, and Commodities."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "V66jzE8XxcMd3j8V",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region spanned by coordinates [0.836, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart legend, which contains representations for each of the portfolio categories: Bonds, Stocks, Mutual Funds, Real Estate, Cryptocurrency, and Commodities."
+            }
+        ],
+        "id_raw": 657
+    },
+    {
+        "id": "1l6bIDCtPMqMntgy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main subject indicated by the coordinates [0.308, 0.021, 0.655, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, called \"Offline vs Online Sales from 2016 to 2018\"."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "ccHDfP2aLwy9XXrQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.908, 0.063, 0.993, 0.249] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "NslOK0pSg4Kc5vJe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat constitutes the object at the coordinates [0.162, 0.478, 0.197, 0.498]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text indicating \"20.0%\"."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "yB56ewgpKi79d9Z0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to the year 2016 as defined by the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color that corresponds to the year 2016 in the legend is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "wbCYWOITYjfGCCQw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text nearest to the coordinates [0.354, 0.891, 0.389, 0.911]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text nearest to these coordinates is \"21.7%\"."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "atacbNn1TDn1DN0x",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the coordinates [0.601, 0.6, 0.644, 0.62] correspond to any text and if so, what does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to the text \"2018\", representing that year on the chart."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "HmG95OS6aY4Ts8Px",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.262, 0.516, 0.57, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart colored \"#66b3ff\" which represents the sales for the year 2017. The slice has an auto-labelled text indicating \"21.7%\"  at coordinates [0.354, 0.891, 0.389, 0.911] and has a text \"2017\" at coordinates [0.409, 0.716, 0.453, 0.736]."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "y9KyYY68S32g444k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the coordinates [0.283, 0.168, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice with the color \"#c2a4ef\", representing the sales for the year 2018. The region contains a text \"8.3%\" at the coordinates [0.283, 0.172, 0.318, 0.192]. It also includes the label \"2018\" at the coordinates [0.375, 0.324, 0.41, 0.344]."
+            }
+        ],
+        "id_raw": 658
+    },
+    {
+        "id": "bVVfcQjF06FDixvn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.875, 0.071, 0.917, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Books\" in the legend."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "s1BrxNSKYw9KY1Ye",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.428, 0.063, 0.46, 0.083] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"25.6%\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "4C97z8QUreWWoLeA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.252, 0.021, 0.711, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Education Resource Distribution and Utilization in 2010.\""
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "Gil7VZkToSe9N2Qh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice does the \"Books\" label from {the legend}[0.831, 0.063, 0.993, 0.219] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {first pie slice}[0.223, 0.278, 0.482, 0.661] in the chart."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "5wkxRu96eGoH4q1b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in {the legend}[0.831, 0.063, 0.993, 0.219] corresponds to {the pie slice}[0.281, 0.109, 0.654, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the label \"Staff\" in the legend."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "kLpeMO21TgZ5X7hu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to the {subtitle}[0.252, 0.021, 0.711, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Books\" in the legend."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "IaMot4jlS0NrtTP8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.2, 0.05, 0.8, 0.25]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the subtitle \"Education Resource Distribution and Utilization in 2010\" [0.252, 0.021, 0.711, 0.047]. This region is located at the top of the chart."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "PY6RahwacMDV4CXS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_60.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region from [0.2, 0.8, 0.8, 0.95]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend [0.831, 0.063, 0.993, 0.219] and its associated labels and color handles for \"Books\", \"Laptops\", \"Lab Equipments\", \"Libraries\", and \"Staff\"."
+            }
+        ],
+        "id_raw": 660
+    },
+    {
+        "id": "UpxqyLPtQFvscSa7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.295, 0.021, 0.668, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle labelled as \"AI and Automation Usage from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "Ry5hsthy65nKGQhU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.908, 0.063, 0.993, 0.249] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "C4LZ3QZaDTfxZJc1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the center of the pie chart."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "N0kEPB3ozSGMHlN8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the handle located at {coordinates}[0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"2020\"."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "WVduL3j5ySPBGpDa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is directly related to {the autotext}[0.516, 0.102, 0.551, 0.122]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object directly related to this autotext is {the slice}[0.283, 0.146, 0.714, 0.516]."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "Po0v26HppyTbYMHy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the handle}[0.913, 0.073, 0.941, 0.087] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {slice}[0.231, 0.278, 0.482, 0.516] in the pie chart."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "nHRZlbO2dpT5TeZ4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the coordinates, [0.482, 0.352, 0.741, 0.843]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This part of the chart contains a pie slice of color '#c2c2f0' representing the year 2019 with the autotext of \"24.6%\" at [0.753, 0.631, 0.788, 0.651]."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "OVgPJmtyFzGuQ1eu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can I find in the region [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In that region, you can find a small handle with the color '#f08080' corresponding to the year 2020 in the legend."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "hCTqaCwRXQ7ijeZb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_62.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area marked by the coordinates [0.223, 0.146, 0.741, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the entire pie chart within which six slices are distributed, each representing a different year and carrying a percentage autotext."
+            }
+        ],
+        "id_raw": 662
+    },
+    {
+        "id": "h38wNGYvV5xu4sQ7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.332, 0.021, 0.631, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, \"Global Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "Y7ALbgJx3mJ3i1g6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at the coordinates [0.924, 0.071, 0.967, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for the label \"Indian\"."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "RkiKov7yJjcpxx3z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.646, 0.883, 0.69, 0.903] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\"."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "qAdaopeYdMVWnn85",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is related to {the legend}[0.924, 0.191, 0.97, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}<r>[0.482, 0.173, 0.701, 0.516]</r> is related to the French cuisine according to the Legend."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "r9np1sZ9T4KG7Dac",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is linked to {the legend}[0.924, 0.251, 0.954, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}<r>[0.283, 0.157, 0.482, 0.516]</r> for Thai cuisine according to the Legend."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "kDl979a9NkrArDdo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich original data corresponds to {the slice}<r>[0.262, 0.516, 0.482, 0.885]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The original data \"15.0%\" or \"15\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "V8deHySeGSqX9U1j",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the region within coordinates [0.158, 0.053, 0.805, 0.979]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the primary chart with the pie graph, which contains slices of different sizes representing different cuisines. Also, it includes text labels showcasing percentages and cuisine names corresponding to each slice."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "vv1Gq1i4DCUkvPm2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region within coordinates [0.88, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend which has different colors corresponding to different cuisines. The cuisines labeled are Indian, Chinese, Italian, Japanese, French, Mexican, and Thai."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "dHEvaulXg2ODerPQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.197, 0.146, 0.741, 0.917]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the entirety of the pie chart, marking it as the main visual focus of the figure. Seven distinct slices can be seen, each representing a different cuisine, labeled with their respective percentages, and named."
+            }
+        ],
+        "id_raw": 663
+    },
+    {
+        "id": "JH4qax2qAOgewhOt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.298, 0.021, 0.666, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's subtitle, which reads \"2020 Budget Allocation for Different Sectors\"."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "4VGLA0AVUIr8LVDW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.918, 0.071, 0.983, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Transport\" in the legend is located at these coordinates."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "jXc4ZS30R5Zz5p5L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.875, 0.073, 0.907, 0.087] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend's color marker for \"Transport\"."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "tgCC4KmzTNLw3Wt5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the proximity relation between the text \"Transport\" {Transport text}[0.918, 0.071, 0.983, 0.091] and its corresponding color marker {Transport color marker}[0.875, 0.073, 0.907, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color marker is just to the left of the text \"Transport\"."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "an86e7hWOmkI5mWi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart corresponds to {the text \"Parks\"}[0.918, 0.221, 0.955, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice that corresponds to \"Parks\" is located at [0.283, 0.202, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "mi9xVLsu7DLIJqS7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the text {Health}[0.918, 0.101, 0.964, 0.121] related to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Health\" corresponds to the pie slice located at [0.247, 0.553, 0.695, 0.923]."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "nKn0L92pCO0yTXT0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region bounded by coordinates [0.874, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, located at the upper right of the chart, we find the chart's legend. It includes the labels and color indicators for \"Transport\", \"Health\", \"Education\", \"Business\", \"Crime\", and \"Parks\", each label accompanied by a specific color marker."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "jQMOm8jEWooplXlV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the content within the region [0.197, 0.146, 0.741, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart representing budget allocations for various sectors in the year 2020. It includes the sectors \"Transport\", \"Health\", \"Education\", \"Business\", \"Crime\", and \"Parks\"."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "mfm7X5Na4b8TusJm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the sector at coordinates [0.247, 0.553, 0.695, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This sector corresponds to the \"Health\" pie slice which makes up 33.3% of the total budget."
+            }
+        ],
+        "id_raw": 666
+    },
+    {
+        "id": "iMOqYwl5oZYBs5LF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart found at coordinates [0.209, 0.021, 0.754, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Traditional Art versus Modern Art from 2012 to 2020 in year 2020\"."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "dkypjXOdCAfZtpr7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at coordinates [0.845, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend for the chart."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "TFYzZlyOqUlGSWpK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the text at coordinates [0.889, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the text at these coordinates is \"Traditional Art\"."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "ileev8fuHyJLTnCb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFrom the pie chart, which slice corresponds to the text {label \"Traditional Art\"}[0.889, 0.071, 0.987, 0.091] mentioned in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice of pie at {coordinates} [0.223, 0.146, 0.741, 0.886] corresponds to \"Traditional Art\"."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "SGTcp6lDUKagfFCT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe slice at {coordinates} [0.223, 0.146, 0.741, 0.886] has certain percentage written on it. What is it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at {coordinates} [0.669, 0.813, 0.766, 0.833] indicates that the percentage is \"95.0%\"."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "LtMCx3d1l83Onpdl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to the text {label \"Modern Art\"}[0.889, 0.101, 0.967, 0.121] from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates} [0.283, 0.202, 0.482, 0.516] corresponds to \"Modern Art\"."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "JZEu9YMUzoN5QkOv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region around the slice for \"Traditional Art\" within coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the pie chart is occupied by a single, large slice representing \"Traditional Art\". It is labeled with the text \"Traditional Art\" at coordinates [0.562, 0.674, 0.606, 0.694] and the percentage \"95.0%\" at coordinates [0.669, 0.813, 0.766, 0.833]."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "HANmbRkC104W2lAq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region focused around the \"Modern Art\" slice located within coordinates [0.283, 0.202, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region of the chart contains a smaller slice representing \"Modern Art\". It is labeled with the text \"Modern Art\" located at coordinates [0.362, 0.338, 0.397, 0.358] and the percentage \"5.0%\" at coordinates [0.217, 0.199, 0.295, 0.219]."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "6Cml0m92JaQInK4N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area covered by the legend within coordinates [0.845, 0.063, 0.993, 0.129]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend of the pie chart. It contains two entries - \"Traditional Art\" with coordinates [0.889, 0.071, 0.987, 0.091] and \"Modern Art\" at coordinates [0.889, 0.101, 0.967, 0.121], with corresponding color markers."
+            }
+        ],
+        "id_raw": 668
+    },
+    {
+        "id": "bVujr8tkOeWcsQiX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the subtitle located at [0.325, 0.021, 0.638, 0.047] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Entertainment across five data points\"."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "NIiTkrWrjjRosgva",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at [0.94, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Point 1\" in the legend."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "0fqZ3qah0t57rgGe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the coordinates [0.199, 0.275, 0.247, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"3.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "7zxnlM33MwB3fro2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the text object {Point 1}[0.336, 0.38, 0.371, 0.4] connected to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a slice of the pie chart with coordinates [0.255, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "ozAR8hlzqMBrN8sh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element in the chart corresponds to {the legend entry \"Point 5\"}[0.94, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice [0.283, 0.146, 0.741, 0.726]."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "DhOA7hLGzOKIPmRi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects {Point 4}[0.497, 0.76, 0.541, 0.78] and {24.2%}[0.545, 0.941, 0.593, 0.961]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both represent the same slice of the pie chart, with \"Point 4\" being the label and \"24.2%\" being the value."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "Zq6YhbjtPtTP87Jt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is happening in the region from [0.223, 0.146, 0.741, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart slice \"Point 5\" [0.283, 0.146, 0.741, 0.726] and the text \"48.5%\" [0.654, 0.182, 0.702, 0.202]. This portion represents the largest proportion of the pie chart."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "fVqy1Yl6bhI5NobL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_69.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the items contained in the region [0.223, 0.516, 0.741, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two slices: slice for \"Point 3\" [0.223, 0.516, 0.482, 0.83] and exploted slice \"Point 4\" [0.35, 0.552, 0.7, 0.922], with corresponding percentage texts \"15.2%\" [0.189, 0.715, 0.237, 0.735] and \"24.2%\" [0.545, 0.941, 0.593, 0.961]."
+            }
+        ],
+        "id_raw": 669
+    },
+    {
+        "id": "RVe9kmqUZh8Zl54Q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the chart present at coordinates [0.316, 0.021, 0.647, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is named \"Family and Household Living Conditions\"."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "BvHcPOA3OSqzmqA1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the component at coordinates [0.858, 0.101, 0.916, 0.121] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label for \"Children\" in the legend."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "SCXnlKwAzQC79Ejd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie slice at coordinates [0.414, 0.151, 0.482, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents \"Overseas Student\"."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "i4gMfIyHM82iUGtY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to the {pie slice}[0.322, 0.355, 0.757, 0.915]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Children\" [0.858, 0.101, 0.916, 0.121] corresponds to this pie slice."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "gNrSnc9kilLPpbKF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage associated with the {pie slice}[0.414, 0.151, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"1.5%\" [0.297, 0.108, 0.421, 0.128] is associated with this pie slice."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "zlqUxsrhMb7bq3Nf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the colour of the {legend item}[0.858, 0.191, 0.905, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colour of this legend item is '#c2c2f0' which corresponds to the colour of the \"Cousin\" slice."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "4cyXVnRHjVP5emFo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is within the region [0.223, 0.146, 0.757, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains eight pie slices representing different groups, \"Parents\", \"Children\", \"Grandparents\", \"Uncle/Aunt\", \"Cousin\", \"Overseas Student\", \"Rented Roommate\", and \"Others\". Each slice is labelled with the group it represents and its percentage share."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "i0Ay5k6HTFvlkERc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the plotted objects within the coordinates [0.819, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a legend with eight different categories - \"Parents\", \"Children\", \"Grandparents\", \"Uncle/Aunt\", \"Cousin\", \"Overseas Student\", \"Rented Roommate\", and \"Others.\" Each of these categories has a corresponding color code."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "GnU4R2dQsNCL7zfE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_70.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region specified by the coordinates [0.482, 0.151, 0.597, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice labeled as \"Uncle/Aunt\" [0.513, 0.295, 0.548, 0.315]. The slice has an autotext of \"4.5%\" [0.571, 0.119, 0.647, 0.139] denoting its percentage share."
+            }
+        ],
+        "id_raw": 670
+    },
+    {
+        "id": "GPpOea9WzgFPeaCs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the box with coordinates [0.136, 0.111, 0.285, 0.129] provide?"
+            },
+            {
+                "from": "gpt",
+                "value": "It provides the subtitle, \"Tourism and Hospitality Data - 2016\"."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "gSwtiFO8bybny8Sq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.421, 0.146, 0.498, 0.16] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They point to the legend entry for \"Tourist Visits (Millions)\"."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "WcqHzPK230VXj4yL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.383, 0.423, 0.425, 0.437] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"71.4%\"."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "xLAlv0jddZ2WrL3O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend item {Hotels}[0.421, 0.167, 0.443, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice located at [0.064, 0.313, 0.211, 0.762]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "9I4YuDgGUHhVnRnz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice is adjacent to the slice {Tourist Visits (Millions)}[0.098, 0.311, 0.211, 0.5] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {Hotels}[0.064, 0.313, 0.211, 0.762] is adjacent to it."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "FlX5qDqdZEm7Xc8b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to the pie slice {Restaurants}[0.113, 0.201, 0.372, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item located at [0.421, 0.188, 0.462, 0.202]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "07azPX9d1Hxc9Kzk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists in the region defined by these coordinates [0.064, 0.201, 0.372, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with three slices representing \"Tourist Visits (Millions)\" [0.098, 0.311, 0.211, 0.5], \"Hotels\" [0.064, 0.313, 0.211, 0.762], and \"Restaurants\" [0.113, 0.201, 0.372, 0.788]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "vLMOmSfIdP9SiVJb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region from [0.895, 0.14, 0.997, 0.207]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend entries for \"Tourist Visits (Millions)\" [0.917, 0.146, 0.994, 0.16], \"Hotels\" [0.917, 0.167, 0.939, 0.181], and \"Restaurants\" [0.917, 0.188, 0.959, 0.202]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "Ramg3KvGd7Ee2HMR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the area defined by these coordinates [0.524, 0.133, 0.89, 0.867]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart and a subplot title \"Tourism and Hospitality Data - 2017\" [0.632, 0.111, 0.782, 0.129], a pie chart includes three slices: \"Tourist Visits (Millions)\" [0.594, 0.311, 0.707, 0.5], \"Hotels\" [0.56, 0.313, 0.707, 0.76], and \"Restaurants\" [0.609, 0.201, 0.868, 0.788]."
+            }
+        ],
+        "id_raw": 673
+    },
+    {
+        "id": "Ev858F4k9FccW0rx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart with coordinates [0.304, 0.021, 0.659, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Online Graduation Rate from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "ZnmAhdQXhJWDPzCc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.908, 0.063, 0.993, 0.219] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "DZSlceVsQTHhB1mw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.163, 0.46, 0.198, 0.48] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the percentage text \"18.6%\"."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "Mr9KV7w7PjcnxDgG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.223, 0.278, 0.482, 0.684]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.251, 0.516, 0.509, 0.886] is adjacent."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "L9xeZJMllXvWsUMN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage text relates to  {the slice '2018'} [0.482, 0.516, 0.735, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text {\"19.9%\"}[0.672, 0.809, 0.707, 0.829] relates to the 2018 slice."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "2mIWK0fAK515sTXb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items are associated with the colors of the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items '2016', '2017', '2018', '2019', and '2020' are associated with the pie slices with colors '#ff9999', '#66b3ff', '#99ff99', '#ffcc99', and '#c2c2f0' respectively."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "OkTuXKCwmvjIAjUU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region in the chart within coordinates [0.223, 0.11, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie chart that represents the online graduation rate from 2016 to 2020. Each slice represents a different year, labeled with their respective rates and year name."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "GgT4LMFb7MJOum5o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region [0.482, 0.185, 0.741, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice representing the year 2019 colored with '#ffcc99', with the percentage text {\"21.0%\"}[0.739, 0.33, 0.774, 0.35]."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "lGSzuRSaS7KZiCHe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region defined by the coordinates [0.482, 0.516, 0.735, 0.884]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a portion of the pie chart for the year 2018 colored in '#99ff99' with the corresponding percentage text {\"19.9%\"}[0.672, 0.809, 0.707, 0.829]."
+            }
+        ],
+        "id_raw": 675
+    },
+    {
+        "id": "RdOJTfM9316qOnKn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.223, 0.021, 0.74, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the subtitle, which reads \"Rural and Urban Development Comparison from 2011 to 2015\"."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "uzZf02jHa5gEtq6k",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies the coordinates [0.197, 0.146, 0.766, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main pie chart that illustrates rural and urban development from 2011 to 2015."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "PR0ah6RmYxtsGRUe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.861, 0.063, 0.993, 0.368] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend that labels different categories (years and development types) in the pie chart."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "7G3873X5yIlStH0b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich items in the legend correspond to the slices {slice}[0.507, 0.284, 0.766, 0.636] and {slice}[0.482, 0.146, 0.686, 0.516] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices correspond to the legend items \"2013 Urban\"[0.906, 0.281, 0.987, 0.301] and \"2014 Urban\"[0.906, 0.311, 0.987, 0.331] respectively."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "VTPX9uS41mPo2Nnf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the {slice}[0.197, 0.484, 0.456, 0.622] in the pie chart and the legend located at coordinates [0.861, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the legend item \"2014 Rural\" located at coordinates [0.906, 0.161, 0.981, 0.181]."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "byMrR5fr2MI1sRrj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the {slice}[0.224, 0.39, 0.482, 0.516] and the legend items located at [0.906, 0.131, 0.981, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend label \"2013 Rural\"."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "w6PHEGvAj3miliS0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.861, 0.063, 0.993, 0.368]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the legend of the pie chart, covering items from years 2011 to 2015 for both rural and urban development types. Each item is represented by a specific color, which corresponds to the color of the slices in the pie chart."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "IiErJzVBQWGDxiPv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_77.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.197, 0.146, 0.766, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart representing rural and urban development from 2011 to 2015. Different years and development types are represented by different slices in the pie chart, each labeled with its respective percentage."
+            }
+        ],
+        "id_raw": 677
+    },
+    {
+        "id": "68ZwMkIUbnX4mb1M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title text at the coordinates [0.264, 0.021, 0.7, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Predicted Increase in AI Adoption from 2022 to 2026\"."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "qIzjlZ45J0Vm28Vo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is at the coordinates [0.201, 0.146, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "i2spd9KbUctqLbet",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find at the coordinates [0.952, 0.191, 0.987, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2026\"."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "9R7fVMOCdlqMCyko",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the slice {2023}[0.201, 0.536, 0.46, 0.87] match in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It matches the color label {2023}[0.913, 0.103, 0.941, 0.117] in the legend, which is #66b3ff."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "p39RbIKeR7MzeZqG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the text {2024}[0.952, 0.131, 0.987, 0.151] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2024\" corresponds to the slice {2024}[0.369, 0.516, 0.701, 0.886]."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "5S8rPHO5qzdNuQkH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the exploded slice {2023}[0.201, 0.536, 0.46, 0.87]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {2024}[0.369, 0.516, 0.701, 0.886] is adjacent to the exploded slice."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "iTFoiGR2YO2rkQ6T",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find in the region [0.808, 0.071, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, the legend containing five entries corresponding to the years 2022 to 2026 is located."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "GuKN39VIIUw4sNva",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.201, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the pie chart, which has five slices representing different years from 2022 to 2026. Each slice is of a different color to differentiate the years."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "BXthv6IcLE2g8TgW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_79.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does region [0.369, 0.516, 0.701, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the pie slice labeled \"2024\" with an autotext s of \"23.2%\", indicating that the predicted increase in AI adoption in 2024 is 23.2%."
+            }
+        ],
+        "id_raw": 679
+    },
+    {
+        "id": "E4prHvvA1YXMzc9k",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.356, 0.021, 0.607, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "hnWt3k6KkorCJFcJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the coordinates [0.857, 0.223, 0.885, 0.237] represent in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color representation for \"Elasticsearch\"."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "ZlOfQcANTuRwtDrA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.514, 0.948, 0.535, 0.968] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"25.2%\"."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "W6O0PNL3mWktjRcG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the legend item \"Hadoop\"}[0.896, 0.071, 0.95, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at coordinates [0.223, 0.278, 0.482, 0.592] is associated with the legend item \"Hadoop\"."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "f0Yw7zXaPMECe28g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item does {the slice}[0.28, 0.147, 0.482, 0.516] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item \"Elasticsearch\"<r>[0.896, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "DDUEE2ib3ws1guXu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart does {the color}[0.857, 0.193, 0.885, 0.207] in the legend represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represents the slice located at {coordinates}[0.466, 0.146, 0.714, 0.516]."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "QuITBXIMrPX6TxQI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.228, 0.516, 0.482, 0.802]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with the color \"#66b3ff\", corresponding to the tool \"Hive\". It also has an autotext \"10.8%\"<r>[0.207, 0.717, 0.238, 0.737]</r>."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "yUjHr0FQQ7OBU32e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.32, 0.553, 0.686, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an exploded slice of the pie chart representing the tool \"Pig\". Also, there is a text \"25.2%\"<r>[0.514, 0.948, 0.535, 0.968]</r> indicating the size of this slice."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "jGa3Bjvq0lSAS3G3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region with coordinates [0.223, 0.278, 0.482, 0.592]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart which represents the tool \"Hadoop\", and an autotext \"14.4%\"<r>[0.151, 0.407, 0.205, 0.427]</r> indicating the size of the slice."
+            }
+        ],
+        "id_raw": 682
+    },
+    {
+        "id": "0ADSS0gxPR0ADZ2i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.223, 0.021, 0.741, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle, titled \"Environmental and Climate Change Impact from 2000 to 2006\"."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "9exVpVWanEIL0JvO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the legend of the chart."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "RJXCwpptu01WRzl0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item placed at coordinates [0.732, 0.769, 0.767, 0.789] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is a piece of autotext which reads \"20.0%\"."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "iBSODNkCjlIdsMnQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is related to the {legend item}[0.952, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {[0.283, 0.19, 0.482, 0.516]} corresponds to the legend item \"2006\"."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "TQ6BDGsduCfzakmo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is closest to {the slice}[0.358, 0.146, 0.594, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"15.0%\"}[0.44, 0.099, 0.475, 0.119] is closest to the slice."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "Bl6LxVfurQj2HqAB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich two slices in the chart are touching each other in the upper right section?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two slices touching each other in the upper right section are {[0.482, 0.182, 0.741, 0.519]} and {[0.502, 0.538, 0.762, 0.891]}."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "vRYqmjFfWPzsF0fU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the description of the region encapsulated by coordinates [0.283, 0.19, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice with the color '#a9a9f0'. It contains the autotext \"6.0%\" located at coordinates [0.266, 0.19, 0.302, 0.21] and the year \"2006\" represented by text at coordinates [0.366, 0.334, 0.401, 0.354]."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "2rIZw9gR5eFfSsvA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the bounds of [0.502, 0.538, 0.762, 0.891]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region holds an exploded slice colored '#ffcc99' indicating the year \"2003\". Two text objects can be found within its bounds. An autotext \"20.0%\" is positioned at [0.732, 0.769, 0.767, 0.789], while the year \"2003\" is from [0.606, 0.66, 0.649, 0.68]."
+            }
+        ],
+        "id_raw": 686
+    },
+    {
+        "id": "c4mcEW9GfEsJI4zC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the object located at the coordinates [0.273, 0.021, 0.69, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this is the subtitle of the chart, which reads \"Resources Distribution in Health and Medical Field\"."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "35ActB0Tw0hO8wlP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the object at coordinates [0.869, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is a legend item indicating \"Physical Therapy\"."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "BynBeUvI6CNZx527",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the coordinates [0.361, 0.1, 0.457, 0.12] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an autotext showing the percentage \"25.0%\"."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "s7dRhypNnktpAcdJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart corresponds to the legend item {Digital Health}<r>[0.869, 0.101, 0.965, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to \"Digital Health\" is {the slice}<r>[0.283, 0.146, 0.648, 0.516]</r>."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "215Wvn516yScvTaC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the centerpiece located at {the coordinates}<r>[0.482, 0.516]</r> represent in relation to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {Physical Therapy}<r>[0.225, 0.269, 0.743, 0.923]</r> and {Digital Health}<r>[0.283, 0.146, 0.648, 0.516]</r> are centered around this point."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "XZJsyi8m5vcCwRnQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the legend items that correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Physical Therapy\" and \"Digital Health\" correspond to the slices with coordinates [0.225, 0.269, 0.743, 0.923] and [0.283, 0.146, 0.648, 0.516] respectively."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "JZjhJJ9A1lzMKqNG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region with coordinates [0.225, 0.146, 0.743, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the slice of the pie chart labeled as \"Physical Therapy\", which occupies around 75% of the pie chart. The percentage is noted with an autotext \"75%\" with coordinates  [0.509, 0.948, 0.627, 0.968]."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "okB1uRTgQIS8ALJr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.283, 0.146, 0.648, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region shows a part of the pie chart assigned to \"Digital Health\". The color indicates it's representing the \"Digital Health\" category from the legend. Also, there is an autotext that mentions the value \"25%\" with coordinates [0.361, 0.1, 0.457, 0.12]."
+            }
+        ],
+        "id_raw": 687
+    },
+    {
+        "id": "Hw8hrffVgLnBd714",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is being represented by the coordinates [0.223, 0.278, 0.482, 0.539] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie slice labeled as \"Friends\" is represented by these coordinates."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "iGRBwhYIfX642kJL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the object located at coordinates [0.885, 0.071, 0.935, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend text \"Friends\"."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "WYU4DXZjOy7JvbH6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element of the chart is represented by coordinates [0.161, 0.379, 0.211, 0.399]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the auto text showing the percentage \"12.1%\" for the \"Friends\" category."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "RFlxzAAUwoUQMOrW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the pie slice at [0.223, 0.278, 0.482, 0.539] and the legend item at [0.885, 0.071, 0.935, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They both represent the category \"Friends\"."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "tT6fS0MpL9nwhPIO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat other elements are related to {the pie slice}[0.223, 0.278, 0.482, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"12.1%\" {autotext}[0.161, 0.379, 0.211, 0.399] is related as it indicates the percentage share of \"Friends\" among all categories."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "PW6siPBPF3u2VdCm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is associated with the label 'Online Friends' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "According to the legend coordinates, \"Online Friends\" is associated with the color [#ffcc99][0.846, 0.163, 0.874, 0.177]."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "tRY2NoNpxWt4R2Qd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region inside the coordinates area [0.223, 0.278, 0.482, 0.539]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice corresponding to the category \"Friends\" with autotext \"12.1%\" at coordinates [0.161, 0.379, 0.211, 0.399], indicating it represents 12.1% of the total."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "uFiKaGomsZk0pels",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region at coordinates [0.841, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend with six items: \"Friends\" at [0.885, 0.071, 0.935, 0.091], \"Family\" at [0.885, 0.101, 0.93, 0.121], \"Colleagues\" at [0.885, 0.131, 0.962, 0.151], \"Online Friends\" at [0.885, 0.161, 0.984, 0.181], \"Acquaintances\" at [0.885, 0.191, 0.987, 0.211], and \"Strangers\" at [0.885, 0.221, 0.953, 0.241], along with their corresponding colors."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "WOFI6EFWWx6pJxEK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_88.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within the coordinates [0.506, 0.158, 0.765, 0.683] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents an exploded pie slice, corresponding to the category \"Online Friends\" with the Autotext \"27.3%\" [0.774, 0.354, 0.873, 0.374], which suggests it constitutes 27.3% of the total."
+            }
+        ],
+        "id_raw": 688
+    },
+    {
+        "id": "anqJad7WfjhZy1bs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.261, 0.021, 0.702, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's subtitle, which reads \"Distribution of Patients in Various Medical Specialties\"."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "LcuDIef7LRMh7q2m",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.905, 0.071, 0.966, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend's text 'Pediatric'."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "1htTSn6syhbr7806",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the bar at [0.223, 0.278, 0.482, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This represents the slice of the 'Pediatric' sector in the pie chart."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "PET5SCBXSPwp7j9v",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat bar is located to the right of {the Pediatric category}[0.223, 0.278, 0.482, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Neurology' slice found at [0.221, 0.54, 0.462, 0.856] is to the right."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "uK0JB4j8irl2lgNx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice follows the {the Neurology slice}[0.221, 0.54, 0.462, 0.856] in the clockwise direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Gynecology' slice at [0.347, 0.516, 0.529, 0.886] follows the Neurology slice."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "jmu6KqOEwofx0L3q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is plotted adjacent to the {the Radiology slice}[0.456, 0.146, 0.668, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Dentistry' slice with coordinates [0.283, 0.148, 0.482, 0.516] is adjacent to the Radiology slice."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "9G71GE4m6KBpJ4L7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region enclosed by the coordinates [0.221, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the region of the pie chart showing the distribution of patients in various specialties. This includes seven slices each representing different specialties namely 'Pediatric', 'Neurology', 'Gynecology', 'Cardiology', 'Oncology', 'Radiology', 'Dentistry'."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "VoZhShk9qDkSwxm9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region defined by [0.866, 0.073, 0.894, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, you can see a colored rectangle in '#ff9999', which represents the color code for the 'Pediatric' sector in the legend."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "ffTUXsDViIDSLAps",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_89.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included in the region [0.456, 0.146, 0.668, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'Radiology' slice of the pie chart."
+            }
+        ],
+        "id_raw": 689
+    },
+    {
+        "id": "lduYyD9ddFlWMSJH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.361, 0.021, 0.602, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart subtitle, called Gastronomy Culture Analysis."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "bDmd1fpk9yJf9K92",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.876, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "4toC1lbxdhXdi9SR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is present at coordinates [0.223, 0.146, 0.741, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "6WvJh2Qee7Yv6ZWt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.482, 0.294, 0.741, 0.615]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Mexican}<r>[0.92, 0.191, 0.978, 0.211]</r> corresponds to the given slice."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "fPOmA2Z9YrBgorET",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.223, 0.278, 0.482, 0.522] and {the label text}[0.17, 0.37, 0.213, 0.39]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label text \"11.4%\" represents the percentage of the slice."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "qFsgZo90oCigAruP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the legend item}[0.92, 0.071, 0.963, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {Italian cuisine}<r>[0.223, 0.278, 0.482, 0.522]</r> corresponds to the given legend item."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "pA9Gat07hhNNuQDH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region within coordinates [0.223, 0.278, 0.482, 0.522]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart. It corresponds to the cuisine \"Italian\" with percentage label \"11.4%\", found at coordinates [0.17, 0.37, 0.213, 0.39]."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "RzdyMLjpyknrAVK5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects in the region defined by coordinates [0.311, 0.552, 0.572, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart slice for the 'Indian' cuisine category, as per the text located at coordinates [0.43, 0.762, 0.474, 0.782]. The percentage label connected to it, \"17.0%\", can be found at coordinates [0.388, 0.944, 0.431, 0.964]."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "dbmeGCOivSxU4Ci1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region within coordinates [0.876, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a chart legend, listing all the different cuisine categories: Italian, French, Indian, Chinese, Mexican, Japanese, and Moroccan. They are colour-coded to match the main pie chart."
+            }
+        ],
+        "id_raw": 693
+    },
+    {
+        "id": "YQezrfYFfCFg0DnQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.297, 0.021, 0.666, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the title of the chart, named \"Global Financial System and Policies in 2020\"."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "uMHvGOhDOlpIpfE2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.723, 0.225, 0.833, 0.245] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text \"56.0%\"{text}<r>[0.723, 0.225, 0.833, 0.245]</r>"
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "VUvplxDeas7G89Ca",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.877, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is legend text labeling \"Monetary Policy\"{legend item}<r>[0.877, 0.101, 0.987, 0.121]</r>."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "MQT2gdDNfkA9RuuO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to {the Fiscal Policy slice}[0.223, 0.278, 0.605, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Monetary Policy slice}<r>[0.303, 0.122, 0.761, 0.818]</r> is adjacent to the Fiscal Policy slice."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "Cd0p0W1SFkpWStZD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend item that matches with {the Monetary Policy slice}[0.303, 0.122, 0.761, 0.818]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#66b3ff\"{legend color}<r>[0.838, 0.103, 0.866, 0.117]</r>."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "fDMAfgMl6WKQlT6w",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart does the {legend item \"Fiscal Policy\"}[0.877, 0.071, 0.96, 0.091] match with?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Fiscal Policy\" matches with the {slice}<r>[0.223, 0.278, 0.605, 0.886]</r>."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "FUVyGMCwBxQWUE0R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.122, 0.761, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the area of the Pie Chart. It contains two slices representing \"Fiscal Policy\" {slice}<r>[0.223, 0.278, 0.605, 0.886]</r> and \"Monetary Policy\" {slice}<r>[0.303, 0.122, 0.761, 0.818]</r>. The \"Fiscal Policy\" slice is painted in color \"#ff9999\" and the \"Monetary Policy\" slice is painted in \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "usvdv0ojqXO0Jwg9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what is inside the region [0.223, 0.122, 0.761, 0.818]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a slice of the pie chart representing the \"Monetary Policy\" {slice}<r>[0.303, 0.122, 0.761, 0.818]</r>, and the text \"56.0%\"{percentage}<r>[0.723, 0.225, 0.833, 0.245]</r> indicating the proportion of Monetary Policy."
+            }
+        ],
+        "id_raw": 694
+    },
+    {
+        "id": "AgCko3uDkQipVMHc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at {coordinates}[0.937, 0.071, 0.971, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text object representing the 'Goth' in the legend."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "GOrudFnTJe9138dO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the item corresponding to these coordinates [0.665, 0.194, 0.715, 0.214]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, that's the percentage text \"50.0%\" for the 'Hipster' slice of the pie."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "WpafzzwNDXh3TfKm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents located at coordinates [0.265, 0.818, 0.298, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The contents at these coordinates are the percentage text \"50.0%\" for the 'Goth' slice of the pie."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "ZsueIbDSAcBCmZkI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart correspond to {the Hipster label in the legend}[0.937, 0.101, 0.987, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.283, 0.146, 0.741, 0.754] corresponds to the Hipster label in the legend."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "U5UDABibxxkhNWgc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is next to the {slice 'Goth'}[0.223, 0.278, 0.68, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice 'Hipster'}[0.283, 0.146, 0.741, 0.754] is next to the slice 'Goth'."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "tDs0PrJfkA82ovem",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does {the handle}[0.898, 0.073, 0.926, 0.087] in the legend correspond with on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle corresponds with the color of {the 'Goth' slice}[0.223, 0.278, 0.68, 0.886]."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "OrwIG0jXfQmaOJqQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.223, 0.278, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of the slice labeled 'Goth' in the pie chart. Within this area are also the text labels 'Goth' and '50.0%', with coordinates {label 'Goth'}[0.36, 0.676, 0.404, 0.696] and {'50.0%'}[0.265, 0.818, 0.298, 0.838]. This slice occupies exactly half of the pie."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "NECRHpC45sjZWP26",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the materials within the coordinates [0.283, 0.146, 0.741, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This set of coordinates encompasses the second half of the pie, which is associated with the label 'Hipster'. The region contains {the label 'Hipster'}[0.56, 0.336, 0.603, 0.356] and {the percentage '50.0%'}[0.665, 0.194, 0.715, 0.214]."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "93pz7HdezUGvp7fC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the spatial bounds [0.355, 0.021, 0.608, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region includes the {title of the chart 'Subculture and Fashion Trends'}[0.355, 0.021, 0.608, 0.047]."
+            }
+        ],
+        "id_raw": 695
+    },
+    {
+        "id": "EeShOvvl3gxGiA4E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object at the coordinates [0.239, 0.021, 0.724, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which reads \"Entertainment and Gaming Industry Development in 2021\"."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "LqtHD7zp9zOm8n9A",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated at the coordinates [0.845, 0.063, 0.993, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "53id5RLwnUVSg3fb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.552, 0.939, 0.649, 0.959] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"79.5%\"."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "nuKtb55WjQS83TBb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart has {the color}[0.851, 0.073, 0.878, 0.087] associated with it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is associated with {Video Gaming slice}[0.228, 0.214, 0.747, 0.922]."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "x0fH6Fs1PViJbeUd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text near the object located at coordinates [0.501, 0.758, 0.544, 0.778]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The nearest text is {\"79.5%\"}[0.552, 0.939, 0.649, 0.959]."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "XiEoTFaVO0KzWDgc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat segment of the pie chart does the element located at [0.89, 0.101, 0.943, 0.121] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the {Cinema slice}[0.283, 0.146, 0.587, 0.516] of the pie chart."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "HsUuVV4G43agbzkZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within coordinates [0.228, 0.214, 0.747, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a slice of the pie chart labeled as \"Video Gaming\" with the text \"79.5%\"[0.552, 0.939, 0.649, 0.959] situated nearby. It represents the dominant portion of the chart, associated with a rose color."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "JfhWhGcdqqAlMbsM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.283, 0.146, 0.587, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a smaller slice of the pie chart labeled as \"Cinema\" and is featured with the nearby text \"20.5%\" [0.364, 0.109, 0.417, 0.129]. It's colored in a sky blue shade."
+            }
+        ],
+        "id_raw": 696
+    },
+    {
+        "id": "F6DhAx9q2FheLFcj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.908, 0.063, 0.993, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located there."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "o6JuEJ6HN4D25jHy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the chart display at coordinates [0.223, 0.146, 0.744, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart is displayed at these coordinates."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "3isfqgbkDNFnlbmR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at coordinates [0.415, 0.911, 0.451, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text representation \"15.4%\" is shown there."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "NStpFSVnWMPjTnDU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels correspond to the slice {the slice}[0.337, 0.516, 0.577, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {\"2016\"}[0.443, 0.727, 0.487, 0.747] and {\"15.4%\"}[0.415, 0.911, 0.451, 0.931] correspond to the slice."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "MLyzl7ezAzHwpFmu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color handle corresponds to the legend item {\"2020\"}[0.952, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle is {\"#c4cff0\"}[0.913, 0.253, 0.941, 0.266]."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "NeFvl1PrwlPRiHjh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the pie chart does the legend handle with color \"#ffcc99\" correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend handle with color \"#ffcc99\" corresponds to the slice with coordinates [0.5, 0.542, 0.744, 0.886]."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "fm9aSvvBKJDutrCy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.337, 0.516, 0.577, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is one of the slices of the pie chart in the middle part of the plot, represented in color \"#99ff99\". It contains labels {\"2016\"}[0.443, 0.727, 0.487, 0.747] and {\"15.4%\"}[0.415, 0.911, 0.451, 0.931], indicating the development data for the year 2016."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "zRrY5nmglG89qgLR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_97.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.146, 0.744, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region displays the entire pie chart, containing multiple slices, each representing a different year and the corresponding urban and rural development percentages."
+            }
+        ],
+        "id_raw": 697
+    },
+    {
+        "id": "9xXA3pgARI4mMdjJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is displayed at the coordinates [0.268, 0.021, 0.695, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, called \"Factors of Human Health and Disease Management\"."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "hTaIkRSjMySCZQiH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the function of the object at the coordinates [0.792, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart, which helps to identify the categories in the pie chart."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "WwLz4KQsOaJ711xj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.151, 0.387, 0.209, 0.407] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object represents the text \"12.8%\"."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "Sg0fMKl14482BYSh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the slice}[0.482, 0.246, 0.738, 0.516] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {\"Mental Health\"}[0.444, 0.11, 0.666, 0.48] is adjacent."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "0DoR0qgTcCLXAhAq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with the label {\"Exercise\"}[0.837, 0.071, 0.895, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "{'The color'}<r>[0.798, 0.073, 0.826, 0.087]</r> associated with the label \"Exercise\" is red-ish, identified by the color code '#ff9999'."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "6pKtqWHkRfPwP7yE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the label {\"Sleep\"}[0.837, 0.191, 0.875, 0.211] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Sleep\" corresponds to the slice with coordinates [0.482, 0.461, 0.741, 0.746]."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "7o1ofhiYH8gZJsp8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's within the region [0.223, 0.11, 0.741, 0.886] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the main body of the pie chart. It contains several pie slices representing factors of human health and disease management, labeled with respective percentages. Specifically, we can see labels such as \"Exercise\"[0.311, 0.441, 0.355, 0.461], \"Healthy Eating\"[0.326, 0.62, 0.37, 0.64], and others within this region."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "ZrDL6VkH9RBU5tez",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the coordinates [0.792, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the legend of the chart which shows the names of health and disease management categories and their corresponding colors. For example, objects like \"Healthy Eating\"[0.837, 0.101, 0.939, 0.121] and the corresponding color square[0.798, 0.103, 0.826, 0.117] is in this region."
+            }
+        ],
+        "id_raw": 698
+    },
+    {
+        "id": "2uOYVivyza4nsLPd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the major object at the position [0.223, 0.146, 0.741, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "65OqqytOIoqa6XX7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me about the object located at coordinates [0.908, 0.063, 0.993, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "JmqDHnAsm1x1Ju4S",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.301, 0.021, 0.662, 0.047] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subtitle of the chart, which is \"Future Prediction Trends from 2022 to 2026\"."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "BXMF4LckNsPeeBam",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExamine the color correlation between the legend and the slices in the chart, particularly {legend item \"2024\"}<r>[0.952, 0.131, 0.987, 0.151]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice for the year 2024 in {the pie chart}<r>[0.223, 0.146, 0.741, 0.886]</r> matches with the legend item \"2024\"."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "qMWs9lMnikfV7Sj3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes the text at {coordinates}<r>[0.338, 0.644, 0.382, 0.664]</r> correlate with any pie slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it corresponds to {the pie slice}<r>[0.227, 0.516, 0.482, 0.856]</r>."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "2MY6EHiL3jDy8edw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"1.0%\"}<r>[0.215, 0.21, 0.25, 0.23]</r> and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"1.0%\" shows the percentage for {the pie slice}<r>[0.264, 0.236, 0.462, 0.491]</r> denoting the prediction for the year 2026."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "nw3MSi9EmUVuAyny",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region enclosed by the coordinates [0.223, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The enclosed region represents a pie chart which is used to display the \"Prediction Type\" vs \"Years\". Each slice of the pie chart represents a different year and the color of the slices corresponds to prediction types, which can be identified using the legend at [0.908, 0.063, 0.993, 0.368]."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "sjId0H2cvyIxJ4w8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.908, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of the legend of the pie chart. It has items and corresponding colors for years ranging from 2022 to 2026."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "GrWlxRPnAB4fADOS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.301, 0.021, 0.662, 0.047] describe?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains the subtitle of the chart, \"Future Prediction Trends from 2022 to 2026\"."
+            }
+        ],
+        "id_raw": 700
+    },
+    {
+        "id": "211seQ6OiK3U9jmS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object within coordinates [0.29, 0.021, 0.674, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which reads \"Annual FinTech Investment from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "ArBpYqs6UTJ77ctm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.913, 0.071, 0.941, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color representative for the year 2015 in the legend."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "APQW3PiJRPZilMVm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.318, 0.869, 0.353, 0.889] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"19.9%\"."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "t1RHpC5uCcQmAr2O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the {the slice}[0.504, 0.382, 0.763, 0.899] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.223, 0.422, 0.482, 0.686], [0.283, 0.146, 0.718, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "RNinm89lCqrmz5n9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich elements in the legend correspond to {the slice}[0.231, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {\"2015\"}[0.952, 0.071, 0.987, 0.091] element in the legend corresponds to the slice."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "59UyXVjinYNh0nhK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the autobus text in the chart with coordinates [0.163, 0.551, 0.198, 0.571]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2016\"[0.952, 0.101, 0.987, 0.121] corresponds to it."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "ZU6cCTXfbepJB3Gh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.146, 0.763, 0.899]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region appears to encompass the entire pie chart. It contains slices reflecting different years from 2015 to 2019, as well as their respective percentage texts such as \"19.9%\" [0.318, 0.869, 0.353, 0.889] and labels such as \"2017\" [0.389, 0.704, 0.433, 0.724]."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "wT8ocpjZetRPqD5e",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_102.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the region [0.223, 0.146, 0.763, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects located within this region are the individual pie slices representing each year, their text labels such as \"2015\" [0.326, 0.404, 0.361, 0.424] and \"2018\" [0.619, 0.629, 0.663, 0.649], and the corresponding percentages, including \"7.0%\" [0.193, 0.319, 0.229, 0.339] and \"29.2%\" [0.755, 0.717, 0.79, 0.737]."
+            }
+        ],
+        "id_raw": 702
+    },
+    {
+        "id": "iW1XBKGsIhO6vSxn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the diagram according to the coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Risk Assessment and Management,\" according to the coordinates {title}<r>[0.336, 0.021, 0.627, 0.047]</r>."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "22VrXg2G0IWCoELh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.936, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Factor7\"is located at these coordinates."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "eGnwwkQzK9RKliR1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content is presented in the region [0.177, 0.319, 0.229, 0.339]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"7.0%\"."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "1T6GeO6G1eIsrco0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to {Factor1}<r>[0.936, 0.071, 0.987, 0.091]</r> according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to Factor1 in the legend is the slice at {coordinates}<r>[0.231, 0.278, 0.482, 0.516]</r>."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "E3Z6TeKr7nndCPoE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the {Factor3's slice}<r>[0.223, 0.516, 0.482, 0.885]</r> correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponding to Factor3's slice in the legend is '#99ff99'."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "Dwxqppi3cb8dMliJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich factor corresponds to the color '#ffb3e6' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The factor that corresponds to the color '#ffb3e6' in the legend is {Factor6}<r>[0.936, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "1SuNljM9e7mtEYrp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.464, 0.516, 0.658, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice corresponding to 'Factor4' of the pie chart, depicted with the color '#66b3ff'. Inside this area, there is the autotext '13.0%' represented at {coordinates}<r>[0.576, 0.89, 0.628, 0.91]</r>."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "wpbdrX1RzoZ8CR43",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.891, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the chart, including colored squares and texts representing each factor, such as {Factor1}<r>[0.936, 0.071, 0.987, 0.091]</r> with its corresponding color '#ff6666', and similar items for the other factors."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "EIaQZYtpxER4NGT2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_104.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.286, 0.109, 0.712, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing {Factor7}<r>[0.936, 0.251, 0.987, 0.271]</r> can be seen in the region, with the autotext '31.0%' also located inside at {coordinates}<r>[0.513, 0.064, 0.565, 0.084]</r>."
+            }
+        ],
+        "id_raw": 704
+    },
+    {
+        "id": "wqLITt60gpc2YwNp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.174, 0.021, 0.789, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The subtitle}<r>[0.174, 0.021, 0.789, 0.047]</r>, is the object located at these coordinates and it is labeled as \"Revenue Growth in the Entertainment and Gaming Industry (2012 - 2017)\"."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "nF837m8KGxgpoGRR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is presented within the region [0.908, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within those coordinates is a {box}<r>[0.908, 0.063, 0.993, 0.249]</r> that serves as the legend of the chart."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "LKEldVX9Tr9W80U3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.262, 0.846, 0.282, 0.866]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The percentage label}<r>[0.262, 0.846, 0.282, 0.866]</r> \"9.9%\" is represented by the object at these coordinates."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "1jrs6bFecM7g7oZj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the legend item {2016}<r>[0.952, 0.191, 0.987, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with the legend item \"2016\" is the {slice}<r>[0.482, 0.252, 0.741, 0.731]</r>."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "I1b8XfjLrJTnxcVb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label corresponds to the slice {slice}[0.456, 0.55, 0.703, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" corresponds to the {slice}<r>[0.456, 0.55, 0.703, 0.92]</r>."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "833mAcGspZQcU9Aj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to the {slice}<r>[0.482, 0.252, 0.741, 0.731]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {slice}<r>[0.456, 0.55, 0.703, 0.92]</r> and {slice}<r>[0.283, 0.146, 0.664, 0.516]</r> are adjacent to the slice."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "5x4jfzjLi37lSXfH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.223, 0.146, 0.741, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly outlines the pie chart with the individual segments. It consists of objects like text labels {'2012'}<r>[0.32, 0.422, 0.355, 0.442]</r>,  {'2013'}<r>[0.31, 0.568, 0.354, 0.588]</r>,  {'2014'}<r>[0.38, 0.697, 0.424, 0.717]</r>, {'2015'}<r>[0.531, 0.744, 0.575, 0.764]</r>, {'2016'}<r>[0.615, 0.487, 0.658, 0.507]</r>, {'2017'}<r>[0.452, 0.284, 0.496, 0.304]</r> and accompanying percentage values in each respective slices of the pie chart."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "SxfjafCdVSeShP1F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the features of the region from [0.223, 0.146, 0.741, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers the pie chart in the figure with various segments and elements. It consists of the pie slices which are color-coded and labeled individually with their respective years such as '2012', '2013', '2014', '2015', '2016' and '2017'."
+            }
+        ],
+        "id_raw": 706
+    },
+    {
+        "id": "85kZ8XUhXdhjoEBc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item in the legend corresponds to the largest slice in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chinese<r>[0.921, 0.071, 0.976, 0.091]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "pRZuqQeJOqnr4hZk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat auto-generated text percentage appears within the bottom most slice of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "7.0%<r>[0.242, 0.183, 0.309, 0.203]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "L6icbt6rHHEcWCGG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the slice directly to the right of the Italian slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "French<r>[0.441, 0.285, 0.485, 0.305]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "gGKWxr14XAxD8ZFp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the slice that shares the smallest angle of the pie with the Japanese slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "Indian<r>[0.55, 0.687, 0.594, 0.707]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "yuV4UC4B2lkTG3p4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is directly adjacent to the top side of the Mexican slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "Thai<r>[0.61, 0.563, 0.654, 0.583]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "z4qRfBpBGHud9NUt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color from the legend corresponds to the slice labeled 'American'?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffb3e6<r>[0.882, 0.282, 0.91, 0.296]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "fRvIOVFLEPy1iIHp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region containing the 'Chinese' and 'Thai' slices."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the Chinese slice with the text \"25.0%\"<r>[0.117, 0.545, 0.172, 0.565]</r> and the auto-exploded boundary<r>[0.197, 0.281, 0.456, 0.803]</r>, and the Thai slice with the text \"10.0%\"<r>[0.757, 0.61, 0.787, 0.63]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "T2NIKRGW8I96Vg1T",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat region contains the slices for Mexican and French cuisine, with their respective text labels?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the Mexican slice with the text \"12.0%\"<r>[0.74, 0.335, 0.798, 0.355]</r> and the French slice with the text \"10.0%\"<r>[0.402, 0.102, 0.448, 0.122]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "t5OcIkHgH0Y6n4en",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area encompassing both the 'Indian' and 'Italian' slices and their associated autotexts."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the Indian slice with text \"12.0%\"<r>[0.648, 0.837, 0.69, 0.857]</r> and the Italian slice with text \"9.0%\"<r>[0.613, 0.145, 0.656, 0.165]</r>."
+            }
+        ],
+        "id_raw": 707
+    },
+    {
+        "id": "4uTR0MSCNSgcihwo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located in the coordinates [0.355, 0.02, 0.645, 0.046]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the title of the chart, which says \"Efficiency and Risk Reduction in Innovation Project from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "Q4cQNJIuX8w48NvE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object occupies the coordinates [0.117, 0.16, 0.382, 0.9] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart displaying distribution of innovation projects over years."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "eNUBl6FjUm5gatte",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is mapped to the coordinates [0.928, 0.078, 0.971, 0.234] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the second pie chart in subplot_1."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "yaSV0Ey4351ymAY9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is near to the legend item of color '#ff9999' located at [0.931, 0.088, 0.945, 0.102] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2016\" located at [0.951, 0.086, 0.968, 0.106] is near to it."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "YQB6bBjJnzghB8ts",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the first pie chart, Which slice's color corresponds to the legend item of color '#ffcc99' located at [0.456, 0.178, 0.47, 0.192] in subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at [0.255, 0.452, 0.382, 0.9] corresponds to that color."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "QyO8GZ1LFaom69zx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the handle color represent for the text \"2018\" located at [0.951, 0.146, 0.968, 0.166] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a slice of pie chart with color '#99ff99'."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "qkb0NjqpIMe49cdJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region falling inside coordinates [0.117, 0.16, 0.382, 0.9] in subplot_0 chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a pie chart showing the distribution of innovation projects over years. The individual sectors of the chart are colored differently representing different years with associated percentage labels."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "SihB28sDy55Js7Y1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the region defined by [0.592, 0.127, 0.847, 0.888] coordinates in the second subplot chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, this region represents a pie chart in subplot_1, exhibiting the distribution of different factors, with each slice of the pie representing a different year and associated percentages."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "u0TzjQZFCe01dEi5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the summary about the region marked by [0.453, 0.078, 0.496, 0.234] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the legend area of the pie chart in subplot_0. It contains five items with labels corresponding to the years from 2016 to 2020, and each item is represented by different coloring."
+            }
+        ],
+        "id_raw": 710
+    },
+    {
+        "id": "zplfNqa4bpEwiRfk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at the coordinates [0.31, 0.021, 0.653, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle which reads \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "WLxEztm4EUJKyxPw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates from [0.891, 0.103, 0.919, 0.117] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color handle of legend item \"Chinese\"."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "Abezlsv6p23pCxAS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element at coordinates [0.504, 0.757, 0.547, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"Chinese\" placed near to the relevant slice in the pie chart."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "zQsNH0HmGII439Eu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is linked with {the legend item Spanish}[0.93, 0.221, 0.985, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The piece of the pie chart associated with \"Spanish\" is at these coordinates: [0.344, 0.146, 0.5, 0.516]."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "kyg8BUIUmG0aH4Oa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color handle associated with {the legend label French}[0.93, 0.191, 0.976, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color handle for the \"French\" is specified by these coordinates: [0.891, 0.193, 0.919, 0.207]."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "tpMwDI0K5a74OvKD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage is associated with {the slice corresponding to Indian cuisine}[0.482, 0.205, 0.733, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage attached to the \"Indian\" slice is \"12.0%\", identified by these coordinates: [0.713, 0.269, 0.756, 0.289]."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "57NkqXAVM7tikAlb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region demarcated by coordinates [0.391, 0.552, 0.686, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a part of the pie chart, specifically the section allocated for \"Chinese\" cuisine. It contains the text \"Chinese\", coordinates: [0.504, 0.757, 0.547, 0.777], and the percentage text \"20.0%\" marked at coordinates: [0.557, 0.937, 0.612, 0.957]."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "YnyiJxx6uY0697B4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the elements within the space outlined by these coordinates [0.197, 0.287, 0.456, 0.868]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this space delineates a personally significant section of the pie chart. It includes the text \"Italian,\" whose coordinates are [0.284, 0.569, 0.328, 0.589], and the text indicating its corresponding percentage, \"30.0%\", which is located at coordinates [0.137, 0.613, 0.18, 0.633]."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "sc1VAcRNV8vF9ScC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents in the region between these coordinates [0.885, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart. It has multiple legend items such as \"Italian\", \"Chinese\", \"Mexican\", \"Indian\", \"French\", \"Spanish\", and \"Others\" each with its corresponding color handle."
+            }
+        ],
+        "id_raw": 711
+    },
+    {
+        "id": "1X5aw2CpL6rJs5VL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.323, 0.676, 0.367, 0.696] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at specified coordinates is \"Residential\"."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "gMzTInCb0LeKMRCl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the color indicated by the legend with coordinates [0.848, 0.103, 0.876, 0.117] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented by the legend key at the provided coordinates corresponds to \"Industrial\" data in the chart."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "f2lZplxIvwGUq31p",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the bounding box [0.345, 0.021, 0.619, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title, \"Sector-wise Energy Consumption\", is seen within the specified bounding box."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "nBBPPMCkrZO3GiGv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does {the legend}[0.848, 0.163, 0.876, 0.177] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item pertains to the slice with coordinates [0.318, 0.18, 0.482, 0.516], marked as \"Agriculture\"."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "9V9BVn8wXGSG9ufV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend}[0.848, 0.073, 0.876, 0.087] and {the autotext}[0.17, 0.797, 0.247, 0.817]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both represent the \"Residential\" sector, the legend is the key for the color coding, whereas the autotext shows the percentage contribution (45.0%) of that sector."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "rHoDpoIWRrydB8nK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentages are adjacent to the slice with coordinates [0.507, 0.138, 0.766, 0.821]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentages {8.0%}[0.33, 0.106, 0.43, 0.126] and {40.0%}[0.783, 0.399, 0.848, 0.419] are located adjacent to the specified slice."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "BnGTtjFOyfAWvNzi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the area defined by the coordinates [0.507, 0.138, 0.766, 0.821]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly includes the \"Industrial\" sector slice in the pie chart colored in the '#66b3ff' shade. It also includes its associated label {\"Industrial\"}[0.636, 0.443, 0.679, 0.463] and the percentage contribution {\"40.0%\"}[0.783, 0.399, 0.848, 0.419]."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "9fo8LvGTAMvA4hbz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.203, 0.302, 0.599, 0.91] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of the \"Residential\" sector slice in the pie chart with color '#ff9999'. The corresponding label {\"Residential\"}[0.323, 0.676, 0.367, 0.696] and percentage {\"45.0%\"}[0.17, 0.797, 0.247, 0.817] are also found in this specified region."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "jm9aHtShDZiUYw3o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_116.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.283, 0.229, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a small slice of the pie chart representing the \"Others\" sector colored as '#c2c2f0'. It also includes the corresponding label {\"Others\"}[0.355, 0.348, 0.39, 0.368] and the percentage contribution {\"3.0%\"}[0.235, 0.216, 0.282, 0.236]."
+            }
+        ],
+        "id_raw": 716
+    },
+    {
+        "id": "TlPEsjes1wsuEppR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at location [0.145, 0.021, 0.818, 0.047] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle \"Local Government and Community Development Expenditure from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "zVtPLoHfEOnzyZRP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is at the coordinates [0.223, 0.146, 0.741, 0.922] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart symbolizing different years of expenditure."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "TGonrpmN5IOIgss5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region at [0.908, 0.063, 0.993, 0.279] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the legend of the chart."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "Tzx1EnacQQ5n6a79",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is related to the legend item {marked as \"2015\"} [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart slice related to '2015' is at {coordinates} [0.223, 0.278, 0.482, 0.558]."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "l6MWF5MJywZL03P6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color relation between the chart's legend and the chart's pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors of the pie slices correspond to the colors of the legend items. For example, {the \"2015\" slice}[0.223, 0.278, 0.482, 0.558] is of the same color as {the \"2015\" legend item}[0.952, 0.071, 0.987, 0.091]."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "GwQT0xZISUiVNykc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any relationship between the pie slices {marked as '2016'}[0.224, 0.516, 0.482, 0.799] and {marked as '2021'}[0.283, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, they are both a part of the same pie chart and represent the expenditure of different years."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "8JmqohHJo5DxEaXd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's inside the region [0.223, 0.146, 0.741, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with its slices representing expenditure of different years starting from 2015 to 2021. Each slice has a unique color and is labeled with the corresponding year and activity percentage."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "VM8TtqU8yrbHLo9z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.908, 0.063, 0.993, 0.279] depict?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region depicts the chart's legend; It contains color handles and text labels pointing to yearly expenditure starting from 2015 to 2021."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "nvDXLTyKyF7EmXbW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe what is located in the area [0.145, 0.021, 0.818, 0.047]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the region where the chart's subtitle is placed. It reads \"Local Government and Community Development Expenditure from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 718
+    },
+    {
+        "id": "BvkfrgYB7nz4gw7P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [[0.213, 0.021, 0.75, 0.047]] on this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Food and Agriculture Industry Analysis' - Grains vs Dairy in 2017\"."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "0RlqAInbTlVOTwuB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nI notice there is an object in the coordinates [0.898, 0.063, 0.993, 0.129]. Could you tell me what it is?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart {legend}<r>[0.898, 0.063, 0.993, 0.129]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "ICQkjZ4OxPEQIsh3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the object located within the coordinates [[0.387, 0.907, 0.432, 0.927]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text element showing {the percentage \"66.7%\"}<r>[0.387, 0.907, 0.432, 0.927]</r> in the chart."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "hQVvVEPNztFPvd5U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you let me know what color corresponds to {the Grains slice}[0.223, 0.278, 0.741, 0.886] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color that corresponds to {the Grains slice} is <r>[#ff9999]</r>."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "KTpIdLkbv4Tm9oOg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at {coordinates}[0.942, 0.071, 0.987, 0.091] in the legend correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Grains\" at these coordinates corresponds to the {Grains slice}<r>[0.223, 0.278, 0.741, 0.886]</r> in the pie chart."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "EF9bRadbgRnnrahD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between the object located at [0.942, 0.101, 0.98, 0.121] and the objects present in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text element at these coordinates is the label \"Dairy\" in the legend, which corresponds to the {Dairy slice} <r>[0.288, 0.109, 0.73, 0.48]</r> in the pie chart."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "8deEXfr9tMWopfKe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's in the region defined by the coordinates [0.223, 0.109, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two primary parts of the pie chart: {the Grains slice}<r>[0.223, 0.278, 0.741, 0.886]</r> colored in \"#ff9999\", and {the Dairy slice}<r>[0.288, 0.109, 0.73, 0.48]</r> in \"#66b3ff\". The labels and corresponding percentages of the slices are also within this region."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "fEe5rM0KmRPBrhNm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region bound by coordinates [0.223, 0.278, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly includes {the Grains slice}<r>[0.223, 0.278, 0.741, 0.886]</r> and its corresponding text objects located at {coordinates \"66.7%\"}<r>[0.387, 0.907, 0.432, 0.927]</r> and {coordinates \"Grains\"}<r>[0.433, 0.725, 0.477, 0.745]</r> within the pie chart."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "TtsZl9xPK0Kt08rM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the specific region that's bounded by [0.288, 0.109, 0.73, 0.48] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a part of the pie chart, specifically {the Dairy slice}<r>[0.288, 0.109, 0.73, 0.48]</r>. The corresponding label \"Dairy\" and its slice percentage \"33.3%\" are also within this region."
+            }
+        ],
+        "id_raw": 719
+    },
+    {
+        "id": "wd4DhhNww9d12efX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.194, 0.021, 0.769, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sub-plot title is located at those coordinates. The text is \"Innovation Stages: Idea Generation vs Project Execution (2010-2015)\"."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "PfrbnEDW9jmMmIBG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.867, 0.071, 0.979, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text of the legend item \"Idea Generation\"."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "cc5bxE6FO77Oaxvm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.202, 0.296, 0.237, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a textual representation of the pie chart slice percentage - \"5.0%\"."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "SUfh1FvOU1YeCEJb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does that {legend item}[0.828, 0.103, 0.856, 0.117] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Project Execution\" in the coordinates corresponds to the slice {slice}[0.286, 0.516, 0.601, 0.886]."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "ZkrykZpRLAwXDrTt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the labels of the pie slices adjacent to {the slice}[0.381, 0.109, 0.598, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent pie slices to the given coordinates are \"2013\" {slice}[0.482, 0.185, 0.74, 0.516] and \"2015\" {slice}[0.283, 0.175, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "KGA1IfEHCQL5SaFI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the exploded pie {slice}[0.505, 0.443, 0.764, 0.873] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded pie slice represents the year 2014."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "urGCBtVshBKsWNtL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.277, 0.482, 0.772]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie slices representing different years and their respective innovation stages. The slices with texts \"2010\", \"2011\" and \"2012\" {slices}[[0.241, 0.278, 0.482, 0.516], [0.223, 0.377, 0.482, 0.548], [0.224, 0.516, 0.482, 0.799]] are included in this region. Also, the autotexts are included that display the percentage distribution of the respective slices."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "Kx1NAEAsrUEgkkm0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.223, 0.109, 0.764, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains all the pie slices of the chart along with their textual labels and autotexts representing the years from 2010 to 2015 and the corresponding percentages."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "qMzG5AzyloawIMfL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the main features in the region [0.382, 0.087, 0.776, 0.846]"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slices for the years 2014 and 2015 with their associated labels and percentage breakdowns. Slice 2014 is the exploded one in this region. The color breakdown markers for these slices in the legend are also included in this region."
+            }
+        ],
+        "id_raw": 720
+    },
+    {
+        "id": "bZe2nG8vUDrr1V5u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text with coordinates [0.346, 0.926, 0.381, 0.946] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It signifies the percentage \"8.5%\" associated with the exploded slice in the pie chart."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "ahJbmQrdnSOe4J29",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can we see at coordinates [0.223, 0.146, 0.741, 0.92] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the entire pie chart for the data."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "JhE4cUUKObkYuAdO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at [0.913, 0.402, 0.941, 0.416] stand for in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the color legend handle for the year 2020. It matches with a slice in pie chart."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "TZrAiZLbEkXxPvDv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is connected to this {object}[0.913, 0.402, 0.941, 0.416] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" [0.952, 0.4, 0.987, 0.42] corresponds to this handle object."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "iOGUejf47BGG5eq8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object in the chart does the color indicated by the handle with these coordinates [0.913, 0.103, 0.941, 0.117] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This handle color refers to the pie slice with the coordinates [0.223, 0.47, 0.482, 0.671] for the year 2016."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "jGACSmkpE6C4Hzeu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the percentages and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice in the pie chart corresponds to a percentage. For example, the slice at [0.246, 0.516, 0.482, 0.822] corresponds to the percentage \"8.7%\" [0.229, 0.769, 0.264, 0.789]."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "RrtxzB5jHOV7YxV2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the objects within the region [0.223, 0.146, 0.741, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of the entirety of the pie chart, which divides into several slices, each representing the data points or years in the dataset. Each slice is accompanied by a percentage text indicating its portion."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "d1hQk0kNylsF9j56",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within the coordinate region [0.908, 0.063, 0.993, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this coordinate region, there's a legend that distinguishes the years, from 2015 to 2020. Each year has a color indicator, marked by a rectangular block and the year it corresponds to."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "whqrgv6V2lICxWiQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's enclosed in the region [0.283, 0.166, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a slice of the pie chart associated with the year \"2020\" [0.376, 0.323, 0.411, 0.343]. It also consists part of the auto text \"8.7%\" [0.286, 0.17, 0.321, 0.19] indicating the slice's percentage of the whole pie."
+            }
+        ],
+        "id_raw": 721
+    },
+    {
+        "id": "DJ1aceYowfOI7e29",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.22, 0.021, 0.743, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Entertainment and Gaming Industry Development 2015 - 2021.\""
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "MLT33nI01KELV0pk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\"."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "5yBIAoXmX4R9B6nF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.283, 0.839, 0.318, 0.859] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.4%\"."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "8PiYRv23utjHLqU5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year is related to {the slice}[0.263, 0.516, 0.482, 0.877]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year {2017} is related from the legend."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "JmsvcpV5XYf5w8Nl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color is associated with {the year 2019}[0.626, 0.608, 0.67, 0.628]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is cyan according to the legend."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "nHPqheSHFtjUSzQa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice component corresponds to {the year 2021}[0.412, 0.256, 0.456, 0.276]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice component with coordinates [0.276, 0.11, 0.562, 0.48] corresponds to the year 2021."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "lSXeZByBCyMTBstQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.11, 0.764, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the pie chart of the Entertainment and Gaming Industry Development from 2015 to 2021. It contains slices that represent each year's contribution. For instance, in the chart, the year {2015 has the coordinates [0.224, 0.278, 0.482, 0.516]} and is marked with \"9.3%\", indicating the contribution for that year."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "3rZUqdMpcKZpyMyi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_123.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend, which includes color codes and labels for each year from 2015 to 2021."
+            }
+        ],
+        "id_raw": 723
+    },
+    {
+        "id": "wZQkfe5OsPeGfrqR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.19, 0.021, 0.773, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, named as \"Innovation and Technology Entrepreneurship Ecosystem in 2018-2020\"."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "DL8nRhMo5iiTnIig",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.908, 0.063, 0.993, 0.249] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "6J24sAYwr6nP5N9l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.162, 0.483, 0.197, 0.503] refer to on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the text \"20.5%\"."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "rO8SwDONtJ09LlEF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.223, 0.278, 0.482, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.504, 0.498, 0.763, 0.865], [0.482, 0.28, 0.739, 0.516]] are adjacent to the specified slice."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "P71vbl86ydnGWHF6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the pie slice}[0.504, 0.498, 0.763, 0.865]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2018\"}[0.608, 0.421, 0.643, 0.441] and {\"2019\"}[0.531, 0.309, 0.575, 0.329] are located near the pie slice."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "qxDvDGl1mSAnCvO1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend correspond with the pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legends \"2018\", \"2019\", and \"2020\" match the slices with coordinates [0.223, 0.278, 0.482, 0.721], [0.266, 0.516, 0.597, 0.886], and [0.504, 0.498, 0.763, 0.865] respectively."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "MRmfpNomke8rvl8I",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you characterize the region within coordinates [0.482, 0.147, 0.681, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"12.8%\" and related to the text \"2019\". This region is part of a pie slice in the upper left section of the chart."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "C61HTzjLjmyCVMlF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key items within the region with coordinates [0.223, 0.146, 0.763, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses several pie slices indicating the years \"2018\", \"2019\", and \"2020\", each slice also has its relative percentages such as \"20.5%\", \"23.0%\", and \"19.2%\"."
+            }
+        ],
+        "id_raw": 724
+    },
+    {
+        "id": "uqMvtOG7F9dbtl3z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title located at coordinates [0.286, 0.021, 0.677, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Population Migration Trends from 2013 to 2019\"."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "1ckthmb6Gi0DyTRV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.952, 0.071, 0.987, 0.091] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"2013\" corresponding to its corresponding color key in the legend."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "IbMJLK2xkaSJXn9c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.617, 0.906, 0.652, 0.926] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the autotext \"15.4%\" associated with a pie slice."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "3DyX7zj6AJcAmFX8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color key in the legend corresponds to {the slice}[0.223, 0.278, 0.482, 0.53]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color key labeled as \"2013\"<r>[0.952, 0.071, 0.987, 0.091]</r> in the legend corresponds to the specified slice."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "rdzPUzIrIonwpten",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the text \"2019\"<r>[0.952, 0.251, 0.987, 0.271]</r> in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\" corresponds to the slice at coordinates [0.283, 0.146, 0.518, 0.516]."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "XnbO31asVUnHdGyw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotexts are next to {the slice}[0.482, 0.413, 0.741, 0.736]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotexts \"14.6%\"<r>[0.762, 0.578, 0.797, 0.598]</r> and \"18.3%\"<r>[0.685, 0.17, 0.721, 0.19]</r> are adjacent to the slice."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "BPvE7rFqgflB4fy8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.223, 0.121, 0.747, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area within these coordinates encompasses the entire pie chart with each slice representing different years and their corresponding population migration percentages between 2013 and 2019."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "J8E2s1Iop5aMc6KB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between [0.482, 0.549, 0.701, 0.919] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains an exploded slice of the pie chart, labeled \"2016\" [0.538, 0.739, 0.582, 0.759] with an autotext \"15.4%\" [0.617, 0.906, 0.652, 0.926]."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "VfdBC3s0G5wjnGdF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is detailed in the region from [0.223, 0.516, 0.482, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart denoting the year \"2015\" [0.393, 0.707, 0.437, 0.727] along with an autotext showing \"13.0%\" [0.324, 0.874, 0.36, 0.894]."
+            }
+        ],
+        "id_raw": 726
+    },
+    {
+        "id": "mWtujEGfQszlSM1X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what does the object at coordinates [0.312, 0.021, 0.651, 0.047] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is \"Entrepreneurial Spirit from 2008 to 2016\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "6WaW1TGM7CtxLv6r",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object is at coordinates [0.908, 0.063, 0.993, 0.338] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "qJt1MVgMgrurHHiA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat feature can you identify at coordinates [0.169, 0.601, 0.205, 0.621] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a percentage value, specifically \"10.4%\"."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "CZfmev96hy7rXvhc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years correspond to the {red slices}[[0.224, 0.278, 0.482, 0.516],[0.507, 0.459, 0.766, 0.694]] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The years 2008 and 2013 correspond to the red slices."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "ZqzefFllG632cUHT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the year linked to the {green slice}[0.463, 0.146, 0.643, 0.516] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2015 is linked with the green slice."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "aKbeX1HffGqhouuP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value associated with the {blue slice}[0.482, 0.516, 0.711, 0.861] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value associated with the blue slice is 11.5%."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "dU9e5M51UI9uvQOR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please give information about the objects found within the region [0.223, 0.146, 0.766, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the given region, there are pie chart slices each representing a year from 2008 to 2016, each with their corresponding percentages displayed. The pie slices are represented in various colors."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "gy2Xo6hvQ6V8jzq5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region with coordinates [0.223, 0.278, 0.482, 0.714] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two pie slices, with the texts \"9.7%\" [0.183, 0.35, 0.219, 0.37] and \"2008\" [0.321, 0.421, 0.356, 0.441], and \"10.4%\" [0.169, 0.601, 0.205, 0.621] and \"2009\" [0.309, 0.558, 0.352, 0.578]."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "nXxORENrTRrFUHDR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region [0.263, 0.516, 0.482, 0.866] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The regional area includes the pie slice labeled \"10.7%\" [0.27, 0.825, 0.305, 0.845] and marked as \"2010\" [0.363, 0.68, 0.407, 0.7]. This specific slice is color coded as light green."
+            }
+        ],
+        "id_raw": 728
+    },
+    {
+        "id": "OSFaQKJ0wM9nueJJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at [0.745, 0.271, 0.899, 0.291] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This text represents the percentage \"30.0%\" of a pie-section in the chart."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "skxLotHN3HDdgKi1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located at [0.785, 0.063, 0.993, 0.249]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "WF1P9yZUl7stGgkg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.186, 0.021, 0.777, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart \"Company Investment in Social Responsibility and Sustainable Business\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "ou47ORri1u8lNdAA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of {the pie-section located at [0.385, 0.516, 0.722, 0.886]} with the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie-section correlates with the legend item \"Workplace Diversity\"."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "gHEA2BjWbLydxqv9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text value is associated with {the slice at [0.504, 0.127, 0.763, 0.635]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.0%\" [0.745, 0.271, 0.899, 0.291] is associated with this slice."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "VHs1nONWR6LAE6hi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWho is the neighboring pie-section of {the pie-slice located at [0.344, 0.146, 0.5, 0.516]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring pie-section of this slice is {the slice}[0.283, 0.202, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "HGY0fIzRdkqoM2Rl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region indicated by coordinates [0.385, 0.516, 0.722, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie-section for \"Workplace Diversity\" identifiable by its unique chart color. It also contains a textual representation of the assigned value \"25.0%\" [0.593, 0.881, 0.732, 0.901]."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "rIV5knYIrgfTZTXi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region bounded by coordinates [0.223, 0.278, 0.482, 0.712] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie-section representing \"Renewable Energy\", and the sector value \"20.0%\" [0.068, 0.478, 0.197, 0.498]."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "TxYKx8pZ7Qo5V3Xj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_129.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe what's in the region [0.504, 0.127, 0.763, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes an exploded pie-section representing \"Environmental Impact\" and its corresponding value \"30.0%\" [0.745, 0.271, 0.899, 0.291]."
+            }
+        ],
+        "id_raw": 729
+    },
+    {
+        "id": "tE23cBrSy2RwhFqH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at the coordinates [0.161, 0.021, 0.803, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Vegetable Production in the Food and Agriculture Industry from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "374a4TpEVQc8wdcf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the legend item at coordinates [0.913, 0.193, 0.941, 0.207] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item represents the year 2019."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "hQvuqUzrWDDUNkSj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.171, 0.401, 0.206, 0.421] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"13.9%\"."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "Okf032dcl6qpSY2m",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year corresponds to the slice at {the position}[0.223, 0.278, 0.482, 0.58]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2015 corresponds to the slice."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "qcOIqabjaPeBhadf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice that corresponds to {the year 2020}[0.395, 0.106, 0.43, 0.126]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice that corresponds to the year 2020 is '#ffb399'."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "IjaRnTu2If5rEicm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item color corresponds to {the slice}[0.482, 0.516, 0.739, 0.836] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item color for the slice is '#ffcc99'."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "fP6cBVShFAm2qE7G",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is visible in the region with coordinates [0.223, 0.146, 0.764, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there are several pie chart slices. They represent vegetable production from 2015 to 2020 and are color-coded according to the legend. Each slice also has associated percentages and year labels like {'13.9%', '2015'}[0.171, 0.401, 0.206, 0.421], {'16.2%', '2016'}[0.225, 0.763, 0.261, 0.783], and so on."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "IOFfzuhyQdkdyufD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_130.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.223, 0.516, 0.739, 0.836] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the '2018' pie chart slice, indicated by coordinates {'15.0%', '2018'}[0.721, 0.727, 0.756, 0.747]. It's color-coded based on the legend."
+            }
+        ],
+        "id_raw": 730
+    },
+    {
+        "id": "4WbiLQt6S1dBXI81",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with {the slice}[0.258, 0.553, 0.654, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text associated with this slice is {\"28.0%\"}[0.401, 0.946, 0.441, 0.966]."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "8sIVK3Zw2bc9FGIf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice matches to the {legend item 'Over 55'}[0.923, 0.191, 0.979, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice aligning with \"Over 55\" is situated at {coordinates}[0.283, 0.148, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "vKNr2oX6GxtwESzP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat other objects can be found close to {the subtitle of the chart}[0.298, 0.021, 0.665, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart, which is located at {coordinates}[0.223, 0.146, 0.741, 0.923], is the closest object to the subtitle."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "alMTXHCJda7PUUBM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat probable contents are there within the region [0.100, 0.500, 0.600, 0.900]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Some elements in this region include the text \"Under 25\"[0.133, 0.478, 0.197, 0.498], the corresponding pie slice[0.223, 0.278, 0.482, 0.712] and the text \"25-34\"[0.401, 0.946, 0.441, 0.966] with its corresponding pie slice[0.258, 0.553, 0.654, 0.923]."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "M62It4OynZoBn9lJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description for the region [0.600, 0.100, 0.900, 0.500]."
+            },
+            {
+                "from": "gpt",
+                "value": "Main elements in this region seem to include the text \"35-44\"[0.614, 0.535, 0.658, 0.555] and the corresponding pie slice[0.482, 0.32, 0.741, 0.788]."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "BqeXVCtyi0nnh08w",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found in the region [0.100, 0.100, 0.500, 0.500]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area, the text \"Over 55\"[0.386, 0.311, 0.43, 0.331] and the corresponding pie slice[0.283, 0.148, 0.482, 0.516] are found."
+            }
+        ],
+        "id_raw": 733
+    },
+    {
+        "id": "7zkXAim2NY8kdp4e",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.118, 0.396, 0.207, 0.416]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"13.5%\"."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "2NtmwHtVU9QYq9Qg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.898, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Legend's label \"Young Adults\"."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "FfCJkiJLCatC6LDy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is labeled at the coordinates [0.608, 0.655, 0.652, 0.675] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Adults\"."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "ZGeCEVCJTvkCvljq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich section of the chart corresponds to the legend item \"Young Adults\" {the item}[0.898, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.223, 0.278, 0.482, 0.572]."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "HUmqjowJ3hKpq73I",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the label \"Millenials\" {the label}[0.898, 0.191, 0.963, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "sqNnuqxIIT0II5kL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to the slice {the slice}[0.487, 0.391, 0.762, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}[[0.226, 0.516, 0.482, 0.885], [0.482, 0.265, 0.72, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "8Smi9sdhoyu9B430",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region designated by coordinates [0.223, 0.278, 0.482, 0.572]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart colored as \"#ff9999\", accounting for 13.5% of the chart, and is labeled as \"Young Adults\"."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "JzKShtH4fQ4RsW6R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details are in the region [0.487, 0.391, 0.762, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that represents 'Adults', colored in '#99ff99', making up 32.4% of the pie."
+            }
+        ],
+        "id_raw": 734
+    },
+    {
+        "id": "5j0janMalD7rbwtn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.903, 0.071, 0.943, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label, \"China\"."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "bbQjq0ITiyIKLCe9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could be discerned at coordinates [0.223, 0.278, 0.482, 0.523] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart, belonging to China."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "tMYzQmVLMOK4jt2t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is found at coordinates [0.217, 0.021, 0.746, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Consumer Behavior Index in Different Emerging Markets - 2021\"."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "wqqlI1b62oirAo1O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between the slice located at {the coordinates}<r>[0.459, 0.552, 0.616, 0.922]</r> and the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice corresponds to the legend label \"Russia\" <r>[0.903, 0.161, 0.947, 0.181]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "UwCPRAUgGNkx4vwH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend label is associated with the slice located at {the individual slice}<r>[0.482, 0.179, 0.714, 0.516]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"Turkey\" <r>[0.903, 0.251, 0.948, 0.271]</r> is associated with this slice."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "UCA3g0AuM85GtOqE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice and legend label are connected to the coordinates {the value}<r>[0.223, 0.516, 0.482, 0.784]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates correspond to a pie chart slice for \"India\" <r>[0.903, 0.101, 0.937, 0.121]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "zoEfNk6rS82yRZ5g",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region specified by the coordinates [0.482, 0.516, 0.725, 0.837]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, we have a pie chart slice corresponding to \"Mexico\" <r>[0.903, 0.191, 0.951, 0.211]</r> and a percentage text \"11.2%\"<r>[0.7, 0.768, 0.748, 0.788]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "TKhYEhlQI36dfAg8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects present in the region [0.223, 0.146, 0.741, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily consists of the pie chart. It includes the slices of China<r>[0.223, 0.278, 0.482, 0.523]</r>, India<r>[0.223, 0.516, 0.482, 0.784]</r>, Brazil<r>[0.303, 0.516, 0.482, 0.884]</r>, Russia<r>[0.459, 0.552, 0.616, 0.922]</r> and part of the slice of Mexico<r>[0.482, 0.516, 0.725, 0.837]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "3PUdVtwjw9lDLb98",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you specify the objects within the region [0.858, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the chart legend which contains the labels \"China\" <r>[0.903, 0.071, 0.943, 0.091]</r>, \"India\" <r>[0.903, 0.101, 0.937, 0.121]</r>, \"Brazil\" <r>[0.903, 0.131, 0.942, 0.151]</r> and half of the label \"Russia\" <r>[0.903, 0.161, 0.947, 0.181]</r>."
+            }
+        ],
+        "id_raw": 736
+    },
+    {
+        "id": "W8Q7Z9ktAl2RvR3L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.337, 0.021, 0.626, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle named \"Industry Market Share Comparison\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "AT4H2MIKyLmsfstj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.386, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the sublegend text \"Transportation\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "mUUQmY1L9AEYVZ8u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.124, 0.439, 0.2, 0.459] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text \"17.0%\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "cSP7V1A1T9ePsYuh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice adjacent to {the slice}[0.482, 0.147, 0.648, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the {adjacent slice} is <r>#[0.223, 0.278, 0.482, 0.65]</r> \"#ff9999\""
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "jNrTim1Jw3sb1wNf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do {the legend handle}[0.847, 0.163, 0.875, 0.177] and {the pie slice}[0.482, 0.516, 0.732, 0.861] share in common?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both the legend handle and the pie slice are in the same color, \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "TfRZIjnOuD9QceLe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the autotext associated with {the legend item \"Agriculture\"}[0.886, 0.071, 0.963, 0.091] located in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext associated with Agriculture is located at coordinates [0.124, 0.439, 0.2, 0.459]."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "r0I1pR4KNuF4wGOM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.223, 0.278, 0.482, 0.65]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, you can find a pie slice with color '#ff9999', representing the 'Agriculture' sector with the autotext '17.0%' [0.124, 0.439, 0.2, 0.459] and its corresponding text \"Agriculture\"[0.306, 0.47, 0.35, 0.49]."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "hRxGyisOi3FMQwPW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the region [0.847, 0.193, 0.875, 0.207]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one square handle for the legend, which is colored \"#c2c2f0\" and associated with the 'Retail' sector in the pie chart."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "uFS3qYOjOkl5P10L",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained in the area defined by [0.886, 0.221, 0.987, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains the text label 'Transportation' which is a part of the chart's legend."
+            }
+        ],
+        "id_raw": 737
+    },
+    {
+        "id": "IV5kbG0gUFMRcz1X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat primary detail can be found within these coordinates [0.318, 0.021, 0.645, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary detail at these coordinates is the subtitle \"Economic Indicator Presentation - 2016\"."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "3nijJdK5zqJsXG01",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in these bounding box coordinates: [0.223, 0.278, 0.482, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GDP slice of the pie chart can be found at these coordinates."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "QReAbvwSVNi4xRaG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the pie chart is located at [0.326, 0.146, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GNP segment of the pie chart is found at these coordinates."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "LsFFy3meWkmn86L8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is next to {the Unemployment Rate slice}[0.265, 0.193, 0.464, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The GDP slice located at the coordinates [0.223, 0.278, 0.482, 0.833] is adjacent to the Unemployment Rate slice."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "lMWL4RGixf0Ubg36",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the corresponding slice of {the legend item 'GNP - 2016'}[0.795, 0.101, 0.874, 0.121] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice is the one with blue colour located at [0.326, 0.146, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "ShBCYb0OvHznvoWz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item in the legend identifies {the slice of the pie chart}[0.265, 0.193, 0.464, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Unemployment Rate - 2016\"[0.795, 0.131, 0.987, 0.151] uniquely corresponds to the identified slice within the pie chart."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "0t9ZeyyYASufhRzD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be identified within the region specifically defined by [0.265, 0.193, 0.464, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the \"Unemployment Rate - 2016\" text at [0.34, 0.318, 0.375, 0.338] and a figure indicating \"3.7%\" at [0.076, 0.183, 0.268, 0.203]."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "rpzvWItizPz14DJ2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the region marked by [0.326, 0.146, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the GNP slice of the pie chart, along with the text \"GNP - 2016\" at [0.615, 0.495, 0.659, 0.515] and the associated percentage \"68.8%\" at [0.766, 0.485, 0.845, 0.505]."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "yVf91aOkURvkeF9a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within [0.223, 0.278, 0.482, 0.833]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the GDP slice with the text \"GDP - 2016\" at [0.306, 0.543, 0.35, 0.563], and a label showing \"27.5%\" at [0.122, 0.573, 0.201, 0.593]."
+            }
+        ],
+        "id_raw": 740
+    },
+    {
+        "id": "QJG54SiKbtkOT6Pz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart with coordinates [0.199, 0.021, 0.764, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"New Energy and Clean Technology Development from 2012 to 2019\"."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "HjQxpGLq1NEzBZYn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the position with coordinates [0.903, 0.071, 0.938, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labelled \"Solar\" located in the legend."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "EKDzIIXKLOz3kTwe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.202, 0.296, 0.237, 0.316] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the auto text \"5.0%\" attached to the pie slice."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "cPlPBlcvRgSMv71L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend item {the Hydropower label}[0.903, 0.131, 0.987, 0.151] and the pie slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Hydropower label corresponds to the slice at coordinates [0.315, 0.516, 0.544, 0.886] with the same color."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "LnEEKMapk1DZSu35",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat other legend items are close to {the Biomass label}[0.903, 0.221, 0.961, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Nuclear\" and \"Fossil fuels\" labels are close to the Biomass label."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "fokj01FPUaE81c8U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the pie slice that corresponds to {the Nuclear label}[0.903, 0.191, 0.956, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Nuclear label corresponds to the slice situated at the coordinates [0.482, 0.173, 0.733, 0.516]."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "6N75DjScPow0xSbi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region in the coordinates [0.223, 0.146, 0.763, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart depicting the different energy sources. Each sector of the pie corresponds to an energy source, for example, \"Solar\" [0.241, 0.278, 0.482, 0.516] or \"Wind\" [0.223, 0.377, 0.482, 0.799]."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "PfKH6uUExGAYi3br",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the objects in the region defined by [0.482, 0.173, 0.763, 0.894]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the 'Nuclear' and 'Geothermal' pie slices, both labeled with percentages reflecting their shares in New Energy and Clean Technology Development. There are also autotext elements indicating the corresponding percentages."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "GjO1lo6pLbXuiTxp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region [0.858, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend of the pie chart. It contains color-coded indicators and corresponding labels representing various energy sources like 'Solar'[0.903, 0.071, 0.938, 0.091] and 'Wind'[0.903, 0.101, 0.938, 0.121]."
+            }
+        ],
+        "id_raw": 741
+    },
+    {
+        "id": "vlWAAGM33LWy1CcA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.314, 0.021, 0.686, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart, labelled 'Future Technological Development Prospects'."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "lcAIrhjQKWQHJdN8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.596, 0.063, 0.817, 0.129] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart, indicating classifications of data."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "mvFvm6C7f014umgM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the object located at [0.5, 0.516, 0.5, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the center of the pie charts."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "Oc8GjUhOHXexELMs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology corresponds to the pie slice with the coordinates [0.5, 0.427, 0.759, 0.761]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponds to {AI and Machine Learning}<r>[0.601, 0.073, 0.629, 0.087]</r> according to the legend."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "QlcWoU4o037dYioC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.5, 0.406, 0.752, 0.516] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinate [0.5, 0.365, 0.747, 0.516] is adjacent."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "eEauEYcY36FdoIhX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice label is located right after {the label '2017'}[0.632, 0.446, 0.667, 0.466]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of '2018' (with coordinates: [0.628, 0.428, 0.663, 0.448]) is located right after '2017' label."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "LAGxjwvHwA4Ht90A",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.5, 0.427, 0.752, 0.761]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the development of 'AI and Machine Learning' in '2023', with a percentage of '15.3%' stated at the coordinates [0.777, 0.603, 0.812, 0.623]."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "f3fDTTgxtFldZTBH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.5, 0.172, 0.674, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice representing the development of 'Blockchain' in '2021'. Its percentage, '5.7%' is located at [0.65, 0.16, 0.685, 0.18]."
+            }
+        ],
+        "id_raw": 742
+    },
+    {
+        "id": "TuQJK5BwbW0FvZ9d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.225, 0.021, 0.739, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Education Technology and Online Learning from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "mbfwC7xDorDx6GHJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2012\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "YOoiPkhliMSJyooS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color representation for the year 2013 found at [0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for the year 2013 is \"#99ff99\"."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "ByLhObQLW4KX23M7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are associated with the color of the pie chart slice at {coordinates}[0.283, 0.146, 0.584, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2020\"}[0.424, 0.29, 0.468, 0.31] and {\"20.3%\"}[0.381, 0.11, 0.416, 0.13] are associated with this slice."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "972Z5oBUaYuNdCD8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object in the legend corresponds to the slice [0.212, 0.146, 0.765, 0.886] for the year 2014 in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item for \"2014\" {with coordinates}[0.952, 0.161, 0.987, 0.181] corresponds to the slice."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "JWyVXOQtjBIRHUQl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are associated with the color code \"#ffcc99\" in the legend found in {coordinates}[0.913, 0.163, 0.941, 0.177]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2014\" is associated with this color code."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "xAPs3xkcD8z87Hax",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the nature of the objects within the region [0.482, 0.176, 0.74, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region are two objects: the text {\"19.4%\"}[0.492, 0.202, 0.524, 0.222] indicating the percentage represented and the text {\"2019\"}[0.587, 0.379, 0.631, 0.399] indicating the year."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "UabnRGwulP3TjqpS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_143.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is presented in the region [0.23, 0.2, 0.369, 0.74]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart, representing data from the year \"2011\" [0.334, 0.384, 0.369, 0.404] with the corresponding percentage \"3.8%\" [0.208, 0.283, 0.243, 0.303]."
+            }
+        ],
+        "id_raw": 743
+    },
+    {
+        "id": "2g4xcWmn75YietJz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located in coordinates [0.275, 0.021, 0.688, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which reads, \"Consumer Spending from 2009 to 2016 in Billions\"."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "roy0rOcCklObv7A1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend is located at these coordinates."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "yFypzOVypdM9FjuR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.223, 0.146, 0.741, 0.923] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The primary object at these coordinates is a pie chart."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "1jiPkMJV6KfW5LJA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is positioned nearest to the {sub-legend located at coordinates [0.908, 0.063, 0.993, 0.309]} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2016\"}[0.952, 0.281, 0.987, 0.301] is the nearest to the mentioned sub-legend."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "YKYYkmEQ162es9Ui",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the colors related to the {legend items labeled \"2009\", \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\" and \"2016\"} in the sub-legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding colors for the legend items are \"#ff9999\", \"#66b3ff\", \"#99ff99\", \"#ffcc99\", \"#c2c2f0\", \"#ffcafa\", \"#b3b3cc\", and \"#c2f0d0\" respectively."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "9QfFdKVaqcZ2urxh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years are associated with the pie slices adjacent to {the slice}[0.283, 0.146, 0.532, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slices correspond to years {\"2015\"}[0.482, 0.153, 0.72, 0.516] and {\"2014\"}[0.482, 0.37, 0.741, 0.698]."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "xeQMx1LAaaZxdUcn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found within the boundary coordinates of [0.223, 0.463, 0.482, 0.676]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these boundaries is a slice of the pie chart that represents the year \"2010\" and this slice is colored \"#66b3ff\". It also contains a text indicating \"9.4%\"[0.165, 0.568, 0.2, 0.588]."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "nTbIXDebcChRkGZM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between the coordinates of [0.358, 0.553, 0.542, 0.923] comprise of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an exploded pie slice corresponding to the year \"2012\", which is color coded as \"#ffcc99\". It also possesses a text stating \"11.7%\"[0.41, 0.947, 0.445, 0.967]."
+            }
+        ],
+        "id_raw": 745
+    },
+    {
+        "id": "RVX78mlQnfgrIWeB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.333, 0.021, 0.63, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Global Concern Distribution in 2015.\""
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "RvWk0VQ8UCu0eCJr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located within the bounding box with coordinates [0.872, 0.071, 0.901, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Year.\""
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "TQ6upeSieEPtDU4i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.226, 0.17, 0.32, 0.19]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"1.8%\"."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "82C6h130lgpwQz9e",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is related to {the legend item \"Year\"}[0.872, 0.071, 0.901, 0.091] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice in the coordinates [0.223, 0.146, 0.741, 0.886] is related."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "k4m8AJpDcrDxXVau",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is related to {the pie slice}[0.283, 0.223, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Air Pollution\"[0.872, 0.191, 0.955, 0.211] is related."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "zk3H9nhPbnvv353G",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are related to {the pie slice}[0.401, 0.148, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"Climate Change\"[0.431, 0.289, 0.466, 0.309] and \"3.2%\"[0.31, 0.108, 0.421, 0.128] are related."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "VQ9TpRKh8h22eQyx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within the coordinates [0.827, 0.063, 0.993, 0.219]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is hosting the chart's legend, which includes the labels \"Year\", \"Climate Change\", \"Ozone Depletion\", \"Deforestation\", and \"Air Pollution\", along with their corresponding color qualifiers."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "fNgoMMdGYeaYZxpw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you provide a description of the region with coordinates [0.223, 0.131, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice denoting the \"Year\" category, associated with the percentage \"87.9%\"[0.616, 0.865, 0.646, 0.885]."
+            }
+        ],
+        "id_raw": 746
+    },
+    {
+        "id": "97ZD03liEn9S18DS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Influence Distribution in Business Management\"{the title}<r>[0.285, 0.021, 0.678, 0.047]</r>."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "38UdJ88FipJCGFPL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.916, 0.133, 0.944, 0.147] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The symbol for \"CMO\" in the legend is located at these coordinates."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "dzQBOPjvSPqnD9u9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.688, 0.787, 0.717, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"22.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "tGdBYQ1p4bYQ2VWA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with 'CEO' in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "'CEO' is associated with color '#ff9999'{the label 'CEO'}<r>[0.916, 0.073, 0.944, 0.087]</r>."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "BuoLEEE9jswaXVNN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {the slice}[0.2, 0.146, 0.741, 0.904]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.2, 0.296, 0.49, 0.904], [0.482, 0.147, 0.741, 0.542]] are adjacent."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "PEJVQ7UQjNxLaRFk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich figure in the chart matches the object at coordinates [0.916, 0.163, 0.944, 0.177] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object matches with the figure 'COO'{COO}<r>[0.283, 0.146, 0.5, 0.516]</r> in the chart."
+            }
+        ],
+        "id_raw": 747
+    },
+    {
+        "id": "RY9yjI2cx2qU75pI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.303, 0.021, 0.66, 0.047] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the subtitle \"Cybersecurity Incidents from 2016 to 2021\"."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "FcQTu3M61Bgq46Qz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located at coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the year \"2020\" in the legend."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "aelFvXmMSy6QkYxN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the purpose of the object at coordinates [0.913, 0.223, 0.941, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the color key for the year \"2021\" in the legend."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "ksYO5zt2PMSyQCej",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the {incident figure}[0.279, 0.109, 0.611, 0.479] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color corresponds to the legend item with the same color at coordinates [0.913, 0.223, 0.941, 0.237], representing the year 2021."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "bv0NgpARmm7tJGJT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich elements in the chart are related to the year {2018}[0.952, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with text \"2018\"<r>[0.437, 0.726, 0.481, 0.746]</r> and the color key in the legend <r>[0.913, 0.133, 0.941, 0.147]</r> are related to the year 2018."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "CBXURoWiqBTK6eTi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice is associated with {the legend item 2016}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the text \"2016\" at coordinates [0.321, 0.42, 0.356, 0.44] and the same color as the legend item is associated."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "UtQ2UPc2PQcHJrAJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.109, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices representing the years 2018, 2019, and 2020, with their corresponding texts for percentages and years within coordinates like \"18.2%\"<r>[0.406, 0.909, 0.441, 0.929]</r>, \"2018\"<r>[0.437, 0.726, 0.481, 0.746]</r>, \"20.8%\"<r>[0.733, 0.698, 0.768, 0.718]</r>, \"2019\"<r>[0.597, 0.611, 0.641, 0.631]</r>, \"13.8%\"<r>[0.718, 0.278, 0.753, 0.298]</r> and \"2020\"<r>[0.588, 0.381, 0.632, 0.401]</r>."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "ieJslhGQXn531T5R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_148.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.223, 0.109, 0.482, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two slices of the pie illustrating years 2016 and 2017, with the corresponding texts \"9.5%\"<r>[0.184, 0.348, 0.219, 0.368]</r>, \"2016\"<r>[0.321, 0.42, 0.356, 0.44]</r>, \"15.2%\"<r>[0.182, 0.656, 0.217, 0.676]</r> and \"2017\"<r>[0.315, 0.588, 0.359, 0.608]</r>."
+            }
+        ],
+        "id_raw": 748
+    },
+    {
+        "id": "I6LPTSgyR7c5Pyjl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.295, 0.021, 0.668, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart called \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "TqV3iWpGjaov2u8Z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented in the bounding box with coordinates [0.481, 0.986, 0.483, 0.987]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the x-axis label, but it's empty."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "ihHGR0eEtWowN3vX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at the coordinates [0.961, 0.071, 0.978, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"13\"."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "zuYlVj7cW7ineQSS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the chart is adjacent to {the slice}[0.242, 0.285, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.279, 0.278, 0.482, 0.516] is adjacent to it."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "mQ2Lqhm1sJVhsNjB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is closest to {the item in the legend}[0.961, 0.341, 0.978, 0.361]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"50\"}[0.961, 0.34, 0.978, 0.36] is closest."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "uhXqtzHd5U32Vouw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart does the text at coordinates [0.961, 0.161, 0.987, 0.181] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"200\" corresponds to {the slice} [0.225, 0.516, 0.482, 0.578]."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "CpGEoCm2xfyNbkpf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.112, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with various colored slices each annotated with a number and percent, corresponding to different categories in legend situated outside this region. It\u2019s the main plot with center at coordinates [0.482, 0.516]."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "PYzr9UICoSEMUbEC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat unique objects are part of the [0.916, 0.063, 0.993, 0.368] region?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region harbors a vertical legend with color patches and numerical labels for each category present in the main plot. Each label is associated to a unique coordinate on the pie chart."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "g2FVEoWNXP5AZQAb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_150.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details of the region [0.416, 0.112, 0.642, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is part of the main pie chart plot, encompassing at least five different slices, each represents a category ranged from small like \"13\" to large like \"200\", as labeled on slices and related to the legend on the side of the plot."
+            }
+        ],
+        "id_raw": 750
+    },
+    {
+        "id": "Ftp2IiTXO1tJzvp5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at the coordinates [0.334, 0.021, 0.63, 0.047] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart named \"Facebook Usage from 2012 to 2016\"."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "bnGPsHa2kUXqjcNd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"2012\" in the legend."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "DSKauk7h4tH09CoE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the textual content found at coordinates [0.17, 0.405, 0.205, 0.425] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the piece of text \"14.3%\"."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "lXIKKWS7dcFaiDSz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the color markings in the sublegend and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors of the handles at coordinates [0.913, 0.073, 0.941, 0.087], [0.913, 0.103, 0.941, 0.117], [0.913, 0.133, 0.941, 0.147], [0.913, 0.163, 0.941, 0.177], and [0.913, 0.193, 0.941, 0.207] in the sublegend denote the colors of the pie slices from 2012 to 2016 respectively. The corresponding pie slices and their text labels can be found at the slices coordinates in the pie chart."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "74TUp0GMLyf9jccJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is related to {the '2013' legend item}<r>[0.952, 0.101, 0.987, 0.121]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at}<r>[0.228, 0.516, 0.482, 0.886]</r>, shown in #66b3ff color, is related to the '2013' legend item."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "YpClqxV4wyf9qYrn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the pie chart represents {the legend item '2016'}<r>[0.952, 0.191, 0.987, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.283, 0.146, 0.6, 0.516]</r> in the chart represents the '2016' legend item."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "Vp8DdSDib5qIYTPM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.223, 0.516, 0.482, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents the data for the year 2013, with the text \"21.4%\" [0.263, 0.818, 0.298, 0.838], indicating the percentage of Facebook usage, and the label \"2013\" [0.36, 0.676, 0.404, 0.696] marking the time period."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "fCEneD2ZmeHfRM6E",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you provide details about the region [0.475, 0.516, 0.733, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Definitely. This region comprises a part of the pie chart corresponding to the year 2014. Besides this, this region contains two text details namely, \"21.4%\" [0.654, 0.83, 0.689, 0.85] and \"2014\" [0.554, 0.683, 0.597, 0.703]. The first text shows the percentage of Facebook usage for the aforementioned year."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "cHIIl8Cbfn6wvwyc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_151.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what you see in the region defined by [0.482, 0.187, 0.741, 0.607]."
+            },
+            {
+                "from": "gpt",
+                "value": "Sure, the region refers to the year 2015 on the pie chart. It includes  the text \"21.4%\" [0.741, 0.339, 0.777, 0.359] which represents the percentage of Facebook usage in 2015, and the label \"2015\" [0.601, 0.415, 0.645, 0.435] indicates the year."
+            }
+        ],
+        "id_raw": 751
+    },
+    {
+        "id": "YSHvLIYefmFrMAcF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.303, 0.021, 0.66, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the chart, which is \"Healthcare Expenditure from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "5yiiNV6WCwpM92Ky",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.223, 0.146, 0.76, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the pie chart that represents the Healthcare Expenditure from 2010 to 2019."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "arpnQIO4EuLrqBqG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend text indicating the year \"2010\"."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "uLxRm6ADUucFvW4n",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.283, 0.147, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.458, 0.146, 0.628, 0.516], [0.504, 0.191, 0.76, 0.496]] are adjacent."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "G2YpNMxg4I0hGUGn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is located close to the legend handle at coordinates [0.913, 0.073, 0.941, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label \"2010\" at {coordinates}[0.952, 0.071, 0.987, 0.091] is located close to this handle."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "k4Y3U1wouhRy7Ll6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color does the legend at coordinates [0.952, 0.281, 0.987, 0.301] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend at these coordinates corresponds to the color \"#9966ff\" which correlates to the label '2017'."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "iGszta8xNhwWMwET",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.146, 0.76, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily encompasses the right half of the pie chart along with multiple years represented by their respective slices. It includes years '2015'[0.554, 0.682, 0.598, 0.702], '2016'[0.611, 0.555, 0.655, 0.575], and '2017' [0.613, 0.369, 0.657, 0.389]. Each year slice contains a specific expenditure percentage."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "5LwUgWIr2d8qYrJx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily consists of the whole chart including the pie chart, the subtitle 'Healthcare Expenditure from 2010 to 2019', and the legend representing the respective years and their color representations."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "Bb4ObqneEvdGV2Ed",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies within the region [0.223, 0.146, 0.482, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region essentially includes the left half of the pie chart covering years '2010'[0.326, 0.403, 0.361, 0.423], '2011'[0.309, 0.5, 0.344, 0.52], '2012'[0.326, 0.607, 0.361, 0.627], '2013'[0.382, 0.694, 0.417, 0.714], '2014'[0.468, 0.728, 0.503, 0.748] and '2019'[0.388, 0.309, 0.431, 0.329]. Each year depicts a slice with a specific expenditure percentage."
+            }
+        ],
+        "id_raw": 753
+    },
+    {
+        "id": "OtyfITBtB7HYBneK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the figure, what object can be identified at the location [0.286, 0.021, 0.677, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Education Resource Distribution and Utilization\" can be found at this location."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "7AR0kIAIu6Usdn45",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content at the coordinates [0.882, 0.101, 0.987, 0.121] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend item labeled as \"Online Material\"."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "ONXt3FyspZUoCR3r",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the provided coordinates [0.201, 0.146, 0.765, 0.906] in the chart diagram?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the pie chart itself."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "lI4xwYxW9oaQYn0T",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text linked to the pie slice {the slice}[0.482, 0.173, 0.701, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Interactive\" is connected to the slice."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "M1VjxOYZQgHoHrH4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat colored legend item matches with the pie slice {the slice}[0.388, 0.146, 0.5, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Software\" with color represented by the coordinates {the handle}[0.843, 0.193, 0.87, 0.207] matches."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "zWW4r6meYBDn3rpd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, which slices is adjacent to the slice at {the slice}[0.506, 0.334, 0.765, 0.889]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}[[0.201, 0.298, 0.522, 0.906], [0.482, 0.173, 0.701, 0.516]] are adjacent."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "eUVp1v4wwmXGNt0r",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is in the region [0.201, 0.146, 0.765, 0.906]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with various slices, including text identifiers such as \"Book\", \"Online Material\", \"Interactive\", \"Library\", \"Software\", \"Toolkit\" and \"Others\". Each slice is annotated with a percentage representing the resource distribution percentage."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "vazHpxsfohVEuiS3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is encompassed within the boundaries [0.837, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legends for the different pie slices, which include \"Book\", \"Online Material\", \"Interactive\", \"Library\", \"Software\", \"Toolkit\", \"Others\" as well their corresponding color identifiers."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "7LwOC4l3MACd7uUi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_155.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is present in the region [0.882, 0.071, 0.916, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a part of the legend, specifically, the object named \"Book\"."
+            }
+        ],
+        "id_raw": 755
+    },
+    {
+        "id": "fzY60XjANEXoHKmp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.845, 0.071, 0.969, 0.091] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represents the legend label \"Gender Inequality\"."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "bc29BD9JpriIFfkd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object that exists at the coordinates [0.482, 0.516, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center of the pie chart."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "e5Qh769Ci85E9YJ3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information does the object with coordinates [0.107, 0.31, 0.232, 0.33] convey?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object conveys the percentage information \"6.2%\"."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "lsPE0whAC465FajY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the label {Gender Inequality}[0.845, 0.071, 0.969, 0.091] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Gender Inequality\" corresponds to the slice with coordinates [0.235, 0.278, 0.482, 0.516] in the chart."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "fRJ4tkZyDLSooK89",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is placed directly inside {the slice}[0.503, 0.537, 0.761, 0.869] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"{LGBTQ+ Rights}\"[0.61, 0.652, 0.654, 0.672] is placed directly inside the slice."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "uDwGdNg8SSPOQeA8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item pairs with {the pie slice}[0.398, 0.516, 0.596, 0.886] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"Healthcare Rights\"}[0.845, 0.161, 0.969, 0.181] pairs with the pie slice."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "BQn1NJjALG1JDMEW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.2, 0.1, 0.8, 0.9] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers most of the chart, mainly containing the pie chart slice with coordinates [0.223, 0.146, 0.761, 0.886] along with various associated labels such as {\"Gender Inequality\"}[0.328, 0.399, 0.363, 0.419] and percentage indications like {\"6.2%\"}[0.107, 0.31, 0.232, 0.33]."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "ndtB9r7GrniUUEOx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.79, 0.05, 0.99, 0.34] on the pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart with labels such as {\"Gender Inequality\"}[0.845, 0.071, 0.969, 0.091] and {\"Racial Inequality\"}[0.845, 0.101, 0.96, 0.121], each associated with a different color represented as handles located at coordinates like [0.806, 0.073, 0.834, 0.087]."
+            }
+        ],
+        "id_raw": 756
+    },
+    {
+        "id": "hieN4fgrirRIdC7S",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.867, 0.071, 0.981, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"Education Policy\" in the legend."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "42CAhhUvMBTmVDi7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present in the chart at the coordinates [0.185, 0.818, 0.298, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"50.0%\"."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "R9z7qEwExEyGq0M0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.3, 0.118, 0.757, 0.725] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a pie slice of the chart labeled \"Healthcare Policy\"."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "4wgSwWqJ3xXvFOU9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with {the 'Education Policy' label}[0.867, 0.071, 0.981, 0.091] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with the 'Education Policy' label is '#ff9999'."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "LF3guHurppjfxBVq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does {the legend item 'Healthcare Policy'}[0.867, 0.101, 0.987, 0.121] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'Healthcare Policy' legend item corresponds to the slice with coordinates [0.3, 0.118, 0.757, 0.725]."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "JBpXDnSDYeS4CCmB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich labels are associated with {the pie slice at coordinates}[0.223, 0.278, 0.68, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels associated with the slice are \"Education Policy\"[0.36, 0.676, 0.404, 0.696] and \"50.0%\"[0.185, 0.818, 0.298, 0.838]."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "DR86jOXIPnGIADrb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the elements in the region [0.158, 0.053, 0.805, 0.979]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the main part of the chart. It includes the pie chart slices, the labels for 'Education Policy', 'Healthcare Policy', their respective percentages '50.0%', and the legend."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "10QXglWNmfAQX7MF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are present in the region [0.223, 0.118, 0.757, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is occupied by the pie chart which includes two pie slices labeled \"Education Policy\"[0.36, 0.676, 0.404, 0.696] and \"Healthcare Policy\"[0.576, 0.307, 0.62, 0.327], along with their respective percentage labels \"50.0%\"[0.185, 0.818, 0.298, 0.838] and \"50.0%\"[0.681, 0.166, 0.802, 0.186]."
+            }
+        ],
+        "id_raw": 757
+    },
+    {
+        "id": "hrZFAaKl8UcYK8Jg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text found at [0.594, 0.672, 0.647, 0.692] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"AI and Machine Learning\"."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "GvTeqHF0nyD1Wq4V",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at [0.283, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart denoting \"Telemedicine\"."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "8eeJC4xzo78UXyj2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at [0.816, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"AI and Machine Learning\" in the legend."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "KlNuhcghMi3m2Srj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to the {text in the legend} [0.816, 0.101, 0.906, 0.121]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.283, 0.278, 0.482, 0.516] corresponds to it."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "0wz8ZSSp6Kvl9k0J",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage denoted next to the {slice} [0.242, 0.17, 0.761, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent text \"100.0%\" [0.72, 0.791, 0.891, 0.811] indicates the percentage."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "PguqLkRZiuNdEquD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is paired with the color of the {legend handle} [0.777, 0.073, 0.805, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"AI and Machine Learning\" [0.816, 0.071, 0.987, 0.091] is paired with it."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "ZdnOxEQyNA81jQO1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.283, 0.278, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart, which denotes the usage of \"Telemedicine\". The slice is labeled as \"0.0%\" [0.173, 0.244, 0.263, 0.264]."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "swvUFGfC2VmZvMkR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.242, 0.17, 0.761, 0.91]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice with the text \"AI and Machine Learning\" [0.594, 0.672, 0.647, 0.692] and its associated percentage \"100.0%\" [0.72, 0.791, 0.891, 0.811]."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "GaAW4ZeUw2qOuS7i",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region of [0.771, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, displaying two items: \"AI and Machine Learning\" [0.816, 0.071, 0.987, 0.091] and \"Telemedicine\" [0.816, 0.101, 0.906, 0.121], each with a corresponding color marker."
+            }
+        ],
+        "id_raw": 760
+    },
+    {
+        "id": "zOsypnn0IkcRYss0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.939, 0.071, 0.987, 0.091] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Laptop\"."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "ftKfz2ccfqzViVTK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.223, 0.134, 0.765, 0.878] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "ixZNEK0iZWjmcwJ6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at coordinates [0.338, 0.021, 0.625, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Product Sales Observation in 2020\"."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "QV38k2Rv7veTe0nr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which pie slice does the {autotext}[0.776, 0.362, 0.819, 0.382] belong?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to the {slice}[0.308, 0.134, 0.765, 0.874]."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "sOLiqO8Wh8N5sz7i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice corresponds to the {legend item}[0.939, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice for \"Laptop\" with coordinates [0.223, 0.278, 0.482, 0.878] corresponds to the legend item."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "B2qGFsuh07hTqxCg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects with coordinates [0.776, 0.362, 0.819, 0.382] and [0.631, 0.422, 0.675, 0.442]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Both are texts in different slices of the pie chart, indicating percentages."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "KoztdrUVjm6SmrU0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.278, 0.482, 0.878]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart pertaining to \"Laptop\" product sales, demonstrated in the color '#ff9999'. Within this slice, the percentage value \"32.7%\" is present at coordinates [0.164, 0.637, 0.212, 0.657]."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "Dk307OsrsQDpnWsX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.308, 0.134, 0.765, 0.874]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of the chart depicting \"Phone\" product sales. Inside this slice, the text \"67.3%\" appears at coordinates [0.776, 0.362, 0.819, 0.382]. Also, the text \"Phone\" is located at coordinates [0.631, 0.422, 0.675, 0.442]."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "gN14O1xYZ1cGqHst",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_161.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is notable about the region containing coordinates [0.895, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, which includes two items. The \"Laptop\" text is located at [0.939, 0.071, 0.987, 0.091], and the \"Phone\" text can be found at [0.939, 0.101, 0.982, 0.121]."
+            }
+        ],
+        "id_raw": 761
+    },
+    {
+        "id": "AJPMUqpJukbpzYEQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the sublegend of the chart."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "LZty4KPgxKLoAE70",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie slice at [0.225, 0.516, 0.482, 0.83] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This pie slice represents the year {2015}<r>[0.329, 0.626, 0.373, 0.646]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "TnsAFZDOaYZd7JdP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.048, 0.021, 0.916, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, titled \"Tourism & Hospitality Industry - Tourist Arrivals (in thousands) & Hotel Occupancy(%) from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "6RXU8IOA0QIespwi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color in the legend corresponding to the pie slice for {2020}[0.283, 0.146, 0.513, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the legend item {2020}<r>[0.952, 0.251, 0.987, 0.271]</r> is '#c2c2f0', corresponding to the slice for 2020."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "P8PY7tay2gYFDauo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the autotext located in relation to the pie slice for {2014}[0.223, 0.278, 0.482, 0.564]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {13.2%}<r>[0.173, 0.392, 0.208, 0.412]</r> is located to the left of the pie slice for 2014."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "ZktUYTVxrjXRYtX7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color for the legend item corresponding to the pie slice for {2019}[0.482, 0.149, 0.706, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the legend item {2019}<r>[0.952, 0.221, 0.987, 0.241]</r> is '#ffb3e6', corresponding to the pie slice for 2019."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "mcMkib03r5FGac9b",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region from [0.223, 0.146, 0.741, 0.891]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains all the pie slices for different years."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "R4fJEdmko0e2C7Jr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by [0.908, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sublegend of the plot, with items giving color reference for each year from 2014 to 2020."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "5XHad4JnloN80zqw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_162.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are contained in the region from [0.344, 0.516, 0.569, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for {2016}<r>[0.344, 0.516, 0.569, 0.886]</r>, as well as its corresponding text {2016}<r>[0.443, 0.727, 0.487, 0.747]</r> and autotext {14.3%}<r>[0.415, 0.911, 0.451, 0.931]</r>."
+            }
+        ],
+        "id_raw": 762
+    },
+    {
+        "id": "wFrJUXMu2UwN3MKM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the primary object located at coordinates [0.3, 0.021, 0.663, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle that reads \"Hipster vs Grunge Trend from 2011 to 2018\"."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "xfDLOfMtugGuWyxV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat purpose does the object at coordinates [0.908, 0.063, 0.993, 0.309] serve in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart, used to decode the meaning of different colors used in the pie chart."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "LpzGFKaWWDT4NjYZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.482, 0.516, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the center of the pie chart."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "tWnNLAbLhSZ30xt0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item relates to the slice {the slice}[0.242, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2011\"}[0.952, 0.071, 0.987, 0.091] corresponds to the given slice."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "HFYg2CmxivSb5nym",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me which slice in the chart is associated with {the legend item}[0.952, 0.281, 0.987, 0.301]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with the legend item \"2018\" has coordinates [0.283, 0.164, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "KSVGeQH345bMlLhC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart has the same color as the {legend's handle}[0.913, 0.133, 0.941, 0.147]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}[0.223, 0.516, 0.482, 0.719] has the same color as the given legend's handle."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "aoax8MRwbig0P0ev",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region specified by the coordinates [0.482, 0.516, 0.739, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists predominantly of a segment of the pie chart with color '#ffcc99' representing the year 2014. The region includes the autotext showing a value of \"14.7%\" [0.723, 0.721, 0.759, 0.741]."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "vCfCS1ul5xcgKo6t",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the contents within the region [0.283, 0.164, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mostly consists of a segment of the pie chart with color '#ffb3e6' that depicts the year 2018. In this region, an autotext shows \"8.9%\" [0.287, 0.168, 0.323, 0.188]."
+            }
+        ],
+        "id_raw": 763
+    },
+    {
+        "id": "U7yfKdcE5BaujGBR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.273, 0.021, 0.69, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, \"Gaming Industry Development from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "YIAqoU2YP3yZTgWK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.952, 0.071, 0.987, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2014\" within the legend."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "HFKThOkzaDZQguW8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart element does the set of coordinates [0.223, 0.146, 0.762, 0.922] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "These coordinates refer to the central pie chart."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "Bsc4MfhpnB2Rae65",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend corresponds to the area enclosed by the coordinates [0.223, 0.474, 0.482, 0.702]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend {item \"2015\"}<r>[0.952, 0.101, 0.987, 0.121]</r> is associated with this region."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "z54qMXQvEE3pa4Xg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to {the slice}[0.503, 0.155, 0.762, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item correlated with this slice is {the text \"2019\"}<r>[0.952, 0.221, 0.987, 0.241]</r>."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "JYlUxVSyzp43gs7T",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chronological progression of the slices of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices follow the progression {\"2014\"}<r>[0.952, 0.071, 0.987, 0.091]</r>, {\"2015\"}<r>[0.952, 0.101, 0.987, 0.121]</r>, {\"2016\"}<r>[0.952, 0.131, 0.987, 0.151]</r>, {\"2017\"}<r>[0.952, 0.161, 0.987, 0.181]</r>, {\"2018\"}<r>[0.952, 0.191, 0.987, 0.211]</r>, {\"2019\"}<r>[0.952, 0.221, 0.987, 0.241]</r>, {\"2020\"}<r>[0.952, 0.251, 0.987, 0.271]</r>."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "AxKXTcGr7Py9yS2O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.223, 0.146, 0.762, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart. Each slice of this pie chart represents a year from 2014 to 2020, with certain slices being exploded for emphasis. For instance, the slice for the year 2017 is exploded and is identified by its coordinates {\"2017\"}<r>[0.425, 0.552, 0.668, 0.922]</r>."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "iJ8C3vw3VHgpUKZm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region enclosed by [0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend, which has seven items. Each item corresponds to a different year from 2014 to 2020. The first item is {\"2014\"}<r>[0.952, 0.071, 0.987, 0.091]</r>, and the last is {\"2020\"}<r>[0.952, 0.251, 0.987, 0.271]</r>."
+            }
+        ],
+        "id_raw": 764
+    },
+    {
+        "id": "kdZ4FzkEtGeHO5DB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.295, 0.021, 0.668, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is entitled \"Human History and Civilization Development\"."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "9mHd7xGeJ28mhjO3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.803, 0.073, 0.831, 0.087]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend color patch associated with the \"Stone Age\"."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "NlwxWxqWPLN3p11h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.766, 0.483, 0.909, 0.503] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text representing the percentage \"30.0%\"."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "JYONMfoTyXkDgZDR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are placed next to the {slice designated as Renaissance}[0.343, 0.553, 0.647, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices associated with {Medieval Age}[0.24, 0.516, 0.482, 0.827] and {Industrial Revolution}[0.482, 0.205, 0.741, 0.803] are placed next to the chosen slice."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "hDz1HLujQJcFi6VV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items are directly linked with {the pie slice corresponding to the Industrial Revolution} [0.482, 0.205, 0.741, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Industrial Revolution} [0.842, 0.221, 0.985, 0.241] is directly linked to the chosen slice."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "1ZUnhtRkkGo5zpMH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near the {subtitle \"Human History and Civilization Development\"} [0.295, 0.021, 0.668, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {Stone Age} [0.842, 0.071, 0.913, 0.091] and {Bronze Age} [0.842, 0.101, 0.921, 0.121] are located near the subtitle."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "tm5GeRe5JIrml3Ry",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.797, 0.063, 0.993, 0.368]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the legend of the chart, containing ten different legend items with their respective color patches and labels. Texts such as {Stone Age} [0.842, 0.071, 0.913, 0.091] and {Bronze Age} [0.842, 0.101, 0.921, 0.121] can be found here."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "eMxQXbGeGB0uWOkO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.223, 0.146, 0.741, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with slices representing different phases of human history and civilization development such as {Stone Age} [0.255, 0.278, 0.482, 0.516] and {Bronze Age} [0.225, 0.335, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 765
+    },
+    {
+        "id": "vpiXbWoPHrUTQOtJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.213, 0.021, 0.75, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle, which reads, \"Education Resource Allocation and Utilization from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "9yyBF4drEaE4kwyr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the coordinates [0.908, 0.063, 0.993, 0.338]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "hVs7WRiObv1xuWpv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.223, 0.116, 0.741, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main pie chart."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "rMmVs9c2Mn2kN0JQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color code adjacent to {the 2016 label}[0.952, 0.191, 0.987, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code is #c2c2f0."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "WKZY9IhrS3vDHVIx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the years adjacent to {year 2015}[0.605, 0.586, 0.649, 0.606] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent years are {2014}[0.457, 0.728, 0.501, 0.748] and {2016}[0.552, 0.323, 0.587, 0.343]."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "sj7rKddc65OLgJbh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are adjacent to the {subtitle}[0.213, 0.021, 0.75, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects adjacent to the subtitle are the xlabel and the pie chart."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "5jP4aoGAmatlDUq7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.428, 0.178, 0.664, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2016, with its color code #c2c2f0, and the corresponding proportion \"5.8%\" at coordinates [0.643, 0.17, 0.678, 0.19]."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "rwioBitCzxPSqw29",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.482, 0.406, 0.741, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2014, with its color #99ff99, and the corresponding proportion \"21.3%\" at coordinates [0.747, 0.653, 0.783, 0.673]."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "MhZvvAT30x4ZeTii",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.329, 0.153, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses the pie slice representing the year 2019, which is colored #ffb3e6, along with the text \"7.0%\" at coordinates [0.333, 0.133, 0.368, 0.153]."
+            }
+        ],
+        "id_raw": 766
+    },
+    {
+        "id": "v3eoC1uCBMpVlbD2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.311, 0.021, 0.652, 0.047] referred to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, called \"Consumer Behavior in Emerging Markets\"."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "ShdCbMd6SlqRKRHQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.903, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a legend item, \"South Africa\"."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "4GmDeRDPK0rrM4sM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at [0.498, 0.912, 0.537, 0.932] in the chart indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label displaying the percentage \"26.0%\"."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "ET4uyGhHlhgXo7VP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does {the Mexico legend handle}[0.864, 0.163, 0.892, 0.177] in the chart correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to the Mexico legend handle is {the slice}[\\[0.496, 0.127, 0.696, 0.485]]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "MISCZjBNoKpvUx9A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with {the pie slice}[0.223, 0.146, 0.548, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"South Africa\" is associated with that slice."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "BNIBOV7YjjNDg40k",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices have autotexts located near them?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices for \"China\", \"Brazil\", \"India\", and \"South Africa\" have autotexts {[0.498, 0.912, 0.537, 0.932], [0.767, 0.509, 0.806, 0.529], [0.163, 0.529, 0.197, 0.549], [0.311, 0.118, 0.395, 0.138]} located near them respectively."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "xeCPWRpYX7yzZXLJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.482, 0.282, 0.741, 0.754]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the slice for \"Brazil\" with a corresponding percentage text '22.0%' nearby located at [0.767, 0.509, 0.806, 0.529]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "KbDQyySCii2nTSeM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is observed in the region [0.223, 0.278, 0.482, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice for \"India\" along with the corresponding percentage text '24.0%' at [0.163, 0.529, 0.197, 0.549]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "KwHr4b21kXihWClf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat makes up the region [0.223, 0.127, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes multiple pie slices and their corresponding percentage labels for different countries: \"India\", \"China\", \"Brazil\", \"Mexico\", and \"South Africa\". Their respective coordinates are [0.223, 0.278, 0.482, 0.784], [0.303, 0.516, 0.68, 0.886], [0.482, 0.282, 0.741, 0.754], [0.496, 0.127, 0.696, 0.485], [0.283, 0.146, 0.548, 0.516]."
+            }
+        ],
+        "id_raw": 771
+    },
+    {
+        "id": "JSfoW7upLNLCLP7v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at the coordinates [0.314, 0.021, 0.649, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Market Share of Different Building Types\"."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "KbAWyrq2v40dVr6a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.228, 0.918, 0.365, 0.938]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"60.0%\"."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "hIbWejAWZPpyFdG3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.843, 0.191, 0.927, 0.211] in the plot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Skyscrapers\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "Mx49knkEEqW6TWOk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is associated with {the label \"Commercial Building\"}[0.843, 0.131, 0.987, 0.151] in the plot legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color associated with \"Commercial Building\" is green as indicated by the corresponding {color patch}[0.804, 0.133, 0.832, 0.147]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "Yh7uK1wFSWGGQAa9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds to {the legend \"Industrial Building\"}[0.843, 0.161, 0.969, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the \"Industrial Building\" is the one located at {these coordinates}[0.318, 0.157, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "7zgsPr8tmNPOyKRZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow do {the legend item \"Single Family Home\"}[0.843, 0.071, 0.98, 0.091] and the slices relate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Single Family Home\" corresponds to the {pink slice with label \"60.0%\"}[0.213, 0.312, 0.73, 0.92] in the chart."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "ti4H6blULlUAoQl6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region defined by [0.419, 0.146, 0.5, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region defined by these coordinates includes a slice of color green {slice}[0.419, 0.146, 0.5, 0.516] with the percentage representation \"5.0%\" {percentage}[0.313, 0.1, 0.457, 0.12] and associated label \"Commercial Building\" {label}[0.451, 0.285, 0.486, 0.305]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "Uv5HrbySZUiuATIH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region defined by [0.213, 0.312, 0.73, 0.92]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The contents in the region include a pink colored slice {slice}[0.213, 0.312, 0.73, 0.92] of a pie chart which represents \"Single Family Home\" {label}[0.392, 0.746, 0.436, 0.766] with a market share of \"60.0%\" {percentage}[0.228, 0.918, 0.365, 0.938]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "dpzkcp4hqSLmTnST",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents of the region [0.318, 0.157, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice {slice}[0.318, 0.157, 0.482, 0.516] with the percentage \"7.0%\" {percentage}[0.228, 0.142, 0.354, 0.162] displayed, and labeled as \"Industrial Building\" {label}[0.395, 0.307, 0.43, 0.327]."
+            }
+        ],
+        "id_raw": 773
+    },
+    {
+        "id": "VW0XeqSvGG2Is4tH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.312, 0.021, 0.651, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the title, named Social Culture and Value Change in 2019."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "iCUjXo5y73Pfjfas",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items in the legend located at coordinates [0.785, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The items are Social Cultural Change and Value Change."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "1yD9fq3Eu4K4j3oG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at the given coordinates [0.586, 0.885, 0.745, 0.905] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"84.2%\" is located there."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "ktJU6ZWZINpyxfn3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {Social Cultural Change}[0.829, 0.071, 0.987, 0.091] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the slice at [0.223, 0.148, 0.741, 0.886]."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "CTq4ws4s40jIKwKa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the {Value Change slice}[0.274, 0.111, 0.503, 0.482] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {Social Cultural Change slice}[0.223, 0.148, 0.741, 0.886] is adjacent to it."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "6HRZzrnjFtkAqZiS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does the text \"15.8%\" correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.8%\" corresponds to the legend item {Value Change}[0.829, 0.101, 0.925, 0.121]."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "217Zt5iRopCGTrUg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region within the coordinates [0.223, 0.111, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the Social Cultural Change slice in the pie chart. It is colored '#ff9999', and its label \"Social Cultural Change\" is at [0.517, 0.712, 0.561, 0.732]. The percentage indication \"84.2%\" is located at [0.586, 0.885, 0.745, 0.905]."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "TXLOogRw72vhowOF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow can you describe the region within the coordinates [0.274, 0.111, 0.503, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the Value Change slice of the pie chart. It is colored '#66b3ff' and has exploded from the pie. Its label \"Value Change\" is at coordinates [0.393, 0.265, 0.437, 0.285], and the relevant percentage \"15.8%\" is located at [0.272, 0.093, 0.367, 0.113]."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "LXm5XSld9CMTaZrc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area of coordinates [0.785, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents the legend of the chart. It contains two items, \"Social Cultural Change\" placed at coordinates [0.829, 0.071, 0.987, 0.091], and \"Value Change\" laid out at the location [0.829, 0.101, 0.925, 0.121]. The items are color-matched with the slices of pie chart."
+            }
+        ],
+        "id_raw": 774
+    },
+    {
+        "id": "eWjgQrBAPUHd8ymP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.291, 0.021, 0.681, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Knowledge Economy and Innovation Industries\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "VTECbZnlTnFHMH8g",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object situated at coordinates [0.849, 0.071, 0.992, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "zT6VTB9e7R9YeuUC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found on [0.849, 0.34, 0.898, 0.36] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"EdTech\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "PdEZUhT5z4Ug7nOZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice corresponding to {the legend item \"Artificial Intelligence\"}[0.849, 0.071, 0.992, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #ff9999."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "JGl4ZxurqDGJP0Yl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat 'autotext' is associated with {the slice}[0.241, 0.516, 0.486, 0.861]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 'autotext' is \"13.8%\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "lKHKlGSpTros1t25",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the label associated with {the slice}[0.392, 0.516, 0.571, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is #99ff99."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "A9nSxCPgnRC7lONs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region from coordinates [0.752, 0.654, 0.802, 0.674]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is part of Fintech slice, where the autotext \"13.8%\" is located."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "nCSKIjhBX3LygZj1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the area encompassed by these coordinates [0.645, 0.168, 0.722, 0.188]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the autotext \"5.2%\", which is part of the 3D Printing slice."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "QvQDvZyxBowTy80M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.248, 0.202, 0.297, 0.222]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is located on the EdTech slice and contains its associated autotext \"4.7%\"."
+            }
+        ],
+        "id_raw": 775
+    },
+    {
+        "id": "aCoyL9Uj6hXP5v6A",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at coordinates [0.211, 0.021, 0.752, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Tech Adoption Rates in Future Society & Living Scenarios in 2021\"."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "XeUH08WmFIjTyVGw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the details of the object at coordinates [0.797, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart legend containing labels for 5G, Blockchain, Quantum Computing, AI, and IoT."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "1U9vtc9TzxHRObeR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is depicted at the coordinates [0.322, 0.516, 0.584, 0.886] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart representing Blockchain."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "0lAnxSjKKRaqMd6A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the object {at}[0.803, 0.073, 0.831, 0.087] and the object {at}[0.197, 0.282, 0.456, 0.811]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The square at the first set of coordinates represents the color of the 5G slice on the pie chart at the second set of coordinates."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "SCIAJBpJ2HqtPd4b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any slices in the pie chart adjacent to {the slice}[0.322, 0.516, 0.584, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slices for Quantum Computing [0.482, 0.516, 0.669, 0.856] and 5G [0.197, 0.282, 0.456, 0.811] are adjacent to it."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "DCO1VXNrYIDqqrzr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage adjacent to the {slice}[0.507, 0.179, 0.766, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage is 29.8%<r>[0.788, 0.43, 0.801, 0.45]</r>."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "5VrylJr2J30bEUes",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is inside the region with the coordinates [0.197, 0.146, 0.766, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the entire pie chart, which contains five slices representing different techies, including 5G, Blockchain, Quantum Computing, AI, IoT. Each slice has its corresponding color, percentage, and label."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "fpT8zHgr0uI9pBln",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_176.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you see in the region [0.797, 0.063, 0.993, 0.219]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend with colored squares indicating the colors of the pie chart's slices and their corresponding technology labels."
+            }
+        ],
+        "id_raw": 776
+    },
+    {
+        "id": "GjlBf4qiWPQmpmHd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text written at the coordinates [0.297, 0.021, 0.678, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "KzeiGdHYNkyAiJkq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.783, 0.063, 0.999, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend area."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "br5YUx3b2XbSAZbj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.229, 0.146, 0.755, 0.916] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "0tyhNYz3nKeZNc00",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item matches with the slice located at coordinates [0.289, 0.215, 0.488, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \u201cCommunication\u201d<r>[0.828, 0.281, 0.937, 0.301]</r> matches with it."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "yoJ62WWD8yJJWA1R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext is located within the exploded slice located at coordinates [0.485, 0.546, 0.755, 0.916]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"22.5%\"<r>[0.673, 0.863, 0.834, 0.883]</r> is located within the slice."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "LeT0ip6iAWopAzI0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend color matches the pie slice [0.229, 0.278, 0.488, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color matches the legend item \"Training\"<r>[0.789, 0.073, 0.817, 0.087]</r>."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "fhpiuaTr3AwH8jrW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within coordinates [0.229, 0.146, 0.755, 0.916] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises of a pie chart with various slices representing areas of efficiency improvement potential. Each slice is labeled and color coded, with percentages displayed within or near each area. For example, the slice for \"Training\"<r>[0.229, 0.278, 0.488, 0.516]</r> represents 10.0%<r>[0.168, 0.353, 0.223, 0.373]</r> of the chart."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "Ld3loqJmA1T326Xl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_177.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.783, 0.063, 0.999, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart. Each item in the legend has a color square and a text label. For instance, \"Training\"<r>[0.828, 0.071, 0.883, 0.091]</r> is an item of the legend whose matching color is given at<r>[0.789, 0.073, 0.817, 0.087]</r>."
+            }
+        ],
+        "id_raw": 777
+    },
+    {
+        "id": "8ywMGmMDmYSQj9yr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.192, 0.021, 0.771, 0.047] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which reads \"Ecological Environment Sustainability Assessment from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "Y7zLor7xNWhH0eOP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.875, 0.063, 0.993, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "S0OmiCkZgJwpVBXN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at location [0.919, 0.071, 0.983, 0.091] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"AQI 2016\"."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "w49f8Dd4GMDWD1OR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie is next to {the slice}[0.228, 0.278, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.223, 0.438, 0.482, 0.631] which belongs to AQI 2017."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "wy1bg8le6iUXkLYa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the legend}[0.875, 0.063, 0.993, 0.368] and the pie slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each item in {the legend}[0.875, 0.063, 0.993, 0.368] corresponds to a slice in the pie chart, representing different ecological indexes in different years."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "OMnNcJPkbVabpLvz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend pieces correspond to the chart with colors '#ff9999' and '#800000'?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"AQI 2016\" and \"WQI 2020\" correspond to '#ff9999' and '#800000' respectively."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "D3r41TwK8zeJ9ZQw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region [0.482, 0.516, 0.678, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice \"AQI 2020\" of the pie chart along with its corresponding percentage denoted by the text \"12.6%\"[0.609, 0.87, 0.672, 0.89]."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "vi5MsgndTGbPqxZw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.223, 0.146, 0.767, 0.886] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the entire pie chart representing the Ecological Environment Sustainability Assessment from 2016 to 2020. It includes ten slices, each slice representing AQI or WQI for a particular year."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "trnp6nAJC6mePIB4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content in the region [0.875, 0.063, 0.993, 0.368]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region [0.875, 0.063, 0.993, 0.368] contains the legend of the graph. This legend contains ten labels, each label presenting information about Air Quality Index (AQI) or Water Quality Index (WQI) for the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 779
+    },
+    {
+        "id": "264CkxGNFA83VscE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the chart title at these coordinates [0.171, 0.021, 0.792, 0.047] say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title is \"Capital Sources in Innovation and Technology Entrepreneurship Ecosystem\"."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "6o5xOWtoclI3LAWj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at [0.802, 0.223, 0.83, 0.237] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend symbol representing the \"Crowd Funding\" category."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "Fu8MXsmli3pwXF1Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.633, 0.118, 0.741, 0.138]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object here is the text that says \"24.0%\"."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "4m6OvNz3dgOhZvxs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the {sector}[0.432, 0.114, 0.741, 0.484] labeled \"Venture Capital\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The sectors {for \"Private Equity\"}[0.482, 0.404, 0.741, 0.754] and {for \"Crowd Funding\"}[0.318, 0.157, 0.482, 0.516] are adjacent."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "QTI0JPZ6YNt1psT1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the legend item {with coordinates}[0.802, 0.073, 0.83, 0.087] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "This legend item corresponds to the {slice for \"Angel Investors\"}[0.223, 0.278, 0.482, 0.75]."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "CCraufMnLFt3xvSr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located near the {slice}[0.432, 0.114, 0.741, 0.484] in the pie chart say?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text adjacent says \"Venture Capital\" and \"24.0%\"."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "sfKTn9UlSXZjcRX1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented in the region [0.223, 0.114, 0.741, 0.886] overall?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the full pie chart, which represents capital sources in the innovation and technology entrepreneurship ecosystem. It contains various slices to represent categories like Angel Investors, Startup Incubators, Government Funding, Private Equity, Venture Capital, Crowd Funding, and Others. Each slice has a separate color and percentage."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "EpM3S56dI5ISyLx9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the area [0.797, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the pie chart. The legend labels all categories in the pie chart with corresponding color symbols."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "lxdJrsNXNo6hvzR0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.281, 0.516, 0.544, 0.886] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice labeled as \"Startup Incubators\" along with its percentage, 18.0%."
+            }
+        ],
+        "id_raw": 780
+    },
+    {
+        "id": "Zh8yyAtSVSM3aLCp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at location {[0.349, 0.021, 0.614, 0.047]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Global Trade from 2015 to 2020\" is present."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "JkUvdh9EWkjiOZQV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.223, 0.146, 0.766, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a pie chart that visualizes global trade data from 2015 to 2020."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "3YsEPPBUrTp7og4n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell what is depicted in the area marked with [0.908, 0.063, 0.993, 0.249] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend containing year labels and corresponding colors is depicted."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "vG95sLAVUet0j6VX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart are adjacent to the slice {the slice}[0.418, 0.516, 0.669, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}[[0.233, 0.516, 0.482, 0.875], [0.507, 0.37, 0.766, 0.777]] are adjacent to it."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "j4BHxR7vSwfb1Wd5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near the exploded {pie slice}[0.507, 0.37, 0.766, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2018\"}[0.638, 0.549, 0.682, 0.569] is located near the exploded pie slice."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "c4NXw8taHapMSyTE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend items {items in legend}[0.908, 0.063, 0.993, 0.249] relates to the slices on {pie chart}[0.223, 0.146, 0.766, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items each corresponds to a specific slice on the pie chart. For instance, the legend item '2015' with color '#ff9999' matches the slice at coordinates [0.223, 0.278, 0.482, 0.621] with the same color."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "OBYNMQEyN26VYqDJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.223, 0.516, 0.482, 0.875]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart with color '#66b3ff'. It includes the text \"16.5%\" [0.249, 0.799, 0.284, 0.819] and \"2016\" [0.352, 0.666, 0.396, 0.686]."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "HCluOZy0tV0iQe4h",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat details can you provide about the region [0.283, 0.146, 0.485, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a slice of the pie chart colored '#ffb3e6' featuring the text \"14.1%\" [0.328, 0.136, 0.363, 0.156] and \"2020\" [0.395, 0.304, 0.439, 0.324]."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "hAbjYY1SErmBsIpM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain the area bounded by the coordinates [0.507, 0.37, 0.766, 0.777]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents an exploded pie slice painted '#ffcc99', containing the texts \"18.8%\" [0.788, 0.58, 0.823, 0.6] and \"2018\" [0.638, 0.549, 0.682, 0.569]."
+            }
+        ],
+        "id_raw": 782
+    },
+    {
+        "id": "cTQvqaYNyryvQeHy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.064, 0.085, 0.34, 0.111] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle of the first subplot, named 'Tech Innovation vs Intellectual Property - 2020'."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "kGKjCdQJiLMkLnaJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated within the coordinates [0.544, 0.193, 0.85, 0.807] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a pie chart visualizing the data for 'Tech Innovation vs Intellectual Property - 2021'."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "WGiPzDCNZR4afvZa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.346, 0.127, 0.504, 0.193] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a legend for the first subplot."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "kE5jVpkQsE8MnPJV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with this text { \"Intellectual Property\" }[0.193, 0.307, 0.224, 0.327] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Intellectual Property\" is {the slice}[0.085, 0.193, 0.332, 0.5]."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "8NlBGVN605SY1Ycg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the second subplot is related to this text { \"Technological Innovation\" }[0.704, 0.668, 0.735, 0.688]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Technological Innovation\" is {the slice}[0.544, 0.216, 0.85, 0.807]."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "9VuoI6xj5Owp3NLg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.011, 0.117, 0.394, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie chart {pie}[0.049, 0.193, 0.356, 0.807] demonstrating the data for 'Tech Innovation vs Intellectual Property - 2020' along with a subtitle {subtitle}[0.064, 0.085, 0.34, 0.111] and a legend {legend}[0.346, 0.127, 0.504, 0.193]."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "LefNFXZLLnRvqnP8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_183.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are within the region [0.505, 0.117, 0.889, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart {pie}[0.544, 0.193, 0.85, 0.807] which visualizes the data for 'Tech Innovation vs Intellectual Property - 2021', a corresponding legend {legend}[0.84, 0.127, 0.999, 0.193], and a subtitle {subtitle}[0.559, 0.085, 0.835, 0.111]."
+            }
+        ],
+        "id_raw": 783
+    },
+    {
+        "id": "qg6Ovs9lusekMhEP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at [0.176, 0.014, 0.296, 0.04]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Product Sales in 2012\"."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "vbpVCzor6RhRn26K",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the coordinates [0.036, 0.692, 0.066, 0.712]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text located at these coordinates is \"37.5%\"."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "aYgsMi8YXYUBXC4P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content within the area at [0.937, 0.065, 0.955, 0.079]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend color box with color \"#ff9999\" representing the product \"Mobile\"."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "hrcJjLUpoDKNuj48",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the slice labels enclosing {the text \"37.5%\"}<r>[0.036, 0.692, 0.066, 0.712]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice labeled as \"Mobile\"<r>[0.129, 0.606, 0.158, 0.626]</r>."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "zLmLzelERx6aaSEs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the product related to the slice that exploded in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded slice corresponds to the product \"Laptop\"."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "f1yfbsZmkbizeNzO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subplot has the largest representation for the product \"Mobile\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {first subplot}<r>[0.018, 0.046, 0.454, 0.979]</r> has a larger representation for the product \"Mobile\"."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "Xs07JetoX9dQjzz0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.933, 0.056, 0.998, 0.121]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes the chart legend, which contains two entries: a pinkish color box denoting the product \"Mobile\"<r>[0.937, 0.065, 0.955, 0.079]</r> and a bluish color box denoting the product \"Laptop\"<r>[0.937, 0.095, 0.955, 0.109]</r>."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "Dw5Stxb46SUq0Fkb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.524, 0.115, 0.886, 0.885]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents the second subplot which contains a two-slice pie chart. One slice is represented by 'Mobile' and the other by 'Laptop'. The 'Laptop' slice is visibly larger and exploded."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "z6hTEB7RhsUOqkas",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_184.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within [0.062, 0.139, 0.41, 0.885]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a two-slice pie chart representing sales of 'Mobile' and 'Laptop' products for the year 2012. The 'Laptop' slice is visibly larger."
+            }
+        ],
+        "id_raw": 784
+    },
+    {
+        "id": "5JbuRvsp37oyNh2g",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart which can be found in this position [0.31, 0.021, 0.653, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "H0ecl5uOwYNXfIBd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.924, 0.191, 0.982, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item, \"Mexican\"."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "IcTz5SLdLKibiCzZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.536, 0.906, 0.592, 0.926] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the text \"10.2%\"."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "RCE0vEni8AB660g0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart shares the same color and category as {the legend item \"Mexican\"}[0.924, 0.191, 0.982, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at{coordinates}[0.482, 0.172, 0.741, 0.531] shares the same color and category."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "p7sGu0Asg9CJOlhr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the adjacent slices to {the slice}[0.449, 0.516, 0.609, 0.886] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.266, 0.516, 0.482, 0.883], [0.482, 0.516, 0.74, 0.838]] are adjacent."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "ujSL2EX1A4YiEyVD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item has the same color as the slice with coordinates [0.283, 0.152, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Greek\"[0.924, 0.251, 0.966, 0.271] has the same color as the slice."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "OShm75RJ7X4rm6rV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within these coordinates [0.197, 0.276, 0.456, 0.719]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of pie with the text \"Italian\" [0.279, 0.491, 0.322, 0.511] and \"20.4%\" [0.128, 0.481, 0.171, 0.501]. This segment on the pie chart represented the Italian cuisine portion."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "9Cety49lhkN1LzIc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key features within the region [0.266, 0.516, 0.482, 0.883]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of pie with the percentage text \"13.6%\" [0.285, 0.852, 0.331, 0.872] and the cuisine name \"French\" [0.378, 0.695, 0.422, 0.715]. This indicates that the slice represents the French cuisine in the distribution."
+            }
+        ],
+        "id_raw": 785
+    },
+    {
+        "id": "WkfNp7gku9u9YElF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object appearing at coordinates [0.279, 0.021, 0.684, 0.047] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle, reading \"Electric Car vs. Gas Car Sales from 2010 to 2017\"."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "8Ayb6CYZGcfw3lkf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element present at coordinates [0.908, 0.063, 0.993, 0.309] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's legend."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "Td1kLeJLfTs0ojPz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPoint out the object located at coordinates [0.186, 0.342, 0.221, 0.362] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"9.1%\"."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "OYSLuvmOCotpVsKt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the legend, what color patch corresponds to the year {2013}[0.952, 0.161, 0.987, 0.181]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color patch corresponding to the year 2013 is colored in {#ffcc99}[0.913, 0.163, 0.941, 0.177]."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "6trnwVZCbBCiKTzF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice in the chart has its corresponding information displayed right next to {the legend item showing the year 2010}[0.952, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with its corresponding information displayed right next to the legend item showing the year 2010 is the one located at {coordinates}[0.225, 0.278, 0.482, 0.516]."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "X89oPQemnpcCF4dW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you point out the pie slice in the chart that corresponds to the {legend item showing 2016}[0.952, 0.251, 0.987, 0.271]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to the legend item showing 2016 is at {coordinates}[0.482, 0.15, 0.716, 0.516]."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "oz4VrouFpdUeDjtQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features lying within the region covered by coordinates [0.482, 0.516, 0.741, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises slices of the pie chart representing years from 2014 to 2017. The autotext for each slice, showing the percentage for each year, is also within this region."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "j2wkJppWBOgwfCiF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you indicate what's inside the region defined by coordinates [0.223, 0.146, 0.482, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices of the pie chart symbolizing years from 2010 to 2012. Autotext providing the percentage for each year is also part of this region."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "6795KsSHFzcGxE0P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat appears in the region marked by the coordinates [0.908, 0.063, 0.993, 0.309]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend for the chart, which includes handle-color arrays corresponding to different years from 2010 through 2017."
+            }
+        ],
+        "id_raw": 788
+    },
+    {
+        "id": "1nFK845JqCQRc61R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the box with coordinates [0.104, 0.454, 0.199, 0.474]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the percentage text \"18.2%\"."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "As6sW3Mcg2VjUXuc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item present at the coordinates [0.797, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend for various types of cybersecurity threats."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "2V1rjmbF2WYjQOiS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is indicated by the coordinates [0.356, 0.021, 0.607, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the subtitle called \"Cybersecurity Threats in 2017\"."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "6l0HXwnsC8F3rper",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich auto text is related to {the slice}[0.482, 0.434, 0.741, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the auto text \"11.1%\" located at [0.764, 0.557, 0.895, 0.577]."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "NYAUUoe0R0myWdus",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is affiliated to {the legend item \"Data Breaches\"}[0.841, 0.221, 0.943, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's correlated with the color '#c2c2f0' from the legend item handle."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "zi9efehHve3Qef38",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does {the slice \"Data Breaches\"}[0.485, 0.116, 0.75, 0.487] relate to {the legend item \"Data Breaches\"}[0.841, 0.221, 0.943, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice \"Data Breaches\" corresponds to the legend item \"Data Breaches\" as both represent the same cybersecurity threat in the pie chart."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "TT8PfoXOU11CUMzr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.223, 0.116, 0.75, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The large region encompasses the entire pie chart, which is separated into several slices each representing a different type of cybersecurity threat. Each slice has a unique color, a percentage text, and a label text associating it with a particular type of threat also found in the chart legend."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "8EidlSFBU1E4UYhy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the content within the region [0.797, 0.063, 0.993, 0.279]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend for the pie chart. The legend itemizes the various types of cybersecurity threats represented in the pie chart. Each threat has a unique color and a label written next to it."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "riFgz74qWxfwAUa8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects inside the region [0.152, 0.516, 0.482, 0.516]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's an empty region; it does not contain any objects or texts, only a portion of the background."
+            }
+        ],
+        "id_raw": 789
+    },
+    {
+        "id": "WkZyF4iCaPaBb6Fg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.235, 0.088, 0.79, 0.114] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's subtitle, which says \"New Energy and Clean Technology Development in 2016 and 2017\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "ePBIZ4n4eARrkSD8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.632, 0.202, 0.72, 0.222] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"19.1%\"."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "O3VBrhmQZMNIMRQR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen at the coordinates [0.803, 0.287, 0.903, 0.307] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the label for \"Nuclear Power\" in the legend."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "etrvzuxuE66cyMjP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is associated with {the legend item that reads \"Solar Power\"}[0.803, 0.138, 0.885, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with \"Solar Power\" is located at [0.297, 0.307, 0.512, 0.757]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "hqhhez811bz9QjRc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to {the \"Geothermal Power\" label}[0.803, 0.228, 0.931, 0.248] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to \"Geothermal Power\" has the coordinates [0.457, 0.167, 0.509, 0.475]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "xzzEcVfwKxHbSFFX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie chart slice is associated with the {label \"BioEnergy\"}[0.803, 0.258, 0.873, 0.278] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"BioEnergy\" label corresponds to the pie chart slice with the coordinates [0.418, 0.177, 0.506, 0.476]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "Z7M1ta7kOnpddIxO",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features are within the region determined by the coordinates [0.803, 0.168, 0.884, 0.188]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the \"Wind Power\" legend item, with handle represented by [0.764, 0.17, 0.792, 0.183]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "gAtpB1LNynfbqf6w",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the contents of the region defined by [0.803, 0.138, 0.885, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend item \"Solar Power\", with handle marked by the coordinates [0.764, 0.14, 0.792, 0.154]."
+            }
+        ],
+        "id_raw": 790
+    },
+    {
+        "id": "TJqMDrk8cRD49xeH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.299, 0.021, 0.664, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Government Policy Effectiveness Evaluation\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "yf9ipJtjnGRwXOUm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.891, 0.071, 0.987, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend's label, \"Foreign Policy\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "HBJqyMwbIj96IXfX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object occupies the coordinates [0.213, 0.146, 0.741, 0.92] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main pie chart."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "mvm4Sg75BRxutVZV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart {the slice}[0.213, 0.312, 0.73, 0.92] corresponds to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Foreign Policy\" slice of the legend is corresponding to {the slice}[0.213, 0.312, 0.73, 0.92]."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "SR9EQaVRRZkufg6V",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is next to the {handle}[0.852, 0.103, 0.88, 0.117] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Tax Policy\" is next to it."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "bAUaq2GBppvjyl1O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation of the pie slices to the {legend}[0.847, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices of the pie chart represent the categories defined in {the legend}[0.847, 0.063, 0.993, 0.129], i.e., \"Foreign Policy\" and \"Tax Policy\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "aULnewfyUQW3MN9N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.213, 0.146, 0.741, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the main pie chart. Inside this region, two slices represent data. One has coordinates [0.213, 0.312, 0.73, 0.92] representing \"Foreign policy\" with a corresponding text \"60.0%\". Another slice located at [0.283, 0.146, 0.741, 0.542] represents \"Tax Policy\" having text \"40.0%\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "xPp6rmThdwnzdRII",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region with boundaries [0.847, 0.063, 0.993, 0.129]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart. Two labels can be seen here: \"Foreign Policy\" and \"Tax Policy\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "f62iOxHCC9fEnn2L",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.213, 0.312, 0.73, 0.92]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a pie slice representing the \"Foreign Policy\" labeled as \"60.0%\"."
+            }
+        ],
+        "id_raw": 796
+    },
+    {
+        "id": "9YKziO3vylrBkCeZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at coordinates [0.266, 0.021, 0.697, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle, called \"AI and Automation Growth Trend from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "JuzmoYckcgZHqBo7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.952, 0.191, 0.987, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend item named \"2019\"."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "tKEFDZWQIlpFkB7l",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.334, 0.88, 0.369, 0.9] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"13.0%\"."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "3DP1hqaSKjxkOuyo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with {the legend item \"2017\"}[0.952, 0.131, 0.987, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with the legend item \"2017\" is {the slice}[0.293, 0.516, 0.482, 0.886]."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "XyEUmtmUcXoSHVoM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the legend}[0.908, 0.063, 0.993, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no text located near the legend in this chart."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "dCI3pF9NxBf2njWo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the position of {the subtitle}[0.266, 0.021, 0.697, 0.047] and {the pie chart}[0.223, 0.111, 0.741, 0.886]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is positioned directly above the pie chart."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "TbJg6HdXP2nigupA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.223, 0.111, 0.741, 0.886]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region hosts a pie chart representing the growth trend of AI and Automation from 2015 to 2021. This region includes different slice elements, each having its own percentage text inside and representing data for different years ranging from 2015 to 2021."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "bMz4d4w56JljGV2m",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.482, 0.156, 0.73, 0.516]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object in this region is a pie slice labeled \"2020\" with the percentage text \"16.7%\"."
+            }
+        ],
+        "id_raw": 798
+    },
+    {
+        "id": "jL1YbHH0jydADwg3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text placed in the coordinates [0.397, 0.02, 0.603, 0.038] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main text is \"Social Networks Usage by Principal and Professor\"."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "LM4eHaCK8rZTnNKu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described at the coordinates [0.65, 0.026, 0.846, 0.044]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It describes \"Interpersonal Relationships Usage by Professor\"."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "p1IKGz3tqjfBxyHO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is written in the region [0.671, 0.736, 0.693, 0.75]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Twitter\" is written."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "esBTp2oe16U8UN4t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is related to the pie slice at {the slice}[0.551, 0.308, 0.914, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"Twitter\"}[0.671, 0.736, 0.693, 0.75] is related."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "MhWSTG9dwDVQGuvB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text corresponds to {the slice}[0.052, 0.142, 0.439, 0.918] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding text is {\"Facebook\"}[0.15, 0.699, 0.172, 0.713]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "tfVpdQq1iZE4Ybk9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text pairs with the slice found at {slice coordinate}[0.052, 0.303, 0.366, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Facebook\"}[0.15, 0.699, 0.172, 0.713] pairs with the slice."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "TXCoTBfo3dRbxAJb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region in the coordinates [0.052, 0.142, 0.439, 0.918]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart region where a pie slice representing \"Facebook\" is located along with its respective data represented as \"47.9%\". The \"Facebook\" text is at coordinates [0.15, 0.699, 0.172, 0.713] and the data \"47.9%\" is at coordinates [0.064, 0.834, 0.096, 0.848]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "YmcDFiVxF7TTjOSL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_4_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between coordinates [0.551, 0.308, 0.914, 0.924] reveal?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region reveals a pie slice represented by \"Twitter\" and it comprises of 55.4% total. The \"Twitter\" label is found at coordinates [0.671, 0.736, 0.693, 0.75] and the corresponding data \"55.4%\" is at coordinates [0.611, 0.898, 0.634, 0.912]."
+            }
+        ],
+        "id_raw": 799
+    },
+    {
+        "id": "wkGUd2EWXUqDgS4i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.084, 0.076, 0.916, 0.101] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle, called \"Sales of Ventilators and Telemedicine Products (2015-2021)\"."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "PTCUqMo5P4LgcMDS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.199, 0.318, 0.199, 0.318] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text, displaying \"3.7%\"."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "DTKiHrePOyBWfSNr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.259, 0.123, 0.809, 0.45]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice with its corresponding text displaying \"32.3%\"."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "fpkhu0nLeqlKuFXx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.423, 0.515, 0.699, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.532, 0.393, 0.859, 0.783] is adjacent."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "vWjZ2SGTnX10H2kY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage representation for {the slice}[0.259, 0.123, 0.809, 0.45]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The representation is \"32.3%\"{at}[0.561, 0.094, 0.561, 0.094]."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "hrqnhXscfr5bg0cK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has the lowest percentage representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at}[0.207, 0.305, 0.5, 0.515] has the lowest percentage representation of \"3.7%\"."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "DTEsPxZxm4V1qz5F",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.173, 0.123, 0.859, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire pie chart in the subplot. It contains 7 slices with various colours and sizes, with each slice representing a percentage of sales in a particular year. The text labels displaying these percentages are also within this region."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "gWmrHRLIZcC0SOu2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.207, 0.305, 0.5, 0.833]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a section of the pie chart with two slices along with three text elements showing the percentages of \"3.7%\", \"6.2%\", and \"12.4%\" respectively."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "3dRvEkeaGlbg6O4N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_4.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are found in the region [0.423, 0.515, 0.699, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart representing the category of \"14.2%\"."
+            }
+        ],
+        "id_raw": 804
+    },
+    {
+        "id": "WZwWLjhUxCnUGWmT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object marks the proportion of countries implementing Financial Stability policies?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {13.5%}<r>[0.228, 0.438, 0.228, 0.438]</r> marks the proportion of countries implementing Financial Stability policies."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "wmHwLjxMWbviCpZy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the color representing Inflation Targeting Policy within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inflation Targeting Policy is represented by the color {#b075b2}<r>[0.49, 0.299, 0.702, 0.512]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "nhV92PTTcBY6wOrD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object contains the value for the slice of countries that have implemented Quantitative Easing?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {6.3%}<r>[0.443, 0.244, 0.443, 0.244]</r> contains the value for the slice of countries that have implemented Quantitative Easing."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "roO2e7fA6NUqtX56",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color code adjacent to the slice representing Monetary Policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color code adjacent to Monetary Policy is {#66b3ff}<r>[0.245, 0.512, 0.49, 0.701]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "Lb4Og4k8Cnjjy2qD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is located closest to the one that specifies the Accommodative Policy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest slice to Accommodative Policy is {Quantitative Easing}<r>[0.3, 0.281, 0.49, 0.512]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "Qd0WWRK6n8y1iNcv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the Fiscal Policy slice in relation to the Interest Rate Policy slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Fiscal Policy slice is {adjacent}<r>[0.245, 0.512, 0.49, 0.701]</r> to the Interest Rate Policy slice <r>[0.242, 0.353, 0.49, 0.549]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "YhZer2IXJf66Qbq6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region where the exploded slice is located."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the exploded slice of Financial Stability, colored in {#99ff99}<r>[0.325, 0.537, 0.573, 0.785]</r>, and the autotext {16.9%}<r>[0.439, 0.805, 0.439, 0.805]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "eWYi8nGPld3i8xkW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found in the upper right quadrant of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The upper right quadrant includes a {Interest Rate Policy} slice <r>[0.49, 0.492, 0.738, 0.652]</r>, its autotext {10.8%}<r>[0.754, 0.582, 0.754, 0.582]</r>, and an {Exchange Rate Policy} slice <r>[0.49, 0.512, 0.695, 0.744]</r>, with its associated autotext {9.8%}<r>[0.659, 0.726, 0.659, 0.726]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "mRKPAdfE86DToWcY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_8.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the contents of the region delimited by the largest slice in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The largest slice, representing {Fiscal Policy}<r>[0.245, 0.512, 0.49, 0.701]</r>, contains autotext {11.4%}<r>[0.252, 0.645, 0.252, 0.645]</r> and is near the slice for {Interest Rate Policy}<r>[0.242, 0.353, 0.49, 0.549]</r>."
+            }
+        ],
+        "id_raw": 808
+    },
+    {
+        "id": "nTiTmaITFfzKP553",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the subtitle with coordinates [0.135, 0.092, 0.865, 0.125] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the title \"Culture and Arts Industry Development\"."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "6ExgWlS1K8wPQMZ8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.186, 0.653, 0.186, 0.653] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"10.4%\"."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "PtYiAw8RuIeBesz7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinate information [0.369, 0.313, 0.443, 0.336] in the chart correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text in the exploded slice of the pie chart {\"\"}<r>[0.369, 0.313, 0.443, 0.336]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "CMxYkTsTjepsIoZE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice close to the slice {at}[0.493, 0.21, 0.746, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates}<r>[0.427, 0.551, 0.587, 0.862]</r> is close to it."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "HHHTGjGvmfoMKYrK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the location of the text in comparison to {the slice}[0.427, 0.551, 0.587, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"\"}<r>[0.508, 0.893, 0.508, 0.893]</r> is located above the slice."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "Wj1SsoiqHvFbfAnU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has a correspondence with {the text}[0.594, 0.642, 0.667, 0.665] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates}<r>[0.5, 0.52, 0.796, 0.818]</r> corresponds to the text \"15.7%\"."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "cN8Qx9KhhNS8kwWI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the features of the region within coordinates [0.5, 0.331, 0.81, 0.613]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart, colored in '#66b3ff'. The associated text located {'inside'}<r>[0.836, 0.461, 0.836, 0.461]</r> displays the percentage \"15.3%\"."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "V2ErRpEPSw3BnYQN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescription of the region [0.249, 0.182, 0.487, 0.492] please."
+            },
+            {
+                "from": "gpt",
+                "value": "This region consists of an exploded slice from the pie chart that is coloured in '#ffcc99'. The percentage associated with this slice is represented by the text {\"13.5%\"}<r>[0.339, 0.184, 0.339, 0.184]</r>."
+            }
+        ],
+        "id_raw": 810
+    },
+    {
+        "id": "HFcA1z2mEhcOm6xO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.053, 0.14, 0.95, 0.174] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, named \"Innovative Businesses in Technology Companies\"."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "o2fzLNSKI4cVlmQ1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.211, 0.453, 0.211, 0.453] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percent label \"15.0%\" of a pie slice."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "3MWAoyYZzUqjU4V4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.793, 0.587, 0.793, 0.587] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percent label \"35.0%\" of a pie slice."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "tWaUpiAkfVU5sd4L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {the slice}[0.502, 0.312, 0.773, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.238, 0.52, 0.567, 0.792], [0.487, 0.249, 0.676, 0.52]] are adjacent."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "WTFzA1TRc320Q29g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage labels are near {the slice}[0.358, 0.249, 0.502, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage labels {\"8.0%\"}[0.411, 0.236, 0.411, 0.236] is near the slice."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "dBKW2Ld3Ds6Ww2Vd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any object adjacent to {the label}[0.306, 0.295, 0.306, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, {the slice}[0.294, 0.29, 0.502, 0.52] is adjacent to the label \"5.0%\"."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "OrR7s7A6sWaCirAV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.23, 0.249, 0.773, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several slices of a pie chart, along with their corresponding percentage labels such as \"15.0%\" [0.211, 0.453, 0.211, 0.453], \"25.0%\" [0.348, 0.776, 0.348, 0.776], \"35.0%\" [0.793, 0.587, 0.793, 0.587], and so on. This region forms the main body of the chart representing the various innovative businesses in different technology companies."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "Cs5gtAKfrOjactUB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_11.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.487, 0.249, 0.676, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart [0.487, 0.249, 0.676, 0.52] and its corresponding percentage label \"12.0%\" [0.596, 0.237, 0.596, 0.237]. This section represents one of the innovative businesses in a technology company."
+            }
+        ],
+        "id_raw": 811
+    },
+    {
+        "id": "5vu1k1vYabXk0iBs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.056, 0.083, 0.944, 0.111] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, and it reads \"Investment Return Rate Comparison from 2012 to 2018\"."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "x1DIgETUmdWCfTYd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.184, 0.312, 0.5, 0.517] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice in the chart with a color of #ff9999. It represents 8.7% of the total."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "LSIWORuzTUXJWXgK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at coordinates [0.482, 0.517, 0.743, 0.837] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice in the chart with a color of #ffcc99 and represents 14.6% of the total."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "yEGhQ9tfnAvI3uuf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.482, 0.517, 0.743, 0.837]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.227, 0.517, 0.5, 0.837], [0.5, 0.501, 0.82, 0.726]] are adjacent."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "CuneL6mx5rFWjTUR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.184, 0.312, 0.5, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slice is {the slice}[0.18, 0.468, 0.5, 0.684]."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "OO2J64WN59G9mYsp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices touch {the slice}[0.248, 0.166, 0.62, 0.486]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.5, 0.224, 0.82, 0.517], [0.184, 0.312, 0.5, 0.517]] touch this slice."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "UDm2vQeM0VPLfpTG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.18, 0.166, 0.5, 0.517]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored with #ff9999, which carves out 8.7% of the pie. This slice's central auto-text label with the value 8.7% is found at coordinates [0.18, 0.372, 0.18, 0.372]."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "ZF7knepBU79K2c4a",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_12.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you identify the objects within the region described by coordinates [0.482, 0.517, 0.743, 0.837]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored with #ffcc99, which occupies 14.6% of the pie. This slice's central auto-text label with the value 14.6% is found at coordinates [0.638, 0.841, 0.638, 0.841]."
+            }
+        ],
+        "id_raw": 812
+    },
+    {
+        "id": "xTJAaju3AACWYixN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.026, 0.17, 0.968, 0.203] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Investment Distribution in Venture Capital Markets."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "nzOLbLJyXywVl1ZY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.738, 0.694, 0.738, 0.694] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"17.8%\"."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "2jk3SDAWR5FsGHOm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.281, 0.687, 0.281, 0.687] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"2.9%\"."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "rVVgyrZVYqQdnVf7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.428, 0.52, 0.59, 0.768] and {the autotext}[0.511, 0.793, 0.511, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"10.7%\" represents the percentage value of the slice in the pie chart."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "gxheB8oafVfdBr9X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the text {autotext}[0.751, 0.365, 0.751, 0.365] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.518, 0.28, 0.766, 0.523] in the pie chart."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "O1UgBw5DwrvN1tuM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is associated with {the autotext}[0.354, 0.752, 0.354, 0.752]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}[0.315, 0.52, 0.497, 0.758] in the pie chart."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "SnQQgOGiP6gXj07s",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.249, 0.272, 0.766, 0.768] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with various slices representing different percentages of venture capital funding in various investment areas. These slices are color-coded and some are exploded for emphasis on their proportion."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "1CsxxUYsy7LRbjsS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects found within the region [0.249, 0.272, 0.766, 0.768]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The regional area contains a pie chart divided into various slices each having an autotext indicating the percentage of a particular sector in the total VC funding. Specific slices have different coordinates and percentage values."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "iYRKEnBeoonOAvY4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_13.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region specified by the coordinates [0.187, 0.21, 0.807, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encapsulates an entire pie chart which is subdivided into distinct sections representing different investment sectors."
+            }
+        ],
+        "id_raw": 813
+    },
+    {
+        "id": "KU7vOUei7YrNqxN4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.143, 0.02, 0.857, 0.067]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the title of the chart, \"Career and Workplace Development Trends in 2014 and 2015\"."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "hP8u4wqcteVhEVeg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.23, 0.064, 0.277, 0.097] in the subplot_0?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2014\" is located there."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "5YCOvycbLBLv3Yqy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned in the coordinates [0.607, 0.542, 0.643, 0.565] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a slice annotation, displaying \"24.0%\"."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "yoVfLoHs6j6u94P2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in subplot_0 is connected to {the autotext}[0.296, 0.167, 0.296, 0.167]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice{the pie's slice}<r>[0.211, 0.192, 0.365, 0.54]</r> is connected to the autotext."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "SCvJ7AqEYfkb8ys3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in subplot_1 are adjacent to {the slice}[0.627, 0.54, 0.848, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}<r>[0.555, 0.318, 0.729, 0.794]</r> and {the slices}<r>[0.747, 0.52, 0.921, 0.823]</r> are adjacent."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "BwJAiOevBOAzJlRR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pieces of autotext are nearest to {the pie center of subplot_0}[0.253, 0.54]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"25.0%\"<r>[0.045, 0.576, 0.045, 0.576]</r> and \"20.0%\"<r>[0.24, 0.922, 0.24, 0.922]</r> are nearest to the center of the pie in subplot_0."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "gne0LZxAbsQnDQd5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.555, 0.192, 0.921, 0.888] in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region is a pie slice. The color of the slice is #ff9999, and it is associated with \"24.0%\"<r>[0.538, 0.563, 0.538, 0.563]</r>, indicating it comprises 24% of the whole pie."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "oDXy91yq1bMoKbBu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_14.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.062, 0.192, 0.427, 0.888] in subplot_0."
+            },
+            {
+                "from": "gpt",
+                "value": "Within the specified region are multiple numbered pie slices. The slice displaying \"25.0%\"<r>[0.045, 0.576, 0.045, 0.576]</r> is color #ff9999 and exploded for emphasis. There are also slices for \"20.0%\"<r>[0.24, 0.922, 0.24, 0.922]</r>, \"15.0%\"<r>[0.417, 0.737, 0.417, 0.737]</r>, \"10.0%\"<r>[0.439, 0.447, 0.439, 0.447]</r>, \"5.0%\"<r>[0.398, 0.288, 0.398, 0.288]</r>, \"15.0%\"<r>[0.296, 0.167, 0.296, 0.167]</r>, and \"10.0%\"<r>[0.152, 0.215, 0.152, 0.215]</r>."
+            }
+        ],
+        "id_raw": 814
+    },
+    {
+        "id": "Auin4oKHo1tBxpsR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at [0.277, 0.434, 0.321, 0.448] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Text mentioning \"Ancient Artifacts\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "Vn4ITA7A5vs7LRmB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the annotation at coordinates [0.545, 0.883, 0.65, 0.897] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"4.3%\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "AnXLoXgCXcfNe97t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify what is placed at coordinates [0.152, 0.054, 0.837, 0.074] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle: \"Worldwide Recognition of Different Types of Cultural Heritage\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "iN0mYq0cFAstLO9W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.495, 0.421, 0.842, 0.756]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices at {coordinates}[[0.495, 0.512, 0.741, 0.847], [0.464, 0.165, 0.83, 0.512]] are adjacent."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "dGPioKsCtNRNth0R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the text \"Old Languages\"}[0.582, 0.685, 0.617, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.495, 0.512, 0.741, 0.847] corresponds to \"Old Languages\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "A8nz57IK64npTIUH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does {the percentage \"22.2%\"}[0.713, 0.192, 0.767, 0.206] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"22.2%\" refers to the slice at coordinates [0.464, 0.165, 0.83, 0.512]."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "YDOuTpB0wbXmSIyx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found in the area defined by coordinates [0.123, 0.165, 0.842, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area covers the entire pie chart, encompassing all heritage types and their corresponding percentages, including but not limited to \"Ancient Artifacts\" and \"Ancient Buildings\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "D1qnYv3xeweDfapZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the elements within the region [0.495, 0.512, 0.741, 0.847]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a pie slice labeled as \"Old Languages\" and attributed with the percentage \"8.3%\"."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "4nCMXjOCUY9FtkMp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_16.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region defined by [0.464, 0.165, 0.83, 0.512]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region captures the pie slice identified as 'Folklore', with an associated percentage of '22.2%'. This slice appears to be a large segment of the pie chart illustrating Folklore's significant recognition worldwide."
+            }
+        ],
+        "id_raw": 816
+    },
+    {
+        "id": "2DKZof0jbhFQMt00",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the object with coordinates [0.021, 0.076, 0.979, 0.109] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, named Healthcare: Medical Technology Usage (2011-2020)."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "PIRf6IfDD5yDrW1t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.383, 0.825, 0.383, 0.825] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text field displaying the percentage \"10.0%\"."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "VcWLjMU2GYDNrwLD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.5, 0.241, 0.794, 0.504] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "z3rSbPIyp233e264",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice adjacent to {the slice}[0.195, 0.305, 0.5, 0.504]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.19, 0.447, 0.5, 0.596] is adjacent."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "k39heqkxmh96SftD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located near {the slice}[0.455, 0.194, 0.665, 0.504]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {11.2%}[0.57, 0.17, 0.57, 0.17] is located near the slice."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "0kaJuQHJgfhjVes5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the chart correspond to each other in terms of the same color?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slice}[0.195, 0.305, 0.5, 0.504] and {the slice}[0.5, 0.405, 0.81, 0.607] are corresponding in terms of the same color."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "8iY4BCG1wAyL7CS3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region bounded by [0.19, 0.194, 0.818, 0.815]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a number of pie slices and their corresponding percentages. Notably, this region contains the slice {the slice}[0.5, 0.241, 0.794, 0.504] and its percentage {10.9%}[0.768, 0.293, 0.768, 0.293]."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "7iesInOifvYLQg34",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents in the region bounded by [0.455, 0.194, 0.665, 0.504]"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart {the slice}[0.455, 0.194, 0.665, 0.504] and its corresponding percentage {the text}[0.57, 0.17, 0.57, 0.17] which is \"11.2%\"."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "Cm8j8BOsJLfWPxYw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_19.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region bounded by [0.309, 0.504, 0.5, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart {the slice}[0.309, 0.504, 0.5, 0.814] along with its percentage {the text}[0.383, 0.825, 0.383, 0.825] which is \"10.0%\"."
+            }
+        ],
+        "id_raw": 819
+    },
+    {
+        "id": "gXvzFu70JprkxkAK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.22, 0.099, 0.774, 0.133] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named Social Demographic Research."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "fTG5NkOJi3mhQQfS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.262, 0.687, 0.317, 0.710] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.3%\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "GkxEnNmjOYE6hLrR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.162, 0.518, 0.162, 0.518] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"22.0%\" of the pie slice."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "RhUkKC3r23RLhVPU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to the {slice with the color code '#ff9999'}[0.192, 0.325, 0.497, 0.713]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice with the color code '#c2c2f0'}[0.497, 0.3, 0.801, 0.716] is adjacent."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "WiKOnHLF9Xdz0wCy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the {the slice}[0.264, 0.216, 0.707, 0.52] related to the autotext \"26.0%\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this slice is \"#ff6666\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "xsb42qxSXDvz3ZPy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the autotext value of the {slice}[0.497, 0.3, 0.801, 0.716] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext value of this slice is \"24.0%\"."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "P0GjmjOGgES8CVEv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.192, 0.325, 0.497, 0.713]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart, colored with the code '#ff9999' and has an autotext of \"22.0%\" with coordinates [0.162, 0.518, 0.162, 0.518]."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "yI20cCRYFfJ8QUbb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.264, 0.216, 0.707, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a pie slice which is represented with the color \"#ff6666\". It has an autotext stating \"26.0%\" located at the coordinates [0.478, 0.186, 0.478, 0.186]."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "W5EL2Eg4z607ekkd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region with coordinates [0.292, 0.551, 0.732, 0.855]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that has exploded out. It is color-coded as '#ffcc99' and has an autotext of \"25.8%\" located at [0.519, 0.885, 0.519, 0.885]."
+            }
+        ],
+        "id_raw": 820
+    },
+    {
+        "id": "hMGLbMX9HovTvcCX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is present at the coordinates [0.235, 0.33, 0.235, 0.33]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at the coordinates is \"2.5%\"."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "4GjHu8LCw0N2EkF6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me the color of {the slice}<r>[0.237, 0.52, 0.754, 0.793]</r> in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice is \"#99ff99\"."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "SXfflJabtC4KTk4b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the pie slice}<r>[0.246, 0.329, 0.479, 0.504]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the pie slice is \"#ff9999\"."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "zGEPgGuwlczBFCek",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text near to {the pie slice}<r>[0.501, 0.267, 0.774, 0.622]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"25.0%\", located at the coordinates [0.778, 0.403, 0.778, 0.403], is near to the specified pie slice."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "vqbOfxve7c65d5K3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage represented by {the slice}<r>[0.293, 0.249, 0.501, 0.52]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents \"12.5%\" of the total, as mentioned in the coordinates [0.363, 0.254, 0.363, 0.254]."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "ywicksYWYRmZMzzY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any text close to {the pie slice}<r>[0.246, 0.329, 0.479, 0.504]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the text \"2.5%\" at coordinates [0.235, 0.33, 0.235, 0.33] is close to the pie slice."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "aupmPhKsBXfXerKk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region comprising the {pie slice}<r>[0.229, 0.38, 0.501, 0.586]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice in color \"#66b3ff\". The slice is not exploded out and covers a theta range of '149 -> 194'. The region also has a text \"12.5%\" at coordinates [0.205, 0.476, 0.205, 0.476]."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "DBi8IVV5DgUVt4x8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region that includes {the slice}<r>[0.237, 0.52, 0.754, 0.793]</r>."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice that is not exploded, is colored as \"#99ff99\" and spans a theta range from '194 -> 338'. Also, it has the text \"40.0%\" within it, at coordinates [0.481, 0.82, 0.481, 0.82]."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "iR1ncrMEqZvvTrWq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region of {the slice}<r>[0.501, 0.248, 0.604, 0.52]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie in the chart. The slice is colored \"#c2c2f0\", not exploded and has a theta range of '428 -> 446'. Also, there's a text \"5.0%\" located at coordinates [0.569, 0.228, 0.569, 0.228]."
+            }
+        ],
+        "id_raw": 822
+    },
+    {
+        "id": "3blzewnsPDH16wpQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text appears at coordinates [0.552, 0.506, 0.552, 0.506] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"23.2%\"."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "IzuIDKfUPXrGudry",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at coordinates [0.463, 0.455, 0.537, 0.478] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an exploded slice of the pie chart."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "dO0xQ0QmPRFKQ1Nz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.017, 0.399, 0.981, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart subtitle: \"Impulsive Buying vs Mindful Spending Observations\"."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "MgHjLsHk5PFCIRuF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are neighboring to {the slice}[0.451, 0.5, 0.499, 0.529]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.45, 0.469, 0.499, 0.511], [0.46, 0.5, 0.499, 0.541]] are neighboring to the given slice."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "zEKbxmfZ3vyodHnR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the slice {the slice}[0.471, 0.5, 0.531, 0.549] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "6QbCCkCGERKkfOCi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow much percentage does {the slice}[0.462, 0.447, 0.539, 0.495] contribute to the whole?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contributes \"29.0%\" to the whole."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "2eE8nIl1KNzK6Yx8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you explain what the region between these coordinates [0.45, 0.447, 0.547, 0.549] contains?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a part of the pie chart that contains several slices with details on the buying behaviors like \"14.5%\", \"6.8%\", \"5.3%\", \"21.3%\" and \"23.2%\". There is also an exploded slice with \"29.0%\"."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "BC83xWAm2eePIoS2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects present in the region with coordinates [0.499, 0.472, 0.547, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice from the pie chart with \"23.2%\" autotext along with its label. The slice has the color \"#c2c2f0\" and it is not exploded."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "moWYgryrrsqrFESn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region [0.462, 0.447, 0.539, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes an exploded slice from the pie chart indicating \"29.0%\" with the color \"#a3a3c2\". The corresponding label for this slice is also included in this region."
+            }
+        ],
+        "id_raw": 825
+    },
+    {
+        "id": "VHSGnq20rIeTcL7q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.137, 0.076, 0.863, 0.101] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, titled \"Culture and Arts Industry Development 2015 - 2020\"."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "REJRhmtPRgOYZ5em",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.248, 0.155, 0.737, 0.483] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice representing a subcategory in the pie chart."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "mLDoEL2jexbDZa2X",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.386, 0.857, 0.386, 0.857] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label associated with a pie slice displaying the percentage \"14.9%\"."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "qaITOiUSG5AHlxCf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.268, 0.515, 0.547, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.173, 0.508, 0.5, 0.746], [0.5, 0.515, 0.809, 0.839]] are adjacent to it."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "CdgmReRWJ0Romw8R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value near {the slice}[0.173, 0.508, 0.5, 0.746]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value is {12.8%}<r>[0.166, 0.648, 0.166, 0.648]</r>."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "cxP8pAFYyqKYOSvz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice has the highest percentage according to the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.248, 0.155, 0.737, 0.483] has the highest percentage of 26.9%."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "EoPaZlldDKB50ZWr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.173, 0.508, 0.5, 0.746]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a pie slice of the chart in the color '#66b3ff'. Inside this region, the text \"12.8%\"<r>[0.166, 0.648, 0.166, 0.648]</r> can be found, which represents the slice's percentage of the whole."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "lBHNVHNolpPOTYCj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region within coordinates [0.5, 0.515, 0.809, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a slice of the pie chart with the color '#ffcc99'. Inside this region, the text \"17.4%\"<r>[0.729, 0.793, 0.729, 0.793]</r> is placed, defining the slice's weightage."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "rVEhlj5oLoZJjq8L",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_29.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region defined by the coordinates [0.248, 0.155, 0.737, 0.483]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is a pie slice that exploded from the pie chart. The percentage associated with it is \"26.9%\"<r>[0.489, 0.123, 0.489, 0.123]</r>, implying it represents the largest portion in the distribution."
+            }
+        ],
+        "id_raw": 829
+    },
+    {
+        "id": "eGQ4GdPWsCPJ3QLX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at coordinates [0.185, 0.341, 0.185, 0.341] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"6.2%\"."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "MGbG70VPPxbeMkwA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nI need information about the object at coordinates [0.424, 0.867, 0.424, 0.867]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text \"8.8%\"."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "BOr6NF6MAhz9N0He",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the chart, what is at [0.266, 0.2, 0.266, 0.2]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text with content \"7.4%\"."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "wHnFI0W0jHak0wj8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the chart is located closest to {the slice}<r>[0.526, 0.535, 0.823, 0.794]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.5, 0.515, 0.701, 0.842]</r> is closest."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "1AsX9VWviqj1JeZn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage text associated with {the slice}<r>[0.227, 0.515, 0.5, 0.804]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated percentage text is \"8.0%\"<r>[0.258, 0.781, 0.258, 0.781]</r>."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "804XmfCMKiCD2uWv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nOn the chart, do we have a slice that has a similar size to {the slice}<r>[0.346, 0.515, 0.521, 0.842]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, {the slice}<r>[0.371, 0.188, 0.536, 0.515]</r> is similar in size."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "ACUa4QLQ91po8ZDp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.227, 0.515, 0.5, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is in the middle of the chart and appears to be part of a pie chart slice colored with \"#ffcc99\". It additionally contains the text \"8.0%\"<r>[0.258, 0.781, 0.258, 0.781]</r>, indicating the percentage this slice represents."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "jmLVzSS8PdoX1a4n",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the composition within the region [0.371, 0.188, 0.536, 0.515]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a slice of the pie chart colored with the color code \"#9370db\". This slice has the associated text, \"8.2%\"<r>[0.447, 0.159, 0.447, 0.159]</r>."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "OAAtgKNiC4tMLuIs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_30.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region with the coordinates [0.175, 0.515, 0.5, 0.695]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region contains a segment of the pie chart colored using \"#99ff99\", and includes the text \"7.5%\"<r>[0.161, 0.637, 0.161, 0.637]</r>, referring to the portion of the total the segment represents."
+            }
+        ],
+        "id_raw": 830
+    },
+    {
+        "id": "g49NF6FrlY651met",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text at the coordinates [0.409, 0.551, 0.409, 0.551]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at this location is \"30.0%\"."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "guBkjybZoqIO1OQ2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates of [0.416, 0.455, 0.521, 0.62] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a pie slice."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "kdVa5fkQfPvXTcnd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle with coordinates [0.106, 0.348, 0.956, 0.382] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "SgGVXSGhPTYFuhHl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to the {slice}<r>[0.531, 0.423, 0.62, 0.52]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices at {coordinates}<r>[0.506, 0.415, 0.57, 0.52]</r> and {coordinates}<r>[0.531, 0.465, 0.636, 0.588]</r> are adjacent."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "1lnXrIOi7pjb4I4n",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the {slice}<r>[0.416, 0.455, 0.521, 0.62]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage for this slice is \"30.0%\"."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "nReJOmDe9awH5s3Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice comes after the {slice}<r>[0.492, 0.52, 0.612, 0.625]</r> in clockwise direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice after it is the {one}<r>[0.531, 0.465, 0.636, 0.588]</r>."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "XFoLtSJUEju6unuI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visible details within the region [0.4, 0.389, 0.663, 0.652]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily consists of a pie chart with various pie slices representing different percentage values for efficiency improvement potential. It has the slices {'slices'}<r>[0.416, 0.455, 0.521, 0.62][0.492, 0.52, 0.612, 0.625][0.531, 0.465, 0.636, 0.588][0.531, 0.423, 0.62, 0.52][0.506, 0.415, 0.57, 0.52][0.45, 0.418, 0.531, 0.52]</r>, spread out and adjacent to each other, each slice represents a separate segment of improvement potential."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "luU32BxBTBljtEFL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.4, 0.389, 0.663, 0.652] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart showing improvement potential in different areas. Each slice of the pie chart has different color, representing different sectors."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "Zyw75N57iZxdAqR4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat features can be observed in the region [0.45, 0.418, 0.531, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region happens to consist of a pie slice which illustrates a specific segment of efficiency improvement potential in one area. Its percentage value is \"10.0%\"."
+            }
+        ],
+        "id_raw": 835
+    },
+    {
+        "id": "WkPsWzBjujDhorEf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of {the subtitle}<r>[0.21, 0.056, 0.79, 0.076]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label of the subtitle is \"Fitness and Sports Health Trends from 2012 to 2021\"."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "6TAPxL1WDsAQtRNZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at the coordinates [0.448, 0.165, 0.5, 0.502] of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's {a slice}<r>[0.448, 0.165, 0.5, 0.502]</r> of the pie chart."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "Keh0do3XFKJXTorm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's the object at coordinates [0.467, 0.293, 0.502, 0.307]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a chunk of empty space enclosed by the pie chart."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "xrGsSE9jQkmFIqJB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to {the slice}<r>[0.219, 0.209, 0.5, 0.502]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}<r>[0.219, 0.209, 0.5, 0.502]</r> and <r>[0.163, 0.316, 0.5, 0.515]</r> are adjacent to it."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "vOMjCQXEUNfzmdNr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does {the text \"15.7%\"}<r>[0.676, 0.175, 0.676, 0.175]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.7%\" corresponds to {the slice}<r>[0.5, 0.165, 0.781, 0.502]</r>."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "3FiV3eaMiSUh0PmM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the slice in the chart with the maximum percentage representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with maximum percentage representation in the chart is {the slice}<r>[0.5, 0.316, 0.837, 0.601]</r>, it corresponds to 15.7%."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "Gx6i6hRVbyn6aGuo",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is located in the region [0.377, 0.152, 0.377, 0.152]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"5.8%\"<r>[0.377, 0.152, 0.377, 0.152]</r>. The region corresponds to a fractional area of a slice of the pie chart."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "swBekd9bOcc5pUfH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the region [0.163, 0.165, 0.837, 0.861]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains a pie chart. The pie chart itself is further divided into slices based on data representation. It includes slices and their respective associated text percentages."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "nKCSqVaWf8FJQjY7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.5, 0.502, 0.822, 0.799]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region refers to a slice<r>[0.5, 0.502, 0.822, 0.799]</r> in the pie chart, and it represents some percentage of the complete pie. The exact percentage number is not included in this specific set of coordinates."
+            }
+        ],
+        "id_raw": 836
+    },
+    {
+        "id": "QWTdXdekk2hwG4HD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.161, 0.025, 0.839, 0.058] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named Education Trends from 2011 to 2016."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "fLWFxFQ10qCnDVTF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is situated at the coordinates [0.245, 0.23, 0.755, 0.775] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "rMf4Pe6JZANQU65G",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.164, 0.545, 0.222, 0.569] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage label \"14.1%\"."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "GhJuHIwt6NJk6YDg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are adjacent to {pie_0 slice}[0.245, 0.357, 0.417, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{Pie_0 slices}[[0.354, 0.617, 0.691, 0.775], [0.526, 0.27, 0.726, 0.453], [0.379, 0.23, 0.541, 0.355] ] are adjacent."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "TMlgLBJunHN8sCQr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are located near {the slice}[0.245, 0.357, 0.417, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labels \"14.1%\" and \"2011\" are located near the slice."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "pz3tirPyQUo7TBDc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices correspond to the title \"Distance Education\" in the data?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slices with labels \"14.1%\", \"14.9%\", \"16.0%\" and \"17.3%\" correspond to the title \"Distance Education\"."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "B9x6ACMvtSJz7H1G",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.245, 0.23, 0.755, 0.775]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with several slices, displaying the distribution of Education Trends from 2011 to 2016. The pie chart includes slices labeled \"2011\" and \"2012\" among others, indicating the percentage of Distance Education trends."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "c3YOM2vLO9bOOdvJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.245, 0.23, 0.755, 0.775]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with slices, each of which represents a year from 2011 to 2016 and their respective percentages in Distance Education. In this region, examples of these slices include \"14.1%\" [0.164, 0.545, 0.222, 0.569] and \"14.9%\" [0.533, 0.787, 0.592, 0.81]."
+            }
+        ],
+        "id_raw": 838
+    },
+    {
+        "id": "yYhwLmTCTUWpp12i",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart from the coordinates [0.22, 0.123, 0.874, 0.156]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Importance of Organizational Roles\"."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "sLHxWY9Z380RUoDB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.421, 0.827, 0.421, 0.827] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"60.0%\" is located there."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "XtRnKQlMosr2S8fD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.809, 0.351, 0.809, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\" is at that location."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "1SwbincblZUvoUyD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is next to {the slice}[0.547, 0.254, 0.828, 0.534]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.398, 0.234, 0.652, 0.515] is next to it."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "i4SVzhr8GZ0FKp8q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does {the slice}[0.256, 0.36, 0.817, 0.822] represent, and what object near to it shows this?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice represents 60.0%, as indicated by the {text with percent}[0.421, 0.827, 0.421, 0.827] next to it."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "pWEVEcZcyVNQDnnn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the slice that represents \"15%\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {[0.547, 0.254, 0.828, 0.534], [0.332, 0.277, 0.547, 0.515]} are adjacent to the slice that represents 15%."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "Xh3VxFvNM378zews",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the main features of the region [0.256, 0.234, 0.828, 0.822]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a {text}[0.421, 0.827, 0.421, 0.827] that indicates \"60.0%\", and a {large pie slice}[0.256, 0.36, 0.817, 0.822] that is highlighted in color and represents this percentage."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "ZuIE0bKpUxEH1XB4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.398, 0.234, 0.652, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a {pie slice}[0.398, 0.234, 0.652, 0.515] and a text object {[0.52, 0.207, 0.52, 0.207]} that displays \"15.0%\" value."
+            }
+        ],
+        "id_raw": 841
+    },
+    {
+        "id": "Y7PF2sBpOdpMgGxk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located at these coordinates [0.831, 0.502, 0.831, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"76.0%\"."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "8K09ZZWwEDvqBYfy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.206, 0.329, 0.504, 0.736] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "NrLArvAjgHzrIW6L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.219, 0.108, 0.789, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which says \"Living Environment Preference\"."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "FY5PIVPK8dbU4jSj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is adjacent to the text {Nature}[0.289, 0.519, 0.362, 0.542]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice <r>[0.206, 0.329, 0.504, 0.736]</r> is adjacent to it."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "seOeyFwS9VFT3M4M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the text \"76.0%\"}[0.831, 0.502, 0.831, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is associated with {the slice}<r>[0.276, 0.223, 0.802, 0.818]</r>."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "boKjf9CJNrr8SjEO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between the slice [0.206, 0.329, 0.504, 0.736] and the text \"24.0%\" [0.177, 0.539, 0.177, 0.539]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the percentage written in the text \"24.0%\"."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "hhePbwWVJFXPOTlv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.206, 0.223, 0.802, 0.818]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains two pie chart slices. One with the text \"24.0%\" [0.177, 0.539, 0.177, 0.539] is colored '#ff9999' and covers the range from 140 to 226 degrees. The other, with the text \"76.0%\" [0.831, 0.502, 0.831, 0.502], is colored '#66b3ff' and spans from 226 to 500 degrees."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "OSd3nJk2hR6We0pn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_42.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.276, 0.223, 0.802, 0.818]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart associated with the text \"76.0%\" [0.831, 0.502, 0.831, 0.502], which is colored '#66b3ff' and spans from 226 to 500 degrees."
+            }
+        ],
+        "id_raw": 842
+    },
+    {
+        "id": "eOz2LcVxL8uEmeda",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title located at the coordinates [0.012, 0.212, 0.992, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's title is \"Prevalence of Technological Innovation and Intellectual Property Areas\"."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "zHOexxIN5whHNFK6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart corresponds to the coordinates [0.502, 0.301, 0.704, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that represents \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "r0nIlJ2Dbxxbcgmw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart comes with the text \"16.8%\" at coordinates [0.414, 0.269, 0.414, 0.269]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that represents \"Quantum Computing\"."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "yMY38crorwTt90uD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the slice with coordinates [0.502, 0.301, 0.704, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.502, 0.432, 0.72, 0.578] is adjacent to it."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "EcKEehKbnKyXrMXG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice represents the same area as stated in the autotext at [0.525, 0.754, 0.525, 0.754]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.442, 0.515, 0.6, 0.734] corresponds to the same area."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "eGMFtJG2qDyEKuoI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.605, 0.5, 0.66, 0.518] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text corresponds to the slice at coordinates [0.502, 0.432, 0.72, 0.578]."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "6cNVazWlWOXwkYkb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.284, 0.276, 0.72, 0.734]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the entire pie chart depicting the prevalence of various technology and intellectual property areas. It includes slices for Innovation, Patents, Technology Transfer, Open Source, Virtual Reality, Augmented Reality, Artificial Intelligence, and Quantum Computing."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "aJ0BBWMp0h5bhGPa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the coordinates [0.327, 0.276, 0.535, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region covers a slice of the pie chart representing Quantum Computing."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "jw9nLmBLaN2FbCAh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_44.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.349, 0.515, 0.502, 0.725]."
+            },
+            {
+                "from": "gpt",
+                "value": "This particular region contains the slice of the pie chart that illustrates the proportion of Augmented Reality."
+            }
+        ],
+        "id_raw": 844
+    },
+    {
+        "id": "Yix53N2d5tnqfTmo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.173, 0.233, 0.821, 0.266] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle labeled as \"Risk Assessment and Management\"."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "QajH3nCmil0ZYeIL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found within the coordinates [0.299, 0.323, 0.695, 0.718] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "seCqKjv3hQLxm8Wa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.447, 0.733, 0.447, 0.733] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text displaying \"13.0%\"."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "VDC5bwe70wbI3PlW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is near to {the slice with a theta range of '281 -> 304'}[0.497, 0.52, 0.609, 0.715]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"6.5%\"}[0.581, 0.721, 0.581, 0.721] is near the slice."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "iOEsSlPVHcgKydRt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice with a theta range of '234 -> 281'}[0.380, 0.520, 0.534, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {with theta ranges of '226 -> 234'}[0.347, 0.535, 0.484, 0.695] and {with theta ranges of '281 -> 304'}[0.497, 0.520, 0.609, 0.715] are adjacent."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "LbAKeQrDU32yVUFd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_47.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are adjacent to {the slice at position '469 -> 500'}[0.345, 0.333, 0.497, 0.520] of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice at position '419 -> 469'}[0.434, 0.323, 0.6, 0.520] and {the slice at position '140 -> 156'}[0.317, 0.393, 0.497, 0.520] are adjacent."
+            }
+        ],
+        "id_raw": 847
+    },
+    {
+        "id": "vaixnzuV9o3ed7d0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nature of the object located at coordinates [0.292, 0.346, 0.657, 0.694] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a pie chart, which represents the distribution of various production sectors in the Food and Agriculture Industry in 2018."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "mhXFdIkJTCVk1wFY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is at the position [0.048, 0.262, 0.917, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at that position is the subtitle of the chart \"Food and Agriculture Industry Analysis in 2018\"."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "cXbmLYi24t8zyCM1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.292, 0.403, 0.466, 0.529] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "That specific object indicates the percentage of beef production in the industry, 12.4%."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "V8aJM7bgIShGB6jw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the {pie chart}[0.292, 0.346, 0.657, 0.694] are close to 20% in terms of data representation?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices located at coordinates [[0.309, 0.52, 0.482, 0.685] and [0.349, 0.346, 0.511, 0.52]] are closest to representing 20% of the data."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "6zcrQd3RlR2GHfP4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there any relation between the specific slice at [0.482, 0.437, 0.657, 0.619] and the text at [0.674, 0.53, 0.674, 0.53]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, they are related. The slice at [0.482, 0.437, 0.657, 0.619] represents a data point, and the text at [0.674, 0.53, 0.674, 0.53] represents the percentage value of that slice in the pie chart which is 17.5%."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "3gDfQKHFagNmugO5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat other data shares the same sector percentage with the sector at [0.309, 0.52, 0.482, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.427, 0.52, 0.626, 0.694] shares the similar sector percentage which is near to 20%."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "hdfiSm265o3JqNEg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates vertically from 0.346 to 0.694."
+            },
+            {
+                "from": "gpt",
+                "value": "The region from 0.346 to 0.694 contains all of the sectors of the pie chart. These sectors include Beef Production, Poultry Production, Vegetable Production, Fruit Production, Seafood Production, and Grain Production with their respective percentage values."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "ZS9hyd9zhbibomxH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell about the region ranging from coordinates [0.482, 0.348, 0.635, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region gives information about the Seafood Production in the Food and Agriculture Industry in 2018. It also gives a proportionate area of the pie chart at 14.4%."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "a2tGKpkr2F8OhXrX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region of [0.349, 0.346, 0.511, 0.52] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the Grain Production area with the percentage of 16.5% distribution in the pie chart."
+            }
+        ],
+        "id_raw": 851
+    },
+    {
+        "id": "4FXzuoQBpKRVctGB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.291, 0.277, 0.726, 0.733] in the figure of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a pie chart covering the central portion of the overall chart."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "mnkS2ANwCdVJRjAW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at the coordinates [0.19, 0.212, 0.827, 0.237] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which is \"Efficiency Improvement Potential Assessment\"."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "nZGsxo5dR1SzRXWY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.357, 0.548, 0.412, 0.565] in the chart's figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "An unlabeled text could have been found there, but it appears to be empty."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "DkfB0y0dgMt8yuiJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does the text \"Retail\" correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Retail\" text corresponds to the {slice}<r>[0.291, 0.375, 0.509, 0.515]</r> colored in '#ff9999'."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "qxSJDL6RDDINg7mQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice does the text \"Healthcare\" relate to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The \"Healthcare\" text relates to the {slice}<r>[0.291, 0.5, 0.509, 0.658]</r> colored in '#66b3ff'."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "TBB2TLmMvPNcYtKf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the annotated percentage of the slice that the text \"IT\" refers to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"IT\" refers to the {slice}<r>[0.509, 0.515, 0.726, 0.627]</r> in the pie chart which is annotated with the percentage \"7.5%\"."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "YPtonWFUNWoAC80K",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects and data within the region of [0.400, 0.373, 0.749, 0.843]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region of the pie chart, one can find a slice of the pie as well as an associated percentage. The slice {slice}<r>[0.427, 0.515, 0.624, 0.733]</r> is colored in '#ffcc99' and the corresponding percentage \"15%\" is denoted."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "4wKkkOGI09psVDNv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_54.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region [0.250, 0.300, 0.550, 0.550]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses two slices of the pie chart, {slice_1}<r>[0.291, 0.375, 0.509, 0.515]</r> labeled with \"10%\" and colored in '#ff9999', as well as {slice_2}<r>[0.291, 0.5, 0.509, 0.658]</r> labeled with \"12.5%\" and colored in '#66b3ff'."
+            }
+        ],
+        "id_raw": 854
+    },
+    {
+        "id": "FEzC14OdD5jaWWf5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat chart object is situated within the coordinates range [0.519, 0.287, 0.763, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart, specifically the one representing \"Ripple\"."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "lwSq815upOZOvpEy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.292, 0.718, 0.292, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text displaying \"45.0%\"."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "W8YctLaXixxzblla",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is at the coordinates [0.046, 0.165, 0.992, 0.199] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, identified as \"Usage of Digital Currencies in Financial Technology\"."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "HATKhyH4h5IeC1gv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the Dash slice}[0.49, 0.269, 0.614, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slices are {Ripple}[0.519, 0.287, 0.763, 0.52] and {Cardano}[0.414, 0.271, 0.519, 0.52]."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "fmB2Y70jsZrLpZkY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the texts close to {the Monero slice}[0.332, 0.347, 0.519, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"0.5%\" and \"Monero\" are close to the Monero slice."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "RSLWweDApjo04WWc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is connected to {the Bitcoin slice}[0.249, 0.375, 0.634, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"45.0%\"<r>[0.292, 0.718, 0.292, 0.718]</r> is connected to the Bitcoin slice."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "mAkyzOU7a0aaOMWI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region confined within the coordinates [0.360, 0.292, 0.519, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The regional area pertains to the Cardano slice of the pie chart, with the inner text \"4.0%\"<r>[0.373, 0.286, 0.373, 0.286]</r>. It's a lower-mid section in the left part of the chart."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "cZNFuivWYHrxOSlT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region of coordinates [0.490, 0.269, 0.771, 0.788]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of the Dash and Ripple slices, associated with the texts \"8.0%\"<r>[0.556, 0.246, 0.556, 0.246]</r> and \"20.0%\"<r>[0.776, 0.624, 0.776, 0.624]</r> respectively."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "yj1hnLpbjp62PC0s",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the area within the coordinates [0.249, 0.375, 0.771, 0.788] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes Bitcoin, Ethereum and Ripple slices with texts \"45.0%\"<r>[0.292, 0.718, 0.292, 0.718]</r>, \"20.0%\"<r>[0.776, 0.624, 0.776, 0.624]</r> and \"15.0%\"<r>[0.728, 0.339, 0.728, 0.339]</r> located within them. This area dominates a major portion of the chart, depicting top 3 digital currencies' usage."
+            }
+        ],
+        "id_raw": 857
+    },
+    {
+        "id": "lVPjzaGZkOhWAY9u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at [0.279, 0.676, 0.279, 0.676] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"40.0%\"."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "3rIjpzOtkzBwQLqL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is shown at [0.262, 0.376, 0.567, 0.778] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is one of the pie slices, represented in the color #ff9999."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "WVvvI30vl9LVAI52",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the title of the chart placed?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title \"Effectiveness of Different Government Policies\" is placed at [0.097, 0.173, 0.96, 0.207]."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "RBY2A6EjhhjAtVu9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the overlapping area of the pie slice of coordinates [0.262, 0.376, 0.567, 0.778] and its text?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The text \"40.0%\"}<r>[0.279, 0.676, 0.279, 0.676]</r> is overlapped with the pie slice."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "mILWGIW6gjWlYsU9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are part of the pie {the pie}[0.262, 0.275, 0.774, 0.778]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects that are part of the pie are the slices at coordinates [0.262, 0.376, 0.567, 0.778], [0.528, 0.461, 0.774, 0.758], and [0.341, 0.275, 0.766, 0.52]."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "MkomKfzTW8v9GGtO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects at [0.262, 0.376, 0.567, 0.778] and [0.341, 0.275, 0.766, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "They are separate slices of {the same pie}[0.262, 0.275, 0.774, 0.778]."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "Vsng3HhWgGg06dRh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the section within coordinates [0.262, 0.275, 0.774, 0.778]."
+            },
+            {
+                "from": "gpt",
+                "value": "The section contains a pie chart with three slices at coordinates [0.262, 0.376, 0.567, 0.778], [0.528, 0.461, 0.774, 0.758], [0.341, 0.275, 0.766, 0.52], and their corresponding labels are 40.0%, 25.0%, and 35.0% respectively."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "mFbuTEcNgImECMkh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are within the region [0.528, 0.461, 0.774, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains one slice of {the pie}[0.262, 0.275, 0.774, 0.778] and its corresponding text \"25.0%\" at [0.76, 0.659, 0.76, 0.659]."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "A10F39TuqiXNlmwA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area [0.341, 0.275, 0.766, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a slice of {the pie}[0.262, 0.275, 0.774, 0.778] and its label \"35.0%\" at [0.589, 0.257, 0.589, 0.257]."
+            }
+        ],
+        "id_raw": 859
+    },
+    {
+        "id": "bcr2JJ56xahRSpMf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the main subject represented in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart represents \"Investment across Fintech and Digital Currency\" {title}<r>[0.1, 0.172, 0.98, 0.205]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "CFxeNQpi6VBVlkVb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what slice of the pie chart has an \"explosion\" effect applied?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice representing a certain value in the chart is shown with an explosion effect {the slice}<r>[0.56, 0.534, 0.804, 0.75]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "U9B6vtZ7gTkXBS0n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is colored in green?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}<r>[0.355, 0.52, 0.54, 0.752]</r> is shown in green color."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "ixjaI4ZN3x647Nqb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage of investment does the exploded slice {the slice}<r>[0.56, 0.534, 0.804, 0.75]</r> represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded slice represents \"14.8%\" investment."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "bEqYkGTyLaGRWks5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there any slices that share a boundary with the pink slice {the slice}<r>[0.54, 0.311, 0.786, 0.554]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}<r>[0.56, 0.534, 0.804, 0.75]</r>, <r>[0.49, 0.274, 0.67, 0.52]</r> share a boundary with the pink slice."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "33Ins31GnlfuEgGz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the percentage values represented for the slices next to the blue slice {the slice}<r>[0.455, 0.52, 0.658, 0.767]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices neighboring the blue one represent \"14.8%\" and \"18.3%\" of investments."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "5W2i1EOPVovmdhF2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region between coordinates [0.55, 0.275, 0.70, 0.55]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices of the pie chart: a grayish-blue one that represents 18.3% {the slice}<r>[0.54, 0.311, 0.786, 0.554]</r> and a turquoise one denoting 12.1% {the slice}<r>[0.49, 0.274, 0.67, 0.52]</r>."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "CNT3IDs8xLxSTpcv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.3, 0.3, 0.55, 0.7]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three slices. One green slice with 7.9%, a blue slice with 13.6%, and a blue-gray slice exploded which shows 14.8%."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "UVrz2sJk2cGWapAr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.25, 0.25, 0.80, 0.80]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers most of the pie chart that shows the distribution of investments across various technology and digital currency. It contains all the slices and their respective investment percentages."
+            }
+        ],
+        "id_raw": 866
+    },
+    {
+        "id": "DAsSrKHdN8IjhZZ8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.289, 0.18, 0.731, 0.208] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, termed as Population Migration Trends."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "PpU29AYWjkfSmmiQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.244, 0.528, 0.244, 0.528] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A text representing \"23.5%\"."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "0W0wpglwCVVdSt9J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at coordinates [0.323, 0.328, 0.323, 0.328]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2.9%\"."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "aOAc3ExXuZJtfglY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.338, 0.517, 0.586, 0.76]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.268, 0.362, 0.51, 0.688], [0.533, 0.389, 0.775, 0.755]] are adjacent."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "GvID2fTrShBVB7OB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice {the slice}[0.51, 0.282, 0.71, 0.517] and the autotext {the autotext}[0.662, 0.298, 0.662, 0.298]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"11.8%\" is located within the slice."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "itp5nj2dD2wrKIUm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices border the slice {the slice}[0.356, 0.287, 0.51, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.434, 0.275, 0.567, 0.517], [0.268, 0.362, 0.51, 0.688]] border the specified slice."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "PMfpfTRXxUyvVYFm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.268, 0.275, 0.51, 0.517]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice [\"#ff9999\", 23.5%, Southeast Asia]. This region is located towards the left side of the chart and comprises a major chunk of the pie."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "iBRcStZwS5UxTbmq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_73.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.324, 0.33, 0.51, 0.517]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice [\"#c2c2f0\", 2.9%, North America]."
+            }
+        ],
+        "id_raw": 873
+    },
+    {
+        "id": "UKJac9AgQkSeyON3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.008, 0.091, 0.992, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Environment Sustainability Measures (2030 vs 2040)\"."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "iHBAcbf0wySaYLTK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.807, 0.67, 0.807, 0.67] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a text \"16.7%\"."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Yb3kUDg5XlhNYeD7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object could be found at coordinates [0.138, 0.43, 0.138, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"14.4%\"."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Y8XxqgkqzBk1x0Wo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the chart is adjacent to {the slice}[0.196, 0.52, 0.5, 0.808]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.384, 0.52, 0.674, 0.831] is adjacent."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "W6GOnphzIzNwLTyd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located inside the {the slice}[0.159, 0.313, 0.47, 0.577]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.4%\"[0.138, 0.43, 0.138, 0.43] is located inside the slice."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "we4tchzi4m6tgMRm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is closest to {the autotext}[0.238, 0.74, 0.238, 0.74]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest slice to the text is {the slice}[0.196, 0.52, 0.5, 0.808]."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "TxD7LkfMC2W4wJwE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.384, 0.52, 0.674, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a part of the pie chart colored in '#99ff99'. There's also an autotext \"15.6%\"[0.536, 0.86, 0.536, 0.86] associated with this part of the pie chart."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "ziM6I5qNBSoh0WkC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.254, 0.179, 0.609, 0.49]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored in '#ffb3e6' with an associated autotext \"20.0%\"[0.41, 0.159, 0.41, 0.159]."
+            }
+        ],
+        "id_raw": 874
+    },
+    {
+        "id": "Vho2ms5tLfKwHUEh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title or main object located at coordinates [0.084, 0.092, 0.916, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle and it's called \"Fashion Trends Popularity from 2015 to 2019\"."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "dPq6LEH1tlLS5TYM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object in the pie chart is found at coordinates [0.802, 0.738, 0.802, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext, which reads \"27.9%\"."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "5BLiM7eOFEGeI7RG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object seen at coordinates [0.384, 0.677, 0.457, 0.7] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an unlabeled text associated with a pie slice."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "pYd2U1DLtV6ZvPDH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice [0.525, 0.461, 0.836, 0.848]} in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.262, 0.21, 0.801, 0.52] is adjacent to it."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "i6Xy98AkC6Eta1r5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main elements adjacent to {the slice}[0.195, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main elements adjacent to it are {the autotext}[0.191, 0.374, 0.191, 0.374] and another {slice}[0.19, 0.463, 0.5, 0.7]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "QzbvcekgxUVl2BIF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBased on the coordinates, what elements are in closest proximity to {the slice}[0.19, 0.463, 0.5, 0.7]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest elements are {the slice}[0.195, 0.321, 0.5, 0.52] and {the slice}[0.247, 0.52, 0.521, 0.831]."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "94GvcpJPKkgZEIez",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.525, 0.461, 0.836, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with the color '#ffcc99', and it's exploded away from the centre. It also contains the text \"27.9%\" located at [0.802, 0.738, 0.802, 0.738] in the chart, suggesting it represents 27.9% of the whole."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "nEKaMNfZhbx54mve",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region defined by the coordinates [0.262, 0.21, 0.801, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region appears to contain a pie slice, colored in '#c2c2f0', with a corresponding percentage autotext \"34.9%\" at [0.576, 0.187, 0.576, 0.187], which indicates this slice represents 34.9% of the whole."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "TGjwnTk2LB20ZcOe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are contained within the region [0.247, 0.52, 0.521, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice with the color '#99ff99'. It holds an associated autotext \"16.3%\" at [0.354, 0.829, 0.354, 0.829] in the chart, suggesting this segment makes up 16.3% of the entire pie."
+            }
+        ],
+        "id_raw": 875
+    },
+    {
+        "id": "SxrRCVytTjY9bPyM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.022, 0.092, 0.978, 0.125] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle: \"Urban Development Investment from 2010 to 2014\"."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "dlgmFBrE9iW3ehWJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.189, 0.661, 0.189, 0.661] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"14.3%\"."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "mw4xJkvsF4Ckn1cl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.547, 0.182, 0.547, 0.182] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"32.1%\"."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "81e6oeyH9pMIFP7q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {text}[0.547, 0.182, 0.547, 0.182]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text corresponds to the {pie slice}[0.262, 0.21, 0.783, 0.52]."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "VgdQ7xhsKgNookow",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship of {text}[0.453, 0.859, 0.453, 0.859] to the objects in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.9%\" is the fraction represented by the {pie slice}[0.301, 0.52, 0.628, 0.831]."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "c5wlM9QRSEztWvEK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a slice that corresponds to the {text}[0.181, 0.399, 0.181, 0.399] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it corresponds to the {pie slice}[0.19, 0.321, 0.5, 0.52]."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "6LwvMNYwAtDwFGRq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.19, 0.321, 0.5, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to a slice of the pie chart, labeled with the text \"10.7%\" [0.181, 0.399, 0.181, 0.399]. It is located on the left, next to the center and covering part of the lower side of the pie chart."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "ySBIDQeQkXEJgRex",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.301, 0.52, 0.628, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice and the corresponding text \"17.9%\" [0.453, 0.859,0.453, 0.859] is in the upper boundary of this region."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "DvCOmoyEZQRtiCRi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_76.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's notable about the region [0.529, 0.404, 0.84, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to an exploded pie slice, suggesting it might be of particular interest. The text \"25.0%\" [0.849, 0.652, 0.849, 0.652] is in this region, showing the proportion represented by this slice."
+            }
+        ],
+        "id_raw": 876
+    },
+    {
+        "id": "PuCeYvsRzV8ra3jU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart with these coordinates [0.027, 0.092, 0.973, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Manufacturing Production Efficiency: 2018 vs 2019\"."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "z0SH1j5ynAOoegmJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the location defined by coordinates [0.674, 0.456, 0.747, 0.479]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a text element, but it is empty."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "Sx0aRbV2YpjMeKOa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the pie chart coordinates [0.19, 0.202, 0.84, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart demonstrating manufacturing production efficiency for the years 2018 and 2019 is located at those coordinates."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "lvJ0ghoUYreMcxjz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart is near the autotext {30.0%}<r>[0.169, 0.603, 0.169, 0.603]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates [0.19, 0.321, 0.5, 0.808] is near the autotext 30.0%."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "QsSOFl0xucWRD3GQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice at {coordinates}[0.292, 0.202, 0.84, 0.823] and autotext at {coordinates}[0.861, 0.43, 0.861, 0.43]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at the coordinates corresponds to the 70.0% autotext, indicating that it represents 70% of the total."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "2GQ7GAmqFbxu18uz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nExplain the relationship between the slices and their autotext?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotexts {30.0%}<r>[0.169, 0.603, 0.169, 0.603]</r> and {70.0%}<r>[0.861, 0.43, 0.861, 0.43]</r> respectively correlate to the slices at coordinates [0.19, 0.321, 0.5, 0.808] and [0.292, 0.202, 0.84, 0.823]."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "uwTAQ66GiF69QvH6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the visual content in this specific location [0.19, 0.202, 0.84, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart split into two slices. The first slice is pink, covers about 30% of the pie, and has its autotext at the coordinates [0.169, 0.603, 0.169, 0.603]. The second slice is blue, covers about 70% of the pie, is slightly exploded, and has its autotext at the coordinates [0.861, 0.43, 0.861, 0.43]."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "RDH92BIx8kc78Hap",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region [0.19, 0.321, 0.5, 0.808]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region illustrates part of a pie chart represented as a slice. The slice has the color \"#ff9999\", is not exploded, and covers about 30% of the pie. The autotext \"30.0%\" is located at [0.169, 0.603, 0.169, 0.603], within the slice."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "ToqzNtzDcfzYFgie",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_78.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main elements in region [0.292, 0.202, 0.84, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains one of the slices of the pie chart. It's in blue and represents approximately 70% of the whole pie. It also includes the autotext \"70.0%\" at coordinates [0.861, 0.43, 0.861, 0.43]."
+            }
+        ],
+        "id_raw": 878
+    },
+    {
+        "id": "ItZXoaj9u3GvEe6H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.082, 0.076, 0.918, 0.101] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, which is \"Population Migration and Mobility Trends from 2012 to 2019\"."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "IaDQwBvr8x1s0nJr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at the coordinates [0.425, 0.865, 0.469, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, that's the percentage text of a slice, and it says \"13.9%\"."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "fFiJVWV0MHzD60Y9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies the coordinates [0.336, 0.515, 0.612, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a pie slice from the main chart."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "OKKdtoKGvmFDa4aj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.336, 0.515, 0.612, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.194, 0.515, 0.5, 0.798], [0.5, 0.515, 0.8, 0.823]] are adjacent."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "J7P4u1TLT89iuCoT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the corresponding category and percentage for the {slice}[0.238, 0.157, 0.539, 0.485]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding category is \"2019\" and the percentage is \"16.3%\"."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "KX9KUSshH49ZWuFN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice appears to be more significant than others, either by larger size or distinctive color/font?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponding to the year 2019 {slice}[0.238, 0.157, 0.539, 0.485] is the most significant due to its larger size and exploded presentation."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "VVFBiFlDl5t9xJcz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.173, 0.157, 0.827, 0.842]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the entire pie chart, which shows a collection of slices, each representing data for a different year from 2012 to 2019. Each slice, such as {2019}[0.238, 0.157, 0.539, 0.485] and {2016}[0.5, 0.515, 0.8, 0.823], has labeled the corresponding year and its percentage."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "1qgigO5Z4P0dF2TF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects lie in the region [0.336, 0.515, 0.612, 0.842]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is one single slice of the pie chart. Its label and percentage values {\"2015\"}[0.455, 0.702, 0.51, 0.719] and {\"13.9%\"}[0.425, 0.865, 0.469, 0.882] lie within this slice."
+            }
+        ],
+        "id_raw": 880
+    },
+    {
+        "id": "7bX7BtJ2RHZKro6H",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.005, 0.092, 0.995, 0.125] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, called \"Creativity and Design Innovations from 2015 to 2021\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "6kZzWYPGrQeK2mtd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.838, 0.676, 0.838, 0.676] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage label \"18.8%\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "iaH30wFtVtanh9Sd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.194, 0.368, 0.194, 0.368] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage label \"7.5%\"."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "yYMibWoZ59ydgMoB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.381, 0.52, 0.662, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.218, 0.52, 0.5, 0.807], [0.528, 0.484, 0.839, 0.798]] are adjacent."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "0Ths4krqg2c6lOz7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located in {the slice}[0.19, 0.451, 0.5, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"10.4%\" [0.16, 0.555, 0.16, 0.555] is located in this slice."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "oHuyW59yaUG980vM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located close to {the slice}[0.5, 0.255, 0.806, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"13.8%\" [0.783, 0.329, 0.783, 0.329] is located close to this slice."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "nfGFzu7qKbzUBax3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.381, 0.52, 0.662, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice from the pie chart colored in '#ffcc99' and a text box containing the percentage label \"15.0%\" [0.526, 0.861, 0.526, 0.861]."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "89RHBFx2GZpqURhS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region [0.218, 0.52, 0.5, 0.807]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice from the pie chart colored in '#99ff99' and a percentage label \"11.9%\" [0.263, 0.766, 0.263, 0.766]."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "ZZzeVEix2AtjK1aa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.528, 0.484, 0.839, 0.798]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains an exploded slice from the pie chart colored in '#c2c2f0' and a percentage label \"18.8%\" [0.838, 0.676, 0.838, 0.676]."
+            }
+        ],
+        "id_raw": 882
+    },
+    {
+        "id": "co4j5IeqGabYJjQm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.046, 0.251, 0.997, 0.284] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, named \"Usage of Financial Technology and Digital Currency\"."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "wL5stMPIfqK3INI0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object positioned at coordinates [0.338, 0.339, 0.723, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart showing different categories of financial technology usage and digital currencies."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "2eD6o72v6SfZurvS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.74, 0.536, 0.74, 0.536] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the text representation of the percentage \"60.0%\"."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "uJocTQzsdC8zQLZ3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart touch {the slice}[0.338, 0.403, 0.521, 0.69]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.471, 0.339, 0.723, 0.705] and {the slice}[0.381, 0.343, 0.521, 0.52] are the ones adjacent."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "bpo3mkPKoW6FklFi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual percentage value associated with {the slice}[0.381, 0.343, 0.521, 0.52] of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"10.0%\", which can be seen at coordinates [0.415, 0.35, 0.415, 0.35],  is associated with the slice."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "8uH4avZ8ACypSOPd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe slice at {the coordinates}[0.338, 0.403, 0.521, 0.69], what is its percentage and color?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice occupies 30% of the chart as indicated by the label at coordinate [0.326, 0.569, 0.326, 0.569] and its color is '#ff9999'."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "6WF0onts23jKS2CT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe everything within the coordinates [0.338, 0.339, 0.723, 0.705] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the complete pie chart. The chart is divided into three segments with their relevant percentage labels as follows:\n          Segment 1 with coordinates [0.338, 0.403, 0.521, 0.69] and its percentage representation of 30.0% at [0.326, 0.569, 0.326, 0.569].\n          The largest Segment 2 at [0.471, 0.339, 0.723, 0.705] corresponds to 60.0% at [0.74, 0.536, 0.74, 0.536], and finally, a relatively smaller slice located at [0.381, 0.343, 0.521, 0.52] contributing to 10.0% to this region with the corresponding label at [0.415, 0.35, 0.415, 0.35]."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "0Xr4ptJ8Y64YyNl8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_83.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region inside the coordinates [0.338, 0.339, 0.723, 0.705]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a pie chart. The pie chart is divided into three slices. Each slice corresponds to a different color and percentage value representing the distribution of financial technology and digital currency usage."
+            }
+        ],
+        "id_raw": 883
+    },
+    {
+        "id": "eDLupMvq61Vbjc0R",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text within the coordinates [0.066, 0.115, 0.954, 0.148] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the chart, 'Equities vs Bonds Portfolio Composition in 2019'."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "Dp7p0GFtFctrDnaO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.218, 0.228, 0.802, 0.812] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the pie chart."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "o1eKVvrCyWZLX8mq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is situated at the coordinates [0.675, 0.244, 0.675, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext '44.9%' associated with one of the slices of the pie chart."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "wwARffYHVIdoBnPn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the autotext {autotext}[0.675, 0.244, 0.675, 0.244]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice{slice}[0.287, 0.228, 0.802, 0.628] is associated with the autotext."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "k2eY9i3mZX9h5JBB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the connection between the text at [0.346, 0.796, 0.346, 0.796] and the slices of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text '55.1%' is associated with the {slice}[0.218, 0.333, 0.782, 0.812] in the pie chart."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "3VBRR1mjbt7NnA1X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the autotext at coordinates [0.346, 0.796, 0.346, 0.796] relate to the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext '55.1%' represents the percentage of the {slice}[0.218, 0.333, 0.782, 0.812] in the pie chart."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "vLuM8h9ZkkzknZcY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.218, 0.333, 0.782, 0.812] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a pie slice in the chart, colored in '#ff9999', and it is associated with the autotext '55.1%'."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "gJX9J3OCx5iQv2ln",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.287, 0.228, 0.802, 0.628] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of the pie slice that represents the portion of the other class, colored in '#66b3ff'. The autotext '44.9%' refers to this slice."
+            }
+        ],
+        "id_raw": 884
+    },
+    {
+        "id": "JB615UVD84lrJBpF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, what does the object within coordinates [0.123, 0.092, 0.877, 0.125] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Global Internet Users from 2012 to 2019\"."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "sL5L2jiNq8Sj7GVg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.188, 0.381, 0.188, 0.381]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"8.9%\"."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "T8wNCt9OaPBzra8d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the entity represented by coordinates [0.19, 0.477, 0.5, 0.666] in the chart refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to one of the slices in the pie chart, colored in '#66b3ff'."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "IvI8q5tskjhwKQwr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich other slice in the pie chart is adjacent to {the slice}[0.377, 0.52, 0.603, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.5, 0.52, 0.784, 0.813] is located adjacent to it."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "CCGbJ6GPmhqmenbQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text near the slice with coordinates [0.5, 0.213, 0.777, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"15.2%\"}[0.701, 0.244, 0.701, 0.244] is located near the slice."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "6Ftv02W6kYRUFYWr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice corresponds with the coordinate text {'s': \"13.0%\"}?[0.731, 0.771, 0.731, 0.771]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.5, 0.52, 0.784, 0.813] corresponds to the text."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "UKPF1xeGJfdaiD1q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.377, 0.52, 0.603, 0.831] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice colored in '#ffcc99' and the slice includes the autotext '11.9%', located at [0.488, 0.861, 0.488, 0.861]. This slice is located in the upper right quadrant of the pie chart."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "1FUveHBHndGUCtVm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found within the region specified by the coordinates [0.19, 0.181, 0.81, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains eight pie slices of different colors with percentages ranging from 8.9% to 16.3%. Each slice also includes a corresponding autotext percentage."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "AysTiZNbIgMddttf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_87.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed within the region [0.5, 0.213, 0.777, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice colored in \"#c2c2f0\" with a corresponding autotext '15.2%' at coordinates [0.701, 0.244, 0.701, 0.244]. This slice is located in the lower right quadrant of the pie chart."
+            }
+        ],
+        "id_raw": 887
+    },
+    {
+        "id": "Km7Egr4CF5Tt4OMZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.108, 0.173, 0.894, 0.206] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart which is 'Human Population Through Historical Eras'."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "h8NTvtbPDEgvRFiE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.255, 0.275, 0.746, 0.766] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object at these coordinates is the pie chart."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "3oZuvQ99MKF83WP6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.735, 0.654, 0.735, 0.654] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"94.3%\", representing one of the pie slices."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "SqXrYEvqTtujvD1S",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the {pie slice}[0.305, 0.365, 0.501, 0.52] and its percentage value?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value of \"0.6%\" is the ratio of the population during the Iron Age to the total historical human population, as represented by the pie slice."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "HmlYgtsRxivuTdGl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the slice at {coordinates}[0.311, 0.363, 0.501, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slices are {the slices}[[0.269, 0.372, 0.501, 0.52], [0.293, 0.347, 0.482, 0.505]]."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "56PwkOs5c2twOhca",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich other slice shares the same color as the {slice}[0.311, 0.363, 0.501, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice in {coordinates}[0.293, 0.347, 0.482, 0.505] shares the same color."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "K5IjHgfIpGHaRQRL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.194, 0.213, 0.808, 0.827]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses majority of the pie chart which contains slices representing different historical eras and their corresponding population proportions."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "hfNl2quQXASJ0eNd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.255, 0.275, 0.746, 0.766]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the complete pie chart, each slice represents a population proportion in different historical eras such as the Stone Age, Bronze Age, Iron Age, the Middle Ages and the Modern Age."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "mcnNtzIUaF6mmdYQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.293, 0.347, 0.482, 0.505] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice as well as an autotext with the value \"0.1%\" corresponding to the population during the Stone Age."
+            }
+        ],
+        "id_raw": 890
+    },
+    {
+        "id": "qPIh9Hrd9G926ojC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies the range [0.172, 0.337, 0.798, 0.37] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Social Responsibility Performance\"."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "Pc50PvfcZvCcxXH8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is shown at the coordinates [0.453, 0.642, 0.453, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"17.4%\"."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "C6bX5fFoXGp9gz7z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at coordinates [0.371, 0.406, 0.608, 0.637] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "2U2YfrVudq1Y0Zdz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color is the {slice}[0.371, 0.447, 0.485, 0.541] within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is \"#ff9999\" or a shade of pink."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "u4GFT7rgnQELpZ6Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart is adjacent to the {slice}[0.494, 0.528, 0.608, 0.637]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[[0.485, 0.441, 0.6, 0.533]] is adjacent."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "IDZ3Qu5G3l097A8x",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the text \"17.4%\" within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.403, 0.52, 0.518, 0.635] corresponds to the text \"17.4%\"."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "RAAyymQkBoByHcKs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.371, 0.406, 0.608, 0.637]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the main part of the chart which contains a pie chart with seven slices."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "mRS0PKUi24MgoICj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of content is in the region from [0.403, 0.52, 0.518, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart in the color \"#99ff99\" or light green, representing 17.4% of the chart."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "njKtckfydLK9fg7D",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within [0.494, 0.528, 0.608, 0.637] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes an exploded slice of the pie chart in the color #ffcc99 or a shade of orange, representing 18.6% of the chart."
+            }
+        ],
+        "id_raw": 892
+    },
+    {
+        "id": "meNHCESdhHQmTbur",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart subtitle found at [0.2, 0.092, 0.8, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Annual Sales from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "Ygl2dB9HsUira8Tx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item in the chart is located at [0.205, 0.348, 0.205, 0.348]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.4%\"."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "jzcwFlK6KZR6tXbd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of pie is located within [0.193, 0.52, 0.5, 0.749]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice colored '#99ff99' which represents a portion of 10.8%."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "WkXtnP9BwH8WJQ9n",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage text is closest to the exploded pie slice {the slice}[0.281, 0.55, 0.524, 0.86]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest text is {the text \"13.5%\"}<r>[0.384, 0.874, 0.384, 0.874]</r>."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "IILt5gk8qMavt24t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to and shares a border with {the slice}[0.189, 0.411, 0.5, 0.567]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with the coordinates {the slice}[0.209, 0.321, 0.5, 0.52] is adjacent."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "Zzb2eSMCjqr5U8LT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the pie slice with theta range \"276 -> 317\" next to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is next to the pie slices with coordinates [0.281, 0.55, 0.524, 0.86] and [0.5, 0.52, 0.81, 0.732]."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "QenHVEZZ5XDYqO2P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region between [0.189, 0.21, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of the pie chart, specifically a slice colored '#ff9999' which represents a portion of 5.4% along with the corresponding text label."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "fxnpwJ1z5zoJqUTN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are in the region [0.5, 0.52, 0.81, 0.732]?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a slice of the pie chart with color '#f6bf26', which corresponds to a portion of 11.9%, and the percentage text label \"11.9%\"."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "8HgQz5QDqwbWEKd7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_93.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content in the region [0.262, 0.21, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart which is colored '#9e9e9e' and represents a portion of 13.5%, along with the corresponding auto text label \"13.5%\" attached to it."
+            }
+        ],
+        "id_raw": 893
+    },
+    {
+        "id": "hOh3Db4P3oiGv2Pz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label at the coordinates [0.212, 0.095, 0.763, 0.115]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is the title of the chart, \"Subculture and Fashion Trends Popularity in 2020\"."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "DUGAQQfi3pIpBfb6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.204, 0.709, 0.204, 0.709]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"10.8%\"."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "uJnvO8VL8uN3hlhH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice with coordinates [0.307, 0.543, 0.721, 0.858]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is representing a subculture with color '#66b3ff' and it is the only slice that is exploded."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "VAtpZGUpZ78jYt7N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.487, 0.444, 0.802, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice with a color of '#66b3ff'}[0.307, 0.543, 0.721, 0.858] is adjacent."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "m6CLqdTee5G54faD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the slice with a color of '#66b3ff'}[0.307, 0.543, 0.721, 0.858]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"23.1%\"}[0.525, 0.887, 0.525, 0.887] is close to that slice."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "gSyf7CFgA0CoN3zl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {text '8.5%'}[0.478, 0.167, 0.478, 0.167]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.397, 0.198, 0.562, 0.512] corresponds to the text '8.5%'."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "hJXX0yZtFSZ1yJ3q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.173, 0.198, 0.802, 0.858]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the whole pie chart. It includes slices representing different subcultures and their corresponding percentages positioned close to each slice."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "dg8qJv77WPn2PyWH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.184, 0.512, 0.487, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with a color of '#ff9999' and the corresponding text \"10.8%\"[0.204, 0.709, 0.204, 0.709]."
+            }
+        ],
+        "id_raw": 894
+    },
+    {
+        "id": "SV4xheAW2M3KKxEG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.124, 0.138, 0.866, 0.171] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named \"Frequency of Different Emotional States\"."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "uOdJB87R6py4a9iR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is specified within the coordinates [0.276, 0.764, 0.276, 0.764]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text reading \"23.7%\"."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "hBxxSpMF8hsYzeqJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.495, 0.52, 0.604, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "l21CqwfC1XGrUjnt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slices are adjacent to {the slice}[0.495, 0.52, 0.665, 0.771] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[0.495, 0.52, 0.604, 0.794] and [0.495, 0.52, 0.767, 0.734] are adjacent."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "Qf6TDKyHTwn7Bd2j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is closest to {the slice}[0.204, 0.541, 0.48, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text denoted as \"23.7%\"[0.276, 0.764, 0.276, 0.764] is closest."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "oYRMDiYL4dkEKsQ4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the text at [0.208, 0.431, 0.208, 0.431] relate to {the slice}[0.221, 0.345, 0.495, 0.546] mentioned?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at [0.208, 0.431, 0.208, 0.431] corresponds to the percentage of {the slice}[0.221, 0.345, 0.495, 0.546] in the pie chart."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "rZmHIJ8HgHLFgbcM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within the coordinates [0.495, 0.52, 0.786, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains two slices of the pie chart: one slice with coordinates [0.464, 0.227, 0.786, 0.519] and another one with the coordinates [0.495, 0.52, 0.604, 0.794]. This region in the pie chart represents two different emotional states."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "UfOocfk5Mov0Y3w9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail the objects within the region [0.204, 0.541, 0.48, 0.814]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice [0.204, 0.541, 0.48, 0.814] of the pie chart and the text \"23.7%\"[0.276, 0.764, 0.276, 0.764]."
+            }
+        ],
+        "id_raw": 895
+    },
+    {
+        "id": "ZLoOKTdZ1hNlnomY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is {Trend of Grunge from 2017 to 2021}<r>[0.088, 0.069, 0.418, 0.102]</r>."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "fv0MAyPd034Uf0BU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies in the coordinates [0.143, 0.443, 0.18, 0.466] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text object at this coordinate is not labeled, it is an empty string \"\"."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "kzKW0D64j4kgz9sv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the element with coordinates [0.434, 0.391, 0.434, 0.391] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The element is an autotext of a pie slice saying {20.2%}<r>[0.434, 0.391, 0.434, 0.391]</r>."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "kuJbXsYDY8IYJ5xU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice in the second subplot of the chart is adjacent to the pie slice at coordinates [0.747, 0.405, 0.905, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {exploded pie slice}[0.662, 0.157, 0.842, 0.472] is adjacent to it."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "Q1GiDCXvYnbRQtpF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the exploded pie slice represent in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded pie slice represents {20.2%}<r>[0.434, 0.391, 0.434, 0.391]</r>."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "KwoojHfaxA9jlxzy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the last pie slice in the second subplot represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The last pie slice represents {4.8%}<r>[0.632, 0.243, 0.632, 0.243]</r>."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "okt4GN1R92AxQ18c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.095, 0.188, 0.326, 0.557] in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the first pie slice of the pie chart with the color \"#ff9999\". Also, it includes the edge text which is empty ('')<r>[0.143, 0.443, 0.18, 0.466]</r> and the autotext saying \"13.9%\"<r>[0.086, 0.414, 0.086, 0.414]</r>."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "u6VdExpsfrPMj5wA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the region [0.589, 0.301, 0.817, 0.819] in the second subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "This region features the first pie slice in the second subplot which represents around {43.5%}<r>[0.611, 0.719, 0.611, 0.719]</r> of the total, as shown in its autotext."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "f6x5wXXXenfNjera",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_96.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the region [0.626, 0.238, 0.747, 0.504] of the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is composed of the last pie slice of the second subplot. The slice has the color \"#c2c2f0\" and it represents about {4.8%}<r>[0.632, 0.243, 0.632, 0.243]</r> of the total, as indicated by the autotext."
+            }
+        ],
+        "id_raw": 896
+    },
+    {
+        "id": "taTAmALgAxylGZzZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at the coordinates [0.157, 0.255, 0.776, 0.288]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the subtitle text - \"Distribution of Social Interactions\"."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "VvtssNyZmQya8Ubv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart with coordinates [0.287, 0.405, 0.467, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a pie chart slice colored in '#ff9999' with a text saying \"30.0%\"."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "teHBAHGUJs2eqpdw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the visual element at the coordinates [0.346, 0.336, 0.664, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to an exploded slice in the pie chart, containing the label \"70.0%\" and colored '#66b3ff'."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "lQARLF81tzTYmJf3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.287, 0.405, 0.467, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.346, 0.336, 0.664, 0.696] is adjacent to it."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "XgIdM7ykgfaQx1Ur",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage value is associated with {the slice}[0.287, 0.405, 0.467, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value associated with the slice is \"30.0%\"."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "0D6vE7xqeqt0XJbc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage value does {the slice}[0.346, 0.336, 0.664, 0.696] occupy?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is \"70.0%\"."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "q9FqwEiNgruWhmEn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's in the region demarcated by the coordinates [0.287, 0.336, 0.664, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a pie slice that is visually differentiated from the others because it has been exploded. It is colored '#66b3ff' and represents 70.0% of the total."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "vtetifSI38tyn0nC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region covered by coordinates [0.287, 0.405, 0.467, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates there is a pie chart slice colored in '#ff9999', representing 30.0% of the total. The region does not include any other object."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "HpxfkyF8L3BtQnXv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_98.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me about the region defined by the coordinates [0.242, 0.296, 0.691, 0.745]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the axes of the pie chart and it contains both pie slices, one of them exploded, along with their labels."
+            }
+        ],
+        "id_raw": 898
+    },
+    {
+        "id": "7a9sOZArVwYkqj6u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.235, 0.079, 0.79, 0.113] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle with the text \"Investment allocation in 2019\"."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "qAaYpiuRJPuUGkif",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.834, 0.323, 0.834, 0.323] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext with the message \"61.4%\"."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "nWUr8TDpBijDFE7a",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the coordinates [0.205, 0.307, 0.56, 0.813] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice of the pie chart with color #ff9999."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "VoM5MBZmVbbwfczr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the pie slice}[0.303, 0.182, 0.847, 0.794] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at the coordinates}[0.205, 0.307, 0.56, 0.813] is adjacent to it."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "QV3f1NLkv5MPfeAk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the percentage {text}[0.218, 0.672, 0.218, 0.672] and the {pie slice}[0.205, 0.307, 0.56, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"38.6%\" represents the proportional representation of the corresponding pie slice in the chart."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "jBxieMWGCghKMKPY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext is associated with the {pie slice}[0.205, 0.307, 0.56, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"38.6%\" at coordinates [0.218, 0.672, 0.218, 0.672] is associated with the slice."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "tHfofIXUvv4Wj1lw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.205, 0.182, 0.847, 0.813]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mainly contains the entire pie chart with two slices. The first slice is at [0.205, 0.307, 0.56, 0.813] with autotext \"38.6%\" at [0.218, 0.672, 0.218, 0.672], and the other exploded slice is at [0.303, 0.182, 0.847, 0.794] with autotext \"61.4%\" at [0.834, 0.323, 0.834, 0.323]."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "EngHLWocAMneCsGC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.205, 0.307, 0.56, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart along with its autotext \"38.6%\" at [0.218, 0.672, 0.218, 0.672]."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "G54ReSvjFTQOl6Bt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_100.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.303, 0.182, 0.847, 0.794] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart that has been exploded out, along with an autotext \"61.4%\" at [0.834, 0.323, 0.834, 0.323]."
+            }
+        ],
+        "id_raw": 900
+    },
+    {
+        "id": "FETiMMAoxxwOTbNO",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.271, 0.496, 0.271, 0.496] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a text showing the percentage \"18.7%\"."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "8GP2tRmUDE7IjH7k",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.441, 0.647, 0.515, 0.67] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no object at these coordinates, the space is blank."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "8e5v94jESlWrrmMo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.389, 0.407, 0.462, 0.431] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a blank space at these coordinates, no object is present."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "GNAKaKFwmzPDQWeV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.488, 0.363, 0.686, 0.524]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.488, 0.52, 0.686, 0.587], [0.443, 0.322, 0.608, 0.52]] are adjacent to it."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "Oghi5OtsoIIzmK8M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.488, 0.52, 0.675, 0.635]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"4.4%\"}[0.681, 0.621, 0.681, 0.621] is located near the slice."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "G9q4YLCa9MYuM0Ou",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage text corresponds to the slice {the slice}[0.31, 0.54, 0.648, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"32.6%\" [0.471, 0.758, 0.471, 0.758] corresponds to the slice."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "Jp0whzq8ycTprEsZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.336, 0.327, 0.488, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice [0.336, 0.327, 0.488, 0.52] with the color '#d9bdf0', indicating a pie sector. There is also a text \"10.3%\" at coordinates [0.374, 0.334, 0.374, 0.334] which denotes the percentage of the sector."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "VJlCHhlAS4tmiunK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.29, 0.322, 0.686, 0.738]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie slices and their corresponding percentage texts, for example, the text \"18.7%\" [0.271, 0.496, 0.271, 0.496] and the corresponding slice [0.29, 0.393, 0.488, 0.611] with the color '#ff9999'. Another example is the text \"32.6%\" [0.471, 0.758, 0.471, 0.758] and the corresponding slice [0.31, 0.54, 0.648, 0.738] with the color '#66b3ff'."
+            }
+        ],
+        "id_raw": 903
+    },
+    {
+        "id": "36n25SfUcaHPLl2B",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the title of the chart based on the coordinates [0.277, 0.025, 0.723, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Stock Value Distribution\"."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "DooLveL0NNKKyNTz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.899, 0.548, 0.899, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The auto text \"20.0%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "kx62uKllerT3yrtV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific object is situated at the coordinates [0.323, 0.676, 0.396, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is essentially an empty text label in the chart."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "rmixJ85CUQaHqdDn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the slice}[0.519, 0.126, 0.827, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that is adjacent is the {slice}<r>[0.5, 0.328, 0.864, 0.754]</r>."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "jLjNuJiRb3hqNbSX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is closest to {the substring}[0.403, 0.132, 0.403, 0.132]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest object to this substring is {the slice}<r>[0.307, 0.157, 0.525, 0.52]</r>."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "iy6SHvoF3NmG6CV0",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext and slice in the pie chart associate with each other?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"50.0%\" aligns with the slice at coordinates [0.136, 0.286, 0.779, 0.884]."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "lMSYcJhSO3wnY0Sz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within the coordinates [0.136, 0.126, 0.864, 0.884]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with slices representing a breakdown of \"Market Share\" and \"Stock Value\" amongst various companies. These slices are color-coded for easy differentiation."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "W3U8jemTbqwvxLoB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements within the region defined by coordinates [0.519, 0.126, 0.827, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's a portion of the pie chart representing \"Diligent Company\" (if the order is from the given legend), with the exploded slice portion and with the autotext \"15.0%\"."
+            }
+        ],
+        "id_raw": 908
+    },
+    {
+        "id": "sG7ACQwKYSdAf2IS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.125, 0.122, 0.894, 0.155] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the title, labeled as \"Cuisine and Gastronomy Culture Analysis\"."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "gSsGD9v36nmkHNIf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.199, 0.234, 0.798, 0.83] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie chart visualizing cuisine types and their popularity."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "nefrOoDztg4iVWWF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the object at coordinates [0.248, 0.75, 0.248, 0.75]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label \"45.0%\"."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "ZtVChEMq5Uu64lGI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is inside the exploded slice {the slice}[0.199, 0.356, 0.64, 0.83] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"45.0%\"}<r>[0.248, 0.75, 0.248, 0.75]</r> is within the exploded slice."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "RP4tG2xrOvn5Rp7y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the relationship between {the text \"45.0%\"}<r>[0.248, 0.75, 0.248, 0.75]</r> and {the corresponding slice}<r>[0.199, 0.356, 0.64, 0.83]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"45.0%\" represents the percentage of the corresponding slice in the pie chart, indicating it's the popularity score of a certain cuisine type."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "XX00t5pOdMb3Zaeb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has its autotext at {the coordinates}<r>[0.803, 0.641, 0.803, 0.641]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates}<r>[0.509, 0.453, 0.798, 0.767]</r> has its autotext at these coordinates."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "bZugdFObfw0xxpXX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.199, 0.234, 0.798, 0.83]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the pie chart. The chart consists of four slices representing different cuisine types. Each slice has associated percentages expressed as autotext."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "zBitluQ9tDELT9lR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.199, 0.234, 0.798, 0.83]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region entirely comprises a pie chart with four slices. Each slice indicates a different cuisine, and they have their percentages, shown as autotext, ranging from 15.0% to 45.0%."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "mfW4tNkyAqxAew0q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_110.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be identified in the area enclosed by [0.509, 0.453, 0.798, 0.767]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a slice of the pie chart colored in '#66b3ff'. It has an autotext label of \"20.0%\" located at [0.803, 0.641, 0.803, 0.641]."
+            }
+        ],
+        "id_raw": 910
+    },
+    {
+        "id": "3VBwilhzGRx0OiXi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is identified by the coordinates [0.154, 0.509, 0.846, 0.534] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is named \"Media and Entertainment Industry Trends in 2021\"."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "GG6NMSHlkYEVtmEu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object depicted in coordinates [0.513, 0.772, 0.682, 0.932] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie slice representing \"Theater\"."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "Xi9Yb0zKbG3920jG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be found at the coordinates [0.694, 0.743, 0.816, 0.761] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text that reads \"10.9%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "YfKQndPlMgEm7Ou5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices in the chart are adjacent to {the slice} [0.513, 0.772, 0.682, 0.932]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent slices are {the \"Television\" slice} [0.407, 0.761, 0.574, 0.937] and {the \"Virtual Reality\" slice} [0.5, 0.694, 0.677, 0.813]."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "Vb0HBRaPYzZ9FLy3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are nearby {the subtitle} [0.154, 0.019, 0.846, 0.044]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"Music\"}[0.382, 0.806, 0.436, 0.823] and {\"Theater\"}[0.566, 0.833, 0.62, 0.851] are near the subtitle."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "Lfw0sn6NA3FjJWvx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is associated with the text \"Esports\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice associated with the text \"Esports\" has the coordinates [0.5, 0.588, 0.664, 0.761]."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "jFrJ6lb6NazTz3nw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.323, 0.647, 0.5, 0.77]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a pie slice marked as {\"Movie\"} [0.372, 0.718, 0.427, 0.736] and the associated percentage {\"12.0%\"} [0.264, 0.691, 0.316, 0.708]."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "DpYK8oNY3nRdl8dy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_111.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the coordinates [0.5, 0.694, 0.677, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, there is a pie slice annotated with {\"Virtual Reality\"}[0.578, 0.747, 0.633, 0.765] and the corresponding percentage {\"10.9%\"}[0.694, 0.743, 0.816, 0.761]."
+            }
+        ],
+        "id_raw": 911
+    },
+    {
+        "id": "31tOsUCcsShQ8RAf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.021, 0.201, 0.939, 0.234] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle of the chart, \"Tourism and Hospitality Industry Analysis in 2020\"."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "4ub2OoYMQfNLfxpl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.502, 0.364, 0.575, 0.387] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This coordinate does not contain any text."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "SQm9StCJ65PSo1iS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.388, 0.748, 0.388, 0.748] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"60.0%\"."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "M9ZikvvmXHzucRuJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near the {slice}[0.257, 0.377, 0.703, 0.743] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {text}<r>[0.388, 0.748, 0.388, 0.748]</r> is located near the slice."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "Eh1aBZBN6ybjdsTP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is closer to the {center coordinates}<r>[0.48, 0.52]</r> of the pie in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.257, 0.377, 0.703, 0.743]</r> is closer."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "oppjBgN4zzNW6IAY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDo the slices' coordinates correspond to the pie's center coordinates [0.48, 0.52] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the slices' coordinates align with the pie's center coordinates."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "HpNfzmWjaiM1rpcw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.257, 0.277, 0.712, 0.743]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice [0.257, 0.377, 0.703, 0.743] and the text \"60.0%\" [0.388, 0.748, 0.388, 0.748]. This region is located in the upper part of the pie, coded with the color '#ff9999'."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "ujwP7rz1CCaQXzy7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_115.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.257, 0.277, 0.712, 0.743]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice [0.257, 0.377, 0.703, 0.743] and the text \"60.0%\" [0.388, 0.748, 0.388, 0.748]."
+            }
+        ],
+        "id_raw": 915
+    },
+    {
+        "id": "MwgLKhAoKuusC2KE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.736, 0.717, 0.736, 0.717] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"21.3%\"."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "RINmTt98z88YGDfA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what the object at coordinates [0.92, 0.506, 0.92, 0.506] is in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is no object at the coordinates [0.92, 0.506, 0.92, 0.506]."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "1jYPgAjKY4yM31Iz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what can be found at coordinates [0.556, 0.317, 0.628, 0.34] in the graph?"
+            },
+            {
+                "from": "gpt",
+                "value": "The exploded slice of the pie chart is located at these coordinates."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "3YmVQPo8VxGEk9GO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pieces of the pie chart are adjacent to the slice located at [0.248, 0.33, 0.52, 0.506]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices at {coordinates}[[0.311, 0.251, 0.52, 0.506], [0.247, 0.476, 0.52, 0.681]] are adjacent to it."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "h8DoIxi27sm4Puvu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located near {the slice at coordinates}[0.431, 0.207, 0.779, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"24.0%\" at coordinates [0.643, 0.202, 0.643, 0.202] and the slice at coordinates [0.311, 0.251, 0.52, 0.506] are located near it."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "uubZHikelrQwu08a",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is closest to the text labeled {\u201c16.0%\u201d}[0.410, 0.786, 0.410, 0.786]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {coordinates}[0.310, 0.506, 0.555, 0.78] is closest to the text labeled \u201c16.0%\u201d."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "vkxVrJPM5M28fdbm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects located in the region marked by coordinates [0.430, 0.180, 0.790, 0.490]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a piece of the pie chart at coordinates [0.431, 0.207, 0.779, 0.481] and the text \"24.0%\" at coordinates [0.643, 0.202, 0.643, 0.202]. This region is roughly centered toward the bottom of the graph."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "o67e1LzbuF2E6cfD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.240, 0.320, 0.530, 0.520] of the provided chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region of the chart, one can find a slice of the pie chart at coordinates [0.248, 0.33, 0.52, 0.506] and the corresponding text \"9.3%\" at coordinates [0.243, 0.388, 0.243, 0.388]."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "nswRKtEe8SZCtayJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_118.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.290, 0.200, 0.560, 0.790]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains slices of the pie chart at coordinates [0.247, 0.476, 0.52, 0.681] and [0.31, 0.506, 0.555, 0.78] as well as the corresponding percentages \"12.8%\" and \"16.0%\" respectively displayed at coordinates [0.232, 0.593, 0.232, 0.593] and [0.41, 0.786, 0.41, 0.786]."
+            }
+        ],
+        "id_raw": 918
+    },
+    {
+        "id": "JZGKqKECmp66fUPj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object positioned within coordinates [0.223, 0.134, 0.777, 0.168] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is a subtitle, which reads \"Social Demographic Research\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "CPJ2MX0ihxk2wzQv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object can be observed at the coordinates [0.789, 0.5, 0.789, 0.5] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object located at these coordinates is the text \"22.5%\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "kdOMaIOkjsFJdN4v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object found at the coordinates [0.498, 0.214, 0.498, 0.214] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object located at these coordinates is the text \"27.5%\"."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "Ng3kC7sfHLQHS8Ig",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the slices adjacent to {the slice}[0.213, 0.326, 0.475, 0.518] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices adjacent to the mentioned slice are {the slices}[[0.239, 0.503, 0.5, 0.739], [0.299, 0.241, 0.698, 0.503]]."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "Gi9UWfC4t7bjxGAG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the pie chart are next to the {slice}[0.387, 0.503, 0.531, 0.765]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices located next to it are {the slices}[[0.239, 0.503, 0.5, 0.739], [0.5, 0.503, 0.61, 0.763]]."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "DA74XwmU7q0oSmfw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices border the {slice}[0.5, 0.331, 0.762, 0.672] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Bordering the referred slice are {the slices}[[0.5, 0.503, 0.639, 0.741], [0.299, 0.241, 0.698, 0.503]]."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "cGKam0fuIHxBFDCC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region encapsulated within the coordinates [0.213, 0.326, 0.475, 0.518]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart that represents 12.5% of the whole. The slice has the color #ff9999, indicating it is distinct from neighboring slices."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "DNmWHDKYFp5weVpg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.5, 0.503, 0.61, 0.763] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie segment representing 5% of the whole pie chart. The slice is colored with #ffcc99."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "aaJOCpwt8xARvZOE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide a description of the region enclosed by the coordinates [0.5, 0.331, 0.762, 0.672]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart, which represents 22.5% of the whole. Its coloring is #c2c2f0, distinguishing it from the surrounding segments."
+            }
+        ],
+        "id_raw": 920
+    },
+    {
+        "id": "DU0jUiTHY3TT9DYC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.169, 0.174, 0.794, 0.199]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle of the chart, titled \"Research Fields in Health and Medical Sector\"."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "B2nRJaUCh1h99Fww",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.213, 0.22, 0.725, 0.753] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart, which contains slices representing different research fields."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "vl6O1R3s4ibdcZ4Z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.562, 0.764, 0.562, 0.764] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.0%\", which is a percentage label for a slice of the pie chart."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "8oersciknKmQBRPe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the chart is adjacent to {the slice}[0.464, 0.509, 0.635, 0.753]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.275, 0.509, 0.481, 0.752] is adjacent."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "LsoDaE4LUb4gwszh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the slice next to {the slice}[0.213, 0.351, 0.457, 0.636]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value of the slice is \"15.0%\"."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "IZF3TxVmOuXoKFff",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, what does the slice at coordinates [0.213, 0.351, 0.457, 0.636] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the research field assigned 20% of total research, which is the Cardiovascular field."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "LMH19nCkcbkuQzDC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nProvide descriptions of the region within coordinates [0.213, 0.22, 0.725, 0.753]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region constitutes the majority of the center of the chart. It contains a pie chart that depicts the statistical distribution of different research fields in the Health and Medical Sector."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "uFm1vdRlg4oCxp9T",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.481, 0.278, 0.649, 0.509]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slices of the pie chart representing the research fields with 7% and 8% of total research, i.e., Respiratory and Infectious Diseases."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "PjCXouVq8lECcCW5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_122.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region [0.213, 0.351, 0.457, 0.636]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is located within the pie chart and contains one slice that exploded out, representing the Cardiovascular field. The corresponding text for this slice is 20%."
+            }
+        ],
+        "id_raw": 922
+    },
+    {
+        "id": "145h7G3vw8quZMv6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat statement is located at coordinates [0.265, 0.674, 0.265, 0.674] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percent figure \"11.2%\"."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "tXbSzYRQRaaC1BwW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object with the coordinates [0.49, 0.808, 0.49, 0.808] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the numerical label \"20.2%\"."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "b9cJdRiZR1kBJbpI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented at the coordinates [0.045, 0.18, 0.913, 0.214] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the chart, which reads \"Artificial Intelligence Application Case Analysis\"."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "HmbrOWEJLLmnVC6O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object neighboring {the slice}[0.296, 0.283, 0.479, 0.52] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is adjacent to {the slice}[0.447, 0.281, 0.67, 0.52]."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "bQ8Uya5pa20bJCIO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie section is adjacent to the exploded slice {the slice}[0.345, 0.544, 0.629, 0.784]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie sections with {the coordinates}[[0.248, 0.52, 0.479, 0.718], [0.479, 0.52, 0.719, 0.708]] are adjacent."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "AoFErSsW58KtfD3O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are surrounding the pie center [0.479, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The surrounding objects are pie{slices}: \n[[0.24, 0.366, 0.479, 0.584], [0.248, 0.52, 0.479, 0.718], [0.345, 0.544, 0.629, 0.784], [0.479, 0.52, 0.719, 0.708], [0.479, 0.376, 0.719, 0.525], [0.447, 0.281, 0.67, 0.52], [0.296, 0.283, 0.479, 0.52]] attached with percent indicators such as \"15.4%\", \"11.2%\", and \"20.2%\"."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "vJ46mTUcehRJeShl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is contained within the region [0.24, 0.281, 0.479, 0.718]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes pieces of the pie chart, color-coded with the respective representations, attach with the numerical values \"15.4%\" and \"11.2%\"."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "WUQzJvhmvfiPORLD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_125.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the content within the coordinates [0.296, 0.283, 0.479, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a section of the pie chart that represents \"11.8%\" of the distribution."
+            }
+        ],
+        "id_raw": 925
+    },
+    {
+        "id": "Ckwr82FPp5gqvcOn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart figure with the coordinates [0.112, 0.132, 0.888, 0.908]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main pie chart."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "soVVkH1DCmzmcOeJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at [0.016, 0.092, 0.984, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, \"Building and Real Estate Market Trends (2012-2020)\"."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "d4sySwkM4nyaZlx8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.204, 0.351, 0.204, 0.351] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"5.7%\"."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "K6x4VME8kEzy4g0N",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is to the left of the {slice}<r>[0.5, 0.225, 0.802, 0.52]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.262, 0.21, 0.596, 0.52]</r> is to the left."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "6n3F2orD9DJbUA9W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is next to the {autotext}<r>[0.285, 0.786, 0.285, 0.786]</r> in clockwise direction?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {autotext}<r>[0.454, 0.859, 0.454, 0.859]</r> of the pie chart is next in clockwise direction."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "imyDfJe6ZzLoSfhX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies directly beneath the {slice}<r>[0.5, 0.52, 0.74, 0.826]</r> of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.5, 0.447, 0.81, 0.717]</r> lies directly beneath."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "RqIpteANmt1iizAG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.367, 0.52, 0.553, 0.831] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice with color '#c2c2f0' of the pie, as well as an autotext \"9.8%\" at coordinates [0.454, 0.859, 0.454, 0.859]."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "EGOmKsCe6JZqUWgN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.5, 0.447, 0.81, 0.717]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie with color '#66b3ff' accompanied by the autotext \"14.7%\" at coordinates [0.833, 0.596, 0.833, 0.596]. This part of the pie chart corresponds to the right middle section."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "yiwjUNGVrVfmwdSw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.208, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a pie slice encoded in color '#ff9999', along with the autotext \"5.7%\" located at [0.204, 0.351, 0.204, 0.351]."
+            }
+        ],
+        "id_raw": 926
+    },
+    {
+        "id": "sQ5VBILODFRBAF2c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.102, 0.092, 0.898, 0.125] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, called Climate Change Impact from 2010 to 2014."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "Lvv1f64Fx0JgBBbU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.5, 0.52, 0.5, 0.52] in the center of the chart ?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie chart."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "VQ2BsVsycnOMjIVK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat decorative element in the chart has coordinates [0.103, 0.52, 0.103, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The y-axis label."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "bpXGWlQ9R4DR6r2i",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are next to the {slice}[0.246, 0.52, 0.5, 0.826]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slices}[[0.19, 0.495, 0.5, 0.698], [0.452, 0.55, 0.701, 0.861]] are next to it."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "e36Ya4Zp2YGamLka",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the ratio of the {slice}[0.5, 0.299, 0.792, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The ratio of this slice is \"7.1%\"."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "d4hHMH6NIKwvvMHh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices are adjacent to the {slice}[0.5, 0.415, 0.81, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slices}[[0.5, 0.516, 0.81, 0.763], [0.5, 0.299, 0.792, 0.52]] are adjacent to it."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "6kJ6sMOT7jjgh5lP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.5, 0.415, 0.8, 0.6]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains three slices: {the dark red slice}[0.5, 0.415, 0.81, 0.52] with ratio \"5.3%\", {the blue slice}[0.5, 0.516, 0.81, 0.763] with ratio \"14.5%\", and part of {the light orange slice}[0.452, 0.55, 0.701, 0.861] with ratio \"13.6%\". This region is located in the top right part of the chart."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "mS16Y0YY9tNy63xE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists within the region defined by the coordinates [0.1, 0.2, 0.4, 0.6]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of {the light red slice}[0.191, 0.321, 0.5, 0.52] with a ratio of \"9.8%\" and part of {the blue slice}[0.19, 0.495, 0.5, 0.698] with a ratio of \"11.0%\". Also, part of the chart's boundary is in this region."
+            }
+        ],
+        "id_raw": 927
+    },
+    {
+        "id": "LWE93XOpice3Hk5d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.082, 0.054, 0.943, 0.077]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main title of the chart, \"Ecological Environment Sustainability Assessment for 2010 and 2020\"."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "PVTNUTBQmTrwLMZB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.325, 0.421, 0.364, 0.436]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Water Quality\"."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "yancqRTTOTUP4qLW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.851, 0.501, 0.933, 0.516] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the percentage \"11.7%\"."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "kMV5Mo0awMAZ2Lyy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with {the pie slice}[0.415, 0.536, 0.664, 0.844]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated text is \"Weather Patterns\"."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "SW4xPKMV51zjddZd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich piece of chart data is adjacent to {the pie slice}[0.536, 0.525, 0.822, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {the adjacent slice}[0.415, 0.536, 0.664, 0.844] is adjacent."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "dTVcfA8IvGdxXJB8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the pie slice}[0.277, 0.197, 0.611, 0.505] and {its associated text}[0.438, 0.319, 0.487, 0.335]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the \"Weather Patterns\" category."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "nBM91LG5adi0cfKQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.205, 0.197, 0.822, 0.844]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart with various slices and labels such as \"Water Quality\", \"Air Quality\", and \"Forest Coverage\". Each slice has an associated percentage value and color, representing the proportion for each category in the Ecological Environment Sustainability Assessment for 2010 and 2020."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "pVSUVE7t72Apb2wn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the objects in the region [0.512, 0.397, 0.82, 0.619]."
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice, colored with #66b3ff and associated with the text \"11.7%\", indicating it represents 11.7% of the total."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "meLtQHS7Kse8fP1J",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_128.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by [0.512, 0.213, 0.801, 0.505]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie slice representing the category \"Forest Coverage\", and its accompanying label showing 14.1%."
+            }
+        ],
+        "id_raw": 928
+    },
+    {
+        "id": "JBKE1GNNQQYSYlqo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object in the chart rests at coordinates [0.217, 0.139, 0.769, 0.173]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Cryptocurrency Value in 2020\"."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "FZ9Pqb2yqwbPtbC7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.395, 0.832, 0.395, 0.832] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"62.5%\"."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "5pcBWeDdtGWtr9M7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at [0.285, 0.298, 0.493, 0.52] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Slice with the color '#c2c2f0' and the autotext \"4.1%\"."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "n8rWerMm4f1JuxwH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {this slice}[0.285, 0.298, 0.493, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{This slice}[0.336, 0.264, 0.493, 0.52] is adjacent."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "awiV7euSW4GPJDqZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich subregion is positioned near {the center of the pie chart}[0.493, 0.52, 0.493, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subregion {the slice}[0.493, 0.312, 0.765, 0.52] is positioned near the center of the pie chart."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "wleAIULQaxGtQ7Zt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe autotext \"4.1%\" corresponds to which slice in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"4.1%\" corresponds to the {slice}[0.285, 0.298, 0.493, 0.52]."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "uEUYh5hHwAVXp3o5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region within boundaries [0.213, 0.248, 0.765, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slice with a center at coordinates [0.493, 0.52, 0.493, 0.52] and colored '#ff9999'. The autotext \"62.5%\" at coordinates [0.395, 0.832, 0.395, 0.832] is also part of this region."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "JB0W15hX5Q0zHHGa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in the region [0.402, 0.248, 0.493, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a pie chart slice with the color '#c2c2f0' and an autotext \"4.7%\" at coordinates [0.435, 0.226, 0.435, 0.226]."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "h8VaNlvEuW4qMcYr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is in the region delineated by coordinates [0.213, 0.248, 0.765, 0.819]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses an exploded pie chart slice that is '#ff9999' in color with the autotext \"62.5%\" found at [0.395, 0.832, 0.395, 0.832]."
+            }
+        ],
+        "id_raw": 931
+    },
+    {
+        "id": "3WBraXpAhjuA0FvL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Number of Graduates in Physics and Chemistry from 2016 to 2021\"{subtitle}<r>[0.087, 0.017, 0.913, 0.039]</r>."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "5HQUim0CIzMHU0U2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at coordinates [0.877, 0.081, 0.956, 0.096]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend item for \"Chemistry\"."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "jPFPI2wG5pO5QqAR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the year \"2020\" mentioned in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2020\" is located at these coordinates: [0.447, 0.729, 0.486, 0.744] on the pie chart and [0.488, 0.28, 0.527, 0.296] on the legend."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "uR1pte2g3N9DYlJn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring slices to the slice at [0.5, 0.514, 0.74, 0.887] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The neighboring slices are at the coordinates [0.349, 0.514, 0.544, 0.889] and [0.5, 0.514, 0.85, 0.802]."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "w0WBVoXIuWiagFRh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices at [0.124, 0.41, 0.5, 0.576] and [0.129, 0.514, 0.5, 0.739]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Physics\" at [0.877, 0.058, 0.934, 0.073] and \"Chemistry\" at [0.877, 0.081, 0.956, 0.096] correspond to these slices."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "9Okcaj6u5rCjqaWi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has its percentage displayed at [0.663, 0.886, 0.702, 0.901]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at [0.5, 0.514, 0.74, 0.887] has its percentage displayed at that position."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "DHRHxXEsUKKese9p",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.124, 0.118, 0.876, 0.889]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart which consists of several slices representing different years. Each slice corresponds to a different year and includes text and autotext objects indicating the year and corresponding percentage."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "UV5NX7h1I0kTaQBF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents within the region [0.828, 0.051, 0.962, 0.103]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a legend containing two entries. The first is a label for Physics represented in the color '#ff7f50' and the second is a label for Chemistry represented in the color '#daa520'."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "ZA57KEVeFrHYqm7q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found in the region [0.255, 0.493, 0.294, 0.509]"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, the text \"2017\" associated with the pie chart is located."
+            }
+        ],
+        "id_raw": 933
+    },
+    {
+        "id": "kEzYXbiOmqb5mnz4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the main title located at [0.049, 0.025, 0.951, 0.058] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is labeled as \"Population distribution: Asia vs Rest of the world\"."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "KPhumpfCzREGMqBn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.591, 0.119, 0.788, 0.142]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"35.1%\"."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "0o1tw5HP8Y7Nqu4d",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.513, 0.296, 0.586, 0.319]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Rest of the world\"."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "8JnD59xUWYluHHa2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does the percentage text {35.1%}[0.591, 0.119, 0.788, 0.142] correlate with?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice} representing the \"Rest of the world\"[0.221, 0.157, 0.854, 0.52]."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "MfGuJs8Ki0mEIHhF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slices {Asia}[0.128, 0.322, 0.855, 0.919] and {Rest of the world}[0.221, 0.157, 0.854, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The two slices form the entire pie chart, representing population distributions of Asia and the Rest of the world respectively."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "ANP87nAF8vkmGjCl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is correlated with the percentage {64.9%}[0.352, 0.934, 0.4, 0.957] located on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"64.9%\" is linked to the {pie slice} representing Asia[0.128, 0.322, 0.855, 0.919]."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "G3qzCoShNhhSp8EQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region defined by the coordinates [0.221, 0.157, 0.854, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice denoted as \"Rest of the world\" and a portion of the pie chart representing 35.1% of the total."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "XydpCc8ShsR2zruC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_135.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.128, 0.322, 0.855, 0.919]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a pie slice associated with \"Asia\", representing 64.9% of the chart which is substantially larger than the other slice."
+            }
+        ],
+        "id_raw": 935
+    },
+    {
+        "id": "098ILnYdr7y1jmbr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could the object with coordinates [0.318, 0.603, 0.318, 0.603] in the chart be referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "Percentage text, reading \"34.8%\"."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "Tiw4m2jYK66p8UU0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich element is situated at [0.32, 0.355, 0.648, 0.697] coordinates in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The whole pie chart."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "V2VsuQNmDBiaynIz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the location [0.051, 0.267, 0.918, 0.3] reference in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, that states \"Referendum Votes for Different Political Trends\"."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "0FoQzSg887nmQLdI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices lie adjacent to the {slice}<r>[0.351, 0.355, 0.477, 0.519]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slices} [[0.32, 0.428, 0.484, 0.697], [0.476, 0.369, 0.562, 0.533]] are adjacent."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "J9fTyJv43ZPWFToM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the text located concerning the {slice}<r>[0.484, 0.389, 0.648, 0.621]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"26.1%\"<r>[0.659, 0.487, 0.659, 0.487]</r> is regarding the slice."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "2N8ZNQY93wUtQYn8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is closest to the {slice}<r>[0.471, 0.533, 0.623, 0.697]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.4%\"<r>[0.565, 0.695, 0.565, 0.695]</r> is closest."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "7xRt0xXqINxGLBYA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.484, 0.389, 0.648, 0.621]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie chart coloured in \"#99ff99\" and the related percentage text \"26.1%\"<r>[0.659, 0.487, 0.659, 0.487]</r>. This slice represents a section of the pie corresponding to a certain political trend in referendum voting."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "CclCtyGfnphgxhB1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area marked by coordinates [0.476, 0.369, 0.562, 0.533] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "It encloses a slice of pie in \"#ffcc99\" color, and associated with it is the percentage text \"8.7%\"<r>[0.524, 0.357, 0.524, 0.357]</r>. This section represents a certain political trend."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "3b5FFSrYX3NNx1CZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you inform me about the area within coordinates [0.32, 0.355, 0.648, 0.697]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the whole pie chart which is divided into different slices. Each slice has its own color and represents the referendum votes of different political trends."
+            }
+        ],
+        "id_raw": 936
+    },
+    {
+        "id": "TniWDlqcWnyqDDQV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label denoted by the coordinates [0.275, 0.429, 0.319, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is blank, it appears to be an identifier for a pie slice."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "gisIPcuUUj6mLfVp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data point is represented by the coordinates [0.217, 0.666, 0.217, 0.666]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point corresponds to the text \"12.6%\"."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "HgNae4rN0iaBsQ7P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.324, 0.58, 0.368, 0.594]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a blank text, which may indicate an identifier for a pie slice."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "1E0VLQY61VZ9C3Yo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data labels are related to the {pie slice}[0.526, 0.547, 0.734, 0.847] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The labels {\"10.2%\"}[0.667, 0.847, 0.667, 0.847] and {\"\"}[0.581, 0.704, 0.625, 0.718] are related to this pie slice."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "IEQRuax2nCGHZ16O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to the {slice with the label \"13.9%\"}[0.269, 0.191, 0.501, 0.492]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {\"\"}[0.5, 0.191, 0.724, 0.492] and {\"\"}[0.169, 0.29, 0.471, 0.531] are adjacent to it."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "rRDOUNrXFDrFTHJn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the pie slices are next to the {slice labeled as \"13.6%\"}[0.169, 0.29, 0.471, 0.531]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {\"\"}[0.5, 0.191, 0.724, 0.492] is adjacent to it."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "kFlj0nxZLgjvFAaA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.323, 0.492, 0.536, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area is a segment of the pie chart with the slice color \"#99ff99\". It is associated with the text \"11.9%\" [0.417, 0.814, 0.417, 0.814] and does not contain any explicit labeling [0.433, 0.661, 0.477, 0.675]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "OO5EHosyTwNX9rRp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_139.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region between [0.5, 0.29, 0.802, 0.512]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart segment with associated text \"12.7%\" [0.813, 0.383, 0.813, 0.383] and no explicit label [0.649, 0.426, 0.693, 0.44]."
+            }
+        ],
+        "id_raw": 939
+    },
+    {
+        "id": "Q2zf7h84oNvdxKfW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the pie chart in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Gaming Industry Revenue from 2014 to 2020\"{subtitle}<r>[0.079, 0.092, 0.921, 0.125]</r>."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "GI6cJvc1W105gvIM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sector corresponds to the coordinates [0.193, 0.321, 0.5, 0.52]? What percentage does it represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice is colored '#ff9999' and it represents \"8.7%\" of the whole{slice}<r>[0.193, 0.321, 0.5, 0.52]</r>."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "WN93dSL7LKdBSBEq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is located at the coordinates [0.189, 0.379, 0.189, 0.379]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"8.7%\"{text}<r>[0.189, 0.379, 0.189, 0.379]</r>."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "Lc2EEzRY76uzeGRJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice with color '#66b3ff' lies next to the slice {the slice}[0.193, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [[0.19, 0.473, 0.5, 0.662]] is next to it."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "7Qltz4HO9XkCTImU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the text next to the slice {the slice}[0.224, 0.52, 0.5, 0.804]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"10.8%\"{autotext}<r>[0.266, 0.769, 0.266, 0.769]</r> is next to the slice."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "xFbaYFPiqkcQ9kk3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the slice {the slice}[0.262, 0.21, 0.646, 0.52] next to the slice {another slice}[0.5, 0.246, 0.81, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, they are adjacent to each other."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "InyHcEkKarUK5EtH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.19, 0.21, 0.81, 0.862]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains seven sectors of the pie chart, representing the annual percentage share of gaming revenue from 2014 to 2020, with each year represented by a different color. These sectors are overlaid with text showing the percentage they represent in autotext. For example, the slice at coordinates [0.193, 0.321, 0.5, 0.52] has an autotext showing \"8.7%\" at coordinates [0.189, 0.379, 0.189, 0.379]."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "G4JfRQ4JyT7HCPah",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region [0.5, 0.246, 0.81, 0.544]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a slice of the pie chart with an autotext reading \"18.4%\" at coordinates [0.799, 0.356, 0.799, 0.356]."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "trrf88HPcpJzimK4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's in the region [0.262, 0.21, 0.646, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a slice of the pie chart, which represents a certain percentage of gaming industry revenue. The percentage represented by this slice is \"21.7%\"{autotext}<r>[0.435, 0.185, 0.435, 0.185]</r>."
+            }
+        ],
+        "id_raw": 940
+    },
+    {
+        "id": "Uh0vPlEBTSvMw5n0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object positioned at coordinates [0.069, 0.256, 0.784, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's subtitle, \"Importance of Global Financial Policies\"."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "L62dncW3VyCb80H2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is present at coordinates [0.661, 0.325, 0.977, 0.716] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "SvTzdHUoc8GLbhpm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.235, 0.476, 0.235, 0.476]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text that shows \"15.0%\"."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "oDHAfywAO6VWKLNm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are adjacent to {the slice}[0.426, 0.383, 0.578, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.426, 0.426, 0.605, 0.533], [0.426, 0.342, 0.541, 0.52]] are adjacent."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "2MD0QxmR8f4aN80P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near the {subtitle}[0.069, 0.256, 0.784, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Texts {\"15.0%\"}[0.235, 0.476, 0.235, 0.476] and {\"Interest Rate\"}[0.736, 0.369, 0.885, 0.392] are found near the subtitle."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "aCYxQVoy9dVmqqYc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"Interest Rate\", \"Inflation\", \"Taxation\", \"Trade Regulation\", \"Financial Regulation\", \"Monetary Policy\", \"Fiscal Policy\", \"Forex Policy\", \"Public Debt\", \"Private Debt\" correspond to the slices with coordinates [0.248, 0.405, 0.426, 0.564], [0.253, 0.52, 0.47, 0.699], [0.426, 0.52, 0.605, 0.694], [0.426, 0.426, 0.605, 0.533], [0.426, 0.383, 0.578, 0.52], [0.426, 0.342, 0.541, 0.52], [0.332, 0.341, 0.439, 0.52], [0.294, 0.354, 0.416, 0.506], [0.284, 0.376, 0.414, 0.508], [0.276, 0.385, 0.413, 0.508] respectively."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "JwGMJLizeyhVEd3N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between coordinates [0.248, 0.341, 0.605, 0.699] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of ten pie slices indicating the importance of different global monetary policies and the respective percentage text inside each slice."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "7h120SmeyRyALHHt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within [0.661, 0.325, 0.977, 0.716]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend with the title \"Policies\". There are different legend items inside this region corresponding to global financial policies like \"Interest Rate\", \"Inflation\", \"Taxation\", \"Trade Regulation\", \"Financial Regulation\", \"Monetary Policy\", \"Fiscal Policy\", \"Forex Policy\", \"Public Debt\", \"Private Debt\". It also lists associated colors for representation on the Pie chart."
+            }
+        ],
+        "id_raw": 941
+    },
+    {
+        "id": "tyWkGNSdSt3NapK2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.346, 0.453, 0.39, 0.467] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at these coordinates represents the text \"Ecological Contribution\"."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "XluZXYohCXkqbumN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.368, 0.59, 0.403, 0.604] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"Resource Utilization\"."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "2q8lZm1eWbvsdPel",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the main object with coordinates [0.22, 0.192, 0.78, 0.212] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle of the chart indicating \"Ecological Environment Sustainability Assessment\"."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "rn7pftYdjKVTdWYx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}[0.5, 0.34, 0.703, 0.512]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.467, 0.253, 0.669, 0.489] is adjacent to the indicated slice."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "XPn39lrIW7iGN82m",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near {the slice}[0.5, 0.512, 0.736, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Ecological Contribution\"<r>[0.607, 0.565, 0.651, 0.579]</r> is located near the slice."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "1BftNAZ3XlgT56Dp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_142.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects near {the text \"Resource Utilization\"}<r>[0.593, 0.416, 0.628, 0.43]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice<r>[0.5, 0.34, 0.703, 0.512]</r> and the autotext \"4.5%\"<r>[0.703, 0.342, 0.841, 0.356]</r> are located near the text \"Resource Utilization\"."
+            }
+        ],
+        "id_raw": 942
+    },
+    {
+        "id": "HRKkZSmvqRjjwMlF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart located at coordinates [0.128, 0.079, 0.897, 0.113]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Investments in Stocks from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "ZBVqYYFOr7aTFJn7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the percentage located at coordinates [0.205, 0.364, 0.205, 0.364] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage is \"8.5%\"."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "GdFq3xhQk1o024WV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.512, 0.354, 0.82, 0.72] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie slice."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "7SVaC6jJZfp841fx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.429, 0.505, 0.733, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.249, 0.505, 0.512, 0.801] is adjacent to it."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "KB8woUYI6A9JQbV3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage text is adjacent to {the slice}[0.512, 0.354, 0.82, 0.72]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.5%\" [0.848, 0.549, 0.848, 0.549] is adjacent to it."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "4iP3rVjE75iZi9Xb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices and the percentages?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each slice corresponds to a percentage that indicates its proportion in the total."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "vNkIWO3DzZ5MTukI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content present within the region with coordinates [0.205, 0.166, 0.82, 0.813]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart with various slices representing different proportions of Investments in Stocks. It includes percentages for each slice, such as \"8.5%\" [0.205, 0.364, 0.205, 0.364]."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "7RaOC6QUMLgbWThs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region between coordinates [0.429, 0.505, 0.733, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart colored '#ffcc99' and corresponding percentage \"17.1%\" [0.6, 0.832, 0.6, 0.832]."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "pdJHwwQg4WmH4ZZf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_144.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region spanning from coordinates [0.279, 0.166, 0.784, 0.474]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice that is 'exploded' from the pie chart colored '#ffb347'. It represents 30.7% of the total investment, as indicated by the percentage text [0.547, 0.137, 0.547, 0.137]."
+            }
+        ],
+        "id_raw": 944
+    },
+    {
+        "id": "dW50W8lqQJfgFbTT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the object at coordinates [0.295, 0.024, 0.705, 0.058]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the graph (Innovation Levels for 2010 and 2014)."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "e39HaTNbccyYwL78",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat item can you find at coordinates [0.5, 0.53, 0.5, 0.53] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center point of the pie chart."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "7V56POBb6ekJbWkT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.414, 0.704, 0.45, 0.727] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a label within a pie slice of the pie chart."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "wsNhfSQMPM3DIAdf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is connected to {the autotext}[0.565, 0.174, 0.565, 0.174]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice <r>[0.489, 0.19, 0.622, 0.5]</r> is connected."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "2qIzuCplJ4hv8FCg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the center of pie chart}[0.5, 0.53, 0.5, 0.53]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {9.1%}<r>[0.383, 0.847, 0.383, 0.847]</r> and {11.2%}<r>[0.701, 0.594, 0.701, 0.594]</r> are located near the center of the pie chart."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "x7eitm7G8jCOX4im",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the object at [0.516, 0.311, 0.56, 0.334] and the exploded slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at coordinates [0.516, 0.311, 0.56, 0.334] is the label of the exploded slice {[0.489, 0.19, 0.622, 0.5]}."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "t73ffzZkSt8x8pmX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.319, 0.331, 0.5, 0.53]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice from the pie chart colored '#ff9999' with autotext {7.5%}[0.317, 0.378, 0.317, 0.378. This area is located on the lower left side of the entire pie chart."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "v6qLpTKHJS1nMVxr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region [0.114, 0.19, 0.986, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "It encloses the entire pie chart, which has different slices and percentage annotations corresponding to each slice such as {7.5%}<r>[0.317, 0.378, 0.317, 0.378]</r>, {7.2%}<r>[0.295, 0.53, 0.295, 0.53]</r> and {8.1%}<r>[0.319, 0.688, 0.319, 0.688]</r>."
+            }
+        ],
+        "id_raw": 945
+    },
+    {
+        "id": "CWCAj0MPfftWY3Ao",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.118, 0.127, 0.886, 0.16] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, named Cuisine and Gastronomy Culture Analysis."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "4QFeZo1lOtHwUMue",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.816, 0.647, 0.816, 0.647] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text indicating \"30.0%\"."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "d0NKDBGzSsxwgZFc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat specific item is located at the coordinates [0.219, 0.238, 0.81, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "qB5bl4r44B3tbMXd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices of the chart are adjacent to {the slice}[0.528, 0.381, 0.81, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.246, 0.52, 0.57, 0.803], [0.502, 0.239, 0.741, 0.52]] are adjacent."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "I8ozeL50102kwj9X",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich of the text indications are near {the slice}[0.502, 0.239, 0.741, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"15.0%\"}[0.662, 0.254, 0.662, 0.254] is located near the slice."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "YBDSZxd9TW38vf6B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to {the slice}[0.246, 0.52, 0.57, 0.803] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"22.0%\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "gQZbSIOYnOAirFt8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.528, 0.381, 0.81, 0.805]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with an exploded label of \"30.0%\" [0.816, 0.647, 0.816, 0.647]. This part is highlighted, in the upper right area of the chart, corresponding to the green part of the pie."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "V2rkYaN7cH2Lvi4y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the region [0.246, 0.52, 0.57, 0.803]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice labeled with \"22.0%\" [0.368, 0.8, 0.368, 0.8], which corresponds to the part of the pie colored in a light blue shade."
+            }
+        ],
+        "id_raw": 947
+    },
+    {
+        "id": "YWePldqLZcp4gaI1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.197, 0.155, 0.855, 0.18] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the subtitle \"Manufacturing Production Efficiency Evaluation\"."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "jEZViQLGu2FVM4YL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.776, 0.379, 0.776, 0.379] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the autotext \"11.6%\"."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "aWRPqeg2NLqENJ94",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.634, 0.426, 0.69, 0.444] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a blank text field."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "mOhfjyihckEBgX6n",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are touching with {the slice}[0.526, 0.317, 0.778, 0.502]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.526, 0.479, 0.779, 0.672], [0.52, 0.249, 0.698, 0.502]] are adjacent."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "s8IxquahBZxZxQb5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are inside the {slice}[0.51, 0.526, 0.722, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"14.8%\"}[0.634, 0.786, 0.634, 0.786] is inside the slice."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "BP0GSpoC0hTlLuqg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs the autotext {\"14.8%\"}[0.634, 0.786, 0.634, 0.786] inside the {slice}[0.51, 0.526, 0.722, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "63RGujNHv2Oe6wPM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen in the area [0.248, 0.249, 0.779, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the main pie chart with multiple slices as well as autotexts indicating the percentage for each slice."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "fwlECxfhBPyNxbRh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within coordinates [0.274, 0.502, 0.526, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains part of a pie chart slice and has a text \"11.3%\"[0.277, 0.627, 0.277, 0.627] inside it."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "dTicVVBJ00W9zVGR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is inside the region between [0.526, 0.317, 0.778, 0.502] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart with an autotext of \"11.6%\"[0.776, 0.379, 0.776, 0.379] showing the percentage of that particular area."
+            }
+        ],
+        "id_raw": 949
+    },
+    {
+        "id": "aVTDrZuhtHYsbslR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.198, 0.214, 0.802, 0.248] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Chart subtitle that says \"Product Sales Trend Observation\"."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "SiVzgNjrKyZc5NDL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.307, 0.616, 0.307, 0.616] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Autotext \"12.3%\"."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "aM2uQtUUF9UHNjhQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the center of the pie chart located on the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart center is at coordinates [0.5, 0.507]."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "FUQ7924UudZtODEe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.515, 0.521, 0.716, 0.721]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.5, 0.413, 0.702, 0.52] is adjacent."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "mDKwShhHJ6JYRS8R",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice has the highest percentage and what is its color according to the pie chart structure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with highest percentage is {the slice}[0.515, 0.521, 0.716, 0.721] with a color value of '#ffcc99'."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "ewz8ZRlMOo7ukCtB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice corresponds to the autotext \"13.2%\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice that corresponds to the autotext \"13.2%\" has coordinates [0.298, 0.378, 0.5, 0.533]."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "oBKbWvwOewt0NlZL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.298, 0.305, 0.716, 0.721]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several pie slices and their respective autotexts and no text labels. The highest percentage slice, {\"21.9%\"}[0.677, 0.673, 0.677, 0.673], is located in this region."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "koltNlSvsNdGTz1X",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_153.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is present within the coordinates [0.298, 0.378, 0.5, 0.533]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates is a pie slice of the chart with the autotext {\"13.2%\"}[0.287, 0.445, 0.287, 0.445]. It is colored '#ff9999'."
+            }
+        ],
+        "id_raw": 953
+    },
+    {
+        "id": "YHixC4tTVRABJHHF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at coordinates [0.188, 0.381, 0.188, 0.381] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"8.8%\"."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "hxadaygYv81ARdnz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at these coordinates [0.262, 0.21, 0.604, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a slice of the pie chart."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "gmmLb9NeSJVfktpN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.414, 0.329, 0.488, 0.352] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text on the chart located at these coordinates is null or empty."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "42VR04F8xXRzPZHR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the pie chart is in relation to the text {the text \"14.7%\"}[0.859, 0.62, 0.859, 0.62]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at these coordinates [0.53, 0.47, 0.84, 0.736] corresponds to the text '14.7%'."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "DMdsBh8NUJ0dkLPr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text {the text \"19.3%\"}[0.41, 0.191, 0.41, 0.191] and any slice on the pie?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"19.3%\" corresponds to the {the slice}[0.262, 0.21, 0.604, 0.52] in the pie chart."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "QVEW8g5vTwWTOCyg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which slice in the pie chart corresponds to {the text \"8.8%\"}[0.188, 0.381, 0.188, 0.381]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice in the pie chart with coordinates [0.193, 0.321, 0.5, 0.52] corresponds to the text \"8.8%\"."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "ZGdYNQlfNCkL09i2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region in the coordinates [0.222, 0.52, 0.5, 0.797]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {a slice of the pie chart}[0.222, 0.52, 0.5, 0.797] with visual representation color '#99ff99' and the text \"10.3%\" [0.257, 0.761, 0.257, 0.761]."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "kGxiBdNGRrVmwBTC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region defined by these coordinates [0.193, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains {a slice of the pie chart}[0.193, 0.321, 0.5, 0.52] having the color '#ff9999' and the text \"8.8%\" [0.188, 0.381, 0.188, 0.381]."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "kKszNmf3bY6G6pTR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_156.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.36, 0.52, 0.534, 0.831]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents {a slice of pie chart}[0.36, 0.52, 0.534, 0.831] with color '#ffcc99' and has '9.2%' autotext [0.44, 0.856, 0.44, 0.856]."
+            }
+        ],
+        "id_raw": 956
+    },
+    {
+        "id": "z0sTH5TtSjoZJ2ac",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at the coordinates [0.096, 0.261, 0.858, 0.294]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Human Health and Disease Management\"."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "b3Okkljj90l9cOga",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at coordinates [0.601, 0.373, 0.601, 0.373] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.0%\" represents the percentage of the category in the pie chart."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "W24T4AAlwlX537D8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.296, 0.345, 0.652, 0.711]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a pie slice of the chart."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "hDDE3PziHahSkpdA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.343, 0.351, 0.477, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.435, 0.345, 0.652, 0.532] is adjacent to it."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "LWNyG5LN572sTS3p",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the texts at coordinates [0.399, 0.715, 0.399, 0.715], [0.601, 0.373, 0.601, 0.373], and [0.375, 0.357, 0.375, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These texts represent the percentage area of the corresponding slices in the pie chart."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "LpOqEse0OCFWQKL2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDoes {the slice}[0.435, 0.345, 0.652, 0.532] have an associating text?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, its corresponding text is \"30.0%\" at [0.601, 0.373, 0.601, 0.373]."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "4KsJfyaWwVbCIl80",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.296, 0.345, 0.652, 0.711]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is a part of the pie chart, representing a category. The region contains the pie slice \"[0.296, 0.424, 0.645, 0.711]\" with the color \"#ff9999\"."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "UEtU1b8cWBGTuuzh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region [0.435, 0.345, 0.652, 0.532]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart colored \"#66b3ff\" and it has a text \"30.0%\" at coordinates [0.601, 0.373, 0.601, 0.373]."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "wtdEyGHcrHvcf88f",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_163.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements in the region [0.296, 0.345, 0.652, 0.711]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is in the top part of the pie chart representing a category with a piece of pie slice colored \"#ff9999\" and a text of \"60.0%\" at coordinates [0.399, 0.715, 0.399, 0.715]."
+            }
+        ],
+        "id_raw": 963
+    },
+    {
+        "id": "rwRYxLw7b5Pf2OQv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart indicated by coordinates [0.1, 0.258, 0.928, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Proportion of Startups in Different Technology Areas\"."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "Ie0nL9hnIGcABOsM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object occupies coordinates [0.327, 0.639, 0.327, 0.639] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"40.0%\" is found at these coordinates."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "09MEb5VsF27knMnq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.469, 0.339, 0.632, 0.523]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is one of the chart slices."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "wF2BFDmrHy9bKmhn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.514, 0.382, 0.698, 0.535]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.514, 0.523, 0.697, 0.701], [0.469, 0.339, 0.632, 0.523]] are adjacent."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "lgRyY6JIJyQmbiJb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart slice does the text {the percentage}[0.372, 0.379, 0.372, 0.379] belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It belongs to {the chart slice}[0.373, 0.38, 0.514, 0.523]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "17bmlcqWXtsUfzqP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice corresponds to the text located at coordinates [0.327, 0.639, 0.327, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponding to the text can be found at coordinates [0.314, 0.414, 0.543, 0.716]."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "99FFLDAMCUF7lNbJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.373, 0.38, 0.514, 0.523]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice and text \"3.0%\" [0.372, 0.379, 0.372, 0.379]. This region is corresponding to a slice of the pie chart representing \"Virtual Reality\"."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "Ble5jI9BrGoqGNvK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_166.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is found in the region [0.314, 0.339, 0.698, 0.716]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice and also contains the text \"40.0%\" [0.327, 0.639, 0.327, 0.639]. This represents the biggest segment of the pie chart, denoting \"Artificial Intelligence\"."
+            }
+        ],
+        "id_raw": 966
+    },
+    {
+        "id": "RVaajxxwiPpt6nyV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text label is found at the coordinates [0.412, 0.898, 0.412, 0.898]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"25.0%\"."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "JAh6Sr1Lh9t9wvup",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.892, 0.543, 0.892, 0.543] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"30.0%\"."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "UrifoaHiGububXau",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.163, 0.076, 0.837, 0.101] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, and it's named \"Automotive and Transportation Market Research\"."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "aW8VC2VATOwT7SiY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart is outlined by the color \"#99ff99\" and what is the associated label?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice outlined by the color \"#99ff99\" is found at {coordinates}<r>[0.533, 0.267, 0.86, 0.795]</r> and is associated with the label \"30.0%\"."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "R5amNW5Qj4xyQqqY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text corresponds to the slice at {coordinates}<r>[0.14, 0.156, 0.86, 0.874]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at these coordinates corresponds to the text \"20.0%\" at {coordinates}<r>[0.108, 0.488, 0.108, 0.488]</r>."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "6v0qXmwSLLY0vmgq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slices with coordinates [0.428, 0.156, 0.718, 0.483] and [0.14, 0.303, 0.467, 0.686]?"
+            },
+            {
+                "from": "gpt",
+                "value": "These slices are adjacent to each other in the pie chart."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "5lLIftu8SbmRdXOw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.14, 0.156, 0.86, 0.874] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart including all its individual slices and their corresponding labels."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "RWAvd5ru0cz8dArG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region with coordinates [0.533, 0.267, 0.86, 0.795]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a specific slice of the pie chart associated with the label \"30.0%\" at coordinates [0.892, 0.543, 0.892, 0.543]."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "SCGj1Foia0nkiP6d",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_167.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the features present in the region [0.263, 0.275, 0.5, 0.515]."
+            },
+            {
+                "from": "gpt",
+                "value": "The features in this region include the slice of the pie chart associated with the label \"1.0%\"[0.247, 0.259, 0.247, 0.259]."
+            }
+        ],
+        "id_raw": 967
+    },
+    {
+        "id": "zBRcVAUux0lLFhvH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart title and its coordinates in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart title is \"Trade Handled by Shenzhen and Hong Kong Ports from 2010 to 2015\"<r>[0.02, 0.077, 0.98, 0.102]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "qZqqEQVh28OOgcTt",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.324, 0.417, 0.368, 0.434] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"Year_2010\"<r>[0.324, 0.417, 0.368, 0.434]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "D3CFieHs7O7qEKIm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.782, 0.347, 0.871, 0.364] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the autotext \"7.5%\"<r>[0.782, 0.347, 0.871, 0.364]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "xlAp5DnHx2Ypr7U2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to {the slice}<r>[0.209, 0.428, 0.5, 0.566]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} <r>[0.215, 0.507, 0.5, 0.698]</r> is adjacent to the given slice."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "mNfke7KbViwXU6pr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich part of the chart is related to the {text \"Year_2012\"} <r>[0.322, 0.576, 0.366, 0.593]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The part of the chart related to the {text \"Year_2012\"} <r>[0.322, 0.576, 0.366, 0.593]</r> is the {slice}<r>[0.215, 0.507, 0.5, 0.698]</r> with autotext \"8.1%\"."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "4AYePHyjnBz2TevZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the autotext \"10.1%\"<r>[0.242, 0.227, 0.331, 0.244]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to the autotext \"10.1%\"<r>[0.242, 0.227, 0.331, 0.244]</r> is the one with text \"Year_2015\"<r>[0.38, 0.35, 0.435, 0.368]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "JWVBo12VUSJZ4WFa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the area from [0.215, 0.507, 0.5, 0.698]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area contains a slice of the pie chart with the color '#99ff99' and the text \"8.1%\"<r>[0.124, 0.64, 0.213, 0.658]</r>, and the associated label \"Year_2012\"<r>[0.322, 0.576, 0.366, 0.593]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "PKUSJ4OWGPhn4ZeC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.41, 0.536, 0.582, 0.827]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an exploded slice of the pie chart with a color of '#c2c2f0'. The slice has an autotext \"9.6%\"<r>[0.406, 0.847, 0.496, 0.865]</r> and a corresponding label \"Year_2014\"<r>[0.476, 0.702, 0.52, 0.719]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "EHx5OkZIrycIzod4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can I find in the region between [0.277, 0.225, 0.5, 0.507]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice colored with '#b3b3cc' and has an autotext \"10.1%\"<r>[0.242, 0.227, 0.331, 0.244]</r> and a corresponding label \"Year_2015\"<r>[0.38, 0.35, 0.435, 0.368]</r>."
+            }
+        ],
+        "id_raw": 969
+    },
+    {
+        "id": "heaB5fNqo6erg6DH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title located at the coordinates [0.163, 0.092, 0.837, 0.125]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"FinTech Adoption from 2013 to 2020\"."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "jwnk631a4iQvORAe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object lies within the coordinates [0.262, 0.21, 0.529, 0.52] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a {slice}<r>[0.262, 0.21, 0.529, 0.52]</r> of the pie chart."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "ARnvRT6p3bU1iNrK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at the coordinates [0.677, 0.229, 0.677, 0.229] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"14.4%\"."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "o9DqOXMGKvHm4x4t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is adjacent to the {slice}[0.368, 0.551, 0.616, 0.862]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located at {coordinates}[0.5, 0.52, 0.795, 0.808] is adjacent to the referred slice."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "OY9Q5y7X5vGQW4we",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices border the {slice}[0.5, 0.211, 0.761, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {coordinates}[[0.5, 0.353, 0.81, 0.617], [0.5, 0.52, 0.795, 0.808]] border the queried slice."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "Lrq1hDifVEyvVSHq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is present inside the {slice}[0.197, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text inside the slice is \"7.7%\"."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "4BxjdFeEZqPSfIYi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.19, 0.21, 0.81, 0.862]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart. There are multiple slices in different colors, each labeled with text such as \"7.7%\", \"9.2%\", \"12.3%\", and so on."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "EOr6I2G2KJGNFMbg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.5, 0.21, 0.761, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart colored in '#ffb6c1', and inside the slice, there is the text \"14.4%\"."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "CbfoPk4pLwA6xtN4",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_171.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.262, 0.21, 0.529, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the chart in the color '#808080', and includes a percentage label \"15.4%\" inside the slice."
+            }
+        ],
+        "id_raw": 971
+    },
+    {
+        "id": "C39DwwyyVYRnGwm8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the sub-plot having coordinates [0.093, 0.281, 0.943, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is 'Efficiency Improvement Potential Assessment'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "HeiH76Iki6lAyVq2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice located at coordinates [0.534, 0.482, 0.693, 0.63] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice is '#c2c2f0'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "Yb3xHdvI4mfduPFm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label written inside the slice with coordinates [0.4, 0.52, 0.518, 0.679] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label inside the slice is '12.9%'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "gZBLrjJXZp7E5TmX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value of the autotext located inside {the slice}[0.529, 0.355, 0.683, 0.509]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext inside the slice is '16.8%'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "850bywnoqlsxQbax",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the theta range of {the slice}[0.513, 0.52, 0.637, 0.679] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The theta range of the slice is '268 -> 318'."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "HlGVXlkNsWYv7ZLF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is located adjacently above {the slice}[0.513, 0.52, 0.637, 0.679]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.534, 0.482, 0.693, 0.63] is located adjacently above."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "iIHEi8FS79ALFUpB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.518, 0.52, 0.587, 0.681] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises of a slice of the pie chart which falls under the color '#ffcc99' indicating a certain category or sector. Inside it, an autotext '13.9%' [0.587, 0.681, 0.587, 0.681] label is present."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "p1s7sZUR2u8xn6Pb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_174.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do the objects within the region coordinates [0.534, 0.482, 0.693, 0.63] signify?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart which is represented in color '#c2c2f0' that represents a specific sector. Also, an autotext label '15.8%' [0.704, 0.564, 0.704, 0.564] is present inside the slice which indicates the sector's percentage."
+            }
+        ],
+        "id_raw": 974
+    },
+    {
+        "id": "22vnfX72NzhFp2Xd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at coordinates [0.101, 0.356, 0.114, 0.37] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"10.4%\"."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "LI3NY7eLzfXW1imz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the main object located at the coordinates [0.333, 0.675, 0.369, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"3D Printing\"."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "yW7EwJw2EkoEttuV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the chart's coordinates [0.576, 0.95, 0.668, 0.964]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage value \"7.5%\"."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "Zbyec6AKxjo6Ngo3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage value is associated with {the slice}[0.183, 0.512, 0.5, 0.852]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage {\"9.0%\"}[0.149, 0.816, 0.227, 0.83] is related to this slice."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "1nvSyC6htySR0cOy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice adjacent to {the slice}[0.33, 0.549, 0.491, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {to the right} [0.482, 0.549, 0.654, 0.925] is adjacent."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "5AsKdYUNzdrrqxD8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between the text {\"Blockchain\"}[0.265, 0.564, 0.3, 0.578] and the percentages plotted on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "They represent the projected percentage investment in blockchain technology for the year 2025."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "Cvv4htb8S8jaKNyG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within the coordinates [0.5, 0.406, 0.876, 0.664]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the sector for \"Cybersec\" with the percentage \"11.2%\" [0.913, 0.532, 0.977, 0.546] displayed. The sector is in a light purple tone."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "JGBhzEkYFfyjCsYc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region defined by [0.5, 0.18, 0.861, 0.512]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a portion of the pie chart representing \"Big Data\", with a percentage value of \"12.7%\" [0.82, 0.243, 0.881, 0.257]. It is shown in a light purple color."
+            }
+        ],
+        "id_raw": 975
+    },
+    {
+        "id": "kwdk9SoCIDUfCz51",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.156, 0.182, 0.802, 0.215]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is named Distribution of Social Relationships."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "xyQWxR1ZJXjVoYM0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.24, 0.259, 0.717, 0.759] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "A pie chart."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "dtIzRZ7TiunIhfAs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.230, 0.438, 0.230, 0.438]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the piece of text reading \"12.0%\"."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "6VWArRPGMcpZKJji",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the chart is adjacent to {the slice}[0.24, 0.299, 0.709, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at {[0.479, 0.457, 0.717, 0.586]} is adjacent to it."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "GhIVMhLeGFCQLDzP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice contains {the text}[0.45, 0.781, 0.45, 0.781] within the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with the coordinates {[0.241, 0.52, 0.708, 0.759]} contains the given text."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "5D5NgIK9oDVUzdAk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with the exploded slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates {[0.479, 0.299, 0.709, 0.52]} is situated next to the exploded slice {[0.29, 0.259, 0.562, 0.497]}."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "AzICyhj0EjexVZ5W",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region described by coordinates [0.24, 0.259, 0.717, 0.759]."
+            },
+            {
+                "from": "gpt",
+                "value": "The specified region contains a pie chart comprising of slices with different percentages, with one slice exploded to stand out. The five slices indicate different social relationships, each with its associated percentage."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "JOEO5itfnp2WylVq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the section within the area [0.29, 0.259, 0.562, 0.497]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains an exploded slice of the pie chart that represents 20.0% of the total, as indicated by the text in its region."
+            }
+        ],
+        "id_raw": 979
+    },
+    {
+        "id": "NjOiZeXCBw8kY0kA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the chart with coordinates [0.266, 0.237, 0.716, 0.262]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart is named \"Patents filed by technology type\"."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "MRbEjDXOak4UrKJo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.277, 0.551, 0.277, 0.551] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the percentage text \"27.5%\"."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "xPhi3MSycSeJoOm6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the coordinates [0.348, 0.351, 0.348, 0.351]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"5.0%\"."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "4dAusn55i6AmUjgL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is associated with {the slice}[0.293, 0.388, 0.491, 0.685]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"27.5%\"<r>[0.277, 0.551, 0.277, 0.551]</r> is associated with this slice."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "9J3GjXk6KDpUDTyF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the text \"15.0%\"<r>[0.654, 0.66, 0.654, 0.66]</r> correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}<r>[0.491, 0.515, 0.683, 0.7]</r>."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "8cHUynLmP0DLp4Ey",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a relationship between {the slice}[0.385, 0.298, 0.576, 0.495] and the text \"16.0%\"<r>[0.478, 0.278, 0.478, 0.278]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, they are related as the text indicates the percentage of the slice in the pie chart."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "N4519jq3BFp6kcgy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.293, 0.298, 0.689, 0.713]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is the main pie chart area where various slices of different colors representing different proportions are located, such as \"27.5%\"<r>[0.277, 0.551, 0.277, 0.551]</r>, \"2.0%\"<r>[0.705, 0.557, 0.705, 0.557]</r> and \"11.0%\"<r>[0.704, 0.469, 0.704, 0.469]</r>."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "rwsYWzY8EYkhT2pA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area [0.385, 0.298, 0.576, 0.495]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains a pie slice with the percentage text \"16.0%\"<r>[0.478, 0.278, 0.478, 0.278]</r>, and this slice is slightly exploded from the main pie chart for emphasis."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "fwUI1dx48bTJk1yh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.293, 0.388, 0.491, 0.685] encompass?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a slice of the pie chart along with its corresponding percentage text \"27.5%\"<r>[0.277, 0.551, 0.277, 0.551]</r>."
+            }
+        ],
+        "id_raw": 980
+    },
+    {
+        "id": "I2pvf8FcK9lf5yFE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at the coordinates [0.236, 0.0, 0.764, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The phrase \"Market Competition Analysis and Comparison\"."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "Z92ZCE0JYXP4qryd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at [0.161, 0.232, 0.345, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Consumer Segment\"."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "EQOYJu0libJDwjVf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.126, 0.287, 0.39, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "One of the pie charts representing the Consumer Segment market competition."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "RF0E1pGbhlsmCw7q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the slice at coordinates [0.263, 0.549, 0.39, 0.812]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates {slice} [0.253, 0.456, 0.38, 0.541] is adjacent."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "J3IVUF7AjbBngDhu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of the text at coordinates [0.797, 0.806, 0.892, 0.847] to the slice at coordinates [0.747, 0.541, 0.812, 0.794]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"6.9%\" represents the percentage proportion of the slice."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "wbWED1FugT4wPW2K",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship of the pie chart at [0.126, 0.287, 0.39, 0.812] to the label at [0.161, 0.232, 0.345, 0.266]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart represents the market segment competition data for the segment represented by the label, which is the \"Consumer Segment\"."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "viQ7sRDB9QmEqcUI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.094, 0.224, 0.412, 0.859]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region is containing the pie chart representing the consumer market segment. It includes slices at coordinates [0.139, 0.378, 0.253, 0.541], [0.126, 0.432, 0.253, 0.694], [0.152, 0.541, 0.258, 0.795], [0.263, 0.549, 0.39, 0.812], [0.253, 0.456, 0.38, 0.541], [0.253, 0.328, 0.373, 0.541], [0.172, 0.287, 0.322, 0.541], and [0.156, 0.346, 0.253, 0.541]."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "Jfs1a51fkQdSJegG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_181.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the area framed by the coordinates [0.588, 0.224, 0.906, 0.859]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart representing the Industrial market segment. It includes slices at coordinates [0.62, 0.378, 0.747, 0.623], [0.626, 0.541, 0.747, 0.752], [0.676, 0.541, 0.761, 0.795], [0.747, 0.541, 0.812, 0.794], [0.758, 0.554, 0.885, 0.772], [0.747, 0.356, 0.874, 0.548], [0.747, 0.292, 0.834, 0.541], and [0.649, 0.287, 0.77, 0.541]."
+            }
+        ],
+        "id_raw": 981
+    },
+    {
+        "id": "NWwG6iY5Ej4llQ2z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with the coordinates [0.204, 0.092, 0.796, 0.125] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, titled \"Occurrences from 2013 to 2019\"."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "vNMCIi43ACxlvETe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.53, 0.335, 0.84, 0.826] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Pie slice in the color \u2018#ffcc99\u2019 which represents 31%."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "fHRcW7j60gAAdn1c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.443, 0.857, 0.443, 0.857]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\"."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "IvsS3OwiOw3eBUEx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.393, 0.21, 0.594, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.5, 0.225, 0.742, 0.52], [0.262, 0.229, 0.5, 0.52]] are adjacent."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "0KvNhxDCTyIklR16",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.5, 0.225, 0.742, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"9.3%\"}[0.693, 0.239, 0.693, 0.239] is located near the slice."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "gzJLLojbUynfTwj1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices in the pie chart have the autotext near them?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices with coordinates [0.19, 0.321, 0.5, 0.545], [0.191, 0.52, 0.5, 0.769], [0.315, 0.52, 0.592, 0.831], [0.5, 0.225, 0.742, 0.52] and [0.393, 0.21, 0.594, 0.52] have the autotext \"12.4%\", \"13.5%\", \"15.0%\", \"9.3%\" and \"10.5%\" near them respectively."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "VpWZvkSiKUcfNc0U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.262, 0.229, 0.5, 0.52]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice in colour #a5a4f9 and the percentage text \"8.3%\" [0.304, 0.241, 0.304, 0.241]. This region represents the percentage share in the pie chart."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "EI5zMNSQJ43uBT9G",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_185.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.191, 0.52, 0.5, 0.769]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice in colour #66b3ff and the percentage text \"13.5%\"[0.201, 0.686, 0.201, 0.686]."
+            }
+        ],
+        "id_raw": 985
+    },
+    {
+        "id": "oaH7Y15Y0tDysXKG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.176, 0.171, 0.799, 0.201] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle \"Family and Household Living Conditions\"."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "6e87srtNRJOPd8qa",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.429, 0.295, 0.578, 0.536] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart representing the situation \"Living with Parents\"."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "5EZIuoT8pFxgI4GI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.275, 0.306, 0.275, 0.306] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2.0%\" which represents the percentage of people in the category."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "ziMG1p8IbnxphODP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.362, 0.23, 0.648, 0.434] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.252, 0.311, 0.5, 0.597] is adjacent."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "xq8gFvPXYPPMVooJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the text}[0.333, 0.321, 0.333, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2.0%\"}[0.275, 0.306, 0.275, 0.306] is located near it."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "B42kYpGWeI2Go6Ko",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice in the chart with coordinates [0.435, 0.278, 0.642, 0.428]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Living with Parents\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "7qBJHeyR1tDldzVg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.405, 0.409, 0.543, 0.595] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with coordinates [0.487, 0.313, 0.692, 0.536] representing \"Shared Housing\" and it contains the text \"10.0%\" at coordinates [0.658, 0.333, 0.658, 0.333], indicating the percentage of this living condition category."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "9Tx1ANTFJLE010wW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.185, 0.235, 0.379, 0.433] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with coordinates [0.246, 0.381, 0.487, 0.76] representing \"Renting\" and it contains the text \"30.0%\" with coordinates [0.23, 0.6, 0.23, 0.6], indicating the percentage of this living condition."
+            }
+        ],
+        "id_raw": 988
+    },
+    {
+        "id": "F8HM0NPMhhWBZqhc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the main object that exists within the coordinates [0.161, 0.117, 0.836, 0.142] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the subtitle of the graph, which reads \"Investment Portfolio Composition by Asset Class\"."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "o2wgFJCsRboNTjJw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.201, 0.533, 0.201, 0.533] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "This object represents the label of a slice in the pie chart, with a value of \"20.0%\"."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "3hrodcRseK96bt0b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the coordinates [0.499, 0.899, 0.499, 0.899]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a position on the chart where the x-axis label is typically located, but in this case, it is empty."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "007wuYjG9dnGtC1S",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage text labels are adjacent to the slice {investment commodity \"Stocks\"}[0.269, 0.554, 0.499, 0.824]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent labels are {20.0%}[0.201, 0.533, 0.201, 0.533] and {12.0%}[0.589, 0.838, 0.589, 0.838]."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "So4lQaqkxE13lfRU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices does the {text \"6.0%\"}[0.593, 0.271, 0.593, 0.271] lie between?"
+            },
+            {
+                "from": "gpt",
+                "value": "This percentage lies between the slices {Commodities}[0.499, 0.317, 0.757, 0.554] and {REITs}[0.401, 0.283, 0.534, 0.554]."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "9bIsXva1QnW7fA8E",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is connected to the percentage {3.0%}[0.289, 0.342, 0.289, 0.342]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {Derivatives}[0.291, 0.344, 0.499, 0.554] is connected to the mentioned percentage."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "GT2YJCNkJCW1w75l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTalk about the area that lies within the coordinates [0.228, 0.283, 0.769, 0.825]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily encompasses the entire pie chart representation of the \"Investment Portfolio Composition by Asset Class\". The pie chart includes various slices representing different asset classes with their respective investment percentages."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "0ccQiJYVwLSFSpo8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with the coordinates [0.327, 0.301, 0.499, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The indicated region lies within the pie chart and contains a slice of the pie which represents \"Futures\" as an asset class with an investment percentage of \"5.0%\"."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "sln7dqhWnwfKl2Km",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region occupying the coordinates [0.499, 0.317, 0.757, 0.554] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises a section of the pie chart, specifically the slice representing \"Commodities\" as a part of the overall investment portfolio alongside its corresponding percentage label of \"12.0%\"."
+            }
+        ],
+        "id_raw": 990
+    },
+    {
+        "id": "OI9r7k4TTYsBUV6E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThere is an object located at coordinates [0.228, 0.113, 0.772, 0.138] in the chart, can you identify this object?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle to the chart, with text 'Product Sales Trend from 2014 to 2020'."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "yGRO74GBRVccQlDf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.213, 0.215, 0.787, 0.789] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie plot is located at these coordinates."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "YhtNSvZwkK7hrVrj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.267, 0.755, 0.267, 0.755]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"8.9%\" is at these coordinates."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "hd7INC9wCvE8Iv3j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of {the slice}[0.213, 0.403, 0.5, 0.518] and how does it relate to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of this slice in the chart is \"#66b3ff\", which corresponds to a legend item on the chart."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "cfz6BJdTLIiyCO1e",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow is the {text}[0.228, 0.113, 0.772, 0.138] related to the {pie plot}[0.213, 0.215, 0.787, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle of the chart, identifying the pie plot as \"Product Sales Trend from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "Y8ZFgyOW1VfYfCq7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow are the {autotexts}[[0.227, 0.344, 0.227, 0.344], [0.188, 0.455, 0.188, 0.455], [0.199, 0.596, 0.199, 0.596],[0.267, 0.755, 0.267, 0.755], [0.439, 0.812, 0.439, 0.812], [0.615, 0.796, 0.615, 0.796], [0.771, 0.663, 0.771, 0.663], [0.815, 0.505, 0.815, 0.505], [0.8, 0.406, 0.8, 0.406], [0.747, 0.306, 0.747, 0.306], [0.657, 0.229, 0.657, 0.229], [0.543, 0.19, 0.543, 0.19], [0.42, 0.197, 0.42, 0.197], [0.307, 0.253, 0.307, 0.253]] located in different {pie slices}[[0.231, 0.318, 0.5, 0.502],[0.213, 0.403, 0.5, 0.518],[0.214, 0.502, 0.5, 0.652], [0.236, 0.523, 0.481, 0.78], [0.372, 0.502, 0.522, 0.789], [0.5, 0.502, 0.678, 0.788], [0.5, 0.502, 0.783, 0.727], [0.5, 0.46, 0.787, 0.549], [0.5, 0.371, 0.784, 0.502], [0.5, 0.283, 0.755, 0.502], [0.5, 0.232, 0.685, 0.502], [0.48, 0.215, 0.596, 0.502], [0.376, 0.216, 0.5, 0.502], [0.28, 0.243, 0.5, 0.502]] related?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotexts are the percentages relating to each slice of the pie chart, indicating the size of the slice in relation to the whole pie."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "ABesMNCYU2SeKk96",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region in the chart delimited by the coordinates [0.213, 0.215, 0.787, 0.789]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the entire pie plot. It contains all 14 slices of the pie, each represented with different colors, and it's located centrally on the chart."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "OaoVTKYhU7Xt5fcZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_191.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region of the chart within coordinates [0.236, 0.523, 0.481, 0.78]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart represented with the color \"#ffcc99\". The slice is exploded from the pie chart, and it is annotated with an autotext \u201c8.9%\u201d [0.267, 0.755, 0.267, 0.755] indicating its proportion in the whole pie chart."
+            }
+        ],
+        "id_raw": 991
+    },
+    {
+        "id": "81SLODXWEWq8rZFl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what object at [0.17, 0.092, 0.83, 0.125] is in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which reads \"Popularity Trend from 2016 to 2019\"."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "eCIhIkss2kxmf9Rl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object at [0.834, 0.592, 0.834, 0.592] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text indicating \"25.9%\"."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "HSwglcrCaMViiZTp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.646, 0.548, 0.719, 0.571] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a text, but its content is not available in the chart. It might be a slice label or legend."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "Gl3OZfYLk7W3zmip",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is next to {the slice}[0.19, 0.321, 0.5, 0.696]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {at} [0.244, 0.52, 0.662, 0.831] is adjacent."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "b5TSsukrpaAblruh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice located next to {this slice of the pie}[0.264, 0.179, 0.757, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice located next to it is {the slice} [0.19, 0.321, 0.5, 0.696]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "pGrHXjx8SEvLsUJ1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is associated with the {text} [0.429, 0.854, 0.429, 0.854]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with the text \"24.1%\" is {the slice} [0.244, 0.52, 0.662, 0.831]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "WXIFF7FzeN7nBAYa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region within [0.264, 0.179, 0.757, 0.489]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a slice {with coordinates} [0.264, 0.179, 0.757, 0.489] and its corresponding text \"29.3%\" {with coordinates} [0.518, 0.148, 0.518, 0.148]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "J5xvQ5BeRgvbAmfW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.244, 0.52, 0.662, 0.831]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice {with coordinates}[0.244, 0.52, 0.662, 0.831] and its corresponding percentage text \"24.1%\"{at} [0.429, 0.854, 0.429, 0.854]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "e5BrUlV1DL7QNisW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_192.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the content within the region [0.19, 0.321, 0.5, 0.696]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice {with coordinates}[0.19, 0.321, 0.5, 0.696] and the percentage text \"20.7%\" {located at} [0.159, 0.504, 0.159, 0.504]."
+            }
+        ],
+        "id_raw": 992
+    },
+    {
+        "id": "2XDwv4wXjrNmWo7J",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.073, 0.141, 0.894, 0.174]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Successful Cases in Medical Sectors in 2020\"."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "lutoUCGzd62UV7Jd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.304, 0.759, 0.304, 0.759]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text representation of a pie slice, \"51.8%\"."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "1w6DELAtLjGdt6x0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.483, 0.52, 0.483, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The center of the pie chart."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "6NNzr4KXuBvwMHI4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label is closest to {the pie slice}[0.292, 0.227, 0.771, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"48.2%\"<r>[0.678, 0.26, 0.678, 0.26]</r> is closest."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "nRnqbYUS8OSzuvPh",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to {the slice}[0.212, 0.346, 0.709, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.292, 0.227, 0.771, 0.649]</r> is adjacent to it."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "JCpeu0rhYld5TEH1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice's text label corresponds to {the pie slice}[0.212, 0.346, 0.709, 0.792]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label \"51.8%\"<r>[0.304, 0.759, 0.304, 0.759]</r> corresponds to it."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "m2zMR4TwJ4JznW4N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.212, 0.227, 0.771, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region outlines the entire pie chart. It involves two pie slices, one colored '#ff9999'<r>[0.212, 0.346, 0.709, 0.792]</r> and the other '#66b3ff'<r>[0.292, 0.227, 0.771, 0.649]</r>."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "pLQqeV0FeblTW60R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the pie slice[0.292, 0.227, 0.771, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice contains the text label \"48.2%\"<r>[0.678, 0.26, 0.678, 0.26]</r>."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "hGPcYtXgHOduizvu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the slice[0.212, 0.346, 0.709, 0.792]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text label \"51.8%\"<r>[0.304, 0.759, 0.304, 0.759]</r>."
+            }
+        ],
+        "id_raw": 993
+    },
+    {
+        "id": "3HtVZEiIULd0zNIK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.047, 0.28, 0.978, 0.314]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Innovation in Technology and Intellectual Property\"."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "JK5BtG2p3kcx63Y1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage adjacent to the slice at coordinates [0.353, 0.418, 0.513, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"17.9%\" is represented at [0.339, 0.496, 0.339, 0.496]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "bJNxlKUDOX8vR6DJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice at [0.403, 0.35, 0.684, 0.649] seems unusual compared to the others?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice is exploded compared to the others."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "rAiRVIPAJuEUadZS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the slice [0.367, 0.52, 0.591, 0.68] and the text at [0.458, 0.687, 0.458, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"26.4%\" is the percentage representation of the slice."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "r1t76KMyDInwwaAr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is related to the exploded slice [0.403, 0.35, 0.684, 0.649]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"55.7%\" at coordinates [0.66, 0.398, 0.66, 0.398]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "HSjKnX3hswFDiUG3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich coordinates reveal the center of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart center is at [0.513, 0.52]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "wPD7WBBl7Y3fpT2k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.353, 0.35, 0.684, 0.68]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That region includes most of the pie chart with three slices located at coordinates [0.353, 0.418, 0.513, 0.586], [0.367, 0.52, 0.591, 0.68], and [0.403, 0.35, 0.684, 0.649]. Each of these slices has a corresponding text representing the percentage of the slice."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "Qqtg2jiUEk0jQttA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within coordinates [0.367, 0.52, 0.591, 0.68] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a pie slice colored in '#66b3ff', which corresponds to a percentage marked as '26.4%' at coordinates [0.458, 0.687, 0.458, 0.687]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "2hsAM6bvyDm1AisP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are included in the region [0.353, 0.418, 0.513, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a portion of the pie chart colored in '#ff9999' along with the associated text '17.9%' at coordinates [0.339, 0.496, 0.339, 0.496]."
+            }
+        ],
+        "id_raw": 996
+    },
+    {
+        "id": "acspbSmfckeLfH8b",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object displayed at coordinates [0.214, 0.092, 0.786, 0.125] in the given chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, named \"Energy Use from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "QijMOFgdLTSIIafA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at coordinates [0.19, 0.377, 0.19, 0.377] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a percentage text \"8.5%\"."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "SPiNljHQ8mHnrWgu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.681, 0.517, 0.754, 0.54] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is a text, although its content is not specified in the details."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "itAzeOjrLQ8zAC2C",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are adjacent to {this particular slice}[0.202, 0.52, 0.5, 0.742]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.19, 0.469, 0.5, 0.607], [0.27, 0.548, 0.486, 0.856]] are adjacent to it."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "zGJHr9vZaPtwxWfq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near to {this text}[0.285, 0.519, 0.343, 0.543]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The auto texts {\"8.2%\"}[0.207, 0.696, 0.207, 0.696] and {\"10.3%\"}[0.338, 0.856, 0.338, 0.856] are located near to it."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "dXsem71MA32fhxhp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the exploded slice in the chart that is adjacent to {this slice}[0.5, 0.52, 0.784, 0.8]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the {slice}[0.531, 0.419, 0.841, 0.647]."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "ciRU3TQ2mgWrMMYq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.5, 0.19, 0.841, 0.856]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains several pie slices with different colors and respective percentage texts. The most prominent slice is the one exploded out with text \"12%\" [0.872, 0.535, 0.872, 0.535]."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "cZIyyXubADU3mNTf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.5, 0.248, 0.793, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice of the chart and a percentage text \"11.7%\" [0.76, 0.299, 0.76, 0.299], which shows the part's contribution."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "LoZ7ogWIiGPJvLO0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_5_199.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.194, 0.321, 0.5, 0.52]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses a part of pie chart, painted in color '#ff9999', and the percentage text \"8.5%\" [0.19, 0.377, 0.19, 0.377] indicating its share."
+            }
+        ],
+        "id_raw": 999
+    },
+    {
+        "id": "5v4H0i054rFWxRHn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text with coordinates [0.238, 0.073, 0.282, 0.096] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the percentage \"10.1%\"."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "zOIDfebIt2KZ4HWf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.919, 0.083, 0.963, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text denoting the year \"2009\"."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "Ljj7WRFKiAcP2Mjg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the meaning of the text at coordinates [0.303, 0.269, 0.358, 0.292] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the year \"2009\"."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "pkekIAGvDqjCMhRM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie does this legend {2009}[0.919, 0.083, 0.963, 0.106] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.205, 0.103, 0.388, 0.283] corresponds to the legend \"2009\"."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "G0hnVKAptbww5z40",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend associated with {the pie slice}[0.556, 0.255, 0.697, 0.586]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend \"2015\" with coordinates [0.919, 0.292, 0.963, 0.316] is associated with this slice."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "P5DAKNm1RPxcTnOb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label is associated with {the color slice} [[0.502, 0.565, 0.692, 0.864]]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2014\" with coordinates [0.919, 0.258, 0.963, 0.281] is associated with that slice."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "jFLmy61QRWLwI5OD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.205, 0.103, 0.388, 0.283]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with a color code of \"#ff9999\" and texts for the year \"2009\" placed at [0.303, 0.269, 0.358, 0.292] and the percentage \"10.1%\" placed at [0.238, 0.073, 0.282, 0.096]. This slice corresponds to the year 2009 in the chart."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "UKubTNkZjP5gxB9J",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.307, 0.759, 0.551, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice with a color code of \"#c2c2c2\" and texts for the year \"2013\" placed at [0.387, 0.747, 0.442, 0.771] and the percentage \"13.1%\" placed at [0.437, 0.951, 0.481, 0.974]. This slice of pie represents the year 2013 in the chart."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "pQmq6rPhRcEmJdJB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_1.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the items in the area defined by coordinates [0.08, 0.103, 0.697, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the entire pie chart detailing the creativity trend from 2009 to 2016. Each slice in the pie corresponds to a particular year with a specific color and percentage."
+            }
+        ],
+        "id_raw": 1001
+    },
+    {
+        "id": "xv1HeVSW8YPBacJe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main title of the chart with coordinates [0.144, 0.025, 0.632, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Social Responsibility Efforts from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "mOtaXUoiqNqsnHQF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend of the chart."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "PGzd3KsXSm4uXyrl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object in the chart with coordinates [0.25, 0.068, 0.294, 0.091]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"8.9%\"."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "xidaykCGATpJHwAJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is sitting next to {the slice named '2014'}[0.077, 0.348, 0.189, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {named '2015'}[0.083, 0.577, 0.25, 0.84] is adjacent."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "es6vISIhZSNa4q7A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is related to the year {2016 presented in the legend}[0.919, 0.223, 0.963, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the year 2016 is {the slice with color '#c2c2f0'}[0.191, 0.743, 0.391, 0.934]."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "MRg0xTnO1Fny3fg7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the text {2020 in the legend}[0.919, 0.362, 0.963, 0.386] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented by the text \"2020\" is '#99ccff'."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "QFRxsC5j6L1KjjWN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.224, 0.104, 0.388, 0.272]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region possesses a slice of the pie chart, characterized by the color '#ff9999'. This slice represents data for the year 2012 and accounts for 8.9% of total social responsibility efforts."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "ZVoBwQJvMN6jvVDF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.077, 0.348, 0.189, 0.602]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart depicting the data for the year 2014. Its color is '#99ff99' and represents 10.0% of total efforts."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "zEs1rHlIycRfOudL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_5.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what is inside the region [0.535, 0.537, 0.699, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a piece of the pie chart corresponding to the data for the year 2018. It's depicted in the color '#b3ff99' and signifies 12.2% of the total."
+            }
+        ],
+        "id_raw": 1005
+    },
+    {
+        "id": "SbDomPq1JGfQyrJR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key text anchored at coordinates [0.083, 0.025, 0.693, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is \"Social Networking Frequency Based on Relationship Status\"."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "VnLrqeu35rSescQk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text represents the object located at [0.547, 0.507, 0.602, 0.53]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Married\"."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "UMGmuh2Nipm8n2lT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the coordinates [0.494, 0.073, 0.571, 0.096] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the text \"10.0%\"."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "oUFLnSRJSG2EZiuu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pieces of the pie chart are associated with {the label \"Single\"}<r>[0.886, 0.083, 0.939, 0.106]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie piece with coordinates [0.077, 0.104, 0.571, 0.934] is associated with it."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "zhSShgBPL9VgudWT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor {the handle \"Divorced\"}<r>[0.837, 0.155, 0.872, 0.171]</r>, which part of the pie does it correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the pie piece with coordinates [0.388, 0.104, 0.571, 0.284]."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "2tV456tzG3An591t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend and the pie chart sections?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each item in the legend corresponds to a slice of the pie chart. The colors used in the legend's handles match the slice colors: \"Single\" to [0.077, 0.104, 0.571, 0.934], \"Married\" to [0.516, 0.183, 0.699, 0.854], and \"Divorced\" to [0.388, 0.104, 0.571, 0.284]."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "QCFCoG4HFbebW6yV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.516, 0.183, 0.699, 0.854] in detail?"
+            },
+            {
+                "from": "gpt",
+                "value": "The designated region envelopes a slice of the pie chart and also the text \"30.0%\" and \"Married\" that are located at [0.73, 0.507, 0.797, 0.53] and [0.547, 0.507, 0.602, 0.53] respectively. The slice is colored in '#66b3ff' indicating the \"Married\" category."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "I6dxj33QHYT0iBTC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_6.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.077, 0.104, 0.571, 0.934] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a slice of the pie, as well as the terms \"60.0%\" and \"Single\", located at [0.009, 0.648, 0.063, 0.671] and [0.183, 0.584, 0.238, 0.607] respectively. This slice represents the \"Single\" portion of the pie chart."
+            }
+        ],
+        "id_raw": 1006
+    },
+    {
+        "id": "IINmW4AvuJ36guw4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.14, 0.025, 0.637, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, called \"Running and Yoga Popularity from 2017 to 2019\"."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "Yu9ojBpWNyfCt6eZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be found at coordinates [0.919, 0.083, 0.963, 0.106], [0.919, 0.118, 0.963, 0.141], and [0.919, 0.153, 0.963, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Those are the legend items that indicated the years \"2017\", \"2018\", and \"2019\"."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "LsFkN5xtSA7Z1Uj9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.535, 0.417, 0.59, 0.44] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2019\"."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "9mRY3W4bM0c1EYjf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart is adjacent to {the slice}[0.089, 0.598, 0.598, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.388, 0.104, 0.699, 0.825] is adjacent."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "uHBP60fOGglWijXI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the texts near {the \"2019\" slice}[0.388, 0.104, 0.699, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"38.2%\"}[0.707, 0.342, 0.751, 0.365] and {\"2019\"}[0.535, 0.417, 0.59, 0.44] are near the \"2019\" slice."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "Ryynew61zLtl8IVs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the pie slice}[0.077, 0.104, 0.388, 0.632]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {\"2017\"}[0.919, 0.083, 0.963, 0.106] corresponds to this slice."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "AfwW4J3e07cP3qkI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.077, 0.104, 0.388, 0.632]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice from the pie chart with a percentage text {\"29.4%\"}[0.071, 0.232, 0.115, 0.255] and the year {\"2017\"}[0.212, 0.357, 0.267, 0.38]. The area reflects the popularity of sports in 2017."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "PUNuqgjAls5X90pk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can you see in the region with coordinates [0.388, 0.104, 0.699, 0.825]?"
+            },
+            {
+                "from": "gpt",
+                "value": "I can see a part of the pie chart, precisely the slice showing the popularity in {\"2019\"}[0.535, 0.417, 0.59, 0.44] with a corresponding percentage text {\"38.2%\"}[0.707, 0.342, 0.751, 0.365]."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "e2ocCqohA0cmij1k",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_10.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region with coordinates [0.219, 0.598, 0.389, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains part of the {\"2018\"}[0.31, 0.746, 0.364, 0.77] slice from the pie chart with a corresponding percentage text {\"32.4%\"}[0.25, 0.946, 0.294, 0.969]."
+            }
+        ],
+        "id_raw": 1010
+    },
+    {
+        "id": "s9WQNleVcHBLznh6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object positioned at these coordinates [0.185, 0.025, 0.591, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, labelled as \"Building and Real Estate Market Trends\"."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "1qIZ943RZEtNRztQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can you find at the coordinates [0.845, 0.188, 0.963, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a legend item, labeled \"Infrastructure\"."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "l8FINk7XYi9MOzhE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at the location specified by these coordinates [0.729, 0.55, 0.778, 0.573] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the auto text saying \"19.0%\"."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "0G9sMms6bi0XyTmR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice {the slice}[0.079, 0.104, 0.388, 0.482] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item}[0.845, 0.083, 0.949, 0.106], labeled \"Commercial\", matches the slice."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "IwUgKiWyv7sUSYRs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is related to {the legend item}[0.845, 0.223, 0.926, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {the slice}[0.388, 0.104, 0.484, 0.242] is linked to the \"Industrial\" legend item."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "MEQBE2TuXZ1inJby",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice adjacent to {the slice}[0.079, 0.104, 0.388, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {adjacent slice}[0.077, 0.467, 0.628, 0.934] is adjacent to it."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "SmOAn3H9ckjL8OSl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The given region almost covers the entire pie chart. It includes five slices representing the segments of \"Commercial\", \"Residential\", \"Retail\", \"Infrastructure\", and \"Industrial\". Their respective percentages are also displayed within this region."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "735YvKn8IJsuVHer",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the area demarcated by the coordinates [0.789, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region hosts the legend of the chart, where each segment of the pie chart is labeled and color-coded."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "aS2WHPTEahzllJmP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_15.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the region [0.455, 0.124, 0.661, 0.379]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes part of the pie chart showing the \"Infrastructure\" segment, which corresponds to the color '#ffcc99', and its associated auto text saying \"12.0%\"."
+            }
+        ],
+        "id_raw": 1015
+    },
+    {
+        "id": "fszFsoeR8JuoetPi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the element found at the coordinates [0.263, 0.015, 0.511, 0.033] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a subtitle indicating \"Emotional Frequency in Males\"."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "0ihDVEM9fFPgOv9u",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify what is contained within [0.892, 0.667, 0.944, 0.681]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In that area, there is the word \"Disgust\" from the legend."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "VTBHfKC1PzCJQ5CC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the pie slice at the coordinates [0.494, 0.189, 0.592, 0.4] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the emotion \"Sadness\" with 17.5% frequency in males."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "9q3piRaclcGeI0ta",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich sections of the chart are adjacent to {the section}[0.258, 0.058, 0.387, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The sections}[[0.387, 0.058, 0.461, 0.129], [0.182, 0.103, 0.297, 0.347]] are adjacent."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "tl2LdYfr90XXbykZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the elements near {the legend}[0.847, 0.536, 0.976, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart showing \"Emotional Frequency in Females\" {the pie chart}[0.249, 0.549, 0.387, 0.649] is near the legend."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "rvTF9yqegQeqUCON",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the slice at coordinates [0.305, 0.359, 0.54, 0.468], which emotion does it signify in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to \"Excitement\" in the legend."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "gOniXDSOi7pIWc9P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region defined by coordinates [0.182, 0.058, 0.592, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the entire male emotion frequency pie chart, encapsulating slices labeled as \"Anxiety\", \"Happiness\", \"Fear\", \"Excitement\", \"Sadness\", \"Boredom\", and \"Disgust\"."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "8XswOyuDWUyG2IyT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_18.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements can be found within the region [0.847, 0.536, 0.976, 0.687]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend marking \"Anxiety\", \"Happiness\", \"Fear\", \"Excitement\", \"Sadness\", \"Boredom\", and \"Disgust\" for the \"Emotional Frequency in Females\" chart."
+            }
+        ],
+        "id_raw": 1018
+    },
+    {
+        "id": "79HbKb8Pg2TwBbic",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is tagged at coordinates [0.164, 0.139, 0.23, 0.156] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"3.0%\", which indicates the percentage of Sharing Photos."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "vEF4IfYpTNMlexVv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the key element with coordinates [0.894, 0.055, 0.995, 0.165] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart legend."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "GfObxNmNRDzbi5M1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at [0.172, 0.347, 0.199, 0.364]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Messaging\""
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Bnhbitwb8xKTJWXv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are adjacent to {the slice}[0.205, 0.18, 0.414, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Adjacent to it are {the slices}[[0.216, 0.18, 0.247, 0.284], [0.08, 0.372, 0.218, 0.838]]."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "VnHEZ4Icxc6nwowR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend matches {the slice with color '#66b3ff'}[0.096, 0.185, 0.225, 0.414] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Messaging\" in the legend correlates."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Q6HMC7vAK9w3TpaU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart corresponds to {the legend entry 'Others'}[0.922, 0.141, 0.951, 0.158]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.205, 0.18, 0.414, 0.848] corresponds."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "rBMKu6QtaKyJg8ke",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.08, 0.18, 0.414, 0.848] comprise of?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several pie slices of different colors each representing a different social network usage namely Sharing Photos [0.216, 0.18, 0.247, 0.284], Messaging [0.096, 0.185, 0.225, 0.414], Posting Updates [0.08, 0.372, 0.218, 0.838], and Others [0.205, 0.18, 0.414, 0.848]."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Y74DbSe8B5vm74xE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area spanned by [0.216, 0.18, 0.247, 0.284]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area represents a slice of the pie chart labeled \"Sharing Photos\", colored in '#ff9999', and corresponds to 3.0% of social network usage."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "XfrrSbJW1599qfKR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_20.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat do you observe in the region [0.08, 0.372, 0.218, 0.838]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a large chunk of the pie chart denominated as \"Posting Updates\" with a green color and indicating 28.0% of social network usage."
+            }
+        ],
+        "id_raw": 1020
+    },
+    {
+        "id": "Sg6WvOB7hZbrqzsx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of this chart in the bounding box [0.185, 0.025, 0.591, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Fitness Trends: Yoga vs Running (2021)\"."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "oqIMGrXF0IJd2pJy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found within the coordinates [0.892, 0.083, 0.933, 0.106] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Yoga\"."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "Ul4brzn9MKB1n7Yi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object does the bounding box [0.725, 0.591, 0.795, 0.614] cover?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"55.9%\"."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "fEwu5L1oFJdFUCEu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice is next to the slice {slice}[0.276, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {slice}[0.077, 0.104, 0.388, 0.905] is next to it."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "9P3CAgd7aMpwjejH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects are located near the bounding box of {object}[0.892, 0.118, 0.963, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects {\"Yoga\"}[0.892, 0.083, 0.933, 0.106] and the blue color box [0.844, 0.12, 0.879, 0.136] are located near it."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "iE4kWj5Uz3ePgbzR",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice with the {coordinates}[0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Yoga\" corresponds to this slice."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "HlUYhU8p2F9hq9XV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region defined by [0.077, 0.104, 0.388, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the pie slice for \"Yoga\". It comprises the text \"Yoga\", given by the bounding box [0.177, 0.461, 0.232, 0.485] and the text \"44.1%\", given by the coordinates [0.011, 0.423, 0.052, 0.446]. The pie slice is in \"#ff9999\" color, which is light coral."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "RZgYy9MrrFRGyGX0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_22.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information found within coordinates [0.276, 0.104, 0.699, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for \"Running\", with the text \"Running\" given by coordinates [0.544, 0.553, 0.599, 0.576] and the participation percentage of \"55.9%\" given by coordinates [0.725, 0.591, 0.795, 0.614]. The slice color is \"#66b3ff\", which is a shade of light blue."
+            }
+        ],
+        "id_raw": 1022
+    },
+    {
+        "id": "fEG5D8L4I2GCyBIP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart at coordinates [0.028, 0.025, 0.748, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Expenditure on Social Welfare and Public Services from 2012 to 2020\"."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "Xlsd4QSzzSN64c2c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.864, 0.074, 0.97, 0.395] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend of the chart."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "0NErmw8tDhPxj81P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.509, 0.347, 0.553, 0.37] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"2019\"."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "roBYynlMRj7xR2Ph",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to the slice {slice}[0.589, 0.36, 0.699, 0.629]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}[0.505, 0.168, 0.676, 0.408] is adjacent to the queried slice."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "KDSn1D7zt3NgaLqe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich colour represents the year 2014 according to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colour #99ff99 represents year 2014."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "JwPAbOAxpM6DeRXt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the legend item {legend item}[0.919, 0.362, 0.963, 0.386] in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2020\" corresponds to the {slice}[0.388, 0.104, 0.555, 0.273] in the chart."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "UvHAVSLaMsLdNb4l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.336, 0.748, 0.579, 0.933]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with an autotext \"13.2%\" and its label \"2016\". The region is located in the upper right part of the chart corresponding to the expenditure in 2016 as indicated by the colour #c2c2f0 on the legend."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "cr9AmmyfZLgVPfFH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_23.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the components within the region having coordinates [0.081, 0.216, 0.239, 0.474]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a portion of the pie, specified with the label \"2013\" and the corresponding percentage \"10.5%\"."
+            }
+        ],
+        "id_raw": 1023
+    },
+    {
+        "id": "1R1KM50QdLqEIXcX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle for the chart within the coordinates [0.305, 0.503, 0.695, 0.518]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Bond Types Distribution on Instagram\"."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "ptnjZ05MfucycOKN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located within the coordinates [0.816, 0.567, 0.966, 0.579] in the subplot_1 region of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text \"Indifferent Bonds\"."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "aaQ4v12hBIjr1oE7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.583, 0.154, 0.638, 0.165] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Indifferent Bonds\"."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "noAuKkbcz106nEdS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near the {pie slice}[0.5, 0.052, 0.798, 0.216]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.0%\"<r>[0.703, 0.069, 0.852, 0.081]</r>."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "4YAzrVLJ6HZaVXGW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text at coordinates [0.105, 0.12, 0.221, 0.132] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the {percentage of the Strong Bonds slice}[0.187, 0.052, 0.5, 0.326]."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "k4xosGNP2N1B1BvV",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the legend items and the pie chart in subplot_1?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each legend item corresponds to a certain color-coded pie slice in the pie chart with the same name."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "s29NU3whPioPz5iG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.019, 0.031, 0.981, 0.491] of the chart in detail?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the distribution of bond types for Facebook represented as a pie chart. The pie slices within this region represent \"Strong Bonds\" with a percentage of 30%, \"Weak Bonds\" with a percentage of 50%, and \"Indifferent Bonds\" with a percentage of 20%."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "bTtxqhGgpp8oqe5V",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.187, 0.052, 0.813, 0.47]."
+            },
+            {
+                "from": "gpt",
+                "value": "It contains three slices, which are categorized as \"Strong Bonds\", \"Weak Bonds\" and \"Indifferent Bonds\" to represent Bond Types Distribution on Facebook.  They are colored differently and labeled as \"30.0%\", \"50.0%\" and \"20.0%\" respectively."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "fbvZERmdDVFLgqgB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_25.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.246, 0.543, 0.5, 0.666] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains a single pie slice representing \"Strong Bonds\" in the Instagram bond types distribution."
+            }
+        ],
+        "id_raw": 1025
+    },
+    {
+        "id": "65OkFP4sNAA5XS6z",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart which is located at coordinates [0.119, 0.025, 0.657, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is \"Product Sales Trend Observation from 2013 to 2020\"."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "EV8Kl1Py4pDdVnS3",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.864, 0.074, 0.97, 0.639] on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart's legend is found at those coordinates."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "VsI25WovHdanQ3at",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the coordinates [0.919, 0.083, 0.963, 0.106] in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"2013\" in the chart's legend."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "d09DrS5V6PIeaSMl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color associated with the legend item labeled \"2013\" at {legend item}[0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated color is '#ff9999'."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "iP8bxNirlGlR8re8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich other legend item has the same color as {legend item 1}[0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{legend item 2}[0.919, 0.362, 0.963, 0.386] also has the same color."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "caFWl4RwqV6qtUwP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color corresponds to the label \"2020\" in the legend at {legends}[0.919, 0.327, 0.963, 0.351] and [0.919, 0.607, 0.963, 0.63]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#ccccff' corresponds to the label \"2020\"."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "Js85URzQGEUaXn1M",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within the region from [0.08, 0.103, 0.696, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a multi-colored pie chart representing product sales trend from 2013 to 2020. There are 16 slices corresponding to two products' sales each year. Each slice has an associated color, percentage, and year."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "gzE8ZG09zsiaQqqE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region between coordinates [0.864, 0.074, 0.97, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend for the pie chart. It lists the years from 2013 to 2020 twice each with a color box adjacent to them. Those colors correspond to the slices in the pie chart."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "pHwStWOq3O3XXaiW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_27.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects can be identified in the region [0.871, 0.364, 0.963, 0.386]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there is a legend item labeled as \"2013\" with the color '#ff9999' attached to it."
+            }
+        ],
+        "id_raw": 1027
+    },
+    {
+        "id": "Y0RmrPLS1Ek6cEJE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.769, 0.19, 0.83, 0.21]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the 5-year data label \"2019\" in the legend."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "AMDU2mrFwdoL0win",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object corresponds to the coordinates [0.285, 0.677, 0.34, 0.701]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the label \"2017\" within the pie chart."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "yTDSYrBfNEOBV7Bs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at coordinates [0.019, 0.062, 0.757, 0.975] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the main plotting area of the chart, containing the actual pie chart with percentages and labels of each year."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "UMfGswU3pbwZ4IrS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the pie slice associated with the {legend item \"2018\"}<r>[0.919, 0.188, 0.963, 0.211]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding pie slice has the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "vnsiAwD6n2c0KGrd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the {legend item \"2017\"}<r>[0.919, 0.153, 0.963, 0.176]</r> and the {percentage}<r>[0.206, 0.924, 0.25, 0.948]</r> in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage \"20.6%\" in the pie chart is the value for the legend item \"2017\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "O5JJoK3CwWpbqYpw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend item that corresponds to the {pie slice}<r>[0.388, 0.104, 0.698, 0.497]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding legend item is \"2019\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "xAaQHLUeR4mmpwWr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.388, 0.104, 0.698, 0.497]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice representing the year \"2019\", along with its corresponding percentage label \"23.8%\"<r>[0.621, 0.172, 0.665, 0.196]</r>. The pie slice is colored \"#c2c2f0\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "ATDPoIV3YuzR0mOh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the area that falls between coordinates [0.388, 0.488, 0.699, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a portion of the pie chart that indicates data for the year \"2018\". It includes the percentage label \"22.5%\"<r>[0.664, 0.776, 0.708, 0.8]</r>, and the slice color is \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "OuiUQwYLHhjmtVms",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_33.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the area with coordinates [0.077, 0.294, 0.205, 0.72] include?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a piece of the pie chart representing the year \"2016\". This segment of the pie chart has the percentage label \"17.2%\"<r>[0.002, 0.492, 0.046, 0.515]</r> and is colored \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 1033
+    },
+    {
+        "id": "RC7xTz7DrC5BjfU5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main content of the heading with coordinates [0.064, 0.025, 0.712, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, namely Rural and Urban Development Comparison from 2013 to 2020."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "A06ZA1Oy3aou92dy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies within the chart coordinates [0.864, 0.074, 0.97, 0.639]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "44Z2t2Kzmr4KUNdp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object found at the coordinates [0.464, 0.133, 0.595, 0.301] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice part of the pie chart, representing \"2019\"."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "rwMCcMgnB6oRaOOt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich are the legend labels that are related to objectives with the color handles {[0.871, 0.085, 0.905, 0.101], [0.871, 0.19, 0.905, 0.206]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend labels \"2013\" and \"2016\" correspond to these color handles."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "eicNeRUmXzWS8ngB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice has color matching with the handle at {[0.871, 0.225, 0.905, 0.241]} in the legend of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice labeled \"2017\"."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "E6PzWtAoZpjnGgKU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you mention how the text labeled as \"2015\" at the coordinates [0.549, 0.535, 0.593, 0.558] is associated with the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" matches with the label in the legend having the handle color at [0.871, 0.155, 0.905, 0.171]."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "rXIouaGQfYH5mp6D",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within coordinates [0.08, 0.516, 0.201, 0.716] of this chart."
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there's a pie chart slice of color '#c2c2f0', labeled as \"2017\" [0.188, 0.566, 0.232, 0.589] and having 8.1% [0.017, 0.619, 0.061, 0.642] as the percentage value."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "YTASU7SArjG3ZbKB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find within the region [0.256, 0.773, 0.462, 0.924]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice marked \"2019\" at [0.342, 0.747, 0.397, 0.77] with the color '#93d7f5', having percentage value 10.9% at [0.31, 0.951, 0.354, 0.975] and part of another slice at [0.44, 0.668, 0.648, 0.912]."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "J4ypsoP8YWHdVYnk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_35.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's inside the coordinates [0.919, 0.083, 0.963, 0.106] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside these coordinates, there's a legend item indicating the label \"2013\"."
+            }
+        ],
+        "id_raw": 1035
+    },
+    {
+        "id": "vWxolQVtgyG9Y87V",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at [0.12, 0.025, 0.656, 0.055] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which says: \"Financial Assets and Investment from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "c2WU3QfozpTDBXzV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of detail can we notice at [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the position of the legend."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "t3ZsZikKgGWtLc2O",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the the text at [0.156, 0.126, 0.2, 0.15] refer to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the percentage \"18.7%\" associated to the 2016 slice."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "xUjXALh6s9amRTib",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are closest to the {the 2016 slice}[0.104, 0.103, 0.388, 0.403]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {percentage label \"18.7%\"}[0.156, 0.126, 0.2, 0.15] and {year label \"2016\"}[0.258, 0.297, 0.313, 0.321] are closest."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "oTL04yqrhbOiD3Gk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice associated with the {legend item \"2018\"}[0.919, 0.153, 0.963, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice associated with 2018 is {colored green} '#99ff99'."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "QAhv0FBz7oa1vSod",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich is the slice corresponding to the {legend item \"2020\"}[0.919, 0.223, 0.963, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to 2020 is: {2020 slice}[0.388, 0.103, 0.69, 0.455]."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "44dwaUfHbCON38mE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.494, 0.429, 0.696, 0.873]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region represents a pie slice of the chart labeled \"2019\". It shows the percentage \"20.2%\" at coordinates [0.697, 0.689, 0.741, 0.712] and it is labeled itself with \"2019\" at [0.529, 0.604, 0.584, 0.628]."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "SpwZ0VQHPNkrTMbx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_36.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within the coordinates [0.175, 0.722, 0.539, 0.925]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice representing the year 2018. It is labeled with \"2018\" [0.337, 0.747, 0.392, 0.771] and it shows a percentage of \"20.3%\" [0.301, 0.951, 0.345, 0.974]."
+            }
+        ],
+        "id_raw": 1036
+    },
+    {
+        "id": "gGSVKuyTyg8q1Mrb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at coordinates [0.182, 0.025, 0.594, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Medical Expenditure from 2013 to 2020\"."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "ES7IcOOC6lxOOppS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of object is located at [0.864, 0.074, 0.97, 0.36] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located there."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "IXuqSNC8KHyPaFV0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text inside the object at [0.528, 0.604, 0.583, 0.627] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2019\"."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "pmQo2z7xsncNDaDO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.388, 0.103, 0.69, 0.461]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.496, 0.439, 0.695, 0.867] is adjacent."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "Gl5X6LXZKjkHe1o6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to {the year 2017}[0.919, 0.223, 0.963, 0.246] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#cca583\" corresponds to the year 2017."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "nvIYVyZ0v8Awq7es",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" correspond to the slices with coordinates [0.304, 0.103, 0.388, 0.237], [0.198, 0.119, 0.329, 0.288], [0.105, 0.191, 0.255, 0.4], [0.081, 0.352, 0.19, 0.62], [0.091, 0.588, 0.29, 0.878], [0.248, 0.761, 0.543, 0.923], [0.496, 0.439, 0.695, 0.867], and [0.388, 0.103, 0.69, 0.461] respectively."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "UPHrpBtAFUyO6B2H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.091, 0.588, 0.29, 0.878]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"13.3%\", corresponding to the \"2017\" sector of the pie chart, which suggests that it was a year when the medical expenditure was 13.3% of some total. The sector's color label in the legend is of color \"#cca583\"."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "nzGFtVU1z72D9szd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.304, 0.103, 0.388, 0.237]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"4.4%\", suggesting the medical expenditure in the year 2013 was corresponding to 4.4% of some total. The sector's color in the legend is \"#4eabaa\"."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "frx7uRGTu20fnpT7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_37.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.496, 0.439, 0.695, 0.867]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"19.5%\", indicating that the medical expenditure in the year 2019 was corresponding to 19.5% of some total. The sector is marked in the legend with a color label of \"#ab8fad\"."
+            }
+        ],
+        "id_raw": 1037
+    },
+    {
+        "id": "9zto3Kua7X0ol4jr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart found at coordinates [0.075, 0.025, 0.701, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "E-commerce and Retail Industry Analysis from 2014 to 2020."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "R7zBOeeSgK9geq4v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.325] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "jkDncvgTHqcgegBA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is represented by the rectangle at coordinates [0.32, 0.103, 0.388, 0.233]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2014 data in the pie chart with a relative proportion of 3.6%."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "cPjkdGlipxx1U5oj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice adjacent to {the slice}[0.581, 0.332, 0.695, 0.528] represents lower year data?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.569, 0.523, 0.695, 0.735]] representing 2016 is adjacent and represents lower year data."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "6ykaLs44gVT9YA9G",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the text '2016'}[0.919, 0.153, 0.963, 0.176] correspond to in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color {'#99ff99'}[0.871, 0.155, 0.905, 0.171] in the legend corresponds to the text '2016'."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "tYDXPUwMo6ITLcoQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item color corresponds to {the slice}[0.144, 0.688, 0.323, 0.904] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item color \"#bcd56a\"[0.871, 0.295, 0.905, 0.311] corresponds to the given slice in the pie chart."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "004Sg7yKu7OvQUJz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.103, 0.695, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses the entire pie chart, containing various slices representing data from different years, each with their corresponding percentage values and year labels."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "TepeBTY6sPIfSbWs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_38.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend, including color labels and corresponding year data ranging from 2014 to 2020."
+            }
+        ],
+        "id_raw": 1038
+    },
+    {
+        "id": "5gNln5xhQ54o0aYj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the plot in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Financial Market Fluctuations and Trends from 2007 to 2014\" {The title}<r>[0.077, 0.025, 0.699, 0.055]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "bnI53CJtrMv5Bvzv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at [0.919, 0.083, 0.963, 0.106] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text corresponding to the year 2007 {the text for year 2007}<r>[0.919, 0.083, 0.963, 0.106]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "joWXfK1dK9DhQFmq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.274, 0.06, 0.318, 0.084]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is an autotext, \"6.6%\" of one of the pie slices {the autotext}<r>[0.274, 0.06, 0.318, 0.084]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "WJ9dYz5WX2WqT5Iw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year is connected to the pie slice at ['coordinates': '[0.263, 0.104, 0.388, 0.252]']?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2007 is connected to the {pie slice}<r>[0.263, 0.104, 0.388, 0.252]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "IwZf3Z9cZ7I9VLfp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the year 2012 in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for the year 2012 is \"#00ffff\" {the legend item for year 2012}<r>[0.919, 0.258, 0.963, 0.281]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "LXirLXaBESvPmjOO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart correlates with the text \"13.1%\"?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.094, 0.138, 0.301, 0.423]</r> correlates with the text \"13.1%\"."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "UnokkqHVt26qIfo3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found within the region [0.372, 0.705, 0.627, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, we have a pie slice of the chart colored \"#00ffff\" and text marking the year \"2012\" {the slice}<r>[0.372, 0.705, 0.627, 0.934]</r> {the text for year 2012}<r>[0.435, 0.735, 0.49, 0.758]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "vKuw0L6MoTVHJVyc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_41.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the contents of the region [0.263, 0.104, 0.388, 0.252]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored \"#0000ff\" and text marking the year \"2007\" {the slice}<r>[0.263, 0.104, 0.388, 0.252]</r> {the text for the year 2007}<r>[0.328, 0.263, 0.372, 0.287]</r>."
+            }
+        ],
+        "id_raw": 1041
+    },
+    {
+        "id": "DyfkEBKvZqKO55xe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the pie chart at the left side of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Primary Activities Value Chain Analysis\"[0.106, 0.021, 0.321, 0.047]."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "BHqn8MwhPYjY8oNZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text that occupies the coordinates [0.423, 0.191, 0.49, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Manufacturing\"[0.423, 0.191, 0.49, 0.211]."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "q9oV1CrJpLdYwjBP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object that uses the color '#ffb3e6' and it's situated at the coordinates [0.397, 0.223, 0.416, 0.237]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it's the legend key for \"Procurement\"[0.397, 0.223, 0.416, 0.237]."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "FFo7foC8q1lYFtFM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to the slice {at coordinates}[0.189, 0.683, 0.335, 0.869]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#c2c2f0'[0.397, 0.193, 0.416, 0.207] in the legend corresponds to the slice."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "WCNtZLPbGcyCxbtZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell which slice {the label}[0.423, 0.071, 0.474, 0.091] called \"Operations\" corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Operations\" corresponds to the slice {at coordinates}[0.119, 0.161, 0.214, 0.312]."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "TRdIspXXB4iQaicd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the label \"Distribution\"[0.423, 0.251, 0.477, 0.271] and the color it is associated with in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Distribution\"[0.423, 0.251, 0.477, 0.271] is associated with the color '#ccec99'[0.397, 0.253, 0.416, 0.266] in the chart."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "vcBaiqA6oUL6HKlj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.546, 0.161, 0.876, 0.869] of the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region features a pie chart of \"Support Activities Value Chain Analysis\" displaying various percentages and activities. Notable activities include \"Operations\", \"Marketing\", \"Sales\", \"Services\", \"Manufacturing\", \"Procurement\", and \"Distribution\"."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "6GXdAdW1QCsNGFHn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_49.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat would you find within the region from [0.891, 0.063, 0.991, 0.279]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there is a legend that contains labels \"Operations\", \"Marketing\", \"Sales\", \"Services\", \"Manufacturing\", \"Procurement\", and \"Distribution\", each associated with a unique color."
+            }
+        ],
+        "id_raw": 1049
+    },
+    {
+        "id": "mt9jGm0MyCQC9jS7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the coordinates [0.274, 0.025, 0.502, 0.055] indicate in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It indicates the subtitle of the chart, which is \"Pollution Share - 2015\"."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "rwtxRVtIydz0SJAq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at the coordinates [0.905, 0.397, 0.963, 0.421] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents a \"Others\" category on the legend."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "1DsnFZNKVuN3ojh2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.418, 0.27, 0.473, 0.294] refer to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the text \"Others\" on a slice in the pie chart."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "ZyKE8AzeLJYdW4zy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {CO2 slice}[0.077, 0.104, 0.388, 0.519]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {O3 slice}[0.077, 0.519, 0.186, 0.671] is adjacent."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "AwawMvFn2Oi72mwE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text label markers are close to the {NO2 label}[0.292, 0.738, 0.347, 0.762]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {CO label}[0.429, 0.738, 0.484, 0.762] and {PM10 label}[0.534, 0.599, 0.589, 0.622] are close to the NO2 label."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "VfUKOY0nzhVdQx2Q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the {Others legend}[0.905, 0.397, 0.963, 0.421] correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Others\" corresponds to the slice at coordinates [0.388, 0.104, 0.571, 0.284]."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "pOFpxO41NSQ5hccK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.077, 0.104, 0.388, 0.519] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region encapsulates the CO2 slice, including its corresponding color-coded representation in the chart and the percentage value '25.0%'[0.109, 0.184, 0.146, 0.208] as well as the 'CO2' label [0.229, 0.331, 0.283, 0.354]."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "LaJd0kX4eWfacelr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region [0.572, 0.296, 0.697, 0.482]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice representing 'PM2.5' pollution, including the numerical percentage '7.0%'[0.71, 0.352, 0.763, 0.376] and the 'PM2.5' label [0.542, 0.423, 0.586, 0.446]."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "sxvVZ1efQ1NWPJFV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_51.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents of the region [0.516, 0.183, 0.601, 0.307] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for 'N2O', along with its label 'N2O'[0.485, 0.315, 0.529, 0.338] and the numerical text '2.0%'[0.606, 0.155, 0.644, 0.179]."
+            }
+        ],
+        "id_raw": 1051
+    },
+    {
+        "id": "LRTQIvRloHODCwXl",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the coordinates [0.155, 0.025, 0.621, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "REka1BUhsYbSfBZp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technology type is represented by the color at coordinates [0.733, 0.225, 0.768, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color at these coordinates represents \"Robotics\"."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "vtOoRpKTZwp5ruuW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage value could you find at coordinates [0.038, 0.77, 0.113, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value at these coordinates is \"20.0%\"."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "YML9sUOqsu2Hnw7U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nBy checking the legend, what color is ascribed to the {text \"IoT\"}[0.782, 0.188, 0.808, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color that corresponds to \"IoT\" is represented by the handle at coordinates [0.733, 0.19, 0.768, 0.206], which is '#ffcc99'."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "hDUKubZB1vk6dbbi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the adjacent slice to the {slice of \"Big Data\"}[0.079, 0.515, 0.321, 0.907]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice adjacent to \"Big Data\" is the {slice of \"AI\"}[0.079, 0.103, 0.388, 0.515]."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "1tEywhqIjrD9F4vf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the legend item with the handle at coordinates [0.733, 0.12, 0.768, 0.136]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with these handle coordinates is {labeled \"Big Data\"}[0.782, 0.118, 0.857, 0.141]."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "86dx8ItNFP9ufmqh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within coordinates [0.079, 0.103, 0.388, 0.515] on this pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region within these coordinates includes the text \"25.0%\" at [0.131, 0.183, 0.148, 0.207] and the label \"AI\" at [0.23, 0.329, 0.284, 0.352]. This area represents a pie slice painted with the color from the handle at [0.733, 0.085, 0.768, 0.101]."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "emMNuCemXEDePrMV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the objects in the region [0.563, 0.273, 0.697, 0.642]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region specified, there are two objects: the text \"15.0%\" at [0.724, 0.433, 0.797, 0.456] and the label \"Robotics\" at [0.544, 0.465, 0.599, 0.488]. This region forms a slice of the pie chart."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "L1QTbmrzHuvuku7X",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_53.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the main elements of the region located at [0.455, 0.124, 0.638, 0.346]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the text \"10.0%\" at [0.588, 0.137, 0.682, 0.16] and the label \"Blockchain\" at [0.47, 0.304, 0.524, 0.327]. It is a slice on the pie chart."
+            }
+        ],
+        "id_raw": 1053
+    },
+    {
+        "id": "H4KdtB8WpwAgzNub",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is at the coordinates [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That is the legend of the chart."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "5lsKQzl6eaqqMzta",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is contained within the coordinates [0.178, 0.025, 0.598, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, which is \"Fitness trends (Yoga vs HIIT): 2012-2020\"."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "3RzahrXdeBA8ZXU8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at the coordinates [0.871, 0.19, 0.905, 0.206]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the handle of the legend with color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "a6mITvgRSKeVwspk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the colors associated with the years 2012 and 2019 in the legend located at [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colors associated with the years 2012 and 2019 are \"#ff9999\" and \"#a7facf\" respectively."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "KIbrWeyzH5WcaIyP",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn this chart, which slice is related to the legend item \"2018\" with coordinates [0.919, 0.292, 0.963, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice that corresponds to \"2018\" is located at [0.383, 0.695, 0.635, 0.934]."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "CawWSDH09kdRrU0j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAccording to the chart, is the color \"#99ff99\" associated with the year 2013 or 2014?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#99ff99\" corresponds with the year 2014."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "9UlMa7drOwEpQvQ0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region of [0.383, 0.695, 0.635, 0.934] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region is a slice of color \"#d7aefc\" representing data from the year 2018 with the text \"14.9%\" indicating its percentage."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "zJRZvk2nFdFdX6mg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the details within the region demarcated by [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily consists of the main pie chart area showing nine equally divided slices representing data from different years (2012-2020). Each slice is differently colored and labeled with a corresponding percentage."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "WQNhspisthJiEReX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_55.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are situated within the region bounded by [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart's legend items. Each item in the legend consists of a handle with a color corresponding to a year and a slice in the pie chart, and a text indicating the respective year from 2012 to 2020."
+            }
+        ],
+        "id_raw": 1055
+    },
+    {
+        "id": "fycepFJDN0GcOMqd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main text with coordinates [0.25, 0.02, 0.75, 0.042] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart, it is labelled as \"Comparison between Oil and Gold prices from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "rvw8lkbQgQSbYUFf",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat content can be found at coordinates [0.909, 0.567, 0.994, 0.834]?"
+            },
+            {
+                "from": "gpt",
+                "value": "A legend to interpret the chart."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "TznvS12pZiHGSK7q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is placed at coordinates [0.015, 0.559, 0.837, 0.981]?"
+            },
+            {
+                "from": "gpt",
+                "value": "An area which represents a subplot."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "zW64NG3DC2ZBfaq7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is next to {the slice}[0.417, 0.407, 0.518, 0.491] in the pie?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.491, 0.359, 0.564, 0.453] is adjacent."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Gmd0Akf0eUrmFsS2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near {the subtitle}[0.384, 0.531, 0.468, 0.553]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle is near {the ylabel} <r>[0.009, 0.77, 0.009, 0.77]</r>."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "xXx8FIDIQNiBP9tp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice corresponds to the label \"2010\" in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.336, 0.107, 0.426, 0.19] corresponds to the label \"2010\"."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "Y2RXgDgvWmqSXMbD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region defined by coordinates [0.272, 0.578, 0.58, 0.962]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the slices of a pie chart, each slice represents the Gold price for a specific year from 2010 to 2019. The text labels for the slices are contained in this region."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "ahXKhs5vZ6W7B3X1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_57.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region with coordinates [0.909, 0.567, 0.994, 0.834] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend of the chart, which includes labels for the years 2010 to 2019 and corresponding colors."
+            }
+        ],
+        "id_raw": 1057
+    },
+    {
+        "id": "ICiLVBxUjAATwIQY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object does the region with coordinates [0.219, 0.025, 0.557, 0.055] in the chart represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subplot title, named \"Product Sales Trend Observation\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "kqR07Rb7TiMC5YNI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object at coordinates [0.864, 0.074, 0.97, 0.325] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the subplot legend."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "sDt0LJvUpRBCzxn0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object located at coordinates [0.312, 0.269, 0.355, 0.292] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the year label \"2014\"."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "mOdvOSwTPIe00wP6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is the color handle {the handle}[0.871, 0.155, 0.905, 0.171] associated with?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's associated with {the slice}[0.077, 0.452, 0.222, 0.787]."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "iMnhGARVRPxQjD1H",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich lines of text are linked to {the legend item}[0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's linked to {\"2014\"}[0.312, 0.269, 0.355, 0.292] in the pie chart."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "aZPZC5igNNL3lJsw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the {label}[0.919, 0.262, 0.974, 0.285] in the legend correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"2019\" corresponds to {the color} \"#990000\" in the pie chart."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "RGbe9sGAWm7G1J0H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained within the region with coordinates [0.214, 0.104, 0.388, 0.278] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises a pie slice labeled \"2014\" {the slice label}[0.312, 0.269, 0.355, 0.292] and labeled \"9.4%\"{the percentage label}[0.244, 0.071, 0.288, 0.094]."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "MXWwm8yWV6RC1ip8",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area within coordinates [0.077, 0.707, 0.699, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "The area hosts two pie slices, one labeled \"2017\" {the slices}[0.284, 0.734, 0.339, 0.758] and marked \"14.2%\"[0.204, 0.924, 0.248, 0.947], the other labeled \"2018\" {the slices}[0.453, 0.723, 0.508, 0.746] and marked \"15.7%\"[0.558, 0.903, 0.602, 0.926]."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "7G3FRNjtOluX4zXt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_58.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat lies in the region marked by coordinates [0.388, 0.104, 0.664, 0.384]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with label \"2020\" {the label}[0.457, 0.294, 0.512, 0.317] and its percentage \"17.3%\" {the percentage}[0.565, 0.116, 0.609, 0.14]."
+            }
+        ],
+        "id_raw": 1058
+    },
+    {
+        "id": "ommrgkBoT4WyQFU9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart at coordinates [0.231, 0.025, 0.545, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"Energy Usage by Source 2020\"."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "e9GuuajGo0IhrScZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item can be found at the coordinates [0.86, 0.223, 0.927, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Nuclear\" can be found at these coordinates."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "qhTcUF1rbDV6X549",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.666, 0.773, 0.705, 0.796]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"24.9%\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "MUWKhj5NBjXhxNno",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {slice \"Coal\"}[0.424, 0.453, 0.699, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pies \"Hydro\" and \"Natural Gas\"}[0.574, 0.302, 0.695, 0.472], [0.079, 0.55, 0.439, 0.934] are adjacent to the slice \"Coal\"."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "yf0QCTeXWCXwRIXn",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color corresponds to the {legend item \"Oil\"}[0.86, 0.083, 0.884, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color \"#ff9999\" corresponds to the legend item \"Oil\"."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "lhClSVHLHBBIvLei",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text on the chart belongs to the {pie slice colored \"#66b3ff\"} [0.079, 0.55, 0.439, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"25.9%\"}[0.078, 0.871, 0.181, 0.894] belongs to the pie slice colored \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "8SV6stILjmBQ55Pn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.424, 0.453, 0.699, 0.928]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses a slice of the pie chart representing \"Coal\" with the percentage value \"24.9%\" [0.666, 0.773, 0.705, 0.796] depicted within. The slice is colored green ('#99ff99')."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "gCeut47n6xBz9dLd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located within the region defined by coordinates [0.805, 0.074, 0.97, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the pie chart. This includes six legend items each consisting of a color handle and a label: \"Oil\" [0.86, 0.083, 0.884, 0.106], \"Natural Gas\" [0.86, 0.118, 0.963, 0.141], \"Coal\" [0.86, 0.153, 0.899, 0.176], \"Hydro\" [0.86, 0.188, 0.912, 0.211], \"Nuclear\" [0.86, 0.223, 0.927, 0.246], and \"Renewable\" [0.86, 0.258, 0.955, 0.281]."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "u2R9lUqlZW6uHT0R",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_59.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region defined by the coordinates [0.494, 0.156, 0.654, 0.367]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a slice of the pie chart referencing \"Nuclear\" energy. It includes the label \"Nuclear\" [0.495, 0.327, 0.539, 0.351] and the percentage value \"8.2%\" [0.625, 0.178, 0.692, 0.201]."
+            }
+        ],
+        "id_raw": 1059
+    },
+    {
+        "id": "JBqpLNCFWj4jsdg5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located at coordinates [0.919, 0.223, 0.963, 0.246] in this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2014\"."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "ev7INz2ricgjgBZK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.325] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the pie chart."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "3gZtbKauoVnGlaM5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.571, 0.893, 0.615, 0.916] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.9%\"."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "yd2h0cEx0SqYWdqT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the pie chart, which slice is adjacent to {the slice}[0.171, 0.727, 0.418, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.409, 0.668, 0.655, 0.932] is adjacent."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "YGZLbyt3c0qplNOB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is associated with {the slice}[0.171, 0.727, 0.418, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2013\"<r>[0.919,0.188,0.963,0.211]</r> is associated with this slice."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "BjRIjsF5bbyBTJxX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is found near {the slice}[0.409, 0.668, 0.655, 0.932] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"14.9%\"<r>[0.571, 0.893, 0.615, 0.916]</r> is found near the slice."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "jRqASp7OPgxPrdG7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you find within the region bound by coordinates [0.360, 0.370, 0.850, 0.860] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within the region, there is the slice \"2016\"<r>[0.388, 0.104, 0.664, 0.384]</r> and the slice \"2015\"<r>[0.575, 0.327, 0.699, 0.732]</r>."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "BZVje8ZwGHkLIemX",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat exists in the region with coordinates [0.100, 0.600, 0.300, 0.800]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region of the pie chart, you can find the slice corresponding to \"2012\"<r>[0.077, 0.492, 0.236, 0.816]</r>."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "cWOP1CA73zWZP71l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_61.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the area bounded by coordinates [0.500, 0.200, 0.700, 0.600] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes two slices of the pie chart: \"2016\"<r>[0.388, 0.104, 0.664, 0.384]</r> and \"2015\"<r>[0.575, 0.327, 0.699, 0.732]</r>."
+            }
+        ],
+        "id_raw": 1061
+    },
+    {
+        "id": "MFns7mAG1NSbv1As",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.101, 0.025, 0.675, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The main object is the title of the chart, which reads \"AI and Automation Algorithm Usage from 2013 to 2019\"."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "Z4CPyyUNlhYFqxoP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.325] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located at these coordinates."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "lWv9Hu0QbGjCBN5m",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.599, 0.149, 0.643, 0.172] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"21.4%\"."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "ffZUYnbMpViqMx33",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to the slice with coordinates [0.236, 0.756, 0.552, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice {with coordinates}[0.503, 0.422, 0.695, 0.86] is adjacent."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "terUmws15mpn5pBF",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near the text with coordinates [0.396, 0.952, 0.44, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts \"17.1%\" and \"2017\" are located near it."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "Hm47IoI55tNqzen5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2013\" to \"2019\" correspond to the slices in the pie chart."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "00Oz3niEbvCZi1mD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.503, 0.422, 0.695, 0.86]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice for the year \"2018\" and the associated percentage text \"19.6%\"."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "zAowJLXVG8CvKzvx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_63.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.213, 0.103, 0.388, 0.277]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice for the year \"2013\" and the associated percentage text \"9.6%\"."
+            }
+        ],
+        "id_raw": 1063
+    },
+    {
+        "id": "TWhkhvqXSzo9MBCY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nReferring to the chart, what kind of text is represented by the coordinates [0.016, 0.025, 0.76, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads \"Growth of Knowledge Economy and Innovation Industries (2015 - 2020)\"."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "bocXDihZYjOXGHI9",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at the coordinates [0.864, 0.074, 0.97, 0.5]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "6h0lsaCrOtKexiyI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich component of the chart is represented by these coordinates: [0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text of the legend item \"2015\"."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "XWCD2jvLq8XCWI2P",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is right next to the slice at {the slice}[0.541, 0.545, 0.695, 0.805]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice}[0.556, 0.256, 0.696, 0.557] is adjacent to it."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "PM7R7t6bkIbpvc9b",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIs there a correspondence between {the legend item \"2015\"}[0.919, 0.083, 0.963, 0.106] with one of the pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, it corresponds to {the pie slice}[0.293, 0.103, 0.388, 0.241]."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "k9noxIzCMQy9ofXT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does the slice at [0.422, 0.718, 0.606, 0.92] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the legend item \"2017\"}[0.919, 0.153, 0.963, 0.176]."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "9ORp9m8uVeCOKwk5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region between [0.08, 0.103, 0.696, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mostly contains the pie chart, covering slices depicting various years from 2015 to 2020 along with their respective percentages."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "jG5kGlcOBfDSI0Jw",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region that falls within these coordinates: [0.864, 0.074, 0.97, 0.5]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily covers the chart's legend. It houses labels representing the years 2015 to 2020 in color coordination with the pie chart slices."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "lm0r4zZIZTTV5aXs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_66.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be explored within the region [0.203, 0.123, 0.321, 0.284] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encapsulates one slice of the pie chart representing the year 2016 and its percentage share \"5.2%\"."
+            }
+        ],
+        "id_raw": 1066
+    },
+    {
+        "id": "qRWSn3h16JVJ3nH0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.07, 0.025, 0.706, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, titled \"Global Warming and Internet Usage Trend from 2001 to 2009\"."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "jrNk56JghMaSEsO0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich item is located at coordinates [0.776, 0.085, 0.811, 0.101] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item with the color '#ff9999', representing \"Global Warming\"."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "BFI2DVcQrHVgTiXi",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.327, 0.051, 0.371, 0.074]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's an autotext indicating the percentage \"1.6%\" in the pie chart."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "qlNCY6hAvhEhwnCr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label corresponds to the color located at {this handle}<r>[0.776, 0.085, 0.811, 0.101]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label \"Global Warming\"."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "5ZQyFtPL7OXX9XpX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is associated with {the text \"2005\"}[0.466, 0.711, 0.51, 0.734]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice}<r>[0.46, 0.715, 0.609, 0.902]</r>."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "76k5O2eQOtFx161L",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor which year does the slice at coordinates [0.08, 0.103, 0.697, 0.926] represent data?"
+            },
+            {
+                "from": "gpt",
+                "value": "That slice represents data for the year 2001."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "xCaVOYb7pESgAuia",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.08, 0.103, 0.697, 0.926]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie chart. The chart features many different slices, each corresponding to a different year's data on Global Warming and Internet Usage trend. The data for each year is highlighted by a distinct slice in the pie chart, and each slice has the color-coded text representing the percentage of percentage of each category."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "cSxEMnfZTus9tULi",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are in the region [0.388, 0.103, 0.575, 0.286]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the pie slice for the year 2009 with autotext indicating '10.4%'."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "MKqm5IMyVtrFANZS",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_67.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region with coordinates [0.769, 0.074, 0.97, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart, which includes the items \"Global Warming\" and \"Internet Usage\" along with their corresponding color handles."
+            }
+        ],
+        "id_raw": 1067
+    },
+    {
+        "id": "8k6OObQeU0cmtiHq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described at coordinates [0.099, 0.025, 0.677, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, \"Social Dynamics and Political Trends from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "qjb07OhJhZSggUHL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart legend with information regarding the years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "YaVzfbXmyR1zciCe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.079, 0.103, 0.697, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart showing the distribution of social dynamics and political trends for different years."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "LWZQartkA6qfzWv4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentage is positioned close to {the 2017 slice}[0.079, 0.343, 0.234, 0.804] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The provided coordinate is for the 2017 slice, and the corresponding percentage is {\"19.2%\"}[0.01, 0.581, 0.053, 0.605]."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "JFIFfvgtQFYmhL3d",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the legend item corresponding to the {the 2019 slice}[0.488, 0.428, 0.697, 0.88] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color for the 2019 slice corresponds to the legend item with the color '#ffcc99'."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "Lz0s2uixbmbhi7SX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the pie slice at coordinates [0.388, 0.103, 0.69, 0.454] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the year {\"2020\"}[0.919, 0.223, 0.963, 0.246]."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "O8mkyRD5f5QcUj9o",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.079, 0.103, 0.697, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart representing social dynamics and political trends from 2016 to 2020. Specifically, it includes five slices representing the years 2016, 2017, 2018, 2019, and 2020, corresponding to their distribution percentages."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "8twZVpl7S9qMGXNF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the contents in the region [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is designated as the chart legend. It includes five items representing different years from 2016 to 2020, each item consists of a distinct color and the corresponding year."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "SA1lsWcyy8p1n5Zk",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_68.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the coordinates [0.388, 0.103, 0.69, 0.454]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region corresponds to the section of a pie chart. Specifically, this region is a slice labelled as \"2020\" which represents 21.6% of the total distribution."
+            }
+        ],
+        "id_raw": 1068
+    },
+    {
+        "id": "OqBg0VpeyOCKCRfv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWithin this chart, what is the main element with coordinates [0.188, 0.025, 0.588, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the subtitle, labeled as \"Patent Applications from 2005 to 2014\"."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "0rZw3IYEyLXMlsOe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what is at coordinates [0.864, 0.074, 0.97, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "Gzq2NWZ4LOH4MDAb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.322, 0.052, 0.366, 0.075]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a text element displaying \"2.1%\"."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "GXtsu9vS8bfrHhCk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {the specific slice}[0.143, 0.161, 0.278, 0.339] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {adjacent slices}[[0.23, 0.123, 0.322, 0.267], [0.085, 0.265, 0.217, 0.464]] are immediately adjacent."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "s3iwMWyAWC7jBEM9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text elements are located near {the 2011 slice}[0.105, 0.625, 0.294, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2011\"}[0.227, 0.671, 0.282, 0.694] and {\"11.4%\"}[0.099, 0.812, 0.143, 0.835] are located near the 2011 slice."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "17EpsV1paPpTNcFg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIn the chart, which legend items correspond to which slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2005\" to \"2014\" correspond to the slices with coordinates from [0.348, 0.103, 0.388, 0.229] to [0.388, 0.103, 0.688, 0.45], respectively."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "1NL0oun0Cinitvrb",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the region within coordinates [0.081, 0.265, 0.217, 0.464]."
+            },
+            {
+                "from": "gpt",
+                "value": "The indicated region contains a pie slice with the text elements \"7.6%\" [0.034, 0.321, 0.078, 0.344] and \"2009\" [0.197, 0.403, 0.241, 0.426] displayed. It seems to be associated with the patent applications recorded in 2009."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "xa8uv4BYp6qfyXfu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_71.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.254, 0.756, 0.551, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region, there is a pie slice with related text elements: \"16.1%\" [0.406, 0.952, 0.45, 0.975] and \"2012\" [0.37, 0.747, 0.425, 0.77]."
+            }
+        ],
+        "id_raw": 1071
+    },
+    {
+        "id": "zk4ZNWJtYC8nHpn7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.171, 0.025, 0.605, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named \"Global Internet Access from 2000 to 2020\"."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "5cuMuGVEvbJV89fJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at the coordinates [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the chart's legend."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "BaiOce0Zgbys6TaJ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.213, 0.085, 0.257, 0.109] coordinates?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"12.5%\"."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "7SxW6fT3Ajaqjbp7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of pie in the chart is adjacent to the slice denoted by coordinates [0.388, 0.564, 0.695, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.388, 0.104, 0.699, 0.584] is adjacent."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "18NRtL2Olnd8Vuy3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is near {the legend}[0.864, 0.074, 0.97, 0.255] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart {centered}[0.388, 0.519] is near."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "DMXnTgVw34t3Oj8W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat texts are close to {the subtitle}[0.171, 0.025, 0.605, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2010\"}[0.251, 0.708, 0.306, 0.732] and {\"2015\"}[0.482, 0.696, 0.537, 0.72] are close to the subtitle."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "SvTSgHwVbwi6sRzC",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.092, 0.608, 0.388, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area is a pie slice and its corresponding labels \"2010\" [0.251, 0.708, 0.306, 0.732] and \"20.0%\" [0.143, 0.876, 0.187, 0.9]. This area represents the data for the year 2010 in the pie chart."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "l5gb9ffaUzGXNspT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region defined by [0.388, 0.104, 0.699, 0.584]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Inside this region is a slice of the pie chart with its respective labels \"2020\" [0.503, 0.345, 0.557, 0.369] and \"27.5%\" [0.648, 0.211, 0.692, 0.234]. This region represents data for the year 2020 in the pie chart."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "4aETFaFJwoouhgKL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_74.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are located in the region [0.077, 0.225, 0.234, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this area is a pie slice and its corresponding labels \"2005\" [0.179, 0.449, 0.234, 0.472] and \"17.5%\" [0.012, 0.4, 0.055, 0.424]. This area represents the data for the year 2005 in the pie chart."
+            }
+        ],
+        "id_raw": 1074
+    },
+    {
+        "id": "DTcJXYiPMDG4HFHz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.05, 0.025, 0.727, 0.055] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a title captioned \"Education and Training Technology Innovation from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "nqqweMPVvkQvDJaI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.864, 0.074, 0.97, 0.29] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "qkkAzPZ8sDTMNJzH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is found at coordinates [0.388, 0.104, 0.679, 0.416] in the chart figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice labeled as \"2020\"."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "lk2E6fQsmxX3rmqq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to {the slice}[0.287, 0.725, 0.605, 0.931] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.54, 0.372, 0.698, 0.814] is adjacent to it."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "i2HZy9tks1b4ZT9U",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object from the legend matches with the slice having the coordinates [0.149, 0.104, 0.388, 0.333]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The colored handle labeled as \"2015\" <r>[0.871, 0.085, 0.905, 0.101]</r> corresponds to this slice."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "iOEn2cnxbYzwrbpE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to {the slice}[0.087, 0.589, 0.318, 0.909] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color #99ff99 <r>[0.871, 0.155, 0.905, 0.171]</r> in the legend corresponds to this slice."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "ChsiVagaE6a8n1Mn",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.287, 0.725, 0.605, 0.931]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart slice of color '#ffcc99'. It is labeled \"2018\" <r>[0.401, 0.748, 0.456, 0.771]</r>, and has an autotext of \"17.5%\" <r>[0.463, 0.95, 0.507, 0.973]</r>."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "UsZiTBOeqBN0bHfF",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_75.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are present in the region with boundaries [0.388, 0.104, 0.679, 0.416]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice colored '#8f99f7' with label \"2020\" <r>[0.467, 0.302, 0.522, 0.325]</r> and autotext \"19.3%\" <r>[0.583, 0.132, 0.626, 0.155]</r>."
+            }
+        ],
+        "id_raw": 1075
+    },
+    {
+        "id": "x83Jz5Is1jzZQYFK",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object locates at [0.128, 0.025, 0.648, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle of the chart, called \"Gaming Industry Development from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "B8ceXKqL9L3Zvx2v",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is inside the bounding box with coordinates [0.864, 0.074, 0.97, 0.43]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend of the chart."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "yxKtr2T25ud5O3Su",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's chart object is within the coordinates [0.388, 0.103, 0.598, 0.303]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the slice labeled as \"2019\" in the pie chart."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "U7XMxldN1AO3NaV1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart is adjacent to {the slice labeled 2018}[0.535, 0.214, 0.694, 0.491]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice labeled 2017}[0.561, 0.482, 0.695, 0.757] and {the slice labeled 2019}[0.388, 0.103, 0.598, 0.303] are adjacent to it."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "EAtf8CFvNa0Qhjhy",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legends are located near {the legend for 2012}[0.919, 0.153, 0.963, 0.176]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legends for {2011}[0.919, 0.118, 0.963, 0.141] and {2013}[0.919, 0.188, 0.963, 0.211] are located near it."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "xnlU8kFwFJCATMJp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice labeled 2015}[0.307, 0.788, 0.474, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2015\" corresponds to it."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "EhcpTZjU8W0tnmSP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the contents inside the region with coordinates [0.081, 0.103, 0.695, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes all slices in the pie chart, each representing a year from 2010 to 2019."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "GotDaSTLxmDCeJSA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region [0.871, 0.085, 0.963, 0.421] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the handles corresponding to each label from 2010 to 2019 in the legend of the pie chart, each handle represents a unique year."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "JbBia8RRnoQxa1oR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_80.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTell me about the objects within the region [0.081, 0.103, 0.695, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes ten slices referencing years from 2010 to 2019, each of which is a part of the pie chart depicting the gaming industry development from 2010 to 2019."
+            }
+        ],
+        "id_raw": 1080
+    },
+    {
+        "id": "8FR09Q0uo01NNySC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the location [0.036, 0.025, 0.74, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart, which reads, \"Automotive and Transportation Market Research from 2015 to 2020\"."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "6fBHmYOzY8Tjr6la",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object positioned at the coordinates [0.864, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That position contains the legend of the chart."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "puiGw4btiinkYZgE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is present at the coordinates [0.279, 0.281, 0.334, 0.305] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2015\" is located at the specified coordinates."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "CJWXYbRGOyE6w5aM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart share an edge with {the slice}[0.257, 0.741, 0.577, 0.925]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices at}[[0.1, 0.615, 0.296, 0.885], [0.521, 0.399, 0.696, 0.838]] share an edge with the specified slice."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "xJfPWrFsmQAxEio5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre there texts located near {the subtitle}[0.036, 0.025, 0.74, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "No, there are no texts located near the subtitle in the chart."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "Y0IK1JBlRmqvdo2l",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.388, 0.103, 0.684, 0.433]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2020\"[0.919, 0.258, 0.963, 0.281] corresponds to the given slice."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "5aGrEkL4NoqAlRyr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.08, 0.103, 0.696, 0.925] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area is occupied by the pie chart, which displays the distribution of \"Automotive and Transportation Market Research from 2015 to 2020\". It contains six slices, with texts and percentages for the years 2015 to 2020 located in the slices."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "4NJhNzNXB9K68IrP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_81.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located within the region defined by the coordinates [0.388, 0.103, 0.684, 0.433]."
+            },
+            {
+                "from": "gpt",
+                "value": "A slice of the pie chart is located within these coordinates. The text \"2020\"[0.472, 0.305, 0.526, 0.328] and the percentage \"20.5%\"[0.591, 0.141, 0.635, 0.164] are located in this slice."
+            }
+        ],
+        "id_raw": 1081
+    },
+    {
+        "id": "QtvYQtYA7kXMBLIe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.204, 0.025, 0.573, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named AI & ML Projects from 2015 to 2020."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "WRMhKeLfRNVRirFL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me location of {legend}[0.864, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Located on the right side of the chart."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "g9ormlLapHHTivU7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's indicated by the coordinates [0.584, 0.488, 0.739, 0.510] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"28.6%\"."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "kiI7wpZNtmZ62BYp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart corresponds to the legend item \"2015\" at {the legend item's coordinates}[0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with {coordinates}[0.208, 0.104, 0.388, 0.282] corresponds to the legend item \"2015\"."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "IDyDaZ6cwDb6sLnM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the legend item \"2020\" at the position [0.919, 0.258, 0.963, 0.281], which pie slice in the chart corresponds it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice corresponding to it is at {coordinates}[0.388, 0.104, 0.699, 0.632]."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "KP6cvDROsVB2uBHZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie shares the same color with the handle at coordinates [0.871, 0.225, 0.905, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with {coordinates}[0.382, 0.598, 0.687, 0.934] shares the same color."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "MNsnS4WA7CSpkIqj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what's there within the area [0.1, 0.1, 0.7, 0.9]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains pie slices with different colors. Each slice represents a year from 2015 - 2020 indicated by the text in each slice. This region also contains the percentages of AI and ML projects executed in respective years."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "lLFdpfanQgOgqY7B",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects in the region [0.07, 0.1, 0.69, 0.93]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie chart that presents the distribution of AI & ML Projects from 2015 to 2020. Each slice of the pie chart is labeled with a year and the corresponding percentage."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "MYrq7UWzK5DDjwh2",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_82.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.86, 0.07, 0.97, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a legend, which specifies the color codes used for different years from 2015 to 2020 in the pie chart."
+            }
+        ],
+        "id_raw": 1082
+    },
+    {
+        "id": "ut8CfvTRZcIC859L",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart's main text located at the coordinates [0.218, 0.021, 0.556, 0.047]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the chart title called \"Future Technological Development Prospects\"."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "foWOdL1wNWYDmzlb",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.805, 0.073, 0.83, 0.087] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend box for the Artificial Intelligence category."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "YbIgFnHU7NjEykFR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at the chart coordinates [0.512, 0.925, 0.605, 0.945]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the percentage label \"10.0%\"."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "ns3sbYN2v1sG76QA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich percentages are associated with {the slice}[0.523, 0.228, 0.646, 0.443]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The percentage} [0.645, 0.283, 0.733, 0.303] is associated with the slice."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "4QMxIIbMGyrfBF4j",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the neighboring pie slices of the {slice}[0.119, 0.516, 0.277, 0.856]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[0.119, 0.095, 0.387, 0.516] and [0.23, 0.754, 0.42, 0.937] neighbour the slice."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "jV7zfZ9RNCd0NDw3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich technological development does the {identifier color box}[0.805, 0.163, 0.83, 0.177] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents \"5G Technology\"."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "5Jh59NtzjYKjAaUc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.805, 0.253, 0.83, 0.266]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend item color box {box}[0.805, 0.253, 0.83, 0.266] pertaining to Cybersecurity."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "6HAndnUYomwbLzyG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is within coordinates [0.515, 0.589, 0.646, 0.823]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this region, there's a slice of {the pie}[0.515, 0.589, 0.646, 0.823] that represents Big Data."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "5LM2goUvLDgmeJum",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_84.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region located at [0.841, 0.131, 0.952, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It contains the name of the technology sector {label \"Internet of Things\"}[0.841, 0.131, 0.952, 0.151]."
+            }
+        ],
+        "id_raw": 1084
+    },
+    {
+        "id": "TTGTU1e1CK6SmDin",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the central object with coordinates [0.081, 0.103, 0.696, 0.923] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "xSOuyAmMcClvPKie",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.148, 0.025, 0.628, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle titled \"International Trade Volume from 2000 to 2005\"."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "Fc62QIuo1aMcUsBh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.594, 0.144, 0.638, 0.167] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.8%\"."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "hHiuzD31eqFxFRkH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the pie chart are adjacent to {the slice}[0.512, 0.407, 0.696, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.25, 0.748, 0.566, 0.923], [0.388, 0.103, 0.685, 0.439]] are adjacent."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "U4AWEnRiYOp70eka",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.148, 0.025, 0.628, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"International Trade Volume from 2000 to 2005\"}[0.148, 0.025, 0.628, 0.055] are located near the subtitle."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "G4hufhrDXmanPbzC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2000\", \"2001\", \"2002\", \"2003\", \"2004\", and \"2005\" correspond to the slices with coordinates [0.158, 0.103, 0.388, 0.323], [0.081, 0.241, 0.227, 0.578], [0.084, 0.558, 0.292, 0.88], [0.25, 0.748, 0.566, 0.923], [0.512, 0.407, 0.696, 0.848], and [0.388, 0.103, 0.685, 0.439] respectively."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "H5xpOoZd6FGOSYVq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.103, 0.696, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart which comprises of slices arranged according to years from 2000 to 2005, each slice representing the International Trade Volume for the respective year, along with the corresponding text percentages."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "O44kc2hmd7prFkZg",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_86.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.864, 0.074, 0.97, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the chart legend which includes color boxes and labels for the years 2000 to 2005."
+            }
+        ],
+        "id_raw": 1086
+    },
+    {
+        "id": "et9r5r9c38EEkybv",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the object at coordinates [0.871, 0.19, 0.905, 0.206] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "#ffcc99"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "b581nLTK1QdHaznV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the subtitle of the chart, corresponding to the coordinates [0.058, 0.025, 0.718, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Transportation and Logistics Industry Trends from 2013 to 2019"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "geWkUoelBSjZJqf4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.245, 0.884, 0.289, 0.907] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"9.5%\"."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "TcODvz8Xr5TYwHNQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart does the legend item {2014}[0.919, 0.118, 0.963, 0.141] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[[0.089, 0.176, 0.265, 0.44]]"
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "4x3ieMxH72LNDng1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow many legend items match the color of {the slice}[0.213, 0.104, 0.388, 0.279] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "One, the \"2013\" legend item."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "ih6JBL16FcNKBnzA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text items are located inside {the slice}[0.551, 0.367, 0.699, 0.793]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"17.5%\"}[0.725, 0.586, 0.769, 0.61] and {\"2018\"}[0.545, 0.55, 0.599, 0.574]."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "U3fHcUigWjV0QX7Y",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.342, 0.711, 0.621, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"15.9%\" [0.505, 0.936, 0.549, 0.959] and the text \"2017\" [0.424, 0.741, 0.479, 0.764]. This region corresponds to the pie slice of the year 2017 and is colored #c2c2f0."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "PfKPdeP2YO5T4mJl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found in the region [0.077, 0.406, 0.199, 0.726]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"12.7%\" [0.005, 0.564, 0.048, 0.587] and the text \"2015\" [0.175, 0.538, 0.23, 0.561]. The region corresponds to a pie chart slice representing the year 2015."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "OVGRTVYYNzMhLCwa",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_90.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the region [0.119, 0.664, 0.356, 0.929]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region spans a sector of the pie chart colored #ffcc99. It includes an autotext \"14.3%\" [0.151, 0.884, 0.195, 0.907], and a text \"2016\" [0.255, 0.713, 0.31, 0.736], indicating that this slice represents data for the year 2016, which had a percentage of 14.3%."
+            }
+        ],
+        "id_raw": 1090
+    },
+    {
+        "id": "3OxEQdESAaWjGGx4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.085, 0.025, 0.691, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the subtitle, named \"Building and Real Estate Market Trends from 2010 to 2019\"."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "LrXsADbRtlMQQpyR",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.864, 0.074, 0.97, 0.779] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the legend."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "PFZt3Ga7kVKrDeK8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.919, 0.432, 0.963, 0.455] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object is the text \"2010\"."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "yh0rw0YWqEe1VAcv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does {the handle}[0.871, 0.434, 0.905, 0.45] in the legend correspond to in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color #ff9999."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "VUxPPfOpkHv156SA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat handle and text are associated with {the slice}[0.299, 0.787, 0.47, 0.922] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The handle}[0.871, 0.748, 0.905, 0.765] and the text \"2019\"[0.919, 0.746, 0.963, 0.77]."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "kXPFgLbIekrvjLQb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is associated with {the color}\"#66b3ff\" in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2011\" [0.919, 0.118, 0.963, 0.141] is associated."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "QbpKlZq9qN6vnTm5",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.103, 0.695, 0.922]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily contains the pie chart. It contains many pie slices, each representing different years from 2010 through 2019."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "7NU3LIO55VApwngT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_91.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.864, 0.074, 0.97, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the various legend items that correspond to the years 2010 through 2019 and their colors displayed in the pie chart."
+            }
+        ],
+        "id_raw": 1091
+    },
+    {
+        "id": "l9mgoj4QWOTX58Nn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object at coordinates [0.813, 0.062, 0.923, 0.08] indicate?"
+            },
+            {
+                "from": "gpt",
+                "value": "{It}<r>[0.813, 0.062, 0.923, 0.08]</r> refers to the legend item labeled \"Financial Sector\"."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "NyolK32OlaErYqOg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what's depicted at coordinates [0.578, 0.78, 0.676, 0.797]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the data proportion {\"28.3%\"}<r>[0.578, 0.78, 0.676, 0.797]</r> associated with one of the pie slices."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "tnJxc5Z10DFZgfjL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is present at the coordinates [0.157, 0.019, 0.675, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text at these coordinates {is the subtitle of the chart}<r>[0.157, 0.019, 0.675, 0.041]</r>, reading \"Resource Allocation in Financial Management and Risk Control\"."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "61j3tnsmj7jQWAak",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is related to the legend item {\\'Credit Risk\\'}[0.813, 0.193, 0.888, 0.211]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with 11.6% proportion {represents 'Credit Risk'}<r>[0.551, 0.318, 0.664, 0.528]</r>."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "r93mQBdm6FW8Lkqt",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the corresponding legend for the pie slice at {coordinates}[0.172, 0.553, 0.373, 0.815]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding legend for the pie slice is {'Stock Evaluation'}<r>[0.813, 0.141, 0.929, 0.158]</r>."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "xFfgZSFC6FeEAGZv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item does the slice with the text \"28.3%\" corresponds to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item corresponding to this slice is {\"Market Trends\"}<r>[0.813, 0.167, 0.911, 0.185]</r>."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "VkFYKuUiwKnxK9xN",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nMention the items within the region defined by these coordinates [0.769, 0.055, 0.959, 0.244]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes several elements from the chart legend, including the legend items \"Financial Sector\", \"Risk Mitigation\", \"Investment Portfolio\", \"Stock Evaluation\", \"Market Trends\", \"Credit Risk\", and \"Operational risk\", along with their corresponding color codes."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "GzfcMDbrUA796p3D",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe what is in the region [0.168, 0.204, 0.664, 0.824]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region primarily includes the different slices of the pie chart, each slice representing a different aspect of Resource Allocation in Financial Management and Risk Control: 'Financial Sector', 'Risk Mitigation', 'Investment Portfolio', 'Stock Evaluation', 'Market Trends', 'Credit Risk', and 'Operational risk'. Each slice also has a corresponding percentage."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "x0SqDo9Tijyxwc0c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_92.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found inside the boundary [0.416, 0.204, 0.608, 0.377]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart depicting \"Operational Risk\" with a proportion of \"14.1%\" associated with it."
+            }
+        ],
+        "id_raw": 1092
+    },
+    {
+        "id": "sa2HnBNTSjFDAwsk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich data point has the text '10.5%' associated with it?"
+            },
+            {
+                "from": "gpt",
+                "value": "The data point associated with the text \"10.5%\" {the autotext}<r>[0.027, 0.334, 0.071, 0.357]</r>."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "RP9wDZPY34nr6zub",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color represents the year '2024' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color representing the year \"2024\" is {the color}<r>[#c2c2f0]</r>."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "dDPw2KRAgt193uFS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhere is the year '2020' labelled within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year \"2020\" is labeled at {the text}<r>[0.283, 0.28, 0.338, 0.304]</r>."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "GRmrg1sfWYR7iVva",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the chart's exploded slice represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.154, 0.104, 0.388, 0.327]</r> represents the year \"2020\"."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "h53HeJOKwdzrm7Zg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item is located next to the color block with coordinates [0.871, 0.225, 0.906, 0.241]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item}<r>[0.92, 0.223, 0.963, 0.246]</r> is \"2024\"."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "wBEgNy2CHkyXpssS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the theta range for the slice indicating the year '2025'?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.57, 0.333, 0.699, 0.746]</r> has the theta range \"327 -> 387\"."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "6AO3SJ9FgQhg4Czx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within the bounding box where the legend is located."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains {the legend items}<r>[0.92, 0.083, 0.963, 0.106]</r>, <r>[0.92, 0.118, 0.963, 0.141]</r>, <r>[0.92, 0.153, 0.963, 0.176]</r>, <r>[0.92, 0.188, 0.963, 0.211]</r>, <r>[0.92, 0.223, 0.963, 0.246]</r>, <r>[0.92, 0.258, 0.963, 0.281]</r>, and <r>[0.92, 0.292, 0.963, 0.316]</r> corresponding to the years 2020 to 2026 respectively, along with their associated color blocks."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "uwRBhCc3nsjPDp6O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the spatial area between the coordinates [0.077, 0.104, 0.699, 0.934] feature?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area predominantly features {the pie chart}<r>[0.077, 0.104, 0.699, 0.934]</r> with all slices representing different years and their percentage values."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "aDIxVWCKBOaeKWXh",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_94.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region stretching from the bottom of the pie chart to the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses {the bottom slices of the pie chart}<r>[0.152, 0.708, 0.648, 0.933]</r>, <r>[0.395, 0.678, 0.648, 0.933]</r>, <r>[0.57, 0.333, 0.699, 0.746]</r>, and <r>[0.388, 0.104, 0.666, 0.389]</r>, as well as {the legend}<r>[0.864, 0.074, 0.97, 0.325]</r> which is situated to the right of these slices."
+            }
+        ],
+        "id_raw": 1094
+    },
+    {
+        "id": "Q5vzoXvbTTYFsDWQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the plot found within the coordinates [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart containing slices for each year from 2009 to 2018."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "k3riCgw7DOfoNxdH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.919, 0.397, 0.963, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text for the year \"2018\"."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "d5AU9392PJyswtiW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart, found at the coordinates [0.092, 0.025, 0.684, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"International Trade and Globalization Trends (2009-2018)\"."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "bpd7Y1R5pIgHBAeG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to {the slice}[0.085, 0.585, 0.254, 0.846]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color that corresponds to this slice is '#ffcc99', which represents the year 2012."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "9OdSUL2RoqiH4UuI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the value represented by {the slice}[0.223, 0.104, 0.388, 0.273]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The value is \"8.9%\", which is given by the autotext located at [0.249, 0.068, 0.293, 0.092]."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "xiPlOs8n357zThCe",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the slice adjacent to {the slice}[0.104, 0.167, 0.272, 0.4]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice adjacent to this one is {the slice}[0.077, 0.35, 0.189, 0.613], which represents the year 2011."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "QJe276KEiDHWT32P",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region within [0.188, 0.585, 0.354, 0.846] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a portion of the pie chart, specifically the slice related to the year 2012. The details of this slice are represented by the text \"10.8%\" located at [0.056, 0.753, 0.1, 0.777] and \"2012\" represented at [0.203, 0.641, 0.258, 0.665]."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "mQ0BbtzGWgbuIU4D",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.519, 0.188, 0.685, 0.432]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region holds a part of the pie chart depicting the slice for the year 2017. The slice is associated with the text \"9.9%\" at [0.668, 0.245, 0.712, 0.268] and \"2017\" at [0.519, 0.364, 0.563, 0.387]."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "bEf2K89fw44KpBGQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_95.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is within the region [0.468, 0.104, 0.656, 0.287]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the slice of the pie chart for the year 2018. It is accompanied by the contribution percentage \"10.3%\" at [0.497, 0.074, 0.541, 0.098] and the year \"2018\" at [0.42, 0.271, 0.475, 0.294]."
+            }
+        ],
+        "id_raw": 1095
+    },
+    {
+        "id": "y5EF4z2pHCphWg0P",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object with coordinates [0.275, 0.06, 0.319, 0.083] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents the text \"6.5%\"."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "4KFWzgPvzj1dPurg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.329, 0.261, 0.373, 0.285]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"2016\"."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "5I2fkXpcfQVekJH7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2016\" in the legend."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "whsDtoZBidn52nnz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart does {the text \"2018\"}[0.919, 0.327, 0.963, 0.351] in the legend correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice} [0.08, 0.289, 0.208, 0.563]."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "cxmMx8EMRYROPaLb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the text \"13.0%\"}[0.05, 0.729, 0.094, 0.753] and {the slice} [0.082, 0.548, 0.259, 0.845]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"13.0%\" represents the percentage portion of the pie chart represented by the said slice."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "Bi8Zkd3lyRclRPuE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the connection between {the text \"2020\"}[0.919, 0.397, 0.963, 0.421] in the legend and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" corresponds to {the slice} [0.388, 0.103, 0.636, 0.343] in the pie chart."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "J77mFP0orF1iM7os",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.08, 0.103, 0.697, 0.926]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart, which is divided into multiple slices with different colors each corresponding to a different year from 2016 to 2020. The slices are accompanied by text indicating the year and the proportion they represent."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "vgCFlp91EQdWRFJT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region with coordinates [0.864, 0.074, 0.97, 0.43]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region comprises of the chart's legend showing mapping of the years with their respective colors on the pie chart."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "jN946wdcZo5lElVH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_103.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region bounded by coordinates [0.919, 0.083, 0.963, 0.106] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a part of the legend, precisely the text \"2016\"."
+            }
+        ],
+        "id_raw": 1103
+    },
+    {
+        "id": "8yCzvdO7ycZVpoEq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.093, 0.025, 0.683, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is called \"Cultural Heritage and Cultural Integration Over the Years\"."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "nNQI07RCGkdU9upZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.855, 0.074, 0.97, 0.255] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "Fsz1dfQZKP1n6dbD",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.721, 0.58, 0.774, 0.603] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"8.9%\" is located here."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "ghkH73MqKJN319eW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.388, 0.103, 0.586, 0.293]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.526, 0.199, 0.694, 0.483],[0.283, 0.771, 0.523, 0.923]] are adjacent."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "z0qIrLHPRvhZchaS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotexts are located in the same quadrant as {the slice}[0.139, 0.139, 0.301, 0.344]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {\"8.3%\"}[0.122, 0.151, 0.175, 0.174] is located in the same quadrant."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "vk8woLqOAlKd5DMd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the slice with the color '#c2c2f0'?"
+            },
+            {
+                "from": "gpt",
+                "value": "\"Year 5\" from the legend corresponds to this slice."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "3fFHZNLRMijhfw0j",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.103, 0.695, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the pie chart with ten slices, each representing data from different years. The corresponding autotexts and text labels for each slice are also located in this region."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "AgnzSHTcKpEqK5fW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_106.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.855, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend of the chart. The legend has five items with text labels \"Year 1\", \"Year 2\", \"Year 3\", \"Year 4\", and \"Year 5\", and the corresponding color squares."
+            }
+        ],
+        "id_raw": 1106
+    },
+    {
+        "id": "qkF0f41bqb5YLPm5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.142, 0.025, 0.634, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle that reads \"Resource Usage Percentage from 2001 to 2007\"."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "HUqp7EnShQrDwv7j",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be found at [0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the location of the legend."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "TL5YXjinxR0LwNmZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at [0.516, 0.183, 0.699, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is a pie slice representing the year \"2006\"."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "WreiV5hN7kqS0qkp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice is adjacent to the {slice \"2002\"}<r>[0.077, 0.183, 0.26, 0.519]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice for the year {2003}<r>[0.077, 0.519, 0.388, 0.934]</r> is adjacent."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "Kplr4ktlo9lEOoxN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color in the legend corresponds to the slice in the chart for the year {2004}<r>[0.388, 0.754, 0.571, 0.934]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color represented in the legend is {\"#ffcc99\"}<r>[0.871, 0.19, 0.906, 0.206]</r>."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "PulO17vuuaQ1C4q1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near the slice for the year {2001}<r>[0.205, 0.104, 0.388, 0.284]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"10.0%\"}<r>[0.238, 0.073, 0.282, 0.096]</r> is near the slice for the year 2001."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "bBK0XMgjDCgt2xxP",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.077, 0.104, 0.699, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers most of the pie chart, it includes the slices for the years 2001, 2002, 2003 and part of the slice for 2004 with their corresponding percentage texts."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "5z2ss3iFao7gzlx6",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.516, 0.183, 0.699, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region specifically contains the slice for the year 2006, with its corresponding text and percentage."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "ezhCr9SE27IStVlz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_107.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.864, 0.074, 0.97, 0.325]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region on the far right contains the chart legend. Each entry in the legend consists of a color patch and a text label representing a different year from 2001 to 2007."
+            }
+        ],
+        "id_raw": 1107
+    },
+    {
+        "id": "U7a3sAWHNpe3gfsT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the parameters of the object located at [0.152, 0.025, 0.624, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle \"Market Competition of Major Tech Companies\"."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "mRlMMYSdHyPwgQWk",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at [0.882, 0.118, 0.963, 0.141] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text for the legend item \"Facebook\"."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "E5HRCu54OXVHPyCd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is located at [0.543, 0.914, 0.614, 0.937] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"15.0%\"."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "FiJ3SWwxUowBUwrI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to {the slice}[0.388, 0.689, 0.64, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Amazon}[0.882, 0.153, 0.952, 0.176] corresponds to the slice."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "smXcxVp51Qox9IfU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the slice which corresponding to the text {Microsoft}[0.882, 0.223, 0.961, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the slice is '#c2c2f0'."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "2eypctnhcdi4kR8t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage of the company, whose legend is placed at [0.882, 0.083, 0.943, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The company's share is represented by {the slice}[0.077, 0.104, 0.388, 0.647] with the text \"30.0%\"."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "HdUxXQWymCChTgVy",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area defined by coordinates [0.826, 0.074, 0.97, 0.255] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes the items of the legend, with various labels like \"Google\", \"Facebook\", \"Amazon\", \"Apple\", and \"Microsoft\", along with corresponding colored symbols."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "qLLso6wWAcY2eH79",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you say about the area between points [0.077, 0.104, 0.699, 0.934] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains every slice of the pie chart, each representing a different company and colored differently. Each slice also has a corresponding label and percentage."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "SgCcTpzNEUXOVK8O",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_108.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat elements are located in the region [0.077, 0.104, 0.388, 0.647]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a slice of the pie chart colored '#ff9999', with the label {Google}[0.21, 0.361, 0.264, 0.384] and the percentage \"30.0%\"."
+            }
+        ],
+        "id_raw": 1108
+    },
+    {
+        "id": "JpN9UcC5dLkPz4hC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object at the coordinates [0.196, 0.025, 0.58, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, called \"Social Culture Value Change: Value-5\"."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "umSRJSd0i8EH9JB6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is defined at the coordinates [0.742, 0.074, 0.97, 0.151]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "SNzkbJxeVEkzjmQh",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.388, 0.104, 0.699, 0.934] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a pie chart depicting the distribution of social cultural values."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "isQR0ysNLCE1vNMj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich label in the legend corresponds to {the pie slice} [0.077, 0.104, 0.484, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label 'Faith' {at} [0.797, 0.083, 0.839, 0.106] corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "BhHQ6F66OhQyzlGT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the percentage value associated with the {pie slice}[0.388, 0.104, 0.699, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage value associated with the pie slice {is} [0.726, 0.436, 0.892, 0.459]. It is \"45.0%\"."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "9VodZIbGMenMy70B",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object adjacent to the {pie slice}[0.077, 0.104, 0.484, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object adjacent to the pie slice is another {pie slice}[0.388, 0.104, 0.699, 0.913]."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "rr5Ae00dVdA4aMxK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's within the region with coordinates [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a pie slice. Inside the slice, there are two labels: 'Faith' {at} [0.176, 0.546, 0.231, 0.569] and '55.0%' {at} [0.008, 0.578, 0.05, 0.602]."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "UFEAgzsxfrQCGf3H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me more about the region [0.388, 0.104, 0.699, 0.913]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie slice indicating the value for 'Religious Tolerance'. There are two texts within this region: 'Religious Tolerance' {at} [0.545, 0.468, 0.6, 0.491] and '45.0%' {at} [0.726, 0.436, 0.892, 0.459]."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "6iAMP0qXfufD172B",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_112.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe what's within the coordinates [0.742, 0.074, 0.97, 0.151]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart legend. Two labels are in this region: 'Faith' {at} [0.797, 0.083, 0.839, 0.106] and 'Religious Tolerance' {at} [0.797, 0.118, 0.963, 0.141]."
+            }
+        ],
+        "id_raw": 1112
+    },
+    {
+        "id": "VTtEX4S5QdpzB5M8",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is indicated by these coordinates [0.084, 0.025, 0.693, 0.055] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This refers to the subtitle \"Culture and Arts Industry Development from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "QQ0LOm9F2URROkhN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what is located at the coordinates [0.864, 0.074, 0.97, 0.325] on this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend of the chart."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "kGqdDzJn7dmWxvFY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at these coordinates [0.229, 0.078, 0.272, 0.101] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the text displaying \"11.0%\"."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "l9a9EagYHVasWvzY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice is closest to {the slice}[0.19, 0.104, 0.388, 0.295]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represented by {the slice}[0.08, 0.199, 0.249, 0.481] is closet one."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "T8w9nyutyD5gkhN7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects can be found adjacent to {the legend}[0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The objects {labels}[[0.919, 0.083, 0.963, 0.106], [0.919, 0.118, 0.963, 0.141], [0.919, 0.153, 0.963, 0.176], [0.919, 0.188, 0.963, 0.211], [0.919, 0.223, 0.963, 0.246], [0.919, 0.258, 0.963, 0.281], [0.919, 0.292, 0.963, 0.316]] for years 2014 to 2020 are adjacent to the legend."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "U5nNZiFnGIlNKhC6",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat handle of the legend aligns with the {text of 2020}[0.462, 0.298, 0.517, 0.322]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle that aligns is the 2020 handle with coordinates [0.871, 0.295, 0.905, 0.311]."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "cpGRJ5l0GvbW6Grm",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within given coordinates [0.146, 0.701, 0.382, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In the region, it includes the slice part of the pie chart with colour '#ffcc99', autopct value '13.8%' [0.192, 0.916, 0.236, 0.939] and labeled as '2017' [0.278, 0.73, 0.333, 0.753]. This portion is situated towards the left bottom of the chart."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "x8Heg5L26d3ySjJM",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat components exist in the region defined by [0.079, 0.0199, 0.249, 0.481]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, a slice of the pie chart is present with colour '#66b3ff', auto pct is \"11.9%\" [0.045, 0.286, 0.088, 0.31] and it's labeled as \"2015\" [0.197, 0.387, 0.252, 0.41] and is towards left middle of the chart."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "hSRHMm61nJpZQ5WR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_119.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region [0.388, 0.104, 0.672, 0.401]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie chart slice with colour '#cfb3ff', labeled as \"2020\" [0.462, 0.298, 0.517, 0.322] and has percentage displayed as \"18.3%\" [0.575, 0.124, 0.618, 0.148]."
+            }
+        ],
+        "id_raw": 1119
+    },
+    {
+        "id": "aOfhcJrz38NXvlf7",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.601, 0.019, 0.829, 0.041] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the second subplot, it is \"Human Life and Living Environment in 2014\"."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "YhSHobSXqqfVfTTe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.896, 0.246, 0.942, 0.263] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the legend item for \"Recreation\"."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "5PT4NwExuLdYHZCm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.119, 0.548, 0.146, 0.565] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie slice label \"Air Quality\"."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "xFUDmCDyzyWM17cD",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to {the pie slice}[0.319, 0.343, 0.384, 0.63] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The pie slice}[0.264, 0.595, 0.373, 0.811] for \"Physical Activity\" is adjacent."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "pOhT8hkJV8zvPLD1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the subtitle}[0.111, 0.019, 0.339, 0.041] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"Quality of Life\"}[0.175, 0.329, 0.203, 0.347] is located near the subtitle."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "jJ5oBWj5WgG53r2Z",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the pie slice corresponding to the legend item \"Water Quality\"[0.896, 0.088, 0.955, 0.106] in the second subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the pie slice with coordinates [0.602, 0.197, 0.715, 0.358] in the second subplot."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "Xp2cM2rGBCSbqjkt",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.067, 0.474, 0.132, 0.687] in the first subplot."
+            },
+            {
+                "from": "gpt",
+                "value": "The region is represented by the color '#99ff99' and contains the text \"Air Quality\" [0.119, 0.548, 0.146, 0.565]. It represents the share of \"Air Quality\" in \"Human Life and Living Environment\" in 2010, with a percentage of \"11.2%\" [0.009, 0.583, 0.055, 0.601]."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "ESCtiGsdqiKYpjZe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_120.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.068, 0.288, 0.147, 0.486] in the first subplot?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes the text \"Water Quality\" [0.126, 0.421, 0.154, 0.438] and the text \"10.6%\" [0.009, 0.351, 0.069, 0.368]. It is represented by the color '#66b3ff'."
+            }
+        ],
+        "id_raw": 1120
+    },
+    {
+        "id": "mwEHe0ENr0pBXJ9n",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.118, 0.025, 0.658, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, which is 'Living Environment Health Index from 2011 to 2018'."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "zcxKuZF6mUD6ytwG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the color of the handle in the legend item with coordinates [0.871, 0.33, 0.905, 0.346]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color of the handle is '#ffb347'."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "CzgdSzqzNce1x1ch",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects at these coordinates: [0.294, 0.274, 0.349, 0.298]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2011\"."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "nmsjzPIiRchjoavs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color of slice in the chart corresponds to {the legend item '2018'}[0.871, 0.33, 0.905, 0.346]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#ffb347' corresponding to the slice in the chart with coordinates: [0.388, 0.104, 0.619, 0.324]."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "WXIhz3xKb8ZaE7PW",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text located near {the slice}[0.526, 0.544, 0.698, 0.839]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2016\"<r>[0.526, 0.623, 0.581, 0.646]</r> is located near the slice."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "3idQAEKAhUkL04nC",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text corresponds to the autotext '13.0%' in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text '2015'<r>[0.412, 0.746, 0.467, 0.77]</r> and '2017'<r>[0.538, 0.428, 0.593, 0.451]</r> correspond to the autotext '13.0%'."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "6w3ApvC87v8NdzB0",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.526, 0.544, 0.698, 0.839]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice colored with '#ffb3e6' [0.526, 0.544, 0.698, 0.839] and the associated text \"2016\" [0.526, 0.623, 0.581, 0.646]. This region represents data for the year 2016 in the pie chart."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "py94sJ57ansVCn0x",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_121.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.847, 0.258, 0.976, 0.281]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend item handle with color '#ffb3e6' [0.871, 0.26, 0.905, 0.276] and the text \"2016\" [0.919, 0.258, 0.963, 0.281]."
+            }
+        ],
+        "id_raw": 1121
+    },
+    {
+        "id": "dRNkU1skP04ChcR6",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.245, 0.025, 0.531, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle called \"Social Media Usage in 2020\"."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "xWCFJWPKualCOwtE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.819, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's legend."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "DIlUPD9TVycluNyN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located within coordinates [0.493, 0.683, 0.547, 0.706] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text label \"Twitter\"."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "O7Ajn67LlDyILbxf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.388, 0.519, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {the slices}[[0.077, 0.39, 0.388, 0.934], [0.516, 0.183, 0.699, 0.519]] are adjacent."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "1lLPIHOGP0l3zMs9",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices have their corresponding legend items near {the legend}[0.819, 0.074, 0.97, 0.29]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices {\"Facebook\", \"Instagram\", \"Twitter\", \"LinkedIn\", \"Snapchat\", \"Pinterest\"}[[0.092, 0.104, 0.388, 0.429], [0.077, 0.39, 0.388, 0.934], [0.388, 0.519, 0.699, 0.934], [0.516, 0.183, 0.699, 0.519], [0.455, 0.124, 0.571, 0.284], [0.388, 0.104, 0.484, 0.242]] have their corresponding legend items near the legend."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "ftaGDbddhvbjmWyd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located near {the slice}[0.092, 0.104, 0.388, 0.429]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Facebook\"[0.251, 0.306, 0.306, 0.329] is located near the slice."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "7HRoHPLKaWezWsna",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.455, 0.124, 0.571, 0.284]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"5.0%\"[0.543, 0.1, 0.625, 0.124] and \"Snapchat\"[0.451, 0.285, 0.495, 0.308]. This area corresponds to a pie slice representing Snapchat's usage percentage."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "70iWRtNUKBGgOI13",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_124.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.077, 0.39, 0.388, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"30.0%\"[0.023, 0.775, 0.111, 0.799] and \"Instagram\"[0.21, 0.653, 0.264, 0.677]. This region represents the Instagram slice of the pie chart."
+            }
+        ],
+        "id_raw": 1124
+    },
+    {
+        "id": "1NA9OvVF9vs48Ct4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.085, 0.021, 0.689, 0.047] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Predicted use of Autonomous Cars and AI Technology from 2030 to 2100."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "je9wApk8jNm8TS6h",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.891, 0.063, 0.976, 0.548] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "c9MoRyW7ql4PgWVm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is in the location [0.935, 0.191, 0.97, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2070\"."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "6lUrg6hDiqD0xaK1",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.406, 0.667, 0.563, 0.888]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.247, 0.631, 0.406, 0.870], [0.563, 0.707, 0.705, 0.928]] are adjacent."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "X6JbntyTVyoIiUim",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color matches with {the label \"2050\"}[0.935, 0.131, 0.97, 0.151] from the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color {#99ff99} matching with the label \"2050\" corresponds with the slice with coordinate [0.262, 0.11, 0.333, 0.248]."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "9dZ9Mcdpy2IH2yBI",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relation between the {index labels in legend}[0.935, 0.071, 0.970, 0.541] and {color handles}[0.896, 0.073, 0.924, 0.536]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The index labels shown in the legend on the right side of the chart correspond to the color handles next to them, which indicates which color represents which year in the pie chart."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "JOVgoUKx25cgtd79",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.021, 0.976, 0.969]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the whole chart with a pie plot in the left middle part of the chart, a descriptive subtitle on top of the chart, and a legend on the right of the chart."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "sM6fEzlbrISeNhck",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.096, 0.095, 0.679, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists multiple pie slices indicating the predicted use of Autonomous Cars and AI Technology from 2030 to 2100."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "3225QD9r3Fyxmj86",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_126.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region [0.891, 0.063, 0.976, 0.548]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, this region contains the legend which describes the color mapping between the years and the pie slices in the chart."
+            }
+        ],
+        "id_raw": 1126
+    },
+    {
+        "id": "ywPaKEtXmM2gxUkm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.082, 0.025, 0.694, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, which reads \"Percentage of U.S. adults using nutrition apps (2015-2020)\"."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "MejVgN7DzCqIr3Kz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart legend."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "4CKtkUHCfrDHJWNx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.585, 0.135, 0.629, 0.158] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the text \"19.7%\"."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "pTx6Vjr8I6KSxZAG",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slices are adjacent to {the slice}[0.388, 0.103, 0.68, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.531, 0.381, 0.697, 0.824], [0.167, 0.103, 0.388, 0.314]] are adjacent."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "1fC01ooRkOscmCAK",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.082, 0.025, 0.694, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text objects {\"2015\"}[0.289, 0.276, 0.344, 0.299] and {\"2016\"}[0.184, 0.427, 0.239, 0.45] are located near the subtitle."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "7QZ6nnVpztxXd5w5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items correspond to the slices as follows: \"2015\" corresponds to [0.167, 0.103, 0.388, 0.314], \"2016\" to [0.079, 0.227, 0.234, 0.573], \"2017\" to [0.082, 0.556, 0.304, 0.895], \"2018\" to [0.268, 0.732, 0.592, 0.927], \"2019\" to [0.531, 0.381, 0.697, 0.824], and \"2020\" to [0.388, 0.103, 0.68, 0.421]."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "mBzHdOWJtG7gwGPV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.531, 0.381, 0.697, 0.824]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes the pie slice for the year 2019 [0.531, 0.381, 0.697, 0.824], the corresponding percentage text \"18.8%\" [0.717, 0.619, 0.761, 0.643], and the year label \"2019\" [0.54, 0.567, 0.595, 0.59]."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "GxDhlRGqckCYx9r1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_127.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region between [0.079, 0.227, 0.234, 0.573]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the 2016 pie slice [0.079, 0.227, 0.234, 0.573], the text \"14.6%\" [0.021, 0.362, 0.065, 0.386], and the year label \"2016\" [0.184, 0.427, 0.239, 0.45]."
+            }
+        ],
+        "id_raw": 1127
+    },
+    {
+        "id": "ZkYCxa8hch9mS396",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.118, 0.025, 0.659, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, titled \"Fitness and Sports Health Trends from 2018 to 2022\"."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "F3eiWzTh5cD2JLVd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.255] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The chart legend."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "70kQDEpq3CVGUsXE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.199, 0.094, 0.243, 0.117] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The annotation text reading \"13.9%\"."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "DuzqND8zaofcGQJM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.149, 0.104, 0.388, 0.332]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.077, 0.253, 0.221, 0.687], [0.388, 0.104, 0.699, 0.527]] are adjacent."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "jQkynKYMBF3eQCa3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the slice}[0.414, 0.524, 0.699, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"22.8%\"}[0.642, 0.813, 0.686, 0.837] and {\"2021\"}[0.499, 0.674, 0.554, 0.697] are located near the slice."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "wfPxxv1YowlEBlPv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item corresponds to the {slice}[0.104, 0.636, 0.425, 0.934] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"2020\" corresponds to the slice."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "riMSgRjdN4FKTlpf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.077, 0.253, 0.221, 0.687]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart slice \"2019\" and the adjacent percentage text \"17.7%\". This segment of the pie chart is colored in \"#66b3ff\"."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "iItzGjF6ZxaWot3d",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_131.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.388, 0.104, 0.699, 0.527]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice for \"2022\" along with percentage text \"25.3%\" placed within it."
+            }
+        ],
+        "id_raw": 1131
+    },
+    {
+        "id": "OqpWZnXdbT8VymAA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat label is at the coordinates [0.321, 0.019, 0.679, 0.041] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label at those coordinates is \"Solar Energy Allocation Over Years\"."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "ERrpiMx3MfxKaENe",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the specific coordinates [0.299, 0.067, 0.701, 0.468] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "There is a pie chart located at those coordinates showcasing the allocation of solar energy over the years."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "I9HTsZBQE1MqgkJs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text is located at coordinates [0.922, 0.551, 0.965, 0.568] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2025\" is at these coordinates."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "Vp7qkZUINV6YduKL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice color in the solar energy pie chart corresponds to the year {2050}[0.922, 0.682, 0.965, 0.699]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice color that corresponds to 2050 is {\"#c2c2f0\"}[0.873, 0.683, 0.908, 0.695]."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "3GZ8PVOoua9Z2uiU",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the textual representation of the slice at the coordinates [0.299, 0.219, 0.371, 0.348] in the solar energy pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text inside this slice represents \"2045\"."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "PKwOmqn4OsY7GWqk",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich years does the legend found at coordinates [0.866, 0.544, 0.973, 0.758] correspond to in the nuclear energy pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend corresponds to the years 2025, 2030, 2035, 2040, 2045, 2050, 2055, and 2060."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "uICYulrCAmodj7dq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.299, 0.555, 0.701, 0.957] in the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a pie chart that represents the allocation of nuclear energy over different years. It includes slices with associated colors and percentages of allocation for each year."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "QEXokc30LTmvmLlp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region marked [0.866, 0.544, 0.973, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the legend listing the years: 2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060, and the corresponding color for each year's energy allocation."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "PZlA6tjWfOYLloe7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_132.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects present in the region [0.299, 0.067, 0.701, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie chart that displays the distribution of solar energy over several years. Each slice of the pie chart represents a different year with specific energy allocation percentages."
+            }
+        ],
+        "id_raw": 1132
+    },
+    {
+        "id": "FJVu6AngBsqcM1IZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text content with coordinates [0.26, 0.295, 0.316, 0.318]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Medical Device\"."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "JrqPA9yyGQ3sItQd",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.776, 0.074, 0.97, 0.22] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart's legend."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "BF9l31ZFb8OIlAzH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.38, 0.515, 0.38, 0.515]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the center of the pie chart."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "W7psqPgNd1CzniL4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend item corresponds to the pie slice with the coordinate [0.388, 0.103, 0.697, 0.757]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item at position {Workforce}<r>[0.832, 0.188, 0.92, 0.211]</r> corresponds to the pie slice."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "dWYm6UaclYKadxs5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is associated with the percentage text at coordinates [0.075, 0.121, 0.206, 0.144]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {Medical Device}<r>[0.109, 0.103, 0.388, 0.392]</r> is associated with this percentage."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "FSvCIfYdhWl7cEWr",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to the slice with coordinates [0.79, 0.34, 0.261, 0.848]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice {Hospital Beds}<r>[0.207, 0.685, 0.638, 0.927]</r> is adjacent to the slice."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "cUMyR7eB3alu6ZCp",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found within the region defined by coordinates [0.207, 0.685, 0.638, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a sector of the pie chart that represents \"Hospital Beds\", with the corresponding percentage \"{25.0%}\"<r>[0.441, 0.951, 0.56, 0.974]</r>."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "bPXfMDdbm8slQCGV",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region with coordinates [0.776, 0.074, 0.97, 0.22]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the chart's legend where each medical field is represented by a color accompanied by their respective labels, including {Medical Device}<r>[0.832, 0.083, 0.963, 0.106]</r>, {Drugs}<r>[0.832, 0.118, 0.883, 0.141]</r>, {Hospital Beds}<r>[0.832, 0.153, 0.951, 0.176]</r>, and {Workforce}<r>[0.832, 0.188, 0.92, 0.211]</r>."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "BgGNzOxaR4jPkpLq",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_133.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the coordinates [0.079, 0.103, 0.697, 0.927]."
+            },
+            {
+                "from": "gpt",
+                "value": "This area comprises the entire pie chart, with each medical field represented by a unique color and specified by a labeled slice: {Medical Device}<r>[0.109, 0.103, 0.388, 0.392]</r>, {Drugs}<r>[0.079, 0.34, 0.261, 0.848]</r>, {Hospital Beds}<r>[0.207, 0.685, 0.638, 0.927]</r>, and {Workforce}<r>[0.388, 0.103, 0.697, 0.757]</r>. Each sector is also annotated with a percentage indicating respective proportions."
+            }
+        ],
+        "id_raw": 1133
+    },
+    {
+        "id": "DOCZWNHcJLrH27sg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.098, 0.025, 0.678, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart subtitle, labeled as \"Investment Return Rate Comparison from 2016 to 2020\"."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "Weru0cYgfFT0kCBE",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.864, 0.074, 0.97, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the chart's legend."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "zqYPViahRrRHmcHw",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the intrinsic meaning of the slice at coordinates [0.522, 0.191, 0.697, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the return rate for Investment in 2019, approximately 12.5%."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "Ug73Cf0Yd072ptq5",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the investment return rate for the year associated with {the color handle}[0.871, 0.225, 0.905, 0.241] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The return rate is approximately 10.5% for the year 2020 as represented by the slice [0.388, 0.104, 0.579, 0.289]."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "0ttRSUzoZbtj2hZq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is closest to {2019's slice}[0.522, 0.191, 0.697, 0.483]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice for the year 2018, which is located at [0.565, 0.468, 0.699, 0.761], is closest to 2019's slice."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "EDgDYYidlfslW2na",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat year does the slice of coordinates [0.387, 0.689, 0.64, 0.934] represents in the time series?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice represents the year 2017, as indicated by {the label in the legend}[0.919, 0.118, 0.963, 0.141]."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "YqUE3pKyqq6zJvQG",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.564, 0.466, 0.70, 0.7597] on the chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the slice representing the return rate for the year 2018 with the label \"11.9%\" [0.72, 0.62, 0.764, 0.644], and the corresponding year text \"2018\" [0.541, 0.569, 0.597, 0.592]."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "XUM82o9FdPTqA29l",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_134.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are presented between coordinates [0.077, 0.104, 0.197, 0.261]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region houses a slice representing the return rate in 2016, labeled as \"7.5%\" [0.264, 0.063, 0.308, 0.087], and the corresponding year text \"2016\" [0.323, 0.265, 0.366, 0.288]."
+            }
+        ],
+        "id_raw": 1134
+    },
+    {
+        "id": "iOdh3ziaYRxFZJow",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item at the coordinates [0.147, 0.025, 0.629, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle, \"Deforestation Area (sq.km) from 2010 to 2016.\""
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "zJEpWKMrohXW8Ddq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "rUh6HCTf5x8VMRo1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.204, 0.091, 0.248, 0.114]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the autotext \"13.6%\"."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "BVEPNSNjopjStOMM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is next to {the slice} [0.578, 0.319, 0.696, 0.689]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice} [0.388, 0.103, 0.659, 0.377] is adjacent to it."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "b7UwUHmAKr59KgXz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice at [0.388, 0.103, 0.659, 0.377] and the legend items?"
+            },
+            {
+                "from": "gpt",
+                "value": "This slice corresponds to the legend item \"2016\" at [0.919, 0.292, 0.963, 0.316]."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "eYVPk0Ee6qhh7iZ4",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the connection between the legend item at [0.919, 0.153, 0.963, 0.176] and the slices?"
+            },
+            {
+                "from": "gpt",
+                "value": "This legend item correspond to the pie slice representing \"2012\" at [0.085, 0.561, 0.289, 0.877]."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "9PUQLgi3xL4QEAEl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.081, 0.103, 0.696, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encompasses seven pie slices, each with unique labels and corresponding autotext percentages. It represents the entire pie chart in the plot."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "28Rc94rIa2RH36KE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_136.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is included between coordinates [0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Between these coordinates lies the legend of the chart which includes colour-coded rectangular items and their labels from \"2010\" to \"2016\"."
+            }
+        ],
+        "id_raw": 1136
+    },
+    {
+        "id": "HKg2cCZ8ujt80O6c",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the main object within the coordinates [0.134, 0.025, 0.642, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle, being \"Physics Research Paper Distribution (2015-2020)\"."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "gPNhhoeWGylj1pPx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the object located at [0.864, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the sublegend."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "qpfl3KvKZG7qxQix",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is represented by the coordinates [0.115, 0.837, 0.182, 0.861] in this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That would be the text \"2017\"."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "scbCbrNrW5BVzGi3",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are adjacent to the {slice}[0.388, 0.103, 0.694, 0.484]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"23.4%\"}[0.615, 0.167, 0.658, 0.19] and {\"2020\"}[0.484, 0.319, 0.539, 0.342] are adjacent to the slice."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "91ZR4L9NlrsKdAhi",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices share colors that correspond with the legend items in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices denoted by \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" match with the colors of the legend items found in the coordinates [0.871, 0.085, 0.905, 0.101], [0.871, 0.12, 0.905, 0.136], [0.871, 0.155, 0.905, 0.171] , [0.871, 0.19, 0.905, 0.206], [0.871, 0.225, 0.905, 0.241], and [0.871, 0.26, 0.905, 0.276] respectively."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "osBY8DPCK4IEksAm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the object at {this location}[0.134, 0.025, 0.642, 0.055] and the other chart elements?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at this location is the title and it describes what the chart is about, which relates to all the elements within the chart."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "NSzaSMyLLd6gvmqB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects within the region [0.081, 0.6, 0.295, 0.882]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the color slice generated by the pie chart, which has a color of '#99ff99', a text object \"2017\" [0.221, 0.663, 0.276, 0.686], and an autotext '13.0%' [0.089, 0.797, 0.133, 0.82]. This slice represents the data from year 2017."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "QCGOeVcVR5htulNf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.504, 0.471, 0.696, 0.858]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region includes a pie slice attributed to 2019 with a color code of '#c2c2f0'. Along with the slice, it also contains the texts {\"2019\"}[0.527, 0.608, 0.582, 0.632] and {\"17.5%\"}[0.693, 0.697, 0.737, 0.72]."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "4jh4C4bVQoY1DO58",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_137.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you see in the region [0.081, 0.103, 0.696, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region essentially covers the entire pie chart, including all slices and their labels and percentages. The different slices represent the distribution of research papers for the years 2015 through 2020."
+            }
+        ],
+        "id_raw": 1137
+    },
+    {
+        "id": "ZDiyixSp3LlwBwON",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.252, 0.019, 0.6, 0.041] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle, labeled \"Popularity of fashion trends by Subculture\"."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "dE5H3OuXzdByM46M",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object exists at coordinates [0.865, 0.055, 0.994, 0.322] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is where the legend resides in the pie chart."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "MU9lfP835flHwfGq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.612, 0.905, 0.692, 0.922] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the percentage text \"6.8%\" for the 'Steampunk' section of the pie chart."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "sYP44700yXDTzO18",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slices are located next to the {slice}<r>[0.498, 0.73, 0.653, 0.913]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The adjacent pie slices are {the slices}<r>[[0.41, 0.793, 0.529, 0.934], [0.585, 0.66, 0.718, 0.823]]</r> representing categories 'Cyberpunk' and 'Grungers'."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "Derouw3BROcIdcjv",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich items are adjacent to the {legend item}<r>[0.909, 0.167, 0.985, 0.185]</r> in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's accompanied by {legend items}<r>[[0.909, 0.141, 0.943, 0.158], [0.909, 0.193, 0.989, 0.211]]</r> which represent 'Punk' and 'Steampunk' respectively."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "iQAqr5riJnphS6gz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the legend relate to pie slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Each label in the legend corresponds to a specific slice in the pie chart, all of them collectively represent a subculture's popularity depicted by proportional areas in the chart."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "04i2RTtDF32cigNW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.088, 0.291, 0.32, 0.889]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a sizable portion of the pie chart denoting the 'Hip-hop' subculture's popularity, given a color of '#99ff99'. It additionally contains a data label saying '26.4%' at coordinates [0.015, 0.631, 0.069, 0.648]."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "ubf4wE9GrSowYDft",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are found in the region [0.138, 0.112, 0.349, 0.357]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the 'Hipster' category in the pie chart, colored '#66b3ff' and a data label indicating '11.0%' at coordinates [0.143, 0.14, 0.193, 0.158]."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "jMfR91LvHN0ZoO61",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_140.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you detail what's in the region with coordinates [0.426, 0.089, 0.684, 0.321]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there lies the 'Indie' slice of the pie chart, which is colored '#ccff99'. Attached to this slice is a data label displaying '13.8%' situated at coordinates [0.583, 0.081, 0.617, 0.099]."
+            }
+        ],
+        "id_raw": 1140
+    },
+    {
+        "id": "fvExkNogwUDxg2cm",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat text is located at these coordinates [0.107, 0.025, 0.669, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is the subtitle \"Percentage of Innovative Start-ups from 2013 to 2021\"."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "fQv856kqEcVS3YRq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the coordinates of the object represented by the color '#ff9999'?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object represented by the color '#ff9999' is the first slice of the pie chart with coordinates [0.092, 0.104, 0.388, 0.429]."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "NNE1jDBuFwSZC7m5",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the content located at [0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend year \"2013\" is located at these coordinates."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "MhKTJBYDLa3Gt7cu",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nTo which pie slice does the {autotext \"5.0%\"}[0.543, 0.1, 0.588, 0.124] relate?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"5.0%\" relates to {the slice}[0.455, 0.124, 0.571, 0.284]."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "dwmJAZ6jQVbCv6Tc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is adjacent to the {slice '2020'}[0.415, 0.107, 0.484, 0.242]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slices representing the years {2019 and 2021}[[0.455, 0.124, 0.571, 0.284], [0.388, 0.104, 0.427, 0.23]] are adjacent to the slice '2020'."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "prq9CyvzIOO3VkrL",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the color '#c2c2f0'}[0.871, 0.225, 0.905, 0.241] in the legend and the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color '#c2c2f0' in the legend relates to the {year 2017's pie slice}[0.585, 0.342, 0.699, 0.596] in the chart."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "1WyOV7InReEHQ00J",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe region includes coordinates [0.077, 0.104, 0.699, 0.934], can you provide an overview?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to the main pie chart area containing offerings from 2013 to 2021, with each slice of the pie chart representing the percentage of Innovative Start-ups for a particular year."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "UM2GSlMCc0aZyhtE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the key elements in the region within coordinates [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the legend of the pie chart, which matches the colors of the pie slices with the corresponding years from 2013 to 2021."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "FWPH1Y6kJtjv86xv",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_141.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can you tell me about the region represented by the coordinates [0.019, 0.062, 0.757, 0.975]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is the major area of the plot, encompassing the pie chart, its labels, and autotext numbers indicating the relevant percentages for each year."
+            }
+        ],
+        "id_raw": 1141
+    },
+    {
+        "id": "2sqfe23KzuGwktWo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.142, 0.025, 0.634, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title, labelled as 'Financial Market Fluctuation from 2016 to 2021'."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "U6twYj0AXy9HaBWQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.29] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "fBEUCJnbLKBnevPy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.919, 0.083, 0.963, 0.106] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labeled as \"2016\"."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "4D59C3i8XP2zaNzd",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat legend corresponds to {the slice}[0.356, 0.727, 0.605, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend for {2019}<r>[0.919, 0.188, 0.963, 0.211]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "hfv9JjqyIJBdFFtp",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices are surrounded by {the slice}[0.113, 0.655, 0.366, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.077, 0.237, 0.228, 0.714], [0.356, 0.727, 0.605, 0.934]] are surrounded by it."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "5hZqxCZZ9LowmLKm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the slice}[0.54, 0.423, 0.699, 0.816] and the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice matches the legend item {2020}<r>[0.919, 0.223, 0.963, 0.246]</r>."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "OriH6mK7n7QTsPFs",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.16, 0.104, 0.388, 0.321]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"13.1%\" [0.207, 0.089, 0.251, 0.112] indicating the percentage and the label \"2016\" [0.286, 0.279, 0.341, 0.302] representing the year. This region corresponds to the first year slice of the pie chart."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "JfEzLYgS9WI6U99w",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_145.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.356, 0.727, 0.605, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"13.9%\" [0.501, 0.938, 0.545, 0.961] and the label \"2019\" [0.422, 0.742, 0.477, 0.765]."
+            }
+        ],
+        "id_raw": 1145
+    },
+    {
+        "id": "ySWl8qirYFMVddA4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.17, 0.025, 0.606, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, called Online Learning Usage from 2011 to 2020."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "qp2mu7OoORQTE3UB",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "V5cfEQWbjXOowSGW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.292, 0.056, 0.337, 0.079] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"4.9%\"."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "p7NIsc0BIyGnRUkf",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.114, 0.193, 0.254, 0.383]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.196, 0.122, 0.323, 0.289] is adjacent."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "JFqaPmz0DWnulJ1O",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the subtitle}[0.17, 0.025, 0.606, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text {\"2011\"}[0.338, 0.258, 0.382, 0.282] and {\"2012\"}[0.279, 0.284, 0.323, 0.308] are located near the title."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "3Jtgehy3X0VnL4RS",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend items \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", and \"2020\" correspond to the slices with coordinates [0.295, 0.103, 0.388, 0.239], [0.196, 0.122, 0.323, 0.289], [0.114, 0.193, 0.254, 0.383], [0.081, 0.328, 0.196, 0.522], [0.081, 0.519, 0.207, 0.735], [0.13, 0.668, 0.319, 0.901], [0.29, 0.776, 0.511, 0.923], [0.474, 0.615, 0.675, 0.889], [0.574, 0.306, 0.695, 0.659], and [0.388, 0.103, 0.653, 0.368] respectively."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "YoGHnq64ifzbolko",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.114, 0.193, 0.254, 0.383]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"6.8%\" [0.082, 0.217, 0.126, 0.24] and the text \"2013\" [0.223, 0.346, 0.267, 0.369]. This region is located in the lower left part of the chart, corresponding to the green slice."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "bdaevTVD6zZefpGB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_146.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.574, 0.306, 0.695, 0.659]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the text \"14.2%\" [0.725, 0.464, 0.769, 0.487] and the text \"2019\" [0.544, 0.481, 0.599, 0.504]."
+            }
+        ],
+        "id_raw": 1146
+    },
+    {
+        "id": "poJcnsb8fJbvrl1T",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.218, 0.025, 0.558, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the title, named \"Sales Volume from 2001 to 2008\"."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "HViIOzheTn3i8qFr",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is situated at coordinates [0.864, 0.074, 0.97, 0.36]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "cevpPM9kzluAP6NT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is positioned at coordinates [0.557, 0.111, 0.601, 0.134] within the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text object displaying \"16.7%\" is positioned there."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "knwvz779wqB3OFGj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice on the chart is adjacent to {the slice}<r>[0.388, 0.103, 0.654, 0.37]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.574, 0.308, 0.695, 0.624]</r> is adjacent to it."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "haFs76YA3Uc4y6Cc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the texts located near {the title}<r>[0.218, 0.025, 0.558, 0.055]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2001\"}<r>[0.92, 0.083, 0.963, 0.106]</r> and {\"2002\"}<r>[0.92, 0.118, 0.963, 0.141]</r> are located near the title."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "WdY45kamC1NtYTkA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to {the slice}<r>[0.388, 0.103, 0.654, 0.37]</r> in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The legend item \"2008\"}<r>[0.92, 0.327, 0.963, 0.351]</r> corresponds to the slice."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "E2PHLBKAekluL5UD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.103, 0.695, 0.923]."
+            },
+            {
+                "from": "gpt",
+                "value": "This is the main area of the Pie chart. It contains multiple slices representing different years from 2001 to 2008, each has its unique color code and a label displaying the corresponding percentage of sales volume."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "tMtNFE8CdBxLRHis",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects encapsulated within the region [0.081, 0.103, 0.695, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encapsulates multiple pie slices each labeled with the percentage of sales volume and the corresponding year. For example, it includes {\"The slice marked '2001'\"}<r>[0.215, 0.103, 0.388, 0.276]</r> and {\"The slice marked '2002'\"}<r>[0.097, 0.174, 0.267, 0.422]</r>.."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "asr3clnrKDyHcknj",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_147.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the content within coordinates [0.864, 0.074, 0.97, 0.36]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the chart's legend. It consists of years ranging from 2001 to 2008, each year is represented with a unique color according to the pie slices in the main chart, like {\"2001\"}<r>[0.92, 0.083, 0.963, 0.106]</r> and {\"2002\"}<r>[0.92, 0.118, 0.963, 0.141]</r>."
+            }
+        ],
+        "id_raw": 1147
+    },
+    {
+        "id": "Dldyul83Jf55yet2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the chart element found at [0.231, 0.025, 0.545, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle of the chart which is \"Big Data Integration Overview\"."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "BgkwT7WQWpdIOwAV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.893, 0.083, 0.95, 0.106] on this pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Volume\" related to an item in the legend is located at these coordinates."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "IspJuxpRNuSpvraN",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the item located within the coordinates [0.591, 0.14, 0.638, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"20.4%\", which represents a percentage in a pie slice, is situated at these coordinates."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "7wNDfjkNHU5dcCuY",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nAre {the autotext}[0.106, 0.156, 0.17, 0.179] and {the text}[0.242, 0.314, 0.297, 0.337] related to the same pie slice?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, they are both related to the same pie slice."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "Vylu1D2DeDZ4Rl5q",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat colors are associated with the descriptions {Volume}[0.893, 0.083, 0.957, 0.106], {Velocity}[0.893, 0.153, 0.961, 0.176], and {Veracity}[0.893, 0.188, 0.963, 0.211] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The descriptions correspond to the colors: \"#ff9999\" (Volume), \"#99ff99\" (Velocity), and \"#ffcc99\" (Veracity) respectively."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "xYBqnaKxwUwqhBYT",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice is associated with {the autotext}[0.591, 0.14, 0.638, 0.163]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext \"20.4%\" is associated with the pie slice at coordinates [0.388, 0.103, 0.684, 0.432]."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "vZ30icYN2Hyg1tAd",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the objects found in the region defined by [0.181, 0.728, 0.518, 0.927]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region encloses part of a pie slice depicted in the color \"#99ff99\". It encompasses the autotext \"18.6%\" positioned at [0.269, 0.951, 0.337, 0.974], and the text \"Velocity\" at [0.333, 0.747, 0.388, 0.771]."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "mjOgBMrYFkxPENah",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be observed in the region [0.084, 0.103, 0.388, 0.464]?"
+            },
+            {
+                "from": "gpt",
+                "value": "In this area, there is a pie slice represented in the color \"#ff9999\". Within this slice, we can find the autotext \"22.2%\" at coordinates [0.106, 0.156, 0.17, 0.179] and the description \"Volume\" located at [0.242, 0.314, 0.297, 0.337]."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "AyQbOu5FIP3iEx2N",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_149.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease provide a description of the region [0.393, 0.18, 0.697, 0.471]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a portion of a pie slice that is displayed in the color \"#c2c2f0\". It contains the autotext \"20.4%\" at the coordinates [0.591, 0.14, 0.638, 0.163] and the text \"Value\" placed at [0.471, 0.305, 0.526, 0.329]."
+            }
+        ],
+        "id_raw": 1149
+    },
+    {
+        "id": "xHDbozoaTX2ntC7q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.08, 0.025, 0.697, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, labelled as Workplace Training Program attendance from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "FA5jxY7hciJVvhfG",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat element is situated at coordinates [0.388, 0.103, 0.689, 0.453] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.388, 0.103, 0.689, 0.453]</r> of the pie chart."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "FXRW1Q7N8kCvBbX4",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located at coordinates [0.919, 0.223, 0.963, 0.246] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend text for the year \"2020\"."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "QCXFCW5LsIg5Qp7g",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is next to {the slice}[0.388, 0.103, 0.689, 0.453]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}<r>[0.507, 0.427, 0.695, 0.855]<r> is next to it."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "KQEYO680NUAdmaVH",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item does {the slice}[0.109, 0.103, 0.388, 0.394] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to the legend item with the label \"2016\"."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "WwinQNSvG7hSWMBA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the text at {coordinates} [0.601, 0.151, 0.644, 0.174] and the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text represents the percentage quantity of the corresponding slice in the pie chart."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "qmlhngNiedzLSuF1",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.081, 0.343, 0.255, 0.835]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with auto text \"21.2%\" [0.016, 0.608, 0.06, 0.631] and has a text label \"2017\" [0.181, 0.559, 0.236, 0.583]. This region is in the upper left part of the chart."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "d8GBFODT3Vhp8w24",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_152.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.198, 0.738, 0.558, 0.923]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart with auto text \"19.9%\" [0.33, 0.952, 0.374, 0.975] and the label \"2018\" [0.353, 0.747, 0.408, 0.77]."
+            }
+        ],
+        "id_raw": 1152
+    },
+    {
+        "id": "sWtoGAdjGXqmNWtx",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text label located at coordinates [0.174, 0.084, 0.231, 0.102] in the first pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"4.7%\"."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "rV618Lwgx3BmZ8XL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.646, 0.005, 0.849, 0.027] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle of the second subplot, called \"Fitness and Sports Health Trends, 2015\"."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "cwhsybTRX5TOw7hy",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is at coordinates [0.233, 0.269, 0.255, 0.287] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Bodybuilding\"."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "EggoIDakqTf7843h",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.136, 0.142, 0.223, 0.305]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.207, 0.126, 0.261, 0.249], [0.075, 0.222, 0.173, 0.471]] are adjacent."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "oVmPO4o0whxUGnCN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext is located near {the slice}[0.38, 0.459, 0.448, 0.651]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The autotext {\"8.4%\"}[0.464, 0.554, 0.492, 0.572] is located near the slice."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "yy8nSb7UyUnNpP7A",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice does the text \"CrossFit\" belong to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with coordinates [0.22, 0.682, 0.395, 0.874] in the first pie chart and [0.748, 0.645, 0.903, 0.874] in the second pie chart."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "S9kMUrpam2iL1mOu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.074, 0.126, 0.448, 0.874] in the first pie chart."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains multiple pie slices, each with percentages like \"4.7%\" [0.174, 0.084, 0.231, 0.102], and associated labels such as \"Bodybuilding\" [0.233, 0.269, 0.255, 0.287]. The slices represent different fitness and sports health trends for the year 2010."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "mGChXMdT1t0paRvx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.561, 0.126, 0.935, 0.874] in the second pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains pie slices such as the slice starting at coordinate [0.66, 0.126, 0.748, 0.269] and each slice has a associated percentage and sports label like \"Bodybuilding\" [0.709, 0.274, 0.731, 0.291] with \"7.8%\" [0.641, 0.092, 0.698, 0.11]. These slices represent different fitness and sports health trends for the year 2015."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "ACVKKP1ZTE6JAfJB",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_157.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects contained within coordinates [0.514, 0.033, 0.981, 0.967]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the complete second subplot, which includes a pie chart representing fitness and sports trends for 2015, the pie slices each have a different color, an associated percentage, and the text displaying the type of fitness trend. The pie chart is titled \"Fitness and Sports Health Trends, 2015\" [0.646, 0.005, 0.849, 0.027]."
+            }
+        ],
+        "id_raw": 1157
+    },
+    {
+        "id": "zR6S6BL29j5ei3pZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object present at the coordinates [0.065, 0.025, 0.711, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the chart subtitle \"Rural and Urban Development Comparison from 2000 to 2008\"."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "7Nr5xKIeVmph3iEp",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the part having the coordinates [0.86, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the chart."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "f87wjxw5TWUTQunz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nIdentify the object at coordinates [0.549, 0.239, 0.699, 0.552]."
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice of the pie chart representing the year 2007."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "GgKCol4XbEAvkpJj",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat handles in the legend are associated with {the slice}[0.549, 0.239, 0.699, 0.552] of the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The handle for the label {\"2007\"}<r>[0.92, 0.327, 0.963, 0.351]</r> in the legend is associated with it."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "ZzU8RNToHFlvoU2G",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich pie slice is labelled by the text contained within coordinates [0.527, 0.62, 0.582, 0.643]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text in those coordinates labels {the pie slice}<r>[0.53, 0.542, 0.698, 0.833]</r>."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "lcPOzmbUEdEZOHp8",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the nearest pie chart slice to the legend item with the label {\"2001\"}<r>[0.92, 0.118, 0.963, 0.141]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The nearest slice is {the pie slice}<r>[0.11, 0.16, 0.279, 0.388]</r>."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "PoR1TUh6MAEQ7cFK",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region between coordinates [0.08, 0.56, 0.241, 0.824] consist of?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a slice of the pie chart representing the year 2003, colored in '#66b3ff' along with its percentage text \"10.9%\" and a label \"2003\"."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "Q6kdUWVnPbWpegnz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_160.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you give a description of the region [0.178, 0.733, 0.382, 0.933]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a slice of the pie chart for the year 2004, represented in '#c2c2f0' color. The slice is labeled \"2004\" and has the percentage representation of \"11.4%\"."
+            }
+        ],
+        "id_raw": 1160
+    },
+    {
+        "id": "S8RGBLbvlgN92qbC",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.109, 0.025, 0.667, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "subtitle, called Future Energy Resource Allocation from 2014 to 2018."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "Gp7zsbAA3nDvgFPX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.864, 0.074, 0.97, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "rHhSMnMpvDs8Kvdg",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.221, 0.081, 0.265, 0.105] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"11.8%\"."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "7E4hafqNTnzsZoba",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices in the chart are adjacent to {the slice}[0.495, 0.625, 0.671, 0.869]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slices}[[0.578, 0.319, 0.696, 0.673], [0.4, 0.763, 0.541, 0.923]] are adjacent."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "RBqpg7GlD3ky4bgb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located near {the legend}[0.864, 0.074, 0.97, 0.43]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2014\"}[0.919, 0.083, 0.963, 0.106] and {\"2015\"}[0.919, 0.118, 0.963, 0.141] are located near the legend."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "Edj3XwwfYwM6TJO2",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend items correspond to the slices in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Legend items \"2014\", \"2015\", \"2016\", \"2017\", and \"2018\" correspond to the slices with coordinates [0.18, 0.103, 0.388, 0.302], [0.085, 0.212, 0.242, 0.463], [0.08, 0.441, 0.188, 0.666], [0.103, 0.62, 0.254, 0.835], and [0.197, 0.739, 0.34, 0.913] respectively."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "WFj9xEqlwKZYwtjA",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.495, 0.625, 0.671, 0.869]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains a single pie slice {\"2016\"}[0.495, 0.625, 0.671, 0.869] which holds 10.4% of the allocation in the energy resource chart. It is located slightly towards the right side of the chart."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "1Q3gaAs4BoqfIkzu",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_164.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects in the region [0.085, 0.212, 0.242, 0.463]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a single pie slice {\"2015\"}[0.085, 0.212, 0.242, 0.463] that holds 10.4% of the allocation in the energy resource chart."
+            }
+        ],
+        "id_raw": 1164
+    },
+    {
+        "id": "PZ1qQC6jOTe4Fksn",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at coordinates [0.92, 0.362, 0.963, 0.386] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the text \"2028\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "pnTu4GJNJnGn8xRX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object lies at coordinates [0.055, 0.025, 0.722, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subplot's subtitle, reading \"Future Technological Development Prospects from 2020 to 2028\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "gISojN3fRauXVQWL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found at the coordinates [0.871, 0.364, 0.906, 0.381] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a handle in the legend, with the color \"#ffcc99\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "EjbUG7BbRuKRXm97",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the text labels associated with the handle at {coordinates}[0.871, 0.364, 0.906, 0.381] in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text label is \"2028\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "TqwIwVbeMFY70T70",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart corresponds to the legend item {\"2026\"}[0.92, 0.292, 0.963, 0.316]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding slice in the pie chart is {the slice}<r>[0.529, 0.548, 0.698, 0.834]</r>."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "rFuHIAYihSrAEQqN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the legend item associated with the slice at {coordinates}[0.554, 0.249, 0.699, 0.561] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated legend item is \"2027\"."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "jhYcYPEevbryRMFY",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.871, 0.364, 0.906, 0.381]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region represents a handle in the legend. Its color is \"#ffcc99\" which corresponds to the year \"2028\" according to the accompanying label."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "hUzjmTSeLPvrghWx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region with coordinates [0.864, 0.074, 0.97, 0.395]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses the figure's legend which lists years from 2020 to 2028. Each year is associated with a color handle, the coordinates of which range from [0.871, 0.085, 0.906, 0.101] to [0.871, 0.364, 0.906, 0.381]."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "4KzjgHpqYVg40WJW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_165.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat integrates in the region with coordinates [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region accommodates the pie chart. The chart is composed of several slices representing the proportions of different years from 2020 to 2028, with each slice being assigned a unique color."
+            }
+        ],
+        "id_raw": 1165
+    },
+    {
+        "id": "cFTIlTfY8wGQMdIW",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the title of the chart at {[0.116, 0.025, 0.661, 0.055]}?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is Specialist Distribution in Hospital from 2018 to 2020."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "VjkdShFhQSBwiSQz",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is display in the bounding box with coordinates [0.919, 0.083, 0.963, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2018\" is displayed at these coordinates."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "IIMvZmmGaT9Iqi9q",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell what object lies within the region [0.703, 0.329, 0.747, 0.353]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"37.3%\" is inside that bounding box."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "xCwERlZ6lYkrVUyl",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slices share the same legend item as {the slice}[0.083, 0.576, 0.611, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "{The slice}[0.079, 0.55, 0.598, 0.934] shares the same legend item."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "hsW63n3hh2gsHN58",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie slice corresponds to {the text \"2019\"}[0.286, 0.845, 0.341, 0.869] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice with coordinates [0.083, 0.576, 0.611, 0.934] corresponds to the text \"2019\"."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "SlDCo9N7UkZAigSs",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's the name for the color represented by the handle {[0.871, 0.085, 0.905, 0.101]} in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The 2018 label is represented by the '#ff9999' color."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "EAsfFj8E849fqX1c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat could you tell me about the region between [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region mainly encapsulates a large section of the pie chart, which contains 3 slices with the following labels and percentages: \"2018 - 28.2%\", \"2019 - 34.5%\", \"2020 - 37.3%\"."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "TbUEV1G507GTdRjl",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the items in the region [0.864, 0.074, 0.97, 0.185]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region holds the legend of the chart, and it details 3 items: \"2018\", \"2019\", and \"2020\"."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "GIprwKpO3fpJFKYI",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_169.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the contents within the coordinates [0.083, 0.576, 0.611, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area of the chart contains a pie slice labeled \"2019\" showing 34.5%."
+            }
+        ],
+        "id_raw": 1169
+    },
+    {
+        "id": "SG4towqkpMHRE28Y",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object located at coordinates [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a legend listing years from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "s9T4ugRJ1JigqPMZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text positioned at [0.252, 0.303, 0.307, 0.326]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"2016\"."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "uJWxRfQGCIkWilVc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat's placed at these coordinates [0.919, 0.223, 0.963, 0.246] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"2020\" is at these coordinates."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "jHW9ZCqYSQEVdx28",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color swatch in the legend corresponds to {the year 2018 slice}[0.22, 0.753, 0.556, 0.922] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding color swatch is {the third item in the legend}[0.871, 0.155, 0.905, 0.171]."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "Ud7pfjUj1ukf3E23",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice of the pie chart does {the text \"2019\"}[0.531, 0.595, 0.586, 0.619] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the slice} [0.506, 0.429, 0.695, 0.856] on the pie chart."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "D1aQs9CY2s5MUYyw",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow does the {legend item \"2017\"}[0.871, 0.12, 0.905, 0.136] relate to the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It represents {the slice} [0.081, 0.388, 0.271, 0.856] in the pie chart."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "gUBkLcXAbKsN5e2Z",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within [0.081, 0.103, 0.695, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region constitutes the pie chart presenting the social dynamics and political trends from 2016 to 2020. It contains slices for each year, labeled with the corresponding percentages."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "1pBCq2Nrl5AF6d9r",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is contained in the area [0.864, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The area includes a legend with color swatches corresponding to each year from 2016 to 2020."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "tHy9DqraV1LT6avL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_172.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the essential content within the area [0.096, 0.103, 0.388, 0.425]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area includes a pie chart slice labelled \"2016\" with a percentage of \"20.1%\" indicated."
+            }
+        ],
+        "id_raw": 1172
+    },
+    {
+        "id": "5kY3W0mObla7nIEQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located at the coordinates [0.052, 0.025, 0.724, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the subtitle named \"Market Shares of Company X and Company Y from 2020 to 2025\"."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "coakw7J9twaAjzyV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you point me to the object located at coordinates [0.863, 0.118, 0.962, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend label for {Company Y}[0.863, 0.118, 0.962, 0.141]."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "fesMUAm4R5oiQPfP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is represented by the object at coordinates [0.319, 0.104, 0.388, 0.235]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie slice representing the market share of the company in the year 2020."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "QA4Cxw6mY93LHtYo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the chart is adjacent to {the slice}[0.564, 0.588, 0.688, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponding to the year 2023 {the slice}[0.598, 0.449, 0.698, 0.618] is adjacent."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "IEcMWufJGpPtQuiB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat shares are represented by the slices adjacent to the slice for '2022' {the slice}[0.094, 0.176, 0.266, 0.427]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The shares represented by the slices for '2021' {the slice}[0.213, 0.114, 0.34, 0.278] and '2023' {the slice}[0.078, 0.389, 0.182, 0.58] are adjacent to the 2022 slice."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "UYtcyT257f3sVvPO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich legend item is associated with the slice at [0.564, 0.588, 0.688, 0.758]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item {Company Y}[0.863, 0.118, 0.962, 0.141] corresponds to the slice."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "JBcorXf5rGnjcAOT",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region within coordinates [0.319, 0.104, 0.388, 0.235]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Yes, the region specified contains the slice representing the market share of the company in the year 2020 with the text \"3.6%\"[0.306, 0.053, 0.35, 0.077] and the label \"2020\"[0.345, 0.259, 0.389, 0.282]. This slice is colored '#ff9999'."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "2mquHNURA4WzNou3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects are contained within the region [0.213, 0.114, 0.34, 0.278]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region consists of a slice representing the year 2021 with the text \"6.0%\"[0.208, 0.089, 0.251, 0.112] and the label \"2021\"[0.292, 0.278, 0.336, 0.301]. The slice in this region is colored '#66b3ff'."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "KDTvkID43QQt8GJL",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_173.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area within the coordinates [0.598, 0.449, 0.698, 0.618]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That area contains a part of the pie chart representing the year '2023' with the label \"2023\"[0.552, 0.515, 0.596, 0.539] and with the associated text \"6.6%\"[0.728, 0.524, 0.772, 0.547]. This slice is colored '#ffcc99'."
+            }
+        ],
+        "id_raw": 1173
+    },
+    {
+        "id": "5jZJiUurzDhkEpej",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object with coordinates [0.067, 0.025, 0.709, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called Education Technology and Online Learning from 2010 to 2019."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "nzecgJHawmMbDGWX",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is described by the object at coordinates [0.919, 0.397, 0.963, 0.421] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label for the year 2019 in the legend."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "oZdL0v6WU58YqFJo",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat information is located in the text box with coordinates [0.729, 0.53, 0.773, 0.554]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text reads \"15.5%\" which is the percentage of Education Technology and Online Learning in 2018."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "k86RiHOx7Y5v8yEM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat color does the handle at {the coordinates}[0.871, 0.085, 0.905, 0.101] correspond to in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color is '#ff9999' which corresponds to the year 2010 in the pie chart."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "HZpwB9ICpy5hOgNN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich autotext is closest to {the label}[0.919, 0.362, 0.963, 0.386] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest autotext to the label for \"2018\" is {\"15.5%\"}[0.729, 0.53, 0.773, 0.554]."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "qBqwORsRhxeAsHTZ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the pie slice at {coordinates}[0.388, 0.104, 0.67, 0.396] and the objects in the legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice at these coordinates corresponds to the year 2019 in the legend, which is represented by the color '#99ff99'."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "Vm5HTYNkdmaEYzAQ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the region within coordinates [0.864, 0.074, 0.97, 0.43]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region located at the right part of the chart, contains the legend for the pie chart. It lists years from 2010 to 2019 with corresponding colors used in the pie chart."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "xIUTO3a0YEoTujY9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region [0.078, 0.104, 0.698, 0.931]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region comprises of the main pie chart, divided by different colored slices each corresponding to a year, and autotext percentages adjoining those slices."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "sTw5fbx0Vs36NWHH",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_175.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located in the region [0.321, 0.104, 0.388, 0.234]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a part of the pie chart represented by the color '#ff9999', and it corresponds to the year 2010."
+            }
+        ],
+        "id_raw": 1175
+    },
+    {
+        "id": "QGIzRdoJXZebayKs",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.165, 0.025, 0.611, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "Subtitle, named \"Cybersecurity Incidents from 2011 to 2020\"."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "ldeXA1wEJcN6rlxF",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich object is found at the coordinates [0.864, 0.074, 0.97, 0.43] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "mPABxujrb674hhlQ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object can be seen at the coordinates [0.081, 0.103, 0.695, 0.922]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "tyNpgXd67ZdSlht7",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich texts are located close to {the subtitle}[0.165, 0.025, 0.611, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The texts {\"2011\"}[0.919, 0.083, 0.963, 0.106] and {\"2012\"}[0.919, 0.118, 0.963, 0.141] are located near the subtitle."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "qBPI74rOa7lJYesc",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color swatches in the legend correspond with {the slice}[0.296, 0.103, 0.388, 0.239]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color swatch with coordinates [0.871, 0.085, 0.905, 0.101] corresponds to the slice."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "m5S4K8loH9KEShVo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich year does the slice with color {the color swatch}[0.871, 0.155, 0.905, 0.171] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The year 2013."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "2nPw3ANcMteqDC2q",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you describe the region with coordinates [0.864, 0.074, 0.97, 0.43]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains several legend items for the chart. Each item consists of a pair - a color swatch and the corresponding text indicating the year. E.g., the pair {\"2013\", color swatch}[0.919, 0.153, 0.963, 0.176, 0.871, 0.155, 0.905, 0.171]."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "Jw74qFkv2n5HVFTf",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.176, 0.122, 0.324, 0.305]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice representing the year 2012, with the text {\"2012\"}[0.273, 0.289, 0.316, 0.313] and the autotext {\"7.3%\"}[0.173, 0.113, 0.217, 0.136]."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "xanVF1TSaHiKvIfR",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_179.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat objects occupy the region [0.087, 0.216, 0.24, 0.455]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is occupied by a pie slice representing the year 2013 with the text {\"2013\"}[0.205, 0.382, 0.249, 0.405] and the autotext {\"9.7%\"}[0.049, 0.283, 0.092, 0.306]."
+            }
+        ],
+        "id_raw": 1179
+    },
+    {
+        "id": "FRJU399VGVahhxQT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object at coordinates [0.068, 0.025, 0.708, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle of the chart, stating \"Healthcare and Medical Technology Usage from 2014 to 2020\"."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "edgqQkkxJ7xDaJwj",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is found at the coordinates [0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend of the chart is found at this location."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "Ha8IKwlbxyFqlBTc",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat represents the coordinates [0.187, 0.104, 0.388, 0.297] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a slice of the pie chart representing the year 2014."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "ux5daZhF3Q2cfXox",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice of the pie chart is adjacent to the {slice for 2014}[0.187, 0.104, 0.388, 0.297]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice for 2020}[0.388, 0.104, 0.629, 0.335] is adjacent to the slice for 2014."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "yt3wi7l0YnuZ94Mx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich elements of the chart are next to the {legend}[0.864, 0.074, 0.97, 0.325]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie chart itself is just next to the legend, but there is no other specific text or item adjacent to it."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "SZckuyirxLvd7Bra",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nFor the pie slice at coordinates [0.557, 0.256, 0.699, 0.664], what does the legend tell us?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend at the coordinates [0.919, 0.258, 0.963, 0.281] tells that the pie slice represents the year 2019."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "CfXUFhdLu7tfFkwJ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the information within coordinates [0.077, 0.104, 0.699, 0.934]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region mostly covers the pie chart. The pie chart itself is subdivided into several slices, each representing a different year and showing the usage of medical technology, from 2014 to 2020. Each slice is colored differently."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "CvpLjTsI0Y7S71PW",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be seen within the region [0.557, 0.256, 0.699, 0.664]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a slice of the pie chart that corresponds to the year 2019, with a label showing \"16.6%\"."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "rTVQV8mN1mAVAdmz",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_180.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is present in the region with coordinates [0.179, 0.734, 0.455, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within these coordinates lies a slice of the pie chart representing the year 2017, labeled \u201c15.2%\u201d."
+            }
+        ],
+        "id_raw": 1180
+    },
+    {
+        "id": "TWS0UeTust1qRBgq",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object found at these coordinates [0.13, 0.025, 0.647, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a chart's subtitle labeled \"Education Resource Utilization from 2012 to 2021\"."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "MWYfkJDIKu5OwCII",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the coordinates [0.864, 0.074, 0.97, 0.779] are referring to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It refers to the legend of the chart."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "NHqYoqLXR9qeqRSL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.371, 0.103, 0.388, 0.227]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a pie chart slice that represents data from the year 2012."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "jsiOjIFeddzIX5aN",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich color in the legend corresponds to {the pie chart piece}[0.523, 0.624, 0.673, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to the color '#f5c2d6' from the legend."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "TPqJZCtFJqfgk8lz",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat pie chart piece does the legend item with color '#66b3ff' correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "It corresponds to {the pie chart piece}[0.336, 0.104, 0.376, 0.231]"
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "bm3H6RwHoSilXSqb",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich chart component is closest to the legend located at [0.864, 0.074, 0.97, 0.779]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The closest component is {the pie chart}[0.08, 0.103, 0.696, 0.925]."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "EIqKaeKL1xeFh2co",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within these coordinates [0.388, 0.103, 0.666, 0.389]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region encompasses a substantial sector of the pie chart labeled '2021' with a rate of '17.9%'. It is highlighted with the color '#f0aac2'."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "Jf3wLKaGzoosITGr",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is in the region between the coordinates [0.523, 0.624, 0.673, 0.835]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a pie chart slice representing the year 2019, with a color code of '#f5c2d6', and the rate '8.0%'."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "sDQcDy3fFyqOxI7U",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_182.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects found in this region [0.08, 0.103, 0.696, 0.925]."
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers the whole pie chart with different slices representing different years from 2012 to 2021. Each slice has a unique color and a certain percentage."
+            }
+        ],
+        "id_raw": 1182
+    },
+    {
+        "id": "nGPHQKR39KmnTvBZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you tell me what the object is at these coordinates [0.104, 0.075, 0.374, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subtitle and says \"Fitness and Sports Health Trends from 2017 to 2019, 2017\"."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "UHwVmI8GTnnhY2yP",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at coordinates [0.377, 0.075, 0.647, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the subtitle that says \"Fitness and Sports Health Trends from 2017 to 2019, 2018\"."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "iUAzpFTPXsnNrZkI",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at [0.651, 0.075, 0.921, 0.111]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's a subtitle, and it reads \"Fitness and Sports Health Trends from 2017 to 2019, 2019\"."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "npmi2TSxuxzFL9KO",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slices are connected to the {slice}<r>[0.7, 0.197, 0.786, 0.772]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {slice}<r>[0.743, 0.197, 0.872, 0.813]</r> is connected to it."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "Vt10clC5ZlEh9qKQ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is positioned close to the {subtitle}<r>[0.378, 0.075, 0.647, 0.111]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "Close to the {subtitle}<r>[0.378, 0.075, 0.647, 0.111]</r> is the {axes}<r>[0.406, 0.12, 0.619, 0.89]</r>."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "oxPmSo1w2WlpCQXA",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat part of the pie chart is associated with the text {Gym Membership}<r>[0.457, 0.592, 0.482, 0.62]</r>?"
+            },
+            {
+                "from": "gpt",
+                "value": "The {pie slice}<r>[0.427, 0.197, 0.591, 0.813]</r> is linked with the text {Gym Membership}<r>[0.457, 0.592, 0.482, 0.62]</r>."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "HwvaslHjIXvnytl9",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you please describe the elements within the region [0.153, 0.197, 0.325, 0.813]?"
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there's a pie slice with color '#ff9999' representing 'Gym Membership', with a percentage of '48.3%', which can be seen from two text objects {48.3%}<r>[0.077, 0.473, 0.145, 0.501]</r> and {Gym Membership}<r>[0.176, 0.481, 0.2, 0.509]</r>."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "U7mQKtQxZAKkISq3",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_187.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the area marked by [0.7, 0.197, 0.872, 0.813]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there are two pie slices. The first slice has a color of '#ff9999' and represents 'Gym Membership' with '41.7%' as shown by the text {41.7%}<r>[0.627, 0.403, 0.695, 0.431]</r>, and the second slice has a color of '#66b3ff' and represents 'Yoga Classes' with '58.3%', this is reflected in the text {58.3%}<r>[0.877, 0.579, 0.926, 0.607]</r>."
+            }
+        ],
+        "id_raw": 1187
+    },
+    {
+        "id": "CH1NlcquVPUNPlbZ",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart located at the coordinates [0.366, 0.019, 0.408, 0.041]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title of the chart is \"2024\"."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "ZbwBYnYjcNODndYH",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is located at the coordinates [0.866, 0.055, 0.976, 0.113] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend is located at those coordinates."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "71UghgbzR0Copv8F",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat data is represented by the object at coordinates [0.047, 0.089, 0.523, 0.939] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice represents the data for 'Artificial Intelligence' with a proportion of '56.5%'."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "RictYCLVjdR57aMX",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich text labels are adjacent to the {legend}[0.866, 0.055, 0.976, 0.113] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text labels {\"AI\"}[0.91, 0.062, 0.924, 0.08] and {\"Big Data\"}[0.91, 0.088, 0.97, 0.106] are adjacent to the legend."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "tkFhpmogBtXMBAtq",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice does the legend label {\"AI\"}[0.91, 0.062, 0.924, 0.08] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend label AI corresponds to the slice at coordinates [0.047, 0.089, 0.523, 0.939]."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "UMH4oIxxom1DZPqm",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat percentage does the slice {pie}[0.387, 0.089, 0.727, 0.904] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The pie slice represents a percentage of '43.5%'."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "YdAqPsRMiLJKqTTx",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat can be found in the region between coordinates [0.047, 0.089, 0.727, 0.939]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the data representing 'Artificial Intelligence', labeled as {\"AI\"}[0.166, 0.557, 0.21, 0.575] with a percentage of {\"56.5%\"}[0.008, 0.6, 0.021, 0.618]."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "BmmN2TsJOjU2k33H",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects within the region [0.047, 0.089, 0.523, 0.939]."
+            },
+            {
+                "from": "gpt",
+                "value": "Within this region, there is a pie slice labeled as 'AI' with the data value '56.5%'."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "QoW0v3OULcOcvWVU",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_188.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the region defined by the coordinates [0.866, 0.055, 0.976, 0.113] contain?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region contains the legend for the pie chart, which includes the labels 'AI' and 'Big Data'."
+            }
+        ],
+        "id_raw": 1188
+    },
+    {
+        "id": "qbY1NQ44ctzcP3pS",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the text located at coordinates [0.111, 0.184, 0.146, 0.208] represent in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is a percentage reading, which is \"25.0%\"."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "hjPl51kJv0kUP7rA",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the name of the legend item with a handle at coordinates [0.826, 0.155, 0.861, 0.171] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The name of the legend item is \"Gaming\"."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "O1ywIE3eoChwiC4t",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the data present at coordinates [0.229, 0.331, 0.283, 0.354] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "It presents a pie slice label, specifically \"Film\"."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "8sCIg066QvFn1p4Y",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the legend item with the handle coordinates [0.826, 0.225, 0.861, 0.241] correspond to?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"VR\" corresponds to the slice at the coordinates [0.388, 0.104, 0.507, 0.25]."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "QBGfy3v8LOSYoL1t",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat kind of relationship exists between the text at coordinates [0.482, 0.696, 0.537, 0.72] and the autotext at coordinates [0.610, 0.854, 0.678, 0.877] in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text \"Gaming\" and the autotext \"30.0%\" signify that the Gaming slice represents 30.0% of the total pie."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "rbliUvCZriYs9n36",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the slice at coordinates [0.077, 0.519, 0.337, 0.922] and the legend at coordinates [0.875, 0.118, 0.924, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at these coordinates represent the category \"Music\" based on the legend."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "Vj2Cd9aPkaQlHzOE",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.077, 0.519, 0.337, 0.922] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region corresponds to a pie slice captioned \"Music\" with a value of \"21.3%\" as seen at coordinates [0.214, 0.661, 0.269, 0.684] and [0.07, 0.789, 0.119, 0.813] respectively. It is located on the left side of the chart and is signified by the color '#66b3ff'."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "XGxXI0W01iXlEBhe",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_189.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich objects appear in the region within [0.388, 0.104, 0.507, 0.25] on the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a label \"VR\", as seen at coordinates [0.403, 0.263, 0.446, 0.286], and a percentage of \"6.2%\", as seen at coordinates [0.455, 0.059, 0.479, 0.083]. This region represents a slice of the pie chart colored as '#c2c2f0'."
+            }
+        ],
+        "id_raw": 1189
+    },
+    {
+        "id": "QbBJlS5SIxM4ApE2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the title of the chart at location [0.197, 0.025, 0.579, 0.055]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The title is \"Public Concern on Major Policy Areas\"."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "fzLelIjOVSwAGXoL",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object located at coordinates [0.845, 0.188, 0.892, 0.211] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Taxes\"."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "j8dHhGLoLVeiKpYT",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the label of the object at coordinates [0.845, 0.083, 0.931, 0.106] on the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The label is \"Education\"."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "6qiMXurWDKpDPayo",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in the pie chart corresponds to {the legend item \"Economy\"}[0.845, 0.223, 0.924, 0.246]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice with a color of '#c2c2f0' and located at coordinates [0.388, 0.104, 0.571, 0.284] corresponds to the \"Economy\" legend item."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "6ECQs8AUQA2Iu34W",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nThe slice at [0.092, 0.608, 0.484, 0.934] is related to which item in the chart's legend?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice corresponds to {the legend item \"Healthcare\"}[0.845, 0.118, 0.939, 0.141]."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "WcEHl0MTwjlaRpIa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart aligns with {the text \"Infrastructure\"}[0.493, 0.683, 0.548, 0.706]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The slice at coordinates [0.455, 0.608, 0.684, 0.913] aligns with the text \"Infrastructure\"."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "yNrvlTLRvLrZdhFD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within the coordinates [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region is part of the pie chart that incorporates different slices representing various policy areas: \"Education\" [0.21, 0.361, 0.264, 0.384], \"Healthcare\" [0.276, 0.729, 0.331, 0.752], \"Infrastructure\" [0.493, 0.683, 0.548, 0.706], \"Taxes\" [0.538, 0.43, 0.593, 0.453] and \"Economy\" [0.418, 0.27, 0.473, 0.294]. Each slice has associated percentage annotations. This region constitutes most of the pie chart area."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "bmaZ3IJeCv2NO4NZ",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects within the region defined by [0.789, 0.074, 0.97, 0.255]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region houses the chart's legend. The objects within this region are the legend items: \"Education\" [0.845, 0.083, 0.931, 0.106], \"Healthcare\" [0.845, 0.118, 0.939, 0.141], \"Infrastructure\" [0.845, 0.153, 0.963, 0.176], \"Taxes\" [0.845, 0.188, 0.892, 0.211] and \"Economy\" [0.845, 0.223, 0.924, 0.246], each represented by a colored square and labeled text."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "YUiCLnUu2DAfkM6B",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_190.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.455, 0.608, 0.684, 0.913] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains the pie slice for \"Infrastructure\" with its color as '#99ff99' and associated annotation text. Also included is the percentage \"15.0%\" at coordinates [0.63, 0.83, 0.749, 0.853]."
+            }
+        ],
+        "id_raw": 1190
+    },
+    {
+        "id": "TDeVHGRM5LjHgcZM",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located at the coordinates [0.781, 0.537, 0.978, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "It is the legend of the second pie chart."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "p7kxZcozTKlKsOJV",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the text in the chart at the coordinates [0.825, 0.668, 0.949, 0.682] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The text is \"Young Consumers\"."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "eSonBlaXJ1bTYmv0",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you specify the object located at coordinates [0.786, 0.648, 0.814, 0.658]?"
+            },
+            {
+                "from": "gpt",
+                "value": "That refers to the color handle for \"Ethnic Cultures\" in the legend."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "i0C2T3Uvoe07Dahx",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich handle in the legend corresponds to the slice {at position}[0.314, 0.55, 0.5, 0.695] of the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The corresponding handle in the legend is {for \"Comfort Foods\"}[0.786, 0.543, 0.814, 0.553]."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "DjIkfDHzW11xQcGa",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhich slice in the pie chart does the legend item {at position}[0.825, 0.584, 0.906, 0.598] represent?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend item \"Innovations\" represents {the slice}[[0.295, 0.226, 0.361, 0.315]] in the chart."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "ByTOEw8FePIZ4hhg",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between the objects at coordinates [0.825, 0.542, 0.927, 0.556] and [0.455, 0.058, 0.5, 0.123]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The object at [0.825, 0.542, 0.927, 0.556] is the legend labeling for the pie slice at [0.455, 0.058, 0.5, 0.123], both represent \"Comfort Foods\"."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "PSIBw0Q2q9cRyV9c",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region within coordinates [0.781, 0.537, 0.978, 0.729]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region is located right side of the chart and contains the legend of the pie chart in the second subplot. The legend items include \"Comfort Foods\", \"Eco-friendly Products\", \"Innovations\", \"Seasonal Offers\", \"Health Conscious\", \"Ethnic Cultures\", \"Young Consumers\", \"Online Shopping\", and \"Return Policies\"."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "rRZJBNjBNDKfmafD",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_193.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the area located within coordinates [0.295, 0.058, 0.705, 0.468]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This area contains the pie chart of the first subplot. The chart includes various slices representing different consumer behavior trends such as \"Comfort Foods\", \"Eco-friendly Products\", \"Innovations\", \"Seasonal Offers\", \"Health Conscious\", \"Ethnic Cultures\", \"Young Consumers\", \"Online Shopping\", and \"Return Policies\"."
+            }
+        ],
+        "id_raw": 1193
+    },
+    {
+        "id": "5FN9VTCJRVyMowWu",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object with coordinates [0.079, 0.025, 0.697, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The subtitle, called \"New Media and Digital Content Creation from 2010 to 2018\"."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "rv9Xtsfrdi10Itp1",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you identify the object at coordinates [0.864, 0.074, 0.97, 0.709] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend of the chart."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "ZhGWRx99HNLNWel2",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat does the object located at coordinates [0.919, 0.397, 0.963, 0.421] represent in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "This is the legend text \"2010\"."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "D0jjxmqkvbjYtBHJ",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat object is directly beside {the legend text \"2010\"}[0.919, 0.397, 0.963, 0.421]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The legend text \"2011\" at coordinates [0.919, 0.432, 0.963, 0.455] is directly beside it."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "0PaOqbDHyBZYEjiE",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the relationship between {the pie slice} [0.574, 0.541, 0.695, 0.722] and {the legend text \"2014\"} [0.919, 0.537, 0.963, 0.56]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The Pie slice at [0.574, 0.541, 0.695, 0.722] represents data from the year \"2014\" as indicated by the legend text."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "u54VUZmBJqg1sX7M",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the correlation between the {color area}[0.871, 0.744, 0.905, 0.760] in the legend section and the slices in the pie chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The color area in the legend section represents the color code for the year \"2017\", correlating with the pie slice that represents data from \"2017\"."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "rTIpjV2ZVj5PwFSc",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nHow would you describe the region within coordinates [0.271, 0.108, 0.355, 0.248]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region covers a segment of the pie representing data from the year \"2011\", this is indicated by the slice color, matching with the legend and the corresponding percentage is \"3.7%\", as decoded from the autotext located at [0.252, 0.067, 0.297, 0.091]."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "1nkrDYuIpMmJTsom",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nDescribe the objects located in the region [0.434, 0.78, 0.505, 0.915]."
+            },
+            {
+                "from": "gpt",
+                "value": "The region contains a segment of the pie chart representing data from the year \"2011\" with an indicated percentage of \"2.8%\" located at coordinates [0.489, 0.933, 0.533, 0.957]."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "ho5vQIliHPYY1393",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_196.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCan you describe the region with coordinates [0.271, 0.108, 0.306, 0.296]?"
+            },
+            {
+                "from": "gpt",
+                "value": "This region includes a segment of the pie chart corresponding to data from the year \"2012\". It contains auto generated text \"5.1%\" located at coordinates [0.252, 0.067, 0.297, 0.091] indicating the percentage data of that year."
+            }
+        ],
+        "id_raw": 1196
+    },
+    {
+        "id": "aWxqgRbv7UFgPFlU",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the main object located at coordinates [0.101, 0.025, 0.675, 0.055] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the subtitle, it reads \"Education Resource Distribution and Utilization in 2020\"."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "ewIlEqxXwm01t1TY",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me what object can be found at [0.868, 0.083, 0.92, 0.106] in the figure?"
+            },
+            {
+                "from": "gpt",
+                "value": "It's the legend text for \"Books\"."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "XeDd92o3kWYMCs3E",
+        "Chart_type": "pie",
+        "QA_type": "Referring QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is the object located within the coordinates [0.64, 0.198, 0.735, 0.221] of this chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "That's the text \"26.3%\"."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "6UnLNyxXCm5TuQqM",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat slice in this pie chart is associated with {the legend item}[0.868, 0.083, 0.92, 0.106]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The associated slice is {the slice}[0.077, 0.104, 0.698, 0.934]."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "4eyJIcUkS5kbfBmB",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat is located near to the {text \"Books\"}[0.223, 0.676, 0.278, 0.699] in the chart?"
+            },
+            {
+                "from": "gpt",
+                "value": "The percentage text \"73.7%\"<r>[0.084, 0.816, 0.136, 0.839]</r> is located near it."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "KT3AGUXchPVLQd6s",
+        "Chart_type": "pie",
+        "QA_type": "Relationship QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nCould you tell me which slice in the pie chart is linked to {the legend item}[0.868, 0.118, 0.963, 0.141]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The related slice is {the slice}[0.388, 0.104, 0.699, 0.553]."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "pPfV7ZLDSOvkify7",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nPlease describe the region within these coordinates [0.77, 0.318, 0.964, 0.536]."
+            },
+            {
+                "from": "gpt",
+                "value": "That region holds the two types of resources. This includes the text labels for \"Books\" [0.868, 0.083, 0.92, 0.106] and \"Computers\" [0.868, 0.118, 0.963, 0.141] from the legend."
+            }
+        ],
+        "id_raw": 1198
+    },
+    {
+        "id": "uWCYRZKLbOuatC8V",
+        "Chart_type": "pie",
+        "QA_type": "Regional Description QA",
+        "image": "png_pie/pie_6_198.png",
+        "conversations": [
+            {
+                "from": "human",
+                "value": "<image>\nWhat are the objects located within the region [0.077, 0.104, 0.699, 0.934]?"
+            },
+            {
+                "from": "gpt",
+                "value": "The region has two main objects, the pie chart slice [0.077, 0.104, 0.698, 0.934] and the associated text label \"73.7%\" [0.084, 0.816, 0.136, 0.839]."
+            }
+        ],
+        "id_raw": 1198
+    }
+]
\ No newline at end of file
diff --git a/data/mapping.yaml b/data/mapping.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b36ca4783051865c278ddd0a6587191d511cad59
--- /dev/null
+++ b/data/mapping.yaml
@@ -0,0 +1,6 @@
+image_paths:
+  BASE: ./
+mapping:
+  final_0721_bar: BASE
+  final_0721_line: BASE
+  final_0721_pie: BASE
\ No newline at end of file
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0f57a01dbf6a3e66fe87d14a7761d794edd9437
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+streamlit-drawable-canvas
+streamlit_chat
+seaborn
+pandas
\ No newline at end of file